From a5bb6d0b1c898678b20df280eb62cb69117caf04 Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Mon, 16 Mar 2026 17:44:19 +0000
Subject: [PATCH 01/11] adding discovr epic

---
 examples/docs_4_discovr_epic.ipynb   | 4354 ++++++++++++++++++++++++++
 examples/testing_more_products.ipynb |   12 +-
 2 files changed, 4360 insertions(+), 6 deletions(-)
 create mode 100644 examples/docs_4_discovr_epic.ipynb

diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_4_discovr_epic.ipynb
new file mode 100644
index 0000000..8d8d5fb
--- /dev/null
+++ b/examples/docs_4_discovr_epic.ipynb
@@ -0,0 +1,4354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "556f084f-684f-41e4-b004-4fde273a8217",
+   "metadata": {},
+   "source": [
+    "# DISCOVR EPIC L2\n",
+    "<br>\n",
+    "\n",
+    "The DSCOVR Earth Polychromatic Imaging Camera (EPIC) observes the sunlit Earth from the Sun–Earth L1 point and captures full-disk images of the planet in 10 UV–visible–near-IR spectral bands roughly every 1–2 hours.\n",
+    "\n",
+    "Level-2 products are geophysical retrievals derived from EPIC radiances. \n",
+    "\n",
+    "* Cloud properties: cloud mask, cloud optical thickness, cloud effective pressure/height/temperature, cloud phase\n",
+    "* Aerosols: aerosol optical depth, aerosol index\n",
+    "* Trace gases: total column ozone, sulfur dioxide (SO₂)\n",
+    "* Surface/vegetation: surface reflectivity, UV aerosol index, vegetation indices (e.g., NDVI)\n",
+    "\n",
+    "This notebook will use two DISCOVR EPIC Level 2 products.\n",
+    "\n",
+    "* DSCOVR_EPIC_L2_AER, Version 03, aerosols\n",
+    "* DSCOVR_EPIC_L2_TO3 Version 03, total column ozone (TO₃)\n",
+    "\n",
+    "*Note: In a virtual machine in AWS us-west-2, where NASA cloud data is, the point matchups are fast. In Colab, say, your compute is not in the same data region nor provider, and the same matchups might take 10x longer.*\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "The examples here use NASA EarthData and you need to have an account with EarthData. Make sure you can login."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69c969a2-cad8-41d0-b1cd-f78228b188b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if needed\n",
+    "pip install point-collocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9b6e22e-f993-4aa0-a03b-6c310cbe11be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import earthaccess\n",
+    "earthaccess.login()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd008cf7-3bcb-4bf4-8535-7f0e142170bb",
+   "metadata": {},
+   "source": [
+    "## DSCOVR_EPIC_L2_AER\n",
+    "\n",
+    "### Create some points\n",
+    "\n",
+    "Random global and in 2024."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2b12e80a-5a0b-4a8c-a89b-e9e4877655b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "31\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>land</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>9.100711</td>\n",
+       "      <td>11.216734</td>\n",
+       "      <td>2024-07-21</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>-67.209878</td>\n",
+       "      <td>-62.734423</td>\n",
+       "      <td>2024-04-16</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>87.228338</td>\n",
+       "      <td>-29.718344</td>\n",
+       "      <td>2024-10-13</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>34.701655</td>\n",
+       "      <td>-20.040258</td>\n",
+       "      <td>2024-04-21</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>-8.043907</td>\n",
+       "      <td>41.779801</td>\n",
+       "      <td>2024-08-31</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           lat        lon       time   land\n",
+       "35    9.100711  11.216734 2024-07-21   True\n",
+       "43  -67.209878 -62.734423 2024-04-16  False\n",
+       "83   87.228338 -29.718344 2024-10-13  False\n",
+       "190  34.701655 -20.040258 2024-04-21  False\n",
+       "222  -8.043907  41.779801 2024-08-31  False"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df_points = pd.read_csv(\n",
+    "    \"fixtures/points_1000.csv\",\n",
+    "    parse_dates=[\"time\"]\n",
+    ")\n",
+    "df = df_points[\n",
+    "    (df_points[\"time\"].dt.year == 2024)\n",
+    "]\n",
+    "print(len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85f3095e-a159-4537-b267-3c2b4124341f",
+   "metadata": {},
+   "source": [
+    "### Create the plan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6153eef7-d94d-4c2f-8bd2-a860d737a620",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 403 μs, sys: 0 ns, total: 403 μs\n",
+      "Wall time: 313 μs\n"
+     ]
+    },
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'point_collocation'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mimport point_collocation as pc\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mshort_name=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDSCOVR_EPIC_L2_AER\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan = pc.plan(\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    df,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    data_source=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mearthaccess\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    source_kwargs=\u001b[39;49m\u001b[33;43m{\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mshort_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: short_name,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mversion\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mV03\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    },\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    time_buffer=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m1h\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan.summary(n=2)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2572\u001b[39m, in \u001b[36mInteractiveShell.run_cell_magic\u001b[39m\u001b[34m(self, magic_name, line, cell)\u001b[39m\n\u001b[32m   2570\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.builtin_trap:\n\u001b[32m   2571\u001b[39m     args = (magic_arg_s, cell)\n\u001b[32m-> \u001b[39m\u001b[32m2572\u001b[39m     result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2574\u001b[39m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[32m   2575\u001b[39m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[32m   2576\u001b[39m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[32m   2577\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1447\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1445\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupt_occured:\n\u001b[32m   1446\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m exit_on_interrupt \u001b[38;5;129;01mand\u001b[39;00m captured_exception:\n\u001b[32m-> \u001b[39m\u001b[32m1447\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m captured_exception\n\u001b[32m   1448\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m   1449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1411\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1409\u001b[39m st = clock2()\n\u001b[32m   1410\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1411\u001b[39m     \u001b[43mexec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1412\u001b[39m     out = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1413\u001b[39m     \u001b[38;5;66;03m# multi-line %%time case\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m<timed exec>:1\u001b[39m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'point_collocation'"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "import point_collocation as pc\n",
+    "short_name=\"DSCOVR_EPIC_L2_AER\"\n",
+    "plan = pc.plan(\n",
+    "    df,\n",
+    "    data_source=\"earthaccess\",\n",
+    "    source_kwargs={\n",
+    "        \"short_name\": short_name,\n",
+    "        \"version\": \"V03\"\n",
+    "    },\n",
+    "    time_buffer=\"1h\"\n",
+    ")\n",
+    "plan.summary(n=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bde99fb2-5f41-4aee-a2d2-df20bc31cf23",
+   "metadata": {},
+   "source": [
+    "### Look at the variables\n",
+    "\n",
+    "We will open a file with datatree and see what groups it has (if any). It does have groups; the latitude, longitude is at the base level: `/` and the product is in `/product`. We want `vertical_column_troposphere` which is the NO2. So we create a profile for TEMPO. We open a dataset with the profile to make sure it looks good."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bee93b40-db7f-4185-b07b-46a1ed843fd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "open_method: {'xarray_open': 'dataset', 'merge': ['/', '/product'], 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n",
+      "CPU times: user 1.92 s, sys: 438 ms, total: 2.35 s\n",
+      "Wall time: 6.56 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 732MB\n",
+       "Dimensions:                                  (latitude: 2950, longitude: 7750,\n",
+       "                                              time: 1)\n",
+       "Coordinates:\n",
+       "  * latitude                                 (latitude) float32 12kB 14.01 .....\n",
+       "  * longitude                                (longitude) float32 31kB -168.0 ...\n",
+       "  * time                                     (time) datetime64[ns] 8B 2024-01...\n",
+       "Data variables:\n",
+       "    weight                                   (latitude, longitude) float32 91MB dask.array&lt;chunksize=(590, 1550), meta=np.ndarray&gt;\n",
+       "    vertical_column_troposphere              (time, latitude, longitude) float64 183MB dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;\n",
+       "    vertical_column_troposphere_uncertainty  (time, latitude, longitude) float64 183MB dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;\n",
+       "    vertical_column_stratosphere             (time, latitude, longitude) float64 183MB dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;\n",
+       "    main_data_quality_flag                   (time, latitude, longitude) float32 91MB dask.array&lt;chunksize=(1, 984, 2584), meta=np.ndarray&gt;\n",
+       "Attributes: (12/40)\n",
+       "    history:                          2024-08-07T13:18:52Z: L2_regrid -v /tem...\n",
+       "    scan_num:                         1\n",
+       "    time_coverage_start:              2024-01-03T12:52:54Z\n",
+       "    time_coverage_end:                2024-01-03T13:32:40Z\n",
+       "    time_coverage_start_since_epoch:  1388321592.148252\n",
+       "    time_coverage_end_since_epoch:    1388323978.7368417\n",
+       "    ...                               ...\n",
+       "    title:                            TEMPO Level 3 nitrogen dioxide product\n",
+       "    collection_shortname:             TEMPO_NO2_L3\n",
+       "    collection_version:               1\n",
+       "    keywords:                         EARTH SCIENCE&gt;ATMOSPHERE&gt;AIR QUALITY&gt;NI...\n",
+       "    summary:                          Nitrogen dioxide Level 3 files provide ...\n",
+       "    coremetadata:                     \\nGROUP                  = INVENTORYMET...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-aab8919e-e084-4d52-8d4a-38348a4a0586' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aab8919e-e084-4d52-8d4a-38348a4a0586' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 2950</li><li><span class='xr-has-index'>longitude</span>: 7750</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e6356620-8943-49a0-a12e-d8a3ec2a012c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6356620-8943-49a0-a12e-d8a3ec2a012c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.01 14.03 14.05 ... 72.97 72.99</div><input id='attrs-d6d88def-6e99-46fd-ae87-e5bdd7630460' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6d88def-6e99-46fd-ae87-e5bdd7630460' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8c13918-8fc6-411b-8f05-b18fb27a5b54' class='xr-var-data-in' type='checkbox'><label for='data-e8c13918-8fc6-411b-8f05-b18fb27a5b54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>comment :</span></dt><dd>latitude at grid box center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([14.01, 14.03, 14.05, ..., 72.95, 72.97, 72.99],\n",
+       "      shape=(2950,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-168.0 -168.0 ... -13.03 -13.01</div><input id='attrs-12c72e31-0492-4a60-a4e8-73fe9fb2902b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12c72e31-0492-4a60-a4e8-73fe9fb2902b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd511b23-375b-4a37-9127-e482d39a9dd5' class='xr-var-data-in' type='checkbox'><label for='data-dd511b23-375b-4a37-9127-e482d39a9dd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>comment :</span></dt><dd>longitude at grid box center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><pre>array([-167.99, -167.97, -167.95, ...,  -13.05,  -13.03,  -13.01],\n",
+       "      shape=(7750,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-03T12:53:12.148251904</div><input id='attrs-07224519-53d0-45a3-a8d8-b1a9a28be46c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07224519-53d0-45a3-a8d8-b1a9a28be46c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18c90b5a-44a9-46a3-bfea-698b3a1c40bc' class='xr-var-data-in' type='checkbox'><label for='data-18c90b5a-44a9-46a3-bfea-698b3a1c40bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>scan start time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-03T12:53:12.148251904&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-850e03d9-a6cc-4c85-b7b1-3c9ec72be723' class='xr-section-summary-in' type='checkbox'  checked><label for='section-850e03d9-a6cc-4c85-b7b1-3c9ec72be723' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>weight</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(590, 1550), meta=np.ndarray&gt;</div><input id='attrs-6250efed-83c7-41d8-998f-0477dc3ea137' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6250efed-83c7-41d8-998f-0477dc3ea137' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bab9109-0f58-4506-b370-80192383bddd' class='xr-var-data-in' type='checkbox'><label for='data-5bab9109-0f58-4506-b370-80192383bddd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sum of area weights</dd><dt><span>comment :</span></dt><dd>sum of Level 2 pixel overlap areas</dd><dt><span>units :</span></dt><dd>km^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 87.21 MiB </td>\n",
+       "                        <td> 3.49 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2950, 7750) </td>\n",
+       "                        <td> (590, 1550) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"95\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"36\" x2=\"120\" y2=\"36\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"45\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"45\" />\n",
+       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"45\" />\n",
+       "  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"45\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,45.677419354838705 0.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"65.677419\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
+       "  <text x=\"140.000000\" y=\"22.838710\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,22.838710)\">2950</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_column_troposphere</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;</div><input id='attrs-75116e43-91f2-4b40-9669-c20900dd9558' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75116e43-91f2-4b40-9669-c20900dd9558' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e976d88-7f2b-4d0f-b49b-3d89a1350f6f' class='xr-var-data-in' type='checkbox'><label for='data-5e976d88-7f2b-4d0f-b49b-3d89a1350f6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>troposphere nitrogen dioxide vertical column</dd><dt><span>units :</span></dt><dd>molecules/cm^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 174.43 MiB </td>\n",
+       "                        <td> 10.91 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 2950, 7750) </td>\n",
+       "                        <td> (1, 738, 1938) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"11\" x2=\"24\" y2=\"26\" />\n",
+       "  <line x1=\"10\" y1=\"22\" x2=\"24\" y2=\"37\" />\n",
+       "  <line x1=\"10\" y1=\"34\" x2=\"24\" y2=\"49\" />\n",
+       "  <line x1=\"10\" y1=\"45\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,60.626017304593105 10.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"40\" y1=\"0\" x2=\"54\" y2=\"14\" />\n",
+       "  <line x1=\"70\" y1=\"0\" x2=\"84\" y2=\"14\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"114\" y2=\"14\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"26\" x2=\"144\" y2=\"26\" />\n",
+       "  <line x1=\"24\" y1=\"37\" x2=\"144\" y2=\"37\" />\n",
+       "  <line x1=\"24\" y1=\"49\" x2=\"144\" y2=\"49\" />\n",
+       "  <line x1=\"24\" y1=\"60\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"54\" y1=\"14\" x2=\"54\" y2=\"60\" />\n",
+       "  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"60\" />\n",
+       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"60\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,60.626017304593105 24.9485979497544,60.626017304593105\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"80.626017\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
+       "  <text x=\"164.948598\" y=\"37.787308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,37.787308)\">2950</text>\n",
+       "  <text x=\"7.474299\" y=\"73.151718\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,73.151718)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_column_troposphere_uncertainty</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;</div><input id='attrs-c9e8a675-bbb0-43e3-96d1-533f144ec06c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9e8a675-bbb0-43e3-96d1-533f144ec06c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59efa2db-1d19-4bfe-a60f-247c92168161' class='xr-var-data-in' type='checkbox'><label for='data-59efa2db-1d19-4bfe-a60f-247c92168161' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>troposphere nitrogen dioxide vertical column uncertainty</dd><dt><span>units :</span></dt><dd>molecules/cm^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 174.43 MiB </td>\n",
+       "                        <td> 10.91 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 2950, 7750) </td>\n",
+       "                        <td> (1, 738, 1938) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"11\" x2=\"24\" y2=\"26\" />\n",
+       "  <line x1=\"10\" y1=\"22\" x2=\"24\" y2=\"37\" />\n",
+       "  <line x1=\"10\" y1=\"34\" x2=\"24\" y2=\"49\" />\n",
+       "  <line x1=\"10\" y1=\"45\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,60.626017304593105 10.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"40\" y1=\"0\" x2=\"54\" y2=\"14\" />\n",
+       "  <line x1=\"70\" y1=\"0\" x2=\"84\" y2=\"14\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"114\" y2=\"14\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"26\" x2=\"144\" y2=\"26\" />\n",
+       "  <line x1=\"24\" y1=\"37\" x2=\"144\" y2=\"37\" />\n",
+       "  <line x1=\"24\" y1=\"49\" x2=\"144\" y2=\"49\" />\n",
+       "  <line x1=\"24\" y1=\"60\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"54\" y1=\"14\" x2=\"54\" y2=\"60\" />\n",
+       "  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"60\" />\n",
+       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"60\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,60.626017304593105 24.9485979497544,60.626017304593105\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"80.626017\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
+       "  <text x=\"164.948598\" y=\"37.787308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,37.787308)\">2950</text>\n",
+       "  <text x=\"7.474299\" y=\"73.151718\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,73.151718)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_column_stratosphere</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;</div><input id='attrs-953182eb-07fd-474d-b8b3-3976b4a3608c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-953182eb-07fd-474d-b8b3-3976b4a3608c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ea73119-0478-45df-83a5-7d25445bf27c' class='xr-var-data-in' type='checkbox'><label for='data-8ea73119-0478-45df-83a5-7d25445bf27c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>stratosphere nitrogen dioxide vertical column</dd><dt><span>units :</span></dt><dd>molecules/cm^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 174.43 MiB </td>\n",
+       "                        <td> 10.91 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 2950, 7750) </td>\n",
+       "                        <td> (1, 738, 1938) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"11\" x2=\"24\" y2=\"26\" />\n",
+       "  <line x1=\"10\" y1=\"22\" x2=\"24\" y2=\"37\" />\n",
+       "  <line x1=\"10\" y1=\"34\" x2=\"24\" y2=\"49\" />\n",
+       "  <line x1=\"10\" y1=\"45\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,60.626017304593105 10.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"40\" y1=\"0\" x2=\"54\" y2=\"14\" />\n",
+       "  <line x1=\"70\" y1=\"0\" x2=\"84\" y2=\"14\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"114\" y2=\"14\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"26\" x2=\"144\" y2=\"26\" />\n",
+       "  <line x1=\"24\" y1=\"37\" x2=\"144\" y2=\"37\" />\n",
+       "  <line x1=\"24\" y1=\"49\" x2=\"144\" y2=\"49\" />\n",
+       "  <line x1=\"24\" y1=\"60\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"54\" y1=\"14\" x2=\"54\" y2=\"60\" />\n",
+       "  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"60\" />\n",
+       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"60\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,60.626017304593105 24.9485979497544,60.626017304593105\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"80.626017\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
+       "  <text x=\"164.948598\" y=\"37.787308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,37.787308)\">2950</text>\n",
+       "  <text x=\"7.474299\" y=\"73.151718\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,73.151718)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>main_data_quality_flag</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 984, 2584), meta=np.ndarray&gt;</div><input id='attrs-d584e624-a6fa-438c-b0cc-7fef15316d7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d584e624-a6fa-438c-b0cc-7fef15316d7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c957554-6d13-415a-8021-b15a2bf19132' class='xr-var-data-in' type='checkbox'><label for='data-9c957554-6d13-415a-8021-b15a2bf19132' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>main data quality flag</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>2</dd><dt><span>flag_meanings :</span></dt><dd>normal suspicious bad</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 87.21 MiB </td>\n",
+       "                        <td> 9.70 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 2950, 7750) </td>\n",
+       "                        <td> (1, 984, 2584) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 9 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"15\" x2=\"24\" y2=\"30\" />\n",
+       "  <line x1=\"10\" y1=\"30\" x2=\"24\" y2=\"45\" />\n",
+       "  <line x1=\"10\" y1=\"45\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,60.626017304593105 10.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"50\" y1=\"0\" x2=\"64\" y2=\"14\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"104\" y2=\"14\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"30\" x2=\"144\" y2=\"30\" />\n",
+       "  <line x1=\"24\" y1=\"45\" x2=\"144\" y2=\"45\" />\n",
+       "  <line x1=\"24\" y1=\"60\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"64\" y1=\"14\" x2=\"64\" y2=\"60\" />\n",
+       "  <line x1=\"104\" y1=\"14\" x2=\"104\" y2=\"60\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,60.626017304593105 24.9485979497544,60.626017304593105\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"80.626017\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
+       "  <text x=\"164.948598\" y=\"37.787308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,37.787308)\">2950</text>\n",
+       "  <text x=\"7.474299\" y=\"73.151718\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,73.151718)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-bb6993d7-8fbd-4538-a83e-284f9b452c3a' class='xr-section-summary-in' type='checkbox'  ><label for='section-bb6993d7-8fbd-4538-a83e-284f9b452c3a' class='xr-section-summary' >Attributes: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>history :</span></dt><dd>2024-08-07T13:18:52Z: L2_regrid -v /tempo/nas0/sdpc/liveroot/temposdpc/ops4/etc/l3.cfg\n",
+       "</dd><dt><span>scan_num :</span></dt><dd>1</dd><dt><span>time_coverage_start :</span></dt><dd>2024-01-03T12:52:54Z</dd><dt><span>time_coverage_end :</span></dt><dd>2024-01-03T13:32:40Z</dd><dt><span>time_coverage_start_since_epoch :</span></dt><dd>1388321592.148252</dd><dt><span>time_coverage_end_since_epoch :</span></dt><dd>1388323978.7368417</dd><dt><span>product_type :</span></dt><dd>NO2</dd><dt><span>processing_level :</span></dt><dd>3</dd><dt><span>processing_version :</span></dt><dd>3</dd><dt><span>sdpc_version :</span></dt><dd>TEMPO_SDPC_v4.4.2</dd><dt><span>production_date_time :</span></dt><dd>2024-08-07T13:18:53Z</dd><dt><span>begin_date :</span></dt><dd>2024-01-03</dd><dt><span>begin_time :</span></dt><dd>12:52:54</dd><dt><span>end_date :</span></dt><dd>2024-01-03</dd><dt><span>end_time :</span></dt><dd>13:32:40</dd><dt><span>local_granule_id :</span></dt><dd>TEMPO_NO2_L3_V03_20240103T125254Z_S001.nc</dd><dt><span>version_id :</span></dt><dd>3</dd><dt><span>pge_version :</span></dt><dd>1.0.0</dd><dt><span>shortname :</span></dt><dd>TEMPO_NO2_L3</dd><dt><span>input_files :</span></dt><dd>[&#x27;TEMPO_NO2_L2_V03_20240103T125254Z_S001G01.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T125934Z_S001G02.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T130611Z_S001G03.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T131249Z_S001G04.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T131926Z_S001G05.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T132603Z_S001G06.nc&#x27;]</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((57.6000 -110.5000,53.5600 -108.3200,49.2200 -106.5200,44.3200 -104.9600,39.6000 -103.8000,33.9600 -102.7400,28.7400 -102.0000,22.8400 -101.3800,17.2600 -100.9600,17.2200 -85.2400,17.4800 -65.1200,21.9400 -64.1200,25.7600 -63.0400,29.6200 -61.7000,32.7800 -60.3800,36.3000 -58.6200,39.4000 -56.7600,42.1800 -54.7800,44.8400 -52.5400,47.7200 -49.6200,50.4000 -46.2800,52.7400 -42.6800,54.7200 -38.9200,56.3800 -35.0200,57.9600 -30.3200,59.1600 -25.6600,60.2400 -20.1000,61.2200 -20.0800,61.2000 -20.2600,61.4800 -20.2800,61.4400 -20.5400,62.8000 -20.5600,62.7200 -21.1000,63.5600 -21.1200,63.4600 -21.8400,63.7200 -21.9600,63.5600 -22.9800,63.4800 -24.4400,62.6600 -30.0600,61.5200 -38.4200,60.7600 -44.1800,59.9600 -51.1600,58.7800 -63.3000,58.2200 -71.1800,57.7800 -80.4600,57.6000 -90.7600,57.7600 -101.2800,58.2000 -110.5000,57.6000 -110.5000))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lon_min :</span></dt><dd>-110.5</dd><dt><span>geospatial_lon_max :</span></dt><dd>-20.080002</dd><dt><span>geospatial_lat_min :</span></dt><dd>17.22</dd><dt><span>geospatial_lat_max :</span></dt><dd>63.719997</dd><dt><span>time_reference :</span></dt><dd>1980-01-06T00:00:00Z</dd><dt><span>day_of_year :</span></dt><dd>3</dd><dt><span>project :</span></dt><dd>TEMPO</dd><dt><span>platform :</span></dt><dd>Intelsat 40e</dd><dt><span>source :</span></dt><dd>UV-VIS hyperspectral imaging</dd><dt><span>institution :</span></dt><dd>Smithsonian Astrophysical Observatory</dd><dt><span>creator_url :</span></dt><dd>http://tempo.si.edu</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>title :</span></dt><dd>TEMPO Level 3 nitrogen dioxide product</dd><dt><span>collection_shortname :</span></dt><dd>TEMPO_NO2_L3</dd><dt><span>collection_version :</span></dt><dd>1</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE&gt;ATMOSPHERE&gt;AIR QUALITY&gt;NITROGEN OXIDES, EARTH SCIENCE&gt;ATMOSPHERE&gt;ATMOSPHERIC CHEMISTRY&gt;NITROGEN COMPOUNDS&gt;NITROGEN DIOXIDE</dd><dt><span>summary :</span></dt><dd>Nitrogen dioxide Level 3 files provide trace gas information on a regular grid covering the TEMPO field of regard for nominal TEMPO observations. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle. The files are provided in netCDF4 format, and contain information on tropospheric, stratospheric and total nitrogen dioxide vertical columns, ancillary data used in air mass factor and stratospheric/tropospheric separation calculations, and retrieval quality flags. The re-gridding algorithm uses an area-weighted approach.</dd><dt><span>coremetadata :</span></dt><dd>\n",
+       "GROUP                  = INVENTORYMETADATA\n",
+       "  GROUPTYPE            = MASTERGROUP\n",
+       "\n",
+       "  GROUP                  = ECSDATAGRANULE\n",
+       "\n",
+       "    OBJECT                 = LOCALGRANULEID\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;TEMPO_NO2_L3_V03_20240103T125254Z_S001.nc&quot;\n",
+       "    END_OBJECT             = LOCALGRANULEID\n",
+       "\n",
+       "    OBJECT                 = LOCALVERSIONID\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = (&quot;RFC1321 MD5 = not yet calculated&quot;)\n",
+       "    END_OBJECT             = LOCALVERSIONID\n",
+       "\n",
+       "    OBJECT                 = PRODUCTIONDATETIME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;2024-08-07T13:18:53Z&quot;\n",
+       "    END_OBJECT             = PRODUCTIONDATETIME\n",
+       "\n",
+       "  END_GROUP              = ECSDATAGRANULE\n",
+       "\n",
+       "  GROUP                  = COLLECTIONDESCRIPTIONCLASS\n",
+       "\n",
+       "    OBJECT                 = SHORTNAME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;TEMPO_NO2_L3&quot;\n",
+       "    END_OBJECT             = SHORTNAME\n",
+       "\n",
+       "    OBJECT                 = VERSIONID\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = 3\n",
+       "    END_OBJECT             = VERSIONID\n",
+       "\n",
+       "  END_GROUP              = COLLECTIONDESCRIPTIONCLASS\n",
+       "\n",
+       "  GROUP                  = INPUTGRANULE\n",
+       "\n",
+       "    OBJECT                 = INPUTPOINTER\n",
+       "      NUM_VAL              = 6\n",
+       "      VALUE                = (&quot;TEMPO_NO2_L2_V03_20240103T125254Z_S001G01.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T125934Z_S001G02.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T130611Z_S001G03.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T131249Z_S001G04.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T131926Z_S001G05.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T132603Z_S001G06.nc&quot;)\n",
+       "    END_OBJECT             = INPUTPOINTER\n",
+       "\n",
+       "  END_GROUP              = INPUTGRANULE\n",
+       "\n",
+       "  GROUP                  = SPATIALDOMAINCONTAINER\n",
+       "\n",
+       "    GROUP                  = HORIZONTALSPATIALDOMAINCONTAINER\n",
+       "\n",
+       "      GROUP                  = GPOLYGON\n",
+       "\n",
+       "        OBJECT                 = GPOLYGONCONTAINER\n",
+       "          CLASS                = &quot;1&quot;\n",
+       "\n",
+       "          GROUP                  = GRINGPOINT\n",
+       "            CLASS                = &quot;1&quot;\n",
+       "\n",
+       "            OBJECT                 = GRINGPOINTLONGITUDE\n",
+       "              NUM_VAL              = 49\n",
+       "              CLASS                = &quot;1&quot;\n",
+       "              VALUE                = (-110.5, -108.32, -106.52, -104.96, -103.8, -102.74, -102, -101.38, -100.96, -85.24001, -65.12, -64.12, -63.04, -61.7, -60.38, -58.62, -56.76, -54.78, -52.54, -49.62, -46.28001, -42.68, -38.92, -35.02, -30.32001, -25.66, -20.10001, -20.08, -20.26001, -20.28, -20.54001, -20.56, -21.10001, -21.12001, -21.84, -21.96001, -22.98, -24.44, -30.06, -38.42, -44.18, -51.16, -63.3, -71.18, -80.46, -90.76, -101.28, -110.5, -110.5)\n",
+       "            END_OBJECT             = GRINGPOINTLONGITUDE\n",
+       "\n",
+       "            OBJECT                 = GRINGPOINTLATITUDE\n",
+       "              NUM_VAL              = 49\n",
+       "              CLASS                = &quot;1&quot;\n",
+       "              VALUE                = (57.6, 53.56, 49.22, 44.32, 39.6, 33.96, 28.74, 22.84, 17.26, 17.22, 17.48, 21.94, 25.76, 29.62, 32.78, 36.3, 39.4, 42.18, 44.84, 47.72, 50.4, 52.74, 54.72, 56.38, 57.96, 59.16, 60.24, 61.22, 61.2, 61.48, 61.44, 62.8, 62.72, 63.56, 63.46, 63.72, 63.56, 63.48, 62.66, 61.52, 60.76, 59.96, 58.78, 58.22, 57.78, 57.6, 57.76, 58.2, 57.6)\n",
+       "            END_OBJECT             = GRINGPOINTLATITUDE\n",
+       "\n",
+       "            OBJECT                 = GRINGPOINTSEQUENCENO\n",
+       "              NUM_VAL              = 49\n",
+       "              CLASS                = &quot;1&quot;\n",
+       "              VALUE                = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49)\n",
+       "            END_OBJECT             = GRINGPOINTSEQUENCENO\n",
+       "\n",
+       "          END_GROUP              = GRINGPOINT\n",
+       "\n",
+       "          GROUP                  = GRING\n",
+       "            CLASS                = &quot;1&quot;\n",
+       "\n",
+       "            OBJECT                 = EXCLUSIONGRINGFLAG\n",
+       "              NUM_VAL              = 1\n",
+       "              VALUE                = &quot;N&quot;\n",
+       "              CLASS                = &quot;1&quot;\n",
+       "            END_OBJECT             = EXCLUSIONGRINGFLAG\n",
+       "\n",
+       "          END_GROUP              = GRING\n",
+       "\n",
+       "        END_OBJECT             = GPOLYGONCONTAINER\n",
+       "\n",
+       "      END_GROUP              = GPOLYGON\n",
+       "\n",
+       "    END_GROUP              = HORIZONTALSPATIALDOMAINCONTAINER\n",
+       "\n",
+       "  END_GROUP              = SPATIALDOMAINCONTAINER\n",
+       "\n",
+       "  GROUP                  = RANGEDATETIME\n",
+       "\n",
+       "    OBJECT                 = RANGEENDINGDATE\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;2024-01-03&quot;\n",
+       "    END_OBJECT             = RANGEENDINGDATE\n",
+       "\n",
+       "    OBJECT                 = RANGEENDINGTIME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;13:32:40&quot;\n",
+       "    END_OBJECT             = RANGEENDINGTIME\n",
+       "\n",
+       "    OBJECT                 = RANGEBEGINNINGDATE\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;2024-01-03&quot;\n",
+       "    END_OBJECT             = RANGEBEGINNINGDATE\n",
+       "\n",
+       "    OBJECT                 = RANGEBEGINNINGTIME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;12:52:54&quot;\n",
+       "    END_OBJECT             = RANGEBEGINNINGTIME\n",
+       "\n",
+       "  END_GROUP              = RANGEDATETIME\n",
+       "\n",
+       "  GROUP                  = PGEVERSIONCLASS\n",
+       "\n",
+       "    OBJECT                 = PGEVERSION\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;1.0.0&quot;\n",
+       "    END_OBJECT             = PGEVERSION\n",
+       "\n",
+       "  END_GROUP              = PGEVERSIONCLASS\n",
+       "\n",
+       "END_GROUP              = INVENTORYMETADATA\n",
+       "\n",
+       "END\n",
+       "</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 732MB\n",
+       "Dimensions:                                  (latitude: 2950, longitude: 7750,\n",
+       "                                              time: 1)\n",
+       "Coordinates:\n",
+       "  * latitude                                 (latitude) float32 12kB 14.01 .....\n",
+       "  * longitude                                (longitude) float32 31kB -168.0 ...\n",
+       "  * time                                     (time) datetime64[ns] 8B 2024-01...\n",
+       "Data variables:\n",
+       "    weight                                   (latitude, longitude) float32 91MB dask.array<chunksize=(590, 1550), meta=np.ndarray>\n",
+       "    vertical_column_troposphere              (time, latitude, longitude) float64 183MB dask.array<chunksize=(1, 738, 1938), meta=np.ndarray>\n",
+       "    vertical_column_troposphere_uncertainty  (time, latitude, longitude) float64 183MB dask.array<chunksize=(1, 738, 1938), meta=np.ndarray>\n",
+       "    vertical_column_stratosphere             (time, latitude, longitude) float64 183MB dask.array<chunksize=(1, 738, 1938), meta=np.ndarray>\n",
+       "    main_data_quality_flag                   (time, latitude, longitude) float32 91MB dask.array<chunksize=(1, 984, 2584), meta=np.ndarray>\n",
+       "Attributes: (12/40)\n",
+       "    history:                          2024-08-07T13:18:52Z: L2_regrid -v /tem...\n",
+       "    scan_num:                         1\n",
+       "    time_coverage_start:              2024-01-03T12:52:54Z\n",
+       "    time_coverage_end:                2024-01-03T13:32:40Z\n",
+       "    time_coverage_start_since_epoch:  1388321592.148252\n",
+       "    time_coverage_end_since_epoch:    1388323978.7368417\n",
+       "    ...                               ...\n",
+       "    title:                            TEMPO Level 3 nitrogen dioxide product\n",
+       "    collection_shortname:             TEMPO_NO2_L3\n",
+       "    collection_version:               1\n",
+       "    keywords:                         EARTH SCIENCE>ATMOSPHERE>AIR QUALITY>NI...\n",
+       "    summary:                          Nitrogen dioxide Level 3 files provide ...\n",
+       "    coremetadata:                     \\nGROUP                  = INVENTORYMET..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tempo = {\n",
+    "    'xarray_open': 'dataset',\n",
+    "    'merge': ['/', '/product'],\n",
+    "}\n",
+    "ds = plan.open_dataset(0, open_method=tempo)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6cb81767-0d32-4328-8dcc-eaf1a64fab9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 20.8 s, sys: 5.08 s, total: 25.9 s\n",
+      "Wall time: 2min\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "res = pc.matchup(plan, \n",
+    "                 variables = [\"vertical_column_troposphere\"], \n",
+    "                 open_method=tempo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e0319b52-da04-4c17-8357-c61256741f27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>vertical_column_troposphere</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>44.355987</td>\n",
+       "      <td>-95.126999</td>\n",
+       "      <td>2024-01-14 16:55:19</td>\n",
+       "      <td>3.574082e+14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>44.355987</td>\n",
+       "      <td>-95.126999</td>\n",
+       "      <td>2024-01-14 16:55:19</td>\n",
+       "      <td>4.644599e+14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>44.355987</td>\n",
+       "      <td>-95.126999</td>\n",
+       "      <td>2024-01-14 16:55:19</td>\n",
+       "      <td>2.405348e+14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>42.919232</td>\n",
+       "      <td>-107.118997</td>\n",
+       "      <td>2024-02-07 19:04:58</td>\n",
+       "      <td>8.827827e+14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>42.919232</td>\n",
+       "      <td>-107.118997</td>\n",
+       "      <td>2024-02-07 19:04:58</td>\n",
+       "      <td>6.472115e+14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          lat         lon                time  vertical_column_troposphere\n",
+       "17  44.355987  -95.126999 2024-01-14 16:55:19                 3.574082e+14\n",
+       "18  44.355987  -95.126999 2024-01-14 16:55:19                 4.644599e+14\n",
+       "19  44.355987  -95.126999 2024-01-14 16:55:19                 2.405348e+14\n",
+       "20  42.919232 -107.118997 2024-02-07 19:04:58                 8.827827e+14\n",
+       "21  42.919232 -107.118997 2024-02-07 19:04:58                 6.472115e+14"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# full res shows more info about matchups, like the granule lat lon\n",
+    "res[['lat', 'lon', 'time', 'vertical_column_troposphere']].dropna(subset=['vertical_column_troposphere']).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accb1ce1-e75a-4030-bcbc-81597ba345ef",
+   "metadata": {},
+   "source": [
+    "### Plot our points with data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "386a0026-746c-427e-a542-29aca71d0016",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "open_method: {'xarray_open': 'dataset', 'merge': ['/', '/product'], 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds = plan.open_dataset(2, open_method=tempo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "253e9418-1dad-441c-96b2-47b84beb31b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'molecules/cm^2'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds[\"vertical_column_troposphere\"].units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "407c5482-1dc5-4a33-91ef-e51073ab8acc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "\n",
+    "fig = plt.figure(figsize=(10,8))\n",
+    "\n",
+    "ds_plot = ds.weight.coarsen(latitude=10, longitude=10, boundary=\"trim\").mean()\n",
+    "\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "\n",
+    "# plot xarray field\n",
+    "ds_plot.plot(\n",
+    "    ax=ax,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    cmap=\"viridis\",\n",
+    "    add_colorbar=True\n",
+    ")\n",
+    "\n",
+    "# coastlines\n",
+    "ax.coastlines(resolution=\"50m\", linewidth=1)\n",
+    "\n",
+    "# optional: borders\n",
+    "ax.add_feature(cfeature.BORDERS, linewidth=0.5)\n",
+    "\n",
+    "# plot dataframe points\n",
+    "ax.scatter(\n",
+    "    df[\"lon\"],\n",
+    "    df[\"lat\"],\n",
+    "    color=\"red\",\n",
+    "    s=10,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    label=\"points\"\n",
+    ")\n",
+    "\n",
+    "# zoom to North America\n",
+    "ax.set_extent([-170, -50, 10, 80], crs=ccrs.PlateCarree())\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8155c497-801f-48f4-babf-46c2cb3c7669",
+   "metadata": {},
+   "source": [
+    "## TEMPO_O3TOT_L2\n",
+    "\n",
+    "This is level 2 swath data. Let's take a look at how to get some matchups. The granule planning takes awhile so let's start with a few points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "5be7b2ec-4379-4a27-a094-d61909be5d39",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>land</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>32.033815</td>\n",
+       "      <td>-87.504205</td>\n",
+       "      <td>2024-03-12 19:18:24</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>36.649735</td>\n",
+       "      <td>-80.935069</td>\n",
+       "      <td>2024-04-24 22:48:36</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>29.592103</td>\n",
+       "      <td>-95.889008</td>\n",
+       "      <td>2024-03-30 03:06:23</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>37.789200</td>\n",
+       "      <td>-98.045855</td>\n",
+       "      <td>2024-01-26 18:04:05</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>44.349757</td>\n",
+       "      <td>-72.301856</td>\n",
+       "      <td>2024-06-13 10:50:15</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           lat        lon                time  land\n",
+       "22   32.033815 -87.504205 2024-03-12 19:18:24  True\n",
+       "127  36.649735 -80.935069 2024-04-24 22:48:36  True\n",
+       "138  29.592103 -95.889008 2024-03-30 03:06:23  True\n",
+       "159  37.789200 -98.045855 2024-01-26 18:04:05  True\n",
+       "161  44.349757 -72.301856 2024-06-13 10:50:15  True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df_points = pd.read_csv(\n",
+    "    \"fixtures/points_1000_usa.csv\",\n",
+    "    parse_dates=[\"time\"]\n",
+    ")\n",
+    "df = df_points[\n",
+    "    (df_points[\"time\"].dt.year == 2024) &\n",
+    "    (df_points[\"land\"] == True) &\n",
+    "    (df_points[\"lon\"] < -60) &\n",
+    "    (df_points[\"lon\"] > -100)\n",
+    "    \n",
+    "]\n",
+    "print(len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5852aa2a-07cf-4539-ba0f-dc57643322c2",
+   "metadata": {},
+   "source": [
+    "### Get the plan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "642aa215-2dcb-47f6-bdc4-5cecb59b6841",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plan: 15 points → 12 unique granule(s)\n",
+      "  Points with 0 matches : 9\n",
+      "  Points with >1 matches: 4\n",
+      "  Time buffer: 0 days 01:00:00\n",
+      "\n",
+      "First 1 point(s):\n",
+      "  [22] lat=32.0338, lon=-87.5042, time=2024-03-12 19:18:24: 1 match(es)\n",
+      "    → https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_O3TOT_L2_V03/2024.03.12/TEMPO_O3TOT_L2_V03_20240312T190833Z_S010G04.nc\n",
+      "CPU times: user 2.7 s, sys: 265 ms, total: 2.96 s\n",
+      "Wall time: 39.3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "import point_collocation as pc\n",
+    "short_name=\"TEMPO_O3TOT_L2\"\n",
+    "plan = pc.plan(\n",
+    "    df,\n",
+    "    data_source=\"earthaccess\",\n",
+    "    source_kwargs={\n",
+    "        \"short_name\": short_name,\n",
+    "        \"version\": \"V03\"\n",
+    "    },\n",
+    "    time_buffer=\"1h\"\n",
+    ")\n",
+    "plan.summary(n=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b7b5461-18d8-49f3-827a-cf4e34d7e592",
+   "metadata": {},
+   "source": [
+    "### Take a look at the granules\n",
+    "\n",
+    "These are grouped netcdfs and we need to figure out what groups we need and variable names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "dfe6c0fe-714f-4aab-b1b8-d3954ea0ceec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "open_method: {'xarray_open': 'datatree', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree&gt;\n",
+       "Group: /\n",
+       "│   Dimensions:      (xtrack: 2048, mirror_step: 131)\n",
+       "│   Coordinates:\n",
+       "│     * xtrack       (xtrack) int32 8kB 0 1 2 3 4 5 ... 2043 2044 2045 2046 2047\n",
+       "│     * mirror_step  (mirror_step) int32 524B 132 133 134 135 ... 259 260 261 262\n",
+       "│   Attributes: (12/34)\n",
+       "│       time_reference:                   1980-01-06T00:00:00Z\n",
+       "│       scan_num:                         1\n",
+       "│       granule_num:                      2\n",
+       "│       time_coverage_start:              2024-01-03T12:59:34Z\n",
+       "│       time_coverage_end:                2024-01-03T13:06:11Z\n",
+       "│       time_coverage_start_since_epoch:  1388321992.512726\n",
+       "│       ...                               ...\n",
+       "│       title:                            TEMPO Level 2 total ozone product\n",
+       "│       collection_shortname:             TEMPO_O3TOT_L2\n",
+       "│       collection_version:               1\n",
+       "│       keywords:                         EARTH SCIENCE&gt;ATMOSPHERE&gt;ATMOSPHERIC CH...\n",
+       "│       summary:                          Total ozone Level 2 files provide ozone...\n",
+       "│       coremetadata:                     \\nGROUP                  = INVENTORYMET...\n",
+       "├── Group: /product\n",
+       "│       Dimensions:               (mirror_step: 131, xtrack: 2048)\n",
+       "│       Data variables:\n",
+       "│           column_amount_o3      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           radiative_cloud_frac  (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           fc                    (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           quality_flag          (mirror_step, xtrack) int32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           o3_below_cloud        (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           so2_index             (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           uv_aerosol_index      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "├── Group: /geolocation\n",
+       "│       Dimensions:                 (mirror_step: 131, xtrack: 2048, corner: 4)\n",
+       "│       Coordinates:\n",
+       "│           time                    (mirror_step) datetime64[ns] 1kB dask.array&lt;chunksize=(131,), meta=np.ndarray&gt;\n",
+       "│           latitude                (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           longitude               (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│       Dimensions without coordinates: corner\n",
+       "│       Data variables:\n",
+       "│           latitude_bounds         (mirror_step, xtrack, corner) float32 4MB dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;\n",
+       "│           longitude_bounds        (mirror_step, xtrack, corner) float32 4MB dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;\n",
+       "│           solar_zenith_angle      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           solar_azimuth_angle     (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           viewing_zenith_angle    (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           viewing_azimuth_angle   (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           relative_azimuth_angle  (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "├── Group: /support_data\n",
+       "│       Dimensions:                        (mirror_step: 131, xtrack: 2048,\n",
+       "│                                           wavelength: 12, layer: 11)\n",
+       "│       Dimensions without coordinates: wavelength, layer\n",
+       "│       Data variables: (12/21)\n",
+       "│           ground_pixel_quality_flag      (mirror_step, xtrack) int32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           lut_wavelength                 (wavelength) float32 48B dask.array&lt;chunksize=(12,), meta=np.ndarray&gt;\n",
+       "│           cloud_pressure                 (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           terrain_pressure               (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           terrain_height                 (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           algorithm_flags                (mirror_step, xtrack) int32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           ...                             ...\n",
+       "│           step_2_N_value_residual        (mirror_step, xtrack, wavelength) float32 13MB dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;\n",
+       "│           ozone_sensitivity_ratio        (mirror_step, xtrack, wavelength) float32 13MB dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;\n",
+       "│           temp_sensitivity_ratio         (mirror_step, xtrack, wavelength) float32 13MB dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;\n",
+       "│           step1_o3                       (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           step2_o3                       (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "│           cal_adjustment                 (xtrack, wavelength) float32 98kB dask.array&lt;chunksize=(2048, 12), meta=np.ndarray&gt;\n",
+       "└── Group: /qa_statistics</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-832acc53-21d8-4132-89ee-561fa4bb9a25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-832acc53-21d8-4132-89ee-561fa4bb9a25' class='xr-section-summary' >Groups: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/product</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b3964da8-09d8-4482-89ad-667781ef9cf5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3964da8-09d8-4482-89ad-667781ef9cf5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>xtrack</span>: 2048</li><li><span class='xr-has-index'>mirror_step</span>: 131</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3afabbb9-5430-4313-816f-f9288125745e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3afabbb9-5430-4313-816f-f9288125745e' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>column_amount_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f4c97298-359a-4357-869f-4a62b8caf387' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4c97298-359a-4357-869f-4a62b8caf387' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372624f1-e685-499a-8bf1-499cdd827ffb' class='xr-var-data-in' type='checkbox'><label for='data-372624f1-e685-499a-8bf1-499cdd827ffb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>best total ozone solution</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>50.0</dd><dt><span>valid_max :</span></dt><dd>700.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radiative_cloud_frac</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-affed888-eac0-450c-8e34-fb7dc42d2b88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-affed888-eac0-450c-8e34-fb7dc42d2b88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b023b2b-7ad7-4d25-9287-a8520e6acb21' class='xr-var-data-in' type='checkbox'><label for='data-9b023b2b-7ad7-4d25-9287-a8520e6acb21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>cloud radiance fraction = fc*Ic331/Im331</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fc</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-1ca58e7a-0d19-4cb5-93f8-4c5368951449' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ca58e7a-0d19-4cb5-93f8-4c5368951449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90ed82c6-97c2-4e75-8abc-51e3ac185f77' class='xr-var-data-in' type='checkbox'><label for='data-90ed82c6-97c2-4e75-8abc-51e3ac185f77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective cloud fraction (mixed LER model)</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quality_flag</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-54f2234d-6221-41da-b182-6f3536de86a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54f2234d-6221-41da-b182-6f3536de86a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4bbd666-c0a3-4c3b-9dbf-5c33e5588b19' class='xr-var-data-in' type='checkbox'><label for='data-b4bbd666-c0a3-4c3b-9dbf-5c33e5588b19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>quality flags</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>65534</dd><dt><span>flag_meanings :</span></dt><dd>good_sample glint_contamination SZA&gt;84 360_residual&gt;threshold residual_at_unused_ozone_wavelenth&gt;4sigma SOI&gt;4sigma non-convergence abs(residual)&gt;16.0(fatal) row_anomaly_error missing_TEMPO_cloud_pressure geolocation_error SZA&gt;88 missing_input_radiance error_input_radiance warning_input_radiance missing_input_irradiance error_input_irradiance warning_input_irradiance</dd><dt><span>flag_values :</span></dt><dd>[    0     1     2     3     4     5     6     7     8   128   256   5121024  2048  4096  8192 16384 32768]</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> int32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3_below_cloud</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-7292a891-d58e-4ca7-b62a-8e9f4a360678' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7292a891-d58e-4ca7-b62a-8e9f4a360678' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe9dcf6f-796d-4c6f-8984-e8cfea5a8ab0' class='xr-var-data-in' type='checkbox'><label for='data-fe9dcf6f-796d-4c6f-8984-e8cfea5a8ab0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ozone below fractional cloud</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>100.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>so2_index</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-70960714-2037-457d-8565-f6830532b341' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70960714-2037-457d-8565-f6830532b341' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92b227ea-16e9-4251-92ef-ca27b0b74309' class='xr-var-data-in' type='checkbox'><label for='data-92b227ea-16e9-4251-92ef-ca27b0b74309' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>SO2 index</dd><dt><span>valid_min :</span></dt><dd>-300.0</dd><dt><span>valid_max :</span></dt><dd>300.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uv_aerosol_index</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-08513761-1e83-486d-8c67-2766ef462704' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08513761-1e83-486d-8c67-2766ef462704' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcf86f09-31e6-4380-ab37-4bbb3bdbbdc1' class='xr-var-data-in' type='checkbox'><label for='data-bcf86f09-31e6-4380-ab37-4bbb3bdbbdc1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>UV aerosol index</dd><dt><span>valid_min :</span></dt><dd>-30.0</dd><dt><span>valid_max :</span></dt><dd>30.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/geolocation</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b7d7286a-b6e6-4a08-9835-9d3969e90954' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b7d7286a-b6e6-4a08-9835-9d3969e90954' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>xtrack</span>: 2048</li><li><span class='xr-has-index'>mirror_step</span>: 131</li><li><span>corner</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5045796b-8dd1-422e-8631-3b7e1b6d3f04' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5045796b-8dd1-422e-8631-3b7e1b6d3f04' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(mirror_step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131,), meta=np.ndarray&gt;</div><input id='attrs-241ae507-d5d2-4a2b-8680-04c75a463bcf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-241ae507-d5d2-4a2b-8680-04c75a463bcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-808367e7-91e6-42d8-ac2c-0b77c27a7fc5' class='xr-var-data-in' type='checkbox'><label for='data-808367e7-91e6-42d8-ac2c-0b77c27a7fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>exposure start time</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10000000000.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 kiB </td><td> 1.02 kiB </td></tr><tr><th> Shape </th><td> (131,) </td><td> (131,) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> datetime64[ns] numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >131</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-84e25c40-9b09-4676-bb1b-8e3518e24c51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84e25c40-9b09-4676-bb1b-8e3518e24c51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2912a7b4-6ead-40ff-a8ca-552e051f6f8f' class='xr-var-data-in' type='checkbox'><label for='data-2912a7b4-6ead-40ff-a8ca-552e051f6f8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>comment :</span></dt><dd>latitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>bounds :</span></dt><dd>latitude_bounds</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-aa0c873a-1979-4fbc-be69-1811a572be7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa0c873a-1979-4fbc-be69-1811a572be7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-444b9a3c-a758-400d-baea-2e33646c4161' class='xr-var-data-in' type='checkbox'><label for='data-444b9a3c-a758-400d-baea-2e33646c4161' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>comment :</span></dt><dd>longitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>bounds :</span></dt><dd>longitude_bounds</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6cdc291e-d6b5-465a-bc5d-e3dc1cf4370c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6cdc291e-d6b5-465a-bc5d-e3dc1cf4370c' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>latitude_bounds</span></div><div class='xr-var-dims'>(mirror_step, xtrack, corner)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;</div><input id='attrs-571a42db-9f34-4d3c-9501-534a2ed6179f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-571a42db-9f34-4d3c-9501-534a2ed6179f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d89a65b4-0224-4b89-87e1-56d2b388eaa2' class='xr-var-data-in' type='checkbox'><label for='data-d89a65b4-0224-4b89-87e1-56d2b388eaa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>latitude at pixel corners (sw,se,ne,nw)</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 4.09 MiB </td><td> 4.09 MiB </td></tr><tr><th> Shape </th><td> (131, 2048, 4) </td><td> (131, 2048, 4) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_bounds</span></div><div class='xr-var-dims'>(mirror_step, xtrack, corner)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;</div><input id='attrs-68d90b8f-b4e4-446c-88af-8480817f608b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68d90b8f-b4e4-446c-88af-8480817f608b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27a89457-fdfb-4402-868a-0b8c9e4ad727' class='xr-var-data-in' type='checkbox'><label for='data-27a89457-fdfb-4402-868a-0b8c9e4ad727' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>longitude at pixel corners (sw,se,ne,nw)</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 4.09 MiB </td><td> 4.09 MiB </td></tr><tr><th> Shape </th><td> (131, 2048, 4) </td><td> (131, 2048, 4) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_zenith_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f4d1cb73-97db-4b9c-8e62-84dc777a11c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4d1cb73-97db-4b9c-8e62-84dc777a11c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2893211-d46f-402a-b9e9-befabbe48ed5' class='xr-var-data-in' type='checkbox'><label for='data-a2893211-d46f-402a-b9e9-befabbe48ed5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>solar zenith angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-78de3838-f903-452f-8fe7-87672433efc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78de3838-f903-452f-8fe7-87672433efc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a122d1a-aa2a-4e07-b6fa-2a80defb4d10' class='xr-var-data-in' type='checkbox'><label for='data-7a122d1a-aa2a-4e07-b6fa-2a80defb4d10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>solar azimuth angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_zenith_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-1341a0de-f0f3-472a-a790-5e2446bae017' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1341a0de-f0f3-472a-a790-5e2446bae017' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9abb0fb8-7874-496f-a9ef-dd34834b74b2' class='xr-var-data-in' type='checkbox'><label for='data-9abb0fb8-7874-496f-a9ef-dd34834b74b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>viewing zenith angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-14347bbb-1a32-45f0-89cf-268d21310683' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14347bbb-1a32-45f0-89cf-268d21310683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-992f8304-64c2-429e-9796-a349106df96b' class='xr-var-data-in' type='checkbox'><label for='data-992f8304-64c2-429e-9796-a349106df96b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>viewing azimuth angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-67814c9a-2d51-43f1-80c1-376f91e6578f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67814c9a-2d51-43f1-80c1-376f91e6578f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bdf8211-fe64-4945-a258-037f9b59f37c' class='xr-var-data-in' type='checkbox'><label for='data-2bdf8211-fe64-4945-a258-037f9b59f37c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>relative azimuth angle (sun + 180 - view)</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/support_data</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a306ffd2-4386-4589-8671-55382e912c0a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a306ffd2-4386-4589-8671-55382e912c0a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>xtrack</span>: 2048</li><li><span class='xr-has-index'>mirror_step</span>: 131</li><li><span>wavelength</span>: 12</li><li><span>layer</span>: 11</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d775754a-6ddd-4d71-a383-0b4a2d9e21ae' class='xr-section-summary-in' type='checkbox'  ><label for='section-d775754a-6ddd-4d71-a383-0b4a2d9e21ae' class='xr-section-summary' >Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ground_pixel_quality_flag</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-d3471317-5e7c-42f4-8ce3-c6593f9eb13a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3471317-5e7c-42f4-8ce3-c6593f9eb13a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fa998b3-0558-4a30-ad84-46ff04b8bc8f' class='xr-var-data-in' type='checkbox'><label for='data-3fa998b3-0558-4a30-ad84-46ff04b8bc8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ground pixel quality flag</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>65534</dd><dt><span>flag_meanings :</span></dt><dd>shallow_ocean land shallow_inland_water shoreline intermittent_water deep_inland_water continental_shelf_ocean deep_ocean land_water_error sun_glint_possibility solar_eclipse_possibility goelocation_error</dd><dt><span>flag_values :</span></dt><dd>[ 0  1  2  3  4  5  6  7 15 16 32 64]</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> int32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lut_wavelength</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12,), meta=np.ndarray&gt;</div><input id='attrs-eecb0384-ecd8-4aa4-9b2b-cbe49eb10992' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eecb0384-ecd8-4aa4-9b2b-cbe49eb10992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e7788c5-5e8e-4c3c-a593-fe4a0fc86e03' class='xr-var-data-in' type='checkbox'><label for='data-9e7788c5-5e8e-4c3c-a593-fe4a0fc86e03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>lookup table wavelength</dd><dt><span>units :</span></dt><dd>nm</dd><dt><span>valid_min :</span></dt><dd>300.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48 B </td><td> 48 B </td></tr><tr><th> Shape </th><td> (12,) </td><td> (12,) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"87\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"37\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,37.56358655585696 0.0,37.56358655585696\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"57.563587\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"140.000000\" y=\"18.781793\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,18.781793)\">1</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cloud_pressure</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-269968d4-0991-4e03-9050-546648b18f60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-269968d4-0991-4e03-9050-546648b18f60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc312388-bc02-431c-8e68-35aeac87cb53' class='xr-var-data-in' type='checkbox'><label for='data-bc312388-bc02-431c-8e68-35aeac87cb53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective cloud pressure</dd><dt><span>units :</span></dt><dd>hPA</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1013.25</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>terrain_pressure</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-571e892c-6318-42be-a3ed-0f42df966cd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-571e892c-6318-42be-a3ed-0f42df966cd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-509edc13-634f-421b-bd3d-90220dbe3019' class='xr-var-data-in' type='checkbox'><label for='data-509edc13-634f-421b-bd3d-90220dbe3019' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>terrain pressure</dd><dt><span>units :</span></dt><dd>hPA</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1013.25</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>terrain_height</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f2e3990c-ebea-4876-bb7e-342d756409f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2e3990c-ebea-4876-bb7e-342d756409f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cab51cf2-b80e-42c7-86f0-09bbcf07d6b9' class='xr-var-data-in' type='checkbox'><label for='data-cab51cf2-b80e-42c7-86f0-09bbcf07d6b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>terrain height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-200</dd><dt><span>valid_max :</span></dt><dd>10000</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>algorithm_flags</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-835ab3ed-6fd9-419d-89ce-b7345bdfb137' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-835ab3ed-6fd9-419d-89ce-b7345bdfb137' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-138145a1-23f9-4daa-9741-bb0c6e359a66' class='xr-var-data-in' type='checkbox'><label for='data-138145a1-23f9-4daa-9741-bb0c6e359a66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>algorithm flags</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>13</dd><dt><span>flag_meanings :</span></dt><dd>skipped standard adjusted_for_profile_shape based_on_C-pair(331_and_360 nm)</dd><dt><span>flag_values :</span></dt><dd>[ 0  1  2  3 10]</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> int32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_priori_layer_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack, layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 11), meta=np.ndarray&gt;</div><input id='attrs-04193c8c-8be7-4a76-8f7d-eb210b5bb399' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04193c8c-8be7-4a76-8f7d-eb210b5bb399' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02ef0393-9a99-4836-84b0-f31f7e8c0e5b' class='xr-var-data-in' type='checkbox'><label for='data-02ef0393-9a99-4836-84b0-f31f7e8c0e5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>a priori ozone profile</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>125.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 11.26 MiB </td><td> 5.50 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 11) </td><td> (1, 128, 11) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >11</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radiance_bpix_flag_accepted</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f65eb6c4-1e9c-4282-a1a9-0facb52c867a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f65eb6c4-1e9c-4282-a1a9-0facb52c867a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-190b58ef-0a5c-4419-afd4-5491f7148729' class='xr-var-data-in' type='checkbox'><label for='data-190b58ef-0a5c-4419-afd4-5491f7148729' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>radiance bad pixel flag accepted</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>65534</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> int32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layer_efficiency</span></div><div class='xr-var-dims'>(mirror_step, xtrack, layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 11), meta=np.ndarray&gt;</div><input id='attrs-026e0b6f-1aac-414a-a2a2-e8206d3f2eba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-026e0b6f-1aac-414a-a2a2-e8206d3f2eba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde64a96-b58b-497c-9d74-dacf6919557c' class='xr-var-data-in' type='checkbox'><label for='data-dde64a96-b58b-497c-9d74-dacf6919557c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>algorithmic layer efficiency</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 11.26 MiB </td><td> 5.50 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 11) </td><td> (1, 128, 11) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >11</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dNdR</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-526b5929-89f7-4635-a34f-4669a2c687cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-526b5929-89f7-4635-a34f-4669a2c687cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d69ac6f7-cb94-44c2-8516-1647c221b49d' class='xr-var-data-in' type='checkbox'><label for='data-d69ac6f7-cb94-44c2-8516-1647c221b49d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>reflectivity sensitivity ratio</dd><dt><span>valid_min :</span></dt><dd>-200.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_value</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-f2259922-db3d-44b2-811d-e3847be1db86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2259922-db3d-44b2-811d-e3847be1db86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35660612-d5b4-41b3-ac28-2190e9e2a6ba' class='xr-var-data-in' type='checkbox'><label for='data-35660612-d5b4-41b3-ac28-2190e9e2a6ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>measured N-value</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>600.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_reflectivity_at_331nm</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f52ddb03-a381-4c94-b42d-5523e231c929' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f52ddb03-a381-4c94-b42d-5523e231c929' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6d681b7-cc95-4b1b-9418-fe92cfb5bb1d' class='xr-var-data-in' type='checkbox'><label for='data-c6d681b7-cc95-4b1b-9418-fe92cfb5bb1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective surface reflectivity at 331 nm</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>-15.0</dd><dt><span>valid_max :</span></dt><dd>115.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_reflectivity_at_360nm</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-140e0c70-27ba-4072-936e-19678c61fbda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-140e0c70-27ba-4072-936e-19678c61fbda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c59565e-94b6-4dfb-9620-a7603a73bfab' class='xr-var-data-in' type='checkbox'><label for='data-4c59565e-94b6-4dfb-9620-a7603a73bfab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective surface reflectivity at 360 nm</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>-15.0</dd><dt><span>valid_max :</span></dt><dd>115.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_value_residual</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-88fbf7f5-8f75-4f78-8326-b0403a2d33fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88fbf7f5-8f75-4f78-8326-b0403a2d33fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcddd858-e625-47e4-bee1-6dd88a10b88e' class='xr-var-data-in' type='checkbox'><label for='data-dcddd858-e625-47e4-bee1-6dd88a10b88e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>N-value residual</dd><dt><span>valid_min :</span></dt><dd>-32.0</dd><dt><span>valid_max :</span></dt><dd>32.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_1_N_value_residual</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-f293121f-6477-4d2f-81a1-13135a8f6ca9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f293121f-6477-4d2f-81a1-13135a8f6ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93b902da-2516-454c-a154-6cb2c20aeae1' class='xr-var-data-in' type='checkbox'><label for='data-93b902da-2516-454c-a154-6cb2c20aeae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>step 1 N-value residual</dd><dt><span>valid_min :</span></dt><dd>-32.0</dd><dt><span>valid_max :</span></dt><dd>32.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_2_N_value_residual</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-d2ff2930-7a42-4689-9de7-2ff1f33f977c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2ff2930-7a42-4689-9de7-2ff1f33f977c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4fbf506-e096-4b57-9cd6-9d29dc058806' class='xr-var-data-in' type='checkbox'><label for='data-e4fbf506-e096-4b57-9cd6-9d29dc058806' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>step 2 N-value residual</dd><dt><span>valid_min :</span></dt><dd>-32.0</dd><dt><span>valid_max :</span></dt><dd>32.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ozone_sensitivity_ratio</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-5a258cad-8749-4abb-b219-2230528cd101' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a258cad-8749-4abb-b219-2230528cd101' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc9d05d4-f4ab-4c87-b875-8f947ced008c' class='xr-var-data-in' type='checkbox'><label for='data-dc9d05d4-f4ab-4c87-b875-8f947ced008c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ozone sensitivity ratio, dN/dOmega</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_sensitivity_ratio</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-9b132247-9268-431c-b962-0d77f37f0eaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b132247-9268-431c-b962-0d77f37f0eaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3cb390f-fbb1-416e-ace5-9e759c1083d2' class='xr-var-data-in' type='checkbox'><label for='data-c3cb390f-fbb1-416e-ace5-9e759c1083d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ozone weighted temperature sensitivity ratio, dN/dT</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step1_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-495f177e-f5b6-477b-b160-65f5e5c11184' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-495f177e-f5b6-477b-b160-65f5e5c11184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10d57ea8-70ed-4dac-9585-44549de7923e' class='xr-var-data-in' type='checkbox'><label for='data-10d57ea8-70ed-4dac-9585-44549de7923e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>step 1 ozone solution</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>50.0</dd><dt><span>valid_max :</span></dt><dd>700.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step2_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-af1fa7d0-47d7-4341-81fa-e43736cd14ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af1fa7d0-47d7-4341-81fa-e43736cd14ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07cf6150-815a-46ec-8e73-d648f2455300' class='xr-var-data-in' type='checkbox'><label for='data-07cf6150-815a-46ec-8e73-d648f2455300' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>step 2 ozone solution</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>50.0</dd><dt><span>valid_max :</span></dt><dd>700.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cal_adjustment</span></div><div class='xr-var-dims'>(xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2048, 12), meta=np.ndarray&gt;</div><input id='attrs-65d57f4f-28a8-4bff-aec3-2afe72f238e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65d57f4f-28a8-4bff-aec3-2afe72f238e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b92213de-158b-4a6b-9eea-639263641944' class='xr-var-data-in' type='checkbox'><label for='data-b92213de-158b-4a6b-9eea-639263641944' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>calibration adjustment</dd><dt><span>valid_min :</span></dt><dd>-10.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 96.00 kiB </td><td> 96.00 kiB </td></tr><tr><th> Shape </th><td> (2048, 12) </td><td> (2048, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">2048</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/qa_statistics</div></div><ul class='xr-sections'></ul></div></div></div></div></li><li class='xr-section-item'><input id='section-1192019d-018f-4c6a-b6c4-56eb5d62ab1c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1192019d-018f-4c6a-b6c4-56eb5d62ab1c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>xtrack</span>: 2048</li><li><span class='xr-has-index'>mirror_step</span>: 131</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3981dd64-7d29-4081-a3ee-f65263b260ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3981dd64-7d29-4081-a3ee-f65263b260ab' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xtrack</span></div><div class='xr-var-dims'>(xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2044 2045 2046 2047</div><input id='attrs-db1061be-6518-4271-877a-5f51706af7f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db1061be-6518-4271-877a-5f51706af7f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35bf2a81-7c04-4fe7-a694-10da111b8191' class='xr-var-data-in' type='checkbox'><label for='data-35bf2a81-7c04-4fe7-a694-10da111b8191' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pixel index along slit</dd></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2045, 2046, 2047], shape=(2048,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mirror_step</span></div><div class='xr-var-dims'>(mirror_step)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>132 133 134 135 ... 259 260 261 262</div><input id='attrs-49f7dcef-8715-4d7a-bf09-04b73c4c8255' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49f7dcef-8715-4d7a-bf09-04b73c4c8255' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a00a3dca-25ce-4594-aea3-b85c8ee9eb68' class='xr-var-data-in' type='checkbox'><label for='data-a00a3dca-25ce-4594-aea3-b85c8ee9eb68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>scan mirror position index</dd></dl></div><div class='xr-var-data'><pre>array([132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,\n",
+       "       146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
+       "       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173,\n",
+       "       174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187,\n",
+       "       188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201,\n",
+       "       202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215,\n",
+       "       216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,\n",
+       "       230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243,\n",
+       "       244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,\n",
+       "       258, 259, 260, 261, 262], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd7104cd-60a1-4eb0-954b-a6934592c388' class='xr-section-summary-in' type='checkbox'  ><label for='section-bd7104cd-60a1-4eb0-954b-a6934592c388' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>time_reference :</span></dt><dd>1980-01-06T00:00:00Z</dd><dt><span>scan_num :</span></dt><dd>1</dd><dt><span>granule_num :</span></dt><dd>2</dd><dt><span>time_coverage_start :</span></dt><dd>2024-01-03T12:59:34Z</dd><dt><span>time_coverage_end :</span></dt><dd>2024-01-03T13:06:11Z</dd><dt><span>time_coverage_start_since_epoch :</span></dt><dd>1388321992.512726</dd><dt><span>time_coverage_end_since_epoch :</span></dt><dd>1388322389.4111006</dd><dt><span>product_type :</span></dt><dd>O3TOT</dd><dt><span>processing_level :</span></dt><dd>2</dd><dt><span>processing_version :</span></dt><dd>3</dd><dt><span>sdpc_version :</span></dt><dd>TEMPO_SDPC_v4.4.2</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((58.7460 -63.7219,54.9606 -66.7865,50.7340 -69.3919,46.0275 -71.6021,41.2245 -73.3336,35.7360 -74.8531,29.8413 -76.0882,23.4120 -77.0890,17.2992 -77.7607,17.3834 -71.6776,21.3196 -71.0525,25.0933 -70.3146,28.6897 -69.4649,32.2082 -68.4707,35.5012 -67.3664,38.6396 -66.1259,41.6019 -64.7509,44.3806 -63.2414,46.9641 -61.6037,49.3638 -59.8332,51.7108 -57.8152,53.7180 -55.8028,55.6668 -53.5377,57.4343 -51.1466,59.1001 -48.5181,60.5875 -45.7741,59.5539 -55.2095,58.7763 -63.6414,58.7460 -63.7219))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lon_min :</span></dt><dd>-77.76071</dd><dt><span>geospatial_lon_max :</span></dt><dd>-45.774143</dd><dt><span>geospatial_lat_min :</span></dt><dd>17.299183</dd><dt><span>geospatial_lat_max :</span></dt><dd>60.587494</dd><dt><span>input_files :</span></dt><dd>TEMPO_RAD_L1_V03_20240103T125934Z_S001G02.nc, TEMPO_IRR_L1_V03_20231228T053116Z.nc, TEMPO_CLDO4_L2_V03_20240103T125934Z_S001G02.nc</dd><dt><span>local_granule_id :</span></dt><dd>TEMPO_O3TOT_L2_V03_20240103T125934Z_S001G02.nc</dd><dt><span>version_id :</span></dt><dd>3</dd><dt><span>production_date_time :</span></dt><dd>2024-08-07T08:06:32.318 UTC-0400</dd><dt><span>day_of_year :</span></dt><dd>3</dd><dt><span>project :</span></dt><dd>TEMPO</dd><dt><span>platform :</span></dt><dd>Intelsat 40e</dd><dt><span>source :</span></dt><dd>UV-VIS hyperspectral imaging</dd><dt><span>institution :</span></dt><dd>Smithsonian Astrophysical Observatory</dd><dt><span>creator_url :</span></dt><dd>http://tempo.si.edu</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>title :</span></dt><dd>TEMPO Level 2 total ozone product</dd><dt><span>collection_shortname :</span></dt><dd>TEMPO_O3TOT_L2</dd><dt><span>collection_version :</span></dt><dd>1</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE&gt;ATMOSPHERE&gt;ATMOSPHERIC CHEMISTRY&gt;OXYGEN COMPOUNDS&gt;OZONE</dd><dt><span>summary :</span></dt><dd>Total ozone Level 2 files provide ozone information at TEMPO’s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on total column ozone and some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The retrieval is based on the OMI TOMS V8.5 algorithm adapted for TEMPO. For further details, please refer to the ATBD.</dd><dt><span>coremetadata :</span></dt><dd>\n",
+       "GROUP                  = INVENTORYMETADATA\n",
+       "  GROUPTYPE            = MASTERGROUP\n",
+       "\n",
+       "  GROUP                  = ECSDATAGRANULE\n",
+       "\n",
+       "    OBJECT                 = LOCALGRANULEID\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;TEMPO_O3TOT_L2_V03_20240103T125934Z_S001G02.nc&quot;\n",
+       "    END_OBJECT             = LOCALGRANULEID\n",
+       "\n",
+       "    OBJECT                 = LOCALVERSIONID\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = (&quot;RFC1321 MD5 = not yet calculated&quot;)\n",
+       "    END_OBJECT             = LOCALVERSIONID\n",
+       "\n",
+       "    OBJECT                 = PRODUCTIONDATETIME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;2024-08-07T12:06:32.000Z&quot;\n",
+       "    END_OBJECT             = PRODUCTIONDATETIME\n",
+       "\n",
+       "  END_GROUP              = ECSDATAGRANULE\n",
+       "\n",
+       "  GROUP                  = COLLECTIONDESCRIPTIONCLASS\n",
+       "\n",
+       "    OBJECT                 = SHORTNAME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;TEMPO_O3TOT_L2&quot;\n",
+       "    END_OBJECT             = SHORTNAME\n",
+       "\n",
+       "    OBJECT                 = VERSIONID\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = (3)\n",
+       "    END_OBJECT             = VERSIONID\n",
+       "\n",
+       "  END_GROUP              = COLLECTIONDESCRIPTIONCLASS\n",
+       "\n",
+       "  GROUP                  = INPUTGRANULE\n",
+       "\n",
+       "    OBJECT                 = INPUTPOINTER\n",
+       "      NUM_VAL              = 3\n",
+       "      VALUE                = (&quot;TEMPO_RAD_L1_V03_20240103T125934Z_S001G02.nc&quot;, &quot;TEMPO_IRR_L1_V03_20231228T053116Z.nc&quot;, &quot;TEMPO_CLDO4_L2_V03_20240103T125934Z_S001G02.nc&quot;)\n",
+       "    END_OBJECT             = INPUTPOINTER\n",
+       "\n",
+       "  END_GROUP              = INPUTGRANULE\n",
+       "\n",
+       "  GROUP                  = SPATIALDOMAINCONTAINER\n",
+       "\n",
+       "    GROUP                  = HORIZONTALSPATIALDOMAINCONTAINER\n",
+       "\n",
+       "      GROUP                  = GPOLYGON\n",
+       "\n",
+       "        OBJECT                 = GPOLYGONCONTAINER\n",
+       "          CLASS                = &quot;1&quot;\n",
+       "\n",
+       "          GROUP                  = GRINGPOINT\n",
+       "            CLASS                = &quot;1&quot;\n",
+       "\n",
+       "            OBJECT                 = GRINGPOINTLONGITUDE\n",
+       "              NUM_VAL              = 29\n",
+       "              CLASS                = &quot;1&quot;\n",
+       "              VALUE                = (-63.7218704223633, -66.7865447998047, -69.3919067382812, -71.6020584106445, -73.3336029052734, -74.8530654907227, -76.0881729125977, -77.0889892578125, -77.7607116699219, -71.6775817871094, -71.0524597167969, -70.3145980834961, -69.4649047851562, -68.470703125, \n",
+       "                                      -67.3664245605469, -66.1259002685547, -64.7509384155273, -63.2413864135742, -61.6036949157715, -59.8332366943359, -57.8151702880859, -55.8027954101562, -53.5377426147461, -51.1465682983398, -48.5181427001953, -45.7741432189941, -55.2095375061035, \n",
+       "                                      -63.6414031982422, -63.7218704223633)\n",
+       "            END_OBJECT             = GRINGPOINTLONGITUDE\n",
+       "\n",
+       "            OBJECT                 = GRINGPOINTLATITUDE\n",
+       "              NUM_VAL              = 29\n",
+       "              CLASS                = &quot;1&quot;\n",
+       "              VALUE                = (58.7460250854492, 54.9606132507324, 50.733959197998, 46.0275497436523, 41.2245101928711, 35.7360458374023, 29.8412837982178, 23.4120216369629, 17.2991828918457, 17.3833675384521, 21.3195819854736, 25.0932579040527, 28.6896781921387, 32.2081680297852, \n",
+       "                                      35.5011901855469, 38.6395874023438, 41.6018981933594, 44.3806228637695, 46.9640960693359, 49.3638229370117, 51.710807800293, 53.7180252075195, 55.6668243408203, 57.4343490600586, 59.1000556945801, 60.5874938964844, 59.5538940429688, 58.7763061523438, \n",
+       "                                      58.7460250854492)\n",
+       "            END_OBJECT             = GRINGPOINTLATITUDE\n",
+       "\n",
+       "            OBJECT                 = GRINGPOINTSEQUENCENO\n",
+       "              NUM_VAL              = 29\n",
+       "              CLASS                = &quot;1&quot;\n",
+       "              VALUE                = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29)\n",
+       "            END_OBJECT             = GRINGPOINTSEQUENCENO\n",
+       "\n",
+       "          END_GROUP              = GRINGPOINT\n",
+       "\n",
+       "          GROUP                  = GRING\n",
+       "            CLASS                = &quot;1&quot;\n",
+       "\n",
+       "            OBJECT                 = EXCLUSIONGRINGFLAG\n",
+       "              NUM_VAL              = 1\n",
+       "              VALUE                = &quot;N&quot;\n",
+       "              CLASS                = &quot;1&quot;\n",
+       "            END_OBJECT             = EXCLUSIONGRINGFLAG\n",
+       "\n",
+       "          END_GROUP              = GRING\n",
+       "\n",
+       "        END_OBJECT             = GPOLYGONCONTAINER\n",
+       "\n",
+       "      END_GROUP              = GPOLYGON\n",
+       "\n",
+       "    END_GROUP              = HORIZONTALSPATIALDOMAINCONTAINER\n",
+       "\n",
+       "  END_GROUP              = SPATIALDOMAINCONTAINER\n",
+       "\n",
+       "  GROUP                  = RANGEDATETIME\n",
+       "\n",
+       "    OBJECT                 = RANGEENDINGDATE\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;2024-01-03&quot;\n",
+       "    END_OBJECT             = RANGEENDINGDATE\n",
+       "\n",
+       "    OBJECT                 = RANGEENDINGTIME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;13:06:11&quot;\n",
+       "    END_OBJECT             = RANGEENDINGTIME\n",
+       "\n",
+       "    OBJECT                 = RANGEBEGINNINGDATE\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;2024-01-03&quot;\n",
+       "    END_OBJECT             = RANGEBEGINNINGDATE\n",
+       "\n",
+       "    OBJECT                 = RANGEBEGINNINGTIME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;12:59:34&quot;\n",
+       "    END_OBJECT             = RANGEBEGINNINGTIME\n",
+       "\n",
+       "  END_GROUP              = RANGEDATETIME\n",
+       "\n",
+       "  GROUP                  = PGEVERSIONCLASS\n",
+       "\n",
+       "    OBJECT                 = PGEVERSION\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = (&quot;2.2.1&quot;)\n",
+       "    END_OBJECT             = PGEVERSION\n",
+       "\n",
+       "  END_GROUP              = PGEVERSIONCLASS\n",
+       "\n",
+       "END_GROUP              = INVENTORYMETADATA\n",
+       "\n",
+       "END\n",
+       "</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataTree>\n",
+       "Group: /\n",
+       "│   Dimensions:      (xtrack: 2048, mirror_step: 131)\n",
+       "│   Coordinates:\n",
+       "│     * xtrack       (xtrack) int32 8kB 0 1 2 3 4 5 ... 2043 2044 2045 2046 2047\n",
+       "│     * mirror_step  (mirror_step) int32 524B 132 133 134 135 ... 259 260 261 262\n",
+       "│   Attributes: (12/34)\n",
+       "│       time_reference:                   1980-01-06T00:00:00Z\n",
+       "│       scan_num:                         1\n",
+       "│       granule_num:                      2\n",
+       "│       time_coverage_start:              2024-01-03T12:59:34Z\n",
+       "│       time_coverage_end:                2024-01-03T13:06:11Z\n",
+       "│       time_coverage_start_since_epoch:  1388321992.512726\n",
+       "│       ...                               ...\n",
+       "│       title:                            TEMPO Level 2 total ozone product\n",
+       "│       collection_shortname:             TEMPO_O3TOT_L2\n",
+       "│       collection_version:               1\n",
+       "│       keywords:                         EARTH SCIENCE>ATMOSPHERE>ATMOSPHERIC CH...\n",
+       "│       summary:                          Total ozone Level 2 files provide ozone...\n",
+       "│       coremetadata:                     \\nGROUP                  = INVENTORYMET...\n",
+       "├── Group: /product\n",
+       "│       Dimensions:               (mirror_step: 131, xtrack: 2048)\n",
+       "│       Data variables:\n",
+       "│           column_amount_o3      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           radiative_cloud_frac  (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           fc                    (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           quality_flag          (mirror_step, xtrack) int32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           o3_below_cloud        (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           so2_index             (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           uv_aerosol_index      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "├── Group: /geolocation\n",
+       "│       Dimensions:                 (mirror_step: 131, xtrack: 2048, corner: 4)\n",
+       "│       Coordinates:\n",
+       "│           time                    (mirror_step) datetime64[ns] 1kB dask.array<chunksize=(131,), meta=np.ndarray>\n",
+       "│           latitude                (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           longitude               (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│       Dimensions without coordinates: corner\n",
+       "│       Data variables:\n",
+       "│           latitude_bounds         (mirror_step, xtrack, corner) float32 4MB dask.array<chunksize=(131, 2048, 4), meta=np.ndarray>\n",
+       "│           longitude_bounds        (mirror_step, xtrack, corner) float32 4MB dask.array<chunksize=(131, 2048, 4), meta=np.ndarray>\n",
+       "│           solar_zenith_angle      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           solar_azimuth_angle     (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           viewing_zenith_angle    (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           viewing_azimuth_angle   (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           relative_azimuth_angle  (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "├── Group: /support_data\n",
+       "│       Dimensions:                        (mirror_step: 131, xtrack: 2048,\n",
+       "│                                           wavelength: 12, layer: 11)\n",
+       "│       Dimensions without coordinates: wavelength, layer\n",
+       "│       Data variables: (12/21)\n",
+       "│           ground_pixel_quality_flag      (mirror_step, xtrack) int32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           lut_wavelength                 (wavelength) float32 48B dask.array<chunksize=(12,), meta=np.ndarray>\n",
+       "│           cloud_pressure                 (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           terrain_pressure               (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           terrain_height                 (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           algorithm_flags                (mirror_step, xtrack) int32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           ...                             ...\n",
+       "│           step_2_N_value_residual        (mirror_step, xtrack, wavelength) float32 13MB dask.array<chunksize=(1, 128, 12), meta=np.ndarray>\n",
+       "│           ozone_sensitivity_ratio        (mirror_step, xtrack, wavelength) float32 13MB dask.array<chunksize=(1, 128, 12), meta=np.ndarray>\n",
+       "│           temp_sensitivity_ratio         (mirror_step, xtrack, wavelength) float32 13MB dask.array<chunksize=(1, 128, 12), meta=np.ndarray>\n",
+       "│           step1_o3                       (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           step2_o3                       (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "│           cal_adjustment                 (xtrack, wavelength) float32 98kB dask.array<chunksize=(2048, 12), meta=np.ndarray>\n",
+       "└── Group: /qa_statistics"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = plan.open_dataset(0, open_method=\"datatree\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fca64cc-cf4f-4d14-8975-db386ff7844e",
+   "metadata": {},
+   "source": [
+    "### Ok now we know the groups\n",
+    "\n",
+    "We will make a open_method dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "284f0557-d4cd-4820-a56e-bd653928b40a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "open_method: {'xarray_open': 'dataset', 'merge': ['/product', '/geolocation'], 'coords': {'lat': 'latitude', 'lon': 'longitude'}, 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 24MB\n",
+       "Dimensions:                 (mirror_step: 131, xtrack: 2048, corner: 4)\n",
+       "Coordinates:\n",
+       "    time                    (mirror_step) datetime64[ns] 1kB dask.array&lt;chunksize=(131,), meta=np.ndarray&gt;\n",
+       "    latitude                (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    longitude               (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: mirror_step, xtrack, corner\n",
+       "Data variables: (12/14)\n",
+       "    column_amount_o3        (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    radiative_cloud_frac    (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    fc                      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    quality_flag            (mirror_step, xtrack) int32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    o3_below_cloud          (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    so2_index               (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    ...                      ...\n",
+       "    longitude_bounds        (mirror_step, xtrack, corner) float32 4MB dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;\n",
+       "    solar_zenith_angle      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    solar_azimuth_angle     (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    viewing_zenith_angle    (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    viewing_azimuth_angle   (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
+       "    relative_azimuth_angle  (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0a448c48-abee-4677-85c2-806aa1e7c574' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a448c48-abee-4677-85c2-806aa1e7c574' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>mirror_step</span>: 131</li><li><span>xtrack</span>: 2048</li><li><span>corner</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a00c09c9-2a55-415a-9f5e-698faea00109' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a00c09c9-2a55-415a-9f5e-698faea00109' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(mirror_step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131,), meta=np.ndarray&gt;</div><input id='attrs-c4a9f7e1-fe67-49f0-b919-29613c849e9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4a9f7e1-fe67-49f0-b919-29613c849e9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f317f95c-a5a8-4ecf-bdf9-45529e80599a' class='xr-var-data-in' type='checkbox'><label for='data-f317f95c-a5a8-4ecf-bdf9-45529e80599a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>exposure start time</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 kiB </td>\n",
+       "                        <td> 1.02 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131,) </td>\n",
+       "                        <td> (131,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >131</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-1940561c-8259-484b-83b0-273279db409f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1940561c-8259-484b-83b0-273279db409f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e3552a7-bf0a-47f9-babd-73b629628c51' class='xr-var-data-in' type='checkbox'><label for='data-1e3552a7-bf0a-47f9-babd-73b629628c51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>comment :</span></dt><dd>latitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>bounds :</span></dt><dd>latitude_bounds</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-8471be32-ef1e-4122-ae51-c24974b65727' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8471be32-ef1e-4122-ae51-c24974b65727' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09bde79b-9834-47fb-9c1c-83a676dbcc38' class='xr-var-data-in' type='checkbox'><label for='data-09bde79b-9834-47fb-9c1c-83a676dbcc38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>comment :</span></dt><dd>longitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>bounds :</span></dt><dd>longitude_bounds</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f127462a-ea38-4dff-a756-167b180380c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f127462a-ea38-4dff-a756-167b180380c6' class='xr-section-summary' >Data variables: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>column_amount_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-ed1c451c-7ee1-4049-873a-601230b735a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed1c451c-7ee1-4049-873a-601230b735a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48594ef4-6f8b-42ae-a011-8fc9303fcff6' class='xr-var-data-in' type='checkbox'><label for='data-48594ef4-6f8b-42ae-a011-8fc9303fcff6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>best total ozone solution</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>50.0</dd><dt><span>valid_max :</span></dt><dd>700.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radiative_cloud_frac</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-fb43d5c3-4a6c-4e93-95c0-e67c670e2625' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb43d5c3-4a6c-4e93-95c0-e67c670e2625' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72f9d397-9f96-4261-804f-9a55af1db418' class='xr-var-data-in' type='checkbox'><label for='data-72f9d397-9f96-4261-804f-9a55af1db418' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>cloud radiance fraction = fc*Ic331/Im331</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fc</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-4ae9e2f5-ebcd-475f-8c4b-c4c508e9f591' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ae9e2f5-ebcd-475f-8c4b-c4c508e9f591' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ab253c4-9c2e-437e-9d57-665e9658d710' class='xr-var-data-in' type='checkbox'><label for='data-4ab253c4-9c2e-437e-9d57-665e9658d710' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective cloud fraction (mixed LER model)</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quality_flag</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-8d86d060-0275-4fb2-b96d-7ff2afef2334' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d86d060-0275-4fb2-b96d-7ff2afef2334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c50eb24-c5a4-41d0-a340-1bf75c504b20' class='xr-var-data-in' type='checkbox'><label for='data-8c50eb24-c5a4-41d0-a340-1bf75c504b20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>quality flags</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>65534</dd><dt><span>flag_meanings :</span></dt><dd>good_sample glint_contamination SZA&gt;84 360_residual&gt;threshold residual_at_unused_ozone_wavelenth&gt;4sigma SOI&gt;4sigma non-convergence abs(residual)&gt;16.0(fatal) row_anomaly_error missing_TEMPO_cloud_pressure geolocation_error SZA&gt;88 missing_input_radiance error_input_radiance warning_input_radiance missing_input_irradiance error_input_irradiance warning_input_irradiance</dd><dt><span>flag_values :</span></dt><dd>[    0     1     2     3     4     5     6     7     8   128   256   512\n",
+       "  1024  2048  4096  8192 16384 32768]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3_below_cloud</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-4d2f396a-cc38-4d28-b2cc-b55f3617bed0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d2f396a-cc38-4d28-b2cc-b55f3617bed0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a218ad8-7bfe-415d-b79d-7b97f1870deb' class='xr-var-data-in' type='checkbox'><label for='data-8a218ad8-7bfe-415d-b79d-7b97f1870deb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ozone below fractional cloud</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>100.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>so2_index</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-631f975f-c8ba-4c2f-9750-6d23a993acc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-631f975f-c8ba-4c2f-9750-6d23a993acc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0848da3-c8ea-4363-ad58-32304495ee0a' class='xr-var-data-in' type='checkbox'><label for='data-a0848da3-c8ea-4363-ad58-32304495ee0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>SO2 index</dd><dt><span>valid_min :</span></dt><dd>-300.0</dd><dt><span>valid_max :</span></dt><dd>300.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uv_aerosol_index</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-30ef9f33-fa02-4900-9273-e47017c80103' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30ef9f33-fa02-4900-9273-e47017c80103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f973f43c-e21a-4f0d-bd39-159bda81eb6d' class='xr-var-data-in' type='checkbox'><label for='data-f973f43c-e21a-4f0d-bd39-159bda81eb6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>UV aerosol index</dd><dt><span>valid_min :</span></dt><dd>-30.0</dd><dt><span>valid_max :</span></dt><dd>30.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_bounds</span></div><div class='xr-var-dims'>(mirror_step, xtrack, corner)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;</div><input id='attrs-7a949263-1b9e-46b5-b59c-46ba47eb2b67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a949263-1b9e-46b5-b59c-46ba47eb2b67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dae8cd09-5f38-4605-a05b-5d86abd07567' class='xr-var-data-in' type='checkbox'><label for='data-dae8cd09-5f38-4605-a05b-5d86abd07567' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>latitude at pixel corners (sw,se,ne,nw)</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.09 MiB </td>\n",
+       "                        <td> 4.09 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048, 4) </td>\n",
+       "                        <td> (131, 2048, 4) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text>\n",
+       "  <text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_bounds</span></div><div class='xr-var-dims'>(mirror_step, xtrack, corner)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;</div><input id='attrs-29308863-8a65-4a2d-b994-6960f8f93c95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29308863-8a65-4a2d-b994-6960f8f93c95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-854162c9-709b-42e0-b65b-72da41384ccb' class='xr-var-data-in' type='checkbox'><label for='data-854162c9-709b-42e0-b65b-72da41384ccb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>longitude at pixel corners (sw,se,ne,nw)</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.09 MiB </td>\n",
+       "                        <td> 4.09 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048, 4) </td>\n",
+       "                        <td> (131, 2048, 4) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text>\n",
+       "  <text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_zenith_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-c4f78054-77ac-447d-b578-b1f24dcdf3f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4f78054-77ac-447d-b578-b1f24dcdf3f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb1777b6-e6bf-4525-8f34-729f5a4cba21' class='xr-var-data-in' type='checkbox'><label for='data-eb1777b6-e6bf-4525-8f34-729f5a4cba21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>solar zenith angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-8ab8b869-1015-4b4a-863b-cefb075fbe32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ab8b869-1015-4b4a-863b-cefb075fbe32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5976b6eb-7935-45b9-8da6-7ffd29607067' class='xr-var-data-in' type='checkbox'><label for='data-5976b6eb-7935-45b9-8da6-7ffd29607067' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>solar azimuth angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_zenith_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-1c8e1ef7-f96e-43c5-91af-d8b73112788f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c8e1ef7-f96e-43c5-91af-d8b73112788f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ef69357-d9c4-45d9-abca-b88c52b39a29' class='xr-var-data-in' type='checkbox'><label for='data-7ef69357-d9c4-45d9-abca-b88c52b39a29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>viewing zenith angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-a77e5178-54ca-4fa4-b4b7-20b99c4e072c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a77e5178-54ca-4fa4-b4b7-20b99c4e072c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a0d142c-df54-4a73-8a72-6d49a66c0c73' class='xr-var-data-in' type='checkbox'><label for='data-9a0d142c-df54-4a73-8a72-6d49a66c0c73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>viewing azimuth angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-7d994732-1300-4a2a-a905-02e9cd15fa11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d994732-1300-4a2a-a905-02e9cd15fa11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-593a529c-ff68-460a-a283-dc01f13f9a33' class='xr-var-data-in' type='checkbox'><label for='data-593a529c-ff68-460a-a283-dc01f13f9a33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>relative azimuth angle (sun + 180 - view)</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 1.02 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (131, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 24MB\n",
+       "Dimensions:                 (mirror_step: 131, xtrack: 2048, corner: 4)\n",
+       "Coordinates:\n",
+       "    time                    (mirror_step) datetime64[ns] 1kB dask.array<chunksize=(131,), meta=np.ndarray>\n",
+       "    latitude                (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    longitude               (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "Dimensions without coordinates: mirror_step, xtrack, corner\n",
+       "Data variables: (12/14)\n",
+       "    column_amount_o3        (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    radiative_cloud_frac    (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    fc                      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    quality_flag            (mirror_step, xtrack) int32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    o3_below_cloud          (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    so2_index               (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    ...                      ...\n",
+       "    longitude_bounds        (mirror_step, xtrack, corner) float32 4MB dask.array<chunksize=(131, 2048, 4), meta=np.ndarray>\n",
+       "    solar_zenith_angle      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    solar_azimuth_angle     (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    viewing_zenith_angle    (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    viewing_azimuth_angle   (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
+       "    relative_azimuth_angle  (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tempo_l2 = {\n",
+    "    'xarray_open': 'dataset',\n",
+    "    'merge': ['/product', '/geolocation'],\n",
+    "    'coords': {'lat': 'latitude', 'lon': 'longitude'},\n",
+    "}\n",
+    "ds = plan.open_dataset(0, open_method=tempo_l2)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "378dc03b-5bfb-458f-b914-4505720b7928",
+   "metadata": {},
+   "source": [
+    "### Now we get the matchups using this open_method dict\n",
+    "\n",
+    "It is roughly 2 seconds per point. So roughly 5 hrs for 10,000 points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0b17d129-7642-4650-a492-85388e0fa90c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 13.2 s, sys: 754 ms, total: 13.9 s\n",
+      "Wall time: 31.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "res = pc.matchup(plan, \n",
+    "                 variables = [\"uv_aerosol_index\"], \n",
+    "                 open_method=tempo_l2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "096a16b2-88be-43c2-8a57-910d1b390651",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>time</th>\n",
+       "      <th>uv_aerosol_index</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>32.033815</td>\n",
+       "      <td>-87.504205</td>\n",
+       "      <td>2024-03-12 19:18:24</td>\n",
+       "      <td>-2.219414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>36.649735</td>\n",
+       "      <td>-80.935069</td>\n",
+       "      <td>2024-04-24 22:48:36</td>\n",
+       "      <td>-2.591015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>44.349757</td>\n",
+       "      <td>-72.301856</td>\n",
+       "      <td>2024-06-13 10:50:15</td>\n",
+       "      <td>-1.152082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>44.349757</td>\n",
+       "      <td>-72.301856</td>\n",
+       "      <td>2024-06-13 10:50:15</td>\n",
+       "      <td>-1.889225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>44.355987</td>\n",
+       "      <td>-95.126999</td>\n",
+       "      <td>2024-01-14 16:55:19</td>\n",
+       "      <td>-2.514838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>44.355987</td>\n",
+       "      <td>-95.126999</td>\n",
+       "      <td>2024-01-14 16:55:19</td>\n",
+       "      <td>-2.738163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>34.035328</td>\n",
+       "      <td>-90.559024</td>\n",
+       "      <td>2024-07-31 23:17:20</td>\n",
+       "      <td>-2.717468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>34.035328</td>\n",
+       "      <td>-90.559024</td>\n",
+       "      <td>2024-07-31 23:17:20</td>\n",
+       "      <td>-2.282551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>34.035328</td>\n",
+       "      <td>-90.559024</td>\n",
+       "      <td>2024-07-31 23:17:20</td>\n",
+       "      <td>-1.936824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>45.961109</td>\n",
+       "      <td>-97.761663</td>\n",
+       "      <td>2024-07-01 12:06:54</td>\n",
+       "      <td>-1.305435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>45.961109</td>\n",
+       "      <td>-97.761663</td>\n",
+       "      <td>2024-07-01 12:06:54</td>\n",
+       "      <td>-1.612602</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          lat        lon                time  uv_aerosol_index\n",
+       "0   32.033815 -87.504205 2024-03-12 19:18:24         -2.219414\n",
+       "1   36.649735 -80.935069 2024-04-24 22:48:36         -2.591015\n",
+       "4   44.349757 -72.301856 2024-06-13 10:50:15         -1.152082\n",
+       "5   44.349757 -72.301856 2024-06-13 10:50:15         -1.889225\n",
+       "6   44.355987 -95.126999 2024-01-14 16:55:19         -2.514838\n",
+       "7   44.355987 -95.126999 2024-01-14 16:55:19         -2.738163\n",
+       "9   34.035328 -90.559024 2024-07-31 23:17:20         -2.717468\n",
+       "10  34.035328 -90.559024 2024-07-31 23:17:20         -2.282551\n",
+       "11  34.035328 -90.559024 2024-07-31 23:17:20         -1.936824\n",
+       "17  45.961109 -97.761663 2024-07-01 12:06:54         -1.305435\n",
+       "18  45.961109 -97.761663 2024-07-01 12:06:54         -1.612602"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    res[['lat', 'lon', 'time', 'uv_aerosol_index']]\n",
+    "        .dropna(subset=['uv_aerosol_index'])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ce4c95b-aabe-4a8f-85be-cc3e1040f8d8",
+   "metadata": {},
+   "source": [
+    "### Here is a plot showing points and swaths\n",
+    "\n",
+    "This is just one swath. The matchup routine finds the granules matching each point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "30c6b96d-1dce-4331-b375-0bc79dbcb769",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "\n",
+    "var = ds[\"column_amount_o3\"]\n",
+    "\n",
+    "fig = plt.figure(figsize=(12, 6))\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "\n",
+    "pcm = ax.pcolormesh(\n",
+    "    ds[\"longitude\"],\n",
+    "    ds[\"latitude\"],\n",
+    "    var,\n",
+    "    shading=\"auto\",\n",
+    "    transform=ccrs.PlateCarree()\n",
+    ")\n",
+    "\n",
+    "# plot dataframe points\n",
+    "ax.scatter(\n",
+    "    df[\"lon\"],\n",
+    "    df[\"lat\"],\n",
+    "    color=\"red\",\n",
+    "    s=10,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    label=\"points\"\n",
+    ")\n",
+    "\n",
+    "ax.coastlines()\n",
+    "ax.add_feature(cfeature.BORDERS, linewidth=0.5)\n",
+    "ax.set_xlabel(\"Longitude\")\n",
+    "ax.set_ylabel(\"Latitude\")\n",
+    "plt.colorbar(pcm, ax=ax, label=var.name)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1da88a23-def7-4cb9-9c70-e8c393cf3d3e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/testing_more_products.ipynb b/examples/testing_more_products.ipynb
index 0ae068f..2012cac 100644
--- a/examples/testing_more_products.ipynb
+++ b/examples/testing_more_products.ipynb
@@ -9,18 +9,18 @@
     "\n",
     "## Level 3\n",
     "\n",
-    "* WORKS `short_name=\"ECCO_L4_SSH_05DEG_MONTHLY_V4R4\"` level 4, ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4)\n",
-    "* WORKS `short_name=\"MUR-JPL-L4-GLOB-v4.1\"` level 4 SST\n",
-    "* WORKS `short_name=\"MUR25-JPL-L4-GLOB-v04.2\"` level 4 SST\n",
-    "* WORKS `short_name = \"TEMPO_NO2_L3\", version = \"V03\"` level 3 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/TEMPO/how_to_examine_TEMPO_data_using_earthaccess.html)\n",
+    "* DONE `short_name=\"ECCO_L4_SSH_05DEG_MONTHLY_V4R4\"` level 4, ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4)\n",
+    "* DONE `short_name=\"MUR-JPL-L4-GLOB-v4.1\"` level 4 SST\n",
+    "* DONE `short_name=\"MUR25-JPL-L4-GLOB-v04.2\"` level 4 SST\n",
+    "* DONE `short_name = \"TEMPO_NO2_L3\", version = \"V03\"` level 3 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/TEMPO/how_to_examine_TEMPO_data_using_earthaccess.html)\n",
     "\n",
     "## Level 2\n",
     "\n",
     "* `concept_id = \"C1962643459-LARC_ASDC\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_explore_aerosol_data_from_EPIC_and_EPA-AQS.html)\n",
-    "* `short_name=\"ATL03\"` level 2, icesat-2. [notebook](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/tutorials/Harmony.html)\n",
+    "* WONTFIX (line) `short_name=\"ATL03\"` level 2, icesat-2. [notebook](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/tutorials/Harmony.html)\n",
     "* `short_name = \"PREFIRE_SAT2_2B-CLD\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/PREFIRE/plot_PREFIRE_L2_CLD_with_earthaccess_and_Harmony.html)\n",
     "* `short_name = \"DSCOVR_EPIC_L2_AER\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_compare_TEMPO_with_DSCOVR_and_AERONET_uvai.html)\n",
-    "* `short_name = \"TEMPO_O3TOT_L2\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_compare_spatially_TEMPO_with_DSCOVR_uvai.html) [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_compare_TEMPO_with_DSCOVR_and_AERONET_uvai.html)\n",
+    "* DONE `short_name = \"TEMPO_O3TOT_L2\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_compare_spatially_TEMPO_with_DSCOVR_uvai.html) [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_compare_TEMPO_with_DSCOVR_and_AERONET_uvai.html)\n",
     "* `short_name = \"DSCOVR_EPIC_L2_TO3\", version = \"03\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_plot_ozone_product_parameters.html)\n",
     "\n",
     "\n",

From 7d9c0b9854274d1602c691fe88827f3c9d7f2b8f Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Mon, 16 Mar 2026 17:51:13 +0000
Subject: [PATCH 02/11] epic

---
 examples/docs_4_discovr_epic.ipynb | 312 ++++++++++++++++++++++++++++-
 1 file changed, 301 insertions(+), 11 deletions(-)

diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_4_discovr_epic.ipynb
index 8d8d5fb..3dd936d 100644
--- a/examples/docs_4_discovr_epic.ipynb
+++ b/examples/docs_4_discovr_epic.ipynb
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "6153eef7-d94d-4c2f-8bd2-a860d737a620",
    "metadata": {},
    "outputs": [
@@ -188,23 +188,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 403 μs, sys: 0 ns, total: 403 μs\n",
-      "Wall time: 313 μs\n"
+      "CPU times: user 579 ms, sys: 88.8 ms, total: 667 ms\n",
+      "Wall time: 11.7 s\n"
      ]
     },
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'point_collocation'",
+     "ename": "ValueError",
+     "evalue": "No 'GET DATA' URL found in granule RelatedUrls. Available types: []",
      "output_type": "error",
      "traceback": [
       "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mimport point_collocation as pc\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mshort_name=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDSCOVR_EPIC_L2_AER\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan = pc.plan(\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    df,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    data_source=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mearthaccess\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    source_kwargs=\u001b[39;49m\u001b[33;43m{\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mshort_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: short_name,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mversion\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mV03\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    },\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    time_buffer=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m1h\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan.summary(n=2)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mimport point_collocation as pc\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mshort_name=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDSCOVR_EPIC_L2_AER\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan = pc.plan(\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    df,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    data_source=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mearthaccess\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    source_kwargs=\u001b[39;49m\u001b[33;43m{\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mshort_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: short_name,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mversion\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m03\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    },\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    time_buffer=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m12h\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan.summary(n=2)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2572\u001b[39m, in \u001b[36mInteractiveShell.run_cell_magic\u001b[39m\u001b[34m(self, magic_name, line, cell)\u001b[39m\n\u001b[32m   2570\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.builtin_trap:\n\u001b[32m   2571\u001b[39m     args = (magic_arg_s, cell)\n\u001b[32m-> \u001b[39m\u001b[32m2572\u001b[39m     result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2574\u001b[39m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[32m   2575\u001b[39m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[32m   2576\u001b[39m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[32m   2577\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
       "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1447\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1445\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupt_occured:\n\u001b[32m   1446\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m exit_on_interrupt \u001b[38;5;129;01mand\u001b[39;00m captured_exception:\n\u001b[32m-> \u001b[39m\u001b[32m1447\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m captured_exception\n\u001b[32m   1448\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m   1449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
       "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1411\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1409\u001b[39m st = clock2()\n\u001b[32m   1410\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1411\u001b[39m     \u001b[43mexec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1412\u001b[39m     out = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1413\u001b[39m     \u001b[38;5;66;03m# multi-line %%time case\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m<timed exec>:1\u001b[39m\n",
-      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'point_collocation'"
+      "\u001b[36mFile \u001b[39m\u001b[32m<timed exec>:3\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/plan.py:680\u001b[39m, in \u001b[36mplan\u001b[39m\u001b[34m(points, data_source, source_kwargs, time_buffer)\u001b[39m\n\u001b[32m    677\u001b[39m _plan_validate_points(points)\n\u001b[32m    679\u001b[39m buffer = _parse_time_buffer(time_buffer)\n\u001b[32m--> \u001b[39m\u001b[32m680\u001b[39m results, granule_metas = \u001b[43m_search_earthaccess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43msource_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    681\u001b[39m point_granule_map = _match_points_to_granules(points, granule_metas, buffer)\n\u001b[32m    683\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m Plan(\n\u001b[32m    684\u001b[39m     points=points,\n\u001b[32m    685\u001b[39m     results=results,\n\u001b[32m   (...)\u001b[39m\u001b[32m    689\u001b[39m     time_buffer=buffer,\n\u001b[32m    690\u001b[39m )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/plan.py:845\u001b[39m, in \u001b[36m_search_earthaccess\u001b[39m\u001b[34m(points, source_kwargs)\u001b[39m\n\u001b[32m    843\u001b[39m granule_metas: \u001b[38;5;28mlist\u001b[39m[GranuleMeta] = []\n\u001b[32m    844\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, result \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(results):\n\u001b[32m--> \u001b[39m\u001b[32m845\u001b[39m     granule_metas.append(\u001b[43m_extract_granule_meta\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult_index\u001b[49m\u001b[43m=\u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m    847\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m results, granule_metas\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/plan.py:865\u001b[39m, in \u001b[36m_extract_granule_meta\u001b[39m\u001b[34m(result, result_index)\u001b[39m\n\u001b[32m    863\u001b[39m     granule_id = https_links[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m https_links \u001b[38;5;28;01melse\u001b[39;00m links[\u001b[32m0\u001b[39m]\n\u001b[32m    864\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m865\u001b[39m     granule_id = \u001b[43m_get_data_url\u001b[49m\u001b[43m(\u001b[49m\u001b[43mumm\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    867\u001b[39m bbox = _get_bbox(umm)\n\u001b[32m    868\u001b[39m polygon = _get_polygon_points(umm)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/plan.py:922\u001b[39m, in \u001b[36m_get_data_url\u001b[39m\u001b[34m(umm)\u001b[39m\n\u001b[32m    920\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m url_info.get(\u001b[33m\"\u001b[39m\u001b[33mType\u001b[39m\u001b[33m\"\u001b[39m) == \u001b[33m\"\u001b[39m\u001b[33mGET DATA\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m    921\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m url_info[\u001b[33m\"\u001b[39m\u001b[33mURL\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m922\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    923\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mNo \u001b[39m\u001b[33m'\u001b[39m\u001b[33mGET DATA\u001b[39m\u001b[33m'\u001b[39m\u001b[33m URL found in granule RelatedUrls. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    924\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAvailable types: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m[u.get(\u001b[33m'\u001b[39m\u001b[33mType\u001b[39m\u001b[33m'\u001b[39m)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mu\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mrelated_urls]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    925\u001b[39m )\n",
+      "\u001b[31mValueError\u001b[39m: No 'GET DATA' URL found in granule RelatedUrls. Available types: []"
      ]
     }
    ],
@@ -217,13 +221,299 @@
     "    data_source=\"earthaccess\",\n",
     "    source_kwargs={\n",
     "        \"short_name\": short_name,\n",
-    "        \"version\": \"V03\"\n",
+    "        \"version\": \"03\"\n",
     "    },\n",
-    "    time_buffer=\"1h\"\n",
+    "    time_buffer=\"12h\"\n",
     ")\n",
     "plan.summary(n=2)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bbc032a7-f6db-4446-9d58-c3f21e00eb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5454 file(s) found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import earthaccess\n",
+    "short_name = \"DSCOVR_EPIC_L2_AER\"\n",
+    "version = \"03\"\n",
+    "\n",
+    "results = earthaccess.search_data(\n",
+    "    short_name=short_name,\n",
+    "    version=version,\n",
+    "    temporal=(\"2024-01-01\", \"2024-12-31\"),\n",
+    ")\n",
+    "print(f\"{len(results)} file(s) found.\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7031dc65-3a16-444a-ae8d-b21b1bc2fdb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "  {\n",
+      "    \"meta\": {\n",
+      "      \"concept-type\": \"granule\",\n",
+      "      \"concept-id\": \"G2834307209-LARC_ASDC\",\n",
+      "      \"revision-id\": 3,\n",
+      "      \"native-id\": \"14d63e3b-733b-497c-a4bd-f2ae5d69b262\",\n",
+      "      \"collection-concept-id\": \"C1962643459-LARC_ASDC\",\n",
+      "      \"provider-id\": \"LARC_ASDC\",\n",
+      "      \"format\": \"application/vnd.nasa.cmr.umm+json\",\n",
+      "      \"revision-date\": \"2024-02-28T14:30:30.916Z\"\n",
+      "    },\n",
+      "    \"umm\": {\n",
+      "      \"TemporalExtent\": {\n",
+      "        \"RangeDateTime\": {\n",
+      "          \"BeginningDateTime\": \"2024-01-01T00:50:27+00:00\",\n",
+      "          \"EndingDateTime\": \"2024-01-01T00:57:02+00:00\"\n",
+      "        }\n",
+      "      },\n",
+      "      \"GranuleUR\": \"14d63e3b-733b-497c-a4bd-f2ae5d69b262\",\n",
+      "      \"AdditionalAttributes\": [\n",
+      "        {\n",
+      "          \"Name\": \"CENTROID_MEAN_LATITUDE\",\n",
+      "          \"Values\": [\n",
+      "            \"-29.0628662109375\"\n",
+      "          ]\n",
+      "        },\n",
+      "        {\n",
+      "          \"Name\": \"CENTROID_MEAN_LONGITUDE\",\n",
+      "          \"Values\": [\n",
+      "            \"158.555419921875\"\n",
+      "          ]\n",
+      "        }\n",
+      "      ],\n",
+      "      \"SpatialExtent\": {\n",
+      "        \"HorizontalSpatialDomain\": {\n",
+      "          \"Geometry\": {\n",
+      "            \"GPolygons\": [\n",
+      "              {\n",
+      "                \"Boundary\": {\n",
+      "                  \"Points\": [\n",
+      "                    {\n",
+      "                      \"Latitude\": 49.242334341,\n",
+      "                      \"Longitude\": 180\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 51.832531981,\n",
+      "                      \"Longitude\": 164.070672645\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 51.58342674,\n",
+      "                      \"Longitude\": 150.521901857\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 48.755913035,\n",
+      "                      \"Longitude\": 135.196423106\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 40.629900009,\n",
+      "                      \"Longitude\": 114.900441024\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 32.505686033,\n",
+      "                      \"Longitude\": 102.875346141\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 18.510237064,\n",
+      "                      \"Longitude\": 91.436527461\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -4.320684176,\n",
+      "                      \"Longitude\": 76.606744451\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -32.495595277,\n",
+      "                      \"Longitude\": 61.512787862\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -53.183237539,\n",
+      "                      \"Longitude\": 43.881807105\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -60.378418353,\n",
+      "                      \"Longitude\": 33.365866226\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -65.551350292,\n",
+      "                      \"Longitude\": 20.321451787\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -68.866104203,\n",
+      "                      \"Longitude\": 4.096261764\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -70.197331676,\n",
+      "                      \"Longitude\": -14.892098025\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -69.540596659,\n",
+      "                      \"Longitude\": -40.890441788\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -66.868546368,\n",
+      "                      \"Longitude\": -58.151990679\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -62.307636931,\n",
+      "                      \"Longitude\": -72.238765996\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -55.829717645,\n",
+      "                      \"Longitude\": -83.497149509\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -47.033139963,\n",
+      "                      \"Longitude\": -92.766503949\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -32.495595277,\n",
+      "                      \"Longitude\": -104.401948018\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 9.831930064,\n",
+      "                      \"Longitude\": -128.446662073\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 32.505686033,\n",
+      "                      \"Longitude\": -145.764506297\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 42.729291469,\n",
+      "                      \"Longitude\": -161.804070826\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 49.242334341,\n",
+      "                      \"Longitude\": -180\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -90,\n",
+      "                      \"Longitude\": -180\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": -90,\n",
+      "                      \"Longitude\": 180\n",
+      "                    },\n",
+      "                    {\n",
+      "                      \"Latitude\": 49.242334341,\n",
+      "                      \"Longitude\": 180\n",
+      "                    }\n",
+      "                  ]\n",
+      "                }\n",
+      "              }\n",
+      "            ]\n",
+      "          }\n",
+      "        }\n",
+      "      },\n",
+      "      \"ProviderDates\": [\n",
+      "        {\n",
+      "          \"Date\": \"2024-01-11T10:34:44.093Z\",\n",
+      "          \"Type\": \"Create\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"Date\": \"2024-01-12T06:59:56.682Z\",\n",
+      "          \"Type\": \"Update\"\n",
+      "        }\n",
+      "      ],\n",
+      "      \"CollectionReference\": {\n",
+      "        \"ShortName\": \"DSCOVR_EPIC_L2_AER\",\n",
+      "        \"Version\": \"03\"\n",
+      "      },\n",
+      "      \"AccessConstraints\": {\n",
+      "        \"Description\": \"WORLD\",\n",
+      "        \"Value\": 4\n",
+      "      },\n",
+      "      \"PGEVersionClass\": {\n",
+      "        \"PGEVersion\": \"03\"\n",
+      "      },\n",
+      "      \"RelatedUrls\": [\n",
+      "        {\n",
+      "          \"Description\": \"This file may be downloaded directly from ASDC.\",\n",
+      "          \"Subtype\": \"DIRECT DOWNLOAD\",\n",
+      "          \"Type\": \"GET DATA\",\n",
+      "          \"URL\": \"https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5\"\n",
+      "        }\n",
+      "      ],\n",
+      "      \"InputGranules\": [\n",
+      "        \"epic_1b_20240101005515_03.h5\"\n",
+      "      ],\n",
+      "      \"Projects\": [\n",
+      "        {\n",
+      "          \"ShortName\": \"DSCOVR\"\n",
+      "        }\n",
+      "      ],\n",
+      "      \"DataGranule\": {\n",
+      "        \"ArchiveAndDistributionInformation\": [\n",
+      "          {\n",
+      "            \"Name\": \"DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5\"\n",
+      "          },\n",
+      "          {\n",
+      "            \"Checksum\": {\n",
+      "              \"Algorithm\": \"MD5\",\n",
+      "              \"Value\": \"9cff3629b6211b4ea5127af39ad64374\"\n",
+      "            },\n",
+      "            \"Name\": \"DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5\",\n",
+      "            \"Size\": 168.895170211792,\n",
+      "            \"SizeUnit\": \"MB\"\n",
+      "          },\n",
+      "          {\n",
+      "            \"Checksum\": {\n",
+      "              \"Algorithm\": \"MD5\",\n",
+      "              \"Value\": \"e8cd84cbf58487e22450b0aaf8d82165\"\n",
+      "            },\n",
+      "            \"Name\": \"DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5.met\",\n",
+      "            \"Size\": 4.7353515625,\n",
+      "            \"SizeUnit\": \"KB\"\n",
+      "          }\n",
+      "        ],\n",
+      "        \"DayNightFlag\": \"Unspecified\",\n",
+      "        \"Identifiers\": [\n",
+      "          {\n",
+      "            \"Identifier\": \"DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5\",\n",
+      "            \"IdentifierType\": \"ProducerGranuleId\"\n",
+      "          },\n",
+      "          {\n",
+      "            \"Identifier\": \"03\",\n",
+      "            \"IdentifierType\": \"LocalVersionId\"\n",
+      "          }\n",
+      "        ],\n",
+      "        \"ProductionDateTime\": \"2024-01-10T09:10:39+00:00\"\n",
+      "      },\n",
+      "      \"MetadataSpecification\": {\n",
+      "        \"URL\": \"https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6\",\n",
+      "        \"Name\": \"UMM-G\",\n",
+      "        \"Version\": \"1.6.6\"\n",
+      "      }\n",
+      "    },\n",
+      "    \"size\": 0\n",
+      "  }\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "print(json.dumps(results[0:1], indent=2))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bde99fb2-5f41-4aee-a2d2-df20bc31cf23",

From 6d1fa331e9107dce42f6c8300cbdfd959e16ccd0 Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Mon, 16 Mar 2026 18:14:10 +0000
Subject: [PATCH 03/11] epic

---
 examples/docs_4_discovr_epic.ipynb | 112 ++++++++++++++++++++++++++---
 1 file changed, 103 insertions(+), 9 deletions(-)

diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_4_discovr_epic.ipynb
index 3dd936d..c224c9d 100644
--- a/examples/docs_4_discovr_epic.ipynb
+++ b/examples/docs_4_discovr_epic.ipynb
@@ -42,10 +42,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "e9b6e22e-f993-4aa0-a03b-6c310cbe11be",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<earthaccess.auth.Auth at 0x7eff8db42810>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import earthaccess\n",
     "earthaccess.login()"
@@ -180,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "6153eef7-d94d-4c2f-8bd2-a860d737a620",
    "metadata": {},
    "outputs": [
@@ -188,8 +199,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 579 ms, sys: 88.8 ms, total: 667 ms\n",
-      "Wall time: 11.7 s\n"
+      "CPU times: user 613 ms, sys: 32.7 ms, total: 646 ms\n",
+      "Wall time: 8.33 s\n"
      ]
     },
     {
@@ -199,7 +210,7 @@
      "traceback": [
       "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
       "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mimport point_collocation as pc\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mshort_name=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDSCOVR_EPIC_L2_AER\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan = pc.plan(\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    df,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    data_source=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mearthaccess\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    source_kwargs=\u001b[39;49m\u001b[33;43m{\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mshort_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: short_name,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mversion\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m03\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    },\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    time_buffer=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m12h\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan.summary(n=2)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mimport point_collocation as pc\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mshort_name=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDSCOVR_EPIC_L2_AER\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan = pc.plan(\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    df,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    data_source=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mearthaccess\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    source_kwargs=\u001b[39;49m\u001b[33;43m{\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mshort_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: short_name,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mversion\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m03\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    },\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    time_buffer=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m12h\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m#plan.summary(n=2)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2572\u001b[39m, in \u001b[36mInteractiveShell.run_cell_magic\u001b[39m\u001b[34m(self, magic_name, line, cell)\u001b[39m\n\u001b[32m   2570\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.builtin_trap:\n\u001b[32m   2571\u001b[39m     args = (magic_arg_s, cell)\n\u001b[32m-> \u001b[39m\u001b[32m2572\u001b[39m     result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2574\u001b[39m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[32m   2575\u001b[39m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[32m   2576\u001b[39m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[32m   2577\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
       "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1447\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1445\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupt_occured:\n\u001b[32m   1446\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m exit_on_interrupt \u001b[38;5;129;01mand\u001b[39;00m captured_exception:\n\u001b[32m-> \u001b[39m\u001b[32m1447\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m captured_exception\n\u001b[32m   1448\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m   1449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
       "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1411\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1409\u001b[39m st = clock2()\n\u001b[32m   1410\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1411\u001b[39m     \u001b[43mexec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1412\u001b[39m     out = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1413\u001b[39m     \u001b[38;5;66;03m# multi-line %%time case\u001b[39;00m\n",
@@ -225,12 +236,12 @@
     "    },\n",
     "    time_buffer=\"12h\"\n",
     ")\n",
-    "plan.summary(n=2)"
+    "#plan.summary(n=2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "bbc032a7-f6db-4446-9d58-c3f21e00eb65",
    "metadata": {},
    "outputs": [
@@ -256,6 +267,78 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7f3d6945-7b53-404a-9d11-04750a758e7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5'],\n",
+       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101024318_03.he5'],\n",
+       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101043120_03.he5'],\n",
+       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101061923_03.he5'],\n",
+       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101080725_03.he5'],\n",
+       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101095527_03.he5'],\n",
+       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101114329_03.he5'],\n",
+       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101133132_03.he5'],\n",
+       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101151935_03.he5'],\n",
+       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101170737_03.he5']]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[res.data_links(in_region=True) for res in results[0:10]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c16ae264-c16e-4a88-909f-4ab1552a1dff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any(res.data_links(in_region=True) == [] for res in results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e6ccc91c-e376-44b2-906c-435e2fe57b0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any(res.data_links() == [] for res in results)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -524,6 +607,17 @@
     "We will open a file with datatree and see what groups it has (if any). It does have groups; the latitude, longitude is at the base level: `/` and the product is in `/product`. We want `vertical_column_troposphere` which is the NO2. So we create a profile for TEMPO. We open a dataset with the profile to make sure it looks good."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71522438-3b18-4000-b413-03d0f5fe9b62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = plan.open_dataset(0, open_method=\"datatree\")\n",
+    "ds"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -1640,7 +1734,7 @@
    ],
    "source": [
     "%%time\n",
-    "tempo = {\n",
+    "discovr_epic = {\n",
     "    'xarray_open': 'dataset',\n",
     "    'merge': ['/', '/product'],\n",
     "}\n",

From 3ae5e7858ec903739206b17f1b0e490b9d4dd69b Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Mon, 16 Mar 2026 18:57:05 +0000
Subject: [PATCH 04/11] epic

---
 examples/docs_4_discovr_epic.ipynb | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_4_discovr_epic.ipynb
index c224c9d..7047914 100644
--- a/examples/docs_4_discovr_epic.ipynb
+++ b/examples/docs_4_discovr_epic.ipynb
@@ -262,6 +262,7 @@
     "    short_name=short_name,\n",
     "    version=version,\n",
     "    temporal=(\"2024-01-01\", \"2024-12-31\"),\n",
+    "    cloud_hosted=True\n",
     ")\n",
     "print(f\"{len(results)} file(s) found.\")\n",
     "\n"
@@ -269,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "id": "7f3d6945-7b53-404a-9d11-04750a758e7d",
    "metadata": {},
    "outputs": [
@@ -288,13 +289,13 @@
        " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101170737_03.he5']]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "[res.data_links(in_region=True) for res in results[0:10]]"
+    "[res.data_links(access=\"external\") for res in results[0:10]]"
    ]
   },
   {

From 0b5e5171816560f99f8e72e77c94d10dfd040a9a Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Mon, 16 Mar 2026 22:40:06 +0000
Subject: [PATCH 05/11] testing

---
 examples/docs_4_discovr_epic.ipynb | 7861 +++++++++++++++++++++++-----
 1 file changed, 6694 insertions(+), 1167 deletions(-)

diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_4_discovr_epic.ipynb
index 7047914..dcbfd38 100644
--- a/examples/docs_4_discovr_epic.ipynb
+++ b/examples/docs_4_discovr_epic.ipynb
@@ -42,17 +42,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "e9b6e22e-f993-4aa0-a03b-6c310cbe11be",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<earthaccess.auth.Auth at 0x7eff8db42810>"
+       "<earthaccess.auth.Auth at 0x7fefa734ad20>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -62,6 +62,27 @@
     "earthaccess.login()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "85551467-0165-4a1a-9877-9b2d972b9b9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "set()\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "from dask.callbacks import Callback\n",
+    "\n",
+    "print(Callback.active)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cd008cf7-3bcb-4bf4-8535-7f0e142170bb",
@@ -191,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "6153eef7-d94d-4c2f-8bd2-a860d737a620",
    "metadata": {},
    "outputs": [
@@ -199,27 +220,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 613 ms, sys: 32.7 ms, total: 646 ms\n",
-      "Wall time: 8.33 s\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "No 'GET DATA' URL found in granule RelatedUrls. Available types: []",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mimport point_collocation as pc\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mshort_name=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDSCOVR_EPIC_L2_AER\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43mplan = pc.plan(\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    df,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    data_source=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mearthaccess\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    source_kwargs=\u001b[39;49m\u001b[33;43m{\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mshort_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: short_name,\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m        \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mversion\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m: \u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m03\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    },\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m    time_buffer=\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m12h\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m#plan.summary(n=2)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2572\u001b[39m, in \u001b[36mInteractiveShell.run_cell_magic\u001b[39m\u001b[34m(self, magic_name, line, cell)\u001b[39m\n\u001b[32m   2570\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.builtin_trap:\n\u001b[32m   2571\u001b[39m     args = (magic_arg_s, cell)\n\u001b[32m-> \u001b[39m\u001b[32m2572\u001b[39m     result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2574\u001b[39m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[32m   2575\u001b[39m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[32m   2576\u001b[39m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[32m   2577\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1447\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1445\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupt_occured:\n\u001b[32m   1446\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m exit_on_interrupt \u001b[38;5;129;01mand\u001b[39;00m captured_exception:\n\u001b[32m-> \u001b[39m\u001b[32m1447\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m captured_exception\n\u001b[32m   1448\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m   1449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1411\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1409\u001b[39m st = clock2()\n\u001b[32m   1410\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1411\u001b[39m     \u001b[43mexec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1412\u001b[39m     out = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1413\u001b[39m     \u001b[38;5;66;03m# multi-line %%time case\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m<timed exec>:3\u001b[39m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/plan.py:680\u001b[39m, in \u001b[36mplan\u001b[39m\u001b[34m(points, data_source, source_kwargs, time_buffer)\u001b[39m\n\u001b[32m    677\u001b[39m _plan_validate_points(points)\n\u001b[32m    679\u001b[39m buffer = _parse_time_buffer(time_buffer)\n\u001b[32m--> \u001b[39m\u001b[32m680\u001b[39m results, granule_metas = \u001b[43m_search_earthaccess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43msource_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    681\u001b[39m point_granule_map = _match_points_to_granules(points, granule_metas, buffer)\n\u001b[32m    683\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m Plan(\n\u001b[32m    684\u001b[39m     points=points,\n\u001b[32m    685\u001b[39m     results=results,\n\u001b[32m   (...)\u001b[39m\u001b[32m    689\u001b[39m     time_buffer=buffer,\n\u001b[32m    690\u001b[39m )\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/plan.py:845\u001b[39m, in \u001b[36m_search_earthaccess\u001b[39m\u001b[34m(points, source_kwargs)\u001b[39m\n\u001b[32m    843\u001b[39m granule_metas: \u001b[38;5;28mlist\u001b[39m[GranuleMeta] = []\n\u001b[32m    844\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, result \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(results):\n\u001b[32m--> \u001b[39m\u001b[32m845\u001b[39m     granule_metas.append(\u001b[43m_extract_granule_meta\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult_index\u001b[49m\u001b[43m=\u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m    847\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m results, granule_metas\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/plan.py:865\u001b[39m, in \u001b[36m_extract_granule_meta\u001b[39m\u001b[34m(result, result_index)\u001b[39m\n\u001b[32m    863\u001b[39m     granule_id = https_links[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m https_links \u001b[38;5;28;01melse\u001b[39;00m links[\u001b[32m0\u001b[39m]\n\u001b[32m    864\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m865\u001b[39m     granule_id = \u001b[43m_get_data_url\u001b[49m\u001b[43m(\u001b[49m\u001b[43mumm\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    867\u001b[39m bbox = _get_bbox(umm)\n\u001b[32m    868\u001b[39m polygon = _get_polygon_points(umm)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/plan.py:922\u001b[39m, in \u001b[36m_get_data_url\u001b[39m\u001b[34m(umm)\u001b[39m\n\u001b[32m    920\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m url_info.get(\u001b[33m\"\u001b[39m\u001b[33mType\u001b[39m\u001b[33m\"\u001b[39m) == \u001b[33m\"\u001b[39m\u001b[33mGET DATA\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m    921\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m url_info[\u001b[33m\"\u001b[39m\u001b[33mURL\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m922\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    923\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mNo \u001b[39m\u001b[33m'\u001b[39m\u001b[33mGET DATA\u001b[39m\u001b[33m'\u001b[39m\u001b[33m URL found in granule RelatedUrls. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    924\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAvailable types: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m[u.get(\u001b[33m'\u001b[39m\u001b[33mType\u001b[39m\u001b[33m'\u001b[39m)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mu\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mrelated_urls]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    925\u001b[39m )\n",
-      "\u001b[31mValueError\u001b[39m: No 'GET DATA' URL found in granule RelatedUrls. Available types: []"
+      "CPU times: user 1.62 s, sys: 288 ms, total: 1.91 s\n",
+      "Wall time: 15.2 s\n"
      ]
     }
    ],
@@ -234,916 +236,6471 @@
     "        \"short_name\": short_name,\n",
     "        \"version\": \"03\"\n",
     "    },\n",
-    "    time_buffer=\"12h\"\n",
-    ")\n",
-    "#plan.summary(n=2)"
+    "    time_buffer=\"6h\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "bbc032a7-f6db-4446-9d58-c3f21e00eb65",
+   "execution_count": 3,
+   "id": "7c827935-f077-4416-8d10-11858f71db7e",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5454 file(s) found.\n"
+      "Plan: 31 points → 90 unique granule(s)\n",
+      "  Points with 0 matches : 9\n",
+      "  Points with >1 matches: 18\n",
+      "  Time buffer: 0 days 06:00:00\n",
+      "\n",
+      "First 2 point(s):\n",
+      "  [35] lat=9.1007, lon=11.2167, time=2024-07-21 00:00:00: 0 match(es)\n",
+      "  [43] lat=-67.2099, lon=-62.7344, time=2024-04-16 00:00:00: 0 match(es)\n"
      ]
     }
    ],
    "source": [
-    "import earthaccess\n",
-    "short_name = \"DSCOVR_EPIC_L2_AER\"\n",
-    "version = \"03\"\n",
-    "\n",
-    "results = earthaccess.search_data(\n",
-    "    short_name=short_name,\n",
-    "    version=version,\n",
-    "    temporal=(\"2024-01-01\", \"2024-12-31\"),\n",
-    "    cloud_hosted=True\n",
-    ")\n",
-    "print(f\"{len(results)} file(s) found.\")\n",
-    "\n"
+    "plan.summary(n=2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "7f3d6945-7b53-404a-9d11-04750a758e7d",
+   "execution_count": 21,
+   "id": "50e8b368-dc72-4c09-9327-da17a4de931d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5'],\n",
-       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101024318_03.he5'],\n",
-       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101043120_03.he5'],\n",
-       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101061923_03.he5'],\n",
-       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101080725_03.he5'],\n",
-       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101095527_03.he5'],\n",
-       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101114329_03.he5'],\n",
-       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101133132_03.he5'],\n",
-       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101151935_03.he5'],\n",
-       " ['https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101170737_03.he5']]"
+       "'https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/02/DSCOVR_EPIC_L2_AER_03_20240208000830_03.he5'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "[res.data_links(access=\"external\") for res in results[0:10]]"
+    "url=plan[0].data_links()[0]\n",
+    "url"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "c16ae264-c16e-4a88-909f-4ab1552a1dff",
+   "execution_count": 2,
+   "id": "91a014bb-ab2e-4800-8685-b2b911b55cb7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "any(res.data_links(in_region=True) == [] for res in results)"
+    "import xarray as xr\n",
+    "import earthaccess\n",
+    "results = earthaccess.search_data(\n",
+    "    short_name=\"DSCOVR_EPIC_L2_AER\",\n",
+    "    temporal = (\"2024-01-01\", \"2024-12-31\"),\n",
+    "    version = \"03\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "e6ccc91c-e376-44b2-906c-435e2fe57b0d",
+   "execution_count": 5,
+   "id": "d78fad1f-5499-4352-9623-c69a2c0a08b0",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2698ed4667c64cd4a764e8857e23b3f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "True"
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "any(res.data_links() == [] for res in results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "7031dc65-3a16-444a-ae8d-b21b1bc2fdb2",
-   "metadata": {},
-   "outputs": [
+     "output_type": "display_data"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "  {\n",
-      "    \"meta\": {\n",
-      "      \"concept-type\": \"granule\",\n",
-      "      \"concept-id\": \"G2834307209-LARC_ASDC\",\n",
-      "      \"revision-id\": 3,\n",
-      "      \"native-id\": \"14d63e3b-733b-497c-a4bd-f2ae5d69b262\",\n",
-      "      \"collection-concept-id\": \"C1962643459-LARC_ASDC\",\n",
-      "      \"provider-id\": \"LARC_ASDC\",\n",
-      "      \"format\": \"application/vnd.nasa.cmr.umm+json\",\n",
-      "      \"revision-date\": \"2024-02-28T14:30:30.916Z\"\n",
-      "    },\n",
-      "    \"umm\": {\n",
-      "      \"TemporalExtent\": {\n",
-      "        \"RangeDateTime\": {\n",
-      "          \"BeginningDateTime\": \"2024-01-01T00:50:27+00:00\",\n",
-      "          \"EndingDateTime\": \"2024-01-01T00:57:02+00:00\"\n",
-      "        }\n",
-      "      },\n",
-      "      \"GranuleUR\": \"14d63e3b-733b-497c-a4bd-f2ae5d69b262\",\n",
-      "      \"AdditionalAttributes\": [\n",
-      "        {\n",
-      "          \"Name\": \"CENTROID_MEAN_LATITUDE\",\n",
-      "          \"Values\": [\n",
-      "            \"-29.0628662109375\"\n",
-      "          ]\n",
-      "        },\n",
-      "        {\n",
-      "          \"Name\": \"CENTROID_MEAN_LONGITUDE\",\n",
-      "          \"Values\": [\n",
-      "            \"158.555419921875\"\n",
-      "          ]\n",
-      "        }\n",
-      "      ],\n",
-      "      \"SpatialExtent\": {\n",
-      "        \"HorizontalSpatialDomain\": {\n",
-      "          \"Geometry\": {\n",
-      "            \"GPolygons\": [\n",
-      "              {\n",
-      "                \"Boundary\": {\n",
-      "                  \"Points\": [\n",
-      "                    {\n",
-      "                      \"Latitude\": 49.242334341,\n",
-      "                      \"Longitude\": 180\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 51.832531981,\n",
-      "                      \"Longitude\": 164.070672645\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 51.58342674,\n",
-      "                      \"Longitude\": 150.521901857\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 48.755913035,\n",
-      "                      \"Longitude\": 135.196423106\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 40.629900009,\n",
-      "                      \"Longitude\": 114.900441024\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 32.505686033,\n",
-      "                      \"Longitude\": 102.875346141\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 18.510237064,\n",
-      "                      \"Longitude\": 91.436527461\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -4.320684176,\n",
-      "                      \"Longitude\": 76.606744451\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -32.495595277,\n",
-      "                      \"Longitude\": 61.512787862\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -53.183237539,\n",
-      "                      \"Longitude\": 43.881807105\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -60.378418353,\n",
-      "                      \"Longitude\": 33.365866226\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -65.551350292,\n",
-      "                      \"Longitude\": 20.321451787\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -68.866104203,\n",
-      "                      \"Longitude\": 4.096261764\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -70.197331676,\n",
-      "                      \"Longitude\": -14.892098025\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -69.540596659,\n",
-      "                      \"Longitude\": -40.890441788\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -66.868546368,\n",
-      "                      \"Longitude\": -58.151990679\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -62.307636931,\n",
-      "                      \"Longitude\": -72.238765996\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -55.829717645,\n",
-      "                      \"Longitude\": -83.497149509\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -47.033139963,\n",
-      "                      \"Longitude\": -92.766503949\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -32.495595277,\n",
-      "                      \"Longitude\": -104.401948018\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 9.831930064,\n",
-      "                      \"Longitude\": -128.446662073\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 32.505686033,\n",
-      "                      \"Longitude\": -145.764506297\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 42.729291469,\n",
-      "                      \"Longitude\": -161.804070826\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 49.242334341,\n",
-      "                      \"Longitude\": -180\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -90,\n",
-      "                      \"Longitude\": -180\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": -90,\n",
-      "                      \"Longitude\": 180\n",
-      "                    },\n",
-      "                    {\n",
-      "                      \"Latitude\": 49.242334341,\n",
-      "                      \"Longitude\": 180\n",
-      "                    }\n",
-      "                  ]\n",
-      "                }\n",
-      "              }\n",
-      "            ]\n",
-      "          }\n",
-      "        }\n",
-      "      },\n",
-      "      \"ProviderDates\": [\n",
-      "        {\n",
-      "          \"Date\": \"2024-01-11T10:34:44.093Z\",\n",
-      "          \"Type\": \"Create\"\n",
-      "        },\n",
-      "        {\n",
-      "          \"Date\": \"2024-01-12T06:59:56.682Z\",\n",
-      "          \"Type\": \"Update\"\n",
-      "        }\n",
-      "      ],\n",
-      "      \"CollectionReference\": {\n",
-      "        \"ShortName\": \"DSCOVR_EPIC_L2_AER\",\n",
-      "        \"Version\": \"03\"\n",
-      "      },\n",
-      "      \"AccessConstraints\": {\n",
-      "        \"Description\": \"WORLD\",\n",
-      "        \"Value\": 4\n",
-      "      },\n",
-      "      \"PGEVersionClass\": {\n",
-      "        \"PGEVersion\": \"03\"\n",
-      "      },\n",
-      "      \"RelatedUrls\": [\n",
-      "        {\n",
-      "          \"Description\": \"This file may be downloaded directly from ASDC.\",\n",
-      "          \"Subtype\": \"DIRECT DOWNLOAD\",\n",
-      "          \"Type\": \"GET DATA\",\n",
-      "          \"URL\": \"https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/01/DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5\"\n",
-      "        }\n",
-      "      ],\n",
-      "      \"InputGranules\": [\n",
-      "        \"epic_1b_20240101005515_03.h5\"\n",
-      "      ],\n",
-      "      \"Projects\": [\n",
-      "        {\n",
-      "          \"ShortName\": \"DSCOVR\"\n",
-      "        }\n",
-      "      ],\n",
-      "      \"DataGranule\": {\n",
-      "        \"ArchiveAndDistributionInformation\": [\n",
-      "          {\n",
-      "            \"Name\": \"DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"Checksum\": {\n",
-      "              \"Algorithm\": \"MD5\",\n",
-      "              \"Value\": \"9cff3629b6211b4ea5127af39ad64374\"\n",
-      "            },\n",
-      "            \"Name\": \"DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5\",\n",
-      "            \"Size\": 168.895170211792,\n",
-      "            \"SizeUnit\": \"MB\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"Checksum\": {\n",
-      "              \"Algorithm\": \"MD5\",\n",
-      "              \"Value\": \"e8cd84cbf58487e22450b0aaf8d82165\"\n",
-      "            },\n",
-      "            \"Name\": \"DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5.met\",\n",
-      "            \"Size\": 4.7353515625,\n",
-      "            \"SizeUnit\": \"KB\"\n",
-      "          }\n",
-      "        ],\n",
-      "        \"DayNightFlag\": \"Unspecified\",\n",
-      "        \"Identifiers\": [\n",
-      "          {\n",
-      "            \"Identifier\": \"DSCOVR_EPIC_L2_AER_03_20240101005515_03.he5\",\n",
-      "            \"IdentifierType\": \"ProducerGranuleId\"\n",
-      "          },\n",
-      "          {\n",
-      "            \"Identifier\": \"03\",\n",
-      "            \"IdentifierType\": \"LocalVersionId\"\n",
-      "          }\n",
-      "        ],\n",
-      "        \"ProductionDateTime\": \"2024-01-10T09:10:39+00:00\"\n",
-      "      },\n",
-      "      \"MetadataSpecification\": {\n",
-      "        \"URL\": \"https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6\",\n",
-      "        \"Name\": \"UMM-G\",\n",
-      "        \"Version\": \"1.6.6\"\n",
-      "      }\n",
-      "    },\n",
-      "    \"size\": 0\n",
-      "  }\n",
-      "]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "\n",
-    "print(json.dumps(results[0:1], indent=2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bde99fb2-5f41-4aee-a2d2-df20bc31cf23",
-   "metadata": {},
-   "source": [
-    "### Look at the variables\n",
-    "\n",
-    "We will open a file with datatree and see what groups it has (if any). It does have groups; the latitude, longitude is at the base level: `/` and the product is in `/product`. We want `vertical_column_troposphere` which is the NO2. So we create a profile for TEMPO. We open a dataset with the profile to make sure it looks good."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "71522438-3b18-4000-b413-03d0f5fe9b62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = plan.open_dataset(0, open_method=\"datatree\")\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "bee93b40-db7f-4185-b07b-46a1ed843fd6",
-   "metadata": {},
-   "outputs": [
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2ab341a1ae44870ac1c6922e2e5740d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "open_method: {'xarray_open': 'dataset', 'merge': ['/', '/product'], 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n",
-      "CPU times: user 1.92 s, sys: 438 ms, total: 2.35 s\n",
-      "Wall time: 6.56 s\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44881ae0506b4caba8337d8cf74f058c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, white)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       "html[theme=\"dark\"],\n",
-       "html[data-theme=\"dark\"],\n",
-       "body[data-theme=\"dark\"],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "  line-height: 1.6;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-obj-name,\n",
-       ".xr-group-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name::before {\n",
-       "  content: \"📁\";\n",
-       "  padding-right: 0.3em;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name,\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "  margin-block-start: 0;\n",
-       "  margin-block-end: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84cbe513aff34234a3cb233f50212fde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "260f9a0876324aceb338e81e385dc6bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d279d665df5d44bbbd94d1d2fe6a00c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5f265e72706472693a56799fe9d319a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c69d1d3f3c2a4533a7a9d12eae8ad305",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27446deaa6ad42e6b7a2e79eae671cb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e11a2d90de3457b9125be920fe2aef5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32eba1e25a00440f9219ee0d9604a9e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d77f588b191542f78772990ca132f2a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ce24f5fc488410da429241b19939bd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca5418a74d8344699e94d078aec8e68a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b97c16dda2d44322af73d54b5dde69fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89d9935bcd5c42edab27c9a6f6a0e9af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2c70050cec349a09a357120a1de24fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fd21b31ed9e4a05960b0d2d324c8543",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74ecd00e827240be9ed51e4a329d5f16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ace215f8822744899a3f9503c09bd9d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e5996a853c244e8b8b32fd74e71eed3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9601a1682cc436c94cf8dc607f744b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc13c1a26cc5473b8017d2a513456e37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee159dd6264c4f16835d30dd7310458e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed3430081efd4bce8367da611bb11b3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c073ff9d16444f793515de1f8c82b22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "925e7c6ac98e41a79e7d4619f199c111",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d640460d4734f46834914c23b6ece16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "748258386b464f24a21ce672698bedfd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6c62c1c4e444a44ad499a49b8c20522",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4947ae2025884c129dd196b7138ffcca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c91e0343c784497795a2abc901ed2206",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b80c8eb96fe74234b8e1d9c5a02fb3c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb2da380751f450ba21c3d34ff587d5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d47732bef3304abfbd77a32d589e740d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e704d1fcdd60439899e486739e555b79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ac789c1ad2f4ccfa4e2ad58a9aa967d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24d59f069f4a48029bc6fd0b3d704958",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd5eefd93cf84e90a36789f8822e9a1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6839e76ae5048b095c8d503a57971e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e86a315d48242d7acff05d5be538dfa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be35c9c3e71b476ca70babee72b3e2c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "249c202121ae4be787abe7fe9a659a81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "580d1fb8881248c19b5b1b5d358927c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00dc966fdc954278a9120a5a976c8a4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ceba2d746b3c460785cc21597c18bf5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9de1a553a6b640ca85cebed251ce1654",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae43ea9860f3490898311f838263de8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "584eb6c9e7144124974b8ebc4bfb4477",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32648e99851046529f3c767734258180",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3df4790e55d4f8a9e11c9651de59567",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70b2d68a2db54848a1a6062bec5af0a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48c2cd6b150a4611a26cf63f5d12013b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48bd6fd66a594840aee21adaf78e2a72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import contextlib\n",
+    "\n",
+    "with open(os.devnull, \"w\") as devnull:\n",
+    "    with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n",
+    "        ds = xr.open_datatree(f[0], chunks={})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8c945542-8ed6-4323-9f9c-e6d3927b65af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from IPython.utils import io\n",
+    "\n",
+    "with io.capture_output():\n",
+    "    ds = xr.open_datatree(f[0], chunks={})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e21299f0-46d0-4e49-9501-c48b672d366b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_12837/472810330.py:2: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
+      "  ds = xr.open_datatree(f[0], engine=\"h5netcdf\", chunks={})\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "903588d3de12453f90f3acf1f99b5daa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "deabeeb8166848a6ab66b8ee848f246a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6051b45d3d5d46d7880779237ebe9d2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77134e1c31e343b3bbeb0e3def7bf740",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4657601c0534c96bebd137b09a67098",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f88a5791bcf499997e68f7a2cebcf56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67864c8afb2b4544861de2dff0069d90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b61f56a4c8a4a4bb16a395ef6791fc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8eaf5b63683440daf51c9459087003a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ca13cfd28bb4aec8e5a212bf49eb272",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d93d4043e79a48bbb04ff97bdc21187a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a7dab9661634fbcbd1d5c9b089a296a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae86ad9a424e464c9d9faf096e62ef8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df6e3223234b4569a1de1877aac6e0b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ba8d04e3d58487ca35a04dd4316007a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f45b0fc8565a4c7db293a33780fb8179",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b080065295164439a8bca7eddbfa55db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5638cc8caf24314a1993ad49ea1d915",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb1e91f4ff0a4af6b549c8540871fa02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd7b2dfdf10a4c3792ea5cdcab593999",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3e9d0323b0f4558bb883dfc4cc4107a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "348c3f08098d49ba9068138df4a74210",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0423b3987bd748a087a7221c112b71db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af3468a31f90497784e7fe342b392294",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faf819cc6ee54e7d9d1d764dca78ec3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1db7b829727142c89bffbca50119db08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "649c87430ca647f98b737bb81f60f67b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf1c575f695847dbbc0b9aef586076e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60dd720b3ee04064bc7c2277d99a3545",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cb211b64d6046839b70666afd04f055",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "052c8279a3b14e62bab67e2038b0943a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed95bc1c0be14888b54d7baa127d85d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33b9514c2c5540b3a0f7d608d4c61633",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8155c7cd149e42e2a7b73cf0d7869e64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2192a0f2824444f835cb552aeea04c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63186749cb9e481c87a34f55692ceb06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46a9bdc2766246a28824bad61e1613d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8adec6986d364bb3aba1b655fd58cf88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f99022d73144852960f52b5ce3d0ff7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02c4d1b0f108423590d77b6f19972860",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ccc1a5d87d2942718dc0aacec4ee623c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4000e041446d4a2a8b2f14f71e70d63e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ce3e66b903642a5af063add76af559d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a29fdb71e20747b9a1a305797e195490",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9b60c7cc11144459f2f7c23057c7cb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fecc46a5221441999d3247406d17875e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d14ff728b234e1b8ff55fdde2ad6a56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebe77389577845259660259d81e991df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b1e0537987e4f47a1414c6f7e06827f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2dea070b422e472593372ab9129083cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c55576e7bbf474080c7d10dba9ac043",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b80d1fdff5584632a0e672e0569bda26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78e6d898f30e4748a8d60c86eb981710",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad0d58182a114ef6b97be9b22484ca33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree&gt;\n",
+       "Group: /\n",
+       "├── Group: /HDFEOS\n",
+       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
+       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
+       "│   │           Attributes: (12/22)\n",
+       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
+       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
+       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
+       "│   │               AuthorAffiliation:   NASA/GSFC\n",
+       "│   │               AuthorName:          Omar Torres\n",
+       "│   │               EarthSunDistance:    0.98332226\n",
+       "│   │               ...                  ...\n",
+       "│   │               ProcessingCenter:    TLCF                                                ...\n",
+       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
+       "│   │               ProductionDateTime:  2024-01-10T02:33:28.0Z\n",
+       "│   │               ShortName:           EPICAERUV\n",
+       "│   │               Time_Band340nm:      2024-01-01 00:56:09\n",
+       "│   │               Time_Band388nm:      2024-01-01 00:55:42\n",
+       "│   └── Group: /HDFEOS/SWATHS\n",
+       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
+       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
+       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
+       "│           │                                            phony_dim_4: 2)\n",
+       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "│           │                                       phony_dim_3, phony_dim_4\n",
+       "│           │       Data variables: (12/29)\n",
+       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
+       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
+       "│           │           ...                              ...\n",
+       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;\n",
+       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
+       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
+       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
+       "│                   Data variables:\n",
+       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "└── Group: /HDFEOS INFORMATION\n",
+       "        Dimensions:           ()\n",
+       "        Data variables:\n",
+       "            StructMetadata.0  |S32000 32kB ...\n",
+       "        Attributes:\n",
+       "            HDFEOSVersion:  HDFEOS_5.1.15</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2c907fd3-d7f7-47e0-84c4-7022c45753af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c907fd3-d7f7-47e0-84c4-7022c45753af' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d8041205-6749-4ad4-bf37-5b2bacca28d2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8041205-6749-4ad4-bf37-5b2bacca28d2' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d1f597a4-c3db-4f58-a269-77ca216f4e49' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1f597a4-c3db-4f58-a269-77ca216f4e49' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL/FILE_ATTRIBUTES</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-80858094-567a-4a1a-8268-ea481e78b122' class='xr-section-summary-in' type='checkbox'  ><label for='section-80858094-567a-4a1a-8268-ea481e78b122' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ACF Filename :</span></dt><dd>acf_epic_sm_ffxt_aug2019.txt</dd><dt><span>AIRSCO_filename1 :</span></dt><dd>AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G16130181606.hdf</dd><dt><span>AIRSCO_filename2 :</span></dt><dd>AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G16129174722.hdf</dd><dt><span>AuthorAffiliation :</span></dt><dd>NASA/GSFC</dd><dt><span>AuthorName :</span></dt><dd>Omar Torres</dd><dt><span>EarthSunDistance :</span></dt><dd>0.98332226</dd><dt><span>GranuleDay :</span></dt><dd>1</dd><dt><span>GranuleMonth :</span></dt><dd>1</dd><dt><span>GranuleYear :</span></dt><dd>2024</dd><dt><span>InputPointer :</span></dt><dd>epic_1b_20240101005515_03.h5                                                                        </dd><dt><span>InstrumentName :</span></dt><dd>EPIC</dd><dt><span>LocalGranuleID :</span></dt><dd>EPIC-DSCOVR_L2-EPICAERUV_2024m0101t005515_v01-00-2024m0110t023328.he5</dd><dt><span>LongName :</span></dt><dd>EPIC/DSCOVR Near UV Aerosol Index L2 Image 20x20km</dd><dt><span>PGEVersion :</span></dt><dd>0.5.0               </dd><dt><span>Platform :</span></dt><dd>DSCOVR</dd><dt><span>ProcessLevel :</span></dt><dd>L2</dd><dt><span>ProcessingCenter :</span></dt><dd>TLCF                                                                                                                                                                                                    </dd><dt><span>ProcessingHost :</span></dt><dd>Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP                                                                                                                                                    </dd><dt><span>ProductionDateTime :</span></dt><dd>2024-01-10T02:33:28.0Z</dd><dt><span>ShortName :</span></dt><dd>EPICAERUV</dd><dt><span>Time_Band340nm :</span></dt><dd>2024-01-01 00:56:09</dd><dt><span>Time_Band388nm :</span></dt><dd>2024-01-01 00:55:42</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c8d522fb-dfb4-4c63-ba71-1564cc489cde' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8d522fb-dfb4-4c63-ba71-1564cc489cde' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-23feb5fc-3a70-40df-aefd-855d0cdd41ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23feb5fc-3a70-40df-aefd-855d0cdd41ce' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-13905af1-3d32-449f-93e4-9d1cd8c88241' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13905af1-3d32-449f-93e4-9d1cd8c88241' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9ca0a550-ccaa-4e41-b548-0e3b4aab005e' class='xr-section-summary-in' type='checkbox'  ><label for='section-9ca0a550-ccaa-4e41-b548-0e3b4aab005e' class='xr-section-summary' >Data variables: <span>(29)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-1ce92145-7ab6-41b9-9bf6-aa82d7309854' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ce92145-7ab6-41b9-9bf6-aa82d7309854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19fb21c1-7068-4dc4-ae9d-941b99a801da' class='xr-var-data-in' type='checkbox'><label for='data-19fb21c1-7068-4dc4-ae9d-941b99a801da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-14c9b5e3-43b3-44fa-8cf2-1d650449a5ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14c9b5e3-43b3-44fa-8cf2-1d650449a5ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fe9c6e4-e1d6-4de0-ad28-533f248ea5be' class='xr-var-data-in' type='checkbox'><label for='data-1fe9c6e4-e1d6-4de0-ad28-533f248ea5be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-8cb789d0-1e97-45b8-b37d-13a587b8bbbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cb789d0-1e97-45b8-b37d-13a587b8bbbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-853869e9-64c6-4463-a636-ab660d7dd7e6' class='xr-var-data-in' type='checkbox'><label for='data-853869e9-64c6-4463-a636-ab660d7dd7e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-798b2e72-6b72-4036-b57b-aded468283f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-798b2e72-6b72-4036-b57b-aded468283f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e1f329f-6009-4807-a535-775f7f2b00f9' class='xr-var-data-in' type='checkbox'><label for='data-1e1f329f-6009-4807-a535-775f7f2b00f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-49bf2367-25f0-42c7-809d-44de8d0a2541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49bf2367-25f0-42c7-809d-44de8d0a2541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b5a1c33-0da2-4ee9-add0-bbdd2cf4edb6' class='xr-var-data-in' type='checkbox'><label for='data-4b5a1c33-0da2-4ee9-add0-bbdd2cf4edb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-2dca004e-f37e-4389-8eaf-3c192a6e2bf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2dca004e-f37e-4389-8eaf-3c192a6e2bf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd2bbbc5-42b8-4512-8bb2-236f4fd81ec6' class='xr-var-data-in' type='checkbox'><label for='data-bd2bbbc5-42b8-4512-8bb2-236f4fd81ec6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-edf54a5c-9d47-4b4c-90a5-d62cbe3fa071' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edf54a5c-9d47-4b4c-90a5-d62cbe3fa071' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d59692b-18fd-46ea-b85e-4ac581af84df' class='xr-var-data-in' type='checkbox'><label for='data-4d59692b-18fd-46ea-b85e-4ac581af84df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-3f40aec6-744e-491a-bd2b-f74a3a0719a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f40aec6-744e-491a-bd2b-f74a3a0719a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f2e6513-54d0-49b9-8aaa-ccfee0e53af0' class='xr-var-data-in' type='checkbox'><label for='data-7f2e6513-54d0-49b9-8aaa-ccfee0e53af0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-3b077a92-e7e2-4560-a9d3-999113efbcd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b077a92-e7e2-4560-a9d3-999113efbcd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c748699-3ad5-4176-8c34-c1b0b8236f96' class='xr-var-data-in' type='checkbox'><label for='data-0c748699-3ad5-4176-8c34-c1b0b8236f96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 80.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"25\" y1=\"0\" x2=\"95\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"95\" y1=\"70\" x2=\"95\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c19aadc2-305b-494c-8b6d-f876fdb5e15f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c19aadc2-305b-494c-8b6d-f876fdb5e15f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb6da25e-e1fb-48a1-8a89-0e9e293475ce' class='xr-var-data-in' type='checkbox'><label for='data-cb6da25e-e1fb-48a1-8a89-0e9e293475ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-0afab51c-10b5-4af4-befe-0ca03537a44c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0afab51c-10b5-4af4-befe-0ca03537a44c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d97b16f-77cc-4b35-95c0-0b99d36cbd51' class='xr-var-data-in' type='checkbox'><label for='data-5d97b16f-77cc-4b35-95c0-0b99d36cbd51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9fbee36a-dd0b-4ef1-b45c-2448cf0d6402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fbee36a-dd0b-4ef1-b45c-2448cf0d6402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2673031e-fb79-441e-9918-6d3862ff3465' class='xr-var-data-in' type='checkbox'><label for='data-2673031e-fb79-441e-9918-6d3862ff3465' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-de44a161-e853-41cc-8d70-9b2757ae2c31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de44a161-e853-41cc-8d70-9b2757ae2c31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fb284f4-b4d0-4de0-8f89-ef64e079869d' class='xr-var-data-in' type='checkbox'><label for='data-9fb284f4-b4d0-4de0-8f89-ef64e079869d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-cbff513d-3f2e-4602-8f2e-127b98556943' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbff513d-3f2e-4602-8f2e-127b98556943' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d3df329-4986-48fe-8664-5814fd151d53' class='xr-var-data-in' type='checkbox'><label for='data-9d3df329-4986-48fe-8664-5814fd151d53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-e4f86327-66af-4494-ad8c-baeee845df7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4f86327-66af-4494-ad8c-baeee845df7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dce646a-8d95-4811-937d-9f216dc664da' class='xr-var-data-in' type='checkbox'><label for='data-2dce646a-8d95-4811-937d-9f216dc664da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-ed00427d-5983-4505-ad99-0dab7499073c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed00427d-5983-4505-ad99-0dab7499073c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f5af1e-c73d-4936-901b-ec5d894c0edc' class='xr-var-data-in' type='checkbox'><label for='data-30f5af1e-c73d-4936-901b-ec5d894c0edc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-cb632dab-1e45-466b-b87e-af7c066fbd10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb632dab-1e45-466b-b87e-af7c066fbd10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbadcb51-910a-48ce-94dc-0174db92c9e6' class='xr-var-data-in' type='checkbox'><label for='data-dbadcb51-910a-48ce-94dc-0174db92c9e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-95f931af-e290-4355-b1de-116f937d7a52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95f931af-e290-4355-b1de-116f937d7a52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc088845-022e-466e-ab5d-64e9bc1b89fa' class='xr-var-data-in' type='checkbox'><label for='data-dc088845-022e-466e-ab5d-64e9bc1b89fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-38e76f9e-c4c7-4873-9ec7-f53128e15795' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e76f9e-c4c7-4873-9ec7-f53128e15795' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b894a6c-05a8-4655-bd14-ea08ac4bc30f' class='xr-var-data-in' type='checkbox'><label for='data-5b894a6c-05a8-4655-bd14-ea08ac4bc30f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-0108e824-5827-4865-9d79-9850a284bcde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0108e824-5827-4865-9d79-9850a284bcde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e064011-ca78-44a2-add8-ce80c825870e' class='xr-var-data-in' type='checkbox'><label for='data-0e064011-ca78-44a2-add8-ce80c825870e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-ec94c4a0-c2e3-4b92-a646-734d74b75ae6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec94c4a0-c2e3-4b92-a646-734d74b75ae6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25f1e12c-9775-4bfa-a4d5-b09fef4a9b03' class='xr-var-data-in' type='checkbox'><label for='data-25f1e12c-9775-4bfa-a4d5-b09fef4a9b03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-13d1de46-9594-4880-9c5a-b41a7598ba0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13d1de46-9594-4880-9c5a-b41a7598ba0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8d28dd8-29d4-484b-b31c-1ab8db51165a' class='xr-var-data-in' type='checkbox'><label for='data-a8d28dd8-29d4-484b-b31c-1ab8db51165a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-1352369b-aa3c-4b55-aec9-922f9b318269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1352369b-aa3c-4b55-aec9-922f9b318269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-632ddc6b-bc6c-42a9-aa2e-1400ed2be7c4' class='xr-var-data-in' type='checkbox'><label for='data-632ddc6b-bc6c-42a9-aa2e-1400ed2be7c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-53f0ed8f-7a1a-4bd0-a904-01b043f924f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53f0ed8f-7a1a-4bd0-a904-01b043f924f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ba3ce14-de34-4e7a-b7e8-cedc73440827' class='xr-var-data-in' type='checkbox'><label for='data-3ba3ce14-de34-4e7a-b7e8-cedc73440827' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-e2ba669d-d424-476a-a646-7dc6d9356b5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2ba669d-d424-476a-a646-7dc6d9356b5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fb4488c-80ff-4121-9c95-0501ba31e205' class='xr-var-data-in' type='checkbox'><label for='data-1fb4488c-80ff-4121-9c95-0501ba31e205' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-e5def52a-236c-4b57-a345-73e2dfffcebf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5def52a-236c-4b57-a345-73e2dfffcebf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e33ec6c4-959a-4479-aa87-76d11a46faf3' class='xr-var-data-in' type='checkbox'><label for='data-e33ec6c4-959a-4479-aa87-76d11a46faf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c84bd895-92cf-48c5-93b1-c405f10f9d51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c84bd895-92cf-48c5-93b1-c405f10f9d51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60c4f2ff-8310-4a19-8811-d8df8d85f79c' class='xr-var-data-in' type='checkbox'><label for='data-60c4f2ff-8310-4a19-8811-d8df8d85f79c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4e747d63-fa50-4682-8676-3f544dcd5f74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e747d63-fa50-4682-8676-3f544dcd5f74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11f8dfdc-cb2f-48aa-b98b-b6807af4d156' class='xr-var-data-in' type='checkbox'><label for='data-11f8dfdc-cb2f-48aa-b98b-b6807af4d156' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-661a8c33-2cf1-44bf-b5e6-d539898ae9e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-661a8c33-2cf1-44bf-b5e6-d539898ae9e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca79b032-7820-4ed5-bdf3-9227ebd865bb' class='xr-var-data-in' type='checkbox'><label for='data-ca79b032-7820-4ed5-bdf3-9227ebd865bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12 B </td><td> 12 B </td></tr><tr><th> Shape </th><td> (3,) </td><td> (3,) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"93\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"43\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,43.88583450710766 0.0,43.88583450710766\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"63.885835\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"140.000000\" y=\"21.942917\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.942917)\">1</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8be816d1-5f9b-4aa5-9df5-1e30b07141da' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8be816d1-5f9b-4aa5-9df5-1e30b07141da' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_5</span>: 2048</li><li><span>phony_dim_6</span>: 2048</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1a4841d9-21d0-461e-9b0c-3e1b56d342b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a4841d9-21d0-461e-9b0c-3e1b56d342b0' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-45c02cd1-fae3-424d-8177-eb77b9cf6d3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45c02cd1-fae3-424d-8177-eb77b9cf6d3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cfed04a-93c5-4c7f-aafd-c7cb8d155e37' class='xr-var-data-in' type='checkbox'><label for='data-1cfed04a-93c5-4c7f-aafd-c7cb8d155e37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-f0cdefa4-d09c-40ef-9a91-a8f8b3d2a16b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0cdefa4-d09c-40ef-9a91-a8f8b3d2a16b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-896fe660-6543-4622-839b-f685df3759e5' class='xr-var-data-in' type='checkbox'><label for='data-896fe660-6543-4622-839b-f685df3759e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a7a7a557-5aec-468e-8854-84610431e147' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a7a557-5aec-468e-8854-84610431e147' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9444d10-cdaf-473c-b635-b1023df471c6' class='xr-var-data-in' type='checkbox'><label for='data-c9444d10-cdaf-473c-b635-b1023df471c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-fa3a391a-432e-4c1e-a147-b6f5a90798f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa3a391a-432e-4c1e-a147-b6f5a90798f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d84f8206-62cb-4a26-bef6-7e5b2a57713d' class='xr-var-data-in' type='checkbox'><label for='data-d84f8206-62cb-4a26-bef6-7e5b2a57713d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-840bf9b7-8593-4e66-8baa-b5f41dd3d5a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-840bf9b7-8593-4e66-8baa-b5f41dd3d5a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fe84a51-1958-4ce4-896c-42a4433fcf08' class='xr-var-data-in' type='checkbox'><label for='data-7fe84a51-1958-4ce4-896c-42a4433fcf08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6e041492-ae27-41de-ae4b-69825f61a912' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e041492-ae27-41de-ae4b-69825f61a912' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87e77af8-fa10-4df7-887f-2c66c781b86f' class='xr-var-data-in' type='checkbox'><label for='data-87e77af8-fa10-4df7-887f-2c66c781b86f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4b11ca8d-ef1d-4429-95fe-4924eae82d44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b11ca8d-ef1d-4429-95fe-4924eae82d44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e73cc0a5-f997-40ba-b947-1c077e133c4c' class='xr-var-data-in' type='checkbox'><label for='data-e73cc0a5-f997-40ba-b947-1c077e133c4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS INFORMATION</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9a34c645-6499-4d32-b818-a05a4b7077b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9a34c645-6499-4d32-b818-a05a4b7077b5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8f4d0ccb-8187-4acc-bf17-c16f512ca5ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f4d0ccb-8187-4acc-bf17-c16f512ca5ba' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>StructMetadata.0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S32000</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ec33d3f5-229c-44af-bbba-59de49b2509f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec33d3f5-229c-44af-bbba-59de49b2509f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14e304cc-7b0e-43ad-84c3-befd4b15b12d' class='xr-var-data-in' type='checkbox'><label for='data-14e304cc-7b0e-43ad-84c3-befd4b15b12d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S32000]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c4a4ca1f-b125-42c7-89ef-213335c969fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c4a4ca1f-b125-42c7-89ef-213335c969fe' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>HDFEOSVersion :</span></dt><dd>HDFEOS_5.1.15</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataTree>\n",
+       "Group: /\n",
+       "├── Group: /HDFEOS\n",
+       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
+       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
+       "│   │           Attributes: (12/22)\n",
+       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
+       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
+       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
+       "│   │               AuthorAffiliation:   NASA/GSFC\n",
+       "│   │               AuthorName:          Omar Torres\n",
+       "│   │               EarthSunDistance:    0.98332226\n",
+       "│   │               ...                  ...\n",
+       "│   │               ProcessingCenter:    TLCF                                                ...\n",
+       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
+       "│   │               ProductionDateTime:  2024-01-10T02:33:28.0Z\n",
+       "│   │               ShortName:           EPICAERUV\n",
+       "│   │               Time_Band340nm:      2024-01-01 00:56:09\n",
+       "│   │               Time_Band388nm:      2024-01-01 00:55:42\n",
+       "│   └── Group: /HDFEOS/SWATHS\n",
+       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
+       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
+       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
+       "│           │                                            phony_dim_4: 2)\n",
+       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "│           │                                       phony_dim_3, phony_dim_4\n",
+       "│           │       Data variables: (12/29)\n",
+       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
+       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
+       "│           │           ...                              ...\n",
+       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>\n",
+       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
+       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
+       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
+       "│                   Data variables:\n",
+       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "└── Group: /HDFEOS INFORMATION\n",
+       "        Dimensions:           ()\n",
+       "        Data variables:\n",
+       "            StructMetadata.0  |S32000 32kB ...\n",
+       "        Attributes:\n",
+       "            HDFEOSVersion:  HDFEOS_5.1.15"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f = earthaccess.open(results[0:1], pqdm_kwargs={\"disable\": True})\n",
+    "ds = xr.open_datatree(f[0], engine=\"h5netcdf\", chunks={})\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bde99fb2-5f41-4aee-a2d2-df20bc31cf23",
+   "metadata": {},
+   "source": [
+    "### Look at the variables\n",
+    "\n",
+    "We will open a file with datatree and see what groups it has (if any)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "71522438-3b18-4000-b413-03d0f5fe9b62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GOT HERE\n",
+      "open_method: {'xarray_open': 'dataset', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n",
+      "BEFORE OPEN_DATASET\n",
+      "set()\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c22f39495b241c9b018a4483c0f21e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff0970dabc824aa896aa0ea2f6a6495e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c6b4085354545feabc19035d88e946e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9e68a37ba214f9892ff16c1b54f8e76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78986c85628c4b97aad57685e3af403a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0b44aa588e246acb9a7722e772c63e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GOT HERE 3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<File-like object HTTPFileSystem, https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/02/DSCOVR_EPIC_L2_AER_03_20240208015633_03.he5>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = plan.open_dataset(1, open_method=\"dataset\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f73990e0-f5dc-4628-a7b1-aec2a53d4b5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fc0930322af4272878c26082cb84461",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05e454dae56a4acf9b5a27f440d956ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8b2da6752b34cfaa7ecd6ed1caa09c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "989f9baf04db470e9d6e84dac178f534",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9c5875ff50f41ceb314b691227ec096",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7ee932782964c50b5d74d1663348474",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 0B\n",
+       "Dimensions:  ()\n",
+       "Data variables:\n",
+       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0b75d328-7c78-4a47-9498-0202822f1e60' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0b75d328-7c78-4a47-9498-0202822f1e60' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a3570f9d-1a1f-4f19-a670-77493737dc45' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3570f9d-1a1f-4f19-a670-77493737dc45' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 0B\n",
+       "Dimensions:  ()\n",
+       "Data variables:\n",
+       "    *empty*"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "xr.open_dataset(ds, chunks={}, engine='h5netcdf', decode_timedelta= False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a8b3bef1-e77f-4631-bfbd-48ff6908d86d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fe3d62917374a299d591debc50b6999",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f36c66a7a54a4c54bd515f77b2b87a81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17b3cad8251e45ef96a21d1d9378a699",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0705dce810754efbbf7804df7320753a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc150c0dc81e4339901f58e0471930c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cf65172531941f8b32b67810c23ca0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 0B\n",
+       "Dimensions:  ()\n",
+       "Data variables:\n",
+       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f8a6d47f-71f3-48f8-8f0c-15994a8a7be6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8a6d47f-71f3-48f8-8f0c-15994a8a7be6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1932bf10-fff2-4033-bf68-fe8e0e006602' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1932bf10-fff2-4033-bf68-fe8e0e006602' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 0B\n",
+       "Dimensions:  ()\n",
+       "Data variables:\n",
+       "    *empty*"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "xr.open_dataset(ds, chunks={})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64d26059-d6cc-4703-b9f6-805d2f081772",
+   "metadata": {},
+   "source": [
+    " It does have groups; the latitude, longitude is at the base level: `/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields` and the product is in `/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields`. We want `UVAerosolIndex`. We create a profile. We open a dataset with the profile to make sure it looks good."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bee93b40-db7f-4185-b07b-46a1ed843fd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "open_method: {'xarray_open': 'dataset', 'merge': ['/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields', '/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields'], 'open_kwargs': {'phony_dims': 'access', 'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13f0f23d73294f88a72abab222ea1ff3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eeab332e7caa4c72a688d430567b18f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68e107ee593a4059827246f81243e75f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1d815d793e0448b9e416d63e89a3744",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8529af93b7844469855d052111fece7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3a82e9cabb645b0a71de995a7b314d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd50c4e50bb84a2fbae4d4e0c63e8baf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2ef95d4e8c84c8091121e92fb2f7785",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c827eb5657f4737bb86c8a6181a4ed6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a716bc1f921c45b1af1d58332ddcbad8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5884d59606a14e008844312e947a20fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06181b3561f846729ba0cc1124419a0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 270 ms, sys: 61.4 ms, total: 331 ms\n",
+      "Wall time: 13.2 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
+       "Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "                                     phony_dim_2: 5, phony_dim_3: 3,\n",
+       "                                     phony_dim_4: 2)\n",
+       "Coordinates:\n",
+       "    Latitude                        (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    Longitude                       (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "                                phony_dim_3, phony_dim_4\n",
+       "Data variables: (12/34)\n",
+       "    RelativeAzimuthAngle            (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    SnowIce_fraction                (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    SolarZenithAngle                (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    TerrainPressure                 (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    ViewingZenithAngle              (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    ...                              ...\n",
+       "    Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "    Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "    SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "    Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-48e1283a-312b-445b-bc68-b354579a7ed8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48e1283a-312b-445b-bc68-b354579a7ed8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7002e10c-0b7c-493a-8741-fcc16d0cb27e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7002e10c-0b7c-493a-8741-fcc16d0cb27e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-5df47a39-9c9e-4f18-a921-8b3a1513dd29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5df47a39-9c9e-4f18-a921-8b3a1513dd29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d1d431f-f69d-4a8d-b5e6-2f92e20467b9' class='xr-var-data-in' type='checkbox'><label for='data-9d1d431f-f69d-4a8d-b5e6-2f92e20467b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-3357a138-9a6e-4696-9d4c-c3e782bc450f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3357a138-9a6e-4696-9d4c-c3e782bc450f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d17b4f4-8428-46ce-97a5-d9318c786735' class='xr-var-data-in' type='checkbox'><label for='data-7d17b4f4-8428-46ce-97a5-d9318c786735' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-81e122bf-d119-484b-8136-ff04367d4f22' class='xr-section-summary-in' type='checkbox'  ><label for='section-81e122bf-d119-484b-8136-ff04367d4f22' class='xr-section-summary' >Data variables: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c4b8c706-4ac4-4c5e-b155-759dce2ad7f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4b8c706-4ac4-4c5e-b155-759dce2ad7f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04cc7966-364a-4184-b112-ae43a57df2ff' class='xr-var-data-in' type='checkbox'><label for='data-04cc7966-364a-4184-b112-ae43a57df2ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-aa9d4e6b-1fd8-4d40-9860-f00ee0c0922f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa9d4e6b-1fd8-4d40-9860-f00ee0c0922f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1928007-b642-4977-ac6c-a14c7fd8ce92' class='xr-var-data-in' type='checkbox'><label for='data-d1928007-b642-4977-ac6c-a14c7fd8ce92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6ed03a6f-bfcb-48e2-9df3-b526104ee180' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ed03a6f-bfcb-48e2-9df3-b526104ee180' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1879abc-a684-44ef-a690-514fae135964' class='xr-var-data-in' type='checkbox'><label for='data-e1879abc-a684-44ef-a690-514fae135964' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a7c00bf2-662d-447f-8dbe-6d8542ad7170' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7c00bf2-662d-447f-8dbe-6d8542ad7170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74a77dcc-c664-43d9-a820-8ddf490df098' class='xr-var-data-in' type='checkbox'><label for='data-74a77dcc-c664-43d9-a820-8ddf490df098' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-eaf2871e-a9fa-4c6b-bcdf-8226a41cc217' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaf2871e-a9fa-4c6b-bcdf-8226a41cc217' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62f6a825-22ee-4854-9ad3-1f081ebf3c73' class='xr-var-data-in' type='checkbox'><label for='data-62f6a825-22ee-4854-9ad3-1f081ebf3c73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-cf6c3bc8-3701-409f-a838-b218c6bc508b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf6c3bc8-3701-409f-a838-b218c6bc508b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0ecd527-58aa-454a-a322-f95e97931858' class='xr-var-data-in' type='checkbox'><label for='data-e0ecd527-58aa-454a-a322-f95e97931858' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-1de46cf5-60ce-4ab7-b54d-f502263d37df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1de46cf5-60ce-4ab7-b54d-f502263d37df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78780253-cc7b-4640-9b43-62fa6db68ad3' class='xr-var-data-in' type='checkbox'><label for='data-78780253-cc7b-4640-9b43-62fa6db68ad3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-a6646869-7dcf-4de4-ac92-580806f67e80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6646869-7dcf-4de4-ac92-580806f67e80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64fb610a-07f5-4a06-9a72-e4994e6d4570' class='xr-var-data-in' type='checkbox'><label for='data-64fb610a-07f5-4a06-9a72-e4994e6d4570' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 240.00 MiB </td>\n",
+       "                        <td> 15.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 5, 3) </td>\n",
+       "                        <td> (256, 1024, 5, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" />\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" />\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text>\n",
+       "  <text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c6e657c6-b114-4736-b0f7-470bd46f3cda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6e657c6-b114-4736-b0f7-470bd46f3cda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f55c3472-e7a8-4ecb-ab84-013e52c6b571' class='xr-var-data-in' type='checkbox'><label for='data-f55c3472-e7a8-4ecb-ab84-013e52c6b571' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-2f3a1445-392c-467d-9580-1d91ca23ff4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f3a1445-392c-467d-9580-1d91ca23ff4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71cde08b-a305-4b59-acf5-502d216cb809' class='xr-var-data-in' type='checkbox'><label for='data-71cde08b-a305-4b59-acf5-502d216cb809' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 48.00 MiB </td>\n",
+       "                        <td> 2.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 3) </td>\n",
+       "                        <td> (256, 1024, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-1af0a9d3-8feb-4848-96e1-3649d51c2aa6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1af0a9d3-8feb-4848-96e1-3649d51c2aa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-834d09b3-c28c-446c-8fbe-3baec6f6b5fb' class='xr-var-data-in' type='checkbox'><label for='data-834d09b3-c28c-446c-8fbe-3baec6f6b5fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 240.00 MiB </td>\n",
+       "                        <td> 15.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 5, 3) </td>\n",
+       "                        <td> (256, 1024, 5, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" />\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" />\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text>\n",
+       "  <text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-11b90939-238a-43eb-a6d3-59dc8d613413' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11b90939-238a-43eb-a6d3-59dc8d613413' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cefe0ef-87c4-4b42-ac9a-20844f1bd6d8' class='xr-var-data-in' type='checkbox'><label for='data-0cefe0ef-87c4-4b42-ac9a-20844f1bd6d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 240.00 MiB </td>\n",
+       "                        <td> 15.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 5, 3) </td>\n",
+       "                        <td> (256, 1024, 5, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" />\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" />\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text>\n",
+       "  <text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-53a06e44-9da9-4a17-a236-a1ddfbc8bc39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53a06e44-9da9-4a17-a236-a1ddfbc8bc39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-753e90af-82dd-43cc-9fc3-8beea7c7ceb9' class='xr-var-data-in' type='checkbox'><label for='data-753e90af-82dd-43cc-9fc3-8beea7c7ceb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-4968be7e-2bff-4dbf-8e59-578b909e8b92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4968be7e-2bff-4dbf-8e59-578b909e8b92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a157f9e6-83de-4741-99b0-691b1f06236d' class='xr-var-data-in' type='checkbox'><label for='data-a157f9e6-83de-4741-99b0-691b1f06236d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.00 MiB </td>\n",
+       "                        <td> 3.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 5) </td>\n",
+       "                        <td> (256, 1024, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"95\" y2=\"70\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"95\" y1=\"70\" x2=\"95\" y2=\"190\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2bf688b3-1f71-4321-b016-1d4e5283ef2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bf688b3-1f71-4321-b016-1d4e5283ef2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-505671ab-5b9f-4713-9327-1620f6c17cff' class='xr-var-data-in' type='checkbox'><label for='data-505671ab-5b9f-4713-9327-1620f6c17cff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-54403ef4-deb4-4888-b2ce-8144a607d7d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54403ef4-deb4-4888-b2ce-8144a607d7d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b5e3281-cd61-40db-a890-e678b5b7336d' class='xr-var-data-in' type='checkbox'><label for='data-3b5e3281-cd61-40db-a890-e678b5b7336d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-84e4860d-0b36-41a8-a705-8ca580e5ca98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84e4860d-0b36-41a8-a705-8ca580e5ca98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d91af4cc-d860-40ad-bed4-adbd6470e846' class='xr-var-data-in' type='checkbox'><label for='data-d91af4cc-d860-40ad-bed4-adbd6470e846' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-63938d07-4d0d-4d37-b005-b3b4b39ff329' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63938d07-4d0d-4d37-b005-b3b4b39ff329' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee94a8f2-6bdb-472f-bf3b-6825015f7dc9' class='xr-var-data-in' type='checkbox'><label for='data-ee94a8f2-6bdb-472f-bf3b-6825015f7dc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 48.00 MiB </td>\n",
+       "                        <td> 3.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 3) </td>\n",
+       "                        <td> (256, 1024, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-507f8a0a-2907-45e2-a45d-9f7fb55683bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-507f8a0a-2907-45e2-a45d-9f7fb55683bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4836f28b-ce07-4c13-8647-281bd2b77035' class='xr-var-data-in' type='checkbox'><label for='data-4836f28b-ce07-4c13-8647-281bd2b77035' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-8bdcb4dd-f895-422f-ac86-af5b44b09c47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bdcb4dd-f895-422f-ac86-af5b44b09c47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d77f0f24-8d7a-4004-b365-727886eae89a' class='xr-var-data-in' type='checkbox'><label for='data-d77f0f24-8d7a-4004-b365-727886eae89a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 48.00 MiB </td>\n",
+       "                        <td> 3.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 3) </td>\n",
+       "                        <td> (256, 1024, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-0c1b7a5e-f227-4b02-bb12-dad9e0e96076' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c1b7a5e-f227-4b02-bb12-dad9e0e96076' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7b81d40-9752-4e06-a053-33be5577cfd3' class='xr-var-data-in' type='checkbox'><label for='data-c7b81d40-9752-4e06-a053-33be5577cfd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 48.00 MiB </td>\n",
+       "                        <td> 3.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 3) </td>\n",
+       "                        <td> (256, 1024, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-42a7dcba-2214-49c0-a29b-8f1df7119b6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42a7dcba-2214-49c0-a29b-8f1df7119b6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2174edf6-aab0-4ef5-a271-372eb92031e0' class='xr-var-data-in' type='checkbox'><label for='data-2174edf6-aab0-4ef5-a271-372eb92031e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-top: 4px;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-group-box {\n",
-       "  display: inline-grid;\n",
-       "  grid-template-columns: 0px 20px auto;\n",
-       "  width: 100%;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-group-box-vline {\n",
-       "  grid-column-start: 1;\n",
-       "  border-right: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "  width: 0px;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2199e83f-6574-48a1-805f-d300826d6e3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2199e83f-6574-48a1-805f-d300826d6e3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc09115a-ace4-4903-9578-aa4880427275' class='xr-var-data-in' type='checkbox'><label for='data-dc09115a-ace4-4903-9578-aa4880427275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-group-box-hline {\n",
-       "  grid-column-start: 2;\n",
-       "  grid-row-start: 1;\n",
-       "  height: 1em;\n",
-       "  width: 20px;\n",
-       "  border-bottom: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-group-box-contents {\n",
-       "  grid-column-start: 3;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-7223f3a4-631c-4abf-9e89-67a49db3d81e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7223f3a4-631c-4abf-9e89-67a49db3d81e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00bc933a-0f4d-4d47-b65b-0259754727c0' class='xr-var-data-in' type='checkbox'><label for='data-00bc933a-0f4d-4d47-b65b-0259754727c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 32.00 MiB </td>\n",
+       "                        <td> 2.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 2) </td>\n",
+       "                        <td> (256, 1024, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2b2a954b-3883-46ec-8d0f-25a88554e996' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b2a954b-3883-46ec-8d0f-25a88554e996' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f7456e7-b19d-4fce-9486-33068ce62131' class='xr-var-data-in' type='checkbox'><label for='data-7f7456e7-b19d-4fce-9486-33068ce62131' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-4e606902-44cd-40ca-9bfe-e5d6ab95f164' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e606902-44cd-40ca-9bfe-e5d6ab95f164' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d57d6bb-86fe-4f36-b9e6-3d490f6cc81c' class='xr-var-data-in' type='checkbox'><label for='data-0d57d6bb-86fe-4f36-b9e6-3d490f6cc81c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 240.00 MiB </td>\n",
+       "                        <td> 15.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 5, 3) </td>\n",
+       "                        <td> (256, 1024, 5, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
        "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
        "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" />\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" />\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text>\n",
+       "  <text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-99952786-addc-42cb-92eb-5c0e18c30a5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99952786-addc-42cb-92eb-5c0e18c30a5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7918573-e246-4a3c-baf0-1bac53e5c132' class='xr-var-data-in' type='checkbox'><label for='data-b7918573-e246-4a3c-baf0-1bac53e5c132' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 48.00 MiB </td>\n",
+       "                        <td> 2.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 3) </td>\n",
+       "                        <td> (256, 1024, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 732MB\n",
-       "Dimensions:                                  (latitude: 2950, longitude: 7750,\n",
-       "                                              time: 1)\n",
-       "Coordinates:\n",
-       "  * latitude                                 (latitude) float32 12kB 14.01 .....\n",
-       "  * longitude                                (longitude) float32 31kB -168.0 ...\n",
-       "  * time                                     (time) datetime64[ns] 8B 2024-01...\n",
-       "Data variables:\n",
-       "    weight                                   (latitude, longitude) float32 91MB dask.array&lt;chunksize=(590, 1550), meta=np.ndarray&gt;\n",
-       "    vertical_column_troposphere              (time, latitude, longitude) float64 183MB dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;\n",
-       "    vertical_column_troposphere_uncertainty  (time, latitude, longitude) float64 183MB dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;\n",
-       "    vertical_column_stratosphere             (time, latitude, longitude) float64 183MB dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;\n",
-       "    main_data_quality_flag                   (time, latitude, longitude) float32 91MB dask.array&lt;chunksize=(1, 984, 2584), meta=np.ndarray&gt;\n",
-       "Attributes: (12/40)\n",
-       "    history:                          2024-08-07T13:18:52Z: L2_regrid -v /tem...\n",
-       "    scan_num:                         1\n",
-       "    time_coverage_start:              2024-01-03T12:52:54Z\n",
-       "    time_coverage_end:                2024-01-03T13:32:40Z\n",
-       "    time_coverage_start_since_epoch:  1388321592.148252\n",
-       "    time_coverage_end_since_epoch:    1388323978.7368417\n",
-       "    ...                               ...\n",
-       "    title:                            TEMPO Level 3 nitrogen dioxide product\n",
-       "    collection_shortname:             TEMPO_NO2_L3\n",
-       "    collection_version:               1\n",
-       "    keywords:                         EARTH SCIENCE&gt;ATMOSPHERE&gt;AIR QUALITY&gt;NI...\n",
-       "    summary:                          Nitrogen dioxide Level 3 files provide ...\n",
-       "    coremetadata:                     \\nGROUP                  = INVENTORYMET...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-aab8919e-e084-4d52-8d4a-38348a4a0586' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aab8919e-e084-4d52-8d4a-38348a4a0586' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 2950</li><li><span class='xr-has-index'>longitude</span>: 7750</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e6356620-8943-49a0-a12e-d8a3ec2a012c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6356620-8943-49a0-a12e-d8a3ec2a012c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.01 14.03 14.05 ... 72.97 72.99</div><input id='attrs-d6d88def-6e99-46fd-ae87-e5bdd7630460' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6d88def-6e99-46fd-ae87-e5bdd7630460' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8c13918-8fc6-411b-8f05-b18fb27a5b54' class='xr-var-data-in' type='checkbox'><label for='data-e8c13918-8fc6-411b-8f05-b18fb27a5b54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>comment :</span></dt><dd>latitude at grid box center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([14.01, 14.03, 14.05, ..., 72.95, 72.97, 72.99],\n",
-       "      shape=(2950,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-168.0 -168.0 ... -13.03 -13.01</div><input id='attrs-12c72e31-0492-4a60-a4e8-73fe9fb2902b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12c72e31-0492-4a60-a4e8-73fe9fb2902b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd511b23-375b-4a37-9127-e482d39a9dd5' class='xr-var-data-in' type='checkbox'><label for='data-dd511b23-375b-4a37-9127-e482d39a9dd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>comment :</span></dt><dd>longitude at grid box center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><pre>array([-167.99, -167.97, -167.95, ...,  -13.05,  -13.03,  -13.01],\n",
-       "      shape=(7750,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-03T12:53:12.148251904</div><input id='attrs-07224519-53d0-45a3-a8d8-b1a9a28be46c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07224519-53d0-45a3-a8d8-b1a9a28be46c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18c90b5a-44a9-46a3-bfea-698b3a1c40bc' class='xr-var-data-in' type='checkbox'><label for='data-18c90b5a-44a9-46a3-bfea-698b3a1c40bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>scan start time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-03T12:53:12.148251904&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-850e03d9-a6cc-4c85-b7b1-3c9ec72be723' class='xr-section-summary-in' type='checkbox'  checked><label for='section-850e03d9-a6cc-4c85-b7b1-3c9ec72be723' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>weight</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(590, 1550), meta=np.ndarray&gt;</div><input id='attrs-6250efed-83c7-41d8-998f-0477dc3ea137' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6250efed-83c7-41d8-998f-0477dc3ea137' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bab9109-0f58-4506-b370-80192383bddd' class='xr-var-data-in' type='checkbox'><label for='data-5bab9109-0f58-4506-b370-80192383bddd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sum of area weights</dd><dt><span>comment :</span></dt><dd>sum of Level 2 pixel overlap areas</dd><dt><span>units :</span></dt><dd>km^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-1ab5d4f0-56ad-42de-a4b3-41dd1d0a551e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ab5d4f0-56ad-42de-a4b3-41dd1d0a551e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ca5d996-0a67-47d1-9d7c-770c90f31809' class='xr-var-data-in' type='checkbox'><label for='data-6ca5d996-0a67-47d1-9d7c-770c90f31809' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1158,18 +6715,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 87.21 MiB </td>\n",
-       "                        <td> 3.49 MiB </td>\n",
+       "                        <td> 32.00 MiB </td>\n",
+       "                        <td> 2.00 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (2950, 7750) </td>\n",
-       "                        <td> (590, 1550) </td>\n",
+       "                        <td> (2048, 2048, 2) </td>\n",
+       "                        <td> (256, 1024, 2) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1179,34 +6736,65 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"95\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
-       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
-       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
-       "  <line x1=\"0\" y1=\"36\" x2=\"120\" y2=\"36\" />\n",
-       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"45\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"45\" />\n",
-       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"45\" />\n",
-       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"45\" />\n",
-       "  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"45\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,45.677419354838705 0.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"65.677419\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
-       "  <text x=\"140.000000\" y=\"22.838710\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,22.838710)\">2950</text>\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_column_troposphere</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;</div><input id='attrs-75116e43-91f2-4b40-9669-c20900dd9558' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75116e43-91f2-4b40-9669-c20900dd9558' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e976d88-7f2b-4d0f-b49b-3d89a1350f6f' class='xr-var-data-in' type='checkbox'><label for='data-5e976d88-7f2b-4d0f-b49b-3d89a1350f6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>troposphere nitrogen dioxide vertical column</dd><dt><span>units :</span></dt><dd>molecules/cm^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-ae24df48-437b-4951-a228-9bfec3089c39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae24df48-437b-4951-a228-9bfec3089c39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b98f3188-db43-4268-8ada-655fa981903f' class='xr-var-data-in' type='checkbox'><label for='data-b98f3188-db43-4268-8ada-655fa981903f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1221,14 +6809,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 174.43 MiB </td>\n",
-       "                        <td> 10.91 MiB </td>\n",
+       "                        <td> 32.00 MiB </td>\n",
+       "                        <td> 2.00 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (1, 2950, 7750) </td>\n",
-       "                        <td> (1, 738, 1938) </td>\n",
+       "                        <td> (2048, 2048, 2) </td>\n",
+       "                        <td> (256, 1024, 2) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -1236,67 +6824,71 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
        "                    </tr>\n",
        "                </tbody>\n",
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"194\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"11\" x2=\"24\" y2=\"26\" />\n",
-       "  <line x1=\"10\" y1=\"22\" x2=\"24\" y2=\"37\" />\n",
-       "  <line x1=\"10\" y1=\"34\" x2=\"24\" y2=\"49\" />\n",
-       "  <line x1=\"10\" y1=\"45\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"45\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,60.626017304593105 10.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"40\" y1=\"0\" x2=\"54\" y2=\"14\" />\n",
-       "  <line x1=\"70\" y1=\"0\" x2=\"84\" y2=\"14\" />\n",
-       "  <line x1=\"100\" y1=\"0\" x2=\"114\" y2=\"14\" />\n",
-       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"26\" x2=\"144\" y2=\"26\" />\n",
-       "  <line x1=\"24\" y1=\"37\" x2=\"144\" y2=\"37\" />\n",
-       "  <line x1=\"24\" y1=\"49\" x2=\"144\" y2=\"49\" />\n",
-       "  <line x1=\"24\" y1=\"60\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"54\" y1=\"14\" x2=\"54\" y2=\"60\" />\n",
-       "  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"60\" />\n",
-       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"60\" />\n",
-       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,60.626017304593105 24.9485979497544,60.626017304593105\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"80.626017\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
-       "  <text x=\"164.948598\" y=\"37.787308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,37.787308)\">2950</text>\n",
-       "  <text x=\"7.474299\" y=\"73.151718\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,73.151718)\">1</text>\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_column_troposphere_uncertainty</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;</div><input id='attrs-c9e8a675-bbb0-43e3-96d1-533f144ec06c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9e8a675-bbb0-43e3-96d1-533f144ec06c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59efa2db-1d19-4bfe-a60f-247c92168161' class='xr-var-data-in' type='checkbox'><label for='data-59efa2db-1d19-4bfe-a60f-247c92168161' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>troposphere nitrogen dioxide vertical column uncertainty</dd><dt><span>units :</span></dt><dd>molecules/cm^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-efda2ac2-9ed2-442b-8cbd-ae0cf1a21490' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efda2ac2-9ed2-442b-8cbd-ae0cf1a21490' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d585a7ed-432c-4ba4-9566-21535618d0f3' class='xr-var-data-in' type='checkbox'><label for='data-d585a7ed-432c-4ba4-9566-21535618d0f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1311,14 +6903,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 174.43 MiB </td>\n",
-       "                        <td> 10.91 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (1, 2950, 7750) </td>\n",
-       "                        <td> (1, 738, 1938) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -1326,67 +6918,40 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
        "                    </tr>\n",
        "                </tbody>\n",
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"194\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"11\" x2=\"24\" y2=\"26\" />\n",
-       "  <line x1=\"10\" y1=\"22\" x2=\"24\" y2=\"37\" />\n",
-       "  <line x1=\"10\" y1=\"34\" x2=\"24\" y2=\"49\" />\n",
-       "  <line x1=\"10\" y1=\"45\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"45\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,60.626017304593105 10.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"40\" y1=\"0\" x2=\"54\" y2=\"14\" />\n",
-       "  <line x1=\"70\" y1=\"0\" x2=\"84\" y2=\"14\" />\n",
-       "  <line x1=\"100\" y1=\"0\" x2=\"114\" y2=\"14\" />\n",
-       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"26\" x2=\"144\" y2=\"26\" />\n",
-       "  <line x1=\"24\" y1=\"37\" x2=\"144\" y2=\"37\" />\n",
-       "  <line x1=\"24\" y1=\"49\" x2=\"144\" y2=\"49\" />\n",
-       "  <line x1=\"24\" y1=\"60\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"54\" y1=\"14\" x2=\"54\" y2=\"60\" />\n",
-       "  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"60\" />\n",
-       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"60\" />\n",
-       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,60.626017304593105 24.9485979497544,60.626017304593105\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"80.626017\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
-       "  <text x=\"164.948598\" y=\"37.787308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,37.787308)\">2950</text>\n",
-       "  <text x=\"7.474299\" y=\"73.151718\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,73.151718)\">1</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_column_stratosphere</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 738, 1938), meta=np.ndarray&gt;</div><input id='attrs-953182eb-07fd-474d-b8b3-3976b4a3608c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-953182eb-07fd-474d-b8b3-3976b4a3608c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ea73119-0478-45df-83a5-7d25445bf27c' class='xr-var-data-in' type='checkbox'><label for='data-8ea73119-0478-45df-83a5-7d25445bf27c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>stratosphere nitrogen dioxide vertical column</dd><dt><span>units :</span></dt><dd>molecules/cm^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-321ec0fd-211f-483c-84e1-af2c26633cfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-321ec0fd-211f-483c-84e1-af2c26633cfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f69b381-5783-41e7-a0aa-dc8f5ec39e4e' class='xr-var-data-in' type='checkbox'><label for='data-7f69b381-5783-41e7-a0aa-dc8f5ec39e4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1401,14 +6966,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 174.43 MiB </td>\n",
-       "                        <td> 10.91 MiB </td>\n",
+       "                        <td> 32.00 MiB </td>\n",
+       "                        <td> 2.00 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (1, 2950, 7750) </td>\n",
-       "                        <td> (1, 738, 1938) </td>\n",
+       "                        <td> (2048, 2048, 2) </td>\n",
+       "                        <td> (256, 1024, 2) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -1416,67 +6981,71 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
        "                    </tr>\n",
        "                </tbody>\n",
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"194\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"11\" x2=\"24\" y2=\"26\" />\n",
-       "  <line x1=\"10\" y1=\"22\" x2=\"24\" y2=\"37\" />\n",
-       "  <line x1=\"10\" y1=\"34\" x2=\"24\" y2=\"49\" />\n",
-       "  <line x1=\"10\" y1=\"45\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"45\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,60.626017304593105 10.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"40\" y1=\"0\" x2=\"54\" y2=\"14\" />\n",
-       "  <line x1=\"70\" y1=\"0\" x2=\"84\" y2=\"14\" />\n",
-       "  <line x1=\"100\" y1=\"0\" x2=\"114\" y2=\"14\" />\n",
-       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"26\" x2=\"144\" y2=\"26\" />\n",
-       "  <line x1=\"24\" y1=\"37\" x2=\"144\" y2=\"37\" />\n",
-       "  <line x1=\"24\" y1=\"49\" x2=\"144\" y2=\"49\" />\n",
-       "  <line x1=\"24\" y1=\"60\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"54\" y1=\"14\" x2=\"54\" y2=\"60\" />\n",
-       "  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"60\" />\n",
-       "  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"60\" />\n",
-       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,60.626017304593105 24.9485979497544,60.626017304593105\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"80.626017\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
-       "  <text x=\"164.948598\" y=\"37.787308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,37.787308)\">2950</text>\n",
-       "  <text x=\"7.474299\" y=\"73.151718\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,73.151718)\">1</text>\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>main_data_quality_flag</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 984, 2584), meta=np.ndarray&gt;</div><input id='attrs-d584e624-a6fa-438c-b0cc-7fef15316d7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d584e624-a6fa-438c-b0cc-7fef15316d7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c957554-6d13-415a-8021-b15a2bf19132' class='xr-var-data-in' type='checkbox'><label for='data-9c957554-6d13-415a-8021-b15a2bf19132' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>main data quality flag</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>2</dd><dt><span>flag_meanings :</span></dt><dd>normal suspicious bad</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-fc896f32-d1a3-4337-acfd-30249f39c52e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc896f32-d1a3-4337-acfd-30249f39c52e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11a37291-fbf4-43ba-9ef5-9ba14a248979' class='xr-var-data-in' type='checkbox'><label for='data-11a37291-fbf4-43ba-9ef5-9ba14a248979' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1491,18 +7060,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 87.21 MiB </td>\n",
-       "                        <td> 9.70 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (1, 2950, 7750) </td>\n",
-       "                        <td> (1, 984, 2584) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 9 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1512,234 +7081,192 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"194\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"15\" x2=\"24\" y2=\"30\" />\n",
-       "  <line x1=\"10\" y1=\"30\" x2=\"24\" y2=\"45\" />\n",
-       "  <line x1=\"10\" y1=\"45\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"45\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,60.626017304593105 10.0,45.677419354838705\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-25d5bcd5-2a0d-4868-a8f7-2b56ea471c6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25d5bcd5-2a0d-4868-a8f7-2b56ea471c6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-243fff77-3c35-45d0-add1-b652f39d8b12' class='xr-var-data-in' type='checkbox'><label for='data-243fff77-3c35-45d0-add1-b652f39d8b12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"64\" y2=\"14\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"104\" y2=\"14\" />\n",
-       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-0506a9ca-53ba-404e-ad81-d3e74b808c29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0506a9ca-53ba-404e-ad81-d3e74b808c29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5d73d7-35f9-4c4a-8aca-c4376d8846ee' class='xr-var-data-in' type='checkbox'><label for='data-aa5d73d7-35f9-4c4a-8aca-c4376d8846ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 12 B </td>\n",
+       "                        <td> 12 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (3,) </td>\n",
+       "                        <td> (3,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"93\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"30\" x2=\"144\" y2=\"30\" />\n",
-       "  <line x1=\"24\" y1=\"45\" x2=\"144\" y2=\"45\" />\n",
-       "  <line x1=\"24\" y1=\"60\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"60\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"64\" y1=\"14\" x2=\"64\" y2=\"60\" />\n",
-       "  <line x1=\"104\" y1=\"14\" x2=\"104\" y2=\"60\" />\n",
-       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"43\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,60.626017304593105 24.9485979497544,60.626017304593105\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.88583450710766 0.0,43.88583450710766\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"80.626017\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7750</text>\n",
-       "  <text x=\"164.948598\" y=\"37.787308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,37.787308)\">2950</text>\n",
-       "  <text x=\"7.474299\" y=\"73.151718\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,73.151718)\">1</text>\n",
+       "  <text x=\"60.000000\" y=\"63.885835\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"140.000000\" y=\"21.942917\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.942917)\">1</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-bb6993d7-8fbd-4538-a83e-284f9b452c3a' class='xr-section-summary-in' type='checkbox'  ><label for='section-bb6993d7-8fbd-4538-a83e-284f9b452c3a' class='xr-section-summary' >Attributes: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>history :</span></dt><dd>2024-08-07T13:18:52Z: L2_regrid -v /tempo/nas0/sdpc/liveroot/temposdpc/ops4/etc/l3.cfg\n",
-       "</dd><dt><span>scan_num :</span></dt><dd>1</dd><dt><span>time_coverage_start :</span></dt><dd>2024-01-03T12:52:54Z</dd><dt><span>time_coverage_end :</span></dt><dd>2024-01-03T13:32:40Z</dd><dt><span>time_coverage_start_since_epoch :</span></dt><dd>1388321592.148252</dd><dt><span>time_coverage_end_since_epoch :</span></dt><dd>1388323978.7368417</dd><dt><span>product_type :</span></dt><dd>NO2</dd><dt><span>processing_level :</span></dt><dd>3</dd><dt><span>processing_version :</span></dt><dd>3</dd><dt><span>sdpc_version :</span></dt><dd>TEMPO_SDPC_v4.4.2</dd><dt><span>production_date_time :</span></dt><dd>2024-08-07T13:18:53Z</dd><dt><span>begin_date :</span></dt><dd>2024-01-03</dd><dt><span>begin_time :</span></dt><dd>12:52:54</dd><dt><span>end_date :</span></dt><dd>2024-01-03</dd><dt><span>end_time :</span></dt><dd>13:32:40</dd><dt><span>local_granule_id :</span></dt><dd>TEMPO_NO2_L3_V03_20240103T125254Z_S001.nc</dd><dt><span>version_id :</span></dt><dd>3</dd><dt><span>pge_version :</span></dt><dd>1.0.0</dd><dt><span>shortname :</span></dt><dd>TEMPO_NO2_L3</dd><dt><span>input_files :</span></dt><dd>[&#x27;TEMPO_NO2_L2_V03_20240103T125254Z_S001G01.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T125934Z_S001G02.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T130611Z_S001G03.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T131249Z_S001G04.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T131926Z_S001G05.nc&#x27;, &#x27;TEMPO_NO2_L2_V03_20240103T132603Z_S001G06.nc&#x27;]</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((57.6000 -110.5000,53.5600 -108.3200,49.2200 -106.5200,44.3200 -104.9600,39.6000 -103.8000,33.9600 -102.7400,28.7400 -102.0000,22.8400 -101.3800,17.2600 -100.9600,17.2200 -85.2400,17.4800 -65.1200,21.9400 -64.1200,25.7600 -63.0400,29.6200 -61.7000,32.7800 -60.3800,36.3000 -58.6200,39.4000 -56.7600,42.1800 -54.7800,44.8400 -52.5400,47.7200 -49.6200,50.4000 -46.2800,52.7400 -42.6800,54.7200 -38.9200,56.3800 -35.0200,57.9600 -30.3200,59.1600 -25.6600,60.2400 -20.1000,61.2200 -20.0800,61.2000 -20.2600,61.4800 -20.2800,61.4400 -20.5400,62.8000 -20.5600,62.7200 -21.1000,63.5600 -21.1200,63.4600 -21.8400,63.7200 -21.9600,63.5600 -22.9800,63.4800 -24.4400,62.6600 -30.0600,61.5200 -38.4200,60.7600 -44.1800,59.9600 -51.1600,58.7800 -63.3000,58.2200 -71.1800,57.7800 -80.4600,57.6000 -90.7600,57.7600 -101.2800,58.2000 -110.5000,57.6000 -110.5000))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lon_min :</span></dt><dd>-110.5</dd><dt><span>geospatial_lon_max :</span></dt><dd>-20.080002</dd><dt><span>geospatial_lat_min :</span></dt><dd>17.22</dd><dt><span>geospatial_lat_max :</span></dt><dd>63.719997</dd><dt><span>time_reference :</span></dt><dd>1980-01-06T00:00:00Z</dd><dt><span>day_of_year :</span></dt><dd>3</dd><dt><span>project :</span></dt><dd>TEMPO</dd><dt><span>platform :</span></dt><dd>Intelsat 40e</dd><dt><span>source :</span></dt><dd>UV-VIS hyperspectral imaging</dd><dt><span>institution :</span></dt><dd>Smithsonian Astrophysical Observatory</dd><dt><span>creator_url :</span></dt><dd>http://tempo.si.edu</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>title :</span></dt><dd>TEMPO Level 3 nitrogen dioxide product</dd><dt><span>collection_shortname :</span></dt><dd>TEMPO_NO2_L3</dd><dt><span>collection_version :</span></dt><dd>1</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE&gt;ATMOSPHERE&gt;AIR QUALITY&gt;NITROGEN OXIDES, EARTH SCIENCE&gt;ATMOSPHERE&gt;ATMOSPHERIC CHEMISTRY&gt;NITROGEN COMPOUNDS&gt;NITROGEN DIOXIDE</dd><dt><span>summary :</span></dt><dd>Nitrogen dioxide Level 3 files provide trace gas information on a regular grid covering the TEMPO field of regard for nominal TEMPO observations. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle. The files are provided in netCDF4 format, and contain information on tropospheric, stratospheric and total nitrogen dioxide vertical columns, ancillary data used in air mass factor and stratospheric/tropospheric separation calculations, and retrieval quality flags. The re-gridding algorithm uses an area-weighted approach.</dd><dt><span>coremetadata :</span></dt><dd>\n",
-       "GROUP                  = INVENTORYMETADATA\n",
-       "  GROUPTYPE            = MASTERGROUP\n",
-       "\n",
-       "  GROUP                  = ECSDATAGRANULE\n",
-       "\n",
-       "    OBJECT                 = LOCALGRANULEID\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;TEMPO_NO2_L3_V03_20240103T125254Z_S001.nc&quot;\n",
-       "    END_OBJECT             = LOCALGRANULEID\n",
-       "\n",
-       "    OBJECT                 = LOCALVERSIONID\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = (&quot;RFC1321 MD5 = not yet calculated&quot;)\n",
-       "    END_OBJECT             = LOCALVERSIONID\n",
-       "\n",
-       "    OBJECT                 = PRODUCTIONDATETIME\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;2024-08-07T13:18:53Z&quot;\n",
-       "    END_OBJECT             = PRODUCTIONDATETIME\n",
-       "\n",
-       "  END_GROUP              = ECSDATAGRANULE\n",
-       "\n",
-       "  GROUP                  = COLLECTIONDESCRIPTIONCLASS\n",
-       "\n",
-       "    OBJECT                 = SHORTNAME\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;TEMPO_NO2_L3&quot;\n",
-       "    END_OBJECT             = SHORTNAME\n",
-       "\n",
-       "    OBJECT                 = VERSIONID\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = 3\n",
-       "    END_OBJECT             = VERSIONID\n",
-       "\n",
-       "  END_GROUP              = COLLECTIONDESCRIPTIONCLASS\n",
-       "\n",
-       "  GROUP                  = INPUTGRANULE\n",
-       "\n",
-       "    OBJECT                 = INPUTPOINTER\n",
-       "      NUM_VAL              = 6\n",
-       "      VALUE                = (&quot;TEMPO_NO2_L2_V03_20240103T125254Z_S001G01.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T125934Z_S001G02.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T130611Z_S001G03.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T131249Z_S001G04.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T131926Z_S001G05.nc&quot;, &quot;TEMPO_NO2_L2_V03_20240103T132603Z_S001G06.nc&quot;)\n",
-       "    END_OBJECT             = INPUTPOINTER\n",
-       "\n",
-       "  END_GROUP              = INPUTGRANULE\n",
-       "\n",
-       "  GROUP                  = SPATIALDOMAINCONTAINER\n",
-       "\n",
-       "    GROUP                  = HORIZONTALSPATIALDOMAINCONTAINER\n",
-       "\n",
-       "      GROUP                  = GPOLYGON\n",
-       "\n",
-       "        OBJECT                 = GPOLYGONCONTAINER\n",
-       "          CLASS                = &quot;1&quot;\n",
-       "\n",
-       "          GROUP                  = GRINGPOINT\n",
-       "            CLASS                = &quot;1&quot;\n",
-       "\n",
-       "            OBJECT                 = GRINGPOINTLONGITUDE\n",
-       "              NUM_VAL              = 49\n",
-       "              CLASS                = &quot;1&quot;\n",
-       "              VALUE                = (-110.5, -108.32, -106.52, -104.96, -103.8, -102.74, -102, -101.38, -100.96, -85.24001, -65.12, -64.12, -63.04, -61.7, -60.38, -58.62, -56.76, -54.78, -52.54, -49.62, -46.28001, -42.68, -38.92, -35.02, -30.32001, -25.66, -20.10001, -20.08, -20.26001, -20.28, -20.54001, -20.56, -21.10001, -21.12001, -21.84, -21.96001, -22.98, -24.44, -30.06, -38.42, -44.18, -51.16, -63.3, -71.18, -80.46, -90.76, -101.28, -110.5, -110.5)\n",
-       "            END_OBJECT             = GRINGPOINTLONGITUDE\n",
-       "\n",
-       "            OBJECT                 = GRINGPOINTLATITUDE\n",
-       "              NUM_VAL              = 49\n",
-       "              CLASS                = &quot;1&quot;\n",
-       "              VALUE                = (57.6, 53.56, 49.22, 44.32, 39.6, 33.96, 28.74, 22.84, 17.26, 17.22, 17.48, 21.94, 25.76, 29.62, 32.78, 36.3, 39.4, 42.18, 44.84, 47.72, 50.4, 52.74, 54.72, 56.38, 57.96, 59.16, 60.24, 61.22, 61.2, 61.48, 61.44, 62.8, 62.72, 63.56, 63.46, 63.72, 63.56, 63.48, 62.66, 61.52, 60.76, 59.96, 58.78, 58.22, 57.78, 57.6, 57.76, 58.2, 57.6)\n",
-       "            END_OBJECT             = GRINGPOINTLATITUDE\n",
-       "\n",
-       "            OBJECT                 = GRINGPOINTSEQUENCENO\n",
-       "              NUM_VAL              = 49\n",
-       "              CLASS                = &quot;1&quot;\n",
-       "              VALUE                = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49)\n",
-       "            END_OBJECT             = GRINGPOINTSEQUENCENO\n",
-       "\n",
-       "          END_GROUP              = GRINGPOINT\n",
-       "\n",
-       "          GROUP                  = GRING\n",
-       "            CLASS                = &quot;1&quot;\n",
-       "\n",
-       "            OBJECT                 = EXCLUSIONGRINGFLAG\n",
-       "              NUM_VAL              = 1\n",
-       "              VALUE                = &quot;N&quot;\n",
-       "              CLASS                = &quot;1&quot;\n",
-       "            END_OBJECT             = EXCLUSIONGRINGFLAG\n",
-       "\n",
-       "          END_GROUP              = GRING\n",
-       "\n",
-       "        END_OBJECT             = GPOLYGONCONTAINER\n",
-       "\n",
-       "      END_GROUP              = GPOLYGON\n",
-       "\n",
-       "    END_GROUP              = HORIZONTALSPATIALDOMAINCONTAINER\n",
-       "\n",
-       "  END_GROUP              = SPATIALDOMAINCONTAINER\n",
-       "\n",
-       "  GROUP                  = RANGEDATETIME\n",
-       "\n",
-       "    OBJECT                 = RANGEENDINGDATE\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;2024-01-03&quot;\n",
-       "    END_OBJECT             = RANGEENDINGDATE\n",
-       "\n",
-       "    OBJECT                 = RANGEENDINGTIME\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;13:32:40&quot;\n",
-       "    END_OBJECT             = RANGEENDINGTIME\n",
-       "\n",
-       "    OBJECT                 = RANGEBEGINNINGDATE\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;2024-01-03&quot;\n",
-       "    END_OBJECT             = RANGEBEGINNINGDATE\n",
-       "\n",
-       "    OBJECT                 = RANGEBEGINNINGTIME\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;12:52:54&quot;\n",
-       "    END_OBJECT             = RANGEBEGINNINGTIME\n",
-       "\n",
-       "  END_GROUP              = RANGEDATETIME\n",
-       "\n",
-       "  GROUP                  = PGEVERSIONCLASS\n",
-       "\n",
-       "    OBJECT                 = PGEVERSION\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;1.0.0&quot;\n",
-       "    END_OBJECT             = PGEVERSION\n",
-       "\n",
-       "  END_GROUP              = PGEVERSIONCLASS\n",
-       "\n",
-       "END_GROUP              = INVENTORYMETADATA\n",
-       "\n",
-       "END\n",
-       "</dd></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 732MB\n",
-       "Dimensions:                                  (latitude: 2950, longitude: 7750,\n",
-       "                                              time: 1)\n",
+       "<xarray.Dataset> Size: 2GB\n",
+       "Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "                                     phony_dim_2: 5, phony_dim_3: 3,\n",
+       "                                     phony_dim_4: 2)\n",
        "Coordinates:\n",
-       "  * latitude                                 (latitude) float32 12kB 14.01 .....\n",
-       "  * longitude                                (longitude) float32 31kB -168.0 ...\n",
-       "  * time                                     (time) datetime64[ns] 8B 2024-01...\n",
-       "Data variables:\n",
-       "    weight                                   (latitude, longitude) float32 91MB dask.array<chunksize=(590, 1550), meta=np.ndarray>\n",
-       "    vertical_column_troposphere              (time, latitude, longitude) float64 183MB dask.array<chunksize=(1, 738, 1938), meta=np.ndarray>\n",
-       "    vertical_column_troposphere_uncertainty  (time, latitude, longitude) float64 183MB dask.array<chunksize=(1, 738, 1938), meta=np.ndarray>\n",
-       "    vertical_column_stratosphere             (time, latitude, longitude) float64 183MB dask.array<chunksize=(1, 738, 1938), meta=np.ndarray>\n",
-       "    main_data_quality_flag                   (time, latitude, longitude) float32 91MB dask.array<chunksize=(1, 984, 2584), meta=np.ndarray>\n",
-       "Attributes: (12/40)\n",
-       "    history:                          2024-08-07T13:18:52Z: L2_regrid -v /tem...\n",
-       "    scan_num:                         1\n",
-       "    time_coverage_start:              2024-01-03T12:52:54Z\n",
-       "    time_coverage_end:                2024-01-03T13:32:40Z\n",
-       "    time_coverage_start_since_epoch:  1388321592.148252\n",
-       "    time_coverage_end_since_epoch:    1388323978.7368417\n",
-       "    ...                               ...\n",
-       "    title:                            TEMPO Level 3 nitrogen dioxide product\n",
-       "    collection_shortname:             TEMPO_NO2_L3\n",
-       "    collection_version:               1\n",
-       "    keywords:                         EARTH SCIENCE>ATMOSPHERE>AIR QUALITY>NI...\n",
-       "    summary:                          Nitrogen dioxide Level 3 files provide ...\n",
-       "    coremetadata:                     \\nGROUP                  = INVENTORYMET..."
+       "    Latitude                        (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    Longitude                       (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "                                phony_dim_3, phony_dim_4\n",
+       "Data variables: (12/34)\n",
+       "    RelativeAzimuthAngle            (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    SnowIce_fraction                (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    SolarZenithAngle                (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    TerrainPressure                 (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    ViewingZenithAngle              (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    ...                              ...\n",
+       "    Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "    Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "    SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "    Wavelength                      (phony_dim_3) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "%%time\n",
-    "discovr_epic = {\n",
+    "discovr_epic_aer = {\n",
     "    'xarray_open': 'dataset',\n",
-    "    'merge': ['/', '/product'],\n",
+    "    'merge': ['/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields', '/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields'],\n",
+    "    'open_kwargs': {'phony_dims':'access'}\n",
     "}\n",
-    "ds = plan.open_dataset(0, open_method=tempo)\n",
+    "ds = plan.open_dataset(0, open_method=discovr_epic_aer)\n",
     "ds"
    ]
   },

From d080164d8008cab0de226c4d08a57d0932633c36 Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Mon, 16 Mar 2026 23:28:14 +0000
Subject: [PATCH 06/11] more testing

---
 examples/docs_4_discovr_epic.ipynb | 3050 +++++++---------------------
 1 file changed, 697 insertions(+), 2353 deletions(-)

diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_4_discovr_epic.ipynb
index dcbfd38..5dd650c 100644
--- a/examples/docs_4_discovr_epic.ipynb
+++ b/examples/docs_4_discovr_epic.ipynb
@@ -62,27 +62,6 @@
     "earthaccess.login()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "85551467-0165-4a1a-9877-9b2d972b9b9b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "set()\n"
-     ]
-    }
-   ],
-   "source": [
-    "import xarray as xr\n",
-    "from dask.callbacks import Callback\n",
-    "\n",
-    "print(Callback.active)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "cd008cf7-3bcb-4bf4-8535-7f0e142170bb",
@@ -97,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "id": "2b12e80a-5a0b-4a8c-a89b-e9e4877655b8",
    "metadata": {},
    "outputs": [
@@ -184,7 +163,7 @@
        "222  -8.043907  41.779801 2024-08-31  False"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -212,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "6153eef7-d94d-4c2f-8bd2-a860d737a620",
    "metadata": {},
    "outputs": [
@@ -220,8 +199,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.62 s, sys: 288 ms, total: 1.91 s\n",
-      "Wall time: 15.2 s\n"
+      "CPU times: user 1.04 s, sys: 126 ms, total: 1.16 s\n",
+      "Wall time: 7.67 s\n"
      ]
     }
    ],
@@ -242,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "id": "7c827935-f077-4416-8d10-11858f71db7e",
    "metadata": {},
    "outputs": [
@@ -266,2241 +245,39 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "50e8b368-dc72-4c09-9327-da17a4de931d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/02/DSCOVR_EPIC_L2_AER_03_20240208000830_03.he5'"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "url=plan[0].data_links()[0]\n",
-    "url"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "91a014bb-ab2e-4800-8685-b2b911b55cb7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "import earthaccess\n",
-    "results = earthaccess.search_data(\n",
-    "    short_name=\"DSCOVR_EPIC_L2_AER\",\n",
-    "    temporal = (\"2024-01-01\", \"2024-12-31\"),\n",
-    "    version = \"03\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d78fad1f-5499-4352-9623-c69a2c0a08b0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2698ed4667c64cd4a764e8857e23b3f6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a2ab341a1ae44870ac1c6922e2e5740d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "44881ae0506b4caba8337d8cf74f058c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "84cbe513aff34234a3cb233f50212fde",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "260f9a0876324aceb338e81e385dc6bb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d279d665df5d44bbbd94d1d2fe6a00c3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b5f265e72706472693a56799fe9d319a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c69d1d3f3c2a4533a7a9d12eae8ad305",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "27446deaa6ad42e6b7a2e79eae671cb7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5e11a2d90de3457b9125be920fe2aef5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "32eba1e25a00440f9219ee0d9604a9e9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d77f588b191542f78772990ca132f2a0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6ce24f5fc488410da429241b19939bd2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ca5418a74d8344699e94d078aec8e68a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b97c16dda2d44322af73d54b5dde69fe",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "89d9935bcd5c42edab27c9a6f6a0e9af",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c2c70050cec349a09a357120a1de24fb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4fd21b31ed9e4a05960b0d2d324c8543",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "74ecd00e827240be9ed51e4a329d5f16",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ace215f8822744899a3f9503c09bd9d2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0e5996a853c244e8b8b32fd74e71eed3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a9601a1682cc436c94cf8dc607f744b9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc13c1a26cc5473b8017d2a513456e37",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee159dd6264c4f16835d30dd7310458e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ed3430081efd4bce8367da611bb11b3f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4c073ff9d16444f793515de1f8c82b22",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "925e7c6ac98e41a79e7d4619f199c111",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8d640460d4734f46834914c23b6ece16",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "748258386b464f24a21ce672698bedfd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a6c62c1c4e444a44ad499a49b8c20522",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4947ae2025884c129dd196b7138ffcca",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c91e0343c784497795a2abc901ed2206",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b80c8eb96fe74234b8e1d9c5a02fb3c7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eb2da380751f450ba21c3d34ff587d5d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d47732bef3304abfbd77a32d589e740d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e704d1fcdd60439899e486739e555b79",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7ac789c1ad2f4ccfa4e2ad58a9aa967d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "24d59f069f4a48029bc6fd0b3d704958",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dd5eefd93cf84e90a36789f8822e9a1b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f6839e76ae5048b095c8d503a57971e7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8e86a315d48242d7acff05d5be538dfa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be35c9c3e71b476ca70babee72b3e2c7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "249c202121ae4be787abe7fe9a659a81",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "580d1fb8881248c19b5b1b5d358927c1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "00dc966fdc954278a9120a5a976c8a4c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ceba2d746b3c460785cc21597c18bf5d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9de1a553a6b640ca85cebed251ce1654",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae43ea9860f3490898311f838263de8f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "584eb6c9e7144124974b8ebc4bfb4477",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "32648e99851046529f3c767734258180",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c3df4790e55d4f8a9e11c9651de59567",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "70b2d68a2db54848a1a6062bec5af0a9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "48c2cd6b150a4611a26cf63f5d12013b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "48bd6fd66a594840aee21adaf78e2a72",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import contextlib\n",
-    "\n",
-    "with open(os.devnull, \"w\") as devnull:\n",
-    "    with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n",
-    "        ds = xr.open_datatree(f[0], chunks={})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8c945542-8ed6-4323-9f9c-e6d3927b65af",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from IPython.utils import io\n",
-    "\n",
-    "with io.capture_output():\n",
-    "    ds = xr.open_datatree(f[0], chunks={})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e21299f0-46d0-4e49-9501-c48b672d366b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_12837/472810330.py:2: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
-      "  ds = xr.open_datatree(f[0], engine=\"h5netcdf\", chunks={})\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "903588d3de12453f90f3acf1f99b5daa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "deabeeb8166848a6ab66b8ee848f246a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6051b45d3d5d46d7880779237ebe9d2e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77134e1c31e343b3bbeb0e3def7bf740",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d4657601c0534c96bebd137b09a67098",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6f88a5791bcf499997e68f7a2cebcf56",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "67864c8afb2b4544861de2dff0069d90",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2b61f56a4c8a4a4bb16a395ef6791fc8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b8eaf5b63683440daf51c9459087003a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ca13cfd28bb4aec8e5a212bf49eb272",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d93d4043e79a48bbb04ff97bdc21187a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a7dab9661634fbcbd1d5c9b089a296a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae86ad9a424e464c9d9faf096e62ef8b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "df6e3223234b4569a1de1877aac6e0b6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ba8d04e3d58487ca35a04dd4316007a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f45b0fc8565a4c7db293a33780fb8179",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b080065295164439a8bca7eddbfa55db",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d5638cc8caf24314a1993ad49ea1d915",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb1e91f4ff0a4af6b549c8540871fa02",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dd7b2dfdf10a4c3792ea5cdcab593999",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b3e9d0323b0f4558bb883dfc4cc4107a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "348c3f08098d49ba9068138df4a74210",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0423b3987bd748a087a7221c112b71db",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "af3468a31f90497784e7fe342b392294",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "faf819cc6ee54e7d9d1d764dca78ec3b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1db7b829727142c89bffbca50119db08",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "649c87430ca647f98b737bb81f60f67b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cf1c575f695847dbbc0b9aef586076e1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "60dd720b3ee04064bc7c2277d99a3545",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2cb211b64d6046839b70666afd04f055",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "052c8279a3b14e62bab67e2038b0943a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ed95bc1c0be14888b54d7baa127d85d7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33b9514c2c5540b3a0f7d608d4c61633",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8155c7cd149e42e2a7b73cf0d7869e64",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e2192a0f2824444f835cb552aeea04c8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "63186749cb9e481c87a34f55692ceb06",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "46a9bdc2766246a28824bad61e1613d9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8adec6986d364bb3aba1b655fd58cf88",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6f99022d73144852960f52b5ce3d0ff7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "02c4d1b0f108423590d77b6f19972860",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ccc1a5d87d2942718dc0aacec4ee623c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4000e041446d4a2a8b2f14f71e70d63e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6ce3e66b903642a5af063add76af559d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a29fdb71e20747b9a1a305797e195490",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f9b60c7cc11144459f2f7c23057c7cb5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fecc46a5221441999d3247406d17875e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6d14ff728b234e1b8ff55fdde2ad6a56",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ebe77389577845259660259d81e991df",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5b1e0537987e4f47a1414c6f7e06827f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2dea070b422e472593372ab9129083cf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9c55576e7bbf474080c7d10dba9ac043",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b80d1fdff5584632a0e672e0569bda26",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78e6d898f30e4748a8d60c86eb981710",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ad0d58182a114ef6b97be9b22484ca33",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, white)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       "html[theme=\"dark\"],\n",
-       "html[data-theme=\"dark\"],\n",
-       "body[data-theme=\"dark\"],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "  line-height: 1.6;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-obj-name,\n",
-       ".xr-group-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name::before {\n",
-       "  content: \"📁\";\n",
-       "  padding-right: 0.3em;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name,\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "  margin-block-start: 0;\n",
-       "  margin-block-end: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-top: 4px;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box {\n",
-       "  display: inline-grid;\n",
-       "  grid-template-columns: 0px 20px auto;\n",
-       "  width: 100%;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-vline {\n",
-       "  grid-column-start: 1;\n",
-       "  border-right: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "  width: 0px;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-hline {\n",
-       "  grid-column-start: 2;\n",
-       "  grid-row-start: 1;\n",
-       "  height: 1em;\n",
-       "  width: 20px;\n",
-       "  border-bottom: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-contents {\n",
-       "  grid-column-start: 3;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree&gt;\n",
-       "Group: /\n",
-       "├── Group: /HDFEOS\n",
-       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
-       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
-       "│   │           Attributes: (12/22)\n",
-       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
-       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
-       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
-       "│   │               AuthorAffiliation:   NASA/GSFC\n",
-       "│   │               AuthorName:          Omar Torres\n",
-       "│   │               EarthSunDistance:    0.98332226\n",
-       "│   │               ...                  ...\n",
-       "│   │               ProcessingCenter:    TLCF                                                ...\n",
-       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
-       "│   │               ProductionDateTime:  2024-01-10T02:33:28.0Z\n",
-       "│   │               ShortName:           EPICAERUV\n",
-       "│   │               Time_Band340nm:      2024-01-01 00:56:09\n",
-       "│   │               Time_Band388nm:      2024-01-01 00:55:42\n",
-       "│   └── Group: /HDFEOS/SWATHS\n",
-       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
-       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
-       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
-       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
-       "│           │                                            phony_dim_4: 2)\n",
-       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
-       "│           │                                       phony_dim_3, phony_dim_4\n",
-       "│           │       Data variables: (12/29)\n",
-       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
-       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
-       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
-       "│           │           ...                              ...\n",
-       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
-       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
-       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;\n",
-       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
-       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
-       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
-       "│                   Data variables:\n",
-       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "└── Group: /HDFEOS INFORMATION\n",
-       "        Dimensions:           ()\n",
-       "        Data variables:\n",
-       "            StructMetadata.0  |S32000 32kB ...\n",
-       "        Attributes:\n",
-       "            HDFEOSVersion:  HDFEOS_5.1.15</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2c907fd3-d7f7-47e0-84c4-7022c45753af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c907fd3-d7f7-47e0-84c4-7022c45753af' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d8041205-6749-4ad4-bf37-5b2bacca28d2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8041205-6749-4ad4-bf37-5b2bacca28d2' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d1f597a4-c3db-4f58-a269-77ca216f4e49' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1f597a4-c3db-4f58-a269-77ca216f4e49' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL/FILE_ATTRIBUTES</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-80858094-567a-4a1a-8268-ea481e78b122' class='xr-section-summary-in' type='checkbox'  ><label for='section-80858094-567a-4a1a-8268-ea481e78b122' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ACF Filename :</span></dt><dd>acf_epic_sm_ffxt_aug2019.txt</dd><dt><span>AIRSCO_filename1 :</span></dt><dd>AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G16130181606.hdf</dd><dt><span>AIRSCO_filename2 :</span></dt><dd>AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G16129174722.hdf</dd><dt><span>AuthorAffiliation :</span></dt><dd>NASA/GSFC</dd><dt><span>AuthorName :</span></dt><dd>Omar Torres</dd><dt><span>EarthSunDistance :</span></dt><dd>0.98332226</dd><dt><span>GranuleDay :</span></dt><dd>1</dd><dt><span>GranuleMonth :</span></dt><dd>1</dd><dt><span>GranuleYear :</span></dt><dd>2024</dd><dt><span>InputPointer :</span></dt><dd>epic_1b_20240101005515_03.h5                                                                        </dd><dt><span>InstrumentName :</span></dt><dd>EPIC</dd><dt><span>LocalGranuleID :</span></dt><dd>EPIC-DSCOVR_L2-EPICAERUV_2024m0101t005515_v01-00-2024m0110t023328.he5</dd><dt><span>LongName :</span></dt><dd>EPIC/DSCOVR Near UV Aerosol Index L2 Image 20x20km</dd><dt><span>PGEVersion :</span></dt><dd>0.5.0               </dd><dt><span>Platform :</span></dt><dd>DSCOVR</dd><dt><span>ProcessLevel :</span></dt><dd>L2</dd><dt><span>ProcessingCenter :</span></dt><dd>TLCF                                                                                                                                                                                                    </dd><dt><span>ProcessingHost :</span></dt><dd>Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP                                                                                                                                                    </dd><dt><span>ProductionDateTime :</span></dt><dd>2024-01-10T02:33:28.0Z</dd><dt><span>ShortName :</span></dt><dd>EPICAERUV</dd><dt><span>Time_Band340nm :</span></dt><dd>2024-01-01 00:56:09</dd><dt><span>Time_Band388nm :</span></dt><dd>2024-01-01 00:55:42</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c8d522fb-dfb4-4c63-ba71-1564cc489cde' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8d522fb-dfb4-4c63-ba71-1564cc489cde' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-23feb5fc-3a70-40df-aefd-855d0cdd41ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23feb5fc-3a70-40df-aefd-855d0cdd41ce' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-13905af1-3d32-449f-93e4-9d1cd8c88241' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13905af1-3d32-449f-93e4-9d1cd8c88241' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9ca0a550-ccaa-4e41-b548-0e3b4aab005e' class='xr-section-summary-in' type='checkbox'  ><label for='section-9ca0a550-ccaa-4e41-b548-0e3b4aab005e' class='xr-section-summary' >Data variables: <span>(29)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-1ce92145-7ab6-41b9-9bf6-aa82d7309854' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ce92145-7ab6-41b9-9bf6-aa82d7309854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19fb21c1-7068-4dc4-ae9d-941b99a801da' class='xr-var-data-in' type='checkbox'><label for='data-19fb21c1-7068-4dc4-ae9d-941b99a801da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-14c9b5e3-43b3-44fa-8cf2-1d650449a5ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14c9b5e3-43b3-44fa-8cf2-1d650449a5ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fe9c6e4-e1d6-4de0-ad28-533f248ea5be' class='xr-var-data-in' type='checkbox'><label for='data-1fe9c6e4-e1d6-4de0-ad28-533f248ea5be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-8cb789d0-1e97-45b8-b37d-13a587b8bbbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cb789d0-1e97-45b8-b37d-13a587b8bbbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-853869e9-64c6-4463-a636-ab660d7dd7e6' class='xr-var-data-in' type='checkbox'><label for='data-853869e9-64c6-4463-a636-ab660d7dd7e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-798b2e72-6b72-4036-b57b-aded468283f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-798b2e72-6b72-4036-b57b-aded468283f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e1f329f-6009-4807-a535-775f7f2b00f9' class='xr-var-data-in' type='checkbox'><label for='data-1e1f329f-6009-4807-a535-775f7f2b00f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-49bf2367-25f0-42c7-809d-44de8d0a2541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49bf2367-25f0-42c7-809d-44de8d0a2541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b5a1c33-0da2-4ee9-add0-bbdd2cf4edb6' class='xr-var-data-in' type='checkbox'><label for='data-4b5a1c33-0da2-4ee9-add0-bbdd2cf4edb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-2dca004e-f37e-4389-8eaf-3c192a6e2bf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2dca004e-f37e-4389-8eaf-3c192a6e2bf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd2bbbc5-42b8-4512-8bb2-236f4fd81ec6' class='xr-var-data-in' type='checkbox'><label for='data-bd2bbbc5-42b8-4512-8bb2-236f4fd81ec6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-edf54a5c-9d47-4b4c-90a5-d62cbe3fa071' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edf54a5c-9d47-4b4c-90a5-d62cbe3fa071' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d59692b-18fd-46ea-b85e-4ac581af84df' class='xr-var-data-in' type='checkbox'><label for='data-4d59692b-18fd-46ea-b85e-4ac581af84df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-3f40aec6-744e-491a-bd2b-f74a3a0719a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f40aec6-744e-491a-bd2b-f74a3a0719a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f2e6513-54d0-49b9-8aaa-ccfee0e53af0' class='xr-var-data-in' type='checkbox'><label for='data-7f2e6513-54d0-49b9-8aaa-ccfee0e53af0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-3b077a92-e7e2-4560-a9d3-999113efbcd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b077a92-e7e2-4560-a9d3-999113efbcd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c748699-3ad5-4176-8c34-c1b0b8236f96' class='xr-var-data-in' type='checkbox'><label for='data-0c748699-3ad5-4176-8c34-c1b0b8236f96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 80.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"25\" y1=\"0\" x2=\"95\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"95\" y1=\"70\" x2=\"95\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c19aadc2-305b-494c-8b6d-f876fdb5e15f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c19aadc2-305b-494c-8b6d-f876fdb5e15f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb6da25e-e1fb-48a1-8a89-0e9e293475ce' class='xr-var-data-in' type='checkbox'><label for='data-cb6da25e-e1fb-48a1-8a89-0e9e293475ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-0afab51c-10b5-4af4-befe-0ca03537a44c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0afab51c-10b5-4af4-befe-0ca03537a44c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d97b16f-77cc-4b35-95c0-0b99d36cbd51' class='xr-var-data-in' type='checkbox'><label for='data-5d97b16f-77cc-4b35-95c0-0b99d36cbd51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9fbee36a-dd0b-4ef1-b45c-2448cf0d6402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fbee36a-dd0b-4ef1-b45c-2448cf0d6402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2673031e-fb79-441e-9918-6d3862ff3465' class='xr-var-data-in' type='checkbox'><label for='data-2673031e-fb79-441e-9918-6d3862ff3465' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-de44a161-e853-41cc-8d70-9b2757ae2c31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de44a161-e853-41cc-8d70-9b2757ae2c31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fb284f4-b4d0-4de0-8f89-ef64e079869d' class='xr-var-data-in' type='checkbox'><label for='data-9fb284f4-b4d0-4de0-8f89-ef64e079869d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-cbff513d-3f2e-4602-8f2e-127b98556943' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbff513d-3f2e-4602-8f2e-127b98556943' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d3df329-4986-48fe-8664-5814fd151d53' class='xr-var-data-in' type='checkbox'><label for='data-9d3df329-4986-48fe-8664-5814fd151d53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-e4f86327-66af-4494-ad8c-baeee845df7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4f86327-66af-4494-ad8c-baeee845df7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dce646a-8d95-4811-937d-9f216dc664da' class='xr-var-data-in' type='checkbox'><label for='data-2dce646a-8d95-4811-937d-9f216dc664da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-ed00427d-5983-4505-ad99-0dab7499073c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed00427d-5983-4505-ad99-0dab7499073c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f5af1e-c73d-4936-901b-ec5d894c0edc' class='xr-var-data-in' type='checkbox'><label for='data-30f5af1e-c73d-4936-901b-ec5d894c0edc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-cb632dab-1e45-466b-b87e-af7c066fbd10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb632dab-1e45-466b-b87e-af7c066fbd10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbadcb51-910a-48ce-94dc-0174db92c9e6' class='xr-var-data-in' type='checkbox'><label for='data-dbadcb51-910a-48ce-94dc-0174db92c9e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-95f931af-e290-4355-b1de-116f937d7a52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95f931af-e290-4355-b1de-116f937d7a52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc088845-022e-466e-ab5d-64e9bc1b89fa' class='xr-var-data-in' type='checkbox'><label for='data-dc088845-022e-466e-ab5d-64e9bc1b89fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-38e76f9e-c4c7-4873-9ec7-f53128e15795' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e76f9e-c4c7-4873-9ec7-f53128e15795' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b894a6c-05a8-4655-bd14-ea08ac4bc30f' class='xr-var-data-in' type='checkbox'><label for='data-5b894a6c-05a8-4655-bd14-ea08ac4bc30f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-0108e824-5827-4865-9d79-9850a284bcde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0108e824-5827-4865-9d79-9850a284bcde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e064011-ca78-44a2-add8-ce80c825870e' class='xr-var-data-in' type='checkbox'><label for='data-0e064011-ca78-44a2-add8-ce80c825870e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-ec94c4a0-c2e3-4b92-a646-734d74b75ae6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec94c4a0-c2e3-4b92-a646-734d74b75ae6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25f1e12c-9775-4bfa-a4d5-b09fef4a9b03' class='xr-var-data-in' type='checkbox'><label for='data-25f1e12c-9775-4bfa-a4d5-b09fef4a9b03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-13d1de46-9594-4880-9c5a-b41a7598ba0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13d1de46-9594-4880-9c5a-b41a7598ba0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8d28dd8-29d4-484b-b31c-1ab8db51165a' class='xr-var-data-in' type='checkbox'><label for='data-a8d28dd8-29d4-484b-b31c-1ab8db51165a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-1352369b-aa3c-4b55-aec9-922f9b318269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1352369b-aa3c-4b55-aec9-922f9b318269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-632ddc6b-bc6c-42a9-aa2e-1400ed2be7c4' class='xr-var-data-in' type='checkbox'><label for='data-632ddc6b-bc6c-42a9-aa2e-1400ed2be7c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-53f0ed8f-7a1a-4bd0-a904-01b043f924f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53f0ed8f-7a1a-4bd0-a904-01b043f924f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ba3ce14-de34-4e7a-b7e8-cedc73440827' class='xr-var-data-in' type='checkbox'><label for='data-3ba3ce14-de34-4e7a-b7e8-cedc73440827' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-e2ba669d-d424-476a-a646-7dc6d9356b5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2ba669d-d424-476a-a646-7dc6d9356b5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fb4488c-80ff-4121-9c95-0501ba31e205' class='xr-var-data-in' type='checkbox'><label for='data-1fb4488c-80ff-4121-9c95-0501ba31e205' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-e5def52a-236c-4b57-a345-73e2dfffcebf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5def52a-236c-4b57-a345-73e2dfffcebf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e33ec6c4-959a-4479-aa87-76d11a46faf3' class='xr-var-data-in' type='checkbox'><label for='data-e33ec6c4-959a-4479-aa87-76d11a46faf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c84bd895-92cf-48c5-93b1-c405f10f9d51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c84bd895-92cf-48c5-93b1-c405f10f9d51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60c4f2ff-8310-4a19-8811-d8df8d85f79c' class='xr-var-data-in' type='checkbox'><label for='data-60c4f2ff-8310-4a19-8811-d8df8d85f79c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4e747d63-fa50-4682-8676-3f544dcd5f74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e747d63-fa50-4682-8676-3f544dcd5f74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11f8dfdc-cb2f-48aa-b98b-b6807af4d156' class='xr-var-data-in' type='checkbox'><label for='data-11f8dfdc-cb2f-48aa-b98b-b6807af4d156' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-661a8c33-2cf1-44bf-b5e6-d539898ae9e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-661a8c33-2cf1-44bf-b5e6-d539898ae9e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca79b032-7820-4ed5-bdf3-9227ebd865bb' class='xr-var-data-in' type='checkbox'><label for='data-ca79b032-7820-4ed5-bdf3-9227ebd865bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12 B </td><td> 12 B </td></tr><tr><th> Shape </th><td> (3,) </td><td> (3,) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"93\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"43\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,43.88583450710766 0.0,43.88583450710766\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"63.885835\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"140.000000\" y=\"21.942917\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.942917)\">1</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8be816d1-5f9b-4aa5-9df5-1e30b07141da' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8be816d1-5f9b-4aa5-9df5-1e30b07141da' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_5</span>: 2048</li><li><span>phony_dim_6</span>: 2048</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1a4841d9-21d0-461e-9b0c-3e1b56d342b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a4841d9-21d0-461e-9b0c-3e1b56d342b0' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-45c02cd1-fae3-424d-8177-eb77b9cf6d3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45c02cd1-fae3-424d-8177-eb77b9cf6d3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cfed04a-93c5-4c7f-aafd-c7cb8d155e37' class='xr-var-data-in' type='checkbox'><label for='data-1cfed04a-93c5-4c7f-aafd-c7cb8d155e37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-f0cdefa4-d09c-40ef-9a91-a8f8b3d2a16b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0cdefa4-d09c-40ef-9a91-a8f8b3d2a16b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-896fe660-6543-4622-839b-f685df3759e5' class='xr-var-data-in' type='checkbox'><label for='data-896fe660-6543-4622-839b-f685df3759e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a7a7a557-5aec-468e-8854-84610431e147' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a7a557-5aec-468e-8854-84610431e147' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9444d10-cdaf-473c-b635-b1023df471c6' class='xr-var-data-in' type='checkbox'><label for='data-c9444d10-cdaf-473c-b635-b1023df471c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-fa3a391a-432e-4c1e-a147-b6f5a90798f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa3a391a-432e-4c1e-a147-b6f5a90798f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d84f8206-62cb-4a26-bef6-7e5b2a57713d' class='xr-var-data-in' type='checkbox'><label for='data-d84f8206-62cb-4a26-bef6-7e5b2a57713d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-840bf9b7-8593-4e66-8baa-b5f41dd3d5a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-840bf9b7-8593-4e66-8baa-b5f41dd3d5a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fe84a51-1958-4ce4-896c-42a4433fcf08' class='xr-var-data-in' type='checkbox'><label for='data-7fe84a51-1958-4ce4-896c-42a4433fcf08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6e041492-ae27-41de-ae4b-69825f61a912' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e041492-ae27-41de-ae4b-69825f61a912' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87e77af8-fa10-4df7-887f-2c66c781b86f' class='xr-var-data-in' type='checkbox'><label for='data-87e77af8-fa10-4df7-887f-2c66c781b86f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4b11ca8d-ef1d-4429-95fe-4924eae82d44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b11ca8d-ef1d-4429-95fe-4924eae82d44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e73cc0a5-f997-40ba-b947-1c077e133c4c' class='xr-var-data-in' type='checkbox'><label for='data-e73cc0a5-f997-40ba-b947-1c077e133c4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS INFORMATION</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9a34c645-6499-4d32-b818-a05a4b7077b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9a34c645-6499-4d32-b818-a05a4b7077b5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8f4d0ccb-8187-4acc-bf17-c16f512ca5ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f4d0ccb-8187-4acc-bf17-c16f512ca5ba' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>StructMetadata.0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S32000</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ec33d3f5-229c-44af-bbba-59de49b2509f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec33d3f5-229c-44af-bbba-59de49b2509f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14e304cc-7b0e-43ad-84c3-befd4b15b12d' class='xr-var-data-in' type='checkbox'><label for='data-14e304cc-7b0e-43ad-84c3-befd4b15b12d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S32000]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c4a4ca1f-b125-42c7-89ef-213335c969fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c4a4ca1f-b125-42c7-89ef-213335c969fe' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>HDFEOSVersion :</span></dt><dd>HDFEOS_5.1.15</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataTree>\n",
-       "Group: /\n",
-       "├── Group: /HDFEOS\n",
-       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
-       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
-       "│   │           Attributes: (12/22)\n",
-       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
-       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
-       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
-       "│   │               AuthorAffiliation:   NASA/GSFC\n",
-       "│   │               AuthorName:          Omar Torres\n",
-       "│   │               EarthSunDistance:    0.98332226\n",
-       "│   │               ...                  ...\n",
-       "│   │               ProcessingCenter:    TLCF                                                ...\n",
-       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
-       "│   │               ProductionDateTime:  2024-01-10T02:33:28.0Z\n",
-       "│   │               ShortName:           EPICAERUV\n",
-       "│   │               Time_Band340nm:      2024-01-01 00:56:09\n",
-       "│   │               Time_Band388nm:      2024-01-01 00:55:42\n",
-       "│   └── Group: /HDFEOS/SWATHS\n",
-       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
-       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
-       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
-       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
-       "│           │                                            phony_dim_4: 2)\n",
-       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
-       "│           │                                       phony_dim_3, phony_dim_4\n",
-       "│           │       Data variables: (12/29)\n",
-       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
-       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
-       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
-       "│           │           ...                              ...\n",
-       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
-       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
-       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>\n",
-       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
-       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
-       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
-       "│                   Data variables:\n",
-       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "└── Group: /HDFEOS INFORMATION\n",
-       "        Dimensions:           ()\n",
-       "        Data variables:\n",
-       "            StructMetadata.0  |S32000 32kB ...\n",
-       "        Attributes:\n",
-       "            HDFEOSVersion:  HDFEOS_5.1.15"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f = earthaccess.open(results[0:1], pqdm_kwargs={\"disable\": True})\n",
-    "ds = xr.open_datatree(f[0], engine=\"h5netcdf\", chunks={})\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bde99fb2-5f41-4aee-a2d2-df20bc31cf23",
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "91a014bb-ab2e-4800-8685-b2b911b55cb7",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Look at the variables\n",
-    "\n",
-    "We will open a file with datatree and see what groups it has (if any)."
+    "import xarray as xr\n",
+    "import earthaccess\n",
+    "results = earthaccess.search_data(\n",
+    "    short_name=\"DSCOVR_EPIC_L2_AER\",\n",
+    "    temporal = (\"2024-01-01\", \"2024-12-31\"),\n",
+    "    version = \"03\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "71522438-3b18-4000-b413-03d0f5fe9b62",
+   "execution_count": 32,
+   "id": "e21299f0-46d0-4e49-9501-c48b672d366b",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "GOT HERE\n",
-      "open_method: {'xarray_open': 'dataset', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n",
-      "BEFORE OPEN_DATASET\n",
-      "set()\n"
+      "/tmp/ipykernel_12837/585827809.py:2: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
+      "  ds = xr.open_dataset(f[0], engine=\"h5netcdf\", chunks={}, group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\")\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2c22f39495b241c9b018a4483c0f21e5",
+       "model_id": "823e6847191245ff99d46a089c044ef3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2514,7 +291,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff0970dabc824aa896aa0ea2f6a6495e",
+       "model_id": "d8c2bdbb61e248f2ab692759f748c5d4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2528,7 +305,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8c6b4085354545feabc19035d88e946e",
+       "model_id": "5107661e25a14e2799dbc08e394ab35c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2542,7 +319,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d9e68a37ba214f9892ff16c1b54f8e76",
+       "model_id": "60fedfc76b594713a51271598449002c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2556,7 +333,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78986c85628c4b97aad57685e3af403a",
+       "model_id": "74982de8f71f4dbf93c506f405480b26",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2570,7 +347,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b0b44aa588e246acb9a7722e772c63e5",
+       "model_id": "090e19ff9b5041b2bf510de9db9ca526",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2581,39 +358,40 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GOT HERE 3\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<File-like object HTTPFileSystem, https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/02/DSCOVR_EPIC_L2_AER_03_20240208015633_03.he5>"
+       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       ...,\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "      shape=(2048, 2048), dtype=float32)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ds = plan.open_dataset(1, open_method=\"dataset\")\n",
-    "ds"
+    "f = earthaccess.open(results[100:101], pqdm_kwargs={\"disable\": True})\n",
+    "ds = xr.open_dataset(f[0], engine=\"h5netcdf\", chunks={}, group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\")\n",
+    "ds.Latitude.values"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "f73990e0-f5dc-4628-a7b1-aec2a53d4b5d",
+   "execution_count": 46,
+   "id": "4d1e0b9c-9d2e-49da-842a-861e287a1949",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8fc0930322af4272878c26082cb84461",
+       "model_id": "1ac1e89b100a4d1ebe384852d009d949",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2627,7 +405,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05e454dae56a4acf9b5a27f440d956ea",
+       "model_id": "56f4eccafdbb4ca6bc7ad60af622d32a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2641,7 +419,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d8b2da6752b34cfaa7ecd6ed1caa09c3",
+       "model_id": "44186b330f344a86a7a38734a34ff28f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2652,15 +430,153 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "text/plain": [
+       "[PosixPath('/home/jovyan/point-collocation/examples/data/2026-03-16-b2ebee/DSCOVR_EPIC_L2_AER_03_20230201001752_03.he5')]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "earthaccess.download(results[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "c30e1de2-57ab-4298-b675-fd9be8c9d174",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "import numpy as np\n",
+    "import json\n",
+    "\n",
+    "paths = {\n",
+    "    \"lat\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields/Latitude\",\n",
+    "    \"lon\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields/Longitude\",\n",
+    "    \"uvai\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields/UVAerosolIndex\",\n",
+    "    \"aod\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields/FinalAerosolOpticalDepth\",\n",
+    "    \"wavelength\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields/Wavelength\",\n",
+    "}\n",
+    "\n",
+    "with h5py.File(f[0], \"r\") as h:\n",
+    "    lat = h[paths[\"lat\"]][500:700, 800:1000]\n",
+    "    lon = h[paths[\"lon\"]][500:700, 800:1000]\n",
+    "    uvai = h[paths[\"uvai\"]][500:700, 800:1000]\n",
+    "\n",
+    "np.savez(\n",
+    "    \"epic_aer_subset.npz\",\n",
+    "    lat=lat,\n",
+    "    lon=lon,\n",
+    "    uvai=uvai,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "77857ef4-72f7-4f0a-a0c6-b8e6975480e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "sample = {\n",
+    "    \"lat\": lat[500:510, 800:810].tolist(),\n",
+    "    \"lon\": lon[500:510, 800:810].tolist(),\n",
+    "    \"uvai\": uvai[500:510, 800:810].tolist(),\n",
+    "}\n",
+    "\n",
+    "with open(\"epic_debug_sample.json\", \"w\") as f:\n",
+    "    json.dump(sample, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "db2d2f92-150d-4a43-9310-3a7cd52a234f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_12837/2386113382.py:1: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
+      "  dt = xr.open_datatree(f[0])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       ...,\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "      shape=(2048, 2048), dtype=float32)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt = xr.open_datatree(f[0])\n",
+    "dt[\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\"].Latitude.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e5f1e060-0840-4c45-b416-b00af857c4f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "short_name = \"DSCOVR_EPIC_L2_AER\"\n",
+    "version = \"03\"\n",
+    "\n",
+    "results = earthaccess.search_data(\n",
+    "    short_name=short_name,\n",
+    "    version=version,\n",
+    "    temporal=(\"2023-02-01T00:00:00\", \"2023-02-01T04:59:59\"),\n",
+    ")\n",
+    "len(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "54ed4aed-102a-44da-9a64-a6fdd62d4544",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "989f9baf04db470e9d6e84dac178f534",
+       "model_id": "cf56c7382e97477caa0e9d9d153ad4a4",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+       "QUEUEING TASKS | :   0%|          | 0/3 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -2669,12 +585,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e9c5875ff50f41ceb314b691227ec096",
+       "model_id": "d2f6c0e0102f4b4582b116b1a1c331b6",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+       "PROCESSING TASKS | :   0%|          | 0/3 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -2683,17 +599,87 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7ee932782964c50b5d74d1663348474",
+       "model_id": "76f3ba01ac334ae3873dd3d9c2a42fe0",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+       "COLLECTING RESULTS | :   0%|          | 0/3 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_12837/1323344285.py:3: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
+      "  ds = xr.open_dataset(f[0], group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "f = earthaccess.open(results)\n",
+    "ds = xr.open_dataset(f[0], group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "83e8fa6c-2b40-4145-9e9c-3bb9660c1546",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_12837/1882213165.py:1: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
+      "  ds = xr.open_dataset(\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds = xr.open_dataset(\n",
+    "    f[0],\n",
+    "    group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\",\n",
+    "    mask_and_scale=False,\n",
+    "    decode_cf=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "f08e47db-1a6c-4de2-876d-a16614e270ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1.2676506e+30 65.678856\n",
+      "[-1.2676506e+30]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import h5py\n",
+    "import numpy as np\n",
+    "\n",
+    "with h5py.File(f[0], \"r\") as h:\n",
+    "    lat = h[\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields/Latitude\"][:]\n",
+    "    print(np.nanmin(lat), np.nanmax(lat))\n",
+    "    print(np.unique(lat[:10, :10]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "0bb168a4-a130-40b7-9a22-3f4114591930",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/html": [
@@ -3175,38 +1161,177 @@
        "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
        "  stroke-width: 0.8px;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 0B\n",
-       "Dimensions:  ()\n",
-       "Data variables:\n",
-       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0b75d328-7c78-4a47-9498-0202822f1e60' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0b75d328-7c78-4a47-9498-0202822f1e60' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a3570f9d-1a1f-4f19-a670-77493737dc45' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3570f9d-1a1f-4f19-a670-77493737dc45' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li></ul></div></div>"
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Latitude&#x27; ()&gt; Size: 1B\n",
+       "array(True)\n",
+       "Attributes:\n",
+       "    MissingValue:  -1.2676506e+30\n",
+       "    Title:         Geodetic Latitude (deg)\n",
+       "    Units:         deg\n",
+       "    _FillValue:    -1.2676506e+30</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;Latitude&#x27;</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b431aaa4-6f57-446d-a7ac-03db26765e98' class='xr-array-in' type='checkbox' checked><label for='section-b431aaa4-6f57-446d-a7ac-03db26765e98' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>True</span></div><div class='xr-array-data'><pre>array(True)</pre></div></div></li><li class='xr-section-item'><input id='section-058d4ef9-e9e2-45a2-9008-17d7f574bb8a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-058d4ef9-e9e2-45a2-9008-17d7f574bb8a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd><dt><span>_FillValue :</span></dt><dd>-1.2676506e+30</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 0B\n",
-       "Dimensions:  ()\n",
-       "Data variables:\n",
-       "    *empty*"
+       "<xarray.DataArray 'Latitude' ()> Size: 1B\n",
+       "array(True)\n",
+       "Attributes:\n",
+       "    MissingValue:  -1.2676506e+30\n",
+       "    Title:         Geodetic Latitude (deg)\n",
+       "    Units:         deg\n",
+       "    _FillValue:    -1.2676506e+30"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fill = ds.Latitude.attrs.get(\"_FillValue\")\n",
+    "(ds.Latitude != fill).any().compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bde99fb2-5f41-4aee-a2d2-df20bc31cf23",
+   "metadata": {},
+   "source": [
+    "### Look at the variables\n",
+    "\n",
+    "We will open a file with datatree and see what groups it has (if any)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "71522438-3b18-4000-b413-03d0f5fe9b62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GOT HERE\n",
+      "open_method: {'xarray_open': 'dataset', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n",
+      "BEFORE OPEN_DATASET\n",
+      "set()\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c22f39495b241c9b018a4483c0f21e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff0970dabc824aa896aa0ea2f6a6495e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c6b4085354545feabc19035d88e946e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9e68a37ba214f9892ff16c1b54f8e76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
-     "execution_count": 5,
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78986c85628c4b97aad57685e3af403a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0b44aa588e246acb9a7722e772c63e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GOT HERE 3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<File-like object HTTPFileSystem, https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/02/DSCOVR_EPIC_L2_AER_03_20240208015633_03.he5>"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import xarray as xr\n",
-    "xr.open_dataset(ds, chunks={}, engine='h5netcdf', decode_timedelta= False)"
+    "ds = plan.open_dataset(1, open_method=\"dataset\")\n",
+    "ds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "a8b3bef1-e77f-4631-bfbd-48ff6908d86d",
+   "execution_count": 5,
+   "id": "f73990e0-f5dc-4628-a7b1-aec2a53d4b5d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9fe3d62917374a299d591debc50b6999",
+       "model_id": "8fc0930322af4272878c26082cb84461",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3220,7 +1345,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f36c66a7a54a4c54bd515f77b2b87a81",
+       "model_id": "05e454dae56a4acf9b5a27f440d956ea",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3234,7 +1359,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "17b3cad8251e45ef96a21d1d9378a699",
+       "model_id": "d8b2da6752b34cfaa7ecd6ed1caa09c3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3248,7 +1373,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0705dce810754efbbf7804df7320753a",
+       "model_id": "989f9baf04db470e9d6e84dac178f534",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3262,7 +1387,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bc150c0dc81e4339901f58e0471930c9",
+       "model_id": "e9c5875ff50f41ceb314b691227ec096",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3276,7 +1401,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2cf65172531941f8b32b67810c23ca0e",
+       "model_id": "d7ee932782964c50b5d74d1663348474",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3771,7 +1896,7 @@
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 0B\n",
        "Dimensions:  ()\n",
        "Data variables:\n",
-       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f8a6d47f-71f3-48f8-8f0c-15994a8a7be6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8a6d47f-71f3-48f8-8f0c-15994a8a7be6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1932bf10-fff2-4033-bf68-fe8e0e006602' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1932bf10-fff2-4033-bf68-fe8e0e006602' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li></ul></div></div>"
+       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0b75d328-7c78-4a47-9498-0202822f1e60' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0b75d328-7c78-4a47-9498-0202822f1e60' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a3570f9d-1a1f-4f19-a670-77493737dc45' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3570f9d-1a1f-4f19-a670-77493737dc45' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 0B\n",
@@ -3780,14 +1905,14 @@
        "    *empty*"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import xarray as xr\n",
-    "xr.open_dataset(ds, chunks={})"
+    "xr.open_dataset(ds, chunks={}, engine='h5netcdf', decode_timedelta= False)"
    ]
   },
   {
@@ -3800,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 29,
    "id": "bee93b40-db7f-4185-b07b-46a1ed843fd6",
    "metadata": {},
    "outputs": [
@@ -3814,7 +1939,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13f0f23d73294f88a72abab222ea1ff3",
+       "model_id": "a3da6a0d0b0d40659547c986e1004f96",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3828,7 +1953,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eeab332e7caa4c72a688d430567b18f4",
+       "model_id": "24c42a0d50eb45b7a7695b92bc141c7b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3842,7 +1967,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "68e107ee593a4059827246f81243e75f",
+       "model_id": "5f170c2a240f4ed58be72fab740a4cf2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3856,7 +1981,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d1d815d793e0448b9e416d63e89a3744",
+       "model_id": "deec2c3df89d42daa17fdf0cca449491",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3870,7 +1995,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b8529af93b7844469855d052111fece7",
+       "model_id": "c72b4876ca6342adadff8a87c9f15d46",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3884,7 +2009,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a3a82e9cabb645b0a71de995a7b314d8",
+       "model_id": "6b900ed19d1f458489afe5e4df6dfa6f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3898,7 +2023,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bd50c4e50bb84a2fbae4d4e0c63e8baf",
+       "model_id": "afca26291d17416fa666201218e3c009",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3912,7 +2037,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d2ef95d4e8c84c8091121e92fb2f7785",
+       "model_id": "80df650fe60f4116bede2a2c9ea8420e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3926,7 +2051,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1c827eb5657f4737bb86c8a6181a4ed6",
+       "model_id": "57e5b5ad2ddc4833a4577bb6dec56a89",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3940,7 +2065,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a716bc1f921c45b1af1d58332ddcbad8",
+       "model_id": "7f29632999224af6bb0f71c3a36db93e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3954,7 +2079,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5884d59606a14e008844312e947a20fd",
+       "model_id": "2dd3b579f93d49389f8b18058119e2f4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3968,7 +2093,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "06181b3561f846729ba0cc1124419a0a",
+       "model_id": "c7abf4de9895480da2c81c46b4ce6124",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3983,8 +2108,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 270 ms, sys: 61.4 ms, total: 331 ms\n",
-      "Wall time: 13.2 s\n"
+      "CPU times: user 254 ms, sys: 52.1 ms, total: 306 ms\n",
+      "Wall time: 4.28 s\n"
      ]
     },
     {
@@ -4490,7 +2615,7 @@
        "    SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
        "    SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
        "    UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "    Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-48e1283a-312b-445b-bc68-b354579a7ed8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48e1283a-312b-445b-bc68-b354579a7ed8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7002e10c-0b7c-493a-8741-fcc16d0cb27e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7002e10c-0b7c-493a-8741-fcc16d0cb27e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-5df47a39-9c9e-4f18-a921-8b3a1513dd29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5df47a39-9c9e-4f18-a921-8b3a1513dd29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d1d431f-f69d-4a8d-b5e6-2f92e20467b9' class='xr-var-data-in' type='checkbox'><label for='data-9d1d431f-f69d-4a8d-b5e6-2f92e20467b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ebc3240e-e692-4312-b451-0f173e44eef6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ebc3240e-e692-4312-b451-0f173e44eef6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff873cdd-9790-43e7-add7-a17717a33257' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff873cdd-9790-43e7-add7-a17717a33257' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c1e27422-4deb-4585-adb0-c405aa1a78f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1e27422-4deb-4585-adb0-c405aa1a78f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b42dbad-956c-46b0-b6c2-772ef78975e2' class='xr-var-data-in' type='checkbox'><label for='data-6b42dbad-956c-46b0-b6c2-772ef78975e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4553,7 +2678,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-3357a138-9a6e-4696-9d4c-c3e782bc450f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3357a138-9a6e-4696-9d4c-c3e782bc450f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d17b4f4-8428-46ce-97a5-d9318c786735' class='xr-var-data-in' type='checkbox'><label for='data-7d17b4f4-8428-46ce-97a5-d9318c786735' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d6fd07d6-28c7-4aeb-9240-03c203beedca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6fd07d6-28c7-4aeb-9240-03c203beedca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58cc0e27-a422-4e34-aaaf-927f629f5350' class='xr-var-data-in' type='checkbox'><label for='data-58cc0e27-a422-4e34-aaaf-927f629f5350' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4616,7 +2741,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-81e122bf-d119-484b-8136-ff04367d4f22' class='xr-section-summary-in' type='checkbox'  ><label for='section-81e122bf-d119-484b-8136-ff04367d4f22' class='xr-section-summary' >Data variables: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c4b8c706-4ac4-4c5e-b155-759dce2ad7f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4b8c706-4ac4-4c5e-b155-759dce2ad7f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04cc7966-364a-4184-b112-ae43a57df2ff' class='xr-var-data-in' type='checkbox'><label for='data-04cc7966-364a-4184-b112-ae43a57df2ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b72a9cb3-8f3b-4add-ad31-5f4b09639879' class='xr-section-summary-in' type='checkbox'  ><label for='section-b72a9cb3-8f3b-4add-ad31-5f4b09639879' class='xr-section-summary' >Data variables: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4b85c129-d4c6-4e6e-8313-388fb5942b21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b85c129-d4c6-4e6e-8313-388fb5942b21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c26597a6-b964-49f0-99f1-a37a9167085c' class='xr-var-data-in' type='checkbox'><label for='data-c26597a6-b964-49f0-99f1-a37a9167085c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4679,7 +2804,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-aa9d4e6b-1fd8-4d40-9860-f00ee0c0922f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa9d4e6b-1fd8-4d40-9860-f00ee0c0922f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1928007-b642-4977-ac6c-a14c7fd8ce92' class='xr-var-data-in' type='checkbox'><label for='data-d1928007-b642-4977-ac6c-a14c7fd8ce92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-bb2160d5-13fd-4557-9360-2da0a50e7e58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb2160d5-13fd-4557-9360-2da0a50e7e58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8ba5c5b-e268-4a4c-bafd-b7237db7c9f2' class='xr-var-data-in' type='checkbox'><label for='data-c8ba5c5b-e268-4a4c-bafd-b7237db7c9f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4742,7 +2867,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6ed03a6f-bfcb-48e2-9df3-b526104ee180' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ed03a6f-bfcb-48e2-9df3-b526104ee180' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1879abc-a684-44ef-a690-514fae135964' class='xr-var-data-in' type='checkbox'><label for='data-e1879abc-a684-44ef-a690-514fae135964' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-904cd54c-579e-4f68-8f0b-0e984d5c03d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-904cd54c-579e-4f68-8f0b-0e984d5c03d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19200ba4-33d9-4ea3-8813-ddb3cd86dc84' class='xr-var-data-in' type='checkbox'><label for='data-19200ba4-33d9-4ea3-8813-ddb3cd86dc84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4805,7 +2930,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a7c00bf2-662d-447f-8dbe-6d8542ad7170' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7c00bf2-662d-447f-8dbe-6d8542ad7170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74a77dcc-c664-43d9-a820-8ddf490df098' class='xr-var-data-in' type='checkbox'><label for='data-74a77dcc-c664-43d9-a820-8ddf490df098' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-ead9d357-b48c-4b19-9a99-a2de77b62400' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ead9d357-b48c-4b19-9a99-a2de77b62400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5488d697-d368-424a-9050-c3110b0f480f' class='xr-var-data-in' type='checkbox'><label for='data-5488d697-d368-424a-9050-c3110b0f480f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4868,7 +2993,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-eaf2871e-a9fa-4c6b-bcdf-8226a41cc217' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaf2871e-a9fa-4c6b-bcdf-8226a41cc217' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62f6a825-22ee-4854-9ad3-1f081ebf3c73' class='xr-var-data-in' type='checkbox'><label for='data-62f6a825-22ee-4854-9ad3-1f081ebf3c73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b65c4055-c6b0-402a-8081-45a687f0134c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b65c4055-c6b0-402a-8081-45a687f0134c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bd569b7-46e3-4539-88a3-6d1fc3c67435' class='xr-var-data-in' type='checkbox'><label for='data-7bd569b7-46e3-4539-88a3-6d1fc3c67435' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4931,7 +3056,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-cf6c3bc8-3701-409f-a838-b218c6bc508b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf6c3bc8-3701-409f-a838-b218c6bc508b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0ecd527-58aa-454a-a322-f95e97931858' class='xr-var-data-in' type='checkbox'><label for='data-e0ecd527-58aa-454a-a322-f95e97931858' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-5e62f92a-4d7e-4fc5-96e6-e4f346de99d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e62f92a-4d7e-4fc5-96e6-e4f346de99d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dc0d129-14dc-4346-ab59-19ad40a4dd24' class='xr-var-data-in' type='checkbox'><label for='data-4dc0d129-14dc-4346-ab59-19ad40a4dd24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4994,7 +3119,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-1de46cf5-60ce-4ab7-b54d-f502263d37df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1de46cf5-60ce-4ab7-b54d-f502263d37df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78780253-cc7b-4640-9b43-62fa6db68ad3' class='xr-var-data-in' type='checkbox'><label for='data-78780253-cc7b-4640-9b43-62fa6db68ad3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-714cf159-484b-4b03-8b05-336cc650bb3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-714cf159-484b-4b03-8b05-336cc650bb3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b9482b0-7905-4c21-9dd6-01b57d1268b9' class='xr-var-data-in' type='checkbox'><label for='data-3b9482b0-7905-4c21-9dd6-01b57d1268b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5057,7 +3182,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-a6646869-7dcf-4de4-ac92-580806f67e80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6646869-7dcf-4de4-ac92-580806f67e80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64fb610a-07f5-4a06-9a72-e4994e6d4570' class='xr-var-data-in' type='checkbox'><label for='data-64fb610a-07f5-4a06-9a72-e4994e6d4570' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-3e3ca523-62cd-4f6b-a69e-c5e3bbd51bf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e3ca523-62cd-4f6b-a69e-c5e3bbd51bf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1648bbf8-b283-4ad2-8f32-b3ff38c52e01' class='xr-var-data-in' type='checkbox'><label for='data-1648bbf8-b283-4ad2-8f32-b3ff38c52e01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5160,7 +3285,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c6e657c6-b114-4736-b0f7-470bd46f3cda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6e657c6-b114-4736-b0f7-470bd46f3cda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f55c3472-e7a8-4ecb-ab84-013e52c6b571' class='xr-var-data-in' type='checkbox'><label for='data-f55c3472-e7a8-4ecb-ab84-013e52c6b571' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9b56ec9f-eed4-435b-bfac-bdb1b50efb6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b56ec9f-eed4-435b-bfac-bdb1b50efb6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b664c2a-2fbe-4fbf-b296-9a0751d3115e' class='xr-var-data-in' type='checkbox'><label for='data-0b664c2a-2fbe-4fbf-b296-9a0751d3115e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5223,7 +3348,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-2f3a1445-392c-467d-9580-1d91ca23ff4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f3a1445-392c-467d-9580-1d91ca23ff4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71cde08b-a305-4b59-acf5-502d216cb809' class='xr-var-data-in' type='checkbox'><label for='data-71cde08b-a305-4b59-acf5-502d216cb809' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-5537349c-93a0-471e-afdd-eea848a71c72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5537349c-93a0-471e-afdd-eea848a71c72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed5ef41c-73b5-4487-9834-ae1a4a49daff' class='xr-var-data-in' type='checkbox'><label for='data-ed5ef41c-73b5-4487-9834-ae1a4a49daff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5319,7 +3444,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-1af0a9d3-8feb-4848-96e1-3649d51c2aa6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1af0a9d3-8feb-4848-96e1-3649d51c2aa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-834d09b3-c28c-446c-8fbe-3baec6f6b5fb' class='xr-var-data-in' type='checkbox'><label for='data-834d09b3-c28c-446c-8fbe-3baec6f6b5fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-22489b99-0177-465d-8df5-8a90277719e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22489b99-0177-465d-8df5-8a90277719e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7faed58-7495-4ead-9d8c-ff088b9bc94e' class='xr-var-data-in' type='checkbox'><label for='data-d7faed58-7495-4ead-9d8c-ff088b9bc94e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5422,7 +3547,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-11b90939-238a-43eb-a6d3-59dc8d613413' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11b90939-238a-43eb-a6d3-59dc8d613413' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cefe0ef-87c4-4b42-ac9a-20844f1bd6d8' class='xr-var-data-in' type='checkbox'><label for='data-0cefe0ef-87c4-4b42-ac9a-20844f1bd6d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-3143013a-9982-409b-90d9-c0986526daf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3143013a-9982-409b-90d9-c0986526daf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35576394-0dcc-4aa0-9dfd-645dd8f28d21' class='xr-var-data-in' type='checkbox'><label for='data-35576394-0dcc-4aa0-9dfd-645dd8f28d21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5525,7 +3650,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-53a06e44-9da9-4a17-a236-a1ddfbc8bc39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53a06e44-9da9-4a17-a236-a1ddfbc8bc39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-753e90af-82dd-43cc-9fc3-8beea7c7ceb9' class='xr-var-data-in' type='checkbox'><label for='data-753e90af-82dd-43cc-9fc3-8beea7c7ceb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-19fad9d1-a6fb-45d3-af5a-3a62c9c9924a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19fad9d1-a6fb-45d3-af5a-3a62c9c9924a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-373a2504-61c7-4d90-a6a9-fb1d328dfdb0' class='xr-var-data-in' type='checkbox'><label for='data-373a2504-61c7-4d90-a6a9-fb1d328dfdb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5588,7 +3713,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-4968be7e-2bff-4dbf-8e59-578b909e8b92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4968be7e-2bff-4dbf-8e59-578b909e8b92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a157f9e6-83de-4741-99b0-691b1f06236d' class='xr-var-data-in' type='checkbox'><label for='data-a157f9e6-83de-4741-99b0-691b1f06236d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-6f3180e2-4649-425f-84cf-582811bd81a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f3180e2-4649-425f-84cf-582811bd81a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6e684da-62d9-4eef-9bd1-4f4e955abedc' class='xr-var-data-in' type='checkbox'><label for='data-f6e684da-62d9-4eef-9bd1-4f4e955abedc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5684,7 +3809,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2bf688b3-1f71-4321-b016-1d4e5283ef2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bf688b3-1f71-4321-b016-1d4e5283ef2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-505671ab-5b9f-4713-9327-1620f6c17cff' class='xr-var-data-in' type='checkbox'><label for='data-505671ab-5b9f-4713-9327-1620f6c17cff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-111a3a96-0cd0-4983-b6d5-9168473b2190' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-111a3a96-0cd0-4983-b6d5-9168473b2190' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a91a6d2-7a5e-4be4-9726-e0b060cedfeb' class='xr-var-data-in' type='checkbox'><label for='data-4a91a6d2-7a5e-4be4-9726-e0b060cedfeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5747,7 +3872,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-54403ef4-deb4-4888-b2ce-8144a607d7d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54403ef4-deb4-4888-b2ce-8144a607d7d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b5e3281-cd61-40db-a890-e678b5b7336d' class='xr-var-data-in' type='checkbox'><label for='data-3b5e3281-cd61-40db-a890-e678b5b7336d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-48c1e0cf-b260-4c85-aafd-601c5204ce4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48c1e0cf-b260-4c85-aafd-601c5204ce4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05dbdd09-c46e-45e9-8a7a-50828d28fb45' class='xr-var-data-in' type='checkbox'><label for='data-05dbdd09-c46e-45e9-8a7a-50828d28fb45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5810,7 +3935,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-84e4860d-0b36-41a8-a705-8ca580e5ca98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84e4860d-0b36-41a8-a705-8ca580e5ca98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d91af4cc-d860-40ad-bed4-adbd6470e846' class='xr-var-data-in' type='checkbox'><label for='data-d91af4cc-d860-40ad-bed4-adbd6470e846' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-82537dd9-967a-4936-b021-fc7176d819da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82537dd9-967a-4936-b021-fc7176d819da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a32e7dab-81f7-4270-b4da-b29cd5a9752c' class='xr-var-data-in' type='checkbox'><label for='data-a32e7dab-81f7-4270-b4da-b29cd5a9752c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5873,7 +3998,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-63938d07-4d0d-4d37-b005-b3b4b39ff329' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63938d07-4d0d-4d37-b005-b3b4b39ff329' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee94a8f2-6bdb-472f-bf3b-6825015f7dc9' class='xr-var-data-in' type='checkbox'><label for='data-ee94a8f2-6bdb-472f-bf3b-6825015f7dc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-acc80b02-6aa7-473d-814b-315d883ff537' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acc80b02-6aa7-473d-814b-315d883ff537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be2beaa9-4023-433c-ae84-06169da7b4d4' class='xr-var-data-in' type='checkbox'><label for='data-be2beaa9-4023-433c-ae84-06169da7b4d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5967,7 +4092,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-507f8a0a-2907-45e2-a45d-9f7fb55683bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-507f8a0a-2907-45e2-a45d-9f7fb55683bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4836f28b-ce07-4c13-8647-281bd2b77035' class='xr-var-data-in' type='checkbox'><label for='data-4836f28b-ce07-4c13-8647-281bd2b77035' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-e0d86e9e-7913-48b3-9b18-06d52e5adbfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0d86e9e-7913-48b3-9b18-06d52e5adbfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62414347-5ebb-49a6-a0fe-fecec98d6616' class='xr-var-data-in' type='checkbox'><label for='data-62414347-5ebb-49a6-a0fe-fecec98d6616' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6030,7 +4155,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-8bdcb4dd-f895-422f-ac86-af5b44b09c47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bdcb4dd-f895-422f-ac86-af5b44b09c47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d77f0f24-8d7a-4004-b365-727886eae89a' class='xr-var-data-in' type='checkbox'><label for='data-d77f0f24-8d7a-4004-b365-727886eae89a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-38e89965-4a32-4d56-b8ed-4b0753e0ce59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e89965-4a32-4d56-b8ed-4b0753e0ce59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36cd4d17-897e-476d-9d7c-1dc11872c47a' class='xr-var-data-in' type='checkbox'><label for='data-36cd4d17-897e-476d-9d7c-1dc11872c47a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6124,7 +4249,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-0c1b7a5e-f227-4b02-bb12-dad9e0e96076' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c1b7a5e-f227-4b02-bb12-dad9e0e96076' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7b81d40-9752-4e06-a053-33be5577cfd3' class='xr-var-data-in' type='checkbox'><label for='data-c7b81d40-9752-4e06-a053-33be5577cfd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-6c235fc1-e904-4f1f-8e38-20a35edda07e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c235fc1-e904-4f1f-8e38-20a35edda07e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e44c7f96-ba50-409c-84c7-5106e2aa5365' class='xr-var-data-in' type='checkbox'><label for='data-e44c7f96-ba50-409c-84c7-5106e2aa5365' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6218,7 +4343,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-42a7dcba-2214-49c0-a29b-8f1df7119b6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42a7dcba-2214-49c0-a29b-8f1df7119b6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2174edf6-aab0-4ef5-a271-372eb92031e0' class='xr-var-data-in' type='checkbox'><label for='data-2174edf6-aab0-4ef5-a271-372eb92031e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-79ab2549-04af-4ed9-9459-43f16a0cc053' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79ab2549-04af-4ed9-9459-43f16a0cc053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f9c9edb-857e-424d-831b-8eed7ab602e4' class='xr-var-data-in' type='checkbox'><label for='data-8f9c9edb-857e-424d-831b-8eed7ab602e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6281,7 +4406,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2199e83f-6574-48a1-805f-d300826d6e3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2199e83f-6574-48a1-805f-d300826d6e3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc09115a-ace4-4903-9578-aa4880427275' class='xr-var-data-in' type='checkbox'><label for='data-dc09115a-ace4-4903-9578-aa4880427275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-349c2d22-ecfa-4cff-ac7a-00adf6b1415f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-349c2d22-ecfa-4cff-ac7a-00adf6b1415f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45f021c9-90b3-4419-b382-85ee2c039841' class='xr-var-data-in' type='checkbox'><label for='data-45f021c9-90b3-4419-b382-85ee2c039841' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6344,7 +4469,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-7223f3a4-631c-4abf-9e89-67a49db3d81e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7223f3a4-631c-4abf-9e89-67a49db3d81e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00bc933a-0f4d-4d47-b65b-0259754727c0' class='xr-var-data-in' type='checkbox'><label for='data-00bc933a-0f4d-4d47-b65b-0259754727c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-ca60a5f2-2222-4eab-bf6b-709f53d4cf79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca60a5f2-2222-4eab-bf6b-709f53d4cf79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e945ff8-4e8d-473c-8bc8-8a4aa826dea6' class='xr-var-data-in' type='checkbox'><label for='data-6e945ff8-4e8d-473c-8bc8-8a4aa826dea6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6438,7 +4563,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2b2a954b-3883-46ec-8d0f-25a88554e996' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b2a954b-3883-46ec-8d0f-25a88554e996' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f7456e7-b19d-4fce-9486-33068ce62131' class='xr-var-data-in' type='checkbox'><label for='data-7f7456e7-b19d-4fce-9486-33068ce62131' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c8d5fcfa-b6d6-4d82-987a-135554c8634a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8d5fcfa-b6d6-4d82-987a-135554c8634a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77187b66-3fa3-488d-ae5b-1f373adfa707' class='xr-var-data-in' type='checkbox'><label for='data-77187b66-3fa3-488d-ae5b-1f373adfa707' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6501,7 +4626,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-4e606902-44cd-40ca-9bfe-e5d6ab95f164' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e606902-44cd-40ca-9bfe-e5d6ab95f164' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d57d6bb-86fe-4f36-b9e6-3d490f6cc81c' class='xr-var-data-in' type='checkbox'><label for='data-0d57d6bb-86fe-4f36-b9e6-3d490f6cc81c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-fed35bcb-dff3-41b3-932b-084653c136fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fed35bcb-dff3-41b3-932b-084653c136fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8eb31b4-f41d-4348-906f-2c7497d70cdf' class='xr-var-data-in' type='checkbox'><label for='data-b8eb31b4-f41d-4348-906f-2c7497d70cdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6604,7 +4729,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-99952786-addc-42cb-92eb-5c0e18c30a5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99952786-addc-42cb-92eb-5c0e18c30a5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7918573-e246-4a3c-baf0-1bac53e5c132' class='xr-var-data-in' type='checkbox'><label for='data-b7918573-e246-4a3c-baf0-1bac53e5c132' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-03f174be-34a1-4d02-8bb7-83977f6a527b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03f174be-34a1-4d02-8bb7-83977f6a527b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55e744c2-1621-4ca4-9946-67ad6b4559db' class='xr-var-data-in' type='checkbox'><label for='data-55e744c2-1621-4ca4-9946-67ad6b4559db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6700,7 +4825,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-1ab5d4f0-56ad-42de-a4b3-41dd1d0a551e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ab5d4f0-56ad-42de-a4b3-41dd1d0a551e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ca5d996-0a67-47d1-9d7c-770c90f31809' class='xr-var-data-in' type='checkbox'><label for='data-6ca5d996-0a67-47d1-9d7c-770c90f31809' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-2260260b-5b67-45e1-b168-a14e46252ab9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2260260b-5b67-45e1-b168-a14e46252ab9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e74d8a91-92d7-4c79-8716-78095cc173ba' class='xr-var-data-in' type='checkbox'><label for='data-e74d8a91-92d7-4c79-8716-78095cc173ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6794,7 +4919,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-ae24df48-437b-4951-a228-9bfec3089c39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae24df48-437b-4951-a228-9bfec3089c39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b98f3188-db43-4268-8ada-655fa981903f' class='xr-var-data-in' type='checkbox'><label for='data-b98f3188-db43-4268-8ada-655fa981903f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-5524929d-0e32-4dea-b783-9049fa03fd0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5524929d-0e32-4dea-b783-9049fa03fd0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a27a790f-7fa8-44d5-9679-302d0c6da505' class='xr-var-data-in' type='checkbox'><label for='data-a27a790f-7fa8-44d5-9679-302d0c6da505' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6888,7 +5013,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-efda2ac2-9ed2-442b-8cbd-ae0cf1a21490' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efda2ac2-9ed2-442b-8cbd-ae0cf1a21490' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d585a7ed-432c-4ba4-9566-21535618d0f3' class='xr-var-data-in' type='checkbox'><label for='data-d585a7ed-432c-4ba4-9566-21535618d0f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-ac5c7d57-4d26-418b-9ae1-91e74c6af624' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac5c7d57-4d26-418b-9ae1-91e74c6af624' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-591ae0f5-0611-4d85-ab5b-e4284222bc15' class='xr-var-data-in' type='checkbox'><label for='data-591ae0f5-0611-4d85-ab5b-e4284222bc15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6951,7 +5076,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-321ec0fd-211f-483c-84e1-af2c26633cfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-321ec0fd-211f-483c-84e1-af2c26633cfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f69b381-5783-41e7-a0aa-dc8f5ec39e4e' class='xr-var-data-in' type='checkbox'><label for='data-7f69b381-5783-41e7-a0aa-dc8f5ec39e4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-585aa806-7586-4a1e-b160-61653d700e61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-585aa806-7586-4a1e-b160-61653d700e61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13ba941d-7e37-4eb0-a478-9ad56d4c754d' class='xr-var-data-in' type='checkbox'><label for='data-13ba941d-7e37-4eb0-a478-9ad56d4c754d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7045,7 +5170,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-fc896f32-d1a3-4337-acfd-30249f39c52e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc896f32-d1a3-4337-acfd-30249f39c52e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11a37291-fbf4-43ba-9ef5-9ba14a248979' class='xr-var-data-in' type='checkbox'><label for='data-11a37291-fbf4-43ba-9ef5-9ba14a248979' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-5d509427-785c-4d7c-8763-381889129171' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d509427-785c-4d7c-8763-381889129171' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0364567-d6ba-4e0e-8260-6624e9ecf13c' class='xr-var-data-in' type='checkbox'><label for='data-c0364567-d6ba-4e0e-8260-6624e9ecf13c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7108,7 +5233,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-25d5bcd5-2a0d-4868-a8f7-2b56ea471c6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25d5bcd5-2a0d-4868-a8f7-2b56ea471c6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-243fff77-3c35-45d0-add1-b652f39d8b12' class='xr-var-data-in' type='checkbox'><label for='data-243fff77-3c35-45d0-add1-b652f39d8b12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-e7cafc80-6f0f-4501-ac67-20421b9b67fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7cafc80-6f0f-4501-ac67-20421b9b67fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eba82fb-5739-41d6-b94f-032fca3061ca' class='xr-var-data-in' type='checkbox'><label for='data-5eba82fb-5739-41d6-b94f-032fca3061ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7171,7 +5296,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-0506a9ca-53ba-404e-ad81-d3e74b808c29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0506a9ca-53ba-404e-ad81-d3e74b808c29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5d73d7-35f9-4c4a-8aca-c4376d8846ee' class='xr-var-data-in' type='checkbox'><label for='data-aa5d73d7-35f9-4c4a-8aca-c4376d8846ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-e1bb9437-7d64-4f48-95a0-abfe1a89efe3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1bb9437-7d64-4f48-95a0-abfe1a89efe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11c44a2a-5d3f-4143-9c7f-83d493da35aa' class='xr-var-data-in' type='checkbox'><label for='data-11c44a2a-5d3f-4143-9c7f-83d493da35aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7254,7 +5379,7 @@
        "    Wavelength                      (phony_dim_3) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7266,30 +5391,249 @@
     "    'merge': ['/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields', '/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields'],\n",
     "    'open_kwargs': {'phony_dims':'access'}\n",
     "}\n",
-    "ds = plan.open_dataset(0, open_method=discovr_epic_aer)\n",
+    "ds = plan.open_dataset(1, open_method=discovr_epic_aer)\n",
     "ds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 30,
+   "id": "b6aae09c-a92b-4ecf-b2c5-ae49e8c08b3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       ...,\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "      shape=(2048, 2048), dtype=float32)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.UVAerosolIndex.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "6cb81767-0d32-4328-8dcc-eaf1a64fab9d",
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f37d9b361e74c589a3b9c3f90370aad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "532793261c9447c49c934d4628fa29f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9450a7e5c93040f69919f476baf0dde6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39085e638fa24d18b4b09b2299380168",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c869f8c89b4247fc8d7e69636586e4c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "086cc97a2ada400b8f495e452c9f68c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41d86561f00e447593f9122c6cd51c38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "211b76a6c0d845c68bc90911a352e367",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b1a9ff89f77417cb4202d009e763a8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0456206ceeaf451daa92ec8d75d6712a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3d20ec538894743931b71514259a94c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70ae2cf13e66479882158bbb33755263",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 20.8 s, sys: 5.08 s, total: 25.9 s\n",
-      "Wall time: 2min\n"
+      "CPU times: user 1.18 s, sys: 223 ms, total: 1.4 s\n",
+      "Wall time: 13.9 s\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "data must be finite, check for nan or inf values",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mres = pc.matchup(plan, \u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m                 variables = [\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mUVAerosolIndex\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m], \u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m                 open_method=discovr_epic_aer)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2572\u001b[39m, in \u001b[36mInteractiveShell.run_cell_magic\u001b[39m\u001b[34m(self, magic_name, line, cell)\u001b[39m\n\u001b[32m   2570\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.builtin_trap:\n\u001b[32m   2571\u001b[39m     args = (magic_arg_s, cell)\n\u001b[32m-> \u001b[39m\u001b[32m2572\u001b[39m     result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2574\u001b[39m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[32m   2575\u001b[39m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[32m   2576\u001b[39m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[32m   2577\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1447\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1445\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupt_occured:\n\u001b[32m   1446\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m exit_on_interrupt \u001b[38;5;129;01mand\u001b[39;00m captured_exception:\n\u001b[32m-> \u001b[39m\u001b[32m1447\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m captured_exception\n\u001b[32m   1448\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m   1449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1411\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1409\u001b[39m st = clock2()\n\u001b[32m   1410\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1411\u001b[39m     \u001b[43mexec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1412\u001b[39m     out = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1413\u001b[39m     \u001b[38;5;66;03m# multi-line %%time case\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m<timed exec>:1\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/engine.py:283\u001b[39m, in \u001b[36mmatchup\u001b[39m\u001b[34m(plan, open_method, variables, spatial_method, open_dataset_kwargs, silent, batch_size, save_dir, granule_range)\u001b[39m\n\u001b[32m    280\u001b[39m spec = _normalize_open_method(effective_open_method, open_dataset_kwargs)\n\u001b[32m    282\u001b[39m effective_vars: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m] = variables \u001b[38;5;28;01mif\u001b[39;00m variables \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m plan.variables\n\u001b[32m--> \u001b[39m\u001b[32m283\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_execute_plan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    284\u001b[39m \u001b[43m    \u001b[49m\u001b[43mplan\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    285\u001b[39m \u001b[43m    \u001b[49m\u001b[43mspec\u001b[49m\u001b[43m=\u001b[49m\u001b[43mspec\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    286\u001b[39m \u001b[43m    \u001b[49m\u001b[43mspatial_method\u001b[49m\u001b[43m=\u001b[49m\u001b[43mspatial_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    287\u001b[39m \u001b[43m    \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m=\u001b[49m\u001b[43meffective_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    288\u001b[39m \u001b[43m    \u001b[49m\u001b[43msilent\u001b[49m\u001b[43m=\u001b[49m\u001b[43msilent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    289\u001b[39m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    290\u001b[39m \u001b[43m    \u001b[49m\u001b[43msave_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43msave_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    291\u001b[39m \u001b[43m    \u001b[49m\u001b[43mgranule_range\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgranule_range\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    292\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/engine.py:693\u001b[39m, in \u001b[36m_execute_plan\u001b[39m\u001b[34m(plan, spec, spatial_method, variables, silent, batch_size, save_dir, granule_range)\u001b[39m\n\u001b[32m    691\u001b[39m     _extract_xoak_batch(ds, rows_for_granule, variables, lon_name, lat_name, time_dim)\n\u001b[32m    692\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m693\u001b[39m     \u001b[43m_extract_ndpoint_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrows_for_granule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlon_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlat_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_dim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    694\u001b[39m output_rows.extend(rows_for_granule)\n\u001b[32m    695\u001b[39m batch_rows.extend(rows_for_granule)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/engine.py:1263\u001b[39m, in \u001b[36m_extract_ndpoint_batch\u001b[39m\u001b[34m(ds, rows, variables, lon_name, lat_name, time_dim)\u001b[39m\n\u001b[32m   1258\u001b[39m     ds_work[lon_name] = xr.DataArray(lon_2d, dims=lat_dims)\n\u001b[32m   1260\u001b[39m \u001b[38;5;66;03m# Build the NDPointIndex once for all query points using the built-in\u001b[39;00m\n\u001b[32m   1261\u001b[39m \u001b[38;5;66;03m# scipy adapter (ScipyKDTreeAdapter).  No tree_adapter_cls argument is\u001b[39;00m\n\u001b[32m   1262\u001b[39m \u001b[38;5;66;03m# passed so xarray's default applies.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1263\u001b[39m indexed_ds = \u001b[43mds_work\u001b[49m\u001b[43m.\u001b[49m\u001b[43mset_xindex\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1264\u001b[39m \u001b[43m    \u001b[49m\u001b[43m[\u001b[49m\u001b[43mlat_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlon_name\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1265\u001b[39m \u001b[43m    \u001b[49m\u001b[43mxr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mindexes\u001b[49m\u001b[43m.\u001b[49m\u001b[43mNDPointIndex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1266\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1268\u001b[39m \u001b[38;5;66;03m# Build the target dataset with all query points at once.\u001b[39;00m\n\u001b[32m   1269\u001b[39m lats = [row[\u001b[33m\"\u001b[39m\u001b[33mlat\u001b[39m\u001b[33m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m rows]\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataset.py:5019\u001b[39m, in \u001b[36mDataset.set_xindex\u001b[39m\u001b[34m(self, coord_names, index_cls, **options)\u001b[39m\n\u001b[32m   5013\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m   5014\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mthose coordinates already have an index: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mindexed_coords\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m   5015\u001b[39m     )\n\u001b[32m   5017\u001b[39m coord_vars = {name: \u001b[38;5;28mself\u001b[39m._variables[name] \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m coord_names}\n\u001b[32m-> \u001b[39m\u001b[32m5019\u001b[39m index = \u001b[43mindex_cls\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoord_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   5021\u001b[39m new_coord_vars = index.create_variables(coord_vars)\n\u001b[32m   5023\u001b[39m \u001b[38;5;66;03m# special case for setting a pandas multi-index from level coordinates\u001b[39;00m\n\u001b[32m   5024\u001b[39m \u001b[38;5;66;03m# TODO: remove it once we depreciate pandas multi-index dimension (tuple\u001b[39;00m\n\u001b[32m   5025\u001b[39m \u001b[38;5;66;03m# elements) coordinate\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/indexes/nd_point_index.py:276\u001b[39m, in \u001b[36mNDPointIndex.from_variables\u001b[39m\u001b[34m(cls, variables, options)\u001b[39m\n\u001b[32m    271\u001b[39m     tree_adapter_cls = ScipyKDTreeAdapter\n\u001b[32m    273\u001b[39m points = get_points(variables.values())\n\u001b[32m    275\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(\n\u001b[32m--> \u001b[39m\u001b[32m276\u001b[39m     \u001b[43mtree_adapter_cls\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mopts\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m    277\u001b[39m     coord_names=\u001b[38;5;28mtuple\u001b[39m(variables),\n\u001b[32m    278\u001b[39m     dims=var0.dims,\n\u001b[32m    279\u001b[39m     shape=var0.shape,\n\u001b[32m    280\u001b[39m )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/indexes/nd_point_index.py:80\u001b[39m, in \u001b[36mScipyKDTreeAdapter.__init__\u001b[39m\u001b[34m(self, points, options)\u001b[39m\n\u001b[32m     77\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, points: np.ndarray, options: Mapping[\u001b[38;5;28mstr\u001b[39m, Any]):\n\u001b[32m     78\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mspatial\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m KDTree\n\u001b[32m---> \u001b[39m\u001b[32m80\u001b[39m     \u001b[38;5;28mself\u001b[39m._kdtree = \u001b[43mKDTree\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/scipy/spatial/_kdtree.py:360\u001b[39m, in \u001b[36mKDTree.__init__\u001b[39m\u001b[34m(self, data, leafsize, compact_nodes, copy_data, balanced_tree, boxsize)\u001b[39m\n\u001b[32m    357\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mKDTree does not work with complex data\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m    359\u001b[39m \u001b[38;5;66;03m# Note KDTree has different default leafsize from cKDTree\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m360\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleafsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompact_nodes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    361\u001b[39m \u001b[43m                 \u001b[49m\u001b[43mbalanced_tree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mboxsize\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mscipy/spatial/_ckdtree.pyx:560\u001b[39m, in \u001b[36mscipy.spatial._ckdtree.cKDTree.__init__\u001b[39m\u001b[34m()\u001b[39m\n",
+      "\u001b[31mValueError\u001b[39m: data must be finite, check for nan or inf values"
      ]
     }
    ],
    "source": [
     "%%time\n",
     "res = pc.matchup(plan, \n",
-    "                 variables = [\"vertical_column_troposphere\"], \n",
-    "                 open_method=tempo)"
+    "                 variables = [\"UVAerosolIndex\"], \n",
+    "                 open_method=discovr_epic_aer)"
    ]
   },
   {

From dff791df06b7c2841cb71c49c6274b09c6d4ee25 Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Mon, 16 Mar 2026 23:29:47 +0000
Subject: [PATCH 07/11] minor

---
 examples/docs_4_discovr_epic.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_4_discovr_epic.ipynb
index 5dd650c..3b5137c 100644
--- a/examples/docs_4_discovr_epic.ipynb
+++ b/examples/docs_4_discovr_epic.ipynb
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "id": "2b12e80a-5a0b-4a8c-a89b-e9e4877655b8",
    "metadata": {},
    "outputs": [
@@ -163,7 +163,7 @@
        "222  -8.043907  41.779801 2024-08-31  False"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "6153eef7-d94d-4c2f-8bd2-a860d737a620",
    "metadata": {},
    "outputs": [
@@ -199,8 +199,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.04 s, sys: 126 ms, total: 1.16 s\n",
-      "Wall time: 7.67 s\n"
+      "CPU times: user 1.53 s, sys: 219 ms, total: 1.75 s\n",
+      "Wall time: 15.3 s\n"
      ]
     }
    ],

From 66baa68974b5c9499ecdd582796b7384a350e423 Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Mon, 16 Mar 2026 23:54:46 +0000
Subject: [PATCH 08/11] add plot

---
 examples/docs_4_discovr_epic.ipynb | 700 +++++++++++------------------
 1 file changed, 258 insertions(+), 442 deletions(-)

diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_4_discovr_epic.ipynb
index 3b5137c..12416a0 100644
--- a/examples/docs_4_discovr_epic.ipynb
+++ b/examples/docs_4_discovr_epic.ipynb
@@ -199,8 +199,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.53 s, sys: 219 ms, total: 1.75 s\n",
-      "Wall time: 15.3 s\n"
+      "CPU times: user 1.46 s, sys: 275 ms, total: 1.73 s\n",
+      "Wall time: 13.6 s\n"
      ]
     }
    ],
@@ -1196,7 +1196,7 @@
    "source": [
     "### Look at the variables\n",
     "\n",
-    "We will open a file with datatree and see what groups it has (if any)."
+    "We will open a file with datatree and see what groups it has."
    ]
   },
   {
@@ -1209,209 +1209,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "GOT HERE\n",
-      "open_method: {'xarray_open': 'dataset', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n",
-      "BEFORE OPEN_DATASET\n",
-      "set()\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2c22f39495b241c9b018a4483c0f21e5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff0970dabc824aa896aa0ea2f6a6495e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8c6b4085354545feabc19035d88e946e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d9e68a37ba214f9892ff16c1b54f8e76",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78986c85628c4b97aad57685e3af403a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b0b44aa588e246acb9a7722e772c63e5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GOT HERE 3\n"
+      "open_method: {'xarray_open': 'datatree', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n"
      ]
     },
-    {
-     "data": {
-      "text/plain": [
-       "<File-like object HTTPFileSystem, https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_AER_03/2024/02/DSCOVR_EPIC_L2_AER_03_20240208015633_03.he5>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds = plan.open_dataset(1, open_method=\"dataset\")\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f73990e0-f5dc-4628-a7b1-aec2a53d4b5d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8fc0930322af4272878c26082cb84461",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05e454dae56a4acf9b5a27f440d956ea",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d8b2da6752b34cfaa7ecd6ed1caa09c3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "989f9baf04db470e9d6e84dac178f534",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e9c5875ff50f41ceb314b691227ec096",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7ee932782964c50b5d74d1663348474",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/html": [
@@ -1893,26 +1693,134 @@
        "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
        "  stroke-width: 0.8px;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 0B\n",
-       "Dimensions:  ()\n",
-       "Data variables:\n",
-       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0b75d328-7c78-4a47-9498-0202822f1e60' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0b75d328-7c78-4a47-9498-0202822f1e60' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a3570f9d-1a1f-4f19-a670-77493737dc45' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3570f9d-1a1f-4f19-a670-77493737dc45' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li></ul></div></div>"
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree&gt;\n",
+       "Group: /\n",
+       "├── Group: /HDFEOS\n",
+       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
+       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
+       "│   │           Attributes: (12/22)\n",
+       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
+       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
+       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
+       "│   │               AuthorAffiliation:   NASA/GSFC\n",
+       "│   │               AuthorName:          Omar Torres\n",
+       "│   │               EarthSunDistance:    0.9862366\n",
+       "│   │               ...                  ...\n",
+       "│   │               ProcessingCenter:    TLCF                                                ...\n",
+       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
+       "│   │               ProductionDateTime:  2024-02-11T01:29:46.0Z\n",
+       "│   │               ShortName:           EPICAERUV\n",
+       "│   │               Time_Band340nm:      2024-02-08 01:57:26\n",
+       "│   │               Time_Band388nm:      2024-02-08 01:56:59\n",
+       "│   └── Group: /HDFEOS/SWATHS\n",
+       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
+       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
+       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
+       "│           │                                            phony_dim_4: 2)\n",
+       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "│           │                                       phony_dim_3, phony_dim_4\n",
+       "│           │       Data variables: (12/29)\n",
+       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
+       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
+       "│           │           ...                              ...\n",
+       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;\n",
+       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
+       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
+       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
+       "│                   Data variables:\n",
+       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "└── Group: /HDFEOS INFORMATION\n",
+       "        Dimensions:           ()\n",
+       "        Data variables:\n",
+       "            StructMetadata.0  |S32000 32kB ...\n",
+       "        Attributes:\n",
+       "            HDFEOSVersion:  HDFEOS_5.1.15</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-00f9a390-1252-4999-b704-0db1de96ee0f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00f9a390-1252-4999-b704-0db1de96ee0f' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5d594e45-0f7b-4db9-ab06-d5774f9e5b51' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d594e45-0f7b-4db9-ab06-d5774f9e5b51' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9f941c95-b34d-450d-8f5f-61048ae8fb06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9f941c95-b34d-450d-8f5f-61048ae8fb06' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL/FILE_ATTRIBUTES</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-49b0db5b-a90b-47f5-ad7d-d71a0d2ea308' class='xr-section-summary-in' type='checkbox'  ><label for='section-49b0db5b-a90b-47f5-ad7d-d71a0d2ea308' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ACF Filename :</span></dt><dd>acf_epic_sm_ffxt_aug2019.txt</dd><dt><span>AIRSCO_filename1 :</span></dt><dd>AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G16130181606.hdf</dd><dt><span>AIRSCO_filename2 :</span></dt><dd>AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G16129174722.hdf</dd><dt><span>AuthorAffiliation :</span></dt><dd>NASA/GSFC</dd><dt><span>AuthorName :</span></dt><dd>Omar Torres</dd><dt><span>EarthSunDistance :</span></dt><dd>0.9862366</dd><dt><span>GranuleDay :</span></dt><dd>8</dd><dt><span>GranuleMonth :</span></dt><dd>2</dd><dt><span>GranuleYear :</span></dt><dd>2024</dd><dt><span>InputPointer :</span></dt><dd>epic_1b_20240208015633_03.h5                                                                        </dd><dt><span>InstrumentName :</span></dt><dd>EPIC</dd><dt><span>LocalGranuleID :</span></dt><dd>EPIC-DSCOVR_L2-EPICAERUV_2024m0208t015633_v01-00-2024m0211t012946.he5</dd><dt><span>LongName :</span></dt><dd>EPIC/DSCOVR Near UV Aerosol Index L2 Image 20x20km</dd><dt><span>PGEVersion :</span></dt><dd>0.5.0               </dd><dt><span>Platform :</span></dt><dd>DSCOVR</dd><dt><span>ProcessLevel :</span></dt><dd>L2</dd><dt><span>ProcessingCenter :</span></dt><dd>TLCF                                                                                                                                                                                                    </dd><dt><span>ProcessingHost :</span></dt><dd>Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP                                                                                                                                                    </dd><dt><span>ProductionDateTime :</span></dt><dd>2024-02-11T01:29:46.0Z</dd><dt><span>ShortName :</span></dt><dd>EPICAERUV</dd><dt><span>Time_Band340nm :</span></dt><dd>2024-02-08 01:57:26</dd><dt><span>Time_Band388nm :</span></dt><dd>2024-02-08 01:56:59</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c36d42d7-91f4-4169-a034-b964e0d9bf06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c36d42d7-91f4-4169-a034-b964e0d9bf06' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2a1035c6-c61c-48eb-b3d3-53de2b3ac606' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a1035c6-c61c-48eb-b3d3-53de2b3ac606' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2b8e4c9e-bcba-4a0b-ad61-182e0948cc86' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b8e4c9e-bcba-4a0b-ad61-182e0948cc86' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2121d946-35b1-4545-a49f-364adef14a6f' class='xr-section-summary-in' type='checkbox'  ><label for='section-2121d946-35b1-4545-a49f-364adef14a6f' class='xr-section-summary' >Data variables: <span>(29)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-344cca44-6a0d-4bea-a611-50daeb85cd3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-344cca44-6a0d-4bea-a611-50daeb85cd3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4557071-5725-404b-8bb2-8ec529c25ec0' class='xr-var-data-in' type='checkbox'><label for='data-c4557071-5725-404b-8bb2-8ec529c25ec0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d8a74ca4-9564-4336-b782-3e5cde5ca1b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8a74ca4-9564-4336-b782-3e5cde5ca1b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cbca9be-fc2e-4302-9274-bfb91bf7ba53' class='xr-var-data-in' type='checkbox'><label for='data-6cbca9be-fc2e-4302-9274-bfb91bf7ba53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-cfc3c750-9083-4819-ac22-b76eaa8259c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfc3c750-9083-4819-ac22-b76eaa8259c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bac451e-ee00-4123-aa05-11bdfa725180' class='xr-var-data-in' type='checkbox'><label for='data-1bac451e-ee00-4123-aa05-11bdfa725180' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6bd91939-2319-4568-a0a4-735400c6b6e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bd91939-2319-4568-a0a4-735400c6b6e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-798892a5-f086-4329-b0fb-1fb10f7d40f4' class='xr-var-data-in' type='checkbox'><label for='data-798892a5-f086-4329-b0fb-1fb10f7d40f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-f9d57173-14ca-4273-8bc9-165ee3f96c7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9d57173-14ca-4273-8bc9-165ee3f96c7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd8168cb-9c43-4e8b-a1ef-35307b09ab4f' class='xr-var-data-in' type='checkbox'><label for='data-fd8168cb-9c43-4e8b-a1ef-35307b09ab4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-e075b246-287b-499f-984d-0b9ea33aa1e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e075b246-287b-499f-984d-0b9ea33aa1e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b67464b9-6846-40b3-b454-5193a634485f' class='xr-var-data-in' type='checkbox'><label for='data-b67464b9-6846-40b3-b454-5193a634485f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-f8914832-2462-4085-94e3-411a13e7b412' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8914832-2462-4085-94e3-411a13e7b412' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db36c802-0b6c-44fa-8fe8-d377122a653d' class='xr-var-data-in' type='checkbox'><label for='data-db36c802-0b6c-44fa-8fe8-d377122a653d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-29dd0408-9660-4bc1-a37b-cc0c48a34846' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29dd0408-9660-4bc1-a37b-cc0c48a34846' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89c4acc4-8318-4991-b424-59a62b0d2a18' class='xr-var-data-in' type='checkbox'><label for='data-89c4acc4-8318-4991-b424-59a62b0d2a18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-71425967-63a5-4286-abb1-cb87791389af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71425967-63a5-4286-abb1-cb87791389af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-780d9620-0b7d-40c9-97c3-65636b50ae4a' class='xr-var-data-in' type='checkbox'><label for='data-780d9620-0b7d-40c9-97c3-65636b50ae4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 80.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"25\" y1=\"0\" x2=\"95\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"95\" y1=\"70\" x2=\"95\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a9e969a6-9b07-4b97-9bc4-6470bdedf8e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9e969a6-9b07-4b97-9bc4-6470bdedf8e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a55ed79-6d35-403f-90dd-cd356efdba7b' class='xr-var-data-in' type='checkbox'><label for='data-5a55ed79-6d35-403f-90dd-cd356efdba7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-ce9c78d9-2467-4e17-8246-5383d2ca4e45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce9c78d9-2467-4e17-8246-5383d2ca4e45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b317472c-b7a3-4794-a73a-a520cfb021b5' class='xr-var-data-in' type='checkbox'><label for='data-b317472c-b7a3-4794-a73a-a520cfb021b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-fbb15d52-d9ad-402c-b012-710435798404' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbb15d52-d9ad-402c-b012-710435798404' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eabd9d33-93bc-4685-a18d-d8f7bffd1620' class='xr-var-data-in' type='checkbox'><label for='data-eabd9d33-93bc-4685-a18d-d8f7bffd1620' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-74c3b0b4-18b2-4f2c-be2d-20152db2f629' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74c3b0b4-18b2-4f2c-be2d-20152db2f629' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fd4ae29-a4af-4ea5-b6e1-c551ed022b6c' class='xr-var-data-in' type='checkbox'><label for='data-6fd4ae29-a4af-4ea5-b6e1-c551ed022b6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-f83ffcb7-cd0c-4817-a380-711a7f9d09d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f83ffcb7-cd0c-4817-a380-711a7f9d09d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3aa3db7-3c55-4730-a6a0-99b92872380a' class='xr-var-data-in' type='checkbox'><label for='data-f3aa3db7-3c55-4730-a6a0-99b92872380a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-81c3d50d-8c2d-431a-8bd3-f1978d019080' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81c3d50d-8c2d-431a-8bd3-f1978d019080' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96416707-2ec9-4f9f-af91-0a5d743d0adf' class='xr-var-data-in' type='checkbox'><label for='data-96416707-2ec9-4f9f-af91-0a5d743d0adf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-a21f73ae-8e06-429c-9aea-342c5ce7db68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a21f73ae-8e06-429c-9aea-342c5ce7db68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ee99fd4-1146-4114-8ff2-1cbd716ada17' class='xr-var-data-in' type='checkbox'><label for='data-9ee99fd4-1146-4114-8ff2-1cbd716ada17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4c6bd9fc-50d7-410f-84b4-d3b205eea6d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c6bd9fc-50d7-410f-84b4-d3b205eea6d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab80560d-fb32-4be8-9461-5867bd818bc8' class='xr-var-data-in' type='checkbox'><label for='data-ab80560d-fb32-4be8-9461-5867bd818bc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-df7dbaa5-2f99-477e-80a0-a61289505017' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df7dbaa5-2f99-477e-80a0-a61289505017' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-699494e9-ae8d-4f08-84fd-2f7d69530057' class='xr-var-data-in' type='checkbox'><label for='data-699494e9-ae8d-4f08-84fd-2f7d69530057' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-70699725-8353-4917-9b99-b5fb28317f73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70699725-8353-4917-9b99-b5fb28317f73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7187566e-e179-4be9-9d3b-a5a973663ebf' class='xr-var-data-in' type='checkbox'><label for='data-7187566e-e179-4be9-9d3b-a5a973663ebf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a34f4b57-3734-45f3-aa34-dda8d1f8ed52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a34f4b57-3734-45f3-aa34-dda8d1f8ed52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61ebc82e-e8f8-4349-9856-bd71f2ef555c' class='xr-var-data-in' type='checkbox'><label for='data-61ebc82e-e8f8-4349-9856-bd71f2ef555c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-f2c49263-40df-4899-8cc3-c4f3e6618e38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2c49263-40df-4899-8cc3-c4f3e6618e38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe267491-6411-49b5-bd79-04552fccbbad' class='xr-var-data-in' type='checkbox'><label for='data-fe267491-6411-49b5-bd79-04552fccbbad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-d2537db1-b5e4-45a2-becb-ee9733720412' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2537db1-b5e4-45a2-becb-ee9733720412' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba97d160-717c-44ae-820d-0e49d4f32850' class='xr-var-data-in' type='checkbox'><label for='data-ba97d160-717c-44ae-820d-0e49d4f32850' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-213d1cab-3330-41ba-af9a-eede329f10a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-213d1cab-3330-41ba-af9a-eede329f10a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-174702d0-5dc0-4213-966e-fc4bf9a2f9c6' class='xr-var-data-in' type='checkbox'><label for='data-174702d0-5dc0-4213-966e-fc4bf9a2f9c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-0afdc9d4-672a-4ee0-86f7-aec8ecfbf954' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0afdc9d4-672a-4ee0-86f7-aec8ecfbf954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca12d348-c198-49b6-a901-70d3ca01b7e9' class='xr-var-data-in' type='checkbox'><label for='data-ca12d348-c198-49b6-a901-70d3ca01b7e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-01fad95b-2887-47f4-ad78-69b6aea9841e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01fad95b-2887-47f4-ad78-69b6aea9841e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80f935f9-e0b5-4ed6-8366-1b0fff129f85' class='xr-var-data-in' type='checkbox'><label for='data-80f935f9-e0b5-4ed6-8366-1b0fff129f85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-f864b073-a891-46f3-8001-393c8721129e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f864b073-a891-46f3-8001-393c8721129e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a07811f8-ce77-492b-9038-5da4e3976fc4' class='xr-var-data-in' type='checkbox'><label for='data-a07811f8-ce77-492b-9038-5da4e3976fc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b45c7fc3-9218-47c4-8b8e-62a559379b91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b45c7fc3-9218-47c4-8b8e-62a559379b91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c85d8ebb-9464-41ac-ad83-c04bd8214acf' class='xr-var-data-in' type='checkbox'><label for='data-c85d8ebb-9464-41ac-ad83-c04bd8214acf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d1f8faaf-7b63-4331-98be-b9f105023328' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1f8faaf-7b63-4331-98be-b9f105023328' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ba9bb8-e5ac-41d5-8bf6-15bfd1cac23d' class='xr-var-data-in' type='checkbox'><label for='data-57ba9bb8-e5ac-41d5-8bf6-15bfd1cac23d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-d7e5b143-cca3-41e1-b2b0-11a44ae37fdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7e5b143-cca3-41e1-b2b0-11a44ae37fdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e8eca97-29b3-4e69-bdf5-80cc99949c53' class='xr-var-data-in' type='checkbox'><label for='data-3e8eca97-29b3-4e69-bdf5-80cc99949c53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12 B </td><td> 12 B </td></tr><tr><th> Shape </th><td> (3,) </td><td> (3,) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"93\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"43\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,43.88583450710766 0.0,43.88583450710766\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"63.885835\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"140.000000\" y=\"21.942917\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.942917)\">1</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e5f50b63-2288-49f7-81c9-86a94143ae8a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5f50b63-2288-49f7-81c9-86a94143ae8a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_5</span>: 2048</li><li><span>phony_dim_6</span>: 2048</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6aa7d461-0a3c-4b80-813f-27a5c6371ace' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6aa7d461-0a3c-4b80-813f-27a5c6371ace' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6548e3ec-c146-4fd5-ba94-b545f3f3c960' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6548e3ec-c146-4fd5-ba94-b545f3f3c960' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1efb1f1-5145-4e55-b92c-519f7e3ca878' class='xr-var-data-in' type='checkbox'><label for='data-e1efb1f1-5145-4e55-b92c-519f7e3ca878' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-bf67c52a-91ab-4586-ab56-62a2cb1e41e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf67c52a-91ab-4586-ab56-62a2cb1e41e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99d8ce32-edab-4329-83cf-24af83168af6' class='xr-var-data-in' type='checkbox'><label for='data-99d8ce32-edab-4329-83cf-24af83168af6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-8b2adb00-0bdc-47f4-9336-1f7ee1c5c415' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b2adb00-0bdc-47f4-9336-1f7ee1c5c415' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b23f65f4-5455-40d1-a3d0-163f0092764b' class='xr-var-data-in' type='checkbox'><label for='data-b23f65f4-5455-40d1-a3d0-163f0092764b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b1df4134-c2a6-49e4-a92e-19f56a68edfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1df4134-c2a6-49e4-a92e-19f56a68edfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f93112bb-5801-4368-884a-6fd9c7632eb6' class='xr-var-data-in' type='checkbox'><label for='data-f93112bb-5801-4368-884a-6fd9c7632eb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-0d199d71-bed0-4ca1-8f92-73b1d89169e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d199d71-bed0-4ca1-8f92-73b1d89169e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fdfaaa6-ffeb-449d-86af-16f035e75df2' class='xr-var-data-in' type='checkbox'><label for='data-3fdfaaa6-ffeb-449d-86af-16f035e75df2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-92cf6635-3a2f-4605-b74e-2cf405ab6784' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92cf6635-3a2f-4605-b74e-2cf405ab6784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06011614-9c33-4f1a-a80a-b08bece7d2e4' class='xr-var-data-in' type='checkbox'><label for='data-06011614-9c33-4f1a-a80a-b08bece7d2e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-85007e5d-7cb4-46c0-9405-ea00ff0f9cfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85007e5d-7cb4-46c0-9405-ea00ff0f9cfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b90c212-85be-472e-b8f3-91b9a8d4f0c6' class='xr-var-data-in' type='checkbox'><label for='data-4b90c212-85be-472e-b8f3-91b9a8d4f0c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS INFORMATION</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3fb7197a-c704-4df1-9c07-139fdc8779c3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3fb7197a-c704-4df1-9c07-139fdc8779c3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1afe8b2b-da21-4c28-bdf6-670322e97aea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1afe8b2b-da21-4c28-bdf6-670322e97aea' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>StructMetadata.0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S32000</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31d7ee7a-d7f4-477d-96cf-3486a33c7c13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31d7ee7a-d7f4-477d-96cf-3486a33c7c13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf89095b-b7aa-4519-aa31-3aff7abc70b3' class='xr-var-data-in' type='checkbox'><label for='data-bf89095b-b7aa-4519-aa31-3aff7abc70b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S32000]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-51591833-277e-4892-afa1-0904b05aa21f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51591833-277e-4892-afa1-0904b05aa21f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>HDFEOSVersion :</span></dt><dd>HDFEOS_5.1.15</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 0B\n",
-       "Dimensions:  ()\n",
-       "Data variables:\n",
-       "    *empty*"
+       "<xarray.DataTree>\n",
+       "Group: /\n",
+       "├── Group: /HDFEOS\n",
+       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
+       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
+       "│   │           Attributes: (12/22)\n",
+       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
+       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
+       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
+       "│   │               AuthorAffiliation:   NASA/GSFC\n",
+       "│   │               AuthorName:          Omar Torres\n",
+       "│   │               EarthSunDistance:    0.9862366\n",
+       "│   │               ...                  ...\n",
+       "│   │               ProcessingCenter:    TLCF                                                ...\n",
+       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
+       "│   │               ProductionDateTime:  2024-02-11T01:29:46.0Z\n",
+       "│   │               ShortName:           EPICAERUV\n",
+       "│   │               Time_Band340nm:      2024-02-08 01:57:26\n",
+       "│   │               Time_Band388nm:      2024-02-08 01:56:59\n",
+       "│   └── Group: /HDFEOS/SWATHS\n",
+       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
+       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
+       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
+       "│           │                                            phony_dim_4: 2)\n",
+       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "│           │                                       phony_dim_3, phony_dim_4\n",
+       "│           │       Data variables: (12/29)\n",
+       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
+       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
+       "│           │           ...                              ...\n",
+       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>\n",
+       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
+       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
+       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
+       "│                   Data variables:\n",
+       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "└── Group: /HDFEOS INFORMATION\n",
+       "        Dimensions:           ()\n",
+       "        Data variables:\n",
+       "            StructMetadata.0  |S32000 32kB ...\n",
+       "        Attributes:\n",
+       "            HDFEOSVersion:  HDFEOS_5.1.15"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import xarray as xr\n",
-    "xr.open_dataset(ds, chunks={}, engine='h5netcdf', decode_timedelta= False)"
+    "ds = plan.open_dataset(1, open_method=\"datatree\")\n",
+    "ds"
    ]
   },
   {
@@ -1925,7 +1833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 4,
    "id": "bee93b40-db7f-4185-b07b-46a1ed843fd6",
    "metadata": {},
    "outputs": [
@@ -1933,183 +1841,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "open_method: {'xarray_open': 'dataset', 'merge': ['/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields', '/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields'], 'open_kwargs': {'phony_dims': 'access', 'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a3da6a0d0b0d40659547c986e1004f96",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "24c42a0d50eb45b7a7695b92bc141c7b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5f170c2a240f4ed58be72fab740a4cf2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "deec2c3df89d42daa17fdf0cca449491",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c72b4876ca6342adadff8a87c9f15d46",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b900ed19d1f458489afe5e4df6dfa6f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "afca26291d17416fa666201218e3c009",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80df650fe60f4116bede2a2c9ea8420e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "57e5b5ad2ddc4833a4577bb6dec56a89",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7f29632999224af6bb0f71c3a36db93e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2dd3b579f93d49389f8b18058119e2f4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c7abf4de9895480da2c81c46b4ce6124",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 254 ms, sys: 52.1 ms, total: 306 ms\n",
-      "Wall time: 4.28 s\n"
+      "open_method: {'xarray_open': 'dataset', 'merge': ['/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields', '/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields'], 'open_kwargs': {'phony_dims': 'access', 'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n",
+      "CPU times: user 335 ms, sys: 592 μs, total: 336 ms\n",
+      "Wall time: 3.89 s\n"
      ]
     },
     {
@@ -2615,7 +2349,7 @@
        "    SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
        "    SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
        "    UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "    Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ebc3240e-e692-4312-b451-0f173e44eef6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ebc3240e-e692-4312-b451-0f173e44eef6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff873cdd-9790-43e7-add7-a17717a33257' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff873cdd-9790-43e7-add7-a17717a33257' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c1e27422-4deb-4585-adb0-c405aa1a78f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1e27422-4deb-4585-adb0-c405aa1a78f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b42dbad-956c-46b0-b6c2-772ef78975e2' class='xr-var-data-in' type='checkbox'><label for='data-6b42dbad-956c-46b0-b6c2-772ef78975e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ad388002-f32e-4f68-83a6-b63936feef12' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ad388002-f32e-4f68-83a6-b63936feef12' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5724ae2b-5327-4c3a-ab4d-0f9deeb57fb0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5724ae2b-5327-4c3a-ab4d-0f9deeb57fb0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-07a0cbda-251d-42e9-8de9-3ba9b8194162' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07a0cbda-251d-42e9-8de9-3ba9b8194162' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dbe1cee-dfff-4220-b2cc-eeb415d5857b' class='xr-var-data-in' type='checkbox'><label for='data-7dbe1cee-dfff-4220-b2cc-eeb415d5857b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2678,7 +2412,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d6fd07d6-28c7-4aeb-9240-03c203beedca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6fd07d6-28c7-4aeb-9240-03c203beedca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58cc0e27-a422-4e34-aaaf-927f629f5350' class='xr-var-data-in' type='checkbox'><label for='data-58cc0e27-a422-4e34-aaaf-927f629f5350' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-f215e591-059b-49d6-900a-07f044e0cae7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f215e591-059b-49d6-900a-07f044e0cae7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ada8af48-25e4-4d97-bf96-d282907442d0' class='xr-var-data-in' type='checkbox'><label for='data-ada8af48-25e4-4d97-bf96-d282907442d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2741,7 +2475,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b72a9cb3-8f3b-4add-ad31-5f4b09639879' class='xr-section-summary-in' type='checkbox'  ><label for='section-b72a9cb3-8f3b-4add-ad31-5f4b09639879' class='xr-section-summary' >Data variables: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4b85c129-d4c6-4e6e-8313-388fb5942b21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b85c129-d4c6-4e6e-8313-388fb5942b21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c26597a6-b964-49f0-99f1-a37a9167085c' class='xr-var-data-in' type='checkbox'><label for='data-c26597a6-b964-49f0-99f1-a37a9167085c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8df3e6e9-2f68-47e1-864c-bc2353c706ad' class='xr-section-summary-in' type='checkbox'  ><label for='section-8df3e6e9-2f68-47e1-864c-bc2353c706ad' class='xr-section-summary' >Data variables: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-be95afe6-8526-46b2-99e3-5252b327ed37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be95afe6-8526-46b2-99e3-5252b327ed37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dd34263-31f3-48d4-b431-0193d405b4e6' class='xr-var-data-in' type='checkbox'><label for='data-8dd34263-31f3-48d4-b431-0193d405b4e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2804,7 +2538,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-bb2160d5-13fd-4557-9360-2da0a50e7e58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb2160d5-13fd-4557-9360-2da0a50e7e58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8ba5c5b-e268-4a4c-bafd-b7237db7c9f2' class='xr-var-data-in' type='checkbox'><label for='data-c8ba5c5b-e268-4a4c-bafd-b7237db7c9f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-5f9eda47-3c8e-4604-a026-37ee5c14b1e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f9eda47-3c8e-4604-a026-37ee5c14b1e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58673807-c923-4fcb-8f38-f8b113ea4942' class='xr-var-data-in' type='checkbox'><label for='data-58673807-c923-4fcb-8f38-f8b113ea4942' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2867,7 +2601,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-904cd54c-579e-4f68-8f0b-0e984d5c03d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-904cd54c-579e-4f68-8f0b-0e984d5c03d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19200ba4-33d9-4ea3-8813-ddb3cd86dc84' class='xr-var-data-in' type='checkbox'><label for='data-19200ba4-33d9-4ea3-8813-ddb3cd86dc84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-bec00f0b-cbd3-413b-9f35-fafea78bb709' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bec00f0b-cbd3-413b-9f35-fafea78bb709' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c72ad49-85fc-4d6b-9f55-6c26cab91f2d' class='xr-var-data-in' type='checkbox'><label for='data-4c72ad49-85fc-4d6b-9f55-6c26cab91f2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2930,7 +2664,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-ead9d357-b48c-4b19-9a99-a2de77b62400' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ead9d357-b48c-4b19-9a99-a2de77b62400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5488d697-d368-424a-9050-c3110b0f480f' class='xr-var-data-in' type='checkbox'><label for='data-5488d697-d368-424a-9050-c3110b0f480f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-22cfd2ee-d06d-496f-afac-a529a36ff609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22cfd2ee-d06d-496f-afac-a529a36ff609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c304f7b4-8b56-4ed9-9c34-c26297e86704' class='xr-var-data-in' type='checkbox'><label for='data-c304f7b4-8b56-4ed9-9c34-c26297e86704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2993,7 +2727,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b65c4055-c6b0-402a-8081-45a687f0134c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b65c4055-c6b0-402a-8081-45a687f0134c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bd569b7-46e3-4539-88a3-6d1fc3c67435' class='xr-var-data-in' type='checkbox'><label for='data-7bd569b7-46e3-4539-88a3-6d1fc3c67435' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-8626be37-db7a-4ef9-a0c9-9dfca77c06ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8626be37-db7a-4ef9-a0c9-9dfca77c06ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1282f234-81b9-4530-a5e0-95cbf13b8aa5' class='xr-var-data-in' type='checkbox'><label for='data-1282f234-81b9-4530-a5e0-95cbf13b8aa5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3056,7 +2790,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-5e62f92a-4d7e-4fc5-96e6-e4f346de99d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e62f92a-4d7e-4fc5-96e6-e4f346de99d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dc0d129-14dc-4346-ab59-19ad40a4dd24' class='xr-var-data-in' type='checkbox'><label for='data-4dc0d129-14dc-4346-ab59-19ad40a4dd24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b7381166-28d6-4f35-a638-e0fcaac5ed1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7381166-28d6-4f35-a638-e0fcaac5ed1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e02c7c-84d5-4ada-82d3-07e69723edc6' class='xr-var-data-in' type='checkbox'><label for='data-f3e02c7c-84d5-4ada-82d3-07e69723edc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3119,7 +2853,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-714cf159-484b-4b03-8b05-336cc650bb3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-714cf159-484b-4b03-8b05-336cc650bb3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b9482b0-7905-4c21-9dd6-01b57d1268b9' class='xr-var-data-in' type='checkbox'><label for='data-3b9482b0-7905-4c21-9dd6-01b57d1268b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-aa379eee-7b9c-4376-bbac-58508e183385' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa379eee-7b9c-4376-bbac-58508e183385' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2769e51-797a-4633-b983-306d082574c1' class='xr-var-data-in' type='checkbox'><label for='data-a2769e51-797a-4633-b983-306d082574c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3182,7 +2916,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-3e3ca523-62cd-4f6b-a69e-c5e3bbd51bf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e3ca523-62cd-4f6b-a69e-c5e3bbd51bf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1648bbf8-b283-4ad2-8f32-b3ff38c52e01' class='xr-var-data-in' type='checkbox'><label for='data-1648bbf8-b283-4ad2-8f32-b3ff38c52e01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-01ac85c9-1fd6-4342-9a81-1f6a6defa19a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01ac85c9-1fd6-4342-9a81-1f6a6defa19a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e5eaafa-d42f-4926-90de-0eb4d32121f2' class='xr-var-data-in' type='checkbox'><label for='data-5e5eaafa-d42f-4926-90de-0eb4d32121f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3285,7 +3019,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9b56ec9f-eed4-435b-bfac-bdb1b50efb6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b56ec9f-eed4-435b-bfac-bdb1b50efb6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b664c2a-2fbe-4fbf-b296-9a0751d3115e' class='xr-var-data-in' type='checkbox'><label for='data-0b664c2a-2fbe-4fbf-b296-9a0751d3115e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-7583f85b-8660-45e5-8e81-8ec1b4288145' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7583f85b-8660-45e5-8e81-8ec1b4288145' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05e52e57-ad36-4251-95b0-acd625c7b944' class='xr-var-data-in' type='checkbox'><label for='data-05e52e57-ad36-4251-95b0-acd625c7b944' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3348,7 +3082,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-5537349c-93a0-471e-afdd-eea848a71c72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5537349c-93a0-471e-afdd-eea848a71c72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed5ef41c-73b5-4487-9834-ae1a4a49daff' class='xr-var-data-in' type='checkbox'><label for='data-ed5ef41c-73b5-4487-9834-ae1a4a49daff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-52d7baba-2e56-4e8e-adf5-ebde3942434d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52d7baba-2e56-4e8e-adf5-ebde3942434d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91f77969-a2ed-4d50-9c82-1638e376e027' class='xr-var-data-in' type='checkbox'><label for='data-91f77969-a2ed-4d50-9c82-1638e376e027' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3444,7 +3178,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-22489b99-0177-465d-8df5-8a90277719e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22489b99-0177-465d-8df5-8a90277719e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7faed58-7495-4ead-9d8c-ff088b9bc94e' class='xr-var-data-in' type='checkbox'><label for='data-d7faed58-7495-4ead-9d8c-ff088b9bc94e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-bed55f0d-73a0-4709-936a-6cb1fabf2cc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bed55f0d-73a0-4709-936a-6cb1fabf2cc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0813567d-8bb5-4638-9dd1-1035c94b2482' class='xr-var-data-in' type='checkbox'><label for='data-0813567d-8bb5-4638-9dd1-1035c94b2482' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3547,7 +3281,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-3143013a-9982-409b-90d9-c0986526daf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3143013a-9982-409b-90d9-c0986526daf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35576394-0dcc-4aa0-9dfd-645dd8f28d21' class='xr-var-data-in' type='checkbox'><label for='data-35576394-0dcc-4aa0-9dfd-645dd8f28d21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-bed429a1-0457-42d9-8381-20a014e9c96c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bed429a1-0457-42d9-8381-20a014e9c96c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4da2c3d-d19f-4e4d-9a8d-81584f120e94' class='xr-var-data-in' type='checkbox'><label for='data-b4da2c3d-d19f-4e4d-9a8d-81584f120e94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3650,7 +3384,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-19fad9d1-a6fb-45d3-af5a-3a62c9c9924a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19fad9d1-a6fb-45d3-af5a-3a62c9c9924a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-373a2504-61c7-4d90-a6a9-fb1d328dfdb0' class='xr-var-data-in' type='checkbox'><label for='data-373a2504-61c7-4d90-a6a9-fb1d328dfdb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9142b48f-4c01-4d31-919f-a06bfc5cb21a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9142b48f-4c01-4d31-919f-a06bfc5cb21a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04db58b2-fa6a-4bae-bde5-1290d491037a' class='xr-var-data-in' type='checkbox'><label for='data-04db58b2-fa6a-4bae-bde5-1290d491037a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3713,7 +3447,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-6f3180e2-4649-425f-84cf-582811bd81a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f3180e2-4649-425f-84cf-582811bd81a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6e684da-62d9-4eef-9bd1-4f4e955abedc' class='xr-var-data-in' type='checkbox'><label for='data-f6e684da-62d9-4eef-9bd1-4f4e955abedc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-9d7ab331-b10d-4a6f-87a1-f9d9ac0f5f21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d7ab331-b10d-4a6f-87a1-f9d9ac0f5f21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbc04636-4405-4815-ac19-dff3768d7628' class='xr-var-data-in' type='checkbox'><label for='data-cbc04636-4405-4815-ac19-dff3768d7628' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3809,7 +3543,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-111a3a96-0cd0-4983-b6d5-9168473b2190' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-111a3a96-0cd0-4983-b6d5-9168473b2190' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a91a6d2-7a5e-4be4-9726-e0b060cedfeb' class='xr-var-data-in' type='checkbox'><label for='data-4a91a6d2-7a5e-4be4-9726-e0b060cedfeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9fca4638-9e3b-4191-81b3-82d9594bdeb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fca4638-9e3b-4191-81b3-82d9594bdeb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0841cc0-f9bc-40c4-a3c0-249a876b743e' class='xr-var-data-in' type='checkbox'><label for='data-d0841cc0-f9bc-40c4-a3c0-249a876b743e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3872,7 +3606,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-48c1e0cf-b260-4c85-aafd-601c5204ce4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48c1e0cf-b260-4c85-aafd-601c5204ce4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05dbdd09-c46e-45e9-8a7a-50828d28fb45' class='xr-var-data-in' type='checkbox'><label for='data-05dbdd09-c46e-45e9-8a7a-50828d28fb45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d29a0952-96c4-491f-9b09-2c7d466777f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d29a0952-96c4-491f-9b09-2c7d466777f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9462352b-f4c6-43e2-ad02-f0894b650e43' class='xr-var-data-in' type='checkbox'><label for='data-9462352b-f4c6-43e2-ad02-f0894b650e43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3935,7 +3669,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-82537dd9-967a-4936-b021-fc7176d819da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82537dd9-967a-4936-b021-fc7176d819da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a32e7dab-81f7-4270-b4da-b29cd5a9752c' class='xr-var-data-in' type='checkbox'><label for='data-a32e7dab-81f7-4270-b4da-b29cd5a9752c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4417013c-8184-46e1-8caa-a723e319506e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4417013c-8184-46e1-8caa-a723e319506e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8f363c0-837a-4de9-871c-027db025bd08' class='xr-var-data-in' type='checkbox'><label for='data-c8f363c0-837a-4de9-871c-027db025bd08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3998,7 +3732,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-acc80b02-6aa7-473d-814b-315d883ff537' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acc80b02-6aa7-473d-814b-315d883ff537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be2beaa9-4023-433c-ae84-06169da7b4d4' class='xr-var-data-in' type='checkbox'><label for='data-be2beaa9-4023-433c-ae84-06169da7b4d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-13346b05-152b-4b21-aeea-fa04a012075d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13346b05-152b-4b21-aeea-fa04a012075d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d4af539-8822-4886-b0c6-6d33afc2a0e2' class='xr-var-data-in' type='checkbox'><label for='data-3d4af539-8822-4886-b0c6-6d33afc2a0e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4092,7 +3826,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-e0d86e9e-7913-48b3-9b18-06d52e5adbfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0d86e9e-7913-48b3-9b18-06d52e5adbfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62414347-5ebb-49a6-a0fe-fecec98d6616' class='xr-var-data-in' type='checkbox'><label for='data-62414347-5ebb-49a6-a0fe-fecec98d6616' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a94cc12f-2111-4d98-a967-e11836e0058c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a94cc12f-2111-4d98-a967-e11836e0058c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42f51b7d-9d97-4857-9cf3-ad2e9f31b10f' class='xr-var-data-in' type='checkbox'><label for='data-42f51b7d-9d97-4857-9cf3-ad2e9f31b10f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4155,7 +3889,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-38e89965-4a32-4d56-b8ed-4b0753e0ce59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38e89965-4a32-4d56-b8ed-4b0753e0ce59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36cd4d17-897e-476d-9d7c-1dc11872c47a' class='xr-var-data-in' type='checkbox'><label for='data-36cd4d17-897e-476d-9d7c-1dc11872c47a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-f6c18e0d-3b1a-4327-ae73-11d8748bbcdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6c18e0d-3b1a-4327-ae73-11d8748bbcdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae245b8d-296e-492e-99c5-33254bee3f15' class='xr-var-data-in' type='checkbox'><label for='data-ae245b8d-296e-492e-99c5-33254bee3f15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4249,7 +3983,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-6c235fc1-e904-4f1f-8e38-20a35edda07e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c235fc1-e904-4f1f-8e38-20a35edda07e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e44c7f96-ba50-409c-84c7-5106e2aa5365' class='xr-var-data-in' type='checkbox'><label for='data-e44c7f96-ba50-409c-84c7-5106e2aa5365' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-4ea1c80a-fc8c-4f66-a2a5-5a35049b5bc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ea1c80a-fc8c-4f66-a2a5-5a35049b5bc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bef827a-5480-4d86-adac-bc118d954c9a' class='xr-var-data-in' type='checkbox'><label for='data-2bef827a-5480-4d86-adac-bc118d954c9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4343,7 +4077,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-79ab2549-04af-4ed9-9459-43f16a0cc053' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79ab2549-04af-4ed9-9459-43f16a0cc053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f9c9edb-857e-424d-831b-8eed7ab602e4' class='xr-var-data-in' type='checkbox'><label for='data-8f9c9edb-857e-424d-831b-8eed7ab602e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-099ef20c-1c7a-49d8-abb5-151da9741261' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-099ef20c-1c7a-49d8-abb5-151da9741261' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbd86854-c628-4113-a92a-34dc38df5896' class='xr-var-data-in' type='checkbox'><label for='data-cbd86854-c628-4113-a92a-34dc38df5896' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4406,7 +4140,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-349c2d22-ecfa-4cff-ac7a-00adf6b1415f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-349c2d22-ecfa-4cff-ac7a-00adf6b1415f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45f021c9-90b3-4419-b382-85ee2c039841' class='xr-var-data-in' type='checkbox'><label for='data-45f021c9-90b3-4419-b382-85ee2c039841' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9742efad-4315-48bd-8c51-7fd57d04de3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9742efad-4315-48bd-8c51-7fd57d04de3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81397121-bdd4-49e6-aa35-5a9036f0e40f' class='xr-var-data-in' type='checkbox'><label for='data-81397121-bdd4-49e6-aa35-5a9036f0e40f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4469,7 +4203,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-ca60a5f2-2222-4eab-bf6b-709f53d4cf79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca60a5f2-2222-4eab-bf6b-709f53d4cf79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e945ff8-4e8d-473c-8bc8-8a4aa826dea6' class='xr-var-data-in' type='checkbox'><label for='data-6e945ff8-4e8d-473c-8bc8-8a4aa826dea6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-ca15c775-576d-42aa-b10e-2f53fb66bd97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca15c775-576d-42aa-b10e-2f53fb66bd97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1139394-eb18-4336-a423-4802eab7085d' class='xr-var-data-in' type='checkbox'><label for='data-b1139394-eb18-4336-a423-4802eab7085d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4563,7 +4297,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c8d5fcfa-b6d6-4d82-987a-135554c8634a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8d5fcfa-b6d6-4d82-987a-135554c8634a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77187b66-3fa3-488d-ae5b-1f373adfa707' class='xr-var-data-in' type='checkbox'><label for='data-77187b66-3fa3-488d-ae5b-1f373adfa707' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2a43b360-35d9-4984-ba73-d680961791e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a43b360-35d9-4984-ba73-d680961791e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f893811a-f4d3-4936-b4cb-00fc6969fb90' class='xr-var-data-in' type='checkbox'><label for='data-f893811a-f4d3-4936-b4cb-00fc6969fb90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4626,7 +4360,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-fed35bcb-dff3-41b3-932b-084653c136fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fed35bcb-dff3-41b3-932b-084653c136fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8eb31b4-f41d-4348-906f-2c7497d70cdf' class='xr-var-data-in' type='checkbox'><label for='data-b8eb31b4-f41d-4348-906f-2c7497d70cdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-36631f8d-5d8a-49f7-a448-ee03c445e209' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36631f8d-5d8a-49f7-a448-ee03c445e209' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e850d8fb-2490-486b-8752-5b036f771d96' class='xr-var-data-in' type='checkbox'><label for='data-e850d8fb-2490-486b-8752-5b036f771d96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4729,7 +4463,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-03f174be-34a1-4d02-8bb7-83977f6a527b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03f174be-34a1-4d02-8bb7-83977f6a527b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55e744c2-1621-4ca4-9946-67ad6b4559db' class='xr-var-data-in' type='checkbox'><label for='data-55e744c2-1621-4ca4-9946-67ad6b4559db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-ff86bd48-ca82-410e-9e2f-e4538caf38c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff86bd48-ca82-410e-9e2f-e4538caf38c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f8f1edc-dda3-4a44-8286-6445373d9b68' class='xr-var-data-in' type='checkbox'><label for='data-6f8f1edc-dda3-4a44-8286-6445373d9b68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4825,7 +4559,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-2260260b-5b67-45e1-b168-a14e46252ab9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2260260b-5b67-45e1-b168-a14e46252ab9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e74d8a91-92d7-4c79-8716-78095cc173ba' class='xr-var-data-in' type='checkbox'><label for='data-e74d8a91-92d7-4c79-8716-78095cc173ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-cd8ff24a-a1d3-4339-98f8-2bbc79e05345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd8ff24a-a1d3-4339-98f8-2bbc79e05345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-334017a6-167f-45cd-9dbf-5eb419d9c7d8' class='xr-var-data-in' type='checkbox'><label for='data-334017a6-167f-45cd-9dbf-5eb419d9c7d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4919,7 +4653,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-5524929d-0e32-4dea-b783-9049fa03fd0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5524929d-0e32-4dea-b783-9049fa03fd0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a27a790f-7fa8-44d5-9679-302d0c6da505' class='xr-var-data-in' type='checkbox'><label for='data-a27a790f-7fa8-44d5-9679-302d0c6da505' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-437db2dc-7bf7-403a-a08c-d82a39c2a2e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-437db2dc-7bf7-403a-a08c-d82a39c2a2e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09997e5e-baa2-4147-ad66-83d5acb9f24f' class='xr-var-data-in' type='checkbox'><label for='data-09997e5e-baa2-4147-ad66-83d5acb9f24f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5013,7 +4747,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-ac5c7d57-4d26-418b-9ae1-91e74c6af624' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac5c7d57-4d26-418b-9ae1-91e74c6af624' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-591ae0f5-0611-4d85-ab5b-e4284222bc15' class='xr-var-data-in' type='checkbox'><label for='data-591ae0f5-0611-4d85-ab5b-e4284222bc15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2876b59b-f776-40a8-a688-e524085ed666' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2876b59b-f776-40a8-a688-e524085ed666' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23ea04a2-c0e6-4f20-9a60-e61a763a3824' class='xr-var-data-in' type='checkbox'><label for='data-23ea04a2-c0e6-4f20-9a60-e61a763a3824' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5076,7 +4810,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-585aa806-7586-4a1e-b160-61653d700e61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-585aa806-7586-4a1e-b160-61653d700e61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13ba941d-7e37-4eb0-a478-9ad56d4c754d' class='xr-var-data-in' type='checkbox'><label for='data-13ba941d-7e37-4eb0-a478-9ad56d4c754d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-8c65c97a-a7aa-488f-b473-1720f06ccca1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c65c97a-a7aa-488f-b473-1720f06ccca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72ad6fbf-5026-4734-90b6-583caf864ef1' class='xr-var-data-in' type='checkbox'><label for='data-72ad6fbf-5026-4734-90b6-583caf864ef1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5170,7 +4904,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-5d509427-785c-4d7c-8763-381889129171' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d509427-785c-4d7c-8763-381889129171' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0364567-d6ba-4e0e-8260-6624e9ecf13c' class='xr-var-data-in' type='checkbox'><label for='data-c0364567-d6ba-4e0e-8260-6624e9ecf13c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-0ee95ec0-1512-4d22-a5e0-9348475acdec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ee95ec0-1512-4d22-a5e0-9348475acdec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f543a021-b264-41cc-b725-c6ef89ae393e' class='xr-var-data-in' type='checkbox'><label for='data-f543a021-b264-41cc-b725-c6ef89ae393e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5233,7 +4967,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-e7cafc80-6f0f-4501-ac67-20421b9b67fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7cafc80-6f0f-4501-ac67-20421b9b67fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eba82fb-5739-41d6-b94f-032fca3061ca' class='xr-var-data-in' type='checkbox'><label for='data-5eba82fb-5739-41d6-b94f-032fca3061ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-537a310d-0c3c-48aa-a471-c110710dc6d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-537a310d-0c3c-48aa-a471-c110710dc6d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7486216-2561-4c0c-81ac-7c719825b736' class='xr-var-data-in' type='checkbox'><label for='data-b7486216-2561-4c0c-81ac-7c719825b736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5296,7 +5030,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-e1bb9437-7d64-4f48-95a0-abfe1a89efe3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1bb9437-7d64-4f48-95a0-abfe1a89efe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11c44a2a-5d3f-4143-9c7f-83d493da35aa' class='xr-var-data-in' type='checkbox'><label for='data-11c44a2a-5d3f-4143-9c7f-83d493da35aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-fe3a27da-c528-4cb1-94ba-85280c98547c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe3a27da-c528-4cb1-94ba-85280c98547c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09bd018b-00f1-49b8-a8e5-4dbba27ef438' class='xr-var-data-in' type='checkbox'><label for='data-09bd018b-00f1-49b8-a8e5-4dbba27ef438' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5379,7 +5113,7 @@
        "    Wavelength                      (phony_dim_3) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5397,30 +5131,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "b6aae09c-a92b-4ecf-b2c5-ae49e8c08b3a",
+   "execution_count": 6,
+   "id": "c92f60a5-a648-41a5-89bb-4a6a8177b16e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "      shape=(2048, 2048), dtype=float32)"
+       "<matplotlib.collections.QuadMesh at 0x7f4286fe6120>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAGxCAYAAABiPLw8AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XV4W/e9+PH3AR2xZFkGme0YwsycNCmlzCutXWndusIddNvdHd+tY+y2brdb23VlZkjbUMMMDppiJlmWZPGB3x+ny71dL3S/dk26nNfz+Hli+0j52rKlj7/fDwiGYRhYLBaLxWKxnATE470Ai8VisVgslo+KFfhYLBaLxWI5aViBj8VisVgslpOGFfhYLBaLxWI5aViBj8VisVgslpOGFfhYLBaLxWI5aViBj8VisVgslpOGFfhYLBaLxWI5acjHewEfF7qu093djdfrRRCE470ci8VisZzADMMgHo9TWlqKKB6/PYbzhRD39OwiFAodtzWcaASrc/P709nZSUVFxfFehsVisVg+Rjo6OigvLz8u//fevXuZNGkSNbhoMRLHZQ0nIivweZ+i0Sh5eXl0dHTg8/mO93IsFovFcgKLxWJUVFQwPDyM3+8/LmuoFJw4kGgjyaq31zF//vzjso4TjXXU9T799XjL5/NZgY/FYrFY3pfjlRrx5ptv0keWyynFj8xFC5bRo6esVA2s5GaLxWKxWP6p6LrOJ5avYCo+HEhMwkcclaeeeup4L+2EcFwDn7vuuouZM2fi9XopKiri/PPP59ChQ++6xjAMvvWtb1FaWorT6WTJkiU0Nja+65pMJsOtt95KQUEBbrebc889l87OznddE4lEuPrqq/H7/fj9fq6++mqGh4f/0V+ixWKxWCwfqeVSEWk0JmCeTtgQmYGf6y+5nGw2e5xXd/wd18BnzZo13HLLLWzatImVK1eiqiqnnXYaicR/JmH96Ec/4mc/+xl33303W7duJRQKceqppxKPx49dc8cdd/DMM8/w6KOP8vbbbzMyMsLZZ5+NpmnHrrniiivYtWsXr776Kq+++iq7du3i6quv/ki/XovFYrFY/pFSqRRbGWYWecj857HWaDxICJxit6q7Tqjk5oGBAYqKilizZg2LFi3CMAxKS0u54447+PKXvwyYuzvFxcX88Ic/5NOf/jTRaJTCwkIefPBBLrvsMgC6u7upqKjg5Zdf5vTTT+fAgQOMGzeOTZs2MXv2bAA2bdrE3LlzOXjwIKNHj/4/1xaLxfD7/USjUSvHx2KxWCz/q+P1mjFHCNBMkgsJIfDufJ6jJFlNmN7hoeOWcH0iOKFyfKLRKAD5+fkAtLa20tvby2mnnXbsGrvdzuLFi9mwYQMA27dvJ5fLveua0tJSJkyYcOyajRs34vf7jwU9AHPmzMHv9x+7xmKxWCyWj7PBwUF2EmUOee8JegAqcZKPjSV5lcdhdSeOEybwMQyDz3/+8yxYsIAJEyYA0NvbC0BxcfG7ri0uLj72ud7eXhRFIRAI/K/XFBUVvef/LCoqOnbN38pkMsRisXe9WSwWi8VyolpaOIoQDspw/refFxCYQ4B9xOno6PiIV3fiOGECn8997nPs2bOHRx555D2f+9vyO8Mw/s+SvL+95r+7/n+7n7vuuutYIrTf77eaF1osFovlhNXc3MwBRphN3v96XSF2qnGyvHLsR7OwE9AJEfjceuutPP/886xatepdHS7/2mL7b3dl+vv7j+0ChUIhstkskUjkf72mr6/vPf/vwMDAe3aT/uqrX/0q0Wj02NvJHB1bLBaL5cR2Wt0k6nGTj/J/XjsLMw9o165d//iFnYCOa+BjGAaf+9znePrpp3nrrbeoqal51+dramoIhUKsXLny2Mey2Sxr1qxh3rx5AEyfPh2bzfaua3p6eti3b9+xa+bOnUs0GmXLli3Hrtm8eTPRaPTYNX/Lbrcfa1ZoNS20WCwWy4lq06ZNtJNiBu8vYdmLzHi8nDN17j94ZSem49q5+ZZbbuHhhx/mueeew+v1HtvZ8fv9OJ1OBEHgjjvu4Pvf/z719fXU19fz/e9/H5fLxRVXXHHs2uuvv54vfOELBINB8vPz+eIXv8jEiRNZvnw5AGPHjuWMM87gxhtv5Pe//z0AN910E2efffb7quiyWCwWi+VEZBgGF8xdwiR8uP+Ol/Rp+HiEbl577TVOP/30f+AKTzzHNfD53e9+B8CSJUve9fH77ruPa6+9FoA777yTVCrFZz/7WSKRCLNnz+b111/H6/Ueu/7nP/85sixz6aWXkkqlWLZsGffffz+SJB275qGHHuK22247Vv117rnncvfdd/9jv0CLxWKxWP6Bnn32WaLkOJP3FvD8b+xITMPPFWecQ7+aetfr5T+7E6qPz4nM6uNjsVgslvfro3jNyOVyFCouJuFjHN7/+wZ/Q8PgMbqYRh6rjcF/wApPTCdEcrPFYrFYLJa/zylKCAGzK/P/DwmBWQTYxjDJZPLDXdwJzAp8LBaLxWL5mFFVlW0MM4sA0n/TrPD9qsWFC+lY/uvJwAp8LBaLxWL5mMlms6TRKcH+ge5HQCCE/aRq2XJck5stFovFYrH8/5MA6f9/wwcA4STL9LUCH4vFYrFYPqZEQUD6PyYZ/J/3YXzAyOljxjrqslgsFovFctKwdnwsFovFYvmYkoQP4ajr5NrwsQIfi8VisVg+rqQP4ajrJIt7rMDHYrFYLJaPK2vH5+9n5fhYLBaLxWI5aVg7PhaLxWKxfEx9KFVdH9JaPi6swMdisVgslo8p66jr72cFPhaLxWKxfEx9OMnNJ1fkc7LtcFksFovFYjmJWTs+FovFYrF8TIl88B2Mk2u/xwp8LBaLxWL52PowjrpOtqMfK/CxWCwWi+VjSrSSm/9uJ1ugZ7FYLBaL5SRm7fhYLBaLxfIxZVV1/f2swMdiOYkoU6+jcu7ZtG98kdLppxPvbiLe00z90gs4suoZLrj9ZlY++irFY6cQDHk5vGknF125jK17e7HZZXRVJxZJceaSUTzz0kGqxxXRfnCA2fMq6Q4nifQlOH1+FRv29yPJAg6njaGBBLMnhHjy4VWUjGng8JrXqZi+BICWtc/hrxxLtP0A2Z1/Or7fHIvlY+jD6OMjnlxxjxX4WCz/bJSp1yEpTrRsimDdNApqaji08im8JbW4CysoLPfD3LOpaChgzgUT2dIcRtcNCq76JPPqC7joRzewvjnMkvoC9s8oo8TvIJXVAFjUUIDfLvPrN45wxzXTKPc5YFkdB/tHmFdfQJnXwd1vNXHl4hpaBxJUFrhwSCIVfid5N5xGMqsxc/I17G+NcPWSUTxc6UcQBVLxKVx2/xbiaZWtr6ynctJE9r74OPmjJiPKCt2PfeY4f1ctlhOTNaT07ycYhmEc70V8HMRiMfx+P9FoFJ/Pd7yXY7HgW/Jlpp9/FnteX8c5V53JmtUtdG19GV95A7HOw8y+4mqyKRVRFrnz3HE0hRN47DJBl8KeriiTyvw09sSYVOojmdOZWeZjc2eUkMdORtMZyaiMyncRSeWwSSLVeQ4USSCZ0wHI6QbDKZWMpqPp5tNIsUchmlaJZzW2tkeYUxVgSsjDps4Y5T4HBS6ZkZyO3y6xpm0Yl02ksTeOxyGT57TxwJoWpowKsuVAP2fPruA3f3iT0XPH098epXvfNvyV4+jZ+QaFY+Zgc/spKPOx9ZunHc+HwWL5b/2jXzOSySRut5sfeGpxi9IHuq+n0v1MveVT/OxnP/uQVndiswKf98kKfCzHmzL1umP/Dk1eSu/uVQTrpmHoGrUzJwIgSgLZlEpRyMOoQg+fnFGBWxEZSuUociusPRphcrEPSYTOWIYCl42uWJqZZT4yqkHQKZHVDV48NMhpdUFU3aDMCRFVxCYKeIQcfVnzSVYSBAoUHV2yEc1oxDM6HkVE0w0OD6WYFnIzmNJI5nSCTpnhtEa5z8bhcJqAU6bAKbGxM874IjcjWZ1Dgwl0wyCnG5R47OzqjTEl5GNXb4xCl8If17WSzagUBV20NA+RX+xhqG+EyeOLefWpdcw/aw4v/e4+0lvvOS6Pj8XyX1mBz4nLOuqyWE5A/zXI+Vu+8gZ6d6/CXVjBSF8bBWPmUFXqZePbRymrDXLG7Ar8LhufnVFKR1zFbxfx2yVcNpErx/hJYSOtGdTk2XGN9NKQX0w4pVHhEUlqkE+KS8YX4VQTGDaZlKGQT5LenAOPAiFiDEt+ApkBco4QcnKIgkyCPH8p8nAnutNPsSeBkc0xKtaJIdvJBEZTKKbICDYmFijEcqAbMLrAhVMWKXJKxDJ2pgagNSWjSAKl3kJSqs6N00p5qy3KN84aS9twinGFHp4u7mFGZR7b2od5dcNRXv3VNdx83za+/oPPs+CHq7hkQRWPr2vj8MbtnHXZMu6/YtpH+OhZLB+dD2dI6cl12GUFPhbLCeB/C3QAZIeHSWedC0AmlSM3egLefCd1lX48DhtVQRdjLvFxWkMhRW4bApAzoMIjktYFfP37yRWMQkqEcbqDOAUAG5ovhCcdNXdqRB8KBoZgA8CQbKBrpDQDh91NAQK6IGNIdshqGKKMpKYJi378efnI6WEwdISmLYhFVRh9zeRqZiAPtSNnR0DLYjd0tg1LjC90YRd0NEXCJ2lIkW6m+QMMG25CbgFZFFAyUTRfHm+2RllW42c4rVHqtVPp0mlYUkN/QmVMgYczxhSxuTPKZ84cTZnPgcch8/z2LnTdYOaZ83nzlT0E7n+OxEAHgJVEbfmnYg0p/ftZgY/Fcpz8d8GOK1hKMtwNwMJPfYqGUh8btnWxcGY5mmFQ5LUzqdRHiddOmdfOwcEk5T4HQ6kcQZcNSYRwUmWyN0N/2oMgCBTIWTRXADEVRc0rR460I+TSaN5CBEEkpfhxR9uR+4+gu/MRsimc/hAAgqbidTuRI+3oTj+CmiWsBAlmwxiyQsxQ8NtFJDWNPNSO6i9FLBsNmQSC3Ymw9w0oH43YtBlt9AIwDKYUO5AzMeShdnwFoxCTI6TzKjEAuwEZVUc3QBFlpHSM2WVe7J27KJIdFDi8GKqXEUOmSo4j6ClqSssodCt4FJGsZiAKULx4FD9+ci9jSopJTR/F1JoZPP74ZiS78z3fdysQsnycfSiBz995/V133cXTTz/NwYMHcTqdzJs3jx/+8IeMHj36gy3kI2IFPhbLR6Ti6vvp27cWAIe/EH/lWLRMCmcgRElDFal4Fk3TKa8PMrMmn8N9cTbt7CYZz7D90AAOl43yiSGq81yEPDYKbCqDLoXaPHOHJqvpFNptVHltJDU7HkAQBEZ0Bdlbik0UODyUZnSwClU3UAwVDB1vz26yZZORZDtCNoHh8CKmogjxQQxPPq5oN48nKrgolELfu5rArPMwJBlDdhCIHcVQ3GieQhIlE3EOt6O37kYuGUXm8E6UiQtAFNFGL0BQM4RxUxhtJhushfxKpESYQVcpBUMtiMlhdHc+3kwCQ5RhJIxaNR2fkcawOdHtHsRcEl2UCA0dAl3DyCtBGe6gASClogYqKXC6ATfnfW0JW7pGuHV+Fa83D3H+hTN59umteEtqESSJWOdh4L0BqBUIWSz/uzVr1nDLLbcwc+ZMVFXla1/7Gqeddhr79+/H7XYf7+X9n6zAx2L5Byq7/A/Y3H66t7+Gw1/IiltuoungIJeeXs+be3pIjmQJlXiZUxukvtDDtBIvBweT5DQdj0PmqlmV/PDZRkRR4OeXTOKZfb147CIjOQ3DkBgdlEiqBkGXhE9RADAESKlm5ZQt1oPmLULMJUnJbordNrriOUQBFFFAEGQyeeMpT4SRRgbJhsYiZuIYkoLQthchrxTdJXNh33qSnnPQZl+GJzeMmEkgRHvR8koBEHIpHJkRsoEqbDYnmqFjm3kmqGmyu9diK6tFsDsoiA+jj1mALdaD0LmfVONW3EDO52fnz5+h6pSx5JJpnEEfBSvOR9CyCIc2oKlZ5MIyciXjSYt23NkEQmwAIZdGyIzQWTCZst5tyIDL7kWK9xPOH02hW0EUBK6YWERrRR7XzKzgxns2MndaGX/83uFjPYT+q/8aCFlBkOVEdzxmdb366qvvev++++6jqKiI7du3s2jRog+0lo+CFfhYLB+y//rCOX7FJQRLvEjyWRSW+0llNTx5Dv7jzxv59zuWoRkGz+/q5qaZ5ThlAZuh4i/zYhNhcsjDvv4EmqYzZlQ+z+zrZXl9IU5ZxCEJ+GWd5liOemmYsBJEECCZ0xlKaYQ8MqKhkfOVIGdidKpOykmgSi40XcAui2iGYR5ZKS7AQaZ0AiNZHbcjD1u8FxSHufNj6OjjFuFARUyF4egehFANgqEjD7WTC401c3sycWy6hu70IyYjoKtonkK0U67DNtwO2RRa2wGMdY+hRcM4xkzHPe8M1LxyjH1rmHTTcpxTF5FrP8z2f38Q2eVgYNevSUdSeMsDeMoKyZs1hz1f/S01p42jcPFCWh78GWWLplBc1UyyswXJ6UKwO5DHzaJgYC9BuxvVXYEU6SDoqsRvF3n735YylNa4dcEvEAVY1TrED+/dfKxM3p0fpPXt59/zWFpBkOVE9GHN6tI0jVgs9q6P2+127Hb7/3n7aDQKQH5+/gdbyEfECnwslg/Jf32RdAVLCdZNxZfvxNANfvK5eXjtMu1RsyqpPTqOnniax9e18YsrpjKc1vDtewZh6mkEEmGywVpSOZWcpjNjfDHLRxdS5nOwsydGVZ6dnb0JlpTZqRcjqN4QPgOk1DAjugdJBEVNIWhZYqKXSNZBoVNiSHfit5n1G5oBTlnEwIUU60FXPEi5NIFMHATz7z+jciIMtIDLj7bmEWxVY8l1NbP/988y5cffQM+kEPJLYOvz2EqrMdQcBMqQ23ciONwY2TSSmkXJk1HzyhEzcZQxMxl8+kH806YjFFWidTcjpRNokoRS2QCuPASHm2lf+QT2MdORnS+iZ1WGDrbhG1OP5A8y97dfp+epJ9BjYQzNoOXFzfRsf47CcQVUnTqJcGMrlakEwnn/gq3xTSK+Udh8VRSIGl1JnZxuELBLjACjpBgHPHZe/d6ZDKWW8/Xn91Pgs5McXoS/uIDDbz79nsfXCoAsJxIzx+eDj6zYsmULfr//XR//5je/ybe+9a3/9baGYfD5z3+eBQsWMGHChA+0jo+KFfhYLB/A3+aHlM1cQbDEy+E1b3H5eePIqjpXTS8n3yHxdkeUmWV+s/+Ny8YLe7p57KZZpFSDkEfGVj0OY2SAXEEdzcMZ9vbG2dY+TM9wimhGxZNWWVFfgE8RGRVwIqgpNG8xvPo7oktuIl9SCNk0opqMrrgYyTrwyQZpzQxk8kkxlHESkFSEXALh0DbEoip0uwdt91vYGqYDoHUcRC6pRs2vxMgvR5ftyHPPRxwZwO7NY/ytDrRwD4gSyc2rcTaMB9lOrnwKwqankGsmYABq9wGkQAJZzaA7vGTza8CnIn36LgQ1hqapGGMXIYwMIOaFaPvBt6k4/0ySRw6hpjMYiRjuGYtYed6XiCRyHP7DNlKaTjSno4gC16xoxxlwkOhLMmvvRnjgmziLC7HneWlfuRll03WUnXUqnhd/inPe2RiKE+zlBOwS0Yx5FDgkBlhcJdAcyTC+0MWdp4+mwu/ggu3dPHD7Am5yypy9oJr7H9tBz8433vOYW0GQ5Z/FrFmzeO211971sfez2/O5z32OPXv28Pbbb/+jlvahsxoYvk9WA0PLf/XXFz9PcTUjfW3HPj7x7EtRnDZcHoULZpYzuyyPEo8NQRAIOkSSqoFHjWHIDg6PCIxLN6MGq5G7G9GDVSScBaxtj1EbcNEZS9MVT3PhmAJ6EyqSILCnL85ZdQFUAxzRTnRXAAwd3e7FNtjESH4dDlTQNcR0DM1TiI6AiIHSvRfd4TWTkXeuRC4sQ0/GSOzZgajIeOYuJ7H5LVxnXI0h2xEHWhAUx7HbGIoLIZMAQUAcbEPtbsVQc2S62hFtMs3PbaRswTiCZ11MvGwatpd/TXj3Icqu+wxqVxNyWR3xVc9i8/mQgiFs5bXo8WEIjUJQsxiyQvL1R3CddjmpNx8D4O1vPEPJ9BCPvHCE6Dsdo/9KEsydq7FeO3V+O2WzSsirC1F67lls/fKvCU0vo+X1I1QurCLeGWXcNcsZPtKOv7YM58zlaP5SOoQAkiCgSALRjEaRS+bpAwNEUjkmFHs5XW5l1oNROvcdpGb6BNr3NhPrOkwmPvSutVgBkOVvfVQNDP8jWI9X/GB7GA+M9DHm5mv+7gaGt956K88++yxr166lpqbmA63ho2Tt+Fgs79Pf7u5IihNnIET+qAkkw30U1lQx2GW+ID797RX0JzI0R5K4bF7q3CryYBceZwBkBXnoKOPVDJ+tu5S7u15DK6plSPLTG80yp8yLIAjEszIjWZkjQxn6ElkyqkaR204koxNQQIj2Ikg2pOEuklvfRFp+NU41wRBORET8Tj+2o9uOBVSSO4iaV4bSdwipbjKZfRsQPXl4Zy1EDfeSbWlEz6kI4XZyLY3YKhrQomEYPRchm0LIJpESYQzJhmB3IddONquwRnWhFtUzad4Z5IoaSL/yexydzWjA679dT9Uze+jvT7B2MMmXv7CQ/Y/tZvtgku60CsCiAhfLv3wqg3taqLn4VDZfdhOh6WU8+IdtzAg4uefpQ5xT7qM3kWVENTgQzzA/6GR9OAXAzKnFxHtGuOfpQ3zmEpHMA4+SiqS5//dbWVSTRy6RoWbFNFqef5ux3/se+7/6rzQUliGHEpQ07WF4yU0UHHmTYMM8+nMisyvy6I5lKPc5eLy/gl99qp7dvaNJ5TTWFrmJRBvY+Jc/M+2iK9jx1MPv+dmwgiDLR+l4JDcbhsGtt97KM888w+rVqz9WQQ9YOz7vm7Xjc/JSpl53rPrHGQjhrxyLpDhxeewUlHmpKfGRVTU8DhuKJFLks/Ov43RSgWqGUhp2WUARBbzJPnK+EpRwM1F/Dd7MENrbT2CfOB81vxLNmUdnLIffLuKyiQwkVaIZjc5Yhrp8J0GnjFMWsMd7EQdawB0AQUR35yP2HUFQHKj5VQiGjiHJyEPtGHY3uuLBkGxkXEEciQEENYOYCGOoOQTZhjrQhVxYZjYr3L7K/LesIDrdiMXVGIoT3eFF6j6A4PIf6wlkDJnHXaI3z7yP4ipybftB19CiYQSbgq2iAUPNog10odRNou13v8ER9OMqCtCzcR/Nr7WSXx/gz2vbue3qiTz+xAGyukFWN5iT72THcJqhrMY55T5GLa/m4CvNTLhgLG2rW8mvy+fx15pp8CjMO6+B5GCKqlMnceCRTUy+aRmtr2ylZ3svgiTgCjqpXjaG4rNWoA10IfqCSIFCcq2N2CfOJ7N3PVJhGWpnM/Z5Z6MGKnnqSJxTa/PpimdRJJGWSIoZpR7CSY14VuXm328incgh2yTiQzFsDgftG1889nNjBUAnt49qx+eBwoYPvOPzp3gv9Z9+/zs+n/3sZ3n44Yd57rnn3tW7x+/343Q6P9BaPgrHNfBZu3YtP/7xj9m+fTs9PT0888wznH/++f+5uP8hiv3Rj37El770JQCWLFnCmjVr3vX5yy67jEcfffTY+5FIhNtuu43nnzcrNc4991x+/etfk5eX977XagU+J5+am56kdlKIHS++RcWUGUQH43jy3OQVujlzRhn9sQwleQ4Aoskc8bRK0KMwrzqfySGzl0VAzJGT7IRTGiVyGnFkgFSgGhs6wqanyHa24Bg7DS3ci1I3yTz2GmhGyytDd/hoTsps7YpyRYVGxF6IDgQPv4lQPhbdFUDIJhGyCYT4IFpRLWI6jtFhlmdLheUYgoigZUltfQN7w1TEQDHoKmpPG4LDhSBKiP4g2cM7kYsrQZTQa6YhJsLH+viI2RTZ0FgENQNbnkP0BxlatZKCSz5FtnEjwrLrELQsbHmO2IyL8coG3UmdylwvqZUPAWBoOlo6i2vsRLRwD7byOnJtBxDsDpAVDt33PMUz6mh9dS9/XtvOOeU+1vaNEM3plDrkY7tDd1w/BcXrwl0SpOn5XUz7yidItrVhD3hJh6PkEmnS4Rh/unsDl100BkmR8FWXEJw5hf51W3CXFdC35SCKz8lwSxh3sZvQrHF4Lv4shiDCoQ1IgSLU/i5ysy5EMyCcUsnpBh6bxC/fbmNGVYACl43RQRd/3NZJodfO1771Z4pGT8butNF35AhDLbuP/RxZAdDJ6Z858PmfXpvvu+8+rr322g+0lo/CcT3qSiQSTJ48mU996lNcdNFF7/l8T0/Pu95/5ZVXuP76699z7Y033sh3vvOdY+//bcR5xRVX0NnZeaz3wE033cTVV1/NCy+88GF9KZZ/InP+/Q2+fP54Vn7vdG58aCf3/+wmntjRSXgkj6oCN1lVZ0yRhwvGh9jXP0IklWNqqR/dMDgcTlDhd5BnA3moDd1TiGugiXj+WDS7h6TkQjAgnAX/nEsQnvkx2aY92Bdfgg5o6x5HTSWQS2t4ObCYs8pV8uvyIRsmP9GJrrjRxy1FProd0dAxug4hBktJV81EGWpF0FWzDN2Xjxooh32rkcvqEGQFMa+Q1OZX0dJZREXGVlyBXD8NtBzK6OkYuQyG551yVEM3d26adiGW1yI1vomh5pBClRi5HMHTz0Hvb0epHoO+5zVWX/tdFv3mdvjj15AvvYmqaB+9TzzCrns3AvBCp1kmG3I8SW9a5co5ZezeP4gkCBTaJYpH5VGYVTmwtx+AvCo/51b5ESSBTCzD3B/eRLbrKK4p8+h46CEC8xcy96KrUXvb8BWWoQ50sfcnLzH/t3dipBLcVlZA0ws7mHj9MpI9YUR/EEfQhyhJeCuLyCVSjPvU6fRt3su2X61kybQ5SMXV4A+iF44iXTUb6Zkf4zz9OvId+bx8ZIiL6jzcMq+K9e3DPLGrm+WjC/nM7AqymsHi++9gU+cwP3pwJ2MWTGHDfwl8rGowyz/ShzGr6++99cf9oOiEOeoSBOE9Oz5/6/zzzycej/Pmm28e+9iSJUuYMmUKv/jFL/7b2xw4cIBx48axadMmZs+eDcCmTZuYO3cuBw8efN8ttq0dn39uX31pPwAHemKcPamE+qCbnG5QneegJ55la9cwqaxGTb6LKSU+wskcHdEUZT4H1XkO8p0SbdEs6ZzOpDwDKd6HITugbTeCbEOrn8ugZschC3TFcwAEnTKh6BFS65/Hccrl5Da/iKFp2GecSrKwgY5YDkUyE29Tqs4oI4zu9CN37gF3gPhrj+G65DbkcJt5pNXbilE7AznSieYpRBoZAMDIZdAGujAyKQS7Ez2VQB3owjl1EbjyIDkMooTa3YoW6UeZcxbagU0Yug4LPoEt3IKueKBtN1JhuZlM7fAiqFmEXApDUiCTILFxJfH2PrLxJI/9bhP5isS+WIasbj7F/CJ5gB/lT2RBfT4jA0l2DKePBUGuAheeEi9VF57Bqpt/g6fEQ2hKCQWT6sglUvzl269wymk11F+8kJ71eyhfsRS5rBZtoAu5vI5c+2EkfxBEkczhXcRae8gbXYPozTOP49xeMs37sYUqsDVMR+tpJrl3O866MQysWcdI1wCCKBLvjDLhtstQRo1H9xSAriIM9yI4vWRLxiNm4ugOP50jKl2xDGlVxyGLFLoVUjmd3X0xppX4uG9LBwd7Ymx++pVjI0j+ygqATg4f1Y7PQ8Vj8H3AHZ//iPVQe9MnrensJ5q+vj5eeuklHnjggfd87qGHHuIvf/kLxcXFnHnmmXzzm9/E6/UCsHHjRvx+/7GgB2DOnDn4/X42bNjwsZktYvnHqLnpSb52w0wml/sZV+ihfFE1TptI8p0KIl+yD39BMZV+O0eGUlT47SiiwPQSN167RKHLhk0ERcvQ4BUQsinI6mhHdiCHKtFGz0Ps3EtX1kaZkiErOZEEgSq/DSUbx4j0AiBkEthqxmMU1xJ1l5DJ6oTcMsMZDUkQSOV06N6LUD8HPRFD8Bfjmb0ETVLQHV7EdByxsBKjYx9a6RjEgRb0QBlivB/Dk48QjyCVN6C27sNIxpCDIbItjUjBEOgaUrAEPRlHTycxOg8hhyoxAqVwYDUUVSL0HsEYNY20pwh76yYEUcYY6kEIFKN1N2Fk0sTb+1B8LhxBH2edW8+ula3MyXey9PNLSfaGabz0XGaVenl8bz9jvXa+fvgZXl94Fa/v7GWs187awSSTH9/PjY3P0PmrHxAcX4PkdNG3fQO3vX03qDna7jN//wc2bKNwHsT2HyS7bgPeymIcsg0pUIRS2UDRhDmI3nz6HruPonMupP3++ym74BwzQOprA13DNX4KAEWnnUphJk3/W2uId0ZZ9y+/Z/53LkNLJYkcOEro7DMRczlsuR2opeNh1f1ULLwcn+LEa5foHcnhtolERYErxwYwRJlvLK/lxcNhRhVdwqp1bSSGIqSGe4l1HrYSoS0fKkEUED5gB0NrSOkJ6oEHHsDr9XLhhRe+6+NXXnklNTU1hEIh9u3bx1e/+lV2797NypUrAejt7aWoqOg991dUVERvb+//+P9lMhkymcyx9/+2o6Xl42/FPRv4yvUz2d8T59qZFeiGgSSazwD+uDnJG10lf2AjecX1hIo9gIphc5LRDcbZR9if9JDvlCkIN6EGqxmW/BREGtEbZqC5AkjxPvTCUZTbMvTkHPQPp5kUEJDDTWjuIKLbi62s1ux8bHeCruLJDOG2e+hISwQcErGMxnglBpUTEUcGoLQeMTFkTj4Pt5nrFGWEbAIkCXXrSxjeAJIrD0PX0Zp2IYcqSW962UxYzitCLgiBO0Du0DbS7W14prtRqseArCDINnoeuo/g3FkIigMpNIroupUY+muMdA1iz/OQP3MGoj8Iukpizw5co8dRtOIs81uWiFMT8FJ12gwOPLyO4JJTkNavRRBFpvz4Gyw+vJMHr/ktR392FxXzy/mkz0l6OMm8ykJ+8KM1fK78TAAkYQ2aAZ8+r4GDP/oltZedTunSWaDrqCMjaNEwwTPOJbF1DUpJOdnOFjL7dmNzO7FX1GCzKQSmT0OLhin72o/Rt76E6HSjRfoxsmlsFQ0kd21A9vlwTFlEcGIX2XgCLacj2GzE9vdQvGwR4bVrCEydTLanE88iO8MLr8E/3ExQzWHY7Dg81QSHmxhxj0KKtJPKH0Uqp7N8VD6iIFDktbP2QD+xcIheTz7ZZPTYmAxl6nVW8GOxfMQ+NoHPn/70J6688kocDse7Pn7jjTce+/eECROor69nxowZ7Nixg2nTpgH/fSKWYRj/Y4IWmNNnv/3tb39Iq7ecSC67fwsrJpbwh0snsbc/wXXVOurGPyGcdhOCmkJIJRG0LLrDj5iOopVPBENHTEYwZDv62kfw1E0Cdx4BVx5FTglVKEdMhAlmEmDoaL4QaUHB6cyCKNKjOih0yZRISXTJTTSvFgBPfhVC8Wi0XBqhfxtEw4jlo9EcXiqVDOSy+PQsgHl8lk2bR2e+EFKsBy2/EnmgiVzbAbLdR3HNWoY45zzEZAQhHQfFiWCzoccjyIVlHLnvSUrmjsNVXW328YmG8S2/EG2gEy3Sjzh5Gbm1j1N80xcxOg8wtGolAZsNQ9cJXHAtef3tGGVjEHqOkN6/BceSS9j5u7dY9PLVGLKDYVcJPiGLMmEEqfcQU+vGIdTNJA+IPfQYancrz1x/DzUhN4//YSttydyxx+WGM2u5aEyQ/V0jzJ1Xhs0hM/rypQztb2Hr7zcAr2Fzmw3VfDUlFCw/nUzjFmyBAFu//QB2v51D23qY+4mJVNQ00PXgA+ZOUEkxtvY9CP4gTD4Fo2krosONUFCGe8n56O58jME2bNVjKbU7KFmQQy6rxReLIo2dg7ujlcjO3Xhv/wk5PYtTFskU1KMMd2BINoqGj6D5ipAMgXT+KNydO3D4QvTbQyyqyqPS78DvsuGzyzyxzcveNXtoWHYhLetfR02PWDlAlg9ElATED7zjc3Jt+XwsAp9169Zx6NAhHnvssf/z2mnTpmGz2Thy5AjTpk0jFArR19f3nusGBgYoLi7+H+/nq1/9Kp///OePvR+LxaioqPj/+wIsJwT/Kf/KrV+6mm+eMQZRgGhGZ1mRhiF5EZdfB1oOtr2IXDUGzR1k2F5Anq4hZJMYsoJxZCuC4sBWUo3a3YqtvBZ/QEQ1wDncjdbbilxUgeYtRg634cwrQzy6G6GgnFDAAaqKbveidO9luGA8HptI1hVEFmAYJ56Jp6Psfplcfg1ZTceT7EeI9pKpmolycDVqxQSzj46uYShOxOFuxL42yCvE0HXsteMAEDMJxEwCw+ZE8xaSfONpXJfeAdteYty3v252Ud74EoJswz55AXqkD7G0FsNbDOk4kj+IuvMNbJUNBObOJ3VwD4FTViDkMmjZNIJkI7V3E85Tr0Q7sJHFL/wHO2+6jZLZdZRc/kkQJSKvPoV//jLk4goSr/wJ16Lzqfniv5JY9TQNM0vo3tl3LOhp8Cg0J7LY/XamfmYJI99/lfy6Avr39vL4Z//MpqEUDR6FsnCKdFOEt/b0c+aCCL2bj5BN5MirCeCr8LHh9VYkAQJjqsj1tBGcVItyypX0/fb7SAeaKL7sU+QObGBg1Wr0rIrN5yLVH6HsvLMQq8YSLptJYdEhSERI7V5PrLUHe9telJJytHQW25anGdm3C9/shag9bXRv3Enh7EkIigPRH6S8fDRGWjFL+RNxCmoDpBUnE4pc9Ixk6I6lmV9fwC1LLuBnrx3CcdpZNG/cQGLA3F20AiDL/w9BEhGkv7cTz9/eiRX4nHD++Mc/Mn36dCZPnvx/XtvY2Egul6OkpASAuXPnEo1G2bJlC7NmzQJg8+bNRKNR5s2b9z/ez/sdzmY58X1n5SE03eCN+7/EpIDACDJOWUTOxEjKeWQ1g+DgETOYqGwALYegpskPHzTLux0+tLWPYZ8wFzVYjbb5eZRR40nvXAulU3H1NmIko8jBEOkdq82E2ulnIA13YYgSmjuIkE0ix3oZLhzHqlwVNWkNEVAkARs5gpkIYmQYobCMuKqjG6D5QtiSEQZTKoVjl2CLdKA7fNC+F8Gdj6A4wekzq7LqJqPHhxBkG4aWRRvqRRvo4tD9zzPmpkuRw21ks2kQZbSmXTimLiLbtAeb023O2JIdSN1mgrcwZhaCpmIoLsRAKS6HC0QJIxklM3kFjr2vYWg6aFmiO7bjHRlm/E1ng6yw+85vM/E3v0RLZ9n9jZ8y9qpT6N24j8RzGxl99RkIkoin1Iu4q//Y43N4JMu0PAe/eXQ/lzYO4nQrvPXQHnYMp7l4XCE/uOsCnHVjOHjPIww0DnL9FxZz8KldzP238+h4YxslcyeQDke58au3o6cTdD7+FBWfuBQ9lUCKdhO6+U6EdJyhFx7CP3shofMvInNwG6LTTctLT1P5hSkwMkTRYCP68AC5jsM4F12AHNxI3yuv0b25hfIFDUiOQzQ+uB7/2n14ywLkEmnW3vkgdWc2UHrqQtKH96GUlCN68zDq5yAeehtx/Kl4yHLaqABuUWMwK9ISSTO5Oh+vQ+btPAeJWIbdzz5K5dyz6dj8ihUAWf4ugvQh5Ph8wLjp4+a4Bj4jIyM0NTUde7+1tZVdu3aRn59PZWUlYO60PPHEE/z0pz99z+2bm5t56KGHWLFiBQUFBezfv58vfOELTJ06lfnz5wMwduxYzjjjDG688UZ+//vfA2Y5+9lnn20lNp8EZnzzNYLFHh64cgqaDllJxJtLIA4NgCDicoGv/wjIdoyeJsgrBFlG7ziIkUkjjl9oHo34zF43hr7dvGPJ3C1x9O0j13EEyZuH2tuO6A+iR/qRWnagZdOo3a3IFRPgwNuoE0/BLeSYW+4lktZI5HS8dhs6dgS7B9VhVn54M0MYigtbXwv6yDBlqc3E1q1EuObrSCMD6OOWmgnJI4Nmbx1BBEPHyKZRKyZg2FzY0nGM2hl4V65DLq1h6KXHEEQRKViCraKe9K61SP6gGSi9ExAJrjz0vjYk2YbuzgddhcEOtIEu1ImnoT7+A7p+8WtqrriQ1f/6NJMOHuXgk3vIJt9k4TfOxlFRweirT2PvLbdTPKOWt1YdZeJXaimcGqfE7SDZ2c3BRzdh9yn4Krws13Te6E/wyUWVzL7/F1z21tPYPG723vs6z7ZEAAhNKUIJ5LH5q/cgiAK+ci+CJOIr99G3ZT+lCyZhKyzGs/hcIi8/hppME5o3BaGsHls2hSHKHBaKcftKCF37NeKCA9/hVeTiSXpe38LMH95Kxy/MRGotnSXc2EL5iqXs/PQdjLv+TPLH1+AK5dO//RB77ttEfn0+vsoCHvjNRj5x5QSCdQFK5k3m1Rt+izfoYvxVs8jFkgTbD5PoGiAAqF3NODJpUmfdjmHozM7XGBlbRFcsTbw6n10tYRqWXciSuZVsK73sPd2grQDI8r/5MI66TrYdn+Nazr569WqWLl36no9fc8013H///QD84Q9/4I477qCnp+c9k2M7Ojq46qqr2LdvHyMjI1RUVHDWWWfxzW9+k/z8/GPXDQ0NvaeB4d133201MPwn9vDOTtqHU5zRUEix24bfLtEUyTBea0e3udD2rDY7FBdWICaHybUfhlnnkZacuGOdCOk42caNiC4veiyMXFKDINsQ3kmORZTMUQ7N29CjYVItRxBtMkpxiZlQHA2TPPsL5Ce7EcLtZsfjcDdUTWLYUYTXJtCTUClVzOMeKdaDobgRE0MYdrfZkblpO1LFGDJbX0f0B5EnLsKQbEgjg+hOPxF3GV3xLBOEPnI73kCpHoPg9KI7/ejuIEJmBHXLSxiJGMqC86H/KFo0jFQ/Db1tL6InD0GSwBMku289tilLEEaGMDIpM5dIcZDYthbvknMxchn0QDm07zXXGywFXcWwuxFSMbItjdhGzwBRQlAzxN58FpvXRf/2Q7iK8tA1nT9993UiOY0R9T9nbs3Jd3LpPZ9i9+9ewlXg4siadjTDYNYnp/PDn67DI4tcuqwagKnfvo3XLvg6i39wEZLbQ7z5KJ6KYvq3H2LoUC/jPn0+w3v3c9e/vcznv7gIe54HURKRHAqx1h4qv/RNGDhKruMII0ea8NSYDRuNXJb9f3yFgf2DlEwPsfHNo2R1g8GsilsSmV7po2RaiPyxlWSGR5BsMh3rmgg3R0hpOufeezORPQfInzaJyO596FmV3m0tNFwyn5GuAZqe30XbkSEuefiL7ySQ20mXTCCnG8QzGoMplZVNg9QF3fxpQxuprEZX8xBHN72Bmh4BrODn4+ijKmd/sn4SPumD7WH8rq+T6muvPGnK2U+YPj4nOivw+XiY+/03uf2sMWRUnTkVfqpdBsLOV5AqxoChk1j9LM4JMxALK80EZqcfQcua+SsDneYYg+42BJsNI5fDVl6LISkYsUHUhoXIh9dhlDSQW/ckueFhEj1hQpdehRYZ4Mhv/khwQjXuskIkfxBpznkYigupeQtCIIQ+0I4UKCJbNhkxEyeMm3wph5BLYthciPtXI8g2uqoX47KJBGKtxPw1+I9uRCufiDTcDek4eqAcMR2j11dHStWpzrRj9DQjegMINjuaO4i+by1aNIwy9xy0w9uQAoUAiN58dLsbQVcxBruOBX6G3YOg5dCjg+Z1vnz02BBCIITmKUDqPURq93pcc87AyGXA6UN3+hEzcdp/+SMqr72WzIHtKKPGo8WHMbJpNnzx92xpjjDB78ARcOAqcJJX5WfXK81c9NhXEGwKvS+/xqv3bCSlGYwJufGVe5lw7VIOP7GeTCxDfr35B0zlqbOJnHYbhcQRmrbQVrWUmlwnan414q6X6X72eQqn1NOzfg9DTYNM/e4dHH3gLxRNH4NSXIIWDTPS0UfT87uYcsvpyCU1ZFoP4Rw3DbWvnZHmVvyz5hLfuRVD13GPGkVs/0GGm7owdJ1YZ4yp370DdJ3U/h0oxSVk+3pwTZnHC7bJnOfsIL3tTaRgCClQRKR+KXkHXjcryNIJlClL0boOw7hFyEPt5nBZMIfBho+SqpzB+o44jf1x1h8ZJM+l8Mgv78MTqmakt80KgD6GPqrA56mGKfjkDxj49LZTdY0V+Fj+hhX4nPiuf3Qnty6soSbPjjMXR8ilEdNRiIcRFMexMmaprIH464/jrBuD4HQjVk1A3bMGadppiINtZA/vRBkzAyOVACDXcRhbRQOiN49s0x7kSYvRm3cyvHkjnpoKRG+AxOGDZONJClechyBJZg8cwywlB7BV1KP2tiHWTGbEV8HGzjgLK304hloQchmEXIps20HEmWchJiOM+KtI/fpLBBcsBKClYQXVu584tusk1Eyh5RtfouaaTyBWjSf67P14JkzBVjka1AzqQBeiywdFVRidh8yZW+EuRH/QzE0SRZS55yCm4+iKE81XgtS6DdGTR2L9y2gX3okn1oEhymYDwFQUIZdCi/QjyApG+TjUzS9gHzebXPFobL0HSKx/maanN/D75w6ztNDFgXiWGQEn+fUB8qr8NPzLLSR2vM3gniZKl85GcPnQ4xH6Nu6mePZE+rc10rG2CdkpI0oivnIvib4EHTt6ea0vwQSfnX2xDD9+4FqizV0UfOVXqAb0jKjkOyXyho4AoLUfQI9HUEZPByC15XUEUUJNpjj42NtUL59IYOFSpMJycu2HEN1e9GiY8KYtBCaNRSosQ48PI047HXm4k2TpZBxH1kFRDZq3CFvfIYxMEiOVIL5lHd5zrkYwdNRAJUI6jhztNvO6ug6S62kj3dGB9/zrobcZIRAiWTQad/ducsVjsPUfxpDtEB8k27SH1LJPM5zR+M7rR1BkkSf/9Dy+8tF0b3/tXT/rVgB04vuoAp+nx07F/wEDn992t1P5ySuswMfyblbgc+I6/95NfPessUiCgN8uUZxoo8NRSdmBl0C2kT64G8eYyejR8LFmfdmWfTjmrIBEBC0aRgubPZ2UukkIdhdqfiUcXI8++QyU3gPHZl5lWxqRgyG6n3zyWNVQ4rn/4OgbOxn7LzcghWrIloyjM5ajwiOi9B4gVzwaIRVFaN6G4HCx1j2Vae/M8nIKmjkjCrC1bEIvMsvc06/ej1JahegPEtu8Ds+YcUjj5mHYnIgDLRiBMoR03Dxq0rKoh7Yhl9eBmkVPJRCdbtTedgS7EyOTQvQGEIsqMWx2BDUH6TiGrwh6mtBrppp5QpqKsectjLmXIEfaMUSZnL8Mcc2fEf1BopveJrDkdIS8IpBt5vfE0BGyZvfmW2rO/8/HZFSAZ1si1LoVFi8ox1PkxhH0suqBHZT67Iw6rZZ4Z5RI6zCjz59E0cJZICsARLbv4Edff5mUZj411boVIjmNa66ZTGj2ODynXYYhiOR2raZp+lU0eAHDwNbTiKG4MSK96MkYqYN7UIpLiOzaR2Z4BDWRIvROInTevMVokX6kYAixsBIkicyGF4kcbCM4ezqiN4/UwT3YK2qQgyGGVr1OYPEyjJppvNAjck6lgqp4aBxIUeVXUCQBb/gIhs2BmleOLkjIqSEO3Hw9E77xReIb3sQeCqFMXUqucSO2ygYMfzF6y26kijEIatqswnMH0Xe8RufUSxlM5njlYD+/uftZFJefxEAHqch/9h6zgp8T20cV+DwzYSp+2faB7us3XUepvOrkCXxOslxuyz8TZep1fPWl/fzkvPH0jWQp9ZgjIMRMglKHjlxWhxbuxTluGkYqgRruRWmYihbuQRk1wRy3kEmbScy+ILaKBjJ7N6INdiMc3oQeDSNseRYjPUJv3mgMmxN54iLEvEJKbv83lLpJiOkYrktuo2r5VNA1BC1LRtUZlWlDMHQypRMwRBkpFcEYPY/oqAXMKvWQ1cxmiUIuhZhLIY0MYOSXo/pCYBi4Fp3PyOzL6HryKTzXfA0pUERuy8to219Fz69ATIThnWCMxDCC24cgiiArjGzbgOD0gqyg9nciegOoA10Ihg7hLhgJv3NcFsdQc4gd+5AHmpHiZiDwZsNsclteRt32Km2fvgTbmFkMr19LuLGVI7/6LSQijLz+GPS1obU1kjt6kOTaZ7nu9FFcUJ+P3yYy/vJpXDQmyLXfPQubQyZ8ZAhH0M/WSJpYMkc2nmZg/yA2t0LZZz9Pz1sbWHf7bxlcv4m7/u0/g56xXjs1bhtDWY3Kc5cRbeoiu/0Njv74uzT+9mnGhbchRbsRtCyavxS1ZQ+iLx9j0mk4x00lu/Q6UgMRfNUhkuEEm+56ga9//hnuO+Mr5Pq7ybUdRG3aidbWiOgPEvrkp7GNn0v68D5sPh/xxn2IgWK8DbUYtTMwFBfnqXt4fexSOm6/gkmHnyUYa8F94A00byFqoNIMerIjCNkUVcunYmTSOBvGmzlVmYRZ9ZXLIkS6kSrHkiuqR3cF0J1+pMFWcguuoFJOkMxp/Ov8En71nauYuHgSP//RzYjvBId//fm3WCx/v49FObvF8reUqdex/rmfMipgZ0tXnAWVPuzZOIKaRu1pI7fpVeLhKN5PfZ3cq38AUcTeMAUAuaSGoTdfIW/2XHKdTQiihDJmutnNd/GlGEe2mtVY5bXmxHJdg199Hv3mr7yTDJtB6GsmNmY5zjd/b/atOdyBaJOR2g/jWehFza9Gf/M+jFQC8cxPm12aE2G89ixIMrLDT3dCpWqkGwTBfOFzBxG1nNlQz2bHs/VJvF++C33942iKw+y4XDMBIRFG85eScBbg1NMYwVHYnE3oNgfqnjUA6MMDyNXjEccvRBxoQSmuRnPnI0QH0SomIKhZs1u0bEPwFTCy+lkcYyYjVoxh+r6NKJEjZHetou4z14IkEVi8DN+kATqeeYX03o0cfHQ9hftaKJhUx6573kAQBcrmVDH2ikUsW3YZujuI76tevL17cVeuJdHehT3Pyyeml9Bw/hQSXQM8dzTKZ2aVckvlOYQcMr1pFb6z8l2P84F4hnzF/PtMqZuEuHUX7S+vo/LsJVTfsQCttxVJlHh40oVc9fpPMNQcI2tfREtncV/xRbx9jSjL5oEoYdtykEPdZq7M5BklxNv7aFvZyIRrlyC7nCT7BsmvmwS5FM6zb0DbuRK3KJJr24+49JMI6RhSvAO1fi6n73gWJBthJUh+LgK1QeThbrLOAHJ6mF2XX8G4q5fgPfVi1GA1crgNQcuS3r4K0ZuH6HCjh+oRug8huQJm76euveDwktMMRgQvs0pFWuM5LilTmXLhRFoiKeqXnkPXnu2M9LUd+z0Aa/fnZPZh9PE52RoYWkdd75N11HXiuOhPm3nw8sl0xXPIIpQbETOfZsfryIVlhN98FcXrRtc0vNPnmDdScyDbUNsPY6sei+jNI9d2AC0axjF5AUYmbR55DXShjBoPss28TUkdkcfuIW/BUqRAEcnNr6OceQNiMmJWTh3eRK6ziZ0/f47p37gWeeIismufRFrxGQQti9i6A4praJZLqNXMRpqap5DujETIY8PetYf+gvHmi6esIOTS6A4fUryPmLcCt5Gm53v/Qvnt/4ow1GlWbdndaL4S5HAb4fzR5OUi5o6SpKAGKs0Oz6KMHOtFjw5ilI3BEGXEXAoh2otgs6P2dyC6vAh2J1poNFK0h0zRaOT0MJkX7sE97wzz/gprEZMR1A3PICgORpqacBUXYJ84F8NfbB4t2RxIvYcQZBu6wwuCiOYrQcglidx7F3nTpoGssO3f7mH2b75O79NPkBmOs+exvbzRn+CdzR0kAfw2iaGs9q7H++JxhdQsN48AS5fNw8ikUeomoacTUD8bQc1gNG+n9YFHKZrawJvfeoHTf3wJ237+CnVnTaBg1mSUiQuIvfo4vVv2Uzi1ngOPbGL6588j2dNHtLkLgIpLL0RQHHQ+9gShRTOxVY1B8BfRc99vKLnkE+AtILt7DdmBfnKJFJ4x49CjYWwLLyK3/hkcUxaRObAVZfIi8/hSENFbdqP2dyJIEsr05ahHdmKrGoM22I0W7kGacSaG4kJ988/YFlxkfh+i3eiuPITYAL0lM1F1gxJjmIwriGOkj/Oe7iboUWjvG2HjX/4MwNQLLmfzN079R//qWf4OH9VR1/NTZ+C3fbCjrruPtlF+xSdOmqMuK/B5n6zA5/irvuExfnDrAqaWePHYJIJ2kIfaUPetR/TmIfmDGLkcqb2bzPyYCYuQkhHUriZEl9fM6QgUocWHEcfOI/Hcf+AaOwnRk4c+MoyeiBHZuZvCFedhZNMkxi7H17sHQ3bQ+pPvU3HxeYhON1JBKbmiBrTX78U+dgaZxi3HGgIaag5x8ZUMqjZKhhoxJAXNH0IOt6H5QuaLodOPISm0RHP4FJFQuosRXwWe4VYM2YGgptG9xfRqDsoj+9EdXrbkiphY5EISBAaSKkVuGVukA80XQkyEMewexJEBc2yEsxjvhr8gzLsYOdqFISnoihtEEan3MHpBtXl9vA8GOhB9+ebEdaef7NvPYp+zAt3hI/rY3fhnLzQ7E7u95LrbEJ1uhLJ6kBT0tj2I1ZMQIl3mGA1/KdrOleaORs1kGu+4A3eRl/IVS9FTCZ69+U9U1AUYfdEMdvxuLSMjWd7oTxx7fAvtErVuhfmXjkdUZJyFAQ4+vp3JNy1m/0MbmPnNa8j2dJLoDROYPAF50iKzmi1Uw9CTfyL/1LNJ7lhHxxvbabj9ZrT4MKhZ5NIas4zfm4fgcKMW1qFvfo544z6cRQGUUeOJrFuNr2EUUrCEwTVrsOd5sAe8SN48hhZeT0l4L4bdjeYOcuiWG6i9+BT0dBKlssEc/lpYi617H4biJrP5lXfmmJn5ZFJBKWp/B6n9O7FX1GBk04gOlxno2h1IoRr0/naomojh8CKk4wi6CrpqVn/pOtgcHEkqVPpttEWz+O0Smzpj/H51M2PL/DR2DKPmNFp2HKHrkZuO2++o5T99VIHPC9NnfuDA59dtrZRfbgU+lr9hBT7HlzL1Oh578HucURegM2b2vhmVbEbrOowWGUAZNR4jlUCLD2OrHI2RjCI43OjRMNm2A9jHzyZ7cNuxai0t3INUWEautRFD05BDlejxYZSGaRjJKEYuR/uf/0zltdci2BTwBlEPbSM15zJ83TvJdbchefPQqyYj6CpifzNaZAC5rI5cYd07O0L5DKkyBelexFwSY6gHrWYGQ7rZEdxlE/EMHGK/vYYGt0ZCcKBIAofCGcY3Po6eiGErr8Oon4Uc6QQtR7ZimpkPZHNgiDIDqkIo02OO1VBciKkohiCiHjWHYNpKqwFQC2vNJoXpOFIijB4fwsimOfrAXyj7yZ+xd+0h27wX0ZtH+uBubIXFJNvasLmd6DmVVDhKwfVfQor1oruDGO2NNP7od1Qtn4quabzwzRdRRAHNMCiv8KG4FXRNx1Xgon1rNyuaNnC7dzLf/dkFDB04StvqNsKRNEVFbo50xdkdTXPZlGLK51Sg5VQKJtWieN2MdA0gSCLOoB/XxOnokX4MXUcdGkBye1DmnkO3o4ziHU+QajmCe/Iscp1N2BZdiiHJqKsexj7rdEgMo+dXYNgcCGoWdf1T6KkEguJAqZtErrWRTP8ASiAP+5wzib/yCL7lF9L6y59Q/t3fIhg60nAX6Bqp4rHoj92FvaqO+L7d2PO8hBtb8VYU4Z060+zgXFyCrbIBwekl19qIFChCDBRjpEfI7N2I4HSjDg8h5xeinXIdrt5GNG8RtOxADNWg2z3o7iAjKHhEzWxUqasYsgO5ay8blHEcGUoQcNjY1RWlP55B0w227u7Bk+dg3Zfe2x/N8tGyAp8TlxX4vE9W4HN8OGffQsPSs3j49gWU+2xkVB2/pKK99h/YyuvMwZpuH0YmxeD6TRQsnA+ihByqNPNr/AWoXe90BxdFxKrxEO4i13EEQRSJHzqMPc8LgGvSbMS8QjB01IEu5OIq1J4WBletIn/aJOSSGgCEQAgj2o+QX4LmDCAlwpCKMbL+NTKROJ6KEMm+QQLnfZKBvDqymoGqG+Q7JA6G00wNSmiSHc0wkEWBZE7Hq8ZoyboYZU+j2n0okaP0/cfPKPrMV8mtesScqZVXiuHwom95AVtlA0Ol08nLDJol+4De3YyRTROdfC75kSMgiuQObEGQbcg1EzBsdpAUc+DqyCCZw7uQiysQCyvNnjzpGFp3E2pnM8qo8Qj5JebIjt5WRnZsouvtvXjKCzjy/D6eOhimwmljMKuS0gwaPArdaZVlZV5e6Yhx+eJKXtvYxTW3zEVNpCiYVMdwcxclyxeihntxzV2BHh0kvX8L4oV30v2lT5KOmJPRvWUB3CVB/BPGEdt/kPxlZyIEQjASPlb5ldq+CseEOQh5xaBrqIW1jNz/7wROO5/B5x+n4IIrzR5Nwz0YqQSGmsMYPQ+5/wi6t4ihh+7G21CLtOgTaG8/Yc4n6241f05kG6LTjW3UBLIHtxFvamOka4Dyr/0IDryNXDoKI5NEC/fS+fTzhOZMJDeSwDlmEtr0c7EdWE3u6AFETx5aNEw2PIRok/EsXIHuDprf61QUvW0PcmEZWrgXoWKsmWwuyqTWP4/kDyLPOouMpxgZnUj2P5vrjmR1yt0izVGNjR0RfA4bJR47GU2nyu/g0l++zYHXn8MTqmbwha98dL+slnf5qAKfl2bN/sCBz69aWij7xGUnTeBjVXVZTljK1OuoXXgmP71+Ji6bgKob5BkJhL1v4Ji6BC3cY75wRPqRxs6h5Krr0UeGkfxBAAw1C2oGRJHkgT2IDje5HW+S3PE2AHoqgbuyDNnlxHn2DeQ6DptHUe4gubYDZul3wxwKb7wTcdY5ROsWodXNYfjFh9AiA2YezdBRcoV14M7DPWMRgYVLQbaZQz21HPl6nGKbSjlRcjoUu23kRAVBAMVQ2dlrHvUkHv0FdXofL3Vq2Pa9TsRTQeiiSxEH23j2cw+ilYylRylG6tyLOONMsk170O/5MmI6Svixe80GjNEwiCKBI6sQMiMY/e3mUNXqcWQbN5o7PmrafIHVdeSZKzDq56AW1BB3FaP5S2DMfHIrbkUdd4p5zJKIIBWU4p4yE0M3GOkcxJHnYEXIwzifwtJCN9edPgowy86rl1bxs22/Y9QZk7jo/AZ6th5l99MH+POXn+HVe7fy+0t/yhev+hO/mHwFWv1ckj1h9l2wgooLz0aUROovWUrVpz+Dt6oUQXGQN3uuGcAd3o7a34UerEIrqsU15wz0MnMoq+HwIrdtQ02m0T0FHHl2B2qwGjEZIbltNYLbh6Fr6BueQgtUYHQe4Oil30JacDHqm3+md/UmEvt2YRs1Hlv1WCR/kN0/fxzDk082HEbLqST7owze8z3iu7eTPbQdLdKPnkrgry3DPmcF3sVn0/nks9gOrUVPJ8hFIuaOj03Bc86nsAUCaJF+NHcQY9tLCNkkkjeP+JoXzeA9HUfNKzUfH4fL3JXc8Try6vuR9q4kYBfx2EQK4m0YBqiI1OQpLKnJZ1lNHgABh401bUN8/qKJVMw6nbnnLLYqv04Cf53V9UHeTrZI4CT7ci0fFwXn/IDQ5KU8/sVFzK/wktUN8jq2oG95AQC17yi26rEk92w2jxHScYxMEmXqUjNfJ68U0e0j136YTPN+nA3jabvvAZSJC3BNmYdcXGEmK3f1YJ91OpEHfopt1gpzlyCTQAqWYMgOaNtF5o0HkQZb8cc76Pn328k763KMWecjqFk23fhVso/9ELV5D4IkMbTmTezTTsHI5VB7WtDWPIJ8dDsj9nyG0ipOm8jQdz/LoXCarhQ8trObwZSK68o76XOUcq5/ED0aJi/dD94CkttWc96vrkAeaCKkD2NoGvqm5+h8aRXeqlIyG14kb/ZckhtfZmjLNoxMmmzbATMwqxiPMPV0coV1SMEQDByFeNjMHYlHzEnvsh053EbewH7EZAQxGcE10osU70PIJcHpQxvqZcOtP6F1dTsbXm5mR+MgL/eOEBpXwOKmrdgcMld/5yxCb6+i4UufR8gvwTemnsGDYdZu68HjUbjpL7dw086H+dyex/jxA9dy24FnOHjdJwhedA3jrzkFta+d2rsf4POX/pbkljc5/PhqNn7lD6SbzMGpUqDQHKPRfZAhyY8+PIA0MoiWV0baX05s3UqS/cO8OuNiZj3zEOgq2ZZG4u19rL/my0ildYhzL2Dkyd8ilI9l/MZ70Le9gmPiXIqmjyYVNnfNBNmGXDeV6T/5KogyrrOvR/G6aPjsNeSNrcUzYQpKw1SkQBFK7UTco0aR2fIaqa1vmINK921CLqrAs/hc+p562KwI7G9FUByg6xhvP2a2JmjcQObQThI9YRAlNE8BhuxAC/eizD0Hx7Ql2MbPRY0MmLlZe17DcWgNht1LpRvEd7Z/KuUEOd1gfKGTSr/C5JCPEq+dX906j462CHd+/0v4lnz5+PwSWz4SH0bgY1V1Wf5b1lHXR0eZeh13fv9LTCj1Mb7IQ13AjjQyiBzrBTWDYfeQ2bYS0ekmdfQozqoqwlt3ERhTTeRgGwXz52AbNZFc234Epxt07Z28nl7s42eBJ4jW04w20IV9zHQMpw+i/Wih0YiZOOnVT2Arq0WuGovW24qRSiBMX4HcdwjD5iR3aBvp9jY8Z11NbvtK7GOmM/j842SGR8gfX4PodKPUTcLILzfzMmwuEu5iJFFgc9cIC0sdxDSJjKpT4JSwNW8gWjWXrd0jLKryYRhgi/ey76abmPjtL6In4wijpplHVMPdJDauxBYIkOkfwB4KIZdU0/vcc6jpLKEF01CmLydbWI/StvXYeAu9txWx1KyMIh5GkG2kK6Yj6TnEeD/iUAc4fceOwwQ1Tfrt55BLalh5xQ/oSObYHU0jCbC00M05j3wZYcpyxKN7IFSL0XmA2OZ15C0/l2cW30x7Mke+IrFpKMW/fmUpbW8domB0kNZVR6lcUM5IX4JdO/rYHU0DcHaJl+CYfGKdcQpGBymZXYe/tozDj62lcvlkEl2DZOMJar7wFdTWfeR62rCd/y/H+hnpjeswkjH0+DBSMIStsoHY6pdQAnlkBodwX/FFxESYXH4VcrQH2veiJ+Iw81yEXa+gR8O0PPoy9bfeSK7F7OmDKCEFChGLqhC0HLGicQx+5Vqqvv5DxOwI6pGdIEpEt20heNkNJFc9hZxfiH3MdHKhsWSf/jn22nEk9uxAv+obRDMalR3ridQuwrftSSR/EC0+jNQwA+3wNtSeVuTCMuT6aRiyAoJINlBFStWRRQFnJkLsLz8lb/HpECyj/affo/zrP8FQzJ9xgJTo4NXmCBlVRxQEBhIZHn6jifhQikO/Pv84/UafnD6qo65XFsz9wEddvzzSTOkll540R11W4PM+WYHPP54y9Tomnn0py2ZV8N1TKpEHW9C8hYiZBOrON5CnnIKYipJt3vvOX9Dmk3269TD2skriBw4QuPQmhEg3AOpgr1lZM3YuYiKMHg2jRcNEd2zHU1OBMvtMEGWMjgOIpbXHOhFnNr2CVFiG6HRjNMyBg+uRiyrMI60dryDXTGCkoAHPUBNHf3YXpWcuQ5xxJp3f/SKlpy9BChSh9rUTO3iE/Gu/QMxZhEfIkcJGWjNI5XSK3TYSOZ28VB/b034qfHYMw8Brl2gayjAlvgu1uxVp7BwY7DArz2JDUFhB/IUHsef76V63k5/8Zgs3nl3P2KtOQa5sQKibiZCJI8QG0IrqkGO9qD0tZjVb6ThUuw9brMfsRyQriOm42QrAF0KK9SIM99DxwH10b2rFW+ohFUkz0DiIM+Bgb1uUA/EMv3jt6whzLkB96bd0L7udw9Pnk8pqNI1kaUuaiefXLK0i0Zdk8g0LiLf3sfbPO9k0lKLaZeOqLy5hxx828nLvyLHH3ikJx5oWNngUzr1mCg//cQd3PvUFBMWB4HRjpBK0P/USjqAf2e0gePPXMWxOmkcE6hxpkrIH56bHkIIlZruC9sOI4+aj71+PrbyWeNk0PP0HIJMgvXMNSt0ktIEu5JIaEjvexjVhxrHb7fzRQ0x/6jFyr96LY/IC1P4u5OrxkI4TW/USajqDPc+La8o8Mge34Zi8gGzTHgSH22xyGe9HHehCiwxgHzcbDB20HMh21EA5hs2Jre8QudBY2hMgiVCm5MzZXdmEmaTe3ogUKET3FiFEe1FLx2MbbEF7p+cTwHOtSc6r9Zr3VVRPU1wg5JHZ2BlnYpGbn61tJZrK4VQkduzvp3nrdivv5yPyUQU+ry2aj1/5YIHPLw41UXLxJSdN4GMddVlOCMrU6xi/4hJe/dIi7pqi05EEDAN5uJsRbxm2ygay659jZO2LGMmYuYuTTWOkE0gOBT0aRvG5obeFyFsvk+s0d3RsVWPQW3aS2voGiV0bkWsm4J8yBWXUBOIvPIh+dB+i24vmDJjBgq8EwekmeWAv+thFtH/9NvMv80EzmDLSCdT8KpyZCJqnkJKlc1BqJ2Iobqru+DK2MbMQQzUoDVMJzF/Ii/1mBZcU7caVHcYmClQcXYtmGCRzOjlfCdNdIxyNpvEoIup932D83kfM3Zf5n2DH9beYRySiTO7oAQYe/C32gnyy0Tili2dw5ZwyhpqGkEtrzN2OeD9iJgFOH4KWM8dl1M0Ed4D2nBN7/yFzPIIkIw+1I6aiiOk4cuceBh74Fasu+RKHntlL9anjSQ6mSEfSTL5uNplYhsWnVvOVOxfT9MDTbDzlLL537X3UxhoZc+E4zt/1LDVu88n39GI3RRNL0HIaajpLst88RpoZcDCrNsDuP21m9DkNnFP+ny8Gf02QvuUT4zjz4jGUnbaAG752qtm0cNJSHjvvm+jJOKO+8nVCt/4bxZd9CqFpC4YoU+1XzAq3L30SQ9cR3WYvIbl2MhzdS+ZoE0Y2jTMbNUvSC2oQRMmcxzXHbF3gnrYA0R8ke3gntuqxTP/2Z+j+wZfNfLG//qyFu+h/5lH8y87BP2M2Skm52RFbcYA3iDJxAQBCuJ1caCxSoMicdJ8eQe3vILN/C5l9G0g+8Sv0tx4gV9SAkIoyamgXQadMDAfycCdiKgr9regJcwcLLYvhyUcaGTCPKfevRzq8HkFNk1F1coJMX/5YhHScekcSX3qQqSEPbcNpPjOviiKvnfpiD7IisfiCpfx4TdNH94tt+Yezcnz+fifZl2s5ESlTr2PWJ67mitPqMQyDXEEd1d0b0XqaybXtxxPrYGjli2b35ZrR2CctRB41Cbm4EkPNIQdDIEp45i5Hi4bxTpjMvl89hp5Nk23eazaZqx6La8IMso0bzRdEwH/q+cd2jTi0Aa1sAhg68qyzcF/yOYTdr1Pz2c+SbWlEKihFe+1ec8jp4fVmH5vNzzPceIj2P/zGbDpn90A6juYrRo+GET15rKg0Ax/NW4zqzMe9/i/oVZOJZXRKhBhDKRXNHaQmz4FT0Ehf+S0i8z5JtnEjvy2azIzf/oihN19B621Fnn8RxRddgToygvu8G3FMnk/tWZOYfedZCA4X4Y1baP3lT1AD5ejufPqVIgA6pQK6vLUUumS0vDLU3asRO/ahdreYM8l0FbW7leCixSz587dYfPct5I2uofrUCTgCDh756Rpqltdg99l5+rcbGWqK8Oj2Hm66YwFvnXcros0Gvc2c37KRCqeN1/oSHF3VwugLJrL256sAKAk62RpJ4yn1MJTKofjcjL9iKhfU53Pb1RP54b1XccYFoxl17jxGf/XL5MKD5nytcC9iKso5bduxzTwTw+Yg7Q2Z666egphLkdV0Yn/8LjU33YCtYTq6p4BcayMYBlKgEFsggNrbjrb+KXRXgPijv0QqLCO+bzexv/wUbeKpaPVzMfwhkBW0ghq0mefjry5BqZsEniAdz7wCQMHy00nv3QiiiK2ywRyGOmoCavMeSMWQJy4y86tsTvTCUcihSnOSfXwYKVgCgHfeMuQSM/laHu4ktX0V7mg7PiGL5i0m5S9HsClIY+eSGHMK+tFGhOFeDJsTRBmpYTpaw3zSz/6WS3pfwLbpCYJCik78iMkIhqwQtMM87wiaDtfMqCDPaePfzh9P30CSux/YYiU9/xMRRAHxA76dbDk+VuBjOa6Uqdcx87KrWDalhJDXQSDVi7DxCYx0Erm4CrmoDHqa8NTWYB8/G62vnezhHRjRftL7t5Bs7wBdQ/QH6X3iEXbddR96LMy4a5ajZ1WU2okIisPsuZNKoFSPMQdslo8jUz4FdB21ypziLRx8GyGbROg5jDRoJqTm2g+T6e4yjypEkaOzr0Vw+xDbdiBNWUbR5TdSOKWez1euQD+4CRxexJbtiG4v4ddewNZ7AI+RJmrYsfcfIjLnSsSjuymQMgjZFCXxZtK6QGGinZwgM5zWGMnq2Mpq+dzR1WRLxuOfNp30wd3oe1ejRQZwnXY5CAJaNIy7JEj7G9tp/MFv6NncRDaWNHsIuQIUp7vJCjIVHespS7TiGTiENNyFPHU5ejKOnojBgXVo/lLkUZOI79xK95NPsv0797P9Z8/x9PdXMv33P2f2+AIMTUfLapQ5ZXpah6lw2ogc6mHJX75L96Y2DoTmMvDjL1LmlFlU4KK1M8bqe7fglEQee+ogzxwZ4hPTzRf+i+//HMNNfaTDUSZcOZPnnzrIl2/4C62rjvKFK+5FDffS8uJmfvjTdYjvHGk6tz2D0XkAIZvCsfc1enUXR7Q8pK59eI6sJW/BUjIHt5Hdt94c/1BehxF9p+fPaTczcnA/uYE+1Nf+iChJ5pR5UcRVVoKYM4esGqKMNOc85FgvSssmnFVV9D31MGpbIxXnLCPX1YyeiCOd8Wlzl8cdQLA76XnqCWyl1ajlkxDC7WTbDyPtWwm6hlxYRnrTy2bbAF++mVOWyyGU1KL5QiQ2vIpr9mkIwz2IiTBiMoIjbh7Roqt4jm42AyZvAULn/nd+DmWkli24555qzp6L9KOteZiqkWY0fylCLk3GEEm4iqhzq9Q701w5xk9XLM28cUUsOqWBitlnUXvzU8fvF99iOY6sHJ/3ycrx+fD9NYn58illyBJUeW2I254zGwpWj0fQVbRwN2pXM7HDLbjLClETaeyhEEYmzfChVtwlQewlZfSu2cTg3k4ysSyOgINAXSGl3/8jrHkQubAMRAnRX4Du9KM1vo0ULEEfGUYuriC+7hWcF92GmIqy5RPXM/uBX5lDLz2FZLwhMn/6Br6LbwbgoFHAWK0Tdc9abNVjGSqfRdtwhinOOJo7iDzcaebMHNqKPGoSuYNbEOecz964wvjGx82k2THzkSOdZEvGs6MvSaFLIZzKkeeQqXHpfH9DL18PtUOwjMRrj+C84LNwaCNGJk18zw6chQFEpxs9lWCko483f/wm+2IZzphSzLTbzsCx9DJ0VwBpuBu1rRG5qAzDV4h2eDtGNo1cXIk+MoyRSdG/+m3+/LO13Pyj8/GMGcfAug2kw1Gqrr6CvpdfAiA4dRxSYRnDG9fTt/0IBZNqOPj4NhY9+wfzGCg2iGBT6H3iEdpW7ufB9R3MyXeyNZLiq18/FXcoiJrOwPXfY9ukuYy5eBLZWIKf/8cOAJYWugBY8sVl+CeZgaow7Qykzr3m7ommIY6bj5BLIcTNqjexqJJcaCy2vkOQjpNrO4Cw4DJS2Dh63UVM+M6/kms/jDD3IlZPP4Wlm19GbNuFXjEBDrxNumm/Obx03Bh653ySqqE9ZCumYTu6DT1YhRTrBS2HoebAHcCwOWizlbK6dYhPFUVAktAVj9mPZ6CdXPth5KJydn3vD5TOrqXksisRFAeRV58ib/m5CDY7mi9En5RP6dA+9ESMXGsj9kkL0e1uMHSSKx/DdeplRJ64F/81X0KKD4BhIGQTaH6zh9OA7qTApqLLduydu8iFxiIPtqL1NJvz5YrKEWunoituUs4gqm7gj3dgiDI9SjGGAevbh5lb4efmx/fQfiTMgZ+fe3yeAP7JfVQ5Pm+ctog8Rfm/b/C/+GnjIULnX2zl+Fgs/0jK1Ou46d/u4NLJpegYVO9/AfnwOoT6mcgFIbSOgxjZFEOrVhLeuR81lSVy4ChKIA9BcdD+2gZSAxE8c5eT6emiYFItyXCKunOnMPErN5I/thrjzT9hpM1p2LmjB1CD1RiiTPtTLyE43WjRMOmda7GXlGFse4nc1leY+ZMvYtjdGKk4OV8JmT99A/eoUSS9pSDZcEgigqaiNExFjw/jkAXKH/o3Wow8BC0HPU1o/lKzfHwkjFxYhtR7iElKxNztURwMiV5yxaPpT2nkO22UeW2UexUKXWZfoBtnlqPVziYbrMV19vWImYQ5TFSS8E2fDYCgOEh0DbDnvg0ciGeQBJAdMvaGKWb/HUB35ZHrasbIZVH3rEUOVRLb12jmpTTMJtPRSjaeYEa5F9lpJ9t9lN6tLXSsP0qmaQ+iTabwiz+m8Q8vEl67BsXnZtS58whMn0bNaWPo99eh97eTqFtIru0Au+7dyKizJtHgUZh9bj2KKBA6/yKGm7vIu/J21tTPJG9UHlo6w6uPNnLzhaO54/opnHHXhVyy6c/4P/cDIjt3YyuvRbX70ItqMSafRmznNt6YdR70tmD4Q+QmnUGqeCwYOl2+OgBsY2YR0WS8nduZ8K0vo/a2YyRj9Pz77Sz60dXkXr2XzMFtiLkUUvV44u19Zim5rlGuh8m2NJIzQIv0k3YXHptTduRnv0BzB9EOb2PvxDksvPdfUNsaMQTRbFwpSRi1M3BMXkD3i68w9opFtL6+3ww0w72MXP5NtHAPja7RSLFeUqpBi388etk4jNM/Q3r7W6j51Rg9zeg5Fe3gFvIvvBZ15QOoR3agu/PJth1EivchRTopOrwSuW0b4obH0d1B5HAbuaJ6hLqZ2GedjpHLEnnsHgQth7drB/5oK4YoM+gI4bdL5DslcrrB84cGmF9fAMCEL754fJ4ELB8KURI+8NvJdtRl7fi8T9aOz4dHmXodTz98F+MLXUiiQLHdQNjxknn0AvS8sY7imeOwnXIlXT/5OqIo4gj6GNzTgrMoD29lEYamk40lGekaINBQQWZ4BEESKbnuc2b34e5WBEki3dOHa+xERLcX0RsANWtW6JTWMLL+NTzzTydVOQP73tfQomGUusnk2g+ZRxf5VaScQZx7Xqa19lRqjryKUDudPqWYAqeEaoCy/TnCb6/De/tPcHXuMHM7BjsRnW4QJTIHtmGbfwFSrJeewikUiil0xY081MYhsYxKvw0bOmndfPJxpsJgc2DsfA1hwuJ3eu0oHP7qndT/9DfmTgQwsvZFsvEEvZsPUnX6TCS7Hfu8s81Ght4ihu0FRDMaNckWSMXA6YNEhFzNbGy9B0iue55Dj66j6tRJ7PjdGmpPq0PLqlRfeg6iN4CeiHHP+d/jqu+fi7MwH8HtQ5BtqOFefnbDn/nsD88jExmh6OavIMX72HHb15h29w/B0Nl4zRconlKCaJNxlwRpen4XgiRQOqeG4aZevOUBKq/4BHoihlwzgUxoHMpwB7rNydunXMji5/9gHmXWL0TJmMnXgJnMW1BPXyJHuTZozinLJpAinfT66ync/xKC4iB9YAdKZT25rmaUygZz1tg7ScrZpj1mxZ7Li9rXAWDmbS24BNQsgpoh5i4h8f1bKLnwAoTysebR4fAArffeR80NnzLvt2EqTT/7ObWfuYlcVzO5gT70nIp76hyzzcLu9ThOuRzt0BYzz8fhpck1CkkQqLSlELJJxOQwmt+ctabu34Q60IVz8nzwF6HbvYiJIQDUonoMQUQXJJRwM9lgLZKeM6sefcXIQ+0AaL4QgppBjPeTrZiG9uxPsVWPJbz6LYovuRpEGd3uZh8l+O0SiiRy1QPbyWU07E6ZQ5sP0/nQ9R/108E/rY9qx2fVWUvIs3+wHZ+f7DlI0TkXWTs+Fss/QtEFP+P+P32HKcVuyrVBQsSQj243P6lr6NEwnrICtv/iRQZ+fxdaOgOAqyRI9VnzKJ49kbyFpyJIIp7yQirPXUZg8TLUdJaiM84gueYZMkf2osZiKGNm4J21ELl6PFKgCBxeuh99GLlmglnOXFyCNtCFI9qJUD4aZdR4tIFOMq2HUIvqQVawi6BPWUHVjkcRR00FyUbrcAb9xV/jOLQGqXIs+Z/7Hko6glo8Gq3zMKLbhxYZQC+oRrAp5g6MbCd/3R+R+w6hdOxAdwepd6ZRDBWp8U08sQ529SZANH8lBZuNuD3fTJjuP0rdZ65F7DuCISkk1r9sTje/dx1j7rwdyW7HMXURw8/9mfTO1UjRbvzqMGXr78WQZHNG1WAnWqgBW+tmBC2La8YSxl69FE9NJYt+ci1lpy2geMYYwpu20PHo4zTf/wSaYfDyt15k/wNvYGRSiDNWMLinma8deBL3qFEUnrKU/nt+QGrrG0x+4D4y+zYwsvZFfBVeiqaP4Ue/2MCO364lvz5AcHQBskMhry5E1Ze+Tmr/DuTSGnKhsYir7iO79kkMu4f5G9+k6fvfQbAp2DY9ge7wk/aXI4TbGfCNghd/RaFLNn9cEN6ZX2WjuH09Qu10pGAp7vkrSM+8EC2dfSfAaQc1i1YyBlvVGKSSWoT8EpS6yejxCKInj+Z/uYGwbJaJ+xI9lN18B2pXM/S3ona3ILh91HzjLoRgGeIp16DnV1D98wdA17BV1OM8y0wWNrJpjPxybGW1qHvXIkxaSrx8Osm1z1K54Y/UpNuQB1sQE0MYihN5oBl1/yakyacQOXAUvXAUe279Aggi8ZVPEi0Yg5CKkkNETgwS9dcg6TkMUUZ3+unU3GRLxrPdVo+QTWDIdnRXHhkd0md/nszkFThv/bFZ5egKQG8LE3rXU9GxnuJUJ/dcMYVLFlThddmon1lP/hnf+eifFCwfiCCJH/iNk2zHxwp8LB+Zymsf5NEfX820Uh+FdgOh9wgceNuctdTfiegPsu++lUSbu2i4YBrZeJLUYBw1nSHW2kOyb5Cut7bQ/9JzOAsDeOYuZ88vn6T/5Zdwh/LRwr24pi1EstvNUuPOJoRgGUak1ywN7muj7FOfJrPlNRwT52IfN/vYLpOgmhU1iCL2i+5ASMehcTVKxw76kmZAZkg2xL4jTNv8O0ZOu4X06MX0P/wHdJsDKRFG6tpHfPd2c4fB5UX1hbCV1WLrPYAW7kaZvBg9EcewOc2RBZFO5KE2+uuW0e0oY97g2xh2LzuGRYxMGtea+xBzSbIt+xBkG3pRLWrTTjb8+/NEm7uYdMNCtMgAkYNtaJEB8pacQaK9i3jJZKTew8hltQiaSm7dk1BSZx7NYL44A8hnfw5pyZWow0MMbNxJKhwlNRCh7LyzqLn4VE49u44V3zqbN9a0g5pj8zmXUfGpGzAiZvKtkctStOIslKWXk3vrIYxcFntFDe5QHs3PbQLguaNRCqfWkx5OUbh4IaVnn4nRdQTP4nPB6UM1gKXXIp5zG/fsGeK1ownq7riNeOUsYjMuNnO0Vt/P+uu/Tv7+V5FLq3EcWsOmhA/dMJDDbaTeegIt3Is42Iba1YQeG2IopSK7HBiVE7FNW4ZeXI+gZjFKGtDd+Qi5DNnDO8gND6OPDDPq6ouRBPjJln50bxEjBQ1Iiy9HT8YxclmMTAp19ypy+zfSPCIQtheiSzYQJRJ1C6F1F95TL0abfi5iOo7o9hLZvgNtwzO4G19HTaTN2V3tB1AHusyyel8Iw+ZEHj8Pw+6m5ObPI+gqE3/zS6R4P+7zbsQfPgyAphtElXw8Rhq0HFK8D0OUKSdKf9pgWnIfQrQPKd5PduNLOPQMbcNZBpMqI1md5jHnoHuLaK1YhF41GXXMYtJ5lWQ1g9PrCshzKQx0xqiZOYuJd7700T85WCwfIeuo632yjrr+/ylTr6Nq3jl8+4aZXFKmkvEUo6QjZkl1lzlYU41F6duyH2dhAC2dJTMcp/z0+WZC79FuMpERtJzK9gd3MOGcehJ9CR5/rZlSh41xZR5m3HYqjooK5OIKjHQSKVCIFg1jZNIIk05B7NpP9uB2lDHTMdJJcm0HGDz1Vgrf/hNycSVUT0aMdKLHIyTGLsfXtw81v5LkE7/CPXUOkj/IYPEUjkazTLUNYggiiBIYBtHH7sY3cx7GxOXYuvehu/IQRobA7oZMAkFxYtjs7L/zy9RffQ7ivItI2bwYhoEnehTNX8pAVuLfXj3MV5bV4ZRFymOH0T0FqBufpWfepyiza0gjA/y8/izOvWIC5UunodRNQo+GzZEd/iCap9BsgHd0N1rDfKSmTWZQ2ddOurMD55VfRRwZxDi8GbFuOmK8H91TgBDrBzWLXlyPbvcgqGnkwVbSO9eSG0mgFBYhFZbRet9DVPzyYeTUEJ26l3IpgdTVCO4AWm+rWTlXUE3qlQfwLDobI5MEbxBjqAc9GUcqb0BXPNC+F6G0nhciecwp9yHd+69EPvld8h0yR4ZSzAgYx46vAg7zeCf11hPsmP85FshdRPPrca3/i9kdO5tGrZrOkZs+wdjvfQ9BTaM7vGSDtUR/8QWKLvgEaul4EEQzWLA5ybgLcfXsRY8NIfry0YcH6K1ZTELVCdgl8hOdaN5ixGQEIWceSWUqpyOlhkFTEbv2o9XOJqrJ+GUdfeW9iMuvQx5shXSc12lgeomHQKrXnCPWfwTD7qHrnl9Qetu/ktv+OraSanJdzSBK9Mz5JGX7nsdWWk22YhpyuAXNX4Yh20mqBv7Bg+j97QyteZOCy24gs+lldDXHwM4jZGMJsvE0e99oo2p0EJvHxpjLFjC4p4mq276IYJg9oNT8SpBsDOVEcrpBaySNyyaR55DZ0xcno+pU+J3cvbaZpuYhPnlGA7fPH3W8nzo+1j6qo661Fyz7wEddP9pxgMKzLrCOuiyWD4My9TqCddOYM7eCiSEvujuIPd6L1N9M7uhB5JIakp3ddK3dha+6BNmp4K8ro/qbP0QKlpCNxhk6cJSebW1s+fN2BjIaskOhdUs3N940gwtvW0DJtBD2gnzCW3fR9eRTSNXjCb/6PGLNZES3D3m4k1xrI9Jfq5nqZ3H01Y2UD+7GPn4OW+/8GdLIAFpRLUY2jSs9ZDYsFETspWU8Kk0jVzIenyIyPb3fDHoAMRVF0LI4CwNs+8ovOXzj5Wb5dHAUgs3Ofkcten4FmreIzKZXGP2be7FVj0W1+5AE2NGbQMsrR3/jTzx1oJ8/rCinxiNQvPVhtLxSDMWFkcvie/hbSC1b6L3nR8wcE+Tn/7GDvi37QdeRgiUIRZVkyqcg5pIYdg96Mk7myZ9hlI0xexAFS/Cdci5SrBfdFUCsmYiQjqOnEv/5QMkK4kALtv7D0LgWNb8KW/VYvIvPRp57PoxdyKgbr6Xj9ivQt7xIqUs0gx4gs+U1c7K5J0h21SO4Tr+SXMcRcp3NqM176HnCnE8lpGL8eeyZqH0dZNY+xTl5Q3hf+imuz/6A6n3PMpRW8SiyOYYiHac42U7jQAq9dS+/qbiSed4Reu//Lb6Wt5FLqsmFxmL4i2HjkzTcfDWCrprHioIIL/6K/NmzEUQRubsRKdYDPU0IuRSu3kY0d5DEjrfJlozHKB9HcMMD1KVayU90oitubANNiOkY7T/9Hpn9W7ANHUV3+EGS2ZY3AwC7LCL3H0EuqcF4+zEQBHIt+1i4/fd4V/0BMZdEPLoLPZVAUDOU3mKOzTAyKQS7A2XqUv545a/wPvgN5Orx6K48VAMGvdUIuoqt7xDONffR/+gfaX/4UQ49tZ1nZl3BN676D+6+6UGe+f1mWt9sRbJJXPD/2HvvKLnKK937d0Llqq6urs45B6mVc0AoAkISyeScTTa2wYABY4MxGGOMAQM2OWcQIARCCOWcs9Q5d3Wo7gpd+YTvj+PLWr4z370z4xl77qBnrbOkpXq7VKfr1Fv77P2EF67DkWNHTaqYPB481cUImkJ87zo0mxtpZIA4MhZJwG2RmJjnoC+SZEtngL1dQaIplUP9YSaXZZBTkMb2Zj9z/7D+H71dnMB/AIIo/N0H369JF/I/+wWcwP9cmCdczaTzLmHptCKqspwUuUxIoW6EZBTF14GcU0znW2+RXlFAyRnzAYh2dGKvn0z/nx9D1zRavjrMF3t9VDvNzDqnlqzxVex4/Gs6oimsHx8ld3w2tZecjHXcLLQ9R7GkO0lsWYGrshQhFYeCKjTAPGE+SushqJuN1N9MxbWXkupqRldSTH/5cVRXNuqG99BiESzlE9A01QjqnHwalRdfgvDxB0gjA+gWJ7rVRfyTZ3BMORnfB++SMbaGmvOmcezdbah5dYh7PketmkqBzYQYCEOwH/OMJQz8+WHc1WWYIiECNQspTbeCmiLe28ctkyJoSZH1MxYz4ca5pI8ZRu9pxDxjGdEP/4J/1eesfGojZ917Kpc79uIsyEId7jcM9lIJ5EA3irvACBr15mKORVD3rUHy5hpxHGE/RFqR/R3oqRR6bgVaVwNCPIrg9qK7MhGGe4wg0JrpiCODCJn5oKbQbG5jXFcylvJrLkevnc3azgil2VPIWf4oluwsRub/EElMYVp0JSSMUQ+jT0Jv2EbOktMJfPMZWkrBbZLw79xH3B8k+eV2Ki4/h3WdYaZPO49iSUDauZzUcD/dM66ibKSR+sNvc3DcJVxkl/HLAjt+8EtqMh0Up5nQAXNaHsKUZYgtuwAIuYqQ3v8NjsWXga6hpxL43FXkdm8jWD2PY/4Y05L9CGYbtjNvoOW2Syg5axHRtjYsdVM4fv99VP/0Vhpyp1OhD1B4/+MGwVzX6IkoZFjTmBLbjR7OJfHWM7jOOB8pMx+9aiqakkScdR6WxAgpVy4BRSM9bCjHEERiGeWIn/4e68xloKnEvJX88JvfIeaWGflpgOopJyvQBOFBki2HsYyaSobdhbhrJ5k1EaweK66WAJWLq0mGogw1DmNyWIzOpiTizHEwtP8YzSsPUtE/DIAz8BLWC+7EqsZIyjY6QylMokC5x0aGTcZmkjjQHUTVdPyRJMFAnJ6OAPFIik8O9XJ2fd4/Zf84gX8bRElElP6+Hsb3TdV1ouNzAv8lKLrsVezefGSTyM7WIc6scOGI9CH4O4ht+hQ9GTeSzNOdWPNyGD5wlITPh7N2FMpAN57aUlzFOWw/PMhVl49l5pnVKLEUA/saqTl7NCOKRs2ZNeRMLGXoUCvRnWtwFedg87qRvLlosQiarxW9uxHd7EAf6kXOzEUe7gKLA3WgGyYvRZyyBKWnBdoPYKmfgam0Dvpbkb25RL75AKH7GJM+/oC1faBLJoY/fR0xFsRSVIZgtpJ7xlkk+gexFxeRt3wlqEmkwhqijhyc6giaxYWWWYratI+Mmx/i6KurELOKsS5/jEIpwg9XtOG4+A6i3koQRCbcOBfXVfcz9M7zKL1tCKk49oI80idO5OTzRpEIjFA8t4b0GbOM9PnsalR/D+qBdcj+NgQ1SbJ8OnJOEaEDB4g1HUPv70AtqEcrnYieUcjAV5+jNe02Es+rp6OFh43MMk8+StF4dIuTaHYNoZx6IwIiMkQqpxZ0HTG3jKTJwUJzFyUuE9pF92EePw/3wRX0JSWO33o9jffdhdLdTN8zDxlxHwPdpC88g4yzLmPJCz8kdOPvKb/9J5RfehZS7VQeW3UcR8N6BF3nK+9cOqZfSeHudxjJrkMQRcb6NqDpOpltm7HKIoqmY2vZgt3fhPhXbpXgyUXpbiK9dy+ueWfRYc5HbdyDklFMTqSNTxbcRvrAEco9VgSTBf/bz9Pzu3upuvcXKEMDKNE4h+01VD3+NKLNgapBauNHqJs+ZG08F2G4h+J4BzYtTmTralSHF8+lt6FklKCbbWg2D0Iqhr53Fboow2d/wB1qR/cUoIYDqANdWA+vxjpjKWp6AWpGMQBaOMBOrYBvpFEgSpjXvIAuyaiFY5CyClh/3o/wb9lKy1cHANBVnbRCF5Z0F7LVTCKUYNXbB9n79EqGGoeIDsYwp9mZeNtpZIyrJfeKG3HOPwcxOoxpsIWekRQtwzE2dgyTaZMYjKZo8Uc41B1kR7Ofo+3D7HzvTSLBBAONh7jwil+ccHk+gf9xOFH4nMB/Cf5y72LuvOdyzp5ezN2LqhGPrEUK95Hqamb4aDstby5HiSWxed0MHzIInLbq0SQ6WzEVVTF0pIWeTQe4/Benkggl6N7Ri5rS0JIqw00DnHF6BbLDRubkevJvvgNd1Ug77Xws+QXomkbU56dv5RdIWYWG8Z0nF6W/21BFrVuOpX4GgecfYEh0IecUofp9hlld1TREVwapgrE45yxF8fuQAj0scPpp/vntuOrHoTXthunnkGw7hhaPIEgin5b8AMfLP0cK+jhsKkUUQGzdg3ZkM7EvX0O0OZCD3Yx+/zMApPPuRmzZzUuzJACET37HI/tiuC+8lYGUTPqsOUhZBQhKAtPsHwBQcfMN5PzkIfLOu9CIQMgqQO46gOTNN/LLRAnVmYWUGCG4cTXHP9qFJTeX+LHdSJ37EWJBhESErCtuQ6iagu7JN0Z89Yu+I9oOKib8ug1rZABX127DN2fTR0gNm/+aSL4Ha+NGNJsbMeInoeqksqvQYxEKIq2Meuy3lP3pHQSzlUNv7iJYMgPLqKnokRCN9kpMk0+lsnElyYJxSJ4shJEhvpoxjJBZgHRgFUstHSRVnbYJF9IVSiHOOBvRkUaxbwdawSjmF1oJxhXDOVlJIahJBLMVzZ6OXFCJrqRQPIUUJ3tITj2HiGAFQWThQ2eSOLCRrO6daDY36VOnkz11DLrVhX3eD7DnevE8fwea3YMWi1AXOcK+Z1ZgKqmlxG1l+NuVxDPKSXz0JNYzbyAu2dCbdyOF+4lv+hRT63Y0Z5Yhz7d7EZfdhtq4B83iQMotQ8wtQ6udwxatCFJxmmJmJFGAyUsZl2Nnbp4MjnRMExcgJiJIPUfQhvvRVR0tpZAIJVm9vIFwzwhaUmPPX7by++d3kTU6kyyLxJhr5pFRlcHYH52Lq6Ya86lXIy68Gs3qInV8lxF6KpmwySLfNgzgsZp4YHUTTrNEXpqVEq8Di1kiMBClYMrpRAZ9hHub0f8a6XKi+PnvC0H8+7O6hO9ZJfA9O90T+Efgy2N97Ok2gilPLvUyNXEUye1FtzgY2rUXS7qTiqsvRJBEUpEYnvpqkuEoQ1u3YBs1EaWvk1VPb6RrW7dhMgeYnSa6tnbjbxxCTankTK4k1DGIuXoCyqHNWGvGI+ga5sqxmCvGkD57HnlX3kji0BbUxj0kdn6NqbACogGGjrWjJ+NkzFvE0K0XgmxBmngKeiSEsuYN40v90Bq00BDmynEEvNW0mQupfPC3MOUMhNqZiIfXIogi6kA3wiX3c27WCK6SfFK5dWTZZTQd1KCf5NRzMBeUoFdMNozkHr0NxVuKpXkzoisdXZLhwDd0Lbyd+8qDROzZyJKA7M1FC/oJr/sMKeLHVFyNkJaJPGSMqsQZ5zCUPwlcXpJN+42MqfQChIPfIBzbhGQ2kT+1iNCxRsyltYa8/shmhFQMdA3N7kF156M6szD3HERd8QwJZw4eq0SGrNB8z21Etq421GeLrkZwpBmRDqNnEyqfDe0HORhz4tWC6N+8BIDe30F07UcEUmCuHMuEH87GFe0DTSXRsJecj3+D6syiu24JwtYPiNQtRCkcy/r0GWgWF0JBFcn8MeS89QtsskjF/neR/W1sstTTlD2Fj7sFdvQrJFQNJauCYGYtCKLhX6OmUF3ZBqcp0IMYCyIJAs7EEEfv+ClPlV1B9zeG+7U4MohUPRnTrLMNvlawHzkji4x6QxIu2hyk2o4y5stViK4MilwyGUsuoCWQxHL+nQiqQjih4qs7nQ5XFfKSm9A9BQCoHUcxKzEiqoBUM9UImLU4SGVXIw+2MEs5hpgIU5NoRQ75kEK9mI6uQxoZILl/A4GPX6b3zZfwffAOLe+upPb8SSQCYbxVHs796VwcOXbKl4wlpz6LielW+g4MUDouBzktjepLF5Ma6GN4/yG6Hv4ZQ0//nIEXH8dcNR7Cgyith8gxq1w/vZgcp5l51Vm4zDJpFoPxUF/gxu40I8kiI31t/+JzfaL4+W+KE3L2fzdOqLr+jTih6vq3YV93gK5Qgv09QW6ZXoSzfTtK4Vj0bZ8QaThG3B8irSwPQRKJdA9gdjl4/a5PuOa5SzAVVNC36hsESSRz6jj0VJLdj38CQDwQR4kpzHrgTOL+IBaPC8fkOQDG2AqQc4tJdTUj5xSTOLYLOauAkWNHAOjdeoSaJ58jbM1EePNBBMkgLpsrx6IOdKP0d6Etvhnr0W9Ra+cgBf+axi5bOHzjD7F5HeROG4W9fjJ61VRDhl83G3XLJ4SOHMN962NIwR465WxKhw6QbDqAPG4uPbYisuwypuMb0HOr0E0WevU0ekeSdIfi1Oc4DWPEqqnoJsPVOZrSKG7fgF48hs7f3I0l3UX2aYuhdByCphiZVQU1hvIo4idgz8OtR0FNIihJwyF45zeYy+tpe/VNov4I2RPKyZg2DdGVbrxRRUYkiObwEnz5YdxX3IlusiM1bGa4ah7/i+84FFcpdIjIgS6ju5KMsU2qoDjNgnfLa8bvP+hn6FArhVddyyFnPTVpIIV8jKQVYdPiDGkWPDveNbpeeaUkWw4TOnCAjEtuIf7tO8hn/xRhy/vG+zzjQnRdJ813gFROLWLEz6A1l/XtASySyGvb2vnLeWMwvf0Q9kvvxpeUydeG0CUZMTrMSHoZNj2JkIoCIAV9hL56H8f4KVA7C7/gIrNxDaLNwU7XBCY6omgOr9GNXPsOcn4ZUnEdvrRKcnt3Gl5MqRiaxYWy9m1MJ5+PmAij+VpRettQF16HvfcgqaxK5GA3vY4y8vr3IogibRljKTQlQNeQfMdRc2tQ1rwOgLm8HtGdSfLYTlKDffTtOELvbuM6HmwNEFONbTnNJFFxahnNq1oxOU1oSRVN1bF5rFQuGU339hZK5o8iY/IE0FQGtuyid1crzmwH3vpSBva3UH3DZUjeXFS/j4H6ZWTYJKSRQRpTaWzvDlCV4WDV8X5UTac/nOB4ewCrw0Tjng56937zLz7jyb0v/2duGf9j8Y9SdW297HQ8Vsvf9VyPbD1AxqIzTqi6TuAE/r14Y08XxwYjjMqyc+PUQkQBNG8JctDY0HVNw1NfhSkzh+Gj7QiiyKHXNzKuOoPu9fsY2rqFjPoydFUj3NCMdcJcJt9zAaMumYk13Url4irMOXm4qiuwj50KgOr3IY2eja5p6KkUpuJqRIcLS+1kJG8ezsvv4ZS+BYz61f2II4OkNazFtewyLPnGHbpSMAbR7mJw12FMOz5GyC7G5DtqeO0MdYGmMubJxyl94hVcc89AKKxhxJSONmEJ+oG1SCddgOWm3yJsfhdB1/DaZBIN+zAVV6N3HSfTJiMmoySbDiCkosiBHnLNCvVZNs62daCosPaqR9Gbd9OStCMCiTsvQUzPAk3FWZDFjuc2ET++D0FTEPwdJBr2obrzMQ00oR3ZjHfwCKTi6GYHoff/RKqrGVNpHbEjeyhcPAdHtovMa+5Ezi5AzCw0xi6ubJqkPDjwDemX/hgp6ENb/xZq1QzuXHGMluEEDlGlyA5iPIjm8CIkYyieQiY2LMez6kkSc68y0s9zipAdVgY/ex+vTUaTLSS3fo4gCKzuTpGZ8pOYdTFS6WgATPmlOAqyiK1+C13ViL32EOqsi5ArxuHqO4w9FTJiP4LdhB159EVSZDvMVHrtfLjES0bbZuyX/AwhFcVlFlGdWQTldELuMpzBdnTJhKAkQTIjqEnSFl9IovEg2tZPyPbtgaLR4MpkaqoBbddKtJV/MgJF88voHbWU94a8qLqO/leCuJpeiLZnFUMLbkIXZXTB6PQNzL4a6ZsX0BNRhv50P6orh/zB/SgFY9CcmeTbRXyq1cgcyyxDHupAzi0mfspNKH0dRLeu5NhLy+n8ZjfhrgBqSqO5YYj0PCdjFpZSVOmhfGEpH7x7hBW9YVr6ozQPxwnGFbJGZ2L1pjHh7stxVxSQ7O2i9YMvybvxDsb97EqqHn+ajCt/Ss2Tzxm2Du4ckE2YRBA0lXbNjdsi0jUc4/PDPhZVZ+G2mzh7XD4ejxWnVSYt081JV131Lz7nJzo/J/D/Ok4UPifwn4IFT20kmEgxu8hNgTmFMxXA0bUHOg5CJECk4Ri6quHfe5TuVevp3NjIode3klaURuWSeorPWMDggVaCTd0kwxEcBVn43n6JhM9H2rSTmHL/paRXF6FFwiiBIZTuZpJNBxBHzQI1aXQyNBU0laGvPia8bR1KbytysJttt40icWgrqisbrXIaSkYpw/sOYS4fjam/AcHmIPenDyFXjEUXZRpdtSS/eR1l0IfQ10wiswpxZJDhLz9ATERwd+5ACvmMKAJNwbrhNcSx8/C/8RT26AC7Hn4bnF7EnFLkXcvZMahjmbSQQVcpj7W76YhJWA6vRnXnU5tsZc4T1yBnFfDOvh7StTDCo2+SzBtN8L1nOPT6Vubcdzq2MdONL9yC0VjnX2S4+bYcRBi/EC08hG5xgqaSvuQiUJIkGg8yeKAJU1E1xQ8/i5CMkGw7hqApBpFaTVHgMsGY+eiCSGTdcoQFVyN3HeAvi/NJs0qGKmrjO2xbfC5idBg9FkY32embdAGJpT/Bvvczvs44md/qMzlywYN8s+BOdveG6Y8opP/6CNsmzGJRgYlGNZ2hB67n0QYLumwlWTkLy6ip2OechZzmxpqXgynYTXzbShRPIQO4eGRfDF224iDJzW/s4WRTDzZZZOG7PfQVz8I00IRqzyBtpBtFh/TEIAlVR3N4ARBSMfYHJZIF41DT87GeegWm0jpSLYfQHIa3kB6PoMy5jGBDqxECOv40VF1nYn4aGVaJ1qI5dI4/H10QMRVXk/7lE4jdRxBSCURPNlmbXsZSOwlEifTRNbTccT1KZhnqV38xFIWCSOba50m2HCZqzSDVcRzF10F6vxFS6tt6iOoLFzB43E8ykqLu/Mmc+9RFKHEFV3EW2w4O4NvXR0jRmJ5hY/rcYq775F4W3rWIinPmGsV+PIK5ciwmj4fCeRPR7B6ESaejpOWiy2ai9izMY2YDoCfidFx0BqFnf06xTUUDphV7mFGWwQf7epBEgbZAlJvmlDMSVzh5WhHHtzdQMOX0f97GcgL/V/wvVdffc3zfVF0nRl3/RpwYdf3/Y+4f1vPKZRPJcZgwhXrpM2WRk+xDHGyj5923UVMKcX8Q2WomZ+oodj+1ihX7+8ixyNz64R00vfEpZUtnoMaTWKvrEcxWOt9+l9Kbb2Nk40qcs07lwD0PUn3+XDQlha5qmHKLkMbNJ/GNkb5urhyHFhxEGehGHjPH4KMEe0AyoZttpA5vxVQzGSLD9OZPw7vhBdpXrKfywd8axM9YGN1bjJqWizzYQmLHKqTTrkf2txnmb6KEqecQuqbR+cqLyFYz+ZddY4ybYkGSBzYgT11C9x8epOiqa9GCfppL5yP98mqKH3sRqWEzyCbwFtD84P1U/Oq3KPvXMrx3Pxm3PEzsncdwnn4JmtVN2JJB5Dc307L6GOOuORnXGVcY45jhLuN1ZRUg5xajxyLoRaMNNVHXcfRkHLF8HEIqQerYDhInXYaz/yjJw1uRPFkkJixDAKIpDYssYkuFkYK9JHNqMLdsQykci2mwhVTLQQY2biFr3lw6P/yUvFlj+f3Vr/KjwYPEFA2PrGHyHWXPrffQttvHOVte4abK85mXZecHX/6OvVkzGesR+OWGHi6dWEhd9BgrEsWc6vua99Pnc16tB38CnGYRUQDL7k8RamfSKXgoCTeiZJYbv2/fUZ72ZXLuqBzMkoB38AgtrhpUXafMFEUK+hhZ8yGWvAJMpXVoGUXGKFC2fJeDJQ8082mynMI0KxNMg6hHtyLVzWDAlk+2bw+pjgZMdVNZHvDSFYpzS2GYaFY1tqPfGsW0bCG2/SvM8y9BCvcBoAx0I5qtDHy9kpzzLkOzudFlK+JwF9EdaxBNMsJZdxB94V4Slz1IbrAR3d+NXlCLbnHgF1x4D35O54fL8R/vp2LpJOL+EBnjaulZu92wKkgqZJ58MkpPK40fbaBrazezf3kGZq8XS/0MEkd2kPIPIkgivq2HKLv2KvSi0ca1eHATpoIKjj3+NJ2buyicUUCoM0zO+DxSkTiOXC+7X9nBvAfPxH+4hdLbf0ZXWjW9I0mODIzgsZoYle3gw4M++kMJVq1rQdd0Wjd9hsnhJhUx+HsnRl7/Z/yjRl07rjnj7x51PbxpH575S0+Muk7gBP4tqLzpY2ZUZ5FvTmHxHUE323BZJMTBNtRwACWeJNo7hGSSsaS7WH/3R3ywp5cRRePiO+fx09N/g8lhYaSzj4MvrQVRQvJkk794AcpAN9bqeoa/Xo67LBtNSWEurcVcXIUgmxAHWrBUjgUMqbnoSsdcMQZl/1roOGg4H2cUozQfQB43DxIR1KJxeDe8QMPbX1P98/tA11DTckl1NSMko8SRUVoPYRkzg9g7jwGQkm0IB78B2YIgmyg452xybvo5msOLsv1zlKwK9EQczeGl84dPMLJ5FZIni5KtL1F09mIQBPSi0ej5tWhWN+XXXUm3OQ85r5TM+Ys4952jDJ59D0F3Gd24sa/5M2pKIdQdxjX1JNT0QlCStDqreLfiEtQZ56PmViNkFyMmwghKEqGoDi0WQTc7UDuPkervwbr9A3SzjV0Pv43kySaU0LAGu6g57zHskT7EWJBYVjViKo4gm2iNyfR769CiYfKuvBE9GsL64MsETvsxP2/6nC8a/GQQQzy0hubHHyN7XDEzbpqN0t/JU1t+z7nfPs2xJ54l7dfXMPfp3ZxWm82dnx7ihcEcljj6iE09l/PqvCxvDJB8+Aa2dIZov+5c+kcvpU33MJLUCGTWIgW7kQea0SUzt+YNkRs4jvvwl6TajlCa6KAy2oLU1wiA85QLkHOK0T0FCEoc3WRjZ9SFZnUhhfrQNY3Kh6/BahJRHV7E+jkk1r1HZsqP7sxAHj2TDlspiyszuKlS4MtoLvLXz0NuOao7HyU9H3nJTcY1klNLfPdaqDuJ+LHdZF/410DPwU4Udx7J0ikkz/4ZLXNvRU6EcF94K2ZJYI9YglYxBe3IZsSIH48Z4k1HUGJJJr3wR4LN3biKc7CMmkrJ1VfTu72BnEt/iFRUi3XCHMoWT2HyrSez97k1CFYHumxFzivFWl0P595FwcLpDK39mv7nHiGxYxV6Isb2Wx9GMkvE4wo9u318uK0LV3EO1Q8+TP7Nd7Dkg/sZbuik5K5fMfz5WxT27WLcwXe4PDfM4op0ilxmfjqjgDEFafz44vG88tOTGLP0/O+KHjgx8vrvAkE8QW7+9+JEx+ffiBMdn3+JtLl38cRjN3FlQRT62yG7BAQRze5BHmyh8bHHOPLZccacO5rVbx7g8ucu5fBr31I4u4Yj7+yiYGoezoIsHv3NGp74+gEkTzYDX68k1j9M3oJZmEtr8X30Pp7aUrrX7Uaymim6+EJSLYYLM4CpuAZ1oAvBamdk+zpcp5xvFDNdDSCKiM50BEkiXDoDV9tWEkd3E2xqJ9Y/TPNXxznp99cSn3MF6qsPkHb+zUghH8mGveiahjr/aiPAVDGyrVSHF3XLJ3RPu5wShtHsHoLP3Ufm2ZegJ6Lo6XmGM3IsSGrnlzS/t4rqP/4Zv+whY99y9GSc6IyLMEsCx/0JUprGFDo5bimjSvCT+OYNzKdfD0c2oHQ1Y528gGTjPqQJiwwlljOTGCZGkhrZwgi6bEUKdhsePJLZiGuwuAi9/QfseTmYSmtpeeFVyi47HwBt0jIEXeONw0NMLnAz2hQwpP7BPhIHtzK4r4FXn9zIld37jDHOqKlsFCqZHdtPaONqHsq/lEsmFvLYmgYeXlKHWRLIS/ahN+6E+rnElz/Lrj98hfuLrylIM9EyHGd8joOYotEzkqI4zUwwoeI0ifRHFZ7Z1Ma104vJcZgAyJAVhP2rkHNLjTGbZEZNzydmdmM/tAqhqA50DcVTjJiMICQiqK5szL2GE7NutoGmISgJEmn5WAMd6LIF3WxHSEYRlATKvm+RJp6CmBghllXNUFzl3pXH+NVpNeQ6TMi7lqOnkphKaglk15Peu5fY7rVY5l5AavfXyFkFUDoO3WRBDvSApnzX8aFtP6mxp9EaSJJulchO9iP4GhEy8lCaD3zHI8LqYsBTRcaRrwy1YyKOriQRCmqgtwmtZByqMxPT4TV0vf8+mWMrjNR2UUJPJUl1NiI605FLRyOoSbSRAGgaauV0pKZtDK9bxeCBZlzFObgrCoideSfeUAtK414EmwM5pwQ9GkQNB9CChmpS8uYi2hwIVgcIIoqnkJjFw4G+KDVeK5GUxob2ANtah3j72feIDfu+2wdOdH7+dfyjOj47rzsLj+3v6/j8esNePPOWnOj4nMAJ/N+w4a17uELfi6ApKLUnIwz3QF+rQeb0+3AV51A8JR/JauHcBxYbGVzBBM6CLGweK86CLKzeNB556XLEMXMZ+HolnnH1xP1hBrftMTg8ZpldT3xOztRRlPzwRvRkHE1JITpcCBYryWM7jQDPkvG4Fp1L2x8eRQ8NAqBFwghpmeiJOJZvXyTV0YC88HIyr/oJJdddz8kv/xxTcTUufyPus640PFl2r8VUNw1TXilvHOgDNUmrtRRdMiMFfZhHz6C0azO9Ugba2tdxlhQY3CFvCYCh2tE1LKOmUvuzHyHGg2QHm9hYcAraSZdgkgQCv70NgIqPH+KgXEK524S6dzX2aacghfqQc0uxzFxqRA1MOg29YTtSdBjx6HrsapRsLUCX6jCypywuQ54d7iPy1Vu03294DQWb2tErp1J21SVEJ5yBVFSLHOxh4v3r8NhMjFHa0S1OEhs+Yv2FPyMxFCRz8mjuGtxL5uqnkfNKUXrbmJ06ipZTRdrSSzjWG8Jjk7hyegnloaNIgoA42EZX3RKkUB+htl5m//kugktOwSMkyHYY+UGdoSRZdpnDA1GSqo6qw56eEPcuqKArlOD9w31s7AgyrMp84T6Jb7UyUtnVpBr3IsbD2GJ++qoWoosyI+4SBCWB5DuObrYhhfvRQkMo6QVGCKnFiebwYh1uQ23YhZgII44M0CdnopkdhtO1ZMKfXklrMEleaoAXzqmjxLcDa/c+pNLRtNWfhR4L4zryNeGCiVhnLAVfM5bq8QjFo4mveg2x8xCqMxPd4uDWrUlUVzaSNxdrzwFqfFt4dU834kALeAsQUgkkTxZybjENv/0dvteeI6tzG8Mb1333WRLMVuhtgpxS1C0fYx5oRC8aTdGNP8ZcXI2uqqTajkJ2GR2frWHrHc8Q2/yZEa2SVQKiiPrVXwDwzF9M8SlTyb/sGsxn344voqC4CxBMZkS7yygqMwqRvblInmzkMSch5FeBOxswlIzyYCsjSY2pWRKCIGCVRX5y/+ucUpvND398CTZPLgDFM5ae6Pz8kyGI4t9/fM86PicKnxP4D6Hypo95vnA8kicLxVNs3HUH/ZBVRO8H77Lnvj+Sdepiyk+fRNEll3D4rW1oKQWL20LfjiPUXjgdT10JA3ubsI+fiRTswTvnZPRknPzZY8icXM9Iawf+Q21MvuMsLCWVJBv2Eti6GduEOYi5ZagD3UZxk1mILltIHd1O6e0/A4w7WNmbawSJ5pQi5xSjRcKGv8vIAFpWuXH3LptRe5pIbv2CVOthgi1dAKhVM7iuJAWCSGnPVoREBAQRf0YNosNFnjqEaeJCg2vTuAN191eII4PICy5HCPVz5IGHCJbPBlFGdecx1zGEpCawHV9P/iVXMqbtK9yTp1Hn0pCHOxCnLqUxbRRqegGJw9vQB7vRrH/9kpq4BM3mRouEIRUHUaQk3IiQihtS60iYxJEdyHYrtodfpeWND8m+6Dpa77qJwOYNWEWdVkcFqiub6VMLWVjugfAgwZcfxlRax9hrTyIZjuDbtAe5cx+WcbPRK6cilo0hsX8TLzZrpLzlfFJ5nCJ1kIWeKKgpcgPHCW1cTXHnZvRAH+nVRey+9ymqzx7HgGJG1XX29UXId5rQdch1mtncESCcVJmYn4aXCMVuK3kuK/5oknu/bGBZRoi5tn7koQ5Ssy9GObgBOdBFthhF6D6G+Ys/Iuz/mtjutUi9x9Bb9tBXehLCt68w9OrvEfZ9haApqOmFSDVTUY7vQus8RuzeKxETYVJdzURsmXhiPg76wrzY+tcN35WJP3ssr/SmUeA0ES2fiTZ6HrbEMIKmQE4ZmjMTzWTDfPbtBMtnG6Gwoswz9SHSZt2KmpaLklnOYPlJnD8mDzW/DkFJofS2INocaJml1Nz/C/r3ttL/+ccM7G9BzR+FUnsykcqTSLYcRjc7SPkH0Qe7jXFx5zGaX36b/s8/4ehfPuGW/EXINjPTf3Mt1vrpiA4XusmGaHOgjIzQ/uqr4C0gvvQnaA4vI5ixyAIRwYpcUImQnoPqzEIY6jLiSQprOCLkGzctmoaSWf7dNZVFmIGUjKrrSAL0vHczC8vTGVPgZvd797Dmo9/RsXUFcGLs9c+EIEl/9/F9G3X9UwufDRs2sGzZMvLz8xEEgeXLl//N41deeSWCIPzNMX369L9Zk0gkuPXWW8nMzMThcHDGGWfQ1dX1N2uGh4e57LLLcLvduN1uLrvsMgKBwH/x2f3PRcnVb/PlLxfxeOQoSCaEg9+QajmIlG/ELuSe9QPyJhsdA1Oa3UihBszeDCqWTiJ/zngks4lQay81Dz6EmJaBMtDNgYf/TOvH3xpSc9nM0NF2ciZXI1isIEoIZiuuylJ0dw6qzYM4/wokby7oOkI8DLLZWOfORg36ESxWdJMVQVUQCmuwTpyLZnaAptL0s1vQIiHQVF4RJyMsuw25cgKZ194FiQhiYgSAUFoJ8aqTIDKMPuzDIouE8icgDXdCbxNSUS2C2YqcU0yy7RhoCr3vvE7tbx8jLT5IPL0YdcO7DDoKkf1toKnoFidiWgaiM52Ou65H7zyK0LyL4m2vIg13YZq4AK1kLJrVjRToJqZLiLEgculoxEQYeaiDkW8/5vj996EcWM/2Wx9mwx2vIrm9tCw+hfhwHP+Hr1D26DOkjapF0BSKpTDC/lXcv/dJjiw7jcThHSRDUdZe8TDO2lEIokjRheeDI90wcrSmk9z6BeZZZ3DV2CwG4zqmstGI8ZARxpk3Gt3qQjTLRPdtYfjbL7FNmkfRSdVk3P002YRwyCI1XhsxRWcwpiAJApPz3eQ7ZNKtEprZgU0Wqcty4AvGKfTYSGWUoMtW/B++QuzZu5HGL8D/+Xsoq16is2QO0tJbYOxCrKOmgj0dLRwgb+gwwpyL8Vx3L/r40xCSUdj6IVrLXhAlxr4rc3X5zWjOLKSaKcQUnYgzj/F5aVwxLhcxFaPfXYlr2zucNzqbaEojper8ae8ggqqgOrwM2AsZsBcyYslAjAVIi/ahIKKm5fF2tIzAlmfpFL3ogohn73IssoC65RMCK95CCwfQzQ5GPnqe4ZXvUXfDuQweaKXu1w/h++1dsPZVrCiYTj4f3WzHPnaaUWiFA2iRECWnzSC9roLcaVU8ve1JCh59BVNhBWrlDFIVMxGSEXSTDceMRXRv60DvbcalhNBsbtyhdsrMcZxalKO2apTGPeiSCWQzuicfze4hz2liqHgGmjWNjpQN1Z3PSNUc1I2Gt5JH1kjXI995I51V6yWh6EzLUFn4w2u/2xdOFD//HBjuy38nz+dE4fOPQyQSYdy4cTzzzDP/v2tOO+00ent7vztWrlz5N4/ffvvtfPLJJ7z77rts2rSJkZERli5diqqq3625+OKL2bdvH1999RVfffUV+/bt47LLLvsvO6//ySi99j3evO9USoVhRlIaiYObEe0uo/MR6EfvaSS6bwudG5sY2nuYN25+G8ntZeLNi0gFAvRsPkzU58dWWYt35gyUpr2E131Gz2dfUHnWNEqXzCRw8AjdX62n6NSZOIoLQFNRelsRzFaDg+BrQVnzBmz7GKWvg1h2DXKwBz0eIbFrtaHsiUVANiOE+iE8SMxTSnTbVyCKaLEIJafNMO6SiydwxfAqRMUwmtP2r0HxdeA3ezlKNlZZxNayxRi7uTOxRwcIJTU+SZQRqZ2P0nYY1e9DdHuRxs5FWfMmyVufYCS9DKHrCLb+46gLr6PljMV8FMxCLx6D6s5DK6on1dnAoU+OGoaIxWMQFl2H2t2AZnUz+OxDSAdXo7mysSsj6NEgmtWF3nUcpa+DVCTGcEsAPR6h5vypzH32Vr68+hnSCl1k1hfhqq5A3fIJ4qzzkAJdxD99ntbX3qXgJ79k/E8Mzo93xlTmv/VLQwY/aw7+2kX408qJ183n6PnLMBVXE1z+Kqa+4+SkBkgc3MyGs65HSctFGhlASEax5BVgO/0qujcdYfftD/DCzJ8SVzR8pCGJAqGEiiwKiAgkVR27SSCc0hEBfwIGokmsksjJFV6umFSAaaidsCMP9/UPIFz/CJ2P/BxnVSWx039Enl3E1HcczWQDTUWzupDrZ6NZHOiiTNSUZty56hrqrItor1oMwL5HFlKU4yQo2BGUJN6Dn9N80ZmYfnU11t5DiNFh2oMJDtaeA4D0xi9JC7Zy83gvumxm0JJNZsogJTtIGtejOw/z/pWY+o5xUV6MUFKlONmDcPAbxFGzyBWjRE++CnOaA2nSaRAP4zz7OtIuuJXBqRdT/cI7BDNrybnvKUSXx3iPnDn0qna0knHonUcZ2bONSFMjptI65Nxisk4/E6GoDlPrdvS0LLTVLyKNDBgcr6FeYrvXMuPztxFtDlAV0DWE8CBJs4uY7KAu1mAEunYe4uhDj6K6cuh/8n5MIvgiKXSLg2BcRXV4kUQB05TFZB9dabhbCyJBOR0BMOsKsgRHImae+sEY8iedit2b/0/Zj07gBP4j+Kemsy9evJjFixf/H9dYLBZyc3P/1ceCwSAvvfQSb7zxBgsXLgTgzTffpKioiG+++YZTTz2Vo0eP8tVXX7Ft2zamTZsGwAsvvMCMGTM4fvw4NTU1/7kn9T8YGac9yMO/vJypWRKifwjHly8TCYRxyGbkrALix/chp2cQ8RmEyYwJo7ny9dEcefIV0oq9uIpzqLnpcpSeVgDU4X6CDa3E/SGyJlRhm7mU6MbP8B9qI2tcOYIkYRk1lci21UgOJ6Ir3SCDmh3IZROQB1tRg36sHbtQhvtRAkNYT70CPTqMqbACJacGcWQANS0PExrxSBxbfzOp6pOwOtPRLA7Eo+vRTroItn+C5ko3QkpFmXSzSNrXf8ZcPZ5A+Wy04hmYhxsRI0Pk2VRmf/kEoVAU8/SJRthnPILW1Yxl8kIKCSL39aB7ctEtDsyJIJXLJjDN4ye5+RvUU27A1rKFI699wRnrnidl8yAKQUzNW9BLRtP+0B3kzZ4IokjMXYhtuA01vw40DSom03L3LZSctYjSRSMgm0k/93qG3n6W/DHZjPrJNfR9uQp5xlnGm5YYYc38y8j4Zg3jqlcz/MaTZMw7BQAtGUfKyEWvmg66hjfpp1fKIKmqbLr7z4wrTKBv34iSXYWp9zDCtKWMvqQNcdM7XN5Tz0vnj6Hk9jV08BX5L39MmlmkOKkRTWmMJDWK0gwidqZdJKFq5DpMtAaSZNgEoimdnnCSEreVmGJ0WNwWiTY9nxte28MXV9TjPrAC00MvIupJPANH0IKDpCqnI4V8aJXTEBq2oFVOJSC6yBhqw5JZgRCLMWjOIjPYzX1f+Tl9zDw2fNbALXPKkQQgFkIsH8fYl14AQDFZaYzbSbNAWbqZ1kCSqmknIaQSsPsLYs1HGTz9TrwmkfWdEU468CqR026hoS/Ka92V3F1dwaNrm0kqDTx6eg1bPW6WjnQRz6snlVRxj5vJoNlLerEXU8s2cGWS3bGFlurTKU0T0JERrXZ0swMByDUrCJEEejLO3ue+JaPKQ139eASLFa2oHiEZg/Q86GvFXD0BYbgTdXiARNMBZE8WqfXv0zfvRtIsEumdO1DzR2GODxtRJ0rKMGDMr6H6pitQvvwz+ZddgxbuZlQigmrNpdhtRhrpxxQLGqTtcadi9h1FG+6DqnmMpDR0XaRc8COICd7qsHLbFZPpGhrNi394A/OEq0+Qnf/BEEURUTyRzv7vwX97js+6devIzs6murqa6667jv7+/u8e2717N6lUilNOOeW7f8vPz6e+vp4tW7YAsHXrVtxu93dFD8D06dNxu93frTmB/zvME65m6+u384O6LDSTFbW7gUBDJ3F/iPCxBkI7N2MuLMc87mTUeBJPeTop/yANb37J6lWtPPbkFrrWH0bxdWCdMIdkVwvNH37L8t+uwTu6DGvtJFRfK+bCcnImV2N2OZDzykg2HcBaNxFLzQQkbz6aI8Mwidv0ofG6yuvpfu1lxJJ6rHUTDcKvyYLmyEA4tgn625Hbd2Nq2YZjnOH2HHnpF8YXQHcjHW++jXDga1S/zzBHzCyn7eknkA6tRlh2G2rFNMQ3H8S17R102YzStBe9aSfei28ga/Y0RKsd0epAT8QRxs5DtzgYsWQYKis1iWZxcPiqy8m46AaDQxEKYm3cSHTPRsqXTkMd6EYeaiP28TP0vPs2YjRA5thKho80g6Zh2b8SBjtBEA2VWvt+gu1BRLsLz6gKvr3xzxy+/XZEs8y4Oy8lcmAPOXc8gpAIE1vxItqhDdT9YBwTR/Zz/JlXcU+eilYyziDR9nXw7Rk3Iw+2cMtqH/tiLnQdPm8YpCLDQe9zj5M2aRpy+278n7+HoMSJXPlrBJuDV88oQQ7303Zhgr5pl5E5cBAhFUMEQn8teoJxlRJThPXtISySiDnQSZ5TJprScJpFvjzeTzCh0h9JUpFhY01rgKSq88Dpdcid+0DTcA03Iw+1o2QUo+fXIIV60Vv3kTA50CunIgV9BmHaXYAcMGTwWZEONGcmT5w5mlFZTp5YVsukTJn0gSOoOVWIiQi6bKZXzgRRplr0U/DFY5gHGsl1yEZoqyub2wfrCZx1Nwd9YTp0NyZJAFEkrWkDY3e9zI9OKiN37wc8XdXHX8aP4DCJLOEYyYY9SKko4YSGVjIe24ePGEWPwwOxEOH9u6mKtRgdGSVhFJ9NO7E2bkSMhxB0jf5v1iDbZMY88ZiRMaepiK17kSLGTYWQkYcei7Du3B8j5legn30n5topdH+7g+JIC57+Q2gZRYjRYVKrXibZdAA1twbZ38aBpAc9laJv5xFS2dXEv32H5KHNIIik7XgPobcBpXm/cc0JIkp6PnrRaDKGDQsBrx4GNUUyvYgFZR5OLvUyuTidc66/4Lu94gT+cfhPkbOLJwqf/zZYvHgxb731Ft9++y2///3v2blzJ/PnzyeRSADg8/kwm814PJ6/+bmcnBx8Pt93a7Kzs//Fc2dnZ3+35l9DIpEgFAr9zfF9xh+f+wVpZonMvn1Yug8QO3YAW3Y6WlJhuKETc5qDWNMxErtWkzlrOmXnzKf50218+UUTWRaJi6fmkz+zhsTgEB0v/YWXb3kHk8PM4h9OwzHvHJS+DoOsPBIg4hvCNX0uYnYx5topRnK6Jx/N5kY5uBF11QuYK8eiDHSDppKzYA6pPd8gZeSiunKIZZQTSS9FysxHdKWjFo0j1dNmELELxuA5/QJUXyuUjCF3er0xqltwKabCClqTVrz15ezOno20cznycAfuBcvonXi+IR+vm45YNg762pA82cTbm2kqPImf95TydZ+EEOjFFR9Es7kZzq5HUBXqX3iR3x6TUJr307zgdtSBbvyHW7FWjiJ69ABaxxFM6elkzz8Z3WQj2j9MxoTRrLn4V+iREKLDkMJKET9bb/kNSkxh969eJNY3QM2ZtWSOLibY3E2suZG00y9C9rcRXf0ezZ9uY+OtT7Pv1Z0kmw5QdcVZmIprkPqbwN+NafIpFK1dw2axiudmSFR7rby8q4srfZ9wtD9MwdU30l2zmEcnX8fIZQ+imZ0Uan4+ds5CHupAjAfpmX8LWeueB1Em/OrDaEBS1ZD/upE+dyTK/FI3A9EUYixI+sARNB0GogrXTSkkklRxWWTKIs1MzHNRrXYzw9KPYLEj55eT3LuWY+Yy4p88g96wnWBaCeqEJdhbtiC27EY32wxp/+b3EfqaDQKyxQXAq3u6GedW6Y8oiCODJPLrUW3phHNGo5vs5AojxGQHYnIE54JzSexYhVMdMTpKe1Yxs9zLmtYhxuelYZUEJuU5sUw9FbJLkOdfSmXXRnRNQ/krh8106Gu0zFLaJ15IXLJR4DKhS2ZsZ95AsuUwmjOTcOEkPIvPQzdZkA6vwdR7GNWZhZRbhhYNozftZOitZzj05i5qfjAJNS0HPR4xwlU9OX+VzltAFEl1NlA0qxghFkLVdJSMYsp/9CPCq94zRtCxIMq+bxFECXnMSYjNO9Hs6VRmWAju2oEl3Yncc5jVP30Pedoywm/+DqluOkpfJ50fr4C2fejr30RsP4AYC6JZnGSG20BV0I5sJqXppDSd4XiKjkCM9v4RymafAZwofv6R+Lv5Pd9DH5//1oXPBRdcwJIlS6ivr2fZsmV8+eWXNDQ08MUXX/wff07X9b9p3f1rbbz/fc3/jkceeeQ7MrTb7aaoqOg/fiL/j+PRtY1cNjaH7OHjJI7soO/91wh39NH57UESgTDZE6ox55eQisRoX7EB35oNbPjZG7y7som2aIqL3vsZRSdV0LnuCFt+s5IVz28DQImlyJ03C22gA8mbh+hMxzRxAUVXXYvS14HSeoiR9Z8hTTwF/1vPIvg7kDzZCBYb6nA/WiRs/DkSwFxaS+LoTpSty7EPNODsP0ri8DYi+eOQug9hqpuK4vchqEl00Zjw6rIF5YyfohWMImk10srLtQGki+5loiOK5MlCSCXQbWnkH/oMMTpM2yMPMLLiNcT0LKTMfKwX3EllqpNH6hNIAgwUTEH0t6Pu/gpXYgghFSNkzeSW6UVItdMYZY8jjZpJ5gPPIxXX4ZgwHWpnIS6+EWHsfOOL/MJrMJePZtG61zGVj0FJL0SMBfG/9yJ9LQFyxuch22TspaUUnXsm9mwPzoIsLJkZKK2H0FMJ9v9lDWWnT2bWb6/klNd+TMf0K5GzCghkjUL1+/jN9Ju4pWgJFd1bKHj6VjSzk32+CLGkirl6ArdOLeCApZKieAd3rLifkqEDxF256BYn53oDtKXVcHPxMrwfPszGe94GXcN94a04TCImUSShaKRbJW4Yn0Xnjy6m0mPh5cEsDtuq8G5/i5Gkht0kUr/nVQYiSY5byymLt4Eo86c2C4mDmw0X7WnLKPrqcbTz7kYuqMSljiAHuhHMVnRNRUjGEBIR5PpZkF1mRFZIMuLIIPeUBpj15B6cZpF7d8ZpDSQRNBVn/1Fighnd7MAigurM4ri1nL6Tf0jXr25Htzgw1UzmrFovV3p87O4J4d30Ms6B42hWN5H0UrStn9BRMofw9IsQpy7jS72aSN1CfrEzypH+CKqmIylxBp+4C91sR5x3OcNmL5GUznB2PerxnWjhAEpfJwgCvtf/DED3R8uJ+0PMeuBMdj65DnXzR3TlTUPy5qJZHIyseA0p3I8+1Iu5egLFj72ImpaLI9KH7G9D9ffinDQLy9iTQBCRJp6CXFhhdJccaQidh7EpEZxlRdizPfhXfMjS/SuIrXiRtHNvQLO4EGb8gOxJtUiebPw79xkiAUmm35KLmIyhW11I3lxsiWEAar02itNtnD+9mEvOqMPqzgJOFD//KPznyNn/2Wfxj8V/68Lnf0deXh4lJSU0Nhot19zcXJLJJMPDw3+zrr+/n5ycnO/W9PX1/YvnGhgY+G7Nv4Z77rmHYDD43dHZ2fmfeCb/72DMz77g9umFmHd/CpFhTGWjiftDpCJxbJl2EqEY8UAYZaAb1+h6cqeOomdbK6mRFOefWsGPr5vI8ot/xxt/2krbbh+fd4WozLRTk2XHt68fLRL67kvMVFxtGMFZXRwefT6q34e1chRC9zFcJfnoGYV/VVAVgaYhub1ItVMxldSBbMY0Yb7x9/Agmt0DmoqjZz9KySRUZxZyTglS10EOmkrRhvvRLU4coS50m5vTnt5GwJKJFPFj3r+SLtwoxRPQo0GERAQt0I/v9T/jrijAWlyK4ik0TAu3fAiSmX5PDYtcQ2RGulBza9CTcaTYMOFPX6H/tgtxBNoYeO0pWpJ2Duk5yJveJuyt4ubWYqRQHwldBJMVIdiHZnOjhgMAqM5MpP4mxHiYiG+I9Ew7mWMrcWY7kHOKUAe6cYyfgqskHzm/FLmkjkTJFGZ89DL+wy1Is89Dcnsp1/2QX41942sItTO58TfLePB3Z9Lx+us8Mftu0DWyHGZCZyzBv/ITpJCPfKeJrqcfY9cvXkDp6+TAKacgJKMMf/AixZ2beabrS9JOv4h5r9xN61NP8NjBJAJQFzmCqoP09fOsaAmjP/QqNn8Tl9Zn0h6Is6XybPKcJuwjPqS5l3BSWoRqpZMhdwVixM/N5SrSnAuQhjuJrXwFS1kNqg7DWaOQho33S/H7kNxeiIcRNIVYRjkpTxFoikHslUxENq/kmztO4s39vdwwoxirLCAdXE0spw57fAg0FSnYg75/DZVyiKJ4B1kPv4SQjKJklmNJhlGHfJw7KhNL/UwQBDSLE1sqjDj9TGRRYEN7gBY1jaq/qtcemiBxWqUHuyygyxa8dz6BvuVD5M59uMUU3q2vY5YE5Nxi1IFufCtXoe75msyT5xDZt5P8B4wO3aZffMKMn5+GPPk0CqOtiDYH6vGd7HnmG7pf+TOq34dSNB5BTbI+nIagJknl1CBUTkHILka1exjwVCHGw1AzE83hJXFwK/1friD48sNYRk/DPno89jwvesN2rJWjUHZ9hdC6hyHVhG3J1ajOLHKuvwPdnYNm9+CxSvRl1MGuFQiSoTQUBYGUplPktjGnJIOFVZnc8NPLT+R7ncB/a/w/Vfj4/X46OzvJy8sDYNKkSZhMJlavXv3dmt7eXg4dOsTMmTMBmDFjBsFgkB07dny3Zvv27QSDwe/W/GuwWCykpaX9zfF9xI/OrYe1ryLnl6P4OhhY+SlKJIZkknEWZGHPdjN81OjE9Hy5hqPvbiERSjDxptmEukI89eIe1g5Euf3Zi8nIcXB2VQbLW4axZ9qY8pNTjQ3U5kAsHmWEjAoiqSNbyf7zTxEsVqTCavT8GqwT5oCmoOdVIZitiGVjjIKpt5lI9cl0vvIiek/jd4/rLXswT1qIZvdg6j6AoCSNUVl/N7UeE+LUZUgtO1Dc+bx/PMgfL56Ad+AQijsfffQ8SkaaYcenxA9uRbO6UCNhcq69Hc9516JFQkg9R43k+ZwihESEbN8efLYiBuyFiM07EedfwUBaOY6xE+m4+0WOiwVc770U+aFrcZgkDtaeQ8+Iwq0nlaGmF6BquiF3xxhpydlFpA5vhdZ96KkkuiBSfPU1jLt2NslQhPJLz2Jk1xbMlWOJHtzNSKePns++4MjP70P7+DF+cxiK73mYjpSNvqqFiAMtJDd9wsCWXey97EoiviFsNz2KdN9f+P2pJawadpL/2aM8ET1GxoLFDL/xJOmHvqDgnLOZ9POLSXU1UX/VyWgH19G3qwFtJIAYC9LzwlP0f72agkdf4Ue+d+keSTGcMxZP714so6ZyZkaQLLuM1tNM7LWHSM2bz6wcExlDDYjRYbQdnxsmg/EwNllATcsl8MHzDD77EIOfvY9jyslEjx7EvvZFXAdW0Pr7Rwm+/DBKRwOpnjZDYZVeiKNrD9LuzxBSccTOQwipGKbsfA70RTm/PpfS0HFWNw+BKGLv2oOgJBjBsD+QXOnIIR8rQ16soR7E/mbEkUHEiB8xqxg55EPxFCIkRtC3fYLUdRApPECuFmBWsZtsu0y5YPCT4unFBOKGovTwYAKTv4XwzEtRCsci+9sIz74Ca6gHPS0L6+QF5J6xlG33vUKs4TC+HUdYN2k+JScVUTijgKhvCGXHF+iSGV3TMBVXM/PRq8icXE/jS++hrX8LMeLnpDwzWvshhHiYqDMXNS2PQWsuWcONqO5c9l16ObpkxjxzGZk33U/ahMmkOhrYfscf0VUNyZtL/zdrUIN+Ot9+l6xoF0L3MXSri9Tur0ntXo3U34yw5iXkV+5Dj4bQMwpR3fnkyXESqk5hmpkCl0yW3cyC6izsTgvppfVYJl33T9u7vjc4Mer6d+OfWviMjIywb98+9u3bB0Brayv79u2jo6ODkZER7rjjDrZu3UpbWxvr1q1j2bJlZGZmcvbZZwPgdru55ppr+OlPf8qaNWvYu3cvl156KWPGjPlO5VVXV8dpp53Gddddx7Zt29i2bRvXXXcdS5cuPaHo+r9g0v1fcYWwH1N5PUpPC8pAN02f7QPg4Ju7SQTC6JpGztRaggcO0rWljcGGIWp+MJHjH+2hvzvMWTVexrmtHH17I5sah9jbHSbLIlG2qA5zVjZyXhm6LQ3l4EZaPGNR964G2Yx3XA2mggrDpt9kMRybO4/9tZOjwUAngs0BgFVPUnzN9YblvyCiW5yGLb9kOAdrsQgJRxZ611G6x56Nqb8Rjm5ioGQWKUQuyo1Q7xFAEBHUFFK4D0GJ07dmA9bZZ6I5s1DPuhNhuMfwtpm+xJDVt+2H7DIiaz9Gd+eStecDdvaEaS2ag9xzmM0dQfRxp7LQ3keJ28Tz543l4A1/ZFtXkLLlvybTJpFhlUmaHLgivaRaDhrxBakEuigTOnQYXUkiOtMhFkIN+vHMWYjnpHmYy0eTdtIiBJMJe91Ydv5xLYUXX0Lm2DJEs5WfzS5GUOKUKj5yUgNEy2cSbu3EVZxD6cIxvPrUZhouOIOYoqHJFhZ2rsA1ZzGhpIquJJF++IgRglo1FWV4gF1Tb8B/qIWuT1firS9FsNrRlRS5t91P3lU3c/UHhwgs/gmZ7z9EeqDZ4LSEA0TSS0kfPIa/dhEvjrqW2Q07Dfn4Hx9DtzgQHS40sxPN7sHWsoWIPZverUfJuewGIr4hBlcuR1NV9FgEyZtH2Z334ijMwzphDnJZPVpWOWIijGZPB01DHOpErZyOz1rA9qpzmNa7hlyzgj9zFKd8cB96xWR6MsehWxz4RhQSzhzIr8afOYr5pW701n2oBaMRNAVBSdL6+G/QRZn2lINQ/gT0OZew3TmeLmcFutmOTRYJJzUitkxGkhrbukeIKxr6mpcZawmQ8pYTTqroJhtqej42WSDy2UuIiQhqWi6CbGLWn+7Cuewqik+ZStWSWlo2dFByymTybvoZsXnX0mHOR8kfTSq3jtCRY5grx5I7rRY5p8gI2N3wNnJOCWL3EcxrX2ZAsxkE5GiAg5dfRsn80bDnS3TJTMvPbkIL9BM6cIDa86cysLeBZMthvJPGYF58Lbkzx7P9fMObR9/zFfHOTqSsAvo+fJuROVeRNmYsyb5ehGSMkGbiWMSELAqoOrgGG8iwSRSmWXnih9MoGV+PM6eU/Aue+yfsXt8f/GdwfITvGbn5n5rVtW7dOubNm/cv/v2KK67gueee46yzzmLv3r0EAgHy8vKYN28eDz300N/wbeLxOHfeeSdvv/02sViMBQsW8Oyzz/7NmqGhIW677TY+++wzAM444wyeeeYZ0tPT/82v9fuW1bXgqY08ff44KuUQusWJuvI5Ij39xP0hEoEwPTu7iA3HyZ+US3pFHsFWHwNHBqn5wUQSgRF2vL6bmKqzbSjGWeUe7Jk21KTKF4cGWFSWzpzHr0S0uxAsNlJdTZhK6/Cv+5ZAQyc19/4cXUmh+DqQqiejHtuO6EpHdKQR+PYL0pdeAlHD3E3MLSN1dAem6kkQD6MnYggWG9Hiydj7jyMocVRnFr3mHPLbN6H6exFdHoSiOpqkPMpsCmJ0GDEeRvUUwpH1JBoPYhsz3Ug7zy1D7WkyXJ41Fc2RgS5bjRgJby64vGgmu5FRZnPDrhVINVMQkjH63nqB3PMvpT1jLDlrnuGDyku5qNJG/xP3kP2TR4hKdmxaHOHwWuP5BRE9FgZXJkrrIUSHCzQNLTyMrqQMqb2mog73o/yVrA2g+n30bNyLZLVQdPvdBJe/invGXISMPARdQzdZUJoPMLh+A+3fHiWt0EXtY4+j7F9Hw/iLqXGqSI1b0ZJx9PoFyJ37ECx2lP5O9DEL6Y6LlAzu487jHh4YfJfWlTsZ9eeXUNa8wfF31jL6zmtpqjgNXYdKp/ZdQjrbP8FUXE2yYS/tky+hxGXC3LkHZItR5CXjMO9KEhrYkkGEph1InmwGs8bg3v0RgsnMnPWZvPnDaRTZwdR3HNWZaYwtA10IqTiaxYnmysbUe9h4D0YCkFmE6soxzCyVhFEcCCJ+Wy4eMUXqs6ew1k8HJYVeNBrVmYVpoIlkTg3+qEK2FEds22OQyjUVzZ5OKrMCc+8RWhxVlPfvwFcwnQybhOn4BrSCUSCKKLYMw0ZIB0v/cQQlgR4LEy2fiUmAiGKQgcMJjSy7hKtjB0rhWKSIn87fP0jm2AokiwVz5ViDj2O2GV3OZJxU2TRkfwsMdDK8bhXdm44w+rk/G/+ZKCFGh1FdWehmB0nRTFLVDT7UUAdDK97BUVqCuvA6RlIatg8fwbnwPDRfK6FRp3Bs8alMf+OPpI5uJzX3SkzrXsW3fhuy1UwqEqfgzCWIlZOgvxV1eABtJIA8bRm62c6egMiapkHOH5NHnlPGF1Eosqps7kuxqXWI91c10rFrI6F1v/3nbmj/BPyjsrqO/OIaMhy2v+u5fvH5RhxT5n9vsrpOhJT+G/F9KnxuEEo5/fA2ljj60CWZ1L51aOFhfNsOoSUVurZ1koykkEwSZQsr6NjQStaoTNIr8+nZ3szLq1pYVphG/qRchlsCeMrT6d7Ry5r+Ea65YBTuslzsuRloKQXP/MUI7myIh0m1HUV0pBnjrdAg0T2bsJ1+FWJyBM3sBF1DGhlAySxHWfM6osuDXD8bYagLLacK1BSaw0vqs6ewLLgE7dAG5IpxxLasIDEcJv3Mywl/+Q6Os29AUBIIiQipnBrkI9+iRcM0Vp5OrdJOKrMSfc3LmCYu/C4/CSC6+j2steMQKiaR2vghcn4Zra+8RcXNN6AVj0VQkkTtWfgiRiCnFAswgItMk4KgJBjQHfSEk9RmWjHrCiFVwtO8AaWvA1NpHf25E8mQUkgNmxFsDvS0LARVIfLtR9jHz0Tp6zCcn5NxpNnnEfv0ecxZ2eiLrsfSfxzVnQe6hhgP0/PUb8hduhihaiqHb/who156gy0Lz2TG47cg1s1EHO4iUTIF7ePH0FWNTVNvZEG2Ckc3AdBVs5jOYIKZjiC61YWQjKI5vMTeegRd1XCfei6JQ1sIHD4OwD0l1/DiPBfq8R0I9Scjdh9hsHQW/qhKtTSEbrYjBXsZ/vR10k8+FYBjj/2R2jtuRXRnAhDIGsWr+3q5rVJDTcsl9tYjuGafwlDhVNxKAL/oJjvQiOItxZeUybFLmAabUJ1ZiPEwumRC2fYZ5gnzDK5YTwOCxcrwtytRrngQ30iKojQz7mArQsCHml+H1N9MsmEv8tQlaM5MpGAPe9VcajOt2AcaUHuaUHoMjo08bq4xTq2eiWZ2ICYjRvGqJxESIwiaQsiWTUrTUVSdLLPKZl+SGYUuYimNtJFudNnCsNmLN9CEklGCZrKif/EM1nGziW7/GnNpLaHd29m3+G5mHX0Hcf4ViCODKLu+wjJqGqq/x1AT7t/E0VdXMfbFv6C6spEDXfTZCsmJdYGukdzyOX3zbqSoeyuiI43Wp5+k7NbbaXj0UYZbhhlzzQJs42aRbDvGwOZdZJ80lf7pl5FjVhn43R3k3HI/w6/9HvfESUZhZXOAKBLYtBbP/MVGMdZ2DG3JrejAPl+UygwrQzGVwjQTCUVjR88ImXYTd76/H7NFZs1tJ/3D97J/Jk4UPv998f8Ux+cE/jGo3rCGGYVpIIrg72Zw2x5iA8NoSYVDnxwl7IsQCMQxO03oqsb0By6k8ORxSFYz/QcGuGB8DhWnlpOKK7x/sJ8/f9rAcFLhuovrMTks6KrGwL5GXHV16Mk4ur/bkG1PXYqcV4pusoCSIuLzc+Da60kd3ITesgehtxEtEkLf9QXWMTMw1U1FUJOouTVGynhvI6b+BiyLLqf7Dw8yMu0CNEcG9ikLcM9agOZrxX7OTQAcvPFmFG8p5u79rL30F0hFtdQmWiDsh7WvIs67HCEZY2T3Ztp/9xBa+2EcMxYR3LYJQUlgKqxEC/opfuptkk0H0Pd+jTTcia7rlDWvpuSiZ+l59C5yoh2Yeg5RcetKoikNj02m5qpXSQqGskytmoE4/SwUXwfZgUaklh0IGXmkSqeQyqxAH+rFOetURHcmgtWBGvQz0tpB+N0/0rDopzROvgLLYCOIImHJaTgqJyLknLoQ6uez45zLqL54EeqmD5jx+C0Mbd2Cbrah+nsx+w0ZdsMHm1nkHKRbcyHlVxqmdaLAxDwHiBIxkwvd4sTUexjbJfeQuPh+0DVCxxo5Xz6Xd2b/hGf0FbQ9fK+R8K1rkF2GN95PWbqZNrx0Kza6nBVknH4ew99+iTrQjeUP7/KpPJZ+bx1d6bWYJYFbxroRgj4EJYGtejTvJSrZ3xdBDvQgvfhzBDVJd0KiIN6JFOxhJKPSiNdIL0LoPoapqBol3RgBCY401Nwaho+2c3wwxrOb23ApIVR3AWrBaKRQP2pmGeYxs4l9+Rqyvw3N7mGcK4Gj9yBCYgQpvxI5txjTmNkIqYQxTk3FDVK0ZEIQDBIzshmxvxlNh2BC5anN7Qhqikl5TsRklJiio5lsaIc3ktLAl1aJuvI5Rp67h+5vthHfu4G4Pwij5mC/7mFcN53P5jrDmkC3uuiYehmx7V8Znj8WB8KSWxj7wvNGB1KUUJ1ZZMoplEOb0BxeLFNOoeDoF2ilE1l/3o8oOv8c1LRcSs9fxsT7rmHn418Y2WCxCPnnX4B/xuV4VjyOtu5N0krz0I9twT1xkqGaDPQjWO2Etm8k2NyNIElIGbkM7jmKacu7mHWFLIcJVdMp9xjdpriq448m2dIxjN1h5uDanbjm3PFP29P+J+M/Q9X1feP4nOj4/Bvxfen4nPS7tbx/3VQy4z7oPEzfis9xFefQv+sYQ01+lJiCmlIxOUyM9I4w7WdLSATChDv66N7WTt6kAgItfuoum8fvr34VmyQyc3wOE360jMYP1pI1tpS0MoOcHmzqxjttEpGmRtLnL0EpmYSQCKNu+hBp5jnIIR+a2WbczSfjKD2tyPllpDoavnu90oRFRop58w6Uvg6kCYto0Lz0RRLkOi3URhuI71yN5M1FGjUT1ZVDW0ykItUNuoZyaDOSJxvR7UVPy0Zt2IWcW0zfh2/jqa9GT8SRi6tBU4kc2IO9ZhSp3jbsUxagaxqCbHi17BRLmdS/Ca1mNknZRsuV5zDqqWeIuvKxoqALIk3XnU/lFecYY7kRP/GDW9n5+y+Y8/7ToCmk2o4i55eROLiVVaOvZGlsJ6LLg+7Ohf5W9PxaBMXwsDqkZVHw/q8wpzlwnbwUXbaipmWjm+x82hplaVUGlv7jhFe9x84/rOLkjZ8iJiNozXsRamci+tsZ9Sc/X8rvE/eH8Tz1Ljt7wswpTkMWBSLP/IyMK36CNDKAbrYZvCpdp+k3D/L8y3tZd9ezfJ35NSaHDbPbhWXcbNTcGmIfPYVv+xGKFkzBOnkBWmiIHbf/BvvbnxGMK0zLd6DoYPcdRo8GUQvHIMaChJwFuCK9tP/6HvJPmoB8ylW0puxs7giwrDqTIwNRpjV/ipxVQKT6ZJxDTehDvaCk2H3346SXeSheOhdTSS2CO5tUdhViPEjC6sE3olCW7KDTWkxh8xpCo07Bte8zpNLRxrjLZPnuejKMEDtRju9C8hj+X2r1LCOoFODQt8i5pehmG132MrLsMklVwx4fQnN4UXSQBeiJKLjMEmlCkghmJFEgpmh4Yz60I5sxFVYQyJuA+bPH0c++E0syzIjkxN25A//KT9ix5OfMLUlDFAQsviM0PvwgBXPGY59zFqnsKqTDa9CrpqPt+JxIwzHUy36J0yzRceuFlD/6DFLEj5CKkWzYS7Kvl+g5d5E9eNjoln79Mj3rd1F07pmgqQxt307OeZeRLBiHFOyhTcymcNsrmEpqGSydRfrm1zEVV4MoofR3I3myUAe6CU45n72+CCXpVsIJlQlyvxGAqiYBGBJdvLGvl1hS5aV3dhPuaWKkr+174+z8j+r4HHvoh393x+f+5euxT5p7ouNzAt8/3CCUUpTjxGMGofsYiYZ9pFcX0bv1EL17ekgv8+BvHka2ymRUepn1y3MxeTMZPNBMqMNP9dkTKbniUiY8fj9H31hLgU1m6Xm1uEvS2PyL9/j080bUeJL+Xcc4+NIaRroHiHe04Zo0HTW3GuHA12jbPsU8zhiVKL0tBu8lGUdPy8JUWodgtqIO92OungiAdngjpv4GdCWJqbgaMTlCsdvEnOAOqsQhQ+Jrc6An4ww7ixhIyZQ6BJT969BcOUgTFqFrKrorE/XYdgCSTQdw5HkNUjEY3SirA8cFt6GF/IhWO/G9G0ge24WupBDUJFOVJtT6RQB0hlKIf3yXgD0PkyjQHQM2vUvFRacj5xSh9bWRqJhF/6IfMfvTl0l1HGfg47eMSAF3DpZxs6nPdkLxGPREjOSWT9FGAmj716DZPYixIO+UTcS34zi2UROMMMuBDiN5vmkb5+iHMB9bR/uTvyVt/hnMff0BpMFWUnu+QUzLIP7ly+iuTI78pJjC+x/H89S7ZAcaKfPYsIo6tr6jZJ5/DZ2P3otutqF4ikke3MSGs3+IqziHO395GtuXRkm/+DZSF9733fUj9zeSisQo/f3LRPsGSbUfQyufzMS7LqLSYyHnkWtJ6dAaTKL0taOUTeXj9hTHyeHtQ30E7HmsvvQxTPMuoj3loHDNU1yZ1olJhFmWPo79+X2jCwkQ9jOw8lN0TWXMDUso//UTyHmlaCMBdNmEHOgyrAmUGAPRJElvBUWJLnQlCa89gH/MMtS0PDSbGzEa4KtwJvv1AlZ1xgFI9XVCTilCdjGCmkTq3G90gDzZjGxYQSqzkoKOTdz3dRO+iMKA5OHJbV28ut/H0zu6Wdk4iCTA8bDA4YEYhweieBMDIJmQ88tozxhLet8BzEtuwBL1I/mOk9ayCezpZJ51EVM+/zUmXcEU9qE076fyxuuwn3w2SssBzD0HoXQ8Qsp4rbGBAD0jKURdpeSM+QiawvbLbyW6/WtMY2Zj8mbi2bsczeJA6j6EnFtM8UXnwuSlCGPn473pV6Ry61B0oPMwFSPHScy/FrV8KpktG2lfvprut95EHe5HqhxvcHyqJpLRspEMm4k8p4k0q0SfrRAxMYIY8RMzu9nRHebUqiwqMx28fu8pWFwZ//Wb2PcQJwwM//04UficwHc49sRrPLKkDnPPQZS+DsIdfbSt3E6wPUhaoYsjnx3HbDeRPSYXZ0EWottL2yffkF5dRHplLuk3PoSYVUyy5TDOfBeSIPDHVw/Q8m07waE4uVaZ1tVH6d7WTt2Fs4j2j6AmUygdDagb3jPcaWecTWLHKoNToBkhi9jT0VoPkmzYC5qKdeYyUi0HMVdPRM42SMeSJxscHpSMUuwdRkEijQxi6j6AdPLFxOdcgad5AxogKAnWV/wAXbYghfuQ8isRkjG6Pl1Jz4qvMI+egW3UBHQlRaSnH9HtNcjVnYcYPtSApW4yUlYB5uoJaJ5CNKuLZOM+mgMpBCVOcZqJ2o61jCRVxI1vcfUbe/jy8ifYNepC9Nwq1LGnYu3cTUnoOAx2Ilis9O9tRpqwCPrb0dOyKbamcJz2EHpuFZI3DzmnmFRfJ4NP30/iyHZskkj5GTORCqsR3ZmINgcjX7yB6u+l49VXSTbsJXtyrUEAtzlQisYjzr0UPb8W+8STGHAUo2RX8czhGHaTiOrOxW2RUDBiCtYrBWgphaEPXyb+3u8wjZ9L6epvyJg6mT9P+RF6LILfmo1bDSHYHAhmG9+efTue+acjD7XjvuhHmEpHGQWD1YG87lVKz1+GvWULFRue5WP7dA74FQpcVlKaxnWj3by0u5tzR2WjunJwv34fgmxGc2aSVHUEVWHMk4+jlU7E2bmLwRUfod3wGCO7toCmsenUC0g07EOomASAGB1GSS9C9rcxySsRUzT2a3kM1S8hddkvyfbtQQz30SxkkcqtY1GBiXqPQLHbytp4LrZJ84zRVipBUHSiBv1GlIOvA/tpl2HyHUUrHc9vTqngzk8PI4mwqDKLA51BzqjL5oLROQQTKqPVDibnO3hmQwtKWi6xFS9y8N5fUxI8ii6Z0bZ8hLrpA0Y2r4KsEgBiO78h4/LbYeuHpNa/j5xbjFY2Ad1sRxsJEModa3C9mnYSaTiGrmqMavsa00ATm+56DWXHF0z8ahWi2Yradpj1P37F8A3yGJ/NwfXr0SJhxMNrab7zJvSdn5P44PfogF45FaW3BRspVNmK6M7EVZyDZDUjlo5BTS+E+vkQGUbPr6Eu00ryrzlruSMt0LaPpLcCSYBsh5mRpEJHIMZ9Hx+kfNIocsfNw3PKA/+0Pe4ETgBOFD4n8FeYJ1zN61dMIk8IodnchityPIlklqhYNpGurT14CtPIHZ9NfDhCMhxlxwOvUnL6bEY6+siZMR55sAVG/HR/tR5LuouqukzGua24ch04nWYuffFaNFVHlEQ2PPAZf/60gUBjp+EsK0pEjx5E6j2GuXo8qjsf/tf8ecSPVFSLqbiaVFczSuMetHgUdA1kM2p6AbqSQnV4ERJhlPzRaJEQifx6Url16KKM/96rUf29BBMq3SkLk1b9FnXFM0YEgMmK2tNE6R33UnzrHQx//hbacD+iIw1HYZ4xbkrG0eNR7NkelP5uBjdupvO1VwxFWDSAuXw05R4zsU+exRdRaCtbAMDw1Av5zdn1nN6wkcmH3iLxzRt0/uQyVE+RkRcWj6J0NFBz2zVozkyU2pMJuYqQhzqIfv5Ttp11KYIk0fr0k5iX3EBaWR5tH63i7k/vwTpmBprFZXS17G5s5VVGUrzZRGxgCNFsReltI1w6A339m2yYMh9dENFyqozATl3jjNpsHMMttP/8FsySgCXsQzfbmEszZbfciufCmwiffTdqeiGZK39PaNqFzCzN4Ie+etwbX+W+TX7k6WeghYeYs+lzUl3NJLatNDKiOo6TOL6Xtdf9gdCRY0jjF6CUTuYnF/6ZCX+6lbFemak5ZkY5Ff6w28+Pp2Tj3PAKx4YSeC67nd2TriGx/gMkUUA324lt+MQYT3Y1kXnR9eT27sR25QMIJhMnv/cEuqoZ/JfQgOG9JFvRHBnI3QeRRYExjijBhPqdyaRm91Dm0FnXaxgeiofXUufSqMqwkSiZgqApKJ5CPOF2pPxKBkpm0VF/NoP2fGPUpTrQBYHlM6Mc6o9yfHCEsUVuRAQCcRWTKICSQkxGeWOOiZbhJI4ZiyiYPYpUbh2aIwPT6JlosQimtDSiq96iN62S8Ok/Zmc8HX3m+cinXI1gc5Fa9TIpTxHSnAtxDTfzbrcJta8DUZJw3PU0+rhTUTxFLPj0KUx5pZh8R5ELKxCsdib/aD79uRMR9n1J7OSrSK+roP3DLwhsWU/ZBUsBsJ1+FfLXz6Pa0pHzytFFGRGdVHYVnrF1yDazYQ8hiPSrVvRUEnGgBXN8GEXT8cgaWk8zFI3G2nuIhKrTH0kSTqi4bSYWjc9HSaoIokRk4PtpBvtfhRMcn38/ThQ+JwDAfY/dhcMk0k8aQngQNRYlo76KiidfJjE8wmBCwWSV8dYVEuoK076uibJFdex6fDmZYyvp27oPQU2SOLyDmH+EtjUNeMrTsUkC7+3rYyAY5/3rXyJ3fA65kwpIL07jgd8sofDM00HTkHOKDV8a2YSQZqh8hOrpKDk1qMMDCMkIsf2bkcvqkSvGYSoyVFxKVxPqnq9RB7o55Y1mOL6V1OfPcOgPb2Bu2YbcsRddtmD95Uvs+uXLjPLvonhwH4GGTuTiaqOj1NeKaE9DtXlQWg9hz8vBVFyN5MnCOm4WWtBPomIWR4vmoSZThmMwUHjhRWj9HSiZZXyaLCf64v04Tr2Ix9Y2U6r10xVKsrMnTEcwjrr6ZQSzFducsxEefIV2POwUihEdLsyzz0I0W5GatmIabMJ17Bu0vjY0Vw7bDg7wm1Puo2DxfO7KnIa5tJaqW36I5MkicXwv73bJjGgSvW+8gBaPItgcpFcUoGsa1olzEV3pOBs30D75EuZ9+y5SuJ+wNZMVjX529CsUS2GazUVkP/4G0ZSG35qNz1oAokzi8HZiq98idPtFhF74JfL5dzMUV1moHuGliRGkmWcTOmMJ2oG1aNEw6vp30CIhTHMvJLFtJVqgn8F5N7Do21dxlhQg9jcj7P2SBwOH2X/LM8gDzQQVEc1k46xRObRFBXbUXYDHKqNbXYzOsvFe1eUc6o/SIOSwcsy1NMasvJE2HyEWojd3Mn1RFcGeRqpgLJYzb0EK9YE9HV22ouk6vXImSsEYbEoEIRnjsW+b+fluxQizla2oq15gfnrE6KDUzoYdn5J3ZAWSmiCVUwO6hubMQsksJyvWQyl+AnEVzeGlSB2kZTiJv2g6czwJZhS5qc10cnQwgiRChk0mnFVLd9JEq6MCAM1TiLOqEinQZRS+gT5ERxoty9fzs6tfJ+3Tx/DG+5l47CNjXBfoJtXZiOWks5FDPqRgLzFvJVML07CMmYVr+lwUTUfuOoDsb6P79ZfRC2rpe+sFBFHCX72AVCROZsqP5PaS1rQBgNKLf4DJYePAk+8Sa2lE2bocNRJGDnSjurIR40Hk4Q7kwVaiHZ24KkoQehuRB1vIHzrEYOksUh0NxD99nqxYDx0REMxWIxImGSOYMAq/TLuZco+d0bkuTBaZoaY95E1YeKLr858IUZQQpb/v+Pf6+GzYsIFly5aRn5+PIAgsX778v+bk/otwovA5AW78cD+3TC/CN6KQaRVASSKnZ9Cxagt9v7uLlq8OMZRUCbQH2fvSdoZbAnirPOSddwH9bUHsNaPInTeLweXvgChR8+zLzHr2Z8SG40w7o4plhQaxzyYJNHzRRNb4aipOH4O7uozo8SPoqoquqUb3JuhH6+8wVDNNO1HXv41od6EOD2CpHIvSvB+tvwPBYkO3pYFsRh57MtTP5X31Y3bf+xSWhZcx/rf3ogb9fMYofL+8Aa9VZNr7LyLYXOhp2ZT/+A7kbMPrSYtHDCfi7iPIJXWY5l6InkqhFoxGNzsQCmsYjqsUffoIgcZOIns24RlVgdLbiuTJIiC5OdPaTvySX5L0VnDn3AqU9AKyHCbmFKdx2vY/IZ16Ha95TuW9QQ8l2iD7fWGmRA4QLZ+JLplJjV6Ar2gmgfQK2krmIcgmtM0fcMu7P+LsZVV0rfiGXzx9AYqvA2QTgtmKZdJCzq9xk969m6wfPYTkyaJv826SoQhml4PE0Z10f/I5b2mjKbVpdJpy0UODNA8nGJXlJJpSCUhuKhPt/OTzY+Q7ZOovfZpLXtmF6u9h+68/IH7Wzyh6/n0yzr6CcEKlK5RAza1G8xQSs3h44t0fGqNAsxXBnoa84HIETcFUWod08kW0DsdJ7FqN6HDh/+ozqJ+LN9RCLKUy4KniD5vaSbzzCGZJoNSmMSXfidcmMfDkvThIcvEoL1PynUSSKmcFN1CbaGFcbhr9OeORRIGi4DHk0TMR9nxBTHYgKAmC3moENYlJTZCXGkAzWRFiQQQlzrPZB3no5DyG3BWkZBumk85Fcedx/7puxJEBkM0w1jA/lf1t6CY78mALcvdBeix5qM4sStxmfJqdhCuXsnQzIymNTQELz23toG04SlzRKBTD9EVS2PQk/qhRYNb0b0Nv2I65fhaazY0c6GL42y8BGPXgA/wmdISOb3ajSyYjhNdTjD7UazgldzciDnUSyKhCB8rkEfRUAt/yj0g/9AXR4snooUGy73mSHlMOlh/9nkTTAdJNkPODizkQNxRuqe5m5OxC/Js2ocQTTHzpT9gqazFNOQ3zqUa2lm6yotgyYKAdQU2SDEeJ9fYZXDmTFUQZEZAnLESNJ0nt/pryoX30lMw2isSeVmRRIM9lwWEWqfba2N42zKgyD+6iOlwZaajJ+Iksr/8kCJLw9/N8/p0dn0gkwrhx43jmmWf+i87qvxYnCp/vOW4QSjFLhvus9NerQRn0oSfiZI6toOmLQ7y1rZuYqrPWN8K+gEGodBZ4aX/5ZbpjCqsv/R1fXvlHujcdZfhIM9rOL2h//R1yJ5djy/ZQe84oSqsyWDsQZU8gzoZ7P0E0y6CksBYVGRydvAoSB7cieY1EdUQJsaTekLdrqlEIRELoSgrR4TI6Q7EQYt1M6GtFSERInzGLics/MTKVssqR8io409xC0U0/Qdz1qZFbBGhmh2E2qKZQc2uQCqsRzFZGtq8zgh1DPvSCWqRAD3qwHyEZIyfShuO8W9jy1n5824+QOv1W40tStmAziTzW6aUnnORAf5QMm0QspVHZs4UFv92AJb+AYVXmwvpsltV40SUTS0vtRhbYe48QduQhB3vJFkZwpQIUbnkRwVsAooQa9DPqzhspvuAcbCUlyLnFCBYbSvVJKN5S+hICqZLJSF0HiTccInfBHCPewOOhe9rlFNz/By6sdqHLFgqVfjoKZlCXaaU83cIC1zDBhMYevZAnz6glpcP+12/l7aunIGaXkL9qNW41RErVETSFjLbNfFE3lWNxB4dvugHxo99imbYYQTaj+H3Io6Ybv7Ouo0YGWyrOLOsAwpJbaF++muMf7SL0xu8Iplcw+v4rEV/4OfePfMq79deR6zAR1EyIa1/h0ECMxM2PI8ZDRFSBLV1hxpuHECsnsU8qZZwzhvjCz8mKdrH/Jz9Ha9mPVFyHTYmgurJJC7UjpBLInftAEDD7jqHb3AgBH1rIz4/cExm641KsR79FdWZhHmjkV4sq6H3ucT7NmIdmdhBUZRRvKbpkwp9VT6pkMmlmETERxjeSwiYbH5a+SApNh/E5Dq6eWoTTIpPntNCSchojOtnCRKGLDX4TavlUdt7zJ2N0Gx4g2XSA9Et+hKmomjZ3Hem9eyk/Zx5iIozq92Fq30Wy5RBaJAyl41CH+3GH2rFE/Wg2D7733kT+yZPoiTgD916D6MpAatpKQaydSEpDcqWjf/MSmjOTMV1rUNa/y3tXPIMa9JNWlkdieAQGOxk5chB8LSCb6bfkEpacyPEAqZo5dKaPwuyy47rkp+BIR7O6SGVXYV/xe8SIn7SllyCddAHqQDf5sU50UUYuqydPGWRMqo2UptMXSXHppEIWj8rh4gunI5tF3EV1jFl6/j9+wzuB/xQsXryYX//615xzzjn/7JfyH4L8z34BJ/DPReqdTzizLptgQiXXIWPqO45eOZ7Q3l20rtoPwPQMGzFVpzOWYpzbQlqhCzWeoGtbJ8MpFUeOHTWpUXv1UjjleqSDX1N27VXoSorooV0ce3cznU3D3HheHStWNFG+sBR3ZQmiK90oZFzpJHZ/A5pKquM4gsWK7M01ZOw2B4LZSrLlMKbRMxD+avWv7luD7M1FbTqAqW6a4WXi9zH81L14b3sYMTpscDT6OtEnLkGSZPRUAkyG5wrxoKGw8behDvYYI6Ell5Hc+y3CgqtpD6eoMMcQ0jLRBrvoKZ9P3pEVnPfmjzGV1qE3bUR3e0k27ccWHuLHkyYzrJvRdZAEiKR0hgpnsf3GHiLuk1GTGhYRBDXJkMlDIqlT2Lsd08lnQN8BY9RidqCmZSNYHQx98hq2bA9da/dwdNx1nOluofGN9yl48i2sR79FDnQR//YdMs1WpNJa4k0HMHkzaXlzOWXnLiI271oyBdAx0RoR0PQUVZ2H6c/2YDeJmEWBHmsBZdF2jpuKGIqpOM0iogAWSSCQUUXVwBFSOTWoKmhWF/4PX+OXf74EaxqId99sFDzeUuRogN6J51PQ+DWS20uqowElFEI9foSIz493yngqb7uZsnAAsXgU8c8eJ9w7wtGz72dGhsJVoowQ9nEo5GDSyVfywufH+MWiKvwmL8f7o1Rl2JCGm1GdWYyTB1BNWYQ7++j76Y+puewU5LxSup5/kvxzz0VLxlGH+zGNnok63I+YiBOrORnTjo9JhvxY6mfwc/915DV/i1Y5jYQuIvU0E0wrR77jKc7SwyR1eHRdK9dPL6bcEiFNtiAkU8gmIyKlQAwjRGM06Fl47RKqBukDR3BkluMt8+CLpKiONDCcNYquEYVBrYBqr5lhTWDC6tUoqSgp2YZVkknt+IKBzbvIqttGIj0DJRSiSchng2bl+oHtmCvHctXBDF5JRflfviONqTSqw33kLD0DIl38f+z9dXAk99n3C38ahlmjEbO0WmmZGc322o6ZMeiQ7TgOOI4dBseOYwoaElMcJ2Zc464XvMwkacUsjUbD2HT+aB9X5Tznfc+TJ/ed3En2W9W1VdNTvd0zo19ffV1fOPzLp5n22NNkLC4cYjuvxIqY5wBr0yyUuoVEf/ttgusuIr59CxfcczHS8gsR972N0TGAFg3jamjAUBU4tpkyXxDB7iYWmoZv5Cjlgx2Mh2O4D7yHOu9M5OEj6MFalEQGo5Cj0LYHI5tGTaWwJqNmp6qoFiTZLJbtMlMtSV4dEWkNuSlyWBhP5tkPVFT7sM795H+MvP2/Cx8rs/6ug5zg+JzAfwiscz/J8qYgiyo9/OjtdmRRQO05jHp0O4amY/PaeOfoBENZlQPxHGtDTgJWmXyigOxy8NqBMZbX+Zh+5RJWPfJNLNXNWI5tRAzVYFRPZ+iFF+l5YxczP3smc85s5OVXj3PRVTOpOX2hKT33BZFDlRi5DLbWBYi+IJaqRoSmheSP7jRJ1tk0hqogz1huqmCcAQRNIdV2FMqbQLaazsKagrTyElzlQYRD7yJloqgj3RhKAXmyF7GQRQ01mnEH48cRUpPoqRjqaC+iy4M63IMQH0MKhLCMHiNgkygU1TMZbEEobzQN86qakWevNRf6sQGyR/ciz1xFoXkVwwULoYHtBI0kYxmVZw+P0hvL8ecxF870GAG7hKVnB1ksSIJAZWEEfCVEQ9PYLk+hwzcTbagDeaKH8UVX4J7ShH3tpTR9/jOcXZJnsHg2klVm96qTePeSb6O178Sx6nzGdx9Bi4ZN5VZskqbPXIm04iJcoobDKGBIVjOWQ4Dj1WvYMRgjVdABqEx0oruCFDkkKgsjWCWBkuFdDCYVFN1AcwbQNzyBv/1dei0VGJ/7Ke7Fa9HeehjJF2TlmxYMyUJPaD4143vYd+dDqKP9WFZdguukC3HU1lJy8w8AyB/ZiTHzFCaf+Q2i3cnSn3yKFWI/+o5XuNG/gHcnHXzQFcGVGOTXp5ezdySJRRSYU+ai1CGQqZjN82N2dLuHCdVC7Td/wMN/PvaR+k+j4svfNEM2//QXANTje0GUoKwR5/ABjFzajFmwOCg//i7q2AByuBO7nic/83QC+16iOD/O5S/2YUuNcfesAlv6orRlbOiyjd1RgYPjGZKy14yF2PAMNT4LXquEXRZIlk7HsDjwHXkTiygwVtTKPZt6cVtFip0WOiJZJEFANwyz4BYg5a/HMv808rEkufO+DoDl8m9xYDTJha0lSNWmJ9GvL5hOIfgRR6jbfBgR80m05uUYooyvvoTkk3dhzUVJvfccq2p9lLe9gVo1i+4bLsO3aCmGrmPze3CtOQ8xGyfTfpSKK68j39NO4mgbmcO7GX7pFSZee57wc4/jTQ2R2fwKzDwJyW4lvGEjcnwIvagaQcnimjGHjrvvxVLZSCEaw3bhzVhmrkAtmYJhc5txInYPJflRdGcAt1VmNJXHbZU5rbWERXMqaDsw+o9e8v4t8V9BbhYEAU3TSCQSf7Xl8/l/9uX9t+BE4fMfjD889n3W1AXw6BnuO286h8YzZihoLs3hJ7YxuG2I1bU+pnqstHpsbAhnAFh+13W0PXeQC5ZVMeezqwheeC1ysAyjahp63RxSoamI2TilS+cw4/57sS7/BJW//jO3Th4mG0nT89p2cu37MfI5Rl99lUJ/B4ZSwFLViG73oB94D9uslQj5NELFFLR4BGPoONFnf0P6zSfR3CGil30XdNU0MLS5EJPjaFufxxoqIT/3HFRvGXJZnZmblU8DIB77gOwLD5E7sJXcvg9Mx+U5ZwIguLzoxXUIVjtkE4xlVCQ1hz83jt5zCN3mYTwwFUO2oYaHMHJpXAtXI2aiWNs2Up3rJ7VjA32KizqHzmfmVzC3zMW65qB5LrkYOLw4D7yGd++LjP3uHjRPKWNplRKXFZskEJm+Di0eoVydYGzzTtMRWJSY/MPPid5wMV+Z9RWW3vMlVv/ieroe+xP54imUn7oGa9NsbLNWYp86B2Wwk/jjdzP8nS+QeeqnRO+7lbt2hHFbRCQRzmspocgumaGopVORxo6TKGhkfVW4hg8Qq15Erc+K1yoxYi1FkC2oI73UtL1OaGgXWjxC7FgXejrJj//yLUQlS42cRq2Zy/yffQNx2nLoNtPPBacXebIfQbZgbZ6LHO4icM1XEF1e0p3HyWx+BcFq5+70MdYfG+eGvQ+S9VXx091xFlZ48E8ex923A1HJklJ0llX7QBAJ2mDdi+MsKXKAKJGrns+Yowq5YRb1N92KGChBnjIPY9Zp6N37MCzm+x7s0IkXTUGwO5FWXkqmYjbyZJ/pl1MzFSkV5qpFNRg2Ny+lyrhOOkzbRBpRU6j12an12Xnj+CRxTcZ6yjVYddOozy0ouAd2I2gFOutPpcZrJVHQuH1tPbuHUwTsEk6LSLKg8UZnFMvIEawDe/GOH0XdZ9pB+A68gnX2aqRdL3FZtU6ioDH+zMMY8XEyio59YA+ZmeZvtX7TL+m950fk/nwP6s7Xqb3hiwxvPQKSFYvXi2f/K6ZgID5E0wOPIpfWYKQT2JtnkNn8CsZQO67LbkbzliE7HRSdug5D03F+61fYgz4ih3tRdryGtWYKUnSQwmfvouzc80DTPo560ZNRmr92C3oujfOSm5GjA7wUCyJFB5Enukk4SkBXEQwzu21GiYtlFU4yisaUoIuGkIuW2WUAJ7g+fy/+S3x8YOfOnfh8vr/afvKTn/yzr+6/BScKn/9Q3CDUcVJDgJr8IEI+haIbhJetBjDJxIpGcUuQqefP5HAiT8gmsTbk5MytjwOw4KaTKJpShHfuAgyLjcyu9xBzccYNN65YL4k3nkFedj7K3vcAcOSjHL1wHfaAg6KWagqJNEYuTfGi2VjK61AGjiM4PABYaprRo2NmEGn/UeTSWgS7k6LzrsZ92qWImSi1YhwhNgq9+5En++ktmsWLlZ9AiUwgvvxz0+hOU9A8pRg2l5njNPM0rKES7LOXY1/xCV6bcgUT99xKvnE5ot2FYXWSbj0FPRljqiWJPNGNONGLtvA8jP3v8HD5bJSNf2Ji63YsC89Eq2hFmxjGqJiKoKl83vIJhpN54roFCzouySCe11B1AwSRbGkr3U1nwPx1FK9ehZgKM2VgI5VbH6Ho5bsIZQYRPQHEiV6qbv42QiGNFhkheMUN2B58lmevnovUvIBCeJyGq84jp+pI05aBoaNFhs3RjsuLd+YsXGVFeM+8jNJLr+cry2sI2QxEBJwWkbxmIGbjAGjlrdQfX48kCCTLZ5vhlqOHEASoyPRhnTIH0enByGfB4cVQFGS7Fb1uDku+dyXR336P+B/ugu0vkN67Bb1tO4auocfMLtTk68+CrvH86begDXYgZmIo48PEu4aQi0Imn+u5u7inqoeiG39K20SOq+ZWIEsCk4EpGH7T5dsuCZTYBfoVB71JjSsW13DpB7/CyKVJ/+Y2SrODGPFxAISGeWjeUuSJbrOQNQwsFXXcXB7GJWoI3mJGf/o1Bm+83DQwTE1gCCJKqIlz8ntJiU4U3UCZfjILKzzokoXS3DCSCOdODZrO1o5iJlUZuZDiWNygULcQgEaXjqIbNDDJa8cn+cnLR/jyi0eYU+qiNt3NecktJD94jdTWtzAEESlUibuxHnXBeaTe/QtSqBIx0ke9JYPzxnsQPUUEChHUkinYJQF1tB/l7JsR73gYR8ssRLcfdaSXxvNWom/9C4ZaYFPZyTBjDbktL5vqtVwao3o6AIamo44NcDRjx7A66F/zRZ5WW/GsOhPvjj9hr21kyhc/hVRag77kQgyLjcr8EKLHb3ZMPaVM/OX3IErkD21DDlWSff4BBCXLOVUihUNb0EZ7sEkCalGdOXIupInnNQZSOkfDKQYTOdx2mdGR5Mfr0Yni55+PRYsWEY/H/2q77bbb/tmn9d+CE4XPfyh+nDiKCBgWB+OWEGXtb3He27/gprW30/XEC5TPK8PmtWJx2rEIAo1zSjnnwasxJkeQgmVYXA5y0SxS0xyIDGGftghtsIOy7AAIIr7TL0LZ/BxGNo1SVItQyDL3Vz/HW1dO2aVXMbjpMMq4GbgoV9QjhyrRY2F0d8jkG8gWM5pCVTByKTOl3OEjv/MttH3vkHUEURqWcPTuX6O5Q5S89QsudfTgWnwS1lOuQUpH0ALVSD27QdPQR3swXv4F1pkrUEun8maqhE/U2XFXhcg+egebPPPJPv8A7uQQAMbhDxjyNWN4Q8jxESz10/lW95s89dnH8NSUoh3ZgtC1G6NxAYc/9zm+c9TKd7b8hFK3FaskYB0+REwxi4dEXmdScDGeUQk6ZLKCFQCxkGXstVcRvUHTgdkVNDtR3UcwBo8x/oeHUMNDbDz9OvpiOSQ1R9xTjWfNuRiFHI4Pfo/mKTXHf1WtWJtmmYnxSy9i8uofoJRMQXMFUTSDgiBTa0lTFO/ijQbT5C+S0zkQlzBmnoI12ofdKBDKDpMpn4moKTw65KbQvofCcB8sPp/7B33osXF+1PhpMHSszXMJrFxDcN1FdE6/kLY/bTX5WaW1GKpCvvMgzspy9HSSC9+8GykQQlBzWMqqqb3lNkS3Hz0ewdB00vu2Yxk9xvfXt1HhFPnMswd59sgYut2DIYi82jFJThdom8hQZJe4sjyDFhnFOv8UApd8FkOUyU1ZiSHKhB/+GfJkP4giRi6DUEhjKArqWD/dX7qSTRd8nsixIRru/s1H30MKpawVy3gHWssqnLLAggovrP81NcnjWEfb0FxBUgUdRyGOUzJwqikEAXZHBRoDNizRAVKGBSGfIpJVGRCDnNdcxBtfXsr9503ng7442mA7PVPOwDVvBdbKWjRPCXrLKoxsGuOle3AvXkuqegE9wTnsjlvwjB0xvydrkPasDUHNY1t4GoXffYtyl0z7Q3/g0D2Pm+Pc065n+J3NWJtmsSaxE83hJxeJm5y4ZAz98CakQAnuVWcTO3SU8qfvoO/2G2ka3MxlLX4SG16nMNSHFo8Q3bwBY4bpQ4Vk/TiCQsjGkRIjOMuCpoP1wCjKYBeuBavM/Z07sc5dCy3Lied1crpAsmwmAFZJQDMMzmgKEs0pFNktZOL/nmOUfzQE8e/v+AiigCRJeL3ev9psNtv/9wn8C+JEVtf/Jv6dsrryiUnkSC9t9iaa5RiKuwRr20YEmx2lv4Pwlh30b+zAVerEVeKh+pyTSbZ34Koo4dAjb+OvD1A8qxEtV8C/fBXqcA+2Raebpmf5NEYhS7h8HgFJRZ7oAUPHsDoQ4mOMPPs03euP4CpxMfvX9yHmkuSP7jSfdK12hupWE3RIWAtJpOGjCFY7qr8Kju8AVUFoWYa27x0sNc103HU3DQ89TX8aGvK95khL19C8ZQhqDsHQUY7tRE9Gsay8iJi9BJ+eYlx3ot71Rcq/fT8AYtsmjFyGiQ8+IBuOUv/FL2PY3Cjtu7FU1JE/tge5rAapphV9tIfU3u14V56KVjmdZ3tUyj02eqMZTmsKMpjIs8CnIMVHyZe2ILz1a0S3n7F5F1NGgt0JGwuG3jM9T6paQVdBEEm99jiO5ukcf/iPNFywltEte6n8xDoK/cexn34tYqSPo4G5TNn3R3ZMvZjlqb0YpY0IQ21oLasQCmkzmFPJIShZbthr50sr6pEEAaskIAhQ7dAZK0jMueJe+l74GmlF57X2CaaVuFnMAJNFzUzmVOrFBP2GD4soUOqUOB4r0NK/AT0ZZWzexRQ7ZP5yNMxVRWEEJUu0fC7ezBhiwSxQBTVvdvBsdgD02WdgHT5ErnIOE1nV5NiM9KAm4kgOJ5aVF3Lgk5+l8o+vEBSyxAUnLouIpOZ4vjPFhc0+hrKg6AYhh4xz2zNMLLiU0uPvIpbWoTt8fOa9KD+ffIbU5d+hMjeAmI0zVjyTIjFvFsGeEiyjxzByaQSHx5T6L76G+nQXalGNGe6q5NB85QhKDkMQEXSVPVkv8/w645qdiokDvKU3saDCQyDZhxqooTepIQkCsghuq4SqGxTnxzFsLqTRdrI1C3COHqFQ1oqYi9P99S9Qd9FZWKYuIL/7XawrTEPE4lQ/E+4agtlRusUQdXYVRXZgzceZFD1s7I0xLeSmpectYjPWEYweZ/yZhxElEWdZENs5N8DRTTDjJHSLAzGfNN2rD2zE2jSLkWeeIHTj90BTkdIRslteZuJgF9VfuAWSE6i1ZjEcVmSKd/4ROVSJ1rIKXbIgJ81OmpiaQDm+zwwHziYo9LaROHgQR0kA63k30vWla2m47CykBWeieUoR82bXVClpZlK3UdAMyuwGAxkwDPjRu8fZsqWXdHiYSOdegH8rovM/Kqur77e3EfQ4/65jfePJN7C2LPnfzupKpVJ0dnYCMHfuXO69917Wrl1LUVERNTU1f9e5/CNwouPzn4idLxMvbqH4ydsRc3F0w0AZ6jIDNws5MuNRAg1+DM3AURLg+JOvIkgiY7uOMue26wk0V+NqaKBo7WmItTNQEwnS7/4Fvf8ogw//kti7r+C3mAulNtTBZLCFQlE9as1cSr78HSqX1DD3599F69iDkUtjbTDDIpWRXoLr78UeH0Ts2YuejIGuwbEtyKW1ZjzD2HGMTAK1aha1ZyxFivajGQbJN03zPGQbxkf5S0I+bfJf5p8ChoHv0GtwbDMlUo7qa68nK1iREiPoyRhSdQvBFSuo/+KXUSpnkSpuRpx3Okp/B6nTvmh65yhZjJYVPDHrsygNS5Dio1zY9UcCdguLqvy83jHBEq0LzeHHsNhIFDTEk69HCpZT0beFQd3DvBI7wpRFGI0LwNCZdNegO3x4Tr8Uo5CjfOk05Ip6vPXlDM04F8dJF6MdeB8jn6OVcfbe9QyLj79gkr89JeZ5qXkQRITJQQRN4bVCHQ+eUUOZS6bRJ1Hj0AjYJcIFib5Ynp1PfoXvvNPF4fEMlV47oiDwUrKE3lgelyxScAaRBNAMA7ljM1MOPMvG636AtvxyfDaJPx4a44z3fsZndwhkqubhS/QhpSMQHycbasYQZSw1UxEa52PMOs0MR3X4kHSF0r1/MTk/s1fjWn4WUqgSw+pgxs1XEDy2nqToxJ+fIFHQwNC5oCVIe1xH06HOaeDe/wqJxZdR7JAQS2rIbXuNU57q4+6zW/Cvu5zSHU8CoMcjFEkKQiGDbnOBYXDQ0UJm72Z6H7oPKVhGXWGQeLAZOT6EsvddNE+IsOFC9ZQgDhxm6OffYYE8hm5xcOFD2/hDopqGIgf+zAhpfx3RAvjtEjZZ4NB4msmciscq8nifyJlPtKNWmOMlpbgBS6Qb4fhOimc1okZG0YY7kU++Cka7KM4MM+qopjgzjJiN0zS+C+HIBiJZjYjgIavovH5ohBZnntSss/GJCrrTj6+hitRQmETPCIgymSP7kWJDiPmkWQAnJxA9AdMl/Ms/Jyl7QZIplE7FOP9rVN38bdavuJrehx9m/K6vor/3e2yP34Ho8qJFx0HXiOc18/O0e01vpClz0TylxN8zw1G9s2bhOOlikCzUfWI1lrpW8u88AZg+SImyWYiZKA7ZDGntT8OhsRThTIFrFlUjWyQAgk3z/vHr4L8JBFH6u7e/tRTYvXs3c+fOZe7cuQDccsstzJ07lzvvvPO/4Qr/63Gi8PkPg3XuJ9GiYXwTbYQuupa4vxHbkXewTVuM7iqi7bHXALAFXDScs4hk/xjuyiB6QSU7HkPPJCkkM+jJGMpIL8mXf4+tuh7HtHlEN2/E31yNd+FyRCWLlI4g1s/G9c6vkHY8z/HPXQGAIxTg0Ld/SKb9KFo8ghaPgGzBUl6HaHfCaBfHfvYAUnEFejZNYf4nzM5IOgqiRGTfUeTe3dgXnMwxqYqKN+7BGiwif3Qn6lgf8lg7WqAaBJHwc49jxMcxuvciBUrIzjoLKTaEYHOS/OmXUQ9sNHO+BAFLVSPKYBfsegXPyAEiD/8EYeXl8PC3oHUlmreMwmu/4ku+HtqvuwgkCVvrfOYwwLSxbVw/K0S6fCbW8HHUojpCiW76v/5pequW855zHsWv3o2gq8T/9CCFNx/hC1uy+PY8j9i9h4Ff3YuRSWD1upCDZQROOYfiV+9G7zlEfP9+jEIOZdebLPjpTciz12I9+SoUQYZQrXk9H3kURYqnsbzaS1dGZiChYIgSr/WaUQ0F3WBhuZOCZnBXWSetxU4WVXqY70iwoWOCcrcFSRQoaAZZ1aDSpvHLVBPJZVex86FnkLY+g6tzMxe0higkMvymcQBH7w7EbJz8wc0IDg+2dBjD7kF3+MwuVDqCHOml31aFFO0nvfhScq0noY/3M/LUo0jNC3iiU8Eo5JDK6nFJBobFTkDI05OVieZNXkiRQyL52PfRs2mKercSK+hovjKE827lnWubGP70hYw+8Vuzc5hLoufS5P58D9JoO4bFiSHbmObMYfvElyj6wSOINdPQfBXov78DtX03RjqBYXVRmhtGLGRoL1tGyaKZJLy1sOVPvPmV5Vxdo1NvzYFkwZkcJqfqWEWzo1YfcNCgh7l/2wBXZ7bwwMWzebk3x1BKAUFk0tdIctpp+Fefjq15DlJNK+g60Y1vMfLLnxK54WKGbeWMB6YyWrmE+PYtlO54kpLRvfhsEo+eUcaI5sQ/fhgpFcY4vpNCPEntddeh5QroFgdc8W0zgkW2Y4gyeqDKHC/2HyIkF3DveBYEkYd2DNITK5BZ/yTzDm1DzRYoveUHxI514pnajNC82PQRCnfit4pI8WGyghU1PITacxh956u4FyzDWVfHxNbtIJh8OuviM4m88SL2GUsQC2mGA9PwDu9DSkdwZcbNSBRZoMZnJon3xrJ86bJZ1M1txRWq+Hh9OoG/EaJoKhj/nu1vlLOvWbMGwzD+l+0Pf/jDf881/hfjROHzH4bEh78EUUQrqkEJNeHOTxL94D3UsT7avn4r7nIvSjpP2ZIZ5KNJKlfNoZBIm+aE3THuveQ+fvj9d3j8c49z18W/MA8qSuTa9+ObNRNdUcl3HmQSB4aSR/NVUIhMcvzhP9J09SfIO4MYX7yH2lPm4poxB7m0BtHtRy41fX2sS9aBKDLtpz8BQ0fwl2I98AZjxTNNoqrDS9kFFxPbuB4EkdbRbVgCASRfEGvDDARRQnf6Ube9RP7IdorWnooWHkIuqTZT1zs+oLDvffTEJJXXfQbRF4SQaaKoJ2NYapqR66ajecvw1pUz+dDt2IM+8lYPUmoC26nXoNYvourR51nx5DiTNUsBeFqfjmDo2I+8w5ZCOWnVQAk2cPiLD2IRBdaWgv28L/DOQA792u9jPfsLPHR6FVp4CEPXkCwyqZ5+XMvOIHdgK4WPfHniu3fgX7CQ1N7tpHv7YOpSCh++jGF1IgoCaV8N6BqFDc8gOH0Eujbx4rEw7RNpfHYJsZBhXrkHTTdHRWnVQBKhrWYtxbKCL9FHN0X8YrmL/kSeIospdXdZRNJYuftXG9AMg282ZREWrKO/cikdkzlCtz+EWFyFHqxFDTUhLzkXQ7ah29yo/ip0iwPdU4ohW1GHu6m0KgiGjq9vG5oB+vS1lH76ZnS7h7OnFjO0+BrUYB0vtEd5tC2DoOaYzCqMpRWmhdyMplQCp5xDdNFlqGMDbO2PIw0fwxI3OVkzf/oDXOVB8tNP5YhzKlLVVFxnXAmuAFJiBHmy13QcTo4hiQIH9HLEbBzLp36IMO905FOvZSgnkvNVIaXC1Pgs2KYvxr3/FS7rn46qG0iJUUZ1J0Ihje4KIt93My5BIZxRWfOphzBkG1/qfYLeaefgs4mc3hhgSrINY9vz+PMTuIwcosuLOtKL1n8MOTGK/4obKf3aXUy/7SZSBR1BgOI9fyZ47uVY6lrpK57DHw+PkX/jEYYuOIuRpx4FrYAcqiRypNt0EW+tRY724x3eh+4MoEg2aP8QMdzN2EvPodfORihkmNy1G0HJcUtDlkqPheGzbsVvl6g971S2rDmP0NVfYOitDxDD3Wj5PKq3DEFXMSQr7lgPQsM8M7OukENPJ5F8QYqXLkR3BRF0FbGQpfiCq00jUCVHaWGM8dI5FMqn00uQKjmLQxap9lqYzKqsqg1QZLdQWuJCtkh4yhv/CSviCfwn4kTh8x+EG4Q6kr/5FoIkIWTjsOd1MvYiAtd8BUGUmHL9RaTHzc5BeihMPpYi0TvCZGeETe/3Ee2OMdVjZW3IyYF4jma3lZ2/eJtjv3yaQ4++z7bbHgXAUlqN/dkfoZVNhR0v4miezpTrL2LwtXcRXryb4K4/4T77WtOJuZCDUC1GJo5RyKEd34tQVI4+3mfydYY7kaqayf/kC+b+QBV6YpLkwLjJHSrksNRPh0WfAEB0edDadppJ704PYqgGS02zuc/tRx3pwTr/FNTwkHkjrJtJl7Ua0lHSzasxbC6Utp1IiVEE2Upg/jxs9VORXn/QVIZ5y5AnunHveJaNNy0ikOyj3VLLpa1FPH0sij51BU6LhEeJIegqVV47frvEtohIe87BrFIXfi2OsecNpMQI4jk3IshWys+/gKLzrubod36AtXkOo5t2UQiP4/vk7ez94aNEjnQTOOksBE3FUtNsjvEme3C1vY/acxh53RfI7XqH648UMxDNEskUKGgGYdXKREYlrZjZSb2xAj6bhEMWOf2RA7SLlaTyOogSlR4b2msP4Wl7F79dwn3wNQ7+5nJCsU72i7VIw0c59dtvs28kwU829ZEMtSCoOaSRY4ixYYzYGJbxDrrjCmIhjSHKxGW/6UdTyKD6q0GUcCaHETQF3R2i7IpHsP3xB9RYstzkm8fF5Xnq/A6E4zsZTORp9YLHKrL6s78CoHR8P1pkhHNqrOQbl6N7SnhvwkKhYy/bVt1E8v5bmab2Y8hmqKZa3EA2UIch28lbXKQDDVgNlTq/FSk5jkNJom9/Gd3uoy52BOfoEQyLA1suSqZmAeqC83jgghm80j5hHk83KBTVgyBQctt9DGRFCqrB1752CYbNjWveCo6Op5nIquwZSZGrmIU8YwUxWzH9OQvHXNMQ7C60yChGLkX3HV9l9KdfQ/QVY5cFgrlxpJZFGJKVkbIFlDhlzmkOET/7Vhb96VGKvnE/Yj6NGmpEL6iM724jNRSG8T4KVXOQkuNYc1GYvgalv4OST91Cu+Jh4jc/InTtjYz/6kfow134urfQpAyg6Qby7DWs+PWtANReczVG3nRnFwcOkzUktKIaol7zmq1T5pgk+qmL0NMJep97A3SVmOBCDdahhJrwrlmHcfB9xFySIi2OoCmEnBIxwYVDFvAJeU6q81EjJWkocjKj0sfsueW0rl4GnOj6/M0Qxb9/O2FgeAL/rnhw+31kx2MgW02Vh8ePZ6LDdEwe7CS+fz9Vq2dQuqCZ1FAYQRKxB70kBpOEbBLeag+LPrmIkkoPF00LUVLiwua10b55gMxElsxElljXEIlDB7HX1CHkkogeP6nD+5Er6qm7/lokj6nkyfprkAIlZhdnrMfM6MqmkctqUNt3I8hW09XZaUrcqz//FZT+DuRIL6K3iMqv3Ik2OWqOyVTFfN3lwcjnkKcuQK5qRJ4yz7xBZNMoPUew1LagJ2MYE0Pm/wsYVhdNE3sZePIJ7FoWQ7KaKfAlzaZ6raYZuXEWtiVnkt/5FsarDxB9+QmeufRnplw6OUGjS0dKR1hVGwDJQvmjX0dKhhnKiYylC/xh/wjzyl0UO2QsosDEr36APGUuQj6NpXcXQlE5ykAH5NO03H0v2QMfUnPT10n2jzH8468y5423qLvdLCTFsePkju3F6DBHTFp0nLF3NjCqWBla+yUunV+NVRZpKHJS6pKJ53XG0gWqvVaK7BJzGCCZ1wk+9yN+dflcnBaB8XSes58bonTHk1iXn4sWNrso6mg/R8JZlFATzRvuQ62dz6vfPZVLp5dwZ/Uozx0Nm27TkRHUqlkoPUdQx/pp2Ps0ujOAZbyDosGdqKFGBE3BMnIEwWrnzUkXxq5XUd74DcO3VGKvrEDs288D0Z0II8c5ObwB0Rek9SefpiDZ8Pzxu/z8u1dyxD/XDKw971Zu9C9AUnPIk72cmt5N1+zL8H76IrScadqY3/Q8YmwYKTGC4/hmBDWHLRfFJoI82Usir/FGvpqk5GZ8ydV8OJzh5Vwt3Z6p5rW/9xSSICBpeYodMqc1BVFLplDpALmQoiBaiaoiAbtEU5GNs6aW8NZAjnccc3FaJFo9OsVOK08eHMMQZQqaQb02SrMYQZ65Ci0ZY7JqEXWXncfY5+9FcxdTZJfQnQF0uw99rBePTcKWGCZV0E21pFbA2rkVPTGJmEvi/PHjqLkCgfnz0BsWIObiDFhMbxwxE0Vc8gk0V5BWxim77FqExDglN3zTJIRnknRaqrGlxlADNYh1MzEsTkR/iPi2jTjXXojgcOFUU8hj7XglDQSRfOUstMgISqAaubyOhmsvw9j7Fh6bZI5b97wOooRgsRILNpsPF5kortQI0bxGVjWLcdvoUYz971LstHDSlGLOmlmOxSZROmPVP35h/BeHIErmw+zfs/2NIaX/6jhR+PyHIJXJIpbVE2itRfIFzdiFqmmk3nuOsV//lLHVn8NdXcb47g4sLgeGrvPUPRv51TdeZt9Qkvnnt1DUFGDbozvwlLvpHk5ysD/O4m9fwvLPL8fmtdJwSh0DmzqZONiFGhnFOL4LgHwsRWfxPCitwzZjGaLHj/TmLxFkC/mjO9EiI4huP1o0jOEt+fjcBKsd0eXFmBhCt3uwNs1CmxxFzySREqNmrpfDBbLF3D6aV0df/AOi3YWgKR8rjMQZq8jtNRUuet0cJI8fdaQbevcjyBb8d/yaBHYSnmpTwt6/D7mk0pSKKnmUI9uwTV+EfMo1eGbN47SvrEF3BtBKpyBHelE9JfTGcgj736TklJN5PBygLtHOmc5RLp9Rym1vHiet6BQ0g6Jb7kHzlnPtdhnDG4JsgubnAxTadjNgrcBxyuXkt7/J2N5eAs3V8PbvYLgDceioGZ6aKyBVt6BFxxFsDorntVIdPYyiG0wNOjl3Wil1fjvDKYUml8q0kJPERzcdzVtOfaEf94U3UOaSqRndyanp3fz6kll4b9/FHQcExOUX48hHEc/8PPG8yu8PhlHO/zpy3x6skoA3OcAO50x29kZpu/GGjz/r/CmfQ2haiDxrFbFHfmR2SKpmYRk5gtG5y/SuKeRY5xxFmHMqttUXg1owOVbBSqTBQ0y+9ybZjsMY+Swtt9yAPTmK7dM/5PJagWf2DWFMXUbywa/x0O5fInXvJPHa09zcW01LZBdLnryf4m/ch5iawD53tRkDMtgOqsKkuwYpPoqUHENzBbHLIg9u6CSnGmQUneUlEmend/C5Z/azJ+NGPuUa9oyk2T6mYBs9ylhKoT2um9wZiwOrXkDT4ZnDYwwmFM7//ruc2v08J5cJKMyK8pAAAQAASURBVLqBlByjxZnn6lml6K4gGUVHt3tQfeXsvf5z2NdcTFH/NvRUjNbNDyKoeVxH3mbrmIKcGMUo5HB1bibhKqfWZ0FQ8oj5NHpxHXomAYZO/KbLKGqtBUDs3YuUGKd6YKvJaRJlk3sjSqj+KpTeY6bj+I5XIZdEkC14H/4GusPPYFqH0W4Miw1DyZO94jtmcnw6gTR8lMjrzyGmJhCUrDnyq5qKlI2BrpPrPIoWDWM5tpERS4h8TzuaO4RU3ULmri8jFDIYVgcDcgmN2hiSAG6rSKqkFWH+WYgCVHhMyfQpM8u4+KIFeKuaT3R9/hb8vfweUQJOFD4n8G+IrGrQ/+A9jO9pQwqE0B0+tD3rsbhdpo/KTZdhm72C6lPmc+SJzWQnktQ4LVxwwVRueftHTBwNUzK3icYF5UR7YkwWNJYvqWRi92EmDvfjqXATnF6PzWcjfHSCjV/7C+2/eoJvL78Z/Qt3U2/NEfnjbzByKazN8+CsLyEEK0n39pEf6EELDyHIFsRMFD0ZQxg8imFzUejYZxJfU2FTyZSMIXn8JCvnYWTiIIroNbMwRBmt3CwG/Bd80oyh8FeYT8kzlmPYPVhXnI9QVI6UCqM3LkSqacVQFdTwEFZJJHB8A954D4z1ogx2EX3/jY8/P2nuqWhlzXB0E9pYP+mhMEbbhyYPqZDG0r6JtfmD9Ew5g/HpZ3Nls5sOl9k58tgkfnZWMw5ZpGpsN6KSRezdyx/mxEn564m9+wq+0mK0dJJK4hhD7djnn0TLFauxuF3kR0dRw0Nk9m6Bklrci9eg+cowmpehLTwP6azPEy2dRY3XwocDMao9FqJZc7SVEuwcj2SxyyKP7xtGyMbROvezK+PBteNZnso3c+gH9+N58k5GNj3Ez799DwWLC33na8jtm7ngits4Ppbi+AXr6H/sUSo2/ZZXJ71MZlV+fvZUpn7hGtTIKGImimdwD0b3XsRCFt9nv0OyuBm5fx+at4yl673IbjfKYBfaQBtY7BiyDTFQAq0r0Dr3M/bSc/iv+xqelWci+EvJHTMlzs7+3cStRVw6pwJj9+vM3FZPtvUkBiqX4jvtAn6xwkO8dimCVkBQTV5Zbt8HZOoWI5bVk9qzFZ+Q/zjaJCz4sEkCXzu1mdLsII1ijJ6sTF/DyTx97TxKXebvcGHH81T7bGRLW/lwIMpLR0aJ5nViCsj9+8hrOp+eU0Zz//t88OPTkZZ8guNZOw0BB0qwHiGfxj6wh7huoTuapUdxkirozH/4fqKeWjINyzg852qs626gEKjFaF7CSl8WQ7IgWO0MVC7Fo8SwT3YTDzajuUOo/kpi27aCrlJ/wUn4Zkyj7bd/Jnd4u2kZ4TDzxMRsHDk6iJBLYggCLL2Il+VZSIEStMgIUrCC0ouuQGzbRJ0yjJ4xR36H7/wxof0voPcfNbuQDi/eT3/HdHQfasc1fIC3s2UIHdtRp67Evfpc3r/pSeJb36NMyuFauBpl64u8vvQqKj5t2kKgqfhsEoZkwWER0Q2wCTpoBQTAZ5MIOCxYZZHJdIF8fOKfsUSewH8QTvj4/G/iX9nHxzr3k7SfrDC6t5/S2ZWULpqGfeZSBp58grKTV6Eno4gOF7t+8BSZSBaAHQMJltb5aLlwDv7manrf3IlkkahcPZt45xCaogIQPjxEvC9Ow2lTSA5GkawSsl0mNHcKnhmzmdi8laFtx5l/3/cIl88j2PEeXTVrKH/tZ3hWn22qlQa7kINl6A3zkcNd6E4/WttOhDmnou98FevU+RiSFaVjD/H9+wmeug7B4UIQRXSn/yMSrQ2pbROix2/6+Bjmwgow+vB9hNauMbsL05ZBuM/MaiqtxiibgmF1mB44k4MAGJoGwUqMoeOIFY0YE0MUOg8iBcswcmmiBw7ja67HPnMphsOLcmwn2a7j6FfdiSSAd+wwx5wtaIZByClTEu80jRfXfRnX4F6i5XPxJU2vmUL5dJNPpOZMNVI8guBwkTuwBevJV2HIVqT4KMqxHWa2WXkDufIZyPkEkV9/j9C1N5LwVOOUBWIFne5oDosoYpEEknmNErcFuyTisYrE8xoVdp2hnMi5P/2AO6+ZR0uxiyK7KSl+qT3MqtoipumDpN54GkEScZ9yMae9nMBmlXjpsiYiuChoBtGcyouHRynz2Tm/NURx71b0mlkUbD5s6TCCmsfoP2Iq81IxUo0rcOYmEQcOg66RbjnJdIm2CFgH9pLbtwn7/LWmy3CwBka7TP5XwzyGxSLcVpG2M0+n4qU3yGsGN/xxHzed2szCCg9bBuL88u0ONl4cwIiPM1m3nNmf/j0Hl3URWHO6OQJ1eEFV0AJVyBPdZCpmkyzo/HJbP3esqiKtS7j3v8L49LMJHXyZ7ILzcaopVJuXVEHDJ+SZ0MzOxGhKYd9InOvLE6j+ajYOF1hc6cb+4TN8T1nKyc0hThK6UMpakZLjFPzVPHlwjNV1ARTdoOaNu3FcdDOdX76exusuxlhyIXlD5M3OSS5IbCY//xPYk6NsTrqZU+oyg2NlkcGvXEX97d+j01KN876bKCTTpEbiVK6YhntKE9bZq1ADNVhGj6EWmV4qKYsfb7yHsLuOgBViChRnhjEGjzG54R20gkpgzgysdS0kNr+DraQY6bRPm6n0eShRIwjDbWj1C5CHj2D4yswoGa1AoXw6wod/RotHsM9eYarJLDYY70PpO4a0+nJSshf/+GH0VAzBV4LmLkZQ8/SKJZS5ZXrjBV45OsY5raX0x3M8u2eAF37zNEo6jitUTfTt7/0DV8r/WvyjfHwGnv4pQa/77zrW1x9+HkvjnP9tH59/dZzo+PwHoH/jg/zmga0oaYXy1QtRkhmMfI6yk1chyBYmdh8hcbSNquUNWOwy7/TEWDU9xKJbzWwgyeGk6cp1VJ8yn2jHAPHeMcKHh+h6s43EQJLKhRXEuiP4m8pouvwM3JUhZLsNPRGhePUq6k6ZSaFjH34LZA/vpqH7Hez1zby84lOo40NYmueblvjvPgGSheFf3oNc1YSUjaJMjKEMHDc7K7KF4OnnIHr8CKKIMtwLkhU53Ak7XjRDG21uMp4KxOQ4gpJHC9RQ/ukbP1JP6SYhOpcBXUOQJKTkGMr6R9B7DqD0d5hFj6ogxEYpdB9GyCYQPX7kk69CmroQMVCCqyxIZmgE3elHObQFS+sivGvX4evchOvYu2S2r6c1fZRWaZKgHidfMhXbzKVYd76A7irCl+hj2F6JWlSDdfQYhmxDa9sJgogaGaXQedD01ug7iJQMowbrzPyp8gYixdOw9WxHjvTi+PLdpN98EvfhNxFzcVTNQBQEqrxWfDaJ3liWSEYhVdA5Es7SFc3Rkxa4e2M3931uMXZZ5PBYkqcOjBCyapzfUsKB0SRHxSo8K04je9FtfKBU8Ma0TqKTWR46lCSox6nM9nF0PMVLG7r509ZeApIKxdVg6NhSY8RsxSi73kSLR8jt3cjgH58m+dMvM2kJgCgi2Oy4EoP4tITp8eP0f1z06KEGDMmC6PGjDHaiH9lMZbYPf9823tszQv6bV/OZJ/bw8JXzKHZamXbBDyhxWXnvszNpszUQrV+BwyLy2A8uxnf9baSmrMJweNG8ZvTFuO5EKZ+OjDl2XNkQRMgnGUmpTHzwAfG8jtw4C1dmnIzsNiXrkoBmceKyCLgsAtNCdq5pdhD3N9KTEVld4+HVjkmkOSfz3dWVJPMqG2kkZVgYspZjjXTxaVcnz+wfNsNvL/4W6rt/oOXO27FUNZJUBWI5jXM7n8GYt45D4xkK7z/Nwgo3+Y+sBTqjecp+/iS5D56nOd3BZMcwxbOa8DeVsfPe91Anw6SKmsgJVjR3CMPmQUpH8MW60LzllEaOkHrkTjKKTu79Z4hNPYXicy+h9KrPmV5ZrgDetesQJAn2vI6UGKV88giKuwSjtBEpHSG97R00TwmCrlI4vt/87RZySL4guX2bUDr2IKYnARCcXhBE3FqKQvl00HXUUCNyYhRxopc6Y4KCZtDsgfOnl+G0iOiGwbKmYoqbF+IMVpAOD/yzlsx/KfxXhJSeIDefwL8VrHM/SdHR9aybW0bx1KDJkymYZoHoGugarspiHvz2a2z5w27yiTwBi4S/1ktmPGpmaa2+BNHuJDsexRnyI9tlbF4bhq7TfO4MHCV+fLUBLE47ejpBLhLHGiwC2Uq+px3/p24jsuKTWPr3mgqUjzDzoulIU+YRDkzhLb3pI3JyhpKVi8DlR+06iGvZGUhTF2FYbMiNs8kd3k7k1WcxbG6MRedhDB5DHek1E9wnusnvfAtXtNv0aHEXk9AkDNmObcEpH0UmFJACISZ378NQFPJte8iMRhCdXiwNM9Aio2bquyhhW3MpiJJp5ibKJpk4PEQhmSZw/nX0//xHSKFKhGyC/kd+hzrWj1DeiO2069BdQTRPCZqzCEU3wOU3+UiGTi5QRxkJpPgous2FvvctDF0nf2Q7ud4uk/TtcCEVlWFYnejvP44+5yxye97HIxuMPPs0hijj3PcKnpVnmuT09Y/gtorYZZHJrIbLIlLndzC/WMZvl1hUItMcdCAIcMW8KtomUjQHnQQcFsq9diYUmTePT3B5aZIWexrB7iYU66TYaSW1+FI6d+zFa5Mx9r+L5i1nXoWXk5fW8OYXlsBu0/tJzEQRJ3oJdG3COmsV1roWrEvXUfGNn1D++VsJDe1C8JWYYxPJgpQKo7zxG4REGM1TimB3oW57ie/XnIzu8JnGmPPPZ+Pp1zFZs5TPjh6g4drLePpTCxlO5vE7ZHY+ezu/397H9z8McySc4qaXjvD68Um290XpKzjQDUyfnn1vES9uQRAEMz9KV6lKdLA2pCKNdiCJUHL9jbSofSjFTRhde7CLBi5BYSKrcSScRf+oNz6eVulXHHhTQzRYUlhHjnB6Y4AP4i6EXJJVtT6WVbpxCwrV8TZ+0WVjtHIJt62sxmER8R14BXn1ZajFDejpJMcnsyaxfMk6hGyccreVgZNuJKvoOGSBaE5jWvwg+0czyBd8DT06Ru0pc3G2zkIvKFQtrURXVAzDwJUYNLuf8WE0bzkjjzxgpqrHwngXLkPRDV5s/SQ+LUGqegHaUAfJji6S7zzH0e/9BKnU7BSJuSQd7hbCGRXVX4nqr8I5ZxliasLsOM5ei+7wYWmYQXbJpRhqAUtNsxkoXNmCnowSvv8OOPQ+1sH9CDY7UrTf7AiJEqqvnKxicqZcFpFUQaPO76DMbaOiqZRMZPjj9esE/j/wX8HxOVH4nMC/E7IvfMkkNWZVvPVlaFUz8Z9yrvmU1t+Lnoqx95fv45UlDsTz7BpLM5ZXsbqs2P0e5CXnkn/nCZSxAexBH9EO8998Ik/x1CC977cTPjxEz3tdFK9cjq6oBFpryQyNkB8ewhIIUHjjd5Rqk+jxCMVf/A6Sx0920UXUXHoBm7LFFKcHOTm8wVRT9XcgLj0fY7wfKRBCLW4wb6j5NPpoD9YzP01gzekI+RSWsXb0ZAyxYTby9GUgW9CyZoK8GOlDyCfRf/MNDNlG4cAHZA/vBl1DHRugZN0nyB3dibjqcnzX34ahFsDmwlI3DfXgBybPKG5ya0SHC6FgdomszXOxuBwox3birgwx+OxfGHriMaweJ6IviBpsQIoOYIgyUjqClEtgEQUMyWp6BgHO4QOIfQdRe48gRIdRw0Po8QiZ/gG8J52LXF5nqtk0BTE5jhQsR0qOIZ31eeT2zZRf93nTvK9lGcnNbxKZvg75nC/jSo0QsMuEnOboaiJTYOe4yvs9UUbzEl6rSNPkAYJOCwcH4iTzGi3FTlbW+gnaYHVdEW/EA9x/KINS3MAWtZKmrb9FfOr7dD/zJco8dvJ9ncjxIRrsBYJuK3IuRmzndoToMMZIFz2h+eh1c0z/F4sDw+I0C7zeQyQ+WE/y7T+TP7KTjKsUfbgL0eU1u29aAUO2Y6lt5bu7f4d2fC/yZd9iy0CCVR++iVUS+OG7XXQ3n8U3XzvG8sRufvthHwfHUvzg0IOU+e38blM3H27q5tTN93HV3Ep8T3wbiyTQP+si4nPPw7XnRVTd4EjBixztJ73xJfTdb4LTz1udE3z6gyxKcROqAZmZZ2Lp3cUfjsRw/vbr2C0iGUXnN7uGsEoCxQ6JCWcFeXuAQvl0inq3omg6UnIcl2TwemeUuCZz0NbEVysncP3lRwiGztaBBMLUJRiHP0Ds20+0cRVNATvy7DWMOWvg2BZqY0doUgaI53VcmXFKXTIAdlmkK6agNy3Gff5nkEKVlK9eiDPo4i8zP0NWNTAki6kMcwaQx9opuel7aK4goj+EWDeL2t1Pc/H4G3znw0nu8E5Di4wSuOpGvGvW0XTlOoamnAbz17FfqsNtESk3YiahOTmG6C1CnBxANDTQVbK+KvKHtuEdP2oGC8cjiGX1GMd3MrxhJ2UXXkLywB5T6KAUQJTRLU4Em5P/m2CRVAXcVpGWIhsZRaPKa8fQDUqmLf/HL5Yn8B+DE4XPvzmMyRG+d+b3aL18IRVXXkf4/jvI7tlA5thBbGVlpHr6CdT7mVvrI6XqrGwJcuPnF1K+dDrelaciDLVhKa9DcnnIReKIkkisc4xcNIeSU3nnwBjpsTSNZ0yl548vML6njUO/34JeULFXVZPo6MZa14Juc9NdfxL6thcx/GW83B5BdHpYld6HMdSOMfdMRioWI7q8iN170GtnY5Q2glpAcwXRnQHE0jo4tgXBXwKyDc1fYSqvClkMiwM0DWtVg5n/dWgbSnEjzi/9DP3QRqQVF+OctRi1ZAq0rkDwl+BYdjZiLkH2+QdMrlH7btSxPiw1zUiBEKgFRKsddbQfQVMwLA70QBWuBasYfvM9Bj44ymT7KJMdw9iDXrMIS0fQXUUIo8cRI33IE91YYwNmjpjDi+avMtPiq6Yh17aCpxjn4tOQ115B4IwLKXQfQU/GkBtno7uL0bNppKpmjOOmt5AWj9Bmb0J3BEg7ihk95+uUJLsRlCyKr5KgQ6Io2UtBM5ha7KLMbeXUxgBDyTy98QKPxGv4sD/KT8afZubhP1EzupOgQ6YvpeF+9Ju0Frs4r7UU9fVfsaTUwtiOQ3jOvRZNN5AEGDzzVgr7NvBqf4GvTL6E0LEd/xd+iOD0IZbVUze4FTEbRymdijbciRjuhmwCsX42niVr8Kw4DTWVwj3RgVhWj3XmCvCVsClTBKmIGZ1itSPMWouiG5xt62f4x1/l9Y4I964tpnbXk5w3p5Jw/UquX1TDuU0+SlcuYndPlOeum8frPziTxpds1A9txbdgEYpmYJMFhlMKO2vOoHTHk7T6JdOx/JKvYZkyF7Wohk/OLefR5WbBaM1FGU2r7HLNotJrx1tfTrMHNANuWFjJWNrsAvktYMtF0V+5j+eYzimBDNpgBzld4HxbDw6LyJb+KLnKOdivvgOpcxvlbhs5dyksOJs3LLPwFSbxH34dgNJ0L3JlE4bFQbaogbe6JmjTAkxmNZ4vNBJwyLitImL3boSR40Reew7RX0LVurVca+zDKgmo3jISCmahLlmQkmFTMODwYfQfQfQFmdy9j5uP/Jq7EweRS2vQ3cW0/+QutHiESuLsDGtEcwrel39G1hHEng4TsZmu5P/330KvtQrp1fsQPX70+ARS1VREjx9jvB+heQmemlJG/vIn3C3TKHQfodC2BzGXRNv/HkY2iW3iOAXdwJ8zC8WJrMbcoITLKrFsZhn51OQ/b9H8V8OJjs/fjBOFz78xnj80jKEqWEWBWMcAkdefY+JIP7de9Rj2mjq0ZAx70IdklXjuaJhGlxWb14avsRLnlKl8d8Fn2X/73eT7Oul9eQPRjgEESaTjw0FKZpagFTS++fvrmfPZtVi9LsqXziAfTdO0bgZWr6ku8dRWIJfWkPzDj2hgkvElV6N17ueSkVcA6Prlb8h37GfjUJ7y4R0841xuOhknxxHzKQyrE81bhpiNY8gWxNrpCLqKFhlm3HCjjvajdh1A2/s2GDrS1IXozgD2BSeTV3Uc0V7kKXPJv/EIRmULQ1kQdBVBK5Df+RbieBeOlllo4SFEX9BMk+/vIN+x32zJR0aRm+aiHtqEMTmCfuxDMnu3INttlMyuYda3v0jT+cvxXfhZrI0zP1YV6ZkkqAXybXvQXEEypa0oJc2I6Ul0uw9huA3Dalr3m2n0VjRPKWp4iHzbbgpHtsFYD7EP3jJ9cBrnYwweI7fwApqOPE9vwuTu1Ox8EnQdQ7IiCNCfUFCDDUxkVep8Vvx2iXBGZSxVYCxV4ILWEFfOKqX73G9xp7aK7IGtuCc6GErk0T53Fxfdu5ljE2nq/mTjsarFaN/4FUJ0mHBGo9Jrp8maQvQGOTe3G0tNM+0PPIzcv89MUZft6NUz0HsOmHEVbj/qhFmsZd99Br1+HoY3hHv1uQAo7bsxJkfYkCujMeBAcHg4o306w8WzkUY7cI8fo9C+B8kiE8+r9N1+I9bZq/jdpm6+F5jO7MmdfOOtbizzTmFxQxElK7+E3y4x+urtHCheAgvOxkWBivwI2wZiLLWOIi46h51jBUbTKnXXPc5ndwikBTtWLY9hsSOlwozjpVGYZJ4rw+nFeWJn3gK6RolLRtEh5LRQ6pKxDh9C7DvIwNovcn50o9nhq24hpxn0Fc0imtP4XKOAXEgh5uJQXE2xU8YZ6UTMRCl328g6gjB1GeEnHkLZ/TaoeUhHsaBzxYxSJEGgPZJhWZUXqyTgkAW0KUtJ791C8flXIpfVoITH0FtX4clPYjm2EUkUSDhKUItqyJdMJVdkdk2Fqqlkjuwn8Zm7CJ5+Dm81LUWonIJ4ZANTbvwCk4d7EDSFer+NJZVu7BfcaJpNqnm8Vgln/25SRw8hqDmq3LJpSxEoQfCXoLTvBk8QyhoQw92ELrya0NfvRQqWIwfLTA7fUCd6OoE6PoQ+2kN113to7hCWsXY8NgkxNcFwMs/lcytZd+nJSB/9fZwYd/3/x38Fx0c4UficwL8Lzo5s4Gdrv8ZpiyooXzqD8L5OSuY28PM/fho9HiEXiZMdj1KxbCrnNQRYu6qa0LQSAiedSe+fX+XCdU2UzqvD0HUMzSDWEyXWHaEnrZCdyBCaVkrkYBeDmw4yuqub9EiExmsuwB70Yg0WmQ61+TzpD9cT6xjA6NqDzyYhTltOYe0nEX1Bas87lew5t7Km0ka2YRlXVKkkW06BbALd5kbMxpHSEVR/BYbVBZLVJFiKEuXJLqTpK5CrmrA2TGcy2IJhcaD6qygc34/ryNtonlJyW17GctHX0Nu2U5vqMsNGc2lsS84is3czhd42wjtMQrHo8SOX1iD5gmZeUUUdSttOrE2zUYa6GHvrXUSrTNlZpxNatQyl9xiWqiaUXW+aYZiCiNy7Gy08hBaPIPmCJhE5PogUH6bt5i+i7XsHvXIahmwn+vITGO4ipOgg9B/CseIT2NZciiBbmHzvTUZ3tCEe2YDRsx+hqhVXtBvB5qBBSuCxSSRXXkc21IyUHEMwDJrsOfI6ZBSN145P4hUKBGwS9QEHrx4epT9e4PhknrePh/nM4hrEC74O6SjL+9+kVJvkvLUN3PbILr5/28W4ZYG8aqBVzaTaI/Orytl8f0ccYcZq1KEuhKqptPzsHtSauehWF5qnBGmiB0QJfccrCL4SpEAII5tm7/IvIabC/LLPwZ/iZejOAN1PvYRQUsNJYg+P7R5E9VXw7pXVlGUHMHyldP/sx3hv30XZTd/hUy1OHjv7+3z7oMTzjvV8PXwIgpXcuPFHPBd288kZAT5/51eoP+nLDCRNxeEvdo0jT/bBcAfX9DzFdw+JHMi4WBSSePbAMFt/fS33ntOC9tHYRdn5BlJ8mLLckGk66AySd4VwWkTk+BAFzcwjUnWDgmbwjtbANYeCNGpj6EsuRHMG0PwVBNveoVLOUmpRybtLEXv3IqgFJt017B1JMuisRz+61cxGE0DQFEquuoH+ZZ/isVgVhq8UOTZIQTcockisrPGi6AYOWSSnGQj73jQVkaKMYHUgSB+dnzPI4ZIlAB9lnjlIFDQiWQ3dFcSwOHBe9CU8Vgl1bICz1t9L7oPn0WadTkf5MorntYIoEdr/ApJocqGkdAStfSdyIQV2D+7rvg2HNyKmIwh2F1IghDZspnQrxU2mH1CgktzejYjJcdSpK9FXX4M85ySE5sXYZ69AT0QwpixBdHmwRLpBEPGMHADJQnPQgcsqURt0Ujn/5H/KuvkvB+FEx+dvxYnC598Yemycs89uIj2eZvCDA0y0R/C3NpEeCoNswdB0vDOm073+IL5aL5UrWyhdNI1C50G89eUUNVdQ8ekvU0ikef2FdgRJIHx0gk9+7yxefqsbZ1kQQRKxuKxEe2Ko6RzK2ADH/rSDkU270SKjWCtqkYtCVN/1KELTQqwbHiP+pwdNEmYuTe/8KwmkBkAQid99M4KawxvvQZAtJu/D7kFKjCL27EMopE3FTzaO6A9BOooQHzVT5R1eguHDKJ4y5MleLK2LQRSRowNYG2ZgHT6Enoigj/WiHthoEmxjY2RGI0guDyVrV1DoPkx633aUgQ60yAhadBx1tB8jlybx7gtIQdMVd2znUbTICEYujeBwYWga8uJz0Dr3IygZ09Ctuhl1uBd1uAfD6kBzh0CUKPrVXxCWX2J6+eQS+FeeilpUhz7ejxQoQTm2g7i70nQintbC1Pt+TWrvdjNg9Z2nyW9+EbF+JoKuYteyeC3giHSi+avI67B+WMcmwkJXmpPr/RxLQAkJKtwWLp5TQYXHQqqg8pVlNdRro1jiQ2glTQizTmJACHDbqlpW/uLLXJ94j8uOvUVL/AAZ2Y11YC/3v/d97lhVxSUvDSIvvxChkDXjIESZ7+xIICVGweaiv+k0IouvpNvZCMXVGEsu5C/7h3lm3MfMUg8XTA1gdGxn6ve+xw6tAkO2cf6MMrbF7Rg2F2FXDVFPLbs+ex8Ab4atGBYHt6ys486TG3CfdAE1Q9vIFTVQPKuJMxoDJHQL2w+NMvTBQxwcS/Lbbb2c21pKqrgZSuuInH4zd/qPUue3kRNt3GZs4mg4w0hK4dc7Bkg9/iN+F/oEsdJZRN3VhKUAkihgMVTcSowPlQryqo6imxEaVklgXrmbJ+clMAQRVTcYtpQSwcX7/mUMqQ50i5073+nCqGihjwBOi8iaOj/lUgapeQGKbjCaVs3fhyOARRSYW+5DzKfJ+muwSwKGAbGcRlc0x6HxNC6LSLbjMJq3jOyHr6H0tyM5nCjFTUiiwAxhDF9qiLQmgGHgt0CFGibrqwJAKGRNifrSC83OZSCEuP8N6v1W9tz7MlJyHLlxNsKmpxHyafRYGGHu6ea4zFWE1LXD5Cg5fLDoE0yUzgFdR5q1hpxmmLlg+TTWhumI+RRSKkxO1dG8ZRgWG1p0HGnNlfQrDpSGJaaCspA291udVI7vwzDgi0uqUXK5j9eyE12fE/ivxInC598UsVSGsW37sXltlM0pxd9UQaDBT/uT65HsVvLjE0we6wPA0AyUtIK/tQnZ6wVdZ9cDH1B6+ikYFhvp0UnOv24Ok8ejlM0p5fhLeym2Sozs6OTg47vIJ/L0jKbRdR3H8nNZePtljO0f5vCj681uzPSlSJ3byLzyO8aXXou7ZRq6xUFi8zvUbPwlQmwEfcMTlNzwTTJvPwOi/HFul/bBHwFM/oBsR5wcgHTUdJb1lyOIokminRhCCTVhSY2DZEXIpxBDNahdB9DTCRBEpFAlangIS8MMUwnV14bN72HyQBsjb29kdPthet/aQ98r79P1l3cZ27qHsfc2YeRzxLqGSO7bRcnKRXjryoke7sBQCtimLUKwWFB3vYEway263QetK9BTMUY/3G/6DRWyJvfH4SdEEnmyl5yvitirT6EMdJgBmnWzzXOcdxr+oT1UvPRjBLuLjavOx33+Z9hz83fIjpt+S7qnlAExiKAVYOMTZIJNZA0JUQCfTSZW0OkjgCwKrO8IsyliwSaLNAcdvNsdpcZrYzStIOTT/GXEBhY7cnyYSquC+sLdPBDdSXrp5WScIbI1C3B3bUENDzE570LEdITnT3chhk2nXwGw9O/lm6N/JPLUQxixceoj+5nMqVglgXaxkraowssv7UUSwCKKqIioo/0gSsw9/CeSwSnMjO9nqS/H26MCD2zt49B4mguj7wOwotqLkI3z6J4h+uIFs2ivaMF26C163thFWjHwH3ub2S0hkgWNEpeN5Y1B6nxWNN2gQ66mavIw4ZYz8FgE7KgYa65hTa2XIrvEt2aIHD3rm3yxQcMpC3gsAiEjjmGYSrWI6GOpbZw3Oycp6XiHyZyGM9yBxyKAO/jx35zwkStxtc/ON187xr1b+7lkTgXdhh9VNxhKKbgpkLd6KGx9GaskUuoy1YKvj0pU2nVmT2wnWTrddIY2THO/oEPCJolMCzmRBHCddS1CIY3k8qAMdWFbdDpSYtTsSA21k3v/GQLjh8kKVsR8EjETxRnuQFCyCIaO0fYhhmTBduHNIFsZ/PPzDH7lKpa+8AeMXIpUcTN9cy7m8YkiXrDMJf/yQzDeg5iaQKtfYD54pCaQEqP4lShSVTPq7vW4EwNI2aj5WRSVIyhZtN1v4ptowzJ6DMPmMTs9qTCaYdCd0MiWtqKUtdInBAnrDnD5aQhY8ZPFYrf/I5fMf138l6Sz/88uBd59993/n/t++9vf/s3H+599tSfwfwx3+wbG9vaTncgQPjLB9t98iGyX0Qo68a4hsz3usBLZsYfQtBKmXjgfKViO6AkgOFxMv2QGcnEZyq719L7XQeebxymeWsTo/jESgwmmlLrwNwRZ8d1zSQ2naGkuwhnyM/L7X9L17FtULWtk1o0XU+g8SOT5x1EGjhPtGKCi+33kaUvQ976F7LIjV9SzwzkTyRck9scHsJaWQ7iPgSefIHlwL7bpS9BjYXNslDC7O3qowVQ1KTk0fyW6zQ2hanPcM3gU3RkAwLDYsNS2YKlpptC+BzlYhrWuBVTT2FCwu0gNhQkf7GVgcxfjh0awBxxYPU5G9o6SGZlksmMYuaIeURQ58uRWJnbsxbdoKf7WRlAV9GQMLRpGLq1GGu+CY1sQh46iJ6OM3vQg1qZZZnfJ5kLq3E5M9GCIMvbjm3GUhgBQgw1E7CWogSrE9CSxd19hsq0XYe5p1J/ciLr9FRY89GMCV9+MtPpyk68BjBpuJpZcjS0fx7bx9+gGtBY7sIoCqm5gEQU+Oa+CjKKRV3USBY1Lmpx8/91O3uma5Ii1nkv0A4Tvu50uTyuGxYH1zE8z8dB3cFPArmWxH3uf3JSVFOaeg3/LHxAnBzBkO1pkFK1jD47eHRTaduM640rGrvohT2jTaLp3jOFEnnIhQdEfbmOaq8DY4U1c5upjsT+PLTWGYHOQ3/4GUiCEJ9pF7sAWpIkezrL28ZnF1awRehBnrOLB33wHVTcwHD5uir9G7bZHyQabzJt3IceMB+4jq+oINdP58sp6EnmN45NpVtb62dyfIKXoNBtjaO4QxVKeIxN59Pd+j5wcZ/eqk5BEgairEkXXETNRBpIqgpIFyYqk5YlYzcLmN/12TmkoIjPjdEJOmULpVKSUac2Q81agaAZlJHi3O8axcIrPLK/n1kUllLgsbB+IU+8WqLdk2BnWcPbuIH36lyiWFeypMXSri084h0AQEb1FOFOjph+PbqDoBoNJBYDi9KCZbG9zoXXuR/SXIMoWlFATqq8cTTegdhai3cnEi0/jiXYhpiPktr+BMTGIIVlhvIe3i9fSm9QQM1EsrYuoufUOrD/8A9qRLeihBuw7/kLN9j+wpi7A2VOKcK69kK7yJaglU7CEOzEsZkEixEZAttLpakK0O2GkEzRToad2HcCwOBBWX2Uq/Gwu5PHjGLINIRGmQYhR7JDIKDpxw0atPoHbKqI5AljDx5nEwZWfmPZXa9qJrs//O/7unC7pf/6oa926dXz1q1+lUCh8/Fo4HOacc87htttu+5uPd6Lw+TfEDUKdGV55OIxW0Jh64Vymn9XIRFuEKRcsNU0M01mqzj8HZ0mAxlu/jqe2gsS+XUiBEFKwjLrPf5HUjg10PvsOxVOLKJ9fRiGtMOXsqVQsqiDYFMDidBDe10HzhQtoPGsm6dFJ0iMRdq/vRtd1EkfbQC3gmzcfKViGr64cpfsIyu63ie7ZS3bcfFqs+PUtIIqIFhlL62KMihaqP/8VPNffgRqoMr1B6udhWF2m9Nvh+4jsbEWKDSFlooiFLIx2I3qLELJxDJsbQVfRbS7T42fOSSZXKVABnmLy7fsQXR6CC+dQsbQFm9eGI2AnF81i6LppkJhTsXltDL7wCmXnnk3VsjqCC+fQ99SfGdm0GylYhhYZwTZtMXo6aXafZp+GIElYapqZtfWXGNm0eZ6pMIgixeFDCGoBwVuMtWkWtmmLsA4dwGUR4dgWDJsLXVEpaqlDGjqC64e/JzcyZhKmnQFGDTeqv4pKKU3ALrFrOElb1kHn/Kuwa1m2DyXxxbpoNMJ0RvM8dXCUdZ4wfiVKnUNHHj7CQ6dV8Ml6A0UzWPu+k+DCOUw//Sakw++gOYsI3PoLM2xy75scu+eXWDMRHOEOJpdfh5HPmoohQGqeT/HNGxCsdvTeQwgCLKz0sXpNA7f8dge/a88ztruT+w8kyLz6VdTwEOlnHwBdI7JrPyMfHkAd7EI9vg9rwwzafLNJlM6gwimy+oU0g2KQX714hKcOjhI45XbqnxR5uf4SLnliH+pwD3JpNeEnHqLs7V+g7HiNgmpQ5JBpKXYRcsrcv6GTzsksHUKp+blufIoih4R17loA5mx8j7G0ysttYeI5lQH/NGrlJAXZgZgKY0hW/FaRwIdPcOn0UlTdwHHwDex6Hjk+ApqC5q/AIkAsr7F+VOTM/pfY0RtlcaWbrGBF0Q0CDjMCQ7X7eXbfEEf8c3FZRMRsDAwDQc1z3N2MFB9G6WvDsNiYzKpm10MUqPJYKHFb6LdWIAkQk3xIgRCWCtPYUtBVBhMKFskcb42u/hzCZ3/CqLsBw+rCUtPMWMMak3tVPYO1+x+mQUqgu4sphKag7HkHt1VEnroAgOyii5BWXUbZew+xfyxNMtBInUug8PzP0Z0BUq89bioOi+sQlBw1XivijFXoDfPNaA1vGVKoEiM2hnh0A7ozgOqrpFA+jUlVRrA5EfJJ/HoSvxLFt+8ljI4duMPt9Ok+dIuTo+EMr2zqobh5IQBzz7/8n7GU/mvgPyCdfdOmTbz66qssXLiQI0eO8PrrrzNjxgxSqRQHDhz4m493ovD5N8SDoxs4dO9TiIKAq9TFD777FhNtESqX1BLrGGDo3W2mlb/Dheyyo/S1Mbx5H/6Vp9L72BNEN29kx+duJ9k/RiGlkB7PoBc0CmmF958+yL73ekmOpBg/0I8jFODwEx+y77cfsv6RXcR6olz2zK0k+8OIVtkMBc0k0cJDyNd+h4erLiW25rMEb7jDDEUNTKXyq99DLm/Av+YMBF0lYS9GLa5He/sRxIHDCHNPQ0pNoAWqkDx+pORHhoK5JHoyRqFjL3pyEiFYiZHPmKovTwi166AZXeHyI2aiGO4ihEIWRBEpWI463IuRTiBIIqEZ5YzuH8dbE2Rgax+ls0KUPvwctWcsRVdUErs+JNBcw9jmnRRNrwdAT8WQpi0jt28j8d070Z1+jA+fI3tgK+OvvkB2PMrGa76LkJxA85SCqqAMdmFMjphjqpJaChUzUf1VONo/IN91lNyGZ/F87oc4Gqegjg9RMrgd5Yo7eH/SzrEEhJwygpKlM+9kMqvx8JYeDowmSRc0CrKDOr+Dh/pd/OSQOWq6ZHopaBrSeCeCmkMvqmb1rw7xYLuG1ybx2heW8EL5OQxufJDMtFOxRHrIKjpySSUsPBe734lg6ChHPqQs1o7o9pMJNbN2awmar5KeF76BFo9w44qvMz3TzvqOMDcsr2Pft+fzeU83O77+Oy6cXkrCV8/42+/gvPo2Jn5/L+WXXklRSx2j2w+ZCjgg5JRovOQX3LdjmDe+uIR4XuOt21bjOGcd/ppW7r79Qr7zyC4evWw21ua56K4irB4nnS9sRV56HjNdGURgflCi8NidLJ9SzG+39LBnOEF3wYl17loqrAqaI2DGYCQGmCLFuD4UYUWND4cs0J53Ib//GEqwnr6kYhKl116H/9jbVOiTFGafhZiNfWzEFzZcHI8VqLTrLK3ycp/jFL62up6uaN5U3XmtLKr0MCAE2D+W5qur66n3W0krOrrdi6Dm+NOwDcMAQcmhLLmYvUk7GcVgd1QgrehoBtTbCpS5LMiigJ8sRnkzuQNbkUur+b9NcaxanoSzlCpLHsMwyKo6usOPPu8cgjZQvWUYNg+WulbU9540O0xKDtuMZWi/vY1koJG8K4R39CBx0U3seD/zi2Xc8T6GspgCBVHGOX0OUtsmBE1Big4g5VOISgYhn8Zl5DCsToSictJNK1FmnIaYHMcS7kTKxggaSdRgHZq/ihHdTfiXP0BPxZArm8hseokah0baXU6py8Znz2nFU2I6bgdLXP+MpfQE/odg8eLF7Nu3j1mzZjF//nzOP/98vvrVr/L+++9TXV39Nx/vROHzb4jsG7/ntQ19tFzQSrQ7BoChG2x4fC9d649SPKsJweFCGezE3joPcdpy6m/9FvGt71F9zsl0vrKfoilF5CJxKpY1UzqvhsDUctwlTmYvKOe0r6zB6rIy55FfMb6vB9kuc9L9V3P69fPw1wfo/fOrVCyfTmZkEnV8yDQNjE3SdfV5fLLrCYJHXkfs3Y+1aRYl0XYwDD65XQR3ED0xibftXcRjH2Bdeg7K1FVmfpfdgxQdxCiqQoj0I2bj6LGwaczm8SO6vGgDbQhWO4z1mG38mqmovkqTPOwKgmSFdBQ0DdQCgs2OoWsUEhlku43iqUVouQIl04qZ9rO7CA1sRwpVUnXu6Qy8fwhbSTH+xkqy4RiVZ56EkTfztURPAP/S5QipSQojgyiJDCWXfQr/stWsXf8YgsWGPNGN6A+hjvSAKJrS7/6jWEePIU30MP76ywiSiH3aIgrP3oVgtZsdpYpWfKLC2jKROp8VKZ/i/WEFn01i22CcF69o4ZzmIur8Nqx6gXXfeJHPzS3lmwsD1LsMgk6ZQlkLncXz0Le9iDDUxoabl3BKYzHbB+NMZlVObyrCt+8lbFv/yMAvfoyvbxvqcA+6ZKHknifB0BGXno9WVEMkNAMB2PDpZh7YO8G33+ok9MA41/XuRRk4zhWzytk3kmB3wsy1ur7FxUA8z2ROo+LK6zA2/ZHgmpMAsHid2IM+LJWNxFpPo6h3K5/94vmc01LKSXdv5tBYioCokH31dc6+cCmf+tSdALzRMB+9fh567yGk675H82UnIwy3YxzcgMNiPr0GzroUn9PCTw8/yCWT72CTBPru/QkAKVsR8kemkobViRpqJDh+0ORBeUWGFl+DnBil2CFTKyeRo/1sD61E9ZSQ1wxidtORO2oLIYnQIk6ydbTAZE7lqtnlWCWBaW6Vx/cN8053jNBkOyGnzCLbJAbQcem5FMW7kBIjHNDLmVnqZkqhD0OS6YsrVHisAJS4rGQUHYsokJVdiJgFzsm/O4TuClKIxkBVkBIjlLhkBK1ANKchJsewv3AXNXLafJI3dMRCGlExycJysAz5tOuRe3YS1y3oDh+TbX14ol3YEsOoRTUEeragfP5u5IkexPQkFS6Z4m//EqNzF0xdht640MzCEyUzcsUVRMynQJSIC05UfzWxnIakZDBUhUL5dFS733zw0FXimkzR6/fQ9cYhJvccRB3pRnI4kRIjOPQczf3vYxEFbrlqLqGWJYyPmMXxiXHX/wv+Q3x82tvb2bVrF1VVVciyTFtbG5lM5v/oWP/UwmfTpk2cc845VFRUIAgCL7300sf7FEXhG9/4BjNnzsTlclFRUcE111zD8PDwXx1jzZo1CILwV9tll132V++JRqNcffXV+Hw+fD4fV199NbFY7B9whf943CDUIdmtiECsO4Kr1IVDEoj1J9gVzfHs/jEGNuxn7Lk/kh0ZQwsPoW5/BSM6imfuQkY/2M6M61dTd8k5eGpKcZUFeeKBrRx8Yg9/ebeHA7tHUNM5SmaW8s6yS2i+dBUVi6qJd/ZRddF5eOvLqb/qYkRJomhGPUYuDbqGc8EaSuY2YJsyE6FxPogiQlE5ht2D7gzw6EoLT0WCUGp2U4TyRgA6o3kei1UxbPloXGF1ga/EJNaW1FDoOoSeNsM95YoG0+nZ5UHQVJRQE4JWMNVGwx3odg96SSOatwQpWI6eTmDoOt76co48sxdfbYDaKy5i6tdvJrPhebIHtlIY7EZPRmm57kwEi5VEzwi+xkqsU+ZgWX0JRiFrFl5OL0Y2jbW0HPeCZabipvcYmiOAYXWgu4JMvv4soieAFg2jtO1EqmjCKGTB5sLmd/Pcjc+Q3ruFiYOdyGU1FDr2mzEZbz9Cd87K7pE0AAvf/hl5Tacx4KQ3J+M68jbFmWHCd9/K+z+/EEHNgyBiGWtn36h5w7vukV1YZq7g3B1evvVOD/tGEiyp8lFdGEbRDV4uWouldRFlK+axxTEbS10rxusP4VDMG86g5sIQZQ6Np7G3f8CNoRUsqvKjXnQuHa//mHK3Fbmshuwd1/OpJplF0V3opVMI33c7S4uh8NUr0HwVCKuuwMimyR/bje2Uq8nHUsSbVrGlP86cJwuEvDbe6gyz7WyF0WSON/qyrK4L8oefPEDbO/fzidOmUHhjPXHRDdPX4I71YFtwCoWOfRxsOAtvdpzRvMSR2+9kWsiNs8RPqu0o5U6RynWnIqXC+AZ2UpobRvGWYwii6eL84Xrsep6CIHM8kuFPgzKBkX1krH4Y62F+uQspM4l/aA+eD59iMKEgiwKpgk7EXsK8MhcNTBKyGXREciRFJ7fVxZhR4sKYHOHrb3SgBKpNflC6gDHezwuTAUpdMr/a2osaqKEQbEQQoMQuYJNNnlaRQ6Kg6XhGDjCW0eiLKzx27QICp9yO59SLED1+Jt01WCUBQclhGKD5q0ie9w1Uu59wQSJviGh2LwXJhpCNg2xj8Pu3kKpfRqAQQYj0m2NaUTYdnyd6GH/1RV5qGycSmEKHz1QRStkYTFtFzBJAHj+OGmzAUBU00WIWNNkE0vFtFPVvQzj4NuVGjAnNRu7AFixj7diHD2J07sKweRhNq+gXfoOFt13M+L4e1MEu5FOvQ1Dy6FYnQuUUTmoo4uhwguLaKsIDkX/CavqvAdOLR/r7tv/h5Oaf/vSnLF26lFNPPZXDhw+za9eujztA27Zt+5uP90+92nQ6zezZs3nooYf+l32ZTIa9e/dyxx13sHfvXl544QU6Ojo499xz/5f3fuYzn2FkZOTj7f/J8r7iiivYv38/69evZ/369ezfv5+rr776v+26/pl4cOcDqJkcPouE7JDJJ/Kc0RCgPWmSws4u91B/9hIcQR+2gOejxO96Jt97E3QdX1051tJykkcOc/SZnez+xXrK7DKBOh9rKjzMnFtKvHcM0SpTu6qGY09uoHzFPMZ2d9L9+z+SiySI796JvbIC0e5EClUi+kvIH9lJsn8Mbawf/ehWclNWki1tNf1AlAzvZkq5sjSBMdSOFChBzKcREuMEfnMrq+uKKNn8MLozgKApCIWsOTqCj12WhaJy1KIaDMlqjsk8IfMpRhAZf/RetHiEgs0HuoZhdaFnEtia55IeCmMpq2bW9UuwB32E39+AlowBmJljFbWMbtlL9HAHejqBs7wIAM0VRBjrwvCVIVrtCDY7WjyCXF6POtKLHKo0Dd6yUYRCFiGfRrbbyA0MINdNJ9ffizbYTmbneyjHdqArKq3zyrBffAvyHb+jULfQlMrLdg4u+CSyKLCiyk1f3obzMz/i/e5JZnvy2CURY8oiCpueo+TL3+HDgRiCodNfsJEsm8nUoIMvvN7DhgW9aN5y/nzNXO5pjeO1yRQ5JAStgH/Hn/jExLtkNjyPYLWzQh7iuUwNPcs+i5gKm15KgsBwXmJq0Em0cRWPzTmJCo+NVXu3EHTKlEsZlJp5NNzyddPEMR6h8O4TJAfGUK1uGn7zJ6T4MIKmsLXmTKyLz+TdqBO++SsA5pS5eeorK1hY6Wfd1BDK9JO5pXSEKq+dprfvIbnpXjQdRuJZVtz/BVwWESk+QuqNp1G7DpA+8yaCTpnw7+6iOmLO/I9HMhSfdR6OT/2A8ZyBFo+gO3wc8s0h56sip+qI+RSapxTLxd9g65iCVc2ytvsFzmsxfW90A9LNq5HzCRBlUtULEJdegE0W2DWcotQlE8loeIf38dq4jaEszDP6cQsKhihT07cJobiKBxtHSBV0Gq/8NfNeecXMgwNe7Zjg7nVTmVBkBpIFDAP6UhqGAYpukFMNUgWdreKUj2X0QYfENdtfYb/cgGFzk1Z0c1SWT1JrSSMd30bIiBPOqDgsIo5sBDk5jmv0CHlnkEJZKyULWrCJYFidIErUX30JYj5NQXagp5OUnHk2n5/hZThlEqvFw+8hRweRUmGKx/aT2voW8mQvgsWGbbzd7PbYPei1sxAcHuRQJVI2SklhHPvc1RSObKNwfD+i04suWaj3W5EESPcPYvPa0FUFebLfHIPpBrrdR3c0x+eW1rJsQSVVzWWUzz3lH7aO/ktB+C/g+PwPx/33389LL73Egw8+iN1uZ/r06ezcuZMLLriANWvW/M3H+6de8ZlnnskPf/hDLrjggv9ln8/n45133uGSSy5h6tSpLFmyhAcffJA9e/bQ39//V+91Op2UlZV9vPl8vo/3HTt2jPXr1/PII4+wdOlSli5dysMPP8xrr71Ge3v7f/s1/qOR2LGZI09soslnw9AMMhNZusczdKULOCSB3nSB8d1tpIbCyMEyols3owx2IlplIls2kxmPIgVC9L1z0FSAjacpsko8vX0Ib5UXX5WX4T2jdL99HNEqIVkl1FSKyeNRPNWlDGzuYmjLMQrhcdREgvi2D9j/o0cQbHZqzlqJkc9hqW0hnteJ5TSTqDxwGJ/dfNrs+u1j6A4faAqCbCH7xXuplZNIKy7m/v0xBK2AnoohHN8O6ZiZLPxRNpSg5JCSY6DrcGwLcu9uXm9ZS/6Gn2Hk0ji6P0QYakOe6EEKlFDoPYa3ZQp6MoqWKxBasZjQKSfDlMU4TroY3yVfQM8kKWqtY3xfN5GDXTgry7HVt5gJ8A4Xqq8CIVhpdnfG+jEqW7AsPNM0Z5u+DK33CJq3DKVjD6mhMM7WmRT2b8B56S3IoUoA9GQMT2sr8396K4gSleke5ENvM770WgDm+VQqtz6C3LGZhvGdiBhcM8XGtNs/pLIwgtizj2TfMIgilzdYyMouaixZRtMK4YzK5fOrmFhwKe0ZC5oBhao5rEt+iDczxl8mi8gvvwI9k8Rx6pUkV1xLoXQqFxRF2TuSYPFvBzh409eYcsqNSKJAiRLmlleO8vk7v8L002/i4vI8Z/xyO2I2jhzt5+cDfpAsyGU1WOpaafreT5DzCSzjx0lWzgOtwJ+nLEQsZDk1vRuHReSujT0UO2We3T9Mjc9GtceKZaILw1vCnLHNuOatoPziB3DIAtcurmXaz+7CGulCCTWxZeVNyBX1vHAsTFX7m5R86hbUolrqHn2eT8mHKTQtxzZxnJLdz2JZeRFiJkqrR8ce68dFAbr3mj5Fw4dY6U2zdVyjMDaCPR3GsHvwtL3L/tG0qVDKJbHrecY1Oy6LiMcmcWjcbLV3+WdxVp2TSrvO8/EQvbdcj+YpYeM130UN1iH4S/B8+BRvPvBJxCMbKARq+cTEu5zaGMQiCpSoESrdFlNdhkCV10Kdz4pDFigRM5S4ze/OaREZSinclz7CnPQhDsm1TGY15PQEgpJHSoVN/ylHEeVShqyiMy76eazbIPHuCziOvc9IRsd22nVYxjvIyi6MQCVSTSuTrz2D+PLPzVGsqpC3uHD/5LMmETppxl4QHwfAse6TZj6bbEPQFPPmmUui232owTqUslY0RwAxG0etnIm84AxTkVk/Dykbw54aQ3r7N8h2G4V0gfY/bUYJNYEgkFJ08s4gK8ut/HZbH985tYmefe3k42HcpXUnxl3/D/zd3Z5/ATn7oUOHOPPMM//qNYvFwt13383bb7/9Nx/v//hq+/r62LFjBzt37qSvr+//9DB/E+LxOIIg4Pf7/+r1p59+muLiYqZPn86tt95KMpn8eN+2bdvw+XwsXrz449eWLFmCz+fjww8//Iec9z8KNwh1FJIZ3t05jL/Wx+j+MYb74vRmzG5PVjM4nMgztL2PeM8oAy+/zejuTvpe20QukiByuJeiudPp+NXjhI9OkIxksIgiPWmFS+eUkhpPEzk+yYwrF2Lz2uh8o4uyxVPIx5KIVol41zCVS2oIza7F4vWaKeYnncWcb1yDYLWTGRzGNnMp2sQwpd0bKd7+JFr7LozyZhbEdoOuU/XAM+j9RwEwJCsNE3voVtzELAG+OPESGDrJD99HClagF1VjqApiJoo2MYyYicFIJ6KSRQqUoFW0cs6+lzgSzmAUciBKiP4Q2kAbiQ/WIwVK6Hvlffrf3IKmqKbqJZ1E+/AFjJEuDJuL/tc2Yg0WMeWilVi8TjJDI0g1rTjUNIbNjXXkCIJmJlPLVY1IiVF0iwO5vIHCgQ+QalpB1xDnnU5w4Rziu3eaX9bRD8h37KeQSDO6/ZA5EgRShgVByUPdHEJOmfdjLjaFBeSl55E7vB21fhGRnE5E8LBoSTXOs34C5U20P7+HvD3Ac70qjnyU90YN6lwCsigQsFsoVScodsiMphViCqiDXRz5wg2c11xEPK8hrr6S9xI+iob3YBs6COEBLtP28uE3ljLze7eSef0bfO2Vo+iuIOtmlnOXayepB07j1GcG2fbUEwiRfvrv+QGf637CTAYvaSQ76yz0/qOI6QiFw1uxffAHtC3P8e07T+W9dZ8j23oSFlHgvjt/ju3AG1z2+FcpdUrIe19BzCUh3IcUrOBY2VL2PX0LxXaBVcUaz04EKAQbMUSJM+1DfGCdzrUzi/8v9v4zTI7y2vqHf5U6h+npyTlplLNQlgAhRM45mWAw2MaYYGPABmMwBgwmGzAZm2ByFkkCSSjnPNKMJufQ093TuSu9HwrrOfxPeA4+xi9+Duu67muk6aq7qqe679q199prIRdZYopSfAhlxXMYdbOIpHXQdeSJC8l4ChGi/dYib2hgGghlo5FFgQbXGEy7hzyXjdjJv+D5VhPD7sYYeyjzM7sR40OEnEXsDJvk23ScsmVaOj1PpmskTYWbL81WVQYSGSof+Asdpp/Zm1ZZZrpOPy1TzmZCQIDqKQwmNaQxs6gQokhqko+H7EiiQIlHoXMkTXdMxRHrwxFuozXroMptcTCcsoAkCIiZGE2ByThkkY5oii78ljmp3Ysx90zSmoE81EJJeC9+h0RtrgvjnF+xo2AepUQtsv9QF85sFABjoB3Hpb8ldNTVoKmY2TSu/gaqr7icX62LYqSTZNe+g9rTZlmsAGpPG9ldqwnljUMN1oA7h3AWeu78+UHF9VBgFKakICXDGMkYYiJETPZZelyALZCDIIkUzahBGulFFW3oBti0FB1JkbJcJwMJjelHTCGvuhbJ7vynrKXf4duFvLw8IpEITz31FDfeeCPDw5aX29atW6mrq/va833twOf++++nvLycmpoa5syZw+zZs6mpqaG8vJwHHnjga5/AfxfpdJobbriBc889F5/Pd/D35513Hi+//DIrVqzg5ptv5o033vhKBqmvr4+CgoJ/N19BQQF9fX3/6fEymQwjIyNfGd92/LHlTdY8sIILfjyHUHOYnpEMgxmdqGp8ZbvmxmG++KCZgV39DDcN07ulj61PrGNo/zB7Hn+Hts87SKQ0isblEajJYVzAQeVhtRz6yI+ZceNZjLT1Ijtkxp4xHoC/3PIBM39+IjXnnkC8N0qsox/R7SX36FOtQCLUh1xchXfydPRQHyOb1pBta6Bv5Xo633yf5HtPYcQjdJh+FC3FE0zDSCUQDA0MnfroLvySBsdeid6wHu/J30dt2YWYiWEEKzFcAfRQLwCmoaPtWoU20I2YirLhjEs4Or0NKViMqWYxnH6k/FLcZ1+NXFJNzYVnU3f7PZSedBzitKMQpx2FlF+KUFyLmIoSnFBNqref3jU7AfDU1WHYPcj9+xF0FS2vBjOTxMwtQ+1oRO1qtjrJDA25uIrYx68gdO212uttDhxBP3oiZj1pyQrhs26h/NyzGWlsQSioAEALVrFP82OLdDLlnds5ZNWDmA4v4YZ22PAWW3pjbOqJURl0k/zgF+j7NjDnvZewqwk6IilMm5spRR6GVevm7LaJfBxy0p9QUUSBeb/4COnoHzDuyWdZ2x2ncOc7sOUDDuv8ACO3HM1fglkyGjGvjI6kSGvxHILnP8/Dp4znp56JnFpqcvSeOj4vWMSBzQcIXVPBMmUiuy+7j/Spv6D2911IPQ24wy2IFePIrHoDMzFi+TbNOoGCa+7g8M/+iqu/AacssndeHGPy0Yz9yYWEH/gZUsVYsi17OPDwo+xz1fPsxk7ylz2M0rIegJ/88nns7ZtIqQZ6oIzDjEbkSBe6Jx9Bz5ItGkNsfyOoadKageHOJe4tRTNMdvmnACBE+jBWvIDZ04RiZGkOJ7lhRR9lXoW4qlPudyKNDBB64AZ6i2ZgKnYCQoZJQRkxGUY1TN7b3YeQTVDmc9A4YiAYGh91a1w6uQClfz/ViWZEQUAtGovZvAWApW1JwvZ8ymIHGPGWgySDKHFEtZ++uEpCNagNOAk4JPqVfOL+Sj5tDtEaN/FJOm5BpdalsSNmp9pldY0d9sWDFLhkTElB8xWxvDWKNzXA/jt+h2F3o2CwsG858b+tA5L8ZZZGIi77oKcRgNiDP6PAKaENdlvK5DYngs3BXeMSaIsuQfIHwdDB6bOCrPAAyrTF5PZsQWnbRHr1O+RlB8n79ePQsQs1v44cM4GYiWEqdnrnXkw2txq3ZKJ2NDLS2EJkfyvpcJq2T/eS/ORlImmdjG7QmVEIOiU2tw7z6o4ekmkNQRSonzP9m1tA/1Xxv4DcvHPnTurr67n77ru59957D3J033rrrW9ex+f222/n1ltv5corr2TLli10d3fT1dXFli1buPLKK7n11lv57W9/+7VP4v8GVVU5++yzMQyDRx999CuvXXbZZSxevJgJEyZw9tln8/rrr7Ns2TK2bt16cJv/yIDNNM3/0pjtzjvvPEiG9vv9f1fL3D8bWnsDywYSPPPIWqKqjm7CxCo/i/8/raDrh1PsiKb5YFMPH+weZLAvTl9ao7klzLMfNfN+bwx/roORrhjbdg0gO2USfRH2P/U67e9+xub3muhsGMJbXoiWSHPihVNYf8dbhLftQFJECqaPpveztfS/8RJqRyPPV56DmU4iVk8kvG4NnnETUcrqKL/wYqpu/A1KTg6mqlKdaAZRYk5ZACMewUzF0MODlv6LqRDN6CBKCOFupPHzwTAQs3HE/iZEfxBBS6N1N6OMnoFgd4AgMvUnxyK6vSArCLnFRF5/wsrAJELg8KINdFlP/4CxeSndd92IXFBO+LWn6Hn4LjLhOM7CfIrmTMBbUYgyfi6GtwC1pw3D5mT1gmOJr1uG2dmAGktixCOsOeYsKxPldCOdf7OlYqul0UO9bF7wE5TiKhBFbKOmUB3ajtbdjG/cGAZeeNzia0gKdQE7a5IBAgsOw33shZib3sNZkMPwtNM4EEpQ4XeQ77NTcc1nCNOO5qVOiQMpGwurc+lJi8SzBl0jWVyKSCStUeyxE01rbOuN8favF4MgILZuY2yeC6Yfh9bbilIzEXGoDe3zl+i+71bIJLDLAu3RNAPvXE8gG+Lhvs8x3EFel95lz0CMjt+Mx1ZWw1WPrGVsnpvcWBsNJw+Cv4DHO10IasbiOwUKLPPZRIhn96e4facldLe2K0b1z25Cff0eops3kLfoSMvGQFaou+1uRrV/xj2jw9gWnm51CO5dReeFInrRaLzJfsubLa+a7Lr3ELr2ou1cib17J77v30yT6sNnl2jSc9g1kMLTtIrcR66BTe+S2b/NCnBFCal5A8f6Qtw9201KM6mW48wq9fBJupi8K35Jb1yl1/RhynYGMwJJdyHDKY27j64jpuQwNrGXcq/CY3viVAecCIZGZvMydilVGKZpketHz2WUEOIE1eIfZQrH4NHjCNkUnSmR3QMpNAMyuknQKZHVrfUpmjE4ZUwBkbTGiq4UjTEQ9Czj852kBBvDKQ3X+Tdgy0TpueeXCIbOkeUODGcOydv+jOEtRGlZj1g7ldK+zRR5FNA1MiUTCNcuZEN3DCG3GMkfpPC0c5EjlpWMMut49JwyTJvbUjhPh60HmAnziOaNwRRl5LJay9Os6hAi5TOx109F6N6HomcQimtJ6ALC/rWI6RimIFKZaEEQsEQQs2l61u4nNRgmFU4z4eJDsRWXUagNUexRSKoGvng3l82rZlSBh/PmVnLGkXVseeNV4Lvurq/gf4GOz7XXXstFF11EU1MTjn+j6H3MMcewatWqrz3f1wp8nnjiCZ5//nl+8YtfMGXKFIqLiykpKWHKlCn84he/4Nlnn+Wxxx772ifxX0FVVc4880xaW1v59NNPv5Lt+Y8wbdo0FEWhqakJgKKiIvr7+//ddoODgxQWFv6n89x4441Eo9GDo7Oz83/2Rr5h2KZeghGPMNZr5/D6IJIgIAmwvTXC1kiacqfy7/YZzuoAfD6YZGskzaZwmkK7jF8RWdo0jKRIHH3BZERJoPzwKRTPrCe0L8Shl82mYkI+oT2tFP/oemxeN9VLRgOQ+JLz4Aj6CC48FPv4mRw7Ko8VF93O8F+fwFtbiej2YWbTdDzzNKnlr9Dw3Mckpp/CcG4977cmGL/rZcTK8ZZwoW6do3+4CUkAc/ZpGLEIgpbGlG2Yw71oFVOt7X1F2MfNtBSBi2tRty5DOOkaMo3bMWIRNH8pgRPOI1Yxk90/vRbSMRxTFiJE+xFsDrbe+QJ5k6zFfLihHXdpHoFxtaT6BxFtDhxjpqM1bUV9+36yHU0Q6mb+Yz/jpeteRw8P4Jk+D/uY6cxZ+QmxsungzcOuJjA8ebSqLpoP/QkTCyxBObGgEkyDgbf+iplJk+7sJNrcjSQIpF+5B/ODR5jT9xlaRyPZVa/Tv3wV3nETcL1xJ0ePyqc/nuWy6SW0Xu5Gs/s4u95LnlOi0u+gayTL7oE4iiSwsStKjkNGkQSaw0nssoRDEpl04+eYZePIT/VgSgq2RefRGxiL2taAvOQSys49D0FWKAvvZZHYSlq0c+f2FEPP3IutZxcDZ/6avkiabfYx9Ew6hfqJhZStfQqjfQ/K2FkYPc38QNuAGe5DChQgTT0SsXwMZjLKy2vb+OUhPhBlDvUnMJx+HEsuIHD2j5DyyywTWrsDw+GFmmm8ka2l31GCPudMWuqOxnHIkYjxQVJLn0X/6E9gGDgmzye8ahlyYQUjhRMQ9nzOKEcSt2TywKoW5sS2YFRNoejnd6BUjsE+wzofMVCIOnohQnyYRiNIQaaPbsNLNGPw+vYeYvZcRuXaKbLrdCUMvHaJgYRGXDVojqi0RbL8eLsTd7yXcycWUe8xEXYt48Mx5zM2R8LZtgFTEBE7d/NZxE1H+TwEQcAwTQZNN/1KPpWELTkCSaDKpzCQ1LBLAhndIMchURTZz6hcB22RFFV+G5gGa7tibOiO0xrJsLQ9xSBeyq66AUOQOPHPu0mLdiY5RmjP2NEqpmLKdnqLZlDYsoKsK4i4+mWiGZ3KHCdasMr67q7/FLOrATUctiQjkmF0fxGpmadz15YYyAqEuvFFW0noAubExYipKPaOLfhH2mm4948Y5RNgywfoOWV4RB0pvwwhHSOVW4PuK0RKDoMookxdxIQbf0T+lHqqjqind91uTE1Fig+hGSY5Domop5RYRmNfXwzDMDmkPIf5F55Pwbh5/4QV9V8HgiT+P6/cvGnTJi6//PJ/9/vS0tL/snLzn+FrBT6hUIjRo0f/p6/X19cTDoe/9kn8Z/hb0NPU1MSyZcsIBoP/13327NmDqqoUF1vCV3PmzCEajbJx48aD22zYsIFoNMrcuXP/03nsdjs+n+8r49uMxIe/ZNW1T1PrsWHoJn6HTHdKI2uY6KZJZ0r9D/eLa/+nDJZrk3BKImceVctPfjKHfV0jtC5vZuoPD2Pzg5/SuXIv/kofXWubqTh0DMVzJpBZ+Rr5c6ZSdNhs4h39TLx0Cb5ZC/BWFCL5g5ipBEGnxII/XIqrOIgaiSDIClIgn+JFc7GVVDLlnlvI6d1G7nAjEwo9tM84D1N2IJWNtkQWi2sQMnF0E5SBJtprjsCUHQjhL6UNtn4Igojh8GEqTpSqsbS6awlv3429ZzeZnm6EuacjpqKQCONO9DP6xXdIrPsUU81YxORQL1OvOYVtf/yEzNbPqLvxV/hPuQTRm4OrotwSYkwnQLYhub3YymrI7tuC6HBz0Qs/sfzAimow3EHkgSa8oSZIjTCME8MVoEaIUOm34VZEtGAVhsOH7i8hMH0ahqYiSCLGb/+MZph0HXs92dAwpqqiVI0ltOsApT+6lmP31tFx7M8BmFzkRgDWVBxDx0gWTIMvOqJ83Byi2GsjntVpDaeQJZFlLSFcisiMEj+KaHUFvXnTIqRoj6VqvOxphK69KE/fhK1uEtJIL4JklTyMeAQ1v47BpMZL7+8j+4O72Xb1L1nZNsyMygBzTrwWRRQIh9PYJh+KOWmJZRlSNRkmLUYPD0BhFSOvPIxp9/Ljid9n+Sl+TLsXLbeChKsAw53LPjMP+prR3UGSjlzE6sl06F6+iDo5uczK0L60q5/KDc+hduxn8n0teBYej3z4uaScQRBl/DNmoUdDNIezqG0NiMkw7XGdRaPz6a+YB6KMHO7CVDOYioNmWzm6K4Ac6Wb4wzcxMNE2fsD2vjhNwykumlnB4xu7SOsmpmSj3GmQ1U0qHJaOUoHb6oz75aJa0r4SRAG2hHTkwkpOKDYYc83HCA4PWy+4FAoqKbzpAqqiDWQ0g6Rq4LeLFEcbidjzSGkGHdEMAymLPxRXDSRB4K+7BxjMHc3ugSQX19vZ1BPnk34rwDVMk4kFTuqDbgaTKiO+SlKawfPnTeGNhkESzjzKt7+KrrgQB5opjjbyu+E6ZAGkMTMp3/MuLWHLLFcb7MZ+7KWoLXtwjpsGsg0hE8OUbEQzBr+YlY9w3JVo/Z0Iuma5yhuaJW7Z04apOBl18WmImRhKxWjMVS/RkwIMDT1QhiPeD6LMsOQns/Z9Qi8+yvDK5SjBPOJdQ7iLgpiZFPpgF5IgMJjQcMsCIxmNiw4p48W17fzsiY2kEyrL7z+DJ5/6zTe8on6HbxMcDsd/SDfZv38/+fn5X3u+rxX4zJw5kzvuuANN0/7da5qm8bvf/Y6ZM2f+t+eLx+Ns376d7du3A9Da2sr27dvp6OhA0zROP/10Nm/ezIsvvoiu6/T19dHX13fQr6O5uZnbbruNzZs309bWxtKlSznjjDOYOnUq8+ZZTwVjx47l6KOP5rLLLmP9+vWsX7+eyy67jOOPP/6/DOL+1TDy+uMkRzKohsGezhFyRwUocshIgkBUNahyKUz2/9emf8NZneZElhc+PMALj2/k1J8vYsZVR5IzbxFFUwp48v0mou0jyE4ZT1k+/Zv3WZYLdZNItLRQPH8S8c4+jFgE0eEitWMNYk4+yvrXCG3abpkqJlJW2cnmIH7gAHo0hB4eQM8ppcNTy6iRvdSlWkl9+Dy6rxDB4QLTQMurJmADTIOqbBeG4rQW55xCxPELEAwNOdp9UD+n3GlQeP7lpLetwLvwGIxlz2BKipVFatmKrWU9wlk3ktmxmmx/L6I3AKLEvGd/R7y1k5GPXkXb9QV6eADR5cVIjqD1dSDYHSiVYxGcbuSKeoxsGtGTg33mUYgZi6BsuHPR3UG00ol4FBExGUZMhrEtfxLx82fJ2P2Wi/u2Ty2No1lLkBw2fDYRuyxQvfpx3FNnIxdVYI6eS/GPrkfIpnj87CkUumT8dgkAWzLEvEKFt/f288zeEVTDJKMZrGgd5tCqHHKdCpOLPHhtMs3DKVTdZCCRJaOb1O15Ay2/ltd39RJZ+H0oriNw6BFWq7/Dj5lT9CWnw+CvTQn2DibYflQPuR/cS9GMGi6YVMia5hDxz+/CAGaOycfw5BE2FLqkPAxPHnKoDXnUVOI51bw180rCtiD3JfdhuHIQEyF2xWzs6E8iRXsYp3WgjV5AM0FOf24r/c4yFFFgbqkHOdxFvpzlrH3Pcs1J9+G9Zjnh7i5ejZdiyg7sX/wFM5tCqhqPNGoaHdEU8vFXIoR7KHTJnFpq4pQF5KEWtJ4WEus+RYj0YpqWz5bp8OK/4DrqAnYEh5tCj435pS4cssiSUfkEZAPVBCGbsIxJJYVCu4lpmvjtEk5FRF72JD0/OJ1DnCNWd1LDahovs5HZvZYZD99Gdv0HjPnLm6gF9TSH06R1E3usD8MVwCkLtIbTVOZYCs45DolYxuCZzV1cNM5PXqKLiseuQcimmJ8vcLRvmJpMG0f6R3BpcT5vC1EbsOMWdTyijiIKHFUbxNuxkWx3O3JmhJWOSahN2/jx7PKDAYsxEqIyx2EZmbp8mHYP9tnHMvDRh8TXfIzhChA2rUyU3LWTvrhqWbnsWUtKM+nSnKj5dUj1MyzrjdmnWKrp7iDKxPmUijGypZORevYiaBnLOBh4re588o4/DWd+gERbO9XHzyFv5mQck+cj5ZchrXkZmyygWXqN+O0SF8yv4rpzJ3PG/EoufX4Ll136629uMf1Xwz+E4/Pt7uo66aSTuO2221BV6wFeEAQ6Ojq44YYbOO200772fF/r3T788MMsX76cgoICTjnlFK644gp++MMfcsopp1BYWMjnn3/OH//4x//2fJs3b2bq1KlMnToVsOp4U6dO5ZZbbqGrq4t3332Xrq6ug2W1v42/dWPZbDaWL1/OUUcdxejRo7nqqqtYsmQJy5YtQ5Kkg8d58cUXmThxIkuWLGHJkiVMmjSJv/zlL1/nrX/rYRoGO6IZPAEH1W6FpWu7iGsGzYkss3OdSILAjmiafLuEX/mvL3tKN/HIIj1rGxEkkT13WzpLP71oEoGaHOw+O6ZuUHvrncQ3rKDl4UeQHXaGdjajpbPsuvd5MgND6GkrE2EkYwRnTScbCuE7ZC56qA8zmyZwwnlW2SuTRvcWUuRRMNxB9JxSlt/4BvtSTrKN29G9BRgOP0qoBWOoCwQR0+lHrZhmnbChkwrWYdi9iA4X8pKLEZNW+23ngh+wxzcJcdGFpJxBftBeR3zCMbTkTce5fyVySTW963Zb5GhDJ7l5Bb5xY/Adew6mpjKwZT+iN0C2bZ9l4Fo9BdHttYQfe1qJbVmPXD2B7I5VVpdQNokUG0Bo3YrUshH7SA9ifAhBy2CrGY/g8iG+8wdM2QpCzXSC6Kdvs/KGN8h1yhQM7mJg8x4iqz/H9BUgpGOWN5LTT4VDxQDybAa5Roy9GQ99WZmLppUwo8RPgdvGjBI/NbkuHlvXwawSN1u6RxgVdDEq6CTXKVGV48Rvl3gx5wjMLR/y8wWVOF6+3VLCHrOQG3cpmIKI2dtM66OP0v7cc5xrbKNjzqEIC8/FXjuO4vMuxjHcgnn6iQw+eDPDl53GpbMqMGUbTlm01JN1lWbPaAa8NdgkkeNH55Gz+wMazz+JXjmPASWfEq9CwKkQ91eyR6rg7Jd2UaPEufukCWTvuIISbRB572d05oxDyCa5SDuGh9c/wHW//RmvdLzAkpoAUiKEPHGBZUfyZeC5sNKPmIoQ/mwpzo2vI2gZ/N1b0AY6YcJhCGfdyFbPZNZ2RsjqJpsjEl342dGfpHfamczwq+wLq0xVhqgPWsRleybKvrQbMRVGCncgaBkMQDNMAoN7UQ45hvqnXkEwNMRkmKa6YzHrZqJFhsHuxjbnBN7cH2Z/TKA3nuHDphARZyFJTxH2kR7K/VaAAbA/lGa00c05U0rQP3qCsKec4tufIOQoQLd7MFwB3o4ESfvLQBD5wWg7sihY5yQpvLCzjyte24mRW45z3DTikoekarCh9iQC/TuRh9swFBfRQ79PvcdEdwagqBY51IbWtJXic75HdiSBsflDImkdpywg2F2USgmksbMPKq6X2nV02YHuK0LQswh61hLsdHgxbFbAKqgpWvOnW11esQH8ssFZY3Mt8+I9rVY7eyRO21vLMDUVzVeEUjORUo+CLMDx9UFSX5KyD6sKsK09QmTQKqd/x/P5EsL/++Tme++9l8HBQQoKCkilUhx66KHU1dXh9Xq54447vvZ8XyvwmThxIo2Njdxxxx34fD5aW1tpaWnB5/Nxxx13sG/fPsaPH//fnu+www7DNM1/N5577jmqqqr+w9dM0zwoWFReXs7KlSsJhUJkMhkOHDjAgw8+SG5u7leOk5ubywsvvHCwO+uFF174dy3x/8q4QqgiHRqh0C6TiWZx5ToZzOg0xrN4ZJH1wymyhsm0HAcTfHbybPLBffPt0lf+7VdEFhe4aU5k8Zb5+fhXb5MZyTDSFWO4KYzkUBj7kwtJ9IYw2ncjSCK+6mJCe1qQHTa8FYUUz6rDUVyI4vNhJEaI7WskcaAJe2kFg598guQP0vPXl0iseBvRm4NcNR5hw5vYu7azQ8tHt3s4ccNLjDO6EN1ejN2rEDMxdLdV6lRzKzEkBXHbB+g5pYihdmzpMIY7iHDclYht260On579VMtxDNPiXWzpjfOnk0bhyQxT3bkKvXYmpq5TNGsc9glzEGwOXAtOZP9z72IO9yJ6cnAX5dL95lvI+aWofZ1klr+IafcgurxISy5FcTsJv/NnlGmL0XpaMOwey14jtwgjGbOIraJEtmk7eKyumJE2q2NGDhYxtHk3zspKckcFCKd1Nl95EwDx7iGab72R7OcvE27s5PkJJzFs2IllDEvj6MBGKnw2wmkNEQi6ZKYUutnaO4IkCFw4o4yntvcRcCosaxqieyRLqRjjkCIH9uY1XOhqQakej5SJ45k2G324D2XwAHeNS1g8i/5Oyo5fTPFdz2Gmk/xw56vIbZtpHXU0mb0bEXSN/qdfoei0M7n9uNsYnTqAseFdXIl+8hJdluO2HKNwaBfTb/qEK17bhejJYdzVF1MoZ8l1SuQ4JF7Y0oVdFhnd+D7L//w67ZoX1y3fw/frP3HkSx3sKJhH03AKwxXg1cMVjPGH85t5eRS/+xEr26NgGqQCVZYdiSjT4yzHs+EV3uhVkC+53eKEuQKY3jzEinFIkR7s2Ri7BmKcPb6AvYMpRgetB4OmUJKhpMYNq4Yo8iiE3aUcGM4wnFRpyboY40hY4pmBClKym4JQAz4FBoJj0XLKLE6MO4hpczLa7CX52kPYKkYRyRuD7i1kUU2AdZ0R5pf7OX3TIwTinbijHfTaCnEpIroBW3tjbOiK8vqQjyqfwvARPyKU0tg+kCK/exOftkQwFScPL2uiO67ydnuW97pBGWqmPWNn8QNrOWF0Ps+fM5lOuYDwmCMZTGkc2b2UfUMWnwpNpUcpZFV7lPUDGk8f0EgsfR4z3Ice6qP7z8/grijFTCeodlleYYniiRzIuNByq2jLnYSWX4uhOJATQ7zRFEP3FRGXfYjtOzBF2fKFs7sRTAOPTcS0OVE7m0hjrT3CkZeRO7aSePcg9lw/o+64BxxeALTuA/iHmzAFgZyhfRR5FCYWeHlkTTtjir3Iisjiyy/9p6yt/wqwlJv/h+NbHvj4fD5Wr17NG2+8wV133cWVV17J0qVLWblyJW731/dxE0zzS4e7bwB33XUXV1xxxf8TQcbIyAh+v59oNPqt4/ukP3ma3Q+8xJvL25hX4KZ1JMPukQweWfwKh8cpCaT0r17uv/3Or4icOLOU5Vt6OXJOKYMNoYP71k0vIrQvRMnMEjzFfr54YQfnfXQ3gx9/SKyjn8rTjyPb3Y6ezrLvtXXUnzrTUoW2ORja2kDxNb9G0FXULZ8gOiyl2OENG8hfciz9779L3qELUSpGkymZwHuNw0wr9uJWRPKTXVbmJjECwVKQbAjpGNldq7GPmwmijGFzIuga6bXv4Zx+OKbTujZCagS1ZAKCmuK011p57fxJfN4RZ4nRAO4ApEbAnQOxEBg62bZ92CbOx5RtGK4AUqSHZOFY7DuW0v3G2xTOmYxj6mFo/e3ooT5skxcS8tUQHN4PsoLuLcRUnGAaKP37rRZ3NUts7We4Ro1G9AYwtSx6qA/7uJkYiRFSO9by8wuf457nL0L2+VHDYXrW7CR/yij0dBbTMBAVi2hdcewCVow5l3fGzuLR/S8SKppKLKuzsXuEUq+DCr+dNR0R3tjWzQ8X1FDms1PlUxAzMeKSh5yB3ZiSDdIxfr4/wO+cGyxBuWQM0ZODoChI/iDaUB+Z1v046idYxrKzT4TOPfxVmoYkgHTUEo5s3ozbTLO0I8OFVz7A8P2H010xH69NxLtvGYJsw0gn2FB4GHOimzCSI4gON4kxi3BH2hjyVJCrhjF3foZcPcEKTBo3WNfN4cKsnwt7VxJdv5p35l3NRdJuOl54kZKjDkNwuDludyVHTSziqun5iIlhRlyFeLURxI6dGBWTyNj9bOiOs0hsZfozIV67ej4VjR8SGn8cOQ6JFW1R7LLIlEI3WcMkIGn0ZyU8isiewRSHFLvIGPDc9l4un1pI2hCQRAHhrXuwHXUJcXsuyS+zEGndpBIr4BHWvgpzTkeK9nzJOfNare/BavYOpplitPPCUC7nVEs826QyJs+N3yEzUWtnq1CBIglc8exmXr5iFuc/tYkVh4+wzD+HIwp0pIFmtrsnMjbPQVY36I5prO4Ic/q4fNZ0jnBIiRfdMBlKaVTn2BlIaPjtInn929FzSumR8iiy6yxtS3JCZiumodNccRhBp4xfiyBolrVLYu1HRBo7cRXl4pkwBSlg8aPcisBwWqfUpiJ37WSg5BBSmkGF2kfaX0ZcNaxsXyZMRPJjl0U8rWvJ1s1DysRBzyJ27iZaMx/bW7/HVlaDHh7AzKaJNnfjLg4infNLbNkY8lALCCLZ4vHIDSsYqT+cZa0R1jSHWFgXZCip8v6OHj5+7Emy25755hfYvxPf9D0jmUzidrsZWP8BwYD//77Df4Frf/cQYm4p99133z/o7L7d+EYDH5/Px/bt26mpqfmmDvFPw7c58Nl49CK6t/fjL3DT3RNjOGvQmVKJawaSYJGWSxwKgxmNnrRGrdtGZ0ola1iXPt8uHSxvjfXaGL2gnFQ4jc1tI5vIsm5TL3MOKWb/jgFOf+gcbEddAg2r6Xz9bcrPPoM9d/8JPauTN7EMZ9CHqMjYc7zILifCSdcQuf869HSWkgsvR23ZhVxUgTbUZ7mb+4MINgdiSS3ZLcuwjZkBgOkrQN+7FnHiYairXsM+fiY4fRY3ITqEHupFGjcXMR1D629HLijHtDkP+g292aEzvdiHTRLw2kT2hVJMzxVoTsrUG72oG5din7QAMxnF1HU258zgkMROhstm4lZEbKFmhv/6BDnzFqKH+ki0tGDzumn/ZDO1py/CVjUWrb8DW90k0uXTcXRvt3yLCmoRdA1tzRvYJ84js2c9xpcaNqnmJlyjx1n2FuFB4k0HaPjrBmZ+/im7Tz+RMecdiqkbZCIxfNNnWX8Xt4/k5hUIksjGu9+nYmEVeXc9R2dMZXV7mKocJ3PLfdhFWNsd5zCaafaNZ/dAnAkFHirc0Je2vJ90A1I3X0zlr+5EHO6k/cknKJo7BfvsYzBF2Sp3uQJWVkAQMWxu1vYkWeiOIKaimIJIpmgck3/2ISU1AabUBPn9kRU8XjKTE1s28+7+Qa70t6L2tFnXtWw0Zk8TWnczqSMu55U9A1zY/jLyERcgpcKkgnXY0mGQbJiSQuvVF1I0cxzq2b/CJ2QxVr6ImUrgmDwPw52L0bIDc+oxxE0Ff7wbehoRc/JR8+ssgu76d5FnnWBl04Y7MGU72cLRHPKrZWy5ZS6NCQm3IlLRvorwqMORBHDJAsrQASLv/Bn1glvx2SQ6RrLs7I/jt8vU5joRBRjJ6NQF7DgaPkNw+8hWHUI8a+CVTeSGFWj9HTROOZfRXpOooRBI9SElQqwwq6nLdVI2tIOBwikkVYNij4KUHKZd8yKJYJggCRDN6MQyOn/Z3MnDx9UiNa7hdXEiZ/j6ieaNwSULvNsUZnaZD0UU+PUnTTzkWQeHX0RMNRlOa+Q5ZQwTAvFOdE8+WdnKZi1rjVAbcJHrlAg2WOKdoeJp5KhhIkoA7eGfUXDxVRhOP8bG9zGiIcJLrqLgwHJC9UeQVA0KXFa2RtFS9Gs28l0yopZh44DGxAIndtmSUfDrI1aQ/TfeyKZ3SRxyOu4tbyFMPgJ9zRtI/iBGLMzQhq00vbuTuSs/RDA0DJsbZfAAgpZBy6tBGu4glDualkgGSRCIZTV29cdIZXUeenwZQ42bvrXBzz8t8Nn40f888LnjAcRAybcq8HnooYf+29teddVVX2vub5TR9A3GVN/h36B1ax9Zw+TjfUPsHskcDHpsomBZFBgmO6JpetIWKT2lG2QNk9m5lgrqwuocPLLINY+czegF5Uy8/UY+XtfN4N4htm/tZ/68MhSHzCkdW3nuRy+S/fgZEru3kzepDgyd8T+/lMm//gnFxyzBP2Ecntpq+jfupXP5JuRdn5C36EhERSazey1yUQWZ/duswCWQf9AnRvfkIy08CzOVAKfP+v/Y2dCxC1vdJLIHdmIMdIBpgKFbxN/u/ZjJKKLNgZmy1LqFcA+G3cvp+XFKvQpFdp3WSJZD3Al677iGWpel2KsnYqgd+9GjIQRJYmrz+xixCL1xFXn7Bzzd7cFdmo+ZGMHMpPDPWoCttJKSeeMx0kkEpxtxzikYrhxSmkG2dDJaTyti524wdOzjZqLlVmCbOJ90KIo0eRGpUJTUActBPrJrL/4LriPx/FsM3/cz1LSGKCvIPj82rxvRk8PIhi/INm6j6/OtpENRZt14IvYcL8tbI/hsEnPKc5hT5kUzTPqTOjNLPBiJEc5+eA1j890kVJ2+tEBvPItbEVnWMoyvqph2qQAjv4bys89EqRqDkBpBzCSsLi6bGynSg2FzI6aj1D19PWZXA7HP3yaUOxr7UBP3XzmXDX/9K79fXI4h2zmncyvFbpkrS0fAE8RWNQbRm2NlcurncnTzVNyizryKAPbJ80k5gxg2z8GgR8gmMWQ7tZddBEBgpBXzi5eRFpzF0BE/Qi0aS/NvfokULILN7+MfaSebU47ozaEzZxy64iLpLsQ29XAS7kKr5BUYh+4OsncwzbKbD6c5KdM4lMAmCrwuT2FHfwKPNoLSvplBXw2uqire3z+ELdTMqNBWlu0bYGy+i9J1z1LqMBAFgTs+b0UfPR9BVlD2r8IrmxbvrHoq6lA/lR/ew8edaQKNy9F9RRh2N5OW3kWh3UTLryUgZChe+yzdMZUdcSeVSoKRjM6ajgi/X9FCvkuhwm/HqUgofQ2EaxcyJs+NWjSW057ezPFPb2FBhZ/pFz9GYWgPjywpIff2BsREiB39Car3L8UfbeWWT5p4ssOOmB4hpRrYQs0c57YkPVyKCGOsxg+fAtLAAXKMGMGf3okWqLDsXkSJWHsPOR/dj1hQiW5AVWQPHSMqGd1k2LBTrA4ykNCQYgPMcQzhykbojqls7Y1bmU9JQdDSSNFuRG8Ovni35ZMnO9AXXUJo4glkertxFwUZfdo0hD2fI6aidMYNdsmVlqTBvtUIpsH+kOUqn+eSeWdXH32RNM++uQfT0P9pa+y3GoL4Px98+0pd999//1fGTTfdxNVXX82tt97KrbfeytVXX81NN930dwknf7up3N/hv4X8Ig9uWSJrmAe5PMDBjM7flJurXJaWz5QcB2O9dvJdCscXe+kbTHLOBZN47prX8JYFeHfRjylxyGgpDb8i4ilws3tDD88VT2HxGWPJhGMoPhc9q3cysmUDyT3bSe3dilI+CmXifJTKMZQumknFiUeQbtgK1VPIROJg6HS/+ALZ0DDayAiCzYEUKMDMppH6m5BGBuh7+w1MmwulrwFt1xcAaL2tiL4galuDxZUpqrHsJ77sxNJjEQSbA1MQMfJrUAYPoPuKUHZ+RFa0MdEZR1vzBhWX/RD902fIrn4bW0W9pV8x/jCLezPjeISK8eQ6JNL7dnCpvwPH1EORR03DVjUWKa8EpaIePZ1FKa4ie2An5pYPMVp3oRsmykAjwtQlDFXNQ0yPWEKLmQRqwwZ8p1+BaXOSe919+A47jr533kH+8T28MuZIxj9/A/mXXc+Ua89ES6YQcwqwV9bR/MfHiTZ3A5BTX47N62bf/CspuPEBphV7eXlnL/W5DprDWavEIAsWkdrp5hdnTKJrJEOuU0Y1TKpy7PQnNM7PGyZ49ImkNOOg2rVZPe1LnaNhzPGHE7PnIpgGXQkDQdcoveBC+t9/D/dR52CTBNS8Oqa+egtripoJGwp7B9P0JTRGsgZIktXR5ivCzClGDrXB7s/4dPYASvdOxthiGDklePt2IXTtBVFGSEXZp/mRMnH0aAjvwmMQIn2oQ/2gpint28ywYafo/hfRiyxDWjGTsJy+bW7Kkq2MfKlH1e6swmmkEWP9dMWyvN0j0hxOEs8a1Pgk7vtgH43DKSYVelkYVEnZ/Gh9HWzuiaNMW8zcigCm4iJTPZtrPryFd/cPYpu0EHP1K9TnyIwp8nLz5xa5XvAXII30WUJ+Q600v7MO74JjmF7sJbL6c5qiGoN/eZTACechZBPsiNkRdnyCFCwinNIYSma5a3OUXKdMTcDFGVNKyDejzPrB0xw5poDPjGpEAerXP8WHzRE+uHwmr180jZRm8Oe7v0dv7ngyNi9/uP9a9mY8uBSRDaVHou/byB1HWc7mAH5RpdNhKYLX+QT46x2En7yD7oKpCLpKpno2ScUSZ1Ra1pN+7wkSjfuQXQ5sdZNAFClMtKEWjaU6x0ZaM8nLDFhEZtNE9+Rj2NzElBzynBLFXjvdaZGoZt1aDG8hYn4FYmwA0ZtDWrSsOQoHtmMLBhEkEZvPZT28yHYqxSjj7HHEZAQjFgYtQ2WOnS/ahtEMOGtqCVV5brIZjdCB/yNS+78aX5ox/8/Gty/waW1tPTjuuOMOpkyZQkNDA8PDwwwPD9PQ0MC0adO4/fbbv/bc32ipy+v1smPHju9KXd8gbFMv4bbGL5hS5OHAUJLhrM6IZhwMgv4rTMtxUFPipbc/zppQijMnFhAdSHDEnaex/fFlzLj2BB6/9FmmjQ6SU+mjcEYd/ZsPEBxbRqIvRN6kWga3NWHqJqOvvxojEcOIhREnHoaxawXp9ma8h51IevNyBLcPQRQR/RZB2YiGkAsrUDsbLc5LKoGg2ND6OlB727Add4VVrpAUdG++5S3U3YTgcKG27kEurUXKLQJdRe1oJNPZir16NOnmBrwLjqEnbzKJG77Hiu/fj2oYXDi5CGfTF5jF9UjxIQy7m/CrT9Bx+q+ZPLAGLdSHNOUIS5JfUtC8BZZnlSBaJM1oP+kdq3HMPhbT7kbd/DH2MdMxUgn0mpkIehYxEcKUHYjhLvRQL0Ysgq1mPP1vvIQoifgnjAPZhjbYjePQ09APbCeycT3e6nIEuwNpwZmIbdvReltJd3Uiux04553I0KtPs+uUWxhOqeS5FObnC/RqDgpdEgYCwykNv0Pi87YoR7a8gbboEoaSGpe+tJ3nBp6j9gOTG++6nqvmVjCY1JBFgQohStYVZP9ZJzLlzhswAyVcWXYMt0f34pQtxeBcp4QSaiXur8Sljljk3f2bECcs5Ix3+7hu0SimbXgM+fgrkSOWwKfmLz1YXum/+TJKb7jTsizoabI8njQVKb8U01cAhoagZtBzSmhKu6gXQ+hbPmL3I68z7vvHENrWQP5cq/QZmfc98g6sQI9FkGsngWFgyjZSgSqcwy10OyvJcUg4M2FMu0WSlQeaeDtexPGDyzCSMTaOPYveeIbpxT7ssoBmmDz4RRt3H1XDB81RFlT4iasG5fqQ9XlzeNHdQQ4kZIZTKklVZ2GlDznai77tU+KN+3EE/XQccTUOWaAqsoeNSj3T/BpSrJ9s/igi91+H8KPfk3dgBWtyZjHPGeKPrQqLaixz0loxArpqEfgnHc6Aks+Uix7lo0cuY4LfYH9cYiiZxfmD06l9ayk5ZoI+w0Vp32YwdIYr5rC1N45LkYhmNAzT5MiaHEJ3/oRVJ/+aM9nNGt8MFpgH2OkcQ/GLN5N3/GloA920jj2BMq+Cq3cXmV1rSB5xOe51L6OU1aL1dSBOWIjuLUQOtbBXLKPMq+Dv3IiZSiDklaH7i2lKOajzmsRNBd0wcSoiumHi0uJWR6fdy4gKuYkuht1lgBWMSY1r0GMRjMgAoicHcfIRiIlhYp+8invyTISK8egN65BrJtHtq2N91wgpVefw6gDnPrmJyGCCeCTBSNd+hj+65ZtZXP+H+KeVujZ/SjCQ8z+a69rf3oeYU/StKnX9W9TW1vL6668f7AD/G7Zs2cLpp59Oa2vr15rvu4zPvzhSb15JkUNGz+r0f8nhiX8Z+PxbFDlkPPJXL/fWSPpg0JNrk3h11wAf9ydYcdObuIJOPv7pC5Q6ZRoOhBEkkUhjJ76KIGufWEfVmSfQu66BkvmTGHPrrxEcbqRgEZ1vf4ix5wuEaUdhL68mu38LgtuHnF+K4PIhBYuJbdtktXX7g0iFFZhfauEINgfiuHm4Zh8Ne1aAlsFw+hGyKehvQygdZWnmTJiDmFeGPtSD4c5FHjUNz7yjkPxB3Eefh2kYBFc/Q833v8elhWF+VBg6mMg1mzai5tchZFO4K0qZ6EkTWfMZotPN8HN/YOBPv2f42XsYvvunxFe9jz7YBQPtZBo2E+/sR2veYZXh8kvJtu1Dbd+HuHcFYtIS7jRbtoLTh9bTxtC6TWj9nbiLgthzvKhD/QiiSOv761G3Lkfv78BTXsjQ1gaaX/mYzluvtuw0vAEEScQ5/yRMxU74wjs4LLmN40flkueyodk8FDoFmiJZUprBUEqjLZrlk4YBemd/j+Zwhtf39DOzNkjxj29g3bv3ce38Sr7/yk4eWd1G6bpnLf2Y/gZuPu43zHxFpc9ZzkOhtThlARPIk1UE02SvUMInLZGDgpHihIW0/OpaXjmznnBaZWjJ1SAImKKM5i9FlZ24BhsRlj9NydGLENNRtB0rkHKLECsnWKKGoV7EVJSov/pgwFjx6X1IsX6k/FJGnTaHWHM7jqAPubACW/1U8kdaMConI9dOQm/bg5CJY9i9OKNdJAI15LtkHHoKgMyr9yBs/5Dwey9yfLWbzbc8yW3C4cwXOzilxk2uUyLgkHhkTTv3FDRwy/I27n2vgZRmUuRW+CLmIZI3BlMQUQYPAKAaBvPKvQyndHZmAyhltfyi4Hw6jriaKcdcTfHqp0iUTMZtk5Bi/XyeLmJjdxzjit+TS4oVvpmsbAkhDHdx9Kg8Trl9OYmswaaEG8MdtLhs3iJawml6njyD8bteBkkhdeHJLHQOUf76B4R/fgH66tco3P4mfcWHsNY1hVhWZ1Khm0Ma32BOmZeJBW5ME/J/dg9H1uayMXAIoiCg+4qInHQMuYcfSe8rLyLVTaHKK+EMWdlRqbCClGqg1E8ntPQtWv7yOurKV7H17ELQVMq8Cjn9O8EdsNzfm3ewNSoz2uylLSmQ1kxUAxKqgSQKaHYfpmRDRSTHiGG070YSBXw2K7sg+oOY2TRmJk2yaT9Czz60lp24p87GzKbpe/z3CHYHpmKnUNFwyCKzyvxEMzrXnzAWxS6hZ1LE+9v+17e1m4L4Px/fwlLXv0Vvb+9BDZ9/C13X/0Nnhv8bvgt8/sWh9xxgOKuzfjDBYMZK+Tulr36ISxwWKfFvwdDf7CvKnQprQik8sohbEql12/DIIkv74gzuHWLciaP5fDDJguNqSYfTjLrqR/z+gbUce++ZpJqb6NveT6yjHy23gp6/PI2pOJHdTjB0jPXvYIQHMBIxpEA+ejREsmEXgqzgrqlBKau1/KyqJmFmrBq+lluBmImjFtQjVk+mOWcSQm8jgmkgBgotgUDhS+Vjdy5C2WgMd9AiZKYSCIqC0WJ5ISnl9aiTjiaz4UNGlr2Jq3cXfeVz2Vu1BFNSMCMDCHYnmfceJ7DoWLS+Dry1lTiCPvSsRu70SSiBAEYsjNbfgWB3kugNsfvBlzDXvwWGzuAqS0/KSFltzmJimPS+HXQ//gCp/kHsOR4GP/ucTCSGZ9Zh2KvHINdOZvSPvocaGgJZIdE9SE59ObljKyg96lCSOzcg2By4jzoHc7gXPacMx30/QcgtJq0ZjFWilvGmYMkQeASV4ZSKWxG54+hRqIZJoVthZlkOp04sxnD6maSEGExqXDCrgrk1uRiJEUQ1BZkEb50/ng2XlzOU0hgU/EiigD0ZAqyOpbFKlBPqc5G7d7ErMB1TtlH3y18jJsNMKHDz0YEQt3/Rg6BlEXYtw7ZjKVrTVgSbA8HpxrS5sdVNQgtWISbDCPtWI9ZMxYhbnJBY8WSMzn24Zx+JHupDH+zGe/jJHHh3O60f78BIJzBiEatrzuZC278ZuaQGw5WDPNyOmAzj3P4etnA7gmkgNG/GXlmHEQ3h/f6tVkD2/FvcdoiLDu8oBC1NSjVw9+7izCklmBMX88tFNXzwkznYJIFffXKA2oCToZTGw02gFo2lNkdhoWuYaMZAN03G5TvQyyezc/8g+4cStCx/CDGngL64RsAuIagZVN2gP5GlMNHGqgGT2oCDl97fx97gDCYedTV771zI2I1PMa3AgTzcTtHFL2FvXc8jq5rRfEVIM45ByCaY+tormDY3hdl+yh98idYZF8CsU8iTMsyJbeG0+1eTn+oh092BWzJ5fU8/cjpi2VgkNWbKfczMl9ij5jBn5SdQNYWCxUdgdO6z1g9PPoKaQm1rIH/rawh6FldxkMxIBlv9FNTOJlJr3sWz+0NGiiZZHY9jZiKXVOO7+UJ0fwmVHonCdA8HhlPoBjhifZY7/HAHjpEeTNlBb/1R5ESaMRBY36+SWPsRmc5WDE1l13OrrCxuzSSr9D31GPIPP4yRLRvQvYVgGhwTtOYGWL5/kNMX1dK/exWuYMk3saz+a+EfwfH5Fpa6/i2OOOIILrvsMjZv3nyQO7x582Yuv/xyFi9e/LXn+0YDnwULFuB0Or/JQ3yH0XOZUp1zMOgB/l3Lek9ao+9LYrNNFA7aV3SmVCQBZuc6ybdL5Nsljh2bx/VXz6XumFHEe2P88Iyx1Jx8KP5KPy8svoa7nzof58TZpEJRcutyLWuKSA/Fl17FsiMvpuzqX7H3kZctNWR/ELmoAj08iBEN4SgtIbH2IwAMhxcjp4SopxQxJ59Mwybk4Q50bz6qZMewualJNkNeOUI2idbbYpFl03HEli0IWobdFKM7fIjJCARLMfJrkPJL0fOqEZxuJC1Nx2E/xjt/Cc+Fi8j9/HHK3rgdYePb6NGQpcXj9hBbu5z+DbsY3rkfZ2E+udMmYSZGiDQcoOu9jzGiIfq/2EjR3CmUzK3HzKattvQcD10frrLKY3ErWDBUDXdp3sFWdFdBAFdRELNoFJ2vv012zzoAOpZtIdrYiqe6HCW/EE9pPlp4EOeEGeihXg7c+kv6332L5h+eS+GcKaxVS/DFuzElmaxh8vjmbrb3xnhy1zB9sQxlDR8Qzei0DKdY2xllVokb1TBozzo58uUudvXHOaFc5uVNnSxqmsXAfTeC04fYsBIxGWFibBf5ehhh1YsIpoEU7cbXsw0xPsTIQz/H9ORS/OLNRJ+/h+cHAzQYefjtEl6bxA9nV1jE1S8DWKV6PCM7dyJVjMW0uTAVJ2IyTOi9Vxj67FOETAKttw2/XUIRBQRRQu1oJLl7M68VnYCZTfHpll7GvfkBH51+G9nGbexN2BBMA6VqLEZkEPrb0PJryZROQqg7BEHPIvfvx9RUTE1FnnwY2Tf+wNtNEaateYQmI5fybA9RyUdKM2nxjeXDfQMIpoFTjZHSTD5vDXP1gioreySJLK7NY/ewTlo3UXMrkUQwTYhldDqydlZev5C9MxcSzRhMf9/Pzv4YeU4JtWAU3SNpTs0N0+moIKnqlKt97LpxAmPNPrZ/+ABZxQ3HXolgGpg2F8udq1C7mvnzafWWn5VpYjj8DJpuVE8BpsOLZKjUObMImRj6h3/CqJzCyrp1DLtKsJdWMJA2OX9yMauGJOqCLkq9CnfvVxgxbYy3jdCfERATw8jFVYS/WMGICsa6tzBcAewLT0MPD1qcpfxSJv7merL7tpBq3INz7vHog924473omz6w7EZKJzL69t8i7VuF3LSGEU8p9UEnDlkg7CjABJAkKxjVswSdEoO+GiQtzdzhtbjnHUvHsq3IgXwqDhuFMut4DFcOpt2NoKsIikLOGVeApKAMtZBwF1LhV3BIIjMqA0STKgsuvpj80dORbP/L7zGC8D8f33I888wzlJaWMnPmTBwOB3a7nVmzZlFcXMxTTz31tef7HwU+AwMD7N69m507d35l/A1Lly496Jn1Hb4ZdNx6LVtaIswL/sdffptofajz7dJXiM8AHllEN2HZQAK/IjKY0REkAUfQzx8e34y70M24P/6Jz376F1qXtXLkVQtpeW8j2QM7KTrzfMb/5Gw8cxaj7tuIumcdC+48l+EXH2HSXTfjnrWI7g8/I9O678u2bC9GYgSloAS5sAL62zCbNuGPdaL7irBNXohaOAb2r8PZsZk+MYdQYBT6/k0Mv/kcxtTj0PwlaBVTobgOMRVlnCuNlIljuHKszI87l2zjNqShVvTwAHLndqp3vYFePIYLKkE47kpyjz7VKrWMX4Bgd7Dr8Q+Q3Q4K50wm/9AFKBX1GIkYgt2BKIkEx9egZzIUzptOeG8zroIAtprxSPNPJ3jsKRTMGIMUKCDbsofM3o00vrmeoZ3N2HO8hPe1Y8/1Wxo+BzbhKgpiHzOdnXc+iSPgxltRSKShGcHmwFZaiVJchVhQiVJRz6irf8Lgzjbq774P+6QFzKMVU7ajfvwMDklgSrGP6SU+BkcynFGc4cCoY8lzynSPpDmk1Ed7TKV5OMV7+wd489JDmFDgYewNX3D9sz/jlcQL5F9/H7o3HylYQrZ8GkZuuVWWGuzG2LEcs7uJtsf+SKZoHIZu8NHMswnMW4DssLPg6WsZn23Flx5iXIGH3wXHo3c1Io2ZhRQoQMur4cB722i44QZo+MK6+Q13Yc/x4gz6McN9yPmlrGgL4+jdjR4eoOejz/AsPoPz/D10PPUE1z/3fZSNb7KwaSP2WccwQRxE6m2weEIl9VBcR9bux9a/H2PXCgybxxKWtDmQJ8xH378J97xjOa3Isrep17sRRgZQRKhw6pQ7DSqDLqThDvYmbBQmO5hXYXXofdYWpVhO47WJTAgIxLMGAwkNjyLiUkTssoh5y8WE0zrv3vAoqe+fwsvXLeD+pfuZetOn7Bw2mFMe4EdrVXx/vQ1REBDSMZ5plzFFmYdXtxJK6Th6d9OfscqEY597BaWiHnm4HSkRwvDkYSDgkAVEUycueRBSUXZEJR5rSPPZJKu8YzvrF7SefjxSoAD5UcvHzW+XSasGiihw+sQieuMqr3WJ5LtkkgWjMRUnod1t+GwiUrAYebgDIdxjZWb9Rex5+K8kNq20HlzcDgxXwMqgrnmLpr+8h7bubbozErHgKDbnzWGwch6+A6vIcUh4zDS5iS48ZDEF0TIoVbxkdRPdAENxYI6ajZmKUTC1FnWon5YPGywBRVHGcPitrsvwIBgaYnwITAOXOoIsCpR6FTw2iUhKJcdjQ81kv8v6/C9Afn4+S5cuZd++fbz22mu8+uqrNDQ0sHTpUgoKCr72fH9X4LNlyxYmTJhAcXExkyZNYsqUKUydOvXgz+/wz8EVQhWh/YP0pFXWhFL/4TZ/K2/9LSO0fvj/bPc3zk+VS6GsPsjUSj9NbVE+uX8Fxxd7ceUHaL7xKsrmlFAys4ThfZ34q/NQR0bYeeNtxPbsJl45C7m4CmX0DHY/uZSceQvZfcvvSO9YTfVV1+KefSQApqbinH446sILEL05VumqZjLa7tWIiWEMuxdh5ycINdNoz5uCTRIIRg4gjZmFqRuoiCihFgQ1hZBNYdittutrl/VYj+GAmBjGNvlQBFFE7+8ge2Anoj/IIF66hAC2wSb08CA9Cy9n7YgLffqJzHjyfpxTF4KmgqETWbuS9NFXopTX4xszCtntYKS1F7m4yhIV1A1MfyGCroErB9eEGYhur/U3KK8nOLoAxe0gNRAmMRAj0tSBMnUR+2uOwn/1H0hvW8WEq87C5nWTDkXJnTmD6PbtRLdvx0wnMAbaMQpHodbOpXT+OPZTiFowCjMd59FGjdDOZh5a10k4pVLkltndHeUPDQZ18ghDKY25FQFK0t2saA0zKuiiyGPnk5YwW3pHaLyxjnkvPkjpdb9ByCYQtCxafzvK0AEMhw8h0key18pcUTmRovnTsEU6CUyfxpybjkYYNZO1v1tK6uanWZYpwbB7GC+FeHLyIoT6WQAINidDD9/MlCuPZ/RVlyHaHKiFo0GUUAIB3KdcQWrXesyycZwfDGGKMpt+8zylp52M0deK7i2w2v8LK9j7wPO49CSmzUnrbTei9bSS2bUOMTaAYXPj6tuDKclWiWzHZ7wRzYfCKj6K5qDPOZMHeoIIWhr7sZdidu3HyCnBpScRMnG+6M1S6nWw2qigNmBnVSqPrb0xVrSGkQSI4KTErjOsyZQM78YwTezxfjKawdEPrmXkpqcoirfw/tXz8Dz7Fg2DCdSMxk0XTEM3TUY505w3vQzfIfM4xtXH7D8PcerYfN4ddPLQQj+lbSvR3UG29MSQoj0YK1/ECFZiOHwM+mqIaiKSoWKTBNKGgCczzCeTjqHQo/DUOw2ceu6NdGtOlJb1TL31h4j+IAXfv5aADSYUOCn2KITTOlVuAdfvLmNykY/euIqqm8SCoxh90/VkXroDPdRrqU3rOr2frqD/vpuZcN1FqImUVRK1OTBbthL54lMiDc0Ex1ciuryUbn8dp5Gm2GNjJKtb34FIN6ZsZ9BVRndGIhGowRjqwj3SRSxrkC+mkCPdSD17yezZSGDeAiKNneSOCjC4rRFBVzGcfrIfPI69fgqmzY2p2NG9BYjpGEq4k2hGp9hrp8Brxy6L9Gz5mFT463M8/p+CKP7Px79A1gcsI/QTTzyRk046ifr6+r97nr8r8Ln44oupr69n7dq1tLS0HLSu+NvP7/DPwSNdH5LoTzDWawf+D5fn/8vx+Y9Q7lRwSyJOSWDepALWbu9HTWSpzHeRNUze742x/+2dVuZh7xBaSiMVTlM0eyJf3PwOYy4/E0PVUD76I9kDOwm/9yKSIjG0fBn15y5Bzi8l27Sd9LZVSP4gostL7IsPcQ63gChhRIcwJRvKqKmY4T6E7n0wei7ahveoijaQYxPR/cWYokzukcehYBxURzZtThBl9EAZN+7+IwgCUnzQUnl2+DCdPmz1U7GPmY5QXEthsoNSMYY51I3gcOFWROb6kgyndDIbPkQvnYB94hx6PvqMWEc/+44/Gj0WwTZpIXJ+KYXfv4qWJ58jf85U3FNnY3jykWIDGG6rZVjPKbWImuXjqTj/XPy3PYXtijupOmo6pm7Q98R9jLYnMN78PY6pC4k1NOAsyMGZH0AOFtH41lZcRUGGt+5EH+xG3/KRVR444SwEAczVr9B4z31cWZFAv+4hPA6ZpGrgFnVuPWYMp08o4oM+iYXXf0CpV7HMJZNZ1naEGU6rzC33c2qpSTK/HgyNBxo0pPggmCZ6f4flcbXxHeIbPidn3kKrA880sM84kufHn4BSPx3JpqCtf5fFe1bgtYkc4Q3TdfOPaCKf6K8nYuz5whI/dHjJnTYJPZWkf+kHqN3NsO51Kwj15ND/yO1kRxKYLVvJ7lyFKSvMffoOy9jWH0Rb/y7eikKMkRCTHryH6DN30Pa7m6n+waXI1RNwTJ6HqThJvvko/a88y6fxPGxjpvNB8TFoRyxm6UiQ+eVe3t43RF3Qje4vwdj6Men92zH2rUdoXItp9zCv3IsiCWzsiiAJAgt8CRyyyLkTCzhKaceliLRddwlBEuxxj6Ui1gSmScnQDo45pIyx65/gtI8TmE/cyCOr25hU6OGFK2YjCfDO7j6W95mUeO2I3hyeHwzw6o/n8MuPGqnKsTSHKKoh/fHzHNXwF9LbVqFMW4wpyUixfnL1KAnV4J3mGI5YHzWn34ugZWh+7R26Tz+OLTcdQmjNH/HYLCmHDcVHoHUdYOm0U4j96VeEkhr5cha3IoCuUn7Xk8SyGuVmGLeZxrn+FULvv45n0alIhxyH4c5F7Wyi/MKLyZs9DdHpxjdrAbZZxyAoCpFxR5N77BkUnXMxheddhugPolTU03nTFRTZNDyKJWPQff9tZExLWqFMTuFAwxh3OIYrQP6yhxETIZp/dR2IEu0frWNkywZM3SBvQgXF8yYhJUKkUHCMm4nhLbC8xxx+MHSETAJTlAmaMYaSKrG0RnNbhMknn42Wjv+vJjj/I8jN30Ydn38LXdd5+umnOffcc1m8eDGLFi36yvi6+LsCn9bWVn7/+98za9YsqqqqqKys/Mr4Dv88tHaNkDVMihwyRV8GPv+W4yMJcNoYq4W81m0DLCXnzpTKuICDQrtMw74QM0YFSKQ0uoYsA8CTKv1Eh9MceOiPCKLI6pUdzHjkd3R8uoFZP19MYs45ZMJxJH+QWHM7gRPOo/zQcQRnz0QKFiPllyLYHNgnzydzYCdyWR3uo85B3bqc7IGdCDkFiMOdliZKTgGCoqCvfwcpUIDuK6I3ofFep8bzHTJ69QwEQ8OU7QjpGKZkQ8imGHzwZoou+hG0bodExOIH9DUhpEbQQ32oPW0ImoruK2YAH0M1C0iPWsCyljBCzz6KRg5gn3kUScmFVj6F0pvupuL63zDrsduQ6qZg2t3Wohvpo/qCM602bE1FTIQssm6ondTeragrX0UqrUf94nXEvDI8G14hmjFwnPVz+ja3EJw6jru2xBBEkfSWz5EddpwFARxTD0Xtbmb0aTPQ01nyDj+cvpXrMbNphGwCtXAMY+J7MTWV+tvuoP/5R3mrYYD3tnTjUkS6UzDW7COe1Vm04Y80/n4BAwkNQU1z5awyppf40Q0TzTAx7R5e2ztIatMyrq1KEMmpJfHOkwgON2pXM2p/J95Dj7cc2ccuACD02jNc0LwCkhE8sw6zSpRAKVGM5m2U3P0c2R+fwaY7X2N42mmIzZtIfPgX1KF+3HOPJv/QBXR9uIro9u0ADG/aTNGJJ7Pudx9hRENIc09l6UiQ9I7ViIccR2bHahAl8uYcQu/ci9G79mOoGhVnnYpZPApkBS3UB4kwrjOuIv/CqzgivBqzsJZJhV5OeuhcJhS4Gb75Us4siHG8owulZzfa/HNxzz4SuXIspqaSEmzctbINhyxy9WgBe/ManmkVmF/hI5zWyZZMxD7Sw6ifXYdh91Lv1kEQyXiL+EIazTmTS7DNO4lXx3bg+fHd/P7Yevx2iXKvzKn9S/GfeRKLc+KsbBtG7WjkN4+u5ulNXTxS2ohNFvD270GIDeE88jwERcE+cQ6mbEfs3G2Rh2ODuBWRkwY+5sMpJ9D3wiUc9lw7PxS38fzn7Ug9e5l762d4FYGB0UcxV+pCcPsonVGM77JbKWxahqk4ccd7MRUn3WmRSr+ddgIgKQxNPxNPdTnJL95l8E93knrpbhItLRixCEpxFaahI7p9hF97Cmn+GeSFGsjs3Qi6ytBrzyLYHKS3raLinNPBNCge3sOQp4KKy36I/cs7SlzyEDckVEFGjPWjHH4O3fZS6n58BdjdVJ10ODmHHkXnFwcIHjLFMgI+sAOnkUaPhjBlu8X/y8RAUjBtTgx3Lpojh7llXjY2DBAscDOqOvANr7D/AvhfQG7+6U9/yk9/+lN0XWfChAlMnjz5K+Pr4u8KfI444gh27Njx9+z6Hf6BMFp24FcsUvJgRrO8lP6Dz+8b+6zyRXPC4jv8bZv3ukaYWuola5i8sr2fCSeMoqbSz+KLpjGSVNk7ksEZ9DPunBnMm1/G5itvou43d+IuK8Yf70ZXNRAlBne0oO7fjCPoR7A7MVXLk8qIR8jsWoetop7MrnWEX3vK0uzwBy2H9VQCvbsREmGyLXuQZx5HqtFSPu6NZ6nPc3Ox6wAjOFAGmpBiA0jxIQDUxi3k/+TXkBpBKB2FPtiN2dmAkFuM6fQhF1raOFqwCsHQKMr0EkwP4Awd4LSxeSTqFvDKjHPYIFTSf+35RB67mfZfXkX/E/fS/ZfnIdRNx+9/TbxxP6kdawAQKyeQadp10AUcpw/XtPnYZx5F+4O/x1YzAcOTR8+U0y0NnIEmtLSGbfKh/HJM1uI6eXNIR2LIwSK00olI+aW4ioOoiRSDy5ZTOGeKRRTe+TnSSC96qM96L9kUheddxoVTivng8pn4HVZnXtRbzlivgfPUK0m88hCRtEbNtZ9hAhMKXCyqCbJ7IMF1n3RwQZmKnF/KyNKXcX70CJ7p8xDmno4681Qch5+FlldDtmY2QjrG5myQ4BmXkHnvcUw1S3LraqRgER1phX1ZL/dkp7G5J8GkO2/hjRv+RH6ig/iWNdiCQZ658mWGl76GXFBK8fxJxLsHMRIj5M6ZS2zjFyx+5icAyJEujpebaf9wA+aWD7n27D8hzT8D0R+kaOWfkItrCF5wFaauo655C3OoG6moGj0WQf3oKVafeBGiy4u65i2CTgnn9MMJvHYH0q+e4L1wDlqgzBJTBEzFifPUR9hQeBg9cY2fL6ikLuCg0cxnsHw2l1Ra+kZuReSaDxqR4oPESqchD7eRlZ2Y0QEUPcPs9qWcfNcKWuUSslNPwDbcivn2vQB8UnMIiXnnc/TRNfyk+AguKk0SmnUe+/94Csu2dLOv+igiKQ0tWIWZGEHd8D72ifPQBrotIcDKKVxZehSmzYXHJiEccgKuVZ+zfEjhw8IVND7yDM9MWcSPx1/EK9cuYENPwuIbSTLdo5YwdO+LxE2Fjdf9AWn3p6R9JWzoSdAZzbC9L47fLiEmQhQPbkdedD6uhSdTdM7FuE6/ktzjz0HrbaXv3beRyurRyqeQe8qFyMPt1nfI5sDwFqBddhdmyRhsY6YjF1bQmJAIf/gauWoYtaMR1YSEapIzuBevkUQWYMhbhZiJUeCWQRTR/UVIwWLQVEafOZPBNZtRSmuRJ8wn8viv0XpbMRw+crq3YCpOhJ59YBpI0R7sfXvRTfjeolpq8j3MHZX3HceHf4CA4bc84/PXv/6VV199lVdeeYUHHnjg36k6f138XYHPU089xTPPPMNvfvMb3njjDd59992vjO/wz4GRsiT4+9I6ugk7ohl08/8QmgH+lvz5G/k51yYd5PucN7uUt5qGiWsGk/0OHnh6O3taIrzw+EaqJhdywY/nEG3rx9QNCiZXEBxdwJaLfoRgd/De1JOx/fJx1N42Ko86hJHdexjaeYDYji20/fVt9MFui3BZZGUJ7GOnk3vUSZZlgyhhhvtAVhA9OeAvQJl2hLXgLzkL0+5mWuPblHoUIpVzCIy0gpZBTEWt7ilBwEwnEJo3W0abnnzE6omIJbWWerNidRJJRdXWk4yaRt+/EaGvCaID2Iea8Azu5/SGT6h8/ibqrvox3soSqn9wKUWnnWl1ZRWNomjuFJz5AWwV9YguL/S3Em3uxowMYKx7k/Ta98AwSK99j9LbH0PIKcC0ufDZJQwT1Pw6Jl66BEQJzV+KfdoihEPPJzB9GnJxFelX78XMpMmEY2y8bzn5P70d0RfEPmkBUv0MDnm0jQOPP2PddCKDkAgTTuu83zTMmKCT3pOPxWskMT57nj7Tg+/Ycyh++nq2TN5GR1QlMLgXn03ieEcXS8YW0C4V8Ef7QhylJfzOcwKvipP53boBrny7gSF3GabixNa2CVO2M8MWIlMwGvsJV5AetQDHSVdgOP3UpFpoCiX46Zxyxuc70Qa7GVfiQ8wkcNaNYfjQHzBnbimBk76HGSixVL7dTgRRQnR78R1+HPsmnok8cQGmprLNOZ7R11/NW/lLWLRjLfrq18i27ME+fTGbLv852bXvYGbTKPNOYVtwFskv3kUuKCfS2Mmcu35A+LMPSS35Eb6W1QwVTkF2O/j1x42cKB8AXUNMRXFGuxgIjGb5G/dQ6rMRSqp0x1Xe2jfEKGWEWMZA2/gBxlv3Ypjw4KJ8jGgIV6wHwx3EEetD7Wyi97c/RbA72HbTVIo/vZ9Y1sBUnGSOvxZBgMN+dyppzWTsT74HwOCfHyHw2WOsnXUoz1w6k41dEcp8NlKyG7NiIvZJCywNnfoZhDIgRXt59MCr6N4Cjnl0Pa0ZG69u62ZMnotMJEb+5Bquvu06ws++yu6BOHZZxCeqIMr4/nobR7gHyencSM37HxFdtxJXfwPj8l3Mzhc5vMLD3sEkUqT7y4yKA2OwAzOTREyGSRaNRzr8AlI/vg90ndTLvye15l3UnjbEls3Ikw8HQ6OofyuG3YNZXE+2dDJjulbiufCXCIZGdvYZ2Le9R0F2AC1YRbdqR9Cz5LWtYY9UgW2wCfIr0T593lq/ag/BXVVJtK0fIx5BjA+Re/iRSMFiTNkODi9SpAu9ZiamzdI7EkzLf7DIY2c4keG5jxoZc9hh/5wF9zv8/w02m426urp/2Hx/V+Czdu1aVq9ezW9+8xvOOOMMTj755IPjlFNO+Yed3Hf4v0DLMpzVqXDJ1Hts1HusUtb/V7zQI4sHyc9/s604JOBgzc4BAEZ7rf3OnVnC4jPGcvlvjiE5lMSe46HqmJnsfnETfdvaSQ6MYPfZ2Pvkexz/zh0Elv2Rgc37EBWZnDnzcBcH0dNZfNVWqUsP9SIFizAzKTJ7NoKh4114DABCXilGPILgL8AUZYSRQYR0DCGbQsgk6J12JpphYphw9QYdPVAOgNrThmlzIc09FbWjES1YRVzyWItiNoUeqCD14fOohaPRc0osMnTrVkS3F9GTg9bXgTnci7pvI6l3Hsd2zf00PfAw2WiM2PoVdP7lz3gmTCH98fPIJVVoyTRyaR1dr76KHurFW1GIkFNAqrUFPZ3FiEewH3YWGDrxgrFgGvQnNNzRDtjyAVJ+Keqetazo1Xg7XsRDm/sR5pxGa/EcPPOOQhwzG/8plzBl5zrkoRbEiYcxEByL0byNDccnuf+Y2yzez+j5PJuso3zXW1x61YOs7oxyeGIUTUkb4pGXUhbeS99zj2K/7kGcBQFqd71GrHA8x9+9ko1SDbUBF/cWTKRlIMEvzUXccoiXGx9exegCD8/MNrg1Z7xVXmtrQF/9GpkVryAsfxpz2yfYR3pAsrEqlYfec4AT4usYSGp400Mk92znqDdvsbyVpiymqHs9U375A96aeS7djnL0dBZvRSGmoaP1tpE9sJP6tU8gpqIIoshksZeev77Eme5OTi4DIxZmYPM+hl59mmm/uwbHxDkIssLP1iWZ7Eog5+SSbd5F4YmnINdOpvnUm+m44GTMwlr8oorzyPN4/OTRmL4ChpUAm8Qq0v4yBpMa0YyG3y4xPt/Jo2vauTC6jKQjF7ssYKufinzcj/AqAsb6d9DHLARJYVfMhuHJQwoWUfzL+5HKRiOqKbbM/pHFv7J7iGZ0cvUoj1ZdQOdIhhXFR/Lgp7+27E3mncLR0gRmn30HU4v9FLoVNMNEyMTI7N1giX2ODJCnaKzWSqm9u4MhVeaz0wNUy3GOGVdI8eaXaT71ZjYc/0skUeAvJ1Uw4d7LmbD6EZS+BsR0DM+Ycej+UobLZvLMlm5yFhzJ2nN/giTAtjBkTJHZhQp6eBDB5qDj+kuRgiXo4QGM1l1oL9yO/sUrVOt9mOE+1ESKdChqZU37OzBFmdCLj5JY9ynSSC+m3QOmgTlqNiOmDWmkj3jW6sYyPHlI4S7KM12WAnZhLWPFIbTcKrRABfJR38ecvARTlLGPmU7dOUcT2bDuoLmtOOMYxO1LrVIzWPpPagop1o+ZjuNND+FSJAZGMkiSSF9b6BteaL/l+F9Q6rruuut48MEH/2H+n39X4HPVVVdxwQUX0Nvbi2EYXxm6/p1x3D8DVwhV9K1cT2M8i9Nto9QpH/TkkgTwK+JBknOhXT6439ZImhKHTNaAtqR6MFgazFg6P7LDxmO//pDKw2rZ8MgqDry5Fm+xh3EXHEbdeccx+uITmXTLT0AUSR99JZlIHNHtw0jEyJk2Dclhw19ThhQsQi6tRQ8PoEdDGJqKqVqdU1RNxnD4EasngyAgGBqC3YXZvOXgedokEZskEEnrPDjDRIoNoHbstxSg1TTINuyjpyJoWbzJfsSOndZCvOZVXNPmI255DznUhtC4HsrHW9wVQSQ+6yyGP/0AYd6ZuKfNx7/jXWq+fwE2v5doW691cM0qCRqJGPGuQTI7v8D7q8eQ8ktxT5tv+W8dfx6O+gmYuo6gpRG0NK7kIFK0l1ynBP2tmNk04emnsW/yORxW5uSYxpf48cwy4tio7l2HVj7FInULIvm736PjycfQ1r2NJAgYqQRi5QT+cPxo9Hnn8H5rAo9d5rOSowg/soS55ZYbc53HQIwPoRaOpvi8i/Gufxnbadcx5YM8hlMa82eWkeOQsUkCW259gsvnVnL3LCc3rInw88vmUJProtkzmmemLKLP9GBEQ9imHI5j9rHYJi9EqahH9ZciZOIcqvQgFdcieXOo7NuI4fDRf/INiL/4I6ZsszJyooQULGLyqWMplpIk+oZJ9IbQkmnU0BCCbHFa9PAA2mA3Pc5y1KsfQO1oxJQdfDThEtK/eJzci65je+ECjESMxCGnc8KvLkHq229xx2afQnK7JR5Z6bcz/tk/YzZt4os+1bI5aFqDmIoynNL54xctvLhrgFG5diYWuElrlq3CfdMMxFkn0RzOUtK+GiMaIiU66E/qvFJ4PIMZgRYzh0k266YbWvEZykAj+2zVvBfycPq1z5JrWoTb8u51CFqW8ycXU+hWWLD9aZqefR17jofIy48QX/5bpp98HB67yBsNQ2R1E8MdRJm6CLlqPGYmhbDjY5pCCfbfNZfuc05AUFP84KM+nCcdS2TOeUx3RDmyJoc7xibpuesXJH77Z1yHHEG2ZCIDwbFIY2ayZt4SeuMq16kr6CifR8X7HxNK6TSFkrzRMIgU6cJIjJDcupran9+AERsm27YPJi4iE4khOlyIsQEERWFw2wHcFWVE16/GSIzQ+8BtBH5wM7azb7Q81hrXWmrluz7D/fEjaMEq/HYJ84jvw7rXUQtGYbiD6Js/ROjeh+EOgq4Szhj06i5aUzL74xJafi2iL4invAi9vwPTMJBig0hl9Za/VzaFqdit4CebQpAVBDVFnsvGtMoAQz0jDB/YSsn0o77B1fbbjX8MufnbjdWrV/Piiy9SW1vLCSecwKmnnvqV8XXxd73jUCjENddcQ2Fh4d+z+3f4B+CZKYtQRxKUOxUESeBAXKX/S56PblrGpH8jOf+N2wPwwzPG0pPWmHZIMaePy6fUKfNxv1UyC7VHWffXXfSlNQZ39+IvcFOxeDK+Mi+CKIKWRVt0CWJBJR0vvIS/cyO1l5xLpq8PZfQMhtasJ9rcTbJ/iNjaz8AwEKsnY6ubhHLiVQh5pegFdYjJMGKoHSGTsLqwhnsxnH6kkjoMu9u6gQKCIGCTBEybi+S6pSglVRjV0xDjQ5jbP8UMlKCte9tSYk4nSZdOQfIHMZIx5MJyi4js9iJGeixxNEHEl+gleNalNP7gXBJbVxNa/QVyYSWC20fepFpc+TmE1m/ENW0BeqiX22/9GNv0xfj2fkJ693rU9garE6av1SrTAYKaoev2n4FpMBwYRV6iC71+Hhg6gW1vM9HoZH1fhrdrzkaJD7CjP8HuvJlENRG9fh7ZNe8gVk+k+JZHsE+YS6B1Nck556B78jnhTxuR1rzMcbV+To2vYYmzD8HhJpYxSDx+IoKaRPMXg6GjDXYjzD0dMRFiz+V+dg0keHhUPxc/vYmd/TGev3w2N3/QgDjUxp7uKG9s6MQwTUYyOql3r6FYHcQx4whSa95Fy6tBa9qGmU7g6N6O0LkHc7iX7O41xDd8Tm/JLOLY2NYbw2cTiYhebqg6Dr14DKmqWbRc/Sia3UfhrInYfC6Gdh6g6c11dL2/zLp52Rzog93ku2SyuolcPQFdcXHYyvup17vRt37ClP4vEJxuPNvfZdF7j/Hjid9Hzi9FivWjJqwbYkHXekybG2HMXB77ooWBz1aiD3aj5dVQ3/0FtywZzSWVGmI2SXM4RUF2gHVdMUI5dUiRbuadegMUVmOkEmR1E9efb+aCnF78Dolch4zhCoAg8vL8azFcViYt16mw+ZkfYti9GJ//GT08iL59OQVSmsLPH0M57GzqH3madGgEm89N6E938MHlMwklVVY0DrKyPULG7qdZKSWeP5r+innsqljCheY2tp97PmPf/ICB/Ik8rL3Lib6pFAzu4sANP4WPHkP3FhD89ePUBOyYmoqw9lXyYm0sHQly6Et3Up8js2vcGRR7FDw2q8PqqNoAFxZG0XauAlHCefyl6K4AWn8n9iO/R1T04P/J71GqxqKHB0ntWENyKImUX0ruKReSDQ3j/9WjJF6+Dyk+SM/Dd5Fp3I5pcyGX1KAcdja9hgc5Gyee1el8+0N6kwamZEOcfRLpsYusxoRsklxZoyzZSo0SZ1SuHcPhRy6qoPntL5Ar6sGdYymz270gKRa3p68RI/qlQGgiBoZGVY6NohwH2USMQM1kerZ8/M9dgL9N+F+Q8cnJyeGUU07h0EMPJS8vD7/f/5XxdfF3mZReeOGFLFiwgEsvvfRrH/BfFd82k9LMihfZ9+gLNK3sIK/cx5q9Q+imSc+XCs3/GY4t8rA1ksYtiXSmVMZ67YwfnctLG3sAWJjnIuBWKJ9XhuxUcAT9pAbCjLrjHjru+Q0V3/sekS8+xTtpGpI/yJbr72bipUtIDQ4D4J93BEblJMRUFHXTR8jzTiPtzsfV32A5lusqal4N0kgvhLohvxIxHTtoBIo3iDnQAbKCUToOsX0HRvVUzK0fW9mE+umYNrfljC3ZQFYwZQemKCOGu6xsjWzjQ7OexZUe5GiP5feVjlldZN4ght2LONSGICuoXc0MfbEG7co/ULTjTQSnGzm/FLWgHjnciTnci5lNkzmwk0w4hiPoJ9E9SN6JZ9L36gtkY0lKTz8NoWQUg+4KhlIa46I7LWfp8vG03PJz+O1z1GXayax9H9uY6Vbpat45pDQDlyIiR3sR+pqs44w/EtsWKxAyWnfBxEWgZTE2vscf7EfwizE6/a4Kclc+gbDkB9i6tmPa3HR4ainTBuiSC/DZJdKaSdAOf9rWz8+v/C37Pn2QErfMj9/Zx/cOKWdCgYtV7VHmV/hJqgbloR1ohaORwl1Eg/X4h/ah5dWgDDSSLpmEvP0DGLvA+juqSQQ1Q+idF7n52rf4Y+vb7HfUMFq1gtmup/9EuLmPcdddjqll0XraSPUPEm3uRk9nKTvucGx1k6xSaKAAI7ec9Cd/wdQNlHNu4q2GIU4dE6QrrjF8wUlM/ctzJN99gu7jrqdhME5trouJahtmqJu+d9+m4Ce/JvzM3VzsO4d3Twhg2tz0Srn4bCL7Qmme2dDBvNogS2pz8dslrn5vH3cdU89xD69jzZlusqWTkcMdGE4/7F4BkxYjxvrRD2y3vMGcVueQNNTKlbt9PLTQz8qom8mFbnJS/YT/8gA5l/wCDAM52kOieCJpzUQR4fO6Qxi9pAZ/bQk59eUk+0J8eth1nDomiCkIiGqaEdNG47FH4SnxMPbRJ5EjXawRRzG92E3yyV8iKQqJs35FcaKVQV8N+Z3riVXNwb7sT0iHnoOYCCFmU2QP7ECcdRJZxY2MQVIX8KaHCD//B3LP/RH7fn4tdecdjzj3NCKCG7+kIQ82Hywp7aWI0Y3vY4yECG3ZRdFlV6Mf2I6ZHLFK1+FBlIp6zMTIwYcL3V9CxlsEgGJkSaHgjveS9JZgF0EebrN83rQ0WqDCUmVWk8jhLvqD48nLDCBm42S3fU6mrw/jnF+hSAL2jPXwI8UHQdcx+tsQykZb5eyeAxjTTuDZHf009cdZ+lkzLaveASC77Zl/+Fr79+KfZVLau3cLwdz/WXfbz265Hcmd8601Kf1H4+/K+NTX13PjjTdy0UUX8Yc//IGHHnroK+M7fPNINuyia103cc2gvyNKtVs5GPRM9jv+0/0+7o8DkNANCu0yO6JpQu1RzppSiFMS2D2S4Z32KHafE0mRsXldVJ9xDIKepezWhxByi+lauZtM634GPniHnOoAeiaDf8GReM+7jsy+zaTeepTIG08h5ZcihtpxdWxG0LMIWgYAKRFCUDOIbh9CpBe1bS9CbAgjtxzdHYTiOgYr57E57kTIK8PY/CH6vHOswGlkEKG3Ed1biN5zwAqSDOt9qy27MfKqiJZOZ1GVH1OU0HLKMLd+hJiKog10g2FY3kQdjWT2bUGpHIOaTJP3yQNku1pI7tqC1tuGmB5BdwdpeuhRTC1LpLGTwDFnIBdX458xk2zbPvIOPZTio46wuEZ71hJs+IhRu14HwMym0TZ+gKc0nyqHZi36TjeCJPFW/hLe3jeEP9qKPNKHaXdjJGNoE5egmyBWT8TsbUYuLEdMhBiUAgjzz6Jz8RLCrz5BSjOQDr8AOdKF4cqBeIjira+y4vAzyXPJpFSDnI/u54XdQ1wxOUjkV+MocYkoA4380bee+bZePm0JM7/CT1skQ2mqnXDxVMvXKq8a/3ATurcAtnxAm38sK9ujMGae1XKdiqLuWcdJy1R8U2dw7wuXQDrGp81D9PzpAZr/cC8Nb+xg/I0/ZWTTGrSeNstFfE8rRXOn4Aj6kfNLARAdbgRZQRxqw7n4HNyLTiOa0Tm9HNJ/uZ3g63dQf+ps9C0fYQsGqfUKnBQI8/vlTfxqj4337NPI+cVDbE24yDv2ZG45ZgxiOkbCmUdJphfnxtfZPGkOj86zk9YM1neN8Pjmbo4ZV8iewRSrLqmixT+eDT0JWuUSxI6daH0dYBogiCjloyA6ALKNzNKn0Hpa+YP5Mc+0CkwudOPf9T507MI/YyZypAexfQeJ4ok4w234tQiGCcdvf5eqkw6la3UjALGOfp7+opUXS6ciR7o57vndRG+6iOmffoLwwF+Rh9txnPEE1zy/GUfnFqZtnYJ/yamkNJNoTq312QqUYJgwuPAyS9bBU0Vy3VLk8XMxN72Hs2MzMU2gN65hKg5yjzyOQVcZVcfOYsPoM4hJHmRRQP/gUZAVOmwlpJa9zKOlkzGTI6S7e3AXBYm+/RxabyuZ3m7MbJp0WzOh918HUUIP9VoPIokQugmOSAfCrmUMJnUMbwHS2/cgRa2HKSnaA0OdiJkYgp5Fd+WCIFIY2sPA43cx9PITJDs68R1xMm5BJaEaZOx+9iYdkIigte1Bm3w0uq8YMzKAVDYaQctwyTgfe7qjRHs68VeM/ccusP9K+IdYVny7Mz7/aPxdGZ/q6ur/fEJB+H9SxPDblvFJvnInr/7oz6R0k560ymBGxyOLxDXj3207wWdn90jm4OvHF3vZHE7hlkRSukFY1SlxKNhEge8/dh57nvuc2hOmMbitifypo/DU1aGFBy014r5h2n/wB8TzTyIdSbPgwR9hqxkPgsjwB68QPvtWKm0pjI3vWRo4Djfi2LkIahI9UIHS14DuK7LKVZEBaxunGzOnGC2njPhTt+A7/zo6dC/lLlAFGWnZk9jHz8a0OS0RQkUBf4Gl4BwZxMimEasmIYS7MRIjtFUeToVDRe7aiRGsRAy1YwZKMSUF3ZOPPNyGmI5ZLcSuwEFuCoYOpmG9llNCVPQgv3gbthwPzkMWY2oqmV3ryAwNY8/LtQK7cfMwmzZiZtIMfbEGm89FdiRJweXXY+xdgzBpEb1CDsVymr1xmTEBBWnPchrLDqP8o3uxn3EdSv9+siUTUVo3YORVYbiDSI1rEP15GE4/ZlcD+oQjueS13eS67fxqcS2/X9HK746sBkHEQEB96bd4jjidLVdcy5RXXqUlbeP/x95Zh8lRp/v+U9LePTM9Pe6SmSQT94S4QnB3l8UhBIddFlhkkcVdgsPiEgiBkIS4u4xl3KVnpt1K7h/FZg9n955zYNlzuHfP93nqeTI1Nb/pmnRXvfW+X7HJIgfHTWXavSfBqbfSe9dveHXuHXy0vIZ95wpoFbORm3YQKJiIs269QUwNDmCpmIQW6EOQJJTuNqQhk9CsLuJfv4KuathGHUG8fAZ88TimkmE0v/0u/bU9rL39JeaUeBihNKE2VxJrqMI6dCyaz0vvhs0kFWdzo/VEns44gJxVQO8XH2C/5mGs+75BkE10Fs8kK9rGsw0mRmclMU1sRh/opvap5yi75nLiFXPpCiXIdJjwxVQ8lcthyFTkvmaU9nqEsgmoSVmYmndy5HcC2SlWzh6fz842H1dPyiOU0NF1HbtJxKn4Wd4B80tSEJUYwr7vQFORcgYRP7CJaEsLjtOMLK2YIx2TGmNdZwKLJDL3lJuJvn8JyBaUlBykzhrQVCJ7NmBeeCmCqjBgSSNZGTBu8t++xObfv8Pke84hPPtSUpo2oXQ2E66tRjnnLpp9cUySQIXQjZKcg77qdVZd+SL5W9axtqmPy0ak8udqP7OK3OR1bQdR4o1gIeflJWg3ZZIb70BJzkHydwKQSM5ldflEnN+vYlqikmD+eMySwKH+2OHRYrbah++9J3nyls+4+JojyL/xLuisQ+3vZsnJD3DyHfNxT5lqdHV+GDPp0TCSOx2ltxNTfhmqw3PYXyv02UuYXHbkY67ios/qeK2ghj1/fJWR152GMP0sJH8H8c1f0Tj1N+QnmQg+eZPh2jy0EM+Rx6H2tCGOnE2l4maINYRmTcbkrUcXRJT9GzCXDENzZYAaRz20m8TEkxmIqnxW3cOrX1ZRs+ZbEiHfv2bHp2r3P97x+d29SPakX13HZ8yYMQj/hTHczp07f9K6P9vA8P+2/f9Y9Pwa0bu7hjFzi0g2iUg/vDH+XtEDsN8fQxKM70sCRFWNzqhCXSiOTRI5YXQWWVaJOceUctO5S2h49G1aVu0je8pwnOWDaRl/LuFjbyTl3Bvor+7Ace0ZTH37cbJGZ4ISJ7pnPXoihnvecZj+dA3q2j8TqqlCC/np27QRKdBldHhCXtSkLIOE3NeBFg0hpGYjmK1GkGU8RMr0+Qi1Wylq3YDcU4etqxLziGnokpmvAunE6/cjmI0nQV22QPYgZE8W2qEdqLnDEfKGkuGQ2dGnEyychNjfSrxkslHQhAcQ1ARqSh5aYADt4AZkbyPqrhUoW5YiBXuQgj0E0wfToztIIkrSsecgmK0E1nyJIIoEm9owpzgxFQ1BrDB4PHokRO+6DQiSiKMgl8zLFiOFjJuF2LSHHLWXTtXKkAMfs7M7ih4N4371Vpwzj0caaKXvq/eR9q9AC/kROmoQA90oHQ0EMoehWV0ctcmN5O/k0kev5s2n38FpErltVjF3rGjg5Dd2IWkJhn8/iPj+DYy66TzEQBeDEi1kbnuXUZcegenUm6nqjVJw6W84tiKTs48dwjr3FEbdsYqt1/weZ6ANrWAkwsjZWComobQdQnSlortz0JUEAx+9ROzLlwCIn3QLXQVTCb98J5Vvr0LIKaP1umcY/cpzXOn9gq7Zc3ik4lSEsgnIqenEhs0ncHA/ADdf8DrPTxKQS0eitB4i7dhTsAa7+C55CsrQWXjsMugaJw7JIMdlQdAURFcKbXe8ypbr/4h5oIV0u4wcHSA93Epg+EIkfxdqbztSei5SyIs80MoXyiDOnlzA0+FPUHVYUJbOvSvrkUR4e08HrqrvAJiQ40JUEwi7viYy8mgj7DQRQZlzMYeOuRWp+xBiqA/rQDOr2mJMq/2I3CQzree4SOQMJ1G/DzEWQvMUEqvcgXX2GUj+bnTJIJMrtlRENYE4+3wGHTOcqiVfEnvyRsOeIBrGcdZiTn15K2NiVQyxhtiVSMPcWckfTXOZ99oNJFSdpbvaSYhmTm/5iIM9IRLFk9hpH870AjdSyEtutAW9+QDflk5C2foVieRcpA3vUX5sOYPTbHi//Ah7qAtB1znkDRNJaGSJYe7aGsR/9t3cvf0l8m+8CyEWMgrtcIDL9n9M2hmXosciRPdsgPxhkD+M3jVriO7bhORON8bMLQcQ4hGkQDeuhWdx4I1ViLEgb851IhcNY8wfFmEePA65bR9Cfzuh5jaGhGswff86G574ntShhVhSXHQv/RTR5SbsyGSILYJPTjHCW0WZm4uONcQT9hSj27hzJVLZWExaHFWHEwenEw0nSMn/1+34/BLk5l9GK/XL48QTT+SEE074T7efip/V8flXxK+t49P//G1oqsrDiz/Gl9CwSQKlDjP7/bG/e3y+zXQ4lf3fwiwKHJnjImt0JsNvOA9dSdCzdiM5Z55N9cNPUH7jtSQaDoAokfD2EvX6UBMK/ZXNDL7tRpT80chNO1AKxyH52tBlKxtDyUy196NWb6Xy6TcZcvnpiA4XeiKBKafIMCXTjSJN7WlFzChA8RQZfJLc0YirX8M/9XzSevaheIoADHVWyVijQ9RZaxglOlMRVAVdNpyco5uXYSoaSnzU0Vj87XSYM8nCj2ZzIwV7UJ3pKDqYYz70HV9jKhpqvJZYEC0UQMsfjti0B7IHIQx0sGPxvYx96Da83yzFlu7GMWEmekoW8U1fYZp+CmIsiNbZQKK5hoHqBjJPPoP4ob0k/343Nxz4nitvmE7e6acTq9nFlgmXc/7vv6DpjxNRmyvREwkESWLXvS8y/pUnDC6REkfMKKTPXYbTLKJ+/DDmY69C2/I5Lzpmc6W4C6F4NCMe3M9z1x7B2CwHm1oDHGlu4cPJ51MwPB1vVR9zX7oCedJxBKxpPLK2kXumpoESR6jbTqK5hq7ZV+IwifhiGtXeMEe6g/TbskgWEwhKFPGHUaSaWoDsbSTReJB4az1yajrBGRdh+fiPOCbMJLxzHY7J84kVjEPa9hnO674l+uFvuKrsTM46tJ3xW1/ANukolNQCEl+/xO5nv2HyW08S27mKSEcXwbN+j6br5Km9KFuWUva+g3fuPYEVNT14Q3GeHR1BdWUScmQiiQKWXUu5vmMwd80bxHf1/Zyd3s/BW26l9NQ5WCYeyUG5AJsskuM0YT64Eq10AuraP/NR3slMK0jGF1M55Q8r2fPY0fDpI3xRcTFnmKoJFE3Brhp+NoKvk1jhBCwNm4kd2Iq08HJaIiKpVonWQIKYojEuetAIxm2pMjp9a9aSeezx6JqKIEpohaNAkolbDNJlMKHhFmJ03H8D3oPtjHnyQdAUyh9r54UbprOxsY/rpuRjVw3X9PU9Or6owpT8JFKJcPqHh3jXtRrTnHPQJRkxFiK+9iO+GHI+pzvbUJJzoHYLu+56hjH3XoMwaAJiqI/4/g1I7nTEohEcknNJsUqkbPkzprxS1IxBqM40pMgAYjSALsqoe1bRvXo9GbOnofS0YS4aAoOPQOqspuuzj8g85zJ0q4uulx4l46o7EXvq0dKK0E1WhENbSdQfQJ57HrrVxZ+r/ZxrqeGtmddwxhvXEKiqwTWoCH9NPQff20pSXhKOTAcmh5WMcUMwF5TRP+ZEHCYRS9hrvLZYELm3HqW9HobPMoqzvhZ0d66R3xXup0X0cM5LW2naU0NvzbZ/yY5Pe/Xef7zj89u7kX+FHZ9/Fv7Lhc/ixYv5wx/+gMPhYPHixf/hsf8//vF+bYVP9NtXaXjvc558fS9ZVpnOf0dq/reFTrpFIqLq5Fhl6kJxJEEg0yJTYDck8C0RQ9Z+9M1z0VWN1vXVjH7/A9ofuJHuPS2Me+wu+r75HPechWi5FUSXvkQiFMHqSaZz834yxg3BUjzEUDlllSJGfGhmG3jbDCPD9FzUnjbk0lEIatxQEegaiDJ6NGhI2WWL0QUqHktQcpLStZdEax2CJKGPWYhYuQalqwXJnY6UNxgCvWiZZQitB9HjUaMF39OGMGgCutmGGPJyyJTPILUDvaUSSsYaJGc1gVC13nhqjUfB4UZNzkLuayZ2cCumCQvRrC6jMCooR/V5DcdlmwMtMIAaGEDOyDVGZ4JIbOU79Fc2EWjupvT8k9GVBHo8SsfqzWTc/yomJYLy1XN8VH4BA9EEx5Snc93H+1h6pJW+z94i5YiZSGk5oCbQlQRKdxtixVSkYC+xvetYPuhMjnd2EcscwoKnNiGIsOiowZSm2rGZRKZe8hxdzyxEiAV5sS8XXyTBXYsfZOXHj/D8+nreGuunKWsidpOIoupkKr3U3nQtSU+8R4YQRDi0ldiw+ajv3U/lvMXkucyoOmTueB/V24FtwjziOSNoCWqsa+7n3NhmwqOOxa6GkVr2MFA4BfvqVxDmXcINyw5x+Xu38Idj7uXDWRK9nqG44166n7uf2NWPkq90c232XB4NVWLvPAC6Ro1zCGWRepTUAvo0C/64SppNRn/td/z2+o95pvVrYms/xjpmJtFda9gy4XJmxg9wKG0s+eteQnQkIY2eCy0H0IbORKrbgmCxsdsxgjS7TG1fhFn2Xh6qkjixIovBags1cj7lWgcNcg55SSbkgTbEvhbUjEHIfU3oJhtKw366V60h9+IrGUiv4JnNLQzLchFTNE5nP0pPG5LbMDqUy8aiNleijTsOU1e1QQrvqiKRNdSQfGsqgq5xV9FRWEWBS++cjyXFSd+BBvIefxtpxxf8tm8Y00o9TMx1IQkCnp79aLZk4z0bCxnjz0AXmsNDj2YjzaTwTpWPcwYnccb7Vbx19iisoR7EqA/N7ERor0awWFFzh+Nbcj+pJ12A5khFSETQ7G58rz1I6skXoiZnIwW6iHgG0XzF6RSfPAeOupLEew/gnHokSmqh8ZDSuBsxoxCtuwkxLQ/dZEFJLUKM+giYUkiK9iK0HkRMSiWQNQKzJCBt+4zeNWtIGVpK395qMo89jsYlbxIdCLP1m3pmnTcayWrBcvtzZPgOEc8cjBT1GzE2Pyg7xYF2NJ8XvXQ8yGbCHzxB0ryTSWQORoiH6MHFF9U93HHXa4R6Wv41C5/afXhSU/+htW668/fINtf/l/fuv4f/cuEze/ZsPv30U1JSUpg9e/b/fUFBYNWqVb/YC/y14NdY+LR9tYLt7+1ldU/48P4cq/wjZZfh6SPRF/+rv9LkVBub+yI4ZZHLLhpN9dIaBs0vxupxUftVFRWnj6H6gj8yO3GA1afdTO7EbGqX1zP/+UvYct8HjLpkJtbsTKTMAhg6ne7H7yTr8ptAUxCCffhyx+FU/GibPiVUX4974WloVhfoGkKwD92ZihiPoHTUo3o7kaadasha+xrRmg4g5Q1G721FV4zCTZBNiO5MlNQCxKbdaOGAIYnOLiWaWoKtpwa9txXRnWlcEJUYJKJG2rPdzceHgpwuHkTPKjPGbLXbEMxWhIJhxFPyMdesJbjle9R4guQps4gc2MbBt9Yw/uXHDcVLZx1CSgbdf36V9KOOQ3SloCsJghu+wVYxhkRjJbqmGTyEwAChmiqcFSO4fu5dPOnfjS5bWN8aZGZKhLA9HVfXAQKZw7CHuowi0ZqEZkum68EbyTn1VHRNxT9kHu6uvSQyytHMdqq9UUb078B23juEl97I0oFUjslSOeWTFj49xhgDZN28kWBnI0teuJVTM8JMfbmR8+YPYvFVfyD07d0Gr8rqQnVl0BIRqfWGWaDs5+oRl/BM69eoSdkA+HUzqf21CEqMcPYIzDEfQixEIjmXQ/0xgnGFix9fz54H5hDUJAQgouhk9x0gnD0C/2OLaTz3PsbsfYevLnuR42rX0adZyGjfhu2cN+lc+wy7u0JMSxeQvY1oVhd+Vz6qDg6TSPDZW7FnexhYuJh0k3JYaivVbSF+aC/fDT2XYxxdqEmZiCEvmjMdIRpAc2XwVOY4rm/+ntdqE5R57CRbTDSMn8rxH/0OqWgYnc4SHCYBZ6Dth4IilVo1hUHmIGLEx4EbFlPxzPNo1iQaFl/MvTPu5MVTh5NQdZYf6uOMlG4StbswFZSjufOMLpGmoEtmdLON2JoPkY+9BinYQyQ5D6u/nWhSDgd6Iozc+gqRrh5STjif93vdnOloQkkvZez926lZ+QnhJaejdDUjj5pNzlVLueaqY2hfcCSfn3oTp589k0en2GmSMjjYE0IUBIak2Vnb1E9FupNT7/iUd+49gVGZdpK69tP6yvPEAyFkh42Cy6+l9eVnSR6Ui/X0m5D8HYixEIo7D2mgHSERQVcS9H+3lJRTLkVNyaPapxFOqIza8zam/HJEhwvNlUGXNYesrp0kCsfTG1HJaliDXjAC1eFBjIcOqyr7BAduSTGMB9sOGA8xZkN0IZeOQk3OJvLxU1iLy5HKx6F4SpB87UYAsaYZo2Jdh6jxWU9kDTVy+vavITHxZGQ0/AnoCav4YgnmnHYnmhL/1yx8Dh34xwufO+5Ctjl/VYXPf5XfAz+d4yP/54cYWL169d/99//ivx9XCEU89ufLMdltrO4J/5DVpf5N0QOGp09fXD3s71PuNLO5z3BxnpXuYMnru5lf5iG5NAfRLJM5Ip2O7Q2URhej3vEQ466Zi2Q148jy0LxsHZPvPZ9YRxtqKADeTqouPJfcaUPRzA60rUsxjZhGZ0ghy5GENRTAfdQpaBYHiV2rEMxWzINGoXXUkfB5kdwZiOPmI/Y2GC9WMiGm56KZ7YjJHhKNlWjRMKb8MnRB5GDIzHAAJWHkffl7sIcHjNT3ijGoGWWYOiuNtn1qATvPOo/xS57i5I71iIPHoceDxg1KFJHc6aiCiGn/tzQXz6ZwQQa6tw0x2YOltIIJLxyN4s5D37aUaFMdtooxJJcXg6YSr9mF5MnGPnw8sUN76dlVi66qFI6bTUPpfApmmIm+cx+33TITuWkHsepdzB4/H8Lg7KpFd7gJxFRcA22Gv5DVham7BslqRhk6i9CSu/lAHUkkkc4EUWdYusYw0wCduZOpXjGRNxv7Oa8wwRMH4nw8rIUu+0gST11Fy7Vz0Sdfjrl9Hws+VrjsqHKuuvweQi8cD427UYvHIugaK1qizCpMotYL8UFTeSRUyQfVXqIdfo4t86Cho7rSUVa8wZ+LMzlpaDorWzRODe0lO304c+5bz3PXHkHlgMr65l4uHZXBXd/W8bCyku65Q0lf/BhjVi1hadGppGW+jexrI7VuL/7KfXg3PIu09AlcU67Aq5vptZWRZpNJD7axR0mn1G0maeRIfLt303vZKUivfkLq9g+QPNmo5VMRWmo5Khs0xY2QiJLYuZKVZWcwY/MbCGfczuIDH2Kb+1uCzx3D8vhUJu95g9yaragWCbG/hczGdexJm0zZd29hHzmJ0Lcf0DTjesrcMcRQHxU3Xo6gxPC/+gdum3wbS04axujFyzj3pGEcOzQTpb4SU14pOFLQa7ZAei6YbRAeQE0vxbTwNwgHv4e8wVii/fgd2aDqeGwmrKOmIns7ie9azekTFqJYS0EQOWthOW+rJ7I/YzIV0RCJHd9y4I0ryJ15Dbva9/LErncwD46QOLiL/kHHcYyrhzZHMaqmk+200OaPcujlMzF117Ji1EXM2r+R7EV3oR1YB+OPpfH315D3u0eRfJ0oWz7lltAEFk0fgqrq5DRVIoycDdWbcI0cS9RdREzVGexMEJdtIErEa3YR9wWInnEnWf56GjyjKfLW43KXoA2ahGpxYu6ppdlWRJ4aQoiHkD96COGC25F8nTS//jr5552H2t+DXDwcdB2pvxVBFA3fLWuy4cpstiH+kMKum6xGIrsrDV0QDQGCpiLnlyHsWoo+6kgSmkxfJEF9fxibO5NQT8t/63X4V4PDeVv/yBq/PlXXiSee+E9b+385Pv9F/Jo6PlcIRTy5+n4qn36TFz6pBiDLKmMVBRrDf8vjAYPL82+jLCan2sh0mQlFFIbMKyLmjyPbZExWmZbNbcz77LHDqqxE5hAabriQgiOPoP9gHU2rKkmvyMCS4qK/rpMhV5zFN+c8zPAzR5J358N4ZTdOs4i9u5rdUhGjujcgulLQnGmG9Lyr0RgZlY4kcWAT8qjZiIFuNGcaQl+rIVs3W0lkDiH+6RNoCYXdsxaRn2whr/prRKuDREfj4Q6L0tOGefhUNEcqMUc65riR4F4XEsl0yDQOxBlp8iKo8cOeQbrFhZCIoCbnoG/4AFPZGEPh1VPPoYwJeE87lsmvPUaifh/b717C2FtOY9Nv32b6ivcRe+oNx1vAlF2E4MlF62mmv3wuJz2/hZcvHM/r21q4L/UAQuk4hK46PheHc3TDR5hHziBRtRXRlYIgm+kqm4f7mycwzz2XxJoPiPcPYD/tOqNAM9kwt+xkv3M4qVaJdLNKW1Qk2SLxzaE+Zha5STMp9MQlcvr2oybnIPbUcyBlDEPsMXTZglS7id6S6bjFBKaeQ3S++yopNz7ORe/v5eIpRUaaeVTBaRLJdhjkYgUR60AzmtVFbczJH1fWcNeCwVR7wyRUDZMk8ru3d7Lpt7P408YWrpuSz7NbWrkl3wuCSHfaMDyRTpSUXDa1BpiaaeL2Va0UphmhqboOJSlmLIFOBt59irg/TNaFV9H11gtMODCGnqrNfPTOAyxo/BRx4nEIaoKYKwv1vftxzTqeQNYIHL5mdNmCunslKHG8U87n69peLkr3csgxCF2Hct8+rhp8Dk/1bWbg1QcxJzlwnHQFmjWJwGt/wH7Z/QBIShTgB7l+P2LLfr63j+GIvW8gLrwSub/ZsFtIyfnrzfmHG43mykDY+ZXhSTTzLIP0K4iH7RB0UaIvopImxVBNdoJxlcBdl5D74Kt8Vefjsz3tvDbbQfCrt3iq+AKOr8hEFATGH3MD69IaiL/4ESVuK+lVy2HodFDiBC2puCLdiOEBYplD6I0omESB7t+cyuCLT2Lr719n6vvPkTi4CWHG2Ui+dhKeYoS177Cm8DimbHwGc24hTD8bfcXLyNlF6NEwm3Lmce9XlXzuWY0gitjGzabtzSVkHXcckjud7szRhBMaqVYJV+sOBJPF8HiKB6mPmimLN6E60xHrdxAZOgezriBEfIbZ6NQz0ZY9i7loKF0ls3BbJaRNH2DKKyWROxJTxwESGeVIDdvRisYasRjdzWgl4xCUOJrFiexrAyWB6spAs7tpCqoc7A6RbJU57sIH/mVVXe11lb9Ax+d3yFbHr6rj88/Ef7lM/PcW0f/R9r/452LJ6Dmo3g6UyF+7Oz0x5f9a9Ez12IhrOpJgGBjm20xs7ovweZOP5nCCaH8UZ7aLdz+r5qm39gFQ//QztLz1JonWOt4rmkL+3PHE+wdwV5RSNH8YqcOKCff0U3Hj5ay79mnGXz2d3HueYcDkxmUW6YuoJKq2MtIyQGXONCI7VrM2nIqgJBBT0pEzchHiEcTJJ6LV7zLIonY3WkYputvweZFqNmAfPwtzZjZHOHwUdm5Fj4TQlQSmsfPQIyHDmdlqJ1G1FcnfiTnaj7r+Q2RvI6VVS7GvfoWKHa+hyxYSnmJ0yYyalI2gxAwH6UA3wtTTIdRvjLWcHgqSzIhvfgZA4OB+Jr9wN/Gebqa9eCvoGk2vvoogikbhFY8a3iT9PdhNImvOzSQ/ycTFE/Pp+W6l8aSals9J9lY6124jtuM75FGzkdwZBHduJqdvP1GvD91sh2Ovo/nYW5F8nYjhfk54bSd7HcMoS7WwtKYXdJ3UTx4E4NRCkSSLyKd1QWRJoOre+6nXU2jNHI+m69RGrMQ/eZz9GZPxxHqMoNSUHNKuuQdpw3u8dNoIZmeJpO36GEkQCCd0xHgIfdXr2Jq3o6TkMea+HTjNInluO0uru3GZJd7Y3ERxio3+Lj9fHernmCEZfH2onxvGp3PuRgk9EWPVDzyrcEJjaqaJhrDIoulFHF2WTnnl53xT24PV1wrN+3j398tIHTeSRPV29px6N8sfOwP/hqc5RjuAPGoWUmc1utUFgGPsNFSHB0fNGnTJTNez9yPaHJjLx/BNnZcza9/gs2AW7+5qp3DL6xxwDeeJcCVCzWYOLd2Fa9oC9JrNhDCTPGUmfPE4/VEjckRu22co/5r2ogUHmFb7ESgJTN56Gs15qBmlhF05hg+UEkXQNfpthkKR4bMwFQ9Dl62oK5Yg7P0WzZqEoCmY+ppwWyVkXxuarpMabifv/hcZct2X9IYTvP+n57h8XRTHmMncPL2QFYd6yXz7t3zy7oNU/fFNJns3sKszyPbsWbQqNvqXPITw9r0oW79iwxlXUdkbZUfFJDxKP0Nffgs0lSnP3IbqzoPZFyI3bjfe75qKPHwac1392I65GGnkLL4ffsQPY6wkxJJRDEq18c1pWViHjCN67GIOuIaTddxxiA4X0T0bSN3/FYXBOiyyCA7j87q3O4zsbaTYrqHLVlAVVJ8Xa6iHXb0JxHiIHYOOR9HBMm4eAxtWoT+xCFOgEyadhJJaiORrJ5QzCjEWREjOQPxBEal4O5G9jUj+TkKCFaV6O4KuISYiJBBJs8mUptqJKRpmu1FYmMdc/MtfcH/t+CWcm/8f8fHZsWMHb7/9Nu+88w67du362ev8lwuff2sPnZSUxMqVK9m+ffuPXtDKlSt/ln30/+Kn4eLdq0gMDBAP/bXQUXWDxPxv8ZesrppgnGSTSKnDzLLOIEfOKiDLKjMq2UpNME6gI4iuady/5Tme+OZ35IzLQjTL5J93PkvPeIAZ54+h6t016KqGPO98PEdMwV5URNmia9E1ldkfPoKWUGDn16SGWjFpcTK3vYtpyEQENUGSWcI8aCSTd7wCkkS8djd6Sjaavw8x4kNOz0WMhYyQUjVhjMYyh6ArCdQMI5FX2foVtekTEF1uJHc6gr8btWwKuqoi5w1CTPageIoQYyFEZwqa1UXf6BPpmXYxTyYfS7OejOnAStSOOjb3aKjOdPRoiA5zJqbOSrz5kxHD/ajJWcQUjTGpIuFNy0g59TcIVgeu6QuRPVkcuPoq0seUoysJQltWIWUV0/fV+5iLhmBv3YkYHuBmx1A87/6e9AULUFONdPrah/5I5pRRiAuvRE3K4pVAMf2n3Yl36ftYPcn433wIeftnvLerjftrbXzd5+ClM0aR5TTR7I9z0tB0NncrLJ90NUnhLiIWN7a+eqblJ5PhrWTwHbfwRWU3/b85hVSbzNBwFcf3zeFgd5CPO0x8esiPvvs7ntkX4K2kOdiDnQYPatzRlCSb6AjGjHiAqaegJ2Vwx4oGzlhYziPf11Oa7uCYwek4zTI3ziljU0s/D1w9lQc/2semlgGKU2x8UBvk9eMLwepi4sEtiBEfqqbTk5BJtUrUeCN8sK8DsWIqa6t78Dlz0YbM4Mqv7mFxdBra9HN46OsqshwmagYUcHrQ7G5eCw9ij09CXPoEzflTkUJeekum023OIOecCxGGHMHrAzmcG9nI3qlXM2f9U/x+kJ+6978h1SqzuzuCYLUz6LgxCFYnn7mm4dhmmEz6j7qOjHg33boT3ZWGJkjo2WUwdBqT1hbiO/I6dl+1mELdy2etYA92GkVYwAuagssi0RgW0Cwu4jW78WkmOPoa9BHz6ErImLpriaQUYO5rwJdSirj0CTSr0Wk86tVbOXloOgN3DMF01kl8ahnPnq4wNwxK0HzmvZx89u2ca6lBdLkZnekgw2Em9ZMHUSJx1ESCDYNPZ/LyT8h48UaOqVyJbnUhhry0jD+X/jUrESI+tC+fRs3/IQ9P16DjEJrZgW62o7kymLPjG/pLZ9CWNR69tZos/yEEbzOJlhp44/eU7XqX2KG9KN1txBdeY3DvZBOm6rVo7XX4Pn6JMQ1fo9rdBDH/0El1IhcPR+pvYWzztyRSC5kcr8LWXY1mT8GemYauakjBXlpuvcQoZKIBzFrc4BrJJtSkLOLpZYb7uaaSaKrCGetDcCShizLR5Dzs3kP0R1UyHDLdofi/7piLXyir61c46vq36O7uZs6cOUyYMIHrrruOa665hnHjxjF37lx6enp+8no/a9R166230tfXxwsvvIAkGTdbVVW56qqrSEpK4pFHHvnJL+TXjl/TqCv23Wv4du/mu4dWsLY3TLJJxJfQKHeaqQkauVx/4fTAXwnP/3afTRKIqDoT3FbKylKRTBLOHCeiJNBfP8Ck+y+jd8Nm0m54kK4Hb8SWkULKWddw6PYbSCnPx+ZJxjVtAaq3E9XbQdzrRVc1LFlZ+KtqSTv2FLS0IryWdIJxjeJwPYgiel8HQkomicotCFYHgsWK5M4wpOkDneD0IMRDhw0FdSUBLg+15kLWNPZz4fBUpJoNkFGI6so0YiUGuhCS0hA0BdSEocSSzUQHz8QS6qFLSiWrZSNkFKM17qWv4ijS+6pRMspoCOoU2zUGnv8dKWPH0jvudDJixpjGfGgDidY6TEMnodndCK0HUX1e9GgIObOA6OCZmGM+tC1fYCooN/yI4lFD+l+/H0QJefjUw3ECpvJxIAjoJiua2YEY9RFMKcae8BOQk0jpq+Vdr4eKdCdD06z44yq+mEpM0Um1SmSIYWJmF5ZECCEWRHVlUjsQpyLeyAFTEUMcCXo0G8VzrgVg3Gnn8NolE0ixSnzfOMBpKb0oniJ0ycyke1az7ZbRNChOcpwmWvwJSh0aly+t50BtLx9cPYUtbX5EQSAvyYLTLLOrw49JFEhoOk6z8bmPKRpOi8yMgiRCCR2nWaRhIEZF80qWOqcwJttFXNWp8YZp80e5zFbDA92FrK3u4asLRyBGfGwOOKjtC5FmNzNj/VM4x00ltHsTrtknEs0eTlzVcAQ70M02JF8nkY1fGmG1R5yKvvEj6kacxuVv7uD7eWGQTYS2r2XPS6tofeEDci47jalfv0+37EEUIHnl81jGzjFGcillhBIaW1t99ITjnD4sE48e4MBF5+N95B3sJpGEqjMiw05PROHTA13caD+AFvIjDp8BDbuJjTDCMa216+gtmkoqERSzEwBN19EBsxJBCvYghvuJ5Y0mpmgUnfgAHYuyOfDcJww8+Wcm5Tqx7PicmevSeX7Hw1wz8TaePHsMRSlmIo9eT+rIwViGTeLmajePDg8Tzh6BJdSD4srA0raX/bbB5LpMHPnoOo45opA77bupKl1InsuEKIAj3A26zoftJk5nPy/HhnDJYCvSQDu+tCEkBVrQJTNvtUicdnAJvXvryDvjNJreeNtQbZ66mIawiE0WydzyFg+b5nDHUIX+pGKSxAR9ikxG507UjFKk9kq0tCIa77mVkmuvQfV20jP8ODKj7ejNB4gd2ovtiGONTmlyjvE5N9uMEFIlauSjiTJSfysDnnKSwl0o25ZhGjMHzZkOahwhEUNIhDko5rGhuZ/H3tpF08alwK8ntuK/a9TV1lCHx/OPjbpuvu0OZIv1VzvqOuOMM6irq+Ott95i6FDDs+ngwYNccMEFDBo0iPfee+8nrfezGFFLlizhpptuOlz0AEiSxOLFi1my5Nfxpvv/GVJ6Lv01LbgdJoDDqex/KXrgrwUOcJjwPDzJysVHlnBiiZtypwWAbf1GZEXWOGO81Fvlxdvkw79vL4lwFLFyLXmXXU3zqv0o25cz+M47iHT30/TdLvRY1EgntzoINHcR7u6nZ9MukkePpu2tN9D2riZV9eE0izQ5SlCScxEsNtSkTMyDRiLaHEiuFNSeNgJL3zJS3K2GegRRNooITUWIhSja8Q4XlZmQe+oMu3zA1HMI3WxHj0dRUwvQBnpQeoxYCj13CCbdsOyXRIF4zW7WhlKQcgaR9N3zRnEiyBQlmTB115B27ClInmyyvfsQowFMld+jZZYhulJIVG4h/t2bKB0Nhmu0KDGwbgXW/h/S3zWVyvsfQQv5QZTQYxHEqachTjwWva2WpqTBiGOPRHWmEfzuQ5SUPOrVJBotBbgaN1F91cW0BxN8F81mSJoDt02iJRBHUXVUDVY3eOmPqUjtB+kJK6gWJ02CB7lqDdGExtVFJ3D1Ozu5ZGkjxXOuZceyx4l8cQPv/mYig7QuntnYzJhsF9dvF5j11HbEWIDv7pjF6h7DxdcXVdncOoCgKSyeVcqS30zi5MfXYzdJ+KMJPtrTwTPrGzjQ7qcvmuBAu59wQiPDYWHJhkZWVvcQV3UGoioxRaMjEOOPweE4zTIfH+hiaVU3FknEJAqEBk2n2OPg8VNGoEtmPu2QGZ1lZ/qrizk6yYvryDPwl8/G7PGQyCijL6Jg2/w+bY/fS4+QDIKIbcwMNJ+XmsvOYuHBMrqPns+3JdsIDp5NpGw611hOZNpbj+CLKUx/+2HEnnoyw81Uz5uHePTVhtt1TzMus0jerg/IcFq4ukQlve575L5mBr37OTOTgviiCoNSbZglgRL6uHGYCb10PHLxcGpvuhYpLYfIi3cY2Wo2BykmaEtYEHUVk6+NiKITV3WCgpUeex696SNQNB0bCb6KruHgi58x7KqTmVr9AQlVp/6NP/PqxRMoO3U6a08yU7HnHRKajnnxE8hzzuVbyrltVjHV992HrX4j3bIHKepHsyUzbGAXyb4GNszu5Q7LDvpGHsdgl07yQB2qDkpSFkpyNmeYqnk5NoTLLFVEzMmonQ04FT8t5hykQBdVHQGa5y8m676XIX8Yzvtf47i+OUj+Dp7IGkmyRaT69S+4bYIbzeHBaRYRogFSTRrfUo4Y8REpOYKQM5uCU44hXn8ARJHsnt0Ghy6ziNjxN6EPdKNHQwBGQTvQbkS3WFyothRQ4yRqduCQBYT2aswlw+i05UMianCsLA50s4OSFDPBmIIo/ro7Fv+LfwzLly/n+eefP1z0AFRUVPDss8/y9ddf/+T1flbhoygKlZWVf7O/srISTfv77sH/i18Oos1B+7Y2/P+O0yMJfzvu+guGuizs8UWxua1IZpE9PoPQefN1k7FaZQKt/WhxlS01fVitMp5jTsWZm4bS1Uxg9WckF7qJd3UQ3vIt8UAYURLQoiH0eBTTsClkXn0nWWddROXHexBEkUOXPIxcNhahbjuuZY9T643gx8px30scvPQCwzhQNqIn1mbNxXvqnYaJoCsDIR5Gl4wE70R7I7rJhmnENGpjTmptJehTzzzsBSRGfIZp3KZPjLyr9kaE3DKizkwkfydHv13Djo4gpumnMDVDoidlEPLMM1GTMrH42xE2fQiqIeUVUrNJtNahmyzoOYOhcTdSsgdz2Wiavt5CtLXFkNq600meMtPwWMktw1RQzqALTgYlQaK5hv33/olO1Yqy4g2k9DweSh8Bshkp0I21dCjq549THqyiUOul9omnKT11Dqqms711gBFVn2CTjSfr4A8F7YlDMshxmujIGs/+buNmYZNFWvKnMvLQUp5tXsoTax+gpr6fx577HU+vayDgLiXVJtNnz+H3Y8zEFJ3sFCu9bX4Ohsx4IwqlqTZe29pCSNE4p9zJ/Zt7GR6pZnlND49cOJ59HX5yk6zkpdqwmSQCMYXaziAjcpMZl5NEbV+I7BQrs8rScK17nRK3mfZgguue3si2hj4KUwz5cpbLSprdzEXZRsF0akUaug6KDh2BKAMPXEPJHb8n7ilFN1lx7v0SLRJCbt1Ldud2fBNO5815d1DtDbNRy0fpbqNv7lWsv/45vq6oZer3X9Ey8wpCCR17/UZSHRYSrXVc2PkpAN7cCagpeVg//oqwohPfu9ZQFAoCpqETmSPUoTrTUcunoiZlsbszjNeawezmL9nc6sMc8/GN18bCD1oRYwF0s439N70IagLPcWcg5w5CSStGCvaQY/7hMylKpES6cBLHKSTw6AGSxQT9UZWKm77j2d88RuprnxCYfiGm0bOwSzqD7r6fUqdO/hsaSwbyqB17LroO39T1836Dgt0k4YtpuP70LvXPPk/g5nOImFw8VScTLJzEU0NPIjL+JBh3DGmBRir9QGgAlxbG5G3A1N+CnpLF7OJUBtatoCOYQPZkETEnk2aXUV2Z3Dc9nRSrhBgPoznTSO+vZfnVk0EQee7QBzj9LQx9+S3Ept0I8Qi6DhGbB58icqSpCb29FvOOz3HWrUcePB5L+WjkrCJiVTvQdyyjI2kQsigYju1Wh2EHEI+AICL3NaOb7UjhPsSW/USPOAtTV7Xx8CObyMCP+oNAQrEkodlSaPIlaO2LEIv8fePWfwn8ElldP7NufO655yguLsZqtTJu3DjWrVv3y57bD9A0DZPJ9Df7TSbTz6o5flbhc9FFF3HxxRfz6KOPsn79etavX8+jjz7KpZdeykUXXfRzlvxf/AR0vPcm3W0B0rOcP9qv6hyOr/j36I0bXR9/S4Dkwr/ysJKKsknKS8LsMKOpOqlmia5AnOZXXyIRitKzaReHPttCoHWAnc+sINzhZf/SWswuKwMb1mIaOpHo+s+RBtpQ+zqZ/eotCKPnMy1NR03KhIIRyMdcRTihYpUFnjl1BBWLL0FNykTxdqJZk5mZZASnClNPN+b8ZjtqcvYPY6aJ6H0dqA4P5UIPgxItyP1GMKna343m60WwOTCXjQYg0tWDGAshCQIDH77AxxeNY0GOhOZMY1c/pNWvM3gFfzFIGzEL3eIk6bybGVj6tiGvbdxnjM0KR6J0t9H22ovkTBtBqMNLrKEKpaMRXUkYvCQlgZo7DIDQ7k2Ycopove9NtrX5qf9iPWgKV7fvQe6t5zdbBITR85GSPeiRAFrlRtZc9iQXBWaQZJG4elIe2wadSH/UGG/ZZJFclwmzJBCMa9T1Ryl123FPX0RfVGHOHd+wxD6DD/tS2XTHS4iyyFXpXdwyu5SnNrXwWVUvEUWn35ZFWaqF+p4Qux45iiSLRFm8iX1dQR6cV0iO00RdWOa3Y+30pg3j4rE5PL+uHlXTue+zA7z5dTU3zizmnvmDKMtyYpIEtrX5kASBskwX93+4l7ZJ51PfH8dhktj+8FFcNaMEsyRw4ZgcSlNtvLipkVl/7iWtdiWbWgOkWCXM8QCXdX5CoLmbd3vcmPubEBJRRHsSuqah5I7glUAxvHwHF4/LpTzVxqSu73lWmsySHW1cWqwjTjyOWz0TKNj8OuGEhu28d3h4fgE5j7awZ8wF6LEIV368H23FK4xxQ1L9eqQZZxLLG42lu5r7q8yGx0wijNy0g36zhxSbzOLPD5KYdjYf7Gjl+YOGmu2Nc8agmx20P/Mwp7i9KGkGWT6aPRwp0GOMaAFBUwjZMwg7s2iOSCREM4o1BTHcT1cwga+tltePLyTbLlLtjaJbXCxyDOPNThdoKuXTjuCoQR6G2CJkdu/m9IFVnDI0jdHfP4Hl8esQBIHUh99k97XPYtu7jFMqMrlzeS2XdO7BWbuWlqiEbnaQapPxr/4KoWYj3a89ZYyQuhopObScr6YuoiDJjD9nDNZtn2CJ9hscr9Z9pO34gN7Hb0fytaO2VNFzz5WEkgtQnem0WXLpUKzsck9gRzQZk68N728vIXnXZ8a5m62Idhda4Wg0azK6zRjxSNNOhQnHE1I0XF0H2Ea+EXVjtkPzPrSuRnTJZDzIJGLEq3Zgjw+AJKEXjDAMIwM9iBs/oMtegMnXRhAzHrvEkGwX2SVp//iF9f9VCML/CLn5/fffZ9GiRdx5553s2rWL6dOns3DhQpqbm3/xU5wzZw7XX3897e3th/e1tbVxww03MHfu3J+83s/i+GiaxqOPPsqTTz5JR4cxdsjOzub666/nxhtv/NEI7P8X/Jo4Pi2/vYS9b2yjORA/3Lkx/9DqTTVLmAThR67NaWaZyoCR13XiYA8fV3l/tN5Uj43B47MxWWU+/LqO048dRPHRE7CPnEjt0y8jSAK2DDeNK42E6dKjKsiYMxO1vxtT3qDDcRQbr7iX4vlDST9iPKrPS+2Ma7DdczGFT76DuHsZWmAAYerp1AV0BscaiG39BsuEBRD0sva8O8gel035LTejpBUjtuxHkCSQzQAIJguJpiqDXzN6DkIiihALopsdEPSCKw3N7iax9gNM004BQSDsyMTZtAXvsk/pPPteqnpDnFhoRrU4kYK9iLGAYSDo8KBLJqRgL2pPK6E9W7Hk5B5OEe9dtYKkomz6q5pIn34EupJAcmcgpedR6yyn2GQYSCrfvkYiGMKUkoJl6Hg6P3yP9AUL8H6/CkeWB8lqNm4MLjf9O3biueoevm1XSbXJpNvNNPmilLityKLAxhYfs4pS0HXoCSvkuUwkt2xlX/JoGvojTMlPIpLQiGs6JlHAIokkW0RSp15N/cqnyPYfwucpJxDXyK5axnm1Rbx6+ghiqk5vRKFE99IipdHiM56UA3GVna0DPP/SCra9eCG3L6siEFXYub4Wi8PJJacOp9sfw26WyHXbGJ5hKK12dvi4+Zr72Lv8CRKazk2f7uftc8cQjKu4rRKfVPUiCQId/igXj80houhkmRX29usMSbPSFkgwSOtCiIfpc5cRjKtEFJ1Sh4ZPM+Hp2ImaPRTNZOOz2gFO6v8eddIp1PRFufKNHZwzu4Qr8sPs0XMZ0bgcfewxNIZ08l1mzH0N+JMKUXVIbd5kWBDMuwQxESUqWrCqEcNZGQwLhXgIraWKiUttfHXzTL6s6eUSazWVGZMYIvtY1WdlQo4T9ZU7cc+Yh698NsG4RjCu0RdJcITQhOIpQqrbimCxEi+ZTEcwQbbTRDihIYsCjqpVhtR784eYCoeww1TGKHuI6oSLaELj6+puFk8twBfTEAVwW4wQWlFXkfubiacWI4d6kQLd6FYXbeZsqnrDzNOrUTNKQTLTFLNQrLTTZcvDaRZxDDQSSinCqseR+5sN9ZWmsGz8aRy1bzmyrx3N30f04FasQ8ZB9iASW5dhHjSS6rQJFK5+ivhAkOS5x7HDWkHhB3fjO/8+ipV2tLpdiHYXytBZhkoyHiKRWkgkoeEKdRgcHl2Dte9gGjoJIn60lBwqtTSG9W1Hj0VpeOU1cmaMxjLrDDSb4elDaIBEwViDGxX5QfWoxtFcmagWJ2I8TES00uxP8NmBTt7/uoba1UaH71+N49Pa3ITH4/mH1rr5llsxmc0/ieMzadIkxo4dy/PPP39439ChQznxxBN58MEH/6HX8+/R0tLCCSecwP79+8nPz0cQBJqbmxkxYgSff/45eXl5P2m9n9XxEUWRW265hba2NgYGBhgYGKCtrY1bbrnlR0XPhg0biMX+hVuQ/0RIZol8+19bf3FNJ67pZFpk/IqR1A7QE1OJqEYrcPG1k9nR7AdggtsYQ0xOtdEXN77/yhc1nHFCOUs+q+HgO+tofvtdMicOofTSc8kYO4Spz95K2uBUkoqzkXOKsUw8ko6yeSSaa+hb+TWjr5iH1ZOEFgogebIZLvYwaPENyP3NiPlDMJWP45Bfp1zrMG4QR/0G3deNFgmx4mAvOVNHGiaG4X7U/h50VUXNGowgimh2N3LpKExFQ4muft/oJokShAdQiicSyxiMFOjGMnS8caEUZSKKhppVjmiWSbfLnFDqQmrZQzCuIQ+0ojnTSTRWIoa86HU72Hfb71A6GnBNW2A8tQb6Ce/fjjM3HVPRUJKKDGfjwz5DaSWUBWuMcNO2A1hGTEEQRXpnX0HH+++QNnMGgmxCiys4Jswk6vXRvnorpsHjSb9wEWKgi6OS+sh2mvHHVGZkCFT3hrFKAuNzkgjENNLiPVhkgUBcQ0vNZ5jZz8GuAOGERo03wqDubagabG71kTr1akadeCa+mMbt+0y8u7+LbDHInPXp/PbIwXSHFZyiyvLaXrrNGezqCJBslQknVG59eSt3VGjMO2Eyty+rwmaWufiIIi46awJ/bn6ObQ19zCpLY83+Thp6QgxKtfLM2jquGJHCoaN0hN9dSCiu8vlFY3F+/woLfv8d39YP0NofYXaxm/F5KezqDBFRNOSW3QxNs/L+/m5kUUAM91NvK2EgqmKWRL6o7EJu24dZEtCdqQREOwFFYGfzAP6xJ1HZG2WoS+Nj7SMuG5bMIVM+I5q/RSoYyrVf1VHoMrGxNYBavY2kQ2v5pLIHNXcY4sxzkIK9CLEg9oMrCAlWdsdT0RweNIeHa/KPgYqZREMJTnhiPW6bia3J4yhKNiMoMeY6erhvVT0Hj7+TeMVckmtWkz9wkKGBfUzIthPJHIr2A+esL28iqmZwtJr9cbpCCrqu8+iMRciiwJbbX6Dj7VcZbfWh7fyGj/Z2MPWEG/lkdR19UZVAXMVtxnif6jpSyEujOQ8RHUxWNFsy7U89QLYYZMSHv0fNLOORXUGqwya+qeul3ZpL2q6PcQw0otbuxBdTDWNAUSbhzmeZz83sum2o6z8kkTkYMgqRHC7aP/oIXZRR/D701DwG927DVFCOJTWZLVfcQZJVwnP0SRTKAcOTC+gbNIsEIlJnNUpKHlKw1/i/M9uQ+xqR9q0wPqsRP4IoIibCuJ9bTFPWRFre+zPOgkwsM09DsyUbHZ9YCBwpmJp3GqPFvo4fFJ8KqHFiioYQDyOLAqlWiYFwAiXxV2f6fzn8EnL2n6jqisfj7NixgwULFvxo/4IFC9i4ceMveXYA5Ofns3PnTr766isWLVrEddddx7Jly9ixY8dPLnrgZxY+/xZJSUn/12p24cKFtLW1/aO/4n/x72DPMhj8ss0w3s6x/tWAe48vii+h/Sip3a9ojEq28shTmymwm5AE2OOLMTbFkLNnWSX6avsZ6rLgynVz1swC/K0BdFUj6YxrUTqbkTxZCFY75ddcTP3SrbS9sYTYxi/J6z+IFgmRMmkKkZ5+4v4w8f4BJHc6dDcgyCa8bz+D4O9Bs6dQLnppkHMI6SbDDyU5A9GRxH373sQx60TU3nYS2789THyUvY1okZBxMQz0oifiWCsm0nrvYiOby2JH7q7F5O9AScoCm+GfgmwmLdSK1F2H+/jzSA820hDUEezJpFR/R6K5BvHQFhAlVG8ngighSgKC2Yp/zXJMBeUAOI46Bzk1nYOPvkSguQvV58U0eDyqtwO5dS+6KIOSAKeHQMFEbAsvwCwJZJ92JpIrBTG9AKsnif5Vy+jdW4d7SCF6byuCEmXgkyVUijl8Ud1DbpKJhpiZIWl2pB+6d6qu812fDYAMh0y7KZNG3Y3TKrOnM8g3Vd18QQVLtrXQH0kQWPMo7101hRK3mfsn2nn01a304GLtaS7WNPZx5Qd70QWRq0sMtVh3KE6zL8rONh/r7pnL+SuDXDCpkFvmljG11MPDSyuZUODG9OJHzB6aQW84zncT2qnpCvDb5TU4rSbO/qgW5x3P4r3jZS59eiM3fFXDguoRLLlxBrU9QQpT7TyxrpE/fHGAzmCMvV1BY4SpxRmVlUSyRUJJzuGxtfU0+aI4TAI3Z3fSlDbaMMnTNTQdUlu38uDwGG5/A0UpZpSvniPz5DNoiZlYWtVNU9lCBj9Ux2PHDqb6gpOZkaaCKKEVjeYydyu6yYYU7EGzu4nYPGiDp+GLqeS4zHQlZNoi8PCS89njkzhwUwlf3zidoekOREGgJ6ww681W1JQ8JhenkmY3Y23Zgb98NomWWnRbErK3HhkNUU2wOX06NlnApMboCcexSiKDZD8JDc6+bhqdwQSTVnyJ5+bH0M12InW13Dy9kOBzx/D+omlYJUM5Z+quJeHOZyCqolmTSLNJRFUdIeIj5Mwm69aHeL9BIe2KOxHiEW4bFGGwPcEV5oMEYhrxyadBdxP6pJNxmkR2LDgKrfkgpt46juxeifT1s4hzLjAS6Q9sRErPJevYhQBYF5yHVreLtnfeRrQ64PgbmPjaU5TQh5o9BN3sMEbKRcNISfQTV3WUgjHookTc7kHGuP7oZgdSeh6hmio0n5f+bz8jcWATmTfeT2H3Dh760zpsnmQj7qZ6I2LY8NPSrMlogX6U5Fx0JQ6iRJ+7DN3iwhHqQowao+r+mIrHaUZJqAw98tR/2jX3XwGqquL3+3+0/d8aF729vaiqSmZm5o/2Z2Zm0tnZ+U97jfPnz+faa6/luuuuY/z48T97nX+48PmP8J9N0dauXctxxx1HTk4OgiDw2Wef/c3P33333eTk5GCz2Zg1axYHDhz40TGxWIxrr72WtLQ0HA4Hxx9/PK2trT86pr+/n/POO++wD9F5553HwMDAL3GK/yOo+3IHmSPS2dDmxymLfxNTUeowxkNmUUASjMiK0+49hiK7ibW9YVTd6BDVheIMdZkxiwK6qrHfH6O3shNfk5+s0ZlkTqyg88l7CLd1IIw/hvY/v4dv83pceSnkXXol5ukng5ogMTCAFvKTMnEy2Wedj+uIuQiyGT1nCInGStLOuBTdnYvQcgCtciN5W98kmNDQd31LfP+Gw2qo+J41SOl5yNnFmIZMBE8uumwBTUOzJaPHoghJaeh5FeTf/gByei662Ybafgi9fqdxk7S70cM+xHA/WsMeo/0fDSAkYow8ahGJ5mpD9RWPGgRLk5l4/X4Es5W8GSNJ9HYRD4TQbMnGa9r8Fd5tuyk6chxWTxJxbx/xfesNDkp3G+gaWm8rSuMBusIKO2JultV6CW5ZzTLbOF4feyZJk6aTeuxZlF5+CbbMdBRvJ+HVH+P4zf3ku0zMLEpFEgSSzBLBhMbBnghmScAsCUwrSMIsCoiqMbp85Ps6hmW4mFOcwoNz8vCG47zz4Q78MYWndvVR1xfB3HEQvz2T/MEZbG71UWkr58TB6bx85ijEcD+f9DhIMoucPiyDqKJx+kd3cMzTm3i9rJnuYIy3treys3mAuaOzeXdbM5e/vp2q9gA7mwb4Q2QsJ43JJc9t4/wJ+dw2r5zvGgYY4rGx+q453Da7lD9fOpHecJwvNjTxwjc1lGU6OXNqERZZZG+bj1dcc1i0vJGOYIzcmdfQRjKFHjvTMyWcgTYSjZUUJDr5Wi/nkKWQuKqjpZegpJUgBPvwx1RsE+aRKJ5EgRzi+lFJOF+9ja0PHYUsQMUzzyOG+0lMPBkEkXDeWGRvI2LIi5iI8OGBbgQlSo5VI82k4LZK5NrAftR5jPXv5IBUQONAjHS7jMss44+prBi8G7Z/yfAMJ2WHltGUOhLn/q/pGXkCalI2anIuAUUgiswkdwIZDUGNU5FmI79/v6FoEiDzjsfRAT9WoppR4H4//Xr290QYqDiKZItE2wUnMUQwrAdMvXWkm1WiogVXbw0RRadOSOekV7ZzMGTm9MHJtJFMpZqKmpxtZJaVTiCqaFhDPax1jUMMeUkeqMPqtoJi5NehxBmorGPDtKMQ4hFMBeXEG6vYmTcPKeRFjAXp37SBYFsvgiMJSYmiuPMJ2DIMLpC/Ey04AJqGGA+RVPUdYrgfU18TsgABRUCXrYg99QC4F56G5E7HvfA05PRcpK5aer/8mCdX3ott0BBi6z5FzCwyXhuQWPuBEXAc7DE4cZJMTNEIaRK61YXm8GAJ9ZBulylNc+DOdFL5zUf/LdffXxt0QfiHN4CtW7f+yK8vOTn5Px1Z/fssLV3X/8v5Wj8FDz30EO+///7hr08//XQ8Hg+5ubns2bPnJ6/3Ty18/jOEQiFGjRrFM88883e///DDD/PYY4/xzDPPsG3bNrKyspg/fz6BQODwMYsWLeLTTz/lz3/+M+vXrycYDHLssceiqn9tfZ599tns3r2b5cuXs3z5cnbv3s155533Tz+/fxZGXnca4d4ISfJfx4pZVpkf/AqpC8WxSUZExV9k7Qff2URI/TH7/cRp+TSHFWwOM4NPGsY5k3MZ88arfFbfj7fKi/dAAwN1Hdgz01DX/pmca27B5LARaB344aKnEM0fh37GHZiGTEQLDhg8HnsygsmEXrURyZONZk1CrdxEoq0OlDimcQvI7d6FYHPQt2MvSlcLiDLy5ONRupoQrHZ0bxuCvwddNqOWTkTZstRQkfV1IPbUo1mT0E02UFVDoZM/hKgjHdWVAWBEPow6Et3iAqsL3WShesWTiA4XoT1bMZePpfGJh4k31xg5UN4OkhaeiTmnkPSTz6H1qT8ijDsaNRQk+6zzsebl4z7qFBxjJmMpH039259hKhiMPtCNIJsRHS5K2zYyztLPhSnt2EdOZGFalPM+vxvN5yV2cAuJjkY61u9E6+9GcjiJJDT8cY0Kp8K+7hAeIUKO08SIDBt/oRu2BRIE4hqCEmNFnZdjh2eharrR7tcUphe5WfPoiVw92sPikgiWc07g40AWe7pCnDujBFEQEAToCCbIaVrPvoiTzQ19RFWdZze34A3HOUo/i2goTl/FUYzLTWIgksDjNJPvtnHexAJmjcqmJMPBURWZ7GkZYGfzAOeOyWVTcz/jlDo6AlEWL63imU3NfLC/kzNf2cr9H+7l3jNG8erFEyhx25mSn0KGw0w4rrKlzsuJI7LpCMS4/4k72N0Z5IIxOdy9rtPgWo1dQL8jl6NcvezpDJJqkxiwpCH31oMjhTy1lypnBVKwBwSRPsHBskdWIokC4s6liKE+EGXs7XsOZxipSZmgqaApnD0iEzEawK+ZkAZaWd/sP5zxNlA4haFyP9lOE2ZRoNUfZWeHH466koahxzEkXIMW8pPtkFHHHIOqgxjsoSchY5YEnC3b0WUrUqCbiMnoTqiuTAQlhrtrL4KmkCdHOHTiQhwHvmWP30QwrjImzURKop/kNa8ybMnrCB21tMUkuhxFtEQMb6QmZympJo0trT4eP20kFfYo0qFNZDtkUqzSD5EbPsRgD73hOMGPnmMWdYhRH7ogkv/2Z7QNPYZN/WbEEbPwTJvGzA/+ZPjhJI/EOucsXt7URP/XH4Kmkn7yOZRefCY92WNpjkjcva4T5+4vWDP1OOhpQrQ5UNz5hroyq9iwcjDZ0AXBkLnrGlp6CZrVhZJaQLyxCj0RQysZh+YpJO3YU4woG78Xy4xTSBzcZDywmOxInmx6t+xEGOhATclFTc4l3awS1/TDvKyA1SA0hxMqo4ZmIP7AB/xXg67/AhswceJEfD7fj7bbb7/97/7OtLQ0JEn6m+5Od3f333SBfgm8+OKL5OfnA7BixQpWrFjB119/zcKFC7n55pt/8nr/o4XPwoULue+++/5uzIWu6zzxxBPceeednHzyyQwfPpw33niDcDjMu+++C4DP5+PVV1/lT3/6E/PmzWPMmDG8/fbb7Nu3j++++w4wJPbLly/nlVdeYcqUKUyZMoWXX36ZL7/8kurq6v/W8/2lIFgd+Fv91IXih0danVGFCW7b4WMiqs5Uj/F1udNMzB/DKv71v9ssCgzUD+BXVOLhBDvf3YPNbaX1kbu478lTyJ9aQO0X+xl23930VzVS/cZXND18D717D+EenI3S1YJmTSaqaJglEc2egnnYFLTCUcSzh0FyBgybgehKQdmyFFNeKZInC3HELMRAN0p7A21lC0hfdL+RvtxcjW62oceiiDYH5JQjWOyGkVnIi3nQSOTsEoS0PL49+npUk51DtmIEJQoON2I8gqrpmHoOgcON5GtHGmg1ktolCWGgk/xoM9rgadhHjCOWMxzxdy9jXngpT5z0R9SpZ9H93sskulpoePoJdFWj+orzUUJRYvs2IXmyQZThB+nk4NtuNLgQPW2IyWkkhi+gr2Q6Yl8LejRE/NBetP1rUdobUL3GxUG0u8hdOAdkE317q7lvVT0xRWdpc5xpaTq6IBJKaPRGjAu8L6aypdXHB3va2TkgkptkJddlJdVmIq1rNweDMhcv2c7OjgBdCZmdeh6/P+tBTnW0MiLDgSSAP6bQNBDBY5eJDp7JmHgND84r5LmNTVx/RAEH2vwsu3c+Oy72cOaS7dzwyX4yXBZsZon1h7x8ureDskwnk/NTSKgap47L4+iKTK7/ZB9zS9O4YCMUu+3kuW0MznBy7cRchha6OXVWCd9UdROKq7QFoqxp7MMkivQEojx49BA6gzGuveIeLh6bwye724irOrfNKqZLTqP+t4txB1u4cZvGyekhLM07SFYGqLIOIuAuRXVlkG6XUVwZPLnHT5JZYkbVNmyxfgSzFa27GdXhIZE9jPD7j2GtXEW37qQjfTTVURtDr/6EU5f1o2g6/UnFlHnsdL73GmFnFi7Fz2Xf9ZHQdOzr3qC2L8TZw9J5ZF0TfZEEC5ZFCRxxLjX9cSzNO0hf9Rz9rkLS5TgDUZV40QTeqvTxaZcFiwiNQhq6xYEfK2pSFlLLHtAUxn7+OZSMZbRlgDNKzLRFoFtMQZp2quF6XTaFFItEIK7y7u52CpLMCEBvXGR4hosRShNyXzOd+Ueg6MaFPCqYUZ1pqO4CZqcrBM74LYmMcrSWKuKeUpbsbCe39lsmZxpeVOKQyaCpaM50hnZvQYiHeXlsGHtBPv7lH/D5tEvQJ55Iel81+U4RsyxS+fQbFK35nkTDASNbLx5GjAZQGw/8QEBOIEUGDH6TxYnq8BB3F4KuGaaTomgo96I+g3+XUYilYiJ01iNnGl5cUrAHOS2L1OFl6DHDywtBQAp0IwIJdz66ZMYqG95Qrf0R4opKWvmE/5br768Nmq7/wxu64cX3F+rKXzaLxfJ3f6fZbGbcuHGsWLHiR/tXrFjBEUcc8YufY0dHx+HC58svv+T0009nwYIF3HLLLWzbtu0nr/c/Wvj8R2hoaKCzs/NH5CmLxcLMmTMPk6d27NhBIpH40TE5OTkMHz788DGbNm0iOTmZSZMmHT5m8uTJJCcn/4ckrFgs9jfzzl8LetdtoGh24eGv/9Lp+YvC66/7BWySQE0wzsdV3sNKrxNL3Jw8JotoVGFWjovqQJzvukPkTh9C2shSwh195D/2FsPPn4z3i/ewuJ1ULL6EgnPPpnVzCya7FUEUQZJxqkGCcRVBTRhPeDu/wVS3Ec3iQvJ30/D0EyS8vbwTLKTr62+QQl5CeWMZ2L6NZIuEEPEhZxZgKhiM5OtELhxqGBi216CbbcbTZKAbgHjVNjS7m9mVm/HHVUrbNhr+K8nZKO48HJFedNlMn2cIiexhCIkYYqALIRFD6e1ErdpK9MPHUHva2NMV5tu6PrTtX3Nq406CcZXM0y/APuVo0keXk3/DHQiiQNv6fSCKxhOtEkN0pYBsAXsK69U8TAXl6NEgvfdeiWfgEInmGnRVxTpmJoLFaoSR2hy0r1iHXDISObsIy8Qj6d3fzENZh9jYMoAoCNRGrMh9zcRVnXBCY2ubH19UQdV1phan0htOkJtkpbo3yMGeIDM+jXP1WztZdc04Rma6aPXHuHd5Fa9dMI4611Dc/gYuEPZwwo7nyUu2sq5pgM1tQfozhtMbF7mt9mXuX1XPBRPy6Q4maHdX8IcThvPJBWMo8tip7wmx/ON1HD8im/NzY5x+52fc9uIWvqvs4tTLHsYsi9y7vAqzLBlk67jKnz7Zz2lv7SYYTXBt5/vkpdroCsVJs5s5Y3gm43a8yp+OG4r04q3kJlnp2/AsvRGFNd9VkqP1YV7xAh3BBG+ddD+aw8P8IRn02HLsLhH8AAEAAElEQVR4uscoOjMcMpGnbyamiwTiKsG4xpFl6Uy4cwX5LpkGxYleNhkht4wuxYzka8fsTkErHEW6HMcqC6RaZdY+cjzvnzWM9FAzkgB5O96j9qw/8NbeTsT6Hbx4fCnf1vXROO4csp0Wht/4NccMySSc0Pj4onHEVZ0KrRUkE6YZp+EOtYEgkq0PIOg6Fwh7eH1jI6Nu+QZvJIEuyjjXvnY49JcDa5H7mqBxN01iGroo0+aPG0aDtlQ0Zzoh3YQvpiKLAr+ZmEdfVCXZItEXVRhp7kezJtHlGYbNJCILkCFFEQUQ4yFU0ZCFZ5oUZG8jO3Nm0xky/k96V36HsOcbqN+Jsn05AImvXyK45Xt0k5WB7DFsf/B9rNmZTLt1AYKaQO1sQFv6FHcNjjL0hVfh9xchHHkFelu1EUCaiBmcPiUOkgl0DYu/nb6ISlQzOo66xYUWHEBJLTSKLZMdKdCD1t0EgF4wAjElHT2vAqWrGd1TgJjsAU1FHGhHCnSjJOdgM4lIwV7a4iZaAnH6oyqjc5PZvqMdX8vfesv9L/55WLx4Ma+88gpLliyhsrKSG264gebmZq644opf/He53W5aWoxYkuXLlzNv3jzAaJD82+nOfxX/1MLnH5n1/aWF9h+Rpzo7OzGbzbjd7v/wmIyMjL9ZPyMj4z8kYT344IM/mnX+pdr8NSDQ0sXKT/7arVJ1Q50V+bd2zcB+f+zwvnKnmdnpdgD6/DH21/ah6vB5k49cm8xF84oJtfVQ/+UWWjfW4X14EWsfX42uatgz0+jfsI6eb75mwo3HoEbjCKPnG67Fgkjytg/QrC5akodgyiulr2AKUrAX3WTh7gk34W/s4IxSK1nHHYcuiPSGFVKu/AOqDkJHLUJarjEKqt1FonYXQjQAsgmlbu/htHbsKZhKhqNZnIjbP8cXUyGzGDEaQO6tNzxDzHY0WzIOk4igGtk/misT1ZmGnGVcSF3TF+KvqmWMR+KCEWmYysZQ7N1Nav06NIuDvi/fwzFhJpGV75M9pYIhN12LZfx8I2nb4kTNKiew+jNUZxpTPBp6yI+enEn2iSdAoNcodFKzSTRW4l27BtXnRbS7sGd5iO9ejdLRSGzz10hmiY1p02gdiHDN/UtxmkW0rkaueH8PH+/rYP0hL7lJFspSHczJtZDpMOMyixS5bUzOT8ZkkdA0nWo/vLGjlW+qe2io8SIJAgXWBCtD6cSGzcd+6jUkWyQCcYWl+zuJ/GCK6BpURHqShe8bvEx2hchU+9jQ1MeZ7+zh1e8O8XRxK4MmjuSVDQ3osoUrL57OonNGc+6EAgRRouFQHw+tf4AbZ5WytrqHvS0DHFj+GXcvHMrlU4u5TjiGC0dnc/LZt/N9bS8fHexGWng5Q896gknVk7nptW183+Tn0wNdbMj7ntM+b0dY8Bs2tvRz9FDDxHJu3UdoOlypbUUMeYmrOmmXGG3tbKeJ7pBC2pLbeO+mGXSFVYIxDbFpN7pspSek8P38c3n7N0tQN35Cv26hJ6yQ1baZhKazv081OCgC6NEQFlnkKk87Wz2TaQ6LlHnstPpjOM0yb94yk+Ep8MGuNpr9cbL7K4mklqA6PDQKafTZc2iLSURsHsSdS6nJm8WQ7CT2/2403aE4R79+wOiG2t20pw6HkfMQEhEE2UxR81o2dKtMqPmY7Jpv0D55GF0QCSc0ki0S+TYNVYM8h0ggrlGaYkFNykKXTGg6OImjIeDVbZiVCMEv30CODqA5PNy+qpWZH/hIvvtC8pVuLOVjyDr9XIPMb7Ya4b6NldjGzcZ21i0Ek/JRdRh56WwEexJt6w8SeO0P6CE/gs2BmpRBg+Kk8PyzMHccQErNQrA5wOJAd+cYajNrEgFTCi1yBulCCFk0yOGoCeScEqTeBvyO7L9K1MHoPgV7QdeMru+IeQjRAPG2JvTcIaCpqNVbkfubMesKc1+rJc8UQxIEcl0m5hWnsPPBBSy48LR/8pX31wn9F9p+Ks444wyeeOIJ7r33XkaPHs3atWtZtmwZhYWF//kP/0ScfPLJnH322cyfPx+v18vChQYJf/fu3QwaNOgnr/c/Sm7+r+DnkKf+/TF/7/j/bJ3bb7/9R7POv1SbvwY88epuipN+3IL8y8jrL8GkAGk/5Ck5ZZG4prPfHyPLKpOV7aQ4ycLqnhAnlaWSnmbDkmSmeW0jJqtM3hGlAJz0+R9IqhhC7cdr8ZxzFaG2HgSzFVOSnYOXX4I++AhkbyPC+GMIm5Kwm0QGVn1F6P4r2XLpjVSLubwxRSPn/MsIvfsoiCKxnaso9Fcj9zWR6q2CzGLwdWMuGoI87AgjKT3iR4+GMeWXIdhcqK5M1LYa1J4244kSKNF6CH39lsEfSCsxZLP+DvS9q7F0HkQaaEcLBRAbdyJGfCh5I9ECA+jxKOknn4NYucbgCsRCxGt2ESqfieZMJ/mMaxBsLuzjZ+G88LcAxHetQrclo3U2IDbtxZKdixTpR4z4iFbtwPfpEhLNNQS3rCbRXIMQ8SO5M0guyUO0ORCTPUaO2cjpmMtGYxkxhY8ufIxP9nbw7e52vn3sTJbW9OC44guWnTeEeWXpzC5PwxdVKfNYeXRrNx3BGIoGvqiCQxb5+JLxLL/+CMYfcwOXTMhjcqGb5u3rSLfLaGa7kf0U6kHf/R17u0I89NwaxhaksLMzyNe1vTzmOob5g9JY8tlBxKiP+HdvcsGYHFxWGXeqjf1ZU7nluKF4nBbOXNpBx0CUeSUeHv6mmoyKSVxxYgVfXPQYZkkgEIix9tUlvPjCnZz5x9VMzpCJKyonvLCFa+5ezDlj85hZlIpr+g1kV4xi+5Mn88ruP3FU8gCLRzm4//6V7NpYT0gVOGt4JkM8NnZHXFSPOYe4qrE6Yw6VYg4RRaNLTiOc0DjUH8P96q14Fv2RXJeJ5I8fIPmh3yA6UxCjAcbEaxh50RFcuOx+3sk6gbRAIz2hBIcyJpBkFsl0yDSJaTjr1rNy8NlYZZGB7DFYJIkUq8RMi/FQNKMwiWZfFCnQxaOhT6iI1tG/7H1UTadVzuDp7JGk9ewj1Sqh6zo9FccgiTC1JJVDt16H22piae4W1NQC7vSMJ9UmI0QDND77lJE3FxhAEgTEKSfTVrYAy4ILEWNB0s3GU6xPlemLKnSENdLtMqGEhuRrR2w7SJpJQYwM0OiL41H6kWo3YT7vLqSOKgCOHZbJSxeMI/OpP9MqZ6AOmUFkx2pMExaiBQeQswoQrA50WxIJyYIj2IEvpmLJL0YaPZfSE6ZgvfQ+pPRcTOPmg2Sm6OBSRKsD/Qe3ZbFkDLrJgmZ3o5idqLIVh2zIzHXJjCXYRZJZBMmE4ilCzSzDFes7HKoqpWYZ+XEWh1GI1u9AX/8+utWFpXCQMUbzedEnnkjUXYRPEXn2nLFs7xdIs0n4YiomfweRhMbUsn9NE0NdB+0f3H7unfqqq66isbGRWCzGjh07mDFjxi96bn/B448/zjXXXENFRQUrVqzA6TTMezs6Orjqqqt+8no/q/C5++67aWpq+k+PCwQClJSU/JxfQVZWFsB/SJ7KysoiHo/T39//Hx7T1dX1N+v39PT8hyQsi8XyN/POXwvuvHMuvn+n5OqLG949EVVnVLKVVLNETTB+2K+nMZzAbZKIa8ZbXDJLnDoqEy2ukjc5l659PbS1B/C1+okNBOg90MzePzxHoLqGoddfhBjqw5bhZqCyDmuKi/Iz5xoEYsmEEA3gCHej6zrOC+5EVzVGX3cixWufNeTpLbU0HX8b4b1bDZm4plBvK4GInzZLLoI9GVxp6LIVubsWwWJHyC0DNYEeCRByZCKUTURKz0Vb+Rq+UcdD0z6ck2aDZELua4bOepSUfLpGnoDmTDMKoo4GlK4W9L4OpEA3cslI4o1VaIEBg5i5+l2wutBCfpw91eiS2SDM6hpq7nD8L9/N9tSJBvlZNKGOmG88ffq8RqCirxPRbCVp0nQEmwP78PGY8gaRaK1DzCjAu+8Qcm4pzW+/S8/eRrref43o9pXosQgXjMvl4QVFXLegnEEuneMHp+Pb9BwdipVgXGFSbhLBuMI9Kw4hiQIN/WH2dwfZ2eYjM9FDw0AcSYDW75/mruXVDEq189jDV9EWSOCLqdhIGKMVi5Vp655k0LgSPt3VRlTRePSd3ZxYkcXB7iC775lGq72YBU1HkNB0frugnMumFXPRc5u4/on1fPHed5wyJpcUu4nv6r2MKnTzu0smUOZxMKvEQ0zReeyM0XSve4ZvDnTy4g3TiIoWFlRkseXdtzi6IpPXtjZz99dVTDjjXO46fyxz/rCa+j++hd5SSecjd3K//yCDRudzyft70YCIonPRUxso3/gSnacfy6gsBzlOE1kOmXQ5jkcPUJYsY1/0J8RgL669X9K+fh8l112PZnEQ37+BndfeTtLVf6T8VYXzE1vochQxJstOkV2nxZ9Aw4j92JM6kekbn2Zk7BDjblzGaLWRlMpv8aWU0h2M4Q0rHFuWSmzNhxycs4j6hx8g5bhzMUsiyRaJx1q/pT9zJIIgcMuyGra2+TnUF6E/kqD81psZlGqld/YVaCYbV3XsIapotEhpZNz/KqGUIqITTmZUph19xzIKIo10yx50k5XYh39CePteDvVHMYkCfRGVuoEY7mALYVcO7flHIOz6mm1hF4Oj9VQnDENJS+dBtPQSblvTzXSnn+IUM44t71MQqqf991chnXADPmcuqreDge+XE2s6RMszj7K1PYjm8FDUugHf7t0om7/AfMqN2Jq34yuZxr6rr0eXTMilIxGT04gd2AKaYkRODHQihvsJJTSsbbtRdLAn/Ji6awjYMjBLP9xmfkhiF3QNob8NXTITyh5hGByCIWiwWFFmXYgumTAVVaD7uo338Q/HBBMaLrOI45Zz2dMVZnt7kAFbJs3+OG9+/f8mZ/Mfha7r//D280uf/x6YTCZuuukmnnzyScaMGXN4/6JFi7j00kt/8no/q/BZunQppaWlzJ07l3fffZdoNPqf/9BPRHFxMVlZWT8iT8XjcdasWXOYPDVu3DhMJtOPjuno6GD//v2Hj5kyZQo+n4+tW7cePmbLli34fL5/CgnrvwOlX+nklaT8qLsD/LWoEWBUstER+ounz+x0OzXBOH1xlea2AM19EdIr0kguTKZpbQuf1vaRl59EX20/fYe8ODJcJKIKiVAUbcwxxGp2kzq8jOTSXLp2VCHPPRdh/0rU5koEJYoum5FEgUNBkYJrb8JUPAzrmBlQPBqlo4GRsUOYUlLQy49A7e8mf/cH1HrGEkpoJNJKUJOzEaN+FE+RkYGViKHbktDdObj66xDiIQTZhGncfHrCCvPWp4DTY4SURoOQPQh5oIXcUAPoGoISRR45A9HmQPV2oIsy/V+8haViEroSx79uBeYpx6CkFWMuGU7i4CbUb18hunstPZ+8g9TfStJv7mV4uo1E6yH6Igob28OE927BtPA3JFpqEcw2rOPmIJitoKkoHQ1oIb/RmYr4ST9iPKFdm5GsFoZccRaiJGKecSpq8XhyLSpiyMtxeSJy8y6yhCAHe6LYZIF57jBpNokpHo1nJomcNCyTuKLR7ItglkUu+KaXkVY/fVGVT6t6ebW0CY9N4twRGaRYJdJbNiOoCaSQl/iY41gy9BLuPq6CecMyGZbu5OXrpzHm6EVkOC3MeW43H+zv5PzZpVT2hHl1Swuf7G7jqCMKmTKtkMFHjOdPn+znrNG5ZDktbKvpocMfZVe7jz+uqMFuErh3eRXvH+jhtwsGc8pVz5A+7WouueQuar97iqNPvxVV19m/q4MTpxQQSais8HyNyyxxT99gnjniJnZ1hqjaVMkHpxRxd8owzK//lubt6+iZcxXu95biljUSmo6i6cjeRoML9f5D2KN9CF11MHQaRS9+iJqSC5IZ07gFuF7/FH9co/6OUoIjjyUz3Gx0wCQz6Q6Z1Q39tPhj9MyazVFds6l3DWb348cQTB9MV9k81jT5GJHlQhAEZDTCnV4s159J4QPPoNndmILdmCUBvzMXJ3HaAgmeXlhE8e8v5GhzEw+9sZO+nHE0+WKomk5HWKMg1sqMe1bx9q52zGEv39X3o+s6UVUnMvFUEpVbiasaEZML+cw7MCXZ2dnhp8QSJckiMcht4YUmMxYRcn01aONPYOSGZ3mpMwWnSaStaCZbhEKk/hb+mLofzZmGpW0vTDqJSHo5BdfexL7eOK5YH2oogOvsGzg4ZxFpv3+BiVueB12jJX8qFyWdhWB1EEXmW8pJadnKsNff5pvmCHpvG7rJgrloKLGqHbS/+bIRVixbsckCanKO8Xc22dAsDnwxlf6oajxM2FLoseUY0R62JJBNmDUjaV2M+NDzKhDsyZhjPjRbCqGsYWj5w5FyyxED3ViCXXhsMrn4GPLqe0zIcTKzMAmXrGMziaTl/HoeTv8Xvzzq6uq49tprmTdvHvPnz+e6666jvr7+Z631swqfHTt2sHPnTkaOHMkNN9xAdnY2V1555U9mVweDQXbv3s3u3bsBg9C8e/dumpubEQSBRYsW8cADD/Dpp5+yf/9+LrzwQux2O2effTYAycnJXHLJJdx4442sXLmSXbt2ce655zJixIjD5KehQ4dy1FFHcdlll7F582Y2b97MZZddxrHHHsvgwYN/zun/j+Pi3asItAdxm/4qZ49qOucfWUKySaQzqvwosX1sivXw1zlWmeZw4nCRtH9vNwVT87hoXjGubCdpQzwMOmE8+z46QOfBXhqW7yfx0SP0795PrLePqnfXYE930/P0PXR9tQypYCiCkkAK9JDSW8WgJIGri05AdCRx4Pf3E/lqCZbyMWygGMv4+TTFLGgVszHllVIqDlAm9iGFvCCIxs062IMuWwhmDEWIhdAlM5rZidawj3jNLpTUIoYED7LqGAnNloxmdiDIJgR/N6rNTTy9zOj9CiJIZiRPFlo4gBTykjJ9PvGqbQysX03S0WchxkKokgUxswjJk43i92MqKMc9dTpqWw2xDx7B2ldP1/zriSg6s8QmrCddg+xtBCVObP9GEo0HDV+gRNwwWrQ6sIyaZsR5DJ2E64i5pA4pRM4ppubUuwkl5SHVbkLYvxJdNqN8+xpKdxuLV3Uz0uTFIovU4aGmP86oezZTbSqkzR/jwjE5nD4sk1nFHl4vruM7r4XKnjDtAxE6yo/k9d0dLHxxGx1BY5zjE+woniK2tgeZWeSh2RdlUm4Kzb4It32wh0X33khVb5CGXdVUdQR4eVkVL21o4JsNTVTWekl1mjl7QgGvXDiO354xirr+MMMznCRiKsk2Ex6HmTy3nbd2tRNXNK694h4UTSce9nPjfTcx/aKLOPr+1QC8ev+TlI/I5M5FDzCjMIW0S29lduPnrK/qJtVpZl29lz/ceBRVERu39+4nZdps+p9bSKpNYpDSRkiTiClGgaCm5JDIHEzgxFsZMLkJlk4jZnVj8ncQdWaipOShmWwM6t5G8o6PieUMJ7mvFiW1CCEWQNj0Id8e8nJWoUBhsoX1z7zL3aeMYGurj1HXf8ED7mFEVZ3qniAmUeBPaxtoDKi8P/k6Ov/4FvqelYjeJjRrEpYDK0jp3o8umchPMkawg887CiW1gA0PLGBTq5+haTYK/dWc/dJWnqo3c9zsEi4Ym8tn7SLHZ8TwxTRS9ZARUeFKIT/eTvOlp9C2+FzUE2/mqEFprOzUKenbTfD529lS56WqL8bbfemGcvGUW/BFElT1hkmxShSnWA3yPTDpgQ2oznRkbyP6Rw+xRc8jzS4jBXvpOfIGWjUXveEE5kQI6+wzCL/zMHlqLx+lb6T6lY+wrHuLOe3fIMgmtvRJzDvwJj3F09F72wxvH08WnnEj0BVDNCELEHOkozqM+IQWawFZThMZihdVshCIqbgtIrpkRtAUdEFEiIcQo350yYwYNzL7pK5apEObMesKusWFkl5qCB0AZ081VXEXlX441B+jN6zgT0B3MEFn48A/8ar768U/OubSfpC0/5rxzTffUFFRwdatWxk5ciTDhw9ny5Yth0dfPxU/m+MzcuRIHn/8cdra2liyZAltbW1MnTqVESNG8OSTT+Lz+f7TNbZv386YMWMOt64WL17MmDFjuOuuuwC45ZZbWLRoEVdddRXjx4+nra2Nb7/9FpfLdXiNxx9/nBNPPJHTTz+dqVOnYrfbWbp06Y+iM9555x1GjBjBggULWLBgASNHjuStt976uaf+q4Bsk5lS9New0b64ys5NbfgSGrNGZrBzwOjCzUizs3Mgevjr/oRKV0xhvz9GxphBLLx6Ku6hBXgGp2N1W8keX0TdlzuYetM8yuYWkVaRTtwfIn3+kVizM1nzfRPeyjYyjz2e3AsvM+SytmQI9KI0VSL31vNkYA+x/ZvIevEjqt5fD7KJwZ/ci+LOp7htAz0xgdbM8XjNHt5tkVgbSoH9q1DceeiijNjXgj3QjurKRDfb8NszkfKHYB48Dm3Zs8QP7UXt7zaeElcuAcmElpIDkowU9RsS994W48JqsWOpmIhmcYAookVCuIYNR7O7ieaM5NH1TXS9/SKIIo55p6H5vIglY9BCAWSnEzEawCwKRBSNT0M57D7zTEIbliGl52KpmIiU7EH1diDaHEiebPSwH5Q4ms+LoCkIZiuOKfNR+7uZbB/Ass5430muFKKfv4Bl8tEUPdvPU6OjVGppOPrrafXHGBar47Zzx3CoL8yUPBfdIYWesEJfJIFv1PFUpDuY2bSUu4YYF/8P1zXy/FmjGZZoZFCkgS1tARpiZiRBIBhX6AxE2dI2wKPf1qBpOrfOKmZKnpvKJRdysL6P8tJU7GaJi48bQmFhCn3BOCMyHKxu6OPbqm72dfjZ0eHnkbPHcKDNT21XkEnFqZhlEV9fhJOuv4JUq8TEU0+issPP5bNKOfXIMtrWPMPBb5/krAn5XHLn9cQ1nedqFL7OO45pQzIoSLHRHTCIqm1+4z16Q3MRTRnjuNkxlIi7CKfix2U2RktxSzKCEsMbUUiWFJ7a1EL0xTuY9uIh7F2ViJF+BCWGmjsMwWRGCvfR5ChB2PIJ125W0IIDzC/18O3Ihbi3/pl7S40U+Tuf38wJx1Rwefse9nQGmFXiIdtp4pyxebT6Y5w1PJPZhUlok09B8xSir3sP0eUmtGGZ4RQO+DQT4vxL6dSdpKg+LJJIsz/OVqmE9eekccX4XBYdeJ4sU5xRWU4OKG5SrBLVEQtmSaCpdAGa1UX73a9TetMt2IOdmCWBQal2bGe9hu3yB3j/T88RVTSKUmwUXPs1gZjKokk5pNnNWNa8TnqwkT/7sugcdizbz5DRbMlcuF7BdNqtTJQ7yd75AUpaMeGETkG8nZnbnqchZjYy6869iyYxjS/LzqbstNn86eRHiLfW8/2pN5D79LWGgs0iIrpS0EN+mt953+j2aKoRNaEpmKPG31+Ih8gVA3jDCqrDg8nfgSwKhuGn3c1AhvEZFFQFNSXX4AmZDd6GnpSBlj/cIIAnIgixAPKAEU7Zl1pOYbKZhGqE+IqCgD+uUdsXIuT/5ScP/y/gf4rc/N+J2267jRtuuIEtW7bw2GOP8fjjj7NlyxYWLVrErbfe+pPX+4fJzZqmEY/HicVi6LpOamoqzz//PPn5+T9yWvx7mDVr1t+dN77++uuAQUq+++676ejoIBqNsmbNGoYPH/6jNaxWK08//TRer5dwOMzSpUv/RoGVmprK22+/fViW/vbbb5OSkvKPnvr/GJaMnkNKYTJt3eHD+2ySwM6BKFlWmY6GgcP7e2Iqw38gQp8yxENENUwNh7osfHzfNxz6aj+OLA+iSaZpbQtJxdmMuOYU/I0d6JqOxe2ie0c10erdAFzw4AnkzRxGz7fLaHrhWRI6aPu+J35oL0pHA2pSJtqqNzCXj8HTe5DSY8eh+ftIHjsOzWxHKxmPZ+3LZJkVHCaR48o9VKTbEconIzbvRYwa5pSaw3M4QT3p4LfoFgeJllqsI6ZgGjwePeSnL7Uc0+hZAEjhfsT6HQZfQJTxfrPUIB/njDRULPEIgsWGuWQ44oRjEJr3sa7Zz/VHFGDPcBPcvhHV5ibR1YIY6KZ/4plEjroGtb+bzHgXJauf5ARtP6PfexfrgvMQJAk9HjX4PhYb31/7EoIoEu/qIF5/AF3TUF0ZaKEA8Zpd/GH2LXQ8eR9ydhGJjkZiB7ZiSjc4Zk2Ls2lJqWCQ24JusjNbbiF+YBMzClOYV5xCIKYiifD8xkZm5dnY0makm9eNOI3V8Ry+ONjJyVMNh2NBiaE6PPzxi4OUBaoodVtZXtWNL5xgfE4yJ03IY/X4RtoCCYanSuztClOQ42IgnGD92kZWV/4f9v4zSq7yTPeHfztVjl1dnbvVQTlnCYSEJCRAJBNMtE0GkzHRmGAMNhhsj0k2YIJNMpgcRRAgIYRyDq3Qrc45Vo47vR+2zIz/c95zjmWPD2uGa629JJVWPV29qurZ93PfV+jn8nk1TKnwc9/nB5ldHuCcaeUMJfOcMjqES5EoDTgoCTjY0hah3O/kjPnVdPYmGcxoXLyglq6uOPMq/dw53cUtH+znujd3c0ZkJY29Cb5oiXDFlBAjQy6qQy7sssjUygALRgRIqzrZn5zPseOKSKkGD6f3oQjQqbvxt62nP6WxbzCL7vCR0wwOxOGMiaX8euSlrL20ysrKcvgtc8IXf41UORZB1/D/5V42VR3Pw4FtNM+6kGKXROSDj5CmLQVJ4eR7P6d+aTNLxxZRqfUzKuTm6fVtSAJs6Y5xzzt7cCki+4dzaIbJB32WyaJWMRn99FuJ6DI2zXLbTv7pXu74cD8xOcDRVV5GH/iAqfveoN1di7T2FdRLf4mYjbOnL8k4r0Eip5PK66RUg2hWw9z2CQv2/ZlN8kgMdwGbu+LELzmd5KOWZUds/eMsOusuClwKrX/6ATZJYHVHimlyP1pkAAY6OLNMZd9AGn3kEcidu1g0tojcy/fR7qjCzKboy0uUemSE4U4GT7iJOrWL1t89TMeFpxF2yZxeEEFZeA7ntm/jwF/WcNTKt6j6/vcQehuR4r2oJePo++A9nOEgUjBMZu92EoWjSRoSgpqzVFrZBKbswGuXSOkCrYJFSo/kseJDROFQWKpumRa6goj5pMUZzGcsMYHda3VuRRnD4cVUHPjI0hLNWVypeBaXIhJ0SKi6iaz89wvH/r+BwT/e8fmmY9++fVxyySX/6fGLL76YvXv3/t3rHXbhs3XrVq655hpKS0u54YYbmDZtGvv27WP16tXs37+fu+++m+uuu+5wl/8W/xvkt/+RaFuMQvu/uzX/Vbbem9VYNfDvBdG+RI7urEaZQ+bLlih1bhthu8TkugCTJ4YJjy/kox+/QdeGNhb99vvYy8rZ+4d3yEZShCaMoOzUU7AHPGx8cDnykafR/tlOOlfXk+mPIDtsmK/+EqVmAvbJ84nsb7Ps7ytHE1253HLhtSlIoRKGN24EQMjEMHXdyqq6/xo6D5FxV0edCL5CqwXuKUCOdqCF6ywnWrcXDA3JG7CUWQ4v8piZ+MgS99dY4YvZJKamIg80YUoKoeNOtlyeMZFCJTT/6n7MXAahsByxdQf6qCNYHExjkwR+X3chvqOPR452IodKMDIpfr26hUDbeoTSOgQ9j1JciT7UizzQBB31VtK3oSMXlSOXVrP4uR8j101hYEcD8f2NiA4XUnIQwpWYap671/+eklNOQigoxUhEyMcS2MfO4KCzhoGxxyMJIG18k59uybAiV07v3POxSSJZzaA7qXJwOMOs6iB1l79GTtMZzmi0xzJMLHJxw7wqSrwOxrpV5rwUZX3MgWKX2KqMIqOZjC3xctL4YpJ5nVPGhFm6s5btPXGWPL6Fa5/ZyK9PGc+Pl45mx0MnEollufMvOyhy2zhjShl1QRsTi1wMp/I8tK4dn12iIuDklmt+wfSqACeMKsDnUDhj3giW7+/ng13ddO5pYMalz+A+9mdMrvRz8tQyzmsdw4IxYV7f3MHbTUl29CSYWe7H71Doi2dZ1RrhpBEOmm98AlEQ8Nsl1nUlGczoRDI6u4MzsMsCYwsdbOlOMe87N5FWdeS7L2TRtedhijKOSCtK62ZIDuEZPYbdSjUpZyFdp1vqPCMZxWMTmXnX50wt9bI17cFIRtl1XAfJY69mmbOHDekAdlng+gW1/PTTJpaNCrHy/Gp0w2RcQMIZaWVZrQ9By5JBwZ0ZJChppAQHed1E/cHP+OOxhbgVESney/aaZSwvPwkTyB5xDuFtr5N2F3PnH7cQM+2UJpoIOGQUUWBC2IkwdSn2yfPx2WXUdx/h5OiXHNzYBeOOskju0TzRB4/kx+/WI6aG8fXuojbo5JVeN/ZjvkdD6ZGYzdtYHN+AsGsFetFITh5diGfx6dhEgeTCS4nldLzpPsxclnJiiJkYRTPHMmLJNPpTGrk1b/PBpGVUxw9QtXgiG048i+jalRhVk8l5SxD3rSbZNUAumsDIpvHMWYRLt0JDdW+RNbbOpdAVFzZJwJfppybfTns8h2FCPG+giAJiNo7uK0H3hBEaNmDYPJYlhcNLXpDJGYCet9ygFSdCLoWgZQm7FBRJYGSBE0WEL9ti+B0yai7/r9yGv8W/EOFw+GtKzH/Ejh07/pd2Nf8nHFbhM3nyZObOnUtLSwvPPvssHR0dPPDAA3+jpz///PMZGBg4nOW/xf8FtIxGR1rFKf3/fwtHeywL9yl+O4umFjM94KAplWfZnHKOYwKF48OoKZXqKcVMPH8+HZ9uAEOnZ2sv8c4EJedeRHzzOvRsnvHnzmT7+RdT9525jPneEmw+N6ZuoIQKUTsa0fraLKdVxY7atg/TMBh+6zmctaMQbA5Cl/0E4+3fkPWVISg2xNQQRT95mAm5Jup6NnBUuYvcls9QC2stszO7F7lt69eKD0HNWbLW8rGImRjN9mrkoVYCfbt4J1mCGSyHmqkYmRS07cIsriPfug8p1o3h9FN7+92YpaMxFJeV0VW/CkHNkHrqDm6dqIA7SF/hJDbf8zzqmAXcs7QOvWISeqACNVRDxzsfkZ1zJvmKqQildchzTkawOTADJdaptWYSurcIUVEIXPULOt//hOEPXkHQNZR5p5Hdu4n0pGWYsoNkRx/OkWPJl01CEQUG0ipFQpKVJUu4feg1Xt3WxckPrGYgrfLxwWEKnDI1ASffr5V5+5enM6fcR5kTSu84nz39abb0pLjj0VV4Ft7KbadPZEapm/cvn8UIvw2/XSTksjHDleTzhgE+ahzitctmE3LZePDMyXx620JWt0Z5aNVB2uN5ZEXCPBSJ4XfItMdVhjM6R9SFOHdKKQPpPPc/t5VA9US0pUtpjebx2iTWNg6S1wxqwx7WPXEBP7nxZH507018Vt9HfVecvbv7kESBc2ZXUh1wMirkQhFFFgtN3DJ/BN+vFln8+A5cikRO0zEevJpPDwxQNriTZF5jvD1pZZnlDeZp+4nfN4sit8KIR/6M+Mmn5HxlCIlB9Eg/ZkEFcnEV7+3tw6Ul+aRxgFKvjaEFl2GYJjtuqGMglWcWHaRGzufLMeewtSfJZ7kyCpwKHbEchmkyotBFqTrA1qwfWQBh23KESDf9WRNB13AnexCzMaREP4ok0JfWCA8fQNvwHsMZjccPmjhkEftxx/HrL5pZ3jCE4HQjCrDnEjeqAZF3XyDskqmM7EHEZPu5PyC3aw1jojtpenc9P2iqxbbyc7TPX+SL1hijpCj52afz1gXT0Da+z37PeDriWVTDQF3zJo4HryA9aRnHrg0gldSA4qA/pYEgYpMEelMqI4N2hM69ZMYtxtyzGjObYu+LX+AoLaZ8zR+QT7meufs3obbuxTd5MtWLx+CurUXqqmc4q7P310/Qt7MXxe1ELq4kt28rAHYRUrqAkEugloxDN01Lau3woQcqGBNyUpJsJmCmyGgGpuJANbEOSOEKDHeBlclnc5LXTZxqwgpDTVuGjYY7RI/pwyYJOGSr0yOLAlNLPDy9uhnF/j81suIfV3V90zk+l112GZdffjkPPvgga9as4auvvuKBBx7ghz/8IZdffvnfvd5hFT5nnnkmra2tLF++nFNPPfVv+DR/RTgcxjCM/8Wzv8U/A7pmMKrc+zfJ7GG7xLyQ82u1V0PSOgHlDZOKebWMXDSCRWEXpbNG8EbvJva+d4BsPIee12n9ZBvJngTp9g6CtQFsboWh915hzf0fU3naMopPP5uB+kEGdjTQv6We7FAMyWFDCpez7oZHia79EsHuwNz2CaaqYg94cVeVM7BmneVZAgwd9yNc/QeQZ59IPlSHFO9l+ZHnY1ROxBQl5u+ajNK7j7ijEDETQ62ehaBrltw9m0Cum4KUjmDEBqk2+sms+wDT7uaEljcwDm61Tpr+IsSiEQh6HmnpxRbPRs2hBasYsodR/eXovmJr/BXrwzfrSItz8P5LFMgavkofnTd8n+QjN9NvuOjLS9iaN1B9290oHz5Gf9Zk8/lXISYH0X0lCLqGVliD4S3GdPpxhHzsv/gcfNWlSIqCum8jUjqC5A/hibWhbv6I0DHHk9izk7gKVW1fMt6exLS5+bJpiPalN/L2M2/w4/Omsrc/ybyqAKX6MLIESSXAVNswzcuORe7YwcjT5zGcUemKZ7nvukUAlPsc2LIRelMatkOfg0lFbuKOQn4wowKXInHhyzs4dnAVWc0gmdc5tzTDz08cxx/WtXH8lFJ+ds5UFjW/xc7eBPsHU3zWPEQkneeVnT384uMDnLhkJKeeu5hdz75OeyzDjs4YAZeNGZUBemJZfvHZQYaTeRaMLGT6iCCdkTSTp5SwYkc3fYkcU4pdTCsQuWP5XjYpo0mrBu90i5x6RBUf7++nP5XngV+t5rpdv0cb6GJMoZN2009XQiWc6cbwFNI/9wcUuWTETISFFU7syT5wBxCrJxF94ynMivE8+8pW0rKHgXiOTZ0xSlpW8+zmTlIf/5npJW62CVUMZzQWB9NU+Z3Mr/KxsSvKJb/8lG09cRbXhpj/dBOFLgXNBH3WqZgFFZQ2fsYBew2NhOly19BrL8UZ68Rvl4gWjmXoqIspi+7n6hEZSjwKM/Zu4JHFYc4S9/Kq80haonl+OzSC4nQ7Bcefzk3v70f3laD07mPy+x8hLrkYvXgUYy4+jSseuZaRIReOaQuo8jvRPWGk9x8G0yC3+FLqPCbqCcdxQXEMQVFY+4NfYZNEPr54Ip0+yzpilCNNbvMKCtLdjLGnkKNdiMFi9p1+InLdZMxclgkXLKbnqEtxTDkK/cMnKNjyGkr1eORwOd6qYoxEBKOwmhIpS/1PnsbQTfx15Qhlo7DVTgBASg7gT1h+Z6oJumGSUg2ykpOOjIgADPlqSUoePOTRHAGcsU4E0xoLK30HLG+gbAL90PxFHmxBUDMMqDIRQ8EuW8VbudeGI95NUjUocMpcdnQtbeveJ7/9j//le+83DcY/4fqG1z3cdddd/PSnP+Wxxx7j6KOPZsGCBfzud7/jZz/7GXfcccffvd5hFT533XUX5eXlh/PUb/FPQu3iETR3J74ubgCmBxzUTQhz4pi/NfKqrA7gKS+kYsFkFjVuZvhAL4pd4pO+FM+vasMZdOAp9THuwuOQbAqjTp2O4rGRj6dZ9sKNZFub+Pi465h310lUnHwcxUdMRXLYKZw3l3xzPYERfmw+N/mBfuK7d5GPRHFOmkump4/A6Eoq7voNgpajddmxmHY3XQ/di224BWGonWU7P0BKDSHHethw7VhMmxuvGkV3h8iZIkIuiZEYxkzF2XHNzRh2N6aWR+hrQj79FvLhUXTOvQi5uBJBzUA6imlzofvLQJTQPWHIJhBTw/jtEsqeFSRlH/KckzFCIywyc2oI35z5Vvr2vbdT9atnKPr+FRQRpzzWAN4QqU9eYd3Ui0jf+n2mv/sugppBiveie8NgGOyOyyT/dC+FS46jZtksbD43uqpiZlNkt65EEEXUrZ8ihUpoe+FF9Gye4azls2PuWokpiNw708nO3ji7Xr8dSQCnIvHkhnbe65VxyyKxe37I2/1Oup96nVz9RrqW/oh5lX6+J+/jPNsBtix/iJlSD9qql6lNHMAhCURyOi5FxJ/sos4rUFfg5P6TJ8DkJRgmFDhlTnt/kFEBG52RNLph0jCY5K3SkzlvUhF1QRe1QRcfbeuieSDFxHI/0yoD9EStbLSLb3ySkyeU4LHLvL6tE6dNwqlIzKoK8urWDtY3DtLVFSenGYws97PkgR/yws4+Ln33ID3dCeqCDjZ1JTi1As6aWMLlcypZVFPAw6l6ctEki9YU8sGBQdKqQbXRb3UEFCc2SSCtGhjOIELOen/VUC0tv7iLF6ZdhZiN4Ql48XZu5b6SJir9TotgXl3AyjlX88beAYYzKhXGEJqvhPZYhh+8spMXv2plw2+/w1njwxQ8dSuf3TiPAofEwUgOOd6L7i+zAmn3v0+ZR6bIKSFLAhl/BYmcgWfr27gUEd0dYthbjUexttj9WTfD1fM4pyzHD5/bwvfXPQT9bWjhOp46toj85y+RKR5HT1KlJSXwSrNK67Rz6Hz2TR5f20quagYj699k+cEI0glXMpg12TuYISvYOGbVnzFFGTUS4bvd7yLn4qQEByV2nd1JB6bsQFl4DqakYO747NB4qZCZv38AUxCJrl2Jc9YSRgzuIL1xBanOHox0gtgnb6CPOgLfsnMsL6ttKzA3v8+Z2jZm3XQi237/KRvPvtwiOwui5bysOEGUsZka7nQ/XkVAN0x8NhHdBFkUcAsqw7pCXjcYcJYBIKgZ6yCRTaAFq74mQiNJ9DkrUA2TosF6AjaRsMs67ImZGEHJIv2/sP7/7Cv33xX/jJDSbzI0TeOFF17g3HPPpbOz82tT4c7OTq6//vrDSog4rMJH13WeffZZzjvvPJYsWcLixYv/5voW//XIxfNfK7X+iuG8TjaSJT2c+fqxiT47Rl4n2tBBNppAf/xWAAzd5Jbr5vLr5y8kOKYUR8hSiA3vayU7FKdm2SzyiTRfXPk7fItPQbJJZIZi7P3dK/R+tY1AXTmb7nqGTH+EcVd8F4ChPc14aqoY2N6APtSD/4iFDBx3g5WQ3rab2as+JffFq1RedClGu2W0JqajCPkM0ZcfxVQcrFLLkfqbrBywgQPokQEIlSPYHUx6/nn0xm1IwSIExUbLVWdTf/qJjGhaAbIdU7LxUqbukA9QBiGbsFybyyfRbytC1HKIhRX4o01I8V6k5ACi/xCJumoS+kAXprcQOdqB7i0m++6TmPFBDJsHe2UNR/tTjLr5JuTBZvRgJeMeG0Bo3ESL4WNMyM5ZxikIFeOwFZeipjIobieIEvZZxyLYHMizT0QqG0n3pg4Kz72ckUaf5YRdUceYa94l4rBm1V+2RUnkdXb3xJlXG+K59W3MvvJFBq97lAlhD+ubh7GPm8mo6G4ODKUR7E6MTIpxcgSGujCWXU28aDwJ1UREILBvBQARTWRaiZsCp8Q7bXmODmRwGDlcNonr3j/AadPKmV0ZYETAic+h4Ix1IomQzGtcvLCO1v4kPz+6lJyms3PdQfb3xJl6wjH87IO9BFwKo4q9JLMqX23t5Dcf7+fz9zfjcsiMrSvggrkjqAg6GfXWcr4ztpDzZlby0Q3zyGgGRW47H/eK1hiqN8lxP/kApX0bo1dIPHH+DC5U9mGXBfQdnyNkE5iKg6c2dX7d0RKzCdLF49DffYjK377Iix83sM2s4JLTxltqP8OgwKnAnNOo8NlZWhvgqBFBFgfTXPDxADv70lT4Hfz+jImYpklR92YUU8N/y8Moeg5JFBjnF4g7i5AHm9nomQqzTsGVj5LVTVqjORz1nzLSJzDzwwAuWaDfVkRSNdAMk+UNgximiXfVU2zNBVl99RSGL/wlxojJmIoTMRtDPvlaHPtWUmkMUeCQGBVyUfLuAxxXF+RHC2o4MJRFmLGMk0Y4iBkKBjAnvZv2eB5188cgiEgOG7H5F/Jxt9VpH1RlPHaRD9sylvLS0MnOORM50k7UUYSp5kCy4Zt1JPmG7eQbtqMUVzK8r41kUwu/veF1lO496F0NdH/0OZGde1Cqx6EP9ZLs6MPUDWYtf/vQexBHyKdQPUX0Gi6ShoTqLbH4PJKAUxHxKgKiAIZsJyRkEASBkJBBinUjRHswnH4SrmJyhtWlNiUbpuxg3i0fUOCU6QyOJ5IzGEhrOGTBOugIIj6byIIx4f+ajfZb/D+HLMtceeWV5HI5ALxe798ouw8Hh1X4XH/99Vx//fXous7EiROZMmXK31zf4r8eumqptWziv1e7myNZdrfGvo6vAOjIqNh9dppWNLHnhQ1se3wNT3/QyGc9CcJTR5Ppj5Dpj5Duj9D67moKJ48kPGcyssvJ0N5uRp8yjuzOrxh9yjh2/OErimfW0butHbCKr+xQnHxPJ77FpzDiupsRbA5y8QzilGPQ+tqx/f5m2m++iMGVnyIfWEP3MdejlU8itWOzpXrqawVdxX3FLxFTQyx0D9NVNI2dCbuVyByuwLS5MYrq0D59HqVyFEYqjhGuJTxtFFOee9biMhgahivI90qSSJFOAARDI+stYVBTCJNA0FVMhxdDcdH5zBOYNifCyFmY8UHMg5txTF9I9ovXUevX83G3gfOkSxEkCTETY2jWOZiiTHLtJ4e6Dg7qL3Zgjp7LyFwbkfuv4dy5Vej71qNFBrB53cRbepCLqzBlhXx3G1JyAD1QRuCtD9EKqhET/Vb+l7+MA7/7DsGcxYmzyyLjwx4W1RXSFc+yYflXvHTfd8lqBiGXhF0W0UvHovV1MCbk4sZ9AfrqFiNFu9C6mpAxcKKypTuBUxZorlmM64RfYpqwpTtFeyzH5GIPL7cLPLRlgFHFXpyKxLKRBUwtdnNsXZCRBU4+jfloGk5jlyXOHl/Iz08Zz62fdzGlxEfvzlXYZBE1p5OMZtnZEaW+K8ax40sIhN1Uht3MXjoNmywyIuTm4U8O4HHIeHLDfNoUQTehP61RluvhjV3dKKJAX0rluLog+35/OrkRs0iseQivTeQNcRJlHgXj6POJu0vRHT6+P60MV3bYcgNODuLq20frwusY/rcbqf/wdeqCdi6bUUbKUwo1U0nkNVrSIuPdeZ7d3oPfLvFyh8Tdx49hSrGLar+N7T1J/nLxTJoLZ9CasgwTtw7pJHJWCK9dEjjorKEznkMebEbMpXB89RIzHDHMkbMxJIWVdy7CFATCegSPItKb0gg6FZqG07QfeQkTw05M2Y4kQj8+2PIB+VAdg6pMauxixHSUa9+uZ2zIyVvTr2BNe4w6vY+Xt3XRnHeRl5341v+Zoi2WlHxC+gAH51zMF6kC1JNvJGATWVDlI6OZOGUBn01icU2AiC6zIR3AqaVYmw3jE1X0kjGYsp3s/p2Ymkp8fyOd739C3Q8vITsU567Pf0lm6yr6V3xK1fe/h+u636BH+jGOPp/QMcdzvDiZ6wIzrZT2ngb0QAVyapASMU1eN+lNqWQ0E90wEQAxl8AmiSRyOmnJhV0wQM1iynZMfzGmZMOpiIgCBGxW3p6hOGn4zSJskoBHETk4nEVEwKbnMO0eBnIC3UmVnuj/TCk7/JMiK77hXZ85c+awffv2f9p6gnkYgVqFhYW88MILnHDCCf+0F/JNRzwex+/3E4vFvhHxFVcI1cwKOtgc+dsv/GiPjQKbRFdGoyOjErZLHFniYc9ghvFBBx92xTl1TIiCkQUUjCnBV11C0wdb8ZZ68FYVEz3YjSiJhCZW4xtdy/qfvsKoUybRs6WFCRcs5uDba5n0y3sYePNF8vE0hTP/3V6g4eUVjL38LLSBLvRsnlw0gf/CHyN11SM43OiRfiKrP8ddHkY49WaUPSsws2kEm4ODT/6Ruku/j1Q2knXnXcuRf3kcU5IRksMYwQqE3kYEuxO9dBxCJgYd9YjeIHrpOOvml4lZhoeyguEtRsgmENS0RYoOlJGx+XHHOy1XZzWLemALkj8EtdOhdQdGbAgjGcU2ehpqZxPSlMUIhoYWqEDpb0APlCMmB9B2r8E2aiqfn3QlhWNClD//NgWpTjA0i4iZiSHoKmr7ASudvXqyNWKQbUjd+5j3jsmXV41HjnRaajTFDqJMJlDF8sZhThvp48F13fxkip02IcS5j6/ntz+YwZ7+BBdMKUHScyRNhae3dHHTRDt/bDYp9zkYFXIy/fS7efqxmwA4akSAsnQb3a4RKKLAYEajymejM65SP5Ck0KWwqSNKbchNfyrHjVf9nDt/9WNum+lHyGfQvUV0pgx6EnlGFjgsbo3i5K0uAcMw+aJxkCKvna/29zOuMoAkCLhsEgtGFrK5PcLOjiinTSsn5LLx+OomlkwoZldHjMmVfkaG3JR7HSy+4FdUzZzH6zfNZzij4rXLjHHmYN8aDtYspdhtZVNt6IwzudhDWzTLnHIPxz+8jlU3H4Woqyi9+9AKqnihMcfFBT1ky6cynNEIOCSklX+k/4gLKLFZPjvxZ+7Be8X9CFqO6BN3kbr4fsqd8ElbipPs7WjBCjZG7ewdSHLhhABKfyO6K0izGKbQKeMRVAQtR7/houDzx7HPXYY53IPgL2IoOIpd/WkW9H7OdQtu44HEXgYzOlW2HC05G0G7ROHQPgZD4/Bvfg1mfwdVdhLLGQxcejrVz7/NuweGKHLbGB9247eL9KU1Tvn5Sj6++xjaYzkKXAojt76E5A+xonARxxbrmIqLlV05lgTTiAPN7PDPYKrRRqxgFP1pjbZolmklbiI5nQKHTEH3Vg4EpjBGbSNXOAo5F0ceaiW77QuMfBbJ7SW5+IcEDnyGmU0z9NUaDry5jaOevhtjxGSkeD8nfJji/Wm9vKTM4nxhN5TUWgaRaoZe1UaR2xpFDaQ1ihzWwUyK92I6vGh2HxnNwCPqh/x+0mheq9MpajkELUdatvx8vK3rUWvnoiEiY5DQBEzTcml2xjrJ+ivIaCYDaY0b3trNqqef/UZxfP6r7xnpdBq3282uxlaCBaF/aK2f3XEbPqeN3/72t/+kV/fPxeuvv/61l8+MGTNwu91/8/+TJ0/+u9Y7rI6PzWY7rETUb/HPRXnQ+Z8e685q5A2TEofEgkIXlU6FvoSV61SzaARnzyqjqz+Nu9hNaEINqa5BgjUFpAczDOxso2TOWEZffwWiIrPy2j/hH+HHV1NK7QnTqH9+JTXLZjG8/FW8dSMIHzkTweag6fXPkEKljLn0u6y/7SmkUClKMIj/BzchRzpR2xus0dGEpUTO/znb5lxJXjcQSuswcxnMfJYxP/kxoidA/OPXmPv2CwCWz4c7gKCmLXdkXxHyUCtSvBexsAIjUGb9u/eAFTiajtL6wD1WQSEp6P5ytKad6GvfxIGGoGVRt32GWjQapayaX0THWvb5gOD2IZfWkKvfhBQMgyBgttdjCgItrlqkrj202ipomHw2Wm8riz/+I1N+dTeOtx5E94TRdn2Jtmm51VUSZUS3F9HmYOC5hzGbtiL1NmCkEjyz7yGExk2YmQS6t4j8xo8Qoj3Y9BzH1gYx177G/p44sx/Zx96BFO9cN4+AU+bE0YW8vLuP3pxEX0rjhhkhxIFmXljVxISwixK3wvC907jw4p8yKuSmZN+HbDfKyOsmE897GIcksqPX+l2PrQ1QHXAwuzJAld/BJdNKafzMIqC/0aoRdRbz/O5BMppBhc+GapgcoJgLP+xh5YEBtrRHmVtbwDPPrKCzcYj+eJa8bhDNqPz89V18tt0ym2vsT2KYJufOriKZ1Th5UglnjC+mJ5HDbRP50Y/P5/Wb5lPhVShwKjgkESnSiRHpp9gt85OPGgAodtvQDRgdcjL3jk9Zd451Y5QHm8mXTcKz+HbmVAbY4xpHd1JFEARu+6gRefaJZHUDQ3HQm5cJnnMV0byB0rcf/7W/IuSUeXrXIEeP8GOqOeRoNxPCTuoKXGDo5EsnYDr9jEw3WwnjpskQbvKGiTL/DDR/OVpXE4YrQECN4LFJtI89kXuj9bjUOAemz6Pr5zcwMtPCcFbHVOy8d2CQrSNPQR5uZ2tPioI1z+J58g1u846neTBFVjNY3xnDO7AfhyRy98UzOfU3a1i+t4/RPhFh8YW841/AMnMfYiZGnyqjGiZpTwl62TjcNpEebx1u8pR7FUaHnHQnVXQD9g9mSFfNZJQURS0cia3hS8ts0+nHPnMJjoVnItgcJO6+FGP8IrT+TjpW72P2T85AcLox7V7MSC9veD5FLh5B2dnfITtuMXOebENuWEOvaqPEriPqKhnVkqx3p63uc6dcRFr2oJsmWc3EkBSGTCfoKspQC0qkg7xos4JNZRFJFNBGzEBKDaHoOeIq5HWTWM7AOdyMkE2gmBr9KY0Dgyni/4M7Pv8McvM3HWeffTYtLS1cd911zJs3j6lTpzJt2rSv//x7cViFz0033cQjjzzyT0lf/xaHD3eR6z89VmiT2JfIsS+R52Ayb7kO6yYzxxfStbmbxsZhZp84klVv7Ed2OVHTGcLTRuMfESQTyWLkNfLN9dgDXuqOqyXeGSfZOYDidpKL5TANA9+sI+n4ZJ3FW5m8gHG/+LklNU/HqfnkU5aXLgPZhrbqZSIfvgqyDXGgGUWAYpfM+LCT4busYDlprBU+mm+ux4gP45k+FzrqWZ4qtronmTgDLz6Omc9iNG0nu/0LS7KeSyEOtqIWjaLld4/xyTGXoXW3UH3HfVZXxzQQs3GU2knYpiyAjW9bpMmZxyNmImg1s7ndvhXdX4Y+1GtJ0ouqUOafQf7gLow9X5Jv3oMc76XsiyfAG2KEOcTE5B4kfwgz2geSgvOEi3huX4JMWxtK5SjU5t3oPU3E1q8GUaLwklswc1m0/i7uW3gz5m9eRgpXkB85DynRz44ZF9FTMpOunET7hadhzP8etx4zis9uO5qeRI417VEcksVjmFMZoCLdwtRlP+LD9hwHC6fz/tVzCTokbJLATxLT2fXxwwym87SMOp6AQ6bMo7Dmj9dycDjNuEInLkWgcThHRyxHoctGpc/OSU9t5p39/Xy+q4esbpDXTY6uDuJRRAbTGje8U0/YJbF3/wBLxhbRG8vQNpRGlG1MnF7GcDxHffMwmbxGoNCFrhnkNYPJ5X5EQaDQpTCy0E1aNTjnDxs5b1IRIza/yMUzKwi7ZNZ2JKiV4rTFsiAr7J9yLs6Pf8dPFtdRIqaZU+amL5Uj9P6v2Xt1IWrxWKTkIMNvPYcpCBRPXED09BOIZTU8NpHPmof5amsnB264ml+tbCKR03m/YYCUr4Jw9CCL388hR9rpT2lcMq2UtlgeMxXng2wFzlXPsCCkkhSscF9TEFGLRuGURbKinaBdZO9AGmPvWgZ1O0rtRHRvMWlHAcVuhc+bh9nVn2Z5l4nni5VUXXszuq+YGo/Aq4NBjqgM0jiU5rNjL6bEa8M2bREVm1/iup5dbG4Z5hTxADPLfCTCY/HbRY4bWcCbN81nUrkfKdpJVIWlax+1iPteywBzYpEb26o/0ml46UvmkSWBrUM6DUM5nIqIzy4RdEiMLHAQy+oI+RQdCQ2jciKimrE6oCXj0H2lZNrayA4lOBAzSLV34Qw6UCpG8umJ1xHXJYzamexecC0N7tEc/cilRLM662+eiV4zk4rEQfKijbgu4ZKsUZvfLhFXQTMgljNIqYZ16DF0ClUrqsZwh76OuUAQEHUVez5hHWYMHUNxkFQNMppBTb4dRJl86XgAYjmVTW0RWrfv+xfsuN9M/DPIzd/0W3lLS8t/upqbm7/+8+/FYRU+X331FX/+85+pq6vj5JNP5vTTT/+b61v81+NJsxU1pf6nx/OGSaVTodAm4ZREwi6FsF3C0E30vMHmSJZIc5S5C6to/3QjisuJqRuk+hM01Q+w/ak19G+pJ9rQwcEPmyidUYqvptSKrZgQJtLQgVg9GckmW0THgztIF4/DPnoqypyTqIzu5di6IK+VnoLgdJPuHcaI9GMGSrB17cTbvgnPxlepuONX0NeK3rAVweZkeMt2xJpJCDYHkj/EUZU+EET0QDk2rwtGTILJS1COOoN8ww4EmxP8RUjxXmpvvJXjVj4LooTRust6nieMFO+1+DiijFxSRQaFqKsUDIsXIo2cys9WdyFNPQbGL0Br24ex50vURBopVIJ9/GwEXcV2xIkYvS2IuQR6bIjU5tXk9m5C7WjEtDm5xHEA99RZ6JEBsm1NSMEivOMnQkkd6urXYPoy5OoJnNu2jRK3QoN7NNL25azIlTMh7CL05dN0xfO8evFDPPhlKwCRrM4RlQEmFHkocss0DueYkD6A5i8nvu73HN/6FpWrfs/Jv9/AyU9s5AbnWO47biQtkQxLAkkiGWu8817DEAeH0xxTaHVC8obJcEal0GUj7JLJ6QYfzeghrxlcvLCOx9/fR+yQEsytiEwscvKXSX0UDtbzy/Nn8Nv39jJtRJDbjq7m9LPmcf9J43E6ZGwOme/PquLHx45m5rgiFo0ronkoxYbWYW5/cRu7u+OU++z86ZJZvLqnn7VjzuKE+1bRl9JY2PUxxpYPcSkiWkE1L2zpxFY7gf2DaQZMN4KaYVKRm9RpP8ZwF7CyK0fOFeIs4TS+GDuH9x84lfA7H6FIAh2xPGdNCJNJ5PE99iq/PXksFQuv5aXPm3DvX8lwcBSv8w5asIqarrV0xlXSqk5m3GJqgk6UGUtBlHHKIo3RPFnJSUNUs1R2uQgApR47d6VmoDx7O43BKXQkLE+fCq2f+SOC1AYdFLpspFWdZOFoMjY/MU3ku7UOtOvPpnkwxW1n3sd7+/qZ/VQX8pSFXP78Vr54/i/UB6bROJQmpZo4RBOvFkcSYFTIhSk7KDAS+Jeeil5YQ0NK4r0DA+zsTdI26wdEMjpTS9x82RYj7LIxMQCNQ1mSeQPDhIxmYACpQDVeu0in4SXrryBfPAZl8CDRJ+7Cc9plVCyaRvj528lFk6QHM+hDPSze+gnujx6hJWejwmdndHI/A2u3UOCUyStuOFQgSoKA30giqhkkUcBjZtFNCDklSmwaiihQ4lFQsb6jYiZmjaV1S50qaLmvSctScgDB0IjldAAqXaAHKjBsbuRoF4akEHAo7OmKIdn+c/f7W/z3wYgRI/6319+Lwyp8AoEAp512GkcffTSFhYX4/f6/ub7FvwbB2gAe+W/fwu6shk0UsIkCTak8u4czJDWDzGCatV1xFhS6cBU66ds9QM/WHjrXNdH11W7qV7VhEwXWd8Spf3k73qpiiiaHGdo/xFB9C8muQbyVxXgri4i+/iRaNk/LYw+jlFXjGjqIqamYgsjQe68Q+flVnFeeR551AqZu0LNuJ4gyuifMnFdzKDUTMG0ua7xUOxnD4aXwmCVWaGikH2Q7vuavUBu2IuYSBE7+PmI2gdS1B/PgZuzjZhzKBxtC378Rw+bEcHgRJy7AzKRoSYsoLRsxHF50XwmGK4iRiuNExasIaO5DDtE2Nz9zb7e8Q5q3IpdUIddNwTNrgeXzo9isPCFNRSooQW+tR+vrwD1nMaLTmjELBzfx2ek/JjflBARFwT31CPLVszg44QyuWpNi+JirQJQQ8ilq5CSh3u3UMcT+EUuYXOzB3bCavTMuoieZI6cZzKoK4lasDo5LESlwyKxsidKTzNFTMIGk4LDGNeV1SCdcyar5g7z1wznM3LiatliekQUuNn33QmYOrqfKZVDqsbOgykej6kMzTEJ/uQdFEkirOkWRA9hEgQfSU9jQNER9d5y1N83i+NvepcgpEc8brO1IQPVUtP4Ogk6FqXddysKaEEseXkdPLMPL27s4fXo5o8p87OqNU+V3cOrkUra0DLN8SyerdvZQMSJAIqtR7rNjmDCm0MPYkJPfXD6bMo/M1toTebnweBaedguPbunj1oU19H3wHtNKPRSnWkFSCDau4u4VjTT85FaW1/fRFMnxyUXjWbxrFU5FZPLxP+LdPb2IgoAS6eDui2dyx4f72dWX5tUX7+Pu707iu9tDvLG3H+nS+4jkDAZr51OTb2eW2IU9n0ARBd4a8qM7fNiGmhijJHCgMdatUqwPgygj7fmU2InHctTlZxM8/wZqHXniOZ02qQgxE6PAKfH81i5uf3MXCw++QU4zcOYiVCy8FlNSKH76DX6c/IA/XXUE108N8L1jR7HTKOXzK6dQNGEeY7wmpV47L+/qIaEJZGx+ypwwtf5VyyAzE6O7YCLtph8RgSnFPloiaap8NmyywL7BDBU+OyMUa6wZcinIosCGzhj5Q+7uGc34+u/RrE5GNdAClYTP+AGmbMcxeiKSIlN60dXUnjDV4ud98WfsU45iZK6N0nwfpmyn5ITjkAwVR6wTKTmAhohkqIjpCGJyAAXDeu9s4FRE2jMSvkw/Oc1AEUDp3YeQT1lcOofFE+s3XFZMhWmQL5uEoThJ5AwKHJKVvaflrEy+eC8Z1SCaVclrBpL9f27hY5jmP3yZ33gnH3jxxReZN28eZWVltLVZ9gUPP/ww77777t+91mEVPn/605/+t9e3+NfA7rOj/4ceZbVLYbTHxtgKH+VOi2DYmlZpSOZ5ty1GpVPhYDLP4IEh/CN8DHUnGWwYpvb0RRT47BSEnJxx6mjyqo6uqtjcCoIkkO4ZRpBEFLcD75QZeMaOJ9YWY81LO628qtZ6cAdR175N5+p6yi+/Fn3XF+RX/hkAb2Ux+v6N6Lu+YO0tczl9lWF1Z4IVDL3xJ4R8Bqm0DiMZxUgnLP8OTUWwOchvX4UpyeR3fYng8CCMmg2yDWGoncRXKxDs1kgit/p1TNnG8PQzKHHLqO0NGJ4w8mAzhsNPT91ipGgnUrwXJdYFeh5xuANx/DzirmKrcPMVWe6xNdMRJy3kT8kaxNZtGO4Cdt16F0YigjY8gKnm6fpsPYKiILp9PHrL79nRm6ZzzDK0oV5ymsHozi+QRKt4EVPDaMVjkKPdmJrKJ8Muxgv9BFc8yqPJUVQHbNQFXcyvCzGywMme/iTRrM4XLcNs6oozpcTDjq4YGzqt7LJZ17zO7Z2VSIk+ctNO5o6PGzhqRJCBlIrfLjLnkdvYc/+jLP7dVubZ+3DpaQbTFs/LfukvGF3gZDCdRy0ZR0oz8DhkXjvGwfSqAJ905jn4yDJUEyrcIsUeGwlHIVJpHTNtQ9zcv5tRBXZWXTGRn58wjsoCF9NK/TywbDRBp8LuviStkTQvnVjC/WdMpqLEw8RyP6UBB4oocGAwxT0f7qO0eyPlPgeeWBs9iRyLqoNkXvwejX1J9vSn6b34QZyyyLNdHsR0hGtmXs0tC+vI//JFHp2WZ/n+fs59s4n0n39FtVvgkSd+SmckTZlXYezPd3N0dZDzZlYyttDJktoApV479580nlfXt/GLlc0UiDkyqkEqWEsiWIeQS1FrS3NqmUFGNehwVCHoKtsHVRKiC7Gv0SKwjz6So99/miW/uxChaz+NaRu++y8lrRoki8aRyBlMLvPx6aRGbFMWEIo3o37yR5599l4iukw40siRW8fTl8yDaXD1tEImHnibh7YOsenfTuLTzhwNQ2mun+AgltOxSQLtKRDnWTL02FvPYD/kWhxySYTdCkdXh+hOqvQl8wymVWoCDqKiFzE1hGaYfNo0iMcmU+CwRl6ablLkEBAAmyQwlNHZOqST3fEliCJSqBRPTSVIErLDTvvr7zGwcRda50FigTrE4Q4SoVF8HFr4tX+PoTixZSPIHTswXEEi3kOn8ENj50ROx8CkUwyR1U0Sqml1Zv1lpPxViJiHVIsy0nA7pigjGBpd+PHbRTzxDqREP6Zko9/0oAcrieV0/HaFBWPClNYWfaOIzf9KHG4o6X+8vul44oknuPHGGznhhBOIRqPoutUFDAQCPPzww3/3ev9QSOnAwABfffUVa9eu/Tae4v8BBEn4m45Pa1qlNa2yumkY3YS5BU7CdonhvE6JQ2ZbNEuBzRp77d7ex3BexxN2cc+5j9MbzZKL5RncP0TxmBBv3b2cA2s6kGwSgweGyA7FaP1sN/Htm9EGupjx3OMs+/EScvWbQBRp+fV9iN4g/ppC1p13LZ3vfYKWziLd+jtCZ1xg5VpNWoCQT/FKyVbihoKg50l2DaDuXY851EV21Hzie/djynYorkaQJOTSatKfvopt2iKMxDDqmjcwPQXosSFsfi+mqoIg0rjgGvrxEW5Zg9PMU//718E00N0hpOQApfow+VCdlSUkKaiFdZj+EgA8ZpbM3m1kPnuF/ddfhbntYwyHjwvL07wvTUJMDbP3zmeRQqW4Zi4ks3MdwbEjkCrG8KY2muczrzMh7KTcYfBO8fF4W9cz9z0bj9X14Gv+CtPuxtz6IaakkKuZy9JyhezKV0gtu568ZtAazTNF7KGuwIVuwBGVPgzTpKEvyd6+BK3RLCML3TQPp5EEaHrsBG5fVIPuL2M4o3Pb4jpqpTiKJFA4sBtkhbGXncGrl8/moFLJzphEpd+O1y6hYBDJ6SxqfovBjM7I1EFKPHY6PXXEstZ47JEdUVTdujH57RI53WSzUMWwq8wip+omaHmq/TaOHxkip+sMZ3XmVwcZF3Zz021/4L1emc8aB7hw7gjuyn7IFbMrKHErHD3CzweXzECvnMJYt8o+sQxREBiz9HraSmZzy8I6JoRd3PjaDnqSKt+bVIS2/h3uGq4nfc1ZNA2n+UWTl3e+auW1uRniZ93Jbzb0kFF1njvaTv1AmuU/W8It7+/lsS+aCCbaeH3vAIpoGd/96QfT+XJbF2v6dMYsvR7HxtfpTmrs0wt4q11nS9KJf6iBm9/by6C9iIlhJwKg1cwGw0DIp8HQsc09keSoBXw4aiarL/o3nt3Yjnv/SroTOU6uEIkf8T3mPNNNj7sGKVTKWaM89Kc0TMXOy9cdxRJ9Lye/3Mimfg25rIaFtSEe+KKFCWEX36nz0m76qY3sQvjiBUo9MlK0i980yHScdgc5zWDvgOXVJYsChS6ZErdMWtXpiGXIaAayKNAhFyEJAotrrXDZ7qSKzcgjCAJSaghREGiL5alyGczJ7kWpGInYsh0jNAJBsaF3NpAZilH52xcJz5uJMOtkbnx/P53FM8nqJktqAohqBt1bzLASJKkEiJXPQMin8WtRxGwMIZ8ipllxGf1JFZskIgkCBbEmzKEuxHySvG7SndJwyCLJvIFgaMjD7ZiCSJldJ6OZGDY3hiuI1LAWuywSsYWwSSKD6Tw90Sz7Vq78l++/3xT8o1J2w/zmFz+PPfYYTz/9NHfcccffJEXMnDmT3bt3/93rHVbhk0qluPjiiyktLWXBggXMnz+fsrIyLrnkEtLp9P95gW/xT0E2kmW0x0ad+98zavKGyUnzq6geb7k3D+R0wnYJ36ECaU88x8fNEfyKyNjRBcT6U/hkifa0iq/SS3NbjGwky8m3HsP4Y6pxF7mYesUSspEMdSfNINk1iK1qNKbNjW/sKORjfgCiRO0NNyP5Q5Qdt5CSqaWUzZ+G54glFG5/k3z9eoSZJ6If2ITU34R97AyCqS5alTJGXHcztpGTcV36JvZkH8qVD4KhY9q9CGPmkpuwFNf0ozAlGxgG9hlLYKADyR8i1d2PoCgYdi8T+zdQku1i368eBUFg2kM/R4p0Yjr9VtaP049hmgi6aqnC1EMqkJYdiPu+RLTJOCfMYtQPTsY48iwihsKXmUJOLBcwnH7O1LaRO7iLvrdeRUtlCSw+kVjBKL5T7cD7w1/g/PwPzLl/LasbBzEyKVbVrgNAjwwg9zciTphPo6OGT5oiyJ27mLN1GrGczo/mVrCzL86fez3kNINSj8z6jjhpVeeaeSOoK3RT4FT4w8qDLKwJ8fTWbu7bOMxAWmdfVCfsktnZm+SpRpWgU2aXcyxqSz3ped/H+fI91LWuZKojRpXai5SNkzUE2qJZ3ik/BQPoDYxhwYgATlnggslFHF8u8yPXXpyf/4GEahJ2yXzWHOGaP25m1Gn3sbJ5mC3dSc54qwM5F0c3Tbx2mcG0Sq05RLVPYfopx7NsZAFhn51tXTF2zbwYt2Syqz9NLKdTP5hjf0ph/iPb8SgiL21qJ7H6N8w6/1FSqk6BrPHJlbOoleJIK55EX3IZHfEc2r+9wol1fi6cUc5X87rI7V6PSxEZSuaZUebnmW4fzotOtwqsMUW8f1SGcffvx2+XieVUhrMaK5qGue+8aYwOOen84jGkcUdQueK3zDzxBk6r8zDbPsxPdissGldE0C7iGG7Gl+ggpom0m352ZX2oxWMwXEHyusm1/dtZWFPAncfUoY1fzLhCJ+90GPi3v8Ou915l8LIzmPRuAYKWZbzQz3a9hOq1T7HaNoHnvzeVGdv/xED5LOqCDrwOmaRqgK5SmziA4QogHPldfvppEy8NFnDriBiFTpl43mCuO05QNrBJApGsxpr2OHPLvcwuDxDPWcVChZwhkddI5DSmlrjw2CSSpkLIDt1iAaVikrGFDlKmQrR8BrvKFpIffww0b0OZtcxS133/h6ivP4iw+ELa77iKHz5zAyXbX0fTTaL/doM11srGKUx345JMdBNSriKQbAj5DDlHEEEQiOUMSjw2JNHqMmn+cmusHesnkO6h0hhCNHU8NhHD7kYPlCFmYyh9+yk1o5h7ViPFujArJ+AT8gTI4LeLuBSJfV0xMpHef+ne+y3+tWhpaflfqrfsdjupVOrvXu+wCp8bb7yR1atX8/777xONRolGo7z77rusXr2am2666XCW/BaHgTltFVRU+mhKWcRAvyIStkusWtfJC1+2s+GQg/NRVf6/ibbI6CalNQGi7XFcPjsdGZWFk4uQHTKn3LUMXdVp+diqot2lBQzVtyDZJNo/24mRVzFScQ7eeg3dn69DSg6S3r2V7T+6g87X36Tj3RVo2Tw7Hv+IXP2mr0dR6kdPIZdUgcPL4DuvIORS1AxsRe9sQC2dQPy+WYj9TXh2fYDhKQTTQNvwHs6BBit/K5sAUYR0FNFXgJnL4qmrYd8jf8JwBTFLR2G07ITfvoIp2b5WvCh9B9C9YeShVmQsabMp25ESfWiBCsSSGkzNSl8XQuUoE45ETvTzxIYOwm4bT+xN0/bzH6O27sdWPZaScy7AN38pRmE1yxuHee1gmj9s7+PX7hN48dp5PHakA7V1P7LPh5nPonY1oftKuOzzCKphclSVn6/sE3n/9oWYJmzsTqGIIooo4LVLbO5OUlfgosRjo1Qfxm+X2dIV47YTx+FSJM6eVMIp40tIqTon3/YOsZzOieUCA/EcWyfM5fsPfYX/nt0c88BqgsvOxDR01p5wLgDyUCuDGZ0xIReLqwPYJIGQQ6TAKeOziUh7PmXjkMCG0Dzs42fjVQTs7VuZUebjvCUj+bcHfsj29ijLHF0k0yrjb/+Kd/b141FEphWIdEiFGAi8dMEMmqI5LphSwq0TFWaJXUjJAZ7f3IFuQoFTIq3qXH/yOK54bRfvP/YH9iVEHt/yZwaPXYrUuJ76IRUxn2LvjIv4oHGY5uE0U+QBmuM6JWKah6SjiDV3EurfxY3bHmFSkQvVMJj26adohsmpq39DvnU/X/ziWIrcdl7e2sXK5mFSeQ2vXWLxnSu485NGfr5LJ3bSzZx901Vc9FYDZns97312kKV1IX7kGkevawRCtIeAHsMpi5R5FaREP526m4G0TkfSoGLzS+wdSNOWUJFFgZNHF2Cm4xz8/FGefOsAb96xmDdGL+bTmI9RH/+apY3TWeAaBsBYdjVOWeTMZzezZFSY0W4dZeAgQ+/+GSQbP1vTywPzApwzyk1u70ba4zl8NpFd+SBiaohSdQC3IjGywEU8b+B3SBQ6ZTTD5NXmPJIgML3IgQDUJg7g1S3icSRr8WociV68mX68Rhq3TUTOJ9EnH4eQSzG8bRfq3vV0f7WLwQd/hD3gZep1p5I/8hxeq++j8JglCPk0Qj7FgLMMqf5zgo2r8DR9ZXkD+cqIZHWiWR2vTaTAKaGIAs7MELriQpi6FLW9ASkdgUOHEmW4zbKxwMrpMlxBpP6DxLdvQdv1JabiQMxEAcjp1nhsyojgf+1G+w3HP0XR9Q1v+dTU1Pwv09k/+ugjxo8f/3evd1iFz5tvvsmzzz7LsmXL8Pl8+Hw+TjjhBJ5++mneeOONw1nyWxwG8tv/iKvQ9XUoaUw1GMjpxDUD/6GMoAKbxNuN1ib717T2apeCltGYc8MinEFrk3EXuSmdNYKVv/yEyRcdST6l4i4JkIsmECQRSZGoXDwJR8hPpqUZQRJxlYQwnH5SZ97BuAuPI1BXTuHkkSR6ktSdOIXI3iZrY/OHcM5agplJYbgChM6+FMMVwPSFEWsmIQ+3sf6O58BbiGBzMPjMg4jpKEp5Hbq/FGOgndzejQi+Qkw1jz7cS751H4KsMP6WK5Fj3WiBcnJNe608rv4GK/fH0NB8JUiJAUybCznaibR9OeZwj+W1k41huIKINVOQwuU0u+rQ3SEithB3FbUyVopweeQjam+8lUzfAGYui6k40XrbUb0lfM/XydRSL9/f/gS3zqtgdeswyQ+eZ83kC7GNnEz/xx9hO/EKECXOn11J99GLeHf/AIPpPF3xPAVOCZcicXR1AIDuRI6ZZR4M08SjiOTcYWI5jdoCF3ZZpH4gydbuBJIIg2mV+29YQks0S1L2cdyYMEfs28hHjrfJvPg9tl4c4g8DRYg2B0e9/wIRdzlaQRVVQozuRI7nd/SgGzCUNRBNHVvXTm7urKE26GBaiQs9WGk5X1fO4OXtXTz79l6eW9XM92ZUsNdRx4vnT+eFW48mllbZP5gGUaLMJdIez7PgJx8z44QbOPuF7dTcuBItWMUFHw9w8ewqKtwiSdVgWhCqg07uPmEsyVUP8GTFVE47sJKxa1bxhjiJqXorO/ViJjvizC73MabQQ4tUwu0f7EVMDXHDrCLCF/4IbaCLomvvZkdfiitG5DFME3+shfAZP+DDEafxxt4+IlmVa46q5oWVTRw/KkxfMs/e+47k8SMVlowK41REjh4d5rxZVZhanl33HEHYKfP4gT9j/Ppa8IZIKAFCdijM9hJxFGETBUo8MgPpPMJRZ1Plt+NRJEQBK+V+8gI8isjCHWuRBIETW7dS5LZzcmwpOz54n9RHL9Kf0vjz7n7csXZev2QWYbfCnpiI1tuK79K7WZ8p4Mb51XwZsaP0N5A++iICDgW7LBJ2y1bB7/BSo/fiUkQq7SrFbpnX6vsodgocXR1gMJ1nVXuSN/cNEi8aD1oeKdrF+NYVxPM6aU8JhsNHr+5gdKoBdn1G8uk7yZdYRb449zvUXnYhRdffQ9Gtv8Y2dibKF89x2rs/QxwxETU8koh3BIX5AYxUwjLt9BYA4Iy0UiIk8dpFNMPEw6HQZFcIQQDT6Udw+TAF8ZDxZx5TsiH0NVld2lTCiq3wFxNcvAxl/BFgGgzZw+QUS722pz/Bmh3d/6ot9xsJy4vH/Ieub3jdwy233MLVV1/Nq6++immabNq0ifvuu4/bb7+dW2655e9e77AKn3Q6TXFx8X96vKio6NtR178Y3lIPGf3fP7ZOSWA4rxNTLVuq4bz+NQ+oIWkRXFvTKsHaAOn+KK6QkyVFbiLNUQJ15Sx78Lv0bjnI2r2DNK9opPqskymcXIe3qohoQwee8jCO0mJG334nomRtWCXxg/R+tQ1Dt0jR0+65jlTPEIIkYl9wBsgKpmRD9AasXCwOObl2NYIos+qES5n3+K3WGCpYRGDcSEybEzEQtuInKidgZlKWzFVTEcNV2GcuRakajeh0YxzcSvqZu2h6dz27pxxJbtMn2Hrq0Qe7kYfbwDTR67/ClO0ItdNBFMn5ypCi3aCrlvOz001d8gCd+Ckc2oc2Zj607ECadwb5knHYLr4X0e2DVMTyBhpuYYM8ijHNn/DazKvZMZDjyrI4AOX3XoQ+0EX46rvY8p3vIugqnH4io9atZnyRB1EQMEyTwYzGxCInadXguJEFTCpyk9dN0qrOvsEMOd2kOuDkyHIPE8MuJAFyusGWrhgem8SUEi8j/A7m3/05M8I2Ok8+npJrbkdPRHktWc7znx3EqJ3BC112gskO5IEm0g7rpuRzKHhsIk5ZIK0L9IQmcduiWjw2kbUdCQynn66MpRg5aXwxpbVB7E6Zn76zh6Bdwu+QKHLbmFUVJOhUaEma9GdN8rrJO3cvoW3VY/R1xXlx71P8fE03zy8r5tEvm3h57xA7ehJ80aMxqcjFv608yL3rh7g3Ws+dXw2hGiZHVPiJhkYzyZmkWyyg0hhiTMiKeXjjeC99riqMz/6IYXdz5+BYBF3lqECOfUIJrt56xHQUTIO6AhdTS3wkcxqfNQ8hSSJ7+pOMDlny61Z7FVNLXDQOZzl9XJjjCtLsrjoWU3HiS3ZhOn0MXPUQqc9exy4JxFXQfSVfB256BJWi31yJKdtJqgbFmU6SeYNqt4CYTeCPNnH6CIWRHgNJgDFfPsq1x40h9vpVtBx/My9t7SSr6eylhN+uaWUgpTJFHmBv9bE0pUTmBFV8osr8EoW2wAR86T5GCUP4hDyl+jAYVvTDPorI6QZvt2TYP5jlgqmlNMY0VrVEWFymsLjCwcLqIANpHRSHZfGQy+JRRBxoJAUHxXIe3VOIWDMJ3wU/wda6GS1UTd4RxBi/iOy7T9L3y5vIbF1F7ugLKbvncbRgFWIugV+LYso25LrJCDaHFR9jd3/9XS/q24FLERG0LG5ZQDNMTBOETAy5egLmISm6mIlZROnq6QhqFiFQhGBoCGrOElEEKyzlnJHAMMGlSMQyKsM9iX/JXvtNxT/Fx+f/9S/xf8BFF13E3Xffza233ko6nea8887jySef5JFHHuGcc875u9c7rMLniCOO4O677yab/Xe3zEwmwz333MMRRxxxOEt+i8NE3+5+nJJAtUsBrDGW9P8Jq01qBmUOS+X11xqpYV0nTR/tR81qlE8oxFvmIdrUhb1mDPZDGWBjTpvMvsdfJXHiTZR85ztsfW0P2aEYwzv3k1z9HqHzrsCM9HLgrjspP/l45IvuRXbYUdsbkB12AuPqMNr3IlZPthyUvcXo7hBGbwtmPotYVIXh9HPUv1nmg9nNn6K2N6BUjsaU7agt9ejdBxHVNLbaCcS/WI6RTmAMtGP0t5HbtxX8RWQa9hDZ30ZoXAXH/OFKBLuDLVfdilln5QjlisfC3EP+Uu27IVSOomVAVqzNVhAxPIUMhsbhUUR6Xnyabf1ZGn//LFJfI1JqCOmj3yOW1XGgYDrR8HgywWrmSN2Yk5Zw3MgQU7Y/z4G77sR53o+pOHoK2kAXUqKfOS88hrZzFUd9+QHbexJ47TKLqv3s7k9Qp3bRn9I4OJzh0+YINkkgoxmMDDrwO2QkAYrcCtv60uwZSJNWDRZWB5hZ7sdtk1jVMsRlr+7kwzsWsX1QZe67f6ZZDMOYIzi9SqJ56y48i29nf08CId6PkUmx98TjmV3uYUqJF7tg4Pz8D+imVVDt6kvx8u4+jnEPENFluuJ59g1maRxKY5NFKgvduN02Cpwy9myEMo9CTtOZXOSi1pFHBFTdpMyj0BbLMesXV1D44QoWjyrkhi+j3LRoJIuqg8wq93FMdgd7v3MCL3pX85PomxT27eDkCSX8cmUT7j/fTd1p99Os+wg5JdYlPNiTfQycewqt9iqUZ2/n4JyL6TK83D8Nsp88T+r13zFO7+SLU68CIFI8Gaci8pvPG5lR7qN9MM0j500jpxmMMbo4mHUw/tjr2dGbZmrYTv1ABrNhI+U+BfWdhxAzMYzuJqamduNedDqOA6vx2UQEXUWOtOOxifTmJPz3PwdfvMBwWqVFLqO0fztSpJ1I8WTQddi9ErltK7ZcjN5jb+DY2gBTfr0fVTe5bE4VV0wJcey1z3HLgmr2DiQRYr08s6Gdt+t7GXj0bkxRJmIolNlUpFi3pZLKJemRClBlJ31pjbqAnUpjiKBDQTdNuhIqQbvEkZUBhg07UrSTUjNKhU8hJrh4s0ehY9JpyAKIiX50w0RK9Fl3QEFEaFiHWjWdjREFe6IXqWEtksNG+eXXYl96Pu5IMw8WTkOK96I7AxgOP4KaQw3VovtKDn2fwhjuEMOil3TFdBzRdkzZgZgawp3qw9G1Aw6sw7S70erXYUZ6D8nVrXuK4fRjOqwgSnX/Jvo/XI7YvIVssJqNEYVYTkc1TIIuG7lE9H+sout/Ei677DLa2tro7++nt7eXjo4OLrnkksMSVh1WVteePXs4/vjjyWazTJkyBUEQ2LFjBw6Hg08++YQJEyb83S/km45vWlbXXzH46E28dffyv0lqd0qWW7NHFr8OLLWJlnndX1HmkDlh0QgGDwxjcysYukl4QiFjrziX2LatdKzey8TrzyPf3YZjzFQia1bx2J0fcNXdxzO0pxXZqdD0STPHr/szpKKYuQzGiKkI+TT6NisNXO3vxjX7GMxUnEz9ZlzzT8FwFyDFe9H62pGCRaSr5+DqPwCShN5aT9MfX2bMXT8lEh6Pv2EVeiKKUjUGw2755hitu8k27CFx6o8BCO//mP6PP0Zy2Ej3DuGvK0cQRQJLTsFIWIZz5ugjMUUZpb8B0+ZEdwZh31cMTTqZwvwAZtNWy2Rx8UWYy38HJ1yDtPFNzNmnktIFfOk+zIObMSctoSktM0ocBkHk+RaT08aFkUUrddq27i/IdVMw+tsRZIV8cz3iSdciJfp4r8/Gd4IR1udLCLsVilwyw1mdSEZDkQTUQ52eIwo5NIaL83qnSFY3WFgd5PmtXZw7tYxU3qDKb0MSYHVbjGKPjWklbpRIB1vyIaZ3foZcUk2kaCL1S5Yw7/kH0QtrMJ1+66a5/SP21y1jYnIPjQEr/DOW1YnlDFyKQOmWV0jN+z6ZQx3DIuJUXPIXen45i93eSYy3J/kqaqcrnuOVTe28/b3xmLKdhGqFevrtEoMZjfJEE7miMTQMZ/HYJGrSzQhaDjU8kpTgIHn/1aR/9IgVWKnqhN026twGxud/Qlx6KRFNZFNXglhW41xpLw9Fa7l+kgv2fUXwx+vZ997PaRjKMKvMQ1ssz+g9bzC8aQvOkJ++rfsZ/ZPbuXmPi/quGC//YBqfNkcYH/Yw3ujk7BVJdm7pYsdvlmFLD9Fm+HHKAinNQDegyqcwnNXRDBNJEKiM7UfraSYx+SS8WhxTdiBHOzBsHjqkQsIuGVU3Gc7q1MbqiRRPxpcdRP3yNQRJwj5+NnqwkjaCdMVztEYznDk+jLzlHaRgmCcStVw+xk5U8hPPG1RJCXrx4VZEZFFA+fAxlAVnst8oYFTAxlDWoFBW6chKVNpVIqZ1UBnK6FQzxKAtTHdCpSZgwxdvs4j9Dj/KUDNoKqbdzQGhlDH0ofnLkIdbMbsaMetmIrZZgaVicTWJFa/hW3wKZjaFGSjBlB3suuwKJr30CjHBRSA3iCmIpB0FZHWTAj32dZdH0PJoBSPICDZskogS78G0OZEjnehD3Zij5iL3N6K2NyC4fUjBIoRDnWFTsWPKDrK+MhyJXg7ceA1jb74WffQ8koaEV0+yO2GjPZbho719PPfLR7+Rhc+/KqtrbX0LgYKCf2itB376Ewo99m9sVtf/F6Zp8tFHH/HMM8+wfPnyr5Pb/29xWB2fiRMn0tjYyC9/+UumTp3K5MmTeeCBB2hsbPxvWfR8k3Hn9W9Scqib81f8dfSVN6zip8Qhf130HFdsFRCzit00belhIJbF0E2ig2k613eh9rSS6h1GsokYySj2UZPoeutt7EEv0wMObF4XnopCHCE/S1+7G4D0ps8Z+PBdDl53CZEXHmLfk29gGzWV7q92ASDYHbR+tAm9twWxvwkjEUXrbUfrbceupiAdxehuQqoYzZg772TLVbcivnQvgtMNhm75+gx2oAWrkCrG4Dr+B4TWv0Bx8xeIDjfFJ5wIQOV3TyXa0EHfpv2oVdNRJx6LPuEY5L4DCIZGrnwyQj6Dsel9mLCAkBZBSg5iJKIIDjf2zh3Ix5xPMq+DobOxJ0NgqIH+Jx/AzGUxvnyFOrdBn2yRr4NOBbsk4EoP4BxuRi6pYu/tdyL6QwiFFbR/sg5b925MUWZZw8uYsoMDg0nKvQpPbemiypZjXKEDv10i6JSZWOSiPW+l0ufcYYJOhca+JMm8wXFjwpanSyzDxq4E/rb1lHrtTC9y8Fr9AE+3KcxKbCcz9WSiRRPxiDrTVn2O2t5A4i+P8NCmXlKmglQ9gTEeHa2whlohSl9Ko+O7J1Lz1ZOMWXo99+hH4ZZMy5HXMNmWcPD2wNtsc02kaThNs+5jarGb08aGePA7E+jKSYi5BF7ZxCYJxPM6FYM7MZx+YjkdpyKyfzDNNZthDbV05BTc5AkfMY1RkZ1M8eaY9uWjjBnczPHP7kL0hTAFkZXNEY6o8CEJMPDhu1w3s5iEEkCsnkTsL5cSdEjMLfcQy+lEMirdH32OpzzMjqdWUX36cQQve4eJ5T4un19DYKiBaaVePDaRib8+yKyaAr765XH0pjRQHPjtIoMZjTqthzG5FhQtgyQIlG17jRKHSe/Lz8KYI63Mq7VvksJSGBquINWxfdiMPG5RZ3nDAGrRaK56cw+YBvaZS5FLa0BSMBUHmmESy1lp7SImWyqXcvmeAs4YV4TYsp17P2uibOXvSNsCDKQ0K5F84AC2I05ETEcYn2th90CW4azG1iEdpyyyLyHikARWtUaxSQJ9ciGyKDA6ZMepiJg2NzlHkJwp0uWusYoKUWak18pfAzB7mtD62knaC0iNXczAp58gaFn2/WUtwx++Trr2SPRABflVr1AxfyxCNoHx5I858vH9yMNtOCSBAjWCoOYwXBbRWAtVI6aGkESri2m4gkg9+zEifVA9FSGbQC0Zh1I9jnxzPWYmhZnPWmafmgptu7FnIwh6nvE/vwfRG6A3K+CWBaKCm7qgjXd39dA5/C214r97XMV/RHNzM3feeSdVVVV873vfw+Vy8Ze//OXvXuewOj7/E/FN7fgAvBwez5eDaQpsEhnd+LrwmeJ30JDMoZtWETS3wPm1l0+xXWZnLMsFi0YQGOFnuDFCJpJlxIIR9O/uwz/CR/HM0fimzUTva8d+5Elo/nL6fnkTuqpRMG4Em3/7CXXLxlJ5w+0Yrbvpfm853soi+jbtZ+QPf4Da3oB97EzMfBatr51o/QGKTjsHfagHyR/CCNeCIJBZ/kfcs47GzGXRE1HkEePQOxvQI/3YJ8y1rO2H2sAbwhzsQvQVoHU3s/ryX7Pos5fQdq+xOixHnkP2D7fju/RulP4G1PBI9E//iG3OMsRcCnQVw11gcQYUO4bTj5hNICYHyW7/EjWZwrP4dHLbVmKfuZTUyjexFVv5RY7KSqR5ZyC27uALzwwmF7nwiyrSwQ0YlRPR173F0NbduG5+BP/gfvRAGcOil6KhfajFY9jUl2daiQtFz/FhW4aTinKsibs5sszKW8saAt5IE7o7xM6kkyfXtfLEkjAZZwinliItuRAEgfqBNLMjm7l0XxELRhfyqxe3s/e6UrTuZoyZ32FdV5Ijy1x81ZXmaF+SsXdvY/2Dx5PXTR5d28a9x1Qz/Nub+cOs67l8diXNkSw1ATstJx7HEW/+kaSvkvn3rmLHxT5Wy2MpcCp0xq3T1Am2Nj7IVXFcpYP82w/zzsRLOaLScmq3SQIFDgl7/aes9M1lUZGBFO3m5UgRz69r5dlzpxJ871f0rN9D4w1PMLXYTVI1GDW0jXztXKSt75GZejK9KZXr3tjNOxfP4MPGYcp9Dt7d08svJuvWOKWwln0JkQm2OIPPPGh9nopHIRzchFk3k497RYIOhSOFNgx3Ac0UIAnWzTerGkwIO2mK5hjt1kkJDjx60iLUGpqleguNozmaw6VIjJMjrIm7GVfoIpbTqdn3PtKo6XT/7ld8ddbPOaskgykpDNnDBGwi8bxBKN7MkK8Wr11CSg8j5DP020tojmSRRJiT3MFXzimsbh7iuiMqscsi8s6P0Qa66P1iA1XX3GT5PkkKpt1zKJ6hkz1GmFhWY1NnlLMmljCY1vDZJQqcEsMZSwYeyVqmbh2xLLt645wwuogKn4JumHjTfRjuArKCDVeyl06pkPLGFehDvcizTkAcaCY64gh8mX6abv8RlcfMpG3RdYxR29B9pRhrX0cur+O53Bgm3XEh4YmllC2djzT9WNLeMtzRVstyQlJQPUVkNYNoTkcAJFGgWNGQYl0I8QHMQKlVIO1bgz7UizrQh2PsFLTOJuwzl1jjtnwK0+6BVAStapo1enQF6cpASjWQBIG8bnDKHe/Tu3PV/+iOz5d7mv/hjs+v7v4JYY/jG9nxyWazvPHGGzzzzDNs2LCBpUuX8tFHH7Fjxw4mTpx4WGsetoFhQ0MDTz31FL/4xS+49957/+b6Fv9aZHRrLOGWxL8hOu+MZcnoJgU2CY8ssi2axSkJFNokdNNkos9O+54B8ikV0SYSGOEj0jLMpEsWUTJnLAVLT0Ipq8Y+YTYkhtCWP04umsBbVYR76hEsWPMuvuoSDKcfPTJAwdgRRBs78NeVWf414XIIVyKW1SGXViNKImYugyDbrKDR9t0IWg7X5DloVdOIrf+C5J4dbL/6FvShHuwT5qJ2NCL0NiI4PGgHtiAES9B9JbSNOZFFn79skR9lBSMZxZHoJRdNkpfsRIsm0pCSUBacBYKIVlhLYs1HqNtXIuh5Sx6/7yuE4U5y+7din30c7pkLENQMgiSh1q/DXllD/5pNKD4XB/70HlKsl4NlRzK/WCK4/1N68zJG5UTk4TaU8joCoysJ9GwHwNi8HN/Hj6IPdCLu+ZywW0Fa8SQb+jVGhlxsSHpxKRLb+rPsj6iIrz+AYfOQffdJ0qrOgbYonqU/ZWVLFFO282V7nERO55EvmjDzWc6eXk7zQIqS6gDNvnEcGLkMQcsRdtsw3n+UhentGPvW8dytC3F/+BBeu8TP3VvZ1p+l+s0cF84op0gb4kilm6IDn3Dka0+ibvmEyM9+yNTJxTyfHMHxZ/6YCdIQy5w9SAL0F07gmJ3PkMJG+9IbmVTsoT+VRxSghDiaYZIev5Rj3NbMXS+oYlF1gFElXkrsOpPXTmDkjTeQzGuMO/VnVH3xe9qLZiDqKkLdDIYyGrVtX/DIGZP4si3OKeEM0/0a/3bnbxh+449kN3/K9ghMpAcxG8P/o38j8eVHyANNmJqKvu5tLr/rVeaZTSSLxrH3mitRDnUcntnQznBGpTWWZ1TAxspuFUkUwNCQh9ss+fXWlRQ0r2F6wCD09K3onjBHlnuoH0gznFERJi/CcAUpuOtxltYVoHuLMJVDDssd2whg2UcEs/3IsR4Mh1VY53TLw2ZMyMkxn8ocETIYXeRhY1eSk5/Zglk3k9zCi6j80W1cuQl0d4icp5gu1Y4U76VDKcEhixS4FGaU+xlMa4wO2SnxyHhEnSo3qIZJncdkpCPLzDIP18ypoMKnIAng0eJ0SoXkRRue4YM0GCF296cQRs5CmbaYlLsYM5fFn+gAoOah53AccRK1O/6COdiFkIlh5rPkG3Zw8he/YebTDxGeNpqmad/DbNxM/qnbEWJ9iLkkpuJAN0188TaKXDIlHgVJEBjWFaugsbsQ4v0IB9ahdTZhqx5LdihGZM0XyCVVZDd8SG7zCnR/GYbDhxkoRUxHMCWZLDJ2SaQ7niOaVXl8bSt9u7/8F+603+JfjauuuoqysjJ+//vfc+aZZ9LV1cX777+PIAiI4uH7Lx/WM59++mnGjx/PT3/6U9544w3efvvtr6933nnnsF/Mtzg8lBwyMOzIWKGlf1Vx/dXYsDerkdQM8obJkrogYbtEQzJPuVNGN6Fnay9bt/aS6Eky+cbv0fD6WrwX3sGv51xB+7NP0fr00yTXf4aezROeNppMf5TWPz3PumPPwHvO9UQlP9rwAJmBCOUnLiUXTWAfNwtpwlEY3mKGfLUIFeMIHXM8g9XziK5bjTlqLmbNdIR4P6I3gLb8cQJLTiF4/BkA2EZORuttRS6pwogPow91k+9uwzzE9anNtdJuK6PnmUdJ7t/L4JY93Fl5DAWTx9D9o+8hvXY/EzKNljQ+m0AebMZxzo+xTVlAbt9mdG8RcnEVaqcVKqp3NqD1tGIqTmyjp4FsIzP3bJJdg+QiCSY88QeMvlZGpQ8iaFmkcAVlxjDa5y+S3b2eztdewzF2Brn9W8ltW4kR6ccx8xj+YExBn3wco2O7kY88jUlFLso9CuU+G4UuBUUU2d2XpPnYG1mT8PDK+EuY54rw+ZVTSHz5W0aH3KztyVITdFI/kOayeTVQO5055V6uPqKKP/5gOvd8coDueA5z7WvUffk4ytFnkd2zgVzLAWZs/xPK8Zfi3PUhzDqFZze003aWk8fWtnFt0QJ0ZxCtu4X3YoUMz7+E0p8/xU1v38H+ngRDN1SxLuVH95ewuNJFazTH/qOuoT+t8WFDP2Pr38SlSNz54X6GRD99aQ27CKHzraiSnTEJj01kwiVnAfDynUt4NVPDtvYoQ+/ejH3SEZQf+Agp0k63Usxru3sZGn0Med1EkQSk5ABfDQpkX7+cgtMvRDrhSqb78mgF1eieMIIAA6f+BOe5z2FOPIaaP2ksf/gC8mWTaIvlGbr/BXb2JgFYOraIzxsGmLrsR/z444OMCrlwqgnkwRbUA1vQBzrJL7sGobDccmcGxO3LMd57mKMDGWoCDoSmLTxbsxBHpBXhubtoSVlZWnV6H6bipC1nZ8hXS7ccRjA04ipEQ6Mpc4nURXfhlkw+XyYiD7cz8VeXk8xrjCh0I6WGWHj/FyCI3L10FL2qjbRqUGbXMdwFlIsJRqodVHoVxoRc1ARstMbyZFSDlqRJFhmbJNCUFMjZvESyOgeGsvSnNBQ9h2H3EnRIuAYaGPbXMYY+Jha5MWUbaqgGl5bErJqEqThpJYS59jX0nibEOafQ9txzmHY3iYOtIIr4J47HlBXyp93KqOaPkYJhCk6/0CpodBUp1oNNz6EFq8jpJtGsjiRCUbTRyuIb7iVXvxEpVIKRz9L3zhskuwbIDMUxMimcs4/FNnoqUWexlaenOK3OlzOIXQS/XWRepZVCH0vnMQ39G9nt+Vfiv7OPz1NPPcWVV17JihUruPrqqwmFQv+UdQ+r8PnFL37BfffdR29vLzt27GD79u1fX9u2bfunvLBv8X+P9zvjX/N8/Mq/E5r/amz4H1VeXf1paqaXUGiTGMjpTP/OaDZ1JzhiQRW5eJ7Hz3iQ0Pgy9l10LnPHFRKaUEPZg8+x/y9raXxrPfaSEmw+F5Wnn8T0q5ci9R6g4OAXKCWVCKKI2ttB1U13kCiZZHFzTIPETy+hy1ZK/0cfUJjuxn+B5bsgpYYQbE5MTcVQNbTedhBEpt53iyWLHXMkgqwgBcPoA13YTr0OMdGPkEtgijJlW16hYOIoUl2DpHqHuO2Rs1CPv5oRpx+Pc+xk+kPjcF33GzR/GQDyQBOGzYMcLsc8sOFQkTMdweFC9ASI7dqNYBqo1bPId7Vh++h3jFg2B0PVENQMYlmd5SjbtgsjMYzQvR+5ajSNr35O+Y0/Y/9vHyc/0I99+mKU6nEkv/yAM8YVIcW60YOVNJkFXydNt0az1PRtwqGI+B0y7+3tI5bVGBVyIWhZPmzPsStiIongkEUmpA9wdKWb+cUSUqKfpkiOX69u4b7PDvL46ROs0dLUYxD9IYy9axEdLuw1Y1gz8Xye2JumfeSxvNaY5OWHnsEZ8vO7n/2WD867i0fqs9jHTOMUdw/FzV8gD7cy+pxj6IykGTjhJv60sd3KRvrgd4wqcDCu0IFqmFw/yUVi7rmEXQp/On004aYvGJk6iGbCwJvXYdrcTAg78XVvZ2njZgpP+Dn2a8+hOujkjsW1GJuXo1VNo/2VN0Cy8fHBIW6YESIoG+Q0A79dYZ97PHVBJ6myKei+YpSBg1z4fgcGAme92Yq09T3CLonMa5chNW9iy9OXsKU7xsMbOgk5ZV7Y1MHimgDr2qOoukFPzOrI/Kasiep8J8Ih9245XI5QMQankUU7sAVxqI3Co62g2qYFVyG076ZAUmHEZH646VmESDeuy+4j5JSt99PQMAY7Kfcq+CWN8kwbKV8FHpuEL9XDzR818XBfCZt7syS+/Aitr41x993Hz17czhPLKlHDI9l8/TiywWoKZZUvWiP4ZYNhXeHFvVEMZxAkG10Jje6E9Z22SQIHI1kCDgm7CAV6jCqfRVSvtKtMMjqo2vUGKWwYgoR3qJFEaBTBhPXaKrV+BDWLqKvWiEpXoXkbpR4ZW+0EjEg/UdFL9Q+vIPHnf8MzohzJH0KZcCRGy248ZpbeDz5CcLitLo+3yMoyUyxfMDGXQDVMBAE8iojhLsDMJhFsDuSyGrSeViR/CEESWfX8NqIHe7HVTcJUc+gjj2Aoo6H5y9D+uq4gYOvejbTyj/QkNZbV+rDJh3/i/+8Eg39GSOk3Ey+88AKbNm2itLSUs88+mw8++ABN0/7hdQ/rkxOJRDjzzDP/4R/+Lf45eNJs/TqS4q9qrr+iwCbxH6ZfbBjO0Luzn/Ovm/f1WMwji6z+oo13miMsPWkkJUdNZ/yPLqKjNYqtopaum8/HP8JPxfyxdH++jr/8YgXXH/NTEu195A/uQhvqRfQGCM5fhK2iFgQR+5oXLUJjoo+K+/5A6d4P0K76NYbdjbHpAxKSB73zALqnEMHuwjV9PnJROWYmgZFNgaQg5lOWyiOXRQqVgqRYLfC2eqtoGTUVpXosajpDeOoofMvOwdO/DwDR5SPcs428bhKzFaB2NDL89vOoX72JOfYoBJsDo78NM59BCpWhR/rRMnkrHHHdazjHT0cuqUJ0uvGOGY1gaBbXKJsg37Adra+DoU/et3gJPju5z15k5AWnYysuxbQ50Qa6yA7FKGz8HC1QTp9UQIFDwiGL9Kc1yrx23jTG0TCYQjdMlq9vx3X6iRwtdfCDVVl+9tJ2fA4JQYCRQQdN3nFoiHzapZItnYjPIfHgHCeSKOCMtqOvfROzZQfJhgNEt2zGVj0ODJ2F5XZOHB1mBBGmlHhJvXIRRU/0sv3Dh3n2pydz/sZH6Cydw4pcOUbdLNTtK7HPXMJPjxvDr8KT+ONcq4hWT/oRXjWKpGUZoyTI2IMEE22ESZAVbJhVk6xspz/czpeDEtsHVQbSGgPv/IW8bvK9NW8w6aVXmB3bikPPsKLiRKR4L03X/o64p5wV9b0MaDa6MjBp/1sUumSq/AqeV+7BnouhOQLEi8Zzx9IxvLN/kD+p79A1Zhl9aQ1TspHbu4nd/SmOHxki6LKxpj3KbceM5IlNnSyuKaA9lqGlK8HFO1bypmMWvc5KTElGKxiBWTHe8qTJxBAnLUQrm4BcMxF5zsmMcWRAlFDfexRMk57wVLK712OLduDPD1OW60HMxOisWYRumghajoivhkRO5/mdvbzYqbBodCHXTHAytcTFheIpbCldyHDBaHZeHmLIdKIhgihhAvJwG+dVaDTEDfyf/p63tnVZ/lWGRsAhUeiS6UlqBO0S04schDK9iPkUuqsAe6KXsE0nLzs5aB+BUjOBjGbyWUuUXNEYHJLAMX/pwx3vRMwlwdARE/1I8R7Mxs3ok5ZiSw+hFYxAnH0yvo1/Id+wHWdxmB2/+4DYjh3kd65mYOUqYk/dQ9H82XQWTETIZxDyKbLlU0n6KjEUB3m7n6CkIQmWbw+6itbfhdpSbx1mKkaT7+tBjac56balFE2rJVe/AdNbSBYZWTxU6KSHMRx+olmdvtAEpKPOxGsXeetAhJXLvz1kA+jGP34Z39DK57zzzuPTTz9lz549jB07lquvvprS0lIMw2Dv3r2Hve5hFT5nnnkmK1asOOwf+i3++ZgzxVJojPPavy6CwDIwHO2x/U0x1JJS2ffqDhYeX0vtBedwzLI6JlV4ue4Hk+hc38WXt75I74efMGZmKWt/9DtK502m9oJzqH9pI44CP7OmFTO3wMnArlZS7Z0YiQi5xt30vPMuRnyIPZQizTgeebD5a1dWsWaSpfbZ8C7K2NmWZ83+XYj9TVaxk0qgF40EQKyejD7YbYWVihLa+MWkJx5HVwZ25AJI5aORkoPonjCMmEz1lVcj2RTyO1bxu+4AAEbNdPTiUQSTHTjf/w1mOo4j5EewO9E+fhrRH0IKlZFv2I4+1I007ggK58+DdBQpXE56zxaYuBAtHkMur0Nr2oWQPxQMWTWa7o8+xxHyIx97MeGpo3BOmUeqYT/p1lb0fRsQZAXvyGr0iUvpTaqU5Hrwy5aR3WfNQxgmLBtpKWAKXTbOO6aOReveY3mmjLuPH8PbuZdI5gyKXTLBTC+VHhHn0EGOqfGzbzDL2vYoz7fLPHZiHWCNBgdWrGDD0lsJf/cCzIrxZGZ/l6l3rWZ5wwA5d5gxe9/CSAyT/PReKj/+DRPCLoJHzCPklDiqyoeQSyHPORkyceoY4u5IPYniCQxnrZRwsWU7cscOUBxIguW1IqYjOMw8WXcYuXwkvkmTWeToRdVNyltXU3jCqYyVIlzStt0KmqyejrBzBcfW+DGcfpbKrWh/+Akvnz2esE2nOn6AnulnUda8EmesE9/Z19JvelBiXbhTfbREM3w3u5HuU28no5ns7U9hDnVhX3g2b+zoJpLVuDCxkqmPXU2pR+GSGeVUOVSq/E5WXjKKh1P1zCn3UWRELZVY114ELYcpShhOP1Kij5Tg+NpHJm/3Q9EIbCdcTtpTgvLs7cgnXgXdDZhOP2qwkr7XX6TQJZPXrZgIn6Sz8M4VXDLOw6x/u4Ln1rchNG7CNdDA02dNZjCt0hTJki+fgiwKyF88R5MRwKZl0APlGE4/NkngN4Hv8NNlY1FlJ4YrSDSrU6n2Mi6935K59zfSby8hJriQkoOovlKU3n30JjV8NglTtmM+/CNOyO0g8dgtsPpFVp7us1Lbm3Zahws1je4rRaoYjRxpByDjDCHmEkhTFmOfeATykvOZ88S9eGoqMdU8Rccvw3f1A6wb/V1K1QGMgXYQZfK6gUMSEPNp7KkBxEwMn6TjNA91nr0BpKPPRfKHUBu3o6YyxNv7MXSDgumTkYJFVrCwYVJly5HZ/Bns+wp570oKxBwhI4Zn0W3c+O5ednfHSfQ0/ddtqN/iG4Xq6mruueceWltbefHFFznjjDP+f+y9d3ibVZr+/3mLuiVZlnuvSZw4vXdKAgkhhN577wMMDDAMMMBQhj6UGVrovZeEmpDee+LEiXtvsizLsvpbfn+8THbnt9/d786wO1/Y5b6uc1229OroyLKOnvM893PfnHvuueTn53P99df/3fP9pwOfp5566vAoLy/nzjvv5MILL+Sxxx77m/ueeuqpv3sRv+DHw5RiCBjuCcbojP1tKjCQNMor8zIdSALUhOJ81hLkhQ9rePWE35M5vpRxl8+haOFUciZmM/7y2cQHQvTXBfh0v4+2ldvp+Gw5s56/lZT8DEoWjMZtErly/C3UfrSN7vU76d5UTdbRczCPO5Ix8XqD8OzOBX8HYrif9qcfJlYwEY68kH2mYgbjGuJZd6CnZhtEZ0Ae7Eawu402WG82aCrtKWWEkjpWPUFh2wZiiobmSDMyRZqCGA2idLfiGD+NwdpGLg98heLvRu5rREgaFhOhRb8mecSFKOEYlpFT8e+rByWBFurHVDQC0eZAcediKh5JfN8m9FgE27AqdMmMbewMhLQcQ+o/1IMYCRDZt4OC2x/AtuRK9l9wNracLHo+/ZD4wBC+0+5Ezi9HGj8f6cjzENQENpNIpyWH6n6VzqEkswo95DlNNATiBKJJMlNMHFXqZV80hUK3ldHH3kD1ZY8zVvah6nDjhjDiruW82ZuKmIgwTmsh3W7GIkuc8c5+kmlFaKm5ZF16A8emRbh31JmoO77ms0N+PvzNEVzu+4xH1rXA1JPwFc0k/MEzbH/iawYTGr2jjueOb+ppCBidboozEz01B6H9AJpu2C94bTKfHfIjpOcjWOxIg12IgkDEnIqgJDB1VmMLtqN01GMqrkRIxpnmGEQZeRRaRimaLZXxgW30RlXEIR9i6Vjk/mYu/KKNczcIiGYZ1CRy6y4UVzZFwRq0yrl8GXAS++plEpqOFAnQY8ogzSazVJjI4DmLGR6t5wRHF6Lby7ONEn/OPkCBy4zi66Dijruw6gl0XWd1l8LITAfXZ8zilb0+cm0gdhzA4ztAz6cfGRpPgTYCugVdMpOiDhl8lUA7Nn89YjyMkIhi1eLYc7zIrbvoWfY5aCqm/Suxed00BOL0hBUWD0tDCrTywJXTWPzWISYezOZXR5bzhjQB3Wyoby/IFRmWZkWMBUkxi4jTT6I/mmQQK2Ljdjb7NJ7f1Mplk/MZk2knqugo1lS8dgnN7iGZUU5c1dHNNrKDdXgGm1A3f8bG9hChnLEUpIhkBg6RzByGfNOTJBqrCdS2EaqpYWD5O3S/+xqxqachhvsRknGE+BCJnJEEnEWoKenYkiF0s6G8XOMYaZSr13+LYDJjLq1C6TECpBm1HyJ0HkKvmEZy65e4eqoRNAXNbEcXRIOHFQ0ihv2Ifc0IFiua1Y2SWYEgisT8g7hLslFjCbTQAJGavdDTjIMEUqCdxMAQwrBpxCqPQowPGZ+95bcytczLx1/XEgv6/tfze4AfXeYySl0/zZTPk08+id/vP/y7IAgsWLCA999/n87OTm6++WbWrFnzd8/7n25nLykp+c9NKAg0Njb+3Qv5qeOn3M4OcKVQ/B/eX2w3kdB0euIKqm7o+ewJxql0mhmzqJzCY6ZgKq5k6cI7UHVDuG3eWVVYUp20rK6l/IRxhDt8dG5r5/u9vVx4/UzcxTnYiopQAj4CNS3knnk2gs0JuoZucTDs/loOPHI0Ycws3dnJTaVR4pu/wjzrJGLufMwRP9tPOZupf7kXwWxlg1jBTJpQUnONritBNPyIRJmEZMGSCNGh2Mi1ash9TSjpJci+BpSWGsRRs4l8/gLWkmGEqvegXHAvvojCyOBe9JQ0tJQMEsufQ3I4MU1egLJvHeZhE9AFkWSd0Yml+ruI+YO4Js88HHjpNpfR2eLJJr7lKwCG2rpxjhiG5MlAziklvH4ZuqrRf7CFvFNPYWD4PNQf/LYaBwyjyNZglCl5Tr6p72dmYSpOs0hDIM6WjgGuKIXsi95m7dLrOPOJdXx482zu+foQvzm6glBcpSjVQpZF57vWCLMLXdiifnSbm76EyNLt7cwoTqPs+ZvIP+M0JE8mXd7RxFUNf0ShMt1KVNF5b38P/UMJytMdTCtwc9FrO4iFk5wws4jfpBygq2Ie2WKEoJiCi5ihPbPuHeSqmayOZjKnby3auOMwd+0nmVGO7G+m950XSbvuAeRAK35XKcG4hqrrlJojtKsOUkwi3kAdako61G3hQOHRjNba0Ps6DEsQQUQOtBL+6g2GTr6drGg7e9QsqtIkTB2GBpRmT+WdXjcnVr/EwSN+xXixk7d6XJyt7ECrOtpQHnZlY27fTXPaGHLtImIiTNzsNP7ugwl6hhLc99VBKrKdPDNVRLV5GHz7CRylpVhGTDQyV+F+NHsq4W/ewXL2HQibPkCYsMAIeLsPQYpBqux7fymukSOM7kRJQp5+Ir3mTNa1DjC3KJXntrQxudDDyWffzsW7v+dW3z5WNvZzfrkJqbeB/a4xDPOYEaMBQ2IBeK5Z5opyiRY8FMdbCXtKsSlh2LuC1hGL0HUoUbvpt+fiifUiqAne6XZwZHEqmWKE+piV3V2DlKbZsZskhtcu47O0I5ma5yKmapRqPnSbm17VSlbSh+hrRLA5+W7hlczdvY7GQZVhXRtRy6cREy3Gc6sJ5IFOtGAfb867kbOX/YFE43446RYsoW52XXAJ495+k8ZkCqWN36GHBw0TYmc6qsOLmIyimWwgyegmm1FWiwYRE0PooozeWYfS0UD3+p0MdfipvO0GBElCT81GGOhGDfoRrY4fSNdWUBIIukbEnoH06SPcYlnC0vv/9JMOfP5Z7exf76rD7flx7exP3nsHuW7bT66d3ePxEI1GOeGEE7j00kuZP38+giD83x/4f8F/OuPT1NT0fxyNjY00Njb+ze+/4J+P5/Tmwz+n/B9If4GkSk/8XzJBQ4qGSxYZNiOf7t091Lyxis5PP6d2KE6eTebk385n8wf7efaxNdQe8tP8XTW2DA85E3K5+aXzyb/+NrY/9R2mwmFEjv81AOFta3grmEubpwqlYS+HrnAgqAlSkgP8OqeHV7pcWCccgaDEsPXUIIV6kcwS2FNJtjcw3dyN0lGPFPaDr8UIoAQRMTqAWTHKTDkOGSERRvXkIyhxozzhTEUK9WI//hIQJayX/gHPtvep7N0CjlQa7r0TqX0fktuLlFWIoCmYK8YxuOJjtL52JE8G0rijMZePwXPMiYjOVCPoMTtQ3HmIrjTaneXI+WWYR0zEOWIY5tGzEMonGxyh027DMfUoCq66kfb3PmAgpuKN+5BEAbMkUBkynO5jis6iYV5kUfhBcl+jzGOnRUznyT9cgCTCo5dMptiqcNqEfMo9FjIcJtyfPISQCDOUUNB1HWQz3TEB61u/J8tlpaE/wnt/2QwFo0jkjeWUZzfxTb2f8aY+BmIqVlmg0G2jPN3B/NWPURysYeW8BOoPMgj68BnkRtuQ/c2YJYHOhInumEBk5rl024s4wtbLnryjCC+9C83q5LhX9tH6zGN8e8xtbOhOIMTD3JU6CvWHM1TE5OLhjNGY37sfAM3u4Wv3TNwWidj6z1jrnIgU6jWkCPrbsRaVkWI2MgTPb2qmPylyT5OXRP44Ou1FnJUZ5IvRlzLWHmZlOIMjij30VMxDFwxuzGAS1NQ8bD/83wvJGP57rmS/L0pJisB0T4IROS6emuMmnl7B170ynmNORJq2hEDWGKN7yOYCfwe2k65GjIeQy8Yihv0EzWlomWUgCBAL4SzKxZRTTLy3D/OISYi+RtKlOCeVpVDTF6E03cHRJYa20WWtuwnGVaYWpHIgakdLzSXNKiEmwrSqTrbG02gzZXPZKDeir5HiRDvfDRkZF4Dg2BMIxlTCSRUlNZ+kBugaQWcBp1V6yRSG2D4g4QsnmJjnYqJtkFGDexHGzWdqnos0m0Rc0dFS0lEtKWSYVbT969jmHI8uWyg9uhRB18h2yGhDA+iSCVs8QK9qRZetaBYH0eFzOX/bG7S+9R6CKKK8eR8fVC1i+GkzSHz1EuXJNpLNBq9OKZlimP7GQ6Aph7WREESkhq2GLYaqElv9AWrAx1BTK1avi5zplaj+bnB40GUrSuF4BLMVPW8EumxG6j50WHm8O5zk3YoLWHr/n/4Lds3/GfifnPHp7u5m6dKl9Pf3s3DhQoqKirj77rtpamr6UfP+w7T4pUuXUlVVhdVqxWq1UlVVxUsvvfSjFvMLfhz+6sf1166uf42oqqPq/+LVtcEfpS2apGefj3BPBNkm4yzI5MKTR1B1UiWB2jY8ZpnZ6Q7GTstjqGsI395mPnp9D03LNrPp1EuZ/aer6f7ofSwfPsj+d3Yy1OHjjOhG8mJtMHERatkUrvdMoUt3kWyt5cK8iEEeTsZRvMV0uIeR9tbnDK380BAtdOXQ/eU3oKpohWPQzA4j82C2I4V6kIJdSKEeElYPQiKC1L4PMT6EGvSTqN1J4I0nEarm0n79WYhuL4n6vfS++RzlN91IeNsawo2NhkpzPEyyaT+OsVNQ/d3oSpLwpy+ghUMoP2zAiq8D/4evGMRP2YL5hVtB02h49jkG9h1A7axHaD9AonYX4rq36P/mM5Akiq66jo1tAzTjRYoP0T4YR/UUMPz5G9nbM/SD+ahGX0Qh1WqiPM3OkgdXc0plOgMxhZkFTpxzb+bYmjfQgVJrgpaFtxAxuTixazmOaB/axo+QRQHt/HsYmZHCdVfew02dm/igy0JPRGXcnZdy8UgXyR3f8ptlNUSTGn2RBGe6u1l19C3o8QjaYD/vXDODW0f+cHoSRJLZlVjVKF1DCcySYXHitEjUygWUeiy8NvZKdMnM/NHZ7L7wYc6KbeJQX5jYnvW4Pl/OHctruOD5LaTUreWZtuUMdfjodldg6q1jQaZCnhxFWnIj5Wk2dKsTuWkrSvEk5PLxOLv30RyT+dMJI7DJAjfOLOTb5hCuDx/gfZ+bU4tEMFmZ2/IFuWofad89zarWIV5qBE/ch+rMYr8vQucdl9EpprH57AeoTLeiffcSEXMqIy4+DTEeQkRnSp6TJu84kGTWtgQNsczWGqIVRhZKCrST2PU96BpudRChqxattwXBZEHOLyMx8mjaT7wdzVuEUjgeKewnqMrMzLEypyiVSFLjxv2r6YskeH5TC6qm81z+OB7er2KRRdi7Al2H1LsuJL9hJW8cDOHPm0yDKY9gXEHqPMDjOwP0xxTMssBwrxVT0xbSbBID9hzMkoAcaKUl6WC410ZS07n45e3oosx+1xgORS0UBA+y3xelaqgaua8JS2c13XEJcdRs0u0mlIY9FDz+BtLBtaR27UIYMYPQ879DtzjJZBC5fS9iPMyalkFCqz4la8pIBpu7cB19IuOPL6fpy20cfGsNgqogZxWy9rpnaItgBKMAvS2GflZaIUIyiq4kSOSOPnxQ0WNh/PubCDZ0kDp7vhF4DfTSZc4ydLbS8w0jY9mKmlmGFGhFF2WiSY1le7sAftLZnn8m/ivIzT9VGWOLxcI555zDihUraGho4KKLLuL111+noqKCefPm8c477/zddhXwDwY+d955J7/61a9YvHgxH3zwAR988AGLFy/mxhtv5He/+90/MuUv+C/AOZdP+nfv+6t9xb9GVNXRVQ13kYtwT4T+/U04Mp3kL5xD+e/vp+L4ClJyU/hyVQsAgiRy2qUT6TvoZ/Kd59I36Qx0TUM0yagJlbRJ45HyhhFOLUYa7EIOtPFM+1dIAohVc1CdP2hzRIIIuobHKlFkjvLqjR9APIw05CP/okvRZRP6jq+Q+pqQ+xqRAu0GzyI+hBgZwBwLIIb7weFBdWUhTDwOeeyRpJ1tnNbL7n+CwJqVWMcfQdZp56Gl5mIpKCF15lEAqIFe5NwSItXb0ZUkclYRluxslK4mRLMVNBXJm4P7ynspuHEVl2xQ8V55J5+cci/ZT7yFdPXDyBl5iG4vQ0dcijDtJByFeQwue4t1lDKjIJXScB3Kty8zJ1OAxp0IdzxPhddOqlUm+YN1gSiAqutkF6ei6To5KWbmPb6By393A+YZi7HEgzTFzRS6TFg3voMa6EU/uJGWT78jXYqzvtXwRRp/0llIbXtYtq+L/MFaHgnXIIV6EMxW4oqGLArMK03jy3gBS3q/4fK9bsSS0ZRGG/G/8RT3bvKjpOYT1SXE2CDlHitRRWNT2yCWDW8zYugATnWIC8flMPjBn8lxWZm383kiY4/nknIZ+cizude0gWAkSTyW5AvTWJI7V5Jyy59wmAR0kwUkGXHIR0QV0HWQffXG+/fNixALoaQVUhE6iKDryKLAeW/voTzNjuuoE/hoVwdyXxNCIkLz2NPQHF765l/HUflWTh+VSa2aRl9UZYTXRsb9SykIHqQ41YYOPHLWs2g6nPf4aSh1uzD11eNN+HGZRRRrKgvq30WvOhqhYorhGVe9Gq23lVB9M7rZgdSxn9DG740PjJpEzirE3N+EyyzRLngQD60n6s7nwNHz2OFLkGMXOe+t3Qy+/QlFj1zOU0UtZLx0KwUrvmVxZRZdQ0kSE5dQFqrhqTf20V52NAsr0nGZRRyyyKkZQwwVTeXcsTnkpphIt8mIiQhqXhWCruOUjTJ0qzmXgZjChwd8xBSNxVMLWBuw0BtOMCpyCC3Qw5hMO3Wpo2n84x9QXNns6RlCUGLk73qf5nc/xdRzCDXoR0kvQYwFiQ8MoW94H6mzBqVoIjvkMhbauvBXN6KEYyjhGA2PPkzJWUsY/dgfGXf/LeiymadO+ANHrngT74f3Iw31IcZCaJFBBq3phzW0lJ42TK07kUI9xDo6GTxYh8lhpej4uQjpeYgpqZBVQk6iB33PSogOojkz0UxWEGV0s4OI1SjntLUM/Fdum7/gZ4K/Epybmpr4+uuvycrK4tJLLyU3N/fvnusfsqxIT0/n6aef5qyzzvqb29955x2uu+46+vr6/u6F/NTxU+f4/BV/5fpIAn/Txv7v4dgsBxVHFx82Ky06qpLvn15LhtuKK99JxYkT2fHMaqweK1mjM2he1UL+9DyGX3shSmczT134Iuf95ihi/UHyFs1HnLIYsWU3gisdZBNCPIySUY5msmHyN6K6cgz+QH8rbZ4qvDYJk64YnA5RAkFET8YRZBOxXWsxzzoJeptIthzEXDoKwWwl0bgfefRskju+Q56yCDE+hGZJQYwP0fHck2QdOw/fqtVkXXUbyXUfYZlyLEJ8yGgZjoVJ1O/FMmY2qr8TLTSAriQPa4hgcxmcgq56YpVHYe/cQ51rJKXCAGJPnbFGh4dkzRa00AD9R1+Nd/3LfJh/MtML3JSGDtHlqWRze5Dsq86g4POvGIipOM0iuRaVgGbCG+ulTvciiZBQDY8rmyzybb2f00Zl0nHV6Qy//wEjUIyH6TJlkDdYj5qSjhTqZeyznbx54yyK3WaskoB8cA3R4XMJJzWu/GAfHy50o+xbx4qiJbgtMhkOE3d/dZCrZ5cyvXMFLxx/N5c3r0FQEgxY0umPKWxtH+Ss7LDRKQe0xgynb6/NcIlfXtfPmXlJ1gymsOjM37Lny8cocpsJPXMrR3XP54VrpzOx+WuE8cfSj43q3giiAOub+rlmWgFOLYIQH2J3zM1YV9Igpe9ZhWn4JCLrPmfj1KuZL9Shaxpaai5iXzMduVN5e08XN413c/mX7bw010YirYS+qIJNFnErA0jBbhRvMTUhkewUE+nhdnps+aTLSaOUlogYMgRhgyBZ+6srGP7kX1BTMkjqYFbj6JKJkCLg9VWjDfjQ80caTuiSCT0aMrJS7XvRnekEnEUGB0oQCWom0vv2M/7FPj68aTaCAKIA+aY4rQkLO7tC/OX7er7IWM1t1hN5aGEFUUUnrb8WNTWX29b0cvm0Qvp/EB7NsJspMoVJWNyoOlhRkPsa2S0WMcY6iC6bjXIPLpoCMer8YSbluXGYDCX2crUL3WQzCOqfPYFgcyBPP5Hk928hHXcV6rcvIdoc6JqGZeRU9t12JxWnzEHyZiPnlqDmVSENdKBLMsKgD+yp9Lz5POEOHxnjhzHU4SPnomsMfa4frCRCr96Pa/IM1J5WBo64nMyBOvRAtyEEGR1EV5JEtq/GNmoygsOFFgqg+ruJd7RicrkQnakIVgeizYFQOAo0hf033sSw849Hnnwcms2NoGsEBTsdoQQmUWTC8b/+WQgX/rM4Pp9tO/SjOT7P/OF3FHjsPzmOz7+H77//npdffpmPP/4Yi8VCIBD4ux7/D2V8VFVl0qR/m12YOHHif4m40C/4x/HXrE6W5V+MS4vtpsM/nzoy42+u/6YnzDNv76dswUhsHiufP7GG8ceWcsRfrmXCHRcw2NzFgd4w3goPg+0hJlw9B3umCzXgwzx9EeffPo+sE06i4NQTeem0h+l75m6SbXXog31EVn2EFhow2mcRSXpL2ROUEGMhVGcWQwmNwpMfQaxeieLvRgsNkGyrQ/V1GClxbzZqzSaE1Cz0ZIJka61xCp+4gMT6T41Apmk3hANIYT+6bMY7pgw9MojZaeezsSdgmmuoBg/mjkezucGZjmXMbMMsUdMQHS5QEsS2r0QwG23DQjKGriSx9dTQ5qli4JwlfDvheBozp3DAM55kRjmD08/BXD4G88t3ILq8nNL0LlFFI5k1nOzBemYXupn+2mPkqP2MMIfI1/yGL1lSo1lIpy0Yo8BpZphDJTfFRMb6l7HIEtZtHzN432t8HkxnrU8gsmwpaVYJ3eJAUOIImsK2P8yjsuYTHljVhDTYTbB8Dr47LkHV4L6V9xrE8DnncKxniHPu/ZKzntrAd+9+TfNAlOvn3MaC+u2IsRBCMkpafy3l/XsodFvRrE6khq1oZjtfHOpF1cAp6zhIcEapGdWVzRH2PrZ98SgVsSYO+GK8PPFadtw1g3yXha2FC9g5IJLeuI6xWQ7mhHdx2yQ3K5sGUC0p6GY7B3xDdKl2EqkFiNNPJpg+Ase0+RyVGiaUN4H3oiUoW5ejp2bjMImcODKLfsHB7fPKCX7yMqZgB6qmYxLBJ7jpTh1Ov2ZhWJqVXWNn8On4k/m8ZCKqbCVhchwOevyyBySZEQ8/StfjdyNu/wzz5g/QJRNiPERa5w6SLQcRbA6kIR+6zYWSVggpXsRo0HC4l6249QhCIsKL1QFUHRpdlay64wgkEXZ2hUizSqiWFNJtEuOznbx2/gT8C27ikUXDUHXwdu+i583nea8xwd3zyhAEmJhhZoq5Dw2dfUNWvmkI0BtWECMBVGcGw70WGrRUtgdNrPWb2N09RE84wY7WAV7f3o4sQqk5Ar4W1JQMTIE2enceRDziXILv/Anp+Gt5dref5MAAWjSMuXwMLX/+E5UvvGYER2Vj0dIK0DZ9DEAyvQw1exiCmiD96Hmkjymn+Zsd7D/7fhLZI1ivFRrBSjRI6OzfE6/bh3DM5aTHe1FdmWglEwDQLSmGX1/JCASHC9XfhRYOoSdimFJTQRQNWYlhkxCzio33ItxP0TyD4yMoscM8odRIF3lOM8F48mcR9Pwzoer6jx4/0UrX36ClpYV77rmHkpISjjnmGDo7O3nxxRfp6ur6u+f6hwKfc889l7/85S//5vYXXniBc8455x+Z8hf8F2FeoUGs/Nct7c2R5OGfv679l9ZAt0nELApUuSzsWroFgNJMO2pSIznrbOSR03AV53D6TXMpOX4aWRMK8cw+goMfH2Bg3wG6X3yS1F8/gWAyEdi0gYkTs/HOmG4QE3OGYZ8wG61oDHrHISxhHyZfPaOr30VQEyBKZKfItF9mN9rZlaRR548MEq2tRmvcg+hMRRw5EzQVObcEadJCBMVQ29U1FVNeGULJOHB4iO/fjBDq4+uqi7lyYCrNK/Yx97ZjEJJR+j0VpNStRbO5UZqqQYmjlU8FQPJkILq9P6jJNiLEQ8aGnFmIlpJOzoFlTH72D8grVlDcsorKzvVIQz6Up29GzxuBc8Qwo2PF5iDzjd8hRIPEM4eT3rObOw6mEDB7kYb6iKdkERPM9ISTqLrOiHQ7zcEET+zws7s7wrl9UznH1c4y75HYTRJLbG3MzpJwzl6ItW4dmsmGFAmgyxaqe6OcUDuM+zPreLvdCHDzFs3HY5WofOwJ+tJHMfPBdXwfdFJ7+hBzJuXT+OHNZKdY6Hj+HQqFIMkty9CsTqP0GB5k1PIH+dO+CD3FszEF2ri2yoEkwqrWIdpiEnsGTezzxdgQy2BUshlCfWztGODySXmYemvJUfsxSQITnDEEmwNfRKEpZzrdegrDvHakSD8tCRtVmU7ye7Zj2vs1YtjPM5vbUN25XPBNH6ZlT3JS28dsrzoHNTWfb+r76RlKULfgGIrtOo7SUvw2o93dMdRF+o73EQXI8u9H2vAOJoeZ9AIXe4IxLN0HiKs68kA7utVJln8/qAq62UHG3X9BtDvREzHkmtVomz6h47WXkUZMRfMWEdn8tSGKaHGjth1El2SkoT6E5A9O4CYrl41JZ1tnCFEAkySg33cZlQ9eiqv3AJvaQwiCQKpVIqpoRBUNKTpwmLScfeYFnJmXJLn0d5TqfvoSImgKZW3rcFslJue5aAnGCJi9+EU3LcEk2zoGyUkx0x9NklQ1shxmFo7M4qIpBRRGmznlgyauqMnAd/+1JLd9Re4t97P9uCUoF9yLJpmYmOvGMWkOlknzSTbX4B1Vwqslc5Cnn4jqzEQ7uJmB6eegm+3IAx3I/a2oDi+Bdau5/ep3GXHF6RzRtpyhhMZMe4Ba50gEJUZh91aa5t2IFOrl4A3XoJvs6KKMklqAZkkxFNhlE5on3zgE2RyYS0eRHBjAlF+O5MlEa6lGTc1FdWah+LuxV45BrppF1FPMgbAZNAUxFsIlJHhvV+d/x7b5s4auGwKEP2b8VAOfWCzGW2+9xdFHH01ZWRkvvfQS55xzDrW1tXz//fecc845WK3Wv3veH01uvvTSS7n00kupqqrixRdfRBRFbrrppsPjx6K4uBhBEP7NuOaaawC48MIL/81906ZN+5s54vE41113Henp6TgcDk444QTa29t/9Np+iljQsOs/vH9I0TguOwWAYFIj3SxRPRgntdBF74E+MkemM9g2yK4jjyax4XMESWTtnzdgn3w0OQuPASVJ1ugMtITCUIeP6pOOI16zg0fuWM6o8+YieTIRbQ6DmKiqqOs/RMgbbgiwpVWQ6OlCiBslldSBBnxHXIk26CfRWosWHkR0e7EWlYEoMrhlHWLU4LFI2SVotlSjNVZNYp21hM6iWaiubNT2Q5jLxxDe9B2LC2QWj85mwoO/IaWkkKHlb5Aa7yO6bzNyXxN6IoaeiMGBNYhur5E1SiYRU1IN6wIlid7fZXwZ7FmJWDKaSP4E5lk6UavmE608Cs3uIfvMC9Cq1yKYrYgOF6bKqXiPXoCga1h6DhI/uIM/lPSyecRUut0V2Lv2kdKyBY9NxiIJ1PVHGTF0ALfNxLhsOz2+CDPfD7OozM0otRXVlQ1bPzPI1ukFKGveJZw9CsIBVjf5+e2xw/nENoWz8xVc0V7WXvcM5kALiXUf45YUtl6exxEdXxOZfQEnj8mhrj/Ors4gH51RgU/ysKnqXISG7STTSyF3GM6L7uTYigwyFb8h3meyUbzjLfJcVlY29uOxyWQ6TOQ6LTTZS2nJnsKkXDehhEabp4ouKY0Jjgg7Q1bei5bgMovku0ysaOzn2jd3gSDyxaFeshwyjekTEXPL0DsOcev0bHTJxFLXelqPuIZLBqbjtEgsawpzamwLM4d2MuWbL/ioLoR25EVktG1G8eSjunPRZ55J2u5PCeeMxlRciTM3hWl/uIg/De1DN1lwhY330dRdQzJzGFI0QMxhZDxjlUchj5mLOmKOUZ41y4iRAEJ3HfbpxyEkotgbNyKWT0Rs2kV0w+eIsRAdSQu9qpUvm4ZYkCsyGFcxoZEzYyyuZ94nkDGSApcVXdfRdZ1sh4l8pwkOGGaaLWljQFPZHU8l9ZRL2XXBJWS1bkBJKyZaeRQl4QYSqs7MAicb2gaxm0RiikaF104eQU4sMjMqM4VhXhvzS1MZ5lBRUzL4oHAf99T8mfTfPYs061SoWU/mx1/iTfgx++qYJbaidDUTWfMJ8ujZ2Becx5Lb5xu8pf4WTAUVpOlhVIeXoU9eREkvRbc6aV93kDvuOBpz8QjUoJ+uoSS6KFMRbTTeA28howZ2oTfuZOQdv0YeaEMaMg45Yl8zpuKRiMVjjMPCnPPQR8xCDfgQRBHJm40ejyLl/iBcGvajtDcgpqQajQ1Alej7QRMsm0MhgbXb/2fu27/g/4zs7GwuvvhiPB4PX3zxBS0tLfzhD3+gtLT0R837DwU+1dXVTJgwgYyMDBoaGmhoaCAjI4MJEyZQXV192Ldr9+7dP2pxANu2baOrq+vw+O677wD+xjJjwYIFf3PNl19++Tdz3HDDDXzyySe8++67rF+/nqGhIY4//nhUVf3R6/spYkmR+z+83+I0M9ZtZViK+XBmaPXeXpJDSTp3dCOZJcI9YfY8/zUt3+3l2LsXsWzuFQgmE0pvO6mlXjb9eT2BxgCufBdydiHnnTSc6ldXI7rTSbbXo6SXQnqBceLztSBWryS1v47IybeiS2YExTAyzDYrSN5sJIcTyZOJ0vmDMWnQj724GOR/KdOJkQC6yUqfNZt4egUDMRU50IoWDhGv2W4I4e37nhOlWmIHtoIo0bl+H+qOrxEkkWRnM+KUxai5lYZfVzyK4uswPINsDrRwyOCFSBLUrCfe1oTiLUVGQzfbiKk6jvadyP5m4ge2Io6ciervouPjTxj8+n0AkmvfJ5Q+DN+si9E9eSzY/y2KphPe8CWR4qkMxlQy7DJjsxy0p1VxeWob4aTGyqvHs/TiyciBVjocJQafY/qp+EYsQLN74Lhr+ao+QKR4KpdNzMVtlTnF2c2cpY280SpyxNpPGHIVMNTWjVS3yThxR0IMxFVm9K3Ha5eZXughYXLg3fYuwZjCy9pY5P5WlNR8TD0HyXnv9+iCSFBMQZfMiDNPoypWR47TQrZD5rWdHbQEY7jMIm3BOONsIbw2iYN9EaKKhhzsJNUqc2x5GtlNaxCVOAvLvXxwxRQ6VAfXmfeRYhYpjTcbZcbsEsOSZPd3dM+6lDJxgPuPG8GNH+zlRKmW7TlH4CuaSUC3cFLHZ1jbdhDbtwlq1qMtexox7GfVJY+i6dCXMZqxn3+JnFOMVLuB5PZvCTlyCOsmklkj0GULnw54Sag68mA3tuYthN2FiEN9ULeF7JNPI7ZjFYChM2OyoOZWIgQ6DA7YcZfTlzGafLWPzJovWZSro+/8mqSqY+qtwzxiIpIAOXOupUTtJqHqhJMaZknAF1G4pKkUsWEba4ZPoSVtDPd+fZCWh+9h3N3XoHQ0oAkSsiigm23YZBEpOsCiAhOhuIqm64xPExGTUdb7DP8rrxLgy7p+tLXvGNmpjlYS1z1O4NEbuW1dP4mJS9jYNgCAZk4hmV2JuWIc1oUXG3o60SCeS29Hr9tGsr3BEBuMh0EQcS65CKn7EJ0P3oI5xYSzMAstJR05t4SReieRlGx0fwdDr/4BzWQjWTwZffQ8SPEawoXOLDSLg2RnM7rJcljM0OavRwz1oHQ2YR0xFtWdi+DOJJk5DCERQQ52Yh0/BzIKiFjTkASB5I5vDS6a6KQ3nKDm28/+y/fLnztUTf/R4x+g+v5TcNddd9HZ2cmHH37IwoULf5Qj+7/GPzTLqlWr/lPj+++//9ELzMjIIDs7+/BYtmwZZWVlzJ079/A1Fovlb65JS/sXolcwGGTp0qU89thjzJs3j/Hjx/Pmm2+yb98+VqxY8aPX91PEZy1BqlyW/+t1Nulf3v7OmIK7yIXNYyXUNYS7yE1rtY9EOMG2J1ZgS7WgdLciZxdiy/DgTrPiznchW810fLacwbZBBElgy6W/xlxaRdTkRAh0IjpTUUunoPg6SOxZQ5oaRHVno8sWZH8z2pq3OPD4UqSMPGKHdoOmItjdSBOOMYImDB0YooPGQn0teLUglp6DjBrci95ZZ2SYPJmY8sroHnU8keKpRLr8SHPPZvhvbqDl468RT/4Ncno2g6//ESQzqtOw+BCtDhL1e9GjYfTc4eiJKPFDu9DjUcw5+QiagqlxM2I0iCQKJJoPorYfQph/GfuvuRrR6SH/9NNZ+fsvQJQwT12Iq3svG9uCbE94WeWTufy9PZhPugFNhwl6K+2hJM7N72CTRfY5R3PWKzvY4VcZpveg7FlNxsbXDDLulk/IOPi1wTn69FFm5LvY2RUmpuqE4ioD6SPY8cFbNPrCoGs4fQdJO+daliYqGdTNmAoqyN35PlooQL4UZlqWCenb55Az8lhUYOLiYo057/YTUXSU9FJSz7oWze7BKgs0DyapuPFbGp3DmZbnxKTG+V1JgKpMO+GkRnmaQaIVBYHmgSiSIDCQWUWJTcGpDPK+OBa5dRdOs0jGUDPZKSa0qqMxSyLJmq3o/V0IiSjRV+/BVDiMLIeJZt1Dmk3mubPHoXmLmNSxkvR4LxmBOkw5xXw253KevOQ1ml57F9HhRLekMOqsCZglge6wQiCuoRWNQxk+G/Hoi7DKInZJJyZa8Mc0JuQ4jQ7D/jZweHAMtpN05SCUjCM5fA5y4TBEZxq6vwMhGUe3OA09p4opCMkYLhMISgxtwmLiy15A8maTapXZeNY11GdPQ9XhwLd/IpiSh1tMkmGX+bKuH4dJ5A8LR5Bsq+XcvR+T0HQ+PiGT1IoC1PGLMJePAUAOdjHoLCChavSLTjqSFgRBwGoSkfuNzspZvrXkLH+UwMt/5CR7O92rN6Ms/zN9e+vx2mRcNz3OwxMFfvX5Qc4YmY5mNUi1fxWelIKdBL/7lEDGSBRbGpInA2HicYixQRAExLAfrWkP/bkTybvxLgKNA3Su30d8zQcIsgndZMO66V0CFUfCBffQ9NvrkUK9iDVrqLeXIihJTL21hr2LpqK6chB0DSnUYwgbxkLICy4zStQYApW6IBBxZBH89mNUZxaqKwdnXy2moV5ME+aRyBpOTNX58kAPmpL4hd/z/8N/iY7PTzPu4eabbyYzMxNJkv5meDwepk2bxscff/wPzfuzsrdNJBK8+eabXHzxxX+j3rh69WoyMzMZNmwYl112Gb29vYfv27FjB8lkkmOOOebwbbm5uVRVVbFx48Z/97ni8TiDg4N/M34ueE5vPqzl4zb927d4e3uI8hwjlfxXV/diu4lt1T4ESUBXdQbbQ6RYZKwuC/O/eIIRp4yjZdlaVH83WSeegmSSiAZiHPjoAM7CTGa8+2cqz5zJ2GsWk2yvx/zdcyi9HSidzbDlE6MkZHciNu1C2/4Vig6Ktxg9Gsb65w+Q8odjHT4Oc/kYdMlE+POlmAqHoQs/ODtHw8Sd2WgFVcSXvUB41cdcP/I80DS0aBg5twQ5Mw/h8V/Bh39EiSXQtnxGonE/umb8LbTMMqyX/gE0BVr3gaYZtgOFw1D9XQidh+h47WXCrR0G30hJoq95EzV/NIo7l329EfomnUH7h5/CurepeupJBNmE6Pay+K2bEVIzQZR5sCWNJcO95KSYOSo1zBvnjGNZc4ShhEaro5RSSwx9zjkc7ItS1b2B186fQE6KmQ5zDssLliB5s0ls+QpphMFDEvvbMM87n0wxQonHii+i8N6uDnZ1hxnY+GfuqYzT/+pjbDjzWny2XC4IrjAyDuWzkdxe1ICP4Kt/RO5rQp52AlpkENnfzJWrBrh8fgWtgwn07ctJuvO4a2UzpvVvU96+jsZ7RlNgh5ZgAs1kNb6UNDCJAukmha6hJKZgB+c2vkGhKYpDSPJdR5IuLYWTfd+g5lUx895V6CY7YiJCd0zA0rKN0P5qWl9/Hf/n76CfexfLlDI2tofI+PRBLCKUmGMGmdtsRQr7Ud3ZCN48Zt48j7ZoktI/PI44ZTFs+xzv2OHIwS5SrRJePYQQCyEPtGPqrcXasZu+mI5JFEizSeRaVFzde0FTUZqqDWuUZARB1zB3HUDKLafLUwm5himtFGgFZ7rRSt9Vi6BrxDzFmLv249/fhFo+nZzvnmDy9ytwPH0j2RbD00wWBYYwE1d1FhfIdIcV8mJtmMcdyYZYBoUuM2J8iOqFtyImY/RmjcPSsIFecyZmSSCU0PD8EDiZJYGeoQTxzOFojbtg+HSivgDeoxew87rbua3qOpb4j2D/+9XYIz7Er56lWsjjhWkC0pAPqXErutVJJHuUISYoyqRMmEZqyyY0XSfZWotmNojzumxGjIWQCisNK41YiJnv/4Wc6aNoW7mdQ08+j7r9K+Ty8bj1CLavn6HkkRcQo0EGR8wj9+vHEdQEan838a3fGNm3QDtCPIxucSCF/Si+DsTG7WgOL2JPHWJkAF03uuGcU2ajm23IwQ7jMy/JaFYnAAMxlc+/qf3v3Th/plDhsEbbPzr+rfLbTwOffPIJH3/88b8ZTz75JBMnTuTcc8/lgw8++Lvn/VkFPp9++ikDAwNceOGFh29buHAhb731Ft9//z2PPfYY27Zt46ijjjosatTd3Y3ZbMbj8fzNXFlZWXR3d/+7z/Xggw/idrsPj4KCgv+W1/TfhfF5xuk28YPtrvSvVL7bokmqzp/GkKIRTKosznfRHEnSl1Dp7QihqxpWjxWLy0Koa4jvT/k17rI8/vSq8aWBNw9XvpOx9/6ajEovA7VtJA9tZ7Cpi5Zla7GOnk73+p1EDh1AsDkQJi3CXDoKadQsQlvXIXuzEQUBcagP69iZVPh3Etv4BYMjj2HffX/Cl1qOJTsbNb3E2IgH2hHdXuzd+0EQebbgHB7LPZu+l41shmC2IpgsaN4iMu54hmBDBwDJ7ja08CDusjwSb9xrZDPq1qGseB0xPR/RmYri6zC8h0QJpasJd3kenqMWIueVIXmzDR7Q/tWIsSD5LgvpNomCM09H8XWgpWQgjZ9PPH8cyZ42klnD6bbmMSIzhd6wwrMbW4g7s+kOKzyzst7QLRrYbwReX/+F8dl2VqZMJqHqHP/AalqDcRZ1LEO0OjAXV6KZHaj+bmK71hCypiP31pEbaWG4KYT1zBM5/bq/8Pa+HtYoeSjRBNOWv4/HDIEpZ5LVvA5bPIAa9GOeuQT3eb8meWAT0mA3YslYOl7+Cxu3tLFw1aMUuMwMTjmDzqEkf5jlZWDqmYjOVPyuUvoSIkVuM/1RFaH9AF6riAaokoXClrUM2HPoO+YGhqQUEERm5Dsp8O9BnLwIabCLHdeXMWDLojtpJufAMpSCcThyM0kfU05KSSEpyiALghs4wtGP/dRr2dgxhNxbh759Of2rvqPrjRdR173Prqt/jS3Tw72PLEFIRg2Np5IqLNMWEl/xBplb30Y3GcKIyZ0r6HpzKUPrviQ73sWWGXONEldfo9GurxklbiU1H+3716C7gWTdLoZWfIB3y1v4Xn2S4DcforUdBFFk2zW/I1G/lxNe24v10BpiOVXk3XgXh4Ia6om38PD6VmSHkZ3MtApor9+DK9qLU4vQpdqpSLOgunKY826/8VmMD6F4i5mcm4JmstI9lCRZNoMUs4iMRqHLxKBupj2UJDXaQySp0hdVkLOLQZTp2lKLlojhe/xt5o/M4pojyphVuxUp0Ia04Aqq6EK3OOiVvSSGzWEAG6qOEZTYPWjhQfS0fKyH1iDMORtTw0bDNb1uK5rNjWZOwVSzGtWdjdKwB9EsY3JY8VYVE21pIbzqY4Iv38+6MRci+wxfvtS2rViOu5T+5e/R++VykkNh9OwK1K4GVE8+/MCdU9obiB/cjrD3W/S0fHSThaGEiqNr3+H9qctRArqG3N/KkCmVaFJjbUs/rZuW/fdvnL/gJ4UlS5b8H8cFF1zAs88+yyOPPMKjjz76d8/7swp8li5dysKFC/9GsOiMM85g0aJFVFVVsXjxYr766itqa2tZvnz5fziXruv/oefH7bffTjAYPDza2tr+y17HPwOf1PWj6oZIIRhlrZMq/qUE2PT1PuYfW8LRmSnkT8/jjHFZlDlM+OIqolnC5rGiaxqCKJCSk4J/fxOSAOZxRyIMdDP27ut4Yvb1tFb72PpeNa+f8Qiyw8qwu+/Fv/xD4gMhnCdegpyRh9R9CM3hRa3ZhPPES1BGHoWl+wDtciZDG74hvn8rckYeroMrGHn9uWR0bKN/137kH5RbxVjIkNC3uZEGOrlgfC53lw/yzhwRacQUxBHTQNfQ9q1G0cH5m38xyq1+eSXu8iKs+QVE3niQTc6JmIpHoPd3ET+wFS08iDLQj3/jJtSgH/uYKXS++w5KVzMDG9ay+77nEFNS2XrOVQaPZaAd/4j5yN5sfJoNgNXNQdRYguAzt6HqsPDAazy3uZX7bFuxDrSS9/EfWDEvibz9U9RAL1LYj3n8Udiat7CxuR+TKNB1YBfXvbQFcdqJqBXT8X37JULDdvR4lOTim3hodRMTXxsEyYy29Qse3v8Gzzx4MXOL07DKIoJkcEDk/mZDSyWnArFpF+bysdTedjNiPMS+qjMZzB2Pz56PZDWTXZSK/eqHOP2VHWzvDFHi2wFARttmQ1pAM07iLhNkiFH+OFDB/r44QwmNr+r72ZUxg4GYytgzHjLKTTEB11AHvk/fRfY1oLbW0GPLZ+DW8xlKaITGnWCQaY+9BMf0+ZjHH4VmdSPllKG31dDz+J2k280gSiQ6W3CWFZE2fhSmvDLs6XYSg2Hck6agNe6h95N3iW9fwY7LfsXN577M06c+yjtF01haPIf1t7yMPdODY+pRrA2nUr6oCmfHTrTeVqMzTlURK2cgqElMU49H6Wmlf+t2Yv4gphFTsGd6cE47AkGU0Ps6GH7adCwTjuKNc8axPW0K4aRGcsMnJFWdmr4ol0/JJ/XGx1AlC4Ku8da4K2gTvQyJdiyygBTp5/bv2/n06mnMP/U3vFMfRTq0HlOwA3HzR4zW2ggrOp8f8rOzN8ZBf4xgXOWZ9c2ormwW5IrwyHXo0RDa5k8pfu0TIvt2MDrTwfHD00m3m3G3bSWZN4bQ0t8DsCmaRppNwtK0GU+0G2fXHsNOIhmBKUsQ+tsNwc5wP1pmGeLoIwzvs2gQKdiJYHPQfu9NfHfuI9iPvwST3cahj3YC4Jh6FMlwjJFv3I7q76b/tceJFk+FmvU4L7qT7AuvJhmOGmTx0gmIkQDEwyTb6jAVj8A6/XikvGHobTVoNjduWUMb7CfRXIM02ENOsBZpsNsI0IDdPWFuuvo+4Be15v8T/mtKXT/RWtf/Bccccwy1tX9/JvBnE/i0tLSwYsUKLr300v/wupycHIqKiqirqwMMVngikfg3Ake9vb1kZWX9u/NYLBZcLtffjJ8TntObybbK2H5I9Qz9oMSbYZEAqN5rlAM7o0m6dnSzqz5AmlmiIZwgOZSk76AfTdVJhpPoqs6LT2+k0mlh8Kt30eMxxOwSLv/T6bRGFLweK7MWV9CzvYGuV54l7eQLKTlzCcqeVQhmK5onHwBT0QjUmk2YemtJZg0n3S4TP+U2LJUTaXr3M5LNB0lOOw1EiYzr7wFNIVG9wTiZW53GadTmxm4S0Qb7SWYNN8ooA52Gl9GMM7G1bielr5a0KYbOVN6McqNkpmlY0tOYuOsVtJFHomsq5jFzkLw5iCYZ76xZxPxBVF8HmXOnI7q8uCdMpOqi+cQPbGXc1Qsp1f2EPl2K47NHkEfPJkMNoNnczC1y07v4Vp4efTUJVSf1oTp+79iFnF1IdMU7WL1uevOnQdUR7M+dQ8RbTsdzT3LQM4F3lx0kV+2jd+lpbLrrKPSdXyO37iJ19EgE2URyYADr+jd5YKLMjou99NtzWV6whETBBMOAsncr0xIHEa97lCYpG61pH02OMnot2WjlU9FC/bj/9C6axcm6ln6crVvJiHaSdcHVfDc3QtrMa5hS5iXfbaU1cyKIMmrBWNa6p5AVbiY96SeQACER4aop+Yx2Kaxp7mdOUSoTk3X8ZVMLfa+fh81fT26yByU1H8/ldxIvnIgwbBo2WSD/iTepMBmlYvW7l7n22058ORO4v9aKqecQumxCG3Uk2o1/YqRvC5rViTm3iHhvHwlfL/HaXaSPKcVRWora04pgtaOrGi9fupR9Wzu5Yskwfr33bc7d9yklXhuZozOxXXg3TRkTmR3YRM4ppxE/uAPF14EeCZIcPsdwn9+/jsRGo3Mu0u3He/QCkCSsZ9xiZCrzh6NWTCd2xh0k9qxhKKkRPXEhyrO3sHHMBaRYRDIdBvm+ayiJL6IwpEmcUplJUtPpGlJINYsMSG4esG3h1V2dNJ1o4uxCneSoow3/qUnHc/9BM7IosKA8jb5IEkkQeHp9Mwe7BukMK6zs1sm+7RG0zDK2VJyM9PHDdK7fh+ebJzngizDd1k/HG6+hizLuqbPRLE6mZMpIsUGSrbWo7lz6v/wAveMQWmcDps5qcKZjGjUDof2AocdjzzLKum0HiWxbierrwJHjZcG291F3fYft9mcZfsoEEqEw8X2bjAzQ+Eq08CCpU6YRe+l3iHYnm45ciOrMoH1NNUrGDx5n3Q0km2vQ41G00ABaTzO6v8PwxNu/lrAmoQb9mPLLiWQMMwLf2l1okRCuWB8mUcTq/lv9sV/wL9D+S8jN/69fxT+GaDT6z21n/2fjlVdeITMzk0WLFv2H1/n9ftra2sjJyQEMUUWTyXS4Gwygq6uL6upqZsyY8d+65v/XmFORdjjjA9AdU/DFVeak2wEYaBkk12aivi9Ciiwy/oJJHFuUiiAJeEpTUaIK7iI3ZoeJs04eTjCpEekNoJVNRpfNWHLyMIsCdq+N9V/UEWwPGfYVrTUovg5M+WVoxeOQgp1G27nDS93S90jsWYOpdSdmLYG3exfIZgqOnkSkqwfT5g/QQgE0ixMEEVNBhWFoqWugJAin5GDV4qhlU9C+ewndYnCV/LIHkxpH8+ST3L+RZHcbfXsb8O1pMXR5rHb6q+vY99xyTD2HEPIrQZKQswsZautB9XWQcuEPdis/lEJC1XuI+vox5ZfTN/86tH2rsZ93O44ZCwi8/wJiWzViJIC5v4nUt+7izjn5XPTKdgZuq0D1dxEpncGOp76h7ajreW1XJ+2qg5GtK7GGffRc9Thlbomm9Z9Tq6YhhPqYdf9qo+ymqbyTvZjVril0barGXDHOaOl1ZRNVdBaVudnZE6E3FOfzedcDkJYM0BKMIQybSoH1h25FXaM6fQpRRWP9gIXFwzPB4UGMBIhv/YZVZ99B6+qnCRx/HKNC1eQ3rYK9K1BXvcGsDIFXO1PoM3nJDNYTc2Tg0GN0P3QL0aSKe/9X7DBV8PBEAV0y0/Hnx9Ebdhh+WA3biL9+LzFHBinxfnrDClsGbWgv3cFready07e/xywKXDOtgGc6XIaY4q6vKAgeREjLwZdSTKKzhXC3Hy2hsPHeT3CWFxPYU00iGGLFaXfiGVPJgvrtHH3ZVDzDc2h8/GGU6vXMeegsqn5/K6x5g9LBGsSMQsgqwTJyCqGDtehp+UjbPkUN+ok11Rr6UM5USm65A9GTRXLfeqSBdjRPPvVyHi9XB9jVNUT06CvIsMvIH32Je1gJk3IciBiHCkkQyDMn6YsoJDUdt0VC+kHH5+Yv60gLNbOmaDHXTc4h+4KraP/jHZh9dQi1G2kYErhtopOU/noUTac41cbGtgA7avt4/OTRvLajg3HZKcz+0y5o2cuUvBQOHvErpEff4unsM5iVriOG/bh/92ekvd/gHzGf+JcvIUYCaBs/MjKMoR488xYj5A1HdKaSzK5Eba2h6+VnUHpa0U1WHKJKsq+H0J4dWOadR6RmL66RIxCjQcwV4+i/+Vy0hELqhAkMtXWjxBIMNbXR8OoHmHKLqft0O12ffEz+tAIOxhwUHDkGuXUXYn8bqr+LUI1hZCpn5qErCQSHCz01Byl/GPprdyM6nAiFo7DEg6iuLMzlYxCLx6DZ3Ly6tZVY0PdLtuffwf9kk9L/G1588UXGjx//dz/uH7Ks+GdD0zRKSko466yzeOihhw7fPjQ0xO9//3tOOeUUcnJyaG5u5re//S2tra3U1NTgdBrEuKuuuoply5bx6quvkpaWxs0334zf72fHjh1IkvSfWsPPxbLiX+NKoZhiu4nmSBKzaJhOFttNDCoaHpNEVNWYmG6nPhin0C5TMjOfrh3dyDaZxFCSvCk5xAcTpJV72fD+fjb3RzlvZgHJmML0hy5HcLjwVy5g84gpOL127Ok27Ok2yk+eifWosxBiIeLbV2A64kx0STZKVpKZoY+f561xV3Du6CxSBpoO3x7fvgJdSSDaHJiKKiGjAM3qRtv6hZG1STMyR0OuAhzhHkR/C1paAb2WbHZ0hRiXnULeYD3J2h0IooiuaYRrD+I871YSy/5MqLWHjGOOg6xiEEQQRIR4mMAXb5EyYiSizYFodzKw4Xs8J5yHHug+HAQF1q7A7HLQumIn5WfMRzjmch7e0M519a+gJpOkHXcal223cNua+yn53X0kd31P0tdD6MRbSZeTbF+4mKpvvoV378dy1u0IapLVnQmKPVaeXNvEk2l72HL7c1ScOJ70eceiJ2JcP+s3PBE9iBQfYk9QYrzcS2LTF4TnX83ipzby9PkTqZs4k5PfuIFoUyPOEy5AS8ng206VhTaD56ELIu92Wji9IoWEZMGEhrlrP4NZVaTUreWBvhKef3kN658+i4N9ETw2ExOtQcTEEENp5djiAcSWvWilkxh87UF2L7yNMZl2zntrN7ceM4y4onGMVsM2xxjGZNqRtnxEz5gl5EZajGBV09io5jPVFUXqrWfg++XYL72P457bysoT7AxkjMTdd9BQzFYTxFa9h7l0FKqvAzmnmKHtG4n6g2hJBe/E0Tx+5tMU2mXycp3MeOgi5LwyBJMZ3ZNH5Os3sC26GLGvGaVwPNrqNzFNPAb10FbEEdPof/1J3FUjkSYuQNu3Gnn4JDr+/Di5p56KrqkMjphHiqga7/m2z2HyCQQ1E25JQRrsRknNR4yHGBCdeAcMyQZdEBlIQtreL/g2/Uim5Kag6pBiFtnYFuLI/nUMVS3k2k/28/r8VDZF05iapvJOQ5zTmt9DPupcBCVBrZLKXyvv5dYYUqAdX9pwRpzyIP6XTqXRXob04BUM3fIXXtjUgumMJTzw4fUk/H6csxeSbG9AGDcPfe8q5PRscKajurIQW/ei5wxD7GsGmwsls8J4fbqGvvkTxMmL0PesRBv0M9TQhCM3k77dtdR8tIdZ95xM07JNlJ1xLObxRzLw2eu4px9BT+kReL7/C5YJRxHb/CXSib/G1LKdHTfczegrFxPr6sE1ez64M9H7uwiu+w5bThbWcXPQJTNK497DEhI4vUYrff1WxOwSQ+PLmYU05EOzOqmV8ph+9kNEA90/u8Dnn2VZ8crafThTf5xlxSsP382wDOdPzrLi39MCDAaDbN++nYaGBtatW/d3Bz8/i4zPihUraG1t5eKLL/6b2yVJYt++fSxZsoRhw4ZxwQUXMGzYMDZt2nQ46AF44oknOPHEEzn99NOZOXMmdrudL7744j8d9Pxc8ZzezLgfxAoLbCbKHGYyLBJzi/5F3Xkorhg+P0cWsea7ZmzpdqypVlpDCaKBGM6cFHr39TDtpOFcfGwp2RPzKJxdAtNPJdlcQ9ruT1n4+k04suyEe8PUr29n26PLDbn9znosU45FjAUR6rYiRgxrCWtOFqeOzMSmJwh98QaJvLHGtZOPwZRfjmnKcSjDZyMk46ibP0OLhtGTSYR4GEFNYBV1QvYssKciDfnIjHez0BslL9SA1tOMIJuQswuRPBm4Z89HXfce4Q4fruIcEs016F0NCMm4QTIOB1BjCXY99DpyVgH9q77FNXmGYZzp60DXVPS8EXgXnYp01h2kluUg2F344xgeVCdegn7hfeixMPccU0HEH0aMBtHDg9iPOYusSCv3re9i3X0vE0lqSGfdQUNQBUFgZa0Pl1kioWhoQwNMuH4BadNn0P7eB0iFlSwpcqN+9DDikA+XVaLZlIucmY9nqI2rFg5nXUs/p9avpWvSWQROu4MvAqlIAx0c641yyFpKtyWHPms2Z2WH2dBjlFAEXaP5mScxf/Nn4ge303nMsTx0y3HYX/4t81P6mNC+Am3favTeVhwHv2fLgIX63BlIbXtIPe1K5njivH+glzfOGcfMLBO5d12Inkxwwxs7uf3rOoTxx7K7ewitZT8H5EIG0ioodFvYe/4F7EwZi+eYE+kaSvJ52ve86MvA1bgevb/L8H/qqidQ08Lgto2YJh1Lon4vm//4FakVhQx1+OjftZ8lJw2jKZzkQEuQGxb9kWvHXU7fso/Q6ncQ7vaz/6orUDqbkFt3IWcVgKbwlDCVQ7++nkBtm6EeHGhHHj4JpWEveeddgODNI1azE8e61wgt/T1SsANx1GykwS7SmjcgB1rx2fPpjemgawwmNLpd5SQEGTERxiwK+EcvptRjaD2d9MwmZFFgfLaDxtL5PLO5jXsXjmBDxMOULDMHIxbOzlcwT12IGA2iWV3kOWUUTedQXxht86f0e0eQ2befts/vYNbbfq56fw+rz3mIkhVPcOXHt/Nw+wrE+ZeinP5but55HV1JIsbD6IkY621jDcVy0Ynq70bd+S16PIaamktAERGUGHKwA5QEQu1mBIsVMSWV+MAQ1snz6d7VgivfyZ7nviVv1mjM5WPoffM57Pm5kF1K/N7LECw2hGQUU+EwTH31hDd9R+XZcxCsDhxn3mB8XpNxlJ42XJNnYpk0Hz0ZRzdZEEfNRtdUoykhOkivngIFoxj69j2S2ZVGhri/G8VTyPObWogG/v0mlF/w4zu61J+wcvNfNQH//6Ovr48FCxawf//+/7kZn58Cfo4ZHwDz+Iu5ePf3ZFgkfHGVsW4rFXlOtjQNoOo641KttEaSVA/GmZNuJyffyfa6AKquM3t8Nlnjcuiv7yPSF0WJKsy462SDH1M+Bn3YDPQdXyK5vcQO7uDmc1+mymWhzGOl9JgySi853+CptNUipmYaG63DRc/yL8k998IfuraCiJEBADrfetXgDhy72PDrikUQU1IxFQ5HszpRqtcjZxUg2hwGf+DgBoSScYYm0EA7fRmj8Q42orbXUvPES4x+4G663nqFrONPQCsZTzcuclvWk2isRnJ7EZ0elJ5W5KxClK4mwq3tOArzEVNSDSn9RMzQ94mFEZ0eBndtw3nJ72HLJ6j+bswzFhNf+xG+3XXkX38byp7VrLr6OY79fikbKGFPzyCLh2WQ+ulD2JZcaZiMxocAaE9a+NO6Zu6ZX8aBviij1z5N746DZE0ZScf8G9jQOsC5ZWaCr/6RlKseJBRXSe/ZjebMRN2/nm9yF/Lc2kY+PaOU4beuZ+UDx5JnA9nXgO7v4HV9NGeOykTevdwgoM68kJz+/aw+6VqGnzKezKtuJ2zPxKbFEONDqLu+Y03RYo7IkQ3TV5sbQU2yb1CmKk1i1qOb2HDNSG5a088Nc0p4e3cnd+R1Mbjma54svZBr9v2FYEMHBUuOQZhxKogSbVGRg30RjslIsrxbYrG6j9X28cyseYf850J0vXMF+rYvEN1e9IppaFu/YM8jb2J2mEgtzyKtsog1v/uUjqhChkXi6N8vxn/qHRSa4+y/4Gye/8wgNR6XnUIwqWIWBWZfPxd3WR6CKKFEojgmz0UN+JAKK9GsLvT6bYZ4YvlEhJghWJlsM/iA7Z98gSXVSda1dxJ3ZGAZ7ETsb0MpHI8Y7kcK9aB4i2lJOnBbRNyyRl9CJK5qaDp0hRLc9v4eVs0NGAHWlMU8vivILeUxDpmKGDG4Dy0c4qk51/OrTX85rFOlunNpFLw4ZJFAXOXDvV0s+OMVTH7iDlbbxzM3NYoU7KT2oYfwjiohfsF9ZEoxNh69mOErVpDVX4NustDvLsPbdwAt0IM6bCboGlLTdlR/N5I3G61wDFKwG0FNGBnWbd8a9hEdDYguL3oihm/tRrzjK6l+8UvG3nwegs3BQ/N+y7lXTiFv8QLQVEIHqulYf4DBtkFmvvUn0BR60kcTufsSlDteoHDjUixV01G8xUgd+w9nasPuQlL6642GhUgA1eE1Os3cufQkJPIG61HSS0AQkQbaWR1J51d/2Uzdqk9+dtke+OdlfF5YvQ9nquf//oD/AK8/8ntGZP70Mj7/Xfgl8PlP4uca+ADcai7FLAr44iqVTgspssDoKbm8/E0juVaZOaMy2HbQz6CiouqGwWntUJwzp+bhrUije3cPntJUAo0DTL75OFb85gNm3jyPtPmLEB0uku0NBLdvpW3NAfoO+fmuaYB7374Sy6gpACSba5DHHQWagqAkiWeNQBBA0FR0UUJc/w7SiCkoBzYTmnYW7v1foQV6MY2cTnTD51iGjUPIrYC+NkSHy7CcECU0bxFiLIjavB8pIw/BbCNevRFTXhlKTyumyqnoA72IrjQ6Xnke7/hKTIXD6PjwI7JmTzHKIDWbGNi+ja4ttQy/YBFJfx+JUBjPvMXo8Rja0ABSYSXJQ9vRY2FMeWWsOvcujnz3Qfq/W07akcdQ+6fnqHjkKQQ1QfDjl7BcfC+jrv2EusssSNkl6IN9RPdsJD4QIu34s9AjQXq/+BjPDY/y4NoW7hylMvTlW3D273CEe4iveIPgcb/G/u59pEyYhmC1U33vY4x88RU+btM5cVga5s59bJbKGJ/tQFTiPLurj2vl3QiiBMVjEf0ttGRORNOhSOtD6K5DdKaxgRJynGZGHvMrdo/2Ufrcu5j8jSieQoRkFNnfjB4Lk2ytpXfaeaiazlBSo8Bpwq5GGBLtuPsOAlDvKKcidJBGVyWtwRhvbW/nseOHY9MTPLHdx7ljc8iQE0iDXYjxMImcUTy8oZ1LtzzJjOYjqL93HFrTHpTuVkz5hm2BYLUj2hyoQT9CThmaMwuSMajbgmh1oMXC1D7zMv31ARyZDl5cVnf4//yptQ9x4MlX8LzwEXmxNqIr38NSNhKhfDKIEtJgN2vP+BWzP3mBm0tO4MHQAUwt24nuWEXHmt2IkkgyHKfs/JONbsFE1MhEqUl6MsdhlgRSSBATLdiHuvFbMxGBVC1Et56CVTIsc9Lat7LGPIrZ7ii9sheHSaB1MMmqJj9pNjM5553ErMcuQzBb+TJzPscV2djqU+kNx5mU6yJX7TM0ddqq2eqeyBRzH7oo02XOInv/MvRxCxD2fsvjifHcktXBeksVMxxBdKuTxpsvp/y+R1D2rUXyZJBsrTW0nIJ+Bmoa8M6YTqypFslqxnrEaejth1ADvaiBXoINBqE51NpDuMuPKImU/upXdL/3JtkXX3u4E1Jye9HCIeT8MqTccjpffArv6HLUeJxATQvpEyoZONRE1rlXQGQANa8KIRpEdecixkNIwS6EZPQHU+JagnMvwRv3oVWvRc4uBJuLZHYly+sDnHW+wbv7JfD5t/hr4PPc93t/dODzxqO/pzLL9b8m8PlZlLp+wY/DwjFZmASBY7McBJIqbVGF3r0+js9xYpNEYoEY8+YVIwkCJx9dzJRJ2Swu8RDujdC5oxuLy8LOta3INplATQuZ5WmkTp+JnoihBnrpqDoBZ3kxNo+V5FCSIUWjv7qO+P6t1D/5lHGi7zhkGBj6O5Cj/QiaavBcmoPEpp+J740/I044FuHVO1HaGzCXj0Gp34Vt4pEoHQ2GEmxGEQCC2UqytdYIehxekE3orkwATBOOJrJ3C6biStTUXLSCKhBEMmZNxTTndMSMQgovvwaUBFrjLpSeVjRVI3N8KX1b92ApKME9fS7J1lqSHQ0gm1BbazANn4Rp8kKE9HyOePVOlK5mUoryEN1ebJmpxolczMF57i3Y23dyYH4j6vhFDCx/h2SLESg4L76L90M5REtnkHH+tYjbP+NXMwpR63dz2xVvk3JoFWKgHcnh5L60UdjKKvjg+N+ilk8n5bkP+bQdTlV284fVzUx9c4BpSh2W1h0IsRC/Ko4QqlqIFgrQIqaj5o6kQA+Q5ZCJpGRjv/BdvowXMKXxcwrs8Gr7Zob/5gZMrTtRnVm033EFsr8ZNSUDLRyCI84n7avHkUSBys71LHl+K50P/JoUdYhg+ghWKQV0DMbRrE6KGley4LRbuX/hMMpOf4KOu65mf0eQTAbpTprZcuaVbJXLETSFE0ZmsfOU37P8voX0OfIJ7dpGoLqOZGsticZqEvV7AZC8ucTSSulWrYZBaPlkEEWE0glU3nkbb2/txFOewVWnVXL+nEIumleCYLbiLskkeuu5xFZ/gCk9C2HYNMTYIELrPhJ1uwm0D7J0zGk88OnNmNt2Evj2Uxo+20TRyQsoPGURwx56lMDk02kwFxDPrUJJL0VJLyGp6QwlNIYwY9XiDNiysMkiqckA/aITRdPRMIT2Xhgs5oBviO/7raSbNSRRoDUY48JxOUzIcWH54mued85DmHgcx2fGaY4a2/CozBTyoi0k17zPe80abymV9EcVVoY8aM5M8jq3IBWMwNRzEDmnlF+n1KAGepneuQJBUxCbdlJxx118Mf5EtFDA8Lpzeog21tGybC3Zp5+LMGEB/upGRNmE3n6I6IGdmEtHYR0/FyUcI9IbQBBFCo+bTdfOThK1u8i65Hp0yYzvy8+wzD7J4FVZrCQa96NZUkgbVYKuqdjnnkT6OMO01ztrFqFv30dPzUFd9QbdzzxAw0ASIRFBNxnSFEJaDubSUaTt/QJdlJHcXpIlU9EcacgH1/DFPqPE9XMMen7BTxu/ZHz+k/g5Z3wAHncOY0yhm69r/QwpGqeOzECJKagJlX39UdwmiZ64QpnDROWUXLp39xJTNdKyHHhHeIn0RYgPJrC4zOiqTta4XPIWzTecyaefTOS9x7HmF1D3xhf46wJsONDHrz+9DXPpKMNYNDqIHh40CI3AI5Mv45aDH6P3tqKMPAq5dh2CK536B/9A8WMvo294H3PFODo9I8npNSwkRIfLUHwN9aIG/YhODzhS0UUZIRlHs6ei7l5JcPduUidNBkCqnI7WuMtwjE/EWH7KPSzZ+Br777gL76giMo5dSOubb2NJdSJZzWSdcjaKvxvRaqxTGxpA8mQYdh3DJxmKtEedS/Lrl7BNnkfLc89SdPmVdKaPJcOsIsRC6JYUxOgAiisbeaCDLnMWZknAJouUnfknOl+/kA+bFRYP99Jy2amMePhREhu/QDz+Oky9tbQ8+UdqPqzm2E8fJNnVTPe08wklNEaYQ/SbPCQfuZ74r56ksP5b2t79gNz5s2n+6BvKHnuOTQEzU/JSWN0cZO6+13gi7URy3FZO2fMC/TUtZM+aQN2Ui0mzSrSHEmxsDVDudTAlz4mi6qRbBUMPyN+M0tWI6u/Gffdu6lc+xe7uIT7e3cnL0zR00VDVbZczOf+V7XxbsZtIeyeNJ/yWfJcZVdP5g7eKcxp2MN3cTbOlkEhSI9Mh4zJLmELdJNd9aJSdHE4Eq4NEeyNyahpoKpYxs42gVhAQ2vajayqi04MW9KMPn4HyzVIEhwtz8QiSpdOQ9q8EQOlowFI1HT0WJn7IMOw1T11oEOhjYQZWf42WVEhbeDLRHauIdPnxzD6ShhdfpfzeP4KmMOguIaro5Ph2k+xsRhg3H5/uIF1Ogq4R0C14E3502Uy/6CTFJOKLKCQ0nWhS48HvajlzUgHHlnloGkgQjCeZ1LESZcIJyIkhtC2fEZtxFkkNHCYRVdexJEJs8ou8sqWV3218CPO9L5PXu4ueD9/G5LDinjaLZHMNpoWXHw5Q9dotdFQuoiDWiqAqCMkoiDKJg9sQPZmIxWP4ZsopZI3OIHtyKWannfjAELbMVNq+30fV88+TXP0uprmno5ttiG3VxA9sNTSpGjrIOelko8vwiPM58+193P7e7ZQtnoDJYcOx4ByinmJsdeuMDSa9gOS2r7GMnEIifxymxs3o8Rh6wSh8L/wRLamQc9E1aFYnYiyE5mtFDfgwl44ikjsWcyLEgOAgLdQMgkiTKZ+5171JX+22n23g88/K+Dy78sdnfN567PeM/CXj8wv+p2F8uYdIf/SwlcWuliDjr5jFgUCMNLMR9JgEgaZwkpe+aqAvbrS+a6qO/6Afk1Vmw4E+XlvVYnR9DUZo++QrzKWjEJt3Yj/tej6+4GnqV7Vg81i54JYjEGQTSlohutlOongyWsl4urInoRSO56av7wMlCaKEGPajxyIIySgVv/kNpq79aOFB1P5uondcaPh3mcxoNjd01pLsbEbyZKJmD0cz2aG7ESWtEDEaJNbcgHvcONBU5Mw81JpNiHajPBbYsI5FH9xJIrsSz7A8Qq09HHjkL6SNKKZtXS1Zp5yN6imAkXPRlQR63gi0UADcmYhOD2p7LdL8ixl84xG6N1fj/+I98pYsQg34yI60oEtm1g9YGNTNNN99M6ISp0XKJCfp44Wt7TgGmlntWofUU8eZjha+bQgw4o8PIygJTMUjkEI9AKTe+xLzXrsZLRZGPPJ8ACYtuhE1JR3L2/eRfe1v2dczxO57nqXkmusYqqtny+d1pBz9O0ySgBzsYr57ENMRZ3JrejMXutroXvwbsu57kYVNE/iipoc0m8zk6H6ulXczd9MzZAzUkylGMDVvY/C1B0mmlyLaXZhHzyL8zV1k13zJxBwnm7e0YT3lzwQ+f4MaLR1Vh7cumczSrBNpW7WXYV4raVKSYFzj5XFHMc0ZRnXnHf4/lASBk14xhBK1WART4TCjbFJShTm3CC08iJiSipKaC6KEbrajVkxHHXU0ak4l2qgjQVNIDoURrXa6P3ofVr+O0tFAYM1K5JmnoPS0ovR2UPv2d8iZ+QgD3egWB/Gymfh21+EcVYXq60A0W7Flemh48VVKH3oGBBHN6iZl9+dkxTqJ7duENHyKUf7TDWJzn2rBbZEYtKazJ2TBJosE4xo2k8jW9kGK3WbeGtvPMK+DmGKYlOakmOmtPI5wUkMM++mZeAav7emmayiJqusEYyr1MSsem4nnl1TQd8tzpFklXhksJHDJH3GNHoNotiIvuhrZ30xo9efokgmluxWHSeSLgTRqfvtbwwJGlNnzxPu0vP4OO86/nCNrt1J1/Rk0fr2fu275jNwrbiDS1U/+3FHotVsId/YS/nwpkQ+fYWDNNwzUtjHU4SPnmtvYcZuhiCsHWnmy7i+MvnwhacefhX3MFBIbv8DRvhNf0UyE1CxCn7+GnFOC6s4l+tp9qKEB1l50DzTtxjOmEkEUUdsOojftpuuVZxEzCkl2t6F4i7EMdtKhGGKgwqAPgMZAlL7abf+MrfFnD/1/sFfXfxd+CXz+l+DIXVs4FEpQbDeRZpZoiybZ+ee1zJqSg9sk0h1TaIsabe9LitxGp1eOg/WNASP4qQ8wzmvjonklxAIxIn1RHHkZJNsbiB/YSt+z9zDmmBJGLCzjlRVNmG96EtXfjb53FULAUKiVgt1k7P4YU28dclYB/rTh6LnDEdQkottLsrOZ7ndfQ/F1UDvtUsOHS9UY+H65YRYpmdEiIYMHoKnIvXVIkQBCej6mvkaUpmo4+3dGN9YPQVXrB5+j9LTiW7GSZDhGrP4ATdechSPbS9Gpi8ibNZLGL3eQP6OcRP1eEiteB01BDfgYtGUeJqBKngy00ACyvxl7Xg7WNDcx/yAdny03TsW+NorPfYHKdDvmDx6g8I8v0JeUybDLiIF2Lvz+IfpTCmnf3Ikej6InYhzf8w1fh9JB15G8uShbl5Os2YJl2ePIuSVoQT/qsmfII0jwvom8sKOTo9rn8nyTyPw9L5I9vgjd5sJz+uUcf/dxRN84h/d2dSL6Ghly5vFum8guz2T2OkYx+tgbENH581njGZnlxFT9LddUnkf0wC4sBSUk9qxB+e5VtPRi0hacjLb2HbRYGKVhD4orm/j4xXi+fYr3bz2C8Asn4pm3mBH2OI9mjmZFYz9XlEtU3fVr4n+5jZhoIZRQOPuWaxnzwG5MPQfpDSewm0RSzCJfHudA2/uDE3oiZgQ/fe2GAe7EI5HLxxtBrCMDxeKiLW6idkBBl0wkRDMc2oS1qIzWj5bjyPESbWkh4fdj8ThRty1n7/3Pow0NMPKGi5BzSwEQElEsLdsou+xCog11IJuI9vgwnXYrw267DUFT8NlyQTYjVExGUGJYJs5DdWYS/PZjMiw6SdmGKEAwrpJQdYpTLfgiCg6TwEBM5YwyK83BBB+KoylP0YgqOnlOEyZRIBBXcRFDdWXj/fpxFn/4O4Z5zFgSIfpjKuGESvNAFEGJM0nqQlrxIqeNymS41oH4Q+u3GA0C0HX8b9AcXpq/WEfi8RtwnnECpScfifL9m7Q+8xiTnr6XQFM/439/FXz5DEO1hxh13kxuvGwC8c1fkXX7Y6RUlKNFwzgK81ATSVKOOwfnmAkMNneRcdlv8L/xFKXHjUOwOlD2rUO+9VmGWjoMEdHQADWvf8fQhm8IXH8mkTWf4D7mZERnKlrNRlpOuA2GT6f46HIGt20gsLeGWH+QWP0BlPYGMmbPoPOlpzHnFhkE7EiAwqEG3EIc3ZOH6sqhrj8M/FLm+s9A1fUfPf6XxT2/BD7/mzCpwkOKLDIsxcywFDN9QwlsHis5JamHFZ3DqkZtf4ySfBdqQmNmsRt7uo2qcybjKU1FMonkTDAsQ/z7W6h98V1Ep4emr6vZ9U0jkb4oxXYTd6WOwlw6CqWriWR7A+Ko2UaXhycT1ZmJ4uvA8uGDiIF29MadaJ58pIoJpF99J2ga8m/OQQ30UnLBmTgnTmOoZAZ6026U7laSjdUGvyAaRvV3orbWkGzch6mgAtvWDzEfd7nhJB0NU3bjjaCpJCMx0qoqSIajWFJTiPQGCO2vJhmOMeLsuaQOK6Dr+40kBoaQ+1vpnHA6np69yLkl6H0dRHesQs7Io/7BPyAfeTZml534QIjc4xdgHjMH0ZnKGu8aMoP1WPIK+eMWH0NJlUXPbEbJG032bx7kqo+qmfPwOTww7WoApIw8pix/gL73l6K4sonMvQhh5ukIJ95slDWKKxEsVtpxI5itXJfVy7ZrSvnNTU/QctQN+KrbUVtrUJ1ZJAYjPO4v4pGjcngvOZxgXGNstpP+aJIx8XpiH1zOymFTGRE+yJwiN8nmgzyz8znsVZPo+na1kZ0bGkKMDBh/16Cf9Vf8EcmbQ9dQEnvvIcxzTmVU/3YGhs9j+6/uYf+F5/Knwd2UeezcsTlEc+50tMseoGjJ/fSGExw5PIOPbj2C/Y5KnBaZquNuYXtnmH2mYgKTT8c6cgpiyWj0RAwt6Ce2bxOxgonosqGGrOnQEzbMOivFPsRwP5ZYACmnjFhLAyW/u4+UiTMxueykHHUy9jFT0KJhcmcYPBM5pxjFk0+ysxmlsxHV34WUkY81Lxc9PIjF40RQExxMGWnoCAmwZ9CE35ZNyFNGf9owvmuLYSspJYZMJGlkS9MjnaRaJcySQJZDxqbF8Lx2ByRjjNj/EceUeqgegPequ/mmIUDG7o/xWCSDa5QI033MjeQcPZOwoqObbFSkWdjYFmBijhN92xfE0kqxjJrGUELjnrLjUToa0F0ZiJEAyab9FLtMiLu/pGDeRKxeF6XHVGAZNRXR4aLwmhsJfPspZccbytmCJGF2OkiEIuQfOYGWL9fT8+Cv0UIB9EQMPRHDlpOFenArAO6yPNQ935NSkIVr7HjkzDzC9XW4lz2Ka+QIWh++m1D1HtKGZyPbbaSPKcU2ehrJjHIE2YQ69RRGmQbQd36DI8eLo7SUri31dO/owFY1CTUaASDv/IsNwnlnDYnanajubKSwH83qpDMusXTZwX/m1vizhqbraNqPG//bGC+/BD7/i3Dkri3YJIHN/VEkQcBtkoj0RfGUppJuljlnWh6+uEpNKE5KbgqSWcSaapiV7n1lM+HeMG07u4n4jS+Xzh3dDLaF2PfMR6SPzKB8TCYffdtIcyTJeTMLIKuYqO8HqxDd+NIQnakIiTDahMUkw1GwOpGzilCdmQhKAjnQTnDsCQx7ZimPnfwISmczktuLZd0bCLIJ88wlSN4c5KxCtEgIMT0fyZNhmInqGtqgHw6sZWDnThL1e4kf3EGyr4fso+ew7o536dvbAIAjx0syHCPS7cdyxBn0bD2AEo4CoHQ3Uzx4CDQVLaOU4IaVyDklxA5sJXvqSHoevxPX2PHkHTMLbWiA/k9eI1G/l7J7/oiSVsS3hSdw7vhcKgYP8Nalk2mKiLQkHdQe7OOjK5byq2fOJNneQP93y/GcfyPpp1+C2HEAx7rXkIZ8xgksEUPNHoZlzGyKgzVIY48ikDWGB/ZD7vgjyHPKBB5/GzmnFLm/lfTr7mPR8Ez2BCWOKvWQF2ujL5JgUm4KAxkjaU4dxcJVL6CkFWJd9jhtc68EUWTzDY+QfcQ0kn09OKbPJ3lwK3osgm38HNzLv0UrqKIw0UkyswLUBEp3K15fNZNeepIxD/wWqXYDMwe38+Rdj1HUvAqPpFD38W85ttDG5Dw35bYEIwO7MEsCQ59cx52fVZPlMOERDBNhQVXQ41GkkTOIH38TbaEEircUzeElpb+eXHMSWyKIFAkgDfkQknHD7sAkQ18batBQd258+AEOPfk8yuAgJocV84hJ6LEwcn8remQQLTyI5PbS563EMmoaic4WZE8GCCLuH4J+h0lk1KbnSDWLdIQUTCLkuyzIVTOxH/iOrZXTyAw1ossWOkJJRAEsiRBS2I8tw4M80I44eRG/dY1knNLI9eUa0wtc7ChdxH5fhIXvtxO3enh7dyem8UeR2l9Haxj8EYWWvghpa15Am3UW5h2fsUEoI7N+JXcEqom0d5LY8hWqMxNxzJEIagKhdAIrJlxBPDDExYVXkGyu4YaFDxDb/CX+6iZ0TaNHSkN0ebEuuRLvrNnIi66m+JRjyT3zLAabu5Az8rBOO47erftQg35qn3+LPS9tRDBbefKS15CGTybZWovV68ZSMZruNZvJnDQC2WohPhACUcJ7/KnoORXIgTY0eyqW3kOg62y98yXc1z5E35adKDGFrHE5CLkVWMpGYioZhRrwoU8/jUTzQQSzFbm/Fc1kQ7Wlsryuj/1ffvBLtucX/Lfhl8Dnfxk6ogq3/no2e4IxNvgj9BzyEw/GSWg6FreFq06rZHG+i5e/acTXF6WzIUB8MM5nLUEcmQ62BWJoCZXG5XvJm5xDwexybB4rA00B3PkuKp1mANbt6qb+93fQsf4AvrUbkQY6jC+hRMxQTdY1nCOrCK/6GN1kQQ52ongKUB1ebLJAfdzO7XvfwDp2Jlp6MfLYI0lWHQOa8kOgk25kjzrrUYN+9Ipp6LIVU1Elbe9/jD3ba/BAXF5M6YYn26izJhL1h0mGY7St2s23T6zGnu3lo7FLcORlYHI5MLnsdC/7ytBC8RQgxEO4px+Bf/16Yv4gyXCUjBmTEEfOxL9jH5InA+sVD2AeNgFdthDUTBwX302u3VCGNksC5d2bKbQmOfTdRxz3++OxjRyPFvTjHDEMWvaidjUgpGahhQZ4olbAJIB49EWILXsJr19GMrsSvXEn9lUv8X11N26vnducI5mVn0Kidie6ycKrxbOIJjW2dAzgEZMMuoqYmJNCYyCOY8cn5G5/h8iWbxGUBJZh4+mYN49ky0EyR2ex65H3MKVnkajdhTDrDFR/F1pGKeOj+zkQT6HZlEv7by6i4d470ZUkodWfsyqRy+Car9l49f3ED24nsuwWrptyPdr3r+G79gw6YiIVYj+b+uDNaBkl5hjCQDfvXzwJb83XAKjl09AlGTm7EF22kNq1i1Ldj7lzH+KQwfWQQj1ETC6UtEI0mxvR10iyowE1lsD35WegqSRCYVKHFVBw1Hhsx19K2onnoTvTUXMrDTJ8aibmYRNQ+rrxHPyORMM+/Pub0EID6IJIdtc21k2dh2XHZ7xdcjZJHYaJfgRBYLjXiqAqSG4vEw9shh8CihL/bsxqnPA7j1MvZuGYPJfothVEPniK0+u2ofd30SBlkbrpLSamyxzZ+z3vXTCB5mCC26Z4Ud25JLKGU4zxPA/OK0LOK2NXdxgpI4/p1j4oHofsbyT1qEWIC69CWfkGyVXvIPsaELrrcFtlMm64n+9mBdGVJPc+sgQlEiOtsgjP4nPwfPMk0oipRD58hi9S51B9zhnIWQXoiRhpkyeh9LQS3biM7KPnEDvmajLHlzHjzsWINgfn/WoWWuMekM1YZyym89PPyZw0ivqPNyCaZMxOw/YmUbsLvWk3rU8/ijDoQ03NI/jeM4y7aj6m5m1oCYUx15xA9vQqdNmKUDEF3WxDHzYNMRYk3NiIWGUoOgtqku2dYR77gQP2C/5z+K8QMNT+X7+IfzJ+CXz+l+GuaD31yw9Q5bIw1m3lm54w1Vs6GZ3loHVXD/6DfkacPJKzp+TicZgomZBNapGbFFlk1bYuhqWY+fjrBloO+VGTGvGBEB1bu7B6rHTu6KZ2KAFAcyRJwzeNWFNtZPz2KRR/N0IibEjUu7KRB9qR88txHHEigEFCrlmLZje6E0pSzajuXPo+fx+9YQda/Q7MB1cDoPg6SDbuQ2mvR5DNyFmFCMkYSlohq1Mmkj2tCuuxF2DOykEb6CXe1YFgtuIZUcSIK05HMsuoCZW8TDuRbj+qDpmLTyZzwghCrT3k/Op3ho5MPIRSvQHBYsUzphLXub+ma1MNptGzEHqMzFFv5XHYe2rQE1HEWJC0xnUINgd1QYVkdiU2WeD8PR6ab76U3quycU2eiWi24t9XjyCKBNasRB91JGprDZZRU7iq/W1Y8wZiqBc9dzjaKbciKHFEu5N4dze3H1fJkjklPBquQfv8SRrGnokumbm2eAblaRbS7WbE2CCOLe9hSwSZoBveX5Ink7RHm9kbc6FUHsFVc39L9bjzKL/pRsbfeBKi24s8/UT0jR/SN+08459FMjEmVkvBng8x3/syBUdPRgsP4lhwDrlOC2+Nu4I7Tr2fP+eeSeuj91H90CsI8y7hovLrMYsCrzULzLT0sHDVo4SkFAaKpmM3iURGL0SXLUjBDrSmfSi9HYjxEPGDOwyPKZsb9eBW9N5WBE0hJdSBkIwZjuK9Hfh37KN9zR7Sps9AGjEFR2kpaadeTMrMY411i5LBh9E0dEFEG/STqN+DnJ5tZAb/CtmEHOwkUjyVOeu/QJu4mNI04wu9y5SB+NEfMXcdIOStQPUUYHv/fnSzHeHpX6O7MtFMVixpbtqPOppE/V4sIybhGD8NqyzyFmMoOfQl2tzz0WQLjJmHc/fnNAWiSAMd9CVlBF2nRvGQGe+mLQL7c+cwKVVFMNv4ot9l8Gla9uP/8hPEXcuJ9weRMvL4/9h77zC36nPf97OK2mgkjUbTe/e4jXvHFZtieu+9hBBIKAFCCAk1lARCCxB67x2MMWCMe7fHY4/Hnt67RiNpVFe7f6wczs09+567z90pe2/8fZ7fYz+jJa21NJq1Xr3vt/g/fJWRb1axyDGM+u0rNBYsIbzwUjJOvwjn9KNwZHkZXfU2ydEx+v/yR2w5ORTeciE1v70epf0QnW+8hR4Nm3lYsgWpZgnu5g3Y0lxEBwNE9u3AmZ/Btl8+gR70U3vdL8meN5WG174hGVGw5hcjWi1svOMdtPAoancLucsXgijRdsvVyHYbcnombZkzcJfmEu3uxVo2EUFXzdw9JYEYDyPGw3jOvhYMAyWrklBKNmsOD9Kz88sj3Z7/A/w9srp+bCSfI4XPjxCx0QQHQgn64yolKRa2jcRIRhWCisbhzhBdmztI8TkYCCexuW1s+ryJPLvMjNxUcn0OpnjsTD2+nKH6YQb3D1C0oABlLIlkFbn8OjP4dXqancUPnsnzL9cihQcJbtuE5i1E8ZUhxMNmrIM7CzWzHGO4B2n8PIbHrUBIRnEMNdIZStInpWO95gFiNSuRisajBf0kN38GgGBzIFdOR/JmgihD6x7kQBdLlXrs80/CaN6JrWoqtgmzsZdWYckrwT7lKATZQtEF5+Aq8DLrRvNGOeO0alr//AyBQ+2k5mei7jDdjsVkDEv1bJCt2KpnIHYdwOFLNaM0AkPkXXApWQ1fog92UuucTIejBLJLUVoPoOkGQVWkd0zlxiUVlN3/GKknXMSTycloVQvwTRmHoeukzVuA/t2rCPYUlN52HNMWYS2bCJIFRInUrl3oFgefS5NRIjHe2NnJbdJ2bOF+ejfuZXx4P9fnHk3ssxu57uN6puW66MCLseBcFv35ALXX/ZLO9z/j8BPPc3ntd9TYQ0j1azluWTln37cWw52FkYgTbzyAONCEHhikcSRmFiApXtT+dkSXl4LAQW469y9snHQx2uGdlGx8lu0tft6/ejaXTM1F//Vf+MmKSgxRYvNJGgt/9RWnbnuCsbRSQpfcZ6bWj7YQUw1s371ARBPQO+qRc0uQc4oQkjGs05Zi2F2o3iLTVXuoB62zAaOzHt3uJtlaj6WoCkEUqbr2EgAMSwqCKIKmgc2JGBlB621GyRkPoojmzkFcdgnSlGVo+RMxUs1MI8kiYxs3DaO7AdvuT3mtKQGb3mGJ2IEtESTLITG88mbUrEr29EcYtWXgPO0aRlLyyLjpIaJr3mQ0rmEpm8isHRuQFp1DsnEvdw9XUpnuwJdipblyJR82DCMmo7zbEkfyZjIuIwXVV4LbJiLGAkygn7g7j+GoQoXXRsLqQg+PcGy511STxSKkXXoL3eOOx1kznXhbI67rHsIzczaG1YllwWlUNn/J56Uz0Jw+AuWLiPQMkRgdQ7JbyVgwl2hnFzOefxzJ46N3/S6KLjyf7tUbMJSk6bHVtJOxnRuw5eXT8PZOtKSC7LBx1Ot/wLLoLCbdfCn7n/2ccecuZOIlyxjasAVbWiqLHr2C2GAAJRRCWHA2vW+8QuUvb8ZeUAhAoV0j0ucnGY4iZxeT3PKpaVGgJtCbTf8pcbgd3ZnOQMygK5Tkg68a/8lXw//60P8O5Gb9R1b5HCl8foQ4M2c2d9xxNKmyiEMSKXRYGEqoJP/a79zXOMK+bT3k+hxEBiLMWlzErEmZ2NxWvGVpZJel0bOzl5QMB/X1Q9SubsHmsWH32smeM5kzqn3sGY3z64tfZpLbxvplZxNq70PoPYSCCLKVeF6N2fkZbkMd6kG3OfHaREQlBqKI2yqxusmPLApY171EfMvnSK401Kjp+Cp5MzFGTfm32tOMoWsQHsZQkkQzqxCspsGa0tmI6EpD9febXjw5RYBJ4tx87xcokTjJcJyuzZ3kXPJT4v4g0c4uBIsFIxqE6Cjbrvw1eiSM6HCSt3Q2RnwM1KR53JEwRtVcxm95hqwUmUBqIaEFFzPRMkoaMdIdEoUeK0KvSdZMHncc8nArgs1OoPYAetCPYLUTO7DrB8dkdaAL49AWrs9aRJN3CsKW91hZkkLqTx/g5fEDPCHNR6/fSLh7lIN3/57HVt3G61PP5rFTJlA51ohy6wV81hRg5f3XIEoCn5//EFXXXMRFCwpZP+rgl92l3LGsjPoVrey+5BrUqMlt8n/1GbbZx6KsPBYhMYYhiGb3JLuExtRq/tz+KY+va2bu2kxsc4/nqdMm4pV1BqIqxQQ4LzuMFA+hVS1AkkXCnYMcHI5RFj5MukVnyF2GRx/jp2NH4R06iJxdRCC7Bi1/EgC61cyVExNhAAJ798H4oxCzikyV1YTZGJKV7FPPQPLlIjpdiPEQ0vRjIDpqytGHOpFcaUhNW0GyIiox01vJ7kKMBVHrNjCyYxeiRUbPrkTMKUWfeQoXVzlQjjqfptQq2pQUxNovKRrcjRTqZ6F/MyNxFW3vN/hGmxHqvkY981c43rsfyZvFlq4QQjKG6PFxT5V57MelhagUR5iQmYplsJFT9j2HnlGC40+/QB5qIfrUrRxOODlIDl+3BJiWYWEwquLs3YdeMh1hzTMQCwEgtO3B8849ROr2EPcHGYqqjM04DeX7d3i1Uya8bzdz63cgKFFSv/sL8SsewDvVfE+TPR1Il97N4Tt+Tf97b1B4+onoQT/F9z+BHg2jh0dpe/09kqEIRixC8dIyUooKUWMJku2HUDa8T9/qr8mclE+orQ/J4yP7hJVo8ST+nbVE+keQ7FbE2ChjPUNEtn3DyJ46BKudrUuPJ9w1gJ5UUftasRRVgSCi/jVCQ2nYjlYw2SRt6wZv7emhdcOnR7o9/81w//33M3/+fFJSUkhLS/tXHw5wpPD5USK59yU6v28kxy4DsPKYUnYG4pT6HIwkTXJzZWU6aX8NM02rMDkydq+dyECERChJxjgfgdZRitIdjJubTywQJ39OGX++7AWqz5hq7kc32DYSQ42rCKLI1mvuxrLhdRJWF5KWMOXpdpcZJnl4K4YgkNzwAQQHyYx0clyFj8e3dpmGdkvOgdwK7BUTEOxOOl58EQDD6kQqGo9ULgZ3PwAA/OdJREFUUIWeVU7gu9Xob9+PIEqIM1cSm3sO0l/N3CzF1abCzGrHkell6R/Pw1NuesyUHF1B8+/vIdI/QjIcxTZpHn3vv4Pa38m0G09B6WrEsDiwTZyDnl5IaH8dQ9+uNR2gt36MYLMTTOj0RxRSrRK9Ugb7QzLpxChacj0Aevt+bt33OkZoGNuE2aSNL8dIxol3d2HNLcCSX25yUfo7kfMr+OjUn1O89UX0o85D2LsaTTcQPT4un56HOHkJE264hJQsDzec8BC7nnsXT/MG/BkTqHj4KZau/QPX3HsCNffdxq9+fj+JtkNMu/EUCv/wE/6wJNPMNDvhemY8fBv2U69FicRIyfXR/9pfWLrza4ykmeFlTDoaJImRs08kvOZd7l45nmd3PEzP04+SEh3iwIiG5c5LiK9+CS2tgE7FgTTazXFHV5Bz1HTu++ow/W+9yEDCNHB8oznOc8fnoVsdGJIVlx5FHmpGyR5nqp7UOGI0wKGipaT/7F7ErgNovc2IrbvNMWgkYGa19ZsGeMBfu4eZ6ClpiA4nydZ6EEXEsSEMQcSwpjAmphBf9y6BfQfwzZ2NzZsKmoJu9yBG/IwITpx9+6mItlIRa4PxCzE82eZrVx9FocuKkYzT4yqnr3olCc1AvvBOwrlTOKrITWz1q+iLLyaaOxmrJICu0mKkM0UawJCsHFj6C5AsZC2eh5IznrRr76PKZaa4LyzyYAgiRR0b2CZXkrA4sVbPZCRvBoLNwa7bHsE9ZyEp4yZgS3PhW/MYd7gnAHD+5GzSTrqQdIeEoCoYmkbWxufN0dI5t/P1rz7EFWih5C/vk33RNSh97Ri6jtCyC33KccgrLiV9vOmIPnKgifwTVnDw+c9JKSkhXH8A/77DZC+cTcf3zbiKsun6fC2Hn3gRT3k+GXOmU3jRxVjyy9lx+kVUXvcTRhu7SMnyEm9voWhhKcWnriD3oqvM7DKnG8PmRBvqIbZ3A9ZxMwDw2zLpDMZZvbH9H3zl++8JTTf+w+sfKepKJpOcddZZ/PSnP/3H7eT/EEcKnx8ppt96FlUnVKAZBqJV4thsJ7v7xpjgtpIqi3ywqw8tqeEtS6N7cyuyQ+Zg7QAf7BswoytaR8mZmk3ujBx8E/KwOi0c/qSOk86dgBKJcfGioh/2NRSM8+STW/GWpSHOPRXHSCuCEvvBsE8vnIRQOhVpbJiWRdeiFU4BwzR/W1ruo+x1kcA7T5vp7NXzESSJogsvQA+NYFjNlGhDlNGdPjyX3oZz+lHED+2Ghk24hg6hefIQgv0giFiKqhC92ci5pYw2dTKwqxldM//qo4NjZNSUI0oi+375W+w+N3rIjyBJJPr7TeWZxYYYC3J30WXkXngFxrLLWFFfjWXOiaR+9AATEm0Mx1RyG9cw2ZVEDA/QsCiKkVOJmJpGsnGvKU9WFexTFyHnluKafzSxtlZUfz/Jxj3Y5h6P7vBw+idPsHfqxcj7vkLOLqTtijNQ2htwqSFmP1bPoaffIP+UE2h+8g0umVXI55YpONc8BZqC5ar7afpkD8vWSHwW2ovtxKuRjrmSwmPnI4UG0BxexpI68YM7UL97A0PXCbX14cz1IUYDqEXTMKwpSGND6JYU5vzlfsZ6hpgePcDMl56g8fKH0fd/T/HH91J+/5+wnXg1lp46Cna+wbEfDmOVRbZOupBPZwyTdfkNZG56idS2LVycG0GMBjBsLgQlhti8Hc2dgybZUNOLEZIxDMlKocuC1LQVPTSCVDTeNJPsOACpZpimXDwey/jZaJ5cM7KkrwVBTfwwltRKZyImIvQmLcQlB57RFqwl1aSNr0Dy5ZKSm43mzkEK9hK2pZMR6abXO4HE7m8xJBkhGYX+Vvq84xGjASQlinH0FRQM7yM/2UfWaBPGBw+R1Ays294n5dgL+LZtlJ4xhY5gktf6nJS1foMYC1JvK2fqzhdpUDy8m3k8lsFGRlSZiGGhQAyj6FDvV2guWMhs/zb44CGUoum4trwB448i/4NVZj5WyI/z5CuInnATf97/InJBOdbRLvTBDrIChyE6inX+KSBKCPZUUlq3cFTjDoiMYhs4hFK/BfviM7DklUDJVJLvPoTUexDXxEmklhZiaDqWvBKm3HMz/p21uCfXkH3CSsaampnx82NRI3GKzz+T1HwfhqYj55aiDveDmiQWiJNsriP32ltJjIbNcZndhrVsIqFvP8KIRzA82QiHtzCytx5rSTWGKGNYHIQTOrV9IQ5/8+GRbs//D/x9Cp9/XOVz9913c+ONNzJ58uR/2D7+T3Gk8PmRwnHyzwl2BLnmhcuwOi2UzM0n3SqxZiCCJJjdmp17B6hb1YwaV0mEkkyYms0CXwo7D/lR4iqHt/WgjCVRIzG0pE5sNEHbt20ceKcO37gMCh0WCh0WNvtj/OLmRXRv64X671EPbCZm9ZBIzTZlyr0NqNs+QxpuY/zgdtoSVoTRfjTdYHJWChufvgT/+XcTyx6PODYMsgUhPRc5r8zkoQx0ottcCHtWsStoesDYJ83F0DTUln0AGIkYSvtBojvWYiRjaH7zJl+4tIbKc44m1OnHXZxOyryVdG88hN1rx7tsJQBqyDSOa/v1z1G9BRgWG4/49tP72vNYW7fxzc9m02PNxXHGz+lzV5DhkBHKZ/DdjGNZG8kko6bC5Jo4nFjHzUCuWYSeXkhi/2bG9mxD6Wwk5YJbEZ0uAAIfv4Lg7+TJbY/hvuNi9PGLMFSFCXfeiqWoiq/7BbYuaMf19HugazTX9uCySZzsGkRa+VOQLEQUA+nNTzn97p+QkuFASMaQgj3IWQXENn3KrjEH6bvew9B0UBXi/hCZK47FXlBoBlyODTGSksfY5y8j/VVh5chMQ/cWoPjKqEx30DDuZDznXIffnkXj9VehdDYiZxex+pLxPJTTzCL7IGO7tsBAG7bqGeD0moWNxYHRuI1k+yHIKcOw2ElqOrrFjjbcixTx4x44gOB0g2xB72/DaNqJnFWIERxEcLjMzoE71+zqyHaMyrlo7hyTdOxMB8lCOHsihYqZ96RbUxFTzNgAdagHwWJBTEZA1/A0b0BQ4uSGmpFWXA5DXXSLPiLVy8iQEug2JyHs2HvrWH/OTRiyDaVxN5x5G+m9uxGmHYPu8LCsxEOR28rE0b1cMM6NWDyJvjdeZMLAVuScIiIXnszZfZ9hDHeTPdKAKMCI5MFjE6mx+KmMNgPQueImpP3f8G7m8UTt6eR1bUEd6ELKLkLQVaySgJ5Zhmh3ou5bh9rTAqJEon4HwQ+fo3ftFvMzXjiFdC2I0t4AwPD2PQij/eD0orqyGOsZIrT+K4K1tST9I6x9ZgtCioeBzz4m95wL0MdGSbbWE+kfoXdTHaMtPfg3bSSjphzHpJkoHQ2MbN3C6J49LHnrfuS8UoZefQKry4lnwdEUXPlTBNmC4/zbsM5YTpetAEQR35wZDK9bRzJ3AjFDomE4wosfH/xnXPb+W+LvUfjoBmiaRigU+puVSCT+1af3D8GRwudHjJe/bUPKLiIeiBMLxDnpFwuZm+4gz24h0yYRVMy09rRiD3sO+7E6rcQ0nUybxMdNI6TKIi27+mj5uhlD0yman0/jSJw1A2N89U49p58zntPPGc9VJ1by4MPrSclwIPlysVbUkDrYgGPwMKgJcHqxzDwG3VeM4MmipO5D9MwyMm0G9nA/bquIJAioumHGGERCCLpKon4b8nArcm4Zh5Iu9JmnMGXrM6YcOzSCIElYSsYjBXtR2uoxknGc848DZxqCJGHLLyK1egL7n11FxekLCHcHSBzYwuRbr6D0nBNJth/CMudEHMvPw73sZIqeeAu/6AF/D0Y8Qt7FVzH63SrEvasQ/ng9QjxMlhTHEu5H0BTKjylnwa5ncc5chLVnH5o7h2RTLYduu9V0qz76MlLGTcBaNQ1j28f0Va8k2deN96QL6M6dw/E7M6h89k3Ehg2ovW0kSucykDWVSp8DY9ll5G57DXHCAnY8ciIle99Bd3iQgr2oriyyVD812/7CmR17uGLmLYhKlNGPXkJMTWNgx0EmZqYQmHX2//RjmTUVLTBI//ptyJMXwUAbnt0f4l52MsOfvE3Pqy+RtuwE1K2fII92k3vwC7Z2jbLoxVbSD6wi5fF3kCumofS1sy8omaaEbfuRnXbUvjb0SBg9NQMEETEWNOMwKqb8YG0giwK27lqE/EoMUUYd7DI9njLzMVQFPRLCkKwY2eVo7hx0q5mlJhg6usUBho4YGUGt/Q7d6cMycBi7FgMtSepgAwgCejyCZdZxWMbNRA0FUde+Zo7H3OkIiTES2dXERDvoGvmWBNInf0CRHQTlNFwWgQOOccz59Rloe79BmHcG37aOEt+7Hs2RhpCM0XnN2YhKnLO3OZB79qPsWkPGL/9IZ+ECmLyM0o+/xFpRgzbuKIz4GGN/JdU5WregH9xMt7sKPR6hov5D9HCA8wtUUoMddL/1Jkp3M0YkxKaTL8UmgqAlQRQxVAVbzUIEJYZ9ygLSTjiP/BNWoOWOJ2lxQsMmWHQBmreAcOcAemYZWloe1ubNZJ9+Dmo8wdC+VpynXcOFdR+RbNxD5jHHMPzFhxiahmPaIqJ9I2RNq8LucyNKIq1fbEfy+PDvPUhqfiZKJE7vO2+jBf04fB6cU2dBTjm63YWaPQ5DsqK5c8kXw2hDPVgmziPzJ7f/1X5hjPf3dNO07uMj3Z5/MXbs2IHH4/mb9cADD/yrD+sfgiOFz48YzxrtpP9mF+7idIIdQQb2tLPo4mmkW0WGEhq9cZUxVSceSjBvVi6b1nfikETaowqFDgs2lxWHJOLKNUmpBzd0UeGxMcvroCrdzuOv1CFaZJ7/ogmALQeGaLj3QbTUTAAM2UKkYDpEAgiGjtG8EwQB/7YdiMkxNNHCxnAq3qSf0q4NOBu+pd9dgejyYoiyaTAYCaF0Hqaq9i1kfyuCbEXKzDd5Q6KIbneZZOjMfLTAIEp3C8NvP0dicAjRlUb44AEmX3kM4c4B8o8y+UN6OECwbj/1T3/Eb0uOI7L6dcY2fomtu5as/j2ofe3I2UWog104MtMZXreOzHnTGPzLw7TdcjVDzz1E+JMXyV8+F9Gegh4NE9nyFeJwO3JuCVXXXoLmyqIuYKD5+xnImEy8vYUcS5JbL3+N2w/YyFMGWH1WPg9tG0AoGGf+vnb1wDO3Up7sIvrCnUgLzuD63KPpj6jINYsRIyMw0IaoxBH9HVwUWUbinqv4ePQFYr4KUnKzCW/7nuJf3YtdT2B7615CbX18+NOXaXn7S0SnG1dhFslNH6NHQ2YobHiUYEsPeWefY0q2J81Da9wFsoVpuW7evWYOufc3UWgEEJIRkgN9VH//OGKKC83fh+Poc8xOQ1oWAIKuYgx3mzEfuooQGsSwuZBEAUOygmRl1FeF7MsBQQBAzio0oxOa9xJw5BC2Z6A6M+DgevM5FjNWxLA5zVFmxE8ydyKWfrPToQ93I/Q1mp+X1n3Et3xOuHOAji82oHcdIpI7Gc2dw0hMJSU+gmCzI7buYuzEm7Hs+AiHLIChU5pmJXTMz5AnzEUe6WBRsQf7vBM57I+jubIYd+99CGqcV86toTV9Kv7d+4m/8xC3fHYQebCJrKH9xIpmYogyta6ppFpFvJKKVjCZD73LyBXHEMpnIDicfF94AobdBYJI4uYnMU65GUvZZOav/ZyKK99CO7Sd5KHdWCfOQx3oQEvNJL53A+FvPkDOzEdo3IL98HoAeu+4isBf7saR5SXw+mO03HQ1WmAItbeNtKOWYugGm445i+DHL2GoCg1/egFHppdPrnmJD5Zcy6vv1LP3me8Yrmvjhbu+IhFKEFz7OemTKtHiSbzVxeTcfK8pKAhFkFxp6A4PIXcx8sBh5NFuhFgQfddqkgN96NZUIo4MDvlj1PWHWPv5kUyu/wj+Xhyf2bNnEwwG/2bdfvvt/+Y+77rrLgRB+N+uXbt2/ZPfiX8/jhQ+P3JENv2Jpi8O48x2mmnt8SRn/Pli0q2mm21DOMH67b04vHbGZabQGTWDFVNlEYtdNuWQSY2B/UPkFrgwNJPQ3BVMYBUFPnvrAJJ5/yKuGxi6gTDQQix7PHqKF3tkCD0SJrz6bfTAIOgaWSedhm5zISdCzM03xz/JCUez73ePk6X60WMRBEPHUliFkYyTaDuMpaiK8OevI7rSULqa0AJDJi/C6UOPRxDKZ2CfuxLR6cJ34XU4p81FG+pBslg4+Oq3OHNMf5dQXR3JgT5i/hDjzl3IL+5diXPlJVjzi1HTCjBiZoaQHg2h9rYherNQI3EU/zDZp55B69ctpI0rRZBELAUV3HTmU/R8+Akps49G6W5h8POPEUuncLdvOtOFbr687lXSJYWek36Fbk1h8y1/5uq5RYy89hiH9HQuW/8wyW2rEGwOrp2Zh/eGP/JVOIPUSVPRNr7HmY078TlkNHeOaQ2gJpFHOmjzTeWu46tJjI7heuJdbPXfsO3eD3Afdza1V1xDzhl/wl/fynvP7eTMF66mYPEUwvt2Y3U7MdQkgmwlsm8Hal8bDZ8covudt5Fnn8AatZShdd/D+IVkpFjIsgsMPbmCRjWNetckGlfcjH3cVOLjl2GZdRzK7m9IXv8oalblDwWv6PGh+vvNrlBaHnFkpLFhM8pk/wbch75Fd6aj21IxZLuZ5+TNRD/qPNL0ME5RQ1LjMG4emiubhMWJNDaEPNhkcoCsTqy9+9HS8k0uUV4lotONWDwRZAuiy4sgiZSedwp7f/uUmTAuiOQGG5H6D5uuwvnVpGtBpHGzcQw0ICYjKJpBZtc2DooFaJ5cukJJHu9yMaH1K1REElnjEJQ4DaefgOPPN5F1w724lpzMbe/dTrxwBkp2Nfq7D5AwRLKdMinxERrDMCalMivfg19woWz+mMHxKzk6ugep5wDKji8p3vUm9tFOlOxx6FYne2ceQOlrx7rwdBAlZF8O6o5VDOw8iPOUqzDUJK3Pv0Jw81oMRaHo1rvxnX8NKZlpBBq7iA7HUPs76Vn1DaHtG3HmpDHxgrk0frSDQO0Bxt94JX1bD9A8lmTdUJRsm8z+9iC137Qx2WMj0DoKwFhHj6kG03Vo2m66jSsqY9vXoW/5kLqlR2NYHGb2Xd33xNtbGNhxEN3hIakZaDqsaxj8L53A/p8B+t+J4yNJEm63+2+WzWb7N/d53XXX0dDQ8L9dkyZN+ie/E/9+HCl8joCT3dP4rm6Q6nMX0L2ti661O5nvS0ESIKYZjHNZ6dnRx47eMB6LxPycVBySwNhQlBS3jWBHCG9ZGsmIwoRzTALbnlEz9b0lkuTMKdncesN8+uMqKVlu8OXjaN+OFBok7sxEysjDecpVZrdluBvBnYF+cLMZQWEYSIEuGi88hZo3XidgNQuURN1G9KgpHRatMkp3C+6lJ2ApGY+1ogZBtqDHIoiddYglNYgjXfS98gxiTikIIkJuOUKKG01RmPrAbaRMP4pQex+iVcZWOo6s2ZMRJJG06dOJffMmcnahKbPOLMRSUI6cmc9YWxfx9hayj12OY9JM1L52ljxxBUoohL2gkOiBXdz/5FnkLl+IIJrE6qzLbyCWVsQv7l1J5Nv3mXnpTPR1r5H3ye/h2xc5cXEpe/rCpE2fTlWqQd6Fl1LxnhPBasfSsYuOsMLRLR8gF49HTE1jYbZE+vrnEDQFy8BhhPxxjGVU4bSIVEsByp58nc3Vs2kpWsJR33+CYbGRfPo9dlY1cOijBm7Z/izDuw4w2tKDIzOdwd2H+MPlryDkV5J6wkUgW1n51aOkZKYhjnSxtP1TXnh4HbEPn6CYAC1BjUjuZMo9EvmpFqb2rWf4u2+QBdj/s19wdv8s9LuuQIz4Yd0r6OERNgoV2KqmYrgzGU3JxaGEoWGTeQ6V0xBdXpTdJjHYsDow4hHE1DSszZuRxoYQI34EJYqgJpGCPdjrv0Ft2oseHkVzZyGODZvPE2X0+o0YVifx/KnoqZmMTDgOyZtpRjj4cph2109R2upRNrxPsn4relY5Yulk+uQMDsZSzJu2t4Ahw0lMNWjNmk1fOIG+5UOcFomrZ+TRXLmSdw4MMhBRaFC9lK2cRs7VNxH76Cm+P+06Jl93Br23XMyIZiF14UocySBxzaAbD181DmEAWZ8/RPZIA7v/+DE58R6Urkbi+zYhzz/NNBqMBRGSEW77qhmL00Hdc9+iNe5CUGJo3kKsFTUUX/Mz1K2fEG/Yg29SKd6V56DNOYPAO08z8s5zuCdNpOyqS6k+fzGCw0lGTQXBlh50ReXAa1vJnJBNan4me3/7FKUXnc3Cah+T3DbGVJ3euMJQQqPqhAryZuRw4LWNtK2pQ7JbUUJRJI8Pa9lEBve2MrS3kbon3id7cibJ+q0MvPAYsi8HW1YmpXfejxgNEErqDEeTfPuXF44UPf9BaMbfh+Pzf4KMjAyqq6v/t8tut/9jTvjvgCOFzxGQ3PsSpxxbxraHvkRL6gQ7RwE4PsdFpk1iKKGRVDQme2zk2CXaRxMUpTsYU3WsTguCJBDsMAnATz65lTMnmN/s++MqAJGBKM5cH7/+1VJCHSMMvf40uq8YIziIff8a0BTTZ2XSIoyq+WZg4bjZCGqSlL79aN5C9tz+PIbNRXrrRlNpZbUTazxgJqZn5iO60sww0vYGBIsNo3AiQl4lSlcTYjRAomEXueddjDHYiXZ4B3pHPUY8woHXtqAXT0VIzyWjpoK4P0Sys4lwSweCKPLdlY9hP+5SjOxyGO5C0FSTXGtx4J5QjS0r03QEdrqRM/OxFFaRuuBY07ME0OJJLAXlKLkT0fInEXIV0n7FGaRfchO3Xv4aoiiy9prnsaal0r1qHddufYRTi62syTseaWyIvrdfo+vBOejjFxEumEFFrI0vi08jmTuBizurkYM9iMsvZxQHwxkTOUgOqW1b8FrBsNgJajJn131KmS0OooygKsxyRgh3j7Li8Eb0WAS7z03eicdjPepUmlc38tt9r4K/B3X/RqQpy1B7WvCdcyWjhbORs4v42UAdhqZjWFOodMSxjw2wvS9GimRwx2Alnd8dhHWvUPbmp3y6TKDw1OOJrXqJ6MJLGChawPyRLXxsm0lkzds0jcSQ+g8jZeab483eZlCTWIqr0VMz0Dx54MpAtznRcsahObwkUrNBEDFsqQij/RiqgjbUY5KhdR3NlQW6jthzEHGiyX+xjA2CIJLlb0ComEW8+SDxQ7vRw6MEDjRin74Ey7RlGLKVRrmQjtEEGQ4ZPdUMB013SNQNjFF04GMWFLoQ559BukPCsuUdqga2Mb/IS4E2TLUjxuh5d6H4Skk95hwWv/MwfXMvpvjOh0j58AHi+zZh7P2afJeF4lg7p47PJmXtX0g56UrUnmYqVn9NPK0IadG5OOYch1a7lqY/PoLqLQDgweMrsV38W+Y8cw/DU0417RBWv0TXqy9jyHZEtw9rQRmeOQvN4N66r3BfdRcp1z7I6LwLUHtakHy57H/qQ4ZqG9n5Zi1NXxxm0sXzyF8+D4vXy7iz5vHdpQ+x6sAQB0IJJEEgwypT5bLhKc/H4UslrcS0ujj0zjaS4Qg4vSidjehJjWBHgPWbuokOxwgdPIR3QjnklCFY7QiJCC1CJps7R/njFw3/xKvcEfyr0NnZSW1tLZ2dnWiaRm1tLbW1tYyNjf3LjkkwfmyxrP8/EQqF8Hg8BINB3G73v/pw/u64RijhlGIPxYuK0BQN2S7jzPHx50fWE1R0rKLACeVeOoaipMoijWNJyp0WvFaZ1LxU9KSGIAlIVolw7xgHA3FaIkmOzXayZsAcD91517Hce9caLpibz7yn78TIM7krYjSAYXchJKOoGWXIw63oDg+9UgZ5ysAPqc0BVzGhOy+n+PLLGdv+PVaPC//+ZnJOOglE0czuCgwieXxoQT+SN8uU0SbiCA4nIwWzSR9pREia6eOCbEWPR2h86iWyplXgqq5C8vhItB1CV8yizbX4RHS7i2ZLIZXRZozQMGpfO0Y8gq1mIXpwGLWvDcHuROlrx3b0BQhqnOTmz7AsPhtl44cgitimL0PzFpC0urBoCSx99aa3iSuNZHMd4cONuKfNRLBYaCo7jsrmLzGmHIMiOxBXPYlt7vFonQ10vPEezvxMsq64CWX7F1hmHYfmyaMnbhLA0x0Sjf4E+W4LXlGhIQTjfHbGnrmd4IX3UNK7lcTBHXSt3UV6dTHuCdUA7HnobQRJIGNcJmW3/hotvQjqvkUqrCax82usC06BvmYEpxulaDrxN+5n4NRfYZdEBAFyxChCPIzuyjIDaXd8imCzk5x2EvZgN7rNyajkwSoJOPQ4lv4G1Ayz+yaN9mJYHWgN2zBUBWtFDegamjsHQY0jKAmIhVCLpoGuISbGTMl9Rpn5+QkPIuiqaYSXOxHlkz/hmLEUwWIDTQEwlV7DXSQO7qDpg41U/+RsM63dl4vm78PQdWxHX4CYGKM7tZyMrx/jq0mXc0LXpxyach4TbGMIbXtoLFhChcugOSygYzAx0oDu9GGIMrEvXyZ10YlEcifjaPiOZGs9ttnHmh24UDej7z+L69I72DyoMTsvlZTefWipmRh2F2NSKsGERr4U4et+gQK3jXE+O6IS5/kDAa4Y+hzJl0Ng80a88xYglk1hbNXrWH3pWOeegBgPo/a2Mrx+Pd6a8RiJOIgisb4B0k65mOS+DWbXMrPIzD37/g02/PIVmgYiVGSkUHF8Jfs/qKdkbj6iVWL1qmYkQSDfIVM+NRt3gYuiY2YjebMI7N2Hd8ok6p54n5qfn4WcXQTZJYx+9BIbf/8VdrtMJKZSvbyE0hPnMbDjIEW33g1aEsPqpEv0ccnLu9j+1uv/rbs9/+h7RjQaxel08sv3tpDi9v6HXmvNXx5gfomPRx999O90dP8Tl156Ka+++ur/8vN169axZMmSv/v+/j040vE5AsAkOvuqfYwNRnDmpAGgKSpWUSBVFlmUkcL2rhAHQgk0w6AkxcJQQqMrqhAbjhIcjJCMKCRCCWSHzPLlJZSkWH4oehZlpGDzpvLziyYz6eKFHPrjk+j1GxGUKGpHg3mDS4yhCDLEQghKjFzZDDQNZE0i5C7m4FCUkhtuhYxCUmctQrDZyVw0H3Wgk9FN60zFkMMJkgUxqxhDVcDpNTsCWRWkjzSyUyjity3pUFyD0tWI2t1C2clH4Z5QjeTxMfTdOg6/sxHJbsXQdOK71yEoCarGDpndiIxC5Mx8pOwilHZTgitnF5mkWnsKRtNOhNF+LGUTUbd9Rrity4xfaDuAYXHw3O5eBDWOml7MglVWNkrj/koKjZo34FiERn8EsWIGyU+e4KvSmViKquh/6SkOP/UKxfc/QfYl14KuosciNIh5hHQLuakWZFEgqRnUWPy0jyYQtCTj0yTGkjrpx55C6dBuks11CLKV0vNPZ7iuleY3v2B4+x6SEYWsybm0f9/G2HcfsXnJCYT27mTk41fRk3F0hwd1qAeA5LsP8fxNH3CwZh6F/n24rSL7w1YiqblIjZvNYsabRWzqSWzsDKE7PGYkhSSQ2rQBqW0X7ek1dKgugq88ZHoxJWMITjdDm0yuiO5MR1DjaN4i/B++CqKE1LIdMRZECvaCYSBG/Fh66hD6mhC0JErOeOSRdiy5JSjtDeijQyBZ0IJ+xla9TvOTTyM6nFScvoDA7j2IDieiw8lQbRM962vNAnuwg/z29dgmzObEMheHppzH+MOfmYGae7fx3LYOBpISZV4rpR4rybzJiOFBNHcOFyjHE8qehFVLIPjykXw5EB4mdbABPcVL2lnXIHfu5agMAwMwLA78jhyCYipOQcH+/K8AWFzsZkJKnPjLdxF64W6uHu9EXHIhX591L2mz5yKmpiFGRrB60xAsVoRAL0YiiqFpZF/4E/y792OtqME2YzlpJ12I2lJnkuzDoyR2fk3ykydQR0eQLBLTp2aTPzuX3p3dbPbH2Lm+k2febyCuG8Q0naU3LWXy1cdT8cCfsC06A3HOyXiqShE9PqbeeQ3JgT6UvnbUA5tJW7AMiyiSNzuPo+86ibKTj0KLJyk8/1y0hq3oTh/D1kx29YZp3lH/T7/G/XeFpv/HCc7/yPbHK6+8gmEY/8v6VxU9cKTwOYL/G2Z/9R2GZtDxfQt9u/uxulK47p6VLM1JpbAmC49FIqkb7BmN0xNTCSimy3ODP0aq1872rhAj/hitg1E2fNfBjCI3k9wmOa55LImh6RQsncbup9YyfHiEgW/WoR3aQeO4EyFo3jxs9d+gFk5Fb99vqn60JJ8eHsZJkrlZMsn9m9Bb95mjLVHCiEdAlHDmZWEkoiSb6zAkKx/MOg9sTtMkL68cebSblwe91Ox6ibsLe5FC/cjZRdhmHUNsaAQ9PMqWGx8nfdpExp27kLg/iKOs8n9a67tz+PlRt+J/82nIq0JypSGXTkJIz4WcMtSBTjMLrHIWan8n6DpKIICWVNHDASKzzsQQZX42PYtR0UXs02dZk7OOo8ROtAXnkblsKQBt73xK5/wl9D/7MLbKyZy2/ln0SAjrjX+i6rm3AGi1lfBGzenseWI1FdtfwiLCSEzDLgsMRFQMm5NpPgkhGUWoXY1bSPLLmstoe/ppbONnMrB9PyPbt+NvGkGNq3RtbGTzQ6+y/a06cqfnMLS3kQVrPybY0oPn8juwlk1E9rebvKbNa2j9Yjunte1m5Vu3oWZVkhIf4ab3anH5m9iXMRe5q5ah0oUkNYPlvgQjgik9d0YHaXvuBYzcKorHWigb3MHQeXcT+epN05G5aAK519xE06OPmTfzniaMze8R94fA4UYrnQmiBLpGZN1H0FaLnpKGkYxDcBBL7wGU/ZuQy2qQc0tQBzpR+9sRrCbXICXTi2B3Imfmk5qfiZRdhJjiImt6Ne7SXPbf/jsonIjgdDNcsgBptJuJllH23PciQmiIlDOv45G5dtLtEs/s7MEaDxDVBPoypxJK6tx+TBWdIQUx4jeNEOedhaEoBDOqkfoa6H3i9+DKwLA4cPbuI+yrxDAgreFr4oKV/u1NDOHC2b0HMR4m5eI7cF99D1L/YeRgD8fWfo5UNRO9uIZt1mqsVVMRU9MwFAXBloJUMZVk3QbSJ1UipOci6CpK/RbknCIspRPRQ35EpwtrQRmGplNx4gRGmgI4c9J4a0cvY6rOntE4pxR7OPP0cZx17gSyTjsX6fifoHjyQZTpN1KxLDgNOacEQbbStnon9U9/xNCm7dQ/9BQTzpmMntTo21qPnFeKvWoSYlYxcukkxHgYQYBfP7sNf/Oe/9bdnn8m/h7kZv1HNvg5Uvgcwd9g4XApBXMLyZmajaHrhDsHmHrlPLSkxsoLTOKyVRToiiloBj+MvZJjCosmZrJhOEqOXaIhnPghDHVlTiqTPTYEScRePYXW3jD7GkfImFrFgcfforp7PXp2JequrxAdTjTRQs/HnyPGwxiyjYuKQfnsCRAl9j7yAWp/J0Y8wmj9YfOg1STKWIT4vk1Imfkw5ue0j+/CCA2bCrBEBASRy30DCDYH9ekz2fPzOxCsdgybE9eMuVgrajjq05ewTlmMfdxUk8Oi66YP0IzjEJQYT7d8QMZZl2F0N5jy6qxKCA8jKAmkgnHodhdhZy5SZr5J2F56Os/c9zXWOceTNlBH100XIQV7cdkkbIWlbF30C9SMMmbf+S16OEBicJjHXqxlZfMuhBseY3Dcsah5ExEnLCCc1DAkC2JHHWWRJi6qfY+U91bh33uQuGbgs4FDFnFbRQzZbo7zUjMZHL+SgxedzYNf/ZaMB19hwwW3YWgakkVm9gPXUrpyFqUff8nVswo4rnUXklWi5I77Sdq97L7iUTNF3eNDyaqi8fFnSZx5O/kvf0yhQ+fgE2/wm41DPF4f533jY0a8lbSPRjljk4XY3VdxyxeHCFh9ZITbEbQkiW9eI31iKeJgizluySihamAbqcvPMn+n3YfB30PF9dciOFzosQhGMk7BVT9DObwL8fAmtL3fYFgcpC4+GUG2YPSYVgmIEmpmOdaqaaZ/zEAXojcLOTMfdaCL1DlLyFo421Qe+fuw5hUjFVShhUdN87/RMcZdeSbicDvaUA/pWhDNnYvQ18iM995E7W1D6m3AEGXsh9dzydRcpPAQfWMqGbvfI6LozHKEeDp/Cpo7B0tBBYHHfokWGCSpG2yxTiD3xt8R9lXSEjboTp+Ec6yP2v4xWkqW0RlKUn3lKeQM7DELesnCcFLEMnAYLbsS3ZqKXrcOLTUT3eElcdpKjOxyU3r/V+NLBtqR5p/O6OE21JZ96OERBKvd7CQqCSRfLkKKG0NVGDnUgSiK5EzNYrih/2+uAYULCsiaVkHxyqPQ0/IQ1ASioaGneMmLdaF58lByxiNY7VRdfCITrjie1PxMWjd0MnSgj97d/ZSevsz8O/DlomRVoXly6LHm8rMPD9C59YsjRc8R/EtxpPA5gr9Bcu9LRAbDKHGVsZ4hZKeD2FCAgcN+is46mXOmZhNUdCQBMqwSx0/PAeDz7hBd7aOcMSmLtojJq/iyf+yHf2vOmcxwXQtydjGZNomiFJn2VVvIm19F5xtvYchWpPmnmRfq9x8k/44/oDfvRtvzNWMfPI1j0WlIjZup+Gw16BqHM2bh8HnQwqMogQCpC47FWjYJPRJGsDvpeu8jxFRzFKD2taP2t6N68rBOWcQEvZvJt16OYLEgJEzfEdGVhrp/I4YkI3p8uCZOItHVRveLfzGDOqMBVF8JWncjRjxKsnEvUvNWtKAfoqMIagLNnYs71IHodKO5c9h9zU1c9tM56B31tD72KMXnn0n9DTcg7/8aecJcCu+9nDPfbmDPnXPpm30hYz1DPDm4gWIxyOqmYdIdMlmn/pE6xcfA2Sdi6dzDN45pjPiq2a7lMcmjk/LLx1E1g4QhIsdGyBveZxYBjnQURLKEMdJe+BDB5sBZ/zUVJ0wi56jp2H0e9NFBlEgM8flf03b2icQfv5mMmnKU7V9gC/ejGwYbe+NsvPh2xGiA3HkTcVhEnKsfR1ATtN77Cvct8HJ18yt8vPAm3JLGnA9+xwcn+Ci+6XYePamayL3X8GXIxwFyKXodUidN5aBvJlKoz0ygzyzGGOkz+VhqktH1axBsDgzZhqVoHNZJC+h99S9Yxs82na8rpqA0bMcQRIT0XKTMAgw1CQ43xl4zY0zQVXP0mOLCSMaxVkxBHxs1FX9V0xAdpt+P0rADOSuf2MAQeScej6VyGnosQqLtEFJ/I8mPHwNfPoKhI5dPQek2HZIFh5OxpM7wuy8wbnAbwozjyfjyEQ7p6Ty57TGs3bUI9hQ8ZQVo887GIgrMTRnl/R4Z9+BBylMNcpMD0LGfFa4RKqPNjNd7iS2+jL6saSQKpoIgkh3tJNm8D6mnHqNxO/K4mYgNG7AMNrL4/ccRlShGzTHo4VGzW5VdghTxk33m+VhKJphmgUVVGIm42YUURWKN9Wj+PrJmTsSZn0H5SbOQLBKT3DYuP7aM6Wl2HBku0ubMQzr2SsR4GHm4FRURtCR6aiatQcXMORsbxZJfTuBAE9seWg2AZBEZd0o1+5/9HD0cQA8HkP7qrr29J8SGTzf8Ky5r/63x9/Lx+THhSOFzBP8Lpn64hmBHkJRML9tf3okzP5NTNjxHoqOZiRfM5aoTK5nkttM4luTNbT0/PG+zP8Z7+wexigJz0x1UpVopdJgREn94YhufvF1PdOuXHP3AGXw7GGH/Vy1o8SQA8U+eRlCTGBYH9mMuwti9GiYuQnS68aw4FSEWQi+chGfne3z58zfwvnw7w6fdjuzLwTFpJiOrPyJ6YJc51tA1HJle3hvLR08v5FD58dwyOB452IugJGh/8G6M2aeC04s+1Ik62EPi0G4s1bPB3wN2F5aiKixpaeSeegpiPEg8fypSoBvJl4Pa34kei6D5+80E6/YGkk21SC07iHz1JkpnI4dv+CkTL1lGyXU3oIdH+eDlvaj9nUz+/e8Y27UFPcXLOxf+kTekVbx8KEJ+09dkLVtM7IsXMA6sp7YriP7RwzzzyLV81TjIpEuXEsyfwcIiN8GERstIjOBzd9M2msRjl0yvJF1HyZtEQBWRdAVb1E9YdvNB/QA9n65ijXseAILFSmwogGC14z/QhmiVsfw1sFaJxEG20n7XLQwsXMaiwXXMW7caZeOHeI45HVvUj7WiBtnfzmm2NtB1HKdfx8z7rgRNIfPWR0mmFZLcuw5P1w60257muBydPX0h+t+5Brl0EuN7N2FYUjCsKRiyjWTjXoSMAsSyaXhPvggtuxJjuBstNQPiYXKuugGlfqvZBbI5kbOL0Ie70VO8P3Q11I4GtKEecPlMvpAoIkgS6mAPut2FlFeBFvSTPLQL67RlxPdvRXQ4SezfStrykxFT09AHO5G8WaQsPQMtpwrHlPmIyZjJb/KVIM4+0TRB9BWT7RDwXXgdGy76DbvPOAeAip2vog71YOi6WRQsPpv3Dw4RiGs8dtjgrOQuEnmTzNgVQMosQE/xouSMJ5JWgis2aJq/ffcyA1K6WXT4zC8W/RNPpPcvj9H+yhvmuDfoR1ASqF88RbK9gfSqPATNNIdM7N+K0mn6EcX3b8VSUG66mUfCpvw8HDVDdVt62PvMd1ScOodz7z2RmhvO5cqdr1Dyk2sQquYghQcQdJVo3hTsTRuRA92o1lSqI4ewDLci55URrd1CsKWXQERh0mnjESSRgx8epGzlNBKNtUgeH4ISo+vX1/DMd80EOxuOdHv+zlB14z+8joy6juAIgOXJaupe3UX5zFy61jcQXPs5I4c6ECSRwsUTyHfIZNpMk8OVOal4LCJW0XQqtIoCe0bjTMxN/YEcDdAeVWj7Yhv++jauu6SGvHIv6RPK0JMq4c4BxJEuDLsL/eBmpOrZCMkYTFyE7vAwmjsNKRZArFnKyYfX4VuyjNJoK4aqIHl8eGbOwVE1CbWvDXWgi/TZMzkt8D2CGmfS2AEm5LlJNOxES/FSdsNNiHtXmWTorGJ6ak7DVj0DtbWO4MZvIB4GZxpyThFidgl6bwv2vgMMv/ciiYZdiB4fki+XWPMhpL/+31pRQ2z/NlwLj8dQFQqXTUONxvlOL0UP+rmpeyOWoio0VzaiVWbMksZvZ7kYO/12LiuXaHv5TeJtjWjxJIeefoM/unaSCIQ5o9LFmZNzUE67lRRZwJoIUtT8NRZJwHfW5Ux2qzgC7RiAmAhjiDJpVhFLXz2GNYWkZvCzwY/IXb6QLybMYfAnjyDIFkabewgfbqRvTx8p+bkc2NTFy7//FkESaXn7S2xpLn528COMWARj64cIDifqYBeCmkAd6KTOXkWrt4YhyczfqnnhOeSRTrqiYO+tQ557MofTp9M8dxGDuLmwMgUxGkDvb0P193PHjigAQjKCfdYKlPotxL55E6KjcHADojcboa/RLGBG+83OhdWBun8jsfqdCFa76W1jcyB5s8wuTtlE88Nr6BgON8nmOuSyGvS2fWbXJrsYKTMfQU1gGz8TOb8C2/wTTSJ0NASAHvSjdTeib/uUhgcfI77zG6RQP5b+BpOjokQxLHYs/laSmz/FV+3j1XUdHH5nI7aJc4ktvsxUi3l8CLrKkhIvmSkyJ7x4I5I3E2vrNlOBlhhDyawg8Pz9SHVrcDauR9AUhqIKcnYRuYN7CedMpvHxZzEScewv3UH2LQ/x+Rt12CbMQcgqMs9T1/HvbSD32lvRepvRk6aCUXS6SAyPoMejKJ2NRHv6iLU0Ibs9WL1pfPOT54gOBsmbnUciEMZZZsrNNVc2akEN0tgQQngYIxpE+u4l1J4Wkod3E3vxTtTBLnSrA93uIjEaZseqZhZdu4CCE5aixlXypmWTkp+LvXoGanoxutXJ8C3PsvnVV44UPf8A/D18fH5kdc+RwucI/m0k975EVk0msUCczr0DbL53FYP7uhht7EKJxJn5s4UASAJs9kcpd1pJ6gaLMlLYMxonqRvs6g4zs9LLmGpmEpU7TSPEwX3drP3oMCkZKchFVRQ9/AIWp53QulXozbsRXWkIoSHE4Xao34DmysbTsRVjsBNxuB192yeo3S3ogx3UPf4Be2++C8mXg5GIIXmz0MMBBFFE6WmBoS60wBBXZgfYN/k8BqzZ6JEQYvFEs6NgGOTbNAAsBeV4jzkVLTDIoKsMsWwaydp1qAOdxLZ/hau8mOFdphpFHx3E6ksn3tVForkOta8dS1YeSm87iQXnI4gi9uJyFnZ8wa/Ofw4ObuCzlNko37/DHWln0zemMmrxkkkYKRag9MGnSJx5O7LTTsVpC5B9ObjPupbmsIDHJtEfUdG/eJJ+I5WBVV9ypr7fHMuFB9BTvFgE0J0+xGSEsGKgZFVhGWwke6SBlo++J7B3H49/8zumKc0Idifu0lwAfJVemt5dy/wLpjAlzc5Y5wA5c6o5/EkdXY89yNvnPEyiq43EksvQ/P2msSQwPk2iNN5OphjDb8+i59G7SNZtIBjXMGQbXwzaqJBDLDqwlSwxiti8HXSVbz3zkKYs496lBaYyrW2/eXPNzMdWORnN348gW0ke2okRj2LoOlpgCBxu9P429EiIlOnmZ09PSTNDZ+ORv/o46SZJvrsRY7jHJEKP+Um0HCT07UcMf/Q62lAPYxu+MEc/ho7m9CFPWoBYOgUxuwTRmw2qQqS1FU9JNqLHTKvXXVmoaQUIYyOILTsxJCvDexoYqh/mlp/PZepzT5Bo2Ikr0oecU2Jmkzk85BqjuPr3k1ZVgJGMY7gzCboK0Ty5GJKF6OW/xyifiR4N0SJlM7F3Ay2FC4nuWMvI764mNT8DdaATR5aX8OsPcUPtG2hD3RjDPcRrN2AtqiLvJzeYvkaJOIJsQbDaSTTW4pq/DICRnbtIBMZwzV5I20ffMbB9PxUrSik+dhbFJy8j4+SzTQJ7Vr5pDFlvKiQ1f5/ZGbOnINcsInTwECkFeciZ+SR95Rjdh7GluVh82Uwy5s0i2t6OltTInVOBIEkIvnw6dA+7xhzc8t6+f9KV68eHv49z87/6LP65OFL4HMH/K2Z/9R2h7jBZJR4awwmUMYXyG29krGcIQ9MZSpghpjHNoPevZoX/g98D0BVTqG8dZXmWk3Nn5DKr2kebP4aumYXQ1D/8Fs3fjzzcinfx0TjKKxn8dq3prixKaOFRBKsdqWUHRnoBhq6hp5u5TdaqqVBcw7T33mbcu5+hZVWYN5ZkHD08iqHr2Jec9cM3ea3rEFP2v03e6CGMZJzGX/+KrwZlOh5/GPHQBrTAEFrAjM8wIiHkl39D069vJtTYiuIfZqxrgNGGFnJPPw2lvws5txTR4cS9+DgcM5YSazxAw7MfINjsNJ97MinHnEfDM+9gKRnPbx46mdGtmzlx6FvODy/lN8vL2T8Yxvqu6WJL2M8VX/aSMVCLJacQwWonUruVJyuOZZzWxV1pE6lo+hLxhJ+RmxxAueExIns2kfSVw1AXhmTF0rELMRYkKqeSHmxBTIyZ74NkYcLDD5F1yllogSHWLLuCke3bGdzbirMgF0EUGP+Lywg0DVGxspyMmgrSTziH/Fl5ZE6rZOGFUwg29zB670+pffQ9+tasxVo9C3mwyeTZHNpEZt8eXl52O8KKq5jUsgpBiXEyB+m695e8uq8fKdiHOmEZse/eRzMww0oNgzHZjVg6GT2jxOzalNQg51eAKCJ6fET27UCw2MyxYncjUnqO6cqd4kF0us3OzcorUfs7TXM8q90cX0ZCiK40rGUTMXxFiBYZ1/yj8UydipSZT6Tfj+hKM32ClARa426ze2J18OZoDsgWmj/dhRKNEWszPaV0mxNL5x4Md5apmEpEyDn5RJa++yAlV12FbvcgLrnQ7Er5SjCq5pqGgokwut2F74YHMRQFMREhzd/IiOBEjAcpTPbC4S3EDtVRkejAqJxLSd2HWE+7gcKbfkP+NTcQX3QJks3G2Hm/Q00vgpIpCBYLkjcLdbCb0bRydIeHaMN++j/9lMDsc3FMW0THSy8hOV14f/EwuRdeATllFB8/B4Bx996HY/JcpKPOJFowHUNJomeWIYz2IxZWE173CWpvO93r9qDHo/jfehZBEhHsTuL7NqEZBlJmAVo8SUZNOZIvB7vPQ9bkHDIWLsBWs5CxzHFYJYHOYPy/vWfPEfzXwpHC5wj+t/i8O0SoK0yOXSbQOsqG83+JoesM13dy8zUz8VhEkrrxg0tzV0yh3GkFIMcucyCUoDOqsLpugH2NI+wLxtm9u59St41vT/4FYmoaiT3fYVTMNiMdbrwfIRYyjQhdaWhBP1r5bEacBUi+PD4csDMy9VS0innoBzcT++gp7E0bEeNBrONmwDFXo6sK3Z+tIbHhQ7ShHpTORsQUF4neHrTUDKJ1O5D++CYnOHpRbn+Wm+f+AkqnImXkofS0oEfDeBctp+qWmxAtMpF+P9HBUTJ+9jviE1dgq5yMoWvEOjrYfdM9JEtmkQiEqXnsYdTuFnLnVKA7PGz6th0jGcdVXYUaT6IN9fDeWRX4oxpnJHfTdNwtXLYuwuCn73PXzj/SmzEFcfZJDO7YT3QwwCmXT6frsQf5XaCew0+/hmWomdpkOrY//xLHKddgCfWhl04n/v6jKMUzEaMBnLFh1PQSEl+/gh4N0ffSUyj1W8HlI9zWxayblnPovV3kzKyg48tNGLpBz8efkze/CluaC1u6h0N334sgCYiyxTSL7BkmEQgz59nfk3/z3cT3fo8+3I2YiCBmlxDdsZbr5hXR86vLcN241uQ4Zcwib8VCzt71Z1RvIZbhZjbc8RGTs5xIgU5ibz+MZ7TFdJPWVdA1MHSSuRPRxy8msHkjzmlz0UMjyL4cpKLx6KERtIp5qN4CtNRMBCWOPNJhcmqCfkR3uimLd5rKJSHFgyFZsM9diaEoyBXTkDPzyTr3CrRJKxC6DyLoKpLHZ55LLMjZo9+h+fvJn19B3tI5uBeuIPzZqxi139L/7hvE172L1t/G6zMvAECwO9ECQ4g9B83A1MNb0a1OBMX0pdJtLsY8xejfv4EWGMSQrOgOD+lKgITdC4LIwOo1OGcuQk0vQQr2IVdMoy0qonccwJCtOHZ9jG3O8eSpQ0gDTYjJMQxFwVIywfTqGWnC2PMV+oW/xZaWSuzuq9hyxR3kH7+MsbZOBE0h2bLfzD0DMu55Dr2/Da18NnKgG4saM4s5LYmRiBP95l2s2bk0vb+OnDkT6P1mIzF/CGdRPnJ2EdaS8Tj76/lVvQPPsWfSvnoHicZaYkMBCk4+FjmnCN3uoies0BNOcOufvjtS9PwDoRnGf3jp/LhaPkecm/+d+O/u3Py/w4ELTqBrczely0vxH/KjazopGSnY3FYG9w+xqWnkh44PmBL3/zHeKkmx0B5VmJ5mJ6jodMUUPBaRcqcVhyTglCUK5uVRfso8nAtWojl90FVv3rzcmTDQjp6MI2cVomaUYUgWM3Vb1+jNmELW4TXEalYif/YIydExXCdfAoaOmIjQ98aLeCdVYq2owUjGEV1ecKahW1IQdJXvI+lMyXYSVXQU3cDzxm/xLlpuBp12NiI43Wj+frPVX1BB21NPkn/MUci5JSjth4gfdx1jv/8Z+RddYkqrdR2l9QD7J53LxEwHb+0f4OQNj+L+2YO0X38eeQtq+OLWD5h9ziTUeJLu256n2GPH+dyt6IqKpzyf4boWiq67mebf30Ph0bP47sa3WP7CtcS7uxiua8aZn0nG8mPN7paikGg7jHH27T8Ewdrqv0HMLEIfaAdRZP2l9zLutBqyli9j+21/JvbaJ8xZ/xiRPj+xoQCOTC/G9Y+gGRD+uUnSzZpWgSPLS2wwgBKJ46nIZ+RAG3afG6vbSaTPj6ciH2tJNcaUY5HCgxz7fj9fXjGVmCHxp82d/LYqiqAlUdMKoGk7Tx1zO2d37DFfX4qjrX+LzrmXUhFpRk/xmp2vSABkK3paHkbLbtO0sKAKrb0eObsQzVsI7bUIeZUIYZMgrAf9CLIF1d+PIFuQy2owgoM/fBYFm8Pcxmo3uzwB8zFBNovzyJ5NCKKIc94KEvXbkbxZyGU1hL9+D/eSEzBUhZ7XXyX72OVY8kpAsqCneNHqN2HMPxtL5x6iO9biPOpEDMliFnCihJpejJAYQx7tRuluQS6vQfXkIzVtxSiYQOj9p3Gf/bO/psbrGGqSpiefJ+fP75Ky9W0kbyaB9WtJu/w29G2fIiw6n967rqPwhl9hWJ1IYTOCQw8MIKTnmrykcIDBjTuofWUnOwMxfv3OdQCmE3aqj7G1H+BacjJGLAxOL4ISMy0Z1CRG7TfmWNHfh6Woiu4PPiGjpgJboWlWaCTjAMiZ+WiePFNu/+z91L28DYD5vzkJiy+DaGcXrvNvxLA6aY8KPLmxnefue+xHWfj8s5ybL3phHXZX2n/otba++kdWjMv6hzg3/2fEkY7PEfx/YtKbq6g4vpLB/YN4y9KIDJrE1OHDI2RNzmROoZuSFMsP27tlkVleO6mySHvUHH1ZRYFZ1T4W+BwkdYOgolO9pJicqVmoMZX4qbdiKAkMuwujfCa4fCDKDJUvQXKlYYgy1H2LkIygtDfwjVGJyyYh5ZYz9scbiPQMITvtaIdN52QMnczlR5vS8sCQOQaxOTGGe+iQsuix5VPtc9A3puBzSOSlWnBffQ83dxaBbMPQdaScUqxlExmafhboGok7XsBWPQOhYDzWsomkDR0kY/p4PtGqeHIoF0EUsY6bQWW6ja5fnM/J4zLwnXMlUqif8vseIdjSQ05uKvnL51F+/59YcPg9ClNFsk8/B/mmx5DTMym+9heMfPwqJWefRP+2/eTOyEEZHiAZilBy+91kXHGLyX2pOQaxei67Fl5PSnSIlO49pPTuI9FYy/qTr6bzrXdINtcx/55zUONJxg7UUrL6axY1vIkSiZGxYC7uklyzWPv9T8kVQhi6gRJR+PKBrwm19TG4t5n0KdX88crXyDv3PL49+hbW/OYTsq/4OcGVN5OcdhJSeBCtfhNrzsxCUBPw1n3cMTuNPk8VhmQl9umzGBOX8oudL+K/5iwydr2LFOpjaOteCjc+B5ijL0FXTXO/SAj94GbEFDei1Y7atBctMEh8/1ZztFYwHqO3ieiOtQAI+ZWIrjTkzHzkshqUxt0YedWmwq3jkGk7kFmAXjoddajHLAqDfgLlixBdaaQuOJbUxSdjuLN+6BQla9dhy8lh+LP36H//bVJy05E8PuJ7TSl24vt3CdbWIo92o/a2ERsMEN36JcZIH4bNiWFNQR7pQA71o2ZWgK6xXctDDnShVc4DQ8d73BmIsSDaUA8ja1cz/O0aDt/6HJ6OrSj9XQiylfQLrgNBZKy5GbF+HQW/vMssEmNB1N5WjGjQJDPrKtQsx1AVfv/gOtKK3PzktmX0rtuOYHcS3rKWxM6vkVPsGKKMoWn0PPckqr8fGjYijQ2bRH6HE8njQw/6ifvDOCbNRC6oMAn83iwMRUHNHoeYHCP01p8ItvTwSWuAT1oD6IpKz9ebcGRnoqWkE9ItDI4pP9qi55+Jv4+c/cfV/zhS+BzBvwuVz3yAZJGIDkfJGJeO7DAl0MOHR8ibkUNSN34YcfXGVRrCSbwW6YfOjlUU2HnIT1DRCSo6Y6rOKx8dwpJqJRaI46lfjZ6WhzzSiRTx/8BbyEgOYfiKIBbCmHocMZsXZp/CsrQIzkArxMMY1z9CSq4PQ9OJHj5IfP9Wwuu/ILx35w/f8EWnG324G3LKKaz/jKRmkBtqZrw0gmXb+zQHEmiihRXVWai+EuTJi2h1VqLlT0LRDdTeNj4/NMCnyTLob0Hpa8eIjyE4nJzqGuTiuud4c6yYOnsVugF5j72JJAr0p5ZhWGzoh7Zx4MLfU3J0BUKKGynYi7joPNjwJlrQT/hXl9D//TbiO78h/bRLiLU2kX/vM0y56wYsZ97CcF0ryU2foO1azdjUk4kJVm7bHmN2Xip+q4944QzQNQZ3HWLmjceRu2gm9snzsM1cgfWOZ3FWVJLb9C16zAxmbXr5AwZ2N+HM8XHogzqUNS+RVp6LqyCNCbNy6Vx/iJw51dgmzeO2d39O+/MvcGrrOyy9dQVJXznZ0U46QwqGbGNk5tl8OPU0dIsDf30rQvMO1Id+xtXbBZzHXUBMtNNw74NUnrEIefIitNRMcm+6G9u4acS3fYk8ZCqGjNEBECXknCIMNYkWHsVIxpF8OdgmzkFMhM1uXk6pSXC2ORETEQyr6QzNmB+OOteMtNA1bJPmI/81WkQa7UW0O0nWbUCumIa3aR2qrwRsTvTgMHrHAdPV2ZeDtXomluJqerceYqxnCPe0Wez77SMM7DzI0Ievs+eJ1aRfcB1q7XdYiqvxnXMlkZ4h9HgEQ7YhBPvBMDCSMRIWJ1L1HGammrJ4qW0XdOxHzShDt5tdAO/CpTh8Hk7S9iNY7djKJ0BWMQy2IUb8DJz1O/oql2PYnKblg2w1zTe9eajdLSTzJjP46O2ga1yytJiC+SUokThplYXse+RtAGyT5yF5sxB0FbWnhZyVx5q8qhQXxMMonY0gilirpjG0ZRfVv7sTPehH7W5GHehC7W0zfy+ijN7bgmy34czxcdGCQm69YT6RPr/ZDc0rQQ7182WTn7Nve/tI0XME/ylxpPA5gn83RjuC7NzSw9hglI6NXXywrZt4IE5/7cAPye3/Q7o+pppjLYck4JBEJAHSrSK9cZVTy7xMcFtZ4Euhe2sPrrxUIrU70VMzMCw2kpmVpI00IahJM4nb0DE82UihPlIHG5DGhlDcuSCIqL1tWF64HfuSszg3cRyO4mJsVdOw5ebjrKo2bxDJOMnWevPfLWZ4ZtHBz1DTi5Aifkamn0FV7VtYAx0cM7IeDJ2frB+jVOtHCvVhALUPvcaKikwK3Hb0eATL9OWo/Z1YCitBkrgz63zOGJ9BTbyRTV0hOoMKmm6QSZgRyYMx+1S8DgtvrPwdmr/PDAr99TUmeTY8SuFJR5NRU06wtZvOpx7BlpPD2Cv3IWQVIdWvJRlJEu7opfebjaTIAg4U/jBVYyhqjhhtbdtIFk7HlubCMWkm26ZcQrK1Hq2zAd/2N2HRBQSqV2ApGc9RX79PoHWUwqVTifT7WXj/GRx6az2D+zpxZHqZ+ecHKVpcjWy3gSCi+IcZaR7GsuA03BOqkXd9grp/I5a7L0d1ZRG/7xpO6tiD/vkTGL95nj2//hMPPrye5+YYiIkIKftX89Rb9cjHXYVuc5qhtIKI0tuOtWwShr/HlJmn52KoSdTBHpO8LEkMrt+K2ttOZOs3iLEg9DWT3LfBDJnVVVNiryXRx0YZKpwLm8xOlxb0ow11I2UWIOcUoTbvhZxybBPnkjywGa1qAWLEbxKXS6ZC5RyEqSvQi6eQqN9O91tvkn/UBHLmTCCwdTPWVAs5i2bhKi1k1t2XYwgiojeLxP7NxDZ8jO/C69AGOgm89ie0IdM7SE8vZEtXGM2VzYjFSzw1GyEtG0PXiMqp5ud9wbkYyTju5aeTOLgDPRZBLJmMYXGALx8kK1Wd35HhkE1+TyyIEBwwzQFTM0HXkA+tJzYYYLShmVmP3E7u4lmkledzx/XvEx2OEu4coO2pJ7GUjDfHXGBypsIBjHgUPeg3vZGCfsY2fomh6aj97Ui+XOScIgR7CqLLi2CxIR7aQGTfDpRIjJTcdCpOnoqh6wzubSPRbyryNFc2r2xso+/96/5FV6ofF/4eHR/9x9XwOVL4HMG/Hyd01fGL0vmEesJIssgxBW4yJ2Sw8JErGVPNBPclmU6qUq1M8djJs8t/HWuZ6q8x1eAnvz2W3Bk5eLKcpGc7kawSakzFUVoGu75AbanD1rkbJasKMR7E3vAdhmwDQ0f1FoFsAUHEEuoDMINKT7+AsVWv88l5VehLL+OJaLUZUKom0eNR05V5qIfgjq3IeaWo3S1EG/ZjGWpGtzrw1a9iZE8dWmomUn4VGDpPnTqe13psaJ0N5Kz/CzMevo0p8hDTxF7k7GKSGz/CmHcWyapFDLnLuH1ZOYG4xkbKOIFD2GQBtwWERITMUCtRTWCaT2LyRaczuPswPd9uRYnE8de3kuzp4PvrnyNl8Wk4c3y4irIQ3T5C7f1o7fUIeZVYnVY+WfxLCn/9IBNv/BJxbBitswG7LOKVdbT8SdQOxshctpTBL1exZsIcPL+rRSqZyKby04i8dBfexBDaQCf6/u+Z9Z3p2eO69QmCzT107uyl+vzFpM+oIb5rLQff2UXXulq2XnIzuqIy9e23aL79Rvb96T3kvDIsE+ch2a1Y/G18/JftWBs3IK+4hNJoK5lvf8bTze/R89yT7P/VnQQ2fs89fzgFfetHUL8BBJEtx53Fq57lhHZuxtA1BFFC2b8JyZtlFpNOL2p/J1mL5yF6fFh8GahDPfx2/s+xTF8OYP6+cst/kF6n132OZdxM5JrFZop7VpEp/fZkI+eVmvL1kX4suSWIsVGT0KxE4dBmpIgfsWUnQssuoj2ms7EgidgKS3HmZ1KwqIahrXtBFDHiETMYtXwG8oIzkJwuaq+8lp5vt+I763Lk7EL01Ax0RxqtgSitP7+YsaSOBR0iAYTSqcQ1Awwda9cepKLxGMmYWXyUTEfvPIjQ14jedQgx4kf0+JATIbSMUnRbKnp4FMHpRmzdhWXx2ey8+QEcWV7Sr/+92bWZuhTX3CU88taVVJwwiZScdIovuRDdW0CyuQ4pMx99sAN93FEo3c0YahLRm03HJ9/grzcNLeXsIvSxURL1O2j5y0vokRCGkqDngw9R4wl0RTW7StOnk7toJjUP3on76FMxymfylz19bH71lX/ZderHBl3X0f6D68io6z8Z7rrrLtPN9P+2cnJyfnjcMAzuuusu8vLycDgcLFmyhPr6v03+TSQSXH/99WRkZOB0Ojn55JPp7u7+Z5/Kfwsk976EzWVFskp82hEk3DfGnofeZtzMXNwpFlb3h3FIIqU+B+NcVjQDxrtsjPw166t/exOe0ix81T6iwzHUmEpKRopJwFSTyFn5aN5CpAPfmHLgtEx0uxtBVRATYVRvEVrDVn62PogQ6GXo27W0//kJRItM8KX7iT5/Bz/1f4YRixA8cJBon9/MWXK6sLqdiFY79ulLcM1eSHzXWsR4GLF4IrLdity5F7W1DjQVW08dy8vSiU45EcmbRbJ6CZrTh9HXgpaagXjSzxGTEcT1r5HZt4dMMYbPBgvG9qDlT6Jw3we8un8Y/eBm01Swby/aqqc55k/nkbNkLmU33YrFaSdrRjWh9j4mXzybxrvuJNTeh2y3oYf8ZM+aQLKzkchXb5I9u5or5AM03fwzXrl1CQ16BskZp2CVBAYSAjHZiXLuSeiRELGhAGe37CGy5rckMyuZnuPEevk96I40dtVcjOTLoT2kkHLSlXxVOoOULC9rBiK0frYFy4xjGNh5kMUv/Zo/v3OQnBn5tH25i+SXz/Hpew1MvuYEQms/IblvA7lL54Khc0PtG+DLp0N1QXgY/a4rAPDVlDP+xivp39VM1/qDDMw6H33mKbTJeayuG+AqbzeehStA19H/Gjbb/ucn0Eb6UVv2IdjsqP5+kj0dqKMjaAOd/PbjXyIkIhiJOPE1r6JmlqNbHci5pWZ3b6QPhjrMQkeUkLyZ0G921YRExAyxza8m+M6TZidREE3H5/AwSl87I+vX4sj0YimqYqxnGKW/i3DnAJLdyuFP6hipO4ylsAq9bCZiIkx89UscfPFLXv6qhaL7nkLrbSZRNAMMA0FLclWxwq3zbifHKSNoipnDZXMiAu1RwZTq9zabBcn4eUiBbgRRgsxixKIJ6HYXyUO7kEc6EXTVLLisdgTZwsja1XTLWcz48F1yrrkVMTKCHg2bCeyNe5Ez88k7/yI8Z1yNmFVMfM2rWAqrGP52DUb5TIxtH6PHo0gF49j/qzuxup2k5meQd/FVGJIVqWg81ooaUvMzQZRIHtpF0RVXY3U5SQTGsKe50Pz9SMdehe4rJpo7md9sDXLLdfcdGXH9E/H3MTA8Uvj8p8PEiRPp6+v7Ye3fv/+Hxx5++GEeffRRnnrqKXbu3ElOTg4rVqwgHA7/sM0NN9zAxx9/zDvvvMOmTZsYGxvjxBNPRNO0f8Xp/JfHsY17UGMqZ1T7UGMqalwlvSIDd4Gb5VlOMm0SklUkMyeVacUeRpIaldlOQt1BAHJXLCFrWhklS4toCCV44vX9BL/5BD0aRpAtiANNCLIVMeJH9Zi8H82dhaAmQRAZnnYGf17sQbDayVi8iLwVC1HP/BUA/dsPMbLvEACeSRMY2N3EWFsX0e5eAo1djKz7mr033GG6+hZVAaAd2sG3D32D4c1Dqp6DPNpNbMfXZKx9Gtt3LyCNm0Vc1ZEDXVAylS7RNLUTI37EmSu5pyMTACnUT1vuPA5HLWy/43nO2P4Ejc+/Q8xbArLNNFnUdbPIA5z5maQuXIlkt5I+pZqq396FGk9icaVgyS9nuLaRob1N+A+0smbBL+gtXUzeUZPZ0xekyG3hu/YgrtrPyJHiiAJUfvU18uSFxAMRBiMJAO5f14Z7rIeUgQbY/jGDkQRq1UIC553E4DMPcHr9VyzvXcKfPr2JCXfcTOfDvzPJr5XH8NLUZZT85BoypxSjnXoLN/VsZqyti5GGdgDk3BKMnibiu9aS3LqK0r6tqMP9aEkVJWc89mmLMSYdTfHyaXhf/oj8eBeWjl0U1X3Az+9YQbK1nvCW7xDzygnu2MpY42Fyl841vZSScSz55YiyhUi/Hy2exFpRY1oWJCJogUHs46cjHlxnml1mFSFl5JnFUk8L6Bpq2wHT1ykeQXS6EJQYlqIqBp55kLTFxxLd/jVGXwtiahrJ5jqGNu8yu3B1LcQP15I9byqOKfMZbexiYMdBcqZm4ynPx1CTGDs/Z+SDl7CVT2Do4DA3/GSmWZBNOprBiGqq1ASRt/scvHfxNGQBYoIVw5XBqOjCG+un9PCXGHnVjE44Dsu0ZWgNW03X8NKp9D75IGIsiJiMYRz7U9SeZuiog6xic//xKBknn02BNsyuoIWSX27AsDnRg370E67HPvNo5Ipp4PIhhgcxRgewlU9gZN3XeKdNQQ50Yy2bSMqcY2h/9AFKj59F7rL5uH5yH5o7B8PqILlvPcGt3yPZrWj+PsSjL6PjOdPPJ2P6eNzHnY21ogYxGiDiyGBbzxiP33WEzHwE//nxX6LwkWWZnJycH1ZmpnmjMQyDxx57jDvuuIPTTz+dSZMm8eqrrxKNRnnrrbcACAaDvPjiizzyyCMsX76cadOm8cYbb7B//36+/fbbf+Vp/ZfGF31hvKVpJEIJnFlOWr5upvXQ8N9s09YdQnbIlDothIMJ+msH0DWD3q++A8A3qYSKVAvnzsjl0PtbUfzDaN5ClPL5CA6nOa7oOkDywGZo2ETcmYkYHiQ73ovu8JBsrkMfHTQNDT/7I55LbyNnTjVZxx3PWFc/yYE+nDlpCJJI6gQzWT59xQlYUy0M7jkEumbK3D0+znj/DozeJvS2fWipmURW3siKlpmIbh9GbxOerh0QCRCw+ihS+kmm+EwnX0Pn5t63iEoptItZFO56A8f9VzH/ubtwTZrCxHt/iywKaClejAXn4lhwMkYihtJ5mOyzLiK+dwMpWV5GG5ppvOcusmZPRnQ4UYd68FTkE+kbIf+UEzg7K0xU0Wk67haumZxGihbluHyZFy1zUa2pXPpOHb74IP3PPUrWtApWxPbSpKdz54JsAs58Ap+/yb4/vk7eL87D0radWa89jW/ebNAU1t++mMEFl2Mk4vxi/PW8etxvKezZytj3D7P/zgdw5viwfPEYPffdjGS3knf80RiREJRMRcivxFo9g+fzziSyawPBaadScu6p1PlVDHcWX3bESDn7BuI3nU+DmIdgdRDaX0fOORciWO2EOwcg7Md3ygVo8SSKf5hYYz2CbCFatx3BZidj2QpsOTkIdqfJ6+rvxFpRgx4xv9wYShJ9sBPd5kTPKEF0eU2zQKcLPRJGzi3DSC8g2X4IKb+K3Et/iuh0Y58wG0GU0MdGifcN4B1fzNC+DmSnncHdh2h+9xt633mb4pVHUXDsAiovORU5LZ2xXVtQBrrwXHILWmCIOb88lpJLLzTl7ECqVUTQkvSpds4e/Y6ootMaVLDJIoZkRdExw06tdpQN7yGJAvS3Ik5YAHYXhsVO3vW/wlAVjGgQa6ADsXwaG73zEJIxEhNXIGcX0uCajObOYVqOk9Ynj0do2oZcsxirEmFswxdmIWRNRc0ehxGP0vD4y7iqyrFWTkVz+tD8/SQObCV79gQGdx9Czi7C0roN3eFBCA5grZpGSlEhelJFzipA+/oF8lauAEB0pZnb+fIxbKnsH4zx3t4eEruf/+dcgI7gB2j6f5zno/+rT+KfjP8ShU9TUxN5eXmUlpZy7rnn0traCkBbWxv9/f0cc8wxP2xrs9lYvHgxW7ZsAWD37t0oivI32+Tl5TFp0qQftvm3kEgkCIVCf7OO4H/iWaOdF1a3EEtqPP9Fk8nTkUQ8WU7SMlLY0RFiTDWQLCKZHjvVy0swNINkJIkaiaEnVURJZPKZEzE0ne+29eKvbyPw6iOIez5HkC1oQT+GmsQ6+SjkzHwcgXa0PV8jxMPInXuxVtRwYPqlHPjT61hyS+DgBlKvuo+BL1ehKSpaPEn+SccR6R9Bzsona3o1WmCIcdddihZPIMhWlJJZdJQsJVy3xzTBszsRDB1f1zbWr4iZMt+sYoxE3Ay0PLCKUGo+js5dCDs+Qd+3FuPs23F176Zs0CSnqvEkRm4VQs0yDMnK79MmotWuxR/X0dLyUPvauX5wImpfO7aJs0ldcCwOnweL047o8jLW1omx5GKaP9nOuBt+wve5K2i3FtAbTlDosXLZJy3IA4cZ1OzMzPNQPxTjz2dMQt35JZm3/BHP9BnED+6AWy8g+ubDpDV8jXjZvUx5/yNm3P1TRtZ8SvSbd2l7+1M6H7iDXzrHk5kiIzjd/Om0SdwxO432vHkI8TATXnoTa1oqajSGLS0VZ1kZotOFlF0E7bUMvvIU7y2/getzR7CkpdF+zklI3iymRw9w1z6dydmpbB+RqLz+KpwP/xQlexx33vQxoXWr6FuzFtEqm1YBB7eTNnMWhq5jy8rEUjIeOcWBFgkztsf0ijHiEdT0IsZmnIZWNhtLXgmCbDUJupEQYjKGFOpHLJuCkTcOQzWLH3QVDB1p2goEXTXT3eMR9LFRYvU7GWtpQ4nE6NlQS978caSNL6fowvOZcOetpOSmgygizz2Z+FEXImcXkXrmtdiWX4QYD2ObMAfHJXciFIzDsLsQdBWbJBBPKyJPHcJSMoHhmEq+y4I/qqJs/JAsZQijaQeUTKFr4dU4ZBG9Yo7p+pxexBAuhNE+cKZh+IpQa79DtzqZnZfKOrUQW6gXJb+G0jQrCR0sgS4zrqNwIklvMT333oj9lGtQvIVEv3gRaaQT0eMjY2KRWTyCeayyBWv1TETZgqHpCA5T+j744E20Pf00yca9WKcsJvuqmzCUJLaJc0yfo/RMBLuTJjJJZFdzOGrh1ndqeeWBJ/6FV6QfL/4eIaU/sknXf/7CZ86cObz22musWbOG559/nv7+fubPn4/f76e/vx+A7Ozsv3lOdnb2D4/19/djtVrxer3/r9v8W3jggQfweDw/rMLCwr/zmf3Xx7NGO1klHiQBtg1FADjYEWR0OMqKRYXUlKehRBTsXjtqXCWrJhPJKmFxO9EVlWQoSmQwTOaEDCpSLXz1wk6S4SjBHVtBlJHSc8z4CjC/IQcHkKctx7A60LMrUf39TEmJMPUPvzWlualpiLVfEr/uUSI9Q9hzs4k1H8KSYidxeC/2GUsRx89HG+qh5Kc/A1HkhX2D2CWB1CvvYcstz0J2CYnv3wVPFkbBBLTAIKq3kGTrAfqPvg61t52R2y81c8EqphKbew72re+gpxdiKAq2iXPwTSwl/tUrxD59lsSONdy150VC8y4gu30jXb/7OfUTz+aBwTcxNI3mx55AsNhwLzuZgpNNY8KBXY1YhlsoeetTrp/9c4IJlSJLjCnZTvrHFJ48bSKH3JOJawa5qRa8DhnXl3/CVj0DcWwYadwsLNmFVD30KK1f7qbl+VfoveYsDMnCztv/jO/k83CccDkVD/yJwnPP4uaB/cRUHTVvIkWWGFGLm6JDq7hkzTA9ig05M5+4P4gz10eirwe1uwX/pk0MrVmNM9fH2d8+hh4cZmDxT3hpTStDX31Oc/oULvvqbjJTZN4sn0Fk7zZyH3gJuauWB54+l5Txk2ld3UBaVSEbLvo1h1/+DCMeweJ2YyiKmb/lzSI2GMBRXomlsMrM3hJEvEMHQRBAthEft9jk8YgSyeZ9ZhdCTTL20V+QswpRyuaaRU77foRkhMTB7SjDAyQadiN5M3FMnGW6aisqvomliBYZyeMjsH4tan8n6SvPwjZ9GYKWxOVvQvJmIkZG0PZ+A33NoKtIEVNhhmEgjg1hD7TTc8MFYOhsppQSh45di5GT6MOy5FzULR9jKCbfJ8dpQU6EkAcOg2RFatpKphFEy6ow4zQattI3+0LUta9jD7Sz2BNBivgZe/keLAe+JpjQGLDnIRg6+qFt7DvhWHKPWYK25WMsIx04p84DIHloFzlnnI086SgMJYG+43MEXz59b79Gx1dbScnxkajfgS0vn/zLfkLpdddjLZuIcngXyo4vsRRVoeZPRulpQbSnIFVMpVIcoW00yfauUXa//+aREde/CH+PrK4j6ez/yXD88cdzxhlnMHnyZJYvX86qVasAePXVV3/YRhCEv3mOYRj/y8/+n/j/2ub2228nGAz+sLq6uv4DZ/HfF4u2b+G08Znk2S10RhXSrRLfDkb4ZF0Hsl1GdsjY3FZGO0IoYwqhrjADezppXnUAQ9PxjS9AjasEFZ18h8x993zDgde2MPzR6yQP70ZIRNAHO1CHejDScul94veE3MWga+hTjkMwdH7T4iNxaBfxA9tA1ygda8J+z0sEDjQytLcR0Sr/4DyrbPoQIcWN0tmI0lrPT/NCpDtkum6+hDm/PoOh159GdLohOAid+wls/B4xGuC7CRcRUwzkpeczUNtHsrkOhrqwrn4KYeYJqDtWoXQ0AGB1Owm29JA6bzmiNwuls5HUb5+BzEJKbriV4o/vRXbYEKYcTeVtvzJvREE/4vRjsVbUkJLlQYyH6R9TeXztPfgXLUNQ4ji+fJwJjii9YwoZDglRgEBCQ7n1AuzzTqTutt+BZCHy1ZsADL74KJPe/oCMmnIqLjmdrpsvIX9BGWpvK8rGD0isfRNt2gkUH16F9PZ99CYtPFO0kLhm4N+0kdcmDvLWvj6CtbXYfR6G65pRYwkEp5usk04jrbKIZMgkDAe+X0Oh0s9T3atp/qyWisGd5B+7GGeom8fX3kPK5BnEX76LxP6t2AsLkTw+ihaVYC2qYt5DV1Ny7AwGN+4g2jfA4K56k+cTj+DI8qL/tfOHKIGuYkgWBCWG5vThGGpEc+cgZ+VjGTcTBjtQ2w6QOn0uCCJiMoKRXmCG0jbtQUxx4Tj6HOScIoa//IT99z6O1ZWCEjJNOS1OB1pgCO/FNyKNm2Umtw92mmaD3YcxbKkIyYiZg6YqKN0tiPEgmtNnJporCQyLg9KLzkZPzaDIY0OT7QxrNtCSZrDsiiuRKqdjCCKDEZUhw0m0YDpoSQa++Awp4kcO9qKleJErp5Hy0q+RTrgWPcXL//hqnjphMtqkFSQ1g5yebej71iJIEjMfuJHEgvOR55yEIcqm39ToIPK8U8GVgZCMoRRNJ7HgfLS+FqyuFOpWNeObNRVLSTWW6ctRuppMwrdsZXDdJpCtJA7tRqj9inhXF3LldHRXNt8FUnhxRyfX/uTuI0XPEfyXwn/6wuf/CafTyeTJk2lqavpB3fX/7NwMDg7+0AXKyckhmUwSCAT+X7f5t2Cz2XC73X+zjuDfxvL6nVhFqCnykO62sTInlZGkxpvbepAsEpt29CFKAoIk4Kv0EgvEsTqt+Bt6aPqsDneRjz2jcTYMRyn5v9j77/C4CnPbH//svaePpmk06r1Zzb3gjo1tjOm9E1oKhISQhLRDGimQQggQQgsBQu+92LhhG+PeZUmW1XsbTdP0Xb5/bB+f37nn3t/NuZyQgtbz6AHJ0tbMaPbsNetd71o2I329YXbfu4mxXftRAiOI2SV6n1IiQsFVV2N+9149C2bTU2iCyJ2Ls7EsPg/5vNuRCqqROxvJC7eRfdZ55C2ehtFuYXh3k64oLL1UN87ml9K9dgeKMwdz9x4yinNIn/l15KgemJce6GL43XfIXL4KcayLee/dhaJpHIlnUHfVIkynrEF0uDHPW03rLdcjLbwA0/RTGXrpWQ48vJGdj3xC16OPkFh4BcbCCkwLziF98CNkbykWr4tUOMqWhWehWl0oOVUIdif9v/oBB7/zMwRJIvrJWmoSbUjeXL6y/1libz+GlFOM0L6XaruCd/wYhc3vUeUyUPnVLxL58GV44CW6NA+OU8+GeedhtFvYdspKOt4/wPjuvZRedzX23EyOPfQ0yppbeDD/Cl5tGqOp/AyMDhvZ6+/nhgevwLXtKbIvuJxw9XJGwkmusV3KyL4WCs5axZs/foeJ422oE0G63vuE8eZutFSCzAuuRQj0oxzfz9TrT+XwD3+NcepiVLOdnlfexv/RJhzzlmCqnIbk8pLqaKTo4vMQK2ZiqF+IbepszJ4MLF4XVq+TdFczos2B5HBjKKxAzC5BGe1HM9mRvaUoVrf+HDBaoPsISsiP3NmImogiOdxIvkJkZy5iLEDgpUcQU3GMxVMwlDUAoMajeBYsItQXYXBHE9EhPwM7WrDVz9Ab2ZMRGOpAqluI6NJTxKX8SogGSPe1Y56zEjG/AqlqFmlvOUI6iZCK6QGL0XGC085BjAUoSA1iSATJTo+iZvgY1JygqUTdpSgZWbgtEpKojyv2hs34br0TVL01XuhvQUhGcd9yF8KBD1AtLr60MYBqtjM840KMw8cosiike1rx79xN+MAehIIqjKKAOHwcMTmB5M0FXxFiagLVnIGSkUXvhIo90IGWTJC5Yg1zr5qBIadYTzhPRvXHKaeIie3ryDv/PMQMN7GuLlI9rQRae9AkAx+PahwYCPHn+5+fJD1/Z/zPJDf/ve/FZ4t/OuKTTCZpbm4mLy+PsrIycnNzWb9+/cl/T6VSbNmyhYULFwIwe/ZsjEbjf/qewcFBGhsbT37PJD49bhxvYW2rH1uWFWeB4+TXdx0aJqqoqIqGZJKIDE6QNSUTZ5GDeCBBztRslESK5T4bcz0WumJptvvj9MbSNL+8n3RXC+nmXajeEjSjGcHqwFw3D81oRlp4AYKmohnMqGYH5hPhiYLRRCq3BiXkJ9jaS2xwHE91MZqiEHrhfgRRN8iWP/gcqtlBbPdGXOWFGN+9j+wf3Ic2Yw3R1haMdgta2Szih7aTedXXqE91krz6XDIu/ApK6z7iezagSQZqf/Urti45j9TRHeScewFzv30Wy390JvnLT8G6+1W23/gjtP5j+PccROrci3Xh2bjmLaBsRQWJzS+hWlwEN7yNszSX6ksWkTWtAkHS78uuW36OMtqH5dLbecq6mH3ZizGOtiEoKfwfb0MTJSiZhqW4lIax3ZQYIgxkNrB79TlYvC6Wffg0s++5g9iQn94XX0aQRCxuG8PfvZZvajuYX+ii8LWf0/3BLg7+cR3xkYAe/hcYwWIQuWeBjQ9Olwh2+JFH+zn9q4sAMOSW4q4upOC+5/TQOk8hry77KuuyV2Crncb03/0CFAUhFadg2WzcU+sQvAXs+fbdxJoPE2rt1DeuOg8hhIYJbPsIZ00V5uoZOOtqEOxOUgPdeveVqkBkDLFsKkI8hJCKYoiM6KpPyy7USEAP8itrQEsmEOxOVKsLMR1HSMXxXPplALRYSFeBEhEkj4+RDRspWVZJRmEWufPqaPjN3SdX4IV4WN8GS+ibhqrFAYKod1xN00dfmmhAkFMYAj0gCKjmDP33mO14g20IShrV5kEKD58sVc0wiUxoRkQB+sJpZFXDZZawJcaZ7ZVImV0giqh2L2rFXBT/EIbufQgWG13hNI+dnk3AWUaulGCPUIz87oNEjrXinT+PjEp9PGZq3aobvzVNL/pt1ZVTKeonafNSYoqDZCLd387Wa39MyZe+rBPH0X5Sh7YgZxbT9+TjWOtmokYCGPNLcVz9HcQLv4v/m39k3FHKpuNjPP7KEUKb7voMX2Um8b/DZGXFfx+Gv/cN+L/h9ttv55xzzqG4uJiRkRF+8YtfEA6HufbaaxEEgdtuu4277rqLqqoqqqqquOuuu7DZbFx55ZUAuFwubrzxRr797W/j9XrJzMzk9ttvPzk6m8T/HO5Jd3CTUMrZeQ6muywcCiVonUgBEO6P4C5xEg8kGDs2jhyXyarJ5ON3jpNpkmib0MtL/73g9FAoQW2lh7H9zRRe/0WYGNWNrQYnDvsg0sQoqt1LOiMbQ3QMzWRD0DRC3mpGbeVUDh+D3GLsuV7sl3wNMR0n+v5fcM6cC4AaCSAe/hC5qxn74rNRzXaE0DDynrcJH9iLc+YcxJr5CN2H9MC7WADF7mXWHdeS2qRf7M1LLkBMRJC9pSx/7V590wno27yfqu//G9HcekyazOIX6tASE+Rd8QWSR3ZgqpxG9PB+ym76Cm0P/JFC8Xe4lqxCjUUYevtdhg50U33hfI7/8mfM/rcrCe/ZTvD5F7nkF0+wpTtEpGwq1rifrNNWMRiTyT64EWP9An0sKBoQH/wOc39yLYLFzpC1CNMLj5Azrw5L7SzCu7ZRfNkFGAsr+GrV5XjencNPaur4TdkNfGP/AwzvO07lVdUkjx/B5nDziW0GhY/8lJm/+TfSfe1kFMQwZ2WSPLoTo92CbfAILe9so8xq5/wXvw8DH9C/dhOpcJTYyATFK6fjXnYGSmCUx2Zczlk3L8BWOw3TQCdqYARTxVTCG9/EWVeDaepi0s27MJXXo3kKkLoOM/rhh9jzRrEUFmHwFmMY7ybZtBvRnY1YWo8hvww15Efy5qKO9SFNmYditiOFR1AtDoREBFGVkd35iBYHQjpBqv0IQxu3MrCzk1Oe/SPI6RMN8TLKaD+m8np91JZM6N1WgWGUnlbEukUIcgJprBM1GYdoGFzZqBk+pP5GfUvQq9c6vDDtfK448rbeMSeKiLEAUmiADKsL1WBGVNIomoZZEhiPK1jNmRglAfOh9xG8uUjiIGpPE+meVgynX8/oAz+h/JZZCKpMZuA4mmRgxtGtjLV0s+4P27jyiWJEp5fk/k0YcouRPNl6FcVAF6n+bjQ5TfCUy3GjMiFlYN33Cjt+8RbTb1yMPNRDpPEQmqIytHsn1Rlucs9cDTWLGP7NDxg59Cwlq6aRecG1VGVVY5EERiJJ2h668O/2WjOJ/8C/k5dPg8nk5n8w9PX1ccUVVzBlyhQuvPBCTCYTO3fupKSkBIDvfve73HbbbXz1q19lzpw59Pf38+GHH+Jw/Ifq8Pvf/57zzz+fSy+9lEWLFmGz2XjnnXeQJOnvdbf+ZfHEjNNojSRJ/S9n0vtDEzRt7SU+FkMyiiQCCZSUysIzK3C4zFx89VTGU8rJXi+AKRfNwl6QReLQdrRkDCk4gEkSSDrzSRVM1y8mO17WyzHlFMbhY9gMAuVCEMWZQ7p4FtrVPyb5/uN0/uwHGDPs+D/ehuTNRfLmITncGAsrSfccg6EOIh9/iGi146it1csabR7UVAIlMEIqr57U5hcwVs3Uw/KsdoTgEOFNb7Nl4VknUqKjaKkElf/2Y7ZcdAvW49sQ9r+HkpFFuqtZr7hYegmDr73CeEsXnY88SufGTno+3M3uW39BquMoqqqy45N+Dj62mbb32wk3tWD2ODj+7jGSisYZlR7axpM8djzN+JaNrK2YA3IKIn6COdNo/+YXyb38Wr2eIBEl8u0rcdRUY8j0kWo7jPusKwBINu3mm1+axeqf3Ig82s/dDUmyvnk3jmIfkZZWutftofORR7n5oR1YfvgI1mueI9x4FFVRSAWCJPoHsGZ76H74D1iz3WjJOOFDB3jnqnsAKP/572h49FHslVXIxTN5yb6Y1dfPwlmWB4CUU6ynaA91YSmtwDhjmR4+KEr6Rt+x3Sij/Zg9GThXnI9h5krEyAjpvHq9ad1qP0FWFES7g1THUQSzBSEVQ4wFUO2ZaCYrgsmqBxwGBwBIHdiEGhjBW1/G3Jf/ghAPI6gyif0fke45hiGnGAwmBKNRD0Gc8KPJadRYBHG8FzERQUvGQU6jFjUgKCmk8R60eBRNURAjI6Q2PUfsvbUIcgKhcSPqUCcYTETypjOWNuCPyYRkkQyjiNUoIomQocawde0i3nSAVMdRUh+/iWDQ1+NT7z9G9le+i2zX06A1owVhYpzE0mtxluZx8W8vxlw3j9ixJkyV0zDkliIYjPjffZV4+3Es1Q0ET7mcTOLsH0mQcfBtut9cz5xbV+GoLCW4dw/OmXMJdw2SWVOEmogRnHYOUmiIgq98g9prlgPQ/+j9ZIS6OTgS57FLZ/zNX0smMYm/FQTt86Zx/T8iHA7jcrkIhUKTfp+/AjcJpQBUZ5iIKxqKpuEz6xUWU3w2esbjmESBGcuKCfdF2HR4hHyLgf3BBJkmifGUHi753dsWUnrLrbr51+UF0C9+chIhrgciCsPt4C1AM9nRRANibyO2G15mYtNdiPEQqtVF9MX7mDjR4O4ozkFJpLCUVOilmNkFhLetx5ydheTyooT8mOesgsgYSu4UxN5G7lr4Nb43shflw8cRJAlDQQVaIobkzSW46T2seTmkVn+VyK++TkaBD1VRiA2Nk1Hgw5KXg7jietSNTyKHQ9jnr2LPLT+kbPU0jj6nr2qXrqgmPhJESSs4in0M7unCU+6h9MLVhBqbsPo8yBd/n0hKJa/lfQbefo+cBTM49sxaHIUeSr/5fX7SbGZRWSarHOMgiPT89k4KL70IQ3YRangc0ZWFJojI7YeIHT+GOdOF/0ibHqA4axpqJIihsIK+V98k1DnGlCuWEzzeg6M4B8sZ1yGOdaFmlaIe3kxqoBvzuV+FI5sI7tqBLc+LweMj3t3Nzl9/wLIHbsQw7yyk8BCa1cnYC4+ReeoKuspWUD62j2TzXrRUAuvclaS7mol3HMdaWYNUPRuldR/Gwgomtq/D2jCHRPN+zGd+Ec2cgWGsg/TxAxhr5+lECUh3HkV0evV+L4sNebgX45Q5eg+XxYlxpBV5tJ/k8SNIVpteYDp1AYlDH2M95QxSbYf04wzpCwym/BIMhZW6EXv6ImJ7NiJabJjrT0Fz5SK78nUjcyqKEBwi3deOYfpyXd3pOYLiH+JI3SXMMAdPbJklENr3kqxfhSgItI6fMNorGtl2I5lWCet4B4Iio0kGtP7jCAYjQnYxsrsI5cPHGVt+E3mpYcR0DNlTrAdojvfqJv9oGMFgQjBbUDOLEPpbGHj1VZ00VpdjLKpGdPuQBzoYen8dB5/aQ2c0TV1BBvVXzUNTVDKn14DBhLl2LsPeelxmCUvvPtp//3vKv/dD+hyVxGWVT3qD3DCn+LN5Efknx9/6mhGLxbDb7Sz91bsY7a5PdazW1x7g4pmF3Hvvvf9Dt+4fG//wis8k/rmhaDCvxMlAQqY3nqbYZmA0lKCyyEmmSaJvxwCSUWLN4iL2BxMn6y3+HfufP0Sq9YD+rjvk1z0TLVsRI8OojmwC1lx9jT0WRJCT9OFCK6ihc9MfENp2szWWiRgLYL/8NvIvvxJXdRmSw425qAwl5Ee0O0kPdOE473rMtXMQrHaMxdWgKnoRZrt+gf7eez+GPW/T/e5WGmddh2i1I1bMRB7oxH32VTQ/tQ7jO/eSNX8WE/2jqGmZYPsggiRiLKlF+fBxpJxiYkN+lJCfumtOJdw1SMMXFuLIy2D9n/cQDySwejPo396BZJJoe78d05TZuG+6EwBnqJPCWCfqzLMItg1hmrEcV1k2Zbd8nU1rbsR1yXksa32JI7d8A9lTTMk3vosa8pPOmUK6t1UP+LNnMrZ9J+ZMF4bianLOOpPeLS30vb8ZVU7z4SU/J3f+VKY+9Ad2//ZtRg936QWYz92vbzCN95Ia6EZNyzTdeA2x5sN4lizHVF5P23PvYnJn8FZ3iJYn3qVLzObFRV9CNTuweF1ED+0md+3vkEf6MRZWkgpOoPiHEOxOlFQaJTBCfNMrhA8dINXVgrlsCmrIj7miDmmkDUPvQeTuZgzeXIRUHC2nAi2vWk8Prl+C6PKiTgQx5BTpTe+ahjQxqldEWOykwlFdIXJ5iR/Yirl6JnJ/28mNP3NFHbEhP/GO4wgmK8biajSrE+v0RZhKa/VRVs9RpMgIAIKcRsmdov9/Koayby0A0UVXM9Mwoq/cixIc3YqQV4E5OspESsFqFMnPMFKTZaEw0oZRADER0StRTHaEgiqU6kUQGsE40opx0QWMf+ki1MatIKdJiybEpO49wu5BMJjghG8NoPvpZ+jadIysS29EXHkDWkEN0bypRA7tIxmM0DaRYmqpi6wpXn754w/4+U/XcdflD7L9e08B4Gv/CHHL0ySO7KDogjUAZNsNHByKTJKef0BoqvapP/icyR+Tis9fiUnF57+Pm4RSVufY2e6Pk2M2EJYVKuwmXEYRq92EklKwZVkx2U3sOjTMudfO4J4/7v4vx7mgKpPSZaVU3PxlRGem/i5aSaE48xBSMTSDCSk0pBtMLU4QBLRjO7l16fd54OPf6BcfOU26r43h3U2U3HgjwY/WYivMRw6H9EA2g1HPKhno1FWgsgZIRkn3tOo1GnYniWMHsa64DLXjEPt+8jB1Vy3hwMMbWfiH21EDI6iRIKbqmRy847cMHxll5ZPfZHTbJ7gqCpDsGcjhMGpaxmDXE4uf+PmH1DlN2JxmvJUedm7vozee5hs/Xo17ShmSx0fPa+9RfNFZjG77hEBrP7X33k/3r36E2e0g4zv303n1+dTedDE/POdXfOn6GeQvnsaqnsVsuW0u63sTTPHaKMXPi30GdnWO8wv/ixjsFkylNchDPcgngjl7NuxDMkm4KgrwfeUHjD3+a7JOW0V4z3ZSkRiZc+cwvmcvnpnT6Xr1fTzVRSQDE2TN0PN1tt3xIqc991OUwCjHHn+V9ESKrn1DVJ1aTN0fHuLQ9V9kxi+/o3dyyWnUaBhT9SwA5OFu/WuRgO6tkVMY8soQa+Yj712rjyYdbr253OHRvVSihHzCj6Nk+BDjIX20lYrrJnhNRRsfRMxwM/7eS4weOE7+4mkYM+xgMOoeGFXRy2xV5YSpORvBYNRJdlwnSZI3Fy0RQyicgiCnUPqOgSgR2vkxmavORnPnIshpneTEwwgmK0pGFoKcRHHlYxhtR5AT9Hr0bbJsu4GkrOfkJhQNj0HvCxOTEaSJUVAUvdk9I4uEYCKjexdKyI/BV4DizEXZ+wGm6pnIo/26QucrQEslSLYdZnh3E+loAqPdQunNt6DFo6hFDYjdh/SOsLwqxp68F8/0BlLDgxg9HsYPtRBqH6BjQxcF8/IQJOHk1qW3Lh9vfRm2WUvoyZ1HWZbjv5ybk/g/47NSfBb94u1Prfi0vfEgl8z+/Cg+k8Tnr8Qk8fl/w01CKXM9FgCWXTODYIefVDSFv2Wc7Gk+YmNxvFUeJKNEqC+MmlI53DjKcFJmKCGfPM7PfnseosmA5/Tz9R6mkX4EoxFl9rkYIiNogghGCyHBhiSATdLf7as2D4ZAD/KxvUi180lufY2+zfvJmVODwW5BcnnRknGSI2OYszIxT13A8KvPk3vp1aT72vWLodmC5vShtB1ESyVQ/IN66nM4jKWiVk8y9uaT2LdJVx0iAeKDw1jzcjDPOo3h5/6EsywP68yldPzhQULdAXZu70PRoCbXjtFiILPKw7FtvThtRspWlvHa80e5/d0fofiHOPTwe0y/5RxEh4eBDzaSt2IRos3B+K5d+E4/E62oHtXqJqQa6QgmmeVMMS46KF72dZo+vJ/SZA+VP23kgfd+ztnbHiedU4Py4eMYcov12oSeVtLBIJayaoQZK0laPJg0GWH/e2iKwtiWrWROr8FUPZNU22GUSJDYoB97gQ+Dr4CBtZsouuoqOgsW4X7ux1izPfRt3k/v9l5ea/HzwNZfEWs+jGPhChJHdmCumwegr0+b9cJNLRFDO9Gdp8XCGHKKEV1ekk27MTcsREvFEUQRJeTXS03jUQy5xShZZWiNWzBUTEMdaNfHoVYnsS1vYF1xGfGNL5Hwh5joH6Pl1cNUrqkiHU1iy8ska1qFHtLX1YxotWMsq0cNjwMgmCy6KdlqB1FC8Q8hOjMRXNlo/n4ARM+JOAw5iaaqqCE/w++/R86ZZ0HJVAQ5QTyzHFvHJwg2F8czqjGJAiZJJJZWcZlFXGKaCUx4RhpRLQ40g4WkIxfxvT9gWnAWiIYTIzsXhmAfqDLpA5sY3b6X3HPPJnn8CKLRQMIfYmTfMey5XrLm1CMYTUi+AgbefJv4SAA5IeOpLsDqc5MMTuCuKkaw669jhjlnIMgJ1N4WJIeb+KHtjDd2YrBb8C5eDHPOxpzx6S6sn0d8VsRn4c/f+vTE580HuXR20eeG+PzDb3VN4p8bj2hdmGbewEuDu3j3yQPUFzmRTCJd0RRdO/opthkJ90coWVJEOpomb1Y+9WkFa1vgPxGfkQNtWDx2JON7OJefhWA06vUSwX40g5mUzYtRTeEQNTRRwjTQiOwtJY6RDMmEoWoWhIYRzr+dsuK3iR7ej8Gbi7FmHnLbAawOD1oqQar1AMngBKmuFgY+2EjBWat0NSCuJ1Mba+ehHdhM4HAzjuIcfSX7RGmkqXoG/nXvYLBZECUJJTpBunkX2WedR+Mvfk+1xUbByvmo6z5hyWoTakqh8eNeSqsyGWsZx+22EAsn2fpaC7f9+Vr631mLmpY55bFfs27lV4jGZfrjMtan9lPkMuOryyI+EiB3/lSCx3t4//Tvc2Xn82ycfgNzP/wxI9seJJRUSe/fyDvRtdRtfpjA2tdwzJxLOhhEDoeJDvlxluZhX30FYjJKzOLBtPMVDNkFaGVTETSVrFlr0D5+Cc2Vo2+k7dqExetC8uYRaWpESSTpefppCle00763lcMfduLLspJZmcnPv7wUJTDC4I5GMuYuxVhcTfzQJxx+bCPzXn+G9OYXkHKKUcN+Yj29uBYsgxM5P+baORhLapCduSgZWRibP0JLxPQKiaxc0j2tqIc+xlBYgTY+iBoNo6kK2lAPltpZjD79IHI0Qf6Nt9B/+w/InZFDOpqk9LxTMZU3oJxQ6Qw5xQhWO6IjE8GsG6I1yQjxMGokgCBKiBlunfQEhtAKapBiAdTQGFpeFYKmIh/azJZbH2HV2seI5NRjTYXQzHas4x1o7lximeVokTTjcYUGt0LabCapaAiqjDOuj3BVo5UJcyZ2TUaqnAb+flBVtIp5GFq3kapeiql1K9LCC8jPK0V0uAlu30pGWRHt724iPZEmd14d6WCQng37cVfkoSSSuCoKcFcVo8ppetbtYd/7bZz507MZXb8Lb30ZTstWHjAu4fL17yNIIvZcr07Uq+v5KG8VqydJzyT+xTCp+PyVmFR8Ph1WPLCNC370RRrDSZb7bLRNpAmkFWodJg6F9C2wpVk2to7F+NqV9bRs6GJPII5V0tfbz56eQ+VZDZjdGTinTjvRcj4dLRVHc2bTby6gKNCI4spHTIT0NeQT755DSRUfEcSOfSiBEbpefodQT5CGG88g0NSOd+4M/XilNaR7WpEDoxg8Pj2dV1X0Dal4FMmTTaqjEWNhJYLJgpqIIve1c+jh95hx20Uk+gdIRaK4pk1FdHlpuucxCk9twOrz6AGMLi/HHnwKgIwCL0oiyfDBQQRJ32Kz59jp3tbL+0MTXFznY9Hd19D1xgaqbvsa+26/m82f9LFkZi7Fy6pJ+EOMHRvF4jSTe0oVwdZeyi5Zg2nKbNTwOJuvvINkKMXi1l04xlpBkuh98B46P2whf24BzrI80uEYRqcNz/QGBIudRGcr9jOvZd8XvszUm87CPG81iY/fItDSTWZdOcbSWhBFBFEienAHksVEfCSAY0o1ajzKRGcvE/2jhHv8uMqyGW3sx+w0kwwnKTmtHs+SZbpyYnMgzDwd+cMnmegdQlNVJItJf1wqK5EDoxjzSlFG+xFdXgw5xcQOfoK1bpY+ejJaGX/9KUxOO8YMO5HuARwVJSjRCQxOF5KvgPC+XUhGI0anDSWRIhWO4pw6DUNuMfFD2zF4cwkuuIqsjm1ocgrRaj85UgJQ3AWII+2o0QhiTimCnEDxDyH5Ckl3HEHyZKPGIkiF1SgDbUiebD6xTOUUdxIxGQVVRkxEQElz1NFAkcOISRKR1DQJDNgiA6g2D4KmEhJsWAwCQxMy2XYD1nQEcWIUxV0ImoaQnEBQZaSJMSI59dhiowjJCOm9H9L6/Ic4i7PY/fQ+ln5zOe76KRjySvX08RMQTBY0s53ohlcY3t1E3uJpjB44rudUKSpyIoUgiQzs7GTWD67GWFhBX94pPLKzh53Hx9h829LP9sXiXwSfleKz4M43P7Xi0/7WH7l0zudH8Zk0N0/iM8HGW5fw5bV3kWmSiCsakgBFViO9cRmXUX8abh2LkWsx8ODzR7FbDZxW6mZRqYsza7MonF+Ep6YE5/SZDK7/CHHOGpCTYNFLKItCLci+SsSJMd0nYXWhme2YwwMomqavIRfUYCyfqhdhOs0Mfrwfk9NGaniQcFML8lCPviptsRHt6CA5HiLhD6EM92AsrkZ0ZmIqrUVTFZSKecgDnQhWO9O/cgaBw82Y3BmY3Q4Si6+m6Z7HcBR6CLb28uiXn+bxK+7jsTN/xPDhUZSUQvsHLeQvn0egI0j1RXPJmZHP8S09nL39Kb5z63w0VSXS2k7JmYsZfOUl9uweYE6pG1eJk/yv3EbJ3Q9RuqpBz0U63Enh8lmkhgdJNu9BsNpZ8c4fESQBgyigWl20/fwnTPSNUfThBsweB57Zs8hsKMN35nkMrN9GvK0FTVFp/tY3mP3UQ0irbgBFIdo/iru6CHP9PALbNhPasUUPNyyrpn/LQTIqyvDvOYghtxjXrNlE+gNUXbwER3E2RafWoikqZaun073pqJ6RUzkNwaoXZSbHQ7hmzcZZU0XCH9aDJQOjmCunIRhNehBhJIhgtmAum0KgRm8Gj6x7CTmRIjroxzJ3Fe45c0n5xwm29hJobCV29CBDu1oY2t1EqK0fk9eLo/5EYnM0ovuFIkHcu15E8Q+iRoKo8ShaMgGSEc3qREhF9Y0+l1cPK0xEkQc78b/1HFLNPESHm2RnC7Ftb6MlEyQOfcx8WxChfa8+jvKWIXsK0cwZ5NqNOMZaMUaGSAkGLMiMmHN1MhPowymmiadVSkxxRAHiRgdyZimGsU7EeJBRyYPszEUTRKz730IK9JI++BH773kdo8XAM3/cwZwrpmHP9ep+peKptP72XrB7kItnIvsq8b/0OKH2flRFRU3JGO0Wss84A09tCUoiyUTfGE9v7SHS3IxcNo+XG4e4+6y6SdLzTwBV1T71x+dN/5hUfP5KTCo+/zP4d89PjsVIQlEZSsi0R1PElf/90/CiGi9zbl1FsL2fcM8YzuIsir/4ZZJHd2GZuQx5sAPR5tT9GHYn8mAXGIwkp5+JORHQ81vkJB2eaZSm+kjtfI9IezdKSkaQRIKtvWTPnoLZ40BJpLDWTNNLMlMJTBVT8b/9AqlwTPe4VOr/piaiSC4v6b52BFHEWFxNqqsF0eHG/9EmRElEspiQjEYm+kcJtg+SNa2MYGsvQweH6e4JU1GdSfUFs2h7+6BOUKwGZt55Kxsu+wmnv3sv8kg/TQ88S/1fnufgZZeSf0oFviULkUf7kWMJJvpG0VSVVDiK2e1gvKUXVdEoWzMXoy8H0eYAUeJP5/2Mr7Stx2/y4m1ZT3PRciq3P6qvdM9bjdLXihIY0VWBVAJjSQ1klzH0yG9wVRRgyi9BdHkZfP0NclYsJXzkMOGuQQrWnEZqsI/okB9XdRlqIkY6HEOymNBU3bwbG9FrYqxeF62v76T/4DArO/ZiPbaF0bXv4K6tZNcvXmbpi79HM1hQBtv1lXOzGdHhJt7dTcachbrZOBJESyUQRFH33ARGMC+7jPSud9EUheEdhzC7MxAkEVd1GaLVTu87G8lfOoN0JIacSGIvL0ceH0WyZ+ijrpAfLaabuwWLHUN2AYLFjuyr1POAho8T278NU3GV7jsqqECdCJLuaUXy5qGlEozt3E/+5VeSPHYAQ34ZWv1yUgYr5kSAEZxkWQQM410gmdAkI4mMHAIJhYJoJ6rZgWr3IsRDaCabnmGUihIzuU8an6Vgnx7TYLIhtH5C8OPNNL+4C19dFlnTKnjvF2vJ81pZ9tDXMRbrytvQ66/gKM6By+8gpWgkf/cNsk6ZxURbG2OH23WzvdFAKhLDU1OCuWYO8qCepC2uuJ7WCYG1raN859TKz+AV4V8Xn5XiM+9Hb3xqxafjnT9y+bziScVnEpP4W+ARrYtrWjfQG0szmlROjrj+T7B5bXS8vw9HcTaeilx8M6tItezVw+/c+fpWS/6Uk/1WWjpFaOfHmA+9j2LLRMnIAslIYYaBVGYZpvlnkbjxbo4+t5tAcw/O0jw0VUVJpBg73Ea6pxXR7tA3jIKjCJJI1qL5qPEoyaO7dYJlc6AlE4hWO2osQuLQdj3wMBKkZ1MTT965liNPfswHP34bJS0T6AjS/MIe3n1FHzPtHI/jq/cR6Rmm7IwGJJPEzKefoOupZ5n7rZWEt61n6y33Y/FY8D/0Exq+dCb7L/s5hvwyzEsuYGRvC86yPNrea8Sa7UGQRDIKvDgKPKhpGeQUgtWO4h9kzXUz+Xr2UiRBoPHuh8h59of0n3qzrroIIvJIH1o8SrjxKIacYiKfbGL8uQfJPfd8vnPtU3p9hX+IzIYyAOz52WTWlBJuPErCH8JdPwXkNAZfAdaSEp10GQ2EOwfp3dJMbMjPa99/HWehk5nXzkF447cI7mwsbgdSTjFLX7ofLZUg/MGLTDSswZRfopvJUwnkRBJ5uBdluId4+3FM05fqpmNVweArQIyH9LBBs5WcBdPxrVqNu7YS0eEhOdhP8UVnIbm8GB368yvc1ILBnXliTBVGDfv14xlMJ1faAWjcROKDJwh+tPY/Hk+LnVTbYSIH9ujhiSYLos2Bb+lCNG8xxsUXoTWswBDqxyTHiRjd5Aea9Bwjkx1UGUFOYhTAY5FQ7F6UDB8BWUSaGGMobSIlmjCM92DVUthe/zXG4WOMPPkA4vBx4q/eR/ujT/Cjb71BdDhK6UWraX1tL12xNM5CB6mBbtJ97cj97eR+5XaMV/4QkyQw9o3LMbszGFi/DVteDkUr5uCqLmN433GyFswl2NrLxJ6tYDBhWHQRqtHC1DzXJOmZxL80JonPJD5zmLIKORRKUGA1UGA10B+XOb/cw3LffyVA7+0ZQJQEfXV6Vi3GggoMBRVQd6renSQZkSLDyGND2GYtYWzbdpxTpyH3tWMcaydoyabROgVh1+uYgr0o7kLcb/6K0964j1Q0RSoSIz4SJOEP4SjKQbTakfvbkbx5qLEIGZWV9Lz2HpqqYsgvY3z9eyij/XpbfDqFaeZyLDP1cUC8/TgGi4HTlhRxvCvE1rEYD/9+G3mz8sme6qPAauDVZ48gCTB+fJx0NEHO1V9hzmO/Y8vyS7AX+OjddISm57YzOhYnb0EDXesbifb0c3rfeyh5NfiffwSj044h08ecb65h7HAnqUgMQRLJX72MVDhKtKcPua+d3nWfkFlTCoDr0NtUXbIY90138lrZLO53rCHdslsvT52I4qyrIdXRiK12Kl0bjpDub+e+975HOhjEWFiBdsF3QJQwltfjOOcaMor0guDutzfhP9KGPNpPoq+XieNtGJwuEv4QU792EZZMF1c+/XUko4G3Ht6JdeZSUkd3YHTakDzZpHuPI5gsGOwWXL27MeQW63ECBRWkwrETW3cJzJkuIh+8gOTN05OzzVYSe9bruTw2B6LVTrqvTf83g5FkIIKWSpxQiSRseTl6OezoCGPbtiPanGjx6Mm1ePHE70n3taP4hzDll+CcuwjHsnNJdLczumUbSnSCjJo6xAy3PiKbCNL7xgeoXYfpErIw+LuQj+2F3W8xcuvlqDYPmsmqhwya9a6vlvEkKUVDtXoYjKl440P0ZFSQpwWxTAwje0sx9B7E6PHQ/dD9jB7uQkun6XhnN4GOACuz7bzVHWLvL58n5o8xP9/Bx7sHaX9rBwPvr0d0eFBadjEakxE/eRlvfRnjzd24KgpQUwniowFEqx1Ptd5mn3vJFRgdNoz5pUQsWVgtls/sdWAS/zPQNO1Tf3zeMEl8JvF3wSNaF+cONhJXNHxmiaFggsJqL0uzbJxX4qLBaSbXYmA8pdC1sx+j3cJEdz8TjQdRoxEMYx0IySiK3atXPUwEEawOvHNnYMgv01NsO4/gPraBeqUHQ1Yu/mceQHn3QRzLzyd56GMyq/PxzawiHY3rptyuQfo37EAOh5hoPIjk8WEoqCDQOa4bn/vaGG/pRgmMosXCaHIazWBBtXmgbAaqopC/sBqjxcC800q4an4BtQ4zBruVcF+ErWMxeuNpFA2e3tpDMpyk8567SB3dwbKnf0qovZ/S1bNQFZW6pUX0bTlEwSnlZK46W8+r2fICqqKSPaceaeEFbL/zTTIKvHR80Ez+uWehJaJklBQQHwkC8Ps/7cfkzuDiOh/BXTs49PB6Gq+6jG+88HW+dkohBl8BancjGSsuZnz/YSwLz0EwGpl60zkASLPPwHrBV+nInod9SPfopLtakJt2Eh0YIdI7jKMoB4PdwvDuJgZ3NOp/XFGi6JwVGPNK8S6YB3KK3AUN3PT2j1FCfka378WYV6ord2UNKBk+TIXlJ8pRRxFMFlJth/GtXIHkzcVcPw8ppxhztg9UhcjOj3TFxeHGv+8IWipBqLGJlH+cdFczof37cM2ZdzLpW1MVhncdweh0EmjtxVVZoP+uZBJNTiO5dG+MmkqQ7m9HsNgR7E7SfW2ke48jGg14akqQo7oqpCZimOtPwVReT868OqTCasqUIdLZVRgKK4l3HKf0gpV0/fIOaP4Y1e4lZMrkGDmUukwYRAE0leJ4F+JYF/lW0MwZaMd3EzM6iZWegpRTjD03k/REig0X/4BD23pJhlOc89K/schrRVM1ihYVUbq8hMtvW8KU689ltLGf+9f8GEN2AXlN7+L/ZAdyIkXp5efjmDmX8cZOrD5dDbN4nagZWQiWDCwLz+GIZzaZjv+z8jqJf1yo2qTH57+LSY/PX4lJj8/fDm/nNVB0Sj79uwcpXJDP7k3d1Nf7CHQE2R+I4zMb8Jkllnz7NJSUTOasaUi+AoS8CsRkFNXmQWneQbK7DVNeIZHmZjLKihEsdn0sMnMlDLQi2J3IRTMQkhG0/etILbycxrPPoHBhJQl/iGQ4ji3bhauiAGteDtGefrznXIY83IM80IUSjyGZzQh2p76ZZLXrVRoON0PvfoBo0tMhLF4n93/3TUaTCvMzrRTmZlC0qIi2D9oQJQFBEpFMItu7QgwlZM4rcXFoNMblX1tIfCSAyWnDVVGALScLVU4DYMwrpXfqBaytnEPFrq18UD+fOx+8jJZXdqApGnN/8y00WS+ENeSWks6tJfjHf2PkQDvBrhALn/8DiiObyIv3c+yVXeTOKtS9HiYjjtWXcW/9JXzjg58hZrgZKFlM77lriAcSnLrpZcTRDpRIkP7X3kRTFNS0TNw/gaPYh5pKk/BHUNIKVm8GBacvxlRejzw2pJNRUUSNBBFdXhI9Xdinz0O0O/Q28L52LHNW0HLnz+nc3M3in56L9byb0A5vQqqejRAcQlMUBKMJRInhV5/Ht2o1lEwlveVlIt0DqIqKpqi4aysw5BYjD/VgWnCObjDev1HPCFJV1EjgZEAhBpN+20wW1MCI7s1RFJBTaKqqK0AmC5LHR2qgG9FoQE3LmCvqQJRQQ379iasqqJEgvctvocwYI25ykdG9i4HnnyFrTgPGsnqSTbuxzl1JqmA6aQ0kQcDkbyex5TUsdfNQY2HGNm8m99Krie3ZiOnML0PTVj2FWU4z3tRBpGcYi9dF4SUXse1Leht6Kpoms8pDqDvE4l9fS6xHr9twzJ6v112IEsoJY/jwe+/jnT6FdDiMuaAYNRrGkF9GX9XpFHVuxv7lN0kdeOIzPuv/9fFZeXxm/eA1jLZPd/zu9x/mivklkx6fSUzis8K5g400b++j/soZpKJpps/J45ntvewN6H1eh0IJemJp+ra1kFHgg+XXIeRXoRltKONDiPEQhqqZZCw5Ey2VwLNste7bSERRoxEm3nmSiX3b6X70YcSJMYKSC3XxFfDqr5n7zCOMNfZi8bqweOz6OCQSQ4lOkArHSDbtxr91C4LVjmXKDPo/2kfgoN7ELVrsGPJKQZRofu0QWQvmkvCH6P+4icsuquHWa6Zy9k/ORJQEfVRmNTAc0cmJp8xNjtnA166s57THb+NLvz6fvDWnU7RyDu6qIiI9I0hrvgKgG6gLK0h9+0rO/epCTqeV3w1uxlpRReHCSuw5dvZ8917SPa1o004n3XmUu7Nm4p41i+HDoySCCU5/J0rkxftpfX03c37xVQrOOwtbcRG22mmkjnzMrW/+gJ6XXmfHV3+Oz2Zg/iM/pebiGQgDx0g272PPd+7DXVFA3opFlFxzJYVLp5E1rYLsOTX4ZlaSPbNCN4wbjKjxKMpwD1o0jOjyYqqZjejyMry3hVRHo944nqvn0CSPbKfg1Oks+dn5SBYTgSd+jVi3CLX7qK68mK2ke1ppy5yuh+mJIuktL2PIL+Pg49vJKPARG/IjebJJtjchOjzIh7cgTIwjebLR5DSCyYIgSiR6ulACoyijegBheqgXwe4k1nwYyePTKyoSUUzF1Ugenx6WCJirZ6IpKlo6rf/NfQWo0TDG+oUYy+upSPcTkpzYYqPs+9bPyP36D9EUBXmwC8FgItVxFDEVxRzzI25+EqVltx7GWLUA/9YtZJ9zgV7RseoLiF0HGd2wEUNuMQf+8D7bH97O3neOEx8JsvHKOznWE6apO8Qpt6+m6rx5FMzNZ/zAUax5OahpGcnhRquaD4DiH0S0OfDUlpAKBAHQ5DTmWafhrz2D7T2hSdIzic8lJhWfvxKTis/fHutrZ1N76SyCbQOEusM8s11/FysJcEGtD2eRg4qzZ2Ny2pGsNsSzv46YiiKFB9EE8WS6rSan0ZJxBKNJz2bx5urhhB1Hkby50HAaqsmOGA/oZY+xIH1PPo41243JaUcQRTRV1X0/ZUUkx8aJjQTw1JQiurykBvsAsC9YRartMMaZpyHGQ6RaD5y8Ly1/fpP679/C4Z/9AVuWlYwCH23vNRIZirLdH+O0UjdrOwL8Zsf9dP/lWfKXzMS09GLirkJsPXs57pmO+be3wPcfoiR8jEhOPZZdr5zMvzGfsgb/q0/imjWb1y77FZWzcyldORVbnhdL3TwSTbs58ueNeMo9BDoC1F6+iM7391K8cjruZWfoTfFl9bTf8xt8M6uRE0kyKisxngj2C27fiuvLP0FZ9zimhefQ+Ysf4ZtZjWg0YKmZjjLcQzoQIB2N6wZgIOEPkzV/FsY8/XGSh3vQ0mkS7c3Yps07uQ01+MGH5CyYgZZOEx8exVaYj+TNQyyfTmrnewx9vJ/i795JbO0zmEt0k60ai+hZSBlu/biJKMM7DuKbUcXw7iaKLr+U8S0bcc+ahX/nbuKjAbKmVZ5Ua0Sj4eSGWqJ5P6biKgBSPceRzGbM9fPAYGL41efRFBWD1UQ6miBr0XyQU4gOD+qJqgyDN5fY4V2YL/4WUttOvSeregnG4WPIvoqTidfB3TtxVJSczH1KdTSCqqLEY5jP/CJiMoJmtCIF+8FgRnbmIqbjMNDK8QcfJdARJNwXoW0sRmM4yXSXhUOhBKtz7JiMElaPhbLTp+AoziYZmMDz3fsxBXsR/D16w/voCMlABKPdCsDvSq/l35aVcXA4ypHhCb5+052TpOdviM9K8Zn5vVc/veLzwcNcuaB0UvGZxCQ+a6xq3scvfraelg1dJMNJLp2ajc8soWiQCCRIBBIEWrqRzGZig8OYBo4gJCKopgyEdBIpMoKSoftAhMw85JF+Uh1HQTKi+soxTV2MPNBF4vUHkA6vA1Ul4CxD9lUgmgyIRgMje1sYPdhKOhrH7HYQ6ewlHY3r68EG44lQQ5X+rQfxv/sq8vgo6QOb9HqLuWeR6NPJWs2XLmJ03QcYrAbGjwcYa+xlW4sfk83IXI+VLd0h4orG62d9n90vNWKqmY2QjGJORdAcWZQeeoWSr36DkvAx5OFuHs6ehqGsgdEHfgIGI8et5fiP6jlCl7z/K/LmlBHuGkQ0GBGMRoZ2NFJ/zal0buhk1o+/zNjhNhoefRTnpbfo20qxCNuv/iYA1vIqXJd+FXnZdaSLZyGaLCSDE+w//wIss09j12VfJndBA5bCIno27EOLRxnb20i4a5Dg8V7GDrcjJ1LYcjNJDQ+iphJEtrxLorWRdH870UE/bQ89zoavPcXo5o+QjAbSgQCC0UhGTR2Gggqk4lqia5/DkFtM0eWXwkgnE/2j+hgqlSA50E/nMy8jGI0gSiRHRsldoSdB2/O8KCE/vouvBYOJ7Btuw+SwY/JlY3BnYs4rwDZvBYbcYsK7tiHZM9BiYQRJ0n07ooQ81MPIGy9iz/OStWg+ksWEaDQgOtyo0Yie8WQw6uOvrmZsMxYSevQnqBNByCpCTITYL5bQH4eh+rMx5BbjWbAIyeVF8uYiuLMxFlZirp+Hbcm5CEoaNJXQi38gcWArqbZDSCNtaKKBVMdRlJSCIAmMh5M0hpNYJV35vHx2HjVrKiheXMj7zWO88eguutYexGi3IClJNIOZ9EAXWjqF0e3GWVejr/I3zGBRWSadwRR3vHl0kvT8C0HTNN3n8yk+Pm/qxyTxmcQ/FB7RutgwEqX6gulUXzCdc5aVsDrHzkejUZLhJEdePcqxFzYzdriddOdRNJMVKTSgkxKbB0GVkUtmA2AsqsK24EySR7YjhQYQlBTmqQtIRaKku5qR4gE8kW6EZJTc8y9ClCQKls0mcHwUTdHThFVFJR1NEO4aJNTaSbRvEEEU8c2swuJ1IUcTJ/Ngkh8+ha1+BoacYkzVs4iPBqi6bAVFSypxV+Ywy2PF7DRxKJTAIuppzb7cDOZeXMfBH/ya/kfv51bPPIYfvw/D1KX0PPoHPlJL2HfHA5x3aS2fXPl1HMU5mMobqIq1Uf2db+lETE4THfJTfNmF9G7cg5pdAXBSiQEou/3feHfUgqCkudFyEZInm5Jl1bh+9RfGtu8k9PJDaMDHc5chZBfjWzgHf8s48mAHew+P0L/1IMbyBox2M7FjTdhyMzHaLXjry3EU56CmZCK9IxhsFhT/kB6S58sh0jOMwWLCaLcw47q5BNsG8Df3YSoooenP77Pumt+hBEZJfPIOotGAIbcURBEtEcPqdRHftxklMEI6Gqf47GUEP1oLqoKltILIUd1M7Z4zF8PUpaS7mhiedh6h1x8n74ILkYPjJ4tItWScxJEdxEf1PCDR5SXR1oSlqAgAJeRHlESMTidKyI9z5lw8MxqQ+9pBFAm1daMmYkQP7CTS3o3gzsZZV4NUWE3AUcLaQZjevY7SUDM5RllvbRdFhDlnoVpdyO2HUONRBLMNIR1H2bcWITh0QmWrx1Rag5JfhzBwjNjgMPa8THKmF9Af1/+GWSf8Y7YsK4IkYnHb+M4vz+L0c6soPk0PZ2Tn6/rqe/txJJcXQZLQVBXvWRejTT+dmiwbhe//hs23LZ0kPf9CmGxn/+9jctT1V2Jy1PXZI/7m7zn8x7cJdoc40B+hK6YbfVfn2FE0OOf4NtSPnsWw4HxUi5OAasSbHEUzWtCMVgz+LjTJgDAxjmCygqaS7jyKOHUZ9BxBMJgQMvMIvvMsGQ0zMJTWM7HxVbrW7aPivAVoioq5rIZEWxPRIT8WtwNBElFSaaL9Y4S6hik/dyFKIoWlWr9I9n+0D09tCY66BqS6hUTeehJzdpa+pl23lPAzv0UyGjFnuhg72IotL5ORva3ExmLE/HFypudisJr00MTaEoJt/bgrCxCNBtwLT0V0uHlmyc3MO6sS0SSdbOJOl8xBffs+vnX5o/yx43VuKb+Qmy+ppeqS5YzsPUo6HKXkyosRahbSnHaxozfIqopMzH+8nbyrbyTde5zo7AsY/fbVVPz45+DvJ7b/Yyb6R8m99Gr6nnyc9rVNFC8tw11VxLo73yM7287Cn11GqLUTURLJKMphonf45HjQYLeQ8Icwux2ko3FEScLkzkBNyxizckj09ZJxzvWk931IuKkFk8OOIIlYK2tID3YhiOIJxSSP0C49h8ZUPYORd94g+6zzOHLnvUy/9y6STbt04hAJIuVVoNozCfzldzjrajDWL0RIx0l3NRM+cljPPKqsRFNV3eisKnqWDzC4/iO89XpekZJIYXTYkFxeIsdaSYVjKIkUksWE2ZOB55yrSB3dgeTy4p96Dr7u7YTLFwNgMYiImqIHFw53oRVPRRhoQQmMYiyeQnjD69hqp+ljV5sbxZWLFB5GtTiR97xP95vr6dvRz6tNoyfPha+cV43RbkROyJSdOZfx5m5GG/vJm1OGyWkj69IbEeQEgbWvYSvMxzx1EZrJimL3Mqg5UTSNC+7ZxqFfnfmZnsOfZ3xWo67pt7+M4VOOunrXPcpVCz8/o67JktJJ/MPCev43yd/bSMFCkXpgaE8H7XsHGU0qtE6k2OCaxfnlHooXrcd1/4vkBo8hZ1fp2zYGM2mf7g8xymmQEygZPozlUyEyjGZ3IlgySB7YhOf08/XagtEejj69lbIzptL2+namXHOGbqaNxsk85RR6X38Xi9fFRP8ovhlVGJ02ffRmMZGK7MLidVGwcj6WGUuJ7VyLEngLAHPtHFIdR2n9yvW4yrI59sYRGq6ae7JF29I5yNo3jpFrMTDn9vNZ9/W/0BlNM8XdReGCAuL+MHmrliF5c1GjYUrL3Ey5805i297m5dteZN7I3Qz89ClWLLmQO389wOFvfp8zczNwlWUD8KvfbOGWy+tQ/INoW1+mfs4qtN99n64WP2anmZzzgyQ7j6EcuANREpEPbyW25FrsZXMY/t6X8b/3Krlnr8FZlofJ4yY2OMwl7/+K4MebkcNhPLNnEW8/zvFXtxEPJMjIaado+QyURArnzDloqQSGE0ZhwWpHOrGK7qycBlE/aiSAe9YsJlqacM6aj+QrRI1FMJXWML7+XSzeYdInVsn9697BUZxDur+dmuvW6L1ZLq+eBZRTQjK3BlPHThyVpRinLibdvBs1MIKSTGLL9ZKOxAAw5BShhvxEmo4R6Rkhd+lc5GhcV518BSj+IfxH2rBmB8ioqiTR24ucSOJevBy5vx3V5sEw90withxyRhpJVS8hIxVFO7AOsXYhQjqGGAui5pQi9BxByMzDmOEmsWc9jtMvRe46ClYnsrcUgOim18hYejaJ8VHu+/NBMgwi010WFE1DEgRe+aCds+bmM/vH17H/l38hOhKl9tJZWH1u0tf8lKRJwtJ/EPfKc4ls/QCx9ziGsgaORG28eriH3/3wnkmV518UJ1WbT3WQz5f+MTnqmsQ/NIp+8Wfc1UUk/CEMVgOZXisFGSbmeixMd1lw5GXQu7OfY6cuZ/Cph1E3P400MYqYirFnKIGYjKBaHCiufDSLA9XiQDPbQZQAMJbUkupqQY1FEPKrqP/CUuRoAjkuM7rrMFoygeuCGzAWVlB49kpa3zhE8ZrFiJJEbMiPaDTgqi4jo3qKXobpH0eTTBg8Pt3PUTWFdFczqeFBfNNK8VQXUXVWDd4F85C8eRgLKjC7HbROpOiPy5inLkASBKySyDt9YR57tRmr18n2bz3Mh6tvZvjN15j/sy8Q2/Y2zc9vpabOR819f0RdvQr/sw8ix1MY7UbKlhWT/4Pf0vHmFn7zxBeouvZ8TLNXYq6dg2a0UPvYX6jduhlnkYOvzfgyRo+e+mzxutBSCSwfPkTjVZdRuGYpBx/byoNn/BAllSYVCGIvL0cwWdh970aSwQip/m56Nh+iYHEdM797FcUrZ2GfOV/P3ZFTpId6SYfDGEtrMJXXAyCaLKR7Wkkc2Eq0fxQ1FsFeXo6mKgTefwnJm0tg0/v6bZoyA3uuF0NhBXI8ham4CkNOkb5aPxHEkF/O4AtPk+45hvLab8DuYe+vX0U+thd5tJ/owAhKIsXIvmOYPG5CjU2ke1oZWLsJk8NOyU23kOgfIH/5PEzlDagTejmtLTdT/7uG/JjcGbjmLUAN+Yn29HHsW19DM5qxC2nkrHLG4gqGsQ6YfRZS1I/sLUfOmYIwMQ751ag2D+ne45iqZ6IZjKTnXcgRUznG0TbGH7wDa90sIh+9Tfe6PfjMEhOyyqFQgsZwkllz8zh/eQl7D4/w7jX38vTWHsb9cRL+EK4LbsBjUPmkL4LizEXJqcKxcAXMWsNRYyl7B0IEzj5zkvT8C+N/pqvr730vPltMjrr+SkyOuv6+uEko5ar5BWiqRjyQwJGXwdNbe7hyXj4503MRJRElLXPfnw/yhaXF5M8vI+EPoaYUqu/6lb767shGRsSUiiBG/SjOPAz9R/R6ihOZJ5Inm+SRHWz65vPM+NJ8hvd34an0UXjHb/Q+JUF/ryAoKYTjO1H8Q0y0HsPksOM/2knOgukASN483V+SiCLYnKgTQb0nymym+dlNeCp92PO8SBYTSiLFW3evpz2aIpRWme6y0BxJsjLbTobPRjKcpD2QYNVl9ahpmb6d/bzWoisoX1xTweMftHPzJbU8/Eozcz0W6mbkkI6mOdDs5/r7L+Ohr73AomnZLHrmd3y16nK+c+t83L94gjucddwfOcR3XDP4/p1nkH39rQRfexxbXg6bv/Usa7Y8wZaLbqHijDrMngx8F11DsnkPqApDW/cgGQ3knrkaDEaU0X6UiJ6AnY4msGV7EESRUHs/ZncGjprqk+vtxtJaDn7nZ0z/2bcJbluPZ+U5hLesxT5tFsn2JkzFVaR6jmPMyiHc1IJ7wSLkoR7d6ByPAvqWlxLyI0cTWIpLMRZXkzy6G9HlRfL4UKMR9v7wESrPnYEcT+GpKSU9ESU2EsBZmkff5v3Ysj16t5iikjmzHv++I3hqSlDTMuHOQd3QDvRvPYimaFRcdwnqRJCuNzZQddfv0AxmxFgAImMESxbgTIwhpKKEnSU4w92oHYfgxDgNQB7qYaKzl8xVZyFmuBl97Rni/jBqKk0yOMHR99sZTSocCiX+16e/fg5cOIXYWJzoSBRnoZMlY2X0ffQHsvzNem5RXzvMPRdFNNI6nuCWZ/az/fun/U3Py0n8n/FZjbrqv/HCpx519W94jGsWl39uRl2TxOevxCTx+cfAoUvXYLIbeenFJhaXe9BUFWehE19DHkP7+xAkgUQgwUh/BID6VWUYrEaKVy9korsf7yU3oFqcIAgIKf0iqvUfR5NTaMmEboRNRJE82cSaD2Ow623tgcbjZN1+D+03X0nVPX9AtXsRJ8YQYwE0s53U9rdIjo1jznSBKNG/aTeuigI8p67Qu6j27cLq89Dy/BbMThORwQl27x1i1fnVvPf6Mabm2PHVZfGnd48z12OhPy5z3jlVVF2ynG9d+hAXVGXiLnGx+ZM+KjNMuLNsvNo0ilUScBklah0mCqu9KGmF9YeGueyiGh58/ig//PEqhvd3Mf2RB3h/ziU4Cxwcah3n7OtnUvqVm7h33pf5Rv8nBP/0czrXHcaebaPw0VewTwzSeNPNOEsyCXb4KVk1DUddA2PbdyKaDBjtFmw5WXzys9dZ9OvrABjbuZ+8Cy7UYwM8PhLd7VhKKog0NWL1efQAQYsdQZIw5JejhsfRCmpIb34BQ34ZaiSgV0fEoxgKK0l3NOo5PAYj2okgx8DhZnKu+hKxbW8DYMzORzBZ8H+yA8+0WkyV00i1HdbDK+UU8e5uQC9MtZ3oNZMsJmJDfpx1NYgWG+Ejh3ngB29z6QVTEESB8nMXEx8dx+xxIIgiCX8Ik9NOoLmbzIYyYmd/G0UDL1HQVLR9H+hN8oER1HgUy4ylpPKn6h4fTUVpO4hUNYtRezFZqVF6fvl9ClafSnpsmI9//AZmpxlV0egejGASBbb74yef79NdFoodJgA85W4igxNYPRZm3LQS8yXf1vOkkhOIsQDpY3sRHW5eMM3jS1/8yaTK83fGJPH5x8Uk8fkrMUl8/nFwr6OapUuK2LClhwuumkrnxk7e7wrS4DRTlmunYG4+xz7sIGeKF++ULCJ9IWxZNkrPO5Xmp9aRM6sU0Wggc94c/Dt2k3vJFXo7eSKq/9dkR1Bl2n/7K7q3dnPaW38gsuVdkoEIR5/bSSqaxlPupujUWtLhGO7qIib6R3FVFBBs7SXUPkDh8pkoiRTD+1rIrCmhc91hYmMxgoMT7A8mWJibQSCapmJGjl5RcdUSDDYLj375aQBMosCpZ1aQWVOE7xs/p/O7N5E9sxr/0Q7u+eNuJAGuXFh0MusIwGeWuPCMCpq397F1TPeyXFCViRyX2TkaZTSpIAnw9RtmIIgi/Tv7ePnICPet+xFj23fS9l4jM29ewcDHh7F4XUQHxyk6bSYmXzZaPIqSTCKZzaQnopjzCpADoyiJFMlAhOF9x6m4UPchxY414Tz1DD1KQE5hqpyGEvKT6mrBmFNE+Mhh+rYcpezMOVgKi1DjUdLBIOa8AgSbE8U/iOTJJtHZijmvADUaIT48SrR/DMliwlNTiiqnsZx6EeMvPsbY4Q4qrruEZHcbPRv2kbegDltxEaM7DmD2ZCBZTARb9ZBK0WhAkEQ0RcXTUE1yZBRzbi5KJIhosZHyjyNZdKIxsq+FgtWn6gnPsQjGkhrU0lkIqRjpjGxCSQVffACleQdKYITW59dTfeUq1ITeMWYqrWGibCFmEQ6OxEkrGvP921EjQQxlDbTd/QvyF09j4OPDxEYmmBiO0nV8nAyDSE9MpjmS5NZrphLoCJJV5yN7ZjXjLV1kTavEWt2AVFzLLaXnUWoz8p1djxAqWcDR0ThLyr2f/Uk5if+Cz4r41N36Agbrpzv+wMbHuGbJ54f4TJqbJ/FPh29FWrlJKOWiGi8fv9pEVVUmZwKnPftDJJeX8Ja1+I+PI5kkbNkeJgYjpBMyQx/vx+w0ER3Ux0Tx0fX0bu9GTctkL1vMREsT7rOvIt26j2hrC1nTKgn3hdh/6x1MjMSweixUnFFH16ZjvLChk6tEAbPLzMihHqouWsDu37xDztRsSs9ZwtAnB7F4XeTMrsHeMIOCSIxAaz9ZNV7cLX5sWTbmnjOL2OA4fZ+042/sYHBPL3XZdhqunsPWh7bjqS7A01BF8PFf0vVRFy88c5gJWUUSQNFg8Pg4l8/OY2fzGF2xNLUOM4++1cpV8wuQ/DHOLHAyMh7ngvuvYFVxNT9deQeKplF1+7f5VX8OF/N9ODLCbat/zjeum8aL+wbRHlxP5VkNiEYDmqJgn7UYJTCC4M2FvnYEswVLTjGKfxBjQQWxvbs58Ng2lv7mGtKBAIgjAGjx6MnAQTWuJ2hb6uYhj/bjmrcA73lXoYbG6HvxBfLWnI7o8OgjqkgQy4ylJJv3YMyw0/f+ZvKXz8NWXIRrwTJ6X3gRVU4T6Rkm/c4zGO1WStacQvtTr1DxhQspWJzCWlZOtO049oIsJJOR6KAfR3EOgiiSjiZwVxUjeXOZaGnCYDUjmCwoiRTmymlI3ihaLEy8u5vSm29BDfnp+PPTlJy/itac+dT4W0nsWY95+mJcR3aQttr1+hKjCUehh0i7rjB56+aRLp+PNR3FMNZBlrOWyJcu4kBCpnTlVDK9uRStmIv/SBvZM6tJR+McfW4nQwmFqJKmwWnmjMvrGW/zY3aaMDntuFadj622HcnjQx7uxaCkeaj9VdJZ5QzKJtY2j/LFeSV/r9NyEn8n/LtP59Pg8yZ/TCo+fyUmFZ9/TGxbsIisGi+H32tjxvk1aKqKyWEj0h/AaDEwsG+Ignl5KCkFg9WImlKweB1YvC6G97aTCCeR4zKFi8pJ+ENk1pYwcqAdgKaNXThtRhb/7AK23/kWBouBrJpM7NkOHvrTPhqcZpojKVZXemj4wnz8jV3UfPcbqCUz0Ix6KzeiROc3riF/0TQ0VcVcUEykuRk5kSI+EiBnXh2i3Umif4DYiZ4uTVEZ3N1KzZcvZcP1v+e0h7/C1m/9GTku805fmGuXl3DkwDD7g/97L8j/L84pdPJOX5g/Hn0KJTBC8/1P8vArzTzYv47E5peQ7A723/M6R4+OMuOUfKbffg2IEqLdiRoJEGlqZNtda6lZU0HV176CEvLrG1eV0whueJtQe79OKiQRg8WsEyZVJdw5SM5ZZyLllqEMtCHanIjOTBJ7N+penWQcTU5jrJlHcs+HKPEYxku+h3R4HYp/kMixVhxTqploa8PsdmCumqrXYCgKyGlUOU2orR97nvfkppktJ4vkeAjXinNIHNrOyN6jZM+pRzAaUaIRJIebifZOzG4HRm8WAP59R7DnejG5M4gN+XHUNxA52ojZ7cC+/ELkjsMYKqaTOrAJw6KLSHzwBJbV16I2f6KPRGedjvzJG2AwIRiMDKz7iOIvfIHOgkWUj+zWgw9nr0E78CFNDzzLwL4hREkgb3Yu+Qv05+vgrlb2be5G0SClalRm2Tj1/i/S/eZ6BnYPcOrLvwO7m9SBzbS9sJYpt96I1rACRImUYODoaJxQQmb1lOy/6bk2if8ePivFZ8pXn/vUis/g5j9x7akVnxvFZ5L4/JWYJD7/uLhJKOXmS2oJdASZf9cXMTQsZuSJ+3RVIJrE4rEzcmSIvLklqOk0otHI+LEhnIUuzG4H+ZdeRuvv/4ggCaSiaUx2I4Ik0vpeG75aL4lAAjkhY7SbyMi2oaoaJruRvh0DeGsyOXZohAlZZfbsXOquWoLR6cRQWEGkYQ2O5DiCpqIe2oihahZDTz2EyWnDmu0h2j+Ke9as/yjGtNgwFlYS2rWN7vWHKTq1joySAia6+4mNBvno8d0suKiWl545zEBC/r8/MEC+xcBAQubiOh/vHRvjjHIPbxwf5xvXTWPKHf+G5ZLHuOHgJu559gbuuuEvVGeYWHL1dIouv4TxujNoWrKc+T+6GMmbS/jQASxeFwZ3JqnREYwZdlKhiF7n0VANokTrsx9gshvJnj1FJ5e7m8g7dS6GggrGN68n7g+TWVOCtW4W8ab9SBYTksuLHBhleHcTJdddR2TnR2Rc/FWSHz5F+1s7qL7ydLR0+qSKpiRSJ0dWGQU+BEkk2NqLZDGROWcmWiqhG8vTadRUgmQgQrR/DGu2m3DXIAC+GdUYbFbGDrYiWUxk3/R9lEOb6Hr5HTIKfORdfwtyx2GdnBVWgMNL289/QuHyWVhmnkqqZS9yOKRvVs1bQHDHdiSLCWuOj9YXN9Jw/70Iikxsyxv4j3biKs0j0jOMaDJg9bk5+sx2BFFAMkls3zfEQCJNXNGY7rIwb0kRdV86h6Gtexht7MdV7Kbk4rMwVs9GMxjZd/3XmPHaa8RFC5c9fYB1X130tzy9JvH/iM+K+FTf9OynJj5DHz3Otcs+P8Rncp19Ev/0eETrYvrLHyAZRRJd7TDYRvb1t2L2OJBMEuloguhIlHDPGBkFPvxNA7hKPChpGVVVGXnvLXwzKzE5bcTGYhgsJkaPjpLT4ENNqQCYnWaiw1HGjo1zbO8gwe4wVWdXEe6LMHVhIVPyMxg/HmD0QCuxwWGU4R5cE/16mnQ6Tv+MiyEeJvuMNThnzsHg8emjl+FeJlqaMBVX6+3io/3YiwsRJYFUJIY8MUHbOwfo2thKStWIjUxwyeV1/38fD0nQ/T7VGSasksiDBx9j2icf8cuXbmH+987ka1fWY7CY+GrV5YzcnAtA/5aD/GznH2mPpuj48DidTz7HxB3XU3PpbCa6+xGsdjyX3YRo1KfjyUCE6MAIltIK4qMBBIud1OgIJStnoqQUnKcswZBXRu7iWUx09vDcGd/DUVFCpGcU+2kXIVhs2BeswnDuN0CUMJXXkzu/gZ6nn2a8pYv4u49j8Pjo3tZLtKcPTU7pSo3dgm/hHDwNVWiKSioSxZDpw+S0Ee0fZWzHHqJd3ajxKIbCCpRECnt5OdnLF2PN9lB68y2UfPcnJIMRYsNjZM2oJnNmPcLAMcKHD1N2/zP4li9DGWxHMOoqjhaPgqpSfPYyRg8eJ75vM5qi0PXBbsxuB4nWRhw11YS7hjj+ymZqv/4FRp56kJ4/3MPQDj1dOhGMkLNiKb5Tl3Dg4Y+wea3UXbWQUHeIhTNyuPa8ar64poIvPHw1Dc+9ROBwMwBlq6dTeuVFBPbtZ+zlPwMw85WXGFfNuDNsk6RnEpP4f8Ck4vNXYlLx+efBvnNWMeXyJZjK64k3HaB30wHMTivpaJKRpjHS0RSF8wvo2NDFjC8uYKJ/FE1VCXWHCHQEqVxTxcRgiHggQTqaxlXiYmJggnBfmHAsTYbZgLPQgZJWceRlYHaacBZnEx3yY7RbsHhdCKKIu7oINS1jW34R2lg/Wv4URH83rb+9l7wT3VeRY62IRsPJbJzwkcOIRgPjRzvJXdBA6yvbadvVT1zRyDIbmHvbMnb9fjPjKYWd4/H/7f33mSXsksh0n423ukOcV+Liw74wt9w0l30vN+K0GWkPJMi1GNgwEmW5z8bm0RiXz84jZ6qPcF8EW5aVv7zawnWX1mLPy+TXv9vGbTfOwFNdhKuhjt13PsMpv/wSLY+9TPbMcqxeF5bCItqee5eCxVMByJizEGW0HzUeZWzvUfIvvpjd3vnMM43peUqHN5PsPIbRm4W5fj5aOsnAs08R6Rlhyve/TfzAVnrW7dHXyGMRtGiYYy9sJn/hFGy5XkLt/agpmWQwgru6CHt+NsN7mvTeKosJR3GOXj1iNiOYLSin34RRkxGSEeJmD28f83PpFBeKaGQ4mqZQCyAmQqQObMZcN4/IlnfJOP0yiPg58qO7qb35crRUAsmbh6YqqCE/xtJakoc+ZqJ3CEdNNf49em+Wa9pUDt/3IhnZdgAqvnAhmpym790NABRdeDb7f6GPLzVVw55tp2TVNOylJQgmC33vbjiZK5SuXYYoJ0mLJpKKxvHxBPNLMj+bk2kS/8/4rBSfqq88g/QpFZ/hLY9z3bLKz43iM0l8/kpMEp9/LsgH1uJf/x6Z136L6Ft/4vCfP8JgMWB2mgl1h1AVjYJ5ecTG4kgmEUehB8liJtIziiAKhLrDuEqcWLMchLvHychzEh2JYPFYeefFJpYuKCAyMEFwLIbDZcaRl0HFWdMZ2ttG6erZJPwhkoEJsubUE2nvJtIzTM68OgLN3dgLspBjCUSjAbPbwXhLN7krlhI6eBBnXQ0Tx9tQ0zKjhzoIdARp+MISrGXlRJqbiY8EGTrQzRPrOv6vj8FlM3KwZdmov2450f5R4v4wBRdfRGjHFlLhGCMHOsjIc6GkZKxeFx8/vovTf7SGUHs/OXc/Sfovd9L6+i7kuMyuQ8Oc/8U5FF56EemuZgRRYmR/C2elL+b5vofwTStFjibIPfdstHj0ZB+WGvIjebJJ97Uh2p0IJ1KbxaxCNIsDpXkHXS++SeHyWZirZ9L7wosUXnKR/n3OTD6+8jbm3XkdqcE+0tG4vplld9L1xgbyFjQgJ5KA3gzvnVmHGtejCNRIkHQ4zLFXPqH2yqVYV1zGhKuEQEKhwBAnZXLgjytkmESOjycocZlxmESST/wY9zlXQzyMFg2jphIoo/2INgfyaD+WxeeRProDY+0pqEOdJI4dRE3LRAf9xEcDOIpysGZ7CLX3Y/W52fCz9/DlZiBIAnVXzCM66Kf0llvZf+sdNO4aINMkMe0LsxFFEaPThu/0Mwnt+Aj3sjMQLHYiedMZjckUOIw0jsTJd5go8Nj/1qfPJP4H8FkRn8ovPf2pic/I1j9z3fJJ4jOJ/wWTxOefE13fuxZXaR7jLV3I8RQDe/pxFjqx59jp3d5LxRlT6PjwOBVrakiFo6SjScxO68mfNzrtTPSNkVGYhZpKE+7xo6RUEoEE3cf8OI0SuTN0U6mSVlEVFavHQjKcov3gMFvHYpxX4mLON07jR996g5sunEL2TN1IbbRbUdIyGQVZGO1WJIuJSM8wktGgj7kSKVpePcybHQFuWF1OVm0uJTfeiDw2xK7v/ZHndw/8p/v678pNpkliustMZzTN/NosTvnhpYRbO8goymF4dxNGuxV/cx/OYi+aqjLWNMyhvYMnjbWVa/QCVsliQk3JrL1/C7VTs1FSCnVXLSQViaEpKgaLCdeceRiLp3D0jh8z5Q+PoR1cT6K7HZMvG0QJLamrUo1/ep9Zv/w2iBLp2mVI8SBi10FEhxslMEJgy0Y8S5ZD6XTSH7+G4dTLMQT7OPqjn5E1rexkblBseAwANSXjPfMCJnZsQI4n8Zx+PokDWxGsdowFFUzs207G7EUoIT/CtNMYlTz4lAApmxfLeAeCppLyVTEeV8iJdpHyVmAebkEd7mJs03rctZUY8spIHDuIZDaDwYjkzSPZeQxDRgaSx8fA2k0M7OwCQJAEvFOyCPUEafjSmQhmK1oyTtdbW3jgmSNkGES++rX5eKqLkRNJwl2DpKMJPNVF9G1rYfrD96E48xDjITSjmVHBhT8uMxhJYpQEXq6ayyNa19/+hJnE/xg+K+JTceNfPj3x2fYE15/2+SE+kx6fSfxLo/TXfyHnsRG89eWYPQ5sWTZS0RSaomK0mxja34dkEjn47H4ifSGiIzHS0SRGp51wX4hQ5wgGqxElkSQ6FCQjz4WvoRCz00TJFC8JRaV97yDhvgjjx8fJyLETDyQYPz7O1rEYt14zlbe6Q+y+dyO/f+tbWL12ksEJksEJDHYLVq+TVDhGwh9i7HA7nukN2At8pMJR5GicNzsCADyxroOx5iEin2xEkCRcJa7/cl+VE29hxlMK2/1xoopK7owcAo2tRAf9PPSlp+n+qJ1IzwiPvH6Mnc8epGdrJ+PHx0mpGotXlhKKJFn7zCGCbQPYc70kxkOc+6M1uEtctB7zM3a4HXf9FLwLF5B1/hWMbv6Ijnt/Q8WFy9h36VVoqQTmvAKGtu7h4L0vYypvoH/DTqovnI9gd4KqYBxrR+o/ijoRJJI3HYrq8V5yA4LFhhgLMLHyZpRPXkce7GLK167DM3M6I3tbkby5pMIxjHYrRruV8XVv0fHObmzFRaiRoK4wRcO03v8IciKJWnkK0pS5pGxecoPHEOQkRiWJ6shBzizFEBkh0yrpCd6pCfofvZ9k22F9POYrQB7sRDQaaPrLBgzZhUQO78e+/EIMK65m+7ce5uOn9hLuCwPgq8umaMVcpn7tIsYPH0MeH0VNxHBXF3Lvq1/jWz9ZTUaBj47392HL9aKmZIpXL8Tq89DwzWtQHTkY/F204+Wdfrj2uQM8taeX4+NRTqv0TZKeSUzifxCTis9fiUnF558fppk30HWRmfHmnpMjLGuWjehwlKGDw5idZkZGouTmZZBZ5UFTNNyVORjtFno+OkblOTOJ9A4T7PCTDCcRJAE5LnOoaYzGsD5yybUYuPjCKQwdHKH89CrCPX4kk3RyfHb06CglPhslS4sJdYdwlbiQ42nyFtQRau8nd+EMUoEg0UE/E/2jJIJxnnnjGGdUeylbWUHrW82ULi9BkETef6mJs65o4JEnDnD58lLGj4/TFUxSnm1jR2+YRaUuzE4zyXCSd1p1cnNBVSaVa6qJjYQ5sqGLokoPRUsrkYwG9j6+k3XDUeZnWjnnzrPpWneQzCm5qOm0Ts4CiZOP0/yfXIpotYPBhHjKeUSfvweTOwPJk40SGEG0O0n7xxg73EbOvDqi/aNknXsp8mg/bY8+Q8HDL5MxegwAdbSH4YrT8B1+Cy0WRlhyBYb+I0wUzSH58PfJXLFGzxIymogc2qe3j9fNI7ZnI0oihZqSsV31XUZ+8x2y5k1H8uaizViDEA8xJLrJU8YJmrNwKeETiooVMRZAceUhBQdQR3s4dt+jZBRmYfO5cd/wPcR4CNXmQTvwIcG9e3A11BHv7mZodxP2XC9Gpw2j3YKmqNhyvfR8uBvRaMDbUIZkNGJ02gi19ZM5sx5ECUEUUZdfjynYy9Ajv2HnI5+w5Nun4WqoQ/IVIBTW0m/Koz0Q59cftiLLKhtvXfL3PF0m8SnxWSk+ZTf8Bcni+FTHGv34CW5YUfW5UXwmic9fiUni86+D1LYXGd20mdHDXSgplehIlPIz6kmFowzu0ZOQJ0aiOAsdeMo9mN0OooPjyAmZrIZiej46RumqehL+EN0fteMsdBLqDvHyET28b7rLQnm2DUdeBkpKwV3qwlNdgMlh44mfvM9lN8+n7b0mCucX4CzNIxXRFR9vfRkAJqfu4fAf7UCymPFUF9G1bh+SUcSel4m3vgz/0U6G9vdQec4s2t7ZT/pEmrSrNAclLXPkuQO8PzShH08UqHWYUTR9lJUz1cfmjV2cd8Ms2j5opWRpMSNHRv7T6CzDIDIhq/jMEhV2E1MXFpJ3SiWjh7vYtb6TxedUkbegnrg/hLu2gv4NOyn51SO033YD1T/5GZ333EX+8nn6Y9nZiyCJOGprSQ32kbFoNamuFlobLqZ88/1YZy4FuwfF4UMz2Qn/6ad4z7oYzWhFHelBzClFM5pJfPQK6XAMW+1U4q1HsRQWYaqZy/i7L2DLy0GJxzCf+UUwmFBNdqSoH2GgBaXiFKRgH4z2ImQVoA60o9UshsMb0JJxet/8gOzZNfRvPUj27Ck4rvo20RfvA0CQRL1sFYj29OG54DqSOz/Af+gY6WicVCRG+7oOlvzsfBL+EO5FSwlu34qjppr06DAJfwjRZMBR14DkzSPd346WjJMOBvE3dlB89VUI3gK2yAX0h5P8+vmD5Ja62fC1xX/7E2ESf1N8VsSn9PqnPjXxGdv+5CTxmcR/xSTx+ddD+MkfY/R42HP3K3jK3diyncRGwtiynSQDUWJjMew5doYODqOkVOw5NrKm+OjY2EF6Io2rRH8eiJJIRn4GmqKipFTWb+piXoWH3Bk5+I+PU7S0Em99GV0f7CazRq+3KFoxl7GDrRidNj3sb04NR57cgiM/A3u2A1VR8TaUoaZkerc0UX7mbJR0GpNDJ0UGm5We9buQjAZURSUdTRLoCJKRY8eWnUFGgY+J/lH++OcDpFSNBqeZq351AYHWHtS0TCIQpfYXP+ep2VeyJ/CfgxDzLQauvkknLe/8eR+98TSKphFXNM7Oc1AwLw9vbQHOsjyaX9xO1hQfGcU5+Bu7CHWHqf/CEo69soMplyxASaWxV9eQ6Go/GTTomDYLNew/6QH695oIY1YO4oILkIaPozmyECJjjH/wOplrLgRVIdV2GHP9fCa2vsvY4Tbyls9HWnIpCCJCMopqdWEYOIqSXYFqcWEcOY4WGkEZ7dc9Nyfa3E3Tl0JoBHmoh/CRw0SHxjHaLAB4F8xDDfk59PB7FJxSzseP72LqylLyFtQCuok6e+UKBt79AKvXyciBduKBBOHeCAt/fCGSr4BUXweS2Ywhv+xEuWopyfYm/Ec76drYSt6sPD0r6IovoEaCrLXN4ea7PuDgQ5fiddg+q6f/JP7G+KyIT8m1T3564vPJk9y4svpzQ3wmKysm8bmF8/qfATAnmWC8qQM1JWO0J/R8nYSMxWNBSSm6L2giRbAjiNVjIX92Lsc2dVOYn4HZaWasxU+4N0wimKBsRQXnXmShbX0nJqeVmD+Opqgc+OOH2LPtCJKImpYZ2nmEUOcYw0dGkUwiPdu6yZqidywlw3EMViP20hKOPPQmGdl2Dv15C94qD+vfbePy760gc94cnnz6MHcdfILAhncINHez4KGvM77xA6zZHuRoArMng0vmDbP54DBVOXYyLv4qzkQITTIhjPexfuWN9MZlqjNMtE6kTj4uAwmZ39z3CQBzPRZWFrtOeo3eHYxwzZibfE8GRoeN+mtORU3LxP0hXGW5GKxGJIuJabecS7i1A/ecuWgJvRU945RlmMrTpPvbEUwWECUMp5yDsOsdJI8PsaQexWBBi4bBlQsWB645p5BqO8zE8TZEowHR5sB+ymkkL/o+flklyywgyEkETUXq3Et6sIv45vdwnX4hrXffhcXrovjGL6OZ7GhGMxzfT/roDjQ5jWixMbT7GFXXno9gMILBpPe1GY0UnFLOyKEeLv3wXgZefomutQdJRdP46rLpe/1tPNXFRIf8OIp9VJxfQ/B4D5I3FzUWwVylr/Mrwz362LJvB476BvLyS8hdvoh4ZwfOU89AdWTzVrKMdw700/vMdZ/hM38Sk/h8Y1Lx+Ssxqfj8a+MmoZQHPvkdqY6jxAb9hNr7sXhddK5vJjambya5SpzkTC/AYDEzdKAbf4uf3Bk5qIqGu9xLz9ZOfHVZZBRmMXakD4NVf18x2jSGIIr46rOwuK0oiTRjx/zkzcqnY2MH7hIXqqLyl83dnFPoxJGfQfbUbNSUwquvNDOWUmhwmqmu8NDaHqDEZ8PqsWDzWnGXe7EX+Gh7+yAzbl6FMSuH/nVbGNjdy5SL5tC14QgVZ89m9OBxEoE4jkIPAL99YCff+/YSgm3DrFvfiVUSaY7oPqV/J0LLfTZmnF/DWNMofa1++uMytV4rreMJzrlxNqlIjPhYDLPTTNHK2QSau3GW5aGk0picdhL+EI7aWtIjA1hXXYVqcSAkowCoTdtJdreRseRMEoc+pn/DTjy1JYweOE7FDVciD3Rimb2cxL7NSDnFGAsr0AwW5MxihCMboHYxUngE1eZGioyQ2LtRNyQP9yBa7RgWXoAU7EfOKkM7uAFhxkoEOYWS4WPk51/D4nViy/Nirp5BaMcW7KUlDG/fR/aceqJ9g6hpmcFdrWRW55M1o/qkuvb+c0e49AcrufPf3mOux8KspcVM/dE3mJiyHHt8DFQFecebiBlu+t5ZhyXTRc75F5Fs2o3ptKvQLA6aJgy80TjE3d//zWSD+r8wPivFp+iaJz614jO+4yluXPX5UXz+4YnP3Xffzeuvv05LSwtWq5WFCxfy61//milTppz8nuuuu46//OUv/+nnTjnlFHbu3Hny82Qyye23384LL7xAPB5nxYoVPPTQQxQWFv5Vt2OS+Hx+YJp5Ax3nSqTCUXIXz0LyZCMP9+gqBZDyj5OOxon0DBNoG0VOyESHo+TNzsXktJGOJpgYiDB0cEQfoWVZT5KgiguXse/et/DV+xjcN4ivTu+M6tjUTfHCArIaipEsJkRJZOCTY7zyQTtL8jLIPNGyvuvQMHMbfMT8cbyVnpMqhKqoWL0urNlu7IV5NP55LaIo6EGOheUcvPtJBo6MsG44ys2X1FK2Zi6WyjoU/yCDW/Yw1jxE/8FhNoxEUTS4uM6H2Wli55FRXEaRTJNEYzjJUELmK+dVk4qmSU2kaG7xU5Zrp3RZGe7qIob3tmK0m8maVoG9sopETxfm3FzUaBhBlMBgRLQ7Ee1OJI8PzZOP/7mHCHcOYnTacRRlEx8NYvW5UdMyotGAmpaxeF3ERwJ4z76YwReexp7rxVJUROBgI6OHu6i/7z7kg5uQfAVoqQSJ1kY6P9iDpyJXz8c54xyUwAiKfwglHiPQ3E3BNdcy9u5rKCkZZ1kelmWXMPTYvXhn1pEcGcVSWkHf2+swOewn+8h6Nx9ESSlkNRRjdmfgmFKNcepi5MxSpNAASuteJIcbwVsAkoTS10qofg2aprGtJ0TLyAR33v6rScLzOcBnRXwKr/rTpyc+O//CF0+vmSQ+/yg444wzuPzyy5k7dy6yLHPHHXdw5MgRmpqasNt1v8N1113H8PAwTz755MmfM5lMZGb+R7rpzTffzDvvvMNTTz2F1+vl29/+NuPj4+zbtw9Jkv6vt2OS+Hz+EPrzDzHnFRA51oq9MA/R5UWLhVGiEUb26u3tAP6jnaTCUWIjEzhLMhFEEU1VcRTlMHa4A9EkkQwnSYaTZE8vIj4SOHEhD2K0m5EsJkYbBxluHMVd7MRgMZA7q5B0NEGkL0SgM4jNayN3dgG927vJrssiHkjw3oZO0prGdx+7BtFoQPLoeULjew+Qc/7FpHta6X1nIwCt77ay+s27+c4pXyOu/Mcp/8U1FSfJ0+D+AarOn8lEzzATg2Gee6+NS5aV4Cpx8YcnDrLcp59vGRkmABx5GeTPL8XfNEAynCJnRh4Gu5WBnZ04Cx1oqobVm0H+8nkIZiuCwcjxv7xJ7W9/ixAcIvjRWpynLKH90SfInVeHnEjSt6WRkpUzMWdl6tUfiRRxf5hkIELJhWcgurzs+c59nPLKE4iREQIfvkmgpRtPTQmpSAwlkcLidTJ2uJ3i1QsZ2XuU/LPXkGhrQk3LJ7KaEsSG/IgmnYx668sxuRwEWrpwluUhRxPE/SE802pJDg0x0T+KyaETWjUtM3Koh/qvX46pvB5EA+mcKbDjVTqee4OSMxZgmb0czWxHsXoImTI5NBzlT9s7eeP+RyYJz+cInxXxKbjikU9NfAK7nuGLq2v/x4lPV1cXP//5z9m0aRNDQ0Pk5+dz9dVXc8cdd2Aymf5Hf9d/B//wHp+1a9f+p8+ffPJJsrOz2bdvH0uXLj35dbPZTG5u7v/2GKFQiD//+c8888wzrFy5EoBnn32WoqIiNmzYwOrVq/92d2AS/7Rw3fgLAJx33EDw3/Iwldfryo8tQu5iG/ETF2Z3RQHmTBfRgRHUtIySkvWLcFrG21BK96ajGC0GXMVuRFFk/HgAryTiLMslGYww1jyExWOh/LQSJJPEWIufvk+6sGfbsWXZSIZTTIxEOf7uMcxOE0fWtrN5NMYX11Rgz7FjzM4n2d9DfPQYgzuaObapmw0/+oBLp2az9P0n2XHJlwklZLZefyfLfXbiikoorVJTnYmqamRWZRLuCyHHZY69tp+sKZk4Ct2sKnNzaO8g+zd0clGNF2ehk8Z9g+zsCeExSpSFk6SiKRq+sJCJ/jF8M6voWbcHR34GBquRkvNXoUYCRHv6sRcX0PnyWopXziK67gUiPcM0v7yfxcVFtL3fTsWXrkPKyiejpg7R7iDechg1JSPHU/Rv76Dy3BkYS2sJb36PWd+9hIEH7iL37DVY83LQVBVRknSjd0qm9bU9TL/pdOSJCYw2C4b8cmhrwlpexcDTr+OuLiR3QQMd7+7CWZxFbCSA0WHj4OM7KFxQQGZ1PpLFRGrFl4jecxtyIkXH2qNIRomq82dRuKQGVEUf3QWHMPQehIrpVHzRjlg+E1VT0YxWNIuDC/64i4bvX89LWhdcN+/v+nyexCQ+a7S0tKCqKo8++iiVlZU0NjbypS99iWg0yj333PN3u13/8IrP/4q2tjaqqqo4cuQIDQ0NgK74vPnmm5hMJtxuN6eeeiq//OUvyc7W3wFv2rSJFStWMD4+jsfjOXms6dOnc/7553PnnXf+l9+TTCZJJpMnPw+HwxQVFU0qPp9j3CSU8seed5CP7SU10I0xtwh5tJ+EP4TV50G0O4m0tmPxuvj/2rvz8KiqNPHj36pKbalUKpW9sodNdjBBVhdApaVFcRmElmaREVdoGPRptbsd6Z6e1pl26WltaZ1R0Xad7gH1J7YMtoCgIAhBdggQkpBUtspSlUoqtZ3fH2lqjICAhKRi3s/z5Hlyz13qPefeW3lz77n3uPaVkHJpf+r3lhDrSKTxcPtj8qYkG4dXf0X6yDS0Bh3ln5XjKEhHZ9DhcTaj1WoAsGbZeP/NvVxiNbCz0UeVL0i6KYZRdjOZox0MeWABaLW0Hmx/4d6uF9dz+GgDVb5gh5Hb77im/RH51gYfb+9wcnP/RPY5mxl3mYO4NAsarYYYs54YkwGtPgafy4PH2Uzlnhri7CYqalpITzBhdcRx7GAdqYlmPE1tHGn2MyzRzIgFY6j84ii5k4dQf6AUj7OZ/B+MAKBi834yLx9Mc0UdpqR42hqb0elj2gcUdbrIvOE6wm4Xu//wPt5qL+ZkM4n9ksi+dixlaz9vH55CqyXWkYhzywHMSXHoTEaaStoHQ/WUVZN27SSKX3oHx7jBxGZl4D58DGvf3PanuLxutNYEYhz5NH32N2zjJhKoOErz0RIsOZkQDhNwuzEkJRFoaKCt0UPA68O1r5Rga5D43ES0ej2lG47gKHCQeukATJkZ6FIyYeAECAXRBNugfB9hTyMxfYYTNlkJ2TL4wfNfsG50FYYrZnXxUSqiRVdd8cmY+YcLv+Kz7Q0WXje4S251/fa3v2XFihUcO3b2YXculqi/4vN1SimWLVvG5ZdfHkl6AKZOncqMGTPIzc2lpKSERx99lMmTJ7Njxw6MRiNVVVUYDIYOSQ9AWloaVVVVp/2sxx9//LQJkei9Tr49V5c9DP3hzwh7PbSVl/x9uISNhL1urAMH0Fx8hMRBue0v7Rs5gObyKiyZKbj2HsfvbmXQjy6jtaaBtsZmTAkmzMlWjAlWzKl2fK4m1v/lIM3Bco56/Rz1+kkx6rjMbiLdYsCaEYfeoqfyg7/yt//8gpnP3s5Hy96h3h9iZ2P7Y+lxMVquH5pCQq6NrRvKMOs0kQFNCxdN4suH38dgMWDLSyPQ0oo+1kzD0SqsmXaSh/fBPiiIJTWW2FQb+YEgra5m/M0BBo3OACAhECbV3cbAGZdRt/sY+T8YEUns+vzwUjxlNXid9RzaVI7Jbibg9WNMiMPnaiLYGiDoC2JJtVLy5ipSRvYnNtlMQr6dxEG5PLpsNc9Mm4g1O43milri8x3EOtLo96Ncwl43DQdLGXTvLI6+torsawrxlRymzy2TCLrdeI4cJ6GggMqPPiHkayP7H6YTdrsoXbmSzKmTKX/rbVIL2/sGNuwtJsZswH7FRAgGCHk9xJiMWHIySRw9ioMv/DdVOyoYPHsC7rI6cm68GsKh9ie3PI3ElO0m6DyOzp6KGjAWfX0ZNfb+bDjeyKx0MxsfmNilx6bovVQ4jAqFLnAjilAohNvt7lBsNBoxGo0Xtu1vaGpq6tANpTv0qMRn0aJF7N69m82bN3conzlzZuT3oUOHMmrUKHJzc1mzZg233HLLGbenlEKj0Zx23iOPPMKyZcsi0yev+AgBoB0wgfs0efzh2CpClUcwDrgUjdlCqKEGS04rLRVObMOHEfY0EutIQmexoreYcO0tQW8xsXvNQbLHZuIozMTvbsGcYqe1rBoVUgwfmoItNx7nzipC/jDB1iB5k3JI6JfBvrd2cHx9GZfenUW6xcDqJW+R29fOx39/+eBNfexMeGw6B978FI1OQ7pJx9pqbyTuXyz5HwDiHFa0hhg8hxvQGdwYLHpUOIz7uBNLZgpJQ/PQW0w0HCiludrLti+rCClFSIFNr6VPhhXntsNkXTkUv6cFjU5L0BfEXeJE8/c+cyXeAOPTk/BWufBW1NJwrIG0EZntQ3a4W/E1+PBW76JmTw16iwH7gGz+aWEBlX/7HH2sGaPdilanRQX9NB4qQYXCWNKTQKuj373z0cZaKf7dc6SPHow5vw/G7HyU30dC30xsV99A82dr0Sclkz52KJ4DBwh6W6ndVYw5KR5DfCxx/fsRbqihtbSU+oPHSeifjc6WRFt5CQNumwgxeirXb+OSO/+Bus+2kjxhLLokB7qMfoQtiRgs8YTi06lScfym3238UR1n1ggZQFR0LRUOocIXlvgoFWbbtm3YbB2HwnnsscdYvnz5BW37644ePcqzzz7LU0891Wnb/C56TOKzePFi3n//fT799NOzPonlcDjIzc2luLgYgPT0dPx+Pw0NDR2u+tTU1DB+/PjTbuNiZLri+yVyBSi/oP1x+PqtqKoyTCMuxzQC2vZuab8tUl1GyOshNtOBTq8nFAiQNzEPAHOKHRVyceh/dmKym4hNjqX/TQXUFB0hZXAyOoOOcEhRu78Oc7KVMQ9eR+PRCva//SUl7jbGXZnDJ+tLWTitP//5QXH7LakPviA+K56GYw0Mmtaf1IMubLnxuE94yL6iP0Gfn9UvfMHVU/tiy0+lrdFDbIodb5WL+uIGvnh1F9dO60fZ5hMMnjmCgDfAD24bhLfay47N5RzwtDF6YC4r/nyAgnUljJ6cizHeSPXuWvpmJuF11mPLT2faTXD0o/3Ysqz0nT6WoG8XLTVNBH1BkgZlYbJ7MKe0n4++Bh/1B0tpKm0irSAHW79MaosOs+eD/eRf0xdzip34fAfG/IG4i7ZjHToC55oPic9z4Cmrxu/x4vziMCF/mJzJQwlWl2PMzifU5MI0bBzGAT5i04vQ5wwg7Gmkafce9I48gtVlmBxpeD4uImXkAMLNjRjzL0G1uDH0G05ORj6B4wdIuWI8ukFjUdoYlE7PMRJR1kQGpsaT87VjQYieavTo0axdu7ZD2Zn+Bi5fvvysd0S2b9/OqFGjItOVlZVcd911zJgxgzvvvPPCA74AUd/HRynF4sWLWb16NRs2bKB///5nXcflcpGZmcmLL77I3LlzaWpqIiUlhddff53bbrsNAKfTSVZWFh9++OE5dW6Wp7rEuar3tBDvLiV0aDtaSzzhVm97Z9jmRjQGEyoYwPm3zzCn2gn7g7TWNhD0+XEddKHRafB7AyT1t6PT67APyqHqi2LSRvXl+Lp9WDPah8FobfChM+jQ6XX4Gnx8vquahkCIqQXpDJ49nu3PfEzepFzsA7JJnnYrNe/9Ga1Oy/63ttHobMaWasHeJ4EYcwzmpDi2vL2H1pCi8PJsDHF6dq0rYfjkPA5uKCVvRBqW1FhaXK3oTTGs+ugo9zzS/pBAW6MHn8uD1qAj2BogFAhjzbRjTrUTm2qn+UQtpZ8cICHXRsKALGJMBuIHD2Tnv73FgFsvax9wND6exkMlWHPScO0rAcCanYpWp+PEp7vJnjQSg81Kc3kVOpOBuKEj2fLgc3y2v46ZCy7F724l74Yr0Fqs6PMG4T9chAoEIBzCU1qJOcVOW2P7rSxDQhxNRyuIy0zBuWUv2deO5eiqDfT9h8k0HjhK4qhLUcEAhqETQKMhbIwDnZ5wrJ2SZkWjL8iVNz0oT2eJs+qqPj7pNz+F1hh3Qdtq2vnf3HX98HPu41NXV0ddXd23LpOXl4fJ1P4KkMrKSiZNmsSYMWNYuXIlWm33jo8e9YnPfffdx5tvvsl7773X4d09NpsNs9lMc3Mzy5cv59Zbb8XhcHD8+HF+9rOfUVZWxoEDB7Ba2zt93XvvvXzwwQesXLmSxMREHnzwQVwulzzOLi6qezR5POv8G5qKgwRdVYQ9DcSk5+A7vBd9UjJtVVUYbFYai8uoP1BG4qAcgl4fzi+PY8u14a32EgqEiM+Kp83dhgqFqd5TS0KujTZ3G2kj0qncXoGvwYejMB1PZTN1JY0dbm/908IC7AOy0ei0eMqqqTtQhQop8q4dSrDVT+3u41iz7BS9vYePa7z8IM2CNSmWz481MP2G/qz48wEW3T6ExEE5AIQDQap3HifOYaWxpAFjvIE4hw3PiUbiHPH4GltoqWvF6ogjPj+d5opavNVeYswx5E+9jB2/X0vf6waTPGoInqOlWDJTOPjGRg5sq8QfVuSkWSIvcLRmJZD1w0mEW70ov4+Gg6UUv7+bS++9Gp3JgGtfCbY8B1pDDOYBQ/GXHUZnTcB9sBitPgZjgpWYuDgCbjfOLXtJHJhLXG5m+/t+fF7qtmynavsx8n9YQPyYKwhWl6O9ajZan5tGYzJaDRxyteL0tHHrsIzuOoxED9RViU/a9H+/4MTHXfQX7po24qJ0bq6oqGDSpEkUFhby+uuvn9Pf24st6m91rVixAoCJEyd2KH/llVeYP38+Op2OPXv28Nprr9HY2IjD4WDSpEm88847kaQH4JlnniEmJobbbrst8gLDlStXRsVOEN9fJ2+BGKb+Ky1rHkKVHwCtFtOAobQe3I0hqb2Tn8WRhK1vJoZ+w2naspE+P0yguaKONncb8Vk2QoEgldsq+aq2hcKceOoOuojPslK3vxaAhD4JtNS1Yu+TQIurFf6e+ExKieUvr++hIbCL267Ow13u4ZKbh1O+qZgvn93I5ctvxFffRENxLZlDkqHGy9pqL4kNPur9IayZ7beiXnx7P30tR8iJjWHwjZeQfdVg4vKz8f/lE/zNASq3nyD/2kF4q1wk9HUQm9r+vhyfq4mm0iZik80kD8/nwJubyLkyn+SCQTQeOIqnrBqNVovfG2DktflU7qhizE+vB6DxcDmmpHhCTS40MQZ09lT0lmrGPjaLQ3/6iL7Tx5HQNxNTXl+aD+6n7YstqHAYc0qQqi+PkHn5YFQ4jC4pHU9JOUlD8jHEWzCOuobAoS8JuqpInXItyeMa0VoT0NlT0SZnEdTqOKGxc8lVi/AXvcx4GT9LRDEVDndKH5+LobKykokTJ5KTk8OTTz5JbW1tZN6ZXj/TFaL+ik+0kCs+ojPdo8nj9w3b0Hmqadu2Fo3egHP9VnKXPkSo8ggV//MuqYWXoLXEU7uliFZXE65DdegMOkx2E8HWIHWHXJTWttAUCHOJ1UBSXztxGXE4d1Sxq6qZYYlm4rOsvLG1Amgfd2vwyDTi0iy0NbXx3ifHaQqE6Wsx0M9m5JLpA6ncfgKdXscXX1VHxu+6qY+d2iYfIQWtIUVIKa6dOQStTktDST0A7nIPf9lfy51T+2K0GSMDu26t9HDVwCRMdhONxxrJvTKH5hovOr2OGHMMpgQznhNN9L91HAFvK2F/EFtBIQHncWq+PIjFkYQ1P5vj/28TjnFDsI6dSNvBLzH0GYry+3Bt3oStXy4N+4+2v1NpQDYhnx+tIQZDmgN9dn/CzY3okhy0fbUZfZ8hhL0eCIdg1DTQ6YmpO0bImkIV8Ww83sicgnN7m7sQ36arrvikXP+vaI0X1qnes3s1d99Q0OlXfFauXMkdd9xx2nndmXpI4nOOJPERF0tzSyuxlV8RbnJBjB7f3q2Rca8AYoeOIuRy0vDVXmJT7bhLnAS8rfhcHqp2VWOym3Cf8JAzIYumE25SBqfhrfFQvvkEvlCYwT/oQ9WuasoqPJh1GtbXtjBzZBrv7KrGptdyZVocZruJ/95Twz8tLMBzogmdQUfVrmr+Wu5m1uXZmO0m9m4up8/AZFpcLRxxeim4zMFXXzrpk2Glsa6F1lAYg1ZD1oD2wVbDoTBNpW4chens33KCfsNT0Rl0pF+aSygQxOusJ3FQDuYkG6FAAGOCle1Pfsi7+2pZfFchDSX19J1WSMmHOwn4goz6twcIVhyl5UQl1hGFNGz5jJA/SDgQJGFANlqDCX3OAHyHduF3e7GNm0i4xUPY6ybGkYcmPhlltBC2JKH1uqizZFHjDfJ85gjpnCw6nSQ+0Svqb3UJ8X0XF2uGfmM52d0vcf7btL51B+GEDEK7/kbr/p0YHFnEptoxpKSS2ncQ9Vs+x5hgxWi3oEJhYpPN7PjwCHExWoKtQZL6J5I5uj1x8jibMdlNXH5lHhqdlpw9TqyOOLIP1WPQaggFw5RVeAAo+VsJ7x5r4AdpFi6Z0odJ/3uM8gN1JCSY+MzVSpbXj6MgHcsJD0mXpHD14DRaatwEtznpOyyFxtIm/vRZOdekWqj3h2gKhEnxBbnspvb+eXqLCXNqAp6yaoK+IJ6yagBqdx/n4IZS8gvSuW90Bvp4C3GpbVRvO0j2xMHEZacRcrW/c8taOJawpxHb0MH4q53o7Xa0cQnoBo6m6d2VJEy+HnN8ClqfB01SJjHhIGGTlWMxGYRRDJu4FH/Ry6QD6TZ5Ikv0bGEVar+CeQF62/UPSXyEiDInnxjSAfrr+hJ/6QJaPvgRMSnH0CakEKqvwpqThj4tm0B1OXW7j5J1VTaOMQPY/rsNpAxJofyzchL7JWLLtZM0pA8tNQ1odFo2/X4jiUlm+v5wJLc7EvE66zm+vizygsN3jzUwNtHMJVP6ULG9krXVXqY5rLS425g5Mo1jpU0Y44289mkZt/uCxGdZ0Rl01LvbePe9w5E6WMwxjJwxhEPvHaS1wYf7hJussZmYU+zEmIyYkmw0HKml1eXFEN9MfE4yU345gpaaBjzl1bj2V2Lvl4IKhzEmtPfVU8EAurQcwg016JIchFxOmo5WYExown7V1fi3rMHSrz8qPoVwXApuWz4VzQGy/9+/kXDXb+j/jfYV4vugs97j05tI4iNElIv8oc4cBIDpHxbg2fgkuuIt6HMGkBqjJyYjH3/ZYa55eTG+suNkXjmS43/dht/dStn6r1ChMK0NPibcO4HDq7/CW1VP0NtKm9vPoBlDqHxtF7VtIUYmmcmflEvJ+lJyr8yGgy4sabG01LVysKSJHKuB1z4tIy5Gi7fay5vbKpmUEkuVL8g0h5XJ/zGH9cteR6PT4jpUiyFOT1xqLLHJycRlptB4+AQeUzUWRxJJgzOITUkAwFtVT0tNA4kjBhIOBEkako8ly4FpxAS8W9ehNVsIexog6IcYA2Gvm2BDLZl33E0oLgWlN2IwmFg08i6g/SpOIpBoBe76TdfvNCG6SGckPvSyKz7Sx+ccSR8fEc3u0eQB8Lu//gyNwYT/xDGMfQej6XcZOq+LprV/IRwKEQ4EseRkotEbaD5a8vdH3GsAaCqp4ZJ516NavWiMJpwbt+P3tFBfXE/IH6Jw6TQ2PvQ2Pl8QvVZLZWuAH/3HLJqOVtDW6KG1zkOfWyZxbNV6Ysx6rNlpNBw+gTHeTO3+Gsx2EzX764hLtZAzcQChQJDUUUPaX/IY9Le/HfmKCWgt8ejsKRxf8Qdyl/wUVJhgyV4Ih/COmYlep+GRvxbzwFX55Cdf2BhFQlwsXdXHxz7lF2gNF9bHx7vvA+69aXSv6eMjic85ksRH9EQrEgbyhzufZMfyK9C5q9C0eaHNi4pLRMWY0DiL0RhNlDz/PHn/uAAA5fehidGjsoega6zAV/Qpn/7sv8m/Oh+LIwl3SRVxmcmk3ziNw8+9TFxWMmkTCqn+bAetNQ1kTSpAq4+htbYBg9WCMTsfjdmCLusS2ratxVQ4GQD/4Z0AOEf9CLtJh1n5QaOhFT3N/jCvFlXyv7sq2fL6axy9XkP2r1/qtnYU4nx1VeKTcPXP0Bou7JUL3gMfct/NYyTxER1J4iO+z4LOYuwzX2D2pr/wQPUe/CFFgkmHUgq3P0yezUCzP4RZryXWdYRAcj/Uuv9Ed8Vt6Boradn0PvrUDGLScgi3uGkePo14TzlhgwWPMRF3W4g0i573D7sIhxXzF/wzh6eEGPC/OulzI76XuirxsU166IITn5aDf+W+W8ZJ4iM6ksRHCCHEueqqxCf+qgcvOPFpPbSW+24d32sSn+4dMEMIIYQQogvJU11CCCFED6VUZzzO3rtu/EjiI4QQQvRQ7WN1XeB7eCTxEUIIIUSPEMWDlEYr6eMjhBBCiF5DrvgIIYQQPZQKX/hYXXKrSwghhBA9QjgcQiOdm8+LJD5CCCFED6VCIdBK4nM+pI+PEEIIIXoNueIjhBBC9FAqHO6EPj6966kuSXyEEEKIHkop6dx8viTxEUIIIXqozniqSyGJjxBCCCF6gM55nL133eqSzs1CCCFET3WhSQ9AOHjh2+hBJPERQgghepjY2Fg01gzCtfsuaDsq0Eq44Rg333xzJ0UW/STxEUIIIXqgok0fEq47iGrzfOdthKt2oYnL4IorrujEyKKbJD5CCCFEDzRixAg0CfmEqnZ+p/WVr4lwfTH7v1jbyZFFN0l8hBBCiB6q9KtPUI2lhFvqznvdkPNLtIn9GDhw4EWILHpJ4iOEEEL0UNnZ2WhTBhOu3H5eQ0+Em6tQnkoq9228iNFFJ0l8hBBCiB6s/vAmVGsDyn3inJZXShGu/BJt6jDS0tIucnTRRxIfIYQQogez2Wz8/pnfEqrcjjqHd/KoxuMofzPuI5u6ILroI4mPEEII0cPdfffdoMKo+uJvXU6FQ4ScO/ivPz6LxWLpouiiiyQ+QgghRA9nMBj4y5uvEHIWoUKBMy4Xdh0ErY758+d3XXBRRhIfIYQQ4nvglltuQWO0Eq7de9r5KthGuOor1vzPG+h0ui6OLnpI4iOEEEJ8D2g0Gjb/72rCNXtRgZZT5odrdqMxJzJ16tRuiC56SOIjhBBCfE+MHz8ejTWTcFVRh3LlbyZce4AvN36ARqPppuiiQ69KfJ5//nny8/MxmUwUFhayaVPv7NEuhBDi++vQ9nWE64+ifI2RspBzJ5qEPAoKCrovsCjRaxKfd955h6VLl/Lzn/+coqIirrjiCqZOnUpZWVl3hyaEEEJ0mv79+6NNGkCo8ksAVIsL1XickqK/dXNk0UGjzudVjz3YmDFjKCgoYMWKFZGyQYMGcdNNN/H444+fdX23243NZqOpqYn4+PiLGaoQQogerrv/ZtTW1pKanomuzzXtA5HGJhGq3tPlcUSjmO4OoCv4/X527NjBww8/3KF8ypQpfP7556ddp62tjba2tsi02+2+qDEKIYQQnSUlJQVt2nBCxzeAClNfvr+7Q4oavSLxqaurIxQKnfJq7rS0NKqqqk67zuOPP84vf/nLU8olARJCCHE2J/9WdOdNleajm7Ak56JJ7EdCQkK3xRFtekXic9I3e7Irpc7Yu/2RRx5h2bJlkemSkhJGjhxJdnb2RY1RCCHE94fH48Fms3XLZ5vNZsLemm757GjWKxKf5ORkdDrdKVd3ampqzjhAm9FoxGg0RqZzc3MBKCsr67aD+EK43W6ys7MpLy/vsX2Uenodenr80PPr0NPjh55fh54eP5xbHZRSeDweMjIyujg6cTa9IvExGAwUFhaybt06br755kj5unXrmD59+jltQ6ttfwDOZrP12JMVID4+vkfHDz2/Dj09fuj5dejp8UPPr0NPjx/OXoee+E9yb9ArEh+AZcuWMWfOHEaNGsW4ceN48cUXKSsr45577unu0IQQQgjRRXpN4jNz5kxcLhe/+tWvcDqdDB06lA8//DByC0sIIYQQ33+9JvEBuO+++7jvvvu+07pGo5HHHnusQ7+fnqSnxw89vw49PX7o+XXo6fFDz69DT48fvh916M16zQsMhRBCCCF6zZAVQgghhBCS+AghhBCi15DERwghhBC9hiQ+5+D5558nPz8fk8lEYWEhmzZt6u6QgPZhNS677DKsViupqancdNNNHDp0qMMy8+fPR6PRdPgZO3Zsh2Xa2tpYvHgxycnJWCwWbrzxRk6cONEldVi+fPkp8aWnp0fmK6VYvnw5GRkZmM1mJk6cyL59+6Im/ry8vFPi12g03H///UB0tv+nn37KDTfcQEZGBhqNhnfffbfD/M5q84aGBubMmYPNZsNmszFnzhwaGxsvavyBQICHHnqIYcOGYbFYyMjIYO7cuVRWVnbYxsSJE0/ZL7Nmzer2+KHzjpmLFf+51OF054RGo+G3v/1tZJnu3Afn8t0Z7eeB+O4k8TmLd955h6VLl/Lzn/+coqIirrjiCqZOnUpZWVl3h8bGjRu5//772bp1K+vWrSMYDDJlyhS8Xm+H5a677jqcTmfk58MPP+wwf+nSpaxevZq3336bzZs309zczLRp0wiFQl1SjyFDhnSIb8+e/xtB+N///d95+umnee6559i+fTvp6elce+21eDyeqIh/+/btHWJft24dADNmzIgsE23t7/V6GTFiBM8999xp53dWm99+++3s2rWLjz76iI8++ohdu3YxZ86cixp/S0sLO3fu5NFHH2Xnzp2sWrWKw4cPc+ONN56y7MKFCzvslxdeeKHD/O6I/6TOOGYuVvznUoevx+50Onn55ZfRaDTceuutHZbrrn1wLt+d0X4eiAugxLcaPXq0uueeezqUDRw4UD388MPdFNGZ1dTUKEBt3LgxUjZv3jw1ffr0M67T2Nio9Hq9evvttyNlFRUVSqvVqo8++uhihquUUuqxxx5TI0aMOO28cDis0tPT1RNPPBEp8/l8ymazqT/+8Y9Kqe6P/5uWLFmi+vbtq8LhsFIq+tsfUKtXr45Md1ab79+/XwFq69atkWW2bNmiAHXw4MGLFv/pbNu2TQGqtLQ0UnbVVVepJUuWnHGd7oy/M46Zror/THX4punTp6vJkyd3KIuWfaDUqd+dPe08EOdHrvh8C7/fz44dO5gyZUqH8ilTpvD55593U1Rn1tTUBEBiYmKH8g0bNpCamsqAAQNYuHAhNTX/N2jdjh07CAQCHeqYkZHB0KFDu6yOxcXFZGRkkJ+fz6xZszh27BjQPjBsVVVVh9iMRiNXXXVVJLZoiP8kv9/P66+/zoIFCzoMfhvt7f91ndXmW7ZswWazMWbMmMgyY8eOxWazdXm9mpqa0Gg0p4xO/cYbb5CcnMyQIUN48MEHO/wn393xX+gx093xf111dTVr1qzhH//xH0+ZFy374Jvfnd/H80D8n171AsPzVVdXRygUOmUg07S0tFMGPO1uSimWLVvG5ZdfztChQyPlU6dOZcaMGeTm5lJSUsKjjz7K5MmT2bFjB0ajkaqqKgwGA3a7vcP2uqqOY8aM4bXXXmPAgAFUV1fz61//mvHjx7Nv377I55+u/UtLSwG6Pf6ve/fdd2lsbGT+/PmRsmhv/2/qrDavqqoiNTX1lO2npqZ2ab18Ph8PP/wwt99+e4cxlWbPnk1+fj7p6ens3buXRx55hK+++ipyq7I74++MYyZa2h/g1VdfxWq1csstt3Qoj5Z9cLrvzu/beSA6ksTnHHz9v3doP1G+WdbdFi1axO7du9m8eXOH8pkzZ0Z+Hzp0KKNGjSI3N5c1a9ac8kX0dV1Vx6lTp0Z+HzZsGOPGjaNv3768+uqrkQ6d36X9u2MfvfTSS0ydOrXDaMzR3v5n0hltfrrlu7JegUCAWbNmEQ6Hef755zvMW7hwYeT3oUOH0r9/f0aNGsXOnTspKCgAui/+zjpmurv9T3r55ZeZPXs2JpOpQ3m07IMzfXee7vN74nkgTiW3ur5FcnIyOp3ulMy8pqbmlP8EutPixYt5//33Wb9+PVlZWd+6rMPhIDc3l+LiYgDS09Px+/00NDR0WK676mixWBg2bBjFxcWRp7u+rf2jJf7S0lI+/vhj7rzzzm9dLtrbv7PaPD09nerq6lO2X1tb2yX1CgQC3HbbbZSUlLBu3bqzjgJeUFCAXq/vsF+6M/6v+y7HTLTEv2nTJg4dOnTW8wK6Zx+c6bvz+3IeiNOTxOdbGAwGCgsLI5deT1q3bh3jx4/vpqj+j1KKRYsWsWrVKj755BPy8/PPuo7L5aK8vByHwwFAYWEher2+Qx2dTid79+7tljq2tbVx4MABHA5H5DL412Pz+/1s3LgxElu0xP/KK6+QmprK9ddf/63LRXv7d1abjxs3jqamJrZt2xZZ5osvvqCpqemi1+tk0lNcXMzHH39MUlLSWdfZt28fgUAgsl+6M/5v+i7HTLTE/9JLL1FYWMiIESPOumxX7oOzfXd+H84D8S26uDN1j/P2228rvV6vXnrpJbV//361dOlSZbFY1PHjx7s7NHXvvfcqm82mNmzYoJxOZ+SnpaVFKaWUx+NRDzzwgPr8889VSUmJWr9+vRo3bpzKzMxUbrc7sp177rlHZWVlqY8//ljt3LlTTZ48WY0YMUIFg8GLXocHHnhAbdiwQR07dkxt3bpVTZs2TVmt1kj7PvHEE8pms6lVq1apPXv2qB/96EfK4XBETfxKKRUKhVROTo566KGHOpRHa/t7PB5VVFSkioqKFKCefvppVVRUFHnqqbPa/LrrrlPDhw9XW7ZsUVu2bFHDhg1T06ZNu6jxBwIBdeONN6qsrCy1a9euDudFW1ubUkqpI0eOqF/+8pdq+/btqqSkRK1Zs0YNHDhQXXrppd0ef2ceMxcr/rPV4aSmpiYVGxurVqxYccr63b0PzvbdqVT0nwfiu5PE5xz84Q9/ULm5ucpgMKiCgoIOj4t3J+C0P6+88opSSqmWlhY1ZcoUlZKSovR6vcrJyVHz5s1TZWVlHbbT2tqqFi1apBITE5XZbFbTpk07ZZmLZebMmcrhcCi9Xq8yMjLULbfcovbt2xeZHw6H1WOPPabS09OV0WhUV155pdqzZ0/UxK+UUmvXrlWAOnToUIfyaG3/9evXn/a4mTdvnlKq89rc5XKp2bNnK6vVqqxWq5o9e7ZqaGi4qPGXlJSc8bxYv369UkqpsrIydeWVV6rExERlMBhU37591U9+8hPlcrm6Pf7OPGYuVvxnq8NJL7zwgjKbzaqxsfGU9bt7H5ztu1Op6D8PxHcno7MLIYQQoteQPj5CCCGE6DUk8RFCCCFEryGJjxBCCCF6DUl8hBBCCNFrSOIjhBBCiF5DEh8hhBBC9BqS+AghhBCi15DERwghhBC9hiQ+QvQgeXl5/O53v+vuMM7b/PnzuemmmyLTEydOZOnSpd0WjxCi95LERwjR5VatWsW//Mu/XLTt+3w+5s+fz7Bhw4iJiemQdAkhejdJfIQQXS4xMRGr1XrRth8KhTCbzfzkJz/hmmuuuWifI4ToeSTxESKKTJw4kUWLFrFo0SISEhJISkriF7/4BV8fUq+lpYUFCxZgtVrJycnhxRdf7LCNPXv2MHnyZMxmM0lJSdx11100NzdH5p+87fTkk0/icDhISkri/vvvJxAIAPCrX/2KYcOGnRJbYWEh//zP/3zWOoRCIZYtWxaJ/6c//SnfHBLwm7e68vLy+PWvf83cuXOJi4sjNzeX9957j9raWqZPn05cXBzDhg3jyy+/PKd2tFgsrFixgoULF5Kenn5O6wghegdJfISIMq+++ioxMTF88cUX/P73v+eZZ57hv/7rvyLzn3rqKUaNGkVRURH33Xcf9957LwcPHgTak6LrrrsOu93O9u3b+fOf/8zHH3/MokWLOnzG+vXrOXr0KOvXr+fVV19l5cqVrFy5EoAFCxawf/9+tm/fHll+9+7dFBUVMX/+/LPG/9RTT/Hyyy/z0ksvsXnzZurr61m9evVZ13vmmWeYMGECRUVFXH/99cyZM4e5c+fy4x//mJ07d9KvXz/mzp17ShIlhBDnpVvHhhdCdHDVVVepQYMGqXA4HCl76KGH1KBBg5RSSuXm5qof//jHkXnhcFilpqaqFStWKKWUevHFF5XdblfNzc2RZdasWaO0Wq2qqqpSSik1b948lZubq4LBYGSZGTNmqJkzZ0amp06dqu69997I9NKlS9XEiRPPqQ4Oh0M98cQTkelAIKCysrLU9OnTO9RzyZIlkelv1svpdCpAPfroo5GyLVu2KEA5nc5ziuOkefPmdfhsIUTvJld8hIgyY8eORaPRRKbHjRtHcXExoVAIgOHDh0fmaTQa0tPTqampAeDAgQOMGDECi8USWWbChAmEw2EOHToUKRsyZAg6nS4y7XA4ItsAWLhwIW+99RY+n49AIMAbb7zBggULzhp7U1MTTqeTcePGRcpiYmIYNWrUWdf9er3S0tIAOtxyO1n29TiFEOJ8xXR3AEKI86PX6ztMazQawuEwAEqpDknTN5c7l20A3HDDDRiNRlavXo3RaKStrY1bb721s6pwWl+P6WSspyv7epxCCHG+5IqPEFFm69atp0z379+/wxWaMxk8eDC7du3C6/VGyj777DO0Wi0DBgw45xhiYmKYN28er7zyCq+88gqzZs0iNjb2rOvZbDYcDkeHOgSDQXbs2HHOny2EEBeTXPERIsqUl5ezbNky7r77bnbu3Mmzzz7LU089dU7rzp49m8cee4x58+axfPlyamtrWbx4MXPmzIncKjpXd955J4MGDQLak6dztWTJEp544gn69+/PoEGDePrpp2lsbDyvz+4M+/fvx+/3U19fj8fjYdeuXQCMHDmyy2MRQkQPSXyEiDJz586ltbWV0aNHo9PpWLx4MXfdddc5rRsbG8vatWtZsmQJl112GbGxsdx66608/fTT5x1H//79GT9+PC6XizFjxpzzeg888ABOp5P58+ej1WpZsGABN998M01NTecdw4X44Q9/SGlpaWT60ksvBZCnwoTo5TRKvgWEiBoTJ05k5MiRUTEshVKKgQMHcvfdd7Ns2bLuDkcIITqFXPERQpyipqaGP/3pT1RUVHDHHXd0dzhCCNFppHOzEOIUaWlpPPHEE7z44ovY7fYO8+Li4s74s2nTpi6Jb+rUqWeM4Te/+U2XxCCE6JnkVpcQ4rwcOXLkjPMyMzMxm80XPYaKigpaW1tPOy8xMZHExMSLHoMQomeSxEcIIYQQvYbc6hJCCCFEryGJjxBCCCF6DUl8hBBCCNFrSOIjhBBCiF5DEh8hhBBC9BqS+AghhBCi15DERwghhBC9hiQ+QgghhOg1/j+xgIeCi/wWLQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "ds.UVAerosolIndex.values"
+    "ds.UVAerosolIndex.plot(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b6aae09c-a92b-4ecf-b2c5-ae49e8c08b3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAJ1CAYAAADQapg8AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xe4JFWZ+PFv5eocb998587ciTAMachKFgXjYhYUFBOuuvoz7a67uq6KirDr7mIOqIgJA4iBnGXIzMDkfHPqnLvS+f3R915mmCHo4hqoz/P003OrT1VXVYd5+9R73iMJIQQ+n8/n8/l8Pt/zgPzn3gGfz+fz+Xw+n+//ih/8+nw+n8/n8/meN/zg1+fz+Xw+n8/3vOEHvz6fz+fz+Xy+5w0/+PX5fD6fz+fzPW/4wa/P5/P5fD6f73nDD359Pp/P5/P5fM8bfvDr8/l8Pp/P53ve8INfn8/n8/l8Pt/zhh/8+v5qffe730WSpIWbaZp0dXVx2mmn8bnPfY6ZmZmDrnfjjTdy1lln0dPTg2EY9PT0cOqpp/L5z3/+gLatVosrrriCF7zgBSQSCXRdp7e3l9e97nXceeedB7TfsmULF154IQMDA+i6Tjqd5pxzzuF3v/vdfu2OPPJIent7cV33KY/vpJNOIp1OY1kWe/fu3e9YZVkmkUhwxhlncNNNNz2r83XHHXfst40n37773e8utD311FP3eywQCHD44YfzpS99Cc/zFtpdeOGFhMPhA57L8zyuuuoqzjzzTNLpNJqmkclkeNnLXsb111+/3zYORpIk3vve9x70sZ/97GdIksQdd9yBbdt0dnZy/PHHP+W2PM9jYGCANWvWPMMZastmsxiGgSRJPPTQQ89qnb8G8++hfV/np2t32WWXPafPPzg4yIUXXvicbtPn8/n+GH7w6/urd+WVV7Ju3TpuvvlmvvzlL3PEEUfwhS98gVWrVnHLLbfs1/ZrX/saL3nJS4hGo1xxxRXceOONC21/9rOf7dc2m81y0kkn8f/+3/9j9erVfPe73+XWW2/l8ssvR1EUzjjjDDZs2LDQ/he/+AVHHnkkDzzwAP/6r//KLbfcwle/+lUAzjnnHD760Y8utL3ooouYmJjgxhtvPOgxbd++nXvvvZc3v/nN6Lq+sPx973sf69at4+677+ayyy5jx44dnHPOOdx1113P+nxdcsklrFu37oDbS1/60v3aLVmyZOGxn/zkJ/T29vLBD36Qf/qnf3ra7TebTc455xwuuOACMpkMX/3qV7ntttv42te+Rk9PD6997Wu5/vrrn/X+Ph1N03jzm9/M/fffz+bNmw/a5pZbbmF0dJSLLrroWW3zqquuwrIsAL797W8/J/vp8/l8vr8gwuf7K3XllVcKQDz44IMHPDY8PCz6+/tFJBIRU1NTC8sHBgbEySeffNDtua67399nn322UFVV3HrrrQdt/8ADD4jh4WEhhBA7d+4UwWBQrF27VlSr1QPavvvd7xaA+NGPfiSEECKfzwvTNMWrX/3qg277Yx/7mADEY489JoQQYs+ePQIQX/ziF/drd+eddwpAvOUtbznodvZ1++23C0Bcc801z9j2lFNOEYceeuh+yyzLEkuWLBHBYFBYliWEEOKCCy4QoVBov3YXX3yxAMT3vve9g257+/btYsOGDU/7/ID4+7//+4M+ds011whA3H777UIIITZv3iwA8aEPfeig7V//+tcLXddFNpt92uect3r1apHJZMQxxxwjYrGYqNfrz2q9Z6tWqz2n23u25t9DV1555bNq9+T32v/WokWLxAUXXPCcbtPn8/n+GH7Pr+9v0sDAAJdffjmVSoWvf/3rC8tzuRzd3d0HXUeWn/g4PPzww/zud7/joosu4vTTTz9o+2OOOYaBgQEA/vM//5N6vc7//M//EAqFDmh7+eWXE4/H+exnPwtAIpHg7/7u77j++uvJ5XL7tXVdl6uuuopjjjmGww477GmPc+3atQBMT08/bbvngqZpHH300dTrdWZnZw/aZmpqim9961u8+MUv5i1vectB2yxbtuxZpyA8G6tWreKEE07gqquuwnGc/R4rFotcd911vPKVrySVSj3jtu6//342btzIm9/8Zt7xjndQKpX4+c9/fkA7IQRf+cpXOOKIIwgEAiQSCV7zmtewe/fu/dqdeuqprF69mrvuuosTTzyRYDDI2972NgBGRkY4//zzyWQyGIbBqlWruPzyyw9ICfnqV7/K4YcfTjgcJhKJsHLlSv75n/95vzYbN27kla98JYlEAtM0OeKII/je9773rM7fszGfYnT77bdz8cUXk06nSaVSnHvuuUxMTOzX1rZtPvrRj9LV1UUwGOQFL3gBDzzwwEG3OzU1xbve9S76+vrQdZ3FixfzqU99auF1FEJwzjnnkEqlGBkZWVivXq9z6KGHsmrVKmq12nN2nD6f7/nBD359f7POOeccFEXZLyXghBNO4Oc//zn/9m//xoYNG54y53Y+j/ZVr3rVs3qum2+++WlzT4PBIGeddRYbN25kamoKaKc+WJbFD37wg/3a3njjjUxMTDyry/R79uwBYPny5c9qP6GdA+s4zgG3Z2PXrl2oqkoikTjo47fffju2bT/r8/Zcueiii5iZmeE3v/nNfst/+MMf0mw2n3XKw3yaw9ve9jbe8IY3EAwGD5r68K53vYsPfOADnHnmmVx77bV85StfYdOmTZx44okH/BCZnJzk/PPP501vehO//e1vec973sPs7CwnnngiN910E5/+9Kf51a9+xZlnnsmHP/zh/XKdf/zjH/Oe97yHU045hV/+8pdce+21fPCDH9wv4Nu2bRsnnngimzZt4r//+7/5xS9+wSGHHMKFF17IpZde+qzP4bPx9re/HU3T+OEPf8ill17KHXfcwfnnn79fm3e84x1cdtllvOUtb+G6667j1a9+Neeeey6FQmG/dlNTUxx77LHceOONfOITn1j4sfm5z32Od7zjHUA79/uqq64iGAzyute9Dtu2AXjPe97Dnj17+OlPf3rQH5s+n8/3tP7cXc8+3x/r6dIe5nV2dopVq1Yt/L1z506xevVqAQhABAIBccYZZ4grrrhi4VK+EE+kKWzduvVZ7YtpmuL4449/2jbzqQz333+/EEIIz/PE4sWLxZo1a/Zr9+pXv1oEg0FRKpUWls1fiv7CF74gbNsWzWZTrF+/Xpxwwgmiu7tb7Nmz5xn3cT7t4aluo6OjC23n0x5s2xa2bYuJiQnxj//4jwIQr33taxfaPTnt4fOf/7wAxA033PCM+/N0+APSHoQQolKpiHA4LF7xilfs1/boo48W/f39B6S0HEytVhPRaHS/1/GCCy4QkiSJnTt3Lixbt26dAMTll1++3/qjo6MiEAiIj370owvLTjnlFAEckDozfy7n3wvzLr74YiFJkti2bZsQQoj3vve9Ih6PP+1+v+ENbxCGYYiRkZH9lp999tkiGAyKYrEohPjfpT3Mf9be85737Nf20ksvFYCYnJwUQgixZcsWAYgPfvCD+7W7+uqrBbBf2sO73vUuEQ6HF1KH5l122WUCEJs2bVpYds899whVVcUHPvAB8Z3vfEcA4lvf+tbTHofP5/M9Fb/n1/c3TQix399DQ0Ns2LCBO++8k0996lOceeaZPPjgg7z3ve/lhBNOoNls/sn3RZKkhfu3vvWtPPbYYzz88MNAOy3j+uuv59WvfjXRaPSAbXzsYx9D07SFS9sbN27k+uuvZ3Bw8Fnvxxe+8AUefPDBA26dnZ37tdu0aROapqFpGj09PVx++eWcd955fPOb3/wjz8CfTjgc5nWvex2//e1vF3peN27cyMMPP8yFF164X0rLU/npT39KuVxeSEuAdg+wEIIrr7xyYdmvf/1rJEni/PPP36/nvKuri8MPP5w77rhjv+0mEokDUmduu+02DjnkEI499tj9ll944YUIIbjtttsAOPbYYykWi7zxjW/kuuuuI5vNHrDft912G2eccQb9/f0HbKter7Nu3bpnPPZn6xWveMV+f8+nrwwPDwPtnn+A8847b792r3vd61BVdb9lv/71rznttNPo6enZ7zyeffbZAPtVUznppJP47Gc/y5e+9CUuvvhizj///Gfdm+/z+XxP5ge/vr9ZtVqNXC5HT0/PfstlWebkk0/mE5/4BL/61a+YmJjg9a9/PQ8//DDf+c53ABZyeefTCp7JwMDAM7bdu3cvwH5Bylvf+lZkWV4Irq6++mosy3rK/9j/4R/+gQcffJB77rmHyy67DNu2eeUrX3lA3vDTWbJkCWvXrj3gpmnafu2GhoZ48MEHeeihh9i4cSPFYpEf/OAHxGKxp9z2H3renoqiKE+ZkjKfovHk/b3oootwHIerrroKgO985zsLPzCejW9/+9uYpslLXvISisUixWKRNWvWMDg4yHe/+92F/ZmenkYIQWdn58KPg/nbfffdd0CAerAc86fKPZ9/r86/nm9+85v5zne+w/DwMK9+9avJZDIcd9xx3HzzzX/wtp4LT86bNgwDgEajsd9zdXV17ddOVdUD1p2enub6668/4BweeuihAAecx/POOw9d12m1WnzkIx95zo7J5/M9//jBr+9v1m9+8xtc1+XUU0992nahUGihfNfGjRsBePGLXwzAtdde+6ye60UvehHT09Pcd999B328Xq9z8803s3r16v0Cg76+Ps466yx++MMf0mq1uPLKK1m6dCknn3zyQbfT19fH2rVrOemkk/jQhz7Et771LcbHx/nkJz/5rPbzD2GaJmvXruXoo4/m0EMPJRgMPuM6p512GpqmPevz9lQ6OzsZHx8/6GPzy5/cU33iiSeyatUqrrzySmzb5gc/+AGnn346ixcvfsbn2759O/fccw/NZpOBgQESicTCbe/evYyPjy+UpUun00iSxD333HPQHvQnH/t8T/++UqkUk5OTByyfHzyWTqcXlr31rW/l3nvvpVQq8Zvf/AYhBC972csWelv/kG39qc0HuPN57fMcxzkgCE+n05x11lkHPYcPPvjgfj8AXdflvPPOI5FIMDAwsJAv7/P5fH8MP/j1/U0aGRnhwx/+MLFYjHe9610Lyw8WJEB7cgp4orfsqKOO4uyzz+bb3/72wiXoJ3vooYcWRqB/8IMfJBAI8L73ve+go88//OEPUygU+Jd/+ZcDHrvooosoFAp84hOfYP369bz1rW89aMB0MOeddx6nnnoq3/zmNxeCoT+nrq4u3v72t3PjjTfy/e9//6Btdu3axWOPPfa02znzzDO5/fbbD6gqIYTgmmuuYXBwkKVLlx6w3tve9jY2b97Mv/zLvzA7O7tfCsPTmR/U9s1vfpPbb799v9tvf/tbNE1buCrwspe9DCEE4+PjB+1Bf6YKHQBnnHEGmzdv5pFHHtlv+fe//30kSeK00047YJ1QKMTZZ5/Nxz/+cSzLYtOmTQvbuu222w6ouvD973+fYDD4tBOAPNfmf2heffXV+y3/6U9/esCgype97GVs3LiRoaGhg57Hfa/YfPKTn+Tuu+/m6quv5ic/+QkbNmzwe399Pt8f78+Xbuzz/e/MD8K58sorxbp168Tdd98tfv7zn4sPfOADIhaLiWQyKW677bb91kkkEuI1r3mN+Pa3vy3uuOMOccMNN4hPfepTIhqNis7OTjExMbHQdnZ2Vhx99NFC13Xx7ne/W1x33XXirrvuEj/5yU/E+eefLxRFEevXr19o/7Of/UwYhiFWrVolvvnNb4q77rpLXHPNNeLss88WgPjwhz980ONotVoinU4LSZKEoihibGzsgDZPV3v1/vvvF4C46KKLnvZ8zQ94u+SSS8S6desOuB1swNszOVid30ajIV784hcLSZLEm970JnHNNdeIu+66S/ziF78QF198sTBNU1x77bVPu92dO3eKRCIhFi9eLL7+9a+L2267Tfzwhz8Up512mpBlWfzsZz876HrT09NC0zQhSZKIx+Oi0Wg84zHYti26urr2Gxj5ZOeee67QNE3MzMwIIYR45zvfKYLBoPjIRz4irr/+enHbbbeJq6++Wlx88cXiK1/5ysJ6T3UeZ2ZmRG9vr+jq6hLf+MY3xI033ije//73C0mS9htU9va3v128733vEz/+8Y/FnXfeKX7yk5+II444QsRisYV92bp1q4hEImL58uXiBz/4gfjtb38rzjvvPAGISy+9dGFbz8WAtycPLp1/T+07+PD8888XkiSJj370o+Kmm24S//Ef/yF6enpENBrdb8DbxMSEWLRokVi5cqX4yle+Im699Vbxm9/8Rnz5y18WL33pSxfejzfddJOQZVl88pOfXFh3flDcL37xi6c9Fp/P5zsYP/j1/dWa/w95/qbrushkMuKUU04Rl1xyyUJwsK+vf/3r4txzz12YrEHXdTE0NCTe/e537xf8zWs0GuK///u/xQknnCCi0ahQVVX09PSIc889V/zmN785oP2mTZvEBRdcIPr6+oSmaSKZTIqXvOQlB227rw9+8IMCEOecc85BH3+miQde+9rXClVV96tK8GTPVO3h4x//+ELb/03wK4QQjuOI733ve+L0008XyWRSqKoqOjo6xNlnny1++MMfPqvqCzt27BDnn3++6O7uFqqqing8Ls4666ynnHRk3t/93d8dtDLBU7n22msFIL70pS89ZZsbbrjhgAoP3/nOd8Rxxx0nQqGQCAQCYmhoSLzlLW8RDz300EKbpzuPw8PD4k1vepNIpVJC0zSxYsUK8cUvfnG/c/O9731PnHbaaaKzs1Poui56enrE6173uoXJT+Y9/vjj4uUvf7mIxWJC13Vx+OGHHxDk/l8Fv61WS3zoQx8SmUxmoQrKunXrDjrJxezsrHj/+98vFi9evPB5Ofroo8XHP/5xUa1WxcTEhMhkMuL000/f77x4nide/vKXi3g8/qwqnfh8Pt++JCGeNBze5/P5fD6fz+f7G+Xn/Pp8Pp/P5/P5njf84Nfn8/l8Pp/P97zhB78+n8/n8/l8vucNP/j1+Xw+n8/n8z1v+MGvz+fz+Xw+n+95ww9+fT6fz+fz+XzPG+qfewd8Pp9vX0IIms0mlUqFcrlMpVLZ79/7LqvX6wSDQSKRCJFIhGg0ut/9vv82TfNZz5zn8/l8vr9dfvDr8/n+14QQNBqNgwaoT142MbyXmb2jFGZmyOeLFOoNWo6NLLk0bJum5+HNlR+XJQlD0wkpCpok0XTd9pQcnkALmFiuQMy1D+syDcvBcV1cGSQkmo6N53lz25LRVRVdktBlFVUPEA2HSKUSpNJpMv099AwMPGMQHY1GCQQCfiDt8/l8f6X8SS58Pt+z4nkew8PDbN26lS1btnD/veu4/577mM1O03Bt5r9IFElCUzSiukpIU1FVDc+T6QtqyK6gMxPFlFVUx8FNd9KcKXNUXKVzcSei2CAaMJCrTZa/4ZW41TJ09uE8fB9uyyaUjpLfvIfwskFUz6UwXaDVaKIFg6RSQcqOjDU2Q9fRhxDoX4ITT9OqFGm1HG574HH6Yzrlao1tkyUSYZUWCvnhnYzONMlLMi27iedYZGtN5HqFSr2JLXnUbYeW1cIT7UBaAjRVJxVLcvzxa3nB6aexatUqVq1axcDAALLsZ5T5fD7fXyo/+PX5fPtpNpvs2LGDLVu28PCDD/LQHXexdfMWss0ajhDEFJ3enm40S+a4qIaWzpA2DA5XW6g2JJJhWpIgIFSCmQCFsRIETawtE4QHk3hNG1kFRVfQ42HqMyXcqkXnEQPktk3SyNaRogaLX3QkTiEPkk5h+xhmOoSq69jFMolVi8g9todaoY4RNrCbDq4Aq2xhNSzSSzvIHLmUxFFH0Ni9EyOTQunoQ+7ohdwESqoLUS2CrNIa3oa7ZC3uxE60viEcYDpXJ2DlcHJF2LMFZWA5wxt2IvX1IVey/GjjJNlqDWl2jM1NidHde1EaBUrYqIpKIhJn0eIlnHHmKRy5di2rVq1i2bJlmKb5Z351fT6fz+cHvz7f81ShUGDLli1s2bKFB++7jwd+ewOjhTy5Rh1DVeg0gixNRciYIY5e0Y8+WeWo45dTeHQPqYEkxdkKnX0JysM5jKBObaJEfqaOpkmYEYPZbIPevijBhI5dc6lP1xC6jFNuEUwHGZms0JkKMFVqElMVqoZKpdAgHdLZXWjS2xGk2bBJGirT+SaZhEE0qJFeniKciVHcM8P0jjyxdIjxkSIPVGtkugLoIZ3DoxHCqkZ8eTe1iRzRxZ3EVy9HjcVQVDAGV0GtgJXNQqOImu5GNOvI6V7kQAChBqBRQORmqGzfzu6bNiFpMpnV/QS74jTTfRh9izCFy79940dc+bMbOdRRufjT/0A5nGB8ZC+T23bz+Ogkk+Mj5ApZbNchaoZIx9Kcds7pnHDSSaxcuZJVq1aRSCT+3G8Hn8/ne97wg1+f72+Y53mMjY2xZcsWtm7dyu9vu50Nd97NSLVC07UJ6QbLklEGwwEGYjEWSTLL+tKsWjXAzK4ZArJAMnSy22aRHZtIT4TRDdM0mi4uYIU1RLmFIkEmoCF0hfGpOi0JXMcjZqj0dgVoWAJNldBjAYpjJYoth7FSC02WmLAEIUkwKmxaeAjZo+R5OHg0cBHA4URYHTRpeB5dSZNoNEBdsXl8vMCoZLGtVmOFbLJDtqhKLp9btpywJRFKBGg2bJymS02R6cxESA6lCfZ24TYbNLJl9JBBfFEGJRhAtJoY/X00ZksomoJsqLSmphm/YxOV8RKyoWKmgjSagv4TV6IETUYKOf7r5gf43fqtNFyX4447jg+89z0EIlGCeMSFh6qqrNu0h+rmR5lVdB5Z9yAzdpM9Y6PUWg0CZpBYKM5RxxzFWS85k1WrVrFy5Ur6+/v93GKfz+d7jvnBr8/3N8B1XbZt27bQk3v3zbew5eFHma5XsYVHMhxhQFJYlorSicrKlf2sDBu4ikmHIeHZFtldBZLdYRRdITte4bFts6iyRMMR2HWbZd1hig0br2YjZIVcy6Fse0RUmazl0ROQMVQV3YOmYzNrCTpNhbLrUnPA0GSELGG3XCqeoIjDKA0maOEiSKBhImMgY6JgIBOVVVqyxCNymQ7ZpKkIXBk0RUbWJIQETcshKGQajkcahbOMBLIEsiPQdZli3UJ2XbJNwcqYSiRigKEyU25SwsV14fSjFxGOGGRW9ZAbLlDYOUXWhWhHgFm7yfbJLHuyZWZxaZVbNCwX1VSQNRk9oC0M0POQyDoO22bzrF27ln94//uJRCIks+MMdYSwhveiBk1sB4I93eQ3bqSe6eaaTSVmKiW279yBOzPC1uFhSo0qiiQTD8dZuWoVL37ZixfyilesWIGiKH/md53P5/P9dfKDX5/vr9Tu3bu5+eab+en3vs+9DzyIK1w6wxH6VI1D+jowZxus6UqwKB2jLDRi+RJ2y6FmubhICCGQBExVWiR0mal8g1hHiEqlRbZkUXH3fz4NcGgP9pKAIFCRJCJBEzcSRAuH6I4HkQMGjm7Q1HWMgIkcMJCDJkrAwAqoOLqKpKuYmk5QNzA0HUVTkXUNWVNRdA1ZVZGU527QmOs4OLaDY9s4zty9bePaDp7t0LItrGYT3XFoVhvk6jVazSZSyyLsOCRdQch16QooZPfksIsVlHqLFf0hXMfDQ6CqMpVik0rDYk/Two2oZO0mZ776tUinnoTSrBP64dUolTqeJOgYylDPVzG7OzBXH4655kgaU+PonmC8YXP/3Q/TFTMYmRhnw8697BkZY1OpTK1ZQ5Vljjv8KN78rrdz5plnsmTJkufsXPl8Pt/fOj/49fn+SuTzeW677TauuepqbvrdDVTsFkuNAJ3pLs6JB1kSiBJf0Untvl2kF8eoTtXYOlZCQaJqwVBYZapuUbAF+ZaHIkHMUCg0XcIKyEgUhIQUDWEkohjxyNwtih6PoEdDaOEQWiSIFgmiRkLooSCyIuM6Do1qDbfWQGnaeI0mjUYLq9Gk2WhQbzQoNmrsahTQWi6HWQGEZeNZDp5t487de5aDa9sIx8VxXaa9BmNejZJnkfJU0q5G2tNQPYggKAiPnVKDEblJXNI5RkrgyeDIEp4Eg5pGEQVPU7A1iZBmIKvtIHv+pmgqhqERDplUdZmWoREzQyTCARqqjh4wCIYMtHAAPRrC0w2kYABkGTwPqdlEtlpIzSaiUkNq1JGtFvZEDtW1kMsVhK5jvfKlYBi43/0pod3bqesq2/aUSGgKa1Z3ICfiTE+WiHdEEQq49QaBzjThgIRQJBqeRD2Z5qf3Pcq1Dz7KVKWCpqi4nkvcMDn1BS/gje96J6effjrJZPLP/Xb1+Xy+v1h+8Ovz/YVqtVr8/ve/57prr+XXP/wRe3JZ+kJhVughjg2EWCIZuA2XcCaMYjmYEmCq7N5TIBbRydUscg2PiCJTF4KAoeMmYtiJOEY6QTAdp6s3RTMaRY9HMRIR9GgYSZZpVmrUiyWqhRLVYolyqUSlVKZSrVCqVGhUqmjVFoFKC1Fp4DUtHqfMNC0S6EC7d9hEJohCEIXA3H0IhSjawnHaeBSxsXCp4NLEI4eNjUcXBisJYu7THiAkgau6fNUeZwkhDiFMHA3tSZNW6oBFu9daAQwF6nOlgp192iVViQ5TJd+wsQUIAYYKMzbEZXAENASkTVjZEyMYUImmI3i6QdWWMKJh9K4Y9SZIkTBeIEhNMyHa/gEhRcNIoSDQronsVWo0do0glcrYswXCbpOwVSc3kmX3jgl010VVBAUbDu0Jk4yZqEhYikzfkQMQNPBSGcZjXWxbdwc/v/thtpSqtCybWrNBOprkNW94Da99wxs48cQTMQzjOX53+nw+318vP/j1+f5CeJ7H448/zi9/+Ut+9NVvsTc7ScQ0OW5oiLUhHWPaoc+ycW2bXNVhtukhSdAS7XnKwwEVOxpD7UwT7EoT7kqT7O2AVAIjHUeNhnEtm3q2QCWbp5DNkc1mmc7OUiwUKBeKWMUyUrGO6njoyE+6SehzObkSBw7CEoiDLn+y+RYmghks7qJIJwbRucB40NCwLJmEpFD09l/XAFpAlwJ1BI+6dWq4lHFo4XIGHQd9Tg2w5+4lIK1BzgZNgrqAkAwJTWK0JRAwF74/ETjbc3+rQLcpE1ZVkkmDsOsxU3PoiOtEEyZ3bspyaDRAw3V4uGDRYypUbZemAFlTWdoZw3nZmfS96AQae8eQt+xEikWRkzFEIoaWjCPpGm6tjjObx57JoeYLxKQm3kQWbzpLWLawZOhZ3kFk+WJ0U8dt1SmHU+wcyXFvtsGmjRt5dMcuZqsVXMdmsLOPN114Hq9+/es57LDD/DrEPp/vec0Pfn2+P6PR0VFuueUWrvqvK3jw8Q20PI8UQU7u7uDU7jTLezM89sgoJh67J6voEnixGHJ/F+G+TkJ9nYS604S60pgdCYQnaMzkqU9lcaezVCdzFGay7M1OsSU7wWypQASVGBpRVKKoRFAP6DH9Q6mAN3dLzQWWKvv3rs5bGdHZlXS5dXyKQcXkMCXE0kiIzdMNAorE4rDGxpJFa659XJVRhMei3ghSqcXDJWthWxYeDoKGZLFTNDmOpy4ZJgH9QYWRuktAmuvdBWqAS/vfzX3aK3PLnywsQ1iVsDzB0ekgk5UWNUkipMiMVixWJUz2lJqUXVgZ1qh6UGg5BFRYOhDn9u0F+s48ntXveQPFjdtpXvYtHM9jsumiy4KiGeTQxR3EujvQu9K0onG03g6kVAIpHgdZwssXMZtVnIlptHKRmO6RSSiokoQtqySXDlInhFAVBs59KwABM4BltdBkhSOGlvPOj32IF73oRfT19T27F9nn8/n+RvjBr8/3f6hUKnHHHXfws6uu5qZfXU/WbjKoBVjk6XR6JmFhABIK0NmTIjDQhdTdSbivi3B/J6HeTtSAQWMmT3V0Cmt8muJElmQhT3ZshvHpAngCC48RGuyljoOgD5NBgoSfoxnNZcAEwgrEIzomMrLnsrdu02MqbK4eGDYqwKKIxmjMZjYqc8qoDLJMLKQyUbeZKNrUgQDtnlYHMCRYHtEYr9lkQirCEWyrt7e9kxpbqBJFRQdWESbOwS/vq0CHJmELQcFpB7X63HPogCJD7Um9zMrcevNBuEE7XcLap81QQGG04dJpSIQUha6ojqFIbMw28ISg4QkOjQeotFySIQU9HuCOrXksIHPcYRz1sYsobNjGo5/6KoYMTa8dpHvACZ1BEnGNR4erlB2PlTEdG5lMfwq9t4NqOEo1kiDU34Xe24mcSoBlIXJ5QnYDiiWidplitcAldz5IOpEkHIjRqtb47SMP0xGLs2d8jFg0ySknv5C3XnQBp556KrFY7A98N/h8Pt9fFz/49fn+hGzb5r777uNX113HL77xbfZUiqQljRVmmEXC4DAjSN2SqHdmiC7uJbK4j+jiXqKLe1ECBo3xGcqjU9TGpqmOTiFNTDMzNkvUscl7kNIkSrZAkaApXLZSZ4QGEhJLCdBNgAAKMQVKB+vGBIJzl/47VJh9UldtUJYIKRJFxyMmgaFKdAU1TEOBgIpXtunsDmFVbGp1CzSZnYUm4839I8mkAkKSUPpUHnPqfHT1Sm64dRfCBUtA5UnfQhkNEBJNIVgRMxivtJi0nwgMW7j8mAneFegm7mi0PAirYKgypqEwXrIwJZicOx6FdvpCc/+nQQUSMpS8dlAblMHy2kHxvukO0A72n7z+vABgyNARUPAE5JsuhblA9uS0SaHpUBMeg6kAu6drFB0ouDB44uGs/PBbmXngcR75/LdJq+1zHovpdMYCtGSJe7cX0ICgAh0BjZQmoZkaraZDy/OYbXn0BTWEpqL2ZQgv6cFLp2klkwQHeqAjheLYiEKOsekZNuzYwdJFfRy5bAnTSpId9/+eR9av54FNm5gpZOlKdfOGN7+ec889l+OOOw5N057iqH0+n++vkx/8+nzPsa1bt3LjjTdy9ZVXsWHTY2iSxGGhKN1NmZSl09HfT3j5IqLLF5EY6ifY34VnO5T3jFHeM05lzzjlPePURqdI4VF3PKoC+vR2Rm3VFpRFu/eyhsNu6ozRJIxEN0EGCMzl5j7RaxmQobFPPCrTDiKhXbJMAjKmzJ6mh0S7h7PDlEnrKlFVYrxuU7E9UoZCd1AnHGjnsiYCGrHeCK2yRaPUQniCvONx32QNVWoPFDNoB7OTNpQCFmXh8uJoksdmGu1BaEB9bh/mv4wkoMtUEK6HIwTjjstmqhRpT4QB7cF0Lw5FWUoICYHteciyjI1H0/ZouTD7FAH/U+nSJKbsp/9KnB9Ed7Bz+WRBGZKaRN4S1AXEFai67aB7PpDuOWUtaz5wPnuvv5Ot3/klCQXW9ITJNhyKlSZ1FwpzQfxQQKbgeKxOBgmoEkZYo1xsYYQ1ZrItlgxGkVWZar6BHNDwqjaTLmSW9SF1Zih2ZEgsG8Dr7EDSNLIzUzjFPEOLF9FrauwZy3LL3Q9yx/r1bB3eievYrFiygnf+/Ts466yzWLly5R92Qn0+n+8vkB/8+nzPgUKhwJVXXskVl17GaHaGlStWMtQ1QHfVpVc3iS4ZILx8EbGhAQBKO0cobh+mtHOE8u4xGjN5JNEebDUftC4Lq0w1XBQhsAQMBBR21F10xeMxt84u6mhIDBGijwDqPsPNVKkdYDX2+XTPVzxwAMOQWbYsRWW6yshsA1OBkCLR3xkiGDVozNbQZYl6yyWsq0yUm1QdQdkRHJk0UVWJuuWxeDCOmTQoT1TB8fAENG2Xm4YrB5yjM889lgduuJ+HtTon1SOUbY/m3PFGVHC9drBcF+39DMz1yALcRpbFBOnCICMp9IYU6pZHWJXJOYKW51GdS2f4U3+hdSntoFoCojKUvYPnNkP7OAQHBscZTWJmnyB76etfzNI3nM3u7/6S4evuRAZiqoQmCRIBjYm6S8nx8IDFQQVFlsgEVHRTIRw1iKbD5IaLRFIBGqUWO6erHHVEF8XZGt1remgWaziVFtt2FIknTey6Q/KQPtKnHclYMEJeCRPLdAFQLRWIqAqqmWL7737H5qlxHt74OFumxumOxHn/xz/GRW9/uz8ls8/n+6vlB78+3x/J8zxuu+02Lv/3z3Dr7+9hsL+fM04+mRN7+7FdlcBQP4GOJJXRKUrb91LcPkxxxzDV0Snw9v/YKbQDpPlexfmKA7oi4bgCU4VNTp2t1BF4DBJkgCD6UwxUi0jtbVT3eZpFBjQ9mYbr8fLXrCbVEWFmyzh33DuGrcCyVIhSsUHEVGnZgkrTId9y6QyolG1BV0BhuGxxRDpIJKJRLLVoahK2LYgAoaTJxqkqzZbDzD5doxKQCWq8+JVHsue+LXy1Mc255ShCQMVxQJZIqTIjdZeKaPdmV3Gp41LDpYiNhrQwmC0lQ3UuTcGUIKHJzNoeIYkDqkP8KShARpfIWe2TOx/c6nPHWp9rpwFRrT1DXv1J37IHGwy45oNvoeeFR7LuY/9JaccI0H4dU4ZCRJWYbDrkHDg6plO1XQKaggakO4MYpkKlaBEJ61jlFhO24OjjupjeXmTRsT1Mbc8iex5C0wjHdPasn8bTFRJRk67V3cieR2ztGhqqzB5HZ9SWkOMpOrq6MSQJTYCdzXLJL29j5OG7GJ+d5ITjTuTfPv1JTjvtNL96hM/n+6viB78+3x9oeHiYr1xxBd/58pexkDj9RWdy+rHH0dnbjx4J0dgzytRjO8lt2klxy26cRuspt6VI4D7FJ1AgqEkW60WNOjZdBBiaG7T2dB9aB49pWujIdOwzAMyQ2reqB6esyWDJMoNRjd3b83QvieOVm2zbU0KWJKZaLiEJZmyPoZCG7QmSusKGUotjkgE8ySNXs9jZEDhASm33us5fnp9PBQgAhiJRcQX9QYVZx+NuNc8HIj3k6i7C84hHNfIli6bjcqdVZxMVOjEIoS7UBU6gEZQlhPdEKse8qNJ+roZ78OoMf4yYKlNyDoyk51Mz5u/T+1S2CMgQkCWmHYE+dw5s/rB9euEV/4yZjnP72z6BU28u/BjaN1hebMpYrqDpCeK6Qsl2CeoyUUUhEtRIBTUyh6WpjlSwWg52y2Wm2KIjYWA7Lr2rUpT2VJBUBdexCcQCtFoOO3MtDhtKMDpeIpkIMFNssb1LZSzZwWmnnEzHkuXYQkII2LZjLzf97gYeeOD3KIrK2956AR/4fx9k0aJFf/xJ9/l8vv8jfvDr8z0LzWaTX/7yl1zyjx9ny9gwR685nDNe8mKOOuooGnsnqT+2lamNuwjvHWGq1NhvANd84DJgSow0xX45owfrASxhs4MaU7TIoLOM0MLEEQJBC486LhoykSdVbxAI7qPIXuqsJMyRPDFyf98BXEEZTj6ik7Dl0Wo5RLuCNMsW23eXsFwP222nMJRcD1NR0GVBQlep2+2e4D3VFmMWT2k++A3O3c/ntxoSDGtVju1NslIymZ6pYwZUtuaalGSbG5w8LyKDMpfAMf/jYH5K5S4NJuwnjieoQFJXEcJjb/O56/adT2d4JjpgypAwFJIxk/FCnZnWH/+Vqpg6p1/5GRqzee55/+f3e2w+HSYsQ8t7okaxRHvSj5Zo5xcvi5ksWdNBs2ozvrdILKCiKBKlsk3/yiTNchMzZuK2XCrZBtGOIMgykzM1YmGNUrFBodSi4UkEDIVg3GQkJpiIRdCHlpIrVnFllVe98pWoqsr99z/IDTfdwqaNG+jpG+KLX/gUr3rVqzBN848+Dz6fz/en5Ae/Pt/TeOSRR7j8ks9x3fW/Ih6LccbZZ3PaKadg5ivId6zj8bvXIzWaZO2DD3oygIDUvhSeUtuDvg5GINhEhR3UmMEiiMJxxFlOGIFgmhabqdLExZybKa2Gg4nC0cRwEGykQhaLNDougn4CDBA44LmCgBHWeenaLlrldlhaKTXZM1un3HAIqyogqNguBQdMVaLPVOgK6qzP1okZCrsb7n4D1J5seVBhe/3APs+EAhOuzY5gjddrHWwrtdij1NjiNkjJCmfrcbJNhaUBhZGGS6epkGu5C2kDAwGFiYa7UKIsosKKeIBHcw0scWAva0qG3LOMiZ/ueJ5OQmkHo+mQioJHvu4t5Co/2dMNjlvY3qrFHHfJP7Dr57ew4we/3u8xfW59B1hsSOxpCTp0mazlIYCY0u61PnRpkrFsjXKpxdFDccoVh3LVon9xDKvQIDEQpzxRIbUqharK2E0HRVOwGw6e5yGHgqiGyuj6cfKzdQKmRt9gnMFXnYRttbhq+xitYIS77/49XctWccppp6NpOuvW3cuNN9xEsVjkjBeczGcu/wJHHnnkH3FWfT6f70/HD359vifJ5XJ842tf42tfvIyZeo0XvOCFnH7qKSxfuozsTfeQ/8XN2JZDlyGzvuyQVmHGOfikCMbcsqcaEDXPRbCHOgHkhcv9tzCLjISHIIXOIUTollSqol12a1FC57ZCibvJk1E1zpCSdKExYXvcwCynkCKIAhxYtqs/HUB1XGRJIqNKjJRa5CyxkL8aVtrBoCxLGHL7UndPQOWhuRJizWf41hgKKOxqtM+GLrXTH8riiZzmBymQwyKgKvQ7AY5VgwRVlXzTxZFgaVCl6glMCSRVYrrhULahM2lQKrQwlXZZNom5HxfPwbdYlw5TT9Ob/Uz+2OB53r7vn1UXncuil57Muo9eTmnn6EKbfSt4xGTQZAgpCsMtly5TRgcqtkd/RKdmCxquy5HdUTzJI9wVwso3qeRbrDh9EbmdeRKL40iSglO3sBsOTtMmNhgn1J8Gx6UxU6Y4UsZ1HVqFBmrYwAxpKCGD+IpBrFwWfdVKHh6bZcSxWbd1L6f93YXUZ0a59e7bueuee4iGE7zv/e/hfe97L6lU6n9xhnw+n++54Qe/Ph/gui633HILl/zjx7n3sfUsX7KYM9Yew/EveCFqKMzun99C7pZ7sZo2Eu1ARZm7NC7DQlmvP9V4q05NZsb2FurcAugIbiXPJC3eoGVYYwaYarncYBXQkFlDBJd2UBakPZPZPEOTcGxBRpdpeR75ueg8MZc/OxBWyVoenYaG4wpqtoshy2yt26RUGVnyKNkQUqHq7B9YA6QUyLkQViROPa6Xma2zPJJvsTKlsy1n4SJYHNPQXAgGFKZLLUKqzPa6R78hIQmwkai7Ho5o59I2HUFnVCVbcWiKA3N/58U1CcsV1J/0YvQHFEot96DpDJ1z9ZIPVsf32fTW/m9EgepBnuPkr/4rWtDk1gs+ftD1FNql09KGwra6yyuOSPOb9VkMCQ5LGKArNOoOSwcijE7WGBiIILmwdU+Rw47opDJZJZg0MQIaZtzEqlqE+2IoioQaNmnmqoS6EoCMFo+S3bCL5Ip+ptfvwa63QAI9rGNGA4BE3gngHraKMz/+KS77l4/yope+lm9c/UN27tjM9u3bGR4ZpW/xoXzzy5dyxhlnoCjKn/Cs+nw+31Pzg1/f89ru3bv5ypev4Mr/+TLCNDjx0DWcHO1g4IRj0FcMMfnLm9l03Z14ln1AEKRL7eADqV2SavwZeg0DQOOP2MegDEuCGrtr9kIP5xRNHqKEBpwkR1mqBnFdQUvA1d4UL6NzofDZfI/k/GQWaxaH6YoFGdlRIKzAhrK9ELyaEqxM6HQHNbS4iet41HINNpcsXCFImSqOIzAkQSyoku6JMDZZYf1si96gwmzdxeKJgLFLk1jSGWYiW2Ok6dEfUGh57aA6pMqUbQdUiXLTxZAk9tZdEioICbJ2e79NuT2VsEd7cFt5rsxYiCcqK8y/LvLcsartlwVLQFqVyDqCPlNmutkuF/bkHnqZdq/0U01i8XQTXDxbTxVEB+b2t0I7J3o+ZdjsSHLq1/+VsVvuY+NXfrJffvh8STsBRGgHzyd0BNiYa2DI4EkyYUOhN6yh6gq1YhNdlTFiOjOzDVAkDFkhEFDoWxxHMjVCIYVW0cbsDmEEg0w/MoqTDDB05CDJww+j8OhjtGyPK257gGyhSqHapKUIHF1Bsl0adYcpxeWkoUX8w+kvZNkJh6MtXcWXf7eOr33nu8RNlWQiyeOPP47rwflvPp+PffTDLFmy5H95Zn0+n+8P4we/vuedRqPBz3/+c77wsX9my+QYx8biHGIZHNXZjXTGKXSddRLTtz/A6I9+R654YL3afYVpByTPJjDq02DsKXJ+n2x+6uC6gA5dIarKPF61KWPzAEXCqBxJjCmpRlpRWCYFKdlgIbieWV5CBngi8E3IUPDgtMPTdAR16rkG07kGhbrNzoa7UGptKKiwrCOIGdLwWi624zJVsMhbHqmQSn9XmEqhjoVET18EWZFwhMve8TpJXWFqts5Y08EE8nY7H3VRR5DtM1UqVjvo7InqDIYUWjbkGjZFx6VlCwIqzFgQkiFuyIw2PFQgpUvUXUHZnetlp30v0R7w5op2wNgTVJmpOwfMFpdQ2pUgmnPraYChQMV9Ik1BBpIqZOeiyyeniTzdD5f5cxyW2qXlnjwJxrzlUY1y02HKemIH932e+drGKk+UbFv2pnMYeu1Z/P6DlyKNTOB47YA/JIMhS+QdsTDVdNxQ6DRkxus2JRd6DAVJlTAVhZbl4njtNIia235fDQVUGkIQDKlYAg4ZiGLN1Bm3HXRNJaRBpSUId0RYvTxFalUfkxv38v/ueJAJxyKqawSQiHoKJcvGlaHpesRNnYAtWJNI8z9jO/GEYOmSJXz2s19Ai3YxMTPDT354FXsfvpeJQpYl6W4++Z9f4NxzzyUQODBH3efz+Z5rfvDre14QQvDQQw/x35dfzs9+eg1RSeGcTCdnxpIMj1Xx1h7J8re+qj3L2g9/hToxy+bawSPVIO3gxqU9yr4q2svqB239zFTaAZRDO4jSJFibNqk1PSabDp0hlcmqzbBjcztZTiZFjPaUs/eRYyUh4rRH1usIrmeGs8jQqUnItCdTUOf2eXnGoFC1cW3BirDOjkqLkvNE5YCVIY1DFkXAA6tpU3U8yjWXoCYRCxk4CjjVJoapEooFMOMm8WUZlJBJY6pMYVeWvSNFDFVhqtRAkmAgGeSR8QoBYMKFuCZz/KFpFMtl854CriIzVnVwgbQCQUUmZ3nEDYmaLegKaeiaxGTLxWt6rFiWpjpVZKJqU3DaAeRgUMXUJabrDnlLEFWh7LQD+vnAfr5HOiC1J//Q53KXVQ6cHOPJ+dsh9k8b2deTA+X5ShFPzgFOaGAImNonATypQH7uiSJzVRwyRrs0XGmu3Wnf/CSegDvf+SkA+kMqlu1hIBixBBlVQpWl9sQXKuxpehgKRFWFhivQFIm64yF53kKlDBVYEVIZbzo03Ha1ir6ISnfCZKpmM1FscczSOKqhEl+7FGvrKN1rF5HbPkVhuMDi0w/BtprYlQaFlkNI1ogkA1goBGNhbv31I2x366S6o5w0OEBvOoGW6cDrGiQ4OEhOTTCRb9GYGuE73/8Rdz16H5VKidNPPo1Pf/7THH300UiShM/n8/0p+MGv729aNpvlyiuv5D8u+QLFcoG1epRT9AhpYRLWFTJLuyi+7ByMngw7v/UL8vdtwJUgo8uMHKR0VkBupzjEVZnxlodGu0fxyQHQH8KgfZk/KIPtgiRLpHRBQFEZqTssSZvsydX5iTfLC0kuBL67qVOkxauNNOMtd6G38SEpz1IRZo1hMN0SB82NlYEVIYWtNZeUKuEiqDtwdGeIdEQlnA4iGwqBdJiZPQWUlku52KTUcnEsj4bnsHp5kvTyNN0vWIOZTOHaNs1Cg8d+fjfRoMTenQU2D5foCWnMND1M4bHXEqRVUE2VgUUJamNl9pZbCFlCEoK0odAZVkn0RrFKTXbONEiGdVwJogmT4d1FTn3jicjlKlO7ptiyc5ZCzUWS273A/QGNiYYDQqApMrmWR40nSsrtG+gqc69bC4hKUBJPBKzzwe/BBjE+02up8cSPmOIz/DA6WKm7fkPGcT0KTvu9FVvcywn/8RGGv/NLRn59J8f2hBgttBhrtHOfVwQ1POFhKjLZpo2iKMgSdATaAwhnGg6KBLUnVcPY99hMCXoNGdOQiWkqiZiBGdbRTJXEkjTCtQl1xSnvzZHfW6LrmH5wLELdSaxCjUhfB2ZnmlahSG0sR2zFIpxKBS0WIvvQNpr5OlbDZfNoleNfsprEoUNsHziJTH8flWKZgF3j2iuv4Vd33Mb6kW3EAhH+4SMf5N0Xv5t0Ov0HvAI+n8/3zPzg1/c3aXZ2ls985jN87YovM5BI8Kp0iuUlkOuCYFAmOZigccSxSKefROnW+3jg+79mEIeppguSRFKXDhr8DpgKddfjiL4ID46UKT0Hsyos5OTyRBmrsAIpTWa46dHA4zayrCFK91wPbxGb35Pn9XKGjpDM45UGHhIuElksKrisJbaQYyoBfYbEaGv/S+4u0DWXVqBLcER3mFLVZnB5gmAqgGcJtLBKYUcRIx2gMFWlUmxSbnks7Q3TdWQvS9/3ToTdpP7og6id3eQefpzyjnE23TdCqemQ6IygVZo8OF6jItol3yoO2BIkFImACsW5/eoMqURNld6BGBu2ZVFd0Z7NzfUwAyqa7WFFAqSTARYNRAk0bOr1Jo9tzlP3PBRPEFYVRlouCRnqXjuPtizaxxuam3BDmcsHlmnnQnti/wC1J6gwUW+XczPnemTNuddmScZk68wTiS4Hq/JwsPze+efad9a9MO183aj8RPoGQFiFptvumdaAwz/+DlKHr+Du8/+RhOQSURVyLQeBzNKYTq7pkDRl7s9ZLAmouAjKtkfSkNlVe/o3qQSkdYmy1c6rjiiwNGGgI7P8mG5iSzoobJkm2BVGDeggS9QmSgQ6giAp6CGDSH8HZjoOZggCcYx0itoj96PGw1TGZqnunUQxDLbdu5uOhEmgM4bZmyZ54nG4S1bTMBPodotNj+2h1BT8+tvf4P5dm9g9O8V5r38jX/yv/6Cjo+Npj8Pn8/meLfWZm/h8fz1yuRz//m+f5Ktf/ior9SCXDCxjVTKG2rB4vFgioIAxMEj9La/DsRzEpV9jZtMwXsOhoUs0BHSqEpokEVOg5LZLSpU8MGQIKhKu1y4NVvkDA999y1Ttaz4W2jf4SusKYw2X2pMCXwnIYrGOAi8iRUVy+GklxzIpiItHRQgsBCm0/YKvHk2ibouF3FWJ9gQSpgSekFAkWBzVqTUcbFcwO1wmlK3hNATxgSituk3ECBM0VaKDMYL5JrH+GLElXTR378Do6cGJdTB+0zrGd+WY3TZJvmwjgFzDwm21c3EVwFQVXDyKjiDvCJbrMjVJUPRgpuowXXXYnG1i0Q4KVQkiisxU1WlP+9yqEa7VuW84z+KOIMe8eDXxoRXcdf295GxBJCBDy0XTZHTbI62rdCgwXXOQJEFak0hGNLbkLby589+knavrzJ0bZ252NwGs7jB5bLpJWJOYtdszq8ETvf0G7eOq8cR7JSRDZZ8XQKedu1ywxEJgvO8Au5rXXiYxn5oh0ZQEzFUQ2fzFK3nh1V9g+QfOZ/2l36VbuNQ9WBKUCYV1JAVsIaFLoMmCqbpLWH36wHe+51cDqk77CoEEFF3YXrA4NhPEczz0SAAzHcJIRIj0p/AUFT0cQAsbNMt1EisHkVUVNRyjvGMvsRMGkBMZzL5epHCU5MpjCW58AC2VQY1EaM3OYsQjNLNZmusfIqE4mP2rQZE4YVBH9Cxn7eIP8/jGaX718GPcf9M19HZ1c8EFF/L5L37BL5fm8/n+1/zg1/c3oVgscslnP8v//Md/0qsE+XCsjyPDEey6R9WqYAmPEhB45VkEX3k62etuZetPbyKlSmRbLhFZwnYFSRVawmPANEkZCttKFmsyAe6earAirDNWsyi64OWfCGOfKd83bcgkVIlQTGf9xP7DpvbtITTm/m0Dsy2XEi63kOUY4mTmpinuCcpcV8/zoVQ/k40m1zZynEkaXahEZTBVyNsHXkpveWK/yR7mL/lXPEgbEqonUXMFuiqhBWSqlstE1WZJyqDVsIj3x3GbHrVii7FqC8vy6FndhazKtCZGaBz9EqyRCnf/ZiOq5TBabGEqEoYq0ZqqMTrXi55WJRQgFdSwqhZ1D5qehK5IaF67K7butYNOmSdmWdNdD3duWVyTwIMlAYV8tsZDv3mM3uPWcNTyJJuHS8SDOkZAId6boLk7S2c6QDbXYC+wJGayt2ztN6d0Q0AmoJBruAuvhTcX4OoSjBZsJAnqdnuZ22qf3RWdJqOzTfoyQWxFZWSqTFhply4zdBWj4ZCdiz2duddg34GRERnCAQVJkvBaDiNz0yQrEuTsds1lEwgoULYcxn5+C/2vezHhVJxirogFFCyXUN0m2RnBK9UJyjBac6kDAfH0Bdpc2gPshuIGddsjFTPIV23G6w7HrOkkZYBqatSmC2SOWEzk0FW0RkdxXA9ZlbGKVSI9acxMJ0bfEE65gJEqUFn/MGbvJJomI9acjti1kfAxpyGFI/Qk07Qmx2iOj9AqVlEDOlZ2CkPTUZcejrNrPYoi6DZBf8UL6Tp+La845jBu3rqLW376LXq+/z3e+fZ38OnPX0I8Hn/a4/P5fL6n4ge/vr9q5XKZL3z+8/zHFy5lyAzwzz2L6arJVFoewZhBcbLKg7Mt9L4uVn7uzaiSRO5T/8O2zSPUAM9rBz8hSVB1Ia3JNB0PM6AQDZkIycNUJOKqjCo/MThpXwEF9p3MTAOiAYVSwyUkQ29cZybfpJQ7sF5AYC4XE/afDGPEs7mPPC8igYlORmt3gW7Sa5zQjPJwrcIOt8ZLRAeaJNMST0x5u+8uSrQ/5Lkn7bcGGKpMAI+IIlNEkO4IsWR1B9XZKhM7CriOh5nQGRmvsSgcIiB7TNoeu/ItFAEPPTpJvD+JVrMwd26gcd/dLB1Kk5suU8616AhqFB2XqCwh8DCAxX1hepd342ZLRPYW2VRsMZQJMZavMxCRiYQ1xostdpQddNq1lGtee7CaxFzVBUeQiOo0hUDRVGzbIbd9O71Rk8MP62LzjizNlstRJyzmMcfFyldxNJm4LtHVFyaeSGPsnWBLyV44F9m5WeOgHWBn5ybQUICq5WIDugzdmkxurst/12wTzwO35RLtDrE20cEjG2dBglNWJbh1w+xC77AHC7WU53OC6x7U6i6HJAxyLQcJ6A2oOK5H0fbQFRnheVge9OpQ/uUNeC8/mcM/eiG//9iXgPaPly25BmatRY8sk9IUdrnt/cs9xcwqGu2Z4BQZlmbCdHWGSR42QGywh9rwOBMtWLk4Qm7zKE7NQQ8F0FMJ1FAIkYyhqzr1iSmMdBwjHkPUq7h2EykYwejtQ62U8OwmcscimrkZJq/+MUPn/x0iGETu6CEYiaENLEModxEaHKQxOoqzaychx0JWZfAcJM8lmttBPJ1h6EWHs/jUEzhh8WJuuu9+rr/ut3zjW9/i3Re/m09/9jNEo9GDH6jP5/M9BT/n1/dXqVqt8rlLLuGySy9jWSTIeZluTuhKMLw5T+/SDOsfm0AoYHggXngMfRf8HeO/vYvKz26gZjn7jbCXaE9QYSiQCWoIWWJVdwjHFWwdrbAopjNTcRhvORSc9sdlPs9zvjCTLEPKkAkGFDpjJoGAzuhYGceTyFk2ubnyVk/+sAVlaHhPLG/isoESNVxerCRIaDr9UR3TFkQGI3x6905e76W5tDrMv8QHcC0wAipTxRaW1z4ORW6X8TJhYZKLfWvvAiRl0FWJvCU4Im0Sjuj0r0ij6QpIAs+TKQ7nSQzEmB2vgu1SL1uYA1EeeWSCibrHEat6OePlh9CYnCK5sh/FMNl712bcmsX0TAWv2qJge3REdW7fnCeigGHIDA52ELYt6g2bSMKk/9AeNv5+N5rr0dUXZWy0wiOTNUwZ0hGNHSV7IfDtDKnsqTn0JzWUuoNsaDieC6rKmiVJCvUWlZrDyFSFw5YlyQx1U9k6yvrxGsuXpxF1i2xTYBRqbKlahJV2OojCEzWCpbnXZFFMJ1ux2nm/qkQqpNGsOwTCGk3LYabu0nJhUURjb81mUUeQndN1VCAeVslWHY7vC/LIWJ3AXI+2zRPVJ6K6jG15C6kWK1MmTctjpmFTcdoVK/JzQfhxaQNZk2kcsprO957Hwx++jNldY4RliYYnCKvguO3pnqeeYeTlkCmzeiiBZXt0DaXoOGyA1JEr0LoHaQ7voLhnlFgmgWXLVMeLRLtDhFcfjhIM0tq7DQyD1uQkRkcG4ToIu4XZ3Y8QcOeXr+PYz/wTISsPzQbCMKlt20y4uxOrUUfv6EFKZMDx8CpFpHQPXjFLa2ocPWggBwIIx0YxQ9ixLrR6HmQNTzMoGQnGKwqP3LKOG++6m9///ncUijO8733v45P//m+Ew+Fn/wXi8/me1/yeX99flXq9zmWXXspll1xCJhjk74dWcrwliCsqe7cWiMUCBAeTZB8cJRwLkn77a4muGmLb57+Ft3kHU7bAoV2irDOg0BXQKFoe3ekAnuUQz4QwAhpTu/PM1BwaniBfd9sTJnhP5GsaEizrCBAJBxmfLXHoYBxNkyln6whJwvME4aBGKKIxssM66LS3MhBRJOqeAAQbKDNGk1PkGGuCISzLY8mSGFNTNTpSIb48OsKrYhlSVYl/6l7Ed4vTXKB2oGoyHYZKb1eQ9cMVFAQtqd1T2XDb+9tnSnSEdB7NtQjRriMrW+1Bbv0DUcJJE02T8ZDA89CCMrHuMK7tseioHvBUhrdNsuzYQXbvLDBVr3HY8aso7xhFjeqUh2dQdQXF89AjGj2LllMZy7Ph7m2MlluoEqiyREJXGd2Toz+kEY4Z1KsWhdE8/T0RChUbW5FJpQz0XA3XgWhQZ5kq4XnQtSjBrs0zAEznbQIyuLZF1YU1S+Ps3pNnrG6zenGCQzvD1CstNty1jUxHkGPPOZrRx3cRMBSmds4S0FW6DBlVkxGugyy1J88IS3PnBsATBFWJiKFiyhJjNQvPgT5ZIqxr6KI9wLDkedgenPjak9h5xc3EFShUHTIqlGtuO4dXlbAcgQ7EVJms5dEX0ZgqtMh57YBYkiWWH9FDbDTP6EyVarPdWx4zJMZrDouCKnvvfJDkm17Kyve9idkPXErDE4RkwJ2r5vAsSo4ENIWOoQRa2CA6mEFRFLRQGBQJ2XPQVVAzXYQOOZ5YswKyimrV8ITArtYxzSB6LIGW6sCrV6jtKRA8pBM3P8VEvsme8CJWbtmArOsofUuJRFKIah7Fnp671FLFDSSQuwaRJKBvOVLPKhSnRksPY1sWqipT16PEAxEcz0N1G8RUF6M/A688g9ARh3PcC09l76ZHuPqqH3HFFV/mA//vA/zrJ/6VYDD4v/yW8fl8f+v84Nf3V6HZbPJfX/oSX/j3f6dT1/jv17+IExIxqrMVfv2LjaztiVBv2tSbLg9d9xjKkn5WfORC6mPT7P2ny6FQQTJUlkVVArhk0gHys02iAZVMRiexKM7E1lmK5RZyocGOfJOiJRgIKBQsj7AsoctwaCpAzfXo7o2x9PAu9GQG+yf30qq0mKg7LB2IsX1rFhSFqYZFM19/ygkwPGDK9thJnTG1xkopzKlulJ6IyXS9xYlH9TIzXmL1C5awwZBJ/GYMqelwvVPmOOKsVkw22HWW5iGlywhX0BlUKbmCNYkAlXKTiKHgIbN0WQqn1sIN1FAbNqMVi5imYCOTLTRolC1CmRCNYoNYIoBbc1AjKnfePcKZizroXLuaQCqAVa2z8rAu0oMW9q6dOI5FmDCFcplkZ5DGbI1FrzuLd9xb4ezDV7I6X+LxbVmGwhp1zyNbtemLajR1hd6Ewa69ZVING1dq97xnh4sYQZ3Vy9NURst0dIdwRsuYUZNyrkbAUFEsm0VRFafpIMkySR0KsxWmSxYWsG13gZACuixRaLpYVoUZexsJRcLQVYQEddul01BpzKU0eG475aQ4V+5svte85QncpkMiqLEobJCzPWRNwinbeFGdjoDGinSIdY9MQqQDU4WALFN0PcoOhBs2FjBjCSSgy5BQERgSjJVazBcU0SV4fLbBWGuciKbScgWugKAu0ZsI4DZssk2XkgOV719H8gMXEF8+SHH7XjSJhXzuJ7/XnlyqLSZDT0ynMlWj9/g0sqKgBnQkTcPLzyAZJsGeHvRDj0eOZZCtMNjN9oamhtESCVBl5Fgc17IRQhA96hjkQBgvEOYV//RmTCmPMEIoXQO0R+I1QdPR+pcijBAUp5AjGkICz4xR14JItgWWg9Eq4cV6cD2B66nMaAkEMglDYrZmo9kSmUiQzsMX8VgqQccRq4kddizb7r2Dr3/l21z+H//FP/7jh/nnf/5nTNN8Lr56fD7f3yA/+PX9RWu1WlzxP//DZ//lX8nEQnz+5S9kTbmFXHQol/Osf2i0PWOWDCFk6h4ETjqWQ99+LuXrbkbcdA8hy6UvbdJ/VA+ykNBMldp4kWg6TGm8gqRITOwpcP9YBcuFQzoDTFntwW+oILkeqqpwWGeErkQIu24R641gF+vk9myjUrcoVASaKuO6LhNNB1NxmbXEfjOIzacczFdcyGJxPwWWyQE+07+EqZpDUm7nFmfr4MqwYk03Qy86gk998SqilsfP5RnOiib5fnESEFyodjHZ8ihYHiONCi9YmSLWF0MGHr1/nCUDURJLUoQ741Sni/Slh6hP5FhpJBh+ZBtBy6VQtphueSxWFPS5eQUi/XFaDYuSK4ifcgbhlctRsbCrNQY1lSWBAEY8SitXZHbTCJausXdXjuVHLiZ8zFpyd97OYT1BwkuSDPTFyG+ZJNIdZW9LpqtVxwgbKHhkEgZGPIAR0qlWLYxqk0AyyJ5ts2SCGpKAlgNHH9PLA7fsIiRDSoFcy8UQEJIkNE1Gsm0WxTRGKjaLohrrZ1sk1Hat22zDI7e3SDipsWsS1i5OkJ2tYwZUzKZFoifCnuESSQmChoxwwRICxRMUHOjQAFUiYCqEZYiEA0wWWwSDBvElaWZ2Z7E9wc7f3srKRXGcQp2BVIjRiRLVltdOjZFBlqA6d+VBltpviPlhk/ZcDd5S2aYp2xi0Jx0ZCGjUqhYZUyPvttM/Ru59lPD5r2D1+97IPe/7HOWnqToy/5ACdGgSSJBeFMVpuDRnK+iRIEZHGjyBpBsY3Ytwi7NIArAaoGhYegjZCCOXcmiySnP3duq5AoosEcikkTr78WQFUS8T7R6AVgEyvZDshVoRz/OgUkJ09OAVpvFyE0izkyh9SxEINFlHVgSVQIqAItA8F8VpIrQoruPQciRmPANDU9hbqGEjkwrodMUDjNYtWLKCvo5+jlh8AuqGm/nPL17BFz53GR//13/kYx/7GIZh/O+/iHw+398UP/j1/UWybZuvfuUrfOpj/0g8ZPLZV53MmX0djNyxk7rrkZuZZcp2qdYsBgyZiUITFJnQW15FxzGHUf2v7xIY3gsqDCzvIJQIERuIE1/Sg2IY7PntQ6gRFbfR4o6NWSy3nScbkWBv0SKpSxy/PM10sUlvwmTR8k7MiEojW2V6yyye5fDYIzPkazZ7aw4hIGMKNu3Ik3Ug6bUD3/lJDFbEdLaVrIWZxnZRYwc1XkyaY6NBAkJBNC2IqZTqNsGgRn5HjmWv7UVOJtk8NcubFy/mA7EU1Jocp4epSR6bpluEFci6kNZV4ukQ0Y4QVrnJYWsyBKMGoc4opWyNnpOPxmk0aWULGM0KRtREydeJxEy8ukPAkHCbLkIIIos6CNRavO5tnaQGM8jFMQI93RitGqqpI+EhBUJo0SC//cUGdgwX6Ylo9PWnadxzE59foyH97FrMwQTlbBkzrNOxJEUqlqI5MUVy1TJ2/fY+4p1hCpNV0pkAUyWLfL7F6rSHYrlsrDu88YwVJA4ZgkqOww5Nc8/DE9Q9cFqCjpSBpqtkqy3qFkQkwdKYwXDBQgWaDriuoOi0K0WMVWw6ojoBz6Nlucy0HGIxnVVdYeR8k5JlU255LMsEoOWypWihAGVbYFdtGi2HgCexo1ykankYewoYnsCyHLpNjfVbZ4nrEqoDIV3mxKN6ueXhcZqOh+vBYEgj17DRgZDSzjAOqu0qEqW5X0khpV1qbL56SGfMYP1YhbCp0JUJ4XpV6kjs+PpPOfTj7yKypI/K7rFn9ZkKKRK2LGNETSK9ASL9aaojWTJnnIGkG8jhGLIeQOSnkawaGCauEAhJoVmvYaoBnJkJhGaA7WI1LcxOgTMzjj28Fz1k4JWyUMohqSpCkqA0i9y9FKFpEO5A6BEkRYWZYbzCFIqhY8oqthbG8lSEFqThCDpklYbjEcfBkyVqGMTsMqMlgaqr2HN58jHTYGWvys4ZldDgIFbqzQz1HI+y9U4u/cwX+ey/f45P/vu/8pGPfARN056z7yefz/fXzQ9+fX9RHMfhm9/4Bh/70MdI6PCZN7+cV7/gKMxUgh3f/QWTw0UCUYOxssVU0yaiyHjAVinAER97K8gKM//2nwQrNeJ9EULpAH3HL0MNBnDqLcrjReyagxbUqM1U2bazSMlq95CpQFiTGFoSJ9pyWXzyEF2FBrJro4RNZnbP0Co2iHaHmdiZo+QKyi0XU5aoeYLppkdQaR9H2WuPqu/QZaYtj1LLxgPquGxTSsjA3yf76EuG8fJNEksSdLgeniqR7AzTY6qEdQW7t5srr7uD2y98GbFkDDVksPkXj6BWFQLI9Ic8qpZFOGCwMmESTJs0CnWE5ZFcFMez3PYgLsuiuHEH0xsnsYs19JhOLV+jXnNYvCxBWtUImQqNQg0toKGaOoqmYngCb3wXTrWMLCvIpoGRiiMsGyHLBFccQrP1K2wPUp1RAskQpeEpOit11o8U6T20k0LNAWRcy2F2/W6aqoQxXSK4uAu9XsYIa9Sna6xe3cuYLNi6O09PVxi53GJ00xTKQAfN8RKyIjG0PEl+pIpQwDRUtKBCTBJo3Tp7JqvEIgahzhjJRRnGt4yyc6TEiq4wkuqxcayOU7TIdISwgK6gylShxfpNMwRkGBxKkm+6ZGdqhDwPQ5ZJaRK2EDRd0R7sqEpULQ+Jdmm68dkacSEYiOjsnbKZtSAeVMhmawQ1hUP7ouRnGgyXWlSsdvmFigcN4SEELAqpWK6D4s71/LoQlCWcuR9PiuWQjBsUmwJbNFEUmKk4GI9soZUvsfri17PuI5c/7WdKk9rTPwd0mVzdQVJkdFPD7IiBAGd2Ar1nMVIogXAdpFAU0gPgtlDqBSjUUcsFKls2E0jFMRcvwxhcipObRTQqeNUyaiSFHE9Ao4aTn0UJhfDysyipTkQphxROQGUGKdqNkuxCCAd3dhRRK+MWcyh6gETHIixh8MhsEytlUGm0CEZD2K5E0HPZWdXpiSukAhqSLGFKMK07JHWZquWwojvK+pZLatVy7EX9xEJLMXev49/+/Qt8+hOf4avf+grnn38+qur/t+fzPd/53wK+vwiu6/LjH/+YD170TppmgHec90Y+esoQwaGVOLPTlLdsoZ6v03IFoYDGUSf0MbEty86ZGm5PD8f+yzspPLqVzV/7Ka84LEXqxBUkl/dQG5mkka1QGR+lUWhRz5YJJkyadZtS3QIPVkRUWh5kWy5dYY01xwyQ2zZNfvs0U3vLBE0Ju9pk+0wTIcGhy1O0hMSRK5I88PA0ttvOG63zRMkzB0jIkLU8wnOX6feYNcacJu8YXMSajhhaQKeYbSCiGqqp0NkZplRosOSoPpSOPtTBJO++8hccrcv8w/gsHz/hCPoSYZqlFsLzSPbH6F6qcN+GGRb3RUj1xVB0HTNtUpso47oSTtOlvCNHK1+jNF3GqVsgoDReJQYUHY/xqTq1SovBvgg9h/eiBg3wBJ4jkBBIjo1dbeBU6qihAKgqkgJGpovWxBinnL6CFfk6q1d30arbNKcrFEfzdPRGKO3OkT6ki5kNY5TGSnQdPsDopglKw7NUXJu462I7DuVsDWnjOOnOMDt2FWg6Lr0Rndp0idp4mUhUo9V0cEI6S3pC6It72P7ILhTFYLZUYUVAA9Ng50yNk1YFKboOibCKKkMkqhO2bJyuAMVCg5nJGp0Rg2LDZihhEF3cgesJ6rMlooaGZqqU6xYrOoOMFZqsSodYP1ml05DJVh2GIip5x6PU9Ag3Hfp6I4QMhaWuoC6BXLeZyrdoVXMMZEI4mkRUk2kBnWGVetMlHNIo1FskwzoVy6VDEhTnpjPuCyhohkzTgyIStu1ySFeYbWMVAppECxAejPzotyz7+zcS6EzhTeeAp5hERbQnMwkoEsvSgXa6Ra1Jee8Mwc4YkqaDqiKqBQjHkHqWg91EyDJ4Mu7ITtRMN6FD1yA1SuA5oBgoyTRuScErzaILB7dcBMdCeB7N2Rk8x0GTdZRiDrVrEdboLrTeIkQTSJE0sushbJvqI/cSGhhES/VQ9uDSH93Dv7/xWHpGNuAdcxJNRyGtezjRMM2qxXDJYkncbE/mYqqkDYmhRIDF8QBBXaHlCG56YBiRTCD3vJHVI0eT3XQL73zH33Px29/Dt773Td7whjegKMqf/HvN5/P9ZfKDX9+fled5/OxnP+Pii96NLCzOy3SSeeM7eNshEkYwwvSvfoNdKBLoCNG5upf8WJVlx/WhBg2ywwXMI1ax5B/ewtQ1N8Itd7O22yC+KEb68CVUhyepjpdRgyqjGyeh4dByPYp1C6duE4mbDHaHKBctkp0h1HQYYTvMTFQYGy1h2S6yK/F4pcWqkI7lCYyQRsfiBL2ruxi5f4SQIqGGVUYq7V69fae6LXqgIXhcVNmtNDgzkOZDi4boPLQTI2KQ35PFMCQUSac5U0fYLtGYTubk42k6DS668le8o3+As155HL/571/y3uvu5qyeDJm84PB4hGgmhKzJxFMaPylMM54f5fKjj6BcbKHgkd00hWs5ZIsWyZDCo+NVUgGVlu1iChnHc4loCrsnK3SYKlosjNV0iSyOYaQSCD0MbhNJVjC7u7ECFfKP70BRBK7rEsiWQcCiI/rotlyaE7PkdxaI93cQrlSZqTi0bJeO7gz9qQ7sYoXksn4CkSByopvcjl2Ydovxx0bpOWYJ1dk6NnDEYRmGt+WYdRwSqkJnREUJqaiGzNa9JbqXJ6lsHWdJV5T1YyVcF3bNNli9uos9W6ZQXJek4rF5osKRR/VhTeSRFYmwpjB0SBdBU0WLxxGmQsSAzmNWUNk7QatmsfGObURNEIEAS1Z34j48STITZE3MpDxRwnY8VEVhZcpkw2iVYFSnBBTKFoN9MUrpJK3xPKWd08RUmWKxSaXpULA9+iMaHaaK0hlix3iFIDJdqQC7cg00ReaQsMr2ssV43SHiqfR2h9F1hW4gmQrgTVQYyITIWSWqLcjdfj+LL3wVq9/zeoY//RVsAa2D5P+qEuypO6xUFCIxBSNqUs9WMdMe4f5u9L4lkOqHVhU33Ilcz+EFUniyimY3kZcdgRROoCgqUnGa/E3XUZ/MokbiOLUSwnGpt3bSMdSFFo8huxalXcMIx6N44wbMnjSZtSW8WhlRK6Bk+lHDMUSzil3Icv/1j3D82wYIeh6aLAhgsXj6EUq/u47koYeQdBwsNUlAdonpMrKksX6qQiKoEdMVDFXjkKRBtiVx2tIOhot1HkiF0MUSZidnyXb3I5IXsHpiO7mxR3nHW9/Juy58F9+5+ju85jWvQZbl/5svO5/P9xfDD359fxZCCH75y1/ywXe+i3yuwOFqmm994KVUt01Se/gOaqITNx0Du44a0hCOwGnZaEIwunWWbL7J5Io1DL3pZey54moyW7aR7ougB1XGt8+yd9fvCbkOdsNBUiRy+QZ126NgCyi1iKkyLcXi0CM6CeWa6BEToyvGzkdG2D0xQ73lsLo/gtpyyTckVFNidTyEqisUsk2md+SpZuvMthx0VWZpVGF32SUkQUtADYEdsPm9XOVYPcx5iT6SnRH6j1+GbkBzKo/b8ug5pAur0kS4gmbVJtQZxexK8f7Lv8W/ffA9JG+7GSWSxGjIvM3souLCOq/EDZUSyn2TWJ6AqsPbj1rOfz+6lZHHJwnGTZyShRxQsUsWJoJC1UFyBRNli4otWB3T2V51GYqq9Id0FAniAymGH9iFHjEI9vegKC5qLIFkmMiRJHqoTiSbRQkHqAxPYnT3IONR3bMHq1QHRSazIkNtqkykO8bAy1ZT3bmXQH8/RkSjmcsDMl6zxZ6bH2DpcQOM3T9JbCCNG44ihIo20Ef99gfpSAUgbKLLMuVclZCq0DPQgVW20DQVyXQpzNY5/gWLuPM32wnEdNJRneTRvTTzdXSrhfBALdUwogaxjjD9nRF6jl1BefcM6ZPWQrOEVaigKg4yLtlt04R1mb1TNoVGE8Ms4nkCq+4QVKFgC+KaSlyVqTtw7NpeCvkadstjOt8Ay2HX9jxHDERY1hnisbEqx3YFGegK8cieEvLcr6J02GCzUiWoSggVjl6ZJD9eJRnViaUDbB0po+PRKDTY03CIKxKJpsORqzPEEybLSy0es5qEVZnx6+9g0etfwsOqTrBlLeSY7yulSnSHNCYbNuG6gvBcVFMjsqiDVr6EvHcHpqyBGcTTAzSVHkyngaxqEIiBooJdR2oKvEiS2sgkk+tH0QMahWIDXYGHtuZZtShBoiNMfCCB7NjUZmvUpsrMbptCtBr0nnwECBdndgpRKWHXqmz51f3c8LttdL/c5oFNNhcs38Pnzz+RwPprCZ18NGajAM0STiyF16wQlBy8kIntKNgCSpZLIevQajlkoiajpQalSpMXrsgwmglRzMRQ8cgVqsRfuJrQ9OnE7rgFe9vdnP+mC3jbW97OVT/6Hq961auQJOn/9DvQ5/P9+fjBr+//lBCCX//613zgXe+hkJ3m709ZS/G2cXoMDXuqiHAFUqPOzGOjdB4pUZsqopkqhYkypUKdXTM1JsfK9J7/Kha94ChGL/kaXROThIMarZrF8HSNsXILU1VZFFRoIFFvOZQth6INYQVkJAxDwZQlIj1RPEewe7jI6KOTFKsWpxzWAUEDL19FD2pkqjaeLFGs2ei2i1GoM7w9y3hrrn6D65HUFEIKZAIqO5p17qPM4Yk4/7lsDV7Jom67SIqEU3eo7Zoi2Bkh0pUgsriTxkQOIavkNowRD6h85Rc3cNwhy1gVUhkt1Nj5s9tIRBViWpJIR4Czw0tQJQndkJF0ncmxIjONGlGhkOiJEUsY5M0WyUyE+kwJYXto6RBDiszUjllsVWL7riKSBIeu7aK2t4grS8w+PowswdRjYziWgx7S6T5+NeXte5BUFVlR0KIhapOzGOEgslVH7+pBHhsjOtRDYzqP22jRdcJhqAENNRSEngyKqdEcHmbi3p1kTlhOeaaKJjnYtSaje4sMDEo4TQvT0Mhv3oVsQjhsUnMUuo7sRX98jNiSDKHOKPXxPEbcJLqiC3t3nohusOqERQQtCz1mkFi9ktrIBFgNOnflSKVN4st6mJmoga6hh3Q6Tzma6vYdjGycpHcojpSMoRo66TWLMfeOkQsEsLdPk46qmKkEicEeRu/aQtn1GMwEmM63EJagkauTn66yKGrQlQpgNWwCls3MbJVGw2aoP0zdFUyUWqyMmWiGTKw3RiiT5NSeBDN7ZpmtWnSaGgFVwkgYaA2bjN6eGa9Yt6m2PDQVag2XaMRg2+MzVJouqgI52yPyu9vhtWdx2IWvZNPXruFgfZiTtqADSOoKTcsjO1xg8ISlqIqMpOlIiopQFOzR3XilCsbAcmRZRtgtyE5g7dqMnkoiKypOvU7skOXkx8uoroU1I5AVWL4ojqFLbH1wlNlHxjl8MEZ9qoqiysyWm+y9bTsnGRF6jugDt4EaDCJrOrmpIq6AO9RFlBoOiitYVtuOE42iZHqQjQB2ohcUFcMu4SHhGSEOTypg18jWXHY3FEbqLgXL4ad37WHvxk189xOvY08pzB3bZIYyYaKqjOW67M6neCxkks0MEN+1kdjw/bzuNW8kYgT4/k+v4qUvfakfBPt8zwN+8Ov7P7N3714uOO887n/gIS7o6uHUvuUMmUF2DUQZK1qUx0pEusJIqkKzXGfq/r3oqRCBvg5237idbXmLWcvjsH84n+jSfkb/9T/psuvkmzbZls1U00FCoieiE08GCAZUumImN9w3Ts1tlxsLA1FVpr87QjCkkRstU56qosgSxZpFp6kSixpUCy3CyRChnijjM1U8VWG0UGdROkSkZePuU58VASXbQzcEN8pFWqrDhzr6Wb6iEy2oUazaxAM6u8dKzIxvwFRlloWDJI5Zg1svgudhdmcwjQl2RIKsv3cD37j6xxS+9UWCHWFG7tvDzvEKK47qZ/eOWTzJYxybXNLgkakcnmuRROGNi3pYfNxiZE2mJ5HAiAeYvGczgY4oob4MdqFM12H9VMezROMmpbpg+WmHMHbPNipjZerFFonuEMWpGpoygx4zQdrcrgqhSrj1Fg1UjHQE1/LQu3tRAibRxf3I4Sie7dLKFgh1RBAIPDNKeEBh6u4HMTIpJrM1qndtRjRtQjENFBlZlalXm8jBEIEA9C0Os+2mUULLMhz2xpfRWP8gRkRDch0qe/NM5Op0GzLBHoVkd4rJzaNEOyJUajajGybZsDlPS4JVEZmGLJNe3oWaTtE3uIRtdzzM0mgUIatkHx/Ganp4rkt21wzJRSl61/Qwkp/h9BefxMiMTa83ha120HHkIHf/egO6rjBVc6jbgkqzhVpqokoy2bLFyqUJitN1CnWHdHeMYqHFqqEo5WyDyZ153IiB1XLJj5ZYcchyMnKdqO5SGC1RmawTDKuMj5TxhEzVFXiA4wrimkTBFozlauyZKHNoZwinadPhKOxtumSbNqmHNtJ56jFs+No1RGhXE9mXB+woWQwEFSRJpTDTZHlHHKdlYSYS6B2diOwYEi5Ss4pamoBWCykQQtRmEcLFK84iL1mNEssQ0VQW2w2sSp2eU0xqkzncWgPZUNi9Octwrolas0gFVcKmimd5BGyb6Ye2oAiL5LIuRLUJhkFm+WIOz7c4KmXROyRAlRHVPI29u1FyM4QO0yGYANfGC3fQlEwCVglZklHcJsn1N1MY9zjh787jy3fvJRlW6T12JZWWS1hXOX5JCtsT5GsWU+UGTcvluMP62RgKEh7sYWZdnCP3rmeHqPDKV76aZUuGuOHm3zI4OPh/9r3o8/n+7/nBr+9PzvM8/utLX+KfP/oxDunp4/qXn4K8t4Blu9Rnaxx+xgpW6SGUegnJcykM5+g+shs5YNIoOTxy7UPsyjbIo3LkP7+DeCbO3n+/gmijweaGTUpTqdouPSGNxYtipBfF0QIqlYkyw9kGwmvn4npAzQFFctk4XCITM6hvz5E2NVqaQkyV6TBVmuUW49MVDu3vQw0Z9C9NUqw6JKoWcVNmMt+gKTyGwirlhosekFgvKoxoDhcfsoTkcJNgMkCjYaOGVVJDCWY2zTLQF6HheBQbLoHuDqJL+rn1gRlu3rqHkQe2MLFtnMFandd6IZTZXSi6ipkMUyq0B9qtu2+EYtzh1lKBo1JpjtGDvObUpUTcFp6APRsmsesWwcEeFFVGC4foOn45leFZlEAYNRbHdF2cWp1Fq7owE2Ea03lquTp62MSImXiSi6pJjI+U6F+hk9sxTWpRkthQF8LyaOZrBLqTVEamSRylIQWjGN0KnqYTsqGVe4RWrkB9No/dEGhdaZRYlOJsi8VvO5/cvfeQdqvUChVcKUDXIf00dk9SsipIjont6Qys7iEnFAzZho4YoVITWRbMjGaRDZWKbNAaKVIfy2E7FvW6zWDMwFMVPKdFFpnMYcvINvaAI6jsHCXeV6UzYVCfzlMa3s3WLdOYmoIkHKRQBMmeIlSugifT2r2L1SccjSrFafYfieQ2GDi0i1DI5NF1u0kEdRRPZW+uwdKwQjqs4nkCT4FYQGXZqk6qTcFj63Yy0Bfh8CN7ufeBMdYsTZDWFHbevZHkSX3YVYvaVI1ayyXgyfT0RmjUbEZHyjRcSBkKhiExW3KoF1uEYwYDJ63CvvUxqg0HB1iyKMb4lb9kxdcOZ8mLTmD45nX7ffbm60vXBEw2XFTFZkl/BitbJJCOYWVzeLUySqoLSVIQqQEku4qoFrGLs6hmAHPxcrxaEalRQelZiWSaBIRESNNwqmVUfSu5zcPUJgv0dgTYUrEo2x5OxWa01MSQYEk8TiQTQlHAc2023/AAy88+mWWvPYeeEw4hlQZ7273UXYGwmgSXrcAa3YO1ZyNqvYq9+AhkM0LdkQnIClK9hFUq0Ni9h+0/X89hf/cmjuwNc8xx3WSbApd2LWVNkTA1GU02CGoyD+7KcvTiFIdkIkwd1s1PAjHGHgjjzo4zEOhkemIrQ4uH+OznPstHP/pRPx/Y5/sb5Qe/vj+pLVu2cO7LXkV5dpwvvfhkDpMVTFmBwQQTj02jpVWQJcp7JolEDMy4TtdRvXQes5zROzZx/03b2Fq0SEYCnPAv78JQwPqPr2MVq8imiutBQpMIqyrpqEYyFSTcEaJZqjGTa/Lwtjz9YZVq64lJJyZtiDoOnbrK0iVxDF3m3sdzqAIqjiCxLEO5ZBHuilOdzjM5VUV4AtMTVMoWu4stgrJET0BlwrS4vVnk5Yk0HzpkAD1uMj4yjmO59Bw5xNR929AMFVSFfLFJ/2CSlUf/f/b+M0q2PK3vfL/bm9jhbXqfJ4/J46vqlO1qU93Vvmka3xgJCRiQsEJIjBDqQQYEAkkIhCQQEh41Dd20N9XV5f0xdfw56X14H7H9vi8Oozv33jVzpTua27MgP2vlu1grVq6V+Ty//Y9/PM9RYguz/NM/+DStdpPvvv8YM8fP8Cvf92uYVY+63aPxyhuo6TT+wGbi3DhepcV/fOUaDxSK/OkPfzPZ+y5glw9ovnyZwA2I5WIsTU0SHy8QDnoo45OgyiheiJL18VwQVZX2m1dQLIvKpR2ErIPuOxSOFVENlWGrR3uzhTGdY7hWJ39slNpeHzWTIHAj+tUOsVISNRmjcOE4vTu3QVaIz04RdLs4vR6SaeDbNr3dOkYujVtrk5ibYXh1lYRXJ31qHOdgH31xjtVPPI+5WCI3lcLyI0xDws+myI/PYHaGhFYec9HEmJylcfk68RmTdKFBKIqUfQVBalIZiEyNGgy6DqXTs+QCm1K3z9ANmFgcQVTunVgHgwH5yQz1y+uUawNGjo4Tjxxu7AxIdWuIgNNo06oNUYs55EQaQQyQ9+9Qv7HBqY+8hb2rO5w81kdN6yQKaTqfuMTCRAIzobK52yUX1/FNheZOi8srDZJ+wPZuj6NjApoIQwS0roPqhdza6TMuCAzdECSR8sAny71ZvD3/3tSGM0UTzwkYFSUkERKyRCBBdjSFbgw4WG+hRbBfaTG9vc/sR95B++mXGfrRf50TnFeg7d2bIhEhIBv3HsZC18VanGewvk7YbyPlJwjbVSJBJpIlItvG31nDMxOougKeD6UJEEDwPdSRUcJeB03PEjXSSHqN0G1ixlUenkmxvt9FAVoeNAMY1ofocZWW1kRJGCT0CDXoYW81SBWzeNtr+K0G7c0DFFlE1DSIfPpr62itNrIgw/gCcUlDdLtEhHzup36RJ//2B3nX/Y+D5vFNCzEsbKqySgWNjCawstuhJcVIyQrHCjESusxE0mDg+ez2HB44PUFjJMXl1y5RvXmDhBpDHdb5R//zP+JXfuFf8sxLX2NpaenrUzwPHTr0f5nD8Hvo/xKe5/FzH/sYv/DzP89bJyb5J9/wFobrbfSsie95pKZzxPZ6ZI6N43V7mDGB/IlxkCREVaR2/YC9S9ust12EeIzjP/dDSO0Ozm/+Hncrg3sbsYIQWRAIJJGYLFCYy6JnDCLfp3HQY3W9TUYRSCsSRUXCazt0AsiIsJA3MQFdlVFUiSiMEESBuRMF7KHH1IOTbN4pIw4crAgC6d5d3l7PY9RU2A8c/o19wNFUnH+5fIqw7RAGIf2dLkZGIwoh6A9olAfEYzJGzmD1bo/z37bEwA354Z//dR7IJPnoxBj11SZf+NyfsJ1y2ClvoSsiv/+xf00pmWBa0Zk/M8Ef3LnLjz92huwgwkzEiMIIORhC6KLEFNRUBj2bITkzjqckUZwWe2/cITsex+8NULQYladfpF3pMP+Os0R+gNPo4vo+lqbTabSJeg5h3MSXFaaOFVA0hbn3PUj/+k3UsUni8QLYPexKB9tpYY3kkEIHp9ZAnVtEqu6TOHaUsFUneTpL58Z1lNIkjbtb5I+Ns/3cJcL+AFGOkOYSpMfjyKKIaulYpsKw1kOqV3CTOm6lhlfbxZyboxmpCG/eoPiutyLt3KJ+6SbywGX2Aw8x121Tu7KBVlTYub7DwqkC5asd7JrD6PI0br/HsDWgsDxJ7c4OUiaNPHeE20+/RtaMGE3H6e3UKD5xFEGUkSdUQmeI3bfxs2NYmo3dv44YKpTOzFA6f5SrL68itvaYjynoloo2ksHZ76OPp3BWqoj5FFPtAb09D8OQcLyAmZiC3HeQFZlEQubyi+uMnB0hUzLx7AAjphCLq/iuz/GCwfXaEFuTmZnKMBL6+LKC3B2ieAGp6SxBd0DGVP7rLOn2n36Rwo98D2PzJeztKnf6PiqQMRWGXQ8pAjuM2Gk59Bp9UloSKZZALxURY0kEw0TsCiAr2HeuIJkxlPllGl/+PPHZCWRVhmSRqF3Br23jDV0UXSVKFggjMAsxIjeNnomjXt3HkCW2yx3mTYWDoU/H82msNJiUZIb7LeLFBG6zhbZ8P/72TYKhjV1tIisSUsJk98svMv7ICeT8GE51n+HzT2HOb6EtP0DoDPH8ACttoh07hzboELa3EcMAwbAYYYBplYiCgPNFleuOQVr2ERWZ+ZyJpcrkUDAUieMjCSbPF/lPo3Euzsxg76xTe/FFkvLj9AcbHDt2gh/9kR/mF/75LxwuyTh06C+Rw/B76H+4ixcv8qF3vRvFt/ntD72N8xN52isHyIaCmtDJjGYJCZh4ZA4tpjGUQ8x0jNSpE/h+yOrHv8zqC6vcrdtYKYsjP/uD2AdVqr/2+4RewL4TUjJkXEHgZClGomCSnUqRmSvg9Ibs367y1CtlvDAiq4qIkoigwBErTrM2oO2HHH9ggt1bFTRLxes7xCWRXEyh0hiyd7vCwrkx9nbahF2HhCoyeSJNrdGnS8B/caoQhPzkmSVyrkTQ8xAlkdAJCZwQPaMTuiHVN3dIF2OoukxiPMGZeIyNO3U+9fobPJLP8MGlOeo3dwgi+OT+OiNJg785M4mkqYSpNBsb23x1q0zBDfj9H/kO/EqH+FgaoojV3/lziqfHkeMmRikPmkX6wccJy6sE5U0EVUHqVtn80l1k0UFpdDBHs3SqPeprW8QnUwiiyKDcxRpJU722TfWgh5kzmVhMkpwuoCZi6OkU7aGNIIp4nT7ZY5N4ko595RruwEcRHbprB6iVFk65hpbqIqsC/fKAxo0Dgp0BQrePN3SJ7D54Lrlzy5Tv7BDYPrFMgGJayLpI4f5jNG+s4ff7GKrM4M1r95Yc9EErZnnt3/0xD/2d78T56utokcyVZ69TmsojpxKMLheQr+0jaxLZ2Qx63kKxZJRMEqfewWu18Qc+u+sbiHMC5x4/xsoXXqNa7mNZCqm5SczlU4i+h11v0HcjzF6V3S89Qyxn4c+M0X/+BS5dqTD+4EmC+Dzpa5tIeYuXLm6TESPW79bJLI7gux7FmMqOIHB2eRS/3ieU+5iqRHLE4uqNGpMJBcf10OIG8byMltKRDIW7zR0WjxcxtlukTyzQLh+wsdJgqWiBpdPv+wxaA8xCjPNZC9HxqNUVehevU3Ackt/0XpRf/c8AjKlg5U0K0YChE9L1Alwv4s6VMsckgd7d2+jFIvLkEoKZREh7BIMe2uQsRAL9jTXUdBw5lcbr9JBiCYTqFmGnSfvSFbLv+gAkiwiJFEookjTjBI5D6HhYfY/6NYmwM+BE3mR1u03LCejcqjCdNog8n+jpK6QcETUakFqaJ/Jd1NIYtSvXMdImbrONdmwOWYXuzTv0b12nu7oFgYc1WeTc48dwd9ZQS5Pwv54IRwJi5BMLhuDZSPVt5pLTxAUPX0mRdDpoegopsBktJmg4ESlV4PEjAm9ZKJLgGL8wVuLu85eJGjkmYuP821/7D/yH3/iPPPvi05w5c+brXF0PHTr0P8Jh+D30P8xwOOTv/tiP8e9/67f4/gvL/M1jsyhCRHejhm/7ZObzJOYnCTyH4W4VUb8XPM1cCgSFztou5at7HLy5w5vlPlgWJ372B+ntVnjjl3+HKAhZit0bav/AiQKNap9sTkdWJQxLxyik6Bw0+Mxzm4Q+zKdUFo6VaG7UmT4zSnuvzdWdLoEA2bksoecSKybobDZQhYiYLlLe79CRfbIrNbq1ASNxlcbA5dXbWzzVq3I8GedvzU0xVUwReAG+50MYoSY1RFkkEiKsvIXneCTnJpA1DTkmIykqdmdA96DNxXaH//DtHyBVytCdnOM/fvkrnD49w7dMlRi27gXN+GIJZdgiG0A+UBG9iNhYhvjyMo0XXiSWVamtVRl/35NEQojYaTC4c4Xe9RsoiTihJoF478Qa18OtdWEYYQ88Ln/5NkeWCpjFOI22Q06KCP2QmVMjpKfyVK9uY5WShIaOlMqTe+QCRrGAlojhlnfw+kPSMzl8L+LgxTsYYzn6nQHS+BiDfp/QF3ETCYTjCyimRhCKRGeXiQ52qW82UWcWkCbnsCtt2pZBhIBkGkiGRk8u3ftm/+gobq1CVS8QRl0iw0L6tgUu7g3h3R9CiumkowhHEBCATQn8CZehJhP5Ie0gQJQlRFFAOPYolSggPO+SclxQZTqOR/x7pgmaPYLAY11IEBvKBIFE2BfxrBRmMkPLyCD1AtSWg1jI0vXLuNUD7I6LHQrMz2RZHHgMtttkj+SpthzUlE47meDkO8cpjWUI2zXs/Q74Ed7QI6lLmMU4a7UhaVnEkwQKMR2vPaD48AN0r92gENcRGg06oUxKk+gPA3TVI5syuPnGGjMFg+Rogv3rZbIxmUbfR7h4FfO+00gxlWlRxHM8HCfAUmRiYsB0XieQJeKqRK/p4Xd71LerSIUJ1DGNqL6LmCwgGjE8xyPqNTFGxxDHFmBUgVgGymv4/S5RENC/cQkvvImvJsgdP0006ECngWQlaF+7xblcktde3UYTAkbzPkJlgGZ7qJJBGIkcrNdp7H2N0okSkiii5+KomRzZ4wsEjTqtlV1M/yJaKoYxPoJdrlC/chUjb1F5/SZCJJDe2iG+tICUSqGNTiIMB0gCiFEIlV283RUy6X0w4iiTx7GcCmE0QJQVAllCUg3UwGa5YCAIAr6n8g3vOs9XcgVee/oF/FaRsdwM5dp1zp27j2/7tm/nN3/z32EYxte73B46dOj/BCGKouj/+8sOHfo/9txzz/EN73kfJUPmFz/8OAu6hmzptFfLdLZbmIU42ePjyKpM/6COkUvitAeEfkBnp4Pb8/B7A67dbuD5PgeixvL/8kN0dytc+YvgKwLn8gYTS1kmjo6w+8YWuqFgjSUw0iahIPDFT99ge6fP0WN54p7Hwukx6neqyJpMrz7g+fU252YTTC+P0LhbJUIAIeLVWw2EJPxJq0pClnAIOWZarAwHuIrAE5ksj1lpJBHMlEF8LE5zvYkSU+jt9kgvZdDiGhuv7VKazxArpkgtzpB69FH6noBWmMDrdRjceIPzP/gzLB9ZoF+vM+jZGPk0P/TQad5z4Sj7L17D6dhEfkS7PUSJAkJVonhsju2tKseefBCxU0UYn+fuF17k9Pd+mNbtDeTODnguTqNLbGqMwe4Bm8/cQZ0fQ6zUGLo+XUljbibBzqub+F5IopQkSsbR5ibvLTRIaIRmHBSZXr2HOVkgkDVCSUGwLDzXww9CQkUjEGX8kHtbwP6CEPiIYYgQhQhhgOAHSLIAjouiKdj1FrIIRiqGvV8nlk8iRT52tYUoS0iKhGwYeE6ImoghiAJ+t48w7KBms/QP2khel2DgoMV13PYALR3D9wMEQUArlXCrdfR8hn6jhxy30EdKRMMOoWyCO8B1Q8TAwR24+MgIhokXAO4AMWbRUy0sOcATNSJZAmeIP3QIEfAjiBQFJBlBVf5ffm/B91HEiMhxwPXoV9soioDYG6CGPlLo4Ry06Na7ON0B2biEYA+QPZdW2yY9P0nnzg7p+VGKUxkU32X7y5fYs31GLZnb+z0mEgYtScZwBhTHU9zaHRBvD+gKAkPHZzQfR/nnP0Prdz9Bcf0WPS8AUSLse9Q7DlPjceQgQvBD6kOfibkUiqqSWSySu3Ae0dDQZ45DYOPZQ9y71+jsd0ExSb7tbYRKDLN+l/Yrz1N+YxU9l0IQYdh0MJaPYx0/gWVEyLJA7+YNundXiQwTUYnRvX2H+n6P9k4L/ID0TI5kwaK80aQwGiM5kyN9ZIYgElCsGLIZo7e2jqCq4LsIikZve5fya5vEiiaN1RbxsSRex0Y2FcyRFEo6hVlIkViYR9J1/HqZyHMQ42kQQCpOQuAiaSaoOpHnICQyRJ6Ll50jjCJsUeVuy6Pvh9zcbPDpN3Z5/c8/RUwxGHht3MufwCPk6ac+xyOPPPL/9zp76NCh/zEOT34P/Z/S7Xb5vu/5a/zppz7J9x9Z4EOpDKUQJENDTSVIHpEw8llSR2cQggGB46ClE1gTJYj1aL65SuXqHs2uQ9OPuNty0GIGZz9278T3fw2+aRHmJ+IcPZIjltTxewP0mIKsyXhBSND12X5tE7dhcySl8dDjc1SubdPerNEdetDoo2sKY7F7W6G2Lu5SbTlYssjrTo+noianpAQ/MTXDZNZi1xnS1wU+qsUYXyqx98YmekxnWBti5HTsto1v+2hJnWbfpXOtih5XCfsBjY02SkwnklXCdhMn0FFzAUK+ROahd/DX3v4Znr90hxOFJGdOHcVUFS7t7vMEy6gxDT2bYPtug8gIaFZaKKZJr9IiowSsfeLLaMUCyU7AkSfuw2+1cO5cJ7AMiJsMSyk8M8lwOolkjXNQd0icOo+YMDE0nV3dwHurQKBqNCQJogjBtpFD/96PECG6NkIkEfohKjZKPIZiNwgbTWKTk1TeuEp+IoPgBvh76ygSeJ0BcuQhxeMMDpqkTx1DsCwUSaV+4zqpY7McXH4DSQBJE/GlGJRlLMWle9DD73ZIPvoW2N3Auv8ROl/7LM2az8TJccJBF7wmuUgkslsMqk1iapbOQYX9rx0w/+6zmFOTyKZIoGWBCFcNiC+MEpZm8Tp11OoWYSaFryVRK3cRpybxOm2EYRdh6T78Xo+N128x6NaZ1hvYrT7xuUn0VJqdV57DyKSobO4QbVUJw4Dpt51AKeYYtvu44b0HARSZ1m6HxHyJ61fWSZSSBIKIH0mQsbCLI8SOLWKZBpGuE+o6vqqhA8MwRHEceo6D67vIQoQ0c4yxXh+33OB0zkAd9BDvlok5PbyDOufedz9rn36ZpYUsw2qf/b0OyUqN7Afejvpv7mJ64b0rKO0AXxO5sdbiSMlC8QNEIWLoC6RnUjTulnE7z2Lkk5RSOaJend6du6hj05hLE9S/+DnMtEkUM+HYMigGkqET2B5R6DGs97j2bz9FavF1Tjw0S+od78KaHEWOmbiVCoIkIE4XSEzmaGYqBIhU7uyjxzV0ScCxfdpbLZILAXphhKBVI0qm7z0ACQLBwKe7s0swcNALCZS4RmLcx8yY1Oo9vLoLUYhQaSEFBYaKiNMbQuhjjZYY3LyFZGo4V26QWJpGKk0TVHaI3CFSKk9v5S7W2xMIvkeUm2c+IyOHLovZUeZGkvy+GXB9pYb/5iXc899JfONV3vLoW3jrW9/Jn33qvxCPx7/OVfjQoUP/vQ5Pfg/9/+zzn/88H/3wh5mOGfxNa4RJVWXsgQlixSR6OkZ7tcro46eQE1ncZhUx8AAIkLCHAetfvYa7ucfNO032nICmHyGoChc+9oME/SHXf+E3kfyQjCURefDoI+PYHYfsVAo1btDdaXH1egU9hK4XUNAlZFkiEqC0VKB2t4psqVT+YpzU4nSSvd0uYRgiCyLXBz0+7zc5mUrwjZNjWIFIYjyJpAhIioSoSMiGgqjJbD2zhm7puD2X+LiFb/v0KwOMrMHGfoeEIjPoeugShEHIyNE8yz/1fUiGztAag9uv4kws8QM/+tM8ULB4QlaJZS0K545z6Yuv8e/v3OHvLi2hxnTwHPbX6vT2e8QXS3iqiWwZSLkUrmoij+TwYwnM0TyupOJ6wb3rDUSoUYji20iei2QP6K2XSRdiKJGHLkPY6SBFPkKvR+fOLmbConJtm+xEGrMYp71ZIzWZIkSid9ChdGYK1wkQfJedy3uc+qnvZ/3jn0GyW8hmjORMCa/bRShOIbb3ETOj0K4Sf+u7CcvbeEObgTVB+RN/yObOkIUjI8TjAv2Oj+B1UQnwXB+zkEQ7+zjh7l2crk8sCU67hzlSoPz8FbLHx9EnxmlduYk1N0vgB6i5Are+douZ+yaIT40zKHfQNJfQDZGKExygktMlymIGye2jmTFkfAwrjVZfIYwkSBfo371JkCqCY2PX6lhOlcbKLqYp0q90yJ49iV/dQzZk6lc20RIqxmiayHWxy03a2y3UmEF9tczIY+fILaTZeOoig3KPVnPA+t0apWIMp+EwtZQlOZnCGs/jRDo3P/cqo4sl+mqCgeuSmpuiU24SahrW0jRSzGToC2Bo+KKMr2hEhgGCALZN2OsjuQ5Rs40SuITJJMHkFMlnvkT/2iaDSgOt10fRJWo1h+LRImbocbDXoYPIsYUMw8YQP4jIz6YpnFvEHBvFa9bwfIH4kUWc8jbWiQcIui2ikTlkb4C7dpNhvY3XqGHX6tz6zJtM3jdFFEQUvv07SGUNhFiC3ivPIoYugecRBiHe0EFfOsX1X/xtmpUuva6LXohjJU2klM7ZDz2MpIKSLkAY4lX2QJborG7T362hZpIohgII9A/qdDZbWMU4rfU6Ttdh6u1HaG40wbGxpvLES2miMESJ6QwrLRJHZzGn5nBbVQTHxuk6DPf3yT32OJEgwNQJ7MQ4QhQghR5eCFcaIU/frVMt1/nsp7+K2OuiGDL7X/1tPG/An3/6Ezz55JNf11p86NCh/z6HJ7+H/rvV63U++o3fzHMvPMsPTE3zhGJhjcRJzuYIBj6D/QZRGFE4v4gQhfiNCsOdA8xihtXr+3irVS5d2SYfhUDIet9HFEGSJU7/1F+HMGT3V/4zCSFiZsxEC0A3VWKZGG7PJ1ZI4DsekiKSEgQGMjQ9mEpq90LvnSqiGGKYKo4bkM0aTKQ0blyvoyoCL/c6HIguDSngnywfI53UMYsxujtd8svj1K9tI1saWiGJ3x0QOB6yKdMt9zAyBoPaEEmTkA0ZM2uwmI+hWxqdrSZaNoXjBBTe+Shev4dSmgTfx2l3+ZFf+SmeMExODyIcUyDq+QhDIF2geNyidmQRKZ8lUA2C91pImslQlhAGA3BsxNDDlGRUfMRuk4wQQzUF+mqWkbSB4fVxN2/jtStUX7uBZ3uMZi0Syhw4A0LbpXp9heKDJ/A0HX0yhecGZKYzOD4I9R5ua8jAUnE6NrHxPIIq07m5TeCK5GbT7P/Zp9AkkUhR6A08rGEfEImldOwoz3B7h/jsJMMbl1BK4zi3b2LeN40uQ/P2Orl3HcGankJd30aIUsRmplj7s2cozM5x8PTTJEYtzISBOTqOmPLo3bpO5tQCoiIiGTESR5cQBR85k0U58yip5CxKQkXy6qjFAjS3wbeJEll+9pPr/C/vnydZ22E3NU6204byHtHZC4Siim2mUOpluq++zqDZJ/3gfcj1fXoDj/zJ4/R9UOwbCMVxrLiKKIPbHaLqMsN6H7x713bMXJxYPs6g2qJ9a53UmMVgp4ExUiA+lULMWpSv7DA1kyKWt9htRlj9PWY/9B4yQ5XB1eu0dq4zDCPKL92kMBbHimm8+bnnyakKyZkMqqFSUn02Lu8TS5o0ULASJmVfxU9YjIxnsMdGEfpDRKB9/6MID74FVdOJPA+72SbWbOPiETabmP0B4sYBFBRksQaNDpIioyQMJE2mPwjwel16qytomoDT7CAnLJTIQVQ11NklhNMjRPU9YncvM9saIODjdHzU9gHS4sP0UhPIi03Egw00Q2W4v4eUTKOKPke++93sv3iZl5+6yc56g8c/OIkQBXidFoKuIE4mEZJ5EELsrW2iIEAyVFLHjxIO23idHrFShtAJyCxN4A1dBrUB5et73Hxlh+mjWZKLCrXr2+gpg8yJadS0hYCInZum/+prmMUMXrtBd7NMNgoZ3LyGqanoiPRjeXzXRdYMjuY0ctYIqjDCe06U+MMXV7l2bY3xD/4E4Y1nee97P8C5Mw/w+S9+kmw2+/UtzocOHfpvchh+D/03i6KIj3/84/z1j36U5XSKP3jgPBlRRs/FEKOQ7naLYbXP+CMzWGM5Qs+lt9VEMjTMUp7mZpXX/uAVcorATmWAlNY46DgkVAgigaUf/U60VJzuP//3LKVlIlHFSig4Q5/sZBy7OSC/mLl3BzMQiU8XmAgj1LjOYhihOD7JuRGaOz36tQEoAoaiEUY+5XKf7cDhq2GLU5k4H84VWMzGUVQJIx0jVkyjmiayqSEoAnLcIDY+Qv2NW/SqPRLjKQQ6REEIoki8ZNHcbCOoIoPtLtqkTGp2lNjbn4BWE2tqlMDrUT/Ypzdw6OgZTj3+TiYyBWqpJMTjoKoIjkMUn2DxoIykSvi7ZeKRzf6VTQzJJy/aKJrE6LsfoylmmZhJEfo+q7/7RUaLDzIIDOSRUXR6iIEAs6eJzD2MrSqqPSQ5mad9MCB/cpzuzVvoqThqIoHXHSJbBlHfRjNFhgPQkjp2vYuesRAliVghTtB3yB+bRh6ZIKxsU766DaFA9ugo8tBjWO3Q2B8yVcjT17M0RouUb6xw/OEZOtevI6WK+OurJLImj33DWbCHtO5uopoKamEcJZtn9MICSsoiXogRoWJMzCBkc+i+j99uYZ48iyhE+LUq6tg47tptRLFDt9wk19pCcDVsu8/B7SoJpYVn+8RGWjyYgUSvgz/oM2Y0GO7vY+bjtK5dJJuJE/hdbCVJ4YMfpPfi1whliXalR+zIEj0jQZgdIYWLENrYtTbNSgfLjBH6Q+Kz43jtBkp/gJzOIbgDktNplJiF3eyTPzXFMDaGUlvjxDvOkRAEuraNkk6g5I/ReuUlhNDDFAWalkEmZxKqEitbHcaPjxE0OyTCkGRCYbxgsPL6DpEl0x+GlGY1/LaD1uswS8SbN++i7cRJP3CGsLHPYCyPrcU471dpXLyNvjDJ6rUdevUefjaJWkjhlEqEi0cp51JgmBAE9Lsdqgok4wnU2QRKr0VvY4t4TCL46hdJLsxgnjyPP+giFSeJdAthZA5TgtFgSOP6ComlMQR/SNhrERoF5NmTSCMTsHMHSa3iDfsIYg7NFJj/n/4GovxbvP70LXaubnDhG+/DqbUY2A79po0+Pk4sZeDUm+iZJOnz9yHJEW47wqk10dIJQj9ATZgohkL+1Cjrb+yw03Ho3KojuuC5DmLSuvcaK4Y6c4Q9X+PuJ15j+X3LaMUMXsfGq1URRQj2t5EQ0aI1BhNnGHgRvTAkayikB7uMT8cYLZzhzTOTfPqVNbpTE4ydfoSXP/kbFErj/NEf/A4f+chHDlckHzr0f3OH4ffQf5O9vT2+5QMf4sqbl/nJxVnelsqiWArtnS7O0AUvJIgEJp84g5ESEKKQ9t09/J6Dlo3TK3d57U8vEjk+d/o+zRCadYcRTaDrRZz6wW/CmBnj1b//L3k4q2DlLXKzOfSMSb/SYnDQQ9BATRoIgBIzcfsOsqUT+iH0bAZOwPTyElNRSPXKJogyQuCzeaPNF4ctNmM+P//o/Sj7A7SUCoGAllaJ5RK43T7NrTZa0iAKwen06d7ZQEkYBBstElMZBuUeak7HqTs4fYfAC/CkGOKRIs6RacLSGA0thjdZoN+3CZUiUt1BdUN6lQ5hvUXvzh64Nmmvz2AwINnr89vtMh8+d5Qjp5bYvXqdiUeWCASX3HSWzJFJ7P09+sVFMkII2RLD57+M33cIRI1OaJKTfETbu/dFs3yJIDvF0FEYs1z666sE3RUEaQI1aaHoKkEUoRYyiM02SsIiGNjMnZ4hch3cXh8jm0DPxjEKGYb7FYbtHqWlBUKxgHnQovDI/Ti1Gv0bW1hjeUoPnkH192FjG2XqKLdfvcLxh2ZobNeof+0GJ7/xIQLLIDWRBS2OkS9Q/+pXKM4dIZQUxGSe0HERoggCBymZIRpdQlp5HX1mimhnla6cwzp5nuHVV9GyebxARrh7Ea/dRojruM0GVi7PYGUHr9sh2j7gvfM5gvIubVmjFPYR7Qp7z23RnJzDykoMvABD8XE6AyJJxl5dxbvwOG4wQI1ErtZszpYm6A1cCvNHUXvXCPtltHwOyVBxDhx8x6N/7Q7mWB6zkEVAQFBEkseWsPJTdL+4hutCPwxob7aYODbL1heeojSZ4dXf+So5oc/map2H37NMFHrcLfdZv7GH3nM4cjyH70O5MWDu7DiREJI5KuD3fWYeOE88FtC+sUbs5Cz23V2uPv06pbzB2Pp1dk49xv5rm3idAf6lq+RUi5lZCz0mU7vyCl/74ipzk3EIIqyUiZdOU22HiMszWI+coSvrDOOjDE9OE8gqkuegBw76ahll2Ce22yA5WUftNpAnpxFllcbdGksPPYicyiLKIlZ9BXwXr7hIMHcB9vcQhg3segPB6cHOGiOnpjjvuDz7zAq3v3qDxbccob3XoPXqJmNnq8in5hFNDTkeR0znkLIF5LUbDBt9mhsVgvaQxk6b4W6bxHgaPYoYhhAbBrx5q0LbC+iHDY687z5qb9xESN8kO9mmllYIXBffdtEzJpE7RDQ0BEVlcPk1ZF0moWn4mVGGUo62K6JKMWKqxlG7xsKYw9yHzvHlGxVefiWk9Mj34t18hm/59u/iyD/6eb76lc8wMjLy9S7bhw4d+t9xGH4P/R+Koohf//Vf5+/+6I9yplTkjx+9j2QoIQgCw/oQb+DTrA4pTSaY//DDZO9bxt5cJ/JcnKFHGIJf7lK7W2Flp40XgU907w9PgOWZFP0z50ncfxLpN36ToxkFM2Mw/egSqiHSr3WxGwNkU0XSJLSkhagruM0hnc0ajhPQL/cwTIXsUonVP3keMyNhFOL0D7q4zSFvxFwQJH7l0VNIqkxPdzGz8XuhxwsxShnab6zQKQ9Ij3WJwpDACQhUHz2fIFRF+moCez6JOzOKn8rQjiUIEkl6sow0HODLoIYhZreOuLfFqN2i/OItZFFgbCnH3/3sS5yq2eTmxti+dcDSW+a4u9rkjaJIeXeIervBfu8mtZbNZCxFfjZL7j3vxUyqiFsjeLqKZUZUqn2ca6uYlkokyBTHcyj+AAHAd7FffgbzwXcQzE0jaBKyoFLMZwlqu/hDl8jz6V69S2KigJFPM2z38Z2A0LYRBQjdEDkRJwrBs0OUTJJhp0JopYniRZTtOt4wRCxMkR+dQs0XcVavoyTS5JczZDWN7E98L3Z7CzVbJDl0kfIlVLePMxSIFU3s9Rskz5wmkkQapRP0WgIFmsROZwkONoiIEJo7DHY2ITWCnkhiCCLOnTcp5xeZHm4jCTJaPkun26RXt9GtJGF1i9AeoOga+8+9yuzjx+m2YGRBR2rvE2oqkn/ArFin8bU7hDOTxI4vsvbxP6M4VcBggFa+ibx8DsNMcF9lH6c0R6vcxMhaZBca1J++Q284JKFN49sOimUw3KnjdboEtkd8ssCg0cWv1ElFLpKu0NmrIfZdSnmT7soWp06ksQMJ7DaaaYLUoDKAiVKc+x+aoXZ9j0iEeCnB/v4AvVBCt3xiozl6u2U8XaR88TbGmXG8gYPTtanVukzN5BldGqEwPc5uFNGeGCcdhGy9dJnpR47hd3pUdg5ITo0zM1lD8QIO6kPSaZ1CScdeX8N59gC5vUNhMkOsmMZu9dh/cwd1poSfSBIk0nQG0C3m2CkPcKQE4kodU5tGfl+aDTlJzJOIdfrEIhuhWUFLZBiaBeRj96GsXQRJxqsN6V25iOcJFE5MIT6/xsXXthldLGLkEhxc2ufg+j4oEukjU3hDj+DqRWL3P4agqXjtHquvbWOJEdt7AwxNItrtMprRye3LxCWRnuuzZ0dYukRQmiRwr3Lw9EtIpoFVjKNYJvZBndTSNF63g2olkGQFBAm33UVt7qH5DuMTMbruEFtPouGhCiGSpnJW6GGdm2QyY/BHkkRvpkTs9UVWbn2BsfEp/vW/+mV+6Id+6PAU+NCh/xs6DL+H/netra3xkfd9gO3tdf7xhVOclQ2USCSKQobtIc3qAEEUMGWRkYeXKLzlIUiPEDMsBpsbbDy7Tn42jTWawAciRcC2QyxZoEOEGIF5dhnpg09g/qffozCTYGoujqxrqDGZ9kaF9mYdb+CTXEiiahKNW3uocZPWeg0jpaPpMl0/QtRkDu4coJkKt15sk0nqNNs2alLhuU6bn1tcwumHdHebmIpM4Hj4g4DQEOltV4n8iIlH5mh7EpxaxEtl2BcNvGSa6P4kgusQ7JYJylVG1QbDNy5hiR6Z2QKtgw6a08dcPMbwxpsMyl3MxTypsQL6fWf5pU/+GUvFNGcFB9XQmDw7ydaNA/797iYnp47wTx9/gNZeG1mVOPGO44SNOiPveiuEAwQURFWi98wLbJf3mHj729HPnKD6wmVCz0EZtBCNAp4s4R8cYMYVnOuvEfd7RPE0B69fY6Io4tSbtO7soiVN3FYfN2ES+D6SqhAbSdPdLpOaGSE5U0QSQc7nqL+5hhGXyS3PYm/uoJ04Q/qJ91D54z/EPHoC7n8U99JzxAoZhsMI1ZTRRqe58Z8/ydEHZjDCPYZRwOVf+j3O/v3vpPPCJayZKQY7VQrj95ZBWMM6Yr7AfvwE0+U3EKJRBit3kYSIwUCkfP0akyen8CplzNkF1K1VopkSG/ttxjsVQsdFEUQGm2UkyUVSVRRTRdwr07t0CX8Y0B+UiM9PY+/tIWSzaHETLdfg9d/7OBMf+zGinV0cYUiQyWB6ProUgd1HbeygJuL0xmYID+7QEVTUkQLRxBG82hbm0jKBOyDhR6j5wr25yo0aiqHgNls4tSqCphOVK0TxBEG9gd+1KZ0ep7ffJCom2L22z+xYkpTo0bxVJzZikcrH6LgRiekiQVSnsr2HPRKjsd6icKLIcLWCyZA7X75CaS5NdlSlvS8hqzJbF9dxNZP4yQS9TIklU8CYn2V8OsXO5XWi11+lu+ZgjWTQBj385oDrO12Wgn2Ko3Eq+11Wr+3jtPrMvj2JubDAmGJgN1o0X3+T4X6XmKmQPVYiDKDftHFTKeLLc0TFUXoDh6qvMOh4+MjoFLBWy1hGi5iuYGSm0JoHeN0hkSCx/fx1Ji4s8NYPnOTZ332Z2u0DxLhKuzmg1hgiqyKSIpM8Mg1BwPDNV4hcG1GVGJ9O0dpqohKRiCtUawM2PBXLkMAJyeoyu0OXhAx3/tNnsTcPyM8naV05wCgaIEq47SG9rTp6xkRUNNLLAZEIaqGEXa2iSwri6hukcyWGbhsvnieU42hSgNI6YDybZuJYFkW5jz958S71tsuUrrOxdZ2//eM/xT//lX/LM1/5DDMzM1/vcn7o0KH/jcPwe+j/QxAE/NIv/RL/8Kd/mm88vci//9Z30nh+E5seTcfH0BUa9SHVnkexYHLknUcYf+JBIsdBjGUIt9e49VufR4+bJOZKiLJAt+vSsyMUQWAQRKRlGD85j/PB92P82ScR9vYIzCyiLhM4LpvP3KFf7ZIoWUi6SPnNfSYfmKC12URNDHDaNpm5Am7fxnZ9TM+nutsl9AL2ui5qWudPh03uNrr8ow+/E67vsnf9gPRUkuJDR2m1BrBUpOapNCdHcN+SIdBNhF4Prd9Gd/uoa+uEN59lsL9P1hKJRJHb1yqMnR9FGEbEl3I0b++wu16nIIfU15vELAE9F8ecmkBcuMDP/87vcfTEKT5sSjQ3tunuHVAXQ365sskPPrbMEw+doNeyMWMm2SOjyNk81ZeuYBt3Gdy4TeqxtyJJPs2XX2MQj3H3P36c5NFJiuePE8ZTYJgEns3VQYzxO3dJLC0w2Fihd/sm6be/DblRwdUNokggc2KWzso2ycVxwoHDYLdG6uQi7NchiAg9j9jkCFrCAMOgcGGZyO7iizFUr4fz+rPIY3Pk7z9Bc29A6+kX0DUV3cox6HYxJAFv6DB1dhpV8tELWQr5Eoog0r58k8zJBeR0EnN2mtb6AegDkpZFsL1HmD6GU8hh123ilkbz8k2iZBFTHODVKsQfeBg/GJJsD2htHdCtOIiTCvGTZ5E1ldYLzxD5IbGpaZxaldmHF+jtV8nMzxK6Do0rd+hUG4gjKpKVgKbCWClG5dN/zsyTFxD8PkF+GtGxqV9dJbM4SVg7wK/WyJ7xkTSZLw6PcOpMjpgWI1XKEPg+wehRVOslVEVAQKC1s49TbyLKEkHPxqm3kPFYevIC5ZcuE3U7eF0b1dQxFhYZ9gTceg1F8DGmsuD7JKeyZBbHkDUFPaGSSqkIgoSWEGl3QFBk0mMlmq0tegd9YrmIbNagX64zslhi79Y24/E4nZMPsvGFl6jv9lEemGCw08RIqBDPMX3iLBv/4bcZm87w2pV9MlFE0lTRZYFM0eBgo86U52PfuImcSWEtTiLoEu2tK4iyRvV6GcQQIxnD6LeR33wTQV9Bj3wyx6axTl/A9n36bshAjugOu+y3fAaijs0IymgaNdZAee8oB80G0rCBqahU1+rUhgG9ngOqhHCniqzIdA/ajJyZQ52awtnZJj49Tv+gR6dtIyPQbzhcrw2JtWx8QI4iPEQmdAk/FNi7voUSgrjWITViMWi5DPc72AMH/C5xJ45v35swosYNFFNHK+XRLYtw2IPGAYqsIY/PEeoWXnYSMSkQi1ykToX36h2cBxaYyMW5dDVO30rSLS1SvfF55uYW+Gf/7J/wd/7O30GSpK93eT906BCH4ffQ/5vr16/z4Xe/j2atzK+97X5OGnE46CCqElJcwz3o4Ts+FcdHMGTSkxmK52dxGxWkXgdZEGhefZOxd92HXszj7G7y2p9fpn3QRyTCFEW8CI4cHSf2E38d+Qtfhqs3qdYdjGSfRt/FHvi0a0OKE3Fio0nsapfACzjYatGoDYg7IaIg0K12qN+tY+gyO9sdfDcAL6SSDPmT7TX+5plj/C3NIDYyQjM/hWkl6BdLXEumIAiJKhViTg95ZQWzUSOqVGhdPyBQZKxjWWRD5c72PlEY4HakezN4FYFerU/pwgJGIcXw5jZpOSKxUEIdn0aYmSHMF/HiKX71N36dhePH+MFv/SCv/52fZebRIzyzt81XLt/l33zocTJmjEDS2L+5wpG3HkOMJZBTGQrvfCu+7UGvwfaffB7diFBiKlNHSqw8dwdqFZSlPI1Xb5Ku16nc3cV88AmMtE712ZdJPnCBr/3KZaYzCxQil+56g+TSLGomjd+3EQSIgoD08iJus0Xo+vemKagaoWPjdUPc/SqhF6BYMQ5eeY3RB47QXdkhbVooE6NEbYFE4wAhNk7vzcs48TGCYcTep3+P1NIEQSFNZ2WLzEe/i7GjRxncuoPXbqONT5A4sUz35g1aOy1k6Tbpc2dp/dmnYHEC/+4a3twMatIgSCQZnS8RtNs8+0fPcPR9j1JMBPRe+SozM0eI5Dxt1yOfjBM/c5bOyy9i12o41TbSRIHMfedQTJ0AhWDzgFQiS/LkUWRRxNVkpp58GLfnoMYUwihO56BK8fgczlBAVGSiTJ5waxvT61KxZY7nMujqOJoVp2unsLfukJYhLE4QVbfwh30U0yB0bCo39ymeHCO3vEhnfQc9qTL/0Q/Su34Vp1LF7w/p7FeYeOc5nGqN4c4u+1d3OfKB+5Asi+HKOoIskZgu0Nmq03F8krkU/ZEJVEGiW66R0FWssSRub3hvHbAk4vZdBl5Ep+PfmzLywAUmb9+gfmUd1/dQRnIERobCSIZBIDE6ZvH2t86C4+FWB1zcbLEIqED1+gHJoo7bdiCK0LMZFr/xATpru1Tf3KNVGzJ5LkbYt/GQiYY+siQwKFcxei38rTXSoyNkrRJ+dYVIs5CLs9h7q1RqNRoNDz+XpTc6gW1YJN/xfuRGHfPOFvrm3r37xLsH9Oo2mm3TiKlkdBNjZp6YEUMbn0Abvczui3dp7XdwQ9BCKOoi13oBYyp0/ABFlpiaSbG22aGYjWGNJDBzHkY2TvfiNm7HIzWZwnEdgqFNq9wiMZHFHwxRYhpSGCDHPexmHyuRRDAspE4FQU8QhCFh4GOGDh84lmEmb5HPxPmSZrB2fYXw+PtJrr3G//wzP8ev/eq/4wtf+gzHjh37Olf5Q4cOHYbfQwD4vs/HPvYxfuGf/jMeUxP86zOnCPdd3NQAQRXJnh7jua+sEI9CspbKkeUC8UyK8p0DfHuIpMogS4TVHeKz40ijcwzX73LlE6+zs1qn3nfpBlBUBZJpC+MHPkr4yhXqn32OmChhagL1rTb7LYeMqeEBzcqAWK7D6mqTwAu5tnfAiVyMhUeK7L2+R3w0y/rlfWzA9ALWZLi9UOL40iI/PbdAVCxQSaVQfQfDHaJtbyJdusio0KN/dZ2OF1JrD4mrGmrgoOoqufkSru/QK/cYu2+aXm/73sIBSwFRYO7EKJNvP0uz4aNZIqXzR8gj0c5OUbhwPweCwSuvXeaPf+nnePLCKb7tu7+b2p/9HqNLBdoVmz+7tsLPnV5Gbjl02y6ZiRLtRg/VMpASMQLHBtdGi8UhHUd9yzHufPJFYqNJUmdOcjybwm8NUSwT64nHUeqr9Pw90q8/xVAaIkohstvk6NuPQ7/MzuVVjr1rGX1qHiF0SZ44Tu2lV5BNjdC1GezVMEtZfMfHmJ7E3ttFSWcZVhv3NnjJGpmjJfTJCZJmHNlQULJFer//aSbf+xjS6CTNZ26Rnp+n9+ZrJM8tI0U+nu2SP3MEu91G6XQJ2nWqZgGp1kESPCQ5glgKt9XCrZVRGRI1DnCqLQRhg9AZoKZckOLII6MU2SJNH7/eJHN0GnIlZFNl9bUVAmysY0cJS1NI3TLNzRrm1Dj91hAtM44et9DMNLrvIGZyRLt30YojCEKEJDeR8iO4OzsM9/exTy0jjGSo6nnGjhm4Q7i10aboN9AUk3QpQwcB0cyyZxbpESdpg45EUGniBQrIMooq0z9oE3giejFL5LgMVleIH52HzChyeYfWnS30B89jjo8iagpxW2D9hbvMfuc3IZbrRCLgR6TnijhegCRKXHv6De57x3n0IwXsvSruIER0WrR3a+QmEySPTqG3fezKJtLOON2RURLuNeKPPEJ4sEv29Dzlqzusf+JLHHvLPPWbOyTyGfrlBmpcJZ23qOx0kQyJiSjEGYZ0V+4Sz5r0uj6JsRyJmREEQaD1tdV7Dw8Jg/Z2i8LpSWRFwusM6d24it/to5gm3l4ZPWUhaRrO9VeRczlSXo/uG29iqhJ630fNWoRxi0pHIJ7M0joyhzT9GEouzSBwcQ72qa1sMbrZZCqewNQNrAffwdTyAxQevsz1f/dfOG772H6IE4UYgCKKdPyQjCpT2e9z7HSRsQtHcGot2hsN1IzF7AceYuVPn2f7chktIeMNHLSEijewcXo26uYuZjFDUHfZe/46o4KAWi0jZzNIs+cQA5cwMYKYGSMmS5zKKcSUEboe7K6WqVNBnDzNuJyg0V3l+ImT/PTf/yk+9rGPIcuH7ffQoa+Xw/++Q9Trdd77zie5ef0m/+rCfQRXaww2uyRGLbInR/G6Q+5c2aM4YpJ0I0ZOFCCCyW//CKMvv4yRLyHHNIJejyAKERN5Gi8+y50/fYWLt8oYQD+MyCgCNT9i7K9/M16zS/gnn6E38FE0CCIQNRFFFgmjEFMS8eyAO5fLdIc+oQSeD6NjcULbw/FgaMSQ3nqB7kgBf2qC5ZERTjSbSFu7CGubJLfvkksp6IHD9Td2KSg+ztClq+n0qn1GjxfJjaUZlFuUHn4Lldevcuwn/gbO6nX8dhOv2+PsY/O4PRvZ0NBTJokPfytSLoMhmRiblwiRkFQZfekhtrbX+aV/9vcQFY1/+Q9/ktGpSaiv0xx4ZI9M8tQrK5wazYPjkj8zRfnmLi/+3rPkNZHypU1GH46x/+zrjDx2ht6tWyQffBTh9ptMXJihfNADPYF5dBn39hUGe/to0jVqbZ/FJx7FefNZIl8hdfIUoqZQmMqSmJ1mOBEnuTBD2G9AFCCqBrKpo6YsQttFSSWJzc8hGBailcTQYwieg6wpaJk0gqqjxjS8WoXY2DiiGqO7VyN7Zon+zgFx0yC0A9hZxXc84tk0ohgRNcrYjRYNe5OiZqPFVZo3b7Ff3cEgILRdxESOMHBx9zbxOl2kiQL5+48QRhJRt8mgNyDQAEVjfDGPGnQYVMqYxTS7Tz3H6PueYCKnsPW5F6m8fA3z+CK5TERyaZbY6fPc+dOvEqw0OfbuB2hc3WT80ZMM+x66lSIcDFBGxhHaDeR4GkZDiu0OtVeu0zhzH3NJAb+8C/4AvdUjsAfEp3wGgUh48w3U4+eZ0CMETaIvKUiZMaLNA3rtLpYYUDi/QPvOBmEA2vH7kfpV9q9eQRJ89CPn0KaK6HqEX9kFWSZ3/hzm+BQXP/sGm599ismlDL36gNbaAdmxOJapYXdtnDvbNC4cJaXEsfcaZE4uoBwZQ7tyC1lXEAOPfFanWQ/xV65Rn56h02xRbd3i9PsfIZaSiMUijPQYw5XrWONZJEllq9YlGdN4/EMn+cxvvcJMSmPv6hZG0mDi/ml62w3MbIyVr11n4uwkyck8c28RsWtNCCI0S8Pv9Al1jVgpDUGAIIFTruB2+4jCKIM7m4gEaJJKd6uC17YZ9Dxc10Pr2mSPiPSfvkU6Z7K1Uueljs/J0RTv/Y0fpT2epzkyQjmEbUnBbPmkxDopXSFx+kGOf08X7198At/1qbVsoujeKu52CKNhRKtlkyn3QRQx8knCEAhDQnfA/EceY/VTL2C3HbSSjNvz8HoNJFVBz8TQs0lETUMxVFpXb2PUqiTmJpFiGYRUAcGpEaZyyJGPJXqMxA3Oz2Yov/UUl1+B3Y0tymMLBMMiY+U8P/9L/4bPffaLfOWpLx7OBT506OvkMPz+FXf16lUef/ARUqHMP87NMFG2sdM6bhgx/Y6TDHb26dd6jBVi6EmNwPVJLY4gyQoSDsnpPPr8IkG9jKi59PfKbD93m43nbrGz2aLlhEykNMpOgBdFLH7Lk2hTY1R+9l8h9H28CAxDRJIlsotpktUht1dajCQUWnaIEkWAQJRKsXRugsHpBbanJ+Cbi2x4HpurK7Cyzcz6OsLuLtpffFTp+T560eLW020WHpyjv1mml9AQFZH9lX2SmgQCxDMaupyit7XL3JNn0ZMGZOLImkwQihQemEBUwJUsrNE87vQsNXQEPcac6uErGgfDiN/4F/+C6zdu8T//rb/B6aJFqCgcPPsC6u4dAkdmsF3hz1fu8sNPPkAJAb8zIFmKM+n6uDtNOjtNsrUGg3af9tYB1kgJp92itddATSeYjJuEzQpaIoY8Pcdwbx8hCth7/k1i+TT4EUrcAt8FWWTzhTucfeA8ajIOgYeom/jdJoLgkX7LE0CAd7BD7OwsDFtEQYQvqIi6jpBMY0wMkOeOEzUrRJFPUGsTyjq9gwabT13CzCYoTMUYbK5h5CwkTcGam8fbuI5WGMPrdgnRKeY0+ncPUAWbzFgcdfE44e4q9t4BODVkFeyDKkrMRNY1BEJ6lSaqIWOlU8Rm53CqB4T9Du2rN0nedz+SKtNxblKwbcx0jNxUHtf1kbq7dBo+KAayEDB+3xLO1hrezhp63MINRWzbR21VkGIx/KGLb9sMLr5K6twFYgsBfqUNV56j3TmOsrxEzMoz1+/SqQ241hc5KkBWDRD3rtExC8RlkbJVoCD1cUdGSKsRzlYHP1RJnzlFZ6tG1KkixBNYpRyhPaD9pS+QO7WAlk0hJrOo+THAR9Ilzn7jA/Rv3iRyPBraKOmHFpGqt9CTBnJxhId+YAbb6RGkijgDD2N+ieHKTTLLc+DZeO0egixijWfoXdqAMID7zzM3rLO7sk8hr4JjE3kB0dgsiirilbeJEImP5VA0maNnx5CafQYBNPfaTAkS9YMeerWHqokc3CnTKndJJTUKZ2bx+jY3v3CD8YSJd1DDLCRxu30iP8SczlO/tkpnu4qsSYiyjJrLgm4hCpBaKDA4aOMMbABKJ4p01upkVREJ2Ku10RtlCkeO0hYsktkU+3/8n5DOnqcbxqh0fe4eNPDTS5g/++OYO5tMbu9hfe0K5Y0DDC1EFQRyMZn2fo/bn3idxfecQE1o91Yne0OsuXHyx4sIgcDWpR2kCKQI4tMJmrfKCFFIcnGK7PI03sBBkkUi22H/k58k/+gFhusbaO/+VlQ1YCDFMcSIoqnyN95+hM3jI/zyHzzH1q1bBO6AysgSeTPNlfVrjE/N8+pLz7K8vPz1awCHDv0VdRh+/wr7wz/8Q77n27+Db5mY4K1CgkQUoscV4oaB74fgegiA37+3MlaQImRNoXn7gNRUlsGdW8imBoJ4r8mqBvtvrPD6V9dZr/SJACeEnb6HHwnk7j9B9v1vpfmPfw3VHvIVp8WUpLGUSrC51yO4U2ev6zMIQsrxLLmHF4gWpkkuziDGTISDMuL2Lu5XX6K/tcWvrl3lF08ep1u3yYwnuXqnwpn7x0hO56mv11B0lZlzo7jNFpYIiiaSmMrTrA2IpXVECYa1HiOPnuPg1Ttopopf2UESBZxAoFdcpDSSRBoZQ9ESKLKAEk9geA5+1MeWNP7jn3yOP/mjP+LHf/Bv8FPf9z3Yt69Tfek6tpih+erXKJ4+hXv1TX7PblHQFZbmZ6i+fBXfiyD0mX3n/ZSfu05ycZLufgNFiBjWmlijeexqk8atDcY/8ASDG7dwVnYpLhUQUhPoIyGki6TmKrQuvkJ6rIioStj1KrUr6xTn8wTVA5SpIwyuXUGyNEQjBpqJXZxB7jWRCwKtmytYy0fpvv462bfME3l93N0dXC9CNNIE1SrazEnMRJl+pYG2uMzsyBT9yxcZ1vaJT48hSgIREXalguAMUNMpiEK8SGdw+y6J2XGGa3dInH6IsHlAoEsYS1NIVhxn/wB9ZhHBShOW10CUiCkmvusgGDGG+xX8yj6+LxEpKm6ziUTI7H0zBLV91GySzP2nkScXcA92GW7uIUgiuDZSu0w8H0Mw43B3BWHDQ0sU8Ac2CBHDgzbezj6hbcPp88j5EVKezXDNQ1h7E7EUo+ZI5FMaB4MK5YMOTSXJUnvA+MYt9MWzNOQ4ZSnBkWICubXDYG4JUb2Ee+caqbOnGLoSgaAgDoekR+Mo43Ps/KdPoOWbWGmN4eoakWyhjE4g612kQQf93Dn8RoXYao/W2BSJhWnS9jYrn3iB0tseIt6J2O/bWOM5Gm9cRpAVRCcivTCF072D3+0TRRHp6SyV7V3a2TESm32iQY/qCxtkjk4zXNthe73L0je9EyOXRLm6hSIL3Hplg9IjD2EIDp5nM9jco1dukpvPo+ka2l4Zp+MSDob0A4/QspADG1mTaW00CIYe6VaPYa2LGjdRyzUEScLrO8iqCYKIkkoRFyTs3TS13Q6SEKGkDPq1PsnRJPvXKuz1AwJAFgUqL7xBYnGRRNhGdESiRpt8rkixOEmIQHj7Dbr1Cv3MJPVsklY6j/Tw44wPB7C6QbS6QbxXo3NxjWDgcvWzV5laLlJ66Bhus429t4eRuTcDe8QL2Xp+HU1XCAch8bkU/XIbyagQuS5yPIGZT4Es4zVbtF59BSVmIG+8SZgdQ0mPgaQym5SJayonMiq777+fzyUt6uUK++sbNAApUcAtX+bk6XN84uN/xIc//OGvZys4dOivnMPw+1dQGIb85I//OL/2r3+VH5+e4z1TJZyujdt3cbouQhiRXcrhdjtIMRUtrlG700AxVdBAMiT0fBp9ahrB7uE1W/SaDrtPv8HKS9sMOg5+CIO/eL8DN6AwVmDhb38Hg9/+OM+t3eZVr8ucaPCK2ONcI0FiYQJvZhpxZoKJo/Moqkz3zhre7TWULz1Ppl4higLS0ynaXYdvvvgqb8vmiBctUqUEt17bRUdgMPTJJU3GLiyxd2WdVCmJbdc4aLtM3TfJfs1hbC6LrCvomSRKJoNn+4y9/RzqyCiBKCIlMvj9LTRRRJ+cZGjliTyPH/rZf4qiKPzDH/1+Xn3jKr/667/O+9/1Ln7tnRewX3qVG9dXkHd2yM/FcfavY4Q+3Ws3uDRW4Pa1Br/20z9G0OuhzS9iDhpIpUluPX+Fibe9lX7PpXpjl6A+IJ6M2PuzF5g9VkIaumh47GOgNhrIsTn2XniZ0v0nEFybuScfxlm9SoAGoc1wv8rgoEXhxCiEIUJujMbm08SKCfbfuII5M4HgxlE1hWIpByXo5hZQplp4e+tImTyRCF7lAH3tGo5soXaaYCYI69cR6inkVpXMsUkGqwGyLDJsdpE1FQKf0I+oX7qOkTYglsPIWEhiiJZNo+UyRCMl6DYJPBfBHaAtFVAKY3jtNqFrw/x5kBSEOxcxxicZrK2gjk4QDSFeTCKMHyXcuo0e2gSigl+cQu83wIojHzuHfuYxopsvEXRaSMsPIO3eIpw6RnLQRomZqIKDVxjHXr+OMXMEyeshF0eJ9ARRu4wYT5E9OsNwf5+t3/pDet/6XSS7QyqT95F85nfxFJnx99yP+PIewdYKm599luKT70csjBAYFu3rNyieO02Q0lh54Qad2RM4z7/M2Y8+waCZQhkGzH/DExBLYV99GSldZLh/gKxJ9G/fREtbyPkSSnGUTHuDlS8/z/7UGNm3nibxTePELR+7ppFY2cYctXA8DS2bZFhr4PgKqmUSSTL0ukiGRXJ/g9bUJEpgs/XsRc5/60PYjXtzi4vVFrv/5c+Zfs8DTC2PoGQK9Hf6OLu79GIZ5uYskrESzbu7pKayOC0Hq5Qmcmv4tk99u427N0AXI0aPZOjudbD7PnZ/yLDWp7XVxuvbKIaCEtMIXI8oErDLBwzLTbSkhn2ld2/29DAgJsDo+XFiGZVepceRjIqVszATJkLoIxYmiQ42CUIQAgdqW4h6DLuyTbC6QevyU1hpk5lzC7TuNGn5EV5xjN6j5+imC/ARl3BnB+/GGg2nxniuQDCwsctN/PYQWdWwsibZ2TTtnQ6iIeC2h6iWQnejjBzTaF3aYeHDFxDMBJmTcwz3a7jtHrXnn8ccyWNeeAI5FicwCxiRDb7Dt50pcmI0xR+/scUrIfSrMVoHO0ilM6QilY985Fv4sR/7YX7xF38RURS/rr3h0KG/Kg7D718xnU6HJ9/6Du5cv8avHzvOiaksjZ0u1d0uiixTGL03WswqpTm4VabfsymUYix+0wMMdvcRopDs0hSe7dO5voYhhVz83Wfo7HTZ3KwjBwFdL2QAJAToRGCpMnM//t0cPPMSv/G1z3FOifOhpdNUF8Z5231nEaamkIH1W7fYv3kb+ZNf4vbaKhedFiOKyj+cmkUQIkRdQVZFciNxbv+Dv8nvf+JZ/sm1O/yDpQXGZpJIsow1UUBJ5xns7iEJArGEil0WmJ1JYE0UyBk2+ekcsiYixy3EsRl8JY6STiFn8ziBgNiro84YKKPzNIYDtOGQ7//xv8t7H3uY2elJvvcHfoTlrMW//fC7ycxO8vyv/jHluxVSGYPxEQs3LWKkY1TW+uSLJr/+yS/yyX/1T3Hzs/jNayTe/WG48QLCiccYi+KEq9fR3vONLJo29RsrKJkcyrBFOBhgLc6yPXqaL/70H3HhF3+ahSOTSDd3cUUdYWoZNZlC2t9GEiOar93ktaducvYf/iix6i2Uo+dB0ii843HCyga64pMumeiaS+/2m5TtZTIFg/rBJpYu4OwdoBMhz53Cv7tL5PSQHYeoeJTOc08hCSHu1iqiENHfqeAMPBRDRJ+YoH3pMqKqo2dS7D5/k1haR7K2MFJxtHwWbWKGsN9FGJ1HGPYACSSJsDfEtm2CSEVdPA/ugKiwCKcfRxQjlPQ4vpVFjSIc30VIFZAlmcjpoXgOTnoEQRKJ6luEfoQ3dw5JlBElD6Wzi1CcQKquIk/PI6Ty0G0gVPdw9itIZgLfcVFiCSKnj0AEZgxjykS2dCIlwRRlxKmz3B+p/BcPJtUIUVGwJ5aJhz1mpBhU13CCXapeAn1yim5yHGfOwvJMaJThwWWIRPoDFZUO8dk5nJtv4g+G6NNxAk8k9D2UXA63WkWQ10DWMU6e55GpBtU7a3ird9CHPdTp87S3KhTe8jD957+MmUnR39hCnD3FsGNjJeP09xskl5ep3lxnwmnSIsIt5YnFRFr7XXLL8wTNBnI8gd1oI4Uu6fkxmrf3Of3oIu4wZLC3iXL+PG6rTa8TYLa6rD+7hrlYIpE06FbruH7I1HKJXmNArzJge6uDlTQRgghv6BMEIaIkMGw7CAI4rSFiTCd5JgleBT1hUZjPs/LyBslCDAYBYd/BlUQIQvZaLg/N6cRG00T9LmIQECo6I2+5D6daRR8ZIZIUBEGkfHENuzags1Nl76DDwoV5ePkW45qD4lcQ7B32d9r0zAQsL9CZfCvPCSKJsQzZY3Fia1eIEbDzpdconZuBYI3OVpf8okpnp4uZN1BiKpIs0rq7g2JViE2Oo2ViCJFAa20XPaYxfPWrxM4+gmIWCJplZEMnr4g8PKaT1iZYzFuEr77I774psr+zhTdxAik1yi//m//AV597mWe+8nkSicTXuUscOvSX32H4/Svk7t27PHr2PJOWxp9/2xMojT5bFw+4Xh+QECWmsgK+57NZd0nNdPD7Ns3VBvmiSenR8xx88Wl8z0cyVF777EWcgY1mKqxeO6DRcan50X99LwlIKyJ9N2T6e7+BZuhxZ2uTn/yxn0Q5voAoCgS317l18zpv/N4fMFFp8MHZUU4OfCr1IVVRICsp/HByFCOuUC0PGJ9MAgL11QZ2N+T9R6d4+dJ19rebTCyNkj6xiNfvkVycxq5U0WMSomWipy0mR3IkT54krUg4e1vETp5HlMEeW0bstRlmJxhEEYbvILR20ftVRC9PT0vxAz/8I3zPux7h7e96N/VP/wk/MzrKkW95N+HOCu72Jvd/0yPsPP8mMUMivTBCZ30fbSxLaWmExNkzJL/2Gnpjh56rkBop4PZ79EdP0MNiZH4E196lN+wz3Nii9NhDXPrdz9KbX2LBrRDTI6TVyxybSTI/myHYvoP56DvwOg0OxCRZKY4aLyDWDlDTCaxcgv3MLNrKdWQjg0wIrSqC55O/cBJzcpr9V95k9EMfYnD1NaSmRU7REONJuvtN1HwOadgldv/DiLKAUt1ELM0Qv/8CojMgah5AFNHfW2Xvq1cRL8yh5ztI2RKSFKGl4+SWJwhsFyOXAgQCx8d1hgS9A+TSAqEdoFgW1e0apfFJ6mqKTDFBV07SL28T15KE1hgOPn3bRzUNFEHAC3w0QaEWGyFhDEjYdbpKEi9tEG/sI8Y0pPWLiJGLnB8hUk3IjoPTRRAVkEQ8QUFo1glCGcw0mizgRxKaLCPoBsTzRK0ykqqRPnMMNIvwYA2/0iOzd5eSOMVgdxfXC0mOjBOvvs5aegnZb1N57RIjCxMUUgZX/Cz5Mw8wVV9n58oN2oMC8YxJbNBGsPvIuow1OwW+i1tvE9kqYbdDc2UHYX0HNZVCXDsgPj9FQolwVu+SnB1h0OwT1MpEpRROp0dmZpqN51vEUy0y4wnE2UfYef33qU1qREGabNBCs/s0UqMs3D9DNwrxqxXUkTEOXniK7Nk5/MEAWY0higG1N9ewjh3HHMkTxQto2TaF4xJRv8XUw/O8+qUbPPz+4/iOj2yHWEWL9Lll7NXblF2JUl5BTZpoaR9Lh1gpTf/qLkbaQJBhUOkQ9rogCciGjpUziVkajeqQ8XELcySFcdBhNKHhBBEH1S5qwkLMjxEOewilGaTmPt1rV3C21lEnZ3FqNezmEHvg0jzoU9loE2xUSUwk2Lu8w/y7z6EdOcnMRI/O668g9jvI27vcfWkd652P0okn2Rw7gxiFqG/PEfZrpM+DcHkd3w9ITufo7dSJj2cZe/QIURAgyhJerYYcM5A0FUmU8V0bv1whev1ZtMUmimngxWaxQwlD9Fku6BRT43z1lz7L297xjdxOgiMaXL16G3nx/Vy59TWKE3O8+fqLLCwsfJ26xKFDfzUcht+/Ir7whS/w4fe+jw9Oj/OzT95HZ6PJ3csHiH7A7FgcKwiJp3RqO12uNWz6z21xvBQjMBSMQhJ3fwdBEpBEmdqNbTRdZGelyUrNwYig9b8JvgApTUI5Nsf8ex8he+YEApB+21t58Y03yHzpq7g723y6XeXbRkb46aPjuGmL+IhFa6NF24JL/T4/lRvH8UJERWJkNk3g+tR3uyRSKmtXdnjLDzzM/Ru7vNHvcv/j99PbrRJ02kiFMRDewBwroKUSaMsxjHNvIYpCBKeP32rgtboIR88haRo7boGipOKGEYohIY7MI2ZG6EYKf/sHf4Tv+I7v4D3LE3Svvs6nfv0LHBlN095sEm7ukz1zhN5Bi7m3LdO9u0lvq0JiMocgSgQJk+/+1d/jW4/OYxw/S6Rm6Tz3GYa1Ieq7nuTg8jXak5OM7n0BjhuY2ThWyuCNV+5yvR1n5tKr/E8/+S4Uelz4e9+P8OZrVHcrJD70Qfz4LKlEEiXwaEyfJ5fZxhyZ5L6JOahfxZ49SVA9QDxyCvXUQ/S6HcQwoqWYKKldpHQePZtGTOcJG1VaK+vET57FrW+hJtLIwxbK3FEiQ0Zo7CBOLDKwh2jpElF9F9XcJL88hqiKBF6AMZJGyyQQZJmYpKOUJggOtpB0hcZWjdjkLNp4ip3IJDb3AJLfZ1+ZIrAUfM8lL8tUAxmykyiChGs7JDUJT1LBC+lHAhICWuQgCBKhpGDHctj9HlgxtMw4/etXiOcSRIMe0cgsohknFEUiIwGejxAFDCMZLVMi+453IpomwfoQRRWIrBSCHxBGIYIkI2RGiNpVIisD7hB/7SbpkSzTYouoskY8lcQv7yCnMpSSBvrqdXIxh8SROaTI4VjnGtXcIwy0OMODFiOPjSAYFnI/DbpOZOWR1i6z8cJNCvMl5EwBaWQMcWSawbVLaNkEbrvHnd/+c458/zcjOD3CSMC5fR0jrWNv7yIsnUIdH+f4d2Wx6y2CRpm2c4TkhYfZfepphlaa0eUx4s0KtdIMe3frjJ2aoXfQQbJF4ovj2HsV6jd2yZ2c4vbTq+SPT5HIGRw8fw0toTJs9xGdPqqhYhZTzE1nsOsdoiAilYvh9VzEjk16aYKpSh8jqeN3XEpnJhnWW/RrvXtzpDUZURLQ4gq1N64TKyWJBEgemcK608SwbYysyaDcRZYlzLjK+Ufm2NlooE3O4afGkTr7BM4QQVZRMylEVYdeDaKQ0oNLVK5tU60OMZ2AnXIft9xHFgQKR0YQ95tYk2OY0xOYo6MMt9fIJGSMtRtMehX06Vm6Zo7tjk4jNY999H6Uc02EXpU4fQzvCqIkI8kSw04Po5RF0XVCx0ZOp0jMhNitLrGRPL2NDYbbO2SfeBfSoIWhePc+5dDzlMyIh//aR1gPR/mpCY1/vWmSbfsMOm0G0hO4e6+zePQEn//Mp3jyySe/Ps3i0KG/Ag7D719yURTxM//gZ/iln/9n/HBxnLcni/R225SvltEVkYn7Rwk9n0Glj6QqjJyIMX+zwt29DsfyBqfesUB8PEd/t0ztdpnNtQZb5S61gYfnwjCE4V+8V2E0i3HmGFPnjmKeWKA/GGJYMfY+9VW2Pvk0dbfBx50qJ1IJ5lWDf/XAaeJxHUEXCIY+dtvmT3b2uGUP+NnlJeSKQ0IWSY4niYKQ4cAnxEaURLSERme3xSnV5PfbByiZHBoqm597BWP6JcIIwnYXIbbIYGUdc9jCPvIYZr+KFyuiyhF9SUdBQ1RCpCggE3QRIxEnknBCie/+wR/huz/6US54bQbtFN0bN1G7NpHgQ7fGoBchr+9Tu7pGZrZAd7tJ6AdY02NsNgf8vU99ke/7nu/jgWwCv9lg2zCZHJ/mzlN/yliuyFiuSGfDRp2awoqpaNOjCKHPmbeeZLrVwXzbGdLnz+B7AVHoo59YRl0AOfBpjcwQDrqY2Iw6LbybLyM8+Z14syfRe1Wk5Dh26BNIBjVJJrJipIUAT1RIvvv9uMMe4fRxgihCbDdwZk6TmBpBWhcISotE+YCoX0GSZMJMgdCxkWIpkGX8bhtESB+fR1YE+tU6kqVh7x9g3f8Yat6/N8PUs++tsX34/bjZDF59H6wMoiQgSwKjBQW9V6Ghp7CjIaYkEIXgCwJuJDAIBBRRYKPlUoir1G0fKw4JTQRPIpINRoIG13oe6v4e0fxpwoQORgIpVYLWHoIexxM1xIMVpGQadTjA8yNiI5OIvTqdvW2SuTz9a1dhdBpBN1D2tvECGEyeIk3A3lMvUnj4fjI9EdGK07tyiWBiCin00M/eR86u0ooVSfmbqPt3cZNZ9HSBVGeX0B+y9KEnEM0YgefQ8EXk/TJ3uxoT42dInrWQsgbO7gbm+CR6Vkc+cw57ax3ZsoiV4gxu3yCxNI/Xt9FMEWdrlyBWwq/u0PN6iJqClkrRYxw97DP6yFkS3R1qbQen2mVuKUVNENBPLeFV90gtH2GwU0YvJnAEHy2tIyoCqBLRoE9ipkTrokTUrDHc7VJcHqNfbuPtVMjOFtDTJruvX+boNx4BXaOzsoMnJglUheTZU7Reu8zd52+RjBvIuky9PmTisaMMmz1EJ8JtD0ktTKCmUwzLNXqEnPuWx3nmN7/M8sNzeH7ATCbG/De9m5FqCylbwFNNhMwY7K4hTBzDTGTwk6Modhd1qkPSt0kfXSG7tMGtz1zk+laHXEwm9ELufO02miIiKzfRR3PMvevekpfi+UWcWh1/0Ad7QCavkJxIUv7KVxBiMZqSRT83wkZ6Cu8bTpEYtkjbLWLhGoqmIWoydtNBqDbRClncbh8kCTUVRzY0/PVbKMM+6uKD2Kp5b1GI22bhycf4gUabmD7DN+9F/KmmsXH7DvtRgC3cTzaUefd73s/P/IO/z8c+9jEEQfh6tpBDh/5SOgy/f4kNBgPe98S7ufTyi/xkYoJCV+aZeoXH/ABZk8jPpjATOkEYEkvHSMxPYC3OEPvTr2K9tkt6OoWZMukfNBnU+rz55gEbB33q3v/zlDc5M0bhgZMULpwkPl7AXdvk9ksXef7jf8B3f9/3s/fMRS79zqf4bXaIgI8k8nzH7CRpRUHTZDzfo321g6SK/P7ODquOzY+fWuJXb95lwjL54XPHSU8V6FfbqCmR6F6PJj7w2b+ygzYdJ+HE2P/S01SaEAUha3/+EoIskhpP4O7vYxw9Qf/mDfRkCTSVenwMxRCwQpeKLzJpeEh2i8jKYTsurd0Vvu/v/gw/8D3fyzIDys+9QP/aNSJ7iJy2uLvSYNB+Bn9ygvlWg1gphaTIaHGNlWSWn//9z9EKI375+76NW194BeOHPkLbyDGRsRDLPRRDI6H77KyvUZgaoTK5TOIrn8N8+FHSpshDH7ofu2ujxQ1ETcWr17FKMaJhG3FmGaG6jRuJ6KFHpGn0BzKa5+G88Tz+0ftQTAvJbdJyIhQhYqcVMpnSqXXq5FIqgmLRiDRMv48euSgTc5jXrtC7/QqcOocmK7zQ1DidmSUZdgnlFLYYsV4fcNyUcLe38B0wpkZQ8lma154i+8HzeLWnCGr7dNf3SBxdwC/MomdterKErSYYZC2yukTPC6h7MiVLIqWAZUSEnkhS8vBDF1+UUQ2NzbZL3pTQFBHHDxm3BBRJJBIEFKdDFOpE1W2y3QCpNIqyexcpcQKvXccdX0CLZRBDD7HXYrixSqAmMVM6cn6MYDggMpJ0N6pYxwM8M0G0vY51bJleaQHna19AmlpGkBTMhSNoY5OceG8eP5SIZ4oMr1/GPH4SyjsIyQxWIcNwK07Ua0Bti71hnNL9p4kaGyjJDJ07N4hlTNRYln6kIl18BW95gczRo5jCgFXbJL27gUiAbBloDz2JareILS1x8MzrGI06npzASMYQSjkC30e3kvTWtwj7bczREXZfWWfisSMEkofg+WxfXWXyQxdY/fxLSB+aYnj8DMVymuH2KsnFeRBC+ptVErMT7N2tMffwIkG3i9cfkpgbZ9B2KJycorVTA99DTuhUX92gdGGRxKhFfb2M17dh6CApAfnledqrB+xe2iI+mcHt2+ipFImxBGraghAQBczRNOtPXWPsvhnaqwcMd1pY87NMnp3E7TlohoKcSOK3GySPLSEIAqrTJjBSRLkxRFEgikLEfgcEH3lklrC6gZLLM/7eGYJeD+HSBrWGTToKqaw18TWZc++9j+b6NgfPXsEczRIfy6DmM/Q3donUPYxuF21skuxDDzK8cxMaFaSr1xifLSJOTdKU47Tzk2xkZrGCIalOGUuxCRtNlEySKIJgYKNmsyhxk0hQwB4iOE0U3cQGwkgEAooJA0EzeM+STjGh88cpk4t2n6os0+IscnaSn/v5f8HTz73MFz/7SUzT/Dp1kUOH/nI6DL9/SW1tbXHhxGliQcB3CSWivsBG4COIAkZMwZrNEAUhg0qP+HQGLR0n/eB9DLd3wQ9YOJpF1CS8gc3BlR26dZfdgx6NQCB9dJaJCydJP7CMlozTvXyTrU98meDWTT7f2KcZ+PzQd34XIRI3fvfT1ASb83Kcf7S0iGgHxFSVCIHQC6jfbaLEFAI34KFCjv985watW7f40dIEzw46vLJV4T0Lo+i5BE7XxohpOL0h4cCl70bsBwYLhTSR7yCVK0i6THIsQX21iiyniYQAv9nAnDuKcLCGUBinFDOw5SKvXrzJ0888x7UrbyCGPpGoQOAy6Hb5iQ+/j5HVVZor1zE0kcGdVbr7HcbSGjvdPmMTKby0QmY8g3/uPv7gay/z5Rcucaw0wo9/63tItBwKR09SfmGVZidkOF9icn0dJVZgZCpF/eVLTJw7QmC3CT71KmRk1E6ZwJFQEkn8dpPenR30qTlErYVkpbEHQxTXYZgeR3b79EQTNfKx4nHCs4/j3byM/flPE5xaIJg4Aq98ieDoCWZ29glTF1ipu6RiPqzfoB/FSM2OIN5+GU6/hVg4YKjIaMVp1Mgna8VYtb3/B3v/GWVZmt51or/9br/38S68y0jvKjOrKquqq8t2dVe3utXyEkICSSAkIRjMwMKtYYa5I+DOGiTEwKwrLiCEJEBCI69ute+uqq7qsllZVekzI8P7ON5s/773Q4BYzHDnfrpyk7+vJ/Z645x1Ip7/fvb/+T9M+A3MWLIbKjZ2Wzw0kWGdu0DgeeieR6bnqF04wej6VdzHnqH1lS9RWJwCy8G2dSjMI5oDylkP0y1jklERGY5r4QjByKshNIU0DSKp4Vjm4ZpZ3SHJJKlSgCLNJJvdmJNVB31wwAiT7O3XKFQcGjLFiCTawnF020N297B6e4Rjx3Bby4SagzW7iOo2EXMn6e9sEW7u4508QeHT30XW3yDKN+DkEaK9dVZVkZNPPUOvUCN1dCpPP4fW2SUVFnLjPokwKJ4/i16sMLp+FSPnY4R9Cg9fhvIE8f0b8MEK3bdepzRVpnftfdL+CNxZCpUx3O46uWcv4siQQW0Cd+M9ShNV3JNHEMMW0b2bGOMzpNo84dc/Q/2x8ySbS4icDnYZe+4Y3Tdfwzt6AmdyEp0qwjIYf/w0ZrVIsLFD/tRJJq5cx/JcGiemGfXbdPwCpUsXuftPvgEK3OkGaCnKqTD/6Yt4vkbv7jLh9g5Wrc4Hv/sSp188y9atPY5ensEZG0N7skj/zhLS0Fm/toFHSmG6jkolhZqHKI9TvncXIRSFE5NEgyFmlJJlOrnZOna1TrixhlexiQ7amJ7J2Y+cJGvtM3dmit5WFyIDu1zEnjmKXqyhuYf51XprHaEBUhKvL5N5FVR9As1y0afOoE+eQmUpY0+usXN1jfJMiVFrwCiDY5emmP+B72QstUg219H2lol6PXTXJu6PYGsXXSUYxSLO+Q9jFCqoN19hY+MOVsGlOj0ip6WcKEwQ1yfZvPIme2aOrZNPYSYRdRGSryT4BYu028eZP4pm2Ye2GSuPSBNcwwBhMJAGkVOmxpBK1uXJ6SLmU0dx0ohrt5e4fVMRomEc/zSvvvUVxmePce3KN5idnf2DLisPeMAfGx6I3z+GvPzyy3ziIx/lIeHynXaDtSxhKBVjrsaR8TymZ+KPFRi1+qj48BqpJBtffp14c5Ph3pAwiGmv99EMQTxK2KpPUnv+BU5fPocmNJpvXuPmv/o1hu/dZpAP+Wqrha4ER/B55vRFJj/5LG/+jX9MXmZ8qlrj6LEy+ZrL1rUDajWPeBAR9WOSNMN1Xay8ycWiw6unxhiu9fHLHo7K8Vu7+3zKMIh2Oqxe38UXGqWFKrqhI6Xif715j598/BzZMEIYgqgb09vrI0cp7kSZrsyh39/AKbns14/x0mvX+fLXXmZlt8XssZN8z/OP8ze/6wWMsXnag5Bxemy/fZVoZZnO7XusvnOfsUmfpBMSZbDrZKx4ivthj1tv3CO8auBd2+bbPvI8v/JNz5OsLFF46qPsLa3wyk/+GwoPX6R75W0q584h77xFsxvT3x/SfX8Vb2Eas2FQbQjcgke2epdOu4WRyzFY3aRy8SzRzjaDrV2siRlC3cEyLZqvfJ3c8ZOE3Zh4cgyRtzBch9aN67jVEqP3ugS3ljFPnaS1tkltdhIv2gOjj3z7CsbULINvXMHUJkhLY4z6EfbMcTzNQvMLDBPJuKVzazeipkeY27dY1ed55kgJbe8OmqkjdIVozBLdv41TK6MdrJKuufQ+uEX16BTBrWvkHv0woVXCLQrQFJW4idJNpNBxLYdeohjFOgVTsTXM8Akxci67QUzRh9miTaIUC25MqFwiy0KPRxgywgtGxFqEmHsEMeigmTqanWMkbKzZ47SNPHmZsZX55JM+lu8hXYdUacTCoj93HG9tiU2tTFA8x1zVZyQV18UEZ+0AbfIMZnroETbdEpmmQ5Zh1gcknQGqMo0Kmii/iFYaR8gYclVUropz7CyTcycYfv1zGNOP4po5dF2h6TrR5grW7HG8zj56qUJ6+wqZo5OP9ohdC2Owjxj2CPb3kJUx1ice4ujOFQzHRcUhhlBkUmLmfISfp/3F1xh//ASkCZVjEyih07l+C71TYPHFJ8h6bXJFmymtx22jQm8YMvH4GcgS1r78HpPPPsL+1ZsUJ4psvnIXb6JC6eJZBsrhkb+xSLZ+mxMfPYsWB+ilEl4cYZ5b5PXfeodcxScNA6qnpzAdnd6t+5iVErWT45i+Tzoccef9FmM1j+6dVQbNEf5EFc0xqZ+eRkpJNgipPbRA//4Kt79+l/p0GTtnsr7So9bZh2oVDAuiEVg2aWkGGfRICw2GN26ibt4jf2ID47FPIC0LPRrinjhLfv4d9u5voqMxVnNY/NSzaFNzuHs7mM99DD1LkK/+Nu0330LYJukopHN3G6NQwoxC9FIZ9+hJlLiKVfQYbR7g1suoLMOxbRaOzrOgG6Qy5WB3xIFd5p44y100alafaX+MajYkrB/Dj7vo6QgnC0DT8J0S+0NBznFw4iG+7HBhso567iy5epliMcfKvQ021zZg8eMMN99gfvEEL33lCzz11FN/sMXlAQ/4Y8ID8fvHCKUUP/mTP8nf+Rt/k+9w6pwjR5wpYuB0zuTITB7PtxntjqidtfAaedJBwKjVp/PuJu2tPrZvMBilIDWCyUnsxy/hPnaeUhCx+/r7XP1HP0f7xn2mbMVakPIqLS4GPp9UdSI0hOfw0F/5U9z9xc/gbG/z2KUxxo/Wad/ap7feJx4laELDcE2GewFuzSM36RN1IsJ+jGnpXNlq8bXNVQIdfvDD5wnbPZxynspUgGsZmI7OIEi44kmeOzmHmUq8qSqGa9GhReFIgyhnoWuCuNXkZr/Hv/pf/g2VyRmeePoZ/trf+NvMZy3ayxvYoya9l5YJD34bf36cji64/b9/leOPzHD35iYzYx6//sEKb+oBuoK5iSrnnzzHmekS3/2QxHVcRqefQnz+M2zfSEklKNPBrpRxfQu530aPD5jqr2I8+TSD9RbV2XnKlx7nys/9Eo9+7DQqyRDVAuHOHqZvQxRBJpHCREQBBinp7jpilCDyLpbt4Pf32H7lDXLzJaJcGVGbpnjyKJntousuceMElWQH25B0Bgmmo1EzE2DIaO0u0wtVRLlOUj1Kb+U+YzkXbfI4WTRC1wzSWDJf9ghJyZdqXHIN3NYGctQh67fRRiFJPMIt2fRv3yU/WSGL+lTOzcHEApbdJNYdzHiAGXVRgy6654FukObrZApGUUogNXShoes6g8zGUQa2Da6uEUhoiJBI2oSZpGxpmOEAubdOtr1O7sQ5uv0eTmuLbO4M5sY17NIYHLmAvnYHylVs08DqtdFyBdTBLnoUkG8coThoYdRL1Fb26TkSGxOnUKFi+xhOgkmEvX4dWZ1iz66hWVWqOUkUjvBKMcrykdv30CsNVL6KGrZRnX2UZqD2NxHh4DDXOI6xpmaQe6sEw5B0fQ3b9xD1WWR5goYGmTJBJnTfeB29XAKZw/jgHWrHZhBuHm1iEbtWJg0jpK6T9YdkwxFJr0v98imc048S33mLYHMTPVfAMHWcuRnMQol4bYkkTLF2bsLD82x8cJ0TR4+TbN2nMDeG6nWId5pkSUz5xCxOo0jWa6GFKXa1ws5Wh/LRcWS/h/QbZINl0kHE2Uem+M1fvc60b1J+b5XxRxaQmo7pmsjYxSx53PnGEjOXz5MTA9x6EbsypPn+El6tAEKDDOJBSDIK2H79LtpgRLijWO+lTMwV6N68TX32BCr8j9F4WYpII9A09FOP4c+dwUgizKiDUjGaNAl0G782xeyP/GmSf/3v6Ny4f+jLtWwMYKgZmE7uMPnjsY9TqEyz9XO/gFM0aN7exxsrYa/fQUsDDNvg5A9+ktbrbzHoBwhhYjf3sQwTffoYGDZWb5/x+QKTaUS8s0lrFNF0qnywPUDJjDrrTJU8KsEeCg1RHsOIR4w5OWQaE1tFhCZxs5TjNQvjwgzPn2zwlbfu8NlXPPbX1ojMZ0j3rvP0s8/z0z/1j/jLf/kv/4HWmQc84I8DD8TvHxPiOOY7v+U7+PLnPsvfKs9y0nMxNcFOJ+TShEfD0bEMnd7eAMc1Ge60ifsRMssY7QV0d4fopkZoFFAvPIT1xCVs16b56ru8///453Rur/wX53WylC9zwCfNGtXYoAOUDZj+4W9nuLXPFz77O+TzGkWjxP005Ndv3OGHJycp5S2yOEO3dNyKjZmzAYVKFUkYE2WSX+ru8WOlSc6eGGPy2AzBTpeoG1Eaz6OCBKdRZP3KOv/+3gb/9q/8CUSiY3uC4MptDNuhOFVmZOlkGvy/Xn6dfhjzL/7mX6U2XiNLM9o3XkfMTdJ8/S1urTd55513eWiyzNPPXWTz7j5LV5YZK1iINOR/Xtul7hv8s6ceI1ct4FXzFE4tEu032f7GbYapRrz2Wxx57Aj9jT3Wb24ypSX0c3Vmzh7BvvAY2aufJV1bZjfaRF58gsbZI0Sbq5y6vIgwLfzxPMKyyB+bQy+NkeZKFMbv07q2jC4k1RdeQGYSbbJMcO8Wyi1i1Wscf+o8WtglSXU2f+O3qD18CuvoGTovv0ntyFGkMYlXSslGIazfxPY9Ul1hSA27YLP65TcY+54jlCYn0EWKGO6T5eogLEw1YswR6P0mWhqi766jPId00CPtDzEn52gVJ6nur5K19hCzY5BJisePI3sHmAWfME3Qt2+huzlkFtKMbSq+Q2j4oMAyNMJYIaWgHyc4mkYvSllpj8iN5ckbkEqDRBj0uy0awRphHGKXy8QzeaxMEuw3sYMmameFUZDhTRRQwsDSMhKp8G6+guy1kYunELYFhTKariOFQCtUKOSaVOcmkVtLZJUxqrpJK9PRhUnbmaCaryN0h52RpB7tY7a3UMUa9Fsow8Iul5D3rqKVKsjWHsGtaziVAkpmEIUQtCFoo1s2+sY61tws7UDH39tE7WyQjvrolTLJICOvx4SjiOLjlzDSDLau4yYHKD+PmjhF8ubnUH6O0fo2dskn6gXoMqF//X3EsEsWJsTtHt7CEeKNDUbhXXIPP4Z94jGM1z6PLWO6pTpaGtFbb1GabzDY3qV4ch67WsFMMzS/hOa4iOX7hBurjAaSqp/Dn5tHWR7O2TPIsEfryk2+9Uc+gtzaOJwXeOgMSveI7t9BNyKC3S6VIxPUj08R3LmB5dloUmIXPXLTdXor2xSOTKJbFuF+B8u3iXb61Ko5pio6E4+dII4EMgthpCPTFFWdQuzcQzMstLEjuHEfgSKpHseQKT1lIZIB0s5hHT/HqR/+Xjpf/C3WX1ti/Te+xLFLj+NML5AKDScLGeTGkWdLTH1nm+6br6O0fYL9DqPPfpWJJy+QRSGm72LVC/Q2Oqy9dIMpleEPAjxNR1THMEwb1WkikZhTc9S212jUi5yuz3KwdJPtcMCV23uYukatVmNqMCSXE+SMBDBIhcMID4uMhqEoNGx6iUP86AleW+5ieR6GlrG9Wqfr1fgrf/1v8fXX3uDf/sLPYVnW73+hecAD/pjwQPz+MWBnZ4fHzj1M3DzgH9SOcHyyhOcZ3L7VpOJZeI5gtx9TSCUyUlSmi2SjBBTs3muTOi7ps49jXL6EOT1GcuUGa//61+i+e5OdIP2vnrkUZ8ziUtF0TlQd3tkN8S+cpv7YOf6nv/TXeWosx0PFPHc6I+T+CnfSgJxlUpotIhOJnbfJNfKkqURlGaWjVVq39kmGKUjFpG8TdiOufPE2hTTFzZmkKdRONcgsk5/a3+IHX3iMyvGTDHe7hBvrrNxp0t4f0mwNMU+P89//ypf4eKXGhyp19n7x1xjNjRObiq/cXuO9Zov7u/vkUmgOhjSGDpt3tnn9lfsUo4xffecGn9na4W89cZajBR/PdzHzDv658yhdJ04OEHPzdG9voO9s0SsbJMGI4vwkIovwZubIfdMnyZsRg94pkk4bs5+Rn5lG7dyFXJHZF58jjEOs+jTtl7/EaNSkfr5MPDmBeaZBcfY0StOQyRB55BzO/gqpYVC2Y0ZX3kA3JZoGwi1QnvQYXb9O8cIlNvoDamFAb/M2MuqS82zS4YjhsIhXn8DBwZyYphxqpJurmETsvH+TxoWTCK+CoafU9Bi9fYCeRiStXRJpIf0ig+s3MOdPY5sWja33kcEAu1oB00bkXLBdhOkSTpxAGzSRUQT5OsLXEPkZlBrSTxVZGmIaBjv9iIWyy1YvwjN1FmyHSd8i6BxQMGISwyVOYszOAV4tR3RvjVYvxDx9lviNzyBVCe3UMbL2LvHGKu7UNCIK6GR59FCSz5VQvRZCaEiviMhX0LeX6dWO0E8k+dokluOj+wV6yqYdpViawhQBo+o8OdNgmIHLiA/airP1aZSmgTCQ9TlUe53+++/jTs9gFT2sSgWVBqhghJZGqDBEyxUgibEXjhK2how2NiieniQcBehBj+H7K2Reg/xYEbnTwunugJcnNR2yYQ97fgzl+djTR1C5EmZpEnn3Cmm7yWAwwtF38SbqWH4RkhGGrSPGagQ7W+ijNmJjmdbKLqXxIQd+Cc0Nyc2NE2zv0bm9zfiHzzPc3EMLh5jjEpUl+PNzdK68S/38UUxLR8mM0Y0rWE9+CntvCW7vMPbIReLwHNrWEmmrjZQdvCOLJJ19ov0mBS3AnpknWruPNTtPevculeMzaKbAqZVxy3mksgi3dzE8k/X9IWqlx7FH5/BmZvAdh/0vf43aJz5JOzPxMomnKZLaHN1EkCmHYrlOX+RwhaSbgo6NbhooFNaRM/hTbzD1cIzSdEZf+W3Kz7yIqVKEruO7Fvupjff4i0wcO0XGLyF7O+xutBj0Q0oNH3+sjF3KUzpSo7fWpHdvi+igjzANrJ1lxNkPkXV3UKMhRmUcZR4Kcy1LqBdz1OtTpCh2Q9jYbfHG9i6e16FUrbMwNY4jE4TlIZOUSJnk9YxImZQdm2966gTvL7VI0ohcPs+m57Ntuvzq73yRt89c5PWvf4WxsbHf52rzgAf88eCB+P0jzttvv80zjz3JPBZ/Z+YY01MFvJpPZ6nN+JjP7tYAmRncbkecKGssTOfJzxZoLXfoNWYw/8KnYG6e6PYy5jfe5OaX36LdGdHLQP1fnFtAcEeP+JfZNi92yszlysz+2HfxOz/3i3xKuXzy7BQ3rmyzn/a5TsCHrQIH7ZjKlEQJQXuzhyEEd5baXHpqFqvgoFD0wogsgyiV+J7OnSu7LORtZs/USOOMVjfkf/jq5/i2owt8+8eeJcEi7XS49bkrvPbBHvujmFZ7h/sfXOevHF9gwc8RqohfvbHJa1eu0BE6/VFIVQh0ofhLJ47yUzfuMtNWvPQ714mF5D9kTS6EJf6b+hxHywXiUcbazX0ePblAMNDYPX6RGUwax1xqEzmyboegExIdhMw9f/ZwkURxmnHLYGQXcP0K/c/+Ck6pQX64TxAHJIVxgrpOqhTNxgmWHi0hvvAF/FDh3Xsf/clPMOxdw5+aY6k9Yi6OyXoDRBag2XnM2SlGt24gTA3bdikcP0LU7ZNt3MUP91jK8tS7Q5yqje56RO0BYamIvH0bTVOoYYd7OwEn9ZSVyGLC19n/yiuUT25gnnmYxCuD5ZK5RYaDCLF6G2Ha5I6eoLfbw0wHOPUyqZKoQh2lFEwdJ1tfhdoYLc2B3BQVy8fubJI4eUR7i6wxTlmNGCjYHkJrlFDxTLIg4JSvoY+G9D64zeL5BbRRj+DmWxROHIdKEeGbmIsn8e08JhJZKJFbvotIp4lCgXRKyN01qE/Tfe0bGJ5P7vQU9uQsKo5IBxsop4JMJLHh4ugJvbkLhFEfe/IM7UyjZMH9TkK9XkBIicgSBoOE8dtf4/7Mh5l2JMWVNxALDyHjBJmGpE6J7vUblM8eRwGaLhCOiyYEIncouEFDMy1EskF10WbU7NMZP06p1Ef03kYv++iL59HXfgfcHOHuBvrJx9GDHjIaIHUDc2Ie1dlFHqwTGTnS/jrh2gH+qWnsmQWEDmlrHznoo5cryChF003SvV3STgvnxvtkF57h4Or72GmIZRlUL19kuLaOXipTnqpjLR4lXLlDsHofp1oAvc/wfg//+Am6S5tMnNmi/cENqpcvEWxtk47PkZucACSGiskGHVQYYjcadLfXMbKAOLEwK+OY+R1EpUK4dJ9gv0d+psHuO3cxTIlVq3Hu0RnGzi/i+AakETK1MSfq7N9dI+iN0Co5/KMzGMN9YlHFydfRbMFGK8XQoJbT8HQNQ+j0pY5hZZiPfwyx8+/Iz08SDiTSsgCFkhlOElEwTYapS746xuz3fid7v/JviG8fsPXGPYZTBbypLsWChV3NU68W2Hz5Frqu07p6k8rJWbTNuzAYkg57GJMLGLVJpGlhJCNUlkJ/H90rMVEoUq6PQzhgtzNge3ePV9fXKJYrjE/NYOdLJFLhaiYoiWnAh+drVDybN+8fEEQwjkWp7HLT8Fhbf5nZI8d59aUv88gjj/y+1JoHPOCPEw/E7x9h/vnP/Ax/8cf/Ik9pBb6/Psapy7MgM5q3D1AColByd5Ry1hTYQmOq4aFXi6zXTpB+7CKaBtbND1j7J/8e2eoQKMX+MEVq/1n4aoAAqoZgL5UIYMoWGDg8bU/xV3vL3JABE3/mewi3dlh4+QalGY+/9+oHbMcRzzVq/MT0NOv3e2iZJGpH+NNFBps9uoMY1xCkUcr6G+vcX2rzu1GLWeVydzeAZoijQYbk7XjAV8OQ4F6bv/Ttn+CZJy+hOxaDvR5v/urX2d9o89VRkw1CnuwWeD4rcPNGi99xNnl10OVMrYhTKvGtJxZYuN1laqHMT159l2t39og6MT+l1ihKgw0V8r3FCb718TP0NIsgi/EbHscfGcc7coJRa8hMxSU39wIiGUA9x3BlBT9o07U1XE8wunsD+8hlNBXT0Xzm4i76xQtougVeAZkbYxBKdtM8icwoKJ1NvcqlP/Wn0XyBpcUYQQdVb6CplA2tQjEZULEcOP0kzWFC4/430EwTNIk+Nk2yuwlemeD+Pfyjx9FEQOXUEfR0BJrAP1ZA3bxJ3GxhuoJo6Q4Fc5ytr73J2NkZcgtH2X75G+iWTWXhJLowiJ0iGmAXK8gsRK2+i277FKcLDO8vo5mCOFBgGWilGlquhqylZO1tcm6ZrN8mu/su2uIiyZ1r/K44xreaByil8EyPulPEqplkZKw1B5zThuRqk0yeOopj6yB8YlMhhn0m6hOklo3eP0B3fdTeCvrkwuG2M6+MN1cjvLpLuLqCU5lg/vHTpLaP2V6HQgmlWSTrVxneXkM9+TyZbpEaLu0QMnIcHIxYqEArkIzCiN2BjmUIUAmz8RZWvcaULTHbG/Q6I/T7y1g5n2x7hcqZE4T6gP7yJpopMDRJ4eLDSKeAioeQr6AlIQoNuz7G1q/9DjsDReO8QM40cE5fpvnKaziLJzFLZTK3RCfYoappRKVJ9KiPdArYYZc06KOZBm61iLe4SH59DdsRJK1domYHe2ISM29DrohZqaLiGGdm6nCJyNs34aGn2c/VOeKEEAxQSUYiBMN769QffghMi2wUIaXCnz9CvLlM0B6htzpYhSKje7dxZqZxxqpke2vs//ZvE+cMik9cBt3kyq9/hbOXFik+eh5nZZfhnTsULj6EjAKUktDrELZHqCQhTVIKM0W2lrvkw4TxYw2KRyaJ9luIQo3YKnAw/Sj56+9QdRKEHIHQQQiKK2/jnHmCgcwz5mUcDGP6UUpmaCQaFPUYzTDRS1W8sQbi8osUbY/g1lW0Uh1TT9DH5igUJhiaOiQmmpfHm6hQre/x7nvb3NvoUXV3OP+xYwTtEaUT09TOTJEMRxy8d59kMMBa2qQw06Bw/hKi0EDpAi0YkHbbCF1HdnYR4RBRCLEMAz0ZMJPuMzfhMpqpcr8Tce/WDdA0rPI4zEzQjAV7g5i6bzFT9pALFW7ZBjdikPGI2XPn2HRc2LvGo499iJ/9l/+cH/qhH/oDq0MPeMAfRR6I3z+CpGnKj//Ij/Lz//rn+A57nKdtjyPzJdySw/6tPQzfYnenz6trAyqGRqbBI5+4hPHCh+hNzRPfWML/nc9hrtxjbaOPGWZ0pMREo2TCZvKfzyoYAqkkutAwOYzqrJo6dwYJW1HGn2CKwrlFHn/qSQ7+zk/yHn0ONtu84JSYdGwqts3W2pBK3iKOJJqlk44icnUfzTPZ2xvypa8us9NNWCNglYRP6UWEpqGk4j015L0w4EWxyF/55k9wYnEMQwWsfu0KzV6MZ6b8+jt3eSXtcoIcH6PGdhpzlzbtKEEi+KZCDdqSEzNFZreH5CZM/sfXvsETtTp2W/CtuWk6WcwqEX/h3HnGjy3glHwWX7iMtH3k3jr+8bPovocbDFGDHTQTVL6CmDyGM3kKrbWBeXyALsDTt8inh57Qkp6g8hXC+lFM3aCjuaRJQis1qVUqSBRlS2KFA3JelULc5M10guNGB788ye2eZL4sEIbNgV/HkSN2YqgYLvbkNPHuNgR94kEAM8eQQsAjLxD0hwTtfXwL9GIDZEy4t0v57Cmi/T0697YoVVPkQoPWq1fREgN7bhHLlXT7EYmekdgu49qA+0NYnDtKtnYXIQRaYwqxs4sQEOzs4xVNaDyFng5RImbY7qIGH6A1N4inj+Hsb6JlMY+KLeK+drjl7f1XWL7wNPnOLkenizzUvkHjzGNEmmBb5RhPWmhuDmGbaFOLDJXA2lxCjs9hago0jXR/A3N8Ct0yUE4O+8wpkuEAdAM77yOFi16qEmxu4s7OcX3+GU40b2GOTbLdl5iWoBdL8rbOMIxY6RqMUsl83kRFQz5/N+L7LzQoaAm9+jzx/gEy76HnC4j168RHLrBfWGQ2OSBLJIZjkD9zntH1KwjHZ+nXPsvUd3wHTiox0NBVBppG46PPEL7yDiUxIFdcQOYqjOwio3t3sIpl0ttvU4pD5K23Mc89xcZIsEAbuXYbVRrHFAbD1fvkx6dI/Twy6qAKY4iDJvHuLsax0yTjJ7CGI8zJI2h7KxhHiwhDZ0PGdIsN3EmH+N51woN9iGPMahEVDpF7Ad6J0wyuvMFgeRmv5FI6dZRg/wDNcxkurxBLl6g1JD+3QLUTELV7BHeXcB59jMlLlyCnE/dG1M4dQSYprfffRxYuYk7No7ZXKF06j7u+gpZlbF5ZxSoVyfkGhXNnccoFzGIZfWoRU3eo5CqkTzyNJYeofhsVDNDyVfzJGdKwjxoMqXoeFX3I3bTClJWxE5vkdQ1r1Ea0NvAvPorIFehnGt2Xv87KG0uUZ2oc/ROfwD4yIteYRyUhGBYq32D/zteZmc5xc3vE9d0R4zd2WfzYaaL9FvmFMcL2gL1rO7Rv7qA7+1iOiZfEWElIMpKoravEezs409NEe3t4s/NguWhr11DxANU6gLFZrLk5Foo6pYl5Rp1dNrf2uPHOGpFVIHArDKwavSBGKQ2JYGLMZ0c1oN1k+ux5+vvTjMpT/Jkf+XHeffcqP/VTP3m4XOYBD3jA/08e/KX8EaPVavGxZ57n3o3bfBvjPGH5TNVdnJzNzpVNPlju0E8zmv0U5bssvPg41U8+hZbzMe/f4uDv/WOinQNKDZ9mO4QoI5MwljPpDDO2k//yPF1TLORtWqMEQwMU3Bgk/MeENAzL4uG/8P3c/4Xf5lfXb7FoOfyd6QVGg5DtXszKbsowUyhlMzWTpzxXRlg6e3cO2N0dsXYQsBFJMiTval2+STU4VXFYM2N+J2pzJrb5H46fYWZuDDMY0Hxzm921Nq+9fJeDKOO6GnI16ZIXOrfVgA0CqliMmzaRkDwly6jA4D2tx9s37mI6OgrF99eneP6bn8VfGEM6eZJej2QUkMuG+I0S7vEz6PPHyHJ19NFJiEMyw0SMeqhwhAi7qKhPWmgg/Qaa4yOlwuhuE2+s4suIrjeJau8SNo6RpQlYLqMIUnRMkSF06AxjLGHz0dMN1mKLiUKDwZ5k3akxjc58LubeEIquQ0kX7IwEbg46J55ibLBGbmwKDAN/8gStt97Bf/xD6FEHp7tGyyghlt5EVUPckkfhyCxibBp6A8rnThHtH5B12+Rmx8i27lAq1/E+9CKtKx/gPzNFlg6IkUwnu4QHXZzGJIQjjFwBuzGOfeohit4tdNNCeRVUlqB5ZbLZPNtrOxzNFxh0BsRajJH3mfF9ojRm7StXePSRWTabIxYti8DIcfzyZUqeQdzcppP64MRI0yY3M0fW3qBdWcCTCXo0QHPzCC8H4/Nojodm5ZDby6TVaYSbRxUaxErQv3ONVJdYnoeqTlMcGRxkM3S3+zgHK9TOPoojNCzTYN4Ht2AzSlMKtsHyfsxj4wZO2EIaNm5vmzPDLVJ9gtArk6t0ufa//SzWix9j5/Ij1DVBvHoPYQrs6TlEsU7x+CJy9RZR4TEMy4A4RAV97HKJI5/+BGr7LgJJ781XicIIo1hCdg4wazW0UZ+92yuMT85wf1TnWLBOf2UZ78ljyI076KUx4q0V7IkF0r0UaVpIqbAnJwh2dnAWzyEm50ilIgtDjNmT+LpBpROwYxUxDA1ZGyPn5fAkCEOg6Tpi9gxq5w7OzDThzh5JIJBZRtLtkj++CCWPwcYOYW+A2/fxphqoaIA1OU7eMzCnHOxKFVGuonba6ELDnyyhgh5GoUziedhj42jxkN23b9AepkwXI/zJcZxqFa1Ugd4ATUbI4jhS6IysMtKokPdrDHfvkb39KvlLjyG7bXIHy2Rhinb+cWYsk0zT2ehHTLo27K+jegeQZgiVYHeaJO0ucjTiYHmD7Jc/w9FvCTA7B2hpguE4eHOzlOZqjDZazBcStltw516L2vwO5ZNTh53yXoSm66RhSjiMWH97iSRRTDz3IdRoRHSwR7C+xXBjm7Q3RFg2ljCRB5vIJCUdBngnLhNmGnbao+KUGW9UyFfHubXdQevuY3XXUYNNMCtMFGu8k2SkSnHQDrDLk7h6hjBsgn4P88Sn+af/4ud58513+Oxv/xaVSuX3uyw94AF/5NCUUv9X1s4H/CHi2rVrPPPo4+Qzg2eTAhOGzhHPZHauxGY7wMkkL28PscsF5j/9LHMvPonY2aO6fhNva5Xu8j5bK0NKeZPydJ5wEDNshfRiiR4nbA1TlkYpEsgBA2DK0qi6BqvDhGEK/8fxt+Pf/ynyZ4/wE3/7v+NpvcCLhRLK0Vk+GNJJwNPhRNlD9w3GJ/PUj9XYurXP7Zt7pKnkzkgCsCNG7GoBFdPkwJGcrBb5tskZ2us93KJL3tAYmy2xud7h5Xd36GWKN2ljIvg2r8JXZYf1MOb7CuMEWcpvxvv8xPmzrEYpv9jd47//c3+OxmhEcOca/SilcP4SZz71PH58gKhPI0cddNtj9N5beOceBdshbBzDGB6gKQ21fQvDzqOVasj1O4j5M3S+8BvkP/LNjHQPw3ZZjQQLnoLWDppuMHDrpO1tVGOBvBximiYpOsIwSNOEKE7YCsC3TMazNu+OcixvHnA+2SA9+ShV16QhIrpRRM730YM+92OPvGOSdPeZdRXiYIVk8hRGliJ7HbJhk73f/jz5yQK6mRFt7pFKQXFugixNUTJj/+1bTD17EXN8kiRI6L//Ll6tglYaRxarCMMmzpV5tZ/nUrSKu30T99FnkcvXsScnIU0IN9bxzj58OPiVq6KSCKlbmK1V4uGINI6wcjkGH3yAUymgTy4iZo8R33yLQTcFpbg59RAX9APaOGhjixTvv4m7eBI16BA7BVxdILOIURARFKcZEyH0dhG2jzJMtCRENjcQxQZxt42cPEpXOURSo7W7x/rBgHnR5+RcDQyLzMkRr97kduwRO2XOj7vcTgqEaYpMJJNFm429LufGfV5/8xrl6TEe8QOiTgezuY7pmQzXdrBPnMRIIjZXO/Tvb6LPTzBf0zFPPIze2UTzK2i6DkGPJD9G5BTxsyFIiervIpw8JBFkKbIwTrK5RNxsY7mganOYuiTe2+ful95ElaqYFy8zVcow2/vEB7sIO4+Rc2mmOrWZSeT7r6AMD6NQIOq0saYXIRoi5k+hVm+iHB/L95Fpwt7dW7znL/LcTB5bSLLO3uHj+WCIXqiStvYR+Txq0EdGI/or66g4Bdsksyu4D11EX75Kdy+j/tSjyHaL0c0P8E8cRQsHaF7h8L1L+R+j3zSUJjCqYxBHpKMeulugf+s6b3/mHWp5n8pMgbHnn0U3NDj3LL2DfXLlCntmHU2TxJmGqWtkCnJaRm9jhWLRxS+W4P2vkjlFouOPM5AmtqYIZEbRtfGH+6jrr6N0he56JAd77L/8GsOdNipNWX1/j4e+/QJRkGLYJsUzJ9EMg2h7j8HqJvd++328uQJX3txgdsxn6lid2aeOkwxSJJKVL1xjOEzY2B3yyHOzHPn0szhT04SdAXJ/h3C/SToYMmz1aVw8iVMr0r19nzRMKT32ONSmMdI++uIFsuIYWRRzkBnommAQxQzaB+xvbZLFIXdCl+XQ4TNfuIFfLSJTyf7SdT708Fm++NnPo7KEbPVl6l7Kl7/4ec6ePfv7W5we8IA/Yjzo/P4R4aWXXuJjzz7POfJcpIiOhqvDMFW8fW+fjUBRmaxz5sc/xdQzj7D/7i16P/kvmU7baAWH/b0hWZJRq9qYnkncjynO5BC6xmClQzOU9BOJ0EAqfq+zuxMrduKEvP5/Fr7WdIOZb36af/43/x5/zh9j1nUYxhkfbIW/97M1Q8fydIpjPpv3WvT7MTeW2mz8R5ENh1/CvKHRTXUeKxT58LkZRJhhTk2SMy08R+PdNzbZ3uyyPAp5OeuwTcRpcnyyXmO2mmN86PPK1j6/qHY5qxwWfI/c02f597/2Bf7ZP/2nnD9/DJllDN95nSwaogplvOkZxF6MsF2EBtqgjX3xSdKxBcxhEwOFJjNkkiEHQzQFwp5FTS6iwiEbr19n/MXvx7MtzME+E1GKys2gimOE6DRHMdNxmzSrY6cj9G4brTRFgAsfvEFpYQGz0MBMAg5UjrG8yT+53uaz167wo//tBXKmIHJM8tahOBsaHnUSbh0EnHAk2aCNEhboBmzdI9lZRXUPEP1d0laEOT1FPAjJz46TDvtohkYaa+SOLWBUasRhRtYfYI+NwYVnEBu3CLd3SP0K+aDL4uwjNGSZsOtipkOyuaPIzTvINMOZXwTTQpOSzHSJ9nawiCBLMJIhhBHRsItz6hTZvZvojg2aThCm2GkbrT7JRaOJ5fh8+U7MpycyZBTTCxMcpTEwikgiImHzZi/EjQOK9j7C8hFJDF4FKx6h2TkSp0jqjdHD5HYrZbvdI/fmKwyPnyd3+gTCClDoCJnhzZ5gMhIEyiSVATVPsNvTKHsaBimtwYBU+UwWBUfyKcF7b5NoFmbORPNK4AywPY90a4/xRx+iPlOE3XXs40+heTl0WUY6xcPBN9OGYIjpFSFRIBOEEpAmKDuHCkPo7qNXx3E8HxEO0NIByc4eSmrMX5pjb71DZbiMFYQoK4fSLVKp4Y5P4BTnibIIPclwJhuoJML0bIRQpIUGpkqR1QmMfAl6e2A5VBdPwU7CRmfIgq8wcqVDH20co9IEkcuhVybIogSZQOmJZ1HdA/rX32Pp3dssqJTSbIOciBEKzMlJTEeAaaFkCpaNyJdQpkuWJuhCIMoNsmBA3Gxiz8zDqA/5OqVyjtnHTmHk8xilMrKxQKqAsXlGwkLPEu73MiZzLmUjZqhMImUSF6o4VooKBmTjJwhK41hZjH3vCjnfJV+sETlT6LaNmjtO9N5Lhzc+6/eRQQippLfeJ0gkS1+5SasTMj1b5NYXrmPW8hx58hS6LilO5ShOlzg1TGjeafLuNzZwa3lUkqAbghjBKEg5GMRcfWUDpV7n2Hc+jX/8LKlI0T2HuNslGozQtAxz8Sxms0Ph4ee5+TM/y+ITx2ivblF5vIN59kMYfgFfLxz+3zRDoqkGZrHKzl6Lmd0NjllNpj86wSsrGe9dX0N3SnzTwzN88bOguyVYeJ6Dnas8/OhlvvC53+WZZ575/2NFesAD/mjzQPz+EeCll17io88+z7fZVcrR4Y73qgGdSNEiwToyzUPf8QKNR8+y/fUrvPbX/hFs7fLRU1VM38Ib85FKsnu3jRZLgnbE9EyeJEjQNI0kA1/X2FWQE5B3BFYGa6HEExBI6GbgcJhNP1KwwogXfuzbuPO5l/nRVOCNF2j1IrqR/C9EcsUx2B/EZJs9BqME1QnZGqVk//F1Q4NUwUzqMo5L1IYP3tnG9iw+/sIlmirkg6/f46Ab8Otpix6S0+S4RBFX1xAopC0I+7Bhxvz5h44RvNtho6Txs1dv8z/82A9x9vyxw9Wljot+8hhJ+wC7WASZIAcdFBrK8dHK44h8jdjy0YIeRhoQ+lV6OFRdl0D3MFRGVpjE726z8OFTDIIhlh6jDraI8xN4gxaZU6CX6DTSNkZ9DitsEfrjWElMojTscEBomrSMEunGGvmsR9EvoOcqfPfDk7RnnmUsZ9MKMiqWSYbFSGqQJURSoBAMnRKWZaKHA7wkYHT9LaLdfXJHp8mdmMWbnkWzPcZmFtEc63DgqLmNN7lI5ldR/X1Ur0cap6TdEC9XwnAc8nMVKDfQSTlpD0iXl7Fnj5AEAXq5gWjMoYUBqlhHaiZKk+jtDbSdVUajAdbiIrHSkP0ue7mjbO1q+FOPcqIygz4aEMycxW2us02OqfoCRnjApy5OoW6+iebnUUInKU5w9cpNLvhDxLHz5DyLmqtjYaBZOolRR4+GpLk6ml0kQdHsj7iy3OJaz+DbLzYwPvEx3OE2lm0wEHkQGkbQxS7WWTpoM+aCcE3SVLHZDZjKVqmPj/FcI2F3OGJhegxp2DjnLiPiBMc3UV6FXLGBLJTRggDLFDA2QzZ1jLhQw0kD0tw4orcNbgGpCdI772AdicCyQRdouiDNMkSWktoeWjRA7a0xKM9Rmpkl27yN5uexXQ9zYDE/O4uWJoedvWFAtz2icvYIwi3gXvsq4SjGMg1kMEC3bfTqNNKwSWfPoqsYI4pQox7JxhIqjBlUFjANk/0g5YiTHtp1gj7oOowvoPf2UdEITAtR8hCuD2mIMzHOQirxHYlmGlhWigq7qKiLaMyiyYw0y0AIsuYOSmZouoMcNsnCkP7yKkKTaKUxDMvFKhfZXjrg6JMa7uJRtMYMuuuSeAWi1MDWNQwEEwUTQwgyYZGlEp2U3KiJk6Vk3T0yu0IcJFztSY7ZBYr9LUbvvYn/5HOk1Rn0YQvddoj7A3r3t0jjlL2lJo6jM31+ine+epdemFCwdZa3B+xd3yHuDHFcHRUm+KOIibkio+aQ9+91mXx7jdXWiMXZElaccuxjD/PGv/oq/WZA/NWbuFWXuUoDNepj5lyE65OLJebJh9BGA0ofegGpNFS7x3DngO03lsiChFo4xDl1kZzwkZVx7LCJEbYoWZPIepVqtcKtzQPccI0ffSjj7twcv/t+i3/4z38dABkP0W0PbeIimZXjhY++yJe++PkHAvgBD/j/wgPx+4ecl156iReefZ4/6db4eLnKa1sjxmyNqmWyOjXF0e95kfKpI2x88XVe/fG/jzxoU3EF545XKEzl0IRGGqSYvokwBLqtM9FwMB2d1vqALJY4KO6OEhwdFn2HXE5wfy9i2hFsh4fd4IIAJHQV3KTP1HOXmR2fZPAzv0S17jHsRNxshuRMHZ3DtAgJrPcjuhmcLSracUqvFZAApgYTtiDOFM1E0ZKHqRK2pnAyRaXsoaIRTt7mXjPgINVokfJNHOZazlRMjs0UefPGAQeDhE1jxMREkQ+XKrwxpvHdn/4Qp7/3W9FvvYnYvA2zp1BZjCjWIFeBQgXSFNVvHYpfIBo7gRu3EUCan0CkI+zeFk5xltSvYacBMskIhY1teTjj41g7H2AsnEUVi5SSLrRb2LketUzHba4gJhYJnBLoOkm+jtFchShAHr1AM9J47a/9BJ/8B38dN19ngMnzp1z0EyWEDmg6kYK8kChpsDHSyFsGp8dsSAKEU2Cku0TNTWzbw5ubQ69O4s9dwmIEponMj6GlMYHukqvUSWpHEEohvTxmQyKOauzcWcJ66yWc48fIhn0M00SM+siwj5avEOk+RqmIaO+RCYPw2OPE6/foeR6N0R5eYwynlEdkAf27txmYRexeQLxyjYm6y3vdaU4tjBPtbZJdv8GdRLCtV1g4c47AtigODlh67za1Jx/BcXzWuiEHt1fYujDLqd27TJWPk3cM9H6MNCo4/W2kk0NlFmJvBa9Ypv3aK1w8eYEzepuKO87IKdDTBTIxmMyZRMGIeKQoFnRaI0XDN+jj0gwicpaO6U8gRm2k5lDXE/xRi9V2wvR4GdncJhpomHUJ5TESp4w9FpHm6xhRj9BrECqBG7ZJc+NYfgyaQouGCNdHS4ZQrICmo9Bgfw3GFxFBH0Z95KCPZ+2jqlXQdYQmSOMU1WtjzZ5GC/uQhKT6gGC0h1PKMUwzUDp+zkH2HUR5DGSCPjFHX+TJLB+JjacbGGEESYZZH8fr7lCyJ2nHCq0yCZpGNuoRbgtx4R8AAQAASURBVG7jnvgQWhKTbt1FGCb2+BwyTaAygdnvULBtrPkzqLCPVvYQMgFdR416AMj2HsIwDtcW9/qY5QrK8SEYYpfLWGNjHFy9gWWl5I4sYjkOw0GGZ+chitC8IlYa4kQJqVtnJEEpDVNoWEJh6pJRGJCzBaq1jYZAxSGl/etcmDzHdjrN7lfeYezWNYonFpGajtxfJz7YIw0zNt5YQcUpw70BFB1iOrTDlLwuMByd0yfHab+9ypX3dxhJyROTBbKJPPEoJkglkwWTIErZbYaEwwMefmSSUt1kfqHEjaUO1zsp+m9dAwmN87O4DQO9WOTLv/Q6xvSH+VS+hTu1SLZ1n1Pf9wLLv/IlTN9m7dW7pMOA8ShASkXu8nNgmGj5OmNGBuiUbZ2yVeNndkP2NY2q1+TPPym5OX2Cf/fF66zsdJGZRNgeVA8HX5/7yMf46pe/8EAAP+AB/xUeiN8/xBxaHZ7jO70xvilXBk3jbMnCmWpgfuvHefTsMVZ++yXe+6lfIOkP8TVYzJlM1V3Gp/OoFIJ2gFNzGOwOmThZJR0e9mWtvI22HyBlRqLA0iGn62Qo9jspiQITCIGKgIatsxJkXKWL9G2+4we/D/Hrn8GII5Y2I9Y7Ia0MbEP+XlcXDgWwAKpFk2YQs/wfz5cKDEdnv5OQcNhxLnoGaArdd5icKRK0eqxvdRGaADJsBMuM+KZjDfyRwkrh7ESBeCrH55aX+d//7p9ncPU25y4/x4nLR8nVG2TtBtH0GRzTJBRFHBWDMEE3EeGAzKuijj5MGxclXCzVwRx16LtVTNMh1UycqI9hOeiDJiiFoZuIziZq4SwmoAkLZbkYpgsHq8jiGJ6hUNOnyMIBg1yBYhYR6iajwhwHgwBtJCk5gsWPP49ZHzscTLIc0gziVKcmJFOeTobARlDWFE7JRsYBIkz47P0h1YKP7+hcckysyWlM10EW62jFMdhdIitPgmFBax1HpJCroNwiencLvdxAJhF6dw///KP4zSrp7jJKasg0QTMdVH8HbA8xeQS1t4JUGWurLfJzNtlLv0v6zX+WO4nDWSeHVWkcbuvqDylNnUC06/S/8FWKR8/xeDek/8Zr3Kgd4yFH49bEOT5SHGH31ug7DQgTpo82CK++jZ13Gd7dQT9xHKfokZQcyq5FLu0ReFUclZG5BdTWPeJ+hLWwiB6HtLd26V+oM65gtRcxX8+R98okqaQXSjZHGpHKcTyGM2MevVSQhBm7g4Qnxwzi9+/QmZujoCdktoU+SBnrraLNNqA6Rs9ysAt1nCzACDsoN0+iBLrpooSOmSQoKTGiAcgYqRloTh795GWkpqErBd0mMuyiF+uooEdWW8QYNTH9HCpXgSyB0gTR/gdkWUa6vYd53kB4eYLVXXbdCVJ1nTDJSF2HrN1nWCjh97oQBQxXlvFqc6SmIooT9DtXaE/NIXLjlG2fLIvRk5hapcb+xj5pmmKiSFfvI/IFWL8OjotZn0ZmKSqJ0eIQrVhD+nnMoxfBcGDpHYzZGQ73E0eozv7hcpPaFMJ2yToHmPkqws2B66N6TeyFEqK1iVfNoeIBwfYWD/3o92BqMYzPkQ3bmDJDYmAZOqMsRY+GKDtHP0pJU0nZ0rm91eaRImA5xPkKe/Y0M/qAwu5dbubP8G7hDN/3UHZ4Q7t1m8GtmyRRysordzAtgyhI2O7ElGJJ1RDUqy5elFEeL1I9N0c4GPHOB/skqaLTDSl3w8Okm1RRtjQKQscVsDJMmdoZckTXOXWiTtiPcbMUFaX87m+8x1PrBxz7nmdw6yYX/uz30t24jfmh4ygnjxp0cOaPMvV8h2BriyxZRiMj2Nkl7Y/wjjcREwtI3UTEIeMqRokKecfmuy5OMkoVv3tL56v3YKqzyU/82ad45/YOv/zVW2w1ByB0RHkRZMZzH/koX/3yFx8I4Ac84P/AA/H7h5SXXnqJjz3/Ef5MYZwnnSLtMCUyciz82LcjLz3E/lde5/Uf+wnibv/3rokVhEqx0Ykw1vtMHK+QpRnd9R6FySJJEOFXPDobXdyqQ3N/RCeW2JrC1gQHcYordQzF4cKJ+NCVK4SgmUiuqgErBPyPf/IHEFs7OLdu89W9EB1INbCAVvSf5yfzGsyXLIJIstFL2Yz/8/uTwHY/YW7MI1WKnFSUCha5mkdrt48/UWTryjrCMajWPLyDId+ZVVmyY/7h6j1+6akn6AUZnsr439ZW+Yf/7Y8Tizz1P/VDTBgKszqGMHTEwnkMkYJwEKMOWr6AKTOUMpCGjT51FJnE+DJCmpChge1gkyGCLnvuFOOOREVDBn6DNFWU5AhyRUbFQ5Hgp0Mir0IaxeTcApphkJgWseGTuXX0NKSpWSRSoOkOu5lEZgrDElz409/NUi+j24850tAYxQmBlGiaQclUhDJFM4xDnygKvb3DfqoxpmWMGyapsAjdOprXxazVSbwyhpKklRlCp0w+bCFsH6EbKMPDDNqILEJ2dtBtB+V4VHSJyOeRTRPcHLK5i6jWkRJUZRorbDOIM+xhj9bxJ6gmA0LHZ/nzXyZ+9GlO6y6D4hR2axdNJhi6hIk5xr/zu5C6wJsvMnz/KkfrPrmZx3na8JB+AQ5W0a+/hzhyEv3Mw8Ttl+m+/hrHPZNWmjE5PYl5sItw21iVMl2zgpN2SLwa6fv/HuPskwT5SVTQx/csqiWbnfwitq7QsohBajKIFRv9iOYgwrF1NnohtpTsBTHjvslMTsOREfszR5msVck0DUcGqMYCnl9FmTb69Di+phOigwQpYxLNppMKyraLHXVBGGjDJsLy0ZRCuSU0lYBtodIYlSWoZITmFVFeES3qY6YjRNRH2S7oBkoTaEqhmwJneoFo2Ibd+0g3RziStN9/h/rzL6LnbIz1a7QLDeTZR9Df3mewtIRen0aFQ3BqtA5a5D0Xt3mfVn6B1GkQxilWQeLu7QOwM4gx330Dq1DGPvkoYtQkG/URjnt40xQOUEoh4xhheeDmUGs3II2Rbg6RJofdbL+ACoZo+RrkisTLyzhnLkCWkmIhk4x4ag63vYNz6iydlU2Moo9/7BztxKC3tcN4rYwRDdAyaIs8TrJLobdBPHGaznBIqBm08iVEvkpidFFehU1RQ7zyOUaf/Da0YsTJkk3jibPkdgLi7XWig32C/TZBP2G00aQ8VyEZRawNY44dK7O3P+RY1SUaxWgmYGhUx3wm1/ts7o2434+ZHsTgmli2zm5XoprD37N1vXe/SeU33+HCDzxHfbLG53/pTVQqWQ0V0zf38L5yhYknI448+80k9z5AJTFZv42+cB6NDCEyTM9m4uIs6Sgk2GtjWCaye4A2dYwUA6e7jCxNoMIB0ihxumCQZSHNmRIFS+dLr3R46T/c4Zsvz/C//PlneeX9DX7la7do9jJE9TjAAwH8gAf8VxB/0L/AA/7PHHp8n+NPOVXOKp9daeB/x4vM/NTfoqsZvPaX/2d2fvZX/wvhmwNOVWzOn6wyO10ATcOt++TGcpiuSe3MGDJSCN8g6seMWgFxprAFdFOFRENDw9UFOUdHFxo9BSVD48J0joKhcxSfT8wcpfLCk7z2L3+J3UFEDDz18DhlUyMGIg4XY5RMcA1wEPiWxmAUUzIO77Y8DTxd4+hCicnFKi988ixj00VubPRorXZ5f33Ilz57k+2tIYQpDz17ipyhc2msSJyl/KVHTlM8dZwjpyf5V1GHH3juE1z+1k8xfuYIRmMcs1JDWh4D3WdUaKBlCT3hEpoFRsJHmj5KCFR7FwYHsHMXY28Fp7fLQC8ipMSI+4zsIqZpshtq7JFjNzZILBeZhWCYDHCI9ByBU0FLEwKlE1bn0bKEUM/RS3UipTPCYJhqDKMMDYhTScnSGdMzUt1ixgqZLbvoMqK7u8W8EZAO+6hwQC4L0RXsBBlvvb+E7PWoGhpnB2s0d/c4L5rYMkSNHyFZv4+89TbasEUax9hRF627izRtVJaiDfeRhglSwsE6keGiAUbcJXOKxIsPE0ycIp0/SzRxgjRfI4glWWuPKIFu4wTnazpuOiSeOs5Y2ObCcJnR9jLDKKWfGyd28sQfvEmsdMLyDB13gvZghP7QI2T5GnFuDHP3DkYW0U4MDC2hrft0hY/21MdY7mUYZx5FNsbZPegR5Rrk1AjsIrnmfTB8DF3DPf8Y5sw8uoyhs0t+8RgHiWCtHRIGEYPOAXuDiCjL8E1wDEHFMdjshCx1QuqeTcWFiaILuqBeraB0B8200GQKdg7dgMjMY2qCodIxhIaeJQRGngALM+0jlUDGIXLzLnCYDywNBxF2YNhFRQFKt5CmjRw7TlqfR/aaKE0/fD2OwM6h9Q4QSYiWBhjFErQ3cRaOoRkm9Jt401MszHhUhhs4KiDrtNn1C3hRG3vxBFmUkuzv0LyxQixMfC0i36gjVm5SuPky0cZ9inkH0eriz4xjGjoHI0n+5Bn02ji2ZaGArNcmXLlPsnEXbJ+sc0Bw7QqZ0iAYgqahzZxBaQKVhKR+jaA0SzYcEDWb3GrGrE1cJO0eMDrYIVq+xb5ZozscstzWWKqe4/OlS+wd/TB3VJVBfpIvRuNsGlVkcQItV0I3LXTDJNYd4m4LnxFVB8JMYyJncLVv0pYWTr4Ebp6lnmRZ1An6XXJmhhaH9DcPeP/lO+y8t8P6m0tEqWT/9h4f3G1SdU12mwHhKCUJE0aRYv9uG10pxi+f5NjZOhO+QStR7LVC3GoeyzWYrbnErsFjF8d55HiVMIFvXN+jeWuD8VMT1BsFmrFCAcM4I+4O2HvzNsb+Cu7EJMPrV2l/7jcJ9lrIMMCulDDzOfZubuJN1wj2+9jlPAoNuXoNa/ltVLeF1jtA0wRl2cMWCZZucH7M49mjNb7rhXN8+KMf5ro2wb94JyBXKvNP/puP8AMvniHn+4jqccTkZZ77yEd56aWXfl/r2AMe8IeZB53fP2T8nvD1Gnw0X0E9dRn7Wz5GurnDG3/3n9G9uwbAlG+wGacYwJSrc/54mWLJJT+WR2YZcS8mSyAZxWiuiVMrUr84xc676yilCNsRM7MFsijD3R8Ro5i1TJJUYesavimI0pCFkoMlNHxDcCFnUvzBP8m1L7+M3NkjyeVwAN21iFKFLyBUMGYITp2tsbnZp+QbyEIeJ1JMjdms3Gyz1gkpeSYPP/cw5ZpF8/oq9WMTaDd2We6EDCUMBjH5nAmeQ3O7g2sINkXMUhTy342P07u/yr/t7/HMExd58a/+OI7rkzlzGCQo0ycxHQaZjmsYdB0dKcGzNDoJmLZAxfFh16o4gT5sopw8ODnyg02wTIw0JigdQxeCZgqeoVP2DXphQpTlqVrgGWBlEVoSI5beplybZFiaQ9g+JikHgUGYSabyNqMwRheHA3pFA7pxSpRz6IQZe32duh3w2yt9HvZjvLtvYWcpxuIZsnyD28sbVPM6t7Z7TJ4ZZ7I2Sc7QeUhXaAZ4B/eQUmP4wVsEO01qzz2J7pVwbBOVq5DkxzE7myS5EpmVR4gDNOvw82mnJmNxB2nlMGyH1ge3qJ8/Q2o4WPNn8fodsmIZ45UvYZ3+Lsx0RN+o03jyw7RnT9BwRwykib52l91Cg/FiDWHnaaYW5t33KFRs4u0NepFD9cwRVLtJ2BsyiATvfO5tPvYdHyZUFnoY0MvViZaXEcbz6OUq//h37/PTn7LRGlMQj7AMgZQhMtIwJxfRsgg76BJtbjLwinRiGMUJmxnsJgqzmBKFIZcmHBx0DoYJ056kLlLKchcvBGlWGZpFinGTtnI5lIAaetCGLAZhMFAGnqGRSujqeew0INNMirbNQWZgWlXK5h5y2GHk13D7rcMBsnBEZOUR/TaGTMByQGZQaqAMC9XdJR0FmGIHZAoHPXQ/T2oY4OTR8iW0LEamMWrtBq6ekhkmam8dQ2Qo3SKfjRD5GsVLlxh+8A5ib5WqHCCcFDMJUHMn6X7tG1z9jXdp/MW/yJmJGlZ1nPxej24Y4016KMNCTwZkWcJwZQ2rXCHc3iDqBtg5izQIMdoHaPUpOneXKD0yTvszv0FhbhxjUWAMBxys7WCoHQK3w7WkSu7JU4wSSaPYxciV2VY5mDvDYrKLPDbBZNHiILVwBTy6MIbUYnq6j9ffohENiPwKmkw5+Hf/lupTl0lPXT60U2mCiYKN7RbAMAkuP0vJ0jkYKa52BU+OVum/9Tr7V5ZovrXM0sGI8x+aobfbZ2yujNrp08ib9AYpNhrxMGNpf8BDvkGWSaofukgaDHgyknzm1XVyukY4iijPVelvtpismeSKDk8+doz99dcIM8nG128w+cQJnv3BZ/B/4Wu8drfDdi/izDChOG9AFiPDEWmYEmztEqi3qZ5ZwClVyFbW8cbqjHa6lI5PYng2u195ifzCBLrt4B5ZRIw80ASWmycTPkiYl13y5QnKjs5c1ef6To8oq/OVN1f5yq37fOJ4gZ/+8af5+S/d5OV3D2vLgw7wAx7wn3kgfv8Q8Z88vt/v1nnu2EmcH/wOKOTQfvnXeffz79BNJAbgauDqgroBM67J0bM1xs+M0Vtu49ZyyDQjTTqoJCWLJI2TdQYbHXr3D1hf6VEwdNyiTu1MnfZSi/G8TdgZImOFXzQYtEI0BVXPJEolMlF0kpQTj5+FEwv8xk//E37YKzE74THqhnzh62u/1+3N6ToLRZvFo3UqrsHYqUlyYwX08QmCOyvU5saZXukgLA1rbhr/9CLv/Oob+NN1hinkDI3Zms1Y2WVqpop0LTbfXaPhGvz03jofz9X42nv3+XU54gd/+Ad59vQxylMTjLIIw/Ex0oBIuIfdk0yiWRpDzUUqsFVIqCCUOj4pqtRAqJQIDb27gz7qoCcRWm0CbI9EKUyhmMqZxJmGIxRDFJg2BxiUNB0NhTnYQQqBGPXRSgpdaKyPBDlLI40EuiaZyOmHebtJG7/qsxVqGCrFMUw0XXCjlzCvetRKJQ7kMeTLnyfnFMgv+uRcE7e1zbecrmCLEK+/hVfOQ69Fev8m/Zt3kUGI9/QLWEoR5wuIygRp1IV8DSUVaWmcJAMTReyWcUZdCoNt7sk8ptPA0z3sZEA3FYyFPWzdQNcUo8oMKIX7oeeI0pSWWaaY9oiVyXSyx7pRx6pO4Ac9pFfj5e0mL0zncZrrBIUCSbHIjlFHD0JqxSoJsNqRTOswf6TC0MrT6B+wak2g379O/rHncQQkmsGnJhLs8VnQFCIegdDQFBitVRL9UBBIDBSwUT9OzdFwrBzH3JT7YZ7bBwG9UcqFhuKYmzCjJ2gH65Q8mzVvhqIZoDQNKTJMFePoCqVACR2lW2iGTayZeKZGIiFIJbEU2KZDPhkhdYuCjBC2jeHnUUojs/IgQ2JhY/VapNurqFwBS0tJDraJN9exzz6M7Qmy6hzJ9V9Fnzt62G3WTVQmUZ09NMM9HJrz82j1WQwFxCGmYxMv3yeJFWPDfQb37pF/8lnE1DE8pdA3N3AGewjbQUkJpTrlDz3CkfAt4iuvkU3ZmGMTVBydpUGGlmUIGZClEQgb98xFRHcXGXiEa0sYjz+Dl0+J4wQVxexdXaH48Iewix7B2FHixMKrTPOyHzBTdTnSWWJiwacbhDiWTtvwqecLeKHAqddRWoExqRhqDp4pKERNMtOnTExEjjCKKSiFGQ+QMuVgdZfq5RC9uUe+Pkc3ExQsE6kLZCaZ1EdwsE+uvkCUmKRf/iK7b9+lu3bA1EyRrVbIm1e2aSgwa0VOnExYv93m7P/0V4l/+RfZvLlPVypavYjhxgHW9VuUj82hSUHx6g7NUUIpiDDyDit7I/K2ztRihYM7mxxdKHGw3qffCmlevUXj6cc5cfkeKYqwHVOYKWEVvUOrzOZ9ipcuIawP0C4+TvM3f4XJb3oOc2yS/DCDLESYNsIyIYnQpEJFI0Z3b+PNJ2i9JjJXIZk9h5H2kF6RJJOUvcPkjlOTBe7vD/jIYwus7tb4N69cZ+b6HX7gueM8/9AU//IzedZ4IIAf8ID/xAPx+4eEl156iReee56PGnWe/Z7vxv/E02ivvsHc4D5B2KKRN5G9CIHGQCosQ+PChI8Wga40RnsjNF0QDwLcehGzE2CXXQzPJIsS2qtNoiCkWnFwHQM7ZyN0DV3XqZ8b48bn71KqeUxfnuOVX75K0TfZ7gQs+jYykwQI1Hd9kr1f+TzxsE/BrxC0QiIOkx1cHUDDdU0WL88x9fxlShvb1J68DEEfsz5B4Ftsf+M2F//0c+y99R609ti8qdNXsPzuGkeqLqGpsZgz6Ucpl3/007SyHCXvN7j5xgrfOznDv93b4KQzzr/5B38f/8KH0YY9UsOjlVlMqYQAE6FrKAlFPcPKMlCSoeYy0vM4SqefgVAG+aTDrl5DL1Vpa2WmrZgkkziug1AZBS0BZWDIEFO3GUYwpg0QMqJnjxGlElfTiDY3iarT5I0MkYQEep5uLBGZomrrSKUhlCCWkrbm4AsL28hop4pMKjShM+4qjntttr5xh+GZ8xgf/hZiItw4pVbyke4sZhjjyhHW3jJR7jzarXcxXQvv5Hni3R0czzz0B2o6oZND84qkSYqlEqThYWcBWhohTfewC1lsMJdKYq+MFrbQZMzcIw8xkKBpBkIJujv76J5FV6+SjgRFJ2WzGWDkDNJ37mJ87BRFlXDQHuLVEmbGaqR776MPEwaN47humYqVMCzXiHTB7U6f0uI5EtugGPY5+MLnmT5SZmbSwJqukVgRyjR50m6RHRtHGgaxBC/roZR2mJzQ3iPKHPqlBoZhIU89wtpKj+W796gvnqBsu3iWRtHSOe0Lrm33eWbWxenvk1oesV+gmVmUXR/b1PGCLlLp+HEbabgo00MTAkYdbC0lxSFIM6RS+EKiCYGyLJThYkdDCHoo00XViuSjPjh5dN1GFKo4e1uIUgHiFKoTeJYF7XWkPkNi5LCrDeLNZVQcH6YjBAOyMMMaL6IKdYj74B6u+8ZxUWkItodVzmNOnibduU+0cg/TXUfzSxiTU2Rrt5HFCiJNWHlnjfknz3LyIxdpr+/iHTkLWUh9uMU98vSxKIgUpVn079+lMD2BKlVwx2cJ9r7E2hvXWbh8htHUSQzTovSRj9DDxj/7CP0wIzRzCMPk3KVThzFi03NMFiz6g4zJvQ+gUMKNTI6YFmuJw0B4xBqQKnwRkW7cp17IYzgemVfGHx2gTIsszYhXl2n1RiQrSzSXW0x+3KIlfWpOjAgCLOEgkhHJ9dcxC3dZyNUJVjfo3tmmM0iIbB1faPimRiuU+Hd2qFZcpucKOAcbpEnGQZDy2NkGpXoB3TEYbmzhVo/jVH0un63z7vu7mFafsX5C2E/odEIuFh227+wzOVVkd71PO1Fomkb/9l2idsi5x2eIY8jP1pCZIum20bw8ctBFF5L4nS8yXN9k+Zc/j+HbFKYLBFtdyg8fQQuHFE4ew6kUUEnMaHMb2e+A3kdXCm3QYuBWsEwPTWa4GpQcnZKt0w9TLCPGmiqSPnWWuzcK/I1feJdvvVjl//kjT/PZ16f5978qHgjgBzyAB+L3DwX/yerwzTOn+M6//VfJhiOy//X/zWQuRav7FOYrnM8kwTc26SuFKRVJpnAdE0zFaH9If39I/ViVsDMiHsbkZ2oMttuEneDwcbsJsiWZvTxF1o8wbAPdMKicrGNYBvUjVSYfmkKzbDZGMRujhDgD19CIZcbYc5dB1xl86VVCebhco5LTKdYc3K0RVVMnMnU+/aPPMv2xFzCLOczKDazqGJo5hdIEQkhy4wVUmnLt68uc+5BCs33yrkXlfJ1Tj87z9d95i0SD40+exJmawfDGOfYtTcY//hFGK6v84Klj5Os+3YlzZAhyjUkGCEZS0JcmGQofAyE0lNBpByNypoWWprgqpav52Jo6fOw8TBhpUBOQL+RIRYLQTWIEppaRRDEi6WMZgjiF7QCKOQuFRi7pMOwPERYIS2endAQsycpAMdPcZr7XIZo4Tqa5oCRSU4yijJxtkqiMuq2DTAnCEWbOIZAW9shlZtxCBGuEU5fI6TaxsImlQLcEecdF6GVCodOKDMZPXcZwbIRp45yMQBMow6ItXQx1OOyllE7RNhBZhhImdtTGEObhsgTLw1U9hlIjnwZ0rCq//NJtPnxuhkI+j1i5xdrVVaoPLTBoHKWw9C63G4sUspBma4RZHudD2QGr6wFi8QLN119j4eknWA2mmJqsoC8t8bXNLnq+wvOLJnpvj+7eFsVakVEM8UMfoiYS9HAPT0vpenVaww3mW7vYw4j1qfPkhYXKQpSmgemR6ibZ9hp72Rju5HG6SidTOkfLNh/0Cqy0RjiWRdnRWVrb58KZHL6bI9ZNtK0VvHOXCYTDmOGRCR2hIrA9MreIOdhHmD6Z0NCQKN3EUDGxtBmlkikX9DRGShCZRBmgaRAZJnp62LnF9tEGB2jNHbBMLMdGygRNJojV68jjD6MP2gTNfWSnh+3nsGybtNdFGBaZ6SOHKen2KmapgdJN6O0yunML+9KTiCzEOvUo8fJ1KlrIfnfA5vYdZl98Cn3YRpgmyjChc4DKVyg60WE3OQhgeh7D90C38Y9eRLu2xHaskzdDkq37hMeewEp6OOMNovs3MR7/GOadDYLaIo6WkCzdg3OPkQQd7lChsvcBjuEQU+Agy2MaNrphYAx3Oe15mNNHiEwXgg4svUrZq6MHAfqlp5CAs32LjdoRFuJNkAamjBGej2zvY/ol7oQOA8MhGmR4FxfRXY+FQRuVGKh772CkCn18ht7aOsHGm6QJGCIlP56nfXWb1n5CL0ip5Hyu7Y44d65OFiYsPn2Ug69/Hce3ODpTYO5MncbF41ilEmm7Q5akCMPg5FPH6PdCejtDlpojqq7B2iCjuTPAEhrt9Q7NOGVSN9m+ssbEQym1c/NY1QL56XEkBvHuLnGriVWtkw1baEIQrK5jlwtkyYBor0WruU/QG6GZHzD29OM4dh+BBo6PWRii2S7ZcABxhLz6EtbYJPrxJ2hoGRkmc1ZKaPjc1keMF1yqmeTMWIFXig523uPnf+szfO29dX7kUw/xz/7en+cf/rPcAwH8gP/b80D8/gHzn4Tv933k43z6h3+AW7/4GYIvv8oLl8awinnSQYxMFQerXUxT54Svs9qK0BUUxnIMD0aUFkoYnk1uvMBwu03cjRnoLfau7yFTRXEqh1WwKS7UMD2LSCqckodCUTl3gmDngGK9R5alrH7+Lp4QOLqgZkI/lqTKYOK7XmTr53+TVpCggL0w4UiuwMkpnxwavm8x95EzLLz4FFqxRDIcsPfaVeZmjjBcv48pFFmS4jXyDAIDx9YRQmAlIy599DS5E6cpjOd4wrDYXNqj9sgpkmKNcmUcrfRx6lHA/rOfYjuEgh+ThpID6WIOBXlbMebrZJmGqyWM0gyVSXzHYDPQWbQFZTMhzcDSNTxNossUJTWqcoDSXHxD4xtrARfqMZ7Q0UyBBcRSI9nfIi1MslAqoI32GJl1/P0b6LtNOqtL5C89jC0kSaoYz1kEFCAWVLr3CerzDNs9UgT7aZ5epDNdcpFKA2UgDZ2yAVamE5QmSUrTVDyT3czDi1uHQ/eJRTNI0aIBlXoDubWDnC/SLUxjiYBE93Doo4SGNDxEkmJpEl1PkJqNQGInfYZGAWHmMaI+UkqsgyXi0jRGFrH6mS+w8tinub414MLCgLKe4JVLFJ6ssxRbLBiK7to2M+MNtvohMzKl+vgFsp2bbKkpHnYkLcdEC4aMlwuMbI96d4N5vcjm8hrN/FlErsAZc4mRkHS7fQbdAdOLY2R6lVGm0e8MWM5NEwmFCptUm/cxc/NEhsPIqeH2t4i32jgLJ5noDGhnCfmwRSc3zdx4DdPL0++2GbMT3lo94OhkiWLOomLaEAdk5z6Mch2MTFF1dZTMUEqDaIgOSKeAJhM0TKTQkYaLUIdPW+p6hC4FSjfIhIkR7BAbHqHukUmFq0WHdgmhI3JVlFUgC9qIJERHIYtj6GhoKiGdOAaFLlGUkBOHw5LUJJphIswcdr2DCIZogJKAUugTc+AVSbeaGJqJVa6D0NFNKB2bRlQa6NUG0nTQm5uEq3exhEbt8ceg0ID6JNXWJmY8QAkD4Xk4lkU7SJC+i1ltUKVD/84tHFen99pXEQtnKJRtEqWhbawQLt2Hk4/jZgGeZtKdOktvb49jNKmVSodWI6l4t6XxdO8OmdKREyfQ4iFpp0lx9gTN1GZ40KHhGYjaLKW7HxAdv0SWJnDnKpmtoftlom4L7/wjPHT2UYyvf4a46CPv36H3zhuYWYBbsNCEIli7T7K7g0oTsn5A5dFT2PUqnf0Ry9f2MRR0uwnTBYP5Z87SvbeON17CWm1iVzycqo9XymOOTeEtHCHeuAuGiV6oojs2Z57os/TVe1zdGnDMAV/T+OoXlrBtga8JUqmoVWxu39xl5slFxr7lW8HLoUdDtHyZdP0O3SvvkI1G2LUqdr2BtX2AU/Xo3l0hCxL0gonQNO587Qalc8cINzcoPnSRbNBBWAbG9CJqe5XB9Q/wF4+S3n0PLYkQc6cRpoulUkbCZTpvM5Wz2R9loCk+vFglzCRrS5dYe+8b/N2ffYXnLs7y9//6n+Fnfr7wQAA/4P/WPBC/f4C89NJLfPT5j/BD3/f9PHnqPDt/6x/RXN3nIxcapGFGNkzpbvWxfJNmM2Q8Z5BKmPRNqmWHqB+h6YcLGixfR0YJmiHwGz79jR5eyaXbHJKfKeI3yuiuRdzq4ZR9Sifn0dw87ngV3XWJewPCzpDmwYAzc3nurg9opYpenHL+u59h1O7T/sZVDKHRkCZa1UC3BY7vcu65OlkYMXF8DCyXZP0um29cY+faBqnxWTq3Vzny7Fnidpv83CTrv/w1SjUft14gX3LRnDrepYewHIf5h55n6mAXm5SkfhTLMjGTEUllkjBxaUYRO1aRkJSiZdJPFRmQStA1gdQMBrFGL5SM6YIF/zCuLcLA0jJ8Hdb7GXM5Awp1wjAlJzRutUP+7k//B/7RX/gED08WMYZ9NKkh4hAlU4ydJZzcWYZmhV6q4RTHSSqL2GMzRLUJKqMDkuYOdnWSkVum5Vaw1AEHqc4HfYuKZ/Co3WbHqmEpSaayw2grTTBKYtaHGbZRQQOyJKYVxGhJxFTVZoTF+9fu4Gshj9fGEPPHWL1xk9MXzqF8m1BquGmAsn30eEhJt9CzEaQBXauGpVJElpKZBqmuIa59hbszT3MquEOq6RhpSCvV6K4s8/isz8mChuUY9MwircGQUAr03h7pyVO8+vpNPv3cOVRujGzY5Lo+w6Lq4vQTWqUSBXG4pKE+2kGfKHHx/8Pef0XbluXnfdhvzrny2nmfs08+956bU4Vboaurq7sanQOaDTRAAsyiCMCkSAxSkGWatGkOkzJFeXBAGpJl2ZQpDUMEZVIkTZAiEbrRQFfn6lBduW7dHM49Oey49goz+GG3IJMG/UChoSHwfmOcp7PXetlhfuv//0IQsRbD6Df/GeNzj3Mwf4rde/s8rXbZ8VdRleZQpBTbd2iHHr3uKm+8/gbPPXkRSUFuBQ6JMZogaeHnOdIXmPVVSlVDHO8RNAWvb/WZSyOCrM+Gf0D+xndZ/+BzKLWOxM0muH6MKsaExYjCX0NhEc6ixvvYuImJ6lRGY1WAj8U5jRA+saiopCBzHqWV1JQi92qMrcQTAt+WGKMpowjfliidozwfN7eOLQvk8RbSaVxzDozGSI9YWoI0BKMw2Q4m6eDFNcT4AGUMpsgQhzkiaeCChGBlDf3gXfTeA9TSRxCTfXCO9mOPYcd9fKUQnocwGsKI4LHnwTmIktmDnimR7R4oH3u0hZxboZ5E9EcT7NwlZFSnenCP9MplTJAQXX4ar1bn9oN9WrubuFNPU1s8hxdobOazdHQNe/pZhpvbBGqVmi+w33kJcWKDt5mjiDsc/eaXmPtED2ENwXMfZydzlEmdg82HEEN64iJNk7P7cItJVnBiskdRloSdFuOpx3R8i+7lJ0mvPk7/wQNEqCkOjjl49U2Wnr9AsH4SUQ0I5xoMX7lF82SP5mNPYLIRp6YVfv0GD/cmtKxlEQhqIY21Hla7mRxhkpPO1RnsDMi+8A3O/uE5gs4iR9/9LrWNk0RLi/QeN+jSMfnKLagMdyY5nUBwONQ0OzEb9YDNg4LNUcmVtx7S+0yJ7G0gRrswnSAk+M06Xi0lWjuFPTqYVan7gqjXoRzvsPj8Y9z9599i80Gf+B99hZWNJuL6deobG/jdEJk0kGFM2OvNdOjDCfbd1wmODwkfexY3GqB6LS43BNr3uHaU00181lsxq62YD7x4mbdbMW98+SV+45X7vHnngH/3xz/JcrfGhz/6cX7j1x8VYTzCv3l4RH7/Z8JLL73Exz/yUf5XP/MzfMgGvPof/N9ZDSRXWgGyMkyEQOyPKXKDM9COJf2pZWW9Tn6Q4fuCsBFinWW4OcCUhpMfuYJ+5z7KkyAhbkeoRBHVE4J6jN+sYcqKuNkgOXUanEFPMqr+MXpaoqca5xy1VkywmZHriiCNaX32w7z98/9PpsbR8yVXbY1v5n0uDGo05gX5YcbyC+e5/+p9Tre7bH3vJjsv38CUFS//0stEFhqdGKE14NE7O08436TzzBPQ6CKl5Wj1SXJnKFSMXGljhAAhkE4g4hYDo/CUYrkZMzEOzwvRzPRuARWRLrAqxFcOX/qs1X0mxlHH4YSPZ4YEkwNKuUxbGcRwwHQ0ws2dZi/T6MpxcqnD426baDgib68RTA4QTlN0VxFf+EeIQGBXn8BTcGgTIqnwe0vcm0pW4wbl8XV02kGl0PGgME06suKJ1Sb1coBI51kpcyYi4MHeEecXmpReSJZplqKAsXE0Q3h33zAqKxrzPfacRxAonjo9x7HxeGcv49nsNjeGhkUi5qopqW9xfoQoJihrMEGKDlMs4EuLsQIRpHgScA7XXGBkwEYtLBLPaS48vsadd7f40NNn0UFC7jz6h7t0jnbYOHmGN7/wMnunr/LRZIfg5S2Kpz6C1+xSTkP6D7eYb6ZcuHge7/5r7Kdr1Oo+pt5BVlOSZp3i2RcYvPwtzi43iNodOjR4oV3j+Ntfwbtyla/nDV4Ic/SoT2d5lQVzxKap8XBa0sz3SPMx16J5NpodkqTBK32wGE6kbb75zhabo5wXTrc5c3IDMbrH+gdfpFFXTJwEEeB5TWTRR+kpOIufHSHKCaRdqnQOETWQzuG8GGkNRs1qpEsvwi9G5LKGYha3VcdSBQnCCYwDJT3ydJ5CC4Qf4akIIQRUBTauYdU6Mh9hcUiTI5EIJDKsQZUz6W5QlZpKxcT13ow4NXowOcClLezWTczgEEuAShpweH9mrPMCVBojyjE6m6A6PShHiNYi0k8oEQTFAJxFlBnOj2E6QgazhsC5esze8ZCRiBiODIvtLtLzyesLyPNPYrbvkWzeofbkZeLJA1zvJLYs0e0F+lGd6bggWDrH9MEN8sYJFgb3SaYBFxZWcNESx1c/RKna9GoRURTx6uaIUz3whwM21SJr1jEM57H/+BcJF1cxH/0wm1/5FqtiwPTBDtVzP0zjnS9jhUe4d5eq6jPe36UY5hxe36HbXaW5cYrQbGAKS/3USVS3B66idX6FtJdwxnmMb28SNFvYomKydYg1gvxogu8SglqFzTX93T2mWw+I1zewZUl2/y7JiQ2StTVOn3sc5f8zjm5s8/xKi+HugGJQkhcazxf4niQNJLdfecjaV79G249RC2sI5xDNLumlJ3DFZNaEF0i8RsLdX3uD9EQXvxZRZjlGG7Z2J4Qv30bli5xdW2R0/QaNJ65ijg9Q9SbRqQQRpei33yaseXhpjJsMUFi6/ZuIIKWKW6w1a4xLAMH5Xo3Ylzy+3kZ4gtdf+jJ7xxn/h//6K/zEhy7wMz/9U3zkox/ni48I8CP8G4ZH5Pd/Brz00kt84qMf42f+xJ/gh65t4t+/z8W6x+1hznIasrU3ZieznG8HtHs15i4vsPf5MesXOzRqPseTiqwwVDtjPCXwIx/flwgJQggcgqAeEDVi9FQjPEm82MWPIrwoxGu0yO49IEw9Rg/2GNzcIh/mSCWYW25QjEtiKTACLvyBj2LvP0S/dYNeIFltRRzuG74yGFDvweH1Q+J2xJ2X3mFzd4ys1dn+1k36dw/JQkXHh+UL8xT7I3RREfR6rP7Ej1AtnEG0O2gkTmtqoc9wCqVx5EYxKg2dQDLnVxy7CCFBCah5kuFU04gkB1NL4kOMIJJQCIMhJPYEsZ1yrEPGfoRnNaGtEFoTZLvMI7n/i7/Ir/3332HpP/8vOSgMHz/V4a/+zKdJoxJ1dIdjkTAnfN4wbZoFtK6+n6JWZ1Q5jrKcw6nhiUbOpkgIleBI1rG9DXphiDaaUHlsTR1pGLKXOawIqYsQDu4SL0XMhXCQG7qJ5t2+wS8PadRrNJKAzCkWGyELScDbRyVFlfP6juUj5xp86dohzy8FfOTqMr2wAiFQuJmmOp9goxSlc4wX4PwYv5ygbIVTASDIDCQnnuC8dYwnNUalY2prJC7g2adO0K32GMplDvsTDkeOM62Uey99k6Q3xxNbb1F/6mmm+0d0xJQtEbAcaRZXG8TNFodOgQ5pjXeYzF8lSFqURjDSMKyg9+EaWaNBExi5FaLxEeNLz9P0PUxgeaWMkVs3aW2cYe/2PVQtZm5+ldHEcng4Rp84wf/186/zuY8+RUvN2tleOZ6y0koQoUdaq6M9n0nvLFIovGyb2BZMXYhgSl81qIcpgbN4JsfUl5DlCB138I82EVGCasyhioLK+kg/AF2gTEnqlUirKVVKYQSBMPiAlh6BrZgSU1mH5zQ6TJG6ROEwwkP6CUIJhPDAC/BMjvNDrFCz3FwUIklAOoRQEDdQ+Rjnh+Ac5uAhQgV466egyGB8iLQWGYeU2/fxVk+z/+u/wvxHP47pbSAB4Qy+ABfWcdUUi0T4EcI6KMYI51iK4W3gwcN92n7AYb1HLUmY9Ac0qgmDQQVnLhIriRod47xNqp0dyrPPcKtKSRRslR7Pn7vC8bRg4z0vIgPFslfyG3dHvO/kMjdHgm2bclEU2FqdcLjD6rkl1HTMt37pn+I9/TRBmJI89jQ3tycs9RoEC3OUv/xFNn7oY5id+5TDHHM8oPP807igxn5/wHT7GDHcJrt+RP3Kk3Q/8H6kFBDEyNUL1Gpt9KiPMCWuqGhdPgNSMR3k7L9yi2k/52BvwuLTpwgbNYrhEDudgq1oPXWV7J23qY6P8U9t4C+ucfIP/Qi1l77EpRNLHL55lztfeZd7t/qsrTX5xrVD3v/UEm+9e8i3/uHXeL6W0PmRP4LwA1Rch7iJy0fY/iHVqI/fqNO5sMh454g7b+1zJi/JjyY4Ca8+HFFPPDZeLBjdeEC80MZvdJCtOZw1yPYirccuUW4/wPkB1c03Ye0Mnh/iigGBF3CiVuMwmMnATjZDFNDPNacvbNDtNPjNf/RPMVbz//riO1zZmOOP/4l/m4987ON88QuPCPAj/JuDR+T3dxn/w8T3p370x3jhS69hRiOMr5gUlnrg4QnBa0c5Z9KA3ZGmd9JHSmi3Q1YvL9C/f4yIPa7fGnFhtc78yRZCKfxklvTQOr/G9teuEaQ+Oq/wk5Cg2cBPErzuImIyQFhA5xhT4/D6NsXeiK2bx3R7MZ6E+9tjDipDp91g7pMfQP9f/msuLdbI8oqJsew0NReGCSu//1Pc+W/+KWsXe2y+ss3pjTav/aNvc/5Cl4cCTizV6S7VCWOfoBmiVZOFz3wa7/wTHPlz5EbQJGMaNlGVo07ORCoaniCUikFp8ZXHQe7oRQ5P+oQY9owllD6tcDa11MKAVGh8POeouwJ0Tkc5HAEKgxMethjj4hou7TAaCzrdOrcPp7N2uuKIXm2m+dSdVXwlcdZwspOQ4/N2LrATwyVxRONgi3DhLP2jPcKOTxvDkW3RXVpCYImONxnUV6kqjZ/UGBU5lR/y4NYmD8omG5HD2oRz2QFT2mANN0fQMCXNQLLeDGiIijyfcsIrOfQjXlhv0PQko6MRo7MLrDZTfD0GP0QA2gtwYYzwIignKGtnVcymQvshqhijgiaZFqQHN6kWzhKUU/Z1Sl2UBGeuEpdjJgcjYlcS6CmLaytENcVy1EFFARGKMDCMTYie36CpAupBhT9tYJVH3RXYM5cQJmfgFL5SjCtDVA0prt3kbm8NO7Skgc+pve9hNh5jVAUMteKxbkXsCu4MYr59bZMPP3mJ9uZ1En1ELdvjzcUNVtsJz9VzJpubnDmzQivbJ99YolcLOGXkrFQl8FE2B0+B8vCNRjJECgeqBBmhXIEOO0jnsFIihEMcPoD5ZeRIQhAS2JISH1/nEKYEpsSoEOUsTngURpIokMIR5kOqOCQNJc6BUD4ahbIlmZHUbY4Lktn/vIBSegTFrIYWFVAbb1PVOqAlOA1uVhEuVIjM+nirZ0EK7NwqcusahBEkdYQQqKUNRFyntryADmuYoI598xuk6+s4P8b4IdgJRX2RwEzRaQt58ABhLJ4pETg2v/Ud1i93KLo9tK2I4oA86KEP3uDg/kNW2qC9mP4rL6GevEpr7xpGrFOmdZ6a92mWxzyeaJTqYKdDjINas0ndTFgKFF/fLDkbH3JmrLl1d5eVZ9Z48A/+IWPd5MmLC3yncwo5Ejx5ap72YoBXb7P+hz6HXxxRnX+Sw1/8uyx9/IOok1cIJ5rVH2tj+ofY8RCSJt7aKdyoj6y1Ia7N2vaUIGh0caND2u99D14c4jXmqO/tkz3YJ9ud4FvH8bs7zF1ZIurVGd5+gPIV6VPvx5U5fncREYQ4B+GpC8xXOSIfEL74FEEzpf93voESkgvdmKVLPfJpybWHQ0599VWajz2BaLaw0zFerYHrLOGMIXv9e4QLi3QfO0XQneNrX7zD+uk22aTgPU+t8OqrWxSDgv6th0yPxvTfusn8+9+LnU5mtcXTERgLQtB/+Vuo0Kc5v4iTEpk2MLogMlPmPR+nPBZ9TSNMePdgyvvOdumvNbh+6z3svP06Jh/z5p0D7u0GfPpHf5KPfuzj/PojAvwI/4bgEfn9XcRvEd8PfIjTv/o9gnaIVZLDScVmobm6EHP7MCcAWp7EKEm6kFIMc5JWTDmqOLx1zPGwYK0Rsnx+Dj/28NNZAsH44RFeENA40aUaTwFY+dhTUExwQYjzQ0Z3NgmSgMnOMdVkl5vfeUANR6AcOtccbY/RlWWrMDz1xz6CfucW3WqIa0cEh46dPOfN0PDn3/ME/gdfxP3yb1JfnSN6cw8Vx5w5M0fv6TNcCjzOfPQJgm6X6c42UauFOnmO4NwTZPE8WInnHMZ5ICTjylDKmH5esRz7CCmYS3z2xhUI0PhY60BKlJQcFWZ2+HuKms1BSer5waxcwAmcc9SLA1yYgK7I715HJSHu9rsEV97LyT/5xzhbDLjXXGB/WlH4kvEkJ2ikCBcTVhnDpMdeKZDCcbYZQJnxlX3LV7814MqVIVdXejwuphgZUPMcCqgIqNI5bk4UdV+xlxm0g1tHBaOxR69dZ1RUXD8sGXqOs/pdFlfOIlWd5u5N3NJFvLxA6DHCg9Zwn7l6g2lzjtroPn/8mSZdUaBtiu/HuBmjYpTlCL9NY3gfm5eIjsT6EdYJChHhVwNGZUWkYBLUCfbvUgqBUIZSSiYywvcs/onHiELJohfhhKP0ElaX57B+jMZRXfsurrvBXiHxPEdBSC9uIbwQryzQTpM5H+0sTluS/dsUMgQJYdrgTj9n1J8SB/N0KuilIcPjI8YqZr4Wc3atR6c2pF9Y/NNPoF77AneOSmy+w8r7LvF20uSpkw36fkTVWaceBUyFo2cP0bKOQOJXGU4pnJDge6gyQ1RTbNxBYWdRb4CYHmP9GCskbuU8OIMyJY4A7c/a71ABGI0sRuAbGn5M6cDisM5RCQ9kgDYCFFghCbMjrPTAaGyYIGxBaSOUMEihGGqfRtyaZQoLQRAkVF5KbhV1MrxyTJXO46sjnPAgbiCcxYR1VL2LHe0jijHG86j8OmGY4DcTxPgI2duY5cAOD5FJDbd1C1dO8PwYlSQUjWXE9kOCRYmMklnV91qPauEkWvn4tiApBthaC+9TP0y8fR8WuhhtqQdN2L5OsLTE4wsa5Q5IjyYoD1yrTn/zLvXQw9WaXEotVCXz/V2eqrXg5jWiV1+lvXAJk3cZPfk8C0mL3Tev88HL68hOALUIESSo7AB/dQNnDL4xNE4vE526AGmN2smTmM4K6uAO+d0b+EsnEEIggnCWqqJCpC6QgDUlMmmgjvZRzSYEIbULVwjrTcrs87z7jfucboVUw4z6yUWS1ZOQHSPiGtG5JzFhgt28CUWOVAH+uSdxgwNUOaXnB7xXeuy/fpvlD14mDDVJ5HH5ZIv+gyP63/wanReehyLHtedxZYHZfQhRgkWhsynN82sszqfs3euzvTli6XLKcjPg+lHOys19wnpItttnfPc+Ua9DePE5sBU2n+A1m6jBiINXbpBeuEI4v44VDlGMZkbIOEU6SeWFSKm4NJ+wWA/53taQz3z8Kl9IUjZf+w7l6JhRVvL5mx7PffhH+NjHP8EXPv9rjwjwI/yex6N6498lvPTSS3zsQx/mJzurvPeNB5y/2GXj/SfJS0NuLALYPi44Kh3rccBBaVk/32W8P0FPNboy3PvuJp6A7nzCxRfWibsppjDk/Zy913cY3DokmasR1AKQgvrJZYQUOGMZvH2T6a13OX7nHre/+CZ7r9zl4XfukhhHNdV4oUL6kkDCkbFc2ejS+tj7sF/4TYJIEi8mvKQm/II/4q//mT/Mx/7CH2bNHfK+n/1R6qfWmLu6zsLV0yw+tYaUhtMvnkNKhxkd033uOWpXn8Z/7AXKxhyqmpJ6EHmCwPe+n4EL+5lDW0XuwCHxBcynPiv1kFYkiH1J5aAeKJRwpEqAExR+zESESKfBSUR2iJweI/IMmY8oRMh44RyHfhcvqTO2CtXu4nV7xL5kOfXxgoAjHbIzFby2VzGdTHjrqCSrHIW1zA9vE3mCC/NtVhdaGCGoJwlF1MSqgFrZJzCaCsHQb5MVFaPK8druhADN9QdHLKQ+mXF4UvKRU22mQZ1f2o14cKw5lxSc1bssVkd88e0D/FobhkNsf5diMkANtxFKEQmN831MVWClpAxSJkIR7t/C3Hqdyku43a84Eimuv4cuJwR5HzfNKCcjhDF8Z6fivksxcYfV8V06/duYyYhhbtjOLE5IQs9RlxWBsEgpcYAaHBIsbbDaSmgfv8tkfwstfYqogdMFUmeY11/G37pBf5xzf1TyQHWIlGW0cQXfUxxlJe9ZSfm1vZAiqlMe7pKEIZeCCUp6FGGTpLvA6U6KLxzB/DL5uzc5E2uCasz7zs6R6DHSC4jjiHbg6HiGoVfHIVECRl4T49Qszq4YzcgjDhe1qFRMJTwyI5FVhjc5RCDBjxCej8iHiHxWW6xMgcwHCF2CLpFVhnKOqBxT4TN1PsYKMq/J2DgkkBuJ9UJ0kCCKISkFQoZooXB44IVI4WYlBlpjLeRRh7GWVFaAAys8PJMBDqdmemIX1FD5AKELXGsJkbSwRwfot17Gjo4JTl7GixO8Yoy3fAIESCkpx0OcKVGBj0Mi+7vYUpPtHeGihKjeACmQzTli6ZGM9/E8RenXeSvzuJ/0OA4X6EfzBOcu43c6VHlGajLS4Rb9b36D4/0xxe4um1OwQiAmxzSyLbKow+6kohF43Lk3hdOXOP9DTxE02nQvnOPyyS4nLp6hubBA2mpjrEFKD5t00WEdFzfQaZvW1WcRSiIciO4iHhY5t0585hJKWOzhDnoyxQ0OZg8rysMhIUwQUiF7KyAUeuc+qlYnOnmS9R9+H8995gq9Z85jiopodZXk0mWMVWg/xDYXsO01RNrElVPcdIiod3Er55BhgpfUWPr4Rzj3Rz7NiT/yE9RObdA+1UNVjmqUc/vXX6fYvAfNeUQQISYDdDYmXNng6FuvEsx18ZsN3vMT76G9MU+oJF95+T5bw5LmYoObN46I5+vYsmTnq68xffAQ198HL0I123i1JmEtwU8DRq+/QjU4wHkJrt6DMEJt30ZNhyhn6LiMQMxql5NA8dTJDi88c5oLH/gAtaWTAFgH39qtEa4/z8c/8clHVciP8Hsejya/vwt46aWX+OiHP8JPtxf5sBeT55blU11e/cY9bg4LFgNFKAVHlWMtVfgIaoGge7bDwdv7xOsJeIJqckwQ+jOncCcCBCL22XltB+UgrgdkByO8SOEqh0p8yr19AKJui8M3rnN0fZ93bx6x3g1558GIjUaEA2xuyauS9sk2F8c5tT/4Wcrth9T1CJTk59+9xYk45h/9hT9F3Izw3BS5/hxH3ctsFJucnFvEWY20OSrwUUlMNRhQ5QqvOYcLIgovJMj6s2ICIHIFSnmgIFSCKAmIRYkEEt8RKYkxBmcKBJLCejQ9i9MVwveQCAZ5iTUWPwwRbnb4VukS0WSfSXuJEkWNHNdZpDnawcQ9XH+PIPaxcYtv3jimEXo8sZKymCiEFByMpyTtFKEEK6kA6TFlnjjwCZXHTz23wgER4XAHPdeh1n9AeeMt5OMvMFA+d4aGhdijrx2vXXvICxsFi3tHFK1TXFpWxMWIQCxyrpfw1m2f9XvfphMtoYucYnDExy4u0/YMdmEB3/X5nneSx5ICbI4QNfAlobM4HEPjU6Pkfm2DuXpGNR5Qactr13c437QEW7eo9+aoghgTQU0POb+Ykk+nmKBN0ponv3+LIHuXvHOSIKoztZLQi7DFkNAWOCcRZc7hL/9jep/6YWzpiDsLnMwGmEhhnWPoAqJ73yW/d5f6wiKrNcm3d0re2CtpWdjqP+DKxhxffWOHmu1xZj6lKDV+mSP8kGE5prH3gJU4QXgBNFZIJn2SU+d44kcFadmnVAlCWIwX4ytBNz9EaYPzY1KtQYUIGWClInMgpYeMOwghwU9QUoKtkMZSWIkJGzhbYR0IV83MgFKBkHjVBCEFosqokhaeVFBlMxNTdkSYhuD5GGuwQlGTEEqBFcxSKJymSOfwbQGmwlPB7N5AZ3SforaIrMaEpYEgpVIppQXnRbgYhHWYsIk6vo8IUoTTSG1wQYRrLiPYgqRNXIwYdk/TsiOk1YhpH6QEXZI7hT+/RGUtfquH1AX5G98jqDeJT5xjFM8Td3KGD+6Si4CGD7q9gvUDNocV40JzYCKy44xTrRg9GWJPX2V0cMRxEbJUX+beimB5v4/cukXr7JNkDzcp6z3Gy6tMjg/J5s/wC1+/y3s/+EmuLAXEdoLzY5and5FBggoKjFCQDUnSLtKU2KQJVQXCkRNQ37gMUoAuIKyDqcBViGZv9uBiK/ThIUJYhC2ZMX+FkLONmPJjXDlGZCNkVMMJqD12lfPrJ5D1Fvn+MTbL0Ps75FOHZz3CndvItUvI3hrDN14lsoKgs8qwtky9u4pK6ogwIY4iZHuBxlPvIVw7Sf1LX2X/7fvE9ZDJ7btEG+fBgAgiwseeR6Rt5nSF3+mQ33gbKfUsCWdq2f3WbZ58bIFX3j1g/b2XCNs1hrd22Ly2j0oTVOdtGk82cVWJkBJvfoGgfg+sxh1sYYopcuMqQvjIRhcnLP7RJl59DiM1DT9hLg4YVZpWM+R9V08ig4g3fnUPk2cAFLUNbK/i45/4JJ//tV99NAF+hN+zeDT5/QHjf6gs/lMLK3zUb7A7qhhqw+2XH7BzkGEdDLQhEjDRBm0NhXXMNUKqqeHkZ56ltjpH0kmp9RLmHl8AAflginWO8jijtZDgeYr2uR6jfk4xzAnbEcObO1TTmSTAVZrp3hiko+sJqlJzd1SBFNQWU/zURylJbbHN+rOnyC48Rvf6Kyw+sYxQkt2y5M986n1EaQhW47V7bHod/uGmQE8yhBfQ/94bs1WuF8x0yL0F2h/7DKQtdmSdw1IipZpFSUlH5CqmTpG4km6smI+grSw1z5KKEuE0sR4RSUuyfwusJrE5iRkhcfh6zFBL6rpPoiccTA390mKrAmctu1VILkKsFxGKiqzWIw8a7Msak1IjbMFcPeZUx6dhMpQpqHlwaalFGEd0k4BaqLAOdNRAu5nhLvQENWVpl32S/haTV76NnWbsP9hk/3jMeDzm9k6fZnnED60Iht/4Ju/X9/lk44ANvUft4TXmjm/xjRv7LISWdLLP5MYNrB9Tq9c4GxX4ZkI8PUSsnONiVyKTFCEsab2GjRp4OMRgH6cLBjdfZ75/g8BM2QrmKIKUS8NrfPvGkP/3eIHx/pBbuonRGjUdcSvzeP31GzgUngQ9t8L9+ga1dofgaItxadEoyrCNkR5gUf0tkpMbyDAGIRFpG9VdRQmH7O9wZ3/Irk0pnv8Yo+4Jkt1bnG15REqwXSpa9Yg7+2OSxOfa8ZS3Hw64f3+L5u4NasWAzXAZb+EkfhSj20tY6ZO2Wkg0rUZM0Okh/YAk8inr80SuQk6HFMYBELsJToYI68hLy3c3+9wdWshGSKMhrOE5jW81Tue07JAsbJPHc7PPi1DkYZM8XZhNHXHkKsIkXYY2xkqJDmo4UyB1Qaz7+BiS/JC06uOERWAIhMUiqVCYoI40FqELgmKIZ0pkPkIKi28mZH6LcdCmCBJiYUmkQRUDZDHCAVIKTL1HqHNwUAYNXFhHlFOs56NMDvNr+L6PDuo4PwJjwE8AgQlbuLJAdFYZmYCqyLHjCbRXGTeW0GGd1tw8AMfjDBwYf5b0cW+oKbWhoRxtz5E/vMONe9tU04J3BoKws8iD+1v0taD25FX6i2eJraXqLtPr1ql5lq/3Q2q1Gh84XaPWqLNnfJwXoPJjXH8PpkPs3CrUmihXERXHM7mKcxQqwHkhcXGMUxJws9DjYjQrEdEavBCRpBDPcnplaw7hx5CPwA+wXoDzAqgyqM0h08ZMU91ZRS5u4K+fQbTmSE6s46+eRZy8TLy8iKsKRBAjdYFOu/idFtY6JsKfVYU3lqC1gjAldJaxQiDTBtHJc/R+9LOc+clPEM3VGG8dovsHmGww0+O3lnBpm/jKs5gL70PU6uzdO+K1ccqpH/8Q7//IWZhU1LsJH/jTn6NxepXaYoNhaZjuHjN4/Rqmv4exjnx3C9XoUn/8SbwkQSiFGx7h7nwPUY5xpkLoHJI6TkgSV7Bkj1iohygh8ZTkyRMtzq62+OyPfQJk8Ftnluyew/Se5mOPJsCP8HsYjya/P0C89NJLfPxDH+ZPza3w6VaHbFgSe4JICaTWXFpp8O1bR2wVEGPIgPHIcjo1yMCjGJbYbIo1kqMbB4T1kMnukMFRQVifMt4aU05LVKDAm9UM3335Ho995jH693ZRSmKNJp43pOsLjLYG7Nwb0J1PGO9lvPDsEqefOEmy0JqtQgcFzZUm9xqrJIMD5poBNofm+ZN88sIpfuqffpG/XG/znh/9EWi0CX3F8ydajH/tG+SHQ2yZU/Yt1g1Y/MCzyHNXmbZW0Dt32G6cIUYycCGR9EnKHIfDGUdsxpiwjaxKkBJfWCoZUWiLUj4q60M5JlQWB5ReHc8aKhS9yCGzCmzFoagxnirwFBcQ1ENHojRGRgwLQzNUFNoxl4aURCjP52Kr4s3DgrSt6KgCoWKiVDK1it1JSTre551pnfDoASfnUrx6wC+9tk+9mfKcGzN65ZuYxRPM1QX5q99mbvGA+bV1/t4bu5x8fJ7V5QVW13+MmBx1+AD0EHf2MmJ0zPt7lu5Gi6h6nqAY4+bXUFLipKTwayTjY5CK1BQYG+KiOl42wMv6uCLjTh5SlyNsdxUvPyDRGWN/gTDrE0QRK7/xK1z4k38ClYUs9rqM8Hnj/g6dboPRucfJ7l6nNV9nod1k3jpcILg3HRLqgrzSjAtNK/bwqxFqsIN46kWMD9PSkjiNkYq8KDBGcGp0G3X6Al+5OSTVFWdbJyiMYPN4St1TPL4gieotOqMdrp7u8EvvDlhcWGAS9phPBT0bU4YxYRlggwZhtoeNm3jaIJIU66UEtuSYBF+FBFgGjTVCBYYKjg9QROD7VLbG/+m/+k1+9nPvIX3577L8+38M21pGWI3MB2jnEwYKnCOwOUfTEllLCKoxVVRjYCSBFEjrsGGKrhxTL6ayEt9pUi9CCInnLPgx0hT4GJQu0SqksqCERwVEViOrYmZeM8VMihH0qFAoFXA8NSxVOZ50IHykNYgyR8dz+HmfadQiKccom1GFMTIfoKa75PUVAj/G1Rax1mGCGlJLKmbfocBq5P3XGN+4Q/jhU0hp2U/Xqb93VmmcqRYPBjkX3Q4A2f42eW2DnBKLR6AEDWU5mW/x5W2PsJpyeW2B6XDE6WjCYj1g+9QFzsiCQ5kwWT7D8esvs/Hce5lagw3qPLeh2Moqnjp/gn/8zZvEF06y0mgiqyni5GOQjwhqLYSTyCiF2hxK50zDNqUzRKbEL0fY/j52+fwsgk4KfFN+3yw4k5G4pD4jfOEqaI0L05muWwhQ3owEHu9gyyleGGP9EFC4o0043iM+dwXRWSavL+J17rEZzFFLU3q+oRQ+wo/g8vup/JTYZiBqGM/DxQ2MihF+jGe3wBqCxTX8hTV0OcXsbJHfu4OXTQnPXIFujCim2O4qTgUkV57mjf/sV/iFTPPJV3+JP/YnnuKw5rPoeVQPbxMtLjP4Z9/hyrOrhGmCH/mUD26hpxnF3iHRwgq1i5fJ3hgBAtloU27dIWwvIIbHiN4qNkhBenjjPVRZ0ms1qDc02UoTISRrczE7Xo+lx66y/drLv3V2ye45LPCJT36KX/vVX3k0AX6E33N4RH5/QJhJHT7Kp1WHP/7iRabHOQcHBySeR7PpsXkwZaefc2hmr8++f10JHBSGeKVBPFdjundA//4Rx0c5C82A6V7Gg8Mpx+OCvWnFmZNdxlt9mrHP3de3SSPFt3/1HTwP5LBg4+oSx3cPKV+9x8GtIw7GmvZcRDyXcPVzz1H7wMcJhcGODjFlTrZ7wGD+HGdufYfuc++hnztaqyf48dXH+dJLH6B//5i3kg0eCzU9l9Gtl5heB+sEYfcENBZBF7jnPsY46uAONonCgI1WiPE8JJLSzNb2hYjZzw1hEGGto1aOcDKgL2rsFZqTHGPSNv74YDYt9nwq56HyETqoMbCSUFhGXp3YWNIwZPXgHXbnLmGDOY5yRy1x9EtLLB0WQS2QeLnG+Amlg2b/Hhd7ZyitYywTUifwPKi0oaYMbrDP9rffZPfaTarBLTrnzvK+C2uUraco8ibJ1ReIfI+y3iBIT9B98G3k1OOPXEzo1B1yrklYjfBGA8yJx3DllCBuE3maU9YgVYnwfEjmsVaDH1MFdTxTIXx/pnMsC2Qa41yMN9zE9PcxjTlGt/aQfkQyN0cRrBK6KcuBRPXmKRrnWPmpK3QaIGtdAt9x+94OUxtyloyDqMY//o2bvPDCk7y3FWAdjCYTem6KvP8qYxXTChQj3cWLFdX8OWJpGRSzVitXWnyT4Rc5ZW0RoXOmLuScPOZtXeftseB4WnBpPsaTirCRoAfHfGxF0mr7/MyLG4TKw0lJJQwLqsKKgJHXINFTlPLQUkF/B9IGLooRLuRe32MtUoQmZzJ11IotNr/+Btlb3+bU5z5LdfIKw0Kz1qtzeaXGjV/5Bks/8RNUwkNrTSwUuy5l0Q9IqjHSltTsFGFmU+OiEsS+jxYCTxcMjc9eVrJSj+gXFe04wPkhQigcjsqP0V6CtgKtPA4LQVMWCOVRojBeiOw/hHoPTEnlJyB8tHUY63DO4pdjKhmS+wHObyBkiKqmUE0IoiZ4Pm6SI43hOOwR+XWU52MdyPEh5aSEhRPslCFzTpP5HTqdErF/j/Tp59ixMzPYyCpaMkD4EdIU7PbHdBspKI+t/ojyYEq9FrIUCU7WJH//u33+1JUGLS/lXDtkITCM5DJLx9ex1Zg1+liVIF75Mofn38/92gaHhwLph5z0PcrphBXP8eo+9GLJYntmNCSsUwV1kuwQV01xTkKQ4AATNRFGU5dmViHtx4i4hg5ixPiAaX0Fr5jM7mNnNeWiGCGswUmFmA4xtQ7CWURVQVXNpCR1D8IYiwWhEPkId7SHSGrY7jKll2Ccw3YWWZE5+zqhikLUzm00iomMUGWO1lOkdRRJkygO8XUG5RhX7yKGu7ioAV5I48IVzPwcxfY2k1t38NIE2VxA6SkuquNXE7LFy7z4c/82e8MaC/e/RNhtsfiJcxTbW5T375Oev0jvfVdwwyPmXnieaprhRocoKfHiiPJwB+kck+0DolMXYTome7iNkN/C663BYHf2ObUWm6Q46dGebNLKJ3y0t867RcgrQnA4rnB4xPOrCBzZ/kNgRoA18OlP/zC//Mv//BEBfoTfU3hEfn8AmJnbPsKf7KzwpEiIGgmDh2M8YOFsg+F2xt1JxZH+7a/va4h8j97TZ8h39unfOaQqDKPtMXiKE3MxB/tT7g01T15c52uvf5sPrCo2rx9y/mKHd17d5UFu6QWC2oNjdh+OySzEwrG02mTpqRN4cUL76lWqtbOgJKqaooZ73OpepjEa0ltfpFpYRWkPtbTC53/5l/nAix/gxT/4+3lpKrgw1yI4vA6bt/CXT+B1enjtHq7WQTd7DKIew9KyWG+ivBYNWWGVh7aawnk46XFUSCIlyKSkXWXI421c0iBKIhrK4kl/Np3BMeluzOqJnUE4Q45HZSs2M0teebQ9SGIPv8rpxbOEg7jSVE6xO9VcrlkK6SOsQUmwUqCLEuWF1DyonOThxHAqKvjeVoHVsDafsl0/Q3uh5L3GpygvEOsx86dOUTYiFAtoL8ZmGeNpRV3mqKhOUE9YLipkdgj9+8hsCL6PUAobxsTFMaY+h9d/SJbODDx+doiSAcYBVYGTgipqYlSAC2uEQoCzOD9EL55GPLxG0Jvn8O//A2595sd4r3/AcfckDTWl//A27XCdSVADEaAmW3i+4EL/JuXcIkm9y2PDMX/jrUN+3w/XmRAQHd8jmU4QgeVWUWcr9ziVJPgPbiAXe/iLG7jRIQgP78Et+r1TdBoxQfYQkjkO/C539gtq/Yy22Oft/ZjYh6VWyL2DklOp4+uv3+dW5wS/L26x5hsmRY7yA6wKEBYCnTFVDcLDm1DvQlXOiihchcon6PYKK5FGFhWVzZGvfwt99gxf+0/+S96+sc9nP/WHMff7LKwu8pd+4jkWUsHqX/k5hB8iqwwpQizQ8AUVgliAUQGm0SaSGqM8Mi1pSYPVkonWjJE0Q4lPRbc6QoQdKhXgSw9nK4QFr5xQxl0cEHuSwGmsE0y0wFMRebqMwiMSmsIIrHD0S4j0iFQpiuEIr+3jmQLr+4DHsfHp1Xt4tpyt2lureDjGWuKHKaHNGA0HNId7xOkCuZVYa8mSNoe5JYk6FMtNjirFr77b54X1Ggf9Q7wHb7H0vmcYiZirqy0mRlJwj9hVxEpzwo5564Gh8Bs8c6JN0Ix5rB3QUA5//yaJlyD8COMkvi05vrNFrZ1wqlsnjSLe2B7RTeD23piVWHPr/g6nzm4g68vMk+Ed7+OUj/BTXKlxfoVtr6JGe7OHwNKRmOFsjW/0zMAWN9F4eELN/AlhA2EqkBIhI9zwAKIEMdyFMJmtwPbuzFb/KHR3FeXNMpNLZ/HLCSIfYcoSb6FLni5Q3XqLYW+dbqtJmbSYq8Yo0YK9u3hVRnOyz144h7WSWjmBtIVfTRHFGBHEOGZtjc4YhAKv1UFFEX5vGT06ZvLu24jtfRpPP4dsLCBMhRIeSy9+gJ8pCvp3z+EtrhJsnCfwoaqFFDevkczFyM4Sfmcev3eS8a//PYKFJeLOHORTyjvXqZ2/hD7cRQUBrrKMbtyh3V7AVRUcbyHrHVwhydMesZ7ggpjWdI/LrXW+1Yj4zuSAKhux8fRTDI/Hv0V+YUaACx4R4Ef4vYdH5Pd3GG+++SYf//BH+OnWIn/y+fOIZsj+9X2c0SycbTI9yDg4nv4riS/AxZUUU5Y8/Oa7JHXF0vNnidvb7LyxxeubxzRDD8+BBV77whtcaIc051N0OeL4IGc7twgHxxo2dzK2RiXvvzRH+/Qcix98huz2bZIz52BhFZe2McUYUe+BUmxv3uXUxhmkt8IkXSAwFVk+5B/8t3+Hf/53/hZelPJc4XBY7M4dhCkRQYy3chaEYxp3KNIF9ieWduzhVRLhh1TO4uuSwkgKZ1CeYKwNtcDDOYkaH+GqHDnRhFGL+cBHFhVm7wF2cY0ybOJ0ycD5FK41q3+2sBBL9pxi7OCEG5OdeIKBVkhnWIkcohzQS+fx995C9E5hhCKXAQGG9nSPYWuRms0wRUajdLjdLU7IAJGXtNsL3DiYcmf7gGc+9Gnm9Qh18BChJFO/znCaEYyGDF7+OnVVIusC25zD9k7PpBpVjigySBvYMEYUGSZq4YSP9WLKxipTL8XpknptAd+V7NmEvZHmXDfE+jG5dmgvIjTD7xuy5EyPuH6F00az/+QFmnVJHDQIApB2Sn7uaeS9r6HWnkJKDze/jghi6o8/DabEEyWJG/Af/dznaDTr7OWWlg1oRaB3jlgyJatnryDCmFBM8EyGyw6wzhCFKS4fokLBbqFYaCwgcXSme9wpY/63f/87/Pc//Rh2IDhKO8hmj4+ca+CZgtrpiyzVIjzfR6DJvYia54GTlDLAcxYfi3XAzl3KuIPsrDM6PiDpLFOIiK4aYscHmNo8tStPEzLhxDOXuHb7K/yf/9nbVFrzZ3/6M8wnAYGZkiz3sCrEMwWgMUGNOB8zsjFhHINQRFbPiFc+oeF7SGfJimpWme05Gp6kX1qaXoCqMkaqQSIKAulQOqcUkkRUyConVTG5iLFCYHEEOmcUtUichskxQQhHBCQmx7/3Fv21xznyOiw7SYLBuoBJ5TiaZtT9hJCSwq/hSQGmRDhDUOUc3LmG833C+WWCOGW30GQiRGcFmVHsCI+OdNweanqNEDM6JPRrqCee4bW+ZCWuqKcpu8dTOq0m2dEu64z5e79+jfrGGVpzgmdXGyT6kMiP8TZvI3RGxBA9GTPa6HGtn3J6sU5iM/pTDQ7ev97g7rDEw7EaVDxI53DZiMi3qK17CCHQaRftLCapQ1ib5XOXGS5qzNI14taMyKYN0CXoHKFzXDEhtjsQJ7g8p4gbhLrEdNdnmdbTESRd5N4t0BWUGUIqKAucH+G8EKMCfJ1jkzZ4Cqc8wOH3HxDMrXEUzVMZj3ZYw9M5ZuEE3vIGZvs29aVZQomwEwLhKL2IwFmcEIhsCICwFc4YyEaI5hyyKqDKcZVm74tfI2i3ieLWzAD34HWE8khWzsITTzC8fo9us41KEiwOUUzJNh/SvnAGMzpGrV0kvPwezJ03CVY2sM4h45RwvgtlgQhD4oU5su19hm+8itedJz3/OPT38WoNVL0HzpJFXRLh4TnNp8/NMc1KPHGF/aMcW2qS7gK6cpTDPeB/JMA//MOf4eWXv8nly5d/sIfoIzzC7wIeGd5+B3F0dMSH3vs8f/DEKn/o0gl6T28gtCWs+SgpCGKP8VHBvaz6ba/vhpIQeOziPLWVFvvXdyiznGI4YTjImGQVR6Xh7qDgflYA0CwrNk622Hw4pJ9rvnrzmFCCcbDRjnh9b8p86jN3eYWV585Qu3CZ8NkXKD7xR6mWL1PKACcERms2j3OcVMT1GqLRIYgSbNrif/8f/jx//n/9v8GvzQo15r2CpBjitEGsnEdEMXrhFFXYYFBfIpDQTRR1SlxURxYZOMvUKm5PNEoKtktJUTmMc4RqZgpy7SVsNDNoaOHh0i4HK09gGj0Cp9mfOjZHJdPKcDQ1BDojqxzn6o7zbQUIdnVEKB07GQgEzktRxqGjBtZaAqdRxRDlNFYo0uKIm/sZejxhd+eIv/P511BK0pk8YPjf/R1ODO7y+993hsSzyKROuXSOqjbPoHD86lff5Re+9A7u9Hm+IeaJmk3MynlyFCJKoL0MtQ5ECQR1nOcjhcNGNaTV2LhBXY9QSoFwyKoi19CtRXgCKiuJbE4sLcJUsxipsI6vM2TaQJVTFj/0IWx7jaLS+MUYHbZZCjST3nnEu6+iPIEZDnHW4mOo/IQ8aKJaC1yej0gnO8zrA3xTMYjmUKcu0jx/gXogCD2FHyfIKEYcbDKJ5xgRouYX2OpnfHt7wk4uGB3s887I0dFDPvf7P8X1xhmWnrnKexsZ71mJaccBNkg5vdRio5MgBVhrEcIjt4LCzGKYpBBYZ7C1LqI1jzzcAs9Hzq3jgohYZwgcXq2NC2ukFBA3efrn/ix/9G/9VdbOn+bF9z3O2U6NZuRjwpS8s0EV1cmDJlIIJBatNcoahFQoHEGVQVXNDGTCURlJqzxk7ug2vi3RQhJJiSkLvMDnoICRi0H6WD+miBqzVboXI2xF/v2c6Vg6uPcWCovnHFVRsVM45GRAu/+AycIZWrWURq2GCOvYcX8Wv1aO2PCLWba19Onnhv54wu2hZd4NkKZg2FxjN1hmWzT58o7hS7f7TLXjdl9jnEBKeOe4IJDQSjw2aSLDiD0TEAlHg4JKa9TWLU5GBuccr93c4dJqk8dOLfH+HrRCB36Mr3NcEGGWLkJnEWsq/P4ufQJ+fV+y31jn1nHOzqjgta0h84nPqVaAjBKeOtFmx4YEYYybjnH1FjpMEV6And9AYBG+h270sM5QNlcAhygzirCFsxq+P2kuN++Q+ylGO4r9XbSG3E+YeC1GfnO2zQFsexlaC4jGPGb+JMIZjACokEKgkyZZ2EYtbaDjFnLnBm71PK3RfXw95d3DEuNFiGqK6K6CH6Et7BATRSF5PE/hvO+XmQiGIkZUGbaYghcgygHOVDNNsvJACIJWHb8WzibF+RBZTZFRitl/AJMByRPPEs13GL75Jt6l5wiX1tFFgQwCRBTNvi9lBqefRPVWEX6A8H3ik2dxw6OZzObgAIGlfuEcepqz9+VvU23fo7rz1ux9LDOEdVjhk4dttF9jycv5qSs1/sD7NnjyfJf1Myv80Kd+iJPnTv0LZ5PsnmNaO8OP/MiPcHR09IM7RB/hEX6X8Ij8/g5Ba80nP/xRzgU+f/nHP0BtqQlKcHBtb2ZiMo7bbx3yxnHO3m/PffngE/OcrHkkjYjaYpvuSo2Ddw558wvv8vWXNzmYaJYij76FPQ2hgLETjIYZr+zlvDGsGNuZbMIH9kcFqYAP/NEPknRrBI8/jzp9ieP3fBZda81+PIsBAZogP+TB1g6dxWXSwGfPRPgK/tmvfgEviHj2+fdReRFSF9igRpVPUYsncfUOdvk8QgUU3Q186RM6Q01ZYj2eHZyjXXShOdYe65FjYhWJgrXGbOoS6gwXJVRxh0ltiVLFiLxP5cBGKdoYApPzcFThEMwNb7NcU/Qi6MYK54V4XsCRV2dUGeq+ZC71Ec5ibUklJLLM8PZuobMRenBI+ebL2P2H5G+9xt03rnFceaTHm1xcb9FIY5KVdYJ2kzCUpEmCsAbtxThn2bMxOw8fUsuO+H1Pr3MynJB1T3I7Pkm5ew852AM3O8BFdowopmA1wllUNUU6gyonSAnjr3+e+Hgbv5zibMlyAt1w9nmwWAI0kS1mhQkqxn7f5FNqy3TnNvLgLsupJCxGlHfe5X6h0Ldeg9ERspwidcF0fws1PSZ3Eo53KcdDMic4mhTE1jAdDjm+d4fa8AHTYYY8fIA8fohXjnH1DqVfY9o9QZQfsTmqGDWWGEZdnorHvLKdY7dvIcIWd8aCFy8uMg7aWC8kmRySJDWiKCKIYlqhpKYs0jmm+AwqhzFQ8xyhJ7AWhLP4JmfSWEVeeg82alCzY4JyhFeOQCmc8PCrDM8VSCGIWjXm3/8+/uwnLvJjL5ynm3ikQUBGxKH1kUJiVMzUq2FRHPsdXJAgjMHTOaKaIqlwQQ20RecTxkTsRvNMrAJhSVyOv3MbW1mMc6TKooWPkz4RFTgDOErpUxOG0FXUJrswOcZ7eA2LZZp08MOE2tabFK1lsriDcoZYOazROF1gnUZrhykqRNZHFWO2Do+Rx9u8cu+QCsWhqFFzJZ+/l/HdrZwHQ00QeAhguRnTsWPqW9e5P6gYl5avvHPA6WZAZ+cten7JopdTH9ynKjLmRUH28B4AC6uLXH36cTbaAQ0zxnMgvBATNtELpxESCCKYX8W8+W3eE09p1VNiW/DEXERZFuwcTumGilbVxwtC7HTA6YZHKg2y0QUvxgsijPCwUs00vgImfhOkh5UeQghs0mLsQpz0EGUORkNSJ8CyvXvEpJ8xLjQ7VYAUzAywtXkqP8b4CXgBTMeQtDHtNYQFoQ0GsCrimAg3d5LK+chKE8QBstXDdxXtxMOfHIB1qGpM5ceIyYhlOyDQEyqjefPBIbm26LiFkz6UU8zOHdxgFzMZYZImrr9LdbCLiBuE556gc/Uy0mmcKXHZAKcLQGCPHmInA9L1VcJeD6F8RNbHCxRBuwFJCzM4xBVTpDZ4S6fAWmR7EdWeR0iFPtrBZhOCxVVEPqQc5XhpzP1/8huM7+1gj3ew774M1YSEkhE+gwoEjiiO+cTpFn/mxQ3ee6nH+okep86fZvnsRWTc/K3zSS4+ye39ip/8yZ9E6/8/q8tHeIT/BeAR+f0dws/+6T/N5ltv81cunEOUmsaJDsM720zHJZP9CZvbE97cy9g3v/31AXD/3pCzpzr4sUc1zcl2M/Z3xrx1u08MpJHiqJj96EhAAdZYvnFnRM7MLAczOUTqCY5yy5XVGr2nL1BfX8R7/AVEfR4ZxnhKIqQiKo6xwyPuZxJjLRfWFhEItiaOB1v7/Ff/xX/Kz/7Fv0JHzaQU6FkslPACiBoUURcTd8BWVE7iC4PCEhVjZDnGHO8wMj7fHQgKIShVDWMFoXAYZ+kqA8qnTOcxXohSAf5oh3A8M8PMuxHHNmBANDNv4+iUx2gn0CoiFgVSeQinGZWW04nBWEfPK8lUTK5iYmFxnRWypMvO9iGDUcXed9+keOc1dr/6NR4fX0eWU5oL87z/xeeI6zVsZ5X4yac5bq6RhS2G8TxGeJSHexwcDziUKRvveZZ2ovCc48PREWvLCzS6bcLWHDZswHAfOzzEZkOEyXFCUvoJyupZEL9T5FON12xh/JhJ1EXj49sKKSB1BcaLsZ7CWY2nx2BKnDHkb34DavPo3W2S0RZ2OmXv9ZvMH93m+tAnygfEjz/D0fe+Q9Koz4jUw3c5ri2RRj7lvevUpvsIIbhdJeilDaTOZ1Ow6RjdXITpAIxmGrUgbmDjDvXtd9mdWNi8RqkdL4gHlFGT1Zrg4kqLf/LGLqfaAQhB9MyH0ELhy9lnNXQam4+o8jHHxsfhiJWdTaOKEq0CUlfgkhbkIyoZYoSaNXjhZn/WovIBypSIqkCUOcJq/O0brKXgtCZSUA8FUgh8BU4oDBBND8idwDoIPQ+LQFiLC1MMilLG5E5QjY+RaYNaq01dGOJ8QGSn+EmC3LvDYgyxzaGYIKzBL2ebjQqFrysm05zCOpwu2frKK0yCGn0TcEDMO0cF0/Yah6Vkd6LJRkOu9zUHkwLZXmTvW99h+KXfZHz/JtHWDbSX0lQG25jnw+0pxzagnxfsENFuxOxPKx4cZbSSgLXEIaVihSEHSY9QKR5vK/6tJ1uIwx16rYQVhjycSPKFc0xHQ6ITp2k/+QJSCDyTk9gpoTO4uAE4pkXBUSUxQmFQOL+GlAr/9DliVbHe8Dm2Hlo4WonPH3miRp0pu9ojqxyeEDQG93Dfb8RjPKQKG1gnwFpyv4Z1Ah+DnBzi2wqLwNa6SGmR5ZTSi/BshWvNUVlHWA2Iz19m5BSdyCOSmlrZRwiJdI4xIaKczrTF5RRMjjd6OPM2OINBzYxgzoF0lHEDaxWmqtiaWBYDR1RlmOkIpwJM2sW/9AyxJ9HTDH+8h1CSL98b45DUmeLCGH24x+hbX6a69l0md+9Rbt1n8vp3KI/7VIUhXllFBDFm9yHT66+TvfM6Zjxgev0dbFVCVCN6/DnI+oiFU2AgWTvJ6NXvYq3ETkewewuUhPUrcOLxWWV2EKBqDVQUIudXqbIcLwmpn13DWc3ON66hD3fQx3sU2w/wJwdEtqJpMzJ8MJqWV3Gh4bi61uKJtSaffvYETz//DHGjjQi+34goBOrEB/jiV7/DX/yLf/EHdJI+wiP87uAR+f0dwN/+23+b//a/+QX+Uned5TM9VOjjtCHbHXNjZ8LgKMfzFVdPt+j54re9RwkIIVk83WG0NWD71QcE9YAIEA5ujiq+vT35ramxBZSAndxS2H/xXo/1Ejqx4qn1Ou/9Yx/EHWzT+NSPI/wIz04JlSSSAt+WOGMxKuDh7gFLK6uEvsfXrt3jr/+FP8df+Pd/jr/2H/5HLNU8BAZvekwV1pGmQEQphDGByditfI60QmtNYKdgSuRwhyPVZlhfZuzXSQNFT05JqKirglro4zlH/v3VrnUQjPYIRw8xw33s4S7CWbz+FjsTQ790LKQe7+5P2Oue4/phxpHxeTDQHEwNWxN4fTcDFTG2HmOrGGhBaR3p1ptsFj4P84CiMgSewT33QaqDPVafu0xnsTvTza6cRsbpTKIRttCrl+jUI6ZOMMg15eiYMkjo+o6VZsD1nSFf3dLo3ilW1ubxwgiVDRHOziZPcR1nDe5wG1fmVH6E8SIqOWsfE1VG+6OfxSkPE8aUMiJ3AnBUTqDMbIWtRYgN62jpI4UAPcUbHhHFCbkXcfy9byFNQfzEVfz9Tdppgu97+MMt7n/5ZdjfYqx9ZODRCD1GXoMqaRPUGjA65FQromcG6Po88cnziOWzEMSQdhFZH4Pi3sGY4f3bPNg+5q3dKV+8dkAR1PF663QWlmfvfa3OY2sdpFD4viTzYsalY6QFCIFwlltjCIVmqg2+nJnDpk7i2ZxIWJSbufGT6QHSlPhVRhV1KN3MGEc1xVkDQuJkADqnSrv46+dwzmeVIdJZYlfhSzsrSikLQlGiEDhraEYewfcf0kSVYVWIViG5g4YZkWZ99O59mqMdWkc3iPZvo8opXr2Oay2ipGKqUg5ywUg79krJxAZMjGC3cPS1YL8Q3KGD/8M/ST/pcVA4rh1VbI2mWD/maFJwqib4xnbF5mDKm4eaQkak5y8wd/Uy9FYxq6dx1ZRlryCNQsKFddppzErqU683QAnWOzFbD3eptMHlGXOqZDwpOLAep9we5fEOi2KC++9+gX4yh02aHFaKsYhYn2/hp23iZhM/COhPMhASqacU777KJM+ZvPYdvnDjkEGhGcqYQmvK6ZRq6TR67gRn8wc0lSXC0KvHNPNj3KxLjtxYklp99sDtRZjOOgiHb/KZFCQfgjU4LM4ZXBDjlRl4s5STVIjZuj+f4K5/h+pglyg/xqu1yVREN3BEvgBrMH6EshpZjglthVYBNLqI7AC/zLBxe9aS5wwBJe1IUKgQ6QWgNSauIYKYFTdgXg6puuuYuQ2cFxCZjLK1jBelpMWIlJJVO+DV7RG744rSCkxtnuDMFYqDAfnDLYrXv8Xh17+FyaeYh9fJX/8WVX+AWj2FycZMbt5g9xvfw2lNsLwGi2dR3QWqG29SXH8DvIDp7gHWOeIz51FxyIO//w85/uqXmVx7G7NzG7d7e1aconzkqav4p68gdI6pNF7sowQsvPA48UKLva99F6ENbuceFBmN6Q6BsNT1iOj4PmExwnkhzy1GnOnUuLhS5+xGl+W1ebqrJ4jaszxooQLUxof5z/7z/4Jf/MVf/EEdqY/wCD9wPDK8/U/Et7/9bf6dP/Wn+Vu/7/1sbOckSy1MYRg+OObGqzv4OLrrNdK5lMH2mFdu9v+V9zq5klL0JxxtDrEIWos1tLEUdladWv5Lrx/Z2dNLS8GRmU2PfQnNmsemtjz76cssv/AEbuUCeuE0odWApBdZPGEQCEzSZvrwFuPRhFMXrrBzcMRf/0s/x1/+Gz/PExfPUuU5qc3RYYtgOiALGtT6d8k7p2ZaVa3xpMQQEkhJVByQFxbXXGNcOLqDLcxwyKnlkwTAyHmkvqJwkKMIBaCnSOWjpcIRM+kt0Mn3OXYR3foC60pSCgG+4mQzxMYxk6MRh9OK13cLTrQV7chntR5yWMIwr5iPHMel5nRYYm68yuujFkux4FJqSTpdJkYS/fBP4ulsFpYvNGEoUIf3yHrnMdmIwFeEcQqmIPE0QTElz/rsBy02s5JzvZTVhgeTHfIbr6Pf+xmCtD3LFS0VwgsROGSzS9lYYCoTnBMgFH45xEfQ9zskwhLqikh45MJjKgKEg7DK8asCGzWwXoTz/Nk09niH9InnEMUQ050nS5pILyfY3MaOjlneqJGvP40cbXPpky+AF+Hv3KS68iKJ1Ex0RTa3ziQfIY1lNMkpp5a1pRiCCOeAcspURBRhh4dHE25/9xWeu7CC9EM+eCJlsfk4caPBNFBMK4uvFKkSPLEkqYoJcRgwKj1agaAC+oWjoQSrrRp7ZYUnZ/XWUkAsHcar41czeQhCYrvrRNkRIkyZqBjhCfxiOjMqmQovH2AH+8hOj6CcME17xNWIotFloi1tm1ELG4BGDHbxmgtUjQXGuaTtQegKhDGz4gQpEUKQugLfVIgkoiNAYNmPFulV+9gwZeQC7o8tC0ozsYKDQhBozc5Y0MkyeonPm/sFceCT+IaOm3AU1ymmFV4c0AglZ2LBzTzCejGydDSaNYy11ALJdu5Y6sxxGLepK8FAxcQeeEIRmTFj1aYoLak1tMKAp3sRTgV87OkNri430EebDO5fJ4ti3OQd9muL9FYW8UYPWPnge/FVhfZTXjhhcJ6HEimx1XjOEUUR+TRn6tdn5jopuDfSxCtnsMewPShoJgFyMsAcHKDWLpOiKaKUXe2zmkgKa3BpG10YojgiDX0UJSZt44TEBCm+UnjlmDif4OIacTVGW49CJoShRjqLqHKclCgMrjGHvfYyqr2AP51SpPMcYVjzwKsqSgRBOeFItWiLAs9olJ1imov4R/dwXogTHiZuIcspYxFQUxaLoPISGsIyvXOdRpqQO0FYbxJODtFpm0pGeEKBLfGEQJgp0oMsbtF88CofPfE01w4mNBcj+gaWmvM0P/hhyvt3OLzxNWpLbUa3tzBFid9pE853kUkTf34Zf2uLoFMinCXozmP3biNrDYR0qHYPV2T4py9gy4rg9BUoM4ovfI/indtEDx7QPLWMn6S4IEbFMUEQYzvrkLQpj36dKs+J2i2CtQ2UErz7d3+T+vocybkrYAukCXhpq+TyOy9Re/opEuUBio6fo5opX30w5BOXFjgeXuXOzfu8/lZIfjxrCxVhE7vyAX76p3+GCxcu8Mwzz/xOH6uP8Ag/cDwiv/8TsLOzw4de/CH+4o9/mA+fXubQbTK6d8hws8+tu33e3J1Ql+CHHtP+lO+9sTuL4fmXIIBUwPb2mPSo4PbumENtSHdG2MJy9H1D0G+HuidYrwdsLLco94d0Eg/pebz48VP0njyDqrUQjTZGBcjpEXnUQTtBengb01lF6YJbuc/c/ALDacmf/+mf4W/+zZ9n/cwZfDvTAuv6KsoU2CBG2Qpdm8daO4ve8iRNPUEUE0RjnqKK2Q8jUnwW/TGuHLPZWGc1SMilzzgvsVLSdBkB4ERAoDUTQmqDHaadDSJbYaKUfglB3J61tU0qFmRGN4l5c3/Md+/3OcgSXn84JPQVqwmoQOHrjGEBlw5eI+ye4ZX7OWejHk/c/hLtsydIwgCRj0hHA2j3sJ1TqNEeVkg8PZ2tOIWjVCFBNcDW5hhMNU0MRWOJSAZsSEOnldLSQ6ZpF1NPSMf76OkR0g9gfAxqgp30wU9wcR2JJKwmKAEyHyOUh4hbeA5QCmM0UlhCW4CKCfQIg5u1VJkK5Uq08yj8hLjexnk+oj8mjJr0GhFykpEu9CjQmMNtxPwy1HtEGyFVaw05PsThUFWOP5mQRk3ytMOgv8tCbBk3zqB9hRAexg9xd17D8xT7jdN45Zirj53D7zS4vHiCehzQ8QRrdY+xlTgBoXT4SlBTiur2u+zMnSdNA4SUCOtohJK6kGzmgjAMiaUmlA7hHBqJkgKpM4TV4CylF+OpEE/MTH8Gh/v+5Nx4MV45QSys47IxqIxADdFRirWCWjlGODPLQx3vkcUt/DKjjDsEyhAWg1kVsNFM4xYqn5L7CfW8jwkilGhRhC08MwVSRvsDvrMLvm+ZVxWbx+XM1CgVejhmd+rR6jgeDC3D0rDUjIiyY2Szzf7uAX4tZTiYshFrGrUafeF4+XafE82QOPHpBJLCCb5y/ZBnTjQ4GhleWPEIFTgpsaVh3B9STyrulk2csVz2djkVR+xMCp6dU8RUPHQB3V6TfrDAqbnZA3QlLJ6C+lPP4gmDLfqIMMaIEocgmR4hPEVTGTarkqGRdM2E46VLBNoh6h3uvH2fU/Mpp3Qf99pLpE9/CDncpqrPMxQJ06LEpAmeJ8i9hKNKshqC50tk/n2ZVpUjkiZWKYQTeNNDrMkp6j2MdaSuBOmjBbPUhigFAeZgE4FANNpkC+e4Pag41a3hV0PwQjw9S8BQVQZRiPMCSufPpvzKBwRGCUR2hMBShEv41qKdJcEghEdw8SmEKyhciKrNo0aHMDrGr3eRk33KeB6vHIMKcHiE5YR8bplzDcWRDPEx3M4crfoccXiEeOw9zC+eIUkUgy//Gvvfvsb8sw3s93N6RRSTnrtIcrWBGBzguiuIfApRSrB2mmpvE3N0iKhywgvPAAKxsMHJP/6TvPk3/lOqI0E1ntI8s0o1nNC6eBq9cxcxGlKcfx/x2XPUQx99sIuMYoJmjWQu5dbnv8sJ51E3Bv/UZeYCy+HbN7jz1Cd5Jo4Jju5ikjaRsjy1mGCE4LPPrPPFKOCtG7v/wrkjGytU+RU++9kf4Xvfe4WFhYXfqWP1ER7hdwWPyO+/Joqi4Llnn+fjj5/i3/vsi0zub2Gt4OGr22wdZLx6mNPyJGfnEsbHBW/fPCbLDeN/6T4R0I0UpzsxubUUxxl3M0MFHJWGmSXs/xcSaMWCT3zoAu1z64i8j8lLiv0BYRyx8uIF4vNXoN4GLM59XxshQFsQpsRKn4mIODjqc+XxJ/nzf+7f4c/9u/8eZy9cwBo9m6I0OwgB/r3XKdYewy/H2LhNUgyxUmKMQEUxCsPQKQpRo+YZGqZPoWLc0mnmXY1KOPYnmiW/woiIvaKkF0qGRsxWxhYGssOkDCi0Y70R4xnYzy2TouLz7+7z3JygCBXDyrLSCvnGjX0eP9GmGymkzgn8OpHTrIiSQeWTZsfMT4a0V3uobkjhLEgfFzdBa3RUR5U56JKqtYo/7YOzCAQN6ZCVYYpHEspZZJj1caqPF4Y0fEEezuErie8FsLiBlw1AKRACNz7C5RNEowtBhKwyoukQ58cw2sPNnUQrn6IEZwVWhmgLgSsQEioVkHkBylNo60iVwuJTWUfkRWBKaMxBYwk1OcRajSd9RLOD6x9AnKBsDmkHF4SQtpDTPsLz8HWGCbpYA3MLPYLsEEWAmQqC7BCbdnBRig1qNAJozXcgTFHCYaWPR8VaTaCFIpGzyelxaWiGHuNK4i+d51QoOcbRLwydUOJLwM5UVoknSU3B2AVI6/BliTSSyq/hH7wD9S4qcmRhg5bOcEKgxofY+hxj7QikpArqSD1F6GNM2gVTgTUk0yFSZ5jaPDiH8SKCcopQHkpPabsKb7AzI1c49kYFzlgW/T2MF6AQUI4JvBixdYuse55dk3C2Lujj04xjtraPCD1LZhQLacSTrZDre2OCMGClFWOcY9Er+NZhQS2ucTwYouKU7+2VnCQhDSS9UMO0YODqLNdi4v4+69027zwccH4xxcgAg5rVWDvL9u4ey6tLbCSOIBszfvgA0T+ifulpbvYNT6s+Lm4wKDLOxhWRnyIG+1T9DN2eJ/TAyQRhCrQXIU2JVSEiP8akPdp6xH0r2BxM8VOPqRRkRzuI3iq9Zp2pdUjfQ1x8CpUPkIMtTNLGKJ9OGuCEB9KCVHjSkSOJdYUS4E37YA1EKdY6rErwE4cOEnCCQDjAgClwURMbWiSzKnQX1dGjAaFSuHGfU0FAUxlUnkOeI5wG51EXBc4oSj+ldJIAh4rqaDyE5+Pns7W+LwQTK2gXe8gwReRD3IN3sfNtgs4GEos9PiAoxuiohshG+HmOjGNcmYOzyLRFZDTVaI/VtIZfVaxmU1zQRKQtAl0gz5zD6+/Q+tCncDJCD48ZvXuNppK4MMVfXMMCtHrYdB7hZ7MaZuUzvnEXr5mSLPQgrsHgAHf4EL+3RHOxxv7rD6kt17n1K99j6bnTOBSTBzskcxOS9QPkhcehnOApRfbOW4TtGovPX+b/8X/8Jzz5b/37tP7J3+M9/7sXeJwx1R/7CR7Uo5lBF4sqJvhxwEIYkAvwOym/7isW1xaZHi0zPtidvZeAnL/Czv0jPve5z/GlL32JIAh4hEf4XwoeaX7/NeCc4w/8+E/Qrfv8zR99P8fv3GF6MKB+sss3bx/z9lFOBDhrme9FfPONA7JpyXY10+oCpMCJdsCpms/737PMxfetc+FKjzvjiv/vMIjfzlM7H0heeGKBs6fnOPGe06w+exrGE3qPn2DlUy+y8gd/lNrlq4jVcxQLZ8hbqxjh4cIavi2o5/s4JAjBw8MBab3BX/tr/wE/8plP8sKLHyQrLLlREEY4lTAoHWb+5CwWzYK3ex1hLbLI2NUBsirRfsLWsGBaGWo2w7cVB1UAYULT//6a0Vq6012MddRExVbpce245N5U8qVbx1zTTQJfkWmHlYpecUDDjJnkJQ+Pcr5zCE4I2r7gne0Rni+ptMPTmqC/B8NDvvdwzP37D7l1VFJqyQm3j9y7g6im6KBO1Zon9xvQWoD+AbIcYcM6/sE9XNrGQ+Blx8jD23CwiatyIukwwscqoNbGBRHHRuFjicoh2jry2iLTzgZFrYeZO4FdOIVYvQTtFXTSmYX1+zF8P+KpDGsIOQvtr7RFu9kGYCwCjLWMteL+2GCcYqrByJjCCZKyj651cMqfTbbKMfghzG+A56E8H6/ewIUpWInBIaoCIxX7QRcjAkSzhyc9NApV65B3TkJURwk3O/BthQsiir194igkrNUxcta4poTg4RRqVR+EpOZy4lCyEgsiKUgDqEUeMkmoK4BZOQdWUwoPT0oyDQc6pDSCQgZIBNZZ8CJ0cxnnxXhAYA1aKTICdr0OhZhdI51lolJckFD1TjEUdUaqjsqOZ99P4SOcQ2WHWKEgbmK9gCjvz6pxwxQXJkyuv0PylX9K+vbXkdJDeQGyzHAqxqiAUpdsDwtU2mDZHpF6MCo0T3YUG906TwZ9RiLgwVHGuwc541LTzyr6WcU3DyzSWMrK0CwGTLWhWU9ZqSlKbYiikA+eafOJ+iG+KXFSoKVgYgRzsuDYeuwXjkp4MxIXzL4bjfyA6miXl8cx/uoGrVrEelQh3v0e61FFWE641y+IlSVwhuZ4D7/KcF5EGaRMwg4GwdAFCKUovAZ9r0ajtwzOMTe6j5Mew2xKrVljpAXbhyPm4oCJ9RDdJdjbZDB/AYzBqYBWEiCEYD6EsYbIn+nthVBY6aH9GKIaDPcQOkMbPSNbB/eJ999FqJnZsArqs7KIcgrZ4SwvN0pQvTWc9Okc3yRIIhAgqnxmgDQFVFNM2ADpkM4gnEVai5MhY5UiEEhT4qQHwuEJi/d9WQtAsrpOefc6BBGBLfEadQgTptUsds+mTdz4GKYTSulRhg1UkCAnAwJRMYnnUK0F8GKcCrBCIZzE+CGy2aP19NMkp06RH47ID/Yotu5R7TygGI4xOofjbeRgBzcZ4HZuM755j8k7N3CVxiUdqHKKd15h8Bu/Qn2pzcmPXKToj2mdWqLcP2bycBsZpVSjKfb2qwgcpr+PiCKGt+5hkQRJwImTLfTtaxzcfIizFnV0H6/Vo+4JqHKm6TIyiFDKR9iKyGmUhAsLdd73zJmZwXdtA4IYESQzA9zaC3zj1ev87M/+7A/gpH2ER/jB4dHk918D/8l//B/z9a/8Bl/5v/1Vii9/g/xoQDWqmPZLhpUjEDNDbj1QvH5nSCgdh8X/SHwj4MRcxGMXuvQfjJhbbwGQ7c3W4v8qJB6sNCIeO99j6eICzfPrNE+t4cqM5ukV6k99AL10mliPodGjrPXQ1qLxSahmRpKqRHkBttElN4LdrYdcu3aNlbVVPvdjP8b/h73/DNc1Tc/ywPNJb/zyymnH2rV31a7qCp2rW61uNd0lyUISCghmRkiALDQi2qBBMpJAGCwj5LF9eACLsTUCI0wGIYTaQqJRR1pd3dUVuvLOceX1xTc9YX68ZQ7sIciDLBHq+rN/7OP41lrfWt/73M99X/d51SFQOo8L0DOSSip2ZxWjXp+snqFuv9hyePsbBBUzDA2yKajjIdsckrgGXS5wQMhGNE6RiQZNxHJqcMkp5gvHkoKi8aylkizW7HcMqx0DHhpbg1VEd17iyvLjhMbxyHafV3ennFl2/Pxnr6Jiw9c8vs68Dnx5d86DDyyhZjPWPvELyKfej9o4j1gdEMU1jUrwV79EGOVUXuFefY7F2YvotE+uI0w1gWqKCw5MhJgdERYTRN6lEhGRbC0RUkQYV9EkA5Zo/aG1THjm/ozH1rsEpbE6IfcVZTzEhAZBoJYRaTNDKgNCU5guHkWCQ0vJ4bwhjtSbHTDB3AeunFRkWqIVSCvaZR6h0FISPGASAgphS5p0qeXMmqTdck9HqGoOcQpSIfGMQ0QZBPtBkMrAcalwBI4byKQiHF7DKsV86RyRhMY5RDYiIFqvuFI469ChpvEaISyRCChXY5QjdQsmUY8Ogehkj3l8Bqkh855pA5kRBP/mZ0NJqmBIqZHK4L1n4iS9OBDyIdQLZPBIEzF3ChcUVklsUBjpkXikkBCgUil1gJFf0JicmEBhcpJyjqwnqHwJJ9qLnvCWxWzRJurZBgh0Lz/MLF1GCw/FCcI1SClRtkCqiItJg/Yz9kPKWr3PWHYw3R5dozGLhuMk4s6k5G1bXa7sz7lpPV93Puf5UnF2JGmCht6AJQWH4zlvzI/pjQY8sTMi7UTIw6tEu6/RO3OOECTnz/W5v3uHpWwJLwQheCo0g6UB9eSEUM25cfuQdz/yEEu5RigQvsDEhsILmnzE+fVlTLGHX91AlJ0W02c9utjjnlpiJ7Gc+Ji8qblpM+7vl2yKAULc5f7Cs1ZblrIUE2n82PHAakI/VvR8jQhQZkvMg8GZDtZKtGh7KM7D1MJSIgnWUQWJ9gqdDgiLQ/zVFxHnHiFtjhHTE3R/BTfexQqNEQ7pGmyQUEzwaUZjLV7GNNsPkdUTxGiLu3XMubh9lgqdgIxBLLDeg5AYu0Bb29ptgkfFCdo1eB/ANcwbz5Y7bjvKAWQ2QC9OkBffhRBgowyzep7iUz9H9P7TTJJVpJTooImNplQdtDKQ99CLV/DNCLX3PP3NBzjRA0yaYO68AEe3sL11YlcgBkuktkI0C45evEa20iFa30IcXEMNn8Bf+xKN9cjRKiIfsPqh91CPJ8zeeJ3OaANlLdPXrzC5vkf31BIqMaw8cZFka5Nf/rG/RffVfXbe9wjLj5ylmZfIySFkfebPfxGTxhS379FMpjz19GWycBPxje+m+eIvkTxwGZRkqdxDC9BBEiTEvuJIJGgRiIXgPaeGWB+YV5tsrQz4xM/9IvV83j4vG9Bnv4r/8af+Ck8++STf8z3f83/WsfuW3tKvqd7q/P4f1Cc+8Qn+2A98P3/1j30P6vPPcvdz1ykOC8Z3pzz38gECqAMYLTi/nLI3b5jaQOkCQyN5YGB46sEBb7u0RH81Y+fxdbo7y5g8ZnKwQIt/cfX7tlHMV719m2/43o9y+ds+yKnv/B0sf+UHkMag44zhU+9FL62RiYZy+Swn6QqSQOxKtGxHpyG0HNWgNZiM48N9yrLgmS88y3f/3j9AQ0wsPB6wCO7MHVXj2e5odDXGFEcEHbG//gT3XMbc9MhChTi5j1kck9VT3Mk+484mx9k6tRfMLHCyx/1ZhQNmPuKwdDQ6JtGKrY6mKGvesQSRrcipeECcMLeCyam3M3aGe5WikyruHEwZuhnnVxQfPJPx7mjCV6ibPL5i+OK1A9LVLTZWE+b7e0zv30fbgtm9XWw5Q6ydIe0NmH/uE/gXP4d79rNo16Cmu4TxPmHpNEJIFqqL6CwhlrcRaZ9cuDZJSWkqJD7uIZ0lsgWNyihFwvXDsrUsGEkiISiNkG1Qh0W0sbXS4E2McCUqOLQMNE27yKgkpApmTcCGQGUDRsBabpAhoBWY6pjIVxAlCLto096MweZLVCZnhqGJu3iTUHSWETis1PgoJyCwQZEqKKuamzMH5ZjdaYk+uMVx6Zn1tpj2T5FEBmMMyfg21kN5csLRz/xN5JujThNqkkgxTVeRtF70yM7xwZKFiqg8RlRTZD3FYOkKi/EVSTNn0rQ2CCE8w9jTUY5J6TmqBfeKQOmgkaaddhRHIBRxqBFCMEoUpQ90VaDwktzOUfM9jF3QVw7tKlzSpVAagUeGlr0rpEaXJ63NR6XocoxVhvLKy6jeCDVaRd96BbtYUAYNAVyUU9c1z1Udev0OkXBM0xGysay5Y5SUeGtx82MGRvDOFcNyaqitJ5Ee9/prNAeH5NJyOZpwMBdcNDPeNn6R/ZASpwkdI5mWNVXQVEoxOniVs3e/yIqueagPfSNIhSVM9nj+xgHza1exB7dwUnFxLWdTtggwOTukkyvirVNIKdlYX6arILgGWRf4KINmhiqOMb4iwxLmE5rFlBsnBQ7BF798jaSXtx3fouaNo5KP35hzWHm+fPu4JUAoRSIsMQGxdZ7NrmEgGtYiCwikFMy8IhK0zxkhKK1DBEsdICBQqzsoIaCpCYMViFLC2gXAIeoa5xxuMSEIRXHnHofjKeHgJhAooj5u6RSV91QhInSW8TpuLWFNTVzPCNaixveRwbahMDqhnTy4N608PYZ+TPAWjyCYlOA9xCm6v9ReqADijOTCgxgVmDmFEQEVp8x0HysNQngqYdDzA5ifkASLl4qekQTrkSaFk1307D7YCuo5YTHBVQ0mlSQbm+Ab3OERUink5gVE3oHFGPprxA+/g/w9HyJ7/D3s/fzPc/Qrn6fYO6E6WRAvDQmNJRoOkUqyfX5EmkXc/+xLNIsKh8GNjxDZiHjnFAJB56HLxEsD8tUeVAW6qXBH9wnOtqi+k7tIV6NsgUfihWBqwUtN5QNaBB5a7fA1j66xNMrYfuQJsk6G+F8vlVEHcfqD/IE/+If45Cc/+X/e4fuW3tKvod7q/P4f0M2bN/m6p5/mT/+2j3CpWXD7xRtIKTi5OeXwuGBa1CgBkYAnNju8sb9AACupJhGWxx9Z4exTD6KCpZ4uCC6w8vgD6Fjhqook0yzc/6/DNwK2zwx44CsfZes3fxRGG9jOKvroOn4yRhqDWj/bQtCDwpocUxdo70EZCBaHRklB0DGqnGCzEVeuvsDnfuUZfuC/+LNUXqDfHBsmSuIsCCkgWLrTPUR53AYebD3Cc3uex9YFkatafJfJ8fUMl48ooiHGGNLxPYpsm+OiYeE6fPHeCR++sMYvvH7Aaj8FlZEJyav3Dnn++i7vfvgUN+c1+7XgyeESczyH3qC1ANFwsrD0TnZ55ssLfs/7Npl+8TOsXfgKvFAsr3WxfUeQFvPUh9h/eY/3rg+Jpebo9KP0tcO/+Ans5kX+nn6E3/nwEvr0Q0hpYV5gkwxlIoJU6CQizKYIAaGuiCd3cN1VhLeEaMRCaqSryJxlQUwcCt53dthaNfD0pScog3I1SinEfIqI8jZqVQSQhkg4rJfs1aBUwAeBEoKysQQUlfOkRqKlYGE9XUrU4ohIRe2oNu60C2wCGr2E8Q21jFmYLl0p0VFEE7pYFRGXE6yKqJ1lID3ZtV+hHp6nqRuWI4XqpQzmu4i10zQHd9HZFlJIFjJF+gY9vQdYpsQM3ZTapNAIGiSxFJRRD1HN0EIhpMaZDBV3iJs5sppBnDEUbZpbLOHVSUknzjEqoESEIHB/4egmmtJBIiRaGbzsIKopJjQ0JkVJ8NahI0FuK9TkHiEfETUFIY4JShMCeBUR2ylNlCOVJkiNR+NNB21PMErirj2P1hq9tExz61WijW3KxYw4jGm6XeZvvEbYOc8n9yXDTc1aNmSbElUcg29Isj7CNyw6q/hyQi4CpzoJT19c5vb1G/z1q57f93UPkiQxYmbJEsXzZc7DZy7x1GgZ7yz++B7G15z0djirb7EvV9n2V9h1EvnaK6QPatKDXZ6Ldhh96mP4p96DGayQqoDMTTu2lgJvYlQ9w0UZmRZUUiFEwMsIRcBnS0hkWwPamuXqHnuv3OWVfIUkTTnfCZw/vcFuIRjX0JMlRR1I0pj9IvDEesIgiVilQDUlGEOW9hChboNjtKYKCS4IahdwXoAMJCFgb7/OPZWSbWwTxV2iJaCpqXqbGKXAVVgTIwPIao5Nuhy5wAYCJvsMRstI57lyUHB5pS30z0YdCh9hpEYawywoOoPszXjjk9Zio2KcTgjBEcnWKoWQyOkhOZa6u0ktNUnwBKEIJserCCVFG6aBR2U5ddylFxxxcYT3jjLOiYWncQItFXrtNFppQmfAYUhZkwGxOCDEXdjow52X8JvnUSbGLeakp89hllfQ69uIyR5Hr90luXAHsXGeSuWkqWT+3OdJLz+OTlPE2UcYXL7FG//zPyBKFIfXDnnw+96PfvUFDp97meUnHkZqgZCBF7+8x9mjfabPvcLyk5fw9/dRQpJsLKE6HfJz59n79K8wvHQGPVwmFDPc/Zst8URCmzCjkXdfoTj3PirrqHwAIag9JEpgpGRtkHD+/CpR9AR3Xnie6ckYO58gO+u41Sf5xm/8LTz77Bc5derUr+fR/O+FyrKkrv/3TKf//xRFEUmS/Jq81r+veqv4/VVqsVjwoSef4JufepTf/ugDVMdj7MISEs0XXjxgr25NDec7mlEvIY80M9t2xpYTzZnljPPvPMvmex9mcec+Ks8QSUq2c4b5S88R9TqUNrCWGzY7hnntWcljGmvpGM3G42fZ/s1fjdx5iCAU0fQeCIFZ3yFkQ0hyQhd8OcU5T0d4ZDUFnaDiDNWUBNl2Eq1JuH1wzHxR8I6v+RaGmWlTkgIc1+2B0I0FOM+SKFvSQ2el3ZaWCUu5Y0jN9blno59RdrcJAmoLh7ZhvfH0khgvAuredcz6Fu/YSemZwFY/ZVmXuJAgbc1zX3oF8i6bumZ5EDgWMWOhKcsZyd51Rt117gqNqEr+bx+5TOwtpr/EzuXLGN/A0jraLSA1LPAY5WH3HtkTp5lLjbj9ClU1QxweMSsannjkLHr1AlJ6gm3w+SpzGZGKgNQJwjUIbdp2rPN4k0IzJ+iU0gZSI8BofPOmn9Y39PAkQrHwBnRrM1ChwQeFi7roZoGcHyC9I8gYpII0IdEOrUClbcSvDRCHwLR0nO8p5nW7lT5KgHKBH7+OWl7DpgNUcYLvrqHrBc6k5NpgUe0uig94oSgqS1zP2D9xyOEInWrM5jad7gZ74zlnB5oxKavZDDm+iymPkUUK6ZAsTSmjnG4Y0HztN7FAoIpDpF4jFopcKWoPJgQKYtJQooKkERFRVSPiHosowQRLFMBLgQROdQ0xlmmjqTWMYsdRAaPEoIVDNwXaV1gVk+xfIYy2SdwU6QXL812q9DxKvbkIFWU0OmC9IDFdNO17WMoMsIS6ogwNd8uY08YTqZgo7dEs7RBlCWFyyK3sFFW2xnm1QMxPWKTLjKt79Mo54zt7vLQ5JF7P2WRKaArs+nkiHGU6JFvcwM4n7OoR20lNL0m5rnN+x1efot9pKQCL3goboSCNDLXqIwMsFjPu1YZQBTpphu6fYtmASnYY+hkvD8/zOAVVr0tX55x+9+PI7U3y6gBRLJBJRtNZpZEZotchnDjq7joehfOCyjWo/gbBNYhqSohTGhXBbIp0JfGgy6CzzPW7B/RkxCObfQ5u30ArjfCWpW7KSnPES5OMB/uSnijQjcfq5M3lzQJMRJP2UdUMZGgL8RAYJIJYwsLD4cd+gdWvf5obU8twOQYhaLIRQUWIpqBWEQRBg0ZFHbw0BBlokJiN0xRXXkBFCQ88eIbSg4m7xJM9tJYsZEImHE1QKFdQmA5BZ6SJR3iLnu1RJiMkFVRzVLVoA2KiBJQkCIlwBapegImpVU4UyjcXHkv8YBOFQAvAW5zOSPycxDYcmz7LOPypS3D1WWxZMeht4Yjx3XWEr5HlApa3aL78DGyfwaxt4dG4g0M42CfuZIwev0h57XXMySH21NuwR9fxR3cpnqvIH7yIGASS7W2qScGXnzvm0Sd3OPnsZ5i+dpPu2VWOvnwFKTUm1XS15OTlG8g0R2mNbEr2n32N5ctn8EEgsg6jJx4B1xDKOa7xKCUJ11+CwTLKe8JgA18W6OKILVvh3BJKpZTKY7VkXDa8c7vP6WHCzzrPZv8dfOIXPoWdt2ekWr7Eya1DvuEbvoFPf/rTZFn2G3JW/7uosixZSju0hsF/c62vr3Pt2rW3CuB/hd4qfn8VCiHwjU9/Dcu9hD/zbR/h+Euvkq4PmR8ueOnOlJn1xMBDKymrmWF0fkA9rrmwnHLzoKCXabae2GbjA4+jlCRdX0Ep0S7eEJBaUx3PSTqGd1zeIYkBDb2tYQs5X9+id2qFaG2jjcltpiAUPh+2KIhsgFOKExcRRwOcUEjaLqMnIJo2HjQ4hxcwDSl/+af+Iu9+73s5d/Y0iZR47zmuYdrAdt7GzQprKWSOG+7gXCCl5rARdGODp2ZVTGnIqdEQLAsvWBEF/WTA2I7Yu3/CpdUeV2m/D2MXfP75K3zre86i50dUVnL+4nlW5veRQnJMxHpxB59uc+tgwoXFjOhTf5Wd3/x72L12F9VJOX9+m9jPUZGG+RFV/zLKzpHe46MMoRTve9sWkVswvnXAtTDi0qlTZKcvknaHPDYEQY33ArRGBIfUMaKeYUPACY2WCllXiCil6azgfIvjkiFgHVQeXDwiAIWISDPTLsqECk+G9QElNNI2NDJGhRnS2ragnh0gpMAmQ/oaxg4SrSjrhn5qmJQ1y5li4VtTgSmOkTrGLZ9l8jP/E71HLlE88BUt19NkECxWRNjGEUuHqBaE2RH0Vsko2Z9Zql/5ZdY++CFi57HLp+gC3bUeMgRSL5AyQTUVIkuRtsRXE7T0OKUJSR+hI7yAeTLCKMNAg/I14zKwUtzl9df3eeDSGSrVIMs5YXwC6RCyBO8dVBNqkWOcZSnV9MOMsY/QofWLnutKrIRIBKLZPXxvve3I9dfAOZTUSNcgnaMJAW8bQtrBYsAWbYCFMgRbEoXA/Tqipxs+f+uECzsJ/+9/fIPvfixmaWuTxILdukxoZmTO8drf+kW6X/khzp7uEXqrJL5kd/sR8rTmuz/SY3ey4KBI2OhGyO0H0CbF7t0mXo4Q1RwjU1YTidKCKM95+iLYJCeI0DKDg6efJhijaKzn6knBKMkYxpLjyJNHGq/6WO1okgxvPRfPGmQzR69scUlqouWHUM0MoTPc3stw9nGcilgIgxDgOxsUXpN6i4kSpjWMfEOo58h6ge2ucWwlZnJId2UT05Wkc8Gygc8dSC7ECx5eHRLRcO/+LqtpYBVL3Uk51YXC9VjEEkkgkh5VTChVTBkMsWzoyppxSOhEAusEM++JsXQvnOINv0TfTfHeQADVzPHSIOo5pRnQa2bYqI85vE5I+3h6+KSHkYFy/QEGnRQZR3jvWpvCaItkfJdmcAYZKno4AqC9RTYzON5to6ZHW0ShaS8AtiYI2TK2rUNPdqG/AVIj6xnBarTxCB0hymN8PMDjCVIxs5I47qFciZAZwtf03vycVckQHSc0u/skvmDhhzgRE+uATzLUxBOainC8hzp1AeZzkrPn24W+NCXprxIm+8xfe50EKPcOsGVDuhpRXXmJ5KwnVAVpJ2K+sNRVzf1PfIF6VpJsryBFYHBmCfwSw1eO2L96zHCjZUrP9+6zdPkc2cNvo7lzE91JObm+T5JDMhoS7ZxFdkd4IRFxShAgxvuEckFUz9B1gQgVk/4ZnIetjqGoc7QIXFzKkEgOFqvcOljwxuc+R1PMkCpCnP8qnnvp7/Fd3/Vd/PRP/zTiX2Lje0v/W9V1zQLH72CL6N/QjVrj+cv371DX9VvF779CbxW/vwr98A/+IC8+/wU+/ie+m+ruAaFxFPdOWMwtr88cQwVf+5GzHH95l+XzfaJezMaTp9GfvcLKKOX0O8+z+vgplFbQH6JMgr9/i2Zvl+badYL36Czm0je8k865Her79xi/cQuFZ/nygyTnLqA2zhK6KwhbYXWG0h6X9NuEn6bihJiZ0/SMokdBEIEmymmEIg4WgmBmPfLGq/zgT/41vuZrv46d8xfZ6hsWpacOEic9y1EbgKG8R9iiHQfaBXPdJ/KOPJJ4L1iIiCjt4F3FCg0nImbLWKQ27Yg1CC7lNdN0hXUlOSoqntuvOClgJuLW2xxK1pb7ZMsRthijfZdfvDrH3rjBu3sVJxY6gwGpUXzzowN6SyOsiloOp9LQW0EIBbYBqdHBtwzYfo+y8cj1U7ytf0Da6SNsgWnGuGiAJUYJj0UimgVSKvT0gML0yIs9GhUROQs6YoGmDoJYBaIQWFiwQRCpCHxAS0UTFJVIyGSgCYFp7YmlobENdQgMdJ+8Y1tM3OrZ9tHmPbLd2UIRaHzrExdNxbGNGKiKWGnuHhcMsgylBAePfQh6CRZFz1pwNaopSIJnKlKY3mvTrpK2C+pCIE5zdh95F6eMJuR9VLMgxzOPhqTNhIiAdgGiCJ9tQTUDHTO2EucCUdQBWyJEoDIdBBAHh1gc4z7+aYp3PYl69XkOb75E/LXfgjYZpRUM9q9g44hI0HJ8bUGmY2ZeoFzDyE6xpo8sLanWlBh0vUCEAN61i5/OtiEhaRcxPiR0+mgJUXFCyHpE9gQ5O8GNdhD1Aj3bxy6dYzi9y9V5TjRcxzQL3n6qy0/94y/xgQ+P2OoMWXGBobDspeusv+cpDm/e4tnuo7zjVISvPBfr2+hsCZXkbLmGX3ztOqcu9uh2OgSToHTr6RZplyxKcZ1lTDODpqCz2GeeD1C2QbkaK2NiI4mlp/KeWClGWYwLgQe7gsJC1XgOvSRKBESGTn2M6yyjmjmN1LA4Qdqq9a5GOSIbIkxESsB6xcy3nzmNI8a2VBI8tYeqsNRx4KDxLIuYeYjYn9fMazj67Kf5mf2Eb/36D9Fbj+j2V+D+LlpAs7zDjhDUoY1VT5RA+AoALxUVhuADtUrRSiAdCAJBC04Kz7gu2X7kIe7UjlP9DtpXeCFR9RzKGdJbXCWYdrp06wlueoBThq6WmHQJVR6xPOxDnENofeOl6BCEQGqFFhAwoDy17CDLGWp+QogiXNLHd1cwrkJN9gjTY7AVvli0U5m104Qig7SDdDU0FUbFUNcI10A9wyY9aEoS1QERIV1NHCpwDus81vSQAZqNh4g3HkLVE2RoWHhD0BJvMmQ+QL/tKxD1BMoFTA+RKgKjEc4iVjeYv/olkgceQs72yB56lOLlL2JWtvBxwvzV55BaMHhwmyddoLk3Ye4c3aWM2XjB6rkVJq/dJMpTTj31AHc+/QYnznHzs8/T214jSRMgoPIUuiOa3c8jc92SepIYmSTozoBgDJgchEKv7iCKMSHrg4pI3IxVE2NkxIVRTGE9ksCZYUI/j3jk0dM4J7n2zGdQUUx5tIc6/UH+xt/5WZ748R/n+77v+36DTu1/N5UKRST+zYpfFcS/mI/6lv43eqv4/dfoYx/7GD/+Z3+Mn3r6/SQHx9x/8TbaSOaHBbdPCgYS3r7eYf2hdcpbJ4wurpGsLpMu91hbVGwaw8r73oEINWK4ilnZws7G1FWJ8zC7vU9ne4mlJx/FnHoQNzvEneyRb47obG+RPngJuXYWhluEKMbZmFIlJOUJEkcRDPeblFFqEA46bo7EUZouC/vmkpuWNF6RHF/hf/zJn6K3eoE0SVhbWaaxgXFt+an/5r/k3u4ef+qP/iHM5inC0W2qzhJBOKzJSYTH1wu6acTYBww1yAjtFigc94qYQazxXsK0Zq24i96/jRzOcFmP1emUP/+xq3znuzYxEk6OjnkgtZjdW3TXN7l3XPDpZ19jZyWms7VFd3mNl33K4CvOsC4LerN9sn5KYWuMr0BpfHcVNb9PSEeI2R6ZNjDfhd4GVdwl15rc9MELiDKUrdotce8JwdKIGKdyUtfQDDcI3qHrOcedZUaRRUhFaQXWORKpqIMAAYbQWlpCoK8cc6GZlJ57+7v0Bh1mJBgl0FJTWo8LklxHONWDpAfzQ+LqCJ8OkQgCEGvJa3cPeLi8zhudcwxf/wTlI++ln2rKuiQsZow2t6ijDhIoaotBIKKk5aFWi3YcrRRmvIuKYnR5QkiXuLDeRcURQYBcHEE+InUFMoT2q6sIUS9oogE6ViBgGgwGAcJh7BzlJUZrAgrrPGZywGClgzze5dRT7+TkaEquwYtAPBoQ7rxGtFG1OLKoQ2xnWCCxJZUTKOeoaocTmrgsqUXAFwcEYQjTMXhPGO/iPIiBby0o5QK/uNf6nie38FEXKzLMeIqa7SEWEyZyBfvSK2QXn2BvPuHVIvDBcymns4e5OZmyYh3l7JBmFCFkj865HSZ3bsHH/gHl7/o2YpOQDpZohKI7v8Ms2+EdS23gwUzHDFyBjAzWpOg0RyhNpAWqqhEHtwi+IR4USNcm8yVOUwlHLyyYqJSNboKQgmEkKRtPYR2lD6RS0AhBZsv2MheHdqlRSHxnBW4/D1ojLz1FaEqUdyTNnEU8aINTpOD+JNCv58gop/CGILuUsaIrFaNEIscF5b1bFCJGZ32e7Z/lmx7f5PH1mH5m0CYFoJnP6GSKmBorIoQD5SXS15RRh1rnSDyZ8swx1MJjlCBrShqdcnCyj+x0mcZ9zi8vE2uHc46FiOiohiAFoVnQOXyDWfwwwijU1oMQ9yiswRBBZwmh2h6YULL1zEvQxaT9+tUUoRTKpNRViTi4TZP2EEmKdE070SnG7XtoK1SSteuZ+RI4i5nv4RBYIRFKQNxBzPbxybDlRweJs45aBGKjW2buoiYpTpjrAQ5FasCaPql0yNCQVhOUNDRRH+UsPsmpej3S/StQzXFH++1eweZZqGcIbxEmwSyvEKJ2iVJnOXr7HKGqaHyN2nyItQtP4P7KX8Gf2+H4y1cYPrAG5ZhmX6KMQhrN5nsvUt474qVn7vD6jS+g0pjf9B3vRkYaqQ12vmD5qXdx8Ol/irMHpDs7hLokJBqWtgmT/RYRJ1sqhp4d06ydQy9OGAlBpZZJlCExgbul4oFRyktHFZc2+uz0U17b6fKxv/VzIDXCpITTH+IHf+iHedvb3sbTTz/9G3eAv6W39C/RW8Xvv0KvvfYa3/yN38iPffOHOHtiWeyP8aVlftzwyisH3Jg5jAItBS99/A0uvOcM/bObZGdOIUyEGQwJzqGzFNlZR2jdFiuDCF81mNGApbf3YTHBbJ3GlXPqG9eIV9dIV1dJLj5BGG0iymmb9CQNjTGoEHAeChLuLwRCCA5qT98IHO0t/nBWke9e56euwve+LUGvnuKXP/VJPntc8n//jqfpZTEzB/Oi4f/5J/4zLp/bRinF3/j5X+J3f+e3c1etomxMLsELRRZqDkPOzdsTPnVlzHc80WVgF9AZUDaOJVWz8DHr1R6v+RGl63Kr2mDnC1+iPPc2Lg0Dv+u85fSt59DbyzC7jlq+zPTFa7z2pTconvk8H/3D30MTdfgrv/QCf+irL/P4Th+ZddHVhPLq81SjEaloCLMxIe4i7r+Gakr8wOPzZUKUwdIZIqnwSrXvhYwRdoooC0JnCeks0hVUukts5xROInbfgDOXibwkJB3ySOFUH2UXREowrgI+ePJIkmiB8IFJ43ABbtSQRRYXwMyPOQie3khTOcVSFPDWk0uFlQYZ9YGAkQZVL3BxBx8ElQXrA/LjP8eX3/dBltKEzw0vk1cJytXEFeyUe7jRkFJJtLC41YtUKqJjAqKZk6sKVs4QhMTce5kQLFKCMRolO4jxXUJnGdtZR9MQlMGqGLU4hPF9fLlgZpZIqhm2moLqMneBuhwj6oLG3aPRGYUXWGfbdL58hTAvsLJpfeRffoXgHC4Egh/inn+j7UT7gPeeEP717Yh/fkwqhWiL873rbeytkAgBvPmv4AiPaAkmvo0q9vvP463AvvoSSgpsCHz5dps+t+YckwlMheD6nfZrhBBglFOMzvOZzz3Tfg+AkBIpQMhDlITdowOkiVEEhBTIG0cIIdrCTO5hcETOo5xFl68g8iFaTShkSqQETrVZ5E2QKK+x0mAD5EZRNA3LxmOdaHF5JkEisHEHWc9bpFd3iDctR1YWY1ycoX2D9dCNJM3xfZarOTPXw8mUmMBRFdjqdQlBsDutGb9xj4M//5M88ae/n2MP77h8hrc9sEkvVvRlTSElQkjq+QS9PqD2KSbUaKnxocZ5B0IR+RmolodNvITB44QCPMHVjKZ3cWLINBmSohHlGD09ZN7fIgRPODnAdYboldMMmyMIKUQpThoqL3G2Yt+lDLUgLQ4g7SKm++i4g6qnHGebDGZ38I3FL21hbIG7+TL1Q+/HqAhlK5RzuKSH9g7RW8alPYQ5wfW3Ea5E1FNkPcF1VltbmC0JJgEp288oDbGfoXWCDQp1cIdp5xRWdyh9YDkB2cxZOEFXW2QxgWKCXjnH3Eu69ZyAwqHBNm3M8sppDCXhZI+mmKE6a8SnH0D2lqC3ihQO8WZCIyd7qG4fzj8Or32BpccfRIbA9PUbVLMKOy/I1obIOAYJtvZEvZj+ZpfBco+DmwecvHILkxnyzXWS82/DS0Pnzk2EFJjhMrK3TDjeR1QF/vAe5F2CSQjjI2TWRSKgmiHjlGR2nyA1IunhfYepBRkC794Z0okFr2/2GU9mfPJn/xHpYJVCx4St9/Nbf+u38cwzn+fChQu/tofzv6eSgn8l7vRX9RrwVuf3V6G3it9/iSaTCe9993v59icv8vUPn+fgmdeZ3RmDgmuvHVGVlm4EK/2U4VaPm3tzVh47R9LNkNqgN07hRmfJ62NCYxFL24j5EcEk7SGaZYSyJlrfwJ0IfFkwe+4LiCii88jbESaFpQ2qzhrpvZdwwSOqBUrHOKm4GfqoRtCEQE8LHGDwmHqC10N6bo5eHHPlRonPFrxw75i/8Ld+nj/7F3+aq1/+AqdOPUltLT/0R/5TvvIdj/CN3/Jt/K7v+Hb+27/0N6llzCxYQuXo05BTskePonH85Kdv8RXNdYbvfIqm8Vjr8WVJz55guhvIO1fYHDV4IUmFZWOQsRcq5HTKUqwowhI9YbFbD3Fs4eVoCXF6mcfFISvDHl/ccxzPKtKja7ggcacfxfiG8Pj7sNdfITz6FP7WNdRWB1WUhE4PkfYhNMhqgk8HiHJGSqBR4O+8hOhvYANErsHMj/FxByccxpbk1YKTZz7LcPMcThhs3EOHNmK3FAmVtYQAx6UlURrrBTklTS3YLz2zsuYXn73NR588xfmlIdL02dYVV5ucREEvMVjvWyA/gUZIpI4J0jBr4LC0BMAowbkPfwi1ssrYKs6cOcWpvmFSe3rSYcwSC9OOfTWekCTszx2JVijnCWjmXuKrihAy6rt3aZyjGoMt5zR1jbt/iyYInHM0LuCtxTqLc759Vl7/JAJQWqGkRCmNFK0XV2uFjsDqhFh4tDLoKEbFGmFiJB4BaCmp0z4Iw0kT2EktXmckokF4S3P7OjaOSY2i6KwQRQnPfvyXeWV0mWuvXaMebfJkfMw4RHzkXQ/hP/4xsjOnWVPHVGeeRBnBWA9YiQOhmiN0hKnGUJUEpZnWnumnn+F26dh4x+P0O5rX9wtWz18g3HmDTMEwk61f3juUMdQypigLfNoja0qcrXA6ItQNpRAk0702cU0leFdRyQS9OKTROU5FeNcQrMM1ktIZXBOoj6fgGmofaBoLoe1+OudaFtibkkohhORES6qg6KYxQiryWGOUJC7HqO4IrSSRB1PdxniLiHJ0MSaOhwgniOcHTPrnWJElIvbcKzyLug04KZzkXNIwXRxy6v/y1ei8x5qSrHc6dFJJRo1yNYmJMUazKBZUKsEFh1UZqQLnFV61vneERoY2JTLzC4ytcdGAsDhBo+kP+xzqHsNeD+8DYXyANArpGjwKFSe4KEHbEoRCLo6p8gewQbGiKzrze4h0A4HAS404vI2XpiVKVJ6kuYPwFXflgJH3JN6jHnoXJBFGBKreOkYKXJBI06bGETy+v94WwTWIctqmOXrbjvjvvkywDjlYbWPcpwe4tEvlJZmbIfqrrLkF87TDuG5YjiQzZ4ipkU1J3VkjsgXC1ix8A0SMnaEjLaG/hnAVqr8Ge1eQyzvo/Rv4W19G5T1wK0BAqAQRxQgTEXYuUfzDv0HvnQ4xGGDGPdxkzOmvey/P/bl/yMrlVWa3Dll69DT3v/Aq5eeuIQ1snR+x9vgDRM9epTiagwezdRbRHcDrX0JpQbJzBpFk+Pm4tTvt3UD2lhBS4mdT/GKCXlrFlwtEb41GSFQ5gb2biFMPk0eC/bkljTSREnRjhZQF733nJV594Sp1XaDThKmtmc3u8/Vf//V87nOfo9fr/fof5P+OSYmW/PNv9Bq85bP+1eit4vdfIO89T3/VR3h4Y8Af+eDb2f3c60zvjFl9ZJP7z9+hrtslizO9hAcfW6OzPmDjfRfoPfEO6qsvY3oDZH+V3XyTc7NdZHeJsrtKXC8I4z3qrYcxm2fQnT5EBjVap7n+Csn2DvKBxwkrZ5BYvNLo0OCXz+KRKLtAN3Nm6RpHlWXR1CylEQ7BSmRxQbUFSzOn62YcXHg7v2/Dcm/vZX74+3+Yv/z/+YvsLyYkaQY65vf/3u/lq7/ivfzeb/kafuDH/yzf+/t+P9poqjqwlGmCbXAv/gp1Jyd+4O3UaC6udXjPa6+yP38Xg7zL/hefxa+vsnr6HPKFZ1hcv053e0Z89hJrF5bw9wvSqMc8JCyf30T1V4gWuwSlaBrLhx7oYNIM/eBHsPevs55t8QPf8g7C7Wdh6yJqch/ijNgXqFjC7Jiw9QCSGvIeIu2147pqSsiGLV5LaaRvUOUJ9XyBiU5gsIZ0bViDwJEIx9QMSE2H4XvfjxMCWbRRuaKc4xcTbptVZnVL29idVdw9npFEhrNd6M/3uFb2iA7vszzs8NnXDzAXR1yubvC63aaWntoKptZRW4FMMkbOYpRCFSfMujvcPKnQCnwIrZd2fZvMSI4mJV0dKGcTqC3H5ZTdumGyfxXhG7y11E1DVTdcdQ3O2n/WVTXGYKTAiEBkFMouiKUjzXMMHhlnBJMQK4htgYwSXNRBC0uEJzm4il06Q6imSKGod29hzz5BHBlUPeNYL9ETBTiPVu2mflQetAuCrsbHHareMqFp6DceEyckfoHTXZzzaCz79xe8ev024j0f5GG3y/rakBfv7fN7zta8cPd1NnZOUR9PqZ7/HPT7XHvuFdy7L9P3Dc/d82zlB4zzGHP/Kt2zF9q87mzIHEV44xnyS6fYns/R8wPM5sM8OXuZIxWQ22eJmxny8GrbDTQpuj5B1cckUYbFYkSD7bcsVYykLxXSLBGiLrgKn24yd4JuoTjQyywxR/jALOpReagcICEEicGRRoqjyrEWtd7nSARKH5hVnsTOObKae+MpZ7oR47IN3rBNA95iXUVZe6qTKdZaXF3TWEvjPHCzvWzou0htEEIQH7zGPSVJtaDRKbnWTIoKFaeQJyy/810QxUzmcybZCtvlXYTooHDtRMRb4jimqUqy0OC1wgpFGVrqwdxKQu0gaFb9nFk8wMsUhCYApe5gcLC0Rb8pWxza4pDga4Tp0Zvcpl69gHQVQad4oXBSEXmHLKfouEfiZgRl6EiHaCyNyTDFLVg9hR7fZT6e0cljBIGVrqBxkloo6K7g0ES+oAy6ZWlXY3AOr2J0NaXur9MEQdQ0rW2qqfG2QUYAElkctZ1UpQkCvM5w3iOFIiQppqnoCTgrPcotiNMOnfERoZkhU0GTLSNvvkjU2UbVM6ZLl1mf34KoDam3OkY1DhPnCFp2MmnW4ih1DOUEiqLdB0hSOpceQt55Fbl+Fn+0h4xikv4Sm++/TTaU7L9wFdOJmV4/ppzWHE9qHnhyh3jU49Tbz3L70y+TbW8Sdh5FzA8gycgeuIgcLONCQMwmyAffhVgct37y/hpC7cLkgBAkqp4SRM6is0EqDXpYI1TMsrAMMseRj5nbwOuHC0ZJxEon4ru+46P8o8++xvOf+hz56jYzKXjj6j/i27/92/m7f/fvIuVb0QJv6d8OvVX8/gv0R/6T/4Tbr7/Ez/+Bb+PaZ67g7pwwLxqOPn0DUzftYpKTVD4wOLeOjgRLl063Cx9K463DDTbIlIHuAB9nbbymMdSj0xyKAcOHP0C0OIAoxt+/jrnwGN556o1LaF+hTI7wDmkd2AptC2xvHTU95O60biEPRgGBSEOkBIuqoVApFk3oJEgV0RPHfPv3/TD/r7/wE8jBGtMbXyIfrfL9P/jH+JoPfxWPPPQof+1Ld7h+/QZ/4k9+mFdPPE2AVePpx5af+v3/Bcff8wd5T6/g0WXNRx9b5+zbfjv/4FbJ05sLIiz/wxXB9+00RIMOVS/hVv9BNkRKdvXLIDV5vkR3tIqrGiI/a0f+x3skWafd6N+7jgsShWfT7uHjU3igzJZJQtNSHHxou+GLMZXKMJ1um1hnS2Q1w+bLKNe+L9j6n+XP624PkeTEwRJUBDhC3G2Zx15hfI3sjphYQS07RHU72hX794lHLbN24SxSCuZVhUfwK3caJlMoQoH5+Kf5hv/4t/Err+8hAlTEPL9fsjs+5txFRaduF58itYw92meSrWGLwHR6D18W1M4yLSpcU7foNdvC/hECYyLiOCLWCmFikAITJ+Q9g0PSCEMVBOv9jI4ORFgiZ3EmbZeTgkLhqBEkrkB4Rx31cCYjqY7xaoRHMvER2paIZo4bbBBMjBUGlXbQaQ/lK4QXhKjTdu+k4cDCtj2GdEChu2TOQTGj7vUITcXdoxnLbzyDfPcH8Q58OWEqczrrO/SSAn3nKgWef/L6IR9aWfCb3/04zWKZM6/+E/pmCxOOebkZsVIc8fqpJ0jzEd2mIXr5C3w+P8uH1XVmy5tkQmGTLlJrIudods5BlpPXDb4zIvYl5cpZIqVIfAl5F5c8hMWQjm8iygkhHyGkQrsCmjmijlBCUkbd1jeto3ZcH+V4qfF1Q4g6xEoh5zOoK1TaobDt41TSTmHcYkogxliYq4SeqFnIGCMFc9+QxBmdNGKEZNCPCY0kURIlPBDomYBZHDOLRyQ4TFO0C69H9/C2oh5sM1M506LkYNHQNzAvaha2RlYFxaTgyDqapmkLalqLVqQkKj1mbBTRcUlkDEZLfOwQStMsFjQ+EIkGJyQCgZIB72HWeJQEG6fYoKmbQC4qdLCEUFHbQJVE9ERBWBxCOcYunUHIgDQZXsU4lSBtSTUfUw+3MElbgM/QaBXBwX1U1hBcjRvuEK2dgzdJK+neNfxjH+QERXANkbf4AHbvFneTHc72E9LpfZRWeKnQk13EYB2CBSEJdQnCg1LY+3uIlQjiGpZP45Mu0paIAK6zgrILeuWi7cYmPUQ9x3fWURqiYJHBot0cn7RLoYfxGivDNWJpiBczeqJEFZMWD5MPCIsZ9ebDKLdA9JbaZeIohSgjHN+F/hpy7Qz+5A5ydoxKI2R3QFAK1R/h9u/ix/ts/6a3MX3jGtlSj/ntI47vzokSTZoZjl/fQ3Vep3dqmeXL28huH47u4F2N3LrA8T/5X+icqWj275OsbsDxXURvGYTgyCwx6EnURnsJE8ER6gXxZBcdJ5D22xQ9XxJVc7QQFNkaLx14pmXNSidme5Ay6D7GnXtT7j73GVScEc5+mH/8iX/Aj/zIj/AjP/Ijv97H+b9TUr8Gtgf1a/Ot/Huvt4rf/53+6k//ND/xF/48f+23fIjDz7zB9S/d5/Rmzst3SgZKstIxHFWWCMlBE1h+9CwqTcm+4j8iHN6iefTD6NQgpSY3Ct/fQHiHWhwziVe4V2uUCwjTJ9bj1g+2GCMuvRe1OCFxc4RtQASEkBz4mLXFXZreOosQ00Qr3N4reXQ9Z1pZskjT0Z4yKNKwoAkxCzQ+aERV8x9/9/fye3/gT7K1vcNscsJ0OuYzX3qdnoFv/9Dbeb3J+bPf/9385f/hL7BbBE4qy/nUsFTexpmYd3zjB/juz1/jG7/1a7B2znxq2f2nv8S7Tp/mmVsrXHzwIb5t94C5Cwxx3F05x2FnmerggAu1Ix2kDO0x3vVoQnvuBJMg6j18MWEyukBeFSz2DuhduozQKWK+D3GOUQIRNE5pyAZ4qVnoLuZLH4d3/6bW+9mUBO9xOkGVY1xnte3+3r+G3ziPSlJoSkI2QPiGIhoQKUXwkFMRQk1dTClqzY0m4VTHg46okhEb7piZyehGiqGbkouSmyqhPrzOL1454Z1vf5h/uP4wlyY1p/sQ12OuWEVy73Uu5jEvXm0LD2ctzrcmU2X2SaLonxW2Ukd0uj2SOCJPE9LIEExMhWIpFq39wNcUTiEDQIGWEntwj13Tpbx2haZzEaU8UgnE8QHNcBM1X6CzLrIc43pbiMUCZseIo1eITz1EmBwg8hFaQuIh8g7ha5ruGkpHROGEUhhSbXC2RgdPOLpJ1l1HqIggZRtFGwIn5CRyzkmIqRYNw8kdZrcnxMs7jAIcXrvC0nKXwcFLqK0LmKWIzke+Bht3een+McX5R1jTnlv5Ke5/xdNEoxzbX+WcXWDjSzzaeLqyIOgOGw9fJk5GdBtJx5eINwkIFlAioFe3EAF8EvAYKq05Ch0iL4i8RcgOtYxaDNligu+29gcbd0FF6KbAmZzyzVS8SEYcO8WSmGFlgvSeEgUyIwLqqIs2KRKJEW1xKYIjPbpOlfaYNxGZCnxpb8qjqxlVHXDeY6Si8AJJoJNEnNSCSAWMgq6bgnUE2cGrlMg3WKkwEvAWOdxANnNcd0BPpSRxBGkgUZLRwJLEhlRY9m1CV3uCEHQW+xQqpZ6cUCNwzlNb2yKWypK6sZTNMVWxwDnHJz75KYzW6DghS2LiOCLolDg26DhhImM6siLWCSKY9vcQAt41TBeWPEkR9YK97BQDb5HZkNDMMc0cnw2QiyOiOMIjaFQCb1JivDSoKKaJYmZzT44DLNJafF0CGiM9pUx4fexJIigXgdyMeO1owUhr+uUMX07RS5t4pVsiDAIZPMn0LiQZQSfIU5dQ1aT1i6sIn3Txx2PQGlxFkAYVxy0OzFmsjGmkQnmPDI7SK+K0/Vlm3VPkUYzwywgXsBsXiZSC9bMUdUMSx8jJAbN0SFwdo6WApAu9IUz2QRmC1ITpAUiDG24inCdUJRAQg1UoK8LeTY6fewlXlHR2ljl67R7Lp/rsXz2ms5JzfFhQfOEG+vlb7Dy2BXWNvXsFJQUq71HfvUfRFBCnyNUtqBe4/euo9XNIAWU2JJPA/AivE+zNl4nyLqyeQ9iy9eSbFFFPSao5ujPi4lLGL75+yPZAM563DYizj5xlcu8G8+NDok4fd/Zpfvy/+q947LHH+KZv+qbfiKP93wm9ZXv49dNbxe8/pxdeeIHf/R3fwY995dtZKR13rh2zsZxw9e6corSs5BF3S8fB3LI9THn6ow+SfPS3Yg5vEJa2cEvbWKc58rAUS6JQgU5w1RwrY/Z8jBegfKAjAotsnWT/Ddi53D5odYQUgkm2TB5KRFOTJRrbdKl1l2kTmFQQBHSM4riwZArwgbkTLEjIlKGj4JXrd/nhP/z7+Nbf9Xt5x5OPUzYNd199BTx86mf+Gj/7l/48fnzAs7/8ab7xN72ffGWHce043VUtL9YYFB7xf/0unvrcHUrv+XP/4IssdzTFz/9jtr/1m1l/59uYacP6cIY4uAGzY9aGW5jFMXsvvEj0cB+5OIHKoKKIpGmQJiLoGJSimS8Iu1fZy7fpvO0RCGPQBrmYIQZriHICgFQRPjiEa4jinPjyk4jZIXSWsDoFFSFx7bKKrdoFlvXT+PvX0Sub2KRPIyRJ4zgRKcsCrPAIJVkQEe5+kXhQs9YY0u42XoK+d5Xpo+9lUpYsS4u8+2V2O1vsz/boBMdXbgvs8XWePmMpb76IEIImijgqAzu5I0oMJyRk3SVWl4f0osCgPGKSrdCd7SKyAT4fcrOQjFKN9dCJFfdnlq04sFcLhFKEAE5oIl8ilMLXHmZ72GKOdjk+eKovfYbx6gqrmyt4E+HvXiMc3ibZ2kZkA1JXEOIOwnvUfIyYT6niPub4Hn56SCxAbD+E2LtJ3F2mjjK0UFQBct9wIPos2RNmpkdFQlcKOtJTlg1ydodqAc7eQ/TPcHL7DVw5xQ9OE632+PTtBZvXrhOd/1oGviZozUKkCK1JheVrHxpBElFJTeQEZzeXOKVLFi7H39nF9wcUX36OpqyZffDDrO6k9FRKVDRvLipV+Chv2dbeIqxF41ECCkAqjcNTOJDRAOMFsfRIAQzXIeoh5wc0KsGjIOlRCUPtAoGASzvc2q/oDQZYYYjrKV2V4psKmXWYh177txcMXdPgFlOuHpY8tNhDjLZITcxR6RjoGdHBdcadHV7aL4lE4H3Dmnk8QgiJC5C6GkdCiDuYMMMXR0haC0udjGhX8RxWGly+yiSkdAmkoeZUInFS4ibHRNkqeEnwNUeVZDmGOh2hUDDK6cURpp7jZOspnmdLRMEyDQmL8Qlffv6LPPXOJ/B1zWEtEK5muiix1YLJSYmvFpR1QwgBrTWp0URpSmLnFCLhWnHCex5YZ6AlXV1QmSGNh0wnmKNbyHyAz4ZQz0lCRWVaEksmC6SvUVmODRo12gQpKIMkPn6NqCoIqcYf75H3V8nzLrkRhKbiF24WnBkkTEJENJ8hu8v0gqXsb7XLiypBeY/rrKKLQ4gSyIYgRHtxnh+gEIT+OkFGBCHwQiKbgJDA+B5lbxu8J5vtgorI3aS9yHtPLByECkIgSXKckCzZElnOSG3DPNtB6ZiOgkrn2FThXv0M+vLj4B0y62ERMNpsY58XY0R3CYEFoUFqzNYZmuk+41dvsfquh+g88gjafIHD128j3zji4je/h4NX73Dwxeu8ceWY8x99Arm6gzu8A0Jjr75Iuj5Epgl4id27Q7SyQdi7C2tn6VdH1OkAkCBkG8ZTzBB5p7U0pX1EMYX5EWIxxQ9WUSe32R6e5wPnBswbmEWKW9MTPnh5lWH6Vfzi3/55qtmYECL8yrv5zu/8Ti5cuMCjjz76G3LGv6W39L/qreL3TR0eHvLBp97Pd146x0MTuHlllweeOsPtL91mUTSUXuB84PJ7zvB3fu4VHlvOeOh3fBNutAORYRo0SkfEJjBZtAlbEytZcRNcnLNbSCIlSVUgEpC5KTdtztrqOU5qQSokncwgfEUlDYkAbWsiW3PHLFOX4LzFaNjoxuzOazY6CucCMxQ3xyUruWF36pjceoU//kf/CD/4p/4M/TMX6RjBZ18/oGtrfubv/V3+uz/5/US24Nik/M2/87f5+3/9r7IQFR0tWZrcoYxSSiGR4/v08h2+9f2XSKd7vPvyaR5b0/id76Y59zjDZkx5cIM8lsije4T+EjOVcdyUrC7HiNkJZBmsbBH2b6O3LyKmB7j9u1RHu9w9aNjMlpgOt9rDIxniEShOwNWI+TG+vwGuolEZOrIIFSG6q2CLtkOSJHiToV0J5QyNQpQTfH8T1R/Bol0y1HgwCQmOmojYFZSkCCloVMQdn+LLKfXtqyyqmjE57tlnaBrL60AsAqE4Iktiok6XuJMwfv5Fzo9y5jPLpXdeZtzf5vDePe6rDszHxJ//FC+MLvBbvnoDefMV9jbO0Pc16ISZyUlcBST4EAhv3tY9AS9AC4FzAhscPdHQRAmyGKP2r7OIuvzt3Zxv2Vmwt3aaM9kq9ubraDGC0KCWt7hCnwfyhFRUCKGhniMWYwQSjEKnOf7YUb72Mp3HnoSkA1IRlKaxFV62LGYfJEFKpnf3CN0urqlxUtGUJdODCdPZgm4zpzp/DqkT3Og0h7MJYbjBX3/lgPdfXGX0/g9z3Ehk7xSZmyPShEgEjkqJzFbbgoAIMz9kd2I5tSTBdIg2z+KDY3TxAqNYs0hTkjBDqDepD1EK1QJtcoSJUdN9ggiEdIAQkjTUeBR9I5hZWkazc5yJCkS9aL2W9QwvBEk1pjA5lelhRGDuA2XtyLVhJVMEEYBApRJSVyLqE1xkMCFQC4N1nryZcrgo2exFyNE5OtQ0KmWgLRtdaKaG1cizl0f8xZ/7PF/5TefpiIJ5rYm04uY8cGlY44gRGBrTLr2Z4hgDtIkWglnURZqICIh8SaEzIgUieMZmSNJAZBJ6UUVjA9NGUDjJKNM4Ao0PzEKMEYIYQWIr5jolDY64nwNQLhasZZq020Now9xrEi2Y1Z7UzZBJTlU1FIsFenKPY9mF44pJDetRyZUrr7cIMtFaddI0pZMm5KEk63qSriPzNZESkGcks11C2id4C1Ihkz6Zs9yZCbJf+V/g7GlS4RFLG8yLmlmzjx51wMOwm5PHNR0akBKf9NndnxJtDQnKgNJY06GmZXs7lSNVRORDm8joagTQJANUcYRNVnBBoFwNIuBVTBisk8z2IO0jhQRX4GWCj3NkU6OLMVILrMnQ1RQpJCHKUdWMUC1oupvY7ipmMUVd/SLi4rswj76HKu0ijcE0c6RUuN4KarpHqOeI0Q6hmoBUiE5LDpK9ZYKJSJcHSCWRRnL86gHLDy4xePuTJFub2PEMe/WYk6v3WXJgRmswOcTNF/jGES11cWUBIeBVgohT/GKC6MVYH0hcA9WcMDtBr2xD1sWbBBBweItQTBCDDTjeJ/QHJDgud+GFscRpyWYvoWg8X/+OM7xx413cev45JIEpARfmfMM3fAOf//znWVpa+nU/5/9t11u2h18/vVX8AtZavuYjT/NwJ+F7nrzE5KDk4XddoLy3z/y4pteLWNGCfGXA+ijhoY2cTqqpLn8lUkqO8k2aIOlKSWUDWgbmVoD3by4zBeZesKwECsilxYaEJRlQ2pDjCCahwhFcTIanRhKSAQelR2mDrT2dSLI7axCijYqNJDTVAm1iUi0QSL70mV/iL//En+Mv/uRPUmVL6BCwJ/ssH73OUVXxkfc8ztmNFRqd8Cf+5H/OH/9//GFiO0ffe5Fw+lFMPWa/FjRCsloXNN2YJaM5OfR8aLVmPGoxbo3wHN+/x3B7B+kLFs98mruPnaejNQ+rfbo7PaTPIetCPsLfv9l2ZoUg2Bo6a7z26S+Sz6b4D6wSJR7oU5cViTaIN4kIPh+yaOybEaj9lj5gDM4aCO340WmNKOegW14tcRur2YxOoe+/hAiepm44KQPTcsrszS5WsShYFCXWNqiT2wgTM/ARSgh6qxus65rq1ZeJLz9OkvW45VO2uxEmOIJbsL44ZrS+hGs8vpiRnLzIhX4Xl/c4HB9w9vQam5PraPsY1dXr+KxHvbwOnRVkvQDnWVU1+JYsoULDKJYYLFpKlKuYesUgFBjhEPUEt3+LTqfH+xZzjLpEkuSYXkp49CnE9D6kfVJhObuxRBOlJMUR1HNkOSYoRVg/i9+9gUq6eAHp4++FXh8vDXr1FGFxTBQPCVHWspDbnXsqEzGee/qRQy2mdOsF82d/BRHFDHaWuX84Z7TZY2OUEm5/EbG5zM9OS8oGXJ6SI6ltwB7sE6+lgOekSejHhlmATMNERoyuP4eK1iiXV4jzlEQ61rKcJkoxIeBlhvVQZctth/fkLs7XKOtZqBRnDL16QVAKp3OC98RSELRkYQNLukEdXid0VwhBImxFk/TQOqIKmsYKloxjRMV1DLqasJKkBOsxvsIKgxMKIw1ealK7IK5nlDpmEhTX6HKxEyPcAoFF2yldITmpGqp8jTgo6uKIjz60zD+9H3hwNKVfTWHtPINM4fCUFiIZUQVJN5RI77AhQFODhEx6XHAYX+KFpgkaFSwiCJIkofaSrnCt51WUZDpGikAqHZNaYn0gUqq1/SiNtgtiAkaItrADJqVlpZsRTe9hB5vkUhF8gwmOY5+w4WuUMXRzje+cRssM3Ys438ygv8pCJGTzfQ6mcw5Fh8xXTMuG41Jy7+4+i+IWzjmMVqTRVbJIkcUnpHmXnAqz1iWtxiRfeIbZYI0k6VDnywipKXSHcPcG5uVn0KfOEUvLe7aGzN94Cb0Ukx++wcnnXqL+wHuJzr0NKRWEdqmuLBtuix5nk5Y1HIIjxB1oYgiOMl3CB9UWIThq00VLSdAapxLi8k0GtTaU6RKxkvjRFnL/BmKwghQ1nkAwKcI7fDmHJEd4Tyob/OII6ppofoSPc5xvEKqDkxJZzVHzQ3ANbuUBpDG4qNMGqLgG4i7i9GXO/I7fjul1cNe/TLQ8Yv3JLeyipLy3C64hGeVEAsav3GHvZ3+G1Xc/QfTwk+jjPZpXrpCf0qj1C0Sbp7C7N/Flgdy7jRCatJgh8O2lwQPdHmF6iB9sI+++gsy7bfJjMUEpg4v7ROUJQSpOdzNeHysirTheWHYGhscv79BUjuuf+2X6O+cpuwPE4Sf5tm/7Nj72sY+h9VslyD+vt2wPv3566y8P+JEf+RHuX3mV//6hhzl6eY/Nd50h6ifEZokzZUO+1mdxf8yp7/6dfOmH/lsef2iFS7/1Q5D0mLgWBzVQnnkdqHxo422Vp+/bMZ6VPdYzSUJNIzTSO45DxJJqcEGSUlGEQOnBB0kqA9YFxlZw1EiWjWQ5CSQycNN6eonGSNX6NakpvOJCXPATf+1/5jOf/CQ/+t//JUarAw4XFr9/m6KZ0BUFn33jGt/9+Ab6jWf51DzCz8e87/JZpv/k75NdegRdHDKLhuyWCcPM8MsHQ+ai5smNBBcsh8cLdv2Ei2lFWRkmpWD35etcHgXMQ4+x3RzRiTTSzpDZCBEnCJXgXYMcruDrkvrebczGDrFJWfq2h1lJJ4QsR0pJONklbmrwFoarhKSDDA4ZBL3mhNrkBGHa7rCMwNdIaBFIQiOjPsXJLovKM62OmVrBYjaleP0I5z1RFJEmCWkSk+cdRqMhXr8ZPICnEZrIFlRRjkcybwLLgxX2RUIaR2wEQaICYjEhUaDObhN0ivEWAKVSnEm4ICwPnB5xeHabXnmCyvt0P/AViHqOSyJOvMRNZ9yqYZRourEkSQ2mKYl0inINPRnQzYKuyhFH91G2gsWU+uA+8doO57YTdKfLRRYE1UUiEa4mJAPCwVXSdIB3HeT8EL+YEFZPYU2OtCXloiKrF4ilNabZGrF36DjDuwovNVpJRD1BAlbn5KFB9Pqo+QIVZ3gkVdRl+cm380uv7RONzjCflxS399lZybHbF7lyMOfyesJqR3P3aMIDK0OEFxwky2wujttLX5TSiRWTxjOvPXm/S++d76JKYrwDi0DjEFGCDg3GC1Q5IcpWAI3Fo3TEbhMzFB4vFVJo5PwIaxKqLCMJFVIoIp2QNTNiOwdnce7NFLngCUiwDValVN5TS02sGrZ0jfANwWlmImZ5cR9NoOxvYaXGokiURpWTtlNuYk51BXk9YRokpeqSNJZEg+4uIeqS0sFKJ0WoigOvEb0Vajckkrr92xIKBYjg6FC33XelUNUE4WvQMUobgrOo0BIEUiw6WBoVM60CnUgg5seIfEA638cnPXomI8wnJHpEVzsmViFVIChDYyF2JTYZouaHaCVZLGYEuYQODt9UCL8gxBkdvyDEg/a55gNWpy0lAoeNuu0CWGiJFt1mRv/4Dr2L7yKrpxzFW3QVLOo3UYjFMbOipKkritpRFHPG+4cs6pri6i5KQJoOMYsF5ckEH3p8/IXbrJ7d4Wu3tziu9zhZWLbTwEBO2O0so8rAyrl3sno0RZ26hFUJxtdE9ZSymJHcvUG0/jDjMoO8g/MBHQRJOUGUc9ToFLcnilMdQxCSOYKes+jyiDJfRh1da5e4pMTQ4INBxDm6twSypTY0pkMRND0/aS1etmF2dECeaZrRDmKwgWtmSFcSKUMoj7HJoLXtCEl95ypyuIOTEbXUCKVaLJyzEGXEj74HJwTh/g3s7fsM3/UYbjzl5JXX2f/SdXxZEsUGGSvylQ5+OsGNp4T9Owze+xQsxohyjj3aR66eAX2PMN4nXHkW3V+B1R3orsDuNcKbhZjcv4qwJS7bQow6cOMFyN9M4KsL1OEN+r0NNvMh3bjDraMFn79xjBTw5CPb5Pp9PP/xj9NZWeNw7YOom3+fH/3RH+WHfuiHfmMO/bf0H7z+gy9+X3rpJX7sR3+Uv/1d38DiE9forHXYe+EWZe3YefsO2XKP7vqQ3qUHmX3mUxTzmvLeBPGuryLyJVp2SFVAaElwEKs2ASwOHqTCxx0Iga5fYIpjvDaUpsth4eh2DMo7pJQkNNxbQD81xPWc23XC/XnFmX6bTOSFJKZisxfTkZ4kzAl0maguc+v4sf/8xxBRwh//r3+CWWOpHS0H9cZrDFdyrjnNC69eRX/4CZrJAT/2p/9r/qcf+G7cnStIo7G797BJly+MYy72HVfvFSxvnOdUJCjnUzZvPcfuRKD7U7InLnD8d/4W6w9scTxdUI3OMDr1AMrXyKaB5U1CVdBS/WtEsAQC93/u58mffJLERHgkTywHpBrgvW0xP66CrIcojlvPnU6RCDI3Bd8gXUUzm+BEYOo9i/ER80XFcSMoigVVWaKUopMYOknEoNNhe5CR5hk+G5JGEdHsPj4bUuqMdHIPryzSLRDBEwFl1McoQ/AN+uQ2x/kqJ4XlylFFYhSPLRuMEARnsVEH7SpC2kMsThBRhDcZyfwQJrfYGG4hOgmV0ciQ47V+E+MEJ5/9OAdrl9jo1eQ724TKI4UgthZhC2LvILg2vGL/BuWdO20Ua5zh6wpVLxB3XyFeOU2oQNQVwtb4yX2mn/xHJCtLmNMX8VWBK6fIpU3qNCESkurCO4hEg9UJxzZiJZO4EHBJDysMWmui8V1CvsTUCtLdK9xOVlnpjKgReBKkDrjhEufOSK40EZuhINy9ghQj1J0rvNJ7Nw+uDRjqwGpuEcIRhZKsA9qCzbqsCABP10hujC3nE8OxShgqSdN4QHLsDQPjiFwDAUqdkcz2mGcrSCRjM6QJhqACaXOMQEPSRR7cIBUKrTRNPsBUY0xxAFEXkfVRxRjpW+SVufU8jDYYLHWYB0PtICkLUq2ooi7C1vT8AmEL2jBq8Ai8F3ilsVEX6wTKSIyzzBYV982QXKk2hUx4po1nKByYGK07yLPn6ciopSnolEwFjAwEoLDQURqDR82P8EneTjQCBKFwQiJtjY1yrAskB1ewq2cBQUxD6QxZNiASAuFr9nzMbCHYMZL85BpiuEFuNCIIhI6odI6Z30eVE0RTYJSkXBSo8hhsg8QTbI2LO2BytFRYmeGdR0sw8yOUSqi9B+doAnS0AJ1iTz+GRyNsRVecoOIM6X37zMkGCD9mfdBFuAan1tBNgU96uLqkvPMGMyuYTRZMveD4znXOZRVy72U+dWgYJYqAZzJfEIYbnF3tsrYyRAow7/lNyDjD2QJlF23kd+Oxm+dZsgULNUB7i/YWMT2hzlcIMqaWEcNIMraeXhRhqiPcwU3QBi0i9qeWdVmwl6yzNjvAZiPU9D6hWBAGGaKcoiZHmM4yPu/xF3767/MTf+Pv8qf/xA/x9Ac/gDAaqrq9ZBK305gAsp5DU+OzPub0ZZxJsd7jg8cpg6kOQChwFcKk1Mky5pH3EKqGu5/4Iqe/6WmKa69R3D9kfq/hwSc3yNdaK1O0skQIlurohMgHdH+IPdyF4NHLO4iVHex8ihqsgK0IShKakuAt4e5VZJ4jVATdUfusG9+FTp+Q9ggutEl6+ZConrLe65CYhNV+wvO3J3TTiHSQsNa7xHw+5+DeCXWxy73Be/nRH/1Rvvmbv5mHH374N+z8/7dNgjdDKv4NX+Mt/ev1H3Tx65zjN3/kq/nOt1/knA0cbnRoLLhJxZfeOGbr8dOsvP1hkkuXceNj3O4dVja6bH/TR9gdPch5SnId8KHlTSbGI0PANDNqn6KkQVULosUuLMZtik85I/ae3IxQdv4maN1hkwRJQ64E5aLG2zbjO9GCynoGscKpmGESiJsaLSUTBzeP5vypP/oHeeIrvoqP/pbfRqRgtvCcFA3u6B7bXcNLM0nlAt39G7D1IP/lT/0Nfud3/k70qYepDndJLz7O9NUXCCVsrS5z+/4dTg17zHzBr7x2yKMrMffvTxkES++Jh7jy7Iusbo0ozj7KabUgT9vud3OwiypOMKs7BO0JTY3QijCb4esS3R/QVBXBOZgdopOz7Qc1GRImR9zLTrPJCT7uYivHa2XBUDdU8wmzRcViPmU2X1C7QGQ0HQ2dToeVQY/01A4i7oDS9EVB3sygmOGzPgIIbkojl3Fxl1pG7Qa4jvAmQS7GbYpUOQYTE2zN2AqyySHzAqZ6iU+9ssc7zg/55ZsVH9g0HJEySiBIi3IOki5NUEQndxDj+4BEVXPQGmMrRDUjZMu4IAgERu9+J4+pHJFkyHIPf/QG5anHiU/uUGYDkmKMn+wTyho6Q0T1GshAceUO0eoq9o1XiM9cQPbmuHKOauYEF7A3XqXa2yMUc+rpjPyJ96BXtgnBIZyl2b+LWDoF2uCBo5MZ0SCjbNr/nzaW80sZUiUEobh6MCOdS06zy3F8piUceEhf/QJHow02igO6O8vEr1zDnb1ILRqSM5f4wNIygywhCo4kUgQFToCRIJKMYFKMb6gQxEIwiCXeW46rQC+SzKqG1Ih2qiIdojjBdZbBgqrn+HSFuYWGmMZ6okzikwE+eJSKUXFKHXXQbs64UZja0WRr5ItdqOYoqQgmxUVdxFICJkbh0cFiPQQsjU6JAdksAI/trSNsjQyhTXoTDer+VRhsIA9u41e22SsEabzEqnZY1S7X5dUxpR4ihCZXUCmwecb9ScMago72iBDIcSy8oCMlaTNtrTH1HNtbQ2KoJcTeERA4k4NvUE0BUULhNFPn6UrbFqTe4dDY7jb7c4meHyHWh2RNQbBF+/PO96iSZSIKrIxQtmkZ2JGhrGts3EUhAYXLBngp0bbCKAjWIwRvMntb0ootA6HTa5d8hcHny8T1FCZ3IXrTmlGMSWTU+q9Njs8GLKgwOuKIhHUKKpVgtKMzXCYXsFIcwlBT5oq97jnyUHH1pTdYyyWTZs7tBtz4GnVjuWYMWZqRdXJGkSLv5pjOEIImjjVJdYzsLpObmrgYtweBUXhtqNMusWwX3Q4ryGRgovusSsUi7pKXR8g3vsz8oUfp2iN2o5xlrVsub73ARh3U/CZ1bwWZDXEh8OipLYZ5wo//N/8dTz/1DiKjub9oWMojGiswzmKTHspbxKiPbBaovAu2JrIFRkDjEji6CytnkNMJIVfEx7eRUUZY36D/yIPt78M27Hz1+5m8dhWTp9QnxxS7J3ROLVBLnunr1+me20YPR8jI4Bdz6vEe0nn82cfRkYGmamPGmgrRGeJ2n6e6/QbppSdbrno9ASQMt9vi2FfQGYGtCVK1fHEp2OolTJcdNw7n3D5e8OBKh3e8/SL/6BeeJUoTZjPN2Uffz+/+3b+bT33qUyj1llMV3rI9/HrqP+ji98f+zJ/BLk743sffydHLe3S2epTHJS9cq6iB+f0jeg9s4+cTbv7iZ1haT5GJpv8N30RtDHOVUFjIpcNJSYKn8JJG5WTS4YMg2nsNEac4nYBvCHWJiHPWwgSze4fZ6gW0UfgQWM8UnsAkJOSxIYoE1ZuBUI3zJKotFAqVUTSB29dv8P3/6e/nj/7Af0bn/GPYAKmEnpEsasdFd0iZxRzMO2Qdw97RPs986pe5eW+fP/RH/yNuHI25kE5ojvaJVja4/sYtnl3OeHqzSzU94Ys3ptx86Q0+/DVvQ5w/zdIg4sY/fZZycsL6Oy6R6BkqNIiTE4SrIe+2UblSQz4kzI5pTk4QvkF2h1Tv/jC98oBgYlg9BfUC312G+2+wkBHNYsprx4ccW8HxyV2CuM51qRn0OnQVrI36bGxskkhYFjNEgJB2cVmfSnc5qRxSKCqdkrkSXImXKyhf4XRO6QWFi+nVY1SaY5MhRZCIVJNLi0wGSN+wCJLdecNg+RzzuWOpm/DoVs778ynPzDNiqblXK3qqoRIpHdWgXYOc7iGnuwRtUDrB6whmR8jmDYg1UkpMnNPIlGr5NCpAWo0RzQLvPFVVUz7/Jfbe/nUk40M265pQTBHdAXp5FTc+RCcpQilIO4TBGraYEZxF5AO8mzO/dgUhFeV4QaoVwbaszoPuNodTR2b6RE2N8S0RIb//ClcPlogiw4Xcsn/3iCg5zbU65VQiyLKU3kyhhadezFFGEWuD3D7DpFSsnH+YVEUsP/QwVdLneFaSLi+Te5g3gZ47JghJEDBTAyJsuyWPgNB6y41dsJzEWO/ZzCSFE0wryyA1qADjxrMiIyqRgPLYOCcEKDxUFiDg0JR4CJ7UNgSpMfMDfN7nuHasJD2EFASTtp5z72iSDiIfgetAvQBXkzYtqsnJCAJ4WyLG+4ROH6dipj6GBnQImPE+QRlCNWNawGD3KjvSU41Os6QVCy1IFofI4MiNRBFDPaeSGQ7B2Y4k0GZ0OKnphZpOcYjPRjilMeUJYXxEvfQAJk6wjWupH3i08sjFAoox+9k6wUsq60iihJrWInXkNJ7/L3t/GjVtltZ1or893HPMEc/8PO88Zb45ZxY1Zc0ghcw2CNhOhQot2nrA9tiNtrQcW7EBRRvwaAsCjba9AEsGgSqpKoqipqzMqsysHN95eOYh5oh73Hv3h8jj19O99FB9qLrWiq/Pioj1xL6vfV3////n0YtL/NkEnVlsu4fD4JsUyhITK6SBvmrR9ha49iCaMhkMENoHqaGaU+qQMJtipWBSWJo2w0hFZhSB3yQYH2KDBsYLwORUUoEryUVI3S+xOKqwjZeNMTrECp/KOGKToZXE+iGJEWAsaVGhqwqXzzHdU6DvAAIdh2y1QjLR5NEzx/izY3aWr5DZhFORI0pC8nGffgaD8YT9fp/J9h5lVVGhWOo0aZYjWquaehzi9MIcKcs5opgSmGKBWpYVXppzv6xRj3zs5gM4q6E4ZrK9S/zEm1F3X8Jefg8gEF6IjWsYBCJq4OIeuspR8z5v/bqv51effJj7o4Jp2KJhSuYF1GKNtjmlTohMjs5nVP4y1qshqgJVjDFSL9I+qimu1kHYAlcuQDJy/wXc2kVkrUXz3BZq7RTcu4b0Q7rveS92cMTs5uskpzcBR3XnNZRWzHcOcJVF1SJcNqc4+H28OCB52zLucBexfgExPsQKhVAaGdcQkxGYApIOBR5Zo0tdO+TJPYS12OVziPuvIFbOIBBoUdKLPd6y1cSTgtjXbA8zOknA+tlTvPSRV8A5bmWrZKPr/ORP/iR/9a/+1S9ZH/B/p/qK4e0Prr5scSu3bt3i7/zQD/EjX/tWmJZ0Li1x+k9+B0vveRdXHlvn/FKN5rkVjp97DXPrGtuvbOOMYfnxi1gV0PAlI+OY5Ivcz7KygEC4iqlTZPgIZ8FYTFlAawUTNKCzifUCjBVkQX2xTpI+ylZY6eGlQ6KoRoUiN29gUKUgte6N2B9Bbh0f/f1P8gN/9S/xoz/+D3nrW9/M8bwi9uR/RMpu2WN0cwVz+y5ROeH0Uou5F/LD//Cf8D9+35+lGhzygBxQ3Xx5ETQf+AzRvO38Eq/tDklfvc6T3pi/sDWmrqFZEygpCGPJxnvfSbx1mtDNMce7mOE+zoH2NLrTxda7iHyOqwpmt2/A8mlEvctaaElCn6FqcH9iee6513jmldt89M6IT9w44N6dO8xnc1qtHmF7jdOTEy6fPUvROY3qrNMJfOqNBnEUgJPY1gbDqEcmY6aF4yQ1lM4RKYn1YlxRIsZ7WCRDETHJHcPxhGzUJ6tg9sb0KhIWI33mTnCSw+HcsDOYcTTK2CkkWWlIogAV+JxMc27OFMfTksNKo2wBWMineON98EIwlsoUuP3bIARCScqoh5MaZS2uzDFOEdgMXc7IVEi1dIrjV14n9RuoO19k/9f+PeNJyTPxA/SDFuXKRezyaeJHnmR2d4dr1wfc+8jvMtFNjvsFMxHSV038zbOoZoNiMie88gguqGEqR1bA+OiQuQp55WBOOR6yX3nEJ7tUSrNZ9QlabZZvv7qAZPghDtjQORunl9lNNePdHfZmBmctcbPO1lKXsNFmSZfk7U1sWGfgNdH5kHkpMI5FvFoxo6wsw0pQKo+ZjJhVgpFd6NZTq8mtYFxCohfT4bOtiIYWWGe5MYGZ16CfOaZGMFQtTgpLIKATCja9nMKCFI56OUSWE8q4jXKGQnhsMKcuSnxXkMfL9JMNTH2Fob/E3AjmRnEimwvccTFDF1O8bIiqZhROYIMYIz1cmTKtBJPSUE5HGCGpah2GLuTDk4Sq3iPsbhAHAZUK8ESJcgahQ5QUoBRjF0BVUfc1K75DCsHELrygcxGACkForI5wfh0ZxngmxaAIRYWTGoMEJFJrpskKJ6XGsfhbQgi0VFipOZwtmk4p1YLQ5yQuai9IiMUcE3eQyuO48phWkpn0SaMeQW1B46Oc41yF8xcaVpmPqLwaEphaja6yxftxhh3VYRJ0yWSA8kK0dAhTEpkZRmmqsE7mNAOviZSSWjXBx2B1jGdLgnxEKTRUBYF06HyMcA5Z5ehGc5Hw0Vl7w2wW4CsJ3Q36WcnNQc7L44pXj1LiRpPu6hrdjTM8vNzk6UbOuW6XZz75OrePC14fwSt3d/n9F1/nd56/wWe++Bqv3Nnl/vGQfgYmzxA3nyOeHtAKxCL+7NaLWFtidMjme9+FKAtEmeNlA8R8iCkzyniJqjK4uItnSnTWx+1cR6UD2o0GFwevk4x2cZMTTp+8SDA54PWXv4jMRshZn9xJhClBgDApzo+Z4mMt2LCJqHKs0JgwxnghNqzhZkNcNofxEfn1F5BSMb93BxcmyNYSyVu+mnDrLKrZQdbqRJcuES23mR8NuPfbz+AQTK7fQeQ5Yv8mIqph/Rq2vgJBDeZjRGeN8PRFKHKErfCmBxRWICqD2X4N2T9AlAX2cBvhQDhHNDui40EvUjy50eJNp1q0Yg+rfYSt8JMEqSTLlx7mpPc0f/Nv/k1u3779JesFvlJfnvVlOfl1zvFNX/cNfMuFTR6pJax99dNMb95ClikrX/deZK1O78znqaYZrrIcv36Pq0+fw5aGtXc+BTalXzqOZhWxLxFoDuclLhIEAmqyIJpPkfkU5/mQTZDpGFvrQZlBkWL8OrLWI86OEdqn8FsE5RSvmjELWkROMswKIk+jpSDRCuEWLux//vO/yOc//h/4Z//yF7nQ8bkzKfHkYt3hcOjxCY16wK1PvsLa6U3McY4Ia/zRP/LVnF1fZakVU+28SHZwgIgS1M6rFO11Ll3ucfOwj3WwXCsJohR95gwiH2DzKUW8QfOtTxPM+5igCaM9/HYHg0IkLYTQuPQucriHmZxgls+S13foH48YV5JxmjNLc6Q8phlIojhgpZNQ3+ziDw8JihmsnALfwyYNsiTgYjfi/o0TSjvHdkNePKm4utwmUg6HASfwdq9T9i4uDIASTFUCgryxjHACl2dUNkfZgnqgaWDZzQ09NcdT9QUUI4iYqphiPoW84m21KUeZY2YV45173D7JeDRZolI+xWTAv/50n+/9mgvkmeVCWGAP7iG665Q3XsRr1MGLFqS0pXPMdcJBJtmsKWSVE/RvMTue86y/yrJnmYiYh3oBTe8efmsJk6xy5fEz3JsJxvWY12eKi0s9BsEq6/1XSZMlijDjtRev8Z53j3And7Brq9Rnh3gPvRl7+mGaT86Q3Q7mZB83HbPfV2yudWiUA+zzn2Py/m9kdzDnwsoGvWqEf+4yJzLk4OxjDGaatm8Yjie0tENa8Ne26LVmOF2RVppYBhhrccaQyZgCiClYq3m8OvBo1C0rvsTKAJlbKru4Z89Ly9FU0IotdQ3SVowqh5oPqSUJAkduQUlJ4RypdWw2fE6s5HCaY6xjqxnhS0tdV3hK4FnD3FX4lEi7uHDq/ATCBF9KVHmClQkuqDFxEryEiYyYFoK5gcpJQg9yr0lYZphaB2+4g60saMmwtkVhK5yR+NLRtWNmh3cRyxvI4QFzarzlTAPXqqFEBmVJMZ8gkyZWechsjItaGGephwqdjSm1z6wKCG1JrCSekKTO4XRIZhwBGWRjbHsN3zny+RgXBCAVWjgqp9BVTlKOSPxNcgMNX3CSWZZixcwKWr4jqxzSliRSMsgNzaikEDFekTKvtSgslEDkCSoH0iiCpI5zjlwl+Fhwjpqbg/QpWGypCjx86SiNY1RVpLpFV0umVhKWKRELYMX82ouEV59EoNDFBKImXv8+xosw+Di/xkzViShxOIRSC0RyEECwjpOLs0UrudDFpjP85Raut4EyJecKh/U9tvSUzxxIPrV9l0cff5hgtIeRjllWEJ1e51v0Ebc/9mHid7+dTr3NxpWrZPM56WyKmQ7YPRkzne5RVSWxp4hNRU3ukzRaWB3iVzPmOiZYOUMqApZEQVpvk1UZ3s5tvFOX+NB9w/uuLGGdpe4sZvk0rtYDW+DOPIJXb1LdvU64cZFf+w8f4zv+wl/mj33j1/Pzf+9vUKydxhcl1iwojL7N8Y/vIJodPMEivg9H1TqNQ+Ham5gvfBT/wsO4/Xt4wsHKGl6RIWYjhJBQ5qjeOnbvLioISK6cozzcJXnTOnn/33D/Y88z35/QubBJOThGeyGiuYKJWkilcYP7yJXz5M9dQ4YhRvr4XkTn+Bqy2QY/QSY1bDFBaLXwbeDwigmdIGTq1VlKFBafP/H4Gr99vU9WWJRSbH3V09z8zCfAedRWrvA93/M9fPjDH15ANL6MazH5/U+VPXyl/s/Ul2Xz+1M/9VOc7N3jr/+JP8LyU48RXn6M8nAPeeVN2DJl7Wvfxc69F8hnJShBPpjy+x+7yXu+7kHYuIwLQiaFxQroxBoBRFoxM4JSONbcDHl8C+F5CxBD1EIWU6xpISdHuBsvELeXEH6EimJMZ5NaeoISsKN6RILFpNcJ9ic5a/UQq2FnlPLf/tAP4znDP/rnP0sz9DkpKu6MMyoAYVmPYFrWoaxIenX6zifyBfVajW/6s/8V3eyQ6oXfZ/TiyyilEKKi9cQTbK88QBhoJq/c4s2nEqLmKexkQLFygTA9gtYyWWeLcHZCcfMa6tEuTvlIKbBliekfkpYFAxqMDueMxxWTnWt4skVrXtAIPZY3Vmk0mijfJ8pOMMYgy5x5cxk/HYOCPC2ITMEFv2K83EbsX8cVEZteRtRe49TnnmWSn8dfruPnE+KGTzWf0DRjJsbnaOYIJreYLG1idYNKBcTPf4ilSw+RG4PTdey91/Au9MjyOVJ7KKFwsyGxnJMhF8alwuKSNlFu6aYp5x/aINIlb+kaKqt45/wan767hO8pymWflfop1HiKf7SPsw754GWOU8erexW3hydURc6jSwGpgwvTQ9gbUNv0GfhLnCv2GNjz5Gtn6DDlMHf4wme2eQVpFQ+6Pcppi6OjnLC5SvvCGU6fPaDxlrdSaUHtwhVm0zHl8RGyyCmmltqlK8i91yhODqn6Y2pnzmHDOpM71xieeYCdfsHD2T1co831vYqGDNmdlZy+fIq98ZREgD/aozr1EGWV0ZMGcXATL66TTRzB+hl8a7i9P2Mp0TSaTXSZ43shdyaWpxsFmkXuqS80YyPwpeM4NUwrWFOOhhlhvQamyonH+zS8DsZP8IWPASIlSKWg5UvuTUoCpYh8kFLgmwIhg8W001Qk41uAxMUtXDZESH9hkqzShQZVhVgVUWXgacm0gron2B4XdBOflrIgBJkMcEaShz1kVbCTQRIKnPCQJufeOKeRVNikib3+Kmpzha7OWe2swuwEEYQMK82YmNh4dL0EgVqY2Ozi/80FtUX+rRLE2RFCe1SqQYUGZyiFj7GKuHUaNTtaND7FhMIWREqCCLDSxwmN0iGBdORWkJgpcwu29PGG+1RBl3YkORin2FGfqfCIg4B8PiRoRDi32FzVhMEgqQwI6ajV68AilcHzBKqY481GFN0tpKsIlaIyMDGa3MJRKmkHkCiYO00hfKK0TyU88lqLxOaILEN4PiqfIaRkKiKag9uU9VWmYQ8lFyk3NkgW0WReAvkU59WxtTYqHWF1gKx5yGy0+K48RS0fsuYq6kqx9uLHuRnU8c40ifo30ZuXOKyf5XzTp/s9383mjevkr15jcuUqONBRnTBIWFntkhOgREGaVmSTE4bTjJOTE+7fuYO1hvBoRpA0WW0l+EkbF9XRwuHlA0zSRmufN69I5qVDaR8rNFl7nTAfI7IJwdZ50qBFeEZjtMc3PHKW7/zGr+O3f/9T/Jf/7Y/wL//XXwShUJM90DXIR6jeFggHVYrLJ4iojh5tY6MG4vAOotZCSElVlPhxiD57lfKVZxCtFYgbkBc4KvTSGkIobGsFv7VMNR1S3+py9OI9pJKkgzHZ+DW6nRXk5AishTChWL5CIAXT69dpvuM95LpOZaCW3YVGB7myiZtPEVUOYQI6QA4PcXGDSBp8ZRlay9m6JvIUZzoJT7/lHL8zHjDtD5BBjM2mTJIrPP/i7/BzP/dzfOADH/jSNQb/N6ivyB7+4OrLrvnd3d3lr//AD/Az3/PNdFc3CN/7rbgsxesu4zXqiMLHHd1lev+E0f6IRq9GPszZ2Z5QTFLmjVW018CrBEu+hycl88oQKAkCNA4hNGV7Y+GYHx/B4T3YuoxQmuK153B5Sthdwb3BbZdHdyjamwgsY6dIEHgsUiBMWdE/PuSf/sLP8plPf4q3f9N38ie/49s5FVnuZ4bKwFbNY5gb2oGik++TRi2EK1i+eJlnn3uBdm+FenrIkd+luvkK2cERtTOn0K0G2e3rC3783j2KZptHVgIYHZFPRlT7u3hRF9wYr7tJy6WMD/bxOz3m0zGzecmwFPRnOZMiw+JoxCGNOOTsaod4/SwNM0ON9qm6p9DpBDvdQUQtrNIQNnFVhp+eoGo1bOlTfeFzVKsdzAPvoF1lTD7/Gc6+47uotRNyBOurIYe/+6u4J68i106DaRKubZIdXGe9fYp53OPeMKLol5zqJMzynOjSUxjfg9kQNbjP3EswwkMqTZ7nyHKCKjPU0Q673acI45D1jkc0nFFLj+HsFdLXn2PUXWfgd/nBn/x1/sbbVri4GqElaCl5/kgQ1Jbx1p7irE0JZiUDG7By+CrPX5vRu3iJX/65X+ZP/JlvZdw7zw0taJDxQDsgllvs7+3gbEnev01tWuKfOc3lYoeXg9N8tlzm8Siitv8s5e0ps1NDxN5Nzly5BMMJ462HcEKhkphDEXM0vMuj5QzrhaQX30H8xY+yvlrn9dIja59jqdsgm1uod6if3OJ9ly4yAqSz3JhJHl6qc29q2Vo5T47EipBWfoKtdzmJVhHFDAgQ0nJsDefSfYpGG+HFeDje0ymo2xxTekjl4WxF5QSFWWRVr8QBTTdfNKRC0ooDdHwGV80pDOTCkWhHYQVOCLR0WAtxoFgNYGwdDW0oAGmLxcQwrIG1iHIOYQtO7iH8TcTsBOsELulSOUnNTRiV8YKo5gmSQKLkIr0hSgdUQY3MOsY2REsPX8O1/T5XWh7t6X1MuMqxapEFIacf7GFuPIt34SkEJV7Wx0QbxPmQer1NisWqgApIzAwvG5HV1/CkQzqDkhqXdJDlDCUsvgCkh1Y+e4VhIwkxRU6sPFyjRzDcp2iu4Kd91HyGiFsUUZsQSc2WiNKyFIWYaR91dJNaV+BImBNQLZ2iPjwg/+RvUeUW/ugfI5SOjg9RNeNO0SDyJBZohR4A0+mMZGV5gS5ur5BLn8AVzGzEpLT4CrSEbqSoexLfppQyRimPNGhCWRCtbZLKGEnO/HhAGEwIKNBJiLYtZJWyXxpaQYVAUcUdLAIBCBylAYGCoEYZNpBVhjIF/YN9ks3zJGJAs90FV7K23GZ1dkQlPMJzD+NXKXZ5C18aXOjjHn2KqrnCTIfY0Zx2s0akHRk+Sgp8Z8mDiMhbxzXBpCUXI0mQHnOQQToasLu3z2R2k1cENJJtGrWEJoqYkHZxwLFt0JCauY7wJgcMvQZdKamEXuDAw9oib3j1Ej/143+fl2/f5eW7x9zd2ePf/auf45u/7Y+zulyjNzpi1tikbjPs9h1Edx3jJ2jpo6sU114B5VG01gmefBfs3sKlUwgCJA5rKsz2TdTZK+DGOFMtcoKjFs44srlh8+lLTHaO8JeWiZZ7qKVVXJBg77yIt3GeWbyBJw2Nt74T6QcI59BhskicEQKpA6h5uCBBrZ5dEDm9N9DQuodUAm2gpkuEK7naC1BXVsiyR3j5lftMjg+Y5zOE8hkkD/EDP/ADvP/972dtbe1L1B18pb6c6suq+XXO8c3f8M28/8EzfOPXvAv70PswnRXUtU8SXHkceXQbETdJb75C68I62y/s0N5s0jzd4u3zksaZJZSQi/UXIN3iiJ6Vlk6o8EVFUKUgJVl9Db+Yw/gAe7QD5x9BlillOic8exljKpSz2LBLoWucqDbTvKCRKFJjyaykJub82N/9W4wGx/zxP/kBvv17/x8sJQEr4+tU8Sks4HD0AknNg8IaELCsMozUVLduMZU+54/uMB2ERK0WJA38ZpvqeI+gk+DFEbfUMvuZgTv71Bo1juYh76pHhFcfW+js5n2GJQwO+/RnBUOjKPq7xNLSqoWsBo6zWxssRwInNYVfw/NCwCGEh6u1KfwGeny4oGpVM6bhKTyt0AhQIVUQI4sp8ZUHcJ0NkBqZj4kfeJhay0NWKcIL8bcu0HunjywnDIMewXSCH8fsBZucLkaQNOjdeBY/jrHHPRrFjHTrMimWl7I2Na+JER6rd68xTecE7RpJJBBaIxttlhohfjGFWUoUtghf+30qaWiO7nJ49y7t9/0xVNrnUzzI90RQm52QRV28bgBhHTMUDJcvExzeJW+ucGPqc6Wdc9Vts74m6b34e1Rvex/nayU2aHJtWPGEP2L/2h3OnF+immf0VlswvIdq9nhkNebYhejRPTpXr5Ac77H9kY/Qiypcr061cYE9GmzGUITniO69RHdzi+rwBi7PicMWXqQJPculVkTeqTEsLE2/pIjWuaub9JKYrDC0Ak3pSW5MDK0oYIykgWRmSowOEJ0W8xy8pEdNa+aF5eJSHXO4Q2UFSkt0mRGN7uCWzzLXTUK5aGo8CzXlCJRECsm8CvC0BiRCC4RVlEHAuLTMC0siHXklCJXEIujGkoYsCOVirmHw8VyBrHLKqA3GLCaVwhGoDLMcEGi1QCD3t3HtDbzJAcF8yHb9Mk07waoaWvpkxhIqReBHzK1iWDgyY/GlR1oWjHb3+GS6xLtjj/bgHrufucfk3GPsbaywdfFxpHSYqqJqbmDGY4L0mFljlVBDnmfUFCiTQRBROPCtQTqL5wzKVqA8chaJKWCxLD53hcDFLUqhyK0gra1SGk0LSYVECkvpJJW1xG5x7syFT3Znm11T49zLn4HeGutLp9DNHjP/Ar18Qk04ZJlR+A1sUZHrJkVqaAQaJcEzOVIIsukYu7KCDeuYskJJxbRcINqbPljnCKQk8TX9tEJpjRCLfGaJZCYDfC/ESUUWLPHFSnKuFhJHBl9YivoyatYntjPkfI6rL2OExJsPGJaCxmzKIG6wIktyHTPHx/d8SgrGTU1WaUYmoVfNybyE3aTNhitp1OpIk3Oim1TGI9MRYXnCSV4Sz7bZnvvcncCfeDBHJTETERKrEuMcrx4NudSNGWdw+2DAxdqQ/eZpTq14hHXBKL68QEJPx0wKGE1n3BxMme5+AV8r/EbBvNMirtVoDfZQvYiisUIlI5QAPZ/gpEDmY2xjlYsPNLn4ZIubr7/K/+sf/Bg//CM/yq/85u/w9RuC8PguuAKzdBqXtPFNQaYjAgFydA8Gu+j182Svv0zSiHBVjnzwaVwY4g7vkF5/CX99Czcdkt2/jdg/IHjyHZSdDWpf9TRsv0J63MdfWSW88hD4NcS0jx0c4tbOEGjAWbyVTZyTxOUQpT3wA8qoTTi4g427OC/GecUCZe8ENh3j+RHG1NgtGqzUFlnLvcjn0TWPSG9RDz1Odu6RDo+JW21W3vJOjv/DgO/7vu/j3/7bf/tlK3/4StrDH1x9WTW/v/zLv8zN66/wS7/4E+irj2NbHey1z6CUR37redTmearRiKqypCdj0nnFZHdIa6vLmSc2kELghZp7M8N6w2daWvKiQktF02U4CZ6zOO2jhAWlELUOauM8zklkMSe88iiy1sbNp1StLSZ4zFTAtUHKZiMk1oJRZhhNx/z3f/Uv8g3f+QGuvuUdbNV9Qk+hhOOTu5bnPv4MX/vVb2Yl0eTWUgsUwlQUsk7qFOV4SD+1JGFA/eSQSiV4jAmvPMb9f/cbVCeHJOtLhJeuUsRtTuV77A8OqJdDzl9YZe5tMNjfoz/YZjDLEVLSqkW04pDVRhPtoO2meEubkI0RlOA0J+EqP/uJa3zbw8tsNvTCUekkXjmlrK9ilcbLx/gKvOkxzo8xYR2Uxvl1xNIW0jn0eA/hLAqLzCa4Yo4K6wgpsaceJL32ebSnqW5dgwsPsl6dMIu7vHSYceHqm5HKkU1ndOMWuSuJfMWDnQBpC6TzaUlBnx5NbbCNFqpKGXzuM3Qam8g7L2ClJeheYCITWvdfwVteYe3KGb4wmfFPv/UM0ZmIaHgPd/c6jdVlgmSZNGgSz+4iBlNeOy4Y5RLiNitNj1VzH/0t30RtcEA0v4+aDxg+f5+l84/iry1x7uIyjeEutGrIMKFavYgWhqCY0CJHSo+eNgSnVknrHuHb3gdra6RBj5oX4quC3XlBfekMjaiOlEM4uo88eA3VW0PmI0KtCKSkMIYo9pnPZgxzi8oMNTNnImMagebepEAKQyuE3WlJWhqyIGDLr2gGIdYt8MJawc6sQnZOM8sNy06SygixcgHhB5RIlNGE1z5Fo3sa1V1FOkkpAxJPwhu42dwIRoWjl0gCK8ikYVJYUifoejCvNB1f4E8HmPoKPiVKwsxphBbkRjMxkt2ZpRN6NITFJB0a2hCagjKoI62lmg0x7TV6ZkJaVnjGMS0KjBOsxhqsYpiDUrAaLHT889ywGTmmds6wucHh7h77xzlL5XNs195Nd71FWM3xqCitY37vddKzjy4yVK3EVwKVDin8mKDKiOQUZSwqG4FUgAOpUaFmbjyc9vFMRqw11kJmYFKU5E4SaEE9PeT11Gej0WUuPebDIZUfM5MhgZTMzUIHXzu8T37lEcI4oNO/yajZRiifoN0GFnpcr8oJqhlz3WUpWGQxO6cWaSVKkWYZJ6ljLYZSBGBgagQNbdBSEFczjK7jyil1qUlZpHMktkDdfY5w4zEsPgEW5ykejVIiDPgdZFVSSoWKmywVGQUBQTFFSY3KhnizFOH7pLMU4U1xvkUEDYSAiVXYIEHjeP7OMU8/cgYNbD10FakexYYexkZYK9gSCkeFK1J47lO4vZs8sXrEpat/hPrwJtrbZB4EaFOhj26S3p+T5g109xSPLAWYvWMOvE1iKVn1YxQG5yfkkc9GNGN5dRm5azlWDVyYMDrc5eD4hOmtWwhnqY/usBw4wtWztOMAPI9CBNgYPOGwVExmE7YuXmJ67xWuz0O+7r3v4CdPrfOP/vr3cPnUGtSXcOYNdLP0EVQ4KRHNLtYYGO7D0pWFwbq9ipseUx7uEl15hCxeQq94eNMB+x/5KOvr64Trlygfegq78wq6XqM4PiYWHkwHVI0lvCtPIpRHMtym7J5GRG2Mt4CZyHyy+N/JRzgvQVQlzitx9S5iPqTcvYWKErCOQsXUPcEws0zKReb06VbA5W7C0bkeLz3xKMc7u6xefZx7zz2LWn8nv/vxX+BXfuVX+LZv+7YvWZ/wpSz5n0H28GWbYvB/sb5svqeTkxP+wp//8/zYn/tjrJy5iF06hxztI/dvY/I58/0Tir1tdj/8u+QnY45euMvmxS5Ht0YYB8n6MrXLF3m2rzmYFyi5MLdNzSLQXdsU7CJg2llDSIWrMuzxffTaGQQOaTJEkcN8hF09R+5FHBSK3BhaocdyrPmZf/Ev+FPf+nX89f/qz/O93/dXePzpd+OcY8lNmZWGu5OS//d/eJ1//L/+Fi/uj9mbFpSVJRKOyGTsmojKSZreIuporRHgRQFyuIvMxsgqZfmpSwuqVZCQ+w1643sc7J3grXXoN+p89t6Qz9/aY1QJlmoBD+mcd55f4ckHLnB+uU5X5hwdHCO7qyAXqGXKHBM26R/s8plnXmf71h2cFyDyRUzOwf/+r7BSkMoQWKQsmCDG6ACdDhDWIIxBBDFCSIQXgRQ498Z8e3CEefVz4ARSOtSFJ2iJlNZKj2R2RNhsE46PuBCWdCJBLEqaKyuwtEVUj/GFYJkRS3bCUiNC+4qeGCHaS1ilEVVJGIdUn/0wgS9Qvk/il/inz6LWzyC1R2BTtiZ3WclO6JYTuPZF9oMOdukM5miHxv3ncFefxuvvsrnzBd7STPn65THhb/4b7Mk+3id+C73zEur4PqLRI+41aZ06jRcENDyJ52mKU48ikgauucw8bDILWsxVBFUOvQ1wluBN7yTwFce1TQ7MIjJqUkEtirFJh+DmZ6kmUwbX7qFXz8LZRzFL59jLDJWx1PNj/Mk+rekRyzuvoF76NN3IY1PnRONdLrd9SmfJK8fBrOJMwyfUirmICLViVFRUxjIrDIESxIFHWsHYQlpZTkTCTISEwhGZCfmg/wayWS3kJQ7E4heBwKEFzI0D6wi0INaStiqpe4qkmhKQg7OM/RaZExQqxMiAaSmpVMikslgn8JRiWlnGJEwrx36hGYmQPOpROsGLcoNXsphAKbKgyetjS1oYVJmzP8mp8JCwmAK7EmVzmPQJVtYpraAyFn95i833fjWv6lXCKmMyT3FlhshTmI2Yn3mSka6TuByoUF6I8HwqlUCV42cjjOcjTYHMJov1vi3xbEarPKEoMvS8T1ROmEzGHM0L/GLO/WGKMQ5Rpjzz4k1+7/oxN/spVZaxMy4Y5WCVojQOfflR1t/2NCdhi/8wSBgkmzih6ZkRNm7hggRMgV/OKL06vplTVxVVUZCWBusUnqeY5yWBrFD5lMJaHJZQOkIJNeYIBz4FYZXSMiMaIieWJSKdIE2FygboaorEgXHQ3cRvLwEg8xn+aA+KAul5aO3h7GJgUHkRSbO7yMPO9qjqy8yjLoE0FBZiLXEWQmV56lQDp0OsUiRRgAwCciMpjaOjHUIIShS2tky5t0Nw6VE2L5xjXQywy2cpvIiGrJiWlhMbMgw67NKmtn+dldDizlzlQtNnZiyFDlDOYaVGHd7GGgv3rnH3I5/ihb7Fr3fobJ7l4asP8N7HHuCJC5s0uksMZhmvvPIyn3jmWT738k3u3L3iklA1AAEAAElEQVTLODeIYo6oKtqqwNca6cWsLbX5yb/7tziZTPlzf/vH+PS1HTxb4s+PEcWUsJqDtZh7ry+ymkf7JI+9maq2hJ2O4PozC9Pg2hlkMSUoJ29QATWNS+dwR/cQ6Rh/9zXCSw+y/PRbKYdjqvvXIK4zUwkiTKAqKeIOVnrgHMaLmHoNqIoFnt0a0B5idoSwDnl8f7HVmw6xRY4tCvz7L7KS76GF5Yu7E47mOX42JEcQeZK3XF7j3FvextGde7S2LpHNZsw6T/KX/tJfot/vf4k6ha/Ul0t92Ux+/8tv/07eduU03/6t34RbuwDjQ2af+Pe4qqS4cYO933sVFb+CyQsGzxVErYhyXjLLKrxQE693kY+8nX//0pg//dYO06KisoJIC1peBbNyoeETBpXloHzc3h1EGCOqEhs2sASwHFD4NYyu088cwzwjcJZLSwm/8ku/xBeef57v+a+/n1Ory5y9+jiToqKxf4sv/sankN/6p+lEHu9/z2O8+dIyp4OSoqxItc/cKQ7LkKYqaU23OdZNxvOMhxoOYxxmnhJtbkI6wymH/1UPcydc4WRgGJcGlUS08xkrnSYP9eoE/W10ZxnXqFEtd5l/7DeR73w/oraEN9ihd+Ei0uULAtVsiPBD8u3rnLx4wne/+yKPXeoilQIvwqqQ8NIDCOUvEM9+nUqFWJ0Q5iOks5ROoPLJYt3lzGLSkWeotbOgAwhi/CUPi0ULSRnGiKIAJUhffxXv0oOozhorox0oUlzvNIGyFIBWAjk5WUAK5vfBT3DFInPZliWZCvH9iOjqk6jJBNFqoqZ9yCbEN1/HeQraXezBLdqHB4zvHiFu3OHGp68RPfkYbLSYnP8qpBlSq2aYMCFOAoJsjPMT3GiCMZBfv4ZYqeMlV7D37qAvPERe67A9y6mt1PFDn37coz6dUN56getqmfX1VWQ+BqXxhSCPV9BRH391C2cFW0EF6TF3xBLTomSz4TFKBZ4pcOvncI0lrLFUXkR57SXuJW0udTUnuaW+tMYXx5rRwRFPpAKNR7e+xFqoqJxDSUEv1kSywkeBq5gUGnBM0oLbRyMeWW8wMYpAwcG0BCmIhKXuxouorukx6dV34dVaRMIx0Qlp4WiFkmnhCKRB25K65+OlfaqwS6jBiZioKsmth48hcz5zIjwjqMsc8Ubkl3GOUW4QwlE6h3YgizmpChEO5kpS5Y5JrtjLK1yeU/MTJBUNkdGwBccf/Hf87oPv5dveeoHcWCJfML/zGmXzNP2TE04vrzKfa3IZ0vQk3VaHszcPuV8uc5g18OOQTjVCRnWUSvCRzIUgkJoK8JEEJsUqb5GSIiTOi5FlH6s8LA5R5qj5CdvBKcJnPkN87iKN5TV0kVEv+/zm5x3vfWSLq91lHj9TcqDr3JtY2p2IZD5lcDLkaNZiNZEMKkHcaLMkp7SGA9L6OqeqAcded5FOYy2+sKgqpW81LU+gixRnFNbOKaMYL4jIs4yOmyPKObUoREmJLxUeDoRClHNkOcZN+8ybp6jZjMx56GxEunwBwjqByRjaBg2ZLRorUVA4QQDk8xkMB7itiyhnMX5tMT2oLaHyCdZbIWxvgStoupSMOpEoOCgVS4lHZQSmvU6BJNQRns1xAialoesLtCnwlcCgMVJx5h1vZjARzON1JiqmHtbomjFpOma2d8CSSnn0zCVq1ZRbkw5fNTuhwkPUPeJIUqiAsiwRzlH1thDFIaa9Qu/qJfrTfQb5FucZg60jfY9mu0UcNAg7AWXYJD/eZs+ETAZ99g6uUZQVjVpCp14jXvYIGzUiYfm6974LFyT80i/8DJlfA1stUi9mI1RkcTjMZISLuwg/AumYew2S8REiCHGmRNWbuNZTyCLDTo8RS1vUpEKECe54FzcbYM49hqofIQ9OkL6Pmw6JT44otVgAVtqn8Qb3EZMTqrhLZFOsEIv0iiIF6eGiOk4pxHyEbS0v/n46Y/rJDxF1m/ieZiuMWYkEby1uUHKeeV6w0fApqhqTJ8/z28OS+5//ODgDzdPY4j7f//3fz8///M9/KVuGL0l9RfbwB1dfFs3vb/3Wb/GpZz7Ncz/zP8L5xxcPnOd/l3J/FxkFjF65RdAIuPPMPTprddrnOswPp1hnaUYa5SvwAu4Ep3jqnCHQkuO04nRdUzqJb2aobMgizUmANWDn4EdI7WGjBfXISYUwOUXU5XBW0S8MZQVbnYTf+LVf5zd/+0P88E/8NI3QZ5Iv1s2TvOL2D/9Dnv/0y/y17/h2ar7kvQ9toR7eJMkHOF9wMhxThm2a0rDkZtj5nMP9PTwlqMUhRRBhLj7EbaMZDaCvTuMtlXSdYqMuOXfzJTpbW+hmC3N8D+Wt4oTAZTNclqI3LmKdRR7cQJ2+imuvsBQk4CLM6ABX5Ki4jl1aZXJ6iaeCQ0Tz0mIK7ofgFMmb3g5KLcLcgxralhigVBHK5qi0D6bAWIvGUCUdVBAj3kCuitVzkI7Joi6V0ARUi6it+Qx1/gH0rA/DHZQXYsYnqNYKTgh8U2GC2iJnFA/d2MLPR7ikxWT7JmWjoB5aqqSHdBXCjxHVQp9psxSXtPG6HaqDe3jNLsXBAVEzpJgYrnzLu/BERjAdsKzuM4m7CDsF7aMin2I4RgYl5775XcgzD1FbPotOx9juJvZkn0rXmM/nzE/GtKtDjlfOce94yhoFO/EaqaiR7d5nYgSDWp1LOze4763geXV8qajnxxSlQcY16ihGhaEtcvZu3OP02x7n2O+S3XoJv5lQbTxMXE/wJzu4jYcJrcVonzdf2mR7ucu8UqzVNCmKpobmIq6Y2JOUOOYOQuPIjWNVpYyNz+nEkqE5ySyRVgzzilbgMbEOPz9i3m2Sh138sIEF5lYhBCjhCExBURT4gUdQzfB9iTq6h1hNiKR+QwOrsFIhbU4gS0YEKGuRVYqJWnRVztwEKGG50c8IfU0zUIzSjHY74u4gwyU+06zgziDD9xW+lmgJSTak0b/Pteg8Dz9xnqjno52jkR5gdY+jZJWVWsDByhbT3BCHmo1YUQmBtY7wiXfSknWcsyRegcAH7VGXiqlxOKnBwQJbI5CuBC9CCodxizPCOofTAbkM8Xdfo2os0y/hlUHCN/k+H98teJvqs9tc4cG1+cKQpnxUbw0/MzzajMDBbFaQHu4w7iaY3T3uFh7f8PAyHVIerFUsy+ECkysUXjbBKZ+XpoLN5gKB7AkB2ieSmrpJ2Z0ZosBnNp2ibU4RthFSUTqBA+YIQsCrZlRSU3bPEAuBxXFcBZwSHhNdJzk5YNZZwQooRwO8MKdK6uQiwA/rpP1j9Op51PgEIQzz5hk8BD4VskjxTU5WX8USE8wPcUojpKTjSayCrFIYsdCSOytQZcoYSW26R9beIjQzfC/EYMgqybxzjkki2Gj4KMJFVJv18UXKVsPHNLeIjWZq65zuWEzSJRzeh6LPJNgAKzBOL6beUUIZbhGPd4jXW0yKLS76Dv/khLxXx0qfUvgEo13K1hpSKOLuKhuqRrDcxhYpRTplOMk4Tgt2XnmZV6uKVqtFr1HjbW9/G1//2Fmq5UvcuP4yF7bWwY6gTBHpFCc10pXIwmL9kEhK3OlHKaMG4fwQI32kLXHTE6QfQa2LdBYhBMPuBRquZKIS6k1H471fjxUSgUHvXKPY20HWO3iTPcRsgMjmBNUUqTRlYx1hS1zQwEtHuLCL8UNE7xTKFIjNS5jt1/nQP/1N3vn3/xtmjfOcMynv7gjCPOa+qlG6kvVY8+J+wUo75uzlNfZeChFSUs6njFpP8MEPfpDv+q7v4v3vf/+XsHP4g6+vpD38wdUf+uZ3Mpnwp7/ru/iRv/ynWXvzV+OCmOxjv4LZvs5sf0D98mlme1Nal9bZuLJEkZsFxaoVM9wesfHkJkGnBrUux5XiTDcgrywNT1ErJ0yCJuQlzhqYDRd6LD9GToe4OMaFdQjrC8e59Ci8mP1ZiStLelFAanP+wf/wNymzjL/9Yz9JZkAWFXvTiqKqqIeah7/nT3HuXa8RhgnjSlAaOEhLNuIarnIQ1qhREmZ7THa3uf/cDSahJjl7li/eO+TYdjHSoyUyVjpNHlg2JPM+1hWYwTGi20L5GuX5EERIz6OqCkSyThZ2CKIGycULqKVNRJkj1MLRK8py4VZXCtNcw1MhD1yMSQ8yuhhEZQC1WOvKYBE95QzaFEiTo918EeavE0Q1xzqJleCKHPwQKxeaYZlNcEKQNzewQlMZQ5hPcIf3OemdZ8VOYbKHHQ+gt45Yu0jhxQTZCBO3ENZgJlP6Lz6D/+hjLLcbjD7/WertmKM7N8gfeQpPSMT+LuULn1hMftdPQZHCdEgxPaZKC/TmefxOFzMdUt9aQfg+0mncbIRfb9MZ3cMMjrjz6x9m7fEr6LXTpO01vOmIcvk8LF9EKId/fIes0cPDEB28hjru4/ZvUneGhw63GVx9B9V4xn4m8cIOm6EjsXNKXaMtKsTaGt78mF/f1jyyErMc1qjlcxSacj6j+/Rb8Dp1hv/63xF97XtIm12mxmPU2GJ15TQy0OSVw3OS0BOcbseU1tHSFfczQVoIxgUshQKLJDOWhBxZzoj8LsPcY5zmeGELKvC1wOBYif1FXqwVZOEplJLcNTFrDnZnhpVEUdcViStQ6YxGNkJWISgPVUxwtTZROaMIFlFb2tNY60hLvUD1GsfUOTy/Tk1AUI4ReAg/Zk8IsrLi/iTjo188ZDUZsD2v+LNvP8VyNWSMA69FVlpsNuNOPyNpXmCzXuMoeIjTScBuWlJmjqXEQNhEOMODKw3uzAwrsWZcLoxxu3nFStymJw1ROkZbBUKgbc7QJMRaUlpDaQRdzzGWEQ0K8iDBL2cY6VGpAKNCpIOyKMiOD5n1pzRXL3HqiYf49DAj7N/i9dOXoQq5eiYh1IrcwOkYhqXEOpjmJTqIOJIttu8OeNgd8+DZUzxzd8TjZ3rMopiap6lPtmmbXbzhHtc+/QrZ+/44oxJW3Jj07i5m6zylgJqSRFIvLszGkHs1nBfi2ZLKCDI0oQZhDNiKMmqC8imRWKnpaUhpEpkCc/Ml7u0NuPjIA3zu2PDEGZ95JYmUobKCoN5mqhNs4LDK5yRz9EJBqCUuSJA5iKpE5wOMXtDOSKckxYyifZpKGDzfJ68snoRUJlgLOmmRW4szxUKHqutMreLG1Oezt4f8mYc1TX9O5mJMkeFRkumAvDD4vkLdv4F+4CqV1gRBTOrXCShIncYJwdyFJLLg2PgsN1c4nrU5364TKUeedHHCA2kRUjHwW3gEhNmIImqRG5A6RuqQxFUkvmDVWyL3WhzMMopxn6N+nxv3tvG0Itr/In/rb/8Qb3nkKv/9X/h2wrLExS30Y+/E1rrIdMhY1klwqCrF2RAX1HF5Csf3cI1VVJVhLUghIEiIlUQ0ejQpwA+pwiZVmZOkA+ht4HU3od5CHO+A74NwKCcgm6G8ECc8nJBYz1vAN5TEJV3IxqgaGONjc8PNpcfoyBA53SYOE4jrNH1N2AQtBON5STsOedsDy9x5+CnuPfMxAIL2BkX2Fr73e7+Xl156ifob0Xtfqa/Uf876Q6/5/Qt/9gM8eGqFP/Wd/wWm3qP4vQ9y/NGPk+716b++x+zmNlVVMbx7iPAEyw9vMtmdIJSgsV6nsdGidfUq89OP4CtBpMDgWApBCotnDcgAggSmfUBBVYIpQYdQ61I4ELMBufTYTR03TjJagWC0c4vv/zN/nHe++Sl+6Ed+nCj0yY3jen+OVrA9ztmMYO3NT/HYd7yfmfP51M6c24Mp89IwM4qp1eweHPOpW8f80vOH3N4bMthcZ7J2inlhUHnGg1HJe1YEm7deZmW+vyArzUfkd24gpUS1u8jGEkIuDHrOOTjzELbRY/7cZ7FFjpj0wQtwkyNwBpFPwBaIqIZrrZJOZhycTGmZOVsrTVSVLYADtgBnkNkMlA/O4uQbhKpshlQaBMyCLjYI8fIZaWsDmU2Q5WyBozUlqUw4mFtGmeELu5PFyjOoIcPGQnsW1ynHY4z0IIhwFsjnoEPkfEx2uE3VWsIaAVWOt7qBq3Lqlx6kdIoqzdj79Q/hr2/iALt9HaYDPF9R7txHugo3HaECj2qWo+IIV1aIpEE57KOCBDcasHtjn+HEET7wKKnXROQG6h3G0xRe+D3UyTZ5kbNvE/71Z7YJPA8mQ9z5R3j11iHcv4d/cJulVp1LHZ+zdcmyVyBrLabNTU5Ui2YY4A7u8Y5lR6fboeZJaoHHSixJT45o2jFzVaf5nj+CrDc5kQ3aScRmKyIIfcaVYJpX9NOKtHRMjcPXipnzSDzJUeaoaUFYjsHBICvBOZSpUDhQPkEY0gwkxi0OEY+F6TLyNL0AMkIGmaUlcgKbsxYJDBUSi2dyEFBFvUV0VZUhJkeQdLFKc5gvptjCGnQxxSqFcQIpoBNJJkYCDqNDsjSjdA6TTamLiuNxylvO1RHK0jApn703YeR8iBMakYc2c9rlhHXfsl0IRndfBwfOFLQCOCsnYCo6AUyMonAssMJKMCwslTE0AsXRvGRWKbxiDFKTeQmFTmj6Ak9Yimoxkbw/t0jPowSsFQxcxMHUkKVT8msvcbizQ+UE17qPchIv0erfYefaDe5MBBfPbPLi7RPufuElxplhmBuUM+ROcW+QszcpaMyP2SwP2Lm7Q1KMqM49xKNdjZ8O8LGs+SW5FzNtbhKZnGL1Eq1TmzQ+/RGCuy9zc+eA8nCHUb/Pdj9jZCGYHZGrEOccc5ksmk5ToWyGr0BIiS5mC5CC0hwUUBjDIM2QxZy59Ml1zLXGWaL9+6jxEZvrSzjpkVpHtX+fajQgklAb7WDyjEoF9HxL6PIFwU5pnNL4rkRMBzgHrqrAVlR+A+EMiSiYZjnOgQU8T9IMFYQJWgpSK5kYTXl4h2D/JpuBIS9ybs8ccv91ZD4lLGfMbt1i53c+w87dXSZpycmHfhM1PuLFwzkmbCzOKBROGLRwzC3slx71QFMiKIXPRmDwpGOcrFMoTeXFGBUykA2ickwxHXM4LVECCgujSmD9BGSAmhzhtCaMYs5trXH5kSd495uf4OKVK1ih+OPf+Sd4+9e8n3/z2Rt89t6Ak8zgaj0yFVJELYz2qZRPqTwq4S2i4XDQXMO2NyFpI7SEWhe0hz87fOMMn+H8kLkIicsZ0qSIehc6GwgdLJyfSRPR6CJGu8gqR+ZT5OA+opggRICcn+Ad38NPRwipsHEbGQe8///5nVxe8jgvh+BHi+ehKQldRiwNSsCpTsLdkxl3jyZ0unWE9uleeISNR95M98lvJmgs84M/+INfos7hS1P/n8nvf+rrK/X/vf5QT34/8YlP8Ov//td55hf+IdR72Gc+xOGHP0ay3MTkBV4jWGBgV5r4GgSSYjrDOkM5SamtNvBqId7py9yOT9HPKtbqPqWThMpBJYnKKXJ6gA1biOYSLqjhlMJNBzhrcdYyCdo02zHj3FKlKaPM8HP/+t/yex/+DX76J/8xq6fOcmNSYREknsSvBySeYlIPSHxFUk7JvQRhcwSWU42YyjhadsbRwQHJ8Q5Dq+hoC9pneXbCXW+DB24/Q/Opp/FPnWX0sQ8zubtP44kn8HREfu8mweXHUGoR1yZshW0sI6IKpiMcEic94gcfQqZDbHsZwhocby8OMsAmnUXOapqzW3r884/f4s+88xKX6znSFDjlY7WPMtmiKbV20VgLjbBTXNSEqsIojRAC5RzCC9BSI53FpWPyJERWjupkD1k6MJYHWi3EzefwLzxK3XOIWYqTmvTwhCp7lviJt8ONl3DrW7jBHq4qsPvbLF19EtHrIaoRwfop1CRgpBJ6/VtktWWWzjWQFx7HvvBxZNLAliXV2lnCIET5PtX2LfTSGo1GG1fM8ep19OoZjj/9y8gzDzO6vU396gP4T7yN/VaPUaWpuZxqNiOdHhOsn6EsSvywhvjC53jn2TNgKk565+m8+lmEapE8/TXMGqsIZ+i5lFY6gjAi82qczApW6yHCTMlPPUpjPsZpiXAOpSRGSsrV84xcSr8MKZY20aFkJfBwboH/BcnOLCc1juVkAVpQAkprKR0kwpE6R+QL/FmO8R1bkUBXGVZKrHM0fcHNuaUTLY6P43nJw7WCmVHEfkySDRn4XWJpaYo5gpiWnVHqJrYsmVWSxryPrgw2jpDGIASUUpMLH2UF06xiRZfo4TZp9wqZtbQChXOWmJKjGSRVxY5s4Z8MqJTPmk7Z9yoeXG9x4eQOXF2mbCbcmxZYLdFFRSwld0WblSTjdJZxMpyxceoMeZHie5o7/ZTToy+ioib9xipjGdP2So7TxbpcCugG0BCGVPiUcQehNE5GVFIRC4vIsgUi2qSMckU3UpyYmMxYZpkhIievLCeZQCUdbo/hYkNSeJvcOgypm9dorLa4X2VYbXi0BfNQczgrmM0z5KSPFXXunMw4OTlhLYK10xu0ajEvH6c83GtxYSUjrww1aZDVHDk7oSoLyrhD8/HHKZ59kft3D1hdqTNe3mJJpnQnu7jaOQLtqMUBB0BWVgg8asKh5OKiKkbHyNkBzgsIsSxXI6L5mDioIaZjhnf6hCur1E9fhPUtjrV4g+ZX0hEVx9vbtNwI9+BTpEjK175I8tATBFGCn/WxenlxVlgDXoJoLmG9CJGNmamIOO1TRE3CeZ84N4jeKSqgdAr3hgmvxCNQgkCHTKykuf8ytTOSb3pomWbkM9ErqCKjH/Ro+DvUujFrLc1keoT/Dd9AONnjM0eOp2o+yjlSuUSgHEKYBQzF5UwLhRYRS/4EbWHqQpAghaawgqx0OGuw2qNI2nQCRUxK4Tw0AisEua4jmjU8M6epNMIZPBmQqoSldki/P+S/+5Gf5tUf/6/Zbvp87NkXmFQCK3Zptdt0ul2WOj4lAXm8jJYSU83J4iUiv4YqJguDZb6IwhNCUyBQQQ2Zz5BViScLpMlwUoGtENUMORtRKB8PidAaN+rjwhzX6OGKlNJroOf7kM2R2Rw3OkIsnyJvncJfXkc3GojD11HtZayOkMUYOx/hFRk6buOylK+yY14RDfLM0mtGXH3fe3FEvPa7vw1CcswFfvZnf5bv+I7v4Omnn/6S9BB/0PUVze8fXP2hnfymacq3f8u38t99+x/h3OYKxbMfZe+Dv44XepiiZH48xcwr4tU2cSNG+R6HLx1isoLGehPta0xekpw7Q7V2mVh7+FIhUDSDhWbPKR9pC4R1WD/GRS2cH4NTIBVuckLuJTTKMUhFd3gL+/Lv8pM/+Jc5OTrgJ/+Xn6G+fo6Z0YRKEisBOLqRTyNQXE5KEmVx0mOqakhTktiSg1uv07/+Ai+88Dyj+zdJxsdclSlvzu5y9vAGoixJ5mPUfL643d99lfTGDU5/+zfhL62iwxAjfJSSCyxx0kAkrUXOIw6nPaYkTIWPH2pUOUc1e1BV0FiiCpu4uAs4rJA4qUmaTbob69x9/Rpp3EM4i1Me+PFiLRYkGKAIGmAqrA5wgJwdYAwLnd9oB1HmICSlgWw4YK/QOD9G7N1EzgY0I0EgJd7mhYXuzSzMQuZwm8aTbyK6cBkxOkSWKSadYe+9zOhjv025v7N4WM4PsCpEOIc7uEfcv4/GLIAk0Ru6bGeQvQ0wJbK5jG73ENrDehpRzNG9ZVRSQ9Sb5McHXLs94viV69in3kO1cYXxPGVysMvx88/QLAY0Ox16ZzYXNLw8I7WK9lvfxbmmwKtmrK53kVee5MpTj+CW1nFRE3X3NTSgwhCNY29mqL1hFhKmJNIWb7SPq3JmBip8Wp6knUTsmGSxEi8tSeDhYTDOEtschaXpK3qRZlpYKgu5cWyPcvxqzsk8QwtBlWeUXsykslTpjJ1xjlMR47xcOONzS2UdzsFyoilUSENWzCsLtqKW92mSI+6+AtkMkU5InUcuA1QQ4YIYFyZUtXWY9heXF+tzkoOSjsKBcxoX1tAup1ce4Y33yLI5nplTHe9wO1PsT0qSesxsPKaajMkMvLQzYOWBi6jOCusNn5VOwmo9ZJblWAH+eJ/XhhUbvRbtU1tE2tH2DLIqic4+SLp8gVncoK0K6lFAI/JZSXx6vqDmK7xsiFAhDpjpBrmOsVKhTI6bLxCvubFU0qcXKUBQCUFWWIZZSb/UDOubnGw+ygsDRSs75pW9Kbeu3+SLd0e0z53nXDdmJRbUfMlg7RxFUXK5penFPr/36hFbdY8VkXNrrlm9cBZTa7Kx1mYzkJxUik6rSctb5ICbIiOU4HRA6AoC4Vh/02NcfGiLrYZiKfEJAg8bt6jPDwhmR6w3EgCGkymVE8zxEUoiyznm2Q9hZQA6BumRTPeRpkTu3aWIuqQqoLb/Gks1j3Ytwo2PyLPFdNfLp9xafpCXw7OkUQ8XNemvX0LYAm1zhKmQRbbYLDkLtlxc/KuMQgeUOiFvrjOqBG46JPVrzLMCUxYIYRFSEJgMTwqqoIFWgoaqGM4UQjhqyjHMLCfzitw4nr8/YbTxACtPPIzqrtKbHdLZXMH3PS7JEccuodIxH7kzYloKyixjZ16xPYdZXhBkI14YC9Ssz6+/fECjGFIah7WWwMzopQeM8Eh1jcSTqDc+U5MZmVMUKmQoE4S1BNJhRIAwOaM0x1nLhZUmX/Oed/CLv/IRgg99iPetKH76J36cJ88s0/Ql+zvbfPqzz/LKi8+zf+8286NdjBejpQBXIZwDL6YMEqwXYIQkbawtDNhxC1FMScoJVvoQxAtdurXYqEG1cgn8eKFf720h5iOE55O2zxAUI5wXgRdAvYtrLFFFPYSrkDhkWUCZ4nQIfoiJWggvxukAZQvc7RfpHd9kd5DytgeWWF1qsLq+yoMXWnhJC5PNcDqClcf4c3/uz5Fl2ZemkfhK/aGtP7TN71/7K3+FraUGH3jsPNmt6xx85JNoDdJXFLOM0e0TvGbAycs7zPaGOClJVmO8JMFLInQtJF7tED3+NFYIPL148PkaImmgKkiVD36I80MK5VP6CdYLkWbxQ7XTEdf6Gd7hTe4dnvDXf/Sn+Hv//Bf5u3/tL/J93//fcFJ55FbQzy1H8xJnK3wlqZxF24oNxuiTu8yO9jm8d5uXX36ZavcVTJEineAJPePy3S/gf+ELdP0SnTQoZhnT9gq9bovmV/9R1LyPjCKW3v021IXH8P0Fkjg5dwnjHLbMwVS4MscNdheNitaoVpdIC1SYYOrLiHpnsYqsL2FrSzgEVgWUukbpBXzx86/R9AXP3JosYqycXRjNLIy9NsIZCuGRyYjSKaYiwFQlpQzR5QRlzSKySChsOiFXPi//2kdJ9q4jq5LZylmGXhuvVkeENWTSAmlRxQinw4V8Q4Kut7BZSjYYkN+/jfNj8qMjwsRHxTWErXDWIqeH5N3T1Eb3UUpjs4xX195MXhjKlbPMdA3T3ULdfWkBKYnqBBcfR2gfoTxUdwMzmzN7/TVqVx9iunWVg0rzzCdfQpczNjZWebhRopMWIhvxjz90ixtqjaqzQbyyST1WSBxuPibcfY3aShexvMH8N/4N4Y1PUwV1wp3XEK6CMuOsGLCuc2JRofIZqABx5ipHlU9lBb7JCKmYFBYlJQhHpCUOi65yBI7MSiySyJNMygVHK5SLqLHYcxykbqHhs4a793fZHc3pD4a4qE6UtHBScZwuULpriUc/s2yPMzwpSZ1HETTJnSLTNZwOcGVGevcGlHOKxjraFkQmRSmJTXrs+mscl4p55wxV1EYoQdNztMnYiCx6dkwWdghFhZABwpRsH45IdY243UZJxXJN89x+zuv3j3h1UBE06riozmHQohPoRfKEtpxuaC7mO5xvebx6UjDFw6VDQldw/2RMHneIzIyOyNhTTXSzg43bOCFx1tGONQKBfuPBLqXDF+B7CoFAuoqonKHH+ziTs2wG1IshXc8gEGSlJSsKRllFJC2uzMinI/LhgHp6wit3T/jUszfIghr7dqER3rURT23G+L7HupyCtZxqepw6u8Gn7o65N5hzNt8muPMSTwZDcIL3XqixEViEUJRCMo+61KsBKp/gexKdT9D5GL8Yscwc1V4ibjUoZUjYamMbq5B0IAgBUOkYXzlKNLkVTD/224Snzy4aHj8CZ7H1HvNklUMXoMopFy6sYc9fpVbN6KS7hEmDpUAQ2JRKwIMtUCunKJ0inB4TH96mwEOP9v9j2otIJxC3wBSobIgrC4wXgxDkKsId3Gd0/xhhDA1REGJQWIRzZKqOEAItJP54n7DZgqfexShoEijJs9tDjkqfqijpZyW/v+9IhUaTQ3eZk/EcW2vzZJzip32C8S43jmekleHEhfhKUQsCAlNw72jMqh1jtU/88d8ky3JUMSEYbOMNd3hxrHmtX5CVlpPMYlVIUEwxeISTfbj9EsaYRSqO0EgsanyAh+HkxnW48zI/8oGv57f2jmhfvcJgb5/b27vcvb/NpbUub37icZ566k30ej3Gh3t84ZXXeea5z3P31g2m4/HiMm8Nha7jvBBVZji5yPS1Xoj1IhASWeXYdIrL5zDtI7SPxi0AFs4tMOH1NqqYE422kcO9RUR1kUHcRNZaGM/Hn+zjwgays4r0QqgKRDbBxD1MvYdpbeKEwp15BPfIu/jWJ9aJfI31Fae6Ed16TNxbI2gtE/c2sCuPI3TAD//wD3+JOok/2FL8Z5A9fKk/xP+f1B/K5vfzn/88//IXfp6f+msfwE5n7P7e5xjcPMSvx2ChGM6QnmS+NyUbzghaCVG3zvrbH+D49QNMZYmWWyy/+2lkZ5WhiFiOBL3YQwuBZyuoMuaVgDwDqfGcwQkWMVumWkgEehu0JgfcPJnygQ98N9/5jV/Hz//0P+Lpsz18KZlXjmFW0Q0F88JwnNmFm3peku/e4Mb1m3zi1W2evbVLdv826+MDTj/wOI899DAPHL9IXRuk0Cw9/iieFhQ796m//5sZyYS1jRXKlXPosw8ilIcOfI6Jef65z/FP/8XPkBcFx7/zcUb9Gc5aRJnihofgxwipiSnxTQ5RE+dH2LAB2QSnNFVlMMqn1AFjqzi5s8fxC1/kXA3e9+5HFi7qqI2TirH10VozKy1kE+ZFyfTwDvdHFcfjCROvSaEiTkZThjZkhsa8+gy3f/mDnGxcRr30DBZoULG13sNTUBMZrkpR0wHKOZznQzHHWUfZP8BVBaOb97n1wd9l/PnnaF69THjxQeykj5sOEf1t2L+DqnVJr72MtQ7ZWqZRzhiVJdvty9wuIiayhghCqumI4uZLCCmQvQ2s1Iz6KcXKZdTGaby3vZdoqcHzU58v7k/pXrhCGEY0NzaQcYTvBO84U+coK6nVEyrto10FxRyCGrrewJv2CfI5QxPiY2h26iRr6xQ6ws2n1GYHCCS+zXDKQwx2sVKxmh/QcHN0ekJISVo5PCXoeiVaSpyxzEXAtBIU0mdaGmaFox4skhEa5ZBWccLW8AbGQhT6RFqSxx2OZxlz4TM1AryAUvqEHuyOc2qe5P4kX7jHs4qTrCKvFuSywk+Y6Rp52EQ/9m6y9imskMTZEVYIhJQU+FQGPFeiMVigNI5kfkB4+BrtyT2YnTCrLKkM2aNGGdXoG4/P3T5hL/co93c5Ohqxvv9FHmo4DrZPkM6y2oj51O0Rde24OTJYFP15xThe4vYMwnaHtHSkqWFVpZxk8OJRRqEiPM+nGSxSFfZyTWYcR5VHRMnMSYJiglSC2GaEssLLp3hmjrYlHN/HGYdfzdBKIqUitCmVg8iXdEXOqWbI3XHO0awiSPtcPdtDbD1Ic3ODq72Yriz4qnhCnB6zXIuotds8GGWs+wvCmixmPLni85atGuvLLaYrF/jCyON0K+TuyZRbM0VbZoggpikyPE+RxUu4yRE2bCAc4Bx2cACtFUzcgqNtlNRooPJihuEyuRVIKbHzIZGGUDjCbEy+dx/a65DPcEW6MMA6R2imNNtNRNSCuIlf6+JRIfu7NH27mP4VOQpHI/LYaicE5ZT89efJ2uuEcUSWLGGVDzjEfAzZDDnpo7IxjI+gyBbGQlPRMGPywZD2ZAc7PFzgrMsMIRzjCrSweLbABQlp0KEeeei4TVWVnBNTlmoRRZHzyKkOeVHS9zvkBBSVxevvkckAceFhalIwq+DKUo1P3RkTeYpGIFFSUHoRzV6XxBXsiSaPP3GRQbG42KTzRbN+Vk+Iigmeyfjk/TESi0xHpMZC3MS1uvTEHGyJogJnKZ77FDUyTDYhK6C3usq5rRW+5V/+73z0xg4f/Kn/iccvnsJKgT65RdPNONdQPPrIo7zrLY9zrhMzn0757Ku3+PgXXuXlO9vMRicwPcGZnCQfMkxWkKYA5MKHUWZkn/s45d1rC0OzkHg2h2yECxvY8TFm+yZ2OsK1VmF4uAg28oOFXn96vDB1Cw1+9MbE1180zYM91PgA7ALrjVLMmluYWoeHl2u0Qo9hatgd5ewcz3n0iYt4Ycj8eAeTzblpzvETP/ETfOELX/hStRR/YCXfkD38p7zklykd7/9q/aHT/JZlyTf80W/kr7z/7WyNBxRlxWx7SNSJcFrgDGT9lNpGA4QmWW8TJCHVLMWvBXQvr+I3Y1oPPYBur0A2o97qcFQIAl8wNw6pNCpokucWUWZYQCjNay+9zM/9s5+i3z9hNBrTi33++Y/+MN/9o7/I//z3/w5XRR/r1yjDNs5W7I8zessRgZmx0QyYj8cM795h0j9izxo61YyL7Tptz5G/epdif4f2W96KcSWTkyHu1HnqV87jbV1Ajg9oXr7IFIUFqrDBv/jQ57j+e7/BzvY2TggefOT3+cxHPsTGSo8/9fa3IGWJmc+hrnHdTdzgEBvEiHyONClOSqwXUCkP6Rxycoy78xKiu4FtrVGNh7xyf0yyf8jbvu5t0OtRr8WUSlCIGFU5Apvj8orfuzvltJ6hwwR3PKQeZ8z8JuH0GBkl5J/+HYa9dbxTF0j2DxnWu5x/4lGiu5/FiyJcrYtUEZXVyCqDg7vQWgKtwXrgBVTHe5w88wWaF08BEoqK2qNP4pk5TmvK+7fQvRVcNkfWmwz+3b/GO3uWTIVMZMys1uDTt4Z8Q3QHv9PB3bkGvRZmtFjxecM+FokpS1yjRxUnzB94B0vDfTq9dd7TUIzXniZ2E+zRPoUT6PEhWe8s72ymTKyHMmOUi3BOIDobcLQH2RSWtxBSUHvHu/H8Eg5u4dYuIucjbJlCbnCjAeLKWzE49oMV1o5v4TuH8CWlChlVkkA5Ei1o2ozCV2hnKWTILKto1TWHc0ukHKFweFIgCsPz98Zs+pa1VkUp48VFRfsIb5mLXY9JJQi0xHcVm7FkqBxtWXC6Gb5hiKsIPUVSjTjIY0LloaWkcIpwaQ0rNV4xRVUFpVigeb10n7XtawRLa7j2OqJ/D63maGkowyZyPgId0B+O6ZSW0I/YHlvuHk35X37+V/mffvDPIkXAq7sj3nP2InJkeCo+5GaWMfI8SlOxVwV0yhPEMGWWrHGiaqxGmjNLDYrjfe4NZzxy8TwPTEfkgQ9qQZ9rj+5yGG9wlJZ0IuglHloaIiHRWYowJaI4Imwso2xBLpt4+Yx8OsMv9rG9r8I58R+nMNY5pllJKiKWdcWvPXeNN19d4+JDbyZVPgNjubBcp1+c5db9IX/sobNM7t0iOXeKJaYIJSmDNo1XnmVkYHn9FL3OCi8cpmysdqif9PlXL+ds9EDKhImMSY1EO412jpGsEa4/QOrV8JQicgUOgfCSRTZunKCmR/STJQIkM6eIrUFKxbxavP847yO8gKVv+jbK5jLB7Bh3fB9Ra+PCGFmV1IRhFDYIhUXmx0hT4tIxHhWinOKTw+AA0ztPzY0RQQ1/dZ2znRgv7WPbm4j5CIoZNDqI0T5UBVVrE3X4AlFURyIZFIa0eZaNBwRFOmI3PsV6ItHCLiA4wuLbClnOqYIGYdpHmpLMX2E8yamJksPC0g4bTOZz3hqPuDupYRoRrfYmtZND/PkAoUNme/dhdYtH3YyJqpjmDebGslHzQEpaRcmsVmP8+kvIi5dpJgk2HZF/7mOkb/1alhoNvCwnmztafgzFnDKsI5RPXpUErRYWTY5PgkFJQfTom5jogNbwPlx8CtNZ4x/8k/+Z+9eu8/BysDgLx/uQTxH5DJOsII5uYc++Cd/M2Oi22EhGVInHSbzB3jjlpWs3wTlWuy1WvRTRbGErRyEEoZDgDOrcA5Q3XiPodGB6AlFzAa4oc9x8Qr53jzhJ0M1laHSxJ7uIqI4oThA6JCinC9nE5ABnLaa1uRgGZTNUOsT07yM3H0BUFb6YYtAU1scBbzrT4mCU4azjhZMZVTpZbB2zGTJZ4swj7+K7v/u7eeaZZ/A870vaY/z/sv6zRJ19pff9P1V/6Ca/f+dv/S1aIfylt13l+IXrDG4doUJNUPMoBnNme32i7gIo0DjVpf3gBawC6XsUgxmtcys0L5xC1ruUWw9QJR1GRlMiAYGWgsIphLEs6RI5H+K8iF/61X/P//A3/wZ/8b94Pz/9936Ip976NO/5mq/lC0cZxovprW4gO2sMZI2hiImFRUpBkaW8fmeP/ZeeZXjnNUIKztx9kbeJEy6VfbyNC2gpaFy9QrTeRZ/cQd17mWKeEVDgJQnu8C4iaaBaPU5ODmmHmg++PuTXfuGf8Te++zv41X/z8/z4z/5vfOMjWzzx4AX+5FseIbv9Gv1hSTMGmbTB85G9DSZRD6IG+AnWq2GVR44H1pBJHxHGFFEHMT1h+9YdwqM72KtfRaMWYv2QeqCwaJSQBC4nqia8/vyrjO7dY1CFPHvjkN1gneZ8SHJyG1sa+vOc4MHH6EYx+pMfRYQ1Gk8+xenlJtGDT5C+9CzZGw5/Vy5IV+nRIcQNRJ4hhtsQ1anmKeVkwuCl68x2j1h+00WCThuQmKMdpne3Gb34IsNrtygnMyY37lJceJRXqibPDeDZnTGiv0+npkjKCXTWyDYeRKxfYJDVyfojnvsn/4qJDLlOh+z5Z2iHhnLzCpUf0QgkpyJDMT5ifNwHqZH1JlExIZzs09IWkY7RVYYo5gtNY62BmU+RSuGEYsWbI8tskZAxO0ZnE5hPIGpgpgMwBdOwh41qyFoPWuuIdMweLeYiYMkz1HxBpmJaogAgqubMjcHLh8QmpSpy5vOU0GX0CdmvQq75p3BRg5uDgmFuqQUeRsL21JBXlpqs8F1B+EZOLr7PcgjLvgUEnUBh1WLaKKXAVxVTI8hZmORUOcdlM4wFZxxeNoOjbWSYYHBoIMrHuKRDUV9crNx0wGZN0R+cYIwlNhnd8oQnnniQr2pb4vSIP3MJzvQSrq5FXHct3rIsOV/XfMPZkNAVmHoLTwrONj0K4zhKS6IqZcUMiVtNjJIU23cJvIVZ6jg1FHGXUW7pRR6TwqIRzI1a5FJ7McaPwFvIAlA+pVMYKZktn0WefgAnPAq50LRb5RNruDfOyM1CenLreE4rn1Iag60KktEe3tE2zUjwjU+s0//sJ8hffoVVnZMWBYEn8YSj0YpYrvk4P8IUc852E86HFR/84ojLyxEre69SG26znysyp2B0hGcLesyQykfPJgwKwVTGlK0NrNIYFb4hZergJy1KAw0NpRUIpd6Y5juQilIHiOYKRmicBVHv4cIGIk9xQjIIl/jItT7OWg5Vmyzp4VYvUHoxA+rkXgMrPcr5GDM6QbiKcPMi0eAeKoiRRY6YHSOrDJfNFr+PpEl17xqT125johZHzmNYwP/2qdu8Kpd4Xa6wkSjkqA/G4KTFEyy0yUENhKV0Ejkfoc2c9WKPq5tNfCT3U8XrQ8eLaUg/LSiN47WBwZx7BGxFPNtn8tqrRCZDD7bZFFNElfOrX9ghqxxBmS7OOj+kEXnEtYREW3wq8jv3yfw6+dE2WdihXQ3ZqmnMfMJYt1Bpn2eOHA6NHG1TGMvM6UXT2F7D1z7u8feRdzcIPAUWHrh8lry5DkpTyWBxRluLcgW5XycoxkjrqBpLmFoPt/UojZV1HgpnvCsccOX8KQrh8fnXbvLCc5/lhb0xI+NhpcJ1TmEvvIlgtYerigXExBhyvw62QK6dJ7j6FmTShFl/IXsZHsH0BFtVWJ0gTYFIR+AcLptSBQ2cF2LWLkMQIooM8hmiygj2XiUpxigca4nPey4s8f4HVznVjUnikKjZI6w3SHrrdM9dYa/+OFmW82M/9mP/B3t/Hi1Zdtd3op+995ljjrg37nxvzllZmVVZg0qlEQmExMwDY6AHG9ws6MZGtlfL7m5j1DZt7Idf93PbDxsj05jBDQZsM1iYwQiESkiiSqo5Kysr58w75J1ins64935/RDVedC8/bINRi+ffWmflirPOuTcybkSc3/nt7/fz/Tx0FX+86/u///t56qmnqFQqtNttvu7rvo7r16//vuc988wzPPnkkwRBwKlTp/jIRz7yR/Bs//Dqj1Xz+8Ybb/B3/t7f5f/zp74SM5lRzDLywYyoXcUNfPZf2CXpJoRLFUobbcK1FeLDI/QoxmiNLnJmhz3c9hrx9l36To1p2GSsJYECV8ynOSVHoJTCZR6g8HO/8jF+7dd+lZ/9mx/ioVObFH6Jz7x0hff/6T/H9/3df8i3/4UPoepthEnJcBjGKTu729Q7N9i/fgWSCQ+dO8sXnW5zqnOb6rTHVISMDsc4d69h3YC8e4iqVOeu22TK4pn5spXNY+TaWURQBsejN4ppViI+/Us/w1/9s9/KyvFt7PCQuBC8+7FLXNve58u+6iuQRcLGUxeQUkBUwfolbGMVKxxsPMKkCRPrkhYGspR8eMQ13WRY3yBzI7q7R2wulvDOP8Zy9x6p41HyXRxrSAx4WIRyGQxGdCc5zekhjicJixkLsyN2oxXizYu4tRaxX8OsnKFy5gxL73sfla0tzrZLSM+FygLj4wFq3CEsYsTggFhr/NUNkC7kMWY4TzBywoDSiU36Nw8oL1VpPv00wgswWYzTWgEM/kKLbJqz+6lXWf3ar6ReCqlEZdp5n8viiG94cp00XGTi1BG+jzjeR65sIdtNtisnqH/5+/nRVwZUK1XCp98HlUVqJsZqjR73yYVCqxBz6lH8+gLKavzJMcYJUNi5sbAo0P09kBLT2oD1s+iogQ6q4ESYcgNa6xBUSP0KxeJJZjJCnXmCPMmRUtFWOUIoRHcH65doyJQVJyFIughToJH44wOcYoYUgvVIYXTBcnpMOh5y9+4uh4nkSLucWW9QLQV4jmS54hK5ElvklKRllBmUKHCLGDnukuOSCwcjXErCUPJgqezRkHNeahT4pFowyRRxrtFG4Mx6kM8JCLK/g1dMKPZuElx6KyYeoG5+jrzSJlk8B0IRJH0St0R6/yZq+yqLoeI4TvCzEe862+bD3/AkfeszHsbsmirEE6JiQthsErsV1oOEdsllyTMsVELy+ip+2uPRcsGJmk8YDxCrZ1mvl+YT2q1zBDojLGaE0lC4EYuBmAe9AJ1ZRj+DflxwTEQRNMnCOkIARuOmI6zyyZ0AggpOb4dgcgBFjHFcCis4UfeJ84Kbg4xve0sDIR3M7nWm3Q7rukvp/k1OVSzNfMi/NieJLj7M9iAhDRawUuFNe8yqqwzWL2GDCtcniqbMcW+8wLtPV2ktNtirn2H7aMKCHpJryzEl4mSGcTysUrhxF18ATkSufBAS7VVQ1pC7AdbCrJDkxlLxJb6j5kYyLFNVYcb88+blY1Q6wEYVtBtg/RJJeZFDW8L35kQOhEvPKPL2KWK/he86KNdHtzZxioS82sLR8VwuUqrNg0AOb2K7DxBFCtLDWoX1SiTVZQhdTDbFdXxujwyfe/E6/+zVPqVmC21hdP8u8uAuSmuEMBRugFUuMT7Wi0jciIrMqUcur48VqXKZ5YIL9bnx8zxdXCXpjWd0tcdR/Qx9r8XeG3scOC26SxcYVNdRJmG9NDe06TzhTuoylhWmJy6SOyWmToQf1Si/9UmW+jcxS6doxvvEtU2aImFQSDAF7v1XOVn1EK9/ithvcWuQ4KZjZDZlJjwKA0XYxJUCR6ccxGIeOGE0RmuK+hqFX8E21sijJsXyOYTj0SskAjClRaQAb/AAYQxm+TThwhqXzp3kSy6f5+SJE+h4wqsvv8RnXr3G9f0eSZIjwzLFeID1IopZHzXtYcMGVNpw8jL4ZfAicCPsmbeQdQ4wQX3eBAc17M7r2OkYhl3c6RFMjjClBjaeICp1hDGAgv4RMplS9x3qvkPFk2zVfN55ssU3vXuLtXNnaa6ssPXwWTq3rjLtHvLAu8Df/Jt/kzfeeOPz12T8R64/qOThP4QW8cwzz/Bd3/VdPPvss3zsYx+jKAo+8IEPMJ1O/63n3L17l6/8yq/k3e9+Ny+99BJ/9a/+Vf7CX/gL/NzP/dwf9CX4I6s/Ns2vMYav/tIv4zu+6j1cDB3ySYJyFdFyhclenywtKOKc0f6E6eEQ5SnGN++R90YIJXFKAcU0RUqBMBnB5gkoCgqraAUOAosnDdpYPD13zVsh+JfPvc5HP/oL/PD3fBAVj9BBmatHCafPPcSvf/pZasvrrK+tUMk63NdV7lx9mXsvP0enP2J5eYWnHr3I49GMlh5TDHokN6/itlp43V3q7/8y3PEh45dfRNSX6XrLiOYibr2Os7iCWFxHLZ9Eej74ZXQe09cKJ5vQuXmFL3vLw7itFiKocbJskBKCsIQIfCoXLyOMYfj6NYxyME6E6D2gnHbJvBLjT32M9OVPYfoHdA4P+OSxw9QoXrt6lztHI6orLfwoojTrUgkgrNSpmxnFuAvDIxwdo5EMMsHd6/cQWxfYu3dAsX/MZvcmW2XDuhgRBR71UoTvKFxb4LgOvqvx+vex1iBmfRpvewclmSOTPpnyOcpcivYprNHoUQdZqmEObpPtbeNIQ9CIsFpj0ylmcEyRZBTjIdGFRxkeDPAvnGfhfe/FP/8w3viQNhPO9q7xZDjCS4ZsyxpOY4E7tTMYt8RRZ8r4M8/xwsijurHBN15eYsufUXb13ETmhRgDxi8RzDpcu35MQ+q5JAMxT7lTClen86nv4S3I8rnBz/Wxy2fQQZW+LJGW2+ThArn0mDgVpnHBHWeZV+QKB9EqwfSYIOni9eeaWLN4AlteoGxmeKMD6OxBlsxJGlhEPMLIOY93oGo8N/IRYYWlrXXKYUjNd8iARuiihMB3HBwl2Yp3OakPCSQ0PYeJcZlUVhAC2pHEYsFkGDs3Gs60IjeCTIakBhwpkFIQxh38YkrRO6SoLCCCEt74CKVzOLoP3SOkM1dfFVJis4Sit89s41GE65F0j6noCau9GzRHezi/8yuUZY4XDznVChG64BdfPeJHXkmYaovyAoxbpaIKYidCCsMwKbifBYS+z9WjKa/rGgke91KPXDosipjS5AGySGilB1SGezTNAE9AyVVUPUFhLKEjOJoVGCGZCg8jXGInnMuMLLTGDzDGQJYggdyrkFpFXlhudVOa0yMWIodP9n3M4gre0hqXggl+e5XW00/SKHkMj3o8WZ5yXF3hdiemKlJmuQVpUdmYmmNYl2Mut0tUyUia65x/9FHKgcPQCanUSrQih4aZkTkhuVNlYgMmboW8fZrQsYQywzMaORsghUBLhxIZhYGSJ8iRWAu+75EXcwRjjENRgFYeKovRTsjRr/wKOx/9JcbhAlNVxlOSt23WmOHgCEuVHFXkBDajxPw7UzkOutSicnwbmSYUjsekfhJtgekAWa6i/TK6uojt3IN8Rq1VI3znV/Hy2KVmY5Y8zdNvu4jUGYkKEH6EePgtKCWx1uAAWngkBYxyi/Ui+kTYNKYXLTPr99msuaRZxkl3TDHskXplBIJ+YhlmhlHhsE+Vy9/xrQTVBs1yQC0bMpvMeFfQQaZDYuvz+uEYYTVX9qdUZTbHTB3fg3ob4XrIcZ/QpFQjj2iwQyQtkQS7cprF2T7ajQgGOyw4Fre/Q197xAVYazAW/GRMrkJaYobJMtzOPbI3nsOZHDEREXgRhfRRjkMuPXrmzXCgZIhMBsjuNmLlNMXSOUrS4uQz1OSAtd4Nntxa5H2PnefE6grdwZDnXniB56Yl9qJVsuNdZp/5OO6sSyYk2g3mjWtQwmYZIp/hZBOOf/03IIjIF08jsjGElXkwhh8ijUbmOW5/ByslNpqbZm0xw2m158250sRmfpPdCl1cKSgHDl/6rnNUllfp7O8TtlYQjk+sGrz/q76eb//2b59/zv4Y1ueD8/trv/Zr/Jk/82e4ePEily9f5sd+7MfY3t7mhRde+Lee85GPfITNzU3+3t/7e1y4cIFv//Zv59u+7du+oCbzf2ya37/7d/4OupjxoXddIpvEmCzHrwaEi3WEK0h6k7mRrRHglTyS7oTRXh8ciVuO0FmOjlNMYbDG4jUXyJ2AWIOQgtxYykoTyZwUB5XHPPPx3+Sf/u8/zo/8lT+Ls38DEZYpamvc3T/ihRde4Af+X3+LL/+yL2f39k0+9dIb3D0eslANefjsaZ5YDAh+69epHF7HzKY4R7fJb7+GCgIOfvslShcfx92/zvjadcJGBRtWqZcV0otg2EVGNUR1AdtcAyFAOoyOjpHAP/zffpxv/o4PYscD7HiAOLiJdFy0E1B2XO6+/DKyUmPw+nWCjZNklRXUrAvTIbbIuH0wIiktUl5awAsjDihzoemCKQjrVa4eF8SlRWwywQl8qu1FSjLH1wniaBtn9zpZPENkCYkT0mmtM8WhNDjg8rsuU3rynUSVOp7rIrFoCyWRgReiph3M0R4ynkI6mQeG7Nxg4tUgy3CmE1aG9zj81Y9ihh3M4X30uEe+c490GhN3ujQvrBOtLZFPJuS9I+L7dylGQ2Lhk7klOo98gPDhx1FSQKmG9+pv4I8PUM0lbP+IpUDjByGtSgm/UaV24RFOfMt/yRc/eZbahYtsba7gDh6gjUFZTSaDeRMUVJjKCmtveRx8H+P4GL9JP1xGhzVEEYPjYqqLiK1LGK+EMBaFYYaPLyypDEiNxY5HjD7zSQKTsBZZluplWiSI+hJGeFgEeXkBG9UhnZAFNUwQYhrLxL0jxGTAJGwTa0G3M4R4QMmDgQzZbIY0ymWs1fhK4klF3Z+nhwGUXNjzWuzKBs1QkSLZzRSBtJRsSikf4+oMI31y4RI5ggkOfQIiZSkrQ1nEKAQ6amIMTCYxAphqB6SHNhY9HmI2Hp5D9fMY3xZkXoVRf8hk0OP+qXdTPPrFmHKdcGkTb3GV6qlNKlGISsYsF4ecNg948tGzXDq9zFIlILeCsXWYOnUyKwiV5eEwJTUOO6kizw3GCnqJJnQdQmmZ+lWwlslwjNUGVcxIVUTZkzRcy4pviBxLqOBUaFBFQpQM0caS45GrgGI2wQyOiZ0ytlzHBGViVWKaW0Z5QZbldGcan5RHTi1xHGeY0QivVAIhKJUiHGtxMbQevoxXbvDedkaWJgyTjAyP3/h//jBpGiOxNLJjjm9fx5cZG3UfdMHjbpeTC2Wscmg7KYuvfxqpp8ySmERLUuHg5VMK3tRgJyNyIUndMkgfT2gMgkBopIDA89DaoIymkk+o6iG4Dla6mMYqYu0kRa5JmcsgmoFEmYKQgiZTxMG9ecpakSB0TiEcpNG4R7dx0NioitI5QoB1Q1RreY6KdEJs/wBVXQDpgXJxfY9zVRehExqOQR1uc3prkbZjOB6ntAIQJiXNcgqjya1kWMy7ACOglAyx+7fxhvs81bIsF33aJY/ZUZeveGSdrohwsxHnikMOj3rs98c8e6/HqLGOsZZ2CNLzsfevMQjbaBSlpMcTqounY1pOSm+czhF98QT/wS1sZZEkjVHNZbx8il8qo48P8Dt3kBasTnHXtpD7t1ktutijbX7+6gAvHVEc71KkMSNVZmIE5fgIR2fYwRHJ2sMUkzG+njL8zG+Ra42f9lG2YMnLSYWHTMbgVRCNVexsygRFoi1GupjpFFXMUMkY5bpsNCIevvQYb3/6rdSW13kwSnlmb8bNzSfo5pJU+IhkjBx30H4Esz5meITMZjTf/jRS56g8RhzcQS5tQmUBWV/CjjoYJWF4jG1sQdSEoIp1SqBcjFfCNTlLkSB0BMYacmPYqPisNwIW2nVU2KLIc6pLawS1Br/xoMmDBw/4wR/8wc9Xq/EFU6PR6PdsaZr+O503HA4BaDab/9Zjfud3focPfOADv2ffl33Zl/H888+T5/l/+JP+I6w/Fs3vvXv3+B8//D38L1//RZjdY4TvID2F16hisgzlKNJ+yuKFRRYeWWayM8Bay+o7HyXpDBFKYOKc4c4AJ/QY37iN9SKs1ZQcgS8sa5FCak0gBakQ/M7zL/APPvIj/Pj3/SV8oRH1RcSpx0GCF0Y8/fTT/KUPfidOOqbpw6mz53j07AkWahWyYQ9neAC3XuL4l36B9PgQkhn0D4hWl6mfWiM92EWPB/OmoL2M09umVEzmDutKE8p10BmyyBCOh+7v07MONRc+efOA977zHZjeAbJSm0sarMRYQWRiZkLB8JD6pQsEp8/j6ByRxXOo/GzKqpvjPPwYncZpdqii3QB30OHG7pCXHsSEIuH+qKCTClrtRaQSOMrFYjH1NiEZYT4k6NzE2b3FB96yyWrd59EveoqTq02cUgUhLNYtowU4JkeZHIoEEMR7OxSHO/Dqb88NF8kYX6eYPEP2H+Dcfp78zk3M9jXMoMf41Rc5/twrTO4fo+OMfDojORoyuHKN49/+LAD67FOM7uxRXV7E+czHyAedeTLUbIbZ38bZOMH0+hvYUY9mPsYIga8ERdSgCGuwdoZGKFGzDmI2QEsfG1TB9Sg7ljIZoecwC5s0Sj5OGqOKBOIOWig8DMIajFDo6jIYzViVUDqhsAKyZD4hG24T7ryCWlihXgsJylVkpcGmlxMc3QKT4eopg/pJhv7inMssFEY5zIJlZl6dWXkBr3MXZ/d1Rvdu0f3Fn8H4FUDwzq0GAkVqDMexYVYYlBT0Yo3EUPIcWr5DvdagUi4Tugop5LzhSMe44yOcIsN//RM4NkdKS+Aoar6i5hqCYkTDxjizAYFJGIqQQa9PuLpJnuTogx1Sr8Rk8zKifQIbT+a6QK8yf33SKSNRYpoZPjf0cNz51Dl2y4hqC7mwgZoN8KVBnn0Cu3SaE60SZ5YqnKj71H2HQApyFK7j4COIHDjjDFjyci66Ay4ulfAcSTtUZMYSywATNRg/9yzu6BgVBPiuh7aWBTtCYViUMWmWUc16uKM9/GyAT0GU97GvfprbL1xhsHwRPx1hBVgLeTwl14Ykt6hkTHWhxVQ7nGoEPNxw+ETPQfolQOLGQ4pKm+ZDj2L9ECMtbnkBqXOCsEzmVRgsbVLRCVYFhMWU1YpPbes0gzinnPSI04JEQzcV+EkX5/zjHIwyGjsvEk0OkMkEo3zGxgGTU0xHyKPbSJOBgIqZojCU9RQnjwlNgtYa1d/F33sVNesgtZ4nKmpD7dFLrP8/vpa6maH0lJJJKNuYoJjhmQRVb1AIiTFghwdoC0LnuEGEiOpMiBhYHz8fgRDkfg3tVxDpBAvY9sl5rLkKMEJS8yWqu0t4vM03P9ZmydVc3z7gc/szCuFDZRElDKNcURiDEsxxjbogzCeY7iF+NpknEna3cY/2+PTIx1XQSo+5vT8gFR7LQcry8S2aZZc8STieZhTCIRsN2K6c5sJSieP+iKlXYbksKWYTNltVypGHl4yRq2fJL7wV13XwHtwimc1wZgPSV34HwhBbaeCO9onKEXL/Fnb9PN1gkaw/4FSxTzU+xssTqtNDQhtTjnvI7gOsH5G0toi6u2RuiHvzOfrPfQ5XWdx0gsgT3DeexRqNnPYhHmD9iHz7BqX+HkKnWOmSLJ1DnHwcotqcA2xyCqBcjFhaXeeJRx/hHZfOEQQhL2/3ePmzz3Ln/g6pNVi3jBQWO+kjioTSuYsgBBJBsXAarADHR8+G82N0gW1vQjbF5vE8dKi/D8aQhw2cdMKCq2m9yR3PtSUrLI6UPH1+gcWtNZRUTLqHJIMu2XTCu77iP+O7v/u7uX///uer5fiPVn+YsoeNjQ1qtdrvbt///d//+/5+ay0f+tCHeNe73sWlS5f+rccdHBywtLT0e/YtLS1RFAWdTucP9iL8EdUXfPNrreUbvvbr+OYvfTtvbbeIVpvkoxnFNGO8fUw2zRhsDzFW0Dy3jCg0ySQFC/HBEW4pIu6M8FsVGmdXkL6LzVKSu9dpuRqLIMyGFELOdYvS5carL/G3/9cf4Cf+1n9H2N6ASguzsMX9KfzaJz9LmI14xxe9h/WzD/OjP/wRTtgRrcPrVGxG6e6LbDox3HqF+ull/LJPeucW2cE2+WCCCgNaX/mVeK7GbbZpvvuLEK1VzLiLXFiFZIqdDRHSIqyam0KsgWGXoarQn6a0zz+O88v/hLzXQZVqPKiepNy5xfHt1+ilORdPLKOqDaKHH0cGIXLaQeQpstJACEtpZZ2qDzEOvi1YZUxnUrDZ9KmtrtMqhezuH/GPXxhRCBftlgFBETSYlJfxTl9CCoEtNSnfvc65ZsDj7ZCmnVCkM+S0B+MeANpYfAoQDjIZY+IxbjnCWAkmR+qUePeIO4lHcvVzqCCgGE9YfOIx4vu30Bb2fuMFOle2MUVCNknYf34bTEHv6i5eq4n0PIK4w8pD64h0hv6tXyH5jZ9H9w8w1hC+7YuZLD+KiWr4pRDrOFipaLiWQnrkKKSweDrGnXYhTZGVOlKCKxWegijuIrG0AkFuQToSjEZkMbW0i8zjN7maZbzpEebea1ij52SN/iHh5AFJlrIzTDHlFtJ1CB66AAvrgMDLBkjXRU365F4Nx3EojJnzT/0qMi8QwuCkI0LXJS+3uOUucqV8nlN/4utohnMjYugKegk0PagGLklhaQQOoadoMcWX4KFpeBBJOJzkHO0eUBhLVhTIZADpBF1tk6sAYwQCzTSDSIDKM8TxPShSqmmHLCs4qm5hhMONoeGX7mQ81zFEpQrDqE16tE1xsI08vo+TjQjzMc2yh5zEtALFwIZ0RcRgPOWYElltFVXMqLgaJ6pxRJkCSSt0WfENVswDMhwpGMYFaVGQChfXD3Ecl2l5CSMUoYJcGwo9jyHO3RLty+exC+uIN5uCkjI4EmQ6wY2HNMQU6/gI5SLk3AQq7r7CzK+xe/UOdTNF9/eZiYjnjix5ktCZZZAmvL2Z0g5B5QkHU821keBtD63NTZHTHvLoFu6sRykAf3JM7eAe6JQiy6l6DiWZc+qbvwlVbmCwJKU2Qa0Mrz/HM9cPyZyQrfiAxuFtWsP7WL+CXwspFxMOnRYCgzQ5WnhU8hHaCUBr/IPb+N37YA1SCOpJB2d8hJcMKYlizniddufSosoKatpFzAaIozsEwz2CpIuTDCnbDDcb4Uy7iON72KNt/M59/N4OXu8+QjpYPUc52tYGptoGJfH8AO2W0FIyCZsU5QWMALmwgUhHiDTG27vGsBDEBnB9wqVlFjc2OL0YcaVn+PSnX2KkJXHUxHVcBJZY27mfQUCBwq6cQTRW8GtNxMIyvcoa55cD3rkRMFUh5yf3uKiO2bz2cQIvIK63eagVoPIYz5EczizScdnaWISwRqvdxnEdsuZJ3ugmlH0P5ZcIswHS97AL6ySThHzrMvupRBQJzqPvomRjRLkJYRVRWgRr6WiPhsywWxd4a6OA+jK+pyjCKlIIzGxMVl6iKDR5awtHzzj2FhjWN6h/25/Fwc5Dl/KUWXUJYyELKqBTrBvgrZ3A79yiND1CDvfIogbWDeBNyYsxliDtoIVLIOYR2l6twblzZ3nbWo0zjYDOUYdnrtzl1VdfpjNK0O1T2DzDZAm8OfhwRgdz3NzwEN05QLZPgZpL8hgcUpSaiGQw/9wodx4KgwKTU3YEBkHZc7BiztRvln2eeGiJh55+Ch1P4M3ksp/5zAFf8iVfwnd8x3fME0P/GJV8E1X2B90AdnZ2GA6Hv7t993d/9+/7+z/4wQ/y6quv8tM//dO/77Hi/6Qt/j/+Fv/n/f93rS941Nm/+lf/irt3b/HRD/9NzNXXkK5DfDzCFJrx3hi/njB+MGbri04gPZfOzS5hM6KYpYSVELfkEyy3mdzbQb75agTtJtHWabpG4giQOmGWVyhZQ+/gAX/9e/4qP/XD/5AKEzKvwl4xZfvGHoPhiHvbu7z/a76eFzo5/8uHv4P/4S/9t+SHu4QmJzu4hR70mcav4pcjTJ5TXl8in87Ijrt49QoqiDBFglg5g+g/QFVqCNfBLm7O45MngzmGSnlYm2NVBftmglp/kPOx51/jT77/XUyf/WWW/vQ3k9SW6ZuQlVmfH/rl3+E7/twHcddOw2wI5cYcVB4PMcqFqI6urzIqJNOsYJDDgplRyIIr5TUmmSaUFp1OEZ19HjuxzNQoql5AJh0sgrIHUkZYpZgGdZa/+qspBCg7n6BUlAdGQ3MNk4xxju4iSw1krQ1CwmyMf/IC+f4d1NLmPJLT9xD9faZXXsF1XbLJhGzvBXpX7lNeX0AoRevhNUxaUD3RRjRWqT9+FoIr+CWFinwIa5gHdzHdfRYunqAY9Ji89grq8jtxaqugLcEjTyI6d7HWYpEom1JYH18JkIopJarLp7GDA9xZn9wP8az8P76T51xkwLMWo1yE62OciHB6jJWSImog771EUW4hgxDx3K+TLS3gkJMMZ/gbOZ1wmY2WgnRK0dhEInCLN0NTornRykoHX1iCYoCaTZiFC6QqxLUgZ1NEWGcUtogin0ddFxU5ODolNwpHKWKrqUtJpCBUCoEkkBYFlGTOOMlJrUIjGAwmRNevMn3safYOj3m6LYhLSySVDUIBFClekRGpEs7kEGMtjDskCydIRUAcZ6wHBa8MI44yTafcJpxpZvg86PRYDhdg2iMih6JK4QQMWqcx45jpKGNZzrgyVRxfvcl6MaRxoo7ZOIPs76GyEb6sIE1BVKTk3ty0VVhQEqZ5QU9KhPSJlIcVDhvVObrLU5JxWuAFLoXOyPFg9SIqLBNQ4BY5Vth5rG6eIoVBJDFFUMERCQyOGIky+5+7jnUjGhdPYm3OT15J+JrVNzDNS+xpDytg9fYLDB9+Gs9k1CNFKwh5YX9MreRh0yEmnSH9CHO0jV9tcHTjHstlgVO0qB9cRel1dGuDp074iDyeGyOVh0bjr6a85+6LPPPZFl96+Sx1X8yxfU6FyniX1XqFobuKb0YUQRkpBU48xOTzaa+Z9pGTMmQxYnELZ3pAsX0bsXmeyMzfd9oYhB+BKaC3j9E5KptBYwnhhxinhHUjKBLEuIudDaG2giiYU038EFtto9AIIbESMrz5hVoJwCE3ao7/K2bEfgNPCJQKYHaEPdrGK7U59hosLZ4gTKbEOIyty9Mtw3a5holHjKManvRoTO8h/Ai33ARr5smCbonS2ctorXGEwD++g21tIqMmYyPJTz5KKx7gPfEO0oUF8lSybPt03Bo1V3Cjl7Ku6rTKITMkFohc6OVQLodIJdHWchgus0RK6HvoeouaIyhnKSQZTrmJdARGpwjPRxRThg96lOoZt3KHSwsLGBSyt8e4uYn/xrM4F59CH+9iG4sURUpIgV06TaMYkTdXMW6ZSjZBaE0uJZX2MulsRE/VaIQuiQqohCkon9lkQinw8fMYUcToQpM7EcqLKIzCpsP5EEIUzGKLjCqE1TIlt2DZN8zciO3jAS+PwRnvsVX3WY0UrlTgl+deBr8NwqI2ToMUcyOvG+FVFxDGIsIatlWlQM5v/k1OLubJoiVXzXn1haYeuhhjuLBaYa3+EK998pMU8fh3r/ud8GHe+NQP88u//Mt89Vd/9R9Jr/GFVtVqlWq1+u98/J//83+ej370o3zyk59kfX39/+exy8vLHBwc/J59R0dHOI5Dq9X6D3q+f9T1BT35tdby577jO/nv/4uvwunuk09i4sMe1dMrGG2QUmJzQ/vhJYpJSj6aEjRKCCkIl2oU0wQhBG6lzOyghxCQ9qeYAqxyGBkXIQUmqFMxU6wwfOi//Yv8z//9d+Epw5Wh4BPPvched8hv/tYn2B+lfNU3/1c06w2e/ckf4Mu++ut4+4UTiO4RyqaYQY9k/5DJrXsMb90lWqxi0oz4oAtA+dJlTJEi+gfIWhOsRYQBQilk4FM8uIU+uDc3DWgzR5JZjc4LxmmORvDsZz7FV7pHtP/0t5AuneK34yZnpnc47CQ899nPce7SZUxYRToe1g0xUQNTWZwn+TgeUmdU8yE3ejkXquDHPa6lIUKC6zhMZgW9vT021tq875FVmv483SsFZgb8WRfiAWAxKIqggh0cMXXKBPEQRyqQAosg6Q8QxuJlE0QyQs/GyFIF4Xrz4I1KE6RD9cwJTk/uUFpfohh2GL1xl+G1e5jCMLh7TG2zgQBKq3N9afXCaWStzuL7P4AThThRmfTqZxnf3UNFFZxqBYRD0hmipj30bEZFFvPn4YfIURdlMnIj8KWZSwusi7ZzCgLDQxCgjAajkdmUzK8hzDyVTKXjeUMcj5E6RUctrDGo4SFaGxgeI9onkc0FjF8G4eCfexTT2OB8u0TiROSlFsoWqOM7OPdfwYRN8soixg/xsgF5keNIMEEV33FQUpIjOQpX2EsUuVdhrepR9h1GmSWRPpGEJNMsBwLHZtREMic7CHClJJM+UkjG/T6f3RuTDAdsvPJrXLqwQMvLsQsbJAun2U0UVjpoA5XxPgqoxvuINEa4IXb5FIPCwbMFV45SpjLiztjQsglftFjw9KLCFCm7ziJFrY3vglk9z9htEGeGyvSAlVbE25o53vFtaoM9brx2n2tpgG2tUOTpfJqkC1r3XyB48BpR2kHoAixkuUagWQ8sUyPwxBwpmKQp5BmFsRQIqp4iLQzuYI8wPibXhkFmyYRCuC6ZcMndEC09jHIQ6Rh5fJdCuGSdQ8avPMcbtU3aus/J4zu48YiT50/SKa1xXnVhNuJclPOqu4CZjRnOUvLCUpGGpp7gWBAHtyn6x6THBzDtIkbHrGytIk9c4s4rdxi7VYwQpKpELpz5ErPNUTajCGs4C5ss1Ku8fatGs+zg+B7u8BB3/zpF2MLxS9SjgNyr4NsCt7/N9NUXePAvf5nJi88hHR8mQ2Qyxs4GCOHOdenb1wgCD4CZv4DZfh1x72Wy+9dIX/kddBpjkhzT24fufeSNZzFBGeP6UF4gd0vMFs8wq6xDUMPqDGULQKOVTyIDTJbiYDHCJTOQW4vMYhxhYdIHNLaygFpcIejco+G75NKbf69nGWu+phwFnG95vD6Q3B9bxpkl8aooJZFYlM4pFxN8YeY3126AnHQJ0Dj1BSq+ZL04olyMMd0HVNeXaBBzoiLBLzPVgp1Rhu8Kqo06iZUUGnxX4uQTciSZX4dJh/Tma3zq6hGFsbx6mM5T8oTEcT0Kr4Sc9bFvMtOtLhDGUnvyaWrrG0R6hik30ft3GfhNbkwl+uxjFE5AsnwSsbBBFrYZP/sMM6eCKNcJ3zQS2t1rmOkQJxvhKIF/73lqh9ewd6+g+oeIeIwYd8gbmySlRfzpERoX0d1FZVOM9CnrFK0CwqMbGMfDGx3iFDFieAjVpblkZPt1zhVHvGfV5YwXczhK+MT2mCv39xnHCaJUAwTWL0NtDZGMMGEVlIMJK8h0MmcUZzGzsEXs1Uj82hwVaOU81jw3jHPLiUaJE80SO/0EKwQbTzwN/Jsp7/M3jviWb/lWPvzhD/+xmv4KJf5Qtn+fstbywQ9+kJ//+Z/n4x//OCdPnvx9z3n729/Oxz72sd+z79d//dd5y1ve8gXDYf6Cbn5/8Rd/kbyI+c/OrzO4fp90PCMdzigttZBSkuc5lY0Gq+88i1SK2sUzLD95mtrmAm6tjHDU/F+tsVbglEPizojyOz9AVlqg6rt4JkWanCAd8ZEf+Qne/953k/g1nr2xh1YuZy5e5hOf/DSFV2L9qffSTTIe3L9Ld2+bP/m1X4scHONIi3ADws0TuOUQoRwcz8HgkI2n6CQjWF1FRmUQApPOMGEDm+fYJJ6nl+UFTCfIlZPY4TG23JjzFMcDzOF9hm4FP4tZKzmUShF7ixeQxrCSHnHr4Ihv/St/nT//l/47bn7icyRhC+s4c1yNE8y1q1LQCxYo4ow8TnnKOUYbw4tpBaVcliNF2RN0JgmjDM55MW7nDp40GCnJkpxAGIQuEMmY3K8SUJDIgLh1AuF4+L1dfEeAE2H3rzP66M+QdrvYSQ/b3cM+uIVVLmYyxLnwNnR1BTMbkhwfke4/YLDdY3TrHsFinWihTuP0AktPnMYtR7jlkPK5M1jlke/vUDy4h9NaQjUXsdZgoybqnR8gOncBZ3mN1rvfQeNLv4pAaEqymNM7bj0/n+mEEe7eNdTOVdxkhLIF2miiwzdQkyPIs7moc9xBDHaReYxUzvxcIec3TNLFhnWQLtYWGGOQboAUlqJ7BCYn2jhJWCkjagsoAf7kAF9BoS1ydIhAIEyK6e4D82Vcf3QE0iVKehTyTdasG+C9OYBeiWDTL4ic+eOZBoWgsILECjJtcIVFColvMxwMjrAEomCsHQY55Ie7nKlJdq/ewG+0EP1jNvyCs+0qMxXSCiWBmEtWpDBgNE42wVTa6MNtJtaj3LuLPz5gq+Lgo3nHgmRzskelWqXsu1T1jI2aT90TuGHAtbHEj4dcmQW4zSXcUYfK9RfR2rBy4gSP1hTvWtIUuSbfuQ0IGB3/7nK+DZp4SjBJMnqzFJKYeu8mbTumlwteG4AUkmmh8bQmoEBJSLOUcbSEpxRD4yOw5FbOG17hUOAgBGi3TFzfRFuB2buFjMo000O+ZBWqW23qm8t0J5aTYQ71BfbykC2GOI6iiFOuH43YTx284xuYZMhmWRHlM2SRMNvbZ1pdmV/bhSDt9olHPe5cu8f+ICPrDrjaTYiNg3V80skEmccIa3FsjopKrLfDefiyWyIqlwg79/BdhXzzvTeTEWI2wFhLsNImfPwtJIMJNgjnYTZJgh4cAxYhJXbpNM64B1jGe7cxkwH2YBs96pONhvPP2N2XMfevwbiLstlc69naZNo+S5IkZCrimq5gpgNGsxSNwkoXjWKQWdL9e+g8Z2wUISm9RDNQDQBit4zxq+CG4JUQJx4mDFwCkVM4PrbSwt+5ykqrwrJvEL5HNVDUmRJ5ztwQbDWJCpCCubEuT5A6Z1pZRp96gpmKKNScPxsHLfIgAmsZaBeFJTcOkYKSr4gch8IKbnRjdsY5/cRQ+BUWmLEYKsSoz/beEauH17G9Qyb9DtIYrnZibg0LCiRdt4n1A4wxHAfLFKZA+yHGaDZn+5ClsLCCGByzVIzQYRNP58ROQDDaI0g6KApUPqGrXcK4h5j1kcZiZhOEG2G6B+StLdTqOWy5OY98Njn4Ecrz8IUlLrWRk2PM0ql5Cp7VuLMe/uiAorWJLQpMa22enllkZId7iPUzsLCOarRRyZS1jVUuHV/hHashEsuzr17js7sjuqMZxZ3XkJMjsAYnHeBMu6ROCWUykJI8qDEqJIKCmQjwhSGzllAJ4nwe0lPxFFlhiTzJSj3g/e88h9/8NxpTIRW/dM2wu7vLv/yX//KPsOv4j1tSiT+U7d+nvuu7vouf/Mmf5J/+039KpVLh4OCAg4MD4jj+3WO++7u/m2/5lm/53cff+Z3fyf379/nQhz7EtWvX+NEf/VH+8T/+x/zlv/yX/9Bei//Y9QXb/Bpj+Avf+ef40Fe+g+2PvYzrOQghkJ7LZPsQ5SqyYUY8ybC6IGyWyIcTijRDm4JiOCVolAkvv4MsSdFxRnI8ovHwKWyektWXqJARzI5hNuSzr92gtrzOyQuPsNCs8663vYUzp0/xymuv8+rrr/OB//zbiByHVqj4+3/re/jmb/rT5L/4ozDq4lWrmHiKTWdkk4T6Qycon1ijGI5w6xUqW8t4y2sIx4ckRkRlKHKsUtjpaA6SX1xHrp5ClesIFPglMiQ7L92k/8obbB90eHDtGu/bquO85UvZmO3y4Ad/kP2rL/DffM/f5oPf+7fZfPr9nC/HSN6cGuczTJ6QC8mxqPLa7UNeeOUacb+PW6oDMFUl1ioON6/dwzEFaxXFIw9t4jYWsK11BBbXGm7f20dN+5jpAOGFpDi42ZRSOsDzfEqjB/Rzhb1/jeLmC/Q/8ZtE7QbewgKUGtjy4jwfPkkQrsfMayDGHdI7b3D86Zcwox79a3fJJzHZYIw1BaawDG9uz5c2jwccfvpVsm6X/edv4J+/jOk+wEzHpEcd9q7ch+03MMc7OOkI2VrBI0O6LnTvo6cTRFTDIOYX+niISgc4B29QmnUQ919B3HgWqzyELjDjLrZ/gL7zGnoyROoUJp35xVp5aOnPEXJSkKkI2dlBJCMUBU6jCUqhPA+hFKKxikpGuOkUaS2VtI+yGmyBLS/A2SdQ6Rg56yGKhEIbpHBwBnugFKPMIhFUiIkmh0T5mJI0JEaRFQb5ZkNdWGgywy/GuDpG5AVuPiM3oN9skMP9G/zqeIFP3x3hnzzFwkOn5nIcinmzJQAETjYisDmFBZvPGFc26KsSPeOwl7qI2YDCCNQv/Awim7HImOruFc4UB/ieg3E8HooygmIyN2J2d/BKcyOaVA57skrn3NuYdUcMX3qexScu4Jy8QHZ4gNNoQ5FQ1NcR9UWor0J/F33nFZo7V9gc3SVGIZvL+L5HxZOcaddYUgmVKMKmQ9xkSGYt7aJLIjwm05i26bEkExSCaW4ojEHa7E2j1gyfAmkLMJbE8Yk7Q8yDA6zjUdy7zpUf/VnW7YC1/Jg0amCjOvv9lPTBDqdKhgzJcf0cBzpgrWSJpg/IO4fEDz2N39mlGA+hXGd68zqziWHz8QtUzz7MrLVGWeRU8h656xMc3yY3Yv53cwLsbIQc7NP51Y9iBwfoSgvv5EWco9tILIkW5HYex2u8EmbjYernTxE+/R7wSuCXQCpsUEUnU8xsTH79RWwyAWB09zZZr4vRKdk4ZnBrD4xBjwdQXcAOOghnngSGcknygmw45s4gY1n36ZgQE5Tncd7xhMQIDscp5XyMnwwpD+6ihCTXsDsrSIY9JJrMq2CUT1w/QeGGqM5dRDympFN0rgmiEP36S6ytNDhdFnPjaTHF9zxcnWOEJLMuwhQwOca+GUFvHQ8PjZ8OEcIQRy3CyQGeUtjBEYUuOLp1F5X0idOMF67ep+wrtNF4xYwV37A7SonxmAmXpcCi4iGLlx+n/vB57qgmj+XbjLSg/8lPIYSlR4kkSzF5gRgdo43Gbt+giKfYqIFZWMNYg4maVONjljuv44sCaSwlP4AsQymPzsV3Q1CmKVOkH6GCMrK9Be0TDP0m+e3XoMhRaAbLDyNaa+TRAjpLCfIpun+Ag0aEVYruMQiBm0/Jihzhe+D4yOEh1ovmpuT2GZKghnUD1NbDSNdHlqtz7Jk2lGZ9HlmMeO/FLWqVMi/ujXguKbPfHaKzBHt4H+OVybSdxyhnU4wbUtZjwtmAkjSgXMoiZ1KAoywnGj6JsSgl2WzOqSth4FBpLYGUuFGZlcvvxm48xTf/qf+KD3/4w39s0Wd/FPVDP/RDDIdD3vve97KysvK728/+7M/+7jH7+/tsb2//7uOTJ0/yK7/yK3ziE5/gscce4/u+7/v4gR/4Ab7hG77h8/Ff+A+qL1jN77/4F/8CIzR/6hu+npf/x4/Ao6s4YQAYetf3QMJsnFK8/IDmiUWC5TpZf4DJNSY1OOWA0lvehVo7y/4P/yjpNKG03qJyagvSKYECkY7YOR5yZ/+Yw6MBl0+s8PBmG1pbMOlRKJff/OVf4IPf8W1sVTy00bz+G7+ASlO2XvkUxi+wR/eYHXRwoxBRWyQbTAgXGrjlOk65gpSgFlfmE14/xkqBs3YGkQywC6tYAcIUyGSK8FysMcjlTWyeMp6m+MUQHcCt4yEf/eRv8//+2qehs41vDeVLZ/mf/9GP8Y9+6CP47S2YdlBnHsKbDebpU8NjjqMNyoHHYHhIKfTITj/CHdfh8f51THCWE8e3SPQ6O2/co76+yrlkn/VaBUdWyNwSaE1R5AS+j4qHSDQ2jXGzEUwGuDZD5lPE3VdorSzC7jVmB4cMrt7jxHf+1xTL55DJADEbospVZi98CufUI+Sf/XWKaki6v0Pt/BZ5v4MjLdloihCCIstB5cTdCdXNJo2HT/HGLz3PxmMbnPmWbyTdeIRw5wrToyE6TlmoezhH23MjjOcw63+SytYK1i+hB8cUt27g1Gt4pQbF7m2E56GCCIRETrvYq5/GOfUQZniEsBYzGqCWNjD1NbTr4yYj7MEtWDmDdEIKxyCTEW46QgT1OcmiSMH1cReWMDiQjDFCYtMpVgoKAzaNyZMpbqlK30ZUI5cZDiWb4A33KYxEdq5hNx5GFik2HpN7ER458vgOkrlO1cvH8zAGY1HCEGtByYWgGM+f/4Pb2HIDmcWw9hRWOuTZDKVjvuJcm93c5cRihJfkIKtYY6lmPbTxETi4owdI1ycJ2/SnY17Zn3Cq4UPjNHUPZtMKItf8erjOVxYhD0128J54B/LoNk5hsAurqHxCVl0mFQHb1mXZupyKUkRhwFpejX1qtYepPfdJNtc7lJYfJz79CO7sAFwP4frIbIqptBBCIpRDuVknrbZItCZ1KuTxhHI1gnSGiMfIYoIbRcQoXCHwXA9HwnbqU/dCKmiEzdBa4EuLNz5mqiKwEPmS17MKJ3oPyB5+iDdaI55sWvKzjxNWXuOxjUuo/RuEuFSzB4zPPUJz9xXOnWjgv/gcZy+/DX9hnbpjiQqF9kokqaAsMmSRIgMf4YbsDDWnl9s0lY/rOkxUFR+HIigRoBDNVRz7b+goanENIyTuSh/9xudwzj2OcXyUKdDCJXQMxiisXyFzQeVTCKo4wweYxIcgQGQpILGDA/JhHzsZUSQTRLnFtACrp2hbkPd6VLdWQEr0JMXXOWLzIczRfUR3B6dUoxInZI0ybSdDqhrF5B61zh3c+gK5G5IiOVuTRFPN8cRQlZqRdQhlxtE0pSg1yK3AFA7C+JTFjHiWEU4meKbA1lZRv/lLhOc2kE7OYiWkHVhkMYL+PunBAcHqGo7ysMqnMAYRVshURJiO8I1F6JRAekhryG69gFzbguYcz9VOj3jQ72KKAac3TpKfWqWqNJFNeLHTY7l3n4fOXqLfGzCSAWfqCnXyMmtSklV8ZtMJ3umLZFpy6vgqJfk0w1yxP3KoroaU8hjH9WDtNKOZQTVraK/Mr99N+IqHSsj2BkoKiu3XsQvLTCihSg2GokQ7cECFKGsxBLhFgpBzpGA0OyQ/OkCfeZoHVAiUxOqCiVdFXHgvAQnFwklcaRCjQ1yjsblHdnjApH2KBWKceAiVOo7NKITEE5piYQvtSowEWW1DPkMPjgg3tuZkkixFjfqcXVrlxMYqewdHvHHY47qQbDkxq5uSSnyAjerztEdhKekZ1g3wkwGpX0MpiWstWHAkhGQ0Q8lqeoN/8EaZz97qUV9bJqyWiScZh288j46n/FyjBp0OP/dzP8c3fuM3fl57kj+UUhIh/4AzSfHvJwP5d5GN/PiP//j/Zd973vMeXnzxxX+v3/V/p/qCnPxqrfngf/Nn+R++9RspNRdYf7SNKSz1c1vEnQmj3TG9N3qkiWb50WWMsPjVMqXlFsFCDb8SUjr/EKJUI335GfI4pnFmlel2l8nde+StTXZ29vjtl15n+7DL6y+9QGm6z6ObLZQ18+ng8V3UbMCdG9e5fOYEWTLhpY/+ND/xI/+Iv/Un3suCHdB+4iFMoRnd3scUGWQTaqfXkZ6akwLKFZyghAwistHchOJsnscOukhAVhYRyodkDEUGxmKUhw2rTD75awQHNzFLS7werLG8vMJ3fcOXcfKxJxDTAd1Oxqc7E9712KMstxfoJzlyPGKx4iPHHaxQjO9tk3z6N5H9fZqO5lzL41LTYasekC1u0S880tGE3m9+nPTcBZoioRl3qNRKCKVw8wkKgZKSM1GMYzLIM/JKG9cU8w8yBjU8QERVwmQM0sGkCdVzG8hSmSMbUkQ17NE2ejJCuAFSxzjDfQ5+61MEGydRqkAISZEa9CzjwesH3HrxkFlnSnW9gYkz5MIyD/3FP0N5awknDHE6O+TbN5C2wG2vMHj9OsZo8smMwY09kr0d0uMDxi+9QO+zr9F79SokCaa3C9Ywu30L4fog5+Yzd2kZWWki3AAcH5vGWNdHSTsPb8hjsALrhZAMcfMpUgqktTjFDJbPzU1G4wHGDbF5TKZ84tIStwcFHXeRJDPEsxHx/iFpMkVgSZ2Q3EqIZ2gvQoyOsBuPoIMy1JbIwiZL6QHy8CYynnOaC6+KKQqEgIoS+BiMtXTjgnT3ztyR7Sjs/g3k4IBG3kXqnEXTRzdW2Vyq8/hahbLvzJ3cTgBOgKsk4XifaLSLmA0xQYWZlQgrqbz6HHef+QSHnT791BKVKzAb8TXvu8xGoHFMPp+4n3oclU2x965BWMZJRxgvYrkRkaqAyOYoJQl0zGo1II+q5O/7KsKz5/GzMY3keD4RRyCTwXxVZHgMsxF26SQqChGjLtmdO4xwKPkOIh4SpUPi/T2K/XsExYSSmaHQFFGDNM9Z91Py269RTCfEnWMa+YBo/3WGcYLev8Ot2EPFI+q9PcKyQy3t0L70KIObt5h1OyjfIyxG6NzitRZYy4/ZGN6gUo0QS1vEFx4n9AX1WQfXsfNmfTrEvfwOnHtXGHzueUwSw6hDKxDsTTSBzajOjmnpAUtFn5GW5Lrgs7c77E8tIovJ3kyhE2GF6Ml3oGp1cudNiUOtDVKhTE5FT5BFip+P8Ye7uDtXcJWAZIzevU1y9wbJlc+y91vP0XnuVbLBiLTXQxpNnAtG9/ZJuyOKLMdIRRFPyUcTECAaa+hBB47uIWYjvKhC0FxGuA6jHErS4gc++bCL1QYPQyPpYMOIV3/qn9M3AZ1xihj3iEZH9Pf3CGxKagXa8ZD3XyO2isnqRfLqMkInCJPzeuzjnXuEupngWDO/OawuYR7cQ+oCISxRMX9/Gesw1RIj5/piO+7h5DEin5H4NUZ+nThYRFQWyIRLfPI8wcZpwukhoe8Q2IQw6VEJXHZLG8RJyvYk57XtPoPMkksfNx8jHJf4yhX645ygGNJ+6xMkTpn17lVGRsLhPbKghi8EuVdlfPCAIp6Qz6b81mdexRQFsXExTkAaz2DSxbvzGpNEULMzHNcltoLAZgz298gHBxR5SmEypITwwiNMf+fTIASRTRBvSpqsG2JQOJ5iLMtQXYDFDXS0gE3GNMyYUdgEDMJKUhmQOGVMElMe7+DOjpB3XyGvr5DX1ubfuc0l0Boz7mH272D7+yghOLG+xrsfPce5zRUe6JDf/uwL3DyekMXTuZQrm3sDtF9FFAleOgCgpAwrZYc4h4oybEZQv3+Fn/mZX8VzHWrNBstrbRY32tgiJ2y0meTwjf/Ft/LX/tpfQ2v9eexK/nBKyD8Eza/8wqAtfL7rC7L5/Zmf+RkCx/LNZ1t0f/WjhO06UauGLjL6t3sUaQFKUF+OaJxdYXD9AKkkbq2CVJLK6Q2U65Nf/SyDZ59DKYfKmQ1qT55n/6G38cnrOxz3+lw6s8nltTq/8tFf5Gu++F3kfhXrKEQ6Yrazx+3feYbNZoX8xhVe/te/yD/733+M//VLnyLSU8rri8Q795jsHVIkGcJCvPsAv1nFby8joyq2yLA6m2ushn1UcwXhl5C1hTeXiGLM7nWENVCd63RNWKEIa5ijbQa9Ea1Tp7m638UUOV/6zrdAcxFKdZzjbX7lsy/wvq/4GuqqoLZ3E20KglkHTIGejund2mV08mH8wSF1R5MmKUWhqeYD3FGHg1HK1dpJkne8m7edbtOZFujL7yD0FNYrI4oUgZ4vlxejuRQhrKIo5k1juYkuLQCSvHPA5MorOIsrmHKL8tveg/EjTBpTvPLbFNMh6e5dRKmM3rlFeniM40rc5iLx7iHRqROsvvsSWmvOf+BRnvjP30lttY5XDomHMQeffoFoZYVwsUnn1z4KVz9FOhgSNCtk9+5RXl8EBMJReLWQxlufQCmJf/4STr2MySzUF+cXhVoLd2kdEZZxKnVwfNTyGaTrocddxm+8hskLGPYBgZ/0scqDlVNzhBAC6UU46QTj+hip0PXlOaWjvoSwhsmtG9w8TkiAbiY4HCXci9b557c193d7dFWD8uyQSWoYzRIOY43VYPICNe5grnwKff913MNbiN4utrMLQmFLdbzJIe7edUyRE0we0ItTunFOliakfoNCF1Ck2CQmy0AcbiOLjMB1cMIIjaVsUnJjscrBRk0Q80RDoXOKcpu4vkkqPfpXrlJRlnNPnKPf3CLs3efG/UPiqI6JKpxr+ZT238BoDUVG8eA+js3xTj4EViCGh5STAYslRUlZ3DzB0ZoiNzT9Oad1qSxRC5ugHIpbr5Dv3kFPRjDqkrkRhV+iONpF5wUTv0Ke53jTEXWp8SRUFDjZDJlM0O0Niv4RjhPgTw+xVlKNjylHPsutOtN+l/D+Kzi2QNmcjqqThzVOVB3Gfp3luoejBE4yozo5wp56lPr0AFWu4VdDsvs3MLs3KZ86jVttzjWtd+4Q9fcIA49ouI28/xrD8Zi9f/ELOPvXkRiijRVkqQo6I+/1UW+8wG5vgjQ57uiQYjYmsgV2cIC5c5ernTmG0cumpHFOMRnj+AGi0qCYxVCqk776HINf+mn0a59C3nwWZ+cK7s5rc6PmdIh1fJL9A6Z7R8z2DinGY6zOKdL5zZFTqSJ1QYaDLQzDOw8Y3T7g4DPXyIbjOSorzjCTHtYJEX6InfQQJiX3QppRwKLu4y+uQVBmMk7xZ12K6YiR3yIutTnx1EWKoESJjGByRK/T44004NOHlrpnqJgp2dpFpABXGJTrIkfHNN7+NmpLbVABqtpiqEpM6uvIsEp+6W3kYY2JCnFNQSeTiGmHkp7MTWjJmKLcJisM6biPd+phtnWNXi4wpQbpg31aocB1fbKwhZSCmYiYqjJri3UurNdIf/tfYzWcXgjJjZ2TM1SAPzpi9VQb//5rHCcaVa8xzMG21ij585jqj107xj++hZuPWejdpTfOSazD05fmdBdrc3K/SrL5CLP9PpV6leD28xRxglEB+zODnHbo17c4DFaJhY9UHtKvkG4+QviOL6JdjDiazWPOHanAZrimQM6GSGvA8Um8OnbaQZWryN4uUXebSWWdNGrgO2oeRFFZZFReI3VKmCJBJWNyJ4K10/NrkuuhpyOE45Lev4EZHs1TAL2A1VrI04+e4/KZdYajMZ98+Q1u371NLn2schkawcyvgynIrIMVElcpSp5gYjwQDuOnvoZv/OYP8PbLa5TLAeOZIYx8wkabuH9INhnwI890iOP49yzT/6f6T/X71Rec7KEoCv7Sn/+LfPhPvAdH50hr8No1nMhjdG+foO4zOZ5RXgxZvLyOTTP8WkDSHSI8h2w4JVCKeP8B8e7unIkU+exGK+wtLFMLSpzf3GK93aKwDv/gb/91/us/+RXYcpNpaZko7mO3ryN1ys7Lr7O3s803/Nm/yLlT6/zYh76Fo2eeRTfL83z5aYKUgqBZnt+VvrmkIaTAKgc96OJEIWDxTj0EQYQtNZH5A4zOMYMOFAU2amCjJnJ6hBSCIkvxlpbx1lZQ3QfoSY9qeQszGTBduYibT7HNKgdHx2w88TbU8A7LpQI/HyOqDfA85KjH2nveTrO5hElH6KhCbEu0432mUQsaJzGDjEp8RN48SxbHvK82gNoKhQgohMS3GoQiysbY3jH2wrsoHB+ZzfBmHXR5kUIo7MEu+awgOHUKC7hPfTGBSNHSY214h9ndG+TC4LdaZHv38HyXZDyj+dAp9MF9vFqFYjTAK/s4votwBa2HT5E+/haSK5/FWWgxe9DFmfaJxxOqJ1YoRkNk2KSY9AgWm5TOn8OMh2SdLv7yElIqcg1eq4X/pV9B9XBnbvQZ9hAY/EfeCliMGzAL6kipiLp3SF57GRwP6ShYPoEtL0I2RkuXVPr4ysEGJZASIyS63EbmCbl0Uc1NbOcuVnncGhRsqh28pMqFgys4Xk7nwgd4/0bOgalTc8G+8EnqZ5/Ae7CHU6tjp0eYZIaMhyghsZMJIosxsyHFeIhcPUNcXsUtJuQGjoczWvkEjzq+AG9wQLe+iQ0EtXSGGfTIOx2K4y5lX5HXVplJl5IxqGIe11o4ESiJLCwqm2Bdj2C8j47HzI4VrfU2Mp9Sa9Wod3KK6hrpTKKTmPZkF9GskS9uocMS/qyDGo+xO9sI5cLyaegdYMqLOHlO7PpM/AVqOmFFDxgXZR5VM+TNG/inTmOiCoMXX6F8/jR22GXSn9F47FFS4zNtncI7vIvb3kREEc6ZNbTjYQCtQsx0QLC+ycQtY+M+PePQVB6xVXNjVeDgVhP0aEx07hHSPMUunqM1nuA3F5FCMzCSyuIaYrCPsZpGMUKtNVDCYnavUUxmSCURQqCERUd1iuENsknOoOSzQIYjof9rv8ho/TFUcxEBqNYifjwPKbBBheBdX0Qr9DD1iCT0MEcPeNA+QxgniEIx3jjHY4sW/AhpEsSog02Gc21394CgKaCoI6IA7t5m8PFblE6sEp06jxUSu3gSWY6RFYsSHhWb0n/2M/jVCm6jxmxvH6dWp//qdXjyBJl1yGcpCJjsD1l6fItiNEO5CpuMEP09nAtvxU4HSD/AKhd32p8v37aWsdaQE5Hdex5TC5EhTKRPWWnCJ9+G2L5DuLJEHpZ4x4blerSEoyRukVNIh6DkILOMIO1hwjrFbAhll9V8hJx4WLfKIDEcjxWXm5bMr6PTMdqtUaQzFAbPFKROyFA7HNkKS55PFFbov/4ii7UJG401ZkKRCp9SrYQNfUaFYUiFqitBKIqgSuhIYmOonzhNUYnYqgqEI0kLg++GBDLDSGitLRJ5Gl1dZKXsIkWLUq4xjTZtIbnntmnnhunmJUwYckTA5dNLJEmC8Sp0tceammG2TqLSMfrCU+xmPqfHHaQok1dWCI2DLyT+7ABvvI8edmHtEpRcDg1kVqLTlJFfJnQEWQLO4IhSO8DmGTOb4sVT7HSKrjUwjRX2Ytio+XjkgEIEZUapIvR9iq0nsUpRmjxgsHCGWnyEMAaT5riVCraxiohqqOEhttScky2QNNprLKmMo6TEjU7C9vMvc3JzA1NfwSuVmVpFWSdYodDCQWhNbAUeklm5yfueLPG5O30eOVGnFkju7/ZpbWzh+i6jB9toXfCer/wmvvd7v5dv+qZvwnG+4Nqa363/EMPa/+Vn8J8mv/8u9QX3LvnHP/IjRKHDNz39CMV0PGdxxhmUQvq3j8kmGW6gqJ9uEi1WEQK8lkWFPsJxOLpyn5WnzzK9cRsdp/RaGxx/4EtRacLlVkB1fYOk3AQEqpjxsWc+zYd+/B+A8nCknDetww7ojLd94H38/Je/C5vn9D73PN7CEv5iExWFZEfH+I06YNBpjk5zosUG2XCE5/pIL8DdOotAIoMQVW7M03ZmI2yRYro9zLCDWj8DUoDVWMdHOwHug5eQFx4jcSJkrCkFAUtlB+OX8fMZ09oG4vATlMtl1kKLSAPC5SU8HSP1bM7krAgYjTDXXiC9+FZ836eVpsxQaOEx0ZYgPiaprnAmgo/f7+JcauB1b6OXzyMQWJ1iEbiTI/BcbDLA8etY7PwiKxSJE+FGTaSRSB/0sE+l3kYf7uMUOSZL8So+k7t7OIGPXw6xUtB8/DKKDLW0hTruImqLjF99CYQgG0yxpToqL/DbLfzTj1IZDEkmMVnu4JYCdGbwz14ieekZSufP4zWaaNfDO/0oBCFMh8hSdf41IQQqDBEL69ijbWwSI4oM64UYNyRWEVXmCXOqWiM6eR6bp5h4hAgbYB1yFaLfRFFZv4JFMgzaeNIlyDpvUhpg0jqDs3OdF/yTnL2whkhnuKQESxusZEd4JVjbqiL2r2IXV6C+wPNHHu/pvojYuoCa9JFWYxqrjHNDzWHesAd9rBMwNooAH7fSxE0KRuEKjdEhh7aNlXXWheDBJKcI23wKn3e/vUE+GeLv3WSyc8DBmXdxMZqQ+RWCdIL2I2aEhEKj0im2fwhRBRFVmX360yxcvkhRbSNdl/cva2ZmzEnlk+/vIkSMSRPE4AhpmljHQ4wOKIISajrGphPE6lnIZtSGh3McUmmFnaKEaJzlhBzhp2PM+gb0j7HVFtHmGrMbt6g+/Q5mLz1D48wJ3M1LVEyOTWZzHm86JBE+mYEgGdGTmsn9Qyb1ZRrtiN3FC5h+l2h1CVdnpH4JUQzRuzdRq+cQYZUxIaHIiep1HqQSx2giZeYYp6iGGHXxJh3kwiJm3KPoHmEsFMkM40cUSAo3wiyf4OzOJ8iWHsIEFfTe63Ru9anmr6IzUF/0xJyDOxgjl9Zg3Kfp+1Cvk2WGgRcSltc4IYY83w15xBlz+VSTlp6QOkt4aYq3dRYbTxCzPqq5Ol8hGg+Z7R6TDYYcvnyfhSRFayidPI0wOaLaREqHQDjguDjRy6j2KjKbEC40cVsLWHF9rhsOfHrXj6idbBA0I4SjmBx0UY6idO4sMqogJJj2SShiChWiZgfMvAZSORxPC1bcgsUnLqHbm9TyGb4n0MKlrALGB/dx1tYoN1vEtNlMp5TsDCcL8KYDRHWJkQgJlSZNU5LKJoGZUspiTFBm8MJnKa2f4EZWYVhfY2igJiXl2SFJ2KAuLblpoZXHdDQglSUK5eLODujFsOCPcYWloQw2GSAbbR7ElsB3MY5kKTQcpwWpUCgFgXLwHnmSWjYlsgmpdejlDjVpSfwWhbeIp8ATCocMRYY/PuKyzUmFw6PdV7jvPMyRqLGxucFMhriFokYCd99ARAHh+gJulkIUgI3xG0usDHu421dxFh8lD0NaNiF3IzybY3pHWK2RUlC+8SmCxz6AO+tRjAbI0pwIUrgB06XzNGaH6L03KDslqLcRaoRZPEEhXXqpZtVahABXWKyUKKnwsyHW85G6wDoOntAIqRCbFwl8f27Iq9TnxJ6Nh5h4VSKboooMkU3QQZlqo8Fb2pqjacbdO7dJdvboLW1wcb1JmE4onBaiyHDzESMxv6ks+y6NQNOuB9zvzvjiSyv8ZGeG9EsUWY6VDkF1gX/+8oQVrfmpn/opvvVbv/Xz2aL8gWo+HPuDLciLP0bot/+Y9QUle8jznL/2PR/mr/yJLyY7OiLtjbB5gVCKpDsi7SXozFBejHACj2Iym2N8ZhnpYEI+mJD0E8Y7xxwVITcffz/HJy6y1b/LE/aQ1lIbJypTUiCNRsV9gsBHKYmRCq97D7l/g/jufaY3b+MuLaOHA1QYoEolkoNDglaDoL1EtL6GWy+T9oYIJSmmMdZYkApnZROpHJyogmq0wS9jpnMTmkgnmPEAM+4jW+vQXMMKB9IpIp3iphNkewN0jnNwE1musdhuU3QPuP/p1xGTHo5SRIFHMp2A1nRVjSKZsDOKMV55vkTXWGO68hCu5xCZGRoJeUweNPGyKc7uTVKnxENNjzd++wW+aGsewCHiAXLWRWUjrHBBQHrz5XlqT+c+ophigaS8gtx+FUdIzNI64cmz6DhF+T62t4+sLZAfbGMnfWyWUr10Eel5OK1l3JVTuLUWxi1DewPnyS9BuB7V05u0Lp2itL5Icv8WZv8WZjxieu1VpolklruULlwgH/SZ7WxjH9wkXGpjjcaEVYrmMnrlLLqxjtl8BLWwiqi3ETpFWk0eNZFhiLO6gfAj9N5N7OEdSvkIOdzHGov/0GPIWhsRltH3b5Lv3UAf3MHdeZVwtI/Ipmgp5/gzNNPMkvk1pB+SeFWS7btIDH/iLVtESZ+omBKcOotZPotzfAf74AaO5yPzBGd5C9yIi2eWEZffh6guItYfZtbYQC+eYttbY+rVyTcuIh56J9aL8OMBH7vRZ2gDHK2ZGEHW2cWTguVahdLkED+f8WBUsBPD7ali1BnRKa9wv3KS4Pg2dvsaTjzETrugDW46QN97ZW4ak4rcKZG98SrOI29lECwgjEZNR4h0ir17g+ITv4xz83VEWMXEE9y4h9i/+buyD2dxC7mwBKNDcFxcz0WGAXJ8jH3+41RnfW50Y6Q0yDeDE4pkjMwmlB59gur5k1grWP2Sd8HSBkrkFNUlnMUNcOY3IG6pQlhMsfGUkgPjNKN9+AahI0nzgjIp1liESWjZGSqbYZsrcxasyZHKRePg6Rl1x+IVBUVhyNwIO+4hojKmVMOmY8zhPYrpDKMtbqmKPj6Y3/zNRgySgvJDD1NKRnMp0GzK2td/OY2LZ1i8tAaui+0fECwuzmUhwyOCcsSDT76ArwyF4+GubDAkROqMHbfFj/3OAzqFyzDRTEVIMZtQaIEttxClMnb5DNl0RvZgh3ySUXv0LOWtNfRsRrxzF20tokix8Yi00kbkMeVHHseGIarSJHz4CWxYo3x6C993EKWIdJpS2VqmdnYNnRaAwatGpPv7WMcHL0BaDVKRe2VkUGLgN5FYFkSCMzlApkNSK8EJsEiEF+AoQXTpccYyxAZlYq+KX63Rc5pvBmFIRDrCExrr+gTTY7zjW7j1ZXRjZZ4y9tBTRJ7knc2Uhp3SkBonmgcnGOWhvTIjt4nRBatmzGbVo+yAUIrV2QG2toQwBn9yQDjtYKWlRMbUONR9Fy0UC3kHJSyOEBTMJ3QWMw+JEBatNeLBG/QHIxIrmRlFpi19vz2PobeGQvoIL0REIQsLLTaSXQonokpCo+ThYBDrZ7ghWlgBqfIplA9RBb+IiRzFdPlhlgODm89wsiG+jucpbWmMiiL82TFZVMft7eBmY7zAw915FRkP0eNjwnQEeQz1FRj2KQ726JfajIc9MgRrFY9ZNr/eCmOQsyElYciliy4KyGNSv4lvcqwbIKRD0j4Hnk965XPYcReZxfg2JZY+1mQ4sz7WK+HEI1ybshoJnn70Ida2TjI43OH5l6+wO86wyDnjWUgW7QRXvEmoCTwuLVfxHMnpVsRbHmqTJzPS6QQ3CEn6hwCsPfLF/I2/8TfI8/zz06D8IdTnA3X2/6/1BTX5/cgP/iD1asQ3vectzG7fRUhFMhgjvPnySnmzymR3iHQUXuCQzeZomdqZDYZv3EesOERPX2T7oSdIyzUW7lyhdXSPtDdltNZGvdUnFQF15WF0gpsnNCtljjvH1L0SxUu/hao1MfGQ8YMxje4DikEPUW9RPb3F6PU3CFfaqFJljgKaDvCaVZQXMN05wF1cRBTFnGO7sIItMoQXzSe7/XmErKwtYKcjRG0B0VyksApHWITRYAqszhAYis4+anmLeDIhKJX58Z//WSZHxxz8s5/hn/xv/xD/8ruZTH6U33r2c3z25g7P/MJPs7S+QffwkAsPneN9X/YVPPboZezSFl5QpkhmZFELxl18PWE8HLKx0OanfvM1vuTSMvXWAs5sF9IEkaeoLAFpEbrKdGcbv9lEVNrgRWS4yNkAYwr01ReJt84Q7t/DOAFHL75G+8IGozduUWqE4DXw2yuI+jK2SJBRFYoM3dzC7eygD3fJtItfqqB7e4SLDUZ3twmkwGk0yYYzdNyn8rb3k9ZWKHau4jggXYm/0MbEE+SZJxn5LabGoeJHeMUEZTKsKcAYRBAhxj0ccuTiBgaBEGJ+UZz08Y5vw6gLi1vIchM77SLcALV6AovC7N8Cq3GUgxWGpLqOr2MqOiFzPaTjY9wQxxqCuEf88NsJXZApQEGycBLPauzyGWRvh9yroNIYkSc4sy7LWYYo1RHdbQYL57me17hoBKHncJi5LO/fZ7JyGmEExpVc9I/oTBpcP0zYWg5IFy6h84ITpZS7Xp2RdtGB4Es2NJs1iyitMBjPeDCr8Nkjy58ev0EUj9CzKc5ZcKRET/uYWh2BnUs6wjKvf/x5Tn75O3n17oC3rwqoLuG2Mqpb55iFC1gVYw+3sa0VrOMRqzKBG81Rfvu3501kdRGCEpQXsdKn4pUQL/0yT55+ijRbwpmOKXZuEDz+HnLHx959BVUqU0y6iFZrPuWQPiqfp2a5TMmrbUxYx8+nUFtAOyW6q4+y2FBk0iW5/jKfoUrTTFkvCdZEDKUaxqviuC4zq5DWMjGKSHi4Ch5MJOcYInUJ0VhBxEPYfAQbjxFRFRmVcVptiskMEXrIoIyioEkC7ZOUSgO0SeHME9jf+AWCS5c4+MTzVN7qU/jVuaZ6NMAKH1lvYtIx+b0b1J86gZ8lhGKCrka4uy9zcXWTq8cpj9VmCKG4tT9kQw9AWbzTjyAmPYo716g//ih5dQWVTWBwhGi0GR92KBcxKIejcJ1SpUzR38U7fYn0lU+hzlyi6BzQu35AZfUknhEI18evlpmOBdX1ZaZ37lNaXiDtjHDPVea0DTdEFDHWgJSAG1J3DLbQFH6IETVmTo25JUmgjcWRIHSGjSpUSZkVAbmBQlq6RNQCQzHtoPwqsQjIrSYqL6CdKk46Qtg5w7a9VGdo25RFTmIsDVdgrKKr6oBCasEo0VRchfIdkvGYqumD61N+8t0U5QZOnoBxwUI+HZO5IUVhcJ0CI1yE4zKOY3wvIJSaAJdxnFCWms8OHErKkEiPhXyASQxT40Hok6SWkg/G8XG0ZqBKBOeexvcrULqIn01R2QTXq+DqKXFYYz2oIS0cUqLmKspFQmYMmV+i4sQciTKOa+knEQ3pkLp1ohMXyXBwlOBo/Qnqtz9D6Aj0+XcSqHtMb7zOuLZI2AxARTjlMvJ8C0fnZKqFIw194+NKSUUV6MkQX4F+7VPUty6gq8u4cZc8apE5PspmyHSG3b1BceqtmFIDp9lErZ7GOg5u5z66fZpMhoRFB5EMSb0avilQ6YhedZPTC4rWwlMcHBxy/c597u8dcuH0CaJaA4EhNYpJliMA31FsNCJaJY9q4FFt1UnXTpAMO5QWNxjt3+X5To0Nx+EnfuIn+PZv//bPV5vyn+oLpL5gJr9pmvK93/s/8T3/5degtKZ08iR//+Of45eu3sNkOdlsRnI0nU/nagGqXsbkGmtBeQ6mVuXmwkPsvvVLKY06PLb9LIsHt5HWEnfGFP0u6bMfx0tG5AiwFq08Lp7a4NWXXqa48TKj63dID/dJDnvUTrUoHtxBhQGT169RjGe4kY9TKoE26DSeBwnU6/ira9QuPYRTaSCCEKe5gqzOU9XMdIDNE0RUQ+/eIr93DSsEejwEoRC7VwGDdcL5cqvyMKUGstZCNNcZxJqKr+jEGb/ywqvs54pCF4h4xNd98Tv41DOfpC5yfvKf/BN+6O//fX7sZ3+BrzmzwavPP8ef/IavpyoybJoijrfpj1N8nTIeZxy2TvFPP3fAVphRXtmkVQ3nEafVBTjeQcRDrAWRTSm9+ysQtTbWC+ZxsMNDtJWYqMm4uUxtcB9hcvzNkzDuY3WO51pkc4mi34VSjWJwQH64OzcuGaC3z+zVZ7HTIelnfp18OMakKTaIkG6IkB4614QnT+AtLuF07hMlfdxqDa/VovqBr0MsbiBWT9EL2lwfCYos4W5niBsPkJ0dJjduQX2Z7HBnPvG7+wrCcckXTsKoi/I8ip0bCDcEx0X6IWRT7P4d8HxkuUF89QWKXhdcH4vB9B7g9Hexej4tCcnQ2mCNwStm8PA7CKVFmpxZaYGivIRVLmCJozZ6+fyc8VyqYcMKNoth+wrsvEZ29IB+4bIZWvxZj4PDLl0bEEcNbh+OkA9usp0oatN91jtXqQUOa25KaHKmwyGv9Q3PvPYAde82s6ND4lKTgSgxygzh4jKXGor3XVymuPxebLmBnvQhnYKwyLAMfhmrNSaocnzmHYRf8uXse0sMtcestkly/x65FEQmxkQ1jqvrsHWJNGiRW5fYSmL9/2Xvz4Mszc7yXvS31vrmPU+5c67Mmruqurt61IzUkjEgRgO2GTyA7ePh2g5jH/t44BDYSGBZGILJCF/DwccTIBsBsoywJkBDq9XqbnVXV3XNlVmVc+bOPe/9zWvdP3Yfx71xTsTxxb5Cjqs3Yv9fkVHf3s/3vs/ze3Jy5SBKdQQCnaWzS4QUDKprqEKJwsUnqDuCtLtLikDXFsjtAiqNZ5cSDN7KGqJQhThEJhPc3euoeABxRO6VyaVLahcwWcw0F5xsl5lYAcMo5bWkTNG30RuvMt3e4rfvRuSH9/H2bxLfu80kFyTGUJIZZtShGA3YHEZ0vTn6okBYWyapLYLtkNbm0Y0VnFIZqz6Hu7SMvXKGbOsGx7nL0f1tnO4mO8+/iN7foufXEe019PwprHd8Pfmwi8li8skEnSRIYYhf+QJ6f5Ps7GVkPEEKsPKEqisoVas87RzTCg8R0wnp4Q52Z4/Rs5/DKldBWRBPYfk07tlH8KplnJXTeCdO4ayeQTzyZkyxifFLFG2wwj7j9nkQAmd5bWZrcQIK7QrZ7gP08TFaSFbe8RAmGpDs7+NWC+gkw1+oYxVLM/+20ZjpGIGebbizlCDqYZHiJRNwAsRrn8fLEmQ6wpMZXjREZRGeY1EQGldC1dHk4ZC2CkFIRo1TTISNrydMc4updGf/F3NNXJgDBI5OkWLWEjYRPkLndBLBXurgKoVLjC1yKoSEmUR94j8g+/uIPEIVirjxCEvHyHgEvT1EnuHrmGEYYYRLlks42sGaDigmPUa5INM53v1XydKMTm/IaiDpiBLGm/HkN3Mf8pT9YQh5SpaE6GKDkmeRKRc/nTG9ZTzFeEWmaYY0OQeJRV0mhOPBjM6SC3KvSqRmSwVlWfiWQEibki0ROiGY7DHx5jh2qgzsGo7r0V17mmlzjSwNidafwvJtake38UgYYxN6VbQdYISk6WhCMWMfO8rg7r2GGHZIe4cwHSIPNhB3nidLM7RUWEmEDIdox5sV9CjQjVXM+bchyo1ZpXV/H+fwPkYIUr8G2qCyKezfxlg2Qli4YYeyBa2FJZ548g20W01evHKNL127SR4nSAFzvoVtSVwFFd/iXndKtahozldZPX8anUHvwc0ZnzoJWbjwDt7znveQJMkfolr5g88fRsPb/7/O/zDi92d/6qdo1wp8y6VVjOPxvg9+lN/40i2WVtuYPGdy2Ofodhev4lI7t0Q+CpGOhVsvsy0q3Hn63aTDCedf+Ritu68wuHaPydEAr10j6k2xCwH3P/r7qNc+B8NDyFJSv8bD60t86dYG4cY9tBHE+/u4JR+34GOyBHSGkjlkEcrzMOEEJFiVGqoxj5pbRlVbkKezxpv1S8hyHSxnxn2VCmM7mGIFLIds7wGq1ka2ljBZiqq10X4DXaySSZfcCZgU5hGLZ8mVRdeuEhRL/I2//8OgLP7tz/8k1s4m5Cl/6Xv+GD/wHV/HX/mGJxESkvGYQE959OGH+BPf8HYePrmCqLRJhc1EeRzsbHFl5HBofB6Ktnl4ucS73vVmlnyNp/PX28sUIp7MksDKIxqOEZUWWlqQp5hMY6IJcb+D3r1LeXAPu78PSYgZD2m++WmEX0QFwQz31pgj3tpAOj7CK5Jsb5BuXmf02Y+Rxznh9VcptOtg26jFdfJxyGAASAurMYdlWZROn8IqFYiuPI9BY7VXsNqLJEEDU13m7mGfF+4dkCcRnePu657qXZxGBfIUuzYH9SWi+xtoJNLk5K11jJAwt44oVhBpBskY4xShWEMkMWY8RNVaWO0VZKmCqMzBuI+X9LGyKYemxEFiIUyMFfeRcUgxPsZNRxgNCAtpEqwsQQ0PZkHIcIS8/ypmOkJv3YDeHqbSRgJ6MmS5e5WKjhE64VRVMJ8cYw2PSMMpY7dCQcEv79b56LRBvVRAKZtWdIhvKaba4sUb+9zWLoEnaDmCl/cnXB85aFWgXVScVscUPYUoVrHXLmBGM+uDthwGVpWdxkUyv0YvTKnM1TmK4J7boDjchVqbyJ/jtr+CFx1zMMn53J4mj8fs3r3P/v1t4lwyzAR9r0F25o30yicIvQYmS2dBzWiM9HwiaZPlCl1skBbqbIWK6NXn0OEEuXyOXnER015HV+chmiAai2gjmBQXEXlGagyxtOiqCq7U5LkhN4KSIzjT8Bi4ZZ6a86iSoNMx0isyGkbsb3UwSUhAjJeNmWpBN7M4XXXZHOb0MkFmJF2rTig8MunOKCEL6+hBB0oNzHTEyCrh6pjDtUexihVqrQDhelTiPu5TX4M1HVANMtJ715DxkGzvAVIaZKGEVSxSPblE0ZaYUQctbGSxykbs8elhhYPlS5yfD+CLnyQYbFHzJM6FC0wPj0iyHCY9bBMRY5EEdczhJnFqyI+2KG29QiYkIk2wbZs40UjLIT3emwWUHBfhl3GqZYI3fA1uqYg2MDnoMrh9SOfGLuO9LslgTDZNkH4BJl1EPIZoTK48rHAwu/pojZp2cQZ7kMa46xdw8imh8BCZQRzeQ00H+FmMsG2mWuBMuwxliYqtiYQ7K78wcrbdNzkemjDXHBuHydERxnboxTlHoyk73SFZmjCaTBFoyjYoY1BGMCciVDjAFGrIR59ClGaV6GrcQUV9xPCY/P5V9P49zO4dogxWx/dR/W0sk2DXmlQrZVypycIxJpqQlWtkoy4XV5p0Y0PoVNiz50i7HRaslFTYlIpFjkSR4+ISofK5M9BsxxZkM8ya9ovoTHOrn5ELRdkyyMN7jEZDqiIjyyHNMgJSlBRo28MyBkyKp0OsaIhOItywQyc0BPmYqogoFwKkW8DOE6aySHLqaeTpx/Esidy6hUoj1GCP2K+TJ1MmWlCUKY7QyHiM9kqEQRO1eAK5eAppOSQ3X2WoFf70YEaWQYIbIIxAI0mcgNTy0YUmyWCAGOziDPcQ9gxnmSsfHA/yfNb2Fk1mNBMMRVJW1tZ56umnydOYz7/wIkf7O7iWwLckdVdQcBWTRHM8Srm8WsX1bJxCYYYBNQadRnxuI8b3fX7xF3/xD1Wv/EFnJl7lf+Pnq+L3v2b+hxC/URTxvve+h7/3TW+dpdPdEp954Rqnm1Xe9DVPYRBE/RCv5BDMlcmShNLJJZyHznL73FvYb66zfOPznNx7DdEbkI6mpMOQ3q0u3etb5PGM/xp4An2wBfevocYdzLXP8sipE7z4yjUKly6Th2NUsUgc5ti1KqrRJtcSy/fIpxFOow6WNROvlo0sVLDqbRBytglFINXsC8OkETRWMO3TCLeCcItIz8dqzCGkRNYXSZtrmPoSIovRQhIGDXKvTIpk6M+hkpBxkiOCCpNXPs13v/tdzHXuE5ucX/73H+ab/qe/xXf/zb/P8dYDujvbfM+f/A6yLOeV1qP8u48+y5sevYjOU+ywSzQcsVL3kZbFRurz3LTEmZUWCnCUhGiIyVNMqQlLZ5mUFom1YDoaMBlO0EGdoVtBG01iFdB2gFq7hBcNQefoOCbbeBVVqc6+qOrLiKWzs8Dcwglk+wTK97FPXUIFBUqnTlN6+9dhL60yEEWu6DLh3hGqtUTrmbfj1Ms4S6ewT5xHlGpov0Yw38QKSujli0ydBmO/QU8VcLTmdLRPobvFubkKIFELp7FPPYKxfUxrFbN4Gm/tHLuyij08JLZ9KDVwGg0EEmNJdO8IdI4q1TFpTO/F5zHNBezmHKZQJ9u6RXq0h56MmLh1SkmXufQImeeYXJMbA7ZN5hRw8ile2EHkM7+gyXLSzCBHxzPo+NysX91EU6LWKXLAXzmFGh1jH91japcZGp9IQxKlrPU2EEGJeHTMuy7OUa3WGeaK/7wxZa+2RqNZYb7q8643P0RPBHSdGnePRpzTXaqf+k2mL3wac7hNpgLSQh0Zj5GuO8NZHdwna65ibr7IZw5y7u3sEmnB9f0RD7cLfPvpAlatides0ReKOxPFv7ttuNGJSOOQsVXGv/Qk/TBmLFxkd5e+8bgSB/QzRWeaEdoVJBohLbLdTbytq5StFG6/jNq5w9F//h1GrdMwHmBqi+ymDqlTwkiFcQLMrLeQ6OYr7N7ZQCQRfjRg2O0wSjRxlpPnGeNcsXByhQsLJR4Ul8lGPRb62+R+kd3yKoedLr3OEdpIUBZxeY6d3Afp0BZTbh5NuHI45TjUTDW4ky7GLdC7fhfhFtCWw563gIgTCtNjHhOHaJNTmGvRiaGTSkx3H4zGMxmiWEH6JZxzl5GNBWivYc8vMfeWd2Afb3LU7ZNOhxgh8Y7useIlVGWEVaoSnLtIeO8u+f0NTLFK4pc4uH0H4ghZbtDLFNbwgNz2OfiPHyUZhYj6PHLnDulwSL57B5NFWINdssyQDkcYFGJ+BevCY5j5U/hnLoGUhId90DmDe8ccX9sjHScIIRBegJwOEXkG9UXk0T1UMiEpzZHZBeRkjFByZj+qNhi9+iJHW/uYSYesUCVzPUQ8hHiMmfTInAKNgkvulUgzTZD0EUaTxwmMjumlBiseEeFwhEXiVnhtt0952uV4OMXTIUexQBqNlJKcnJePUmKp0H4FYbkU59vo6RAz7kOeI/IckUwgDjHDY8Yvfwn7eIvi3AKqs4U53CQLagS2RN6/xWudBLO/iSqU6FfW2OqOGWrJ7jSmH2dkJx/G6JwvDRQJFqNMocdDinGfD107Iss1O7FF5paQOsNMuyzbmm4MxajHF/cS6o0W1aSD1ppeJsDxCKQh1wJ3csTu8QAhDAw7TO9vYB/doxEf4aUT3AevUBjtcaDqHKkqGYbQuES1E0RBG3XyYdS9F8i8IkPpkxTmmPcVsXEoxT1MNIV4gj/ax3hlMqUQno969G3UxzuYYRe6O8gsgjxGPbiKFQ9wswm5FaCDKtbJS5j2OqK7M2PXSpssqDCZOw86pZgPIYuxh/tU8y52dIzMMwLf5/Slyyyun+P+5iavfukF9HTEXKBYLfu0CjauJTgcxyzPF1m/fAGs2eUMQFoODz31Lt773vcSRdEfhlz56vwPMv9DiN+f/smfZLFR5uvPLCPrC3zw95/nbUttEgzOYEA+iUBDda1KcaXJ+GDE/WCRaytPUpz2Ofulj+PeuU3l7ArydeqDHTgIKejdPCTshRy8sgtZSNztc/Dxj5Pt3iHbvkfDkXT3D5hu3iXpjnDn5ihfeohkMMJuzGO12ox3utjNOsp1wVIzKsN4SN7vEN2/TbZ9F7fenPl8jQAEpneIcLxZ0MxxMOV5wp1trMVTYNnEt16Z+RnzfFadm8YoATJLKZgUV2T0VJHRZEK5VOQwTHj+hZf4az/1L/jeH/15psLj3/3sT/De7//j/Nkf+Vn+9nv+CZcef5L/8P/8aXyledPTT/AvfuNj7N67iTneIe+PGNpVzs4VaTdKZFGCNprMLZJIj9wIjF9BjI5BWgwO9xFJhDISiwxj2XgCCHtURtv0sYjub6Bay7Mgn+NClqKPD9GA25qbncLa66iFdUQeIwol7KW1GZTeD5CjDt7qGtaTb+eurtB9/ktINPLgBla9gajUMEmEaZ9GL5yFM08xLbTQ1Sa2H2DbNvuR5NWR4gZVgkqFYq2C9AqIZMrUqWCkQhcaZJaPffISDRGRH+/NNvXDDigLPR2Qdw9mDOZkTHK4NQuYJCPsSn1GeJgOCTduo6OYbHcDL+5SOt4gSnKOYkNWqKMLDUJ/jkNTJPMqJFaRyPEZDftov4gtcyItMItn0cU6urlKuvoI3uFdzHgw25rMn+R+sMoUm8+9cIv6XBvZbLFbP0ktGbAxVpwswwWrS8kxzNWKbHZjjkdTPJFxYr7GE+tVcg0HkSDzfarnTnP84D59Y5PaAZGx0H4VTA6FKlmxTpZkKAvWezcYhjH1/ZusOTEtK6Xtw8QqoNIIUZnnZGAo+9Au2Dy+VmePAnmuWV6aI1AGaQxlleF2dvDiIWrU5X7sMM0MwnIwxuB4Lumrz+GfOk8Sp/jnzhO0Wgwe7GD273PWHGKZfNZINekhDjeRUkES0kv1zEdvudT1iFGcUeveYfu3f4dxp0vF1qwFsJzu0Wg38M8+xIEpsN70OfnoGZrVMr1EE6WC4Rc+Tdky7A/HiLsvcuV+nw9+4hWmkyGf3+xjOlvI3iHOY08jF9YgmqKqDQoLC4jj3ZlQu3uDrNImGPXQWU6yeA6SWbW4ngzIJiOi0XT2nAw7SDHDymmvgleogOWSu0UWT83ICq2kS/Tip7HLJTa8VfSbnqHkgX24SS0dkg97xInASjNUUCTNLGpPPIojMlRzkXw85LV//xE6pTUSq4glDF7BxZpfJplGhNdexowGTJ0KiV8FIShcfpjSap3KWp35J09QOtmm9uRjZPtbsxeQ1xnQproA/T1UmhD0twCDyHMyt0R243lkvUl99ACO94mcKnkG+GVUGlHvb+ILTRB1IckohvuoeEg0HNKTPtvap5sqXGFwbYuptrDQXCgLjBdwZqlBQScsOClBPiGwBAGGsifQ0mFiHAzgjg4R8RSMBp39F6uIKNYQc8uYN/5RqC/OnoXli6TFOrk2xJkm2bjJcnxAPh5z9+4WjjG4ShJv3SPLBJV0wkAWKIqcVzaO6I1jNo9GFLpbaJ3xxGqNRd/QF0W+uBMxzARxoUX3w79B0Qbh+sRzJyk7AlWqUtJjqr5HlEtEFmKnY0R3j7bKiKKE+NqL4BeJFi9S2bvDwCpjakuMtYUShkkUE0YpqcnxTIjKEzxH0Vl+irs08STYQlKIu1Rkgkxj7hfXOC4uYHkBorVCGCzMGvL8ItOgxaR9EaEztOUxuX2X9MFtUq0IvSrueA+TZ6SVhVkLo+tj8hwBeKMDcuWCW0JlGmN5KJ1ixRNEliLTERkCY2DiVFi69BR+scLNq1/i9r1Nao6gEbgs1Qp4rkWr7HHyRIv2Q4+/rhQEJs/47WshhUKBf/7P//mXXav8t85XA29fvvmKF7/T6ZQf+8fv4+9/+zNMdo+wn3wH//uv/CaVHL7m5DL7z17D5BqlFMV2gaFdZvfrv52+X2fthY9zYriJzNLZj2GWEvVHxP2QNM7QOme0NyJPDZOjCUrZZGlCZbFJ/7ln8U+dRwYBjXKB/Ruv4VRLkGUUHv8avMVlhMnBDqicWcap1jC2jfKKTF75/CxAMxmQdo4QxTLCshBGYEoNSCOEmj2oUodkoxGMj5BSkmpD1jkAHWN1NpDT/sxbqzO83gOs/jZKR2igl9ukccRuavPZbIlv+hPfi3A8fv49P8hf/K5vpShzHr1wjn/ww/+Qr/uWb+Uf/cN/yH/85Gf4xZ//BZZdw1y9TLBwgt8L6zx3f4t//P6f4Plb21TDDqfOLFEsFLCUhSUMsreDONog6x4wyCS13gbRnatkrUX8ShVhWVhSIpKQvLFMe+dV7GYV0hihLIxfQToWwg9AKaTrYiZ90OnsZDrske7toLsH/P6//Cj//sd+FTPqQTSmER3yLYVdiksNbJFgOxbKL6OFhbFcMuWQC4lII5Rlk+BiAMukLFoRZ1pFnrp4gry5TKwVieVh6ovYjoM1PUYkU+zxAegUJ5kSb2/iTHuYg01MmpDvbZJ1jtCTHll3n/jOVeL9bYqXHkFPh+St01CoY198GnflBLJYJb/3Gmmhzq98boPnd0MmssguJQ4jw3GU0w1zjnb2mU4TlM7o2zUSY8HiaWR1jp4qEzVPkxTnkPMnkM1VtLSJ45jG0R2mmeDbG31qSZfGeIdApQyDFqdbRaRfoTAecDrcZq5gszfJ2OznHIYGPR2BsKi7koNJxkgElJ56E9M3fC1bssmR8YmlDUGZpLxI5lXYr59lNBgRLj/M2ZU5vGoTb2kN11V0BwOGIkBO+iRuiTvDnFrJ5WzDpxUoSiLlRLRPbedLFPOQ0d07jOurXBlZzDWrjPwGe1euUCRmJAtEpTaWNGTdDket8+jpGIC1tkf3+j36cycZPv95lOPBnecxgyMotzDVJrlfwzl5jsaJEwRqluZPm6coOpJf/swDbly9h3vrFbrXr3HzzhYlk1Gaa3F+bYkv7IYkfoN6e46C79Ic3sd0HjB46RryeIvTkwf03QYiDPmu8wV2OzHnNp5DWBa797dQ2zfQO7expaad9LEmXSzPJWqdRFsBdPYonT7LUnqAoxNMNEaPeujplO7nv0Dvtz6EGXYQXgncAkZrhFeienwXZ9ojEx5JblgNt/BdG+naJHaRwkoLuXhqVqJw6izFs+fBaESzTckxmCQl37xGqV3DFEvkN1+A9UeZe+JJSmmf+tE1rHEPnQuEyVGnH0MGHpYES09JlAuA9c53U33TW2k9ukZxZQ7Hc7B8n+zS25jcvP561XlGVprDeGVUnmIkUFkkKzVm28BKg+LCInJxjfHNWxQOXsM1EZHlEzol8oMtZDREZrN69CA8xmAhdYJSklU7w+iMzZHGziNkUCLe3yCzPfxSiQwLyg1sr4AjJRWVoaZHnCsLJplgpBWZzmde21ITU26B68N0iKi0UNW5WWX48Q7lckAxOiYY71O2weptE4z3cN/+bi4UEpywz3jlAqmSzLua7QlURYhX9MkBuXOXP75qWC7AqunzarDOCx3BStWHJGKdHl/cGjDNDGNc0kuPMHVqRMrl0brAmxxh7AJFGRPmYCcTnDxBS4VcOg0vf5pUQ3LhLbjLJxjgMj7sYwmDKlUoF1yW0wM6gylNO6ViZXQigdx4kX4CgTR0w5ydcYabjbCmXTIkhANqgYvINVmpjYjGeNkYLBs37OOnIyyZI2oLJDIgLrRI97aI4wmWAG0XUZtfYio8VDiGxgpytI88uIPxCgR6gokn5MKAXwadg+1jhGSqSiAgygypNriWorV6ktMXLzMaDrh75YvIaETRkZyeKzGJUxpFG8cvgLQAg/KKCMvliXd8Mz/2Yz/GdDr9Q9Etf9ARQry+Kf9v+Iivit//mvmKF78/+eM/zqmVeb7xDY/gLCzyrd/75/j6Rp1P7O7zdctzWIFHnuYIx6b7hmfYvPAW2qNdTr/6KYJxD4whGsfYpYCw2yfuTkinCek0YbA7RQUOTtFGKUX/QZfpgwPcR58mDxMoVTF5xuMnFvnda3coXzyDsT10uYldqzPa2ELpGGUL9HQCaTrD2jgOqtZGleu4zSZi8RSiuQiWQ+KUyPOMxC6Qjfokt64gLIUcdrBb84hSA1koIWttUsvD2C7GLaHCERw+gDxFCkGsXMqTA2zb5ge//0/Q++JHaVqab/nu7+V//pH3obfuoVbPI5fWefMjF/kL3/5NOAo+9G9+kUdOzvMXf/ifMIozvvGP/2n+t3/20xz0ejxWt/jx972PSjKgVS0xSCDODTJPkEf3EUKQD/uo3j5i1KXQbFBJekiTkDoVMiS5dBlvb+GJDGd4iFEWstrCjPvIYgMQWCcfgyxntH8w89XaLqI8hyqV6D77LNd+9yqTvSOoLyAXzyGSGA+oP/4o6tRl3DMPI8sNsmF3luxXLnY8QSgHqVP8dIiKx2gUjlJcmC/wcAVcCb5IyO0CMo+x02jmc0zGqDTBmByTJpj2EsL2kctnERhMMkUVi1BqkO/eZXI0ItramtUEH21yQEDUWidzA6LqCfaPMjY//hwHoeLC+VWeTO/RzQW//mqH/3R1n99+7h4br17j6MY9olvXiAs1+rlNLzZMpY+RkrLK8HSCm4XIeIIolMj376OSiICI6r3nKBdtxNZ1zHTM+aaH79o0AxtLCcZrj+A02njJiMttj7cue1x5cMydsaBoGZaLissrFZS0ePUoplEucipIqBPi2Q5jq8SkukwS1DkcRPQL86hCldRyOFNzCDyLdnSI8gLyPMfVCdqvIoVknEmWrZCCayOUhet7jOsnqYx32cpqRJkhTHLuDRKUEHyq8NBsq5YkqCxEtU8giyWCcY98Z4PSfIug5GMOHrD58n3GTomoNyY86JC6FUw4xrhFMsBPR3iWnIVGpUXVgcWswx95fJW5r/9aCpceRgxHpB/7j6hyFZWneIM93jxvEQuFLlRQjk8h8Clahovf+FaWdY96ycVqLDAd9TkZd8huvsZau0B662U+96u/zzQ05N0DyDLYv4Pe20RICy/uE5Rm1beWkkgp8Y/uIYp1VHMJe36J4toS1TOLZAd75PGI+PCAWBUYWz5Ba56pdBhj4yd9xNwKQtl4J87i6ZizJUF9eoiKRlCbJx8PEDAjEuiZzaH00EWE6yKTBJNMMZuvUjt/EvvK7yOGHUQ6QW++CkEVFfew8xhsByuOOOXPsFHWvSv4BRjtHCOUQ9QfI4MyVnMJHcUzXGNQx5r2wCsisxCMJBcZuEWUBOV5IMB1HSpveBuqVEUX54ixGKsiorWKJJ99x0VDsn6HZHBMVJqnHHZRV55jtzfCKI8sDBkNp9wKPWSxhm9iylEH4gnHUQa2jZOMia0Ad9LDsyQ1RyK1xlTaGNuZEUcO7oM9axfLkwjheDTnqqij+8h0gmep2Xa4toTlFRBOAVYvoc4+ylq1wBxjmoMt9jIPp1CmKwogBHJ+iercPLW7XyRULo7j4ikoRl3uTzSJ5fHtl1pUTUg3s6kuzxOYkDE2kREkykMLhbZLWMqQ64y+cZC9XWQ4wG01SOOIuLKEF3Vxpz2Kp08gJz0iI7EnXWyR03AFiXSx8hRfT+m2zlNOhlS6t3k0iCjJhJ7xyLTB0TG5U8Qedynf+QKDOCN0K1jT7izHEg0xyqanndniJx0jzz+M8+QzlI43EXkK0ZikOIcl81n1NQaMhHjKxHgzC5xfJg6aCJ3NkHdookITR+RYGCwpOFX3ZpxfG6RX4OHHnqS1tMrR3WvUo0M8R7HbjXj1tR2Guw/gdcGXR2N0GvNbL/WpVqt84AMf+MOSLl+dr/D5iha/4/GYH//xH+eHv+87yJqr/PVf/zilPOZtrTp2waNqKcprLTp9zeE3fgdhscajuy+wOD1guj/ELhWwA5/keERxsTlrd6sUyMKUycEEZUmaZ2pMRzHKkfS2Rpg0Rx8eUHzqTZjRgHQa8ae/5Z18bPOQD+4k2OvnEEZj2uvY5RImT0i6fYRto4WFUArL9zGDA2SlTtzpImrLiPI8xrIx96+RRxEmikh37pN4dYTjz7yviycwrkcmFFaxhKnMY/wKmAzi4SxRXWoh4gmuzhn3jqg6gu98+iKXL1/mT77zab7h8XNM4wRx9jL6wTXy8jzCpGivSOYEdDOPP/Gn/xwf+le/xC//r3+NWsHjB37wh/neP/N9/PVvfxfWYBfRWsHPxuS9A7b7U4TJEe0TM9tDHpG/8Hvg+KhkiDU6whgFUiBNzkj5lPMhqr2CDirQmKXireY86sQF5NpF4uoi2biP7fmMWmfQtg/FEqK1jHPmYd75vV/LH/vbf5xk/TFyJ0BUm7gra7hnH4FSE2lZs2DLICG2fJx4MCMmCAFZgpp0sXtbKJ3gSoMf9yiS4CUDPCWwkxFa2jPMkl1AuxVSv0ZcWcWkY3zfh8EBWWkO4QaoYhVZnSPffwCVFjoXFE+eRLglOl4LKQUyDblirfKyvcyn8gWcb/g2Knmf9UaBQqFElGmW431W6gFX7/eQpSrdlVPcH+dU0yHTOMWzBJ7UqDwlFg5aCHQyJXfLCMdD+D5KSaRU+Hs3iLdvk3f3Ma+TGLzBHsdxzt2dHkpKjkzAUnKAZbtsTQwCQ7vs8qFn73G9l7BadrnZzfi1F/b4pY9f42NbMeXJPlFucCS4UmKh8bpbyEKJaW54YeATY7Mb29zzFnlxa0J5uI2IpljHD2h7OREOD1QDz1Ik964zjiJ8V5FWF6gf38eXhvPOBN+2EINDTrbL7N2+y84wJY0jcFzs1TNUGSCVRpAjsozq130Ldv+AKHO5+Tufxz99blawMDrC7NxExVOceECjd5dhMiO9iCzESSc8tVLgXW88T609R/vyJS5/6x+Z+RB7R2Q7d1hIjylYisQukil79tLm+TjpAOXYFC3BmZrDn3nnQ1TOXeCbv/FxnHMPo5ZO8rXf/QyuIxDNFUwaYwoV1KlHobGA8AoIx5kxZw82MJU5RKmKdHykVKhiFf/UQ3iPvgF54iGyzjGv9AVZsYayXXTrBAWlcaXmUJRQxQqpsMAvkWFjkgTR3yNvniAXHvHtV1Hrl7ClBi9AmQyVJ2DZCEdhFYozS8XRFiqb1RkbA0aA7u2T3XoBOTxA372Gd3SLQmcDMGRG49bq3L+xT+/S1yBsG+H7uJMOpTe8FcukpNKa2aKEQRhN5gQMVJnM9giTnDhoguUjCyUolaHYJBczpnop6cPaBTK3jEwmmCQmHM3KZtTdLxH//m8z3nrASsFF2DZMR5y6/nFWSgKBYRjBtHOA6e7hjzsgFDKNEGkEEqppH1snBMNtUtslLbUROpvxyS0fMepA7wBZbaGWTpP6ZcLSEhTrCKFQJkUnE+TokHT7DlobAsfCMhly+TRvONdmrRaghGCv0+O2anOQWOi1i5yb8zlXtugpnxPjDZoedAio+RZ6d5OKr3BKVZTrUZRguQUGboPAgkj5KMCfdHGPt4ncInmWEi9f5PlxkbvdCePyKqEWfDafZyJslFQk5QXi8jKL1QBPamRvn9Jwn6Jn4U6OELUFyqNtFuIDyukAcfQAdXCXYTCHqM9jTj5KKFyEX8Ioh7g4j9KaVPmUHcD2cQ/u4JIyXHoYEZSx8hi2rqEKJXLpQBLN2NXNNWR9AV9kaOWCVDjJaEbYSUO0sJDJBCeLSLTEYPAsRS/KGaWGFIFlMlZXllm9cJlo0KXdv8ucL4hSTRLPiBr/75NreNO7voX3ve99jMfjL794+QOOVPK/y+er838/X9F/pX/yYz/GuaUmX3v5DL/63FW+5unH+fG3PM7Lu13eeP4k8TTmzp5h8L1/Fv/4gAvbL+KlISaOqZ5eRNqKNIqQtiSZhtieg1320ZkhTzSFukc6SUmMYfWPPkJtrUISRhz8zsdw5+YgKGHNrVBuNPk3H/gJ/tPz1/js7T3EwV2S2gp2s02ys006mpCPh0R7e7Pto+uRd49I9h7grSwj8wg96jB+9SWM42PSDD0JyaOIQrNBevWzGCHAL5C6NWivQ3URNxoi4ily2gPpIBrL/B/GficeMBpPKNmCP/vN7+Q3P/QhosYJPvW7v8873/5WVKlGUmzPAOxehVBLhM45tMpYEgqlIo1H3sJ7/tJ38Qvv+fsgwKk1Odks8mvPb7D9YIdyd5NqNmKcGCg1Zx7laYjUIUIKmE4QpRqZX0XtXIfpAEcKpO8SFeokixcJ3Rqmf4TwAky5ia4tkwuXZO0JnPkFpLIwe3cwWc640CY4e46LX/84rbc/g5Rq9uPV2Z1RNPKU+NoXQClMc5Xhf/r3jLTNxEgwBqFT7CycbcrR2MMOobCx85RMKnCKZNLByiKM5QAaIzTGdtBegdSaFUPo6YhBeQkjLXSxiShVUNUWOhojTj2BVa5gLa+DSdmuX2CzF8HxLkpKDkJgZZ3J3Aqv6So//4kbs4NAnFIt2CxWXd72+Aqy1UYbQXl5GXH3VRaPruIqg68jcuUgpWQgPESxBsU6SIWuLqA7OygFtmuhJxPQmu7vf4r01itYOzc44yVYRY/cGHIgM3Aq3eWiGvKWExWeXi7x6PgWudbYUZ9zLZu/9FiJb3lknkF/wtW4hIpHWI6LylKOuyMCE7IUbqGHHVZbZcYprFZsVsSEk3Wfw50jTG0BuXuLU4WM9r3nkVJgjGC3uEzQXCD2a/RyD+fpNzOIMjpug4n0SKKQk7pDYCtOiR57dpvcL6Pn1vHOXZoVezSXodKmaGke+/Y/Qmm0S/viMrZtobr3ydMUekdYO9fIRwPE1i1K00PcvRvY/e1ZoNAp4CiFbyk8qfHmmiAl/ee+wLQfza4WCJxsit3fQ0sbMx3BdISMQ8Ski7vzGqe9iNH9u6je3ixo2+lSOXuW4toZyGOMX6a/PyTqjtFOgbi2hig3kWuXyBfPExXmMUF19iz19hGWiwlK6EIFp1rDqjVYPrqNa0ks28LYAcr2cNIRdhZhhSPU/i3EZEB+7wrjK88zfO5Z0msvwt6NGVJMSEyhRh404egeeZZi0gxpOTNcYRqRXnkOghI6DDFJirQd0ntXyQfdmVfTUahSYxYKA453+0RRxKmnTiL7Hfz2HDrPMcNjDAI5OkTmCVnzBMIYNIa83MSzJblR2Bi2RBmUTWoFxMpn6hRJkeQ6x7YcUBbG8tBRhM4zJAbZWqI0OsANbIaPPcNiekDj+AYHwTy102dxGssYZWGRcFQ7RVhbo1ypYZkcA7NNpV0nQ2CPj5GZZpJLJsKD1ipm4QxGCoxlIytNtF/F5Brz4DbGCLLOHkZrchT9YIGRXcX1fMTRPbyDGwjl4JmEsyWwRU7Nzlib7uDlERv7XW5EPsYpUpAR60WbKwcRuXQoWIqjwZjJ4nkw0EsUaZqAAN+WJJlGJDG9KCXNIdu+g52FXE+rZJUFYjvgyWDEUSKY2GWcGy/ypd2YX7oaszEWKAEDbbE5FWihSUstdjMbEY6YTmLIM3TvECsN8SbHjGprHDpNHDKMyXAqVaoujFJDvHkbrRzE6JAgG6OxYNInu3sV0pQyE4zR9EVhlqfYvUlwvEHiN4i8OrnlogGrt4vMYsRgHysNEXmEKVSZOkXs0T4iS8mNpmRLMgPLBXCEwFUC8oz09aDg8+k8nczm3bUu56sZUb/zf6kffuPFPq1Wi5/7uZ/7/71Y+e80X0WdffnmK1b8DodDfuZnfpof+u5voOs3+bWP/Gf+4p//PnQK94YTVrRm9/STTJ75Wkq/81HOZLuQ52it0drgt8oYrRnc3qZyehmpFPEkZnD3EOVISu0C0pUoR3LyjatMj/rIHI5f66CTGOnYJPUVlCXI4wy5eI4/+//4m3zowx+BwSFuf5dk+x54AcGJVaTj4jVrCCnRcYS0bFStgd1ehzRExFNEmqCkwC6VsX0L+9yjSNcj2dkiKs4jphMK6QBL6NlPcR6Bycm8CtPCHNlkgNA5xnJIBz36+DjlKs7px3nXo2f48K9/kH/127/Ld3zP9xObmd1AIEiwCIY72Hs3WVdjJlhMhUdiB6x+zbdRLJZ4+fptpuUl7MYyzuSArtfGWb9IfriL3Lg6q11WDuH2Nvk4xF46+Xpj1RA5OiLPUrJBD3dwROrW6JpgxsK1ZluqpDQ/S+VLhUVGN5MM66fwyJjOX0AHFbKghhj1ZltOtzADz7tF4vZJ8lEfM+yiu/tkB1tYSUzzmWcIH9yGLEWODtDjLvp4G7P9GrgFpAInDyE3GNsjdwKkBjk+RsVjRBqRuBVEnqGyGD/pIV0PpiMCobGzCDk5JjvcJT7YIRtPkb09vHd9K3mhRVpe4EIhpe0ZhuVFtJBoA2891UQoxbzvYDR8Qcyxku6xaBtMlvPYapklT7N2cpkTcoCyBdbeAyrpEI2FQeLnE4oyx84ijMnRQCYd0lNPo8d9aC7htRfIEdi+Q37vVWR7BdXfpRoP2BuGVKMjDu0WyWsvIZ77BOuTexSjPm9f9dk+GM5O+AXD7dtbeHeu8FQlJsTFnvaxe1s4YQdTnyebO0XfrqMPHlAMfFqeIMwMxaiDdCxKZ85jjraxTj1G1u9RWVjgjBMz7R3xysRhkhiC4R5jYdPfO+LWUcxgmmKExWfGBYblBZ6dVkjdMuNcEHs1Eq+GmTuJXD4LC2dnmMA8JKiWqK9VmGvYJIe7xF4DncZgciQGo1Oyw23kjedJrz4L3UOMXyFRLpYAQTYLkh7tQJpQfecfoXBiEcu2EUYj0TjxcHZFSGPU4inyQmUW4im2MPsP2BnnCKkR+xu81tWoZEp+uMGkskzW3eOFjQ6i0UAbg5OOMePh7PSfx0zu3SBPc/SoB68LN324Q5QpsqMdpKVori3gpBPsSY9EKPq5hc5y5u2cxK8gik207eAsLOItrOAtLOL6FtbcEsHSEqJcn23YlAKvhEgjTDyB3gGTG1eJjo+xKiXy0RgdRyS7m2Rhghn2cAoFhOOT7O+S2QH5eAQGYrdCdPMG808+hP79T2IFNtnuJjpNyO++DFk8K+wREmMkSdBEGYPGItQSKQ1COaSWjzECISASHv60S7m7OTuPR1PyeDKzQgmBVasRb9whd32cC5dpnjyBLQXVWp1GwcGqV4ktD8ey8MtVioWAzPIYuVXCJGWCRWwV2R1l3OxmdF76Aml3hzSfBUspNskLsxdLhEIgZnmMPJ6d7U1Cf3sb0d2dlVR098jdIr3yIruHYBWrkExheEx/MKKfaIrxgEbFp9bZxNu5SXf/ABMOYNRn3oroz18gt2cb+bqI8VyL8GALRUb+hU+AkFgmpWGnRHeu4EQDgvEhYvk0YX2NlYrPSLvYlsWDPGC9COHt6zxbPMfyXJEz7TIbx1NGmcFPepzVBwTHD7DvfQldX+WuqXK48gjdYIH4xKPoYY9MWIyrJ4iKbbzpEfcnEiUMgciZRBHdM29EpdEMK9jfIxQKo3NktY6fT3DzBJKQwvEGsjqHNBo7GaMHh2RCEmaA4yOYLSfEsIPQ6WwBYQfIPAHLR2RTCvGA1MzEe9lM8W2JpxRDY7M3yUl1zs4o4VdfS/jVaxl/dAX+yrsfwnPU/0lDhEnO9/zp7+f9738/w+Hwyytgvjpf8fMVK37/8Xvey8OnVnnLO57h+/6Xf8RP/rU/hUqmeI0AXatR/abvJp+bp/6r/4ZgZ4N0OGFw74DJzhECQEiS0QS7UKD6yEM49QqjB4ekUYKyLYSryBMNWmAJw9HVXYzUIA3SUmQ7m0z6fXQcoc89gfALPHzhDLc3HkBzEb17m3DrPiKLyKcj0skYq1wDYTA6Q9Xb2PPrUKiiwylCpwRPvg2VhTOkUzRCdbdnifrWKqpcRbeWZ4LXr4JyyO2AqNimL30wOSJLMPGY8e4WO//+g4RRzNQqkVWW+IG/9Of4tx/6MFo5qHKT+LXnGf2nXyGLxmQ6xw97UGrgmyk1WyNFzvYgZLD/gONJxEK9QiJs3GKN7qsvYRyP5w41onuEdeIMue2TxVO8+Tby8psRloNOQnQ4hoO7HNfOcFA5Rb8/YNo6Q6VaBgMChVk8w8htkCqP0ChEPOTHP3KV24OZ48slhTShtPUy49Ic8cJZsFyEEAxkAEmEaa2BMbjLa+hSE5IJwYVHma8V8Q/voHuHiL07sL/5+qnThr07eKMD6G6h8gSdZyRZ8joaB7RQCMuZeZrjMZYxCGVhlSuooAi9XbKN1xh6bXY++nuMewlHX/wiw/6Qw2CZ8cJ53MCn4UluRy4VV7HRGVNwJbZr02hUOHeyza2dHtfjIu5nPsJKLWBvnNPv9plzQRUrDBYu0p07Tfz8p+D+VWQWY4UDvGSMmA6Y5hY9t0XqlclMBq1VJutvwJx4mDyM8B9/C4XLb8Aoi34wR3u6y5Kc4G3doJIcYZ1/guCt78KedhE3Pk9xdYlHNj7PlmgwufEaz4YVrpZP08rGtEwfXW4gDIjeDt60S7ngcSwKqKXTFCzB4TiByZD90jptNaVm5Ux7XUwaMnVKjIsN7t3fw9EJT1UyjrcecH9sqF79JJujiEdqGZ1RSLHosf7KswRK8rXny/jFIm0nJxY22mjy0hxUFhAmxygLEYUwOERVaiQHO0yvX2Hy6hVIMtBmVixj+6hag7xziLIkxuTI3ZvE0icPR5hcw3B/Vt6RTpHTAXoSQhQSJhlCqtmmzw5mQU1lYSyfvFDBbrYR8yeZ79yeIfKM5qTsIWyX4VGfcW0NMX+ai0+cxy1VkElIJmxYPIMOh+QGnu1ZiHEHvBI6qEI4wgjwoh750Q7J0Q5EE9Lde5jXPosfdvEO72EOH2B6R3jTLiKeIIZdMuUjyxXcoou3chIZT2bfJ/FkFl5KY2gszz4YsB20luTGxWmvYBUK7P7eC0w2t0h7HbTRs79Pa5FsOGT0+d9ndO01hBAEFx4iWF0kj2PyQZ/pgz3Gd+4S376KdH10dQWjbEw8BksxMja5gUwbcqMJcQAxI4nkCRiBH/dmdgzbITvaovuJTxAe7pG4ZXSa0B9mKD0lXzxFjqKQDPBLBYSAxt6rmDijMt0nyzMY9/mtV/bZGUxIJz2iKOTghVexwh56f4PG3ZfZnT9HUmpwe6uPmh4joiEyCSEaE1aXyBbOIlyfqfQRzUXccMRw9WHSwx3i3Q3yaMqNl67g6ikcPUD4BcZWQIgg290kSlKS7W1EUGF8a5PR0llarTLXtgd8YexyrB3WFpr4ylAUCYGJMJZLyzUsixHJ+qOkWpMlMd3cwZlrM8kVYv82AhD9I4ajKfvTmDv9hLmiRbc/wi0VOLfS5FLL4+JSiTDXBAoeHPbx0jEjFaCXztAPY5azDtpInj1M+c0t0JU2qeVQzQa0fIee1WChIIm+8ElQFiXPwbUcYumS1JbRxRbDMJ+9CJ59AzrLyC0HU23j2Qq8IrRWEHPriIN7hFqRaTDSRsTxTPDOnSJ3y5jXQ2oYyJ0CWjpY/W1sYci1JrUKKJGhBAgDrx1OuHc8YRJmjMYZz17Z5x/8m1eZq5f4J3/xHczXC/8nHfHzH73H0tISP/3TP/1lUi7/bfPVze+Xb74ixW+v1+Nn/tk/44f+9g/wQ7/4Qf76n/oOHn708RlE/eIlnvl7f5err16l/psfpFoxFNol0smU6eEQadnoOGW8sUvcm1A+s4ywHPLJFAGk45RpL2S4OSAdp2TZjPhQWQrIwhypJMq2MMUa+nCHw/ajyHKLSS4oi5zVhRY797dI9x7gVMuzbUGcYNIUVahgChWsUh2xeJrcCTBZDL19KFQRQQnRWMS4PqJQRloKbbsEb/tGhBNggjrYhVkDzrRLx6qRZBnF4/v421dQjUVGOHzs6jHTM4+hjaGS9DFKoRtL/OTP/Azf91f/Jr9785B/9dxNjlcvYYZdzPE+Lx1OmfotRm6TKDcU4yHf9c1fx3t/9B8TRhF/5S//Zf7G3/pbvHb9GtsHh+jpgO1+xK9M2tweaDp3bpHdennWXbry0Cwg0j0im05g1KMw2KLiKsrNCtKxUXnKODVMURhpM7jxGmoymPnwpM03P7HGmYaLSMYok6EfXEMlE+zqHLROMHJr5KM+kyQnTsEUq+S1eURjkeTEw+S1RYxTxPUddFBF9w5nrVs6Jzs+gGQ6S73299GVObLxEJGE2CZDF+rEpXnCYhsQiHwWgtNZSp7EmNYqo1iTZhoqDUS9jTW/TFdW2TlIob2CUIYkgzzNeekoJc4Nh5OMbzhd5k4vYTjNifOclUaVdz+8iLZs+u/8zpk1Yhjyvz+3i9i+yTSM6UwThsUmemmN/N4raCXRXhVMTs+t088F/VRyOyuj3AJ6OmYiHNLmSdRjz9A9+Qb0ysPo2jLG8jnyFwnyKYP5h4j8FllQQdZbyFIV2ZgDARcWisxHe9wtLvMD7zzJ15ytUT15ko6qU417mCxCuwUQkp52yHSOY1tooDuaMgpzbL+ECqr0c5thouh+9jO0httMrQq/Nyzy2b7LUkGzWMjwTMrn3LOsqpDbocubG5qSpSkWbJYGG7RLPm1H02SMbwlcM/PwyWSAUc6saKPcwMytIr0CzvknKJw9R7kZIE2GSSPSW9cwxiAsC2/1BKq9gqy0EZYi0FMKhzcxOsfYAdRWyaZjXvnXH2bv1iaf/vFf4PDzz2J6u1AoAwYsi3F5iVwpssoSKp2iSHFH++z7S2TSpdyeJ3N8SvPzNO0MUa6zsNBGDvdBp6QbN4isgFG3R4rD6fOnUMYwrS2RHOyhMQitsSs1sBTjK69ANCHfuYNSNmL3BkGlTOIE4LpYOga/MCvAeXANnRkIiug0gSRGj4eQJtidDeT+bYwBWawgy01kY5nSI5dx5+dR86uQRATLi+gkntWnYyGUhegfoxrz6MERxQsPIQXk4RCrUCI67CEshfAcpGPjnbmEXFhHTrvINCR3Shhh46JxTIIf96hmY3ypqTsGWxhkEpPoGdM5sTy0XyOrLWOtrmMmI67SIrEK7I8hP/kEur5I0jsgtMqkcY6yHRK/xEjYyCzjxit3iMMJNc9C5imT8ZSSo4hcHzfqE9+4SXW6RXv/HolXo1q2qEc9dDhBb1whn04Y2HWEMJh+h2MCTGsdpSOWsyOscpVrpoZXLOJbAieeUH7n13PwpSuoYYcv7aU096+zkh0Tj8ZMNZS8CSsFizkrZS3QrDBklBoaVkol7SMlOKUqtlTYtRaBMojFk1TyEWGaY/W2Cd0q2C7p4jlCYeM0GwQ7V5lEGe64S9HzaTXqBO0Fqg4URYIjBGU9ZjgZU7SgU1rmyG4xcOpYJiU4vk9BZpypKJ69fUi/ssrYa+D2txHRMVmecDPyMc0VdJJhtMbv3WMY52SZIZMOAjmrsDaS0CmihQ2lFsJyIZnOljbGxpx+kpoe4sic2PIwbkCiCmg/YKJKpHYJYQxDWUB4RTKhSCorFEU62yjHA5SQeFZOqg3DKGWzEzIYhWSxxil4HB4P+JF/9TleuLnHj/6Ft3F2ufb/oSU6o5g/+b1/hp/4iZ+g3+9/+UTMH3C+6vn98o31h/0P+L+a9/3oj/LkYw/Tzy0cS/GNf/SdGK/M9oP7XPdXGP32b3H/xS/gnDyBUJKg3WCyd8TcY2vYJX8mSCchbrnA5MH+DPcTZ4y2x4w7U466EfPNAL/uoeMcZSl0qNFG0zjbxm9VGN7YYPPTz7H8j99MR7oYDe3uHca9Ln4yBcvB8lxIE4RjoypV8iRETMfI5bMYaZFtXkVJSXqwjTd3AgadWdGFHyAM6LVLs3+rUmTSxpp20LaLSBOM5fL5nTHf1IxRo8NZ2UEm2BhM+FzHcPbxNpP9LqNRjMg1JbfMqRVJMd3k/l6fn/3nv8C/ay9SsAR2nnBve4vPfOx3uJZVuVgY8Bv/8TdZWV7m/f/kffz6pz5HuL/Dt73tSV46HLOZlCnbkqWqy3J1lf/07Ku84/JJln/vPzB/cZXszsuwNA+Wgx4co5bWCI7vY1VKpNX2DNNkUowqUSBGdB7wL7/zr/H3f+2HcC+9iUxYvPWUh0OOyFPi2iqBVCR+FduykNKQo7iSVjjHMXuDkGbawVs/SxAP8ZSFiSeY8RDL84gbJ3DHXbJyCzF3ApVn6P27CL+MKM+T2gFWMiK027jjfYb+HAKbaZZREzFm1CWNO6j+AelkxLSRoSZ3MacuElkuB3aNxmNvoPbwZeIs53fvT1gvjTjRrpChudHJWKz5LJcU876GWGEw7E4VDy24TO++SmOvi+1A3tnmidOPca5+gZ++ssXffATuHGueDo6wqwHHJx6npgI8kzKwa+RC0tQRh6lNpCGyCnjFEs28j8ogsyS+SRgOJvhKY7SkcHQHWV8kG/bw10/iR8e4uY0pNgi9JmpyjL92grM2nK64SCfB0hlGSS6qHHnnVXT7JJPSAuVJh27qUBz38X2B6R/QaK7hJYpPvLbLt15eIti+wqesVS7P+wTNFYK4y3xZcaFpIZNjgtERQaHGeL7Cxv2QcjigbxWYUz3W3vUWPNejojKE0eDNLgZjY1GMR4hojLYKxFgU0GReFeWXsUpVEJo8SWDYIZ8MwBKYzi55mqC0RrrB7GWtdQp70iHq9+llD5C+opn20WlK4amnSM48TJxXkdsb2A81SQsL6M4D0lKbkVWlVvaIJkNcpdFpinPhSTyVYrs+6SNvg3iKXFzD1iGpVUXFx5DE5MM90t0DOpmi3zzLipKcc3O0U8fZv0M2HZOP97DPPoGIBujJmGw8xSQxQlrI5gJKp2SHDyisnEf0R+TCJ9u/RXjnNoXzFzCNNrq3S3a4i7u8jmmfmm18o/GsLfBwE41GGgVkaJORb90kNyFZNKXxhseZ3HgVWQhwm/Pk/SNMd4/h9du0nnqSZDJGCE18fEw4vo+q1mk/s4hTKWNVmrMNffcQu1BCIbBKLbRlo5REa8FYFWlOd7HCHi5iFjBLE7AcRJYQTUNsV5HcuYGOEsrNgGrUx/PhzIUVYr+KyVKsSpMHicC9+Rq90w/TbCxj949JB10qm7vkK0+z+bnPcfabv4Y7ssqiMqycbLE9UCw9cgaxd5tiOuXo1WtUHU1aCYhOPIIb1BG3nqUwSZEqJh12sb7wJXj6CfLxEV5rmdSps1yqwKDHIwsO0y9+ivDk0+gwpJParLaLFNpvJyy2qD5RR0+HDMKMpc5tXE+iHJ8JNlVfM0wNSioi4WOkpp9ZDMcRZ6oBpXSA6G5TyXKiMCcqVmkEFkdxCft4m9vlk7TmT1MQkEwm3BmktHyXbjihaRsypZiGY3LLI7VLLAYxe3YJ0dnHqQSULYE++Riv9g0qjvjLb1rGpFPKlmDs1SiTU4t6fHDDY/XiCveONW9w9hFS4RAjM41zdAdVd+gUytTDLQJLMg0qWJ1NsiTBKjcwkwFDq0FQWUSmYyzLQsVTTHkOa7QHQQVlInK3iOlvU5Ye2BYqnWKKHhNjUc4GGCnJtMSTGZaQnKoHbHQnLFQDppEmy6tUlk/Svf0y//rjr3HYD/mhP/Nmfu43XuIL1/f+i574lc/ssba2xk/+5E/yIz/yI39Ysuar8xU2X3GvCMfHx/zsz/4cf/fv/QN+4V/8C977A/8TGMPG/jGvpSXmv/gJPnPlRb77iYfwm4XZdi/PkELgtxsIKbEKPlbgEazNkRwPOHjh9swOIQzpNCdMDU7ZQWuDU7bR2lA716ZxboHWk2fIk5T4wV3i/WPkzS9SIMYRBrFzm/FgQCHsIaUgDyfIoAyeD8oh7RwgVs5BpQHDDtneA5LN2+hck/cPyboHmONt8v0NsDykkLOGH5MjhUGkU5gMEEmI9uq8tZHP+tCziKy+zAv/5reIhkPedXkZNe0TT0JKKycopGP8uIsz7mJuX+fyqTkaS6v8nZ/6l/zQP/oxfuC9P8O5c+cpBzaPmj3+0Xt/jE9duYOlU/7RL36Qgkm48uCQ+VqZ1sNvwbRWSIIy0yhhve7w7reeZXVlgfo3fCt64SRpZQ60RpZrGG3Q3UOcWhM16mADhfEOLjmBDUpKjHI5d7aF69tkjo9WLl40ACDxmzgK0sYqOAWUTpH5DBm3GEg+e+zx2bjFuL6MMjmJ4yMxWNEIa9Kd+Xg3r2LQTH/vI+hwgio30N1D9GSI7h8iN19GYWC0z0T5DMKQBxubdPcPIcuI7QL7uUfeP6KbFJj83ifIX/osKguxxn2aHjiPPUXfLlNI+jxRmrBYtNDS4crY5eGFMg1PccKL6OcWVTfHzkI0mmGqOfTnSGwPd3kJp1KmZmlOtgJOtMv8y9cmnNi5jtteYhLmVCslTJqSZzEREoUgFRa2lCwEkEgLsXiGxEhMnrHjzOMlI4yCaZyQpRnh+mU+eGXI7+zNwj3ROMTkCZlbJpQeQtokjoccHGIf3cOa9MEtIpIM595L6DxFpSEeOWl1Acf3qZd9Pjv0CFTCEiOKnkV9/waJzskLDZ7M97leOcnLYYEkmnJZdTglh6RBi2muyBZO4ZTKPNZSPOJOqamYuf4d5vIeJWtWsWucInJyRJpmZEaiDCAU1mAfV8eQxaiDuzOqkWWBZWOVq7PN/MIa7smHAIOzfB7RXIJyA5knCB0j0pjJUY+jX/81dq/fJ6wsYtXnmD+7SqmzwRufeQTvnV+HKDYwlo/wykSFFooEJ+xRuv8S8ngPwiHB6YsE8QDp2MgsQvd2yd0iubBAgI4mZIMO+d4mamWN8s4NzqseVRNjJiOyYoPU9nHLPlZzfpZVGPSYPtgnPBqQHHewzj2OAPLyPLlyMQ/uwrhLPBgipGKydYBOU2SeodwSotogr8wx9ZsYATSW0XOnEFpjuvuYcg1dasGJx3GXToCUKK+EICdYP8n0wR5kCULZTG7ewrEMOp5gF8tIo0nSDGflFLJQwa3XcOYWUY02yY2XiV97kXjzJiIcIrr7IB0wBjAUpEYDJhyhD7dQJkV6PsKkSJNSCg8Jc0FUbpIddxmqgErBJ62vYvkFsq3bWFmCl8dIJO65C7Rdgxj3yW+8Ont+fZuqzPj2R5fgc59kPI0Z5DY3diaMBwO2XrqHOvckwWKbZWdE+NEPM/zMx/GO7qI2rpDu72LvXEM4LtLxsIWBziZptw+Wj8k0VZmQvvY81rRL3u3T/d1PYimLxnib8vYttOMTpCPMuE9sFC+2H8OL+0hlkeWG9IXPc5goPvPqJrnl0Yth6/4ueZ7z/l95lgdTRWQUSXme3EgcmVLpP4BXv0CmDSLXLHsphcDhnBtRrZdpFmwq010KjoVwHFzbZrnmszbX5CCete+VlWTx6CpW/5DQLnAsiqxVA45SSc/Y7Kcue6nkpbyFzFKyoMo3rEhqRATEjKVHVl+gZgvk9nV6cxdwLYnnWMhSGZHHjBLIB0fE9ZWZl3fSoxHukAOHooRl9MxGZVJUMkFO+7ide2hpEZXm8aM+ZAly0icULr3UzAhHZha4m+aKxAgWyy4n6j5/4W3reIE1Q6jZ/syzDfznL27w07/+In/12x7n3W88+V80xe3dPt///X+On/qpn+L4+PjLpmX+QPPfw/LwVdvDf9V8xYnfH33Pe3jjm99IMjzmj771KSzH5cbBkDv7XR53h7gPHnCcpiwVAwrNGjrTjB4cU1lfBmWjXIfJ7hHpcIKZJuR5TnQ8prczQEuD5UhOrJTQuSEeJBTmy1i+QhhD+7HTWI7Ltd+9yQu//gJrl08QWgG+DomiiNSrgRDExx1Gt27iLa3hnDiJXalhELjLp5CVOejsMb17E5nnOIsrBE+9C7P88OsP9OvkhPbJ1+1OEtIUgyRya6R5hskTRDqiMdiEQQdqCxy5C1hPvonO4THvaOZMR2PWWmWa1RLlpIs92ENkIaPLb+ef//KHaZc9/sipEt7LX+CTH/0NlpaX+bvveT8f+PXf5sMf/i0ef+Ob+Rv/yw/ysf/8MVonL3DvtVfZG2Y8NOdyvurwpXvHeMmQYZTQqNfwLMWodYJbYg65uA5ZjHTcWTVxqTZDOlk2MuwhBch4ihuP0EJham2+7f0/AO01jBY46XTmAUsmKAmRcMmkhRP3IA0hnuJe+xTN6JBHlop826OLZEGZWHkMZYFMWgiTIEs15KQHQQnyjNFrNxm/9AXMtEs6GZP3jjBaE5YWSVTAKFWEcUbj6Bbz9Khd/wLCZIRGMTk6InPKeI+9keTiGzCPvAmGx9yQDQoix9cJJU8x9FssWyE1lZOEE/I0o+oITtpTStmElivwhWQ7hBe2x3z8+jEXjm8z//QjFJmizl3itZFg494mz7Ri3nhqkZd0FdsWFPq7FEyIde+L9DMLZQwyj7AF1ETEipNRIkMOjlCTPgduA09ovhSV0UmKYyD0ymAXecu6z6rocfPZ57l7HEIcw+Em5bRPfnCP6a17YAzm8MGseWu4D90tmI6JjzrgeNjpBCuasORmFAKbx2uGVLp4g20mwmfp4nkqJsKu1nGKLk8Ob3Jw1GUzL7NetrGHh0yvfJFRZYm9cU7l+ucpDffxFxYpNNrk7XWk7WLv3Zr9+MVDjFvEy8b4ymCEnAk5S6EAk6cIvwj9IwhHCNtDSDVjyBbKCJMjK3VkNkVU56DQQCfh7DkLx4hmjdWHl6g+uEbn8y8gLJvJCy9SP7FCzUxZKcxIKiiB4wV4OqEx2UMND2HYQYdD2L2DFOnsstHfJ9vbRO/fw+nvEoURurc3qzVvLWOdepSCq6g+8dSstMCyGQQtYmwGCxdJFs8TLz0C5Ii5E2BZuLUSTq2OMIDlkimL3PLRna1ZQn/nLjrPabzjGaL794i372BGHaZXXya3PbQB7VfJgjpZY3XGs9WaJGiQB1XyQh3rzCOIk5exz11GFqsox8NpNGfhO50TdYcE7SYmS8hHfYTO0cohPe5RfvhRrIV18t4R2f0bxJ0upClCMGN593eQYRc3GSCTCd7xJvRnRAjR20FrgQiHqN4+xilgOQ6mu0e3P6J4/iyfOA54tu+wn9hk0ibPU7IsJen1kQ+ukxTqWHaBI1HgsL2OOnGeE5fOYtKE5qVzFE6tc76QontHrA9u016o4eZjdn7nU/Q2+uAWKC+30EmCmYYw7iN8nzxOEIUK+XRC+d3fSbT6MPaJMxgJctJD3H4J7+E3w+IZal//bSx+7TPMXTqN21rAajQxGrLt60R7WyjHZ+70KdTiOvuVkwwXH8JzIcgTuoMU1+RY0YDCzRfobm3xHc88QimfYJDsTDRuscBw4QKmvoBbclGd+3QKS7SdnPp4G7FzkzU7pGaBXZ3H2XqNGIspktA4aGbb5am2ibOMw+Un0bt3Wck6lLIRthS8e82nZefEuWFjALYAE4/Yp8pCe56N1Od01cXzC6TSQx7cm9kZ3Ao6mhJ0HyDGXchySlGXo7mHCbr3yN0io6XL6NIcxfgYTxi6CeSWTWJ5ZKU2WWWJ3PIYJwbtFkFayOERujyHFw0pOwImfUR3CyebojUkuaHsKE43itR9h1LBwfEVSfcAtzo/E8HACzf3+ZF/9Tn+2FvP8H1ffwn5ug78vVsTzp07xz/9p//0y6xo/r8bKcQMnfnf8vlqycV/1XxFid/Dw0M+8IEP8MN/52/wb3/tP/CnvvUbePX+ITuDiCfdIe7uBmEvJklzbKNJwph0GjPaG6J8l+7LNxjc3SU87KNzQ55kSCWxizbPf26L7v0xbsFBGkN4HGF0TjZO0KEGS7F/7QG3P3WVK1f2qbpQPTWP15yjlzkoQPoFpNHoLAOtUbXmjL4gFfbcMqJYmVX4phMsclS1hjr5CMLxsUyMZc9sEiYag9SzzY60ZhvGdIoWEuqLmKAK8QSRZ5jR8SxMYKYcNld5Ti8CkFXqLDUCnGQM4xHks7a5hVqBW5v3OXX6PNnWXW70dvmND/47At/n4x//OB/5yEd48zvexaNPPM3iQ49ybqHKVDh0JhOOqovYxvD2ecN4NMaNJpRMgklCOr0+x50uk9zCmvZn50u/iA4nyPr8rEFPCsS0B8pFihwlNFYW0skd9PojmN4OSqezZPXwYOZHlBJhDO60i5ACFfbAcrGzGHvaYUFFBJ5FwVIoKdCTEZPEYIIGpthAmQxZbyFcn9Yf+w4Kjz5FurWJyTJkqUSeG7zX0UFDq4I1OuajkzmsyjzlxQb28X248yrms58gWnkIZUF1rsqouszEq1MolNgbxYgshjjGtmxCp8IwaFLRE5oy5mg45V989DomifDiASKPuFDMeWbZ4WsrHczSOty5iqjUSdOc5eyYBc9we+qyeHyXN6143J2CffoSeXef/NYrWN0tPJHj6hhvvId/fBc7GeLlI3A9dG0JORnw4vaEwFJ8dF8R9Q+RScr9CA4oMgxaNF0bf3kdghLKpDhRn3TxAtbDT6LTBIFAug4GA7VFrFMXcR99K5lfxuzcxrYECo22y7TzPrZtkSmPQjbkxZGLtjwkIOcWKJ48zVvCu8ylA4Z2gX13nqEqU7zzEvvdCbW1ExSbdQrhMdVsSNdrE3ot8AswmbX5CQwy7ONMjhAmw/g1UruMSEfoyiICkLUWJqiS1JbQQs5EcFCesV2rc4iggLFctBRgFzHREExG1VcUm1XaCy6V0R1M75DKE5fxfWvGVPZ8Mq9Kkgm0EBiTo0wGvo9qzAECUa5jjMCkKclwAjrlRbVGnGSoaDi7hLglkCBPXESuXiJvrDBtnmZsV8mlx8FEkwqHUWkJ3VohX7mAKVSpvfktVB+9iGzOE+Ji6ovEWuEurmKdOIfwi/ir65hBF7G4jlWqoKwCstpGvOWbAUmJGA7uQRbhhF30dICqtGYVyUhSkzOurxPWVjErFxFSwsIpim96J8LxkLU2lTe8Gas5Q/GZNEZZCgoFpnduI0o19PxpRKU5ewH2A5y5OVStQXJ8QLq/hcwTVJZi4oR08yom7GHufYm0e4TevEL64Dr69gvkr3ya9PqLOPdeonVwB3n4gDeXJqzWXIr5mDQM2ZV1CCpoW7F10MOJe0we3MNVkp62UNEAa3CEpUNKmy9z9uI656qKiq0JLr+FpN8h8zyGDz2Mv1gl6+xTetu7qL7pbdiTDmrtHPbCCYL100SH+1jFClbUI8ZGKsHAaxKtPk66egFZbZLZBYSyqM3PzYSZsukUGqRb1xHzZ3DnlpBxzHI+4ECW0VJwbwT31p5ED7q88/IqWTRh3sk4vvQ2vKLHymIVnWmmcYSrII1i5OEuUqccVlaZEzExgijLMUGdbnmJdBoSG9hLFF61Tm40bRmhkQSWYL2ssPIJzYNXODYeVxae5lbkYdkOgWVwSTlRdhjEGpFEXD0cg5QsxPuI/g4r/VtorRnmEj8Zcey36C2cxzYJrfsvkHoBIsuI66s4nkvVjBHlOVKpsJUEyyV2a2RCUtZjRJaiUYy9xuw5qc3jCDNrj4uHmN4emQYn7OLrGKMNJhpTi48JnNlLaWogTHPu9qZUPZtqzcOvz71euBGCmG2A7+z0+cFf+gyPnW7zN77zSaQU/M5zd/ie7/kefvZnf5ajo6Mvu7b5rx2h5H+Xz1fn/36+ov5KP/IP/yHPvPWNLDgGlcXsDGIGKTxl97B37iBMjuUK4uGEwYMOey9usnv1kOHBlOMrd3GqBQYbh4w6IZVz60TDCdgWCENdChxHzlBoOQRNn/q5FpOjKbnRHG4es3d9h4NXHrBe8lh6ah1ncQUTjtlJbSyTwuZVTJbglCs4lRLStjFOcYYDymNwA3Q8RQ+HgMGqt2eM1tfZvJRqSD+A4dGs2WZ0iJESMRmgsww/HWNJa1bVaGaMSpOliDSl8OAl3rbsscY+h5EmyjSlRgupMwh76HCM7h1gH93jSeeQr3/iPL/wwQ/zkedfZmVlhXd+159nZ2+f9/zVv8Cge8zS0iLGGOLpmF9479/jF/7Zz3Gm6rIb5shoxJ846zG2Ashzpv0xBkg1LN37AsHxBgg1O2VOBlCsY5wC2inOGpCEhCSdJamTkGvHKSJNENMxOk+R/V2Sq88i8wRjDJ6OkXkIwsI4RchzRLE2qyGddFDJlEmakWQZH3puA/WlT5A7RYTWICWWjjAa3HIJs3QGghL2ymmEV8TMraGGh4g7L9LOOjhb17lo9RlrB6daR9eWCM5cRF9+EnH1OTwdMywucn+sSeZOMFfymGSCg+P+jB1qckKvjM4yTJazcOOTPMIe7eU5unaDXndILxEoZbMWwKnuHbJSDbtYxklDPGIuFFKWGHNirox/+gzz83XWffM6csnCrtcoDXbod7qzs7VXQUZ9RBpjLB/teBxmNr3Pf54FM8BNBmgsXnVP0N24gxMO6GubxZMnmLSXmdgFdu0Waes0kSpTLARUizaW7YDtoeZPwLD3OltVIMM+MhwDGhmOSJWLI2LwSvS9FlPhMh2HtMYHZMMOqV+lWp8jsMG/9CSNog1BnUTD/coye+tP8UQ9x6vWyU4+iSjUkGEXXwmE65HXVugX2uROAKMuSdBAaQ3TIVFQIS1WydwqSabJ9++DVIgsAeWQuSV0mmIcF+H66EEH4xXJnQJG2uSNJXShCeUWslxHFku4C4uUH38aMx3iZ0P04QN0qU2Ix3FuczzNmKgCQ1kgLS+BW8YUGoigiGiuoktNcttnfPpJpvPnKC2sMpi/hGquIJvL0NlCHz5ARCO05ZBpxdipkaCINJRsQ8URJBrS3JBIj6lbRdTmsQoFhO3Pzvr3r1LMBsh4DIun0O2TiKCKdBzGDzYR5QaqNPM1J5U29rSPyHNEFmMd3IUkhiRC2wFWPsUWgsKkQywcPAWJ9Ajnz5P5VURtDlmqYa9fovDoGxCVFna1hdVexRICoxTFk0sYN5g1nikX/BLewvwMqag1+bCPGQ/IHtxCd3dJrj+HHnQR1RWE66OqTYSy0OEEoSyyjddmFpY4orpQwREJi/MtzsTbqCxmdOcO98c5YtKjV1zh0kMrTEWZydwJvGKJR9aX2JE1kiQhbaxymCockyG72yhyVBpyaHwWZMy5pSLu8gqjg2MmV1/BtiV5/5DB5z6NPtpDK4vxxhbGK2AlYwpRDzMZYOkcRYyXTwFB7hXpFxeQ0iKur6LGXbzeFmLhDJaOcUtF7P42pWoVr1ojyQ1zKiHPNNenFq1AsZfapFaBQwrs9ENqSnOcCb64OyFySsTDIabzgGQypWhibl3dYNkKEf0OL448NkWNODdk0sXXIaFTpphPybMUW2napo+OQ0LpkzVPkwrJ6UaJSS54bSRmdIUoAWWxUHZ4fLHA+XqB+9asiMk+uEvBJBTDI4xyUFtXkcrCSUaUR/vYjSaRVUYX6kyEi85SEsvFeEXso3t4431yobAcFwuNLcAOh5DFHEUGMTogyw0FG3IjmNTWMc1VnLiHUQqnt405eoAeHCNqC6RGIcXsNzTKDC9sDTDSYNmKhZOLSMfHqbTwanP/RUsc9qb84C99muVmib/0TY+Sa8PUanL58mXe//73f3mFzVfnK3K+YsRvp9PhF3/xF/lf/+7f4Sd/6V/zfd/7XUxyeFJ1YPsOOknJo5gsyzG5JhklTPYn7O1NZ2e5PMetlNA6J53E4HhMto6ZHg5J+jHrF5sUFwOcokP7UovSUplSq8A017QeXuHGCzt4nsNgFLOwXqH58Dqi0uLOxCXVmmp0SDac+VSduTZus042GoClyIdDRH2BbHeD6NpLZIM+VnsJWWuCTjHJFPbvYsIRxnaRtTamvz9jWwpJVqjTk0VSt4rQGbK/g9m/R3a0C9qgd27Qu3abuHvI+3/wb3Nz4z5RmhEUAsy4CwJMHGLiEZNRzPTWDT7wgQ8wHR7z57/nO3nTW97Ka89+kj/zfX+OB5mivryOlYyxleL7/tr/zP/2r/8t586dRUhB0/cY1dZolDzecmGFtYbL2A5YiI5ZvPU5GufWZg1NSiFOXsZpLc5qfnUOSEw0ovPpT6KHB+hoAk7A2bpLkE3prDw188LmgLLBaDJpgW2DFTC7HSYIcli5AI4Pox7W+JDP3Dnmt17cpZRN0FGMRkI6BqdI7hSJK/NMpc9hLyI5/ST5mTcRNU+i7r44q9TcuI559VmiWzc5WZaU9l7DKpUQ4RCr3ODM5QvE2iAGB1RrRR74C7x2FHJoXE7uvci+VeVBLBkLDzEdcPSRD9E/PiY48zAlPeLtlxbIbRcKJZpiQu64GOXgLyxSvfF7+LUSV9UKMp1iOlt4pRJGpxxaFZL6Kr6lyKWFJMeaWyJvrpDcuzELJ+U5eBWwHFQWozNNSWY8X1inNdkhCSc0Kx5bU8Gnp1XEsMMjcy7lbMzq5hdYDeCVownP9Syu50XCTICQiJWzUKxiJgPSo22ym18EnSEQCGEQlRbkIXaeEMsiYXGeL/7yB+kdD+hZRYK5JnezIgw6WGRIYfBdFzdwkUrSrJV5ck7Sblbw/AJSaByhZ3VibpGCJfGiHplTxLUUI6dBIhy0chj7c6SlFrmwsIUglD7hb/8KHacJlkdcXsCedomDBkllAZwC1JcwtXnC2onZVaG/izncIItjtF0AaSEMWIvrqHINuXgS6Rdh2EWMOgTHd7GGB9hSk2NhS41JJrMCmkoThER3dtDakEuXzA6IpctDdYuiIzHSxuo9wPT2ibY2EGGfXENqNJ41czxVbU1ZZThZSJam7IwSzKSPM9gj2ryFWlhHrD+Cv3oK5Xqz7be0UaMOZtQH28Zae4iCJyCoks2dZlqYwwl74HqY4T64BUxQJd+4hqktwfgYGQ6RUR8pJcV8grBdjhKBLrXYslsYZZMsPIQIShi/hHBdhK2Y3Lg6oz0YgfB86O4id2/A6BBZbf2/2PvPaNvSu7wT/b0zh5XX2jmfHOrUqZxUJalkSWBZGBDGBss2tI3d0G268e32td1OYGPfNjYG2gQzQAQDMklCgASFsqpUOZ2qOjnsnPfKYeb5vv1h1fUYdzSN7WtA6jH0jDG/7b3HXnPvNdfz/v9PwDx5EYIBUuaY1QnSUUTWPiLe32Bw7SbG8mmEyJHH78UwLZRbIsgstPnj6EtnUVkCx+5GoEhbhyTNQwb9AetDwcbVVeSgh99cY6Js0ct19LBHo1pCN2yCV58l2dkgnj+LqesUT54j9+pjP4JpYaqYxvwC1cffiXG0gaFreAsLlI4fQ8kEY2oR/56HiBfOEdtl3PsfQxSrKN3GKE8gDAunv4s1aGLbFkYakCltLO86Wmegl0knT9CYX8Kp1RmaJfQ8RSQBzvAQd/USCsXGYYtT1oCpWomNXoxUkOQ5qZLUiw6TeRuFzsp0jQUnRwQ9yisnWM19nLCNs7JCpegh0hAzj6ma0KmtUNZyGgRYnS3iTHIkyoQZaLqOp+fUd96gaxSpuxa2Lri3kqNlGYYAfdDC3rvBnK8Rmx5nGi5uZ4uOUUIt3Y02dYw1Y4pq3ETkMZFbxRQ6LX8KdINSeIQyDPxsgB716GlF8jRiUFlm5E2hyxizu0spOHw7h/0Ia9RicrCBGvXxhvsYQQdt0CIROkoYRKV5Eq+B2L2NNr2MXqqhhEApGMYZgySjYusMopQoyqkWHCanikwcO8n02XvJ4uD/h1MMw5R/8cvPc9exCT783nP82K8/x7d8y7fwkz/5kzSbf3gxxlcami7+WK6v4T+Prxry+3M/93M8cP+9TBdtJuYWUX6Ve40BVsFHmR72zBz9tQPWRM6ZWhmlJLo5LhWIckWe5IRHXQzLwKvYrP/202iOzmCnT3d/iOEY5COJVbDI4wzLM9Esg6nlOlNPPoqZ5qxt9GiOEqQUOPUyaBqnT8xwsqAh0oT+YIgL5MMBxsQ8ycEO/VdeRJg2Mhghe20ME8yZJfTKJKo0hegfITIJpoPml1GdI4RhorwqmVsHTSN3iuhpiL5/DdHbR0+CcWKCEGi1SQZvvUkwEHzsd36XWq1KkEk0TWBaFiQJeCVEsYqoTMGdy9z17nfw4Yun+bvf9df42O99hg+878/w9V//AR5699fx2x/7GO/7zr+DZplMF02+9ZEzGH6Zf/DPf4iv++a/yO/91q8TtvYwoiHIFKff5MT6lykX7XFO794hKolQWY4o1NEbM0glQQjE4SoowZd+9D9yUFhB+jUy3aTqWsTlKXK3jBYP0QpljIvvGus8kxC9vQPDNhxuQHMLEXYQUR+hMmS/hUKj32pTaa8xmFggbCxi7N9AqbFcRZo+rc1tYqXzSlQic8pI1wOvSDh9CqQkO/MAdh5QvnAvpm1SWFxGU+O0CRWNMA430ewSI+FBHHLYDWjYGm/u9MkW7+KiPcCzTGxS1no5C15AqVRkZJeRlVkmjYSyitBth9QtUzIg7hwiUGT1BYzhETsxHIWKzK2QIai5JlN5HytsM1Q6YtQnnjqN0AxkbZ6V4xOka7fZuHmFrl1HCQOFIm8ekRoGp84d5/P7Ji6KN24f8N4ZA89x2DHq7CqPZ1uCt2YfIJM586N9RJ7xxtaAV5sZB6lNYFVQSYT0KojyJMaJ+8mdImr2NLI0Nc5yrsyCpqOERqIUg9ll9mJBMR9wlOgMNjZpvnaFPM/GkyalodwShaSLN9zB1yT1zh1E0AZNR88jRDJCKDWuLTZMEqnQlEDTdbLaImYeYURdBpmOnYbjkpaohT1ZpxD3xqYOwwbNIBEW/cIcuemQFqdQ06fpa0V6KeSGTV+aROs3GV59i97ePrgeSndQbhWRjyuAxcQiYtRGlxmlwQ4VPcXMI6w8Rdu8AnGAdMvI2hwyl6jNa7D2Go3eGlO9VcxkiNIM9P4e2qCNyGLMUgUhNPS4hzJdMCxMIXG7G4jBEcmgS9XIUELH0DUSYYJTIHNKZEKMp9vTx1B2AQwTdbSFyEJkFoHtYE4vEyyeH8d9lRpopkNu+qhCnVxKCHpkWYoM+2iVBrldJNU9QnQslZLlCktkxElOwRSgGZikJE6F3C1jFOto1Wnc5WV0w0BqOnZtchzbqEC8fYin1EC4PppuQLFMNnUMUarRDB3i84+THu1D0CVz6miVCQ7m7iO++CT55AmMu9+JXijD9BJxeQbt4jtpFyYZVucpGym1J9/FsZJEK5SR3SbFchHPtxnGCj3qcFReoC77FIpFIqmjFctIBZldID/aQYURU2mL1C+jls4jpaRw6jRGpQKjAWL5HGa1Dt1DZGUaw/PRLBs5bKMnQ6hO0HKnGZoFMMdbPSfqIJRClSdws/748FSoomuQOqXxfSwUMbIAS4VMGDknGw7MnaNW9jnuS5YGq9h7qxSCI3pY9J5+hnq9jJXF6DvX0WpT9O0ylWIRBgOW7RRn1GbYOM6KFZIPu7i2gRceotk22soFhkaZQSxh0EJDQxk2g8GIogqYthI6oxAdxXK6T5mYfm3h7f/5HI8EkcfUR3s0RIwYdUijgOYwIh8fxzENg4KZ45s2qVslRaBpGtbgCA1BXQWovXUSwyMeDemmBv1cYIVNDHKUrpPv3qLQWSe8/ibCK8PuDTRdUMhGiJ0rIHKkYcP0MsqvgV8mUwIDhS4Ejq5zpx0wijKKrsE9K2WOT/qgmViuR3l64f/CK9qDiB/8ped48t5Fnrh7nunFk5w7d45f+ZVf+VPnOP8l+FrO758evirIr1KKH/3XP8J/9+Fv45d/+ykefPzd3Fc3sOMeaDZOo0bWaTI6bPPMtS3u9jyUgtJcmS+LHp+2A9xGCZQi7sckg5i0PyJsBvQ3+wTdiMHOECFAt3QM16QwV8OqFtA0Hd31KZQcCCIef/IYi4+fRCYpRqlCNTiiljbZLh5jvddjoeAS7e2juQWigw5Z8xCzVECkIRoZ+tQcYvkuBBI0gSxNjXvs3QLSKyJO3If0qjDqkFsuZmsbs7dPYbCP3LiGlsWkh9sw6qPPH0fqFlG7hzM3zcf+4Av8jb/2YdJc4jk2oV5A1edQjWOI8hR5fRH/8ffynf/vf4Tz5F/mE7/3B6xM1Xn47AqnGw6/9JP/lp/6x/8zHzheoiwksRTYpkaFlNu7Tb7vn/wgUZLx3d/93Vzba1PrrvJsR2fz2UtI1+dKbJM8+3n6V94aa3WzAKEEyrDIN6+hhIbm2Nz9jY9jazlGMmKYG2PdpOFQ1DKE0NGH++MCg9YOZnsduX+H/NbrkAYQBuP4uHhIYnoMdvZIhgNOL0+wKCLOmgGf3c7AGK/slVJIKWlNnmL7MOTBcohl2RhJiCMT8m4f4RUpzC7jzMxhNSqI9h64FfJ+BxX2Ub1DEs3EXZgm2jukmYwlMvtBPm6HK01wpZ2NjWdRk0pwCEqjV1nk6etHGLZLtXWbOz/yw3iWiafBlWaEjEfEezswvYTuuDxZ6NJwdLbdWWKnTCsRWJUKhbhDLAUtq4rwKsjGAiTxOLPUnWaSlNbVK6j2LiQBxtQChTzmOD3ONQr8wZHJeidkb++IYsFhqugw2V2n7HtMTk7wG9e6XMprnK/rvLEz4Pm1Fs+9ucrhUYeRsuiIAmllcjyB7x0i8oxECbBdwERqGraMKO9f451ni5zwErrSxGnt8eXbh4SDHprloBkmiowIB7wyhqajvCrUF0gKEyjTQwiDzLBQjg9pjAgH+MNdjN4umpDYMiEyC3DpS3z88iHG8BA9GpDpNp2zj+PWqijTQddACoUtY0KlMxQFEtMlFTpKKTSVc+hMY0/MoB07T3/mJIN2QJ6mSLc8luZEQ5RhIefPIwp1RLGOmQwxk4BUjbcA8cwpksosue6glMBsTGFMTKObJtmbXx4fEpVAy+Kx+bJQRfgVtLkTSKGhaxqG0DBljp91EKZDnCkcGWJbJitOhnn7ZfTLXyDdvkO8vYps70Mygu4hmB55kqAZFpppE9lVQn+OQZrRanc5HGYoAYlVII5j8s3r5L0eKhxinb6fo+deJtjbJxWCTGXs5eNNSa4ExXTI6jCnpEukXSS1SqSajspSEJDtb6EtnkHPUxQCY2acEiHq82iVBqrYQLw9ZRXGOOFh//Ia0fYWdh5TduLxAWVzldGnP0bv1m30JKTg2Ggo9LBNfuJBlGHRrJ3mtrtIOzOIwgjftri21WJpoog+uUw2e5KSbRMkikGasd4KKC4vsXGUstHq004NHJWj9feJNlZZd2ZoTZ0j9ypkuj02AnklesV51OQSsr6ACEdIt4hXr1MYNTFlgLIKyEINrAJpqqiIAEdlSMtFGTbs3SFNElJvAkNAFicYow609qgM9sh3bqKZFmrYRcQRTh4gum1Mt4AfNsdmyEqdvc98gWNFg0apgHX+Poxhi14iaZXm6BpFpFdjwgG1cBK9c0Qn05C6SaLb6H6Zkglav4kN49Y7S7ASb1NurqGCHgcRHMzdQ5SM5R+LR1cJdQ9jYp5w2Edv75Et3U2sNEZhRCRMstzE2LgElkNo+hyruoy8SQ4qx+mEGXh1vK1XSXNFoPtkXhXKDYRXwksGZPPnqYqQen+DUtrBMi0SqSNGXbLd1bEOfDSk89plRGcXEASFGUwVI0ybXNjoQZdQd0nNAs3CIkehZC/IsAyNOM8I0pyTUwU6w5TOKKU3TDHMMdkrzS3+ofxirzXiX/7y8/yFd57mqeeu8sQTT/CRj3wEpdSfFsX5Gr4K8VVBfl955RUG0ZDTizOcu/8R7ptyKWV98kGPaOMWIgwAhYozrvT6PHJ6EdO1OLIUfxB1KdomU4/cTWftgHSY4k0WQFMM9oaYBZNizcXyTUpzJSorDUzfJmwH5HGGIqf5zAuYusLWYPL0HOXlCfIwQvoNDBmjxQOmDi7z7K0dHj0xTzoKCW++gSCl8sQ7x+t6pcijCJFLhGWj/AqxWUAaLiQRFBu0vDl23VnSiWOo+jJGGozbmgZNhGmi+yVUNBrnIFYn3n6IdiifP8kn3nyTv/zeR3juDz5Jo1LG9XyCMGLU7yHVuL5XKImYXKCveWwZZX7zC8/yfX//H5KbLp/53U/w8MMPEfmTFLWYZm4SxRmJVYZkyJyVsuAbfMtf/iv8xW/9i9y4doWsNAXoREojvHWVC53LWBWfzo11lMwRcYRUGWxcI9vfQBQq5OGIEx/6IFVboGlgazl22ALUWKqQpSjDwYj6CNtCJAGEA5ASmWYorwiDNsq02cs8ei0wXI/JgkPj9AnS9RssLUxCeQotjcmsAmbQZrrqERYr1Gtl9DxEP7jD8OZ1PFeOddNSQmUKWZ9HVCehf4hobiGyFGUXUJpOVp7mUHf45LUBOoIbd3a5J9mm3etxqWczSHKujzxmZQdx7kHMOGA53ia3faRTRC+6yCSgf3jA6aIkv/Ia5sQU5f4ewqtiKEXHLLOTORzkLnkuMWWGFvZpBLuko4TVQU545xbZ658nxyS77zEKk3NkL79C166hR0NE1KZ4dIeCY1CdqzFVdDk+U+aT+xrdKOWTL97hqj7NK6/f4PJhSJQonrnd5/L6EY8YTY67kkLBYlic4FLxHK8cJKy9fo1upwUI1KBFEkdEVokhJoFwMYIuidBJJ49xaWBz7eU3qDuKR84vYD3+nrdJtI9MUzKpiC2fpLY0nmDmMRoK0gClMvLSDKDIs5QEAxGP0Ht7eMkAVI6XRzhT09xbkSAldnuNJByRGT771dNk/hQJFigNqVsUDUU3F/QTaGcaFS2ipGJ8x0OPhuxlFkO/in/xARJvktjyEVFv3IZmOWjJECUzRBogNA0r7uEEbZRUOK5HhIPII5LDHZRmjKeccYyMRsigh5aNcOIWqjg5NhHOHENrLCDLM+RSYMoYMxuihkMy3YBSA832MJMAv3UbOWihJSGWLfAbdcxiFYEG/cOxvnbvDqo+A14RzXQZ7Gww8meZOLjDvJ1ir79Bob9PJNphvQABAABJREFUd3eHtDjJMFHk7UMEEueuB1COg3m0QXDtMmUZ0ss0vrTRYysxmS/aRLqHJhSZXcCOB5j9PaRhofTxNFqz7PHrtmxEbRrl+KTlWVR7F3mwBqYDKESeM3t6lmh9DS/rwq03CdfXwbTwp+qY5To1hgyHfYbKJJOCG2kRTR/LZKJ+l2Zu0TVK5MLAL1dwq3W0LECis90e8db6Lhv9nPV+jrJ97FN3UYo6lGSfWLMIcVjVqkyUXQqE2K6HhsbILJGbPo7rouU56co94JeI/Abp5EmkaSHdKlLXwHYJDI+2P4eRjNDVOK5NiwckUUQ12EHbv0V+8xUYHtBKNJRpYagMMeogZIIo1jAak/iHd5jsrZHs7+P7Pik6qe4xd3aWyWoJ62ANUa/w+UPJea2LUahiWSZBktHqDnhzYLFpFLHjAY7jY+qCk+aQFAPllxGGTZ5lBBtXSbsdZJaQJCHDXKO2cwNv/xpmnqCVG1iXv4gQ0MsUh1aDg8zGyQJsU6NsaXQvXUKfP4Nl2RTliPrRNfywxZFRZqMTkm5cQ+sfYTS3MQwdiY7oHJAKC2namOkIPQ4wJhcwdYEVtkj2txjqHtrUItnEMnkwRPdsZNgb63uHR8SGR16ZZT+1aeplQqPIXmqwFWq82Yy4ejjiufU2zUHKYtWl4BiYpsbV1Q5b7YAkGBF020SDEZpd+EM5xupej3/9ay/xgUeO89g73sHq6iqvvvrqnwq/+a/B1wxvf3r4qrhLP/bDP8I3fcMH2Q0FU3mXSUuhkhi9WMZqTCEtm6gzpBfFCEOjUHJpnJ/jh166zLedWsLTdOTbWkXd0WndPKK31ScLc2QqKc+VkEqiGRpRJyDuBgz3evTWD9l6fZsXfu0lnrvTw9A0lMyRgLO0TGqYKClRSsM+usPL1+7w4LF57KmJcSC/AM200ByfbG8DYVqIcg0RDsAaP2R1GSINBwQcxjpJmqOQCHK0oIWKQ7IoQd+9he75ZIdbJEcHY4uc4cDiXaRumd/43d9n+/ZN7r3vPmaqRWzbYetLz3C7L4ilJLDLkIwwhaCkS8KrX+CbPvA+2kaZPOjzMx/7Pb73r/9V/NllzH6TcnBIniuCXpNnnv4S2zfewJ9eppAO0RyPo2FIqHu8Q6xz11/9i2C5ODLFnphAq02Np66DJrJ1ALVpjJP3IoSGGnRRpUkEAqnbuOkQiU6agynkuKfStNCCNkQBQrfRijWUTMnae+Sb10HXkXYZ88ZrlE/MkAgd1zEZbh0wd2KJ+8sxQklyBX3hIrr7+KbOot5Dv/kimRRouoZdLWK6HloaofodVGWK3CzRL8wgOweo6jSqUMO0dHS7hLN+iVrV56Offov9fsxuoJjxFFc6igdPzZAojSNs9GMXCauzDOOEhekasntE25ng1BP3ot95jVG/T6G9ARcfH8tdDG2cirF5Bc0wEaZJECWUTYUR9hHlSTTdQg9aDN58lcP6ErqmER/skmUZQanOwje8n/zVZ+jdukW+dhWiPmbUx5cjVsyQC1M+r13boVZwqZZ8tjspSwvT1EoO05bgxEyRa6HNnlGmmem8uZ8i45TzpZyJ6SmGK/dwJakxLM+hjtZx1t9ECo2rnYw4F+QyIwhGGHfeoGOU+dj1Ph1/mpPzk9zopbyxP6QbRARGkXaU4QhFLsbGNCElRmcbLQ7HaQ1hGxEOEWEfrdwYv1fKE3C4Tr5xBW1wRDRzgoulDJVn5GaR57s2dUfgl4ooY9w0J1SGgcQRCitLKCR9CvEAKxmBZeOLBEvXmNUjJj0DTx/nLws0lO0hbJfMqUFnjzDOUEEfLA8R9NHDNuaoiRb1cPIBuqbTqh1H2f74sFubRveKoNtk/Q4iS8G0GfiT5EInMMbRS4FZYJAqtkbQHwwhTcaT4GQwziDWDcLtXcK9JsL00d0Cwq+CkAjLRcgUuXyB3KuRORU2E4veMKHkO9gTE/iGRE2toMuQSj4guX2dzZ0B2twJsusvUyprWFEXkoTQKBK8/EXazTa+rTNhCyp6hkVOrnQM5NtyFDHeks0cQ8gc03ZQSiItD2EVoXc0PhgrUMM+KgmhUCXYO8TIM4oPPIRBSrB9yP6X30SzDCzXRERdtO4hWrGCUDmr2gTFUYteMKIUd7jLi1hODtgYKcLDA6anJxklKbFeQMszdkc5zyeTvHFrF71QxNPhiy2NA3uSzPDpZ7D9wku0Gid58SDHGTYRQuJrY29BZzDEkRGg0G0PiSBVAt32oDiBNC1yw0ZYBQ5iRW8wQLYPUbdfxejukhUmUMfuBd0mLk5jdPdxejsIt0giTCK/DsfuQynGm4TaDJplYU9MM7h2FSPuUUnbWK013JNnMMM2CxMl6LXwLBdThhS1HGHZlPrb2FnIGSfkN9YhnDiGpSlsNa4TtpIeabcNYRt90KQ8PISjXeTBDlGzybQpKS6t0LIbvBUV6FeXoTYP7T2sPEHYLrd7Ga7I0Q2HUQqlRx8j6+yR5Tn020i7gB8ectYKWHRz8lGfUXES48QFIquEOTxAyhw6u8R2EaUk4uAWQkqGX/4c2dVXUHMn0Qxj/P7wK0R7e9Qffmi80egcYSKIjSJqcolavM92KOgZRbYHGTshoBSjTHKjGfB7Nw5wDZ1+mHLXfBlhCFKp0E0TUyQIoWPaPsL0/lCecXmtyb//nUsoJXjyySf5uZ/7uT95cvNfCU3/49D9fqVfxf8z8BUnv0EQ8PHf/gT33HORnc01Hr7vAsorIwcdNNfHqE+RHuwRHbX41LUN3lmp4NaK/Oqr17ngF7CVoO7aJJ0uWZqyeq3JtctNRu0QmUnskk1xsUIe5yglCVsB9XOLlJfqmLZJe2fI1e0+C5Mupx9ffrswQ0OoHPNwjQyD1p0dlMqJFFiGQbR/hOYXcaenkTurCMvEXDqNPrUIk8fI/CoijbHCFhE20i6QxgkFExa2XiAOYvT2NpnwyOMIqlPovofSLeRowO4nPoUMR2SaTmz6/NOf+w0WThynZVX4H773exkF42mphWLn135rXHiAGNdEKoXe2+N7HjvDp377t7lz5RI//6sf54nH30FxapEyEZZtYYucH/rRH+PD//33cenyTf7ND/5T3FKBNOxTbUyhjfqkSQqVBr3dLa7nRUrv+jrMyWkqZ06jNI3crSBsG+nXMFwf5VZgconQmyDRbQAy3WRg1xAyx477IHNU5wARDcGvkherYxe956NGISrPkHGIDHtUphsot0AiNaLRgNt3tqmLBFsH+gckUiOIYpLiBINUUPI91MQS6dYdUruAEY2gPD12YluMV7S6RuZUyOfOoIQ+/iDfuMHoxS+R7m7iX32JB0/UOFa1+ZuPTDOwyxyfn6CTgo7glc0ub8UFKrtXKcoR3sJxNjOPXruLsXgKM4+Z3LmE3L1DwbcZFSZRuonQNVSpgaHrnKsY9NpdJkbbRC88RW7Y5Con+/QnOVeIcW5dwT57P+7MLFPtG+jNLUSljrzwGKOrV+hrHhgm8mATr9+GK69RGe7wrQ/OstYaUp+dolxy+J2X1rjVCvn1l3fpdQO+birH0uGTX36LD5wtY2UBl65vorf3yCt11jsJ3WFKfHiItnCa3PQoJ11acUaUwag4g5hb5iBU/E9/5f1MFC0OI1j2JJ12kzBJ2R0k6EpipSFudxOEQsocpRvjD8ckxEgCcIsY+duh96YNMkcrNjCiAfmgjTzcIclylOWiCjWO1V2EUliaRE8jTKFQdhGDDKlyhKFjZH3MV58am7GSEYw6iDzB7WxRHe1gB0fjCL7OFtKrkdaWyKwiqVNC318jvn0NlYTIURe1fQsjCyEMsUYd9O42E2aMigPUoI3WWMC8+E6kX4LyDE2tggRiTCLTRYtHcLiKsGxizWKUKSK7SC83iJSONDxEHEChhjW3CK5HXGigLAeRxygpSGrz9PQSTavOviqwp3y2Rznp0jkOU53+1Alyf4L0YAutf4g1f4zk7ncyr48g6KOZJiIKOPjis8jeEf50g6P5u1g2BpytmVSyLpnQsUWOigZYYQeh6yjLJbeKgEJ3ixgqH+cHOyWI++hkGNEIUZlEWziBaMyjWS725CSapTHYOSTc20OzLSYvLBL1AphYwpyahto0an+NfekRaSaayLgzMug4NfadaSrDQzxT56A4DabFxlDQykxGmLzUzDk/V6E62eCZy9tYB+so3aCf6QRSoygTDo/dw1Sjil8sk9VmCTOd7WYbrd/Es3SCRDHyJ8k1g4PMJJIaejoi0iz6rSaJ0ol7XVqdPnVfkJsuqjYHmkHOOH829WvoeYQ89RC5P0FJT0jtMi/vJwRRylB5yGGbcBgiS1Ponk/l3U+iZQm6At3xyJu7SLtM6Jbp1Zc527pBy6iiRX2EZuD3D9gQFVrSYLJgj82//UM+uycZHB4SxTl5mpH2e3RffAFlOXjn7kE2D/AMhb/xyniYUpsnUBqBEpgLK8Q3r+D1DtBGbSYcDSk0MgGlpMVO9QSvD11ur+8S3blGXl8gLM6SI6h6Fk7URp9Yoit8dAFCKqJuh1y3sGWKWr+MlkvyzgHtLz2DNb+M5lcYferXQeVYQQerVsE68wCiWCVfPIdQCVYaQBRQOrrNoqco2CZV18Q1NAqWTsM1mC87TBRshklGyTGpewb3HKujAdWpKtWFBZIoRKLwGjP/t3zj2cs7vHJjn8cff5yPfvSjhGH4J09yvoavSnzFye9v/dZvMT87RX1ikm9610NguqjmNnqljlASlcbIOEG3Tb7c6/Lu2gTr20d88q1VvuvhM1xu93j4rmUGGwcE+wHNbsxBmNILcry6w+TFWbJYgtCw6wUqxxqUTsyTpzHttQ67o4zjUz7v/6uPUj+/hN2oolkGcbtPIgWyNEF1tsz1jV3OHV9itNMjbPZQaNjHLyDqk+BXEI4PC+dIhU5uuKRxTNZroTr7iPY2YvsaU9EhwnEpDjcZ3b5C1D0iTlJ0FKp7OCbetkvjkXsgjQndCZ755Md45c0rxKbHD/7wjxJZZQaDEEYBdsHk7ifuwtE03DykJ22yzWtYcR9Zm+EnfuTf8E//zvfy6kvP8+c+9JfJ4xFWHhLaPonUeeGzT/GLP/i/8aFT57lrZYnDYUbuVfFdm/V+Si3p0fHnsBdPU/VtlG6gLZ3GMCXx3g7R4S44RYzBISqLSZ0CQkmyJGRPFUh1i8TwkEphywSRhkh/AhWFKDRkY2Wcw1qcRJ89jT41i16bQtkFxOEWYaFOPn+cF/o2w+u30E6cYbedIAs1UrvMlt7A0yRdo4LIE4TtkzkFekEKuaL94ksMc52sUEPzK2iGAabFIJVjrfDUcaJCHemVKJ4+hjRLmKfP8gNPNvjQCZu5YItRdR4dja1OONaYWjrP3DpC1KdwdA195waTg43xyn9/nawwiTOziFw8B5ZDZHgQj5BZSn/2PNebMVmWkcQBcSrRGtPEYUDfnaH8wW/GqpSpLs6gtq+hpRG+jND3bhI89znCUUj0xDfy+lMvMmh1ycM+/vJJGvdc5PRgDbtQ4J1uny+9scqnvniJR+5dIUpyluommia40pE8slzm2x6Y59KRpFwpoeKQF3czjutDSu112vnb2s7Nq0jdYtpRTOoxlqExxYj9oeDJSZ2FosE9dZPP3unx06/2cQpl7FGLLz39BlGeMxQWSuYgJbnfIBsMENEAogHoJnL7OppM0HoH0N1H6i7IGDG9iGjMk1x5gzQT9EQBTddZsjMcITFkjpDZOEXFsMg0GzseYGkSTSo0S4M0RvUOCa68QrqzTlicRkYhyvZRE8fR0wQVj+jrRazRPrFujzX4vTb54Q4qS+i98jIyHJBv34T+PtnWLczDdbTu/rhgY9gaGwLdGn3NYbWfk0QBw3YLiUBPY7JUYbZ3KWcDjlkRdS2lZGqIJKCbKlRrG/IEQ5OU738I7a4nxuZD3USZNklxhswem7h03cBIAqZ9g0UnJZYaPeXQET6qdwSFCpoQKMfDn59Es2y0ah0VDll/Yxst6FG24NTyFIZMKWuS1K9jygwj7GIe3UAoSWr4oJtkjo/o7SKGR+OcXxQIQW7YSLuEfngL4gDl11ClyfHB5sz9iPkVxOEmhmNRvvsCRsnFYCwHMObPoCQUCgXyHGxNMVvUOG906CWCq52cL9gn2G2HVD2LXpDwzOqIYRJztRnznhMNTB0aBYf7F0u8lZfoDQPqvsXNX/84Io0oGJJUwmRpXHM/SHNGZpEvHpns9CLo7BBi0QkycjHWygdhQLi1wV7qcvMwIChOMlsvYaUx0ex5xNwJhGGR6A6mDno2wrYNhrqPTGP0QRtkxkTnDkdHRzy1r5M19+i/9hL99VWUXcTPB+BXAYXwSujLF0hljjs4wrcsZusO5dEh+s51TF0QHhxRDg+40hH8ufMNSmEbvXfIqXCLZ1sOGwEYpklH+Yj6JOHEMdhfo7u5R75yL6rXxYz77IaSKd+kQkSu25SnaxiaoFFw8A2FLhQOIFr7NPIexuQSh+VlrJN3E2OyJYtgOdQGexjVGmzeQIv7FPLRuLgkHKKX62MynMVIv8rgy5+mcd8pBm+8ibFzE6/ig2mT3HoLzj5K2twHp0hQnCfdWcVqryEvfR7hV6gHO1S0lBlXMEgyLF1wvq7zwGyRd6/UqLomd80U2e0lNDwTxzXoHA0Y9GOyoEceB8SDzh/JOX76dy9x/MRJarUav/Vbv/WnwHL+yyE08cdyfQ3/eXzFye8Pff8/4x3vfBe7azeZMZKxy9+2wa+i3AIqHBEnGZfeXGW+7DN7cY5/8cwl/t7D53EKLgOhmJ1rYBQL7BwM2Y8yDmOJ7ejIRGIaGrquMXHXBF6tTOnYNJ0rdxju9nnr6iGOrfPwN9zFxIUVCjMNyqdPIhHIQZehP4k93MMw4Nmrazx+zzmmv/5d+I0ixszCON9Ss1DlSTAt8KskuoMhE8JWk86Xn0HaRdpP/e64CEJJtLmT6DInry+QXn2N2GugRX3GgQkKY3qRwtIiwinQaXf5Nz//q+iOx4/9o7+DLVN61y6RaDrdTkBcqVF55HGE443LAQZNRmFIb6+J0ZhhfnGJf/8T/44f+tf/hqM/+DhS02DQ5mN34H//2V/im9/3LqhN4z7+OKlXYU4P+cytPjd3+6y3h7zWNYmlxigXzMxPowV9RDBC7W8weONV0ptXkGr8hpWVmXEW8LCHlBJH18jjFNDw9XS8IjN9lGFAsQKOj1AZhD1E2IX6HJphYdQmodRAJRFxs0Vo+PSjjEGpytL5kzydNdg/ahNioPV22dlrcqMHJRGP11/dPYwT58gKdW7O383otRfH7mfTQgqTvURH5Al3EotuphFOniJcup/s9GPEj/95otm7qJQ9jCwmkRqzdkyREFuHXpgSJjmPzdjcGWoYhSqiUEYfdik/+9s0L10nbx+i/DK4xXEkmWuRFRuEu1sUVMSFKox6HXr7ezSVw7X6RZRbxFEhtu+haQp7YoJ46w6jK2+Q6zYiipCHWxz+zM9izcwx/773kR8dItoHGMmApYLEnZjixIu/w9lJm284U+PRlQl8DVq9iPecqbNYtzkQRZ7bjhFxzGTZASkZiAIPLFe4GriIySXeWmsymjyObmq4QRNPRRS6W5jDA4RpEV96jRf3Rry50yPtdVioeDxxZppuIlmSHd5ZjahHh0TSolteBpkxyHX2f/e3kcUGhAPkoE12sEnePYB4AEEPlQwRgy6i38LIIgaaQ/7859i/doNBEHEwTEmUIsNExUNGmSCSECtBpgDd5EA6qOnjBMVZhFvEERG6Y9F97jlUZRqcIpgWyi8ipSQHVBzhh23i3V1UmoDlQByh4hStOsPRlTvEW6ukm3dg1EMlAcK0yO0CsVtHDA6IjnaJ0en4C0xYkiBTxIVJmvUTDId9kksv4qqIpDSN5XpY/T1aqUGujc1AuEXEzElsxxrrz2VOVpxEKg1NN6hoCRmCvjTwZUwUp1QdjSxLiaVieOpR4uoSEkFFDjDnVoisEhILvBL3/Y/fhlo8D5aLF3XJStOYnW2sPMZqjpM4NMMGKQEJwsCSGQodBi30sDeOjksD8vriuL55dw2BRBoOI6MACDKriJy/m8IH/iL68mmMYhk0yIdd8p3bgBxLTUrTzMsjJvMuyirhVOpEUpAquLQTUTJybm8dcev2BjOTRSq2oD0MkEpSdXSu7/UYYrMzyrmrZmKt38LrHPJKV7A0U+domGBrEpmGeOmAIExpVAp0M40ta45PXz2iH8R0M4N5+oTSoB3nyNWbvLXVwk4HbHQzPnWtQ6bb5HaFJAclJYFeIk0kWB7JneskdhkpJUoT1H2PQZDz4PEaqjbL3t1/BrFyHqXraGEfEEihI/+/0qfBiFEGAoFdKqBFPXK3jK3lWOfvY6Hsc+98iXqlSEv3OPSmKb7xPJ5Keb2jI2szeDtXKR5bwtYF4c4e5uQMKLBKRUKzyH3FnAUnJ85TyDOixbvRwha9OCOJYnbzAnR3yaIIVZrgXM3g1GQRlcckccSULbG33kTuraGmjnNUW6KARF99FU2mWNU6etBFRQOykw8hZIJdqyAKRUZr62SHOxhln6TbQxSKGJ198iRCjNpYQYv4zlXU4S7i5P2IYh3N8rD7OxTUkLIlWCpqTBiSk3UXx9K4a9JnoWhyvOETZRLX1NF0Hdc1OH3fOarzy2Sj3h/JOYI44z9+/hrvete7+MhHPvInyG7+66FpGpr+33hpX3Fa9/8IfEXv0urqKlfXV2lUKnzXt38r9Jrkoz5CaMiDTfL2AdnBFs3XbvGbb97mO7/uET61ucfZgsfZ43NsrR1Ssi1kkqMZgjd3hnRyyICDXkI4TMZaX13g1YvorkU2DBkd9ECC41jcc6FBcaaKbrtkaY4oT2BNLWDU6ljbtyAKkf0uz75xnQdmKuTNHQrn78Koz6DCAaI6RV6eRekWIh1iC4mIehiORenMGew8wqvaaGmIvn8LrX+ASkLcuSXsYyfw6g3QTQxvLIFQtkd4cEC2eIF//Lf/OtONGn/1Qx9kxtcR629y5Q+eBSHw5mc5PeHhGOMPJZGGNHqbFF0T9/g5LLdInGYsVhw+9h9/me/7mV/gDz7+a1zfafIf/sX3cXu3xxMf/m42KCP9EqlXx3A9VkQXZ2aJPA54+hB+6otrmNvXkVHAYH2NbH8Tc26B0dY+l3/yt8nbh8jCBNIqEu2s0QslWhJiCIU93GWYKsBEZCEij9F7e9BrjZMdkgi6+2TlWSQaqjqJ8Mpjw2B9juLCIhVdUTYFKyeO4aoYvVDg2Zdv8eJejOvZLPRuU9EzwlyQScmwPEdJV7y0GzF5bIXC8WXk4Ig2RY4yg71+AlLhOi5XBjpBLhCmQW4XsCs1hpmg70/zUy8P+PT3/x/c6gnamYFuGuz3Y567vIMoFNkQFWQWsVdYJFi6SPGRdxLe/25GM8fJlUQ0t9C6B1g6JG6F3bn70FCsdjKe77m8EFbJ3BqZWyazS5hZSCsMMbwS+d46aW9A9/XXufGbnyRyq/gLs9Q9if/65ymcvgt58d3E7T7pa18guvYGqljizPveTcXWeFTtsDTpc7amM1d1eOXKOl424mJVcPH4JK+stnhls8/rN7dZcTKMtbfYCiT7ZoWHpw0c20IU6xjNdbA8Aq8BtUUyr8bMe9/DPWWJ195hWy9z32KJJ4+XyXtd/t0rQ6KlM/QznWEux6tio4Tb22HisccQaQRZgty4BklK3jpACG1s8MxC5OE6wrYgGtA4cxL/4UeZPn0SdfkltJtvEGeKMElJWgeIoE0/N5AS1mITmSvsnauk1Tn09hYUathLZ9DnTtK4eBe6rpOYPpkwSJwqu6JMcxBxJa+AAvfiozinzpNNnyCOMioPPjCODXvgXaStNjLLyHtNhNDJ60skxSm62ASN46xb05RcCwyTQX1l3KaWJVTzNq/FZTKpII0pjfZI0oh85gRT9RLMnB5HpC2dRxo2WmuLSBiAIDNtIjSGWU6sDOIc5osm5d4mA7OEq8GCK/BMgSMUiTDRTAfNdDF1g3z2DKqxAPVpnNlZkmaT4d4eNLcwervQ3kHLEowsRqm3J7vDQ6x4AHIcwSYXLpBXZjHLNRSMU12ynC1VolWcJxcWZn8foQvSYgMzi7CJsJZOYhy/iJIp9sQsyrTH2tD2DoFTZS+zMHSDMiGjLGc/AldmNIcxW3ttpioepqmzMNdgqe6yOVS0ehEbhwNGscRKA5aiLbSjbVqJYns44soTH2KuYJNaHtOuJBkNSTPFf3zlACUUIguZFSFmr0Unynh5Z8B+P6QQNkkUrFpT3Cwv0qiWEdGIo17I+VPLY+09kB5sEL78HG5rlTYWz+1JCmUffdRmNTQ4ki7e/CKV5RWqRo5en+V4ycAzIGO8jUDlCHJS3SP1angi4dVXVqG1jcxyDgPJMDG4dJTzXN/ijiqz2w0JjAIWGa5XoPC+b2Tlrc/wUOcmTeWQHLuXwGuQjELcpTkaTzzBXqfHYOoUmu3gZz2sg1vYpoXQdQwUWqGBH7UpvfwUVthmsN9Em15AGg7W6JDqYJ38cAe9tYPX2ybujzBO3Qt2kVK1gXa4hhb00PIEnALpaEAeBFi6jvCruHc9iPfQn6H+4L3o5x5icOUaUjPRJ+aht49h6SjDQX/pU9hTs+iNCZLKPCIZjWVouoUxOOThmmJSBKS6g8oiSFN0AWU5xDd12kHKMEgQQpHnkrtOTVNdXBkbWf8z+NTzd3j3k0/y9NNPs7a29ifIcr6Gr1Z8Rcnvj/7Y/8GDD9xPMmhTbEwjCmXk0Q4yiRBCIw8GZGFEpiluDUcUpeQTl+/wLfVJ0kHEK7f2uVAvk2cpm89cp/f2/7wADsKM4oRHHmeYJR+r6JGnKclgRGG+TtSLuOfRBYrTJWpPvJtwkJD2hgxu3MJeOYW9corhlz+P3F0lD0f0M4l1eIRRqmGvnAXLgyRA80sY/cPxJCATqDxDKR27MoEzNYm2dwNzchY1bGMYOtGbL5Bs3UEfHmIuncUxFCqNyJIYgj7J2hUMv8CP//TPUEpDBu0m3/HhDyNNh/72PnOn59CV5LSfYg4PMdu7GN09YruMUZtCbN9EuS6X1rsEccrP/OanuPXWJZ79Dz/Cz/3ir/AP//m/4oN//fv45g//dcqlIpcOYyTQDwJ2E51eaZYs02i1OixNl9AKBX6tVeY3Lg/IT9yDMbWAAPzFOXRN0bt6g4FehMM7sH4Fwytiey4pkBWnsHUx/jB1qyjDQeYp2fxpcs1GDMcpECqO0NrbCL9Knibka1dovvg6WtTD7Oww7wviTCFbO9iWwfRjT/CbVwfcjIqsX91ltnmd5I3n2c08OmYFafmsxhqTvT2k5dHOTf7lx1/kt9/Yo+bqfH5zxNEwYaFkstVPSDJFvL9DFEXMt9+kNNznvmmDyXMneGF7SCvMeX29y4vrLTY3j5BJxrEJnzAzMHWodNfR2oekvS5NZ5KkPE3e6ZBWZwmsEvnN16hsvEq4uYmTtNgfZdyb7hNIHdvQx3pX08TVNXLNJHPKIBVG0aVY81BJiJg5Rv2hC8idVabpY7k27unz6I1ZOHYebl3G9UxwfPxakUdbl5iXbZYLig+/9x56I8nrt3bJpWJ59Q3OluGnn17n5mGPqfse4e65GnfPV7EnZ7FUDLX5sVknG2DbLoRdPF3SmKgyd+IY9ak6Ya549pmXMQ83cH/pp5henCfOwdbB7Dfx2psQD7EGBzjHzyE0DeH6oAmM+RU0TQfHB7+MkoyJcecI5dexZpYRC+dJ/DpXFh/BvOsBerHidi/j3696EIXEaUq/16OsZfjhIYWjLXxT4RgCkYQotwSGhbF8msCqkIhxBmmSS9JcchDB7UHOW9os7bmLyMYipspg5W6cmQW0qEtDdtBsi8FBl8GdtfGGQuWMlM7Ta20OpEOhUmO65OJpEhWGdFKdrdwnsWvctTBB+dxZcq8K7R16eOSYoFno6RDZ3kU2dwjjFC3sYybBOOtUZgDYmoZNStUW5IaDU6pg6hpGHpMZNt0oJ80kWTwiiwN03SAr1MhlzsCpkVfmEKVJZG0abe8O0nTQkgAxewqlaWB7EPbBdBFuBanbiDSEPEXTNWRpirzQAEDqLubmWxQ238IyNdSojcwz3DwismtoeTYuu8lTHDK0hXM4Z+/DO3sRa+UswrIxEAwGI97oCmLN5ii3eOMox9QVV3d6JFHEtXbMIS5Fx+FgkKJrGsszZQap4qPPbTI7UWHHmGC418TV4eV0gmEiqao+B8OMLAx4sZmxq0r4joHueixWXapWji8j7pkrcHmzxSCRdA0fTSqWKxbnV6aZKHukpSlmpyp4tsUbW02yNGO7dBz/9CmaRpmXDiXtOCFfvIvEKHJ9p0PzsEkkTHrCJ8BGInDTHobvo6QYa7n7h/TeeItRLhgpC6tQ4uTDFxkOAt4KLXZqZ4nDEROexgk75EY3w9MlH7/a4gtbCcPDHSLXY+bdTzDTv0XWOuQLPQehcppHLcJMJ7j6OlO9dUShipkMSaQ2jhnUHIz1txAHG6hSjZFbp3zv/ZTNnFK0jxUP8bSU0CyiOwXE+ccoDvfJXn8aPe2Pn+G9AyqdNaLqHOLYPSi7gFYoYwsJoxZGews16kCxhsgS0oNtrPYG1ql70WWMPNocW71318i7TayJKZLBANnv4h5cI8tBmg5SQW4WcGQCmskgU6wPoG7lpElKIFw+/dYeO+0RWaaQmWLQHXHQGmE71n8R95AKvnzliAcffJCf//mf/2NmNv//42s5v396+IqR3zzP+eivfBTX1Pme7/x2RB4h3CJatUHebYFlQZaRxjH5bAPdsfjnTz3P3z13gsbxKXqrTe6IlPtWZumuN3n+pZ3/9LNtAcu+gV20SYMEGSUkwwDT87AqJXRDp3KsTu3YJBMXT6J7BUzXwj52AqPgotKEZHuV0tI0eEU+9qWXePTuszgrJ7FnFyAaEPuTqEIDoiFaMkJ5JezDO2jNbfSoh5YMSDuH41zW2iTDK2+R7G0RbmzQfflVRpdeRDvcgGjA8I1XCO/cQZkuqt/lCxtNXnvhOW4cdvhXf+9vI3oHKL8Bnk95fgLX0rGPVsl27qD6R+hZgIYArwDTKxjhgKM7t/mn3//9bLz+Kj/wLR/AmTnG/+v7/zW/+q/+AbXF45w/s0w/kTyyWKIwPCLTLG4dDJlxJe8600DlOZ6pUasUSEyfaDAi0UyS/U2O3rxD9Z67Ofe934o+N0/v9z+BONrCrdWwR23MPKcocqLCJLoGSZaTmg4qDYn8aTKvRlJfIq8tQH0R42gVABX2OfrM79F943XWn/oso8tvkUnFTMXDGDUJ/UmONVzqvsvj56YYKh0efQ805ukevx81aHEYC2SWMaHFBFPLpH4Nv+ChO0XOLTbI0JgoOGz2I/ZGGTMFmzgM+Py/+BF+8VOv0vn8ZzCaG9STLkf3PsGxgkKPBtw86NMNM1YW67SVQUnFbL36Kl/eTRiVF7g9eRZ3aprZ/hqZWcQ8dz+R38CKBxyWj1HQEg5jEydLWN9ucaG/zqIZMqcFmJ0t4lFAqbfD8NpVehPHMB5+H5quM/vYgxTqDdTBOu7MNDLLEVGPTEmSu54ElWLqOWLlDMp0ycIRYuYMlUf/DPNuyp+tDejFOR9+4iSqUueLr9wiPn6OGgn/nbVNy56grfvMFE1ONhxqvkdoVBBRn3x/A6EETjpAmQ7KsDHyCJucA7OOrcE9VcHGr3+c9c+9zNcv2TzYfQXr6gvEP/VvGfz2LxE+81vIfgsGTRh0xjnPi6fRyhOImWXGdX8KYRcQU8uI6jTK8jCzEJWGFEXMufk6aaHBQFi8vNmnZ5b4hbcC2v0hzqUvMtG5iTNq4Z65iJ4lxIUpkCno43KOTHfHiSEqwLrzMkpmHIWSJMs56KXoXoFk9Qbtgw66qWNb4+puTSpc10avVPEXF/AeeR95lpFaBQphm4Jj8czqkEGcowswDjfxRcJhqNB0nY5yqdoaaXGCgXCRM2cY5gZ21sc9uE66foN0a5XWZ/8A886rIDPM3evjJq/2Lr7IEJqgmRochTlhPHbp26QE6GQKHEMn82tkwmYobBLDZiAcbF0D3cKIBoCGPXcMa/kkvfoJhrVjJHYRkSXkWUraWEGNBuMkC90AFNqohejtI6IBhjE2r+YyRciIUsmmODlLkAlGw5C40+KtgxGDJEWaHhKNzC6g3BKpMKA2i/ALKLuIFx6yovWoNO8QS0Hc73N2uoAWtnlk2uCDj59Gkxk7nYj9IGHeztEE6JrGxYUK7faAF7cH5JpFf+UuXr15xMpSg1utEZ/ZjOjFkhlGLFUK3DoYIv0SlqbRyXVuywpH9QV81+LW7S0mPaibOhMuVAsuJVNgOTav74Vous7aW9e53U04HCS4xSLxnVWS8jx73YBJFXA4CunqLktiSKngYGqCXirJpSLWPfLKAmLQgtsvkymTzKvRXd3Ddn10lYDQcYol7L3bZOh0ugPCiWU006Zkm1zfH+KbYIucVzb7/NKGxuWRTTx5jM79H8DqHLA02mc9tvBPXWRYmkTe/U4K9SlKaQ/R2SP3q9h5SDHaR/NcbNcmccvMFk2sQgHd9tAvPE7Tm0IKQUcvj+MjTQvzzIPY5x/GOvUgZm8X2T5gEKc4Wo7qd8blJVmCslz0ow2UYRJ6E6RpDuEI07fJERQv3gPL96C8EmLmBEZ9auzrmT2O1H3yXgexe5tkZxW2rsHmVbT9O3C0CbrGxmCcwLQeGnQyGOQ6W72Irb0hoyBDNwxaazfY60TU63940sMfhs+8usF73vMefuEXfmHcovlVgK9Fnf3p4St2lz73uc+haYpqucSxpSX0QWucBWrYqGGHrNdC5jkCxdJsg9/823+Bn/+ub+RYvYRddZGZ5MjRmFWC5rUD2oEEoKTD+ZrDybumKC3V0SwdJSVJP8SdmcRwTIQQNM7OU7v3FKV770IaFtbyGZwLjyA0nbTTovfWdTTL4s5I8KuffY7/9a98I+6D70Jp5jjpQLcYvfEiyjARboEIE2SCrmJEGhDfuMTgjddRUhFv3CbcPSTe2eDg0gaDnSP2v/gi+099mtHrL6AVyohilbAyy1qs8aO/8FFGoxH/67d9gDNzMwzXVol2NpDX3yBMMhwU8fY6+BW0coO0MDkO529u0awc4+rqBj/wwz/Igw8/wj/8+negp0M++2u/TH2wxyujInfPlXj20hpkCcslA8NzWU73GY0iLF2nmPXxoz7nJl2OVS3uOz3H0soUe0YVNXcS49gJtNo05Qv34t39CDOPPYQo19EnFzG6u2NHvUyQSudglKOZDqFRRBk2dtREGA4YBpnujPVyQpA3t4hef4a9z7/O9c9cYnJpCuPMBXrlZXYjnRU3o14uotB4qxlxbbdPzTboexVuBhZ7ymfZDLEtm0jBGTthcOcmavcOTjrib73vAnd22zhkHK/YPDBbpBdLlICSlrJ97BwP0mb/5VuM1tYpNLeoexqn8ibXXrnMXzpfwbd1picq1H2XpzdHFPI+zWef4852i1KtjqpOIGdOUZQjpGaBghEGebHBqn+S0sljfP5AcWLC4yf0i3xsPWMrNRk2A0ZvvUhfLxHeukZ6uMvz2SRhN2Hw+mvkaYLu+qRbdyg9+DBNrUixeYdQd8gGffK1G2z5S4h4OK4V7e9ixy2cQg0tT1lqXmNrJHmPWuMdJyf50De/A0cFxKMR74rXuLTTxQ67lOUIW5O4w31UewfSDBn2EckI5ZTI9PHkzu1tslBxKNomk+fvYv6b/jx/5Ye/G/lrP41avYZ84Wks+qhBh+xoD9lvIgdNlGmiDAsq82C5CMsFw0FpJiIdoWkCVWy8HWOW4gz3cbqblOMWZUtRd03OVgTHJoucLcRsDhTt4/cxePV1VP+IrDpNrhv0vSkCr4FCEBo+ghxfhsSaRT81yaRkWvaY9jWsg1UMAQcRKNNAGeY4faJQRylFXp1BLV3EO3c3hmcj0oR8b4vtL36BJM+olBz2+iFRDma1TsusUnNNLE3DMgR5khBrLgMcIruMq2dY0ZD9X/wI7c8+xd6XX6X5xh30oIVMItSwhbrxInprA0Nk6CiGmaIdxGz2Y45iAcIgTDXCDCxDw9YFnmlg2R6aBhKDQGqYKkPkKXmeIbt79M0ixbSH3twgyiF1qsjmPjcSfyyRyDNEFiPSeDwRHvUw0gBTf7sMQLNg+iTi5EPcpk5/6gyjxgqHazs0+puMrCo94dPLdQKnRuxUGFWPMXBnkf4ExAEZOl61Srs5zui+KSt0RwnDVKdWq+FZBhfnqrxrWnCirDNZsGgPQ1abI5I044MPLzFVdriy06NRKzDdKGAqxcMnZ1DCwjU0bugNGgWTURTxuctH3GmPSHONLg7NRGfSVHz7I4vsBoqNWOd2VkSonDjLiZOMihbhkNOOY85PetzopniaYndyhZ1rVzk32sQctoh0l3Z3yNSpU9RLBdSgyXK4j2lbjLKcw9SgY08iizVUscIoExQWKljZEFdFpOEAlScYdz2IkydMTpQpqBFe2GWo+7xrpcDNHlyoW3z9XdMMMp1WpLgTWzStEodL9zAzVaY5yvHzAaI6h1lrkNYW0DUdVZ1GVxIRDhCagSYlolDGNh3MJEDza/SNMug2DRs8DVxdJ3WKaKMW2v6N8Ro16pHlilzTsSfniYwC0cEOsnOAMGzU3hrh3DmuW/N8flCC7hHS8TCXz5E2Vuh5U7TMMunSveQLZ0lzhV6ugkwpHD+GtnhmbKaOhmhekc78fTBzikwYGFlGw9UZpIrDIKXqmBRMxf0rdRoND8PUsRyDQmOKYBhz+flL/CdaI96egP7faGB3W0PO3HUvcRzz+c9//k+Q7XwNX434ipHff/mPv5+V5WW+53u+Z1x6b5jkWYIIhuN+9VyhkhTTspBpimaZ6LaBWfHJgwSraNEfhUR3Wuxv98gEzDs6JysWgzmTE3/2HrypIkhBGiXITBLtHSGzDKPgYJV87OPnIIpIC3WSudMoBc7iMr3nnycbjkiSiO/7gf8PP/EDfxdr8Txi1IGVe0jmL+AOd9CscaCefPvBQqGObnvkwYjB9WsEm/vk7SOyThPN1hls7qNpMDwY0N9q88y//31Ga6ugKfxzF7kkFvlffvw/YLs+f+GuZZ58/BHar7yI3Fslu/kagys3CIIEJx2gWRbGyQdQXoVDrYLo7yOlxD+8w9/9R/+UH/6ub+ODx+YoP/JObu93+eH/8Bv8/f/tH7D2sV9AffaTVMyUURSRKUjQePn5l/j5v/8dRMLmEy9chckl3twN2OnE9OOMmaoPThE5d4obp5/kUC+SIDBViJxeRi+UEfEAe+E40nCxswgE1E2FEbTw0h4iS9CFGBv/lMJUMRzeQfZaMBpgFktM3rtMsNVkEOXkG3d4vRlSd3T6mYYXdlioOJybKnL3fIXDKGe2aPP0G7d5Ya3FTmwwCmNA4/ruADm9Qsm3kO09ZmSbu2aKhMM+8/EuVQPun7AoG2DYLu9+74MsXDjJyrd/CKVppIsneXjOpjRRpxNmXHrqae6pwkTZoTuMuNzM+A15kolsiCYgS2KEPi4CuJWVeOYgAyUZpZI32wmt6jyZ7rJYtLh7xqbQKFAr2NzuplwvLRF5NY6e+j0Kd5/FKFZYlj2mvulDNC+tMnz1JfSlU5jVKmLuNAOjSLqzRvHwJvrEPOb0DNPBNlgOolAfx4AhSbodnuv7TNQLVHavs1Y5zYSvoy69xD0Xz/Lkt76f0vEVHtEP+N0vX2MkHLq5RZwrssos1KcgDhAyRwlBJDW0qItyyyxrfZaGd6jLASVXMjHpYomA7vU1Ku/7eqxT5zEnJnBmphBuAbwSuGVyvwK6DnYRYTjjNsP9O2MzpF8DTSeziuRWESF0hFsam2NkQtmCK0cRdSMmdzzumzBJNWNc05znDMwaI6NE+vIXGT37BWK3ASj0JMDMIuI0YU0WkHtb3A4tynrGvWcWUVnGwoRLY2kJ0ow86KOEINha5d9+ZpXcsQnKcwRWif1Y0O4HvDV7Lw9WFGcaLqflEQVCOsJFCZ1hnLI/iIlTxQu7I242Aw5HCXfawTin1Cgio5i400MTgsqpOYRXRO2tIZWG6regNIGKI9qDIZ0gYcUKKVkaPc3HyAMsGSHefi9JqbC1HKmZkMQomRJIQaoUyjDZVx47rSFm/4grXY3hYQd/uEeumzB/iqJrIpQCckRnCyUUctQdN/blKfrGmwDo/X3wqmA5+J6DEIJi2sdbmmd2cYGCrRHmikyYKM1AWBaZZeMakFolKFTopgKZpMwaEVsjRZRJdgJJanucK8RUXJ3tfsybuwGR1NkeJEyZGYMgoznKKJUc6o5O0u7g2QYXViZ4bavHVMnGdkxGccruSHEwjDlK4NRiHVdI+qOAdpgRRCnro4yzyzOcrXtopQbXu5JffnWf2uiI5s4+HRwWkz3yWoMXVrvcP+WgtTboehP0946YLBXwix7bvYTFoqDouwhdB6+CViySphJn1OIoTLkysjiYuEAPB3HzNW7P3IMhNJKN66gkoSRDEq/OTM2nYgnKpk7VzJH7u1QdkweqEsP30Q2dU5MemRLEWc7BIGPBTjFVxtn91zGDHoz6bAQ6UtdIdRtpFRC6wXDyNLkQiOokueWTCY1USkT/gFgJtGyEqylSqWHpAhQw6iI0nezma6Tr14k3VhFSYusKU2ios48gXA9t1KGbwJa/hFKCaU+HUo1NY4ps+SJ75gRDaaHlKW3hkUpFtnw/WD6y00S47lgKVZ1GP/8OyDP8cpnIq5ItXkAIScmAYyWDKJUUDIGrSR5dqfD4iQYXj1c5s1KhNj1DrWgj8wzDL4MQaHYBuzL1tpHzD8fHvnSN973vfV81xjehaX8s19fwn8dX5C61Wi2ev/QqutB4x4XTEHQRKGRzd+yMdf1xn7sCvVAEXUNGKaP9Nm6tiFHy0HUNzTLJA8neSLLgGwz9jH8b7/GpYYcsCjl8YxtQHLyxR+3CMfxzd+EvLuKfOI07OwOGjdGYQZWniTUHYfvE2+tYjSKlpRn+yce/yHdcOMbsqQuINEB6NbLDLbIkg3CAffw8yvSQfg0r6aHlAQoFMkdKhVUrojk2SW9E5/YBaILph06RDiKef3GXpJuQj0LizTVkmnDlV3+MV6/d5s+9+zG+9YPvJdpexy77uI063devkOgG4WiIqymM2gQibJO29+l12uS6xSDKeObONu988B6m3nyDwed+n/b11/j+T3yWH/2Bv88vfs+38ZvPP8et1cu8396htbOP2dsnSVL+2U/8NFOTk/SP9vj0Zz/HN3z4OxgkGZO+wamGRxbH3DloEgoLheBTzRJ7zhy9WIwjlqRCkI8b2nQNTJtMSrxshNndxoz6mP0DhMzR8wiDHDQTTWjoro8+MUvSDynMT3Lfd74fEBxe3WHpSx8nu/oGmlPkrcAizcZayLPTJWYKFpf3RizOT3JhrsIvXh5wsmbz1K0mjyx7FMycURAzrC6R3LyCVXDxHAfLscjynKnBKpqQWIM9Kt0d/mAHkuNn6beHrMxW0bwCrmnwvifvxZ2Z5eUXr/KOGZMLgxt83XGHZzZjXll6gG17AiMNWF/f5drmEb1csNqKcGSETHKKOkx1t3CyEQU14qkXbnP/YpWH5wosF3Vu7XVYm7+f2nuepNUO8R2LxYaH4bvM/62/hV4oktllRBKSFWqkgz7G5ALRUYt85zbSrVK0daRTBJkjCmWy6iyu73Ai7xCU55go27Bxh1/9yO/wxd0RN3s5e8+/xJ3KcYJRyJJs87NfXGNvlKJXp0jqK2jFGqIyA0oi0HCPbqLHAfaoid28jegeYPT3Yecmeb+DXavgTpSJ2iPQDdylFazK29F2g/b40JOnkMUoIRjZLiockjcWUdVZVKFO6E0wlBqBU+PQmyYsTBFWFjHa2zhJl7mJEs9d2cFzLIa7W8zIHnmpQl6bx1QpNzYOeH29w972EcMwIlE6uVKgGVSCJscnyrwSlDhVFlSPriMslzyPKFfHpjs1aLL50lXuPPc668+8xUd+/1UGqcnhGzewRm20QoVPdEvUFhYINYcrRwF3Eg9TSFZ7CYeDkGEU8/TtJqudEYeJxu1WQDvKQNfZGylKJEx9/XtxqiX8uTq1cysIy0VlOb1XX0PpJhKBlidMMMLSNV5rjlMYENCWNjvRWFutCUECbzfe5YRhgGkYVI2MUGrkhk2pVKRQr5MPR/zOWohqHY0NvpqBJlNmbAlJAGlKunaFrLlDtnoVdIN86ybmuHJn3F6XBOgyp2oIrKCDWahRtzVcXeBnIyxN4GkC3bQIc0FByxAaxKaL9KqURYxIhmjHjjOvuvi6oBmkIAxaR01O+HDtzg63uznXDhMsw6BcLnF82qcXZswXLd59ss7kTJ1BmLLaDmkFOW/uDii6NuecgJdvHdKNM3Kl4emChm8ikxhTF3SClM1BNk4TCSNao4T+KOLasy+iZMo9NcVKUWfHrNOwdUqmziDJuZ1XmOht4t59H3lrjzv6LMloSFsvYgqFlac4nk9NTxkog16qUTQVLillPSIZ9tk0Gyy2bpAFA0b9iL5d5Wbs4GQjrN4es9E+lkgxhaSQR2i6geX5JJnkuBjwgRWXk1qX2bxL2TWwbIfYdEmqUzx1a4B7/Tlmg01uHEVcGxkcxAI5aBOnKZrpkNsllGaiGyaa7SAdnxIhRjgk1yxCYWCIHF0oRKEBbgnRmMVqTINlIWdOQO8Is7eLMh0o1NE0wYuVeyk7NqZhcNEZommCgmuylhdoxYpOojhKdG51JasjQVadYr9xGjW9Qri9ByjSxiLs3SBfvIijUkJlosdDcqHhJh1qvs3pyQL9VBJJnQcrOe9aKXP/YoUz8xUeujjD+x9c4P3vfxivUqO6dBrTK6I7/h/JRZ69vMNjjz3GJz7xCdrt9p8o7/kvwX9z0sPb19fwn8dX5C79zM98hMXFRb77O74NPYuQBxvk4QgMk6x1QNrtkDQPkWE8LgjIJLpjMdhqkcUZrbe2ODoYYuQKUExXLc7eN8Vn4y4fPrvMk4vTNK/tMDwcEg9jklGM4bjoIseeXcCcmkGvz6A5PvnKRUZiHESvmlsEqY63uMyv3dyhXG/wiGeRtg6Rts+mKqD3dnFuPoMyHLRSg9yrINIIEQ5Rfh2l28TrtylMlLGrJUR1iq3rB7z+hdvYlSL99T12b3U4HKUsnKuhWzre9BS925f5Fz/5Ef7Hb/0A//1f+vMYpTLCtBG6gbF0iu5uF9KU3HFxfB857CAPtmm+8hZL0R4HhWOsbbf5+V/4Rf7GN/1Zbuzv8x2/81n+wUd+kw8/fj9n738Q7nucKE5417sfQjvY5D1Wk/C1V/jBf/j3+da/8G08/K738LO/8Ttsrd5k7sQ5Kr6FZZvMiIjBlddYMWIGw4BTFSiUSuwNE/qRRO/tk+/dZnT5DUavPgt7twiMAqYAU5fjZiHNRCmJEhpGPEQb9UHw9kO0jNJ08iTFmlugdOYky0/ci1yYx5k7jrXxFunGFWSe8epWjzjLydKcXEG9qLOfmQiZsTxZYLvZxTNMQCDdMoMo49bTr3BYPY5QkkG3zVpf0YkzNCnR4hFZGPKvPnWHJ07UuZTVSR77AE5vHxXFGDLgpDzk/efrBEnMyZkqz37yBapZzLc/PMvaYcALqz3e2A95s69THx2w1U/4s7M5aXOb1VCn4erI7iG3+zmliVm0yiSFYgnbtimLmHefneGpq4dcv77J8PQj2J0tAr8BQmGVHJz5BfT92yi/ghp0KXzxd8iVorC0iL10mkHtGEF5ljYOmV8jNTz0zcvklQalk2cwNQMxucgFo8/7jTZkir2tfa77S5imyU892+SeYxWWy5LLu23WR2CoBOWWQWbIfguttYHpjIsmVK+JGvSQSYjcugFZjKbp+AuzWFMT2EUTP+/hLB5DTC9gzB1Hs8ZrdHI5/psbDrlTR524H+k3yBAQ9cnzFCcboSWjcYJBPMIOO+NSAAzeM2vwNx6c5LFKzJI1oiSHVLUA3S2ixSOu7o34tX2NS/VzNJ/7NEEq6IlxnbGeBRRLNnfPuQRhiCZ0Xt3sUdIUTmcLMeggM0mzMoNWbZDNLPFXapBtXKN++hTKsmj0t/imRbjLGmCaBssVl8nJGonuc9IYUchHXJguYRg6692ITEqO1x1kpgix6KeKbpSR9gd485M4kxPIXCKHPdA0jHKRPE6QzX10TVG0dCYKDvfOV3BtnaqIsdKAqWAfp7eHNs4aJEHgpQNSs8AwVSipoxCM7BqhNHALRfILT/BNJ31q999DnmbYeQBJiDlqIwTjHO5eH3ptNNcjd0qQpViGAUBqOYgshCTCSfpUdy6hWybqaAu9v0eu6ZT1mGLawshDdAGpZhFgk0mNRGq0rAZbuc/h2hahV2Wx7nHzYECKwGjM8/ROxF86V+bEfI16ySIVOuvdlJ1uRNEUbLcCZmzFvJ+zULKZrzh88MIUF2ZKBLni87s5e+0QT9epuiaPn6wj0dkZwWY35PR0CcvQSaQkdAsEccx7Fx0eXShyEAmCMOEwEly6sUOpu8PtN6+QH+1QIaDmGpyWR3QSAyvoc84LmXMz7FGbcnSEGbbRLJOqqxO6ZQyZM+UbDOIcW0j2S0tEyxcIO20mFmdZf+51jlsRetBmU9TQq5OoaPw3qa3McaykURjskWFQMMDtHjKrurQyjaoFfjZiuxcz8Krcu1wnvfsJStmIeTum2x9w+c4uRxEknUO0YQcUmHEfPQ3RdRs0Ez8boWyXWLNwkx5hHDOIJWlpErKEvNshn1jBPvcIhpDQ3kdUJvGDJvn6ZRKls9Co0EoVSsHQm6TvT2MYGmvdmCkzwdcVg1SRSIUwHHIJqe4j0oR47QZpcRo7GUGWEheqjJSBrlJyBYe5S2SVAUXF1qnLEUKmWMMjzlojFqsuF+fKXFiscm66yHvunefYvRdYuPs85blFklH/j+Qi3WHMSHlcuHCBj370o3/CzOe/AH8cet//SvL79NNP8w3f8A3Mzs4ihOATn/jEH/n1X/ziFxFC/F+u69ev/ze88D99fEXI70/+5E9ioPjQ179nTIiCPrT30XQTGUfIXgejUMDwXbJOCxAoJUmHMcFeC80QbLX6TCBwyi7Hzk2w7ijeUa/xUrNDL8v5zk9+md/c2ydXiv4wZbixR3Kwi0oCNCFITYu4MsM2NYY5GConHgyonDrJZ1++zDM3t/j+/+VvU1qcROtsI/1JNKeAUZlAhSPEqIOSOSoaIg0HFQ9RmoE83CbXLIyJaVSuGN26zZefukbNNwiP2rRuHpDkkqohmFyug65hXngHP/Rzv86fPX+M//mJC4xWb4Bh4czNIRZOEd+6SuPMAnOPniNWGrZpopKUzHBwji+jlapkScCGUabie+xs7PEjl2/wK3/vu/jpf/8TfOhv/k3yJOYnf/9ZvuuvfZi6npItnCd/7RV8X+POm28S1U/x/sfu47O/8tPc+/h7OApSTE1SdU1amcar+hzX8hKDMKElC9w7V2RCT6h4JrR3CdfW6F69weEzLxC98fy43COLEe099HiAHLSQydi9i2ZA0EGEXeJblxGmQ9wfoFVqmLMn0CwTp+Kj3/co5aKGfd+jtK7c4Vwx55FJA0sXrPYibB3u8RLee2aK0zTZ7OX84pc3GMmcF9fHD/Itf4YpK2S/OIXKFZOO4Gor4fnNIdL2KA82CYwC83efo1Lx6e/uYKocWWqw8wu/SCB8Wnt7VFTEtz+wyKQWcuJbPsTBm6/ynpUif+PdJ6l6gju7XT7xwjqVhUU0AZN5j9bWLis3vsi8MaTiWrzZSnlha4QpU05P+sTDHoFeQGQpD6VNntNWeHG9y83Mg2d/l0EQk5g+3ckTCM8n2tsh/sLvMHlmns4rlwieforcK3G0e8CtjRa/fOmIl7dHrA4FyvCwu3uM9AIqGRIfbVM+e4L7v/e7+PZvfpyHiwFGpULVMbhwfpZBbYl5LeIBb0gnhdWupONMEWsOOEW0PAZhILKYvLVHurtB1mmiuz5auY52/F60ygRuvYFX8iieuwtRbCCq8zB9nGT+HKo2P64ElxJQFJIumpCIWy9i9PfRDlbxm2u4u2/hRF2m1QBnuI8RdglXb9EdBbSlhVWqQGcfUZwgrszhTtShf0RXWTx47xn+zp+/D+fqm2z9zudYW92gnI/GKQSmA/02EyKkmTvkuoFnWTiex9FRh06uc/Wzr1E5dYLa8hKLTzxEVxcUHIOSq3AcB8OxmKVHPTliwjU4Xnc4X9YoyBFFI0c3NGxd410nalyYdKk6Jq0gpRkmOIbGkhVhb18l2dsixUY/fve4XKJ9iIoC8l6H+OCA7M4V1PoVRDSkTsCUkVIwNUq6ZKZg0/At/PAAL26TpSmmBlLolHl7va+genCDKE3xgkOkZpJoJoslC6pz9N0GRtCDaIBIAgBU9xDj/j8DC2fQbI+k0EDUpjAqNQDyTBKZJdIsw+xso5fePvjPn0UNu2gSjCwGTUNlMVLpRJnGIBNEqaQvTfYSk9j0GTYWaA1iTBQrNRcQbPVzrjZDDswKZDmJAt8y2RvGvKOcMFE08fWELBrx6Ol5hknObMnl5ESB5apNxTG41Ze8/+Is075BEMU8t9rmte0BhmPQHqZ8/K0D1o5GXN4foQvBdMkl0UxmHn+Cz6zFqEKJJInRj/bJN/cYdHskzQO0ZhPh2jTXNnnTnqK3scXczARePEQkEciUQGkMjTJuHjFhKQ4jULcvo2UxX9qVnKh76Hu3GTllBqOI43efohgc4qiMRSejKXzk5HGkUiTBkFECaWmSk1ULT89RugZTxzlTd5kYbCOO1qmVCtieR6FWJzNcBodNYqnzYL7Jg8s1DnIDz7GRhonSxThuMBpAniB2rqMQKKFhRn30w1WceICUGVkcI6VEv/fdhJUF8so0QirSmVNIy0Xr7hEVJng2qhMrmLah4FgcJjpSaXhBh1NFQbXgEkjBzjDGNwU126AZSnxLJ0tC7OUV9DwGXWNr9n6QOXGW4qqUjvAY5RqGoZPnimkHQmHgpCPi+jK5aTPh28yXHRYqLgqwTZ1zp2ewHZOVU4vUj53DLjX+SD7ymVfWv2rrjv80MBqNuHjxIj/+4z/+X/V9N27cYG9v7z9dJ0+e/BP6Df9k8KdOfl977TVazUO++f3vwk4D5NEWolhHhuMWLM0yMadnETLDcCyEJrDKRZJhSH97AFLDmyjgSp2RpaHbGuWpIi/uHfHQQoPrgxG2ZfHn8hqre0P+2ctX6A0SrIKF5noI24VClf7cvfTsSY4iiaYZBBnEqc5bLz7Hj3/sKf7d3/hm1n/5N3CWl9FP3A/NVaYcBbqFNX8SqTtkoxHJK59H7d0hwUJ098mGHbzTZ9l+a4+w3efaf/w8USdg8liN6GhAaWWS7X5MrepwsDPAadTJ99Z5dGGKf/nN7ybbP6D/8itkgz5xq4eWhMStFlPvfTfDW+skwkRvbqJyieb5mCfuwtIFg4MDPvnpp3jysYf4Rz/1s/zsP/mfqL3rG9EHBzAakhUmuH7zFvfccxG9MYtp6sh+k2DxHO6Ji4QvfZnK8jmKlRp3P/lBVqoOaa44XXfo5ToXTs7i6hpr/ZwXNvtYezdwt29RCJpolWnwyxjv/xDVdzyGsgrEn/tNtOc+jhp1EcMO6mhnXAQybJMbNllhAnm0ibFwHBkMUEfb9F59HZllyPYB0d4hE3tvIQZNxPZtSvc9wK12TjOzePP2AVqYYGqCWwOJLgSVep1Hj9f44IPHKNoG+cQybw5MpmslsvufpH/1Tdzbb3I7L/MebZ1jBY04zQlyA69U4rseqqFffhbah1jDIyKnSvmd74RXPk168iGMPCco1rGjPnOTHvaJC8S5RqwEVU3yFx6Y5fzJaV5owpyVIaOc+swktYk6T60mFBdWOD1R4B0nqnznMUVRRHRx6McZt7sJnm/z3kdP07PKvBZVGfp1cqtIfvst8AtoCMzT92PVKhxYVeJH3kfQDNCjHvFvfhTtxqvkr73ML/7uS/zqi2vs62W06iQqGRH7EwzNAvnRDrQPwC8ip4/zgXtnmS9o/yd7/xlsSXqfd4K/N7053l1vy5uu6upqi0Y3PMgGSBACrTgkRQ05M5QbhRQSV5wNaWdC0lCKlaFESeRyViQoihRFEgQIEYRroBto78u7W1XX++NN+sx3PxwEJxTCTIgrAIzd0RORH+69J/PkzQ/nPO//fQx/9pjDb99N+MxNn72mh3uwxvp2k91BiFeeJZ46itBthMzIhEBMLSHyBdRCGZII4RSQB+uopRqKYUDvECHkuEUvX8MvTjPKTyEtl8AqkWoWZAnJ6nXYu0eyegvRPxwT4+Y68e1LiOYmajQkyzdQZEK6cJLMKtL0Jc+/ep3u9h7tO7fBKaEWyqSbN6llPSZMydGaxblHFnnn3EeYrBdRs4DM65OlKcnMSdIgRlt5h3vmLLYOhmEzurXOyI+Q4QBGQzbut9h9/W0++q5FCsU8KhFCAfSxmzxOJKMUZBJhjw6hs4OWRcw4Asfbx+ntoikKD3SvUe7tslgyMVWFqd49XC0jGiWEqU7/zTfIEIBAzZUwahVk5CNjD3m4hYhGFEY7BFLjcJQRGAVGmoVmO6ilKYRMyDfvINMUX2q0IhUvyVCzBF+olL19VN3EVMAhRnEKKINDXFWiRENkZYakMEHmVhFuAVFqoKkqTCyNY9+kRNXG8VFpkvJm32A1sfE31yCJUQ7XUTIfFAU18RFkpFYJGcf4cYYXp+PyGzmeUBctFRSVS62MMBOsdUbMlR2KpqDi6KhCMEgE13YGeEFKnMG7ij4L7Tvs3LrJQ/4KrWGIZjnMlUwUIRhGGWu9CENROFa3mSwa7AxiNrebtLpD7tzdptkPQYH1wyFnJ/OcrlrcbwfseylfXemSpGM9a6Vaph4PmVYChik8/uhxpl0FHJN/+hvPU33wId51YpIn3vMwOhlSqISBz54Yk15VZsTdJqMErKCLIzJkBvdurpKJjIm5KS7tx2yXjlCZbZA2d/Hv3OBQ5KhrKcH9FcIgIghiVkdwY6ChphGZW8ZozNLQYzQnR61aQk4socQe0937HCQKt5tDblbOkMQBQWEK7eabeJjkdUFqOOOhg1BJNQOZJYjyJJFVIg18lM42SqmBpWQ0ogO4+QKytU1q5/ElKGRI0vFiSWb4tWWuuKfIFQuULRWpajhKQj+MKagxVnOVCTlAUVSCJKPnxSyULfpRQsUQpFLSrp5gZfZx1KBPoBfQnSKKblHJPDzFJKcJUinQEQSZQE8DCplHRy2SotEiT8NRCZNxYoypKcwWLXKWhhAKtqNTma5RPXIW3S0jjG+eBPH6zR0eeugh7ty5wzvvvPPtJ0H/JxDKtyDt4U+o+X3mmWf4e3/v7/GJT3ziT3Reo9FgcnLyjw9VVf9E5/9p4ztOfn/h5/8Bk406f/nHfgARe4gkGI/qk3Ccket7pP6QtD9AMNbeD7b2Oby8hlkwKR2bJOj6nDg3RTdJ2VzroBgK9/sjFg2b//BD38XH7Dy9RGEpdbl9OGTxVJ3cTANz/ghx84A0A6tYBU1jtqBjkmKnPhtvvcrP/cZn+Wc/+AEOvvwa7esrRI98DzIckG3cwRodjrMtswTRP4BiHfXYeYTfJbr0dfz7t1E1QfvVSxzevM/+pVX27h8ySiWl+QqZAkFvRD+WnDk3xeLDC/j7HYJbV3isUULVDczFJfLHlxGFKocvvIZ35wbm7Bxpv00YCTJVw3Zd1GIZPV/CIUUaDlmc8P0feor/+Z/+K37xb/53TL/nezG8Nl0KBPu7kEScO3WaL1y5C+Up4uo8SrmEISTv+8AHeaffIvY9/sd/9m+p1mocDkMMXcVLJIqisJTTeHzK4rQT8sHSkK5WoFhwUEyLpH1AdvpxPt0qEJ19F8bZR9lafjcqCaMb75AMu1CuI/dWyfwRSmsNvbmG3L4HccRoYx3F0MnPTeDdv4Ny+glII4zeAc7x44SLZ/Ea8ywGu+STIds9j8+8dJvf/uJb6G6OG/sDXmyrhJnCZCVHwzWwcharwwzdcvnCRsDx00fIV/KcGd0mvLXCiZvPshPpiCxlP4A3DuA5r4ST1zlsnCLduwvnHiE7+y6mjATDUvmAuo3aWqcR7nNxQtBPMhRFcKMT87srQ+arNr/7yl0G/RGt194g7ffZCXSeOFZnWylQcQ0SoTOoL/DKep/6cBtXV2jkDLJyhYKakCvmuDBf4crMI/zmtsubz76FlqZj3a2uED3y3byQzLJdnEf57k9wWFgifvL9NM4/xAeOV3km12Gqt8cvvHLIm12D6fAAMwvomDV0RSEZ9lF3VrEJsZMBfqrhzC1zcrrEn3nqKBvtkLWRyvTVV4jXb3KvE5MqFlImZFlCXJlHqS+hnXwc1c6hVKeQoY9SqoBQUfJlhJ1DFqvE+TqJaoCiYcgMsozIKJBpBkrYR3R2IYzQl0+S9NtI1QQ3j4xjFLdEZroIKcgMl7whqac9rFGTzr/+1wyPPcKNyulxkkF1Aa3cQDUMNCL2vvYqqVPgA4/MYkQeShaToTLITzOIM3y3wqo9SZopPLhUw0o8Oo99mKEfk374EzRX90j3ttgZxTz27odJ8xUkGqligmXTun2Pl792m+Bgmz+80eRey2d3dY+NzUNkljLa3yF86w2qrXVe24W7W4ecEk0cv0MmJCJfofBdHyFfccgtzAICvT5FFgUYhgoK6FOzqJU6me4i4oAk9HG1lExmyDgZt0LKlFhzCUtzIASZotPDRFNgdZRxSUyyHuoMjCIy9JGjPunLX2F4sI8uUkK3TlacAqGCppPZY+d/qiiMClOILELmqshCHYDAyFOxFRqOirF0HOJonHcuJcLOI9IAursgM3RVJZEZgyAhyyReItnwMrw0YxRL5ucnMRybpaqDpUnaXsxhkNIoGJQNwfcetymaGkGSYghB/vRDnDw2T36wT7l9j41uiB9J0iwjIcPRBFKmtHs+hj/CS1IeOdZgqruFnXqkSUrV1si7Ou0gQUYx5yYcul7ISytN6hYcX57gzl6ftFTFnphgT9gcX56ipRdpG3meu9dHODmq5TwVLULfW0FGI/ytDZzVt4hH3njhEAfsv/EWOimRZfPZS3v88MMNLrcS4lyNmXqOTGgE/ghRbrBVOUrFlGjNe4RhQrKzgYxjGq5KjoivbPnc98eTe1XTSSQI3WSk5gjMIl5ukoWcgqrrVIsuWqGKXp3EPP0gD5dCMsNBG7VR+k2yLEGVGaHmEuUa4zzs0GOYn+RAq9BSc7S1KmH9CEF3SDfR2OxFqEk4Tu0pTOEHIetpnkzTcHSVUSwp+AeUBttU1ZAklcg0wU09ghSqtv6NYQpUtGhsxAR6iUq94CAUFa23+Y1yioxM1dHTkF6YYOoCQ/roIkVtrZMpKgkCXZG4ukQIFVNTKJo6wyjDjyWDIOHkQonDVsjkZAEEWKVJTLf0TSmPFybc2urzzDPP/KlPf7+Vhrd+v/8fHWEYfkvv9cKFC0xNTfGBD3yA55577lt67e8EvqPkNwgCPvP5z3P86DL1chE0AwwHGYZgOaiFEugGWbuFMHSkadNb3SFsjbWCTs0hGgXILGNoCuxRTKsf42sajq5RXq5jlvJ0mh6WraMjSIHJB5cQpgtJMnaWumX8lLFLWlXRRUbQbfO3/t1n+MW/9ueZOn4MQcLsxWXUXov0ztuoC2dANcBwkZqBKE+it9cx3DzB1gatl96k/85lklDSeeMVTr3/DFNPnkOg8ND5SfbvHNBe7XLp66ssFs2x03emxM5rd0mCkN3LW3gHbcxycdxylquQJBLFyaMpGVmvjfvMx1CQ6LUp1FyerLVDpBlIzcSp1Xn3ww/yxr/5pyxOTuKjoho6gelgVQpkL3+WH182efnZL9Je20I9WMPNqRy+8grvs4ckgwNudRKSKGOuaPPOWhdVKISp4HjVpmAp1Hr3qeY05ooqx3IxajIiq84xCmKaWoEnG5BevYFo7dJormDMHOHaCzcYXLlK3GqSZXKce5wkZKMuojpDGCak5TlSL8ScniTYPSS+dxVCj8Ovv8rOV7/OS1sJN7a7rBeXwTD57gcXeN+jy5zOZ+x3A+40R2x0Qk4k+wQSLE3j5atbDPojlCignjP43B0Pq95gZJSJHv8ulIWjNMoFtNjDbt3ngtXCjxO+vJYSeh6D3DR6Z5utW+vsbTf5/EZEOHuKNAiInQpSs1jveDQslfnpMg/OVLFci5W1DrGZx3z4Ybb3BlzKL7K/c0g3lEy5OgdezBv7MUauSFaookdDqq9+nhNTOQZByqm6w4StMlnI8dq9Fsl3/SBuzoXiBKPKIp1QMj1Z5Y3NEb1Cgzf6BnG1glqfpP7YY1z4wNM8cWaODy/n+MXPX2N1qGJcfo553We0/Ai50+cxcxZq7JNZBdrh2Ij1wKRLrlTgwxfniBsz/MKuzkxeo6AmRGkKnvcN17QgMPKEpRmyqaNjacPEEkIIZG0OOXUUMX+adOIEoWITpgp6FmGmPmmWYaQhmQTSFMV1ifsDMj/E39plePsG2XCAPjkLtjue+rfWyA7W4GAdNexx2vF58oF53Ne+wskj0xRbd2DUhZkTdIwaXSzEydP4usVyzSa/dZ3AqeJPHCXUc9xrBaz3fKanJ7jRChiFKaNMMptL2bq2znzRQBxZYur9T9ObOs6GPkVbyeObJQK9QGKVeEtO0T3+IGJ7BUeDuyPYthv0jBKdSGUYKpTrZRrxAWdmS5wabqDIjOrWFd5pasSdQ/QswZyYxllYxqhNjiVZXo/M9zFrDfSFEwhV41ArkFolilGbidYtNve7RN0mUZwQXHsFrbuNkwwRZCjDQ6q6ZMbVKBoas0Ub1SkxiCAd9chCj6DTIY0zZL+NEnsocUgSR6QoyMIUxB6BmmcgTYTq8LX/9V8xev0FAOLQ44RoU0pGpIaNUHUYdeAbSR1SNcjcMj4ameHSCyV5S2cYJ+jf0AU6msa0BZauMKHFFEXKESNglAr2BxF2Go3zjLMEL/AxVYEyaKFrChUtoX/scSg2ODLaJKdnNFwNddSl6SW8vtXDIWYvU7m03mFjr0usCFLdYbJgsd8J2d04YLvj8/LNbW4fDJmzFX7kiQVixaImR4S6yx++dJN+bY7aXJ1SrsiLWwEG8Jf/4g+xd/kaE8MtBsGI1KmwnhZg8RSZ0PD9kGGmQr7CYeBTbkzSqh3lB997DscxqFrqeDetpKIKgaJbJOUpBqUpYs0ls8vsRhly4TS5ao3DUQJpQsMQjHyfIJEcpDb+cEhv0McLAhbUAR2hI3SLRyqCQqmEKlScoItpWzhk6IN9kq2VcT696SKtApHUkAfrpMMO3nDAyCzRSw3u9QU9aZFNnUAefwgvFVzdGqCEQ4TfGzdlalWaQUqcSA6HEXEqx4UUtsuECDBTn8wbMCrNklMlQkgembKxFIH9xhcpKBmGgERKcmo2TktSTSayHsQJUrexRUxDDLDJGKY6uWQAqkEiFYqGwO1ukc9GKEJiaiq2oeIYCmttj4NugKqq5PMG7z3doDFdobq0SHl2Fgzzm3KTl65t8/TTT/Obv/mbBEHwHeFD327Mzc1RLBb/+Pj5n//5b8l1p6am+JVf+RU+9alP8fu///ucOHGCD3zgA3z961//llz/OwXtO/lmn/nMZ7Acm7/91/4KIkvI0nEclIzH8VRoBqptI2MXLJvB2hb+bht7soze80BX8XZbjPaGvNTt8FC1zIX5Wd7xPN59fJ7ykUm0aoMbGy/z/u8+zxc+/SYA1aU6yBiZROM4lUIVKQVBDI4ywm2v8i9/+Vf46U98N0vlPGp9ipn3RCiVOuzdRp8/jqIJsiRAlOso/gCEikiicTVup8n+apNCPyARLxK1+hgFF29rk+psgekHFxjudshP5On0EzTHwKzYRJ0REw8ukCQJd64fUD42A+VphDZgsLpJ+eQc0cEuwlJwlo6izh7BuLMC3UMQCtmoRyo0dL9DzXZQuz202RMkN96kODMPpos1SvAKU2TdFyh0RywXXX7oL/01Hput8SM//ZNUN7f5h597nu/7yf8e2zFpCI2OF/Ch0zUcRdKNEtp7PufdiFjLUUiGiGEPM0mItlY47Es0IgquRf2t10gLDSg3cO9cIRsVsX70z5PldYSIyQ4G6MU6QtPwN9cxjpwi2N5nO2iSX73PzAceo/TkEvHWGlp9GrMV0Ov3aLz4HwiOn+UP0gaXbq5S0iQXzixzQvHJXX6R9525wKfe2uHVaYf3zlkMw4iPPDjNjfs7vL4TsNwoYACpSFgZCOrrl9mXgrqmkc+XGSk5iOD9nUscfeh9OL0tboqjPFZdYK9lcyTqo+/t8uIrkuL8ER5RdGTcwUtLpAJON3IESUZRU7l4ZpavrXZ44nydJpPcOfB5TF1DMwJGxWPEUUIziMkHKd1SDiuXYZ+/gOMo6Gqe6wceQ89DRaNUckicHBvb+8zIAbqVp9xwGFk6tbzF5253KJsK25mB5SskQL1QoVRrUBr1OHWvyf/28g5/7tEzHAuHXB5ZnMqB5ZQxTYtU0xl1U/xMx1YFUktI9TwndIO3jx8hmD5FI+ky2muSixOEWyPLItpKgaKqktWWwOuMS15GA5h06Jo1Bp7PpCIwUo9MRqj9PUKrhOl3IRgihQYqZOj03nwdoSkUH3oQmSgIRaDMHEUMW2TD/lijGEWIQh0GXTTT4vjP/BT61nXo3CXbuUdWGI4rimcd2qnDQXGGW196jntLszjF47xHGtgyRKoZdt7hV57b5b9/9wLLUsG88xbWwgQyjTn98DGM4QFLuQxx7zpn8zV6gz7oAjVnstPqc2oyz/LROT53f8DbPZsfeH+B7V0fpT6F8Dvc3B1yZGKG5XAHZe4YbqixnDbYW1+jUasQX1tj9K6HKBQrZGlMOthHy6LxZN+2yfa2EapANndB01H6TZKpOZQkIdvdQL2/Svmph4iFS2CW0VdvYeRy+MdLGM0N3FpIKzfHrJPho5OlKRkKWr5MMezBd30PQteQfo9Md1GDPgf6BNV4hGk5xNJBV0YECUhDUD9/CmNqGvZDMqGDqpPJjMwqIasJqQQFSZqrkaYpg0xHk4JRppJkMX6SoikKUZZRsjRMVSKTDFdX2AthQo3YT0zqjs7qQZ9ZS1I2YNOzeKBhEgjBO0mVBU9hz5PMlWcoZ/tkexvojUVe3uhTQEUqkoKh08jbHKuZ5A2Tl25scXZ2kjdfusWDR6ooox7fO5ui5w0mclO0goQvPX+dH/roI7y12SUXebz/XA29LshCH6c2Sc8fofYP+fJmg6S5wTPFVQ6v7hMsncJpb+HFCsbSKWh5DAoGB/tDzucS7NkFLF0DXTCQKgXDYYmY21sdzi1OUPyGhGaz7XFcafLGbsZjtRL+lWdZm/9hqr02+9LlnD5kouBwKzTHNcJbt7iSO8GFSh47GtBS8xTTHjI0sTQdV9cJsnT8/aY5WAgCU0GrZWTlOZQ0RsYBNgpK7BOUp+hGKoNQsNnxOF8VqJZKP1UY6RPYacrRqkmS+eMot3S82+XFElsXhClM5TTS1KUpbRJdR1Fj9EKdgTQpi4z9YUy1rGNoEnd2Frl1iWjuIlJKjKCLCAfEpSlMEjZTnWktRZESRXcwEfjSQCgpWr6GncYcBClF00TNEgxNkjcUtnopdVvnS7v7zNUcjtZdlioWSxWH+kSBKIE0zEEa4U4tM9q9/x9xk7fu7PEXv+9BarUaf/AHf8AP//APfyep0R9jLF34L5MPCHVc2LG5uUmhUPjj35vmNyf+f1KcOHGCEydO/PHPTzzxBJubm/yjf/SPePrpp78l7/GdwHd08vs//c//K8VikYeWJki8ASg6wi0jClXUxhTh6i3IJGkYkY4ChrfX0PMWJCl+20dRFbzmiDTOuNTp8u7Ts8w+foQ/urHKn3nPQ6RhTNxusX0worXT4WPPnKKoKUTlInptEqlbjIpzBIqDRBLEMYqqc//SO7z9xut835MX0KcXEaGPahnoxRLG8llkEo+jvAYtZApC1VGUcQtUFvj4rQFBJ0QRgs7KHvUHltBrk+y8ukp7o0/z8iaxn6BoOtXjcyy/ax7DMhhsHmJWchy8vUY+Z1I6eQS1Ook5u0jh6ALlR59EEylSSoSiEo06mIaBNn8STzgMtw8w/B5KHGCnI7Isgd0V1KlZxLBFkiTEUuWw46EunmE4d4H3/eB/y2/9zm+jPfRh/vHvfZmf+e1PQRpxYaZGqxNwVu+y3YuI44xnX7xOEsQEUYwWD5F7G2iDFnLUI9u5R3+nx73f/B1cLUTZuUenFbLbjhhub+CcOk8Sprh3rqJpoHtNiCOEboCmoy2fRQRDIj9A5EvUf+iH0aeXiFUTc3YJ54GHmPjoM0wsTXLyyTMsHa5wbrTOaaVD1G4xUc2x6mmEg4CFuMmfK2xQ6G/DsIXutzEsix9Zgodqgru7XQadHu2BTxLEOGmfTS/hlWEBpdPFUiQV1yZ/4Rzn54q86uWx0wA9HqETE+gm3sQiqW3xyT+8wmurLYbuFMslgz94e4vPv7bC1e0+/+HFO/zUk/N8YN6E8iRuKcfaoU9y5lHqekQUpSiGxnI1R5yk3OnG6JqOX19Gae0wSgSmqbPf7hHHIR98YJq7I5Uvtxy+5E0gSw1o72AbBu9aKnNxpsiRySI5x6UTZuR1yRfX+1zZD1hPHB44vcSPv3eZf3lpxIbWwNEVwkSOtW+xRzfWOGJGmEEXcbDCZ5+/wZdXehT1jL/04VPkdQXfqWGVqiS5KnT3aEuH1W5AHEfIJBhveXf3QCjQP8ANezgyJPWGDPwY0W8iN2+irV+Gzi5Zrkr0xrP0V+7grd5Ds3WsiTpqoYrWmBybSQ0LEFBfImkeoMyfQpuYB0VDDtroRGPJ1MqbkIFaqJIdbOH4TQqZB5v3edAM6H31Oe7cWkWJPdTII8syzMTnqeUi/t3b1Mo5Vq0JIrtI752raMGQxMzz+VfucbVwgl++3MfZuMWLd3toq5dxDI1WYmBa8PEnT3Pm7HFiAaudmN/49PNEoyGhN0TTDcTR88TJ2AGfzSzTDTT6Lz7Hkf4KpoxI7rzF9m6ftppH1hZQqzW0pXMI00CfmEc4ObBcKnKE6B2QDtoMp44z+/hjZEGIkJDYOUSSIMS4KEaUamiahkOEJ1W2+hF7fkYoxwUFo+IcsZ0nQaKQoSQ+iVOhoIL8RuObmngIu4itCxKhMvEjP8nwyKMABIrF0KoR2WUGik2Qm2THmGbk1PCliicNUjHOiW2HkDNV2n5Mmo27Ekwhcb1DkjggSVKm8xaRkaMTScIo5Wgjz+J0nXaic2Emz7VOyju7fdqYtDxJsVjEz+CmZzFcOsfuMGFvGOFrBgUdpis2xyYK2KqKY+k8cWqOxaLBM8+8i+Wsw+5+j+t+nmNVkyhKGA5HnD46yaTi41gGZxZqiCzh+lqT0uE6Rj5Pb+ST7O5S02JmrBgliVi1aqiayuVbu2SlCTpWATdv4+sF/CBEHGzxSCVDRj6mSCFNiHSHrevXGAobRh10kbI5ytgYxojY54TcZ6Wfsvz+pzli+mjlKS5mG5R0yB3e4bAfkIu67OoN6rZCzm+iZjGmZWMUJ1kdSjqKPZYDKILEzBNkCr4w0GVMUJkn1CykYSKTEGXYZj8/RxsHs1hFUyRBCr7USeIAhYx2BPd7IbMlkxYW3URBZmOpiaLAfNEkk5K8DqFe4O5AIVM1VjyV1vQ54kzSCzOO5AX3BwkqKerEPGJ/HVWBhiUI7DqKOTY8pppDzlDGBmnDZSRM+olEVwRtadFXXNZljobi0TWqRKpFFCdoChwOo/HiytBYrLkslh1ONvJI4JEjdU4fq6EZGjPnHkVRrf+Emwz9mOtrLT7+8Y//qWb+fisb3gqFwn90fKvI7zfD448/zsrKyrft+t8OfMfI79raGpv3bvIX/tsfR4zaCN1EBn2wcyj1WeSoT7i9DYqKt3tA662roCh4h0O8/R6aqyMkxF6C4Rhsez5VTzLyfAIpyIcJiR8yWN9nabpAf2Of2pEpvvvEPJ9+8zZfv7XBF++3EUg2BymbvZjVXogA/vav/h7/jx/+MIxGJPvrJIMhw/VNpKqRdXaQ/TZ4XVA15O698dRVMUjqS6z8/heIJo/iuDqaoTLa2EcrOiAzIj+kPpMjP1tEs016ez0Y9ciylP2re0SDEKtRxW3kSL0Ir9XHv32N0duvkuzeJ2ruYeYtnPkFyGKCgx0MIclQELkyzYWLBHFM2OvR7/URtoVuOwQzJ0nilF5ujly9Ti1ukSyeovbwGfpzZ9g3ajz8nvfyS7/w/+R3fvVf8qP/0z/gtbDCzu4B17b7vH/exTE1JmyVpN/hyWKA2LmP3rxHuHmfJPCR/gjVcTj6038Of+oUVjTEffL9DL/2Mv2b9xndXyPo9Zg9NkPO0gnry+hTsySGS6A6qPkS/vJj1D743SyeP4Ojf8MgVZ9HzJ1CkRnR/Dn0pz9G69YqatDn/ZMqP35xmr/3WIXpnM4z7zpD8dgcG6HF9PEjPNhfIx+0Scw8X71+yErLR/N6eGGMIX3SYg1je5WNe/tML87x5FIRu9GgJofErz1LtV4B1WCk2Lx5EPEfdjQqr3yOyvYNPrRs8cDZJT7x+CJfvj1EeG1OyD3UyKc1iJjIGTx+tMJE3KZeLBD5PtOWyqPLFVoB7JaPjid6maDu6Agkd3b7/Nuv3eRWe8SOUuTSVhdXhQlXQ9N0pnI6jgI3BwpvDky8VCDqi1gioqIJio5B0dI5OeFyr+2hJDFP1yFD8upGl53MYNLVSZo7XNno4oqUl+51eGfPZzQK6GcKrg5W0Cbv2nxwSnJc62Ip4JgmtprSDzJcPUVWJklJMRWFxd0rXL69gYJASfyx3lPTQTcx4z5VS0XrbWMlIzRFgZmT3P7NzxKt3kHt7pGmGUGm4RxZxpqYoHvpGv7KHRIUQCIHLaTQSDduMtrYBN0iiXyyQRv8IdGNN4n2NiFN0KYWEOVJjOkFRJZRtwR6pcbGqcfxzj7KheOTmFmE6O+TrbzFQusmHzmW41pcJBEKbrVKrDt8Pp3m5tBkL7UZ7ezyTpCn3KjyTy/5/JP/96e5Zc7iFKu8sR+xqzVQdIOTZZWCmqJ4HR45f5LS1Ay16Ulu9yVto4E36DLU8xiWS+7YEe6NLKxSAeKUYZDRffFZ3MP7yPYemVMh1Sy0+ROE+weEA38c8zhsk23cZHTzbYxohFJpsNEJ0IMeRdfGWjpKb3OPnh+TBkOSNEONRthpyGgwQMqM++2AjdjGS1UO/IxO5pAo43jHNJMU/V00GZMJlUSoZDJFVxWy2KefSvrJuClLFSlCUZGKAkJlfSTopwqh1NASn16U0vMTpARTlRRNjQlXp2ILZm1BWQ0hTfHjjONlkyBLMXSFfNxn72svUbFVdvoRYQrdXo/JnIGpaSSZJBcPiIRKXs3oSYv7fehF4/skg36QktdVMjnWkvpJykLN4dAsk8s5XEkqbLQ9fvpdUxivfI3mzTt4N2/yWF1F27zB8ZJGY3CA8EYMrRy3u4J6weCcG/Lxiw0+WI14aNpCzJ1kemGGkVmi/N4PYk4tYqga7cxGJyXTTcTiKUar2/TCDLu1yd033uJKO+blfoHX17sMooTRyOetOzuUXZNRfoIs1SgXS1Cbobm7i7m/SkX4rLV9hF0gAa75NjOzUxRzNkp9AcstkBMpgaJTzDkMpYo62Mce7eMnCu7uTazBHrHhoqchaRIjRuO680Qz2UpNvrjSwdQU1joBJVtFKiqdWKEdxFiqgpSSG62YrnTpY2PoOqFUCJOUuy2fmmNgZAG+0CnbGk0v5sBLSYWKoQgOAklezchpClYSklo55KnHyCRYhkkzVYmNHJqAnnSRmSTRbYaZxtog4V57LEEI04ydUUI7gEhz2Rgk9DKTq+2Q3X6MokCYpJyYyPHkQhlHVxAKTOQtHp4rMl11WD42heEU8Hr735SjvHpjhyeeeILnn3+e9fX17wQt+v8bvPPOO0xNTf1p38afCN8x8vtLv/TL1Ot1fupHfgBZXwAg3t0aF1zEAcIpoBeLJMM+WZQQtHuQZWRxiu4aaJaGUc5h1x3aA49KwUYzNZ7b3OeDp5YYrO+hl3K4R+Y5vVjkAz/xHgaVBX7oQ0/w3FqXV6+v8LnnX+GTv/TPGSaSAy8ibwj+6N//NovTEzzwxJNos8skgx6rv/cFmldXCe6vkLYPUYp1yFWQ/pCsdwDegODWGyTdLmEiKU2X8VseXmdA0A7I4oRga5XayTpCQLs54uprq7z+/F3uX93h3rUDVjb7eIOITpDxYnfI6R95Cq/bI9BcRgctvOtXSPY20San0RqzKPUZBlvbqJFHdOsdlGCAqQusYZN7v/27JLfvoGUxvl0hvX2FOJEYmUecgUgV9OYWFilJFLM3injUCTAVia6opLrLS7cP0LNxK9U7m0PULMYxBPQOUO+8gn//Dp23r8KwzfDqVSJ3AvPhd5MdOUMnN8FurDNqLJE8/BS2azC6t8LqXshO6Qhf20nZHma0qic5cGa5lxaQ4YiukqeTn8Vu1FAK5XFRgWkjyJCaiaLbDKwiuWc+Tu0nfwb33e/n9BMXWVyu8Yg7pF4p4hw9Q2FhgVFuErXfJXNKtIchrgj4a5/Z5pe/epennD4nnJhOs8trtVM47/0I9vHTDNbWSFDHve5GniwThEJjoeQyDDMOYoXVix/l9epFirbCv7/SJZhe5NFzs/zBpSabX3uNj5+pUZuu8fzVTb7vsUVit4ySBMSKxmB3jXdP65i6yp1ujCoUcobAT1IencnhRSnR3jqGrvPZXZ03twfc2h+QOWUSFEZBxETVwdUUJoo2v7shuTNUKCgppBGaSCkrEYqUJFnGMFVwXRehQJxmxEnK1/cFP3yihHHnGlsHLcr4/NLXNvj9dXBFQqbopLqNrinUTp/h/JEpLJFx7cDndiumHh1gZJJIaoTlhbGBZbLBacMjUAx8o4g0HWTsI00b2T0k84bIfB039UhW3iLbXaXxPR8lnTlGvHGX/IOPkXvkPWilBtbUJM50A6teRuzcQS5eIFy7Q9zaIdlZx8jZyDBAFmeRuomcPYlx8QPIJCEa9In7PZRRF8Vx2bVm8BJJK84oV0tkUwuklWl8M0+cq5F3bXTNwE1DfuB8jcm8ToCKLxzEzAKf3YK93Aynn36Suy2ft+812R5FzFdyPH9tn8PegLs7XZ6/3+PXn7vF1b0hazst9PIkA8Ui8nyMNCHnN7l6OCKdPs6k6o8nVM99iclzx3EXFkgDH1wHLxBo4QhhO+xvtAjCBKHqCDuP3+kjp46TZaA05vHtGtgl3MwjrkyiOiWUNCYKE/Rj5ymLiGzyJL7TAATD/U2sV77Mzv0NWsOx0WXoR1zdG7DV81DbGzQD8OOUb5TAMsg0mplNN7NRkehpxJzqURUjALQsYxBLOpFCN0roxhkpEkMBX5gMooymH9MLUxxdxVIkE7ZGRfpoMkaM8wKYMhNsEWCEHsMwZie20eYWGEQpn3t7h74f0cdivuJiqZJhmPLZ9QhSiLw+oyglSlKiJCNIUnKWxlItx2zOoGKqZIrA0hTiNKMbwWrL44G5HB984iTTis+sEaB2dgizBP3+DdLtTS69cY31r7/Mi7sJB8OYt6I8ctjDt+vcrp1DNXTKJmhLJ5g1Il56+y4UKpT727i9dfYNF1NJOVLSSIRKZ/EsbmuDsqNT9ptkmcL7Hj3BbMnmQCkz6ncplgvMjHaJpMbe9h61nI4btNh0F0icApFq0zPrbDvzLFUcFqp58paBpevoQZdQd5GqiioUGrKPfXCPME5g7RJWf5vMKeJbJfSgj5IEGEGfaH8LKRWEpjJrCyYLJq1IQVMF2/2QfpQSxIJBlDHhalzfH+KnkmGScasZcLfrMYoSOn7MzYMRJVPB9LukEiYdla1eiK2r5JV0PN01BXuxRs5QMESCVDQSt4FMMzKZMcOA2CxBFjPMJBEgEaRpwv22zyDOWOuGbHcCru97JEjaMWz2Q/qxQFc1JJKCpRNJyeOzOSqOQd01yOs6GpJeGNH1YgxLIwzGhvpvhtdv7SKE4MMf/jC//uu//m3lQ/9HUBTlW3L8STAcDrl06RKXLl0CYHV1lUuXLrGxsQHAz/3cz/ETP/ETf/z6X/iFX+Azn/kMKysrXL9+nZ/7uZ/jU5/6FH/5L//lb9lz+E7gO0J+syzjk7/6qzx88SFsGaJoBoQeRq0BoUeq6CT9HsOtQwg80jDEnW6gF12MokXqR+Rma6iWQeLF6AUbTSgUlup84dY6j0pJPAyIRyGam+fIBx+gtbKBq8dMHZ/lb/2DX+Bvfv/7+MX/+W/yyiuvsnf3OseLKkc1n1/59U/yc3/+E6h2DmnoJMMh3Tvb+AcDsmBEtLc7TisYtoi7HWSWEdx4g53Pv4xo7TJzfILhay9zd7XN9SsHCE3Qv7/DwatXCA+H+H7C9rV9rm4PWW36DLoBazf26Qcxh0dm+LF/9EmubB3yP/zSv2ft1dtkQYQ7P4szM4115hGMI+cQ5Qkwc8hcHj1L0Eol5P421Yk6ahIQTC9QfuABtOEh4dVL5OMm2f4axs4t2LmLPb+EKFW41NV5asagSERq6Si9XbR8iYdbN/jZ98/xZ548xUMnZ1k57CJDn9n1Kzy2VCDc2sJcPIremCYLI9wjiyS6i9WYwBjtk7vyVS5rs9xcO+DE+y6QO3se6+hpZJDS/vTvke+1WfdVIqFzEEhMGTKcPIsmUiKpkCka0siRWS6RohPrDl5lFi3oUO2u0ihZGLaDbpkYpo3bqGJVq+hZxKQYMd1b4Uono/fw0/y733uev/9Ln6Z2uM5TVo9ReQZpmOSLBc6IfR5VDnk7ztMPU7SFI7ScOgf7Tayqg+8HjDKFVCRcXCjTKFiYc4vU5+aIyzOcmqvgx/DYfJmHZyy6595DfaLOo4sVVM3g5mHAr1/tYecKOIZOY3aRW52QOJNs9GLe2B2QZVCydISm8dBCmYuPnSdD4ieSWt7k+r7PVi8gTjJ6sSSVgkbV5WjDZapgcrcb8JWdjDBJ0VVBI+2haSpnGi7NIGNtAJdXe6SZ5KHZIhNlm/L58zTefIHCzhZX+wq6a/Brn7vM71zaI1Y1YruMNHMYpo1qOKBqbHVD/vGnX+e3LndphQqj4QCttUZ1uElQmsY5cookzQj0PBKBUqyjCPBK82Q792gl+ri4wnSQ3Sby5pvI2ZOE0yfpVRYZ3rtGFvmE+3uUH34EtVLHmD8B+Qr67FESL0CbnEepToPIxuY3w2CQm0JTMiQq/s4+8b0bZAerEHpkqsLK5h4vbfrs9EOePF7lxEQOQwCqhqhOo0zMENglnPuv4MiEx8sJVdnj/Wcm+J6nz0IcsxkqrLd8bm12GYbw0x86TfmVZ9lqDfneIw4zwx0eXyqw30/YX99kruIggwFJFrHd7NLrDJlQffJxn1zzPnJvndxklYWZPKmUaIaOnsszdWaJ4M5VOgNJ//W3CL7+R0ivj3XyNPrcAorMUGaOokQjFARqPELbvskx0YHmOpqQhPk6brWAliuhGCaOSDFlgj8c8po1z+z9N6k4Go4KJW+bdNhjNmsTGzlq7ftYSko2aKLEIc7ONar7N1AO7kEaIYWKaVrkkzH5RaZoSEYjj56XUDB1Mgn66JBUSoqWStHUUBRQVYkuIwwZYgz3MaSPqug4pkou6CFVC0WBqpLyyGKZVXQ6XsJDRypMFVTe3g/RFWg4BqeLMOqPuDtI+MNrTY4XJK6hsN7xCZOMhmvQcDUmzAyZRpR0lcm8yd4wJGcanKjnsCybhapFKlNiIVEGHhO24I/8OsK2+Z5TBVZ32iRXXmeiWqCFwdu7MUEmiFSba9T4w004iFVeWRtiKhlZEtE86HCvp7AYHRApBu7ObczEw3FUvDDkzetb7Nh1Vu9vs+BKZuousyUboWhMV/JUky7dX/lXvBaVGQYx/dwkyxMlhsVpepVlFqbKaJbDQtFmWhmR9w/QNUiFzgiNWNEJkwxl9Sq5rZsEVplMs9A2rmGYOiOtQJarIHZWGKkGK+XjqKbJeuJSam1wuqrT8hMuVAVFS+PGoYeiQNHUEEDO1EiyjKt7Q5peTJJKtgcJx6sux2oOe8OIVDUp4hOlEtdQmc5rDKXB4TCklnXohmApGUIxUQ7HO6dO1MFKPEyvSSpUenoVVYCtKGRJTCYEaQZlS2et63MYxBx6MUVdpecn9MMEVYwlEa4hmC2YzOTHzYiGIpjMGUzmDe60PLpBzMBPmK+5OMXSON3km6A7DLm92eZjH/sYv/Zrv0b2f1KL/O3Ct1L28J+LN998kwsXLnDhwgUA/vpf/+tcuHCBv/N3/g4Au7u7f0yEAaIo4m/8jb/BuXPneOqpp3jxxRf53Oc+9yeOSvvTxneE/H71q18liEL+2l/6GWRrG0IPkpjMcJCBz/DVr9D62vOkYYCUElXTyC9NYbgmZNm4xjiMycKYeBRz9IljdP2QVm/E7r1Dqvkc7kyZNJG0r66QPzJPtepirK/gtYYcGa2h6Dbp2nWiNGVxYZFpPeLn//bf5C888QCFLIagT7a9RtjpkQQJubkKm1+9jIwDsr11hKKTJRH+zhZSZtR/8AdQCwXkzjp3P/smt/sJ9zZ7tA49dq5s09ts0bnbxjAVQj/Fl2BqCl4i6YQpXiz5pedf4Zf+xs/wV5++wEnN5Ithj8JkGXt6CnVqETSddNAHw4ZhGyoTWNU64fwDIGNi3SUeDZipldGDA4girIcfI81VMCbmyDbukHMMZGuHQ6uOZeq4YY9pA1LdRUWgJAGzixWOKx3MNMJtrvLh4zXeuNfi4RNFtJ0V+mvbbP7WZ6h+1zOkZoHd/RDdcYn6XQ4osdP2WXQkx3fexty+w5eCGu6FB5n5+EcoPP0eFsoGyxMF1gKN2/t93j7IaAdjLZwiBALGW629XfQkGqcCeH3EoElYnEQM2+h+b9xoJRPS8hRhboL7aztsfvHLeDeucaEQ4pt5vnL7kFfeuoNx6iwffWSWmYpDV3P58qHFp3ZtItNlyjukpKZ8eXVEFEXcMWeJFs9xQ5/mKzcPWDYz7m3ss5STHCvoHLcCHF3l0cUyhj7OrTRLJY5VJK9v95goWPzouxY5boe8Z7nMazsjenHKYWbwhWsdXl1to+sq72z2WO8GaIrAC1OyLCM08kRRwoNzJXRV8Mh8kct7HntexETOYrPtc2erx2o74PGZAqcbLtWcTUeadCN428/R9iM0mRFGCe/c3Wc4DDjsBmx2Qv7g8h7rgxjto88g2jsYimChYHD6zCJhAn6q0Up1UiOPqggUAVI1eWja4eHFGle3+/zC17d44do26aCHMjjE8Fq0YwWkJJJAZ3es+R12sfwuw5kziCRh79JtRHUa2ZjDtEC79RJKFrAnc1z69U/R29wllBqUGgSTp4lmzqC0N1EUgXruKfTjF7BPPADNTYKNu0RphnrpWeSghfHAo1gnzoFljct3DZuyKhm1W5BKLEPliBUjAC3xSTSLxHBIem0yr0c2cwKrvYqpZjiJR12NyJkKG/sdFsoGJ2eK/OQTsxyZrrDS1Yne/QGkohKNehw/schM3ORwY51kr03YPqSupcznBKFi03v7Mi9e2ia7+SpReQaDmNJgGy3yxikx0scM2tQND70+xf5rrzN1appcvUhSanDj1g4EPvT3SVQTVWaUjy3jeE2ETNF7e8hcGQTYW9dQdAshJLrMQKbIJGFG9Tlfjli+eIaqa+B2NlHbLSazPg0D1CTCDjqI1haeO4nevIdpWdjpkHJwSK59H72zSSoFam8XGA8x1GREbvs6ti7QFIWGreFlCkkYUlASTEWiKoKBnxJKgZZG44WtZjLKNBTDRcYhQjORZp5I1Wh2hxyub/HSvTZH6w550+TsRI63dwaMgpA7zYiHjzWouQZnZ/PoacQDuZiZksXF2RK3Dr2xzjhR6CaCYZyQUwVSAiJjFKWs9mLCTNLOTeNOLfDgQoknHz/PkfkqW7MPIqaWcGYn6RcmKEZ93jVrc37GwfEO2A5AVTSq9RLO8IBcv8UDSYvV26uEV6+z4O2QUxIGG/fZjk1iYVBq3Wc6r1B5+2ucmTSpFVx6mc6RqRq3ujFpscFS2SGrz2C6Dj/wyByJUGlHUDA1UMZ5yHk1pcpg3Jjn91AkJElMaBUwVYXRyIfODqsjMGYWMWOfqL6IMn8CTUjySkgWhYSVGbZ8A1eBNg66riM6u9RFSDOI2AkExysOc3kTUx/Lj+53A2o5nYZrcqTmMJk3aI4i8rogTDMyIKdLhnqeKFNIhULdNSgpGRLBoS/xsVAVcOM+oaYR2SXU1MdMPczOBmlhAglsj1L2vYhRnNFMTfqRYKpo4hgqYZqx2/PIJHTChGaQMlmwuN/1CZKMIJbUXQMTEELF0AQakglHZRDGDEYxjYKFzCBfdnEb8/+HXOWN23ucOnUKz/P+fzK+6/8bvPe970VK+Z8cn/zkJwH45Cc/yfPPP//Hr//Zn/1Z7t69i+/7tNttXnjhBT7ykY/86dz8fwG+I+T3H//9n6dcLPCu00cgjb+xMhHQ2iEb9fBWV+nd20F3TFAVFENHJcMo5ckvTFI4Mo1q6OSPLJCfq3DgB0xMlPhau8dT9RrV03M4MxPEis3e1S20xhxbm22GscLE4xcRG7foXr3K7u0bWLbLXK3A889+icz3+cjTDyGzCJwS2aBDOgpw6i77N/axbIXh+g5quUbi9RF+D9UwSfo91PYqsr2DXTTpdnwCYMY1CTPYvd9h7Y1d2gceg60Bc8tFjlUsFJmhKjCMwT2aY7ZWInnuRdaev8HH6nWuru/irW+wc2uHLIV08zaHn/9D0tYOMvSJJehJAEmMs3AEfdTCL8+jHT9ONvKQbgXbMJCHO8SNJbSlUyiDNt4w5o2tgKKj8/ywQHSwjfbaS0QSZByi6SqqZSFfeZ5bm32uv/wmj7oh2dELZAfb9O/v0ryyys4ffJ69yhKXjVm2+yrR/ib16ADl4vsQhs3EfB2ZL/Kot8JAuuSTEfNVg75pYycBt7ba45ilKODZ+x32A4iyDFVIfDVHquUwuxtY0RBV0SAKcDXBQZAikShhD5GlZHYBMxnR+soL3JYldvQS0ipSVDM++uGH+Qs/+l2cULvcf3uVja8/xz/71GuUVq/wxq0d7osqW2aJT331KieUDvdaASemS9wzJpkID3m4ITDEOAuz2Ys46A/QdA1FBYFEJUWkCXuihHnrbeo5i71BgKIp4BQ5UTN5bbXDzVbMStvDj1Nu7w1xVDgzkWN/GNIJYm53Ai7v9LnbHNEKJHVHp+zovHq/TSYk292Ayzs9yq7ObMWmntO4P0jZ6QWUbY2WH3G/7XGlGbLZDWlHknvdEKuU58SkTlHPePbGPpapcm2nxxflBP9GLvPI8UmW6y77e01kkrDVD9gbSbIsxMhCJBIvTpgzxovLC2cX+MDJKr1Y8mZQ54+2FJI7lxBpjDk8IK+kyGEPNBN6B4jeDu79N4mlykZ1hqg35N7rt3HOXkCfPYIjUqzmNo0f+AHealzEPnGO7o0bdKVBLBWEk0MKgbpxFTQLqarEpVku/f5XCa9fwpbxmMSTYDXqWEsnSUYjJApad4s1tc7TxyuUNm5wpS8g9NC8DlocomQZfmUR9e5VdE0hswtIoSPcIo4Mmch6XGunvNQU7O0cMjVd4/vefYyTUxZBeZKLMzk6scZON+Tmyhbf8/AC6199CX1ni4mZKbrS4dxsnpVuQBAnvJM/i+O66JoYJyzpJkpzE01V8JwJtNBHu/A+pj/6MdzH34/QVTylSPjWO+jxkOj2ZdR+E6ozqJaDHByOM5VtB+EWySpTWMUiQhHIJEaLhsjIw2u1GAwTFmcniWaOk1clhtfn8u0dTg3voOzdRtx7G9k9wFQVDDLwBhCOUKozqJVptGELGYWQhGRiHAqUCYWtQKU8t8i0npDXMkZxNia2K9dQh4dUjQxXpAySDEUoDIRNz6gwygyGUpAoGlFxgiCRlLSUgiZQdI0ffeoo33d2krxp0PVjyrbO1Z0+dzohrq4wVSvSsFU0t8DbfR3VtMnrCj0vIklTsjRBI6Zsaagy48qhR9lUOFmxudcaoqmCW5s97jZDmJ5id69PurHKu5by1BmSZJKJB89z5vwRksokj1sj/v0XL7EyUHmyMf7fW17KtYHAPX4M4+QZXtwJ2b34forHzzAqL7PdDjlSVcmPdlDWbmKZJosXT0JllqOLFfJhGzVNSNMMKSD2R9xX60z+0Pcz6wr6XoCXaURxiiNjZBqRCQ1pOKRRSJhrMBz0ubLdpxcLrNEB1sufJxp2qc3MISeW0A0NUZmFJEGmKeqoR084iOYOhyEMUDGylKoaok/O0cZGE4Il2WLO32C+qDMIEsIkJszgRM1hveNxpmww6wjut0Y0vZi8Lnj21iGOrtMPJXcGAkMI2n7CMNNJM8lsXuW1NtiaQqC5dEOFDVFH6BaxVcQzCvhGgVQoRBLW2j5rPZ+t4bjdb8bVKRgKpgqDIGXgx2x1fbwoIU0lbT8GBHvDCEOBOJNkZERphqNKkhTqrkkkJTlLwTVVNE3BcFyE+s2Drt5Z2eew2eLHfuzH/lQyf/80Jr//V8W3/Sl1Oh2+8tILfPT9T0HYR1SnwB+O7b+KQtQ6RFEUwq6HZupEgxHBYMRwv8Ngc/w3mWUITUExTVRT4w+v3eMjJxZ59vo9fuoTj6GoGu3bG0y/53GqDxxhdP8u4Sikamek3RYMOmS+xx+8eYNPvP8Jgp07/LN//i/4h//jT2BNz4ynwp194mGP/ddXmLiwSHOtTe2BRTTbxN/fZ3T1TYTvASkHb91i/0tfIbh/hyz0qE/mKCqwOJfn2MUJlCSDFEZRikwhV7UoOyqjGHYGMa6r8nUHfrBRYe/mPqv32piOzka7z2/8w/8Xza88x91PP0dwcEiwf4h35XWkPyLKQG6vkb7+LEkco2zdpCtMouo0B84M0dYaSZKiXPwgeneXSNoM33mTQZri5HP80d0QW1PJqhPklyZREp8sV0eqJlkYody9zIkC5CouD4RrOOtv07l1j50dj9rFZZJ+H+PoaQ6nT/LL+xavpdMo+SqLygD1uc8S2nXaao7csTNYnS3aQx8/V6NfniNp7XKxBo/OFznRsNjvBby52sKPEsLhADQF4XfGTWKqSbp7D+w8UZIyKM6T5mpg5JFJgBpHmH6H4nydE27C1MIMJe8A8nk+/vRZ/sYPv4vKzDwb86d535TJu4MdWqHk//aJizy5UORjcwqZ4XB5c8AyLTzVpaCrvHaYct4aUtu+yvdNB5wy+5hWjv3EIktTcl6TB6fyTNspqj/gF1vTbHYD7m606Hgxl/eG7IwyqjmNza5P24+ZqTqcWyhRtHSO1ywWSzZhlOJFCaqq4JgauiroBgmTOZNMEdi6ytWdPvdaHkLCdi/EUBSeWznk7a0BB6OQXphy59AjySRdP+b1jS5lx6DsWvSlST+GuaqNqipsd0M29j3WD3zSYMByzeLD52Y5NlOiautMODCIxtvIsRwvzmLdBd2iO4p5cMLg0RNTHCtIjFyZXz2YJL9/E1Vk8NazoOtEm3egOotwi4j2FvXVF3hAb2EEbRbe9QiJU6VDgWzuNBPeDscLCdWiy/Uwz6X6Q+z0fOjtIo0ciuVClkBnk2h1hWBnm7Mfey9qpYE6s4RiWIh8ZbwonTsNy+fAzKEdrPKReR3SiPOOT8U0uNlN2IkMROjB7m30LMBaOo4IRmijLqxdGpvoNAfyFd53uo4fZ9y9v89rBylekqEc7PDupQIDxaWdGbQHPpfSGg9NWVSGbbzlE7gyJNQdZvImT73/QX7yYo2jNQvNb5Ns3UHVNbLu4bjLzffJEWCef5Jsbw1jb2UsbbByaPcuU/3wB/E3tzh47S1o75IkIYHUSRUHhl2knUcBYqdOOHWKxCwyTAREPr7Q6e/tE82fxk9SSDPqyghDUzkz7WCN2mMDb5qAIhBeHyPskc6cIawuIISKMMxxgYVpo/kdoolj48KhLGOx7KAUq7gamPEATREQp1wPLVKnhKZq5KTHjKthquBJhd1A0o4VVASZahHYVaSAkdSJooiaAXNVl4WKhSpSJhiyKA/58LEqx+sut27eZb3tfWOLW+d41WKIjh9n5AyFxbJJMR2Q03WGcUYriDGFZLPjs9nzqecMdCQ/fqHGQbvPMEx54LETzMku+WKFjj0Box5ML5PqDnlNcPXVazxdkbTVAlauzKydcrRzl421PUI9R9g8ZLGgYdcqpG6Zzo2bzNWLFEYthKahFOtkuomRNxiaBWq99XET5fo7nK47LBkh5uEKV7aGtJwJou17vLza4dbhABH0KYgA6Q/xM4GXatzspQwx8coLqKpGhQFdrUR5eY5GpYBZqbOT2bSkCXFI2m8T63n09iYVA1Q7z3zZpCxDbvVT2pHgIDdL3tR4oAhmaws9X6RhZFQdjWNWwkRO59k7LRo5g0gKFsNtHp0ukAFv7wyYr9rcaHrsBlC0VHaGIUfLNl4G612PnK7RDVMKpsrmCL6w0iZKJSE6Xa3I636BdihJ0gwhJSvNIbf3h8wWTQpaRoJAUwXDMOOR+QKaCu0gYavr0/MTdvshppqRpJL9YUyQCiqmTsmAXqqw78X0oxQ/gomiTSNvYKkCybiE45th82BAZxDw8Y9/nN///d+n0+l8uynSfwQhvgUlF+K/kt//HHzbn9Kv/dqvUalW+dm/8jNkhWkECugm2WiITFJG65uY5TxzH7qIWS3Sv7dD9/Yu3dtbDDcPSb2QxAvwdju0L9+id/+QF1a2OFnMYwgougZ7l1dxK3n2PvdFZh8/Qdo55Ikfew+aocGghWaqSFVnbfeAhe4WP/e3/u/83b/+F3AcBznsIlRBtLdB//4uXscj6I6wbR2hqYStAZ3XL5EM+uPto71DeiuHyDBGL+RQdJP+YOxIHQ1jtlY63OqGCEUwPZMjFIKtWy2u74wYAaGEjhJhdT3y3YDpU3UmXR3D1Pm55WVu7jf5Oy+9wt/4yrP8zL/5I/7tnXVGqxuE/R5RKsn8CCEUWpsHCKA02MeUEW+FOdprB0jNoIdFWp0jXrmKKE/wcjxJe+M+ShbwtLKDlsuT2nmk4eK//lVSv4+iQu3sESrnH+K94X0sSyUdeISxoLg4S/Xh85ilHIVcjotHGzz1wAKXswrPHpr0y/M0VI+N198h7nfYx8FTctTUCJGEuH6fF+Q0xUqN9WHEBB4PmAN63RFrBwNWVvdQek0GN64Q3L2G8FrEnX3CKELt7TNdtBBpDIpCplkosU+kWZz44NPMvO+9FBqzqF6PRTEir6SUgiY5DdJqnezC45x/76P84JPzDBWD6mifkVD5odNFnjk3hTAsmqMI4fW5tOlxU5tFTMxxJSrz1XWf33nhFjcPh+xHOv1Oh2kjJlJM5u++gF4u4Ucpw50Nrl6+Q9pvcmN7wIcWXEZhAgiqeZPWKORgEPCl2y0+e2kHL4hpuAYVR+fsRB4vyuiGCb0gopo30FSF+bLFudkir622qRXG+sW8bXBi0uVu00P7RpVnaxjiGCrdUUQqM3RNIBXBQi3HXMXBUAWLVZtO85BcTudSS7IxyBBJxPyNr7PdGmAqEu/eXT758jb3ugktP6Lvhxw5MkMURgSKRbVWIXHyPGT3mJ+tsWrOsJe53Hn5BvHuNt69FeSoA7qFNn8MNUtQ7t9CxB5Kf5fNwhLbl2+h9A+xl4+j1xucNXpM9A/QbB0jiwlf/gpZnCB1g7h1SCQMZBah6QL72BnMuSWIAnDyYDkIVSVGI8pNk1g5lOklymLAo/YAx4HScAc/hn/yeo9Vinhhym5nRJgodIszQIIs1LjRCbk3VNjshkw5Kn/uQp0/855T/DdPzDOrSVbzE9SG25TViHktpJ1o/Mwz54nRePwf/C+IfIljVYt00OP5DY+Tp45SkCMmLBAyI+kckiUxwrBBFZBF6IND5N5d2LpF8OqzZJu3wM5hOQLzjS8xWrlD4cn3EguF3/3qNaLP/RbDwy7K9DKkCZlmosmY0CigkuGEXbrdLpnQOFh+mDBfZlBdJkRBGHnS2RPUHjiPPrMAloso1VAnF2DUQUQeajjEyGIYHECWIadPEE+eBCs/1kgKgZKEGKQgFKTlMlBc4iRjLVB48vQcUZIxTBSGwh6bbrJxmYH4RsFFKlN6UUqISmsUE6cJvcGQcNgnSBUqSZdFZUCxWESx8wyS8fTuzFyFMw0LLxNM6wkTRsbVwxGZlJS1lOYowe5t0x+OiFMAwWzJ4bH5EnEmODOZ5/Jmn9gfcXTCZZAJ8nOTZA+/j4HUOFpQmK3lqTga27stlMjj7sxJyo88ylzFZhhLcskQ49gDLFZNloo6G+TJBz1yho5GSv3JJ7mwkKd95TpplBKpCm29RDpzkqnoADMOiZ0avcYypqGjex3uHXo8MVegnI4YJDqPLxRYzFuYXod9LyWIUw6HPp1+D1W36MVwGAlOGUNCqdNNNIbVOXLDPWLVpB9JvnS7Q683IJp/ABQJMycxgh7GzCIVQ5AoFnEmeXUv5konI68kWCTIXIM9KvRinVGU0pEGUSJRVYXpgsXaIIZhl0beZLpgcG13wIMzRZJUkmRj4/Stlk8/imiPAqIUNvoRoyglSKEXg2vrvLMzoBNmDGNJK8jY7EVcb/rsDUNa/YgXbh8SZOOEiO2ux2Y3IEFSsgxmyg4DP6Hlxfzh1R1cU4VM0I9SpJDYhoqhSPx0vLO868WcqTkUXR1NgaYX0277+K29MTnRv3n019srexSLRR588EF++7d/+9tNkf4r/pTwbSe//+Kf/AIzU1NMN2oovR2k75F0mySH20T9FokfIOMEgSTs9DCLLoqmkfgxUoJiaaRhjJ6zUDXBYMKhoWt85c46Hz4+jxAKet7GrBQJ+x7BxhrebgujVEKmKTJLURyX8sWHeWh+gr/7+19mUpdcbIynKEIopP0u6WBE5/YWumsy2GyTr7n0tw4ZbjdRHXOsg1FU1JxD48IstfNHMabmSKOYg0MPX8Ibaz3euNcllJJc2aSyVKIy4eIn2R87qg0NXjJGfMA30Eyd/GSJXM3GtFSW8gV+5sNP8fc/+BT/+s9+iB9wanzh2iq9jUP81TWSVKJGQ1TXoTxVR4l9/PoSzt4Ky53bKI88germqYohst/Bu3EJGtMslDVeuLzOw1e/THS4R85roUzMkqIQTSwT90ZkaOiLx9DiAYXJBtbsLNIyKRw/SeXHfxy/PEVursFWqFFWFRYrFm7OYH2U0Ex1eo8+A5lHNeygeC0OrCLBxHGKtokbdJlXfExD4ygdtmSBJ07N8fAEFJWIL27Dr7/T4s3dkGAwJEXh7tQjKBKE7UAmkEkIWUpilQhSULOMvN/EGh3i7F1HD4fj9rtogEhjjPYm/8MDJt99osaR00fGU0p/xI40eLmnsTpMONrIEdhliqMDdps9Th+ZpBNKfvmlbZZzgrMzRaSmkYsGjOKUgVOl7UV8/eYe9swsf/GJST56vMR/9+55vt/Z4eFCTNra48Wbe0wXHbY6PjXXZKHk8Mpqh46fUC+YvH63iWRsDmmOQlYOBsgM7jY9Kq6JAOYqLrMFi0eWSuNdEqFQdAzuNUfYhkoYZRypufhxRpJkPL5UYcGBCVdHB6bLDq+ttnnz6g67HZ/Hzy8xOVllvuqgqgr//PdeJNpco/vbn8TfXqW5u4vt97l+6PFHV3f5/J0e75nV+fGHGry2F5IJBYoTaI0pPjSvUCs6NAcxn554gs3E4fpn38YTNuGNNxEzJ9En59FnZpH9FvHtyzj7d0iPn0BNAkTkk5ZnUEddikeWyNen2BcOr1UuEG/fZeNwxH2tQTbo0DnxfsTUEvfNCZJclbgyBwhSRSd2G3S9mBGCLiapkYPyIq4GsV3num8QSUm55HJpL8S//BZ7gcL23QNcQ2Vk14kL07QTg6i7R+fgkH4QMaWH/NS7F9lpD3HzJkptht+71uJf/9Fl8uUc33vCJUgliVWmujDNTMliD5eGGFKJ26wOYMueYZRA2mmjFKqoEwuIUgMyQTro0XvzNeK7l0mGfQQpaeuAoDgNhkVhqkbp4kVyy4t8pZvjnzy7xmj2KO0bK8hei6zfgnCINjzEFjEiTUjsEnfCIpqmUS/lqRtQzVm0Eo1IURkJDTUJUKeWUXMlxPRRkuIkMl8hsUoofhuls0WmaEAGxrh2ViQRmfa/kwRzeIiWhnSCBKnoFC0VqelEVhG/eYCeeWyPJH6cEUgVTUhcTaAK8JIxIdIUEAL8KEVoBjOKj9rapB1pqE6OVKZYmoqlK6RCkK9WyTJoDX3US19lsxvwzmqHmZxGIWdz3vFIFJNy3MNUYMYCV0kpGgoLFZcr2wM6Xsjbg/HkTw89KE6xl1hYjOUXdwOHhqtxqgSzJ0/xzBOnKLsufT9mP5A09TKzZZO3WiqrvsrJ5Snq84skg+44OqxYomfWsR58hMHbb7NXO0kn0fFVB0sRSD8kSlO0fBkvTPBGAYfuLLXkgEEi2LGr7PRjit1NfH+Elwie2wfT78GowwlrxNtbbe63hoSaw72hoBMmMOwgdYtOJDBUgW0bbMg8gbBA6MSKSWZYJKqFO9hBMQ0AkjTlrBuwH+sYImUjN8cgTbjVDfBiye1OgqkKbFMjSDKiKOEP4wWiNEEXCo8slKjZOsfrLqM4wU8yFooWm72I9X5I2dbZGYT0w5g0kwyiGFdTEEKw2Q/Z6kVMuDqZTLm0M2AYJWiKIMskW72Im52MS7sDDr2YM3UXR9e4OJ1noWwzV7LQNYW8qeOlGboqmDJCLJGw56WEKQgp0aTAMRWKpkIYZQRRyqDvE/sDACpzR0Ho/wlneWflgJsr9/mpn/qp73jm73+VPXzn8G1teLt16xab+3v83Z//e8hRD9E7QKoawzdfwnBNRhtbyCRj/51V8jMldMdCM3VKRyZIRgGRH5A/d56oeUi0s4tUBDc8nwcLeZ5f3+Wf/sgHEaZB1NpDOzWPXcsTDUeMtjsUDw4on5pHKZTIRiPUheO8+5EH+ZXn3+GvPnkGkUaI6jQkEXLnPlJXSeMEp+ZSPLHErX/3Mm9+7honHmhguBYiyTCOnUePE8xyGa0+jVqq0rz0ee41feq6wnY0doculjSErqBYOtMXp7n5BzeJMigZCq+4A/7q4w8Q3+hRPjWD1HRmnzyKUHVUx8IoFVCqDVb+3ed45Y27qGHMFz59gz/7szMkEixTB39E2OliOjrula+gzCzQcFxiO8c7HcGFwQa/8dP/gId++qMsb6/y8EM1jE88gX/tKsNCjYqlYnR2aU+fw+500I+eHecs5xsU4h5xwWVUOwbDjHTzdayZadyKibLhk2UZ72y2iQOfyXyB/Szit15e42NnK+wUF7BKdUZr2xinL+CrBrGiUZyxaT/7GgsLMxTam1ilGdpBnjklITjcYdnVebPlc6VfZ6kRYK3fZnL5UQZGnVQoGEjUTCByVQKpYBkW+u5NZByi7t5HSoF69inYvQuGTmJYZJpFOQ7G8WVvvoBy/DRL/g5esUboB+xZLjLyKRkaX24bnBB93t8I8A2b0tQ0V3swSgTvPV7BGDap1UwKtmQ7MagUbLLCAxwMPUb4FGLJldJpHrcd3jU74Ld2HIqmwqNzZRKZMohSjtRcdE0hkpIstegHCS/ebTFVttFUhZv7fZIMesMAqah8+FgVP8ko2gaWpqFkGYsVm/3uCC9M6HoxMyWbqaJJN4gxDZVAGJQVwW7L4/VLayTFAo8crRFoKrW8xSAJ2B1E7PZDpFHkzkMfZHa4xd/9whYf+9AFfuxIi7cjeM+8y1t3DriUdHhwqc6UKUh8DzcdsetneKlg0jig0jvgQ9ouq6Vl7I9/Hy999jkmnnyM6O4OOdUmdZaQWZEsl5ENPLJ4l9f3RgjDJFOakEnC9jaJUNCkpBVGfK2toIVNMgS7ox7ZwXXUNCJRt9jOElJFR5EJki4Z31i9KwpCSqQQaDIFRR2bqsQQESacU0AeaNyYPU+8e5+rqcrBlZvEUkHVVASC3TDB0RTkXocrLQ1d22VWJkQ9n3oc0ZgpM2/DQbNF3rYZ7q7T0Swc2wTPw7ANEkWnXoSDUZe9zoAX+irfW0+x8yVUwyAbNPF31jBsh6jbRWQxqu3gzEwyXN3ALl5FMQT2wjJ4A9L1O8wPYv6XH3gIIzig9JGPMHjra1hlGyOKEI05tMNVhG5iF6bx719HXX6aiiYRioYXJYhMMopA0xTQTLLQg1yJ5tdeHLvu3/M+TMNCKgIRN/HrxzGjPqrfQvT2CNwSw3D8uSaTGDXsk+k66c4ehYVT+ImgYmkkmULTmaDqdynZk/ipQkGNyABL0WnHGX6U4EUZoxgW4iZazkHRFVRP54qf57wWonl9hKxymOoYCuw0Rwx21rm35fChC0vkTp7hvhdwdCLP8apBnKS4pSIHL79NuHCa9cDjpOlhRSP8yiw122C72eO9lRQvZ3OnG3JChSyOqBkpo0iQpAoTeR2RxTTmZrAMncNRht7b5bAt6YSwOFlitqBwpGajpREzDugLE6Tr11GnauS8Q7JeH80x0U8eww73WA9ztKwS02qAXbS53vExSVDjGNdtINsHZPYcb271eKSqc2/D4/z5GXr9FjXvkCQr0FYLeJnKzl7Ig06IVXZRVZWDQcz1fY9c3qRlFdjqBTi6zpGqSyoz+rEgL32k5tBTi+gyxd/dxZ+bojkImS45dJKQtf0R765o5JSYFU9BIEBIiqZKL0jRhUARGduDANO0SDKougr7Q5V2EDPvalwbJRhKCEjWuwGLJYeKrXH7MCFMJBIomQaeGtP0I9pBxoQzbqRcsmC/aHK/5XFypsB83UUXgmYYc7KRQ1cVXENjuaATZILJfExdGry00iTOMuIswzVUHFVBHTVRRZWSKeiFkq2ej5fobHcCdE2w3w8wLBO3PIGv6ggzBzL+T3jLtdVDVDI+8pGP8Ff+yl/h1q1bnDx58ttJlf4Y3wry+l/J738evq3k90tf+hLVapXvf/Ic2d49FNOCICDp9THdBv5+B9XU0R2DLIrJDJ00ClFNHUVX0QsOeqVG1m3TO+wS+wlrW4ecKBdRmm3kTge1WqZyao4sDJCBT6oK7EYOs5wbfwlmKUatRnTnKvMXHuWr/2qJweV3YDSAUkLqDVGsHEl/nWtvbPPuP/sY9ccf5nf+0RfYGsaUCgZCqMx/12OolUkgQzE1RJqRNHdp396lk0D5G6UxigAtljR7IerNJlMPzrLXC9EERBNwqtHglFskO+vSOHuENJUk/R7W0bOoWcD+117lhav3+MUX3uBsscyZlsu2CLnz4gqc/hDBxga+nzHx9KNErX1UTUdNYoyt6zgnz8H1L7LVmIMs5DfjJX5+2UaMWjxQcwnf8zBKOEJYLqpIye3dRtUEyep1sn6PBI3O3iajgx7G6Q6F+QXSo6epRgdIQ+fW4hMsqUPsbJfboc5mrHNxsUJBE3zunW0mjTzV+wdUtm+TqxbZl4JeKEisPOrafXp7p+jc3mT+rMZy2uFFdRLb1ii+/QoX63W0Y2W+lpT5LiWi2l2nNXEaS1UojvZRwhFJbQFz2EIPeggJ4cZdRBigTc2iKgrZ/CmyjRtkjUWy8jyZfx+hG+QWF7lizXLGDLm90WOQwdNzDuGoi+NaaPdX+LpdItk4YHFOoTI9wa29IV9+6SYzkwXqeYvHLclJbxf7+tuc/f5PsBqqDKTFp99Y5795qEajAmuRzus7NrZrcDCKeXzO4vXtkJKpMld1uLnZQ3d0bE3lxk4fL07Z6YY8PF/k63eavOtYlSvbXZZrea7tD6k5BnlLJWcqPH95E/viPMdqDu1hTN+PubXbxzY1Hlssc2OrjzFbYG+zw0PzBc6VFQYILBWSNGZKjHDskOZhkwVd4ac/uICWdolKeT58JibYus09JcJll1AonLFT8OHGjUMAeooYtxqmKYlm4OsqehKgTtcw99e4V5xhptJgO1JxN+6TTE8gXZtaqTrOstVNVE1FJgNUUwIJSrFGVy3gpxmaonDN7/Pi7R0+tJxj7dDje49XkDLktl/BdizqdsZWVmLODnEPtulnCuriUXKaIIpjlDRB+j1yuopsH9DPMn57X+VI1eHcfAUz9aG9z/Nfvc7F730U1TIRukWaxMRSpZUaWEqGcrCFaSkIO8chGicKEZ1Ux1QSAn/EME2+kTPrEfQhTRK8jiSVjNu84oQASVVKXt4DcFEEqIqCmjuJqgDHaxi2hW5aKKMuQ1VQiVV0dNQwQFVNslFKY7qOe+sm3bVdpFHCqkyCGpD2m8j2LopbQNRnMK0875qzEP1DdH9AMHGcbpRwwg5IhE2aQiAlB5HFrO5TXJokywR7epU51Ud6ASJX5TA1mXZKKF4CqsVIsclFrfGHWzRCRhLR3SV/7XmcRg1pVLBMlUhCmElQJDkRkeoaKhla5GNJhYLiIHRBpWKSyQy5c5f41KPYQY+WVqQd+Ai9SK/dI1eIEZaDqcc0CiZ3121OORInZ5FoiyxHKaUww/U7XOlrnJ+wyBdKxLrBcystHjgtULdWCUuzDIYBU0rA2ZPH2OwMyAyfcqFMTU8QTg6dlHZUoueH3O0mPJkbYNgTbKUZ3d6I45bOjpNHlRkbvsZ+a8AD81XeasOBP+JsdRI1jUnjCGuwz279CIvJDqOr72Dn9ig89T40r49pSI7IFmGnyX7b421nkfdNwObOAe3dLunkAk/MxhiaztZum5XiDO9eKrDaCzlXMXnlxgE3FYfFZMSclnCcEZeSHCNhMVDyZGmKIObl9T7np/McjCIKxTJWPADFod/vc698kqTvc27CRdMV3tpMef76Fu6Ti0zaCcsFl0Gc8cWVFvWcyULZAVVhtRNxsW7x+n6IVEy6QYamacQp7AeSCVfn7e0+VcdgsaAxlTMomQr9MGEib4KAnKVyf3vA8VoOBcG+FzOrJ+wrBscqLnuDkLmizTAa6+xtTeFIyeBWM6QXJjQ9QdHWqbsGt5ojZss2eV3DUARzRQN11CTUXGqWihCCgpbipQm7+yEdL6Zh69RzJoahUphdZKF6hntvX/6mvCWIUm6st7jwoM/TTz/Nl7/85e8Y+VVUBeW/kLz+l57/fxV8W8nvb33yNzh/9hRKd49obwN1ZgkRjhCaRjTycKZrDNb3KMzVyOIUxVBJRgmZTNm/vEnjwXlazz9P1OujWiaxn3CQJNjNHudzBbIwoX1jg+LRSdIgQnNMVEPHnJwgHo6wJyaIWk2UyVmyjXXk4RaqnSN39jzIFMhIW3sYE7OEvYBmNyI/20DN54nijEjCy2/u8pGijV6qgCoQioWarxCvXcdbW8eeKGMo67TjDAVYmshRViSOppAIUC0dXVNJC/C1qM+//dgzFI8sER0eICwTMRzgLB9FOXaenf/tXyB0kxvra/zZhTkmeoJbyoBeBl99a49HAKdSJu20iNbv4e0dUjr/IEq5Ss52CHSbvJIQNNf43p/9UarVBtfMCkuyS6G5Ta46gVRViIbjNinDRu7eh36TsNVmePMu1twcqmlg1urEmNjCJx0OOawfR+sdci8rUZOSsxM6rYHGykEXU9d45vHj6KMWr26V6ImEp37vM5z/6Z/A2dzmil7iysxDpIHgiccuMLy1QtZa5dxTH+V+cYrnS8cx05Qjb7xD8vjTtPJ1vrbr833uIVbmoQ72yKZOkKUpRhYjghHIDKNUBcsiO9xBBH2EauBXFrBlRhyNSN3qOLe2VOWY5jHSCthmwJ12jKKadHc6VGcFj2ibnJF9fvXSHm+sTHDhqYd5bC7P/EcfZBimvH5zld994RbvrimcCCX5m29zcPMA/93fxbm5IhsjmMr6zLR2eFUeI0Oy0xzwRztNGjM1DEWQMzQKBZOpvEkGDIKEimvQKFj0ghhTF3z9xj5CU9lSPN5aOaRRNJgvGgRRxEOzFlbQ5bA7pJ7TMOKQAgElRcXeP+CimqDtZpRkBj2QQuAKFUXVGCWSYaiS13XcnEI3zLBtHd2yuX7oM10rcnO7x0yxwPnpwtjQg4ZUBFM5jROmT5pKFLfEV9cGnGlYNEyBkkUYIuXOr34SZfkBSifOcNUzeXbYZymo866Ntxg89n6OVmMK6RCRpSQTp9BGHbpWgzw+eafE3Y1DelHCp750iWqjwT95sU+vffj/Ye+/g6xPzPpO9PPL6eR8Tud+++2335wnz2hmNKMIQgLhi9ZQyAbb7Hoxhc3uNbveNaZ8bRzwLtesFwwsxgmQBJIAxVEYhcnpzblzPn1y+OVw/2hfqm4Zl+uWJVx1r77/9+lQp7s/5znP8/1w5uGLFAebnG/o/MqbNrV6jltbHRQx4UPTBfL796g0GuiISFHEHd/kVGWS4c4GM7UKqV6bn3xiFiFfR1p9h/ZbV4gvPsXTH6qSz8qozhDdH4KVJrH7GBNnyWoSoddCSVuohRqmE1EfdBjOXiRORCzBp5dohIlIx/Gp6QJRIrDV7WNZaWYUl16ni1ioI2zdZXTvHmqtSNqUGVaW0Mctwighaq4SFxtEnkMQDlEW5yCdYzToE4xtIlHE8xIgIKzME1YXwBeALHKURYljpChA6QSooxbq/hg5U0RcWyExs8jCPqlYgNE+cmUWeeMG40KdQLPoLd8lNTFHFITkoyFIEkmhRjI4wMBH9Bxio4BodxBlleFgCElCIkiHfefDIcrEArI/IiskRGKWDRuiRMJX04x21slPzSMmHkQ+qcjFUAKKQkys51kZBGRr03T2DhAVkWJW42JNpxvLyLFIVYkZJyH4Dgs5CelYlSj2UESFoeeTNTQywhB2m4TGEYaRSHvmLPvjkOP1EH9wQGaijiWM2FQsjs/XkTUNgi5T5RSCopEWbILRPpKRoiFELDsBDcPgnX2PUxmJlAp+dYa8nNAZhGhJROvNq8wWs4wiqEo+hiZQMSSs/hoCIkomQ46AeG8NrVSgcvws207E9kqH8yfmSMcBG8E0/d42hVc/T+7jP8gf/N63OXLxFNK4z2RGRz9YQZIVjlQz+IM2Ymih+2NWVra58Oh5Krv32UhMjOPT/CAtPnu7x9OPFInjkBt7LkMv5MAJ2B/6HCvm8fabtPIZcrJAWtSpagEHvkBGlXhyLossibhRQiBqHLgxKy0bRRLJKmAHIQVNYH8YcG8oIAiwP/JppFXSikTBUhAAXRKoZzS6tk+lnKLjhVQjicemcwz9GFMSMESJnhMyL4qUDBE3iukjYCQSJV3i8kSOJAm51XW4MJlFEQXabowsCZQNhbYXkzMFqqbMhizx1EKRelpnQgswcVD6B3i5NIo3QNAspOYDpjM1Xu8P8cOQS3MVfuHfvU0UJoiCgOMmKKqB859glysPmjy0vM7zzz/PCy+8wE//9E9/N1Hpe/mvkO8a/AZBwDs3r/Nvf/PXIElAgHhvE7lYQ9YUkjDEqFXwhjZRFJKanWS8uYvTHTLaGxLYHrHjEgcBsRuSKDG5pSl2v/4aBUFFbIcMi0P0fEImifGaQ/RSllgASRbwe33UdIbI9RBECckyAYgjDzmTIxEFEt9DmV1ClGXSR+dZrB+2CYR7m+RyGlt7NrmshmpJYFjEVhnRHcF4QDAcopar5OZ2OVlP8WBvRN2S0fyA8x99mO5yk+ZWE2u2TiTEfMHr8AsffDfldz2DVJnE2l2l/crLyKZElLRRS7vErkcYe5wQ4WsHXU6PNSbTCr1+QCmbIvF9vPaQ3q1t6o+mEFWF4W4bw30ZtVzG279F5ugM0eYOkZSlGI0Za7OsDH2OVUqo8QicPoQeSb+NlGsgptMkioiwv0fsB7gDl8xMme7kcfabbY4MI/xoyMuSxKivsZCxeEuockTXmFU1PvnGFk/MpqjpFr6c48PZgH/vhHzGCzkqqNzJLzBXSIFrc1J2cKQUldMnGL9ygDocMF+QyRfTvNP0ePeTl5lw7vLVgwts9GLQDLyXvox0/DT0myiZmEgyEEQNSfSh3ECSNARFgyQmOVjHqB0h0LPskWJCHBK6Y956fYPahSUyKsjZLI9WZLRogLq4gNfdx1+6xHjs8hPTTdzHTvDtsciBEyNrKttrazw0U0RJp5mqZPjklzxu3vM40esiv/Uyc/Uyvb7Ml4YRpxsTPD5T5GDocP+gyyffXOGj709xba3De05WSasikymRmztd5oyYKTlAjh3coccTqYBQ8UmroIsJ2kQCHB7lB6KEnwjItoIWx/THMrIso1hp2rGAaJioksLFqSyirLDvJrx4v8OdB7v85fedpTdw2GiPERHJ6TKv7/fZ3eoxN6mydeDhbo3RpYRv3mmjfXCK99cVDhITN4zY3dlnctogUdPonR1SZvlQ0rF7G6mxgCCpHH3PE8xIGkq8h9o4TeFig8+uhnxwcZE3bZHxjTucf/TkoQI5XSTlj/DiCGX7AfckG10SOLBldgYRa5s3KMyfpH50nrYbM5TKHPgJkhmRFUMkWSCdNvgnX7rBY48sUd2I+EjDZyyqXN0dc4I2rb0W5E4wU1AoCCEHu+v8cTvD+ycn2fjUJ1n4uZ/j/u3bHDcCbgolFiOf12/ucNH24dhD2KUaGR2c5SuIGx3kh0/ihiKaDLGkIcUShggSEggJYSQyoURoakRPMInyJgUVxJyB9dDDRCvXMQY2VrGBUCqRIKIIQ7BUvFtXkXN5pNoscWcH19nC29lAO3oCUQ1IkiG9O+sIk3N8RjnO4lyJ80qH9uo64dimtbxOZnESIcnhGzlCxyNwuzjdEUkYsB1GRKuH03u5OUZJItZDidz+EFHVEW+9THZuDj1XQm3vYCUyQaaMsnYNr34UH4WhWgRBIBFEyJaI9jYRZ04Tt3aRSnVGSppUb4ulQp2haLIq5jkvycSoSKJEHIqIskIUwSiSqKVlBr6FuHkDbXGRZmSQtiTcwKNsqaAYCO0d8n4PJ3+MpxoOAwf2xj41XSSMY+KVuximxJwectAPaPoiDjLTWY1MZ4Q0OYcc+eihwMmkRd9NU0rJVC0ZnC6iJBJqKdRhiyRVZjKnIoVjXmkf0J1ogHAId9lkxFoSYZkGb9qQK2ZQApHFfJZ/9bnrXDw5zYVqhcntN4kCD1PYQqxMYcoyXrFGdu0e927eQ1soE+xvMVFbopCe4w0frndF3GKd43NVmp/9DPLTTxJGMTMVi89/4Rv48yeplkTWRwLtVI3zaYfbn32VpScvERtp9tod2p0RaTkkG9pkayb7OQtRErGkBJKYKF0ho0p8Yx2OlmHdFhl6ERv9AFkQMDSR2YLJpBpyf5yQCAnHKilOZ2NWHZlG1KVSybM98vmjN7Z477k6d/dGvGuhgCiAKoroUoIbxJRMjY1xzGLBoGnHpFUZTYW2HRLFMRens8wYICoKOR0MRUQVRcZ+SCOj8Ee3ekxkDRIOzxwsRUaXJNwoYSEFyaiLYZnM5TVMRUaXINdbIQkCEt9D278N9oAgknnpjXX8pz/Ipak0r0Qx1/dHtNtDDuXvAkmSEMXJf5Jfbqwe0O93efbZZ/nFX/xFgiBAUf7j/eDvdARRQPj/0tD2Zz3G9/Kfz3cNfl977TUkWeJ9i2VEK4NUqJD0u4d9ubKEbOr4vQHOfhcRSDXq9O5ukZqt4LRs0o3Djt/Q9YnTJm6rh6+riEHIjh1SdkXeurnLu54/hW97ICTImsJou0U0ctCzKfr3VlBSOlG/g5wtgCSD5yKYCYQRceAh58tEiokxf5TJC3XsZh9R3+H4mQaF8piU72A1CgiSTGQPiHfuIYnQu36bwvnTmKU0l55ZYOuT16gZEn03pP7wSeqPHOeor/Dyv/wD/oQ2f+OhM5w5dQS1WCXIVGHYZfO1+5RPNcDv4dzdZf2tDeJqin/+7Wv8xNwsUyWZiZNz+K/eZ24iB67H5uv3KSzNIYgi9m6L/qsr5I+UKD52mcx0nSg3gbyzQxKFLMzW8bWYg0HMpgNpOSKxh4gkCIFPEsdgDxAEAWPhGAny4QqBmSK3c5NkbRf77k3cRx9nfX/MY1MZqpLLjN5h3S2hpSw+cLrKje0ex4OYKAiYsGJ+7PFp/g8/4v/xJ3f4i49P8437HnOWQU/TWBxsIWXzZM6fwx16ZIf7/MTxHK9MWpRTIp/46g7J1JjHsi7xqENg5jF9ByQRIfSQRnsw7BL128gzSwj2ECmdB0UnnjqJTEy8doNCrgb5EuNIwO+22b//gOJDF2ikZTLBkMgbIesao0RluLbB3vx5Fh9O0daKnJAC/rdPvMpCNcX8XANBksl1dxlZKu+9vMSDvQ6/6+aoJzKD+w6PVTWK5QK3m33MwTKPTqrUSw5z76qhOpuU4zH28g6TYsKoAxNICLLCIBRQFJVeGLPnSmy34Mh8GVlW2OgHlLMmB3ZIe+RjeyFheDgJOVpPMxqFPLZQ4sbuiNlClu39EZFi4cYJKSVCREAxdD7xxgaX5wtkTYU7zTF9R2QypzObrWKoEi3bQQ5jpqsFdrb73Nwd8HQ9TyBDLhwQCS6yaBFEAag6ghewYUvUUMhHAZFsIusKmqIjaDrljMZUMeb37wV8VZhgIZ9m1tvlzn2L2CpRUPaZKmRQFJkX/EmO3Pomvef+bwhSzNNPncfzXRLFoJrRuL9+QCSrNN0Iz48JBgKnZgustsaYExNc2XF5dDHFS57GVF6nH3X4lleiJo9RPJdrgxAt8clmJMIo4cupk3zoY9Nk/TZxaxX9+CIHex7zos3ajsfp8xUy9g6KpiIYGfyZC+StFcaOgG4OUZGQnBGGWcRxQzqBSlYViOKYqqURqDK9sUc5bR0+V7fvIWSncMYxTM8iDVokkoI1PgBZBkQkVUZI50lkBXyP8GAfwgj3wX1yz38Q7BGl4w8TDzo8WVigZO/ipycRXJHu2iZyd0z67g3Kp+YR7QilWiMa29y9sc7Se58iCQOC9VvYVoV1tUIp6OD0uxA6eKqCI2o47R5h38ELBfzOJrCJJIrIB/eQZAVNEUmShL1RgCnGKBMniaQ0hf4aYuMoqiCglcuoUowmSWRNBzUYI0UuCYdd4oKeR1BFkkAgSEBwxgzzVTJRiB0HbA8jrM4mxkQeO0loakXywpiCmmCFLrom4AU2rpKm6wVMqjKqoZAOB6iKyk5vzKatk7MUBqVJ/EhA0fKsdGxOGhlq8YCBruHcvUn66CKC20dTM0iGhS9KCLKK6ne5OlR5uNcjXSghiwJpZ8SlcpYOGs8/eYZAMaikFYz+Fn/x6eMoioSdROxpNQrNa+hL54hTFoEg44o6XqbGuXelkEQJP1cmr0HFHWLPZNk/aPLsk+fJJGPunbjEt97c5kfPpLDMEgepEv31fbK5FMlom0ImiwFsVI9SUTNYgYNVqSGVfJyVNW4O4eQREXsYcHyqxnp7SJLoOP0eQ8FkRo+Ik4R05LA2hFCEiqFwbXvEI5MZtlyZnCaSM1Rqlsa6E1JREyytyMGuQ95QOTuT5/p2n0uzRcZeSMWScfyIl9c6TBV0pvOHLTejIKBkykSJgC6CT8QgiNBlmVgSGQcx6cMakMPnhwDNUchc0eRC1WSlH9L1I/KGQsGQCDyPrL1DfLAF+RrTYYyYKaP4Lox7JJ194kwRchVEwyK+fRXl3CUuR6vc0U/x5FyRrz04QNMEnJFPaaZOkiSE7uA/yS/r+wOCMGZqagpd13n99dd5/PHHv1u49Kf53s7vn1++a/D76c/8EfOzM0jDA+KUiWjliH0PwgC1VMLe2SMY2WgZC4D+vTWMcprMTP2w6SEMGG42MUs5oijEGzp889XrPDrd4NU7m9wMhrT8iO+fzEMUY1QKJEmCIAlIioRWLZEkLeI4QsgWwRmRuDZJ6CPlK8Q7qySee1jjYxUQhx0UU6P7YBdroszUyRql6Yi3PnuVVL2K29zHvXYNSTzsG078EGd7m9APEL2Q05fmSXo92l0fVQJJEbAuPsy/+Se/ysfmJjk5M0HtAx8ASULyhvi9DsOtDu/c2OU9P3wJXY759naPP7p+jV+8fAr1wKcwmaH+2HHeVzVY3fbxXZf+RgvfA3dLp3Jmguzp0wxXDidFoWyReDbG0lmaI3Ayk1QZkxltMiqfIRJTiPEecRQi5Up467cgmwPPIRyO0Rs1JF1BLJVh9TbWoM92ukoze4ydF19CGanksxlUwWGwMMek5rBnpTmek7jR9Xn5pWt86EwNLYl5X9Hn2jjh1794g7/1/efYjQy2/uRzLPz4B/GvfZ3EcZEeep7ozqukp3SeCVax2ybnHr3AdqvHsUaa9sEBxvQSgh5jX3sd/cKT0Nwk8X3EXBUhjkhECSEMSFQdJzOBOdrDrS8hewNIIizLoPbUQ9iSxd2WSzAecr6qM8hPo77zJUSzTHZmjgMSCtv3+GQ0y1JB5seeOcZxWnzZV5ieyLG+G5EddkgrcKYUM6WLqPioSYwqueTCXVw9YRzY7PczmIZFVghphQp7sUyYKvHO5gjbtqlMVKkgMJDAtUM0KeHaQY8wNom6oIUDTk/n0TMWAQ6GKnNjs4vbH5GyNG5cbzMxVUOTRAxFoO8ENIoGIz9kuesRxTGX5go0bZ+9gcuVnSEPTWepZXWSBN5abTPoeRTTKmlLBVOkkTP44OPHSGc03uwL2P6Qs3mN6ZtfJ5z/UdxEJJ2ETNlbvDhsMG/VmB36TMoKSWGCKJGJdRMnELjdE5kQQ6JIoDvyUZ96P7kINgYhB/ubfPnqDtr0MdYdDTu3iDIIWOu47G43yRdztDY3Of/QUSQtjRvGWIJAKa0ym9NZ3h9ieyExIjghWUNhe+BxqzlCUWRWui4Dq0HNsug5A2pqwBeubvOBMzVeGcsIKQNh+S1yEyUCxeBCuomcJDz71Bl6ZhnBsEjjHXakWj5KXKT97W9RfEzD0ScRI5XuyCMt+qiiiipCIeoiCDJCEjPBED/WkUIXBAHFH6DZHcifQ+1uMQx9tP1dBFUCoY1crBO2msgxYKRQp2cJ+33CTpvQ85E183DaqqfJJaA/eAevOIljlslnTQr/zQ8heGNEZw8x9AnaTaJsg7klEclugevid/bICjGLTg8pDtHqNTpvvIGWMtCmJ5EMA1HRCDv7JJUZYjPHKJbwPQ/btiGJ6HZh5MdsDiJGIUTBgCTJIl65h6pp6JqOpYoomoYZOqhhTCiB1lw+tImlCiBZjIMYVRI4UArcbg3IGDa7QZZxr88F2uCIxEaZySzgltHX3mY49NBPXKQWerDzDq3sDML9N4ie+WG01jpSb4CaPw2OTUqTWIlSzPge3njMvU5AV/Q4N2UyihI6/YiniIi3l1HqAknoIOcNpH6bwMpyflFB1nRMVcIQE4Q4Rh8foBl1WqqJIomYqsy+lCej65Q0aNpQqE2gDjbZ77rk1RQvbTuUJh2c2OLsdAlv7w5ecYZBojKphqTKJRLDpiaN6PkxZr7IwUoPnARJFnj+/ByF1n1a9ja3N9s0psoc+DLHHjrHO1stLs3IfOqtTU4dqfP2QZfuxh1mFiY4568wCEsUUirvNF28gYSuBNy4sc5jFxZxBRlNiVkqmlzbHXFhMsP2MECTBBRFIhYOZ6M7Ax9bjZkspLHcLpPpDMfqKfSezHxKxJBi9kcRmnzYMnG6YnLgRBiqTGvsctSK6CYGCODHAhVTx1QFRkFMTk1QY49NT8OQRWQBPOB8LUXPDRCEhJIpc2D7pBUNTRIQAh9SOaTIQ241iXUTJIkwBjFXISk06BpVsooImWlOiwpqNEZxZZLEZeSGPPPYIvsdmzsrLQRE9HQJr70H/Mca4ySBm2stlja3ee6553jhhRf+XOD3e/nzy3cNfj/ze7/PD3/0+5A0naS1h5ArAgJCoYq3s4Wz34L/cFkqKwqCKKLmLIbru7gHXWRLJfYCHCEhSmKSIObLb93lv33XeZ60Za4Hbb6q21hzEwhxRDQa4w9sJF0jiROEOELUZIxSFUFWELJFElGAfhdcG+IQybCIu/sIUUQyaJE50qD19gP6y9sUFiZZ/9YNGhMpNt54wOBz16icn2Ln9WVyNYvGiQlaV1fQsjqhpFJeKFDNTnImZ6JXiiRJwj/+h/+YS+dO85Bs0njXU6iGipdtENx8ld7rbyIWcwh720TjMXfuHPD1TMjfPf0U2sY+pqXQ3htjfusK+TML2Gv7YLsEto8WujRXbULH4+iPLpK9cA5HLyK0tnA3NklqJZTcAoIEup7CmTtJLrJJ4pCw20IUBCRFRynXGd+8gqwrbL76gPq7HkKXRJAkiBNkLSb93Md4daXHr/6dn2Liv/sxqs8+h+COSM2NiIcjEnfM7UBhISUiZIaIhZOs3XrAhbOzRActDHeTV9aP8sGTCnYB4te/QrB4Hnl3GXnvAZQnCO68jZhEqL7IMFuBKEQxCwSOTVYMcIcO+tETuEYeVdFIXAcCB9GTwMqTmFkSNYXuDRBDl5SZw88UGCNiSFCum6hOhz9ci5hOWSSahu24OGYVqd8mKmapEjKaP8ITw4Bs3CUIXfqCzEVpC2FnmwVVIwlChqHOwBcYuCKbvsXGdpdMtYCkG7z8zev89z/0MEFaZyglGLKLGCicLRapF0xaPty4b5NPqQz7YyRTQ1Zlxr0el+bSjAOoZEy2V1rYjsWbuy1aPZdGXuZ4VmZr4LO33QZB5O6DPWbLKdJ4tEYSckpFk6CRUfnczT1ShspWx+ZILUOCQM/xuLXV46H5Iq4fM7R9hnbATEPgvadr7A99XBLquoQqirhCzBceDHji5HMsiQqZzjpBsYFUy3J24HC9L/KFa3v8T+cUvFQJIZNHk2WGdsL1TsL3Hc+wkA/4B2/bPPn0FL2+R3f1BltajquBTHCvjSNIDFIlPmAqdHcGRHHAwd4Bd6/fZjoFcW6Cjz05y72Oy3p7xErbZracYlESeXZKI2NZPBjGxMLhMV4cx2QzOhMZjUCAUEuxNh4wX8nw7bZC2/O4mzI5N3EGqbvFlgsTgY1TWcAysrT8BEnT8WKFcSJS9LrEgU/qkSe5NRCoWAkpoO+HJIZBwT1A3G4iTE4Q3X4DcfE8e56AoQts/pt/zdyRAmpFwqpm4eABQr9FVleJzRTB/asEtkv2vR9FdF2i7h6ibiEXK+A52N0+wz/4JLWHzxDtrCMgkFm4jD9zBsG0iO4/YLJ7l3ThBEE8xmu1McoF3K1t4vwkmcYECPFhE8XRM9A/QPfHxLJC0m+RPvcwye596O4hSFWC7TV2X36L/OnjpE6dI+c5CGYWanXGksH97QNkK8MwW8U0LYqmRFZK8DwHz/fwvQBCl77j0/UC/E4Tz/eJYwVZAH14HU0WUXQLdIsoFnBvvIO79CFurA3JuF32aiWKB1tYqeJhNaQ7JFq+Qve1OwxmH+GE0IHBLpOWiXz0JIkASugjqCJnjTH1hozpNXm5YzE/k6EmOpyZSBPc32DbKVLPapRPLeHGEpqikYzbRMMBYmEKob2JrGg8MpPiIFCQkpAABUFVkf2EtKoQjAMkMSFKEhAV+m7MwEuIExFBzzI7NUdmc5f93/06jYsPw6115o7No8UVeoUjWF6f2wce+nSBZDQiY+o8OOjSSElU2g9IWxnWu0MyUynuLa/y5NIswlaHYaqKSULL9ZlIK7R0iTeurOCEGoIsUWwU6Q4nCAWVN1+6w+l8gzkLPtVPkzYyzKR0qicbJBKEog6iy8gLubbZ5cNnJ0ipEkMvIiHkeF7h7aZLSpPoBdBvjnhspkY/FKinY7YHLhO3v8pvi+f4C5cmCRM408iQwWPNl7BUqFoGXhzQcUMWsjLZtMi+E6OKMpLgszOE6bSKLotE8eHKRNGQSakSy10XEMjpEsudkF4QkVNVYi3NOJMj47URFxqsRybz0ph+7TTFsINjVdgfCWQ0CVux6PkJRb2I6LnYYUxEQiGl4ZEwMcyQyWmM+2MGuxZJYEMc/UcMc33lgKl3bvPcc8/xW7/1W/zCL/zCdwuX/jTfm/z++eW7Ar/9fp+13S1+4sPvAy2BOCLxHAh8/JV79O+sIooCdmtMbrGBCNi7HbLlPJEXoBXSJGGIIAj4fZvA8YmjhOXdDvmhh11LUdgcMgwHRIlI4jg4W02MWhmzUSIYjBBNCzkUUGszEPrI+RKRqCJoFjhjpNo8SW8f4gTBtwn6XdKNCkkUo+dSyJkMhekysR1g73Z488o+9vUtxl2PRkbl8eCwy7d1q0PhwgnsjT71j/4AwqAF5UketEZc39zlV/+n/4Hdl98ke/YUrpYhdF3GN26w/tINps7PEIxctEqRr7+9xn//7ks8vjDLg099E0kAaTgm8AIG99bIzE5jJzHrHY/jj+dpfOA5Xvvnv8f0QRvr+HE8x0HuHWCYIpIsIqzfplbK4Ms11MRDkFWE0KP9xlvkTx1DnFoi2VtFnTtKuL1G+gd/BPvqK+jlYzjvvEY46qJPTFPwO3z1t36Zf/jXfpRxEBItnOTagyYHL13nuWCL3MIxHs6YvDlu8PypCkJJoPHwcdRgyNy5eSbqKWS5Sx6d4nueQ9+8yWu/+htc/tj7EDIlov11kmEPZzDC6zs8cq5OPDFJBpe4kCbW0uA7CG6X/gtfpDyVRplYINi4RdBrIVUjkkwFkhh5sIugWsh2C1HNEIkio26f9uYucqnAxNimO5a4bksM+33CICDGQu20DicWZpaa7JF2B4Srd0jPzWOdewTHc0j2tnl7M+CNjX2ml07S3rrLgV7ikdPTfPZrN3j24WMcPzZDKadRTBvs7Tb56s0DRv0+sZ5mvpFHCG1+5EKVjVjg4WMlTFVkpR+wEbq8dm+f29duceH0Ij+e7fBFp4YTBNy/8YB7zoCHLhzj7763we9uyDwym6fZc8hmUty/3STIyZSqJn98fZ/j1TSXp/LcOhgz9kJKlkoxpR3+g7MPWNsfockC0/U0thdSzuoUDYnz5Qz/7toBywc2n3rhOo9eXGC5OSJlFZn2fVKhSzTssBWW6Tou75s1eeOtDn/vCw4/8ozBfKqAO+whCibvmVaQChUy3Xu8f7ZIN5IYBjCRVyBfJVDGBFFCx/U5W01xzAzR5lIsr5gossyJs8e4sefzk48U8cKE2YxKEupc3+xxMPQ5UUvzeEVHN2QyaZnP3G4zmTNoDlwUSeTLN5s8d7LCOBSwRQvNFMlaBn/4B9/msY+eR9YUnNI8VbtNMHse5+tfpPjMuzFlhaHjEUUJkhwjjLsIWgpRTSMPDrD9mEhNM6P36KFj6jLjYZfEziKbKpEoIUoSA8flyu9/kfqPPUas6ST7W8iqhDo9f1glJcYE7hgllQF/jKjrxIFDYg+Jhz3EdB45lybq9RFqswg7q0iFOuw/wLl5HWN2jvrKTbTJMlI6R6Ka7EcWlYzOzh99nWldYZSuYS6dOtyBHPRQilWE2hzJ7deIZYXhnTVS9SzSwgUEr0/QukrhzDEkfJzb7+Bu7ZKen0KenCeaOAOAqqqopkkjozL0Y4y0Rl6JiUQDEhk5HrJPliROKEs+JAHyzW/iygaeqeGOhziCytAd4/c6TCw2WL9zlekEXC/CdQvcUivkdnYxTAvl/hsoSUL5oXMMiXElA6NQJWnvI04dIYoiKNYRO/soqko1GONkCoSDmLURXE5Bs+NTnp4jw5i8JGIksL7Xpbx1QCpjEHoB5v4qUXsHpTSBJCnkVRnJ7+ElWdz1B9izZxGjgJQiocogxxHbI5/JnEmUgBclKLKMlM6SPV3BlQxqs/Ns9UfE2Tq+pGFIAoZjk3qwjFI9T7j9ADtIKKQy3PNK6KVjCKMRtzIVntE1jkwV2HdDKrvXuLj4EN/e9OiN2mzmp0h19imlMnzs0jQ+KneaQ5556BhW4nL35EPgBYw1DTEJWSyY2J5PoVCi6yW0xx5VS2Zz4HO0miZOEjK6zE7fQTRUNFEgCEL2XcjoEuOxwzc2A04XFLqeyLNHCmTMKS4bZYZOwK4TkVIlTlgBi1mFW72IqYxCJxQxZIGuDw09YRAkZPWEkmAjxg4xdYoayO6AlNNnqE2yOw45lZcIEom1kY8dhKQFiZEHRSODqOgkgYzoO1QJEeIA0cyAGxLGMmXZJUpkOl5E341IqTJ1SyQlSPjzRSRRJEzSJAslVjo2YgIv7K4x3ln5MznmxmqLH3/vKS5dfIS/+lf/KoPBgEwm891Apj/N/9vw9l/6GN/Lfz7flZ/Siy++SD6XY7qaI2xuEwce8aBHNO5j72zjNvuMWwM69w9w9jpIlo7XHyFqCko+dVh7FiU4nTGSKjPc6LPe6tPQde7f2sceB0xPZRBEGNy8w+43biAoCunFWeIoQC8VkDI5pJSFqCoIhkns+SSdPURVBwFEVQVJQVC1Q+lGa5/ByiayrpE6fhLBNBm3B/h+xNvX9tkYhVQNGTeBzshne6VFEiV090bceXOZwuUTBEcuo1x6nnDyNH/7l36Ff/Rzfw2xe8DC0xdRersEozHJxh2ibov59z2EGHk4isz6SOBNz+f7njhHbmGayskqesUiXTEY7/dRVAHf8SEMOLlUJBq76JM1jj5zFrfdZ+OPvsLwzVfx1jdQsxnGV95G2FwhvvJV1NBF2NtG3bqBd/XbBCMbDIvQc/AL0ySqiZTKIaZSpI4fRfRs/FYT96BL6IwIoxi/s8+PvOsirz7Y5Wd+6qf4n3/6J0CK2S/MEQVD5FvvUJM8NCvDre0hem+X9J2XqGxdYWJhmkqlQDoYIGsacqXB3EIBvzqH/+AW3TurSLNL6CcvouTzFIs6lbQM6TpGawfF6SJbaYLSHJmcjJTJgCgjTy4iVWdIUjkiRLpjlz1fZnlzm5vbXd68fZ8X377Naw82eeAGrG1s03c9KkpAPp3CGA7Jp3NUVu7TeHCLxVNnWZyd5EQlzWQhy+yJI6idLvuvvs5LmwGfuutwa2vMfDRADQbolTr9rT16bZsferjBey/MkEvJ/PsXrvKJV9b5xMv3kDSBJ87OcGK+xJ39Ed+43ebWIOaVe21kTWNe98mKEQ/2ukSqSSyb1OpFfvluRNuLuDRf4OPvPcXjJxqs37jF1TacnsyzVEkhajozRY3LF44xHnjkdJHT00X6XkTRkliqWJxrZPnmlS12umMEUeDkdJa+G5I2VGoFk3LBZORGfGu5Qy+I6Yx8UppMKWfx2a/cRowSvnVtmxCJtdQ8W57C9tChuPwajqjx/qcvsOZI/KM/vI6wdY8763ustWy+tjzi5r7DGz2F0wUY+RGTikt+foEYkTMTaUbDIY/MFRlHsN4PKPg9brz8KmeqJs+fmuTHP3COh+s6GU1mozmmaCqcqGcYuSGmofC1/YQ3OzG744gjJRMvjKnnDGLA8UM+89Y220OH/XHAkukyYcIzJyf41BubtASLrdgkqRxBCjzUM5cR1m9QjvrIew8whYCo0yRKQIhjVLdDulonv3eTtCYylHMkgsRIzdI9epnNxCKoHCFavUnVb5GWQp7+xb9FNHkcYe4M/qCPlMsjVedIohB/9Q56pY7aaIBmIcgKUZgQOjaxpIDdR9JVJDVFnCqSaCaCEKMkHtlyBj0aYRZTDJdXEYSEvdQUg7nLbFfPoL7/IwSoeH5Ac3mb7fUeUaqM27PZUavYS88QFxo4C+eIw4B42Cay8giLDyGqGvubPQYPtvEjGb/TIu7ukQkGCEBJjcmZh9piS+VQ6SzItHwRy+8c2vfiCFUE1euhDw+Q/DGWN6BqiDRm5jl69Ainjy9xvpJh+8W3GJeP8+Udg5uv3qBgKCCIrLRG3Fnf5rW4zLdSJ3kjc4Txxm1W1jZYdWQOtAzt3R0c30MYdrCrR+kmGpKmYYgCxZTCa+s9XATSnXUyToeS3eLebofuYEhf1LmhThDW5/DOPE2CQBxGhxa74QFqFCApGrbrs9N1QTNIeQc0hCFhDEkcciqfkDcUZMCQRfKaAJJGrJgUputo6UNJjSjLOLGEQkKgp/FyeV7Yihjl6hTLRTLegNPtO8w1cpw7VuF8asiDrosfJ/SUAr6RpVbI8OypCsfW3+Gi0edOZp5xrsHV7QEIAg9lHIzERetsc+LEDFeCNDeaHu+dkKjKIXO9FRzPZ6PvoMgS91oOpiKTNxTW97vsDz1mjAiBhFsdn1Mlk7yhYCoyT8Xr/IPffIHS258np0vUTZnRzDlmCwZ9L8IPIyxVJlLSpIWAqikf/t5IElld5qWNHsQBRV1CSyISQUbzHQwhIk4gESRiRSWMIa0IpASXYtwlpcksFHQyYkhVHBGLMmEsgNuHQZN0NCRBwBAFYi1FN4jYsKHvx+yPfYIoIY5jTDFh0oLL0zne1ZC5PJmmntE5Xk2zNJ1FkP6D1OXPyE57xMjx0XSDI0eO8OKLL343cOl7+a+U78rk9w8+9YecXFwg8V2iUR8xlz+89BYEerc3cfsusiZjFk28zhBmIgRZInI97J0Oo70eqUoes5xBsjR8J+SmM+biZIX9jo8QQlYSEWURoghrIoesqwT9MUQJgipDEBC7HnEC0eYakqEjZookgkg4HiAZaYgDkDXCvS0EQ2Ow0WSwvcy8aaGlVA7aDuOdPtvDkLImcuJMnenjKvdevUcxp9O+10IVBTq7HXZeeYvyD/wIgZriE5/7Io89+gjVYplguIwsJojZCvLOLbxmk+H6HvXjR5G9EZd/cJL//dVNPvzQecb5GcTrbzDYGzEeRWxtjbB7LjPvuYDUFYnCiPqRArGkML56hannn8J3xrA3pHNrjTBoMtreITXVwCg3ME5exN5bIYlDko27kIA+OUGcKWHfvo1x8hzx9hphImC2VjHKRfytB6TPX8JZvk3SbLMWvMHJtEThyBL/26++n9aN2/zzT3+F3uZ9lv7Sj9G6e51c0WJxcJukPEk16pGxu9i3bpJ66HGUzhrD5RXiS48Sd/YY336L+vPvg3iMM3MCfXOFwdo23tIlpCcW+OqWyxlFxPfHlHsDUp6DZZoEZgnpyDHark/PU3C9mPHAYbzfxg8PkEUBU9fQvBGGZWEYJuM45nTOJcg20GSF19si32qOeL6eJr1witlGjtRMlk7HQZIkBFUlHgiM3nqD4rlTKI+cwfmN3yQbX+VbM0+g9w740u6YwZ03WSgZ+LHCu89N8K3NIRECP/XkNK1NgW8MDe7dGrN7Z5nh5Yt87IkFFE0iOloiTBIUEb5574C7pszXX7nOTtfhxPkTzL7vETRLIa5NE4cxD5ojSoLAw4+e4blJk6+/dAOjVKJ0eYYoSZGRINREBn6CpmjogU8updHQQuqWiaVKSAI0hz71XEgYw8W5AqvtMXECVV1GFgW8IObVrSGJAG074JEzM9zb6VM3BV7ZGvKH11tECZybybLaGXFCkXlrd8xy2+bRR0+xt7HH7f0xe7k5onHAOx0o1kPe3Ik5Iu7wxT2b5yZEMqrC45U0bV9kfX2XyVqBpXqa1+43eXcZzh1tMDlR4JGpDFY6zZX1NtmsQCVvcLc5pJwxuDCbo5pSKKZ0miOXW3t9TtczdJyQKIaqrnB6psBW2yaMgCRhV63g2QGmpdMQBFqhSipo40U5lMghrQb03niN/OMa39pP84zeRdEMVpM6jbSJGoxII9DJzYCTcLPtcqKoUUg8fM1EEkNi2eBAaJLW8iCplM+cwR0O8HUDa3oKuVAj7B0qydVCBanSQBAEEtVEyJSRVQP35ttEjoNACIpO6PSIu3sIRy7QdXyy+7eRiyUEM4WZLSKkyoy6I3bf+RqZ938IUxJon3wSYbSHls6w0fNRygv49h5t20G3YzaVMkE6xbTX4jXtcc6bARtOihPyHq4sM5SLpKbT2HeWkRfq+M0DtNIukKAmPkU1wkpsit0dpHwJUS8hhRAKEoQhJdEj2F+nX5gg374Lsno40dYM5NgjTkQkb4zY2ub8u5/AqlpsFkyefvoCM+Usgmbxxm6GR6oK0dUXCUtTOGGEHfvYrs/Yl9j9D3AfNu+iqQrW/h3sSKSRT+O4LtVcg+fMIUFvRHVqksB32LRFtFGHKKsRp0o0ji8RizbbgYRZO4po5oAEQRKRnA6OUQRRRDx6ilTQB1HGUjWmwxFSLJJPRgzEHB0/wpSlw9sSb4AkCgg7d5DsMn6nS3ZBoq1XcEUZy3U4sdCgJ6eYSnyi1AxkizDqIwgSlZSBYk0SOxFXupAOXEpnnmRWD1CaHXb3B3TjHOfMLltShspoizdvuxyraUy88Xlunn6OGUXgSC1LsytwMIZAjJlPm+i9LSzy6LLGqWqKV9Z7XJ7K8ktfuErxIxfoxRFpK2Eqp5OPbLYkkeu7Ayq1Wc5e0NjVmgRRjDJuo5gZruz7vL3eZbacwlAkZBHGosakFrITHOqFm3ZA0dToeQIpKabpQSWJ2dFrLBIxQuXeSKHjpriQlzAUETexMLwxRdEnp4uIy1cQGvOMjSx+KBLqGcQoQQIiPYMYOiSCwPYg4LXNHkvVNIIgoEkwCmI0VUQTRcIgIhv2MTImSaziRQleBJIQIukpInf0Z7LMjbUWyTde5/nnn+crX/kKH/rQh74byPSnEaTDd4/+Sx/je/nP57sCv1964QV+/r/7ccjWkM88TZwpIne3EEddDv/AQHq2fHigFiYEIxfFUPGGNs5BD8XQEBUQFQl7t4NZMrixuk+xkOEH3nOU1tUtgt7hQYlRKSCKAkmcENsjlGIeUT2sNTNOnSc62KF/6w75SxdININgd42k1yYxLMRMBUQB0TAQVBW3Z9Pd6PLK77xIuZIm6Ay4s9YjSuDkVAbN1KgcWyAje4jjgLsbPSoFnbSuMP3wBaLIp+Mn/OZv/haf+bV/jGAPUJ76fuzP/Q7mvIekKNjLy8S+z/3Pv0xjoUT/7jYvvfwav/nHf0y684Cd5W1Wbje5+JFHufL6CkIicufL14gfvUzsekiqSn9vwGQmxejOHZS0jBoJlKbzCP2E9s6A1LyGNjlN3N7Ft8dYgYNYqIIko5+cxpNM7ikO1bdfoRyNEGUZzaiB3UfUTfAckiBEnZ7lm5/4HI888QStjT5ThXcwZ2p84EyRlcEAKxyhB/tEsorTG9D58r/BmJpCPrmIp6S48zufJX96kVxWR9hpIlaqxPMniGSNXiiR9Ydsm3W6yzu0b3yB3fpRSgtH+dpL15idrLCqVynYNuL1ZUbhA6IEDCnBNCNSqTSNQpq028NsTCMYGaK1+4wrk0jtJl4kkl5cYrUz5rTioKU1Hs2niWWVni8y35hANRJCcjjWFNnYJgljElUi9ewPkPTW0Mb7FOs5eo98mI8GDrH3gGcun8PKZahVcnzh+i7ZlE6DfXrtDudmLOpihQXNQo7PMqstomaKvLjaY6dlc6xi8sHjaeylGu/c30eUZW7f36BcyPPWtS0eOjdFEiY8erzO/sjj/ESW1eaQQQALJ+dJx2lGrS5/dM9m4NhUDJGkvcO7qjJ6ErA98CkYElueRl0SUWWR83MFNgceO32HhUqKhISdvkstqzOR0UlrMmsdG1WSqaZ1um7Eg45NoVognbb4kXdleOlul4NWH0WY5kg1ze3sJXaGHu2xz437e7zn3DQ/+8kXqE8MuHBikg9dnGRhvEr3jVf5pH2CI8IqYuo8P/fbL/FLP/kU+ZTCT1/I8PndzqG+tJEjZ/i85/lHubnR4yuv3+dnf+hR0qbKt1Y7HCmZZC2NjC5TMGX2hy5ThRS1tE5n7HN3f0QhpXC8YuKGCbIqcGIyjRPAzsBlreey1XPZH6qUBYm3d4acMBMquo48SvAVE+Gh94LXoTq9iBy3EcwCnmDQD0GVs5jtZZT0LGJy+PlmMhq+kKBIIZKmEW/dQirPoCQ+nQDSqoEUNwm1KsrMEoJu4L7xTcyl00j1OYgCklyVQE2jjrsIuRrqkRMkB5uM7t5BP3kR/cgpSOUQBntYiYidnyZdyMG4TzzaRsik6bzwBRrpLNrOO0gL56kwYi+RiT2J0WiAVGzQEue4Px6idyKOVwRGqPRzE7yzMmRuskw6DAh227jTF5nPbyIUJlBOP4qjSujXX0TwRpCIBLKGlkQoRKBqDCMJpbtLJVskUnKoYZOkt4HkdmiP86ClkLJl4tY2CCL0W4ihR2IWCJpNxHyGwmCHD12osuvV2XQCJjQBdW+TsLqAcvxh5NBD21+jYGaIDI1gf4cVx+DI6SU8M4fnB3RGNtqwT6+1z9BPCFodJBIcX0emS9QZ0M42ePvGOv/tD1xmQvSxBQVJN5kZd+lENQqpPInTAcWAXhNFNlC9iI0oRUOUiTSFRJBIBwMSVSMycngJzFsJnVDAsttEeoZY0pBzJQCSdJEohkI8whELKOkMBdnACGP2/RJTuoIbauilGkrk0uoHLFSyyL7HJfZIrGnuDCKKuk7/S/+abuMogaxxPmySmpymUSph3FzF187iSDrZTIpRKODFMZqp8/K9bd59dpLQmmXCvUpltEeYW6Stl1ko6AgI/NjzJ1ntOOwPHR6ekllSBugEHNNUlHqKB07Ee89PQ2GOKUNHEkJutGzeXBtxpJriSDFFURfZcWKSREBFRAAkIUGXZRppgYMQaqqMKMbcG6tM5DRiMSaJDhuIxrJ0WD8mAFHMyKiQ7a6DXCK2x9hqkSQS6dgBqBXSGRO1vYYwGqCICX5pEYSYMIHNvsuxksUoCIgjhbYTIRgKbSfEUi0ycYwqwcgLmC9ZaOkiw4Pmf5Jl7m50KGVNnnvuOX7+53/+u4FL/x/53s7vn1++4/C7ublJ62CP/+ZDHzjcbytPEyKiKCpCdZrC8Wm6K3t4QxtVU9HKOULXI0HAaw8QZQnZUAkcH1FViePDY7dXtg/Yvr/BB4/P4jbH5CoWkifj90ekZ+qEjoeSTSHKGkngI9ZnEKIQQRRQS3lE3SBq7+Csrx1KJTQDQdeJRz3kQpWg0yQzUSS1MMvNP36Tt7b7RLbHyigiL4OQwMxjJ1j92g0WP/g4zs4eo/QOd1+6R+NEleLcJJKQ8Buf/EOefOZZ4lyDnVaPSUkiGAwIlRSqIRGMXfLHpkjaDhgZmFfJlAtYzVVWv/otot6YwdBj+6VbRDHM10xO/PC7uLLnI4UhXgBTl4+ArLJ/6w66KRP0bURLpfYXPoZ60CKV0ZFqk4evZt95E+mhRxHcIe1rd5FFk9WdNj/7t3+R2vwEn/qF/x57Y5XgYB8hl0aURewHd+guN/E3+rx6fZXHT14mWVrCe/WPKJ2ysAyZaTTYX0EQFXovvYRWqzO2RfQ4YdC3ecGfxijH4CU8eGUF6bOvES0sMPvjH0deu89tpcHljERPMNk7/RjNwYi0EqOEB8xMFwj9MYkXkylZaCQslksoqQzm5nVcKYa8RVuqkdYaiIqJ291niAaZClnRxSpO46s697cHOKU8USyw7/g8MZNieb9Laec6YbGGZOgUE5udUEH5k99j4+IzPKru4m+vIJcmGDz3FzhLjy1XJX7qCS5M5BF8l8DI8rGna3z6ehMpCKgHPQ6+/grSifPovRZ1SWKyXuQrN5q8tjHk/HyZnWaX1bpOKetTrmTw44Rn3vsE9+/uwCji6pu3KVsSP/zu8zw8aXJttUMkKGw0B8zmi/hGhpW9B8xqBtONCqsb+/zJN25xfirH0MojyiJ1UwVZwkkScqpMGCRcnEhzc2+M44d07ICMrmC7EWlNJhYF5koWYz+knFFp22NiRBqVNEpaY6pswp0OYhTx6Zfu8wNPLNFuDblwosabKx3OzleoZFTmj86yvLJNEHg0PfjJCZ+b6QYHdor/pbDD/d6Y9z9zhm+s9DhSSdMfa0zW0hi6hqHK3A4lbFWk6Yx5/eYmvySlKOd0nr0ww1RWJ60pVAyFnaHD7f0RAx8emsqyULZYbdnkTJWCodJ1fE6UM8RRQM9LEISE7jjAkCRyKY23317mg6cLWEaKW5sdlspl1no2M3OLOIMWWTWFoiRosoDo9RjLWbbHIaY5SV70sWKP9x0tUbJkwmGPPWQaoo1fnGEcKtTkkLLkMxyHZEqHO5litowQuKgz81A/ShJFxGYaVzIJJR1lcACahSQkkMqgXHqGYPkW5uXHwSohtFeREwhrR7GjAO1gF88oo0UuqZkJ9GIa3C7R/TfR4wBbKZES1unoRxh6ISfLOi/fHvP4kRR3dwYUMwqVjMCJLCRxhBwHdEonCKtzmKU8TiAw1DK0nJCpufMU1RiabaQoJHTH+IqKkCowTFLIkYeZyMhBSCzriL0DNFWj2rsPSUw4HCJMLiHYHbAHkM4T+D56o0J2ZY1IHJPxfJzJBfTJOSJNZqlmEL7xNZSjxxGsDOQqxPaQxHOJDnaYsvLEN76Nde4p0maKnKSBOyaYrbOuTpJnhD7Y5+X9mDQOB32HtLDDQwtp3rlzH1kUkM00fjaHpUkI4zF+NoWupRDsDpE7wjfzpFSXUmggShE4QyQS0ExCLUcvkkmS5FDwoEnI45AxFvFwhCmqOG9+EzllIREwmL2ImADDJlqqjJgkoBpIIuTsfRLFQN69x0WjyFjIMZsS+fJQ5/2LFvkg4FY/RH33D2Ol02iaxiB/gYIksOfnaTZkZjSZ9VSDR1WHO46OJsc4bsxM2cL1Qwa6iKVoqPU5jNEe7sp93nTneWxOZCmfUBXSNFMq95sjzpsCVjTE2tjFPPIYtzoOjbRGMWNhSgmxC/1A5MMnixQsHVPwafkxUQx2FJJRBFqOT1oRKVkajiAQxZAK+8Rqlu2hgB9CLwZfiJnKmaSMmJiEJBHYswWyhsi2VCUfKyhHH8OOZbJyQt+Psf0YQdA4VplD/vankMoNktopojhgpmBSNGVWug4lU2YcRmz3XTRZYiqrsRUYWOOEnnfIBUM/JIkhdv7TlWd3Njv86PMneeqpp7h//z5bW1tMTk5+p7Hpe/mvkO84/H7lK1+hXiyQcTtE6zuIi5fQ91cPxQqei9sdEgc+ft+l8Ng0Wi5D784anXtNvCghP1UgcRyCoUcYwHC7j6KprA5G/MIPPsO/ePkafz1VwCybuDsRURiiZVMY1QqiqkAUEiMdfmPeGEHRMes1Et9D0HSEJEFK5UicMYKZJ2pug6IQOS7lh0+SxCLOxg73/+QOSZJQTSvocUQurXLvxesIVobNb91g8sIUFz90CWHQp35+FtMScX2fz//+v+XXfvt3EAURYfkGsbPHaKOD/ohM6+sv0r65Q+n0FIIXkDt5kiIeF+7t8o1XXqV6c5XmlWXGQ5/l27toioitqxj5FJWswcb1HZz9Pvr5GeKDbbJTFbbfvEt+onQoAzEzSLuvELpFZAmEMEbSZNy9JmIYYj7yLnbsiJ/7P/8ZP/PR7+eTt+8izZ/G0gzc7U287W2UUhkUlcKl0xzseRizs4SZLKLvokwtIBpZBrFC1tLwOz0Ed4QoCRj1ClNzS4xjBTubo35epS1dYtlzaOabnOuskMrAePk6jiBixvvcaXF43d6zsaKIB2N48YVrxLHKX3n+OM8WQb37KnZrRObhM9innmVDqZDWNXqRRs7tEeZnUZ02uixgqzK9SGBzIDNb1ElikaV0wEF/TNWU8AODkecxb4Ykb19huFslHrmkH32UUjhimMvy9TWHh84XuSHPUF5ew5/Ice8rX6SpV3iltMgvTKTpSTrheEQ+J/GR2hBPzpIrFOlt30YYj3CzOT56VuHg7gMmUzluCAG247G/1+O6npBtbVE7e4rF2UkaS0X+6Ts3qWoap0/Ms7e5w7/92i0sGY5O5ZiamiRMa3zp6i6KoXFpcZL3HMsQZLL0hyPqs1Ms+yLtqw9YOjpDkFPZGQW4ToipixQtg1pKozCv8vJ6HwwBP4wIw4RX1trMl1PMFyxsL+DqVp961sTSZNKahO2FXNsdcXy+wJd2O6QNjUSIkbXDncrtrSZ6o8BXuzZTMxMszdX53Bdf48iJo3xxRyVfzeGYBu9IU5TdIdMTc1hOm3stl3/91bscP3OMM7NZhk7MVCVF3/HQMxrTxxbZHYe0+nsEksrTJ6ucqVk0xyFuCIYsoysibTvAkGVmCgaaItNxfAZeiBxEiKKIrsnoboQgR+w3RzhA4LmcFlu0pQai4OB4Aq6gs9e3ae06SDWHkaLjegItWyGljBm7Mvm8iRd75CWfaUNCEhM0JWHk2PjjFm7tKAN7yNhMsTKIyAgRQaRgKBIEDkFzE3nqOLZRhnBMIudIEh9RSCAB0R+TJDFJZQa91yFZOkliZJD62/gbd1EmjmIIEAwOkDQVJV0GK0MqCkicEXEUoLTXiBUN/dgpUr1NTq6/ijO5QK2d8CMnS6wOAqZKOYIw5n4vYF4YYAoWejBgPT/NxoHD05kQezjmm2+t87y5w7o+SXamBLRBUtGaKyT2EF9OkcQybxnT6IMul4wumq5AGJBkyySFLONEwvL6RFGAbGTAt0l8HwwLodSgMHkGubdB0OqxmJFgvE0QWDi7+yjbKxgzcxC4CLrF4P4DMg89jjpokVh5RH+M2NlGECeR+012yidoe1AxBLydDqnaDDPrbzBxeokhY+7KU0zLNumZOfb2moyCEMd1aLfHeM4Ky5KIoevkdIlMKKD3OuipHBOKRyzqSEgEoYej5kkEGT320AjoYyIlAb5sYTptwo27SOkckq4SdAck2Q7dWkxDHuBqRazIR/SHSFYZYTBEiX3GahmjUCHv2TTHYxJJ5LlLc7yyNeb5SoiQ+NwQspzOmIDI5iBAFhO8IGI+lZDvrDJ85SvElkO6cQz/xl0yc0eIdYkbPXg8B4PSUQ7siEkGqF6fU/UUixZ842YTq66SN1UO2iMkPU0g6QTxJqIz4FjBQJYUVFkg9n0S12FRl2gJOsb6TZTFE7yzOeLx6Qx5UUATBIJYYGfogyAwn1PZG4WISYQiHgLH2zsjtno2j80V8TSRig6jKMEOYva9iOv7Q6YLKbbbPqeqFmKUMHQjgiii5YWokkDOTDFZqCIUGyQITKYVCqbK1f0xfcfnVMXiys6ArhdxsZFBVWT2xi4FQ6HXd9AlkfbQQUlnEVUdSdEJxr3/iGe2mgMSEh5sNnnooYf4yle+wsc//vHvNDb9ab43+f3zy3ccfv/lb/w2Z5eOEPZb3P70Nzn9kxlw+sQI2HdvMNppo2ZNVFPH64zwhzaCJCAbEje/tcnoZpOziwV0S6Z/c5fQi1n8gaPwOfihs0d5+8Yym+MRZ55+gkcil9dafZ4eO+RTaURZBV0iaO4T1wUE1QCnA1FI4rugqOhTMwhGirjbJNhZwW/uIBsWrTev0vjBH2K0t0/hiYcZf/YObgwlNUFRZHITGdbcBHd3h/7AxbfHlOdLLH3oXRTOHEOozfF//ctf52Mf/QhNT6Sq2aib6zTvr1J95mHsG68zXF0/NMuU6uSWSgzX1sjOTfFzH3meH/r5f8zf/8DzrL32ACdK2Bt4VPIGNgKv/fEVGn/jL5NabpLoEsPNA7JHJpBCl6g/xhZC5n76ryOZFlG5jrf6AK3WINxZZ7BnIww3qV08Rj9X4W/9D3+Nv/uhx/gXX3+bn/mbP0s0HiKns1jnJvE2l5ErdaK1dYR4gNTc48LZU2xcf5kzkxbEAf69Nt3bbzFx6SGGt25hlIqkjsxi7+zhmjBeOMFWxyMwLWqJSxCMqEwphFNLdPyQmiFSqNWRjDTLt9Y5du4ooWJQdFocSCbvOlrmG7sQTtXIVYaMwyXGt75OqBosjxIq1WmCwCUaj7gTp1hyXJRYwNpdQ68uYN16g3stl1tejqfnUyiKSL27jaHkWEjGxD0XVRKJl87zbz7zDsJOhwsjkbPvf5L+qUc42/NIZJlCxqKVucjrd3boyBO8tBzwVD1mK05R3rzG7eIxRD+hWMgRJQJiElJ+8hlkp4sTeXiZKerWCu/p30e5dIZeKPDDD1+i7YbI4zLrO20mi11GscKFc0s8vVDkS6s2hekJPjhX4e61Gyx3XOZmY7oHB8SJgD8eca0Tw4GKMbQZtEb0Ntf5vsdPUW6U2RjDzQOXbz/o8P4TZa5tjzk3q6LZERlVxA9j5gsGb68c0B96pHImW22HJIYTdQt5XWCmaKIpIgNXYqfnUcip2F7AwnyFG9dXWd3p8f2XZ/n01V2myxma4xDPdrh5Y5mJ+UmGsQCey0qY8L7jC0ypKl9/c8CjxQKf/cLbLE0VkdMpvu/Zc2z1XTb6EZXMYbMLicB2y+aRE3VW2zaymGe3a+OEEQM35mAc4EcJiiIx9iLWew4zeQPbC+nbAUMvwI0iDFmhkTNIEgjiGFmEzYMhMxNZshmNLzQNnpkL0U0VcdwhpVb537/ZRA0dlpIc88KhTGFeGtAbeJyOR0Q7CXFtHl820BIPUBBCl7oi4k3M4jgOpj/masuk68ecsGK27Zg5fQyKRtTv4B8t4dl9dkOD9c0WZ7MeZnmCKD+B4PRAUhAlmai9SWD7KIMW/riPvbaG2O5iDAcgKCSlMjIJYeeA8OjDSLdfJdy+i6QoKJPzlJMRQnWCkjckHK1BP2Z6scBdx6Q/dsmbGrKewo4dJt026nCfVlhkd6uFdMKgqMV8/5zAsK2zeXedrKWSAIgSo2yN+Bsvslc8QnVpngnRIyIh7XVwb9/FOP8UYbqEJ1tIgYds75Hs7JDUZg+PjTUTegeIvW3E48cw4w6JKiN7A1pvvk16roHe3SSKD1/My/74UE5UncNZ30IpTuDv7WEcP0uyt0xiZvG6Q2I6zIs2hpsn6i9jb1wje+xhdCuDcPwsR6Qc+f4KjpgQZ0s8nIpwwwDHPEYqGOLtrNLT8tiux4Yb492+TxCGmKpM2jJIGToZTSRRCqTxUBIfJXbIySAGAclwn3BzjaS/j7u9BoqGn0nz1lsrnCrPEBaLSMMusSrh6xlESSXpH0CugRCEJN6YaGeDqaxDmCkTlmZYqGbZSHwSNebhko7r+6R1mRRddEXGbq6TSRu804kIzj7KH+/JvG/R5BuhzrmMRXuviZlOkwoGEEYUVRUvN0F62OaRkoB1/xUuKilC0+NP1kb8xPkK9nhMGMBuusF8VsbVLSLPIe11aYoZvroj8EF1E6s2Q3j9LZIjx5nIqLQGNmlLI5JkVto2pbTGZt/nWAZKcsB2ZCC4IV03oJ7RGPkhoyBko+Pz3jmLIAwQZJU4irnVtNE1FYCVroclQZIcKpHFJMELYLPvUz5yGYUAtb1CpTDLfqCgSrDRtpnM6qQNlZYzxlRlbDdEEmDghWz3PK7v9Bi6EdmiSebRJ7jzza/9mTwTJ3B/q8vNzVf+tO/3uwq/4neg7eG/8OP//yXfUfiN45hbN67yq7/4f8deXsFQIoLlmyiNScLuAWG3j1XNI6cMvM6AyHUJOg56MYuW1RkFESsdl/NLJQabI5IkoXauhiiJrPzCXyFVyvO+Spkb/REfsjR+6tEz/LXfe4Hfeuce3//oGf76X/wI8XhILIrEnSaR5xCPhhAFiKaJIiuAQOQ4+LubCLpF/84K1nQdt2cTj/ocvP4AJuqkNYnIC3GDhGrJZHezy+zDp3nh1ZeopxTsnk67qXPiwxeRLJWDROdLb1zjn/9f/yP9WKHXb2JpMpnZEkgSo/U9IiciMTO88XvfpnF5gSNzeZJMGSVT5m+ePcYvf/Xr/NJfeZ7lK+u0uyMO7u4h9m1EOSGOQgpzVdKdHIql0V/eJbs4ReKFpBdPQmcPb3uZ0dl3Y8QS3tY68sQc6UhCOfcke2HIz/zNv83HLxzDllUWp+s8tjhFvP2AWNMQRAX1xGUEe0Dq+EmGD+6jKAlzq/d4eTDgvWvL/NwnP0/TCxl5ASfSOdof+gsI9oihqDDwm4RWlqjZxYtEFEkh0nVEUSE37FOZrvI/fuo2P/Pj7+Fzn/ssn/r930XVLYJE4NzxBaaqRWZnZqmUCrxftynOn2BkNYglC+EjRQI1pOCPULptVscC6WyaitdGxkDVUgzUNHppgty5FKebTezuBqX9hI3ULLpkohaniUZ9tO17KIbC4Pot6nfX2X3kffzqO1v8ytMBDX+Xe32JF60JzoyaVGSdS8cn+LyT8PFnj/PkrI7s9dDrE9Rb23ipBWy9iDLYQxx2kPJVBN/Gzc2gExCdeIipzB0+XCxxEGs4QcxEIUXRKOBqBjMFhfnODivFLMuDiB86U+bTV3aQdIWTJ49wioTte7exyRL3R8wuTKPqLnVLoVSwmJjQ2bjfZX2/jaRpXGnGPL2Y442XrvFoaoo7n/9DZn74R5k/dYIv3T1gp+9wb6fHudkiK/tDnDhhozUCMUFIYgqWztANKBom6YyE7UdkNYm9rs2FuTztjs35I1Xu7g0ZOiG9UYRlqoSJRLZSYrqaZX9mmnJG5ksvXicajXj47BFiw6Kuh5wrq1w+WuCfvbDKydkyx6eL7DshYSJQ0CTaY59TU3lefHsdiRCrUMA0FPKGwtW9IXlToWd71LMaN3eHnJnKMfRCmmOX/UFIe+xRzxoYWZEE6LkhTnT41upz5yfxgpjMqQW+uDxgcbZOmAikVJVrnZDJkkkmXWCxksbJ6qRlkeZ2D3XYoVWoU1FCxMAGrUgvlsl7AxLVIPJDRuMRUiIwI49RZQUNGUm3qGg6maAJqkVy+hn8OEGKAhq6CuM1tlPzHEcgHvTw9laQFIEEmVgxQArwtjeQLBOpNoNz8xpJv494+XkEq4gS+YidTeTIg5RJVJxAGLURRn2SQgMp9sE0EA2NqNchtIecqWT51O0uIyfgaD3HXVdn2l9Fy+SYM0XmZnUUI40gJgj33kJLl7CmJsnnMrB/gJqE3PAKXF6YpRIEmLk0U+6QPcFEzJSRCx1i34E4IUHB3L8CuoWYzSNoOiQqeCMoTSLoKmHgEaZq6MI+8agPoy7r4lnmch3CJEc86JAUygiqiXb8FN43v0iiNEi0FOgWQraIqOo4gUh5uIleyJOMDhByRQzPxUzG+OoUqpam4NmMrTIlv0cqnUG2crh2gEGIjUbTmCCVzdHueZi5GZayKkocIHS2GDkOAy9gv+syXnsNSRSwLAvDSlE1+qSyORKtROisoCcyXvMAzAypapnPp05x4pUv01q8SGWiSjtS0SQNTdZJcnWkyEFpbRK39+heu4KeSWEtHCOszDJr+IxGI9R8mVRk04lV0nEMt94iOTJPlKtw6/U3caw6w9ocTxVidDFgaXGCiiHTtPLEvkvKHbM3CMlXyySaxFtUqEUx5cXTzLa3oLfMR05cID3eJzBqeGHEfCmFnspidDbpSClEp4WUyzFVsGgPVer+COU9H0DorHAqM8FOqJKWYG3kc6pqMgoPj846oYwbJqRjm9jpklJzlAyRynQGMUkoKPBOK0QUYiYyEl4Qc7KeJogiFoomzZHP19f6XJ7OICGx67gULY2MJjPQy5jOJpKqM4gUZBJ0RWaplqbrRhRNGT+IGbgBnTgmrcksdxxqGY2Xl8NDx4Ag4iQKkmYRucM/k2vubHSYLKX4/uef59d//deJ4xjxuwSY35v8/vnlOwq/169fx/VcPvzkRcIbrzD7/qcJttdQiiW8/R0ESUS2DEgSgr6NkjMRBInIDeitdhHjmJQoICgKg77D4rMLmKUUoe1iFHMImorhRFBK4TV7FKcafPJn/yJKyuTDf+/XeOjEMc5WLbx2F1GWCcY2ievi9AakKkWiIEFMZXHfeBl8G31yktjzGN7fIvIihsurWMfmUbw2J+fzvLHaoyHBbndE+yDBS25RBA4GPtlAYGJmgmjrHtrj7+Of/vKv8bM/8zdodzrMGxE7nkD5vT+IcPs1pEIVIRHYurFL/dwsD/+l72P7yjW06gT7vkyh2+TETIP+jXtkanme+Pn3wrDH2otvsDtMuPyRp7gXRsTjIXIcMj6wKZ6ZYe/1ZQozWXIf/D4GX/kjsvNTbHQ8ZvQ8+swRhg8ecL94lM//q9/lxdfe5Ic++hd4XO3w47/1CT719/8W4ZVvoZ44f6g6lmSS/gGiVUBUZLJLx9GPSJyJX+TXP/E29kMnUGsz/K/Pvwu/OkEvVljrjpAUFU/QkcUUS5ZEa32HL2bPsXV7n+cnVU7lBXLOHlZ2hr/7WJ7f+Mf/M3dGCv/wV3+LuVKa1mDEl27tcLB8j2IMm9eu8kuf/TT/6rdP4l9ZYf+tK4zFNHuWxeRjMp9ec5meqdPd2mHuzuuE1RTq1CztuYcoCwq6lSY9nyXV3Sdq7TBND68xiZBEGKpEMneKYGsZOZXmscUi5nGJx+ePkpLgE/cFnq4LqL372PWTZO68hKkmfF+ywfzcBQxTIxk5fPsApF7IJWuFB8EcqXFANlsmFwa41QW82CSd2MhCgtiYhlDlqOzwjb7AfO6w8/PiTAkv9lnpwvGJHILvoUcuTy7W6LgR630BSRSZn55G2Nxl7PfoH+RodQcsU+Sd7REFKWDx3GPUinmU7gFlLcWJgkzRXuaX/s6v8NGf+V/43d/6dT5ht/nwX/ppCuk5almRTpLw6EKBnhPjVi0+/c1VrkgyU1NZ2iOPgqXhRxEjJ+TqyGVjd8T+QANdIWUovHRnnwABURJ57niZ1+60uLfb4+5Wj1CQaUxUeO7Z83zrm9eYnJ8ijOH3vvGA6ZkGr28O2d0+1PsOXR9FhNOnJygaEqokUs1q6IbOnat3WbiUpWQY7I18Rl6EoUg0xx4zeZPpgoUEeGFMjERWA1MVqaY1VFlEBPw4AQRKpsLQC5gqqNzejWkPfP7F567ysefOME5Erm228b2AoxUDQ0zIqyK7/TElJSSq1NiV8lTkwaG1LfKQhi1G6RJZf4xoZVHvXGMwdZZMtkaz6RDJImu9gBmtiWyGRIKIlMogxSLB6jWMIyeoTE8h2B20zU1wR0hTx4jvvELkhIi6RZitIwsKcrFI/9U3kFMp9HoN9/qr0N4isFK4d66SfvQZhhs7BEsPkequIlUa2NkZUvEImhuEnRbS7AkUTaNgH3C8mmUuq2L0dvnansjz9Yi2WqActFmR8wiSjDBuQ+MoiiLwrukSgecD0HJDRrpEbOQoH22wf+UdCqdO0ji4S6xNYhfnydh9krpJEEQ4EZjpIkJ7h0QQQdVJZA3HLKBpKRw7QAts5E4TIYrQKxUqo02SREKdP064epPYm0bM10mWbxFffpo+OhYekZkj8T3GmoV57hESb0gy2MW9fw3BKiHYI7x3voFqD3EnTyH7QzTR5MCLKcguDgU0BTbv36NeMCmVJwkSgTPpIY5uYUoCtqBTL2SpRBaJosK4i5+qYnsB+wOH7mDAg/0e9uoOkiSRsSpIBz1UY4o6PZRcgY8vLCJtC/xRO8NfntXx0VCTmHEEWRLEBNQoILKH2Jv7SHWf8Mo7CLXjWIUKZPIcBJCJXSZlH7yEwbFLuIbBGA10k2JeI5+VSYk+y4OEUjDmwYHC2uom7z8zQSIKSLVpHFHA9z0cH7TBAVKtRChrCFaO3KhJU9HJEyIqIkKmhHf/JllxhOUEJHPHkOKIkwUZNVVDdU3r6/IAAQAASURBVLsovk/7618j/egTWIUFIjSySsJaz6OeMZhSQjqRQpQIaIMmtHc5Vj2CpZZpBjL1qMlU4rOemiAjiey6EQVdpKZHHPgiugSiIGCHEQ86Lgt5g82OTXvkIyUJiqhhJ0Ua9On5MU4UMXB9NEVEFOBBc4guCbyz1WXshNQLFrosUkqp5NMadzZ7ZLIGW/fWMMsTeIM2fv/gP+Kae5sdnjk3xcMPP8x4PObGjRucOXPmO4lO38t/hXxH4ffTn/ks9VoVNXaRKhPIM8cQYp/YHhKHIUgCogChH2BOVfAHIxRTo31nl956j6YX04sSrl/Z4mgjg57W8Po2kqSgFxJab96hUUnzu8vrCLKIPjlN99XXsZ56mp8/eZS/83/+e/75T32ETGeEO/AIh0NSE2X6y3tEo4Ao2KL+vmcx5ueJ99aJhj2sRhWxVME8pyNLIYqu413fY+pUjerjJxjuDDirOPz2p26zf7fJtCLjhDFKwaK8OIt+9CQ33n6b7c1NnnjoAvb9q5g2zKoGTJzCjkWy29fInT5KvdmjubxP4/tSLCyVCLt7aJVJWl/9PNLZ01S+/Rb7L11lemYeVXCZf+8jzNQWGelZ/DdeIxnYIGvIT7+PX//MJyntdVkqVKit3UZYOsFtrcDnv/J1MvYOVzf2aW88YPLEed7/rsf5+Ed+gKwu8Bv/z1/mJ7/vGQxVwu214fY7KOU6SapEMu4hGmnGzQPGiUJXr+A++jwfP/oIN3N5nmq0CPFIdw6olsu8GE9yXA4xdYXRnZs0Hv8oO07MRxfzPJjNc7kssxMqrJBj9Uvf5g9/+7c5WZ7gb/3df8DSbI3bXY9T03U+IsHf33PR1RLPffh5+pHJP/qN3+dvPnsR68wleh2HN994wJp/g9e1SV7bvM88bej2KJ8/jZvOM20JdP0YMQkZDoa8cX+Xb716hdRgjU7rgIfPn+Y9xyepTM8RmzrqqXPkw5iUFlHKiggH9zk2N0OxppO+/hUGikBKF1h+4y1mn7qI1byLUp8jTEK2h7C+5ZLKpahqY1JRD7FvIxgyGDkkISa5+RJKsUJQmiJCQiZkKuwTd4a0UjXSqoy5doO+NUuv7xFEEblklwICLbHOkZzKF67u4E4VSYpTnJqdxQkS5LyFHQlocUBZdviVz7zJj/zo93N7V0Cyt7kzkeY9T1/iQc7ly7/zq8wvnuDs08/xO//636Hi8Zd+6m+QzU+w0rEpWQa7XRfZUDAUBd8LcR3YH7r0nIC+HdDq2DQ7DmMvxnV9NBmONLLca9ms39/jT4YjPv6+UyQ3JW7fb2OkNSbSKnd3FE6fOcLZmTyGobBrSGz0PJZ3B5RzBhNli7mUzNeu7XCkkaWiCxypWGz0PM4fK7O900GIY9oDl42WTS2r0xx7VDMmEQJpXWJ5f8RSPc2xsoWEQN8PyekKfhRh+xGGIhJFMXGc0Ejr+FFEDMxOZPnyn7xMaarOiVqa1f0RJb9Hhk2CsUPwyHFK5QZyKNMXU1R1AT+UUQ2FWDXwYpsgVvAjFUuQkKrTRElCO9ZpuyOOWiPujWXWhjZWIw2yQnbUZTUpIVGg4QaImkXyjU/DsSXi1jaymDBsdkifmiKpzoBRQtm/g9PskCyexVQ89EoN2XPprW+ip3Lox84S6Rmi3j7q2g3sxuxhg40kk0gW4WhMMugQbz+AwGdfrPDkmbMk3gj7xmucPf5e2tUaO+OQp9x1KqUyyajF2GrQE10qlsbI9UmlNACubQx515k8wuwisTfEijpI+6tYooB/9wrK5CKUaoc7zATs5Y8zj4dQniGUNSRBJEnlEASRbqKRNgVSB338VhNBEtBSGlFzh+DpjxKvXkGdO44YOkQ7DxBUhZUky5QWYSoSaCZxe5dW/ii13ipD0URXTcSFy4jemMQfIQcCkTtCvfsKcm0SojaenEUZtghVCy+I+fFf/mN+5W//GJcLPokkkdJiXCWiFcrIxHTkLJXxGonegPYOcqpMSpPQ6hX00gRF0UWOfZojn7i9xesrbawLp9kWwXBkMp09+qUyz5dUBkYV5z/sthohpGSZSEyjaAreYIxRyeM6MWrkkm0vEz54m9QTP0AS2qBnsdw+TpIwUc3jDgekkgBOLzB2I4rpiE19ilnBJrp5A6MyhaWp+Hdvkpw9RYkx416PtjnFKFXBzEWE9oBY1mmJOeRoiH7/deS0QW7+GAPBpKCBKJpEr38VLJ2MckAgSWjZMrKVQQgczLyJIEFhvAO9mCjXQAAsGQaxhqWI3Om6zFYmifZXyd77NurZJ0lZk/hKEdVuUgt6jKQcM+lDGU7BHzCVK+H7LhXLYCKrkTdkDFXi2SMF1noOXgx3Ww7Xt3q8d7FI2x8TIdKzPZYPbFRJ5NbWocjIUGRafY/nLk3i+RGrByOaPZde18UZuQgCRK6DKCt/Jtfc2+pSypq8fXuVp59+mhdeeOG7Br+CKPyXT35F4Tv01fz/dr6j8Pup3/8DHjp3BlHPEKt9BElGypUItpZxD/oABCMXNWuiZCwGqztIusrutT3ag4BekGBIAjebLieO5kEQMKt5tl9aRsloLL+8hpFVyAcJb282ede5AFERSYSEoyfm+F8zGn/n332JX1w6Sn97wMgNWHwcrOk6m9+4TeOxJexOn9T8PM7uBlp9ElU1cQcuwdYGqcefZPDmt7D3O5SWGqSefj+9N96ks7pFWr7NyIdOHFIupVg6XYdykX/6h1/hG6++yf/x936OqHOAuHqX9uJFst6I4O7bjK/fITOpEcQqxcUa1UdOM0p0ZF/GyEjsffkLLN9uYxwNUVSNoJDnzpdfYna+SHp+GiXbwlHLuEMbS1eJHJ+/828/wQdOTXFAzG/0bXq/9gmymQzT1SJ512W+XuDJH/8xpo8eQd5bQdrd5M4/+Wd82oi4st3k5370Bwm6bdTGJJSmGY96dPf2GBbnGKw0GehzKEKCYQ/IjH1u/L/Y+89gSfKzThu+0mdleXvqeH9Oe9/TZrzGyGvkhQQyCLOYXYQQLCyPVhhhF1YLCFhAQkgghJAb2RmNxrue6Zlp36dPm+O9KV+VVenz/dDEbmws+z5PvNK7++HRFfH/UhGZkZFREXnlP+/7/p16HsWIUjn9LK//+fcR9O9m65GHue11b2P5RomN2Vm67n09JSXDfHY3aqvD/IqJu9nki1/4B8ori7z9Ta/ltz/yEV6caXJI3mYtGOJgMUqrXifltji5donzymG6gghveM09fPTX/y9W3/R60sPDXLgwy7fDGtpCmzfcquPIUL62QmpyAmnnIUKriYfCerPDd//6Dzn18hlG9h2lJ5tm8MBhhsbGuH72ZX7pT/+OpuVytD/L2970BsZ27SZYnEbtHUIsDHHL5gxqMAyZHOrMBbyeUXa+cz+ReIygugm+jey7PFD06aR1TMmjS7Hw8n2IkozYLqN6JlFJQOodITTryJ5DWpYQrTZDCQnXrCN7FnY0z4zcwyGtzbSfRRZC5urdfOOZ82SKMu88PkA+qrJWbtMyTdy+HPMLFcZHMlQbFlFDQU3kuetVR9EkgVwhyf0jgySjGuL+Q+R27GfvAw7lxWu89MhXkGSRxL7X8dsf/10kTWff23+O4f4eTl/ZRjcUJgcT+IhkDBkIMS2XnpSO5/vYTkDHC1BFqLcc+lI6sYiMX9KR4gk+/fgMybjKkV0FAkHkoUtb+IFPVzFHfypCy/FQZFgMW+wejXB1rUZO9jkxlGSyVOeFzTrza1UG+3OM5wzOLdfJdWcQRAHbcVmtdghCEMQALR6hL66zbnp0vJC4KjOei7BUs2i5AmldxnRvNtwkNJGtpo0shKSjKpWOR08ygi6LnLjrEJbnM5LWSKYMdFSUrIMrl7Ee+RpTt7+HnBjyzy9f4d7b95BTFHYZCpWWxXIQJx+AYbfwdZ0FL4KuKVTdkBYKv/PtK3i+wx0jcQ4XZDZ9mZW2TCItcMrNsq+j0R+0sfomkLOD+Mtr0GoiEOBGc5i+SnzzBpLnouw5TsI20XQNb3UWa32dTnGCmOYiBQ5L0V56Bsdxt5bx+idoSElqbZ/e0ETVDUjlCJwO9uYmQ4NRcE2WqiYXtkKsQYuGF6MnolBV+oh0qqyLBuVAY2rb545UDtdc5+V1EIBsKkpvJCC48AqtrU3iA/1Y8Tz+jWlqFy5SuA3CTB7Js4iHAUI0CnaDMBrHkWNogUMgaVgOBL5HIqgSWm2kbA7MBpYrsDR8G91GkogaQxAEAi2O6AfQMpk0PEzbRZQFrNImfm4I+ZUnaRZ7Mesmke4ite4xspe/h1+pEsml8T0bb3UZKWagdVqsrW2RG4/hv/Q4kf5RThyYYKy/gCZYiL6F4Dlo5jLReC+CrFBzRLLRFEIYIqS7kRob2KlBJEkiFTgYTouWGieRjJJQbKZ7B+nqG2R3uEI1NcJ2rUHVtGi2yqysLKPrOqIeo5jP0EwYRH2LRrSI3D9GKh5DiEQJmlU6Vy5z9aunODK+m1h1A1uKI+Lh9e6hjY4U08hWZgjzBYT5GbRQIinZpFYvYio+FTXLRCwgUm+z5McZFkwMp0lKN9mrNIi4IoEsojU3SBSztCPdxCQf0bPwZy6j7L0dqW+ccPECyZN34QkCYqOCHjUIpR5wbGwtgbr3FkRNw1idwWmaiEqEYjRHTvGwQ4mr1Q7DionYtpEK3Xg1CVuNEQltQlnBi+XRKsvIsQSS76CpOqGcJurViYYhdcng8lqTHzvSS0QQWbM99hXjXClZqJLA4cE0y6bL9EaT3d0J1msdZpZr6LpKIR3h1ItbhJ6PFovw4pUNmpUGsWySsZ4ElzsuRlyltnydwDJJDe3EKq/9T15jOR5r5RZPX36Ge++9l0ceeYSPfOQjP0h1+m/8sOb3fx8/MPm1LIsbs1f5T7/0XkLPRdQihK5N0DGxSxUq0ytkJnporm7TM7gHu9GCICDwfWK5KOtlCwdQQphIyLRLNoIAzcUSyb4kjYUtLs1WcAhJCQEPPTvF3nyezL6d+NVtsgf3IKsi0ecu8+SpGZK2wGzVQfIDDvzkazDLr1DaNmk99jyjrw2REylCUQa7jdY/Qnv2Om6pQmJiB2IoIeGw/M1HSOV1+o/tJP6Zp4kJAbH+DF1JjeevzPGZh1/krT/5c3zoj/4NPeoaXmmVaD7NSmqIjVAgWV6k8uIr9P/azyG+fIrI4BBEklBaInXbMdzF61TPzbDrJz/A4J0neOk7j/DbL57jI0O9bJW3SOwYoXNjGjHSIrRsFF3hISXBzp1D/NhbX0Vn9hroUegeZC0zQfQ7n6F1ZRa7K09QXqTcXKIVKDw6tcbXLl3hg7fv42N7xlk6dxm3d4BGEKWx5RGEUeKCR8q3GIoK6N19REOTzukrNDfb3FnI8DfPv4Rv2nxpzmaPbjEymKPebhBcfonuzTVK7j38zUPn6Roa5OnFTf76t3+JwR17ye+4ja+9//W4O4/yzHKbH7nNILZ+ntziK8Qn91B68Itc3XmcV8Wb9KyeIrcwQcrtELWr9Ffmsa0SB+stml0iM2KG9ZVNskN93DVR4OqleXbvPEeFGJ1ak3//y/+Bd91/B1/+8peZb4VUOj6Hkz5NUeVoWuDEAz9KSnSYeuRL/NmXvsXS0iq3jY7ymp/YxbjroskqwY1zSKkM3rE3oNe3cBeuUfNkKsUhEqZEXvCJKhBVI2RFEcExUdt1wniGQIviSRHm2wKT6W46Ro5YdREtlUMQoJXsQ45YVAOZAibjEYuykuTSjSrdiQhN06UwPIyuSjw7X+edJ4cprazzjbMNNLnA/Qf7cISAvKGTjsms1h2keJqLizWOjeepOzaSprK3J876ygpfnC6zXVWR976NVGuFy9/4G0bu/wDxZIxzn/ktrPf8OnvHesgldUpNG8tz0WToiUTZ26dSarpkohphKNCxXSaLOabWqly6vMRAdwZLUFifWccLQwJb5bZdOxEFWCm1Ob9aR5RkXri+QSYbp2WHpFIR2qZNJhFhvh7w4FSNY4OTRGwVKRCYWmkQdvk0TZvx3gSrFZN606GsdEhFNSTBI2kobLRsehIGvh/g+FC3Pcqdmzu9iijyykoJRZKQgERE5sZ2m6TpElVlFktNBrJR3niolw3TxQfunijw0kKFxyyD0XSSI30GE2mZrSDKO24zeHHD5KIf0JcrUm+WSRlJ4irMiFmGfQ97dYGu4T7yRpThgQhzAyl6illSTpU1SyYnlDCtANM2uHM4zobp8NS2gBKdZLDeZMfxe1k+dw6hpZJrdSilJxF1g2RtiXY0R9zrEK7NEqzPoBlJ0l4dNRohMLJkl84jKyLi4Vehm2XWzBYbloKhQbTaQJQUrCtX8ep1tKEJrECkSJur/b185vEp/vj9xzi1ZLG7mEITPexQYSACjXwcNwjQIjGyokRlAyZ1E39uA8Hp4OeGodjHVnSQ7u4x8pkMoQihqCFXV/DjXaiqB75DGEhIoUNLi9FxReTQI2MuIdgNnMVryJk85maJdrWBsrcH1XeQZJmgvgWJDEEQoCRSmE9/h5JnEB4/zndfXmFoMMsrtS5+/NgELMxQUaLMNoCBIyQrDxPIKqJZRh8YxDeSTP3VP/DlBy/R9e0/Z7vsMxxd5bfetpcobeTqCqS6CQIfX5KJiy5BGCCHNggi7spVFFUhSPehCBCGPnGniqcmEEUVy4cgVeT2N92FEwQIxNAbTYoDwyTNTZraMKqqsL60xPbGJiXfYn7GRJElovEEmdQwqcIIGbGNbJmEdgejp4v1f/pHvEYDQoH0nmFSxV6sUEZVNYRkDl+JIA9M4l15HmMih2OkaezbyZH2GhvpHcjFHB1PoqNG0SSRlNChKqsEikhw4Tnk0QlavkBM7CDke5EDl2DxKqKkEPgmom2iFIcRq1uIiRiE4Mycwx7aTVvPEO9NIZnbCNEYQdshvT5FttCPqBRoiyl6YzLJUEFslDC1BFZ3F5Gpl2jtuJWUId+cCizrqIGLhchaO6Rf9SgFES6sNxH0NgNZgzNrTfoTGit1GzeE/oQGgoAqCWw2LKK6zIXVGje22jSbNiN9KYqJCNfSBmvL21htC4GQ5XOn2Xvvq1gumQiBz9biNsG/BFz4fkAk00Wnsvk/+c3cWo1yrc1PvfX1/Pqv/zq2baNp2g9Kn37I/wF+YPJ76tQpNEXmzuEiuB0EI4a3Po+1OEt7s4IoCHiOR6wrjW/ZWJUmfscHLyS7I8uNGxV6dZFVK6DjhEiSiN3ssPrSIj1HBmitt5jsjbG63Wa2GrKyVKK5UiW1NyA0LQTfIQxCdrdlvry2xS1hChs4PV1Ce/hlfEni7JPT7BmIU5u6jlbsQmx1wG5Rfu4SxcPjiFKAtbhE4sARTCnG2td/h3oygjhXpn8gQ0OUOX6wl49+5SkSrQS/MTbB2P4RRG+Z+rmzZPeMERiD9FvrXI0M8nQrxh4XcNpgd9AG+gkLQ8iJCK1IBm14F/3HZ6g/8Rh2f4qf/5n3Mv53/8wvPHKKT/7bdyLm+5CDTeJOBTWXYXtmm8cvT/GpH30T1XMXqb58nr63vha3WeX5C4/xyOOX2Z6/zshGFd2YIlB0HNcnO3GA9//a75KNhKzGVAg80thEy+sMjcXJSjaCHiHIZJCcJp4c0giTdHbfTjxzjTu6Cnz71CkOv/pOPvqf/ohP/8MX6Ow4xuXPfYWXx2/j9qEJuoMaF12b64sl3rM7w7eLOcT0EN2FAvUdR8gpPkcHkkRUme2eQ+Tri2x//rOElSb50hzLxQniQchD9RjHd46Q7iqS2TWK0nGwUzGG5DnMo4eZWa5S1XMcLkSZMAJQdL711W/zpedf4td+5z8xPtDHliOiqxKjhkip02Cq5nCbqmN2LDqKinjkDfzqodtoaykeevBB/vi/fprQtbE7LTw/RCTAlzVuPbSfI4eOMNhuoUV0Zi9dxT10kG0b9mcVpMoKYa4HqV0hbFWpZca4Nr/BVF1CGM7Qn9AJ0zfDRWzbZ7UNGTVC3YV6GGM4qiA267RNl5mOw+XLS2T7u1lcrWPENN69L0fH7/CaW8aZa4Q03ABNFhjJR5gvmziOT29cpy+pk9AFXphtEjMcqNdQjQj7dgyC6/D4KzNs+yP8m9/9S/7xT34PJxHl7vd/iIc/9VvEfuo/ko4MExcDmq7P0paNaYeM5KMMpHXmA5/BbOrmDqokkUpGMaUojz11DlUV0NQIejJKLJmmYXnkRY9D/Ql8AvIpnZcuLvHGu3eiyCD5Aq8+2MfpxQrPTm9RKKR4uWWzVm0z0ZMgqnrENJlCVKNuuUiCQH27QeCH3Lmzi7YtI0sizZbPkCxStTyK8ZDluo3tusRVjYvrDeptjx3dBkuVNhFFpDup07I8PM+n0vZIxQJODERo+wI3Sh0GMgYD+SiXV2pMFrI0+3Poms65y9sc7EuysDrDW24ZQhBlzqw22dcrosZ1YpqEGwSMjA8yX7PJKCKKBD9/IIKSSyEGUVrNFjldx02lKZVrROM6OyM+LVcmikKg9+HIAeXxW5jo7UEzJJIRhTNrKvtSg6hLU6hJA2trFSEAe32V6NHbsG9MY7csdEPCdz1CJ0A1FIrNFaTUEDE9xmbZQhodp3tXQHlmkSU5T1yKIjg+K+kxUBboiSkMp+HUcgvXdjhaEEGPoUgalmWz5ihEQwuAuG9S/va36Lr3TiIDYyzYMo1GAzmdpm/PITqBiOmHCJEs4vYGZLoJQxHftVCbZdYyu9HlkIjoocoS/toafruFOnkAJTuM5CukBRF57QbC1gJhsR+huoGgaASdFoJm0D/ci5PKU8iVqU1fZ2DXQaRoBlFSiK5dI5O0SPZmOZc7xK7BbtLL5xEEkeYLz7A0tUpa9ZAvnqL/njejBRbxaBTRaxJ0WpDwaRtdyLKCgotS30AurSMYEfylGdRdhwj0BKLdIBRAlBQkAmwEghA8ZERCIppKydZJZnRiikBLMYj5baphAblrmFI5YO/kGKlWA0WUcMwapWqN+UYDQYBUzCCVHCZ1p0Dt0efwag0K+wfQCkXCaB5FkuigEhUVhOoqUVkjyHcRzF7CVTWemRe4fU8fPeYyYRgymCwgIeGkBxAClyHKeEECZ2gvwenHqN3142QvPII0fgAzM0D02P1ooY0Q+IjFYZAkpEgEgoAwnqFx5gJhcoCqF0PLxPG1BF7/AayCQ0RVqH7mT8nfshthx73EFRHFA6FdoxM4XFAzHBw5gLY5i5LNInkWwcYswtB+NsQcL601GIxvYCVGWTThUEqkN6lTMj2uldpkDIVK28N1A3RFQhHhaLDA09UoNcejbft09aSIKBJDOZ3XnBjks+tlvLaDbbuEgsTchWn69+ygvLRC+cbF/+YwzbUF+F+Eo82u19g7nGPXrl2kUilOnTrF3Xff/YPSp/+GIEoI4veZ8PZ9Hv//Fn5g8vu1B79OT7FAWN2EXBdkemk88wSSDFJEIzqQpTa3SXIgi9fqoMYiiCMFGsvbBI7PnuP9CGdWWLUs1m2fo0mF2nwFLa5Tmy0RBiGZjEGhL8nC5UXkELL7htl84QyJYgajr5tQEHntXbv5/Be/h+3fvK4NO2Tx0gqeL7DvxCCtiyvMPTFFPL9CdmcRLZeifPoisWIU3QlQczn8RpWtcxcYOTbOi18/g9vZ4La3HUQcGOHPPv15crkof/ATb0cQVfwnH0Iu9jD36AskxvqpJYt0tDg7nGV61G3iD5zEXbyONjyK2tVHtVzD3DaJFXTOhjkOHr+F2j99h4W//UfSu4e5/c1v5GNbTX7677/J3+3Zx67+wZvxkabEo89s84e/93FSYZ0Lf/05Xgocnv2NT+AKIseOn+S+n/pF3lC7TCeiM5uZJNIpUwtknADQomzUbV44u8pb9ufZ0RWl3Six8bWHMO46jDpSAATo2ISNFVqbLkZSxatWqc1M8XP7x/jgt5/g/hPH6d+4RtnMIw6Ps/vABM14GluV2bCuY26u0Bgc5Lc++lF+/hc+zAFxmc9tPEsiGqOjxXjzW95GIZNgNRggsfcA4vXriLv2sE8LaG2tE4lOEs3p7B4fZm3uGrvHx1CKXei9OTr5XobkFmE6hlKtIu7Zwc9/7A+J5nv4+T/+W4joLJgwoopULBfL8zlkuIw5JZrxLtTTT1IbP0hvKsViO48mKbz+Pe/l5BvfTk9UZEB3sc0WL9oZulWHqXNn+OtvPsbVqSkO7pnkvtuOkVle4dlOCqsp0Ts/ReZwBqN2M4JWCVzifou4abO4BYpSZMCIgaQx04nghXCj7pKLyKRVEW/2OhvpIfYOGjQbbYpHRymkNb5+1mZ9rcblssNwPoXe8dDrM4R6D1pukCAMCYIQ1w9oWR4nRjKcW6mS0iSG4gL/+MIyG9sV3v+eexkrJLnn5G5UQeCVlTpv+aWP8+Ljj/HI3/5n7n7fL/DQn32Ms3vvRkkOkB8a5vYDQ+RzUdodlzU/IKbJJHSZWttlq9amP2ewdzjD1mKSyuI0v/TTb+FMLaS5us2Z6yV2DafwTZs7J3LMVyx+9O5xBN+m1XQ4e3GWnV1HmSjGb6Y7iiLrpQ6b5RZOq8Prjg6x1fbQIyqZpEapYSNws0/gwkKFtx3q5eJ6E0kSKJs2E/koaw2LvcU46w2LkaxMpe2Sj2tYrk+z49IwQ+6YyPPSfIV9PdmbNXUCPDFTIaLICBLMVzuMZQxWK2026jYbhQhFJeTuyBbLlsbJXd3syQhUOxYnCjJpQ0BeuU56bYkgk0PXJepboOGjpbKkcv0Yqohn2WQlB0G6Gb4jazry7Css9x1iJJfACDqs2CJztsxEPiS20qZqjNLyRSa7E1Rdh4zUwG9VUcb3EVY2aF/5HtquNk6tQfTQSUJFw585fzMMwm/SfO5JsnfcQ0Ua5sXIOG+QQwRNpxnpYrqhcsDxWAxyZCWbt982QsP22ZuBpxYcMlJAn1thaj2gp6eb+YqLZtfp6DfHxnWSvXTffx/u7hPIV54jluolEKJUTJ/umEQ10oVlO/QpHsHCeezuMSq2gyjJxCWHWNBBDhXU0EUI3Jtfs/JFiGbQOg1Cyae5to6SiyPlCgTF0ZsTLAQBz3JQJRVn+iLRniHGh4vYaYPNSISteoPIwA4EM0/h1GM8a91GV+8gSd1H7uoBUcVIrXHg/n0cSSeJR0ScjVUCxceT+9hSM+TzEXz5ZuiIKvqEgYAnKIjNKoQ2WjpJaCTB69CUEyRby9TjgyTcCpoWwUagZIWYnkG3LtMKAjRFQw3bSFoEFZFYZ4umUuD2WB1PgngyTj1QCWQDLV7AdwVysofUrtByOyzlx/HeOYph1tBiIvLwGCnPx/U8fEWCzVlYmEIe3ImX6UGMd6EuXebAwhTJg29BbdZvzpEOEgREqEk6hmuhRnSU2gpSoodwZJL4jecJGyVkp44oQjmIkBN9bFFBEwTwfUTNA0Ek1GKEOw6iby4y5aTpjQRYgkQbFS2i40oC2XvuQbDL5J0yp+sJdlBCdyzShX7SocwyOtmMRkILEMoLN7/AAk3bYrPewVFt8kKH2/oNBpMKNSfA90NcX8CQRHxg3bQxLY/XjGdIGnnumjBYbdisltbJx1SmlmrsKMTIxFVa2xtkBoaIpaM4/RPU1+YpLZUwy1sQev9dYnwL/H+9ZGB+vc4DJ8d45co89957L4899tj/X+QXUbq5vt9z/JD/W35g8vvwN7/FPbcdRurqIzBiCFaLkACr2sKtmQiSiJ6KouUSqLEokiSy/vJ1QidAzyfQEgGxqQ0ArBDOXi+zoxgn158AL0AxVPR0BN8NaF0LODjSjduoYS5skZ0cRkqkcbYuUNzZR0ySkHzozUeRHRfdDxk60I2iG7R0meXrZW69fQfxvXtoXbqI3bBZ/NZp2o5AarTIxNvuRNpaYLMl0NhqMbG7n/rMGsuPTfPolSW+8cs/xrIYZUd3FGupibW0hBY12H7sCcz3/SqSJPDCSoU7VA8hpiJHdIRcN47r8vi8xauOHSEaMzDKLnYoUTw2SWt5k/nzV/n6Iy/y0soi771lD6nFFfTduwhdm+WFBY6NdhPzm3z0C9/k+o1lHji0g39/uIfiXccpx7pp6AFnGUQSIFFfRRVlciM7+drVFkKgcPuuNJ9+oUpnJmTGj9BnDJLcZVC/Mk1clFi9sUr/zj6sy+dxN1osCirjR3cg9vZiVCo8/omPcvmhUxQGcnTkKCLd6CN5ZloyN5Y2uXNnikNry1yamseKS/zer/8HgphB4JjMzK0S9Vv87od+mr/97GeoajHUQyeRdh+i2TSxVYlIvcKYXCG/UWbvQBeXrs0ymU2h5IdAL4IooTdLhLJKI9rLr/3ef+LgXa8md+AudhcTRNoVNjs+KbWLqGMixHVUzaBn5hlWrOPYqSJGq0n92lUak0e4Iyfwd5drPDBikJl+kq39d3NhzWdb88nFfJThw/y7Xz7ChquQc8qcffxbfOQr36DScrgyPExhYjdF8zp7+9Ic8s5h94yzEell15iD5/tkrBVEo0DTDVBkjU7HJiG49NFBiqSYLzfRsgKqonGkCNfWaqws1xE6LX7m1WOs1S2u1j1iukwukueoYuLLLT75Qol0LomuS5TbDtMbJhsVm2hU42rFYXjHKJ6e4JXZCqWmS2/GYDBnsFC32W7Z7Dp5Jy1f48WvfY597/strn75D7GTDbonxplea2KKIu/a383UlslW3WZx26Rpu7xyYQXtaD+1msm+Q+N0BqI8dn6BW47uYf+wwmOrAT4C206IVbF4bmqLZn+KHT0R9vYl2Z72iQo+F5dq5DMxLixW2G508BDZrDtMrdWZ6E5QN11MBxRdYngsRzETZWO9SoSQtKGQi2u0bJ/RuM6p2TK6LDKcNNBlkZ64SjoiM1PusLjZpNlwuGMij6FJuGHASFrnyeslljeaDHYnGStGkUXQZZE37O3m6WvbPH6lxLsOd+HlBnn0uTnecMsQfhBybq1DTzxLToGnFxt0CwprQg93ChVervgcokZcSeJLUcZFm4YQx3VNurWQzPZVYoVRtmtFKmdfpvueV5FQQpJulcttnU9fqvHOsS7i8Rxt08MJfMqWwLSf51VuA1WV0NI5kvv3I8Qy6N1FxIhG6Ac0R4/QKa3jn7+KfeRVBG6AsT1D78QwrURAtBZSyHTIGiXUaoDkuGzUfG4fypLfWmXRi3Gkr8hGy+WiEGFqo039wjkmJwdpWFA0XJICvFCVedOuY2gCCK5DRnAw4hmqhobfqaAYIkkNljoyyZFjGJ0GLS3NestiUnKJ+yahpKG0y+D7SD2D0KwQSDqS6qCFEs3AQ+jUECMJWLxAYFuIXUOEXeOorgPRNfzKFuncIOt+hrhrsVoPSFCj0KkRHrufnCszlhQRy0uEyZtRw0rPADk/QN13K/6l57CuvkArM0gkmma2FaW3IOC4HWRZvtmgWt9GcG2EwZ0giojlZYLAx1Vi6JJIyc7TCQRioobpQ0SChgeBINBwwRdDaraPICvEJY/Al9GiMoIrIQ7tJqZKiJ6FJ6uUTbA9kf6EjKFGMbIxdNUiiBcwp1+hWeijavtMrTdwFs+RjOpkE1HCdplYPHMz3e7is0T33YlU7KenaxJFjyCJIsRSBPggBCRam3hLMwhdRdCjqM11woExepemECf2ImlRDHOLupJHtLaR4r24egbT8dEjWQTXQpPAG9pFWF/lDiPACNpc6MRQN6ZIpZNohT7kVBqrJRN1GqQqTcrdvWSVTbZ9hZm6TUIPiWcTCIpFIIj45S2ahUkUMeRgf5JlJUYemR1yFQ+J4aRKSpOY3m4hyxJtx8O0PBKaQlJXsZQuunyPmVKb+3ZlOT1fQ1Ek5qttelIG/RMj5HtvvvhKYgHXtinNX0ONJbHKK4CAIEloySJWZeVf9Zv59TqpuM5/+exDvPa++/jkJz/J7/7u7/6g9OmH/B/gByK/lUqF+eVF3vHmjyPICsH1CwSCSGBZtFdLWLU2SixCp9IikotBAKIs4dkOqfEiasygvVklZihkZah5MFN2KGgWuAHdO3MYXQlEETamlrlebvLWY5PYZROjJ4PaN0jQKiNGNDqlKtFMhK6Owhvfth+76bJ9ZpaB23cQqHFmnpwmGpF57qtn2bdu0t4ssbjWpI+QV6a20abW6T6+i9paicWZEtGoRunqCp18lEcvrhMEIS05Sdfdr6X15DfIHjvG2te+zch9h2nNLzLsb+EpMcq+gxyL4egxsOqomV6eOrdIu2Pj6TE2lQx5cRE1dChvlPirV6aYrjR5tRHj9oFRepERVxdwrp5D7R9mu1LDWl7mn7/5Hf7dr/wa6k/9Io1alW27TTX00StNsskO/ZNHCKNJok/8E+bCGun9BxjqudlhXbMC7rtrD57V5rFLM2SGRxjzTLrvfCNzVsDLW9u8KuUz0ttDpLNI8s43Ea5P024EpG+/m+Xnz+CvrROuLEF6kOS+vcybPlFZ5CtPXuYX33M7CTfH+LEDzF46z+b0IrYS45qq073nPgpxlZ4bV/niVx7kXa97NY2mzctLFV46e4HV65fZ3FhncGIHH/nge+k9kuTL//B3vOE2GWF1iWQ+Q6CmedHKEmuKrJ55nmaziTx2gvmtBm/sl6htr3Io4sCNOURNo5TfRRmJaCRNZ2GR5HAvp+wEPcMphpUOa22ZtK4ghB5SLI63sYImqVyaK3HPQIWhsb2sWRD3BJLGIMY7P8iP/cyH2Lhyhq8/9gJT1y7y1MNf5a8sl3xXL7fddpKTJ08g5pPkdBlRlGmiIUkCIi4126c3aGBqcbJhQHbnTgRzi0R+hIbpYRtJDmZjzC9t0Pag1LI5PpJjdrtFOVXkaUGgZ7vB3ft6aTg+r8yXecPubhZKJikJPMfmzFIT2/LRolEsN2CjYTGYj/GJj/8Gm6tLNMwOubjOsXf/Ox69UiRlLbHnNe/m8rNPMDBUZPHaHDFdpjsmQRhlMg5Xty2u1iyk0GNmrYXv+TTXlugf6GZHf4a2JPHihUWuLNW49dbDLJVc3rQ/z1kh5Hunr/FEYPKmOw+yYyhFrLnFI09f5/jJvWzXbFRdRlJC/FBmqdKh7QS8ek+Rhu2yWG5z/74eTi/WODxZ5BMPX+cdd44ghJCPaaQiCq/ekaPe8chFZQhC1uoWEVViLBfl89dWCAQJL4DehE4QhJi2z1bV4shIlq6sQRCEbNRtqi2b0Xyct+zr4s+/N8MjZxd43S2j9Bcz9EQl6udf5pXOCL2ax45MnXPfO8f5O+7kNrOOoMOx3QNkWyt469d59FqKzA6Zkl5gw0mQaW3SQUEwG1h6kvFbejCbLUjESUsN+u0VUlqSarSXhukCAlFNZr7usljp8E9hgR1GlGN6AyURwy+v07g+SzKRYjs+Sqvd4MvXBI7HRkl0jTHbshiNCVy+0WaUNorbpr2wTLy6gT40gODB0Z4+hinhx3MYFmQiChPFON+7uo2iqxRScTYrbUZ7kizX2qSAdsenIhj0bE6hFrpwq+vcePwVRu+/E0lXabY7FOwVJL2fWrxIvnGNdn4vdQ+UoMVlv5ddEYkQCAQRIRIj7JhIZokgUUB02kRGJ6gIGjnZI9xexGt3sLKjxLZu4IQinZ0nYO4i2mCUAc3mzFKbEbFFvWPzbNDN3bUlemMZltY6tNoqt9SvIXUPQqYb2Wxjl6p0HBl1dDdGKHLhxgp7DyTxxSgrlRb9WgRRCBFra4SEhNEMUigQpLsJJA3V3EAQbyaC5dsbOBuL6LECSqGbmBxBFmVadkAYguUJmLKCKqp4hOihjSqDIEVRPRMPGcn36Dag5YIgiCREBymEcnKMvNxB23OCtG9SFBQkRcc0m2y3XJqVLRabCngC6UYb+/lpdokqyd5+oj2DuIqBmx9CcGwEPUEjkIkLTbyBnQhOFSQZQTMIm5vI3UP4sSzB1jxuPE+BNs6FF1BOvJbtIEHLF8krAiYaKRyU5gZOMo+xPUdY6KWYiOB1ErTKJQRRwm41sLtGkUOb/uoZlO44YaeNuH6OPcMHkdolZHGMQJTwRQ15ZBflUANRIaaCqEdoS5ASI0i+RVJLExM8pn2fpCZiOjCZixHTJGw/JCkHaJLAlulw/3iOb51dZ6gY5+J8jb23Jrjj2AhrTYtsRMH3AlrZFAIuXqeFpMfw7Q6h7xHN92BV1oDgf3Icy/FYL7dYWl3jng+/lQ984ANUq1XS6fQPQqH+O6J4c32/5/gh/7f8QOT3iSeeIJNOcmT3JFQWwLUJzCZWuYbVaNNcb2JWt8kOpjBXtlFTcQQRFF1BjUYQRNCSBqlilNFSh8WGRcOFYiZCvCdGcqxIc6mCJIdohkY+rbM0t0Ve1RE8j+qZc0SSESLpKK3tJko8wr137qJwcBIlmSLdm0TrKtK8sUAuH2Vrs4Fr+ljr61x/eYOLpZuD2TetkBwuG9eX6L33BJXy90hkFdZXm5TaDmVcjo92ob3qTchdfUivfhuK5qIUe9HzWYzJfViVDexUD+LX/pH2XXtZ2bAIh8ZB2GC+anM8pdBu28R0Bz2VQBsYwzt3gfPbNf7D2CT2aoWr59ZQhlJ0H5lEzPdw+dwFUslumtIkP/f6t9J2XKxWk7aW5NK2wL6hLkxnmeGUxIYUJ+Z6yINjfOG7T3Dv1EVI7qc3qXF6rsJwIcqZeYvhiQEOai1uzYRst11MW0SfGCV3bAK9fp3WwibpiE1z/+3E6xXshRnyB/YQ6+6hvb6NpWZxkBnLRLjSELjzjgMsbtsIxQOMJuKcOLID9o+ztlBie93iNaNxVq7O0H3He/jbv/xdvvSVB0kmYkzsO4wXCOj5PiaK/Xz9y1/kTR/4ELNNkZkbc8iTt6C2q/iySnNtjWJcpVoq85t/+lf81Z//KdfrbW43toiWaug5A0vKUdlaJ9mzixdXOggh7B+/lTPrLknH58jSeS7tuIWxqMz1NrRaLR5bULh78DDLLYeurMDR6xe5GNnHPYKPGei4no0ieASChBy4PFNSSe+5jX/7zh8jHlEpSA6fevwiS9MX+KM//gRLK6sMF+LsHxrggZ/7ZfYVowyFNno2zRM3FF6tm9DokI+p2Il+Nt2QjqASNUSmahYfuHOEV7bb+I5Id1Si5UTwAh9NkgkFib6IxJOLJeqlFo22w5H+BHf3qvzFU3MQeMSjKh0vIBlVaXdczs5VmLp4gbs+8gnqZsCBfMCDf/Kb3PdTv87X/+t/5uT7fgXlsS+xVmlz/vmXUC2ToFlgZqbEnTmfvNimV+tQi9kIUYXnXjnH+99wBHO7TLpT5djOQbpDOHjwOJlClgvzZRwvIJuMMDNdJ5rQqXRCqkISqRNldKQH2w3oz0fJOj6XFqoEYUi+J8FYIcZm2yWpSezrT9JwfBKaTLnjoRsyTdsnY6iUWg5BCDldZG9/hIWyyePz7ZtiJQoc7ktwcm+RfDKG43pkoyqLFZNys0MyrvKqiTSKrOGEPt85e5WuTJSK5SN7UfaOZPjOt57izn0D7Et4PHq5hLIc8qqTKWjV2LAERoYzrGg6L69UODg+zO7tDX7npQqb6xuM7YmyuN1BEVpkskUerKao1Bw2Xn6c9xhN9D1DuJtVNgKV6F1HyaeTvCer04qrzFXaNC2fakfEUETKLZfNaptEMsZZNc/49WX0qI7btlnYdtnYWCLRnWJVzXCpZ4ThlkdMVWmJKqJq4RgpOmuX0fbuw778PFGviGFEGaKCKEVx9TiBotBqWOiKypHeJNWOwwXP52BvkqWGTcqQoQM7umOIVgvBSFHxAvSqyejOPvSVK1wZvJV86GFFsxRVEawmDUfEC0QKhkQjMoTcdOi0TLbqLtlcnoRVAt1AUDTETgskEYWQeiRJze+Qzvcjj+SJrE7hbq9T3/cacBw25QIrSyavSnc4NJDlSinGmc0qo10q2lqdquVirpdQRJD2D+MtzSBFVMTA4ZU/+RR94z1EjRT+ZoWreh+HdRV5aw5fH8ILJVQhBFWnPXUWWQrRdx7Gy42gtLYImxVCRSPmeATrc1RfOkOl6pEa7EPuHyd5x11omoKmqDSdkBAw/RDTg5QskVMFNqotdLeEk+ii5fgst1wS11/BnNjHpuvSlzJwVQPLdfEiGZqBjmLV0RrrRBLdFGI6/V0ZOPMQCy+fx+obotw/yotOEi6vk5+r0L1jAj3ThRpPIYYeJdNHkaO4kRTuVgVRFBEAwXMgDPFX56A4hC1GSbTXaCzOoB62sOUEKQVcBBJBB9VpUtFSrJkhrjJIn6ehyyFiuot4NEnbslCLE7iKQRyJ2JHjCKVFwniaSKGbSLtGGAQYokdb0JH79mA6Li1boTeqIIgiphNgKBJNMhihQ0TwaUsyb+0LcCSPrmicLdNFlSWUwMV2QpxQ4jUTGVRF4tBYjnNXN0ikDboM+WZCoSBgewF37O7i2y2bRC7O4ZEEDz50lvrqMrISEknEiOT76Wwvwc1Mw/+B+fU6A4UEPT097Ny5kyeeeIK3ve1tPwiF+m8IkoQgfZ81v9/n8f9v4Qciv5/97OdJxWNE6qtI2R4Cz0H0PdR4FCXe4fr0PB0Xuvf2EIY+AgFqOklg+yjRCPX5m7N0MuMFChWLdHcUp24jigLxYhyr3KK5VkONKBh5gx0zBt+e3yDWFjj6jlsIPB/PcRFECaMni9ex6d07RvLk3XjrC2Tvup3N7z6MnkqjxFSK6TyqJCEaEboHkpwrdbhc6hAKEAiQ7BuiOb+CZrtcurbFHW/cycZsmXinzt137Sc3NkjguiheG8d1KDzwRtp2gJgtMlO20QKB3nc8wGYgce30KcYqZ3BHxzl24AjxTpNsew1h6zrRVIyO6/FK2+NwVxft7SYtJ6SYjtCKJJn1Y0hugkrvQZJhgB1ske8fRY8nqdk+CUHkSqdMVVSZlXoohQbK0gbTX/8MFy9dxKxW6HKSyKLE+dU608sNKg2L6YvXOD6U5uDxbvSOR3PT4Yja4PCBbiLNDYR0N4nRXvytVTpPv4C8bw8zn/0G+v5hxt/+JqaUPjRV5KmVkFeNhhSiKrsHs8TxWW6HhOUOE8k0fQkYSOe5Jb6OpCXo685Rp0nvbe/lxGCaMXGNT/7jV8mk4txyy0FSozu5+2/+jhulFr2pGGt1m+ee+B6vPTiBExvETYm8vGpx6R//lPe/+50gqjy6bPLMVot//8AgNVcEJ+RyLcJtvky5YVFarfLd7RZBZZ033TXJRv8Odkh1rq2pSFED3wtptE2+MRPieB7FhEG/18KIqlza6hDYFbqyKTR8xCDEbTfoK2a5sFTh8lKJXD5Hsi+J0T1GMt7PXcfu5+p6B+wah6wz/OYv/jRvfeABPvC+9yCuzFEQNeKajm5uY6+u0N53Dx0/ZEiso0STlBsOQqaHN6UqvNiK8fxSnUuzJVxR5vhgnPHuJCIhh3sjvDBT4YsvLBKLaQzENK5ueXR1JVBlgZguk03o6LLAY+c2kGNp6vUmuWyGpqQhhiG7Rod4OGgz9eBf8JrX3E9hOMP1TIYjh3dgKTFWWzXmcymCRIH+5jQ/Oxnwn5frjE4MM1cLuefwLl68usmQKFPKDXLC3kTO9XFlRWKhFVK3XWavTLHzwEHSqQi6p9EOAgaGerBtl5m1OlldIKKKlMsm5VqbTFSjbnkISY25LRNNEcjHFPJxjXLL5sxMmdWoQssPOTyawfEVUqpAXBUoJFQMVWWjZfP0tW1ed2ycnrjKbKnNaqXF6ekt5m8s8PEP3kmXucJqdJC5WoejY3kOD6Zo12r8hz/+Z9787teyFUR48OIWrx6IMuk1+JZW4MKpq/xUfJP1QObljTYffnOaK40kUmCTNiTun4jzvcBiuBClZGjMbbUZ3rxMcuwAl0yTL7y0zv7334mcjqGvLuKcnWFhbITelIJcLKKJAnFFIhWReXauymv6dV7QZF6/v5uIrvLwmSUGhvYSlUxiO44w/80nse58LfFEhmS0ieWHVDseITKbpsnu7gSaX8NstFAHu3k+e5iTuSxG9zBuvUrD6EJbmMIqDHJp02OfouH/y0PfMFR25xRaro/rugCkYxHKq0uIuQwd1WCg/Qp6OoO1uEHvpEOpWqad66VLADcUua70skto46ky7UAiahgsWhIRqU05jCBFskRiOcL6FkQ1AkkjDGR0RSaUDNyeXcj19Zvpb5qOOX2R9O69GOk4ES9keX2b8X6BiYRGz84MGTpoaj9ba030nQeZbF0jTHdh2x6G6CCJCnvecQ9C6BONCjR7u7nNM2mioylR+uMqkhgimeWbd6FTQx3ZTWjWISfghiKS7yO01vDmZggFESkSoSuj4WysMPfd5xi1amTufR34MrYURRZDFLcDso4sSogEzDRFhFQRIdBYb9vMb9YQpW5OyALt2WmayWN0yz4NP0nermFLBpLvg6QiCwIKDq1QJynJJP023VKb1Nwlbvzd52mMjbE6MkzV8fAjCyQSceLpHPlEDFlTUHHwAxEyvcj1NUI9AVoEUW4SiBJqY52gvo4xPIRjJEkhEPVauETROtsIskIhKhH6ESwf1jouw7rIti0ykEzSUKK4qooiC8iNKoKk4KZ7MRtNSOcxPA/bMpHCkJYboMs6DSTiGjQ9iGsikgSGItCyQ0SviVrZYlodpM9II4SgSwKJiIIsgC+IuG6LjBqlqClUXJGUIbN7NE/d9vjW5S329CV5tryNqsgsl9tMDmUpJjS2GxbprgKhFzJ+cIxQVNiameJfE1+A+Y06471pmmaH++67j0cfffQHLr8/5H8fPxD5PX3qOe68/ThiIkXoORAGN9+0GiZ2tY3nhhSSGggBvhsS6+/CKtXJ7h/HabVorNRQ4yqh79O3rwev0WH76jZ6VkPQVWbOrdLXl+Lqi8vsu3eUeD1kweuQzMeI75hETWeoPvsUajLGmVNTjI+PouazmJkx5O0Ngq4+cL6DEtPovX0XRspg7aUrmIGA4/iMpzU6Ykip5VKxfL7zJ1/j3teNs3h1C9ULcTs+806by3T42O2HMXyL+enr5G88h7L/MGE0RkfNorguvudh5LrJig30+WsU7ruTzlNP0p3QSXanWDLTdK29wvbZK2hHxmmvt/jC6Wn+8Nd/CXd5nfmNGurkOIGsYK6tkj7/Mt85d4E3vP3dmO0WxNLMNz1K9Q6HihqTaot9QyPEvQZ//6n/TG19kdsGkxzfOcnDp89ixbtpNdtMrzXp6YpQkAV2He3j2IExxOoSViTNizemyLzhdtqo7BKX6TTquKUKSAWi/b3MvbRA9o13EzYqWLNzxI0mdjrL0eIAVctmo+MxmI1TatqkIgFjSYVVK6TtuISSwnG9gh8oLMYM7tyfZK3R4h++8Fms8gY/+vof5TW3jTHSn2NTiONUSpiCypIFP/6hX+eZs8/w53/5V7zvHQ/w2gfeQa+1yD+W65SdQTan6tw+lOarV01++9szjE0MsVZuMr+wRUXLcP7aFm8odljdXmIorfFPX3qKQ8f3YaIRz2a4L1xnqxUhIgVoiCwubbNtVVnsGuSeZo1Hl3x2D8QJA431VshYLMDyFPq7ouzqSfH0QpMvPzNH/J4Rbok3uBLNIgAnJ1T++qELnO2+m1/8w/fw9Nf+nnf/+E/zGx/4EU7cciuy5IORQE3GcWwXQpHf+fo5bjt+kBNDGRRZpE2OPlx0MUJ0NMO1sk1PPknC0Kk0OlQ9gV99yz7+4qlZQkmiGYYUMgbjvXFiqoyuSARhiOX4xGIKkqLSXFvgrv0jyKKAHcCpq5uMHr+PYlTkIx/6WV5eq/PxX3gzI3056pUqxydz5BIGlh8i6Lvw2m0e6DPQNJUba01G8glGdY85T2UtTBPJpYmbNR7YneLZZZuRYoLixC7qjQ4vzZaJ2DUO7BpGViR2D6fwOhYPffdFAjWOKCtMba6ys+8EfekoLcujZbmsVDyGC1GqZoutskkhbbBe6+CIIusNi535GK9smpQbFjFVIKZqbDVtsv/S9NYVlfECg6i5RWxfkc9tN/CQuOYlcJptbCfg7fuKXN5uI6k69951EDUMGejPcvnaKrf1DnOm7KNGZA6N9jOejsKVBd54yxDZuVP05vdguhqX1xyS5RVeO5JFFpoMJWO4voEZm8QIBZodhx3HT/DVVYlTZsB/OHYP15RxdjVrnOskyeguw7E6Md9DRCIt+Ty+ZtNxPYqJCE4YcttInJhXwk0O8LRVwOuZwFfj9BgJiimPmKGSjMjENRnT8VltOliShhvdw+XnF9g12stvPbbKW+7tI54oML1mc8/ofpqVOq/NOzy8VufVY2mWaw6jGYPVzQpXVz0GEjd3ks5vmGDFUDZdXj8h4ecHsEobJA8cR7GatCyfDn0giNQ7Ltl4nKjbRJQitCplhEw3ki6Tdhw2XJ81QWUoqRKUVqAVQywOEfEttI5DwyiAIOMLAmIiQUfPoM8t4AEoKt87v8U98SajzhrJTDeZWJpg5Sp+cZza9VcY3u9j906ihz7i1iJidy+hJJPbMYZTayJJAemoRMwKaK0t4fQMEvdaNMUsyeoGwdY8WlcfQiaP0GkRqBFcI0PUtwhbFbThcdzNNXDbmGub2KUmqhbSuXiOtuBg9OSJ9O9BMwwEWUeQXGxkqq5Af9rAFaDZ8Zip2Yx3JdlKxJhzZApDe8jHIjgIN2PHfY+I2yBobCGoKoHvYcpxBBnE4gjJkVkEQ8OuN9FFn2B+Bu/iVZb+7qvsfNV+un/y3Ww0VdZXlpBEkUIqTrpQpODaBLXNm1HUdhtJjxFuzKHnBwiTeaRmmbBdIxqGiG4H1d3ETXaDJBKKEvHAwZBF5KiKKAjYoUAktChGNMLQRwoCbC2B7HaQVBUp0420vQiZXtJbN/C9Ik7goKpxipJFK9RABFmAlh/S9iCiSFhBDFEzGYoEdOQICa9FK1QxXY/umIYsgSfGwLVY8yQ6jsPcpokrhOztSSGLAgNpnZh2U3xHuxOMxTUUUeTFa5sc2NvHTDzGm0+M8Q/fOY+k6EQKg3S2Fv8nz1neanD3gQE+8fff5d577+VDH/rQD0Kf/kd+2PD2v43vuzhkbm6OWqPGm157H4KoIHg2dEysjU1aa2UC22OoP0E6qWLXOwhCiBKP4toOISFCCPX5CvWNFlvzda5cWkfSFfSCwcJak0svLoPjI0dkBnYVaJdMinENQQBjsAdj5178WgXFiKClU8yuVBjZswN133FW2i5rq1WUxiZKNoW1Xaa6uE2t4zF7pUSn0qJRNtk1mubgnjyhd7NcZnqpzG9//ml+v7rE81qLvz4zxReuzfOpt99DKqpTfvoJErOvELbq1F45Q+CHJCMSlh8Qi2jkvAp+9wSxZArzS1+l5833IvfkOVcWGBZNtFiU3O4RzNUVNlsmJ1/3APOuymauSL43Q/HSi4w88iW6n3+czmPPMD99hdBuI4QhVdvH6bTYk3RwkfDcDr/yK7/Cf/7tj/IT73kHX/zCP/LS1CyT976Jd/2bX6CYjXF1pUE2piIhcnQkx7EDE8Q0hU0hznkngVzo45tPX+Qrr6xx/soCtpoiduAIrZ5JtMlJHKvDTE1G3H0YBofJ1RYZlxr0LrzCpatLPPbCNKdvbDG7VgVCWr5IRJZohgo1K+BGdJSmbNCvhnz3Ow/x1T/5GB/9mffwd3/0O4zfcpBkPgetFoIepSSnuLU3juTZ9I6Mcet7f5E//f2PUzUd3vLeH+cjv/hhfuXnfoqBeEgmm+ZueZvu3h56u3I88tQZFtdq2LbHd1+4wdlLM3zq2QVunczgNx0Oj/bR0jMsrlZZXt3m8VoCTxQ4d/4GY0mJmF3HrlW4b0DhRjVkdWaGsXycPgMmIh71UOefHjvP9XKb0PF443iMdxzr5bmLy/jxPLmYxtH+FLIi8eoTE5iKwUOXt9jx6h/l/g/+Eh/+g//Cn33kY3zzxQUqSo5Wz2663DL5rasMDgxwcixLxlBJaDKyKLFQtogrAreNpvmRI71UTZu0JtKndNjZlSChK6QjCveMZ9hdMBCCAAGRmCazUrOoWT6t2jYv/fX/BfEeJvYfhhCunH6OgcldSKHD7pOvYvzW13JlrUbSt9EMDd918OdnGRUbVG9c489/5Rd4909/iEeu11itNBgrRFF0haVSg28+O01PUmfcgJov8fJLV8mILqsVkz3dcbLFPuL5HLPXVlGsDqW1LXZLFQYNn3ce6uaNrz3Jxo3zVJZnKQz0Y+gqmiSysGkiywKJiEzFdJAlkZGeOIgCk8MZ8hmDUtOl6XisbZvEIgqPXi1jNevkdDAtjxevl9luB4SCyFh/F3uzMh98zW5WTRcjkeD8aoOYLuMFIRIh37uwxfDoANmkwcLSFvu7oySe/SaRrQ1Omss8d+oS37paYWvsOEXZpu4riNEUVqPCy5sC82GC7WqHZ88vcK0JdU9kR38GJwjJxHRGOsvcvyfDVsfnSxsaLzQNXlRHiBW6kUrzlJotOmtLLDR9IrpG2wu5pT9NCNiuz2ylw9kKyNk+LmzaXO3dzboFy02HhBZyV18EhBAJiOsSTctjyYI5Nc9LdZknFzo8P9/hvzw8zedPzTO72UDQddKJBCnBZndKJCmF+B0bVQz5ry9vsbtokNBvPibOLTSQojqHxrqpBjLB0G4aO+7Aa9WxtSRaIkVeE1ku1VnzZLo0AaGySrS1Saq5Tq6xSLllcV3qYXlqCsWzEOw2XqNO59plQt8Fq4noNLGCANFq0Il108iOIioa4a5D6M1NTq9aSIKIqOnU4j0I6zOIjU0Et0Pp6WdZsyVq6UGkdAFPS6J39xAWBqE4ShBKKH3DlF98gc6NywiehXX6FAnRAd+j7Ye8ZKXpVFo3HwZ6jCA/jFDfRMdBdCxCzyMUZeR0FmNwFN9xsBomRlQillHp3JiifekcwemHkAMPMfSJOCYGLpoUIksCbgAIAl2GwrYNgxmDnC5TSEcR7CZ+CEt1G0+OIvg2sniznMdTIugSxFpbiNE4cu8QdrVGc6mE1fYQZJkdrz+ILwosPnGezmf/nt1+mbv2jzM8OoooKVybmeWpM5c5v22zrmZx1SgtOYq9sogXSSIKAvXLl5HmziPOnUPwA9qnH8dyA2zHpebp6JJEVPBoezfrbXMRiQ4ysiQRtbYRJQEvksZLdhEIIiVXwUnkSQgWteFDzLsGmiKjCKCHDqudgKjkooUdNCFAEkN8BHRzCyHw8eUI6aCFpGlkFIH+uHrzJd4L0GQR1bfo1kW6IjInx3PEVIWhrMHOrhgjCY037Cpg2j4JXeFAd5J8XEEEjozm6e9PIgnQ05NBj6fx7M6/6jrLW026s1G+/sQr3HnnnSwuLjI/P//9KtT/iCj+dwH+/3n9sOb3/wnf91169NFH6e7Kc/vRw4QIhFoU3/XwXQdZlTHyMeJFg0hGp73dRk1GCVwXp2pSn11GSUaxFRFFEkj3J6m0Hb64sMJHVxb42Px1SltNBiZyNFdqROIqSkRlYCyDLArsefu9uIs38HxI7t6Bmoxz4O69rJ6/gCZaGC98l0puBG9tgYXnb7AxU2FutkIYKgwe6GdlvU7N9lFiCnOrTQqGhOlDQMiLrRof7hthOJUk2Qz47QO76d07jlgc4vqXvo5UW8dpWaxcWqB99ToBIZJj09KThFYL1TVpJIuk4jKC0+KhDY2J2hRUlrh6cZ4pIclLiQlK8SyV+VkGtpYYSiXYlQgZHMjgNhwS/Wki+8eJtSzSskiz2eDpz/45A8sXGZXbvHDhBr/1f/0qhaOv5ff/8jO86rbj/P1nP8ePvedHOHrsOLHdt/LyYo2x7gRdCZ2O7fDSWhPXrLPZ9lhzNE6vWKypaW7fWURubLGodlFVojzKCK2+/Zj5cQrvehupfZNEzQbtuok/eZT59A605iavKzq8a9inmNRZrltcWKzy3EyZdttCtRvs0lrYiDy92EGJJzh64jiF4XG+9OUvoxkqJ4sBUbfFZmGSVHWBLSHGI3MNThZk+nvSeI0mr8gj/MhP/jx/8Uf/hZH3/CanVkNuPThI22zy2Aa8+dgw+e40431Z3nRylMG0RMpt8qqdWY4f38dyM8Ce3Mmteh05keBdrzlMd1rn9LV1pq+vkikWwXXI9HXzgbffQzGX5vxKkydevMyXT82xUTU5v+VgOw4tNcm1hRJ/+tAVnpgz6cnHmBjtpmRBYDkIoY8dCuzpSSEqMtfXmki+y5iu8L4/+AwPz1znqa/8PVeeeZ6ptTrbQRQ9leYDd02SjeoIooASeixulGnbAZ9+4gb/fKHE09dKpGmzUm6gySK6rvLyWgPXD1kotXjqC5/kyhd+k4F0hHrHxQ8Crl++xD//3q+y500/Se+tbyMV19loWDz3lc9w8oH3EfUsznz7IXrySerTF/nmU2d5aabKmUtzfOJ8m8/OhNS3q1yfn8PeXuKlhx5h7foCl5YqjHjb7MlpdA8MkI3IaLEom7UOoztGePRGk3uGY7iOxesPdaOGbXaNdlEcH6ZjtdGUCOZD36B/5Tx37Orive96HbfdcZi9411cvrrMzHKJ4YzK1avLjGRU8F3kIODlZ86xvWXyzCuLyJJIVJeYqXa4tmlyaaXO5kaNP/+np3jpWhlDFJjoitKwPVRJoCpE6TIkRnJRhtMRXM9jqJhkRy5K03bp2B7JtMHL06vcWG+yczBPd1bj0b/4OtlTj4EaMrh7kv/44HX+/PE5rvUcZHnwFhq+iGl00aUH5MdGuBHEeHKuRVby6Yqr+GGAH4aMZzVEVUKNaGTTOtNrdVLFPLaoMeNqJHsG8VQD+iaQNR1F1bhjMEU+rrLQsPjO5U2urdToctqc37YZyxkM5RNMFJPkYgr3DKdQVJWO7WF5PgTgE1KMqiQTOrffcYhKJMXA5AgVT+WFc/MsLZWRBRHdbXJl0yQTj9F0PPrayzRabaavrjCzUWeHXAMgIXm8vNjCF0TOl118xaCYNhCTOapamu1IN67vkTEUxmQTxaoT5PqoKBnkaAKqm4zba8zXbK65BsWogNjaQBnZQ2T3ARzbJlQNgo6JGnggSnSkCHoshR3Nk5N91somqWSUfYbJgimhtmrI6QIOMlatibFrF4fvOEpvUkXHx9XihP17sfQMggChWcGZu4iqibTml0DSid9+B14AYRAgLF6iGUhIu44hJHO47TZOu41T3QJRIQh8MOLYc9O4rTZSPErh+H4yu/rxbVCzaWRDRUhlaS+s0bp+FZamoFlGbWyg4aNLIpmIjOMFqIqEJkuAgCRKpJSAuNckJniYnTa274Fj0050Y8b7kPw2qmezaEs4koagaNTn1on2FYjlokTzUWqrFQ4d7UNSJMzVLRqPfhv/lcfoi8CugTxHjxxmv9jA8DrMl1o8uVDj3HqT9ewQVqMEzRpKzCCorBM0qoTVDcyZWeQz3ybi1BAIkEMHtVOhT+7Q8kJEAUqWRN2BdiSHh4wsCvhIeGqUnNjEiyRw/QAjFif0A6xAQDe38UobFHUBK1DQQ5+M4iFycxdY1HTK0W4ieKiygtwqkQxbpFQgDG8+dwUQI0kiuoYXihzuS/LA3i4KUZXhtMZi08F0fXb1JREAVRHQJZH3nBzBcj3unMghigIj/RkC3yf816seKNU7OG5As9EiFotx4sQJHn300e9XoX7I/yG+77KHv//8F9FVlT5DIKit47Va+KUNfMshcH0UXcNtO/jtAC2pIusKgSDimBbOvIWajlMqdzAyGp+7cJUZ1+Ynbn8NP5fV+PgffZ2losyB9Ra+D37HJjFZRNEE5Esi+tAwG1/8IqGkYNx9DHd+EzWQaXXaYJqEF55D236S6VaLU+eXMc2ARFcc75GLKHrI2K5eluZrhH6IW7VYa96c+XeNFiNEWa46vP6Bw2w/cYmNpsuI5aF2mkzcthdkke2zF9n2IgzvO8p3twwOBSVysTyqF/LIpU2mri/ywVcfZeupZ+mfOMqNsoYlysS7+ymosCcbwXryMf741BOow2muPD/Na37mdSxNzaBldJx6i0sDg5w8tBsIeOurbuX3P/oxHnz2EX7sx97JH/75V9j3ro8wtHMPI2md1uYKD337m3z3N34KO2yzUekAAl1pjeVSh7GUQUoPGZJanHViTGRVLm8KzJctrvV1YRQkJqNNppoyZ9ZsjvfWUQIB0UjSk9wmOzRCrd5mZmqFyliC5NguXpxeYmpmjmhhGSPWxcPP13ntXfs5fW6dhFuh2OVhzdcYHxxl/rsvM7fndv70k5/k6vRVfvU3fpM3338vO+5+A72ySlRTiJ1/kn0nbiMeTxITOuT0KquOzFemArbXbaRoinSxQIko9w0E1Msddkg1lk0Lw2mTMyTeMaaQy3bx9TUJvVlhPCFRTQjMMswtzirXFmJ0ZZPck0khSCJDGYONtsdr9g+wUHO4Ug64tmGS6+7m+bkyoaawtmWi6zKFYpaEAi3H5wtfeZx33LeffF8PV2ZLzFU7XFxSyGYSjBZiEIZEZYmnp0sc6ovTHxM49t7/wOVnH+RvX7jE3VqeqbrIaEZnl2Gy6oWYdkAY+HzpUol8Kk49VAgJqbQc0okoMxsdWoU42C66BP2FGIPuNl+5eBlD12i1OjQsnx7N48uf/WN+7RN/zdNzJls1C00Suf7iI0QH9/Hy1W3+4kf3MZPp0DA0fvPB09x6bB9Bs8zr4iv0jiZBMTlV9lF3nKR1dYZXnnkQu7nF2/Zm2TWSwY5HeeOxERRFYZA6O/LdtOwQS+uw1XHZF/dpegb//l2348kKN7abjBYnKQsOi3NVJo/czkrV4i237+Szzy/y9v1dPPlSmW9/9xTj/XnmLy2RGNVJ+TH68lHuu30vmVyK7apJf86g1nEQwpCDYxmeu1YiEdVJju2gmI9xzJ5ncP8RljshqcDEkRQCLYFhBRhSiKBH+PS3T7G2b5yebIzuhEaiYaPEYixW2vTlsjxzdQv9zjdz9z6Z9s6d3DhbQ5Ukcm6VoZhAX1Li0aUOL0+tU8xE6GpWuSPl8uLAEB/7++f5kbfeyqPTJWKGzNZ2g4Ghfqou3DOe53svXyPQDSKqxI1ym+26RMqQGMsrqIpEWgJdgbWWjybAwlYTWZD5bivOrkqdWDqPqigkdYXVpsX0RoVXdzvs7Rqh5nqsbJrE1ZshALbt0xXTiEQjyLKMqLhkYh5378gg+R1s12WNBBeWWvhByFvjKqWFVTqlKjHR4UrZxwD29ye5WPGpdzxMJ+TiRpPj/hLziUECz8eozDEd7UeXRcaFEMms0NH6YGuGRqEPRdXxEz0ktzwSxTSOqCHH84SldYRYGnwBQdFwskMYfgtHTbLW9DEEgYFYFPXaeboaTYyxPWgba4TxDPG4hudDSzKI54t0UhkSmkZE8BHtBq6WoC5E0GQJodNAiKewLp8hevAoSt3k7NefJTawga+EjN9xgJYrczBcRmyLdJQE5SBJtF4hqQeYSpxEbgDBaaOaNcz5ZSLdOex2h0gugVNpIOsRpME01SvzJHdPsPS5zzFw10HUVBylOIRmpFCkFJok0HJ9euIa1Y6LocgYEoSKSEfUiPsme+QmnbCIFerYxHB8mayoEQkdEpqEoMcRcv3k7roDmmWkx14kNd6NmC4Q1OooioQY+rRXt0jvbSNvzEAsRjRRRI/p5JWQ8UyMWXUHbqfNRmmLmatLxPDId01Q0EVigQVWi/Z2HW1pDjGZIzUsI8kyoSgR89uUiCEi4eNT6rioCY0wACHw0FUJN5QIVYOoa1L3VMpeQI9sYwtRfM9HViVE30bQVdpoRJrrNIxeIjI4kRQR2yMSmPhKAl9PQuijhy6KpJJUBeq2R0ZTCDyHlC7gCgrLdRs9FGjaAa4XgiSR+pevPJooUu/4KIrAZCJOTL05MvK6KGJkcjQ2Fv6XvrOy3aSvkADgvvvu47HHHuOnf/qnv1+N+m/8MN74fx/fl/z6vs/5My9x65F9CPg3ow/XFhBVjcALcBoOoq7Q3OyQ7I4RyRr4jocgCPhtn+ZWi0ihwsCBAb589jLd3Sn+yx/8NHKhi9rzT/HmOw/wy488wwP3naB/softqSXcmkngB8QLCZavTFF853tpnzlF48IUnZV1rn77InbMZ+lr32Lu2atUbY+p2Sq1mz0bCFWTZ1duIAC7uqPYrohYDZH9ABMICVkS2/xUpJdKy+bCmSV80yLfMXjmn09z77/tI31wD+31LQQg0p1jeWGL7PYV/N40fdXrCNE0tWqJO/f0MF0vYx29k2itRHJtiUIhT1qXqL50DktVkaMaaizK4PG96IUyfqPCtWevsffu/XRmpnnyxjK/9L73Uw9ULDXJJ//9z/Irf/1PvPvjn+DH3vEekocmGMmrlJZn+JUPf5iPvudNyGFAUzX4wN6Af5xu0/mXNJwjw2lKG5tM0YUhhHznaoNEwuDuvVGWag4jvTkcJUYhnWdnUMavbiGVl4hF8tRG9rNVnuEbF69y7tw5Zj73N0SjUdKTt3DH4X2sr69Rnj+NMH2DR85/nlvvuIvXvutdPP2Fr3MisokimXiqwmQCtlsWtxSjuD/x85z+7hd5/dvfxbVKh8efPM1nP/UpBno/z8+9/8c4evJW5FgMZpextBj12Wv8zESUlakZ9ix2eOGrzxJXVdQ37uf1ew4xsHWGwJpE7xvhHx4+w8l7jrH08jyJgV7u6pZ5+hPfpHhkH0xdZ8vXSL32PtrRDOITzzNy3710RSS+9fIK3z27gigEbJky2kqJpUKCwd4kUq3Oc6cuM56L0DXQTzSq8Xv/9Vscvf9e8l0JPCRq7YAnXniJAwcnODme497JHOdvbGFFYnzqoUu8diLLbR98L5/6q08y9L5341oWD375eSpvvZemY1HUJFYqTSJRg0rH4b7JDOevb9CdNXjmlVW2gwgNN2RfTwLT9iiqAfPPfJeeyf14vssXP/n7tGplzHqF4df9BJFEiurmVW48+NcsSz5d3T285oMf4cXrdR5ZtLjz1tvYskNeffshXnXXYTZXN+meKFJ64nkGYybnxTR9o/tYvHKe237xj5h/+K8wm5vEokU2PBF8n5TXIJQkFEUlFnQ40hentjLPrFQkEDq0LJe+vEZ+IMn5lQZ7Zp6j9ta38KfPLlBq2uxIy5zcN87DL87T6XiMTo5w5exlfvQNR3DjMRbn2+xUJHL5DGuNNsm4gQDMbTbZ1BRuG8+RMmSSkkF5s86pF6fYSkm8vbtGV1cOp1klnkwTylEUyUdXYdv02TNSBEnA9XyemS4zt9HECkVes7+H7ZaDmNQ5JyVAvsTZqsTsjVVkp8HOHbeQkAOQVO7rMsHJ8fWLmzy1uEQ2nyUp2TQIqJSbzGw0mOxPIuoRLrckdrU9IorHif2jXFprEQBV02Gm1GLHYIZcMkqXIrBStyh1FHakVUJU+rMG6bjO1VKLy3Mue3tanBjPIYYhnh/y9YvreGKR/TEf1/YZyxoIAlxcaxDVJbZbLllDIdBEwiAgosQwzCZV0+GxTQlNjYAXsrHd5CFToZiWGdwxwrk1m7fuThLaZSbSYKsJ1ps2AgHrHYG6ZuD6EoqmE8azpBU4u95mPGMTbCzQiPeTVKAkJyi5EbyWgCTC7oE0lbaFEIthezJtIU5vZ5lQcPGyWUBi24ZMRMALREJJoJPshvwEW02LQ3qAILQQ5BybiQFc3yfRvoycDZhvy+wImhjizSAVQ/Q5t9ogL8QYKPQQ2VVDiqUw+ncxqcSZ/YcHCY/cenMsWM84DadNZ+Yi+f4iaUNGN2tIhb0EiPhaArG8glwYJNq/D/vcczS3LCKHD+Cen8OstNFSAvGBAmtPn6WxvE1t6gau5ZDZuUl09BAJ7eYLyVg2QhBCChlVutnoJRMidrYJfA+50EfLF5EjcVQtggKYQYhFhIzYRuk0CaMxIj09uMsWPffdiqgISHtuR7j+CqHn0V7fYOviGrn1Lcz5FXL33Y8QbOBVNhCHdhBqMfRIlHQ6zVBvAbHTorI6z0bDYrHloikGRbuJse8AatSn/sILFLq6CZJFGmoKSQgJfRBEEAKBmCqh+R2aoUbbFyjKAa7ng+uiNZYJXIFpukkpLeq+iq2m6RbbJP0mDgmsUCKyNE18d4H1jkxGV9EVaPgJdGQEJSRwQ2SzTExPo4kylnCzRU2RJbTQpRZCISqTiigQwlbHZTQdZX6zybrpUW67TBYMTi/WGMlFyRsK+ajCesOmqz/H1rSE/79wnuXtBgOFOLPLW9x77738yZ/8Cb7vI/2gJiwIP4CaX+GHNb//T/i+5PfatWt4vseP/Nj7CSSVoFHBN9uI8Rie2aFTsXBMF7/jEUlHCIIAWZFx6i0ERcQyHSrX1okYGldX6uy1JOyleeRYgsATidRavHqkjz9/doo/PDqKaqhAiBLX+bUPvo2P/8UX+OSD70K6cJra/DJWuYllB1zfXOfbyy+i2j5Xzf/xb1wxb+7uhsDVdRMRWFcgEYICqAWR1xb6OSKlKG+2uH5jlXU75ETEZHi0wOYLF8gMxKlsmnTaFvk7TrB8aZri3CWiJ/ew0MqxrZp0RWW0ToWBjEY+ptMpX2DpidPkP/phJNfGWl1HNXTUkV4a1QbVsxdJ9hQxb8wg+Q56OkLNF1hfnqN39yHKcyt4no8yNM4v/NKvMf/4l0ldvk6/e5lrKzof/v0/4BMf/hkKpXXCbC8yApfrPoMZg7rrY8kBW22PvUNFrlZcUobCRJdOPB6/KRKrK5zYm2KzrTOsuSR74sxvNEj130K12iDRqPLRP/t7JoYHecsHf5aD3VFC1WDTjSCrMrMr2/xM1kVo19lQu/mFn/tJ3nHf3RQn+omLKoLlMtuzB2u7RScMWKm24eIFKqZNShPJiy1++0tf5S9///egWeYvH/wOf/a5L/COt/wIR08eoWvmEmK/S0f0SftVkl0DDHUbVKdXcP1duJcu0Kc4VGobPD3d4PV7uzmQd2gf6ybr1miYUZQwYOXF8xy6/xCzp6cJH/8Wu977o0TvOsp3VptEBQ/P9fnZ1+3ni8/eQJd1nPIyrg+3FmRa8RSzNYvHnz1DLnEdI51meHKEC9Nr3JWOcXIsxf5UyO/fCLjy8jlG0ie43nG5ayRKJqpSLvXwjWvb7K6EbJRrfPJrL9HxJUZ6++mNiJxvuHz+0ZfI7NhJTFdYX1zDr8ZohiIv/OXvIKk6le1tXgl9UjGNcr2Noul09Q8T2XUP+a4UVnmV2/ceYLbkENMV5tZrLD78afK3vI3unXupz8+gWRb9XQlKDYd2KJKJCLz31kE6CYPDfeOs2j5TQycgLXLIXWS13YUkuOwZzPDjH/89fuvDP8noR/8t5sS9ELh89UaDkyMZBtIeBasK0STa1Gm6xo/x3Q0RSXfJZRMYUsD09AJX5i1i7nXkWJaR7i5qlToRq82VzRZDvRmsrWVifSNcW2vx80cn2XZKDKcNrtS2SSmwtlFjURAp102EWITTN0pMXDuNOTjG4JBONdtLU44gqTJDYRUrkaMl66TEkIwuYHVsnEDm2M4e5io261tVPve3X8XyBE7csofoviLbYUDJDXndgT5+66vTeJvX8OwWA2MjHN4zxpbv4zgiXbLEfYMK1xd8Hilvsbq6zvrSMrccP4iwMscu1eCe8RG+c7XEoZEkcV0hH9NoWh6DuQh106YroXN5eo22GzJaiFEzLV5erHPvrjy2L5EJW7xhXzfPzFXRdIUbs1W26zaZlE7D8thbTLFjopvr2w47h2+OLuxK6ARhiOOHGH6IKgpUyk12Gx3OLDbZlmB2zuSNosyltQ4Hu3SGhvpxvZCyabK3p0C+J6QvG6Xtu0SAl6oysajIStnkrvE0y1WHuUg/shBiWyG9yQyabTOZ8FiwNUZ33EqAwjkzzmTRxxcUlmsW+4pxNLHDC1sut+gSWSwiXotmbpjE9ix6pIRpFGk6Pt1RmemyQ09Ep5EdQXY6jMkiVOME6S5cPQaCQMdXEbsGiaRSyC0J0XIgmaDU7JCUYdN0UaMRwnYDKVsEu03oeWRzMaL/7sdZXVhFkkQys2dYW2lR23OQQJdJqhJSIocQhuiBi2jV8BevIg/sQMwUEPI92M9fYOPKFiOHJtl8ZYrx9zxASECh2UDsVLErTbZvlJBFiHk+siBQD0VUIaAdQEKVEISQEIGWH5IWJbztFeT0EGoYkFFg2/cJkJBFgbobEAtB7TQIZJ1QTyAN7URwXZRYAj9ZQBrfT0ZVEc8HNMsu9bkVsntHKZ96HjkaQ/ba2L6P2OuQ741QV7sQRRFVFsiWlkjbbWqbdezuPsrRDPPFncQ1iXzWJtYyCQo51tsBcU3+lzGMNyc02G5I1NzE1HoIkZCcFqEQwfBMPMUgKK2yNynTMnpoOBIXV9scKRbpi8k4IcQlH69/L7JvYygqlg9IN+vfk1gIvoMjR7D8AK1TpaP00PJ9OrZPThNQXYuYqhFVJWKKTBAGDCd1BEIODaR5araCG4R0GSq3DKRIqyIxRcJ2A1RJZKAnyWw6S7NT+1edZ3m7xc6BLL/1l1/nMx//IJ1Oh2vXrrFr167vR6X+Oz9sePvfxvclv9PT0xhGhL/5m7/hRw/9Nl6rTuA6NJY2MNdK+ASUrteJpTUEUSDwfMSYTnV6EVETqdYdoimH+nKDiCQR0aC11SB57zBO8zl6dnbzFl3j12sX+dtPPcX9ozl8V6f75DjDt97Gf/zUV1E2b6COjLD1xLMoUYPJgTRHaxW+V99m0k/+f71+/1+W64L5L7/1HewnqDTYWmjQ8QK8IEQG0v0pUgkRKWjhiwWWv/sMXuATzC+w4/huZgopNnIF4p0q/apJTvJw5+fQ+3oxr24SOha99xzGTg+gXH2WxFAvtcUq//Sd59mnqJizC/iWg5LLcPD1e0nv30Nt5gbJcodke4u0YOF4IvWOROPBr3IwJ3NmtcKZv/o0Dzkyn/jrzzCyeYV29zE2mxa2BwstKCZk6jWPQuzmJ6JtGwpxlbgSkI7YzDoGaUMhlYgwVXIoJCM0OjZRQ0PSJXTXxIwX6EnKlOeu8J7f/QNsz6WjyCydfY7Hn3yC5YrJysYWfxM4tDyJN73z3bzqTe/gew8/yN2mzfMLG+y86yh+qsDkUIGZSpuvXCwz0HuQtacf5vQ3/5mvfPtp9u2c5HtXStQXt/i1Q5Ns7jrIX/7pf+Ufv/bP/PLdhzm6c4BGq0JHDZE9m4Nvu5vNC3O4vkF+sAv1xL3MdQwySY9a1xCaDtF0lM70PH4+wq3vfT3h2g0ifXmODPVwZqbD0qc+zV0f+xCHV27w2OU8r9+VYiQZ0HdvP/Ubszw8JfHqgz2kZRMzmeQNtyY4ONmD06jz9995iRP7d9DasGi7Po1aHamrm/uOjPLKmSt89/FXuH13ke9czXJwMM29uwr0d6W4MF/Bze7gwtc/iZLsojlxkNsOj+A7HunhQVKGzIGeOGPRfqpNm2tXNlm+cY2eW97CyfveixLLsHswTbVU5YVLS9QaDa6fvsz7fuodXHEiIGkkJIdkZZov//mn2XXyHiJjOyGiUpZkRN1AbbcZ6yny8koDsW0yFvGYOn0Zrr6AMtxN+kaFJ3ccYm9/F8cLCZbm3sa3/+YPuP/DP8vJH/kwP/KLH+Pj756lfudbEG2fzz89wxuODxMRVUaEgPne43Q9+g0GDr+dcGUOfSDJU9Mlju8f5rFqhbWVJvGBDIYkghTw5OV1rl2+ji3vJd3dRfPsDVI7d/CVcxu0G22WqymeOH2FWw+N4oki7Y5Ls1Tjg7cOsrpZp/XUY+z7zZNsuyJDA1Hmam0efvoC4/dM0BaT9EdFNpsusuhTNcHHJ2OViHQVCFyHe4+M0erYPPbE8xiCS+/OSZ558Qb7fuQkuaEBZq/N8x9/+nXYoch4PkqzY+O4HRxFpmG2ef/+DPfueiMvzm7yl1+weN09R7k2u8brj41S2qrSrrQ4vLuLTdPF9QPOXF5CiMfY3q7S25Vh70QXASKLlQ4hAaOFKLIgEtFElpoqmiywuydJqWWzVbWomg7PzVTx/YDJXBxNUZmpNzm7VGcwZ5A3VB6bWiUe1+k2BOR0nH/++vOM7kpx21gGvdPgrx6Z549+9BC3DkT58vktDiQ1Hnt5jnedHCOUJfb2JBAlma1mk0FgrurQL8rU3YBG22Pb8klHdZKahOgGrJsBA3GVuptE9G0qgUqhNk8m30tKFsgZMl0ERPT/D3v/HWxJVl/54p/0mcf7633dW3XL++6qNtXdtKUbmgYBAuHdgAToCSGvkUVCBklIGiEBQhghgTACupuG9tW+q8vbW1XXe3e8TZ/vjwv8hqeZiacWM/Mm4veN2HHi7Dhn584TGSdXrr32WjLH52yimsbJi+O8eiCOL6o0LIegrR/JrOEHoEjCBnhXZJqeQFgWCRfnEBIdmJ2bWRZiJJsbfuR5KQ6agWQ26CgX8FNp/PIycbGKm+lnX0cE37N4vmiw01snrikEK7NIsThKWxfReDuiLqLVJmhXWmTkOg3SG6ROLLdBSNhlxGYJoWcTghHBC0SUTJbkSDfmxatY7TG0VASnUUPpHcIYlUkGHuZaiYGhPqKb+pDcFjUXIqrAZMkjYchk1YCaJ+AHAgt1n1g8Ras7husFOEGAhbrhyxv46HhYgogTSlJTQzQCmZAiEfWbOAFotWVolQhiaQRVR0lm6H9lN/baGko4wuqLL1BYrNO3vw+tWUcXAmSvQbh3N5KuY4+f5vRf/SN6Iork2RiJKHt/5ZcwWzarvsrq0iJTSyah+hmMZI5Qro1iw6WhSIRkATeAvJxGkSVUScYMQsTqa7S0CFqjiKr4kOvCbAa0hyXmKgJHZxvcty2HKoAggBXNoQkuciBsxGq7HlFFxAkkPFREScLUYniighx4G9/xoOlBxKygaBEMRcYPwAkEqvaGzjqqqxzqT9EdUylbPlXLIwh8FEVGlkROzZdJx3VCmQ5cP6C1MvVvMMPCWpXb9vbxhUdPIcsymzdv5vLlyz858Pv/r/9l9R8Gv47toIjgVcrYpRKBD631AvXlBrWiScvykKo2ggyJoQ7M1QqtfBPb84nnQsS6E1SWFnnDSA9frOd5awu8+St0vOZeCo88CIrMe8t9/OnZS7z6yCZWp2tweYn4rXF6N2+ieOY5YpKMmghj5FLkHItbltI8NjPJJv7H4Pe/9WPMza7S7QrMVmyqrk/Fhy09EbqGc/i2xdyFZaJLLRJ3HaKS6abU249nyfR0J+mQapSOHkXdc4DKxFUkzccuFFg7NcXIf3oj1vRl6k8/gj9zES0e4ezSMkfPTfGnt1+LoEhENvXSmFki2t2OtzJDORqmp0dH0EJQmkRPtyMXy2y/5zpE1+TItm3UXxonffw4VSWBMLSVl4o6MgEjCIQVkXrLomK6RHUZTZZpugFC4JE1VCzRJybBUt1mV3eMkK4ws17m6LnjnH7uKbb0dfPhn/sgGdvn9NgVKk2br37qE0xOTrK0mmf3/v28+sj1ZIdGiWc68X2ff3r+Ki8+9gCzZ59ncWqcWz/7WQ7rImsLK+zs0JmvNkiEQ3zg7p3MrFcRIr/L733xz3nnvi3cc/N1FKo2T4f7eGB9mUYxwn279jB0w24+/a8P8ocPHkW0bSTfxm2Z+JLEtbt3ciTdQTLdRiG7iWi1wb039eMV5xmv97JZFJE3bSNRXsdr7yYeFbDzBcR0B/03jLDe10nLgeTmrTDZIit7HF8W8EMx9vqrDG7WyHkLrNUdzl68woGbDxONhtm/OUN/WwJ/aYbr22Tm0wn+6pNf4JFd2xkZaOe6V1yPe+wy6w2XsOHz3WPjvPamnWgRid6Uwvb3vYfPfnM7oxkfp7bAH//Sh6jbHmJykDe/40006wrX90cYryfIxUM0J97KlRe+xyNnH2HLtbexKXU3gzEJbd8gMdnn0pZuUorH+tIq33rxKJVLj3Hkmh186lOfQgineODsKkZMJRffQSwZJScpSBLkwipV4EtPXERxW0SjOXqMDKk9nbSkBKdWHBary2w+cJi12jiffPgF3nrNLlqH7+K9n/0iNwc5rrv5FuYbPtMlE3yPF+dbrBcaqMI2Mi2H0xMFFPsEy6FNXJMM8Qv3bKfkKXzpxDoty2dyZplINEbYqWPPT9LItXHt1nbu3tnG/FqLZ9cLPD62xuiOzWhRg6Rrcv7kOZaWS3w1HWOkI0b+jteT1uNkQj5FV2S5avPkC1cxQiGuPZDEqZZozS5hbN+KokqsrdZIhDTimsz+vjjDd+wiVl8jFIlQW1zlKy9eZMeOzUyuN8i0Z6FRZ92EzVkVxW1wftXmmk6DgqPQEbJRYj2IJmjRMKFohO39aURNQYuEKZZa3Lcji+y7dIQVpkotRiI+xxYLiHaV2XIdozPLrv4MvQmDqVKTpKESUiRMxydv+qzXbEbbwnREVabzDbSKSUQVEZGYKjVxfI+bd3bSaNqcny7QEdOJNopMmwkObImyZLncum+Q3rEX2HPvraiByJXCFhKZDDGrwHXbulko1Dk4mKJp21xZtaisVzg2V+UNO6MEAYx2xKg7DpsyEfpiCoKi0h3VKZsOUVUkHrjo+KTCBuEgYKLkYuerdA/EqbZCdKseddmn5cN1WThRU4lbFTxfQ3NKJGNteEqIkq+iyyKS4FGyNuzqRFkg3KwQWBa2YjDTcNCFJiDgRLMM1PJ4SghxdZJ4rYzYO0CwehUpGWe6atOXDlNoKRTmz7HanyOUv4TU3se8EKXfM8kqNv7aCmomgTO0lZm5ZSYrTe4cEpHLi5j5PMLQLtRmEyQZUdxYeg/sOqGOJLniOsvFKh1bNvPoZZMbmhPE9u0jJgrEHJNAENA6+6G6iml0k5Js4kpA4EPNg6rtE1UlkiEFITBxZIOW6xEXHXzJAM8jKVhI8+fRo1mkbDeuoKLbLXQvwFEMJALcSBbRrOBLGn66E90NkMNhIqM7cOfHsZo2F88ss5hvctu7bqR6/hLaap6wF+BVaxRPn2LtyiqJaJnTS3Ve/ca9OEsLqJk2ulqrdI8OU1FjrK/nWc2vUJibQIokEFI5jHQGPxCoYqD7ArrgoUgQaCFaYhhdCyNIdcT8MktkyYqQCylEVRFJCJAEHx8JSxCIuDU0fALVYLXi0xOVKHsyfiATQyQQfBRJwg02HKOyhoTpb9iwWYGELIBLQEgWkAWBxVqLnniItojCc7Nl9nfFiBkya3Wb00t59nfF8AOfXd1J7m/VEYX/tm52uVAnlwxRKJYAGB0dZWxs7D8Co36s/ndofp9++mn+9E//lJMnT7K8vMy3vvUtXvOa1/wPv/PUU0/xkY98hIsXL9LZ2ckv//Iv8/73v/8/MOv/9fUfAr9Hn3ga27a5ZvsI9SsXcas1JFXGLjVorNc5PVOhTZXJdUdxWi6B7+OaFlpUY32qQKozRqtiooYUkj7IZZNFwaNjdQkjlSO+eRA9usgBzeD2uQK/c2mSP7j1BsSmiStqdLS3c3x8nusTCqlNXYw/cBrfkDi5WMdHwMFH+XcYWrjA1dl1towMUhZLVHxQgf6kTr1Qp5XIEX7PK1mXFBRJICfYxFeu0n7jzXiqQTA9hpKOsX7qPOLSAp7l0HHNCInhLorHjqNFVArPPEx8MEupLvBfLo7zyXuOkO5N41VreMUC0d4cou+CbrC4vE60fRuNeCeyMYHcLFHZcQNdXhlh4hit1AB3f+Amjp07y9TDX+aaN/8Mvcgs1F1ydoGErnNmfJWRnEIkFWOpapKKaHh+wMnVFgPxDgTHw2xYVBcm+dqnPwGiyK6Dh7ntje/gka99gS8/+DCduw4jSDqvev2bOTDcxStf9wY29fUh+C4VVyas+MiyQs0VuG73MNmu97M5+4tcujpOs7OLSz60927F12Q8SUcSBHRVpCsXZ8kc5Of/9LMcCNXRdIm0GuW26QkeX+nm2MUlvnpV4v1dLX7x536WC3Sgfu0L6OYaOw9uITK6mce+8SCf/JvP0pBsBo7cyWteeQeRgSxu7wjYDqyusVwTibt19OJ5vO4ulMERPD1OVUygjeSYruWJyhL/9K3nONMR5hM3RrkU2YGx9wBxfC5JOTAX2T51nLP9w8yWLHZkujDCIWzZ5+yFOcR9g7zldbdi+BZH9nby4IU8r9rbwRdeypOy1pharHB0bB2rUmJIqnDNQJRNbznAU+N5XjwroO0ZxSxXmD/5KN//58+i1tepvuktKKM3MhQV+ItfeyfPztzHcqFK6eyT/MMf/Q75xVk2X38rd99yA7rd5PhTZyg99yTLyyX6b34Tr/vg61lzffoEl7dc18szMyX2bcnytZNLLK9WOHtpgV+4dzeipHHdlhSffnIWKduL0DvMYxfXGEr7ZHJxBgcyjE2uctv7P8SnfuV9zOzYwujwMNdlP8r4Cw9j1Yvc96a3cP/DR/EVnaHNW7Blg3U1wa5EhEcsDacSY7grzDNXV5i+dIW33bGfN+3L4SsaE217kSWVeDxCrdGg1vIRQlGKlkBH2mC4L0cuHUUQ4LmLa7SFAm45spdvPXKB8dUm5RY0Ghr7CJis2Dx9YoY7D/ZT39HHs8+d5YZrtpLJTzM9PUH73lE8VaM9ZjLWkOm2A8K+gxltIxyO8PH7six++RQf6Wjj1tE0lmvRGZUZvX4X2zsi1B1oeQLtIZuyA6IoIGsRapaLhIyLwKGhNIs1k6GETH9M5mkbWnGD5y4VePXWFIHvMzqQZXRrjM+/MEtaFvnug0/xql/7aWYLTZqWS160WamZ7OuK4/qwKRsibcgUiyYH+hI4XoDtemiKyGS+QbnhkO7RqFo28/k66zWbkRjURI2pBszPL3LLnj5O/sXH6PzpG1kSErzlVX00zRaRcJpOo4iuCqS7k1ieyEzZZFtnmO8+e4H6yCYERdjYpNUKSMZklk2B/oSGJAR0GgIlX0AMNpbvQ4pASA7RK3oUhSEqvghek4SsElJl7MBFkCRShkx2qBu3WUCVfMRWCU8xkFQDrTBLW6wDW5AJ41AwZTx0hJ49iE6LTMRAlSV0wUD0nY1NU5USbr2KEo3imXWc7BBSs8xAIofpgev77OgMYS0tsdK9mWh5BaM7C1gIogyJHF6pQiOQ6QzLeK0WppDCXpimNTWBUi6i5HLUp6aIbN+BKNcQwjGCVovm8jrZnm6q4zOsLAKxLNbFE4jJLJIgIXotGuNj6JkMkQ4btbpEe7SLmi9guy66JKPjEdME/EAm4tTRZANdkmkEAVXTRddE4m4LxSxjCgMooosuw7opkNAUTM9Dlw18yUZxLAinsCYeRtt7LWI0iZ9uo7pmky+3WCy10L55ih272xDEPFq1iKwoWMsFurdkkWWRQV9AlAKsuXHUehG1Z5AgP0dMkIj0bae7b5BSvc7ayirllTmq8xMYyQzhzm4kNYrtgywFOEoE1QsIVAM504F54nn2bN1DIw9q2wBZXUaXN9w2/EBEE8CXNCKeiykECIAoBAiBgC5tsMuGBCVHJiJLlGyLuCqgiCKWGkX1WqT0EBUbJAE6xCbpVIjllofr+YzmQngBxBWZPA5tUZ1jC1W8QCAA4tkMSjhKY3ni32CE9cqGDVo6bhAEAVu2bOHy5cv/ERj14/W/QfbQaDTYtWsX73znO/9fhXZMT0/zyle+kve+9718+ctf5rnnnuNnf/ZnyWaz/0eFfvzHmN9Ll3Bcl4GudgRRxMglqUwtErg+Wkwlq0qokkC8K4rv+tSXCmhhg9p8kZeuFOjKm7TrMpou4ckiu4IQH//HB/n2f/kotfNnCG8axm/W6d80wq3PjtG7q4cvP36Mj/7iexB0jY/eeYAP/tGnOZeN8879W6mu1Yl3hjEEkTY0VrHoxvh3ndNKy2bm8jpZSSGiivS3xTH37KN2y2GCcBg3v0ZyZAfx6hK9Az0Ig/1IVhUnksb3fYxUlOZMkUwyTG2xiFNvQaoLc2GS+lwLLRFCUGU++q2n+NOffzs9ZgU/CDbSgxQRORTCa1TxmiaT82uck3fx9XNFDokyAi6ceI7VS6dI7x5EzIVRzAr/+QNv5e1//lkOXHcjfUGT7u7NNNfWONQ1iGFHqK2uYIkSYS1Mmw4TFZ+q5aArIZYaDglDxUolMEIh/vAvP81szWY4qfPkvyrMrFYx1xts7Wzn7e96N82VWYRML0U7ICULOKKIjcR00SIZUvARSIVVyi2b/oEhVBHWmy4FX6TV2tDia6qAKomoos/e7gQSsODKrDnQoWtcCnUzMqoiajpv2Zfj8y8tsXquTl1cpnfr9SSfvJ+2kkfx6eNsyyT41SOH6d/Ty7MFn6985/t87NjT5HYc4OabrufnrtuEGMBJv4vDRpmabeMXWzTjYTqTFmXN4Ik5ieryGjdt7eGBp04ysX8Pw+0NxFNPUy55uCPXkKkswnXXMjwQ5rGIwebl41wQtnG9EZA/cJAdfRGWpAidS1No2gg/tSNBZ/4KVlvAeixJMpOkIcBaSyIVMchGwmgXTvOOVII9B1J84WyDlUaDW17zet50382MRn1+9l1v5dOfP0Lk1DHiAz2E2ttQtm2iuDPHgRtu45GT06TjPk8fO44ciqCFIiR2340w7JGKGGTCKv96epnXbEvTH5KQEKiYHjs74yRDCkNemeTkaWwpxSUjS3+/x/hqneGGQ3tC57GnTvLmV17DxGyZ1+3KsYbB+37nz/jzj36Ya+94JR9/53389m8/zitv2IXve6y+cD/Li/O8FO2l78jr8QSFR45Ncv01o5y7NM+d/Qrl46c5WWzwz5/9FntvO0x3e5Jj46t0hEMc3trJetPn5MVZBrI6a3WbIKahaSozq3XOHjtBXe1gpjRFvHOIA9dsYXa+gO+7zE3M8M/flymsrFGtVOnvTHLdoZ2MjAwwP7lEb1xlvH0rfb6P4Pks110isoIqSZjrRS63wrxFnaV69iz9N+zjo+ogzUKBK4LEdcNJmuUqKV1CUwKu5JtENQHfNsklExQcF6fZJB4VGfRWmfGiG+EBksx40eS6vgSLVYvBbITFcoXlNZNEd5a0KHH37m6ihsoTz58nJnocv7pORzpMW1RjqmLz4myJgUyEuK7j+gF+EJAwFNrCKs9MFUkaKge6k7y0UOXxK6t0JA1GelOIdoOCEiPmu3REYxy3FU6umFz7Wx/l8YkC2bTEjm3dhDyTcstENuLg1dFlFV+UualT4qWCTFd7Ct9xCBRoOD4dEYUggIgmY9kBLd8npopoQoCuqFQcl4gq0HICOlSLeVFlulxHlGX0SIyMs06lBXU1jCKCEE6i+hUIHIJWE9GdR1ejCM0SEUmikejCR8G1wVIi+AFYgUHd8shFFXRFRq0u4UsKgd1CiicJPJtg9iJSxxAzDYUtTplGeZWymuGRmRbRZ56ncuAQrzi8jb7ApCWH0TwTQVTR1TJtzhoLkTSxqIyOi1ev4lst7IlLOLUmQiRF88IJIjfcTeAL6KP74ORVqBSIRVVeuVkkkA2E9n6q+SqZ9gSNExdwlCRKSCXUKhEYCRSvRSDGSAk2rqqh2DVUUcAPFMTAATR8QcL3wUPElSXwXNDDqK0yrhpCEBU8RcYCGq6EIwoEehrZc5FUAbVvELI9BKKE2D3CyM/cyUsXP49ZaDJ1YgZzuciu63rRBxsYe/aRujaP3jWPJEHvbTGsSoPw4Cb8VgXRCOPn51EUlRobITrZsIY6MEissw+/VaO6tszUpTNoukGmvZNMOoziWQgzl5CG9xIQEN21h0BW0VauEIupWEYvomMi+Q5lKYwhyoiFeTCioIRIh0RcL0CXRVRRwPM9LFdkpWEzqkBHRGGp4ZKLiEgIBIKEFHhIgojhNAnlr3JM30LJFRCDgE1JjWLLoxy4pHWZpuPRDCn0JnUeObNEd3+OKxdm/i04EFQCWSJfadGRTeJ5HqOjo9x///3/ERj1v73uuusu7rrrrv/Xn/+7v/s7ent7+eQnPwlssN8nTpzgE5/4xP8U8NtqtTCM/zaOW15epqOj42WN+7L5dd/3KZTWAUjFoujJGHalgVNrUlmt4Zo+nUmNlK7guz6iImKVLRzLwXU9ND+gIxeifXOameUGT59dhRoUKy2++S8PM/Wtp/BbTbTBUZRkhk27e7ivK8VjM3OsnDxH/ptfxZ8c56OpHIHj8uGvPwZhCS1msK0rSg6VMs6/+7xcoGD5xLYOcOhX3k73n/0GwrZR4mNnOOCv0duexVQMFjt2cNzJ0CwVqV2+jFXKY46dQ23vJtBAUGVEVSa0bScbHhKQPbSXzttvYEHR6cu1sbM7jes4yKEQ8a3DiEaI1tI8vush+C7n6jAsejx/dprls2NUF9Y59lt/zsx3n2ZsagFREBFdC6m9i3f/5h/zS7/y6+itPOGlccZKPq7rckic5UBfjJ5sArtl4ptNNAE6wjqTpRau6yP4Pn19vUQNg4/951/l4S/8DX/5sd9EtBu8/rWv5k39PiFNYaXhIMSyKLJAzQvwbZPAh6Wmj+nD5fUWqiiyKWlQNj0MVUaTJTIhibAis9hwkaWApuNTMj0EAkKyiCqLDOUiJKMG+ZZPfzbGN1+Y5MySyeWazDvuvZ7Dh3bSlHVK8QyPDt3IdHqAC0+c5czz07RQuP9Lz/JEZA9jjHD3Bz/O9Yeu59ylq7zjV/+A5NRpkiceY9Ex+L6Z49Jyg+nzV4hgkfLqdEUVvnd2CT8U5pc++Fpe0kf4w8fnOJPeRbBpBMGIUDh1idDaBDnNZXdapGm5bDr2LYL5abaqFjvEdXr8CiFUusQ6O1pTMD3Bbbuz3HLNKFdn1uiQTT54az933bST+OAgbbceIZ4IccdoireWLnJvh8xb79xLtVKjM6Lyqte/kaf/9Uu07dhMXAsYchdJ1ZcZr/rMewZ337SL3bt2Exo9wute+1qO3HgTP/8zd3DrK67h3W+4mZiu0BuWSIR0/MBnotDk+GwZy/MYaouySy5SvjhO+eJFRElBT6UYHOnle8+NU5qZ5drTj9Mm1GlNTRBOpnlpvsJsXeE3f++PmJqe5p43vZvtm4fZNzxEoWUzfOdbCUSZfZvaOPXF36e6tkxN01mutBgfu8I/PnqJR2thduwZZeCWG3nq5GW+8vw0JVtmodDg9EwJI6TiRWK4Rpi97QZi06Q/rRNWAm47vIN0yiCdzXLq2RdYnZlnqDuGK0BnfzdzV8fRFIF3H+kjZDeJOA0eP3aJRH6Wz50pkRgYRFMV1uen2GdOsS+YpjfsYV+6wq3RMoX2zYT37Sfft5Md1gJiKMYtA1G+d2KOyLlnqZ16jn6xwYC3zthyFTkAzW1iOh5hXcY2TSK+RUIKmG8GXFqp851zKyyXTG7qlLkmY7M3WKM9aLEwOcFnv32MscUqm5MiH3z1LrZ3xujPRciEFUKaSG9cJxNW0WQ4NlXk5GIVQxYZSuikQwo9CZ18yyKkSuQiMj2JECBwebXOQtVjuWpyJONTaPlkkyHWqi1W2wc5V4+QSYeZPjOGJ+tErj5P2/RzJN0K6uoEbqtJDgtTkLjrulEyURVBEBhJh7ghJ9IRVanZPo7vYXs+giCQ1SUQBJxgI3ig6UM1MPARQNGYXihwcrHBmpjEEnRcQSSkyNQtk1azga+orEX7WZWTPDKWp6gkoVVG8lwsVyKuS1iegOsLBIhIskRY3mBzEWVcNYQfSUO1iN9sIioSkm/T5lRAkBGWphFadeTnnqaYznFdp0Q8pKPX1pCr6wSlVUwvoBnvoWzLxMM6rhwm4jaQUzl8y8ZVYvi5PqSeAbRcJ+gx/PET4Flkdg9jVRrIsRiaKFCdy9NaWkR0TQJJRcx101xaRfBcxNIilmwgCQKa4IGiIQTQlAwE18H2PCwxjCvKVCwIAtAkEAMBQhGERhnRLIHv4pbW0OUAzxdwgoCSGVA0QbPrBL6NsOMmnGg7yAaCGiK1axuvuGszIVVAFwQKi1XqyxVqY5fwWk20W15H9o67SR4+QmR4mOT2LfjlVZpXLxO4LvTtxMsNoAU2LdfDRkZGICl5CHqEnsFhbjx8Lb1dHZTWlnnqxBnGJiZp2jZibZlg4hSC09poskzYa+IEErLdQPB9ZM9Bb60hVNcRl68iuBaG6GM6DppbR/AdxMBHEECRBZabHq4nkNYgJHrIno3UKiNZNQLPoeYJXAwNM5ZvISMQELDa9BgvNimZLl4QMFVqUDUdTDeg1rKJxnSqK0tI0SQAgqIhaQYoEoFrs1Ks0xZX+dRXH2d0dJTLly8T/PfMgf+99RMMuahWqz/WLMv6iUzxhRde4Pbbb/+xvjvuuIMTJ078KAr9J1l79uzh1KlT/6b/G9/4Bjt37nzZ475s8LuwsIDtOERCBmldJhAErjxwitULS7TWW8iyRLIvRiRr0MqbRHJRWvkWhct5nj61xGAmRM9gEt/xmSy0yMU02kIyB8wYf/7Qi1xeKeAU1/G7tiBIKm37R0j093JXNMU/nx/D2nUD91+e48/GJnjz9iHuSqb5k/UFXEMi2xGhPaXQG9X+XeckaSq33HYLR/7sl2n/yHtwGi2cv/w053/jkyhzUxROX8ELxTg2V6UvZZC2StTaR5BlAfnyi9j5AitPv0RCbDB9dpG5+TqR0R14uSyRvk6iu/ehbD2AlG4j2j+AoIVRIgaNuSXkiEFzcRkhACkSR8h2cK7WYsyNcm+PRHmphOd69G3t4Hx3nNt+5Y9ZnptENsLYiXb8TC/v+qm7+d0v3Y+cTOArBtX5WRpqnFhphu6UwU0dAR2qR1ZxGQgJxBURybUYNWwU3+F1v/A7vP6d72X33r3su+4If/arP8+OjghdqRjDuklGDbhSdVEQ6DTAUQ1qjo8iBsRUiUxYpm67WK5PXFOQgYShYHoQIJANayzVXGq2jxdA3vSYLTcJqRJVc4MxMrA4P19kZ2+Ct75iM13ZJFdW6nhI7N+UZqQ3R6Szg0LnCNlX3Ul4zx5WEr10Xbud7k2D3HbTAYK2Hr742ASV+C7CmsF3Tk9w9bGTPP3Jz+I+/T1Oqz2sJQYpEUJduMCgbvO+u3ezvU1m1ZZYKdXQ1BBfH6vyzVqCRiRO857X80xiN999ahyrsMaTjQx/fnSRiBwQf/BLUFlDxKV24AbqgYwjaUgHbkJVBSwkBjrTlNB5/vI6j42XWTMd3GgacfchnlI2sXP/AIemjlL/0se5+tDn+bn3vZOj33sAz/cJSx7++jyCKOPNXWFLQqAnKvDVpyf57D89CtUyZqnA9WmH240V3nqoj50D7ZSaDj81JLHJmiM0eYrOsEqvX8SyHAxN5DuVDE9ltnGxbzfn5yrk52epLK9y675ubj04xPb2EE9NVumJGfhui4QiEMch9OwT/ML73kM+e4Tm5ldTffZZjrRJ5FJt3PauX+bYc8/Ss/cIhee+TOv8k9wWM7lzbzfX9sdYIsLXHnyJ584tcvima8h2d5BL6sRzSU5dHOfs+DqdiRDD2TDfPjZJtV4nZ+fpS4TYnxN55Z4O9iQC7j08wOXTpxg/P8ZIR4xbDo6Qbs9Rq1SIxhKI5TW+/dCLBK7HA3mJ/OIcDzx9kamr07Q3likIBmtqO/H6GvOVGj16wMLYBLMz67SJLSbi/VwTtemaPIbTsFjq3Y2f7kArTBEfe4GJqwucLflYkkavanM2b6M2K3ildVKaS0QT2doZIxlSECUQfFhdLfLtQoxvnVrj6LOXOHZxgSdOT3NxoQLhKAQ+bzrQxc7OMBHB41BPlD1tGrmQykLFZL1uE5HB8YINiyxNoWF6TOTrTK/UaI/rKAS4fkDFE9ifgSkhQVgVONCTIK6KfP6R89yws581OcHjJ8eZnp2l2bWVufgQ4WgUHBtt+iKTYoqUoWBEDNpDMpIookoithIiYaiEJag7AS3XZ6lmUXN8KpaHJAoULR+AuiegShs+r6v5Oot1h8nJOU7PV1muWSDAZFWgJSfA8wlXFwGRTCyMs7aKHWlDEXzMIMD3NwgESQiIqCJtYQUQN1jRcBJHDiGE4njVEl55DUFWEBWF8NDQxo1u824yzXVuv3kHdx/oon5xgrBgUjeSiI0i9dlFnIsvUZu6suGTq+h0RiU8NYw/sBPj+luJHr6BIBHn8tMnkboGCOoF0HS87ABqJoMSNohv3Ux822ZCYQHzzAlYmqFw9AkkVSHUlsG3TNzFCYTFKwj1PFLgYzTzhJorRL0aLTlMyK6gKRK6Z244QNgeddunYVo027bgRjMUlTSWoCBJAotVH33tKoog4AkBhixircyg+zZSJAmqji9JtPQUxLKogY8uSXS1h+lqCyPpOoXzCyw/8ChuOIHUuQlv63Uom3biFNfxqiUKZ67iV/MIqoYQTiBdfpGwJKJJG9RO0quiSyKiLIMo0dvZxvY9+7lmczeCpHCipvDc5DoLTR9bNmhpcUQBhGQXScUHaQO0RZwyUrMCvgu+h+aZKIJIWvHRWyVUr4nut4jIkA2pWIFIRJOoeAIg4ekRfD2MELgkvTL1QKUp6uztjBHXJbZkIiR1GQQIKSJhVWK+1EKTRTRZZOdQhtm5Eq3iKuH0BqMYODaBD9gt8D1WKw6duSSff+AYw8PDtFotFhYWXi6U+rESJOkn0gB6enqIx+M/ah//+Md/InNcWVmhra3tx/ra2tpwXZd8Pv8TOcZ/XbfddhuHDx/mj/7ojwiCgHq9zjve8Q7e/va381u/9Vsve9yXLXsYGxsjHDLozaUJV1ap1SXGTi/TlzEItYUId0TwgwBLbmE3bAIhQE/oVNcblFoBa6UW+tlVaDmMdEQIFIHzU1VkRG7zEvzL5Dz3eg6iVYNEDiEcQnBd7rn7CB/62v08/ju/z0/tHeTWa0f5pYdf5E9ecS3xpQQfeuA5Nok642aTD/UNUF5pcLH0P34aCXe30XvndXTdfIDqep6LDz1N6PlzmC2Le+7eRskBt+4QikoEz3+XN9790ySrszRPP422aTPqpi1Ujz5Ca72EqCnkdu5k4dQMmw5vwZVlwoPDxEe6EJNZkFTUVI7AukDt8hhuvU5ldp3owBrIIYyBIcTNB/mLB55H79nGoahP2m8QHm1nrdrisQDGCxbX3nALQm6AkhxFicQ5GBPJ3HErn/rUZ7nqJbhsu3SpFqFoG81wFs/2aKst43ZsoVi2SCg2fdVVxOIimabAUrwP24sRimS5/kCWqASdYZFy4KNG4uQuPUKrfxd9oSgN18OXAvxAxg9cVioObRGVjCFhKAK26xM3JGQRXM8jIksb2i8vQJcExMCnYbkUGg6KIrBabaE6DRrRBJqs0BPXyes6judTWp3k1sPXc2WlTEoX2NqcZeftW/Bsk/XMnQi2Q2WtzrBR4mAqwpcuTnHtJpfbbjhAIPmI2+/kE1/+Sz74G39MauYyPWGByWqLzs29nKtIbE8OolgmIyGZ7hGDL66pvPj0Se7c1cW2PSNcbipsyugk1BDduRBPnFnln56dJRULEew6SGhnFqmyANUSfcO78aqLVJK9eKQQ2jYhXn0WP2Rzx+FNtNyA4xcXWZpd42hE4Z5tUY7OVjmz3MLcdyfNq8f4/rOn+OCv3sK2d70BI5FCHD+HUy0iCAJuENDo3Eq/bDLcLzKc3sp8sZfjiy0y/Wn6+6MsFqvkQgqBCr00WA3irK9YPH+hTscunehEgcV6gVZolAM7+hgfu0JVl5k6eYb+7Vs5f/YSbzjYS0s26P/Qz/Lk2QLfmGoyWnbpVj2iSzNMJ3vY1tbOddcdwLJb/KPXyXUFhxuyAV+fa/HK9/1nvvmlvyeolDjxjb/idbce4N67DjEi1/ibT/0mxatTPHOqg67Mhyi4OjcMxUm7Jmd8gedePM2uTR2ENqe5bmsPn/3WMXb1hHjN7T3s0mGPpPD36928KiNy5PAO/uwz3+WJ+UUkLUQkEeMVdxziKy+e5b2Hukl4Re5/7Cy7Nt/A4J6DJDWfscuLnO8bZvbqLIf25MhpOt0daeqVEkunxkjk0kgFj29csbi7J4Sqpegf7kSIKCS74rilSbTePg6VNDZ1RpgvtdgTcziYVYisrzOv51DGLvHAeIw3XtNNh2gzpAhoFhRchWfnTBRNoaszgdOeYDCtMNWA3d0ZzpV8BlMS/bkoJxcqPH55jU4drhnpYGfXBjhWJJG86TA3X6EjqXP86jqbchEqLYdzixU0ScIL4NxilZPFKj979wBnlyt0xgy2diew18IsrRXIdnUS272XqapNSoZ4Ko4tiAgBLJy5SLpnG2uySt32UUUwBYGlUp1MKEG+YeED602Huu2RMhScQMD1NsIvBEGkaXsbD8GGylSpwY17BpluiczlfYSkBIFP3fJYqHkM9XXj2FXWfBE3iDCSaSIpXfiKgeMJKL4LokA6aGEIDpUgiR8AQoA2/iJCey+aotNcXUNWEzjrs2jhGE61ikiFQJaQMn34F87Qtnc/qlVDv7KIFgRIdpUFvZtkb5yaI6BnMwh6hJnVPLIo4YZF1NkJiKVQo3FCrQoRsYUYTeHqccQOG0HVkLfsI1EoIIajaLEkUd9n5YkX0QeGKF0cJ75lmNjO7ZRfeA49FSOSSCMINoIHthFF9m00p4mrxAgaJVwjjYiPIQNBgGZIyIGPLccxYxEURcbxIRKKkfYCfDmJB6R1CcsXWMuOMii7CKKIj0BrdREvN0Agq4SHetlzo022LcTK6VmUWARZ8hCsCkplBc81qYU60eIedqmCElJRE1Hc1VmUVBeSouAqGjIOLUdB8X1MPYHd2tB724AbyASBTzwcZltHwLC/znKyh4VluHp1lUzGp9OTafcdVLcFtgmyRqBG8QIBIRwDBMTKKrrehFCUIBRHFGQEswqyiuertEdktB9Eeiu+hSVqCECgbqS5RXyRlCMSUiXcQMT3fxCHLIgkNJli00aVJKKaTFtMo9pysS0PPZWjOvMDLa+i4Tvmj/DC8nqJrX0ZLk8uoGkag4ODjI2N0dPT83Lh1P+Ump+fJxaL/ei9pv37yMD/UQmC8GPvf8h8/z/7fxL113/919x99928853v5Lvf/S5LS0vEYjGOHz/+H3LZeNng9/LlyyiSRFdbhmwuS+3sOCXTo0cSyG3vQkkYVGfWkTWZ6nyN5mqD7PZOGs9McnA4zpnpCjOXi/z0W69n4uGTKM6G2NwHNqkajzWLOLUGerMKuT6kwEdMp9n6joP8+XU30RXy6di5F2f2Mv7ffJq/uDzFh7raeVd7Fyfni9zYSmCvtejPhrhYqgAbPr4/hMGCKNJ27U56X3k9iZF+Vp47zanf+zTVtUnmGja7hDApTWZ2YoUDQ3EunJxFj6kM3LwHtXSFhhAlNLINBRNndoLIwcO4boAoBjRX1th+936ir7wPd20FfWgP7oVnUIx1gkQW0bPwamXUzD4cyyLclSZ04GYMPQyixIqY5Av/9A3CQ0d4w+37CWrLVOpN3HCK05cu8xef/yIf/MivIdaKaJ2dyE4NJRCYvnKF7mwb58oCVhDwYj3B9XKcqWIDteWghLtINAoMZNrJai6OmMQwbMT5CaSnnufQPfehymEWqgGdcQFXTBHPX0EwEtj9e9CunGRkz0GKSpS5mk9CFbDcAFkUObNcpTNqEFIEukIBckhntWmz3AjoisiMrTTojCvENIWVhk3L2rBf8wKfsunRbWjULJcl26MtrLE3o3Dyy3/H7/3JX3PfB36V33vPa7g0vszXrjZ4fY9PW+kqsUQ3DxUEEpk4D10tc23C53BfBDUU4tXXRHnqwgKLs2Xuuf1VPHf/lxm++330dMLeo0+Qt3bjD2/iXDrBSNCkKSgE0Sw3x2L0vmo/pfUSfiLLQNhFCUVZtSwGMhFePVJk58AoEadBflng2MPn2HzzKxE8E0OVqYkKVcEgme3Hd12cQMYTZEpmi1d1CXzjySJbQjbFZZFPLZWwFIM3783w0mKLN3/oozww8SvcsHOU6XgPK4USfaLK1a88yLZ7DuNXy2iGjBFpZ7HqcHW1xmBfjntyPu1RjXB1kUDvoOxDP01C4RA9gcjlZo31Up0RDb5aTZA0q2wLTOaXW2yLSwRJEeXaXvZfN8KHLk7yX772JKF4mpuH0/R7DTYPR3luYp0zV5f5+X0ZJkJDfPnvHuaaXVu4fiTHubl1SuUmt+1oI5pLgedyaP+fMb5cZH9/BjmawJXgsYLPna96NfzrN5hqxfjyxz7CwTtexfPFnVwTlvjp2/bhrC/ztaPn+X53D1fOXuENhwfZnpNYqTaZ1ZP0CTUO9Mc4enGZkf4cv3n7Jp5w0jzw+FmE9WWuzPTw9v3dfOPkCt2pGEHU4OTVVR4+Mcd77r2W+UAlLkiMDA+QlANCSsC3/E7yD5wlWajzzruO8J1LNb711e8Su+cQ1/TF2NYRoT2qk7VW8a0WauBwYP8oS6ur/N3z6/ztPTlkLYbs2ViRHmJuHeeMSU7xMf06ffEwgR4mklDY1NbkjiP9bAs1+fTzy3R3JJAkn0q1SdkFx/dJGSo7clFc02NqZhlZVWnPJejQXFQlwJAFnp5fY70awTFbXJy32belnUuzBVxBZqgtQrlpcXq6yEPnlvFFgbAs0LB8Rrf245oWCUNkuCfNSMhj+cJZjK37McfOkhrZQvlQJ0uzSyi2jZLpp+V4NCyPmdUKmbBKyfIZLzSQhQ1tr5zZuOklNYHTqxaiIBBSRQI/oGa7rDccJEFFU0Q+e67Bz90hM1u2yDcdQppE2RV4ZiFgsE3n289N87ObfaKD26g2ayQUF1UPo9plVM+FxasIm25AAQzXRIqnENenN9w4XjyHOroTafAaQmsr+J6D0r0JoVFGMUvE9+9FjCaoP/I43swSQnkN2baIuXlK0SzLeoytskdg1+hzVzdicysCYnc/J2bqHNRb+EtLdGVUcEwIZ/AjbZgohGJtiIfvQbDLuJKKfe48ob4eCsvrxAe7kNNZKiePI0ZTLMkpBlsOYjyEFnjkiaBpEiG/SdMXqMgpImYTVRKIWssYnouk6qypWcKCg+s08KUEsihiK2HiqoQoaMiWT9kKCMkyDRRMSUbzfYpWQNK2MMurCIZG+r43k7phCcGx8e2v4NZrhLuTZI5cj1xaotq9G1kM8HyF8KZN4Fhk9u/Aa9lImorkOdC3A9m3KPoStucTk1UisksQBKhui6avEStNQjgCtXXEwX1kIlnauvuo1RusLsxyri4ye3mG/o4MsWwnmm8TyDKelEaWZAJ8xFoBcX0Gf+AAviQiuTaBb+EKErrgokgaSuDQpgus2QKyKqIKGjUrIKYIqATUfxDyVDEdVus2EVUiFZax/IC6G5AIq7g++H5AxJDp7k0zfbzxAzQCgu8TCAI/zD1eKTa4ZU8fnreRGfDDTW//TynAyypR/JFs4T80BhCLxX4M/P6kqr29nZWVlR/rW1tbQ5Zl0un0T/x4ALfffjuvfe1r+du//VtkWeaBBx74D9vLvWzw+8xTzyDJEkYoRPumYU58+vvEwxKm6aEmQpiVOq21BvHeBPnpMmrCQJRl4p1RpHWBgymDcxfX6djZy4FKnqDcZHplDgewgIFYhFPPnuHwnhtQLRMxngbfRVMlNu/ehj59Gm/yDFb3bt7yvnfx9Mf/mpKhsL0tg7tocT5wKJkelcX6j+YsAKIi03XLNQzedwsIAnPfe5bTf/x53FoDBLDEgEbc59qRdipVi5AY0HR9OjZlWVmt0lGuU1u6QFNSKMwUcaJhhuIWrjJJ5uZbkAqLGOUi9nqJuuOT7uhCqC7gj+xEqK0SFBahnCdAQItHEdV+4lsMhK5RpPoqZqSdwrETaNUV4pu2cd7oYKCzD8WDbDqHkUrSKpewPZe4U0G4coIgHEWIpWmpYZalEAGwVjc5ONqOIm3IDQSnSVmOkJWqrDUdAselLRLBXJklIfnkRrrRvDLr3/8Og/0dCFt24BsxXFdELK2gdO8gMtiP63nouLSHZRq2Ry4kM+85dEZ1Wp5Pyw9wXYFduYCQ7vJiA1qOTNP1SYjQDAISuoIm+axVTQxZYGy9Qbg7RsN2SWog+B7llsinvvsET3z5b3n7f/4EZ197L6PmImY6TsmTGK+GaVu9xE3tXURznRyMd/DEko3e2ck1fQksQeH84+PMlGRuOXg9C099j7ZDBZ6wNVIDO+j0ahRrTdqSIWbEFDIuWmWZbFeazFAGaTiHaZk8MefQnvRpd6uU6xJXpTTXp22aRZHTaob2yDItL8Dv2korkmJ1rU5HfRZJzVKvNRE6huiNhjAkAbNR5AM39fPSdAWzWiMb09i1JYEauHhCwHqsj3A6w2Jg0B7RqJ44y8nh69h+n4jdXEMI6bQlo0ybEkZxHnHNpdqWZI9coeC1cXWtRtdIDw0PxECmEqhU7IAt24bJVBZoNeYJW1W++dBTvPLmnyOpeWRTKRKqx2i4iyUJ3npDP1FdJiiu8sjYHHapwXBQontri3ccHOLFi/O8YneM51tNnj1xiU4t4MS5K/zhB+5BiYSo1m060jpX5pdJZNv4p0fP0D86TF/CYFtHiJ23HGGrV+N3v/RVDr/pvezIiTzw9b+h974387rBNNlUgy1b72WsEtBcCXFiYo1vPzHHn9w1wPQ3T7L2lndxua5yYP8oY8sV5uohnGaB3o44Vl2iuy2GmlZ41Su6yYQF7rh2G6tnj/OtSY8FS+SnUlWCxecJrr2FuaKJ7jm8ImGyfMshFhZW+f6szas7bCa35jBDCTYJTYYiHg4W/vwkjbkF/Bvu5uErFUYyGg1P4FJNoFj3ac37HIheYr1/Dz9/ewO1nqcvHaHhiRTmV7CNNqIhB19TkSWH9aVlxisC77wxxiOXVlgpW+zd1k1Md9jdFiISVriuV6JDL7FoRzhTbnB92sNV01zbG+ZXvngUQw9x6IbdLKzVmbw6i4vGSNdWXBcO7BtgudoirKvEDY2EDldLLa7M1vlAZ4xvn1jhF/eGcXqGGVuqY5tpDjdMEimDpfk5zGqN714VeNvoxs2/FcjYnsd81aJluzQtn5AmE1U3biNLDY/5aouOiI5v+dRNB1UWqZouqxWbwbTByECKRy+vsb03ycnZCu1xFVmC+brN6dkC05Pz/Ekpysdic8w5Ybx4CCcAy9EYikUx4z3ogo9UW0XxLAiFwa4j2CadOwdpqQrOzGWKq0ukRvvxy+sIgYNgNvHnryKMXoOcSJAcFZAKc4iKDD37yHhN/MICohWwouWwtXb6EzpCo0xzeoaMK0J8mFrWJTKyndqpZwjvAnwfTQAxnsZMdhEumdjrq4Q2b0epN5j5+uNkdrVjra5g2QqJa68h3PDJP/t9MjcpCPEkZdEmo+hUpQgRLIJECgCtPI83fxVJlaBr40YvOk3WvBCS7ROSBHxFQQiAACzfBwEEwSepSLQEkeDyCWLdfYi9m9EQCRbHkLtHMTM9aJ5Fz11z+PUaxYuTOOsrKD2DSHoYBZeGHCG8+2aYOI4oK3g92xAFBU9VEH0X3wNJkomIPmrgISoy6y0XQ5WJuSWkZhlBDPATnYBLywNdEklEI6T7snRv3s7KzBQXppeQFtbp7Oiivy2JqCnYioFitwjCCQQ1BF6LQI5CENDUMoBMylmhpXWA79LyJKqOREKBlgdOAHnLw3Z9wqpMw/I5uVRlKGlg+wFRVcYLAjzXJ6GrhFWJSkskrkv0ZEN0jm5l9tjTCLK6EV/n+T/CEevlJpnExgasWr31k7U7+z8g5OLQoUM88MADP9b3yCOPsH//fhRF+Ykfb3Jykje/+c2srKzw8MMP89RTT3Hvvffy4Q9/mD/4gz942cd82Y8YZ0+eIh6L47geiiigRVV628KkezcuUMH1cVoOnu3SvjWHkQwR4CMpInbTZ/OhEW67sR97cZbOngRaSOWHCwuO53NYi/CJoyepjl/CXZ5B7BrGKtXxVueI+SayplI/f5bVqoXaM8j7X3kTX18u4pg2yYgKwFLLpWhuJLzJIZ1Nr3sFN33mt+m963qu/vNDPP2BjzH97Sdwag00cePBbkt7HCGq40kBdt3m6mId3/Ux8zX2vO4WYqO7cIpFjn7+US48+Bxzs1XKKybl9QZr6c0IgzuQN+3GQ8PODtCcW9wQzCsKgSASmE3qgUQ4nkDQVORYHCWZBM8mUAxKgcaZpsZNO0bZ1jzN8bkaSjjMZGIAAKFZo2v6HFKlRKlqY7UNIOHinH2OrQM9vPquV/Dtz/w5Nw+kaXkezWqJhGDRo3n0k8eLpBiOQGzqDJ7ZIrJ+Fb9WQjQM1h9/mlibQVTz4dTTcP5J6hPjNEPtGOV5RFWjKoQJPAdDlgkEyLdcFCFgNGOgSwJZXcEXJOYbPnLg0RvXCakiNw0m6QgHZHSRkLKhrxqMiQxE4N5eDUPwSSsefbpHTIWJ+SXESIL2PTfiSTonx/McD22h2y6SrK5QQ8O5dBVXj2IX1pnyoozkImzKRom5ddqsAr+8pcX+6/bhW3Uy19xGqDnN3tYiz5pxFgf3IEsSPaGA7lSUXSmRWq3FWt1houISN0s8Nd0gZWyAfF+UYWYCNZTg0UoMKdvBzQe30HXXK3m2GqIq6cxXTS7WZObEFL6s80JRZdLa2NjSmzBQUzkKlSadkkuqp5twROep5y5ybqbIxJUFlusu99z3ej7wq7/N6qXzOJt303BFLtVlhOF9qIk0ROKo8TjGyDZef2QzNxgl1qQoXSFwM134okxGtjBljZWWz0TJxJMV2tpT9Oo2t+zo5HV3HuRS3mS+bKLrKrHApCWo2KLMPYdG2Lx6lf6BboYGehl9xXVsue0I/QM5wmtz7OmPMxJq8XPvvZf7XvcKspsGuOmG/QSqwZNnpuhMhpgqtsjGVCKazBvv3M+B/hTxmEEjkJgqe2i7b+SPP/6n+EsXOP/803z4Z96KUlvm19/3M1yyDQJBYiSl8doj27lnfx87t2/mi4sKK7EuYopAKBQiEdEJhSIcPLSLcHcft99xHb/3rtt4/6jC4tQ8ofGLmA8+xPcvLnHGjnJo31Yyqs/fnWuwUDY5NlXguZkK590ke8ImBU8i3NbB4ZEsJTXBHTcf4Kdu2Exq10704jwzpoTXNcTCwEFOThfZmpHRonGSIQj0GCIC37hQYFJrR6mX2J102NEVok/1WB2/AopCu+oxmNTwfZ/zls7N29oY6k7ynbNrLDd9iivLCJ7PWrXJ6eU651eaPFEOM0YbU0WTZyZqPDljs9BwceQQfZ1tRDId2KZHvuUQjsVJZWKslppcO5iiOxNBlEV29MTwHRtJ3pDOdGQi1J2AydkSj85aDOsujhcw56p86oU8dU9k754taO29hMM6jusiiiI3b85Q9UQEQFFk1momw9kIbTGdAJgptsiEVFRJpN0QcPyAiUKLoUyYREhmpWrRkzKIhjUWyy1Wyk0s12exZlIzHQJJ5vYbdxCYJn92/zm+d3KGv/zuVZbLTWbWqpSaJqbjohVnUew6zckxgnqJINcPyQ7sYhmjuUyja4TY7n0IuT780gqoBkE1T1ArIVeWMfoHCeUSeLNXCFp1VMdEDYVIRyPMzS3B4gSiKCCJIs/+3dfw43HamgtIdh1XN/CQEPqGaSLimC0Cs4Up6giChBtKIWW6ETr7UTSZgZ3tCKqIWylSmV1h/eRZGt95AHt9HTKd+NMXGWjNE1+7ihGY+J6PoKgYvgXhBEJbL6JlQW0dxXeQZAlRFHF9aHo+rusj4SG6NknRRnKamJZNrDEPrsOY3oUiSwiagaeGKWU3YwoqeaIIoSjsuxV1cDPpn/pp2H0EIRRDwEMKPFwkCCcQN1+DP7QPX4sQ+A4tNYHnuZiChiEFIIhI9g+CSUTIezKS54JrYy/O4SzN4CGRsEsYTg0TiZrehqKqRLsHOXToEKn2HpaWFnj2xBlmZmaoBQrIKo6sAT6i2SAIfGxZpimGNjCGsMEES66JikNMlwgIcH0f2/UwnQA3CCCAsUKDmCaRi6p4fkCx5TBbMvEICKkinh+Qr5vIksjmjgiyqqElO9BiGQL/h8B3Y0m/UDUJaQqGJlMs137ydmf/i6ter3PmzBnOnDkDbFiZnTlzhrm5OQB+7dd+jbe97W0/+vz73/9+Zmdn+chHPsLY2Bj/8A//wOc+9zk++tGP/k+Z3+7duxkYGODs2bPcdtttfOxjH+OJJ57gX//1Xzl48ODLHvdlg998uUBXWxbBtXGrFdLbOpA9CMV1BFlBb0sih1UqsxUK43lWxlao5escfWYOMa6ix8K0b+tCTUSJb+oh0ZtA+4FcZKnpUV122WlqvPPjn6KyOIu9MIUci+BWStTPPEdQzmP09tIjl6k+8xh7urNcnlum7NjMVzdgtCSAGI8y8pZ7uOmzv0PXtTsofv7rPPd//THLT5/8ry5qMDcemtm9q4u337iX+9fW2XFtL329KaotF7/lMffAs9Rnpkhu7cVwHGxJYDCpkn7T2zhx4PVMjs1Si7TjtQ+gHrmDmN/Es02aehyaFRrTM0w/cJSp2QUGB/qQsz0IooSQ6cbVE5jhNmIK3BC12H/kbl589nEK4yc5/9h3mCnamIEImo4zP8MHDu7gP/32x3nuxZNgt9BTaXyzzrtfcS2J1hrrz3+btOAQwaXdXkOVRBTPQlyewKgtI3T04T/4zzgLc5jFCkE0gxJRacXaqA8cRE4kQBKxTr9I46nHaVwdx3ZcGgj4/oYpeVZ0Wak7TBRbmF5ALqJi/eBVEEUsJYYkiNhuQMsNWHE1WoGA6fpcWa9jujBb9bhQtlhsOLSHReZthaqncvboQ7z2Na9mumSh+BZ9vVn6shqfL8g8c3GezeRJNKrYWpRyop110+f+s6tcXamjShKZyhRb0xrvHPL44KEO3vbWN3Lm0hht11/HbTsyRFSNJ45d4uuXKkQkF8d1kQyVZG2eS2cv82LB41svzbBaaWK5ATU1TqlnK5oY4AcC82KCih0QT8bZsqWf6aLFXz50BSMWJxVSkPBIxkMUHJnxsoMdBCgSPHp+Bc1rcleHS/LCS2wfbOOZS4s88Z1HeeryCn07r+EDv/hrvOsDH+LrDz/GoYzLaJuKVppFKi8QrM9TNH08SWGpCTE5wFcMFlsChhFiudYiXF9HF0Ucz6dn+QILc8tMGb1MC2l+/c+/xksnr3B7h8id2iLG5dOYKFz2k7i2Q3htnEK2n4dnbbr6ulDDIR6frNNs62PHUJJ8ph+7Y4TethSbu5IcGs7ymutHmZhdJlheoFy1CEkBRwY2NHuXf7ARqyuh0am6DE8dRxy/zGMTNXb/zC/TftPr+OTXvsa5vMOvvP/d/OXv/TplG773wlXOL9ZZXFrktm6H/SPtHE0O8+nn5nBth0LL59xMkUBRCSIxKg50hQLa/BKqWeL4mUnGY928KVrlwPYhlvJVnv3+CzS0BF8oprkwtsThhMt3x4rYgoTiWogEPHG1zLKa4MnJKiXLIzAMXFGmTfZ4oWwwH+lizjUwZKhUq2ztz7K8VuPsXJG+ZIgvPXERq7SGQsCxapiV1Qof+uIJvEQHvY0VytUaV5arPD62xoKcZagjRlyVWFiv4+lRlstNlisWy5UWnSGRzd1J5hs+U6UGhbrJo1fyXF6qMV9zOHJ4O2+7ZYjrhlMMt8e4/dpNbO7PsLMzxuR6nZrlYfkBZ5bqZBMG5R9sRNvcHsWRNN5wuI9nzszQdAS6UgavHdTY1hnD0FSu1CQGkyqjuQiCHxDTZYJAIKlJpEMq9bpNVypEZ0yjK6wyWWwxXW4RUiTawgo9P1gJ6oxrDKV0elIhGpaDJos4rsdKycQPoFB3KTdd1qoWligyV7VR0m2ca0Y5v2rTChnYBDxzbp7Hp5qcaMV4bEWgiI4UjeKOn6bZcqj7Bt6mPQRoSJv3oOYymNUq1YtX8CtF8HzkcAR8FyHTjdy3haYt4/oCrRceIfAF5FCEZ6QhZmN9dBugmBVyh/cTdus0sn3Yp56j4anMWDJXw/2MBTkC1WDtpQu4yzPgbOh/nVQ3fiyHZSTRcjlEPYqb6aNtzzDJrgTp0XbcniGYu0R1eg73xBMInoPwxNcQS8tIZg3B96FRQEi04w3soZEcIOxbCIUF2pwCSaGF5JnUnQBFhIhVIFqZIbYySWj1CmVPRrHrZJNhanKCZr2O7LQoiBEKvoJPQAWDVnoQtW8LDO7CzQ5Sj3UhuC6m4yHhIQkCjUgngh5Dq64Q5JcR58dAFrFlA10GWRSYNjXKpk1clVAkmcB1CBx74z7Q8vCUKKIQIFlNRFHA8wIUp0VIFrEDkWi2nUO7trFpeIT1fIFTL77Apbklmp6ML6oIno3neZjoaDJoOLS0JHp1EbFZJCIHyJIAARRNl5LpElFERATWmxZ+sLFhsun4eIGPJArkwgrtEZ3emE5IFsiEdBw3QJUkhjoTDF97LXoiC/zQyWHjtWE6mLZLOmbw8c89+BNlfgVR+om0f0+dOHGCPXv2sGfPHgA+8pGPsGfPnh9tJlteXv4REAYYGBjgoYce4ujRo+zevZvf//3f56/+6q/+p3n8fupTn+KrX/0qiUTiR32HDx/m9OnT7N2792WP+7LAb7FYpFytkkkl6EzFKRw/RWu1THo4SeAH4Ng49Rb9d+1nfaXG+ekSR59f4JvfusTVvIWWTiJIIqFMnNjmIeSIjqzK3LY7R0aTaAF5FygLHDLCfPvsBP76AtWxq9j5POvPHscqV3ErJdRWDdlQqE9M8AvXbud3J8Z5pFFBb0vR+57Xs+dvf4toXycnP/YZln7vvxAbu/rf/QF2dIRYW6nRa8GZWpULagijvY31lkOiL4vn2hTOXmT12GUO3LaZsChSmrrK+hOPkhsZor2+SkQEpTCLmexi/ctfpjG4E2dlHsd0WR9bwNMjTK2W6A0rBLE25J4tiKEoAj62F8DVM4x//2mGC+Poffspr8zwD1/5BoEb0LBctGSWlV2HecXrXsU//P4v8jf/8i0+95VvIo7swRs/j/nik7zn3e/hK//yL5T+7g9RRFj1NHBt/FoFt1okKlokClM0R/ayZKlcvJTn2Ge+wUpR5l+b3dx/eolg3224fTvR23IoK5epvPAigqKjagYN16FuuSi+ye6cwc6cQa1lU7G8jZ3Gno/r+6zbAYLgo0oCi1WbpYbHeNFEEwMGUyGMkEEqZmBKBkgSthwipKm8uFBmumBxfrnOF7/xAL1d/WQNkXDgcJc5SWk1z1fnFF7Ydw/rnspLc02Elsn+gQRbu2MEioSf7kLuG2YkoRJbnaLHs5i4cIFHFjyigssXv/08H3z1Xm4eTqO1SiiCTzM9wLevmtyxJcWcF+I/3bWL9bUiYUUiIXko4ShPTRQYTSlIAUR1BdtxGUiGeWm+RioZQhRFusIKtUCnV25y4vEH+IPf/HX+5aEnubBS57b+MNcf2s16oHN0fIXFhx7muu09ENbpShqcXKhyZGsfv/Opz3FxZol3vOc/Ua1XEWQNMZqCZpn+2hzRxXN01mdwKmVa8/M0my0sX0Cwmlw0IzgB1E2H4wWRnAZnnzmF40ts3buDpWaAUFllzk+Rf/IxDKuEGASUL17kqVqaW/b3kQvLLLcCfEFk09YBcB2+d6lMIxBZtyUkAXriGgEBqhAwXpf5zIUGV9bqIMpYvsxqzcR3Pf78ocs8eWGJ71wqsdS7lbMVkWcny4TCGm+6+zY++/ef5/p9W/mNT36Gydk5/ulr93N1sUw0pPPFT/4e377/If7kF3+O3pQBosharcVMoUFfR5SkDKWlNTbFJJ6ct3BS3Xx/UWC2fZAzoS7ksE754hVGetuws93YZgu/WWK7VGbm+49wcWyaK3I7C1Nz7OpPoYcNCmbALYe3UWg5uIis1R2capFAFBlbrbO9K0FpZpbZksm+lMil+TL7tCovTa2zffsI9UQvjXKZb55dYy3eRvfwCFHJpRHL0ggU5pcr2PNTnBqb4/ELy2zrTZAwdBrVFg8+eppi1aQ/YXBsfA3XDxADDxBoOD6xZIwXZ/Icm87TlwrTlQ5z9PQMWuASUiWyUQ1VFbH8gEbLplC1cRyXB86tst4wWS6ZrNRd1hoOM2WTynqe42s2UuDRpli8ZkeayNIldulV+krj7OmLoYg+SDItx6Vuu4gSHOxPck1PgraITNPzmSo1sV2P5ZpF3XY5V7TI12wGkgbllktMk7hpJEdP0mC4LcLWrii96TCyAr7nE9UkFElkpdxiIt9CMMIYiRg7BpIsVB1GtvSRjBlUHYGZiseUF2c92oWLDGefYuk79+NFEuQHryEiCzjRtg3HgmwaQd/4f3EaTZz8KuTn8Yt5Qpu2IMeimOMTyPU8lh6nuytNIplEE0VoVtiUtlClAKFvlIYURzz6EIlQiLVCBTGw0QSfUELHm54ksjaGVFlFkkQqYhh/cCfK/puJ7LsGdWQ70W1bUVQVLRbGHNmJiE95fAWpvRerWtuwGmsW0eur+NU1/KVJhEYR9BAhs4x46Umslx5DmTpNfO4s0ckTJFpruJ6P1MgTNCoE0RhRXaVh5GhIIXrsFXxRwpQM1pcWSGoCcUVAEqDhBhiiR91IMmPreARYchjZb6H6FppVxP+Be0dLVCCSJEh2oIXUH8gd3A35nl0lFxYxm3UM0cUQBexwGrFjCLmzj9DIKDVRx9NCUF1Hc5skWys4KNQ9kYYLKSlAkGXSqQQ7du9m+84dNE2bF46f5MzsGg3HR7WqCIEDogAEiJL0AzJWwBMkPD/A88GyPaKqhCeAKks03QAEgc3pED0xjWxIw1AkBEHAUEQkSWQ0E+Ga3jgN28NQJV59wwiSAqIgIcn/dlm9UG2Rjuk89uJFtmzZwurqKqVS6eXAqR8vQfz/6X5fbvvvpNP99+qmm24iCIJ/077whS8A8IUvfIGjR4/+2HeOHDnCqVOnsCyL6enp/6npbm9961sBsG2bK1eu4LobWutoNMrnPve5lz3uywK/ly9fJh2PIQJD3W1UJ5cxqzZKWCE+kEVJRvEdBzUZYyZvMlP3WGx65KsOcVlkbraAs7a+YVsxNUv56jzh7iyphEaA96PjND1oL3q8ODYJ3UPUJhexClXyl1coXppG0nWaF09ROT/G1ZNTCOdXeUNmC4d/4Z3c+Ne/jmToHP+Vv+DkH3yGxtgU4zWXiarFcFQhq0KYH0raQQNm803On1th5sQMH7/vbr47Ns9fP/MclZbPiydmaFRbxAd7cVstVi4sowoS1FuEzAbbhDz09iEHDooQkKwvYzo+ydXLNKI5Vu9/kOTebbRfv4/puUW6DBl8F79VBt9FzM+wVrMonTvF6KEtjN5+iNe+5tXs3XktAgFbO6MokkCQaGeMFNrIdsKbD/LJT3+Ob714HqG4iCoGlGyP3/7d3+U9r7qdj3//EexLZ7hohvCdFq7ZwFtbxnJkzs4U+ctLHl+vZansPkLz9tewkshhtXXTPdiLFYBTyhPasg27XEUNSciKRLo6R9TQiQo2lhxDk2QGEjqbYj69MZW4oeAHEPoBw+P7Ah4bMqRcSKFiusxWHBRJZKrQxEcgF1JRJZlnZ8oYisCmqMClNZi6conjD/4T7/35n2ep7vJP5xoMZ8PcK8zxtnSR82tNnr60wJXlGmfPjVNpebTWl7BceKKgcEHuYjI6iNe7hVh7lj39nXQXz1GXdN5x+3YS7R2kYiHOtSIsuwYaLmVLYqXUxA8E8oUKRjhMTA2oeyK2FxALaWDZdEUkQgo0XQHBcznYn+KaTRnaYwrrlsfXvvQ53v+f3sfC0ho393dQGDvBR/+vD/HFv/5d/uQvPsH4+dPsfvWdTGtxNl18CT2Z47sPPYMReEzWfPZ2hvjY+97IR3/xo7z3P/8pxxthKvEuCASM5YsovoObL5K//9vkVi6w8NSjvPTCSc4t1blc8XjiygpKpYR+5gxOs8mcHGV9rcDP3jDA3r3bWU0M0pnSaW3axfSDT9LVXCBTWmCu5vCdk/MMpXRSMZXepMFoV4JoSOaRJY+a5bNas1lv2izVXabLFrbrc+euTl5z+z6WynVyWkC12SIiugxlDJrrFfJVC6tp8fSawMNrAvnpSWqNBnnbp2Ek+PB9t/I3//h13v7eD7CpL4evRXjFYIifuXEXcjiLlmxn6cIx5qcWUTSZpWIT1WrgNGoc3tpJWBFYchV+/7ElDt+0j3e98QjXbe/ipdgI35+rcOY73+ddQx43Dca4/bodXDA6mOjaiuZ5KCdeZDhj8PdfO0p1YXGDkTQddEnEs23O6n1YS4tI//w5dikVGuee44+PLnB6qsji0jpDA1m+f2mdTG8vw+UJsn4FMdfD4c05Tpfhzfddx5NTdb501STvBBse2bEks8tlLl+a4dHLBe7c3UE8pPCa23bSHZYxvYDdAxnKTZtkSKXccpAUiSceeZYnvv194iGdYstlsWphizpPPnWaxdUaW9sjtCwP0/VYyjeJaDIt1+PiSp3LSzV8fDpjGvOlBpokcMN1u3jpcp6K7fNMI4ohwZol4CsGU6dnmFpax7I9qpaP5wdM5Fu4jo+qiEQ1GcsNODqZ5/RMGRGBidU6l/NNpgsNTMdjvmzy3EyZwA8w/QBFEAgrMgOpMG1xjc3ZKJblcuOWLF1xDbtlkwgpDPYk6M1FaNguxZYNssxkvoETbMQBr1sBZ1tR3G03wPosjbk5LMWgaGTJWx4TXgRbjeLuvwOiKQRNRwgcfNvFqxQRkmkk0cceO0d8dBBnZRrVNdmWVulWTCS7AggY6TaEeIq2/Bj1K5fp2NRJ0i6wc+UkO8w5AtdB27wdwgny3/kGQX4BPxAJJIW6FEGy6lhdo4SbeVzLZCK3nerEHJ3UCDyXjlffjZpIIQlg7DoMmkZgNmk89SBBJQ92C6leRDVLyKoKBJSff5r8ww8gGiEiZgHNbeJGsljpfmp6CjHdzkAkQBJlZM3YcAlRFYrhHqT6KlUHdElAF6EVKNhGhrCh05TCNB2fQI1Sl6JUCeELIiLgCCqBKCJLAoFi4MoGcuBvOFEEDooPHToIpUVsH1xRJ1BUiLdjhVLEZA8xCPBiHYjLE4jNIhVXQGTDEcgi2ADSXouQ1yKnwfYtW4gN7UBslnl2bI5LE3M0mzYSAaJjI0kCzUgHXigBnoMfBCRVgagmkQvLRBVhYwOeIDCUMIjpKrosbYTF+AGCAHXHY71hUbFckoa8MQtBoCNmINo+8fY2Yp2D/FDy8MMqVE3SMYO5lRLJZJK2trb/o6UP/1+uVqvFu9/9bkKhENu2bfsRC/3hD3+YP/7jP37Z475s8JvNpHCaNQZzadR4CCMRYupKAavaoj6fx7Nclp86yc7RND802NiU1oirIhEc5LBKq1TFKpcx10uUx5eQwypRVf7RZTaa1uhNhllZLxKMXk9sSx/1pVWU9gTxkR6EUIjG0iqBIPLwiyvM7j7Mlj/6JRzL5LkP/gEXPvmPmHPLDBkSBzfFadMlar5AVpPYFNVw/6tz8oCqA4MxlUvzFYrLTT4wOsKi1aI3qTJXc5AEBX37TnL7t0I6Rv/BTYzevpeOm/fTQZneeIA7fZ61p5/GOv0sPTdfi7+8QDo/QeJV93Lhm9/HLTdYLVdJaRo0SngrczTn5pn5/tMsnT3DspzkVGyEQhDlJnuafVvaCesGK5UKvufRkYxSmpvErRbQkhn8tRliuU4Wvv4gJ4oWP/uP32Z+fp47927h2htuZOL0cfYHy+jFRWovHoPAZeLMVYRMJzvbdG6883oShw7TbOviE2crWE2TrR0hQhOnCIwIQiSBNrwVef+NiPUiillBEQPy7oaVjS46WL5AIGvENBldFhAFqNk+puOjqyIpTaUjoiKKMJwy6EvotFyfuXKLy/kG600by/XoSRo8P5Hn4sVJPvS6wzz8j5/ixjtfxXhFIBvVEGslsrtHaR8ZwJ4a5xUr55i6MM788jq7tw/Q3xZjzolzbN0nJPh4CMihMGPRIb4+3qAoa5xcKKEYIdxAJN+wkUSJsCYxXbJYnJ7jhh2dUCmTVkRmrkxw3/Yk66sFJkoW7SG4JWUhn3gWwW5QNAMShkzJ8YmHFQ52hnn8a1/i3W/9GTKJGN/88ufpv/UN7Lj+CK+/+y5+7y8/w99+5rPsGBnlxXOX+eJvvJc9wykezavcvaeLcxem+fw/Psjvf+UFxqwY4VQbQdcon/ijP+SPfvc3yJdbtKQYk7ERZtctxsbXmNHaEEa2oY/s4Nr+OHG/xSNPXeAzn/02X3noBc5Oz/PPT4zznROLrBbqKFLAB161h0RgIqkKlZFdFA7ewtRCnvvtHNf2JwhHI1y2FWwfBDZYkzVH4ZrDe9jVGSGmSYwt1ZAEHxl4fqlB04VtA20cHkxSsn3GVuuYDZNwNMJv/dRW3nbDJmLpOO3ZGO949TX83Htew1qxseEYk9ARRcjEdN7w02/m7a+5gw+/citdUZWDr3kbjzz1BKnR65l96VF60iGWp+bZkQvxtftf4M+/+DgPvjDJ4+dWECtr9HSl6MtGSUR0Dg9nuHYowwffdidqfz+hrdupWC59NAitLRAIIlFD5GtOO/NNgYnlOprXZFgoo7ZaLFctLqyZjMRVyLWjHbmFzRmd9v5eRruinHz6Jf7o68dZWV7n1gNbGO5KsCUlUZFinF13yagBsbBGPKQyu1rfkP9YDnNFk5YSpXvrEMNdKcrFCouVFlcm5yk2PNpSBk9cWObCdJkTpyd49KVZOpMhRtpjbBsZJNWWw5VEsuENn9ubd3SS7u/lhfMLWB50JQwyEY1QSCERUdAlid5MmPa4gSpJP7BMk9k/kKQvrRMV6vRIFgtFi5Id0B5TkQKX1nU3Mb5cISCg5cFCxSSmiowXGixUTFZqNvNlk4l8g+WqSUdMpz9l0B7TKTRdHM/nmYsrKCKYbkC+6fD42BpX83VWay1UScByXbb2JGi6AQPpMNmEQSyqs6s/yXBbBMf1cZwATRSJGiqSJBJLRImqMlpI54IVQd13G723XIdUKtATkcmbAo6oMlGxsCNpVuT0hl5VVFAGtoAgICkqSjgEIQOpvYdg9jJaYZZ2XSTtV2kKBoSjBKlOAsdBjsRIbB5C3HkdNdtnIbeTOS9CPb+KV8kT6mmnsVyhtbyCWpjGEHwsP9gIhGiVaU2P42f7mF1rEt82DOUiLTWJKLoIfVsRU2n8VIZGop8iBlpHJ0J7P35xBSE/RyDI+B6IhoHjCJQvTmGeeQl/5jJiYR7JadAKtxEKRRA9G8M3N/zBXQfDreEFMrlEiOmWiq5IaKKPJgroUoAnCJiux3rTZa1uU/ckfDb+0x1U2lSPtaZLS0viNyoIvoct6jiSimrXEF2Lii+hKBINI0VI9PEEcIwkgdlADjw0s4LkmEiCR7PRAtdF9jdYddF3aXkCJhLe3AQ2Kp4gofgWgzGFHRGXa3Ztww98zp46zszVyzhegBCwsfdDDuOJG4SWLASkDHVj/gis1E1sP6DUsjFth7rtoisiaUPBUCQiioRAgOkGlCyPsCrTFlLpjMncfXiAdC5KKJUg0rXpxzBQoWKSim+EygA/Md3v/w7Zw//X61d/9Vc5e/YsR48eRdf1H/XfeuutfPWrX33Z474s8Hvq+DE0TSNfqTPomsiShF23OXm5iF1uMX10gqmnZlHDGroi4QugSAIHr+llpemSS4eQNA23ZdNcqaNEdB79ziXyq0129ycIgDveeD23vPt2ZEUiEdIwi8uoiQRGMkL7YBaz3MRZXSY/vc5JM83Bv/h1xGgY+6//ju995vMMCSbX7Gwjowhs649z4I5Rtg7G6QvLLDUcpusWARAVYGtUxgN6sgaWKiFqKpceP407PUumLcrN9+5je3+C4dfdTCibJjI6zL6fuZnd776P7ttuRsZm8Zv347x4lMVv3s/UN5+meGWSqX95kPVnjyPUC0jlJYbvvhWpNI9dq6OoMl6zwfpsia//wd9z7jvP0Dx/ht62KN8+vcr9Mw4TXfuYqEno/TuYm5zA9z28SJLu6gTWyjwNFxqiztzMPH/z4gU++fmv8tGfeS133n4rzWZA0DGEv/NaAt/BOn8cp2liuRqVXdezKsXYOtrPJ79+gm8+dhpVEVheL3NkJIsRCrN8+iq2HoWuQYxXv4VodxuO7RH4Ls21JZKKQGTxHEJhGc8PaPkyuuBSMz00SaRqu5RMh/W6DUJAmyaQkn1USUQWBSo1k2v7kqQNhYgoMLle5/lLS+R0gUR7J+3tbbzy3tfyrntuIe8rXF2pceNwjOWGj1NrsNIQCb/0FB/do3H7rm4uXJ1HFkUODWco21DydaqmhwScePYpvvyxn2fHzXez987X0h5RuaNXIGaX0AKXrB5AfpW//5ej3H/0FOuZHq5Or/P227YRHz/O86eucml6ldWGi7G6xOncKGGnih8I6JJPt9hEKS/zG+9/KxHB4ff/8tPc8sp7ccp57uiP4IdjPFwK8S/PTfDQrMtt1+/n/T/3c9z+pg/ylc99EnX7MG39fbz7Xa9hNt/kmWdO89x4geNl6IppCKbPgV27+c4/f5VauY4Yz3JM6OBFvZtH+27my1dcqnqaVCpO/cJ5Ik6doa4s4U2b8Q9ez1cvlmmZdb5yxWWOJJeml6nMTlI/e4a+rjSuEeNUUWJnY5YvPT+Ll+4gFgmzvzPCldkKJycLNCwXSZaoWi6rdQsHgUvLdQLfRQAevbzGesPFCIfpi+kMtyf4ztGL/NXXn2UpULhcbKLpCpYgkksZiNEwHznSw8FUwLnlBmtigtNLFSQCfM8Fu8WZdRsx3sbQQA+i3yQqulw/FOKGrR28OF3mhpsP8OvvvJUj+4dQRIHL83VWiw1MN6BiudQcj4rpko6oBIPb+NayyAk3zTcmTHZsHWCPtUhHaR1PU3F9nzfesYM9B3ayVbPoS8Dy1DKFco2Zkk2sukpne4qJ8TlmhAz1WDvvee0hNFXi0eev0Dx7mq6oxnFjE9WWTY+zwnjR5rFTy1xZq6MbKmdPnkOVRNriGvPlFpIkcn5ilZ62KP/81ccY2jqMY1qcX2lhVeuMpH12XzjK6+QZNEmgULW47/Y9XHfLITa3R2l4Au3RjeVqX5DYt7kDm4CLazXWKxZbOqIsly00VSSiyURDClvao1xcrdER1/B9AT8Uo2PzMIrbYn61TFQC1xLQA4dOSty3twtFhJbts5RvMjZXxrI9jl1dx/JdHB9uHEhz7WASVdmQW4QkCcSAbe1Rao5Hw/JxgoCVSouq5eB6PtNFk/WmQ1RXWK9ZGMqGZVYirJFNGmzNRdiWi9CfCjGUDdMb0+lL6vQlDeq2y3zFpCOislWtYUoqEd2lOXkFW5AxAxFFElFEWLZkKtMzWFfOIQ7swg8nETuGEGIZBN9BH96B4jYQk1nyjzyOWl1BmL+MujwOvoclhPAzfVhLc8iju2iceJHiyjpiNEZTCbOc3YaWzbL47QchnQXfQrGqqJ6JCCiSiKdH8bbfiFJe4raRKFpnD1KzxPr938dd2GCxlFgGTzIICQ5uKIknawjpHoJmHa9cxF2ZIVifRcm0ocXDqNEQ1vkzNK9cwL1yCiEAkYCsW0SsrCI6JoKs0kr0YKxNIosBUVkgl0oiBh6a4CELNr7dRBcFavaGo0dUVym2HJwAQpJAxRUQRIGWF5D3NKxIjiDRgSSA3Cgieg54JiHZxw8EYn4TEQ8NB6FVQyzOojo1pHoBwazjqSGCzi340Ryp2gyGVSMhmmTsPHIQMP/PX+ULxxapSlFEPNq8AsHwQcIhfSNNcs8eWq0WTx8/xeTEBKpvE4gKdVQCQcITBBQJAj9AlQRWqja6ItJwfZbqNg3bBzY07K4fkDIU4rpKRJNo2j5xTSKqSgzpNnduyzLYHWdk2yCJ9tyPYaBCtUk6qgEBruv95HS//zd7/xkk2X1fd+Ofm2/nHCbnmZ3ZHLHYXSQCIEiQBCCCoEhJJEWRkkAly1a0rSxZybaSJZrBlEklBgliBBGInHYXm/PsTs6hp3O6+T4vhpb/rsdV/4cUX9gun6qumrnV93t7fl09dfrc8zvne9jw9n8KvvKVr/Dnf/7nnDhx4n/IEZ6YmGBmZua7nvtdrdLJk2dB2r4FkxN9yrNbZPf0cPeJXgRBwMNHZrutpNF2ECQ4sTPDi6/PE5Qgd3gQNRQglEsSH8zRLjeZ3Wpz9somOC75hM5Yf5z8e95DUJPpata58q1vEurpJjLcR2V6naXnL3Jj1WH5gcdoZvKc+fW/4MIf/ldqVBkOhRgaTnH0Aw8wPpSg+0gPAhKDe/MYpsdq28V1wQJCMuQDMvuGotxzxwA9PQk+9Bvv49hEmrH3vxUtHqT/nUe5/wePEu1J0Dp3EiUcR1IVzKVZGtcvsXn6Eleffp25V04x/ewV9LjGpeevohoVQgPdmKtrWFsbtFWPn/rsl9GCOoIksH7qDKtNjQuBHgZ+6kfZORBjPZDkh962Fz+RI9PbRawjx849u7kyNYcAjKRj1EyXXH8v0dYmnaUVfvnf/ipmtJO//p1f4MnJVX7okbdxXcqRVX2MVgNd3k4LIBCFeJy+qERDCvLUbIOZtSJbNZPi2Yt8/N+8jzGtgSvJBI/fiSwKzJsariRTOH+Z4jeewF5bJjh1Fqm4jJXsYbFQYXqzwVyhiuB7WLZFUgfDcig2LAzHQcGl1ahSdQQiuoTjWJyZWcdqtbFrdYp1k+FEgKBt0GGXGM9oNAWd7/+Xv0lg/hb3Nm6xdn2GpyYrFL78dRqizKngAIGDB1jK7uSgu859+7pIB0RWyi264jpFU2C2ZPL8a6/zpb/7W977q3+OkJtgemaN5y4t8l8utckEVZzKGsXVdUq+wqHxYaKyx3/9xjmO7upAdi2ea3cR0hViskVW93FzGc4XXHx8cppHWBTwi8v8/I//MD/7E4/z+Ec/jKLqND2BJ6dqJP0a2VaRrouvMBz2+MaLV/js60u0SiX6xsewew4weepb/ONzZ9nRHecd734rgVCMpUqb2c0Gr91aZ+3sRd77rod4+uw5PrkgEouGeceAxuEdndwtrlHcKLCwsMQXLpT401dneOX6KqNxn72BOkO7dvCht+5leGSA3X1hjgTrjPpFbrYDxA8c4sxclZlbU3iORXJokFQ2zqXVOqW6yetzVbLJIFXbpdA0GM2E8D04PV9hqdxGlzyankChVOPkjQ2KbZuK4bDS3vZwZof6eODYBE+dnqFiOMyuVtHwObtY5fpKg9lim6/NtdlsGgSsJvGNG/zppz/LretX2d0RJeY0mK55/OC//DXK114ivvdO/vT3f4uFpXVu748xMZilJul8384MH3v7OI8+eIhDo100XQHPA8N2EQSBTzw3ydEdHbQNl1LbZWL3EGpnF0cfeZCHb+vnvcyg2S0E2+b6wgYv1zRURSfY3OLFyRKzayVEScU5d4pzRY8XZ6pMzmxwas1BzfUQUVyeWBf44kuTfP1WjV//xJMslw2eP3kT03ZRRZH1usniWpPVchtHkKhWTCpNmx17h5jecgjGokxfvEgMgwd359k3muUDoQUO7uxCGx9HlQTycZ2AKrOnN8Hh7gQNwyYvGbi+R8MW2D2UYq3Uptp0yEVUNuomrudRa9l4gk9/Qt/28yHQtj2KtsdYNsJYTxoz3sltO/JMlS3Wmw6N6Wn8ep2IYKGIAsGATrPeZPdAknxER/VFym2XyY06IUVkX1ccPJ+QLCKIEJRlCk0LRVPojmtkQtu+yXw8SERX6Irr24TX8ciFNdK6Qt102NMbpzsewPchqsskwxqCILDWtLDd7UY5TRKJ6CqdARDCCSwfVvI7Ce0YZ7Zq0RWWkXG4sVqnVa2heQ5iPIkQCtJW40jBII7vIYQTGOkhXNPCGzmCfvQ4wtYizavnqZ07jWV5TNc96oKG2juGujGDkcySlj2E0gojmgGBMIpRRw+KpMf7INGB16jQePofyVKjKgSoylGkVB4xHEMIJSHdhXrkbuSxCRp1D3fuMl6rhiVpCPUCBOPMdt6G4/sIkTiu49FemEFKdyBpQRJHbyf3/T9AfMcAaiqNJIsgQGhrGrEwj6zp2MEkMiCJErXsKIoIsiQQFX1E10FtlnBdH1EQsDyIahJpXUYUthMSim0HyXcRBGj5EvmAiAu0gwnwHbTmFmp9A8E2cAIpIjIoroGEgGo3wDbxLAsv2oG7Oovv+wiuiaNFkIM6jWAWMZwl3Fih7clI119H8ExSByfY2x9H8kxwbAQlSEPQERwbBJ9YOMyB8RH2jI9QKRc5/+ZJlhfnkO02trv9f1mRRDwfXH97E5wuSURVmc2mRbFl4/kCpbZFRNn2meuKiO35lFsWhuujKyKGGGCyaJFLhenriJLqzIP4372/pfq27QHg0q2l7zH5/R48/g9CoVAgm83+v443m81/VqnGd7VKG4UNVEVmLB1DUBSCnXG0RJjeiTzBXJhkdxQ5IGGUarx8dRNZgNJ6g9WqzYl9eWJDPUQOHiLYm6e+WeXSqWU8HzwPNopteoMS7XKd6b9+glhfgm7b581nXgLXxjJdSvEuij/wAUodw8z8p7/l9K/8Jyq3FgB45coyB4+OUbVFJM+gsz+OnMsgp5J4pktAFYlIbLeOAVs2tCWBo3cOkx1IcujhgySTOiP37iRz4jhetY0NyL5LdWYJu17F3Fxn69o8TqyHxcuLfP4LL/IHc7P81Nde5QtrK3zu5gJ/NnWTJ5aXaRXrkO3n2Veu8eGf+00CgQB9PR3Mblh84alpcjuH+NGffoTkkcPIdz7MSqgfQQtx+1gHK6UWcSw2V5ZIpbbrBAe2FlgihJQfpIkMpTUO2AV+7j1vQcl0cu7cefr2neAmYf7x2ReIhAKoS9fR3Rqhnjxq/zBXqj5jmQD5RIjfevxd/GCmzfDBXbQshycXPZaLJgU9weVGAKm0Co0yHVkNOayzePoaVsuAtWk2mw6vFHVuLJaZ3mzy8pUlitUGyuYiHaJJWJeImhWubTYp+gGmSiZrhSK11VUeyluslyqMRl2OJky622vsqC0RlV0ikRDpoMRr33iG9sxNYtPnOfD6P+BMXWdGiLC8sEEuGaD59kcZi4mMBRt0N1ZRL77B5sI8haZFpb1dAvHVz/8NOx/6MQbzCTZWN7l5Y4aehM5B4xbXNypUHYmCIbB7uIujDxznyO4BfuH+QeIhlYqWYkYMc3D/KHcOZ6k32lQrNR450EldS25nnFbmkbUQ6VSSgyP9lMp1mqLO6aU2F9cdfu0bc3z2RpPTdorg5CWODoSZLlv88n/+Bk8+d47HfuiHuXrqecY7wsyu1HjPsWEe/77D5EIKkgBj2SBPJXazlt/Bf/jNf8Ozn/p9cEyu1qBt27SEAGPpMDtHejjcF+bA0X0MDHRwec1gZKiHLz5znvsPdfOD4zqblRarhMmPT/DOfV38wVM3mN6sMDu7iqcH2UCiLx9HVkRCQZVy22Gu0kLwfSo1m1cvLzO50cDxPAZSGvvVGjHJZ71Q5c6dOUR8IqJAUJU5PVdmYiCDGgpQFzVOXd3k1ZcvsFqzWa60SIYUvjZncma1iSQINF5/iTdff4XizDU++ZnP8f4P/BC/8Ku/zvOnr9KbCvF3n/4zlk89RSvQy3/++J9Rty1uzG5yeXqDzWabVETnrsEobc+jO6wwkdYJyiLxoIoQiXJ9rcH89CqRoMpbJjo42hVAXJ8jNtKHO3GExyZiHEr4nHvzOsNxgY8/fZWppsrd41ne09FC6e4nd9dx3nqon8ODSUQBzpy/iappzNoRLmzY9Pem6I9JzC+v8R9fWeP4/gFCgs3Tp6ZIRVXuvGs/sipTWl3jnt05gopEKhmmatj0TIyRzaYpNVu02xYnBhP8+XWPuR3HuWxFmN5sMpQOEZBFRjMRFAnu7FAoOwIvX9vENFy+fnIOx/WoGxbXV2vMrjewLJeFjSaVlkPbdLAcj6Aq0bQcGpbLF16bxnN9Kk2Lft0l4Nt87qbFZucY6XScAbeEgE86qvPjd3STDKr0p4N8321dxCQfSYKtahMNn7JhY/s+Sm2TetPm+nqDYsNkrdTm5aki8YCKpkrkozqqLKLLAsWmjQe0XY+W7ZIOKiRCCjc26xQaJpeXKygCLFdaKJLAbKmN4/tossiGAQXCrMSGuEKOaSXLYqmF6/m8uWFRNVxm1sv46Q7e0CdoSgGcepGirVDQu2gHc8iCjxxL4JYK6KkYLVSkYBBJkahdvUBs/iyFpo2oaRjxHoJ9/VxbqLKipLElnajXRJRF6tensZfmsfNDlJse1Tdexz/7PBGnSkAUWW9BI7eLmhCiEe2m0bkL+Z4HiB46gpkdpHHtAobl4KshBFEi15FH1MLYffsQwmHa6xs05uch2YHUMYA8vp/grv0Ed+4DUcb3wfM9rJVp7HAeBwnT9VGNMoYcxvdcPN9DFW1UGUzPJ2A38CWVmOigSgKO5xKTQPR9ik0byaii4eIgkhENYqpAyKpjS4Ht+mFjO0O/JkWo2DKaKGKFEggCFKUoUmUVzzaoJAbwJQVf0kAQEUUZvb2JUN/Ecx0qvsbW4FFMMUBs9x4OxFyixRnE0gpCq0SgsYm0fgtBkBCsKqLZIJjs4MiOAXYP97K+WeDS2TNsrS1SNXxsz8PyfEoth3xYJaAIVMztL+aFlgXAWt0CQaRtO0gCbNRNVuom2ZCKKIisG9s10SIQ0WVUVQZJBnE7RrXaNImGdSQ1QK3R/N8+7ux/ZRw+fJgnn3zyn37/b4T305/+NLfffvt3Pfc7LrkwDIO19TV6u3K8+6EHcTfmyB8Zxaq0aK5X0GM6juEQH0ggyhJtx8dywWtbdEdldj10gEB3P1LnMNbcDC9/4xpXFusERZAFj6As48hQWi6yee08B9++g12Def7LqWuUxSA3O/dhZjykrz7F1FdeZqrm/A+vb0IOc6WwxZMtC/M/fpY/2rWD3sNHCCsW5uoaii6j2S479g6wdnoGw/O5XjSxr1e4864kiQP7aK+v47oim88/T0c0zBtPvEDr2jol22NlMMRX/+gLfHR0B8e1Dl5uNHjZa/Nrg0PIis4X5grccfcRfm7fOD/2u3/OvoEJ1Btz/KeTb/KJD76bRFeaz3/mGSavLdHMdJLeNUHaamPbZdYrDoWZea4UXX7y3bexdOUip8QYF69eo3fkNgCk3BDta2cwJI24rsDuCfylafxmGfB5y/4Jfv3f/hKz167yMz/+MY7kg0hNsDWdW9dukbotjVxtoEaCHI+rXF1v05tK02y3aAdCWKurbLUDZKfPUr/9+8hRRS9sgG8THh5FblosbDlosxcJ7HC4T9NRFBU7kuK6mWDu5jKhpEV31kSLdlO/Msl8534S4QCzmyXWVjYY6MnxjqE0kZZDe2OTm5t1kvEQ6sEj3LKbTGDz9LkFsrkUG7EwI9ISnffdSbao8KlTRcSohCHe4F8NdHMkXkOwbfyTL5AZ6GeiUeNKppOeiMTLL51laXGVXeEYc+sVJgY7Wd0M8sUvfgXj8pNE8j1Ua1WyQRlVkUjEYvy7n/4w85khMmaRC+0wqq7iamE0r8x8XSSS6WHcW6PtD6C41nbLz/WzqDgIVpXmhWleliYwtQChTJbpW5tcWVhFS2R57sYNJsLjPHLHGJM9eb7x2jX+4q+eITBwN298+RNIj/82C+cX+OHbxzh59gbPPXeL8ceO8sPH+/HsFqMxjf6hHbzj3e8jFE+wuDDP+x7/1wwfvYOxXJxaXeT9J0ZZNSSUycs8Odli8+YkOe046dEMrd48EykXc/4mM4k+9ucjTLkKFSXOo3uGySsWV9vbMUi1tkO1bTKSi1BvO5y5tMD50gbf987jtJsmoe4of3uhyr0H49w10Y0a1LhVaLPWspANh9emi+wZiPH6VBHHF7h6bZ14LgeCRzKikwwoWJ5PV1Sl3WjzmtbNxYXXuest91GMjRKyW2w2mzz5qT/AaRT4xQ+8g5/5rT/i53/yY4SDKrdefo7ew3fxwsnrbBVr/NhDBynVWtRaJmPZAFldYqrocWm9RjoaYmdXBK8ZZ9MSMS2bU3ObrBQDHM2J9A330B3qo399nlq1xkRngkfvT7JeNhjoiKHWl3AVDdd3EMQAe8UqUqvGwtQMru+TSifp6YrTXFpgYG83H/zAw7xyZZ13D2sczub5rW/MsrhaoyMmkwwFkKo6Z+cq7OhL0CxX0ASoVqrUKiXu2Xc7Z8/cIHLHTp54Y5bWKwu8/0ceZjQdJKX6lKs1lECItZrJvnyQK8sVlgtNjFKFRURaLQtXVUES6UiHaVsOiZBCfzrIQtlgpdKmOxlgcq1BLCAhBjSur9XwN1awM3Hu3dfNwQl3u8DDGkZwavh+lbAq0vBUNltNmqbLQFylM6LgSAqeJFI1baY3GwQViXRWI6BDwlLwbJf+dJhCywTXZTAZpNi0SYYVVkoWdcujamzbparfzl6ttG10RWSpYlAzHC6u1ghrEpmgRsV0cD2FqCZRND2yIZmKI7BUanOq5dGbCCIAhYbFUELntuFOprZaXLq6hpNTOJa1WM/swvEl2oTpkF30VB7F9nFFBWNxhfjuo1Cu4/fvJvzSPxLfdZj16TmkwX04okZDktgjlTD1MVKijSslyN15iNmXLhD6whfRB3tI7Opn5m+/wq69x7AEkYYLtikQUkVatkjCqRGORtGjAbaKZaStElp5FVMXkSI+PiKGHkcxa4i2gaSIaMkMbmkdqXsHcrOAmO5EiHcgZwfwtDCKsYyfzOMpOorTQLYNaJRRU0l038SxTRRJBbOGaDeQ7CoCIAkuCUfhzapIfzyAJEr0REVkt4LlxwmIAi4KeAKoAVxRwdWiSHoER48QkDwM16OlJ3AcULQYmuchxjK4egTZl/D0KKJRQ6ltIgg+gmtgzk6i5DpIRU0CiRiWJONFsqiNAtQKCIEwvp5CtVoIoShiu4GlBpEFGQ0LAYFkpoPxdD/G8nWmllaRNop0DIyCHsb0IKbJVE2Xlu0RVCRWagaDySAhVSKkiqzWDCRBJBGQ6SdAV1SjZbtEVRFJ9OlNBSnUTbSAiqTIuHYbgKarEAsH0GMpPv7V8/zR43czOzuLYRj/gy/1O4UgSQjSP8+z+889/381/N7v/R5ve9vbuH79Oo7j8Kd/+qdcu3aNkydP8vLLL3/Xc79j5XdqaopAIMhdx49xbChLc6OML0ro+SQCPnbTRBRFIvkIzY0GI10RfKBgurzr/YdI7RxB1HUordEslikYDi5Q90AWRDZsm3BMY2thg66BCMWpTUb2jjDx6Hs419BJNEvcMdZJvrFGNh5AZ9tyrn/7jwn6Ag+JCf7l4DixXIKBR+4jGNWxKlXSt+2lY1c/AVmk6w9+h3d+7J0AHHrkNnp6M8zerNGYnaW9UeD8E2d45k++QjrXwe++dIY/rmzyicIit64U+dn9R/nczZt8/LWXWHR8/u1//DOCyTSJ7ji//Info7HjTmLpJI9levi1107yW197mV/6s8/QkY7jXr5O2hfJjw0xdO8dLC2uU56Zx795hdoLT7Pz1knuDlsEFi4zvdXg3mGd5uYK4VgS1xdoqiEqW+tIG7PI5WUUz0COxZAznbC5yK/81Ee4Z/coP33XMR7pkVAcF6dnFDvVTfuB92AKKpagEpq/SrddwfZ9mjsPsp4b5MxUkeliG2n6Gsl4lLs7fJb1DE4wRe3KNZREDEUR8Jo1xLERat2DdDdnSEg2A84WBwYSvEeaZigq03Qg01ghMLqbdEDCq2zy5rlpfFEkLlustCBVL9AulHhl02dstJ+6C0MJFV3weO/tg1iBGEnFQ46nSLht7hiN8JFRiR/JNXlkNMeuxhyK6NGu1jFKW0S6suTFNqPWJu2VRdbOvUy8c4w3n32eb/75Jzj5939N46lPsvrC33DogQ+wEd5HPX6I7rs/zGf+8nO8772P8UM//xs0NpaZdkL4ns2eniiKIPC5C1tcWGqQjkW42lIRfAelUaAS7UPedye7UiFefe0spzJ7ycdVBtMhVgo1fvad49x7915UzUHMDzCzWeObt0po6TQjB3bTOzhAnRixnffyzT/5Zd544zL/cHKWzsEB6vUmp9ZtIqqAWy2xpWX4sZ94nAMf+BXu+6k/ZNf+Q4yMDtJyPXzB49aVBbKxILv700SHh1jdrJEfHGTKUAllsoxndDbVBIYrstIQuO/EBI/f0cs9dx9C0FS+NNVmtdJmcatFWPLpTwRp2D5lw8GXFbLDI0xtNBmJyrx26ibRRARF07g4s0mx5eB5PnXT4+XpIveN5whIMooiUayYWEYTy/S4Plem1bJJhzV8z+PKSo1AUKdhmTTDg/zFn/wRX/nkH/APX/5H6ptL5G9/iH/4dz/NyaUm806Ex37y54kFda4KA7x8s0IgGieRTvLM5CYXlmvs70uSEg3emCuRCakMZiL0JoOYjseN6TVWNxqUWyaFapPdw3myEYVa28QXJfyOQf7zv3oHcjjK/p4Uhwfj+K7Lgh+mZbnMNWWubzlcqqnc3S1w+4ExDvcl6UxpdKWDzJY9NFHk9JvX2FjZYNOW0FJ5QgGJG2cuc+3CVU6dvskjJ8ZIJzR0ReDM1TV29KdwWgYoYS7OF9m/b4BvXdrg59+1l2hnNxFV5N2DKoNxmYCqUmpb3FipslS1SfkG338oTz4hceLAAJYo0v52s5VhOty1I8ORvgRBVWazYWE7HpNrDSzHIR8JcHgwhenCymqB3OxNVqsmYV1hq2mz6ga4ZW/XowYVhemKSV88QLll8epchRfmG7QdF1WT2Wq5RAMaDdPh3Fqbua0G3QmdB3fniQZkdEVieqNBJqSSDCpEVRFVEQkrAumgTDSosLsjxlbDpGY6pMMayzWDnlQI1/PZnYswuVUnrMhENYmK4cC31U5ZFMilYrSbBtmYxoXVOrIksN5wKBHCD8UZG8rw9JLIn1yVWbt1i4TkowqwbKu0Ev0Y2X6sWJ7wUB++D3oyTlsNYu08QaC1RTHWi6RqiKJIYtdeSGS5WTAQHYNVP4K+7wg7PvhuWgsLKNUSUiTOLTFHc/ISjm0Q12RatotoVkloMhGvSUAVMcolnr64htc9QCnWiRzvwMGn7fmYgoRiNRBiKSRFRR4/gJsdxJu/huxaCIqKHUpih5IIeAjlVaRMF7JRQW6UMVwJK9FNwGkhuTaqAEK7ilRcQqiX8B0b2TXBsoiWZ7lZaLHVstEkEccHL5RiuW4j+j51TyGEQRMd1axjCSJWspuWHEVyTQLCtp3mv2lRtifgJjqRRJ+oYNMUNBBlhGYB8BFcF6/VwMsNIGsqWruC6/kY4TROKIXTt2c70sxq40VybN9imEfxbMBDaW0hmDUEo0bArpPuHWH34duIxZPMXDlPYWEK2XfYalv4+OSDKhFVom441E2HZEDBByzPp2a56IrEjnQQVRKJ6yIRWcTyoC8RwHY8FE1m5LYj/0SZGm2biC5h1sucvz5Pd3c3gUCA6enp75qMAd8Dv+/3oCHufzEcO3aM119/nVarxdDQEM8++yy5XI6TJ09y8ODB73rud6z83rhxg9HhQX71/W+neek1zEqLxuwy4d4cnu0hiNtB0421GqVyC9dykYFMQiczkNlmqoqKvbGMpMq4zn+PNlszXCxj24977I5eireKVHf0U+w/xoFUic/+h9/ndw6MYghteh99K1bja8xvtHDwSQYVXMvlZtFirrxCVFnHDTdwJ3Zi3rpI9cY0vR/6ELmbRQxNwWo3UfNZ8gGJ4o1FDv7Zr7A6uUgzaFG7fIYrcyXy6QCPHdrPA4+8jVSql3Q+Tu36NJMvneXd3YMEckkee9/7ONPSmb3jYW47+WUqf/cP7HvsHaxcOEP6oYf5KauGf+I9UK9RXi1ibNQ4fM9RrIO38eXnVkmNhhnzS7SbJt27+6l+4Sxv7n6Q+vV5vnpzledmVllfK3D78f041RkSHSmKdQPFrINZR5DiuJKO5IPbbuHNXeJobYPwrl4ENYCYzmIvzcHABEN5DVGBQ6E2n3q+xJGqw/CeIyCL9OdixMUOXHWYHqlJ0t6Aqet0hOLIQgszkcKXg5SvT+EvlcgdGKGwPIscT1M8cw65P0XElLArBThyNz2iS1WMUZ+ZYXK5jXbrVb5/ZBx9/346l8+iiBNcteNoSYVjmQCLhRKdgoeCCq5Dh9Dmo3cMoNysETp0N/WvfRWfDo696y3EN2ep1+uUb22yvOMQxpufR5NlnGwXYdui88oL9PYN8onyLcRWkw1LIJbIUpbS3HfiNh4Z7WPwaC+hdJYLp85x/55unKnL3NaX54P/6jf4jd/8Dd7zkZ8k39nPrrFBLqxV6UxHGEyHeWOlyWgyz4WCze5EhIRg8fxLL3Nydo3bH+0mkU0xXWqzMLXK2EAaTRGZ6E+hyBL1jgiyIHJpqczU9UVkXUFzWhy/7yinX71E7q4fYfW5T/KG9TDrzRPsGsojttt8/nKTd8dq1ENDPHN9hlq5wrG7Jric7eYPf/tX0MMxvjU4TF//KLsPDjOoO0SHOnl/rhvnxCCeFsQJgGRv57sO7j7Ebfg8P1/lUEcUXxC4sdlCUmQqbZuQJnGft0xtdIIX5its2i6JdARNFtEDEorT4o3Xz1E8dIi79vahx8PoOMyVGoxmwozlIwwmdD5/fhXX8QhI4Ng2giRheT435spEAgqyIDLREebJc8vUN1p0dw5RveNHqFcrbGytcf7sJeJBmUf/xa8zlA7xynyNR+86hBDq4oWr61SbWyQUm3orTq1lsHO4k7Aq85enS6TjQd6RiDCelOlKJPnUyQV6+jq5MV+kaMKDxyZI61C0JHK6gygr+L5HMJlh1QKnVSGt6ciiiaYlODVbwpEUmnWLoCbzjnsPU7zWwGvbDMQUclu32L07RLK6xnu7BTaP7uLldY+dXT5vPzhIY7yTvo4kf/H3b7DesslGNeYLDfbvH+DIYAKaOV43fPxIlHPzDartNpNKmGOHc3zruXPszt1JR8in5QtkAhJ/9+Y8PVGZnWKFbG8eUx6nIxZitDPMZ5+5jlP3iUc1fB9ans/cZpOQJpEMBrm2ViemKyRD2y1VXQmRjY5u2jmVb02WyEY1zlYU5tbW2D+y7bULBQOMh0LguwymApTbNiXDxfM81msGtzYalBsmuq4wt9pg92CCcssmGVQ4GiizGMtTLLeZLrVIBzVUSWQ4HWJqs0lQ2ya0CV3Fx2e22KRpOaTDKqoE+biOLInIgkBMl1iumaRDKpbrMrNlE9ZkZFlA8QQmV2pMdEbYlY3gGAaGB/mIzrVWlKulAm8ZCrLRMki++CShu95GqeWSDOhE/RaW5+J2jSE1ivi2Qay1gRDwqb/0DF2qRts5jt7Zzc7uFKrTJLyxhFtogSNhZPLEOjTG3ns/NiFqp18n8dhjtK+9hLhVJXPvO4lpULM8qm2bghMhR4BAeZ3bDo3hxqP0ih6CUSYehCUrRFSVELQgRDKoR+/HDyVxtBhacxNiOZxAnLbjokgKMib0TODjY4si8tYSYudOREVBbJZAVsEHyTHwIknE0ipeYQVJ0XGUIIX4IGOCyFBMJSSYbPoqlq9SNlooLriSitIsoekp5NoKRqwPRw2z2XTJ6TKSICGLHgFFpGX51B2IBnV0sYUJeIIIZg0/3o2vBBBEGX18P4YWI9jYhMUr+GM5NGzE9SkqA8dIrM1DugMh2gFqBKwVPDWAaNYRBBnwkeobCGsbtAjAnuNkegeoa3HMjTlWrp3DTXTjxdMY3ra/WcRnqWZwMBemabvo8nYToe35xHWJuuWRU2C5DYog4Ho+jg+tpk0onUYKx3EbJYqlCpoiIbkGpc01BEFgx44d3Lhxg127dn3XhOz/4n+O3bt387nPfe57OvM7Jr/nzl9gR38XlNfwTJtAMogSi2JVG3iuhx4KUa9uYRs2py9tcu9bRmi9ucg9j+5F1hR8y8BamcMpVVBDQZT/zn0x2ebGhulSM2T42IfwEmnWvvAN7rtjkF2Wyd8UG/zkVonU3p0MfvAh7kyeRDOrVGa2WF6o4bPd1jaiy/SLMi/+7ed5cGcvajyCsTjD1htXcN/5Nhav32TcKIPr4bTahJplursjrFxb4NxTF9lyYeeeIfq7IsTuv4d2yyDUM0By5346dnRytFCnfvUiekDjnqhLpBJFn/hx/DdeI7ZwhasVOHB3jhveKP9wfpJfDd7i+qsXER2JXL5G8IWvcoc+wkgE/Etz/ENsNx/zbyLdczdHF15mPdHLD9w1ih1IsnL6C1x45SmOj3Xxla9/jUff9wPI4SieLIAaRo6q+KUVvMIazfk1QvkE4bExhFQXrudx6e+fo+N+k1RPB6vXZtBHRsindJ6ct9ibq6EoCgOpEKFImO6QxJqdZW3LY39IQV6fwUpEMJY2EF2f8vQKXbv7ES2TXDpLvV3DOPoWLnsyEaNN8/AjZFwJwbX4+Ms3uGMgxG3KEqMZm0C3QttdRejp4Y2mxrJlMJbJMVxc4FItzomsyPrzL2KFkohdOfbEbIJHDiNaDSoD++g1FklnelE791H96tOorRKrlkigUmGx5xB//dwsP5cvkLjzHpa/+Ty//ZnPk21ViJXm0MwKbzx5jrv251CTO5AyXTy+K8PNvb388RdfpfvD97GnvsqJqE/63/8Fn/n3v0m4e4TF9SNUiyXWNzf5armC6hns3zXGvlSQC107+Ks/+g2Gezr50t9+jsWyxZRlk44G+c8vnOV9730Li5UWiqQSDKiEunJ4U5PocphGu87CtXn6R4e4f0eKiLSfp1+8zOO/9Sd85T/+a8J5mezBe8glg+QbJRqpHoRak4vTG7xDXubJF1QO3vEQ7//BD3N6Zo2//52fQZBkZusuiqKQE02QJPZ1BlACCrbroakiE1Sp+AHiqktPLIgjSqQDCqtlg0N9SaKKQNsDWdIo2wKzRQPb89jXFefyep1YQKMtqrzn7ccI5HKs1AzuiTaoBKKUahbtuMvMeoNTN9Zp+gKm4VKuNOnuiNDZFadq2ARCCsPZIIW6heB62L7Itcs3qXSmEBQBPznInceOsFA0CSai/Oi9Q+xde41Gbg8f/09/ij90nHQiwtLcMnpMZW1lnV37R9jXHSGlCuzpjnNlrcnJ+TJBq0lvJszergT1ZBhVkXF9D0VTsX2XmCayWBNIChKaZ9N2PQqFBiHRoGKJyLJOrrbJrnweUbR5zfIplYqUoil6UwFePLvJvXtGWQyOc+b8FD/+ziMsPP7T6D/5cyw1LAKFFg/tzvP0jSJR2WNkpJe1Qo18KECpZhJWZSqFLX5qxCORGsVXZf7Lp79EV18XI3ccIBkS2YiGcUWRhZbI85dX6Y8IvPNYL54k0xkPEbAqvHO0l6WWT0gVOTSUoGRL+LJEw3QxTIeVzTqPHOnh7FKVbESlPxWkaDi4gsD+hEhyooMLK3Xars/93RFW6wZnlpfZkSpxA2h74FguZcOk1LJZqRokwjqrFYNSzaTaNGg1HY5MZIkqUZqGjaormI7PV4oi0XCLVESlVDdwbI98d4xrazU0RSIbUpFlCVXyAYHeRBBJFImoMpPrNUZyUTRFxEGgaftkQtq23cF0mS606E7oqLKE41uoagRZlcmGFUIvfYng972PTVvAFyQeOtzNHRnI6x6/98ev8It3uOSDEm3HIyD4tF2fYCBCMyQTri7TPn2S8MgI5lYFPegRuPg8rfyH0PztDF3H8fEKy2QSaWqyTkSSUMMhSt96kUbF52DWRwgfQglqcPZpggfuoeKLXFqpocgCsuYi5PfQdm1Sksym5yPqOYKeQ6o+h6d044sSuDZqrhfBbhJ02viI2HocUVQQHQ9ZYLshDg9bDtKQ4zj5CVQlgIKIG0rhOBaq7+CpOqYcJujY+NUtBKtFM9yJLkiMfTuJx261iId0THzGEzK+KBPEQQREScQNppBkGUQRAQdfkJEkcD3QRIGmB6roYgsyLSmKLYhEfA8vnEZwTETbB9fBT3Zsv6bJk/i9O4nSQqyt0w5mCLl1hFgawfPBrEKrgpDtxZJ03ICGbtdxxTii3Ka8cJHYyAhF26NluWRiYTL5A6yur7E6P4PXLJLsGGDNFYnpMqmgQtPxKbcdeqIKhtWmYdh4noYiiriuSdMSiesyr8yVCKoi+D6eJ6AGI7QbJZqtNq7nEQ1pFCot4HsUd/a9UG7/D1B+a7Xa/+fnRqPR7+oa3zH5feG5l3nHfccA0FIpJG2JyMgg9akpIp1xpKDO4N0jTH/jGg3T59bFFU4cyuO12ijRCL7n41RLuLZLbXaNXk2kJQsUne3qwD0jSVo7dtP4wDsJzE2TePnzdB3cz/qNFQY3Xf5KL/BRRcULhHFPPMLO/XfT/vpncKoGxanthhUBiAYEMiWJv3rpHHf3ZokP9yF4NiN3jWIqNbKDKRaqGvm4zt733Mva1CrJfTtYvLpMcaMOwOiubmq5EWrPvELn7j5IZnCT3URHdhAc04l1RWil8iwXKuTCCsLiDG/uvZ8dK2eQ3/EAKfsGJ3IBQv1DiE+fRmwYdB0YITWUpXZtkrvHO6jJMYRDdzFWa9PsOIKyMEPH+iJ5f52Dok2xMMtdn/8iv/cnH6fdE+fxD36A/sE+fNFEiHXirc0gdvTjWhY4BuZWhcCObpxmG79wlo19j+BWqui9/ZSXlym0XEILS9y2e5hjsSz/5u/e5MS+QSZSGltNh65YEM92OZT2CIY68MUqnijDjmHMUonMO9+1/X4//WXU3kGKnaOEAyJSrIOFtkhlfpa1jVUcXae3XOLAHXfjNUQCfcMo3QMoloVYX2OvZBNJdXAgr/HJixbBlM3fvTTHh/oyOLduELHnkIaHoRaBSIRIfw/zwggRoY7qWGgHDnLp4jRvPPEsDx88wVPmAGmhzGpJRCmavKAP4pc9FglzKD9K1+UX6H3XOwl362x+8zns8T1cbiyh7N1DT08P3T15lp4+xTNOitv2VHnLT/02V599gvL5F4hm8hzuz6L1xYl29jJ75SJ//dJzOMYX+L1f+Tn6d0zguy4pxaG61aRoyBw8spPC9BzlUo5URCUXFrl2fYZQJElKVnj4wQPcWO0HOcDpmxssrhQ5NJYj19nJL//Of+Clr32Ri5//Q/p/7OdJS20aSwsElmb5qdAmlweO0yfZnL54k1Q5zP1DaS6n02xdPcnZv9fofuvdFH2frABhKYEa6kU0a+iBCOdqCjkNigboqoQoQEiTyUW3dzLLkojqu5TUGILjMJ4L4/o+dcMhpEloksBoR4TEUIbXpgp0RHXkUA+qLzHcEeGp07OYosZWsYWuSYyPpJAln3RER1Ekpi+uIPoOyt4MYU1GF33u351HMA7jKxLNaoVgLEk+nyaacMgndPpjMhvNATpUiclbsxTfOE3DhOzuo0QG7uTtx3cTDunUDZfp1TqjmQDxaAizsM5Xz69y37COkB/k2ECcEzkRLxBkpVAioqm0HIHeuMJcuc1OoUCoWuL0K0t0dGRoGxZEEpyavsnxnQ1y5QVq0YPEQjr/9eV5Hn/bBJMdcRxBZqnQItTZwablw+g4VwoePT2wVqxzc6nN1776Ism+fh473MOXXrhEx8Qebp67zDvv2kuPWWV6dpEDx9/CXMVmz1gvqf5eLs4XkQMi4WSUIx0hLm6afHhfFEdQ6I1uq1QRQUKzWiwZILouQzGZj4xJfLkUxXNhaqXKzNQ8fcO9XFurYzgeEU0mqqsEVAHTdmnaAustn2Q4wLXpFRZbadbKBo8dHyboF6HSotkyyUZFyoZIOqRRb5tYhsX8Zh1dFIkrMnNLc8yHoLc3x42ZDQ6Pd3BjtcZEV5QXr28itlsIssRH7xwkHlDxPRhJB6gaLqOZAMXWtt83oIrsyQa5VWrj+AKqtL1zP6yJhCSRLdPCcj2Gk0FqCYdS20aXRQKxMDFF5PJyhQAxhpIZFloCp2eLdKeDxCMqVc/BcVTuvG2ctiCR8po4oo5qG0hGE0/yMT0ZdeYagmuhxFME3v4Q3ktfQ9dA8g0cV6C1PkcrOUw6k6apx6m3bDK6AMlOUg8+DC89TzDfQXGzTCIeplKqE7V9RMdhVLOoSRrrhQpFNNZrLsOWQdvz2ZEJUXFlUpZDwKjhhGP48TyOYaArMgI+fixHTQiQdOo0/CABo7G9w99zsPQ4qtmgKsWIqAKibeKqOpIiI9gNbD2GajVx21UERcGTVVTBR7bKFN0wcafGupTAtF3SukRaF3EEAcVz8EQV01cQwhl838XxBEKqDHhYvoCKg+JDUhO5WfZI6S56Yx0r0Y3rS/g+SJ6LK8oIahCxXQW/hmebCGYLUZTx5ABSPIBeWwPfQwiEcUQV2WqDFkYURGhVEFsb+JE0SCr5O47jRTIEgC3bxhckNMmlv6uTaDLN4uw09ZnLBGN5cuEMSV2hZjqYnkex5VBouSRD0nbSg29hSgqqDK5vfzsqUSOfDrK4ViWa66K9uYDvQ71lEQ2q/0R+x8fHuXbt2ndFxP4bBFFE+GemNfxzz/9fAfF4/P9zkoPruv//n/Q/wXe8SqtrK4z1dyMEwkiyjGPYVK5OsnFmlvhoN5Km4BjWdjYg0NcdwataVOdLKMkUZrHExoU5Fp+7xNrZefxoANffJr5H799D/Cc+Qv5tx4m/9DTyl7/Ka89NceXZ05z5+ptcWm5yZ0+W585eoiVGCGJgZvoIdHThGduRaneMJEgqAhVbIC7L9E2M8rG/e5KHf/uT/NvPfpXq9BpRu4ZXLFE+f5ORwQTtaJrS+UtMfuEbTH79eSru9qKff/oCGy2P9laZzUILs7iOtDpL3dcpXDyPlx7glRULCYHQ7sPQPUT2+ptc9TLcPpbDbRvIRoWDjWsUL02RHMpiV+qE9h0i3Jlk9rk3iL7+daq2y1JdZF7JIVYLqAEwImkiqTj9ozlGC1f505/9IUY7cwjRJFJhFVNQMJUQbtc4Qr0EpVV8xyE50Y1v29QvnmXr/FWufOqvGHj0rTRDCdY9hXLvCMX+PayIKS6VXI7vHyKfTRCWRXaGGjRth90hh7AMgg9GMMHmuUsszGzwYnQPsVQA2ayR2DuO0NGN0D3GaSeN4zh4tSL99SV6Gpscl1a4J+2gX3sN1TKRxw+i1LbwVmcpywkaX3uCPeunkdcnGUn4tJttvn9/mnxfnqETh0jlYni3LmJPnceduoRx+QzdIRAWb2BurrO+VcVOd/Lg7QPMTpygd6iDO08c5HRyJ6ueymIgw56OIMlIgNdWLFZ6duBmsiystgg//Chrmw2uKh002w4/+a69zFQdUh0Z7g2UOdWKMZEJMn7iHbzj/R/l8Y9+hPc/+hA/NBzmPQeG+PB7HyL0lp/g93/55+ksLmBN38QQJDJ+ncEg7M+I9ARl3vOW3RweyrKwUccultg6dQ6vVqQ7IvD0qRsM5+O0PBGzVOWewTj9Y4PUWyZOLMeP/fS/4OF77uQvfvuXmF1ZYmF2ii9O17iqpLi4WuYfXr3J6K4hXltr8/Kyw9sfe5zPfPB93LFnnE/8x3/P47/2W/zNi2cpqElOLreoi2FMF8LBIDXbZbVu0TbbFL6dd70rGwLfIx5Q2BuxyQZVmobDwZ4YsizSsrbzcseyIW6t13E8l8F8BMvxqHkKr88VubJUo9B00YMyekBhrC9GZ0zHF0Suz5RQFJGAaxIOyHz+1DJ1wyEdCVKot8l3p4jFw9x/fIIHDvUwkA2xuyNCWJOI0v/uAAEAAElEQVRRJJmtWBdPTjd46Kf+DUFd556P/Q5dqRSXPvfbbK2vkA7KGK5HPBpC0HRsx2JmpUy9tMVnnpsiGxJYrlk8fa1ESBaoWgLVtoFSXuK12QoSHrM1nwUhyebSPIMxCV32uc+bIxuCZxdb/OLTiwz6LUzbJlZZ4tTFKXpSYV69ucHSVoNAKs6XrpT5ZtdRVlc2mFqponoudcPll/aH6VItrq7UePRwH2/tD3DnWI4HhgIMvvxV5mIdXF6ukgqqfODddxGLxzh3+SYrK0X290QRBdgV9TgorZPWPZwbZ+lsLxFubeEWCwi+TY+9hWu0iVQK7OqMMdIZptS2aNsQC6islBoUqgbZiEo6rLBSMggJ8OZqg9mFLURZJK4r/Nd/PEnKa9ERjzCrbKfMdHVkievbNd+NtsV8yWC1WKc3HaYrFUDyTR57617KDRNV8HF9gaVSi305na26jWW7aKEQx3bkGVRa1GybO4cT3NYTZUc2iCT4eAg4jkupZXN6oUJAEokGJIKSQKllM7vRYK1psFkztxMfJAFFBMv2WNhqIfk+L15aptx2+NatIkt77sJ0PIoti0rLZDgqE43GOFtw8AZ2bKuXnku9VkFoN3DPv4zU3CLh1xAln9BAN5LTxC+s4gYT6HuPoZeWibY3WNJypIQWcqNA0xWI6TLYJq3EAFomT/7Bd+JMXUDd2kLQFBoNC3/pJpZpsTY3jeErfPX16+SiOumwSlz12KibaGxbB6xoDsIpJN/GjuQRawXMVhtf1bBjeXRRwPFFQqKDZBTxRZW2nqTuiATbZTx8HEFG9tooVgMPH0MOUbYlaJTw5m+A6+NFsrhaGE8JktNlZMEjo3mogohpu9i+gOWJCI4JloHtAoKAarVxHAdFBNExt5vuHAPNqSMCWV0iYFbRJRC87XMED3xBwlN0TFHDDCQRRAkplsbMjyII4MU60AozOMEUfjSFs7m07WeWVRA8ZKuFXpwBJYBoNmFrEclq4+hBYrKPJIoIwHzVwhMEFEUl2DlAOTmI2K6gbNyi2WzStD1Mx6VqOkwVGnSGNUzXI16ZQxV9RFEgpG5H8S0WmtTqBq2G9T8QrVrLIhraTn8wLfv/Jj58D/Hiiy/ywgsv8MILL/CXf/mXZLNZfvEXf5Evf/nLfPnLX+YXf/EXyeVy/OVf/uV3fY3vSPn1PI9icZPxkIdXK9LeKhLKJ7n6tYuopk3n7SN4tsPmpRVubDRQRQHJ9dCSQQKZCHajSX1pk/LNNVzXJdadIFA1qbjQ+/YTJH7kEaa//ALi0y+w5DpkVRmzbXPm9VsoIng+2C9P8c3ROEciPXSaFfArCHhYlsOenjihpE44ptM1nKS5UeeR0S6Ue3aSHxjiR/7wM3z11jwf2dmL07IJD+VZuDLDtY9/kb6QQCSg0tcd5vjBCb75hdc5d2OD6V/897znreOsHzzO/I11dlgXuFUJ4uQSBAp1jnak2Zot4Xd4qH19JIwWe3fuQPYdBFHAKa6z9soFYkMZYrt3YWyVcHwf+eidiJe/jjQ/SeriZSYOHie/XCBy6Ai1114gHdNQPAsn3Y2k6aTqa2y267RDMTTfZM4J0C/KiGoAb/48vuNgVeto4QBWtU59vcXFU1OEjydoZI5j+hLu7Crp2mWeyh8g3tvNTNHg/Qe7uLVaYW65wR3DSXRBpN5ooJbXoFGjuOWxNbVA2Q2y910jGCs3MV78KsGhASIn9uNoAslImJfnN8huTrM7YOD5RfSGinL4rXDrLKoq4bRauPUmXizNopgifewexPoG1c4wh4OrDBbnaIl7wChjCgqKIoOs0pqexXn4o2Td68S9Ku3NdSwtRNyXKAo9xLvGeP3kAtGQxmZlnmtVlTfnN7lVaYNynUg6j7ixxRMCWBuXcBWdXC3M2M7D7FKjyI112sUSXjhFeM9evLVNvi8fZcEQeX+Xgds5hrZ0Fs/2sBdvoiUztFK7+Jm3aHQWr1C6MYkydpSIbeG2DarBboy2yw/dOURPQmWPoJK0CiTiEUoP3Me1K5O8rTNOq6+bp8/Ok83n+NG7hxlNSXzqYptDSZdbhouqhtnz0A/wp3fex2c+/wTNzRVW1jb51osb1Nc3CaVi/M2Zv0f0Pc7rOo4cpfnBD/OxE3swM6NUCyt8+fN/Q8H5YZYabRwZyi0TQRQIqhKpgMgO6jxRCJMJ+uQiMroooAg+C5aO59i0HI+w5xNSJTKR7cKapuNxer6MrilcXCjz3tv6iAUkRjSD1w2T2toqEnBkLEtA9SnUTDKagBuUWV/ZYLArxlh3lOcuLjG9EWe93GRPR5jeVJiy5aAApuORCioEwxqr1TZTW21kRWCxZpBQdYYOHKf60qcQYzk6h0b4s1/6cX7mU19loiNGXJepGi5Ny6YlyvzBO/r42rTD5GKFXKdINBlho9EicfkN/D0HeeLMMgf2R3B8ATOQYnq9xhfPLPPw93dydqVARzLKce0MuV1HOLXY4muLNh+9q5tBz8DVVeS8wu9/fZ6fuHeAsi2S6YnxlbUkWxsz3LbD4q6dI3Sb62TuPsbakkDk/Iuc2UrjzVf50XceoDy9QOCt91Nd8Si//C3MdAf3vf0YQxmd/YNJDh8a59EjPdCqYa2tYPf0ILd8tjp3o0YElnyNeAxeuFXGaVt88JDEfM8ESU1hqmBx11iOiwEFTRIw2y5Nw6ZcMzjfsikUGjw63MFXr7VomjYbNYMju3uZLjRZMVX0isVWrUkcWG56TCg2McFGweFQRqZBmOsLJQazQY7t7+ZmyWbvSAdLxSa7u+N0ZsO0HZulSpOG7VNrtzFuGiQMld5ejR1hmy1XIyxL3Co2sRyXmukS0SUurdYothwiusRSxaJi2jQMm6zrsVxs0hPXCVhVXF/GAeqGTT4RxEckoMvIkoRZrWDoERIRbbtYqCPCbKHJYCpIl6piOz7nl5vkkyKWXYVAkHk/wUhpnpYSoZHqo8MqopRXaRcLCEYdodnELq3TvfcdiK0SpqQTqi4jp3uh2UIU7e1ozFgadXUOYe8+ZFmgoyOCFpBJVabp7u0mkNRZsxUCskBcV7m2UqEnHWar5dAV04joEpYHXr2AFBZR4hm8jUUE2aOl57B9H1nWCJllBEnF80x8QUMSoa3HiIg+kuDjCjJSYQGnaxc+4Ao+zF/ZJqOZHjw1iI1ASwoTsyp4ehRXkKkaNtGATFQQcPHwBZmWnkRXQPRcPNPA0IIIgoDhymSFBi0xiO856L5HVBWpOEHCmkDDgai6XaIhmg18NUJV0FBlDVm3kTqG0YvziPEcqGGEYAxHkpCSPUitBoJj4cQ7EM0GilFDlHUQRARBwF6egmgOsWMHgttCQCMoiyR08HwB3/cwHcinErjxBOWlGZT5K4jJXvxAHBuBnR1R4gEZQRAQ9AD2tlkEVYKhTIiTU0VWlksgyDhG+5/4UKNtEQ5sk98XTt9gfHycyclJPM9D/G7VV+F7YHsQ/ve3Pdx1113/9PNv/dZv8Ud/9Ee8//3v/6djDz30ELt37+ZTn/oUH/rQh76ra3xH5HdhYQHHsRnqyiKqGs6NG9jNNrLvkdyRQZBEjK0al28UWa87TGSDJEdzNNYq9O7qoXjhKguvzhJMaFQX6wy/6xCWL3LPo48g9HTj/uXfsPTURXbGNYy2i4eI63gA7NuVI9rbw+LVeWr1FtL6DHa2A//lJ9g8e51YVxw9ptJar5NWfWKZCFa5hYZDtiOFFA3yrw5O8LPXvs6P6wKK6iFWCyzWTSTLZ65ocvtb9tKfiJL6yA8xfvYW166vo5kmkiiQWptiId2P1zNB7+o6Fy7dwNpxkNTsJBdeukhGVOgO6oQmJshmovgrU8g79tC++Arq0ARSs0jNFPDEIIXXTrO1/y72/MK/ILd5Ae3aLF3CGs3nX8V/4G2EuzuQJBsh3U0DlVizvB1Sbja364W7kvRSQS0XEYNhvGgGT48hrK7hh6LYKyWCw72MJjvxTtyL2DfO/Nkr7N89iLpi80F3jtbYfvorFssNi0q1zv3dcRzXpbwwSUITcRYnqVyZonxtmYEHDuGM3EnErmLevE5zo4ovLoD0CvFjd9MbzhJPuPTIPoFEP+2Ofqpf+SJC+wWCA2laC/NI62tot9/HlBcl6jbp6EvSmqkSuHIKZXwfc3Ob7I763Py7l2jcvMnhX/xRJEegdnMLbf4WpZHbUL0abtvBy2V4YsqlqqsUryyxdyhFo2kRKrT4ocVn0UZ2sHpoN8sNl795+QJJyeOtd+3k2NFBvnJ1k1eXDOZEB6M0xw/sT5BdXWM91cOTyx77AzpD5jJSZBDp1gypfBZZdFh//lmCuRhOooN0+SaRRD9yZJRgqUozmkYz16mFkniiSl43mK+J1KoSBzKwZ6yHXOkm0yM9TBUMZqY38admOPDg2+mIBRns70QWXfb3FulPBsiYFvLMdZbrPoVkF6vhXUxNw9vuezt/dizNk7/0p+z/0cf4utfLZz/zD9x+4iB7xXk++We/y6mXdrG+vMBmochjP/3LVF0IBFSevlnAcnwcx2FvXwLHU5FQuK0rysnlGrIEtzabXN1oslFp058NsV43aTkehYaFACTDCi3DJhZWqRguyajG6zc2Efw0t9ZbHB5MU5rVGMgp6LLE1nqJq9Or/ODtvRw5OshyzeLZV67zmb85Sc/oIO3lBQKDA7x6aZnvv3sEe6GKo6g4nsSVhQqhsEapZnBXf5x/vFpgbqnGHDB2+/cRcTbIBGWqbZvTM0UuzJY42pug6TjEgwq2FyK4e5BFTD64a5VVWeKVYps9XXFKC/MsThaQmaarv5tbZRtDahDUFVRJ5IF33EtfdZYP37ETbWmKi5l9XN1weWB/L0PZMGuOylU5S0ZTKE4WeOhoP69OV9mT19jdm+MpQQRBYHKlyttln/bCIl+tjTHRncB29zNAgJYFogtzZy7Tce/tpN0mt8pD/PCAgWFscVCq0XnfPoayQVr1Op98aZLHxuMUXYWMu0E838m1LZtoOEgwInFb3qJkh3hlxeRYTua5jTZDUZlIUMXyPHzPRZEE/vavvkFtOsOjj96NGdIIey0e3JFg/soktXQHm22bdCqKHtLJB2B2rU3MhwtLFbK+yGubIl/+5mmGMkF27xtHsuo8dXKD/f2HGUlJiLkIT13fIBVVWKsZtB2PpunieB7lxVXUZIDWxG6aDmy2QVF9HHxiukTDhAMZiVt1gZ0dEWYKTXKRGIiwVTKJ6zINy+WvPvsV9tzZx2Z7HuHhH6M7ptEX18mFZCpth78/Pc+xsTyJoMcmEgOpMFsbJs/d3CJmVhk7tAPDMJgrmwwFPLZKZUq+QqrzAI5t09LjtCSDeHWFZm4AOj2sN69gTV3GFnQakzeQKw6lrRrKfe8grxgUTRNX1PH1GIaiI7kmkdH9iIqMuz6D6LmUXnoe/eAxkp1dRHWFfTv7WLsxzavtALf3JTh/dYH+oR7CARVdUpiru6hmiGFjGUHy8dIduIEAhguqBAIuwvwVvOGDiLaLEAgQ8FyQA8T9Bm0nhiaAkMihOm0cUSaFg6AHINWFEI4guDayDJYgodgNXD2P6JjoShBdlrCAMC5tUUGxmrTcAEHfxr91CnPiQfKKga0oSK7AmiXQEQyC74DvI8gajqAQthp4bgBf1qBZQgqnUEQdRRKwxRiiXENU41iyjlZZxtmYx0v2gVkBQcBXdATPga1lfNvGz3Tiyyqi3UaMJMBq4rgOrqjSEZbwfEhjYfoiCtAd1dhsbd89ELN9uHYSb3MOQauwqufZ35XE9yGkCLTDXTQsl5AisNH0OdgR4fpajZGRPPWGRbuepjK7zYfapkNA3aZQjVab4dv2Y1kWi4uL9Pf3f1eEDEEA4Z9pW/hnFD/8r4iTJ0/yiU984v91/NChQ3z0ox/9rud+R6s8OTnJwMAAaiSK79gEM0mMcovcRAedR3cgAPWVClstG0mAQ3f2srnR2E6AEEXapRqetf3B6NiZY32txeY734MeCWL+589w/sXL9EYUcjmd3lSAjkyA1jb3pXe0jzt//99yxwffyeBWnde++FeonsPW869T3ywjaxJ220XSJbSESnW1wqnLm2zc8TAbk6u0bk7SmQwQjAVYPzvL0jOvY06tc9+7b+fRn3grDzz+LobGMwQfew/zV+dIRxTuOt7Nez9yO/l7j3L6s9/k2qZDq1Ln+W9d4obcgfzmK5TOn+eKnmaiNo0QC9NbmkNYvArNMoRj2/mErTLLMyXm59uUQhleT+zFUcIIU5exVtdI7xkhuv8wid3jiJ6B4LTxPRcxHCeQyCBnuxFkhWA8hu9YKGaDQHUNqbaGYDVAC0CmCzEWxxcUakKMRu84PW+/F7l3GMOxyGUiWEYT7bY7WHvyZTIrV7k9UCGjSwx2xokHRUQtTEJwsJ98AiwLdh7CtU08X6MrHiJgmyT3jlNtCNiGjbm8yNrzrxA/+xQdm7eISSa6DNGIRMfb7yYsNVAFG79WQtEV1HCE3vnT9AddZEVB9VoomoR/8ywDdgmrXmM62sUFUmy89Dq1s2eQO/JUvv4VZgstPn5yE99zKBdMduwY4gMTCqdurKM5LQbCIv911iA73EO4tE6fucIDXSpWYQXZKnPm2dd48dWL3NUf4r6hILoiEZIlvnZyjn80O7A3ljAbBoFwlPpWgSuFNi+YGRpbJaz1dSKj/aije3GrRZyzrxCtrbJJhMDoBHGjSIkYF5s6JxfKBASPctPg/OQqVU9DdUyK6THenmnx4bsHGd8ziNeRJW9tcbg9C0YVWVZJiC5GtUp7Y521N07x9O9/kuX5RYqr6+QH+vnm+VlmSw4Hfv0XeFEdYHp+g9zAMNWNNZ6dk/jYz/48B972KP/yDz7OX33xCe69+07qhoOEgO/7aCJUGyYBScT1Pa41ZF6aK7Mn5qGKIrc2G8wVm5QNm0vLFRRJZCARIB1U6UsGsR2P9ZpJ2/Zx8QlrMvObdS5dukU8FOKB4TgP3D7O6nKBluFwZnKDeCzMt+Ycip7CvpDBfeNxxscH6EoEWZpd4NLp89y5u5tvnVtAWlvifn+diZDDwbSM6ppUmibnFmu0HIfObJBQUOXRA3kGxsbRO4ZQkj0cOXIQWZJ48foGhYaN5HssVwwUSWCkK0ulaxcZxeXoQJK6abEhRunvS/KpL79Ju2nQkY6Q1EWmN+qkZYdoNMSrXo4xr0DdF4n29NIZC7DR9ulXW3gIdGWjWJLKP56eQ7baRENBDCQmF4r8/AMDTPSmuLbU4MLkIl96cZb6ZhlNFujL6ezrTZHLJXA1ndw9J2ipUSbCcGjvCCO3H6YrINGt2bzFnsJzHKK6SCafBctAd0xuNIPEJZ/xhEJ3a5Fr63WSsShXZzdZqlggKJTKLfpiKvbyPDoO+aDGW0fifOShw3RlIqxsVOmMa3iSRFx2OdnUMSyfayt1NqoG0UiA602FkAweMHP9JqVCmbO3thjaPcbT52ZZuDmD4kt4ksJkoYWMw+RalZ85EGFHUqRpOEwvV7Adn3ZpC0EWaTgCN1YqLG01WHZ0KrU6DctnveEwGpdJuE08z9/2nosCnWGZsaDHYDxIsdKiZTtMjPURv/Q6mb4E2ZDKgXyI3piOJEgkQyqCbTOYCaFHtmPSJNEnkY5x/co8o+IWGdGg7QsUmybfWrJZJUJsawHTcQiWlrhmx/FEEGMZCkIYO5bGFlRa65soooveO0j72gVC7XXkl7/C6pPPIa1M0bA8bCWIiYSJihPdJpJOogPfaKIP7UALh0jqCo4vcP9IBoobpCWfb5xf5fiOPEvlFqbrs2LJ2+RQj7Esp3GVAEI8T1XN43oQFH1EQUKUZfAF3G/7orV2gbInIwgqAuAJ8jbp9By0VgmtvobQtwsnP4wv6iBKCJ5HSHTx1TCerOGrEULa9h4AyxVxRJGmryKKPiGnjmg1oXeCfABESUEWwZaDaIKEKAmo7TK2oCEJAoIgEBAhaJZoCSpCMA6uTVB08QUR2TMRZBlB1HDVAL4kI2Z7CLYK24JOLAOCiK+EEdQA4EGzhisHwXMRHBsplUdxDARcYpJHQjARfIeAWSXhVFBk0CSRlu2S0hUsPcZmYgTPsemszdJoVNFEH1mAuifhCWx/YXN9GraLIIr0poOIgk+71vonPtQ2HQK6AoLIV1+8iKqqDA0N/V/rw/cYPT09/1Py+8lPfpKenp7veu53pPzeuHGDwZExxHgOz/HwPI9wd4JgPoMSDmBsFNFSYXIhhVRKJ9qXobI2RyAdpHRzEWOjBY6P5/gUe0ZpHbsT/c1TxM6fId8VIKF1EI+FSXQGaa3XWbiwzkBM5UbVolY2WCzVqC4s0V8X+MqzL/LYhz9G94N3IVoG66+e5uZrC3QNxLh5vcBSoYnmizhTk5jLG1iKy/q5BYy6xdSFRcb2dRLriVNvN9j1jkcIxkdZeepJbjpJDpfPI40N0BUxifZk2Sq3GPrg91NWRTj3JsHbDtA/v8BmLcH4wcNEzy8T1wSk5Sn88izO4TuRjTqupNGcXWLt1CSIKsl3PYRcKbC1ZTORzKDP16gvbxAaHESKxAjuPYJf2cSXdLxqEVGQCZll0EOQ6UHzNrE2KnjZfrzVmyiyiuhY0G4jixJGto/a5Umkg8fIdCbxNrfoVmawA0muCg49ShOuv8CBx25HMjdxz17g9kN3cM6W0Zo+jbbJjbbEeCRIs62QzFrUB7uxW02CpVUCrQKrL75JbiSDb5tYloNYmMexsxRLNZKH+vHKG0iSgqzKxEYHEfQIajhEe6NEdXqZREcXrEzT3Crgl7ZQ03HkeJqF555FW1hCurpGe/cevJ4u/AWbSDrItL2bTmcLLe2j992DQJrISy/h3X2CPZzl5k2fuN2mR2jjZ3IEB0ZY13NMLVb4w/0CakTifHCcsiNxqSxze1agw5fJ5YO8/uwN5qa3WJvYy/eHr1BJ7ULp3UP/8gpTgs0VKcveRCfe4iLawXsw529gb2xgLszwW6+1+cGDHcxcmifutJCHxjme1fnSrSavn77OB+7fjW+1SSsuVjhE0KvzFnuV5pFhOvMJfElmonYZVfE4O7XOF791g9GUwtLiEsfMOvuG40T7MjySyKCHgzxz+hb/7slJIpEYlhJgbDhLfybArtVLvNwI0Ds8QSwdRxYgn9BYq9tM1UxkSSQWUNgstrh3Zw5FhPW6zXLBYGdnmA6/TEMMg+CzuF7j0EiWhuEwkQ1hex6RgEyzbbPasGla7na0lCRguZBMBNjdG6cvqvLczQJvHY1S98YomDAw0kWtbrBRqPGJb03SlwjRciSCoQBaQGdosItXX7/Cf/mSS7lU4r5Dw6xV6oz2Whwzlrktn+ClvnFeOD9NzXAZGsqRiwhMF9sc6kuw0bB4+tQcB8c7+MmdKr9/qkIqEWIOn4gqUTFdWo7PetPm4oVN3npPP0eSAW4GFZKJfQwWwrw8U8K+ssz4UB91w2a1oPJjB2Jca0lMEqekabw6U8O1XQY60nzu5ga39ZUwhDCT08sUC1U+/Y3zfPD7TvDaVIGgKHJkJE3XyAAtd46nrm7y3ruOMDr5BvU9fZxftLlzr86YBsgiuYBPXGuyUVzn7qyBH9xFQtMh241SXWcwnsCXVB7eLeA1VMKqyGhCQnRNYpJAo1riv7y2xq//4G1MDKSp2iJtJcQjgxZtUWZTT1GfWeTBO3eieiYz3Z28b3+CX36uRIY2wp4d6EaJoFGl5Xp0xAOkwxqllkMkoCB6Lq7nY/kapUCSjlSbgYTMrg/cxzt3pjhTlNhZNpjbajKYSnNIXcUxYvRJPm/bkeHxZ87z0P37uLmxysCefTRtj2dPz3GytcKvfugerhJhb7dAQPSoNNpstH3mVytE40H6UyGOqGVe+Q+f5rNuN6F0Jw/92P0Mv/du9ur7UVJZRnQFSRIQEWjYHi3H48BYB/iQklw2VZFCy0aTRXq747xp+jhbDqqu0WgatFyRQ/YyZt8oq16IoOojuhZi1wibokrZ8ChXPIYGU5irKyiH+2hOLaCEo6gdWcrTazjRDMnVGRKJELbbpC4FiFdnkIJRBCx8RUcaHMOXNIRGDcFpIwkqpUqdxIGDuLNlcnGVN968SueevYRkEevbVdEBReRawSEcCaGiUHUFZNHHRURxTbxcP55jUhQjOI5ERE+iCwK+qOG0DZqiQMy3UJoV0IIIroknhDC0GHppGjecRLMbeEigKLQcMH2RkAJVw8X1ISBLBNw2nieAqmBLMq1UkpBrIrWKuHIIWwsT0yU0PBAVZEFAwqLmSEiyjm42UQBXkBDwkXDwXBHRMXDVMHJ9A1nV8QPbrbFCu4kYSuJrIYR2FdQwBKKIio4gCcjNTdytVVB1CEa31WrfAcFANOrYkTxKs4ToGATVBElNpqTLpIIqyzUTMRqiJA4QNorIizdYcnoZGhjYrkV2fW5WWsiCSMNw2ZmPMFNosLJSo13d+ic+1LY9wqEQciDEc6e3Ce9/qzl+29ve9t0xMkH8Hii///tvePv/xR//8R/z6KOP8swzz3D06FEATp06xczMDE888cR3Pfc7WqXTp95kdGQYJAUkEdcwaG+UkQMadrVGZWaVtYUa8zULRRBorlWwSy2ahSb1pTKBbIjE7i4qb3snzQOHyb3yFOorbxDvSeE1TQaGUgzdM0Yol0DSFeLZIPq3Ffxmu0FkaxlzYZbxRBDd9Vksb6LqOrZtc+vMKtge9bUG52arrFcdbNdB+tbzBLMh2pU6q6sVPNelXrXQ03EcVSY3kOPUX/wDpdk5buTGuVdf5fyXXsIrFgh1ZrENk42X34Cr55nY0cOWFuK4O8fdhwY5tqcHpaeXI9kQdVtm+h+fwh49jGu7NM69iXX1DK6gkxzpYfDhtxC+foaeHX18f7fDWFwivnuC5NHbEHtGcU0bLBO/1cSrbSFH45hrSzjVMoIaAi2CkshjywGchWnK+T2Q7MG1XSpilOKLL9EotgnuP0DHQAeu66HqMpokEZp8nV2BNprsoSbThCISqtkk1JlDufoG+71lpOlztK5dJZjpIn7iDlwU/u6V8zz+/Ou843c+zotf/wqNuXnkVALRa2+nSyTzRPt7sctbRBqbuKUiztYGvtVGCCcRE1l8QNRkWivrrD3xNZqn38QpriKUVgkN9GC3DJzSFtnhLvLveoDW1DQf2aGgr96iqKRpDe9hz8P3s1U3yHd18OTfv8w/vHSF+M03kecvsF81ubHZ5i/fWGBQaeP0TSA6DqnXn6d7xyB7j0wwtqOPh0+MMdqToLi0yKs3N2Fznd1ylbfdv5f3eks80qtT6Bzn9YUGry+YzFR99o/k6fIbtPsPoB29m4XNErfO3mJ6zaVQbnF0JMVrs0V+7dPP8rtPnOP02VvkSrOcGIpz+0Q3u3rjyJJMJZil5kr4oRiCGiDotbmjP8SxvjCGZbF6/Savvnmds5ML/P2lZbaiPTzReYLld32AVzY9spkEiiLT19vJzrE+XFlhXGty8+wlUorITSXJntsOMF1s0p8MMpDQ8H2fZFhnJB1CFAVyMY3bRtKIgshG0yYki9w5msL1PZ4qBEgEJfZ1R8iHVATB5+5Ohc6wQqntIPgwtdHENE1s2yUb0lBMkyPdYSRZoqpEOd+Q2DI8Pnm2TMHwcT0XUZWwfY9mtUZ/R4LBbAjaDTYKVV548RwP7Mrz6LvvwVIUOibGiY+M8Mpym8+9Ns3i4GE8C8x6jfbKApvnTnHh9HWiQZWW6/O5U0ucmy8x1Jfi3M11zpR8dg0l2ay1qLVtaobNSrlFzXTIBRXed2IYRRaI6wo+IkU9wdH9o+jJDJoi8fyZaZ5//jX6IhrnjBhqOML1kks8JOO1WqwsrPOFp88wko2weHORGzdmeOnNW2gBjcOHJpiaWiYe0RgZSPLM1TVqpoevx4lGo3zjjRkGdw0SMCrcNRgkFVTpCCuIgkgym0V1TMJ7dhNJJtEFWHd0YqEAYucQrqTTdH162ytk/DqW65CwS9j1Ir6s81erITpzCUrrm3iWyd6cQtV08SUR2xfYsGVymQydYhNZ0Tk0kmE4DB8b88jSwmu3UGtb5AaHWa+0SYcUuhIBmu3txrYADoIgcmOtga+GODKYICHYvGU0jqVFKFkuq5U2u3timK5HXQlTV2LcMMOUTYcdE/2sbDVRwwma1RbRgEI8FSOQ66Loq+yIOzw9uUXZ9il5GrfsIIubdaKqDIKA59rUAgluTi+T7ulAFiW2TJ9SvJt1V6fUdqi0PHwEDMsjrsn0pMPMbDVxVI18RGO5bFBu2lQ8DSGWZLpm0bZcZFni1lKBeijBopJmctPkby9WSKzeBKfNtaUiuipTEKLguwQGB/AzfSx7AWLHb0NQdMRQiJCmcl3uoBjIotkNIkYZRVURRQk3kMRTdFAC2LMzWNUipXOniLc32NGTJJWIcH/e523DQfx8D7s7YkiiwErDAN8nokCXZBHQA5zfbG3fnldFJN/fvkOuhjHVMFHRRZW3PftR10TyLFTJZ8OS8FwXobyKu7lAY24WqhsEjAqoAQSjiWQbKI0iglGnZvvomN/23vuokkjD8VEEF8F38WQdRw4hiwLy1jyiKBGoLCM6bRTBQ3JtPFFEFmxUp03Ia3+bUG4rs81IB4YSQbRMguV5XCWErQTBA7G2gWTUcNQYhFJgN0FW8ZUgogh+MIIXTuCLCqJr420uIgbC+IEwYqtMU4kgtSoIlQ2U2hrUC4itKoro4wkC/YkgTdvFARK6REhXUFId5Eb30ChuMHntMpLg4vjbinVMlbC87XSUcssmkwvjGE2kcBqAtumiiS5Oq4717Y1w/9xNb74gfk8e/yfhwQcfZGpqioceeohSqUSxWOThhx/m1q1bPPjgg9/13O9I+b145iw/8Qs/j6NH8cubCJKEmojQWt9CUhVsw+TspVVO7O9gcrKAdXYFtWWjRzTaZRN9NEHhgfthc4ueb32ZSFRGfeseQvko4c4idrOBpMg4zRZ20yCY0Zm9tN3FffXyEm9rrBFQfPruGuWxGwv83s/9a/74hx+ieHORoCYRzCZprFW583AXquOgR0NkduRorpUwahbpXIjgmorg+3z9K1c58Z7jaAeOUX3+r9lTukZkZRX3YpGsZBILx2n7Mlo0QaCzwXqhROS5Z5kpmGQzUaKaix6LYBZX2JkUKCOx5/YBQiqorkXD9HANgfieHSRsAzHdRWBiN4FIgMGJAI7YQg5oMDCEYLcxxQDK1iqCICCHYoixNM7qPG7vCWS7BY6NzHYlrFBcwl8r0OrtwV2bYWO+QTiuEu3pZfPydbyqRyXbxf7ePgRVxc23ECYvI+zZhzl5ESmVp+jFyCo15gOdJDpHcFZkLDnA5Re+xl+88C0KdYP7dvbzBx96P7/yib/k4MgATrNNa6tKQ1GpL9WZf/4ZHnr8fhzHI7V/F1JvP5JvY65tEEi6+K4NCOB6uKZNV38IoV3EWKkR7OpG6N+H3uPgr8ygNB1k1+T42ycIGFWiA1myXSPMxfvJ+lvcHje46IislQyOdddJ33UHxuQMqfUtxmWT1O5edtx/kExSRY4MoZh1VK2F3bMT2azhz13BmCywu1pEtD1y3fsRIjGGBMh+9CNsLi7QFdJIROJoqku1UGH65CxnL97gjp/5CTrig6xaW7wZGmFSCHHu099ic/GPGX/sXzPem6PcMFneqvL1lTAjYYMfPZIiILewBZ0NV8MHfFWlnB1Hl0UEx2JjaoaakufNVoSnLp+mYnv88L2HkFWVmdUyr93YQFBVNCTOX7pFb1+O2fUqvqrx1qRDK9THkf1D1PMKZ0s+7z0xSFSVmClaKCJ0RCVG0kGiukLbdnARWCg1CesyguCjSSJTRYOOiMbFpRqyrPB9h7o5v1rn1IaJWinTGQuyVjcJhxSu3FhnoCvKB3eG+NKZJVK2wPftzpKP6FxYq7NebDNfaHB4NM31hQLNapN2y6Q3LhLwbLRwmO58kltLdW7fO8C//8eL+KrCg/fdhiHJZBMhLDFIIJvlr66VyWlp2p7Fo+84zueeucjIYA4Bn4F0mIS2rexulZuMd8X51N+/we6d/WS6O6hWLfq6IhSqBqfmisQCCm/pCdMlm/i+hiqCzXZurNuscu9d+7i2WCWrj7HSFlhdbpKIuXQkdJ6frfLq9RUev3OEyVKKe/cP8LebFc499wb5gQkimszJl8/Q099LUo8wHvH59DOT5DrzqKEAuzsVhJ4Jgh0uo7VbhBLD2Ni4kkRTFPDlAHKmk7ggIvlhxFaJeCCH1KpgCjqO5bHadEmH0qhmA1UFoVzHXN5gdiTFwbFOim24srjMg0MBHE/ED2i8dqvFjoEIQ+kgo7pI0NyioASJiC5FNYGTlrk/WeRWzeVEOER5usby1ArHyiAcOsbN5RYVx2dH0EERBfaOZLm5VsMFeuNRHD2M0WhSKDeRVImcWyeRyfCV8/ADnQp99SINNc1Ef4Y9+RBb/XGe+eZrJAdvo9I0ScUSnFxqsbJukogGyYVCbDYsCjWT3R3braBD6QAXPY3Uhz7ETx/fpCzrSICmirw8XSYdVcmGNDpjMo7pYXg+aQlSuoxjK6zUHPpCIpmARHdMpZAMcmWlzoG+OKs1gx35GGcsm+tVuDfZop4O0VHzUKZv0h4cZejGizzbvIP9PUkK2XE6nAI3XjpP9MSdmDdeRzpwJ2LlW4hunYQmEsZB8H3kzZuQ6ADXQdQlpPoW7uxlWlenUN/+CNYbJ7G6ssidPSiiRzDfhdyocXQ0hq4IBGWRkLSdR900QBQcZN+iJ6IiiiK261P2XaKSii6DZja2S1rcNpIsUzNdkqKPajZIaEnctouMgLs2T/G5VxCOHCC418eLZqFRQPA9PKOJEe/H92zC5SUqyWE0eftaDdcjGAphIePYPo7rokk+Id+D4ip+JIUgB1G8NkKrCmoAzTNwfYGgbyE0K1jhLnxRwRQVNNHF8iBoVJHsJK4ew5eVbeXXaCC6Jr7v4athfM8DWQd8pMoK1VgfQV9ENatIA7uhXcUMZtHtJQR8PFHHrxaQgxEcNYTstJGtOqYXQ2C7NE6XRCx321rjeyDqQQ4dPMSlq1eYvHSBzuHddIdVXA9cz+f6Wp2m4dC2PCJd/bSKm7QaW7TaBh2ZGPHuAcxKAdhWfj/zmc9814Ts/+J/ju7ubn73d3/3ezrzOyK/KxtrjI8M4cohRMvAEwQ8y0YUZQLZFJ47R73tEgvL2KLPm9MlDmSCCKJA/K5DrBw6QaK6Tvz6q6jZAFo4RHSoE7NYxjHaRAe7MMt1BFEi3BGlulgmGlZx2zY7B5OIvkvPrl6ivWl21Ov8ydde5+lPP43VdrjtQCeyJhKIKGR299FYKhDsyiJpIka1jhZUaAseOD6zdYMjDx0mfPQYxViWvjgorRI3vnWRHWMZ+g8NMnlzixunXuSdjz9G/OHHyJcXOfPxJ1ibK3Blpotdbz9KcFjF3dpEyvfQFVBRenuQ6iUco402MEx4dAei3cJfuYVTXEXbdy++UcfxGqiShC8Ajo3fbqClY/ieg1Muoe49ga+FEBNdKHYdXw5QeOFFwhPDOA5s/j/s/XewZdlZ5Yv+ll9re7+Pt5l58qR3lZllskrlSyWpZJGELAiEdBsQIOEauI3rvtAXukV3A2oaEB4Z5FUypZJUVSqbWWkqvT3en+398vP+cdT9muj34glBv3hBMCLWHzvWjhk7VqyYe8zvG98YF27gra+x0oXJu/dhJDOs5MZpRPpwI/OcP3OLXe84hBuJYts2rpUjYQhCu0u3fxdJZ5342Ah+eYmv3qpgLp3Da23wV5//Ao/edxc/+wu/zHZqnLw6wy/9h9/nd173KoxmDbQIrdww314JuO+dDzJ28SZeZRFDh9Ub6xQL24kETcJ2Fb+ZRjRrSLEESiqJpsuIdh1zbBC/00GSwJ6/gZXL0CqVSezaibu5iiqBUSxutQwTSUbbs1vpTt0u586eQTt6O1fX19m/J0f9ySdZO73Aj3/0V9GK/ZjpPMbcScJuC7W1jLQcRZs4QNd2iJSWeECr8Jfb7qc2v8IdqxWevXad+/dk+Ox1CatviAMoTIgWqucyd/0iHSPBJaWIe/o6tx2aZrw+RyeaY6ZzlWavQWH6GOunn0Au7OJV2/Ls3zdCPhtnb97AEl08WaUcqMiSQFdVepJGN5BoeiFhpcTzT19kcvc4S47GjblVDh7Zy5VLc4zu2sn0RD97k3ByscblV65x+fRZyotZRqamiZpw++sfYvSFZ0n6q2yO9DM4qlKzfTpeSNP1SRgKEQWW2i6mptByIGrI7O9PEAjBSwt1dLlHJqqzLWNycaNL1FB48kaZoUwUOxB0nZB0VKNte5TrLvm0yc2bK1QO9fPgoTFansywDtfW2+S0gNtGE5RKDXanZM6c3uT6+as4rs+eQ3t4/vlzSPcd59z5eQa3DdOnBrh6jEYgYRJSrzc5t7DIzr2TtJwALJWL5Ra9ZgfNijC3tMljD99GTJK4tFwnF9XJRHReKrcYjCvceWCCSi9gtW6TkiVqHZ39Q0lqPR8vhFMlD52A+ydjvLLa5rVTKYo70pTmZpmp9tg/nmVvf5y5tRIRVaFue1iqzJ/+xVd57DV38rohl327d5DXfHaM5Uk+doKXnj5LYbyfbeNJIvsnONivoykwMVxA9j0u3lwjJQq8+8EDmGaJsNxECn20TokglicTdNFDH8fKUG11yGsyQRDQ3FiltLJIJmISWb3JjtseYE1KMVidR0rnkBEkhvsZpkk8naAT86ki0ZYjGBsrSNsKJGMmM7UeMU1h2Apot2Tabpek4vDNkkI8lmE9NsBL1ysM7ciwVp/nvY8c4tatZb755Zd59T372Wj10EIJP1QoFrJ8/dQtdu8YxGl1KNdsNmoN3rovR9kJ6FZ7nF5sszsrM9ia54KaZYtyCb52cZ27p4v8QLTOSQlqtS57dudxq1VGxnRqts9EUqfRC3jVcJRX1hQm81HihkbT9YnEImSzKfamLdwgYK7U4UDR4uRyk/FchMGEiaWBKYETQNzU8EJBreNyzZPYNxBlIgKX1iRetS2HYao4no+pyuzbOYrj+8S1kNv0GlFzAfqTdAQog1misk/b83G33UnHryErEVZ9ifzkNG6lhJ5JETNiGGEZR54mbFYQUhwrDFF8G9npIBESWHGI51CabUQkxcwnPsPoT4+juwFLWGwGJnYgcL+rM81YMv16gNIrUdUzCCExlLToeSENV2ApMl0ZXClCUu4hIVBliTAMUHSdjiRwG3WUfHqr8pzqRxNgZJJ0btzAHJtEsjKI0hpSPE7QN8WNhkQqqCJamxi5SXxVxgtDPCHRciEMFMIQarbHSEwG3yPsNhACRHoEHIFjpTDYGlaXvQZKuwKxLLLv4KkapiYhQomuYhBJ9CF7NrIZh2gGvDbCjOGjIGkmmtfD7zkIPYIqhYTRHFa3hmzFCb0e4BKmBghVCwIPK3QR0SSyboFqEcQKKAtnEPltdDuCnCFjfDe+eKVl0+q5DCRNAgE1XyY3uZvy/E3mr5xh++79GPEIl9YFGw2b/lSE61c3CH0fv9cBtjS/EVOnXSmj6luuD1NTU1y/fv37Z2T/Inv4f4t6vc6pU6fY3NwkDMO/d+8973nP97Xm90x+hRB0bZuRhIG4+DSE0Li5huQLBIL6zXkWr68zljWJFZI4wQrTSYOzG132/uiDLB+6m/7qAoPNBex8AoKQ6GAOv93BqXfw2z1kTaOzvmXZEx8roOg6R7sBrheiGDqSFccuNQnaba6eX+dQEOd3rl3lZ5JD2JU2kiSj6grNpSorF9aJztUYvms7g3cfJuz1mPns8/QaHgM7+xl688MspEZ49ukrfHBPETsxzMqlz+N3HCobHVJTQ0RiUWITE7wQnSTVCNl7505UQ6fvjmNEjZBu0+FCcprKSp0H7pzCuvAdzEN30PraJ4nefj9yrwaSihfI6JN7sDULY2MGkv3g2UhhgF9ZQ0kVcOavo/ePQ7OGCHzs8iZS/wTK5gxyMofUa6IgAyHtbkB+OE/11AzVq3PczMjEEj2efvI0+dE+/rrc48OtNq9+/7t45498gIP793IgmcWrbCIXp3hxo8PuzgWae+4gWrzI5//w37FtfJy/+oP/yNDlJ2k/9UX+4OpVvvn8ZT76pvsJZ6t04hmUiImVyPLw/Ue4cn2Rh/NtJJHmxc+8xPQj99A4e4qWcMnvGEREMrRuXCexbxhhtJEjBuWbm9xYC9hz7z78no0cbmAHNmbcQLSbPP2HjzOUtygGHoztRY1l0JUS7tItvEqNE2PDrNk+/vQkenOW4t4xjg/l6Nd70F1G1hy8egm/Ucdrtonku3jNCrZkYAYdYoNDvClcYXXSwMuMMrOaotPfR6zRYOfqK5xdjNCcOERpfpEJW2NjYDdLywuc6C+g2C02ggjrjsTN577Er37szznUF+GD738f23Y8wLnlKo+lNPbu3oYs9bDDCK4cxRQBGalLS4mz3g1Rw4DLTzzPtdU1stunOVNtsrF2C0MB3xO8slGlbF9i955t/NChYabTgq9crHD0rScwVBVhpXhkW4S2UNh+/DaUxiplzyeSSoIT4PouO8IaWqWBnt9FLmxQkVLszJiU7XArAMaQyMQ0hCRTiGmYqoSpycxXuvS6PnIqBBFyaDBB3lQRSYNXblXIx3Q6mTRfOL/BznycJ88t0peNQuBiV+vc3a/w2u0xzs/WefT4NvaPZPjqU2dYXNhEi0X47Jeepy8bZfP6KrGxLG63SXdlk//60kt4nkfSVLnzzoP0J9LULs6Qn97H3Tt0PnOxgtNuU6l1udH2ubGy1Y7+sQd2kMwmeOKbz/OGR44znUtycaPL4nyZZDZCxw04MZ5F0yTKHZerGw7rLZuxbISIFFIWEhM7t1OzQ44MJ5mOCwpPf5t4LoGx5xDfLPuMTm3j7fWzyJm3k5Kh5UlcKrncs3MCoil2JwNmah5lFP7myWu8+6FpsqkkL1+a4f5jO7h3W5L9WZnFmsHQ6AHCMMCXFGTfRavMIqJpsLKkNAnNbdGrN7n6nQssFqa4M50g1IuMmwbx9jphro9A1lDNCGgWaSOCqMwRaTa5ldpNy9dYacTZX15HBFvBJX4QEGgR7NoyV+bXeOJmg+HdU/jrDndOFznSp3OhGlDe2KBfy1PvzzNaarNvIE7eczl3w0eoKoulLklD496Mh3C7PFkRnBhN0R+FuN/hbDLPxdNL/NJYmQVvlEVbwbddVEXm/ukiqajOxf130262aS/c4pubS7xtX4Y7c0WuqBluXLzMUN8AL2z4rDR6BJpMVJUYTVt87dIGnhdg6QrP3KwwmLYQigaSYCRlIYkAw+/RFhp4Aa4PQ3GdgajOl66VeWjE+q49lsKR4SQvLNQZiGuU2y6SYbBPq+KSwXLXsVJp5PG9aKHH+U2bewbXeXxVIxM3/sfBY0B1aMZ2YFz/OyJeFfPQfZjLV9l85RSdzBC63aWbHiQd1JElGSfaR3RIRg5TNF96juzxuwjK1/Cef4LE1BQ7+neSVVROrbfR0Ziv2eQsja6lEwiLMaOHv1miV9iOIRxkSaft+miBQkYNcXwZ05BpYBCXA0xJwgtlSOSIl28iJfO4y9fxKyUyR2+jN3ud2qmXWFr8NumhHNLr301GiTKUCojYOqT3IssSBhKOvxXzKwRYqkTJ9sgYClHZh1QRKZYG14EwQKstcNWcYFdsK1FPcXtbRCzw0II6iqpgqxZa6BEoJigKoWYhkMF3CJUteUglMOijBQGc/E8fY+89h0ju24vf6aBH4gSJNH5yAMlogBlDIyCI5pAEEHjIsSRChCh2HSEEQjeJOgGOANuHQkzDCQSOHzIaN1BVFTcICZDIj0/RWFvkxoWzjE3tptR2iBoqQRgQBi6t5Rn0ZBa3vk7P9bf27V6LwDMAKBQKVCoVhBDfc0jD34Mk/ePdGv6ZuT18+ctf5p3vfCedTod4PP73nqskSd83+f2ejwi9Xg8hQmIRje75U3jdFgvPXqe+WKM1v8rKqTnOXCkztadAft92Qk9gh7D7kSMsHb6H4Y3rpK+fp7tRZ+6ZW1jFDEHPRgQBXttG1jTszRphGCBcH79tk9o+QHujjeqHHH7XPfjrC8hqwOzz8ywvtxnWTAqhxZfbFS7N1VB0Bd8NqCxWKS01KF0rUbm6jGoaCAmkmEbKUIl5Ds1Pf4aZq0v0fetr6LsPcv3L3+bIvTuo1roMZS323HuI1//k65H2HsK5dYXcrbPkju0lcv8jxOMaVlQnXJnn5qe+QE/RSXXWUHYdQvFsZFlCd5uE1XV6s1cpDR2kpOQJhYyUHUO124j6+lZbTDORogm8lXkIJbSRKeTAo72yRuj5iEQe4fYw8xlufPpxNBlyR6YIbJdUNoY9tB2pOIJ/4RKv12cZvPwyR6cy/OpP/hi+lSSSSLMt2GT19CVWF8tcn1un5avc0Pv45BNnOPeNT/Ovf+aDvCObZnTlFZrra3z4018iMKP855/5CdKHj5MYzbIyu0FNS2MffxWdp77NPt3HWa/SWyvRP5oiv2eCzK5t2KsbrF9apKxlsId3cynIIpBRDZ3EcIbEiXtZffkqoecj6yphZRMlGsFt1hk7vp2xN95HY7VNc3kduV0C3UBNppG8Ln35BAeiPgNDWfRUivjOHYz8wNuhU0FJZMC3aV64wMZTLyGCACVbwA8lEr6Dsec42vY9DCRVDo0nyERl3jwhE4sZPLw9yujBvbz60DCjCYnHTuzihlnk7K11BlIG33z6Zda6AfLAOBsNl1BS6L/2CtHQYergUUzJ48iJ4xjD21jrBWw4KptBBGSJXHsJyfcJG1WSdpXe0iwvVGyWOxIjXo17xyzefHCIH//R1zKxY4iR/gxez6Xek0jEogyGLu94cB9HjuzjbcfG+IHDA+TnTpGySwSShBxNcuHl69xaLdNtdxhuzGFk82BYzNY8FlyLtCHRCwRRXWKz6+IEMBA3KXU9xtNR/u7sOl+/uMbl2TLnrpX5yy++zMpqg3Rnk9nNFtWOw7a+OEP5OEJRuLbeYaPjYjpNrl6b58LVZZ47c4uZ6CCSGSOaMllydW4/uov3vfe17D+yl+rKJp1ewFg+ihCCq0sVjFSaXqvF8PQuUvkMpYbNc6dnuGfQIJ9N8+q+gIesGm84Mswvv+MOzpw+z6Vrc/zCEZO0b/Pxx19BdmzuvfswN+bXODPbYDBt8r4Hd3D/jjwtJ0DIEnFdYXcugibD2dU2lq5ypuozU3XohRJj2QhRVcJU4a9+4WOcW+nhCIl7JjL8H2++k/HXPIoc2jRtl/l2yLv2Z0jETNRoBFeLECgmK7OLTE6NkktEEZfOYOkaIvDQA5ulmk3TyCDFEohUP8KMU/FVerEiK3o/XijhKyYoOh07YPD24wy0Vllbr7DSv4uuGsdwauiVJSgv0YsXCY04kiSQFBU9P8S2sMyehM+xtIMsyewsRjmUk9mxeg7X6VFJDrFvNIlIZYnYLe6eiGHKIdtyBqt1m6gUMnvpGseSNg/dPs3cZodoLAYiQFdVHpnO8jMPTnC7N0sqk+bEZIodAxmeXuiRKvRhOz5vndI4P9Pmo08vU8zEUSQwNYWGH7JQ6zI8MYjnw+++aZJfetNh3rUviba2wNmSQ7LX4NsrNqEsU287rNZ6LNYdvnFtk8G0xQu3Kpyaq5K3ZCodh5QBh4bTZKMaruuy2bRpOYKVjstMo0e157PUdhGS4MlrZTxJ4fhYikovoN7sEjc1yl0Hw9IoZOJ8Z9lGKQxzkT7cehvn0gVS3RrCaUGrjtZpUJS6tJyASMTkyVsNeuP7OalP0u0FBIO7kUTAK21QRidoNjpbQSpKHN1rgaIRj/q0NusYXpPYbXdw/ff/BqfeRJLAkrcqk6VWl1KjS8Pd6uK4ZgK1uYl89SReAGa3QssJWG27yAjiQQOkECHJKK6N6vSwfVDLK0goqIqEZjfwFmdZe/JFVr/xHK3ZFRa/copXPvsMn232MdvVafmQVAR+JAW6iSY8JAmSuoSpSAgEhirTH5GwVJlWIONFs3ixLHZ6CLOzjqTqFPWQrqRvpc3FCohICkSIY6UICQmFhOS7wJZO21UiaE6LIPQgDEAEJJQtJ4hQ1hh95FHiE2NQ20BqlRDdJnK7ghp08KwksiwTihDRqqHUlgm/qxEWegRx4yR4DqEAXZGJSoJsREWXJXRFQpclXAFpHVKWihdskamBkXESg2PMXL1ITurgBoJrC3UUVUWPJpFlBSQJz9+yDwQQvgNAPB4nCAJs2/6+CBmy/E9z/TPCRz7yEd73vvfRarWo1+vUarX/cVWr1e973e/5Kf33rOVoMgeyRNDtsj5Xp9d1WTu/ynMvLuEHgquXNjj9d89zZDqDdd9xCj/ydibXr5ApLTJ/dpHls0tszNVwWjbNmTU6GzXi24bQYxG0ZASv6RAZSCPJMtWrS8ghFLdlqV6+hVOp4dRtFpfb5DWJhhOyixjXPZsr6w0kWaZwxx4mHj5C33gaQli5UWL9+fPY62WKo3kCQgbTOt/51mXkgUGOvu/1eJkxCimVV87MU5gaobhzmLG7D9MWMRqPf56DSydJ3XmCzWtLVIfGoJijOXGMVNbixESat03paEFAwq1QfvKryMkkolMHzSRULBS7y3y9h1G6hSRcKC0QlpYRikrYKCEaFeTQxZ67hiQr+Ol+ejuPI+sakt0G10WPGHi+j9xqsXl1nlvP3YJ8lIF7XsWBEYNdd+7DOnon14MuL3/mD/jp3/woQ3GTeNRC7zRYSk8y21QJRnZi5TKY3QpXv/MV5hbnscw0Shjw3Lee5z1/9Dles30b02EKa6xI28qQefe72P/TP0FmzzQxyUedmiaYuURjscb8rQ1GD0xAq8LSZx8nmo5gJQ2k6xfZWCpzbraKu7mCPDGNjIJz/goDeyaoXp5F6HHURApZNRC9LqN7xjEzGZRIhPqLzxN2GohmBcWKoBk6udoCxajDnu4tmudO4y/NIrpN8BykMNiacO44GIU01ugkKArytZew7A0UVUEyImjJNH5+mJQhoWUHUEOfdFQjk4/RF5OYiCuMBmV2jmY5sL2foclJfuPNexm5cYpvn11meXaOTNKiPTuHaJcZHxtnRKzx2r0F8hGFvCmR1GVUTSbudwhUkxu2ztrKJr//9es8vwHXgiR7Du+lcOQ2tk9NcSJR5v17YnzgzhHe8Kq9uJEUk0MJbvRk7PFpNDXA7zRpWWkUr8vs4CEMRSK48ALYbZJek1RlgcXZef7k1CZrNtTTQ8iSRDxicHa9ywtLTRYrXTQJFupdDEVhOGUR0WQ6nkdckwlaHXrrszi9DvuG05RqXS7PlZGEhCRCYlGdqA5DaZPDSpNkIcvktiF27NvG/Q8c48Jii+8sumxWHS7eLPHVC+sEQuLi+RuokSiebfP4V55FkX3CoIeVzPDGNz9IJhmhf/s0j7zhEY6cuI1DOwtMHNjFcyWNpewkN1ZaHNo9xpGdw3xge0haCXlzosTG/ByRXpWpkQwj42O87mAfWUsnnzA5NBBnVzEGQvDNWxVWuj5SKFitdriy1qDS8dlo2uzKWwynLJxOE9fxsPJ9/NqLVRLrs6QVjyNjWez8MJtKlpYrSOoSnhknoSvszWk4rsOVio2aSBG061xcbBL7yhfYXYwTtVRu3phH6CbFiILcayIFLmEoEUoKq1oBO1QIwhA5CPjWfIeF9QrG4Agjd9xBNTLAUF8OWYagsB27uI1ZfYCv3+ggdAtUHdlKIkVjxEOHhOSRj+nUlBRuo4yHSpgfQ5Ng2PIYziZ47PhO3nm4wB6tRn97kYqnYhkqd09l6ESz1Ep1hnWXr3z7LF10FElCDQP6EipPX1ujHcuzJw2xqMVnzqwzPFhE03X29Ucp5JIkT9zF6LZh2rbHjfUWkgi5tdFmPBOh0nH4idsLHNy/i4cODlM7exkRy9CfMulmB3npzE3OXpxneiDJtmKM1YZN1wnohYJYwuCbz7zCzYrNI9uT7Ih4KAjWWx5lJ6QURqi5ITdLXZKazGy1x2zNZiBpkUrE6NUryJJAUmB7WmVHLCCneBzqt7ilFen4EqWZOZ5Zclm5eZ3audMUX/0YrWszDKgBO1Iqqy2fOyIt1ps2kiZzys1yNUxR++KnsLUolqoiJdKstMMte89ElkDIKICQVKROh2K/Ac0y6BpjD+3F6/SQVm9g6iojnSXSYZeGHVCzQwLfJaZIiFiG5an7SAV1PCOOLofszZpISHhGgraRoidpmGEPJXSouhJhdYPo3ElEu44oLaEWB/G7DmvPXyF0PKx8jOMPbOOOQ30cyctosoTi25h2DbnXQA4DCELaHliaRFoX+EjIQYgi+dhCoxnq+FoMeekqntDwEkUSbmVrID7wIfAJFJXQSuHKEUIUhBD0ZGurh6lb6G4bfId1OUMYuBD4RJwGQpaRZBg8chA5nd9yehjbi5QuIrWrCElFFQKpvorvByiNdTxFx1c1RDSFWL8J3608i9BHI8AwFLKGghOAIkHW0slFFCRpy+asGFEwVRknBOJ5/Pw4ue46+2JdXD/EsCwimQyKYYIQ+EGIpsooRgRZ1ejZLolE4u/xpX/BPx4rKyt86EMfIhKJ/JOu+z3LHlqtFpquo9dXEfE4ge+gSdBt9LAMlY3m1kmxqQYYDYe+195D5o7bmbh5Cn19iVqty9zFdY49toczlzf41l++xEPvu5NeqYrwHGRFordZI9aXRDUMoqMDtL9zllR/lEghjqJKdFYrrF4qIYmAa62ANiAhcZw03/KrfPDgLmLjRTo3b2HXOyx3HNx5l0xmlb4jozRn61TCkI2NDmbE4HX6GnLoUn/888xfr7C51mHqkIxx/0PQqaPtmET90hfRMyrS819HxLO8Kt9hbSXEqJUQx+7l8FEfWZdpeArBzAX8egt1chRpcAdCljFUi7Uz55C278LRJNQwhFQOVpt481cRdgfJ6aIYOr2lecx8Hkk1iact9LVLhJUN8Lo0Ll5g+r7DnHMDvGSWz0kBl7/8PPLTlzi4cxtdT3D21gKPHpjiP73vrfzhH/0avXOXydhdNkd2U1M9spbKN6+u88OTEr//559Fdrr88cf+kPNffZavfP3rbHZt3pUf4oETB3nqk6cwjk+ydKPGwOhDrIUatOsMppqkEy5+PoEsZVDKMpvn56m9MIfRrOFEZHpXl4nv6hK9/SHuiKU5d2GIDX2Me46lkP/i87SDJGEQgqHhuz6aquJJJvF8ASWZhvZFYnpIUF5FCkNCBE6lhGGZ4AiUVgOv0cbaMU7n/Cki2RRBo4KUKhLbNYWeTqON7US4NpGREeRI4rtvioSIxGHpOubQLjTJJVy8jD96mE4oE+t0GTFcgswYj6oSnZtzrO26i0hcIM1/ifX+EcqzF7nj0GHib38nL5c38dJjXPj6Z3jXo6/C0oskVVBCF0v1sXs+K0GMXH2Wv26k6Z15kr+dGef9P3AH94wnSegKqlvDmzxI4MFoNIkWcxGqRleSObfc5HikzZmqxKuHZHJBk4gRUpq9zjOtURKRMfpJsz4wRff6BR4aEpx9eYHVO46gSwGGprNW77GtLwG4rH7nOTYLI4yPFDBjOoNRjYYTsncozQ53DfnATsq7dF6Y7bK9GENW4lSfv8WlJQ3X83ni2y9i2z4oGgt2lc7gTkZyMUSo0peJ0FI8RKfDpYUNFhcbrK0kSB0eJ92XwVur48s25vA085cu02tU2Si3UY9MokUTeLUOQ7ko19dtlj2DWEIjLDlcmd2gL5fm4maPeqPNfLrAxx5f4e7ju9h33OaxQcE3VioEWoz9QynOPDfHbKnN7vtGeGWmyno2itBkZkttWl2PUs9nfzZGuWPT6PosNhzuTRm4ns7s3CJv+b1f5C5FxwjWaKIhCUFGdinJOuko+GEIksJyy2MkGWMwplNTbb74xGkUu8d77t7OZ5o296RsKmP9mE6cTNQkE1SguQF2Cy8zSgRBqBhko9Dq9DCVgECNsJKbxm66aGacw7syWKpMMqgj6TFUM86lM/PkC3l8ScLo1iCwcVeWkY0Ii55JrK+fUUml4EaxhUe2L0MgSZQDi4zRIWMpbHoq4+k+6o0OlqEzlTbYk8/QUyLU5DjPnb/BR17Vj4GPEIJGvcu3vnmRZFwlsW0/9UAh3QtJmRIRXSX0egxpPdqhzVnbZGogzdVSi8GkScRQ2JaPcHmjzcatRd78yDauN3326w4pK8Qf2YZShS/ecrl1dZaD+3dy/3SWQFI5t1gnqUq0my3GslE2Y1GG8zEMr8uCiHKwoLPSDdBVHT906QQhEV0ioSm8st5iMhthIGFyxQt5/HqZ3RMWUU1jWWi0zz7PQU1QKz7IbNOmPyJYlYbZ4VW4kTzM3X15bD8kk0ty3+4cfqLAQKeFe/YZskcfJNyocG7N5o0pj7akY9U3iA8McmT1En+5OsEbDvbhaQaBUFBCH+F0UZUQJBnZNJHjGWKHjnDyc8+w6wNvRXMdhnJxgjDCbquBmciwUW6i9JvEjDhyArp2Ey0aoWiphEDcXie0inhCIlR1MBWE1yKhgN5XJDx1hna5ReLgQbREivS2IlYhhZm3SNx+J2Y2g9SeQ2sNIrKTSPVNVM9G0qMIM7FlexZCTjgIVSNwBXNdQVLdksLoioQkCbREGqlXpy0bWHYHpbVJEIkjOjVkMwKBiyMJZM3A8UNi4ruD0LKG7FYJtTiaHqHkSaQVULQQTzIxww4EDkJWkTQdqdsCXSeUZQQqjmohRbOY1QXC4Wl6UhQdBclz8VfnkFI5MCLonSpxWcUNM3R8QSggpilkTI2U5LDp62T1kFBV8NyQIAiI6wqzcoROdIyx9jx3D1lcakZpV1Ns3rqJGkkRopBJRnn5v/4IvZ7NF59+hbc/chRN02i1WhSLxX8wIfuncGv45+b28PDDD3P69GkmJib+Sdf9nslvs9kkFovjXz2HFrWwV+rYQcjacpPbHt2J9+wSg5bCvu0ZtLtuo3fiLpKf+ASF+3fh+inqs5v4usyZb99ED0LkyJZA3EzHCR0PNRnFWdggsWOEoNvD73ZRLINe1cbMxnGbPdYvLFOpdbneCugBBuAAMVTUQOU//Pk3+LET0yiuzXrFYcMLObwtQ6vm4H1nlpMXNwlEyNj2DPGhaS7+t79i6radtOZWWV0qMZ616C1tYDZLVM410bqvYA2l0LMJ2lUbuTxL4JQphCFyWyMSCVF2HaF16nlOuRnu2D5N0PChMEzQaWHfuoZ39BG6+4qkRRe1MLR1QpdkwuUbyNEU9sY6SrJO6If4TkBbMjHmL2MNT9HRYkQTDt7MMtHBfqTiIO2ZEl+dXWXf8aN85IEHaMVSLJ4/Sz2Adz2W4m//+s/4xWdf4F//0BuYeu0D1GbPErPXuKXvpZKMM6A7fOTf/BZ33nsv/2ZngVtfeZzf/b3/xDumRpgQMYbuPE40GnLvO05QvTLDxP5pujfnKVV76DeuMPCON5FsLBMOJ3FFks7CKusXV6CQI9pXwG218U2T6nKV5Pos1eJulrsat9ptjjeukT82RbPRotCXZ/Wly+TvOIrfc5F0nbBZBVUhMtxH9eXz4E0iBnbAwkVCX2DfvI41MoJmmaTvfwTKS0SKI7Axj6ht4icKWIfuwauugapRiwyQqm9CPEewMYP83Yz4m9EJkujEVRU9O4AtKcQ0ibWvPkXyzW8kYWi8rPXTv02jL6oh6RoT73wHj3U0Tj3eRu4/iBNKTBsQmRpl30/+PP/qF/5PfuWPP0sxqjEQ1AniBSSnxzdmfOI9hT0TKV7/zmOcdy1u355BRsIWglBLogiBFjQQsoKQFQ6O5fCE4PJGk6t+jKm8YDDmseBIbI9rDKcUfEtlSU7iegYnpop8truX3f0B1XSH0zdK9GoVjkwV2RWRGUlmKbU9GnNz7KuvUtYOsSEPsmckz3LDZipnMWIbBGqH//jSOg/tH+HFszfomxhm53gOD5Vep8N6LUT4PnZjk14+x/ZilqFiDF+GFy4uo6YSbFybp7S8Qv+OKcqrm2x2hklGo1SkKrlignILfN8n9KG6vklf/gizV1bxUZlbqzCcyfIXT10lnsniqTpXgzjf+fZ1jhwcpy+VpGPGqXbn+PaVDVKJGG7a44E+lT+82eX8WpNCUuf6hRt85rkuUSPKcM5iJGWy2HCIWBrjUYN9/TEursJg0qLZcbmxXuOZa2Xu2pnlwb39RHo2C2KIdkegqSFCDkipghstl5YHkhwyGtfJKg4dI8ZYSuLBQ2NseApoKlP/97+lOzrC0cEkl8s6kizhShGUaIberYsosSKR2k3M4hRqaBJVBcKIMdans9Y0+PatGm/cV6Rh+zSe+grpE4eRimkUv8vDO7MIw0ANekitMkFhkraUwLW7NJfmGcilKHfaqJqK6zjcbMsUohqa30Hrlui9sk5j5z6SGJyv2TyQhcGJPNH1CpGISTq0iY1Z9PflcCVAhBSTOr2ZDd718H34kowWerQ9WNjscHOjSabfBCXBN26ukt+WI2kIhjSNRDpNq9HE13SeemWGD2XXMFtJKisOX1jSOX7Xa7na1KnduMr2eJTE3VPcf2yabrfL9oEsjzd6ZPIWV2bXOXZoCvnAJEMpi/N1B0XzGUxGKEYV2m6Ipsj4bkit57NhefTHDRRZZqnWww0CBgfyLJY65JIRlmsd9hSGSbgdAt8mpkkMJjNcqXhoGYWRfJyGE9K/9DItTSa5eo5u8gHsS6fwBsdYqvY4lFbodjRmoyOcDrbzlo0mY4keDT3GG8eStNptVN1AV0JqXZuEU8NfXUKSVIJ6BUVcwRvdjbr+GbKWTnd1lqf8FBgBR9x5HGuQJxdDtIRHfyJkvhEymIgxLHw8ScfARTYsvBCMwMZTDExZAkXFkGBDzdKfyCB3fHBtkCQio/2I+RX8rkv22N1IoUd4y4ZmmYgkgxnFPv0MciyJfihBTEsgSQpK4GKjEw1b5C2LIBTUXZ+4LGG4LeREBpwOotvAjxeQujXkaBJZU/BVA6W2iZJOYfgOSq+NFk0idUpIskaoRxGqiaVKSEJCFy5N4riBhOXbIKtb+m5CJEVD8lwQILtNhGoQaFGwXALZwAp6eKGOqK2BakAQIHIjyG4bOTNKzxY03ICYLpE0VDwRonUq6JEhmp4gooVIEjTcANcPyUYNZipdbGOQY8llEvEsi7MWimHSq5eQrAKWaXD3L36BWDrNV37t9QAkEonvv/L7LwNv/wte85rX8HM/93NcuXKFvXv3omna37v/2GOPfV/r/oMqv7FYDH3Xfvxb53EabZbbHrIcUJ+rogGplMENK8/43ffS/MtPMCB30WMmbhUa1R6thsOlVgcBPLijyMbZWQp7R1Ati16piplPYeXTdFY8vFqL3kaLZt2htVxGkmB9rsG1mst/T9f2AQUIgIMkeGJ1AfGtNm/dNkxuKEcmsOkbirN2ucRmN2Aoa5FwTepdn77hDNev36BdKpMqRtgTH6Q4mOHWlQ3k5VsokxNQadFYusHwe9+Oa7b43Fef5Ikb8xSmJ1CEQP/Kd7AmduJXKjx49Ajd0TvxxrZtiUlCQS/VT7q+QNIqksqNIkWiaMtXIHSRY2ncZh21fwxJVpCsCGLvDv7rk2dYunmF29/6AV549ilOP/4JfvN9b+NoxuL0V09zSRPcPjzI6NXLrH3jGdKvfQT/a9/gtC7zF9eu84E79vHrH/5x7Egcc2UOc3iKK3Pr7N2h0JFDvviXv8O/+uF3cOC2o2iew1c/9eu86cRx7svGaLVsRu7azcqff4LCA3egHt3PUjkk/cp3GFAl2qtl1MVLoKvQqGIODaPemMdVdTIxAzOfxV22ydx9jNrlaywstnmq1aA2X+YHB6ok4jLn1iP0FYvoeh1HNWD+IvKeY8ibqwQyYNsoO4+RCCzayX6UmWvIkkps+zbspQW0wjCoKoquEbg97J6PuusEYacG8SJIAqm2CU4PQ3MJMkPIQQCRJBixLdP2eIL1LvTnLfxEP4pw8VdX6Xv1q/iOk2BnL8SSQ9pWms8/N8vPPzCCXexn/lIFqbXEfQ/cjeMGfObiGh95NElj9ABfyuWIiy4LJVjvNMn0RbhSU7m+XEM3NO5OmeRTO3lVJIEQEhU7IAD6IwZS6KKZUXwR0HYDFAT9cQPXj1HvORwpKPihhxwYSHQQB+/ha88t8ehug87sDZYqHX50Ms03zi3QNKJszi+SSpssVV1aUpIbMzUqdsi1iWNkJ2IMRKCfMgvVKEsbdUYr83SHCnzmUoN212Nlo4yfHaQvYXFz1SWQPG7ObjA6NUZreY7M7qNMjuQY709ydq5KfzbKzUvXmTy8F9uXiBRHGB8vsnj5MntHk6SkHs1mlr6hHDe/+ATxXIFM3yC+ZHJtpcVIf4Z6s83seocrs3WSmThqo8b4cAYFiUxU5zvPnufRY9s4viPD1MPjdH2JP32pzC+cnOWtb7iHR7erWCpcXGqyq2AS8TvUZBNZkhmImygiYDyVI2ko9DyPe8aTNJ0QvS/KhZUqL1/b4OjeEV6qSchEaHiCHCGi6xPPmvRLHtN6h04kjiMpWIaCEUrYssy0PUNWrVHbdphBtUvh8BBfWhYMeyFt1ycihVs2T7kxeusVUo11ZBFidcuEIo5hJQllKMZ06q5ASOD4EApYefIFhu69G88RxCUFSxd4igahQ6DqSGFAJFtEaVSYjhg4jRqJeAZ7Y5mGHCdlWNhESPlddFXh2L5hFKNOVUmRDFqsuhnyWoicHCDSXCdYnqW/MEC4dJlK3x4QgmJfnve/bpSBpEn1xef569keo7unycs266tVyqk+hjrzHNw3iWhV6bfiaH6dmTDNpXrAer3MgZE4c/p2/JdvsTpygEany5+8tMrdu/r4nY9/leLgAJ/5rR+i4kg8O9dge7TMzkKUeMzgocMTRGI6DKZYqnWRhOC1u7I4gUCVZJqeTzqikTRVBuMKng8zS1XSQ2naQUC947K60aTPklG1HscnslyuGPhzM9SyOylYGmu1DuPZJKtNmflaj4NGl6A4RqSoILfXEFdPcrG4l6tVF+3mde7ckSETTzC0douvDuzk+c02LT3C0YJCQxMM+SUcX6EWJtmMDCASRRKSgRK7hV9Zw49nsJcWmXzwOJ22jRGLIgVposLG8VQuVlwSSoAqCa6WHdquRNMxWBYQUT2aAqo9gxyCpiPY0VlCzo0gJJX5ls9QIoI6tINgZo6w2UDOFBCqSeX6GkZUx71xEX3noS17sMoqqmkRGjE2XzgFksbI8DbkgT3ENBmHKB0fesJEyCqe5xLTFFKdJcK5S0jTRwlVHau5RCuSJDRzZEIXJBlVBt9KogUuitdC0XSkXgPhO8hBDzs7umUPJzw6qEQUQScAgSCQZOTAA8DNjqO4bdT6OsRyBLUNYhIgqwSdCoarEaomutMGK4oydRtuo4wSSX9XAyvQFQhDaNohnibo02ykdoUgMkRSCTGET2CYaD2JuKViaVvaYC2S4OVamtusCnuGTRYvdsDv0apUaLW7tDeWaG8s8dKNO9g7NU48HqfVan1fhOxf8L/i/e9/PwC/8Ru/8b/ckySJ4Lsey/9QfM9HhFarRSIWRUagDG6DUGYgaSIbKi+dXqYDrJhpRj70HrKnn6d2dYZAlnDrdcLQ59unV1nr+HhsxWV2ZkvUVhsEnovf7SGpClosgte1aa9s4jbbtDfb9O9Io+o6gRNws+FS/59cLgLgv6tADBReTYG1eo+fvXCJNckhnzFxbRczaTF6YJDdh/tRVZl2IHHlE0/g1Rq0zt9E1mX2/uCj5PZPUihmsHsyViZC/tA4mDFEt4t/6wxfXFjh/ZPbeNfgIB9+1xv5sYfu5IcfvZuHxvr5D3/zKcxIjGxrhaXMduzVVcxsHtXrMqr1iIsuBD5Sq4xQ9S1dlOug7zwAioa24yDewiLPPP4F7t27jT/5nV9jMJ3gj//043zgj76IOb6dgZgP1SprocQvPPcCP/Onf8JPvvfdfHlzntsH03z6X7+PXaksciLO5sw8aj7HzBee4Cl9gr985hpuvUy1YxOPF1CadVa++QTfuDTDg6NjaJO7iI5P0nzmW2gJk42nXyA5PkJ0bZaw28avNIgmLXC7yLqKms6D52D1Zdhx3xRaVGX40BhOcYxEMUVyz35Wpo+RrM9jJTJMjxkIy6R29QrqylW04UFGXnUXG/OrNOZvYKRjdJc2ceMD1C5dYPPgwziyQTfVjxqJoOaHUBNJ5FgcX4sgGmUYm6b0ta8ju230ZAbF7VD2ZOibwDVTeEGAalp0E334/VMIzSRMFhiNyRRjBh2h0pMNDM3AUMAZ2k1/0kIRIastj9MbDkrEQutWaNkuu602Q30FdM3krh1pJvfu5GQ7ynrLZXDPbXz5C58jGVFxk0Wk0CFpqLz7+BD37CzSH1OJaaAHDqHbRSckpimocogtJDY8jdXeVppW1xc4nsBQJdKGQhQXVYYBS+CjUA80AlnjVMnDdzxKkSR6MsVQc5UPvukeVpo2b7x9G6852MfUYJJXluqs1nrcd2ySipGkFimgx1N4rsPCaomuIvHEzQ6XV7t8KLvGW/dl6LU7XJ1ZZWGtxaW5MlYxz74dRT7yQw/zoccOMFxIoioKXSeAUJAeHKdTrpPKZRgqxpi9tQSSxB/8ty/yuS89xfLMHBfOzWFGYzi9Dp1WB9W0uDFT5eK1DdBj5AYKNEobJJJRYrEIzZbDxcvrRCMKQ2ODzJY7PHF+jaaRYfdEgRN7C0SKRb5wapm5pku/X+dVEwnuLwiWydJ1ZeqtLguVLqdvlul6AYokWGu7DAZlChFIWxo7cxZve2gPEVUmH7M4udCk0fPJWSo5S6PlCWTfJtZapewKUrqEosgII4YqSeQtnYnRIQ4r62RwKaouw0mTiLJVYao6IbaQCYUCO44iN0uEyzcJF68i5i+gihCtvoKGoBjRkGWJ3ncJ3dDPf5h1JUW159MQOm01RiireFoEkZ9EUhSUwCZiRbEiUc5cWcKWVFRNQdFNBrwq/bS2XCWaZVpWAi3Zh+353LcjgyLLbHZd3NIqYa9BkO1DVRW81UXi9tYwSUITTGc1ZK+DbKgMZy1UQqb7ozywPY7W2mBtcQ2vtMzlyzcI7BYXKXJ5pU7b8Tk6lORDt6XJVJdZzE2gGDqOYvL0s2fxQ3jnm+6lb2yUQNKwLIOBiMT51R5JHSRFpj8Tw1IVdFmi5fgcGIhi6QpxTaXmhPihIKJK6ErIcMyg7QZ87eRNQhGSt2SSqoQTwPVz55mMwy6tyUqjR8KQ2Gh72L5g01UwVAVVkXny3BKxaJKzNRndbeAXthGEITsTPrYjOFpQGIgrHLn4ZbSb53hgOk/QrvKVSw2+U9/SYs8bg5Q6Pusdl5qzNdgVZvpR9t2NOnGAyrPPoRYHWeifxgw6eOlBdmaj9JuCYGyK0aTOjpEs/ZZEyVdwQ0FGg6gCn79SBhGC55A2IGoYmJqKFHp4ksZATCVA4GcGcDY36W1u4nfa9CotUjsGcdouq19+nO6zX0FKZxCtGu7qIgQubtth7fkbdK6cRZO3/IWRFVQJQtXAkCVavkxO6iA3y7QvnMMRBoFiIuqb1LsumhUlQEGSJOTmBmplEcVrb/n1igCpW0WoOqGqEoqtlDejs4ksy1sSiSDcqsqFYOtJ6DYQkkSJJH69grASiNVZ5F4TTzVR4jkk1UT2PcLyEkEkgyQ8uv17UN0OqBqqa6NKghBB3fFBQOiH1FJjqAjMsIsna6gSWIqMpSvossxmy0NVZF5e83lyTefN4x5HjkyDrOL7Icr/xKAala30t0Qi8f2T3/9e+f3HXv+MEIbh/8fr+yW+8A+UPUTjcUSvg1wYJ3vPCca+cZEhw+DaXIWsEWP3r3wA84UXufGNp7nnnm2c/Po1MmNZurUeTRdGLYlbXcHuwQiyqlBaadO3WEIaAT0excyl6K6VUSMG3Y068YEEyZE8mQPbOfV7X6HuhP/L7/qfX7GcpqB4cbK+z59cusbdQ3neMNDH+D07KB47QPPKFTh9AbvpUN5ssak5/NVik0m5y3vveYAjYztIDZZIjhWpXr1Obu9uig/chSoFNAMXPR3nyKsP0ZjbYPDIXbS/9nmc7YfZsdHghJ7jj7/8Ld6aNVCn4e/aaYK//SRzG5scuusuCntPcHSwi1oYxTeTnJxpsH/XCUSsj0ZB4vmrc3zza08xPDnBa+46xtGhCbSgxO986vOkIhoztTZDB/ZxuHMOd3yUP/u/fwX38kVSfWnkZJraSofVRQ/vnregVBYZ0WwWnz5FkM0QM2VGsgl+6798nBNHHkLqGyecfZE/+dzX+MFHH8aPx6g4gtGBFKJ/L4Wl85SuLKE0NlGq65gJi9hontZKBaN/ECQFOZklrG4Q370f59nnGLr3KMJzGb97P+2FJTyh4rkrvO7OPbSf+TbupkJgezz6zttREJRrXf7LM5/khZNnsJ56BbvdZNdAH68Pskz3R7ZaS6lhoqULtHQd6cYsS+eXGO4KZK9LfO9upNQA8f4kUr2EKks8ceol9k9NI7wq4eB2kBREPEPghQSqiqJbCFlBFoKkAYoQGMJFEgK3fwpF0Ri22ghUkpEIn3z2Eu+4fxeduIXo+bz0/LMM7DzAWFKm1AnYnosST8SwFME7HrqHD/3q/4WTG+fE8WO4eoL9GZ20Lkh3BLIEhD5qZQXig2Qll2qQ5Gqlh+OFCElive2yWu/x8I4cvgAvECDLdBULSwGTkIqa5NJCjUxMI3L5JJ9edlkLVfZ5Za7UNfyqw8hwgcsrHVbsNnvGctw5muDr59ewRqMoksz1tTY3QoepfoPJrMWnz5V4093jTAwH5LsaN5wItcoSL5xeYfvuPbiazqHBFIeHUwzETYpxDUfAXKVDIqKxsVZC8m00SyOZj3Pj9GXajQb5HbspXbtAr14jOdJHGIbkhseZP/0M6BaaFUdOaYS9HqVKjF6lxPiOCTr1BvGEQ8cWTObinD1/CVlVyOYSuGicROLCcp220CmODlHrBixttFlJaTyglSkFMkIFvWvzxWc3+I037SKuCGjViTsNtpda3GKYSMInUHTSsShHxkwKEZ2Ntsdtoxl0VSJuaIRhSLPngOwjJXKoQqIRGKihQNVlNBnkaBIzmkF0K2DEoFtnT1+MqCqx2fOpOgEBGsOag6RFENl+ZAl6s9eRZTDG92/FlEsKshSQi+l0Ah/bD5GSOQSCza6HrkjENRkJaAUKmc15SGaQOxtIsQyi26SZ7GOt5ZFM9tHqyAgth9FusKpkSEdTPLmh8gNpkyIermyiISMCQWClaKgZ0l6dbqIPZaCKsnIdSKFUV/FFj9XYJJ3sIIcmMvjtNlo0Tp+/yr/99jINLcJdhJStPEtSllulGpVKnUCJkk/2CKJDiFaH8UN9fPXpi1yruAzlEthC5wOv3sMzcw6GsjWVf7+xysnoBJ6sEDN1EobCQsul7QWkLI1tKYOlhs1ATKXW89BkCTP0EAI8SWGz7XBsepheINHyQg7kZNo9BTMhqKhpFstlCrk4+u5XMeyq9PwAD2g5PglDZc9YnoqeZSRp07t5npnpbfTtuI346jXeO11EmW+ikMLKZjFGE5x88XliQ4P0lxv8wZfP8Ts/3k9d6CQNnZQkU3d8vFCjo2dAAml4F8H4KgtmH2HrFmp6EFfVkQLIxy1utCAqyRRXr6Ntn2K20sWUwfdlztdD9g4mKdkhCctkruFT7nlMRmxk3yfUdaKBTT20sI005tg2nM1FKrc2ybztHZT/+PfxHJfNF9ZBhIykY4Sqjr++jBKNk50eoVdtsvjVZ5h+1RsRqo4tGUQVnwAdISTiukTUbhFGUriNFvHSLKIwhtO/nZi6Fbv85EKbB0cNzMAlyPRTJkKeAEWNoFsusmISmAZ60AXfRpJlEn6Drh4nIQssDdRuh54fbhU6UoMgBK2hQ7ieQm77fgJZxTcSaL6DE8ujLl4gGNiJVl3ELWwj4dQI1a2wCt1ro1gRdAkiioxAok4UTZdIaiFOYNILZTRAVWR8P2Sm2iMV0SjGDOyex4Ji8exGwPtvi7KxOIZfL6H+T+z35YszwJbjw7/IHv7/H/8g2UM0FkNK5JEaG9Bt0XdoFKfapNPzGXzfe1g9eZG1TzyOmY9x9qV5Dt49Sa/cYP3sOroElqWjdh3ShspCvcfd9x9ASDaKoWMN94Njo5g6ogGt5Qbx/jjR/gwSEuurbbpslaqj/H3Sq7KV3DKeM9hmqGyUe8TbCt9ZLjE1UWQkriEJn+iOcZq2y6fdFSpSjx2Ozodv20n/D76Wz714ln93+Trzs7N88hd/GGexQmvM5UvXFjh58iSpsMv7jx7i1sUVpn/wzWzMbDJ8YBenSNJ35yP0b/P4s1/+IF+UZfo/9zjSyF5ef2A3dx2+k8urm/zcj3+YwxN53nliL3fs3MXJC022HTpMzw343LkVPvVH/5Xf/fAH2DM5gQhbfOETH+WLGzWOHT3BHT/063yh5PPDOYedR6e5JBSM+Yuoiosaj1PfaGIk4vQqPdyla0hDEQLXZkGK8AV1EGm9yY/fP8kXPj7HD7/7V3EUmbmFJU7OzrJHzbBRzIBpkhzNk4lXIZXGa9/CKW8g6xq9epfMnhj5o0O41Sr68CSSYiBHk4igSubobajb9+Nfeglno0xs+yR6pcLxsElm6Cil3BDaRIK1v/g7Nk5JjL5qL3/77Cv89fOn+cAPv4dX336E7Po8Zy7N8dtf/TrH77iTu3Y1mTQDWq7L+rVlRP8IwdEHqTRXsEa3o/gCaW2TL790g2trEomwxwvnr6CGLn/+mz9PolPG65uiLZuYqo3iNnCsDFIoo/ltLDWKL4Ok6iCFSCgYdhnFzBAKgeI3ecvdU+wpxtFVwG2y78jdPPFvPszQv/oR4qbKF9a73F1Q6Lg+A0bAB//Nv+e3P/IBBif+iIAso6kIVdsjbynUegGmr7CkDaOFOrrwmW92uVnu0HECJrIRVEkiG9FQFYWELqErGl0/IPA9AiuJ361xc7WM+/QzcH2W9P4BCtOHeeWlK/ynF0r09DjpmWVG+zJstgPunCrwymqDuydzPLxvgMvrPXaPZnhqpUYgKxR7PncUZE5aGj0UHix0WClrvDRT5YE7d5EcLDBYzOKFAcdG0vQnje+a3G+RBENTaNs+r7xwBT2RIapqCEWm3bEJfA/DNDDTBXzXZXzHKK1Gi20Fg0UrgZXM0i5vkuov4nYEVlSnXZG5/OILpPpHsOtl+nbspN7qcccdB3j58iLXry5iyIv8yI+8gW+8uMmB6X5Wlpq4aOw+so1vrVaJFhREV+Xg/CnaOw9xZabFc3MtwtBn6to5broSxvROkokkdq9HaBlYcoAfSGQMmfWOYCRtUHeCLR1iCDl3kVp0hISssE3xKSPhhVC1fQqmIOzVkRUFN1pEdeogayiShCckLEUiF9FwAuhKJpIEimYiihPoio5Yn0Nyu7ipfmQEpioYz1gUI1vT/AlDxg1hR8ak2guRkHH8kJYryOkqoraOW9yBIslUwxhSNMT1PW50VGQJvEAiFs9gOjJrkREKoaARCGKageU2kGULyfdx9AhRXSWIWFyphewd2oZd0mC1i+jfQau6RlosoxgGinDZuHCaF4wh6C4ST/Tzleeuc3jbPdw2FuPpmSqby8tcPHeNyOAEUifC23I5rmQmcS4usicZ8LojE/zZi8scGo5zea3KY3vyqGtXKMeG6CSH6JcUrjQEMQMMOcAJQvqiBtmYRk9sOZRs9mQCIRiyFGxJYbntksXD0GTabZtSo8dEPkrJl7g2c5PEapkdHZdidYH24AGu9mR2J0F3OqyYCRqOT19MZ7KYoucFlByZtKaxUW4wopk4uWFSmsAdnUTVNcKJCaywy9seGeIb69A6f4b943u4tNHj/OUrPHj7XsbSKoQSHR8KRkA5MMhJLtajDyLdvElrIIfbaUHKQ/d8JFUgSSpuAOX1TW6JLDHdxAt8JN0kGwlJmluO74rwcZGp93waUQtt5grK5C4qvoqsCsLAJ7FvP6ufukrrxhrJK+cwklHWXp7HU6C91iBolCldWSA+kMHyeqQOH2Xh2UtUr66xfekm8uhOkAUhMsgQ+IK2tzWE3P7O1zHSSeTWBpLfozN0kEx9kcqXvkpj96P0MFF7bWwji6pqSL6NrCjYehJFAtntIisqrh5DECI8jxCNmOqjujZCNuD5LxHc/3Z6qGSCOi0tSs2DrCThxPtxAzDsFqGRQBQn0BQFokkUzcANfBQrgeq08Mw0IQoJAwIkkoZCNwjQZIWQAA8Vg4D5VkDS0Cj1fKKazJ6+GLWey/FdBfxAsF5WuNxq8bOP7eJ3/+ZZFPn/5Tn79ecvAfyL7OGfCP/5P//n7+l7H/rQh76v9f9Bld9INEYQTW5t4PE0mZ2D1K4GuI/dg79ZZeaPP0tEF9w/kgEtJJGNYNfalJs2roDCeJp42ubacotC1qR4+y7E8gx6PErYs5EUmTCEzVubCDdES0awBop4tRrxqAalHpOmQsUNtrQT38X+vElhOMFT5zZ59FCRbSMZnr+4zqMTU/yHhWX6M1GWbizyV89f5LaRAkszVX5laIJurcfRH3wtygNv5NceCmhbOc7/xUf5yT/4JJqi0WdrHLj3ET754x+AmfNUL13Hbndxk/30EkVkscJ35lv8aK7ChXmdT33yk5TPvMyeQZOGkULRDG5JGXLNKsd3bCMp23zoV36Viwf28/pIhPXyvYzUbtI4/wyvfs0b2DVU5OJT3+B3Pv84d+/ZyUfffpwri03+anGV9dDmDT/6MN/6yhfYtivDynqFrGmw8vwFFte73PkjjxG5bTuV1VWk7hqL5xeJP/Aa1HNlxkozqGsW8YhG3RU8+1f/ma9//Qn+9esfJtozycYF/WMFlhbqxK+VUA4cJ717kpc+/Sy7j23Da6pIgGZabL4ySyaaIswZuPEBjG4HOW4g9RoQBuQPHWDt5fNEzZCYlebMZo/2ruNMWE3Sh3fhLK4wf36Gx179Jt7yq7/L2a9+il/47f/IO4/t4fXvfQ/3/lSe//hnn+Cn3vcO+ienGU8kePNtk9RnLvFXL55h9MDt7C/u5x65x3/797/L+bPX+OE3vhtt/Rqv+z9+kbkXn+Rdv/Cb/OV/+xgp4dKzHRxkFCuFE0okazdpJEdBSBiBgyIJ/DBEDxpo7TI1I4NqtxjNJxnXdQxVxick1KMsuW1830MQosgK2ZiO0+0yEoVuLMk9EcHz99xHa/4G2yfuwAsCVjsuczN1sqk4L18vcf/+UUptD9cPWG+7zG+2yMRMClGNp2dq3DeVQwJUZYsIF70A3xN4vs/jV+vs9Er4ew/hyybp2/ZwW6BxvTJKu91hG12ubNYZjoLIDDKWtRC9Nl0h0ZJV7pwqEAL7xjKMiRY0ZrjSjbFuS3zqKy8SvvYYqAl27VPRNB09FqHe8xhMGVQcj7ijIIBSw6XS9tBViZiuMLp3mmbLZXA0S7fjoOsSjhPSXV8hXcxQjAqWrlwCPc7FpkyhkGFjYwMRhNTXNglDl4mISt2KEUulSBsB1p5dBLJGvR3wzOl5TNnBSOV5x4PTzJa6VLseG5stctkYbjug3XMo9wJOhwWiahu1fzsLHY0370kxs7HBmdkqwZ5xXj5/nR/MdRifELitGj4akmpSbncoRRTymqDiBHhhiCEFhPgII8pCR2Z3JokXAgHossRy00MKFWSh0w0jpDwbFAOpvIAWHUJGsENrsRFmCEMJFxkjlHCjOVTho+UFXmWd0IzjCBU97JL0XPpjFuZ3K7w3Kx10VSVjRnDDEGRB4AmyYQOEwFuaQc2Po6g6qq6yNyfhIqFJgogiYRlxLLtOVLQw7A7xTD9adZ6KlEbXOiQyBS5We8Q1m7muylhMoVqtoo4m6FkZoEs6atAK86TVED1UaK4v8bfXWyTSy/zJy+cx787SKNf50y+f5s2vO4LcbPKu/TnmBw/T9CS+ea2CtLHE7rTK2cvXuX9Pknz5ZdyjR9Akwc6oi+VUWV6vou4cRqlUsDPjDMUlLF1hseNhyRLxiMl4SudauUPLFkTUED8I8SUFRZJZ6ThoikRf3GD3ZJ5RqcGtqsx6tUO12sKKZjm5YfPBfJK+nOCGp2FoEpaeYLBSopcZAc/G0iVulrvsl10uDh7j5nyTV1klnMwQYbeFGUvgXzuFFx1ESQ/go7LPu87ozgLxXf1U1m6g211K9Qbblq9hRjOY/YOE0a33J7DShAhSyxdQDz6AaG3gPfE3SIFA27uXRGo7jUBBFIf5zumrPPzA7TiBz7NLTe4btJAliXVbENFUBmIKcTPBRt1FXiszOtyjSZIMEhG/g5zIkjxykKXnbxLfuRPd2WQqHuGZJ64xkomCZrD60k2mXnsEyYoSthpkx/uo3LxE+fHP0f+2d6PFizjRzFa0sRAYqoIsmdgLC6RvO4icH8IPJIy5c8jD25FGx7ijTyPi1umefYlzh8eJGi59/jrUXWqJCfJqSKgZyMLb0l5KCigCUwXPDTBaq3jrS0h77kJql4kkCiiBTZKASBDQiQ+hCp+ub1BXiwwFDord+K7Hbwz8Hm0tQRRQQx9PNrH9rQQ9TWErxEMoBIAdyggRbpF6AV4YooqQgYTOcwtdEBI7+5Ms13uslDp85arL2yYd3njX9r/HkVx3S5/8jxl4E5L0T+D28M8j5OKjH/3o/9fvSJL0v5/8tlotUvEYbiSDKSnIfS7KzctUMsOoI4OYf/THSCLk0FAK0e6QHsty7dQCkZjBhbU2EzGN5ZUmihdw153jpIZS3Pjis2w71E/guhhmDq/TYePMDS69tMiRu0YxEzGE00PIEtc3O2xPGxQViVvlgPG0zlzNBeCONx1k9eQtDBmWFpo89JHbyRyooYYeH7V28ZEvPcO0bPI3P/VO/vTJ5xE3WkSyUQrDCaz7X8+6mSOmdAkWZxiotPj3v/mr9BcG2Yj2URc61bU1jHIbXZNIT4+yevJ5rLe+D19W+QHVpXZzgbf0jVGsz8LCBYIdDxPTdLpanN2iRePZT5LYs4ef/Q9/zH37p/jZn/1pmLmEnTRxT89y7plv8hPveAu/8Mu/jBmP8/F/90tkt+3mxgvP8aXVNu+cNnl2qc77f+xHOXjnq5gUIb/5jbMYjTY/cmA/Ox65mzCRRXPbRDMppMEiI6kRmivr/NirdvDSt2soloHfKPHh97+b19x1nF/+iZ9jTolzb97DvHYRp+EQzNyilU/Qa7/I8GMPo3zmWTRDRitkUNMZhKaRf82rEeVVvKUZnGoDub8PqW8E+9TTGIko7sYq1uQ26pLBla8/Q//h19JcLuH2TeA++Ha0Jz+Nf32V5vaD7O6s8tpHHmJk936+8LE/4K26CsLnw+95Kz/8ugf5zOUq3fVbfPvqLTqldf7PX/gIp2+t8Be//cv8UWWd9773vfzYL/862dExIp299OwGN6fuYXr3s1xfrXAkV8RcOkNoxiHTD9ECvpnEadSIpTNIkkLotqmpCWLdJkGkQNMJyAsbIVlYioShQK0XslKqMve5j/O2d72XhAY1J2AgopHwy3SkQU6tl4i4be44eohXLl7BePQ+Ot5WDOlUIc61hs/JxTpHpwdYbNn4QYjwQzJRg0PDSbxQkImoICCiCmJ+C1uN4ykqvVBis9YjvjrPyWiR2Uqbrl5gddZmeXWNXtdmOCExNjBIZH6FjmLRn9yadieeJBPRaPR8EqbKZsdlLGexN2bxp5+8QDMo8UMPncBxPUIzws58BN8PKfd8RAciuspircfVpSYP7S4ymDIJfcFCvbeVngQkMxFCSebESAQlNBhO3sl6tQVeiBuJct/2Azx/YY7xoT7OzlRwel3WV9YxYzHcXg/dsmjWO6h4DE/v4pdeu4PTJcHnvzOD29xAMuK84aF9PPncLU7N95BFC0MWnLs6hxI45PsKLM6tkshmiZgKn3+hjiYHDA4FXJMtGpLKnfuifP65G1y/scz1pU1+Ppklk8ky5dv4SoQTBfDDDqYiSJkRpG4TQzFwQhm755FNQce2Uc0YCTmk6kkUozKzTZcwOsLJ5RZvHFXQdBWEwHRq+HqKqOhxq9JlR3YryaodCGQzRdypEsg6IjOIQMFDQgsFXSVCRJPRZBlNEdheSMZSWGq5DEZVXC9AVyV0V0CrCukCareMJOskI2lipoIj6YRCIhZ2qAQJcF2iiRzKWpm0Cm4o6LMXCItj+EHAUEJHbVY5u+zhFUyKmSSS08Lvbv2Ba4FNsrsGfROYkkmva/PmNz7AzcUNxvolvuSmyQ9keejOPdx44WXe/tidXDp1mvLIHu7ZmeeZuRbx4VF2Jl32Ni/TZ9iE0Rz3T8ZomgquyOJ0Nzgf20ayq1BQDFRZxVAEddvF9gUnZ6vcPpllsQnLTRdFlpHQ6AWCthtws9qDQHB9rc6DwyrFiRSOF8FpCXQiZG7fywODCtddAz+fxjn/Msm9J/jstQpvHhSkFA/DkGlVS/hagdWazY7JAb55aYMhfPzcKG1PJdAEcbdH5eXzlB7aR16NECgKqWKRv2n0c0JJ4Ud8hkeyWKpC97lTbB4+wbVnnuDV73sbEh6uqqC6HRL7D9P2bVQV1OIQzW89idWfJ52f4Eqtx3Qhj8iHtFyftKnjeT1S7VXWrb6tyr0qs9IJmNZbBBFoHbuHiqKjBxALW6jVJcJWHbptikfG6V49j9E3QnJ8mofvvJelv/oMdsulvtFh4/wc+sgYmhIQSUeot32atxbIz19BGRFgpdEV0ISgp8qEcoTU3feiFoqEjoME9FL9qM0NjF17cb0ApVNFJmCXPUvPNqj3DSM6VfxmBSkVJZBM8H08y0AOPHTh4QcuRtDbCgbqtWlObKdv/lnk6hp+dgi8ALm6iqslqSsRYgZEIhq+pKKEIVK7hGTGkZ0mVjZJL5BQ9ThuIDBkMA2JbigTlUMagYSKQAiIiS5V32AiIXGz4eMEEpIfoMtweb3NvoEkLdtnaaNFebXM7718no9+4O6/H6b23Q//qMrvv8ge/gfm5ub+t67/PT+lxflF0jETSQjwbYKlWUQmR+Xg7ShPfIPRvUNMxjVyuSjJ4RRhz2Vlvs7Zc6vImkyl51Ecy2HFDeY322T7E6wvV1EtHcUwCJwe1SuzeA2brCxhJkwEYFcarL1wjcVuwNH9Reqex464xsHRLTNpRYZI2sRztqJbR/b3sXKrhFdukt0zSUqCj732fn7xQ+9B63Y5s7TJQ8enyW3Locd1VDziqkyj3qL2mb9G6jaZcDss/Kc/IP63/4X8Z/6EtbNXMAYKaMk4jE+ReOi1DLQXUOw2uTOPM9aXZPzWM4QrMzRjEX73t36Hj//+H3FtdhFXS1CNF3n3f/wLbnvwEX7xh36AYGUOc2wc9fzTxBI6HU/w7JWb/NRPfJD/8hu/QCpqATLRXB9Hju3j6isv87WP/y4f++l38/OHtmOoKr/2Qz/I4YEsv3X9CsmJQUQ0jmLFSegSQtG5utjEFRK1dsBNO2AtNcRP/vrv8daf+TV+8md/ip17h9l3523MkyR/+yGsoUEyA0mknYcp3nkQd+UWfeMZKpcXceUoSiqNu7mJqqsosQiK5KHHDUrPv0jvwmlCu4ekb7W2lhfqPHNmHrcwQiKiMRZ1SVdusnlrHn1siOxghtkby5iWiW4aZNNp1hwPf3OJ5uULKL06sWicdx0e4qff/AC/8hPv49f/7W+RGt7Ou+/cxZf/8P/iyU/8MY+89T2E2X7ijSXmPIulnmAsqHD+1MvsnZ7CqZUJtCjh4jVaj38S063jmGliSkCl42J213FbNRxPUL1whp6Q0RUVT46Q1wWKDH4gWG25TMt1Lq5W6B8ZQxcB5uYsMUPGtTIYfou44jHbCbhjIEFl/joJusS9GilLJx03kSWYHi9wrdRBEhK1tkcA9AKBIm21sg1d5epGi8Wmx3IbKrZH21OodAMWNqt8/GKTjhfyyKjGCavK2uIaG7O3iCViXF4oc2h7lg0lwexymRvXF/jOjU3qbkCp47I9F8MJQhw3JGlo1G2PXTGFA3snGMkn2TOW5/b+KAldJaoKqraPIoMXBLR7Pra35URRiBp0fMHuqMOFy/OUN2uszK6hSiEjcZm5tTbNrkOhP8tG3WZbIcpy3SGSzTI1nGL7ZJZsIYOZTHHonttxWjV8t4ulCh49NkraUPjiC1e5udai026SLGbp1Ss89cxl7r1risOTCZK6z1i/iSoFZDIJhBHj2nqPlbUmz13ewPFDFDPKes3mdAVcxUAk0qSHBth55ABLm3V+58+fpuFKfG7OI6d55BQXTdeRDYuiX6NPsZHtNk4gSDWWMRFE589iOg3cUELr1MmbW6b5GS1kbyGKrhuEskoY+Bjz55C7ZQLFZDCmkuhuILXKqIGN7vfAtXGsNP7aIqudEEN43OoptEKVxaZN1/XpeSGWpmL7AYWISl97gfVuQCBA+D4iFCjxJFJ1A1plJBGg2XUSfpOU1EN3W5xfaWJ7grIwcArbkUSIniuiDO1A03W89UWSXp1NM4PnuuwsRNEMA1sx6QgVWZLwfBcpkcVXTFwUyh2H5VAnOzxMcv8xSi2BpSrsnSqye8cofakIzCzz+kyVRNTgB05sJ6b59EstCoUURq4Io7voaQlU38fbWGZFzXNbFoqWjGpGyVsSL86V+c7NErosMVmIIEJB1w1o2B4xQ6Xe8+nYPhsdh6imsFrusFaqk2ys0PMDBqQmAxGB8Dzu356hpZjsjQu6oSAyNkHGFBS0AJ75Emo8RcKt0rWyFCMqx8dTbHR9FtZbbB/L0bIDYmETc/UavXYb48hdrJ25yKlNly+c3+RkM8K9e/uJxCx6mQFOpGySnU2sbISr6y2ivRarbZdcUKUnVDRFQTIjZHUwsgWsdJL0W96G0+yi+h7jUYlVT6eYjjGcNNEUid19MTwjyka5wWhMYrXlktBltNBFlQTp7jqIgIIpY954Eb+6TuP8WVo353FKDbzSJgtPvASagaGpTPzIOzH3HycxmGLj8irVsxcJUTD6sgyPxtGjJi/9xZdY0XOE7RqyLKPKW37poRVF33MUSdHwrp+FVpWoZdCKFFE0g4zs4S3cQE+lSBsqBUugBzZKNEXVKtIys2iNDaTAwwskQkUDFJT6OkIxCBVlq5uhSIjcMCLdj9KrQnuT5c9+CXX2PAESETxMRUbyHYTdg/QgsFX99ENpK3sg0LaGiEMwFEFC9tG8NgohhCF2IAhVi6ShYkqgyjIxTaLrBfjIGKpMqePiegGZTIR4IUVXRPjss7cwdJVUbCva+L/H7v6jBt7+Bf8/w/dc+Z2ZXeLQ8SOEvksoJKonT7K49y4SpXWSZofmXIOdk2kG7xxn+YV5vFaPbeMJvnBynbgp0fDhrh96Hdf+8ot88aUVBoZTNMtbmh9JlQltF0mExEczuOc2UE2d0vlFHCfgxqV1EpqEFAT0XJjus/DtrSm/nTGdwHVxfZ96AFNvuJf6zVtE+kcQvoOZTxM0HLIH9jL7yitUVusM79Zw611S24aZdSNkKmvIckDogaoKNr/0deKGTPmli0T6UqRlF+PEHkqBTNNWCQZypLtLUFmhev46XjbPN18+w1e/8AR13+Ttd+xl5cnn+PTCCgvVCkurJT74lrfw2rf+INLiGfRCH+tCJ6jNUihYfOyXf5od+3YjqxrVtsdLZcGQ6TMQT/CGnTCTfoyR7cP8n7//cazNMu/6pV9BGh7jHa+7i1NfeoFz3Qgjl24QmHE6I9t55r/8NUf2DXGl4ZF2XHYIm//6zWt0mg50yxzeOUZpU6AunsEUMPPcEyTlELvnY8xeRB09hN2xye0bxW25RKwAEWxtKFK3juh1ID+E2u1gZdP4vS6lhkxfn0J3fplosh9vYJJvNmHhhZvckXboVa5hzFdA7RHNpblj4TlObnsTT/3pb3NhdokP/sRP4hgp6udfQd6Ywbr/LXi9HpqmMivnMVVBWhZ4no6UH0T1XZKahomP2zFIeA6dqMWts6fZvW2UjlBZaQm2mRG03Bjx4jh0m5hGwKq11folIoMQ1NdXaSR2oK9VGBlLEPfqyJ5g1kni6xayBM1oHlWHvmIBYXewE/0s1ruMZmKEnQpGtsiNm7PcO6DjeR6J1cv0Ev1ctCPM1jpkIwYiFFxcanDntizVjo0kWxwdTdFwPNYaDotrJRqOICJLzCoysuxxoBClvzHL5ivXueu2nZgIRpNw5NAwRjPN1YJFQ08z0p/k1794nZ/epTNrjfH1F67w7OnrTO/by48+NEXalNnoyGjqVlt8JTDI3Xs/CUMBWcaQBJ0g5JWVFi3HZyQTIQgEoSYIYibjfYKe7dN2PJ6/usajyTo7BgsUUhbnTl9Fz5pcLIcEhkXKlMgkDcbH8vi2T9cL0FSZ+bpNIWqg9snMDQ9y88JV3MYmfq/J1Zc6TCaOcc9EhK+cWcfKaGidGq4Uw3dtbl6/sfVbXBicHKM6P8NUf5rNVg/V77J69RKb8SySrJMfGSBiSDQ6Drqhc/56iWrH5dFjE3zm+TmGh4fY2Cxza63BvqEEa44C1S6W4SKbFpJdJjCiOIpBQrhoQ9sxJI9ecZKOJ6OpgvDJL2C//l1MJmRilVtEU0P4skUgAiw9ih0bpNb1qK3cRJk8yt89e5VjapfVHbfxSKZDsLFAaGTQBsbQqiuYep5Yx8ZXB7HkrSSsxVqPgZhBzQ2IKBKOoqNIW04gkh5DSfZhCpuw4yN5NmJzmTN/8rcc+eB7UZJZhKxx0GqwFsTp9x28aBRZSGihjS+b9C6f5XqYYnOxTYU4hycHEGac4d4inpBoBQqSLNMwC6SDOr12ixJRckOjSMtbh92BYoqfuk+hFV6k3myyXXS4cnWGobe+mb54hZdbPse9W6x4B0mLCm5+hNCr4TTK9EKN1cVVls0ct0cMrp29SuHA7Yz2lmmtt7D9KINr1xna14/sSqz2XPoUnbRlkNBkvn52jlcfHGUsbVBzBHePxygaETYF9JtQq3h4so+iSuTXLuGkRjFe+AYvjN9HIRHnNsnnAXUJcyCFO3sNa3w7+ahFPVQxtBC77ZJOmHz6mxf5jaNR5Mkp1Nww3XYTWZEJ5xcoHjnEZmOVipvkgekMJTdk1+YFrGSOxRWb2GCW1FKFiTc+htqpopkamgRoBkIzUeJxQsem9OIZsq99LUZtkXDlGonJOyk3AvrTERbqPeK6RsyQiVoxJrw2hhTQl9C29mTNRAiB4toYfg9D9rb2qKU5RCKPL1VZvbiKkUvRKbWoXJ5l/ZXLDB/dTRiqhG0b3VLQEyaSolC9PM8rt2rcv72AF+ooZ58jeuJVBKGLrGiYikAggZ6AuIOcTBPWS3i1l/COvAZLlQjMKGGrgjEwgt9p0R07jIWDg8V4UsFHwVu8hrz9ELrXBiUKoYcXyRDIGko0i2KukqSHN3MBdt2OH80iBx6ZfbuxMml6igyhS6gZRNqbhLUVRH4ISTMQuoWlCDoudAOJQISAhBJ2UIQAVSXqtZhzLPpjKl2h4IUhgaSgSj4l2ycbMegFAiEEIymTc/Nl8kmTZjZJZ3icCzPXeNu9O/hXrz/Ev//MK4jv2rLF43GWl5e/P0YmSfCPlS38M5E9/O/G90x+q46PGU8QSBqBkFmyLRqhzuilk5x5fp7R0SRWNkKv3CE7leOVx6+w7G3FCNZtwT337ebrv/d5xq2AqApPPnGNh7ZlQAhC10U2TRTTZOP6IlZco1NtsXFlg1MrbZKGyn0H+vDtkIypkByMUyt3ARjNmAghMbvRIyZL2LMzFCdymIUCod1BLeQxvRDftvnrL55F6wo++fI1NjX4jWOHaV66SWz5ZRRNMPi6h5DsJpvzi3zq757i2vwiXtClWK8z4gUMaT5px6N0/hxPXj7LYtfjha9+k+yzpzk4OsK/ee+7UAfGWAwttp14PQNf/TOKb72fanoHen0d75lvEI7140kGFTnJtvEhShWHMW0DyWkS+jr2tVsM5UZp9+KsaGmmwzlyGZcTb36Yx6wuz339JGsb65w8s8zLzzyFF83y3JpgXI8x1q7wu8/XyCbGGLTyHD4+RBiGGBvPsLx2C7e+wo5tBYpOhWdbCsGtNV6TD5CjcVqXL+PWeqSPjoFl0r0yR3L7GJnt+7Avv4zotQibTeRIAjU3SKfWpvPEk7ieRP+Dh1g4+Tleed37kL71u/Q/sp+vvbTAvQmPjfhuqhbs3n8Md+5TvONjn8IaHEbyHKyTZ/mpN9zHL/7mbxMIH10RDGVz+NdOIW/OEvZP43VK9LkNpPQAEb+NrWu4qMi6jI6E5jos2AoDMZV8aZaf+9jv8cs/+a8IQsHOQoJeGMOQADNCT4sR8TtEI4JMzEQsX0UZ3EW00yEb7dFMj9F1A7qyxdxqCS0q05IMspbG0nrIZqWKb8R5YiMkm5CpOoJIy2a2KaPIXeq+xF9eatNrlGkXtvPUhuB6rUrPCaj9P+z9Z5Rt6V3ei/5mnnPlWGutyrl2jr27e3dL6iS1shASIlviWAJjgw3iGnsI8D33DIxxwICxiRYYDAZJoIRCSy11UMfde+/eOVXtymHVqlUrp5nnvB82xpeDDxaCe33vuecZ4/9ljne+c73vGKvqWf/5vM8T8TAkgROzGWaHYoRBwLGCgYDEvuXjun0eMHp86m6T5kgWSRXpmw66GBBULMyxQ6imT3/Qxk0f5lytQ6jpjC3MoTVN5ooJbi5V+c87EQ5OG7zlsdNcWiyzsrzGJ1/W+GfvPfLnWreSGlIJZfpeQFZVsIOQOzUTJwz54vU9xnIGU9koSUNClWF5v8GNtRbWSIJcXGYhH+Nn//Qap9+UYyaqMjs1xErbx2q22dztMTE7TERVODadZXm7ze5+i4lSllbf4fz5FebmhplemOD8sy+j5YY5evIQgtXnhWsVzgV9njgxRpU443rA7U5IrzPAUWT8AFbv3GVyqkjPhdD0iKfSHBuO0ShnWbm9RCQ7xnZjl8RjbwBBRlFEkkkdBLhWbrO5uovXHZBIJvny0xfIfuARRrUW/+b5Gkenc/zg+B4Dq8OvPfsCxeERvusdjyH6NglZwDKidDwJKQgxTz5IXgrvRWgLAlpvF88DV4kSRFKs+SnSKR3L90hb+8SyQ1T2y8wKA15++irq3Dgn/TphMo+cHaGvRijGW9Qln4QhMS60+ZO1Pj92X/6eub/VhVCgsbFMc2iSjAKl+DD0K+xGppjw9wjWrnLzmUvc98FvI2iXkbIlnNgEliUh4nKn5tJ2PE7HXWQ1YKCnWN31eeF6hQ+8fZThdJTbNYthKUoskcQvNyAI+NzVPb7nVJHP3t5jdiLJqKFyKu/RR8camBzPipRH4nxpdY1gfIJBu8Vb4j2WjWmGRRdNzLP3uS+i5Hzq8/ejFmZZqlg4L7/G1uIqnTd+G9tti09tyfzYcRdJ0+nKceLhgBdbOpmtGkpEpdq0cUI4OhSj57i8Yz7FYbWJG2aYsfdoyjEyMtypWWjlMuH4Cfprq7x1qoR8s0pk7ASarvBfzm3zj99xEKlbJxHXITqPNbAI1ChRAnZdn6gikjMU3jSXZzMaIhcUXNMhyIwT2mtIk8MceEeCyuINJtJJWqkR2hvrDI/kERrrKNNzzDXvsufmuO9kHqtb5TWxyNRYFkPwUMwWfTmOXCvTkSKkTh3CVmL4t29jyznimTHmc1PkYgquH7K0P8AJFLpRg6GCji3IiEJIXvHBB7/fQw4s+vu7JHIpQlHEa3cIQp3arRU8IFoqoBeylFf3UM48zPk/+ARn3v84esJgMLCo39ggPjMFgkyradPdaDGou7zwr36b73vgfkQ9iheqJBgguCIhIAoiXrOJqCgIQcha22EhoyPtLqEt3Iff3kVIp3FlA0OUCVGJODU8ycCdP4uxeRk9mcMeOQy+ixiJE4bBPXJtxJFr61h9E1+O48saEVUhMT/FemoGLQQzlJFCGSSNYOoUEgIEHkgaqu9QC5R7XrAhJNWQfhAjItjg2oSKjuLd6xD3HI9QEGg7IttdG0EQiCgiMVmm1nMZSei0TA/F8en3TKrLtxlLCli2x9hQgnc8dIAvv3oX+FuyOvub4P8ksof/d+Ob1/yaAxLxOIbg0x0MaJx4gMnyLb7xhevUyz0Wt9s8NpuhvtNG1lVuNRysELzwz3K0R2LcfPE2D//gg8Rv7nF4IsX4/WPICQOr2UPPSwiiQH2tha5J3L64g92y2LMC3ny2RKIY49aL64yOJhg6MUbv+bscSirsdxyUZ5fY6zlMRxXE0EM2DJR4gvrKOnrWZeXZW8Qzl2njoWY08sMZ7u5UuPr6DZQvnKMswZu+4wEG63dpDo3xQ7/8u7zr6AG++10PMjU/xV4gUh44XHvtKsJug8LEBHFF5L0PneYfHp8hMTlKb7tG9ORRpHiawu42a+l5ht/yRoRcjlR2iq56CHX1BoLkY156hbGHHkfyLaKzM7Baw+1buFIIMwuMtOuQUWmfu4A0IqNFMkj1LeRCkemDs8wdmuXw1CTvLI7zn65VGc3G+fJLV3nfVIzvPJRgKiayWO5CLM3lP/ki2Ycf49tuXyaqj5PNGEhKl+8c6mGXJljo7SAUh1m9cRMlItOrDUg4MrGpUdTSOF4oQiyFmC2i60mQNYL9Hbh+lejMBHKtgahpHH7Pw9TNXVpHDxDrVZmqb3H4Oz7EpGPz0JDHlZV1fvZTT/H7P/MPSN/3JvzsGELgom9eRRjsIygq1pXziGPTyKOz+LaJ2t1D6tWJNffYS48zkDPsuj5Toojpi6heF18IwHMJfZHffeolJh94hNSj76Xv+wShS0wIENMlhOoqjJQwPZ2s1yVQDURZpqFn8dMJzEEPHY+nluu8bdLgpU6MU/E4aV2maznkIhLDCZ3Xl3cQTZvJQ8O0PInNHpy7VGZ9f8D7jqTZMWMk8iVeurXGbmSC9sChP7CoV3b56DtOokkCXgCPjUWJqArtwYCOKHNxpUazs8tbJnLEFJctU2Q2a6DICvZOlY3UFLlUglrfYt+VuNuXedeBNIJr8mIgcHN9j3e/cZ4rm20Wkj63uiKibRMtFGj1+nz8xTXeeLhItWczrorYvoYuiWy1HfzQ5xMXtkjHdMbiGkIoEFEk0rpMy3S5vdbEGlisbPnUqm3+7psP0Hn0OF1F5uZuj5Ss0HNcXig3mJos0rU95iMy/Waf0DGJp+OIssDiVpv+wKXe6XFwNMUFQcIbDFhfWiGiCqi5cXzb4ytXqkSzIfFUkmheItoVGMnH8Jr7EAZsVHrYDhybG2c6F2NUDdmeH+PulcvY3TqJYonmXgMjFqU+cJifybLfsVjbG5BM6eRHF3jsxATnVxqsVts8Pl5gZbeDgcegFOGal+XSlcskLrzIy899jV/4yX9ARNGRfJO+I7BjwtzUHLIkgGtjZyfRWmUks44oGliaSs0UiYUC0VSOmOLzljM5qtY8+b1FLm7tkCgVUYazEEnfiwhev0o9MUzZbWPEElQCg/kcuJJGSQ/ImF1uNVwy+TxrlocmiMQSEkuLDZpakUJrBzVT5D3/4M20PYGUkQDPIxIxGFUCDLOO6SXJSB5NyyOiJ4kGXcJGH0mEwPeRPIee4xNmU4SBjeu6hI7DZrvP+m6NA3GXTETnbtNkJBaloMnECTAtiZXRY5yWBLYGARcrDu8xVulPJMn4LZaee47C6TMs+RqNnkoxK3Hz9ia3tlwGfY0nNB+10+Ddj51GlEOcRp8vdlIsf/UbCFPHcbodcpbFk4Usu4pCUpdwbZPDOZFIfYteJI3U2mNIqbEp5Vh3k+h6lqjvMDYxgifJYJq8cGObB0cP8b6RErMpEafWJNRjhIFPODqFQ4gqSoxoPo6gEAgC4ymdI0PTXK3uc6Kgo9bXEKSAluNjKTLTcZ87koQauvznJY9/kHZJHLqfwc4OJT1g8doGxeEkTqXMGw+N4i1fxx2awJBMRKeP7FnsSCXGSnO03RCta5N95xmWPvWHTH/gA1i5Q0QiAsuNAZoCgaQwQEAlIGeI+J6HLeootBE9j4wk4hlppEEXT9JYffUW/Xqblh9ijJXwFs7g/dtfI3PfEY6+8yzmbpXRtz3Cjadforq4x2ivT/zQLIfHb2Dko2y+tsTom47hLr2OeuadmGFIJHSRPHCUOKoWwdqv0yx3GHrf+9lsmRzTunxqQ+XbH5olurOIUNRRfZNeqCNIAlIoI3omkiTj6VHkZB7Z7hCqUTwkEEIk38Krl5ENA+PIffh+F7w2oh5HEEKsQCSvgOEOsJEJJYlQi4FnEQYhlqih4ZHWBJpWCME9bW8/ENFVDUGQCUWZqBZCGNJzQ3QF2o7HcFTGC0W6rs9u3yEfVyh3bSZzUZZ3uvTb95LnBOHe2+ff/PJNfuy9R3nm/BLwN7Q6+7/w/zF80+TX7XfJJBLIgzZbN66QEDTaf/o8EUVmJwg4MJEkMZGkv9JgY61BIiJiBDA1nabbD3jhi1dJJRXOv7zKY99xhqjfI3tolN7WPrIiYcsSgiIy+cAEu9e3Ka/2KPc90orI+EMzXPrTa/S7LhPzWQgDQtvj1H0lXnp5B3W3QzuA8bEkqbkx5EQCIQyQIjqe6bJ1aZ35+4b58MMLaKk4BAELEwX+xbkLiF2HaC7K7/zOLlbXonD0ML/wU/83pjVwtzeRDRV9vcLpB0+grfR48EgGe/o+locPMrd3idBPYhppHKGFGhvCbDWIyyHzSh9j7gBmpYp49wJatESn7ZDUZSRFILK7RPPmKok3DmPuNxHECGqxxFBUpGNryLLC6OmDCK0dJPneq6mBlCL1/u9j9+oluoJKZOogIxsNlnYtxFBmZCTPTMRi5fwi9eQEN+oB5UBBvLPE/SenkNM55MDFr+8wH1bwai3q11aJDCXJnznOYHeT/YHBRCaLXa8SpHJsf+EpVLfLcLGIMjyGW99BCAUk0UXL5/HbLZyNO+jzR4iUN4nPjuAWx/jJIyd5pt5npnKHWx2dn/vFf8/v/4t/SkYTsOM5ovVlQi3JYPwUhttBdvvgubz+U7/IyP1TRIpZlNIEkVwScfwQKV2j5QkU4yJSGOBt38YrTVPpBVy7tcyP/dLPUopH+Sf/5t/xwkqdd87FcaQk8fJFzOwERjyD2q/hJorU+x4b+zZH9AT023htE6W1y+cbcTLZBLsdiZPjeXRVIC6HXC5bJGWZg/c9xMb558lMH+f8tsulu1t85yPzjCc1gliSuwOX9x9Jcrv+Pfzrf/2v+e7/9T+Q9tp86Zd/msB1eM/h/8gDY0n6cpRM0KZlG8haFMH1eOfRPGZbZ34kw7kLi+Q0hz3jMDMRgfjBOcQ66AmDk6kpVEngDVNpIqqIIIgMpxQ6ZhQ3CDFrFSbGE9x2YtitOh+4b5bzbYkvvnCDtu2jqyLVpg6yyVw+juu6NC2PEIgbCu8/NkTXCZiKBrRsga7T54m0zRXFYDThsmXJnN9o0Qwktis9EELWBwNq9QH9gYcvijTaFrXdGvmhLIsrt5g+Mk3PD/Fdn2w2yuatJSaGHyAzNoER1di+fQePBO+4f5bqnbv4sQyVu+s0+hFiUYOYLBCL6jh+kvEjh2nv75GdnGGhlKaU1jnbW+K3t6v3/k6ZXSR1Ci+QUQwNb+CyvrRBPJcjHteJzJTo1ExqA5gopblydQP79DBvOjHO7to6FWOWuL2HEIb8s7/zPp7frPFb//Hj/Oi7HsWbOAq+yNWqTejpZKMKhqaRlAWC2BC2JyCEAX01ixvayKJITvbv2TOGA1Q9glEc4tg7H2euGEN0emA1kVwPEY+2HKfd7XO53uLa5TvMH5lj34b5iI88aFE0DGqiwlBUZTKhsNjqU0xGkAYDmnc3KD1wiuQDj/DRr1b4ue97iITfwfFDJETKYRpdhjElZMfWsfsORVXj6JEiYu1Frq+3GE8ZbLQGzCYkbjZ6CL6PFE8ynB+ip0eJaBaGEpLUZdKaiCoEhEoEtu8yM3mERHOVec3h1lAO9cwJ3JUdWnab/7gl88NvHeOFmw2irXXOXTKpDuD4SJH4sXlisSidQY2T43Fc3yWcOIhxocyDp2eRszHUZIp5+zaCYlAT0vT6JtP1JYy4SP/6FSKqimUNkGPDxCWF+1Ii9S0bvdcilsvjXX2VS16aebdMptdjIRPH2usxiBdISSFbQZSUEqMQdvABHZc9W8L0fJKaTs8LaDpQN30yehzJsVA3l9GzY2idPQxRID87y9OfeJX+/e8mmh6htvQ6hYUxEu88hq64NAoHsDp92qaP1WyQHB5iX1QxuwEzeZlWWCTtd9DvO0Gwu0pKF5Crm6SK87RcmWOlOIQCYQiqEBLvbuOlRqijo0sOTixLoERooRHr7BNJZSEn0t65QXm1hZGJQSgQMxQO/aMPY134Bsbbvp3yv/k5Bo0e9VoPsdylv12ltbFLPK3TWm8wbEh415Zx6g00u40qRQmUCPKgQSBqhGYfOR7l2tde4skP/xAPKQa1MOS+OZWXVjo8PjyP6lpE1l6nP3cWSZAgsHHVGIJnIekxrGieSPkazthJRMdGsttg9pCHpwgVHcEZYKsJYq0NrEgGSguMKAAhgnBP84xrE0YkXFGmHyvh+yKhpKIIAYEg3EtNDAWU0AdBJhRFhCDEEENEx0KWda5XOsxlI5QUj0ao07JcvCBAESWqXQtNkQhDH0EIMFJZhKBGCFxZ3GarOsmTJ4eBv9mBt1AQ/xbcHv6vzu83g296lxxrQCwWxW3vsadliVx4ldE3nWBuNEHDg7npDDgBEVlEEeCxJ+bwBBHVFxiLqSSDkBNHC9xYbRKXRbRYlEGlSXulht21cTp93IFDNGcQiWmMpFR6wH2TCVzT5ZVbNRKajO/47F8vk5xIsLHZZt3yWWxaAGTHkkgRFbfVIQwC1GQcs95l8lgJSVMwmxaekWDj9j5vfN+7+NxP/ABP/fbP8rl/8VF+5a2P87HTp/jVH/0Qhdwo8QMHMMaGQZSJLcxiVcocfv/jrF3bRt5fQ48n6IweRm7u0l/b5ulf+WO8eo0rnZAbbZnO0grezhrW+hqq4CCsXGWQymIJGsqDb8HzXEQ5wF1dJHbiflRvgKLI0Non4XcRQx9iaQJZIxBFHC2B0GliXXudZ/txrFQa48A0z/YMRjWHH33zDCPlawxefIYhacBo2KderqDOH+Zw1MOLpZD6DYJ6GdGzEU0TORqn4wkoCycx5qaIPPYElhGhe+0Kdn9A//XzxKMq2n0PE7oO7voS3Vu3MNcWUQvDeL0+6vAEVsskNAe0Ll9HPvEQ0eEx7HaTg06ZkUOz/Op//gT//H/5OyTzRQTVQFy/huDYhJUlHKuPYA/wq5uoxXGkdAKrso8gykgRg0CW6WVmULGRJYmQkKVKG9WIIkgSYW2Lo+qA9FCJf/tTP8lQMsJDKRusPumd6/i2Se/OXbrRAt1oCcl3qXo6P/KLn+aGMEK10Wbzzl1Wl9ZYWtpG6veo+yqZiEzb9ojIAieHE7Qsh9joAW5fvcBbzh7h/mGZqfEstZ5DPxCZzUU5NDdMEAQMxBhDY9M8+3v/nt//lx/j/R/93/jQT/9LfuYnfoQLF65QHgTULBG5XaFpBwSeT90WKA3n2OsG1Hd2eW3HYXuvycvLdciVODObJ/RDplSTvOQxkTJI9PexnYCV3R7fuLhMOBiQjRv83HP7HCjFeJNhMRZ2eP31RWpbO3z609/g85/+Cl95bZFueZtzSxWygsXzF1eYKUR5w2yWqUjIwXhIurNB3ghZLTcYHorw0EIOT01w/1yBXs+k0jR5w0Ie0Q9Yu71EfWuFQd9hZaVBMRPh2GSGG7s9TpyaY/FWmbWNJmuXXmdnYxeMLI1qE1GNMr8wyYH77yMaj2J1u/iyxjtKHj/7tmnedWaawxNJIvkkUU2i6wh0qlX2VlcxdJm2aXOz3KFRd7Dse84vsXSOwHFwBi626WG2u1x+5jl+4vFpNEVCkGTMIGRpt875W1tUay1eWm8jSwFvOnuIIAwo2jWaNjA6zeP3HefWxg5Bu0Hjs58nF9OZTqss7Vtc3jNxwxBBDHBFjW2lwLaUQ9dUNFEgJksIiIjNHYTGJsnWKnpgc8DbRK1vELR2CQc96k9/HckxqZohgawTd3p8+9EsD+UFFBEcUSNwHUIjSXHvBkKnzuWNBrG9RezUKJtSCuex9+Nlx/ASOeY1C7HboLtb4XefX6bSHXCr5SI0dui4In57n87aMmtilu2GyZ1qh0z0ntNINqJxtdym5Sh4noeq6bxhKgOKzJUL19ht9CnEdPJ+HUUC3TfZ1oYIQx8xkUPNjfDw+D33j4XWXTb0AoeffILfe32faDIGVhc/keXtjxwn57SQUnk6q7eJqyKVdp9nNl1udgTene3SH5qi1zcp2wEVJYmHTDGmoikiViyF32qhzh2m4uhEpJBw6y45q0bKbqONTlOXYhiiTzSXozg9wdDIEIoR5UBrEaFVZzdMsN4H3YigCQGurCPaPfYsEBEYiSloskAQhEwXkry0UqMqpsFIEGtvku9tY8sGWr/L129UGZ8cp2apNKUYhZkSbnyIdFxlzyhhGDodx6GVHMLtNJDaFbKyy1UniYCHK0tIAgTZMZyhKYyHnkBMpNClAD8MiUoCiijghQFGvwq+h0SAG4QEroUtRxH7NWxJoxlEcLY2Ec0u9//ddzM+N0wpHUcxRGRNZVXL0llaRwps0kcX8PfKGKpGIqnTWlonmomyudbCapgQQr3dZ/flqwS7y0iLLyH16xB4GIMaotWmu1rm0X/6dzAUmUgkiqwazIs1nE4NJzOOV9mE+g4KAnYQIrgmjqhiKXGwewS2iRMvMfAEFN9Ecm2Ebh0hnsNTDNzqNpLTBz2K+WcpbE4o3QsxkWO4oUQYTREoGn0liS/ICIKIGQg4vkBcFSnq9xweMn4LACH0kHoVor0qGz0PgZCJlEHKUNh1VQZuQN8NyRoKxbjGfs/B8nxESUSQpHuk29Dx/RCAT7+wyLvPzuB53t9c9iD+Dev/BOS30+l80/Wt4pveJd+1EfUoa6vb6HaX4NYSyckM9sBGAzzLJ1JIoKc0TEXCyEdo9j2SMYX1rSZOGFBebfGev/dOBFEgd2KO2FiR/PFxNl5eo7VSJbRs3K5FYjJFcSrFbFTiwKMzXPjCTQxJRBJCNtbb1FZaiKLEy3fbADTcewsp3TeJVW/h9gcEYUBvfZe9q8skR1OEgG95+LLI+DsfpSEnkScWiM3M0VvbIn36GKU3Poxqt7Fefw2/uYeWMIiOjCDG4si6Qq6zxbofpx4vMCG08YMQdWyS9JDGg9/7OOLaNcbXLqDj4LkejUs30JMGYjwJC8fITYwi17ZoSHGU4hjG4+9Ej8i4a3dQDAXRtQjtHoJnErVbqP0aRNMgawj9DtmxNOxuMBq0kTWF55e73K100QyNo7MFhuYmUDUJe/EOb/BXeKu8xTsSffKlJMruyj3/v+IUYjyDPHeMRuEQ0WwezZAQrTZhcZb4936Qwfoa8aMn0CI6XsfErdXxRYPeTpXlz5/DbrYJBgOsRptGtUH0xGlsQSV//0kUr09LjvOKMs3wzARlUcfzXIYDH8kzaSyt4e7t4A4fIMzPIgUuodknTJVo7e4z+eHvI31wEmI5xNIY4ugRZEXC8kVUZ0Cr22P1lVf5ytUylbbLmN8kur5BfXON5thRmq7MSNJgxY2xNJC5qc/jSzJheQV/exkTGfX2Fc686Sz12j5us4nc3Gc9NkVSMrm212c6a5CjTzGq4SKTVEWKBrR6Ls1Ojxsbdaq+zPHZAocLOm+/f4ozU2mafZfnKgI/+PAkD37gfwFCfuW3/4DhsTEKIxO8/b3fz4d+4qf49NMv0XAC5PwkoQBxXWA8bXBptUHR3OR62+d6pYuhS0SMe3G3BUNhOKEz27xDzXLpOR7rjsLNdsCBgsbpYxP0Bn1eXnco2wqtQOScPsbVTsjmehnLdBnsb7GQT2DvbnF7cZPyVoUvLbZYWdtCF0SODRkYZp3koILoe0jOAN+BP7nVZbh6C6VZpW26PDAaZ74QY+B7BJJEPBEHNYZr2VhuQN/1ePpOm92WyeVbO7Q6NlbfJJLNIwigxww2y21UXWO8mODoQonM6AQGEMQTPLXU5Kqvc22nw+J2G8U12a8NsPomvd6A/NxR1m4s8/LNPdJ2m2sDgbGxEnp+hEg6w3eeGqJb2wPAiEbQ0kW2Bx4iAqOZCKIsUm17DOUTZFMathPw8sVlKh2XnOQi6VE6A5edQchQMs5+o0VzvQyyjq7KHNYtHpnPcrqoU1ACHNtBFCGmyQSSiheEaIqEFXDPhqxdgX4bUY9Bp4roWEiKgdcbUNttImXTCFqUE6mA40qD+eptHsg6FBQX13NoOSGOEqfx1c8jBjaNepN4r8ora32+cfEOL9zaYj1IctOK015dwW8H3F2r0Br43FlaYaM+YFRx2Ohp9DyRL3/9AvV6g1zY5bc+/xplW+Tcco3dvkNCl9EiESptEz8MMRSRkhbQadTZ3K4heBZ+p42AjOL00dYvMZ1PMfBAiyW407LQ715nZxDSLy2w3RUppVS69RqLi1v81jOLDCU0VjfKOKLM1p1Fvnynx10xi7J8i61OQLq+zlPNBAQhj+y+htfrkdJk1u5WUAKLpOhx3k7SS46gRXWSQQ8pEkMfn6Z96TWuOSk03+RQ3IN+EyWbJa6LjEQEJAHMnT3C+SPoUshoSiMSWKTsGq4XYGlJZN9DlAQCRAxZIKnL3K70uHxzh3R7C8H3UONx1NDB6zq0Qxgfz3F0Ion00jOorTKGLKBIAlVXQ8Vlpw+6ZpDIlSiOjGIFMp4S4dGcT98XSDbWWenCF//171PV8+xNHMefPIYvqUiCQFp2iEv3Uo0Hsk4Y+Aj9JgJQ9TQ0SaRhFIgrIkldxmx2aV27Q2Ozgvs938fYyQnUmaP41Q00xyJz6iDSoINkyMQnstz/XY9SmMnQ2Wyw+foGgSIQCDAIYavvsX99jd3PfYHmc19HcrogiARrVxGE8F4i5tQ4rqSTNKvk+tsMfJm3TOhYokHjG8/gtZv4oU9AAHtr6GYDVQgJXYfQNmnKCbTARBDv/UANcuNgdrBf+CLdy5fRzAa+GiUSOgiiCIKELoMReiiBhyno9FwQAp9YaAIBfghdX0BExJBDdLOL6A7ouhAiEDYrbGxu8zuvlfGDkLGEiiaJKLJERBGJygK26wEQ/lmwwM56g3a5gufYSL6H492TPlxd2afaGrC+vv63k/D2/+fxxqlUinQ6/VfWfx3zreKb7/zaFtFYlE1bplDbpHT2CF67TSBIpFSRQdciNjFEx/K5U7XYXaziAy9f26NtOnT6Lqu7HdrfuEjmQBGv3cXcb2K2B7y20qS8WMd3fHzPx66bKIbM/aeLuF2LmxtNHlrIsm96rO6bqKpEq9L985wLXYAHixGMbAJRFHG7XQZb21z406s4fY+9Kzu4PYfiyXEqt7eoqgkipRLdnSr9lRX0k2con7/I7NseQy2UmHjX46ixJGIsgTo+R2JhHnVshtb12+Ta2yRjKorVx7pzA8eyCfodEvefQbbqJOubjFSvUQh3iYyNIMkCV774Mv7oPH55GyUVJyaGSLKCJoTIEwcIZo8gFScgmiDsdRBzowjOgDAMEAkQxRBtbJL+nVuMP3CEh558BFEQePeEwG//8Bt5TN5AHNQxV5dInThO/vgEmZPHiOoKmWAfYfUKUiZHWN2611FWNTBS1O+uY6R0NhMTDFZWiKlQGEoRmz+Au7sBqQzpI9O0ltZYkoY490cvkD46iz5zEHOnjGP5qJqCuL8Bpkl0coqekkRRNCbycQRR5nd+8sf57r/798g9/iiD1WWCAJwz78AUdAb5KfxYjqBVIYxnadQttIhImC4QTs4ip4YINB0/FFnvCyx3A1KCzUOHhxHrdco7ZeqXbuBFNCQ9QiYeQW1XEONJMppAU0uRiir0RueIxOL4e7uI+GyurPC2uTjRfBGjXmZn/DierPHeJ89ybH6UiKKQ8loMvIBL5RYbtTYRs84jJ8YYBArVcpVrdZehiERSsBEkGI9LnJ1KM5GLMZQymCoWeNP3/QixeJQH4y6vv3aT733bg/zSb36c5//44/zh557CtzoMiQPKXZdSb5u5QoQryz1+/gef4Pvf/RAf0LdZ36wjtmuEokRcl6kWj6AEDhu1e7n2Kdmj0g+ZjoRUXJl4PsLUVI5nb+4jhA6vrXVRoxk8e8Dh+07yjrMzfPDNR/iO2TgjSR3Xspk5MM+7jhZJBgOEXh1v0KMaGea5pRYjZo21tT0+sSXz2LjOV56+wG98+RrxpI7TbBLRJIZLRWL5IqPzYxgRmbXVOscnMhwfSXB6Kk1KMsmlNI6eXCAzOszIaJaxmWEWpnO0Bw6bVZNUNs7xYR2r0+P6aoN/95++zJ21XSbTCjurq9T26nT3NpBlCU1yGR3PU1le5jsPp1AiMr4aJ5rK853vfQPvSnUZNLZxbA/NUAh9j3PrLRzfZzQTIRvXyeZipLMJEqVh5ofjvP/Nxzk+HOfiWovP7Bp88Nvezoc/8kP8+L/8FXZ3K6RPnabwwAHiTouk6BKVYDh+Txc68AREQaAYdsgNdmgNbPJaiAhYShQhM4KQLAACAQK+pOECfiTJzgCMoQxCrkisXyOs7NEUJMzIEBcW93jpVpVKq8P69j6uqnNem+OrywO6qRJfbxi8vtUim41T2dykefM6N60UF5QcLwSjVH2JnOThDkxi7V1mMwruS8+yk5zk575wk00xzRtPzdCJFJkoJPAGAxKqxEhc4rGFHGEQIGoGax2XG42A73znA8z21zj/8d+mZzl4jg1aFGXQorOzyzM39/nDSw2+4aZomTZre22qtQZXb60TET2SdoODc6M8OqLyroUk0sQ8j50Y5+1vPo3kBbC7zZOTUVL5LF4kTnmzzPLoCY7kNSqkeNXN8krZptnrs991WfKi7LoK0bFZAllDSBXwC1NUG12GZI/9vo3S3sMvr1NUfdAiyLE40eP3oeHg99pgDkjt3yHoNhC9ewedUHUkQUCRRUwvpNJ1uHx7m0cKPrpvI61dxvZFHMsjOTNG/NSDJAwVL1ei9dATdAIJa28Lb/0uq/ttcp110hGV0lCaihXiyAZuq871fQd95w46PlJ5mbwaMPWm+3Acl9tVmxVTpmcHxBXoeBKDULhnjyhFCdUIQruCLgtkIipeKKDWt9i3fRxRZdCX6Hf7NC9cZWIiT+H93wbdFpaaICb7DDa3cNstzL06gRJHDB3ik0UqGx0KcwXqbYvdjoMowXhao2057F1ZYuf1Vbz9PYLaJt1L50HWSb//+6AwSQMDPIfwznnkWy9jKAJZc+/eXg6PE+tXiIYOnhZD2r2D2t3FDWSirW3k/U3k7Ru43HM7CI0kgtlDGx0neeo0BAGBpCJ7HoGsEZUDov4ASQTH6mKHIq4fEAtMEAWqg3vWjJbHnzk9gGTWsIwsAzfEQ6SZmuaSX+DIWApVFolwj+hKwr2zSrtdh33LI64p9K0A1w9R8BAUBVFSUWRw/4z8Anz6hSWWl5eJRqP/P2l19mu/9mtMTU2h6zqnT5/mxRdf/D8c+/zzzyMIwl+qO3fu/K18lueee45nn332r6z/OuZbxTdFfsMwxLEGJLw+oiig3LlJ6oGT6EMFtsttDDEgO5mmvbjNa0s1AMydDgEgElKzIRuVUSMK6eE01QtLCLKAJAu88Lnr7NoBgizS3mhgFNLYHQsjFSE9kqS1WmciqUEYUrUCGo5Py/bo7N5ze9ABN4QjD0+gp1MgCHQ29qm8vsKdxTq52SxOxyZxYJrS29/MwvFRbqsF+rU9omEbs7LHzWeu4OUmEZw+gqojCR6+2b23PXoEYhnkkRmSEwWyWkhi+wayIpF1mnh6HAmQ15dx+h6ZE8dRInHE4hTRfBS/22T0kYdQRJdBZQ/ViJH1m4QIaE4HSVVRdZ2w18S+fg5Ri0Aih50aJ0iOIMTS0K4R7ixiHDiErCsMJ3V0VSHa2uZBZZ/03GGEXhspotLb3sUzPZRYGva3EEUJKTNE0NxHLI4TVDeoXrmOK0C16VC+tk5w8Rzx+TmCV5+i0N9COfs4/twppNDHN9I8EzvIzYqF8ba3oB+9DxEXxwmRA4fk8fuQJg4gHnkIL10kpgK+T0FzaPdNUobO4tOfI/AcOskSxukHCFUD2zKx+h0M34Z4lv6lVxh/8BRyNIn14LtZi4xgiSq7jR7h0utEcRn19yk7KmY8w+hskTOlCC+IJX71lUUeHCniPfd5GnICtbxESbRp+TIpxWciaKBEY1gL96HYPYoHZ6k1O8wmRFLhAGtgEkmnGM/FedNcnoTX4dLXL9Le2+P1Lz3Lb37yGQ5nNLpSgkgw4AMnh3h8MknX8vndGz2SkoiuSOjivU7Sc3frDMcVsjGd55ebbFkSHxq1ELPD7IkpnvjgP2b75gUulfsMKjuMR2VWb22j91pcX9pkupBgTLC466WxfJ/J8nWGoqD5Nh01Tk8wECWFcr1HOOhyvdzl1k6f5bqN6wWYlke30WavY3L+xjoxXeTI6aMcivuc27L44rLJi16Wtx7OUyqkefLkKIIk0gtkbFSWehLt/X2+8PoOz3QjbC7epFLr8dl2nF4okUzpnB1PsbO9z83r6yxttvGckF6jgdkdIEkSdhDS9Xws2+MffeB+xofiOGHIyHiOWFSl17G48fo14jJ0LA/bc/nc1T3atTqqrvHehxd48/ES3/34Ib7r2x9FMxSOnj7CE0+epZBPYWgq4wuzPNuK8NLdOjuVFpFcAUmA/9AsMjQzhxaRWb+2xNDMAoubLRw3YL9rc3ouTy4bwbRchocSfOnGHtP5KMVMlHP7EvX9Gub0g3z2d3+LH/r+D3BmZhQvdAk0g4pvEEaSKP4AK1ARCMnJHtc3qjRdGU9SubbbR5cEvDAk0FO4sRwELs39OneUMfZvr3Ll9z+DVBhlYiiOMXcI0wuxJJ3VxDhKdpzP3m7RXt3kSEnn2vVl7qZmWSbHviPy7e4dGp/6E0JJ5fpymfumspQbNrc6Pnnd50cKDRZv3+GZxSbHFsZIWU3+07kqEUPilZ7O+48NUUzH2byzQX4oQy6XoZQ0iPg9qn2bvCGSEC3CMMQNRXq2z0hMYGQoRdCoMBgqsdHzsLQoYjTJft+i/CefYWlpE3F/hz07pF9v8Mquw/WdNv3KDvOzI3zvW0/xfWcKTI0XSBaKHCnFUGJJjg4nicc0ekceoJhPsBXGOTYc45EHDjB/8giZyhK/d6NNopTBtgPmwwZsLuKWd/B2dqiLcbxYGl9Sicwu8GiwBJtL3LqzQz0+zMCX6foilf/y+zSMAsHeGvrlZ5iKhZzv6jiOjb2xzkBUEQhIqT4JVSAqiXhBQLlrcnY6xdmCQP+1F/EHJvbWJl6rD7pBW00yGtdYbIukU1HkeJpa/jByZ48T268iOQ75qIQhCQzFZfyNO3ytJvPFb9xgPznO5bZMY/gwEa9NZG6BczWRs5EOvmPRcUKCwEOWFTqWy2q9jy6BG0oMRB0xcMD3EAMHLzNGorpKHJPMA8cYOnGY0cceJh04OOV1vHgGL5GlutugcnUFNz+OuVsFu4vkW0TyKSKlJNVOwNRomsHARU/pbHRtLt6ssHarQvVmBfPmZbzyBiECvhbDLk4gDBoYsnjPQWHyAIJnIgUuUmMTbaRE6+o1RCFEci2C4QMIfoAbCASJLG63Q7S+Ti9aRPoz96dAvBf8IpcmEBQFeg1MIiCJSJ0KcuAheSZSv07U69IPJVKSjyAJeBj4Idxz/gf5z7IJQlmnj47t+whAxdM5MFHgTTNZBGDAPVldy3QhhJbtUYho9ByX+aEYwymDibkSWkSnX99BkSVc/79Fzb6+VEHTNLrdLp1OhzAM//qM7H9S5/eTn/wkP/7jP85P//RPc/nyZd74xjfy9re/nc3Nzb/yvsXFRXZ3d/+85ubm/srx3yweeeSRb7q+VXxTu2RZFoHv0+t1GfXqEIK5vIxVbxKRRTYs2FtrEPgC+20PFVAEkbgiEAoCIzmd+WKcd7zvOLm5AvHRNHajg6Ar1OomqgBDk2kihThWo4sgCQS+j2M5EMLUcIJ+12Hf8bFCKDctRO6d1rMADyidnsFqNLHrHRrLNfYubzEc14iWhlj4ofcSHSnQ2y6Tf+gMb8l2yDo1YuNFkvOTTJ89xvxb3wCNbYL9HcJ+H29vBymaIKiX71mnWF3UZIxoQkXLD2F94Y9QzH2MVIbO2i6pmXHiD5xFzhfRCkXE/AiCEae7vkfecIi6PabOnkI0Yvc2PlsiJERwTPxbr4LjIKgG0vgBkDXkQQOht3fP4iiSRIzEUQwNYWgaVi4TwcM0LfpXryFrEqKsoE/NoSbiJJ94O6Gq09vchWwedXgcvzCNu7kGQUBv4GLu73H0+CTFY/OkG2t4rSaypuK2WkQFDyMRxzctLv/WZ6gGUfr1faRokl5pFNvIEZmZQ9Rk9GIJJBnVNxEECUuMUmn3cZ2QC7f3eKqjgQDO7gZ7ZZNdYxhdU1lsuuwOAvqOjR3JYm2t0Vi+Q3/gIseTKLrBSzWBSqNDkB9DEiVeamqMyBZBLMNCAl6sivzxZsDnXj3Pfd//o2ysbrPSDNhqmrxccTia1ajLafR4HHHQpL9ylytfeRFp6Tb5XIqBYLC80aJ7/Ty/+rtfZKMxQAo8xP0drEoVLXSp9gJO+i0WnQTj+TjvePAkn3zxMhd2eoxGRNLxKDfrDt/Y6vGN9RZtJ0SRBco9n6wh4QU+G5bE4QdOEDFU3FBg2KtQGBll39X4N+e6DHwBqzTFoYzEt7/1KFIY8vBklMnZUR5YGKJ94EH2+gKdQCEd0RiN6+QMgdN6k0AyGInCZtuh37JwOx0G3S6DTovl1V0yCZV6vUkypjGbMXBUg62NCjcbAZ9c7FLuuUxnokRFkTudgLbl4e7WqJDgQDbk1k6d6YUZ1GiEctsmFY9x5vAUL756nYW5cb73sQX2N9ewBh6uJ6FHdYaLMXY6JivbLdSoRqhFkFyXfs9G8hx832dzcZn16ze4uNxAUUSa+3129/scncoRmgPmF6bRszm+frdHJBHlDacneOTkFKIgMDIzzhuOjBARBZ65tMaz55fxA7C7LV64UWa9J1CanaTfMhFEkWg6ge14uIQMHI+dRp9my+Llr59DFu+R70CUWG9ZpPNxDh8YZzwXY2Y4x/ihU3z8qy/zuSvrWKNH6XY7mO0O4tUXEAKPsNugHWgk6utYroeazHB/ScNHxpAEWns79IQoFZK8vG2y/Mk/5WY3wcrUg7i9Lsb8Mey9XdqCQdUWyHfLxDtVJsIW/bFZZF3jHacnmJW61E2Xi6stOpUW0Y0bnJnL85H3nkVrVnji2BAnj04Sm50nH4UfePtJhieGGZUH7FX3OTWXRd28w+H5IY7KFu//tjfwwKkZrCsXEPF5eFQnI3tsbJb5wrUanhwBoGBXOJHXeFe6h6aqJDIZvv3+MSb0gF6oUddyGNE4i/Uen37+dXQx4NhomvPbPcbHhhhPagwfPMKhyWHGafFE0ScyaNHr9pFuXkFTVL6y0qZjByRyaaKyjN6tQBhgqCKepJCYmiYVWkiCiKxK1IQYGU3lpTs1nvn0i9i9Dq7tIaxeRl69jNSuEeoxpso3sDdWuSQN01WSMHuYiG8hDE3Qyk0gmD0OGiaN4jEimRieY7Nba2P6Es3ugIjfZ0jymctGeDjYRF65TOfSVdz9PQgD4ocP0spMUGquUpBsjo8mEfsduoGCZXuoyTjh7ja7gUpIyI4tkFJkomMz/PvPnCdfyFDf2mJgu/iDLvuBQWt5kbcs5LjVE2nVWshCwK3GvYCF1ZZFTFWQxRCz08HXokRDi+j+CqrbwzISJHWB0OyzlV0ge/Z+/PFxKhcu0YiU8CrbsF8h+OynqK820Jo7KMkct19ZobFYprdTQcwnOPKBJ4npIk3TZadh4Toh9Z5PpTFg+GABXxCpn79C/NQZTCODJEj4SgSjuoqJghB6qNMHCBUNIVNCz+fxmi2QdPTmBpIAJPPIioyUK6EOT2Jvb6Ek8viSim32EJ0+1s0r2Nvr1L/xHEEosu+E95wdOi3avoyjJpBCH1GLkcQiEGR8UcdHQJcFVFFAkaAfgCvoQEBMDkjrMm4YkNQlchGFqCxiSAJOINCxPXquT8d2yURUJtI6theQjCik9Hta4GQ+QzRbQpVFXO+/kd8whImJCXq9Hp7nYdv2X5+R/U8iv7/4i7/Ihz/8YT7ykY9w8OBBfvmXf5mxsTF+/dd//a+8b2hoiGKx+OclSdJff83fBFqtFv/23/5bPvKRj/CDP/iD/NIv/RLtdvtvNOc3tUv/tYXvCjIluigxjbtffJXKa3cojcQJgb3aANM0GTLuvZqJ5SJkFBE5BN/xSWV1Rh88SKJgEFo2VrOL2+qzbwc8dl+J51/aQI1puJ0BWlLD7ti0tnooMQVJEhgMXDxAAsbSKrEhHe/PFnAopyPpKmari7nfZme9BWHI2e86Tfahh8g++gibT73KYG2b0OyQbGwQixso0RiSZ5GbKaJKLn5hAUE3EHMlRCOC263jNcqIgkDYbWDu7lF85AGkZBp7t4pV6yC5A6xqFbNSxqvuQbKArGooioQwNAGOhbtXIVy8hDpogqIghAGCbYEexx+08et1xFQObXQSQZYRXAsxcAkrqwhOnyBdIhAE3K01Brkp/KEpZE3DjaeJzUwSXn+NsNsgdGy0fA7RalE7fxk1k0ABnLW7hEuXgABh5ADFNzyKYbXJBFWG5ovkHjiNduohuk2L3ewCnVqT8jMv0Niq0zr2EI8MyQwVkhxODUg6PSLFEmGnjhSNYFs2IiHi/iqhotH2Rb663GZtELLy6md4aKHIB3/4R/BlhckT8xQrN5GFkIN5g/G4ApEM3Wie3QfeR8fI0HMDogrkhQGTcYV0LkvNDskrNmcSNoKq0xo4SK7LJ168ywPTEeT0MOPFJL++raEs32JTG8X2QzxC8uKAhhhjTy3w+jPn+NUrDb6mzqINeuiaxsrCw+SPnuStx8aoWTZPv3ST9u4OQx/4Drq1GiuhwapRQOjsEdNUkhGN/a7FdM6gHcicmUiCJLG41ydlKKy3TJoDj1uVNpstm/l8DF2GZTfGfs8hEbpcrrpcL7fJSgFSGDJJjT0n5EtlOHh4AScU0NNpxgfbfM+0hBBLIEgShbjOwA3Y6vs8s9Li1aUmkixy32SGs0dGecOhAquXrrBy4SK9VoODpRQHTpziobPHUGJJ1GyStBTywHSSem3A4p5NzwlZqvXp+wH7A5ftjs1TP/azdC2b3376JqOjQ0wcmOfQoQn6u2WKY0P83mde5df+5BV+/Xc+z97A5czDxxAVkdJ4jtpWmd39PuV6n3prQDKb5MtXd3EVA8+HXELhcFrE6jSRVI3K9j6dtkV9fZl+vY4WT/GWt55ByRcoNyzShsiz55bpt7q8+OIVnv3ay3TbXW5t1HlgJsuxmSGkeJqDB0uI+Oxt7hH6Ar4bIAYeiUKBI3N5VE0moUtU2xaVjkmrWkMQJMptm739Pteu3KE5cGj1LIopg4W8im/2uLlV58SZB/nUC5d4+rOfItLbZ3W9wlf6JfzQR1I1nP1durFRrm13Wa520aWQIm32XnyBvu3RGthccZJ4Y/NMnZgj4XTQrRbV517AXrvL8z/3G8S6e7i9JlqpRO/IfRw/vsB77hvnmOEgeza7psBjh9N8+GiU0qnD9L/zwzye6dHc3+dmM0ROZRmKKpiCymDqELF0FqkwjlBrYBRHeOPxSX7zG3e5bzzB0mc+gy6K9ESD48MGZ46M4XguX7re5KVXbqNGdXznnmF/ujCCiouUKSC29hByw+yqORwjSUxwaYcSn1q2Kc+c5PSxOcaKWWYnitw3kSWSiPHdR2KUCimGxS4tKcFdO8bin3yazYu32C+OoZk1BEGgMvApi3FuNm3udDSazT7nVpu8vNHGDuHRA0OIZpeIJnHbT3MjTCLHY9w9eD8J2UVJ5ekuLtLb7SAt3I/bqlOaKtCvVNnbqyG39/EeejOd6BCN/CxrySl6oUpEkREDD1SD5J0XaF54GdsyqTeahL6Lau4j9Dvo1j77r95G1FVEMSB29CRiPIEajzHk9zBbbTqiRjtU8GyLvF3Hb+wTmZ6hVWmBFzCkBLS6XZp6lv/trRNENQXLDPn1T77ItZ0Br7Y0BqlRrqzU2Ov65KMSit3F8UMcq8ehjE4+pqCFLkY6hSaKdMUooSgguyYpGRzTwWrVEfU49W+8jKzHKR2YJRFVMVsD6mKWV79yhal3v5n1LzxP9vRBapttBrUmd15eo5iN07x8nbE3HaXjh6zum+gSKIDvBCQm0uy/eJ7+zi727i5SYxNXkHFS4xCJ4gQCgedS+eLThNVtgm4LQdVIfcf3E3o2ghZFcB0w4thGDs23Eerr9O7cQbMb7Fgi4v4WYa2MPjaBO3kc+fhD2LtlBALqrgxD4yQln0AQCVSDEIjh4gkyvqQgiCIZFWKKgC4JaGKIhAeNPbTmJobgY/TrhEDDchly66j4EIY0TY98VKfSs5lIqsiCwHBcQ/szHbBte+hRDVHRkWXpL8ge4B4Z/K+k93+29OF/fzDs/4iMO47D66+/zpNPPvkXrj/55JO88sorf+UzTp48SalU4oknnuC55577W/vs/6+4ePEiMzMz/NIv/RKNRoNarcYv/uIvMjMzw6VLl77leb8p8tvpdJAVhbShIJl9nGaPoG/z6lO3ubXeAuDYA+OYHsQ0iaIiIOn39FKBIGLZPmEooOgKbr3F4qUdrIGH3RogClBrWRTSGo3FKk7fobfbw2xaLC03WFlq4FoetYHLQ+NxSqoIkkzhcImUDGenkjzw0Bj9vQb161tsvrbBwA6ZfddJxt75GHgO/u46kVyM7Bsexq3X0EfHCGSVMPARk3lEIwHpcQQjgoBA6DogyXjrd6HfI/Q8PNunu7lP9OgppFSe9EwBLa5h3r5B+vAcvhXQvXGDoFUHILT74AzIPP44gefSfOUV6NcJuw0CPYLQb0JrD39vEyWZQIgkEFX93r1CSNjcRZBVfGuAZPcJmnXEXBFt9zZSOocQyzIIZRTBIeg0EeNpcG1kXUWMJogVU8iJJELgoAwNoc6fQB0qgSCimTXUWAwkFSWZQRmZglC996qousNqZo4dJYf84GOcevMxRkaHOF2MIMeipKYnkTp7CEGA/tDb8WtVREDQE/hhiGp1GUmpHFXavHxrhZ//u9/O0tdephEfJVYqoI9N0ghkmr7KrYbPStvFcWwqg5DL+hSD/BSv7JhEEkkq27soAoxGQ1AijBkByaDP+M4lmmOHuX8ixif+/S/wsQ99gDED7EyRTqVGNuxzev8KWbeFu1fj1arPhi3z0oVl6kqE9c1d/svFMpbvMz6cITc5ycTcFIl+i2v7Nr+9l+aX/uQ1nnn+Fh99ZJQnj+UYLhaptwe8dHuF8YVjtKptvn5plbWmSVITODKc4PBQFM8P2GoOmM/HOD0cx/YCRpI6mz2fcr3HePkWpeIItduX+aMvv8qHTyVY7Ku8O17jLQeHcGWDmidBCGFmmFBLoggiCRm8wEcRfO7u93jptZt88toeN5sBEVViPhmwu7fPj3z/Y7zznY9g+gLVfsDMRI5vf+QA0xM5RlIRHjo9iR/LcLAQo5SPACEvLVapdCzals9thsicOMz1jTZBtMB3HsuyvlXn+o0NThyZZWdvQHl7Dzk9RGl2hs996QIbG3sk0ga9dpcn7l9gf2OHft/EiEURwxAjpnK33GV0KMJ21UFQNWKFIkfOnuHowjCplEEkHiU9PsVLr97kuWcvsrq+y53FTZbOv0ZEclm+vc7LL18iNzzM2maddt+mNXA4d22H9Ng4t29ukZ+a4NjpGcZGE6iKiGYoxDMxrPBeuEetbdFoW+iazPFDYxw7e5y26bC11eTqeo1qx0FwfGazBiVNRhBgsrXMZKaEk5rkj556ht/+5FfoNtrsCDE0TcUXFMzb13ju6YtsXb1DXg1pOSC4FleWd9jxdBZvLDKR1JARsN2AzsQBDr/pDLeevUT1+m1Wjz/C85c3EKIZ1hwds9/l1eUWiXYF+h3KL19gXHWwBgK75y+y/o1X2V9dobV8h02tyBNzEW4s7dJaXOXWXo95zULxPU7lRL7OGPcfm8FTYywONGqOh/q29yCpMoQhS4lpLtze5cuffh7Rdzl+5gizuQjXt+5Zx93c97B98C2LYPMmogh39i1cSWW3ZfLyzTItF9755pN895uP8bY3HuVQTuf0SJR8PErbSOObfZa6IrYW50VniC8OnaUxNY88VKTWszk9Emc0bXBhs0fd9JgfyxKRfFp2QGtznfOVkECQiO2uoEkiU3GR4ZiMFjWItVrEOhWCu5dZu76NfPZR5OYWcrqAu7FEfKSIX6+iqQodJ0BQNdww5GBSYXkgc+O5C9z++B/Qi5foZcepdgKql68xXr6AEoS4F16kePmL1J57GS0dIz13z+Wmf/s6zvWXiK5cxm71cEURIfRZ3G5Taq2gJuIMxAi99BjhxBzr3QD/zmWGzU0Mr834qdPkJIfIgQNkcgmyExPMpSSmxjO0anXeGt1jJuKSMysUJZcvrvTQxJCoLOKjIIkyFSnJlT2TDW2EHTGD7HRQi2PIy1cwDB19fJRwcgbr7NvRxqbRcjn86g5nTpfIP3CQ+uIK9u426eEMS1fKbG+1sFottl68SroY593fdpxiKUFMFhGBSt+jen2HfrmJkY3j7pXRW2UifhfFNbEzE0S3r+PvV+jd3UAgBFFEnjlMOzuPF8vjq1GwOwRaFFNQEAUIZYNbLy9ScxUEz0LMDuF16gipDEIiR+zwaZREjKRoo4shomuhNTeQPAdknUDSEHwHLwjxwxBF8EGUUGURQxGJCC6K2SRMDhF2GqjdCr6k4QUCWUPBEwQS8j29bzaiUOtZJDSFlCrTsj12ug6luE7P9YkaCroqomgiqiz9hc4vQDQaJR6PoyjKt3ToLRSEP7c7+9brntxjbGyMZDL55/XzP//z/91n1mo1fN+nUCj8heuFQoFKpfLfvadUKvFbv/VbfPrTn+Yzn/kMCwsLPPHEE7zwwgt/7TX/j/DRj36U97znPayvr/OZz3yGz372s6ytrfGud72LH//xH/+W5/2mO7+RSJS04GC1Bzh9i0FjwF7b4spWj1MjUfSIii5JpEpJKm6IL4vEVZGFmTQHJhKMv2EWp93BrPe4dK3K1565y/a1Mn4Id5abnHpkFimqYmTj2E2btZUm5b7H3eoA1/Y4NJtmbiHL7HAUSZXIHhzjzQ+NceKhcfILw1SvbnLuxQ0GXZejD46Rf/wRtOnDuK06km4w/razaEaINjGLNjwBQYCcHwNFJ+w2Eawmcn0TknnC6iZIMlajReh5hJ6L39wnMjKEv7uBt7eFMTFG4tR9RCbGkVSR9vVVkpMlzBuvEapR6LUIFQPCADmZID45QgjgOXhqAtpVwsDHa+wjqBphECCoKhCAY4Hng+9BbZcg8HGqe2B2kbv7aNs3KQx2GQwGaPkCei6FGHgIsSyIMqEew/d8YoePIUXTKMVJZDGA7AjioIkUiRL6Lu5+5Z7Be7tOb2Md5ezjBM88TVwOCVZWWT13GzuWZurIKMb+FntyAXHQoL+5ReQNbyPQDGRDxdNiBHoMggC31WDi7nn++JNfojSxwO3IFCNnjrP61eeoOAru8k0Ua4DQqRKJRAitAds1kzsb++zvVknZVY5GBuw2OxjRCK7rE8g6DgJ9NUnFM9j3DCRAw6NabzJSKrG1vM6HHj3IV8wYmxtb9CeOIrkOV5fLHEvCmNjjfR96nIfnCyykDf7hu0+yeGeZ9N46gaBwcn6EzVcvMFNKkSrmuVyFC/IoL+9YTMgeemudfFRmY3ef0Je5eWedjb0uX3v5JgVrn6m0QdUMiKkyoR/StDyeXqqxutvGHHhstGw22g6vk+XQYJeF6TEa3T7n2xpRs8knFjtcu7lOfWObl5YbtD2JppajGaqIoogoCphuwK19i1LS4O2nJknkCriuy+vbXWw00pkk7ziY56EDw6QjOqfvmyOf0AkkkXcdKeDmx7kvGTCaT5KLCwwGFpMZnQOlJOs7+3iOR+i6LLzzUYYHVaTQ4Te+dhfPd9iqDXjq+auIgcugvsvx8RjddpdULsXq65cIgcEABE3B3LzNq089Q6fV4cXLO8wOxcglVa5fWWd9s8aFrS6x3BBmYOBJMvmkwfThBcZLKU4cGWc8H+P3/+ALbNy8wR8/fYVy3eK+Bw6TH5ukP3BJD+W4cu4in/38s9xc2iEZUTDiEYQwxBYkohEFzw/xXQfP9hgMHBIxDdcDXZcxLZ+tfRPNUOk0BiQjKgcPzhHXJJJxlVi/SlNQ8dLDiJ0uBw49QPfOq7QadbpWl53iDBkjQEJAEgP8ow/yihdn+NAsRFMUDFh1YwyOnEXwbVpffY7Oyy8yt/U6o+1VfK9L0/TIvfUtvHa7yuG8yGvhEBGzQSEiYr7+Ktc3avzhisOFLz/PlpJmMOgy0l1lfd/k9dJBXnzpDmPTkzw8JLDRV/AVFTcWJaoriMVJwsCjevs6h8YSXCr36DoB//y772e32iGtQd7v8/z5O7QCjdt397lW7XG1LmA7Hp4XcvP2KmEI/+kr11jb72EaaaSROQTF4IFoD9uHP3xpnZdv7XF0yEBQFIYSOjktQLO7bPZDLm80kUSZjYZLOp1k1xKYyxlYmSKCquB4MFASWH5As2dzbCTJly+t85mXbnJ3r8uhkSTX9xw22g5WZQO743G32qVihUjxBJHOHodmEoT9NoIikclGkX0LqutYm2ts6+N4yRLHVYsdNY8oCGQYkBUsXr+0xH/4zGvslMt0Zg7xqVfuEnoQT6f4/O0W16Rxbtxewjh0GC20CGwTNa4SuA575+7QvHAZb69CsLOBFDrsbu6yVu5ghyJSYQyhvk2AyIoyxKgBPTdEmjvCRhBDVnT6bsBE1uDCRoNy26dswWFni7vlLr/+1eus7bt0XVAJWHA3SUcMygMH0wsIJBnL8+jtV+iaDp4kExdsqmEUdW+Dfm6WhungvOFdpGSfpKEwMHL0Rg8QXblGbnYEVXQ58ZH3M9je49T772fQc3FEiTt36hRPTlG5s8GJf/QhvvtnvpsTJ8cIgZGIzPrKvWZU426Z2lKZoNvCFXUcQsxQhIlDCOkcei6B2+8TFOcQ1Chpv0cgyth6HC+agdAnIkIgCPQXb6MfOUZgWWTunsNNjXI9dYRqfBJFFpH3lsAxiba2MUILggCUCMruLQTXQhAFBElGDO/p1IUwxPBNBN8BBLRBExAJqlt4lQ1kUUBQNCKyQFZ2aSsZRM+kYflEVZmoKpGPqjhhSFZXCMIQwgDT8bBsHxCQtDiarv0l8isIAoVC4Vs/9Pa3KHvY2tqi3W7/eX3sYx/7qx/9v4tFDsPwL137r1hYWOAHf/AHOXXqFGfPnuXXfu3XeOc738kv/MIv/PXX/D/AxYsX+af/9J8iy/8tlkKWZf7JP/knXLx48Vue95smv9FohHQ8QmR6GgSBze0+TeeeoHtuLEm92qez0aCoK8wMGazcruP78MA7DnH4wTES08OoySRKzOANb5hgfCLLfn2ALkLCkEkOp5EkAUWX8IKA1a5HQZMQAFWXKR0pQiAwf/8Yh992kPSp40w9MEWklMIYShL4IWpUJVWMMXTfHOrkYULfQx2dxtnbRZ2cBQR8x6W/ehcxliSwB9CtIwgioaRDZhRJlkHTkAvjKJEoyDJ+s4LbamC1LQbb29h7u0ipIcIAfDWCEk9QemgBKRqne3cFr7lH6Lq4N17BWryF0/wzHfPuOhgxxP110A1C3yVERCmOEdR3CSMpQs+FIMCvbuJXdwlDoN9CNjQkSQDPRYyn0PZWMAMB9CiCAKEoEfounhSjYeRRNQnBdwkJCO0eAH6iSCBKoOjgeQiHH8SuNwkDH72QQ5MFSm9/EsXssPDoCbJzkxTGRzEDaN/dpC9KbH39eTZevo7rA9a9SNBqfIo9T2O/0+diR2a/NMtLX/wc3/vYw6y3bH7jts1nWzEu3timQgp5f4uIbzGqOhxJ+oylI3z/2THedaJEX82g6BEidp9CRGBlc4+GL9PptLEaNVi5jnv3FpdXKnzggMHb3niGX/7d3+MPd2VkQ+P+B49SNiU2rl5CtnpY6QI5t4arGCRPPcyp4zM8/p5HyYt91ts+e/kp2vUWkqbyO1ebXHjtKiVD4uTxKYpZjaF8guvyCA0lxadfu00kW6RYSGDrcQ4cGqdUzPPCrkDPDQiCkLl8lHhEpWu7NHoOh3MRGu0Bju8zljY4cmCYMBXHmDqJW77GYHOV7brJw9NZ7ly4zu5GmUxtk5odIooCPTcEAfquTxBC2pB5/vwyh0px0oU8566u8ZmnzmM2azwxLPJ6SyIXVRgdKzExMcx6vY8kCBRiKvGIxtWBzqt3drl5/TY3z11nKqmBP+BU4yabzT5/fKnOv/vida4PFBZG0yhWl9D1OLgwhj2wuH7uAr5lUW8M2NmosHzpIpHcML2Wiej22Kp20IfH6DU7bFy7ztq167xyZZPtzRpuKGO22wxaAzRdRZAlqk2LiWIcTZMZzxn4rocpakiySEQEQdZYu73M4naDVC5PbWudQjpKZmySgQOB6zAVdRjLRjh1sIAXhKiywFQpSWmqiBG79w8qE1NQFIF+z6HVtbnwwkVqdZPa1h6qaHFiJs/8UJRbFy/zVFkgIXgISpTMmTN4B4/z7r/3U3z+Dz7O3//g95DwB8QiBiI+zUFAQ4pw+sw8MzMjZNsbbLU9KoOQA9MFRCTUiEY0lySVz9E9cpqhiVGGFRdne4vq9BF25x9kJKVjmTZ7r5xjEotUfojr5R6/cKXJU2aKnYHCC3f7fLGusR4ZoT08ySvdJBFZpFAYojrwKRtjZGWfwe1rfPZ6jXI3ZK8f8PrtHfKiibi3gVlr8e9frvCVlR7vX9AIDR1XUciNlrDquxweitK9c4shxcL3fYa1Jr/0mYvcqjpc+KM/pfnKOdKZBCW/wd974yg/8d7THB9JciYxYKZ+Ay30UVpVFGdAylAIRZnJkQyqEPKVi+t8bbFGRBOZSWjcqQ5YbAa0TJ+t2gDT9dm5doudrX0mcgmG4hrfdv80h6UmfiDw+AGd9+YtOqbHw0mTd73rUd5yZhovM44UjWBoAVpvjzAzjGrWyS3M0bmzxERaZMjeZyQu0/cElP1N+tUKb7l/mgfPTGJmc5zMyuwKSc6FSYy5OfpKkuTOEoIWwT/yMEJxAjWXxzh0gtThSUrvew/awjEkTSaUVOqmyyvnrpFWXXZJciN5iLXENMlEjFDVGIoq1MIIbX2ImK4Sdbvc6UKukOfHv/00huCiRKJcOn+DA8cO4qQL1PQSTmKI3ZVdRlI6h3SbhOihCtBdW2dssINt2ciCQKXaxEXGw6X11JcQZYkuKoFsEMEhbe6T3F9EzicoPPIQ7es3kFJJco88SnxymNn5HNGoyukf/SByscTTX1nCSxdR3vLtnP7hb+PYkWHWeh4b1QH1rQ47t/a59Owd7EoZSRBoE0MMBHw9jjd2kszRWXa//DXEwMPXEijVJYJQJBQUBkoSX0+AKOHJUaydXWbe+kbCP/lNwkSGRmggBQ5ddOTAhUSOMFVEw0f0PVBUvGgGYdDFC+55bNOtkmhtowohVihhmFUivQoyAUIYIngWeCbq1GHCMEANTHRZwPZBEwNs0SBlyEQViKoifhBgeoBwT8u7VOvTs30milFatS52p4kuwcB2/xJfGhoaIhKJ/E9PeUskEn+hNE37747L5XJIkvSXurzVavUvdYP/Kjz44IPcvXv3b/SZ/3tIJBL/3YN3W1tbxOPxb3neb1r2YOgG6eIIar6E2bRJxu8Jm0ciMk7X4RvntiidGCY/HOORx6aIBnBoPIEaN8jMjaAP5QkRiBRTTJwe58wTh4mKEu0Anvyeh+ls1xAVGbtr0uw4ZBQJJ4QDKR0jH0EQRSRDJDmaIn94AiWRJv+Gs+ROH0dSFUYfXuBN7zvFzHsfpPD44wS9OoLVQXAHdBeXCJUInmnSPHeeQaOPt7+DvbYEggCJLGEkQajq4DkImoGoKMiJOCEibmeAbXpEJidh/jhaaZju0jJrn32W/pXzuOYAqTSG395HUhTCQYfe1fNIikp0bpburduYlTp2o4a7s47UrSIAoeehH7rv3hes1wDXIiivInQaiMkMoRCCayMOWviDPlazTRj4IAgkZg4RIjDod+mvb4LvEfTaeL0uu1tVhGwBr9shiOXwG3sIgCgICN06IQJiLIG/egs5YiAUJojlUlz/yutsnTvP0O4NcmMZMg/cT8sR2HQ0Xt/c4B//i3/BB3/vc/yuCfutHuX4OO3sNF1k+mocE5W5UpJb5xdpx6McLKYZCU0aF1+hF00QGy4i5Me4RYqk1SLW2kQW7lmSDZQ4eaHP3t0VTMtiUy3wc09v0PEU7nQFavt17pIhms2gxKOcjDmIgsTH/uHf59EHT/C7v/B/55d+97NYe1usfO6r/MnLG/zsV5bIiB6DaoXe5g6peIRjIwlyvV0W7ThP6g2ub7cYy0RJSwHv/8CTnJgpcOfqNeaSAiO9TSRZwdYifHXVZGPpFqXZw7RNj7m5MVIRlZ7pUnUEdCEkrot0LZd8VOFEKcGbpmNsD2xOTaWYSuqsbDU4npE4oMHdfpLNlVv0UQk9jyu3togVcnz6ehkHmb4XYPshCU0iLTkkNJG27TEUUznubPGrz65wpqTy6P3z9CJZLuwFXK0HeKLE81tdzh6b5A0TKZ48MMSdSpetrktt4HBtz2R7r8P99gbpqMyv/ZdnqK2V+WQjw9xQgkMFjet6nq+cu8PZ++bIT41x+PA063fX+Mn3HuX06SMgwMVz5+nXKpw5Os1wMkAMfULfYfnS65w8NsfxI7M4dsBITOD2jWW6lkehmMSI66hRjWxCwfNCJDEkH1XwBYGNnsfGdo315Q3ecuYg//DsJJF0gcMPHGW3OeCRB2c4cHAGTZWYGkkh6zoCDndXy3iOy/JimXxCoVy3QIS4pnBoKsNYPoauKPQHLq1GD890sAY2tUqNWAzmFsYpxBVubnV57do6xXyKZHcDAQ9B1bl/NsfCSApXjvHVW3X6jQbHRuKobg+js4MoCGSjKlktZNeV2Or7mP69ddW+/lXUNz6MPzWPOH+MRCqPmxziwldf5dpKlaYa4+WNLi8sNfhKK44tyjyVO42lqDxwYpbjb3mcvBqQkj389BCnT83z988OMTNTYK/WYX0gc+tumYPJgNJQDOnu61zdMYlLkJlbIFQjvPjadbbbDlaySC8/wtG8xFYfXi4HrO/16e6s4cgaDx+f4L/8wR/xU//rT1OplDFtm6fO3WZ8dozRjMHys68SmG3CwjS626eY0ZnMaKR1mUxMR8gM0XKgpsTpt5vM5A1u7FvEJB/Xtjkzm+Pth3LUWjbXK10OF2PYvQFL5Q4r5RZXX7tGPKpytw0XdvrMJgTePKYyMVFEK43yUlMjsXaJogEHpAZCJIYSTSBYPbrEuPiNWzRsjUHbQVIEYpkE1b5LbHaauBAgXnuB6w0fM5KmeOwUnmSQnT3M42mH43Mj7CsJrq+1yGQSRHUJTj9GgEjH9EgcP0byze9Gnz1M/p3fhrpwErk0hT49T7SUZVdKkYorFLduYHs+DSHGb71Uxh902HF1JDEkokpMRgREUaIYkSnmUsQ0lfFsBLnTpOKqvOdEgaDTZmS8RL/XpkIcJ5oib8gY/oCBL2D60MpM4IQq901kkOw+T5fBDUK6xSOI3/EhIorCsOIR+D6+5xLaA4R+H8Pp4CycwY0VGdy4ghCPIcwex4wmiMVVSkenaVWaDM0UCdt1IskMyTe9jXf97A9TmBxiyw6RFZHLa00WVxqsPH0BubFFTAqwAh9JEFEHVeR4BFlTEfv77IQR3MwYdhjiCveaWwMieAGIoY8xPkLrqa/QW91CiMXpBQqH9QGj/j4BIkI8ey9NTo+jtXbAdxE8ByE/QSjryM1tME0kPYISevfYqmWCICJZbcBDcCyE6ZOg6nhGCt/1kcWQPUtg3wzZMRVkkXs+5U5IuWOz0hjg+gGm57PVNpkfiuH5IbWtLUIgossMrL9Ifu9uVMjlchiG8S12foW/nfprQFVVTp8+zde+9rW/cP1rX/saDz300Dc9z+XLlymVSn+tZ38z+K7v+i4+/OEP88lPfpKtrS22t7f5xCc+wUc+8hG+53u+51ue95vu/OqGgZYtEJTmiI8VCCWJsZjCdExBTyhMKiKN9RqFgyUSxRSTJ0vEogqCLKLlM8iZIczdPURFInd0HiWikM5HEICVKys4HZfcoTEEUSKbMzgwFacYkZk4mENWJRITOQRRoF/tICoy1upttNIoghggRSIkFyYonjlEan4CKZVBIMBLlBCCAPHhN+O6Lt7AIXnsKJomIEdT+N0WoR4h1AzC+g5iv0GoGn/eRZWTGcIwZOW5iwhBQGxuFqnXQZRErnzyeQRcjHyOoNulfeECoiwh6wr4HmoihjQ0TOg5yBEdQQjob+4QWj1CxyR0bURFRRQFwoGJ1+kStKqEvTZoEaShcUQjjpDMgmXi9U3cRgNkldC2aJsCWugxQEXUdUJRxM1OIoYOcrfKQIohaAY+An5tF9+1wRkgFKbA8xHSwyjZPKKhI8oqqBpmZYPxg6O4a4u4UpS+43HhtWv82I/8Iz610eA//sff5MM//1tIhVl+6Gd+lj/8+G+QEiziqsT6QCSqa4QCRGQLS5CpFOeZ1wb8yHzI33n8MMVsDNnukzAUbt/eYkUb5vZOk8HA5GozpCyliXoeOcnDRCYRi5DMJihqAhO1RabUAW0xzubQUXaVDNt1C2NkjJEjZ/nFX/1Ndu5c5fd+5Z/TyGiMzEyxsufwny7v8Y1dkai5hyqJhG7IsxdXMQKX7m6V2aRAc2ONzfaAD844fOCRAzx25iC7Gxu85x1nmSsl2Ovb7IcG1n6F0Ehxdr7AREYnH5FJxnXetJCna3u4bojtw9RQjIQh43s+p4sRVvctvv6VF8lHRXYWF3m+JvDPvucBpseH6dd3OTga49BojNPHpkg0N1nq+miiSBAKCGGIhk9MlRmKKpQ7DukTp/jQG6fZdSWGEgZ/57EFKr2ADVvi8nqbzWqPiCahqDIZXSaiiVR7FrfLHV66skl9e4fhQ/P85IeeYCyXRsPhTk8CIeS9ox7Thw/w3red5fJKne39Ftdvb5NWA3ZdhWHDhvDeIQ81nqLjwFBcx2nvYzb2GC8VWLq7yd8rhkznZfYdCS0aRdF0nFAgO1xg7eYKO5s1OrUG9WqXatfFsj0QRPxkiTcN6ejxOGfPHuLggRL3zQ1zejJFeb/Hu97yAK2Bw8ZamVShwPThA5Q7Adcu36LTM+k3ukznI+zsD4gaCoosUOvZVBp9evUOe3fvomgKpblp7L7JDz95GMsPeX2zQymj82/+/tsppuPIWgw7VLC9kLdG93nUuoH75U/y3uM53jYmM+VWsPfL2Mhc22kzJtr4pov3+nkmBJPr220E16IxeRQhniQTUamYAWIsTv9Tn6K52+Dxj/4QT7zxOG8/PcY7HphHicaoTh7lT1+4w7m7NSpdm5btMzxWxBuZxZo/ytsfmEGKp9nsyHzyhVv81pcu8a9/5yuUN2p84rmbfPXFRb7uFfFkjasND0kWmD04z298/iKrYoaYBGFqiFxosr6xg19donv1C7z++z/LM7/981itbWbe8yMkc6OYAxN1/DTZYgHL9XnL/+OjDEqT+I09nF6PLhGCIMQMREyjCEaSVLdMY2mZlhBhs2XypVfXyZpNfvMbmyzeusulxV1cx6dfrzMaV3l8JsoHDifIuR0ezblcvbPBDzw6jaxHiEkB+8QYK+WYKyVZbYMzd4qDQwn6gYJ84zXqkTyKEUVMZLmVP8xTtxo88+nnqAwdQLx9jmMTUULTxK3sUpOSLMShubpCVmiT0gW6kTzFtEovkuFwZ52feUBjdFClvbZKJirRfvkZSuE+mhgiZoYRZBUxNYQdL+JFswipEub2Bo3P/inf9eYzRKfmKUVkshGZ0WyMC0+/SD4ioYkCmiySlD1c1+GKl6XjBgxcn+v7Fme0JtL2CqMpgQ++aRyhusF8UqTZNYkdPMFEHNpClC/f3GfP9GlpcVZS8/S9kIqjEE9GCYKQqATS6CwxXcEXZYROlfDuVXx7gD9o3Wt8pIaInD5Fv9xCLowTdDqcfP9DnHrsAJIs0m+ahH0L2bcR7T6mkSR+8CDv/aE3IwNuAImISssJufTVW/jXXibi9Uh4LRTfJNBSCJJC5uA4oaKTlly6sRJuIGJ4baQgwApCvBAE3yU6u4ChhYiihNJtMk4TSTHQevsE7RqhGkEZnUHwTejWwfcQhBBUDcUbQOAhGAZC4OETYggOgm2CpCD36xCE+NEE6DFAoB/IXOgqSILAIICOB2YQ4gcCdiAQ0UQ0RUIVBZYbfTQJxtMGHcslBPREhtBziWgyA9v7C1zp6uImiqL8f4Xs4a+Dn/iJn+DjH/84v/M7v8Pt27f56Ec/yubmJj/8wz8MwMc+9jE++MEP/vn4X/7lX+Zzn/scd+/e5ebNm3zsYx/j05/+ND/6oz/611/z/wC/8Au/wPve9z4++MEPMjk5ycTEBD/wAz/Ad3zHd/Cv/tW/+pbn/aY7v+l0ClPUEfUYo+95jMD0MWSBeEyjvNVheCSG2bLQ0nFEWUSNK0y95TBqVEeJ3Iu7RJZQM1k6q9uErs1ewyQETj+2wNDJSWJTwygxjaH5PKXDBVJxlUgxhiAIWPUuds+hsdyks7KN2+sT2CZ+t4PZMhGNGEo2h5xM46tRwngOMzWOkMqzQ4ybN8s0UpOoM/MEvocgg49MaFuE20uwfgPsAaHr4DeqhAH0tzZRhkrogoOaHUIWfbyN2zTDBFMPHaD4xKOoxWGi+TTCoEPzzjJKIo61W0HMlu7JF2p7qMkocixO6IWI0QTO9gYAgqyCqt8TufsegjUARSXQ7v0qForjuI0aoaajj0+gxKI4tTplMcudT3wRQ1Xo9C3kqE6/ZeGJGhQnGUoa99wmtBh9y8EdO05DK+APOnitOubKDYJuHTGdx+928X0XQZKZy0mY6QmUmYOsiRmUwOHf/cyP8ZbH30ziyX/A02UBs2fy2Nw4n//tX0ExYvz4T/4Utu2y13eRWxVyikep10SOpPjis6/wak9HPPMYelSj1e5yuQ/hoMduT+H5l15HCCW8bhdVDBjSoXDiCCI+E2L//8nefwZZll/XnejveHO9zZveVGVWlsny3dVV7b0FGo4AQYpeEkWQek+GovgkPXGkIUMiNRJFSkFKFAUSAEkQDYCEIRpt0GjfXd3V5X2lqfR583p/jz/vQzI4oeFMDARq4kUwtCL+H/Jkxr730//sXHvttZgZSzORVHA8H2ffKcpykoYeZ2BilPrWBmtmgYWNGrXA4JlBj9/54Yf4lR/5NFmvxNc+/2+ZP/0cNz54k8++vcgFL8kbb1/ga196GX+ryAfvX+W8PoRtu8xkFGaGM5hWjZxsc8euPD911zDNeoeZGAjdHoooE9htJgYLfOX8BpbtE7gOUUViq2lT7LjcrHQwZDBlAccN6KNxueLy1u06tUiWhUqf/tY2zvY21UaP++6+h/mXv0jDV/h6I8NbZ+Z5fEjl2TsnUCUBkRA3FHAEBcfbcWPouQH5QpbBlEnS0MhFZCazUQgDmn2PUr3LVErhxGSWm+UeSy2b1brN8xc3efP8FsvzmziOz4u9LH/21iI/v9tivdxiZijOhYVtNLfDeBQs1aBdqfDe6ZsE/R4J0WJ5q85zX3/9L+6FsT3TtByBh+YK3HXXLIf3j5MZGiCXy1Pdf5Rfe3yQH7hzCE3VkBSREIFMJkIoGJipJG6/zfRsgcDf0c/uzkfoWT7izAF+0Nzm9270+PShDHuyBk+Ur+K3G0x5RZy+jSJDXukzmxLZPzfFhx8+zK6swdrKNoWkSdQU6fQdOj2Pbs+jVO0STUYwk1mQRApDGZK5HKNRgbW1GquNPs+/v0hb0Ei3V3E8D726iFFfo/jBFVbFIZTd0xTEHrWvf51rv/VZ2tU6fTdEO/su24trRAWL1//N7zMSVPnETAz53FscnB3C7FZJiC6B3edGU0R9/Clm/87fIhwcZbXaZ7tlMTGYRBRELq82yeRz7B9JIIsiBWsn1l1dm+diLUAURd7b6KHIsF3vc/2DszRElfjEMD+4R2UxjCAn07xwfpOvvHCOb59epNhyEWWZhXKPLdHk33/hOyyV+rTlOK999tf48Cd/kI/+49/g2Y//FJHdd7L+9p8iWzUEWWV6bj/NjsN6x2N98AAvXilz/oPrtLc38JGwPY+m7eEgQKuM53nUImnqvoTh9Pixe8YIMwX2TWbY01tEiUbwu222WzYJp0kqlWKov80nI1uMHTvOz/+tD3PXZJxswmCp7qIKLkprG3t7k9nqDYjv7DeQGkTNplBVjZuuTsxr8tDHHsXs91h99NN8ZUWkZ+bJDg/Qff8NvFvXKTd7lF9+mS9ea9N2JUpr63zxm2+wUOlT/sqX6b3/JjVM6ijkKuv0bI+NhW36ayWEaISiA9gdvPIGUquKHYR4roU1upeTYyIzaRE3lkKWZRq2z+31Esf3jZJ06+hySNRr48omxsZVBhMq0zGBtu3Td310QSHVLaIGFntLV1n+jd/hytdf4bu36lQ9Ed/zkATYPxyn0nMZiyls2AIbHYdcROPuYYMJsYEoCcR0magUoArgpkfAc3DPvkl/ZZ3t5RoEDnI0ifL0R+npaQI3wClVmT+3SrB0DTOuU9pq0UlNYtTXaIsmThCiPPZRTEVEU0SO7E7jA2NjcRQ5AEUjFjoIoYer6uh7jxD0unhGmpjfIxQk4n4XK9RxBRExDFAlaCsxBM3AzMQQRZAyQ2j1DbwQwkgCsXiLMPAI8bCMAciOEmgRQj2yYwNaWSFMDuKXNggCHwsFJfQIUwWETgUkhVBSCRwP+h1CRSfqt9AUFVkIEYC8KaMIEBLi+QGGJLErbbAnZ9JxAoZiBllTY7nWw/VDhicGUCNRTF2h+39gftOJHSvTdDr9/3fZw38PPvWpT/Hv//2/51/+y3/J4cOHeeONN3j++ecZHx8HYGtr67+RHjiOw8///M9z8OBB7r33Xt566y2+9a1v8bGPfex/+HdTVZXf+I3foF6vc+HCBc6fP0+tVuPXf/3X/y+lHN8LvmfmN55KQyjgrVxF1lQGZwawESi2bTRdQU8bTNw1jpaM0a+0qW+0ETUZq9rC6fZofHAGp9qieuEm1laJwPFwvICpmEIkKhKbGsHv9JBUhaGT0yQmsqSmk/g9l9SuHNUbRVavVxGlELdv0d2sEsoa6AZWqYIUiYHdI9BitN5/m7C6TdBrQWaQ8dUzlF5/Gz+V5/bnvoy5awYxmkU2dMJOG3vhKnajTmj1cMsb+PUSgefjdnrUL1wlf2gGPaHjV0uEnsfml79F7tAezJSOEEIoALJAd2Wb0HGQVZn+zauE/S6B5yNpKmomhZqMENh9eltFBNVAMGIEjoPbqCFnsqBoiLpJsHGL3sUPEBAhcJFiaeT8KKIoIAoeoSJz8GP3ERM9ep5A5fJt/NFpat95mSCSRJi/gH/xXXxVpXvjKu7aEurGdWzXo3f5fXwtiitHEOw+4sgu3MwkYTxL/ukPceOL36S/6w5e+uxv8qlP/gA3imUeffwhDo+m2JMzeWAo5JsrPt1Q5cd+7Mc4fu+D/NzP/C02imW+dWGeP/rjr/Dm/Hmyksdrf/jbvLDq8tVtg9CHr7x5ndmcxnU3xqXcPv7DF99iNBfFuPI2UuARiBoIIQtBArG0ydX3LzC/so3mWzQ7Pb748gW+dmaFr7xyll/87e+wT++Taa6xeXuZd//LV7nWkmmmp5BH72Du8Z/gkfufAd/myp/9Z37lP32Z5776Cu9VbQbyccaDDmM3zjCYijKRVkiKHgxM4va7aMtnSBoKVqPOv/z6VUbTBhOmx/riTeb2z+A4DqrT5eXz68yOxMkbIQ9NxknoMhoClreTENRxPHJRlX0jce4+OIWpKbzsDjCXhy+/t05z6DgHRgf5Gz/2E3iOxdXtFqknP0YinSRnKkgipHQBP4CqHSAKIaosEhcDFtaqGBIs1Sw6rs+9ewdodxw+eWKUR+bG0WSJi+sN1up9qi2L7aZNo94lVcgztm+W/OggcsLgph3jxJFpcimD0naTl+sxTF3l8sUF1ESKAIn1lQ1eevcGX3nuRQLZ/ItrY3NhEdt1ee7cNpLXJ6srpFMGZjJCU1T58tUm5ZV1gsDDtRyGByIsXLoF+CxduIogS4xEBTRF4NOnRnl4Nsv0eBI5k+HPhAm8lUU2iLKyXmYlP8FsTOSbb1zj5HQKKZ5habXIt7/9JrrvY0kqA4UMD5+c4epGg3bTxnND4lGVgbhGNmsSeAL5ySH2jcbJp01c3+d3zjTQhBC70yOm69ieh2TbrPkRrLZDJz5KcewQSiyOmEqyud2lEh+iVJjg+u98nsbqBrsmMzy116Tq+AR7D1MLVba++acIE7Nc33bIJnS6PZvOxXMMbF9Dymd5cbHB1VKfr71xld/6o7e4eH2Nzc0y0W6FJw/lmcga3Ddm8CMHYtivfJs3GhLzF67zxkqTgzmVx45Ncd9Mik/cu49f+IE7ef3SBtn5D8gX8uSFDsMJiZUrl3FrVeYvXOGd+RKteh0zEufI7gJPyEXqpRIjoyOUylv87r/6J/yr3/w39C5/lx9//BTpXB4znuEHT43xyGyOq/Pr+P0uEydP4B64k7fVWXpeSD+Q6Lg+iijS2GzRvnSJOa3LRDbK8FCWe/YOciAZcnIyzSuVOBcuzXPXdIEfvGc3a6+9gXn1PVq2iz4zw3wbPnwwixmNUlzbQlA1TL9HRPRJb81z58k5IooCnQqq18XJT+AholhtbD9ganaYAavKzPQIWm6AlcIctqSzMnAIY2wQ+7sv8/Z//H2eOFBADS2+erVLpeXwRzcs/IlddPYcY1zuMjw1wR82kywHMYZ+6EcI6iXat4v8x6+fIfQ8bvzO5/HX51Gqa3SNHI6ZJHL0OIgqheYqZu02IiFDpsdAv4rSKhLrlVBvvkPg2nhrS6RDm0FTZDSucnLYRHdLmNkUvZUVnv83nyd753HavS7HzBZx0UXtVoh7LY5Ee4xFRIpdD1mSMCSZeHmB0ZV3kObPIvQ76GKIGnqIThcTDwkXuTCGeef9CJqE1m1geG1Kl2/gICDGI1gH7sLQRbzKFpeyB9gotVgvlrDTo0RCG0uKIHVqHD46TL/j4HshMRFGZgeQpg/TFwxCRQc/oBWqYMQQVQnBbiP4AZJvozkNRFlCEyFhVZB8C0EQ8RMDbF9Ywu70CW5fwoukCZ0edmSQsLAbAh9LihKYSdxEHnwfwQ8ImmUwk4idGl5lC0GQkO0uQRAiuBZhu7Ezaa1vYb/3Ep6k4UgGgh5jNqvhBCIiAoYiEdVkim0bN4AQSKkiluOzO20SUUTimkjH8kkYMgNZnX6zjqkp9Gx3h8T6c7jeDhOcy+W+L+b3r+70sHO+H3zmM59heXkZ27Y5e/Ys991331/87vd///d57bXX/uLnX/iFX2BhYYF+v0+tVuPNN9/kqaee+r4+93uFaZrMzc0xMTHBSy+9xPXr1/9K9eT/+z/ZaX6j0ShacR7f9WheusLuJ48gyz4b5S75lImRMiAMqVxaorpYZuVWA028Tnw4hrhSwhzY0dm4zQ7R0Sxe12Z0KEYhrWMO5BB1HSniER3KoiYiuH0bI27i2T6xyUH6tS6pco/YUBwtFqFX7yOqOl67QyQbx62VkaMx7PVlpEMnCXGIlG/RuHoNZWyGez49wtLX/4xMJoIUeoSF3Uhrt8AwCLY6eI4LC9fYeO0Mub2j9FvvExkbw/dBTKUJqkWaN+YxBnMM780giyLOxm3wXILAI7BdEvsm8PsWajqxM3mQJFzLxuv0aS8XiYzn8JpNJEXFqxWRjAhBpYhbqyAqeYJeC0GWEPQIilbC73cQI3GCTgMxlgZZQtI0hrw6qD6mG1CUdfRKE+mlP8OYHMF58wXEmSMIty8hhAHu1CH8b/wBhd278TfnCcZmEFUV10gipQcIfQ+pVwO7S/HaTYaeepDWW69RsVw++fFPEpk5ztWOzKP7M3ieS67r8ol8iOL1ETWFRz/8USKJFH/0n/4Fs3tnObZvhmujh6m4RR786DPM7B0ipglkNJGPjMB6EOP+TB+vr3Liwbu5cn2N7q7jqO0OHwgShxvXebE9hKlnqHVvIYkCzdurlKtVuqt1ooMO8aBHZnSMPgJDU9MMNda5RIY7x4YZTOlsLQv4gsTTgzJzH/pZNOfH+ZVf/mWWy1X+5mO/RGn/FNc3GxyVQjIvPcf20AC1kxnMRAGzu0i2WCI9O8iJQY2qHuc/vXydH310lmbPoeXJPHmgQMfxmBjNsjdrktRCNFnYSSIExlSN9UafgZhBrdPn+devsG//Lh7fk+esKrO1ZfPo/fs4c61IZOQUD6pJ1i+/x/Gjx4imUiiyhChANmhieSn6yMj4JPBwNIO6bVFEZ7ep0glCbD9EUEQemhvANFUUScTxQx6azrDS6KNIAoaukEhomBGDBw4O0LA8xkeSvL4kcdyMcn6xxt89Hme73Uf3Fb7+8jK+sIwcG0BQDIz0MP3aFqptgWZiN7cRVYPhqXGSuTTbjsfKjSKh6oEs07IgGZ/iWr1ErVxmaO8sxY0aK+fOkBjdg2d16VU3+dofrHPvRz/EHVmF4kqVsXyEctPiynqPRwoZ9qhtxGSef/17l/DjBR6cnWCv4fKd9XlGhgeRFIHiZpGW5VDth+RTKl1LAFVhOJPD832mClEurjYZKERpdGw8QWRhpcLWtctMTTzIgcEkD8xm2a52KfZcbsyvMJSss97xab9/jcGHH8S/eBpFTvOtVpr86AyiH6JNzJDJ5dFzBeSwjy9H8J5+lotll/z0LEulMt89W+F9ReCOQyYHfZtUZgizvsSDs3t49/o6T6Qtfu1Clc9+7X0+//OP890PGhwVtjH0kPWKwoKV4bu9Em984SUm9h3EslzOvHUTv9/ivpkcHSNGzZOQAxdJMzh59xF+98UboOkcPHoQrbrGMyemWdtu8sqr59k3nuUHHj1K49wHjKgKk5/8Yd68sMhHf/Lv03JF3GqR7N5ZtLBBrdNnf3eT816Sp/YPkIpp7JFlREGgnkuQUD08QUW0HAh9vMEhkoNZ1qQk/ZbHkRQ7bi6Oymazz4ljM7x1YYnzC2vsFk05gwABAABJREFUHdjDf71Y5cczaXRLYmRqgkggk1BcuvhMFSKkTQ0t6CMCsZkZrPgAlhcg+CHy+iLhdBZBNhiLa1ytuUiSzdEH9nH71vvMHZhFbm5hx2IkhB6N3G6ax6I8+tCDSGrIla7KUyd3U7m9Tk1Okjg4yutn17hDFBC7TR4dV3Fff530w7P4h+6kc+kcT87uwQkkhKFRxMIIrFyl8u4FcpPD9JNZUq11Vt48h+5ajA+6PHr3QcJLr1B1uqSPHiH0LfSNqwR2n43AwPFgJq8y1F3F8wXCygZvvHSVsWceY/6ORyhEFCQhJCILGJ5LaFtETQ3Fa+JG03Q8mM0YxJsmvTffp7deZuBZCfY/gB+Co0SRhQB1dAYREYwoI9VV6u++x/bF6wwcmiLe2sIemiQvzONMpOl3fO565BRbv6aQeOclmvsOkwss+qqOsHSdfrlNdiDGyIlJnIvbKDENzBQtD0xEPMVAkmTCXpv6zRWG7qwiKBoxVSNUIwRoaIGNVJwnLExhxPIgSqSmcpQvrSD1G/jzZwnufAZFCAg3l+i1Wuh7D2MlR/BEA1Ho7qS2hQGCECCUbiPvu4tAlJDCgEAyaEkRooJIsH4Tv9tCSSaRQhc/DHDFGKEPPT8kqojAjhdwueeRNhS6tk8QSHTdAEEQ6bgB210PSRKoN1xK1S6EYBoqPcsj9Jy/6JWqzS6ws6T1fTG/36ds4S/V+GuET37yk9x333383M/9HP1+n+PHj7O8vEwYhvzxH/8xH//4x7+vut9T89vr9dCMKHKvQuD3sctlfNsjPhAldAO2NprsGY1jVXtEChJmQieb0tATGr7j4dkeRh5UXUPOSTjdPoIPub05+pUuStRA1KOoqokgiTiVGrKhMXjPHL3tBm63T3x6CLfnYCQieL5H/vhewnYDq1RFS8XxWi2UzADO0jrqwCYk0wS9LrLfY+OPvs7E06cYGjNRnQT0G/iVdUJZJ2zWkaNxRNdi5c9eo36rhFdtkj40RWT/IVQxBFkhmNhHMpFGaJeJTA+C7+M3SwiSguAFxMaHQBLpb5chCFEjJn6jgt/vU1/YxO31Sewaore+RWSkAN0Wnu8jqBrG5C6slSVMI4qgpfAbJfrbFaTOWfRcliCaRhIl3GoN7eA9eHYfKfDQOw16YoY9ByYInCpKO8Dce4TOjQso46MIlS0KIzHs4RSiGeHCf/kqAz/+Y8QjGjcuLnMkZ+OPH8BQZAI9QXWzyf59Ib/6x29zc2uLoWNPcmLPMH0/4ELJ5t54n20xwbEnpzHtKq7dwPZinLzvQT7+0AmWuwLajQ/YrkgMDY2x68B+lipdHp2O0Wp2dnyfvZCeqJMIa/yTD+2j7SloRgRDk1mvtXmulubhVJ12tUt/qsC2q1IYTlFzFJ75+FGGpT6J8gL3PHIfX7myzt4CCI5DOTsAY7u4Um9yT/82f3rZ5Uv3neJeXcdWVe740X9Me/EiX/g3/x96P/ePCYQY37jd535bpvD0PTRub+IK83yuaPK3H7yfQmsVp9Lik3ODXK4OceXCOTK7D+GFAgt1D0SBvgcqNmIgs90PubDZZjYfwQsEyh2X8YxMKqLyzKk9mBGD/TGbrYjCua7CmGMR9+pc6+uEyi6Kp79MbO9dtByfkVBExKcV6Gx3HApRhW4Yst4NOTqqUWw4fOk713j4yAShabIrZ6JJEnvHogQIZA2RMIDdKYMDqR1ZxmbTYiyt47gBuaRGecOl6/o8dniM0ysNookIz9dENosuq9tt7FDGaTeg0yeaGyIxMIDVaWCYcZRonOrNFmZ2kNJmCTOVQtU0UhMjrN7cQJBESqbM4nYVVZfx+i2GC3HWFtYZ2bcXfGi4AkeO72fx2i2qa5t8Yf4iniBzsyWSjEW5deYsjZER0psxFBOGjh1hT9Lg7QvzfHBzhYojoGyto+WG+eRDB0i0t3mzE2MyLnO26NDe2mDusf2cW65xfb1FOqoS0UVimkTPdsnnYiQOT/LR48OUOy4jcQVHiNLYbvNKOMGDQQN3c4WKq3L5RploTeGRo7tIrN/mojrKsakMLVnicmWbhStlTs0NsrW4zuGxJDHdY3h8FMw0P5Rs0V1bZqC2SDWW4gtfv8I/nNOYjW7iUCJr9PnFn3iY+euL3K5bDA7mGZ1KIpaWqcUm6Mo6jzx5N4uugplNc3mhyFg6y0fnhljaKKHVS8TG93Dm5hrO7Xne0w7y0LEJyvUmX3tjnZFMlpPTGbqOyK13v8EffG6Tf/uTj/PFxgbvry/x2D0nOXbPozx9cheVts3pS0t8UBN5OCOSyyVxiyu0NhbZeOBeIo5DYPXIagKmIhIGCjVkQsvG1QJerkh8aHeeoFxlXy7F4qWLJBUBa7lE4eTjnDg6wRNHJji93qLu+Pz0D95PfGycaOgSkTxicohn9RC0KE/vSSJILlJxhTAaxYuPoAZ91HiSlYZGITOKICtkumt0lpfIBDJStoBs6kizcwwZNoEc57ZaIF47jR6LcmxYxpjaR+B7jBGn4lYYHI9zo22x0rQJzTh+d5tXVmBMjTPy6h/SvGuUojlG0vqA3XKHzoUlZj7xYVqWgxLKmHYHxbcY2DNLpFPk5geXCIdzBJPH6Ich67bIzN6DeGYCZ30dJW2hDY1Q6wWEAuRMAUlREKf2YV3pcO3qBs/81G5+8ZVFfuToAB8dtlDsPmG9SBDJIm4voOVGEZQcpuwRU0UUM0Js716Q1Z0oewFkq01bSZHCRVQULFHHdHqwa454pYSChV0pAwH2my+g7ppl8KG7IDVMKu5zYHoAd61IbHsBKz9MtLaKYOogSwxMZ1g7t8ZcwQQEvM1F9JkhhHaFZnycIBDwNpfRsmnCdg1fkJC0GGG/Sy+RQFdEqq+/QvxHD6G4FsG1d/C6Dla1h5DIIttdwuoqjhbHunUDZ20VIyqjGDGCIKSpp8kEFkJ2DL/fxo3lEVMjdJBRPZu+D205RTRShdIyYiKNNX0Pihig2F2avkDTDtBkcEJQvADL88mZyp+nu/m4IWiyiCSK9D2PwZiKGIbcXmsghD5Ot0FEn/5Lbg97xgvADkvZ6/W+r6bsf+K/xRtvvME//af/FIA//dM/JQxDGo0Gn/vc5/jlX/7l/2ebX9M0cbeKhM0yomTQr1sYWRMlatDa2GCl1GNWFhE1AUEWEQ2Z8ePDQEBgeWhJk+5mlYGj03i2jV1rI+gStHsMnpwh6PcQ40nCZm0nlMJxiE9PQ+AROzDHxje+zer5DYb3ZImO5NDHRlAiBl7xNoHjYpXqREY0QlkmdvQ4pAapvfhN1Aee4frLf8zgcIyAEEURMQbHkPKj+FsLaIVhvOoWfq8LgohqauSP7f7z7+CDZrLjZ2YjWy1E08QPEnjlTeRUDkGSkQwTLwgQfY8w3NEmm5k0qAp+o4akG3h9l/hoFqfVRZQV1JFdO0xJZQt1cBxR0dDGpxALk/TMHPbFD5DTWcJuC7QIgdUGI4K4/zjO4hWUwVGkSBLz9jxOahh9agJZjyKqKu2hvfi2ADpYpS20dBt11yxhp0V2egDnla/RzOQpHLqHeqNCwumBkUYIQyY+9ixf+PbzdJs1Pverv8S7t7sMKhZvlnzEEG6V+8S2N9Gnhqh5cXqWgKYJnF9rMDgiksDntSubVB2RbjTObt1lTmxxrqwRq66SGhrH0EU0t8MHRZsl12Q04nNn7xqV/CwjToWFbgd1PENk/Tbm4DFiEZlKvU1UV3BUhT0Zn07qIDOeTVNtce69WxyZyHL3hMfCO6f5/JbAo67JL9yh8WVBpVjvcWWtie16PPD4Q4zv3cd7f/C/IYgqUreNnMpzKuixMr/K3LBK/Lsv8/zwT/Lo9DjuWIpFR+Mzj+7hP/2rL/J3P/Ykk3sLfHe+TKvjcWA4ie/5VEWVUIJ8XGMkqtDyRE6MJfACuF3psncsQyLosOFo9H2bUhDlg2+9w66CgZobZOVGm81ym8zli2w8sJ+5vIkviFQ8mbbjEnMEan2PsZTGzXKb1WKdjz2wl1zS3LmkFYlq1wFCUrpMTnLY8BQKukDLlcnGdA4PJxiIqKy3LJarXfJxDV2WEAWBkbSB52sU6xaOoXPj1i0i6QKtzUXwHAg8MuODBI6FkcmQGMjS3R4gCGUKkyO0eg61Yon0YIpE2qTXbLE9v4xt99m9Z4y6omO3uhw5Osmlm1FEu8VEIclMxOXIxx/lrQur9DUFObRwbFht9BFlhfLWFj3XwWmv8ewPP82F8wvMHNyPZrVwZyUuX7tNKEqouIzPjJPxBKbySe4LdRxrmkvVPndOprm82mQ0a5IqLtKa3EWl7VLYleHsssHN9TZHppJstxzG4iqN22UULcJ6DyJzdzCQSdC9dgMll+W/fu0Mg16De5+aY63eobG+SNBustLwOTKdYyJsktpYoHv8QQhCcoko96se1sRRyttlvBAejWdRRmMgyRxODZKwSkyaBpXMMC+0BB7dO4EZUegoOjO6gdRwEUSJh+8/hCyKpE2FzUafoDDA+9drTK/P88ykydiJQa6tPcDugTjNSo1EMsNYdo1rNxZ5MSXz+h/+Bpm7P42luHzqZ/8pjzz8AO76JdZW8nzsh07gItHxBcx8jiNCC8EWGB7O43RiNK9coFQqoyoFTFkh7VSQRRnLtuiHJuW1Goq+G6HXwenCwhvnuPOBg7x8q00/N87Tc6M0XeiLOlFN4uRMhGTQAyVNP5SRQplOKKCLEoai0Q414pEIvudDLI2giOitDYLUIPSb5D2Lq2EeqRFyUhOxR2dp27DHsKl+4zX4yN/Gd7ZwamVa6QLJ+z5M0inhlmq4osR2EKHetTk0pLPckxAViflmgOy56LsmmRI6FK8sYPzUZ9iwBMSYxK1d97JHCrBm78RUDVbX6sTGT5C4OY8xVEAQAvxIgtnDUxQO7Obziy1CWeVwoBEuXCEMdtNdXSczMoo/MIXfsxhKmoRAS02iaj7EM/hti/eub3HX1F2Mp1VUa4vi579O9sguwukMmtPHnb+AenCQwHMIXIktXyWtR9FmDuIlhvCQ0LoVIukEcmUFJzlElwhafY3ejauohoLcq9GrN2i/+R1sS0Q7lELZtYflb77M2K5pStUWd+7KEm2WUDSBnmrSi+To2x6NUhfDlIhJIm7PYuO5rzP2j+dwEnk8ZMywD5qBpEiEQQhuj2D9Ot3F26QfySA1uxiZFK5ioFfncRslMsdmad4uIoQuzdtbxCIp6NxCSA+SPTCH4HZRQw88i1AzEDptrOVF3MXL9J74myQEEd1qollN/OT4jjVkeYOg30UamKAtx1GCLmIkTxCCF4SYgkjX8bAdH1EUyJgKPS/ED11EQQYCFAnmazYjcZ2IJmFbPp1ykcC1iegy3f5/2/we2z8J7BCGqVTqv7sh2wm5+O9za/g/q/HXCc1mk3Q6DcALL7zAxz/+cUzT5Omnn+Yf/aN/9H3X/Z748Vgsht2sgSxDNEnhjhkCLyA5PYIU0TBkka2L23S3uzSWK7TXOnQ3W9Ru1nB6LpIiE/rQr9bplZsgCPh9hxABz/Lo1zp49SpWeRtB11FMA0RpxylhcAqvZ0HXQktHSR0/gj4wsOPm4DhIukF0agwhlqJ99Qp+v4tkdwhaVYRGCVP1aZYsustrSOkB5FgSQVaQBkYQogkCL0Qb24VaGCa1f4qxT3+M3MnDuP2Ahd/6fQRVgnYdr1HDaTTxO238Xh8CDwIfIZVHUg0kM4YciaElo4imgW85eI5HYFsYuRiSrODbDno+hZRIIcgqUjJL4LkE/Q5KfgRP1fHDAHNiGll0UTIZlMlZrJVVtl97n2ZoIhQmEKw+ICBLKgo+Vn4KpTCCVWtQKlb505s9vt3O449M4y1fw+t08K0esQcfJ/b0J2g0PYy1q8TTKoJt4bkufVHFM0zevnCZT//tz1DM7CbTKtMotalvbHF/QeC1NZuertNfnad6e4GFpU22ai1q7T7dQMBwuzi6yaavkohovLTQYLtY5u7JBF+/UufFyxVOX9ui6wp89FCePUGVF95f4ExLp3HjEhnZ52PpGs2ezbnoLG63zcHBKKNbVwmKW7x0emGHbbUbmIrIwb3jTBw9ztm6gJDfxb57j/HkZIJvd5OM7x7mU/uTJLC5+N5Zli5c5uWX3kdwBR7+1M9y4sQ9PPjJH+N0scanfvrv8srabYrZKR780N0c8Eu8fmubLSPPq1s+GWzWrp7jgf0TyJLAUzNpVEliPGXiKRECUUQS4HbNYr3r0bRd3ltvYrkeu/JRZElA0zRqnsDNm0uMTmQRZJlXr2zTFTQaXYf7B0eoXnmL8ysNbm418IOQMbFDWgHXh6GIhCJKxK68R3t+gaMjce4q6BwZjBPXZXZnDJpWgO369AWZG5UuJSeg5sHBgQiCINB1fGayJifHk8wN7miUG06ARMjubIxjE0l6locZM5AUBT2RA1HC7bbYvn4VWdeJpqI4lo9iRPGsHlvbPVJpcyddyQ1BlJk9tItYNomqyLQsmcxghqvvvU+lWMdpt4mWl/nkI/vxZYHxbILDx2eZmp0gUA0U18JuN4iYCqO7J0kVJrjnoRO8/Z03uLm4wcUr6xzaM8Ijh4Z58I4p9k3m+bPvXOC1dZfLJZfNzRLpqMJAOk5OBdcLODKZ4uCAwe7ZMcZNkamUymRCZTalUlAcnG6X65dvMRMJGRofZypnEBkb4fWFErW2xcFdAwyMDjLW3aRkqyy+8xZus8Kh/hI/ElnnR/boJK0GA/sOELnrQeJSgJQqoFp1dFFA8iyUSAzLSHLH3hGUVB4hEice01EVkYQiM6z0+ciwRz6mY8gCshFHkBX2ZAw0MeDOiQyJyA4zlYuorFU6/I3791NKj7CVneRQXmJCC5gaSvPdC+vcn3V5Zi7H331okpe/9FnufvLTjB28gx5pDv74v+CBx57muX/3z7n25teZzuuMRGSGEgb7CklOZEUEAZa7EjdeeYv4gf3Imsm3LxcpdjxqUpLQteg1O1y7ssTpy7dZrFuEZhJdNzjd1UnFo3z80UN8+sQQE7vGmc1HyERUGlaAqYgYTg1RCImGPXqBiArYgUDbF4krEITgSzvvG0RxxzZLkpGcFrGISjYRJdKv7PiiKzobvo4TiDsuO06XtbM3WRKzjMYlUpqL4PVIxxXiy+fZHVQ5ptaYTcDT+R4/dO8eHvFuk1FDeoJBPKLy+H0HSAQdbrZF3q2GdMZ2cz0yznMLPp6RopkcpuIKLEcHcbUYQq+FHwh4iTSh7TCRT3BkKEb64D76165w44t/gjYwQNBugWpwYkCmEDSI4FJ0FUACu8/xU+Nc3epzcm6YP7rYwvdDmsUGXqOGlxolGDmAkh4gtXqWObmGWJxHMSJsDh/nRmScpr0T3Yvvo7S3QdHw5CimVcPeXKFx7jLFV99DKowz9Nj9bL17BTWiIAgexdfPYY5N0FwpMn1qP8tvL1B89XVETaOVnuCCMsSuXTn6HQs1ptDruwiqQq9UgUYRX4kSVyFWX0aQFEQR7OVbhIKApaVZfuFtVKeHUFnBPHIK2XcJBBnGDxDde4js3mHEVAGrVKFbbqKqMtLENN7yTaRkDiobCIGPBgR2m9uf/QOs7RImHj1k9O1FRN9BFQVeulYmzI4S2n2C9CiSAIIQYgkaPS+k0XcJA5BF6PshpiKiSwKKKBDXZAgDErqCIYvENJmu41PvukTjKrKmoBgR4oZCs/t/HhncarWIx+P/3Q1ZGP6POX+dMDo6yrvvvku32+WFF174ixjmer2Oruvfd93vufnttVswPge9OqHrEslEie3dix5T8PyAhdt1OpUeVtlifbWJ1bZptWxqWx2QBWJjWex6F6fZwWn3CVyf3KFp1l6/Qei42GvLuNUashlDyaZp31pA1KM7OlhBYGCugJqKg2Nhry7h93u4lQbJo4dQsgMY47tQxmdY+5OXaS+vEps7hFBeIz2aI3H33dj1JlohR9hpEzZKWAs36ceG8W0Xb3sNyYwTm52F+ibRiQkGH7uP3NE9WIu3ENJ56NRoXLpCd7NMKCn0tzYRDJOg10GMRJBTWUTNQItHCCwbNZXE7XZ34pIdB8lUCRwXJZvbsYpSZJRMgd7CrR0PxkgCr91B2LyN32mC7wMhnb4AuVHkbIZYRMOrbBOO78frtVAnZ4ji0apWQYuykpvDWl4lWVshurbMpj5CNz+FvucQtpkmsnGVZERh4tgu4oM5FFVizdO41tc4vdXnZk+hVq0yfeQIUU3j/Pgd3L52E/X1V/BuXOPDJ3axkdlNuROgVUtkv/Fl/NOvs1G3WeiEtJQEBx++l791aoyfOjXEp+6d5pvrEtfXa/z0U4dIpwxmh2OUMDi90saM6fzcWBvN7hNmhvFiadzpo8wOmDw6N8CTe1K4gsDn522EiMEsWywFMYJogrBbJ52MMZNVGWss8fvLHj/zH1+kKqr87IO7uXlpjSNTGRLdBnckXUbSJlZlm369wttn55kzTfYHfZ76+X+Gcf9nWL58hvmWzTeXuzw/X2WrL6LJAv2+x1d/81+RSsQIRYGoIpI3JAJgq9VHsjsYYkC552OqIvW+x2q9x4F8lMmgRsKq8ty5Tb5ytkit5/OJ+w9QMGQW1ys06h2K89cxo1HiJ04xMDZAs+tw9soyhuChX3uV8aBMQpWI3r5EVIXpw3u5Oy8yEhFQAxvb9YlIElFZRBZhq+tR6odstCxKvQDXD9FliVxEo+uHzFd73Kha6JLISELHVERUUeTqVp33FmtYXoCZTCApIgO7Z4gO70Y24wiSQLu8id3bSVxUzCgQ4rsetuWhKioRXUYSRXqWj2rG+dmfeAxEBTma4567D1Fb32QoaZAbKvAnr11DTuX52vkS1fVNcprI2mqRXUNJFF1CMaNkhwYZH0vj+R56PIeETb/TIyE59FCYv73F6K4JBufm2Ly9RS4Zoaul+er7a7x+YZFHd8U5kRVYuL1FymshaxrTpovqu6yubHCkscBjLHP5/Qs8fiAPVps7JtJsuzIXFuvIrsXr33qJCxsthnMRyruP8ewzd/FkLuDw7mHiw5OIuw6QmD9PbH2epCpSNLLEsnnCwEXYXiT0HdxXvkLj9iID3S1CQcFFRBREfFmlnRjFNrOIU4cZTmiYgYXqdvEFkZgIWq9GXoO8KXHfoMpAVKPS6CIGLpoq8omHD5D3OqyKSQonTzEUN/gHP/IQrqyyrRdoVPs8etcBVENn72SOH3jiOHPTQ7Qxefkrz/GRJx8l6rYpN5ooQsieqMd6uPNCSeoC8r5ZDKvFHqlL3K7z9nyZ5XqP624CJZVl1qly/0SCgZjG8akcohHh2F2HIZoikh9lMhIQt8uYikwYhiRUET8M8PUkoigg+xbNvovid5G9HsnuGqLvYvshMuCocfxIFldLoXRrCEqUUNZptxtkvRbC1hJsr3Aq4WI4TfS4TGHtCu1aA6/ZpyuYiJIMiEiJLEqzjOJ2MUUwa8vYtk9B9YkPDZJJmiw3HEYyEXKChVwqc7KgsHruJqubNT73J2/R9AWWLQlPkHj+/SUW07uwJI1g/Qbi1iKpRJRbfZGuEyApMrGohpzLkB6IEey7EyE/iri9iP/qc2i3TpOwy5hSgOY0UWM6qWyMh++e5c2FGg8fHeGDN6+wMHoAefoQoizjGDFECYTyOp3nv4anRYnh4WkJ2ukxiCQQA3fHD37xAoHno5XnES69jrf3PmIjOYyETvn1N+kVtxl+4Bje1irtb38NLR1FUySc17+JIcPZ86tcf+08zs3zxDslbpdaRAwNVVexqz2u1yxe/dY1PC/EX93xzBd8F6fXRjAM+qU61XNX2H7pZTa++i2sSh3aVdzCDP7ILIrVIIgPEJhxxEiEzKnjCLJMcm4P6tAIgiTiD83s7KZ4Hu6t9wlrm4j9Fu7yVSQcYifuxZZ05NV5EMEzkiitLT40bSIELp6WwlJj6DIodhM5dNBEgUFjhx0VCHF9H1WCpCrs7FqYKpYfIIQgCQKSIBCGAo4fIkkiTq9LPKIhigKt/4vmt91uf18BDEEY/g85f53w9/7e3+OHf/iHGRkZYWhoiAceeADYkUPMzc1933W/p+Y3Ho/TbrcI+13s2/Og6tj1Nl63g5mMULY9Nm0fx/KJjsQZGI1Tq/ZY2OpQ67tk5qZRkxE8y8VIx8H3kXSZ9kYFLa7j9SzCIMT3fOxyBbfTpbtRRozFCWtFlIiOEtGJDOUJXYfA3dFcquk4iCJBYQq330GdnGHoY8/w5v/2B8iRKI2LV0g98DjRyVG6Ygah00DMDe2ER0gi1tWz6LtmwHfxep2dJBhFJSRAFgMS06OEzQpBu46QypE+MIMgBFjFEm6jiTI2ixiJ4dbKuNUygdVFjidBEhEjCeRYAmNyivj0BEo0gpZJIjgWQbdJ0GvjNcqEno+gmeAHNC9fwypX6Ny4hme5uOMHqa6WsGMF0rPjiFaX7VdP49kuoe0hRGJEpIC2I+AUNzlztYQ2PcOqMUDa6zJ/4QZ6s4TjeXSX1hEIwO5CZhC/2yWUVHzZoOZKrHQ9vvK7v8GP/+TfhHaF1MYH3F2/xIYUw9p3jLedBIV+kc1Gn9tLW4hTe1CffoZ1NcVdAzsv6ltbTV6cb9LPjuCUN1E7Ne44uZ+uJ5DLJPiBu/cwOzXMVCaKEolwtaUipQYY7tXZ3VvFXltFx8dPDCDqESrNNtGF80wMpnj+6jY/8OhJwmadnhSjGyh05Aij6QiyGUWSFI4e2kU/luKYUsI5+QBLtomTGeTv/8RT/OuffpTf+n8/wdMPHuYHnzhOP5tjOYhyarrAs0+eJHnko/zBr/8KX//dX2Xx4hu4t95D6VZ58z//Elsdm1/7m59EEUMCu8dW16MQ0+h7AQudkPWmTUKV2DcQ4+x6k8AP2a42udpVKFs7qW+uqCKLAp6sYXf63H1wlFxMobGyjGe7DI3l0WJJOu0udx8YxHA6iP0OptPDX7hCs2Mz0NvETQ3A/qNsOBL/+huX6XgBXuDT8QKajkdMleh5IfvzcS5tNfmzqyXeWa0ThiGaFHK91EWXRRaqXUpdm4gMN4odtho2C+UdD2ZFV9i/r4BpCOD7uFaHSDqHJGs70wZVoVveQFQ0FFWlVGzjuT6lUo1QkNi4XUQIfWRJJpWNUF5Z5+Zykb/50Ax3HhxlImZgqgovv36eGx+8x8Jqk61Kg9zIGGevr5HNpXB9KEQEfNfF9QMKI1nG9+5F0UW++NI1XruwwpMPHqFv+yS9LrMDKt/+YB3BUPngVola3+c/vnSN4rVLNNfWePnsMr/+R2/wpfeLeLbPYESjeGuB33y/gWT5CLdvsPLOWfRune4Hb8PFd/jQLhXLiCPoMdQ3X2DznXPk/Sp9F5JBh1ihQDWMokzvR3e6WK0qpbZDJ9TQZBk3FOmGMvbuA0wUIgS5IdqBgBhCw4F+qCCpOn1kJEIERUeUBARZI6IIeMDFnoqk6miSwHYvYCpjsmc4wVg2Tigq7B3YsYqKxRJopoYiixwYjKMNjHB8eoCqmUFQ4nz3q7/H23/6RbZrfU6fPsP73/wy//XrL/KJjzzDVtvh3/3JB9zaarNV7/D+UpUQePFqk6C0CarGmx2doqPRLVdZKXVYKnVx43ny9z3AyfvuZHfKIK5JhLLJXQdGaIcqETkkQCYUZZwwxA0hKbvIrkUYeFg+BLLJetfHUmJEsVAVnU6rQ9eFjiugYe+kbS3fwLUd3tmy8Vpl0v0KGdlDyI+Ry8SIuC16xTKh02fjS39C/vgc660AOxBoyXHc/AROuYw2sQdqW1R8BffmJaRkAbOxhmfG2OuucTwvUOk6xE0d5/Z1lHde4EPLL3O0s8A/PhnnAaXCudU645GQO0YNfvflW/zu25usCzFWLYjPHYDxWe6cTJMyFJ6/XCeMJDHkkHBtheqrb9O6dJX+zVvU3zmD0K5QkDz6Ny7hV7apXt8k1e/QdlzWVjcYGEhTXq3SGj6I63qoBASOjTg5h19coyjEKVlQ6u2E66T7O171GEmUbJ5w7RosnkcbHic040iJJGoihuND9doatWuriJrB9qvnaC6tUX/7LUrrFULLwrc9Lt+s0Cs16Hs+yaFhMg/dw/iT9+C5PqYscnmtw+LVIkwdxgnAD0Xswf2EYUj56iq97TpvffkDvvs736TbsKi9+hKiayH22siihCcZqEKApKiosRh+bRslFkNYuoCTGadk+QRz9xCacbpLq3ibi4huF7+4jqwpqJkMy72Q1mvfQdQNfCUCdo9x1cG7ehr1yH3UMZAFAU9PYwsKmiwwoNgoorATJKQrf251BpoQ4vgBI1GFQIDVZo/zG006rodEyORglNz4KKl4lJ7t4ggakmYg61EkzfyLnun7ZX7/J/4yPvOZz/Duu+/y2c9+lrfeegtR3Glbp6am+OVf/uXvu+73pPmNxWJ0Oj3C5auUz1wm88RTaNU63YVFtLiKHlEoNlw0VULSBGIpnSs3quTSOgdOTNDbKuN1e7g9h9iggjpeoLtVpXphgeGTu1EiJrKpEfo+gd0HzaS92aIzfwsznyW+e5T+VmnHFsz3EWQZOZbCadRwy9tUblVIPvEU8pkX0O97lr0PHUUYGCP7oI48NEREVUjEJWRJJOjUwLIRzQThjbP40gEQJSQjCqJAaPUQ9MjO2CgZRRqaxrt9EVEzkQcGMSwba3sdt9kj7DZ2/jvUTcIwRJRk5JiBmEgjKBrR4+MEtU3icwfwKiUI3B0f32YdQZIJRZHY3GFCx8arl+heOoOiitRKbXLdPrGxLS7+yn/l0V/8NJijKLkhlIjB5qvfZfiOObxKEef6bbz9h3Bw8Z//Jlf0Z/nQsRFSwxMUbnyAvPsg/a5FaOhI6QS1pkdyZIDr332XqJYm7V7H2HcX26ubVLe3eDSncL2hsrvVwbj6PidHp1jYrNOR9nBxZJSTxhZGQmdRHyIzIZNKO5zwF7h95hqSZXPP4ZO8ebtFN5ciLkSxXBdNFDm/0WLXoMJUSkVC5fBoltViBRJjFNUUctpko+uTWlqiOXKAvFPBXlrkoqRzQm3x6LOHuNqRmPMrrPTSTGVy+KpOCJx48C6S7RBXmGSv2sF1d1PQxnj90hLpgTwRt48Wqlzpwq4Bk4HkBFfO9LlRLTFYbbJWbPGzP3Afl+bGOBJz+LNbFdz5t/hX/+hP+X/94Ec5+PDTVGyfIc3ADEOKzS7jWZOeGzJu+Ay5JdzoLrxAZq4QZTShcWa1ycZWl4+PCzyzt8BXr1aQJYE3lhsU0hHmhtN0Q5U9KYXXrhY5v9Ig9BwenopQI0IgCTA+SyiCc+404RMfxzcCqj2XdH+LlUaEQ7sG6VguVgDHMwqlXkhUFvDDEM2UEGrQdTxu3mwShBAEIVeXiuy1AjrqAEGwY1g/lFJxfWhbLlFdZnokRsfxiSbieI6FasbYvHqJoN8jOxCj1nAxkwkimRS9Zgmxq+F5HoqSoNNuopkmuUjAucu3sboBgqigOBb/4etnqfVD9LDPxMQE9VqLXr1GP5nku68soWWG6LW7TAymQYni+wK5iIrTt7A8F8+2uP/4bpaW1nnnncusbTfQVZG/s0fGkWHvyQH+8NWbjGUMDu8b49alG6RGxplztkkOj7BebCDgsd73OJFs8p1klvvzaZYzM8yrfVrtDtX5DZKxGIn5K6T7E/yDowZnIhqb2QkeeVDgytlLJE49ghOqTLPNmYUiG7ECdx0fB6/LYELDCPrIRgSrMIvmtImZKno2j9HeZlXQUTWJQT1EliW0ThlFiSDXViGRR7R6oGoQKvR9gaFEBEEUESWJiXwSWRLZn9UwhRArFFD6NZLZFOVQwxRcOoHKds8lqknctyvJ6LN38o1zw3xyeo73PjjDN377fyWiqQwd3s+v/OyPEBEl3qiFfOzkbkYLMQq3LuKmEjiWwP7JNJsfaOzJJ6mIGhPDaS5d2Ob3XljjMz9wD4Jr05NUEopCTAjxwxDL95EF0AiQalvMy2km4hoRq4+sRXFchZYvEIo6tuNjYjGXjSEJIsg6gRAghwIVO8DURMzQp4XJVmSEvCDRDERaepqRwQZGvwG6CY0K6DqOoBEf20Vq1iIaVdEGo2Q0gZgs0CWDmsgjuDWCrRXyg2N0B6aIDWbwOg3CxXOw+w5Gi9ewM0cwZI/JB0/hyybywCLzM0cZTsmcObtOyRaouiJ7Ix5PPXGSdy7fZj0w8AWFB6YLzMVNZFmi0nF46O69nNkcIOeeZ69fJSgMs/b155n65KM4jRYr33gR4xM/gl7exCquUssMcmw8R/1KjenxOKVjjxNf+SrXOhKjmksQtHFWGySmU0Q+8aP0lq7gpvOk8hOklRA25rF2n0CKmagS+CsL+PUKxugezMDGHxlh+fnXCBWNottgdCLP+tkr1NcaBGvvkh5L8ua7K+iiyFrd3pkMpdM0oznmRkK2Ux9h4qXPUjg8RuF6lU3bIT+cQAAkfJBFbF/jurmHwtgwm2duYRByo9xDDzwKb18gsW8WeWiKMJJAkgTIjuOXbyNEE9TfeIu15Rozn/4YVmaCSstjxIygaRGsWoNkMgehgLW9TUCI5wYQCqgPPUUoeUgb12iMHMKzXXLZAXD7qHpAywlRRJ0wFAlCaAlRuq6H64WkjZ2pmR9AP4CYFBLXBRo23Cj3SJsqrh8Qj8ikTI21cocwptPqeeA65PYeQFJ1Au9/Z4G/X+Y3/PPzV8FfL953B8ePH+f48eOEYUgYhgiCwNNPP/1Xqvm9M7+WDYZJbHqChT97DTGTY/uDRYYeuwvP2tmU1AsxivN1kruypFWRvUeHCC0bq1glMjxIctcAiCJKRMeqdogNJbHqbQLXQRAE1KFB1MIwSn6Y8U88hlutEVo9+uU6vu1Ru7KEVaygDhQQRJH20hpuvQ1rS/jvv0pnfQvv6jsMP3QXaiyOsXsvst1E7FTQYnEEM4pTqRCGPnaziT4yROfmNZBV/EYFBBFBUQitDkgqoh5BtDtIe+5CzA3Su3WTMJpEMKN4dkDQbhL2OyiZAdRsAXlyDmlwHGlkBiEziGQaSJEYYjKHnMruuEokcgjRJFI6jxRL43eaSNlB+qtLZI/Mge+juh6V6xvU33gTtVJn+bvv09fTIEgox45BuULt+W9Sv3IbI3DouAFKv4W/vo548xpTukOqt05ueAC7tEntnTdJJBR8QcZKZZFq62jRGLFohLP/9aske0W+9rn/wj/4hV9EKwzQ/KMvcNovoB27l87xx6geOsGJvTm2NktcKlo4lTKxToWqHXJpq0M1PsbQXXeRrG0yl5X55OEcSiSJE6oYdpv5xQ1mclECQaTnhpR6AV4QEJNCtlp9bjRcVrvw9pZL8/XTzNavc6ausO2JDO6fJbXvABFDIyJ6GCNTbHZ9VhyN+UqHjX7ItarPjY0W42IH1dBwC1OEX/sKB26+SeTt51loyVQdn9NFH9VuUy9XEXSNn94jc6igcXimgKHLpHNpjLEJeuogP3bvgzzzM79EMHiI8MZZTEPHkiI4gkKo6YwlNDq2R8VVWJFSVDoeeVMhaajo/QZNy+XeXSn2pTVsL2QwrgMCxzNwtWwRGx6j0rJobm/zT56e4f4H7uf6e28giTJH8hqNUKM/ehgrOYa+dw+yrlP3NDa7HudbKlt/9k2G1q+x2bDJRRQuV22EIGCtZTFfs6j2HDqOhyoLtGstFkpt3jw7T6/dY8NSEEO4f9xko9xmyhDotfocy5ls3Vig17ZYuV3CiBjkx0cwkgMY6QKJiRk6PWiXSrS3N7C7fbavn8NqNZFlFc/zdl6CskDd8qh0LG6fP4Oi69x/33Emxgc4sG+cYqlC3bLxZY279o5w4sA4Tzx8B/sOzvDok/dwY3GLn/zIHRRUl3fPXKPS8hjIpxjJGuhRndkDu/jpjxxn8fo8q9evUzJS/Of3G6QjKo8dzCP3qyyulLC2t1ghxivtOLakoIkup6x1SuUa8cYG6eldXJUKPDQVRTIM4okoQizO+/0olXs/RLfXoyFFaTkCX7rcYLfUoByfQnb6jFVusX1+gbISYWxqiAgWdS3NoOYR89vE3Abm1jUCp0Ov3GV1s8rVW5tInSo4PVSrgeR0CNpVlF4FAR/B6SB3txFdC/BJC12SuowpCQxHJIYiAnkNkm4dNbCJhX0CWUUJPbTQY92ScIKQihXQdXwUMWRgMMfTd+/hh+6a4Ic//WlGjn+Yf/5TP8ov/bNf4MSDDyFZdQ75RQ7lRYYSKqGZphfsaNhHoyGN2Agrtka56/Lym9eo9z3GYiqvvHKG//Tty2x0QyzLQgxDUn6HuGATFTxcSeG6o9PzVeReHR2fvu2x2HIpdgO2XYl2ICO5XdL0kQMbpXwbS42yFRistix8P8ARVJxARE9ncY0kd47H6SLj6UlCI44YhoT9Jk5ihMSeaYTcGJn77yUWi3GHViFhFZH7VQK7h5hMY2UmEMdnUAIXffowgiQhmSn6Y4cRZA3FjJOmg2x3EAkQC+P05Rhj+TRybZV4PsMwHVbaPlfIUchGmNo9ylAhSz4b4ysXK9xqBZR6LogifgjmYIG12XtZZoD4zAS5/YMo8ThXvvomq/Mb3OpIVG6t00mPM/v4fZRH9vHhU9NosszZjRbvRUYRBYENT2PBjbO8XMPuW9hKjFi9xFBzgZgiAx4YMXqiScsXwXHoF7dZ/tZ7tD54B/fa29CoIGky775yE83pcOlb7/Pyd25wfa1Jqd6nslBD9uF61caQwZAFPD+gv7BAGHqkdYXOwVPY9R4lJwDAjChIjQ2U0EcG7FBAN2PkHz6FbMqcXaoTAleqDmsXN+hubhG6fUS7A56L0GsQ1DYRtAiuqLHwzi3szVWUrdtkDRkt6MH2ErGpMcRIDHyLfqWBnk2iZAcQRRExonLRSSGJAkavRkJyQY8g9Jok6VHteRh4+CH0vAArFLEDkCSBnGgRER1cBNKaQFIXUQhp2y6DMZ2QkLblUOu6vHNxg3bbZnRyiGbXRlQVBFmh3yyDIP1Fz9Rqtb5P2cP/mPPXDZ///OeZm5vDMAwMw+DgwYN84Qtf+CvV/J41v61OF2FoD8nZPUw+dIz6xVvYbZvqBzfIxFRMKeT9c5u8c7WE27OZ3J1GMxVkQ0XUVZSEuZPS1u7i9x0IRURFQDY0BFEiBPxGk8DuE/gO+u5p7EYX0TBxGi0ETSEIAkRFQnBtQiOKMTaGVsgx8PiDVN89R+yuB3DbPeRYFAEBwWpBbXvH907XEYwo6uAIomLidrqEkwdpL6yiTOzDt/qErSqh1UdM5MBqIYQBuBaSJBFUiqjpDI3zl3BbFol9uwh6bcSpOaSBMQRRQDRjCIGHoEURnR6oJmIyh5DII8aSSJkhRFVDzhYgCAh9BxwL33Nx11cwR0eRZBFJhJtn17n9yiWyc5N0ljZpn34Te3uT2996DUXTCUQdP54mFxdxEOkUi0zcdYDDd8/iuw5sLvLu197CvXyG+tg+Gut1pGQaSYvipIdI6T5OCIf/2S/QrteQfJu+ZODmpxkbjuGc/oC1iSN0fYno8BjvtA0unb6CHc9zNXeAgcX3GDIC7hoxaKPQ10xac/ey4qisWCJht8n1co84LruGEkhiSNfxEESRF2+UuLjRYrq/xHvLNSZVn6Ij8LFRyD7+CKoUomgq7zRNrpRD6oLGe70YL1wvsbW+zVEqWO0O9Ptc3Oqw+sqbfPP1a3zj7ApvFn2UUOC2mUe94wR77zlGH1juy0znI9xow1QhhdVqo05O8+r5dXanDZbakB/IkhJ9Hjg6zovSIA/IZSKazG9ccqmFOu1AwhZ19qQMZEKyEZlszKTS86nZHqWegxOErNkKh/Qe27Uupe0ig2KPIcUhrQQkoiZPHciTyydJKDB48DDS6C4emIyx676P840vf46bNQtZlGi6IVsdG2v6KFuNHl96/TLDpkClC8/1MvzO+1uMpjQSiojjB8wlRSzXRxKh2feIKgp3jKX40ZMj3Bm12J5f4vR3XqPY81EIee1mlTfOb/BbXz3NuYU6Hyw3ePzICFS32JuVaa2vksmkEQWBRDLG8PQM1WIVLZZETQzQWL0FnkNuYhRJ0QiRCQWJ+voK2ZTB/M017NoWne1VNraK/L37JtmTM3nq6fupFMv0ylv80JN3slx1iOXyzA7F+fuPzXDojn3cuLpIcnyUBx86RNvykMKQGwtFik2XQ1M59u+fIJpK40kmhXyCtgNfvVrn4nKFuw9NIPsW97fXUR2HsLLNdy5tcM/cBO2Dd/LEmMxqW2F41zSH9gwxodgUwgbHR5McCkp8epeEFI1xuuxx3h/ipVfPcCTp4gyNEL9wGuGd1wkjCaJUSfRrGFaXci9kNK6iSgqKJOKG0Juf5xt//A5bV+d589d+B3vhNmJjG6FdRi7fhvkzOEYKBIHQTALs3IWNTSJigBOKbDZttrrujl+r7yAEHqLVReyUEQIf34gjaAaaqiHKEvPVHjEVrpbaVHsBqigxm4+iBSGHSle4h01WXQVRlAlSo9iZMRTBQ4qkEX2fcM8BVHaWey7Ob5GJqaxtlglDcBGRoymWbt0ik4njRpOc2+7SRkHq1VArt1HtNragYnvQV2IMGCKSrKILLqrbwgsCWq5Ho+eQ1iVcM4OnGthI1NQEiDpVy0eTRFqOx0oXvCAAQWBQD8l4NfJKgBZYuGqCoFFGGJpGECVEQUKUFazsJIgi6bCPFrgInksybNGTDBpKGn/sAPR7qGYEXzZR/C4dSUfy+tj1KhoBnUDGy41Tl5N4h+9BwaOvJ9ktNBmV+0xrPRKCiyGJHBpJEDNlVEVEVAVKXRdZ3JmkvFd22Wy7zI5mWJw8STeWRzYNnNwk9VqHwT1jlH2ZGzMPwNA0vbse4U+u1ZEUmW+/ex17bZ2feGyOgbiGJonkMzHU0KKmJkiFLYhFCXcdISmHpCToDezFlELM2m2w2/Q3tjDyEeqXb7D5x1+lv1lCNmQKcZkbr12lu77FkWNjNHsuiKAlFE5+8n4Aui488VMPcPNPvst7v/hLZPwO2fUrdFIjNNdqNHo7gQ4Dc5O41TJIMpYfEoQCQ3ENOT3AWtni4N48ALoqcmO5wbnPfht3dRGh3yEUxB2LtHPn6M1fJ/Xspzjy6AF6iSEUQ8SQJRxUAt/DrtXwkel98CZCKFC8tE6gxRg2BRwjxW+/u836i6/jvv0CoihBNI2fmwBJY8QI0d0uShgShDvsqEjIZFzAcFuo/TZeCKokoAQOstNDlSTypkypbbNQ6ePZPutrTcrLa2j4tPseQRCwdeUsteUltq6c/Yueqd1u/0/Zw/8g/Lt/9+/4mZ/5GZ566imee+45vvSlL/HEE0/wd/7O3+HXf/3Xv++633Pz2+l06CRHEAoTiPVtrHKVZrnH5pkFbpWtnY12UyGTimBX+kgRic5Wm9hEDsXUsbereD0Xp9VDimhkD02QmBxENXXcXh+3Z+H3ewSdLrLgsfknz2PkEuC5O+EY9Tbx8QEi+w8i50fwO22MfIag10WJmgw/fh/V06dRZg8jZEfwOxXclRsI8TSBohM0qkCAqJsQT6Gn49RefInkkUMI0R0fXHFoN16rSRgEYMYJtSgM7kLoNREEaCfHIAyQh8ZRUhkQReh38dt1gn6HsFsnFCUCp0toJHZGGEYKQZAQ0wWEdAExmsTtdHH1JIIaQYolobqFlowjuH3UdAKn7zI0mWbwyARSIUnH9rj81RextutULt9GyuWIzOwic3gWWVXR8FguSeB06ScLXLm8TZAeZnRXgUob1MIwy4Oz9Dc26PZ7WIKBPXucrOIiXHqXXnyEbCqO4fepXL9O+85HSUsutY0i1y5c5Y6ROK4okyjkODYUQc9m2Szs5UzRYSofRZNkuh7Ep6fxrR6lrsN3P1jgxLDOSD7NmVWL9Q4kNJHA6RFxbZrtHiuRSZ7cn0e5cZmw3eTVNY9GfIDtSpejZo/4+DhaRGNTSrIvKTKm+fzc773DN86tEF26Quzbf8ysV8b2PX7kmTuZmNvHTCHOCxsOzbmjlLK7+aNKgoW+RjpqcmOrw8X5Em+/d4PB8jxbi+tcvbnM77+zxuXbVRRJwk4OIroWZibJG/oUva0yP3DfLJn+FonOOqoEihAiSiJesHOBaqZJPOyzsN2htV3hRMpjy5aYGc7w6pbKYtvnUKSPocmstndM6zVF5J//jXsIZZWo7+A1ajz7sWfYnr/CH/7Jt6haHos1i4WOyJvrfdKqzx37RkHReGhumH/5mWc48dSD7MlF6XsBJ0YSjCR0JmIS+YjKnnyUXdkIqiSwXLeYUlw6PZtGqYomCZw+v8C122WGhpM09TRzEwn0wOH1+RZ3TqU5sGeMB/YVmJzMImoKg+PDTI7E6JY2CAIBAg/f6SFoJqOjacZ2pYkmdcy4CYHIwo11psazAHRLq4gh/Oe3V5BKW5SKZWS3gyBKvLnY4oc+fILXLmzx7plLnL2yyk+dGuVZcQXDMJAjUQRd573ra4hGjIf357ljOM6t9SapwiCPHR3lwmKNiKFxcb5EKp/n/aLLzHCKiY98mClnkyOjEaKGxq2uyO+9u8XAyDD1XfvpdjvsDqsI5XUOKHViUZ1OZQPDafNAHu4/OceVpRIPHJrkoQ8/jjA8zaEf/DCDD96DmozTGJ9jKq3BH/8esevv4jVq+CtXWHzrDLeLLeqLazRWtlhF4938LK1olpwWYsyfRnA6BNkxZKuNUy7iuzvxq7g2gu8j19bZsGXabkBCFVEDB1c1CGUV6hsE0QyhJCPabSxPZL3l0Oz7tJ2AYttlwFQwZAlN8FDcPtLIBEZ+kCc/8ij33n0YybPo+qCZMSLRGHnVZaXR58ZyGcFzECSZr718nj/57gf0mh1effsSx6YL7JbbFMMIry47HJgeImtqhEGI0K0juD2ayk6YQMv2GTA1BmhjySaiZpJtrzMT8YkpErP5yJ83ISEyAeq1MyipYQS7Rdf2mUtLtGwfxwfHDwkDECSZQI2QCNto5SWU7jbYbdBiuIh4egQALegR9uoE6RHC7WUE1URoVVG7VUyngVacJ0zk6KLSQsXvdRlsLBP2W0gDo0RNnTAzStfM4CAQjZq0PYGSXkDJFFAyebLpFIcLGnOFKF3Hx/ECFFlkOBVhMKbRd31uVbq4wc5zxw2YGcpSM/IoD34IKTvInh/5ONfHjvDKuRVmjh9kQYgjxeLYgsBWuU2v0+fUoMi+mE/OVJlI6ERlkd3PPkk6GYP1BeJxBVdQkEIbtV1Ebm4iWq2diWB0ECVmoCVMOqUOXqtFZ2GBMIShsSR22+LGRof56xuMDkSZO7ULKW4gWC0EQFQlxg7totNoUr2xQf72+1z53JcwSksgy0gyxEXQcwkERQPXRWIn8tQPBaSBCUZnCgzEFAYVgWxURopptFYqiKqCb8SQ+3UCz8Nr1OkuL2O//woDR6dZK3URjCQxLUT0+wSldVoLm/Svn2ft+de49sI1vvvWMv7adawgIKoq/NCJYeziOrouoLSL1HMzrLsatqgQs0qIbh9dEdDknSU22w9JeF2E+haha6GLIU4ggqgiBDZRTSKiigwndKKayN7RJKohIUsCYq9Bq+fuWEH+HxbMwjD8/mUPfz7W/6uev074D//hP/Dbv/3b/Oqv/iof/vCHefbZZ/m1X/s1fuu3fovf/M3f/L7rfs+yB8dxcDwPVB05oiMbMuP37KbXdRBFiIgitZbD/qEolcUqsixjewKXXrqOIEt4lkNi9yB6JoZVaRIZymIMpHY0QvU2tRurO2lqpkHouMhBBwLwej1UXcMYymEOFlCzgwhmgqDXJtAT9NbL1N54HVGGZD6C1K1Bs0SwuQLRNEI8R7h1m9Dp4TWr+J6N4LvE9h4iffQgRiZKaHXwHY8wDFB3z4HnIqaHwXMQbAt/cwE5PYAsQu4TP4QxOoESSyAmMoTtOrQqoGjQbSGaCdCjhI1tqJcI21UEIQRZA6dPoEdx1pZZSuzGa9UJRZEg8BF0nSAM8H2ZbqXFxEOHyJ84yOTxGdbOrzAxmqHy2mtM7y+gWdsIQQiROO7cA8iVCt7sNHvuPUrjndM0B0ZoeApaIUclPYq2cY1o6NId20e+cZvzJYuuqLASneSbfYOvf+kLWH0br7LOyh88x+oXn2P4kVMcGDIwszlUwWcmrfCJ+3ax1gxoNTpckwa4a9Ck4irETZkEfZJeE7FSZPfNt3l8t8mFok1+5RxP7dIRRAEngMWmz1jxOs86i7j9Hvm4TuLEce6cyJKQHPYYXSqZSRRF5NiIySOJNnv9bap2yJ16n8d2JbnW1VhOT/B72zLtzS2uuRLvLtYxDQXLsinEDVZbDrom0/VDXr9W5vOv3WBhaZ3xiMfFnk5yYpxwbIqJQ4cob1d49/RlEpqErCicX9xkZiRFIZ8me8dxxscLmJoOokzKb+3Eq4oiOVMmpkmMGQJvrNtIQsBMwaDWtvEjCUqWx+6pAvVApWMOYHkClm2z0XG5vVZB0CPcMWyyfzRFWnCZzCf55X/77zHWL/LTn/oIX/3Cf+Hb524jiAKWHGVXVkcIPNq+gGnq3H1gjKbjIQgiCw2bDWtnlFfruyzXelxaqbNR6/LSxS1+89tX6Xoh+4dj3FqrcmBmmMN7h9iq9RAEiXa7x1Amgi7Bkq3yhW+e41pLolTr0Fq/Tblms7BcR4tE0aMaeiyObMTA92m1bWRFIpGJEIkoKBET33MwYwZ794wxPJhl93CGgdERvnVzGyGAvbLN8blZTi/VyMRUTu1JU6pZvLjU4rdeXiGYu5NH4l1Ez0MVYf3aNWQxZL7c5/RKHTsQKC0vQGqA9Z7AkYOTaLEY2ZjO1ECcC1Wfr692+a6dJjkxxak9Wd5Ytjg6neOtUkA0kaAn6uixFEupGXqpCWSnz0Vzlq3MODfVHP34APcd38XumUl0Q2O8v0lc7KGHNhHDZCSl0zh3iVp+hE5xkwv/67+l+p2XuP6lb6HZbRqnnuWZn/sBBkKLT50Y5tgT96Cn0giJNEG/R0eQ2Fzfol2u0Tz3PtbabRpn3sYpriEGLnm3QtaUKfV9Gr6K7YkIYUgo6QiBj9iu4Ktxtm2R200HK/AZjGksbLW4a9Cg53l0q0VEp0tedFAPHSXr9Zhvw5+eWeQ3X1lioQXF5AQ32zKDdom9coWQkHQ6wT//oTv50RMj7BtP8qE7duOuz5P0mvyLn3iCT967h4mkwWRM5NZ2k54Sh+wYgSgjixBVQvK6T6DqeH++WBsoKmn6HDI6SKKAh4CLTM9yCWQFBxFaJXandWKCS95UMOWdMfSwGRIGAQLgCxJhv4MkhAh6dMeHXdORXBf0KGq7QlBax0sO08lM4is6jbffQAxcXMfb8U/3HEDAEHzs+Stofpuo4KLg48o6PUFH37hBTPaJdLdxLYukIZOMRhiJCPRLVdrvvEFCctmdkEmZKgldZSxjoMsCqiTgeAEtK6Dz59OY5YZF2w0Rx+e4HSYY+vDTLOt5JoczrDUtrjkJLiyVEP2Q3//DF/nEpMHRA+N0JRNVDNDEAMmzcJLDGKqMmEyj9OpIdgehXUFQNAzRQ6os46sGstNFMTVamy08NYKWTlG+ssHN91bodD0mp1KkZJHFzS5je7LER9I4HQeh3eLgWIxTB/Oc+Z1vYa1VkBUR33HYc8c09pe+QnIiw1hKpx9A6fwi8r67UJprhAEkNYFkc5m2mWfvY4eRgKGoxkrNIaNKyLqM17MISuv4F75LUFkjcWAfr37uTYL6NuIdT5ASA0QxJPB8EGV8q0e/2qH4ymnq63Vqmy3WWy5fuK3xxQ+2KLsCezIq4z/4CdS5k7i+jyeoeIJIpF9H9H0Cp0cogCYKSALkIwqIMsTzhJpBRPRxghBXEAnUOLIoAALVrkvSVCm2+hw4MEwskycdUai1LXasN0QkLYIg7sgebNvGdd3vi/n9n7KHv4ytrS1OnTr1l56fOnWKra2t77vu98z8AjiNCqIRp3N7lUghRXa6QHwgxv4BEwQYTmi4ro8aUVDjKgurDS5f2aZ8eR3ZUAkCiI4OIisqYjSG17XobTfolru89c2rXP7S+zvjCstCT0cxJ4YRdBMpqqFGTJTBMdA0AruHMnsE6/pFipfX0QdyNC5eQ8tmkQKXsLyGWy/heiJIMqFtEYoq/vI1gu11GD+IoGlE9+xBjKUI+y3keIqwvIYYzyGmCohen6BRIqytgW4QujbJQhZNk9BMmRAfKZkFt480vhenWiGwuggEiL0GBD4IQG0D7D70WwiSjGAkMXZNM9S6TShpO4yPHyANT9G5NY8+ECe3ZwBJEnCjOURVRU/FyN55jBsvnyf30EmIZWFkCrvZIkhmUJNp3EaD1SOPseuBE3Tnl3m5neSX3++xsFFhXshSXlvGbFdheJrJSEgrkKC4zHPf+BZLbYFKtUZk7Tr9ux9gYjyHkx7mUkthaqyA4ts47R4xxSebMumGEq9+cBvLsij3XJqBgS4GJDWJbjxPbHoGZWCYB+Nd3qVAebNEoVdhKqGgiVBLDuBFVOasJWwfcvksF9dbDHkt9FiGoaSB2G1xathEnz9HJmowm1bYals8Gavw7LFBppIijz9xkhtenKlsjHNvv8u331/md15dYqvUJKGrXNtq4bgBx6bSdB2Be2ZH+MoHmxwV69SkGM+/c5M3z93kxx7Zy4dPTPCtS0Xmq10ePT7Del/krrzMRFwiHfbAjGMrJn0kfDVGmhbTKQ1DkYmrIrMDJg+O6hixJMm1S8zmDCwv2Blx+nC51KNSKjM3miET0RjPx6k4IVMDMeq2j7FylbmcRC4W4R/+w3/AV3/r1zg4lObq7/1/+S+//E949fxNlDAkKblsNi3eWq6z1bKZL7XRZQGBgLdXm8RkCcntk1MFwnaD09/4Dt7GMu/eLCJrBj/2E58gtNpYHZvXr1fYXK9jWw6vvLPAq9e3eeO9G7QrJdA1qh2H+naD0YP7CEKBSCrCkVMHiMUMwsDF6dRRBI9msUZpfpW47LFvNMKRgyOMRQOstXmevvsA//SnHkfwbe6b0vnB+/cyonS4845pfvzuUX76qVmWLp5nPOHzaz96lE/eUeCOnE2r36VV22bUr/LohMSPPjzND5/IMOIV6dy6iLB+hZ+8e5D7Bn2OZyz2my0+Ni2R6G8x6Fc4ZLRIil00p4XWLrK1ts5DUxKTSpucW6FW3sKqbFGs11lc3eTFxSY3Kn3GpBZ5Z5v2xjIvn77EhOkQ2byFtXKLzvo8tatnaV2/gF1dR8wPUfj0p5Hvfoio3SL9wL10Bvcx8JFnKMRksqrNkNbj6Ece5b4Zk6TQR/Yd+tcvgeMghgJYDit2DHvvSdqZCaTsIKFqYlsuUd8iWl7F8wIURaLe7VG2QrzhWTygGmgsdwABJhLaztKi43Mk6WOH4C/dohXqiIEHns3Lt+q87ObZnYuQN0Q82aBeKqFLIcmwh9gqERkchzBEFGBUsPj4PdOcv7VNs2tT0AQO7Z/i/gM5Hj5YIKFJKLjMxECLJwFIhBZyGDAQtDDcNkLgofsWoe/uyMG8LhG/gxr6GKHHuWKPtqDTmTyEJELRGEKRwel1CUOBtuMjCCKKJNJFQSRElFWIJAn7XYRIAkIRpVtF9B1olnYaEttCslu4sUHc0hphNIGsmaidIuHkIUIzgYqFJoA6c5ggPYzgOYTJIboohKFLOz5C3w0xCJiUmmTrS5S6DlEzhqjJCIs3kFfnEWUJQ5GYSBnszkRZqPbouyG7shF2ZU0kYKvt4PgeFV+lI+mUHZF3V9sstQQG0hGW+jCSj+Ej8PqZW4SBxzN7E1y2EsxXegRWn6BdReuU8fod7KUF+vPXCFwfu9lF1CO47TqBIBPkdhGICq0X/pTV+TLmE8/in7yH7P2nMLJxlteaXL5YpFztsu9AlnxE5oMPtmguFLHqPbx6h0LeJHBCait1FFlkvWFTfP47XPnGO+haQHL3GJk/j3M3UhHoNQmKK5huC9f3obKKI8g7McAJlWxGJwD23z3J9dt11l5+G2t1Ab/dBNemU+sz9/BeOuvbKKrE6GOP0BWjaASobhe3UiMUoLXV5J0zW2RyEe6/c4R2q8WDkyaqKJGsLiPLIl5qBFGQOb3WYswuIlkNEEL8SArZ6yMKO82vIUu4WgSxVaSnJQn5323CBKeFSMhWd2d5tNHzWNvqEAQ7i52ZmEql7SAZCURZxbe7fxEr3G63/5u+6X/ir4bdu3fz3HPP/aXnX/rSl5ienv6+635Pbg+6riPJMo22RSF0cTs9tHiUMAwxUhGmxx3auQLiwiqNSm9npNKyaTR67N9X4OK5TYbu2k3txgajDx1E0VWEUMTtdIGdzT3fC7BbXRAENt6fJz07gjq6G2vlFiCAGCBlBgk7TUKrDcP7MEeGmXg6j9duEooGTt8iqJaQDRNlcIL+2k0YyBIEHqKiIyga0vBuxH6LwOkhqAaSqiIUdiP2mtCpggihrOwwy73OzgKc3ScEBGtHoxRUNnciuGMJMOKEYYAkSzs2bN0WomoQ5sYQmyVCIGiUkCIx/EgWunVE3UBfmcfvdQj0BGKnhjK2Hz8Q6S+uIyoKai6D2Czi1Nvc+3efpfTmaVL3HuPVwime5Db1a0ucnzjCnRmV5sAkmrhMvLpJd3wU31xid22B+JEhVnqjbDpxOqJGqiezN1zBjU/RXDhN4dAheqs3+Ae/+r+wsfEMKbODfXOJ0iMfoSCoDDm3WWcPZ8+ew5V0HDnJTCZGxNTxm1WubLQZztqUxDSGHFJxRQbSUVphmlalzlbbIT6Q4w9ffJm3XjnNj3/6EbIxHbl4i/ypYzTbTaxWk/XA4O2lOvkgZMYJUASHvFdFjBzA86HdbCN6Iu+Lwzx5qMCUKSDGUuR//3fhwz/M8+/b/PT/j73/DJIsPe870d/xJr3PLG+72vvusT0GM5iB18ARoMMSJLWiEamVF7XSXVLc1ZIiRXIprZaikyFIAiBBgHADOxjbM9Nu2lV3dXVVd3mT3p7M4++HgrjLoG4EBHIjbij4RLwf8mTkE5kRec75n+f9mw88xP/2m19i9qFzfOorb/HAY0dZ3rU4NpZkebWKIsKxyRSqcRjx5kVcL46ZzPOkKfKJL17m2FSWcyWJL164x998corTus+X5neZHCuhCAaSKpATAhzFIKcLWISUuy5RXUQQNeKKhxtCeO0CC8n9RFQF3Q6YS8rsNDq8Ug2wPQibPQqGynguwb1WH0HRuV3tQukIekdgOh3BHTSwjCLpk+/kM+95B1++scpv/5+/zO25/fzDv//3yERkapaDJMKllQ5RTaVpuaw1+txeqdPerdCSTDKiyzufOcMXX77B46bBa77E9eVtfuDhMfx4gsabS0xHHRIxncLsEK5rc7YwSX9nle85MkbQqzF1pIDt+Uihj6k4qLKLGIYIxyaRxan/x1XCwA96eKGAmwmxhyZwHI9ADFCCHkN6QK9e5eBolJI+hOeHbDe7CLaL6/gUUlE2NisMZ6LMFCL4QcC2kkDXbPpuAIrJQJBxA4mdhkO73iSeStO0Q6qOyKFCjNWaRRCAPQiI6irJmIHt+lx/7QV8z0NVJPT9h1GzBWoti6gu0axWKegyg0GHVm1A34PQhqTcZS4SsLu2gmD3ubnSAkEliO0jEATCtS6sLfzZr1+Ye2SPrvDtSNRL63UIfHbaIaJmIfdtZOsekgBh6QAqCn7fRc4UMDa36FbXScSjBJOHaSwtI+0uoJSGqV9ZZOqpR4EZTNGn6YdURJVmpU09NOj3LLIRBQQJUxExFZFENMvFO7sUWg0OjGYRm9uEjQbFyCyZ4VEKUZVuocCkAS/Mr/KRTJRMe51yaox0c3Nv67TXQ3AttrNDvO30JANbIBcb4YEDwwSyitZv48s6oR4lYUiI7AmXZEFlYCZQXAvHzOLJGprXQ7b7e0MBVQdBQvIGGN0KQ0YCURB5Y7fNo6MyLU9mabfLobhMoO1xMAWg7yuoMrSDCHH6BMkCstsnbJYhCBHDgKBdxR/0qF64QfTwAcJehy0nxmi1xuKhZxiXoqjRAEWL4IYB6qBLIEo4Vhc/MUXU6UOnghAdJy66bGkJbNunoCiwdANBENCzEfSgz3B/F+vZ97BbqZHNThA3dBp9nzvlLrfX68ykTLJRFcEPaLsBTihQtlwOZFVqlkvBlPj9qkIuJWMqEq7rcb9l89Zyma3lVY5NZDHDJvO3lkj6HeR0kc2Kj9bYIju1j8Zai4Qb5UBpksHXnsd//CnOb/QIMxEeUO6S6G7hDqCV3If78otce8f/SGFMYvgdFpPrTTaub6PrCoobcuxAgd2NOrkT02zf2aFStsgfHkETAzZbFotrLQqGTONejertMiOHRrA7NiAymdHxun3611/HPPIA/uYiYWKU2tAxim6dnaUN+tU+gSSgA+s3t9ixfL7yR5d5e6/L6JNnCe0BL/zbz/PwO+fQcxk6L3+N5NPvQ+n3EKpNRFVBLQ0jx+4QGzeJvrbClbU273g2x4PRHVJ+Cfo+YnUVFAX7wgtITpvTx9+Bf+VVOP4AjpbFVyMInksghexJ9UIIIIikcQUJGYGIIiIEPgMljh9CQpMZTZmsNWq0ui7dgYeqq6SjHpVqDd9qQ7jHfQ79vbS3druNLMtomvZdgbL/zga3f+n6uZ/7OT7ykY/w8ssv88gjjyAIAq+++irf/OY3/6ug+Dut7wj8CoKAoRvY3Rp9I47XdcDrQCKKIvjsbLaJyDrbjkNKEtm+16A4kaCQNuh1+nTtANnQyD16Cq/bQlRlpMBDK+RxOn36jTLHjxYwiwnsWo3+bpXER96LX9vCbzTRk3EIQvx2HVHVUEvjhMEAPz+MZN9n/fVbTP3QB6m/9DKxo0eoX77E0Aeew5REvMo2gaIheC5SuohoRAh2lkGUEBQVNIOwsYNgxggSRQIjhRh4hM0dhEgc+l3CTp1ASCN9W5jnNqpo4zOEoQACBPdvoYxO424sIedHENwBpEYRHAu/soWkR0BUCNs7CJ0GnhlHGHQBuPC7z7P/6VPk2mWsrRp+f0CvarP25YtMPXgAKVcilYvj3LqBYblIX/pT+LHvoboxz76URFx2GCvmWK1tMnn3EkJSRzt2lJevzHN0LMOj+wtU+h53qyafr/SoFYu8+cd/zGd/89/y2//5E/iBT6m7hrF4Ee3IMcSlVRZLp6lYPk9l0xRMi0ZM4OL5G6w7Ch95qkBKCXjbRIyN8hZnoj7/5l6PRQVGRjKYkk9et0nEBBblKF1rwAMPnODSzdt8+oXLZKfnOCUmCH7/D+l9708iKjrTfpXnjiT4hU8v8e5pE6FQwEnNkfVBOXyK/3Q75O2r3+BvPPVOUobMQs3h8r0Ko0NHmKmtM11KcKsW8EPPnWO+K1HM57l9r8HjR4t0XJ/RTIRUKsLF1TaqJnI5yBI0ZabHZBLJHIWNOlIqxddXHfZnFXxR4Y2KR1YKGNcdGl2Hqm+itctc8nwOliIUYyqeILDZHvDG5UW0Qp7haIVCMcnv3Lb5W8UeVzsBd9sKomjyroMm13Z7LFYszlfrPHdmhKl0FFmGfMxgIl5ipW+T1GM0XIPUxjWOJcaYrwa85+gwBz/2Ea6s3uVHf/B7+fiP/RRCYRo30BiW+5Q3N1ir9xiJSPiayyDrUTQ6BKKIYld5/5k8ElnOfPt8tvttwkGXx8dkukNRfETycZ2rS10CWaPtBWyVO3S7fZyMTNeWEGQZLVQoGCZrjQGvn79Ns1KntbuBEMmgxZMIkkhyZIKd5RUG9S0i6RLJYpra8nWeePJBWoMeD+0vcXtlh4EbIAchL750HrM0yVC2S3NrC1+L8OSRcT78zCEu1wQubJb3hKOiyoiW48KFWzRtEUEqEu1FWKsZdNsD7JiBFepUq33GSzHWGh7JqE7P9/nE8xf5vo99iNTUYX7/N3+RTCLBT/+Tn6HpKvzmF9/kn7xrP2Nej+6gT9WRWW6FrFgiH31iltTGLa5Uehgj45wdNzG6OwiyTChreLvbBOkszZvzVDoyQr1K8tEzqKFFEDdx210CQcLp9QgIcVoNPN3EDwS6voxbb+L5AV4kgdsaELYbBEENkBGUDNRdBkNjtLeqqJUWvXCPZmMJAq1Kg24goPa7aIUYlp5iYOYQNJ1dQWB/awU7EUH1+ki6gR5uMZRJMJlU0BQRNZrggQwcyh9A0QQGmVG0+8tQSgDQExRyM0Ms6Qmk22/Q7AScfOYUYrtKxRaoOgKh4DCUMfAEGcHtgKQiuD0UWyJAou6rCAj4noAhyODaeGZqbzdOUnGNNBnRpBfs4QfXD8hKA9yUSSB49O0+bU8iZwRIIth+QNuBqC7gqElirV1QVYT2Lv7IMeg2wQtZ8LKc8WwEx0ZQBbbEOJsDkYXFFo9Mxkhu3MRJjYKm4iHQKTeRRk3MIMSOl+i7PiICWcVjw1HoKTGar1+levZJHDHCW7bMiaBMc2gOuXWZmOjSFnVahAR+gKIo9L2AhCzxxlaHA4Uo1Z5DWpdp2QGKCDFN5uRYkprrU4ip7HT7fOPleXQxpJgx+fvPHcUrxVDXXUQlhx0ITA6nEA0bVdPw9s2iJh5EidjEz5zGWblL6/wy6ZNHuaGa7JM8ti4tETuwj1wxwZHmIrISR04mUWdnmUjkmD5cYuf1GwxlTDI5jdvfmCf16ANc/vJrnHjgIGYmzqDc5fKbq0QliY3bu8weyPLin17GVEQeevth5K/P07pXIbW+QdDvo6WSpPDolw4jOi1qN1bQclGaux1GDYnllSYeMOi73P7mXUrHplDMEc785Pew8gd/wpl//nZql28jOj3CXg05DAi7bapXFhh6+4Nc/defoTpw2BlAc71J9Px5hLkDqFvzCKks9uYq9//TZzjwY99PUehh1Sv48SE8JULLhYQi4XogigKK4DEIQU4NYRAw8Pa8tQcI7FgiI5E9ms1q3aLSGhCJqURUEctyyMQ0qq3+nwHf/2f9F7Gb8F3EDP9V0Bb+e6M9fPCDH+TChQv8yq/8Cp/73OcIw5CDBw9y4cIFTpw48V33/Y5oDwCaplNvW0jNMqnHHsHtWIiqxKAzwO57JESf9c0evhcSTRuEbsCBo3mu3KkRDQN8L8SpNxBCsOsdJDOKb9lUbm5h7t+HnomQmi3hdjqMP31yb7IaiSNqKnJsT2QV9toQBKBEEPpNwngGOT9E9uAIYWWdxLFD6LkcajpGYA8QjQjt5fvY5QpCt4ZQmEAQRNBMBFkG1cRauAFun0FqHDczjhj4BKIMqo6QzCMkc4R6BG9nkyCSQlAN1JkjhIpGWC8T2ha4NmHgge1AJAmKDnYXP4DA8xBjaXw9guA6CIk83tI8tYX7NDbr7L61jBSLsPH5r6JFFZRYhJFnHmDubacpdwX+4z//z3RbAcZwgVQ8gvvlL2N0t9mePY4hhFRdgfGUiUBI2/J5sxNjdus25uQ0v/gHr3JrvcYL33iTSR3OzaRZcTU+94Xn+dGf+Al+6z98gufmJvjZX/51FFNFW56nee0a52IuhzMyq1qJdqNJOz/Ju04leVLYwty4joxAoZTEPf8qu0qGpORxyF7ii6/cYr27x53WF14hG9MIFY3UUJa//0Pv4vH3PEM/mmfi6ccIjp1m9c4yluez9eJrhNUaY0mV9isvMZnWqV98lRs377KrZPiBYymaUwdYbw0Y2AM+c2mDB6dzBOPDPF+R+eFZhQ+Ha/zg4STFbJKnTs8yNxSl2+0j+AHDKQ1FCMnGVE5lFB574CCKofDmcp1GpcVLby3z0GiMkaTCV29skdAVlrbbGH4P6cIrZO8vcELpcLUNmZjKvXqfF5bqtAYebcfnW9fvc/V+nRc6SYKxGd42pPKbX76CtLJMWglouyHrHZv92QjpqMIDMxk0ScT3PeYrFmOah9LYIa4EdKtb1JtNVpU09co2TmOX8wtbbJsxhg6f5h/9g79PLqqSbq8QqS0T7+/S2d0i6vUIrB4b23V8D4orC9h6kkor5MVdlW8sevzany6wntjPrhvls6sy88Iw88IwW9FJWukp/v031/nDt/r83jcX+ezFLb7y1i5vbIvYZorsUJHAiPKN2y1eu1Hnzs1b7FZqkBjFQ6LftXj8oVlam/cYVFYxEhm0eAZRjxCKEb72pW9gSQaf+sJ5PNsm26/QRqcfCLi2zdK9Lcodj54b8vk3bvHFL79GqzUgl4mQzRg8eXKSVsuiaUHoOXspc6pMNqEzM5bi6r0aG5UuviTQG7isN2zubLRYr/d474/+JF/7/d8mYqr883/1a0ydepi/9yMf48b8XR48c4CKmmTLNbkdKTLkVEjcfJ0fnHQYNQUSU7Nk8hmmmvcxRA9RNwjaNWhWULJp1F6DiOySG2yTGcmy9dKbpAWbxNoNcp11SpLNeDrCdFIlv3SDQzGPY4cPcuTkKU4dnOHEyROcOXuao4cOcPbANIdOP8TRsw9x6sxZ9s/NcWQkRao0ihpL4Qoabrjn6NEJwLP7NPyQzY5NdWcTa+U6vTsXsBYvsaYqtLyAm2tlbu52WfQTpOqLlNdWaDRbmJ7FleUKiZiOKKlEVYV0Mc2aYxKG0K62ueGncQcWtbpFJJ/ldlvmt5f2uPstT6E28PnG/DY9NyAMfELFwNdjyK5Fy8wiCCFa4LHdaNMTVAIzCa61x1vutRCbFTwgGbaYyRhk/A6hrDEclchFFIxBk+GohBsE9Fyfpu0TVWXcIMDsVQjbVQTF2Lvm+jYEAZ7toD52Dm3yEEo8idyt08pMsrhS5vXra7QdkLpNtI2rCN4Av15By+TQtpYpmyWWmj5ir4nrONxuhSQV8LttgpkZeu0ed2rOnr4pkeaV5Sar+f1k3RoR3+J+wyJuyjx1IE/b8Viq99nt9GnYHtudAQM3xA0CKj0fTYL9eZNmzycfUbhXsbjw4qvMTA9zdjrH6Sx0jQxTpTSrtoITBIyoAplikaipMzw2jmEohIKMrCkkJgo88J5HeOJgmpHtO2i9CpPPPEA8ruJNH2akfJd7n/4CPStk+txDBH/3H1F63zMMnRjFTMdYvtek03MpnpnjbR85Q3w4x6DZJv/MY3TtkJHJNGXLI5qPc2hfntWNFomhNMd/4Alu3qsjmQahPaC3sUFY2cD0O/iOy+gPfz/NbkBcVTh8okjOVMgZIo8+OUMyqmFVGki5AtmhLFNPHqV5e4nsqQOgmoTpEYJEAbvVIXn2DK1ri0gKVAagAv2yhWPZiLtLBPUdgsI+vN1Nli+tI6ga7t3rGOOTIEiobg9FlLADCdvzUYQAIQgwRQ88HwGBiNvG9QP6IYgIBCF0bY9632V1u8NEPkq/72Pgocoitd5/HSt9tzZnf11/sVzX5eMf/zjJZJJPfOITXL58mStXrvCJT3ziLwV84b8F/OoGu30fefo4gtVEMlSs3Tr1jR49QSAcuBR1kfxMguL+FMVTwwiSQDGu0nY8BrUmOy9eJwh87EaH1c+/SHdlA4KQ+OFZRCRESST0Q9REjMB1Ca0OWi6PNjyGGI0gDE/jVjYJrTaB6+LencfabhI7fBBJ17A31xAVhfTT78FtVHE2VzGOnELZfwohmduzFBIEhFiKMFHAWruPVhrZu5kBomsjuDaCICKYSbyVW4S2Rej5iJoOzTJBq4qUyhK06vj9LmEQELgOYbOGYJgIgx6+ICE4FlhN5JFZAj8g9FxETSeMpsGIsPDpi6y/9BYH3nsG2e7glKvI6l7+uN+qkz52kOwDxzDSMeIxB3nyAKUnz3L0yaOEQcARo8fieo1dYnQ7XUwRBgeP8cwY5IYTPDNY4kQ84OaNBb758gVee+ECk0uvkbS2mMqmeNs738fm4jUemRnnxpWb3Ap1zLEShfc/x8bv/B6JqMmdSo+dnkt0+z7JTILxiTheahh17QZ37QhS02K7OcASNfK5LKeCCus96AUyV81Z7u92ODgUJ50wSWhgGCqlTIS27XFTKZG4fgl/4Rbf0EYJPItfeN8M2fc/x6evVgn2nSAsb+AgUjBEDukuqVSSF9YC/K01vMDl4bk8VdGkkh3H/eaX0VeW+cCUgmP3UdwBmiLz0uu3WLq/zYFiglsVCy8S5+Zuj7/17AGemkmxtFkhjETpDlxOqBbvm03xu1+fJ2LqmCMTLBtFarqJkclxdG6ckzmViYtfRRV83lpvIwkCP/3RRxkvxhkrpuh5Ag/qNQ5Pl2jWyojuAKtZY2VljRs354m3t3B3lti6c5XanbdQ1q9y7dpVLux02Vq9z+rmLr1Wg64n4JkpIrkSWrZELTJEz8xh5oc5evIs4Ytf5Zd/6Zf5xJ9+nc+uwq9/dY0v3W7z8rpPJpNDOPYIX720SvviDZ47OcbD547yzsePsrzTJZpIMDNW5MRUjvceLfHOCR1REIjlS3j2gFhpCjEM8O0OhiwSInBns8mVhTILNzcIxJDSgROoZhxZN0iUxvGcLq98/Ty7y0tECmN755rn7D2syhJKvIQsipRmZrhZ8Xjpxirb9T6z+yaJZ5LoiQx6MsWTDx3G8UVeuFNnWA8pxSQ2d9rULBdVVUgPF/DsAfFklFLaYH2thqnJRBI6Q3mTSmOAqohEDRk3CNnZ7TEyMcaT3//jfPIXfoZW32Xy5Dme/PGf5RO//E+JttaYSRkMzYxxxrRonXgC550fwejsIjTLeGac/acPMlxUEDRzzw88DBGTaQTVACOCkSuQGM+SHE0xNSzvTXxEAXtrh/pLL+Ks3UVUVFInDiIKIgIBgqTQRkcJA7q+iBqNI0QzJHQFVdFQ/AGFoEk+bpBLJCnkcrxVlZBUk4KhsU8P2F+/z1bdI5kb53Y/ydF8hKMzY0RLk0xMzVAcHiWSyiGqBp1AptFsUdnaYOn2PNt3rpFqLbF8+Tz3b1zi6p173KxYbFSqIICQL6LbLcTKOpGpcTaWN3jpxauUdy3stWU83+f67oCmDbvtAaEfEAR74qRAAFGUyQY99LDPhbpEZWuXHSWPZ7sE9S2C1Tu0Lr/BpTvrKP0m4ykDx/OJij5ZNSSqiBSFHllTQcND9vvoskDX9dlyDAJZRZAUQiNOoMcQezUCz0WKxTld1BGzJQRnwI4YYTgd4UBG47lHphiOqZAdYrC2ymB7HWlniYgRENa3CPUYu2++zq2Gz3K5hShJBNUN2ttbRGQY3j/DyYSHZpjcV0vsVNukAgd/a4Vc2GYuG2EkYbDbHaDKAoIgkDI1CEMMSaJmuViOh66IfONundc2erQGDpsdh3srFZ595kFmRtI8fXaathKn64XETY0jhRiNgY/QLROVQqJecy/+ul+Hyn28xUtIgc/4wQNogy5zZ6cxAo+NS4vkTx0lPZzC3d2gUmvjpfKozXVOxy0kb4Dv+lx6YQGFkEg2yuDqZQonj9DZ2EKOmPgdi6eenGV6Ms/UZIoglaa5UuPkVJb4sWOYDz1IPJvAGB7BGB/DH3hI2SL2+n2EXoPFFy8xce4YqaE0hiIxeyTPuz92jtmnTjLx5AxK1EDKDqFpAYnZMfp3FxF9h6Bd5StrLo6eIAglImMjtNcq+LrObEbFA6ptGyUZw6tsI8YSiPU1lJERnL6PtbJGMHaQIDuEGwRAiO2HBAhYboBIiNzZRULYozKKIKkGPRdEAYaMADcQ8YIQQ5V4+FCeYlKjYzmUshqWG+KLf37jXPz2oPcvY3P2124Pf74UReGzn/3s/yu9/xvAr0mt2UYQQ/BsEEU8yyE7naJreRhJnWTcIDWcQDUVupbP9Us7nD1VIp4yGJQbFE6P01reIjKcJfR99EyC4ulxhJUF8icmCb0Ar2+DJONVtxECH8F3kaJxRD1CUNlAHpomCAL60SLd9W3u/O7n6LcHWJU6sQOHESMxGgt38BwfIT+Gt3QDv7YHbt2V2zgbywTdNs7KImEkvuez26yj1ZZRynf2JhODNogyq3/6DdqX30DJDyOoKkGnDr6H4DiEdh95fD/YfTzbRtAjBO6AEPDu3SQorxGUZglrW/i1DYKVeRDlvYjoZg8tH6N0cJKEHtK8fR+zEKV1fxc1rmPM7CMY9DEKQ3z/b/ws8tg+jJEi3eVFhj78HHf8FOHdGwi9FjIC1+9u4qkxeu0WShggltcQC8P81BGVD82Y/JP/4SkUwWXHgi/+xq8zt/8M6p2b/OQ//Kf8+O/9EbP5LGdMB6HfZebUDKmRIcytZQ5FPWpCHKvZo7N8n+2dkPU7a7SI0HNDokEXd2GRR1I2bwQFJsZLPNy7j+V4KKVJ4lENAZGHizJRVcZQJN57pEgyFmF0fJSvBlnc69eJ3bmIGYsTLwwznE9wJCVgaTHckTnGNBfP7pOZGKYbBLxjLspPjVis3V9h3ktyejpLrDjMyN/8OLWOy/jaLQw8nGqdxyeirC/eRVIV7m7WOFrQsRoNDNHnC3caTI1l+Iln9vO3PvgQ15Z2+dTFXfqSwimlxwemJKYbtygmFDpDc/QDibgcErYrtL70FZ7IeswZA0ynhdBrctTsQPkei7ducqXpMyLUOTqbZGVpEb+2SSbs4dr2no+zoiHEsrSjw3QzM5w5MI2AQnz2JJNHTlIYm2S8vMa+QoyumUOSFc5fXeZPXl3lCzebbFk+yWc+xId+7rfYXlngodSAc4keOzev0lpf4xsLNX7rep3HHzjEzbEj/Jsv38asrrEexgh6HXLDRQxVYeCHWF7Ay4stKu0+eswkkogQLwwjanH0ZJ7N1XW2Kh3ub3bpWB6KKiIiEoQi0VwRSZKJpeOEHuysb4Ddwxv00GNxFE2gurJCKiaSGx1ltJSk6wj4mGw0Xaq7TQaehKoqFMdG0CNRpqeGODw3gZob5iuXbjOwLPq9AeVKh0pvj+ecGx8nnjSwbZvHjg7RslwMWaYY08FxsZs9QsellDIIJegNPE4/+DAnH3ua3/qln2fg+kSzJf7ur/wmn/2tX+MrX/gCNdeHaBKpvsUD+4fZOfQ0b4UFAmTiToegvktYWcNr1wkcG7fVILQ6+J0mfreBFIsjO10ShSRSLIWaH0KOx1ATEeydMsKgh6LpCLkxhH6LQFLQBB/Z7hDvV4mGDmVf4VqtT63v0JJidH2RtpHFVVVUVWEkrDGTUpBTOZyRfcQPnmS6lCDe22Z89QZXnQRWpUxMhrjfIZtJMj1cJDG6j8OlBKdSAscOHeTJYZ1njoxx7uAEZw/PcmR6hJl8glQ2v+f1G0I46LJRqVNtNsmaDg+eKvH2STiu77LV6lBdvEmivYM5qFGt1rnfF7HMLIQBgRpHFQMG9oC+J3JQs1jqhvz7l1bwzBSL92os9gQcM8vzV1aROzVUv0dXSbDeF+mGKrI7wMwPIQoiLTfADmQcDzzPRReDvUS4dJFBdYd6N8AzUkilCdToXty5p0SQNYO5SEhUCnji8ChPFCAmBXixLNrYLH3LQxiewdcTdIb24/VajO8bo+tLXGyI7HZd3H6fmiNiH36YppxkoacgNXeRFZ2PHoqRvXEee2cbp92i5/qkdAVRFJEFgSAIkGWR3ZaN5/lUeg4JXaXctWnaHpIsUOsM+PK1LTTB44lHj3Nns4HTbrLuaCiBB4JAJqaQ1CWcb/0pUuU+YbNCorPOfTGJvReqxmDtLurOMvLYPpzCJLG3vRve9yFalRqs32dQqTMud6jduM3yK1fQFYWunmHj6jp31lpcv9ekVNCRBY9BfIg//a2Xufjvv0LxUImRUoTRZ89y+kffzp1XbxMKIgcem2Zw7y6vmQd4+md/DEEIEFNFUu/8GwiFKWRZwlu5hXv5GvOvvMXEyVEURSB/YpLpp85C6KMYOmYpT+D5SPEUu69cRokZBKJEsDLP3aqL2tlFSaYI7T6xySFKc3n6A5cA2LU8Pvk7r7J94RbS6EEEPYqUKhE3FRY/+VUk3eB+Yj+hrGGJJh6AsMf3VQSQB20IfNoYe/RfSSFviHt0yVCk7YYIgkhCkzE1iaSpISoiuuBT73l4A+u/ipX+MpPf4K9o/fdU73//+/nc5z73V973O+L8AsiqRrneRuz30DJ5WvcvUTw5QSiK5CMbyDGVw49PEB1KYdd7fP1z85ycyxI6HidODe3dBKpNnL5NwvUpnZ5l0OwQySZAELEqNWRFxSxlEQgIbRchXyIMg73ktX1nCOZfJzRiOKOHaVUa9JfWmP2p/wFh+w6ipiKEPmEo0FjaJlWK4xdjCDOH2XzhFWaffhRvd4PAA0vu4CzfJv3c9xI2NwhlHQY9wiBEtHugmeB6RKam0bIppGR2b6tOM8EdgN1DEEWwegQDC7taQy6M0767hpwpIkTTCKKEL8iEzV2Ukdm9E3pg4dt1Vl66QvKRk9DexhwZY+GLrzF1dhzbgZG5fSBB0O+QiHWQJg/A5h38tSV2GgFHoiqFoI0/NoHbjzO/1iCzvUY4Po3Yb9GoVgnLPezObSZmxhnJpJjdus/DE23s4gzru9v847/zAM3tKhOmwCf/zS9iJIr0rr+COlVAt1v0BBU1m+Wg20ROxGjIh9jchPLCJvL2ErOPHsLHR/j+D7HrRkn6A0bOv0p47nFe3Ory/ZN1LCkLmkrPDZDkCBHJY65oEJcC6qLCsYkM4fuf5K3rd3gqGzIxMYRQXobSIQ70V7hiHCMZV7nb8JkVLGpGkv1R2MZkd/o0B7UAM22Qihhoqox0+CxltUT7ykscO9hDOLWPz19e55/94OO8VpdJ7NbY0KMcSAmMyjZSJsnlnQ5zMYUnTwzjWgNeSqUp75ZJTIzSqZT5+q7Jg1NpOpUNbldc7CDYU63/+A9z884yoiTR62hoikxcMRgEKsWkxJ9c6TI1N8E7hzWuuXGGFJGIodItd7hbsXhkRKIe6owYGmEIO6HNmazKa10HQkhYdTZL0xzu1Wn4Ef7zZy/zYGWRkTMP84dfu8CzRwrYo3PMORZSv8l8zccw4zyY6XHqbzzOv3v+KtnxMa4u73IqOuBfXVil1zJpeio/8b6T5KMK5/bn2Gg7BL5AJh/n3k6Ppw5kcDWNxZUGyliCdt9DjRv03ADP9xFFETOZxOq6tLfXkDST0YOztBs9ZE3FBuRIHKdXx25WCAKfSH6UH/zQk3DzJluqhBLRUTsD1FiKTq1MZ9ehMD2LGQs4cmiKhiOQjOtEPZfTh6Z54fouYiLKGzc2OLCvxNbAIwT2jSe5Pr+BrA1TSgnsVi2uX11mxFCQBz0cX+DicoWe7fG6HdCxHeYefRc3Fha5+IXf5+x7vxdXkBl55m/zqf/wv6O7bU6cfoibXpJS3ebqhQVOzpYQrLv4eHidNs6dWyipOK2r11FzGbRikd7SMrIp45fr6OkE2tg0RGKQGsaIpul89o/JPvUAghkBRSdUI4iBixz4IEk0wjjZxk0GqSl0SWBYE/AEEVUWiSfiYLXA0OhIOmnRok6E5VqfkZSBNrWfw+IibrrEtXuv8MGzdRbUIl+70eWjh2NML82jjIxjS0l0MwLqMHG/AYMuQn4Ms7HL4O49UvsO4I/NIPoSnd1NfDdgzRhBkUUqq7s8e3yIXqvBwvYG2ZjOVqVNUvcQJZck4JfbLO34LAsCmiKjayqGqqJIInosQS4iI4s24ykR1emQycb4j5UMT80d4vDgDqJjYdhtAj2DL+k4oYinaPRClUhgI/RbxFJ5HD+g64aMGCK+4yOqOrthSLCxTSK1D0FS8HsdhH4TwQ/BHZBp3MeT9iHHMijrizjRDH6ihByNEK6XCeI699oQGZ9B6NbZig4xaNnookBMkzC1CG8MVC5tBSTMASvNPlpKp+j43NryeOjAERJ6QFlMICIgSSIjSQOJkFAAVYS+G7DdtMjHTXRZoC+LnBiKU7Uc5G+HkDx7MMe17R53r17jb52SydQdrlgxZg8doOULHDL6GKUcXmOXppQgHhMoexLTxTmEN1/E3q5homIWRnFiaRRFYG5GQLp5H1EWwfdo3K9Ryt2mM3kIZBHFEUkMJ0nmm/RrPWRBoLnRpP/aRbrbDVa3BWxrQHQoTXQ4Q2elzNp6k1xMY+A4eEaaB+UKFEq4925gO6tE98chCJCLU/TnzxM6HmMjCfRCltBfJnVklvbN2xj5BHdfucuBVAzHeonI1CiJ2VG6K+v47SaceJofEhREyaP+p39I6uxZkuMp7l27g5aIQa9NOqqwWBtQvbXF7LensKHVIJk32N1pcuz+W5QLjzEsCJT7ApoU0nNCUroMYkggyAjtMpY5hiaALPpokkTdCbAcsFyflCmz25dx2jZ9xyHwAkYSCs1+Z8/n3xcRZXlPEB/6wHcfbQx7lsF/2cHtf0eDX2DP7eHnf/7nOX/+PKdOnSISify593/6p3/6u+r73zD51XH6PQIzjiCJpOaGCIKAxGSJ8X05rIqFEtHorDcwSikeeGic9HAU3/ZRowqe4yHIItauxcbrd5GiJt3VHexWD99xsLbqBI6LKIoMqlXkbB6UvfQ3HBv//jxoJhtff4XaQEANHQrveZbk4QMoiTja6DihIFD95jcovevtyF6X3uvncStVdp78Hvq1JtroFPaRxxD6bWIzs2huByGZR508SKBF8JJFQi2yZzYf+OSe+yDG7GFQTVAVyI0jaHtqZcHu4e6sYFerSKqOvXqXyr1d/F4XMZUl7DURV68hF8YQBAFBFPfcIJpVtitd1ANHMGZmEcM+9dUmcjTOxPd9aE+UF/ogy3gb93BFhcCIExpxJsZNJFFAcm2iyTgP62VGG/McLUXYX7+FK0jETZ3k08+Qi+sIY7PIXo9Bp0+/bZEJByRiJt1YnoEooOXyzM7uY1VOESmmkRLpPb7ukVmCIOSlbpTB7g6dQOJbVhTn4cfJfeiDnO9GGIlJ8OI3OXz6AF9aB2vmEBPdDdLDeepqjvzWDXxnQFroYwfQDlVEUQZ3gK4IDEI4OJHj0XMnmTp9HCJJZCOG3qtgHDnDyYkMY9kIlqTxUhlSUZNE9R45p81vvLKCMjKDQshQXGOn47Db8xidGkZ4+An87CiZmMzjwwqhbqDaPXyrg0LIkbzJ8eEE0wkVU/V5cX6DFy7cZNCtUhKqPD4KpcEqt3YbFLUBd9e2iCgiohllqFRgZmqG/UdPkjrwALG5M4wfOkFpchYjO0S+WKKuFXn/U6dIZ9Jc7ZsIG/dYag5QJYmEoRIxZTqBQiaqUbZ9QiGk5stc00ZI6grBtwM07GiWK+IIhiJxav8EzuGTvPPhg7z7zCSXNy1WN+os31zkH/ydv8vW1/49+aE091Kj2LUdPnxqmGRrh6wuI4c+Dz56ivk76xw7vo8dT6XnhaSMvWmS7faZSJv86COjfOjxfZyazfHo6WHe+/g+ThwfJZmKYPXcPR/fpI7v2IiiwND+ObRIHM2QaVdrRHKj7DHlQBB1zMLEnvhH1Xj1xjb9bIlvfestNm/c5AfzbSRBRDNNAteiUa6xurjMkydGuL+6SX50iDMn5zg1mWB4Ks/J6RzZmIYXwvhIAsNU2agOePj4OEsbLTRFYnu7ghKP4boOS/c3+egj+xDsNvX1VQLH4cVvXOHli3c5/v6/yebKCl/8j/+O81fucXBulEd+4he4+OqLvPrJ3+CIVyEhB0ivvcCXfu7X+bVPX6DWc9i9s4ljDaheXwRJYvPWJm7HQhJDrO0a9s4OXqeDIOsE0Rxi4CBkh4lOjSEPTeI2q/hWF8FpQxjQX7pBYxCgBg5et8u9nRpSGFCq3oG714jXl5FlHV/WED0bSRA5MVGiL8psbuywXLWo+RKOHkMKAh7MeAzls+yPhySiOp++3uSTmypXF9ZIBN/ezYqXEBWT0IjhDyzC2gab9T6VN15HrK+TEFx8UUbXNN5VcHnfXJoTwxFevd9DNpKcPDLN1EiWIDXCUsXnP12oUY9OkhmbIjp2iEP79zMyOU22UMTUVbwgoLyxyvK9e2yvrzPkl3n9Xpl7VsADsTZmb4snMz26gYDXbaEKAYoArufhCTJRKaARyAiyiu177HQdwlCg6orUlAwBIjnVJZYxkWSFvgtBp4Xr+sjbdwjtLhiZPRpav4GtRFBlGdm3CSQTaXQcQTMZioDg+Xz+1dsEoUBcE3l7QWC93MCvNQlFUESBarNPq+NwvyMSUQQKuSQX2yr/5rrPchBjSPExZYFDWQPbC8kYKp2BR8PyOFSMU0rqWG6A5YXU+g5ly+ZwKcZUPkrSUJhfKvNPnpkmqWtIZoxSyiCpSYzGVEIEZCNGkBlDSRVwZZ0wBEMK8QUFu+vRqzXxKptovQpC6BM3TbThUZo3bmEO5Wj2A7rlOqbkU/vG83SuXcE4e5K5kQSiIPDWlQ2uX1qntzCP64cIaZPIsZMkZ4a49fwb6AcPMXFwFEkW0ESR2NkzxJMJyl/8EtXLN2gbOVwvwKlXELtlEGDDhcx0iUGzy8hTx5CjCTLPvpdAULixVGXr/CLVawu4lV1E06DfcsH36MVKpCQfv1nBbbYJ7AGhrJI+vo/p8SyF0Sw3qwOsEHpOQNCpItgdcH16loPjetgNi8m0uScTkgU6rkffD4koApLvYSVG8do1/CCkHwg0XZFBAG3bp++GuL7PrWqPiCqSiapIosBQzsQUPHZbDngukmEQeA6h5+wNxPjrdLe/6vrt3/5tkskkly9f5jd/8zf51V/91T9bv/Zrv/Zd9/2OwW8ylcS12tiiiTg8jWTK306BkZl5fB/Fk+PIuoZiKhAGZHIGkWIa33KRIzqxyWGS+8ZQUwZhCGo6g5bL4PcsrJ0qYRASEuL3+9i1FqHv0b52DQKfQNGx15aRhqbZvbqAb/XQKvchnkWwu5gHTxBWt5Emj+E6IuHyW/gPvRPj6fdgyiGT6QhCfgRPi6IkkzT2n0U98wTe8nXCVpmwuoYfz4ORIPRdwsYW1HcQzQSCphGKCkgqomsRxougGwiJLL5lYVdb+LaN17NYfmsTf+YMNHYIXQe/2UCMZ2FgQRAgFMbpD+/HrXRIzo6xUjiMkkwyfmSYxLHD6OkUYqaAODyLNnEAuTSK3NwEz0M9+zbih47SixaIppK8tdFha63CCaWFdO01wouvE3oeHU/CNHWy++fwl2/ilddQFWnvydZt723VXrvJ1HSJWmKEN16+xbDfQp49jpQq4e1ucXQ8yq++UaMku1xcb3Okdof83AG8eIyt+BCOHXLXj7Bc2IdZWeHhAwUmHzlDe+4B3j0T48WVLm/2Ikx7NZRuhUx5CYm9TPV7Aw0lCKl8OxpTkiRipkpK6CJGE9hqjFDR0QQfUxI4mNWYmZ1gIGiE2TGurzX40acOM6q7tFyBnZbFVEohY8pUBz65qITX7aLqGo5j8UDa50NTAinFZp/WpLK7QXVnndbSFQ76mzw1JtLb3aTV7lO2ZeLFMaTSLEJ2gvTING5smDe3QozCCFUhTjqTxlcj+AJ0bZ8X79aodQek+7s0nJAx3SF0BiQMlecvrVK//Baz2QiCZ5NU90QusqrjOTZxGV5fbdHxBCzZ4PJ6k4wuY8sxYtgU4hFsx2MyF+Ph5IBozGTCayHrBteu3uX24hov7kj8nX/6s7z56f+Df3w6wr2Gz+deXeTvnkoxW0zwey/f4m3Hhjny4Em0aIRdy+FzV3ZY2LX4wrVNxmQbJwhZqFi8udGi3NvjvCciOiNpk2JCIxJREAUBzwto75YxEyp6NMLbzu3D3tphfGqYfn0TM5kgVShx/KETTO6f5MFnnmb64DRvXbzJH7x0i8Zunacf3k+0skY2rTM0d4jkyAyKmUCT4fkL97lzZ40Pn8ixbcHlxR22N+ooisDi/AKaIhE1FVJJjZm8iRcE7BuOs7TZZnunRjpu4IkaxX2zfOP8TT50PMfYSIGk06CQidDpDljdbDD69I9Q69jcf/H3ubbSYm5miJ/53/81iwOVn/+FX6S6usL4Y0/w9JDJlOuy/tYq3WqXzYuLfP4T5/F6NpFsiubVW4iGgdOzMfNpfEElVL79cKxFCKvryJpI0Knjba8RVNYIQwFrdYXexVdw3niZSL9GO1qgSJer210Ew0ByegTRDGJjBU30kKwactAnzJbY7Hg0jCxful2j2vfw03l25RiHv+fDtHyF18sBD06lKWZiOILEy1UFMXDxXQcnDAlEgSA/idyp4na61HZ6lKUEoqJg3b+N7TgohsmMMiAiCzywL8u7j+Z5LGthJjMokSRrdZtqdoIff3Y/fiBSD3QWK13SEYlEIk4sW2JyOM+R6THOml2eHFJ46uAYR44cpTg2SSKikZEdeuVttrsOr+96vLCwxatvXOT+jcus3r3N0r0V6ltrKN0ycVPDwKfj+GiDOhttByuUsaMFzEiE5MgYnqLhBAHB0CzW0h1aHgx6A1oXL1IrV/EFhcvSGKg6C02HVnyY9XaAoGoEpVli2/NMTo/TdXx8RLbaNorn0c6OEsoqx3IaE16NmGBzMh6Q1gLShoqeTPG169skFJFCdwNVEjAUkdm4yL60QTqiMZrSSRkqIiGrjT4bdYu1hkV/EBAzFV64W+WbN3f5yXMlEoU81rFH2ay2aQka/VDADHroqoyUSKIGLm4kzbyXpBBVcJQ4WqlA/NAM3Ss3ESSDnmSCoBCs3kCWQH/0WXKPP8rciSH6W3W2/+grbF+5jdeoM3LuYYoZlZGsjqxr7DYHXHxzFcfzMYfSWDeuUV/YIFXME5SmOPdj70HyQjw0wpUFetEctavzNOdXCVYXad+5y+pXX2LnlddAVnj4w4/SWN5l+atvkZqbQD1xDoEBZjFL3lS59voqjdtbyJqOIIiU3vU2lGwJI+wTOhbCoEegxXDK2/S3aiQmhznxE+/n2HQSB4iIApPHhwmbdbzkKKIqs122qKy38P0Apb1DxQYJkSCErOSiieABXgi9wgEkEXZ7Hl1PoOeGKBIkDQlFErEdl3bfI6FJtPouiiShuQM2dlogyeix9J9hpGx6D/D+pWgPfx1y8Rfq/v37/z/XvXv3vuu+3zH4nRgq0O/1qAcifnmDoO/Q3WkihAGCCNHhNImZUVL7RhAkEbczwG33iBaiewEVpoHT7pI8sp+Rxw8hxLMkpksgSbjtAZHhHG6nD4ASNbA21iHwwExQe+08bq9PX40QHSqS6W2iZAuoo5N42ysIng2xBH63Q+y5DyPmhlHtJtKggVYoULK38Ef2sZmcpNPqkO7XEQQfAg/n3iL4PqzOg+8SNsoIegxBFhHtNiAidGuQmwZJIUwWCFUDJBV17gRyaYjW3U2UmMnJ544TLFwgcPqIw7NIyRQoKogiYmGSwExx3h/hwAeeQuoP2F/SiZ15lMmf+lEESSWQNRj0EQyTMJIhmDyGXa4ihy5ieRVJlWl3HapKDmVjBb84jNypsPzyLSq1Abrj0Lt1EbFbg3QBsV0lHFhER0ZInXsWJZkmYkYxe7v46QK95QUaRpxEr0FQWSdQdKSpg5i9Ms9O61xrKWSzaUinOCS0WVipYNQ3iTldRkKL/v6j2N0mphKS3rlNTuxjCRpPjKqc1Orc6in4WpxyrUuusUzE6ZJWA2pOgOOFNGyfEAE3lLHFCKEoI+k6ou+i2m0kSWAQqpiqjCxLSOkSfTNFJKJR9g2Go3uJbGrgU6/VMa0a7XaL+Xur9O5dR3J7rO5U8DyHYyNRRrMJekqCFSFPat8JdmNzJCfn6LkG/8sfXUdIFlkZ6PQ8hblBmXx9mZH6PcZiIuWdBp/+wiv4fkBOh/XmgI3WgELKZKEjUjeyxFRIJUz6iokmiTx3PM8b8UnKXZvzyzU6fY+txoDQ8/BR2GjapAwFRRJYrPZ5cDxBTrRBVUjqMqEgsC+pkLIr9JCZX1zllR2feMLg7WaT0XOPMZ6JMl9X+fgP/BD/8F//O972+HFGDx/hNWWUbn/ATx/WWdps8N7HjzFXjJE3FR6YSvGlW9vIgsDLlRD8gJfu1VnYalPv2AiCgBv6DCUMYoZCr1whElEoJHTmTswiSSIxU2W0EOcfn47zwYMazx4b5uRjj3L49BFKY3kihswvv7vAXF7GalVJqiEPPbifgRPwcnSGyePH0FSZ0vQs73jsEB/9yNPUrID65havv3qZxbtrZLJJThQU3ryxxcBTqVa7tGodqjsdQkFgJh/HR+DURIrplMStpR3W7twhqYXMDqe4tBvw+IMHeOcjcwSuTTEms7a0ji66fPR7vp+3n9jH9pd+iWHdR3N6JKbOcV8Y4ld++V+Srm9y6nuf4Ikf+ygjZw4ydHo/yrvfQ+oHP442MUMyF8XIxfH7fVInj6JmM0hGhICAsN/FlXSCdov2yjZeo4o8PIkwdZK+G1LTM0SHSySnJgi3FlCTWeIaPMwqajRKdGqWQI3iinupkKEs0/dFms0mI2KXI8NxzgTbNPoOsmZQMCVWpSRXXrkMC7fp1Js8//VLZOwmO1s16qJB1QJ7YOOocfqhCskSZMcQh4rcz+3D2qnwrYqE5/mYiowWjVAYbJEP2ozFRbT7V5nW+oiKwmMHC/zwQ2Pkh0vc2yyTdmo45Qrljo+mRRB9m34gITjdPTtJRKTsCJmIwGRS52BG51AhyokDMzw0VeShsyd5cDzH8OgoQ6UcKcnH6fdY3djk2q1Frl28wPz1q2hb82xubtOpbeP1mtiuh+jbOGYGTzTwWh12ql2s+CgLyhhafpTY0YP0bI+KAyPNNdzAx1VM3txxWFaLDLwAwgBG5zi4bxJVFslENAaqyblCQCaq8GBBZUJ3OVSK8D2JGtv/+pcQBIVQCHA8n4+/8wiNbg87M8zAD/D9AE0KqfVdTFlEUyWsIKQ98Oi7Pgs7HQRBQhDhpcUqTq/Hg8MqkWiMl2oKV+sh18Mch4pxZN8lDEJAxC/O4KaHERSNsUyMiYRK4PXZevE8t37ni9iWS2f+BkldxgkEyulp/K17BPE8cjpHz/KJDaew2jbl5V1i555E7VbIzI1y4olZTpwscfLsFLlQQE9HGD04x/0/foGvffIiuSceISL7mMfOsN5xSB3ex/pL12lcu4rsQ3wyiym4VF78FumiSeWNy6x99RI7F27S3m6ROzBEv95FsHtIrkXr5jwT+3IIEgSqTO3mInLUJDo5jtOooHcrUN8GI0rm2Bzq2Wform9TuXQLsVXj+sUV8rrETExh514FRucQAhdn9S7jkyn6dog4qJOzdpBFAcsPSGgiSa+JVr5LfRAyQEEWRNp9F1kUCAUgDNFECdcLEAgJQxFJFLiy3mI2G2XgemRMgbYQA8+lV9lkL80KPvjUSeCvBW//b5XjONy5cwfP+4v2ct9Nfefgd2KMcqMFfkDjrWtsXVhhUG3Tr3dwXRdZVfFabRTTQNJUEASUeITcoSEgoHHzLoN6l/SR/WgxHa+2TdDvI+kqsYkCoWMjyjKCssc39btdYnP7sDdX9nw+TzxGe3UVMZGhmhyH7Chyv4WoGQTtBl7HIthaRLNbWJOnkDaWUUOXsF3FFyS2ux6uluKenKerxhG2VpGOPI4QjRF0GgjdBlL5HkIqB6EPSgR8B3xvLzFIDPesehAItShiPIPQ3CW2/wCIIkoyRWZ2DEUIEJNZJN9DcCxCq40/NAeaSbByi6jTIf6B70EVA0xhwCCUEcdnkab20/YEwtQQGCkYdBBFBXtrk7DbxN7ZpLVwj42NGp+8VuZCZJbs2m3UVJqpRw7gZYYx+h06vkDYb0MoYhw8gTwyh1uYIiyv4HohI4kobS0g6rQ5NF3koVgXZ+oIYbJAEArQ7zAYOcypyQJPTEQQdY1Xyh5G3ORQVqK9s8NE0UQZneZAPMRODeG6EjXBRL74EkGlQt1TyObSNASDlZZDZeBi31/AXbpOVPJxA3CDgFrPpRCR2ex5KHYDV5LwZR1XkBmgojZ3cAKXuCZgCg4Dx2bS9Lm/U2NrZZlb8/PUFy6xeO0inc0les0q9sCjmE7wtZs1zMIEJ2Mh1+0EheFRZktJapbPznaDL11aYa3VpzfwaBkZTpyYw5NkXr65Sa1c4Zu9OCuxCa7+0v/F2btfx/AH+IHK5fKA7a7HSELncCnC6YLOg3mFITWgOKgiKQbFuI7tBTREnecePsB2o0tflBEkiXzcZLvnIYgSt7e7dF2Pm1st+o5Hx/a534OMriDHkuQMmUlvm3hjl5fX2nzm+csMHT3M7tIKPauNLErc3LG5v9vhjaqOaMa5fukKH33iAGoigRVLs3PiHcSSJogCIwmNJ4Zk1to2LctHNzSipo4ThAynDDZ2O9zdbOF4PmIQMhyV0WWZZ0+N8+SBPJm4jh7VUFUJw5R5a36De7bMF8/f4YFj44SSSrk+YHmjw6OHh3Akg69+8Vvsmx3lg9MGH3/mABUr4IknT6IQkIurPHlimJG8wYNKjZxsMzJa4nbPIKJrbDcdmpbF3Zv3aG2v8OY3X2JxaYdsLkq11ef6WgNFhM1aF8mIcGw0xtZ2lWNjaRLugEo34NWrq7QDmfc9doRYMs3waBFTU9jYrPLwwYN84P0f4B/98A9w/vptFL/PD/3wx/jIQw/zv/zn36UVzWFLIpIKDVfB09KcLbnIERV9YorooaPI6SwcegQ5k8cYKeGnx2gGKqHdpapmYO40QWqEq3/yEvPXtll76za95U1EVSWWSSIIIp4WpZscx8xkCc0UKdlDc1r4ySJ+oogfy+Mj0BCiSLrJwYLJumcwHteQQ5GorjAU17gRZnhzpcqQU+FZd4lBs4HSrfLvv3KLf/XyFmJti1VLoNNp4vseYnuLY0MCp6dSbNU6JC+8iB+GSP02ansXVTMwA4vo3ddxbB/h0gsc99d4suDjBgHxiEk8k8RRTMbiOsv1PnpgEQ0sDLuD4PQIuy0EM0ogiIiCDHoEEnmkfgshkkJJxBnIEXZ3u1zcsNFTJaYnxjgwVuTI8RPsO/kA+dmjjE9Nk86kSRoycafFxuItXn/9Db5+a4erV65w59Z11lbW8Vplbr95E4wogR7Bz4wSz2fZ6niI/R7brk7Utbh5r8Kd3S7zbpwv3mmzsbVD0K5yMgVHog6PmHUKbo1c2GVc6aOJAolsBlURcFttXl3r4DgepiIznDJw3JCarwMiV3ctLlc8ru506PsB9Y6DLovYXshKpUsYhpiqyIF8jExM4W2HRyCS4D9d2ubebpuNusVjMxmkwCXuNXAkHVGWcdUkXijgDfokcNB7ZRTXRonGyEzlUVSB3tULKAREB1XkSBJ1eJxg4z6EIWomSunYOCfe/zgnP3AOFZvQiBEdL1B85Bih7RIzRbLFKEeOlOjM32J1q048H0fWNULHxtndZP9IDMdXyX7PR2n1XSpbTULVYOv1eYx4hMa1RSa/7/3U7+5y4eIakck8ysFDbHzrLXqXX2ewsoysyxT253EHPm9c3eXW568y2N7B212ne+s2/vzr9Obfwr47j726jOj2iZZS3Lm5QeP6LWazEZ5+dJS7bQfHFvaElr5HGArMPn2QqCLibG8SFqcIA4EwCChIDmKnguj2SUt7UdZlN6Q18JHFPXeOrY6D63mUey4t2ycTVVFlkTAMSZgSW9stMobAdrXD/y0t2wOburLHO/5rq7O/2rIsix/5kR/BNE0OHTrE2toasMf1/YVf+IXvuu93DH5jsRjNdoe4HCAbJlbVwnd83I6FrCgoySRSNIIxMU0YBJilNGYhiRo3EWUZs5Qhe+oIratXCByPoNPec4zo9vE6fSRFwbNdQgJwPQLPR0wVWP3Cq7Sf/ADrmX0k7AZjH3oXfT2B1G+CbSFFk4iFUQgGKNNH8SJxtKWLCONziOkibrxAJTJMMWyCEDKRieKWpsDtE8RzaMPTEM8jF8YhmgAjRdDv4Q+6BP02od0j0Ex81SRAhOYOgZmGwN3zC3b6REYzWLvlPaa5beHem8dvlhEjSfAcBMUgECS235rneHcBTRERggA/XmDFj2EqIq4cIeJYBL0OA8/HXb1NuLmANjrOoNZge/4uTq2BtrbCQ1NZjo7nSM/NIo3uo/DOd3Hihz5CaTjFIJFBkBQEpwcDCyFVQAoDguIU/Y5LSgj5g3suQWUT4jmih48RLy/QjxTxYlnIjaH7Np6iEVcC0revcP52mc0wwowmkB8e5lJ0juWWz4g0wO+0mdRtLtQ1zAOHkbbvY/gW7fwcZ+UKkmaytVpjffIclh5DDlxSevBtLpuCJEJRdWn3HAbomL6N6HuULZf7jR6N+4vcfOsyL752gTdff5327hojhocvSry5bpMvFJg4dBJ14hjG8Cy3ri8yPjXFQw+doqkledUbZkT1+INLZSq2yLnJBO9P93l2VGFto4po9zhaXuLDUzKGGPJ4rEEsn8M2orxRCZj6n/42r6aO46oqTz0yR73R5epGi4yhckCzyVRuM1m9Snr5VdLleYyNG8QED9/zqFo+01mFp4cURtMGgiQiSAKaLNC0HJJRmYXVBoIoMJIwOJGCHcuH8jKK10eXQsLKNmP7xvnY08c4dWSCjzw8yXAmyr+75VDq1wgSKWaGUmQPHGAsPsqn//PvkIwojKciHCrFqAYq3YHHUrlLuetwx1JoOx4RTSYT0xl4UEyYZCMqtY1trl24heUEVLo2C9tNmh2b50YCkrqM7YMkiRQiCpoi8fCREeZtjc2NXfq2j2EoPH12guFCjNVmn89uQGlyApQI6aEiX19oYBoyl+5s89jBYTrdHkGzyo3FLRrdAT/0ntM8975zbHUGdOt1HparDAKFY/vzpBMquq6RLWYZTZo45Srjch8/CIiEfd577gh3azZPv+sJrtY8fvdKBbW1RXE4w8sLFcrdkPccK9Db2SSejGGbCf7B71/EHjnNIx/82/z2f/oD1m+/SimtYWfmePyH/w5//3/9Bdq2T0Lxib7re9k/m2O4oOP3LIRMCaU4gn7sMcTsCESSCGYKQdVgZxX3/gLlRh85HidoNSgrRexOk2xERIsKkB+FtRsEw3OooYevROhGhumJOh0pgiyA7HsgyshhQFoVODSSJlSjLO52eccjB6gOAgJCOnZIP1B56LGj6HMH+cKaz3ve9wRThRTZVJxqo83xI7N8rpHkKyt9Xt/oY++sQruBWt9m8d/8Oo3IENulfQAo6SIYMcIgQAoDhLVbKJpMTYhgqxEGrQaKFHJ7p8uHHxhnuFgknY+SNDTEQRdFEBDrawTbqwTRFOHAwgt82lJ072YSegTJ0l7srNMnKvqEk5PIqkooSNhqFM9M0Qx16o6IYEbQk1kKo+Oc2TfGo/tHeGp/iWcOjnDKtBhXbLR2GTsWY0PL4E6XaN59i4vzd7izVeXWbo9ut8tWdpJLdVjsCTwWqzG0epvm+jpDmRgv3Ouy7JoMRB2v30UlgPQwQruCsHYLURKR65tcE0vk/t4/QBFCXrlbo962GFIcynbItuXj+D5OALosMZEyiesyYymdruOzPx9lKhvh4HCcsaSBKMChUoKIrvDGUpWXL9yh2bZ5z1yMQI/Sdzw2vAhCZQ1x0GHgOQzabZJ+A7W9SdBusT3wSR86QO7oDDvrNTKnjxBqJqEABXsbMZLEeOAhPC3G5LufRk1FiKQ1YhMj9N58gxt/8DUC18acnCEIQ2obdZRchKnZLJoh8tL1Kicem2HpDz8Ldpf6i69TVyNkDwyTHMrQH9vP0IkJrrxyBzod/GqV7VsrNO4sUzw5zqGpNJnHH2P+a+fRtJDmtWvsXLhBb7tOb6dJxfZZrlp4Aw+7Uqd9ewG3Z1G9fI3WwjK7V26hj44TrC1gTI1z5fomK5fXKI1E6Vb7KLpELK7AzjKu46GNTtPaqDI7k0QvlQgkBcsLQACpWwe3TxhN4wRgBSJtR2A8peGGIWIYggBXdzos1S28cA8gbbcG+CGs1AaktL0pb7XZ/vZ/+f/2VhC+7XX2lxG8/bXbw1+sn/mZn+HatWu8+OKL6Lr+Z8effvppPvWpT33Xfb9jt4d4PE651mTgC4hA8fQoekQlcH0iI3nkVBIhcMHuYeYz4MGg2kJPxlGiBsZQiUBW0DIp5FQKQVdhMCD0AgRzz6IIAgaVFkokiihJ2GtL6MUcSXHAwMixOHKWY0aHtDCA3RVIZgitFkFPwNh/EuvODYJTz6LEe3j3bsHsMdz0JNuWiGxEafYDDBl8JcL9oVNMdWuEsSRirwmRONR3CDtNQtcmtLoIbYlg4hC+FsNdWURoldGiBhhxAkFGzJQIuk1EI4oYhCAqiIkEfnMv2pRkEaFdRrCaBFoE7dzb0Xdv4/Za9Dc30UZnGQ+rsLNJMHQAZ2MZ2dBx9j+EavsoRgSKeba1DKOZYfSYRkJJIOp9BFFBCVLfTlWyEONpMsU8CysNAkXfkx51aoSKRBiP0EuOYJ3/j3z+5j0+8I4fp1FvYpbXicV1rGiW1ksvo777WQaSSSRjog9a7Nxb5tBDR7mz2Gdps8bazcucevIkD48P8eZWj7nxfajNGs35yxxNjPK1zTjjlsP8nR3CpMdzRg9Btjgwq3Ln5ltcT44Qef0VMlNjRKJZAj1Oc+Cx2uujDGys6iJWt8PA9dB0HdOMEI+axLMFXMUgYer4rstyD0Agb8f43GKF9z6kkYuK9Ps2xIr0Q5GpoSxWEPC7b/TJhz0ejbbZtqdoWQHvmcvRv32dD47NkLB2eF4rcspt0WlJnE7ISF6Ns4dmuLa8zZu7BjFNZbDbJZJJ4XohMTlkrdVnnzMP7TpCukTYrBJG4uyGOtUB+EgEYUg/lJGjBs2WxUTCYKfrcKIUYbPp8NRkkguiRCBJPB7p4jshiqDC9eskx3bwjjxJPT9Hz5XI9HZ5ZCbNlGKhHZrkrQfq/N4rCzz+9kc4WTJ59X6DR97xNEu/92+JWGXaYoYAyEYVXr/bQpJEigmd9bpF6AfEDJlTI0naA4eMJvJ8uceJ6QLxw0McGI5xb7vL4WTARCHK719ZIzYZYTxtUK11yUVEIukIlzfayHqEx59+lNfeWkIe2UfcVDg9nuSbV1ZhMs8v/tg7+MUv3GLTC9ls2iwtbpCMqXzwWIqXdI0vfu08+08e5YUNl5K8i+/AxmaDwGry5eEYMyNFmoKMJrjUbZEfeHwKRRQRx2O8ubBFt1UnnzXxgwBDU3j25ChbffCPz3D+668TWdkhpoEykkRSJX78I4+RFQb8yt0eRx99lGurVXLFIaae+58Y6c7zf/2zn+Tc+z/G+P4jvN5p0Vi+gXJkmozqItXqSKMzKBMaoeeBphHsrGIYBt7QHGq/RdCrI+8/jdBrUWzV8G9dp7y2yrEf/WF0U0Zyu2T8AD2iEd5dB1VH6zURFJPdPnQCkfz2Atv5cfIxDQQR7B6y5OGrOnFVIpIx6Lkig75DxO8zQKQZCKx1XJ588AA79S71fIyHtTbKiMhB18X88h+wfu5DnB2NcHVNYqczICPFkAdNijGN1W99iweee5J7qxsUYzIYEbCahNE0+r5jBISUVt9Cj51loEY5GPFpuwYzQgtPjnP96k1Ovv0RRBwkJPzsOAw63BeyZLQAk73ZQDtQ0KQoTTlGTrSQDQk18IiqKmfal0n3FbxYnn6o4Po+paiCEPiYCihhCKGOZHch8JBlibgSEhvsEFb7tI48xdFiHMFq8eL9FlpUxvFd6LUIvT6NsktEUnBlA0uNYwdlbm+0ODrk88ihUeRElIgi0JYTJEMbadChffkNVF1CMWO8+YmvknvPezFnZmn0XOKmjCoKCEYUQe7QGLjstG2m0wa5mEal66ApIhIC660BUUUkHzeQJaj0HDRZ5kRW4XM3drmysMHDEzGeO1NACEXiXoewuoKTO4LgdCmvVMiOjGJVVlFGZxAcC0MWUDUd89TDhKsLdOvPYzfaKEFIPTApyCG+Z+MbcWShiOC79LfvsX3+Kn6tjBaLMZhfQj30CM72Bm++ukJ+KIZi20QzabKJBoog8Mrnr/Puf/lTuM0W7Y6NIAB+gERALmly/vwyY4ZE5tAEg3KFfU8cYOlbb5LSQq7cLnM2nyQbV1GiBs2VKnumCAGiBF0/IK5JxBMa/VqHyvwmRjZC5dYd4mMpJj/+NKFTw2t3kHLjGD4oCZM33lynMXD52M9+H2a3DLE0ardCEPrsXt6k1XWQcwVCBFRJIKpJCN0WfmEOqb5OJ5rCFkVGNY9eAKKwF96S0CW2BIGxhI7vB1QsF1UWGUrorNUt4jhU2h797l9MuEhG94BZu93+7mkP/BW4PfzlPv7/d/W5z32OT33qUzz44IN/LjXv4MGDLC8vf9d9/5smv1avixOEDHaqRNImkWIKSZGQDR2/2cB3XJxeB2u7Ruj7e36boY9ZKqIVhvEqu0RnZ6lfX6R79x7G1BxBEDCo1PE9H1lXkWQJNVdAz6fxej20mI7b79O+cIHRTARPjZPevErY2IX0EF67iSDJiJKEEPoMvvV5yE2gFEfxmxVEWaTpeKz2YLPrIogCGVNCVVXWgjieHEHUNIJmGWwLVA1Se0lzaCY4Np5l0d3cxK03QNORWls4tSpOtUzgQ/yJdxE9chwlX0Ia3Yc2cwgxWyLUo6BGEAIbv7qOqxng9mH1Ft6ZtyP4HmxvYC/dQLfbKMUSYmCj2BZiKkkYBHiiRqQ4gj4yhhhNokdN5K1FlE4ZVGOPmlHdRmxtE9VVBEI6bgB+HyEaB1lDEAKMQZNPd0RmH36S0bhMIpem89WvM9jYQrEa7B5/lBtfeIm2lqEnGfSNLNrEPlIjQzzZm0dcvEM9nubluoIbQHmnhSeoVM0iucMnyXfKpHYWuLhuUZdijJeSrOlDRPp1jKEiXHiT/WNJrpjDXKg4zK9tcOnyZRZvXqOzs4Hne+SiKkeHYjx9oMijp09wav80+4ayjCU0SokYdiiSsbZJ6SIpQ+a540WeOjVN1wlI6TKVRoficIGMEjJi2BxICjz3wAjnzs4xeuY0c2qPdncAxl5s9XFplzGvjSqH/OMv3OPzb97nt15f58rSLl/8o2/QvrtAb+EGk3mNoxM5mjZEDR1d9BCDgM26zaachUgK1+rjqSZ2NIcpS5wUK8xkDHoDn3gwQAoBQSBpKDi+x6tLdVRD57G5LEPSgFxvk4HjMaK5lJPDOJuriE6XTeK8VQ/YantMrFxAFUOmkxL/4EMPIPd63L55lzdWmizNL1DuO/zAB97Htz75O/ir1/nMr/0LyvUOuiyQiCoUohoTaZPttkPb8UlrkFQFXD/gWCnKR89NkIgaSKFArW3zpbs9JAlOH5thKh9DEUN+9NExhjXQCdncrrF8bxs5ouPICuf25Tg5nmIia3JoKI4cOFzbsTgznaMVyeE4HtlCFkMK+Gf/5/McmsyQyudIxmLEBJuF+SXM6irjowX+5g88y06tza2lbTKexYmiRt+Dby1U+cJbm6x0fJ46OcFUMUK3b/Pl2xVicZOe49Pq2Izmonzkw29jZ2eHh09NMei0WG55yIZBtd1nv2oRuDYnxpM8cXaOsWIK+eCT/Mj/+u/wyvf4k1//F/zqv/wXPP7wg5AoonZ2CSNJguw4JPL4ZoIwmqY+dJJANpD6LTCTSNHkHuCRQ6qegmMmqXV9zPoKihQS1VV00cW9fxsyQ4iVVcJOFUEEv9+h0u2zbivsru1SlRP00bHkBIgCshAQ1yVKUZlSTGIsppHtrmEyQBBDZEEkqsqcGIoynjSoalmyuSRnJ7Ok90+zsLjBYtUmlUlw3yhy7cIm5ZpH/od+lLPvfgCn3gLA1FRCp4tgxAhDEWHsMP72OkZUxaivUOitYwo+pzICamcL1W4yN5ahMggYKEl8VcOP5wgTefpqnM0wxQCD7Z5Dxwu53Za4VHO41w0JZA1bVHEFhaLUJ/QDdLeDG/j4YYjg9DG624i+u7eDJavQqRKqJn4kC/0+oeeSnxphLmeCAKoscjrSZ/2lC0jNDlqySCU+y/jMAdzkEKEWIdKtMDZT4mABgrUbdMobNDeWqO/uELXrBK0q7t3L6GeeQC6O0VlcQsRjPBoQU2UGAUxkonTckAtbXQQ/pNKyaQ32LBHbAxc/DBERCIFat8+dag+BEFncO+d22hYdT2Rpu8679qf5u+dGiJkGr2xamI11dEUi8pVPYvX6JLs7hMvXMCTYEJO05RhbNtiCiux1wekxcmwG9eApAs9DVVRsX2RTyiN16zhba4jFCZBEUnmTa1+dx2m3MKIqna06TqdDMhehmI7ghiK7F+/Q2W6TVQVSksDC8y/iTR0nNpxhIqOiSB6BYrI2kImIAu3OgMShaQh9umu7RDwbxVR54G0H2PrMnzJ3ehw9GyM0TZSISmw0S8+R6fvw0Y+dwek47C6UKS9Wuf/6Kv2WjWYq7H7hi+B67Fycx1paIm7ImMUsaV3C8sFb2yJaSuGlRhDNGIONNdrVHnpCx1lbRghDdFlEFAWIZRHCAOpbpJQQRYS0V2Gn5xKGoIggixKFmMb+jMpGa0DTcoioe9aDogD5uMR2vYfr2Iia+ecw0sfe9yjwl5v8/nX9xapUKuTz+b9wvNfrfVcR0v+lvmPwG4/HcQcWEU0mfngOo7T3ZeL7xgAIHJvudoXe6iblqytIqkxvp4PveHscWkkkMjHN1vPfQo0qhIKI324hiAKe5eD3HdSoiVbM4AkiXijRWd0leXiO/s23SFq7pDubSGKIGHiI+TGC3XW8Xg80g6BTR37wXUQSOkqvjjdyEDIj9AKRo0oXU4GZhILvB9i+QC5qkPYsAtUklFXEWIpAEAnNFHbXobm4inV/Gb9n4SOQLCRRTp0jTI4gKDqBpCBYTfxIAjGeRjRiez5/3QaCJCGqOvgOgiwSyhrVl19Dv/kagiCg5AtE1JCumsRv1HA6A5x6DSmRZyc5h++5eDsbdJU4augQU0IEfEJZ+zMjQH9nFbFbBy265z8cBOA6JHWZVrcPIYSOg7ezTudrn6P7qd/hS1/4LD/+8R9gdjKH2dkmrvRY/cNPsvB//Adiekg/lyPZ3UYM4Z4l0lRTBIGPWGlyPOmRzyQ5KPeo9F2mhxNs2iDJIn5+CunEAyQnpzn7nqd59OAwcTHABq43Xd7Y7tJ+4mF27t0hajepuhJBJMORpMSRjMHZIwc4PDvJVCZCqlBCDgPkwEb2LaR2GbVfw5RdCqqLakYYjhvENRlTCDiZgsPxAF2RmBrOML5/HBSNeK9CVPB5PGWzr5jA1xPUtRR/I7hFQ8tjnHiI0A5I5RL8vUeTPHTuDLOH9/PwubMsOxoLV28RRlLMPfYIRn2XsFPjgdEYX7y+RRyP40kQ6mVerchs2RKemmSncISC1yKnBKiiz8PxPrIi0gxVijGVYlRhLKYwToen9mcxZZmN1oDhfIZ66QCp+n2GEhru1EHujJylc3Oe0ZjCqbEE+QMHCE6/DUGS4NYFisqA//mEyZHWIn/y+Rfw/ZDnHp7jww8d4g8/+yV+4xd/jtLIKG/80k/xNrFMqXYLx/dJGDJHhuOcKyqUWza9gc39tkMuqrNYsXllpcNWu89WpcPNhS22mzZJXabRc3nr5jZ5ecDDx8aQ/ICU4DLiVnGaTR569BSjKZ2E5PPHX79BKhXhPZMRvnBlg+W2S7c74OF9OeL5LFajzXrd5u7mLv/bh48xIlgMFJNiLslbu32OzWSY7Gxz+NgR/sZjB9m+c4fnbzbpDxzG0hqeH+B4Hs/fLLNV7zFdSjEWV9mXi/Ctl6/yyotv8uaF24ynNb7vQ0/wrpPjjI7m6AcCk9qAdDbJ97z9FB87N0lKcliu9dls2ARBwE6ly8TjH+Hf/uqvUIxFaQgGsOfvLOpRAiMJVgNRCAgUA+H6eQaOgzho468vIlht5NCjb2TIyzbGSJFDH3g7piFj4NAWTXrlTfzCGGGiSE+O0UUjBCJRna7lUNBckoqPY9t4okhT0HACAVHc442GwV5SlSG4OJEMjqjTGoScGonT93yWWx5ly+NmbcCO5eH6IZYfMnP7VQ6VIuyLhiyHUS6NnUQ+fobFLsSjIvVKGQSRVsfC6XuE/R7B/auElftg9/Yi4wMPye2Da4Nj0Y+W8CWNXFTAsPvowQBbiRP6Hr4oUvAafHOxuiceCqDrBtytW4CAJUUoi0kGiBTlAZIi4zQrCNt3Se/Mk5Y9evUK3e6AUBRB0hDdAQx6BJJKKCkImoZgxJBFiAc9BElBFiWk7S1qokFbUphfXMd2fLY8jbKnMzI8ghsv0s3sYzM+y6GDh2kqe97s1VqVa0trvLBlccFJsVRtsSGncUf3YR47iS3p1J2ApCZyIqd8291B4uhwkrip8NhkirrlsdQYcLvcoWN7rDQs7lUsKj2HsuVg+wFxXSEI4EbFYn8uwm7bYcdXyGvwYFagnxylkj9EMilh3L/G4P4qwc4KfnOX0LFotjvsankCQcBT4wymTjP19hNIXhd29hTwvZUllho213o6bTNHUN6kcWOZaClBFIHmVoNvvnifzZfn6a+sMXcgh6RLCIqI3epxc6nG0ek0y/U+WqsBzTLikeNkvv/78KwevhBStKoMpVV0RWL9GxdxHJ/6/Bbb13dYvbLJ6Q8/RuGhkxiFHJ7jE41oZA6OYHc8Fi6vkFJFkkmF7IEMq7fL3NvtcX6pwW61y51rm9z65jwbL15GKE5hbZd58md+iDuX7zGU0jieNXn9M6+jjU0QyDphfZO7X3yD+w2bsUMl5HQeT9JwEXB9sKQoUmMTNBPNMBDDEE82yJsS7DEeEEWBquVS6fsoskhUk0jpKp2+iyAIaE6PigWB3SOw+38OIxWzSeAvJ3gLwvCvZP33VGfOnOFLX/rSn73+L4D3t37rt3jooYe+677fMe0hFovhDXo4oYi0tUVkdhq/UUdSBKRoDOvuMna9Q+C63L60SSQXQ09H0bNJ8Fz86i6iGUHPpVAiMrGDh/CruxiZBL3tGlo6ippL7F2Azl8g98BZ7n/1S4yqUeKxEP29PwyCR3D/OqIoICgQ4KFOH8ROj6DUdih7OqV9RxAHDXzZQEkXMJDQ3Qa2rJMaNFgXSiiSSMQQ8CMx5OYaA8tGL40iZkcINhZY/ew3mHzHI3tpc84AubaKkkjvgWRFIhi00UYn8WQT8e5lrPI2ejKBPDRFsLYAqSLYFoLn4GlxfKuLPDqJUcohJOOEkorjuISRGKouIOw/QqCoCO1dJCFKJBrFjiaJCC5yYwMllt676SombN/aA/7b9wgdGwIXIZokbFVBU0km4jQcj1EtgT9YxVme5/yvPk/27CQTuQxnkgO2Xn0Z9e1Pk1I1du6so0gS+kufZz15nHP+AKm6RD46guQPMEQB+/Ap2leu4h3OMi7KxPMGi4ZO2/FJCgqBO2C77xNGdDrrd7F7HUIEItEIaUNBimqko1HaWpL7Fy9xJhHQTGe501KZq88TBB62YCKbSRw1jiIbCLIGbh9H1BBUFV9QkUILP5ZBCfcuEmEIereCGU3Q9yXUuEHV8gkQ6afHSdhVpK3bFKQoanaalhMymDqB0a+gmBGM42cIO7vkc8P8yHSC0B0wcKFYyPAZR+TksVlaXkj7/CIvbhd4tiBxbCyJF9njPirHHmFstczX3rjNe84eQBcFai++gvH+jxKkhnHbFQqGxlI3YDoqoOLjCQFac4u54SMovS1UQ0KIZOh0XcLSfvKSyExa48tXG4RY7J+sYJpJutYAT9bZ8mSGIlHkIGDm5EGKw1sMBmPkkyYTaRHh5n3+w7/4GaShSYzcEF8SHf4///PfRpElfvDXP0MoaaiywJP5gLV+k2w6QTOUqFkeTTvk0SGJL780z9957iR/8OUa1sDjM9+4SiM6Qrkf8Bt//Ab/4/EMgjrB4aMzPBwtYeaH+KOvnOdOdIiphEQgS5xMh1y6s8NwMUXb8uhWm1huyNZ2je2ejqjFuXtzgS8XHuBHhj1u5uf4/7L331GWZPd9J/i54eN5l/nS+8qsLN9V7RvtYLobAAnRS6TMDklII3JnJO1Iu6MVZ3a0u6I0mpU0FEXRyECERJBDkBQBEL4JtO/qri7fZbPS+8znbfi4+0f2iuIMtUcANUd7tPqdE+fkyRNxX7x48eJ97+9+jaJo7BY0okySWmgyXMzy+oM9rm02+LDu8ErhODsNl2q9izpTQnoOHzo9xue/fYcffv44aV3hgjHGmkxw570r/NbLN/nxjy2y1+jzydMjbDS66JbFiCkpaCGTQzlWdys0tSRjWQtVFcyfKHHvsE+YTVKJBxhQDcLYR6aK8OAGrcQwhaESrXYXK65j1DbpWB/G6hwgVYHUTCLdxPY99EKOOIrw7Sy2JqjLJFsP7jNRmsApTtILfOoKbF25QtEoM28ErH3tFZ7/vgW2zVHiTpu+ZuILjbqaIS2h2g9QbJWmH1OyDJQgJhW5zFjQxSCnwxdu1vjTD1v0nICJoTSDyZjc88/SbgV88Vs3uHBykmTYYyU/yv1sie1mj9FWk6qWJiVDfu2VZf7MMzMU21vE1V2ElQDLwrm3SmIxQEmkIJFF930i1cLs1zjYrzI1Z6J2+wR6ClUIEDqlYJ/HsjoJHAqapBXpGJpgIGGQsXTe32vzxGQWI3CQU8fQVZ17bRgLWuhxiJlK8v4bNzk9PMeAEaC5HWQUQX0PNZM78iyu7KHEIVq3jrl5n3jqLIqdxFqcpkAD+2Cbs4+fRtN1JlIqUezjjM3Q2O+gKioykUFJSoa1NqLuEEydptTYIRk3OcCi5Th0+12ibIZ6rUcm2MLQNSpxlom8xXjePvIw/+CH+dW7Bzx3YggnkMTyyP36+FCKuWKKnY7HTD7Bq8s1hrIWr93a42Bzn1Nlg3EjjeorpFMWDVdSTBsY+SSYFvFgEpE06XmCYWeHK30L3RSkIodGZMDeGpnzz7LXdElGCm/s+jxmpTg5ksNVTbq+RWF/n9wj56i/d53yQp6X31xHF4Jatcfxc2dQbt2msV7l5tU9TkwffW+DhEGj5dGrdPDX19EnZnG6EXKgQIUM+ev/mu3DPsVSgi9/7l2mZ4s0t5oQxYwmNKqX3ic5OwtC0t+pMfxDP0jzjW9RXBzHeXWJIOridxy6XZ/3ax7dD3Db9apHsl0jrUL41Zs8+d+eYufGAaVnP84jH3qPd754i5JpcKvuUb2+yujgu0hdo7myz4oTsX1nn8kfMvEVHQGoQtJTTdLEUBrDkxplWUHVbRKKTj/wsTSDnY5HwlDxIkHG1EibGqauEiNoOj7nBxO8syaP7sF/B8HgjyN4k//OUb+zMf5Tqr/zd/4OL730Enfu3CEMQ/7hP/yH3L59m4sXL/Laa6991+N+R7QHp9cl8lxkHBH3OySPnzoy+fBdujt14jDGyKY59tgY2elhBh+eAykRuo50+wSdLrmZYZACRdcRpkEUhHhNF1XXESioSLLHF9l9+VVcKXnwjUskP/QCmiqQRpLocJeoUQPfwbl1E5kpETdryOmT2De/iZ8fxbEy6Aq4WvIIKJfGGBctrOY2Q1GFAS1EiSKElHipQfrVCu7tK0gp8Q92GH/8JBoR1sQEimWilCaQug5+D9qH+OtLqF4fRQiUZIJg6SZRzBHPVzNB00GzEBLq9hArvk1rchFr8hjRwCx0GqT8FpnOPltLe9Qu3yAwbDwnpNzaBK+Pls2iHiwjvD6oOsQBoW4gU2VIZhCFYVA1hNsn1i1EZhCERj6dpOGEhMkMFEcxFx+itDhCZ26O2WPT9NITyKW7uPduoQ8OcfxP/wCL/6c/j2ZnObv7DpHfJ2rXGOxukt++jmZa2KfPk/vkp3jh0TkKJ47j9lp0DraRuw+o373E9Utv093fxI8i8gPDXHjoDOeffIbjZ84ye2wBNzOG73mkqhuEI9No86dYKBic1FrcKZymHWlIt03cqdGPFUJFR0YukWawppWpqiUiqVAJDQJhUHUjSrZGO5R00yNHy86xQwqPnA5BBJGqH3GwyxNgJSnZMGl42KkEQjMRvoNmJZE7D1DcPmU9ZlLrM+euciHp8L1nSwykFEqmSjwwzEN6m7oX8yMnizSaPXQFeokilf0mZwfTHChpOp0u3Sc+iqUKUFRudm0+d6uN1W+w58RH1m4yIk5kSIUd1BuvUmpskJQuozmLjpKgHwoi1+XZ0yPcKM6zESY5XL3H5q//OsobX8Sub1PLTiBCD/3YScwzF/jhJ49xZqpAmCiiz55m7vg87WQZoamMffzP8QN/7R9x5umPc/vqFZKKZDip8YVlSSafZbkDjb5P3w/4xMlBatU6i6M51it9njk/xXBS54VRnfNDKXJJA3vpHl+/sspEzmS+aPD1PZV7G1WS+TyHHY/Lay3Oj9pkdAHJHPWOT0pXyAzk2W87VPYbKLqJbukMjA7jSYVfaxfZ9jWKWsTrmy6606WvmPTDkMWpEj/+Y5/gLd+kMDrCyvImA2bE+m6dgWKW19+9x6mxDIphcn2rQ+T2uHt3nedOjTGdCnn1XoWN3Qa5hM4F9lEru+Q23qP/+isQOFh3LpERHqW0wUjaoJQwmC0leFB3WW+HxHHEPima6QnWL77Pr7yxwWv/8J9x/Vc/T3jvKu2RBRpOQF3P0Rg6zmU3SxQr3N7rcujE9AKIfB/vYJ9ObZ9cJkWcHaPmCVw1QRDEnBB1spHDQZxgfG6Ilw8spJC8eXODS7tdkrqCrik4kUStb9FyXdwgIKHG1N99E/9rv8HVb792JH7zYxK6wmbTxY1AE4JObGDrMQNPPsGJQZOicNBRGE6bXN7qslPp81sbChkLeiFsVR12ejFS1YlcB6Q8cgI49xCEIbIwjlQNmh703YCmmkZ7/KMo2RIyjEj2D4lVHanqaErMo2IHY/8BZbWHRLA4kCJrqQzKLo2eh4hjFKeFlxjAyY1y4OkEQ1MogcPXNgPk7FlafkDHjYmiCJIFFL+L4vaQ+UGMiWOIRBZfTxLV6nieR2YwxfMLRTrFacbOnMJs7kMQUuv5tCONhhMyhE8pqXN5p0UYR6ihzx0nwoxcbl68SZwu8Zl3qqTG55grDzAzVqZQGsCMXdqHu+zdv0G8epXDlTvsb60xaflc2qjx1FwRQxVM5i1GUxpBHDNWSJIxFcazFusNh/sHbd5eqtBoOgwM5fjRp2aoBQZRHNEQGbTQIxIqcmSOoOfgbqzR9lUYGuVeWOTyVg9biQmuv00Uh7ilMYLMIBvpcb7YKpDXIS4Ns9oRtH2Foq0dCc2dHtbscXIzZZIypjicIjNRonFzidT8HHurNTY7AYcdl0M35tXLu4QxaLZOJ1kgbetYty7hzz9MyjmkZg3Qb3ssbzWodUNu3D5kp+1hDGcRqsLS799m/9sXkckyiYkxetUO2WdfIDU/ixocdVNbB12+9sr6vwG+AB4waCpsODHl4RStdy8x9cQir/3Vv03c7LNc88jkTZJCELvdIyGbYhBzBP62Vxqotg2RRBOSrC7JqIBmIq0UKjGaoiGcFlKCqalEMma/45LRFdwwouEE7HY8buy28KMITUI5Kdg67KNZiT8CJR3Vf6Y9/IetJ598krfeeot+v8/s7Czf/OY3KZfLXLx4kQsXLnzX435HtAev36MfHs3oral5RDqHatsEPYcoCJEywsjYlKZKJIeLhO0uqqkT9Bz6lfqRpZiUqKZ+BNxUHVVTsYtJFEND1VQQkFo8ycbSIVPPnub4h8+iDc0R3b1EpBkoyTTa3DmQEvXYGcLb76Js3IFOm3TYQnoOa/oIh1qRvh/jhBIZBaBZKGML5Po1dCXGih2y+3fQwgB/8VFiVcV97zUUYjRLh/wgNA6Iknn6ZpooVUbRdOL9VYJ6lahdRenWUQwTa3YRWdtD6klEpkDUqiCFIGxXOez4DLT3GSxkEIqCGvvo+UE6F19H7TfYvLKE2+yiESLqe8h8GXmwCvU9FNNG2MkjQK1axCjEyRxEMeHANNhJYiMB6RKkC5DKk85mcVyXlq9BeRatNMHMIzPs1CrMTAxx77O/Qe/jP0zHg6B6iDkyjj23yPQz55l5/DyxoqIms4jAQTVNgl6HBC6oAffX1njzrYvcuruEFfQZKOQ4c2yKj56Z5snzp5k+dpzy8DD5RAJdFWSCJmbYI6NGrOhDfHnVYVy0sE2NbqSRTiVZGB8kUm32OiHre02CKKYrDRyRoKflaIcQKTqhhLxy5EFr68qRMbkrQFWJ0AiMNKrTICe7JNUYVQpCI33UuR9fwGjukkzZZGOHlHBBKCScQ3S/i9KpYh7eQyDQUnmyCZWPjap4sUodg35+iKA0xkxeJ9/e4PbNB9xb36e6X2UzWWZdKVDeu0XYd8imTUJF483NNtmUxYm8wtfvd5GKjqkqpNwmamEMvVNBL4+h6EcCHE1VydkG6vZdWk7ARpzmkckMPRSW/DTBh7+X+ux5trYOSLR38aTCRtOnb+YxFclMfwu136CdGyesH2Aoghuf+zLX1+sczygY0+c4uHedKSsgiuHbaw3e3vPp+TFvLNV4Y6VG04v45OMLfOpkiWzKpBooJC2Fr/dLKFmbk5MFHv2JHyFI5Nhc2eS9t6/SbVS5uVlnZmqSw9VVupHGSw/PcK+rEakax4ZTmLbObtXBUgOEotJYu4UpQmLN5MJkhq7j0Wg5bL1/n7HRPF+612J+uszSfoexQhJDgWpiCBlL1NAhH9Q5OVlicSjBswtFrKTNTELw8FSeplUkNTWJPjrF8ZPz6FaCY1aXzcMur+7HOAd7NPUCW9sN6pt7OHYK/8ENEpokZ2sITcE+eEAYuEgZ8YX3K6x1JFEkcB5/gdT0LOGZJ+loBluZOZKTs9ypBsS+x+1qyNXdDqJ1wNZOnZ606KYHqbzxCrd+72XurOximQo9oRLGkBIhpqHgn36aVqyib9zhwlMPkS6PYiczPD1X4vGJPCoxicghDELUOCatSgZoozt16oUJglQGM50j5XXZrXaZLNqUkwbDWZO9jkcYxyhRRCKf5/T8BKm9ZdaUHKWkQq3j8pHTYyQmp/EiyXalywvPn2IsCbK6Q+z0oXGIXh7FHBmB4WlCK0M11Eh5TYJOg6aWIZW0WA7S7Lz1Lv1YQ6LgSQWJJBicA0UQ6RnytkJKVynpMQklZGIwRxT6xFYS4XdQNIN6pU6j1ud//vo9TuQ1nEiSUBWM3gHrvgWqOHLLAUS6hMgUUIZm8MMQdbBM1Rf8va+tkUmnGc+qnDFb3HzzCheXD3jj9g7t/T0Omi6OaiAQdA9qBFLl9V6Gd5sG/+AbS7xROMGb6x1O5ARxLFizxoizo/TMAtMzszw+O8yHFmeYTpsMphN4Th9vd42Rxj3s6gOU2hZKv0m979F2PKIo5t3tNrttl2/e3uf8WJZYwonZEqVSlt957Sa6ZSMSOZKGQi5hIJ02ajqPPjxFbmwAIX0a9gBfuN/muTPjDLtVMqNjuBiYmSwHIsWE5vH4aIJUwsRKFZg3uxS0ENur42xto5WHKD37DPn5CRK2QVYVTF6YRbNV+pUmV24f4kgwJZgchUF4QKsdIItF9FQWzZQYBysEscLNnRoqknu7XXqAG0kiIJ212K31aO52KH/kaTr3HzD4Pd9D+/59tKFhOivr5MKIhcEE1y5uEf0RyQzr3Q/8XIMYRZF4B4c0NiscupKkqdBquOQ0QenCaeJWneBgg7W1JgB6UkdNZ9jtR8QcrbpJIHY6IBSMsI/oHCAVgaGAEOAEkpytc+iErNVdnCBku97jsOOxW+2xX+2Q0GBpeRMjlf93YqU/juDtP4dc/NF1+vRpPvvZz3Lr1i3u3LnDr/3ar3HixAm+8IUvfNdjfke0hzgM6Do9Sp/4U8Sb1xBhSOx5BL0+qq6CEKiGQWZ6FMUw6R/UUS0Tv9IiDkNSc7Ogm+hDEjVbJNjdIgwCjFya9nYLa2gQ6TqYvsvAZIns2CBa0gavg7e7S3poCZkvga4jzAS6EKiDI8S+i2wdYEwt0lNMyqbCvVbIWFrBNjTUGCSS8P5VNF3gHzTQZ+ZQiFAOVykVhtCOncAfGDnyAczkUEKX2Eqh+T2STgvKs6iH68ROH3OgiKKqELtIt49RzOPv7xFnh1CD/pG62GkjXIdydxtl9Saq3yJ46Hl06YORQEyfgJFjPPLDz9OtO1hBF8dIEG4uodlHqlEhQAiFQKhHqU92DoSCCD0UIwmqgbRSiH4TubOEyBYJsxPYts1hs02+aCOdNlG/z2985ff5jV/421z+ld9Be+IRlLkTiLiLTJVQvD4ymSM1VyIIXA7bXRpdl5oH/Z0H2MkUuVyWqbERirbGoTlM0Ra0/Zi8bKM6dWIZY2kCO+gSKgY6EQaCEJhNxoxSJXPhNLsdhwXpoUR9+ukyA4bk0JekDMFScoxiPySpK0SIo+59KLEU8MKIGI2EIsgagITBhI4QIYoMsH0HNYpAi0kLHy+WqDJEpPKYsYfUTYSiIQ0DpbF39PlkisStJnQqR7z0MEAOzuCaGWzfpxQ3IT3Irp1ifshiwNbYemWJ/OAMn7u4xp8oS8ZHppnPKLC3QRAHeEqOVjfgtObwoBNxY7vDCw9PkbckA6JH7HWR3QPCTBllNHMETIIuLU8hb2k8CEzGhMOBmgFFZW9zh2K5iKpb2IpBXU2STcZskOXQCzmRVOhHgi2jxP21FkP5iKHEMP7Fb2G/f5mPlJIcqDpVvUxj6z5v7QWE9Qcsrt1k+rkpBnMWv/5WDcsy2G57PFLO8rW3b3GjFvPJcyOYikP+wgx7PZ/TRZNr2006Iwucz0sKqQl+6+ISjh+yub6DmityfDbP59/b4+HJHG/c3qErbBwkigLtjgOKglQUPvmJx/n1z/4utUemOZPxyCcdfuWSx98+u8HOY5Ns1hyiXg/V83j95g5nTs6w3w156uETPJH1+PX31zk7lufhrMtUTuIGHmlDoygPmZqbp/Pq13ny4fOkMxqNlkM7HdHZ2uOLqRE+kZXoZxbZeO8ajbbPwx85R2irdELIxDFXmyrn5nSCfp+x8QQ3Dzso/RZutcNH2zvYwyOIkSfJJwIUU+N83Gd132e00GR0eJgHq+tMhg5NBplRFd5crnHh6cdx13fQMj6kiuz0JFM7dxifPY2ULnfrBmMzC6R0QTaTxI8F0cQMijh6du06goQakRwepRerpKw0oRIye2yS3miZ4cBAa2xxemyOlY5PSpNoqsrl3Q6LRROsLLNaQJTJ8Lmv1ljvXeVuv8b4I0+yU2tzf7PGdDHm2HCWxcEUOW+XOAxRi0OIfAk1UyT2HGRuBIRK11cI1CT7kUYqFrS8iF//+mXOF8c5F5vYAUe0hcwQPaOIqesoIiRnmHRD8AMNRdMYsWICoRH3OhyQJ+26VH3oLN3hwqnTVCpNgso+jaEsA+0KuYJJoKfRpaTVcDETAq3XIyxNUXU6pFWbr3/jGu82FPacI3/mk6aCNzZLtL7MbpBCEwpNvY/h9qjubvPi8xeIBHiRTmXvgOrmIccGUgxmbcJen2/fb3BmpoTQdE5pHbSDfWoD8wQS1vJJyimTfM6g6UesHdRR3A4pzyXRbxFV15hCR5FphKNR8RPkkgaIo4hkxwk4bHk8MTdPZJl4qkoqDnFVi7QGcTcAItyxE3x51+LNLy/xZ5+dppQ2uXqYI9tskh8o8c52i9FSxGpV8OJCGm69i3/yKfpqmoJTBUJkFOB1FURmkObKLuNZi3rXR5gWd799g5mz46gJExyHWq1PwlI5PpDkzlYbaVuM+4fs6lO895XLfOLYCOrwMX4/GuRUrU8rgKwmjppNMdy9sUfbizg/V8AqF4g7DYLDPcZ+5Aep/O7v4O9tUionmXnxHK1Gn+wbd7l4r47/bwE394O/QxW8no9aaTA6mOLl31/iubk8le0ulTCm9+A+1ql5qq9fJG/rgM9Tf+YJlNE5FAGGAkrkEwuLsDhFoKWxlAgqm4Qz52m4AZoQ9KKjgIiiraMgiSMF09Co9wIuL9cZM0IarqRVq2IkUn8IH+nqUR/R8zyCIPjuO7/yj+/28J8c7+F/Vffu3eMzn/kMn/3sZ2k0Gvi+/12N8x11fgHe22wiEym0wXGiVhVUldgPyR6bxEyn0RM2iYkpRCKFnknRr7VILRwjPT1O6LjIbgtt6gSx59HfO6ByZY3eXoP2ZoX28gaRF3L42lts3K2wcmkFEQf0Lr1K4swFgq0l/JV7RHsryCiEZgUiH2nnEJMnoTDKtqdTcSQlW6OkhUgEPS2LJ3UOKz06+1Xc5SUOv/Ey/cMqIp0jSpcQjT30dBp9aBxRnECxEkfUDLeN2mug7i4hO3WwEyiGgaJpSMcl7rYQSJRkCqVTRWQHEJk8se8SOC7K+++w9I330EcmiVGQcUyUzKGdeBjRbqCm01jHjoFu4m2v4+5u4eztoKZzSKEQ6wYdPUNoJlHiCKobSLeNWlk/WuLZe4DSq0O/TdyooPbrZFMJZKdCKCFE4Te++g4fvnCG4sAQs0+eobR6m75uEaULOLVdtis1bhz0eXtpk9fu77HeCtBUhdnZWZ594lHOnH+UydkFCkNjaOk8mhKjRh4ZNSaw0gSp8pFxe9jBaG0TCB3L6xAaSTwtgSIjkl6LM4MGT44nCRyXSmQcAc44JkYlnckzNzyAKgSdICaKoO/FlNM6TT+i7sb0hUGMoB+AGrpH0Z2KJFZ1YlRiVQe3jYxCrNhH6R2C20Xp14gHppGaiahvIbpVhJ0AM4m+cIa43URYSeLaHmHjgFAK4nQBLV1kUJMMpARTiZheJPhKJ83O2i4fm8lTHZ5hZ3WT/vXrvKZM0zpskbr8TYSUKKZJKZPk1dtVDlouxbiHaB3gpMpEe+scqBn6egaRyKNVNmi2O+y5gh1rhCtOlq16j72Oz4WMSzaVotnrc2k/ZDO0qCWGsaRHxoRuBLnaGrFqMtA/oBmp1FMj/EZvkt+ZfootLcPx42OcKagYImZv7QB7Z4loYIhLtzb55qUVfjhY4mxe4epWi6WeoJ4YpHvzJv/4N99ksyEJBSyUUpwqp3h8qkSxkOSVvYh3uxqF8SlGxkeJ8kWy+Rz1psvdikvX86i2uvQ6HooQ2LZGOpNBVxXSxRFarT4ffu4Cn/3Se3x8LkVmcJDM8BDfWGpwKg+P6gc4Tp/9lsN5pU7PC8kmTS7fXOO3lyIShk5ra41/+dYaCoIgkrx7b58wPchoMcHr0TCiVWXU8jFGJgirWxSGBtndaTDe2cHVEwwuHsN+/AkKYxPUejGtjkdBOoyMDiMMk0sHMboMmbhziVcu36dkOKRyGsVTJ+mFgq6Ro+oJhJWk1nNpiASKKsipEmVkgthM0d3dY+j8o9yqOLyrjSOGpxmI2qy/8i1CoaDGIeb+Ms9M2KQzWYSRIB1UyXfXSKgx7WYVP5LUei4WLik8un2HULHpa0kSqTTFhMKs1qGaGuFO1WGxmGIqZ1Hv9vGCGNMwwG/jhwHa6hVmsklO1+7ztXfu8+jcIG/d3iWVMkBK9uIU/VYLd+UW3bqLnLtwpGoXCsIwkV4PJeiT1WGnJ3ljvcth3+fG3S2eODHCZX2SEJWGG9PzI8JEDl3X8PQkahShKIKcDjlTxW/WSYU9krUNIkUhs3Ufu1thqQ2H9YBsLo2dzbGr5dmsOrQPGhz2fHbiFL6Z4v3f/ib+9iY7v/MlNu+t82Cvz72dPlk15K//2adRNIXlVshmYZ6KXmRmdoiXHh7FfvebXJjOkzQiHpkq0o0V7h86ZFTJ/OljPPfUQ7z00Biv1Qw+OSb56FySeVlhyg5R3n8TxU7QdwOcWGO8lOG1zS7vrNd4cNAmkCpXaiq7epk72gTd8kkOjBJbdZeBqMFxd52n1B2sxgbDdHlmJs3CWAo7n6Hmxhy6cGnXxSKkIW0CK0vNKrP0YIu9hsdMOc2xoo0ddJnP67xdTxKEIZqlcWu7xVA+ieW2sBrbpKIWe57AtGzU/Qdo0/OkF0/StwvYi8cZOz/MTt1j+Y2baIUMyVKGR56YYiihseJEDCSPAjwKtorodQhTRZb2upx67gwyV8a7dZ3/dlFhxztCW+1Q4gPNGBw/ImmonP3YPPW336W710DLZmi9/GXSo3ma201qDZfBR88w//wZzn/8LI+f/ANF/7OLfxAbnD59GqNc5PDOHu+tNnjm1BCGaWCVbc7M5LBKBbxenziMWGv2yWoKpeNjOKkylqYSxjEBYMQuqmHS9gK8WBK26riKjetLdE1FUwRuJMmZKjO5JBlLo5w0sXUFXYOMCKk4gBD4/fYfwkemqR9dg3b7D+Gl/1z/YarX6/GZz3yGp556ipMnT3L16lV+9md/lt3d3e96zH9v8GvbNkIofOW9VbDSKIUy2vAUaDpCUzBHhzELafrVFsQRwjBJlEvkThwjODjAq7bwDg8Juj3w+oS7qwSdPmYugZ7QyE0XcQ9btHerRG6Xsz/+fXjCor9dRXHbePduEjsO+tAI3toDiAXVi5eQUsXfXkaEPpGAZMJmtx9TSujE3SYNx4XGDlp1hcb8w8QzD5F58VMULpzFnDuLEkVEqk3QqCOaewhNRzR2iXo9KIwgNBslmSVuHBJ0OiiqjpLMIlGIavvEQQBuHz2VRjgtECpYKWQQUH//AWbGJDeUh+IIplNjjwzS7WHs3idcv8Xlloly8mHw+ihuF9Wy0RM2QjNAUZGajel3UXwP8+A+8dJlosNd4uYhsnGAzA2DYSFSWeJWDe3O65S0iHani9Kv89a/+iy/eWeVv/DSh4h9h/Ij59ivdqlvLHPx/iZvLFfYqnUxwz4nR4t89MIiT56cY3ZuhkImhTBMhJA4foSUklCzyKkRpt8lgYfeb6EAIoowGpuE2RF0IrSwh4wjEk6DWNWQxYkPhIoGlgZJO0lSV4kUlaLoYuChKoKkrqIgyJgqQhG4fkylF2Cpgq4X4YeSdgChqpNQBQIIpcAz04SJPNLOcxAlcLQ0gZFG+H2EBLW5Dwhiz0FkigjNgsYeqm6iDQwjSuNIp0PY66Ij6QkbRVGJNIMn7Ba620YnZujsSa6u7bJz+w4nCgYPjad5055mqJREGRqmM3WKe5UuqZ2b5P0Wnzpm85nffYe7rklLSWM6La5ac+iaiSCihc2uNU6Aho+OaRlk0gmGyoOcG9CJy9PkNYkVO6DqvHp9i9VOyK6rMGoJXC8gMBNou+ssPTjgjNlhtxvx7IdOMY/PZm6CBkmenExx9uQ5Nn7/n/LUzCCffPo4pzMhX33nNtevvM+fO2GSNxW+eXMPXIfF88d58liJ/Y7Dbq2DrQpMVWW+lEQJQ2JFoeOGhIrGQa3L02emmRlI0XMlA1ZMqrnPheZdSkmVueEMSUXykbkC4zNljl9Y5K23bhJnhgh8n19d1Xhtw2V+LE+qPMEvvr7Ncjvm4eOjZKXHufEko8UE58Zz9Ks1jITJEC32Ao1eaZLbokgQSV46nmFsdorPv7dDcm6OBwOL7FmDHGwfcqmikBka5CPzab5YtyCdp1EYZWZ+EjPoY7/9Lcyt+9xY2cWNBK2+z4edO9y7vsSlwGRhYQ5t8ji12Yfx7QwlwyPZqzJ4cId8a4eh+Tn8fp/dfkjx+GnGRsoctl3MdIKeGxDcf8ALz5ykoPookcvThYDE0DCq08CrHmLFffT2PkrkIcOQWLfRAodqq0fNicmnLExVEEqNsaxN7PVR4wAldNH8HunSIJmUTcuPyFgqBjFj2QSGDopQEL6Hfu/i0bPqkQ9x6iOP8n/7yZdYWttjMHGkYhfAjqfzK3ccXjZPc3vgFIdth36iRKCaeOkyrN5ARhEZ2WUyqDBYytB1Iw48UOt1Ov2Al+/VWKp0EZqNGxt4EbhG8mglLfKJQh/Z3EPfv4ffrWNHXfbSxzDPXKB37x520kJLmtw96LNYULgwZPDa+xu08mX2G132XnuNN1a7jBwb4mIlpj5zmr//zSUi3yERdLDmjtMOdLxYMJfVSNo2Z/KSqWMzJNJJ3PPPcG29wVozJjc9w9J+l6XDHu/dWuWRiTzlwQR9YfLhuRLR0AxJv00yXwTdpL3wNEoiR9UDP4xJqhDKmKIhSZsaCVNjIGVhaIK+F7LZ9LnT0fj6nsE/vi74zEaG31tXsG2bmZRPorrEBbaxapvE7SaGIrmz26EXCTwpcIOY0LB5p53iBx4b5c89NYUTa+x0Q9o+/PCjo+R372MqCgeHbR7OqVjVDd4ef4a6BwNJnY6RxbVLtAfnUHN5tKhH6WMfJzczwmjG4NbVbbxah8RIkWOPn2CqaBEGku2Wj6nDRMEiGh0l8H2i65cZe/YxvIMq8eE++re+igqoHDUc/z/GU80YZkYyjD7/JNlTiwx98kXUZAoNl9gPcWsOLS+ic2cJY+E8oz/0AzzxJ58iq0DeUkjlzH+DPbLf9yn2911W1tqcmc6QTAhQBcOlBJmMhbAM/EoFe6jEVk+yMJxES2WQQkFRgFii+C4iluB2SD94m3tN0MqjSN1mIKUTS4hiyYCtM5DQSBsKhqpCLDE0gedHZITPZqXLH9We/eTTZ4Ajvq+qqn8ojOE7qRj5H2T7T6UuXrzIT/7kTzI0NMQv/MIv8AM/8AMIIfj5n/95Pv3pT1Mqlb7rsf+9wa8QAtNKMGj6KGaS2GnjH+4j+z30dJJgf58g1Ih9DxlHRL0u1kAOY2AQ77DG1uu36e3sc/DubaLDbdTReSI/REto6Ekb3TJQDJ31dzZQkmmSOZtHf+qHKXzP9yOEQCvm8XZ3kb0WsdNDWjaZRx7Fa9ZITC0QPbhC1xxAIEhbKorXRV9+h8zGdczaFvHeJgtJj9RoGfbW0cdn0PWYuL6P5wc4ygdCNb9P7LSIGofIRB4SSRQzQRxFKJMLiEyeqNch7tTRRyfRioMIVUdJ5XHL80eujokspEsYgyXiwiT5iQG0OCCWMYqqEvZ7oBuspaeYXphDV1WCyjZK0gJFQbMtpIyOrFd6LezuAarTQLarBPUKMg5RcoMoAkhmkUYSURwGw0SxEmRTNu1uj1/8W3+Tn//q6/zy//gz7JZmeWPP483DiOj8Q9ihy6mEx7PxNo+PppiP6wzkUqgyIjaS+FYeI3YQgYfSb6KGDpZbRZVHfGk16BGjoEY+SmsXtX0AYURfz6DK6MhcvL2LNAw8u0Bkp/HQsUWMatoklAiNEF/RSCjgKRaKItA0wWBSI4gEKe3oS5wyVRJqTNZQaXkRmoBuqBABASpq5KOHHsJ3AbA0yaEL+0oeaacJK1uIxg7C6RAPLSCNJGg6im4AgrjfIW7XCXp9ooEJbOmjhw5K5KMqoNjJD+JZBYtDKUqzc+gjY/wvr97hbtUnTiWZ1H0Ct8fliuSRbMje/T16Bwd8ZDbD94yF3F6tEKCgbNxkRIvZ7gYcuAog8U2bHU/QdnxShoZtKEzlDMxEkm6soXYOybhdFkYyHJst8/qNLZpOQDcIyRhws60SPrjH4fAxauYgGUtjrdXnxIlJ4n6Prz7oc6Gk8OLzz/OpY0P897/4C9T2dnnzIKK6vcPc80/x+Ws1jmUVsp0Gca/L/EMnkYkczz80zX435P0rtznourQcnzCIGcpZqFKwvdVAGDaLIxmWV9YYG0rw1MIQ37pX427fYLRkEMcxCJUgDHh0rki5YPNDLz3GGWeLHz2W4F/+/gOabZf9lRUeffosTz51gcaVu0TvXiRjSrZSQwSNGtmUwUQpjew10QOPydlJJtOCS3sOS23w9TRbtQ6pTJb58QKxnqCcVOlqCd65t0u722dSj/jSuuD/9dtXuXVjleXdNm+t1lGXb6Pcu83mQfdI5HZQ4a5vkTdjxk4vYiQSNBIlJoZKKJ0qup3EFCEiP0iUzNASNpdbSVqRiW9mOIhNgutv0b78LtsiRWvhDHPRDnh9ZCJP6eQptH6D9qXX8PITuJEAr4NS3cS9e51QT6BHPgVTZVTpM2jptIUNpo2IArS3v4ha2wQZ4Tdq/Mbb21QafU4WLBTAExq6oXN2MI2uxKBpGJPHEEPTPDSW4dT5BZ55/BS+08UJJe2DfaSEM3PDjOcS1LQEmeOnoTzNrQbcicocaEU6A3O0lASVyOLmYUhWBMznNKaHcih7m5TWrnH/0nuMpHRKCYjiD5adxdGzIjxcZ60rEYk0+tAE0swgc6M0Aomlw9Y772N0qzRVhWPDaWrSQlpJ7i/vkisVuXx7jyvqAF967Qat3QY7717ltYOIJy/MMTM9TB+Vgeo6q/U+CRXqLuS0mOmpMlOpo0n13Kl5ZLtBu9ZkKJvg+YUixbTJn3ruJOWUzg+PCxZKFkMphbtRlrzbQkuk6UcSmcnjxIKRnM1IxiDyPRK6yiNjKUbzFk034qmZPJahs1/vI4WCpQhUVZBKG3TdgCCV426QwRmYQ5t+iLA4fdS02dtl++Z7nGSL/eX7eNVtPKGjJnP84GPDGKqKpWskNTjWuI2LTk6X1Adn2Wm5PLYwgCDASKfoKxaOYnHv0MGorBKNLnCgZnDzk+Qih9DK4jb7nH7mOBLYWTogUkwGPvQEA2NZpIAolFxaqZNJmuQXZljVilxQN3Hv3aZ68QoHNzfYv73FQEpjYeAI7BX/LdvVuuuz9fpNNNsgPPYQUfUQe3KK5u0lojDmzKMTtDf38A92Ubw+yZSgF8NjUznuPqiTNo7unZf/u/+Zwz/5F9iqdqm0+pi2weSHjtPa7jDx3HH237hB8tg8K1+7wdmyzcmHRjDHJgmlRkqDUn8bsXwZJQ6QDy5jFfIk8ZH9Lkmviqoc3ade+EE2mlDohxG7HYfNtkPPlxTTJsNpha1a9AGxNvxD+Ojh0zMAHB4eUiwWv2v/2Q+cTP/Y238KdeLECX70R3+UcrnMu+++y9WrV/mrf/Wv/rG8ff/t+vcGvwCD2QJqew9VkYTr9wh21lEMAy2bQ00mufFb73D/2g6Kbh6FLygKslVDSSYozg/QXD6gdn+XoLILiQSh6yEUBS1pkhwtkp0ZJowkQSD59q98BbM8ghr2UJMpWu++h9AFMvCxBsrE1R3i8x/BPPMhRDKJmDlLKBTarkfREKQ7u+hjCxSzFrK2iz4yjVYYRd26g/Pe63gHO0inQ29tC8912R5/jDBZBEWFKEQfmcE3EiAjpO+gHn8YjRjMFGH9kKB6AJqB7Lbw93eRrQqCo+9FYOchVcQ69yi2rZCZncA3syi6QVGL0EWEjHySxQGGcyZWdx9F09FMi7DTQ7EzyDBCBj64beLMECJwEZqBPjyJPnwMUnlEuoAaOMRWGt8uoZankIURVnf28X2fsade5L/8f/5dmmMLSCvJQt7gw8dKPDFqcnxikNTeCpppIhv7xIGHdHoIp41vJrFkiDAyhCjo/Tr7Bwc4WERO++gaqQauMEBRjmzJulWIgqPz7NRQohARR4gwQJUR/fCID+Y6HQh9DKcGUYypKESaRYCGqR4Z+eeUAE2VFA0Yz5mYqkovjPGkwAtj0po4inGMIRQavpZACIna2kFEPrYGYRxR9SRBapA4ihDFcRQZoDkNos3bUNlAtquouoZQFEQyTfPWEtrGHaisY1dWEPVdFBkTGVlcq0jVl3hRzPHJQVZDg+GJUVZ6ClNFk2+te2Bn2BUp+pGk8fgLVM0c74cZni/7GEGf0Gmjzp5jaGIUTcQEoUQP++R0SSLqkwhd6q0+MgjRiHF6PQg8vnpjC1EeZzpr8fH5Ei1f8vnLezitBndX91ltRrw18BBPxWustELeWa/xhHaIeXqRkZ1VLiyO8NY7D2hJk5/+yz/Jn5+d5P/6sz/L5z//JZptn2DiFPmREWrdAGkofOmNmwxaKgvzo9w57NN1Iyo7OyR0hWarw8dOlEhaOt9/ssD3Pj7Bqflh1is9zo+myIuIfMZmY79OffwUViZNxw3Z2TtAEhNKKFgGkaZhjo7yXC7ke86NMSJbTI+VOYg0tNoWJ86O82jZ4mx/mYu3dnn71be5enubP/Xxh7B1k8zEJCMDOSYsiVOrc2YoxVov5nOXd3l4tsSFssXsYJJ0JsczJ4f50+k6e77CW26en3j+GI9MpCk2Dti+e5utwxbLDz9H/eTDBAK2OwExGpeqCvV0iQ/lJQslAzP2ERK89ABvGDMcdiPahRni7AC6UHjmxDCFlInmtKi0PQSSByJN9r3X6SwtEdx8D1VTQDPQNTi8cYdGnCHY2aZ79336926jqCqmoSA9B+G1GR0eIK1FpMMWcf2AdqPBZr3Lq1UN5949/Jvv4GUH+Zu/9AW+fn2bb7x8meVKl44fU+37WE4dJY6QmnXEwtdU7LCDb+XR33+PlGWzUvEYSChIwA+h0e5y8fouSzWPtzb7HIos72w1aISCZmYSAp9ICHJ761y9+YDDTo8Hm1U2x06SalR4KOUzXTgSk6WjNkLGNPo+NA/oNR2ShoIvDJBQyKaRQmUuERI5fdRMltN5hfsyx07LBVVFTRfQLJ336xH5XJYzxyf5y588Q/qlTzJ6cp744mX2f+GXWV5a45+sWRinzyEUwTfu1SlqAV4ksBRB3Vd4vxbRCyI+eizHc7NZ3ry5wdZeg+PDGTTTwAz7BA/u4N+9RaPZQHEdanYRt1EhvXcXQ4CiqDTdiIYTUo9VzoykSZgq6/U+A7bGO2t1Km2HT5wbZqvZJ580MA1BZeuAym4dU1XIWQYnSwmGUjaJTJYVmSNdGGRXG2FmIEfo9NhcXeXmrVtsPLhLr15B71VpuwEGEcbgGMfjQ1qRiqkEjOVMZkyXq3VJNzY5NpAi8lyWtut4vQ5CRsz0d4hDH2GYqLFL+fknsDMaTj9kdnGA/so6vTvXyeVtjo+lSFqC/WZAR4ETDy8wnkuRPzaHoqkIITEzNtdvVzgzlefxZ2Yp6gpVCfoHeGFjv0f71jL7N+9jKILYaSFVg9TYIN2qgykkcShpXnoHQo8o8BktmGRTBk8+NUfPh3PDSe5fXyH15u8zXU5S7Rz5R3dW90gkdarLFUZfeg7l+BMUp/NoH4SIiPwQiipIipD4yitHjY44AjsFvsfkwQ26Dx7gRCqmgCCOaHsRuqLiRTFuIFlruGRNncurVXJJneEkrO010NN5/qDHfVS6qgJw//59FhYWvmtA9p8Fb39Qy8vLPPPMMzz//PMsLi7+Bx//OwK/jz71GO29ddoBSNPGyKaQMkbLFogcl9lPPobhByiJNEpugNbyBl6jTXJyjPffXuNb31olM5ajt7mDc/VtrFIW1TKwBwsEPQ9rZIiUZWINDLD4/HmCnVWi9ABafgApYlRFQckNoA6NowxOEhlpwkQWYo54up5Ds+djuXXUoI+ayRMoJlgWUXkWpbJO4LjUDl3iyy8Tt5ukji+Qri4z7B/QbXUhjlEGJiGO0LbuIvUEUbMGqkG4v4lsHoDbwxieBLeHt79DHLpIRWB29gjtDJGdhTjASFgouoo6ewLdbYGigwS1V0MNXIa9A9TmFsreypE5u6GRmD+OOjyJUiijWAmkYSP87tEdnSlhTM6DnUCqGlLVUYSgE+ustgJ+/e07/OtrG2y3A3RFMDlS5smMz9NGlZNzUwylbbTqFnRbCL+Pki+AqiBVA9WyIfAQuokIQ1SngSDEkhH15DCW26TrerhSHP2YKgpm7xDRqSKTeWS/C8k8Kb+ODF1k5B+Z0MsYvVMldDpYK5ep7O4RuD0Uz0H4HYSMkTJEILD8Fvmoh9E7pCBcdOkTx5A2BEN+hawSIoSCE4OlCvqRRBVHUcICQViYINKToBgkdZWttkNby+FOPgQiPqLjVDehVQHTAt85OseJ00grgz06RPe9i7Rf/hLtV74MzV2iKCKKQzwMCoZgSnN4YkzHb3fpCpPzs0NcX6sxPlrCMDTKisfnrndRZcBEKcOErPNz6wUWioKVmotnpvCtLNc2m9zarFD3VQaiNigGtRB6h7uITpXY87jdkBQSGh8et9k87PHORp37NZ9zEwW+/3SR6x2bRrfHiWzMUGuTZQo8PJwi9/bX+Y3LFd5ZrnJTJDmm9VDtJE69ypZS5OT3fYr/x9//R/gPvs2nnhjhW++tcKPislN3yY0Pszg3yd/6zMtcvPyAm1stXih67Jt5SpbCsQGbuYEUfhij2hYL43nOTeW5s9tmvWvxqbMDRFFEPzlANpthtxWghJKzOfjCmw+oNhw+NFfgdEnhZCbk+tde56URnxcfneG5CZtmpYmr2bwWlGg98hR//ZubvHd/h595osSPLhp85stXUYmZnBzm/FiS8akyWUshiCOabsR4IcOgBUUDyoaklLLoHB6QTGmMZm1uHTh84bXbfCzXxi5n+Ye/+Rprgc2tqMjNMIUsT9DoeTR7AS8+sUCmNEil3aPfqFMPBV2ODP1L2TQ7w6dpOy5xGLG6dYilCY4nAhJblxlw9+mMneKOKKMsznLhwiz6zAKxfxS3u3n5JlfeW2WwZJI69zC1W7cJ2x2igw1kr4W1ex/NaWHGLiKRJ45jitUHbG/tk3/v9+nYRYyZWa60de7UfP6rP/kM6WSCySGbmhNy0AsYp8vllQabLY9YtwnW7iPcHprXRTR3aFdbhFaC4OCApCaIEbx5Y4XYSHJ9vc0//t0b/E+f+SbfWGkSoZD1OyiBSyW2CaVgeHYIwwlwq/vYd69zv+7TQGOyfkA29tC9NmZ7l3Bvmb7nEEcBYSpFvR9iqxD0u4SBi1JZwV6/jLAzFI/PkjA0JgcyrBz06LgRo7Zk/tQim13J8OgA/SuX+fqX3uZL33iXYycn6LTaTJUTTE2N8f3PneQwgKFcgtnBJHP+Hnu7+7x1d5d9T9APImrNHsdnR3huwuDUsRFeXW6hBB5bl6+wvrJBtS2xDlYghmFvj43VbW40oJ8dpelHqO1DbDxWGg5pU+ewG7B06PDRXJ9TWcgldCqdgJYTkjJUvDDGEILhpMKf/fhp/o+PFTk7kqbecbmzUcGPYz5ajDkxmORZdZc4M8CCbPPkkMWZY5O4GNS7LveWHrB2812W79/lWxWF97sKl9fqXF9toUjBxW2HatdlhQJrDZfLBy6N3V3aQydQVYV4+X3MjWsI38PwuqiTs/QOGszN5qkedCmcP0VQbZAeyvLMJ0+RHijw8GgS1wmJ1u9jL11CTedJLhxn+OMfxrJVFsazzF0YRdcFWesIAAYf4AVLEVQ2m5QefwKztY1AsPY7L9PcPCD+wO7UrXXpbVcQw7N4bZdnHxll8OwoA5NFxiyFSscnqwre/OXfAhQWBlIkMxYDDy9iZAz2lysQ+wQbDyg/cYq9ho89kDqiL0oFvbWP8HsopTHU6gZh/RAxMI6iKTSu3EDRNLohDFiCQEpsQ6HrRzQ8n2Y/4EGlS8+NKBoSgWR9u4qqm/xhkgd86vnzANy9e/d/F6D2/4+1trbGwsICP/VTP8XY2Bh/7a/9Na5du/Yfp/P7xJOPs7byAD9SUI49jtA01OwAQtWRls3e5fuMHC/j7OxQv3SNoOMgFBCayt37NcZKFldeXSXsurj7+0RBgFlIo1gWeipF4MWMfuwCu19/jZlPvUDv9i1EaYxIEZjFErGqoeQHCZot3FSR2HfQuhX6xSnCKCSOIs5nPEqqT7i7hvP+ZVrvvItr5VD2HyBNG+/ODYYfXkDVVbyNB6i6gZHJkgq7yFaDSLcQxAg7hRo6oCfwhuYQYR+CPkLViPouIpFGdtsopRFkGMHoCWS7ThQLzOoyMpFF7G9AFNIdOI5vZBAyIogk2FnU0MUwdOi2iTwH4fVQsyWsmUWEqiLyI5DKQ+ijdBtAhIiCozWNOKKtZ3jQjLh4Z5W333mXzQd3qFcOeG5+iI8fL3MsLQCB3a8d0Tl6TUASNSsIOwWBjzk2h1ocIuy2UFIZZG4IgcToV0A5ApRqt0o27DI7UiCvhSSDHi4KgaKjxyHC6yM8BzQVVBWpGrTsIaL9DaRqEJtpXM0kliqxYRNfvXJkoSQ4sp+LfVShYIsAw+ugey0izT7yNhYQA2nhY9XWSAUNEkqEqQhURUFXIIwlmgxQ3aPjfC1BFEtqXoTjx3QCSUNJI1WNWLeJDzZRhmdQjASxUI+uTegiUUk9/XHaSyv0d/fQNAh7Xbh3Ge/1r9KLY1RVUNRjsoUSf/7xQZ4dTzBcsPjxx0ZYGC+x043Z2a5wa3OXb1xa5p+8uUlZc3hsMk02mcI3MlRcSbNWQbt2iUsrTfZaLi/vx5RSBooiGBst4aKhixBV02i4MbI0womiwsrqLgjBpZVDRrSQKcsjk0mw4SuohQGaZgFTBJTGJ5jJqVy+tcaPPH+Ctw8hM1jgsYyC+t5bvOfmeHY8xT/7/L9m/fobdO+/wqX3NyjrHppuoOZLPDufZ3ooz/2bS3zj6iY/8Owpru71eW+rexS0ICVJTWW10iWhKVyYLTIwkuWbN6ssN3zG5sdRdQVFVSimNL5nDH5oRmGyaPPKjS3evrVGbWOHqQGL2uAMs6JBcngEkUpyL0gQGkl+481Vbrs5po9NcmKiQLUfccL2eWY6xUzBxHc9xgbzPHp6Gj+KWSgk+NFHRijoEZ1Yg9gnlpITAxal+Tm2l7f5sXM55ufHaUye5YafZ6/WY3pmko+cncCptylaKsWEzsx0mRMlgxwOWnmCUibJSFrHd7rcPOwzkjYZyacxDIvDew/Y29zl1998cDTBSmQ5RpXzVPjEE8d45MmHeOjcPHY2hwx9/DAi5TaZ+MHvxZ5bRGlV0E6co5PO015ZpeuClh8gSpUJNlYIl97DkRrq2DGm8hrppz7CDz0xRS1VJkoP4C2vYw1P8tJ8ktH5BbpBzOphBxSVl86NkrI1Qj2BsXAOlZi4ukOutol++gypboWMFTFYSNALJNfeucZv//Y3ODGeZHLAolavU682qGzvIbtNzItfJ5ey2K60aZppOu9e5d2WydzpWVbrHp+7ucvGiSfQ+zU0t4tMDXB4+y5Ro0mvUkXPl1nMxFheA02BpN9FCInSqSFkSKQZ3G1JepjcWKvxzlaDb92tkE2bpLMpPNXEeuQJkidOcMVJ8Y0DnZ3HniX7E5+moafYbjj0nIi9lnM0WS6NsXx/l//l/QZxLJkrWPh9l8N6Cy2KyGdM6jubvHZtg5onie8/oDyRI3v6FF9cDfj9Tg7DNCjm0xyIDK4XsmkNE6EzmtJxI0HGUnFjlVwuy9jmVWZKKUZzFkbkc7ycYi6jM5fV+Ns/doHjo1kWtRZhLPmNy1vYlsriQIrhnEWqUKQ8mEdLZnFOPXMU1NFt0dCyDMU+z88UGM6mCYIA0avT210lbu8zkpEclxVi3aYZCnqoKEJw68BBT6RwQxASlJMPI3sdZLeGrydwB6ZJTg9SrfZ56r/+MYximcRHPklhfozeRhUr9Nhv+QyVMxjpDA9+72WCbgc9mUKZXMDvesw9PkHkh/QbLrlSAh3If4AqOpFk8U88gpk0iXQDmSygSg+/2sOyVIYfmyOKI+xyDn9zidZmDVUo6GkbI22xOJHFkZJ+KOn0Q058z8OUChaz3/9herU23ZZHvmBx53PfYO+LX+TSl65QslSKp2aRXh/Tq6F4LYypeWKnS+y00ZIZ0C1kKkdmfhrL76AKaHmSKIoJ4ggvjLhb7TOesTA1hYSp0q032Gr4+IGL2zz4ABH9QYt1rHwk0Lt37x7Hjx//rgHZf6Y9/EGNjo7yMz/zMywvL/Ov/tW/Yn9/n6eeeoowDPnVX/1VlpaW/ljjf0fgd3Fxkf21ZYQaoxo6SmkMJVfEOdjH2dyjeW8LzTbob29QufaAwok57OljKKrKC99/juOPTWOoCkY+hVNpErshViZN3HfoH7Ro1fsIt8Nrb6/jvfMKiWdeQqlu4q+vEPZd7PEp4k6D3toDXCOHIgQtLU+oWXRDMPeWjgIfjARy+hTd5QckTp+DrXVo7hOs38ecWUDp1Oht7aBl80hAz+TQiiNohxv4jQbSzoNuIYojADi5SRRAmzoFUmJe+BDSc2BgBCVbwFp4CMXpIFJZzPY24d33js4hO4hiJrGjLpEiEIGH3dmBThVhWAhVA7cP9QPE2DGU8jiKqoAUiDg6ckNIZMHrEDl9mgf73N+p8vryIRev3KTa6TGcTTE1Nc0/+0c/x+miSbmUR03myHX2abghUa+FmsocZZsnsqiloaOZUyqLkCEiO4A6c5Z4YAZC70gkEPSJ9BQSCU6LxM77aKpAS6RRzSSqqh956OomJDKI2iZKugRRhKOl8VXzyHc4kQNxxN/l6mtspybQnv8ewpHjRJlhpJFCjUKkEEePEUUQ2QV8K0c9UBBCJSFikl4dSuMoMiSrxwRSHpmoSeh6MX4cE2sGnlTohxJkzEBCYzRjIQUkdRVppPGtDNhJlNwAEoFIZogTBYhDZOsAhELp8YfInFwAVQHPJ1R1FOmTDLtEKGCmOZY3SI5Mc7KoMV9OU07qOH5I14tJyT5J3WB5p8nC1BD/7FbI8MQwu0tbjOaSXNnt8tpexPWa4MXmTW4dOjw49BGqRt5QaLuSji/xItho9smGHVp6li1X48VzY3z12g7Pnp3mRj1GahYzQ3kypoE5MsZcKUHs9Pldd5iZU4v88AsXeH035KAdsb97yFBGQY6M88hEko7rMjGY58d/5u8yr/VY+9q/4ECkUYAXF4uMjo9Q0bP8+HNzrLsG9Z7HresPeOf2Lv/jl+6yurzGztY+Gb/PO3f2CcKIjhMzlDdJ2QZtP2amnAYZk0oY/OLFfZww4uGpAv/Vc6P88EcfZnNwgQdPfJLmQYW3WjYidCnisNEIKadUpgdz/ORLp3lmzOJfNIb51ddXOTNRZPatLzHo16i4gkbHpePGjGZt8rZKy/PRW/uku3t4r36dWEqW4iwdkaJxsM+VzTaPzBbIXHmdpxYHSeXSfHjCJK/HnMhLqm+/wWTe5HhaJdy8jxG4OK7HzabCV25VEGaSat+n6UcU6kvEkUO6lOQT0wZPDOpc3fcI7QLtqsdGpcXg+hUKRkRSiY4ieXPDOCGkixYzjbvIXovgYIvM6CiDY6Psm2M0e4JAs9nY2OPw6jU2mz5S01DsJINDI8hiGSc/Qj+QjM3PMzY1ghqFVJ0jq6YhtU+tH9Azc+ipHLaqIGRInC6BDImH5lAzea72TO7dWaaTGcIkot1xUZMZHi0b5BqbjI0XOfX4I1x/5zqvvH2Tv/vF29yxBvDCiDmtx2hSp/v4h3jYbJM6tsB4WuWx6QFKBYs4kQPdJBbQ17K8+c4tblAmZSoYRPhCQ+bKqKaNsNLg+8goYvvBNr/7+j1u3NniQ/MlbMdht+tzYSzDwkCa0xMlZCLN9Y4JEzPc33PRRqeYLihUG10qB1XaYcRBo8d6V7IRp/HHJvnw2VH6XkDf9fhkuctmR/Dl129jyZC/9OIiL5wep7tZ4ezHnqQ7dIxGYphPnizy2HiG1PxJLE1jsL3BvNYhH/eo9gOKtkpak0zpLmk1wDFyZCfGsA2Fkqnhuz4lW+P27XU+tDBIIZtkOqPRTg4zn1MpJQzKORPdb6KlCyiqhpVKMBhW2ewL5OAMK8lJZspZzFwBgWTYiCmNTnFPjFIrHqdYKqF2Dniwtc2Yv8Vo1KTWbDGU1HhqJs9zZ6cYpE2357K8tItaGkUqOk01ja8lCAKFJz5xhoHFURicIJy9gJrNs7XZROn7KEjO/cBjmMODjJ1fJHb7iGQWub9OcqiAnrQIux752TKFQoIzkxkaH1BnY+DWlTWiwx0iPUN3r4Lb8MhO5hl9Yh57ZIzmVpO969s8+K1vULm5g9N2WP7mXYShsPCDT1G0dRxgKGNQGs7x8F/8XqwTD3P9a1dY2emyulwn7jvc/dYdvvHlW9S9CLfaxF17gLpxG6wU6tAczvW3oDSGmsmCUHFvXEKzNKSdQRWCqhsSxZL9jkfHi1AkHB9KkU+adP2QVOSy1QiOhPBR8L+GRv+m/rid3/+Ygrdf/MVfZHp6GsuyuHDhAm+88cb/1/1fe+01Lly4gGVZzMzM8Mu//Mvf1ev++9SHP/xhfu3Xfo29vT1+4Rd+gW9/+9scP36cM2fOfNdjfkfg9/jx4+xtbZAKe6CoaJks4cE2ztoavf0q42fKJIYL+K0e25tNFDuBWhpGURXmXnyUqR/7EUZH0qBpKKZObn6KKAwxcin6h02a1+/j+EdcotV//SqqBs6ll1Ftg8zxY+A7CNMm+cTHyPlV0kpATUkAAs20MYWHuPcuvp6Gwz3iF38Ef+okxpMvoRg2YmQGv17Ba7RQkmlEaQhnbQm5v4rwehjZNPRaRJ0a0mkdCaNij3Tchn4bYdrI9ABhcYJY1VGMBEq/BeVJROiAbhHrCcLaIXFjj76eBF1FjwOSnX1k6xBxsAaagQhd4srOUaeoWEZNFhCZMrHbO0oBi0Kk06fuS+624LWDiEtNQd8PmSsmeP7UFMNzi7x34yb/l//mL/Pf/eWf4rnv/T7wXehWScUOsZT0koMI3UBGITL2UQqjRLUD4sYhKAai10LTNKSVQtp5ROgiNRvXTBMpGqRLqHaS2C4SoKF2Kyi795CxRAQu0rDBTBDbGTAsZBRgair94RMEKIQyRnn/TbRunTEzoJi1iY0EIg4gjhEyQqhHxr2xmSFQdLxYof9BNKjxgY2XmxmlqRUIhY6uCEwZIpH0o5h2oCCtDMQhKUWiKzFJDdKmioIkoQFRiBOrkB8kTA/haxZRfoIoVSQ2U1AYol+v07y7ikCSeOgpZODC1gPW5p/HbmxiEiNCj1T/gFzSImsIBuI2fXRaviSb1Lmx4zDhVXjmqdPksjbvb9e51RbcVXMcdD1mWhucDw540mow8eQFTFVhvL3KWhdeW+mQ8HpMltI0sHimpLITWeztV5nQOrxdiZgZzeH1urz6/ib7vsLV7TYp26C2e8j0eJE9keanXjjGoS9JmDpzU4OcXxhiyyiweuU+1vFTTLS2+afvVLm4WufRYZOf+un/GkPTufzFz7BW7zM/OcjY+BCGCrqM+PiHFrl35QbG4RovLuR4KtfnTz99nJP+IZ0g5LjeIaErWHrMveVDZBzz0FgeS1cYyZqcG8/xE3/yOcoPXeCrl9aYtALotmi3O3x5qYeSK/HNB13u7HRB0ZgtWnzrletIFK6vH9Crtfno2VF+7LFRLssit4aOc/+gS63nYsQu9StX+eblFYQMcHxxJJxMZtHSWdLNdfR+m26kotkJprMazuV3uejnKGkOP/5DH6YlDeoHB9zdqDIyWqIbCt7Y9fnsVoJv102+dGkVH6h0fdK65EQxwfJek2pkEgoLe3ye+4kJ7vsWI81VOtUa8f4q6u4umq6QiPrI9du0N1aIZUxCeMT9HrJTI1q6hruyTMJtsHv1NsWxEg9+85ts+Tp3PvvbCN1E6zUwQ59ObOAFPn4QUPcV2qGGsrvCOxWfDycbTMoeX/72ZS7d2qZaaaEo0IsEPoJYNY9cVyII6gf4ns+HRjUWBtM8N5dBJcLI5hiZmWY6r1OenuTuQR8PHdcLKA/ksAaG+dxKQLJXpe5KTBHw0EPHaHVDkht3GQwb/Pmf/B5eXMgTaxbig0CB7EiJwb0dumYWTzGIdZuOlqFmlJHdGqJbQx0cxuwecPaFx/jTn3qMx7ZvMpLVeHHa5gceHsM2dBQFKk5AJOETTy7yUNnAVOHF02VuhVl8N0C6PWzbQmoaaiy5dnsZS4kYUX3KS5cJYlg2J/A1k217GA3JubTPC+MRf+JMHi2ZxCRCypAZtcN43maxaLLVcgiadbTqFknpMZMRyFaVd9abLLUiTiYcgigkSBdY2u8wa7o0uz7DKZU/9ZGTPDaggpFgwoxIrFxnr+1xbqZEPmHTd3wEMUHo0zcyqNvL1CONrpog1G12PIVrapmemkJN2Hxltc/uQZOEZVAcGqZUKNLTcuQHhknhY1eWaT24RtnZZ0ZvkXUOqff6jIzlkekikZFETWbwjDQzP/5/YOLxBdyDPbzaIYn6Gtb5p8lkbRKGgqerSKdH9/pVsicW0S2LUGi4719FTxkkRwqMv/QYhbkyg+UUYwvjfwgzvPLaMtWlLTrf/CLtGzeo7HUoPPYI83/p05hnHycMoX5vn7uv36PS7ON3PLYqPda/fpPySy9y/sI4pnpklbj28jW27u7QTI1gRZJcQmer6aAYJpEXUEqoVL0YIWL6y3fxahVkECDb+/R3dqG+f3RStW2C/W1aK1tEmoEReWRMDSEElqYCAiHgTqXLVsMhZWqMpgWr+12IA5B/IIwDUD5Yhvd9n5WVlT9W5/c/Vv3mb/4mf+Wv/BV+5md+hmvXrvH000/z8Y9/nM3NzT9y/7W1NT7xiU/w9NNPc+3aNf7G3/gb/KW/9Jf4nd/5nf9dzzObzfLTP/3TXL58matXr/Lcc89912N9R+B3cnISRVV5sLqGVFREIotip1ANHen4FGbLqJpKYqhEppxh6fOvENX2UawE1vQxundvMfLYLGGzg6bruNUajXvbdDf2GfnoI2gpk92b65ydyTP2zGlUr4uRy2MUiqgKCFVD+j7qwDjt9Ci+ojOiB8RCI2zXUKwkdiEPukEngLTbQF++QsvIIDODiFYFBqfRFx8hee4R+tevIJN5/HYP2lX0bA5jcIjua18BK33EtTUyGN3q0YO8XUUk06iqiswNIuIINZ1FGgmknT4im9cPUU4/Q7z0HhlbJzCzxIncEZB0uyiWiSyOIRUT2W2AqqHaaaQC2CmiwOcw0Hj//jLf2uxybatBKDRODWX48LljnJseYSSfJE7m2Hrzq3z+t3+H3/3HP8uJ84+A7yGjgLhZQUlkjjqJxSlI5pHJIhgJQEGYJkppBGRwBL6dJqgaoZlC9jvEdpoYAUJFaBqk8kcRkI0tAtVGC1yMjevIyIMoBDOJTOSIVYNkb5cokkSqRYhA1nehcYg1WMAwdHTdJNE/RIkDROwTCIErwXKahDEEEjTpU7AVYgla5MHBJqCg6RprbQ/HC+jUGqT6VQaES0b4CK9Lsl/DwMMIe+gyopgw8GIQkY+IPLxIoTlylp6eoZWeIErmCHWbln3k2EFuABnFaLkc0cgiUrWI+21aioXcW8fsV1C6Fdi8Qy5sYXQPEUj6Uietq5wbyXDy5DTXt6q88splRhMCQypcfPc2X7m1z++//j7NtocR9DlZ8Ol7AZbXwZ6cY44Gs3nBxd0+aw2Py5tNOhGcFA2qvYg39yWb9R62qdLAYHoox9e/dYUgOJrrP5p2+PrdKr/6xtYRLcUPCQ+2mLZiRgsWj547xtCHHiMKPdzcIH/2wwt8dC6DLSTbt5b4C//nv4FUBJVvfIaZ1hJX9ly+f1Jwd6vCo6MJCiMTXHWTrFQ99GSW5WaM/+Aez5waZ+WgzcnggOfHbaZf/m3yb3yDpwsBD3aanC1qXFyp8X4TLNPmyeN5PvvOLmJ3E5nIce29K5yfHeTZUZ291VUMRaA6PdY3dxnJ6EQRPPXkad54+xqnz5/key6MU59a5MFhj0TC4kFX4XtnDR6fytHyJNNqE+H1kUJFZrKYuk5U3eVj5gZKrkh9ZZXM8CAXFks0SPDUwwskVEg7NT7x0DhR3yVrq5y3GtgJE0NIxgbyuEHE08cG6HiSvUqd9TvruJkypgaajJkdzLK7tMH4zDjqzn1SQ0Uef/48unTw3COnl2V1kDgKUZw+9Z6JMbMIiQzZuUne+Fv/mHfe28C184R/6s9hLN/moY8+jDE0wOTiPKZ0savL+Mvvozd2yGuSKdMnXVlm3m8SptPUrBw//dwcc4uznJkZJOdWiIKAdNAlQBBXtuk3W2hxzCrOVHIAAQAASURBVIGeR82V8UbmeOjYCJqqkCoU+ZHnFjHu3uL9esj9G6ts3FsiCGMCzyObkDS3trETJlOWz+VeisdKKupb38ZM6CT29nn0+DhhZgRHqnT1NAEKmdFxHvrTP8hIzsYmRIQBSbdBJ1aIhE6cyCMKY6BbGErE9bt7MDGO7nTop0okVIEuYKnW5/cfVEiZOmv7TXqtJi+dG2G+u8Z8OcfYSIGPPHIchKCYsbh4f493rtxjLKyQX71MuZxiLGMzV0oiVIX5mSFya5dppwbY60Rw5hFqJDlQMigxKJrGg2bEvY1dHo53WElNEeQGURuHWKs3OWw0kAIeygSodopcUEcRKnMJyYmRNJ8YCRjTPIYLaQLfxWpu4ioGojQCmsqLcxlyekjk9REyphsKTGI2tCJeEPOF21UqvYBxC44N5dg2h+hU68yXUxjJFDUnJGcI1GSGTfIk0imSo7MEY2eZnZnjoOtxb3WD15erNPe3cTQbRZE4+XFELAn6LdShMYxsinD2YWR1C1VVUGybOIyZPTPC1HCa6s0tzOEhglaNMJkh3noAAiI3IHPiBNmTp5BhxMJHTrO5svOHMEME/Iu/92W+9fO/y7f+5UVGz4xjn33kqGESxYw/NoOZMjmo9riz1aPZcUkldEI35rWf/SeUF4f44R97HFWBwU9+lCCIiOsHxKEkn9J58QcvcOf2Dom8zUzBZL5ss391Azwfd2ONeH+dYPUOQkZIpwsohKs3aC7vELhADHbUIKVxlEpqaaiqwqNjGVw/Jp/UQUhGUrDTU4/SVgEU7d+A4NQHnr7Ly8uYpsnExMR3Dcj+Y9Ee/sE/+Af85E/+JJ/+9KdZXFzk537u5xgfH+eXfumX/sj9f/mXf5mJiQl+7ud+jsXFRT796U/zEz/xE/y9v/f3vuv3/p3WuXPn+Pmf//nv+vjvCPwqikKmOMzXrywddfxUDUGMkUuBBk6ti6rrCFVj7FiJ+m6D2OkifQcZx+C0iLp9jFIeo5DDHhok//hj1Jf2iFt1RNfhYK9Oa7fFzlt3cNeXP/CGtRGmBYpGMDIPzT2kUHGkiQIoQZ/k5nVEcQS/fIwojlkeOou88w5qfQ/z6qvUpYG0EqwX59j83a/hl2cxBstw/BHE+DxBq4qSKUC/jUIEoXf0pgMP6Xtg2UdBCPUDNK+HpkikjFHSJUQUIiWobpt49wEMToGiIUKPKDVIoBiwu4wMfKSRQDg9RL58dE2Hp5GmTeWwzo2VTb69F3F7r4Wmajw0nOHZxx/h9GieRHkMaaUJ7Awvv7/B+9eu8bN//+f4p//T30SxUkgrhRJ5oNlgp9BGJilkEzR6HgiBaB9C6CPNBEph5Gip0U5BZgBpZ1H69SOObXYEEfpYTg01dCEKiO0sMnIRRgI1csCyUVr7EEqEAEL3qJNrJJFmikBKzNjDqG3iRioyDtGSSaSRwFd0NFUhNDJ4epKeSKFGMbEi8LQECBWbiEzcQ6oaFEZhZJYohiAUDMoO9b5Pz0xza7eF7rXIdbfQvDaKqhGiHKXzqDqaIoijmHYg6Bg5uhFEepJ+BH2pEgmVthdz6CkI3yFhG1hZk1gYsHkPfWIWe2iQxxNtZK+L2F8nXL1FWD9EkTGyU0WEAbYmyGsRFgFnqZAaHOVgd5+/+ytf5MUPHSdlmVS39vn61RWW25LG+Akaw7OUZmZZnCyxs1PnN253OBnt8dGTQwyldR7KhCw1Al7daPPJUyUSusrt5RqnBpPUeyFjIwVizeL2nstxw6WVGWJuKM2TEzZffus+l25t85VNSWpnhZwheGQyT+rEabZ74KsKqRsXSYY93OoBL1/bYmAgw//9v/8ZBo2QX/r8Fzk3kuRS0+AHnj3NO1sOY+MDfPr7niBO2Hxl3WWqnMZdfIghp8JHz46xGxmEmo4xP423v4f89u+RUGIOXYWEKpgsJDi4c5uE69JMDtApDHNvrUbg+dzaqNIVGj/Sv8FQSuOZU8P8l9/3CAohl9+9SUoJ+N5CG2V/k5dvVQjNFELRebC6xcmhHFOPP8qs7TOSEERWhnpylEi30ByHKF1iKzfNjcOAghqhxgFjZ8+xl5qgrPmUjYCv3K1QGhpkvLrEmXOzuEHM5dUWx8fyfPxYhmcXC5QTgkiBZqxyftBgRO3jOB61rS1SnR3GRJcXK1cR3QbJXIpgdx1/c413wlF8x6FXmGaonEdTBWtr+9y5d0jLiZFD03ihAbMLrC1cQG6sYfdqWEqAfe4RBqZGEYfrsLeMMjDOfmoY386hRh6WIVgfPc/sk48Q5Ma4vFbl8kHAmbE8C7LOTh/07j6KpmA4vaMocN2GVJrU4BimJvjYhWmmussAzA9m2Gm6dAePkVFj/F6PfqdLaWyQy/f2eOPqMoGdRLoePT1F/+q77H7pizQmT/CVr76HeWwGQ9dpSpPVrqSq5ujHGqnOLiMFjUWrjxp6SGKMRJKhsImfKBJlh4mTeWIrw713b3E8rZJdmMNOJokQBKiU0yZF22BhMIWtwd5+jZ2GTyGpkxkskVUjhFBxVIMBWyOtq+QGcmhDU1zz89y2xqiU52m4Ie8fdhEy5umTo7Qzw7xdVWiaeQ5DjX03ImUZGIaClRugZES0sYkuvU1Z9wj3Nqk4MVI3mArrfCgf0pUqd90EfRK8tRdwvnWLUCp0FZvs7nXUXo1VL8G7603CyCccmmY2reFHgpuViMTAGIGUjPlVemaOKJGls7eDKkBRJdOWy6B9ZHG2kZoin9T55GKOK3d3SRAyLLpMFQyyQZueFzDXW8e3MjSSI5RmT3JmJE/W0tnY3ODVmyusPLhH1KkyIbt4y/doLO8TtVq0ph8jVnSU2hZ7lS75qSLP/IVPMvfjP4SiavTW99CNJPrUPEIo5GZGUTJ5ZK+JPVjEHBvl/dXm/wY31Fseb90+oN5yaK9VOfzy71H/2u/g3btJ6PlIYpZbAf1IkkybKJZOfiJL89Yqvb0maqfP2l6foWMDnPkrfxH12msMTOa4vN1h5Mw0cSg4rHS5utPnkaemUBSOBOH99hEFQEpU20aGIaSLdFY22bm2SWIgja/oeGYOSwgMVcVUFExVQVMUNAQpU8WKQ1QB61uHR7GrcGQ19EEN5mzgiPKwsLCAonxHsOoPVSzlf5ANjgI3/u3N87w/8jV93+fKlSu88MILf+j/L7zwAm+//fYfeczFixf/N/u/+OKLXL58mSD4d9NC/n+pvuNP6djUFBurK6jNXaJOE6VQRk1mif2IxEAK0hk6m4cEXY/Jx48R9n0QCsHOGkG7g5400DIZkpOTKMkk9kCW9EQRgSAzVSQnADdE1WI6798icn1k6NPb3EBGAUpxjLgwSipsE8kITYaknSrG5OJR2ly3xfVKn2NaF3fmYbTFhzGyWVI7d5EDk+h2gtSP/jnU22+jHTtJ+NaXMRI2zkEdjCRhZY/EwinibgtUHdGvQ6+J0G1k5CN6NUT7kDg1SNxvI5EoXgea+5DIEo2fQu1UcM0coZ1BVSRm4CJ0HVEcIW5Vieu7RO1Dmn2fuwddXlnv8P5+G1PTeHRygGcKEYvz0+RNcSQCSQ9QM0pUqnV+7Kf/Gl/5+jf51X/+z/ml/+GvkrV0MFJoTos4jpHtCiKdRzFMipk0dSc46vj6faL9NeTuA0QiDd06ws4jvS4yChBuH213CewUIgrRO4covSZKt06kGShRhNBUgtQwUmgoo8eI9x6AZoGZRrhd6LeI7TyxBC8WKMksajaP+fwPopanUL0eqhD09ByeauCoNroKhl8/4voKBU8oRIpGrJp48sg0RyBIeBUMJWRQj0gZkhI9miv36TkeQlX/jXcvgY8UKjFHWtzDXsChE3MY6EgkMo5RjjzSqXkqdSdEVxW8RAFVQHp2GhGHKL0KNPcwJxfQexVkq4L34BqNK9cAkPUdvPs3iSub5PwGJf+Qsuww3K/yZ55f5EcfnWR2fJClVsjkzDh/9gef5r/4kedoD8/SwWB0cpyO6/N7by6R6Nc5O5HFS6bJ5rNMKD3iVI6MiNjqG6x3JbOpiL/+4gx+FHFmNM1cWuHFJxZ4YVrnvZbKHgkylomRSbK0fsjs5BAjaZ03/BxerHDoShqOT9oySY9PsaNnaSzdRxo2L/3JDzNXTqMpgh//b36Gb1y6Rd9z2XIlr+36nJnMMdzcRoljLt/dJZ2xWa/1ue8Z/A+fe4O7e22U+0vU6w6Fc8e4Pf8En9s2GR3I0+lHzA1lSKoKC6aL/89/mSFL8KX7dcYGbDK5DGX3gC98+zp3Zj7ElO5yrLPCJyZM3t9q8NzHn+PSns9bbpntKE3aqSCF4MKZOaaGslxcr1Px4HpbpxMqKGaa3ThBL1bpLj5KK1I5WbK4b02TnJ7m0Ree5aAbMUWbL29AWoWn5gZYv7fCWmGeb93dRw9ClNvX8N+/wkBQZ9KOmU3Bk+k+06KDGoUkpc9Ycx3HD7m10yW+exle+BO8+9vfQJ08ceQN3WmRu/YGSmkUYdnkdIjMLO7xx9h+8uPsfOlVrh5GtHoO5/7if8GPPzVGa3WVbq2OerhBUjjIXodgZx3HLqIaNmPlAqadwFtZwjxYJVHdoxYo3L23zgunR8gWs+RMjdr2Pp3YINQS9EINFR89nSHuNul2fbJ+nYR+FKndUY48WgetmIkMTHzvSzxdgPGZUU6cPEayOERicADFtHh8LMODdsAvv7nOxq113stOs58b5tbKBuvf/DbtzU2EgM1aH1au4QYRbalitPaw2nsolQ2Uxj4CgVZbp/nKN+nfuoSj2DQPD/jGN29y4tQ4KVtyEOtUej6Xt9s0nIBSQiOfMAljmJoaJlss4nkhh6lRVla32e+49IKQej9gopBA0xQePzXO/HgZtTxKV5oMWIIJxSWMJTd2GmTVkAkr5lhcodYPqXQD/t/c/Xm0Zdld3wl+9j7znaf37pvniHgRkREZkZFz5KTMlISEEGKQGAQYDNgY211l7HbZ7uqisL3a7lqu1dhVXuW2BY0x2BgMCCQkpNSQykzlFDnEPMeLF2+e73zPvHf/cRMwtlzVSPJql39r7X/uO2ffc967593f/u3v7/PN6AjLdSmqHtWkxWglS/Gpp5lyItyMQ2a4RpzEZKXCIcC3S9SthIYyGTd9zNmjmGGbQ5kEabn4qeBIARaNNjoI8Ps94lRhkDJRyWBKya2WHkjkOjsMl3IszE/w0FSBgmPRkEXiOMJTEanjcnWnT05HVIpZLjQFSZRwtOaQaM1CJqUvLBSSNFKklofr2MxnNWfvW2Ds8H0Yhs3FW8u8fHONu80O7767Sm9nm36QEpgZ/Ns3eeQHHuP2q0tU3vcMzsws1tAojau3EUIhHRdvahajUCDd2xgkzLaFtjyq1n+6E38jhjQIOXj7Ov7GFjd/5TN89rNXaXQj3Peam5t+ysx8GW1A0I8YOjXN+vVVqq5BsrON02uQGS5juSaxYdC8fJunP/kob9/tDBBrQtHoJqy9fJXuyi7GxCJmqYYzOoYKe6ROlsyheXYP+pRPnxjsEAmLthJUPRNDSkquJE40SjIwtyBguy9wykMQDxJII/MnDm5/5YeeB771Zrdvd0xOTlIsFv94/IN/8A++4XF7e3ukaUq9Xv9Tr9frdba2tr7hOVtbW9/w+CRJ2Nvb+/bcwH/mMP+sJzx59kHeuX6L8PzLWPVxZDaPVRuifGScoNmlceE6Ts5D6BK7NzYYf+5x0s4BUaOFV6+xf+UuwX4Hd2qO9mtfB6UozY/j1McouBlGD2+Rnxime2+DwrHDiPdWMf2le2RGR7HSgIbp0lcOQyLG7B2Q7G9hjE6jlCbbXmfS0jieTZgroNfvYJFg1IaIrr3O/MIJelYft5Qn2V4fVDc12EcfQFgSYZjITA61sYSsDKPbOxDHqF5r4BSTLaJ1il46jxCguw30/iZEfeLGHnJyEbF1hxvl45zMDmMmAVKng+TMzuD3emwpzUYoCKgwEkecrJjUHIEsuCRuCdFZp58bx8lV0VoQWnlyYcRP/eWf5W//2Pdw5vRppCnRUYhqbCGlidpfHyTYOgUnA0DJtEk1dDpdclqjdjcwR6cHoP0kHDjtLV1FlspEgcAuuBi2hGTQza+iPZTSCMNGBm1Sb6ANFOURZL+NKA+jpQAtIYlQbhGdhGjtEAmJtjPk+w16pUlsQ+CETUKvTEfblAGtBWkKWhv4SmIKcIUa/M3jCNPQiKCNSOKB+YcZYcY9qrZLJvV55nCZXa9MlHOQxsA62ox6JHYRI+ximjl6UUI/TshYJpNFl06sSVJFJ0wpZwy2OxG1jEXiuQhDEBwccOHfvMLRs4cxMy6V0yByBexCjmBnm/zxo9gjE4TXz9G5vYQ9OoFTqqFMl9gtMvnc+3AvXUNXc2ijwtV+xJvnLvG+953h7FydvhrwVH/j9VWeORpz2vMZnagRDNdIdIcbSzucKkVIeoyP1TgzavPKnR32DRuxs8fk2BBDlmKu4vDvXtjgTE1z9uQwn393nSiTo+YKnntska+8dYu/9onHEUlMN9XcWtphcqRKP+oTW4Jz1JivVHn5S9d44qlTrDd8OnHKQtHmQx/7fr762U+Tf/gjBKni3HKDp+vjNCLNfgTff2IM15Qsdz3e2PB58IkS49mQ0lSZuJVwJh/xTu4RHhmzeXU9oLnd5am5EherR1n83iL7icEHJz0u9zO8/4n7cZXg8UyXLeWwbVf56vW7nH3yCA8fL1MoZLh74SYPjph89eY2n5wXbNdsxjr38GYP8cKdBiuNkKl6iXLWwWqsYjnDbF27xUZi8+Y+/Mzjo3z3mXGSKMLevEFjM2Li8DjfX0u40jd5uGbzpfwsjZU93lna536vzvd+/Lvwens4hSp3Iocru33uy2iG/R1yjXVOLNTIxU2+sldnKthk+cY1xv/8U9wSGeKNFayRKYp2hsLYYRKdYGfzmGvXCCZOMJP18e6fwhj+GF+/1WTh7GNQKNHfWmKs6uAZDu7CIUwBemgSS6ckhTJJmpDaWSwVk+9vEzQixOoGpUzEp79wncPzP8j8SJF+ohl9+GE6nS4HqSCDQAsDQ4fsDi1gAh4CaZh4SYe9VhMhBNf3QxaSHnHO5etJhr/6VJ132jYvf+0cp45MkfUsTt43RdDssfH5ryEPjRCUR3jz1Ys8NDdPe6/NzkGDqbGQnWaPfqFE8vY5vhrm+cHTdao7S8jiEMnydcKxFLs4hJnZ5fpWzERqsdoVzE8Pc2N5B0Ym2N3rsN4MeXiqxF4/ouCYNPyQIE44MppH2AY3D/o8NJGj0NslnZ/ljdU21YzFra0eo3kHYZqcW27y5a9d5H1/5QMUbAPT8TjoGOjle/Tu7VJ7oEJ89ypjp+fotFus+RZW5FMXPmbYp+cbXDNKHDcC4uII3cTEun0bdfIIoZ1jOi/J9NqEWPQ9gS4Ok2mucfD6OcxiSFFk8Qouk5PDbPdDjHIWN+5jJykNUaaW7rNvVejILFfagjMFwczePZzSPLcPIsZyOdbaMccziuNVi/z+Cl/ZGub5+SygaGfrjO6vYmcN1uIs+4UJ6IQ8c7hK2Yi5HY1SzAgqXg7PMshVqnSLY0zGW2xf3kZ84uPctkycRoOkMsLIibNMjt5C5kuo3W2ivXXc+x5hLDtgMksU9pn30X/hX+ONjuGv3sWdnGX1Nz/PqGewHSX/qfSB4nSZme95kv69FS5++iIbrYiwH3NmvkRGwv5BgCFg89oOu/0UWR7h9lobiUC6FqmbQRx7HMP5fZ5/fIr+TpvOVpcYCBW8/tI91jsJHzhRwyl66J17RPu7xM0mZqmKLgwTrm3wxI+exapP0U9TZBqTKJOM9UeOcJLNbp9OmHJttckRAla74LeaDIQcDHj378XkyID08O3AnKVqML7VOQBWV1f/lM2y4zj/iTMG8R8ixLTW/7tYsW90/Dd6/b/U+DNXfh9++GGWbt9CHX0MMXUfcWkC4WRwCllKJ0+SBiFW3qP24CITZxeRzqCqYBVyeBPjDD2wiJN3CZZu07m3RX+3gVkZRjg2hmky/R2PUD21yMiTpzFsC8IuOgopnTo50Jc2tjGDABON3dkiShX+9YvovXVE0EMLyVAph+MfEGSrGMSYhhg0boVd9PYqxpU3iXZ3iJaXaN9ZIdldR61cRXXb+Gtr6PYBojKM3t0EIdGOh+4cIIpV0sY+qtdDpBGkAxkBcQDSRJoG6fodxPAkY9XCQK/aPaCXpNxUJV6/u8sr/SJNM89CNcNz03lOTdaoTUwjynVUmpBaLtrN0UhNQquAiAOsxOd//rt/m+//0Ps5c3gKmfgIN4ewXXRzD91rEN69PtDo2h4g0XGEoQJK2mdvewvV3EY4LsLNog0LEoXaXoZkAO6PtjYgWxo0/G2toKIATA/teBjR4H6VmSW1MmhpEnpF0pHF93RPGmE5SK0wdcLg2ROQRojIx2uuozsHpO0mUaoouxIhJUmq8bVEOxm0FuREjGlIeM8NKtvbpW+XiO0sUbaGFzXQdo68odFOjmTqfgr5LOgELSTKsIltj46y6GqLbqzoxZoDP2G3HyIE2IYg1VDJSKSGfpSQImiGg6rw1hu3+dIXLnL3069y+/deJtq8x/4bbyK8DEYmgzM2AyOziGwZZ3gYWayhLY+kMEJs2aRRwFJ9kSPxHsfNLq986VXG60W+9MI5/tlvvsxw0mLvoEMvUpz/R/+U+pF5DoKUog39azd5wG5grlzjsBfgWiYtZbHRSzk1ZHNru8NmK0BaDr/99grjQ1nGRsps+oJ6d53+ygZrN5ep1fIsHprkhWu7WKbBTiei3WmRJAm/f26dDV9SXrpJTiQ8M5PlmNWn3+1ye73F9a02Zx96kHMvfpHFeo6Hp6tkXIvfvdNjqZvw6P1TTJQ8bEMyMVJh5r6TlGol/lWjwEonYLhWpmEV+MnnFzhcMillbXa229zZ6zExUmD2kdOofp8nHpinXCvwMz/8Afa2Nxg7dpT3N98mjiLao/MsByZHJ6vMFG0efegwx+eGqBYtvEMn2I8FolAh1YLHpst0V1eItMbtbKN6DbIXXqK9vUfh9tvU92+z6QuCVLB57jV8r4S7t0G6t0soB7sjzV6Ph6byfGzB5G994hHumyyhS8Po4TFWGx122j3GM1ArZQmTFGv6MFPRJo0765xki+86M8OKcBFRn+qZU6zc3UFIjeHayKyHn6kiYx+9v47l77HaCulFmmTmCGtGEXNokmTpEjubAdbxU9TuO8GN6ml0r0Fs2oihKVJpsqcc7vUNdkJBtLJFWh7iSvUI2zst/tJ3P0p7/R6eKWkEEVoItgKDet6jYipCJHpvA1Ws00wlu3g4xGy1IrZFFtMwuHRrk9de+DpvvnaF7xhWbBz0eeZIlR/66OMcnRtlfmqCwLB4dTMlzWew8yVe+INXuHTpOru4fPLHP0R9fAivvc4H5gtk4oBXuw5hpsy7foF1VSCozdGzS7QvvMMyVbqnHkdtr3F9vcGcGWDVyjy0OEEqTdZ22rQ6IWXP5MBPkEKy0fQ5t9xgIiuZqWQ5k+0zbiWow8fJS0U1a7NYsdlp9mj5CbZOGC3YfPeThyklbX7jhXfRWnGs4vD0iKJ430nE+bfg2GO4/gGZ1hbdW7d469o6a8srdL7yRV5+8xa/c6VBYOVJX/w0uf4OpYceRIUR4UsvEne7xKaDb+TwGwdkVY+edukcvg9VqGL6TVpWBd8qsJK6FGRMzjHx3Qr4HYIwoZox6F95l6/c2GPNl6hskXu7bd5aaXJxs4MWktjL00gdDhcN5odzjFfyPFxRpG4W05bc+co5ru4GXF5rcUjvYcsUVwfc6Wrw8qQaLDEgCtRFwLgZcnKiwmPHDzE9O4th2yxdOc87bcWaWWb00fsQjs3e6xeg38YbqRHUZgilB3YWmclgjMwghKDxzru013bpJH9acDrp/EkilDME1+806dxZR0cxwrYo2IITM0Xue2Sa8UNDLB4dYvjMPGmUUh4t0L69SrnoUiy79FdWWfu1f0tarDP+0AJiv4O0JF954QYAIXB1N2DMBBJF9cgkW196EaUUKlsi3VlHJDGq16G/sUuQreKkfZqpxW4vJkgVBpog0ez0o8Ei0TGZymkuLx3Q2bjzJzem/yRDffL0IeDbU/n9dsoeCoXCnxr/qeS3VqthGMZ/VOXd2dn5j6q7fxQjIyPf8HjTNKlWq9/S7+CP4hd/8Rc5ODj4tsz1jeLPnPwePXqUzXtL6NIwWDaGZZLsrGC4LtlDR8iN1bHyOVQcUzi+CDrB8DIYpolVrZN/+GmE1tgTM8T9mKjt012+iywPY+U8dOhj6Ijy6dMcvH0RlaTE7QOssSnSbgtWr5HRAYHS9LEwhSR7+CgkIf03v4oqDuFjo3c3qJiacHMDYdr4b72IYVs0v/AHZOcW0IZD9sGztJb2sExwq0PQ2SdtN8C0MIrDCMsEpRFuDlkdRxgmwhSE924jXA9ZHoVcDVkeQccBIpsjHj1CNz9Ozd9iZ2+fiyvbvPr2JbZ29xjxJO8bNXhwPM/YxPhA/2Z7DERKGp2pkDgFRH0Gy4RYSyI7wxtvvE6n0eCTH/sQMl9FSwvdb0GaIqrjqH4XomCAJisOQxKjCsMoN0/Fgf0IRKog6CNMG+3loTqG2ltHloeQlkPhzKPQ2ALDQmZzg+vJlVDFEYTfBtNBhi2MNEL0mxhBBxH7EIUDXJtKQQi0sNCNbcJ+F9HYIpUGMg3QjU38fpd81EEKgQBcYiq6g0BhyBQhB1VnlWq07SB6TWJpEdkFUtMF6ZFmiwghIY2xDDHgASORvX0CbRAY2YHGS5hEqWIka+CakihWLB/0aPZiTCkY8ky0hvGiS84xCJVGxyHb19YYzli4IxUszyPptGm+dYmtF98eONkNT4PjYR+6n+IjT0EmT+LkSYSBpRShU8BxTYIzz7AwP8JffP4IP/T++3l0TPDASIZfObfDjNUj7nZozh0jCGI+t9RnwgzoTx3hrs7hHrmfXTJcuX6PVd9gZLhAR1v8yFOHyKmQ7W7AmKd5/coW//jffIULGy1Ga0WOXXoR69JbHK3nOD2eoxXEvLrc5NpGm53U5de+fIn2+j1effs6nc0dVrYaXKGCvPEuca9HPd5ne2UdtbHCxNwh5MYtDvyIxWzEfRNF/DjBcyRNP2K8YFPK2Dx5YgJTwMholXPrPrdih5Nzo6x0FedaBuvNgE6qefHaHgXXop8qfvD+Mn2rwIeO1hkpZXhLjPKHWxb3Jk4z2b7JQtnkoB9iCU0UJQyXs/zytZjpkRIvvH2XQ0N5/MIItj1oUBmbn8WPNT27xK20RCuEkck6+ftOcOLIGHnPxu/3aTVbbIgiVjlLVKryT7++xfnr9whTxYzR5SAzwrU2TNgpfr/H55dDrjYE7e0dRsp5sp19RvM2MolQ5RGqZ+7nQ0eKZAseJ555jOjia2yeu8CvXu2gRg4jTJtIeOSVj9HZR5SGIQrZ+tSnuPXaOUr0eeLUDHpvBf3uq7y53iSbs9mOJO0oQY8uEIgMmBafWQNHKBCCXLCPKWKC7T3OPnY/G22TseEiDeVyEGkWZId7rZjTk0Wk5fD6WofffmuDXqQo6z7qyhWsjRu8s94lf+8SKo4wLItjZYMbwye5oSuMTtbJZSxG0yZRHDE0XOYgiBgpODxwdJQz3/Nhzt/Z4YHpHKdzCauXb1Gplsl7Homdg9uXWd/Zx3EszvbvMNrfwq9N0w4i9nfabKw02W328Q0Xf/EBdt59k77vk6QJK5HBTDXDs6cnODWR59xqi7G8w0arz/m7+zw8WeSdjS5RmlKyBFfbmputlK/da+GZkpVOygfvq/PEdIkjuQRHpxyerrObWGQqVdZaIYbUROPH8Msj2A8+zMWezRfevUdtYox7EUxkJa+lw2wffYzhiQofOTVBrA0yR45jvf0VMgQYuSL1bIJq7rDVTVkLNPdEDaOxSXL1LYLhaezaKGL6KPnWGmZrm0NFSOwcRhpjm5Kx9XeI200OtjaxSxW++4Ex+lHE5TDHb15r0w0iso7JVjsApakYEVtendmiy0hW4sR9UiW4a48w7PmcLXYoSkXx7rvEu5skwmaumiVnSlSSkDViCkZK6Y3P0zz3DvHsKRiZoV8cZWzxJNXD91MZHmVb5PhaJ8eV/RDn7NkBKBgQ0kTn68i4h3fyLDpNsGtD7F9b52C9y0YvpTTwusARA9bvH6W/GQn31lpsvXKRYPuA2miBj3/8fhaOjTH15H0MHZ/k8J/7TrKjQ2zu95kbydC9vczMSJYnn58jTRThzi5eZ5vc/DRIwfK7Gzz40DQAMxkDDZQtwY21Dtf/8AJXfuXLqCSide5dtNLQ2ibp94nDlE55Cjvpk2hN04+xpcQUmoYf0g1iCp5FPWdRczXXVxsD19pvEKVCFqUU169f/z+lwYVt25w5c4YXXnjhT73+wgsv8Pjjj3/Dcx577LH/6PgvfvGLPPjgg1iW9Q3P+bPGL/zCLzA2NsYnPvEJvvjFL/5xZfnbFX92ze+hQ8RhwMat68TX30Zu3UQnCVatitpdJTsziTAM2rdXEbkKwjTRgFEdRqoUkcnhTM9ilkp41RLF2REyExPE925i1sZwx6cG2Jd2m+23bxP3+yTtDmpvc2CioBSyu0fZ1qhuG9HaQo4vEK2toDNFrDTCfOl3EFphXn8Fd2ISqqNY8yegNk7h5HGMYhm3WkL3O8z9+Y+DMEClyGye/AMPI7J5dHsfDAOZLaBrU+Dm0d0mYuwI5nAd1diFTBERdVEqQXXbGG4ObJvNjXXeuLPJ1TvLONk8jy9O8/jxWabH6jgSiH2IB1pobUgIfXT0XmWyv4d2stgCdKpIDJdf+pe/zl//q38JlSmTlCdQhdog4bVdRH0GaTlYc0cHlXHThvYO2AMXuKGRURqpia6MQak2kERIE9XcBa0IN+7hb62idIqRr6D2NiBNEG4OLA+5v46IumghMfw2EoUQBkZjDaPfGDje6QidJmBYJH6HzOZNqkmbGInK19G5IRLDwVcuxD20itFKkUm6GKYNSYybBihhIFWKHTQxoghtGOR0hJP0MIQmdTy0dAeUCDtDID3cqI3UIYFXIdGC9U6KQJFzDAwhMQ2DmZJLP07xbINQpRQsgasCsqamlLExhaBkKdS9azR3unzkJx5n8cc/zH1/9QcHFswFE39nn7TbQQRNEjOL2l9HS4P+5XchiTEk+DGUm8scjdapugYHlUkeeGCRXK9JsTbEu6tNKuUSn718QDbn0j37HHQ7nE032U49Rmt5Xr28SccoEtp58kaMITT5jM3hvGQvUCwOWbxxa4c5K+QTT88TS5uJvMeltMbu6UfIVStopQltj+ePDPGRwwW+YwK+5/QU73vwEGcfOcaZ6Qq/tmeghMUXzm+yc2ON+/KKg1YPt1pl6OhRDh27n89++SWypmaymmek4BAniq6f4Mcpfqw48GM+cnyIRydzTBZsHjlUYyiXZcdPCJXBTi+mVnR54vgI3/3gODPlDGEquBt5mChWuxErTZ8nHznGjz61wG4oiJduceTySwileOHGHvsB7HcDHjg5z/j4KO/uKVKlcQ1Bkgo6UcpaapEIg76SeK7FWivBlIKXNgWO5/LZL7zKhfUuSbGOYdtUKy6bPfjOSot5dUBp9RKpFkyZPbQh2ekG1FzJIxXB8vIq10IPW8WI1j6G1uz4mj0jhxxZQNcmkWnEk0dHqC/ez+ITD1EcnaRbmsAwBrQSnaYEpTFEqY5MAsoPPcqTsx6ZoM37KiEZx6F09BgPf+AZ7jqjZCpVFisGoZUjIxV7ZpVC1kUYFp7q4Zgm+Q9/nOzcJOW8x+REhXOffZXReoG8bdKSLp4lyScdDpotWucv8WQ1pJ+twPJNtlPNje0QGXa4atVJk4QAkx0fzGKFE4eG2CpM0rIK/D//t08ThQmfeeU2F8/fYGllj1Ihw/1HJnjkI89xdGGS/9sPPMlf/G9+iLwI2WgH3GnDbVmgSIKbzdHppqjNVRq3lsjlCtiHDnN74ggTOUGUaNaHFrBzLnr/gOXaDNHBHrYh8ByTgisZsQUdP6LbC1ioFzg0nGGjr4lTyA2P0Ygl7Riy3mDX6/pul6VmiOsYxDvr7PdDXr7bpI/JkckqIlMguHOZlc6gyarnFgm0QXl6mnXfpOEOsRGBl80Qjc9y/0SJSTuA0CfOD2HPHSXevIfMFhA5B7O3hwh9Djo+1xrQLoyz48eMljOo4WnszhZ9YWKHHYZEgKdjZBoRJ9CZup9rxhDlrEM4tshwucic5dOIBEfqRRaHchwEMRvtkO2+4g+Xe7y1qxnNCK43YTcCw5SoJKZQyXI7LdGPEnbHjjG8v4zXXGM8Y7AbxFxpJBREiuE3Ce/cZPel19iXOZQWBEpgSoMjRZux4Qpnxso8Oj+CTGMuWXVe3Um5mRQR+yuDHddsFVEYRlTGMCp1qkcnaHRDZicyPHV2QDt434N1Hn1ynqyEiYxJI9a0OhFW0SEzNUyu4pHPGSz8xR/AW7yP4fc/j521caZnudVMaG+0sbIOo4dr2JbJwYUlZn7k+1Br17n9h28zdGyMydPjmIUcACVHMuVKdkLN5V2fUj1Dv9Xn7u++QhqFRAcHNH//35D0Ayaee4yyv4928qRJwnTZI1SavX7CqysN0lQzkbfpNhp0E0EPlz92dBN/kjb9UXPb+vo6vu9z6NChbyEdG1R+029xqG8iSfy5n/s5PvWpT/HLv/zLXLt2jb/21/4aKysr/MzP/AwAf/tv/21+7Md+7I+P/5mf+Rnu3bvHz/3cz3Ht2jV++Zd/mV/6pV/ib/yNv/Et3f+/H1tbW/zSL/0SBwcHfOhDH2J6epqf//mf5+7du9+W+f/Mya/runiVOl9f2Ydeg2TzHlGzjTk8RdrrYBaLxJ0eUavD2m/9HnE/xKiMIMvDCNfDCDogQGjN0NkHyI4OYVo2yg+QpRr25CG6m7v0blzHckw23riNihOixh7RQQORyZGWR8mqgGzUJVm7DUphjE4TrqygkoT8wgwyDdj8vc+iey3S1ZuYWQ8jV8I2DbRhIgRIHeEOD5EmEdHBLlopjFwBodIBoSLw0Zb7nhAnGhhP9NuYo/P0lpaItldRoY862KNlZLiw3eeNSzdpNhrMFw1OHD/OkeE8eSPF7g2STZ2vIQwH3dweaId31tGdfXRjC9VrInoHIAyyIsFQAf7uBl0/YPzIfWjDJs1ViEuTJJXpwda/StGj85gTiwNjDcOE8iiJZYPhUCjXQEhaxSnk/IPoOAI0en8NMkW03ydcW4WtpQHZIfQHx1gW2rBI3cLg/pVCGyaJnSF1swPm8Xuubonhor0CIvaJ/YArpUV86dByh5AqIZUm6cRRnKk5dLaKViBUijDkAM0iLVLpEMUKIQRCxYiggZAmkhQzDUApAuEQKga7CULQU2LgzqOy9PWgESbVGo1EozGFouSalN2Btndp32ffj0m1QMYBeRFjahBoSmkHbRgMDWXIVjLoFLyZI0hLUj5zionvehazXEH02+jtJZqvvEjv4uu03r2IkYbEiSRIFWzcIrtyHq0S/H6PjjJQ9VH+/DGHkyNFtoOUZgyLj57hg3N5CvPzPPj+J3Btk4OL58m6FjthiisEi6JNs9vnwWGHq13JVj9Belk+ktnhDzYk94/n+YfffYhmp0vi+4zOjqMffRqpErKmwWLGxxMJlXKFKFVMVHO4uRz3jzpMLB7hzfWQKS/hneI8WWKC3V26N25SKGZ59PQJ8A+oZSwOUps3lxrkPJupag6tNDsHHQquxX6UcmeviysVJ4azhGnCgR/hGDBacJACTlRt5gomQaqItODVu22+vNSkGSS0ooSyY5J3LKbGhnixcIbO2CSPr7zGXNWlm6b0Y00v1WRckx96ZpGqI1hvBeTizgC6r2JKMuZXX7nDdNEhPXKUX399ncdKISWZMBMfUHE10/UCtorZVgW01PhOCXf5Jo1XXmX3lZfoa5sjFZe6mWDZNont8uHFKqubXe5s7GHPHiF0SyzfXOUzl/boa8X1vo2QErG7QuuFL/PVq9voG1dxdUhQnUUZBlJFJKZHWBhGGiaHHj9JdW4WlShUGCJNk4Px4yjHpWEU2JYlpJNDGILlxCVEkrEM1P4mxsEW3dwIqZfFDTu0Lr7DRuowUnH50rVd2mFCIDxcS+IrQcF1yM5OI2pjVFyLoBejpua5udNnzku47RskSUJGgqiNcXmjzee+cI5zt/fwm11WWopzV5YZL5kM2bDRDAj7IXUrYXq6zvNPnUaeepiFySrLawfsyhJBq8VynMHIWPiY/MaejTc/T6s+S2Q49Df2OfvIPJbU/MNf/Qpxv0ctn6Wz12e5J/m9N+/ylTducn75gFIuw/xYgaJnsdVN+cTpcW4dDHa5rm92CJTANQVjpQxv3dnDFJqRrM3hTMKF1SaNZofxoTymKfjVL15lc6/LdHsV13EYc2HMjnBNwXjBoV7I8Nnr+zx9qMrszDjPTmao5FwqliIvIryMh+q0CGZOEu8fkKJpLzzO5cxhDpwqD3gdPjoeY7geb+WPUvSb9N1h9gpzqGwVM5MbGAmhSVp7HEQJpuVyouogRUoqFO1Ec1eVqWQdHpvNs99PEUJQy1pc2e3z1kqb0ZJNkir2I82myrPajrjVN4iqM6x0NIcnyvSsEhdfukjjyg1k3KeWsRjL2yAE6ZXXiXodhj/4LHnbBJVQz5h0Y4V0HZzEp9PpkSuWmG/d46zYYjor2T844GvX1rh0d4NOp0OSKYE0wHapPPoID3zsIT70w48yPlPGBnb2Ah547gRPPTjBWNEhBjqJZnetjVUsMfbsGVp3G7Sv3YT6POLIQwOL736XTN4ijDTd9SZmxkZFMVbWRubyhJtr/MFvvcXEcw9w6Cc+zqtfuUzJgLutmImMSc6SHC1aRO2Ahp+yf3OHoOXjH3RIWg28WhmhQ+zeDgqDvGsxnrfwTNjqJahUU/Rsmn7MiJuyF1vEQcgfObpJ08H0chhOhmr1T/S+c3Nz/4e62v+jUPrbIX34s7/vD/zAD/CLv/iL/N2/+3c5deoUL730Ep/73OeYnh5U1Tc3N/8U83d2dpbPfe5zvPjii5w6dYq/9/f+Hv/kn/wTvu/7vu9buv9/PxzH4ZOf/CRf+tKXuHPnDj/xEz/Br/7qr3Lo0CGef/55fuM3fuM/SbD4/yW+KSZHrlhl584NDAYVQndqhjRJSXs9pOWQhiH97SblJ56g+c4FNBqhFDhZVBxi2O5gu6QyRNjp0F9bwfBclIZo+Rqm5yAljHzkOe6du0t40CJuNrGqdTAt0lwNI+qju3ukWiAiHyEE3jPfAd0mIvAhTTBdh+DuTdTuGnp/neD2JeTUAvG924h8bVA5tlySpas4M0fQfg/t98DLDT6EAGEfI2oj0gQhJWrzDlInZI+eQCUxq3sd3kjKvCvHkGnEIxXBg4dnGHEt1lQGUw04uAIJUqBNF50Mri/d20A3dxCOh8hX0NvLYHkoJBgWGsFf/5t/k5/86Z8mERYkIQqDRFgE1SnC4ghRtk5keqBikmyFll1B2RmkkCgJiVekUsiy3+4gvDxEfZRbACmR9Rms2UNkxscHKDk3h3yvWU5FAQkaVZkgzQ+hhEC5JWRnFxlFEEek2SFEdYo0VyNx8wgUbiHH+FCZggX3kswgORYWvlWETBEr7qK0xupuI1rbGK1N+sKmkxrshZpEGkReCSEE2jAgClCmh0z6dLVJJ9akbhkhDFwVIZKAvdjA0gmOUPhRSqIFOo0Zkj5Z26QZKiSaTpTgJwMHnNAcWFdWrZCqrTB0ilEdpzA3QuPKPbDNgZ12oYg7NkawuUHv1m1wC3S/9LtI2yFYWcerZoi31/HDhKKpSJXAcmxGMpJYG1RzLodrOcyZ+3hiysaOIj7+6CzXtnqcGvMYzVukjQabPUXu9gWeWsjimCZfeOs2l5IM2bTPha0uqdLMljxipRibqPP8Q/O4KsTP1Xh+OsP7Nt6gUqtRK2R4+do+lYxBqZjHv3sL0/UotlbRWtPox5zbl/y5D9xHV6XU124TVEf4X99oENhlpuYnWe/AplGE5jYjNpQtzTOHq8yVPRKl8GOFTGMypqTkORzNKY60ligZCb1IM1PyKLgmnilxLYPdUDGWk2y0AiwheP54Hdc26YUpJdeiEaUkwOhojfmpOna5RLNU58atdeJUU8mYFInZ7YaUMyadbodUaZpffYHJ7QvcbzTotfsM4dOQee6FJg9VEnq5Ejd1hd9Yd8gX8vzhvZT5cJPtwKDuKKiOEJx5hv2nvofekTNk95ap+Nvs6CxBt8OSb4CTxSLl6HgJo3vAO+dvkUoXp1Tj2kYLs7lFgsVbv/5ZSo8/hLhzlfzUJJaKSaKIg26INh2CWGGmMSpXwz1YRhRqeEQkXoErTYUujNAIU9Bw5yDAJkVqQcExiZSkmrF46coKG32J2FklEBZ68THCyiS6F3P6qVMsvXOZRi/ATnt4psBOfVrKQpeHkELQ9YbxR6ZYrHr82oUGn760T7VWQQhY7hsM1Uq0m21+/NkF3rqywo1L13mwJLF1yvLqPkOjQ+QdePXGJq/ea3N9dRd3f4UFL6a/sUIwPIWKI+JUoFZXWQvyrPdgsQweCcu3Nzl/eZnf2zZZv77CnZ5J/sLLHL/5Bldevco751d54qHDTC7MordXOWiE9KOU5WaIQDBU9nh3o0UvSJmvZPBEzN1WRD9UxEqx1erz1fMrlDMmoRJ8/qvvsq2yHM8mfPdihQ/eV2OhJGjfuE5SHWO5k2IbBtk7b5MhZjqnKbkWdeFzOhcwYseMyAjcDB3hYbW30fk82vZwDh0nLIxR8qxBL8D+HV5vGLR31jCF5KNzJptbW6QqRlkuqWmT2i46W6apLDrlWVwpKKZtXNtAm97A9CeO0FKSMQWvb/g8OZvj9JBHY7/B23cb1AsuU0UXqROGVI8vXNvDjzWVYo6tkeM0I83RssHEzmVmf+gHWX39HYq9LRwJpYxDnMREuwesvHwT+8EnyPl7ZFVIrA1MQxKEMbrTolGdgjTCKhVovHWeqXKWx6dKPHZ0BtM0effydV5/9xLL69uEnRbCdpj8C3+B2iMPkoYxQsLITAXDdZk9Pcvp5w5xYiLL/FgOEDjjU5QefZzx7/9OVr/2Dpf+X/+Mnc/9Ls74FNsvvMGhisfc/XWsjE1+eoTqsSlKizMIlRC3W8yfHMcwBNJ1EYmm6ppkTIHrGARKIxScf2uDnSjhwkqT17+8hBSaykMPYJaKmNVhsFx6iSZrCYTS5CzJaMHm0HCOnU7I+Y0WE17K9d2EsH0wwIMySIEN08a0XWbGasB/eaSHbyZ+9md/luXlZcIw5O233+app57645/9yq/8Ci+++OKfOv7pp5/mnXfeIQxD7t69+8dV4v8cMTMzwy/8wi9w9+5d/vAP/5B6vc5P/uRPMjY29k3P+U0lvx984mHuLC0R7e0iKnWs+jRqbxVh22BYOLUyI2dPYZQLJL0+ydYKeNmBZbDlDqqIoY8OOqgwQVoWolBBLV/D8HK4QzXifkju/gcZO1qncWuLqONjCI3MlTGTwT+UpDxJcO8u9Fro1g5uNkPc2CE62EbkqxTmxjAcB1kdQaeKcG0FkSRAOmiQau6SBD5GNjvQrGoNCGS2DNIkbewOLIYRaDeLMCyEadELY26qIi8fCNbJMdzZ4JlxhwV/jZxrYaoYJheZLboDdm5zAzIldOiT3r2EjhNUroqcWETkCkghSPbXSb0ixCEhJp0g5v/x83+H5555mg8+eBylFMIykUIgUXRTE2W4uMQEZoFuZpitzCR7iUksbWQaoZwiqeVRqlQ5aLZBKdTwHKm04fBjMDyNNXMf5ug0sj5P0u/SuX0bskV0r0UgMygBvcwwvdwIgeFgtDbRcZewMj3g+hoGlk4HWlsMNIK8Y2JEXebKFsryMFWA0ho37kDkE2DRxSL2inSwMOMeLhGe6tOMJFqapHaeJFMldIskToFUSxxS8kZCYGUQcZ982kJsLjFp9Sj0NsjqLmVXkCo4iMBwPDKGpp61yLsWR4dzjOWc9z74ApFG2KmPq6KBrEOkTH3wYRASM18Aw8aZPYF/+zaWbWAP1Ukb25CmFM8+gz06RnZujujG2zhLrxG8+PscvP0uIleknykzU8lSMwdVyabhURur8pGHJqnkbB6dLnAgCwgp6WxvE6QJV+ceo69NVrY7TOcUubjDXMHg9t1NNtoRGy2fGdFiy65QMBQ0N5lp3mKnr5n77u8iYwpO1kw+upjj5kYLX3pckRVanR5xdphJJ+bsdJ7peolWknLfWInDM6OM7Czx0LjLZrPN4YUJzl9f45V379LshbiNZZyggTQlW50Iz5Ac9ELIZlHAguUzUs2j546x3IqxDE3BMWn1E4SAkmuyetDnRlvimgaObZA1DSoZm9lqBkMaWFKQtSTN1OQgleQKJdTwOFahSMW12OolJKbLQ+MFxvMmhpvDFinJ5Aw71hBf3LTJdDYZG6uy2owQwJGTi2z0DGzX5Xu+4yFuNhNu9wVvdDK4U+OMJXus7PWYGc4yOTNKbahENHGUvL/HfFFw4dYaJorLByljtQIZ22a/0yX2E+LRCRanq3Sx6NlFdj/3WT79zz9PbvUCI488wswjZ1jZabG11yC7dZN+rPCXryDShMjKwtAkMuwjkwhbplTSFhGwWPWou4JgZx1TJJCkZIxBf3nZEeys7/Arf/AG//btdbJxl3TtFmWzR+X4UdqZGj/84Qc599Jb7EeS5Xub3O5IXEtyXz1L0m3jK019ZoKxapG//tGT3Nzqc/3110EIfvtan9X9HjUzYXG2zp97aIRXLy3RmziEVapz6cI16La4ee5NHpwfYrZsMtbc4NKnv8yn31rhlasb5CyDxfZ1ehdeZ8QTfHE9oJYRPGb2+eLNLp3NNa7+T/8Yvb/JG3f3OXf+DuX1dTr7DeKHzvKF+glWmyFePst3LGT55CMjpCrluBMhVMRo3qYXK0pZk4mixdPzRa7vdhFSkyhNGAacWzqg5kkcIv78+xe52jEZyRjMZxUVF75+eYO9U8+y1IyJ2y36e1vczUxSbm1imxZ//pCm0NtG9ltIx2MHB88ycFub+NkhxPgRCirAzGRwowZrPTXYafJKlLIZdmqLpNIgU6oQlMbIBk0M0yRrCbZ3W/i3L+GhKdoCKSWrZhVLCMrSp2Apru4FdKMEz5SMFlzOZLtsdiNGCu7ALdCSbPvpwOnSFCwOuaw1e+y3fWoOPFxVNCJNOHUUUaox8+M/zt5BEy/tk0kDdhMLlckx+9EnBxSjzgE7qUVeJpQtyY1GhLe/xFTBxU80Rn0Kd6SOX5mEqEveNjk2Pcr75qvMVLNs+Smv9Avc6Jt0M2WsQ6ewPJfRvMNjP/5hqk8/xcz3fZD6iVk+8rMf4PFHZzjy9CHsQyfRlTFyDz6EZZpsfPU87TffINrdxi44jI1keeviNvUH5ymfOkn+8DzF44vIbJakF/D+n/4g5uwxgv0W40Me+0HKw6dGKA5lWO7FNCPFRjdmM9RsdFLWmgGZsWHIZjFMgVWfIi2OcqejMYSilzJwxzMGKdFGs8/WXp+cDri20iHY30JHweC7Q0Aah6gk5m/+uQ8C3x7SA/wJ7eFbHf81h5RyUBzTGqW++Zv9ppLfZ54+y+XrN9l55xpqd23gdFaqYy+cQuZKWLVRCg8+CpurlI/N01/fJu00STduo+II0hQdh2Da5A4tkLvvNPH+HjqJQWiMTJbcoQWkihl/4iS1xx+kvbwDrkcaBkSGS0/mEVGf3JGjoJKBBqu1TbK5inHkAeLtNWSlAkmM8HLEO1s4xTxqdw1ZqJIsXQJpEF56Y2A+4bfBtNCWiw77qG6LcGNlsBpXMdpwaQYJ59MKL692iZTivt49HpsqMJvRxLeuopRA1mchDVCGjdvbASBpHqD9JmYcYJQq6HaDtDaFrh9CVCdQYR/l5vHv3qG1fJOvnbvAi7/5yxh+mx/7kU8ihcILDtDawEx8jLSPJEUYAiUtuqkEM4vn2tgCemaOQJsoQ6IRDGUtGu3BNQdeFeI+UXlywP81bJg4hvKy9JRF0AugNoOeOokhJWYS0hMO2nARho0oTxKXJlDZCqAHzXc797CjLqgUI2hTtAGvTFn3B1IR0yVFEAiPMFPFTwUdq4qUBkl+BGV47PgJG40eRu+AOIoJvQpCCnzhkUgDp7dLsb+NFzQwkwiVRCRuGSEkmaSP0d3FirpMZk0cA1qxIEWy7StSLTgynGehbDNZtDGlwEojQumAYQ+sqmOfNFMhd/wY0vWI7t3Gv/wWm5/7Is7cYbyzHyb/0BMIlZKfnUQaBpmpSWS5RvfmLVb+2S/xe3/nn3Hni+dJ8yN4KFzLpJFaNIOEkWCbdGh24GiHwHBcLu+F3N7rsz6yyMmixC8M8T9+bp3utYtsmmW+vBqD6VEaGWWz0WWm7HEzKVIqFHBNg7f9AtuFGXj9q7TjmLVOzPCNl9nC5TsWclxaa3G+bfPV88us+ymbr3+d8tYtzNe/gup1OSzbLI0fYssq4earTM9Pc/7iLbabPq9++SXWDnyuWZN0wpi7+31sQ5K1DWbKWYIYpgoOBRkTYTEzlMNzLIIEXMtkrOhwpJblqZkyZ8czIDSVnIWhNe04IUkVpoQo1Qg0W92AzW7Cmq+5GGUwChVSJJ4lUHowJ2hcIZiSXSYygtpYFSE1zd0dgncukLn2Jt0/+DQnq4LfvtrgC69cwpOCE6MuJ0by/NgzRxkeHWbB7tOqLWCEPTImZKTmeuDStXIcVBfJ3n6Hbn6YmgOfeWuFhw9V2UpsVpIC0+NVLl5eYm9zh4zy0f0Wv7csmPjQ09RGK3xs1uCRSsBO6hAVRliyJ9gPwe10CJKUtdCkZ2SIU4VfnaWDx8RwmZqZUs1YeLbB956oYaUxnuph6oSqNVhWlpdu8KNnRvmtz7/G16+tc72teHtPMVvPcSCytHJDTCX7/PqXL5LNZvntl65QtfWgCah5m6G0RV5qIi/P+w6Xef6+UQzHAw1PLw7z+pU1yrLH2xfvMkyPH/rAKfbcEheu3MayTCbKDh997kFmzRajnXVEocCnezXWL13G6/eZySo2z1/hVTHCgXRYHC9S2lzjt798m77tMj5dY2Siync8dR9X7FFevbZK7Ye+H7NWppuk3NttUvckP3SqzpHD4yw4IcOegUoDhi+9hEfKeNEjiBWNfkorNJnNwnE3xDMlH3/sEBPjFXa6MZVCjkq5wHdPafx7t2g3Wjg65LFJCzfrMZY1Gd+8A7VxqhnN3qtfR7e3GSvlcAsFUjtLX1iMZ0yklLzr57DcDIZKkfsrkMSYKuX17QjDMLBro7y5EzA9VGS1k7Ce5Hh7J0agkSrB1ppAG2xkJ7jVCFGRjy0NtjsJZnsTZWbppjazVZdaxuHOQZ8R12CTEp9+Y4Xr+wnX72zx1au7/OY7W9xuJpzb18wNZUnClN/66lVMYka3bvD5S3u0tUPf7xMVqggnS6gMlNZIKemcehbje38MK2jQtEqIOMAgwTFSbMOC4RlCaWO7GXRzh8xIBdFrINwiMumjTBvLtZms5nns0ARnCjGBsDl36QZXN/fIPXaa7/lb34+VcRD1aaxKjczMDE7e5drrd2mtNejbJdL8EMHVK6xeWqMy7FGZn2D7xTeIuj3GjtcZGsmThhHWxBx2bRy7OgRhyPbbN7BEjBg/RHbxCLmKx6Gqx8SxOrXDQ0y4JkUTpNIowDNgpORg5otE95awyjUoDKO1YjyjCLWFbQwwZ/tBQsaQTFc9Rge901y9tgyGHFgbM0h8k6BH7HeYmxiQDb5dye+3k/bwX1Pcu3ePX/iFX2B2dpYPfOADbGxs8C/+xb9gc3Pzm57zm0p+FxcXWbpzh8Lx+zBKFfByGIcfwSwPQdjFrk8gs3nc46ewiiWwbNLGLv7mOmp3A7zMgB7g5bBHppHDk3Ru3UOl0SDx1HpgA+x38YbLFJ75AJZrIcbmEcOTNCJBBxs5Mgej86RKo6U3aEKzTKJ3XsKwTOLdXXAc0t1NWrfWMApFkn4Xf+k6SbuNWZ8g7fcRljtg5va70G+jem1kvoTp5UjR7HZC3ryxxGurLZyox2PJCvd7AUVbooIAkS8i4h791W107IOTx0Th7N8dmEuMLUL7AJ2rgO0gR6exgiYijdBaowUIN4s4/hh31rb4v/93P8f567f44U98HBH1EP0WpBGytY0Mujh7y5RkhFIaXzqULEWGAEcoIg0HMbTI4CcGHWVhZErYjsO9jgIVYUa9wfsiibNDaNMkjUPu/NrvYk3NDSQXtodUKRqBJQXoFDvqEFoezcQEoRFJgIh6sH4d299HWS44RaSOUc7A9UYGTbA8DCGIDBtfeOR1gDBMAjOHbZokhoUSFvcih+u+zX5ksNpNaSoXU4KpkgGeTQqEFJgqZNesIU0TURkBwybZ3UBEPlbUxiNgOGOwH+qBvi7VhElCogYYtVRDKk0MqekI9481UkmmxFc6NUb+0l/GX9/i7r/+XV77f3+B3bcvEy/fHDS4mCbmxCFENo+QBmp/j1d/6y3+8NMX0c0ur5/fJLx7C4eYvIzIOzYXm5rd1Kacz3BhvctuHxbKGXIEvHB1l7InSdAcnShy7Ngkv3Orj2WZnJ6o8uuv3KSRpJQ8i994c43Pnd/g7laD9tISThRwJciRPXKUzdUtyv1dXlGjWJbNTqAZKXucGssTCZs7+yFfbmZpiAzJ3TuMFVzSl77Ao+vv8IWNFF+Ab2S48s41PlHZIwlDRmoF2jEcpDaZpM8ffOktrq82uLPVoJwxcC1Bxyqzm1i0E5NQSWqupN3xWWmHuObAbGQtMriy0+fOfp9QKZpBwmjOZjIjqTlQz1okCtphjGtIJvI2vpZ88lgO05DUMjYnSnogt9AJriVYDQ1i0yUqj3O2nPK/dcb5tb0quUqOXMbjyVMzVG7f4I1ra5AkHLG7zNeyHLK7mG6OO6HD9xT3UZkCdnublQsXyK5cQuiYRFos+/DaxQ3qOYd/9fKtASnBk1zrmwzlTHZ1hqpMuNq1efpjj/Jd//DnkdPHyFfLuDY80L7InGwyPj5Erlgive9REtulRDhY4Nl5TMtAuFk6dgWRhvRiKOk+mVKNUEtiJ4+tQixibKF4+qc+iTc6zRPhHl+8dkBeBSzvBqztdXF1SCVrsWlV0RjcvXGLp+eruEGHmwcBYa7Oza0myf4GvjZwLJMH5iqcPVJDS8lpsUnBb5AvlUgzZVZkAV8LJit5bMugWs6SuHnmZ8fYM4oYpTL5iUm+88NneP6xY+QzGXxtYs4eY6K/xbPDCpWrsF+tM/uRJ3l01GH14l2yH/sY7uQCyxsNqmmPo0cmmTkxx2Oda/ydUynbt25ih11eahVY6gsyUZcCAfbEKMHN64SxImubXN3q0ItTTMsiFQYGMFuy+fCpMTb6KbupzYt3u7zoV/kak2zv7LKSFOiUp7CkQTbjUjs6Rx8Lb3+dieefpZJxcSyL0C6gizU8Q2JIQYzJqbk6pgQ7biMin3DlNmmnyeMLQyitKTkGpYyHIQ3aYYq0TI6MlOhma2R0BAcr5EfGKQzX8f0+uzpDNww4XjHAsskGe4QpTDoKtGKmkmWr2cX1MqSWzazR4czMMO1eRClrYRmC69t9fuXcFlNlm6mxGkkKMpdnPCtY7WpqyT6q28AaGmO1r2kpQVkECCdLaaiOX5slMW0SaeMIBs980UVJibl/j7DVQgxNYlXqWCTEdg6VpvTdElpaA4vf7h6V0QkeWBjnsVEP0T7gcv0Eu6cfo+MVEEEHIQzM0XFaqzvkJIhcjsQrYm/dYeuznyeXtdhvhmQee4bm8h5Jx6e/0+PZTzyEU86B5YBUqDii8/bX6a00sFyb0MyS9vrUPZvDR6sUZ+ok3Yh6xmQ7TNkeIHnppfDgI5OYpTL24fuR3gCdqYRBxjZJFCA0kbTZ7UXca4ccGcoxlU1Z9yVJEg2ayf8oxJ80vt23MA58+2QP32qz2x+N/xoiCAJ+/dd/neeee475+Xk+9alP8clPfpKbN2/yla98hU9+8pO4rvtNz/9NJ78He7uoag7pFSFTABKIA2SuBAxkDcbYPMbYLMUT9yMNk+6dFaLGAagEgUBURxH1GYh9Cg+cJmg0MavjIAxaF66gWnsYw1OITJ7K/YtI20MoRTdJ6cQK0dxGFmuknSYiW4CRGSzHJTMyhsgW6K1sEu43MLJZiocmEIaBv7lN0mwhLRPt+1hDQ4hChaR5gOo0SQ+2BggzldKoTPHa3Sbvrh5Qdgzed3yaQ7KLGfbRQObwfej2PsG9JezZo2Rnp1FecaCrFQbaK6HdAmauACoe2C2u3Bw0mmUqg8SwvQdJirp7mUzapZXA3Tt3+MorrzM8s4AwLLSdHZhkpAFCRWg5kBpYYsDYzbTXEH4DS4UUbAPbMAkUbPmDp79tZMmXKuzt7+OnEuwsTtAEUpBgdPcwkpDxZ58gWykhGmsIrfEx0dIil7ZxwyZCpbS1RyeBAIdIWmgrA2NHBjSMOEILhdPdRyQhMglRdoZAD3RSrUCBUHiGwpDQEQ6dVBAqMKSglnEJhMWtdkIn0myH0E80RtAdECrs7MAERAgcU2CkMdpySNeuEm+ukoYh8t4lvKhDKdpHoJlwE2wTjg9lcE0DV4WYUpAYDgEOKQKrv0d85xLCdPm/fuqrGOUKd792lVuvL+MJuPz/+SK7X3qBxHCQxRqiOo5OYsToPGJigdpDp3j2Jz7AEz/+ATbaMRysoYQgFh65/i7TRYd18tzYC3jqUI0USNEEyuDRmuLLF9ZpdPocCtZ5dCpHYts01jeoTI1xu6VxkoiHpsssjBVwLJNXru/whaDCxuYBhwqC/XyVbnkcTykqkxOEWysYWjGcc3l63OHIZIFeGPHqSp/WjZu8duwDBGHML7ervH0Q8dc/MEe9ViSSktziIv/8esQnP/EhGtubzLfucKOhaDUCnq4azAc7LOY1N9aa7PZjDpQk6vVQnR12+zF7bZ+3lvaxDcnNrTY73YDhjMN00UFrsKTgvqEcliHI64DZnGA6J5ksuLRjRS1jgTBxJVheBscwiFKN67hs92PuNEIiO08SJ1z0c1xtpIjZIzx99ghd4fCb0QR/+R/9LlnH5Kd/6gO8v9RiKzRYjy1UGrMaOvzOjQ5RmOBbBVZbMQ2vxoP1LG/387y+FpBMzPPYkM0U+5w6VOF42WJEtxgRfYrlAo3KNHEc8/mLWyxMjnDk8BxZS9NqdzG6e+CVsYtlTKHIWgaeIfEcF9POULZSnOIQ0hRYcR/ShFZq0NcWUkBiuOxFIN0sfWy0MPBMScnSVMfqxOU6z/3gc5w5MsxyV/LstIO5dYOg06McNYiUxfseOsLm8jJ6fQW3v4NLQn9oga1A8plXrrL86tdoXzkHV99FhD2UFqy8ewXRb3N6PEtse7x426d95zZDBZu/+yOP8dc+/ghLjZg07COkgVkexTJM5ufGOfrQSR57YIGbByFrBwHTk6N8Nchzb6fNQlVyb+OA3/7qFV7fbOMN17jb9JmrWMzuLHGyt8I7UZGR++5n5tEnqV17C9MyeXwyx0Iupbp6CZkrUR8dYaHscXDtBmULnpwv00007dSgowwW9D6FuMnpoqaetanYkkfHHJ6spbSw0YaN5znUgk2Mzg5JGLHjJyTKYHXoGAeZEaTfJrh1CUuHmIDRb9FITMzl8+QsAyNoIQ/WEdkClilJm7t4Ao4XwTQMHpkusx0KKq7BmJMyUXRxEESWS1g/gihWqKRdJmpFbDeDNCws16XnVhHSoGxrLJ2QaGiFCWPNuxRlyA8/PM5Q1uWZxTLj9Rz9RHFxo8N0wWak5LIbwfc+MsWlIEunPMrpoZTLWx2+eLuPbVnESEY8ybsbPkoJsrZACUHPzCIdj6KRYIct1iKTdhgTlcdoecP0X32JyMyi5h8grc7xcsPG31onFh7aySJ0gk4TVGMHHfSwV66x6PicWn6DkqW4lJT4+u0d1ptt0jBi88IK448fYe4Hv4eGsgmyZYpHFxhaGOLQE/M0Pv953JJL/WMfwc5aRI0WKopRzV2Ek4FsicaV28x9/zMY5RqRW6L92ksc+fD9jDwwg+FaxFrSj1N2/71cVQuIWz2MiUOIQw8hR+YQ3X12RQFfm3imwJMDJ9FumNALY/aDhFzqsxaYmLbFex1Ag1DpH808YBw3Gmxvb/+fXvP7X1qMjIzwkz/5k5TLZT7zmc9w7949/v7f//vMzc19W+b/ppLfSqVCoVjiejsddHuqeMC+zA+h3Px7FIHugN/qZcC20WlMb7tJZ3mVNIyQ9ckBI9ZvIpBkjp1GY4LjEu3t4x05RoqNkfHwX38R79BR0sY2Ko7IS0WYKoQB7C1jZrKk/Q6EPayFkxilGvhd8jNjqCCFNMGbPYQQBs7oGFaxgFGrk6YxabdDurUMkY8wTRSC1YMur/azXA9dRrI2Z3MBh6ZGsdMQa+YIKkkHPuGZAkZ9FrNYQg5NYB89g7FyGd3cJk01+mADkoRUWiAM1PqtwapRa1LDgV4TkgE7UJgmst+gGUT8yA98H+evXKNargw8xO0MIFDlMRAGOl9D9g5IkOT6+4N/yJ1drMRHawED4i8ZS5I1BRIYqtXoNg9Q0oI0xEgDhFIDfW12GHJD5J58HmvuKEIrRBJi6gQz7mAlEZYKiZwcWkg8U2JJhZY2WgjC8aNoDIh6aCuL2F1GtrfQaYzyyiRakNU+SRySTQNSK4MQAvM9u+GiHOg06zmLnCnQCEwpEFrQTcA3HJSbR+oUMVDrUky7iPfeL7lzCXIluq98HvpNRBxhJ32KqkshalEyYibUPjYRhoS8GBhbpFrTfO1lRGefzluvYmzf4qeemsXZus7FN9e5udUjAeKMTRrGpJkyaRigey10tkwwvEA0d4Yjf/OvU//L/w2jP/gJ3n92CoTGlxl8DW5zlbJjUjQGZIN311r04pRmmLLZVwxX8ny8sM6rt7bYy4/gWSY//sH7ubofUesd8B0nx/jXf/Aun/qtrzGUkTxx3wQrBxGzw1mGpse5sNpgItykrwUTi4dY60oqGZOLd3fZ7EVsBvCbL91k6GCNn/++EyyXp+kHPudfPseP/+CzjB87xMvnbuHvb1HQAQchXN8OWTqAhSc/xv/0j/5nHlSbvLETsdKPcA1BNekRba3x+o0dDK349EuX+K0Xb2Kj6fdDzh4uM12w6G2t8aUbe7y63CDnmINFhwLbgjGjy92epBVDpE1cQ1JyTSKl6YYxQQrN9wgahpRc3AtxTMlWAHuNJlO3XmGsYJOEEXf32jTaCd87nlBUAe9fHOGlG/u0CkOMV7KczPpU4iblpE0rVjwyleWk3WOpOEtZhuwEmpXMEDf7FpeXdnhnpYvtWPzhvsdaZHNm7zJba5ssZaY4uPgWu72EqNvmWn/AUO0Lh9+9tk9/Z5d0dxO7u0XabaGdPEbcx+7vYQmFNA1kmmDYFk5rg1QYuGlIwdDkZEyGGGmYGKaDZKAlDc0MCDDCHkpIDtcLDI0Oc+Wt6wRekX/wu+/wL6+FyDRB5ktwsMnayhrzE0PkXZtlZ4wTxh5aSA6P5LkauFzb6hJhcTk3QxAlFDybqX6b6Y1bfO7aLi9+7Q3uvnsRpRXH6hZTZZfjR+f4xNOH2QwsCvkcNRngmII5q0vBsZgczWD4XZZ7iv3sMEtJlubOPv/rC8vcXtljxTeYvu8wbWWw3gqQGZcvhlXs6Qmeni4wVRLodoP57/gg2aBNfPMShtKU6xWKliBSGjE2wpFzn2PcVViAYUj2eyFvrrZYl0UswyRnCyZMnzBNKHe3mB0p02wFJE6GThDSsmpY3X3ssEHWNsikHUbyDq5IiA726Fl5dJpwrx2R+B0cEw6GF+lHKdJvISKfdHuZJI6xXRsME5AYhiRjCPpxyrjRRwtJIdzFiDr0U4l0XEwBlt/CUjHCkAybAaY0CA2P1C1gmibSdFhpDeyXt0ODTmpRz5qo6hCNCJ6aK/Gh2RydOOEDUxaHh7PMlDxW1nYYKWao5h3qOYepoTxkc/QMh7V9n0aYMJxzeGMf/FjRjzW630NLi36ScjcpECnohSmRzGA6LoXxEhfCAsJvY8R9ttsx4dIdQqUHfTtKI9wCpCFqf4M0iOjeW8PN2czLDk8MScbzLjcbCa+1LXpzRzjyV34KNXs/OUeybE8wfPYMU+9/kOFHTtLf2GTkfQ/jVArcubBF594eZtZDNXYYGDf56DiheGgaWa6T0z52xiaNI7RKiBodlpf2aEWKwr+X2cSDDjV0r41sb9N65zXE0rs0wxTNgLHfSyFJIWtJTo8X6QUxNSviXsfAKQ798VymO2jYAygVB+5p169fp16vUy6Xv5l06k+F4o+ID9/C+Jav4r+M+B/+h/+BtbU1/t2/+3d86EMf+mOs3LcrvunZSqUh3tpNiDIVsLxBIuLmwfagPI7IlpBhF600ancTY/4URsalu7xJ7+Y14lZzoLPtNdGZPKmdI3/8FOQr2LUa9tFTWLNHEZk8aewjpCBaukq3MEJNNXANQbOyAH6ANh1yJ84gDBN/t4Uoj6BdD292AW984FCS9ntg25jF0sCm2DRJWgeoIBzINoo1NmSRV41JlnabzOQtnqoLZhYXCdfWQAiEaSFsl8zxMwNaw8Zd0kINs1QZPKCeR7K/hU4VRm8Hy8sg9tcw0gHTV1jWew5sIFQEqUYUa4ioj1GqguXS7Pk8uzjJj3/fdyGlQqAQWoFO0VZ28Om2PZJsBUNrRNyFTAlheYiojymhaChSpSh7Jr1EoRA4uQJpFGLGPSKnBNJAhwNtspACkYY4aUCcHyEdngdpk9HBoPGj3wStkWJQoc3aEpuUFEUqJWbcH6yuyxOD7TAEaW6Y1CkiVIKJJtPfY9roYEhFzMDkwhCCvGOQShuNJEg1WggMQ9CJU4QcLBQCw6UtcoRmBmEIROyjhURaHolpYQ6NE67dw787IBrozduogy1yvV2IQ7yNq8j9VXK9bdCQCRs4/QPkxh1+4y//jzTfeI3+9gHbn/0MPzbUoPPuORwUC2N5YkPyzM//JGTLWAdr7H7u92m/+XUIg/fkGC690ijbZplMrcyjP/1dWJVh0IpelLJVO4YSmorncLjscnQ4R5Ro9v2UVj9iE5erssaZkmA/MRnK2jw86vLIkTG2WjEnx/KM1Wvc2Y34g5cvc3Oryc99x2FOFTWLFRthWvzLrSLzJZtOAsKS7FklDMdBJ5pGP+LZ+ydotbocmqrh5WzKZsr4zDSHah4fnTX5V1+6yPWNLq+/eZGH5so8d3qCRqvFD5w9wbnNFjfffJfn7x/nOz/4OJ9Z7vHStU2eH4o5WrPZ6UY8UjMYmxjm7dUmn/v6dWbdmLjTIt3f5uKVVTzb4O3VNmGiuL3X4/WVNjeaCSUrobO6RCNK2eoGxHFCP1J04hStNMutcIC0i1LeXG1xYaNDPWdjSMlabpLDosWJYszDTpfpgiZ1szx/aoTIK/B7X7vEhp9i1kYR/Q6Fm69zaavNdBZyrsta6rIwnOfgoM14JcdqW3O0oPneIxne6Tns9BPGqwWqGYe1mQcwS0P0Yk1j6n6OTZXZ9eF7zx4iUQkcbHOqqClOTpAqhX/3NobrIVYv4/gNjLBLmkZoLYhDf7DQ1orUzWGnAVLHeEkXL+0gTIEnBRKJJQWGEBh+G9KITipRCMbHapQX5rjUEZyaqJFGER1MvJ11Fo7Ps7S0gTWxQL6Y443Lq7SsIkNmhHJLfPSZBzh99BDRwhnGCxYJgrbMsvTAB8iVy1xf2WdMBnxvqc3Hn7uPD4xbjIkO87bPE0NwZjxLyUjRpkUl41BUHfxUEMQpI/srzHiCnUhwcrzE1YvXcUm4cOk6Hzi7SOXQYYqFHEJpHlgYZ+7+I9y0RjFvXiDKDnP5rSucKIQ0bl5HaE3UadG1szQSg/UL11gP4eiPfA8XtnxeW22xetBjQgaYacLVrYC1/R6BsKllHWbiTTxinE6TD50cwR4aY9oMqKzfZju2MMojqNEFcmmHqzs+ZdGnV53GGJ8mNlyGsjZ+YYwg0pRzHk7YGiRKlTFEYYhoawNRHqNopmBZREpTMhQ5x8LSCQYpRhqRSgtHpPSVJCMlyrKpyoGrphYSwjbZsEFqF7CFpolNrBSniyknx7IEGNzrJJy0GlRlyETR5UGvzfvHbcqewclhl7onCPshRcdC2hmEYXHMaFD1DP75uQZj11+hGcvBblukuHvQJ9WKe40+KQLpFLi0HyKBbqwIYk3ONkkTxWqaIzI8ZGuT75yzyD3znZTpDeR97X1Sv4sKA5AGIlek2wZraAzhZLDSiKmyy0PBMlP+LvKZJzjfirlxEBBHCaFKMaaPYxaKtC5c5Y0v36Fw4n76V8/TizTecAGdpMh8CZErkx7s4A4VB7jR6iip6ZF//FncapHWnT2ys5OkUUoqJf+B0Ry2ZxKtL0Fzl+6Vq4jMIHG1DYVKI1KtWe1EHPRjDCGomDEg2Opqevt/4mSmkgEqFOCJM4NK77dL7wuQKv1tGf81xM/93M9Rq9X+s83/TSe/999/kjv3VolzwxAH76FaEoTWCL+ByFfAb5FWpsCy6Vx4l7QfUD37EOHePjuf/Qxpt4OKE1CKKE5Rpo1II8xcjuCNLyIO1km0gXr4w3SuXcUenSLT2KBv5Nnvx1xuKMKxI4OOP6HRjU3ab7yCjkP6Vy5hlqq0bq6ibZf+7WuQDK7PymdJGvuYhTJmIctWN+XVsMjd2OVwTnO2rJhwYoTW6K075E89hFIpOuyBtBBjR2B8gdjvEV54BVEaQlTGkP0OojKKzGTg7nl0cYQ0TTD2lhFeHjE2h/bb6LCH6bfe041KdKogjulePs+ZsRJfeuV1/ul//38ZOLgFXRLLQUsHqWKUOdgWju0chorBsAnKE2gnh3IKGBIcGZOmECeaRGtsqQiUgfByHOw3iLWEfhvpN7CSABl2MPoNpN8gkSYiiUiFxoiDgY1zZwdtZjB1jKlTJJBogRQmCANTJ2hpYAiBFgpdGzTTpTffQmuNE3cRBxs4EiIzg6EVaZqy1UvpRCm91EAIcAxBzTMZy9kEUYplCKQAPxG0YwilC1oj/DYiDlACiGOM+iSZo6dwR+ugUnRrD723gW5todauI1VMunEHvbNMfvc6Yuc25q3XCO9cZWayyM6XXmLrrWWWXzjH5me/ytc+9SK7QYznWcweGyY7PUftwx8kvXeZ1q0Vrv+rL7L92/8WY2PQyb/Zi0m1QmiBV6sg5R81LkA242Eg2I00GUswmndYqHqsNX0a/ZiDADL1UTIzRxh2BQf9gHs6x6l0m9s7DfZ7AUJqSlMTlD2PX/7Mu7y03qNSyFDMenx0XPHw/DDZTI7tfsyZuks7kTx8ZAJTpORtwYfcTcbHKnxpuced7T6Pz5Woz05g9fbZLEzzwQen+dnHR3h+tsL/8m9fZj0wuL9i87W1lJ/6b/8W/8sXX8CVimze44NPHGfPqbBVOcRUsk+73edqZoKJmUned7jGj37gfv5gOWRy5zJBZZyypenHKVsNn1dv7BKniumcJONlWeoZnN9LaQch1/Z69GPFQbvHMaOLZUoaQcpmL+TAj7i122enH3Hgx1xuCYaGStzqwrkDg1+6nbC+tYdbqaLfOccrF1chDlgsmzTvLBEYLhuHnkb7Pvu3biI7uzjZDBXRw63V0VpyfGaIHenxlbWIH3pwHEenPGrvM1W2WSXLeE4QRhETk3X6UUqtVqKWtYi1IHKzTGdTyBbh+FnEmffD6DwqVtw76LG8skOmu0tnZ4u13T47IYTZYaSURLka2bCJEftoaSN9HyT0laCXKhIh6dlFkCYWmkgpMuUhvuexRU7P1LAnZvBuvssv/e7XePHVc8y2V3nl3DWOjOVgf5WJLCQrd7my0eX2QY9cuUyrNsmVnsW/envAHs/kC5SmJqk/9TD/3QcPM7c4y1Mnqjzx4HEcxyRLQKm9grF+FWkYFKRCIxjOaFR2iG6UsqGy7NTnaEuXYb/BSMFmv9HiLzw9i9SK23sRzVhztxFyc73Jdifmow9PY2rNsh7g/cZaO2yKLKWgzfThSV70y5xrOmyoDPg9CgfbxMVhRncusnPQo7u9Rbe5P8BJNrb47dfv8m+/fo+9SHKtJfAqQzTXVjCiHuc3u9z0LUYfOM3b91qcv7VF4+Yl0uXrLAxnEU4ey/MGVulWlpz2aaewFyYcaBffq5IWhukWpojHj2GdehoR9XCjLhkSCqqPpQMcQ3JglVEaepkR7nVSMmmPKEmx4h6xmcNKfSKtMaWgGQnCVMPdC9jL72IbmieKEWV/B8YOIRgUGwpJn/mSw0QWRLbIcM6jnQjKZspSM+a5k2NoNI5t4xbLeNVx5qTP8Yrmtew8RdXhKzd3yZqSo2YDISSLTo9WqDgIYiKlSBQcKTtYOsAXLro2xktv30a399H9LpG2EZaNkSaIIED1WgQ3LqD9PrI2jhImhe//JE4ugyxWMAtF0lvnMct16o7mzIP3M7y3wubqCpcvvEs12CNRCa2r1+mt71PKOex89vOYjsPkeI7y0TlyR+8DpQm315GWR/XRxxCFMpgmXT2Q3WVn58iNFwn22+QkLI7niP7D/C9OiTY3Uftb5CbryNoYk64iJxQeA1fRTqzQQuDHiqjTZr1vsHZ7ldTv//E0KokQ0kQYJr/wFz8M/NeBOfsvMaSUGIbxH41yucyjjz7K7/zO73xr83+zJz737JNcvnadzM516Pcg6iP8FjQ3B0xf04QoRMQ90k6HxptvUj1zgvxQCTObYevt27Su3kQdbKNbe8jtZaLlG+goQPU6mEKRHuzQ+fJn8aImbq2CMTqHGTSIkYRpSj9KSIqjiPYucf0IutWk9Pz7UZ2DQTNSv0PU7SGcLCoa0CVU52BwfVrTiuB8doGbziiTWcmDrRuM1UoQ9IjqhyBfQ/QaELTQjR10vztIvpIQ3CL96dP4Vy+TWhlkvgztXUzbJNzcBC9H99KbJOt3Ce8tge0g4hiRyUNrF3oNUjuDylVJnSza76K6LY5kYGOvwcHW5qA6LCTKyGDEPkqlJE4WjcZKQxIkaa5GameJvRJ9u4AtFDKFmqPRGsoyJmdqbFNQq5bZb7bwoiba8RCGidQppIrEKxBnq4QxIDRKSJTlobJVVGXyvQa3EM9IAA3CBC0IDI/E8JBCIlRKYriEuWFkd4d+/TAKiZn4kC2RugVSBaGS+PGAr9hPIFEaCdjGAOY/mpVMFQbJtBSS6D0yQKoFRtCGoDPYatNgHywjM0XMqQVyjz+LyJeI9rYQhQqiOokWJmlpFNVp4V94c9CAEfoI26M8VuWD3/sAUXeAYtNxwusvXOely7vcC2GvFzH90adRpsVucQYtJPvXdui3I/bO30aFPZRQVGXMjJeyY5UxCxVkuY4tJYnSFLSPVjHb3ZjNXko3TsnbJo+OeBwazjLkCG4caAwSmp0+PWw8S+LWysxkIt68eIfScBW05mNPH+N4zeH80j5Rr4fX3uRGW3Oo6mH19pBasR8JjhY1QazwRIxj2+ymFmJomGYrZL0ryXgOn7+6x14nxEl8/txHHmYfj7e2e/y3Hz5Kt9nDmpthOO0SdzVJv0M+HFRuT0xW+OHn72NyvMa/e2eTIyN5TtYMbNOkknWYGC4g99d5q2niFvLMHppiNm8wN5Th+u1NXAlrnZgJo0fGFMSlOlXHZrKQ4b5hD09oIq3x+z7Hy/Zgp0NrLFMwW/FYOugzVsxglmrg5rhvLMdiVvPsQoFm1+dlNcyPf+QUP/v0FOt7ffrVCZZ/53Osa4+WV6UQNJnWDSa6y0SxZiEvKaRdJnOSdqQZGSpRlREPF30esnc4oXcYLbncaWtOGA0KliRNFDrWlC1N0Ygx04Cr/SyGlxuwofMVon6ILg5hrNzECCOCxj5LSxukF1+HJOW17YRo6x7tfsi2zNPJDNE1spgqoJ/ATiBItSRNFT1toJKAQEkqlkIZJnnhM1X0OHXiMN/5g9/F4sIEbWeI1uQiz3/nszhehvLUJMcOz/CP//tP0fvK5xjvrbHUDKjlXUquzcLkoFM9MnOIoEXPq/DkQoWnHjlJeXIcUyi04aJyVYTpIJMILBfd3MDu7jGkfVSqMFTE5c0ehXvXGL1/kbmKy4WNDj/1sUdpd30eevQBasUMWmkurDXZavp0ex2itTX882/QxMLcvcOtK/ewTBM5e5iecDk6nGdlq0neNtAPP0veSPk3b9xjcrzOs4fKHKmY5GtDfGyoxfePdfjkUJuhc19FXnuDYH+f9pe/hKpN0o4lngHX1/us+4rvPT3G7791l5fuxbxhzzPuaNLWLrZlk2msIZOQjWaALU1KpiDREscywXRopRbCy2NMHSGxMphxF9HZxfYPAEHRCGmFKa9uJ9gk7CcSX3rkTEksTUTcR3p5crZBjEkHF6li7jZDdNQlbwiqMmTfLJEKgSk0SgvalVkir0iZmESamK6Db3iEZob9ICU0XNbbETupQzc1MVwLvzTC2EiVXmGU6WCDM2MZZuINwmyNXT+la2TRKiVKYacT4NmSQAs0mk6Usj95hp/+8Cm69UXifA3fcDGjDq1UDugHYwuYroM5uQDFYaxaHW/nBkwtkubq6Fx5UDQSJvrB95OXKdOPP83s/Q9jl4a5cfsO51abNJSFnfeYPzmMN15GxxHRvg+kWFPzqMhH9XsY1WGs0Sni3S1UtoKlYxLAGpvGymYQOiSNFUubXRIgJ8AAJjyD+ukZvIXDpEEw0Ozmh8gYyeCrLOwRJylZS+LHCtcUiKDN9a2AoBcg3jOMGoRAq4HWeX5iGPj2Vn71t4H08O22Af7/V/zO7/zONxy/+Iu/yJkzZ/iRH/kRfuu3fuubnv+bTn4XFxe5e/sWUZyikxDd7w6oBNZAB5qs3iRtHSD2VrBHxslNjZC/7wQIAyuXYe3WHv3luwR7+xiZLObUYYzRqUFjVyY3cNMqVPDm51Frd1BRAH4XiiMIBeP5gd1ghAmjh9HSxJg+gizXkbaDPTQEtsfIc2cRbg4pBdLLoJOEoDzG5ewM78oRSmmfR/xbTKkmdqkAnQap3wPDIvbKSMMm2bgLuysDtze/hezuIlWI7Lcpv+9DiJVL0NhEZ0ogJJEfE1y/yPZn/xAjmxtwjmGAAqvUEWmCzlbByqCcAvHIYVQYkpmbxTr0AD/6/sf5N195HWVnwDBJGRAhZJIQSxdlZRFouolBIBwcFZGaHiL2cbq7mO01SjLGIB5stWoDUwhmKgWajQNk5wDsArg5UgwSaRJlqqSGB2lKahcw4wDfsFG2R5wfRgsTX3hYUQ9PKkwd04kHXGRlWAPt23vubH1lDqrTlXHsxgqkCSpXITI8uonGlJBoTTljULYFJTPFNTSmMUDLOFIwlrOwJVQcTdaUDOk2fqKIzSxpaZw4V8fo7qByFXSaoDNlzEodUagjXY9keB6tFO2ZhwZaYdPCKhZQYUiaxIjhKUwV440O4VZc2r2Qpc0Ot3YD1HsPxuM/9DS5j/4oSXkCb+UqvXZMaWGI+//q9zPzEz+IME0KUvP/Ze+/Yy3LsvRO7Lf3PvZ687yNeOEzIjLS28qqzLJZ1WW6mt1kO7IpNdikhkOJpIQhBYnQaEBBM2wSxAhDzWgIotlDo+pidxfZxS7TmVmZVVnpfWSGN8/7d/29x5+99cctctTiAEN0lTiYQS3gIgIPeDeA927ss/Za3/f7Tjoj6p1VvKQHzVmyfp+g36ETxkijydOM1ijh6sGQozCj6EgsW9Esubi2jZIW373eoZVKTvsRts6okrB8fJGTFy5y+vgE8yXFzf0un31gma9emmL3sEdgBFOVIuQ5/VyikogcRTVs0c81yisw0duiMGjzzt2A01M+R90e633DZMXm91Yl39gS/DcvrRLkmoNYMOlb/K1TAx5M93lpN2Ro+wS54mt/8Bq+LXGUol5wEUjaxSbPrfZZDxSWJSmrnHgU8Oil06jFkzQ8RT9KcCxF/6iL5yjeurHPhGsY4XN6osAzx6sIy2Kq5NDMujwyuMI7bcn71zZgNMTkKTaaS9M+2+2Ye7M9buwOuNXPub7XRz33h5xYmuTOUcwDS2UuPHiGGTNkcarKST9i+M67aNdmpeJQLJX4tnsOXW7gTsxRy4fYUZtq1qNIxM8tpDx2rEycBCidETtlSmGHLx93MI6L15wmHvbpJRkfn5cUdUbBsVClBqcXGoyMzagwRS/OeWmtjQ4GFCemWGrahDubNLv79O55gru//3t0V1fpZDYHoxTbKTDCJckNkRpHmzu2Is1ybndjDoOUUWmeIBuvz6NcUMkDdoMEZQkWTp/m5y7N8OhTD7F89jSffGCFsq1oLM2TKMXt+z7OG94SG6bGfMWjH2scSzA7ToXlD68csNLfYdFKWP9X30a6Pr+1KshRBGlG3G6RjkaI2gSj3GL9G99g9Mb3II6I4wgTDkniiP1bt5guSHwr43AQY80v8VYr56HHLrERZCTDiNZhnzjJ2do4Ys2fozV5loPiHLFWvHPmE7wVlrlSPcEtU+e4F6Nz6AcRxVKR9Nh5Lp5bplz02Q3gY+fmuDDfpFCpknz4Ls31D7h/0WHJHHGxdZn1b/0Rh9/8Lp3egEcGNzntDbi6M+CDqMiTFxa4eHaOM2WDTEbY8QBP5EjhYGcjmr5klI3PXd+WCJOBspB5RCosBqMhg8oSpAkiiRDhEMtklKRh0pfjQCQhmSk5DHCxjEYJQVCaQKQhtoHYWDRdya5qsjN5D8GxRzlMJEbAWloiyQV7w4TD0fh8H6YQFxo4tkOtu8opJ6TmKR6e8zG5phclBJmh4NpM2CmO41DwPE7PVbnrzPLwfAW/4LMTKu52E/JCnbVuhDA5l2YrdIMU31YkwqET5xRch1ALutoiqsxQsBQDu8pubo+f+bVZ7MUTmJlTMGhhuxLbtomqx0mLDXRuoDqFXDwOhSp7mc1eYZ4k08wtLPLUPcs0Rczm/c/QevarBH6V8jNfIW51Kc0WcI+Nn+smCLAnJhBxiMky3OOnMcrDzUJskZMe7tF85F6U7/9oqDU2pI1+1AM++dAc/nQNVa6RHO4hyk0Cu4jAJss1ufKIjBpvHos2272QsonZCBRRd480HAFiTLfg3zSWgoI/boh/kpPf3PxkXv9LqJ/92Z/lK1/5yr/z+rVf+zX+wT/4B/zmb/4mf/fv/t0/8fv/iZvfc+fOsb+1zsBvokdd8u4+eRRhsgyTZaTrNyAakbX20Erhz8whshijU9L+gLNPrZAMQqQ05EIihm3sQhkZDcfw7TxHBF2SnR3IY+zmFFl7lzzP0LZCqTHAPctzEqeAGragOo1s7WCiABMOyYZDLNch7xyMpRSZ4RoNXk9quJbFk06bk9YIr9pAVptIt0B+tI01MYPauob46GV0GiGyBFGo0r9xlXw0AGEj05iizJAFF47dhxl2wCuSH2yRBgnZcERxto6WFu6xM+j2LunOHXA8hOvDqIsMexANcIcHqFMPYB2/gJpc5MHTx7hydxNQZJZDhMS4RSDFImNkBBhBKdgfEzbyBCsd4A8OkMkI4VZIgYLIGRobJXJ2hwmFWg2lJJ1+H3QMWYpWNkFhmkgWkDqnYvrIZIhMAsgMmRFjTa5l4+QRQghkPg4EibWhMDzEjntkykFmMU4yQCgBjk/FMmPqhV1ACwlGU1aMb9tC4CVDyoT4WYCSCk+CEpAagUZSdCTDBGxLjNdwBrraIvUbCASRW0OXJhmWZojcOnFhArwC9n2f5MCbQhcbVKIjjF/HXjiJPb1Asnod3T0ca7ClACSHN494/p19PrzdZfijg2OiaLP4q7+Ktlxs1yZ67y1+6z/5h9y8sk/W79B+4XnCfozMQmyjIRpQuPp9rv+j3+KVv/813Pe/y6IdMcwFV/uGSsFh2hdcLMZoY7CEoOhYOMrgF1wyIxlkAt9zORZt4VSrTMqAFHhg2qO7u8dvfftDdjKbnf0OG4lC+mUW5QBHxwxv3OWY7CPjHl2rQn3nOjfe/4g/3BYEtVnUxi2u7Q343NkGNw8Cbu4GuEWHg1ZAc2aKV/YMf/nZiwReg+36Cl6jzs8tONxuRSwvTBMd7qPTjCyNGMVj3NhXHjtDs+jQKBWwhKBqC1YmfdraoTDqk968zp+tHXCfalP0HT79yHF291o0PIs2LjkwK4fUXUnDt3i/BWu6Qqm/zUytwLeutpi4/DqXN/s8tvM63b0d3Cxhda9Hv9Vmr5eysXCew9Ti9ZZib3OXNy+vsnYYMDfY4iZNSsvzHH/mCRwF9uCIS8tNXJHh2ZBVJ3EthbIcSoNd/P4RzXxEYdTGDHqkwsbKQ1Qy4lwhweic01aPAhmrkcC8+SKDaLxRujPQfLQ/4mCU89L1XWbmF0jKTdTiCZhZxl1eYfL+88jmFNbF+3l8sUplahrP80g1DJN8PDQwZqzDFGPs0SDKKFgWmbBIc8OtTkKaa4Z2meWiouYqPKUR9TlOTFeZLiiWqg7KsclKM3hK8Z/+xc/yiY9dolgpUvVs5t2MZTeikAcoJVks28hGk7oe0m93eP32Af/1P/kucZazfWuL5/ckauYYWa/DrRdepHXUZ/Pl93nj6iaH77zNWzsBF2UHc+8jfO2DDp2ZE3hKjlnj5QZ3t4+Ikoy81+GHz32fpy5McXyuzv4g4Iv3TfHQUoWDb3+Hn/viIxjb4bAXM1uyCeKUB88tUHcVjfYqpZ0rLJoOWW2GINUIu0AxahEP+sR+jfJcE6/sEx11GagKvudzbMVntl5k4FR47MwsV996l43NPU42faZqBdKtVdqbm8hyHSsNQWh2IkFqlyg6kprn4EoIjUUsbEZGMdIK5VfxRUxenSKpz6OlRcv44BZRUnFhwqOdWuRGUrQktgJrdDQOUdAZg1RTlCmeiXBtm1OTRXB8yoRkbhlLSQZZTj/RZAautUJSPY74RjkkwsXRMbEWTJR9lOWwVLbxlEBnMXYSMF+QLHsZaZqiig2UEtSjQ+bclMmiTS8z5EAr1pxtFmgWbcqWIcwEvpJEGiZKDp6SbKUu7cQQ+01mfZuktkCGhapNkzplRDggnT1LunAPI1xkMiQ73ONK7Tx5OCLC4Z/fytgeZXSTHFsBQrIS7XLs+79PUaS0f/ZPc3W/y/7tQwr1As6Ji5jDDdTUDNLyMKMuyd0rqNokhC0OjYvMMwj6+MfPIj2ftW7CnGcztn2PLy6zF6bx52bRBxtsPPc2lsjJjMRIQaAtcsfHEhrbkpRsxdbeAUEG19b7SMseP/Pzsa7535TjWMAYx3X37t2f2OT3p/XvX5/97Ge5efPmn/j7/8TN78LCApbtsLbXItlaI9pYRe/cQbcPCC6/+iM3aEa4egcGXSxXEa3dpnfjFtKy8Ks+ApC2g97fIttbRSQhOhrCwhn2vv8uslKje3MVGQyQvg/BALF9C1tauFIzXXKQlgWlBmrv5jhBrXdIcucKOs/IDzYxbglVm2A9K/DDQ0NiJA8na5xNd/BdC+kV0KM+ydYqlGqI5iy6vU907T3yYQ9VnUC4RUwS8v/+P/1j8n4P4/iYeIQyKcKrIOrziGpzLCOYXEAUS3jLJyjMzzC6dRPLNkRr18nbR1CeBKcIwzYiHqA6W+OJeTJEemVE3Gd2osHe9hbEQxKviSOALMGY8VrE0imqu4kT9fBMAlkMdhFJTl6oY2wHIxVGKJQAO0uQUuDojMlahYNYYCyfzCshkyGWYhx37NioPEJhMEbjR0dE0gUpSIWFZTJEGkLYRqUj6o5G6RhsD41AmgxpoBC0EVEPJ2yhhED8KADDi9p4UpMjKIgcJ27jRD20zvBlRjfSJGhiA9oI4swQa00/1oxwMEA3NljJACtsj5PflEOKhR13kUoiAG05lLMhqXRQJkcEPYTvQaWJLJSQE7OIYYs8GDJa3+CVd3cINEy7isb4TGNproQsV0i0REYhnXevsbp6yM3rB7zyO98nabdZ/+Z3cA5XSZXF4dS9DA67XH3xCsV6Gdt1cIXhRs8wiDPua9g8tfcDJj56iSQz/HB7hGcJ6iWfCzMlHjle58J0Ba1copuXMQf7KKW4NOszW/L40tP3IgY9tnY6XJhyadarpNLGTwfULc3o1hVWj4Yci/e43pccm6yx3N/k1HyNrizw+XIHuXaNa1dv0hjt8Niyz/bqBm9874e0t7YxSYouFqlNT9KfWGLVrrNw/hQvvvI+oXL51IPHeeH9DaJsLE+ZKFgs1z1OT/icbPoUHUWAQyocumFG8bXvcXNtD7N9wO27LX7unuoYPTUteGkjpGxLNJLULhLl40tP7BQ4qi6w996HjA6PuM9tkzfqfHYeCvVJvrzicb10jPtXmtSdnAtTPqmt2Ao1l07OMmwu0pxucnx5lmFuePuH79GfOsaaN0uyeYtXP1znnrpDx5sgt0qMVJHcKOIkIrc8DpsnEFmKHg1pX76CkmDNr/B+WEK7RQo6xPdcjg83uD5QZIvHGBzs0+32WRRD/Czkw9ff4bd+71XyNGcjVFjJiANRpuAXKBZ8FkqKcxdPYRaOYSvFgi+wdYyybLQGY1lYwpCmOVoLcgOdKGMQZ5QdRdO3GWWGrvFZ8g0FmdMKDdpyCbWikxhSDUIoIiy62mJltsE98pCThYy6GRGMBmhhE0cRlu3wiQm4s3NEVGryzsknuZhs8tClc0RRRLlgcWHKJWkfsJ+4pNoQPfoM3s/+KX7nepdb5SUOY0nLaTC9coJmw6OV2ZyZ9nigqZiyoOwqrl6+y5U7eySjiD+94nCymNILItYTl8rwgKNexgW9yShK0Dl0Y817owJz7dvUo33ufu8H+LevMHf3TQaZ4nP1PvbOVdT2bUK7THDuQYS0mDx3lvSxn+H6yY/j/vwv4dz3KHGa8XpSoZz0+IuPTPOL1T3ONSzmNt6goxWub8PubTApealBNxVYIkUbyIQgM2AYm28HqcCRAmFbJNgEqkyER1Bo0M3H8evdWKOkYS9IafgWRaUReU5eqJBaPl1vgk6UIwXYaGZUyIzuYOuQkknIvRKJMfhqfCksWoJpf5zuFuVgtEFWGxi3gNIZjsgRWch0QVAUKVmWYu3eQrXWqaZdLpZTSjJDxxEtf5KyzFgoKpbtlDM1h1NFQ280NqcbISjIlP1hTJIbHEuR6pw00yihybShpEMSr85eaIgrUwy1ReZXyNwqe6JEqjUDVeVg44B9VUM2Z9kcZvi+wyDOSDIzlnmkGXrQpTFb50zd4uzttwkOD9j+3C8QX7pE3tomO9pD+iVMsQylJlmnDdJBRiG2ycGysaZmCba3sBeWKduKZsHiwqSPBJq2oLI8g7Ako70WUW9EFofYWYTROSIPGWbjxLCSEvSiFD8dcqcvyIIBWhv4N5SBLP63/c/5lXGs7u3bt/F9n4WFhT9pK/XH6qchF//+FYbhf3jOL4zFyI2JGS7fvI2YXcFeOIaq1MnjAMvxsBeOgZDYlQpq9hjKUYy29pFuYTxxE1CYqaFqEwivgBh0SRcvIuoz6LUr1E/NY5KUxoWTyHIN3e/Su3mXNBhgS00/NrhKkmtB6JQwXpno6ps4xSKyVEbVJ3Cn5zi4eZM3DhJ2KwtcqgvODu6S3r2LTjNkpQlKkXY6CKUYXrmMVZ1GNac5ev8G+WgwzvGeWkA4Dg/+/NNQqKCDPkFhCvwyIhlgD/dJvdoYxVKbge4+0ShBhyOyfpc8CFDKwj1+FpGnCHJEmqBHffSoNzYMtnYRSTDm2QYjcmkjlIdMRyhhSKQLfgOjc2wpkHFAOrE8NsHZHjIeQKkOQgICN+oipCDSMBIOS75BhT2mSg4Hg3Cc0WgXEdJGS4Wbx1h5ghE2ueWSeRWMX8eRCqUzRJ6O3cnDDjJPMEZSjNrgl8Fo7DQgtcYcZrezjjAG7ALYDjINUMkAY3noPCXKc4SlQNgY5YLJKERt4jxnFBs8ARkwygxVG6QQ2JagZhvqrgKjEZaHFCDylGp0gJ2Mxnrg3j765lv4119G3XkbYbmI/h4CSXrjXey5ZcTsSUaFSaLdbbq3t/g3R1rNgtONAisVm2d+9RmEW6GsEvJkxN0re8z4kic/fZ7X39zg8of7mL1tciOQymIjEuyvPMnF/+I/Y/Jv/E165z/LoSlSLnjMFG2mxJCwvsxw9gSHUcYH2z2ev3lEkhtSBFXfAp1RTjpkk8voy68T90Y0fYtk1Mcr+fz6587wqUrMVmvEq69/SHSwh5bj9fjZZ59hsNUm88rUVE53FDJ19gRnsh3MsIv/0CM8eP8ZTt9/if/2+3dZqdl86sGTGGlze32f7iCiXPBZ7Ua0Rhm1ks9NXeL4iWX2Dvtsahsz6vDDOx3kjyb0RQmpBs+SKDlexw9ig1KS7pkHeChv0ev26FolXrreot7bo1rxWCgJ9vYP6EU5fTPm/1ZMwIwH9yxP84Y9zz97Zxu/UsaZX8IyMU4ScjWtYJNhxV1udgXPv7tK267QCzMyYdB5xkrNxe0ekEwsU9u/S/jit9jYOmLkVHELZV54Z5XIrqKNGTNHkoCOLPPdTpGJcpFbapJBdZbrCw+R7G4Srd/E930yu4CwfTqpoDkzSbPksFc/xvdvdnjzlbfJ3CKzdZ+bP3yDJ/rX+dpz79EXJdZij3I6wDgFkiyncXAd4RRw84h2f0A/01STDtO+Is6hkI7AQDvOOYxSzk36+LZka5CS63FYSxCn5Donkg4HydjZnuSaXjwOW/Bdi2raZpjmGAOxUIRenY96Cl+HTLmGV7dikjgitz3i1hGPLJa42dN84uMP8Pj9p/m//+UvEvd7CBsmD2/SThWFZh33wgV2K8t8M5zn/PkT/KBfZqpRYdtqMIgzThc0b652qO2vM1eUfMqs0umnPHF2liiX/IW/8FWur+3wiUcv0HQtVkewnvr8XvN+hn6TYP+QjzZabB0NORrEfHMb1o9iftAp8btrCeHCWQaZ5Nr3XmRTVzCzJyklPSYnp9DnHqO9s0vLn+BEw2Pp/CIeEZMMudhQ+PUGMzUbrr+HJQ16OOQEPQrlKsLkDN0GeusOp/Q+qVVCCkmQavZHKcYYbB2zVDQIoJXYaGkzyMAIQepNIIQg1YZeorl2FBNnhiTTaD2e3sZWhTgX7IUSR44b6p5w8XSMpxOKo8MxJg+bkqO43QlRAuqejWdJXDQSQScXCGmTKA/fxCgDFQccHePrgAoJ0dRJsjhFdzoEOEwcXUXFfZo2OFnChJVgxX1mfc1k2qIiMsrKoNMMz5LMVW0Gydjc3A5z9kcJR0HK7iiDoIPKI6qOYCMp0ItSBtVFRplhOmkxyjVHqaTxwIM82JD0y0uk2jBTcfBtQWsUsVS2MIMWsjlJ+cwZpFSIrU3u80fUXnmJo5OXeG21x1AWoTqNONxAeD6W5xCs3SQz0BhtQpaSHx5gWltkqaJoQanoUK26CODCyQZZGBPuHLDx/DvUlhq4s/O46++gkiHFrEesDcMk5yjUBLmmScj1lqa3t4XROdK2/53+5//462Oz27Vr1zh79izi3wRf/Jj1U9rDv3/9w3/4D7n//vv/xN9v/Tj/+D33nOf6rVX4wjPIKz9AzCwj3AJWPCI92sNZPoXrlci7bTK/jkBQOnuWbG8dk26QDEOs+gTR3g520RvzbA97SM+j8vBjZEfbWOUK6c46/pl70eYmrVdep3Dpc8xamp4sM0pyykmX1Fi40/OkR9s4C6fJMdza67LevIflSoGLh+9jtWuM2l3i/RacOYkpVhGjPlalhJpawNOaPIvQoxHlhemxiN4poMM+Ohhx/5c/gYkHGMdi4Anc8iRi/QP07MqYYLB1G3niAbyJOlvffIGJU5OUz56F3iHO+ScwQQ897CFtG6PARCPC1Ru4MwvoLMP4FXQesXnrLtONGkIanN4O4cRpOsZnrn2XrLmI0hqSYOwQt8c3XKHDcWpe3B9HFucJWmtqRESUaHRvIByfZsEmSjJGYUyh5IyT28I2lrIgixEYcr/OSEsqJsAy6ZiqYAxGuRi/Am4ZlQSQRmSFGioO0G4BLA8jBNLxMcoe65CTAAZtRH0Bspi+rBLlBs8aM1/xfOz2GkIIjrICxxsOUqeMUouyJfBsSWwEBUsihMEVkIgqnsnGKCGhsL0SuVtGZCkmywmvXcYql3BnZjGlEsIvEl17l/6NO0yde4DUraAsTX7yXqx3b9GwFEdorg1znl32uOfkAvNf+QIiC8mKdbr+DNWyzc/+yiOc+LO/wNXX/havvrbGjK8Qjk9Hu9w8CpH2LLWqQ8m1mFcu0wVBnEPZy7D2NygunuD/8XaL5qhLzbfJjGGYGqYKFsKA1dul41YIt3pMNpdwtu9wt7RI0tqh2pxjxQ95tbDERFHxz198iU88fJaRgBuHA2ZHeyxafWo71ziztYdTdnEuPIZ473laF75EJ3OIkph6o0hxeo7tTsKMm/PnvvgY3373DqeXm1xd79LRmuMVm51IorKcKTtit79H3jzOubkaxUqBq7d3mb5nmkEYsVQp0knHxpyqiNgTDhXPRpy/xPxiDTsLuXa1z73zJb6567K2m/HVFclo0CGfmibHUM26vLYdosMu84VZzj5wD9Z2l3fTCvd5RdZHPmL7fcruPju6wsbBDs7UAke5zb0q5pX315hemWLf2BxtboMwPFrWfF8tstENeLy7x0H9JOf3X+eWf5HfeW2Nz80K5k8ssbuxSTx3hl5k2A0Ne72IHV3gvVHOhnWSpXbCsdM2fjIgGxzQsmfot7Z4qplTkHXWtrt86d4J3N07iGP3IE7fy8/0P6J1ssSLdwbMeym1S0ukow7p/jq9qMeWd4qqlXO8IBhEA+JSAw+NoyS7iYclBQUl6CbjB1k7TBkmOaAZJZrZiks30TQcAQgybSi6koINRZFhlEVOiYotsQEpJJ7rcDIbEFPF8hTHaxYHe/l4UnZinuuZx8VpgR0M2YxsJssu9elJ8jgBpbnJBK3bqzxwtk5lZYI7b67x4FSJ9X7KpZki370WcCPIOWoPsJTDjW7OucEeL/UEz33nJf63//Ev8fiFeTqDEV/fbvOr5SZ/4/EmyUyBYO+Ai8drvDUo8vSlSUZxTitXbO13yEsz/ME//wZ+DMsfP4+ZW6EUHvB/fjfkNwo3ODYN4uQl0kyTzp0mrq2w1Y3Z3tnj42dnmYj2KBXKnHFsnP1VwrurePPz5AcbRFu7CGkwQUDcD3BXHue2PcNCeRrEmEBjck1RQUllkCRI6Y6xk8Ao1SQaGo6gnxkKIsf6UQPs2fY4GRPYHBqOq4CR24Bck+Yaz7LIdI6tbIyWiCTEpAlSSTRQtBX7g4gwsZgtu4Q5NO2YQQqurQiEQoocJw4xWoJdJE1HCOVCf4+0vkQxj7iy1We23CVxK1idPeLZ0+ylEhFm6CBl2R4SOmXcPIfBDqHbJHEKTIjxgKQqDLoAcX9AqD2mKjZdu0HdpHS1S0Ml2Pu3yfw6GzQ47TpMmAG5XYK9u2zMz9CwNFGmyTJojVJW6j7H5IBs/Qq9tS0aX/g5TBISDlIsNJPJAfNXXuCumuPt+x5g+WjECbeESmOs+iRH733I8OxnWN5+HVOqYc0vIz2Xne+/iltyKM4V2b3bZbZkM396ghyL/MxDFK9ucPD+JlMP7mFX6wxkgZ6QCDP+PXWTlIrIsWROXihTqE+QxhFCSsLD0R/rfT7zxEXgJ2t2+2n98frrf/2v/w9+vdfr8fbbb3Pnzh1efvnlP/H7/1jU4Gc+/hjXb97EsWzU5CK6Po84dgmigHzQR/plyH8UDdjewW2Ux3xLLajdc4q4PUJnGcL1EfVJnKhHdPV98sNd8n4XuXQe6boYaRHcvYn/xV8hHkH56Ba+bVOyJa0oQwhFsnoTEQ0wGg6Fzyv7KW1Z4NETU5ypeXhTM4xu38KbblKcn0CkCfGVt8j6beyFFUy/hbu4Qnz9Mtn+JpWHHkQqyLZvk0cJ1uLpcahGNCLb2yD/1tfRO7cRjXn6/jSkIcnabfK7H+Asn6b51GMUL96PNz03llL0DtG7q2TbtyBPwSuD55MP+oTrt0gO9tCjHsLyWXjwIQ739zEGkvIMUo9XnvR3SYWHkyXglbD6+4jB0ThiWEoIuj+iQ1hIk2HrGM8k+HmAlAoQ2JZDvVrhaG97PIWWFnY0QAKytQlOYaz5BWQSI0ctVBphkyPDzpixqzPIQlK/gcYm9WuQ56g0gDQAx0MLRSwUmeVj5DguWUUdUhQNVyC1ILJ8rP4uIkvQyuVi1TDhaJw84DCIKdqGfgLJjxysdjIiMwKNQGcJTjpCKknmVSHPyLwS2DZZmtK9dpd4bx9hOejSNCYa4Z25AMU6Vv9ojGfrDZn77McoT5WY8hUZ0G9FlGcriPIUZCn93KKcjzj11ElO/blfwDp1ice//ABIweKTpxGVJvupTdVXDFLNZjekF+fsDjJcoWkS4O3eJF2/wV7uUip5HAxTPneyzgPzVbJcE2SaKNdsiirlzh7d0iS3ps6zXT7G/uvv4BXKrJQVN8vHOTZRoFGv8qXPPc5Eo8bexhG91gDh+hw/MUPnpVfY/8Z36b31EdH7b+HOLtKNNYMo5sOtHrOtVf7qJ4/x2NkZ5KjL+wchS/UCliUJjeHr//Jl/u5/9Xvc3utRK3t4xSKe51EuFSgWbO6xeoxGAVe22lzeGXKjFeIpgaNACEnVs1isupQ9i9HUCnLlEo88/TAnzp3i6fuWWW743DoIeKvvMUhSdJpj79/muB2SWy7ocRN478oEU80i3UxiWRZzz36G+dWbzL/7HMHWBq9cvsv5cka0t8eXH14hxqYfZDzY0PzG/Q2q9TLPfvwCG+4EB9KjaMHkfed59mydcBTwr95Yo7ff4nZHMjw84H5xxNZAU/ccNg5HfPJkg6LnMJhdYdaKcftbhOvrhGFC2FxkMe9x+e4eJy+exq43KYwOsF57jo/fO8fiL/0y8yLny9Uh1eAAIyXRlQ/ZKc/xuxvumHNt2fQ6bUJh0c/kWD+vIDAWxoBlKTKtudEKsSSUHIVrKWbLDhOuzYIv8EgoWQZXgW8J6i64IkMpQSfNKechtXyIFXYoqYzF9nWsPMZyPKp6hAH2RppRDraEPEp5eXNIdvsq7tEquV3hUPv0RJkb17e4ppt8Zz3mexsjfNvBcxTEAWuHAzY2Dnnlwy1+59vvIOKY1zqK5w5dvt0rIdwCl3eHXN/q4Sgbv1Lhh29eIa1MUCNlpujws5MjFlu3udPNmdm/xslCxoxvUOt3sbttTvzMJ7j/oQvknQM+3Ar43/za5zj9zCexm5N8kFbZCKCbKwoFn8Wy5Mnp8SbAbcyh/QqpKsLcWYZzZ5Dnn6RfmiWtL5B0Q5LdDZwLDyJ1TGXrCkiLUZKQZDnTjmZpcAfynBiJKwyZhlzDINa0RxlIyUf7ARPxPkWZjb0NnmKhYo+lakIQSo/cjKPZ5+2IrX7IMJcURcrIKOJig6Q0AV6ZJM1oBQnzFZ+SOz6XSq6im9s40uAIQ6wFftwnkRZGpxiTo70ytknJBy3E/hraLaDqFQgHWJZNNByOz1+Z0DcehYlZsjwl2F4nfv17yKBPobdG/2AbnWWUCxajXFDvbvJwfIdL7feofPA8haRHvx9QJKI02Mc3Kfb7L3Ei2mQt81ESqnmf4fvvMlF02A9Sio7iXE1xpuHSKCjUYMxgz/sdZJYQTZ5g+pOPYdSYIqR7I5J/9gdcaN2kM4z54ZHhMFOIcpX6V/4MM9khYvEC5CnSsrGOnadzfZNTTy6TG0mtYPPkw3Okwxj/536N6OzjpGGGZSuMNjCxQDt3OUwERVsDgr1ehIr6HGYOqXRZOHWCLMvJkvTf6X2KhfG6/SeNOfup7OG/r/fee+9/8HV0dMSzzz7LlStX/qeb/J47d45//jtfJ3MK2FPH0JbLWjfmmFfDPVHGSJs8GmGdPEe+dQtnYgLSCMv3KJw4RzoYkAcD1MQUsj6H6GwjZI49uUByuI8IRmRRgt2cYnDlI8pP2Cx89YtklWmcbIRre8yUbDrYlB94mjTqcy3d5XD9kNOTZY5VJBQ89KiHLFTwpifJ+30KJ06Sj3pkoxCdppQa02MMmhYIz0VZChkFUKiSJxHOdHmMOJs5iapOIY52UMlNgusf4t3/BH1t4XtVRGMa4pC83MSfbJJ3dqFcQdQmydeuIqTCJH2EVCjXx4gqBolINeiE4L0fUL7/KfJWi7jfI5Quls6woi6q3ITKBJ40qDgEp4jIE2TYBZOCssi8CpldQOY5dhxiSgqTh9jEYy0jgJBMlR32OiOOBV2MW0Z0dsa0BqcAaQTDLl6yDuUqKglIiw1EGiNMjnHLGLcybrgtBysdEttVLDQizxAH66Qzp1BpgGVyRqqEnDyFa9nY7jiOWQtAgSMF2nJI64s44RgjhYkIZRFHQi8VBJnBtwS9OGdSjXPLjckZCRffURgEQlmoLCI3mnziGLbvEzsDutduY5+5H6s8gTUxi714HpRE79yA+hze3AwMOnzmr/0Zbn7tOT54d51+ZuitdwiuvYM9MUUwVaCcD5n6/OfQy+cxhSZ6GPPUmTrTP/NFYqfMjCURQuFIxfXDITXfpu4r1PqHbL7+PqX+OsPtFlXR5OHjj9JNDRO+xVGYEeWa3YOQp5fKbLUFOyEcb7isZoJ38wqT3Ssc72jSqcc5U0i5HDmU4z6VZoOw18a1Ux4VB4wyn3a7Q9w5RIiM2mKVo8MetlfkZP86Q9/haOoMH60NuPfSAiURc4M6Z/w+E6dOcRBrdrsxTz5wlg9ubjPo9OjPVHBMH3/5InkYkWY2L2xnnCjliBvvE7QjXlu8wHDFsNAs4HsOviXItMD2JI6SuJaklBqOtGTah19/+jQ3RpL3tgesdkJ2u/CgXWEhOUSGKdf2LeJUUPFspJEMU8N0yUWVfMqPP0783A94zLY5c36ZrTffJDx1gctdeO0H7/Bnnn2c+48/gOtaTMsCtzqH/K2fu8Rk+zYHtSLdpAzbOzwxVWC1OE0ny0lKFRbqPjuyzmAQMD1doWJluCLljZ2AP3WmQpSE+NV5kgtLNFodji02uXu3w6sHcP64xWDQ4vlejfPJAQ+cmEIqxebcWap5yAlfUZUJ2+cfZH77Gp87ZhNtXmFr9jyvHdj8/IRCihSRK7IfpSdKNBqIc03VtXlzs8UTyw2KlsRRgmLexxcZIodJUmy/RoGUgk6JhU0pD1EmI+60KBRtyq3dMfll+jjStshth51wfIbPLR9j2k1ZEDnD1TXO5RFXnHniyGN6lBIpjw/bAVG1wZRlk0pBd5QSJJJebjFhMpoHd5gvu/zrtw5YUn22WiHViTK39oe0WwHnn3iEO5stKlGfzcE0Hz89Q3Rg8X5H8DE7JvGqVGbmObb+Bvb0PK/pae6LArxqkRV6PP7Xfo16yYZqFW7c4L4sgxNPj83Ox+7jG9+8zt/41CKWGTJKJJMyQfZ22CxMjBM1LY+DIGHWc3BPnEWnPUpxD+ankSePM3zrFdL1TWK7yrSr6QQR0x6kkQarRGo5eHF/TPvRGftpET8P2E4sKp4NBubLEjvsY0oTFOwxXQcEh2FGyRaMjIfQEKKwjKFgS+p6QCYqRFYJ2zKYLGUY9ZECglSDkDhSULMVRVuy10uZKSlcS5IikBikzlFxn5ZbpKQEiSpgL99LNhhhh23OLtYZbW4iK2XIUkZJTl0HIAoI5fBOVGN+tsaEjglzQU0POeVI9sQMpUxT2L3Jzu/9C449cQ4n04w2D8g31yk8/Dhe5DL68DLeVANv5STW1hWWThYQukZs2agv/ApKKmyRE2U5FwshI6tMIToiq81hzyxQjDOGVy9TuM/n6NotrBNTVE7OcfjeHbYOAu4/2uPBL5xif+0uHx4NmbQ8zpsEy6uQFSeI3/ke1tn7sKRAJzkiy0klbB4FTK/UaZ6bHZ9DN19h49oO5YUy9uQkqXAIM8NsyeEo1AgM1w6GnEhbbIQWt+62SI92iTp74xCP/68qFdx/+/dr167x8z//8z9OG/XH6idBa/hfCu3hxRdf/P/r+/9Yk99z586xeusWeZaNY3rzjJsDgZpdxCpVyds7ZK2jcUOVaezZJdTkIvb0AsIv4q+cZCDL/P7VPqMr75EPWuMmU4AzO4dybHIjydpHNJ74OFa/hUwHiDRGRwFe0MIVGRoY5ZJXb28TK48HZJ/FZnH8oTWafOMm2BbeqfNY0/Ok7TZxqwNaj4MKoxGYnPDmh9ilCrI5i3QcdJZgTc2Tpwl09jC2S16sY45doPTlX6K/O+Tqf/NPKVx/nYFTx6mUMMYgbrwFxy9il4rIagNRrKFKFUSpirWwgtE5Ro6xB978Iu5kE1UsEG5vk3d20dGQY7USL776JvZwH6EURgPFBm4yBHKM7WNKTXRtHmMkRnokTgWZJyiTgFCQpUROBWMX0UpCcQJTaFCZmKE7CsmUjRoegl8Zw93LdTLlsuvNws5tsjhAaE2qHITOMIMOebE+nuYDKgtB2dhxH61sDAJTrLOnmuPGPA3JhSCUPtKk6EKFbqqJM4HWGZa0CYrzRG6VuDRNLC1i49AzFjVPoaRgwhtHNYcZY2kLmtQIlAAtHaTRZEJAEiHjEXmhiX3mIhLwZyaRgyNkPEKHA/Kbb48/p0ZBexvLckjWbzPxzNPc8+c/y/JSg1BDcaFOtr1GeP0K7otfJzvcJTjzJK3MhmGb4myFM/fPkx6/n5FRSClxbcVMyeETKw0mfIdZGRK+/gL/8j/7R7z39dfYfusWt/7x1zhndZireFxvxbyx2sbVCUXXwgnbmNGQPafOvl9HpzHze9dZ9+Y4KM1yuLtHM9zHbK/x0UHE3Y0D3vjhNbSwWZ++yE71DOnGFvvvrqOTHJMkLHz8cUonj2F27jAszzIMM1642eKf/dFHXN9oceNgiDPqcRBq3NYeb3ywxjMPnOD8yTk2bq8SDEf0dla59NBDUCxw9c4BE7bgtdsdLh8kXCqFXCqN+MG7d/hou88wM2QGHKGpKo1CY3SOZ0kcKZkZ3MHyfEom4slZj2Mm4FzDoWXX6MYWR7stzk6VeGy5ynzFpe5JNve7nKpKDmPDQWmK9oPPUP7kp5nZu02vschX7p/j0nKdj+7s868+6rKWetwMLOJRyGK0jxRQnJlnKj7kzGyV31/PMWGP+GifPOwhPI/vbAvCH/6A7L13qOQD3tkJePFuj/vPLTBdcsiVy/sjh8gI7gZgS4GePs65Uozv2ags5Z6DG9w69jBRlNBLJQ/YbSIM9eXjfNQ1dLXD3k6HxtIKe90hTn+fJxbLGA1eHmKZhECDryQFy2ALwbGqi6sgSiFKDUFqKFkCEY/IBm1U0MP90Qo+yiHMDd1MQZ7gFkvkxYnx5bYxT9o8jq7NIJRFKizqMkYIwZwIuNEX3E4KdKaPk5bqnFiaZDdQrA6hG+ZEAtqJYHayRCvIGKYaYSv2Rpr9MKM6VeeF1ZjpIswtLvLwosds0idJc1TvgKLv8fT5aWyTs9xweHqlyvlzi9zY7jJSJVqmTJhqmscWMHHCbz13nY8OkjEX9qF7aV+/ijxcZfOl7yMXTuIvr1BIuozS8Uboyys2viXoaIc7Gzt4nk1hboGFZpkWBQi7KJ2zH+QcxYBXxCs3sJuTjJpLWA89TTYxg7hxmcPQRe7cxl7/CG69TxpE7IoGMo1RvX0SIyk4ivxf/Da/98Mb7A5itJAcL9uMJk6QK5tJ3yZIx4SdIM3IDXgiRQhBpC3coM18URBaRUZa0k80o8xwlNnciEs4tkXFlYDBkhIhNJ6SzPo5jpJIYVBoAuESa0mSaSo2Y0Oy7WLpHOUX0X6VyK2T5Tm5W6BULVEwIT3pIxBYJmGiYNPwLCrTE1RViqpM0pZF9oYJOughbryFa9nc/sYPEKMBpdMrRAf75B+9QbZ+C1tlKK9A/7WXMFlCYf8W6p3vkhtDXp3GpBFzFZf5soso1ii5Fn7Sh8MtRtc+wqpPYs8fw6x+wMTpaQpPfA403Hxjm/UgY3g0wNx+j3nd42OTglC4vLJ6RC+3sIcH9C9/xJYpY9auMv+J8zTPLvHKRwfcaseAwakU8NtrdF9+kSjOqK9MY5UrmEKZyeIYZ1dxxhKX5ZpHWYe8t5MTBTG76ztjXJuQCPXf636fenAsc8iyjBs3bvw04OJ/pvVjNb9nzpxBCcHrX/uniLCHtXuTp8Q6slgHtwi9FibP0BvX8U7eg6pPIFwPKSR4RaRlceUo44Mjg7d8An7E08uDAcQROg4oXHwAa2aB5GALensIy8IO2oh4iDaScnDE9sYaH169ynyjzIXuNfZ+7znSKCPdXsP4ZeTcSRiNEAjsehOyhDRO0MbgzsyQd1sIZSPyDFmqIrKENAiIWl2MX8EM2+B4yDQiN5LQn2K/OI/zs79C9fQSvVdexcQjzMI5jLSwZuZxXYmaWcJojYpHyOoUqtZE+eXxz0ba0DtE1RoIrwCWwvJ9hO1SPHmW/9vf/Cv8v/7Lv8flm2tkXhVpUkSeoqI2Yn8VlIIkIveKmMoUWaGKzCPsw7vINMI4LoQdnHyEFXfJK7Pkjo/MYwK3gV8octAZIIQ1hpELi7zQILIqFGSGPnaJXW8OAyRBQCoscrfMyNjINIQ8w7glsNzxoY4DtkM6dZyqa9DlWVQyoqRymmkLFQ3IhUXNkQRpRjvUpMLBKEOOJLZLOEYTMU6yqliaaj6gkI+wpKDpCrx0gKsMvsmwRY7KU/JhB2nE+PIifQIjUKcepjg3TfncacygNW54pYPyXHKvSnL8XpRXQeQxamqO/pXLyEGL+/5XX+ThX/scUWIz3NpHnb6EqteQcY8sGOB+8D0GL3wHqQOUZ0OekWqIc6g6UPJsKq7FQlHS6N7FP32ej/3KZ4kSw8vv7xLc2cIRmoIlSXXOZ85M0Q1jzlt99kKN6u2yYgccDhMOY82OqNKzPVYjj7oO+HY8z64pskyXE3N1fns15D9/e8Dd1U1W+wnusEunlVA+NkmeGfZeeB7hOlRXFphYmOfBeQ9jWSwtT/EHVzv8uceWCXsDVvd7vHx1i088doo3rm9x9sQsf/ErjzASNq2Nmzz9+APYnsvx5Wn29vc5t1DjSt9mZ/oUhaDLJy9OcXTU5h+9sYWFxjUpdtzGSwfYeUTZAk9C7lfphClv/OB9Xruyye7NW0ybHicGa1jHz+Kef4hKweHSpMeFySITRZtH0nXmR1vIJMDPY07O1wldD3nfIzz1yCnC3ObmZo/zD17i5x+bx16/Tnv/iJVCwuMXFxgYi0N7giKach7gFcv89ivbNB3FYV7k3uVJ8jDgg1Dx0JMXSN58nWdWirj9LuVKDY1E20WOFzRv7Wcs1Cv0g5h2YrjvwnEuJYcszzaYeugBTlQlf7SWsJe5FBzFlEroaZtSNmJ1v0f3aIg52MFfOE57fZuZoqLQuoswOdr28ckwaPyNy2A0DVdS9WweXa4xXXYoOoqDMKdtPLbcWTapEVkOBSLWR5q+8BBCoC2fYHeDikxxBnvIPMQYjRoeYfIcJSWj4QDLsihPTFAvWkwVFH48IMgkEjg9W6Lg2wjb4Z7ZIlM1j8u7fa7v9hmEGecXa/S1TWVumt/fVXSP9pms+lQbdZ5SB1RbB9R9wbn5KrWSw6yn+d//yjOcLuXId1/jTLTBs/UO2+0+7WHIzbiIqc/gdnf56syQwZ27XN+LOAwzPhp69EJ49fY+re9+i7YzwVuvfUTa3ieOYy6eO40K+khpKGQB8dqtsTdBWliWojuKmVAxq90Y17axxHjD1PKnkFpDntA4eZze8XupnztB2dXIJEA2mpQObzDfv43MIoROkY6HJSXWxXtZ2xtQdhQiH6/NlVSkaQaM/4/7liTJoWCrsWFZgC01Pa9J0bHZDuFwlJED3UQySg013yNODQ1PMeMZBIaiUoDGsxS2ScZyNaNpGZeh9Ol6k/QySIWFwZDnOSWZM/IaiMEhB5MnUMIgppfRbomGLakozVonpGQJLNsidivY08uIygTNeJ97htdp6AEWGXNffpbWrS3iQYK1fAH7qc8ihMQ6fg7r3MPjZ7oSkITEu9vQ3aO4+SGl4IC5/k2KMqFqGfrawmjNqLpA9uEPsYpFtr/5XcTUImJyCT19DN1cRPklbu+NqNuS0dGIzve+S9o5wK1O8NC9F1iYqPHWB1dYX1ul8ewXmN19D9NrUVmcpvHQ/XzyZ+7l0kqF0mSJ8skVzKBLYaJK4+Qkhfkp5OwKR940xTwmN2CJsflwyU1JkMRGkQRd0qgHuaYwewzbr/zbvudv/5WvAvDWW2/h+z5nzpz5cdqoP1Y/lT38h6sfq/lVSvHQ+Qs8/51vjaON04hidwvyFF2ewKQxdrVB0jkiO9pDd48gGIDjIGwfu1LjXP82f/PpaWSxhCzVSPtDTJ6DgDwYklx9E7F0boxhmVlBTsyh+20yv0HW3ePq6jZha5fysXuYOX4ad+kUc5//BMEPvkW4tUuWS2StiSnXYNhjdOUDnFKB4sIihfkFnLnjyFIFdIZ19r4xSaA6SbCzi33fx5ETS1gnH8aUamPHdxIxSDIa+RC7UiX/7J/Ce+pzlO6+Q1qZxjp2D8Ier7hUqUreb5NnKRqDHg3G+mdloeMRWBYiCsi6fdL+EK9eQbo+slKjduYS/+hv/w3+1t/7B2OstrAAiYiDMS4tS8HkaOmQWA693MYJO5jyBMb20FYBjEFqA9IidMaYHSMVNUY0mw0OWj2MMORenag0Ra4KxEJRUQI1uUDR9yBLcJM+WlnEEyfIcv0j2DeQjNA6I3NKxEaQ2R7aLuFIRW575MUJlBh/TiLLhSymqARRbihZGnSM1oK9UYZUgqFw8KSgbI0RTlYaEOHiWuApSOwS0mjcpIcWFlpneGmAMBnGq2BJwSAB5RVwJurk/QHd1U3SjRsIodFRAFGA5RTHOuxRj6Qyyc4rb7Px5m123l9l/r4TzH/204jJGaKP3ia795PsLjxAkiakt25x8N3ncGsV8jjD7m5TEQkFZSiZAIWgHY2xbbo2Q372Uc78+p9l+dOPsxdBsejSKszyylqHyaKNa3JakeHyTsSVt2+iBxF2tc6V7SFt4bNmlVm65ySd9oCj8gzlok+12eS+mQJlEfML5wqcPD7NSzsJO1tHuBNV7v3lx7BcC7tgk3llkrVVkoVTjIyNRvKXf+4R0DlnTi0Q3r3DcP40zbLDuQsnidttru6GvPXRFiuzdTZ2enRbR7y5m7HS9Ektj5fvDkiVxScbAf/1WwN+cHdArlweqWWkUcDtTkxiFCLLsKM+Kh5gj44YZIawPEuY5Xiv/4DouX/J6XrO1YHisDiDn3Q47nSRrkeGIDWGk37C3MkVNu6sUYm7pMM2xwqGxZpHIergmpyXdwMqwwMuFVM+Xo3Z+fYLnK7C+7sJd+IiQQq/98EBB7FmP9R86WyZX//ZR3CWFvG27jA4OmS6UcAWKbc/uMZ+aZZjG+9x/toPyMIR/TClH6e0UoXrWATC4bDdY5gY5q2YVnOW3KvgdzfRYYiIh3QHI1aded7b6HL11jYoh0+UByw99RDFgs1UtcjOUY+tQGBcj45xkIMjNFCKu4j9W0xtvkXx6Ca2FMyVXNphhiUyjoYJ3dwiNZLc8ekblw92BrSHCULnFGXG4P2XWd85YvuozShOSEcDbMk4nl2AnY4IgxGe55EJixPNInO+YLrocmFhHJPtpDETvkOSG2Zcw3K9QK7h9FyFteu3+GCjg+dKtFBUigUcK2dlrsaTj1zkm8EE/+RKm7DdJijXaFQ83n/tfV5+9UPeu7XNYHqOf/LWPpt7fXZv32Lvxe+SFSpo2yNJc/7XX3qUzz+8xLHZGicXpvjkpx8itKvcd2qWoNVHphH7qsoxNaCQjQhQSNumYsEFeUTRBDjDg3HssIloNifwCwUeWqwwUxQYxig4adsMb95ARQFeb4el4R2KU7OUGk3E8jnKjTqmPotTb5KWm+T1OVJjoUSOuvdx/qMv3cephkdqcqx0hNSGg1iyP0opu5KqSpkt2XSilF5isKWg5tk07THLueJaCA27/TH7u+xaTBUkRgjiXLLTHnEwiomRbA0yujnoLMWK2hSiNlLZVGRO3RUcjVJcHeIO99G2h5EKjwzjFJi3InIkBqiFB2hjmDUdImyOYkOSppgsJHcKtCgiZk7gVyoInWLufxq7VOXMV58mBw7/8JsUVIo3M0XWXMRICQJMmqKqDaK7dwl2D4ivf4h1/Ycot0BB5JSTFoPE0M0tkBbOzBytK3ep3XcBNWrBqDOW2e1eo/zEx7l4YZrjsyWS/SPiox5WqQyuD7bNibkmjyxWWe8lXB5Isu4RSRyA0cRHh9zzzH1cujBDHqYULj4MnotVq3Li2YcoLM1jgj62UkhrHGwR5GMecL99xNUjWJqrkCc5tYWTYBKCgy2cUoXa8nmqi+e4eGoRgOeff55PfepTKKX+f1ujP3FpbX4ir5/W/3j9WM0vwC/9hV/nlb0O2i1imouIch2CNkba4xud4zDa2EEHQ/LWPqLcRFSaGNdH5xnlZoVS0sdsXEXnyY/CEAxYDv07W9jTC2R3bmAVi0jloIWNCId0N+7w5voRWBb3XziH8cpYWYwqlil87BkKczOoqXms3g6k8VhTPDGDdBTW5CxW0QfGMcyiWMXUZ6FzCJZFdP09/Ac+hjs9DwKElFBbwJicnt8ky3IqnVWKBzdwLAt7bpJtq4HMUpRJMWmCyVNy6RIFOWljCROO0J39cQKeEWTrNxBuCaEU1tQMSInJcnT/R1NokzPhwLOf/hR/7+//l8RCjfm9bgntVzFZiLEcjLBIhcsoh9BtkLsVcqcEGEyhjhwcAnKsM7YUmVWgGB4yX7Y46nbJdYpWAmN55FmMJzVSgpWGVIgwlsXAqoxXaVlINWmP44WFQA0PMFg4nU0K+RAjJKkxICFHkqLGMHHlgl3CHhxgZwMmXJjr3yUQPoNkrG+zjGaQChIDo1wg85TMKdE3DgJDaiSZGpsMZDTETgMsDDILEVEXdIpQchyakmsoVkmHPbpX1xl+9AHB6l3W/uh1sqNdMiOICk30sIfVP8B99stYn/08xQtnGH50E8sTVD/zZeLHv0Dd0eAWafvTuJ//BZyVk8hiBX+mQXr1HfwsQAqDnScYAXMlhxxB7lY4shuEzWWCdpfZiSLNRy9x2BtS8Sy+c73Fnb0OM2WfAS6F5WVOL1UokPCpexf5ykTAlx47zammy+xMncNAc+NgxNrBgDvONLWpSe5stPny+Uk+f2Ge/aM+L088gv/k03gPPEn7xgZVGWNKJZJU07AzMi0oeA5P3LvM/Y2c337hMqVqiaPDId/6ozd49a0rSGl46GSD732wwSdWyhx2A/7oO6/wgw/WEAKeOjfPyxsh25U5gjji6tDmw8OYhWaBX3I3iA73udZOCJwaQWmCxCoSWgVmfIEECl6BT/zGL/Kxz32M2ePLXCiENFWG193BzSLcNAI9RjrZtkfulwkXzhBXpmnZdXraQhrNRi/mo3/9HT5ejZlMu0zd/oihZbP87FP0D4842ZAUO5v84Pou/aMjMPDWfs7szARn5+tsx4qX7nRYCy3mVYg/v0w+0aBSs7gyfYmrxUnuufMq0WiATmJWDwes1F0SI5ifmeBY3SezSwSZwHdsdifP0ZyaZBAZpsseh8OIdBQy2/DZTh2CyjRVmdKvL5IJybMPL/Hh7pBBdYlI+pjt69SyAYW4gz05TyEPsAoVNIb9UUzBFrT7IUmecxBBHvTZ6ses9VIKWcC7a4fUhgcEYcgfrkF9skYvyLl554hbSQUpJLq5QmZ5tIKEOI4pFHzAEBmLWHkUmnUs5XB/U3J9c59hmrPScNH9NnNFRbVg49sWc3NTvPLeJt0gZW23x7XtDmcvnuNffv8qwhbsZg5eHvKJRsqnsi2erXZZvvke7W98nXMXzvD1TYvnr7V4x0zRXFrmqZMl5kuKfX+G+iNPokyGrtQoiYCdWFLzBe8Wl6k++glKX/0VJio+j6gdZBIT2RVyAzLoUow7WPUJ7KXTiNIEZDG2jpFS0csc0sxgC4gsH+1XqVfLTC7OMixPEbtVyifOjCfBtku7PE9UnmNYWSAvTjJUFUZWmXas8eUY53d2ukhqQBhDaGx2U8lCSVG0Fa6CVgIuGb4S2EpiCYPSESPhYecplhhvgOaclGDQYacfcBTmJNogJEi3wGxlfOZVbENJgmVZBKpC6NawBOTKRlkup+ItCnvXAYXKInSeEGc5jlegWCxh5TFX/tvfJo8TPEtg/Cr3lDSzR6ukaUxb1jFGjxNTXR/ZXEYVq4S1ZXLXo/6n/izWp3+BwrmztJ77PsL1yN58HhENod/GnZ1FK5/SyiJHlzfwpiaRlkVQW0IaQ+xVKYuEgonx+7uYUY+X//VHvPHcFczkMkJZ2JvXye9+iOkdcvEL93Luk6dY/vJTWGUfWZvEbN8ahy8hqDmKJxdK2OS8KRbYG2jCdof+9TvY5SLVxRperQS2j0CSHB0hXQurOY1VrlJSmkR6xPmYw56mGoIub64FXLm5Q7FWx/YL+BPzkMUEh1vE/Rajg/V/2/s899xzfPrTn/5xW6g/VvonkO720973369+7Ob3M5/5DG9dvsqBPUneXID58+T725jVD7AaM2OSgwBVrmCEAZMilYWIx+lCVrmIqEyAV0UHAYV7LqEmF0j2dlH1KYKb13EbVbKjfUT/EO0VWXVn+aAVcdxJuG+6wGpSpORI+lhck7MoneOcvEBxfhZj2XC4hVASIW38xWPY00sof+xClrYLSYj0S+hhFxBYCydI1m6j8xQZdtHtHUiGmL11usOAKTPA+FVSy2Fy910qw12mTA+7u4neXyVr78L+GnrQJbhzCzMaIEwOQmDSmGztMtlwSC7GwHJlW+g4ZbR3SDocoKULhSqUG/zGL/882+tr/ME3fh8tLTK3jLYLpP4kSEnyow96nBtC5dExPr1Mon/E7zV+GZEFyDxGaM3IKmKcEjXfx1GSw0igpYsxGksY/HSADNok0kZrSViZR1s+Ih7iDvawB4cw7GCUIvdqY4KDX0PaHiLXOMkQhUHpmDDX4/hPneDqCLG/hh92aCZtnHSIT4KUMF+2wBhWuyFxbri8P2KgbbZTF4WmF2kMYNBjw5pbJNc5Qmu0UESyRKg8VJ7gCsnQLkNtEndumeJMHYEhafcYtBPy/U1MFrGdFxCFKl6lwcRkiaabsXdzne7uAd50A08kyMYcqVtm2so564VYk3N0//RfovSpz6NKZexaHcIBGRKDoGwLyq4FucGKeigBUllMfPppfv6v/ikqf/4vMJH1+Vgt5p6mzSu3jtDDDqdqGaZSJ585jtaaKRHyvSu7jFKD3N9kO7U41fRQShJheG2jx8Ywh4kpBr0A3W1z31yVF+6O+Gd7dV6uXuT2fZ/naukUUZjyoZzihzf2mShZTBcsOlHGKStl6dgU//Rrf8hcCf70F5+gLuFnjtk4tqJSrzJZdmi4AlsnfOM7b7LXi5hfmuHY/CT/4u1DFuslqvNzNBxDS1WJjrqY137ATidAKAtjLA5Sm0x6WMMD0jgix1A6eZr5x5/EqU1hux6+MhiviJk5yWjQh9uXieOIUNhMeBaFapOi73JmqkzRkrhCIyfmmXry43TaLb6rp/nyX/p5StLQnGjyw0PFu20Y2CVEb5/H713m1labw8iw0x4R7Wzy2AQcuBP8nd99nzcu3+XCzgd8e9fnj95Z51gRBnOneIcGxY/eZOfmKo9XQ7Y3dxn2R/SDmDDLyNrbGCm5fBDxQtuhnA545NwsmIyp9fe5Z7rMVL3C/t4hdyKXVVMj7HUYfvQuLeNRjjrkaYwfHGDVplB5iIx65MEQqzHNjtUgyw3DxNAaJdzua/ZHGa0o5W5o8/r6gO9eP+QoMGwfhXzreoeBtrj42ANci4voJKY6NcX6YZ/E8siFIAgi4iBGa03dVTg6JUxzwqvv0lvbpCgSrt/exLtxFU/BD1e73A59+mFG03d4dKnKFx88xscuzrG2P2R784A3377FoysT+I0JXnxvh2MVm18/nvFe6GOHMS9eOUR+7LPUvvBlEuUwPDriFx9b4mm5y5KX4JBRXv2Aph4y6wsiAxOMeLAYUO9vQRjwyIRkuuLR8GJUZ5vR6ScYTR6j4HtULINBoKRA1GYRTolhoY6TjoiVSz+DXGdjT4I2RBoGwkMphTszS3lhCWpTmGKN1PaJLZ+SY4OUOI5NjKKXCq63UoI0H8vPlKBhaZQxWEZzlNvEqUEbTd2V3DgcUcmH7I0y+qmmaad46QgHjW9JBkZSVJrFkmTBSymnfdJMo4WgosZ83ZWmx2LJpmgL2jEkxiZVPh3jshEYlAAjJMJobHJMbzwMUsMWo9SwH0kSaTPSgr4o4DQnsTrbyDzFioZU196ktHOZicEmhYM7IK3xRjPPEWiywgTYLoPCDKbcoFuaQjzwDM0vfRXyBKtYRlgOZmoZu1bHdgSi1MCZnCRr7yP9EhGKXCqiTFCSmkI2Qq9+BFLxsb/8VeoND2lyZH0aNTkDTgnluDQ/+ywzj5yj/OjHKBxfgSSC2dNkbgkGR+id21i2zaWmw0p2yK2p82yIJm6tTPeDj8jTnMYD95DevUoeRXiVEsKAmjmBVi7dOGeUanSu2R8lrB8corXhwBRodyKqkxVGrX0sxwFA5ylhZ4/p6tjsNhwOee211/jMZz7z47ZQP63/ierHbn5XVlaolip875U3iXHGuKnG7HjFFg4RxQrSUYzu3kU1ZtBZThaHiDRCVJvkw+HYuDQxi5pcwKpNo2oTqMlZGo8/gtNsgE4wpx8iHfa5enudjZHmoUrG4vIy6YevUHUlrpIc5j64PonX5Kh5mtwvYg7WMRNzkMXowXi1gtE/CtuwEBhkbRIGbdzTF8esRcvDm5qENAGpyHbukN+9jEZS/u5v4/R2OCrNoRwfOxhh2zZFz4ajLfTRHtn+LvHsPVCbxL/vUXQuMHOnUMcvQpKQdQ5xKmX09l2yzgHCL6N8j6QzJNvdglEbk0ZktWlk1OXv/x/+Ar/79a/z+gc30HYBlQ0JnRKpUyFMc4QQ1D1JQRq0VAwygfYqGGvMThZZgn33HUQaYuUJKAshBFPNBgftHqlQZEmMFfeQaYQedMEInDykq6rU8j5CJwjLxYzamOYCGgvcEkhFXmyAZaOyAFsnZIwP5KLIcUf7iDhEhMNxilw2QqYDTPMYbjqkpnJqIibMYanq4ilBkkMkbDyhqTOiJHJibeilP2IDWy5G2aBTOqUF9lKHUIwPJVuCVIpo/hLW5ALVsyfxzt5L9f4HufiXfhF3cgorCZlwcsTMCYh6uBtj49jUQ+exH30C7VeQhSoTaYsg0wyxybFILB+vVqezeAl18QGC1TvIPMZOhuRuCVeAMRo76aEReNaY2bnw+c9R/8KXUJPT9MvTrMU2T04aLp2YZH9zi2RvnynfYm1o+BdvrJEfbOJIh0rQppGOOB4dcCOwqLqSuWaBasFmFETcc2qBjVDyYq9EqVFheNTlxk6Hf/rBEf/wLrwVFnl7KyTfWOPs1efZf+8KwcEOH1zZ4A83Uj7zxEW+UEp47vXrjEYpf/2XP0Z5eoZXNwLWWyHffmeDfreHcD0eu/c4NzfbDGJNqDVf+tQ9zM5OYByPU9NVXry6zfuLD/GePcspN2SrH+MGB8j1yxxFGXaeMuj3KGZ9fKnJEKz+i99lc2OX52/1eC+bYD+1EOT87revUB7u0A0j0u4RBrDMWFc5kbVx9le5f7bApfMrrPsLPHJ2AT21SF5qkhXL3HN8gmgU8c7dDsXJeda7MYm06O0e0Xr/LZ5bD5HDDr/0sWN88bHjvHP7gDuL9/HZ8iH77iRXA8XM/BSlhQWGS2cp5CPe7Quc3bssNkpEVoEpRxNqiyRNOehrLs1VSfwKk6N9wtWbfP3DNsq2UcmIxUYRgi6iUkPdeAd17Bw7iYUlc7p3b3LU6hGNQrRbIXXLiPosWalOjMVBMNb6v7PVx7MlZ6d8TtaLHK+5LDd9+kHCUQCXxAF//5sfcBTkLDZcjNa09vfp5YL67DxtUaZ34yovvHyF/NXvAFAp+pCG6Nsf8K031vnB828QWj4fhRUq915kPtnnRFmx2R5x9+4W7UHElCdwHYdjUyXWdjqUPcVTD5/m+m6X6kSddNimc9Th2Oe/yOkzxwnvfZjXDgVr5Tn68yfxs4gXXnqHvWFI8uYPiba22F16lHYnoN8+Quc5ndI8mfIpTC7hTMzSD0KaNpgsoeNPoUyO32hS8n2i3JBjyGtzaLeMFgriLjLXJIUmwmj80SEVkVG0BUmeU5Cao8CM5XVuidyvktglYuWzk7vsyCZJBplQ5BpC6VFQhkTnHIUpw1yiNSS5IdEGxVhHLU1KmkQcxWMToglHvHB1hyTJEFmCE7bHZm1lY8UjvKjL1shwuWcTFJqcqUp0rsm1xiCo2OOUy16kcS1JOzVkxmB0RkkadDhgszOiH4aY8gS5X8EaHqCNJtaSqgNbo4w0M3gSzn7xk6RrN9A338HqbqFcF296BpWEFE2IyGL8o9uQRiTKIx91cOMevi3HOmOdY7kW0coFsjNPolbOEVsFgsmTyFOPYMo1DBlzn3wQe+kspjqLLQSZscZBHv09ZGuL/Ni9WEtnqMkR9//MI6ioj6lMIoXEXjyBmFnGkgJ/cRk5OMKrVxCNWfCKZEJhogiTRlCfJT/axrt2Gee3/jsO50+zs3KJzt0Ddt7bofzoJwjbLfoffYjROf6lJ6A2TVqZ5Sg0VFRKkudkxtA+PGQ9tEEJsjiis9dFWTbxcICwnH/b7/zVP/tZAL7//e+zvLzM8ePHf9wW6o/VTzW//+Hqx25+AT7+iad59QcvYeUxAokqlhF5CpaNSMd/hu0u+nAH096D1j7G9TCzJ5ELJ9G7t8fJXK0dol4fHfRxV+7BtPaR08fJkgw5u8KHkU8/zrjv3gvUj58eN9CNOeazA7I843prxFxhHOm5GjnsV08gjEDYPskoQjSmEVEIXhmjU2SxPJYbJBEm6iMaS+hhl2zjIzApZvsOefcQoSFZu4lZu4yVa4KP3iZH4XW3UVOziGFnbGALB6RxgiqWsVwL6fjIY2dwpmdJ60vI+hRIhRn0EUohynWk7ZB2DlGVEtKzQYpxxHNnDz0a8drdQ/6T3/wH/M2//r/j//J//U+JwhGRVSLSkswujJm3WpPnAiU0BUuMBfzGjLVUWmO8Csp1ENGIYtRCBH3IY6brZQ46few8phoejBOvsggRBbjdNYzJqcoMJ+lhiuM1onHLAMROEZKQQFUBiUxiVNCGNIR4hJVEOGiMXYDWJmL7CmL6BLlbJ64uYRwfoSy8uI07OqKXwpw3Dn043fQp2IqGjLHTgDIBjhI4Etw8xgC55RHaNVLpjHFnAiLhMswESoKuTCLLdbxHP429chF3fhmZDYk3b5J9+ENK/R1kFmLynHh7G2d/ncbHHufEZz6BValBniHb2zSIqGxd5iBMOYoMRUtQJMN59NM4tRqmtYNSAqMFgww2+wn7ukDLaSIQJJlhK7LZqi3jW4oTRZCOxztDl8+fneS+C2d49fIaU3mLM1XFH90I+K/e6lHp7JBaLt/r+UxPT9BNJKv7Q7rdEN9WrB6MqA8P6QYJliP5f37rPW4NUu65eIrJ2SmOMsnzt44IvRLfvtlhQ5XIX/gD/rvvvM/JmTpHb/6Qr73f5v4vfZY/f7ZApx+Q1ad47W6PWtFha6fD8vIMmbR47NQsf+7zDzCV9fn959/n1Sv7fPD2VZ4pDzm/WGXroMs7L77Bt//gJfTxFb7z9l2udlLWkgJuYxbXUuRJylprxId7IzpBQiU44PkdTTAIGF2/ycEoZ33rkFI0wFqeJ0eS9Hq8PvKp2gJjMhxhEJvXieIIV0psoXloxuGZ5TJzvqYqYxbLDguNEk9N5xyfLOKEfa7e2OTDvs+fvq+BXDjBkhfx8pbm9GyFv/ixOX7jl5+l55T5O//yXe6/sEBnpHmwHHErsHl+y/CN9QTtlFgdCIpmRIZEG4FVbdJt97hvscjeKOKDVoY7t4xdLHPOg9v2DLF0uThT4sHhHXItWJ2+SGlymufXE/7xLdilTL6/w8itwdE60WELqtP08KlKzY2DIT+43SbKcrbaIVO+Qz/OuLrRZacVcPf2NhthxtJklT9zb5P2IMC2FAp49UDwwUFKYrvsh4YPjzQFz2L/2L1jI6tXJVIetXKRyUvn6QYpA1mk2CgzChKGBy3K/W3q4SH3eV0ebAqu7oestUesNB3+ypNTfOXj53jn5j4vv34NpWF6YZL1kcRqTDA/WcVIizP3LGG7Fr/0yCIbhyP+9i8/yOzMJO+d/BjlE2c5lGV65x4nnDjGRmRx+TBGVycJlEO5UmF/kDHUFnmeU7Mg9auURDKWcKRqjJC0HMhzQlwCVaEQt0GIsYG4u0VXKzyRYYTETUPKriJMDakRGCQxkq7xSLRAS0U/y1FmHCriRG32BhELnmEQa7YCTSdMEcmIJBwx3F8nyjUqCfn+VoxEcKoiebdrcftgRLy/yZF2yIUgtzwsnXC9mzHAZvpHlwltIM1T1jshN1oBDUcj0IT5+BnrKMUg0egc6sowJUO8PKQUt2mFOU5vFwREa3dJb75HsbNBZbjHrIqphkc4lkKQYYxgeO0aye0PEY6DXDyNKTUQxQrCaITrYaREC3B2ruHv38TubkKWMTPcQMQxxoDAsKdqZLVZsBykbUF7n8Hl94k3NkmPdhDNGaJckGiNHxwg0gCSkKw2A06BfNQn2dlC9w4xxmCW7gHXRdgFpFdAeB5kMapcJbcKIAVe2CXvHqKmFiEaMrx2lTSIaKqAi3vv07ErHD316fHzlYxoa4eo1SUNM0RjhsCuspU6lF2LIDUMkrFGtpQNuHqkOby7yuGtK7Q37uLV6kilqC6cwSk3cKsT/Me/NJ70Pv/88z9xyQMwpmT8BF4/rf/x+ok0v7/8q7/Iiy++QCJdFNkYIVZpIvwywraxbJfqyjLYLiaJkPUmJgyR/QNUtYFQAqqTiKlFbEcimgsY20W4LihBmiS8d/UGaRTy0IWzVGSO8Qrofgs1ewy7vcmSr6m7Fuv9DKRCC4l0C+RL5zHdXXRlAoIhsjpBdrgF82cQhTJG6zHAfHsD3d7FpCnCssg272IGB9BvIxsT9G5vYUYj3OVT5HaV1EBQmkIUSiAMDDtjra6yx2jJ4SFycIh0iliDPez1d8AIsB3spROIxhzSLyL9MsOrVzC9wdicBiRbq+hMk733EudXlvjeq2/zn/8Xf4ftnR1+/7svkllFlBAkZsySHCZjLmiUKzLG6WkjbZCMNVxaKvrlYwjLRhg91lsrh7pnIYB++3DM71U22m/A5DxCCvJCA9+EyEEboWNyy8U05tCOj1Q22i2gdYwdtpBRD5PFGKOw0xgjx8YSYwzZ4Q6U62BZYNu0c4ccAyYns30Cr4ErGQd0GKh5ChvNSDgMvAm6xiVKNFIYurnNQJUZ5ZJurhBC4FhQlpooF+QYtAZbGky5xnDi9PhA98vjKOYkZ3TrFvnNd9EIZH2G8vnzVE4dZ3L3Q7z9WzhjIjK6e4BIQ0SpxvThdTpxzjDVuOT4BQ+r2cQUq8RYdHJFlGccDBKu9zX9TJIZg1RwMMoZJrAdGJTrUC95TNsaEXaZTvvMrizxtatD9O0P+WtfPM/PfOp+NpcvcS1QFGbm+O6ojOsKqo7k9loHVwgyI/jNb7zHYlGQi5xfffYBHrywwnNv3abf6yPr02RZipk9Tnl+kZfsk/xR8SwznV3ua6Q8/umPce32Ls/f6dJ1JqjVy3zvo23CdpfO0ZBht4VyLGxL8fmzdXb2unzxXIWnHzhG0bX49BMXeGlfc3x0gLl7lVNTBR6bcLlz/S4PLlR4/nvv8TtvbPPaqEjFFjiuzaNNuGfCI5UOMuzz+Ufn6NRmudJLaHVHDHYOuBIXeaYwGpv1DDyttseXJKFpmCHt0hybpWN0EkMj2GW+aDFnxWRaE1klgl6LieSI9dTn9FyNT56d5FhVkMYRO1S5tdFClKo085BvvHybrUBSm5nizLEpSgvHeOfaIVMTJV44cJiuFKjT58OexXvvrdKfP4levY3JMja2dvj+VoSwfVyRUUiG7A8Tbg0Fo+YS7ycOs80ytz68wZG2qTSK1GSCmZjjg8OAc4sN0ILpmSkKU9M4jmSUCjZEjVRIol6f7mjEyYri3qohSnPKnsUwyYnynNmJCq/faTFb8XlmRnJYm+fEySUeKqeUs4jXP1jn1x5fotiscswfEzgqxxewjp9CW9Y49Mb2sIM+hVqZoSrRnVzgG8+9Q68fMVd3uDxy+L3rCd/bNTx3VOFKK2OtG9Bwcm51My4t1HlqucRMw6folfjFjy1zcmGS/+iTK1hJRN/YnK4bzs3VOTFT5NZuD+34XDy7zFeOCz77qYfZVVWmyg6TRRfHsinLjIvFBFtILCFoqITFoubuKMPojCTXvDKqEkcRetCm6ZgxbUZ57CWgBvvkXgVtOQidj2m486fGz4MsoR3mdMOMIM3YjaGrbVKtsQWkaUzZUthC4wuIhEWiUywMH+4HWEfr+LZAGkOYZgyjjMu3tzDKI44SDlPBq9f2idMEYbv8qw8Pqfoul9s5FWXI3AoqD8mznOlakUQbsCwW3ZSbnYyd2KLiWZjDTXq9PnGSo/OcqicRQNVVbA8zYmNo43GgajR1gCty4onjKCVBp2yMfFqtI/JoSDHuokZHpDonR2GVq5TPncEqV8ZSAr8GpSZWpY6wPKLaIsYtY2kNk4uIqE++cRPr4AZ2PMQxMVYeIk1OikVquag8RY7a6DQhbvU5vHyH/rXrpJZPkRg3HWJ27pBtXsfMncZvrxF8+AbhQQ//5OkxXx6DLFQwXomoOIkp1cDxydv7yHOPYfwasrM7HniZMVpVH6wTdbqUFyaZ//i9VCcbHP/g++S2Q/DnfoXWO29z8MEqSIHbrCF0TjtTXG1FVKycg0QySDImVIbSKW/caON4Hn69wai9R2f9JpZfIg16VOdPU6jPjn/GjPW+P5U8/M+7fiLN7yc/+Unu3LrJ2u4BRtro8iR6+RImCWDuNIV7LmD5HsLx0IPeuMlpbYNQ5PvrZAc7CMdD1KaxppeRyQiDhDTBTMzz7sDC1SkXh6tjZFKWoKVLmErkxtUxSzjPuVhTvLbZI8xytIGqIwlGCSYIsHSELjchHIKlELZHsrNKnkTI5izhzj7ZnXfJhiOk42M1Z8Z6KteG5hLFhVlUqUihIqjdd4kpGaMKBYxXQTTmkbUm6AynXEKYfLzmB0RvD9IYMewhMIhyE+vkfePLge1BuY4/PzMW4zfKqEqdqNVlcOMadqlMebDL3/grv8HS3CS/+gtf5Xe+9jWkNWZ6WsbgKY2rxrdrS0KWGYomIjEWiVNiI/cIVJE/3NZoYSGSkNyrjo0AToGpssvhzhYoG6E1Qkp0eYq0ND1mLvf2IRqQ6fEK0NgeBokzOqSvHQz/H/b+NNjW7D7rBH9rvfO75+nM59xz53nIOZWZSk0pybJsS7blEcuIwYCLCoKgq4tiqHZXVAENTVAQDQUNZbqMkW3ANka2RktKSamcM28Odx7PPfOw5/Gd1+oPO7uqiYAqIpBMBeF/xP547tlxzx6e9V/P83tMjHBEarlkuRkwLaRKATn1Gk86CNeZBlDCMZmQgCA1fUIjz1jmCc08FSbsBVPRKoXAzAJMoQGJEja98Zh7nYiNsWKQCqJs6q+TYrrpHr5XwiOAFIGrAlKngpIwydXRuSrx3AncUxcoP/U+hIrR23dQtodYPI45M0d6sEmys4aKJmRCIk++j9irYlZmsGyDlYLJITdlrA3u9zNSK48oNdgea9pBSpRpxkmGZZggBEGsEQoOV2zKrkGiIVA2dQd8HfHaV76DubfGJ5Y11XyOr+4LrL277LbH2Lk89zfatBNFdxJzea3H2k6fVEje3eqh0fzxn/04B14N2zCZGA7Hj81TnKnz0nffZvPmfT793GP8rd+8TDNU3B1bbM0f5vXKSUaZJP/KC/zcw3WO5TNahRojTKSCDz+yyunlIkdPHSbJUvKujVuf563NIalX5LnTDf7CBxZ5a62FrDV4Y3/MfnEJ35JctucwLY8vXD1g66DHbNkDpWkGKdp2cImxcmWKjqAr87QPP4yXhrQxOS9a1L2Ul/Y13bOP840bXa7c2cCTBqL1ADMJSYRFfWaGfM5nvR/yxk5INu6hgEA4KGnyctdhFGYY4wGbARRnZnn6/Crnlgp84VaPiprw+Xd7XCjFPLzxLr/97RtcvbPDjCP48Y89Rj+IGIaKNzZGZMMeWRizMF/htb4mi1J+c8sg/M7XibUx9XjaDlVTUXAk0XhMqjVBnPL+Dz5GwdbUug/QWjAxXPZGIb0IPBSD3pCfe+YQrzQVW4mJbm3xu2uKUJp8/moPwy/gigx7sIe2bQ6VfY7Vc8RZgiUlCyWHp45WOX5snsXRPheXSjCzSM/KMd64x3E54NVJjrv7AdfaMe12k65y6MbQH46xbZvLexMmwYTYyvHcksGPPPcIT+1c5pONmKTf4XBRYFVLfGjRwLQl37m+zXB7g8e9EWerFnuhpqIn/N8/OMtf/amH+fixIkXPwtEx+0HCa3ebHHIzDMB1DMq25lTD552+xFw8geP5uJaBKwWTLCNvgW2ZVPI2o1Rh6QzLNJGzhzjkG2DYbMYemyMFbhE36mPEI5x0hEWC5eTpOzVSDZlfQ6YhwyhhIFwmUUY3MTENk+3URiuNRJCXCVJnhImiYYFAcXlngGtPm/a0Fmi3yKMzFvfiIoNxxOJwnS+9uUV/OOTMTIlRmOAM96jIiE+uWFgodvsRP3xxkZ86X+bwQoNxotDDNnG3g6lCKo5JPl8giyJCw+VY1SVNUiwUb61P+OZGgJcM8YlJMyCLGCcpWsBQmWDY+JbFjs6xk7ik0oTaMu7CKrVzp2k1zmDWFtizarQLy4gsYWgVsebmMQsltFtknGtAOEJHY5Q0EckImQSIURMjjZjkZ4nys6T3byDiCDXsIMM+pfY6tHYIJgGxAmewi5aSaHMdr1Fl/pPPUbz4EJE2sMIebhYgDHOaY9m5jbQcwgdraC2RlgHVBWLLJ3ZLKL9KYOVg7wGEIaJQQfllZPvBlDXf2cOYWwU3hzAd8isLNN+4je3Z9O8+wMpC/F/9NcRwwNXaCTqdFAwPe+UYsVOkFyacLNvEKYzChKu7Q1qtA0Yyx/7mHnEiGe7vAYKo1yILBowPNmjeepWaHQGws7PDjRs3+PCHP/y9kE//1vwh7eEPbr4n4rdarTK/dJhXv/ttNBIl5DTwEseoXA29chZpWaBTkvGI0d37CNcj21uj/8orDO8+QE+GyMEBOotRhg29XYZejVfu7FASIQ/VTWxbkmWKnlkidactY7JQQHh5/KSHa0s+fLTGVj+k4hgoYdC/ehnRWGJkl8ma26STIQIT+geoQQ8pJdq0KZ45Q9brY51+ZLodjcbI6hx6PIJcmdzRw9iVBkIrMBy8uI+tMzBcCAbTrWp5BlmbQeRKCMtBxxPs9gO0NDCqs+gsQXkFSBL0pAdao5MAqz6H4RewqxXSQZv8Ux+jf/UuWgh0MOJLX/4y//jzv8mHP/Qh/ttf+iWSFEDj6ABDaTwDqpZgogSmFDhZiJXGdLVPoiWRlpxdKKEMC2X7GCqFaASkzFWKHIxiSGMy5HvqMUFJb+rF2roDbg5te+CXSOw8vdRAmQ4agZRiyhy2c6S2i4wGkCVow0QoBV4ZI1eGSR+tEjAdPEMhyYgNd7olJ8MJ24ySlF6k8AyNmUwQwsAywTQFedtE6oySaxGnin6YEqSaRCkyBSEShKBENA1ExSEiCfCSMSLLECpBlWYR+Spy+Rzm4bNkuTyZ7TGsrJItnMZqLNJvp4wSQZ8C2rDRWkOa0F9+FFsl5DffoqAj9rY22UxL3BtLXt8aEqcpYQKp1nimJFPTJrthklGwTYJUYTDFvCWZQBoOxe27hMuneU2u8ANn6zxXm0BthjTNuLM3YmunhWcYrO30aY1i7GKOjzyyyCMrVcbDiK1uRM7IKEVDhlHMubpLKe/z6U+9nyzRvLszpra6hGLaebI9NBDlOl/Yyvj9rMrZrIlZaVDIeyzV85xartDD4KXXrzNKDIY33qFSLHLYGfOJcw3GTpGNoeJsbsyHHjuK49n88Acv8cp2jFNt8EOPH0YIoFznz/7ASYJxSCEa0MiGjI0CqeVwtx+htaZeq7LkgX2wT1qe562BxatffYsP57tkQcJZu8eFhTI7pcO0A02aJvRwyZw8aRwy5xr0X7/M2x1JsxMCAik0frnK5d9/iVK5wvFohyBJCFLNkWCbn16JeTct8ZFzSwyPXUKcPMWFyRZ3f+XXaa5v8cTRGn/s/au8u9nlkxcX2Lp6h6+vh/zEQ/OUSnl+9Yuv8431CbtOiZ3A4MlqxFEr5NX9hBVfYI0HPH+rSWuUMMoEotem3zhOkoQ0vSWqYRfbmnrpo2GfR4sJ75vz2RybfLVb452hoJJ3ufzmNdYzBzUZ81rPZjwc0rAVL6936I8SCqQM+z0+eXaGx1artFfOUnEETy76ZMImNSwOQoO99pCNnSYvXm/x7Ws7dCNF2bOQWUyRkCWGtP05EmlRUQOW60VUFLJxd4t8FmCFET90yGA38/mZw4pPPHSIBJsdUeBUUbOaEyjToarHnKh7oFIqrsk/erXNUt6mXszxZlBkwReMA8Xp+TIfOeLxkXmN6+UoWwLfFPhGxpycUExHpNKkLwpYhkkqDPraAdPByuVIDI963uGjqx6+oUmcPFbQxwr62P1tiobC9Kc4RKEylFskESbjWFFwTfqZYJxpdscZmdYU1Ih+zLQFTqdow8bMAlZKLpNMY5NSmuwRhSGeSHm4MEGmAZ3mCFeFbB2MeH434/mm5Leu9hhqD12q8juXt3jxXpf17oTDc1XOVyWpEEyuv8vA9JgwZaMDNCabeGZGL4houDAIEx7IMpcO1RFegcyw6QcRVTEhiyNkHBCmGWGSIiQkhVlWSjZm0CP1KkSNVSo5j3MLRYTj43o+vmOT2Hn8cQsxe5igfpTevS06sgiDJuzeIZM2YE4LNOJgWlJkuTB3DOvIWYTtokc9suYWhsxIl8+yXLIpOhaivcGgchijMU/hsfdhhEMMnWBvX0MnEfT2QSiE7ZFsPyANJhRPnyQZT0gO9kBrJmYBbVooyyWfjsnmjiDKdYx8GXPcwQgGZNKAcWdqaciVyXoH06C1AYP721iuyf71A6LOhFPt21TDHsHnfo7rr22RHX2U0MyxO4qp5VzaCVzeHeKZgkmnyZWuoFCbIR53UFE4vRkRBtGoD0yr2//0Dz8KwDe+8Q0eeeQRKpXK90I+/VuT8R9Pe8i+58/qP8/5nohfgE9+/KO88K1vIsgw0vh/aw4zHVSugXH8YXDyxKMJ2c59hOVh2DZmfQZ/poI+2ECYFtnOPdLNW4wO9nizJ5iTAWdr9pRduHSKWJoU9ZhESWzfJ241kQYYOiWTU4j2nfYEYUAnM3CXD5Pt3MXevUW6/WAKgW9uwaiLfewcyrShs4NZaUzN7nffQeSKGIUKan+dpNdFdLcgTcGbsnORApFGCJVNvWaTAVOI4ix6MkGEo6ltQ+up9cPJIXMlMBz0uA/BAPotVG8PHQxRgy74Ofwjp9BaIsjIH5pHIxhff4f/7uc/Td51+Ft/7+/z0LkzxBpqjFGZJlACIcGyDUwhyRAk+1tk734XLQQFS2JozckikMWkTm5a+mA5JGaOhjN9w7TDlJ6RQ8cTRDzGTPrIoIco1cnqhzAkU9KCglgbJF6N8nAT/8GbEIVYoxZWbwdl+cS5OtqwyUwXEfWnbXTBCGGYmPEElxRhmASZJtPgi4TAKrHsS0yhGKeANBHCnDb0KSg4kv2JIogzQDBJpk2A6Xsb4IIpKIx3KKR9nKCNHOxhBl0sqXGjDiKLppt8v4LOlUgqK8SLF0iLCyTSIfUqcPIx2re22G9FPH9jl3BvA3e0Q5Kv0sWlIDOUl2d4sEVt6z4bX3+Jjd0WZd+iNU4ZxilBnJHoaUClYEyLCsZxRskSzPqCvCXpJZq91EI++UFu/sbvINwCr9zZY5if4VTDxbYFxxfLXDy1xN4gZHu7w50HbTqTmI3OhEQpfvR9q5QqPr7QvL3ZZRgoru1MePx4HWnaPPr4Se7c3yXn2wyDlFY7IE0yulubvH23w/2Fc6zPneL19Qk3OyGzZZdSyWUUZXzkqfM8eqzKW29fJm2cApVyqXeZZV+TcyRfu9FnNQ+fOF4i0YIfv1jmhY6mlwqe9sZsjxV3+5rWOObFe20GSYbd3WRdznIwTgkygW0Kao6idPEhPrWgMUsVfnkPvEKeupvwwGwwX7LQjs38TIWRUyNSECNZNgMKzTVa80cotvd5u5uiNYzjjNWGz7XZc/yzlze4pquYwy4L4w1WVuewKlV+7qkVHpuzmbdTXrXn2S8tcOjHfpA7kcXtK7dQW1v87IpCGJo34gKbG3t8/aU7GFJQ8B1OHJujP3+UG/d26HfHbP3OF1hce5Ptt24wV89zquGRCM1es0d5ps5DF47gGpJlK+Bu5NGQKXXX5I8/MY+fTSgE+zx6co5i0WX99hbX2jGf/eH3kaTwzX3JmWLGStbm/lBRy9m8+qDLa3f2efHL3+TlrSGRnlaESykpEbJQsOkWF3BLRXzXZq7kcf5IleUjh5krecwXHVApVq7M0UaeubyDYwlkvsog0dyvHuGrl2/TcaqInQf8xhsHPHPxKLq6yHMnG/zQ+Tqh4RFl0EoMMF3i6jJSZ+AWsE3ByTmPZiLwPZMr7YQnnQPOlBSzVkzNEViOT5Yk6PGQNMmwDQPDsAiRVC3wHUk7ShkrST+BgXLImFo1HAOqRkZJjRn7DchXIEmQbglt2zhGihSKvjJpR1NLmGmYpBrS97a9xysuZTOjrz0OAk1znLE5iNkapyCgMdnFuvc24401suYWWTBiP3OZ7G/h5XI8SC3cYonq/BwrjTxnlhtcOr1KkiacangsenC+JqnYiq/f63FnYlISmt9aSxB+BYRkvLtJaxzTtWsYhssoM1H9Jp4B7767Rqo1oyhFkuHGPcTOA06HG4S//wUm716mmvRxpaLsmtRFxH7q0s8ssCxModGGjVQZniFwpSKMU2RlFq0UkZ0jf+wIDd8mbHXpl5YR2zdBxYhhFyENRBbhhn0sFSM8j7TfYbRzgJmE4Fewh02kzqb2svkTSGmgHv0BxNGHsJePIP0C9vAAGY6JH9yAMCRZv401t4gZDzFmlimszCJyRXA89saKXmqRYRNgIoIBwrKQ9UW0Kckqc5BvTAuikgg17KGHPUzXwilPrYfjvTbbb66z8v5jzH7iYyxsXMF/cBvxx36WJIywUORtSaQFnUmKIQRPL7g4pHzz+pBMa/qb97EKJQQaDAOVTNtMtcr4Mz/5AeAPLQ//ucz3TPz+9E//BN/61rfQWTblumoFjo8I+tjpaCpmcgW846cQQkOWQHWB0sOPYy8dRumMbNAj29sgWTjB5TDPfNHhUPs2IlcmuHVtGixyfOxRm14C4sgl4v1dsDyUmWOUmbQSOF7PUTYh1TBYfRiVK8PMCunqGYyFIyjLnvJ9bRcGXciVwfGw8gWY9FHDLtmgjdAaVIY+2IAkRrX2yCYjxKA5rfgNRwiVgT/1mMl4BK4HXo5sfx0tTTA9RDRAuXmy7dvQO0APO1MWcBBAGGLmC9MTbrGKsB1obpI7eYJ48z7SMims3eevf/aHWJ6tIcM+eTVGGhYjHLSGMBWoDJRW2IYgUwb5hTnQghlHk096OKN9jCwktovTEhLDwchihAGznmR/f5+cSNGGg3DzGJ1NdBIjZg+hhYGRhZhaYcc9imaGPdrHVCFi0oPOFkpnaNMjdQtEpoMMp/aW1KuQtramdc5Kgc6wswlKCFKlcWWKNCx2VZ6ckbJshkgyMpVhGhqTqfgVhs3puk+kNLahqXkmeVOQaugEKWEKno6Qky4GQDiBJCAzXGK7TOIUiOKMMF9mLBwyt4CyC0gBJSNDS8met0j+j/4x4r1d/Od/j9dHBZRbBgSWITBGTSblFd6412Fy/RbJXIOjjuZMPUcjb3MwioiUJkw0viHwTMEontaqzudtUgwmSUaUwamqiVg4TG6+zrmtywy2d6FY40pcIGcaVMyUyHQxbZMjR+fAsen0I9642+H33t7ldnvM1d0JmwGcPX0YaRucms+xUvaolzwiLXF9j6svv4VQGXGa0d1eJ1YmG7c3WJzJs63zZIZNszXkYBQjlOBDJ2rsdodMgpgHV14nmj3Hz//d3yczfXZ6Cb0g5jstg2/f7FA2DeY9yfmVOn/1iSJ53+afv3KXn3l0nqbO8+yFJa6t708PLeOIRElmcgbtQNPJbJLCHJVynhNmyAnVpTi3RMsuI/Y3KBkhVn0Rw7DZF0X6ccZWPyZIBZnh8UDncFdW2c3NMJO3IYuJM41vGnzg8ZN8+kyFgmuzRZ4lI0QBSTDhlasb8PLXcXTKQj1PVF/ErxSZmykRItneWMfybVaSPn/uuVWefuYcl3fHXHnjBvOH5kk1xNJkkipeHLi84C1wNS3zqy2fseGzVHM5Uc/hForc6StaQTK9ATIFTx0t007gxIyHaQh8keDZkq1eQKWc5/yZFfK+zbdud8jCCa1RQpok5GcXKdiSQW9CrZrnieMNjjx0gfmSz/Gqx9GCQdWCiXBox5pCpcS9kebbL92kMd9glGr6iSbJFP1eB4CWP8+eLCCFwAlHxNLCD9qceOwCy6eO8KtvNvlG7jRutcqrD9q8sJOQICnVZzlSLyCkwZ32hDia8Mb2iFe3RuyMU776zg4nZ/NsjBS7g4jj80W8+hxPHa7QJEdoF7kXmLRTSRYNsS2TQTzlggvTwgvb5ESKbxhT3DsQK+iGGb04ZWuYMIg1m4HgINQowyKJY2LDQiYxtuHgN+9iC0GkIEPg2wb9ICHvmqRKYRoGobIItORQ3iATsD1M2B7GrHdDvnV/gCMzXrnygHvUEPevEg36HMxf5N+8us637/SYCJM3t8bkshHfvdPmta0ReUvyhVs95lYW+fadLr3U5N3dEUVLMBqPEKUKIHn92hqdd6+w3xtjug5aGCRZxjdbLt9Z7yOyiO44ZrCxRnawSz4agkrp37xGqVpgOW/ht++j9+5T0CP2EoOhnPLFB7FgYhdJlCRKp581Kg6mn43AxGtQnDSxkx5y9w66s0UWBAidkazfQvb3CNduopGEacYEi3jQByST3hjyJVBTUe6RkSLBcHCTCdIvYjkOev4wxvwRZKFCevM1ws0Nuu++i8yXMDwflWWIJMJbWsSqNgDJ3U6AANBgmQbpvSuoXhMRjhnZNTBthOMhHZ9oZ5106w7xYIBYPErh/Dlyy3N07+3j52wQGdI02X75NvlXXqQQdHnr7jr70dTGEiWaRCtcx2TcbdJUPu1mj8H+FioOSYMJKo0xTGd6KwAIaeDYFlrr71vYDf6Q9vAHOd8z8fv000/T7fa4+e5lyCJ0oQGWNRWEaJK7b2F4eazGIla9hg5GJIUZRKGEzBVQvQ5ap1Cd5Z29Mbks5FQuw67W0ZUFstY2eu4o7tobKMcnU4rELeC87xOQK6AMB99QrBZdEIL7g5S8ZeBYBuPlh6alGS98B6UNZG0ZYXvo5jYKiUinPeBy5QRCmkTdDsHWNipJpifAOEJ4eVQ4RmiNDseoKEFPpggwciUozKDzDWSxgfAK6O4+etiBeARhSLr7YPrhkiuAYaGjAJWEyHIdrTRmqQaWi7NyBEYdJg82iPb2Gazt4lV9PnNulTMri/zV//aXcKMhVtTDsyRSZ/jEjN9D4pg6w14+QjJ3EkPo6ZYnGaFVSmZM60+FTkltH5mGaOkzV69x0OlNNwamQ6ay6UvDK5JZeUbaJDU9hEox4wm+DqfFH3YO4eURpsXIqaGcAuakC1KC4WLFI5RTgDBEd/aJbr6JOtgCBLE2MKWYFlKo6TWk1gamqSlkY2K3hMgSlDAxTYknFUXXYMab+n1zjkAIiRBQdkziaRsJDNsgLXRxBmU6ZBhIoUncGv74AJ2qab22YWBFA3QSYQbdadpaCcT8KsHcEtXOGgdbW3SGCezcppoNEds38EYHnDp3msu1o5RWlynWylQ9SZQq+mGCJQVb/QmtIKETQydS1O2ESMP2MGQSa8quSYWAZdnjXq7BS9e22B1E7G7tcfvmJm9t9DDSmAsLOe7vDbm32WNhxqdStCgWTMhSNlpjwjTlK29s8tXvXKU7DHl1a8D97oTufpNyweHsapkjhxqksabkm3z6Yw+Ty7ksHZplkihevbZJyU44VM8zn7fZHERc2RryYLuDpROiOOLJBY/i4lGGi6f57//Jl3jrQZ9PXZploZLj89+9xQv3W7y1F/DKuMT5Q1VKFx/nXz9/nYuHK9zen/Chx0/z1Qch/3TT4d2dNoc9hWtKDgKNIU1CZRBfeoZjec1/+amHGESCneaIRjohGU1QQrIxUuwMY2o5A4VG2A6zC3McnyuiC3mqtQpVGbDsKzyZcbKgSHIlTtYcluIDDkqHeblrU0/7nOzfo7/Xo/nOZQ46fZ7V95lEUyZqoVLn4UdPsaDG3Oln/P6dIQ+fmOf0iXm6B/vcvbVN2Bvwtbe3GZsFmqMU+9hJXohKHDt1mNM1EyuasD+KOTWX4yCI+dL1NvsHfQK7QNGxeLKqSMZDSqZGmDZuscLJmRJ2rsATJ+qsBRav32vRjDSHZgt4wx7/6LUedUcwGI4o2JLr+2NsnXK6ZlJ1BdKwSaRNIl1mHYUhTH74/WeorS5xZ7tPJWeTKs1wkhAOR1PSg+EABr4KSDr79O7dZS3LUT92gvOPP8JnfvBxzhxu8OHzi3z1Zp9a0UOojLlSDkOajKTHxUKKGw6YyRk4OsFvruNPWry5PWKtPeHCYoGKa2IUG+SMjLvdmFGUUXcFNdcgKE5vt0It6GgHYTiEbgWkgW1KtFYIlZIphWuAzjSG0oRhyP7GBgtmNL21sXI0Uwcx7mGOW4jOFl48oKFH2EKTKUWcZVgIPNNAqJRITct1tkcxeUNQtAVbw4jv3ulwbyT48o02iZNjUprnq7s2cwWLr91o4RoGM7UcazstjtVMvn1/zLdeu8MkUxjhgJce9Bhlmny1zLGFEoQx33pjja9dO2AUKtTuHe61YlqrFygVc0jTYxRnzOZdjsyW2O0n/OjHLrFUshlWFugNB4yVyU3/EOMzz/JqUkQePjIN1V17E6u5hTvpcjQ/PWynGPTwiTLF2MwTpBrydRwxtX7ZUmPoFFFqIMMh0jTIdbfR/QPCy9+d3rRNAtJBm7bI0cele+xpxMwhCj/2OXR5Ht1rIkwTTBtLpUg5tRw5kzbKcsmqKwjDQMUhajQgGQSknQ4kE7LeAepgE1FbwikXCR7cQYy6TFLNOEkxdISlE9LhGCUlutjA1yGxmUN3d0l21kj2d9C1JZL+ELVykdKRw/iHjxB0ImqnZ5g0R2AYVFaqOHmHxXtv4boOt65fR2rFJMso2QJfCnqtA9Zjh2GrSTyainydBGghUEmANB2k6XBoZdrqdv36dfr9Pk899dT3Sjr9W/OHtIc/uPmeiV/HcTi6tMpXX75MogD9HuvQchDdPYgCVDzldpJEU4GEnPJai1WyYIwKAq6F0yvyi/PFaRFGYxE5auMePYnoHoAW9P3ZKd+2uQ2zR9GVOcwsJG9kOIbAMSR5W5JkirvtCcr22JU1ooeeJt5dBymI9nbBy6EHLchV0N0mhCFGsQxphjQNsvEIIUAUK8hSBR1HKEBNhtDdJd26i25vTG0UyQQMA+14CMebboABHYxQaUR88zI6jtGjwbT+2C+iwxGGXwSt0G4OggFWuQa2h+3C2tVtiufPYxRKSMviF374QyyUfP5vf+3/gRx3sISikvWRyQhDgm1KnKCN0d9CSgNDSEIliZ0yqjBDZOXRGhK/jkiiafFGGlCamSfTgtYwJDIchrgIb8rwjWyfRLpoBCQhmC4gpn5YaUCphpqM8YMmZjpGSIkfTyAeg8qwwh4yl0f1W0zu3iC49hqsX0UGfeJEMVYWvTCl7hlEwiQ08kjTRSGYaIHMImypMEd7VPSYhh5TlREVPcIkw5YC0zBwpAYEVOYRWQRuHl2Yw4562NEQK53g7N3Eam5iZxFhCubmFYxJn0QpzMEeM75g1hOcO75I/WMf5oNGk/X1LbK77+IEfZAmlk5Yzms+8OFH4MhZguIsUTZ9rfm2SS9IuLE35EZzQsGG5YKJT8zt9phekBKpaUDR1in1Uo6Hn3mUXmORZSciTTSVNOBs9xovbQZ8+c1NSr41tYZYBvWcxdHZAt2DLoNWD9cykY5FFATc2hmwN4gpOxZv3T+gYhncvrtBquH9Zxo4BmxsHlC2FQsljydXK+Rck/edXmSl4rM7DLGlYG8UMZMzeO0bX+Unf+QTaNvjR2tjhLT48JJLf32dXm9MP0oplgt889Wr3Nrqcm1/SHeS8eSZBc6fOcQ7d3f54PES76y3OVR1WfIVX7m8xW+/vUNZhtTsaRNgqKCdGlxJK1yw+ohJn+0sz3feXufKwYQwSSm4BgqoeiZZpjEMQc6xKfsOnmPRDRUxFiaKCAONJGembE4EE2USDwfk33mT9o0HOKfOM/uxj+PmPS5WBbXVE7zP77LkJTjjffZbERuyxOnlCqbv0hpH5H2Hw6fOcPJog1G7w53L72CRsbvXZrMZ8vSpObTS5MIe37w3JIgzhM6IwpReCFfbiv3YQq69S05EVNt38fpbpPk6ws1T8F2WSh6PFwKKtsHZ4wuMlcE4zrh+f5/3zypuNicUXIuzZUGoJFkckDemqC4hYZJNv/AcneHqCUfrLp96YpUnChN0mpJMQlzbIC9ThJBY1hT1FQuLsVlGRCFFc0poOdHIM6+61PMOswWbn336EIetgPJokyCOCTNFO4L1zOfGyGImZ5IKyfXQY2O/x6TVxFAKU0iW8habw5hxonj+2i77kcLzC0gpsA0L1wRHgiElCRJpOYxSk0msyMmUt3b6ZO/d7hRtKDtQKeaYm61Q0CGGhI5TYRjGtM0cmeky1h5xHONnIxoioBQccKakyQRESuMYAoUmzhQ3myGq38JQCp1phO1x+tAsf+flNqWCw831fdLVk3SUy/V7u7S7Q/KmSRLGVHIeRxZK/NzHLvDwYoFXtkZcXMxRcy2eOlJnbxDzJy/luHhqjjv9hPOPX+KerHHi5ConVmZYcBS9ZMoLnstZWJbJ5x6Z4ZE5j0GkOVS0OLw0i3ewiT/pMZI2K4cWeNAJSKqriGMXGePi3n+bcH8LLQQ1IyZnSRxTTpcJYlrKIVAQjXHi927kWjtkysQ4egErXyQdDHAefT9h/TDm6Ye4K+rM6AH3WmNujQzG+TqBW0GmEaK6AHGMRoOQJLaPljaRmUds38IIhqQHGyR7W6RRQjoZY7o2UbOF6rQQM6tk7e3pIikIUFIw45s0x8l007pzB3d5lfjQI1ORbefQ3tSfrMZDvEc+hDG7jJVzkffeIOs3kY7Loecu0DizQG6+yvj6VeqPnKS02qD+xBNcKmmiNGO4dZu6LehMMqpmDCrj1fsD+pt3sNwcSHP6vR0H6Cydhr4Ng1/89GPA1PLw7LPP4jjO90o6/eH8J5rvmfgF+NGf/Um+9vx32BZFtDAQGOitWxCO0dJg8NYrjN9+E7NQQgUjrM4GqruPau4QHLS5cRAwyCQX0l0s30XnimC7U//P0UukWzdIV84SY1J0THQ0wRjtI5EQTzAzhRaCmZyFKQSGgF6Ucb+bcLsX8TV1iHGzgx52SQZdCCfIYgWhUvByJM0dkBKzmEdYJqbvI7zcVKhORgjTJDnYQ4cTlFIwGRLfegc97CI6O4hMoXIVVGkWWV2YMoTHA2S+jFmfxZpdRkVjVDicWgCkiZoMkfWF6RscMS2O8IsQDMjCGGdphdH6Lna1ijp4wJ/90MME+1t888tfRI67UzC+5WMJQd6ZlkukwsZUEb6R4oUdIsMlljaZhlALtDTfS+japLkamZNjpl5lv9lCoNHx9AsBlZIhKCVdzGFzamcxzOmhxvaRQR+ZaQhH2OM2MgmRUmJkMVjTq2gRjSAKUWFA8827qDCGQRM3m6CyBJRmbSwQ0gAxrcAlCXC3ruHrFGPQJJdOkEmIFfRxRjvUOncx713GYZqONoQibyi0tEFP7dcpBgKFEhLt5hCGSZKfYfLOy8Q3L3PjN36LtNBARWM2Xn6H4TBA7a5Pg5eOjS7Ps/vSm6Rf/SKq1CBwi4hciSjfwJAm50oZF2dcpGkRaY1AU7Il/SDlQXOCKQEFljRopS7twYScNbV6WCpExyFhfp5aMc8T77uE8Atc2WiyfmuTI8M2nyl2mO9uc/PmlI396kvX2Vjf543LGxxdqpKTBgfdMVpITl48xXLdR753b/gzH3uIXpLygx+4xCPHZrh2e4PDMyV297uUGzUiDc1hxMPnVpCuQ72R534rwDQlZVuQNfd46cv/mqc/9kl+9IlVHCvla6/fYvHpZ/jk6VnGcULRUBTyLo+dXqWSjRgedFnfOOCxJY/TR+c5UnF5dSvg4qzLnCO44E3I+w5brZA3doYYUuB0HmAAO6OEl1uSK9Yc+WKOQwwJawsMDY9emLE/SjClwFUZIosJYoVtCuqewbzv4FmCrraRyYScKVC2x9GyQxyF3F3bY2sM71SPcH8C8wc3GDk+xuwK9ZyFvX+fnfwy580+W2sH1BzFjKO5PdD84Jk6i3mbWt7loWMNPvLYcSpzsyRKsr+1wUOn55krOUzihCN1lytJiUOLDdqjmF9/p83NgxHzObi23uI3XrjLax2Tzcgj9spkpRkCw2eSCchSEqVJ8zNcXMjz9LEGMwWb+YrP1uI5jhxd4qvffJ279zb51bfb7I9iktlVIiziYEycafIkOCRYowM8Q9MwEi7MFLh46SQPz5i8f9Hm5PA2Sb+FZZks5wxylgZpEDcWEHPL+BKiYRcdTXBFSrfdQ6mUhZLHjXtbvLwveH1rwCRJORhFKCVIbJdffqPNl97Y4Otv3+foqWMYuTyubVJyTMx0woNuxO9eG1Av+ziGREoDE8EkS4kThSEFniGJM42hptSUg0lMP5ZYroMhNKYUbHaHlGSEZwrm9JBYWJjDJqW4S8MVJNIis/NEqw/hiBSVq2JIgU2GJSU5U5C3Jbl0gKMj5vMOtazPt166RmGyz25nwpkZh7WDPqvL81yZFHmyGnM2H/P1127xyJEaJ5ZrPAg0Jd9jlGnG2qCnBI8sFemaJV565TqOyNgeRdTyNguOwvZz/MIHT3FyoUqtVufITIE5OyITkqqpEFkGUlByDBLDplQu4RgSPx4gcxWcMw+x1KiyULQ5V8o43CjgbdxAuy6GbSDzRb7TsRBZipFFKMH0s0CCZpoDmcg8yvYRwQAlBTqcEA8HiM4OpBOiEMzGIlvkCZwyRr5MT7scq1jc3m7x7b33GtEG3Wk2xzAJUoFQCRNlIdIIQ2iS26+jN24yvv4uvSvX2P7mm2idIQxJ3O6hpAnDJsI00IaJdfphksIsR2v56fZ8+ya9r38RanO4wwPIUgDsYRPDsrFOPowoN2DnLu7yKoQDsn6PdNCn9tBZ3GKO2sWTBJtbOKunKJ44jrj4fprlo7w4rqKTmBu3b9OLUhh1WQ8s3n31OsK0pmJXxf+WplFpRBZN+IWf+BDw/eP7/q+/T2my/8jHH9Ie/sPmeyp+f+LHf4yXX3qRONMkbmlKbXBctGmRDntM1nYIdpqoNAYE2c49xKCDjgMGh86xb5V4bN7Hb8wipMH49i3i/S1EltL+4u+QNY6CkycvoSBi+vXDyFGbVEsY90BnuGqMYwhGiUYLwZGKTzNMGIcJ89U8l099AjF3GO/SM2SZQkfTE56SJiqYQJow2Wth5fMIN4c0HbL2HlorkAbOwgoyV0QPOlNO8KCDiiOy98QiSYRUChlN0HEElo0QEqNYBc+DKES8xzA2q7OoyQBh+2A5CL8IhoWcP4x2PHK2QXDjKoXlOobtIhuHUEnEf/0zn+Tv/4svYozbyHiInYYINCaQ5Wqo4gx2MsJQKYbtEAsDIw5xVIwpBFLF0yYkrcFycFprzBds2s197GGTvCWgUEdkMf64hSEloruLsj2USqdtb/EYoUFNelCZgyxDpyFKC0TQn1oPhIU2HeTsIYylY2BZ+CfPYM4dRkYhc05K3oSFos0wzLCTEXbUhY13MSYdhGkghSBOMkQcIpMRCJOstw/DDn7UJW8oalaGqRIgQzk5UivHQPoMlE3ollGWB1oTVpZQXpF4/QHDW3d4+9d+k/UsR7BzwNXX7nCjHRFdfx1teswcWqT0mZ/iQWEZMRhgekW0X0B5tWky3C2SChvPNDCEoORZuKYkZ0nOLBTIOxZbw5T7vZC9QJFh4pkGR+yQ/NXnYdRkoAxGcUatWuTTH32MZ5+8SPn4KmsLZxg0+3y0NODj3i5PNBRGZ5uGiLn11juEzX0OzXl87MwMH10yeWypQDoOePu1t/l//+YrvP3OLT5wehZpCE4fmeeJk/M4OZfP/vCTlCsFbNfmN77yNlqBb0oMKdi8fpf+fpMbN+7z8hf/KYuPfIykNMtjywXmTp3i0uQOZ50x584d4pkFm5vtgNNFwZv7KacPz/Ds4Qpfe/EajMYYAoqVMnNWzL/42jsUhnvcT/L8gNPnB87UeOlWizCO2R1EeCKl7Jk8eqpBJg2U6fI7vRyrSzOYtkXREoRxitBTokc3EQxSwTCaEj8cS2AJ2BgkDGQBVwqqtib1qxRtiWzu4/VafLwxYeHEUS6bhzCvv0vn5g38YIQ+dJLN+9uklXkeWy3ylrlIpiDvmDzojsnHY8pEbA0TXrvf4ZFTc/zgJ55kZmWJe+2Ygu8SpJrTNZdRCrHSnJjNc26xRD/MeDCGRy4d5Xwp4xt3e1zdGdMuHaFnVohSxSTOSJOAg1HEzRFUXZuia3Gi6lJ1DdrjkG9thBw6dQJXR9xd22djd4i2XK7sjrCDDrnJHvbuTeyoz8SvowwPJxlTlDFH3YhECQqVEjVbEaQZmA5xkjLnZHjpiIJQFCxBV1gI2+WV/Qgxe4RhKsilYzSaZmGBA+URBiFGFpNlKXcHGYZhcni+yOpSjdnlRQ4t1jh7dIH3L7m8stnl1khyuzPhxZ2AYZKRpArDkByECf1AMc6m9gPbnIbTUi0Ik4y5nIWUmmNVlywDOxpxyAchTdQkwNi8Tqc7JGjto3otkm6LjV6Gsj3cXIGsNMsIG1snIA1MFeOpgNfWO3z7wYSZYJcgSalefYm0Ns/Vg4SPHMnx9Kzk8WOz/IlPPswPPrpKy1/kyFyF2fkGOldkpWjyY+8/Tb7sM5Y2q2WfqmdhqoynjtRZXlngy1ea2IbgeNXFrM7xyJzH4XyGkJKabyFVQmLmwLDxdERejUmVYrloMCsD9scJq2WLLFcjEhKtU3pWAc+1SapL+PkifqOKN2lhblxjtNskNRxkFhCaeYJk6vt0pJqm/50CwpToUZv4/nVGTgPKC/jnH4dxFxVF+EePIzeuoQ2PzHKYcS1c20YJk5+aGVD0XOxkwhtqjs12CM1NRGsNkUXYyRgtwQnbZKOA4evPc+O3XuL2F95i806bZJIgTIPc0gzDm7emBJ3yPDge2er5aZhcwDjMUL0muZkSFKoYrk1qOhitNcxJG/IlZLFK5hXQkxHG7CF0FKOUIj5oYpaquAvz2DOzVJ79IGHrAP/wKkOnyrcONBdWaqQzRwl6LY47EcP2Ad+52WfS72JaNuGk/+/VNnnfJY5jvvWtb31fw27/scL3//f4w/k/nu+p+D1//jzSMHnppVeIlQQpETOHydo7aGFRvnQaZ6ZMfNDCrM5ClpBpiP0qd2SZM6qJG/RRwQCkyeidt1n/4rfIooDmuzdRro9+51sMk4ycCqjGHbLiHOZwDwpVlGliC0WUaVZygihTFG1BybW405xwuOrTzhzGTomgvEQWRIRbG6R7D6C9jTMzi3Y80vF4ev0hJdg2WWcfPRqQDPqYtVm0ykAaCM/HKJXJei3SXgfd30ds3JgyZAGdJmBY08RoGpJuP5gWO3Rb6DRCeAVIEwj602Y0aUAWIw2bbDAg7E6QMkGnGcLLIWcP4SwdpTa/zInFWV74xtcQa29hxBMckTCKFEoYWKSEThE5akMSUUoHOPEAkwxTQpJpRl51+jv3NxCTPuVSEZSiP+hhGgZM+ohwjBlPiJ0yolRHa01iFYjMAoldQJk2ajJCtffQwxYq30CnybQxTwjo7sK4h8hXseoLLP3As5h+ASwPGQ1xsgDTMik7kmEUYSQhVjiCXBUxc2jammfn8AbbaMOATJFt3SF5cBvh+ZjjNjoeAyDSeIqds32E0ATaoJlITDSxsJGDfbz2JsbwAOGYnH3qHPtrbV77y38D6/RRkm4bd+sOcnYBIxyQG+7j3HiDQ+9/FPfoCcwsYlA6ip7mH6boN1OCznAkLOQdjtZ8Hlkq8b7DVZaLNgpBmGoWCg5PrJQ4ao9xHrzO5J3XEOEIKQUP+iGBEmwFgkdqmg89c5FDjzyE/dgzuI++j+eeOsMPnJ3hv/rhC5RyDmcOV7ny9jtcu3qb166v8frNHXQS88OrFlsP9hgFEb/79be4f3uTVjfky8+/xUMVzTOrZfaClP1RRKnk8ti5Jb78yn3GccJLVzZoPljj5htv8OW/+39FLT7Ohz72ceYKDl++1uK+PcPM2YscKhjEnSaRX+XMUolbExtTp/yVf/hl2rkiM40y//jrtymaGZPWPtdaEX/yoQZXsyKFRoVg/hBef5ths8srax1+e8Og22khlKbmWtR8C61SHqLJ5bsH3G2NeXG9N+UN2wamIZFS4KqIUZSyN47xTUl7kjCOM0IcJJq8qXCEolZwOf3EeU4UE3qdAecOlzFUyI1uyEtbIe1ul4k2eHouYy8QeIeP4aVjfu96j3/5hZf4X770Ni/eH2EZBrksYrs14eW1DpESFCslVmo5pGWwWPa50onoBilVR7LeCzkzk2Oh4hIEIW886LLjVOkpjy++vsYX3tplFKX0wpj7rQkqU+y2x6yUHCItcC2BY1nYpsETDcmVOzuUih7HTxxjxofd5oDWMGKtOWFTVGiqHJ1XXmSUSa72DFQaEzglvPY97OEeZU9SMyK+Pa6TKU1JJLi2OSXFDFsEGYy9MmVLMpe3ed+cQ86yOL1cJTLy5G2Dj5xoUC157IxTOsmUJx4EMTMFlw8dq5Mv5rl4qIFlSCwJuUmLqgqQAqq+zQ9enOfJQ1W6YUKSZox4S4JrAAEAAElEQVQjzVzBwEQzThW9UOEYEGaCkoyoZQPKSZ8o0RQcqO7fRvZb6P17JLsPMIp17LtXyHpDXg/LeCJj1Ysx3xNjCEmQiekhPUsQow7GYI9+d8RA2+x789PD6sNP8NzjJ3j4UIWCY3K5pSiGPc7N2JiGwDUgMDyeOtbgmSNlLh6fw5aCnzw/y0reppq3OVz1iDBo+BaPnV7k2VMznKi5rOQNPBWSFwnB7javrHVwVYzTfoA13CMnUjIhKFqKqmuQ27/DDGPMLMVRCTIJ8Q3B2MyjDROFTdF38ESMnllFlqqM5k+TXHo/KyWLsTZpx4JETQN85nvkSktF2JbDIL9AUlvEjAbESyenG9h8GWEYpHPHia0cNTPFBGw1QdseL24NWHeXmC+4zPTuUsm57Dl1dG0RUZpHJzFIgzZ5NDYyn2Pj99+ks9nhjWsHJP2Q29++TZIKhOMQ9HtkShJc/g5pFCOFJBj2cFUAQsHsKv1ba8jJAOwCgVdD7N9DpBHKsNGTISoKMApFcHIICdlwRO7MOaSXx3noA5gzi2AYtL/7OjqKsU3J/jDmUMUF02ZSXGTvwW2yTPHm7SZx74Bk3INw9O/UNQ+fXgHg1VdfJZfLce7cue+lbPrD+U8031PxK6Xk5JmLfPvbz6MQ771gXaL6UdynPonVmMP0PSYHHVSviawvIGyPm7FPJWhT12Pi9Vuobodk9z7CMth/+x7Rgzsc/cBFnNYaZqFAJWxBNMENOlMBmkwQSiOUJjNzZFrjWRJbCHZGCRXH4MxcHseSlHM2/+xOhjM6QNkeVqVG1m1BHKHDAN3v4M41MCo1kvYBJAlGvoQoljH9HEqAGvVAglGsY9QXMCt1nJMPTUsiXBfdb4KTw8iV0OM+OhgiCxWsuSXk7CrBvVuoOEbkCiST8RQA7hamTETTgXiEe/gkhz/2CMH2HvFgNL3+MU2EFAhD8KefOMnf+Ie/QrB5h+z+O5jjLgo93SpIi0Q6RLkGQkjMNIQkwOzvk2WaHg6YLqnl0/zSF5hsbTDODOZqJXb6MaK7S2aYU+ZyGhFpjXbeq79MQwwVIVGkwYis15qKaNMmcYtkxRm6lVW0ZoqE6zfBthFkuPUaetxHdXdIEhDRmFgJ7KDHUdFBttenGLhcEYSY8ojTYIop299EZwmisYhRnwXbJ9ldx15/C7u7ib73Bgz2EOMOAqiIiAVv6ok0oyHsrzG58ib7L71L4fhJfCegfP4C5SOHmPze72A/+TTzPtilGrK7jajOYmYRZ1ZqxPVVUgQHmclEm9DaQOkUU2v8ZIhlCBwjo+4ZnKz55G2Tgm1Oc38SJmlGJeuRW3sdvb+Bf+4hROMwoyibkjS04H4vYkfnmM0bHJ4pkCvmcKoNRo2jjBdOMnfuFIvHlvnUJ57hR3/s4ywfOcZsweb8+aPsNXuMU81zDx3hiTMLlBYP89XvXuW3v/QK73v0BH/5V16i3RuzmDep5ly2drpcvrpFqz1iGCju393inSu3eP0bv8mnPvU5/tif/Dlqvs3XX73O//DrL6KDMVGuRlpdYM8sshNLPlSLKKcjHj1S58MXltjc73Hu4nF+YTXh+Rv7GP0uH1xxebmV8P56SikZc2h5hu7aDp8+XaDbHXH5tXe59u597rdGvL034KX7Hf7V6zvczB9m4pW4vNbh+v0Wb93YhmjEIEzpBQmFoENRhrQnMfuThIIjGUUKKQXNKGOEjzAM6iKgkgyZLJ0jlyYMjSKlconKiWM8/ZEnKa0eQ+1uIasL3OgmvLAT8vkvvsYzxxs8d6rCuaJmtuFRyNu8/1SNJ1crJJnm2r0Wu/sdbtzdoeQZ7A0m2AIuzhfZ7UfMFRx2emM0cOnIDFmm6SRQPnqIpbkSr7x7h7/0qy9T7u5wpGIwFi5LFY9JnPHO7ohekGIIzSjOWJmrsVDyeOnFK0ykxYeOVViqWrx1p0k57/DC2pC32xnG05/gYCIo6jHs3iPJpk2O2nSpmym+63Fq1gUgKcyQamiFgv1RysEopN/cZaI1ozChnxrstzuUdq/iJCNmXIFrGBytF7Asm4pjsVDJcW6pPLU5kXF6Ns+Z2QKvPegxShSfv6N4+U6X5iilYBsslxwMCQsFh1hplosmRanY6IeUjfg9PKBBrOHdZsxaINlJPXzLxBEZk8UTWJMudhZjVSqklUXsxhyGSDm9UGJT5WmICTKZYPc2SOOInA5Z+/yvcft3vky8fo/90OCkP+HCShXHtil7Js7CKp4lWWiUOeiO6HTHjDHJghHzVsQRPc2oNJIWszkL27KZyRksVF0avolnSer+FNsIIKSknrOp2QYIiYwn0N1h68XL+MmIUTBG5kp0yTFONaH0GRoF0ihgrEz6ozHfXOsTBRMm0kOqBMMwqdkKaUhsqZFpjHILWMmEWq1MtehysuZQNCBIp02YAs0kFeQsQaglCeB3ttBL53AsC5GvoJRiUD9CUD8yXWTMHwfLx496iDSmFWXUXJPUzlF1BePqKjVP4NmaTuEQG8oniwMwTGQWE8chynZIxglnf+QSYwRr7YA3rzcpLs8jhWBwe5/B1av037qCmPTxwi5518Q1NMslj3Gugc5S+t/5fWK3iJNOQJjTdtjWNkRDuPoisr6E7u4iinUQkB1so7VGWjY6VyW5e4Xa4xfIBgNGmUHZNxnGiqprsZV6JHpKA+nv78F7jZ7/vvmlX/xRYOr3fe6555Dyeyqb/q35w83vH9x8z/+Kf+5Pf45rr72IFQ2nYkWn6MWT6MYhjFweZ3EJnSnSfgfpF9n3Z+iHKUvb1zEcl3QyQZgmetAlt9Dg6AdOopTCynmoyRjqS8ju1rRkIRojspSotIgmxYgnGFGPsiURCAZJxlzRRulpyhcN/SjhWCNHqsE+8xhZGKMxELaDzhK0bSENh7TbRFo2aRwjhEJaDsJ2UFv3ELkKYv4Eojo/ZSI6OSjPgu2gxgPUoIfh2NMAXJoi3BxamkjPR+aKOJUywrDAsLEWVlFpCpPBNEgXjcl217Fn5/DqBUzPI2p2wDAQox68J6iXLj7Cz184xk/8jf8Pl1/4FgwOqDMGBKGwkUA7NUmcPLq3h+4doNavYqQTXDllPyYYWLaGUg3bsZmrV9lrtcmSADUJUIUZAreK09tCGxbGuImZBhhakQmD4PVvTzvYHR/dOAQqm7J8DROyBGFZUwJDOEKb7pTpqBXRg1vc/J8/jz7YxEhGmPEQNwsRQoKG+MZr6OYmctxmCnGw0IaJDsfT6uzKLPHuJml3H9PPIyZ9dGsXBi0Y9hDXvoMfNnFUgpuMsffvoSYDnEqewollZHudYr3EmYuLtB56ipX3nad05btUlmYxRl2C0jzkytSefRZRmccJugjDwBHTemO8IrH0sUixdDJFVk2aeMmQkuqyWLDQgGtK4kyhNIhrLxLcfBdx+kkOTn2ITEgacsLBKKITJMzlHFqphalTPBGybMdoYZJKycmGT2L6lPI55ssOprB4/sV3ebA34O7tbR6fd3htaPO5C3WerQR84tIsT15aZb7sIfIl/vgPP8ZX3lrj6vaQ9c0uSabIFzxW58qUfZO1m7dwS0Wa3TG193+MWq3C0w3FoDvgMx97jLlGg7nZKvNGSFs7HK/ajM0ilxoWc1bKB585z/nDDYRhkFtZ4NkTs7gnTvPi7SYdbfHL393mC++2GA/6LK8u8u5ml8dqcPr4Cl+/26Y7GNEcJRxt+JgFj4tnD3NsqUzcajJuHVDpN2mGmjfWOiz5mn+zrWikA2YtuH0wpBtmNHwLU2qUFkRKkyoQ1hQVZ5tQf+oZ7gYWE+nw0KEiRZGSL+aRjakQXJQhrYni//KDJ9gOBXOnTvGLP/gw8/UC335nlwedlGLBZqHkcGK5RHt9nfOLZVaHa2QaanmX7iRmnCmCJOON3QlppnlitcL7T8+w1hyzUva4Pzb48IceoT+KeGvioLVgrR8jDcHl7QF3W0P2uiN2xzEHw5ha0eehM4ucOb7IWze3+K032qwuz/KTF/L0B0NUFLLXD9kyC/z+jSZXuppX1/qMW/u03HnSyiLK9kjtHE40AOBwTiGlQdFUdFObSQpvDvOMJxE3WyGRYXN7JJCui+d5TLTJKJ4KhJ84W2MhJznsK6o6xMhSbrcDSpakGyTsD0NO1Xw+/cgqJ4/OUXANTtQ8jroJeccgyCBJUsJEEWqDndaIwf4+UitMMvJqwlBLysU8vu8xec8qUTczkiSlN5yQjfp8506Tuy+9QjAKyLKMuaKDsjxi6RIJC42BkiatXkyl7qGXT3Lryj2ad7eY8U28eIChU+I0pRL3Mfbus1K2OXNsjnPOtGEx6+6Sz+fZu30NHQdkSjGIMiyhSZSBNAw64xTDMIhTTfKeXWYxb+ARIsjAcsjihG6UkBqSBwPF/X6GGfUZRNCKFJm0MNIEszrHxK0htCLUkl6kGGTmlDikp1kwqTOMoIvu7iGGTWw1wTMNcqbAMmEub5MzBL5poBAEKYxwpoSb6iKG64HtIC0HOjv4KiSrLKIFhHGAN9klckvYvS1KOuTSrEfVNlAaQstnK7E5bEwItSDBoO/OkQpBOWyi99YYvnOV0pEZtl+7y5GCjc4UJxcKkIWI+aOUTx3GKhaxCi6yOjfdDpgmvk7wTYkYd8miBB0MUCrDTMaIxgo6y2DuMMrKYVgGqT21HIZb68S9AUmvP7UdhkNUcxMtDfzZGaxTl8iUpjmYluv0o5QjNRebDFMrnjmWn35H/e/Mx58+D0z9vt9vvm+mvhcC+Pv6FP+zme+5+H3uuee4/s5l9scxmVtESBMvm0xfmFGEWZvBLufRSUIw6HJjq82Z+TJeuQS56XYVAcIvYOc93HIe0JiNeXSWkl5+HsOykJ1t0oM9ZNgn8SoILdBkmGlMUY/ppxKtBa6U9KOEuaLNJM549W6Ho7mY23Ju2vIWTjCqM1N4ttY4h87gHTuJTmLi4Xj6pqstEXdaqCicsgYXjmLk8jBsIxwHY/EYpCnCzSMMCzm3gjYt8IvImWWENJC1RWR1AZGGyFIN/Z5H2WwsT+u3BKhhFz3qTy0DwQSzWid/8gQIkLkiqr2N2r2HrC9gHrvEJ559gn/6F/8kf/FXfo+9t17C6u8iVIwdDTDDzpSGEA3Jdu5BmiDKs0jDwpEgtcJKAwpnTpHubjMOQoxyHcs02TJqZEsnwXYYGT5fWFfT4JpWpJgEVoEQC2vpMEZtFjmzzKi0iKlSxKSHr6Mp49j2EKUaurePjkOkX5heWfkl5i+sku3cxd+9gZYWmC46VyEdHJBsP0D1DuDBVeR4gHZ89Hv/N1e+83X+9t/8u/z0f/8/8Zm//sv88H/xl/inv/LP6e/vE+1uML78bZKDHfSgjWytIYcHQIbwi7jzSzQ++BHk8lkGa3tUF2u8Lzdiy6qQPrjNXv00aWUOy8uhhQGzh5EqQKIxkoRF1SUvY6LaERQgswStFWYWYSQTNAIjzZgxYgq2IEkVAs2xvCbZ2cA5cYl1b5nIyHFf1unEYAvB/jim4UFBJrgqwoqGFGTM5iBg1lEMUoNBmDBb9ji+WOfk0RmOnzxErlrjfjfiV98dcHy2QDNfo7xyhOMlMC2Lxy4d5t6dXUKvyJnj81R8i4nS7N5bw7B9fuTpI1y5vUMwHOAZKYcvPY5wLSypmVUhq8sLfOZDp3n0WIMBLhvRtPK1sLeB7Xm80HWYu/sWL20ENGOJLwXX5Rz9VHCnHZAUa6RWAbs2w/YgoHTvMvr+Gre3u/grR+htPkB4ecqlAhXfopFzObNQYrXu8eRKmU+fyHFqpkBWrvLCnQ5RphiOJwQJ/OadgPt7A7IwwpMZrTChE2QUbZODUUKqBZHhMaofxk4DTEPgpCGvdyVbRpUSEb1IcG0g+CfvDDkfruFu3WOvFXNyNsdqo0TBSKiZGZ+4MEfB0uwNInY293nu3Bw/80NPc3bJ54tv7vDIfIFZV3CsqNk4GPHWWpsgTCi7BmvdAM8xqbgWv/vKGoZnsR9o/uuf/wit9XW+frfDnVbEV+50KOhpKPJQuEtdKraHIbd7MfWCwx997jyPH28wv1ThjSv3WJ9Y7I9TRgoMBEG3z4t3D2iHGffzK0TtFjujjExLUiuPn03oddpYUjDnKlboUnIkI7eEFgaXDk+LaUzPo0yKEpI30xle3x5zvxuxPQ7xDZCmNa3+3rhCtbPG8MZVSlGX62t79CYxp+aKLOQMco7JB47XePZQmSU7YjOyKNoGYZJxsxMgkpDRJKKeN6B7gJmGvLw5oK8cfFMSpoowVSSZZqINKFSxarN4M/OknSalzgFf7RVpL1+gOQwoh206IoeVxWRuDaO7wVAZLH7sWczzj7Fl1ojmlrn4xAVsQzMWHqNoalN55/pdmr0+23sdclLTzc+ihU107W0cx+OKmqXlL0zbKW1JkBmMM0U/YsoQjjI8y8Q1BQV7mlMYxJpRYpBZLrd+/QtMFo6gvQL1aMj8TJXtMfxP37zPIExxTElm+wxSSA2XzYMxrlTMZh1kGiKiCTEGqVIE2kT7VYy928SdLuytEWmJ0JpYTDf7jiWpOAKhwBJqGgrWCsMUWNEYEQ6wt6+T7G6g7DyFsInb30Z4JRzbRUYTUrdIMepj64Q4CTAlWFs3WOncZGjk8fdvsRQfEGiB0dtDtrYwyBBaYZga17XxTMmFY2WOP3YI2VhCrF5k5if/CP7Rw5QvXSK6cw0VhegwRocTSvu3yfW2MV0Pb2EB5+DOlKP/XmOf8MpQmkEuHkeGA1QSEm88wKxUiLp99HulSMHVt/DPPwEC0rljrPVjBklGPWcRJBnOpI+2Pf7FlYif/8hxZkruv1fPeK6FlJJ+v89rr732fQ27wR9ufv8gx/xe/4PLy8vUZ+Z56aWX+cyPfBK660i3gDHaR9eWMVGY/tvgOtxoBlTzZWZnZ4kbdbQAqz6HcNxpuYTt4dYrBDtN/KMSFcdY1Tn03jrG0nF0fR5MFyfqQxIz8Xw8O2WUWWghcA3JKM5YytmMlWatF/DwoQqNvEEaDRGuh3P6IWQwQPh5ZKaQbg7iECkEwjQxTEm6v8347j38uQpi4SjYPgS96RuyNIP2iog0QgNi9hBSq/e8UAJRaqA621BbnhZjRArj2EX0web0xGnaUJlFqAxj7ggqV0IUG0jbw8kVSe5fIX/8MMwegf4B9PaRpTmEUBQeepKCafDnP/l+/vY/+Q3+2yCl/OxzmHGEcCysooUYD5BeDlGZR+fKaJ1NxVoaTmuJl46TJXfo2mUQDo2ZGbZafSpzyxD26SQGJ1bm0GYERoW+LOAiMFSGdfJhMiRmmky35/EQOWmhvMqUXuEU3is8AT3pg19GZSlm3qd24gKquQmtLUShDGkESQxxjJkrkLV30bkJQRjxm999k29+9xXak4hTh1d4+gMf5Y8890kqUQvShH/165/ns3/rnzJnOzx27hhra1v08/+SRx++yGfe/wilag3peijTQgzaaFNiHjqGaTlM7t7hoDmicO4x9pJpJWuGwlEZqTDJhU3U7l3s+SNI26GcBQytEqZWiCygL3wEFnk7T2zlSDKTXNAl8R1S4EIxw1UZ2Qc+zcQpYWmPUawYpyZBqCl5gpWCTclQFHMGI6VxsPGF5pIzoDDsMJg7g2XmSFNNzrU4u1Sh9NEL3GpPsE7MU7QkpmVQ8i1+714fSZknThWweztcjxT/6vnrHKrZzHsGlnDI5T2W54sMDprcvnWfn37qCF/61jf4zJ/4L5hp5FkuOry+E/JHHssR6IScVNwZJPQDCVLz3Z2IcxU1vTY89wS9dw84Olvirbu7fO6RWTIRkJQcZnMFUmUgpGbFDPm7awl/qmHwZz5xlNfX+4y04PHjc1i2SZBmWIbk4apke5RRzZu8MPC48+5tPvDkWfbGCSdMgyt9wUrR4Ne/u8FMo8xPP3GIW/tj5qrFKYZQCFZKU+/vSJm4ZkoiLb6+HrCz2+PDR3JYaHa8BrWoz6mi4NvaoL1wlsfZI0kMSjKk3QsZiYzticXJhsszFZcYh18+aHOwP+CxE3WakcKsL/LSeo8lD85UTZ7ODfjGWkSr1WU5d4JTp+Z44X6To3WfcVAkijOqvkU7SDlpTvjnb9/Em1vC9j3icoFVRvRu7RMUF9CZ4tWNIR89NYNhCk67AQsXV/nt/pB3tgasbx6Qb9R5eL5Ec+OAn3h8hX/19jY/cmGeYaHBQX/Mas5Fp5pK0GMYKzxLQnGWWNso6dAoavoJVB2T9QzqviB591Vmjz9MNxRESnHIM6d0j9Zdxl516kFdOocMOtxs9bloOwzvXWF5pcDxzKPbsxhYOQxS9lLFEXeKp1vUPWLDI7VMhqminQiEVnyt7fHccso4zRgnGb4t0Qj2xwlSp+QtH6Uy8r5NIh3E3AKHqhannAPYfEBfSXrj+/DBn4B0SPrWi7TOfgiRQjx7BC/r88qtfbxylVEujwpSvnl/yDP3Xyb5+MeRccQNc45I5/HHEaslh76STE49Q9Vx+Oj5BWwp6MYajSAFCiomsnz8TDKMMzzLIEoVFd+iE6U86GnqfkreE/QuPY46epJLZcm9tstcpMhHY7QQ9MOEYWwSp5qS52GlEX/hw4cQrksoDGQSYmcxGGXE3gOSueMgTIzFY4xvfxGzVKaXSkpOjuA96kesLRypiaUmR4yRhmgjj6WS6Y1cNCZbvwJxiNlag3EfoTW5wght2jiuSywskr0trKVjzNgenlCoUQ/36FnuRxaXfBPdXcPq7aJmDxPn61jJmPyjj9H85X/Jjav7PPzR06gwZOHDjyMvfAidxBgqwZhdQa+cIdj4NXRrB7MUEzabVKIB48MPY+Vd0sEAPepNW2LbW2SVeVAZ9HYR+SqM98Bw8FaPoE2Lcecaws+jLRdrbgGiCVpaDEWO6wctTszkqXkWX7vV4iJNyNfYDMa8emOLH//ACf7hv3n736ln/sgPPgnA888/z/Hjx1laWvpeS6Y/nP9E830xrzz11NN8/ZvPM0wg8arTrZ4wp8y+KEBKQXuvQ1O7nD60AK117Po8slQn63VRwYik1SQdDlFJhvQ91LCFEBppgLRdVKGOMG1SbWC0NggKM+TGu2RxgqcDCjIhU4qckREgcKQkzTRn5nI0lctv3Qvpff1LyMos2bCHTmOQkI37ZOM+wsvhr6xiVBtkwz6GZ2N4eVRzB4at6fVKvgpCopvrU9+rYSOcPKgUZbvg5qde3TiGYAy9Jtp0kaM+RqmKGvXQtj+91pIGIl+m6c6xk18FQyIrs1Co4p++hBQgK3PIxtKUPDHuwtIpgsXzfPIzP06v3ePz//r3SG9dhmiIAOTuDbL9LWRxBp2vknZb2EEHQ6eQhtB6AIaJe/4J5lyBY0C+Ps+o2wGdsa1zrPiCYyWDzC4gtMK3JDqLiTT0vTkCr0EiDTJtEDt5sErIeIgMR5CmZIWZqU2kUAedYVRmEPkyhBOEFEAGSYB28uhiA9mYx1o5SdDs8MI7t/jUX/ybRH6d/+F//Hv8k3/5u/ziX/8HPPXTv0Bhbolo6Tzuyik++wt/it/+B3+LX/jRj1M6doYPf/AD/IXP/SyVpcP86f/xV/jjv/R3+MpXv0m6eZcsGEEU4PgSZdqc/NQneeTiMqvPfQQEBEpjDDqIsE+MJHZKkKX02i209JDhAEdnhJliYBSJhIOUJoHXAJUyFDZdp04zUMz603IQmUaoXBVXTDdEozRjGKYoFKerNnN2TH88Ikwy/KhHpM2pjy1fZVKYI28Kll1YLju4pmTGtzi7UODZ1TInZvI8dqjCbNEntDz2uhH4Pn7eY3eUEWJwac7FyRL+xTeu4hmCC8dWEeGY7V7M009e4IVbbfJxkzNnz7Fa9ae12C5E0ibQBnfbAUpp6nmbMNHcVXn+3u++w3h3j3/wu2+Sb97n7nqHJ04t8lJLUZ60OTfjcapmMsoUjzQsKmnMZx+fZ3G1zq00z6KXMZsrsNLZ5cl5l6WyxyRJ6WQO8yWH9f0+FxaLCL/A3jjl4qEqRdemkXdZmWvwzCPHuHRiljeb0bRMR4BniPda3gRbo4QgzTANSeBMt5tmsYjtuUz6HVaMgC9ta0Rlnl98ZoX5gs2F1Rpzh2d5MLbYafWw05CS73B5d8LtnqITJbzv4irXdjr86u+9SncY8en3H6eetynpkG9vx3z5rU0+88Qyj59oUJn0uLXTY7C3Tz+IqZZc6nmDrV7AC+/c5W/fdbELNfw0oe5K1g/GfPfBiImX43deuM75hsdi3mAYxGgtmDt8mENljz/x0fOcWiwj8wXau00+/51b7Lk1tkKB49jc2h+TZYrff/EWrF2jPxgho8FUHPke9HaI+y3STGNZFgXHYC/IGAQZpoD+7FGWSy7n5gqcnS2wkDNxdcT1ZsjG3h5y9w77oeTGgwPScplvb0xIV47w/3qlwxdfus7f+503ydIMNRkSDroMBgOyfpvn73QQacyCO93qBlHAWi/hVLzPdx8MKDoWe70xM55N0VBUPYvFtMXeKGKjHzJ0KuzKMvdCh7/9whaN+Rm6hQqz+3donngSDSi/zOT4wxiuR6YU40nE2s0NtDQouFP2uW3AvBjAcJ+d/phs4QgnDi/w+LEGM6UCeZlRcyV+Y5ZEC6quxNchG4OYKFX0gpROKrERBKlCCIHUGss0KNgGdzoxhpSkSqOQnDo6y+YwY3u/w6F6gX/zyh3ejkucXa0QJopWoNDCpEBETWY0SnkSLdFC0reK3BoobnRjek6VcZqhgz4iiXFm6ijbo6GGTJIMpTTmpIOtUgjH5OMu5mCP2PCR6r3buEmfZOc+gztrTJp9sjtXIQ4gGBK/8yJ0NsHx0YU6yfxR/KSHRqCyFOfoMWTYY7HsQhxCroIzaWOMm8T5BmLQxp4/Qv74Ko/89LMYhmLufWconDyOEQ6R/Z0pAlNnqIMH2POLiNlDiNIMhqERi0cw0oCwOyAZjhDGFF0ncqVpJmb/HjIOgQzV3QehMasNkk4baZkgBXrUwzl2FtwcsrHCxjCi5JqcrOVIlGLO0xjJhFtDk2TS47dfuM0z55dolL1/p5b5G3/+p4A/GMsD/J9/89vtdvnsZz9LqVSiVCrx2c9+ll6v97/7M5/73Oem5Vr/f48nn3zy+/Yc/0Pn+yJ+/+hnf4bnv/kNBrEiNHOQJujOLvT2wMkhvTw7/jzzZoxdWwTDROSLaClBKGShikojgt396RdWuYCWJvbKMaTro4IxJBHCcVH9Nrq6hBQaI4uwD+5jRUN2J4qaC9KwqNqSSZpR9Cwu74zw1YQTboBz9mHEwQOCvX2M4syUNaszdKawl49jFMpg+ziNGZxSYepFngwQUqBGQ7KNG+h4QsepQ5qiRp2pKFUpuFNWr97fRHh5ZDSYbnonPXQ8mqJeohDiybRsIppANMCIR5RdgVCKzPbRp55BAnoynBr/lUbv3EUbFkKnRNKFpZP8tT/xc3xnp8k3XruM9itEzT2Gl1/k4MtfmqLIgiHZzVdAa3p4oDMQFsLxUYU6hsiIlCaX8/FzOfb3D6h5Bk7Yw0+HCJ2ghSRWYEuBYVoYUjHWJj1nBqVSkCZaRSi/DkLDuINIQ0QWT0N9Xh6xeHpacqJipOVg1hcRwRgsF+2X0YM+A8vnv/nau/zm1W3++b/8F3zq5/849uwxAumSz/nYhkUQK4S0CZUmqywRn32ah/7YL/JDf/6vcP7P/hX0+z7DB3/qc/zdf/ab/Hd/4Re5fHuNT/3S3+fv/ONfI1xfI31wh/GNd3GSIZWn3ofn2ORtSZoq7k1AGyaB0siDu3Q7Hc4/92P0Nm5jhCO01kRagjCpZH1sCZNU0IlNokxg2w5VV5IpzVgZYNjILEbZOQAmsaLkCh5jiyjTPL/Wx9MJubU3iJ0Krgro6jyu0EjDxExGOIMtcqagLOMp3F8lHK/YnJ7JM1twWChMyzAeO1Ln7EKB7UFKUJzhsQvH+PATZ6e3BlnCpN+naCSILMIvFZG2Sa5/n/rJx1FCkLMMpEqJO23CTNCMDbZbfR70JsRRSs23WK26XKo5ZFff5MihJYalWaqzJY7UfUYpfGlY5no35kE35NRsnsRyWD46T6VaZry/yUW7Ty9MOSMmFKN9yBQlIlKluLrbI+p0uPLm2zSyIf/Nj5/nydOzPHe8TsGzWPYUtm3h+w4nG0Wao5CaZ6MEVMQEY9Jl3Nym1N9krTPm7VaCJRQfmVX82GGLsiXY2WwzCASPrVSwTUFeB6S9AxLLo+FZTAybjlHmOwcGDxkdLtVNHprPcTCKOVs2+DPLE7b7Kb/7wk0Ge3tMxgmxX2T9YMgPPfcIkTAY2wXq7R1aUYrjutRcwQePVDi72sCOY84eX2YURIwMjw+cneOtV94mk5rDKzNEy0ewCkX2gpRHFwpkaUZvHLKbGMzmLAquxcXFEo8dnaF5/z537+/z5v0+ne6Yx481WCpb3FtvcaJm8O7OhPu7bbJEkWaKSrUKts/9QcowUVQ9g0VrSmdZKjoUPJv5hVmUNDCFxJaCSaZpZw7vUierLLORuGxs7PPlX/0SXjji5FKVg9jm8UdOc/zsKX7urItvCSrlKgGSd0cuX7gXEBi5qfdfZRhZghcOONnIUTp2nJWaR8U1qBZz9EZDWjtbDMOUwKuhtWCwvkUnc3AsyUzeZsGBk1WbCyeWWHriUYp5l5ojyDDYc+dJMXjw+jt8dS3lde8wTx8uUncNGgyRaB5eKPJr4gy7Q8XNiQWWS90V5CzNQPqYOmJOTghwiDKDiXRxhKTsmPiWQSpMpNDYpsQyBEposiyj6EjKnknNs8nZBiPtYlZmcXXMm03YnGiWCiYj02Gp5LPeC/nqlQ2EADPsI1oPUOMuxvoNDFIedCfM775DxRYkThnXABEPyZIQ4RcRwzZmMiG+/QY+CSnGNOTb38Pu7zD+ym9BMkYlEZnpoaOAbH8LlWQ4C8tI30XPH4OZJZzFZYyZQwjDYmj4FKp1ssIMxo03pzz+RCHSmFLQJFm7CX6B0dEnsYp13HhEOhkjhi3mP/4Rjj5zlnFzRPGJpxFZihy1EX4ZasvoJCHZuod79jHUg+ukloPqNtG1QzitB7j1EqY7zcxg59F+GaLJ9Fa2sYxIM4zaPMK0SEdDOlfucvD2A/TBFmTp1JooBcnMEdBimrtQUDChmg2JnSJrrZD7l99ktz3mtRu7fOrp4/9OLVMq+MD/Fnb7fs//2Tm/P/uzP8vbb7/NV77yFb7yla/w9ttv89nPfvb/8Od+4Ad+gN3d3f/18aUvfen79hz/Q+f7In4/+MEPsrf5gM7eNiBQKkY6HiJXRg3a6NkF2sU5DolpgEnVV8EpImaPIC88C1Jg2DZOuUj/9hogpuJw1EdZNmmaoTauk/WaiP4+avMa9s4dEstHOz4kMXuTjFhBP1bE2XsBGKWYzVn869sTnilOsJZX0WmCmfOmCCsp0L0WvFfLqISAOJpyCFGoOMUsVdFaoAdtEAJl2gztCiqNUHsPYNJFjfqIcCp2dRZBOERHkynn188jio2pj6lQRmYJUqVTf2ySUJ3sk0vDadWzNDFsZ4pMC/ugEsiVIUvQxek1kCM1w9w85U/9LP/ov/oF/va/+hqdN7/D+v/yeUY37iJEBtGY2HSxZpcZ4pKmGo1ENKYHD5VljJSJIwS+KajPzNA+2MMWkgEmwrARwkB19/BVQiYtlNIkysSTilgY7/GDE8TgvTIM04bqEsa4jWpvT5k70pjWPVsOMl9COB7azU/B52EAvT1+89f+NT/95/4yP/mn/kv+n3//H1Er5KmIGCmmpQZCTK8d94MMrTPeaWW0AkVPeyTlRQIlGWDTTCUD5dBMLZwLH+Izf+lv8o/+2l/l6vo2VqWOe+ZhwhvvQHMDfbBOcbLPITmhaClC259uiuIR+mCT//mFazx76Qy/98UvosIB1vCAginIBy3sZITSgjBT9ONpTbOpM0ymjM2SLcgMhzYeDwYZ4zhjPm9y1p2QExlaGuRlzIPEI1w8i3XndfzxAQ4JI+0gHJdUwcBroLXCSwP8uEdDBgxSiSUllhBUXZu5nMVDi0V2B1Mh+QOHLIRSXNsf8+iMzY+dK2JaNgeR4Ltv3qf5e7/HcG+f9vrbPPODP06QKBwJS66iWqvx+kHMjBVzf3+IYxj8s+/c4ebaPi+9dpPOjVskJ89h5DwOUovleo5BrKjlHQIlMKXEtiXnnBFLdsz5eZ9Sd5PmkYf59j/8DQ7ub7G+eIIr/iq337rx/2XvT6Mly+7yTvi395lPzBE37nzzZt6cK7Myax41VAlKDJIQEgJZNm1GYWzjoRvb2N3gBr+NMbZpz8sGBBJCMgghiUFCaKySaq7KGjKzch7uzTuPMceZz97vhyizll5o2m1T2P0u/9c6H26sE+ueiFgr4jl7P8/zYyxucaSgua8KKxu7nBqukpaazI7XuG2yiNKamZJBzZUYUrPZCbnSDnEti36cUXFM+rkFwzbtCHaefYorl25xbXvIhd2E7dSivHODIIhJ69PsaZOSa4NWGKbJMBMYUjIwXGbKLif2NWhMTWK4NvQ7VA3Nw9ktir7La3qMv/vYPr7nwQO8tNrj2q0tVtsB984UubQ2pB1Ba7fNS+V5MqXxij5TRZOdTsjSeoda1ef6VsDttx9gdavPxZ2E7/+Wk/Q7XVrXr9PL4E2n92EqyRevtjlWNRjTIQfLFjVLIAVkWuPZgvsfvoe77z3KuesbPPn4i3z2K68yV/O57eAEhbE6q+O3MVGv0TNHK1uNsXFyv8rs7D5sQ2BKjZfFlCyJJQUIgTAkSimUyti5eYP2MKGbKgzLZKrk8qFnN3jL8Qkqb30b7cxhoSCYb7i8+VCNYsFnUJ3FN0a7KA1bcHLc5ZtOTFGwNRo4s5OiDQu3XGK66pHYHq9shNhSkCiNawjO9iz6acbFdsrm5hadT/8mO8MYA8HAn+D77qpijM/iODa95pHRd49Q9FPJdMnEEjnkKe+8bz+HF2aYDZdJox6b65vc6KZ85mqCmJjEKxV55EAF3zJJtMQyDIw0QGEihcLKY1qpBmkxUbbRCGqeZMyV5EDDiDEFGBo2Bgk32zFl20AIqBNgCUXu13h0yuTBY00qtmZFF5kvCe4ak9w16ZD1+hSyPnk0pP31r+GriLRUx1o6z243YDB+lHFHURMhQZyhimNEk0cRxx7EmDkM4QD7+muYg212zSqWAMMvohvzWNUKVtDGaG8QS3v0OzO+j/pb30bYGpKdeCukKSQpOCXyzi59s0gpamNEPUCwPXGMOInIcoUOejjb1xGWRS4tQqtEhkCrDFkf5XKsSgVlGpRn6+i5k8jxWbBd8KsQdVBCoE0bTAdZKCB2V4k3V7GCXTpnzqDiDKfZRDfnkTqBaIBMh+hCHdwS6epVKFRJeiFkCcH6HuOn9yMcH1FpgFeFSpPd3GGtn7DciUBAP9G4YQu7MsbllQ5ZFADw6Sev8ugd+6iXvtH7e/vhGQCWl5e5ceMGjzzyyBshl/5fM5cuXeIP//AP+dCHPsSDDz7Igw8+yC//8i/z2c9+litXrvypz3Uch8nJyT866vX6n9NV/1/PGyJ+K5UKjYl9/OGXv0o3g8iroUyDZGeZ9c98muWhQU1meHGHNEtR0oAkwFAZujEHpTEwLczGGKZtEG/vgjTIW1uk18/hTM4Rz54k3Fwj7Q/QSqHTkPjWTUR7nV5lhponudVLGcQ5qRaEaU4/Ueyv+zQqPqZtYwVt8EoYY5Oo3XXy9t7IVyQFursNvdaIJuNYIzKZFIjx/Ww8/SQY5sjS0NtjpnURtbcB1QbSdtFBD5kMRz7gJBq9KV4R/DokCYJRZZhIQ4iHr2OgPfJBF7VyCZGnYHmIvWWMsPM6VW0U4NOmg2zOQToENBmCXCtUdZzq8Tt431vu5uP/7jdxPBP/4ALVUydGpLbWFgaKYnuZRucGyrQh6CHzCCPs4JGDypAqY6o5RrfbI4oCUreKcovkwiRp7EOLEdUnzDQiSxASKo7AJ8IatsiKTaRKR+9jbxe9dQthWOgkRksLvX6dZHsTFYak7R3yrRXyvXX6K1f5qz/+D3huZYNf/9TvcPfDD+PkAbF0GUiXHGhFGXGqgIymrXE2LxEv36K1fItnrmxwa3OPbpxScySG0AyTjF6Ucq0vsEsVfJXQy+AXfvN3iZOI4vw0ycYK6fXX4NLTFPpLWGGXw3Wf1HBxUaQTB3ji2Rf4V3/t/XzuubOk7R3M/hZuHuBELbTpQvY6gcwQVM0Md7hNOd79o8ekyijplDAf/UDapslQ+kRTR2nYgnsbFlPxLlFhjL5bQdkFDJXi5ANC4bIrCji2R5zmQI6RJ/hhl6oOkFrTT3IcAxxTkmjN4fEi672Qn/3cVVQacmmjzU435tChQziVEgMteNPdB/HPPM2zZy5jN5pQrTNfcdntD9lVDmPNOoNEs9GJuLC8w/bKBt951wytW4u0+yHR4ZNMzM/STgWbm132hglZrlFKsb4TsNoaEsSaT339Mq8udfmD13YxV2+SDAfED76NWneNhZka/VTx4lqHdaNKsLFCwzeYdHLyu9+KSlPCDNb3Bnz10hZ5nHKjm/H0+RW+Zb9DyTE4NlFgvmJTdkzyJADTodZf46VrW9y2d52J3Zs8c2mdyxt9vtxyGPp1FvaP49UbWIYkygVRGLKkKySGQ5oqFryUhmtSdm2kW+DYZAXkCARRVwMOH5mji0+tXuadDx7jA/eO4+Y5d++v8IF7Jzk4XeVNdx3k7oMNqq7FnQ2ByFLG3IxXP//r/OpP/ihf+8WfYmGswIPHxrg1SLk5tJkfb3DSj1lZ2eXqZh/TkpycLvLJl9dpBTFRZ49BBmVD40poFFweu2Mfj94+zb2n99M4uEAKlEtFvPo4h6YnOL2vStkVrLZCBJDaHj1lkmjYG6bIPKWFS6ahn+VIrSlbgm6Ycm1nwNaXn2Dt0gXOvXQB0hw77fMdDx1GC83Ju0+SGibndlNONBxe3oo5Pl/lQNNnN8oYPPUVFgNBN0w4WsgoVesIoGpodhON1gY7YYrjeRz1Rmh0Swq04ZKaDvuKJieqklq5SPKt34Vr2xR0igIqrkBkGakwKdiCpqMJM4NIadJc03QN7nz4NHvDgI2dPr0g49JWxk2qDGJFDkw2yxyq2TQcqJkJqZYoDGwhsC1BKIuIPCFVehTPkALLAIucYrSLTgLMJKApE9Z6o0Cgb0lqnoUtc7pRin3zRV7bHpD5VV5cG9BPBe+pdnl8cUiARGnBg0enaAxXObMR0XfHaC3fxCiVyZTg3qrCnJhD6Jw+LmkckQibDIv0+isoqwBS4t3/NhbzComWyO0bDN0qSIl/7DbAwBh2MaM+6e4ul5M6otzAveNOuv4E4fgC6940i0OTrDmPff1FbEMhelvIJGBfMadQKGJcepJ4+RrJynU2n3wJc7iLKzXaKWK2VpDlMQYXzzG4cA4VRjQfvm/UzesURihjnSMMGxAY0hj9bitFdusyyU4LPWhTuON+iqfvQoXhqLIsiZC2TR7HAKSGjTJ8lFMk3dukf+06E/cdpfKmR0agKAF52GHPnWR9mKI0NH2TTCnWt7dJlebMZsbG6vYf6ZWV7T6vXN/iXQ8d+gYd8yv/6IeBkeXh/vvvp1wuvxFy6Rsm138Gtgc9Wvnt9XrfcMSvv4f/pfPss89SqVS4//77/+ixBx54gEqlwjPPPPOnPveJJ55gfHycI0eO8MEPfpDt7e0/9fw/j3nDCuv+p+95N09//QmKliTTJjpR9M++zM7ZJVYyh4nNKyBNxOol5LUX0OEAdIYddlDTx7FmD2G4Ds7MLM54A2NshnzQxypVob2Bm/RJj72ZfH0RvbtJuHSdwXNPkbV2qO5eZ6pgM+5b9JOMOMvYDRIEmk6UcWfTJSpNoXJF1mujTjxC2tlFVhvI+iRGfQIxPoswTfLtZfBrYNkk7Q5aCH77n3xq1ARQG0doMAUIw8TwKmTdNrhFFKCjECFAVMeRtgfRcLR9FAzRhgtOibTXIhsOoL+LcD2E7ZI7/qjn1nYROkXa3qgDuLOOTIboQQvR20EIEy8dUg93sIRGj+/nnffcxlfXN3DKHsWFA5iej87B2roKpoVYeQ0zbKOXLkAckGMQek1kFmBFHaxwj6HhUq9WaK0vU7Q0GbAXQ/Dck+ibLyF0RkP3KWYdDKGxpMbIFTINoTKBSELksI0YbI2gF4YkvHqW5PIZsvYeyfYqBF3yvU3ScMgzizt819/+hzzyvu/jb/7CL2PYHhKIpYPWGUEu2BgkLLf6tMOEVjegsPoye0aJeqVEPY9R7W1a/ZjHzy4xjFPWejGbg5QwzdmNMla6McUDx/nF7/om9tWKvPsv/1UWdzoEWzvoNAW/hNACQ8W4eYylM3TY4tc/+2Xe/Z73UT/9EEEO4eYWRAF2NPIxitdT1EppTAOq0agX1NAKJ+1jdtYRUZ9isMV0wRghprOcKz0YqlEwpawHNOM9BlHI+thRVp0pUsOnoLPXEd2KrVgTKIPILKLdIho9wrSagu0g5cJeQJBpyrbB0UaBE7M1SrUqmVNkrz1Ezhzg1dhnvmIwM1bi0UMVBm95lDjuMXfkOM2CS8OVVFybmm/hFoscrgqe28r4kbefZHmrw1eu7VKfmeJ7HruTeq3Ezb2YiaLJI3cvsDVMOLfRY7LkMtsssLTR5/OvrvM9D8wx6eQEQcBvJnN85eouh+48TrjvBNd3Ih5/8RI/8OZ9TCc7dK0KXHyVI1WDQ9M1TtktZNhn5eYK0fYW6+0229sdPN/l+c2MomWwv+KhDYOmZ+IbkqjQ4OZOwq350/z+rS7tHB41t1htDzBLZVYGGZaUI+86oLSgh0uz5KOVYHPxBkJpyrZkouiwlzvcTG2E6RLOHCXp9zg55mI5BuUb58iUop8a3HugzGTJJREmBcugUS5wcKrG4WaBQqXKL359ndsq0Cj5fNt3fg+2V+fcFz5Js2jR9CT3zZf5+uUd0v3HuFducu7mFofqLsc7N8mlxbp22VU2O4Mhr+2ExGnKfM1mru5R9x0eu2OWmZlxvu2tp9gKMzZDhec71EVEJ7fodduYlkXWaREog1vdmF6SM4gVX7kVjnCvSrHSCWG4S5Bk2HlM7TveTRpE2HvbjO2t8qXVDNex+N3XOnz23Dq//9IGv/CJ59mOBRNlly9e2EXpUbXUa+2cGc9gOxaEWgKazX7KmfWAyxsDNkNN2dQs7gZcXEvx0gECyPKUZtGhaELZFJyuwanTR/FVhIoGZGtLDEvTDJOcnUBhCAiKU3QyidSaNFdkSIx+i97WFlP5Ni+ac0zXHWaaNYZxxtR4gYf2FbHRGMMWtk6QgEDjyhylYZiDZY5S4WGiKVgGUaZIcjBsG8MQDMwy/tZlaq7kRDHHMQTtMMWXir62MQ2J3lzn3E5CnZy9IKPT6bF/vIChJZ0op2kn7KQGvcIEHLmNV/JxbgaC57xjhHFM04iJpUul6LBPhjg7N7H2bpFcPou68hwIMFbOMVRQ0BFbymWYGbC1SOJUiF74Mnl9GrV+nXgQMHH0MGGxiddooCwbqRVRdZb08H0YtXFsmSENC11uImwXxzbw8j7m+Czx+hrJ9iYyHSB6u1TaSzjpkHhtiWT5MsK2Gd64RpaBcep+7KSPWr1CurGENly0kBh+GVmpkV97GQyT7qtnsasVssVLGCfuwzv90IhyKiSJVUQj0PEADAOrt4U5sw+9uYhOUnqL65Tf+RfIoxTqs+jSBJuFBfrCJUoVoKm4DlGmaW9usJYXuLDSZefmjW/QK7/z1HW+6a55TEP80WN3HJsH/vz8vvBn6/mdm5v7I29upVLh537u5/6rrm1zc5Px8fE/9vj4+Dibm5v/l8/7tm/7Nj7+8Y/z1a9+lV/4hV/gxRdf5G1ve9t/tRj/r503TPy+933v44WnnsARGX68h4z7ZEEId59ExjHFzjbhbpvh1/+Q7plnwbRg0B41I6xcQtenkbaLFGCWKyAEzpE7wPEQfgEjDSmsv4Y3P48xPoNz4Aj1e+8l2dlGdDao7lxB5TnzJZMwV7y00qXhWxgIWokmVqALVZIwwUz6mMU6slhG2N4Ia2w5aCR7Z86iwx5GsYLhuujtW+xvGKPrVQqyGFkdQ7iFEfxCZTC+D7wKulBGTu5Hu2W0FgjbRTTnoTk32v437ZEXdtBCZTlCCIxyHWX5o55KrwhOGe0WIM8RhjHijzOy1KIyrMEm2vaQWhPbJSYPHsYp+lxa2UJHQ3Qak69ffb38u4zONaq7R3Lj4qiRwrCwyOmJka86dWooJZhtlFnf3kPloLRG5AnLhQmC5RUyJTDzmNyvImGEl9Zq1AUc9yENyA0L/CrJziYoTXLrBvnmEhRrPLU15Jv/yj/gu//JR/jef/YRvvDieX7j3/xTHnnsW8gsn70oJ87AzFOSDGzDpCgz9ld9mmZGdxjRKc6wadZ5Ki5wvXGEdxyrMDNWYLLq8cLZm5xb6VCwJDXf5HDdo2BbDKpzVL75nXzHX/xB/u0Pfgd/8z/8Fuvj87hHT5G2dknXb0IaY4Z76EGbVq/Ppz77h9zxbd+DtjyycIhTcNGlBvGlFyEejsIi0kCjGCY5iVenV5xCDPYoXP06Yuk87Cwh0oCKkXOiYbE3TNgN0lFZumVjOB56cj95MOSQn+PZBpupJLGLdGOFY4yS5EmuSQwXLB+kRNoFXKmZKNgULYM5V/PirRY1meDbJu946AimlPzAY7fhNZtM1YsM+wE12+D8Toaxb5arFy4S+lO8/UiDRGmutWJsIdjppxi2x6liRNE2+GvfcQ8Pjkt2E5vu4lVOzpVAwpX1DnthRhhl3NwL2eiGLK3sgVKsLa/xVMvn7MDinvki1YLN4uXrfOrMCvuGq9w77XPogYdo2zUcAXVCrtQPM7B8vKuvIApVWq0uk6rFa9dWWBtIzizu4Q1adOMUE83NVsB0cbTio91Rort4+538wEP7mT0wxy9+6TofvxBwyujSrJe5sraHyBOqZopriFH5v+9T9WzSNGayWSYwfTwd4JnQcCHMBVf2Ap5YDgmimGg4oFCusFyb40Q54+7ykPl8F0sKDmRbNAmYL0n2WQkV16ZR8vjg2w7xmZfWcA+9iS9/7neRhx7lS3/4eS4vrnAq22GlG3Nyoc4zaxErtXkGMXz095/nDGNMNUsUTINr7YyXb2zj2wbjeYdpO+PKTsDeMMazDVzPZKzsUbQNDClYvLnEztoaTVcSKYF0fBaf+Co7gxipNYMk46XthIIpefLGLu0449zWgExL2lHOdNWlZ3gY+w4ynD9GMjFFqeDwqc8/z9JWF9M2eOz2cR69/zATRYcgzVmYrPH1TZCWxDtyhNc2+hRcj5XI5Ksv3mCuajPrZux2YwItObM84OrGkJ7pk0qbsysdlJAcqNuUTE25t0KeBEyLDl53nTTPCc+/xq3Ywo322OwneBJM26RsQVMEjBsht3oR4cVX+J1Xt1jVJRYqDjrNGCQ5zaLFvbMVUkwyBEbQZWl7QKoUaZ4TYJGsXkd0t4hxsKQYieM85vx6dyQwnBL9zKaoQvqFccatjA4mwyTn1tYepjCwLINwbD97T3yNXhhzaNznkNFj8s67eNO+KrW8R4mMTqLp+dOUPZvdwhil9goFQ3PaC5ip+Thhh4JOsHWC4ZWQZLjZgMLpe4nXltnMPSjUOBqvUxlu8LWWy84gJtxcoWNX6MyfYuvrz9Buh4ixMTy/QGIWyJoLJJkm7+7gipxDFUFquiTzd6GERCcxConIclR3F6k07swsVqPB2MP3I4oVRHsN0VnDKlXQUYx/4k7Kc1PsfO0ZZG0CEfTQaYoem0WGbWRrddRZ77jkQQ/Va2PYNnG7hxQaEAjDQNzxTaBSvKQDWiBq06NgeG8HLJd8bwPDs5l4+zcTNw+hvQLadqC/DYbGNzRISao0viO5tDqyqG1S4sr1DYLttW/QKzfW28RpxpG50Xb87YfnAFBK/bmK3z/LWVlZodvt/tHxD/7BP/gTz/vpn/7pPxZI+/89zpw5A4AQ4o89X2v9Jz7+n+b9738/73jHOzh58iTvete7+PznP8/Vq1f53Oc+92fzQv8L5w0Tv/fffz9hEHD21XOYeYYo1/AmxzBOnqRZNCgeOkRw/SYqDBgsrtM/+yLhzatEr71I90u/y+CJP8CcOYQzPolZaSArdYhDzPE5hFtA1CYwdUKyski6eo3B9WuYRR974SjZoIe49iIT/SXCfGQAv3e6yPYwZjeIcC2L5dxDDDtkt78VY/UiYnYBHYdkW0voXnsU0ktjnvrw4zyVzJFqCyyJLNd564+8g/TCs+heC/wKuEVEsYrut5HlMbRXRZsOFMfBHlV9EXRGDRDlJpguSBNlGIjZ25DTR8hmjoNXRaCRUQ8ZddBWAeIe9HYQE/vQwQC8MrIxDW4BrRVGfxdt+QgBtkoQfpmf/Vt/mZ9/6hWCxUU0AqFy8kGfbPEiUatDHg5w9h0gb28ipMTub2JIRmhoYeAYkvFKgSTLCfc2sIMeE8NlDs/VGJ5+lNX+aAs/0xKtFTfaMSILya0iMo9Rfp2gPIsu1DEqDdJ+F2dmBqNS59kXX+ZDXznDxz7+cT7827/DL/3H3+Zn/tH/QePgbZhSM1kQTMgIW8WYpoEZtYlzTS9WOFLj6Rjf98hKE9imSdExWeqlRNVZip7PvbMVCpUSeZrjW1CxJY4hqLgGw1zCwdOo+WPM3/dmPvL3/wp/79/+Gq8urZG299DRkOzWZcJXnmbY3uF/+ts/xU/8rz/FUBs8/tTTPHJ8HoIeenOR6NqFkbVDC9CagiWZFAOEIXEZ3ciI8jjDqWPkQqL8Ko6OqViwr2RS9018U2KrlMyv0/OazPqvb5smu6g0ZicVxLkmyTNSNaJtG+REpkuOIhMSK2rRNBL2+aCkwU4/xTYlFdfkeFly35TPkbECU1WfkmfzwL4Cj1/couJZdFNw9IADtx3nzFqXp69sUzAFaZbhDnZ4YaWNY9sYeUSiBN/UjHngyDi/d6nD//7vP4tvS6Rh4Nkme4OYPM3ZG6YcG3fAtnj3m49yc6NDZNp86tkbHJif5G8/cpCnvvgky+U5uhisru+glObxtsNQ2sxNVNi0x1jxxojdKlFhDH//YcYPLXBuscVSO+J3X1lj4tIZamLIHcYehbBFkmW4pkChmasWqM/s50cfO8b3veseJo4cQNQa7KYmM4SoXOMKQckSlE0oypE/dKAtkkITz5IIaVKRMVg+Y5ZirOBwvOkxWbYZXnyFZrDDbUVFdxAwtGrI8jhZEqFKY4ztvMaU6mE6LoYUpEriFlwqU9N0sGgnmhNHpzn8nX+dT//rn+HffPw30Dur7KsVOD5ZIneLNOtFzi+3eHk95NZGn3gQsduL8UtlhokCp0iowDYhSxKWWiETZQeF4kDNo+JKvvTyMuMVl2G/S5wkpJbPL3fn+cyFDmMFg4YjaQUplYrLibECVdviLrPF0Cpz+7jLWii5vtOnrS2cWhntFQkyzTctlJidrjJd9tjcbrO0vE1ZRDSMjIcOjnOqqtnoxlibN3ALRYQp6QYx77t3hlany9yVF3j/XZPsL1v04pw791eZb/q4rse1m6skuWZaxgTCwrAsntk1CFPoujWQBvW3PYbjeFCfYV/NQelR0K0gYow8YhDltAJF7Y67ee+jpxlrNJBSMFnxkGlCmClqhsJYvojQioEySZcuYqmcdixYiyyeaUmcYQspoGFrikSk7U1mnYx+nBIqg1QIfEsS+jW2Mgs09LotbEa2jnMbQy4OLaI7H2C7E9Cs+BzYPMfqEKIcUqU43vSwSnU2Y4Fvmzy5GnNGztGKcoxiCceUZLaHthxEPERJk8itoYMhiTZQC6e5FnpEiUSP7cPMI46OWfgiYaVxjLbyKJiSYP8RPFsi8gTdaxMpiUHGhc0hO1ad9dREqQylBLkwUJZPfvUlCAfQb0GhQZ6nGGMzoyD43CGUWyVevkbwwlPkWY514h7yrQ2i7pDidB2rtYouVBDj+0jL0+hBa9TmlCXQPIB0PILlJfzDh/AmG5AlGMMWsV+jbxYZRjkqy0eLTI1ZRBKhkMSvvUC4dAPDK+E++M0IFOmBu4DXcz3hNoNUUbUlvmVwY3dAvLfJrnJ59kqblQuvAfk36BWt4eyNHe44OFrZ/KV/+H0AnDt3jiiKuO+++94oqfQN82e58lsul7/hcBznT/yfP/ZjP8alS5f+1OPkyZNMTk6ytbX1x56/s7PDxMTEf/ZrnJqaYn5+nmvXrv2XvUl/RvOGiV/Lsjhy5DY++5WvEXhjEEf4B2+nX2gwVvXROsdulJHFAlbJJ1hcZPDaWdJBn8JkncH168Trq2BYr5PCDKhPoMIBJDEiaJPsbmONT4HWxOvr7L56gcHlKwjLJ7y1grO7xIKbUDY198/47K96DGLF/rJBxZLo9g4FIlSSIXWOKNVHlDLHQQ265EKycFuTq7pOvHQVqzKGbsziTY8jLBvZmCI4/xLxyjUEIGxnpFDSEdZZ5CHaLiHylDzojRDBKkUb5ui8QmNElrNdIstH5jGU6phRd7RFZBfAKY880H4NnYwwwjqNUWmC7O+OOobTwah/UlroiQX2jde5f3aCjz5xZkSVmz6I1ppgZZWsu4eKApAm2c4G2eYKortJZbCC1dseBdvQRHaZ6XqJzbUVrM4K0rIpBDtMWTE6z3HUKHyyMoSKpXmqJcn21ke1bqZHigGmjTk5S3zxFczqGLc6ET/3u1/nZ//drxLV54mdOrFwECpFxD0UJjZgGiap5RPmJkuqwk6YUrANOnHO01s54y5caUV4UnCwUWDCSXhmLaSrJJHpcXKmyMGxAnXHIAhDuoOQdpizG+REwsYQFuHhB6jf/Sgf/7mf4Cf/z1/iys0VBjeX6L70IjJL+aEf/0l+4u//BO6Re5goWDz54qu87dRhxPgs/d1tsiQmWl9GS0mGxDQMqjLHi9q4w210npNNHuGSaNKfvZPYG0NogWyt0SyYHK55aAXdRLOtC/gyJ389ZKTilIoFphCM+ybTvuRI1cQ1JFEKoRKsinEG2kS01kHnVIwMJUw+cPc0S4FEq5yqZxPkUDA0c07CAwdqlAou7753nlKwA0qQt5dp6To7vZiSY/KJp25wZS/kZmbT7kXcCE3WE49BrgkbCwRpxtyhw1y7tcX11TbdTownYaHuk8QpABfaOZc2+jy1mXH68BSvXlmH+iS3QpObVpOf+uFv5fzSHr/6hVfZP12ms7pC79UXefxyl8mSRdPI2bDGeW1rwG1TBbRfYqrq4suMo/smKBY9PvTkNdT6LdwzX+TaJ36L9U98DDtoYeYJvpHjmZLMLfHdDxzkhx7eR96cw7Qt5uanKHoWHWVhCk0vTmm39kj6LUwhMaQkxyBQFoEy0UIwUy9RsQ1Ol2LsYolis8lUb4mqBbXBHlbcJg6GkIXEWATNIxiWSa/XpRentMKMXpIz3qxzx5FZ7v3W9/HUpz/K5Ow8f/ff/hrVE/fyv/1vf58Xv/L7GHnKZi/hgcMN2p0eBdtASIFpGSRJznOX1rmy2aclfOJM0ItyCiZ8+aUlelFGPxztDO0GKXpsjqeHFbrDIVprQmXylvsOc2Glw2o7RmuFYwmiJGcnzDi73qXpGdhSEA4D6kbK3RMuS52IqmcRpRn7qy61Y7cxUxAsjPlov0QsbFphxpgLmhzp2JwoQ/PAQc6ttMlzxX66tIYRnb0Wr9ZOsNKLOHOzxW1jFgXf5k2zRb5yeZuDc+NIw2RpqLi00aVXnGZ6vEFsFSkWinStCsV6hWbRxRAC2zDo5aByTaoknWGCMh2avsX5uMjeIGa/lzBIcw7UXOaKgrusDkkS8HzgM1QGg+o8V5wZIq1xpWa8YFGfmEI359mOBXK4x64q0LbHmPANQLA5SPCkIBcmnhTMli2udnOGK7cIcoPtQcxaK+KzlzqYjTEmakWauod57F5aUUZD9di5uUx7EHK4LCmYgoqlOTpV4d13zTIZ7+LuLpF6deytm6gsQ2UpLJ8n7Q9Jo4Bkc5OrYycoOBK37GI4NkH1AHXPITSL/N4y1LMuvdzAnd1HJB0Sv4bZ3WTQbXG9p6n4NnVHUrMEy4HA1AlqsIfeW0PMHYfeFlkYECwtkUuHeO4UeaGBbrdJ3DJWY5w8SdDDPmrxLPbh2/EPH6N0+ADJpZfRuUKMLyCXXkXtbDA4+zy0N8DxoFAh3Nhk99kzRHujSjVsF9lvk6YJLeVgSIm2XcTuMtneGr3nvsbKb/8eaavD5tMvsS7rSJVjuD4CBX4JVajSjTXXWiFpnrOyM+SoH7OcFYnSHJX/yW0Ir17f5vShkfi94/h+YGR5eOSRR7As642SSt8wmdJ/Jsf/kxkbG+PYsWN/6uG6Lg8++OAfwT7+0zz//PN0u10eeuih/+z/t7e3x8rKClNTU/+PrvPPet44SDXwox/8Pp544nGQgtSroOZvp59Lqjokb+1SPHKUyul7qJ0+QenEUQqHjuHN7MM+cBS/WWXt975CFgzIui3UsEtUnkFIQd7dJd9eGa24mhbScqk9/CaSzS0M10QNezjNMXLDwlUJ88MljO0l6p7JfM3lWidj0gjR+45iqBRx+G50v4NKEsyJfQjLHYEX+m3q02XeN96nMD2BbE4jd5ZJNjaRtoX2q6gkJL1xCTVoo6IhOugiwy6yvwlhH7IUtCK9dRUxbI3Cblqj3DpkKTLsobwqbhaj8gztlEfhtkINrTSgEG4RMBD1ScgSdGsL3dsBr4R2yxh5BvEQQyVIt0g+GPLXv+NRvra0wZNPPodKYtSwT9xqE+72MOvT5OtLiEqd5PIZVJ5jxAOE4+BEbYZJykZqM1Oy2exF5KYzar0YdjH2bnE0WSbGJsszxjyTSVdxfjOk9ZnfQtk2ZCEF1R/dsZcaOAePs3LlMj/273+TX/w3/5r5gqJppngmWBI62iKzimRK080EA+GS5IJMaAqmYMbRHLBCKnrIqUpKPzc51hz1wr60uMcd5RzHlHzt+i5BGGNIwVtnTAxyphzF+dUBN1oBAs1uoFBS4umMuHmQ4sFT/LV3fxOf/eLzxFu7SDSpbWILzf2nT9D0LaaLEpeMbPwAHHuY//kjn+WjT58dUf1MCxhhjNEKvXwJtX4VHYeYKqLh22jDIZMmMo/INxeJckGQaqJc0c4kiZZoYRC4DbqJILM82qnEkYJhluNaJqZh4BigVI4tNF1tspVYBOUZAukwNAo4JFRci3FPstaLifOca62Afpyx3UvoRRG5X+LaRpd1XcLuXmXi9keYKkqev7hC0TF47K451joR3devy3EsCrakSMZqYtDPNO+4Y5JH3vYgt5bbjFVsnnj8RSpmzsHJIkcmCnTDHCklrfaAoRKURYYyfZ650eJXv3gZbZm8Sa3gOzZ3zlbol8eZuP0Uwd4OX7neZik0OTpTpOhZbPYTtNZ861jK2/ZZTJdMjh47wMGH7ufqwGFj/31szB1n46nn2PrdTxEEAa1Wm51MEFllbEMxJiOqrsW+ig+Ox0YAUgBKMUhBXXqZiwOTa1tdet0eSZ6PgDWGRc2RVEVCQwwZCBvHMLD9It2nn8UqFBlMHeO5todtGezJOn53laTYJBEWpBEzckic5QSppuAYPHKowYn730zU3+WYNyTJBXe+6RHe8RP/J8888SUWBoscq1t4hslP/PC34VeLPGy2aa2uUvZsvqM2xDMlT17Z4/HVAMcyOeCmvPvOadJ0dAO1HSTkSvL2O2epuyb9bLQludzX9MKIBxbqXNvssxcpSpYkCiJKjsHZxT1+/8qQpV7Er/7eGVYCeHllSLNgkcaKww2P/QVBwXe5R27jCfiuO5r8zbeO0w1iDMthrRcj7QKm63Ep9KjaBjJLqBkJCrirYXHn6QUiTM6t7BFkOVd2+tzqDLi1PeCBOZcszynaJn7Bo1goULahlrUpuC5mMiTJNevDjFfWhwgUrWGMWLvIrZsruGELhMYQCq0Vt0/5ZFoyk/foxSlbmcG2UWEjgPGpSQp7i6y0BmTSQsQBHhmujri9qilGu/hRh6C1zY12wEy9QMMRPHG1TX8wxDY1wpBsx5AqwaGSRV6oM16yWO/GeI7J4abLfM1Ga4hSeKEjKNsCq7dLvO82tpTNMI4pDbY5u5NQdzSGNAisMsPiOLuZZNjqkmrJhqgiXYf+c88QNQ9g1GpsBZLtzMQSGVmmQAhS4fAHF7ep+jYvDEt0KzNMWgklArL6LKvuOC+0JHupwf6yiVGqYYqcsm2Q5hqjWEMWaxjFIrI+zfDqhVFN59gcpudhTs6jD57G2ruFaEzjLRxC1hqjALVKcGfnMRuTaKVAggjb2LMLDG5cwW6OIepziP42hldk9alr9K+vkQ0DhGkjnAL63FdpxZqGDEY1mXmC7uyggj7ZzhaFfVNoneNPNykZGivpkyhBKiy0MOjJAkLAtd0hqRa89/DIJnajpQkGAVk8+BP1yrmb2+yfqDDVLGMYI2n0pS996f+Vloc3Yo4fP863fuu38sEPfpDnnnuO5557jg9+8IO8853v5OjRo3903rFjx/jMZz4DwGAw4O/8nb/Ds88+y9LSEk888QTvete7GBsb4z3vec9/q5cCvMHi9+1vfzuvvvAsnd6Afnkf7UGEZ1t4fhF7dj/W4TuRjo3pWBQOHseensaanMUYm0ZlCqdeINvdQvVa5LvrmMuvQWkcWW6SbyzjzB9ClCooJclWbtF88734R05gLJyE+jRi/jRsL2JYFn7cYbx7nVNGC5mnRFmGHHboX7qI6KxDqTZqZmjOo/0yKtdorUFrSiuvYNYaJCs3QAjs+QWk1oh4gFWt4t35EDroI0oN4lvXyFubiLAP2YgnTp4iHA82b6Dz131N6QCtc5RTIrMLGHmArs+incKo31AKRBZC1EUZBpgSqpOovXXS3XVEno3ulMVoJcLobY046CrGPXE3tbvv59/+le/mZz/5JVZeexUEOPUyVtEl2Vqlv7Q86kpWObrfRg37o+q0wTaWTnFtg7Jj4tgWG/0UbdvI+gSGV8CIBjz+d3+aTz99BZcMZdh874JJ49FH0V4VmQ6RSMhTcqvIH7xyiR/96Bf4hV/4F8zVC5hCjAJEuWaQZvTwiPwxPFNQ0hGuVKRKk2ITJCnFcIeBkiThkGKlQdGRNIJt4kGfetEh9Mc40vSwyHl2ZcCXboVc7GRkQUA4GDDW8BkvjHpwiwZEqSJFooVgxxzjxNws53fb+HPT2M0am6nBVK2Mo0IKpmSQKDzHIsg0oVFktRvz+atrJEpDa4O9MGWjn4IwkFKi2rtEYwsYSjFdtDDJMQBlOHDwLqxsiEOEG3dpOmAJQSpMtDTpS48wE+w3uqRa0E8UKglpJRpPZIynW7h5xExBYpuS3C0SZ4LFTkwWDfB0xG6Y0RomXO7kHCtplAZ37To6i2iUPFY3duikmie+8Fne+d7vwa9WUMOI9SAnQtIomKS55p65Ghc3BtSTPT5/tQVac/t4gX11h+OTJWYrBmeePc8Ddx3k3EbA3uY2Z2/ucffBOnNNH8t1uLjV54e/6Tjfc9zh4ESRxx48yNnVPme2JH/p0RMEdpFnbvR5fHGIQcLzSz1+5/kbbLUjjpRNnK0bFD2PL2xLlrMC5bLL/XM+bz42zuMbOR9fzJk8OMfBH/lBXr7eZu3p53n+lz7C7l6X7WHM+e2ELiUmCiZFW+BZBlVX0BARZtzBNjTjp+9hv6/YXtvEa62OfNYix5BgSU2a5yirSCR9QruAZwv63/YXUUGXkmOgTJurA8lOnHPDmmVvmLAX5axQx91ZZMzRTBUNojhjaWfITM3j3r/wN/i1f/mPmS6b7K+7zE2N0+v1+d3dAgeKgmbJoTNIiLoDnGaD9zQiLt1YpX54gTHfYrsTkGaavWHMb14OmS9Kjk4UKLoWVhZSsDR3T3qU4hYyGqCRKK9AJiVHZqucmqsyiHOu7ga8fHGRC6tdTjUcDizM8XsvrvDgg7fjOQ7dVLHcDik5kknfHIVPewly7jBDJWgUfaatnGbRQWnFvrLNVMmi4NpYvsc9RyZppi12vEleasEreRXfM+mGKY1Gmf3NKgaCvW6MV/F5ZbnHejdivORwoOoSZoo4Tlgfam50Yjq723QiRdlSpFJwvRURJSnrgeDayi7//rLk1bXR9U4vvsBXX7rOVxeHJELSWV8mz3IGuaCqhtxVF2zuhRTyHhXPYrEd09cSQ6UEKQTbG5RMxUZpgfGCjZHGxHFMkCu0aWMBKdDYvcm5rYAxM2Fq3z5y0+L6eoul7T7HGy5TdgJZTpCkrAYQ93r89rZPs2TSSzRpkhBevISB5tJuyuV2xtPrKX2vyQvLPRZn70JkIVUjJxo7SPmxbyOtHyA+8gDHixEnmz7J9SvIqEOSQ8HSLEyUODVX5dr2EE/HaA396RNYQRu1cp26IzhUd5mSI/tEZlhUXYM41zi2gxF30XZhRFndf5R4YgazUscIW6jSGI7noXdWUevXIeoj4xhjbHa0sFMeQxw4hX3yvtGu5+4mUkjc8Qmc2YOQBOhoiIoCBpsDDM/Ca9Sh1ECoFDV3HM82KeQhRhqi85RwZZF87SYqVzQfuodgu0vxwH4qaoDR3cIRKSDIggF5rrjaGuKZBnXPpLu9zpYoMznms3n1Jirs/4l6pTdMWNrs8vM/9q7R370eX//61/9cxe9/75CLj3/849x+++28/e1v5+1vfzunTp3i13/917/hnCtXrtDtdgEwDIPz58/z7ne/myNHjvB93/d9HDlyhGeffZZSqfSGXed/zryh4vfYsWM0Z+b5xKc+g2FIWmFMzTNhYh7rwAmkXwLbQ9YnRr2vjVkUEi1NrFqN0lSZLEpG4bJhH716FW3aiEIJWR1DmsZIjIkU58Q9I3pccz/52lXk6TdjD7cRjg/9Dka5hqsUzuVnOBEvsqFKI+pcrsiXLiNsj7zTgjhEFBsQdDGrTUqH5hEqhTgiWVuG6gR2YwLRnEUPuxiOi1GojMJrpoUwTbpPfw2SCJEkI2Ed9ZC+j5ASkgCC1qgqzbDRto8ZB4g8JfMqqDxBW97I2qFSlFcjLzQgy5DD1ihgpRTCtFDtDUTQApUiw+4ItwyIQhUWTjP+lnfyH37qb/FX/+WvkY7NkkUZeRgTrayQJwn5zhaiUCC4foV8d42svQVBl8lkj6IEXZtmeqzOSntA4lRQThGdZwjb4ZUvneGn/t1ncHZvYqqcku9i7zuC0KMwiJYW3Wce52f+7v/MMze2+I3P/D7Hjhwkcsqkpk2gBK0oY2uQIQXESoAAd7hNMe3gmxqhc0qOQWy4DLMcVRwjkRZ7EchhC8v1ma27pHFArbvG+iBjox8T5+B4Pp+9FbMnixRsiaNiNttDOrEijlM6uUGQKrbDnOHd76KxMM+P/caXuWDPsJQXmJ2bQSjoRglrw5wrGx2mfcHjL53j3nvu5ofe++386lPnUeVxOrFmsmCMgokTB8hvexhdahAUmsg0xE+6WGkfmUdkThFPaCbFkOylr6PTmCTP8aXGMSVV12JHFsnsErbOMLVmLTLoxwqVj6qojCyiJDKk0ITKoKJDtocJZ3ZBxUOqOuCbD9UomRpDKzZ7AcPtHkKbtFtd5moOT7+yyEyzzjvvnGK8YPNNJyepFB0O1gvsq9hEuSZJc2ZqPp+/HnFgvMyFmxsMU4WpU+xCmW+/rcpk1aFQrfLobRN89amLvHjxFg83JHv9iPmxAnfMVHhmI2adCrN1n0qjTrlRJhwf56WVLqenK0zWi5yYb7DbD7i0tEunN+TSzXUe//GfZv1XPsylpW2qvsuXXtviN55d5XNXu3hS82BNU6k3uBx6dKaPsPbQO6jMTDN44Szdz/wmr11Y4vz5W3zq5XW2tvYwkogJQjxDY0VdTDRHChlhoUZXu/jVChvOOIYQDLSBRY5OY4YrN4nzDEsIesolqc8yPjvF470Sl3uKuisYKzn0wgxbJ3zsietc7Gi245xPd+r0BwEOORXX4KvPXeWFS9tUp2aZOnQbZ776BXxD4puSom0QOkUuDgT9JKWtwHJM2tLlxfJRSjrgMzdDLt5cx3RNJksWaZbTT3IuhRYbvYTDDZ+dUPP4tRadKEGUx0jDAYkwKXkm51a6nLm2wwu3OlRtwanpEt86k1PWMW87VmGi6jJdcSiUC2RKY6uMKd9gYcwnESa+43D7VAmky+m6IFxfZcOZYGq4zoQYUs97OMmAg+kWlYLLdNnipiqxe/UyK4vr9EJFlOQM+33unqvSSeHBuTIn949zZLxAc2Kce2YK5MIkzqEV5zRLPtXOLXyR0nIn6cc5SJP9RROloeGbVCcmyGcX2ItynIKJ2tvmy7sWmAazzSKVok8j6VBUCWvdiFZusjlUPK/H+OTFkMAqUK8UaViac1sxSW+PVvMoFwOPRGtiLdiKFNva50jTZbpgsDJMQVrYKubEmMfN2MY0Rt9l040yP/TgHEesIb7jcs9sgd7uFlfWeniuSyvWXNtLmSkaTFR8/Psf4iFrkyjJeWZpj0rRwUIx5hm8tt7jcugSCYfErmDUJkElmJZFtVrDt02cIydxVs4SY9BPMo7VbEqOxbGmT+JUWbXqmI5DIB1mDyxwZMylZCoy0yPKYdbOSQFHKmQagVYMjQLa8jHnj+HPLBBJBxkFo5v5uAfVCZg8gtGcxTh2DyLqo6sTiCxFJgG6OjWSGDoj72xjzx+EOEBtLZOsLkJ7h+KEj+nZDHbacPQ+hFJ0p05QtcEwTPK9LfKlKwRLN5GGwc0nrjC4eAlvagpnvInMQoROcYIWZthl25/m96912OyGHG4WWd7YhWTIc9uCS0ttwn7nT9Usm60hdx/fB8Bv/dZvceLECY4dO/ZGyqRvmP/eIRf1ep2Pfexjf1Sf9rGPfYxqtfoN52it+f7v/34APM/jC1/4Atvb2yRJwq1bt/jIRz7C3NzcG3aN/7nzhopfgB//6z/CRz7yEZb7GXGm0V6ZvDyNbm8htBqRzlQOORhuAbW7QXbrMs7EBE6zyXB1Hen6YAjsuQMolZFvLo4AE5gwtg8xsQ+GLfLpY2jbonPoIfQrj6Pbm0RejYFXQ0cB2bCHMX8cO+rQLBh0itMUxoro6cOkwQBrrAmmQ7a1iNAKrTJKRw4jvBLCNLBqlVHaVOUI10cPOkjXRcRDdJ6iWhtkg4BwaxtdGQPTAA0qyxC2i5YG9HYR/dbIy9zfQuucgeEiNCTSRZsFtO2jTBchTJAWIh4igy7CMBF+Cel66GIVouFopViaIwoOGvIcqTJkFkJlnINHD/N33v1W/tq//DWKb/12vKlppO9SaNYJ9zoEt1aQvo8xcwi1sUhebmLsLWGHLUAyZQzo9AOS7i7aqyAKNVQw4Lvefzc/+5ceQna3RjaPsI22HZQQKMtDpiEOARdWNvmb//vPY9guKQaW1CjhstpPMTRkKmd3mICAAinKsJBZTDneww22KAjQtse4A6bt4YscS2pesRfophojHuKonJWtNkcnijh5wt1zZeZrPvfsq3F2N2NzkDBXVIz5Buv9EEumbA5yCjIHpaFc59/963/O3/iJ/5UPf+V5/t5P/QyTh0/Sd2ooLbHbq+wsL1IKe/zHD/173vXwnXznA7fzB8+8RL6zim+b2IYxWuk3JGltDhuFIQRGHiGGLcwkQNs+GZKuLJA6FUQ4wEv7TOUtEDBIFWGmaUc5z+9kXO/lbEc5lmWitX69GN5Da0WWaYxhByvqj2AsWU7VH7WHqHBIlAlODy5StgRm1MesFth5+hmM3WW8LODIWMaOqnBxL2V/w2PPKoCQhFlOoi2OjZe4Y7bMqZkqc9NjjHsCncPi3pBPnG1Rn6hQHGvyl979EIkwSHLB2956N5vrW3zod5/n9gmX2ZpLa5iQas3jNzqs9yKWeylnb7Y5VLVY2+vxtQtbfO7r59luhfyl0zO851iB4+kmS9dv8aQq8LSa4g9eXub2/U0Ozo7x6G0TbPUzKNbZP1VmrODSW1/j7I0dDtUEexOHGX7v96Nvv5NTkxYz0TrP/s7v8ZO//CU+9Okn+ezLK+xduEzn3DmkSsjT0Q3jdNXn/kNNjs01sU1JMe2i0pwggxf0BC/9/D9n9flnScIIbTqYboG5iSpIg1eWh/z2U9fZ3d5luR1watYhxeBAvYDhF/kPX7nOVpATKXjsgUOcatrcP1vm2Ld8gE9+9Fe5uLrDejfA8QvsLwlaw4ztIOWu+TG+7Y593Of12O6FzB3cz/1jkpVAo1TOze0hJyeLvPnIGGmm2OuHxFnOXMXlULPIv/jCVa53UtI4Rjs+nmFQdE2W94a8cGGFM7c6aCGJ3Srv3ZfQ6of8k498nr1BjCkEm4OYD9xeoujYbPQTRptWGss0CRSUxAhqU3Ml2itwrS94civjpd2Etj9B3TVohRnNgs2UDDh9ZJYTky6+LWj4FoN+yJWdIZd3AvpRQhBmdPtDzDzj0k6IpyJKloFUKYXZAxQch4mqT8U10GtXUEJSsiSD3S0Cq8RExeWH7yihd3e4sBdTOnCMhf3TjBVG9Mhr5YNs94asDnIe31DcCjSuY7F/qsj90wXGyiPf6D/9jefYMOvUjJRWAlNFG980KHgepudzpGoxlneJUk2YZrzIFFtBii9yrm/3sA3Bo4cbLFh9PJFQ7a+yMwj4WrdC2u9h+x5F22S5F48WMtKE3dwms8u8ezpjvGAzXXX50uUdkiRBxgG2aWKqFCU0WpqUZYZnmVRkRo6CiQVUFJOqnJKhqfsWvSSjUi1R9B1M06GTCQquQ6FawrMsBrmkp+2RCEgitBa4YQ9tuWjbxx9skCxfI736CnLYQmcZuCVk1Ic8JV25gVEooGeOIcnBsMD00XvraMtGRANUGiOrExAOEEIivCI6T0huLaHrEwy2BnQW90jbQ0QaoVvrZMLGzSP0sIUatFDJkHCvjy7V2dwckPa6NL7lnTB/go5ZRvkNxGC0o9tSLttBys3diBu7fXaXl7mVeFy40WJ5rTX6vv9TJkwy0nSUXfiVX/kVfuiHfugNVEf/Y/5bzhsufn/wB3+Q6xfPc+XyFZJckxouAoV2vJGBv7cFpolKY9TeKlpKktVF+hcvQhKS9cNRzZftopH0vvx7iGLtdcDELjqJyLdX0BMLWJUKSAPLr6DiEB0PSV57iYHfZKdxlH5pfLSa6laoBzuUom3yJCFpHiC78ir0OxB2Sa+dA6+EdIvIQoloc4Msy7H8MjqNEFqTrV0fYRSrzZFNIcuR5ToYksJEA/IcnAIYEjXsooZ9pF+EQhmBgP4eur2F1hoTReoU0UohsyFK5eRCoEwH2dvAiHoIspGFwvaRjWn05BHE2CxYPkqao1XnPENrhXILJMUJMrdMNnGQN73ruzm5f4bPPn+O4vHjSMvCqFawih5KC7zb7kYU6yO/8+o1+i88g3jlyygE7rDDmJmx2h6M0sZo8s42k/NV3sk1cHxEGozw1VkEKmWYgoz7OIdvpzK7wOrGFv1Y01EWKhigsogpM6HkSPaGKYnSDJOcIEkIvQZKa+i2GJgVlhOJ1Bk9s8ogzUmQFG1J3TM53PC5x21jOA5n5STjRZeiazJXsrjRChnGGSfGi/i2gbRLVH2L6YJJqbNK1cp5bn2IpwJKFoSNAzTveJif+sf/lN/93Od4x3e+j0S67C8K/vBX/hWLyyv8rV/9Hb7/ez/Anffej6g2sOIIWSgiR8V0CA2ZV0VhkOUg85i2KDB8vbkglRa50mR6tKpR2jeOF7XwWjchT7ANQabgSN2kn2iiLKcXZwwSTdO3kIYBWUTe28NcPU9z5VkqG2dJpMNc3WPaVug050YrYLETkDIiXU1VfTYTG6M5zppZJ7AqiPYGx28/ybOXNvn8azsUHBOlFI4hqBcdKrZkI8yxpeDoVIEpX3Di0CRZDkenKjQdgVsucmr/GEfGi0yUbYq1It/+zfdRnBhnbW2PxRsbLO8NaPdCFhc3WN/usdD0qVd8zKk5DtR8zi93ESrhUitl5dwF+sLhvqbF993d5MA73s7KPY/yyD0H+cjza1i+zY2bK1QKNk9c3OFzr23zwmsrTBQdVnoxx6bKjNGnVi2xonx+6ellgvoE3/X+x6hPT3Dm1g6Pf+15nmobPLuZ89pTZ9l6+VV6rRb26gWc4R6+zKkQ4WQhPWVQsjSnZiqcics8c26N82fOcb0VYhsGc2WHqmvz8PFxjh7bT3NiDL9SQRdrVD2LMRFyarLAX3rzQfbVC6y2huRCcqObIxwTGWe844M/zm//85+kH2XUD55k+fzz7IUp++tFTtZtZksmpVqFByoJB2aqfP1an6lmhYVm+Y8APuMll/mqgy/hzEqXi7sRYZLxvrtmRgEflaPcMluDmP1jPpWCw9JahxcvraIVLOYVPr8hWenHPHp6geMzFYZxgiEE2D7HGw5lz6ZoCjI96rT2TEnPrtKZPk65YOOZgrmKgzJcYm1yqZuPgjcapEoJp49wcr5Ow5EkccprmwFxknF83GMnzNkdhKAVn33yIvn6ItZgh16U4fY3iaKYrjNGK8mwBRS2brCqSjy73OPGZpcXzi1ya32PqsxJel2+/spNfvOlLUxL8JZ9NQ5XDTqxZqxaY7JaQEmDY80CBxsFHjtY4x1ijYnhCiVLYCYBf+8dR5m2U9YocaDu4ouchjf6qZwuOvjFMj27xoRvsRNJ9jU9FtsRV3YDThRTxjyLMVtjqAyrNslaeQFp+5hkLNQKDHO4a67Mg/vKCKWIDBdhQNupo9ZX+aYJyVGrT7HokJs2ojbB7jAlynI6w4Q4V6Smj6EilGFRFIrE8keNQXFIzRL4ecihskALGLz+WU4YEe7WNUQWUTAlaa4RKAyVkg+72O01wue/gtq6CRjk64uEF14itxy6yzdwt69DGhHkBtopYe87hMhT5PJr6NYmaa8P2zeQfomwOIO2HDatCYTKUcMuhAGUqmhpIqpjbH3lGXZ2QwatIaV946Qvf5lsbB5Lgh120FuLGHNHMZqz5GHMcHWDh37k7VTuOI2sTbFXmOWVPdBeCbwSmV8DBJ1hgmMIbm53OFrOuJkW2Lx2ifb6FnFv90/VKw/eeYQ0Tbl48SKvvvoqH/jAB95oifQNk2v9Z3L8j/m/nzdc/NZqNe659yE+84mPEecK0zQx0hhmjkOvBeEACahhl+DiS5AmxO0eSWeAsG1UrsijCKM2PoI7RD3y3Q3yYMjg/Mvsfvo32Hn6RfJ+B3nrPForfAOce78ZMXUYvXyJCdWmaOSYpQapU8Qo18G0sL0S2eF78Xeu4kzPARIVdEn7A5QyEVmC6rXRKiPd2YZCGZUkqCxBOkW0YYwSptIcBeTCIXa9geFZiN4eKolHd7uOh1GsgPu61aJQhqANaQSrl4mFTWy4dGNN7hTJpE1iuojBNghrFDbQGm376OZ+xMxRtOGMgnAqRVneqGUhicF2Sf06meGipKTrT6Hnj/FX33YXv/grHyZtLmAWfdIox2nUqd5+ClkoI9J4xE5PApzmJHtPPonevIYolJkrCNZ32qhb59HbKyDA9By6Z14muPIqrF6CoA8IMqNIoEfQD11tYrs+u90ew1Thhy12tU+KQVl12Q5yFuoeZdtgbxDx4k7OAIdto4H0PAwJk46kk4x49SXHpB1luCrhgJtREyGZV+PacBTqcUx458kJzm+F7CtJzm10GaaK/VWfosgwLQfPNmn7k2jpEIUJF7vQDnIQAtfQLHVjlF9lYBYJcvB0yN333MPP/+Kv8bMf+gSPvfVhpFPgpZsbNEpFzr38Ems3LrJ56xq55RNID4FiNzcIDI9US1KnwmbqEmoHoaBqaYTKyOv7yEwXUR7DUiOh2XRBKclC1WOhXuB002bMGxHrpM6B0Za9sXUT7dVQSEiGbA4yfJmxFDlc34t54sIOSXUS2d9jL3c5OOGhmxMsPv0iD03bXHz6aQqNCQ7PVhgMI+aqHlGmQRo4AsJMkeYZNzpDKo5FwfXwbAvfloRK0E/hVitkrdXHNg0802StFXBkusz84f3c7CqubvaZsTRZkoFhYqQJUkpun69TqxUplh2+5Z4ZHnvbvQhy1m5/M2fXBnyiX+dqYQqnMc5WK+DszRa7q6tcvLLJ+OQ4w35ErVHgvtMHefPd+7g0kDxyYgYKFUQacWrM4ERvjShM+NUvXuTixpDvevQ0737Hm/ie976NSxsB7X2HuWhO8MJmzGefusJTv/TbbH/x8wx7LcI0ZyU06UQpKQaTZspbv/87WXj0IQ75GS9d3WSQKkqOSZxltKOcaqVErgWpEvgFl+1hgu16zJRtTs7UOWBHPFLq81Al4LHbm8yWXY4fmuS2O+/hTY88yq2vfoKH3vEeHv+dT9LrRuzsbBFqiWuZKK9K/dyTPOgOyYXBeNHh6kaPRw6PkWQ5t7oxt7oJBc+l7Bgs7wb0o5Ri2WPaHBEmC9Ua40WLrVZM2Tf57red4K5jc3TjlEcON3hoocFktcpks4rlWNxezDgx5nG5q7jRCqhYUEk7dMKUKFcowLVN9tc8jH6bgTfGxlBQsAUzZQ/TkMSZZq0fs9yOea1vsTWIeWKxx+WVDg/NFaktX0UEASXXwHZdDEvyrrtm+eKuxfmNAFRO5lXZHYT0lcF+K0aqmJ5T45gX0VQ9/LDLsq7wyScv8fiNLh+7lJCOzXOi4XB4oowkY30QstQJcW0Lt1Tm1ITPRNljvGgx5ih+/+/+/KhnHYXIYk4c24e0PMaKHlVL0+8PqIgEX4VoCVpKTMsmVorNfozrlbmnPlpUrBkZG8OEnpYod0QEa1oJJ80W+8er3Hd6H2ae4tkWJamYvPUigv8E5hDsNmYQSUy5WOaRuQIVz+Jgs0QvSNhLoGhLEiWw0yE6S7gamOzEmp/+jWf52d96HtNy0JuLhK09/Dxkn6uYS7Zw0yGWY6PWl+hubtKOUgpZnwoB9WQPUZ8Bt0jabUFvl/jcU+x+9evIo6cIDtzLZv0YaXWaeOky0QtfQwGiPoW2PfSwSx72keX6qFe+MYsT7qAFNMJVdG8XWawiak1AY84cpnjfm3E9xV4/JWhF9Jc3scYmUZaPLzJka3kEiSpUyfpdpr71mxFJTPOB+7Cak2iviG2ZlFwL2VlHJyFtp06Y5aA1bzlQpZH2WOoLXr2yg9YmSdCDPPlT9cq9Jw+RJAkf/vCHee973/vHtvTf6Pnv3fP7/0/zhotfgP/jH/0kn//0bxHHMUpD5vhETgVRrYNfRrtFhFck63YY3LhB0htSuf04xvxtTDzyMGapOtouMUycSpng1iKdc6+x8cRLDG+uIkyL5OzX0MMeQ3eM1DBRpoMKBqM72/4uJorSrVdGvtrOJvHrotLVKcIrosIQMX2AbHMVb26O4dnnyIIAsgT/xJ1YY2OkO5voYY+8swulCvneFsIporVCmiZaSNTu5ggXKwXpzQuoKMRo7sMYm0EnMTkGWgvywjhIE8OxEFpg5wmxFoDAjvtYUo4EUnV6tJ0kBNockd+0kIiwi4h6aCFH1LeghXZ8RBwhsxgn6aGFgRYSjU1xcobve/e38iuf+hxpf0i4toFZqkM8JLpwBt3dGol1w8CulUFpdj75MVTYZ6zoIqVkx2mSl8cxxmbJkwQDQeels6jhgHBrg3jxMiLYpWAIlIbcdFm8eYPbDi0w5hvEGERash4ZvDwoUCZGCMF6O0RIyVjBxpKCx5cG7AqPRBlM6i7NSgGAp262GAYhRR3idpdxext0pY9lmLxlf4UDToYpBUmukNLgzftrnN/qU7U0PWyqtkmFIWN71zB1zJHpGm9tZAx6bdIcZJ7R9CzKBviWwDc0HeHz5ne/n2D8MN1U00lNQiwapRILt5/gU19+mk/9x4/yz37hX/G9P/CDtNZXcUmxyClkQxqOYJDkaCkIFQgpEOREhkc4dhDLsogrcySGRYEEiaaTSSZLDq5pUDYlWsPVVkCaQ2566CxF7D+Bqkxyq3YcP+szV3b4/GLEtZ0en3/6Mg8uVCmZJqrQoB0rfv/MLY41HB5bKDD11B9yKN3j4OwY42WHb75zlmPNAndMFsiVppcoJgom6730dUoSRApyLdgLM27uBbyy3se1TWyZs78oaHqCH3tgkpJtEmY5262I7dTgK2cXkcMut803ePTEJPNVj+1hTJTmvHR9l6evtyn4DslwiF2q0o4Fl7YVl1ua2bLNt9+/nxNTPg/MlxkrWURacGNpHc+xWO4mlIio1KsUjZyNToDY3eB8VmXQi/iet5zkew9LznztBR6/sMUXnl9hvZ1wcKLAMT/icCVjtTjOlZ7iufkHuRI7bL7yKoPWHtmLj3Pp+jKDMCYzPe6ab3Df4Rn6C6d48GCDTGukznCNUUdowzORhqAbZVRsg4WaR4xNPxZshzkSTXFsHNv1uUesc7wkuX2mgikFC6fuZXdzg43QILcLLN9a5Gd+6H384r/5FygtudjOeG7PZJAqHrp9mp1OQL3qM4gzxn2LhZrHZMnBMA3edajM0YkCNza7fPLVLda3dlAavvTSKl8/u07BNdhX89kKMu5daDBTsrGkolIsUCq6TE3WuW+mxHJicmEnhDxjiIkGrocGN3aHRFlGaxhjG5K+kmgEe5nFXgoHS9BNUnpxRq6h6Wgut3OOT5bpRYpWJ0RbDpHtcroWkSye4+paj9dWWgz6EZNTTUS5RqMxRqPsoy2LStxHC4G3t8pAm2yaBVp2ndMVxXGzzX2nD/Ajjx3jznLMW+6Y5/LqDq/1TBIMkiyjbJpMlV08UxIqyamJAlXHoGIbpJjc88MfIJk4hNZAoUYiPYq+Q9VIqZoZgWGTIDEGO6hoiCU0htAoAUIwAjA5PlZ3m0sDk+1OCFmOUhk7ykQbDrbtYIucl262OFFSBLnG62/Sa+5HmiaTrkQJi7GpacTYOHvKZi3QHCpoxgs2bzpQZn/VxbBsfEORrVyn1R4yYWW0Qo3tWQysItgGg0KDokhwdUbTiACJpyNEGBCdeIRFY4ylXo427VEw2ZC4eQiGSdbroQ7cQXD5Au5tt5PPHGeQw4HhLYTK2J68HXn7wyi7iCo2kE4RMb6PvN8ZwagGbQQC4RQwdlZwBajtZZKtVVRri92vfgUsB11oMNzpYChFlGiiQYZhmDj9ddy4RXD5PHGnDVEfo1jGmVvAm51CNKYRlkcoLLSGg14O3V123QlSbXBtZ0jBMbmxF3DUD7mVFblxaRGvWmXYbv2pOqVW9rFtmyAI+OhHP8oP/uAPvpGy6E+c/yF+//zmz0X8vu1tbwMteObpp1FCMsQh1wJtFdCNfdBrQzAg2muTtHsYtoU9PsFK6QDOiXswJ2ZHK8SlBhoYrmyS9kOceonpD7wXaQqSfkI67JNJB4WBYVnIoI13/DSDl57C7m+T1Saxrz5LMnEEO+0jkwFm2EEaJmLm8Mg+cOqtyOYMdqOOTmJkeQyj1MCsT0AcoqMAFfbRe+uke7sjH6btj1oX0gijNoYwbFSagFLoQRsdDEYm9HjwejjOhDQaeZ4tl1J3Eau9TN3M0EIgdI6ZDMiLzRGkIo1RpjvKDqQRQiXI9hrKraDcCsKw0fUZcreMcgoYeYY2LFSe4ZERaAkzt/H+97+PL37lq5iVBqX5aSjWoVCie/bcqFYtGiDKNUgi3EaVfNAjunKe+OLLzDTKrMQWojaFUShjVWoYBRcdJ8QbKwwWF1E3zuEELWwVc/7GLX7+Q5+gUvRJNHTClG3tY7o+GbDcU0Ra0E9ybu6N6oukzpECFhoO1/uSszshoTYI45R2mILKUNIhMEvsulMM7Mrrq7kSaRpEwQBjZ4lBq41OE2b2rtPe2cPUOVIKcgm+bZJXxpmxYhqWomibHCmBF+6xlUoajkEl2MCUEkunXNwOOLcVs1C2OBwsEWmDQEv23ftWfvz/88/4e//0X/HT//An+be/8I/5X370B/nR7/9eXvzyHzCWdUe1TEoS5xlFE6QQpBoybSCEwBGKrDCGEgbboWZjkLI5VJQtqJspltBsJQbtSNGKRgJqkEsGzSP0SjO0M0GMRHkNXl7e400TmpVWzHe/82HebG6gBrv0vDpVz2L6yCGGnT2e387ozB8n3n+IoxMlJko2M2WP3TClk2S0oph2lFL3HW6rO+yruqQKXtoYIoTk3rkqR5sFTNNgp5+wl0qu7Qy5sNFjZ2mR5eurLN/cpFr2CVNF7ta4sJdyuOFRMRRjVsp02eXZ88us7/RZaw0JU8Wb7lrg6FSJe49MUh8rcWW1y6u7MWeub5NkOYFVYCBtVnYClDB5/tVFXrq0weVzV3hrQ9Fu91lZ2SKcPUq3F3Br/ghPv7pIfWGBv/9Qg2NzVVbWNvnlX/8smwlc7wi+tgW3WjH7HTi0f4wn5QyfWDP42vIQDpzAzqEbpKNVrmiIELB/rMyBmo+JppcptBbMlh1MQ1KWinLQ4WDFZL7qISX0M0WO5Fzfop8bdJRNx65ipQMEkmNNj6zcZG1lkSTXNO9/J61nP8XpU6dQccivf/Qj2LbNzLc8RjsXLO8MSBW4lmS7H5JrRUll7Ku63DNp8/WlIYebPncuNDk2WWLSSlHSolEtUCx7FH0L15EcnihScSULJYu9GM63c7b7MYbt8PRyl81QU3RMikmHe6cLhHHKxd2MqmNixgN6iaId5mz0Y9pmebQ7ZZoUpOZQUTDuCHyRUoi6PLBQZ9JOaBRMTh2ZYazu8dr6kO2Dd/HEsA7dLlGUc/++EgVT8+ZGTmLZ6DhkqxsTb27ikLHhNBEaDpZtTMelH0aslecgS3E6mzy9NODaTkQxH1IrOWz1Yq50FUsBlC2JZUDFMXEMAyHAIsWRitnvfA9C5+RoZJ5gGxpLaCydIU2HZtFHv3aG3UHCi9d2EUCcQWuYUvMEVpaSABd3U5TpcGGpRZBBurZMqiWbmUPXKNAaptzYCWhZI+JctzBJ5FaQaYzreZRck7oFDZHg5gFTZR9DKDwTbNvBMCxMFObKZX7jr/4jvvrPfpFK0uZgIeWvv+t+/uF3nmKchKIt0ZMLo9rGfg/HNki1RKUJZhZycKJKPWvh3zyDTmIwXHRrdZRNsF0S4bN74hH27niMYZoyle7i6wjlFslrM2QTc1i7S4hslNWIpo5DFKGCPnhFZDIADGRzlqy1Dl4BQ4Aa9rCrldH3eBQQbLRQAlxTUj8+j0oT9PYtaG8R3FjEcH10kiD3nSTLFNHiTRh2YfoQMQ6dRFPpL5HM38n5gclSN+G5xT3KjsWtxWVSJZicncS0TVxbooZ/uvj99L/8GwA888wz+L7Po48++sYKo/8x/03nz0X8Sin5kR/4Xr74hT/EMUaeR1dFkOco04GJOaKNFeKdLt7EGO54g95r54l3tlCtETNa5TlZe4uds1cQpkXj/ruZ/t6/TLx4hZtfuUT7lQukvQC0ohC2yA0HDBPd72CXSxiddfTGImZ9EjPqkxUm0JYDXhmR55giRRWaiDgEDf7x05jVCnJsGiE0QkisxhiiXENFMcnuLtIrIFWC7u8hDQM5uTBKvk7NIjRY+w4iJw6gOhuotWuIYgMR9VCOj2HbUKojkgiWXkOYNuW0A0Iiww5G0CF3ShjBDsq0UG6FxPKJDBdMF+VXyd0K6BziAarYREsT7RRQlkssHLxgGy/YoRh1yKpTGIUKVd+jV6ii8py8u4PRnKVw9Biqt4eoTYzaMbKM8snbcCtFyDPycMicB61OhyQMyf0ahfveglmpUL7/HsyZBYqHD6ErDX75d77Md773u/nwx3+LU3fcwS//458i1xKjs0lRJlzaCxlTAx6dNvjarQFnbrV5cKGBbZpUHBuVROwrQt3S3OwlvBI4nN2J2A1SvqWRYFsmW1HOK13BS32HpXaASjNK+ZCWVWfdarLfSzi3EfLMzTYLruLado8wSDGB3PAYlqbpyBLdWNG2KshyDWVYzBYt/LTDnlHGzgKyTOPLnIXBVZykj1Pw2R6m1NUAZdrkTonM9EhNj9AocPr+h/nkP/9JfuYX/jX9XovErSCEYl4OcaTAFIxQrDrGEGAMNsmFSV9Jnl3pcbWXc6MXUzQzZDokUZpBqhACiqYkSFK+uJaiurvEvQ7PBCUG2iZKcy6stNhZ2eQ9JyqcmiqwshtiFcqYUjBZNJjPeugg4NuPj3PirgMkxRplE45UTMq2JEhzrmyH5JnCFPDa9pBASyaKDrEaCSHHgKJj0otywiRlmGZY0sK2bdy0x7Wb27TXl/ieU1Xee+8sZdvk2HyNe47N8vL1HX7liVu8crNFxYRatYhXLDJT83j6wgZnLt7i1Zs7HJ8u8867Z1ioOXQHOY5t8/jlNk8sBez2I/pJRmW8SrXkI4EPf/ECT+1kmC88wb6ay7WewEDx3ErEQwcr3BLjJIdPEiP4sXed4q8/0CDOc3wTts0yR8YKzJw8QlBucv/t81h+ia8tJtw0m9w546LyFJGGdLTJtb0BJhqZDrANA0uYlF1zFL5VYNk2QRiz1EkwBaMqQjRlR7K/5tFTJpWST1wY56mOSS/JmCi6xNrCKtYwh7t857e8lWx7iTuPHuT0m7+Zm7dusb/mMjk9gV0qs89JKHo2GZr3nBhnuRXxm8+v0o9TXNvB9yyutxKqvkmjaJFlKYnpkjkmbz0+wbGJEg/YLR45WMeSBr1Uo8MhZZGxHYEpYNzO2F/10Epxs2cwTOBKO2Km6jJXd4iUpOkbIDRbgwjTMIhyTcW1RuQzJbi6O2qleep6nxlfsL68Qtzv4diChZrPvobPUysZVOrU7Yx7FhpsJZJ2rIkyhdKa3dTiN15Y4YXCPFJnOL5PQcUsrW1R3LtBrzBBbthc3Yn47RWTuFyn5FrcdfoYf+OBBp1hxHo/YbrkkGhAa9J8BAFItCZTkOYgTItMmCTpCNMu8nR0M56lmEEHV+TIyTlKy9dphfGo8UQFLHeHTJiKa+2QXq+PV27w0efWWdztYUR9tq9do0SMZ2ieXI24d8rmL9wzzphvYxmCrrIpVBrYXoFcS4QUZPGQ3PbJhEGYZjgqpjjcBiBHYEmNEFBamGP42uVREFoIXEMynu6hr72I9EpIQ8JwF58Ea/k8iVVi3WnitJfxwh2ODhbRSjPMNDLq0H/mSdqFGYpHjuD3V6meuANvsEHDM3FcF1WsocIhtkooDrZR3U3EziI66KCvvoK4/c3odIhyCrS8KVh+DZFGZNtrhMu3kNVxlGVRPHSIxKuh164SdQIKpkSaEh0NyIcdspWr6I2b2OPjWAdOko4fHH0+pQZmqYgediEJ0dLAMgRxcYpVXSRV0E9zDjWLTJZsTpYS2nYNK9dMzO9juLvxf6tTHr7zKGEY8oUvfIEf+IEfGOHn/5znf6z8/vnNn9un+7f+9t/mpeef4+qNW7SClEhJdDwANMouEq+ucPFGl1hLjHqdpNVlbuMF8uVLo6236xcJr13Dn5rCm2zgnbgLo1Zn9/mz9LaH6DTDm52hoAOs3ZuYO4uIQgVZrGAVKximjT02jg4G2INdjP4Wyi6QCUleqqGdwqir13JGgbQ8RTbnyFvraMMc8cbdAsItYE3NIRwX99BJsihCDzrgl5FeAWwbwy8hSjWS5ZtorUaBPcd9Pc26hlaQOuWRUA37WOMz4FcQeYKRJ6NAQDTECHoIwwYByi6QWkWU7Y/qzhwfpMCIh8gsRUsTmcWjxgFp4AU7SMvFGO5h6xApcgj7fMvD9/L5rz2DTuPRKnKe4d/xAKq3B6ZDurmCShKk0NjNJub4FEbBx9q6QcNSrG1to22PbbPMz51d4v3//jN84J9+mL/4T36Fv/CvP0lglfilX/8kf/nv/2Pue+hhCkWXqYJJ26lSC3YRWlPJemzFgrmax/6xApuDGMhRQhILA2G4lMpl7puukmSK2YrL5jDhfDxaQQCB0AqTnGxznZ2VRbRdoOKY3AglUXmCAw2b55MyD8wXOTG8zjOLHZI0RQpBJ9GkwmaoDVYHKWkQ8nLb4NJexLousp25iGRIpHJ8v8CqPQNPf5WX4iqrg5Tc8rG7a/hb1/C2ryHzDFdFJNLFPfYgP/GXv4uf+he/jDJdtIK29ugkox+aINfEwkNohVy7ghl2QGt2hhkFU1I3UzYGOV1ZRCHwDZhxc06JTQRwLxskxSZxsUnFGlkl2lHOe6ZCllsZ07pN+/kXkQdvIy81sYTAN0ycs8+zr79MtzlDzVHcfffdfOm5V9iIJNtBgi1hsuIQ5JpUQTdKEejR1naccaLh0rQVO68HboaxwpSCUCk2Bik7lFirTXPi7tvxkiFVmfJXHl3ggcMNZhoFDs7UWFgYpytdnjp/i2+/rc5bjjWZL9tIlbGxN+SpV27y4c++zFNPvcybD1d49+km8zNVJhsezYrL6rUl1m5tYQjB937TUb7t4UM89q0PsZMIuO9REr/KTpSTFSu8/+EDTJQMgjjixT3B+kaL5tQkh++6h4op+a2zHWYqNks7XXJp8PTlDXKl8OpV7prxuNJJEWNT1KsV7HhAZrjMmyEi7rCdWQiVUzYZedAtg4IlqPk2xw+MMVXxRuX+WiCEpBUpKo5F0bbIkaSMxPJEwaJiSmZLFlN3fzPLz32RuarDB//m/8KDb30rt/qKbpjgGoKmrZkoOtx5eB937q8xV/a51U2YrvkcWWhgoglzyULDZ6xgMVuwKNkmhs6ZbDb4/7L3n1GWbnd5L/qb883vyqly6Oqcdvfu3TvvrbiVJSMJgcEGYXzhGh+CsQ3GAZtzDMeyj+8BjgP2sTFcMMImGZCEcpZ2zr075+quXLVyevOc98PSONdfkBnXlsS5Q88Y77caq9aqUWut5/3P5/97RqHidnPE1bUu5+MCiVI0uz0+f26NbpDgWCZ7o5gk1ewvSg54KYcbPicPNBilmmnPpOKbfOl6i1oxTzfSVByDx8ohftTFk4phlNLJTNJehwdyEWYScnCpgalTPt/zqQ13aA8ibKnJuxbH5ouTZbn6DA8Vhnz5ZptpT2LvrHLIS1DBgH1ll35i8exqn0tdjQxHLG29xvUwxxeudbjcijixVGVleYb7F0us1F2eOFKllYIFtIcxFc+k5ggc02C9H6GVwhSTZsY7I4XUkCoDJQQjM0+CxIqGyKCD0bpLLh1gzs5hVEokhkOYaVwpeHyhzLlWQt51uDlQ7J/No5TmoaNzSJ3RPPII692QO5u7NMcpt85fYs4MSTIwgLpvMGVrVGeHqqkQWpElCW5ng7VAUtM9IiNP4lXQWiGkwlQppu8y91M/zRt/6R8DGTqJkcmQwK+Tbqwy0DYOGTpXQ8Zjes89Rz7YIdQSMXMQqTWDfffzfOEUmxQw0LiHj1EK2xgHT5C4Bbygh/bL2GJSmx7W9qFyVaZUE6e/Sbq3TXzzAlgeurdDWp6nOXcvo/Mvk0bBZKG9uU68vYN2C7DvJEJI5PR+TJVAbw+dwW6Y4eYsTN9F1uZQGvRogPOm95JWZpFJwFi6WHu3cefmUVlCnJvCICZvafpmkVRBEGdcb47AEITDLiVbs5r4fPnCBsNRwt7G3tf1JycPzgNw9+5dnn76af7KX/kr3zgz9HWUaUWm/jsvrb4lz/3/bjK/Wb9oeXmZ6YUDfPxjf8z+ex8kck3ybh4jGqPaG5jVCjfOb7JQtYlmK8TdAaWVADm7gBIW3Qs3KR9eptCoYc8uk+xtknWbpEHIyv3z5OZrWDNLyOYq6bBPur5B0tzEakyhhcL08xiGiTZMpO0gOmtor8wwP83QyVMxNFZ/i2TYwxx2wMsjchWyy69gzB8h2lnHmV1G9TsYlSkcrSckh61VRL6IDoaYlg1+GYIe2bCDMbuMbm0hq9MTbMu4hx50IDiHtB1UsY7hl8G2JwttloNIU5RXhizFiHtg+SBshEqw5aTQQrt55M4NhOkhHGdCvhi0MAbbZI39EAzQ5tcqoS1ngkLDRGcpbz86xw/+H1/kLz14nGB7lyzVOLUpdByRrF5Gx2N0HJClKYUH30Dvq5+icOwEuDmmVm9yJdzHv/o3/4617R3+xt/+O9x39ixOFtJJBYVcgWakiBUUTIFvKgbGNMKQzAU73NIeD1QSMlHk0kbM4arDVCnPzfaYcaIIkphZTxLytbIIMmIF0pQcrPkMopReGHMkrwgKLru9EYtLi9BpkXNsojThdZWYz20ZBFnGDx0wuT4yWPZmeWjvNZ6/fYaj8xVsKTCkiWtK1nqT1qfjVZs7Y831dsjp6Rw6FXQjcAzNp9ZT9s+dQb3yKsnSEW7kChy48AxbX34Zp+xRe+sTqMMPInRGZBV48G3v4T98/udZv/Qyzv7T2IZFVQ0ZSZ8kzlCmRX68TbK7jVls4NRyvGWlyLKTYkYD+rkFDEOSpJqGo7GkxPNddPcO+YUFYstBCIfd9S47/YSHpix2/BkeK1/E0A0WD05RLmaYWUyoXXLDXU6cmCF1StyIfabGm7z81S/w+je/BdM0yTtM8pm+hVaKvG2w1UvZGYTkbJOab+EY0I4EYaoYpxlzFY9MKeZyNlGUUfAd7jmwyPVuQOZVuGcxx4yT8crA4qmNLp/6zHO86a0PceP6xGx9+nKLLM04NOVzcqFMbt5h4OT4zFeuEfY6/PaXr1MrlXjo5ALjcoSwLLLjKxyq5SjkbFr9MYY0uf/oPFWZUJ+ZQQu4N6/43z52lVNzZXpBwvJsmTTQvLsRYYuYT3UcHjlcpVItUBIB+1dmOWEP+byZkmSSxw7VeG1rwOHpPC4JaZKgoyEFr0KYL5ElMXnbIE4SHCNjZe81wsoS62aN6ZzN2CkzCBMyoGAZlF1JO8iI9KQ0xyVDBH1qeRdXKu4OI7b6EQ89cB//+Zf+hC8+c5Effvc7kEKz/eoFng4ibnUCThXiCTkk1RRck3GcsT5KKDkmWkoiDYslh91hxL6yQ3ccE4z6eAD5CqcWFIMw4cZWn7nGNE9f2uTUXI44L7gZGBzzDOZLDp84v8VfOFHh4toAiwF7iUEp59FLIg7ZFvdOezx1u00+59LwJJ1QsN1PaI279FPFVNFlv2Fy+dYdSENev5Tjd6/Ms1jJsaNdVvIOa90xR8omfhJhCRfflPRdk4PVEVeuXOfI0jLXeilffe4qf+10GW+5QvfOTW6Z+7FGm3D0DGmS4xE/xrZtir6NoTVFz8TubNNLG9zqCe7sjTmzXCXNQCNJgKprYukE23BwRUqYTSapwzTDNgTjTFIzU4xogBj3EVKjUGhlUNh/gNNJCS0EgfSQZMwWXbrnX+SauUgvCTg7PclHb+oc5uorxCfPcnFvxMiQjOv76cocRqZIlKBogdW9y3D1DsaKIFecIsLA7bdQ+QqjKEPbFlJILK0xMkEGqK07yMIR5qo+F5M6S0lMmmTIYg372BnKakCGC34Na9RGn3kjrdRgqz/k+KwPWYjvmJQ9Rc4yIHPRfp6mUWDO9bFGLbALTKdDzK277NWOUFq7APkSlglpcx1j8SjcfAXGA9IzbwctuK0rTL1ynaVjl0mOPIbZvIN9PEbsv4+hVaA0m5BJk2RznXRrE8M2mS47CKnRSYLa22S0tk35bd+B6u0R5Rrk924wmDuD3W9jVuqIqRVSFAYwSgUbg5hn1rocaxRoDSK6QcJpucOlscf5O9tcu3QLK+d9LZz9p+tzv/p3AfijP/oj3vCGN7C8vPyNskLf1p8TfVPn+r/4z/4XPvvpTxPGGXliaG8jLAfp5bH8HB/4m+9g4YnHaV1Yw5upE/aG9G9tMDz3HM1Lm5iVGsbUEpgWw3Mv0nvtImFzTOXYPLn5Blg+YtQl7TYxF/YhHY+k02Z07QY6+Rp5oTDJ5IrqAoZpkiPG0IpEpRi2g6FSdHWWpLqIjgKM5cOo1UuY+SJZMEbk8iihUUmCjsaT5SPTJbt9aUKBQKLzdeh3MOcPw9wBsr01stbe15plBGlnG4ZdWL2ATiLS4jQkEdopIOMRMotJSvOTn1EJmZtHJsGk9Q0N0gTbR/d3oddCD1uIrauT4gwhwC8TehW0FKjiFCIJCZVBUF6msHSIh86e4em+IOkPab3wCsnOGqIyBUEfo1QF10MnMTpXJh2FmEuHkPkyd155he5gyAf/8vfw+//pt3no0ccnOVSzSGgVsKSmOU4whaRkg8wS3HiEmYZUHYFXrjJUFnuJSWtjG8s0GcaTKlMpBI4lCZVgdxRStgWr3YhRpHAMA9+QlB2T7jDi+lAwiFLWh5qtRHKo6vPiTp+aHjOTdshZkkfLIVf7GbnmKr9/B1ShRMkzufK5L/AHz1yjF6UMoxTHlFiGQYTFjeYIQ0I3ymiOE6pJG5UlnNhX4WInY/0LT3GQNrfurLPe8Yilh1QJVjRGbF1FCskghY4/z0/8+I/xK//il6lETepyiB+38U1BTQQUrQxUgvBz9JwKtmkwW/Twky6GX8Y2Be0gpTWOKQR7xFqQuBOM3tAusRuByFJmih7HylB57XMs3nqexuIUgZ2nUclhWw4j6ZNpkI4Pj7+Lrw4kzdee5Cc/9Eucfd2bOfDYW8nZBjMFh6Jj45uChbKHlIJBqnAsk/Y4xZEwSjXdMCVRGXXP5KHFIjMFlxQYJwlBqsh7JlNFl/sPz1Eq5LFMk0VX8cZpwfe991EuXLxLzcy4fzHHuQt3uPbqVVZbEd1en531PcLukHe9/ggPv+EBbMviznaX166vsbPX4fR8jqWpMoMg48b1Lf7gE8+zNwx46vwGd3qKUQZlW+BbBjrTPP3KTXZTyXKjwLWtEb93RzOvupxdLNBNIR4NObcTYzk2n78x4Imzh7i71+PG3V3CWPHS9W2evN2m2emzcXOTwfaEcrIaGjRjySDRyHiM5+dJei1mzIS6EZOTipyRESUZeanIMs2UNyFoOJYg1DBd8jAEPLs+YLsXsdkNcZIR3V5I0AvI2wapEuSqDa6ff4X/8pGP0BY57g5TVrshnikZpSm+Tnn+lXPsr3q4lkUQT9jSu8OYatyimA7RCIqey0LJ41Ajx2Mn5rh3rsgbDtXoZBZGsULJs7Edk4rn8OjBBneGgp1YkgmT2YLB9iBAZZKdfoTvmMzYGQ/N+gSpopIOSYTDIMzI2wZTOYdbiUNt5QDWwhF+8zrsDCMO1XOkwqI3CKjKGBVHrF+8RjTo4Hg2H7vaoWBJvjQocm5o8fnrfUaFCty+hOEV6OZmudEcM5w/zOWxw4ydUcjlWKnlKLmC6bzFOFGU61U64aQcp5x3qOQsokwjhSbNNAsFmztjQU4mIEwWHYWjY1witkcxAhhkBpFXQRkG2s6RSIdAQ2AWMaWBJwWpStnsBOwvmlxqat6xZDFXcHj/iSpl32JjkPD8wOFWK+R1h6ucmMkzOzfFSBlYEiIFsdJEl14mWDnD7lgxGIakhkUyvY9T+YS9CMIwokiAT0KgoJXayGDA/rxBOOjRz0xMNKZtIQV0p48TCYOd0EAbJmmugTh5P0XX5pTeIrNchGXjJEOOewEVGcG4S5hrYJoW4s45ss1VzEEby7BQAsppD6lCklydbP06nWeeh+n9BK0OzcwkMR1M0+Ro1cM7egIxtUSgLZqFJfSRR8C28LIRYtwiKs2i9jZQ4xGLT5zmwKEKhilRWULa3mN0dwvDL2KT4N54BhWOKTdvIpIQWaxNWPM6RUiLjX7Exy/tcmlzQM6W3G6NqZsJVRnz4p4ApWiuXmfn8mukw+7X9SbVch6tNZ/+9Kf54R/+4W+0FfpT9ee95OL/n/RNNb/ve9/76LWafP6rT00QYdMrKMNCSxMsk0KtiP/om/Eaear3nmC42Wbziy8Qbm7TOHOQpN/DXjlGsnWX4eomvZsbDNsjyg89zPrnz4M0wCtgCDBJ8Y7fi3/6YaSfI+u2UL0m2c5t9LgPQqJGA1K3RKl9i3w8ABRiah+GlyN2i6ThGGk5aNsmarVRvQ5Sa6Ibl5HW5HmrcISKg0mMIBxDFqLtPGL+MDqNkN1thAaBJuu0JlGHYR9jdgVRbKD37iK+hnBDazIhyGwfqRIwnclSQTwmkzYpYlImAQgNethFqxTyNcxcGWV7pIYz2eJVk8ILLP9rxAdB4hYYzp3k4MI0m3dXids9hNZE7Q5mYw7huESrt+nf3SHq9uh96ZPk9y8h/CqqNMsLmUvZyFCWSyYlQaJJDRspBa7UmMmQum+TszSuTjAlqGCEEgbKtlh2ImLTZdqGty+a7IUZm/2QTGuGsSJvmiRKYShFkiYslW0O1nKEmaIVJNxsjYkzzU4/4Yg5pOCZbI8i7oYGyyWX3VBxR1Q4Ed0i0gZnD8/zufWU/VN5nndXGGkDr1DgwYMNPn5+myCdLFt4tuROL+RQzedQJc8wUjzdtciJDGE5HKq7PHqoyKX5o2S9XU7VDGwpcN71Pgrv/z62GsdplfYxziAvM3pRxv0nj7G+uUU87KMtnyTfIMTAMEwEmig/i3HicXreDBaT/CFCElh5olSB1tQ8ycAs4Ksx6fVX0L0OZjqmoYbc6qf4jsQSgvLKEsH8EYaFBbZlhbS+gjBsdne2+Jt//Yf4iz/wV/mRv/0z/MYf/jF7z32eD/74z/DoW9+NYII0800DQ2iW85KlokXFlhQca8JpFZpxCp1Y0xqn7A4Syp7DfMnjkcUyczmbw40CrWFIL8o4M5PDNQ0SYaHzZUamz5bKc//BGd59T53peMAXn7uM4fsU9y1zpGTygUf389ijJ0mV5vrqHiu1POV6hZwjsTyP2ztDnrmyw4MrFapVl2OHZ7h2t8O5F86zvDzFVKPIR5+9wUc+9Qz1eIcPLAkeO1xhabrEck7w4w/mmZ4qcqkZs1yQHCjbvP7wFHO+pAD8yTNXeeZqi2lXUSz5PPvsa9zci/mNz1zj1aHNVVWkq2ysoM9OP6QsE0rjbXZik8D26JeX6P7Gb9B/6jM457/KcrxDXYZ4e5cpWZoaY1wR4UrBOJmUWFSjLsknPsLMzZe5ttFnd2+Ht7zufv7uX34dYar4zOVtpFfkg//rv+fF51/ie//yB/ns0y/w3I0mz9/p8PRXnuSnfuj7eP5Pfo+f/Ct/kaeffJJn1zvkLcnza30Ghk8WDMG0MaTBME5oByn1nIVvSXYyyemFEtMFn9mCS6IEiyWbkzN5KjmbO92Yjf6Yq+tDemHM3e0+nWHMnV6K4zmEccI4Svn4nYwgVUzlLKYLDgVLEsUZm52QkTYY+2UWCh6/9/RNLl7foDkMeaWV8exWynNimqsdzUe+cp7nz92hnDc5u1Lj1Y0hB6ZynD44y+i+N/KF6y0+cqXHpatrPHWnRzcVuK6NYQpudiOSOEGojLW9ES93NDeaI4Q0eHC5Qt2z6IXphDSkYBSnDGKFZ8BQCWpGSF4NqRNStE3Q0ItB2T5pbT9xvsbQLNKPUrA9kq8xVE2gnvcIlMmJew7R1D6nF0rcVAUUBvuLJg89cIzZokPChEneTTRZHGAbkooMySU9sun9bCQW5VKeTgoVmU2WmPWkrKQZaYJMEcUpFin9OCOpzMD6dbqFOfbXC/RzNfZEnrXQRBkuXeXRjSbYNmkZmJaHaXt0vAbpxm1EewthWJjDFrnRDtorsOPU8Hcu0vzcZ9FZQtLaAiEQ4wHar2BOLeEaEoQk7feRoxa3n7nNz/7xDTZHGV5vA9cE9zv+Eml5njhRZJgYd89xpS8YZYJMaezWHdz5eYL2EKucp7yvRmV/FWlaGMUSTqMM4w4agWHb6M42XHoSkS+j1KR4qpO6KCRhqtjqhzgG3GqPmcmbzOouN0KXnVbM9m4XnSYkQQj86Ybwe97xIDBZdOt0Orz3ve/9hvqgr6dvZ36/efqmml/XdXnjG97KH//nD7MdWWSWhxZArozpF3EWlkn3tqkeXsBwHLo3t1FhjFMtMvu2N+BOz5Bt3mB85y5ZmBAPIoSUiExROjKP6mwTD4dI1wPLRu+sYqDI3f84ujJDvHaD8fNPkfV2IYvRUR8AY9BEhBMjLNOIkT+FE48Ibl5HV+fAzWPm8qg0gtIsZqEwKbQwbaRXIOs2McoN1LAL0kZsXobGMmTJpC65OoVszGOsnCBtbaKzFKk1cmphMo3ubaMNe9Je55dJDRcz6KJyZbQQgEHq+thxhDHYRWQZyisg95+B2gLKKzAqzZMVGighQSU4yXAyCVYpaa6OlIJurHj51ia//uHf5XUrUxiehZX36F65Q+uZ54m2t+mcv4TqdXEqFdTeNoZlojo7ZMU6T19f48F9k0WSsLWDITIcCSmCatwkwcCQkoKZ4aUjAi0Z5Kaw0jFr5gzamnCEW7FGlkrM5ySfv7ZHmmakGuI0JUgg59iEiWYUZSitMdDM+4LXz1rYtsk90z6f3RUUTLi3YtNVNlOexatNxV4ETafOctom31vnDbkxS3nBqcUqgRLEB4/j+D6mY5IpxfYgmJgC32K5PJnImVKTasGOO4NC0w4VbrnKD3/nQ3x6DS71LUYnTiGSDp9sulyJPV5sGyRK04wVpk5ROuWv/shf55//m/9AqE0SK4dEEjkFlDRRpoW2ctQdjRCaStwkzVUws2iS9bUN8lKRz4YY8Ri1eZvtvV263S65dMSRaJWCSFjMC3SvSW12mq12n248ee6/8Qd/zI/82N/gB3/8p/hnv/phfvoX/y3v/8mf59Ef/YdUDt3DOMmoeRamEFhS4NkGd4YaraBkKxaLNjM5B8XkWLg1TpgveTy8kOdIw8O1TSKlyVkm03mH+aLNsbpPzpSMYkXRzJBpSs4yOLNSZW8Yse/gMo+94T6+76El3t2AcabYuXWT1bVdPvbUFd45k3DYiXn6/E2ONjze9cAi9WoBz/XZ6UacW+9y32KZhYrLkTMnyVUaqAx2xxkjZfBiG768kfHIfUd5/6kp3jxn8uW7A7aMKkenc3zhao/zzYRur4/lOHzvPs2Lqx1+4r33kjcyfuVj5/iV3/gM9dlpSkWTesni+Zcu86Wux0t3OlxtR9xXSknCkLTbwogDPMOiKlLSx97Er6663F7v0FnfZnNji2QUUBjvEWIRaYdRqvBNQSRtrty6y8bvfpTRbhdDwNq1q7zlkTMMteRaa8wffeUqrmXw6MEpfu4H3seR9/wQH//93+X//Jkf4t//zF/lpc99lF/61f83H/q5n+HYiZPMlfOsd0Ju9ULmKi6JVUSkIdL22B0nrLbHxJmi7JqkChSScZKSs2A6ZzJXsMmZBhmTG0JDCkLhkEqBaRjct1IhnzOZKjrshBrf1Nxujjh1aIEMuLg9pO4ZPHm7jUTzsZfWKDgGvXGKYQkWKnl2mgNaEVzeHdMPEiJpE2LTHWXYaYhrmcQZ9DoDjs/kcS3BTKPKiark5EqD+44ucKqi6fbGXN3ogtYMopRWkHJlZ8S5G02OVWxKvoVvG1Q8gyRT9KOEj9/osT2I2AkyKr5FNzEYJRqhBca4Q2T6zPqSKMtwDYWSktTKTSanUjJtxmy1ehQdg1TDWAss2ySTAulIlosGrmmgkbiWwLVMagUXYQh6sWZzEFNyBNp0iTNIv/hJzCziQuEQh/QOqZbkRUoCjJIEMx1jmjaxhoFyWQsFhaRP3pK86B7GliD9Io7UGFpyuRWyO4q41Q3oRBkNT5JkCkNrXJFiWgaVravor502dr/wSTBsBspAhQGdUCOvXqJ75Q4iyxBfY97r+aM0tY0YtDH628jpA5ROHQPbYeV7v5OTK1OUXZO+9LDCHg4xCJNhkuFZgsyvEUuTxHRR0/uw0hGMejizM+BVKe+fxakU0YZDuNei9pb3gFcmunwOYdmolTOY8wcnxU7NTWKnRCcT7I3Cyf+vYxArwZ1uwJVLG8wYITejHFVfsnnjNkIaoJKv60v+5d/7IAC//uu/zvd93/fhuu43zgR9W39u9E1fZ/ynH/o5rj75Ka7t9tnVLkKaSCGRK6cQKiU59xVyy/OIfJF0HGPkHJLBcIIHCwZk7S0Mx0JaBt2tAfVDUyTdJoW5KuneJsFLTwECnWWgFWlzA5kvQG0B7eZxDx9GtXZQw+4EnXbhSVRtgXGuwUh6hMLECLtEpRmc+X1ke2uwfApz8RBGZQr8/KSpjUkEQYVDjEoDMX9g8kbLIshilOWindJk8lKZmZQ+xBFmoYzZmEMJA8IBoj6HKM+g+03MO69h7d3BigfEbpnMq4CVRycRMktItCIqzCFVQuhVEaY1Kb5wCiR+lcT0yQCZZWSmO8GjhX0UTIopspRLL7/AB975FhYXFrBLeaLOkLAzovXsSyStJggo33MUs1TCKucw9p9iu9PjjW99B6fP3Eth1KKRDVjf2SVHwDDTJEmKqTUdfDyZYWlNT5ugFeXBGrE2Jl8mwqYxuEuYaZzmOojJxu5TdzpIAamGas5iPj9B8yzLIZ87v0lRajzLZDuSFGxJ2lzjRMVgd22djb0+iz4QBTxeGXMiWMV2XZ7JplDXL3L0xHF8P0fOMmj4JpEWbIxSTs7kiTPF7ihBoelGKXGq8GWGFJKVikuQwd4ooRdleLaFXarzyJmD9FKDX3ymyW/uVCiPtriwMcaxDNphSn8U0gxSNseax972XvxKnZ/82z/F7jBCyowk02R60gCXSBMLhRzuYQ33UMJChn1y3TWiYRdfpMRunVQJPvTMdd7x1/4On/rExyFL8eMRB+0QdMpo5jhGd49RomgPAv6nH/3rXLh4mV/77d9j+dAxbvciEm0wVbDpK4feOKUVxDxzt0MvSumMY3xTUnYnx+0k0SSikbOYz7uM4oyiY+CbEkMa5G0LTwputEa8vNNnxtU8tlyhH2a0Ik2qM8rDdewswLVMHNPEMA2CTLOFy5mzJznjJ9xbt/BHHRoq4MTBWdZbAQ+Nb3KPO+a1S6uM2z388YjvL27zgXunGA4DjngxZ+byPHy4wbFDCxyeLXJzdZelssWZEweYzUs2RwnD0ixHKxYnS4q58QYbnRHvf8sZ3JzHWE+OhHOVOj/6+Dxnpwy+48EVfux7HucDT9zDxVevc/PWHn//O07ztgePYdgOfa/Oa82UdpBh9bYYtgb0tcUgP03eUsyYA6brRf4km+PuzAmWx5sEvT5Jv8lODM/e7XJ7u8cojJFSTBoF/9L3kj36BvZ2m5x79issn3mUa80xt9pj9u+bwrcMXlzt8tHLfY4sLPAPfupv8VM//T/zP/+73+Zf/at/yVumDebNmN3Vazzy8EPcMzNZZDvRyBGnKSpNGVt5bnXGTOdtqq6J0hrHgIpn8cruCMs0qNgaiWBjFONLwf6CwduP1HnPiQbvOb3Ag4sVjjQ8yqYGBIsFB2VYzNd8TMvkQMXjwFyRC3sBvinZHiacOTrDcy9dY7rsMQhTDixVefzBwxycKTJd9BgJSAyTnG+xfGiZ733TMdZbY0qGxvE8ajmDkzWX3UFArpCnkrc5UNAUCyUenrPZjiUGmrxlkCJY7cS8/XiZC2udyclDplBI+lGGAvbGKdIAx5xkP7dHKeM4JTA9EqdMKq3JaaEFs0aMgUZJQaqhFysUgkb3BhaKqoxwyLAFODpFmR71uEMnTNBCszeMqbqSl9YGGEDZM1h0EprdEbvDGIWm11iib9eoygjD9gkxmJIx2srRGaeEXh3VWsc3BFIqplxB5hRIsgzDduhNH6FIjKEzZBxM+O9K0xwmVF2TggWGlJAEJNImMXzKZoptKJLbF/iNn/oVdjsB//65DsrJc4/ZhHseonxkMoCKV69AeY4wjmn1xxNmvBQgwJudB9PGu+c+/uIT91IzFU2rBrZHaHhYyZCaK0hSzZ3CAcqOQ9lIEW6FTNrIhcMUzj6MWytSOHGcuXe/mZ3nr2IVCxgGqPVrOCuHidwSG8WDNBuHJwvoxSqZFiw7KbthyjDKeMfRaao5CwGcndasJy5PvnCdO3f2SJMQrdOv60fyvkOllGMwGPC7v/u73xK273+tVEGq9H/n9S19Cf+30Tfd/N53330U8yU++dE/pBloImGiBQjXo73yCKbv4cwvkrSaFBbKLL31QbypGjtf+ArxcMjg1jrjvS46U9T2lUGaOAdPYM/NMDz8KP7RexAz+9BJCPky0eoNQICQiJOvw6rUJ8dXNy+gDZu028ForZEIA5uEjlnEGLSwVIKYmUdrkOEAwwBrZmlS4Wt7k63U9jZCGkgEau06srE4ye2Wp9DhEKSApeOTWEd1FjHqIeYPYa6cgGIdlSYIJwdpTLZxA5EECJUiVEZo+KSWS2qaX6s4ntTVStMkMmy6uGilJnxNKREIpJCTSXIyRpCBkGjTIRUmJikFQn7g3W/io199YVKr7HtU7z2GW81TP7GCNT1D6cRxjHyJcGcHp9FAl+f4hQ9/jP/tn/8if/fv/0Nkocx81mWjM2akTESaUrBNtq0atoSCGqH0ZMKhwzHKzWONWpRFRDDs8GTbZrFznZ3KCv0YTk1PsoitcYwCtMoYjwOagxHPX13j/fUuOy++iGsKomGfC5dX2WqPUMMeFddmNzMQ4wF727uUDIWeWeGo6OAYkuSR98DiEncHEaM4pRVklByLTMH2KGahaHOw4lO0LEqOycYwYj3QzOZNlgourSCj6E2WET0DCiJhIe5wqOZwfGUGK1+g1Jji9W4XoTWjJMOVGfuKBueaCbudIf/TT/xtnnjj6/nRH/wgO7tNVnsxd7oxlg5BC2LDQcYBKkuxumuIy0/Bsx9lfngHZTgIFfGPPvTPMeuL/Mxf/2FyMmX3tetcH9o80za5OTJ57ZXbtFKbRvsOv/Vv/wWnzj7E3/vZf4Rh2UgBQkh822Ch5NAeJWwPIzINjy1VuLo9wDQllpQ4piRKMpIMXtkaECYZZpYQRiFBGLHbGTKOEpRW5OIOKklouAYlmbA1TCl5Fotli0UxRDt5xmYeR2ZYQrFSdrDlBOdUyjmcePw4P/zYEisPnuGp7ZQ3nVzk9D37WXjsEVRjgdfdd4DZXhNuXeYLtwccqRkcmSrSFy4fvzFAStjqjyl6NuMgQbe32W2NmZ2qM1/N0Sj4bOo8h2frTJcLfPCQxYG8Zl/eIJ/z8G1JlK9RzxvMNa9w1O7x/v0WP/TgND/5PY9SKBa40UtZ3+th2pJ2BDux4AvrMReSGiKKGcWal2/v8dxuiqli7lV7PCh3Wal4tJ06w+XTfL5XQe3c5dLNHX7nc6/y6fPb3OmMmbEzjNe9nqJvs+wp/FyOV1sJL611KNiSh4/MIAUcni4yLs+i4ojI9KnPT/O6eY99nsKKOvz2p5/m3e99P2VXcma+yLF6Ad+ySKPR5APXr7Kv7LOvlmdvlNAOYnpRykYv4qnrLYZRSiuEdpjQGqWMMigQUnFtkkwzTjNcSzAIU5TSXNwdMJ23sFXCxe0xu4OQnGVQ9m1ypqResInTlDcdqlGvFemOI05WLYZxxkzVxbUEKzUPS5rkPYOlWo5H9tdZmq0xGkXc3mhzsipZ78fsBgnPrfW52Iq5tTdmMzF5cS/lya1JXteQkqt7I9JUcGOzx0Yn4JmNANeeIPq0hpmCjW3AvqrPOFL0ghSDjOYoYpBM2jmxPCbDDIu6CpBCoZVGZgmWivEMwUYgkFP7sE0Dxl2eXx9i7twkpyJWyj5JdYGpnEXOMnj9chFbKEQyZr0T4EhBogVrvZhRPKkQz03VGWmD6YLPTV0i0BIpFLf7CQ0HnGRA0Ug4XLYxhKRoKVImew+tYULs5GjFE1a6PW6xUPDwbBPXMijaYsIDHnfQdp6NQUqoNNa+w2jLxqxMY7k2P/vFXZ7dGNG2y1hujmR6P5XHXodZb2CU6oi9W1g7tzg0uoXWGUl1BUZtMAzksIeZjvFMiUVMwzMRQQ8zjTC7G/gywzIlsQbfhBEOOg1Iaktkto/Yf2Yy7GrtoccDBmttbn7iBaLbVwi37kA8olU5yDjTEx59ZYa0vsz6KMPOIsYJHG7kGMUZwyjl9nqb0w3NF9chSSBVitL0AiqOvq4f+dBPfBcAv//7v8/hw4c5c+bMN9L+/Df17djDN0/fNNrDf62f+Im/zq//3n/iL3/fD4Bpk+WqyDgk73iY8/uRhRrJrVvUzp7AW5gn2V5n6ysXEQ8fANNm7ekbLNy7QH6mgj07j5QmzvIx9rAx950iufQc5sF7kH4dWW9DawO1u4GozkyaZyp19NYt0q27mMUSoljFH2wT1laoiRHB7HGGiYHjTlG199BbNwhbu7iHTwMSAZM70co0WguEn59kcMuz6J2byEIFM+iAVwFhkhkmMg4QXh7cPCKNIB5Dbw+qc4hojMwVyZRG99vI6jxe2GRkTCGtHFZ5Hq0S8uMdtF8mtAqQCEZ2CdtxJ0tuAkBjaIVKEyQBWphfM1AaLQVD6VMvCE6dPs0/+fBHeWihytlD+2mvtln4wHvRSYRRroPOaH3s02B4XPzCNdrbW9z38KPEqcapL9Io1vG3QnZ29zhYsulbDhgGOZnQFzkqOsZSGtOUaKfCzmCHhujRe/FlrvuneKQhyUtFU2W4jkMnzLAMwdYgInAk075D2RrjC8Xc4hx3Vp+mmxisVD1QFVQ44jef3eHn3r7IsNWkJ2rMRTv0swUGThlRLbDc73MtdKhkEY/MeqwOFZ41aUur5xyao4gv3Wyzv56j7FnEmWIQZThSMDIUr+wFFB0LRwokUI46JLvrXJZ19jfqPGYPGe3ucrKiWIsVfQP6YUq1lGe902HO0Hzk2pjvPZ7ynjc+yvTcAj/61/4aVq7A8on7+JG33MvMqccpuoJMG4hgiOH4JMMehptHSoHq7fLcy6/R6fa4712P47702/yrj7/MF2LJX3nnQ+j3/QhPvnSTQ6vXWKhL/vXnXuP2zRv8o3/4szimIEnBNQTzBZe8LdnsR7zhyBQ7w4h2lDJbdNgbJ1TcyYa6pRXJxmU2d1qcdvNcac6wFN2kVq3y4kvXGVgV5o7uJ+8EnA9MHgo3cYYpned3qDz8RuxqAyEMPBXQtup0IoXKssm0Ps0oOSZHqxblsIUgxvdL3HdsiUNTHYQneWUHpoKQUWxzd2fA4spRniiMONCNeKVncmTBpZsqTs4VcCS89uRzXHEsvvu+abY6AY8u1ZlJW6hcjT1gpmRj3b1GabqCuPEyWXmKUmWeYGqW9GulKkPt4a3cSykZMTIc7FGHo0vL3E0tPvylqyw6AZmC6ZkZxtLFLrtsJwL/0BkuXltnLbFZmSoyFjPI3buocgNTpdwypzgUhZCkbI5CHtlfZxSHfOHZ8wzDkLccneLQgQXc8R7q2qd57M3vwDYki1WPA9U8NztjnrvTY3/d421n9/GHT66yuGLyjkN5ZhjQc6p0xDy/8/t/j5//lV+nG6QUHIlSYEjI+i0QgmLOIWdJbCk4UvdoBhlxBlLCmw7X2erHzBQdkjSjE8Z0A4OaK/AtwdZQkbcnN9ZFW2JVSxSjhFaoMFzJbMHGdyySbILpcmyTom2w3g251YnAy+H2Qp662cPzbYorNeTktpzlikOaCSquyULeoh1m3HtwiiTJ6EcJQkqKjsWoBHMFh+WSx84owjYldSvFzeXwbZOSb9GNU6amfe5GmrMrZY5U8wgyTAlplhHEGanWbA0jDtRzeCqiHrfIVxYYRQnCUKRJinYkdhYQC4ftQFO2JEJOUH/DFHYzTaJiKqHGylKy9du4OZdYVjGVzcGKQy9S+KS0x2Pumc7z66+0ODqVxzVtHs31eFFNUJrphVdpvLlBYhjkHME0I4wLz3Gz+jhH5gWGEhiWSyeBdpBQ81KccA/l11mq2MybIwZ2FTTkwzbk6uyNU+ZLHndbI6bzFsW9NfALOIUVRrGiphRyeh4djvneX/pbTKkC/eIUVzsZ+ws+vlY4jSniKMGqGZNdDwyM9h7jlfvwumtg2qT9HiK3jTDniC2FtDxSJdDCJL9xAVUoMlImSikqnoWfDmjFBoFtYtkuhulRjvvg+qSdDsHGDrlGjoufu0yx5lA6tDz5Gx0zibIEzxUk5XmGqYmTQFdkNHyTfpDSDmJ22gEHo21uWi65YoGVI8tcfv4VRr3Rf9OL/PB3vRGYRB6+1VPfb+ubq2+J+f3xH/9xfv5//SfsbdxGTx0jkR42AZgm5qH7EaMmucPH2XzhNfIakkSRm6+jMkXlzNFJdD0OKczXGWz3wHVJ7lxjfs5gVDxK98knKfSaRG/5QeorAarfRMbjCWtXxRPcWb6CbMwQXnwVM+exs/gQjagPpoNtGJjSZpwpvIV7sLev0f3CZ6lOL2OKBGG7E0auV0QWypAlBAOF195GhCNo7EPoDC00IkvAciFsQjyCaAS2g+63MErTiEEb4iE6HCP8IngVRDImdGqYWYqR9FFOHnPcJnXzBEYOhSTIFJ3E5qALQmVYUqMwSLVA+VWcweYkhoHECHuMyvuwLRjh8TM/8zO8/Ie/zqur2/yLf/lhjk/N8NNWmUayjarMIDu7FE8e57O/9UX+3bkL/INf+FGIAwLpkivNIHo7LDdcbm1ssFg/ga8DpOkzVDYjpbFNh24cMGVoouYmwrAI4pjh6ir3/YU3MMoZWHGMqQSebRJkmpyhWeuFXI4zfujUFKM4Y6pSwAjaTD/6GE2lmXJtTK9IOzP4S0f63B3BSsFimC9jiAFG1Mco1hgGITeHglwRWomgVrDphCOkNHBNiWUKLNNAA5mCKd/mTm/MYtGhOU54dZSyO0pwLZtmmNGOUu5kEqcbsG/RJmdJthAUzr3I1rFpDNNmXkSQz5OgSaRHvXsT066yk9jsCpODh47ym//xPzHs7fGp517jwx/5NJf+9/8ThMZUKb5j8cCRFR5bKHH4nd/LplOlcvVL/MIv/ns+9K9/FbFxk8X3fDf73vvDfOx3/5Bf+pM/QrxwkXf/4I9Rmq+zee0yf/Lpj/KLv/F7CDkprbANA50oHNsgUopxqigJzXTBwbUk15pjSu4E+SZUitKKO2OTvY9/nucOnOXA/fPc9RYYJS61e++juLtOz3CZMvts5ot4o5BnejnUesZ9r36VWDuUz56iIy02hz32lEsjZ2EpwZdutXl4qcIgjHGtCUbMsCziQFGuFHEMzan5KkHk8XhNsrWzx2oMwajIkhfy+LxLtLOJP7uIrwNe6RksyZBnwoTL/TZ/8USFgeojDAt55Tkct0a8eIx8qcBQFiiYFkapgZ2FGMmYyHSIMkHBMUHkwBB4vTYGGSt2wINzPs1WHlOUMAZt0u4uO60AKj7O8iKbdpGv3B6ysddncGwfpxbzvJzNUri5RufCNiKO2Sc0vd0hz2z3SG6PsfM5Xr+/wGrH5eNPX0Z/5CO0b73MO97yRr7zdfdzxRAsVQqYEjZ7IYcbPiuFSUTj5961QqHgUVIhWqTYQvBrf/wpTj/4CI7r0Q0zDMOg5kiUyhj0ehiWQ84SbPYDKjmX2ZxNqhMKjskikGQWm8OIfpQxlzNQ2iZMFVEwRLseqVJY0qBRMBinFjJReLZJlGQ8tTngUK1AqjU3O2N8y6DuWbSCFMs06EcpU2Wf4wslNnsxoLjbGnP/UonmSDCMMgquScOzSLTGlAIpBfuqHnuBjQAavsFswcExBbYpyduT/LFQJtJ0kFJzoOzSHCfsbxRYzjsUPANQdIOYfUXB7XFGojRaCzxHcGlnQM01KFh5GpZJO5pQTaxRBy1zxEaOzLSQSjJWAjLQacqCHPPrF4f4tHnjiUUeyl4hmVthoB3udiPumzUxETimxExTvFGLdn6BWtElZ8MoltQaU7A9ySn7p+7F3LqKMbWfiunidraJpg7wkNlB2wuoZASWS56YvfGQjlekbsF2P2Ah7xLJCqaQZFHA0MxxyE+pWyYXujEq7FNzCgRumcuijh4n7KvZEKZgWqipFfKVZd4dDUGE/Lube9TvmWc23oBCnWymQfSZP6B431n0zCGkVyQxbYzSDE6hRnr9MuluG6++xPl2zKGaSTntovst5LBDVlvAScbsKA8J9H/nP3DpcpvKT/8DTtcyUi3QlkPW6WJPzdF76RXy82Wm1vuAwi4VUF4VmSWgJViSfmYwzmDKVahEECWaK80xV3cG9Jo9Hjxu8VvXBIf3O9xYa+EVyuxeu/R1fciJg/OYpsHVq1d58cUX+ehHP/qNtD1/JmVKT5bV/zsf49v6b+tbYn5rtRrHjp/hI7/725w5/vOgBQWnhIxHsHUd8kWMqQVqKzuM+glog4V3v5HeuZdpv3yeXKNMMgAhDEqHlxhdOE/u6HHo7eEVa9y606Z+OsFs3kI7kzttY3YfUaGOHHXonzuHm5O4s/uwH3kHxqiNnQZYakgqoCMqCAmbw4xNBLOVw1SPnmC4tUm+YGEvHEFoDTqBXhPleATNTcyohXQ9xMY1RKaQK/cggh5GZ3OCKMtX0J1NyFVJVy8iq3NIWZ5EH+YOIwtlMq+K0jFO3Me0LJI0xbAykJrMcBAqRkkLQ8KUbyHTABkOkFKSmR6xnSfRFi4GqbSwxm1QCVKljFNNzrKInBJPvPF1vAmTH3/jaZ6My/zUP/llLD/PO9+lee7JL3L1+i2KtuT1D5zgdXMF7O4qWfUQGlC3zzN3/DGuru/RHIbMlAtYQpEJSaoUO4EmUQYDy8Wlj+EV6Q96zL/7HeRKDomW5N2MtW7EIJU8OF/A7G4RUaAaZQRxwN1eyjF26Xun6YoC1aiNtnLEWnCsYnJZLVJtXcI9sB+jt0dcnSFIYnr9mKqpqRRzbI0TcrbBnUHKIMqYK5r4toEUgoanOV0v0k8Mkkzj2+bX2stSru30cRwTz5LM+AZK2Ywyk3N6ioO4NAzBQbPH9qOvY3VzmxtGiXf2t5i2CrQC8OqLtJI6718ooLXG8l30zh3WxnBqxuPNT7yF2Xe/mRe2E3KOiWFIDngRlz/xO3z+pXP844/+JL5lELb3+LF/+M9YrBbZkIfAyjhca/D93/cB/m31AG+aUXz4t36FPwpi9ppN/smv/AdOLFSZ8gR7AdhCc7MfIhDcag8ZpVD1TMJMYxsG9ZxNwZokn/xsDPGYfnUOv1ZD2HnuqwlebXk8ulDCtkzG5QIzhoUt8xw3HfrpEod0mxdO3kvrQIOZnWt0d3Z4KalwwmtRaRwkGw84vydpuMbEhNkObW3TiyQNKbjRSan4No5QRAhyOZ+KJRjpBvbeiDcebnDp8l0+cb7JQxVNlkEl7VN1q7RPnKLSkSwWCmRegbutiCmnSnHpCPYzX6AZS0YvPUV24ize/FEkFZbtMVoKnDRgaBSRgJmO0b0d9Ool9IFT+J7Hw0ZI4bFDqFTx3N0yOTKOzzWZcVKGwy3MfJn7Ty7Se+oyX3r6Nb4Y9Fk5eRQ1MNFXN7hwY5Wvlhys0ZC3fc9f4Klnz/H0U1fZvzjDhx5x+a5PPYdTX+YH/taHmJ+vcPXzX2Dqfd/NgqO4MVA8slzhZNWkGjUZuFXywG4GoTbQpsHqXp8/+p0P87//+n8GaWBIQEOiNZkSZFGAX6oihCRTkGaKYZQxTBRBnHKzM8mpt8Yx9ZzN5jChGyTkHYvXpI+dJiQKxqkib0l6YUzRtclixWzeomD49DK42RozU3DJmQYIcE3B4akcB+o+N3ZGvLoRcGquxPntAY/sK1AwBHdaI4I0w7NNlosO1zoB903nEVr/XzhWxxBEqaZgwSAVFE09qQpXGbYxqQF2hKJU0CAsZooOFZFRczW98ZC6lfDyriLINMsljyBTdIOUfhSyWHSp5X2iVBGmkDa3iJHUpaLtNHAQ2HJSRZ6kipoe0EwMXMvgpYu7vONonX7jEI7n0U0tmuEAspTIMIjSjLTdJC3NsNaJeOuhGuuDjKMVi2c3DR6YtdjoBRyszTJY6+HFAZYj6edmKRt7LAiNUjFXQp+jMqRoKk70L6KmHkJrk2l6dGObYQYNT1DsrjMoL1HXI3AsHEOj80VipRj4M2z1M/aVzQnqzS1iBCMy28MYrCFmDpOMRrzuiM+CHWC1e6ipfVhSIqp5SGNsnTEqzdOKDeq2hRu1sR94C+HtqyiVss8XGDrDjfuYOiLNlSdDiNI0dgpSpWw8/SL9vsUJOSDf7aMKdfoUsPstZM5nuDWgMFdhME4QpkMWjMnds5/69ks4iw9gA3dHCdM5C1Mn9DHZHYU8eXmHWxt9HiuNuNl3KFerlIsOF7/6NMlowNcjPAD8l1/+cWAy9X3f+95HtVr9BjmeP7u+bX6/efqWmF+AX/p//QLf+30f5Pt/4mdYKOexTZtS2iS6cR7/gTcT5KeRC4cQ1y5SeOgxoq0tiitzXPyPX2ZtoLj37Byl4wfQhsPg8jVUEuAWPYwkxs179G5v0P3iv2DmTfeTf+IDExZumqCLDbyFBZLtu+hwhGV1wM9TH2yw7c1TMB3GsUZnmkxrCrZkdaTw73sHeynMfvn3KE0tIgs1MmEgl8uEz34W17Kwjz6ENm2y1Qsov0hfu5TuXsGYPwAodH4K1W8jertoJMJ2SXY3sA+fJinMYCQBxngH/CokY4RZJPTrFOIeatjFau8iZw4gVEjOdCiYGhUpJJMPGYHCFppASLLiNIm0saJ1tGFh9bdxc7MYUpAKAWmKnj2A7eU4XDrEbz10hte2+jz1hc/w/T/4QywfPYmDwt69jnH7VYw0IT/ehvUrXPjjL3L63idYnJ9la2ODauUevDQiwkKKSauVY2rGmWRg1yns3ebZq12+4+wUg0RTdg1udOBUKaNrFhBaE1p5pg2X6ariEzd6TJVcDq6cxjRtGA2oZk3G4wTfqXIzFOzEQ0qz9xCZisadJ+l6j1P0TAZByAZ5RpmgFyXMFByGQUI9Z2GISQwg0ZBzLGp2xl6sMLOM+YKNIyUDx6Sct5ktOHimoOFZDBNNL0zI1etc2Is51sjwhl3mazM0wxr7mzucG1e5v2iSjDuYrU32RIksmjS0TQlF381xbaPPbCHGzit2Q02jYNELM2yh2eoGnHz7B3j8Xe/j7+/cYOAUuNpWHLjnDGkcsr8oaYkCzVFG1yhx9tA8/+XFm5x9+wc520j5+G6et5w+iJsGSA0lWzJONZ0gYZwoiq5FWUi6YcYgTpktupQdE8sQKDRaZ8QvfYnF/FESE376XUf5P76wyve8416mfYmjY5pGibnBTeRogKzmSecOkp+OeY9MQGWk1ft4cm2AJWJmZYfLSUrZFNR8kyOdC9yIauzaUxSHtxlkJr36IkIIIKOXwGY3wDINGgWLmaKLYIKhW1yqQTfCrhQJB13+cEtwdr/ggdwIWdrHgl+nF0bsn3KxnRzoMdljf4GXXrzN1StDHuIWh5OU9bImf2gez7II44w41fTjjJydR3oBHLyfoDSNbbuYg12WqzMIIVBSMucb1HWO2LAZb96BkkN5pcCJ6n3c2u1xtzXiCy/epOTmaVs53rPoc+j+E7x8+Q4NGfLI6YN8x8OHeXIroqOadLbXecNb/jJWuYLteXx4UOTguXUWgnMkfYuZt78BFfoEGnJhl71BwAd/9G/hug5hqui02/yjX/gQ17sxlplS9yyKjkk7mFTBa5UhciXO7fTZ7UYsVH1E2cM1JRv9kH6cseJb1Bo5ukFCmGqCTFOWmnEqQWpsCZ4hcCyTZhCxWIR2oNgJYCbnMhykGFLSH8Zo38I1JQsll9YoxjIk00WXThBjm5KHFnIcKwpK7WtEQZFQK1YqFpbMcAT0opRunE1wWVKzN0qo+TYFc9LmqJOY3ZGi6BmUbE22dh2Vy2FXG/gW2FJQEBmGaVM1hlwKfS7v9FmZyjOft+jEim6QcLSRRwswDMmdXsTRmoNMgXwdZIgtBUGaEaSQKkGkNLOG5LU+zFR8Hrt3hWnVYVRaphml9EcRp2sOQZJSNCReFmOHHQalBp969Q5vOzlHo+CigYprkWBgyoyr3ZRHFhYYKkmcCULDJ5droO5cwhyOEOVDBE6B/OZVRGOJRKWYW5ewpcuH7yZ85+lZtoYxRnmGvBozxKarbap+xs4wYqA94kzhm8mkUh1FTxbId84xqh3FL8ZIlbBnlbFSRTFpQ7GGkgZGMsI4eh+quYZMQtrCp2JrHDVGZDHStHAOHiOzXGZMA2v3GqbnI5w8g89+isqjj5CaHjkJ3to1zjUDHv3ZHwWvgEhHaNPGjIcMbm3SX90l7I1JopTYMLHyDlkYIsng7g2ypYfpJpPq7GI6YGTmWR1FXG+NaPUi4t6Ah09ZvBA1ONwooIE4jMnCrx95kAL2L0yTJAm/+Zu/yW/91m99wz3Pn0Xqf0Bm99uc3z+bvmXm94knniDJNH/0J5/kb37wuybbqm4B6fno8ix30yL7qnOIwZMYro999AzScbFLz3Hh+VssuLD0LgMsn9J99xJtbRE3h9j5Ol5e0j5/m7vPrxO1uxx74rshX8VQGUIlWIfuwdABabeDHA1RRx/BjIZU2lex/cMI4dAaZ8wXLNb6CYaAllnCtSX6gTczvvwiucPH0e1dklwF++wbJ48lQQFi9hBCp9DZRTgewnLQWoBpT+gWhTKm7SELVfDL6CTBaq2CVyJzyphpgnCLEI1w3ApCSnQcov0SysrhRj1kzkVpSVe71B2FmaYTlrCYHA0m0p0gawyD1CthJSMMJqxFzzJIavsm1brFOjnbZGTNcvzwFPccPojOEjKRQhoiKjMI837ifJVEmNiWQ/3wflLLZ7FWZvXuOtH6DXKuJm+6xMVlBBApQT4bcz11iRLJifkiI7fOINEkOqHoOuwoG6kEzTDl+bWQ9xz0+PLtAbPlHGvdMXdyRdJxwonhLbLpecbaZ2rnCqPyETzXoZ9EfG5LcnD6BPuGd3gxOkg9SelozcYwYC7v4pmS1iijkXdpjROaoxGOIVmq5hgJC1emNIcxFc8i01DNW5Rci6prkmaaXqzI1ARV5FsGuyKml2he7Bd5JFyjVFwgu93kD2+NsN94grJRYbA9YP5AjVakSBJBe29M1bB4ZN5mrE3Wewklz6LuSjItmBpuMNjZ47nSCq/fXyJfX8TMTXNowWUYKYaxQcmpUJSSm92IoZIUlpc53ewxV7L540sbfNcHXk8/yjANyDCJsknhSJRqpBTcO1vkWmvMXMEhSA2CRNEaJxiGpmAZgI0ZJdjNG1j79yN313nDPgc0XOlmHCg6mIYktXy0nefK2GauApgOrjQZZgYvbQ3wXJtZx6SlppixE9YCQcVMoTzFYhwRDttcXW2T8w1mPZuLu2N2bYOqb2PEKbZp0OwqRlFKwTVprTUx4jENJYm6JjIJOJB0ad/aYkxKZCn6zoRDrIRk2NpBKIUlNcfmc8x919vIlOJaokhaLa7LkLxrE2YCw5hgqxwxwEhihGUzljHF4RAziFHqLpmdR8QGuWCboZsnzucm2+HxAMsr8VDN5IF9JW6PJNVGFQ/FlWZEp34cv1plS7e5O9ZULE2l4HFKSZ7szvLEsQN86lf/MXzwR3nn209z5L57efL5KzQWPc54GRf+4++SPznPer3C/lP38EN/5+f48b/7Dzl48jTdMMMzBJFSXGkGTDkmhkoYJya7owQnGqCByC2yt9vn8vaAs8sVKp4FaD57q8ubDlTY6IUcqvtcb41xbIO9foTGx7dMZm3Bej9GSii5BjXPJMwEJdtkkCikNPDtSaazNYg5kLMYJ5qKbdDIWczmbba6Aa9b9Lk7CKkXbQpJl1Ap9tddNsYJx6eLrPVCglRxuxdNqszzFqu9gK1uwNSUwRc2NEsVn7Fnsj4ImNcWDcfBzQLCzRbSsoh1nsvdlHfOCXZDhW343OhEuI7BvqJLpCaUlfmiQ6IEWk2m4IYAC4WbhUhTEMo8G8OYMNH04hRTGszkbW6MLIIkZhgkPLq/ypAAlUInyNjoRzRRPLSvjIGmm5ro8gp5YfDQvioXtoe8p+pQ6G9w1C+ypfI8t9okSzWnH5rDJyKvMjKp0GYJNeoRLhzFVhKpE5IoIqmVULcvoCp1do0SJ6Yl00bE9dAgyblIqVCXX8E++jAXtoc8slwmSTXTeYsw1GjTwk7GYBUYPvskmaxhLUyRGB71LKBa9skiD2VIZH9vwqyPQkShhtq+QVo8RDHaxphahCRCtDfQi/dgxSNElk5ONtMSmB5WrQq5MqmCUQJ+GiBHI4KVEzQsQeLPkSgwgibjzoje6i7pKEaliqMPLuLPzxM3d9FRgBr2STT0Q02YRCxYPV7ommwNIja7EZHWvHFJc7svSV2bxUaBf/tfnkOpr094APg3PzupL/7Upz6F4zi8+c1v/kZZnW/rz6m+ZebXMAw++L3fzZMf+z1+4LvfR9EBGYdYc0tETpmcEIwSl8qDj5Ll6xjSBNekuDLNqcY2pYUiIoywFqZJ9taxFxdJd3eQpsCbLpEMhjh5m1sv3eXwpWfQJx9DmibGaI/YqyFX7mHw+Y/jrqwgwxFYPq7oIKIQ1/TojCJO5yDM5emGGZZh4JiCYOoAur2NF45J2i1My0E5PtZilSQOkIMO0WiEnY5xdzdQ5a8teTg5DKWgNgcqRURjhF/A2LkD8/sJnBq+Ssn8CuZoD6FTtF+ZAMdNG7M8R5hrIBSYbh6YoMG0lCTSw2QAwkArsFRArG1cAZnlAJrAr+OlfTLTxzQlwi8htrZIK/NkiEkVMhInC2DrFtlohN+ocMc/QK5cRMlJG9u++WPMvcUnFQZe0GLaN9lav0PRGGCUa4ji8oQVqhNMy6Fh2NwMFrhn3mR1nJB3DZTS7IxjfEPhuxLLgFreJk1TLu6NeEctx+3dMVGS8fZZQa+8zNzuDbL8Ip96aYszT6zg2QZ3b7c42LAJq3NsrndYmhtzbmyxr+pQ9iw8U7A1+NpxrVIEmSJJFZ1gshjy6mbAQtllaxiSoZgv+dhSkzMktiExDTnhUAtNrBSuYbBYdNkdRSzVCzx9N6SQDgkaB/jOJYPz7RQ7TjlpadZaA8rFIlmW8eLmiJm8jSF9Tqg91mOT+bzF8+uTmtujM7OU6jXyV67TGhzirirCWOBYKdea48mXvxKYQmAb0A8TtBIsnznNfTMeK4dXODlrsRpoYtPGSlNaYcqTtzs8ulSZHIVnmuLXtsF7UcLNTkAvTHjl5iYPH5xhxo7xDpzli1++wPF8wMUbPaYrFq3NVSyh2WhLojThTpYi0ohxJri6mqGyjOxrF0zwMTtfu4QQaCEZSYGWk2IQKSMqeUGWJvS3N2gogUgyhPIBC5VoPMvCMBSGhnGskEpgIOjEGsdwyRWL2KMOPbuM75g4piKXpmSGgZElJIaDTYwjFTNllyxNSTQ0hylBlKLSjDDNJpEPnbKpFCIakQmTpDVmK0tQKkOleyhpkGnYVRmTfoNrk9cpBNIwWDcMTENiGCZLWUbZdzi8KImVYGt7m4MNi/7eDlNlh91+k8sDE8/P84G/8Xd4163zfOjXfoe/9Zn/zHe87f3Mve1xvvLqKt2pWRYeOUmYC9jt9TFDi3EQcP+xg0SmILYm1dcmgppn4BmS8XhMwXXY6EcUB3tIBLtjRc2zOdDI4xqSJNOUHcEhI+TGTo+DNZ+L6x0GocZzTKZyFgcrHu0gZdmO2EASJIq7vRjbFBhCUPYtvDjFQlE3MnTRYcY3GUQpMwUbxxDcW3fIopAj00WMLGbJUGyNFR/rG8wWF8nnJF6SkbcM9gRkStHuhVi25FIz4MtX9vjAffOEvW3udgxmyi6vbPYpORZbw4ijBc2wusS1LMAdmLTSFEcKOgmca4YoIdnoRcyVPRTQGwXE2qKWs0izFMu0aY5jiq5BgqTrVBFKs9qOeGlzwJGp/NeGBIIkVUwXHTZDyDkmSQa/f63PbCVlqeKjhMGTlzd54mCN3ViTd+DLayF1J+SFm23e88AS+wa3+UzPp1YxKPkZbztYZL0bMVIGnu0gyVBqglWLjr+O11qKqaKBuXebYX4KJxig9nZIZw4wFkXeXNzEyhQq9Qhik8K4RWRZ5ExFxbfI2Qaro5DZgkuoBZaUyCTEDgPUzDz+eIfMPgBpiGHb+KZGKQepEkQaoUZdaO/A/tOINGWFDlJFaCSoDJGvYHXXEaaDvH0BVZ1DKo0e9/EaZXRlHpWlKExGpXmW799PiZh+5lP0TPzeNuMwYvZNr6P18k1IYfHRQ4TtIdJxKJ46TfPZ5ygfWsZWEGeKRt6gR521rTHjNMOQoMKQR086fLJZIO8Jdvpjzj35FDoY/Df9x//jA28A4Nd+7df4wR/8QQzD+B/obv5/V6Y04tuxh2+KvmXmF+Cnf/qnOHzkCHc3tzmeryN0QrxylgAT38pQhRqxkTKwp6h2roE0KJ8+wfwrqxQWamQoZGeHrLVH2B7gL8whgiFhZ4xbLXDgPQ9w65MvMXj5BaonHiLBxlIasXoOuXic/P2PEvpljOYWhufBzGHiOGA27WDP1FGGQihBzpYkSmFj4FgWlxr3sGQGWNe/TP3hd2JsXUHojLXpB1hqn0d2WhizSySmRzIeIbtNxle/BLka7okzpOEIz/FBCIzaLCCwWmuwcAiyBCWNyRsgCbD0kKE7Q84XxMICnWELOZkko6gailRL1NcwUhkSoSXNQDHnZWinjKETrCwhsxzMuIcpXMyoT1aZQ0sTTyiMNGCnPaDkmgSRQF+8TjpbQx+qoSs18kkPz7IR0kaV55Fk6GDA9OZFXs3tp/0Hf8LZ9z9OuPw4mRIYhsdAaVwJBZkhDYtaziFC0HA1lulhy4youUVQmKXqWqQYOGgsNG87VqN19w6vnWsyXynTX1ymevcGh/dPs9VXrNQc5MIMZucudyixF0/zfhXz8O5rGFMPERYqtMcTNrIrDLphCkpT8mwqvqYVJIwSRStImbYS7vQ0vmORMyU136BkSxzbnByn2gagudkcsVz1JzGN3S0e3nsZufIWfuEr1/kLB23ubdS4JMu8vNZmwfawXZ+LOwOqeZtawcWSis9dEyzPW/y7j73Idzy0QsF0ybIYl5TpoweJDAcxHPCfXuvyvlMzCCnIW4I41RQ9wd0kI0oVBUtSzdlow+HUdA5l2sznJb0g4ep2n6tbbY75MeNmgGtoNrdDVJqyKzWjKKEWTRihDxgZanWbDUAaJvP7p2jFKd1RSCkzsKOAnGsgTI+c0GTFGl42RpkulutNmpukhWFZXNgd0w5TFis+Nd9CRCOe2oo5O51nqODBSojONJsUsPo7FHM+v3NlTEME3Ly0xtEzR7j+pS+yerfDvU88xNGDCwz8GveUFOfaCtuU1MyMWrlAMWrB2mVGh06Ra97mK948VVNhuzZVSyOlTU4NsW2XXpiRmDYHZMZuahMnGTJVLJQcajKgk0icoIdhmZheAV9kyKCP3r2NWjgGShNY+Unu01D46RgZ9ggMn1EmsdOQLI15/lKX44slYq1pjxLKUjHtSeIkpR8EeCrmpAxJky6jPQ2FEn/vb/4ISarotVvMq23qJ/JgBKzd3eaZqSnqCtSgzcJMA2NvndryAZIsws4XuNsNmc+7tKOMMTbN3TEasJMxme1iGxPGbC82MaRg1oNc3KVay5NkE8zXV1+5wTseu4dMCGbqOSqeyc3OmEsDiwzN7iim7FqkWjFfdCenIxa81okJ45STFc3dIMbFoDeOyVuSNIpxwx6ON0smLOpqxOKMR3+k2EwNTs3mmbI1RT1mypbYWpJqgWNPyjXuXazgWwZxqYHX6dIMMsapxrZguxfxO2st7jnYoBelzBcdtptDjtYMuqKE5ySMk4yzixXGaUZzHBNFGadrGsvwiJIER4IpBXlLEqSayMiz1Yq51hxjGQCaKMkwpSBIMjxDEqcp17dHLBdtbrVC1roRm4OIvG3x6MEaPS2xpMKIepwoS+4OFe1xSDXn0PydT/DMvvfwA3MT5JvGoJS3cYnBdIhTSaY1wjBwc0WOiwjbUHTkIaxoQC7cZCQzxn6F7VbMHIobic+F3RGHpnJY6xvsztyHOYSDNR9XRcz5BnbQY8bUdDCRdy/AeEDpgcf58qDMI4bJ07sJ9824OCrB6qyhe010qTFBi41HkISIyixCpeigi+hsQzxA2z6quQ5xCpZFeulZjBMPQBz+X7x+b+MiG+XjJHaV2UfPEki42UlYdjL0eIBtCuJCmfxiBZ0psjAijjK2vvIC9YfPsvf8RSonj+JbmpowmbNirg9NZooOf/DyFrW8yRvmFPglHjq+yHO3Wnz2wg7ZeIRbnSdsb/ypvuPdrz8FwPb2Np/4xCf45V/+5W+01fkzS2uN/u80r1p/2/z+WfRN5/z+19q/fz9zc/v47Ef+gESBtnNIJ08vzjBUQkkHBLlphGmi0xBRqGAZmqmjsxM0TZwQ7W0S9fvc/MRLBJ0eRq6AXS6xd3GX7q1NDARJt4u6fR6ndQuVq0CaIoIOUijk3UsIEpQQEPex4j5mNEBKg1C6SCBnCDwBgyilGcQUcnm2ZIXNt36QNIkwHBedr1Pv3kLnK1hLRxGmjXniYcyjZ0mjCCefQ7W2CG9d5ZXrOyivhDZMKJSJ7t7G1BmsX0JuX0dhEFk+IuiDVcDUCVqluIYmp0JSJDIJcHWCYUgcFRBIfxJLUCndbGLelJx8gekkAiFIlEYKgTFoofp79L/6OdTWbcyNqzj9XabjJn7cx29MY7/uLWTTi9Q2z1MNt8klPXwpIMv+v01yMweoFFwGN+/yWlzkxX/5R4hnP0cUjbjZCbHkpE70ZHCTu4MIJQ32hiGv7UXYUqG1oLJ1lSRLCRLFbG4C27+w3mUq65MfNRk3DlB8+Sus3d3hRnGFE/M+8yWLuxevcG8FrHyB0zM51ndHtLWFu7wfp7dBIR1jGpK8bWIJQZhlhKmm4BhoDQpB3jZJUoV2ciyUPbRSZBrWuwEX9sZ85VqLm52A9X7EKMq4sTei5lt0xhEz7TWu+CsY65fZV0j5px+7yCc+c44ZI+HW7pDtfkZvNObMTIm8bZCzJO1RwsxMg7KtONhwuT1U3OyEXOybyFEHw5I4YRfXNHmD10UKyVLRIZ8M0HHAsNfFjXrMqQ7VaBezucrdq6/xzPkrPPPMczz71S9z+cWnURuX2aeaZKM+o14ba9xmFKdIx6NWzEFxim5ulmJjFnNrj4/89tN8dTjDCyzSaRxhtHgKa99JRnMnmTlykqUDRzm8PMf+xTkWFxeYr/hMlT3cSoO2yFHyTEzLZaacQ0hJ3XeoeharY4OZvIcyDS5s9RlYZbqjAFvHbBplhF8gCkasHFxCTc9hlSo89B3v4L59s1R7u9wcag4XBaZKcG0TQ0jcnE+kJcIvku27h1hbyPoiUwUPP+/TVwYxFq5UxFHCMEkxXYdODKESaK3xTEHVM0kUbEQWwjBxszE6S9EqI9AmursNpomWE3rAWDiUHAPftvCNDM8v0HvlPHNlnxkzYtaXfOfZFWwpaExNs7iyD296kaPzdZZWDnBsaYp+4wiOclm+/CLZ0mlUaY5+fpGF+QVe+OLnuXR5jZH0SQ2HXpDR77TwHTh/4QLf893fxfmtJs8++xyrF1/l8ovPMLx1ns7qJeTebSrhLs54l0LcRUUBhu1SsQX9OKXkmCiVURIx2s1xrGKSZoI7vRHX1jvc3BsRJTFCC1Y7Ic1RSqxNPEPQi1LGqWLat8kyRRBnKEOwPyfoJxonHbMXa16+dIfdUYKnY5xwgFGsYcJkYQ0T17Fpp4IwzoiSjO29Dn/05QtY6YhnX7vJTidgtR1SdgxGaUqmNHlDMZuzCJOMQ/Ucecek6Nk0teCFrRGrgeRWK+BoRbJSNEnSyYLafNEhZ0uiTJFkioWyz2Zk0o8Sfv/8Llf2xuyMUsxohL97nUzDS6s9NloBUguKjiTvWuSkIm8Z7Iwywiijkrf5g1e2eWB/nTBVDOKMvGPwK7/9eUaRppb1KWdDZs2YvGtzz4Fp8pYk96a3Mm9O2g7z1uTU86AT0o81SZqCEOTjFlJOputF16KgY9ygwyBRRP4U6ak3YWpFycgYVvZRLeR455LNzjilv/9+ZjyDsh4xnTNJB23cYJfO7g6x6VFp3WA0fwJZrJIkCR3tEGmDes4BKRBpSLp9h9TOoQdNhO2TLRwFw4DR3oTZ3m6B0OhcBb13F723QdbZm3Du63MQx4hCDVGoobOU+NUnGcbppEHu3teB5bLaCdDhgKw4hRRA0KV6/2lqJxdp32pSXiiz98o6rWeeZ/rh40gvR4rGFZosm+A8C7ZJnGX0BmPO1BRWbYFhorhxt0ezOQYU0aD9dX3H7/zzHwPgwx/+MK9//etZWVn5Bjudb+vPo76l5hfgn37of+FTf/CfaKUWsTCRUpAzwUaBFNgqJiciosYhRJoQN1tU7z1K6fA+zHyBwfU12q/dYm+zzaf/41dQwqD++scZ7/a4+NRNTN+gc2mN6NJLiGBI4tWQ5QZq2IFojDm1iCUhzjQJEoZdlJAUozZWMiZvCqbslLKV4FqSnUFMyTUpeTZWuc4zA5+RWyUuTOFbAqU149oyAyvHQNoMrSLD/Q+QHLqf3LFj+DMNVlq3CPY6DO0KqV1G2DbS8ZB2DtHaQp37MkYWo4cdRH8bb9xEqgRDZ1jpGCsJsId7iGBApCUD4dLJbIbKwRATgxtrTTOCwMyROAVGwgLDJjFzZElCeOM8t//zn7D6L/8Vq7/0ywy++imMrRuoW5cpRl0KrkE+b+MWK7B7l2w8hCggzRQyHpMqBShEsYJ/6RwL73w9Fy9vs/vCi0yvXySfjAgSzWDtFsPqAjnDxCFFIPjirTa9OGN3GLO+eD/XmwFxprnSSnjvPQ2S4ZA/PLdFPH+YswenmHvfe7nXGTGv2lxQdXK2he3a3OkF7JlFEi1500sfZbjV4qI5x5WOwh/tULGg4Vt4lkHeMinZkk6YUvYsSo6BAgZRhiUNlksOArANgTTMSc63FxCnGf0wo+AYLFQ9tNZsjhTGykGqUUCrfpiH7z/KiTOn+PJuzMu3t3n0+DQzqsf67oAoSzlYcjAFXN4esdENefKVO7x+pczppQZxptFpxOU4x/WdHjc2dljbWCNNhqxffInNc0/z8iuvsHbpZW5fu0LS3sbPxhQMjet59Iw8/fw0/uJhZk+c5czDr+PBxx6ndPgMncYx3NoCTX+Re/fNsK+SIz+3QmIViU2fOReuNQUcPYppWazYmkvrffZGCU6hyG5msDPM2IkFA23TsypINGm+QeLVGSUTpuwoUfTDmDRNkUiW84JRpLjeHLNcm2C44lRzrZtwQ1e43FWYpsl6KHnDwSrjRPOWBw5yrJ7DzBc5/YF38fDpeR5cqlD2bSKrwLGKTd61KMqUvKHoKofUqbB96ybYNoPBiCvbY86vthkECZZQtKSLsHIIJL6EUWZQsA0qORvfNBnFMZW0j2tNMGg52yQTDh1loi2b3sY2XHuZKMnQSmGJDFtFpGaO/pc+zpd+/hcxAMFkardkjShFfXQS8urmkPZwzKVrN/jbf+0H+fKXn0WlivbScV7agZmkw83IYy+AndV1DszNs75xg1qtxp4u8tLqgCubEZ98+Qqfef41nnjwDPevzHHmvvs4XrapzO2nNLtIudbAdlyiJEPEY5xxC9CoUZf45issda6yOLhBunaJ8zfusHPnJrc2d+k1t+j0Bnz/O07TCyIu3+3TDWPO7wypehab/WBiQHVCmGT0Y0U/zNgeRgSZwLFNGjmHfqK400+QvkvBNdkbx5SDbexxi43eGN+UNExFL9W0IkHdM/noa1v0hMsLzYxXbu8Stnd54fwN5goOF7YH3GyOMIDreyOev9FCCNBIJGAbYLku++s5DtbyHJ/OcWss6EkfrQWxUuTtCalFakGYKp692+GFjQGXmyMyJA1nQkLZ3WtxfnPM5y9ucWhmUr+8vt3GlBLPhI++tsmUb2BJGCaKzV5Etehwqznk7L4KDy6U2OzHHFieoTkOMaWGJMRAYGcBviO4ujckmDvE9z8ww16oyHk+ywXJqwOTf/2pCwTppL4drZFJTKoVzUiTWS57vRFTZkjPrSKuvcBQwYnqpNzDRBEbk0w+wx6eI5mRY8wspalc6oy4KircHQtMqVBuiWR2Qi85MzX5rj1SMvB0hOpskNy5hbR84to+ktYORvM2ejwguPQK0cXnEH6OtLU7ycAX6yT9PtaBE2RxjOn56JkD6PIUqAytNcnBB5nKO1hSsFte4eqASQwDh8wt0Pem2Vp8AOvACfyZaUabA9IgJTdboHpkkfJDD5MtHKUVQYKgqW3QcLMTEmeK/aJL4pXYigQ7vYBa1aW70wFAJ8Gf6jfyvoPjWGit/1yyfZXS/0Oub+u/rW9p7AHgO7/zO/mh/+eP8Mknn+M9r3+YuqkoixhUSipdTCICJckPtoinjxC8KUfejrAliM422Yuv0V/rstZOaFQh27lD1A+YOTOPuraHjhKEBXZ9ChWOkMMWIg1Rgw6Up5CFKYxRG6u7iZCzZJ02yZ1VvCMnMbIMt1BHC8meWcUxJfvKHqkWFExBXY5ohTmeCcos2D4HKiYdT+E6OYLcNOfbgpmyjx2HTBenGMaaoitoHF1BlwqEgy690gzeobNoleFsXkZaFsnJN2DHI2huQHUeYdgow8YKeuhRF+FmJEphDJtYcUTgz2BJBUgSYSKUnjTjAMoyUdJEZhkxJmGiqcchcbNDNI4494lXqZiSbDik8dBJao8+goiHmOEInasQ9+4SXL6MV/GxpmZI9p3ByAJ2KTBTcKE8w7GTB7iEIjlzDy998gVm77+P6UKdopljdipPz6sQhfDk+oj5gsP7T0yRt03GkaKdgNYZSkv+y/OrfP/jB3nrPoeN0GbP8BCGiZrZh9WYxW/tMHvhecTDb+JMXbNquxSxWR8llN78RuyLL1Iumly1ynS7PQpig5xbIbIt+pFEodnuBaRKM5Wf0Bz+P+z9Z7Rt2UGdi35jzLxy3Dnvk2PlrFKWERIYTLBFMsGAAZv7QM0YbBNsg5Blm/zsZwwGjI1FFAIhUKwqVZWq6lQ4dU6dfHbOYeUw85zj/tj4vffj2XBtZOu1q97a/Ln3Xq3ttdbss48e2m7MvqvImTrdMMGNUw7ckKpjMFLJMK4NyWuKJQzqWYtXtnucHcmzpZkU2i/g2Q/iRybvvVDD1S12Gz3+7mmd33lxk35xjM2MSW3URIQRp+0+3f4QO2pwc7NP+XCXMZXidyRddCzbJkLHsS1KJQPH1NHzZWadiL5VQWoWu8OQudzRUftWmkENQ64fDlk9iLlvMscJE3wlGUYJpazB68stRit51kOHqN9krqQ4Lw8pj01wtxXzpvc8gNiMaCrBpBNxuQ3P3z7ge9+6iKYJun5EFMdEWZ2cJTlwYyoZE10IHB1KhmS7rwhVTNeLODee5/VGQMbQmS05eFFCVoPHF2ocDkPun8hyq+lja4ooTTGKVWI/YSxr4Hs+TtBnW2W41itwz4iGlsYkwqSgK+oZwd1uhCYjCrbBmC0YUUOUO2BCDCBbZOZwFz9y8EQWOxkQkycvIsYsQaIZ6CJGSwMMlTCMYlId4jDARpFoOokmyUYeAuiffJxC5w5m7FL09vDtMikKQ0WkhTpv/dHvJ5UGwnSI7RKGitjP6Hz487uUywV2b3+W3/vwb3Hvk+/kTz9/iXfPP8DpmTqqMkKSKVAYeJyerRPvx/zWC5d5/G98K6cqBtcOIyZHi5zYeJFPbK7w3v/jA/zaC7u8by7AHjZpFMZZrBVINYkSOs9tu9TLNkHfZ1rvc7gxoHb8IrZjs9Xs4/WHjGTB81waXsogCHFUQNbtYwhFXUoiQ2O41mDecshGGdYHCcOOybnxHK81feoZg1YQkyjFMFI0Y6hlDFa3Pa4v9xmt5jmWgYFmEUiDTmJRVX2ElucgsRnTdO6pGSTL19g4yPPtiwVyT55F39tk7sxZjEHMaM7k9m6Pqq1hJUOu3t4mU6gxljWJ0oRXNtrM1bPMVhzypsZIzmI83KNXqND2Y6byDnlTp+tFjBVs9vptspbGctPFMTVqOYMzdsz1lR3GCxYfWfYoG5KOnhAdDslpCUXL5FbTw1AwX3Y49FIMITjo+ex3A+6fK+MYOsdHczx3+5BQCB5/9DzPXtvgnidmkNLEchvUA51h3yQsKoapQhsOacUGiRIYicdmL0VYNm1fkDfU0eLm/hKD+gkyWkKKwBmZoIPCin3ksENWJKRhQIEAIw2J7RG0VPHKise7ci7poE0miRnL1dDTPCd0E8uKCJwZVvoppwt5xP7LVEtjtOMMjqFj+Yd4L30W4/63Ix2LKFcnfeZj5Obnj/IjtTGCUJEW6ijfhZ27JIv3om3cgWwJ1TkE3UCmKXG2hN7bR6TQmz5HyZK0lEIojUMPjlUthoaOHfa4OTToiwpvvv9h4mc2mXzbvcSDFuMPzJM7fx5ZGqGbHaWY6hgSDgYh+26MEHChbnAsDtm1ZqlmTFI1ZHW9hdvt/oV841f/6XcA8OKLL7Kzs8NXf/VXfwHZzf91KKX+p20LX7I9/OXwv135dRyHd77ry/ijD/8n1vsRKlFoUmIkEbHQ6IoMKeDaFbYTh094VVbyJ+nWT6HsLLnpUZIg5p3f+iRv//a3IXST1uXrOKMVpu+bxak6FKdrhO0WSWOb+NVPHi2tOXlU6wC1+jpR+xAjXzh6as9kCQ93wB+geg1kY5MkTVkbJIRxjEKRJCklWyc0cozlDY6Vj8YQDkSWtchiaxjT0/JkshZFU5JoNqZpsmyNs6yN440eB9NE6x9ibVyn4Sl20hxpaYQkCBDZAkJakC0ivS5i2EKkKaiYNFsmFRLfLNDPjhMB2bRPUU9JgQSJl0LOEATx0VP4gKOk/iCM6QYQ12Zxzt2Ll8nTUDqXd3tcvbxJ98ZdCFz8bO2o6P1wFbNSI7s4Q3jiQcLqHKK5SXD3DhXl4SqdwegpxFe+jwmvwelvfg/3PX6CckUn0U3CwCNy8rTCozBHPWMSpAnNYXjkCzMF19cOmMxqNLs+5+fqDOKUJV9SLeZ4W3nIhO4jSXGVgahPkTtcRewsMRw9QSpMJgomkwWL+Ufu4/hbH2K2aDFuR9z0LHo9n6izR2n/GvN2RGMQEfz5/vlh36cbJgzChJYbstn3SZKUlhuy3T5SvVrDEFM3iOOU5jBkEMfkLA1dU0jNYPEtD1Ps77Gyvo+MQ/7OeYcHxhSX9n3Onxnj0bGYicEyO8u3aO3vkEY+hZyDVh3nUkPj+SuHvMYk6dR5lqxZrKkTdPKTWLVJlpISg/w4+2QJpY3X7fDq3gBLKFQS0Ut1KrbO8ZLJSNbkRDWDFyds9mLWOj69IEEkKTdvXuVXf+0/8dt/8gme3/G42fBQTp4wSimZijBI0Yd9YqXxYt9ivJLl1FiRkZzNuXqWSkYnUYKbBy5elLLSOUpaN/0YJQQFA3KOyWjOZH8QktMFcaIYBkcVVwVDI2/pZC2JnxyNh7S8mGsHLqtNn5uHQ8Iooemn7PqSuwODommwobL0lcZQmMQp3GpH3G157A1jpBAEMei6xm5tkW1fUB0ZwTJN7IVF7ikLLF3nUGXxUzBjFz0d4iQ9nMEBKvJII5+ZnEYcxxR72/iJwNPzGCqhGPcQSlF3JGJkBldkjk5a1t8gWLvDylCg3/ck3j1P0E0EMhigpEYrtbny3Cu8fS7Lr//Mv+TpS1f4yC9+gJMPvI3Grct8/m4Dt9vk2d1tnn75OoNhSMY0+Ke//JuMLZ5lsVZnMzI5UzM4vTDCn1x9kXd974/zFSeLTHq76EQwaBNdv4zTXmcQQinpYWkSiWKsYOF3WyB1tKuXEEiGymB8YpSoOMrCRB27NMJ+bpbs3Dm25QRe/QTe6HG0kWl6qY1pGuiRy0TaZsLfpLNyjfneXZLN6zRXbxIebNLY22H3sEFvMKCtF2k3WowUHIz+PgVdo2cU+Tcv7uOkCtcbkiYxLTfCkAmf2Un5249M8RuvNbB0yZ3Q4thkFX8Q8PxKi8mizZcv5nhuucOzL19jtGSz3ffpDgOWDgd4UYouJVEKlhR8eueojrLqGFgScqaGn0LfC7gwmsOUknsmC5T//IEtFhoff32XT90ZIJ0MW65GnCgmCzaRblApFdluehQcjVfeWCNjaCRITo3medepGgAn6g5ZXTBZyjBesrm83uTxE6Mow0GU6kjdoJ8f57OX7nBtp8+VPY9ne1kKjsXzy4c8d32LqysN3nXPNJZIUHFC24toySw5t4kQEoOYCW1AceUFBrFi/cTbUELSllmaZEjilIoeglIYuRxDZSJNB7FxnUJ/h76eZyc0yImENE0ZzR41guiTiwh59Pu9WCH8IXptFN1tEWVqSCHxHvoy0vEF8PpIO0N49glEYYS4Oo0YtgmEiXHyQVQUIuIYd2uLJA7wggBlOkjDYNoK0OV/tXXAYsWm4UVkCUg1m3rO4kH9ED3o075yk6jVxm/20bIZzOkTJNkauhCkApQ8ClqWbY2ZoslxvYufqbE/VBwOAp67ssXe2iZe87/t8/2v+Op3PAAcdft+wzd8A47jfOHIzZfwRY3/7covwI/943/Im978Fv7Bj/8kRlYceY0SD0uWUAcbmEGfpFSnmySMFy32fEW14BDdvYqRscmOl5m8OINWn8K9cxOAoD2gdHoaSYqecXA3t3EmJmm8eglr/iSkKanbRwhFuLuBLDxMvLOGeewiwfafEE1vY1RHoTaOFgecMF3yyuP5lsloIUsiBJgZCipBKkVeT1hq+HSDmHrWoGBD3tAxzCPvWBtYrBj83J/e4jvfusBU9y7u7L3kdq9T660RpZLAtHAnzrLZCrnAAFWfQRgmsrmFyhQQcUBcmEAIiRdCUYtQMoeURwtemoREKQoGaPKoED5SR63xnrLohwltP2Lc0kgWH+DY3/nbpE9fYu35VykYCYXZOtLKkGRrBEYeXctguC20+XNomsNOqJMpz6JXfXSpIzSTWChGHY/KO95Be2mTqW/4OuyRGhP5Cp12jz2Rp+PFZE2BkIKPvrCGls3wI2+apOKkvKRbvLTaZ7GocLUMFVOj6xToCx29t4t150WS6dNUKqMEW5vcPf92ziYtiHvsYNDzPI47MUb7AM0xSdOYBVMRN9a5U3qAit8g0PMYrz/F8VNv4dqhpOOGJEA5Y7LZ8lgYyVJ2DLwwphdGoMANj6rjXt3qMz9a5tblVR47VsIhJthvYMqIVzwXFQWcNBSxLxjGJtOjBW7d3eVNj12g5Ojc3u6z0/PYiiuYlsHxUo6xuuC14T63NwecaLjsl31GCg7PrnR4eLbI8yttzDRirRswVilQMHPUbR09lriJ5B/98w9w8/o1IiUpliu8/d3v5cyDTxArjU8tt7l8a4ONl/6U1cvPc/qeB6mWR9ldusGtl5+j9bXfjzhVxzJ1ZiS4mmA/FnzFhVGWWx5lx2A/65M3wdA0vChhSMpmL0DKoweoYZhg9wXnRnMcDhISJG0v5NZej8Vahq4X4wcRJ8fydIIUpVKqmuTl1Q6mpqNUitBMDKk4WTRwkRAGFG2LpqdjGpLFWpbJnElOprzW9Cl4B2iFMdbaHllDZ65qMowUWctk30+oGRYnyxH7fh5Td0lFyljBIVUQCJtIaFjE3BnAjNHlwBwhl5o8tzfksdkxurHEArI65CKfvcI8I94uaa6OoaWkxgjKTljbbNJtB7y2M0QzDXKOTTlTIcRguecTTC5geH1aSy/zj3/2owwOblJu9RnoBUreBv/0H32IwKoytnGLk1/7A3z+536NpSuv8LZv+8dUj82S1QQTk2V+9h/9Pd73nT/IaDnD3XbAE4+dY7/fxzJM1qfKVF5+GuvhDFY5z6gFuqlRsnXWV4ZkMg6ric1EENGLUnoNF1MIhobCth02ml06vYCvOFPllf2IRybLfHatw2t7Arsn+aoHF0ndCKFJRqVLKHSurR8y6kh0keJ2GkSex0EYoAPfcMaiIHZZ6ts0WhvM5nVmiyY7qU2006KlHOaqOp6QHOQmcPsD3tjp8/A9Gm/1brEbjjM5XmB7ELHejpnOZqnldN7ylkeYr2TIaSlZU8c7XiVRigiFAPbcCJXNse8lTMmIwNTJWwZaFHN1d0DeNpgp2txpDLHShJqR8p9XOjx2/zHe2OrxnsUyH/yNm4xM1RFSo17OoVsGT07YTBVMti4uMoxi5pyEYWxyLp/wsU3F7jBiJOfw2EIFSBh2Pfa9o4ERMw7wFIw6CiUkQsJ02SGvgWFqnMmG3B1Kdg6HSFLKYQPNHbKcjuLG8CbhYsUefr+Dns3glKr0sKiVTMIkYbmfcJ/cxy+MkksjdCGZrxZoRIKsZiOdHLFu40kbEXVRymQ70CiaYBzcJdAc0AzKnXVE9wBRHcE4dgF6R9Y6q31AlCkSZxyk0AiuXsIqTyANgXzjJeIzj2H5HdIkQpgOslghXl3Hy43huIcITQfdQkQeQ5HFkUcz9xqQ0cEWCW4KFVOSufksabFK6YH72P93v09hvEBmfoG0UEcIgRA6pogJY3lkpUhhc69BNhjSqU4RtQJiBRs3b9Ncug1J+N/lGd/7t94KwHA45MMf/jBPPfXUF4jR/I9DpX8Fgbcv2R7+UviiIL8PPPAA+VKNZz75Jzz2DV+BPuyQWA663+PyD/0Exx+YpP63vpW8VWSm6NAPExwJ0rDQa6PM/vUC0tIREvT6KHl3iMiVsIq5ozGHRGEVHJLhgNI994DUiQ+2Sd0BxsJp9MBHDXsIFZGEQ/LHZmm9dInRr30fIgrwsxPkEw9p57ivErEeKvphSk4HqRKGQUw3goYXMZK1CGJFkByFJfwoZhiBkcDAj7l6a53cl52kXz/BzlBwbPIEptdDa+zgHRzgz97LdmvImZEMaa+NEbeQmRwyCQGJ3tqB0gipsLES/6jeKfZINJs9X1DLmGRlRKJZjP+5Xy1KoBtLSrak68W80UmpZwxyj76VE2fv58yXXUMf9sjSRNRnyHpNAs1E6BpJt43UHYyxcSYShQpc+naem72YIgljWRMNA11TzIyPst7tkm91UXqOoetSLtTIGvC715rEgz5Xrq3wve97gnJ3lYPqKd517xRXd3u8cn2T9z4ywr6XomtH1Vgr1jhTowJ/r4nIT6FfvUJlbJ7B2ZMoZeGv3mI4OsPx7et4i/fgiBituYlTmuLwEx/lnrP3oUc2q2mN1XCHM51NkvocW/2YBMXQj+n0fTKTBdZaQ6pZi/5gyIgeIPpDarGP1w9o9Df58mpE0u6i2Q6p7jDQshwaOQoFg7jd5cSxCf5s0+Od47D74lU2ogwfv7bBbC3H2bTFviqy342p5SyEo/PAXJWHZoo095pcLCQ8szdEQ7F84PHylWXmj81wdiLLSttlomAR2BZRGrPvhhSzGb75+/4BL2z4TI8WWbn6DL/5679GoVhimAiiQZeL7/xqjr/j6/jWB6cwTROSkP/Ht30DpyYLCF0nZ2rE1gj5/iGPHa8SRAnHKw5urHjH7NEYgq8EbqRYaXmoNGW97XKslqHtxfS9lF4pYavlstrymB3LkzF1/FhhGRobHY/xIMWQkvmKQ8eLOFbPMluyiVKFo0tMoVjZ75OxBHNFk09tdJgs2HS8mJmSwzBOGJM+bpSy1tGY0kNGkz6CLKYQrHYDJgsWQpPsDBOKlkHBTNn0JdmMxJaCUMFBbDKMj0YdDkWRiayiapiEUueByRyGZSH9FE0pSnqKCAb4xuTRgqPXxSiMESY2jqkYnbIZtXNojR69RKMbxLQjGyuM2Oj6zNaz/Mnnlxkrl1nUemzVjzOx9TpG9Th3nv0jPvhd38pNc4pf/Bc/inHpEs/81s9z5vGvQcvmKCUuZcvhZz74U5x+9F2EkxdwHIf5Z3+PFx79Su7JCXIbNzmZHSOaXsQWkkGqcbpqEEQRkWERhSGTtRK5wxVe3p+hLj0OIwvLMfjDW23uHctxLActNF7cjTlX0XhhrY3np5yar3F3t0+UwkTRIooV/dDidy/tYhqCKFPksQnBx/Y0qmUDP0kpaQoZ++T1mN5wiBx22e96zJMS7DRIhMZ4Lsf6eoPCVI23zecYtWO+9i1niBE0Jy5yOAipWBItSXn7QpHP7qS0XcV7752mIGJCJXlmq08p44BQeFFKnCrGMgajjo6lS0aSLokSBJgMg4g3z2a50Yq43Rjy20/fxW01GM8aDItVusGR0toexhw/McHS8i5be12+8yvPYxkCTZMQJ9w/X2HM32WnDX2zypXDiCBKCZMjNbVkmdxth1TKWZI0RaYJbN9mMHIeVxjcc26ahyaLnCkIVpseQij2yHF6Pk+pHHFq80WWjz/OCUdjecvn4kSZuJAh093j4CP/Aeeb/w+0sTmmDjYIM8fZcFNIQRu0WFcF7GyeSa3PM02N+8azBPt72CMLaFKhpQn1vA26JA0k1eAAug1UaRJTE0TXX8CaO0mqBEIl7NVOU9+9SzB+Ert3iJl0wXbw5++BVz6NOn0fZqVK0tlDdfYRk8eQbpskXyd74tjRZ8XJk6qjwSW8Phgj9OKYURtCpZjJ62wMYNLw0f02cmKetNMkRSNTsSkdn0ClCfSa+BNnCOOUW+2QxjAkZ+p4UYRs7yBLI1ze7HFyskRz6ON1/7/W3IQO6v93z++HfvB9APze7/0eCwsL3H///f8r6M3/JfxVeHa/5Pn9y+F/u+0BjvpAf/DvfTe/9Ru/QVtaxIUxwuwoiJTauWm0NERrbDGR1cnokvmSTVbGmAun0eZO45y6iBydhiRFNw1yM1MUH3gEq1pHRRGaqRH5Mcmgi1Upk3YPaV95A1koEDcOSOMEObpA0u/hfv7TCMsid+oUg5FTYJjkuusEdplumGKYFqNZQUF5bA9iggSkShnLGGh/HpRYqDjYXoebu12G6ys4hsCPUlYaQ37u688QJAoZBzi2QWgVaeplKNUwqqMU4wH35gJW9jo8MyxwmBknjmPiIEShI90OWtCloAJizSI1jnyxhq4hlMBJQ2JpooCCKcmIBEuCLfnzejTQpeR6Y8hyJ6FpFig8+BjlJ9+CPneaxLSRsYcVe3Ty4wxGj5O2d9GHLTKdTRInj4HiVB5s00RXCdtJhn17hNFalc4w4NAuoauQ1B0QvfAUhc1rnCo75B2L7/maRxnLWSS5KoYmsDXJyWzCk6dquK1DDochaZJQtgQNZfMHSyE34xLNGzcYOhkK05MsPfsK+88+S0rKUzcOeaphE5pZAqtIMH4KogDnbe9hqxOw0UuYzCn2yifYokDVFJysZSgbkory+PIpMJvr6Nu3kGuXOeOvMpvs40R9LNNA5SusmRN0M5NEk2f5/KBGIzNGmKvx8PFJ6rU6YW2crdjk/qkCTZHj0QtT2EtXuXF3iw9/4hWikSlqa69Ty5qAQghB0Tqqd8qLmO3mgMcmHW5eWyFJE1bWGtzZ7LB+0GN5t8/Hr+2RAhN5k8WC5OyZ03zsE5/m204XMYtV3vV138Tf+el/x9/74R/lJ37kH/LhD3+Yb/qar2S8kKGfSibkkGvrHbKWxXzBwNIktYzJ1jDltSDPQM9wbW/Aa7s9GoMIN9F4aatPexiQbWwwndNBge+GNAYBJgmPTTiMSpcf/sCvMSIDSoagZOsYmuRwEPKWhSpzWcibknB/G6PXOPq/yJSRjM7AHZAhxGju4FgW264iowsilXL3YMALa21UkrLSSREq5bETY1imRqm9TZgKBlwZDwABAABJREFUgiTFj1J2ej6GFAiliJMEN0q520nohYJOEJPVUu42XdIo4fJheDQaEsUUggZFETKZERSMI6tGyUjINFaInCqVjCQpTiGlhtIkhibRibGyBbxEsukqDE0yosd0hgHdIKHnx/zCb36UT/7mz/INf/f/YJAZ5Y+WfdzFc/znH/l6/ssvfpDpc2c4oZqMGwmxYfP1f+vv0H7jEzxqrLD0yjN877d/C4uLC3zXd34bZyaK2I7F+r1vI987oLGzz9LMgzhjU/RGFrhNhZbMYxkmWRmiu0eBn5FslrNnFjjc73Es3OedExYPjWexleD5lQ6ffHX1aIa4amJtrbPS9JmtZXl8vsR9i1VyloaXwP4wwPc8Gl2PfM6h7GjYOuQNSceL6PoJsWERmgWyI5OMzh6nuniOXH2G/MI59NF5kmwVS5fUhMvO+hLuxjWW796ht3abvbUl1ts9TlouhqGxQIfP70U4QqFLGLoeMnS53VMsHbrkTTClZODF2IZGwTYp2wZBoujqef7w6gF7fZ8oCPFSyWY3QJOCYZzScKFj5ei7CcsHQ+bH8ux1PfwE/OYBaAZ3D4YIBKYmaASCGEneb9DTbLw4RTMsVnf7gKQXxHTCmDRNubnd45WNHrGCOIjYj3XWBzFPLlaYKTtkox6FnEM+a3P/iI6u6zw6pigYOko3WY/LnMjDes8nUZLU7eFMjHOpAXEck8ln0VXCIBJMFU2MyXm62Bx4CWkYc7xi4acSd/QU+D1iTDLKxzEEbmqwkInQREJQn2e7eIwUDU4/gsxXUctXEApykUt79AwDvcArSRWhQItD9IkZrJkFVBiAStGDHmkUo6qTpLqJTEPiRgPdbRDbRTS/DcEAEXhECtJUcjiMqWcEbio59BISM4OMPGSxhpw9yfpvf4zCTB2lwLCO7nECdZS/GATEiQIBmt8jS0THrNAPYrp+xGcvreD2mpBER2Tiv0F8Hz4/j2kcaX2/+qu/yrd/+7f/+brkl/B/V3xRkF+A7/yu72Lj9jV+/3OXaWIzSMDXcpx+39dSunAe5Xax4gFVXWERHz1d6gYiP4rMltByVcgUSZoHaLaDjAKE7ZDEirB/lP7U80USd4B35/rR5r2foFLFYGufpHdI1O2ikgh9apHs8RNooU/UauFX5/GURjvW6YXpEZlMXDSOviBbiY4hFWNZg81+iC5gtrfEqd4Kz/7IT1PRYqSEN9djijkdx28jQx9bSsIopYlFOz+HMzaDUaxiZHLkMg6vb/u82pYsx2WGO7u4O6tEUQxRhKkCfGmRCg1lmASazVjWODouQqAShRcr0HQsTaBrECtF0dKZKVk4ukaQpEfDA2mING3k7Dlip4Lyhyi7iGEYaPVJOPM4ZEtExTEEAnGwSl/Y5GSKSEOyYQf7xov0NjYZL+c5ODxEWg5jrWV+/4d+hmVZ5t//wbNkHZvFiRILugtWBtIYTSXsRzqdUPCZ1zcoWAazBYOCqVN2NJ65uUsrgpsbByR2FmNqmuxDj8PiaUpzxxirZ3jRK/InV3cYDgcQ+LRefZWMTHhp1+M/vjHA6zXZazZ5dXmXS5depXnzZcT6ZczWGkY8xDF0ukaR28kI/2k9w2e7NcLyLN3cGKXRcVIrx9Wezr6XsDCWZRDFZN0uNT0kTFNGCzYHw5DlVkA1l6E5epJJK2GiXuTM2WP8x1cOacUO4wUHlEIhaHoxex2PJ+wBSkl2ej7f9uRJpIBvftd5QHH31jJ3b23R9mNsXTKSNbB1k69+19vorN3g5cMOX3e2ymJeUNAEI6PjjM3OoQk4WbV5fL7IRM7EHu6D6TBz/n5+82NPAwpBSm/ok7MN7nYiruz22emFrHWGvHzgc+3Q5dJGh4+/eIeXbuxysp5BS1NK6zc5lYl5dqXNnV7KsYVpzltdCsaR4rvT88lHLnsdl5VeTMePuPzSZa499QJpf8DvffYKGRWx3U/Y3Tpg97BD+5OfIDzYxPV8Vg/6PDB+9F5eOuhx4/pdyrZOz09ZLNkwf46qoxN1W9zdbfGnNw9Yaw/ZH/h0gphbTY+cY6JLRZiAHgx4Y6/P7bbPIIasKbjSFkTSIqsCXOngJ7DW6JAOOsSGzSoVtgcJh2mGnlmk4QsioSGSkH0/JWPpVByLSsag4ScYusIRMd7mLRpXP8uv/MLPct/DD9BJJafG81wshUzldULPpdvtc3t5h621LTKTi0w89jbG3vw3+Sc/+uPc2GrygX/z6xx/21dz4EVUCzZ1S+CVRpmYHkXLFziINLaUQ8+qMGIpqhmNZggDo0xrfxshBPrhOml1gsei2+gTc2x85k8ZzcA3PTrNyfECJ07NM/ATjHjApcjmyeNVahmNVn+IF8VstjzCIMJNwNAks2M5Hp4vI5XiupdF0zS22j5dP6JqShSw0fUIUo5saVqRnAabvs7F+XEuNSzi6jyHqsSKPotXXWA1zBKlAsdv095Zw9m7QW84YLi1DL1dHi949Nsd9rQSawcDtps+CznJS3cOcUxJLaNhaIpQQdE22A8Ui+NV7jSGrPcjmr7C1iV3DgecmK2ha4ILJye4MFvm3rkye00Xx9RZubmM70ckwdGc8olalkGQstr1eHalzVp+kTM1h2GQYJgGPT8ib2nEf356YcuUB+fKPDGdZdtVhFNnybc2yGsSUwNL13CFRj5xSVOYq1eYqeaoFvOsTlwkKxUNP+I/PL9FcxihDduwtwJPfAUTRkIsDXyrhGFbWIZGRtdp9wacq+pM5k2EVMwYAYZMCYUEf0hi54gVSK/PjpvgY5L0O6hinboZY/X3MPaWSbZuIQo1hNclk8sQm1kKMmCh4kC2ROr3cbqb6PVJ5NRpUgSMzmKefYQQg0ZxAaVbmMfvgcYOetBDKUEauCSj82x1AxKlaLoxRhqTIBjJ6kihoZGiKpOkTpniwij2SIniqUX06ROo2hTDVEeXguPVLI9P51goWejtbQrjMwwT6HsJz13f5fInP0Ma/cWLbp/85X8IwKuvvsqrr77KN37jN36hqMz/FFT6V3N9CX8xvmjIb61W4xu/8Zv4+Q9+ADdK2RtEuMqA+iz6xSeRuRJyb4lSf4WCu4/YuwtpjBwckLb2UH4fcnmkZUImj7BtyBbIP/AYdrVE/vxFYs8nae6il8qU7rsXo1zC39og9hNan/kzrMfeg33uYfTqBCoMsPvb4GTw9Sx32iGaYVIRHoYGB7JIL4iI0hRdkyx3Q9a6PmXLoCgDjOoUVz/087zlb76ZXH+XiimpRw3swSH9WMNqb2Gg2BomGJpkYxihhI40LHynilEe5SuOZTDjgM9veHx4Q2M/O8phu0e0dh3pdrDCHiIMiVJxNGUsFImuE8QpXgpa6jOIFH56tAZnChBSsNMPyJhHal7Hj1kaagxiCSpBapKkOAKaJC9CiiIiNmxiM09q59mNdPzKDPWMTlYEaKaDpzR2ijPonQNGRuu0+kN6fko0coy3fudXY2QLTC5OcqfhEscJoW6TJhFaGvPURo8IcNwOp07NUd2/yx9dPWC3F+BFcN+ZeY6VLc6emWf/9Jv45JpL6mSZnB1nMzDopzpvuTDFuqfxa5cOOLx1jSWV53MNqO/f5k3FDpdvrbGgdUkjn5VWyJpv02143HvPeYpxSn18grmpCd57YZKLsyOMZwxWVvfQj6g+0yWbx47XMaRkPG+jEsVUUWejG0PgYRsacxUHx5RkTZ2phRny9z7Il7/9fp68Z4HM6Ah71XnyhuDE3ZcpG4owTrg353M7O45VG2HDFdgZSZSknF2scaYC08cWkP0Oj8xV6PoRK+0AqeskUuOf/Ng/43d+/qfYbzX4wyWXa3sDnrt7yGev7XLgJnhhxLmxHJN2QliYoFJymDt7D97mTSSwsd9icfsmBUPn0ek8Eri73eXVpSa3Njso30dKQXN0gc9cXqbZdpkdz3P1R/459Y2rXJzMYTsZ3v8dX86qOcZvvbjNYtUha0gezLuMd+5gaYIkTmlNnuRTHZOtlVVMzWC7PcBKQw6ffp61wyFb5Un2fJNjlz+H6Q95cWvAyZpDuLnCWpRjt+kihCCWkuOTVSxC2o0GlqExdANGtQQvTikmfcqWRsHUyMqEEVuhmybVjAFKcLxksuCknBzJY9oOUZziJZJemHJfPkIzTXpOnRErIU7FUXAKi0GkaIeSVDMo2waGJpgpWQghOfRhLGNwfX/Ab//LH+eXfupHqeYzfG61y3R3hfH2KsXrz3HpQ7/Mf/73n+TSbsCvfuZZxk88SG2syvSxGb75u/82v/anz/FNf/fvU8plcTSJSlJyMmW8YLFQzVDJGpS728xmYay3RpSkWLoEJLZxRLRafRfLskiO30ffKHLfxRPMFW3u6mW0oM90weCr7xnjradGGC87fPp2n4tjGa6sNsmbBr4y+fTlLV5YbXN55RChYL0HiyM5BkOXjX7M5d0hd/cH7HdcLo7m6QcxliYxUaw1B+z1A7K2QVPZzBVtbrQjPvXGBllTox1qPH37gKaX0jfzbMsyw8IMK5lFPnJQoZOZwM7m0ZOAw0aDoLHB/rWXmIs2eeuoz1PXNjns9jE0QeQF7A1Cml5EmKRoQpB3dKaLDnuDkMvbXS5vdDjoh5wYz/PEPTPstwYcm8jjRymLY3mUgKlqhvsfvcC9p0YY+hFumLDUdnGjmDBN6SiH/dThdC3D0A95aKGGo0kEsN8LKR6uUSvY9F2f7Y7PNdem0lgia+ncM2IRJgnrcZ6iv894sMuhn+KGCtlvsB0K3tjvs9kLuP/YGHGcEnY6ROUpLE1yLFqHtdv0/JAolYxqIfm0zxthHj0KMf0+ThpgRH2EgkgJguosDNr84sstwsCnYELqD1Cajt1YxdE1VBLhL9/A395EVuqo6jQiHGCKlJ4fU9JTlFLE0ka4PRh0UbHHcPp+8Ifw51WQjqmjhAbZPEoKtMY6YXGCoLqAJ7IgUnSp0KXE2L2FJQQZXYICZdgM9CypO6B+32mSoY8wTESuispU6EeKCChYGmXpMzzcwdA0wkyVIIG+F7G6tE8Shkjx36cxI5U8jm0C8GM/9mN87/d+L7Va7QtLaP4H8V/bHv5nry/hL8YXDfkF+Fcf+mk233iZp597gV6QoBuSyCqS5Gtg2Kg4Rhg2+sEqeH1S3SH1h0edvUmIaO6i5YvguSRhQLy9ir14CmOkjjY2h5bLI+sTJIM+xuwxNCdH3O9TPD5NfP/b6eRGETOnSL0uJCm94gxUxhmGgqwhqMmQ0MrjJ4pYaQQp+LEiq0um8zZenNDyImQaEQ8GvOm7vp7pY2Oou5cwbrxE0umQFiepudtoMiWTDilYksNhSM7Q2QklnUQcDTBYktrYKPcvVPny43kWFmd5bZhhrXSMVnYSf20Z7r6K/8wfkDS2iIMhQZQgAw8vSrBlSl6EGByl8nOGQAowNYkhYCJvEKcJQaLYHiZs+wpfz6KkBLuEp2cRgIpjdARKpShNxzIMXC1HrDR8PYeUMGElzIyWyD34ZpSVZ6RSYnNtBSujsfCORxnP6bz53CwPz5WYKpj0NYdrXobnbuzw8LhDSRdU4xbW4R6bG9t0woTff3WTrK74ilqH5vomucVT1HImwyDm7m6fjUHKuKWoiJCgvct57YDTxgFXBwrfNugFMSvtmLQyDdPniUZOsCXHWBN1Xu053DLHudxIGCQJlc1r5JRH2Gtx/4TOI4sl3DDh2ZfusNX1mcjbSCl4ZDqPY2jomkZf2MT+gPVeRHMYk9Eljq6x2XWpOTprsYVuGDQSQcONmZzMsbTeYGNpk/5v/DqOSrB7baJ+hwiBk8Z8+maDP3zqMjc3OnzLIzOMCp/3vvUMV+5uc3mjS6IUtiHZDXXcbJV/8E9+lH/0/vez2XKJFMyN5JkbK7Dd9VkfKgzD4uX9kLbIUMnoPHnfWbqbd6k6OisvX+O6LOCHIWfdNR6fyVLJGFzMKlp7LTpdl9WVTQZCQ5brvLi0Q1lPmZuq0hufRychZ0hO1yxGCxm+9aExjhUk90zkWLPH6ZdmiIQkUooTMxO87z0P0woE1tgYf7LmEe3t0D99Eb3bZ2p6BH93m+pD9/LYcB3j0gvstFwyY+OcH7F449YqyzstbE3ixQm9ROOlQYZeBPOjeULNpGBpvNFW9IOjNTBLQtYyEEpw/3iBmZzkYHg0FR7FCTL2uDa06EcxvUhhFEfxNZtQc7BNnZqjUdISqsJHk4K9QYRv5NAF6Bzd0MMkoWBKAmmz/en/zFc/eT8L0xOE3R4PVGGwvEzl0mdpvvYG1565wuj2Mkuf/QMGgcexb3k/K7tddtv+0dF7AncP+7y22uLhEY3N5oDDYUgnPBo+8GJYi0t03IBDq0bB1lDSQHX32ehGmMQMPR87VyAysphhj93CHFZzha9blKx4Fkm/wc2DIUolzBVNcuUiYa7GI4Uh1eiQ6bLJidESjx2rIiyDvWHAXD3D2uo2KxsN5isZhnHCXtdlqpJhu+8Tp4qpvM605rHUdNnq+oSpomhJqo7GdjciV6/hrd/lgtmh0WiTej5mFCF1ndMzZbJayqnZCovTo5yYm+CpO0NWrDleVnN8plXgalPSaPbRB4d8Ra1N+eA60d4Sg70Ngm4DPzhaNEtTRTVrcmGyiCEEhazBufE8caKYnqoQInEsHV2Dv352FFPXOHHxJE+cGqPVCwmTlKYf4YYpe22PsazJoRsdDfpIwbXNLuM06bsRe4OAQax4aaCz2hzi6TbnCyl1fw95+mH8RLHjweEwYhDBoLqAaB2gopDtQcBH1xOWlvd4eb3HjCOYrGV5uBySXn+JG7kT9GLYyS+ixhfIJSF7fY+RNz7JlY5gOqfj6w554XNojhBrDjvDmI1egvfpj2K5bW7s9KEwyoT0UE9/FHGwTmBk0NpbhJqNOv0gaDpxfoREN0mikJJ/SDvRsbvbCL9PolLCnQ0CoRFqFlY8hEEPNWwjpSAT9GD5NdTq66huE5WtkOgWuhTI2OOcMWDOjig7Gr3sFDoxWRkjU5/UyiBUjNi7TeJ6aI6DzBVAKmLNpC59DCnJ6+BGip2NderT83hxcjSlXXbwOi2UlKTJf1/5/egv/ABwVG/2zDPP8EM/9EP/C9jMl/DFji8q8luv1/m2b/kWPviT/4ztrs8gSBFCkeo20cQJ3PoxYqtE2jsgWr0NwxYqlUg7i9BMVByRDF1kvky6t4HM5FBxiHnqQVLPReoGxugM5ugk2EWwHPJnzxBPnuCPxCKv923QTAgDRCaHMGwGMo+lw2TWpDjcIlQaK+0IL044WXXImhrDMGEsK7m3pLBEzGZkYAuXsYtnMAyBmDxOvloCEgypGGRGWbenMYcH1G2NvGViyZTdUMdLDBxNgZToukZiOKjKGItTZU7WHCZHqvzhtsUnWzbNnRbubgP39ZcY7OzQGwwJovgo4d3vs9ePSLwBeuhidvcQsUfFTBnLWRRNwal6lumiRcaQlC3JMNXxlEasGwjDJNYsEiePpkv6icYwlqhUITVoeAkoQSokkV0mpzxMy0AJOFYvsN9z6ZpljEEX5/pzvDnb4tExSVHETGR0jucla56OphnULDioneS+CZuDnT4nzCHnpvJcP/CRacL8mx6n5SsGvR4TSZOSu8Xerdc5vHOZireH73rc2OyRmFWS8jxxZRo/N8rJhUk6Wp7VfswbDR9lSt5xzyTj9Txmrcb1hst6dprf2DDIWgahrlE1U3qR4JEL8xRESKM9JGtI+kHM3jCiHURkDI3b3ZTRYpbNQx+dBF1KTtVzGJpkz4touEetH1GUUrY1jpdt7pnK0ztxlju1GX7991/kt255vPyxp0lee5lk5Q2c3j7H8gZXlrb4zNKAfNZmUnU4PGjx2SsbKJWy1va51fSpOhrjZx/mvje/g70XP8bjCxUWqg4PzZR4ZDpHxtBoeTGrvQhbE9Qdg9/99V/lb3/TNzLqaFR6u/zhaxtkulu4r7zEBdniyWrMfbNFbD2mVszw0utLuF2PsVqO6ZkxirksT/7cP6eVm6Sf6HS9kJan6KWSQ19jzhhSNiWGbrLjaswVbBaqDlNWQKRZ/OA7jzFdsjhRNrByWebqNsN8no4y0YY+45bLzYmz5DMWuweH/MHTb7ARCPIoInV0jJ2mMJKzeHhxhJlqllzGwosFlYzFTCVP04+QAoYJhHFKL9WOAj5KkLM0NvsJBcfAMyt4qcCS8qhSSdNAt3EjRapZlEzox5CY2aP6rDhhZ5DQCGJW2x5ZUxImKUUTdtaWeOoTH+fbH7uIbllMH59j4pO/w41nXyf3yGMsh3nKj95LeG6G3/rk8zz6Vd/BwmSZv3ZxkqX1Jp4b8NrLNzEONlk+GPDzf3yNtxyrY8YJnZ7LTj8gATLrN9ENg/WhwIoj6v4+ndW7LO33aA980iShbxXR0gRr6zo5mdA2yhxoRSLNwrn1MvMli2MZRU5PecepUcaNkJL0CdEI44S3na9TzxjcM11hMmfx4nqParXI7HSNvClIk5RazuLa1du4boyhSV7e6HIgC4wXLGxD53RRHlVftQ44XzMZKTiMTE7w+nqDB0/PUB2pcHK2ymjeoh0kXLm5y3tPlJG6Rhr5PHDfKYQmubPXY7WTcKAX+MyuyYeXJB85HGH++CmsXIHB0CU63GT/+iXuXHmZ/tYd3MNtysqjYB3lCQxdokip5yxsU8PSdR6ZKhGliulyhrGiQzcVXJyvYJo62x2ffhDxsedWSICSpaMULBZ03nF6hHsqGmU9pGBKdvo+mdo4K40hZ8fzqDQiECab5FlpDtFNE01ITKmwRULz6nVGu8tMWTFatsiNdsj77h2jks/QDxMmkh7GxUdRUsPXbPYigytBnle8LFlT4uWK9BJBJuOQTXxSK4cxbNHXctxpeAQK8lOjyJFp/sbZMppp4ksDo1ikP36OA2cCsX2H0Mwhjj+Ac8/DpMIg0PO0nHGQgrwBSaZAX89i2VmMuRO0K8eRmnFEfqtjiOb20X3A64GTQXh9xPgCCDDigFCaYBg4q69hypRpO8Y0JfZwDy0J8FODocwTYiHKo7gHffInF8AwGRRncFNJToVIcVTdeXNtBzuX58VGyt2Gx3ZjwH7HI45AJTEkwX+TU2Rtk/vPzgFHqu/3f//3U6/X/xewmf8xfGnk4n8dvqjIL8BPf+iDbN54nT/4408QpQqhEqRKSK0cgVMGFNr4cRAKbWT6qBbNsBGVMUS2gF6poTQJhoPmZEm37iBNi+DmKwgVI3UdYdu4Lz+Nu7EOpx9lvX6BA1+BLpC2RVibB6dIgk4rlugalNQAhGQYKjRNktElQZxgahI/TomR1MolHh/RyAt1NPU47KKbBroQ2EaKsDJo7X0ywYAVT+PZQeHPPcSCVpBiSYlFiKYU7djgbickF3UQacxAaUyX82RshwvHxqFY59bMo7xy8q8RDwJ49pOE20scLt1kpeXSTgyud1OWWj4HrQbBwSb23ZcRrzyNrUFWJgiO5iLLjokhBCsdj34oaSUmEkmSSrxU4sUCISSRgjCFsqXR8kP2BgF7gwgt6LKr19nvezhpRMbUqBWyNPf3EYunMUZGqPoN8m6LREE+aFJwD/jGiS4VMaQbpdwzZmNnDd76rvt4qAQzRZt6b4tPbKdcf/0yzRsv0d64A/6QTC6HlxvHqx3nE40Cf3LL5fPrKZ/eTqmPlDlvDKh19yhUSjhaSsWQkCYs7/bJiYisqTHwYjrDmBu7A7T6KJ5mUc0XyBeKVMyU8Y3LPHb/KXK2wR+/vkOUKBrDmJ6fcHo0SyFjsZc6vOPsCFfu7hEmKUIqbEOn5cbUs0fhCqkLdENjPGexHAj2sqN8omFh1GtkJkZ5znP45N0mza0Of3bX410Pn+TNo7A2jPitzy2RyTp8+4UaBUOx0nY5dCN6UcKer0iF4Fu+6ZvZePUZEBDGYAjFaHjITF7S8WOOVTJESYouBZevXOHRN7+FXpiihOKdMxa/9sImV449wacaDnl8zi9WeGOzRzHngJnj1uoBlaxJECZkYpdGeYq9YcRK22e95+PGKefKknzOJOm0abU6SAmFrEk/TJBCUnEs4tjl822dLAmffHmVzdduce3Ao7CzykrDpbt4ko+9cci900Xcex/m4VLM1MQIK/2E8bPHmaoXkanC1CQ6GqaEWsbCDxMaXkycKgIFo1mLOFHouoFDQCokhqETo9EOYgq6wpaCQz+h5mjA0dGqLiVefNQiIJUiVgJbUxz6Rwt2WVPixmBKjb1hxDCEK1ttXr/6Bh/80R/m5/7B+3m+V0D29jGHTcbyEbNWgBq6PG3Os7F4jH/zZ58m/+D7+IonziNReEHEEyeKFFo7rF6/ztpAJ409Xm8nPL/eAQ2evr2PIRSXD2PUvY/SDFKOj2TYGYY0cFipnOaJcQ2vfQDAdLVEb9CjWZhGX7vOjaTMVXuRu7t9Dj2Tes6kJ5yjUxGlGAibaX1IKV9gtmBzsmyiC2i6ITMlCySY+TwFx+Kztw7Y7XisNz2u7oR0BiErBwM0UydVioVyhomCASgQJsHBNkpoPDpToClsdjPT+L0Br621KWYdLoxmcEOYHi/RV0cDPLFd4OxUgawmuDhVwLFNDrb3MFAUHZOFsTzXDiJ+dxk+smnwgj/KYPI8SW2WWLfpdtr0Nu6Q3b3G6WSbknfAhBExkjE4O1nGTRS7g5BLW10EKVEcM1/J4Ak4P1ZgEMRM5G0eOD3O4SBkte2iFGSVR5CmGLkitbzDAxN5xvMWc5UMp8eOWg66OGyIIgLJrb0BQSKoZSSmTEnTlOCxd9PWCwjL4U0LJUrlIl6siBAcr2WIizXcbJXGIEBYWaJYHVVVapIEwR9wkqyuUcKjjYGxv0IpOKSX6CyWbc7VMxjTCxCHnJ6uYAiJEw9JHnwXO2YdxzQRI7NkRYibCpLaAoby0VRMJA16RoUR1WNfVOjqReJMASlAj120YZskBRH7KM3AHOwjCVGDHmLxXvz6MbQkIRSSME1ASILDfWKZQZca5s4NPGw6AbjCIEXQC0IIfOxK7kiYmj5JN7XIRR1U4hFECeGwT+9wj7Q8zWubXdr9gCtLTXa3e7iNnf9P0O2/gU//+yOv77PPPsuLL77I+9///i8Mcfkrwn+tOvufvb6EvxhfdOS3Wq3yXd/x7bz42//2aP87TRDxUdJUahqp6ZB0m1jHzoNTRtkZVK5I0thFpAlaLkewvYG7vgpSIuwcaWsPkcakpsPg2mu0n/s8w7t3iPa2CAZ9LDvLIzMFxrI2iZYhKU2Sjsyx6x0VqGsKbL9N6pSJhOCE7VPNaLgxOIbgRNkko0E9bKGERnnz8p9PLMYkvRbKzhIHAcnOEma1il6q8WBZUdNS/uhmg14QM2eFRHGCArIiPJreDVP2VR5Padw5HHKn5SOE4sJEhicvLnBqfpT8xAjRE+8l82V/nXw+SzkNGAQxRUsSaRaFQonXmibrxWMEo/PgD5B3XiTcW+eTdxpsdTzKtmRrEBNEKUmqAMkgTNBSj36oSJMUSz96s+QNiFLF7iDCT2BnELPr6zjxgDhJ2aVAy6gwXzDYarRp3VrCy46zV5qjnRhIlWCELsnhFmUtpZsaHHM3qa5eYufOEq0o5iDw8DeuYcZdLs6UCLQMV5hg7twDFGsjiMokTeVg5zI8Np/na950kp/4+gt85bkKcRTyvJdH6Tpar0np2iXW1g4p2DrHxgqM1yrcN1Ui6xicHS/yyLE6tmOS03Vea0Rc2gm4003oW3n6hwc0Oy53t9qYugChsHRJ3japOSZb3RAhBW+pR4g0ZRAkWBr0ggRH1+l7CdPFLAsjGVZ7IQjJIBVYtRpf/uA87871+O6vegg1NYM7uwjFCmkcU0v6HO4csLGxzVaSo1bL8cMXDJzIpR9EZHQdhIahSZRuc+/Z0/Q3bjNVMBhGKUIl5IYHVB1JJWNSl0d9mMIw+exKh87ApXvqYYzGLufykn//1BJ7AYxNT7DaiTg5YvL89T3OnJok5/co2wZOv8HmU89jqYiKY7DZDfnEqzt89uYh/YHPrByyM4jY9ARXlnbp9oeU4z6WULRklrqecny6zst7LjJfYLkwxf/roy+yXRjlHfYBs3qEb5fIaYrZisNdUWYq9jgr25wcydHyIraGEZFKcHR4aasLKqUxCLE08GLFIDwir26SEEQJqeawN4xJEoUuFGmiKFuQG+yw2vbwAp9ApWQ1gRvH5MyjloN+FLM7THhlz+dwGJKkKV0vJlIKSypypiSME1545jP8P//lB/nAL/07CsMO1pmLeActwtWruGQo3neBrAUX3nwvf/iJj/A3/s4P8c+++904GZu1psvV9TYPZ3zeuHSddz+8wMTcOE4qULHGQavP7145oFzOYXs95pIDylGX6bBDyT4KZq27klpWZ2AU6LYaSN2gbiXIVGFHHh/bgu0b1xmzUmJN8vLIeYJEkDE0lg6HrLWGBKnAnzzLfiSxiZCaTt2CT9085Pdf2+WhySyXVzv0YsXmXp8zYwVmKg718SovLB+SAKYEP4zI2hr9IOGZjQFNN4bRGfrJ0Tzt5T2fz712h6HvcXqiQMGUlHR4aCLLWy/MYKQhOa/N3UOXg0FCJW9zYabC248VKYmE/Uabhy9McXwsyyCRVPIWZcfk1dU2Ly21aSobN1PjID/DZ1olbhrTGKU6GZFQcXeQ65dZ9FepuztUGJLXIIoVxwsaVeUyU8kQpinz1Sz1nMW9i1Wyto5jaJi6oBUbnMqm9P0E3TRZ74aUtIQKLsdqOWoZiyuHEfuDCC9JeWKxQhQnlA1BKxDg5KllNSq1KpWcQ0VL+K57y/zKM2sUDUklY4BusR2ZhHFKt93CsSQLJYsggcN+xPHRMgmgJxF5fGSpRpqpMKp7vL7VpayFDMtziP01TFNHTzzkwQYKSd6UmKZOWhxBIDBin6FwkECUpmR1EFIjHvYwSSilLoHMgFOiFh6SNNbhYJXALECxju73UdJESQMq0xhpCOGQg9Ag198js3sTTYDtt9BJkbUplJUh0DPkVcQwTBkbbqEyRaJOk3jooaSGphKU7hALB5WGLK8uU6iP0U4NcrbOamNAv+sz6Azw27v/XS6ha5L7z80DR6rvD/zAD1CtVr+A7OWvAH8VxPdL5PcvhS868gvwgQ/8JIert/iDj30STxj4mLRjA0ukyKCLd+cqynCgdwCFGiJbRMQRIBBmhqTfx8xZqFSRxDEq8NDyZdqvvMreU8/TW9/FLOXQLQ3LlDh+m0erkqKt0Yh1DBUSo4EAWxcoAWrYRngdihLyiYuRHnkeC7oin/ToRyl2exPbdo48v0KCnYckhkGH4Y0rpGFMrDv4ukVDFhgrmLy17HF3r8fVVkJeBTRCjVTTKcqjJgozTXBjOFO12e4M2epHpHFEKjXiFO4rC7RcloPsOGF9gcHigzyR6WKmITldBympFm0Egj1ZRDzwZkS2QLK/xRRdlve73G647PR8Ol5IL4gQqaLtp/hKQwOkCkhSRYqg5u0SpFCyNEayBqM5g9XQ5DMHknI6YNxO2I91RKuPoensDQbc/i8fJrh1hwEGejhgXVbwnRJbosDB6l0urx/w+Y7Etx20OGDaSDgT7JMrVcnMnOSvnR3lrZ0bqGDIidECmoAnFquMZ00OD10WMxFzVZMLwy2mkx73B9soJ8MfrEualSkeODOKlirefWGMZtcjjBNmyjZZA0JSZitZtt2UQSJ4eb3LbjdkKz9NPSt5MFxm1g4ZDAIOBwGjeYuDnocSkKKIYhgfrVCXLiVTEkUJTTdAl4J+FFN3dBZGCoSxYqRgMVfPc3K6iLj0HJ3dbca7Db78dAVZH2V6vMTq6iblos3yrRXmSxr3Zl1wTNYCg5neFne2uwgFe32fnKmTNQV/7eu+id/7lV/CMSWWrtE1ygyNIjXpUTLhZtPjT595jkGQ8MJyg0pribyWsF6Y4YFMQjab4y3TNp+60WF7fZN6McP8RIHTi+N89yN1zlYk99QtPukXePqzr2PrkmLOpFZ02B6EXHV1monJaN7m/kKMiEI2BgmZ1KfcXqLvhmyFkt/905e4udNlYbzI+OIk3/O33s7s2VN8rpdB5vNkB20+favB9fUmlazN6YuLHGw3aHsRCIGjSwwknSDGixJe2GjRc0P6cYopQRNHQwObHY+WnzBIBAXLoOsH7A8j6jZsd0OWB3C8bJHzOtzY69MKEnZ6ETpQdwRhImm4ARlToxfENL0QBMRxyjBKkW6Pn/nRH4TrT/PzH/gA1ZxN/vwFjuckV158jT978YCbs/dxOHqSz33mM/zMP/2H/MsP/jRvevAMoxOjFE1BEiecmcxj727w2CMnMedOooBL15bImyk3lvY5OVlkKgN1ceQj9wsjXF9vEKWKdphyb81gWvcRaULPi8jmcoReSC7tERgOD5+b5azpMad1eeeEzuJoCcuQVGRM1jGIw4CcqdHZblAiRrR3CJWGmwoemisjDY1212VxLI+Dwh8E7A0Drq23EVFCImCqnGG967PZj2gNQmq6YrFo8dJKk9fbGt1mj2F0pNiXKgWOL0zyVeMhmgZK06g2b6PrkgkbzHDIYsWi6Og4hqIgE6Sh8Z5HF/nWJ+dQqeLu/gBPCHQFJ8byWIbg1toh17a7rDVcMoZkpJLHcHJE2RqVuRNcN+fojZ2mNDJJ2dYJDraoNG5g792g0F7j5l6Pnb026y2XJFVoKFpeyGTBoWDrOCqgm0jMuEdk5cjoitc2O8zTYTRokpVQM2O2m0PyhiKOE/KmxmTeoBQcUE376IMOxo3PYYV9RJrSURZlPSGTMxnGKZrQsJ0MB4OIkmMwooXUHJ2iKThZcbjd8jhez1DP6HSNEhEGO/oIhzKPtX+HR+fKJGGEcHuoQg0vVARIRO+A4mCDjpegCwG9Fsod4gsLITXEoIUd9sjEfXLBPkmujiMTLL+DOWyg2nukqUI3TMiUiMwSaXGCcPkaBB5y8iSRZqIlAUmuSt5rQq6CME2kZSI7e4gkIFm5jrFzh9GsjiCk5O+jLb2C6jVxtxu429ukRgZDKoSUhIDeazDo9ehn6iwf9hgrONy+u8v+0l1aK2/8hTzi3/34twHw1FNP8frrr/MDP/ADX0DW8iX8/xu+KMlvuVzmu7/jO/jXP/2T7A5TrnfU0fFlGqA0m7TfI21uoQIX+i00f4DSJWrYJY0CMmcuoherDO9cp/HUZ/DXl+jcWWP1Yy8T9TzMnE3Ud9GLZYhTypuv4gx2KeshqYJY6BxEBiMZA4MUO/HBtFBOGcdI0LyjLwZDV5hBHyklllQoO4shEoxcgU5+FiUNzJEJSCNMQ2Ceug8tdPGFhWkZ9DCYKue4f7bIYk7SSjUsXbA7TNj2YDIjQQNTF4RovGVcZ9qOWR1AqhQIgWXZ1NMBmtRA6kjTomHWkIZFwdY4dCP23JjNQYInDBoiy252GmPxHPeNWcz4G5R1xfXNNr/z1A2WGy6tXg8nddkZpDjKZ99VuJFCqgSkxEAwW3IoW5JJR1K0dIRh8vlhgWYIQim8+bNMaop9YZJ77HHyYZ9cc4W1zW3Wb13j+eU9lncbCMNkbqzKhcYK94W7zBSzlGeOkUNwccQCKemHgumzx8g1VtmOHc6UNcIoZtSWvO1EkV99eZ9WKGjUZ9DXblA9Ns/FisZ7Sh6bfko/govTJWyVcKvRZ6sbsNPx2OoHXBjJkTclb+z3kQKmahnOTxZY3mzQyFQ5c2Kac601jL0d8hqINOVzqy10KYiV4qmVJs92bC4dxOT1I4WwPYyQQlDN6PTCmJ1OgGkaoCBOUoJ+n3NOwLH77+PF0OHmfoiuaZTyJlfbin/z9DpPnJ9memaaay/eZC+0cEuTtK+vcP+IjmVqTBVt+mFMpGD++EkefeJJ/vlPfZCycgmTFLu1QsZt4YcRv/xLv8jv/NKHuHnlVbwo5dLdfQa9PplanesqwxP6IVV3n2OzVZYbHrPzszy5WKaatfn4nR63Dlyws5xu38EPhuz3A9abQwolm6m6gxslbIUa7eoi62mOl9dddjsef7wU8Kk9k8t7PTJpwFnZ56G5CsPwaOzj/PFJzh8fQ9ZqzHi7vCpHUabB73z6Ko2Bz6wxoO/77PZ9pgsWGUMnbx8FTXteiEQwUXbwI0XDjY56fQ2NME4p2DrbvYBEwYEbM1vO0IsF+z585MaR4vncniJn6nT9mLYXcrfpYkqJY0hKtsFWL8DWJCoVxOmR7/jjf/xx/sk3fRVf/8h5/tXXvgvtjSvk3niWhgvxrct8dFMw8da3sdSL+KU/e4Ff/Mwl/vW/+hDm5CKFjIlt6pyRh3zFqSp/fdHmudc3OJXzyRmS9cMeo/UCI7ZieqLCgu5y3lshVxshXyyy1+rzxNlRDoYxn73bQWgmy56O8oeEUYSZK+Msv87dvomuIhYqFvMn57DyZQpBk1reYrsXsO6mTJczLOYFl1bavPTcZXzfQ0qBIQU5Q2OhmmG8YLPeS5kqWXzklS0unBzl8nKLYsnh9Mk6F2bKvLzWIqNJ1htD9twQxxBsewmzOZC6pK+OxitGbfjO9zzA46cmEb02ZVMnn7i8NMjTcUP2I42JmQnWmy7HyhZhfJSfOF4vUCvmePjkJPfPlKnkbS5OlrjXaDJZMlm9u8nV51/BD1M2Oz7HyhlOTJSwjaNVsYqlMV6wKOdylOujZMYWUFNnGD/zIKWJOYSdYUR1eMze43i4Cc0tOt02c6UMxysOloQgPOrivjs0udOJ6SUa7z2R5+lehptylIVgk8NBhJnEXKxZRCohRlDQFYE74Hi4jeG1EFFMsnmbTBogJQRWiYcWqkihCL0+d9ohxYyJrkmCXJ2sBv0oBaVoDkPcMEECw1gxlBkcU8NwcniVeeaz0JcZzN4+fqZyZBkUEjmxiLLy6JrAiF3SxiaxP+RKM0KP+qjQJ8nVMJIAIw7Jug20yEeTHIWfVYzK1SBNUOVxMp11VDQ8aoEQkI7OoSdHc/WRnqN8eB2RxBDH6IvnUNkSYvsO7WefRq3fQvf7GGmCM9iDTAkxaBF5EZmZSbCy+EqiEOzFGa6tbhIWxnl5e8hLSy3cIGLt6jWkbhG09/9CHvHNX/E4Sil+7Md+jPe///2USqUvMHP5n0eq1F/J9SX8xfiiJL8AH/jgT9HcuMu/+o3f51qjz3o3IIlj0lRh1urI0ghJYxs2rpPqFipbgzSGOELOXUQ7dg+xF+BuN7jzsc/z8n94CgRYeQdpmXRXdkmjCD3sY9fGkW6PaDhAlwKrs8kwBimOakOkAKFbhFJihX1ioaP8LtV0SIxkN83gx4IoWyNIQCQBttSIzBxxtkJSHIWH3n2kUqsETRPkNY0pMSC0yxSiAV1slKbzxv6Ay/suO12frCmxDRNHJjiaQAPyhORNjayIKRoCqaXEdp6yIckakjRRTOQ02pEEjrqH54oWuqaTKHjqToO9QHCpq/NqWyc3Pk3dVkxaKTVH4/lb+3zqbos31huMRTvk/CbjSZNCb4u1js+BVsXUwNYEugqRErKGwT2jRzPLt7sJCYKs8AjmzqJrkp1U8rpZ5FonoZ3oTI1UeeiRh1l1ZpmeWyBvGpj3PEC7Ms/6b/wn3vidjxHUxmnsHjCXFeBk6dglXjtI2Wx6NIcB49LHbe6TZvM8sDiC7ZjIco1LEw9wPS6xnxtluzLBw/eepJo12BwEPLXUoZRzGMtbHA4ibAm6SGgNIyYKNghYaQxpdQfEhk2zH/Hv72jkz5zhlGgwt32b5c0WtYyBUOBHCSdGckSpYrGaYRgLTpQ17p3IYQiFxlEfaDmjUXI0EiCIYkpazHpxgtQw+dz1bSxLxxYJJ0dynL/3BC9u+bx4p4FWLLHw0AWKGZt2EPNUZpFh45CiLsibBk8tNbnbGOLokrd+zTfh7a7yRx/9QwI0spkMfhTyzNUlttZX+Nlf/g0e/tv/mIKtkyzey8Fmg3VPZ1ibYLkx4Nf/7A3Mzg5tu8puP+QTryzTHQbc7UIQJNzwTb72a59kcqrOrb0BG4cu6w2XK8tt7u4NGEYJbqI48BUn5yuMDvbpdgZo7UOirTWqzTXWQsF4Ncc7jtVYoEfTjfCUznsXi1iZDDJNmXDgvVMGCkEvUJyYKpOEKamCNE1xUp+6YzBVtJgoWFiaJE5jUgGjWZMgTpguOigFI7ZiGMUULA1LExQdm/lalonRHL0wJrWOWjz2XR8pJJc2u/ixQqkEx5CM5UyWW0N2+z7L65v8/A9/H5/4tX/LL//w97Owdou7r20Q9Vv89gf+Iy/+xh9iz89TevBN/P6dPsX9NfZu3sZ48L309SLbdzd55so6660BTx9o2FmbYqXKyuwZ/ng/wyde3+T6jTVOz9QZiAzZJGCn0adnFnGV5HZS5mLvNrrpYIqU8xNHAavxnIMfxShAyxTwpo9zsmZhVMdQgN7roaTO5aDAZjcgTmFnEDFVNBl34GLO5XDmDAXlQhCQ8RvkTEGYKN65kOegH2JpEjtrc3Wry7DbpZyz2G14PLpQYbSc5UTd4fHZAgtFmz1f4gUx75w2OVnPsbbfp9No0I8Es9UsFUeyZIywO4zopRqV0TGK3gFKaJimxZvGNapxk/NGl5ojuaB2OFG1KWUtirbOqZE885kUo1Tmmde3UEIjX69hi5SpUgakZConmSvZTBVt2kGKEBI3SrnVdGl5Ib0woVLMUK/XuSXH2cjP89RghCZFCnqK1VhD27zCwcp1ZL/BtYMhvTAhZxrcbXgcT9scBhqVjMGUI0gzBQ5jycMnauRtk2P+ztFIRwy+XSWqzZIGAWLuFL3nnyVNj74DHC3hnok8WV1iC8FSK+D6Xo8gScnffZW9oY8loW6lPDlX5MbBAE1KhFJULLA1KLaWSKROO9XIiwCZr7IZO5RsjTT2wSkh0hg3TgnilLDvEtdnOV4y0FdeZzc7gaZiZOQT724iNA0naBMbGXQBqjQOSUJSnUUPesgkQOkW+ug4Skr03i4y8iAM0YMOojyG2LlJuHKdYXUerbdPsruMc+4+5P1vR+/voa+/DrU5ZHUUEbj0NloYpRqivYWu6bhahvWtDXqh4tWOwdrhgDhN+dzrO6A5pNF/O+D2X/HT3/+1AHzqU5/ixo0bfP/3f/8Xjqz8FUKpvwLP75fI718KX7Tkt1Ao8P6//3389s99gKW9Hm/sdNkMbfThIcbJe6FQJ20fImsTkClBsY6SOnG7gbJsovI0mYlxGstN/uij13l1s0MKDPZ7WNUixdPH0cs1gpWbqN4BqWExSA00pRC5KoYEIw0wgz5IHYXCjDwi6dDMTZJkS4hBEy3xyZgawzjGtStHnYvFGq6CgVHELS+gGRad7Cg9o8QwM4qhKXy3R98q04tilFKMB7uMOToVx6DlRry01mbHVWgCSiKmF6boEmTkkSEiSAQCEEEAmoGduoRxgiTFERFKKQ6HEd0wpe2GzFVsdAkvLrV4+k6TKzt9NDvLXpplZQCTtSyPTxqYhmS0muPzWyGrfQM3UyETtjGuPsUx2YbVN2htb/DM5RVWGz283oC8CY6hUTQFTjhgb2OFl26ts710nVTqpFHA5NwxHqhZnJmfY2RiElfpPKG2UEBQm0RWJ9Acg9zZE8zcf4ZsKYext0I27LHvS5JEcGqqiN9p0zhs86uf3+FP3tgjlzGYLOfY9ySmlCgkOcsgaxrUR+tM5E2qpmAib3NmIser145qfB6cKVIwdZ5b6fLMtR3CKCZjaBQdk4+9uM6l51/j5FiBx06P8VTPYvye82SIyfX2mchZ7HQ9QJDRBYt5jeYwoGhpbA9iSi9+iivrbVYaQ8bzNm03pm7r2FIwlre5Z6HO070cL19b4XuemOGN12/wyusrXF06JPJ8/sbXvpOpsyfoeyl+cqTGnaxnWB0EBNLBiAZYxMzKASstj5tND5TiX/z4j/Aff/M3eeqpp/ncWo+n1/t88td/gTe973u5203IjZ/BSkNytsX8iUnsfovd/R73PXqR3dIifWVzbCSHTsLAC8lYOj/wtQ/xZQ/O8ZbFGs83NbaNMstNl4wp8YMQN04YhDGNYcizq232Oh5fOQbx/h79bodX15scC5vIXIlvemyOzcMeZUeSGbSJooQwTbAyWbTRKU4ujNLpDHn32y6yejjEzdc5efYE5ybyxEoxjBISIRGknBktMltxaA0CBBJbk0ggY2hUM0cWqUAJDocB3UDhhjGDIMbSwJISU2pMmQElS9IYxoznDe6dyLPW9emHKRVLYusab9y4w0d++Wf53X/9o3zrzDg/nHNY+cwlVp+6zt4Ll/j86zsMRyZ54Bvey3+5HdCJFQX/gFcv3+Cd81VuP/cxNlsRneee5bAf8NzlLZaWd7izsc+ty69yumoSVMb5s2dvUBupsNKOGS/bR77OaoVes8/rSwdUMxq9U48ROgVMXdIcejy76R0FjNr7pAg2ewGpU8Dx22y0XG7u9hgeNgl1B2FlaLrhkULb83FUgCkF1YlJpmZGiKROhI6m6QxSyUzR4MbekNGize8/fYOCJTg7XeaxU5OMWoL75iqMmilzZYNTJY35sn3UFqAl1E3FcpJn3gl429lR5mZGOTGS5Xdf3sDu73NIhsYw5Fde2uf2oYstNSpZA6e9yoy3DcMutaxBpbeFbegYacC46IFKyZgabpzStcpcXmnwrocWWFic4sUrmyghiNKUy7su1ayJoUniJCWJUwqmxI9SWl6MJSVJktIJYhZqWQ7aAdPjFc6dWsCZXKRbP83I8fMIO080aJM9vEO+cZeljQ1O5WJ028FIAx42m4z2l+lYVcayBqfrOUxTUuisc9iPuNkMOZR5hlqWuDpOYGTI1AtYaYAadEi8PhlNUNBS1vyjZoqRnEU9o5MebjAiAhqDgETqLOYE99UNojQlTFN0ocjEfRi04cWPU1IuVtAlCQJqGYtS3EUJE4Qg0gxmM4qDxMY89wiW3yGrS9LSCEamSIwJUYC3sUzS3EZly+hJcBQkH7Rg7Sr97CjkKqRJQmDmwcygdlYRUYCIPGhtHd2Hc1VUc48Dq0Z34KOqk8jF+7DvewItjVBhCFJDSInoHOIPBsRuhL+9jl+cxksStrseYWOHP9sxWWm4oATbmy3WlvcY7K/T27z1F/KH93/bl/+/Vd8f+qEfolAofCHpyv9t8FM/9VM89thjZDKZv7SSrpTiJ37iJ5iYmMBxHN7ylrdw/fr1L+wL/Uvgi5b8AvzYP/0J4u4+n//Up+m4MS1fEZt51MRp4vYB2ugMFMdID9Zg/SpKN4EUohhlOiTekOtv7NH0UkZzJsNOwOR730bpyXdSuvdektY+3soqQjNQlSmyyQCRBviaxcEwYhBJIt3AGOwh3D59u0pqZrE1gWzuoAUDDKUohV0mBxuoNCWRNqlmkTc0hsKilRrsahUsTdCIJOueRFOKSn8TXTdJ0LgdOuQMwaQVcbpqUXIMHj1WxYtTGn7KUJgULMlqnGMjzVMIu6ShCwL2lIOIXLqxhqlS/CTFkw51K2WyaKIUbPUjdF2nnjF5x7kxpuoO7WFIkCgOBj7TWY1coUBaG2einmcsa2BldF5Y7vLLn7rLbpRBO/soZS0ks7NK8ok/YG7/Fp9+7gY/9/HLPPPKNV5/5RK92y+T7K+iC+jnJ1gvHGfh9AUs22a72cXI5bGTAY5IiYOQJFsmQSNjmth+l/bIKZIn3kv2noeQI9NUTpwgeP0FguvX2HMVf3anR7M9YHywxVsXbLZCiz987YCDRoebOx2S3XUez3aZtz06IQiVgtfnqY0+F0fyaKbO/cdG2OqDLiST5Qy9IMEbevzOx1/koNFjpGhz/swkk9N1PvLU61xda/LIqTrPNDXy917kiXMTnKTJ/n4DIwy5ULcZNWKMOIbI43O3D2nutzi99SqbzT66EDw0XeTQjZgq2vy1WYffeW6dOFH84aUtbt3e4O3ZDlGsqPoH3Fza5+RkiWzeYcSIcTZucmVvyOs7Pd4yneH6ocvS2g5lt8FqKyRNjnqmP3WzwRUvzy/8q3/Ba889xe/95q/xW7/1YSYWT3LvPRc5XpJcmCrR39rjI09d5fpehynvkHcftzm0apiWycdvD7Etnf2lFcbHaqg05kTNIm9IZBoyzJTxhcm5iQKTFYe+l3Bxtkyj5UISE0UKXYMX7jaYf/wh8rNzPJRL+c9riq3sFEvWOI/VU55fOmSj1aO0fpvpgkU3SGhHGnrG5uLpOcq1Eu7uPjOVDIvDNYZ/7qFOgFBJVjoez662yOhHRKiWMVgo6OwNo6NkuSYYxIKGD5ttn5wpmDFjLBGTpIJizqLpBRwc9rE1wVbLpzGMKDgGW12PTy21ePqZz/EPv/1vsvWp3+RrHr/Ab//kD5F56iWKBYuH/ua7sU2d0uOPUXnsHt7yXV/Dhz5/wK/95kf4j//8/fzKL3yIa902v/XKDcbnT/Irv/1JPtzJYDoFcrUCjy0WmF6+xn/4+Bt84to+kxmN933VIzxydophKqnXctTGa9zspPSlRTnusNmPuebZZEXEeM7midkS3UjgBSHtXh/dcnh9x2N9CEGq8blNn31yGCdOMxoeYuuQ0QW7XRcZBax0FG03QNcMjldt9mSZTb3AS23B0zcPOXBT7hy61DKSk1M1whRmKhb1cgY3VJimhpA6Mo7ZGYCLzuEwIRWC8XKWbpjQJoPwB1wYy7HecTFIGDY7uAk8f2OX7V7Ads8jK2P2+hHLQYZ+bgzXLLKp1TksHQMrS8eL8JXGbFYwl1X4SkckEedPjJItZHnvo4t849vPsNNxOeiFKCnpBwk7PY+uFxGnKVIKFssO41mdIIoZNg94fqXN528fIFUKQpGzNCZyFg03JDVsRHmc4vxZxi88Qm7yGJGS0NzktevXcXdX2dg5wPUCkgTaXkKcxFiDNsHkWaYLBvW8SZgozINllO4Q5MYwpo+h9w/RggGtxESEfXwF9axBxdFxDEnDjYkuvIkeDlOqw62mR5wkjOdMen5CyTHwlURTCfSakCqyMqVvloi9AbbXpCACTBWhoojUylMJDih3VhGmgdB07nYjktI45biNi4JMER5+D2G2TnqwjjCyR6Q2XwLDQfkDlG6ivAGEHkIATh5l5o76eu0smtskNTNohQrG5AK1gkOQGUXYWWInj7a3THD3Jun4SYTXQVTHCBpNctMljFIFX7M4HCRsry4xNAu0EoOsqZN1dFr7h7S3NyFygf++svmTf/+rAfj4xz/OysoK3/d93/eFJSp/hfhib3sIw5Cv+7qv43u+53v+0j/zoQ99iJ/5mZ/hl37pl3j55ZcZGxvjne98J/1+/wv2Ov8y0P+3/vW/ALlcjh/5Bz/Iv/i3v8S7v/I9DOOYVrHOeHcZ//Zl7BPnwHRItu4SNRpkHn4rCQZKRZhpSHv/gKyuUTUFsyM5cgWL0oXzaJkcUe+AYG+f7PwsSWsPY2JAX9jIOGF9oPHU/8nef0bZtt7lnehv5rDmymvVqhx31c7p7H2yTlA6kgBJCBlLBItgNzjdtt0YBrIMiAZD28P3tts2xsYNloSRZYQkJISydHLY5+xzzs6xcq5aOc78zvuhZO71tdvotiUa9eAZ4/2wZq0ac86qGrWe+X+fsFTje48MYWgmZq8NmkGEhhclZNVvxvlYaVC+aQprbaFZNokkIXSLSFEIfIGlSOx5KjYRd/b7WIaOoys4hTkURWIQCQ4XLfa9IfBVWkHIqSGHi9ttBGAoMrcaLooQjBfSNCKVoUyeJBZ4oWBvkJBOGdRDiVFDJgwSXKGgSzEpVSKlyhwdSuEoEZu9hFYYM5oxOHE6w3bXZyxnI6sqvUEAqoEUBuz0YiJJ5b4TYzR7IR+52+X7To9xhCbOfQ+RTO4TYXBPvUY/gL7foS0ZhLHFIxkDR/G5FBl4kcqul5CkSkiNLcTUPK6RJeX3GapvsOZMEfg+iiYTdNuEpXHQZSKREKGzJI8yOpUje3WJecth6mieT6/GBDPHGTRD3jIrE4V9ahS4sVzFHzJ5fMohHMhkNdiRDRQl4B3zZVKGzHDKoGSpmIpMTod9T+b4WBZNk/G6eT79pYvMLkxQbXo8du4ojiyoNftMZwyeqvX42LbHIxmf8xWNR+uX+PxOmWD6frTFq/gdwbo0w7ucfaqPPcHenVu843CWl9bqPDBTRMvZ9ANBS+i8/83HuVL3MHIOt7pN5k5UkK61uP/EMHef3aDWD8jZGgvLd9CnRqlYYNtpVvcFTr5Eo7rEJ5b3yC4cRVIlRlMypycyqIbMIHuE9/6dX+JL17Z574lRprImaSlmdafFXr3NO0+PkA0afKmTY+LYKPuhTNYxee+DM3z16h7FlMGJIzPcaYXMpeDfv1LlvmmHlGWi6jpvnEshEbLVM5jOp6gOQs5NZhgRXa7VQ940NcHT7jhr1T6NZpvh+dP8+DmDTV9wabtH2ZCZy8Cyq1BrNPjL511ueDZ+LJjImphxgInPex6ZpxvryIUJzEhiOK1jKjJ9z+PZ5Qa2rnJ1r0vG0qi5IWsdFUkCW5NJmwqLtR4TGZuJnAlIdCWditpnPVbJWSoFLSGcGKEXwVvm81T7Ea1BQM7U+fhHPoa99DQf/a3fYOjGU1gOeEuvQxyz8N7HcU6cJPNjH+DUPYf4X760xGbO5rU//k28bofpB7+HUHF44yNH2FqvUzwyTxL02d7tcWu7yVatg3nPDG88NM4HzBQ/9R9fQEliTp09xc2NFidm8hwdzeHubeMbDtboNHN+lY4JThyRxAk9BEYS8/iEidTaJQx8MoUy9VrIP/vSIv/TAzmOjWY4mY7ZCm1GVIGIZBzhgyxjCI+Nrk4s2ZwwB2S8Fi0voWrkkJGYKRgIkTBRSjGTVZguTfK16/vcWK7z6JkJpssSF5YbfOzJdebmx3g0n8ZUJKYyGgVb4rcu7NLseLzhUIm073G35fL1i6v8tbceY7s/YKfjEXLQSmkoCkZxhHTgsxjZeJLDrb0ek7mQGTvhQktiOJsiFBFlVSIIYkZtlWwxZmxWRRoqEvoRKVvj61d3+KN6lyOzQ/SjmGs7Hda2WkwMZXh0ViEfdVkJEmI03Dih0eiwtNvn/Y/M8dJaC1VOCBNBfXBQeLHR9pgt2JRtnSido5DJUjJUzKBPrVHj9s4uS00Xu/U6DSVDMFRmUhcUTAVjbwVnaIiOYqPmh3itFnNfSUHLZPGyIxhbN1FKFTKvfx0eeRc7g4NUkvogYjans0+aIVWwG9pMZTS03h6xlGMkZSEn4IYCTXXQZk7jjPaJogCsNMroHFx7GnHoDAMRk+u3iawCoj9gYA/R9jVKMgwbEbHmINCwkghJ1TAUlcCcILnyZZLGDtLMSQItzfbwacbjHnKripg4hu02YGQB3C5JexcxfAhJMRC9BmGqgmmnGZYHRIqM5LVQoy6k84S9NmGnTWI4aP0aUrpM0OgxfN8CsR8ik9BvNQj7bb5Wz1NvdAl9QafrMWh0iMLBt8Qbfu4n3/knU9+f//mfx3Gc7yBL+fZCCJD+O8mr+A7WG//yL/8yAB/5yEe+pfcnScI/+2f/jA996EP8wA/8AAAf/ehHqVQqfPzjH+enf/qnv1OX+qfiz/XkF+CDH/oQYa/GJ37vkyzXe9QGgtDIoFdGoTx+oJGRZLp7TZLcKGF+isjIQJIQ9gMcJB45M8zIoRxTbz2NlAikyEeSVSRVQZs9Ttxrk7R2KG3foNbsUnMDNhoHtbXb/QihqFDbONh2iweoXhORroCsfFOjEyOVxpHDAaxdJ4rA9jp0w4gQKJgKtUHE56/tkdYVap5gy1cIhcDRVTRFIZOyaQRQ9wUJsN8NSRKJl1br6LLMM0tN/vDyDoMw4W4PaoHK9iAiThLWXImcIWNLMaYmgyQRJDK9SKJkq0xldWxZYq3jE0eC/Z5PGCdM5S2OFU0MBbwwYSZv8gNzNtNFi9mSRca2ODya521Hh5DdDjdrLi/e2eTibpNOu8HIcInTJ09w/3QRw8rTM0u8HKVZ7QiO12/yPWxhhC7biYWuKextrKOoKpsihW7oTMo9hup3ECjs5OfY7vjkLYUi/YNikbRGIzPJ5eFjbKg5NLfL+46muVENcRybNV+l2xnwUAX+3r0Ob5myUXdWkKMBLTfmixeXubTj4UcB/SghoyscLxigKGiahkgSqoOAmUKKYjHHj77jDGeGDITrsVjr0YokzsxVcMOYQyWHx6Zz/IeLezTsCkMPP8i7Jw0+9coa+dlZzo7nKOytY1dGuEfdZ6KoEgQxp4d0aoOQrKmiSrDrxsyMFjg2kuX8wgheqsDTexLHpktITo6f+f7T3DuWw1ZV8uPjBKMzZDQYcyTczDDjWRnXh2fuNtlc3cT0Brx0Z4dms8v1tTrNnS2+dG2PdgAzjUXW99vUvZAFf5t3p5p0Vpd5ct2jkEtxfGaYz76+je+5ZFR47/0T1PoBi7sdRlIqipnie45m6YXwzFIDW4mZNCMOe5s8Xk44kRHYiuANBZ982sINodbssNP3uH71NoZjUy5leXW9xZwt+N5jJcZLDsWgxeRImXecKhHevspkKmH75hJid4f1/Ta1QcxKYLLnRsT5YQxFQZNBRTC0eYmhtMF+y0eVZY5XHMbTBhlDIWeqpI2D0ordXoShK5iaQtFSMXSNrpymYKhMrl8mlHXmKxkMTWEub7FQMNgdRDz/kX/KUO0KHzp5hOwLX0LyepAu0rtxizN/5y/jnH8Q4fVZjCXCROfBe49T77bxVZ0f/MV/znt/9L088b2P0DRzbNhlXEXDyJeozE9yz7jFWHuDCzc2+bUv3WG9OMY/+lvvoTI2yvPX1/FCwf5Oi4EXUjQl7hvWUAZdvtG0aHYGzLTvsuYafPrKHq9VQ4oamMmB/rFhlnE0mUouxSfWoZTSsWUYES2e2xUoJLy+0eXwsMNqYHJ3t82F1Q6DVo3m5jpFA3ZcwaWlHXIZi/ogQFVlXt722HUjZkbTPHpiFETMiGPwhkNlHjs1xT1jaaJY0PBiFDlBReKJWYd3Hstzf1ni8EiWF243OXN4lLVOwMWNAbqqMD+a5eyQyWTBJIpDYlVnquiAJNH2IvphwmbXJ69CLRCkDAsj7GMmLomiYZkGp6bLHPU3OeYE9PsD3nRmlDcdLpKxNDRAkySOjzqcH0tRJGTDlSlkLE4M2Vzr6xyfHiKtQcY2eHAqx4ges9cN2Ki59APBwD0oTGm5AZIMI6aMoSm4ukPdKGLPnGb8+L2MZkxyUYfW3cvcXV5hudpjuxcilq/SDBJ23YQ4DlE3rpDkR0DRYGgcOYkQyzcxkgBTU5gxXCppjQVjwLAaoEgJuWyWnKFiSRFxp8qoFmMS0vFjWpGKiAa4WopVX8eOD+I8xcJ9xLurFPtboGv0ggiTGCldpCV0VkKbnC4TRIKmULHCPlIcoIcdTDFAGp4hKk0g4gjPyPDkrkAYaYRuIlpVlCRGkhXIVAiG5hC6c5Bq1N5H3HoNUZokkTXUOEAhJjHTyAgYtFEe/h7MoAt2mqSzjzU+jggi4k6LXqzg762yKRcJZQ1Nkmi1XVav38Hr1gga/+1oM4AP/413A/DZz36W7e1t/vpf/+vfOXLy5xydTuc/W77/p2ulv91YWVlhd3eXJ5544k+OGYbBY489xgsvvPBnfj3/3/hzT35t2+aXPvizLP7x/86T1/a40+jhpstIEki+C/0W2994keatdYhCmuhEikmiaATNLomlcOx7TzP88FlK588gvAFhq4FUmULIBuH2Glplgs1P/D7Nz/4BY9e/TiISZkopLFWmGybgFMDJEyXg9HaQ3R6xnjrYBlIOjFIijgmtItLQBEZvB9VtUjY1MoZK04+xVJmTkxniRJDVZbpBxEbroJc+Egl+DIMoZjKtEwlIGQpDlsZSfcD+IGK+kmY4Z9IPQiIBt/f7bHcC1tsuNTcgb2mEqGiyhIhDAiEhk1B3Ixw1oRXKzGY0vu9QliSGF1ab9AOBSUxOlVgo6BzPJATqwaTjifkykohwa5u4GzfYXbpFMOgznE9z7txZTs+NMzE2ip0tUCmXeMvRAt9/eph7Do2zk5/kuexx5PIws/1VHh/VSZVGWW379HoDukKmYw2RVPeIMiWqIeQci5N5GSX0GAQhK72YSJIYlg9qMt2lDe4GFulsGk1TKGuCNxypsGUOYWcLaPkKpXKR+vBhUioMpVTecs8kT60PeHnH5XNXdsgmHfpBhK0q7PVjqoOA6byNBJwbTRPLGsfnxnjjA4eJRcKZiSxXl3e4s9Xhuds1KkbCSNHkt59e5vIgRRWLe8cdnm9IqCMV1J01Cu4+klMiFw4o3rlAenuJI3qfrK5gaAfd9p0gRlbgxGiKmUoaN4bhfJrXNju4SDimzDmlxmKSpiLavLba4NLdbV584XX0IGBiZoy3n5tgPq/xqc8+TbsfMr78MlNrl5FTDmeHVYwoBN9DVSWcoEcvP4qYnmO4mOZd908ThxG7tTbnZ8pcX2vywt0q3WaTlZ02C0mV4VyKxc197lRdsrrKTFqmevsOkSSzFliofpfsrYvMFNM0JBt50OO9x9Msr23xwLBJyknxgXNDZFRBN5F56sINKga88uyLDGV1eoHEtaubfO6ZW6xdeIEjRhu73+LK1UWubnaZGuxz8cYaF7f6OJaOJUtsD2I27VFOVNI8NJ2lXa1RrN7l8stXyHS2GI+blLSDvN+SrdHxQgxFpudH6LKMH4MiQ3pijH4gSGkKpiLx1cUGt2p9vvgvfwX8Hj8zPEROC2heu46sSGgT8+TOnyd97AjN3AzJ5m0yc8epfulLjGUsvvrJj3D/D/wYfpxQzpicnipSKmYYHyswVbKJJIGhKjw0m+XES1/kA2cy3HfuBJ9+cZHtvTqBaqClUpyezHF8vsJLd/ZpKxbVdoBX3+XSRhPJsLihz3C7HeIYKutNl36SsOapSEgst2LKOYvhrI4ky7ih4KvrA+JOhys7A75ys8o3Ltxkea9HKBIuXd3g6Ega2SnwzJbgkzc6GKHPS1fX+fylbY6qDYI4YaZgU2v1mbQlvnJzj89fWOcjz6+z1/MJVY29fsReN0TEMRuNPnsDwY3dHut1l0RRSXyXR+YypFMWtiZzaNhhOK1zeizH5FiOIUfDvH2BRqvPsA23druUHY2UoSAZDlNZmSmpj7F+A7mxQZU0QRBiqaB4bTR3gK5KnEv1OVo2ee8Rm9GsSTGlc3WzhWwYzJkR7NxldOkl8rqErUk4usahcor/6W0LjKYUTqQj6l2XZ5fqmLqCLCU8PJtnLK0xKXXJqAeGVVuOAUGYKAw5OtNFm/HREc4tTBGOHMMujtDt9em06tzwTYzWBhd2QyqOCX6PxCmgBgMSb8Bwa5Hs6RN4icyoHjNpRJiqwkAxMDWJlHCxFYFBSFiYQpdA85oMYigoITIxdX0YtbOHajrI/SoDIRE5Q5AfxnJbJKqBrQrCBERyoHkuOgbZqMvt6gAtiWkmJrh9pEEPtb6Bf/caYXacXmacIFEoOzroBtgZRH4UkEgCj4GZJk4XSRIBkY+IZYLFG4R2Eb84Bb4LkgxCILlt4pNvRM9kkOIITzHxspOk3/6DOA++CfnMI9xeXCaRZWpShkMVB0VXcb8pUQlaf3q6A8AH/4d3IYTgl37pl/jgBz+IbdvfQXby7UeSJN+WBTAxMUE2m/2T9eu//ut/5vezu7sLQKVS+c+OVyqVP/na/1X4c09+AX7+gx8kHrS49tSXeGG5hSwSkpkzMGjTf/aL1C4tkR7PIYV9XCEjRExSXWXQ8jFyBoX77iP70CNIIiSs7dC/9hpxv4t99BS1Z19i/ZlXaNzaYPvFJV7+Jx9j1ozJWyr94CABQerskQy6ZEQPoWgkO0sYu7cgDpFij8DIE+bGkEUETgkkCbF+k4LiY4ddUppCPxCcGcnhhhGhSMibKouNPv0wxo8FCQJLlYkTiBOBrkps9Xx+8OwoeVPBNFSmCiatIEZKEuaKJm0vIqNrNAYHbt4dP2Kt5WMlAb0gZkgLUWOfm82InXafeRoU9Zi3TVk8PldgKi2z50t4ImGodgdTjgmCAL+xy+K111m58gqNVpvy2ASPnT/F2YVJipOHSBs6eipDrKVR4gDPKdLTM5RsjaKjc2YiR6lcppEq4w/NkqgWUyMVLNuiXtsnpcAmadaLhxgkEhVvD1vVKdIn014n8nyySoQXxKxHBr5qYc9M8FDexQq6DBsCPegjxwFpy6AVQF8oXN/tUfH3udORuFlzsXSNw2NZzo5mqGR0bvV0Gn0fU5EIY8GhgoUhyyQSXNrucN9EhqTbIAxicobKp55fYTJpUG/1eNNcmuc2+7xHWSdvylxerXM7ydCKBMW7V6jX2mTuOcP2Hz3FxqUrRKVxwu0N5JVF3C9/njjwmHEODG8iEUykZPww4dR4lvedG+ex+SLlvM2uK3h9t082l+fNj5yg7bo8MRxzgho/cSKDsXaLsYLNIyemefDoCJFh8cJig8z4JPrUHNeW9ylqsCCa9Iam6EQSGy7Uhc7Xru9xqa/xak3iuVdu86+/dIUzUzl++vFZJhwJwzTw+wOcsRGOZ2PO3foCpdYaxvp1vN1t5KDHnYaHaxXYavtszd7Dy7f3GLJkvrAe8cJ2SEdNs9IV/JU3zuMnCtdubXC6YnMoY+LducxfGe1x7c4WSz2Z4tGj/EEjxT++0Ge1dITPrgbomRRhKo/e2OWoNmAiZxCFAdudgKyh4qaHGMsY7F67zjGthxoFdF9+ia8+d5NC3KXx6gWWVrYIoojdnseUGRMlEqokQJaxVYm4PMl4VkcmodEP2G72+e1/8Dd4sCDxoR/7ARoXLnP3yVsMn11AeCHRzgp6NoNk2sQJBAGkXvwG1w89iLx9A0Kfh4/O0Oj45A2V42WHvK1xajyLrioksUIpo3GhpTH2k3+FPaXIiSPjPPbQCUZH8qxs7HHllSvs7ze4pwiPnRwhRuVmK0Epj/GeB2cp6RJ6rkAh6vDIlI0vBL7rU220iBSda9sdImC1HWDrMvuDkC9f2qLqjLByd42iFvKB770XWnVazT4PnJqikjIwUg5nTs3wma+/xme+fpmdvoJj6jy1k2DELvmUxpWri/zHL1yg1nAplNNsNXoYmoJlKKRNlem8zvNrLTTd4PO36nSEyq2mz1bb57PLPikRcN+IzQPDOsNSh0lLpt730XWdMBSkj5yhVMiw3okoiS7tvkdKhmFbwuhVaW1ss3NniUHPRXGbSMuX6a8sskUKpTyEJMlsBAa2cGn7EgUtYYIuw5bCI+M2XHga07YIS2MEUcRqK+T+iQyHpDal/jaF1csoIubjL63x0EyOmYLJkYxgrR2QiIS85NHwPGqeQIoDFCRytoYXC4xBnWR3iTsDnVDRqGoFjpVtDhXS9I089XaXUusu1aXrbHYCwuY+oaQgNXcIbl0hbDVBUrCiLsIf0AsELgZd1UHWLRAJoZDR+lWC9BCelkJRDsooLOFys6egBj3yUZddrUwqcbE6W4jcMEQ+kWJiiJhOaZ6SX6PhxcgSbEQpZpU21mCP1b5CkgiEdhAtaY6MkvKqJEisdA+aQNU4RIlCpFSWUNHpp4bohDJq6KP2qiSJQCqPoT34FmSvh9bYBq8NwQBUDRQDL1VGjwKaRonQLFBTsyiyhF6u0CzN4dW32TVHyKUMVqp9drc7VJdW8Zvb3xJP+Ic/9U4kSeJTn/oU9Xqdn/qpn/oOspLvDBLx7VkAGxsbtNvtP1kf/OAH/6vn/PCHP4wkSf/NdfHixf+u+5Ik6T+/zyT5L479WePPteb3P8E0Tf7nX/h5fvWf/Rb+u99FLZCopCvYrc1v/vNIKNz/wDefeARKfYPOVz9L0Gwxe98ManmMsLpN5PuEzQax7zO49ArmiXtwJivc/PLrhC7sr9bJZE3murcxZ86ga5Dx6tCpfzO70IOgjzQyj9zdhdADWcWNY3TVQpESvEhGM7IE2y+SGl3DTQ0hyTajGZ3b1T5NN8L1QkqOwYm8TCsWbPcC4jhhNqciUA4mV5ZBzjyohlXMmLudEDeIESJhr+8Ri4RKSqcfxrhBxEYnZDSt0FElLjUTTD1iIavS6ccUTZmxxl0MNQUclGuUJY26lxCJGN/32Il0erdXaTabmHaKVGGI6UMLtJKDLeUGAr8/wLYTomCAYqXRpYBEuIRCR1dVFK9BpGSpeQmHSib9epVNM0u9K1jI61ilUWobS9w7Po6seDR0mxd2BMdsDyMSCKtISjORlq7gz97LnhuQUXXOzJS43ZeRdI1WmDBqAIpBLKs8NG6x0QkZMQU3mzFfubLHuyb30EdnKUkWdUNis97njU6TKIK72jhpJMYdmZSa0NU0uq7Lxc0m88EOTTXNWCVF1PAY9CT+cDHiJ95RYtCqc3mlS2b0OKMkLAYJUhRyqxXzQ0cOI576AvH9Zxn5njfx5Ec/h3pacN/DT7Czv4+9u8b+73yEw29/kPTESbKmypSlcakRIMsH0hRHlZgvp5jKmLy+22NDZHjQUvlqz+LN8TbXzBlGZlJU+lXGiwaGZaF6ff7Kux7iuRs7fNktkLMN7uytcywrM2UE3Ogq7Lc7KFFIWY9ZmBumXa3S2G/x+PFRPn25yqdf3WV6JM+C5dO/eolccYbPvLDMg8dCZvJHWQ4tdlb3+KvvOEy92uLClSX0oSHeMFHgM8+t0Q41OlGWOFE4MT3EVxbrTBs6sipIBX2uNBOGJI+5iVn+3dMvUZRV3nnKIGcnvO6neOxN93FsvEioqtx3bITxnElhyGEnc5ppS8ZJmWi9Nn0lha3r5KWE/SDm+GSeKwONB+cm+KEf1Ll6ZYlbdzZoeApGaxnPcegNTbMkaUxlLa7VPIq2TlZNCNFI1IRruz0Gu/s89dv/gvc8/iDvuu800tgClRNj7N3ZQxw+h93do/fai+Te+i6EmSfj1Xl9qc1TlzZ477l5PvwbH+Ef/szP0ItjZooWHS9CkSQcU2ckpWOqCseH0jiGQtMLGZ55gq1BxFBKp2zDlcv7bKysc/j4ET7zhRfprJf53u99M2tCIpcLuNtymVQCyovX2R89iZtoHJMCpvMWK74Ckc/tBty/UGKnOeC++TJZQ8JQJM4emWCg53jr+Vnuy/QxshoLTo5Oz+druxKzWQg9l6NSi5/4y49jyRKfenmDxvo6npZi3DgorHjrlEG6MotsGVzfbHH+/qmDHNgEruz0aHZ1spaG68UcKpss7g/4ycMKzzZ83nKkjOR1EWGPvFtlUlWpN/bI2kXWGi64PuqQRS50KWV0xhZvcS5bRKpvYQ5s5LSNffUZlO/7QeL6Mrmt61yVhlne6/CukZCWWcCOPRLDYsRv8NmVgJyqcm93mROpFCMWuAuHkToNhsoV4v3bLJhlcqKIvX+bZnqcTCmLrQW8+dQUeVPikekcroAoSXDkmOudhM0gYGFIwxegE1CxDQAioeFXG9hZwaE0ZNQYq1FFKYwzbRZpBoI5W0a0dtna6XPz1iZ5aZmJoTzx9VvkTy2QCVv05RSGLjGeBHhuSMkQBEYKI+qyHjscGjSQNB9JxMg5m83ERh3A8XKKKH+KdHuHQekYnUSlnNQBEKFPPYCSLrAshSRWmcmarLUCdruChVKFQlJHJyTU02iNVURmGGkkTxiFSEgossRc0UJIAYgQT6hIZpZBBEEioXf2SfzOAeEqjyESA7m3eWDEczJIgUtipRFuF6fTxM0UiSQVA4mMIRMHJnIqz9rtZapylqonESUhS5tt/L5La+Put8wTfvFvvIc4jvnwhz/Mhz70IUzT/LZzke8mZDKZbynl4m//7b/N+9///v/me6anp/9PXcPw8DBwMAEeGRn5k+P7+/v/xTT4zxrfFeQX4Gd/9mf58K//P1l+4au8OFvigfEMM8iYwyX8dkD/7hL68Qcoyj7tP/gopgWjD57AyBjI2TKqnUFsL5HIKpKI6W5tkyDhnDhJ4cINVjb32G8FFIcyUBpmVA+htYW49RIcvgdUHUXTwM6DkSE2TJS9RaRMmTQKkZXDlW0GSkIhipDufzv+lWeI3vCDFFUFLxKkDJXDKcEnbre5b1Ih09siP7rArYbLdNZgry8YSmnf1DGDpqg0/YieHyOTsNL1Kdka3TBhp+lyZlzHixJ2Oh6HCim2OgKRJMyXbAahwE00EAHWN7dBNT9ArV8hiSO6hx7l7l4Nx6uzWdsnkhSK5QqnzkySlwIiM4cU9LAMjViSaXiCRDVZq3vMZmxKcUicQAeLWl/QCwL2gpi841E2depejCuZdPoRoYCLOz1szSFjpVja2iE0HMZyEfdmBV0pQ2MQ4qsSq4GBlj9Gt5swiCQycYCQIwwzg2va7LVcgkjB8wQnhwwGUUJWkpjLhBhmjuWVMu2yw5XdmKzhMVzJ0OoOWFZSjG7f5MiUylqnyExaRnX7ZGSNs+1b6Gmd3SQNfshwXmZbkSjmUrzr1BATZkzTlHjbsRIjhSzLaxvkdm5ztxUjF8fpLYyQ0SXCyxeJTp7j8JvvZ/mlS7RGHXInTpPJG7ywv8bQ0m2EkmFibJqtjk8YC8qOzmhUR9upM1Q5hqEelIYkksTtZsBaPWS7u4ozk4axMkZxkpW+yrCUcGcv4p7ZCku9hBdubvOW4xU6QuWZhkKjofD4IYUj7hIb6TGutAQre33+1kPT/PonXuDRh0/z+NlJwnaTLzy/yVNyxOHxAudTISePp9g2bT55Z0AxJxFkMvybV5ocni4zMaei3rlOau40kq7z+PEKd5s+3TBhOCVhKwmKCoZpU3fb3DuXZaSYJR97xA/dg4JArdgs6DKGp/KsGzNWShPGAZWgy1Ba0NBlKjmQiUmiAcuBwXRGYRAlWIGLF6tIo9MUuwF7scbo1ALzY3Nk7l7mkysBkRA4esQPtJ7jc80UP/jgDM/ecHnXwwt4QkUO+9xoxkTri2xfu4rYvcuj7/pZXh8oPKjYDJ0/ROGh+wjyY1hqgr0A6A79QQ8tXWK5r5B94F7+YHnARl/h05d7rGze5fu//43Ml1MgS0ykD5rKUpqKrSsUdAFegJyy0LsRWV0msnP05uY4XIWHTk+zM5GhubbCxVeu0SmOMVmw+fzVDu84ZSJnJ1hpeJwcTmHpEuWUiipCJBIiZ4iiLnGl5dIeBJyeyNN0Q+aGMyw2egjDomalmA+aB0VBmTwLskYsqdRdl22K5App5nIqudsvEekmrZ0eZTlDd2aUB3vX4b6TfG25w4myTks+aHhcqvaYL9o4hsKImfDyxUXe8PBJUsJHqCZnc4JA0RnRIoKddeS0watiirmUYMaKaZs6ugY0a+hrqyj3PIK8cA9qY5VuYqBv32WwcC/lJ96MpIZI3RotHI6NqIyMzCAFHWQtjdnaZFjPotsmR8Zy+F5INHaO96QWWfJVJqMOWiaNXN/BXbxD4f63IPcb+ImKfPNlwvNvYt+XuS8bEMkR7VSRG7UBjqpyoxniq0WUOCIOY/TuOhQmkDXloD3UtFkcP8fJtIzvDkhuXcUfG0MtDpOLQtb6Aj2QOTQyzmHLo9Nss/TqHW5FEJ9/glE9ZnxrET03wpY1QmmwiqOEhKubRPe8DbW6TWhMETpDCFVHdJu4QmMqFfH5pQHndRPLKaJrJo4c4/k+hC5Jr0FcmKKU9FA6LoqdQVVkhhyNW9UBpq6y148ZLRWZMTyS25dh9hSu5mATIFc3kPKz5MyE1ZbPjBbj9T0kZ4CmaMhJhKnJJJEHzT2S8ePIwQA7GRA6FbBK6EEPqbFB7JRpmWXy1ddYMicYMxUkPCxJRhIhy60AN4xwM5M8c2EVyzZpVF0Mx0B4vW+JH7z4738BgN///d+n1+vxV//qX/2OcZHvJIRIvg2Gt///vr9UKlEqlf67zvl/hJmZGYaHh/nqV7/K2bNngYPEiKeffpp//I//8XfknN8qvitkD3Agkv7Vf/hzvPgffpNXlqu0g4Sk1yYJA8buP8TGl19GGdRJb92gf3eZqO+SO3UUxTYR3kFGpHA9FNvCGBmlt9Wic3uR5uvXiGNBZSTFUNFm4d3nkFIlEkkm2Vki6bWJjRSJlSXUHGLdQQ46oJqgGCTNbZTIQ4s9YqAfJihygmmZyHMnMOWEII5REeQMib6k8ua5IgsFg9eDLCk5YsiSsTSFlK7S8ELcKMZWJYLkwPzmxoJrO11UWaY1iAgiAQgUCSbyBpMFi4iEzY7HTi/k8k6X7W7AIE4YldqM0yAzNoOwc0DETqRx+8prhBs3kCKf8swC29lDTE1NIZsOQlYZhDGa2yLjNzFFeJC9GEQcSUXEskYzUmn6ErYcYakyOUNiuSdxp5fQ8AV+EJN3bLKWxuGcwnTRYq5ks6GWqe5uEWoGsWqhZ/LkbZWsEnGj4XN1v0ddMgkErNT6LLdDavtNbEUiTmJMw+C5pQaWIugGCf0wOWim4qDh6e3nRpjJ6ty7MMz9swUOFUwWcho52+C1zGFubjZJdXZpX71MKzbphYLd3BQnwg1ODlnYpkE643D/VJ6spdHzPdabfSbGhjhaskCVeHgmzTums7z/bIWfqLTw+338oWlSlTzBc0/h37iG9YY3Un/9EkMpmcxQmdE33M/t7Dw3Pvc5kn6PIIoZMmRKlsYGaTy7yE4noO0LKo6BLoEXw2P3LfCN8nn0kVEOR1Vu7nlsdAL+4GaD//kj32Cj41FO6QfFDqrKoakKe4HCvfceA03hmjPL622YKWdISRH/+ut3eefJIl+7tMG4LchIEXo6zXI9YDFO83xd4v/19RUWWyHprEN/EPH3HxlHqm4xWcpwpym4rA1Tr3a4b6HCTCHFw8Eyw1mdrOwxIkecy8vs93xWBjJntBaloMtXL62RyWVoSgYXtnwWBzp3d/tkLJ21toutaqTzJbREQLdGOnYRScIXlvqM2QrZ7jrBq8+y1gnxIsFkzuL8WJopKyZevk53ZwN35ihHj85xeHYUr9XhGy/epBT0IQK5ucPN7Qafvlnjtade4XefXCS6/Bof/fi/430f+GvcunCLxavXuL5V45o6ijSxQLyzSs0DMXKIZNBCvfYC9Fv4ksSbHz/NJz76W7zv7/0C+dEhzt97nAcPldEkmaKlcqpsUbQ1SrZGVo1IhV1sQoxowJijktGgoAScnCnxyPk5RrIGQ5Uy991/nlv7Hl//+qss73cZq6SIZYV9NcXxIeubBqwIR1dp7R8YgI4M2fRqdR6by7K8uI6lQt42GbI1hh2djKXQExpfrNoMFJOUY7OQk8mqUHAsbCfNPUMaX7y0w+cHZXKn72Ujlac1NsdzKy3U0w+hGRYTRQfPyBDECXIiuHB1m1s7LdpuiBxHnKzIxJKEj0ZWjqlkLFpuRKa7QyFtoPg9TCKOqR1Wugl5Q+LBfIDIlZEO34MkyZhhm35hhjthCmX+DHISoukywaUXkQyLeOYwemGIkimhNHcx+zXUcICRybKW5CmmdIpZk7Yw6Q/NU7AMmlaBeGiSqN9mMHUKrBReqgxTJ7HUBEmRaWMhBl1kzSDwPa7t9nAMBWSFIE5IGzr9KMFwMvRQkYGMLqGJkGIxT0dy0E2bpDjMNWUUTVb49O0m01mNkbTOUivCt8s4Y9PMqn0Kn/skp2fH8QW8uOvz2q0V5LXLB/nxmg57q0jBACldYLZ+BUmWkZGoOhNIMlyoRozlNOIY/PhAU9sLQmRJBjODun2HfnEaXfiIVJaml1ALVSwiJjI6wykDR42Bgxxx2ckRpwooikIYJ8hOjgTwghhNSvAkjVZxDk0EqH6bVsiBvC8MkFJZEt1CdlsowQBJVtCDHvhdYpHQ1dK4kgGj8+R0BZEkGHEEJNSFxdLqGs7YHJIkgyzTdANMR2P/7p+e5wtg6Crnjs8QRREf/vCH+YVf+AV0Xf/2k5A/A/x5jzpbX1/n0qVLrK+vE8cxly5d4tKlS/R6/5+HlCNHjvCZz3wGOJA7/N2/+3f5tV/7NT7zmc9w7do1fvzHfxzbtvnhH/7h79h1fiv4rpn8AvzM3//7/MNf+Sf88cc/wUPzf5djo0fQBx3y3QG6rSL6fdZ/7xNUb+6j2hbmzg5J7OPfuQopB7wBslBRUhkqD51m9XPPs391k85ej+JciQd+8jEqf+mHwesQGHnE0i2cE2dAVhFhSE+V8EOVYTONtnqZBJDyoyRmigSwCKgOJEYkDa26jqxoiOYOacNmUyogi4hdN+Gsso/ekzg+PkxalzhsGPSFQseL0FWZgqlSH4QULQ1LlQiimMfnimx2ffwwRlMU9iWZKE6IpYRKykSVJIbTFv0g4nbV5UTKoD6I6JFnPtoh0FV2lu6yNUiQdZni8BAnykewpJCGkmWSHivdiNGUTkNOI6Gwr40w7q4jSxpn0zH7zRp3Y5tK2SZrSKx0ZGJbQ5MSupHgoYrKbqKTNTRMw+NOJySryUxaMUJEqLpBMFaiJ7UZ7G0h0vPsBBJFRRDIGgVL0PYCyrZGyxc8PFuipIbolFh2o4MnNUlmNG8RBCEr9S6tIGFhKI26t4SQFwgklW7KIduv00jyHEorpNMqqhQSVrKs7Qp0XaeVRAz3B/hBhKEk6GfvJ6s4jDkayDKTBZsJW+JO3cUgYMOFlBSTVhO2qjFHTx+j62pURIeZQR2vlGKg58AepnjpJYyll4keexxpZw2GRih5e+wUxnh19Cz6K1c5Nmog6yqhZnEhzLMWmyRSgKUpVFI6JUennMDVvZDxmRGyVsTHXtnlfeMJF9Pz3D+aIvXEPSzt96nXu4yWM+x2Ah5fGOLa+hprDQ8l6DJWMCmg8vLNLR4/nCPw0nQ0i0K/QyCrzA07HM/5XKvMcnh+lL2vfB79kfv44ot3OV7WmZ0o8PVVl0eHNV66usn4bIXR0UlSOYPhVof1WpfxmRPcO5BY9kyqDEA1GMsKNpoxe+s1vOWrLOeOIG132Wv7bNcaXFmtcmNxh7/9vodIJBAkeF4PxTRQ16/TH5nGzWS5Z1whVFWqzjRFbYesLXHZizFUGSv06AifKIqYN9rIa9tsdkZ4x+kpTsyN8rVnMqTHRlmONN533wRN2WVvt8mzzYTIc7nS7jA8f4yGWmDY3eb7DpXodXf56FLETS2mnynzw8Y6USGP1NolUmw2X3iJRyfSfOZrX+DIA2/i0TPzrLd8DhUtRtIa9X5EWokwVAUjktCTHurGNSTbIS5MIbV2MbPDyHFAJOvs9UMypsrJ4Swb7SopS+PKShWh6/QHIbaloSsyjUHIbEHnylaXQ7MqhqlQ9bq4qOTyafpBk6VGwPuOZZEUiebAp5JSkWSZWBxo6xa7AYeHyxjIDLwug0Bhv5NgCZ8kiHn7QoYnjTk629u8/4mz3ByoPJBSUNIObnwwzY5tiGWdouLzo+dLpEyFbT9CklWKWZM/vLzD8Yksu5rNl6/XmMkovKhWOCyaDOkyjyo79JUKiYCheB/bbxIaIyTpCvr+MmLQwawvMjl5CiltI4RGPw5Q0hk64yeINIe+LLPmKYyMnsCRAQK8WKYTxqiKggS4IkEoJiKJiYfnqcsyxsRZukYJN5GwZRVb8wnu+z4S1WLQD2gXpkkiD8erkbNTmKpM0dJwtAhBgq5IJHaO0IOUImHHHorfZSwZUCNDaKSQZxZYCAI6ImG2mGLY0ugIiW4YE9x6BdU2MSoj6PJV8pJPMH+K0aDDdttjq91ipXmTYUNmojJF6u7zoBtow4cQmkFXsugPElKSYKvnM2JKIEE/Emiej26p6CIikRSi8jS62yZIlVEbGwzyQ7RCjXlHpdP1SWkyAyGR7W7Ry0/hjp7BEgopPKJUnsiwGcQJaQ2GEpeU64GagQSSOERTZUwlQe61kCzrwAujaDDooRpdJEkQZyr0UyM4iYcnpUk0C1tRkYlR3Cad1AjX7iyhpQtcb8tsNfvIqoLfGqAqMr3t5W+JFzz52z8PwMc//nGiKOLHfuzHvt3U4y/wTfziL/4iH/3oR//k9X+a5j755JM8/vjjANy+fZt2u/0n7/m5n/s5XNflb/7Nv0mz2eT+++/nK1/5Cul0+s/02v9/8V0z+QXQNI1/+qu/wNbTv8e19TpJZgg1V8YYnyF76hjNZ79B/foKmqWRmRuhefU2vaUtutevs/LvP0dvbRWh2QTdAUYpz8gDCwxaLs3GgNEH5xl56+NQHkcOPZJnPo09PAKyiowguHkREbhUfQlfNkFVCZ0iiVOAyD9wtUoyI45GJz2KFMYouk5TdjAHNTRZ4vK+z6mhFB21QFfLMm0JVOWgelWWJAr2QQuYqamMZi1UGTRZJmvqZE0FU5XxhSBjyJRSGlU3xBOw2w9Ya7vEIqYXxhwpOxQsDQXBVrXBhY02L126jitpLMzPMXvqPJWRMbJ71zAkIIEhE0YcjZLcR1UV0qKLK8t0rWG0YIBqpRnNGBy2XWJJJpQ0CrbKU2sdtrs+8aDPzZ7CpNRFJAJHkxlxVLQkYi/U0BHoiccRa8ChyVGSTo39roupSry05dNxI8aNkLR+UMOsyQltP2LXS/BQD6QASOz3A0ayNrVQ4uXtHkcrDmlVws1PMpk1GHIMfAGSZmPo0A5gK7aoy3lytg7ZHMJ0KMzP8eROQmxnmCs5uEqKTV+lG4GjyDgEVBo3OexuYmdzyFHEtbbEbitgLG7hdHcYEx2sdIZkZI6d4jRqqYy+cITgLT9Aqt9nLGwTaBYsXiYyHCZKDtmJGb6w1uGrl3fZe+U1MprKmf3LXHzhVW5sd3jyzh53qj3KeoKlKRwqpTg5nOH2bpfdfsTX/BJzzTXu7va478gYeVPl669tcM9YivmKQ6PvMl4yeWIuh2rofPXaHq8t1XnuRo1/9NtfY6RgsjBe4I0LJdqDmKdvbLO9tM1jYzCdlriRneddJ8p835yOXShgRiG5pEdDWBSHC5wezZG2dfoCNrsR5YzBWqDR6x1Ms4+PZ9EJiCJB6Ac8yyhb88d5932zCEkiSgT3zA1zdn6ENz18jJW6h62o2JKgYKpo1Q3coWkEoKkK43ZMP4yxDZ3k6BnqgURKk0m6NUQUU0466LqMXhnHnFngoaMTCFmiE8NbHjtDuWgwZsncffH1g0Y6dEYmhzG8NsGRR7hy6w6x26KRKVEzS5imzhPJNpe+9By/8/Ev86kruyyuNPCG5ogOnaK1cI7f+PpXufD6NX76p36Ksq3T8oOD2EJVQVZk0n4LVYQYUoC2t4jktknaVRJZIbr9KtLeIpIQRIpBRpeZL9qEIuGRyQxzepf5Uwu8661nabb7JEnC8n6X/Y5HL06YGU4h2zlWWx6EHrXIop/oTI0Nc2LY4dD8DEVTww8jipaOKklkDAU/FoxlLToRXK97bIYmoWzy3FqL11dr/PGtNrd6YKdM/uXTqwjb4VAqpiI6vFqNSESC4jUZ0iPShs5STyE3NIQwUhwbTqEbJr6e5tZ2hyCGW/suN1aq7PYi1jyV1vA8rlnASdl4e2uMuNssjORZzB+jqhaJhEQ/TpDGD2MePctoMYskKSSywq5IYx07hyEnFL0Gqe4OU6kEX0+jtjdJ0kWka88zYQgkKUGSJYqmgqXIZIVLSYvR+k12Ao12ELHTj+mEAl82ka003URlKqehaRo3OxKBleVtFUFjcPB7HU5rlEwZS5XpJxoZU0aPXeRBE7F1B6O+RDluo4uIVBISSRp+GDOfN9gcRHQGIaGA7qGHEIMu+8+8zMQ7n4DdZaRUFru+wYK/yTnD5WRSJ+i3eamrc7EWsdvoEu8tk8gKhgx+FLPVDTlStDGliK4f4UiCqjGMqcjohIil1zEkgSVH9LU0YmeNjHCxVBlDlpC/Oc0+ZCdI4QDimHqkoHZ38RUbpIRGYjKIYrYHMUUtQSQSabdGIkm0lDQFNcLxmjA0SbSzhtxrEOtpEr+HXFsnMlII1STV3zuY6AItOYNKSIxEojvs7dbwem20yiRP3d7ntZUG9dqAxl6XtWt/ytT3m2aplG1y/sQsYRjyy7/8y/ziL/4imqZ9R3nIdxJ/3ie/H/nIR/6r6RL/ifjCgZntx3/8x//ktSRJfPjDH2ZnZwfP83j66ac5ceLEd+wav1V8V5FfgL/9P/6PaHHAH/6H36MjVCQrhXb4LIoiEfe7lI5PUT41jqIqlB97FGukROPGJhe/cYu7f3SR9qXXCPd38Gp1rPERdFvHLDrY5Tze/j7q9m1EGBHv7ZAEPQZXLhLHgnBjkVJvk52ORy9I8MZO0CvOAoKk20JpbKA1NymaCbqqIo3PQ2aI1OILhOkKvTDmsZkCqqKyGZnsCIe9QEeRVXqRgq2CpXxzK02SSEiQJYWVpkfwzdTqjndADntBzCAQyJJEww0QScJWx8ONEvxY0HUDVtfW2Lz2KvH+GsLKMHvqPmYOH4NchaYfQyJImvvIQRdduDiywFFAkyCMInxUeqHghmfSy4xgqDKuM0KhsUxB9OkFMa0gIWco1L2QQafDXttDjwfooUtPSCy1Qq7UIzQZ4vYuvmSQrN0krSmUSiWS+iaNfkAxpXN5u8P2ABCCiq1xyIG0rrDnCi7ux7yw1sRQIGdrlE2Zjhcf1Al3Dipq95Uc6x0fkUDHj6nHCis1jxiJ51daXNrpc2HXxVQVJvI2KU3GUCViWSOys9zqSmy0XGbMkCj0URKBaqcxqisYcsJ4VOeU1kYhYtcs0LYqGImLtH0bv9/FkiI6qSGSOCatKahvezs7egG5s0uQKRMVRhg3I95ypMijZ2f5Wi3mapSlbhW4QYmfvn+Ue0YdzuR06l2Xl1fqrDZdJCQ0RaKrZxianeVOR7C31WAsqiOLmOsr+zx0zyRfvbxBWku4X93naCrCsXU2my62rVHIapw8NcHxU4f5589s8W9+/0WMbo1HtX1+7LF5pJNnaacneOpug3vOzPHMhs/Tqz5PvniLV5ZruO6A8j2neORQgfmcStpQ6bkRtm2SyDLNRhvNsvjqxVVWb9yi1gtxtpYoRy0QEbNjZZZ32rSaPYoZk3ceK6AqMrqmMVPQ0dpVrP4u3fQwXrrMnquSSjuU2mvIcUROFQRRRP/Jz7P8la/QaDb5zc+/jqWDyI5SVyw+dafPq0GGzZZPzUug26SQy3K/M6DSXuWmM4X/8ssML13mfLSNt71K3xecPP8YLzzzDf7Vxz7G3/vgh/hbP/ch6lPTvOlwiX63zx/u2/yL5zdpXb7C81/8DB/6Oz/F7Oxx/sGv/a9MFlNkTYmdtk/TixFIqLKMLEIQMb5ssJ+aQlQOE4yfQvU7JNkKIj1EotsEvkfGkCmbCrdrAxRNIV8c4vESTOseT5wZ48iQxdXVJs++usm1rRaGJKOqEpuNLkkiODo5hCIJhCzR9gR2yiQl+vR8wf43yxoW6+5BnGIU4+gKQ6aGKkm0goQIODqWZmE8SzFtMTfk8L53PcLL2y5Lno5RHGYhp9INfC7XBHe6KqEQbPUCImRm7AihGtgK5FIGH3h4GsdUiRIJVJU3z2VwhE8lqBFrNr3MBKl0FimdJzYylHDJ2xo7PZ+4cghZkojDGOprUNuiKUxKeoLsd5AUhU1P0OsPUGWFlh+BUyTsdkgmF8hGPbJxj4KeIBIIhECXYuywhdXZIf/SFznSWWQopeHoCkKS0cI+phSTUwQyEsOGRKazR9mvU969TtePMRQJXVUoxl1sGbTIw9y+RbK3iqhtIWkWRm8Ho7+LpBoMZINL210abkBWlVBUhZSukAk7vPzbX8B84xOYJ84jjS1QpkPv0IMEV17DvXyRnKkwv32Fya9/CnNzlRsDjRc3euzs7mNGA+ZyGmO2xExapmhqXN+sYXkNHCUmlhRQdHorK8iagdi4SbUbwuwpyuE+c6ZPJ5IoWwpFU6Xo7hCis9sdMOJuYhKh9uuIIKAdJPhhhKrIBLpDnBlGcVv0FQtkFb1fJ+5WaVgjSOUxlNhDSUKkVB7XLpHIKr1IQqpuEco6phShKTGKdFA53Udnc+UOcWGSbggb+wP297vsb2zTb7dobiz9t4nAN+O8vvKv/z4AH/vYx1BVlR/5kR/5jnGPPwuIJPm2rL/An47vOvKrqir/9rd+g8UvfZTXNpoMirOQKZHIErn77qV4dAJnagT7zP2olVGcQ4fxeh6ddsTuSpOo12X35evsPHOF7uIKqiJx5p0nkA2L7pVLxN0mye4K6YU53M0d9l+/Q+/rn0XNpIi3VzhatGgFCQPZRtd0RBQf6LQin0SzsIIu/SghUnSS3SXMlEMsaUyoPrYS0fIibF0hFFAPoRMeuIpVCTRZodoPCZMEL0yQD4QVZHUVQ1VQFJmRjIEiS0QiIW8oCJGgSVCydRwFUr09lK2riFYVszJJMnEKkRuhIxQutiRUSWFME+TDOu3Je0j8PrmwycjWK9hJQKhoFDRIFB2TiIqjYUc9whgsRSAVR1FFTNcPyRoKt7Y6LO91yEcNjqUClkSeC1sDltsR+12fobTGZl+wrVeoCZO9zCQagtzYNO1GjcFgwHOLNdarA/Z6MXUvphUKDNNiKi1zPJtg6zLVbsDdmockDsLviykTJOkgv1UIXtnpUe2HvLLVI6XCqO4TyxJ1L6KSVrmwUsePBB0/Zrvj88pmmyMjGSxNwQ0TLm93KdoGvqSR1wRK5NFNj+LNnmVYjVjzFVAVph2FmZyJ7dapp8bxRw5THuwwNVKmaMn8wY5OZmqCXGUE3VJQc8O8qk6SSgJakYojRfR0h7c+fi+305NsdXwWJgoUZg5xfsRkrGRzZDhN0zvIVY3iCC8UFPMpzk3n+dEHJojmj3CzOiAjeYQDl4dzLm87N00YS/zTr6zwwt0Gg3qdmxtNHlwYxtvdZun2Ft/zwBESWWF4Yojfe3GdL25Ao93nniHYafR4/soGzz1ziU8+dYvmwKNiRuBkSGKVI3mdPH3aviAhoZLReKyQUO+HvPjqDc7HmxwayXI8m/CNG/t4q0uIWodM1uE3PvMSJwoq75uMOGb7yJrOVCnFREahJPtId28gRwFOYwm5uUez2WGrG0GmQK/Z4tW9PqGssjt+nM9uwKQR8Oj9x+hINmuegjQ0TWF4GBeNQ1mZZ27vc62rIEsQF6fI4vIDbzrOpTs73OrBlWdf5fMX7uDubVMcmuaN7/lxPvAP/gnvec9P8r98+B/yLz/2e3xmc5vDGY+M1Eevr/A9v/yv+K2vvMpf/omfYSxQEHcvI9w+uD3KjkHe0lBEgKlJB5OuOEJIEBgpyFeQbQelW0MN+ii7y4TVbZ7b6BNeeY1v3KqRMw+iFRuJxuRInnO5g7i9mZyNbanksiYbzQEXN5ustwMc/8DRHzSqvHZnB11VOTuZxgq6bPYS3jKf5U69T8ZUECSMZy2m0xpZOUSVEvZ7AVlD5sxYltjKcnlnwFOvr9FwY/JZm8msyXovJkJC0zS8xj7DuRTeYMBO12ehYHN9q4Vq5VAlaAuN2EjjxoIhS+Vw2eSNp8bYFxqqmULPl9nVS8QiJJVJMZ5WaLs+kixxbd+l0lgkHQ8I+m06L72AiAQilaMoDcjFLbydbWwFLNOiWh1wc69DXpcOzF1RQMfIYrp1JmpXKSohaq9KVgoYaBkS2SS+cZFMWiGVBIxrPo7oYQyaSO09LL+N0t5DX7/CoXCTWFYJtzeY0T3CfgtZShiECYrwkZMYx60TLV3CX7uNCAMSElBtJFklVnQUSeZw2UZWdRJZwtRkjhQNzG4V9ex91A8/SMMaRpSn8M0itixQhsdIPfAYStCnem2NxsVVhneXuK9xldHOJusbmzxz8TI7mxvkVIGuaQxnTK7uDLjrmQxLfTQpwX/tOVonHgfdJPF83CQhzpag0yCRDYJIkFIiylEVNzfGjjOJL5kYoYuULuOZOboDD0uV2e2HTKZNElkCWSaJAoJ+D0dOkJauIkUBSRQiVeYQVgYRg5cdoZ2dIFYMUtW7JLnCwc9W00gPamhxn3zjLreXFnEyOVKF0sHgZuCxv7TOoNumtX6bxP/T29wWpivce3KWIAj4lV/5FX7pl34JVf2uUnL+F/jzPvn9vxO+68gvwPvf/37kJOGf/PN/w+W2ShyFqCfegDp5DGNknNxb3oWWckBRkGTQ0zrHj5Y48obpg65z12fv4grt5S3MrEXU9ZANEz2dIu40Sbwekp1Cq4zSXdqmduESiRfSuXydLAHDmk/Tj1hqBWxIBeR+C8mwkZMIP4pREMSSRoKEMnYIy2viMIBEYacfoMnw2laTnKnQCQTVXkg/PDCwtQLBIIpRZYkgFswVTAxVIhQwn09RsjViAUdLDlEMBUtDTSLKXpX2nVfRvC6FqQXs2ZNMjA0zkdXxI0FWk4iETCJJ5EULoafY14okisFlL43U76IHHeRbLzEQMml8Rm2ZrJKgJTHdMKHtxUT5MeTuPgUlJidHPLxQ5AenFdadQ9zuK2TbG4yX01TSOmVTIYgEiaywGRpUex6uXeJSzyCfMskUh7A727zp2DCTlTRrHRdHV6j2fTbbAYoEJU0w50i883CWvCmx0fL44uUddhptqh0fRYKlZkjPi/H9iGFH48pOn+f2oOOGDPyQXpAwXrRZqw3YbHncqvXxwpiEBDdOeGGrxzN362iaTKJoRJKEG8UkcYA5NIGT9LG6LV56cZHXayEtL0aT4oMqVGGwmjnEvuQgZIPvndD4/Rt91LBPuVRkIzbJeE1WWz6+56OrKufzcGw8xzseOMLnLu3TlGyutmKaocx0OmEQxJyecMiaOvu9gNt7HY6VLKYzOoZu8OCYgjY0juXWeccDM5SLBbaqbbxYsHD8ECfKKhfrMTlN4pUrixwbL5K3da5dvMLjpyY4OVnhJ991nsKhOVrpUV7d9pnKyLz3zBDTMyN8/0OTHD4+w//jmMwvnBIohka72+NWS9ANIla3GxTViD0v5sERi11f5tWWzu3tNo3iLCcyMXfTI7y+Ued82eTk6cO0zTQXNj2W6i7PrLbQNYVRS5De2+CqWmLbHkWq7yOPTjFz/SWSjSVu+inY3yFOZBwlITd3hHdPKFQ6a4yXHMxuFRtBWo0ZzVnM2hFpDR4rCk4NWXiDDr1E5XV9kkQzGT1ymHZ5lM/vqkiqxqOP30d+dITO7bvc3u3wydc2+NSdmAd/4pdY6cgk3S3ilz/H8pVneceP/FXOv+WHeLaZ4tSjJ3jyTpXf/OTTJLVVTg2lDnYSetsYikSiGSSqii4nlJQIJfQOig1UHW18FlHdINlaZKGzzuuv3KCyd42yrTCdlhg1YubmxhnWBSNxiygMqTZdZEVBkiS6XsTNrS663yVWDM6ZXXqBRKs3oKTEtCMZdIuSefDAXOsHZHSFnAb3DRvUPMGTdxpUux53633m8iYtXzBesDkzlafecfH8kAtbfRbvbnLp9gat3R2y/V08b0BtECFEwot3a4SAH3i4oUCXYNeF7Z6PLMNERudQycYXEl00tgIdLwiJg4CuUcQwLdKNFVK3XkKKI4qZFNGtl2nsVckszOCZWXwjjR522fRVglSeSCQMSQMyhw6T1RTkOKYnVFzNpNJbJ1y/i1zbwlh8GbVfRxE+OhHq/hJq5KLFA4LtVaIn/wBn7xb61nX8l76M6rfBSqF0qmDYGIQomSyWk+Kcewcz9kBKqKk5jMgluv0iW198Ftl2UEdnEEaK0CkxMAuEKKQln/mcwpGiwajsUpQ8in6NwdoiJ37kPSiGxV6oQhKj9/ex1l5n8Ia/RLR4lerF6/R36hz5kcfInLsH2TIo7K1yX7DCidE8zXqNC6+8wsrKClu9mPMzebZcCc8uYHoNgrW75CoVvF6L5PjDTKQ1FEkhQaIVSwziBEkIJM89MBiqMjk1ZlA+RJIk2H6LrmwRJgmmKrPWDtBEhNHdQwy65Ku30fdvI5s6UXkeRwnpSQaRYtB98vN8ZVMQJSqJYiIXxvArR0gyZeQkoi/bSJJKrRvQaraYOrTAqGOx2fTod3zcdp3I84kG3W+JB7zwuwcJD7/zO79DKpXife9733eQdfwF/u+G70ryqygKv/s7v8mFT/4Wf/j6Jte8FJuZWdBMlMkjMDwPTha5XSPp9zCzNpPnxslO5JFkBXs4jRDgN1z8MCZzeJb27RXSR0+glkboXL1G7PpoY1NojkbQGRA266QqWdSLX8ASMZstnyRJiGQNSVVIAh85ClCiAbm4jRa7hBMnkf0eYvUKPb3ISjciSWC767NWcykYCl0/RlUkumFCyxcoskzTi9nrB6R1FV1KiBNBwsEEIYySg8rWlE7fGxDtrWLv3kCOXLzyHNmZE8ipHDlT5eWt3jerdTVURWYiY3CnMeBLW4Ivb/jkdEDRKCkB8dFHEEKgpnPIIsbqbaMHPXQJ2L6Ds/IqjhIRCEhVF+kGCUbcZzyt0dFznB7L8JZJnZIcctgOUEk4UzG4u99HlxMMJeH3X90iFDHPr7Wpra0wMPJ0Oi3GRJPzwzoL5RTzJZu9bsBKN2Sv4+JJBp1YxTF0juUOIp7G0jov3Wnyyt06y9UeGx0XWYapvIltaPT9ENtQMBS4WxsQCRjPW3giQZNhabfPTClF14/pugG2pnB2tsD1vR473ZDdlsdtN8VLNYm+ELhGnkF+lHsncwwGEb/z7Dp/sJOi0+4SxbDYgVf2BuwJA900mXMiLnpZLBWcdI6KIXhA3WGp5XJro45rZiibCrouc898hd9/rUqzH5G2dDzFBlmi6QveOKVzJK/jaApLDZfFxgBDgeWdJss7XXp+QsXR0XMFToxlWRhOc3xulOzkNK8sNvnpN8/S9SR+4zMXeeNDs2y6cGuxykxRw9BUnphQeEivck5rYfYaXG/EXG8k7HcFx0ZyRIfO8JHbEf/7k7d4bqnDzVZEsbeLEzZZbQzoySmCWPD2R08zSJcxE4+9lktxYpzHHjrFIwtZ9t2IQzNjrDRd/PIwSq5I1tJpexEDPQNTh3jPyWFG6fPc128RiIT2/Ckcx6bRG9CdPcMDwzo6MYGQ2EqP8Y1mipRtQuSRGdQoGhKarLDUdPmX/+FZWoHgRLDJaNIn017DMGwG1V2GiwaZ5Vvc6wgmZqY4PWrxnmN5jh8eY68dcvjMGV66ucu15V3e9rYnOH/6jWQX3sJP/+Kv4YUabx01OZWL+MiiTClwEU6Bpa++QvzJjzHY3aAbgpwkeIpDX7IOCmsUA8IeUpIgnCLEMfLoNMrxByjlHQoPP8rp00cZ230NRxYYioylKEjlSQZbuzhKxKGKw7vPjfKj0wrPX7zDa1utA5e9naE5fS/NIKHtx7y679HEJKvAVk+wkNPpBwI/AUOBrqTzyStVHFvDlMGSEhZrAyayBocLJiOVLCI8qM7daw6IEolP3OrxUhWqZpn70h6jBQddVahkDUYzJtuexGbbJQkO0jnSqoyUwG6zjx8nxLFgexDy5cUGpmmwmWS401e4VAu4FBVZkQqMxw3cVo3m9L30zSJ2SsPoVrmx3yPUsxT7+2j5IRo4iDgg3F9jdrCK2d2G9UXSu4tgF1BtC5EpIUc+YvEmkt+j2WzjhTFqroSay0FpHNFvIjV2kVQFfXwWYRfZC0327CGqehElO0RS3STpN9AlgdFvkCGg6cbQ2qRz5Sp+Z4A2cxxGF/AL03hmjnpiIYQgE3cx4gBbkjBbG2T8Bolu0dHLuLJFI0gY1QMGnS6RrBNNnEDSTKSoT+36On43pLNZRR8apXZ5GVmRSfptSr1Nzt1zD/PHTuF7Hvu3X8Nob+H2Dyqfpe3b5MdyGMJjVS7jGw6WkiAlMYgYI+pTMSUSxaCankBLIkxFMNpbxpJiQkVFkhVGHJ2eHzGc0nht7aC0hOXXkDQNLVtE7KwQdbqoUoSnOnRjmTCBaPIwSy0PkUQoIsB3ikSyTh0L3avTlmzC6jbX99oUJw9h6CpeFLO82yXyXDKlIdxWFS3zp8dunZgfJ+PYeJ7Hr/7qr/LhD38YRVG+09TjO44k+TZMfv9C9vAt4buS/AL8pb/0l0BS+Q//4n/l313Y5uKOy7JSwRtZIESmlZtFjB0mEQLF0Bl68DR6PkNnrYpf75EZdqivt/nK0yt4zR7u3j69jQ2IQwZbe4gwJOk2aO32SY0VsOcPoygCq1Qm2r3D2QJUHB3HUBjkp4mcAoQuuttGC3okcYQaBwh/gGQ7WK11ypbCSOpge/PcdJ7lhoeuSqiKRNmUafkRsRDEsSCKY7p+TNUXjGsuUiLQFYmcpTJiClpbSzh7NwnCEHdoAXN8gXpioCoyTS/kdq2PqUh0A4GIYmRJpuKoVFIGFcdkLGsj3C6JlWFK6aOqCdQ3EKPz5PwGseZg7C2iNVdJssPIQyPY0sF0Ojj8CKWMST1Q6LkxQ6qPKUukdQW9UMZIArJRH1SDN0xnmHUU1usu7z4xzH6gcM94lhdbBjNyF88usbGxzqSjUlIiypZMteMiS7Dvw829LkXZxXTreEJmpd7n7aeG+alHJpkfSfHM9R1eWWpwuGgynDbp+yFBGLFRHdByI0YyOqqcMPAjVEVivemiajLLtQFuKFhuuFxYbdDsR9T7IUEi0FIZupHgdt3n0k6PF/cCLnckXslMsjCZ5W33zbLtCp7fDPjanT3GMzpTmQODiG9mmJgcY9wSrEh5EgQp4dPOTXHCXeHilUUGfZ/r+3222gGVjMGJyTxfv7pDFEYg60xYCSdyEgtah5eefoUROeD15Sq7u03SO7e4/Potfuy4zgudFFv9g4n8ViA4kTdIb98l7PeZLVsEksa7Hp7jrQ8s8PqNHX72ved47/EUz3/jAi9cWmazE1KTslzeaDBeynBkJE3GgAfLCZ/+9NfYUEtomRJHDs/w0MlxHqDGV3cSjlgRV/d8VpourUhBVVUOjeZ4/wOHeM+wy7QDrmKiHDpJoZQjSgRjGZtS1uH0RB7HPNB4VgchNztgjE+g7G0wOpxmsyvYzQyzo5fJ2yadMGGzG0G/yXY/5N5yhJMvYRFTl23kpYssDnRW6j2e2/T5wqUNTiy+yPrvfAS/vg2vvkpm+TJzTsLRjYs4993P4UdOIscBL9xu8MkbPf63J29R0UOW13epVvf5wKOHuUfqMmdFZO+9l51AZYBC7txZ3q1tYlsaX3Fz/K03LfC1p69SG53h08/e5LldmZ2uy1Iv4U4z4HbVY8dN2JGKtNQ0npoisjKI0aMEWhp9fIpzcxnyRkRfcYhff5burcsM/JBVT8MZG0LxDx7UkiRhN9bY2+lQVgVSkrArHJZ6gofniry+06fmC0qyS8qUqbsRmx2fvb6Po6vcbgbcrfusVwccHk6jSDI5OSSvCxxdYa3lUe3HfO/QgLOjGe6byDI+VuJtZyd4eDZHrrrMdstl3jmQIam6QqPvMwgCzNhjtdrBkBLCWLDZ8aiFMo6hstvxyVs6uiJj2xZBGLLW8tGtNE9XJZL5s0T5MdzCNKFISGcdYkVHK1UYcvdBUbEdmyTwuNkKeXlQoJYYKGEP3euTSjtIuSESRad6dxvPyJHkSng3r8CdS3Rjmf3iAm6jTtRuEi9fYff5m8S9HtHWCsrsSTBMsobAGJsln/QIDQdt/jRKOo+kyNDeI3X3Oabbi0RbyxDEjD16mnjxMigqUpKg+j2MxCcWMXK3geI16QqJyCkzcCq4ehZncph4f4WuFyJHAX0jw6pIE1hZUgpIssbCT/0wRsZm65W7bH7uC/i1BmrWIVF05OIYSWOL0EiTmzqKOnmcrApW9RarS0u4ZoE4UVDbVUaVECUKMIRHTxgwMkfG76DLCfmkR0aX6bea6MJHVnUMBVxM+kaBCJlBILhddXl1pcFz2x5JZQbZdIisLFK2iHHoOIlicq0e0BIaShwhjj/EDx22KPtV5M0beFGCpqnIEoBOXg64vtfBzuYZGypBFPJHN3a4cnULREwQhAi3Q9ip/amf/y99M9f33/7bf0uxWOS9733vd5Zw/BkhEQniv3P9hezhW8N3LfmVZZnf/9i/oXrxj3julTt87tUtntzssy+Mg/pFycIrzyEvnCV98iSKrqLqGrF/4FwuHRvGD2OqvZjnP/oMzc0G1YtXEVFE7vg8vWtXEI0qZtoAxSTu9fD2q6i5PLFTJEWIJoMqEoSs0jOL+JlR5DBA2V4iGLj0hQJRBE6B+PoFHE1GlcHWZcq2zqtbbXZ7Hooss9YNaHshtqagKRKHChZBkqDJMpF8kO+pSYLa1jq125cg9KgcPkPNHqeTaOz1QrKGSlYDS5VRZQkkCUdXmKtewZDBIsRUZE7kJcZSErKIaSUmoZUnVDO4ToVaIBO5HYJbryElAt/zSQKXZblI3NxFJAmqCLFVUJq73NlvgaKw2Y/wE42qnCYx0qz4Mv1QIOIYhQhV1zjj9BhxDM5XNA6POHStAoXRMbxI0N1dx0/gyv6Ax6azZGRBWYN8OsWnbrbRa5t4+5s8lOriDfoM51MsjGQ5NFVAVSQMVWGr55M2Nd4wW+Dh2Sxhp8+LN6s8eX2Pa7c28NyQnheiyBL7PZ9BGFPtBhQcgyiO8GPBzd0eIgFHUyg7Jr5I+PL1GseHHXIpk0YIHXSOTRaREaiqytN3G0xmVIoEkEDLj4lTRTRNZ8k3GAwdwrQcrNmTvH+4x+21bXQJJEmQNWQePlTgHcfL/PZz6/ixQDcNTDlBtgooF1/CeP0Vtm8vkctaPNfUyGVTdFFZuPENMpbGIIoZSnyEDJOOxruS6zwxbeJGgicv3OKEaPOWe6b53JLHmSOTrF2/zXzRoFlr8Ac3O+woDrcHGqu1LjlNQkrn+P63nWd9fRtVVfhrD4xxd7/Hyms3WenDf9x3ODKWpxnEpE2NrKWSFy652ipZU0a2UgyZMhlLR5EU8qZKIBIqjoEiw2ha41Re4nRKcG8+ptZoUhitkGtvM64LWkLhj1d8mgG4cUIrVvAadXJyhJHPM1/Q2QlVNElhceDwxVfW+OzztxiEgkefeJjq575I3pIYFGe4s/AAdyfOoGfSLDkT3Gu2ePZmjb/3w4+z9EdfoGjA9z5+D9cWd8jZEo1ajdzV13jl7h5C0XnrhEGr4/LwvSfYbA7YOvE4HUXj0WPjNBKDN8wVcA4foa0WaXcGhK0afdcjiBJuN300BQJZoxcr+JJBlCoQayn6iYTV20ELB4SKTWN1l0tfu8AfvbaNf+sSt7ZauOkyaUIeL/vsNrpoqSzZjEXS2EIAjVhjteUjKxLnJ7I0BhHX2wdFI7sdj1zKIE4kQiHYaAd0AsGhsTSWCnMjGfZDDccykWWZcsak7ob4ZpYxO2FuJMNbZmxmCw5dVLaKR4nzw1zd9xGBjypLTBZMTnjrnBkyODOSxlQlzuYSxqyEmZxBWg45VZAYS6mMajHbbZ+VdsD1zRa7HY97KibtCAZxQkdzWO6EJN0mDS2Paw2RrozQF7BU7XD7D/+YK1tddryETmqYZOIkamOTcPwI1ewU9UFAfeYsHaHQy0yQvf9BwuIk04ZPrKgYzoFEQcvnCJtNhKKjZAtItXUUr0cq8tDUAyOyFnvIsUucHSYZnkfSdJKNJcydW/h9D/vUacLBgKBZh90VtLXLaFvXyCkxKa8OSUxsZgkTCc+pEEo6u92Qbm4aZJ0hx8TWJRzhkZd8rNjDGuxhzR9D02Diex5m4T2PsP7UFYbuPYK73yCo1SEMkLweufYGFVulkM3STo8zeugI3U6HF5b3WS8dJZQ1jPYW+v4iTWERySqRloKNW5i9PQaJgl5fwzY19CQG3UKoBp0IbjVDuiF4YULLD/nAvaMMOxpyv0lQmkbyXSQnh18YpyMUSpZKWQeShJzkMyTaWO1NVNNiux8RC7BVmUS32K9WaQ1cjkyOosd9brVjLiw1UM2DUp/29srBh/t/6uf9P8Df/8Db0XUN13X5tV/7NX75l38ZWf6upTJ/gf+L8F39F/P93//9pC2bu3/0b3n98jIv3K5xc99n3z3oIxeSQnv4JPY7fgjJKSCCkMxUiTiIUVMmw9M5JiyJO2stvvK1ZbYubtC9fBEtn2Ows08sBIapMNipUX3lEr2dJnF1F0236QcCNQ5oBAJdEsiSQlvNEadyJE4effMGqd42ojRJt7hAsnAOVyTESIxlLQ5nVSxTJUwkYpHQD2IGoSCMY9wwIU4kFDnBDWNCSSVo7nPntQu0alVmjpxg/thpprM6D0w4PDiaYjJr0QtjVjsBbT/m8JBDWlPImiq+nWPfjyFJUKSErcQhh0ciBFHgUsOiG0YE6SGqPiRWls2nXsLLDCOn0gzabYpSwNUgS6sf0I/BR0PYGR4dSXhmFxxZ0E1UZEki6nU4YvkMKQHh7jpXt/tMGwFrvoERD2h4CaOmxIi3i6PrlHIZlvZbzBdTVEwo6oKRtMFktEsflSNFA9UwuSnyKKrCvBPT8SLeMpvhkXELVYHVpoejyRzOaUwpXYq2znunE2aGUpyZyPG15y7z5a9cYHdlDW8woJLWiUUCEmRMDV2VOTac5viQzW4/4Ot3qziaTHMQ8dZDae7WBoSxoNkPubPf44XlJqfHs9wzlmGz69PoujTdiJ2Oy53VHcI4wZQEBTXmpd0A21DZ8RQG02d4u77BZOMWZVNmvRuhInjzTIrT42lu1vo0Wj1ebclc68HZv/YBjr31Xn7oLSeRVI3LHYORU2d4ua7xujZJUG9wOi+jEfDinT1uBibR1AnsTIqcJdGrtfBmjlHImMwMOdQDmZ/90fu50Yz5vaeWWV/fp5TL8o9+9zn++BuvsbNd5V99+mVeW+vxjpMVTDnms69t0XBjbo6cYnuzSqQa2IbGWNZCkaDX90nt30EeH0dz8vS9EL/dYkQX7A18droBmioxn1OI4oTFjX0qbpXMYI+y5OMPBiRul+SN70YfmeCt0ybvvHeKqbxJLBJGHZ3AizmsddmKUviyRl5JKEsur/7Kv+Tff+KP+fwffIEjQw4PHikhPfF2VnoqcjpHqZRjqLVBoGbgkXewlpvj/AOn2ap2UIdHWOwL1kWK8ekJfuHHn+D973yYwhsf5a3npzn+tseQooR7FkbZ7vRZrvpsxhZvmjKZHk6hdHbg3Gmkuzc5v1Di8nKNp5e6qMJnNtxF+AENNwKkb8ZMHeQLh4nAJEaEIWJnhSRdQJ0/yhejCSqH5/jYC6uMWxJ1P+GZpslmaHPU8OhGgqFyhsmcQitUODuaIQhjru12yRgKunpQytCKVFZaHj0/JhEJMhAnAi+MmS2nWWx4ZE2d+bLDvptQ6x+U2BRshS+uR9ysR+z3ArqRwmhKIqNrHB7JktUE9d0dWpHMyZxM2tQJMhUasXaQq+41KdEnF/cgiUHRsXT1QC4kFBqNJnkl5OhImieXWoxkUzx1a59BmECvhYvObaXMFddmN5TYThy2Wx5r+ii1M29gROpjKgIvSdhIsviH7iMIfTJ+lSQ7hHP0BJluC/32BeLtFTRdQh/UsYIBnmzQ397BN0eYeOwU+8+9gltrEkkySb9BWN3CjwL2fQVfCIRmE2kpBpJxYKw9fJak3UbuNti7skTU7hK0O0T7G9BvIOfKSEEPJegh0mViPYMjC9bbAQ0vph0KYtXilbBITo2QVZ1U0GJI9ohuv4bs9ZAKIyRxRGZhDs3JMHrPFK9/8gLd1X1ad9YJG3vI9U0yuzdQFJhKyViWjuLkuH+myNnZEfYHPi9sttnSimBn6QqNtBojbd5AHZ0mjCK2Ah2js4fSqyP36kRqCh+VxiAkTmRWWx5TeZ1DhRTzGThjdBCxwLPKKH6HrpJm09cIhUTZ1pA0DcVtEyARqDZBZoQoXaJkyvgiIYoT/NYud1fWmZxdIC961EKFO/U+9Z0WTsai12jwrezWyxL8o7/7gwD85m/+JiMjI7z73e/+TtKMP1P812LE/s+sv8Cfju9q8itJEr/7u79NvHWR3Ruvs7Lb5Xatx4tbPXp+RIREK5TBctBOP4bmpCidmiezMEbQ6WGkLepewolJh4Eb4TY8Vr5yifat2/Q3Wyh2ikSSGOw36W23MIYKJF4bo76CFHsMogRIqHsgKTKGDKQKJKUJ5OmjyJpOnK5gd7cJy9PkBnvIKKQ0BTeRGcmYB8HxScL/m73/jJUsT887wd/xJry93t/MvGkqbZkuX13dbN+tppWhqAVESSSoWYwEiGu0MrMaLSmttBotZ8BdrkRqSIqiaarZ7Gb7quryJqvS5017vY8b3h1/zn8/3BYGi1mRXCxJUK1+gINAfIkAIgLxPuf9v+/vcaMj4sNqy6Hp+HTc8Ijk4LS4dfUyBzubLCzMcerco2RyRVQZLFkwZyZMp2R0VWY+Z5HSFTQJdtouKUOlbMJOevpoDi9UcaOEUtxHUjSKmRToFoqs4AiNotdg3oxRGluon/kxRJKQpItcU6fJ+h1yt97B0WxaauGI+5nK0pFzIEsMEgldAVtXebutMBAGe07MGcNhUjRZ8Hb5xv0u+uCQJPAYoFPOmswMNjlev4UiyzR2NymZKlISYesysV0gHQ15ZtrE6TSoZFMcyEUkz+Xh4QCNiKIY8rFRiYKlIlqHyE4b27JZb/rkChmenC9ybDzLxz7xFJ978RHajuCNd++zdn8TbW+NEVNwpmIxXbBJ6woNN+LWXpdCSicUgvYw4E4zoOMFpDUZgYSQoZTS+NLNQ966vcNUwaQZyfzbd3Z5sNHghVFI43Pj0CGjCBaLOpJISGS44VlcTZ9ktL+LPmxwbe2QrX5IM9Y5Pl5AkWT+x9d2KZkyNxsRdSPLtSDL6bkqpmFwfLJAaOX5+Ik022oOf3+T7v4BI0GbOIn5xVc3ie5cZc9Xub9W5wfmdYZI/IdvXEdTFK7XhvSrx3h8rkK6kCc9UsI1C5x6ZJEf+OgTdFWbB2uHbG7v8MU377NZd3h3z2OrPuTRE1Xu1TyCOOHOYZ9i2CcjBeT27qKNTuPqWV5ZHbC83eDNfZ849BExPDGmk9NlJo2Iy7c22bp9B7UwyrWkyu9vCTL5PM30GN7kcXRFMCG6LGYEPS/m7kGf19e7WMfOMJAMnlAPMHWNhx2PJA557HMform/T+QMuXJ7g4/Pplk8Nc2dJz4Hwz67+wdMpSSkaMhYpUCk6fi5Mp9bTNObnCedSrM0nuXRxTLdSOZnPnOO3NQYZVthLKtz+liZqXKGTihzfmmCKT1kbvsGI1u3yacMcqOjxIc1fkDb5oR7iK5IXH/tGtrD6zxbhWR/h8h1GAYRht8hVnSo72AOakRWDnf6HFY8JMlVWPrYcxzPQqs8g2tYbOy12OzHDCSNtQcPGVUjCuUCBQM2BgoTNoTx0Z7Azf0+Qz+iaGnkrCOU1xev7tLoewzCBF1TiBOBrckkQC8Iedj2aAwDtjsOe72AO/tDTEvn8l6flZZLJxRk4z6j8gA7HlJSIj51doz5rErBbzLt7B4tzQqoOxE5Q0VPZSjJPlklIWOq7PsqQQQfnk1RlByO5XUWcyqPTWVZaQ15cq5IMWWiaDrTeZuNbsgH2z3+w7vbfO1Bk//nW7vsDhPOn57nqfk8U4UMlqawPYyoJxaS75EoNjEqYRiQKhgwNo8xPkXUayEfbCCW38OcnkUZP0b31jVQFdKf+4v0HZ2gcgzJykBxBONwhZQmcaMlGLo+bC/jbT0gUHToN1BHxtCPnUbz+gTDgOaNFaRUnng4RJgZVLdHnK6AJEiIiROJf/pb79F0QtpOQJDEVHM2RdlF8waQKYIIiaeWcAZDQklDqk4hlceIDw8onJiltd0ERWLznTW6772DXBrDnJwnTgQ1P+ZMxaTl+BBFlC2Vc+cvsnRsgb1anbdXDtD8HooEycZdpFSOSFIYUX0YdpF275Ns3eFQShPGCSlDxdAkOl6MpRzVlPuOgh46KPkReiEMU2M4hSnSqkpKiUhJLkKSaaWnkCWFUM8xTFWRNJu8GkOSIEuC61tNssUyk9U8oaSx58m8v9Zm+/4Kh9t1ejuryJr5R9b8y7/53yFJEp1Oh3/2z/4Z/+Sf/BOk7zJ/vxf0fdrDn53+iza/AJ/5zGeYnZlH7LzNoOew1XSI4piDYcAwOALkyxIofhd9agpjfIbqC8+jlyt0agNCoNUPOFaysGyN5r1DDq+sk4iY3W+8wZ23t7hxq87goMPqV6/gH9Zxlm/hRRKyJJPFxUug7SWYkgeKfgTgNlOQKiJLCSJTJBV0cfyIfNQhp0tH0HBNwVJlnDChaKgs1/p0goQwFnRdn92Ve2yv3md0bIKlC4+SZKq4yVHkcU5XMNwWxvZN1M4BZVMmQaI2DJjOm1iyYLc5JLl3lRmpjRARhkgomjrp/jZJ7CMkCSvysBOfXDQgliSs3g5BpkL52ElMp4mi6pxYeZUH6hjO5CJ5w0BXJcLAJUrgD+73mUopZHWFN7ddgiAETUdIEkkU4UydQR+ZJhw7zsfmDA6NUQLVQtd1XKtCW81Qm/8Q5YfL1A4OSHttwkRGjQNeXe2Sq91DHXY4GH0EUxGcNPs0mj1kRSbs1GlreU7koSQHnOo9xE6nudGMOVbSuNs3GOvvMJWW+cGzY/zkU9P89F94lHNn5tms9/itr18lWr1HuL/O45kAS0S8/aDOrZ0utb6HhGCxbFPNGNi6yq++vcFB1+P8WIa+H5BOG+wcdrl2ex1FVjizMEImY/NWS0VNApZSPlISMq04RIFDz0/wI8E3r2xxffpZspk0lkjYbznsDULe2emiKhJPLpX53ZuHPD2ZJkHiQd2l7h5tuiuKRN0NGTNlHj0zzVc2YszVaxRsnUImxT/+xDR/79U2e/Ue41mNj5+u8uYHK/wfP3eS+jDgUibgfh/+H1+7wehomtmxPGdGNT58cZ7JsSK7Ow2efv4iH3rqEscWZkhnM/y3nzrNSMakHwrQdFpuiBZ55F/6XTZff4cn54vc82ySYZcPmhGTpQzPGQ1+52aHO5tNlprXeCLvcxiofH5aRq+MEbYOqNoyx8ZzrA0l2nIeXVPQEETpEjoR02bIqZEMT85mMcI+2e4u2sQxHKHS9OFuUqT0V/4a6WyWseMnOXAkfu3dA6zjp3j6yZMouo7veAQTJ/iFl7eQk4h+KDFfMhGpAo8UZTbrHYbtHmNmwmpjgBn75IVLIGKUy6+jtbZ5+2GDz8/IKHeusqQ77O83WG1HTJkJuZEq8z/wHJVihkzOZCEj2P7Sl3nt3Q12exH3fv8bfPB//QUe3F/Dk3T0/dUjBNqgh9BMPDWFJOsgK1xamqRcKbGY9Hn7YYu3bu5gqxJbg4Cvb8RE1y9zYdJGluBWV+PB2g6vXH6IqUrsdTwUoDkMGfpHPPC92pCMpVO2NQxZAlmQMrWj4Bxd4XAYkoiE9ZbLVstlv+chy6DIAiFgGML1vkYnURFGGtUbYsiCkaSNVB6jFes4YcjVgwGrex0aShZ0A8nOEcsqUSxxIitQ8EgUlUq1gmalWO1FzJVMFkspLoyarHc9bjs2eVNi4EeosqAa1pnK20yUDcIoQQhQEQgJTEVmKmiy7wpu+GniTIVBArYqkRQnaGfG8Qvj+NkxEkmnnLcwIh+zmGXs5AypR58hVUojP/9JQtVCIBNqJrqZYtpKOPQl7iijeOsr7Kgl7sXlI1PhDhmMnsGamsBtO2QfWYJ0BgHE/R6SZhKpFrFsEMQSrUDw7MVZBkFE14+oDQImMzq+liXSTWK7SNccwUxZBDubaLaNs3yDZNDFKKfpPNjj9NPTNB40ebDaIgnCI2OjmwQiQZFVsqrEpZRHpNtEjT2M5jaVfI7T5y4xXilxZ/kWy7duI+YeIdYsTE0j5bdRx6ePTkOFIImPTh3LBrhRjK3JZLUEVZLImBrICmSK2FJEx8yjS2CrAtOpoyYxShxxvxsjJRFGe5t00CKRVWIhs9n12dzcwA8iJucXkQcd7nkp3l5vc2+jhWaYmBkbRZGI+s0/tNYXsjZnT0wD8LM/+7NcvHiRT3/603/KDuP7+l7Vf9lQPI66v6+89FXmFo6z8s3fYnL8p8ilDCIh4YYxx4opYhK0YRd9chHSZWTLpuR69DdrLN2tk8/o3NvpM1qyOGh7KLt93IGHnNQQQcKdpsd4oY+dtzm8cpeDqxucHvThuU+Ttw329TIPBwrlio4kAhI9gzasQb+FV5jGEWmqtQ/o5U6SD3xMtYsbWyxkZSJJomiqeFHC6dEsO12Hctwj3NgmNjKMLF1kpJQmiBNabohlSJRMUMMhSBLB3gpyu05lrIdrzZHPmcxaMVk9y0bLYT/KsrSxTPr4eZLtTVqV49TUadb3YbFssCh36PkxuqHRjbMYakySmyDjd2H0GHISoT/yJDNZlU15AQOPfqiiy9DHYL5ksdULScKY6azK/aHECWNIX8qjKhGSqpMPOwSpMnlJQ4QSLT8miEK2I5nJ/AgHfY/J0xewBRzubKDPn6cqhpgpG69yDlcXGIFgrruKKE5Rz6c43dtmjwJjeY1IGyO9tYJaGUUOPJTuHpmRBTKyz1DNMGHA1XbC4uEdzk0sIc5MkLkwSb1xjKu7Q8qdCP9gjxPygLOpKgemSStMeOnuIZIsYWsyKUVmv+VjnVRQVIV6N+Dz5yfYGkkR9RyCOCFtKozlUvz7dzZJVIMzIzZ1X1DMKBCrjMU1bnYFTz16gjutmJc7Lj96tsqb9w4IihZzRRNNknhyvoy10+NmzeHxqRz2TJadjkvRtJnUVdKaxENXoNo2z85luBmOMicKaOk0b6/u8+JHn+TX/uAd/vanznG9q/HjT06woA74ck9ltBJwplJhZHGGFy5NsZCC2wceAwIOuz7TE0WOzZZ57eoWD9whJ0/N0NVMAvpsdX2mqylm0jLPZkOUc8fYLD/Cl9b3kLId0lMWM9UcViqNnZmiGsdciurUozxFLYVQVHZjjQ+fHeVWrceHczV2pBKabPPBZosL0wXKSQhWllAIQknmwOkxkcsQShHx6CKqplEKJPaHEaOmgmGPcOHpD1GsjvDJsxU+eHhIt9Nh/GCHV9qnODNeJeXWWJws4cUJj4o6YmfIfaWIVh1l0TFo9RxqZsxEIUvtsMNENeELDz3+t599kfe3mywldfrDFC+9/5BjK7sMMpOUl85wv+Ogd/YolIo0ZIlzZ6Z5OyyQXZzkJW0K/2s3yI6c4sfSN/nFP3iPhfGPoxfnsEyDrHOAo6axEbSjo5norKHRlyuMvP6vWH/sBfpelsy9W0iPPsbCXIXXpRLTboNElvjUpUm+cm2Hpx+ZYq6aZr3rIiO4ddDjsAV2JsUnHhtHkhUmsib7PQ8vkLAVGVdX2Ox6iDjBiUCWJS5M5vBiQccNMBWVakbDVBQ+2BtyV5N4egSM0ENWDDzVRFUs0jmbvY5PzrYIOy2q9HFEBrO/SapyAhkoxAO8xKKnZFB1hRiJswUFy1SoOzE1V2I8bbAauqw0HI6V0xSSAWMjRfZsixeKPmHrgFSs0zKzVNE4GLo4UcKZrMsHXQ2TiJIqM0x02ukZlusOE/IIk5pPXJ3CCAf4dgmjsYFk6SRj83hWEX3QJfRVAiVA1xWELOEJwcVqiih0kR7/FAYpympMPH0axetD64Dm5evU1hocdgQnzDx63CGtaiSj08iSSt+qsNuP0TWF506MUncCTlZSZHRIyQGqJONrRfQkBklBliS0fhP6LcJ+F1tVME5coP6rL9Nse8jDgPG0QW/9EPWlL1P62GcxMwq+LFAlQTrqM3RinOpJOrUa5aSOn5tgpFrFrExSW73LWzs9ZtU2mfII6RhQQ9rV0zTdkFnVoymbiCgkpR6dTApJQpVhOukgSRAYNqnYR1ZUrGiIFAUkdh6cFrmkQ9aoMlBNEltG0TR0FGJJZjjo4GxvMrV0lowmM/Qk7rR97ux2adS6KHoaORFkRmdpr936Q2v9ylf/BQAvv/wyv/Vbv8Xt27e/p7q+AEki4P/Pzm3y/c7vH0v/xXd+AWZnZ/nv/8//CP/e19jfPeTVy+u8+aDO9b0uYSIYBAnoNpI7JNy8SxIHGJOzVJ8+x7GFPPmsQdXU2Tp0WO+HXF4+5P5Gl3dXOux6IZoENza63Ntqc+srN3jr2h7v/frL8MbXcG9cptK4z4LUxUlA6uyjtHcBCam9g7F5DTMZ4pfnmNAjdEOjE4AcBURIJEIQJYKuH5IhZHK4idTZIzVxjLG5EyiagYgTEiHR8wNymoQpJaiRA3HEYG0TZXIOSxLMdu8gALu7RSQE8yWb7OQch1PnGGAQ9VtYXotsyuJ0RWcYQVPJk/K7mEEfhZgoP0M3iCHxEe1dhDfAyOcpxH3maGJsL5OKB4SKyaTU5Ww+xo0TfvvqPqMZnbIl46s291seBVun7Qv6ShovFjjxUVpZ0dZ5a72Dk4CuKPiJRHvyOLOTo+w5CWWGJFaep0egoMUYkcNQHOGxtrwjhFmlmCFfGSWKY3RJYJsqN8MMbq+NmSkgvCHFoIuqydxsxaw1HLbJIUUh9b7PXMnkyWOjfOjkKKdPLVCcO8Zqep5+IvOj8zLPjqo8M5cjo0C94/H6zRpTI2lkYOjHtPs+KV0mTuCpk2PUBgFFSyVnqrx4epQAiZv7DpNZi1ZssD1M+OpWxLPTKbqDkGre5LHZIruhymdLLrc2D0HI5E0ZXZbY6/mUbI29foClwn4/YLXts9YakrZUCqbGXBqeujBP2VSx5Jg4Fkh2mufPz/EzP/I8B5HOP/v2FhfGDOpSiifcNfqZUe5vHvJXn5zjZCXNSFpnvJhCUhRu73Y5e3qSfhAzMl4kN1Hh4nyZjhdxdbPD+mGfpbEM2t42Vi7HysSHuLrV5t++/JD31jrc70o8XpbwYkA3mbQgSSBnCFJhk2jY5ztbIddefgf71a9S63qs9GQqQYM37h5SdXcYbG+w2QvpRwqSrDKe1pGAvUDDVW26foi1ewvz219EQjCIZD73A5e4tDRKAvyEscEvfWed19aHjAqXP3h7lZY9yvm5PJnaDh+stNhbW0Pv7rG6sk24vcFEyWYmY5JL6aw7Jo22w8cfXaAtW7y6FXBXFHjnYZ35qRxSrcYvfvky126tsk2OW/sufSPPe22D5IPXeG7S5Om/8oM89fx5Pv3caZ4c15j8yAtce7jHvcu3cYIYVA2sPLVA0E5kIknB1o7Mxr6XMDh1mh8aDXn+whzvNl0cJyRVmaClmdiJSyAbJI0WuUqOyZEc7662WNvt4w5DZos2MyM5DAnOjGRImyp9L8LUFE5UbTL6kdkatXWC+CguN0Eia2rIssR8OcXFySzTOYsEGM/bdPyENV/lID1Px6riaTn8UKBJCpV8igsVneMjFikiVEXF07IoUkLPjVjpK/zeqkcUuAwCwZ2aS9FUyMoRfT/ET0CRJY7lDPqJxLzpUSkVsKtTTOoh1sEWY5Zg01NRSeiHCS035gFZ1hyZk3kFQUTJ3Wer53OtNuSt5X02RYplUWLLGCfIjyHK0wzGloiq86huG1VK6L79Omlnj225iDJsoNoWamsPNXZIFAXD1BgJGnQkna6axbt/h/A7X8HIZRhZLNG9ucryL3yBoNUlONzBbXeQu4fYicOYKcgpAVVbYa5okTVURuQAu7uD2tyA0EUdNklHfVw1hV0t4j24hf3ECyRuSPfN13EGAQ83WtgZnV6SoNkKzvYuIoxQkwBDgW4kkSg6eraIbaismRPccNIkQcB+bIJucu74NGcyEjs7u9y+cYu+0Ih0G729iWlApFooSNR8iZYTULFVmo5gf+CSkgKEauBiEaomqgSG10Heu4fRq4FdQFZVRjM6CTKHsU4s6wSJRM0JCfZXSFUnaWOgRA4PA5v9gU9rEKCZOq2Nu7QPGvTr+39ojf/BFy+SSVsMh0P+5t/8m/z8z/88MzMzf8rO4s9eIon/RK7v64/W94T5Bfj7f//vU8zluPM7/5qV2w85bDusHAxBErgRRFYOEYfE+5t4730HIatEWOTGs6SraS4+N8/s8RIFVcZOBJYs0Y0Em8OYoYBdN+H2rsMHmz323JiXLu9w5d+/zMHXv4lz9R0qKRmtt4+j51D8AaK5jyRktHSOrNsgMnIM9Ry6LJHzW6QNCT2OCIMAJ4hp721Tf3Adw85w/PzjZPNF9pwIUwX3aHSKrp8QCAkrHoKikhxskn/mRZJEEBfH0HJVZmlTl0uUNUHRlKhmLAqVCp6exjn+JFFuHNNQGbFUJu0jvrAojeNKOoqIqXkhFgFJv41s6OiKwm57SKRZZEwVoeqogC4i6NSYSFo8XZL4i4+O0/AkDtoureBodqk19ClEHXw0XtvosjsIOXBCwjgmiAUzGY2UKpg2Imxdo5CxMHIl1tY2aAdQl7I4qCxv9Sjt3MPLjpLFpe0J7lLlcOgj+UOu1wYU8dgdJuiqgpQpEiYKbpywNUgIhMz6dgOnMslrezGmrqC5HYI44kJFZ7Pjs1iy6agmV/YicukUpyYrLBRMnpwvovSa7K+v0TlscNDz8YOQ6UqK5dqAME7YHwSUbI2tjsd21yNva0zkbFKWwTCWiKKQ7Z5HPp9BSWW4OFekmjJImRrX1tu8IUb5bNUjp4RMpI9GRmRV5tdfuUvY73N9d0DOVDE1Bc+P0GSZohpza6+HZtnsV05QGBnl7YeHpLNpjMAnk9apZNN89KkTtKQUiYBNOU+9VudBbUjRVul5EZJhck7sMaJDytKYKabYazrECM7NlIgUCUtTeWouy6XxFBP9OieWprnSgnc2epTzaXLj45SqaQ79iIGWYqfrsduLGb/yEvnRMfTqOH0/4c2DkI81btAuTtGNJL653MJImdztysQiJggSfudmk2EM/SDGj4/ieA8GIRu9kJoTUQtVvnyY5pstDTcWtN0QSZGwbRUrk+LtRsK1fYdrrsmeL3M23mdtYx9LRPQrEyw3+rydPs6vLLu8+voVLiR1JuMOWDZm6HO/4+H3B9zfqnO4vsJIpUgmbvP4dIbM2ac484lnMQyVhaJMRfIYmZ+hYgT0tQKvFC/Sj1XGwiZGv4mlynzk1AgHsU66XGak12BlfZtOt0/v3jK2LDDCIUEiGJMc6k4AksSlH/48q034oQsjJOkSX3n9DnHgcXHUIK0JsLPMOutcrNp4jkspb3JyKsvF+TyL5RSyovDy9V3e3GiT1RXu1wfIkkR9GGGpMpYioaky80WTckojZyrsD30MRUJGUOt5rLQc/ChGEoJSSgchMRAySEfdWy8W+HqaohoiFJW+miHSUvRjQU0fZXMgsdbx+M1bXRzXJeU0mIoOOZly6YUJpq5ypppiu+0c8c0HBywVNOY1F9MwaboB1dYKIpMhyI2RT1vgD1HlhDAGVZFZ7ki8tOlwf6Ag2zlEkvDBTpetTkAYJ1zdHXKlGbGe5OlGMpKdo5mdx7fLhEKhf/ZZhoU5dgcxwirgxALD61JVA0bcQ5S9B6j9OqW9B9Qjg6aUpXjhLCPPP4FdTpO2VUQYMtg6YFjvITbvEBh5kliQO7hFKelRcPbJqDIq0VEEsNsljmKU0EUM22ixAwIojJCkckh7a0S7q/S3D1j61DlmRjPYlRT3D138noOsKuAOMKTwu/sHComRJVZ0AkmjmNKw0ikGqNR6AXlcJAFlQ3AmaKHs73Dz5k3WGwNst8mIfrQYniSCjKnixQm2LBEBKREQWwWErKLKCREysojAGyCVZyD0USQJLz2KF4ONR95SsTUJPxZsrTxA1XQKo1NUTZVeJPH6SpN7O33azSFOd4hmp6iv3CLq/+F4s9/+v/03APzDf/gPGR8f52d+5mf+tC3F9/U9ru8Z8yvLMm++/m3CnQ8Ybt1i7fYmy3d3aTkBh25M7LuE4ydRi2V6t2/h3b2G1K8z9tQZxi7NsviXXmT6whynFvMsjlkceBH/34Arg+8+hgm8e3UXNWvj7+4w+ObvYTc3kNdvECy/R+gF+AHE7UPkYIgdtsk4BwjdRk/nGE3pVNr3iBNBe/02qtNk7vR5cpPzpAyNeTPkbFFlEMbISYgbJ1waNdFlQQcTJXTBsFB1DTkOcJU00vCQxEhTExaqpqKJBEtOsFQVQ1NIWWlKloqQNTRVx374PmN6hB56HJAiVAwKukogW/iZEdz0GImdp1TI0gxlQjVNr3KCyx0F/cor9Aoz9NJjZITDSdvF1qAXSbT6A3pOQCZxqQuTDB7zbo1xS0b0u3x9+ZClkRTvbLa4eTBg3EoYUYYkssnF2Qpd12dY38WVVKTXvkZUHkO//z7ZnXvYps5Fs0fO1FESwe1WQs4y6eSn0GQJ1feYSQtsMeRqWMKwM5ypGESaQT52mLLhotXnm3ebLO91SekKbreLTcSMJfHouE5LTpOWQ2ZzJk+MmZxYnOAHn5yjtrLG+++v8KXX73FyJEXHCVgopVne7+PHgoqtc3O/x1bHxY8SZosp2kOfzX5EwdRZqqbpBwkhMrYm03JDHpkqcPfQ5xu1FBN6jB47CGSmciYfuzDD22tN3rpzwJ39HufHs8xU0gzDmFo/oBmobHRCsraJH4bMVNJMpXWuN31sVaFqCj53ZoTjOx/Q9BLOVyTWGgP+woeO40WCzbYLiaCt5Tkb7/K3RrocLxhcGE+xULJ5bKZEVlMZz5q89v46Y0ZCuPwBm5HB77xyk5dvH7K822ekWuCJiTSPVGyCWHCmoDK4eZlNa4Qv39zj64c6r3ds5os2jz61xLlH5nAvPY01WkEAe77CRyox7w1sjPklOu4R47rtRtw+7JMxVBCQ1RVsTebM0hynP/Ic7x5G3KsPkSWQJYmJrMX00x/i0pPn+LHnTnDe6PBaQ+W331zhl79xndrDu9xpRkzlLdbXt1man+AX3ljhV79zdLP8G9cP+Z9+5UscNnpYlRHuS0Xytsri1Ch5WyNuHFKu2Pzzz5+m2ezza29uoDhD9uUSH5+UeOGxRaLhkHwpj908IKrtYnl9zOoEf/kTT5J67knM9Tus7DbxBz12Oy6R77HTGWKoEomAhZzGybKOasjEgc9Hnz2Nmc2yt7FNWYuQgI0ow31zltffvcnLf/AKYRjx1XfWkSSJsiWzWh9yfrGMJMEgOPofc8KQIBY0vQgvjEnpKpamYKoKYxmDoRfTdUMO+iHvbXdoOyESEssHfRqDgLX2kP2+z4Omy9WDIYeOz1rHxzRMhKTg+IJbPVht+9ScgHstl4qt8+EzI/zgrMrtocmDfoIeDpkRbZQ4oOWEnCsIxjMqVVtjNO4SWXksVTAuu4jKDN7ocRwtQ4YAKY7YHYRM5QzSmsLAjxjJ2qiKxp7IsHwwYL6c4sefmQEkFBKWSgaSZhCHHpIso6gKXbPCnifIVyvIusV4Wsdr7OJKNm/K0wSygfT+S8SKzpo5iVLfYlrpU7BhcNAiQMfMpchN5ygulFEsncO3bhC2Owxf/RrKsMHNf/MbJGt3ie7fIO3UqIYNQtnCrxxHt9OoikJQmiEx8oQxxJOnkB/9GJIU09/YpnFri/5ug+XNHtmxPI8u5on8mDgM8bZX0b0uliyOTKmRRmrukSQS40bCRMZAVRQUVUJ06gwihaGWI86NMBYHpKaOs7Ozy1U/S13Oo8UBGTkgg89CwcKLE8YUl1LaRG1tEek2miRQkhCERHy4hdi7T+w5JEIwDI9IJm4io0kyQSyzuntA1G+SnzpGIgSGKrjejHn7Xp1b9w/Zvn0H3dSRVRMRRX9obf93//1PAvDuu+/yS7/0S/zyL//y9yza7Pud3z87fU/9gpaWlvjbP/O38e99lZ1b77Gzus2t2gAvhn5umsHyNdzcCJHj07t7F69WJ3fmNIVzZ1FSacafvYhlaQxlmbG09p99n7mUgiVBGCbs39jFrbfwV+6y97tfwn/vNYJ2ncFbryCrELcOiRKBcHoojXWSu++jxx76sEHbDdlcvoqmG+QWzmFmsgz9GBJQpYhxOiwYPq3oiNtbNFU0WSYrJyRaCmHaR5gZRcGKBhwkNkZjk6oJWuKhJAGqDErskNUkLF1BKNp3/8g89JFJdFkQSVBOmySqiaVE6LqOqkqomoGcRMSqTTHuEnSb9ITKyaJJb+YkOVPDTkI2hxKRahG2a3yoIni2KoHvEPdaXGsGRO6A4e/9DuPOFmfDDRYrNnlDZT4jMWHFtOU00WBAN1KQ9TRTeYu93T1yIkDxB0zv38J54pPkbYEuqzTvPWRK7nK8rDFVzjFTsFGFYKGaRbJtOgEIPc3xssUwFqx1fV44VuSl+z2WmwFBu8XFcZv37u9z6CQs9Ve51w5ZMF0+dXGeSj6Dpurc70YEcczxiRxPPH6Sj75wnsfm89y+fJ3lW2sYUsJ+x8HUZUCgKzIzRZtKWmer4/HBTocrBwPSmoqty+QNlYYTUrQU2l5ESlWYKOicmMhQyRj8/oMBLSdEFz5OmPDIVIGPXZzDMnX8oYeIQo5XbBCCr93v8vhsDkOTOVU5ikeWZJnjZY2xfIorOwNcSaOQuHRnLlHK2owrHg+jAstNn+XagCBOuLHXYyU02bUn6QkDTVE4Vs3w5GKFnK1yrGSw3hgwVTRAM1ifOs2rV1bYaYfMTeWpH3TRpZj3HzbIySGhO+TVlSZ3hyrWpceI6wd8+/YeyBobLRejUqYbJJyeq3BeGpDfuUNVDpARBLLBqekyYSI4GAasd3xqvZAgimm7AVf3h2iaxkha45kTY5wp6xwrp7572UQIHhsxWCjbNJwInZgnjpX48Y+eJ9IzfGPN5fjMODuRSTR0+cZr12iGKpPlDL/+xTfpDUNKKYv+7BkePzVHsbnLB+/dICULzDCg2t2iZ49iP/IkTaPCX/zwaXbubvHVX/8Smf4uO6vr3Nmuc0Ce4fQSphzTECqVjMaFpXGEbrFSOsGX7/XpTp7gi6/c5OXdkLmUQqClKRgqCRJm74BjFxa51Uw4M5nj+QuTVLIW0rCLoqg8NVdivqTxxJPn+PRMmmNlixfPTpLSVVbbPpIsSGSJjyxUqPUDeq7PRstDAEGcIMkypiqhqzLjWZOUrlHJaPhx8l3WuEpKV8gYCuM5g4KtocoyPS+i5oY0nZC1pkPTjdl3BKtNj0SWWOuGrNUdbuz1+NatA97a7LDZC1j2UhgpGzIFrg5MvtPQORhGjEgDDlx4WB8SGimuH/p85WGf7jCgrhVQNIV7LZ/tXoCmyNREirYTktJVmk7IRNYgb6jEQoA/5IkinB3LkMHneE7i49o2Z80BBT0mo2scekfzkE4MTgiSLJGOehSjDo3cPJJhcaycJURh79hzJOPHOBFuY5Wz2KZOozIP2RxhOk/6/OMUH72Ank+xfGUbo5CCKCI7WsB74+vULi/TeOnrrP/6Fwje+yZyr4Xp1IhRkOwMgVkgUm0SRSOVDPHsMiganHwazTJQTZn2gxpn5vLopQLdQwc1n0FRNcJ2C9kfknZrBJ6LFPvU7TEGCRSCNn4s6AcRiSQRZEqYB/fQdAX51EWSp16kkVg8YTioqsy9m1fp1XdRSbDlBFWW6HshKCrjKRnsHJF+FP2sJDFK0CEZdJAliWBkkQ4mgyhBlWSGQcIwFniuw3BnBXVkjnYks1wb8uZmj9rAp9336DQ7BK5P7d4t+jt3IQn+s7U2n7b58c88je/7/ORP/iT/4B/8A06cOPEnbx7+nEgkyZ+A+f3DOcnf15G+p8wvwP/wr/4FppSQ7F1FszLc2e3hRzFCxOhj09gjk6SnRjHzWRQVBg/uEfoR/Tu3UQydiWeP48YwiAUFXaao/6/fY6FskVKhE8PylR0kWULSVRrXH9DfqVG/vcHB2zc4/ParRPU94sYhNHdIBgMaX/4Ch1/5EitrG1yreUxMTTI2v4SqqtiqRDGl0gliIi3NQC9iiZAxW8EJE0QcYuHRCgSObNLMzRLZJTBTyP6ATWWUllGgKHkokY8v6/hCxRrWMeUYTT7als7G3aPub6GAr+gM5DSWqpCVAjTpaOa0rxVRkhikoyNSSbexVYEhQtLJEDlXYbfj4XVqTB0u03Zj3txLKA128cKE52czuMUpZumTTjxO/OjnSDIlYlnhXPMOSSKwFeg6ET3HRaAS+A5+fY+ZjAoIBrUt7KkxssIl1djCXXgC9XCV/cjCc10KkuCM2jmKL419SrkMA8Vk4CUMY5hUXU466xQNmYtjFp+/OM6P5BtkJudAs/jU8QLfen8Nq73PUjY5QgEBD5oufuCz0Xa42xEcL5qYukKpkGN5t83c0jyvX11ldavDB3d2WTscIkkSoYgxVYWKrZG3VF5fbVK0depuSNk+wk8tFiz8GE6WbObKNvcbLk03Rs/YXFwo8/+60oIk4bnZLNcP+tw6GDI1kmWqkuLm/gDHD3llvc3Z2QIJMoMgodH3uFCQOTOaphlq6LrKxek8ORPebMuEVopbHYlw+gyXZnNUMwayLHHecnl9tU3W0JjJKlwO87x655BC1uaxMZtKSuf63oDv3KnzUx+eY88VfPZEhqDT54WTIxwbTfO3P3WaqXKGBwcDfvdKnZW7D3nzYZOLVYPV3QaPXzrJ55fyrOz1kIl57SBBkiDRbFY3ajgvv8SJvODQLFMfeNiqxFTWQCQCVYaxnMn1gy6WplIbBuiKRFEOkRQZ2zQ5GAYIAVMpmYVUTGzk+ETZZefld/idjZi9zBQdNcWHH13kybE0f21JYn4kzc8/M0IiSzz39EU+9uHzDLttPvf4DH/3r7zAVM6gILmcmMjw2apHuLNOmMnycKDSciKqtsTT0zoXKioZNeHb1zf551+8zgd39vD6Pq1332W3EyAsC61ZI+42CLfWGQx9nr+0xF96ZpG4MMnnnl4CTacnNJabDtcPB7gRuEaWqDKLv/GA3O49yimTx09O4Q/7pDIZqlrA8bDN0liGiiY4Xs0wWklzqzZEliTaTkA1bfDyg0NWdxokQiZKEqJEYCgymnI0tlC2DUKRoCkyCwUbSYKsqZA2FZww5mFjwHTRJhRHCMO8pZLVNHKGykbLw9Rkdvo+EdB0Q2IBK7UBp0ayzJYsdrreUXJlJNPxBdcPPDzZYnUo8W4t5MahTyuU0VUFIRvYpSoPezG/eaNO3wvpYvGg1mfc3WbYbvL29pCHu31WWw4tJ2Q6pzOZlSgbgk7jkJG924zrMY+kQxalPtPVAnLgkks8QmR2hzGNdpu08JhJS2ixj0xCUThoVvoojjqJefMg5HJUQg49kvIUanWKxMojjR1DPfssViqFms0y+vwTWPOL3LjfJv7x/4b0408D4DVanP2BMzRvrjPYqtO7eRfn/k3CwwPMvVugqEQCojhB6e2jkiBEgiIS/HQF+8RxJEnhxoMm1ZKFrsbsDQIM+4g5nPguwcY95OW3ySsRenuL7GCHdHsDXzZoDj2cICZOBF6iEIydwExZOLFCaGY5XbHRS0Wmlh5henqK62t7rKytMxAGOTliyvDQQwcNiSg/DlFEQoIkYjRJQ6lOI6kGsZklFjJuGNGPEsqGTEoRrN6/jVWoUK5U6fgRAy+k68Us7/VpHPbxej38XpPh4fYfWc/vfuXnAfi5n/s5dF3n7/29v/cnZxa+r/+q9T1nfjVN4/VXv0m8f41kWOPKnRr7fZ9GZKDNn0TRbcyJcczRKsHAZ+tbN9j4wrfprdforqwgKxJnz1UpFW1Oli3Kxv/SAf5PH9ZOy6esHz3b6/sc3Kmz8/YKvWFEY3mXg6ubvP/GJq3rt3H3D5DyJVBMOleuIQyNB5kR9ps9zp49w9jEJGlTYSSlISFhyCqSJGFGDpYs8MzS0V25KhPLBoqsY4d9GiEYMrTVHCIMkfotzo1YOFqa+MZbSN4QHxVFAimJkd0uIolQkFDiEJEkSJKErBhIskKCwJMtJKeLKnxMVSYybXyhEQmFrpzBT49QMFVCxSQve2x2A75St9i2x0nX1nkmM0CUp9lu9UmpEpKscrGqkbT2sOIuiZDo+jKpUpHmwGXaVsjIIQU5JskU6EUSHc1mUJjjxEiBtf0mrpbDHB3Hq8yirN+CfJW5jKDlCVAgcfrk5QA3EZTVAKGaZNWQbDSgm+gYXg/pK79LtrtDx3GoaQWiKOa06TA7VuCxXMjvD8v8qy9dJhM63N7vs3Y4oBsILj9scHe3Q2sY8PKtA2bHcvzg86f4q5+4wN/9iY9QLKT48JkxVlYPePNeDVWWGU8bbLQ9hkHMhak8JUsjFhJfv1tHliUKpspkRiONTxjHRPFRsMmd+oDGMCSU4E4nOboRsnUOhx6qrvD4qEY/THhw6CBx1LUbyegUbJU7LY+u0Jlw95hIH90oRQgWsjqTGZ2sKTOaNdGkhCv7ffwo4f52hzdX2/zl0zlu7nUIo4jPnq7y2XMVMpZCEITIEqQMlTPjNlcOYm7t9lgJLGamymCmGMloaLrM7GiWE/NlXr2+TqLalOWQt5oKU8UU48Ucx/Ia03ZEUY642ZUxVYVGLJMt5Ll26kUaahbN1BHAwI8QSMwVTOIkIUpiNpoeEpDWZR42XPB6+LHgcBAQxIK8pfKgE5FIGhk5wqpMcvZEieemLaYmSvgJbPd98tUsy2Gejf0uT54b56f+2udYvHgGVzH5W3/pRc5JHfT/+O+xv/Rr1N2QPXOM6UuP42g29x7sMlq2WasPSdp1lIMtpF6NwqVLdGWVlw/gTEVn6q2v8UFf5rmTo7x6v03z1g2utlW8gz2237/J127vUckalCyZcjnHyUqGvUHEld0BFdtAV2SGRoGw3SBwHB4EFmMFk6crMbqc0JUzqIHPO1fvIw2bBPMncHsORVtDRBEV2WM0bTKZM9jreoDMSEZlMm+hStD3I9KqesRBTxKm0jq2JhNECRNZi5lCivG8hRsmjGQsZnMGlioTJgn9IDqKx7VUTo2lCb+7VW7rMiKBasqgnDPIWxrT5TSLZZtnqjKREGiqRMrUyNkaj80UmS7nULJFFkoZyrZGO1apOTGyLJOxFZxYwkvgwqjBvjbCoRuzUDD4gfKQtCZTsDRGLJW9bkBK0zBVhe2RJSwpwnTaRMvvEtkZvK119O4Odn+PqNNixtsjN9wjHw8wZJDjEGGmCRJBUQmxFThWTvGJxRz9bhdHyxKaeaQkoBsmOIaNsXAapTqFrCuM//hf4q/+y59BnZhGy2SQl85jP/8pqk8+Sn5hlMqFedz2gO5OncHlN5A270LgYhBiioj4wVXCThOrvU0gJBQhiJp1NpfrrNQ9AickjmIePT+GW+uRPXkCfWQEZ/kq/Xt3sR6+T9xpkZU8SsMDhrFMPh6CkPDCBKHo6G6bu4ySCMGEFjAWNfHGT6NJKmOlHCeWTtLu9rh16yY6AabTRQ4dXKHgJRIgcOwqem8fSQYpnYdsHlkSeFFCEAsOBz6BpLGzsUqcCCrT87y/00OTZVKGAsDt7S7B0KW1fofI6fyRtfxv/cgLlPIZbt68yb/4F/+CX/mVX0HT/vMnst8LEnH8J3J9X3+0vufML8DFixf5oR/+MVpv/BuCKGJ5v8dmx2XfU4kR6GeeQtF1QseHJCLsuTTv7FN79x47rz8kP1Hg2U8tHRX24v8C3h4xJVRg1wmpf/ekphXB5Xc2ePWtDV56f5d+26OwUKY3DLlzbZ+D91cJlt/D3T+k0+qydv4jWOUSZ9t3KQ33cWIZCTBUmWEQIUsCRZGIFQNdSo6g45FMxpBIxQ51P0E2MwzdADdMMGrr+GYOT7FQNZ1jogGPPI2rZ4kFiDggsPKoXg85OYoalgC1vUOsWCiqQiSg48VEsSAcNBn6MVrQJXB9WkFy9BphgCdkWrEMmomsmTxZlnhkssymNso7js3l2zvcfOsWsi+x0ouIUOhKNn5+HKU4hiYixqtF1KkFXpjLEqUKjOYsXCETyBq6JNhwFHRDZ1iYJEPIrmKimSrFag4/COh7IemgQSGbRjR28Eoz6H6HUM8giQRTiimsXkFqbiIiH9kwScYnkTfvMvrgPaRBD/XOVSLdwDZ1zp4Y58NPnubFi8f5xTc2CB9eYzRu0Xc8fuTSBNOqg+x0WBjNUNxb5eZam2Ykk8mncZH4g3fXEYqMCBPGUyor9T4ZXaHrhYSxwNYkMprEMIx5d6vNIPCx4gFC05mI2zxWUhnJGFzb7PL2RptC2qDpBLy60ePCaBpDVbgwmeWV+x1m8zZXNjoUTYn5nI4qy7SciMWcSsNNiM0UZv+AuVTCiWAboejkbQNFlhmEMa5qcaKSZaMx4FOny6y7CqGsU5BivrMd0wwEVVMmCiLeP/AYhgnzOY2PdpepPVglbna4Xw8wEsFjx0rMFm2u7nZBQN9LOD47wt3dDqYICOw8LS/hd1+/y69+UOOgH9DCJGXp3DvsUVFjzoxq3BtqXLt8i+s7XUopnYYb0fcD3DBmpTnkg4022wc97h300DWV/WHIdcfiRm1AbeCT1RWCJKGSNljtJwRhgKbKJJksw9wE1dYmQRgCEmOFFFbWplTOslk9Q6jYXIr2eefdm5ytauybRT44/iybuVnWByqj8pBBt0UtsZg8ucToiePg9uit7/CQErc6Mkqlwj/9iSf46zM6bnYUd+M+J4+PUcTBLpT56sSHydoq94rH0Gs7WIbGre02iohJaRq2oRIJwUTOIm+pZFTwJRUrn+NUKuHbby6TUiTarg+AqWkE7SZvmAtU4j4z42X6a7e5sdtjtpLmmw965EyVN9Y6fHghz098aIoXxg3mCxYrtR5dL8SPY+7XHXZ7PusdHzdKCASMZgyyuoIsYL5oI0sSt2tDRlI6OU0hSmB/GJIxFCazJkvphKyh0hwGJAloqoSIE/YHHpFISJsaTSxiAbu9gIKtMWZrqDJ4cULWVBEiIhQQiaMUvB87WeD8WJa0oRAlMr1IpinZZFI2iq5RrpTIGBp5S6Hmw2Te4tBLkIg5Zbp4vs+yayKKFZqRzuHIEh2tRK/e5vT+OyTpEg0nIgbi1gHJ7goxEiUxRA49Um6Tgi4wVWimR9kegjfsI0KftCajhS5EAXFximjuAlI2R+a5jzCpDCFVJCzMEy+eQzl2ifKzTzHxw59j/C98AmN+EbUySm9nH6VXR05iNCKU8QWiq68hH25geB3Mxgru3iH1nkfBkNjc7nFwfYu5x6fxOgOCboek16FxbYWo3aL35iskqoFXXECqzJBbuUI+bWKokLV0UrqMmx6lFR4Z0JShYOgKbSlFLqiTJqKtF3j0kZOoisy1m3dwaltEdgFJU7DCAW5yROSRQw/he+D0IFOg6SYcOiEdP6Jka+wcHtKoHSBV5zl0Inp+REYFZImHjSH1gz7t7YckgftH1nBZkvif/k9/jSiK+Mmf/En+zt/5O1y4cOFP0ib8uZQQfwIzv+L75vePo+9J8wvw73/13xA7XYL73+TqSpPXVhrsDmP2tQpReRZ5egl7fASraKPaGvmlKarPPE7rcICZzyBcl0rBojSR4T+RBEdMlScWc1RsDVuW0IAYWBnEtDyBH8Khl3DwsM4THzlG5ES0HjY5fPcm7/7a19k49wLVlMYj1RSFU+eQ4hhLBCRCQgJaXkxGlckqEkOh4KsGcQKKJJMWLmo4pCyGfNAIaPZclNgj1lQcI0dgZoiRke00aiZHpKVQBi26tUMOIpO2kkGEPkEQEDgOLXsUKQ6IEvAigXtYY7fVo5+ZoN0b4EaCeOs2+o03sHdvk6vdIV67g1Xb4PCwjvBclKDPUlHjiRGN0dESydQ839oPeLi2T667g3nrMuGwS8MRiNjHxmeNAsmwi9c4pNhdBfeImBCGMTN0mdFdjCSgEchMnniEw2aHVquD2m+S7O2wrVUQi+exayvooUteDHDlFNm4T7i3TmBkCadPoqdSjG59gCEJxs4dJynPkEppjO7eZ/70LJEb88FBwNWuSmAXKRWzfHrRZnOosns44NryQxbcLT57qkhHy7NUNmgqaZ46ludEQQMJjhcNam7C+RNj9IKYr14/YL/v88qdQ0xVodN3uHsw5EFjyETRxg0FgxAetBIMSaCn81QUFyEEi9Uj0PtOy+PKZoeVpsNe3+NDM3lyhkJoWey2HX7yQxOcrVjsfTfMYH8QkDNVsqZKU87SlFIEiUK+u09KOJREj6KSUEnp3Km55CyNk5UU+bTJZ6dl3lrvEqk6p8az6IrEwwFYmkw1rdP3Q4Q74IFUZFFzOa21edzf5eypKTRFOjquFtDxQ5r1Nppl0kmVGJ2o4kgS43rMnGjzF45bnBqscqkoKKc0Dnshu6vrvFfzKWcNvroeUE4bHA4ChkFEy4/Z6vkIISFJEg+3uqy1jpbzgihh30nY63rs9AK2Og5+JFAliSQRXO3IhI5DvlhAOAMm3v8WJ22P3LCBbKSZVH1eLEW8tevi9Tr0P7iOr9io6TS9+iHLcYrb1SUW8zKVjetcrgmiVAZbjkhZFo9lBaNLs/zFj50n/7UvULZ1svMnGT+xSMvMM/WX/gq3931+/iu3qZbTTIzlaPU8njF7qBfP8fkJia/9was0PKi7IbWBR8FQMDUFNxIIkSALsOwMIyfmuZALyeoS+x0HP5FZzAj+YN3hk08dJ18oUypkGBSmWajYTGRMchmb/bbLxck0tqkznjUYJCppXSFlaBRsnTu7HW7vtNnveSgyFAwFS5EYBDG1oc941mQqZ5IxVJru0YxtNW0gcYQqfHuzw3bXIxISti5zOAwJo4h6z+d3fv8NGsMITVGIhWC9F3H/oI8XJUgCvCjGTwR397t0/ARVkRkEgo4TcjgIaPiC3Z7HO5sdXnrYoukfwRAORIo7tT6ymaLlhlTTBlEco8pH9IlrwyzvNyW+enWbTsdhxZ6jv7eDXSii7t5Hqk5QWjxOvLPKwdYeJDFSp4VwB6iDJtn+FqrbJiNHDBOVnUHCRFrj/a0ucuDjKQYZVea9WsiGb+CrFoFVoCNlkFM5RKpIoluIJECXVJKRWXTbQM0VEMVxUqpP+tRZlPII7Ze/DrsPkP0eUiqDbhz9ztXGBtH9ywwPGpRSR9H0Ik7YX2mjGBqVc1MMNg8Y7tQwK1kSBGrKRuyvEQgZP1NF1DeP0JWKRN5UcUOBq9mMZnSiJGGIgSIiDBERm2kcPcNEWsfRMpw9sUghbfNeU1B3Y7xYRlZkOiF4UYLIjSAiH3IjCNXEViQmbAkZcB2XvZX7FKcWEbrBMEy4MJZGkiT2uy7L60327y4TtPb+WPX7lV/+PwDwr//1v6bX6/GP/tE/+pM3CX8O9f2Ftz87fc+aX8uyePlrX+TBV34ZM2hQ7/k8bDnUfYlANlCqU+RPzFM4OU3x9DTzP/W/IffMM1Rn8sgZk7DnY2R0dEPF+u6nJKkK03N5PvTEOEujNtOpo4wQFciqEgGwtdXhg3tNwq7D6ScmEElCR0kR/OgP4bz0NsfKabRMFmGlkdJ5LKeDEBAkkNclumGCLwSSJOMJFYGEpQpQDFwjT6r+kHnD51LvBsWD++QSl9TggCgzToJEiApRhO859Ha30K6/wtU763z5VgNnZ4ev329x6zDEcDt0YwnF62N7bXLXv4WmSCi6xZSzTYhGMz+LVxhBDgOMyCWXMzGdLlO9DXSvzY5aoOf4lMMG5/UWn7L3+fTjU1iTYzTTU6idXez6Jmq7xkNG2QsNSqZgPbT47eUBoaTS80KqYkilv07fC3jQhXsDiUpKRy9WmcvK3Gm4NN5+F7uUxahtgCyzZU8gK9BTMki9JlgF5O079DotTDXBLS1iVCdRTJNaqKAbMvH8WYyFY4hUmub1ZcxeHS8SlGWHsdptRucX+PEPL3Fyvko90HltF3Y7ARsHPcqddUamJ1huhoyqPmGUsJgKmZ8r85dP5Tk+keNge49iSsc0Ze6sH1IbhqQtlYymEMUJKV0BBK6i0w8SekJjW+R4fjLNI2NZ5kspfujcGJ9cqnBpMsfr6x3yusLtgwGjOYtyziSQFI7lZO7WHT7Y6ZM3VG41fA56PmlNYj/Q2XMFyfxZkiQh29okGbQwVRlDhdG0RqgoNLyEtlZgYTTLtd0hczmV0ZSOrip880EDTUrY6gbUhUUtPcbvHJoMkYnKVUoZjbGcTdeNqB22KPl9XlgsUkjpfObiNOlSATNlUcfkidOTFIs5liYLTNkSVUtjqWIiEsHLhyrecMDk/DRZW6eU0qmmDeayJgddn9GMia2rzE/m0BWJuwc99voBaV0ho8moCpTTBlEMtnZ0pH+mmiLrHXKYqfLKvQ53L34c08qQihwOOz2Sdp1hvUZpuM+PFAfkP/eDjEyN8K1Vj7qbEAvBM8cLVKUh+wtPcnYmj2LovH75AfVOl8bWNumoR0oWrMxdou9GpHNZptUOz1QF4YUP8aTV44Vxg3J3k2p7m698423Wv/0W66HNbmPA4xxwZbtD34sAiV6YIAuBLkM3hFCAEwpS5RKD3/wid5o+ahLgaFmu9Ey+eK1OJRlw2UkjfIeLJ6Z4fCxNSRd89nSZR8ZMdmp9pvQQz/VxXI+dnk/KVOl7Aa9dXSdlKniJoGKb1J0QLxZHQSqWRkpXsDWFjCEzV7RI6zLjGZ04SWj2PZZ3ulzb6fLK5pA7O11EknDQD7m+0+XMyVn8727vH8upSEIwWTBZKpoYisxO32Ov7XFlrUXXiwgTGAQhy7UejcERGeXd1RYDL+H9zRZzeYOFoommKNx8eMjXV3scDH3CCJAk+jEoMjw5lyNVHaU4NkaUr0KuxLAwRSHpQXOPW/UApTRGMnmM0yUZvb1HZqxKPHcOPzNClC4j0kX87U1S0YB2IGgHCk/MFhCGjSNMmn5ILKvIkiAUR6NptibhxgquZCD2VlBEjEKCkBXkqZNExSm00Sna71xGSgLEpY8QtOsc/N7vEt37AElW0MZnGNy+SbTzkKDToX1/F8XQGHoxOf0oJe1gvY2UJIw8/yRCCCRFZvvVFcx8GiWTJx27qPsP0LWE5J1vMGsGiAScCMJYkNGPvu+mD6FqU/D2IRGosUc3iNAVCU2GRyYLTOdNlm/cYNDYRyg6W12ftg+OmkVyOiSpPKGVo+dHbPVCojjhYPUudrHKUM8RRAlpXcWWj5avrq002dts0tt+8Meq3R9+7ARPXTjGysoK//gf/2N++Zd/GdP8o6OPv6/v6/8Xfc+aX4Bnn32WZ575CMu/+S/56KkqI7bBWttnGMsk7pDh5jZxEDP2Qz+INHUKSZbJzRaPZmFNHSkBs5KmbB+Z3M2ujwgirKxFJODxi6NM5g3Ojdm4sSAlgeNG7LR9hKYw7LoEx49Te/YTpN94lcHvv8TOb32BOJYQtU1w2uz6grQicMMYIcukoyFJAooioUkyfgyBgESSEUnEu8kYo9IALfYJDzZJskXUyCfTW0ft10jikJ4bonodyoU0h6df5MR4jg/Lmwinz6WKjlUdoS5nsEWA6nUw198nOvcMeS0iiGXie1fQRMRAzRLYObbKJ7mszrCnlUhGxhnKOko6x6ToceBDaJeQnR45Hea3PuC5KZvZjETx2eeIZs8xOlbhuNbF6uxiaSrHDYcfPJXidlxCLVTZUgr4+QkCLYWSzhNrKWRFw5RjRqbniTyPYKSInCszUb+HkghGrYR42EeLffYSHWPzClIqSyZ22VXK6JqKSOcIVAs1UybOVpGzBdSxKYSexnjkBKerFkVTsN8N+Z9ve3Taber9iNVOgNE7QCXml96vcz4b8h9udPjtt1c5P2bx5t0DSmrMSDJkIQMDP6TTcvjbZ1SyhsrAjajmbQxN5e0HdUQiKOoq9w8H3Kw5zOYs7nUiasOIckrH1mUkVebCWJq0LqPpCudHU3zseBknShjL6GiKzBNTOWTgG+sOS2Wbckpht+dTTOm8ulJHJWYkJVEb+PT1Ioms0Rs9hZTKYgc9sppMwwnJGxrnCxLNUMYUER85WcSSI9Jqwpgt+PiUjpfIyCQ8mR7SERIXzs2zrY5webPDciPgm3cPeefeIfPjZSojFZ6eTfHDlyZ5fsLms9MmFyoavUBwsHbARr1P4dhJ+oMh//Hr7/O1r7xCu93jp44J3r+7haRLmLpM3lRBCDQVNFVmJmdSsDR+5LFJHpvOMwgS8uaRSX9ipsCjk3kujmbImyotLyZvaUiyzIE1iReGjJZtpNIoDV8QlsaZWH2bL+xYXPEK3O3IvGvP8dioRLWQYryU4ryzSnF0hHQmTS1J8XAI+oNlwoMaL54q0nViLisT/MLlLiu1LjdLx6mkDe7fXENDZtXTuL1R53Vzgclb7yE21njl1h5/64U5/vWDHm+9dZ1vv32XkxfP8oVvvofv+5Q1CV2WCBKBramoytHzQzeh3upTWJqmooTIkqAyPsX/5Ve+zYUzMzzoHBExesIkSWK+ePsQPxEossKpqsVHtC0GEfQShXI2zWpzyMCP2Gx5PH52mmOVNMeKFkiCtnt0RO2FMX3/aN5cVyRkWaFiaeRMjb1BwFZniBMnDIOYtcMhb6+3+catA25vNNF1ib4fcWJuHCFkNuoDSARLKcETFZnTJQNbkzkYBNzb7/GZc+Po3RqDIGEhafDIaIqPzueYyZs8uVhipmzxN89mqaZU8pqMqgg2H25zOIy5vdNludZn4IcMvYjmMMBPZEQikUkZTIwUmcsZLFSzBIqNee45soUMUhTTtSoMxk4iIp+9JIVjFKhjg2ahuH3ar3yTYSyx2xqSwWVWc1CzeYYRjBoJOVOlrEbowZBOfETNKCoRIRLR4S6KSFCCIXECaBpxqoTy3Q38xPcwCTHyWaRuA8lOIdw+Uq5E78EDnI1tDt68hYghVzSZy+g86Pps9kNuv75CEsYk3oC9W3vEbkiv1iH2PQYfvIN/9TtEW/dBUXF390nFA6q06PsRDSfECSJsVWYQxLiSwUDNkQBCUpDioyjjWICkasznNJYWZrl//wH1wwZ+mKDIEq6kkJQm8YwCw1jl0EvoBQK1tY0iwez8IlEieNhwyBsS9+o+b6y3efVbb7F7++ofu25/45f+dyRJwt/4G3+Dv/7X/zrPPPPMn4Y9+HOp73d+/+z0PW1+Ab78pd+kubnCm1/+bWaLJiVLxUMiCTz0fIb8xz6Nms2RqCZyFFF94jRGIU9uvoI1UqC72eHUQh4ZaIUCxdAwLBXdUNEUmafPVJiqWDgJ5BQYfjdZMPQi7GceJ/rLP8Ric42q06I8YrP1yk0Ov/AbJEFIvHEHM3SQ+odHi2lCIkhiclqMwn9KfouIk6PEQ0/SyRWK+EaB/eoZ/NlHGO6sE3kOyu5DggfXcWSLN+sxWUOFbIXpnEJdycH8OcT8KXRDp9lo0Yp1BAqdd99mO0yhlkdZ7mukhEdy4lF2O0M0VSH5+u/jbDxkeatNO5D4d/cCfmfLZLknEckmc3IfP5EQUYzot/BSIzS+/AVK3U30fJW8paNnUqT6e9imidhbI+j1sLN5qmmTQtKnELT44CBgW6TJpizG0jpZQ8GUBEm6wsmFadY8BSHJ5BdmsKIe2f4+rpojjiPMTA6xu46/8ChKpkSltcr9hstebFHTq5QlhyhdQo2PiuSqp1Csr+C7fUYthUfSIU9fOsHdrszvXzvE6jaYibok/QbTesI3ru/wExeqfHQxzfGszJPTWTb6CdeCLD92Ks+vvn/IwkgKbeEMj05meXS+xP3bayiKxEzJ4ptXd1BVGV2RyGoKQRIz9CP6QUzbF/RCcIOYOJEopnRK5lGc8kz6aDHpg60eccIRE9oNubI/ZKU+ZCqjUbE13DBhoZzi997fQnEG5CyNlhdxZyCjyjJdLUfouaRVKEsuuiKRUgW3drr4zTrn0gGK16cctVECB820SGkKzZ5PKhzwI6eKPHWswqnFETJTk1xZb/P+SoNGKJgqp5gvmYzGbaZSkIiIjHeIqSnEUULz7kN6VpHhnTv84jfv4ukGjaFgzVV4v6vzzLkZDms9JGTuHDq03JDNjo8kgSzDMwslLF0mb6lcmsxSsFRGUhpOKDDlo1Q0IWK6bkRGPzrmjaOYL14/pJS1CZBIv/M6vVjFqu3y9GKOnY6L48UEmkFdzpAyNFQpZjl1nBeXRoiExLWaxwfv3+aNdx8yROd2UuaN5X2eD9dZPLHI+y14bKFMKBKeKECtOMVSNYWuwqWTY0gLi4wVUzz64vO8+d4dtgcBaipDabTIsjLKT5ywuXl5mW/drbHeGlKwdGKORpwGYYKWBLRv36X6Q5/DdBoIJAJJwfdCPruUZ1QLODuSYqUbcuOgT7MXcLfh0nBDEj+mW5ojk04xmZJJE7C532N5u0nOVDkzWcDUNQQyuixTsjW8KKKSNkgSwd4g5H7DwVCODFHfi6j1XQqWgRBwYSrPfCXFZM7gE+fGeGGpSt+P0WW4ut1jpmDxwkKJ9UHMpgtuJPE7dxpYmsTFkTRnR028wYBj0oCq7KCoKmVbZcKWqEpDLDkmZ+qMXXmTXNzHiWKSBP7bH3uSakrl7ESGoq2y1wvphTFFU6VkJCiGwsNOxAg9dAJkEaFIMoGV55gVQ+hQNaFlVNjOLGBICVH7kLSSIIcOQtUoPX4BQ5F5dNQgHfUwRADIFC0FIemMZ3SapNEP1xDIDISG5PXJJS7K/Bk8RUdKApTIJ8mOIYsExe+TPbVEEscY/UOMyUkyc0fR0z09i2uVyMxOEscRRjnN5A9/HNPUiYSgYCjM2ApZ6YhEsfblt8mO5XEbXbpuzHCnhttsEWw8oHvjFjvffJdI6NCuoRkp/FhQMBW6foKlKEgy7PkqdjQkUi0OIospMyBIBKFqEWkpRGGC0YkpTh+f5+b9h3iDLhOWIJv0Ccw8O45AyAp+mDAiunjdJkl1ASeS6HoeF8ezHA58JE3lq+9t0qvVCLv1P1a9/o1//lNIksS//bf/lvX1dX7u537uT8sa/LnU983vn52+581vJpPhd3/7N/j1/+Gf8sath0xkTVKyQMpVkU0TceIJkuwYij9ELo2TOnMe/dgjpBaPUTxewa7mkAPBk0tFdMBrORgFG12T0bIGdvqoiMymVFwg+q75vdWUqF94ivyVd1BXVph47hyjc2WGg4CNb7xPf3OVwYMHlNfeJRt20RSw5JgtT8MQEWoS0/ViClKIpUggjpJ45owAXURIxTG6lWPsl46TKAru9FniE5dYc49M+UGsgWFTlzIczyjoqTzbgY7ve0hxgB+GNDDw2x2Ki8dJqQrTpQy5qE08fQpPy5AzVYYv/gWmRwp8fkZhZrjH8aLKhx6Z5MpBTE1OkaBibt/C3VjnlXiezZffov3kpwmHPRLfQxcBwi4gW2mUYpXg+nXWHIWCnqAS4BhFcm6H8eEue05MxVLQFQlVEnhCIYNDZWSUQibFyiBBsnIokY+bGSVJQpK9XWaiJv7JJ0mGPazBHmHkEcchsV1gueVzUG/ScnxCWSW9t4yKjFKZYM9RuLPXJzQzGJbOR2bTfPLiBLmJCYyTj9ErzLCw/DIRKoNMhR8w9wmMLO/2DC4WBVNWjK/nePJ4ma4bcK0Z8p3lXbwo4qc/PM+5sTSb2y2efmScV1cabDaHZAyZ7a6HH0PWkOkFEXeaAXnjCHF3ZaPFMSvAExAhkZYTqjkTS5W503B42HZRFZnL213e2OoTJAlOELHWGFIsF1jpxeQMlau7fdKayhCNuieoq0XSqSMGcU4J2fVUzs2WeKmp8Bv3XL61L5EEAct9lXqg0A9Ceq02/eIUs5UMWUPjhcUyUxNlRjMmU2M5PnSsjCYrdHt9ekIlFHCrHbOjlFnthBzLyzz/Fz9KwYSgMs6ZVg3PCfjIhy/ysWwPJ1XiE4tpLENhKmfgBxGTOYuGc7RA03BDZAGdICFlaMwVbfpBhCRL3G8OafsRHS/h9uGQatpgzJbZ6nkUu5usbjdxArhYkBDTc9TvPOTyxIdYbwzZaAyZmR3l0pjN1e0hk2bAZFanMjnOkwtFav2AtKlz2j1gZrxENpclb6r8zWkHZ+4kH1kq8HwlwZYEahwycnKWC9M5HNnk44s53rm5S8+wueLbrN55yPmozYljc/zoR86xeGyS3/j2bR66Fo8XYo6NZmn1PVZbDmntaG45E/cJ9tYQjSZLJ2YJhl2EZvPK/QY/9tFzDBQTU5eQJYl7DZfdTsBjsxm2+yG7XZ9fen2NSlrFTSRascpG1+fuzfu89LU3WSzZVGydoqVRdwMUWabuREjIGKqEpsLQj2i5AVldIW/K7A19vChhpmhTtHU+c7zIcwtFsrZGwVJZ6wZMZnQuTOf58OkqszmNh22HhhPx9QdNXt0Ycv9wSBIJJm0oWwofEpt44/NkbRNkDSSVSDEYFT3OG31Oijqk09gqNP2EuhswVc2QcIRd2+n5PDaRRiQJvSDBERo5OebFhQI5OURLQrTQRccnERJZKaCvFxHIxIngQMqyH6io+TJW4hNYRXC6GNUqZf+QWXcTjCyqbqIdriIBlhRQxgUJ5GyJog7K2lVEfQM96KHKAjnwEf4AKQnYiwxcNBIzS+r843j3bhIrGu13r+LW2sTdJve9FK5VIo5l3Fqf9GgFfIeFH30Rs5zCCwWjGR0ZCa/ZJTjskBnL093rUZ3M0H6wT9R3kFSZ/SsPcfYatDf28Q92iFSbKBGsdjwMVSYSCTldxZAjJFUlQMVLwJFtlDhAlQSxrJMoBjEylUqVqYVjmM1VQneAKqkMYo2mF2HLCUkwYH9jheljpwgVjQfNPgXb4HhO4821Do4f8fCddzCyhT9WrR4tZ/nRjz3Bzs4OP/uzP8sv/dIvkclk/pScwff1X7u+580vwCc/+UmWli7wf//v/ve03ICcV4f2PubSeYRm4llF5DiAzj7h5Dm8uUeJHY9hvY9Ewvyzc5w8UeLcRAqv46GnTUozRWQZcnNl1rY7WJrMmK3z2GwGs5Rj7u/9JM5Lr9P7xttIhSruMKawUGHm4gQqsPfyDfaur9P74CrR9go6gqYboSky3VgmlkBRIBUP8CNBP5LIyxGqImHs3cGNZbqRxr5UoDtymo49ynaSZ38QUU4btEUKNxa4vR6G2yYSCbqI+dZuxGjO4ljnIcH+NvqnfhQ7m0fRVMZtBaGYSMhMl7NEyIxMjZIan8SeOo40PseliTSnqzafOVNhRFeQiAhknXZqjGLiI8/OcSgM3OnzKJELwRDZ6xGVZ1GSAGthiTPjaTJ+iynRwRQRkj8gvHmTvBxTSToAuBEoImCwuU2spTgxPcpe36ftxcipNA4WsV3CtHTkVA4jlaVrFpGikHZ6gtmMxs3dHhN5gwO1yGGrh1AtvNI8kxbcM2f4H1/f4VtvXmf1i9/g8js3cDSLpWiL1zeHnBjROTtX4taJF/i7j6d4Yy9kNTVLFMOMLbi+O+CNnYjdtsPJLExnZSZyOoOhy17Xw0nlMVWVuhtw6+CINb262eWbyzWqKZ2ZvE7WUNEliUgIZgs2I7aGGwpARo5DIiQ0Q+OR0RTVrMHNvQGbDYdy2mS6ZHP/cMgHOx1qfR83TFgayXC3K8hqgrKtUXcCNFmi7YRkTBVbl2gLC0232BnEzJctLEvn/qHLK2s9HogSmiwxYctEsWB+okw9lGl6CRJgGgo5XeEnLo3w4WMVpnMWOUvlINC43dfZ6x7FnW61A55S9pmmy/beIYrvI6dT/MZGh5e+9hLPb73Jr2xZBGj89o0+P/HMIjlDoTPw0RQJQ5VYKqcoGAptPyQIIyQBC3mDvabLq6stHD9EUxRypsxEzkRLfDb6AaosMbh+k9PHJ5mqpilYCq/JJZ5tLdOvzHCmatCp93kkE/Ifv3Ode7tdTqy/xbdXe6imxku3a/zuBzvkDYmV/AKXXnyc+ZEUZ8ZSTM+NcnWnh+N5GI0GH9ze4N5eh0CxULJlJgyBu71DobFOc+okv3erycEv/CL1Yxf45BMn2bm3yq+9tcFffXSEd7cGHIwc4+pWh5m8Ra3vkVEEHT9k/8EKxX4D8/w5KmpIEgbkyhWiMGFxfgRTV3l1O8IJY0bSOne2Olze6hP4IaWUxuJEmUwqy0xW5SvX93htpcNnnn+EE8fmqcg+URSxkNNpDXwUGbbaDuvNIV0/xo8ETScAAT0/JElkEgHjWZu8JrNQsnGRiQVU0gb3mi6SBBttn71BgBdGrLQ91hsuIo750EyBH56V+OiJKqvNPoHvUTLAnHuECT1m11NJmxqqlOBGMTV9hDA/Tl6PyT96hqGSYy4lk5IhLSd8aDrP2XTI8zMZqqbCbFBjQR3Q9yOOWT55TYA4ok505TQCibrQSIwM98M0kqJgaxISR4lknh9x6CQEiUBoJlF14Qhtpmr4WgokBdmyUEIXs76CtfwSOV0DRUVubJPcep+gMAO9BoPsDFbioigqXSmNJisEcUIiAZkRzMc/QtI+ZGd5G3nmBP7JZzle0LEViezZs5SfukTiOGSOH6P47LMce2yWIbA/iNBVCafmoGc0EDG9nT7pkkV3o41IoHFtlfxshW7fw7ISnONPoQdDJECTZGIhWGu5KDLYqkpk5lEJiZKjbqMuJ0hxgCICpDhA9noMhMHk+BgzpSy3bt7EHbSpewk7XY94/Rr+9gMmpmfAzpEzVPb7RyNVXuChqzL//ndfxWvuk/iD/1xp/v/Q2tf/JUIIfvqnf5rPf/7zfOITn/hT8QN/nvX9kIs/O/1XYX4BXv7Wl9i+c4N/9+v/ASQZUZ1Bm5wHAVroEW7dx1lfAUXBlmK8g10SzyX2AnrrTfSUztRYCmKJJEiY+8FnSY+V8RpDYmTGJ3NUsjqVqRKP/sOfpn3tDnf/5z/ArQ/o376JOwgwxydIFVMopkJttcHvffE2oeMRH25SjHuMKS6WLtENZYSskFYEbTlNLmxREH3UcIirpnEmzpDWZSxNJm3qJLKGqkjcrQ9JqTIr9QGv3jngg/UGXTfBu32Fzs2b1FsdigR8aRuSygT5vfuoEhgkqHEEEqCopIVHRo5IqaBKCq7Q0SSBaqUpG5BNBpgSdCLBZpxmu3yKg5ETZOMBRm2DhbECsWrhBCG+ZkN9h0DPEqSqWCcWsBIP74NX6RsVPCHwCtPcW3wGN1HxlBRm0KXrhSTOAGVQ52rdR8uPMJfTublRwxm41CMZ7zt/gChXiYcdVK/HqJ4wyE8ypQdEgc/5qomJ4FhGJrbyaKHD0C6ArjNt+JxemuBvLSYwWuDT5yeoyA6KN+CJ/fcx4yHFtMX4sXlu77mM4rD3nbe5/Pq7lDMmj42onJoq4Q/aHA5jdvoJPTfi9EKFuWKa6/tD+n7MqYVRLk3mSRkqz58bQ9MUNjouo7bCZtthq+NiKkfc3pWmwwuLJZqkSMcDLFVhJmcyZsoYikxKlxnNGpwfy1C0dXRVJomPNrENWebt+wfMZyR2+yGmrjCdNUnLAQeDgFhAfRiz2nLpBjGaDL0gppo26LkRAz/g3e0uu40hmd4BOUPhhXSbEbfO1tYemibRHPpIsgQyPDpqcjwvo0qC5e0uNVcijhN6fY+5bIKVzlCrd/j9msH7mwN+/RvLnH3uKX7ik0/QPvkEuWyG84slPvv0Aoaucn2vy2NzRRRJYiRtkNFVVFlmOm/S8SJG0jpfv9/gzEia1sBn9XBAyZBJhMDSFFabwdHGvCJhPv4UF+bLnCxrbAxlxnImC4/N8QPjCv1Y4+//2HkmBztM2BI/cq7Kw67E03NZDFkhnTY4NZ3H9wI+/fQiV8lRb/UQyNyVRkgpMnuNPms3VzmeTdjfPEAKPXpCo9/vcfnXv8rr11Zpe4IfefECE4+ew7UyxCKm9+3X6La62GGPjNtMm1kAAQAASURBVKWhBANGlIDHJ1OcqKSRIxdTkxmmS7z5736PsuZytXl0lLRDho+dHeMrVw6YVhxuHwy4UxsgSRLHJnM8O19ib7/NXEbmycUqSIJAyOzUOixOlZBNk09cnEZ1u5yumpRNibylc9BzGcloHKukiIWg6wU4UYKiwLdu7NBxPCYyOkgSO8MQGYiFIK1LfPpYnksTWR4ZSfPMdJrZrM4gSLi9dsjZsQwnqmkmsjoZy+B0UWXElqkwpLj8JlktIdXeZNKWSA0OqA52CKKEWiAo2gZefhpZklBFyCASLMltQkllxJIZCxvMGQLTbzLaWSEbdJkTDUynhXm4BiKCwD0yqE4XJJl2onIwDOkHMT0/JhGCM0qLwmATa/066vpNks17RLubhJKCMHPIkUeoqJCuYgUdFLePmisgS0cGZfjmV0ncAWbiErcOEKZNLGuEdom0HCGLkGw8wFGyeFYOylMknTpr9+tIz30SMeyTURLMYQ15bBZ9dJLshYuYo1PEqSITT57iQ5cmGDUVElWmtdGht90l7LuUjpWJnQDN1gDB4f0DFFXGa7mUl6ZQQw/daVC0VXRNxglilg97OH5CgIIWuTjCoOuFZKI+vmSQqBpEPorXRUYiTkCOfRbVHoW0zfsP99jtOhwvWdysB0hmGqM4hqpAydI4VU3RdgOEZvy/2fvvKN2ytLwT/O3jzedNeB83rr83M296U1lZlirKUVVAgQyU1CAaLQSDpJagp3sQ6maQWiPUmhmkGYRGNAIBggKSspRL7901eX3c8O6Lz9vjz54/AmmNuiVBL6lQM5W/tfZ/cWLtMN95n7PP+z4PN/f7vP36FQCCTuOPrc//r//+B9E0jd/8zd/ktdde4x/9o3/0rRMD/wcmTZP/LOsd/ni+bcRvuVzm1/7FP+O3/ue/y5VWQFcvId0S8c03ufhP/inJ/gbOyXuwZIDa3sUuFzBck6Dnk3gB3b0Bu9t9trse7dUa9ddvUji9QOmBczzwwTMoUULOVBl98EOIThfxr56k6hgYhsr2S5vc+fWvcue562y9fcj+VpdLa23SMOGFz79N99YmpDFZ26Jo6aAIRBLT9FJ0GRGrJkavhkgTRolgM87iJym6gIKpkkkGJH90QpMAcZqyNJal5oFbyLI3dR6xdo3MzUu8x7vOCbXDamgSn3+MbHcL/B7qqI2eJqhpiBL7CFIsRSIBRRFIQJNHxU/qJiKNMEyDybxDxdEZL1jsTp3GO/cQ2pWXMZrrmEGfRqQR2TkG/R6EAfVAEGerGDNL2KbGKEgx+3XePZbySMHH3L5K9+mvsr96mxt7PXy7QG0UcaOT4MUKUSpYvXgN2zShOovqD7kmxkEo+C98nbdakGbHSIB+lPIPvrLGYWIwW7TZ63uURntsbu0gXvsmf/G0w+TUJLPLM+wXl9AO19lqJNx1eoHRtRuEQYDhj5g6dYJmlBJNL3BmuIWbcUlyY3zpyy9wbPc6X764zzl7wJgJu82AjCY4XnHZ7/vsNwacmnApOSbbrRGnpvOst30uH3r0QrANFS9OOByEJMDrBz1MXWE/ttGIsTWVRKg0BwEpgqm8xYmSwZijU3Z0dE1htmBzajrPMBUM5NGgWXsUkgpBPxJ0RwGmItjqBlRdg4NByHbf583NNhlT45HjFWSUEqQK602PXTXP1s4epbiLu3uT8hf+JVXvkJdXmxiqgqXEaN06cbeJrcD5xRLvOTWG5TiEQcBOJ6VnFhmV5zkW7TGMU/75167w8OkxPv3YcX7r+ogJK8LQVSZzDtWMxamxHO9aLDHmaExkDExdwTVUSoaGq6uUTUmz79HyI5aqLr0wRVEUdCSkKefLKqamUuuGXAoy3FdVKfZ2mc5pfKLY4fODCk7UZ6niMG4nsHgaJ5OjMxywq1aYLeZ57k4Dw1A43K/xzEYPM465a9whn7Xo1ffJSp9jZYdWN2DmQ08we+I47753lucOQnRS9Fe+yrFPfYC/+j2PcY/WZqMZ8t4f/i7ObV7i4/fNYn/PZ7iz22LizBk+qNb4x7/+FBN5g1u1LgIJQtDzY6qV0tHpnlGgVm+SKDo39wfcPuzxxmuX+bkv3OGJu+ZZrQ2ZK1hs1kfMFkweP1Gh46estkYkoU8nSDm5WOVgEFFxdZQoYGTliPyQYZxyOPB5a6//R0JHkjM08qZGkqTstn3+l995hvHXvoQQR8/FSSIZRikSOOsGFPWUk27EsbTOuWzIvZMuMxnBh06P8WhFcrxkMu5oJL0mIz/kpJNgjjqIxgG12zeQToHc4TWU8hSWCJg2Y5azKo6aYKiCOFsG1aAbK7iGjqFCJWkTHexy2OuR3rlIbJXZ9UGPfZL9NVpOlWsjmztxjlQmRHtr+FHCGwcjyrbKGzs9FCHQFcHIKrJrTpJMLdO2qjQrxxhJlYHiEpg5zPodRCJRBjVEYwfK04jJ4zh+A6W5TdzuoMgY7/qbSN/DSgJo7aGmMcbeNRw1BcvF10wGsUZXyaBksqwsVMjUbhB88deR/gAResTuGLI6j7Zw+ii2fvMyRqnI9OkpXEvlte0+Vt46aotZmkEzBF4vJjeXJ+hHZKtZurf36LU8Lv/+q9gHtxFWhooWMZPV8eKUvGmiqGCoIFQVVVWZyBqosc8wASuNSFUDoUAqBMWwjoJKtL1OvXgMzbRJ65t4B5v0E0Fx7hi7gwRDKCDAVAVfv93gG2td3nrtKijan6g2j5Vz/OVPvZt6vc6P//iP84u/+IuUSqVvkRJ4h3c44ttG/AJ893d/N9PLp/nr/81P48WCJInZ/K3f4qV/8XnMU3cTV+eQRgYK42iZHM78ImbOork3IPVCRAqr7ZA0jDl84w7b37hEdmaCE598GF0XZD/2GObSHINf+i2KGkw6GsUpl8qxEvW9Ltefu8HFt3aoN0fsjSR3z2fJKrD1/C2UwzWUoIeGQlVLUZEcejGFzga63z0yv44jwhiCNMWPYPhHJzRGEtLxfTRFMohiqlrEE5WITywZnGhf5XjFQHvfR5l5+BEqS4t8cFrlnNrBlAHaqIfZq5EKBSESkCmK0IiEdmThZkgcJUaRCT4GiWaRRhFR6JHRBLmgTUH4TBxe5oGpDOWH30VheQEjm4fRgH1fY1uvYtRus7mxhfHqN0hQUcZn6YUSVJWG4nCQKBTrN+m//Dy1yRMsjufJ3HqNoDjDo6WEzPCQjJ0hn7E5KE+jBj1K993D4Cuf4047oNfrY506S29rExH56E4GO/X57nP5o+AQoRDeusnzgxyGSLg6dhcbuy1GYYzI5hgrOPRy84zZKbtXb9MsTaP1ayyoPTKK5PF8wNSZFaoLC2RFhKspOJUyF9sqp6az2KbN7XaIZWr0ukNm8gYfW8pypmrw+nafendEvTmiMQw4aHvcbniMZ9SjhwpFoT6KWCzYjLsWXS9md5Sy3/MJ05TdfsR6e8Rk3kBTFYZ/ZO+UCrB0hZVKhrsnXDyh8ttv7qEImMrapMDeIOHiThdXV6hmdGIpWWuO8KMUTVe4bybH+akc7zkzwcnxLD6CV2ohtUjnwJlk6JSRYYK4+iLGsIEShby50+fp1S6+YpHt7fPglI2q6sxpPhUr5dx8mWg0pDUY8fd/43ksEj79wbt5ZT/lN296TDmS71wyaY9SPnetgRcnzJccBFC1oGhpGIqCq6s4+CiA8eZT3Lh6i8mswZ32iBPVDAejkJ4XkpE+26OEkqFwdz7hCzc7GLpG/1//Fr91qYVrKqzMlhkZWVKZsHfQQoZDmorDsfZNyuMVRin0vYSvv3qHsuPwmeMWb2w1GUu6jJkxX9iSlHo19KxFx86iJjELRZuCqTPn1Qjah8jRAGd2EW9qicmgztx0gds7Da7rVb6yGfP1qwecX5okNmxyzT1EJsfqrVV2eymxhEEskKTkHIP5v/h9fGNfYkmfemKyPOawPJahWslSmarw8HyOD52bYH8QIjWFb95qsFjN4cWSlbzCUM2gCRjGsFSyOT9uc/+xEu1E5Wc/9zb7HQ9dgGMIDgcBKoJl3WOtNeLly5sM/Zi7HziLvbREz0/RVcmxosGposq0o2CoKgqCTNilWMiTVSVjasSnxkOO5yRFRuQ1iUCSEwHzVkBhuEOi2+QffICrxhyJWyZxCkgJSXYCoSiU4xbJqIsnNQLVZpAqjJkCZf82AugqDl9WTtIMUtLSDMHGbb62K4hiSRAJClGbGBW/38JOPAbXb7LdC9EUBYk48mjXjlLLBui4+QJ+boKmM06vMM8z2jGseAiRh5Ap6u2Xibdv4197g+bXvgpJgti8cRSQEfhY0zOIUYdo6QJa/xDVsImkgrSzKJqOkBJXFUQImkGKunwPxz7+AOHVN+jcWCO5+jzx7h3UxgbYWaSbwzMLaE6G0doGrfUGZsGmbKukUYw74YBiEHsRk/fOE41ihgcdVF0l8mKu1n3W3tom8QOiNCUUOllVRUUwkTOwdUGOAGSClkYUDY1ENcglQ0Q4pCccUiuPHnukcYQvBfrJ+zlezVKaO046aNM+3KdfXDxqh1LAVsCPUq4c9Lm20eFf/O6L7Fy/RRr88WEWAHe++D8B8BM/8RO8613v4tOf/vS3ovz/meCdgbc/Pb6txK8Qgme/+Nu88ew3+K0nP4+nucx+9EN8+u/8Ffzx40c9XjJFaAbOg++j+t73sPS9H2D2wUXSVKIJcHSBP4wYBTFj964wuHmD3u115j94N71H30319lvEgxG2Y1CazpObGQdDw7R0Gl5CxVIYq7h8+H2LLD2whOVoaCKl8cUvQH0LRYZHfruqwkpOOXpCf/s5pJUDw8L1D3EMFUsTlG2d5jCkJjMEvsdUxkSkKbsD6A6G5Aa7iF4Tdec6k+EhKgHd/AxqeRInnyezcwWKVaSioDa3oN/+tx8eHYlGhC4TNL+HJiNsTRCqFqgqpq7jEKANDlGjEXphDFtNKFsCuzqOpigoxTFmW9f5ylfeotYcsqmUMCyNOJG8UFO5eOBx2O5Su3iV/PplqM7gLswxrM7glqukp+4j2t/A0QR56dErTDE2PolZq/HG9U1qicmNE+/lQbnFdXeRljPB4qVv0rlzC/whjqHwSDXllNpDI6ZVmOKegqTjCbZ3G2wHFiMzj92tMaf0kdkyyvkHyNx3HyfPnaRkSIq7m9zpBmSKRcqOiq14xKMeTtjhex8/xbHxDDlVUmsPWMzA55+9iicUdvoximlyomqzUR+wdnuH8ZJBqx9iCsEoithoB+y2PQqmgq0pjJKUMEl4fbdH20/YHUnWOgE7XQ9VURCpIEhSdvohW92AKJE8MFtAF4LGKDoaSqq4+OnRGwGB5IWtLmEsWe/4CClojo5OXB1dZTpnESaw3w+ZzNsYmsL5uQIRgmLGZeCOs5uZJvr495Ocepj3LxfIqCknixZXfRvFMLg5VGh4KVZnF7ezQQ2HdizIJj79UPIdD57izFyeMxdO8eGzY5RslQ89vMJzoxItP+I9xyv4UYKXpAhFkMRHLRlJmpAxNKIUHp008RbvAjdPqhwl0L1rxmar5bHe6GG4WcbTIULGCJFScjSe2xrRz04QhDGbFLAcF8O0WcwqVJp7KK0WuUIWeh2S8SluHQ544/YhvUShN+hzbaNOjoiNoWDU6pILe2zr4xwraHQ9yec3Q/qjEbXUZEyJuNaMqd/zPtxSkQv5hJZdpix8fL0IKHT9mAk95MKpKV6+1eRlL48qNXb3u1zc7pE1VNz2Fif0Ad2DXUSUsntQBylZXl4kb5o0+jE/9Rffw6fum2GzMUBX4fJej7yp8VtfeZ3VTshm20NFcqcTMgxTZosWlzfbeEFIS8nwxRsthmj8wcV9DCE4VrKYLdrYpkLwa/+M3/jtp7GSkJMVg+97dJlw/hxjrspZ2+N0ss9U2qESd5C6hVRUYreENmoh0ghpOmR1jqxpNB2nu4MedPGyU9jBAOl7jJ77CuHmKiu1a1jDfUiTo2Nl3SZu1en0A5S925ipjx0OyY8OKPo1/M07ZERMqlocH7Mp5PIExRnMex9habpE0q4jZ0/SVxxOWT5s3SE93MEeKzGZNThZtZmwJFlb51bL52u3muyOwNA0TE3BNnVQNE6Udd5qp3TVHGFmHFnfpvPGawy392i+8CL06iRxwGh9ncFeB+3sQ5hnH0ZXFVLNRPRqRKpFPz+LEQ5QwgFCQFY7ujdGdonsuXOkgU/U84j3NkAmqG6WNIlINQf19iukSUJmbpy5R0+TL9rcf/cURs5ESLj5O88iE0nz2h79nR5W3sLI2xxu9ZhwdfrDkBuf+xoyDhkm0ApjirYKCNpegtXdIU1STBkhhcQz8pheg6Z0sWTESM8SmS53ZBEtjYknlymaOmkUgDxqw6lkTIZhwlLRZHsY0PYibu73Oah1WF3dI+o1IA3+2Jr8O//oxzBNg89//vN8+ctf5hd/8RcRQvyx1/3/K++I3z89vq3EL8DU1BT/48/+Hf7eT/911hp9Bicfxfn499FOTSy/h+r3EMMWZPKoE/Pk77mbxY8/xsInHqE0XWA5Y5JECU7BIjedx56sQBhSO3UP6uYWwxcuoiJJdIUzf/n9zH3ycTRVYXw+x/lTJcp5m7GpLCfun2fm8bs59bELzLznbjQRMXj+KYzrz6IGPWQqyasJeH30xbNEaQqDFtlhjbyhIATYKkybCYoKxzIqRdfksZzPA4tlamaFfizg+D0o08ewlJjM4Q36hzXu+BqenkUZnydRTaSdRRo26sEdIkVF8dogBDKJiFEJVIdUMYhSCKUCmgm6iRIHkJ9EHbUQqkqiGgRxSktkwDBQDIusobC8c4VebpIl75DGwr10djbxhcr1nTolEVN++BHmz5wgyVTJZzXusQPCFCacFDerU/MlkeHQ7I5wcjlK0/PkhM/zl9bYzcxQGR4yk7VIX/kyl5cfZO9XfpWX90eMpE5o5UhWr7DRDjjWusZXb3VRvC767DLVySoZ20EtVghRyFx5mkQYzNkxrTcvcqkNr+cXsWWKFvRRSYjnTiDtIm8eJlT3r2KrERefv4IqIw7aPpNjOdZ3WghSHENlpx+xud8lUyxw404TkpSeF9IcxlQdHV1XeGuvj6mmLOdU7jQ8ToxlACjaOm0/ZSJnU3UNspaKHxwNOhm6SsHSmSmYtPyA13Z6LFdc7p0vQQpNPyKvpMwULN59vIIXp/SCiL4fU3UNbFWgqwqj6Ehk5nTB9Vqfs2MZlsoOd03nKVo62WKZ/OICkVuByhT5Qp7qWJkHjlfY7IZ85Wab3WFMd3uLYSQYBhJVSPbcKWbVEf+n77qH+e4GZysWnoTvK/eYLDjcNV9kzNWIEnB1QVZXcDTBri+IEkl9FBMkKTd6KV9b7/FGYPPpR0+SMzU2mx5OMiJOU05E+7ixD8MBorFH5drXefXaHk9M6Ux98qM8sFzickejnegIJ0NqZMifPoEaBJyo2uxNnkPtHFLr+1RLDuNZiw9nuvzyq4cMrTzXGyFRvsqZosb4RIGMSMlffJX+1j4vbfa5dmePXCVLVzWZmJ5Cpgkj1Sbr6ty+doeTc0V2zTwPr1RYmCzw4mqbiZLL9BOP8NnvfpSZc+eYncxRNDX0NKZ+UKecc/HnVzieSVAUhZEwORj4LJRsLNtgp+3RDBV+47k1+l7EwI95911LBEmMIVLiTg037rPV93nmVp29XshX39xmp9Un8iIeODWOblkomsqEqzGfs7EVhYtn34tGwn/93kUeH1d4bNJEUwXTpiSPj5H6GEEfI+yjNHdR92+hhkOkPyAwi+jxiMQtoo0OCY3s0ec/6NHU8yiJT1Jdxp4/hpKG5K6/QLp5Az0YseNp+Aeb7P/u73L7G6/hYZHp7ZA8+/uYG5dIV98iHfbZOGhSiRqcySdkDY1MdxNdhRUrpF9aoLl3yHqSZ99LeV2f5Xbk4N31PlZMH0ukjLk6i8aIRRvunsqSMVTSOMGLEmxNIUpTlgsmp0oGqq4jTBtKk9gnzmPOLUEmT1ieoTd1FnNqmsq9Z/Dyc8TlOfTEA90kadWQoxZCSqRqkqgWSuRjhX1cDRSZIqqL6IUcuZky5tgk2vgckV2E2uZRn7Jug6rSu7lB8dgECx99kOJ8jtRL6O0OGO12yM2V2dtus/zJh2hutPGaQ0ScMl91mM+bFFyJfrCGgmAYQ5oeeWR7cXpk69k9QGoGLS/GjgckVokRGqYMMRQIFJtBojCUOs1Iw/OG7K1epTqziFsoM9y5Q9E2EKnkuY02X75WQygKSRARhTFHuXz/ccaKWT7+xAW63S4/+qM/yi/8wi8wOTn5rRUA7/AOf8S3nfgF+Jt/82+Qc3L8rb/902yPUlqRRtNLUZMRqaYhNRPSFJnGyOw4z+Qf5KUzH+H097+L8TGX5Y9c4Pinn0DNFpFhiLd8nEF+jMzXv8pzz95GqoIzn7qHzLEl1HBAtpqhsFhm6ewkU6eqoOu0bh0wWtvAdDSc+RmCnkfr+hqjV59B3bxMsP42ShyQBB6iOkPXnoD2AUHlGGWGZJQEfdSh6QuyaYiZDBmPWzQ2DylYJicKOt3KSZrSJRz1kIUJZK5KoXkHsX6LG60AGfnE7Q6RnsHLTiDypSPz89BjFMbEegYvVRCqDkKgkIJMQWhoQiCSCCFjCIYgFFShkCpHPZoDNcfIqRAt3MX9n/1u7ilDtbdJ1hS8nRZ5bCzldLiLbhu8WY/QlRjV7xKtPIQ9PCS8/hp67FMu5KnKAVahwn0Fn1z9OunSCaZmZpgwfJbzKqPl++hFKRlT4UMTIZ3v+kvMt9a4stNiWK8jgxH7tTqF8Qpq84ClkknFVZgqWuzpWQKp8fKWT2u/TW8wxFu9zpozxf32kPusNiVb5R9fDAlbdRKhotXWOOyHjFKDrVqXs0/cz8CLsB2XH32wiumPeOZanZfW21y6WUMzNaYnctQOWlxfPeClq3vkDJW3NlrstD1MQ2O35dMYxbxn3mG9PaLsaky7Ko4OJVvj7GSOUSTZ6fo0hhHdUcBKxWEUQm0QUcro5CwNISTVrM7h0GfXlzimyrvnCiwXHXKWzn0zeYq2BkLBNTQqtoauHlmHJamk6UVstzy+cu2Atw76+HFCLAXtWKEXpHixZBinCFKWs4J3T2uUbZ1RdZ4oM8Z03iJOBUVLI+P3UEMPr7pI2dWZ0BN+9VKNJPBZsBIQginVZ8qIiKOQjbaHpSmstX2yaoIXxcyYCWOpT3sUkSgKFUtlKm/zTF2wmNP53S2Bd+cSm2/f5kYjovnqZeaKOhMZDRSF6aDGQtEkb2r0o5QBOnuiQHttg2U6fO6mx/3hOmVHo9kesVK2MO9cwULyuH5Iz/eYN2PumnNp9HyC3TWyy1N84OFlsrHP5p0NtuIsD05mKDoWrtfA1AR/cLGGP4joJ7A9DLj0xtvMLk5ybGGaiVKWmYVx8gWXB87M84GVEgfDiE1jnLdevckbfYvFkkVFjzFNk8Yo5MbBgK9fP8TzQ3a6AZfWmuhxzOpOh3LW4OSxiaNTOlS69hjtWKc9jOh6CTMVh91Y5+kbTdY2m6xM5Jgbz1B1NLZ7EdutHl+9uE+vNMt/8199J6emK2REhL5/EyUNORyFyEEb4eSJJCSKftSGtXMDefkZlCRByBg18kiMDEoYou/dINZtBJKx/g4IFU1EWJUiretrMBzBqIciI4pxj/jaG5h5B3duEnH1RaLLL2E4BtHeGlF9H3dultPhBtqgCeuXKLZv0//i7yIvP0dWCcB2UTWFg1aHy4cxY4UsM1aKpqiIV75EIeqSDocMhU03hoeLKYvBFjJN6AYR/TDGVAQjaZAxTZAQRiEH33wF5eTdaA88QfWzP0JfuLzpZ1DHpnHmF+ikGomUYLiEQkEZm0W//iKZzTdR/B6BYiG8LkLTqXkpiaKSlibQzj7K+BMPoboZZGECz84h/D4iTVA0ATOnscYrBO0ulQfvQc84uONZGgcDkihFNRX2WiFBu41byWAVTEozWXRFUCg7RJ023TfewtUVLFUhkgJDUZjLGQydKi2zhIgC8lqCVHWEqjEeNZAS1DgAeSSWD/pDer0uN669jZ4rURyfYnz+GCIcIkcdrtU9Xr/TZONwyOpmg26rTzjo/onq8M0v/n0A/vbf/tucOnWKz372s9/Cqv9nhCRB/icukndOfv8kfFuKXyEEzzzzVZ7/0u/yzFPPUOsHJGlKZBeJ7BJ+aY44N0Fs56ll5/mr//iL/NQ/ewr3kfdy+jMPI8OA/OIYSiaL1Ay2jAlm+9tUlirUAjB1BSPjImwXqQiEqpA/MU/+xCzBMEAmMd29HnuvrtG8uslwa5c0jonDgGgwRHabBM9/hThNUcvTiGGLnBqTTh7DibtopBR6G6i7N0ginywew68+if7ik0Rj44jmHgIoaxE7gcFeZp4GLp3sLOnxB4gP9nG3r/FsO0P/5g16d27h9Xr4UicOA5L8BPnhPko0QiHFDHtEUqDKFJsYIcOjV8xJhOzUkHEKuk4MFPAwlYQgVRhJHUuBqjJEtR3yJ1coJ13umimQqBb3npgjp0ouTJgoAgKniuIWEOVJxmemScoz0Nwmcsu4IsKORkR7eywm+2Sn5sjncnQPtmgKl+XODV5deD+Vgkvh2DLGU3/A3vYB3LqCLFd4rBQSVmY5rg0IrDyz/i7ewQ439vv8xlsHaIerXDvxKPoffo6v5u/j/lNV7GKByZXj5PJZzs2XiXNj/IPPvUm9M+Ch1lUyzQZfHBR514zOjaHOhULAZDrg3FyB5ZvPE4w8FMtASRJylsoHHjvB8kyRtHPImxdvc3GjReD5HCtYvHinxQvbfSZdhbMlg66f0A6hZOlYuiBjakznDBbKDo6moCkqSZpiqoKiqbFYdNjt+HT9iBU75sKYzUEvYqsb0E8k+4OAQRiTchSkUHFUlgoW+4OINE2puCabbZ/L+z3e3u/x3LUan3+7xqvbHfb6PsMooe1FaKrgy69tctqNyNsGmerRSU3HLLCWOCjKUahBy0sIqjO8NsygZFyCVEOqBms9aI9STEIMTWU2aeLHMGELdnoBgyClNgwxNZ1OIFkbCmbHMuw1RxzLGTT9hE+cLqGoCr5UeXQxw21zDnHyFDKTQ3zwk/y3751nMBjwpe2Af7UaM+sIDv2E13cHHHoJVTHkmerdjOwSf/6EoLXyMO0g4eRCmdmCyt96scO5qmB7IHh0XOd33+5gdmsMWk2648e469w8866g0xtRsU1evryBToIyaJINuqhxxHseWOGB994HusmFE7P0pUmlXMQpujwwaYNQWMjqLLoJ3SA5Clrw++QvPc+tzTp3BhKVhL5VRqSS3c6I9VqP33lpi6dfu82Di0U+eWGMx46VqfUCtppDpmSfyYLNTEaj6Uk8P+GeuRyaCjudgERTSWTKfNHi6sGANEn5rVd2+PqzFxkrOTywVODR+SKqqlJPdCzpI5KYS/WQvUDHu/U2A6cKkU8UhKRmAdVyEfkyRhri22XiFKLiNIpIUdKEpl7C3b9K0jpACX3ksMNv/9+/RH3jgNXyOZJum8zma4Q3r5C1EyZ7txne3iANPRh00LI5zBMXELaLk8tDHJLs3sF74xkOXrzCaPUWma2LjA12KCyvcN6JuFCIOVkUNAcBe8OIpDJD3G8hevtkwh6potIJQuz6Brndt5kyE9ZbI1abQ0ZRys4oxU8ksWISf+T7iEuzhNlJ7MV5LDVlLm/h5aZIZk+RifoMVYdBohJ4MalUiLdWwXJRG5uoQqKkIb7i0BilDLQMqAaaYaLmSkhFAaFjpxHJwr0krV2k7iJsh9wjT5B54F2o+Qqlu05iZE0iBIoi8Ho+J5eLbL28Sn6miNf1SVNJq+0z6gVU7j1L79Yd/ERi60dtBJ0gRhEKmqaiZUp4ikVeTRFxhDpqYiUeahriS51DL0VTBbdaMQcbaxiagjm+yCCWBKhY5Un6tW2u1wbcXmsReCH7naP/C9X446OI/+X/9UdwbYunn36aX/u1X+OXfumXvq3bHf4NUv5naHuQ74jfPwnfluIXYGlpif/2p36Kf/Tf/w32W11cQ2WIQT0xCW+/zf4zzxB0+9RCWFpZYuXMCezxCSqf+C4K7/4AQmiE9QNGbonAdJlRBtSv7+MIOH56jNxHPoOMY1pX7pDEMaOdGnrWxZ0skvoRbjXDznqbp766xtUnXyb2fKycw2CrzWhjnajVxnvqSeLtG8j2AaYiiN0yiZUnQsPQLUZugcX15wivvUYy6BBsbzGphhTWXqYbQz/VURQFqVtseyq3RipNJUty7+NMTZRZHO7RvvcDxLUdrMY6MlNg1O2iNPdg7zZqdx83HoBMGEoFkYakEkJ0IhTo1ABIclVknKDeeQ21e4CSpgyiFEMR6GmMbOyQZiqQHhXEQtxH277JKx0by1AoWxoyW8UMe6gyJdBd9IyDZljE+Sl03UALB6jhAO2eJ+jkl9GDEePZDMmgjdHaRi+PcTw6gGyFYnuHK2c/xPflD7Fmp1HbNbKFAsErz7E6eR7fLdFwZznmJrjDHv1U4e65Il+60cGQHqWpCQ4Sm1ZqIvbXkarKR86UmSm49NwKF6Mq6uIi24tncdUU9/kn+YBVZ7s1orPf4L7FPGenXI7PlPnI/Ut8x8kS9UHMzb0+tmMxs7SAqajs1dr83u89wzBKeOx4hdv7ffY9wSiGiqWT0VXKpiBNIK8LNAGuqVFwDCZzJs+stkCmDOKUrh9Tyej0goSMCmEqcDTojyJe3Ggf/d0Sia0K8qZgytUJU8jrKts9n9owZKXqoCkKYRgzVnHYqg15Y7vHGztdgiShMYrY6/i4WZMxPaLdHxEKDZmkTGRt6r5EVxToHDAuhkwnbY4f3KLTHbDfaKOrgr/9yfP8wTde55ldmM+qjKRK8oXfJxz2KNs6qgKLBYvdYUzBVhFCYaC5hElKxw8xVcGk6vP6lQ1W6wNW13ZZmh1nyk7RB3Ve81wIffrDIWv7A85PF1gfwqRroApJwVRp7e6TKWTI6QlxrkxPGmw1fS6vt7naFzSFSWlqBjl/jEre5YzWJtIs+ugomkFx4xLPbQ5Yrl9jaf0SZrnClfqQg1qDjcRF6DqTXo/zZY3paoZzGclHT+S5utvlO1eKfP3yNlVHo+QfMowM5oI9WgOP6/t9gofexaW9LoWoA0DPKHJ5p8N00eLYZJ7r11e59sYVHpvW2AsVPnw8i6OlPPf6LU7rvaMwA0Uj62iUFI9yxuTl1Sa7hwNa3YDHL8zjhyH3T1rcqHs8/dQrZKZnGSUJ905kKWshpDFFxyEtzWILeHjSxCyOoVUmcYIOSjDCGB4yWr1JPBxCMECM2iijLlZ/B7VTQ0gQ21fR/R5CSOL9HeSgTfvyJaYqDlNnF5iwBb4XEW6souXyqCKh/9JLeFt7JIMhqddH0Q0U10VqBkmzRjrogqahZrLkl8ZI4gQZhsheHbe7xYQjCfPj5KIexc23j0ScrpHmxlEMByP1WcqpDDBpZqeJ1q5QliMe0GpMuSpG6tH0IvZ7AUPVxphdZBgpgERKBUU3mcvoKJkCvltlPdBoByldNUums8HgpafZf+UGaa5MUp5BSwM8u0oCuKZOmAikUCDoo0wuoc6cIlU19GjEfqSQrl1G+D2UVCIyRZTCOLh5nIkqw0afqdkslbMTiDhld7vD/vU6VsFGA+yCTXUmS6sbkgz7ZBcmsTRQkewNAnK6QpCk6DLGVmKEotLHRB0ckMQRyeYNROTjv/5Nnr9V5/Jen7nkEOKQyZUzFG0dRQi8KGYrzaKEHm9duoMQ0OyFRFHM4GALv137j9bexy8c5zMffpDRaMQP/dAP8XM/93MsLi5+K8v9O7zD/4ZvW/EL8DM/838B1eIXfv5/ZL3js92PkVLym5/9W/yrH/95Wk9/k6Kt8SN//gP89F96H8J06E+eJT79KJx6AGtuhY00w1hvD5HE1G/VOFkxMQsORi4DmQJ+vUPnZh095xI2m+gaqJZOOIyQqSQcRXQPB7z5ldu8+cwt9FKO0c4eUgj8WzcYXHqLaOM6oruPFIJIdzGDDlLTcetr6KVxVn/1D8hMT5H/jk9iXHsRo1IlTQWqolKxVcJEopDiqApZHRTDQh+bozA7w7geoJ6+QFBZQFx/ifTtl5HdOqk3xLcqqEhSO4crUgwZARKJgun18Pt9hG4SFBfwbrxBuH2Hnl5AUwQFS0HK9Ojr3aMQkVFpESrTpL0WXnESZ+s2UrXIBy18s4AS9BGDGp6fINfeRg09RGkGLTm6USt2DmEauPgUrj9LW2osLCxwe3uPplYl3VojkZLstecwj5/mem4FW8YEqcmuzLL1wsucP7zG9ZpHJh7SyEzy4EqGx09P4ycpx71dnl56H3cHm1TliK3QYtMcR1VUhFBpBIK/8R3HKekJTzVVKmbCI+OC9Nx97OYX2UosYgmaprOwNMF+16PTGXBsPMtM0WFhIkut53NiocL8yVnazR6afZTclTVVFqsuf3CtxW4/pKRFCJngx9ALU3RFEKWQSoGuqszkLGZLNrdaHl0vpDkI2e0FlG2Npw4S1noxjSDh/ukM9WFI1wtJgShOcROfrUGMqwn2BgEZXSerq5yqOmR0has3GwyChI/ePcn2fo+eF/HqRo9LO13e2G1TLWVppja77QFvbbQ4lpfkLYWVqsNcsM//8s1rZHSFJ/dM0jMX2B3BUriD1t2jEeh8albjX76wger3+fKhhTNVoH9wQD7us9nz6YYJjnbkJmBoCkXX4q6ZPJ0ITCK2b9/msxfGeNekiba9QdypcXG1gVKcoJzP8ytXBnxjRzBqtXls2mTMPXor8Z5ShHn7El8fFbl7zCLb2WWskOP1zS6pkCiqIGeZfOZ7P8ypqSIXnCF9Pc+xiRxV22DroI3+2osMnAqh6VKfPEb1sXdRtuD5m21qeonW7TUsXWP75hq2AXPaiJmlWTqKy4UxjWVjRPZwjcv7A7K2xWSwQ7a9x4WyzqmpHPHUcT501zRRr4VuObS8mDNTeY6VHPbqHYqlMeZOn0bRdbq9EVcaIR/Ra5xcmuGWPkXkh7RDwSu3D5jMaPT9hMNaB8fS+ej5cT50ssq12ogHKxqnJlwEGpaucmo8Q8ePCKXGbqCyGRp4VhGpKUjdRjg5ovIUGDa90iKyNINz9i706tFQmDzcQ7/9IuLW68i1N0FG+DvrOBtvkvQ6RP0OvTdf5KVf/gbn757CLlokzzxJdP114v6A3IX70I/fTebUSZzFKfBHGMUiSbdJsnaV5GAb2Tkk6bbQylOY5XEqd5/EmpxAyY8Rrl5FKjpWb4+JrVfIb18mW6hw3vEgjjDjIUmmxBplGgH0YoVrcZn0/OPQ2KHY2SJHhBaNGHMNgiRlmCpsdUOGKVgiIk4lcZpS8yWp4bAemTy7OWAUSxTDhGwRZ+U4le/8ONh5RnaFWGjomoLb30flqG2oERvEc3cR5qdAM0A5ikjO3HkFkUL36mVobB0lY/o9ZBgidA1F06mcGEOzdfoHPYjBtDWEaaK6JjMfeoC5+2aZWikz3K2Rue9hzDQ4ip9PJd0gxCDBDyNkKhAkxAiinXUSwyXtdxD1TWTrkF/56nWCziH95iGnzpwjECaWqhCmktog4kbD55Zn8/BkytJCEdsQNLe28Dv7kEb/wZpr6Cpf/+c/9Uf192cYGxvjx37sx/40yv2fCd4JufjT49ta/Kqqygtf/X3e/NJv8uLLL7PRCY+mWTWLZq2NEXdwFIlrG5yrGgSGwyDVeb4h2bDmGI0t0yuMM6d7HLx8iySGlTMTVM/Ocvivf5P+tRtMf8+nmf7QQ0StLqoicMYLuGMuL7+8zcKZCk985gHKRZsbB0NquwP6a/sMDzsM9lq4xxawx8dBN1CGTZQ4RktDhIBE1UmW7iHQc9hzk9z8ta+i2RbuWJ508ybFwQ6WklDSYkxVMIggYx4NFY2ZKQehoJ2ZwBYx9t4NLMMguHODydlJkmwJKSXBoIfgqM1XTwIiPQNJjBW0EId3aFeOEzgFwrde4sveNIfPv86drV1afkLUbhB5A4beCG9sBeH1UGRKqtsIy0UvljiTCVjd7+HbFZJEkowGxFaer1/eIG0dIGIfRZEYfgcRecjyFEmnRsFr0O4GWEGPMcXHMC02N1YJ5ldIUkHxvvs5t/ESr26NiHNjeKMAsXmV6U9/Brm+yjHb53jFoMqQcqnCBbPHrj3FR8+PsxK3MFQwoy4LdoqjwFPbQ3Y9QZqEzFghp+dKzF16liRX5cYALm/1yBYy3FMSBONzeKtXuBrZ1Ot9plSP9dZRy8H52SKNYcRk2Wan4fGx959l5dgMB12fZ++0ydkqGVvhN17a4tBLCZOUJE2Jk5RhJMmaOmOORtVWGYUJZydyVB2DYZSw3wtodAPKrs5K2cTRFUr2Ud9rwTE4HMX0vYiDUcjrjYSprIEXJ7yw3mQ8a/DYQpHaMCIB5mZyvOv4GAjIuQbvXykRpymWoZIkEkUodBSXp3cCrl25QZxIDCEp6eBGPu979BxPbw4Zc1WsYp5hvcYrXYv/+Q8u8ivPrrLnaTxwaopabPD5N2vYD72HL3Ty2O19ThtDen6MpQkkR69B01RyrJpBVRRUKbioTNE08hRvvoI4ex9JnPCuYJVf/cYVHpqx+OSSwvlcyCMnx9mTDhNmSlYX6JaL0m9wYa7ERH2Dp3ZStkcCx9ZRUDg1mSP0fRbnqly5sc7NRogb9VAm5zh44wp/eL2NOVYmu3iMhxaKPLKQx1ha4m57wHvnbdwbb3FRneC3Lx5QfeRBoq07yCii2F4jK0LunckibJdxv05JB+/Sa+xt7dO+vcpbO0MWsyrG5DSWZRH7Hm6xjCIEJ8dcZsouVtbhgyezfObdJ3n6RoNx1+LG/og5JaRazfLNrYAJK6E+iKjXh/h2jnsnHaJuh0fPjvPR43lUXaVqwm+velzc6XLX2WXmx7PcN1fgc1ebbPuCtU5ElEJbWlzrqlw8HHGnG7EaZuna4wzVLMPiEmLuJPH8XYym78afOQUyQTh5hJVBxjHP/9KXCf2AIDsJcUR0WCdjqeRmq+iOi6YJom4Pe7yKli9gVMepPPwwY489TPbUGdTSBEqmgJxaJplehkweMbXEaOokcaaIed8TWGceRFQnSE48SJobQ0YhWjggsmyMjEJi5tGIEZaLqSp0IskwllxvBpTyNigab/dNPKtAtbsG3oA5O8U2VBpDn1SAgqSvuJijFqMwZRDEGDKhaKpcmMox7hqoArbUMcJz78N67IPEioXV28dMQ0Q0Qu3XyRpQMhViCbGRIzGcoweLNAKvT7GYp3/rBna1CkIiZIysLuJdfQ1/Z5PZDz/I1BP3015tIMOUudkcaSy59c0bGHkLZ3KC7PFFSrMZEj9C2BnWehJTEWiK4GAQUZA+vurQlgaNUUJGhgR3rmF2d9FPXCDJVUge/xTf8+AkucEeyyfPkpguCrDV87E1hTuNIWenchxqRaadhIyWUD/oEoz+w6L333D9yb8HwGuvvcYv/uIv8su//MuoqvqtK/J/xnjH7eFPj29r8Qtw+vRp/tpf+2v80//zT9Du9RhGKR/9Jz/HD/wPP0T5Q58gIyJOVRxyBEihsT+KafspERobnkqlUKB47BT1Owe0gPHTU1QeexhNjui/+QbCspn6ro8z/vh9ZO+5QHZlmc52B8NRmXvPOU79yA9y5vsf51N/9d285+Nn6Ox2ePsbd/D7Pr3b66RJhEAg+x207j5KGoHh0tdzDJ0xgtkVpu9bYeYDD5CsXUHoFno+h94/wOlt46RDXF2j4ugYqkojANMwqCZt0iTispdBr45z8Ku/gnviNOqgTlBdJtpZw9EFqRCIYEAqNNI0xjq4jtbYIi5Ok2dAIkzidovi3Dz2x76fyfoqRnsH8+rz2J1dkltXCKWAURuDEKkaqEGfyI/JFjTWLt7A21vHlR7Ut1ERnDmxgDJ3Al8YJIqCv7NB3Gshmtv42QkCb8ThmSfYdGaxWztkb63SaTbIODpvhkXIFBk7dZw/98AUtikw7nmIQrmEv3SesekM07tvoXb2MdwcaueAVbXCqdZ1gsos0zNlzIyNkS3j3b5B3tYxgNowZHy0j+95PLfaouJ1mEqanFqexk0Txq+8wp2ewKjdZnplEW+nzkg3CTSDihYwP6yxULKZHXOwFUG351FrhywuTXPn+jqKIgiTI7/Mvh9zue4x9Pwjs3lHQ1eOPGCnMiYxEKWSOEkZRDE9L6aSMdA1hVc3u4zbAkUIJmydyYzJYsnB1AU7XZ8wTik4OllD45m1FgfdgOmcwUEvwNI1ru52eeTkGMfHHHK2zgMrZZasiOUxl/vnC4y7JvdWBDKFFy9v8dHHz/H0XoiKZOgFKGMTrG4ecmu3xU4n4NJWmxPjFq9dPeDXvvgGRj7H82Ge1nDA+HCfpeksa6FJM5D45Wk6foyUKbXRUX9xydLoBjEdP0KREk0k3GxGSFXDPHEKu1CgknV4vnA3H3/kBBvdgK9vS5YHazx6bpa5rIrZ2uIr1+qI3Rtkkj4TdoIsFvj6jRZv7g1wDJULk1nuWcjT6XTZ7kQ8dmYayzbJBG1cBbYO+nzgrlk6Jx9A37vFlOjT1HNYiqRjFFBsh0LB5v1WC622ydXaCKs6wbNXdrg0NLlnoQhhgB6PmLQMihmH/tJ5Xh86vOCXMWs7KDKlKgdM0AeO2okm8ybNUcK13S5CCsr1O3x8SedcVeflW3tkLJXPxdM8OFugV9tF6Ca2AX/lAycZBCl32gE/9IkH+PCJCnlHJ2doTJQLZHWNYSxZnKtQyTlULI2lqkvHj5nM6PhxRG2U0ApS2n5C24u53BgRJ4JECAaJQl/NsSez1IwSsVMmmruHYOUhRscfI3HLhKbDq9VHWM0dw5xZIHv3PZz5Cx/AGiuhjk3hHDtO/r4HUWZXkHHCwCjSy00SZStE5RnC5fuJzr2PzvgZtqv3EJ54lLC6hFdapD1+llH5GFo+R5oKLmrT1PaatEUGOX8WcfJRvPI8N4YKqVskFAa1WOWUGzFlplyYyTGTs1GdDEtuSD3U2BpJOrl56oEABA4RmgJVI0WXEiX2sC2TnAFaNEKicnosc5R4lw7opBZf3wnZChWUUQtdSBSZYPgDcAuURIApU4pqjJ4GqGmCCPooaYjMVJFeH6EIrJN3E1VWEPVNYjNL5+ptEBrueIXMfY9x/DNPUDw2xsT5SVRFEBz2MHMZ1MoEuXPnGOz26G214HCTsqUwimKaw5ClkkNq2OTSIZaiMIhT3N4W1tJJVCTCtAhKi7RjhSWlzvTSCuVKhTf3+/TCmOfXWrRGPgddH1sTbLV8tocKzrCLnXUJh73/aK39+z/5vcxOlun3+/zgD/4gP/3TP83p06e/1SX+zxRHJ7//6esd/ni+7cUvwD/8B3+fjJ3hF/7WX2On4+OePM3KD/4FlEyZVGjMiB5CCm4NFC7udQmTlDBK6PX75CpjMLXCsQ/dxyN/5cP0DgaIJCHo9ijedZzw9mWC9Ru45y5gLJ9CyWdQUfjkf/f9FM6dh8Y2xfe8jxPf/92M33+axfefI5PRufjUKlF3QHd9h6DRJA0DxO4NVL+PlClGEmJpAl0IrOlZqo/eT9xtITUTtVhG7q2T3L6Eur+KmXqUbY2CpZHII+FkKbDeDMhLn+38KYr3nEF6Q2SSYh/eRp9axNi5igh9hqpJqqpYh6tHdjbekMPtOmFmDHNQw3r0Cd6V7ZFZOYZx+n4SwyV35gK+4ZJZXiEftkntAlLPgkxJ91bx33oR7/gj3D99NLyFqqNMHUMdtZh2FGRpko6SIxI2h/kpenqRSHPRrr7CpjvPtCtZcEFWpinKEdnqFFe3G0xkdES3Tto4IN/bJS7OkUt9qO+gNXboTZ9EqUzSfOsaugqiXWOl6qL12gzjlN7qGrcDl+bmTXrzJzm0SlxId7lLafJ0w+Qr19rc6w7If/p7KXpNFgs6cvksYf2Q7OEqz4hFOlqJXOwzVTDJPv817gwF5y59g889f53prE19FPK+u6aIkVzb7nBypsjpiQyaENzYHzA/liFrqGy3PDRFksgUP0ro+Qn9IEZF4XDks9cP2Op4R6llWZMESdHReXnPZ6frsz0IqXsRSZJwY7/PTnvEYslGiKMo7J2Wx9mpHJf3++wPQ97YaHFzb0DB1rl5OKA2CDg3VeBKV+Fs1WHe1fmeEw75aMDFWp9ytUImkyFvaCjBkHJ4yO2+4Pev1FlammZ/kBJGMd1E5zvCTeZPnuYT9y3yrpMVVBRGepbxkoMq4SNnJ7jYkBwM4FzZoGSCCoQywVQhiCVZS2cv0lgYz7FcctBsm1dqIUM/5naa54k5l1G7y2w1xwvaEka3RS/VSRBMlRxWhyrm3BI5kXJdqfKj1UN2t+voSG7ttNg57PH+YyUsU2czdVjRB6R+QHz1VfyT9/DYqXFKhs5texZPGBQ1QVGECDtDNptlJ7vAb2/F1AI4077GU7s+J/UBXS2H1HSSQZthKLHvf4gJK8bWYNlr8tB9y0zfdZJ1pcRKNUOzXkPVDTbbEVvNEZvtIZfu1On0POYeexfFzjaKUAjDhFHP455MyAu36nz23SdRNYMz3etMZAyef3uf8azJVNFhkRYiSaioIRVb4ePnx3hwtsD5cZPFgoUiJPdOZTEUUIVgox3gxSmGJqjaR/eNhYLFIEkJooQgTvFRURVBx5cceCmb5jhNsuyRpV9c5NG/93dI3CLjM7N0lh8kWHkQ9dEP4nzkL6Jc+CDJ2SfQzzwIK/cSVBcJzDwte4rewn00q6c41Cs0jRKDWBCg4ikuRtDFkTFZLcb0WvTULPXOkAlHwxnVML0uLzZUBorFS8McmioIy5OoikIvEgwVh7hTY9o68lLXRIqYWOTVpEqvdAw1DUBAxVZZyghOZCWGKgkSSTM3h0WKoSpsRg4GMao4CqnRDJOprEpjFJPRQI36RHfeJrn+MiJNSN0KetBDT4fY0ZAoBZEGJP0WsWIgkwha+8RDn6A0gzo4BCCOE4oP3oeWy2PNLSAsl9y5U1TvXSFoD5m5a5KFR+bJnTpGfLCDEJLsfInNKzXS+j6zgw06QYqqCOZzOlIoKKZFIFMmbAXhD1BNi9jOk+oOTS9l++YVSpOzLM1O0/EjBmGCrQmKrk7LSzk2luHKfh+hwGFsMmtHJHF6lFL4H+D43Bg/+QMfIk1TfuAHfoDp6Wl++qd/+k+pwr/DO/xveUf8Apqmcen159m4foW///M/x2Y/ZmQU8HSXbihRb76A6NfIWjpJkjKfN3C1hEGvh7SzpO1d7EqBlR/488z+xE8wanUIRwlavoRiGkSNPWSvTfvpb9K7dIXj3/so4w/fj7p0DtwsojqHUqhg5LMUz5/g7h94HwvnpuhtHNK8eIc0CohRkEKg1DcgTdASHy32yUd9FDePYmfRqpOomRzYWTBtiEOiVp3M+usUkj4mKSX9KJ5UTwIenHXI5zKkmob92HfA4t3ohSKYWRLNQgqJ0tnB7h2g713Hv/4GwrCodwPCp/8Qc/MKQ6eCLUO0oEc29ck7Bko2jzBNzFwZTdNRiAmzY/iKjhKOUBfPMVq5G800ce5+lOL0xNEQiJsnqNdw+vvofo9ZfYQuYipZB9vUaepZtLBHVQxJFI38lecYJTrtJz6Blx1D03Tae1skqaT+0ls8+3O/RD8RqN0aSbeN/9I3KTkKg8IsVkYQRSlDq4glwL73XcwOtimOlWDYJ1eqcFLWaXhQDyA92Of17T6qZmAunmRo5unlJqnU1zHjHu373s2c1+L+uSKbGxt4Fx7h1P6b6BPjuEpKguThY1Ueq0rmMiqbtS7dQcB81eHPfeBomCQFjpd0LFWy2fGRmsFXbra4ejDgcBSTNXWEAjlDQQFyls5C0WE+71DrB8wWLcayFv1QEkYJAz/my9cPWW2OeO7SPkXboKQr7HYDdgcBM2WHJ44V2WiP2GqNeGOjw4mZHLcPB1za6TIMEjQhaUeSrV7IuC2pxSa/vRHTGYX80BMrjCkjqo5CMOzDnWuMO4JHlsqcKSg8MmPhGy6RmWfq/Dz/049/hG6nRylj8L7z01SqRcbyJs/daTL0AnphzMxkmcNhfORDLANiP0IFHFMBCWVbZy6royAxUsn98yV+ey0kSOFQ2kxf/grljELZEPyDL76N12mTZMsEfsx+YYl6ZortUCPvGGjLJ3nMW+fy+gH3z7h8eHSFyM5wV9VgytEIb11Fq62zV1zgWLjDfnjkGrGVZulKk14UESQp467K771yE/WN5+nnp3jw/rPs5Wdxxqe4s9Xhhc0Bt/caeI06O4GC8upTVBobWLk82q0bpLrBpCPoJDqboYkIRwi3yFO36ux1PW7XBqRRwmdPmDwypTOcOklXs1G8EZYCTtalF8Sohk0gBfvZeb50s8l3nBtDQzCR0Yn6PUwVMp0ttrohfiyZzBl8x6kq43qEJmPyhkLO0olSyVLRRhMSR9fIGwpRKilYOkJCN0yPYo6lpKgEdLyIhpewP0rpxzFtP2F1IPGrs2RtjUAq7CpV1qmwU1jiMDvLQC8iLJeeUyUJIwKrCFGEruuYQiJ0i1zcoR9JkJIFOyTX3yWQClYywBh2CRs1GmTxs2V2IhNv4QJ6eZx6ajOKBaahsuJKhDckGrRZUo5+B2FuilzUJOnUCaROI1ToJxpSU9GCEb1mE1XGCM3mcBASC41UKOiqjhr2yKkRASq9QZ+eH5MRIbFqUI46vHu5hKaqHKglhGkgOwfQPUAMDhHakasNrS3iYQdl1Gcru0xT2khVI4kT7JPn8BQXNRoidBMr6hNurqFaOiJfhcI40vOxZ6YxchZ6Rseu5PH2D0m6Xbw7t8jNVLEsldbbq0jbRVckJUdHESqxVEhVnUwaUozaCN+DOCAqzdPX89y6eplssczC4iKhhJv1IfVBxFrDAymRHD2Mq0DG1omsHMtFBX8U4HfqIP79kuLK7/88AH/37/5dLl++zG/+5m+iaX+y+ONvJ95pe/jT4x3x+0eUy2Vee/4bPP87/4Jf+c1/ze1OQD1QyadD4p11Es0kTiQfWSlwTz5GSzwQYJg29JqY9z5BnKvgHb+A9u5PMvGh9xO2OgjNxJhZQpomu3/4MtHQR0kjFNtFINGnl0jqB8j2AVp1AtV2KJxe5t4feD+Vu5YorEyDTJGdNuHuDkmvTapotJQ8AlDjIYlbAN1EK1ZJJcSVBdTiBHL+DEJIRi99E+/Lv0EUhSAEvSglNrJkoy7lpEPRb6AGHmTzJEnM4ZO/i3ftLdY+/wzh7ibdL/wO/q23iaKUtNtksLHL1Ee/kwgVdfMqwjBANSAcoRPj6CqplQFFQQhB2m2hJwFaGiJVHY2IsqViBS3yYQOlVyeub5PYBVKhockArCxKGqM0twlf+CqaTMjs3yEtTxHaJfYTi/TggItrB5TViKXokLPzkzQPD2joRcrvfS9nH1/BVFIGrz9Poxli3H0/liqRb36TTX0c7+WvwdrbpMGQKAoIei2YWmB6egxLxqSKZMn00RwXfXGFv3ChzCPHcnQTFV2FQe2AL7UyNLyE+cNbaHHM9c06x13BAxMKx4wRyoPvISMjopk5Tk3lmTpc5YOTCq+8eoNBmHJyPAtS8M3Le9zZrPP+WQuh6gz8iJMVi9NVA1MRdMMYoRx1wR4MQ0xNZTJjoKsKgpS+HzEME4SQaJrCZE5nFMV0/YieFyGjhO88Pc5Luz3CNCGOEk6OZbi422e7NeTZ6zUKrsbIj7m13WMYxIznLa41hpQsnf2uTyIVDkYJ37zV4QNzLsdLBjNWTL/Tpo9FY+UR6sOEx1afw05DXlmtMYgliJQne2VCFE6uvcGvv7jFdMbgjVrIdnvEXMHkoB9x94TNIIFhAo1RTDrsMR/XOBdvk9EU8sJn+uAyW/2QnbZHZ+iTtzXuXijz4EKe3X7I8w2HsLbHieiA1a19xnpbKIpKJu4yCBIyow7dWMFLIL2zhn/qbj64lGNGdOkfu4cz3jprg4R7Jk26G7tw7DyZ8RmUsUnuzcUMgpBFJ6Z/sMNYIUctOdp7zjY4PPcIf27eIxEq4ytnWNKHJHc9xMcWTfLFCiU9ZoEue7kJ3toLkRLm5vPs61VujnSGQcrra3tIKdkXebK2zmbD43DnkL/02CLH9y/S8WJ8oTOWc/n4tMr4ZIGrcZYrb17ixZ0+NxtDXm4KbjUGVAsusUzRNRUtX2Swv0vDHuP1nS5BKlnIqNiGTl4HL1UIU8EgSrlRHzAIE3Z64ZEFYQoV16Dq6gBUTUlODamoAb1ER8ZH6Y+OIskMDkmSFC+CnV5EPzoa2izaKjnHIBEG/RCCVJIkEr1dQ0qBEAp7kcrBMEYKHZOEnK5SNhRyuiAUJtLNI9OUQLER7R0a0mYq2kdTddJRl0ioaE6G+8Z1ekHMvJMiX/oaymiAPuqg7t/B9btcqw9RRn3UnVWi/XUyd95g1k3pNlpsJRYLcYNBf4gnBY6T4XCUIDkS/amZJRIGCSANlwiJGQ5Q/dHRoK+pIpIUU6awcB6OP0hiuEeOFOGIZOcm3We/gvzqb6AMWyS6SyShp+WJWw2MB9+PJSApzpJmyoRGFnNuHuPEfaRxjGjvok8voNoO5XNLjPZ7dNZq+Ad1GhdvELXbuLPjzN83g724gKJq2JrK2WoGVRUMIokSeBhKjNHdQ+o66fw5fKlx8cpVAqEzs3wcQ1WQCTS9CEsV+GnKRM5iq+3RGoS4lo6qKKw1Q0ahpJrUSeN/f7jFS//yv0MIwe/93u/xC7/wCzz55JOUy+U/veL+Z4j/o4vfn/u5n+ORRx7BcRwKhcKf6JrPfvazCCH+nfXQQw99y/b4J+Ud8fv/w7lz5/gn/49/zC//nb/J5595DT+V2MMDtJW7aFiTFHXJpOqRV2JE55C8bVBKe4hshc7kGXpKDkNRUKwsyvmHcM7eg3HsNGlphu2n3sSquDhz8wjTQsYhsl1Den1kv37U05sroy/fjVqZwJqdpXThHJWH7kWkknTYAhlBYZxQNQmFQpikSBQwM8hhB2G5hK0GfTVz5KxgZ2BsHrNUoPb1Z7B2LxOnkowuCFPJKBGoXo9S7W20zYsYoxZi7ixKGjJY22XruYu89Uuf463PPUv7jUvoMiLqHKIdrqGNDtFkjGUbkApkpoiCgpApOilKKsmOGviKTnDnOmowQIs8Aj1D0B8yXL+BkAlK6MOwjex10A/XUGaWoXWA9PsQefQbXcTcCQQJw60ttPFZktGIibhN5gMfR50+RmTYZIYNMtkMx8oOt7f2kF6d7IMPYx7cJH3gwxQsyZgYMtzaYi+7AG+/gTmziCZDxP4t1n/vK3yjXyQftRhGKfrgkJq7gBu2mcjbRHHEhGtQjpp4kaTZ8yl0d7i/FFDKZ2lky+jHlpirutiVMmmviTJ7jMWCwd1Xvsg1a4IJdcSlvkmwX+fHv/cRqkWL3bbPq1tdpvMm71/OsNpPeWguxyOLZVJFYzpj4iUSW1foehG7vZBBELPd9rANBVsTvLbdpjEMOFZy6Ycxfhgzk3coWToZ42ja/JOPLuBFEU++XeNO3WMYpAz8mCdf32Vjo8NgGOEnkjCVVEsWZ6aLnCo7ZHWVnbbHTs/j199uUHF0PnZhmmrWYX2YciBy7Mcm39wOqViCr2+MsN/9XjIa3N5qc24yw1LR4b3hDqu7Layzd/OJh5Y58BKyjsHJis1juR6LVZf7nQFLWZXtXoxhGNwJbDKFIpZIKY5q7A8T9vUK+12PgmuwqZTwwxRDg/vNNjOiR+fCe1CLVVoLd/OLf/khapUVvBgmHMF03kQ/uMmYpXHWDSg99CD3L4+RmBmKO2vUdhrs2ZM8PGFhCIn23o+Bk8eRHq3pk1wdqMw3bpDt1Sm7OoFqUQ/gC283OG76rBRBdwtsdgN8KWgOQlQpKagJ/URh2O6x3pMEUuPCvEk9Ndi46700Q0nTj5EcteEkKLxdD2n1AqpZkx94zwnOlxP8+9+HLwxEHCAVlcPZ4zS8BM9LOXNyjmfuHAn8r718g+VqFg3JzZ027VFIS7oEbom90GKh7KIKQTtKiYQKqoFQBAoxSQr7gxAJKArs9wKmswaGECSpRBBTkSPGxIhgOKDhJSyaAVlDcHO/R6gY9MMERxfoquBsxcbWBFlLx9BVUsDQBJ0gYS3U2WiP2O2OSJOIppcQREfDuCEa66FNJ4hw4y7tSCB1i9TMUo8Uwpnz5DVJGClYYZdjToKxcx2v24TIp9TbpB8L1EKZenGBrlFkyzMIrr/FBbVONzODMjlP1OqilUo8aA8Ib19lbus1grFFKgwZRKDpOl4kaXshXT8iQKMVcBRHbVoU1BStuUUkEwiHiDQl075DQQlo6CVIInrl4zTcCQYvfY3Ry98kbPUQSQz5CpMWlAjZC1Ws5ePg5AhVg55RpJ+ZYqQ6aGcfB1Ujvvk6aW0DYVgo86exyjnMrIPfHKG5FlY5e/SmRKQ4Y1myEzkEKRldwzUEhoxIEeh+k0iqyE4DkSnQtCZ4+9o1wjjBmDqGRCGMU0ZxwmTWZLnqYqmw3/N58Wad23s9cpbGeMbk5lqTvbaPEzbA0IF/16v3p/+rj3Dv2SXefvttfvAHf5Bf+ZVf4ezZs3/6xf0d/rMQhiHf8z3fw4/+6I/+77ruQx/6EPv7+/92felLX/oW7fBPzjvi93/Fj/zIj/B9n/lz/L9/6q+wtVsjdqvUJu+lEx3lsw+0HKlmEQUe1qiN9fY3EaaDHydYUY+CHCB1g6FRJpw+w3DhAnLqGP7GOp6XksgUfWKc/s3bJN4AOWijjc/hz5yBbAkZ+8hB72hq2c1gnH0Q/Z4nGG3tIiYX6ZcWiNEoSw+LmNQfIWrroBmIXBm5ch9CtxnmJvHtMk17HOPRjzHx/nfR/N3fQX/uSYaRpKtm6esFSGO0fAWlUD2KUvY6jH/0E6S6haId+fTOnZ0BTYFilb0n/5Cx5TkGl99ERCGKnUUkAZhZpKogwxGivQuxj9R0lDjGPPsAotcgvfgNFK+HZhqMVYuo6ZFlEPlxlLBDUt/EaG4hchVkax8lGNK5dQvmlxhev4Hy6IeQUjLdX2MUC7ynvsJUJUuv7xGHCepoQF7VyJg6a7HFUGQIbl1DyWSx0iFkS1y6dYBqaizdfZJrv/qv0O95gmDoM3FsnHtPzZEqBppM6VZWeG7bI0hVlK1Vyu1VVE3l0BjDjxOu1T3k8nnGFY/pzjoTx+bZsieYLDo0dne50Vehvg2hx8R7P8zHL0xg92usGWU66+vM5lSWqy5TFZcojgmEwq2hwu3tNrXOkK3WiESAYVnYmoqtSDa7PlGaMp7VMTSVQZDw4kaLMJbMl1yWiw4jP2EYJriaQn0UMZG3EAjGijavbHVIJWRNhZUxh7X2iN4gYH+3zcR4BlfXWB5z8aOEiqPxxm6Xrh9hWxoDL+ZwGDKIJOfGsxz4KX6UsuOraLrOe8sjVFXhg6fG6I7NYXf3+NHPPMaMq4NusCZdHBlwKba5tj8gpwk0ReHxSRPhjzhZdsB2KSo+JVvD0aDpxyiaQTc7hq5o1EJQShXuKgqyfptn3rhOVgdvZ5utrR1e2ol47OQYbmWcg0RHq84SxpK9UcqVvs5Wa0Tt+OMspXtEqgXZCloc0AsC/N1tTk0WcIplZmoXafiCuDqDr5iYt19lFKY0Q4FRKFGI+hyoJZRBi7mkyUJjjW+sDzEVweu3G5QtlcVol//bk1eYCxqokUdFDjAyGdqjFP30efRCmd1BwlricL3u0fFinl9rUFEDYiPLWM5koeryI/eUOT6e46UmdBWXL6/7HIQaX7vVRBgm71oukygx4+MTWJpCRleYmChjawo5EXJypsjnrxzw/K7Hm32di/t9MkpKKiVVU2CGPYTfIxN2sRKPvBZzvOwwnVU5M+agqwJFwFp7hJ+kTKkx+s5lZL/Ljq9SsBTWpU3Ti6lkLW6MjgYyDVVQdXQ0ceSRGycSBcGMq1BVR2giZTKjY04v0XGq9EYerinImCrNUUySSN5uBYwpPqGRJWtotAdDdE2lZCkMNZesEZO0D6iRJdfbo6053AyydIRDUzpcOvTQsxa5dMSeyFEoZ1DLZYpek1v1PkkcYpRKtN1pRlYBb+EMo4kVlI0b3E7yR846KaiKYL8f0Q8lW4OETpAAKaYOiVCJNZtQs4nNDPneBkbtDl3hkEv7eE4VQ4N81CM82MfIuxQ/9AmyZ84SuSUsAoJhj5KloowvovcbpIlECoV2ahBISJ08aXOX4d4eSa8LdhYlCZBCoXrf0lHhEgK9kEGfnCIaebizE6jZPNR3iGSKE/dIU4GpKQR2FdtrA5CqBrdX1xj0+1SXTnGs5DAMEpp+xG4/QlMVNATrbY9Xbh6St3R8P2ajMeDKjQM233yVwdAnYyoQeEfWQH/E8uw4P/tjn6bVavGJT3yCn/zJn+RTn/rUn25B/zNGmib/Wda3ip/92Z/lJ3/yJzl37tz/rutM02RiYuLfrlKp9C3a4Z+cd8Tvv4df+Re/RDFX4qd+7Id5q6dxuZsipKSTGERCJVU00jgiaTXQcnnC3CRjaRdreIiSRBAH6H6LhnDxhEkqFBa+40GWfuSHEKaLns1jnH2AqLpCsH6btF2jnToEuUnwh0jTBctBGAbCdFEmF3AWlwnXV9HqW7RHMY4coQgFrzRLnJ9CWjkSI3s0IJVKvAT2IoPQypPYOZx3fRD17D2EN94kE3QQioJhaIS5KUhiMC28sROgaghVMPO938v0mRk2rtcQoxAtX+bq/+fLtO/s0bp6Ez2fo5abJe3WEcM2REOIQujWUWSC1t4+iupsbBAqR/GZaqGKcv15hs0uSmUSLRn9UUpcg7RTp/Hsi6iGSbK7ShQmJPublOanicOI9lN/iNo+JJUCTIe1yKW9ch5l1GHira+yW1xC7+2zq+U548bUYx1v8wbq+ASZ9gYpKmm7wd759zG1e4XuQQ9/8RTRyIe585SmKqyYR5GrvjB4uWdz77SDjiRvp2hxgBKFKLpJ1dFYmCzTtif59cEySi5LW5pMCR+t3eJ/eLbJP/3SJVJF5+vrQzThczGt4OUn+AsrgvbCcQ52Dnh8oQgy5fRkjrNzJR47NsYj5xd4/0qRiYxBkoKahJwfz3Bc7VKwdFpeTFZXWC47XDnosVhymC663Njv8vpul7GcxUrFoTYIWG8NKdg6piYIk5SLm12OVV0enC9xpzGi3g/JOTrf+b4T5G2dx46XKTo6K+MZBmHCxZ0eSQKrO126XsxdM3kKpmBvGPLiWgtDU1AFNLyIStbl2YMURRGMwhhXTbFkQGm4QxjGnKyqHE/rDA5rqLpgonWbSVfFJWbTXcBQJF/djvB7Q2xN4EUpkHLgC9qpw2sDEysJ2W0HzOV0hk/+Lo8eHyc3OOBzr++RWFnMQgF0B5KABVfBSwWZ9ibz/i5ZS+fqwRDbtGn1A7wwJVEV4nqNuHGI/YGPkV9aYMFOuBNm2B1JbJHQlwbR3DnORRvoUmKo0J85TW0YkvSbbO42uff0OJMzUzx7vUa/3eb19TajYcz3f+eD7BemKG1fxbZMwumT5Kam6EqbO6JCxTWwNMEwiPnCxV2UJEQVkqFd5sJMjo+dLLLRj4jihFQ1qVgKz9+scfVwyFjOYBSlBEnKNy/WaIxC3n+qyr9+eZtWJFgsOziGwU4vYHfnkFzYY6cX8J6lIl+42eRwGOFJA4FAGxwSopMKlYKhsVi0OW6GzOoh58oGW72AWj/gRmOELiIUf0B6/SXmrAhHRlRsC1NREJpGN0xYKTocDmNMTeBJSZAkJBJ0Abqm45CQMXREqpAxNAqWiTAcVHGUWmhoAi+VTLkGhpNB09SjaGolQ2RkCaWKF0si3ea6tcTVuo896pDLZZnYvkQsFcIEHi2l+DNnURFUkz44RbRCBcbnORjF6IUyfqdFdrCHXbvNuxYLRG6BoUzJ6wkTRgJCYmtg6iqjOKHtx6hCEKdHgTFZERFUFtEEhJqDerhGVJylo7rIFNZjh1EioNck95HvI737/UQLdxNOHEdGKSLwsDbfpDA8RI6GqEEPp79Dmh45vyQpR16/wy6D3QbezhbpwTpy2CNz/+PklhcYv3cBzXEw8lmUNEF3HDJL86gTsxB6DMOEULEZITCUo+8ptq5AvsqNRsCo26S8fBofFUdXkUKSShhFMa1RzE7f4+krB2wcDHj4RJlS2eaZV9Z5/dXLxGF0ZMOYdflfy4nrf/DzxHHM933f93Hu3Dl+5md+5r9EOf8zhUzS/+R4Y5kcPYD0er1/ZwVB8F/s53r66acZGxvj+PHj/PAP/zCHh4f/xfbyb3hH/P57UBSFK2++wM7qLf7yf/3Xef5Oi16Y0vYjdJmiDDsk3Ra646JOLKGqAj0JUaSAcIg5auM01hmLW5TjNqYiMC+8G3HmYcrf+Sl68/eyrxcIC5NQniaKU+zXv0gy6CGLU/hz9yCcAsOtHeTeTeTNVzHnFlDHZpAXn6XQWmWEhRINSBWdIDtGmq0SWzm0JCInfAyRYmkarqGSOjn0JGTn3PvIffC7yNeuo5GQStBkjOw1ARVDRkjdQR5sItKA8myJxYdPYGQtnvqnX+DG195g0PDpbzUwJmfZN8ZgYgFGbRDKUVSn4SBUFXQbBg0YDgmsHGmmQDR3Fq08gT05hZQKMvBACLzDfYYND31uhWjkk6gGim7RurlGEMQYQuB84BOMLr+Fp1gMspOcL6tkwh6Vy18nt7LC4tZrKElAKsEoFJmqb7LmTiCbB+C4bC7cz2j1BudFl6A8Tf3GOqceuxszHWL5dYQCxrCJZtvMmCEreZ2MrqGYFkp1hmjmFIqmMmnKI8cFRSEWKivzFYZjJ5i1JEHg07GLPHb3Ej98Ns9rbZOqq5LcuUqcCnJvfoOGVmSDPLNWTNXb5/U7Le6fzqIp0A0Sdvohz+375CydizttIt+nEyYk8dHgz12TLpaAYZgQJXDPRJYoTXENg5YfUrJ0HsoH3GyOkBJMVTCRt+kHCacmszyyUCSvK9QGAf0g5t3nJpgoOlSyFhnL4K6JLB87PU69H1ByDeZKFr5QeGSpxLmxDK6hkCLZ6QaEiSRBEkQpX68LirZCPh1xz1SWgTB4ebWNsnuHvV7AoTuGO+ryHZMwY8F6J2IYpRwkJllT5bXtHsagRVeqPL/eYq3tMZZ6vHDrkN5giNcfsJCBi2stXq6nWO9+P/uRydcOVLLlPKOxZR5fKBAhWEoatEOBa+j8P1/rsJYWMUnphyG59haBZnO9G9PyU4Rl8ED3Mm6xwFa9Tdho4K1vslrvkyNg0tURbo72ao3rhyPiq68SqBbrrRGt/Bxfqlu8nI7RGwXc2O/grpzh3sUy0WGdT0yNWLF81KWTKP0GQ7PItXZEwtGpd60fULQ0bEMlaxvMizZRCm/XY44XLXQBFVvjStPj7JjLKElxHB1dgYpjsN4Y8dJmi+nJLDMFl7KtY+tw/coG//zLVxjEKZd2u/zg+88wNz3GdN4iZ2sslx32eyF3Dlvshiq92GAoTCIMdEXQ9mNMGeP2dhmTXS7tdLm23yVOUkKhIwMPrTBG6hR5pR7RGEUUHYOZnMGxkkOYJuRMhTgFU8CErWCoR8NSozCljQsyJcMIWxHYpoZuGOQMBYSgausgFFZKxh/ZNqlkNChnLLZ6PjGCAiPC7ATlzjqnqw79O6uUO+tczh3nG29s0Gk0KNVXid98Di0cYVsah7jg5vnSnRGPTOhcqcdEM8s8uZqya08hD7ZxRELZSFnytolSCDp1qkrIuKORJAkDP6HlRZgK9IIEkYZIVcNLFAapihQq/YmTTLsqdeFiWSYoKo3yMaLCDL3KMocBjDLj6Ac3Ca+9Qu+55+h//l/hXXqB4bUrpHaeVhDhJSmOKknDEakw8Bt9FNch2dtAqUwjipOYk5NU7zvH8KCN3+qTWV4k6g0IuyPSRBBOn6ZkawylTttLUQS4fh1RGONOnGH3oMbE8bPomsmN+oiDYcQgjPGjhChNeXuvw4u36jS7AWePVXhzs8v19Sa3375Oe+0aiTeg1x9RqY5ROX73H1VQwY3PH/n5/tRP/RQ7Ozv86q/+Koryjtz402R2dpZ8Pv9v18///M//F9nHhz/8YX7913+db37zm/zDf/gPee2113jve9/7X1SMwzvi9z9ILpfj9Vee584LX+CpJ3+H2sDjRtNjKFXU7gGp6aDNnyC1Mmh+H/weQjPRYx8l8lG9LuawhpFGaMMmIDGSEUFxhitpmacaCr0Y0FS00hjWhcdJLz5NtHoJY3BIUtskaRzF9erlCuSq+Cfuw37wvXhf/X3UzSsERh5LhlhKCjIhUfQjU3YFsmp8NFksNDShoCmSpbyOOneSaOoExaCBKiShlT1qPVAEqt9BqgZSwOjqmyimzuI9UxSOTbG902EUJuRm8uSmi+APOOf6tFMXnBII9ciuRxGknUPSYRvpDVFnlnEUSWpmEbpFaroopg3bbzO6+CoyjqBQZbSxjvnuj1D7g98jXHkYTIfMI+8me/Isws0jzz6A/fATKEh2AoWhVODFbxCffIik3yJj67SNAsdHayhWhu6x+3HTmFVzgvDOTSq2RM3kKc+M44xPMDFuYbcPkW6F5M4VxO5totICXmEaoVuMZ1QmlSGGY9MrztK0xhFhn1jRaPhHxu5CwJmqSSB03PIYT3dtil6dd9+9wExrg4Ie0xUm4uyjPJYboS+sULj6Au+RW5x2AzRVY6FiUzBgqWhx2A/ww4iul5DXJafTOr4U5EyFw1DgRQkZVbA7jJFI7pvJsdkLuV0fEIw8FCnQ1CNh9ZVL+5QzJlttD9KU9cMh56fzVG2dnUFI2TEoOjrvPlahYGmcrDrM5kz6UUrbi3ENlRPjGXqjBMfUMDXoBSn7g4hX1zvMVhyKlkYQJTi6yu+9usNSweK1w4i8CNhw53lYaxJUF1gu6uRdE2Nu+f/L3p8HW5edZ53gb6215zMPd56+ec78ck5JqVTKsowky8ZWubCxKQduoJqhDF3duKKrOqoxTUVR4AaioujCdIPBYGxkY1mWLVmDNaeknDO//Ob5zuM598zn7Hmt/uMqXGHoBmMLXCbyF7H+O7HPvufcE8+713rf52G1coxrb93kwVaL7jim4Fmk4xGajKfKEW/24I+tFHHziMPeBLdU4mE/Z2j7jGLDR44rpvMht606IxSlSpEPv+ssK9WA/XFKw9K8NfbJEeyONe994jT/y8stnp33mSr5bKsKVRuyHBZkjCjUEWHIVm9M6eo3eHG1Q/Opp/m+xgSkwoqHxFiYvQd8b3PCzenLVFxJYFtYUrE0V2VptsHjsx7Hz52lFnZZsSKulo5zuH/I1hhMc5HrTHG3f1Q0zRQclFLsDyM645T5ikclcCjlE9qpjZGSXqx5MDJcNht0RinXDiYoIQiHEStVl7nS0Q7/fGAxVfaYr3v8y2884Ma1u+zevcV7TtV52M84Xg+Yq/gEvs9lq8ODgx4fOTvFQT9ihT6HE81bZopxLrjeTXg4OvK2xeTISQ8tDK88aBMlRz3iI+OR1ZfJz7+bdqoAgaUkw0RTcxWBo9gdJqxUPcapRguFkRaOhNYkY5TmHEaaVpjRSS0muUEBjpQsBFD3j9wmCragKFI6qSTSUJY5GpjzoKjH2OmYbpjhzqyQ+mW2L3+Y2C3zaLTJe5M1xmFIa/os+d4he5TZzjymxBgtHYp+wChTLIzWuTV2eOZ4kdlmjcKgRaXoI4cdTG0eJ48ouxKjDa9v9TnbKHAwjjlTUVxvRWgjiKwiGoFlNHYekRx7FEfBKJOk+siVZRBpUqvAEIeSI0lySTHtY+IR/VdewQpsBtevs/OV12l95etYkw5xpinYkhIJ49IiRlkoxyHaPkA4DuPCDHEwBQtnkbaEXLL5levE+y38+RmsepOseYyO3aA1SdkexkglGaWCXAWsmgoPN7dYOvcIiXSZZJrZkkOmjx5s1/ox3XHCb7+5w9sPOjQbBdJc8/b9FuvX7jDa3zoSSp2QpQm2Y1OcmmX5me/hb/61v8yJxWl+4Rd+gZ/7uZ/jU5/6FOVy+Q9Fx/+oYcx3YODNHLU9bG5u0u/3f2f9/7OW++t//a//GwNp//p6/fXXf99/04/8yI/w0Y9+lEuXLvH93//9fPazn+Xu3bt85jOf+X1f8zvBO8Xvv4XTp0/zW7/5Sa780s/wrz77da5sHe1+GNcnKkyRCovcKSKiPnJ0lHom0eB44HgIbTAC0DnJG18hfvE3IU9xLUnFd1iNbHaX30VUnMKp1bGOXSSbPYXcWyPe3SdYnMfUVyBLYXSIY7uo6WXqlx/BHe1jt1exOxtkeUrkFJE6JbaKpEaSS+doV85IxsYmqswR2iUiI1FBAZSNL0Aqi3zhPCIcogvTmN4OD198k9wqY/seynMI2wPe+95j5L6FSXMaH/geZLGOM+xgvfk5cAPYu49p70ISke2uI7RGFSqETgkrT9HKxc4mjPoh0XhEPn0CPb2M0RqvVKD4yBNk11+kfOkszvZ11OJpHCmI3CJSCurRAXkSIg7Wme8+REtF/b3vRc4sIaaXUQtLR+0kM0vQ2mD+4WuMmscZGsW2P83U4QOCR56kEdhkm+tYlRK5csijEdotI6oNnHyCcQrkgzZ7kSTTmoEsgXQJ0hHZ3bfwLYtn58s4lqJoC/qZxBiYaMHC7DQbwTw7vZib5z7AmapgyjGktkdcbDJYukx69Q0K9Qa7qsJ9ynzodJWb6wc0VcZKzefVO0e70MNMc5AqfvnFexSE4Ubs8URTYg0P2BunKCEYxdnRkXKmWZyt8O6VKkXbsBoqHj9W43QzYBRrVrshoyTheCPgyv6Qm7t93np4yHz5yM5qbxCxXA9YKHlM+Rb9KOPZlQpnmgEvPTzE9yQPOjFZlnFzf0J7kjJVcBglGXvDBMeS/Pj7TuFYCmVZOLbNUslhbq6KnF4kERbVSpmRdDjrprjNGT7x9iHboSHBY6wKPNcQ9GbO89Urm6wNUkQScnDY4drmgPvthGGc4XgOq/fWOXVihbqe8NxyiWfmA94zZZjzDIM4xSVlplklUJKXNvtcqub84HEbg+Fko8BnN1MOumMqgUtdJTR1B+fYaZpWgptGPNyfIEo1iq7ADDvosI+68S3ix19gvvOQK2kdg6E9Tri2O+L9lTH7w4SpuVlOLk2xdPgQkeUsdB/QmzvP0tw0G4lDoxQwW3SYr3rAkSvASi1grzfhct3ieFXhCM1qVuLiQpmH3ZCXHrS5KhZZKNu4lmKYGn78hdP4lqAziXl6pcqzSyWemi0wjlKqBZcf/+Aj/OgPvIcfX4l4vC754Okmy6bH3ighdwPKQjOTHuDawLjP//z5+7iuQy/SPDgMuXkwIbAstF9i1DzLtqlSKfk0ig6tUcJEg1y5RGQVGUQ5vq3wlMJRktV+jCUFBVuhhKFkgW9SwjQnzgUVT+Eqga8kjiUJjToajANSrUEeXWcqPqBoIkTYpxdrUg2HqYWrBKXsqLc6dKvMbb5GVGowbwZMGkvEM6coPfo0ly4f59ipU9QDh9G5i6R3rqPyjOZgHdKQdy+7RMLCXjyO7Xhc9EYUog7DuRMUJIjjj0J/j3zzPh3t81YnxXUUlaTP8QCqDiyUXJSANI4xOiNDom2fkazgGI1NhpKC3Ag2BjF1VzCMc8bJkb/wxC4zWrhM8T3PM2mNGe0MMXmOTiLiN7/OnJOR5IZRmtNOQBqN8hR2s0I2GmEpyaFx0aVpVLlKcaWOU/GxGw2CMxdRM4s81GXW+gmbg4T9cUagIMtT9ocR99c2uHD+PMYtMYwzNIbjVR/HEtjqqOB/bbNP4Cocx8a2BPfWurS22mijIAl/Ryenyg47O/skkyEX6jk/9bHHeP311/mLf/Ev8vGPf5zTp0//ISn4Hz2+k24P5XL5dy3Xdf9/vudP/uRPcuvWrX/r+k4OKc7NzbGyssK9e/e+Y9f8/fBO8fvv4MMf/jB/7r/88/za//hXGHfa3NwfMApmybwK4/EYHB/iyVHBi4EoPDqmau8gnIAYByMFOs/IDw/glS9wybSpuorAtmlR5IGusWOqJHOnsEsl5PIZ1IlzdO9tINMRotpkPHeRgomZGAdz6kl0nmM2bpHeehVHpwghybIUlSdHEcSDHabyPr6tSbUh8ao0RIRQNpEs0LYqjDKDbTKM42OUjTh4yPCVr/HGr30L+/yTDHfaRPsd/HrAIz/+Ib7vv/kYfrNE1t0jXjjH5O51ZGef8MYb5K1tTBKS97uILMEM+1CoYKUjZDxECMlY+DiLJ2j3x4h4gt9ooKQg3VkjmJ+icOoCXsFBz58llRbGthllkn4CeaFGqd5gxxSQ4w5L7RvkUmEmA+TGVQiHpFoglSCNUvxkwqmyxWk74WHq0s8dxLCN2r0DBxtYT7+A5fvkN19BnX2M6HBMZgd4CoRXxDYJfVWhmwgwmqqewNxJMqHoRimzRZfcSLSBqYKFn2fUbc0oF+xOci7P2owWL/GIdYjo7vO17Yhs1MU5c4mOW8MddDhfs3iwN+Rv/9LXGY96uFJy+eQUjhJUVc6FKYeC0IRJzHTR4zBVTFLD6UbAbj9BCsnDwwkfOd/kPceqnK1ZnKkobg80P3q+wt4wwbUlWaZ5/mSTcZTyuRv7fObFO0RpzpnpgJc3+1QCh2+tddFGo4SgWbDxbYtemHFpqYSvJAVHsjU+Ok043vCxhGR/lOK7R16dxysCncbMl11e341wdcRWsEhdZdzaH1MLPAqlIv6d1whkyoc++j7ePethZROGccZkNMR3FK2DEYe9CTeHFlZQoZEPOT8XcL63RpJlPH+6TpqlBI0G80XF8niD2mAdIY92xr++l8JwgKcM83bI/aFideuAz2/lPDVjkRuJu3icmcBi9OVfR1gWvSs3EDffpFxyeeHVX+Ozb62i4wgdDlC2RzQZMSpU6U2f5LtON7jdinjfiRpfvN0iLzRIjaEdZeTGYB5/nrxYZmpuhrlKgcME1gYpuYF+nDEV2DiWQEqYLbmcmanQ8G3OWAMybZibm+Kx2QInKjZJnPNgCJPUUHUVgzDluBrxsJeyvtehP4kY9MeY3/xVlusFfuiZRT76+Bz/xbsWcYslLM/l1HgVS0e0JjG3xzbFUomNESxWPF7sBLzrXBNHCvaGEStVD0tJ+mHCGIe8UCUXivedbvDESo3L80VcS5LYPkZKEmOYKzmcrDlICYFjEaWa+bJLQYFtKQKVsdkLkcmIgjK4UuNYhhkXao4kzAyRhmFsGOUSBUjLIU4ztPQIoxCZjJHS4I7aCAxaOFhZQjJzmmP5Pt4rn+WYm1Aa7SMthXNwj4nlkygf8ogbdhPH98H2kf0Deu0OJ9SAWn+ds1Yf41X5uZe2cMeH6NsvI10PS0ji7XVIRuRpzpwnGMcJTzmH6DShGShSnbM90bQmhkBmFHVEOdxnO5ZobWi6oI2h5tlIIUgyTZRrhIDdzOUgdRmefIbG05dZ/tj7KR+fRscpetClMjnAVZIUG1dpZFAkmKljMokKfOw8JsoMmVdAeAWE0Mw+uoyzfBo1fxzleQwzwyubPYqWxJWCh70IZ+8uN+7d59ETS5Sb0yh5FOARZ1ByFffaE8BwfbvP/e0Bj51q0KjYbKx32V/fI40jJt0DEPbvaORMrcDm9j69W6/wqb/5p9jf3+djH/sYP/3TP82HPvShPwzZfod/D5rNJufOnfu3Ls/zvmPvd3h4yObmJnNzc9+xa/5+eKf4/T3wD/9f/zMLyyf55f/h/8Qn3tzkt7djLDdgNB6TGglBFen6CGkBBjHsIBqzGJ1ikyPdgHScYJcLqHyIc/W3eWJwg6YYAoZ2mHPrMOJAB+xRBNtFHbuIXarA/ip6b5OWqhOVZjnIbSalGczCGShWyAdd2L6DyDMyI3HSIZbJsSyboP2A2nCLWriHn05w+5sEnVWkMWgshqkhMg5bsUUeVMi3bpMnhvmKy87P/X8YrO6h6k3cpeMUzl1i4UMfYu6Hf5T88IDs5ms4jz6LMBnp2i3QBiEkJBGyOQ+2jfaqmKB2dISqU+zOJk4+pnXrFoc7bSjVMEKST0ZE0iHTCmF7qP4BsrPNmrNAMR+hxi1C7TBRZWb1kDfyGajN8/bU44gv/jIqKJDvbiL8ChtffpntxSdxHn2cBmNmAoulWoHbqcOwO+DwlbcYPftRxP0rtD/162inQOYVmGysk2uDTmJCv86C6VN2LRxyHB2RpSnDmbME4S6LdkjN0YRZTsWV+JbC1QOaRRshBBKDVZ7mQFXoDibkecwjfkgh7ZM9/QJuax376svc6QkOhgl//c9+COMUmOSGY00fkHxlM2Kwvc3zF+eZ5JLjZYf9sWZPVVjrTDg/U6DsW8yUPC5PFynbFo4xdJIjz9QExRvrXZ5ZqvJ95xr8sRMVVnshh6OEUydmefrMFJ+7sc9WN6TgKKJM87AbstqLWOvFrPUi7rRHnKwFGA1pbrCAuZLNOMkoeQrflkRJisLwtY2Q1EgedmKuHYxQnW0ebdpoIWkUHPJBl0j5pCcv0Zib5QnVxx21SLY28EXCP/jWHq3egPc+Ok+j5LBQ8zlzYorHLy7T2LvHI4WEi8UU+9h5TlQdLtRs3HBIqVZlZFzCHBatGCUknd4hYZpzKdtnK5Esn1iiP5pQJGOxETBlG5pOgpqdw6DY7+YcnnqKG+UTLD5xgRecA3a8WbLN+9DZJjn3LCvRFmF9mSnf5u39EQ/3+1ycKzNlJ8yUXAySu60Jq6liYCy+FtV5ZT/hYJyw0ZswjjOS1KCQFGyJLyHXcL7pcnuQk456bI8Nz9ViyjIjx/CxJ2d5arHEhZkizYJNomFk+RxveJwv5fiWYqq9wVzeJ9GGaV/hmpSFiofSGX40wOnvEBZm2GgffT9YNllQY+Mw5uLZFd4zH3B7u8M4yam6kobvsNaPWR1qBonAEorzzQLnGj6XZwoUZI42AoXmmJMRKIExAp0dJZ8F9pE7hP627WFqDKe9iMLa66gsJstzgmxCHEUgNKMoIclzEJB39uj2uoycMpF0mHztN3nQjalNtgmERh9sINAwaqOlxDGaTNrYjz1HVaXklofavMnkoM15vY8i44Qz4b3lISaNSIMKAxxK8QCns0mhVGZq52062HzgseMUswFpUEJ7RaLKDLJRZ65oMVN2Oe9F1O0cmcQw7hPnhqojmXIMngX26AAvHuAcrmMJ2JyABqZ8i8ARpGii3DBJDdoYokwT5obNxEOVAvy5GXoPW3Q2eiSDiFS5JLlG2RYFpaBQwW9WkZ6LsGxENCaQmom2ETMr5InGLhUR6QRzsEVWmcGRgnrRph9nFFxJkI25vtni0kKdxtwCQmsGYc50YLNUPQrHcSxBP87pTBIG45Q720M2N3sMhxFxOGJ02GJysHlku/ltZmoB+90J7Rf/V9I05Yd+6Id43/vex0/91E/94Qj2H2H+9+7zu7GxwZUrV9jY2CDPc65cucKVK1cYjUa/85pz587xyU9+EoDRaMRP/dRP8dJLL7G2tsZXv/pVvv/7v59ms8nHPvax/2D3+XvhneL394AQguuvfpWw3+NLP/s/8fc/c5fNiSHPMjqTFGM76FwjCuUjD99eC5FlGKGOPHhReO/9CI7n4xaKjNc3se68hv+Zf87JcB1LGDb7EaEWUGgw8RvI3jaVEwvk7W3yUY+GHDPKBbkWpMpBN5aJlx/DOfUIQqdYt7+OJzSkKUJKRNhHWRYy1xgpkMkIkSSY3h5+3CdQhungSKhTbNqqhvbKmHGf8lyFeL+NzjR7r97EqVTRXgEqTQaPvoD/7HfjpWPy+1dBOeRxjHA9MAZRLKNqU8jaNCKNsdIQo1xkbxc7PCRHcv7UAr5jEQbTjJ06TmMG3VhiW1UZz19CTTqIvTXo7VOQGY3RLuX+Q3bCBD9wad54hcNXXsWxbRxfYS2dwZpdxp+0GXaHpLlBX38FL/Cxl48z9cYXMFJxe6zoXb1OdNgjbbUZbe7jPfIs9u5d4nYPa9LGSInJUpTtItFUHY2TDNmym5RJyPwqtXxEaizS3NCNNIM4ZTcrMtEOjzUE80WbthZcWe/w6UGNf3k4RbNaJvVrjH7jVxlcv0nx6We5XJjw5Kkm040Kc4FN2TsypK8FFtuDGJZOU5IpM05GIHJ8WxJryWo3YqnsMlu0mSk6HEY5t9pjDjLFzcOUsmvzYJBxaa7ExSmXc/EGro65sjXgxFSBSsVnpV5gvT3GUor1TsQkzrm2N+S1jQ69ScwrDw5pDxNaYcYkzTnVLGIrRZzmnGgUeW2zxyhKWW1NeHO9jwE2hjmBzDg5VaRbP45tSTRwqlHk/sf/JXfvb/D1oUd7dY1SOmY9UtjzC5Qcxfc+vowv4PxyE8+xqcscrXxGueSmadA78y5W0wIaRS4VftJHPXiDcGOVPXeGO90EZdnMlx2+vJZyPNtjNHeGSw2HkVPm/VPwIPVwLYUfHlIKW2gh6QqfkhzjhUOGbo1fX3qemomwyREHOwxL8zRcxWj+EVzb4XYn4satTZ4oRihX8fBgwmCS0I+O+pclME5zvrHaZbsXcftgQhhnvL07ZGcYsTuKwQg8k7BgenxjvUOrN0bojC1RZ0cXMELx1l7I3jhjkhnutscUpOZ4zWWl4rNQ8Hh74jHOJd2V86z8mT/DKM5YH2ZkWmID/cIsipzoxNNg4HvP1Xhu1qZMQmwMlxZL2Lbkm5tjuqOIomdxGOUUnCOfVlsqktwwSVPqgc2pmk9Ragr9HVQ6xsuGFMIDyq27TNIcjabmKZq+RfJtv2iXmLGxMblmp3mJvL3N/ijBnnQpddbwTUJZpoiwR4EUv7WK+9bXeLjfJYxTIi04N1Mk9ep04hxTm6Pf7xN/8zO4WYRsrWG1N0lnT6C9CqpQwVo+x/jSc9h769R7G4x39slnjjElJmg7gDTCnlsEyyPZ3yJrrlCcHDI9M4MpVsn8MtaoDeMRnaUnsaVkOjygMt7DG3eYlKaQ1SYlkYHR9IVLUcFYFoEcWawxN9pEIFBCkBiDyQ3SCI5m8A1hqokzTaYNw0TTOv5esjhnvNOjtzGgd/M+O6p29PsxgoJI0Hsb+CtnKF28hPAChOPTjPexsgn4NcoXL+CfvnB0AmnbjL0G4yRjkuR880GLzmGPzuotzi/NMFcNGOPTCjWZgcCxMUawP4552DmyK7OkIctyOqOIJIdJaxuThQz31n+XNhZ9m2rR4xf+1l/EshR/+S//ZcIw5B//43+MEL/b8/cd/t387734/Wt/7a/x+OOP89M//dOMRiMef/xxHn/88d/VE3znzh36/T4ASimuXbvGD/zAD3DmzBn+9J/+05w5c4aXXnqJUqn0H+w+fy+8U/z+HgmCgNtvfoP2ta9x83Mf58b+CMsL2G4dEgVTUF/EGMhae+juAXoyQIw6qHhA3tpAbVxHrZzCeD52oYDj22Tba5S3rnBZtjmhRhQU+JYkNhKz9QBhOURbGySHbUq9LYoqwxJH0bWOyYiCKvnSI8RzF2DYBZ1gbB+pczp2jbA8T+4W6NkN8MsYIYgri+RZhJ3HKGPwLEXVFSipiY8/SWqXOfenvo/KI2eoXj5HMtaoWpNxcZFk7Q4Jkq3KKexLz9JduMBwt4MoN6DchGIFOXMMoxxwPNRoFxWPyeMJJGOU7WENDnAbDfwTpzHtHdrCRy6cxG/dp9l7gFWbIVl5HAPM3vsqk8QQbm+TeDVE/wBmjlF4/gO4Z85hRh2cpePkQpKPeuhxD/PeF6ht3+Pw5DP0M5vMb1KYn+HUwiwDYZGuLJN/40u4Tz8PQqDvvUbvtz9H7fFHsSXkToGhscilhUrHSMsmssvYboDVXmeoyiAgE4KKqxgnGRv9hH4Oea5xbJ/HKxEVx+LJxSLPLwS4hQKpsMj8GocnH2NhqcLNylma3TWeLMfsDRPII4qOZJBknKr5XCzmLOoehahHYe0N3jiIsCQ0fMUzSxWKKifXhpWixZ1OhBYSS0Ca5lRsia0Ezx6rU5c53cIcX9xJGMcpqYYzU0W64xjXVpxqBkf2WklGmOTc2Rmx1Qu5u9VntuLRGsU0Cg6epWiNQ+oFj0szRQ6GCdd3BhyOEm4+3OPZ5TKjxDBTcjnf9EAqJsalKwqUREz67u/m7kDw6ZfWePeiS9cJ2Igs9mM4DDXnk022eiHHqjbPnWjQa3cwaUKo4blTdYSlGGqLjWGGlSXs5T69uQu8vhvSzDoYY0iUQy9K+S/fM8fGRHDciVl4/Qv4rsVDU2KcaOq+RR5UGeYe8e2b7IWa7Kn3gjZ8oDbGKlZ40E1oHNwmf/IDjJwa27rA2kR92+bK0KgWWWiUeNdSBcvxuLYzoD9KGUUZ650xRVuQZjlnpgpsdCYIJIGtSLKcUZIzTBJ2xhmTgy0++9Y+9ugAECwszXOnm7E6zOlNUu7sjfmNG23utyZcbcdojnpke1HKyWaZ9ihitZfwhZbgYBQziHNWY4euVeJKT9JKBG/2JVmec7Kgqe1ew5UZ8wWLJStmkmoOjaRQKaIkTAc2rrJYqnq4liQ1R0WspQSTDGQ6ZuKUSNMMoRyMVyKrTAOGTpTjW5KDSfbtQJaEXLjoHIRlYfUP2FNlPNch9ytIx0Lvr3MQS/b6Ce5gB3f+GNVzj1AsBrRTcN73vZwuQRZNKPW3SByfG2KeycolJijsqVl6sxfwJoe48QDLclDCsFi0MLsbGJ0R7+yShiGme4gd9vGSENPvoC2Lwys3GLx1Bbtco8wYV0A56ZJtPyS++jLdT/8GVjJElRuoUQtFjkOG09ti/PlPsz/OKUhIhKKYdiFNMOMu4t4bzJoekxxao5RWlNNPc2wFSZaxN04YJRmrvZBOnHBo19A6Q7qSykKZPEmZifapOwJPJLjJCFltYp04j6nMIY8/CpZD5hRx0WThED0ek194H0n9OEJILCUJ06OACyuLEfv3OLW0yIKakNkFMjTj1LBUspFSEuc5gzglzzVvbPR4sNUnTzN6+0P6O7sMWy0OV+9B+rsn9J86O0dvnPLkpVP8w3/4D/nkJz/JJz/5SXzf/8OQ6T/65CkmT/5Aizz9d7/P75Of//mfxxjzb6z3v//9v/MaYww/8RM/AYDv+3z+85/n4OCAJElYX1/n53/+51laWvoPdo+/V94J1/73YHl5mS9/4bd44bu+mxe/9AyP//Fn6bb22Z6aYskxOH6F5NYbWMUyetQn9wKk2iC6+hr++cuoQhmtHITQqGqT5ge/B2kJCve/xdODMfbUB4jtGbqpjXX8MYoWpHdvM7qxRnDmIlnzBHEmaHg5Is8pKMnIqZLnGfXFc1jr12DuBKq/S6kyjzCGAxFQJyTDQzRWmODRSA5h0mLHnqEU2AidoyyXoLeGfu792K5grlxm7xuvcf7/+pfBziiGB0SNBRpxiy4BplADv0DlT/wE/U6H8uwcbN4EnUK3BfMnEeMu+d4aY1xMY56CA5YANw3ROqH/6pcof/CHMZU5VBjiOD5qfEBUWcAplkjf2iC5nBOmHlNRyFnfootHefUNZLHBb4fHONe+i12ZId3eZtddYdr00LUak1HMtOugb7+KchzKjSand1Z5ePk5Zl75OkZIVn74IwhyDt+6x/SP/Bkyt4iXDElMldz1cSdtcjtgLH1qImPfqVG99zKcfhRHQCSg7FlYUjCMM/bDiIVSCWMXMbmgHUuerEgem/bw2g9Z846xcvkcdn8D0e8RLZzj/l4Lz0l4La/RLOXs9GMavsMT0zZqvE/g+CSjFG1guajopoI5O0N6AV+61eJks0BgCepVl3FqkEIzyTVhaliuKfYTzV7qcn33gK2DET/41DJnmgF7w4hnj9c52SyyNwgxBpQSPHO8zpvrHc7Ml1mpBQyiDNdRhFnGfj/isfkq28MI31GcnapwfiYjuDTDQqDYGubMeLCgQrqxZpK6FNMeYS5Zrlp8eLbG/bIhdFwulhO+cO2Q5qUZlrM+w6mTfPH1LgtzilkR8czFFX77Tof5RkBXuqwomA4cunHG1TDlCbvDqDxPoX7AQ1PHFZqv3R/ywZMlKoMW951FvHGIuXGT3pkP8O6lKt0w5VjZYT2E1l6f8/UKwd5dvuUeY9YpcH885Jw5IGweQ8zOsaurxHFK2XHIxiESSTvMePLsHJGQzDKmVo3oOVN0Jgl5bnjYnnCyVOX8XIk4z1mq++wPQ6ZKDo6U7A4jWqMES0FZLTBV6+HGe+ROAdcSrO1NiJOcZ1ZqDOPsqA+45OBKQ8W1aGeal7YOkQZevrmD8j0uzJU4Fkhev7tLKB2eP9VktTXi2IqPL6F8eA9RnycqTSEfvo176mkaRYEUFo/MFri2PeaJ+SpV3yLKNF525GOrMASWIskEnoLtxAMhmXE0iVAMrCr7cUrFhsCSRDmUFBQsgxYSkU4Yph7NbIg/VUOECZYn2JnYSHeJMOrQmuSsjgSXy4LYr6Adn5n1TUrTJQI8TC4RUZ9WYYXS/iaPTE2jswpWlmAsG8vxseIhdpqSuj4yGoJSFB57gg1rhoLOKfoeWbvLndICp/QemJTJgweUn3k3A+OQRyFplJPVl3GzMSOGRLMFvGwHs30fe/4sqeWT3L6GbixhxgcUZqZojNdImycZ5hppB5gsoldaolhdoN66Q7s0xYKXcHuguN+OeHJKcfVQk2tDyZE8aEVYtoUtNC/+k8/SubKHEvD0yQb+9jWcxhwqD8ncAhLISnNkUmEcF6+zia4uM86hFvZQCycY2CVCo2g2T6CUhRQpFZnwrNdidmmZk+4YvbOJXrqEnecUXAshDXmmGcU5Dw8n9MKMN2+3GA5iDjc38CsN8kyDEEfOPP8a//n7L/DcM4/x4osv8lf/6l/lc5/7HMvLy//xxfmPOI7jMDs7y97NX/mOXG92dhbHcb4j1/pPlXd2fv89ed/73sd//3/7b/nmP/zv+dTb25hxn4NBSB+f3HawTz+CqNRAStK9bfLN+6hCEdWY+7YHrkQWfITJUVl4ZEcmFTKakH/rtyjce5liHjKqLNMpLyGNxq4WEMUK7QTqTkZiJJlTBmMY55KtYQa1GZQXgLRJy7PftmkaIJXC6mygOhuk2lDN+kc9v1LRjHawgP0ooxMbTLFBNHsCUZlCOC6VH/yT2J5C2wUmgyH9ygo5NlO9ddIoomZr0uocsjqFRtF6/QaM+4hiHZRHXmwiyjWceIRdrCCCGkIb0DmEfeyV46QoZDJGDfaQtVkSK8BdfQuZRYhCCc8rkF1+BjnpMSjMUEz65K5PvnGPp443cWbncQoVvLOPIn/l55A3XuUuFRYqDqXhLrQ2j3oPv/opFqyUcjLk4NxjpHev45+5hHALnPizPwrJBJmNkTphWoZHR3ZphNPbomTG+ONdIreB0jEimZAjGCUaT0mKlmSuYGMryWGUE0mfJJcsVz383gb3uglOFtEapOg4ZFA7xqmZIqNwQo2U2Uad7bFme5Ty9GKV2YLFg5Ei1oJk/gxhfYW5koPU2bfjigWTVDNX9rCVYL7kMFt0udkeUfAcjpVdTtdtwijHU/DGTp+lqsdiLeB0o8DJkmCq5LJYD5ikGs+2mKt6tAYRKw0fy5I8ebJJ2VMsVz1yrdnqhkwyQy9M2B8mnGoGlF2Lp5erPL9SwLIVyyVFFo4Zxpo7I4e394ccZC5vrLZ5OLLYGOR84HiBy8kDdnoJl+YDyibls23JSFj8xON1qtmAPBoz1brOc6cbPDW+ycWSIQMKtqFKjO84JEaQDjtcXihxf6C5N0h57mSTiZaoqQUuNHx2KbLwYz9E1XOpeTYrJQslDFv7LUKjCHNBu3ycx9jlWM2naiaMbt9mPXHoOFNUbMPdTswkyzhWcZhksDOIj3riJ4aWcdnWJS5W4YV5myTTvP/0FFmacnnhaJDteD1gsVKgE6ZMFW1ybRgmKbYU5NLh6ZUyLjknluY550x434qPMoblisuZhs+jMwEaQcOz2BrEXNkdcm17wNs7Q44v1PiJZ5Z4drmGS8pKdsDx+pGH7reubWMsh5MVG6s2w0Gnz+e7ZVKhiDMYC4/tYcKj04WjnV4pGKc5gS3/t1OE3CCQ2CbHFTm7Y01mBJnWoGx6icZ1LCapoejaOEpQ98WRL7fIkUaz4CbEXoU0qBAVpiiZiFLSJ9YCEZRIwoiFoodwijhbd+iNQsTuQ9wswrTWMJMeSWOJ4HCNG7rGIJWIJMQoRe6VKeVD0sIUerCPGh0Cgmx3DTHqsCAnlI/N4u7dZ/zKK2xMBLpQI9u4j146iW5v0K8uMfr6l9GDQxK7TPtwQLBzh9kTKzinzzLZ28NEIaY6Tby9iQrbmAvvxnv0CYaFaYaZoawnbIsqqRAMS3NkfhXT2aWixzSSDr4lWSk7COB43acRWBxOEk7UC6yULWwlOfPkCfphRsmx8GsFpJBYaUieC1K7CCsXETpGK0VmFMJkRFpiCYmszdI//iz78ZFHfTeYoRMLBqMRau8ebmOey9MBjLqIlQtIJGujDIxhksJmd0zBEhijWd0bMRrFJHFEf3eToBIgFWD+zaP0xekyszWPPM/5oR/6If7u3/27PP/88/8x5fg/GTzPY3V19XdZk/1B1urq6nd0SO0/Rd4pfn8f/I2/8Te4cO4yv/I//l/oJIJh54DNYc7IbXDXP8lV+yRq/gTKcTBZgjs9h0GQ99pkh7vkgzEmN+jJ6KhdAIEzPY2e9MjW71DZepP6cANvfIhz8jyNF76LrLpAzZIEJiGI2shJB2OOBk58z+NQe8R+FREPyC0fpEUa1CmQYSYDcHy8B69hbr2MHh6S99q4SuGMWyylB8zLMW1nhpqrIB7DyiNYzTnIc9Lrr9IvLVDurxPEHe62JrRXN5CTISIaQLHBTqKIRil4JUx9AX24BTpDWApvdo4gH6PCDkKnmMEh+aBHqV6nHLVBa0BgD/exbBsmQ5LOAdaxs7B6henARSqJbyKC4TY88l6yM4/wrmkLy/MRhRKyMYvXqFA8fZ7Leg9drJPsb2Ivn0JWpzDxhP6NW5w9uUJiORwol/D1ryG8Avb8EtrxMW6ZrDBFMNhCpBF72keGY7ywS+Q1mLcjzJmnAIMEOmFKxbNIc40rYa5o04tztDG4SlJ3JXEqeO+UYa9yjKfnHKz7b+GHXfL71xjmir4swLhNN0w5V3MJbMk4zXlsssqbaoFS1GGNCjmCtw5iPGXYDiXSZCxXPaYLzpGdmoGq79CbpER5zqytKV/7Gmu9iO4oZfVwzGPH60dCZjsESpAZWO+MCVONb1uEqWGcGQqORZzl9MKMVBuEgV6UUXRtbu6PWKn6zBQc+pOU3iRilBq0VkyphH1R5Jfvhqz1IhYqAesTw8mmR0lqpvIhr/RtslKdryQNrvdybM/mkRrc2OlTrVYRXolEuViuT83KcZbOE6YZ0mj8POT19R5hGHLtxjbp5z/DYXGex6ddzjQCFksKYTnkfpVxrqlZgtQIpgJFmOU8GOQUFLxnWnFycRb3ieepVgosxx36qaZhawr1Mmv3N9gaJLRjgWdJbrcn/NKbuzzojMgNKKC/t0dp3GE23mOq94D5tM2lhTKzZZ+9SHKhKnDymCcaivPTBYq2xBKKRmCxUguoezYImNNdhBCoUoNJmPKuGcWHzjfZb3e5+nCPm1tddvoxOhqwP4r5+Ctb9DojjM44OV1iouGNnQFfvtdjMy9xdXvAybrHDzy9SCYU/TSnk7vcyys0Cw7DmTNkSDZDwa39CZaAd89aOFEPkohhlCGFwbMEsTYoaSgQshtqGoFNjiCSLrkRaAOeUkgFFU9hSRjEkCKRJsdYLlKBNTjAxlBlgt3dxR52mPUNM1aC71hkxqBdBxX1mN58i9qZk2i3RNg8Qa8/QUYTTBRyxgvRXpl06jiZkRCPiL78Gzite2i3ghm2EOmRs4RUCmv9CqpaR+gMv1LC3brPpjNF8uAWYvYEm8EKXtqDC4/BynkmaUpcmsE78xghinhri9yrYJucvLyI/cL34V58ljCood0CHe0i8oziYIdEgwwqpLnBjgaYPMPprDLyGwQWVFyLO32DIwULnmCl5HK6GXDMS1lMdvHjET/wV3+A5lwRIQUEJVR/F1vHyCwktwNyZWHnKWmuyUozDBLDJNMYN+B2aGFLicBggINeH/fgHruiwunjx1BR90hrSlPIbMIoNqx2xjw8HHMwydjsx3zrzVVu3z+ku7vP6NsJXP2dffJ4QjIZ/S4NFAK++LN/Bc/z+LEf+zE+9rGP8Rf+wl/4jyfC/wnied6/YU/2+13vFL7/bt4pfn8fCCF47eUvItOE/+Fv/gxhe484z9lLJf/dL77Bf/sLL7HbvIS1fAajFLJUxeytkj68SrizhV2roE2OLJVI7l8jO9iGNMQqVclGE5IHt7BHh0Rf/AST9U2oTBFbPpmy8Qe7xF/+TcgiuplHN82REm4MFa1ggbgwi0qG5Ei6JiCTDun8ebAchDAw7JIfbBJ+4wtox8MOe5T27+CY5KjX2PKIq0tkXhV3uAML5xBLJ5krQPLiZxl95ldxZxZYLy2SHHZp/+I/paZHTFkps889jkkTCHsw6UMSIo1GhEPM/gZC66PEJttFFErk2mJDF4hSQehVGY9i9PWXmNy7RRomdFYeI7/+OpM4Ju93yCwf4xSo50Pss09QWnsNdfwCuR3AxnWm3v8CbrmIam9TvP0SUeM42A6TzU2Ks01sR6L7A4a/+Ak2nAYDv0J4/y5CKfRkgDGaNDOEwRRv9wVxJjHVJrnlEUkPR8cI5ZD4NQYZLBUlltDMyDGV0TaLukvJglGSoYQmMxpVrlOwNNNpl/Lqy7zmnCLfesBBZZFhrGn4gm01zXTJwbMlDd9mHGUU1u5wuZiikhFOscJ6a8SDfsI4ytgaJmS5wVaKMINMKhw0C0WPy3MlDiPNQFuIMORTb+yANkyXfBoFF19K+rFhJpCUHYUQ0Cg6FGzFcydrOFJwarZInBvGicYgjqzXfIuqb/P4QoVcGyaZ4cnFEg86Ezb7KZMk5iA0nK+6zDUCHPuoGMqM4V5eorq8xPGlabZH4NWbjFPNk1M2s2mHHIsTBYEwKamRBMkQHcdUzQSrs8XGwQBPJ9wfw0ebAy55I+zP/Cb3P/FbPBiByVMqtsDJIxwlEGlI0bHx+xvkpTrNsEXN0lhSopQi90rUCzbFxgxuOmZQXeGcF3JfNrngp7z3+Se4stmjGyZUHIt+mFIrHonJQsXDlpLVh/t88407uEpgtbcwky6n3QjXRByzx7gmZ6yh9NJvEOgxj075FF1JM3CwxNHn0p5k9A9bJHaB17eHdLw6xWzMjAg5Ge3wqDfkMbnPjJujvAr77S7Hah7vPjVNtxvjCU2Y5Lx7qcjF5SmeubjEU8eq7A8jTk4FjAcDjIGvH+QUZc65aJVrXcPD1oR7nYjjJYnUOXPjbbytmxij6Sc5/TAn56hFqekp+vhIKam4R167vkixdcR80cG1BSVbYUmBZzIqeQ/XxFjJGHROiiK//zb2wQMKa1dIWjsM6sc4jAUDHA7jI0uw0DhYzTk8x8KxDLGWKJPjbl7HmRwynjtNy5tilGS0M4nT3cPYAcNCHalzzOCAuLqIyTNAYKRFXqxjbJer/+u/oPbMUzwtt+nkiuCp9yLf+DIsnGZH1OivXMZISS4VpUoNlOCzDyPCldOI2RUyx6OlbUZzZ4luv8294dHJy6LpUrz+JeLGMVCSEQ6LXs4ePhMTQDTBS0csZ7s4EvbHCa1JSsGGSW7I84RK4OFmIeWnn2Hu3ZeZefocXr2MHhyiowkmiUjTHBNPQNqgM4pJl4lVJNeanVFKz6owjHM8S9KZZGzud2ndv44pz5BWZmmYEWlhBoISRlqkVoF+knKvE9KOU1qDiH/6zVW+8eXXKFQcLClJRn3Ks4scrt3icPUO+b/W8vDWr/w/uH//Pj/7sz+L7/v8/b//9/9jy/A7vMMfiHeK398njuPw4NYbrN28wq9/4hPkvQNe3x6y0x3TVyV6qoQuN8l7PchiSEL6t+4zunkHaTuYYRepHITnA5p0MCSfjPFPn0VZAj3s8vAzL7H2+W+SbdxB5Qk2BlmqM7p5lcnbr1MY7fCt9R6fvnlAkms2Rpqt1EFLCzfqMM40oxyksBDKOkqlm1vGTMYULz0OvQNEniMA7ICSOorRzVCoLMKUZ9B5ijn2KKlfxTn/GNbcHGeSbc6UDFbJR84uoNfv4PT3UI5EpynCDZDNZYQx4FcR8QRGLTCQH6weRdD5ZUQhYLp1H0dHjOcuoLZvku5toPIx7twCfqVJUp7l8P4dbM/FXH8JitMooQnyMaQTdOcA097FRCO82UXGt2/gT03jzMwSvf4qurlE/+oNrIVTFC5cRB/s8N4//yc46Rtuu3OYSoVkOCS9+k1A4MVdenaFVgQ7okysAsZ2A1cZcukg05ChLOBbkoIlyXKDUDbG8XG3bjAvetRJqJkx5Aazv0Zil/Dyo4eb96wEyL01qo7mtB0RB3UsJThddanIhMMw4kTNxXrm3ZgvfoYbYp6qK/kHv/YKNx62CI2kHR75izoKfAX744xumBHrnLmCTd23CVyb0RPvp1QrcGmhwuMLZY5VXaI8P7Ix66ZcaHikueZkvUDgSqYLHpnWeLai7CpsS6CNpuxaNIsuZ6d8JIbFssNUwUZKRZSBGPcJxyHT421CbfCUYrrkkOaaxZKLxrBkRwyjIw9eOx5ha8MHlxxeDYusZR55sYaUNvVwH0mOcX3YuE789sucy/b59KsPuNGKsGpzuLv3Kf8f/izV972HoqPoaoe73YTM9pizE+zJIb5JmEn6ZMZmZBc4GEUUbUmoJUOrhJNGsPsA587bvJiUEdGQY6bN4MLz5LbDxYUCnVHMjEpASBpFl4bvMFOwGWeaxZUq3xj4THIQK+dBWFR2rzJretRdiVYOT0y5gGYlPeSiO6RkS6SEqnu0c+aTYJmcqZkZLtYULyz4pE6BIB3SrS9ydq7Com84U7UIbn6d59pvsjRVZKrmc3m5zKyTc21vQCnw+eCFGYpBgXctlVnZfhPPtvjE1Ta//PoeX7nR5mFrxGcfhtxrjXl1vcNvXd3leivkZjdhUpyiu3gZz3Fw5NFQXZIbhAQpBbGReEpiK0nD0fjJEJ1rCpZGCbCVQGuDHPWwd++iBy3M6BAnGaCGHbqiwpVJkcPCNBuzj1Biwu4ooxVbvGfKcLkusLMJhAP2p84y9prIQRunu0N07FEm7Q6bkYXMcw4mObOTfa78818lGQxQ5sh1IWvtkacGrTVm+REGb72J6ewjF85TO3MMKwjwbcNjZhc1fwKMpmLlxFlCXaW0xxFRppFCE7o1sjjkfNlgbr2EcYsoMkRri3hjk9Oyw/p+l0IyxBu3GeSKotRoFZAql7aoUDx+AhNHqGiMM+xgZ0N8SzJJDZ1UUnItikmfRnoIWhM8/T7Sk08zc/k4ynOJtreI7t8kK02znXukTpme8YhkQFcUCRNz5PAyTvnmToRnKzCGe7sHtB9ex2suUJpdYq7kMsLGSIVIYxIrINSKnX7MKTfDkZI3NnpEYYY/tUS56KB8B42FzjKyJAGd/a4hqq/90/8OVyR84hOf4PXXX+cTn/jEO/2l7/BHjneK3z8As7OzvPrNr/CvPv6LvPhbn8QRmqfedYEf+94nKHgOuAHe+cegUCYfDfCaVbxaCbIE6TgYcdS/ppOccGef+LAHcUjSbjO5/ha9jUN2b7cx4RBPp7iTNqa1ichyOi99i9Gv/gsckfH6Ru/bO4GCTpizmfq0RREljo7nrfE+GRJVnUH4JezjZ1EWmNWrkIaIqSXo7VAbrONGbWQWInubaOHQzl0yt0osHA5PPIt47qNElkdp9WXc3Vs05qoMvv45Jm98g3Rzlb4oYNKUbP/h0W5FGmJsF1mqQTSEQR99sEm+v0YejrAbMxidU2ndZXL7DuHaKsHiCumdq5Q7q1w/991MmRGpW2Ly9mtkmzfBaEyuyZFgSbj/KuHDNRK/hNusQThExCHisWcY/Na/ov7M42SDDtbscZRXQD3yJEvxPmXf4baaovXpT6GCMjIaInRKXU8oOhZTgUXbBBykkGpF5FWP+rZNziDOOYw1wmj0qEuoBXrmJJaEUrRHUYcM4pxR8zgyGSMcn6zYoPDgLXjkXZRlgm7OsjDZZrrkkWWa3YlmSmXUw32y1fsUH3uc/UQzpfucOL3ADz6xRGBLKq5CCkFFh6QaKnrM3iRDAiodMIxTOpOIzVgxV/GpF2wuVAUzgcMkM0wXbEquxcNexELFZ7HiIRAUHEU3zGgGDueniyyVPWwpkEJQ8hSNwKXq2Ywzw+4oYZSkrHcmPOoNuN3P8etNGltXiHJQBrqDhDvbA87WCmi3yD9/u8f7821uqFn+xHKGR8ZKI+Aj0ymnZJ+H3YgdWWHi1GD7Dn2vif/u7+GL4yp/42c/xT/9pS8hXZe9yjGap4/xxA9/mKmiy2LJ4oxp4+QJbjwk1Rpx9esMnCpBfxudxHz5/oA8zzgIcySSQ4oki+fJhodUHDBJTKzlUciAgeWC5P3uAWu9iOcWA6YCi358lGjnKlgXdSq2pDuICd0aIksQ5RmkXwaTIRwXW6e4zzxPo17E6W5RkyGn7DFnqy7TRZeFrIOUkqfKMRcLMQ3dw9p/gBGSkiNIlU3aaVE/vIc16sCl93Bm6ihm9j+r9bD9In/qtENgW/i2pOJbVBxJuegyP97gh55YwA9cHjteY2ZmlvtZie3+hDOzZb7r7BTNss9ircha7lOrljmIj55Ly46NMhmznsIWOY7QlF151KsfR4zsEkwGsHkXPeoSRD2czhrizjcYq4Beati3m3RlFbFxi/z4GV7fT9kNltGWT8kRzDkJNd9mWqVI2yeIeyAULVllzxQo6T66Mo1SNr3583xro0dFpJDnWAcbnLm8zOsDl2qlRL67jnX6MbzNK6TDIQd2FWdqBlWfQtk2J3/o+1FxH3vxFK7IQae4jzzJ9NrLPD2+R0HBCn1mevfxsjGt7pDvK+5jY7C8AFsBWpN94p9RuXAaz7Y4kR9AOIJ0gv3y55myMrQw9FLBfNGiHcwROyVG/hS6VCVLUs7UfeYKkqIyzHuaaUejRl10ocbYrWIKddxLT2MMjNY2CDc3yIUk1oJBZtgY5uylFq+3ElpxxjcfHLLeHfPKeh/PEnR7Xaz9e0SlWcLC9FEs+jglUzb2qI2WFqEWDDLNo7NFamtXaA1jWt0JW1s9smTEta+/StgbMT7cobd5FzBYhSqYI6O2H/3wszx5YYVf+qVf4hd/8Rf55Cc/yczMzB+OAL/DO/wBeKf4/QPy1FNP8TN/62/x//yZn2H99RcxRuC79lEMsldG+EWIQqRfxJuZobCyhKpMIZfPgk7JBl1kpULp0iWsUgnyDKQk7o2Qk5QTTy7gLJ1EjHtYk0OiO2/RfnBA5+4uo/v3+IB1wFMLJbaHEdoYAB70U+70oB8Z+nFO365hhT1Mdw8qTSg1oDJ9ZM0jFSQx2a3XkfEQuXOHVEjy/S3E5BBfaVSe4ilDLbCwvRLrhRWUbRPeusbmP/8ESadP0u3C9DKDX/tF9N4DGPfR4RAx6UFQAis4Ek/bIusdIkp1zI1XsW2b8NorsPsAJXKi3Q6ZkeRaoK98BdsLKB47xVbtNJUPfASRZ+AUQeeIYRdl2Vi1Br07a9DaRugc6Qfkjo+9cAwZD7GkwSoUCItNHCvDv/pF8smIC/P1I6/Ux9+Ldj3Mxm1QHl46pOxJmr5kb5SSaEM/NYxSgzGaih6xM4zxHQvLxOyoCv7hGmN/ikNdIn3jqxhlM71/Fa0Nxd3b6CiilThMtrfZtmuY6hyWOep/XutHnCwbBklO/umPk6/f4e1f/BzJ9DwztkHlCX/puUWeWQiwpaAeONhoHGnY6IXsxZLTdQ/PVozwKNmKXNjsjRIWyy5TBeeo59xozlRtPOuooN0eHMXR6iyh7CqmChaBLakFLrYU2PJo6OmR6QI116HkWeSAMVCwFZ+9ecC7Vup0gymeLMb03Cb09pkpWOQYzs84eJ7Ftd0+b3ZyLh+r4ywuk1o+frHMhqhT8T2m27ewgwIoyb2RYKCK5H6ZLKhhWZpjS7P8+MfehypVsYXhjpii3lsjmz1JlZi9Yc60GTHafshG7BBYinzS5XVmkOUqn327xYma4nN3DwHIckNuckbaplaz8RzJr6zbuFMLdO7d5RlvgEZRLJdZmargSMm8pzlV9ThR89kbJihl8199/2M09BBjDGHzBGltDi1tcAt4UmImPezdB1jKRjYWWOvG1KMuQXeThspIhx3cUpm4MEXfn2IkfZIoxUZDew/2VkmVgznYpHDhCbzGNAsliyfnK1TnFlmq+VRKRYQAWwgCW2G7NsP5yxhl0+hu8P5TNY41C0xVPD72zAl+8PIS56dLnJoqsFQLaPg2oVFgcsquha8kVREyp8aUZIIIh8yMNvB2blEY7dD7wm/yoBNxsLnF8LDNq3shgzRnN1J0Sgu8pBdAukS9Ntatl8j9El1TZK7mc6rmcybbIctySpUyJREjJbh6gkhjtNacz/fwXYfUKYPjUwr3ebUraTiS4vZtbu6PYOEk4pkPcDLcZP+V60yGOSJPEOU68YO7HIY58tFnCVeeRIw6WOUyxsgja8ONW1jRCMd20PvrdH/t17CjHo5lU4l7+GGbme49ikogwx65U2QsCkwd3GDmo9+LqjWwMZR9h7B1SJwqmAzQoy4iCVnvxby1H/LFfUm0+Cj72mVglXGSAWVXUhIJgedQtCRysEtWXyZxi2ihQChkntBf3UXHCf5MnXEmkcZwvRVSsQW5Nmx2Ix52Y66udfnW3QPGccrmzh5bd68hm0vMLixScSXtScbxqk9RSkx7k6Qwxe4449bBiE+9vcs/G87zxnqHTmvE4LDDuNXi4P4tWvfePrLcSmNMEmE5Hn5jgQ9/97v4Z//Tn+eLX/wif+fv/B1+9md/lqeeeuoPS3rf4R3+QLxT/H4H+K//z/81H/rgR/gHf/P/zuThLb55+4DDccpABmReGSwL6QdY1TpWpY6sNpBBFZ2k9B/ukPYG6HBM4fRZcqGwazVad3ZJY83sey6THeyh9x6QX3+F9ps32H57j923t4n6Y4J7L/FjS5p3zQUocbR7d74ZsBumDNOMMDEM7QLD4ixZbYHUb5B7FSZTp2DpAqY0hR51EEGJ7LCFsCz8zWsox0b1DyglQ7zxPk48RgqBMikz5QJy7hST1pD6uRnyJCfuDei//DJ2HkI0Rnk+Zm8TjQTbg0IRk8QIJEmvT55ozImLyOEeB198EXHyMTAGf3mezmtXsGcXkbbNE7WMpDTDW60Yd2YZxl3kqIXZXwUvIHeKiEKV0lNPk998GeEFmNo8SZJT6qxSPHcOWZ0mPzzAise45y5jpxN0mhO/+HnOTbbYMR6tgw7JwTbCpIg85qQdcTDJmSu7ND1F1QzpjEJaOkACx4oSW0IsPYzyyBsLOLe+immtoadX4HATq7dD9vo3cCs1xvfvkiYJ9rs/hO8VEVHIuDfGmj3OspMTCZ8zFZuDr7xIMpywMmWR+A0ujO8xcsos1ouILCZHsBgIZoo2Lx7k7PYnpLhYSuLbks2hphPnvLY9YG8UYynBgi8wacI4y3GkoDuKsQW8vd2jNUr42vqQ1jhFCsHxesBMYBEmmgedkL1RRGuSoqRkHGdkuaHsKQJbcaIZcK7h0nAMVn+X3VHC4OTTNAObSyWItWQc5bz/RJVmweGZpTJv5EfF5EB6hEYyiULyuTPYSlAj5HjJxpaSg+VnaTAmriwyVS7xUx97ih/98BNYAs7WFMpxSLUgN4LDccSVic+kOMPn73YY9dpsrbyHi2UNh1uszFf55pWHfOvtXda7IaE2zEw2qZoJByfexxQxjXqJomPxtd2IkVehFu5xIMtYliLONK+uDVgpwGygODNV4MnlKkNt8bB5iXZmgeMzyTRCKAyGYn8dpSQ6jhjubCFsm4vxOjINcTpr2Fe+QJ5nlPKYZDAgyCeo/TUc30cM9tB+CVFpMll6hDDMycoNvKSPmXSZUhMit8DO3j6tiYE8Y38Uk+capTMsW+EXCgz3W9R9iaM0TU+yUPY4VvNZCnKWizYN36bEUe9uabBDUQmm7BT/zotkaY65+RIcrDJKMnpWgYlT4WrxOBvtIdedeXpLj3Bmpsx6bJH4dYK5Y5yb8hngcLUneHE3Z718gjCHuZkGJTMh9Bv0jcsoTKh9OxpbDtqIPCOePoX0fMrE6CxHX32R3dIJXrp3yHdXQ3ADfqg+QNamUa6Dc/witivZbJ5kTc0yKs1iP/8R5q/8JmOniekfgjCY0SGmsYTeXyObjInvvIFob7D6xTcpfu/3kyUJUipEfe4oTGJmEZEnpJt3sXwfK+ofPUAvngAnIBxPSIpN7ILPZHWL4uVH0Vs3KB3cYYUOFU/xeF0wyjTGGEqWQbllYnPkuewM91FbV6HQQCvr6DuzFE4Wkna7WI7Cr5fxTj/KJDO8vTNkEGfEGtZ6Ic2iy6+/uU3RV2zuTzhZyCl015mUFpieXSTOYZzkGDhKBkSiZ07Szn1uHIR8/UGHtze7tLXNVmvM7u3bhL0D4nEXAMsvs3j5SexCFTBE3T3efbLMb/yd/yMbGxv8pb/0l/hzf+7P8eM//uN/SIr7Du/wB+ed4vc7xC//6r8k0R73f+Pv8+DGKr91a49OLGgFi5hCEyMEwiuShxPy/iHhrTfovPIqOopJD7tM1tZACMIH98h6ParHmlz4sXdRvvwY9vyRIXQWjujf3yaoFzgYJCgBZjSkOVzlkt3nVJAxU7CJteFE1SHVoCTMWSm2TkhLM3RxGUufGBvmT5IUp5G1WUShgDn5BEwdxy7XodgAx8Xs3sUM2mijkQJyr0SNiCxLaf6J/4KoPaL78IDh+iHbX36dXLqkgz4m18hak/ThLfKdB2TrN4lXb5BG46Nd4t07TMrzTK6+TvPP/EU0UHr8WRrf9UGCpo+qNxEnHydOIUlinq2DsV3GJ5+GUgOrMo2uLZI7Aen+Fu4jTyFLZeyFk0xuX2M4fQp9uIewXezmDKkqkLe2yO+8ilWp45+7hCqVqD73Yc41XG6Xj2NmV8iiiNyv45Bg6QxPgJ8P8U3GQraPPdgjT1N8W+KaDFsIFNCxq4jpJeyH1+kVZxkbF3fhJM7+fbSyESam9vbncJUm7vdwulsEnVV2KFF1DWkO2ep1Tv3JD2PNLzLz3OPUJ1uIhdPYyQg/7lMgx1OCoquQlk0v0mwPEow4So3yjaY9Ttjuh1zb6rG21UFKjozp9x/iSAstJPtRzu32mJV6keWay8WpAjNFl16YU3EtojRjlKbk2lD1LB52Qwq2xLPAsyW2FGhjCFybdqTRTsCX0kVyrRk5NZrxIeWtt7jdDnlhOkcBls5pmDElS7OcHtBJNJWkT1NGoBT9TLGUHTJVUBT0mHrgoYIq2kBD92lnNh857iOiAcu6Q1KcIYgOKO5ewxm12ToYIXbXeOnmHt8Y11mMdpm58zW60+f5z04oHr14gucvTPPSwxbGaESaoLZuUaxUeJgG2J7Dmwcxzzx9gYrvQm4YGodxlFIKXA5CzTdW+yRpijKGOd/g2ooOHk6xgtIZDoa9cYKZDEmVz9AuI1cuImeW6KkKBZWisgmxHbBFASUEfqUOd97A3rtHnOXks6d4Sy5zLykTVRYYYxGffYqbY5tOApVSmc71t6lkQ8yV18mvvYy+8xY7h13cndv0Dlq8utlnL3OoP/k0UWJYad9DxSP6iSY34I47uGtvMaf7rO20WJJjvLhLELbQb3+TRAVM7l5lUDvBhjtPXl/ivq5zb6won7/Eb97pUywUGKw9YClpc26yQRwOSV75CvZkgBKCs/aI3WCOb24mVFzFVKDQyYS9u3coTVosM8DubJA4ZbLNu+AEeIerJPeu4uoE6djkxy7yma2MctHl0K6QTs0z5aQEhw8pbV9DX32F4rvexWFtmcMY1nUZ41ewiyVsHRFM9hFGEz+4h9l9AMqGuVOk/SHonG43wS8HJG6ZUDiMsZk8vE+4t8XBy6+S7GxheTZeNiGdPYvpt8g3bhEc3Mea9LFsm+b3fhR99ZvI3KAOVmluX+GMHLBsT1jvp6wku6hui670cLMIkhglDL3SHH1/6qglKh6hhMGKO9gLyww3u/gnTjKaPc8w08S5pjWMkVKyPYjZ7Yfc2+zQH2VcqhuOZ7uktSXychOE5mF7zINuyOrhiHGWI6RCKIkW0ItiDgYxBddGSQO5wS3VSUYjdDgGII+H1OdmMXmKX5ulMjXP5/7f/w1xHPNjP/ZjnD59mr/9t//2H5LSvsM7fGd4p/j9DmFZFrevvcL12/dY3P4NVnc73Ngf8XY7pWdVmVSXGRWamOkVTG2OrHOIsCyUY1M6exqTG0SeovMcVShgkoSZS8egWCdeuw+1Wexqk3CYEDQCymWX5HCE8n0QFsmtN/A2rqHSCVMi5KIXcrHpMu9mWMbgJ2M8UjqxIJeKokwwlouIx+hSHWv2JNLxSb0iSfM4prmMri8f3VNrG4TEVgrbpBgpEbMnUbZk5k/+SaqXH8WrBUw9foLDq6vsfvEbtN+6iXA8ejeuc/j1F8kP9+m89jb7X3uF0eoecbtL55d+HnfpOHZQwB60UKcvoxpHO8kkEaI2RTt3cVqrzG2/iREK23FInRJJeQq7VMbsraKLDXwT4Z04jzGC/iuv4YUHiKXToFOMybFOXUK2tlDNBeTiKeKHdyk89TzEE2qLJ5hu1LhymBAqD51EZEayku1SHqzikkJvj6wwQ7m9itm4gejuYJkEyCnaORtDsKYW6V/+IK7SpKu3OLx9l1zaDK9fxYyGsHyWyZuvYG/dQ9mS0ew56r1V+rHBf/OzHP7ivyA9eZmNn/s5dHUKx/OxbBsvi3CiHkYaqoTYJkUC71mu8NHz0wyTHGMMcZqwP47pTjJO1H0GseF4xeXhSPDxvSJr3QmJkcSpoRF4fPfJOp6UnG4WcBXEWX408R9rhklOxbdZqQYsVzxcBVOeZLpg055k9KKUum9xd29IguLiQomVsoObDMF22c0C5gKL5nCbNE8pmzGv7aWs2CHppM+t3TFZnjNx66g8prB7G8eW7I1z8izD6Awz6uBPDgnGe7RiWHQSpO2ietvYnS1UZxfpeBy3IoxT4MG/+AQ/+vxJzlYh93yixiIVR7BrNXlObWNJ6Ox1OJykrP7d/4XcLVDsrHNxoco40UhLEbtFbEsgZo9R8Fy2Q03RxLz7dJ1GNeDa2i5mbw03GtKwck6WLSoyoSsCvPU3UQIkAksZ/M4GZniIFZSpjHcQXolxaYGN2nm62DhewJ4qk116nk5hjrec41wfWRQDn1fv7bAxFoxSybaqk2PhF8pc72SkC6foT2JG557kTW+Fz9/p0e6H7KcWev0W9qiLL4Fem3B/jTdHDu7hGl+73aY7GJLnKcXBDllvn6LvYgnDnqpzK/L4ZtdmcOIprhXP0S3NYJfrKMfnQg16Sc5y2eZ7L85SljlLaoJ1uMZWJAhzxatxjf5XvkT06reYW17mY+85xQeWXZyrL+NhMEjaw5QwzbBGbUZuGaMzxjjk7Q3ibp/9Gw+YKJthaYZ1d4HtbswLKz63+4ayylF5ilQWzJ9iUq5zMHWKpVqR+iuf47jpoid93LNP4B/cZ3LzTQavfwt7do70/hVwA1RzHv/YMWI74PyPfAgtLJRtI7u7RNLDvvA00X4LXVlAPPo+RHMBnUYApNIFv0C8epfhlz6HiQbISRcpDP2r18B10dsPST79Czg3v8Fj6gB7/SqTYJpOKkkRdDJFHtQp52OCbEx+61XyOMY1GdnmHdLddZxqEWf5NDumzO29EbWCyyTJ2R/HvLHWYbMTcrjT5ZQ74Y/NRowqy8zNzbI/iNgfJkR5zmp7zGonpD3OsKVg6NSOhhgRKCnY3Dzkzq1dlqcLZMmEuLd7JGTCwiQRm2+/yfnv/hDHHr3A9mf+OsYYfvInf5KdnR1++Zd/Gct6Jx/rHf5o885/8HeQer3Oay+9yJNPPsWP1Zp8gj/JBy4v8fVUcKq5hBMYap6gogxBqY67eZvx1TfRAgrLy4iFM/iTEbrfBwEyKCCrU1jFAJFMsDyXoOwx3Okxv1glmD7yqO188+vsv/mQhfc9jjcZ48/NI5XAFhIrjxC5QntljFQsZSPsRKGA2PHx+9vk2GDZ5OmYzCkg0SRuBTedoOsLWMUxZtgmD6r0tYO0XKoixgRVRPcK8x/9LqI7V9j64htMnZslOuxx+9e+jluy0UlKuLsPQrP68kNsk9PvJ/DaNhf/8/cwevtNotFL1B89jzd3iuEb3yJ41wfIuvu4wzbz0T5y5hjhK1+kdPpxRLfFeOoUiZTYuzv0Sg2C6UXs219Hz59BlMo0zs/jxj1EtYY49giJchG3X8GaX0EpyAtNrHqTWHiYt7+KfOGHuThb4pXBgNXVNR6ZLWAlIVSnsVo79KtLeJtfoViZIlp+FFuHuOEAnUQUiRBCEFmCSPlESjAqnuD0+Qq7n/wU9onTRLUmjjYUyzWU0dTGYwavXcdrbpEkhn4zY8lVqD/+I7jjDjNPXMB77F2EhRJ22If2JmbhJIlXxRu1aFMk8EApedRyUHaZdRKSyZgz9QK2bTGMUo41y9zvJbRGMe863iBXGgMs1TxsJDVPopRkGKe8sTVgtuIQODYV58gzNNOammdR8iz2Rwm2MBREROI5eMrHtQWXp2yS3HA8gIk2iHRC5pRZKyyynA/YMx5R5xChM+Zaq3Rqi/QywTl5yP6hxvQGdHSM7vVIxja9sMtARGTCQhgNpotGkIs+3zA5GnHUq56vg1IgInSWM6cN5iMfxDp8wMO+ZMNoZJ4gB7cwQrGlc85bByzO5EzWbxFdfpLhYUSeDcjaMQ0UZeOgLMXhHuS2R2YkDWDwYJWseJyqNBSzIbXpOm5vCy8tQmURf7jPgTXDoHGKmkgYv/lNwud+kJlSTixstLJwTY4edGDlBMnhEG3geFFhO2MqvQ6mUOLZmSUm4QTL9fiBiw0mSUZgG5TlEuawO0552E14EGcsN3yubg+4exixXD+O289omwILhTMcaxa42wm5vh1T0Qne7ALrTs5czcZRigehj3PifQy04BtvbfDCU0cJZu1hgjrzKOsjw/HFJg2ZEFouWa5xlU3VBV9pnjtWJA5TOP04zmiLpdGIdLpM/NxT2Ou3UZ09WusbFI+fwfvmp9k1Lkm7TXlwn9OPnsORgu1b15h9bAHVbzFeusDe3Wt0bz5g9+nv5YkcqnGPr7Q1rhQ8Itq8NipjOwHaUsT3rqOOn2dqsok9dmiXHsGUyiTb25jmNLIxhd3dxiQp0i0wuX0df36e7PAAy/FJEkM4fYZCPCFvzqJMTladpSvKFKYEQaWBJyDDInMc+omgOTqk/8q3KDaLdK/cRvkuZr4AtVlkqUqpsUTc7uKdusS9n/lHNI7PYF//BvrR97JllWmEOyTWLJVigDQ5ts7oSw+1t403dxyZJSQ7awxXd6hfPst+4xwPDse8uj3g3ct1olzz4oMOe72QciHng6c9vmsx54vdEh860aQXZdzZGRKlOXGWH9nz+Q5112KY5NjKY3cYIoE4jLj38is4QZk0iuhtPvwdDZO2jU4yDtcf0t14yOjVf4RlKf7e3/t7fPzjH+fll1+mXq//ISnsO7zDd453it/vMJcuXeILX/g83/3BP8af0JK9lb9AeboMWBQCRW4MUsTQXMDOxnhbD9C9HjLwkKND7MYs0XhMHiXYiycRvW2kbZOPJ6AzBrt9uttDTr57mfpjJ8nHY6zApVh2MOM+0f07mM4e3skz2EkMloeRNiLso/0qhTyEXh9dqCCdBXAC5No1KFXJZi5g5zEqmZDbRSCjU1pmariOrswSpgACVxx5WtpWgFMok2w+RJVqlJZqSFuBpTjzsfeC0ORhjFV06d7exC3YdB8M6UcZi8surZfepnnhGI7vYAVF9OE2ur2LPH0J7QYMX/smdrGImVkmeOoFiMfoYp0g7uJYPvFkQvHUZfTNt4i21um//DZTH/oQUirSjbvYcycwaYoc9jFCInWKsX3S268h51Ywd1/FKVcQvR0yy+HRIOLlbsq2J5ifm0PmGXmhhJeMcI6dR0uBZynGaobYqzPKNCWT4JIxn7dJxCK+LfFcB1tVqfzwn8aZdJG+w6A5T5yMqNRqWAsrdAt1iuMWoWMx/uYrdB+bo/zcC5hJF/3eRWzHIpY2Q1WkWKwzcGfwtCR0yuS5Q2xA6JySbSg5oHY3cWszzDkOBkOaKx5p2OyFhs3ehEq5wjJdVpMArQ2WpelE4Flw93DCI7MldscxDd+mH2XY0lBXgjwaM55kqCThTivCMSm5gXGUIHVKHEXsm5ws12T5/5YCJaVkXSmSzBC4A0IsSl4Zf3yIrVP2VIVxLpBK86ifImxDVqtTLaX4lsESoJSDTEZonSOLDXKpkGhUOGD40tew3vshvMP1o57rQYfByuNsdkMCz+f4eBWhCvTffovk2Q/xmWstvuvSLDOk5HGIP19DmoiusPHJKGYTyALiBFo6J8m6hEmKMJpensLhLXb10cT7XqeNJQWWNUSJPRxLEakuHRPj5DuoVFDavkfLdbGLJdxkzKS6hHXvBsWlEf2DHRzbYmrSQp06j9WJ0eGIcmWCXSkzzAW1qYCqMQxjg6dAZQZL5zyxUOLzd0K8yHD7YMypqTL39vucW6gw7xrudzIW5nx+/Uqbe62IsmU4b0eMCXh7vYtrN5G5TdHxmCkpnjleYz9MmSv6LJQFFVeS5uBbNv34aBdxyrdAOpScFLIEpEvTSUj295hMLWC//SlKxTK58oinGrQWT1FffYPkizcIzl6kaxrMlnxsd4FKvcloOKDouXQyj+rGPYpnn+Tw7HuYtV2SxXmKWZ/7kUtrnHBmvkTqSj4ykxJ+67eI9/eQaURw7mmKpy+Rrt5ElZaYfeQE3dfeolr1SHqGdDLBO34aVZ+mt7WDX68zLMxg0hB3ew3n8RQlFIPWgNyWuM05NvoJy7KNrEyznwX0w5hj/V3a9gLBZELe65FkfexGjeLKPNbCCaKH97j3pTe49F/9WbKpeWSxyMJHXsBYLllnH9FrUZmepTHYZdxYJExSQqVoBUsUhMA9dhzR30VMzWMdv4hv15jcucGX9wU7gzFPLJS5czAAIWiPYgyGc6rHqRXFP7utOHa8zBvbPe5s9QHBbM3n6nqfUmAxV/e40RpzECZcnC5xMIz58tUdXvvGm6TxhGQ8YHS4jY7j3/nd6iQEBAjB/d/6GSxL8U/+yT/hp3/6p/n0pz/NxYsX/yOq6Tu8w3843il+/wPwwgsv8KUvfoHv+Z4P8cOW4od/6q9QKypcYcjz/Cj3Xjhk63eRQQGr2iTr7mN0DuEY0ow8ziCNMEpikgSR9TBCECc5mYFgtkb98qMkWlL9wB/H8z9L0u3Qfuk1pp9/kuHVa3iVIspEKD8gzxKE7WMefT8GibZclM4wdgEzcxw57uKFbXSmEdkEqyTQToDjeUzUArZSpFpQcsCadBnZZXIrQD/yXmzndQavv4JAUzpzhsnBW8g8Jp+kKNdBCEnrxj7Hv/vMt4+DQyZ7Q/IowSq3mL+0yM2f/w1OffhJ3JkZtj/+Kyx+8F0cvnGVxiOn8K5+FXniItoIoiSlQIIlIvJCEau7h7ByEqWwTp3CtDexVs6QbT8gd6vk198Ay8I68xRi7x7x2h1MlmKSGLdaJzvcBQRGG9ygxMV6k+v31zFTPtPlgKgoKecTsBywPbq5S8GRODphYvnsxpLlAPpxTiWdYKsqBT0kycD2HNxMkJcaVLyEfKDQ9RmGcYZ/fhYvHqDsEssXE6rjDVq4WEGFh5nkvMiIMhgYj033GEEiaCoDyiHPIM4M03ZOX7vEaU5jahmFQeSSspUTuQohBLMFyYdPVBlryVhUCMc5vVHIWCc4wlCTKfHhkIormIkion6GTBJknhIBa1Jh2Q6+a6OUJLJchLJx/IBZMSaRUMsj5Owx7DgmrExRtg2WVyLJc3Y7Q+anm2xPNJ7SLPTWmFz5BvHFHyBJJCcbPg0rpUiEkZKBrDMSLg3PYGcJ2asvYxUriJUT9ClQ1GPs0Qbud30frWCa5OtfxXvXu6lNL2GVZqg8/C1uTD+LWrpA3c3Z/tl/xM8mj/PsxUXm56ZojWJsb8zi4vN4cZ/x1z/D3gt/mqXxFoHvkDZWSG2PKMnZm+QoIRnFKeiMcDhk57DH+/J1kqVzZJZPvr/OMM7BtWlPoCoESWmOXqtLpBU62yH/dtFsqzrujVVGYYTvBzyonkC1epRVgbw0Q13YTLRDhqFkQqTlEmlFqg2WNJRIkAWPd6/UcGyL6SdmEa5LtagwmWZrkHN+oULFgRfOTXFpuULBVmz1I8BwrllEA5caFq0oZE5KgrkKmbKRliLJoaxyjM5JjAIkUZqSeopRmBMowyBzWJAp2tj0N7eoHG7hzC6g0oj2N76FfuF7qYwOOWicZs4pEdZmuFCsUVU5mdtEKQuZhKjtu6jTl2DxBCYeUZRQKHvsjzPq1QqvHA557kSZJAdfD5h56ZPsvnmNwmyF/y97fxZk2Xrd94G/79vz3meecs6syqrKmoc73wvci8IFQACcCVGUBakpUWqF1FLLTbMlUrbczWh3ONT9YEfQaoktUy3JVksKkaImkCCJgSCAi+HOU81VWZWV83Dmac97f/2QoGWG7WCHIiTRZv2ez0M+nMj1O+tba/3rz15F3Hmd/PnvxzBdVG8PvWigpxMotbAH+9xvXuWCbLMvy/zLyODPioDg1/4lxg/8MFpzDqlysmmPfm9Auv6QxR/6PlwaKKvA5PXfQbv8MX7tns+fv1xncbjNQ2OOi6st8iiievlFtN42yi5gtmapXb5CVF9hkmk0sy7Oxz5F8O0vYn/yj6JlCbNH75EFPt1YEGc6lTxkJ9Z4xgzJyjOoUhMRTsiXLiL3t3Gvf4ZcgUAxjDO+ceuQ+apDqWTxUmHCSSfjv7unIywH1zbY7EwZThIqJYvOMGR7b8Sz5+toAh4cjNDnS9xuT/jH39nk9ocP2FtfR2Xx/3C7V7OLZJH/P4owVvzr/+Y/ZnG2xt//+3+fn/7pn+bXf/3XuX79+r//YvqEJ/w74on8/jvi+vXrfOUrX+JTn/4sMwWDn/lrf51USsJcY17EiDQGx4NSBaZjpFMg7uxj1JoIKUgmAUgd6VXJ7TZaqc7w7dfZeXR8G9RpVhC2g718mbx/gFIK98QJzM0O4zvrJLFCdy3ikc/Cx58FcnTLId++hb/+AOOlz2JM+8jRIeHseUShBvsPoLuHXDh9/Lycx+hSkBgeaZpjCUGcCxAaoRLIJEezCzgnLmBtPiLcOyBLc4xGk3Q6xdAdrEaJzvubNJ9aBSmwXRMV5+SjmP7+hMLsGCU1ik2bpHMApsnBd25SXVvCbNSIDruYtRoy9Mnvv4/bmEW3bXzhYqYj0hv3MRZP4Zy5QG7XyTfeRS/XmG7uoayvUSoXSfttdrQqK3mOsD3Sg0OklMjmAmp/k7zQwLj3GrlXp9l9wKnZOR7cv0fp6WvohkUuU7LSLGMccnkcP6wJDZGDq0EGPEocVo2MUZYyL0ICzcTKI4TKMacdploRyyshdJ0sN1AqIhESiKm7GX7pHOu9EBTsTUJmi0XSFCaJopsIWiWJIdRxUAhgSggw6QcJmhRkpkEvFlSNlHQyoCY0Dg4ioigkC33a0wSRJSRxiMhzEqmBYdI1LSqGiWXb9JRJ0XXoBhl3dn0+c22BVsHC0jRq0ifFwMkDAuEQPLxFgwF5fwtj9SpRdwsFGLaB1Iro7UdMm2dZKh+fTKraDmYypWs30FevMRN1aGlw2C+Qd+8hT67yUDapFkwiPwCZw6RDsreJeOn7CVIDdFBJQp4LRo830C7OUFtt0h9PMI0EarMcNFc4X0jo9Xz8RpOTf/Ev8aP6CleWqmjDfe4NbUq2zaztkWZgmRLD8ZjYy2iZj5FGkOfoecqMW2SSZPQjKFkmK26J6mADceZZOoFkrlpjK5Pc2g+oKZ1SRZFIwcl6gWpnk/HyRcpaynQ8IUeSHT2mG6ZMgpBqtUoQp2TTQ46ihCiOyLMMKTUMy8Q2TQq2SWoW0EyHVNM5jCQrdZvnm5LbY8lCrYQuJfcOxgz8lFpR50zToSojTtccFsoWJV0SpMdBJZfnC5h5iKMLaoYktctEeYLMIgQGbjLBkjoYBk7msyMNlqIOlmpyFCac0n2UWwJpocUDSspHDH2Spz9JJ85pPvssv3yQ84n+NoPZU6wuziM0E8tU6LsPkDOLZNMRbvshecnlN7YiXmh6yDTj7bHkVHWNcJzy3kGMJRUXzCl95WDfeo/J4T5WsYCzfBJr6RyjkQ9xglWeRRwcMO1P2N+dshYHjLUyyyWdTFWxRJHr3a+RLl2m8PLHkA/eR159kRyJvvoUM3MhigE9UYDJiHTShihC84o8NZMiyfGmh5yK24zvrlM8d5q4Oosctom6fYQwmf++6wzNEmZ7k9E7XyefP4XdbCLdIrK7zXgSkp58mhuHU2aLOkOVsmwLMmkSNU/jihSmbfQ0RGvO0Fm5Sr2dMgg1buwMGU4ixp0Rf/5FF42MX96y2RsELMzD/f0Ro2mCaUqOej5bu0MA9rohz5/UCTOFFIJv3jti43GP0VEPlf3etDaVp//DHV+Af/kLf5nvf+XaE/F9wv+meSK//w65fv06X/3yb/GZz3yWTEj+ys/9Z5Q0hehuoimBcosIXQevglSK8eu/g6bpxJ0epdNLJI2TiN37xHPnKYiIm//idbaGEQVXJ89TZLmO8vukj29jLCwj05C5P/4nCI46jL77GioJGG916H1wE6fq4aochl2Gb73DbKtOFviIiy+iREZuFpDlWeSwgzI9GB4ia/PHz/aGQ1+ZlGVMhs5QFnE1QSdRlNIAmfh4a2vE+9vEkwlH64e4ekR+apG4O6B9Z4+VH3iWfDTELDoUlltMDm8jhaC71ad22MPQcnTHond/h2KrxIf/8Cs881d/ksnjPYyVs0i3AK05ZKGKmg4Quw/J8gzz7FXCO7fQ6zXkNETzSoRbm2j1BmY6wQ8M3NklPBXhHx3hlF2iSQ/hVUDTMMpV5MEdRLlK2OuhNxucaM4wzuD2zVs8d/kCiW6yHSomWcrJss16L+R8zWQaKpalz0Fc4GRBkCso6QKVJJhSIJUgMy10leH1HoNpoyU+ltNAZSmJVQGhsP02I1nhrBuh6QbzrkWY5cSZpGhIjKJJmkOUK8JUHEuSSGkPJoynPlrsM4gjgjAiiULSLMPQdVzbxLJdLMem7lYxTYtE6iAlpuXi6fBgGOHoOnMlg0c7I1zHRBMpr16ucbJsUZApU6FzNMywCg5yNMTpvEv03e+SP/00WaIwkOSnnkEGo2MBTn2S8hwFkaIMA2P/HtKpYeQBeWGBtFCmlEaIYELDDAmXzxNIgWmaFHUYjzscUqIoi9j1WUb1ZWKl4QlF8vADtLCHd+qjSBkjFk5RKM1xc+MRZ+o+QXOVTphS/eLfxXjlJcTZa5yIjeMUsjhgqVxiEORYfh9pmGgnLzFvpESZ5L2hxRVLYxLllEyJJqFmSkx5HMmmhMHJk8tkXpF5R+AZOeVimd52zO+8f8hf3/t1hClRf+5nwTFppn2y4Zi2OcOJcB/h2ax3O7i65MTgMXJ+BbmxQTJ3ns7881giw8hCJkFEFgZEkU8SjBn32kRRhEgTtrc1uo5NolnYBRvllHi5mdOpFJnPO9SCLoVKmYiUUaTINXhpuYSGYrZ3H832UEaVBA2SiNmkT2baGHGXzDTRp4cMnTksXdEUKZpUJOGUU2Eb03PJd+6SzJ4iSRVaMOZ27TxXIp9SMCFfvsCn/Aht/hprw0PMTodg5Rk0odH56pdpff6nkHEfNe6SLF0g6k5w3RZH45CSa9DJDTa6HVrjPT59aREtCiEYkAwGBMOA+sUzeJ/4UTKvQL9pc+Mo4uWFnEf6LHNZjxPf/zGGd+/zW3/vi/yJX/y/ItobyMZZFs6fw6zX+OqhxavmDuJwEy0OEIUyhXRCe2GNzcwD20I/vIl1bo04HtMqeTg77xDc+4Du2/dofOI62tJp7MEuYm6BYGsbdfZp7PEuSQ4NW7L5aI+lc+dRZ189jnoPpxzOPc2DnuCtrR7Pr9RoK8myl2E7BRwRIyddcsNF7D9EFMpI3SHOhry53mb/YEo6DPhzz2iIPOMr/Sp7gzG9nX0sfYEwjHAKHlITeK7BdJJg2pIMwWY/oupK9oY+795vc3D/HkLqkMXfq1ACBKg0QZo2Ksv4rb/907z6wvkn4vuE/83zRH7/HXP9+nW+9KXf4tOf/X5MKfnrP/ufkBpFZNiD3j6q0kSfPUk+aBMNJqT3H5JOfWqf/WH+y9/Z4z//7NXjmOFH3+HBB3scJjCdpJTPnQO3CnYJckXWPkAWi5j1BnL1MtrZy+TvfgNv6RDN1DA9AwSE+we4803C+7fRCy6kKUQJyj8eq8iXLyFFDpM+2AUMlZCUl6mJAGM6AKeMMopIIagYCl0Ick2H5hLFi5cYvPc+BBP2HnXod32mBxP2D8fc+Xtfp1pzmW14nFposPDSKtvffkS/XMbvRqjpGN00qVy9gOk9JLl1RDIJ8X7wx8nufgtZmwWrgHILZNv3sZ56hd6v/D2qzRZOxSKv1DFTHzX/DJPDDt7Wh9gLSwTtEeNCE/udr8KF50gOH2LUa0y+/VXM6z+K3VhAeWWkUNAfk02naIUxFxdbvHnzHnfv3eX8UpM4b1DWFF1f4hgagwRajsJo71GuLGOaOnEm0NOQqV0nzaHU3yRtrmJOD0mbJ9CSKak/pJAndNwFyjInTCWRUaAkE0w9Q7cMykZGrhlMwogkjjGjiFEU0w9DppMJceCTZBmmYeA6FpZpUnQM6rUK1ekRtmNhtRbx9QIhGnEKQgriVHG3M+VMzSJIoRtkpBkUXMnhODwe/dAF5+ouUZpSkBlhmhGJhO0InilGBCrH2N+k8son0DyXLE1I+kfoXgXddvFjxXQ4QD16jH36LERT4uGQ4bffpnRpjWKxyai+gBn0QTNw0xF+uc5vrg+Yb0SYuoflFhjgUc27yCuvkKFh5jFZqDCj43u2nimRyZAhEqezDrmGTGPKdhnH0NlbucKVWp00VyRxirI10soC58cHHFTnOJyMWezdYHTiaew4oJhNWSmUSJMYDI+R0iilU0Jh4yeCpgtJDkllESsaIr/5NcKPfJKKXeTcbIHzDZvFzkuMb93A3foA1ZxlPA1IUknTjDF72/Ru3cJvnOeck2CKBOkVEI0WKpvSNBISYVLJhhj7j/FOnOJIO8na6CGJN0dXr3DnaMq8o5BZDKM26ahLdzwlDqaoNOVACibjMd7+PqYUjPUqlmszM9OkPN1HdnfxW6cwHrzH9omPsrr1JkNvFmumihmNUYYNwQT//neQ117EvPUNtJU1xmbp+D60KJAbAeFgRK3kYS0usDQY0JYnKD98k3TpAnOuTjo4It14B9auUAj6BMLEW1nBn0wRlRaqMItOwo/Np4z6XRLN43k34O5Rhx84O0frxjuo3SkjqRjtTnBOXWCUNHE23kbZFRAO5aTDVTeHVHC64aC++E3MF5/l6P5DFmsG+cFDRLFGMeyRnj6LFo8433Ixa1fINm6RtFZBuhSifUKnxSRRNBwdKUBTOW/14dLht9EWFqE2R/HZCslzn4J0fLy4uLNOf+4qLcsiG0kq6Yhc5dh/8i8i9Akax0mBoelyd6roTBMuNlyuVCWDTGPGEQTKoCATlGaROwW00pTYa/BoFHHraMw7b92j5rn89IsOB5HGL9+EcTBkMgpI/Cl7GzsUGlX6d+9QWzpBmlo05zz2t/p0DJ1HhTEn6g7fvdums9cHIdAMDYQOKgUBKIUSoLKUX/rPf/KJ+D7hDw1P5PffA9evX+fLv/WbfOaznyUHfu5n/yqGZSIWI6I776JVZ8j9Ib31fZTKqZ6cxU9hqz1G8ypoKIZvv8Fc2ea9Tsizq1Xs0xdRXhmBgdmcJRv3wLIRaYLcuoG1cAH96RfJNAN1tH18S1NIpGFSXl5h+OAh9uk12H9A3v42CQJ97gSi3EIVigivdLzckoRYzgRGHTTHI8vj741EpJSzEROthnJmsKWGszqmrFvMTgLyNOPNN7bwM8XYT4mPQuydMUstl8psEcPUsWseF66toIZ9NM8AleOdvUTvnZtc+D/9Gdqvv8Oc30ZVWmT9LqI+Q2oWMGotsiQlngTHh/0ti8FXf4P6T/1lRBoR37xJ6aUXiO6/jRbluMNt1IWrxLsbTIVOpVQiJyB54ytkn/ocxriDCicof0oyaiOrM8hxm8vxDm8NF/GGe5y++gKJXeRWP6DiugRpTtHQUYaJGY1QskIuDKSpYeYJO5HJob3EjNBxpGQ/d1mQEb7XRObQTHqoMCdevwOXnsbUJENfkXV36A3HTLNjkUlyhW1oOI6DU6qwYCsqjQqWyhDzayipoUVTcsOjkwjMwRbW0gmGepkoF+hCIaVGqhSWyKhaGu0gZ6M7oe5YHE4jekHM1ZbHUklgk1MzMqZphBNNMZIExw+ol2qI9RuUZheIpz5GcxkGuwjNILMKTG7fonzuDNruJvcb1zhb8Nl89w5RpYj7+oe0fuQHmfgjvK11jGoLKwsZVxfx0yb1oEOxXOGUPiYPM+q9dfL5p4myImK6S6ms2Gr3SKXNSvcI56lnyeMJ6ugxBUyCYou18bvI+3tYZz9OM2qzfuV5ujWHWn+T1doi01yRCJ2ynjDOQm5PLQLnBEudXXbcGU6Vi7SkID/YxJ09xVRY6OEYdA2FhqsUpAGxVUS99SX+4X/y3/CNPxbyN37mx3m+qaNZZbzKKbTzV8jf+hq6bRIOR+RCkNcXSebOsb83QgBzJOiLq0xf+028Vz6Do+kkeYidjskzKDiSdHcdc97kdlalobk0tIS5skPTVujKxptuo63MMq2s4PmHdP0Mwgm+4THu9+j4EWG8y3aasL/9EFuT1AyPYJBRSh20LEYFAfHKSfRwROewgze6hb5ymof+iBe2byAbs6Dp7A8DamaJ0sZ7sHwObdRDC3NEGlFdmKP/9tc5eOoTnMkDxOCQ4NtfRr/6An5zDaIR+u3vMl2/hbjwPLm0eUtf5VN1MCZHbPx/f4Xk46+SZF0WxyGGew116aOEYcrXdwasOm1anX1KMsbyJPK9r2JefI4sjZibdIg6AbY/YdA7YPp4h9pP/HFmXriBaJ0kNovHrxwH98kNG0dCnqakwyGRErhCMWl3mZm1OSzMEMQpSbnFbuEEK1LiPtgjFxE882mKGgipESUak9pp7Id3MeeBm9+md+c+tf/ozxA7FSxhksQxoXAwNIthrUr/0RBL1zhXd1hWXWa8GpqhM1TH59/QdYTQQUgORJmv3j/g9t6YlbrDn33K5u5Y55tti+3NPbIkBiXQNMiThGm/x6TXozy/TO9ggGM3MB0LXZesb3R59FDR7o3pPn5A0DuAPD8WXziOagTIU56/vMpPfe7lJ+L7hD80PJHff09cv36dL/3W8QiEUoqf/9mfJp27jDvqgj9mfPM2v/kb9yjbGtcdk+y3v8jf+PxPo4d9UDn+1g65Lqhp8MznnkHYDnnoo/whWqFCHkzI/TEq9hFpjJH4ZG4RnCqakIg8Bt3Cqc0gdZ3Kx06hOjsoPSPrHJIMhsjuEYVrH0EZx7PGKs9RQkdMe+CUIBpBZR6VZbjxgNSpkGYCRypSqaG8OtayZOZln7DdJf72Y3Ynx0sUGiAyxX434Ftfuc+8Y/L055+l9twzPPwnv0Z1bZHa80/Tf/tdiqdPEm0+pHxmidwpkVXmyR/ewK23yHduI00T/3CH8vnTBHc/wD1zEZKIdDLEHBxipAHBh2+hqZzptU9i6glCpeS5OP7fD6hwgr1whvyDbzHp93EqHsHuNuVXv59cNxBHW9hLZ7ka+LzT8ShurdNYWuFsHnLTnydTULdNYrdJZroEmST1RxQKBcbCwY8THNMkyRWhUcQUijSXtPMClq7R7e8jDh8xSHWm77zHNBUgJZ7jICyHRrPGQqNKs2ihqYyBO4Op64zHYzxbI1IwDFLKJZftTNAdJyyWXT4on+cZw2EUQ5gpPAMcQ5GlMMkEp6sWvUjxzb7PibKDbtgYUrBmB4wtA/IM2zJBWbD+bTBc0vlzuGlASo4Wh2iFAunbXyIcDDGrZfRSmb2Vp3H620TtI04vhST7AfLhI068fInsx/4IQW+fG9+5z5Uf+yROOoHuHnpphlA3kd02z7gST8THcbRpQNXISf0e64OcC03FiYLGVpBhnznLoLCIs3cfXWgkh/tonTZJv4dpSDyZ4jt1qkrHMQ0M26SHTj6ZUK6Uya0W5b1Nnkpj7qRlOlhc1If4SY2dRGdt5x6i2sI+uo8sFTG6G8wtngVZQlMJpshIpeD8mSZvuAWKpqSppiSaReRVueebnJidxzYUerUBoU8l6WKIGTpGkYptkl56CvPxOwjbgs4Oauk8fiap+D3S4gzp8jW6I5/dqYbl2JRMEydsUzXqjDINM5lSXDx9/MNHKswsolquojUbaFJjNLfI5iBkOeiSaRoF5bP79vv0l8+QT4Z0pwHqg7fY1m2s9fs0mTAdBiw1Wyi7xuyVGbSjt1CLZ5F5ztHUYM4B05Dk0y7muM2gtkxldpVpYZ7eL/49ll58BdXfh2EHd+08wtaIhcIIhijHRSsXsWVMun+PSyii199Hq3rY+ZQz5YzOwsdp9B+Tmjp3M48l0ebyqQXKQ4knC+Tf+hLW2fM8/Po7nD5zhZ5WYZ4uYncD88RpKs+9wHjhAlEmmBUKtXsXY+40yikxkh6bWQnPK3E4zliYW0Du38M4/Qzbqc1ioY4jFHMljR37LN1Ecs5KGGwcMnnhCqb0mB89RhbKDLwZhFBY8wuIah0jrFF67jkmRhlt2qM8eoSyPB5lDWQCriEoWCZSKaSmoyyXzLCJ0LDzEC2fktoFEulgDtpM7RN8uDWgFI74iWddvvI45512xsysiemY7G3cwSnX8OqzBMMeYX+A5RWY9gfkSmM8jpGapF6z2XzQ5mBzi2g0IOjtI4RA/Z6xh2OunV3iW//w//JEfJ/wh4on8vvvkd8dgfjMZz+LAn7ur/0c6sxz5IM9Bus7bPdDIlNg1yuIKGQm3EPVz5NvvEs8GNFaqfBcmtP6/E8SuU307ZuoKCIRinw6QvMKx09ZWUImJVOngZP6yDxG2S7SKZDHAUozEOUmFCqoUQfpudAfkLU70DtA6TqZV0baHmK4Se4Hx9nzJy5BOKWSxcg8JpENLKWIc50kybHsMplVwYpTiifeZ9a9xeb35DcDMgUlQ7J16LNwzkUzNHRDMNybcPrzV9G9Amp0m8LaKSbbB5Seep4sz4k/fAv3/BWiR7cxZ2YQwRi5cw9rZZXh4SHxwTbe8ixi5yGIjPoLz5AInc6//tdU5+6TNefQvRIy9TG8Krk+gz71MQwItx6SG8XjqOTJiOjRHYz5VeJOH7nyNKW73+Kc7fF+B16sTSjHEwa0OFu3AejlJl6e4UnQu+v47lVkEiGkpD1NSFONUNNR033ujiZste/jqBiEoOC6uPUyTVLORz20kxd5lLhUjJya65CrnKnKsZUiySQySYgNl7F+PMqgjJxRCrES6JpkkuRUSiViqZFnKRXLJMrAEzlCKAqmQqJoCp+Xl2xsW6eqS2YtMPobeNUlOpnOIIRpDI1iA7VxG33YJ01icn9KeLCJmvpER/tYiyukysCqtjilC9K793HKJeTDd+h9601KP/VX2O51mK00uB07zJ9NCIOEfbPK+RnIpY01bhNnOeVpm21vmXakc6UoeDRJWT/SmSnNgBKoe2/SdOrcqJzBCDXOuTbT0gq24ZBsrGOefQrhWhhC0c00HF2QKsHIbnJ/f0TtxussfOaT5HYJr7lA6MGzO+9zs/UCH45GeJpkJj5EnXmaBA0z6pN1pwTvvok5GKBOnIDKDFrko88v8Mm/+nmunHuelvBB6ujRBN+q4GUpd8sXOVkxGQ5HzHYfIm1BtzBLkitOLbTQ8xSRJRgFC1WbIRn1cG2XVJgw7uAX5imIiDO1Aq5K0DffZlRdwvMUMkkJpc44U5SGexheHbIMIxqQ5e7xHLvTwNB06vmY1KvjYlE9v8i96iqLnoE/6pEIyZ6fI/0h+5MMP0rZ3xvjtW9hlWo8PBhRasaUii4n8jalcUwsdGRtkTwM0DtbRLrgtQdtLv6Z/yPFdEhbr6LPtihkEzK7iOu3icYDgts3KKydQ5eCdO82rShl8ngd/FnqV9cYp5Jc0xk3TlBNuizZUDtqU4n7xHaB6HCM4Vpotkft1U9iRCM+TBq0yjNYixPy8Qj77FUyp0m69xhp6KhgiEDQlkWmFY/BNKXlOqjURp16miNf4MQJxblZxlaVlhQURcxXDjOeapkUVUTSWmS7epaFOCHrt4nLLTIkM0mXtLODNbtK5k/R5k4QYFDvPEKRI70KniYYporhOKZsSbaGIeeMIhuqyqxukEYZShrISZ/EOQUqJ7TKPGz7LIsxH12M+bUtk8grseootg4n9LY2yJOEcDggSwVZNEXTNCadw+PvgQoQAvJc0elMOdzcYrBxC1SGVZ0lCaeo76W4/S5Pn1/m9X/yf3sivk/4Q8cT+f33zO92gD/92e9HF4qf+dm/hqx5zPyRP8IPbA+ZX5mh+cmX6Xzj66i9dcTqM2SDHnmU0lxrUV+bZzR3iUzBbH0RtX2bZH+LeORTfuUqynTAThFSY5rpSGFiGBbCq5J7FdjfgEIVlWUcllbQ3RZ2rqP7ASoMCDfuoXX2Mc4+hYoClNAQ86dg40NUbxtNN8GrIlAYeUImTBQCSyYcJDYlW2LOnsaamWd39G+2imumIEkVK3MelVzQnCmQpSlRENN66hQijRH1eYqnV0iDhNLFi+gqB11DVOuIeMLBF7/I8p/+35EbFrptkE3GFE4sM97YpfLccyirgLBMOHyMChLMWhMzHiH6GRTO47sl3EYDjjYQuoZwC5j1BuG9ddJKhdLTzx0f0FcR/vIVHL+P8MfoH7zF4guv8s76Hi+dmeW8a7BopuxFCZNEw7ENpqmi1prHUBmhP8LxY8bdPkfTEQdpgu0WKBQKzM/Po3klVsoWrgrpUcAyDYxwwHbm4mkafgqzWcRBrFPSUkbS4N3DCWXLwNRhxlYMcp00Ox5lKFk6Jwoavf6AxHDQpn0qRhE/O97g7ieCsiEwoxF5mqCP97laX+EgNyjIiEgZBEaJWNgcxjmOSGmYOdKwGNx7QPHSFcTcafRSmWw8YGJVMb/xq8hrH8fweyR+hJNP6A+GGJZDPtylcmaeHbPEr7d9PjcrCZWJdnaN7m9/lfrlVZJGGb2/xZGqYM9eYo4p8V6PA1XhUvc+ZbNGRVec330H0z1JlER8Rcyw8cEeP/zCCiLycUkgGKEVXKLZVaQmiHWHmaiPshuoXPFOO+H1dx7wx6+uoY26oNUws5BidQbMy1RyA+9Xf4VHP/ineWa6gd96iQRFrbaAMmy08gPSuVXo7JFX56CzjcwVumMzP3qMiA1SzUO4Hm6W03IqNO0quZDYUYR79SW6/5//mscvltGlpKGn0H4IWUq6v4d2KmdblZgRGuP7dyhcvIQXdRmaHvXRLlKXyMTHDPqkmoHauEdzYZ5EGsdJc26b4GAHY+UMSdUjS2NyPWLp8APMag175wNoLhPtbLAy9bHPPYfjagRGEUo2s+OAyXCIrJr0rRp5njBOIzadGbLbdzFNA1M36XsufW2ZM6JEtXUCIV0m/oSV/XdZeekaie1ytydxc50sK6OnkjN2iclMk+pzEn1ukTxLME+cR6UZau40g699hUE/YcarUIq/Q3DuRaQSlMe7aGlAVmiRCp3kgzcpXLtC5o9oLK2QkfJ0A4zuEanQyaMQEfqou79NY26GfNRDK1eZFmfIc41xmlGxdQoqwPDbqMIJzIJF0VDcyQvs7vp8ctEgGg9wjCpVLUcLQyoX15grO8STAXuti8zlGa3pBpHuIqoLGGlEOrvK0KrjiIy03yVee4HCzgcsLTpUyjPsjzX2RxMOxzFxmpOKnEkkkSonzSWZ6SCEwOpuERYq7Lx9n+drMf/dbY3AsTjrmgzChM7eAKU0BILEH5NnOYnfPx5jyFOC3h4IjXG/TLlRprM3xO/uH58vExppHJKHU3THIw3GADx78QTf+Uc//0R8n/CHkify+x+AfzMD/P1kmeKnfvpnOX3mMq/83F84Po9TaVLud8mnY+R0gF4oIQwdu17CW1tjM1aoXJE7c7S0ByjAWlhCzZ4mygWWF5BYZbRc4YQDcs2CgoFCQymJXmoRWgU6icmCZzGav8KM65Ht3Cd6dJe8N0Dcfx/heVCdwyy10OdOoQYHKE0Dx4UsZ5LruPkEI8/QUdwZW5RtE0mGe/kaJ09UMNs+k0lEkilWKxZlU8MCNFvirqyQjsac/COfJR12yHqHGItn0At18s4eaecAsTbPYL6Ku/U63tIC2d4mWrGE0AyIA/SlUxgHeyRHu+i2RVaZQek20cYtWh//DNnRFtnhDkaxiqw0CQ0Po92G6ZjMHqDrJs6lp+i//S61558lnfqk9+5DYJKsrMCgTzYZs3hild7RhHe3+pw6P0+OQT9IKZiC6WREvz9gYzxkNFwnzxV2oUSjXMLavcfg+R+kZJuYUtAwYkaZhmeAlWRUtZwpgn1VYBBlLJckR9OUsdJxRUqoNNJU4Bga0yTnZNVBypySTME2mSYpVh7gjA5YcApE48foTpmpzKiJEZluobKMTLNJlMIZHxLZNTp4FE2opyF9zUZZRbxowIpVwOneR019kt17aMUScaeH8/xp4p0HbGcF3PoSjaefJSlXCb/9BcJIUVxZYnT7IcWLF0g6m5jnrvHm9oiXZg2KUnCm5eDefZdoZR7DMJDkJF6dX/52mz95vYYaHjFXsPEmbcJCk6pMeGGxwPRbb5FqIUF5jo+mB5SWZjkthmS1OTJxHBGsuWWsLCTQKxh5dhyukisGUcbWUYCoNZiJ2jxuV1jbewjzMxhuE02DgmXSf+8tSj/wH+GvXMVHIxm0keVF/DyjceYikw++g3jxZWQa0f3al0jjjEJJR3Y7GLZO7/Zjmj/2E2RahG44mGkAWU7VFeSWx/TaxxmGCYtlG/wxOGWQBs7zi8TFOuWv/iui2gLNiokRdMhzST05Quw9IHfLqDxDdO+hnbJJdQM9mqLbFnmpRt7fJVq/i5oMyJ6pImwPp/uIdOtDTOsymWESrd9Ea8yjx1NQEcboADVTwcpDZBzgnDqLP+hSdU1crwqaxs1uRr1cBJVz2O1z0Oshoi5325vUPROzOkNbuZQuPw8ywxgfca6yzDDJeb0d8cGdHf7UJ9YYBjlnaycxTZswF3hKIfr7xAvnKL0c0TVbTFwH/8E9olBQTwOy7UeopVU0f4jZPYRP/Bj9ox2qhTq5bWNKjRkZEHW7xFuP0FQCwZTkYB99cRXpj9BKVaw0oGBIckcjzAQiHnPfPoHnZ8y7CboQuIbBhWqCPjpCK89gxgptuEciJSpJ8PIpRqmAl02QR7tMbryJevYTJCeeozh4yF7hJIZuUhnt8Gj2CqukBBuPECefw5E5ti4YRgpdE0ihOCUGrAc1apagQsDEaWIpxVSzufFoj7qR8NvDOu6MxCHn7mYPDA3LtdEMm2TSBd0lH3U4fk8DEEjDIfJHBKMJpmnQ29kg9o8lF5WRTQcA5HGE4VW4dmaWb/+Dn3sivk/4Q4v8D/0H/GHluAP8m/ztv/23+G//6/8n/VRnb/YqO6UzpE4Zc+0aeq2JNu0Q7TyGNEOzTXSvTHscsz1OSHSPcOkK9uJprIUVSBN6ymXqNJCaRpkQLZ6CysndGsntN9n9za+S58nxE+X3IjyLBQetuYh25VWYPYVwXDSvQHK4T7Z5n2z3PunhFqJQRvkj0vvvoeIpOhlmNMJUCULTWCoI4LgZIedWefX//Dk++7lLrDVcThQM4iijPYh479EAqQn0Sok8CFG6JOr1EIaFNrOMzBPiXo/kcI8sjCgfPkBNh5TPriJMC5VnqDQibh+RD3uYzVmEEFCoksbJ8VWL/hT/0UOSTgdZKJL0O/jChLe/jrj0EtLzGL3xbcZ376CEYLq9h4qnmJUqwVGP3hd/Df32G6gkIvQj2rnNxVaFGI3Nh+u8+/iIdPsWWx+8wa0P3mc67FMqlVhYvcArLzzFpatPsXLyJMuXLrJYMBinKb0wwRBQNhSxEqg8R9Pk8TKaoVN3dQZRDkIwTsBVAQqBn2RcaZhIIRBArqCoC5I0QSCoJUO0aIhBiitBOUUKxEggV4rCZA9DasgsY8uYJXDrxCkEaU5gFHB0gdPfItBdytMdbJGj9rZJRiOyROKcOsXOJCWtLXDCy5hTE5iO0R++g1GtEj7eYHz3NuUf+DHS6jxBoBEf7uJqgmt1DTHuM5v2qdbr1JaXsQ63iGLFdNBnqeZS/MY/IwgCBpgUmi381irGo/dxgh7Vy5cI5s+hVZvMtqpcOTWH1nmMaG8xMqqQRIgowDjcoOAfsD9NGd68y8E4pmRqvHquzjSFnXfvoaQG8ycZPnpMNxHILKGBz+pPfo6nuh/Q10psjBWGVyJNErzNG+iOS775AC2NkCjcVo3C8jyaYxL2J4RHbVQWEd55CyUFehJhDnbQdz4kO3hMfrRNungCBZyQAeHDmwjDIJs9jajPYwU9ikaKePAuZnOG+OEt6O0ghodk0wnx3i5C5WhZgHbzOxQP7sKkj+wdoLkewqvirCyiFwpokw75B68R5xKh6eTSQBgW5olzWAuL6GeuoqURwi6gpz7G/n20cIQ1OaRkKJx4ArqOShV1V6fRX8cpVujbM9zTFmmcu8baubNUKlX8YZd4+w6DzTt88HCLW2OdHJhkklwpGq0KcSYoWJJ/+ThERCOMPCJyGhwmBg9HCYP5c8wvL6AaC8inP0rdULwRNVCVFnmaoAwb6ZYRjTksCZlZJDOL5L09RDgl6+6ShIKk0IJCjWwcEt99B6HpSM8jNxys2CfJoR9lHKYuuWGw6UsmccIwNaibsBAfIop1+spi0c0xth/AwWPS5gq1e9/AVBkl/5D+r/0qk41d7EIJpUke6gvcH0PZ0uilJguuRvT2NzAXlgmNAlF+PH6zWHE43/RYKhqMlcG8FtDCx4m6KM0iHHV5+94mvpJMmqfpJxKpwc7GLpuPDjjcbpMmCeGoc1w8Up9/I74AinBwRNzbY3y4SW93H797hNdc+Z/UnTyLubBQfCK+T/hDz5PO739AfleAP/XpzyJQvPT5v0jRc3FMi0bzJPn+OqQxe1/+9vGWsh+hFV3mSyaH04y9aYJRnMOq9THDAWrvPnWzwGj+KiIKcNsPIIvJZtbI0MhHPeL+iHz7LrWnThPnOXF2fAaroOlAhnjlx7AP1xHhCMMuknf3Ce/fAMNFH/cwy3WkbRF8+DruSzWUZiGkhhIaM0ZKrln4uoNr2pRfuo4eDbEqRTa/cpPxKGTYD+kGOXv3OmRf+BZW4DOnWRTnWwjDIH50k+5r36V8+Tz6tVcQ7cdYgz4snkWtf3B8EgjQKk0y6ZD1DtGKNQa3HuAEIcIuImOf8eGU9b/zz3n+Zz6PyhXxxj0qow7d+w+pvPIjyCTHm62AV0T1DqlfPkn766/RfOlZ8iRiut0l7raZ7nXI/YSss8VmKsmjhE6g0P0Qu1jg0tkzRNIkNYuUbJ1hmBDrBro6DiyQi6doZAGZbfHBUch8wcPNQyLdJdc9wjCj6uoEIsfUBZo47hRVLA0HkyjLUbbA06HhGlTzEWNfYtsFvrXd5+Mny+iDLll7D1GdI60voh1skMycpZ/CwSTnmm4zjUK82AfhYSQBJZVwp69DxWIp7ZJH0+NFP39KKhT28grJpIx+oc471iJZYuJVXLxHb6HLHAyd+GATdIvytaukfox2/gr6eB/rkz/MnZ0DFp2Mzo2bEAZYV6/gF5oU7rxFtHwa/WgXf6/Dj7z4MvpEIHfuk185CVJRvPNdDEMnGx4RzKwiVYqMEpSEwt5tktxA7K7TrV+hpjvEt9/BOXEKIaCVTcj7j6m5L+OZknvjkB87U8S7+Bnmjm5xYL/CTilhzR8TukXczGekG0R6AU0o1ttjFlY86sMt0ixBHx9S++SnyYs1RDRFuAW81QvEt98g31rHOnOKyW6b9LCDd/AQsWSiunuEj+6TCotRXmavuYBj6qSVFnL3PiqOkO0NsnEXLc9wT64hzcegW6TDIaQpWrUJaYaxdg10DaM6w+jN13CWl1GjPpnrgOEgoym6UwTH5U7xDKe8PrFTxC03ob9PdOYlNI5fimQeMdBKlGWOPHxIVD+JnHS/N3tv8yB0WHKLGB9+mYr08OfPcLcbkJPx6XlJo+JRyRWa0PBaZ1nyJwz6XdLQp9Ptsre9iel4zJgehuOgKUWYwlqzRGZLtn2d6SiC0ilMXUcd3SdfPYcf52iaQZ5GDJTJuLqMsfUWibChtUJ24w3MZh3/wxtUCjbR7hbCKHB46mVaiyP0+UUUOSXHIijOIgoeuSaIMrDyFEPX2B366FWbOVenbEgcPWEvVmRCUGossT3JsCyoWwLhWGjza2DaZMsXMEcHhHfexF2Zx3r6YwgVU9FzvhtoFC2JVAn18SZ5e5+gs4d4+mW8sM+RVqakQ9PVOVdUJLngxljnlZZEkwKhWWwftNlfv83s/AJdd5b25oCSa/Du+1t0Ng+/t6BbYto7IPaH/wuVRJCEE4ThEA97ZGGIVShTaMww2bv3ez55ZW2Rt375v3givk/4Q88T+f0PzO8GYXzq058ljFPmP/OnQc3ySkvD0iSqNEueZWimiVFw8R88YPHqZ9GlzoNuwA5gts7RDHbI772FijMK5Rm6soSjmahCldAsYB89YPponcXPfgy9XAcVM6+m+JmD1HSmuounJpiOCwtryP42cmYZNR6StvfJxgPC7XXCgyO8+Xmiwzby3d/BuPpxlOUhwyHKKqEpRSY0yEOSyhLe7CyFi9dQ4xF77+3xzs4IHZgOYx6+dg/iDN3QkC9dJtvYJesdcfD6PUoXzpDt3scsVcltj3RvG3PpNGmhidq/jxACu9kgHemQhoSjBL0QIqWLjCIKq0s8eHiIOvMM2a3voJeqWHPLzDSWCF77AuLax3FHh4zv3qK4dob4cB9UzvqvfJWlV68xOppw8+YAde4CyUfPIAcBRX/CiWoRRcp6JtnLXEpmBWeyizAt4gTqjkYnzPGIcCyTVBgk5DQIadkamYK9WKesKXJlMRUCW0nMdIpleiQqwxAgRE6kdPZ8xaynk2Up560xaBa7gcYMAY4pGQcxmjuLVtfx4pggzNGdMnEUoucp7anBluvRnyqWdzZozi2h+SkEijmnQUNkJDlkdpVRLKE8iyk11KN3EE4FcbTFfDGlubSE0W4j3DLpxk30Ug2t1iKaO4dxtI1XKjG89x7ambNYpsPS3Q+489YuB1sjslPzXG/vYXR22f72+6Q/fpH5+dM0arMcSI/atVcp7t5iKevRyQvUTAHNZXb1JotJm1w3kF6JwWiMdvtdjq7/BI36LDOTPaSAKLcw8xxNt/EMi+ijnwGVkWQCgWCl4aIGh4wXL6AlEw6UgxuC4RhYWoa4+lEebBzg7uwyaxXY39ljbrDO+up1rsougVmlaEhEPIWLL0E4Qi6cwm0uodVb1E9dQ2s/RjMM8nEHSk3y8APMRolibYZomHLKBBWHmFc/iopDGB1Bv0OiFOb8SVQwOb7GcuoMsliDwSH5xedRXpnQrmClIc7Hfxi9u0W+dJn9KczaOug6cvUySa9NxVD0TzzNYSCozl7F9ntYRonRxGfWlQS5Qy/RcIXEBIRuEpXnSDQba9JmO9LxYommlfBnzvPeQHCiqnHFmbIZaBTSMXL3No/Ka0SxTqV/gF9bw80DRGJAktPpHOImIa1ol/adXWShQi9zeFCYZXcSc7GQY6qYxDSo1ytsRhCGPmgWzf5DTtfPUEtGjO/eQ5Vr7HWhGXYprK6QXf9BIksjP1tAL9UoCwvDUIwyiUVOvHiZ0uE6uSbIay2K3YeMjAp9PeV8w8UTCY5hkyN5MMkpW2BIwQSTD7pDPragMcw0CrOnEVJgHD4kz2E6e5lCv4O7fJq0Mc/NocFJU8OPQ16YdXH7GwgpGT26R/kjn2TgzmJ3HpAUighNULN0bE2yF+TMeAa6Bkexwf7eIe39HRZW1zg5V6eQaLy7IzjX8vjWOKZYr7J7+xbhuI9VrJF/b1b3f4qCNEJYBbzGLMGgS331DNPO4e/51OXTi7z9y//3J+L7hCfwRH7/QPC7AvyJ7/sMf07Ajc/+JCdKK5yWOmLUpXZugTSImB70KJoGehpSd4qkVYf9acpEmVR1D7mwhlVooPbvUqvMoyqz5ECepcjBIUbBxTl9HlFbQIunGO0HWEqhZs8ycaqIPEePxyB18tIMKk8xhCStL2GonPRoi/CwR9rtopn68S5F+xFCkwhACJ9cZbhCJ5ESbA9hOWgnLrLwoyHO7Du8c7tNL0nYGIXMxga704Rz44CHX/gueZwxSXMKlmTj199i9YeeIpiGmOeuod79GqpSwnBd0lEPo7WAsjwQFkKXlJY69HenzJw4gRAV6pfmeHb1LOl4QlpZwI2HTB+u4569TNbZw552UAtnsBEkkxGa6xJMc/LnnuMDUSf43FMwHFFPxzT3brP4wnNs/tOvUb9yApGEdE5+hN5wFzktcCOu8RERE95+C786j12foxB3yFWdPByS6B7CsVkrC3JdEeYGnSDjzuGE5xZKZLkilTZmloLUaFgZZjJhpJXxjBSCETdDk6ecFF0TLLhFSqNNzrtlar0dAreBX1okN3L4yj+lcH4NPVPkus014RDd2mD1+qdQh9uoSp2hW0Y3E2bKLlGW0M1sSnHM9jAgTW1eLk6YBgHJnVtolSr2/QeI7iz64gqcvIIoFAlLs/hBhFEo03/jLXJngJEr7HOXMaVGde0szw6GfHf/NvNxn2ntCtWHH2BcvMysCBlrZcz4EC/oYc0uopmXEUeblOZrTB89xmrv0zx7hTgMiO/fRXv6Jfpug8XTZ9meTkncBsuyg0gyCpevMr57l0JjiKp7JMUWB4cBL85AXjJpiIyvdmCtOKY1vMULboMP/WU2Rj4/cLqEm++zcmKFZrDL/SjiBEOk49JyTSaxxUbucrX/CP/xOtbqOdTwgHQaIGZPIGot9DQ8jmM2LVSxRYzEXbuIKNe5NwApY5aaBbqHhxhPvYCVTNGiKcLxYTxgeuNt7JNnYO8eAoFQOeQZVOdJdQM7HDGyWlQME4IBmt8m6SXoRgmFRliaIVaSqR8y7+lMdSh4NdpujXIq0EwToUGapigEaWEGPQlJTZeen3IwTgknkik5632f+8ESn9IEy1bMatFh0EmYKJdpr81W8Qxd5aFixb6xhAhSLqaPaAmXw+ppDg99ZioObu0Cg+B7y3TdXXZu7OCVqwSqRKFW4dEkZzsrU9t6h3q1QnF+mTSdZ6Zgk+1F2Nc+ij9/hlnNwtFhoDR6uYUnBOtZTiWyKFkaYaZhiZxUCFxNYhYdkkEPWWuSFFv445CSo7BNDU8JEsDQJUrBg37EWtngVj/jqaZJUVN4tkGPWepyhBx30XSLWLdRbgUFkGVsTgW2Y/PRlSpFPUOLfLLmSYxyBdwSo1ww8eaRuiTVTDKVM86hoqdUREg3L/Bg/T7+sIe5eB6rUidNIww0HF3y9noXw9aZ9iYkkx7JOEeo37+O5GlIoTFDEvqM9x5jlaoI3UKlMVfPn+Stf/LknNkTnvC7PJHfPyBcv36dr33lS3zi+z7Dn1c5G82/xOlyA4IBheUFok4Pq1rDLDlo4Qhdt2i4FrkQ6BLaRpXmQgXp90gXzqMryLIUNRlg7a9DrYV9YhUVTRCS4zAM7XhTWkYDpFkA3UTGx6ebZDgmN2yUpiPjCVqeU3zqIxhRyuib38RrFsmiKcl4isn68QzjYI9k5hxCKVKlYWcTtOU1YreOuXqBmoBzZ96if6NNJ1LkKiUTijfe3mO1YDIYx2wOI2bLJqfjjHhyDulK0jtvYS2tglOAYRt97gRp94BE+EjLQSu1mKw+xcxaRm46qPvvohWblIoaYtoh8hpk/oDMq9H57W9QefkF9DhAGx6STAf0zBLbpVUG3/80BUPiPrxH7VtfgoNDzv/lP0N8tI+0LRZ/8vNE2xsoTXDq6B7ezBoPH9xndWmBvWiGhaMtwltvYi0vYdZbUBkh+oeokY9VrzI67GM99zGanUPa3iwN1+SbGz0uz5U44yXk0gCOk/i0JCTXPFxdUrn9DYwTnyS6f4vo0kcpJV2scMJyfES6+ZDyxWv4lo2eJoizl8hnVoiCKbrlUtENstERiWnjrpwgkRoTvcSCNUFMDpkU52lsv408fMyl1hRh6Ch1fLt5tLfPdGJSK3jkWY6S1nFXv7NHWFqgv31IslrDPncFzZ9gLa4w1ovUxrtMvvZF7I99llN/9EeZm26jmzna+cssOHWSN76CvPIJ8vGIwqjL7m9/k+U//hP4G49JD8aUz5wlTUPk7iZH516ltnfAnmywOn2MNAWnox3kJEJFE8L+AKFLrBe/j7D9CFGV+HFGP8gQqYBc8XAUcDY9YM84h9p+j2Z5yPy5JUIsNCnw3/gaS899nGTnAefLVT7UlrgU7THY3uZxcYZGWRIdHvD4V7/Mqc8FGMtrqEzHCEeE5knUdIxfO009HZO4jeNgkvnTpF6dw+1b1JpNktWzWK0xmi4R+9tMHtzDsiUISZ4Buk4+HaFXm2SDNll5jtxwiDDJbIkZ9MAyyBYvIIb7LIkOgWhgux5KGOiGy5Jlcm+ikUqNo0FCy9GQUpErQZikFPScnAQrz3nTL1J3MqJU0NvZoTq/QF0dL2VZBY8cjQuyz4Ame6JIFoW03RpHsWSlqvHBvo9pGnz167f42KdraEcdMHo804Sv70+peDM0ilXOaT6hvcaiEdKNMrYP2jx6vEWlWiUwSsw6oOcBme6g2RE+NuPqCbKKQnMKdKcJFeO4W7rdjThRMnk8THm6oNGwJeuDhIvlnKl0cLWErLoIM0UGwiKbDKmVXbLBIw6rZzAtGy1PmaaSYXy8pOtaBpqf4RkGejollS7mwW1kpQjlJnl9mToB1tIKajJASZPzTZeW5mM5JfRgTO5VUP1d9NYy5DlVXWFkEcq06GY5mgADhYHiKNW49eF7CCmJ5y9gOTbdMKFsGuyOInb6IV//xk3SOKW3cQfyFKSO3978fWvIzNpllk7N0b7/AbEUTNoH1FZOc3q2yGt/688/Ed8nPOF/xJOFtz9A/K4A/9Lf/bt86R/9HR5oM0xmL6DXZzBqNcrPPY999hKyt8Pmd77CF37lHxGNBzS1iOpkj6y9g/LHBG6DxHTRs5TotV9Dbd1H5AnWmWvkC1dQuo3o7ZBp+vFzaxRgPX4PMe6A4R5/KUwbgSA1S0TCgCREekWsmQWqV86g1xqEO7vHEclCR5kFlDQQ6QRJjp0MyXSXtHUK3yihCg3Ewjku/uCzfOzaHKuuznzJZBzDvQOfsVLsjWN6UUZ/kjLsTjn4xju4py+i2RayUCJb/4Bs2EMVKowebREf7mF4JkkGXmuWMNMJt3YwTpxjeHsdzYDJXp+CZ5AEMXptBvfkLMKwGEiHmx2fb00cHgwSCpUKL8k2F3Y+4HxTwxoPmX3xEpnuYq9dRhQaGJpCu/YKXHuVo6+9ziltSEklbD3epDQ6IEoV5uwC07feIjnaI/3wO+T9A2RvCyPxkfEY8843EW/8BjMf/hZX9R7FxGerM2R9mPHOYUAQ+qgsQQRDwihjOBoQz56h5uhk7X3SLERMhkSFOlQWMFcvMrQXsHtbCNNAsw067gzZzCmGXovIKWKcOE2S5WQnn8JvnaKe9NGVglGHgkwRj28hswRt431cMsBAK5bw1k5TLprkUUBcaBE8uEV49wbd198ikwZmFtBc/w71WpH07NN4nkNVS+BgA3/5LMFX/gV0tmDzNgdv36Rr19E6m2gqYVENsC48x436Uxy2J2w+3CK6+FF6XZ/X2jnBwGdiVjiMwFo7R2n7PqFmkikwgiFGsUzXbGKuXePm/It00dn2llnfbiPiKS+3YD+zeDTK+Fvf2KTaauBHKbMVycb8NRZtqLoGm/0Io1JD9HeINx9hSsVco8Zo8RLesEslHVE0JaI1h/u5P4a2cAo0nUGqkSmdfDpBGxyRK0WUZMh0ih6PyMsz7HWH5EqxttAiRSc0iqBZxJZLUJjFWFhFL5axFubJM4USOklpjt7cZfqlJQLdIZIGkVYAt0poVhji0rbniatz3E5LdGtriFzxYVxmI3K40c2Ic41JAm/sTYmTHIWOEAJNalhC0g5yHo1SKjLD1eC5cIOeH3IwmnAwDpECHkcGk8IchtRpGSkiGIJmYqYhmYIbjw6xNMGfbbWJ7ArWzBy6ZRPNnmVmtkHN0ZkdPEQvVHmupfNBVCIqzlFdu8bMuWuUPJe0v8cbfcWd2GEwGhE5FRKVERsuA2VzMD5OkwxSxeEkJs0y8jynZOk0XUmsBI2CAYaJR0polDnS64xynUI8pTLeI1VwqDcYBAlWnhAkOVU15GRRslYzubk/ZbFoUQgO6EqPTqzQb32XHA2mQzTDwozHqHEXrTGL7G5Sdwwq4QCRJYgkIh10iG6+xeTsSwjDwIt7DESRQaYh1PFohRCKo/GEmx98QCAdmD/LTNElV3D3aMyNo5C77Snf/mCTcOLTe3wXEh8Ar7X0+9YOq9zkIx+7SpgIhJCU5pZx6y1Ot5wn4vuEJ/zP8ER+/4Bx/fp1vvbVL/N3/ttf4u/+/X/AxmEX5s+QjcaI+jxq5SqhbvPUj/xJfulv/gJ/5X//Jzm49z7df/zf0/kn/wAx7uFGfdygAypDLxRIfJ9sbwsMm8wtofKU3B8Rr9+EJEJFAVpni+TO64hJB4Q4nktMQvR4jKVp4FXJzALxh9/GKFXQZ5cpnDmLyDKS+hJ+FBNVl1BJjlAZynA4TCUTs0oKxHYJyk1qr36StR9+jmvXWsw3XEKO95brdYfDKGOsICVnc2+KUy8TvPcammOhhCSPYzrffA2h20gJ+XRMtLWOpgmc/i7h298l2j+AmWUq51cxFlYZv/kaejRGPfMq/t13OZg7z7cOU95vh4S6yzNNg4+2dE6eO4vXaNH74A7CtKh/7kfIX/lhOq3TkGeoOCCrLyEfvoPKUqyf+otE926z1rlPPfNZ39zk7us3UElObpbJSy2Cgy75YEAc5iRxwt1f+W12//WXiXs9zGSE/PI/5YVbv8ZHBu9R0nIcKdnY63IIpoLyAADz/klEQVTw9odMM51ZMWGpfw/dcTEGh+hXXmBnkrM1zRndeUCmJGLuND1RYFJd5ki5dGunuNuL0OKA3VGCPdhH6gZSCRQCTyWUpgcEcYp0XPTREXq1QRykDNf3oDqHKBRRQuK9+H14z32U8id+AKs1g1kp8PCffYF47WlGCRSyMdbZK0zffpNy0EWmU5SSpKUam7UzZEvnyB4+ZHTmI3S+9Sb9/oDDqWJ85gWU0DHTKddmTFZ/8sd5nLgUCWjVTBY7W8SLZ4ijlCXGpLkgvfUeQjMYyiJBdZnErmAvrBIoyYIjqAQHLB6+T/raNwgGPYomHPkZWZ4TjHzu9FL2uyP0hZPMlUxSp8KDbog5OUJqAv/+HRLfJ9jvMGdDxcxonVlidaZI2VSo2hzNM8topo0sNbAWTrKveXjJkLw6w4y/j0FKeriNNhmROmU29o/wbIuCDruTCMuQjDKND8QC5adfRNbnEbVZEs0jEw6yWEHEAbZpMlQGqdIwhcJNBiS6zYOxYn+Ssh8J7skFUmHwKLZ5vZsR5YIbnYCyoxNlOQtFg+WqR54mrPd9jHTKWJmUCNmODH50RWfY79ObhEQXX8KVOfNlm0mcc2trwHpnTIGAyvSARSPg1FwF3bKZr3hEGfzp52d42ugx8/gdzL27OCLlhJthHj2iYaTMOjqalMR2mUeJQ5pDjqJi65xolDi1usKVZ15iee0SumFy7+5tPnz3Xfz9LUp7N4lzOOcENDyDiiFo2YILZUHBlLwy72AJsMnRpSRTEj2ZspO4yOmQ8v4Nsm/+c0yVoHSLOTlmNdzF7q5TC/ZwRwcsGhGVw3t4BsyH++RWif3+hOLRfbQTaxD6SMNCD4co3SARLsqtoKsUa3IA7ccYt76GFgzItx8ivAqRXSYrzZIgeK8TIxA4piRMMt6//5hHt2/il+YJKosUDEknTJn1DHaHEd/c6PPGww7tTkgSBhD/mzCKUsH8feuGVW3RqpcYdgNWX/wY0/Yun3/1Aq/94l96Ir5PeML/DE/GHv4AcizAX+LT3/dpEIKf/ON/jPMf+Qxx/SSZ7oBd4e7Xv8TP/Gf/KZ/+1Mv8hZ/+K/zHxRLnz8whTRMZj5GaRqgE9soq0yBEaRp5dxtZaKIe3yLvH0HkQ62J//53sAoe6XSK9vgG2slriOkQ5ZURfh/DKRDZVTTdQkpIjvbRy2Ws0+cReUpiuNjRBKFCemadMBTUHI2iCuklDotyTGCUUY7EXL1E3XLQkpDNb93GvNMjBj540CfMFBVNME0URUfDmauTHO3jnjoDwRh9+Rwlt0G+dx+jUkKFMeO7D6lduU6mW9SuXkAunCAWOlY4gLSAVq0yGAy42d8mbFygOp5yarYB1UUwTYrTLVQSoQdjooM9NMdGq7WwUhDtTcLhhHCujnCLaArk/Cr2rW/SLDWJW6fQB5ss7d7mYeM08SuvED9+i+ZzT3E7L9DMbNTeAYP2lKTX5/wPvkDv7fexFxYQXhF7waH3xa/TqBuUZE5l9RqRbmAGcPSVr9I60UL1D7BPjCn3+4RTxckrVxkO26TDLum2DnMtZhsnUX6MGce82RW8v3vEpRfnCZOIPA/pRhKLMbupwUJBkgsT/8v/EtGoYp+/hDpxGfOkQDv4F4jJEaLcQpQbKMvALKRopkX3/jZ2bYXW5RHV06vsSYNk5RypXWPQ6TH/+A757ByaVeKGtsKsc4g4fR5nZ5u+22TlT/04DI8IP3iTwUs/gC6LNEZHuHYFo7fLM2efx9/ZpLo0i6YpVHeb/rmPUHF0otSFH/4T9D2PMFE45ERpTsuU6NUW8wf3yPwxqePAlafoDCbMNuvkwJdvH/Gp504zN73LzPxJtPlzfOHdLh+1c0qOgXOwj7Z2jbT323TeuUP13Ecw/T7JzhZy9iSxUcTIM2KzTMEZI6eQGSaVSZuKGpLvDVE4pFqGLJaRj++iNJ2oUCUIIy6vtBgYVZIowU0njKTLo17EU80CqVYn37yLtfYM4xTM6T6EYzRnSstrkCroxzlpbCBVjiky2gGsiD43owLvbHfJswyF5FNnm1ycKTCOcx51pqzVquQiZXuaMOOabAYZ+wOfU07KONaRrk4nVtxqD2icrnCyYmLEPr6nUT7T5JwxQhZaGN0NptWT3NiPONfQsKWBK1IaUpJ3t0k+80cJnSJpoYopgOo8dZETSQ2/cZb+JOcoUFybL9JyNTRNwxDJ8Z1yAcVymeVGhfmyw36i02/v8Xg8xvMfMJmtYlZrmCKn7FjoKfSUjqNSjhJYMBLAgDwlNTz6o4SV8TZi9x4iGJHvPUTVlrHi41vk8YdvYJ86DssQ0xHig7e59hGXLIZIczg1fYyXh4xPPo28+y30kxdASvRoAktnyAsV9MTHW38DUaohx32oLWCsXSUoz+NpijxT7GRlbC2iKhP8FLbX75INhwzrp2hWKpiaJMoywjTncJqwXLHZ7PkcDmPGR4cEB49+Tz0Yd49+35oRdHa5/XCbF55a4OFmn1/9r/4ir16aeyK+T3jC/wJPOr9/QLl+/Tpf/sqX+Tt/55f4x7/yz3l3AEeJySQVtBOTx83z/I1f/Ae8d+seJ6oF/knnkOJig7x5ktwqQZajbbxL1mvjXXoa4/QVlGaiT3vHT6ClGrJQQC6eQy9VEK6H3pwjGw9g0gXLhu4u6dYd0F3MSR899tEbc+RxgKjOEm+uQ5ZijNvoky5GGlEVIbZpEksTJXQOxzGJZpPmihiTqDCLObuA0aji1QoUv/cNfDyIAFgpm1yZL3Dt2XnGO22cEyeJ9rdJpYU0NUxbQytUUHaZfDomHk0I12+QjAZIx0Q/eozR30XWmmxnLhvXPsZ7I41a0GW1UeH5xTL1e9/GefweO4OIYWWV1K2RHe3hTwLqV06jrALGzCx6Yw73g6+hpSHxwS6JUoy8OfRKnfS97xDHEbI6i8JgrX8X3TS417yAPr/A7PwslfOnYfks89dfIur0MRyb4qd/CFVfJHcr5FHIzMeexV5dQ4QTSne/TevoFsXoiMJkF7NcRM2eYGKVyUd9xN59zAdvYb75Ncz9Bzh5HyOJMIIhhTyiOdzimtHlRWfKwTQjzjL8ccThYY+j0ZTZyWP0w4ekwZRCySSaPU0ymZDcfpfUcCk++xR5aYYUE9+sMnz3XXb/+3/Ew3/8q/T/1ZdpBxpHT30cuXCapquBV8Hvdeice4695nmiR7eJb30XdIOiZ/NWPkPh4lW84T5eNsbsHzAJBZ3+kK+PTB6bi/RwCCggBm1GN28QdkeI2jyGa7EXgqZSDmKNqeEyiXLKtkZ1+BhbxgRZjh2PEFKRj3ow6nFCDmnrTX5jO6Fmacw7Gi+erFB++D5WrUGqmYyEjkZGOQ8xZhdIdQcSHzG3iHnhKumbXyY72uWN/8cvoK+/ju4P0FRO5jVQmo7IcjSviHBKhKlk4xtv0t0fkqQ58eEBxvxJHt69A0KyUCtSintcVAc4vcdUkx6fXBBovU3EqEO4u43SdIrBEfHGPZRmMjLLjHOTrXHI42HMyA8QWYinw1zJILbL+FFKydapew4vn6pha4K6KShoiuWCxMxDioaGpkluHoz457f6DKKMvWHCSScnFgbPVFN+7LTN3PabLKseraLBC3XBWtPDq1aZKIvXwwadCHJx/HQfJTFhnJDu3GfaPIc+fxLZXCbSPdYjC9+uMtQqbE8zSjImF4qzNRshJI5hoEmw4gn9TMcjQgpQKMLiAqFV49TFq3z08hroJjfuP+LOu2/wYHsXP1aEyiDOoJcZNGyJFg6wNNCkZJofJx8aukRJHefiU8jmPKWoT1g/xai0TBoE9N9+k3w6InrwHioYou8/RLglKv3Hx/JebmKiMJZOg2kDktQqEjaWkeGIfHgIlk1umCihkXhVRrVVuuJ7c/GaTpDDMy0T3/e58d7bkCZEs+ewveJxQIghWCjZLJVs+mHCNMl5f6PHD12bIxgcAPnvqQWTfv/3rRemW+Zb/+wLfP03vsTP//CJJ+L7hCf8PjyR3z/A/O4ViF/8xV/kX3zhi9x6/222+yPCLGOUwL7R4v/9X/w1Tkp4+6BL9cUXGGseiV0hnwwgSZG1FlqxROo1EM1l6GwhbAdRrqM3FhF5hrV8Gu3UMxj1FlqhAoUKSA3pFRGzp8lNGy0cQp4gqk1yoZPubZK291F5jnQcKNUgizCjESUCslxxf5zjJzkHkcY0zUkUREpDmhZ2c4biyRanahYnbUFJlyTAOEqZ+AmFZgGr4qJpCtwKwi6Q+SGyUCba28R96iM4Fy/T+synmb77DlarhW7ahKMBm/ttvrEbsrm1w4m6x1P773Hh0gWqjRYYJsbsMua4jfvuaxiaQLkVtPkVzBNnyM8+R7S3w/jrv41TqVF++ZOwe4/ktd+CW2+w3Z8gXI/B7ghWz2MunqD8zDPops2zMzbKK3BjIFBHB2TDAzRDR15+mdit0X79QxwtwZxfJFi8gNGYxZhdQgUBwfp9qMwikhAZTjBLDqapYWoZcXkOrr2Kd+YsUa+POTeLMz+H1DSEoZMHQ5TtIh0Xp1Dk+QWb6aNHnDJD7v7yv2It2cJMQvpGjdgfoyIfuXaFtNsGjjvB0dd/jfHuAbfv7+J32myMMpLqPPLkGbynnmLmx34Uu71PanvsR5JHU4nIMm5uD6kEfYx0jJpOSKXF4uA+dm8fpds8pM56Xsaoz6CETuGz38/Zhk3JNXnQ9fl6z0CevogsV9A/+nGMy88jF5dJly6Q+2MwLGKhszOOCTJBL8wZluapGorMsJHxhNxy6N9aR682ma5cZnGuRtF10DXJUydr6L/zBQZXXuYL94dEqeDphSJn7JgLTQvfqyOVItdsFj/9cXIEaXUB78x5zn3qOcTj+0TvfBcjj1BphjBsSKaIaZ+8s0OUW1gvvUweRUw2dzDOXiXTTI4mIc2ZGTKnhp4GpGYRqRvkUqdhG+huBYIB+epVku/+OurxTRKnjKovoBs2fppTljmOLoiETcvWeTjMuHUw4dEoo1UwuTxb5JXVCnXXoGrCoZ/iaCClYHMY8cZ2n1v7I37r7W1ePFHgQnzApaO3WZpu4PY3sTKfWuceuuMwNsoEehHpFGhPUw4Tk7af8KCv0FXK08WEStxndvQIKSXCLVAUIbHuEeRQyCbU3ON0x4aY4uoamW5Stg0SpXB1gZZMSScjUt3CVCkpElcXxEiqFtQdgdJ0tEKNxtIqS1deoDm/yORwl/e/+03ee/CYIAgxNY0cwciqk2Y5OZBlx8EcFGpoc6dQzWVUbYHQrSMNHcOysVfPgxRoZ58hGw6xT6+h6QKZTNGTKZYJieVgD3eRloPKUxLdJrKr2ImPGBwwebyNWFhDlGfIooSJ2+TuSDGJjseRQqXT0lOGWw94+8ObNJotls5fwbEsrs4UcE2Nnp+ilMASGZMgwdQEUkp2DkeE7b1/q1oxbW9jeBX+Xz/9OX7gxQtPxPcJT/h9eDL28AecYwH+Et/3mc8yiBQ/+ukYbeY0wvRAKugd8KeurfGnf/ovoM6/xPu9jCstC2N0hHbmWURvG3SL2KpANkUDpOWixn1EbY7MKWL4fXLDgEIdVV8htzxkOEZ5VRK7gRVOUZYLEmQUoJXrKCkRpSp5oYnMUoRXRY06CKGhuxllXdHwLFw9xdQEtpTHIxOZAhTW3AxWwWK+5VF3TW61fYJMMI4zPD1HCIUIAoRhk3e2Gdy7R+G5j+AULMY33se8GKK7Bfpvv0/pYx9ndO8eh4Uau2qegg7nRY/C4Sbmyqtw9QUgo9h5yOiojakD556n8kt/E/3qCcxoiFq5iuOV0CY94oVPoH37N4/vtna2iH0fb3kOhvucGn2JSfMkxWvPUNZD0s4RslQltwrIYMTTpxd472AMnotDiSS3aX7wDoWPfoRxauL3HmMM3sd58ZOoYQetuUw6baM5NuO79/HqFppl4S3NQW0RzRriZQGyt4OcXyJySyTvvU3xhWfJhcHYbmJlMXGuoQYDasWcnlaiuVTiTqBx6af+DKOdWzjvfINcSe6euMLquXMof4o5nDA2C5jPXSW/+SbxO9/FeerT7E7HtLUCzQUdVs7Ty3XKpiCZjImnISCwpOK9kclC1aBlLxLqDrqmyFbOoWsmXuceaw0HIQQ16ZIVHNSll7DTDM8/5HRyyFZq0Z/6dLMKllFgtlWiE2VsDFOmsSTIdL7wfodElwRRzNOrTRwpEW4RnSluPIJMMXjtNaxnXiCZP8f60MbOcyxNULdgvdthuLWJuPQCy7lFLDXOVC16SURDV2hS0BEecekUcwtLdDIL99Kr5KZBrXWCzJ8QJwqvt0sejsm9AioOUd0Dpvfv0fnwId7/4Wd41J1wKdwm6cRsB6CUxtrpVVJNkDs1OrnBouxjAJntopSN3PgAYxRgtmYhDknOv0JmWqyPIuqORdXVcRwNI1eMlE43DFBK4QcpLc9kqaAxThTb45iiKShZJkdhxmubI55drvLoaMwriy4Xny7zbGHM1m4Xu1HDFBmZVyOVgjyMiNeeZz+xaUmNONVoFVIOJymRrnF9rYY32qWkxWTSRK+0EIaDmD+NHgwIsoyyzJCmCRlMEyibEi8XhMqgYuTEvo+ZZ1gqQR7tkJ98mkrUJXSqGFnAduzQcqFiW+hSEOBQscEUCr2wQKtZxd/e4GG/x2Z7h9mFJeqziyjDxCBjkgoMXaLrGpmQBNUlIq9JFocI06UoQagIfW6FYjgBu4Qzt0x09hW0bIolJLg1UDmayqHUhEkbVZtnnGjog0O0qE8+GRItnsNDIPMY8+ILJJj0wwmHvuJay0bkOfvrt+iNfWZPXyC0SshYYUiBFIKiLpjEOU0x5ltbXb5xZ4rmGhy0p7zx5W/+2xcKlfN3/9PP8UdevfpEfJ/whP8/eCK//yvg+vXrfOVLv8Wnvu/T3Fnf4mf//Od5KBq8eOk0iTCpPnsN/cpHGTgzFLPjRQthuYg0gGoLFUWEUYSuEqguIKYdcDyUZpELk8wuIXQTHI/Ua2IGPZQ0yNw6RjxBoUA3UaMOKpxirZwGu0RysIlyi7D/AJFGkEWo2EIoSQZYEhzXIEhSQhRRmlFzDJTQMOqzaMUCXsOlvGAg6wE7O0NqlkYQpHQf9nDna0zu30GlKbphI9qb5NoS7tICHG0RHLXRG1UePD7ioLxA0R+xenCLxQuXkJMBYn4ekcakwQCNZfLWMmYYMt3eQzccSp/9Eaw0IHPKGON98vYeslTBzCLyZ18lMl3GhVlqSxUYHDC5ew/dzhl8/as0Xn0FOeqixgOYXcF94ROEwkCVWqyYB6zv7FM7eRmzMgvBIdqoQ3H5BNG5qxR764ib30JUamQK/PXHFBZaKB8ml17FyBImkwnlnUf0Hu7R+vir5KbHOHV4NJxQvPQiplOkZOqIx3cZza7A6ACvVCcotPAGexjpgIJXw0oyXrdOMnd2mZJKWZidRZeSUCnC1mlGdo137k1ouOcIz5WoJgbKbNAyYpTSKRZc+pOUbT9ntV4nyAL6CZAr9CzDaC2S5WMs08acP0kgYWrXaHgFjoKMhqNTURP6tBi7Nj0/ZLFWpNTZotmoMGcYOGRIqTNKFX0/5rWNHr1+xJVTVfaPBlwsjPnuuMDnn1kiDcYUVE4chcRf/y02/Sn+g0OsV34cRRFbzwnSnNNFgT3c5bI5pP9Df4zXH/W4eLFOMZnwIHGIMwNf02kIn9cPFedVgl9osj9KWa17RHGArUmEYTBymzSGdxGAv7NPtnETlaY4V1+gaRWJ3TLOUy9gxnOw/YCd1KAoMzQBk1TDVhrVeAyRj9R0fKeCl0xR9UWsQki69xCtXKcQHPEoX2IUZ8w5CpGlWFlIYft9/OVrlEyNXpR+r5sKd7oRixULW5fMFE1uHfqkScIPnvRYqJpcLhaJf+2fU/2hHyIIp9SefpZI5FiTHZTKCc0G5qmn0KROzTbJyUkEFA2NqQk1G2atjF2zRCHvk3sVpoZLIVWQK2JpUiJCkxrdWGd9EHCiqJMojVLcQTMqeCoANSK1qiRWCZqSQEkMw6UTKVzdwk8zwswgzHLiRGFKQZLl2K6JQFFJxjirZ5mLp4ymPg+39zjc26G1uMKZpRlCbDRykgzSwiz3+zELCvrK5pSekaUpzugQFfkou0guNfTVC0xLLcZZjjE5wKm0EIcb6F4Z9u6TSQGFJoWj++TtfaJcwzp1BmprZCpCHj1AFaoEWY4uBU3HwIuGvHvjIbaE1pkr7CU6M1IwTTJMU5IrwSRKabg6mSb5Z+932RonKKGx+eEt+psb/9Y14ugbf5NSweXnf/7n+YVf+IUn4vuEJ/w+PJHf/5Vw/fp13nv3HZ567mX+0r07/Ff/5V9n8yGcOneJVqnCkdVECIkuBf0ooygN8s4eslBBVGYoDTYw8oSktkQ8HWFqEiUFetCDaEoeTJGGgemvI5OIfO4cE2yUPO6oOOPjZ2xZnkE1liCKMest1HRIHgXknX1IQ9T8RTo4eEj6QULZ1siFpD2JCJKceU9CNIVSC911WP7EFXI/pNgdYpETTGM8U+Nwc0Tr4hivWcM7fRaVp6jmCkqXaF6JVBjsRhrb7jKWEXK+fRd9e52oO+Zw74j5P/YTiMNHpHubREf7aGvPEhTqOPU+1spF3KRPvlBHSyKy+dOkkY+M7pPtdtHmcuLKKipKUTOrRP8/9v47ytbsLO9Ff3POL6+8VuWqXTvn3rt7d1JqtZCEIjIgiRyMMfgikJEBESQbG5AHnGMEWMYHAcbGHDAYTLwmSyiHVksd1N0756q9K1etWvHLc877R+mMce899x7DGdY4x9L+jVH/V9VY632f7/3e93m2r6NmjuKrCnJnGe+RBRQlyWCEPzmNvnsZ5+BpzMQ+dnKBmljkQGOGG+efJZA+Ewv7WK9M4fgRFc/F7DtO4EHvQ39B7aWvpPbilyOTPnpnhKs1IqxSOFW2UIy615lavoRVDl2/ytR4HTl/hmF7jroa4V6/wMbMaT7bF3x1zWJXriIPHEd89uPE7YQPrRW8SMV4MsI/cYZYRURFj3TtLro5QePWM5xtzpEYH3FwH6FrqVZ8nGsXWF84yf60RyBqXOmnHKjvWWbZUnOgIQnHKR2vQjPLGQcd9NGHuD5w6HgFpjrFfiXxsyE6aFBYQWkE8/WIQe4TzByjUeS0+7eoeC3S3GVdtfHKMW+YytmoKfZPeBSf+BiHTnQQ+x7FMSXu+Y9Tnn4RvQwmHnkM/eSn6X3L93CtLyj7fSquw6RImQ3brI0immWKvn6JcPpRPnZ1m8FCxLkpS1Ztsp0UDMqAuYahVjuBUA5zTkKgHRrdq9ipo1gP2iLGxj10XiBHPXYu3GT6VS9DNjoEr3gDNT0kx6G00OscJl/b4dih/ZQGrndTKq7kRNFDeAF3y4C5uIvYWaa8dRHb3kexsQp+BEqxNog52KqSGMGUa7iVujSmDxC5klFe0vAcHCFQyhIELloLjrZ9gmTAg20fx/OZ3bkCKwVeJWK4fx5fSrQjcF2LUR6x7jAWPg6CUApKqagqjcwTBJalwqfuOzRlTKolk60mG+MQX7oEKHy3YFBKShPQcCwah7jci+Ru2RjGI/yoSu652Kwgr83R1wpRSFTQIS8Njhvi6QKMwlOSXmZQ1mIQRMQ4SuAlMUZKHCyFLcmciE7VMrHYZlMe4OrNWzyxeofphf1MTM/iKEFpHfa3FRVyXE+C2VulElaTNQ9Rax9g5NURncNc3hxTDzyaThOhx4SNSawbka/cRI8TuHoRr1Wn3NolPHEa01ygmXVREtJLn8d56ZsY5QZXQJBs88ztG8zN72Nu3wIXdkpqviQuDKUxTFRcALbTkprvcicxzC92WLq4xbg/Zvfm+f/TvaH36V+mLDLe/OY3c+HCBZ544glOnz7936v13OMeX5Lc2/n9H4j77ruPjZWbpMMB3/v9P8T61fM8f/02o+Y+VsaaYa5Z2k0pDBRpBlmM7W0gjEEFFYTrY64+jdhdI63PYrVGFAl67RZma4n0xvMUty6ihz1skSJMSSZ9hOOQVGbJJw+QTh+jFB5qZwlGPWx3BRHWMTOHkY0ptHIYmb2I39xaekmBFILNccF01cMTIKRE76wQTEzSevQBpCuwcUqtE+JLiRAwc6hJmWm0Ngg3wJ07SPzZj7P14Y9yZ7XHp9cz1sI2J5sej/h9mr7BabXRuKycX6Ys945G8o0VRJmx/Sd/QLcEJSxEFYaVWVSljYjqKJ2h4j4ibIDvM776ArXeTapmhB9GLAfz3C4jipUluk89gx8F6KiDPvggeW+ALHOk8rBWosoCx1UYN2Ti8GnSnXXubPVoVQOqrkuSxVjHI548TvTyr8ICF3/j9xldvUZS38/Wbo/IFqzFUDcpc695HOkqfJvTufwJZKZpjzaYdgt00CQ+9RiL2V2cpWt8uBsR16Youxtst+eZHq3xH37p5/mH7/3XfPTODsIIJmyK0CW23sGb2o86ej8n2j6dwHKgHRF6LjfWB4wnDyB1iZPF5GXJgy2BSMc09Ji/fvIyHzm/yrNjn//0xBJP7Hj89YUNro8Ey8OcSryN3r7LdL5DZ+cyneQukc0JPEVNapqBy0zVp1OP8D0P4Ya4cZd9xSZz6RbHWx4P11MOrD/NvqMTjKmy+Oe/SXH188jdDXxhaGxc45Y/RVlroao+NZvyqNejYjXN288wvHmZUhukEqjly2i/wiO1ETVfobIBN3ZipBSkBmqhxzYVtno9DohdGiQ41iC6KzjXniKSQK2NqrfwpiaY+JZvwz18GulHCL8GSY8WBYHJuDXM8R1FbW4/dxPLre6Ym7sZKgjQCHpOFWUhHwyQQUR+6RlEVKV/6FH61UUerSXs5pp6vsNOIYkCDxpTGBVwqBWx2PA41JC0PcGhCszKmFBnfHIlo+4apkIX2ZmD9gy2NkHw8IvAFnhhnfVM0csFm6KJCiq4UlMIwVah8JI+LgUugkFmkcJwbSywWqOsxlESmwzxZEliHVAOOC5lodmKC6b1LlVPkXgNtr1JNqjQKxQrtkKCz51+SawNkQORsnhCoByfyzsp0moix+LLkpanqekRVZMRjFZxMTDcobQSx90LgIlrM7TaLV68r8V9sw26q7e5+PnP0u/vUnEFdVlQKI9ISbrGIRYRg/oBdp0Gfa+OUS5rhc9fXt2mmxV4roMcbWPdAO1FCD+iyDUyjEjWNlHNJmb+BGq4gVq5iN28iXRdRLXJtB3ibV9n885tTp46ydTifnI8PFfSCB2GucZgsQa24wRtoJ8WfOj6Dudv9Rn1Epae+dz/qX4Q+A69T72flbvLvPjFLyZNUz772c/eE773uMffgnvi938w2u02u+vXefTcg7z7nT/AJz79GT7zwmWKdERWalqhIi0048MvRi4cwxx6BB1UkcKCUuR3bkJ/E+IBhDXM2i2SWzdBKERZUOz2KNduI3buEuZ9vGKEW2aUgLWWkXEpioxh2KHwQuy+U5SLZ4hbi6STh7D9LWqKPfFpLbEGa2G+5lMPHMKki1U+FBnx3RX65y+z9vQtjITaXI2Zhw+w/+UHmTw5w8zrvxK3XkPW64jBFlkccz2c4mo4zULZ4yGzTX3lMnowoBznSMen9dKXcOL7vxN2txFBRLAwR9bvQ3eV6cFt6O8Qmhy7uYztbWLzGAHQvYvdWQFj2frwJ7CbdyhUlVQo5qouc8kqfnsC/8AJRrsDytVbVMwYVauhJhbIKtPoNKby/EepOwopIFEBB0+cZmd1ic9dX8PmQ6aTLbxkwDgv8B1LGTaYfPRhdi/eYuPOCmK7SxI1aXuWq7RJpg5Tzp/AVqq0Tp5g5txpWLuN1ZoiTRjXpvn0uM2DW8+xGAn6tTn+ejvieTWH3HeMH/+n76I6McV/+O3f4TPPPkuJ5HPLIz4yaPDM0i5Do7h1dYWZwU0ik1DPdinynPOrQ/7zRy7w1ErM4Mp5fvsvn6XTu8VEOeDN90+RGqgYzbXtlKu3N3nyxjZXNgbMRIpw9Qpm0KXo9WDURV96lnC8hVcmRHkfF4tC4zoK3d5HHtTot4/iuAom91EVe9HL0fQ8rYOLqJP302ko5LCLf+ohMiMZdfYjlMvyqZfh97fJP/431NeuIC8/hVw8wqaqMZFs8Zwziz89ybm5Ck9sSfad/yAxPhXXUHUEs6FgWuWkTkBQa2OjNrlV5NNHKOIRujVH362STh6jmDoKs8ex+88ihUXqDL8YYpcu4W/fYiecJh4NWZxs4klLNzF0ZEFFlqReHVFojvsF2gsRhx4kywSXPvoC3bOvIQ2ahKKklD5H5JBJO2Cq2GZa72KLFBv3mXUzRqOE2WyVrVGB7zn0jIeUhsLzcas1QjNGeT622iINWvTdFnfUFCuizuc3EzZiTc2TSKCwLkPVwLWCXJcIbWCwjqMEU5HiUCSpKIubj5gwPQJp6BWKuATfZATFkFq8SlNmuKNNqr5LZhVjo7g7KFnqp5QIDBYpwUUyLgXKapDgCk2aZ0w7OS07JigTIj0kET64LmQp0mhMWSABR+eYosBxXExZ4JQjZtyCFxVLzHeaXL90nosXz7ObGKyBTBcYA1ZArnw6Tk5fOwxzw9Y45/GDLY42fVwlcFvTCMdBjLbwD5+hdvgg/kvfRPjS12MfeT0lkjIZQzrCjkYE+w+xu7nGhYsX8LA8fPY0051JbKnR1uAKgdGWiidwhKSblqwOC9JC8+GrG5y/2ycuNJs3rpPtbvyd+8CDJxYZfObf8YlPfIxHH32Ur/qqr+LP//zPabfb/z3bzT3u8SXLvbWH/wFxHIePfujPeO9738u7/umPc/mbv5O//9Y38rw3zaW+w/HZKi892KYTNdHVNhQZyhpMkVF55Csw1z6H3L6NMNOIzizh6RA5tQhLF5D1Nnp3i+5HP4w3OYlbr6EOHSf0qnQrc2QWHLeBbdUYxDF+VCHV4FqHLVokwqNuYDJQOI6inxRsjTPagYcSEmlyhNEgHfypWW79/l+zc2WbyRMdph85QjQ3QxHH+BPT+M0KHHwZamKGu7dvc/nkK2hkAw4tPcXU6aMYt0peFpAMIB2R9mOmTx8na86g7l6EyiQ2yUB4SJlSvvAU/v2PoLXFnZxD5D1sfxPyFL21gpCCcncLoTXCj5BXPkX79GO4IqN//SrOQy9i8IEP0njZi/A6ExQ3X8A7fIqkucimdpjRu+waRaEtTdeS+oqK38BOHibYuE6xsk04O41yBIMMJh0HtXyF5qu/isIMEX7O6igjWF1jYmKaRuhTuB49NUM1bBCtXUImfYKjpzBC8oF/+Wu88rtewwfjBaZe+lrqoYscrPO51ZznLi3x7GKDf3ymRm+3y39470/zzh//Ccw/+HauqEPkUnP4aGPvEv7kfYyVpXAiwmrJV1RS+mXEgYmI33viFk5vhf37p/ibG1v0xncRZYJSHqo1wY++aIq2HfPJrsfhlkt1sMStP/8kxVu/kfLf/jJHzs0Tr9ylurtL59QZxPwJatkGwp3EsZpSWihyKkKAkBROgPZCpBCUlQnwq0yNtuBNr2fbhuwkOf6N83DipXhJTNtX9PQU6vh9bHspl4I2l3cryCBABgW3cp+JffczK8asJQrPyUndkCO7N8ibZ0B5OHbIQrPBKC8ZqiZb45yK41GbP4V2XCoKMuPjFsO9iWnWR7sVZJkjRtuYYQ87HnPXTiKEoHXwGN1S4SvDgY5HrXcLsWIQokCoBZTJkd0lthJQ3/UDrLgzNIxDW1pSI6l7EhNO4tx8DllpMJ4+Rrh1Hn/qGNNVj7E7y0LgcX1QYFGUucvRKR9PGLRXpShKlmKoBi79tGRzlFMi6Oea0FPodMSV2KHuSSargnGZUQ2q2NvPMqzPMtdyEFJhHRfrebhxj9StsV24+NairSAYbSJMjvUbaOGxUjtIWwkAPrPco1PxmK5IWkrhKp9MG7aSgkNBjlIC8KjZmJe1ClKnTlIaEiXBZHihhxEWMbmfXHqo6aN7/7P+Oty9gT3zFSTGgeoMftxFNTrsX1zEb82ysXqH55/5HLWZ/dyvdlCzR4mEZEQFZ3cdWd/Hte2UtNR8xZyLa8YY46KVQg27cO3ziPmDqP33YSottBuSy5D13phm8wgTvXXyqQNc7uWsXl3i8IFFFqouhBGgqZOhyzH4Phu5YF/V40M3d2mFLhc2h+TFngOO50kC36F/99rfuf4/et8hPvGb/4z3vve9/NRP/RS/+qu/yrd+67f+920y97jHlzj3xO//wPzIj/wIr3zlK3nR41/JjZs3+NG3/yOirsuf36lhraB1MIIE2hSY0uLurCEm5pEH74N4BMkQ4bqoag1GO5Qby+SDGMd3cKsR46tX8Bs+QbOJbFuqagc3aFFxXPraYc34VBKL4whcBa3QxVUVAkcy1pbQEeSexHcEdV9SdTQMEhAWay3J7Rt4NY+oE2E1BDNzqGpA9b4HQOfYErLJAzx/a4lh6nB2/wRTqs1oeBMz6CFnGmAB18VptWlNTVBaH/P0x1jb7BFFS1z92AVm9k0w89IzpL0BVBooU9L1ZxiUMNkAuXYVVWtCFjN44SLR4hxy9gjucIfC9XG9EG+ySf8v/4D2wUnC+f2UcR8O3o/NhoxVyMpujtdqk51+hGqZ0Cz7PDeuMedY/KjGoeOHuXLlOmbrBeYPLzIXNbDtBcpnP4197sOEhw4TNVrUn3+a8vkNqo+/hlE0ySgzWARBUGF14jRTboYscxLhsTAf4EYRb20WxKOcW4XDGVnw4o5GthWf+cgHuPP7T+/tZYY1fuu9P8X3/dR7WS7bHFk8xUvC+yn9fdjNZT69mXDbhHz2yWeYyNZ44dJFPFMgJOzogJdPRlw6cJwpaZlYmGZ09zaru7v82tJd3vOWx7i/BDk4ih6XmP4u9esX6XzT1yKuPYnfqEF3HTc7hF0+jzt9EJsOEGWKWr2JiCpQn0D3d2i0PXqVORqyQK1fRU8eBEqotOiMewxtHzl9kpZMCR2N7zukYchH4qMEoeXRgwG//KEbfPt9IZNFxrHcsFo/yO5GyhtOtmjk+xgUGZV0hwJJqWO6MsTLE9ZHkpoHJQ7WCHaMT0cZhBU4CkylSb80jIwkcD06aoAQDeJjLyLob7Ozs83ERAclPbrDArCsx5r65H6yv/wN3MdfT6wCGukWeu0u4vQDFN40k5HHyiBjlAmONavEpaWS7jCOS0yjRmwUZv5+tnMJAna0ZZSWvLAx4sRElXbdIS4scSlIpWJQCLbTDE3JdlIyKg0NT1L3HSqOYn1gcLHUfBdpNa3AoTpeg2oT3ZohlHbPl1s4KB2Th3Vy66JzjSdBGUFZnyIrLSiXi7sFc1WX7XFOzXeZiFwO1wSHzAYOHXa1x3zdI85LavEaQvkUSmJROGFAKTxSSvqFRkuXKeGiTYbKY9zAQ1iNuHsRBluYA2fISwtlgpSCotrBsxqZ9Znv32VhYYqdiZAXbi1zSRlON3ZAG7LWIS7ZJls7CYO4ZH/TI5Riz2WjHCLCGgiQ80fQtTa6OU9pFbkRZNbSLzRtkbA1d47nb9zBVYLFU+dYqCtybQmSIXmljVSQqyqOtdRRjEqNIyVb4xxfSi6s9XnwQIurGyMufPJzoIu/U81/349+C//ga17Kt37rt/KJT3yCj33sYzz00EP//ZvLPe7xJc498fs/OA8//DArNy+z/9SjfM87foR3/diP8bpOyV99KmNtZ443nnWgErAtpzk7VbLjTVJzNI65g7QGlERvr6PCCKfRQYRV9M4mfk1iWiFYGD31BCIMcU49QrMaks+exJERDV9RaKi7gtwYPF/hlIpSaywG18Js5KPSPsJKvCxDJgNozmCGO5TjBCkEfiugc98+HF+hB31wHEQQcUtNs3zhCs3I57EjU3iOwnpNKi95Ldlzn9zzxs1Tti/eZOb1X0k+Sim7Xdz9R6g4a/QvXcZJc8KpJmrxEN7ODvnmHbyDZwhNyR0qtE2Pcvk6wZmXUHa3cOoNeje3ae2sICb3o4WiDKoYr8rgynWq++awVy6QLRyl5StWosNMZbvMANsDi3QDnNKQ5IaJ0GE5LTkx4YOcYuqQy82bF5GyTt0q3O4a+vD9ZE9+HO/r/yHuxjUq01N0j7yMdDRglFg6suS2mqTje5TJmDu5z36RYgebPPA1j7OUSKqbN9nKXT709Mf5nz/4Z6jhGt1unwdOHOBtP/keTi10uCamsTLmve//9/z0v/kVnv/g7/D+u0+ggir5xnWSvMCRLnljHzP7Fvju7/unbHR7yEGfQ+dOcOJjf4h35iQb/YRi9gBlexo3dvmGVw/49d/5HX7o/pPs3vkMd179rdx/4g7+xkWCYB+m4uNFDvkghiwlvnqBcDxAHX0Ic/cyjAaw7yTCFujlq+jOIlgotSGIBwhhGbcWqZQjZFQjcjxMdRLVu4PnV3H1iNI4zNc85ms+aVHQjTN6sUs8MUGnt0rYXaF2+ABnRisQTbLPGWH3nSYwMcJk3Ck9ytxgjSApNYGjGBeGmi+IyLDWRWvYLQRZYXFxCHyQY4OJQlbrxxFFiO2tMTO3D52POb+R41jLQ5Mu5dI1yqFB7Y7QjYLyxgsUQmEmFjH9nKQoee5un7ecnWRUlETJFttrW2BdbHWaejFk07TYSfdemxtgMC44MVUhKQy+u+cCMdICUxiW+wk3txMOTVSYrbo0fYeGB3ZQ0GBMxxkggzoj6VEbrVHWJhHjLqY1R+g6rCXQDCwCS4JLZlywgoon2c0Mc4HFOD7Wwsq4INeGiJJdbbBa8PJOQXXnJmb1FsmjXwPWMuXD1VggggbDQlNYF20EQgS4WCrSIFxwlESUKf5wDYTESXcx60uwvYZePEni1AiXnkYJgXQdTHMesjHCWpTrYtdv0PRavLiScUVXeeLSMocP7ifCMsKlLgoemdV4HmjHAenQD9q0sy0MkMycIjaSqrWkxlLiUC92OVZsc77vMN5cYRy0OXPqFNpC5giWRxmYgLnAUNiQ0kq03Vu3UCiaocPdQcmhiYiG7zBVD/jLJ2/TvXXp71Trf++938dDx6Z47LHHiKKIp556iunp6S9GW7nHPb7kuSd+vwSYmZlhuH6dMw99BT/x7h/mLX//e/m21z/Gbz/5DJOtx4gOd/Bcn9jfx/ldy+m2TzAZUSlHYAtYvooVFlwPp96m7O8yurlENoyJJhrkucUORsjeBwhe8jL+1bPXeM1Lj/FwRyJMjjMesO1NEMUJMRU8V4FQ1M2YUvp4yRZ0Vyn6A+TULKRDkqVbeLPT9D6/xHgzYdpRBLOz6DgmuX6BlcOPcXvlDrN3r3L6wePI8S6m3gE3xMZLqCNnSf7sj6ifOUUw2Wb1z/+axn0nqR4+hNdoEC7sp374CM0zS4QTHfT2NsPnLpB+7gLtd5xjKxNMVTyG4iCVkwXWr9F/6s8JJjvUc40KI6zjEqU9bJ7AnatUjh0jfcnrqHevEVuLznOkb9BOyJxdo7j2JPnJxyBJGEuf/XLAxbJKUI4ovApz0xNU5TGu3bhOdfYAJ+cOsmYHeOElqsM+eXWGAIfNFy5zW9V5Sf9ThCdPMdfyWV2O2coFpU3Yd6hFHk4jx3fQS9s8/cRn+Fd/9AHue8nLeNVrXse3fe3rEEoidI5xIsaOJcphs/Ro+z4/9o63MfOj38NnL96ku7HKuXMPoPwKetAn73fxJzr0S0XFEXQHMQemGnjH3k6vO+D+couy1ubPP7JLEFT4o2ubPL815pfvdvmGN72Rzvws7olvoz5cxa7fRlbqEHepHLoPUW/haYPszCGKmPz6efxzXwH9dYTVuAdPYMqMRrECQQPRnCSTPrmBSHmkKiJVCm0FYXMBawXeaJ361k3OWgfbuo8rqeD7X3+ahZqD47jctXUqQiM2r+LpLmrhMNpvMnTq+NLgmwFTZcYnxxXmay6dAEok1pRUHQeMIcMhzguud0v21yQTKsPBY9froA2MdzcZrK/T8gTNtMdY1Lmv4zLZv40yHWxREL7hzZzfHHNu+yomqHCreRJPQ6Y1n7k9ohY4hKZAX3qK4eIxgl4fHn4pvcJhWMIgz2kHHkMJobIcbwb0C7V3AFbshUgk+V4CXl5ajLXsq+2JsGboIjCcaEOYbVMEdUSZ0Cj7OPkQkVcgrJL6LTItaMqMJFP4QQBWEqJJpcKUgry0uLKk1IphsWfjFTqCVrKGMxxTazZQZQbjHYrOAgZFzbE4OmdfIwTR4MZuSVNLJBYH8LHU1s9Tb81jZYCTjiAdQa2NTUds/c3fMPGqVyLGu0Tr1xF5gpiYx+5sQJ5imjPY/jY6L3Am9mFvXkJvbfLgY1/J0p1VLt24zVwheKBiCDyFm4zBNkio4iQD/EqIGe9yN9pPkUsyA/WNC3jTh4l0zijLeWFjRJplbFcWWSt9HpTQiwuyKEAIGFqHq7s5ALuJ5mA7xBhL4EBaah6crdIJPXbqJZ+5tcuVJ5/+O9X4S3/yM6wuX+Phhx/mLW95C7/4i7+I53n/3XvJPe7x5cI98fslgud5XHnh0/zoj/xT3vsL7+Xy9WX++du/mWt3b7AyVWUy8riDQ923e5ZATgUjS2QB8uAD2I0bewcmc0dwOzuMltcY3d3m2hM3ydMcpxDc9w0vwuQFR6ZDqukusl/gZgNEkdIulhGDHRoLJ/Ecl7QyRaZcZJECDmZlme0nP8vsK19BkRdIz8Xs7LK7mzN1eBo39JC1JrY+wdXlbXa3+zzADp7uMXzhWarzs6iJfYjBJuX6EqI+Se3kScbXbuJVfPqDFEdpnPmj2NE2EovXaqHuLqMU9K9c5u7nb3Lsm9/IxW7JsZZLTeXEBMSTRyjzIY0Xv4Jy5RqNFx9CVOrsuh06o9uYwTYmSXGnZhjXpygHd2kUI6QOaDsla6mPF80z3dggiDfpxhp3/1GqouRE4DDsFdSSTazrM+ckrDWmGK7eZsOXdKamCF7zBkwxpthaJ5iYoNh6hgcf6tA8/jhZa45mskNtukFxd53LF8/zG0/FJCVU8h6XVrvcvnGDf/Kv/wOvPT5F4dcYGQdXSXYKQTrKaUd7Nkt7IsnQzQXT1YD5oyc5d/8ZtuKS0hq6QYNcVpmKfNCWUkI9rHBxrJmMAopOnZnKHLJI+aObT/Otgyf4RHiAv/8v30/vD3+VX/zt32Wj+8scnmry2q9+M6968BQV18FuLSMaE+hxD1VvQbWFHWxhrUUvXUC1pqAzB34FE9SJ+ksUlSZELaROMSLCSoORDqkFaS25dcl0SdVapB9SdUNMvMVJx6PozBDojKXU4LsOWihqh04y7K+jhjHUDrAaFxwPYoZ+gzgfEyjJwbAkEAUb1Km4LkhBiU9hAcdjqiZpBQY/HjJyJtjUHtM7NyiSktJY9h85hptsIRBUCPDQ7BKQ7D9Hd5zRObSfjT/6X6i89R+w0vcQ3ZhxqdnXDpkNLfnyDXbr8+C36Dz4EJva4ZO3u9w3V0Owtz7U/MKqQmwcLIb5mkuWZuSey/owR2ApreEl+ypMuIbUKELTx5USnIDYnaHQFg9JfbSFFg6J9AkaCxRWUREpSpbU423ycB9Yi4NFWAlCMFlVdAvLZ5YHHJuuEkqoBwqn9GhmdxGFiw1byMmDuMLSiFcp3HlKy14oT1mw6Fu8eA3RmkOXKX5vC/obyN1VxNQBslsXKHpjZFShcvgYbs0lufA0/swMyvMw0oPBDjSmKOZOIoucoiZw124jqi3MA6+iOu4i4132L84Tqhbnn38B6ZWcqCt0b4t89jjlXAflRhRZzk50kA/dHjJZKzjYqiI9D1sWLHf73Li1zNhv8vmiTZZY4mxEXE4xzEvGWclGXBC5LhvjgtJY6r7Dxc0Rl1Z6tKsBd3YTBhMRoSM4v7zLX/zN0/RXbv6t6rojBbuf/hX+43/8D7zzne/k53/+53nb2972xWoj97jHlw33xO+XGD/73p/hTX/v9bzy1a/jbUs3+Jn3vIudK89xZ+Ig+yYaVDyFtQ6+AyIrsF4V5Y3RYRVRFux+6kPU9i+g0xRrBDcubnJlKyHyBMvjT/HKYcHXfNUUauU8xmoSv4FrE0bXrpF1B7i1FwjuP0tYbVFMHcLLE8TObYrBDmV/jBUS9+iDOAcz1K2LnMk0QaNB5fRJaM/zwtIGo+oEL6qnsJ1iGlXcyRl03INxH5UOcGf2U6wv4558CLF0G6fRJNo3g99uYcI6jHaRjkMZBIT7FnAcRe3EYU6feYDyvsc4VIypqRpG+EQ2RXXvUnQWcCIf5g7gOA6p36SQinI8gPEQf6qDmD6Ac+FDDK9do3bkEI5vGc2fYbKIEW7A8NCjNLNt0kqDuuuQllAtdgn6y2xU5gie+jDOww9zX8Ow5ndYvn2TPEs44w0pV27uTdoGcOLMIktZykbp4iYb6GTEc0/9Fb/9B3/Cq1/7Ohb2H2BaGJKyxauOnOTwO36YWLg8PTLMCInAMixylJBsxgXDvGQy8rHaYiw0AkVs2Utt0wZfQWkU3VgTemLPVkrAIC8YFwZHKnaeO894bpHjYYjwA975+D7y6Aw/ORHA5hL8wI/wjmyZMom5sb7Fh59+nu/67d8mTRKCap1Dkw0OLMxxsBlw/7lzTE3PoOaOEscDvu3HfpZKFPF93/s2XvySF6Mdf88LWqc4SlH3BTbL8AOXcaHYTTKmo70rfW0hmb0Pm6fUhytUh3cY3b6KOznBljrAoYaHVAIjAm47M9Q8QRVJK3RRQuIJSS/qcCAwBHZET1bwgdJaQiMYGJdcG5K8JC0NvZS9iaFRYC29zJCOBniug1PrINfOU5kIYHeD58UEE8Kj5VsCK6ipjPTAKW5mIZ4Lz6/0KHPNG4/WmRQpu5UWzakZEi1Zt5Jr3QQDuAI8V5CVhinfspN7rI8SPKXIcsOgsGynKefXh0xXPa6sDXn1GQ+lalTtCF3ESDckFXsBFP1Ss6gMiYrYrcyT4zJvMkKZs1t6hAi82iRxsTcFL43FWEPVU2htuDMqicuSa1sjrDW8vraLDQLyyQP4jiIxEOVj7O4G4tA5+trB1ZqqLLA3niSaP0s4Wkf3t7C1JvrSZzFSIz0fWaTYeIwMQ6xySeMx0f4D6PEYqRzk5H7GT3+SyrFTyEoDoTzk0gvI+RPIE+covRpxrgltAVmCnjxKNahw7sgCL1y6yqfVBA97I65F+zigXNZli1u9FN+TNAIXjKXqwiiv8vyV26RJTK+xnw/cHNMdD9g/UaEoDBVXMlf1+ezdAbtpga9yQgeGBUxVfD56fYfPXdnEdR3K4ZiPjGNGK3fY3enRX7/7t6rl504u8vH/+G7e8Y5/zB//8R/zV3/1V7z85S//YraPe9zjy4Z74vdLkMcff5xbN65w9MQ5/sk/eSff+85/xivOaG7FMzi1DsJaTnRCUlEjciSBhax9AN8JSG59iMbp00jPpzrXYm5/k+c3E9LM4t/eptxex3z2b1DtNkVesPPCk3j1kM2rSyTrPaQrOO4IrIXqiS2k60FURecZwlH0V7u0TtSQKagTD1FdXSZa2IetNnl6I6bE4cWHOriDDcz0NP27d6jc/wjltV1yVcXWq7h3L+MuHsc6isrBRbyFQ8iohqrWEOtXkV6AsRBH0/gLDpkB1d9FmQw12CRoTSK3b2Onj+GUGdJmyKQL6QjZ6DCUIdap4eicK2Kak6oPE3PonRWkzqidPIVTiUgmjqCBStYn9yM2tYtbnUZph6FxyLVln+cjAg+WrlE5dYYgHiLG20SlYLDaYwNLtRUxaV0qysGtVHj7//QrdLt9FudnKZVLNfTY167yW//lj5GOS5MYufQc65NnmC23yCPLcukwyku0AW01pbHMVxXDyCFw935yrUm1wVWCrDC4ak8oGw2eglOTARJJZkEBmVE0JFR8j99/x7v52Gu/jpOPdai98Ws5+NA5HAcCLKIZYesdTCahLDl67CGOvfhxvrfMyEXAIDesXXiK2/2Ui5de4Hd/9ffY7g32wgLSnB/+gR/k5KF5fu7f/Ra/8p/+Cz/9z36UzlwThxjpBGTCoXTqVI3Gk5aK59IWMTg+y84EOhfsjB0erk8jlQOXPwy+ZTdcYKqZskODUhv6mWFuqgIIlBAMrM9AS6qeYGmgGVRqpIXl8+s9mpHHsXZIXFqGRcmBimCmt8YwWmCYBeSpZjczTAVVimwdOXsM4/gUBx/krqli1YgDpqC+cR6ak8TRLAjD+IFXsDYoubE9Zr4WcLZcBtnEGoGqtdkYaToVyYev91kbZjy0r0HVk4xLqAWK1Ao0cGlrzOnpGsujjO24YGlnzNHJKtYKHpyv4ogMpRO0FThBhVRVGGvLZpzR9hS7osodISATHKgLnNEWRWOezZEhclxq0mE3M4ABBAiIjKGXGbKi4IGORzPycMZb+MMNSneOQTBBLe0h8xE6TXHmj5BVWji6oK8ljXyLXDs4a9eId3Zxsy02nl+G4Q4Lb/576IWTCM/HqU0z8FsgFLnVRFMHEBa0LSjDKuGDAlmJyLXCS3Yo85SAgpHTJDUujfUXML6HScaIfMBQVLD1Gc41rvFkaXjOmWQqjNhJNMv9HE85bPTHzNZDunHKhRtLeL0VKq0Opn0ApeH2nRWcyMeRkm98ZI7NccaVzZhrW0OakYf0YXNYsD0uudsdkxYFvZ0E6Tls3Vxh6/rnsXn6t67h7/7uN/J9X/dyXv3qV5MkCU899RSLi4tflH5xj3t8OXJP/H6Jsri4yO72HeYPP8C/fc8Pc+N7fpzveJVlrEd8uNsivG+ORuDiGEkWzfB8X/OyFjTPniDfWGG4vEN1rsm5r30xpvYcg40+J07OEXXqlIMBtePHYGIR9/AZ9NoyG1dvs3yjiy8g/Z1P4oQuJ95QUjswj2xPM7qzxWi1R+PMkAEeoXRQfpVgYT8Wy/luTun5nJ2t45mCbNhHuR7hwgLFnds4nVnipz6OrU+ghaHSKJF5jJ1YBCvwFw/hhgE627tOF0GNro3o1AO00XDy5bg3nsE+/wnSx7+RSm0alQ0Ro12EH2F218GvktQXyJIxQW8FkWZM16dIL17H2XcMxl3U9AHk4km2tUcfn4WtW+jWJKux4dLSDsHBDjd7KfvqPrNiCEXCRuMg0c1leoOQqXwLTxqcqf0cDFbQIuDCjqE7cYSz8y2cYsCdzR2+51/8HG8YPYN82ddiLbj9FTZcl1xbhMkppo7Q8hXSttBelYqQzNf3RJ0jJGvDHC1gthowLjRCQNXf+7pbK7ixm3CoFRG6grgs8BC0PUmqJdpaKkqQG4srFULCY9//LSwcPIvr77KbGhbcvVjXW3nAfDNgWAgKvwO+RClJIBzGvkNuFWOlqT7wKh70PV7/hjeSygAjwOQFRjkESuLZjHe/52e5dPEC//Snf44kyzl5/zmOHVzkwKFDHNs3TzWSdMIWQkh8YRlry9A4eNaSa8GuqhM0KoQPvBQz2OZIZMisR5IVaCGo+y5JVpIjcKViYB1KBJu7Kb1MU/McumnJTDUg0RpHCUhTytzi+Zpg8yZYQehFrIs6B5oBt59ZQjgumV/nVi/lUK1BoC3GazC5dgnlaOzUHA0R0Es0F3ZG7AwzjnciHpjymOoqriHZ8Vp0U00vzSmN4eL6kEcOtOhELggFWBwJSWn2on/dvcnzrV5G6AgqnsvhdsTmOONEo0auHXwJI7dKZBJcW2IJEcLgi5KNMUxUQkIMXjlGpzHDqosr873gDzRSClq+RCD2ViWE4SM3dviawxXa8QZu5iDyHunkIQZ+k35SUl+/AdIhnz+OdB20cKgIjZduI0yM42p0dxPhN0iX1vBFjH9wFtGeZlCbx6YxutLg9qBkquIy42i6zjShlCgp9g4Cp0/gO4D0UVkXdfDM3t81uEtWO4iOWqSNWaKb53Gn+jTcGkoW6LOv4Og45/rlixQ3rzJz6DiuEtQ9wW8+scmBlmKf3qLmaLar82S1Di/c7fPoYpOV67d49Wse4dyBFokuWdrIub45ZjDOmawHbPYznri6xakDbbbGOcNxSXd1nahZZfv25b+T8P3zX/ohWl7Kww8/zOOPP86v/dqvEUXRF6dR3OMeX6bcE79fwkRRRHf1Cl//Ld/FH77/X/DCC9/CD3/X13I8HPLMDXj5yX2sxIZQ+XgqRxtLdPpRNv/4P9O7scXkuUNUj5/i5TNNClnD8cCvhEhhEI6LCn1kpYIpBxx+zYvwqjV6F5dYur4NGqrReY5O1PEabfLukKwf41VDdFQjDWt4pkTd/2ou31qmPxjywInjZAYiEWOiDub8J0mXbhLNz2Gn5nDlKusf+CBBp45bHCHYf5yN6iLz7pgsLXGiFmbYR/Q2KeqztFVKrwzACoKwhj54joqSVHob7E4cZLrYRFBAnFCGbcxol3FpqJgUf/MWfjqk99mPMVi5gb18k+axRcRwF0yJ9juYwqKaE5RuyJWVEf/uv3ycH/3ml9ARQ37gB36CiVadhx98kNe86WtwGvNckx1eo9cQUwvIIKJ99jS9T36E6flH2NjZ5rJSzEd7LhGvnBV4+VFyq0isoBq2MGZvBzB2G/iBwLElpXCQRtP0XTwFAkFaWsblnkWXtpa6rwglpI6DLwzdzOJIgSsE24lhfZRjhaTvSaqBS6Ak0pGE2tJNS+qe4sg3fxOTiWHLGKY9B6E0vVKSFJpxqRjkhlboYC1YAz0TIKWgnxb0M81OqjnkCHZEyPY4pxk4aO0QKoERgm7h4EjBzKHj/My//Xeku1t88MlnuL2xxtPP/VdWblyhXYv4xV/+ZQIFl3uCwN9LC+tllmao2M6g4rl4UwcpPvchZl+0gCcm6GUC3/NQUlNYQS8r6USC3Ai0sdzqp1Q9h7VhxmzFIXehLhQVm9B2Yjq6xF+9zdp//QAz3/R1xK2zHCgzLm8NiPOCsrPIZMWl0JqdTLATl5xsKYaTh/FtyuVdy2Sl5A+fW+fF+wL2zUYclH0iEzGuThL6PjupISk1cam51S85Ml1jru7jSkiKElc6FKVlZZAS5yWhI9hJCiZDxbjUHOxEDDPNROQx1JK0DNjnlwhTYIe75Cog8mFO5rTKMXWlUL1t0CU2bJC1DyAwzIQCKxWB0Agh8KQA6bCT5LiB4OxsjcnhbWQ2RjY6ZNU2u+EUVcdQ1WOcsILwArphh6DoIYsMZ/smTtJHuCGyLJDVGu78Ubrnn6L5yKOIow9RVOqMcstOJhn0C0JH4giJVQ43NnOOdDyMVmzEJdZITtYtnk2IwzbVMkYMtrBRi5v9jLo/x35bIqI6VnpYU2ClolQet0YFu/UDtPu32L17g2jqIOujnJfPljSTdW7pkKtqlkkZcW25x93tmNlGxOMvO81XPzDHuCi51U0w1jJX96l4is9c3WJ3WKBcxfNXNgl8h+1unzxO9m4b4t7fql47StJ/4lf4vd/7Xd78trfxkz/5k7zzne9ECPHFbRT3uMeXIffE75c4Qgj+4D//On/1Hd/AG970NfzIrct803d+N29+EG7eyNkNJnl0oYmwcFNOcCJKcZsNZl90iOrRY5hsRDA3T+XwOSQGu3ELk+fgBRhjEbZARBWq9z/C8RNnGF+7TPWvP8ny5XU2zq+i80/QOr1MNNVmKnLxTz9CWRQkaYzyJRs3L7PeK3nk3APURquMZYBNu/hI4mEPb2ICb99hhhc+jywS9r3pFRSbG8jjDzPwGhS/9kuYr3k1jlaUK1cZH32ceu82BQZfGir5CKsCrAVfxzgTM2TXn2NiYoZM+IShi5UOrheSxEOqNz9Lfus6wZFDFBvLiPEQWxZEC5OkGxtUGh3GfpMZEdOMAhJaeHkXsXKBf/bqaYbPP8H7P/Bf+aa3vpk3fOVX8lef/BTv/OEfZTQa85UvfoQPbixx5fZdZifbnDx+kpFTR8YbTDRckjznA5cv8Y1f/1ZMY5bUTjAuBUv9hDOdKoOxZiFUlFJh0HgYMhnglEMyEdBwNGPjIUXJwWaAI2FcWDqBQAjwshhHx/hCUWtW2MkL8sIyLDRVb++gybJ3na6EZVQaSmOpeIpharg2SHlwtrrn0axCtuKM0lqEFYxLTRtBpg1KQGIhTXLGpSXXMFtxGed7kdO7qd6zznIl/dQgQsHaSHO47ZBo8F1BtTPF61/7WspSg7D4juK//Pqv8j3f83a+7e9/Ow+cPUMiDTOmRyVq4ymHbqYRCIxyiI+/hDiappYNWR/4LE54TIWCUkhmlEc/LwiUgxSwM85pRx6jvCRs+Bht2ednhONNnOEO46efRk+0qL3kpciwRq13i9jvsLWyhJCKSmuayJEoVzDIDf3csp45xKVgOmqRjRO244LDfsapmg8r56ksHmG99On4PpmGojTsxAWhp1jeGXNiuo4jICkhLSyBU1JayVZcMkhzSgszFYXjSXRuCD1FwxOMSoPvS1IrEd0VwiJj8w9/n8mHThPc/xiMe+BHqMEKOqyS+BN4lQZautSEQUoDUpILl7oes1OEVD3D6jCj4QUc7VSQo/peappfJwtbSCsxVqBVhFw4DUVKURpEkeKaGIFBzJ/GpH2kzrDSA51T/YqvpphcIPNr7CSGu4MYbQxzNY+a5+IryKXLgp8SKh9TZBwUQ2KjcbZ2cdvT6CLH+BVsOuavNkN2jea1iyGhTjCNNmVjmkK4aOWyOiqxCB7dVydYvI/nnnmGqnRRu1tMGMNO+zAr2yWNwKXqKe6WlpccmyJ0JW99yWECR+E5klGmsdYCcG07JjMgXZDCcu3iTXQ6wovqlFlG99bVv1WdfujUfj766+/mXe/6MX7913+dP/zDP+R1r3vdF6kr3OMe97gnfr9MeP3rX8/VS+c5duYRfusXfpLOv/olHig2UWqH8/Ywh2Y7uL6P7e5Q2T+PW6siW5MUt6+h012CagsWT2CzBKE1Os0Q8RCTZmALnHoT1Z4nrHg0JiOOh3MsfWaFwZ0ddJoxHuac/ZaXUKQl4u4LyLvX6Zcel1WL49MtvOWLsLBIdbSDKQpkOsI/9RB69RbpcITXnoDBDtnaGl49wF77PPmBh2nMdZC9TYQTkLZmicoBwvP449slX388om772GyAKHJEVKfcXCbt7uDdfAoxexyT9KAxBXlMmPUYXXmWbHWTfH0Tk8coBdUDB3H3H0AYjTO7j9gGhE5K0FvFZDlq6zav2D+DM9zme3/ld/m2b/8WXvaGtxLYjG957ct45Ru+mvNLm/zV7/wWrz18hK//zu+n/OhfcsFxSIzk9vNPoE+dYr4iefjhR2g0GmR49LSLKyxbsSZpSawxuKJAE1Bqg6MkRWEpZBUBaFxyYymtwIq9KbC2FsdqZJ4gTYEa7+BZQ9I6Rjf5wrTflUxXHKRUeBJA0M8NUkDVlSRFyZVuQjNwCVzFyiBjQQVI2HNzwOIIcBToErLSoo1hdVSwWA9oeuBIQ2kl3XTPR9dVkn5e0Aw8lBBUfIkx4Dl76xbCWrS2rIwycg0nJyt8wz98G6u3rvPHv/+fed/73se/ed/7GLfbCMdBOtCSkvVRTqXmszZ5H/tVxlosudbNONpyaVSqbOSWrNSsjnIaARSl5mA74mBVoqVPYqAdKKTRICTaCQj37aPwGtQOHSMTCqe/ybJwsemIhcWDyKrPblbSTUs2BykPzFTwbEZDGSplwmkxxHFDDnsbrJWTHFw4RB626A5LPHwGecHGOKc0MEhKlnspRyfr3O1nzDUDAkfQzzTzrmKm4rI9SklKQxp4LAQujhBYY3ElGG3xlKAmDfQ3Ka9fwHcNKowQNke4PnltGh00SWTETm6JLPhWIHWGdASqyFAyYERALzMEyoKArUQzVVPguOSTRyiEQ2kFpbasZSXSOjQDiULQcC12GKMb04hKCycbY6MOJQIdNXGEwovGjIIGFZ3SRVBxBIcbHqHvcrOvcRyXapmyGKSIvECXJaMP/AFT0y3UzD50ewYv6zPy2jiVSe6sw6sXHeqhA90u1q+SSx+hS6QSeNJypi1pOZrYejTqNbqrS8zNzjK3uMgglxxfEGyMMtJSs9AMeXC+xsYgoxm6bMUFpyZCunFGzVVc2YmZa+w9ZH7y2TV0kVOmKfHODsPNDfLB9t+qNr/9m1/NP//uN/D3/t6bWFlZ4XOf+xxHjhz54jWDe9zjHvfE75cTR48epb+5zIFDZ/g3P/QdLL7me/mOr30ZZ7lMTyxS379/z1i+NYPTmka6Po7rovwAO9yhuHUBJQT4IbtPfhydlvSWtpk4NYtqtgk7G2x+6hlsXtA5vo+gWWe80sWvudTx2Xr2Kssffx9FvUKnKVk58xIakSa49FF645iJ734njgC9uQIKhHSw4zHj8y8glWJ3aZuJt34LKl/H5CnB+Y8SPHwOxruMbt3AnzqM6q1iGlPUQo+xFmh3gooc4Zc7CJ1glSR46VdiTIlMR5jhFrK7Ru410WsblAXI0w8ht++i/YMUE03SpRtMCInSKbkNESuXSTuT2Ds3MXlKZgRhuUZw9BSzhw7TOXgKYQuMUFgrqLmKTqPKi7/mmwg8QzAxQeOhR7h/uoZ36DS3R5aJepVQlIwzzdVLz3PlwvPMHDlF4ComKh53RzlpaYm1ixGGis3Jjc+otDQ8S24lKQJjLVIIKq7AUxA5AiskUmfkQQtPF0hd4pOjxJ7Q9JRDklsmq4KWW7KVSly55/iwOkhxlGA3zelEFXpJyfooR2JpBnvT0vVEMlN10cbiS3ClYGkno+45GLEXdW2NYFAKHKHxXYEQlmFqmKvtid7Z0GEz0QjAWBAWGoFAW5/VUYYrBOPScODIMd71z3+SC+cv8n3/+Puptid55OFHqHgSY0FX2pw7MMU4t6QqISpSzp14JVWZgaOolAWe73CoGZFpS08bzs1U8YQm1g6ByNhOwPUcYm+SwGuhG/vYKl0OFluMwxl60X7uXLuMkBJa01zvJbQCBdZwsBVQDxUTt5+jeuQkl66tMDHdZrC2infgflyr2HAqbA80kStZG2f4SqEEzNdcBrlhsuax0PBZGYAnJIOiJC9LdlKIc8Od3YQHFpoEjsSVgshzWPQzPnY35tRMnV5mmHM1cv4IVDq0F1cxU4tYp4I1McZx2So8MCXj0lIgsXmJMZYTqo8cbuNXGmyHi1hbshlbIleRFBoA7UWkKsAzBcNSYACspeMLRiV0pMZPBpjGNAQ1tNY4422s45DUF1CAQIMvcbF4UjMjCxZqisDE9G2HqqeoipggHSJGOxB3UU6AU45RzUMIx6UnI7zAJSktjVqLN58SzBZbjGSNYX0Bo1xGBbRtSu5GTHl7R5wruwOu33iewPeZaNZJ0pSG51DxLEHh0Ikc0tIQOYpAwGzVJdYwLgy3ehkbo4Kha7nbTXnRgSafurhGOs4Ybq4RNVukgwHp7q2/VU3+9G/9OAExjzzyCGfOnOEzn/kMtVrti9UC7nGPe3yBe+L3y4x6vc725i2+5s3fxJ/92c/zCyu3eOs/+HbeIFZJ+ztUjxyiMX0IOdjGuAGivoHTmiS7eQmzuYb7wMuwAoLJKZL1NYphxnhpk+SFZbxaCBhsXjBe2SHtDomm6ghhmX3FY6z85UfJbq9y4zPXGD52luJlHZp/8Yf0XYtUFnvpCUR7AqfZBGux4wFuJSCanyFdXefZj1/lRe96EPsn/5bGw4+gJl0YbcP8SbzqDLq3jQld7poaj3cM0vEZ5JqNLGBffR+RiXEO3U+OQhmDWLuGDBuUu7dY+dxncVyf4TjHbzawJw7DRz+GqFTYPf0Kij/8d0w/fJJeGuENu4we+3uoA2epjvvsXL2Avb1CMD3N0to2bqXGyLhUXSgqkxQaYutweN80zcBhX1Sy03gFoW8ohMd8C4SQ9DMFrsPB0+e4cv45Vi4/R3DyLJ4UlMbSywrG2gdhqVPSK0PiQuMqBYBlb8+36gpCx2KFpOlbrDTkYZvCCggnccoEhaHueYRKsB4XzFZdjLGkRmGspRNCLzV7r9GtYGtYUPNyNkY5kavYijW1cG/SqpSk5TsoBb4SFAYyDaELdXfPNiwuwIi9wIW40CSlZqrm770ux5IXFqXAGkHFgWG+J8prnuBkxyE3hrjcC3FIC82+I8f5xd/8A+7eXeHutYu0Kg5ZrrmzusYzn75M6VZ4YXuZFy7fZLv4dQ5MNnnjN34HBx54MUdaLhXXYVwYpiJBJEti46CUoRrv4BvJpu5weSfm0dkaq2ONtCXLYoJn78YoYXDHPaqT86RZzmE3p92oM+xeY1YV2Moig6COHI+Z2r/AcuYR10Oi0qMWOOymBd24YOwIIkcxX5cYG3Bhc0grdNnfrgJ7+90aS6YNCMEg1bywNmCca2rBnkBzBGTWEluH9eGIR/YpkqLEKo/Y1kk7ExStBXzXwZYjcrfK3ZEhKWE2KKlgsNYhs5JJXyKTGOE4jL0G2hp8Z88OD8B35d5E3g0wCBIUdTumUAGtYC/Gd2QUxo+IpURIha8N0lrSqMMIn6qw+EmXNJxEOYJqOkCHNSq7q0jHR/sVjLY0XKgWY4TO0Ou3UdIiFmZQgY/0QwbPfIZk4WHGwmW63MEtNdNhB7RCI8ELcdFIY9mVFUJdshXn3L11g2Q8ZHb/IWZnZ3HzmCeffoa13oBOewKDRWEpSyiFoJtpVvoJk9UAJSxpodHGshPnPHVlk06oWF7q4wQOpRZ0rzxHOdz9b9bh6XadW3/9c/zpn/4p3/Ed38EP/uAP8hM/8RNIKb9Ypf8e97jH/xv3xO+XIVJK/vT/+V/4oz/6I9769d/I7/7ru1z/hrfxlgemMOcvMTG/yKHFY4SiJKgvQ9RCzB7CsQbcEBkE1L7i9UR3ruBPXiHr9XAHBfFOj63nV6nMVvAbFXSmWfnsEvsfP0a2uYFUltbhSU7UIrqPv5T61fMMLt7FzFSoztYp15bwphfAaOz0IXLlEyw9i280Rb/P2VecoLnyeeTUDCgXNbkPvTRAKIsa7WL8Ksn5Z/EemsCGEaVycLWgE1WxUvL0dsojtTFhdw0hFalQpNevINMBXpFQOXGS1tQExeY2pR7xa+vL/OF//PfMLewjjXMe3Bjyxrfs49yxkzhljOxtMeru4KCpnjnNz/3H3+P0yePMLOwn0xYfi0Wh0HvHXHUfT0r8ckTdr+NgGFqJJyHOSxwpkFKQasXs0fvYvHWFqy88w74TZ8BxafsuGktRGMZeRFyUpNrSFJKssBj2jts8JSmMwVEGKQRuqfd+k3TIyG/RsCW5qNIMJHeGhshVTIaKtdgQIal5EDgWRynmai7dVLPQDJio+GANngQlJYXWxHmBFpLcGipCMsj2RNBU1cFTklyDLwQSu3cMh6XhC0ojaPgSKQAh2BjnTEcOfW2JlOBOWtIMJIEj8R3L0qBklGtKrRjlhrQoMUhaU9McXJyn4e9F7z4o4KjskwVNws0rrNUPsls4uDtLvO/Xf4ubv/Sv+Yff9s181Vc+TkX6e0eCRUEcTKOAHdWm4ms2h5rjE1V2cosBZkL4vQsDkrRgJlmm4wiEVRzzM2R/E7uxSasqkaVgpHyai0cYlAotBL1BSsX1UUBeGkoDU1WPXlbQjlyyIqce+vje3iR3vuYxLDRC7k3BfUcyGbkMM82LFpvEeclc1ePpu33WfQcsgOSVhyeQAsalJbaK7VQx6Wks4OqEgQ3YNg4rcUHNVQjpsHDjr9k69XoasiSxDroyCTolcyuMYk0goeFJfJsTunufhWrFIbQZifCITIrxXWQypqsahApcCnLl77mGjLYwAoZ+i1AX+MkYMeriqQiZDxBxD6EWMNVJtHIpraZe7GLdANtdRYRN5KEHENkY60bYMsOGEea+l1NzBKmV+FvL5FPHQEIRtRFWUjFjEhUxLHJqrmLj9nWWV9eZmJri5LEHGFuXJDdEQUBrep47y3cIah1KC7tJweogo+pKtuOc3VSz2JJ0Ip/1YcLOMKMeOmSF5oPPb7C7vorjaOLuzt9K+L79G1/Fz//ot/Ce97yHX/iFX+A3fuM3eMtb3vLFLPn3uMc9/r+4J36/jHnLW97C859/hrMPv5RP/eo/5+ZX/CPe9PoX8Wq7Qn9nm/3HTjC98AAj6xG1D5JpiEyC8AKqSRc5s5/qxH7Ec09iOyPcqoMtNGuX1+mcmCOcrGEBYwXJ+iZlphms9kiDOuX8PPVnP4Z/dg6wqMCnGCXorWXUwnFKa8ELsYsPYLbWqB47idtsIZdewD3xCLZIIE8Q1RZs3cWdmkK5EVQfxV15gbFbozK/n0Ab8uAY1khWxyV5IyBIhtgiwxmPGPRyClmh9fhXMujtUK+30QfPsDSyvO1tk3zq88/zu+/+HpaGLp9a6/E//6c/Je+tMxG5HMoKztVqiMU6v/e5y5x7/DH+2T/+R4zU3uQzLQWuMGyle0c8oZJUHIs2Ab4jkcM+TiCwWtHP7V7ARGHxBUSBy4Hjp7l76zrLF54lmTzM6YUpJICFgVZYSqquxBOCjD0Hh1JbXGUprETnJUbs7TqWxhLF22RhHalz3EBSEdAOHHp5iVIKX2hKC0oK4lLgOuBrRcWDhufQ8ARpqRBYEIpukmOswFMSISSegKc2Yw62A4QQuEKirUFIgecKunFJ4Dm4UhK5kJbgKctEAANX0s8sqdZcTzPyEoaFpepa7g5K8sLQ8BS7mcZow51hxkzFR1vYHOX00j2hnGlDv96g1AIvamFUQBKnRJMH+JF3/zhR7za/9Qd/yrd+99v59fe/D11tgeuTZzk1TzC0DkmuiLMCE8DqMKMVKp5fS4njlPtmKoiVgqnJCRavfIj+5FdTGcf0pw/TqYZsZQLP83E8xcYoo+UrUq1peA6uKwkdyTAvqXqKXgLjXLORW9qBxhWCTsXbe2goDFgotWEqctmOC0JXMhkFlNZQaPBdZ29ar/dS3R5sFYyp4EuB1ZpW0aNtc6QtGFbmeXI740Rbcbgh2E1BCYjn7sMIiXI8JshJRUjo+kgDgSPoppqzZhuRDrDUGcsWaW5p6DFpOIEYdxFugDAlriwZG4ErUpzeBrrQ6LuXERPT1GZO4I63sVJiRwOk18Vu3sb4NZztJUx9BmVynCJD76whXJfh5z6JfvwbqPkhS4XLnNDImQXuVA+x6oecEgqjLTtem9ANKYyDkIZBUuBJTSkMQdLn6vmrSOVw+uQJ/KjG0LrsJiWpTlHSY2J2nivP3KE3HKCCKjtxiQFu7iYcaFUwVYEQElcZNgYpa72YrZGiyAp27mwwWFumGO7s2Zz8N/j0f/pxji9O8ta3vpXnn3+eT33qU5w5c+aLW+jvcY97/O+4J36/zDlz5gzbq7eZOXyW1b96L7+7/lY+dd/DvP2rjpM//zQbk3N4E/McbEcUEnomouqC3b4GfohTr+K/9PW48S7Fjc9TJBmLzToUJX4jIpyZovWSF1PubDG4tUF1psX4yAOEt64xuLzGMDM0Ki6T982Qdru4gzFh1GDst5DCoS98aofPIbMh5ZVLOM0GNhmCFZjuJspxkJNzWAvCDUlnjuKWOdXdHTY/+AFmvvLVOFtX0f0BD+07y6i3RdSaxuQZ3tRBplsr9DdTFCn12UnUzgq9zmEmm5JG7PEd3/C1/PJHn+EdX/t62pNtXrWvTf1FL2N87Vme/LP/yieuLnNIePzsd309h77uO7GOt7fnqwwlFl8nFMan7kl8FxQWlLfnrGBKQp0hpKSlXBzl0C0tgSvojksmA4epxcMMg4CtlauMag5Os0M3zZmq+Ht7iYGLkoLcGISxWEfhacBqLm/HLLZCCl8hTUIWtgi0pvRr5EaQGoHnWCItURiksOSloRE65KUFLOujjKbnEnl7h27TVZ+dpKAVSpa7Me3Qo7B7oSbrsWay6pGVgDUkVnO46TAqShCSWFsaSpAUBk9KLAYrBOXePRUG0AjuDgraoYe1ljg3ZMaQaksrUmhrSQxMVz3qnmSpnyMkhJ5kbZRzuB2yW1giT5JFHSj3Js6phQ451WaH7/6u72K3P+STz17k8Ve+mp1CspCuMBIdlgeCycglt4anVwZcuNPjwf1VZqs+X3fYob+1zAZwyo25tjJkYCNO7jvK2ER4wiOhQFmBY1JqvuLC1pBW4NFNSg6FLgJB1VVsjks2xhm+s5csVuqCmcgnUILAk5TWYhCUJfiOQooCR0rioiQuoeo5zNZ8XEdgMNxXycgzjQgqNFVBY7SDn+9gZQPrBvSMw6FojOcIpIGmB2Ot6FcWcLUAX2GsRqMQ5OwtNrjMB6BVFReLCCIWihhvPMA2pqmYDKxBjXb29spFjJENtAbV28Sv1Nm98AL1EzleWENYyAa7yEoN2V/H5hkrf/ZhFr77e7B+BWyByVNSp0pgU/yzj3FVNTkpc1KvQWxGZPe9ltWigpVws5ftHQXWZpjOBKnW+A5kFlbHmvXl86TjAQuL+9j1JzmfGKoGrm716FT9LxwHOlRcgd/osLa+SX02YrrqMkgEt7oJpdVEjuLubswHnl1lYCy9YYpJU258+uNYIyiG/+3DtslWneUP/jzPPPMML3rRm5ifn+dzn/sc7Xb7i1rf73GPe/z/5p74vQedTodke4m//w/fxn/+T7/B5VtP8a/z7+DYQptvfdTD39nCOXiULiGz9QDXcSnqs4z8Fs3uNTwvws4cwi9imlai0Ox87jnCmUmqL3k15e2LCJ3TPj5LZW6WrcphnI9/gtpig91n19ECxqs9vGrI7jMXcc49Tul4FKVkOc45Pn2URrJDsLif7tPPUMkKwvsexBiJqHUwUiKCCrFbY5gL6osPUMzD0u/9JJ2zq4TTM2S9dSbqLcbnn0c/cAY7uR9pNdKRNOQqW3/+F9S//XvJNu8yG0l2MsO2jvi6b/123vyN387bv//7mXFgN3cQUYMDBw8x/4Pv4qv9CKlLgnSAGGyg65O4WAIKrDVk0qfi7u3BGiNQ7AVOWBS7fptCQ0uW1DzDkP9NBApcKRmWltRYZvbtZzuzrF+/SDJ/iDjsMF2BO7sJTkeSFprcWKQUdELoxjnbqeb6bsqRySqJhjow8CepqBJrBVtJCewdtcWFJS/t3ittTyKtRQDdpGRcGKTYs3ZaG+UsNgM2xwUNz2GQW45MeMDehDJwBKXe208eFRolJIURbIxLOhWPtNybjA2yEiVhvqLILVgEgbQYKZES5ms+jS/sBnfjktm6T2ksSoCDIAwVpVFoC/MVWI8Lqt6ex3DNsWwnlqoPpQoo85zj3pBMRTS6N3Acye3L13ni05/iH7z9+4mKPrnToqhOggqZrhaUGiZ8wbU7Q6YrgnMtQVMMCdIxV3YHdEKXTLgkr/tGWoGPCT1UagiVIHH2Sur6sCTwPLaTkpl6wLiQuFLRDgXPb8RMVnwONSOmKi5tX4BwMEIxygvqvoOwEDqK0loqrmScazqhR1xqXCFxJCw2fUptCauGWhnT81sYDR2nwJgSkpi12jHqgYNrBTNOzB/dMjyyr0GoBEqCpwSuK3EAH8MYQWElYxGw1I851PTYKEOiIMDFEsoUKq095xNbYlsLmMEGOD6x06QhC4pC4IU1KAuqj7wMse8YZrCFwlIkOebAESgyIn+N1mvfhJAKnSWM3YjYm4apWaQosJ5HI5ckwqHiSi4NAlxHcLDtM85LcivJdEKs95woSmsJc0O2fZfttVUmpmeZP3wMP/AZDXI8B7bHBQt1f+/NkiPYjDMC1yFza4jBBv32PHVPsTFOcaTlmTt9wHJ5bcjtu11m5hp0dwasX18iH8dQJv/N+vp93/gqfuYdb+HHfuzHeP/738+73/1u3v3ud+M499rvPe7xfxX3vn33AMBxHH7nN/89P/6uH+K+R76Ca7/7k9w68Di3u1/Ld7/2GFy/ROxWiKcPcNv1WKg1MQWMKwdo+w7KcfAm5qmWOfnOOo2z9xMeOYZyFWJqGuEEuAeOU+6sM9YRi3ZMZd8ERwIPv1UnG8QUiWZ8a5nQbzPODDtpwb6qS2wcap6Pd+xBxLOfB6vZ+eBfY/ConT2FOP0SYu1RjzdohFVk1KJbGmb/yQ8xaNXwlz6N0DFq5TK1Y4cZXr1BcCbCFwaBIr97l+jsOWxrDu01sMKnG48ZrMU0OxN8x3f9I97zc7/Ie/7Fj9OXAXULSWMerxiTOHWsKPErErFzB4skzHZASJQQ2LzHXG2KgfGwRiPLMbg+RvmsJoqJyGEjlyAFjtqLGc61xVhLxZEIJI6w7HpNzh5rcOfaeVR9TFE/urfjaAxrcUHFVTgSurFgKykw7IVHrAwyJis+jlMhLUFKl4qJWRoYOqFHpg2hI+mlmsII0tLQTwVWWDbjnKbvMMj03lRNCQI0BxsBubHsb4XkhSHyHQa55kDd5dZuiRSCyJOEStAvBNZacq0JlKCf5vSykhfWB3zn3Jhlb4G5isdE0aUXTH9hwispjUXyhXVWY1FCoO2em4UvJJG3tx4Suy610KHlC9q+T9WxfCH8DIVmgoRg5XnCoEZenWLnxvO846ffx8/+0q+RW4UUhkGSM3I9Wq6g4wu2N9Y4EgoO1FaQnSkqo1XSlbs8XfgIBIcO7GccTTBjShqRpJSSug+ezam6DkoK/GzEehEwTjVlqVlo+KyNMjqBz0I1pBSWuYpiOl3DcStkXgcNCKH2vJY1tAPBbgZFoal4Eqzl5lbMA/M1HLnnmNH0QRiLiZo0pMCQs1UGhI19jJ2I57ol900FxGVJVJni3GRGUxliK6CETBukMhQofBWw1stpV0IKDUlpGZVQassnb+/yloOKQKeklTlcDLq0uOWIpLlIrvdWbYwVlE5IxRrssItz6AGQLnr5Y8hD97Nz9CXEhUvoBKjJCmLah+EdnFvnyU+9juvDnDMTklJVyK1lmJWMBVzYGDBZ8djf8HGEop/lNALFVNVlMnKJHNjZWGX17hK1ep2T9z9EFDpsZwpbGIa55k4vJi4sg3HOSj8hLzVV12WmFWK05b58zIXVXe6bb3J+Y0xWlLRCl5V+Sl4a+sOcrScvsbu5jjUCyv/j1DZHSS78yc+wdP0iZ8+eZXJykqeeeopTp059scv5Pe5xj/8G98TvPf4/OHXqFMVgjR/90XfxC+97H5e6N/jV0XcyPTvDm885LJYX2A6mSMtZqr7DcmFYqBlckTHrTzLJDVRUoXr6MWy1hU0GSAwIhaNzxhP7cLYKDrziYUwSk2+uUz1yBPwQ3dth64Vb+I7CUzmxo6g5ikRIRFogbEnroUdwjpzBPPcU488/zfaHPsJcrYpSDYTtIa2LV6swMRhhFk5gyz5iahHR76JsidYpwan7cKSk3O3iBAp1/EFsZw4hJNVKhVLAMT+FQ3WUkLz5Da/lYx//OB/4xBM8/NirGBUWP1KYNKcmRiiTU/pVionDGMenPtigqE1CkSKVQuVjWukmSAdpUmylA9Kl7om9vU4U48wwVzEEgaSfWEJ3zwu35gmWBwVKWGI3ZOroWXZuXeL2lRdozB0hLgz9tGQycnlhdYA720BKSVEYfCXYHOW0A4ehUKSlxgBS+WwM+8xWA4ZZScNz2Mk0gSP3hKrd25ucDF26SU6pDdqA5yiGWtKqOOSlZpAlSKnoJQWdikc31Rj2LuIlisnm3sTWdySlNligG5esDjIOtyPW/SpXt2JcJdFFhBCaiivYiAsiJQkdRdUzjEtNpCTGGEJXEkiIXDBG4VlDJMFVgn6+J5JdCqSUuLrAGa9h0hQV1EmCJv+P/+lX+MGf/gW8mQMctNvkTgtf7O0hW61RpuBQ9zzSi9Drt0huXqNXGsqwST8MWGzXqdTrWAQTSmGEJjUGjCXId/DDOqkIcCpVglLQChUGiS8t6+OcyYq7t0st92y0/MEambNI6RoSIykMjPOSVGtu9wz1wGF5VLCvHrDUy2lEDqG7N/XOCoNCUmqH1FocYUmMw3I/YzqS7ORVzk55eMriOQ5SwlG1yh0zzyDb+yyMc009cFAWdnOL5yq244KtcUbDVzSVpmskD8xERCJhIEJGqWUickmzIcb1ybVkM7MINLlyaDkluB505hDJGNIu0qsAAscL8UrDIDOU1qXiChJvmsoCDAqYjBxCUdAvHbbjkkvbYxbqIYErWGgElAgGSU7NUzgC+olmQuxya/kWYJk8eJy5qQlEmbPc1whH8OTdIYOkQCqFNobrO2NWdxMiX3F1Y5eZmRqz7YhTjmB9Z8B6P+PW9ph+UvKq09MYK0Ebtm5epr90g70Fnf9j3vjyM/zGe76TH/uxH+N3fud3+Omf/mne/va3o77gzHKPe9zj/1ru+arc43+HUoqf//n3cvP6VWza5/Lv/SSf/JPf4ef+6Hme7FWoxtuEG5eJB30urQ+51sv4zafXSVRAOnmUfOEsOqyCEJTVCZg6hK12MGlCmRcoKQmO3kd45mFqp+/D7UziRBXcapW57/xeCGokWtHyPUZW4AtL5texXhXn2ANYIaE5RevFj5L1c4o719n6X/9XNj/yKYo714lfeIri0tM4lz+F+fRfkF17YU+IlwnO1hK+5yKLnEy66OVraD9iowgptMVKhSssNd2nYhLk9Se49sKzbNxZ5lfe/35CZQldwTCHbdUCIRHYvaAF18cxGhtUyN2IImiSVWcRaQ+3iHH6KwihEKMdvP4KMzJGSkH1f4tytXvTtsxC6AikgBDN+jBjkGoub41RQcT0sfvJ8pL09gVG4zFSgLWW3aQkdAUCS6oNrcBlvu5zbSfGF4a1YcbGsOBGv2CQlqz0U5ISNuJ8zzZLQm4s49zgKUVpDMv9DNdRBEowzkoGeQlf2NFd7mVUXUWm94IyRrlGIkm/IHSHqcYaQ+Q5dFTJHz1xA19qXCWYrvqsJIq1YUo/1fSNS1yU9DPDMDdoa+mmBUlp6SUl6Rf0Rs2TGEAgKawhFIZIFrhFQuhAamCoFdIaRFkglYeQEiMVP/uz/4qHX/8WwoUTzIQut0yDXGt8oGYTquvnCYseXqONiPs4YUC6tUlt3yxrU9MIITk2U0ergGbZJ5ce/UJxZTuhm2oyv4Y73kGj6OqQlshISovrSC5uJczXfRy59xABECrIpo9yR7QIKPd8kpVlmBlcIfY8nntLTIUuoatYbDg8MuXjCotnCqpZDxDEGtbGBZe2MnbikrVhxkhrEAJXQKLFFya5hjzs0Ms0WMvOuKATOnRcTVoaNpOSGztj0lJTDrocqDlMZxtMqoIT1YKBitiwVdLSYo2lMlgmkyG3hyWuFIxyjVcmNHdvQViH7hrsriKB8sRLyeszBI7EUZK43DuuLAxs5IqNaB4lJY3Aoa8DEg2r/ZRjnYiKIzg3E1D1FKESOFIyX1dsdnu4W9dYvX6ZyZk5Dp95iMStcXuQc2VQcnE3Z3k3YZBpbndjsrIkzjX7WyGuoyiyEuk5jJOCcVyiUYzijA8+eYfl1SHbWzF3dxIuXd/icx95gv7SNf42wvcvfvkH+e7XHuW+++7j5s2bPP/887zjHe+4J3zvcY//G3Fv8nuP/78cPHgQPbjLe97zHn7qX/4MK9vX+OWtNeaOHOdtbzzJ/uI6iyJCFIscmKruxdU29pGXdi8EQey5AhjHx+9dxZgSHedIHZJe+TxutYZ78CQUCcMnPklw+BB0ZhBWs1sqrC2phQ5WCmIb4tc7iJUr2OEuZtAneNErmNwZES/don1yjuGNu4jFKfzZOUbnzxPfvI3JErY/8STB7AQTD5xGTc9B0mf09BMoX0Elwl27gtcWbDFDPYAmOcYNkevX6N28xI//8u/zrrd/F2df9/WIfEiuGjhGkFrBrlBEXkAEKKsZ42H9DoUR7JR7aw1lZQIn7YE1CFPA7jpSKbzpCq63d4xV8/cmb1mpcRW0Qw+MZqgVs1WXzVHGgVaFotAIR5FNHsYdrFDeuYgzfYQbuzBZ91kbZuTa0gpd9ozFBFVvb8q4Ps7pRB5xVlBYy3a8t18auYpcG6y1lMZgkHjCkmhYG2S0Q5dxVjJb82kELkJAWu69Ek4tZF+w7+olBY4jmQhdOqFLYcF1JCGG57dz9ndqHG0FTNYlaQnjsmC2FuAqQakN/VRTCwTaWLbikk7kUGpDYcXeyoUrMdbuOUlQosXeXvR00aeImiQZdEKJ0QYnG4Hroa3lQurxgd/4Xe52E970jW/CEbCdFvS1JMo0dSfBWXoaWWsi3CoEEUR1VODTPtfC1OpsrqS05vazHkxR80J8ZciMZTsXKLHn0+w5HjpqoYX4f7H33mGWbWW572+MMfPKtWpV6q7OaecIbGCTRESRIEFAthIFCQqiApI5RvSYOAbEfCRIFA6CoEjObPZmx+7dOVVXXrXymnGMcf9YWz2ee+65x3vQq1K/7ufperrqmbNX1Zpff/Ob7/e+JBrmnZwjzYhBXDDINI+YChhklkwafEcyMGCp0M80A0/RSQqUMHhK0k01O6suIvMpew4VkVEKFGHSIRYOfjpCFEO2ZIuN0YhUW7S15GMLWrMzlPQ8l6HWGGsouyCFZCQjmm7BUAumSw4Vf+LrbAX4UtAMXQ64Q9wZheMWWFumkXdRyiN3G5QDSygNqsgQUztJC8u40EwJD19B0ynQF44hW/MIL8DO7iYXAhHVaRcCkxk8JZjyBXORYpiDNgaDg4vFdwSjDNI8x3EVjcDFE4aaP7neAiydYcy995xjq92mPL3A/n3XkBrJoDCsD3MWGwFDDbORYn2c4yuBEAKE5HAr4OTmgEE/pr3cxw1dymGNExc7POqIJY4z4rRgOBxjteZTn95kY+kCw+XT/6/18or9C/zN772cn/zJn+QTn/gEv/Ebv8Fzn/vcybm32Wabf1NsN7/b/C8RQvDmN7+ZF77whey54sEMvvwOTp67id/ofz+P/b4Hc0VpyOLaMVR5lkHWIFAeSmoKoSiUQOoC60Z41Rl0v4czs4NieYA7PYeMInSRYzZWsEWCNQKTJggPdvqWrnU52x6zUA2o+QqSAWbYASEoXXsDWVAlevQPIM7ehbIpUbOBqlYRrkt05AhFt4fypxkurSJ0TjYYIA8vsP6edyH0mOkHXI+MypjZvcjhCK8YExoPJ93i9MaYO754B3/9d5/hmQ+/nhv3zGN7lxAoKiUP7ZbwRE6OIrbgGotBkmOJjSJyJo/iHQwDp0JQCvCjHJHFiEqKTRMoUkKToqSDK3PG0mV0f5KZACI9ZN1W2VOR5LZC6DjEWUZeGBZqAbXWQW49dprp1RNEs3vou3X6WUHz/sjbcuDgSUGgXAaFISsMrhR0M82BqRLDtCA1GmsdlICzW2McRzFX9tAGcm1QEtZHGQsVH1dJyq5CSkGcTWJ0fTV5VJ1pTSct2BeFCO6fbmpLS+WoPOaL5/o8+8YdSMfF5pphllNyJQvVYGIHlmtCV01uoFJwxOS9J6VgpuTiSUHFlfQyQ8VhspSXjKjkY2Q+gHKTqtITzawFiWHr0jme+/LXsPPINTz0ysM8+QmP4EGVHuPKNFvjnIWyQ7MYMxynmFIdUZoiu+tL+Fdch1I+duUs3vxOvn6ujVKK2R27kHqEYwUFCik9QsfglSW+kozyDOVVEEDgSBILZ7sJtcjlivsXDxNtqLtQGEs/m8hJRpnm/EgwyAqGiWZ/M8CVcuJn7DXZQXJ/Sl+CTPoETkB+/Dacw1czLAo0gmak6MQFkSs57OdUXUF7HOMFIWApDISuJBQF1RBqeY4z6mJoENuQYVaQGcORVogX53g2Rg83aZfmqHgRNu1NHCj0xH7OGWyiK9PEiSXXBk9a9vkJbtwhXzmPm8Wofdcg0hgq01DkhDIgweIYwx5nNHFKUAF1X90fKTw5f6gkm0NNM3Bojwv2NRyEFKTjhIsXzrC8ssb0zCyHrn0gxvHpZJo7V3rUApdhnuOrkB0Vn1QbPnNiEysFC3WfLNcs9RJuP9dl0M+IGiHxIGFluUd3q49/pEw3MXTOn8GvNmifvQ+rC4pRd1ILlYvV+f+0Tn70v7yCrYv3cvnll/Pwhz+co0ePMj8//69UpbfZZpt/LtvN7zb/WywsLJBuneODH/wgT3/Ws7n0t2f4yKjHVw5dzs1XzfEI2eHY7bcyu3s/+3vn0Fc8GIwl1hatHYQIEdpS02NyY9Gzu2BuHyYekdiAUmUK8hSSPoXR4EVUHEluJo+9y66kcEo4XkjRb+MKMEWCFRq59zLs8gnCfQexAlAKp8iI+31Us0Y416Ry8ABZb0Tn4x/BJAOKUQJSIWpN0qVzsHg1kQMBGUtrbW55wct42bOfwUMf+hCe9PBrMZ01JBZRmSIwlrgiKCvFSINVDhqLX4zRTpnckTjSUpYSaQuEcCkkeMZilcKWGgi/QMQ9fKFw3QCVJ3h+mcAP0VJhEAhrcaWhEB5Tfk7gCnYEkkuJoOY5BMLQmJ1nWk3TOX+cZiOmubiXXEPNVwSeQllYjgvGuaUWOigpscYwU/IY5wXDzHBkSnLP+ogMScsT9OMCYw2D3BC6Dr0kZzr02IozDkxFSDGRR7TKPtpYcjNxhnClYG2YkVvYVRVMhw7dzJDmCs+RJBriQrM8TEEIFisOI0cyLqDmO3hKYJDMhg5WCCJnsjTnO5K60vikbGlBLBxqOsZdP8Poq5/HXnMVTmWGajKgUDN85c57+dPf/S0GGyu86TWvZv7ah2GxjNICXMlU4HByK+ayhkuMx3IvpRqEjD73SUp7dmPCKezaBdT0Ar3lJTqpx5HZOtOmj5QCdIKjY3B8ZkJopwKpJEPjU7YTUYbvSL6wpOmnBQ/bXQWh0MYyzDQLxRb3ZjVWhxk76yGpMVStJdea2YrH6a2YK2dKNNI2wo9YjSUzSlHWOXlYRw3abEwfJHJqYGA2cgnUZCFuRySoyhoyHRC5AUpJtuKMjUxzQElcX+FaSZB38IsuXTWLuN/dY7bo4JhpUDD262QatJVIpcAU9DN9f1PvUPIjtLG4WKYjj0AYfFtgpYeamkG4PvQ2MI6HndqBkwzwfI9QZAhrcTAUaIZ5wSC3nO+nNEMXTypGmcEIgbaWUa4pcrhwdonlpQtE1TqVfVdjwpD11FIyBV8632GplzAVubTKAZ1xzsow4WsnN7nnbJtHXr+LJLd86e5V8kJTqU1s9PJEs3bqFGF1Cs8k+KrMt752N52z91Ka30ve/6cWZv+zxrcS+Xz2HS/n9a99Nd/4xjf4/d//fZ761KduT3u32ebfONvN7zb/2wgh+MEf/EE2v+u72H/VQ9n45K/Que9h9NaeyMlrL+ORu1yuPneKfpEws96mXGuQaonAoGo74HCdmfZRQpGx4U4xl2tMUMebr6D0DHJzCTHu0ItaRDojN1ALXFolD6RkHDYo7ziMKq8jHYkcbWGKAuqziMYCerBJvuMy3DyG1TMEh0tQpJSvaeG3puh84lNEc9PIuSt41Z9+hB8/uMw1uw6TBDXyoIpyPf7Tr72Vb9x6K//lt/8LN+0o068s4sTr2JUzIMFsraFqOZEjsH6NsjCkVBBI+iakm1qmQwEIMm3xMAQ2xhn3kH6ISPsYv4HQfZAuWE0mPDxZIAFfgW8k0uTkTkhVxxTCY970sKKEYzXTYYRGUkJz00IJ5Xr0amXOHb+HrVP3svvwFQilcIUg1Za5ssdGXLBY9hkVEylEyZcUFgIlONVN+OaFHlcv1ugkOb20YLbsUw894lzTSwWp1kyFDomehC8Mcj2JOY4Lqp5CGwiU5NIgZU8joOQqBlnB3esJWlt2NELGmeZiXrDcS9hVnzgKZAZcJoENniPIC0O15AET3XCFAiEKQgpIMnbkCUm0A6sdxPQCbqWMyWKEThhqSbJyjl944+v4/d/7PaLGFIFSlEyXr3VcosAnEYJxe8zOskusDVu9hMtFG6ffh6kIx3dga4nk7BmCWsRdfQ+XgunT34T2OZzD1zNyQzw7kWrkYjJRVTpjbB1MkVNyDGuZy+dOdXn0oWmU43KpnxC5ivVRxu5Wg4vthExr4qzAkxLXgTjT1H2LpxQBGteRGCUn0hetEViMAONFxM0G7d6QZiNjLvLJCkMpkjQHFxFhmU3to3yPbpyzMsooOYq1ccGUdSiMoexUabKFqzN8AX3p0RQpcv0ExfQuhiJkYCwlYGw9jDsN+USj6xdDcuVzbjyRodRCl81MUzglglIV58pH4SiBsZC5EY61pG6ZFIkrAwoMkfLQahIn7siJNGe65NHLDL0kZ32c41iNO1znmydWcMMyUweu4PxIITPLSrtHVmgiz+H81phGyWO+FjDKDB+64yKDVJPllqv3z+AqQXdUsLo+xPMl3fYA6Xr0OgOS3hZuUGLfjEMvsSwfvxeA0crZ/6H6Sf5Hve8bXvgEZtU6D3voTTz5yU/m6NGjNJvNf+kyvM0223wb2G5+t/ln02w26S7fxyc/+Um+74lP49L6MYbLT+Pu2gyX7Z/jyTfuZHTsblRUJZjdxVyzTugJUlGiV1lkKjhBt73JLjdBKR856mCjKnq0RXbuBNEj9hHlIwpHMeU51EVGJ3Moh5IibOJ4PoU2CDFEDpbADynyFKs8nKBEGjVQ5VncvI8uJuEA+uw9BFN1wpkWztU3sfVfP8lDfvLnOXTgL7j+e57EVVcvUfTb3PqtO/jtd76f3YFBFyNQDqY8g1PtgbAQX0LARL6QjRF+lTAekFZajIVHSVrk/QthvbQgd2B2vAJSQSYRBnAcjKgiwhrGwobxqCpFVZnJ420EJT1mixIVZQjyEUIY5GANrCWs78IIO1mIc328IqFcC6hedz3njt3NmbtvY9+hI7hT0/RSQ01Jqq4iLkAUBZ6AODN4AnqZIS8MniMpuZLuOMcRkl5cEDmSeuiRmYxOXFAPPYpCUyBo+IqKJ9GppeQohJzYmIWuoOIplBT8wZfOc9muBhfaI2aqIZk2aGMo7m+evUzc37T5APfHMlt8aSibIYVbnvgnJ2NMVENlA1QyQFamJtZw/Q3SfEQxHCDjjOEo4QU/8VM86+U/i2gs4KuMtUyy03M55MasC8XuUkhn6yxNafCHltpggyD0yKMq4c79mHIdOiu4JZfVrR4jbydXFOsINHpmLxu5JNMOOJJuZlBSoKSmpQf0ZQOkILY+d6/3eeLlszQilzQ3nO3EHJ4uoYQlMYKiKFisRoxyTaAk68MM35mk1D2gJTnVK3BUmRnHwdgMUWSIrEce1PH9gF3JJrgJQrhYoFQM0EYQexWs9NnSIZ1+iicnHr67awHjQpNrw8Y4Z6wsYWUBd/U4oj5PK6hRVFsEl9aQWYLxSgTK0is0ZQGp9RFq8p5LjaSvXYy1rI5S9roRhbUU0qVvJS3HRQiLKdXBSEbGkgoHm2V08cAKVOSQak3Nd8jN33sWw8n2CKFziq115HAdG5aYO3Alm8ZnNbd8/dw6rVrASi9mVyMkzg1z1ZBa6Exs5mzO2fWYq/fUmYpcXEdxfmvM6dU+4/6QjfVLFNkkdCOLh2SDTXSjyXULU3ztvtV/UuekG2LyiY+vU6pRjCbRxUf2zvH+X3oOL3nJizlz5gzvf//7eexjH/uvVH232Wabbwfbze82/5/53u/9XvrtFfZd8zA2v/h25Nx1fOX85dx1dAcHjxziqQ/0ODS+mwvtadz9+/CCgC23ydzuA9x56gzFTIST98Bq7OYSdtCjf+ICMw/pYtfPUV64DKscvHxIzSkhrYsQmtwpI12BCOoIJDhgohnk/c1jpg1Vm2LyDJmOEG6Ibl+idt0NMGzjRDU+9N738OVj53nVq36aXFuWly+xc26G3/i9P6DlGITjYGREmPYQTogt18HxEV4Fq8fgBsj+OmawhRTglRo0lcAql0FuiFxB6EIgwfglZJEhTAZFikgG4PhgLMaLqBjFWEPF5GxkFiEt2vHZig1eFICCYLQFoz7S8wn1CKsC0Cm4Hk7SQUdNqrLg+tmA00Of4/fezd59+3AaO5DSUuiCk1sxNd9ldZhxoDVZ+qoHLiVXcf3OCs3QY32YcWQ6ZDPJ/sFn1xEQBg51T1EAG6NssuCWaVxp2YxzZks+SggWayHr44IdZZ+rd1ZATnS9VkLFV2Q5tEo+U4GDK6GTG+p2YofG/XKKMjmuKcikQ6JKRGJM7oQoJ4VA4PZXsaUmdvMiXrWC2HUFnfPHuOVnf4UXvvbn2HP5tRg78aeteYJB4VBLNoiSc/j5NIvD8wzvO8p4MKS0fy/m0NU4UZm02sJxHMSgjR70OCYXCIuY+cOHyYYZ4dwCX+34tOKCcjWg7Bni3DAlcrqiRCAFZ3uaeqSo+i4HGz5SKqzJualpqZYs+3yLt3InO/UUrajEmR6EnmJjVLC/ERE4k2Z6d1lwsm84thFzcDokVQ7tTBHJiChbRZbqJKFkLXfwcpdIRLiuoUuZUZYxyCxT96f/bY7zyU/SWlJtJ5Z2UrBhPfz6PqbzLZSQ+GTo3gbU5/F9QeFAGYUrBWOr0caipGQpc0kLjaMku2sBEkPoKqbjFdJ+Fz9ysU6EMRZz/+N/KaBsx3gShlbRSTTj3OA5AlcIGr7LJ+5bZUr38AfrhGFEtOswu2YaxFpSdMec3RjQTQtqueaK2Qql0CVyJKc2h5zbSElqBRoohy7WQj1w+NqZDuc3hgyHGcO1FXrnj91fvf5xkjtev8iNe3bzq+/9+j+pb0an//CxTgZ4pRrv+YXncOKbf8cNN1zP85//fD760Y9SqVT+ZQvtNtts821nu/nd5v+ISqXCxpk7+OIXv8gjHv04dOcMw+Rm7lhf4/y5fTz4odfw2L0Div43KE3PI2qzxEEN6bhcjBW7phdAZ+R1BxHWyOIvI7tr6CTBRRPFqwjlkoiIqL+J8DyEVAjlkKoSo9IswhaUPAeLIMUhLAZIUyCLBNIx+X23UvQH+C2LOXgj5CnG0+w7eJhfevuf8fKnP44//dDHmJtpAYLI04y1whfgKR8Zd9B+DWVycFys18DCZDpVrWPDOjIbExU5xg1YysuUHIUyghRBKawhO8ugJjpeIRTEPazWWCdCKUuTgqENGKSaWuSwnEqMnaS9pdIntICjMNIDbVBZDwRIr0weTjE2DvViCKUqu5s16lMt7jp2grDTp3bkML2kQFtLYSzagCsmS1fWTJLEuuOCZsnlxD0DHnNwmqJj2Rxl+EqRGMOOakRsDIOxBm3opBpHOvieQyAEUliuTC+wEu4jVQJHGG7eO8Wda2N2VCYTT2Mh8hxKvsBVgo1Rzkzk4N0vj0xyTc0R5BaME2ERDEWEGzWQWULhljBehJuPkYM1BsfvITp0OSIb8mvv/DAvftXrOXLtDfSSgkGuaRewuyqIQh+1cJCgt0J++k4KGVLeuZPxxfNkRx5M5gaUwjKpcNCA11nn1HpCscPnitVvoXc3SfdcQds4KJkTeQqlU4xwibVguRBkSOY9wdluzKKFRuiSWkHFFggpCEIPx1X4w03yS6e4Yc+VGC+HKZ9uMknY69wfTtLzFYfdISW/ilKTABIvVJxPJIcigVU+mXBZ0YpOZkjHKZHnsrvsYzWMCnG/lZgGIdld8/nWygBHCTypCD1J1VckBqKwQt9RaMcnpyCqzpBLn8xCSWpiI3HI6CcGqcQkTMVTVHyLsIKqr1Bpl9wt43XbyPYFhFpAaA1mCoQkMYK6HCP9AAdJZCSxmXiR5EYwShO21i4x21lGej7F9F4uX5zGSI92nNOOU5Y6CZf6KVfNl3nQ7gbrg5TNuKA7yji6PGS5PWL/XJl+nGKlopdkLPUV37p3lbywJP0teufv/e8q1z9KGB5yqM4oyTl+ceufFjfzj1+zb6HBu978DF784hcyGAz4m7/5G26++eZ/oaq6zTbb/Euz7fO7zbeFhz3sYYz7G9xwzTUUJz6GXv4mW2eP8+m//gI/957b+cg5yVq7y+D0ndx27DRZNM2ppTV6qswwbJGVWmRzh5l52jPRlWmKQw8mFh55ZRZXGGpS43QvwMX7cPproC0Omq3EkEmfiyNNZh0CNFZ5WFNAqYYZdTDjPtG+wyAlFCnZqbuxZuKFu3LfnTz6kQ/n8p0z+ErhSEkuPTJjyIRApkNEOkBlA4QpAIMJqxjpIoIIXB8qLdAZjDv0c8sdKwOi/gq2iCnZBC0UNojACvS4j8jGMGxjzt2Lm/YojTfxBmtEWydphSCxjAtL2ZGUyAjQ4HjYsEJRn4ciQeRjRH8VR+do5ZMYSSZc0qDFUJapl0Nuuv5qtM6487ZvMhoNCV2HtNBU/cnUNSssjdBhOlI0Sg4lR+IqSZJpjIFq4IIQWANSSjJtaY9zYm0ZJhmjzE6aIQG+IyjckIvDjFbkMcwK2okhchVbiaZZ8rFm8rqagSJUEk/CrqpHP9f365NBSLivD6vZJDbZU4KOiDAC1mxAX5TJvQoWhb/nMPHCVYxVxH1nz/OwRzwcRyoum46YjTyUUoyMpJtCbCVZeZrx3htQpEhy0s6QbtRi1ZkiUz6esGjlkTQWWV44TLi6xPBrd7P1yf9GcO4OrHDYX/fw7o+eXh7lKKCTQt1zsNZy39pwIl+QkhoxlfEKQlhWcheBwCgf78gDkUWOb3JaZgRFiiNgdatLnKUs91I6ooIjJTVf0os1X1saTmQpcQFpjLdxhiYxXhHTHSU0fEU/1wzSjFbocPuFLS5sJcw6Bb1U0wgcrqgrLDkbwxQJDOKYQW7RXokCyWqi2Jy9mg01hSeZ+GkLcMmYq/rsKSsaPsz4lmkXpgKJg0ZIh8Dk2H4H2ZgD4WAxIB38rE9V99FIRJGgBJR8RdlTbPRHtC+c5uLd32DU6yBm9+DuvoK9O2doBBIpBVuJYTPW3Haxg+9KSoGLJyW9VPP5Y+ucaY9BWJK44Nz6iN5I40nBaifhM7dforO6zub5cyx96+v/07rluYpnftdl/MVnjmHt//3zUsJt730TT78u4GEPu5nHPOYx3HHHHduN7zbb/Dtne/K7zbeNIAi49auf5vbbb+fGhz6aoneeePFm0m6LD7U3+fyOBR7xwMM8fHeM3upipcPXjp4inN3FwamQpJCEO66h6cPA+HTznBlHgRMR5ANozCO3VrGdNVQyRvkRC/EYUTrMO+/t8KQrZ9gRGpSQWOUihcAOe/iHrsE2FrCbF0jv+AJuo4XKRzS8CF/HeOUa0nHwMCQaFIZQWgKrIR+BX0HkCdavINwQhGTkN6jsuAwz2gKdUbhldLXEKNXcMBPgjC7RUl2QLtbxEekA4frYYQddZOiNZWw8wM1TnCLGxiPceEjNj8iiKfACqo7GGW8Ck217XA9hCkxUx+ksoYWDsgUUBiVDujpCCEmWQ+R4uGbMtVddzrlz57l4/iimucgyJa6Yq2OZRClXPJfNccGhRkjgSq5brCPvtxmLHAloPGfShKUFrAxSlJQIAYErkUrQcF36ScHdcQ1PWUJ34lOLEBgsDV+SFYZWoNjKASmoeyCER2blJA7YmTgOSASuFJQdi7IF3RxSrchVlXZcUPMtkesgytOYg9cBLvceP85Vu3cwzgy5NmAlSk4CMdYGGZGnaKeaRdfQdptE5WnEhUuUrroBvxjBcMgw2sswE9Qcw8m+xQrJ1Kc/yXgcs3V2nSwRRDen+Jdfw1iExHgEjmCmpNAYCmuJC0vgKoQFQUGpdwE5GhBKj2Cco/waSIWWEtbPIsOQ6qDLwdY+9skM10/pRSFZGpP0C2arUwwKg5KGuarHztASFl2sdCnCKrV4g0MIdvgGUUjuWR1wOnZwhKBZcpkTIxrK0gpDrHLRxnJlMGALS1M61AKJL2JGsWA9hgzw3JB+lpNjCZRknFsK6eO7gAClJBiLMIaNuCDNNZlxmQktqt9GRhmmeRXamWh5hfLpZRbfcTGOoBxvsenUOHv2HKO1VURYYWr/5fil6mTqPc7xlKJvJmEpq/0EpQQzlYAL7RGtyOdiL6U9ylnvJpRLHtoY0qygVvUoRy4r3THt1SG9lTVWj34TjAar/6c16/E37aczSPjKPZf+b5978nddzyt/8Eae+ZTvx/M8vvzlL3Pdddf9C1XPbbbZ5l+T7eZ3m287119/PWl/nVue80I+8N53YaePkOjruNTv8JF2j3uuPMwNB6Z4yIym1ltjZZSSZnsoRz6hkoRiojE0QmKQoDNsfxNbn0O0FpHjPrq/hWmvEFSq6CzlsQfqlJTkrnbGvpKgaguU1biLh7BBBWsK4vvuwd+xiDz4AOKoRSPp8Cu/9Av85jv+DJmOcFSALy2uNTjCTmQTUR1jLMILodBgc4w1CCGwjk/hV+h97EOoJz0fJ/CpyowZx2KpIrqrCDNJOcMYtBCYeAT9DrazCZ6LXT4DtRo4PjIwsHaGYMElyNbAi5CjLYSQGGvIw504vVVEWJt8o6cWkVkCUiJVSC4lCoGjLCPjoSQEwrJncRdtUWV86RR7ogqeLZNql1bkUw8VxVgzEwoyIwg9ReQoHDF5ncZYDjcjZksunXGBlLBQdvBVMEnrEpJUG3qZphK4LFY9MgP9dJL6FjoSY2BjXHCkallLJK6c6Ig9pQhFTtmRRMWAQHlkeOysupRlTookcCzWClwpWSi7jHONP94kCeoU0QzjJOWuU0s0dh9ic6tDZwxJO6E1VaGhJChNIwzoFQ6jYZe5/gpmfj/jM8cJpur4QtOrtMAaTnYSGjal0x9SO3onZjRGKEX7XI+zpz/HNWcvsuPJj2Xq4JUUCwdZ0yFYqAcu1sCFfszuRkRqDAuegx1K8rCKG/eYH7QRG8eQUZnxsTtwGw3or+MEZWpSY2VB7rvUHUMgMjZtCSElM06OKinacUEpG2KEYiA9PJMjHB9Hx9RCn63lJXakCX5lDpVlVKeq7A1c7OpJ3N2XMSo0auMclfEWVePgBfPYoAIiIJAWr+wyMC5Zofn6+S6Xz1XuD6OY6HPnPQepXKQ1pFaxNMg4uZWQGkvNd6iHEenCFXjlGkKAdiMSLWhnHsPMMKcKBlnB6YuXWG0fRZQbpLOHEUFIq1FmkBiakaLmCiqu4uIo51Iv4cunN3nk4RaZNmwMMtpxwvqFBIOgUQsYDBJWewmugjTX1KxDnuVsnD7D1rljkycy/w/sW6jz5Icd5Off+ZV/8vfl0OPoR36BX33rL/HoR7+GN7zhDfzMz/wMruv+S5bNbbbZ5l+R7eZ3m38RXNfl/e/5M+57089y+Y2PQN/3YeSumxkDd31ulZN3NPjE3A5e9j37OFjros/dyWa5RVFu8bWzBdctNthVDzHWYB0PsgQTNZCjTQSG+NidJIVD88Z55PppLmsuoosxY9enV7gsJQGHyhZdjajoETqOcVrzyNYixmpMliCTHr/+q7/Cb/3im/mjn/tpSvuvIbQGKwJkNkYWMTqooZE4OkHkfYTjYS0ENoW4S3LnN4kvrTLlgIulI0O0NfjRDJ4McPrLYC02i7FbmxD3EaUGcnqOYtAjP38U1VpASIn1PITroQab6P46RoNsNDGDHqa7AcEUbJzDnd2DHXWg3ERsnkO29hKRMLDuJMzgfn9kpRzUaIMztkFrukU3KBGvnGbr5J009hxid73KrIgJKyGejkkoc0UzosCypxFwphvjKkHNdzHm75ffJLOlkNzEVAIHi6UzLsi1oRE6VDyHuChoRQ6j3FIPfM51h0xHAQM8Sp5FIEiNITOGijvR+LrWMBIeoU0wMiAXPq6ZSD5qImPDVPFdSSXbIHNCTnQLLg/7nBtKkp3Xcvyv38kttsch0ya/7y7kTd9HubPKbHOOIijBuKBS8hi+7wPomVmq0xUIy/S05EuXxgiZ4gYSd/luVJHjfe6zTF02z8a9KzQWyhgjCRoB+eoF/JkWduEAo0xTWEPJUeTGIizkxrJQ9ukWFq8yD911Kv0V6LfR0kWpMV69TtHpwqHryS4eR8QjTBKzVd1Lc6GJSrrIwqDdicOCS86BmoPsp8ilk8jWHEl9kUAZtOMziAvk0S/TfPjj2XXpbui3sfk8slxFuA5i1MXvbUI8ID36TZJckvoR9Uc9lpFfQdkcWWiGmaTiS66f9ai4BZcSSZIZOmnBXDUgNRZlNKux4fbVERc6MTtqARIYF+BO7ydWiumVb7FUq6EFnO/GRHrMnaeWEMmI+vQMtYN7WU0F05HHQnmynPb3ioMFT7OlJZ4UbI5TFpsl7lnus9aLCVzBvRd7DAY5j79xkdPrQ77wsc8T1hsMtzaJ6lOshS62PE3ca4POENLBGgv8/eRXAJZS4PLTP3gjH/z8cU5c7PxD3frFn3gK1+8OechND2RhYYHbbruNI0eO/CtWzm222eZfg+3md5t/UY4cOULRX+E1r3ktv/Ybv4Gp70Mt3Ei8VRBvtfnNOOMV37uHXbNlRDwiGGyQjFxOeJYkq7BQDxFeiXzmShrKQ4d1EAGitUg0akOeoistjPJwR20W/QoDz6fsBRwfFygEV7ljOPst/JlFrOOSfv3TuJUy7Fjk4I0381M/rvmhV7yBX3vbb3HwwCFyoVBugOf6E/cI65BIRea7NGWCTAYIKbBBFXHtw5k7ci1SWjLpYCykgBYebnmKwvVw4g620kI4PlK3wCtBOmR06iTB3AyDe+/BrZXwKlWc3YfIt1Yxl06Tj8eYnXshzxHJEO77MnrYQUwvUiydRY6GGJ1jy9P4YohxynSdKq4wBK4CBIkKSGNLNZS0qiF5+Up665fYOn0v8ws7KJUyRtOXIfsb0KiwQ3SJg2mSAiqew0wAFkGsYXOc48qJlKGfatz7gxUKa5gpufRSTS/VOFIxHUmkNAgpuXaugraCdmxpRgqLBWMZJIa6r5gKLJkok2qJKx1cChLr4ekUK8HIiZQgEBrfFaxrj3g44MLyJTaDWXqVPbS7faTnkty3xtpdZznwUIHT3EFbRgRpStV12aKOf9OjCXpruJddTj69n5EOeMBsj6l0k6X1TVat4MDSXeTTZaqH9yIDF5MbLr98P/HFVRzfo9jcQGYpQktGuGyNYhqRR+gpenHGIMkZ5IZNo9hdaVEdXUJNz+ME5UkjFk3hT3Wg0GRLZ/BmFhBhBf8bHyebnsedbTI1HuIsHsQqDz9LEdUmbJwDm6AGG3jVaeSwgzvqUS01kHsXYP04Zu0cMogQ/VXGx+5AlmuUrnsgariJnd5F2h2RdMdMXXcFprPGsDOkVK5QGo+YD+rY8ixT9ZBS1sMvevSdKYZ5QSCrZNpyelyw0s/ojHOaoYvvKg5Nl9iKc1pVRS4my3Pv+9Yqj150SC5dRAuDLk/T2nMEKxWho1jwLMYY4sKipMBTFiVAFmNO9QJmSh6ulCgh8BxFmhv6o4LAE6ytDvnEF4+ztNahc/4kg60GxaDDcHOLQb2FHw5Ihx2EclCeTxGP/qEeqbAC6YCXP+V6Lqz3+ehXTgHwpEddxx+88Yd51atexc+94n388i//Mi996UuRcnstZptt/iMirP2fyfy32ebbz/nz5zlw9UMpxm3kzFXI6SMIJ6A+O88vP+cGNmK4vevz4HnLbj9l5FZxpuapVMocaVUoKYvvwGZsmJNj3PZFRKnEMGiRSI+GY3DSIak25GGD0aBDYhwWyi7p2gXKJQ9GA4rVC9DaiSg3GDoVNjJFabDEc1/5Ov74z99LtVqhwFCSIK3mzBBagUBIRc2OJ49+dYGN6hgjiIZr6FKDRLkYbZHW4hUjpFSkKkKlA4QX4I02kOkQ65dg9SxZr0127iQX/uZWpq/eTWVxltwo4pVNynNVknYPNwrRhaa0Y554Y5NwYcckknk8xIxHePOLfKZ0Aw+fSnGTPqPmHnBDPCWwRc6AEN+mxDhErsNmnFO1Maq7wp1LWzhYZg9fzWwokH4JkwzwgpBBocgtRORsaQ9XwDjPOd9NuWymxPownUxwLVQcRT2UXOznzJRcXCmIBKxnBl9CJVA4CEa5wVGThqaX5ISupO5J4sygpaAsNFJoCqMI9QhpNUiH3PHJpY+rE4wtuLTSxa6fJfn0p6hffTn68HV85P3vxqI4rEpcvqtG8L0/SMkVbJiIKadgjEeSpQwLyz7ZQ3kBQrqs2xCZxsws385nVnNKIufqzhlWv3QrpYUZwppHuGMBhADlkCcZKigRXnEDg7nLacea4+2EmfJEe3p8c0TVcwldwY6ay06RUl66Ez3cAjfCxGPs1BxKCsyl06TnTxPt2YOsNij8KsXFswTTU5hRH1VvQF5grcEGJezWKsILUPUWxlrIYkShkTv2kh2/HTMeIbwQp1Qi31pj+Yv34Fx1PY3v/X5KF+9GFzk6aqJGW/Tuuw8hJ44NxkI008S57hFk0TTkQ8Jxl2LjAjkeaRoT3PDdXBw7nO4meAIu9BL2TUUcrrsUUqHzgl3uiB4lLl68wPLyCr7vs+rUuWzPLs73U/ZPRYzSiTNLbg2DdGKfVvYkUiqktTjknOpOPKZH2nCmPUYBn7zjErmGpDfk/L0nGG+tUiRjKFL+fpqL8pCOj8lGYA3CCbBWw3+XzOb4IT/+hMvZ2arw5j/9MlJJ7v3Qz/Oed/85b33rW7n++ut5xzvewe7du/9/qpLbbLPNvwbbk99t/tXYvXs3WfciH//4x3nC05+DWb8HOXs1XSxveZfhtU+5gsPOmPfcGbJjtsKjdhTsWj9B3CnRkXsYVOpUPYdRIdj0SrSaO0hVhDQZnhAY5dFXFSrnv4Y48ACU69LSMTkeK5U9zPoFZS0orng4PREyXXSo6DGDYUxzbgc/9/pX88IXPJffffs7mK8FuGis65JrB8ekeF4FMk0iPXqUqBmDRmH+YVFu4qeLdMmckKAYI2WGk8cYJUG5IJ2JhZLr4M3uoDhzH36zjFQOxhj6J87hRgF5b4gT+GT9EcL1ICgj2CRZX8WNgsk31Bh0PGYcSmxQRnQuEqV9Bk6IM9rEIvACj1I2RIbTBHrMnCpQRYpb9nnwlfs4vtzl1F230dm5l8N7Qk6OXA4oQ0kahOsxKnyksXhKEDoelwYZFjEJvNAw7QmSwnKinVD1HdT9P+vN3DDODZmwVDyHxFpcR6DNxPM1cKDhCvqFpZMZMm2YL0nKUtEuBLtMQhY28EyOTAfkfkBQjJC5ZlpvETRCPnPbUeTmKgf27eLx9ZB3fPVbfPTkOZ76nOfx3aKEkg5V3xIXirgwzEUKcpeBDPGkZZTlOKJg3fqsJAFGaK4+cohILzBjBenSRVRUwt97hHz5PKpUQk6FpOdOkJ68CzXK2bF4gH7ZxZWGcuDgtErMBgqVD6hnW4hT30L3N+mfPU95/14GZ5Yo79pEm4zBuSVMkuHPpVg3xm3MI2dmSNaWcVxQhZ6Eo7geprdJEWcUy6uEOwtyK5HDLbyFPeiV85jOJjgKMoGWAhGUqe6fpXJwAW3BpmOsG5CurxKWHQb3nmD2oddhATMe480uQHcV79J9iKiGNZbul7+CsAWV/btQoy2srhM6ksJYSp6k5Cgib/K0o9/vcsfZZTa2upRrdfYeugy/NkWxNWaQG6q+Q+RKrFUk2nC2M6YwFiEEqfawtmC5P7mJWBlk3LfSZ7risdwZ4zqSODZsXFxm9egdFKM+/yiU4B8/1hnmv9P42iL5J/VHCnjp91/G4kyNn3/X1/jAr7+MY7d9nuuuvYqFhQX+9E//lMc97nHb0cTbbPMdwHbzu82/KkIIHv/4x2NGm3zkIx/hKc96AWb9bpbXr+GNW5u8+bk3872tLu871malO89sJeKJlwVcOn0f0nFw63Ps2rWDfq4wTpn20LAz8shxCHXOGJewtYg3bmO6m4hKDWNKzPkO1iuxFrl0C2fy36U3xbQ7ZqYq2NIO81fdxAt+qsRLX/Bsnve0J/DUxz8GFdVoRtOIPMEfb7KmAy7GmtkypBhGBhKnyfmOYbZUoJEMs4Rm6GPdCI9iovvNa1jlYtMRlOoQ1sjKMziL+5nfuR9lIVu/QHl+mtHFdVzPBa0xWYEX+iRLk+YrXlvnC+/6KouHW+y5ZhfK7/DohVXcpf5ki2z5BOUdCuk4MGxTSscQ1UmFwHN8jHAZyxI112doPXYdmKHamOb4iRN8c2uTUX0XC9VphrmhqgRSCRwDodQUKPbWQ3Jt0BaknAQUKFegzcQ2bW1cMFNy6MUFgeugjSYxFkcIhIVQTRqeyJcIabG5YCZSdGJBqCTD3GARIB2sdNFYcr+KKwoKq/CWj1KREqZ385BbHkXhTTO64zY2T52kNh6yMF3lnnvu4un+ZCpdswUj66EUWKVoURDj4kjBQGusAZGN2eiN2LFzAa+1i6WNNXqL19CszVCyI4p4TNHr4uw6QHr8BFJIZKkGmxdx52a5XGh01MQKTcV1qBdjGK+w9cXPIeMeqbI883c/wMue8xSecNkiMvTQucPg7CobFzq4dywRLbbY970FeXuT+/76mxx6ws04cyW63R5v+8Bf86WvfJOtccx8KeAPnvk9nKnsR52/j4e89OHYk7cjGjMIKRFKYdMEVZuiultTrJxBZSNGS0tk/RFr955j10MuQ/oSVa9DkpDrjGxjFVFcJO9u0T2zhgoCNu84yc6b9mPGCUF3mX0VTd+dppsYPKlY7o3Ie6tkW6vkacbc3Bz7rjpAtRJRIWMltVQdZ2JR5knObI3pJBop4c5LPUJXUgs8HGEZ5TBINSfXO6x1xqx3E+7pjxklOWHgsnZxneW7vn7/pPefj+cqXvYD17FrpsrpDc1bnnE5z3vG9zM1NcXb3/52nvSkJ203vdts8x3Etuxhm/9fMcbwoQ99iKf/yAvBFESLN/CmV/wI0nH5wy91yMI6D3zwIWYqPju9mAPeCN/m+I0ZpuZ2YpSHUnIydbSakqcQxlAbLWHiMePqAoHNKbwyFsuJPriOwnMU4yxnd6Ap2THrskE7zlgeFFTzLp/68Pv56uf/jh//8R/nUd/9WIJLd2Kau/jqoESca/ZPlfCEZXlUUAsctsY5nppkolkBjcChEUgKY5geLqGUJIlahIPViTdwPKA3tZ9q5ww4Ieic/NhX0eMhg1Pn6J1eZ+ba3eSjhHCuSbbVxSmVGC9v8O7f+Ry+5/LstzwJtxShohLO9AIiqmCtRVSa4JcQOsFKH12fJ5UeQkiGmaWTaqZ8uLedcW0r4OygAKPpXTrDuLPBjj0H8BpzzFZcJIJuXNAKNP3CIXAEFwc5JVci73ehaAaTaV5SCIoiJ/Jc+mlB4Dloran5ElcKLBINuFajFAgEw0JQM2M2bUjkWAozURg0O6cZtg6BmeiG3XwIRYYT95G2gEGbL916J7/0Zx/EtYYoHvKkRz2IB+xZYO9TnkPhlcjTmBDNujdHoDRKOTg2xeKSW0k7LSi05fxd3wBruPkBN2C8gNHGMqtjw7g/YNEdI0/ei7t+lvDwlVgEsrOMt+8ymD8IQQm1fIzEaaCKIcarIJI+7c9+muTsGU6sbPErdx5nbmaKLeFy/f5dhEmfvdbhGiEZd2P6G2Pqh3ew57pF/H0HufuD32D/9S3Ozi7wht9/Dz9xy9O4sVThr77+Fb66vEn3xAkac7OMumPGgeDKg3uZn20hlEO8ukQ6HOJPNbmqGnB96CL9Cl4k2Dq9Sh7nlFpl0q0+lZ0zVPcv0jt1EYRFOQ7ZcEw2SPFrPsHCHJXFeZx6CzW/D8KIu8UOzm72KI/bpL0NwjBix8ICO2suRoaM3DKZtgzTlPXYTvybjaE9ypBKsjXOuW91wD1ntvBDh5uPtAiUZCpyue18l7gwnDjTpt0esnriGEL5KDekt3wCEw/+P9WYqWrAq5/5IASWgJhf+ZVfIYoi/tN/+k889alP3db1brPNdyDbze82/ybQWvO+972PW573YoSA7/7eJ/LMpzyO3/nYCVZ0SLXZIGzO8YSH7aNBwhw9SsUIp1ynND1Hvd7AoPAU7CpBlhWkxpBYlzkxxMmG9MMWFxNFSUk2Ez15lK9gNhCIfAhG882+z8X2kAftbuJkfd7+O7/D0smj/PGv/TzJ9H5Od8cs+oIo8BgVAikBpVjuF/SSnExbFms+Ukg8R97vO1uw4GpSt8RUvoUz2sQIh7w0hT/ewjouosgwnY2JlZq2JCuruEUP6wbYPCddXsJKEMZy7msnCWfqzO6dxqvVcKenceb3wtROMDnEIzA52cw+nDzBhlW0E5Bah8IK1oYFJV9wvptwYCpkZZjSSyxToUO/u0W8fBrPD9i5/zCeH5IYwWwkSDS4UrAy0sz6BQkeBsO0pxgbgbWWsh7Sl2WEmLhfFAgqxAwIcZQg1zBl+uR+edIsW0Mz79J26vjORH/qS4vbXaaozNLRLlVH42b9yQ2MFYQbxxHtFb7/9W/jv/7+71JtzUF7CS/vUyQxqrUH45egSLDKIw7qKGMohIM1mqLQpEwcGtbOn6G9coHDV99IrRohhMMwTamRUbRXMOsX2fLrTPsOatQm8iWqyBCtHaALsksX+dgXvsQ73v1RHv2Ay3nRo2+mfdtR/HrA51c3+PMv3MHv/crPs+fIXjYHY5YvrfBrv/12Zq3h8Xv3YGou55KQQaPOfXfdydLaJoQRRgoO79/D837iZ1jUPUrVMmmS4+65Avv5D+He+HCGCYRrx/jq0EO3l3EGm4zvvJ3yfItg525e/Qtv4xHzLZ7x4GuZm6vjzc4j8ozuyfNsfOskO77nQeTra2zeeZEDz3gk40ttVOSCydFpTvPRj0UARDWK6gyrW30utPsMxzGVZgu3Psf+uQaeIyiNNjGdNdZaV3K2l7A5yig5kr89usqhhQqd8cQxojCWzxxfpxV6dNKcxamIQhvyouDOk12UKzl99BKOC6tHbyfPUoSV2HyytKbCCvqf0QQf2tngVc94AKdPHuPd73oXjuPw5je/mWc84xkopf7fD7DNNtv8h2S7+d3m3xRFUfDud7+b577oJwh9lx993rO5lDX5+K3nmTpwPQcu34uxBVdftsBNiyXcUZsw6SKlZG5+gbAxzf6pgJXUoalyBjk4wlIruhRRk/Wxpp1oBoVlKnCIC8ueqkuYtrlYRGwlAs/mzAYwFD5T5YDbP/cpPv2J/8YvvPXXYbxJEHcwlRkyK/FcF+34bAwz2trDFYapUOG5Pkan9AuHcWFQUtLwFb7UVHrnkcMuVJoINKAm0+A0BmHACWDtLAaLjCqY7iZ6PESPR5jhAPwQBm2ElOAGBHsPIatNKNUxjo/or2HK03SDGaS0hI4ixyG1ICyk2jDMDP1UM1NyMVbTji2NYKKNHacF49VzjNpryKkFDu/bi+85bMU5WW5wHEVF5hjlUvYctIWSo9DWIG2Oh8AoQaolmRbUfUM3l3gCNIKInKFRWF1g8hiVDIgLQ1EY0ixD5ylFoSm0JtMW8pTCgrEThwCrCwyQphm+7yEQ3P8bISRSSRAK15EgFUo5WKEIAg8hFFY5RL5Pqi0rZ+6j3Gix99DlGCmwwInNETMllxBDmI+QEkquwCsScFxCk2LzlF57yFe+9Cne+Sfv5S2PezBv+fI9vOYJN7NQmyUu+/z4f/mv/Plv/GdqzpjiwANJrSLYOMcnPvUpvnbHvcSjIeVKneb8ApdffTU7D1/JXLVCqRyiXZ8wH3P70GdHKPCEwXohkQtq0GYYTDPUlvnhEpvBLDPdE2SZYX15k4WGg0xHtAvJ7//Jn+ON+rzsSd+FW63gVGoMzp3nvvd9njetXORb5y7xmTf/NIcf8VCKcgu9cpYwVBjHJ1s4Qt7fZKmXsbrVoxL6zM+2mG5O89GlgsB1uXGhgqMkDTfHL1LWbIm10cQNop0YsjTHcQSbo5RRbgkcxcYwRWuN5yiWOmNOnW1jhUQbAQrOfes4xbjDcHMV/oluV4GS/2SB7f8JKQXPeOQRWmKND3/4wwC86U1v4pZbbsFxttV+22zznc5287vNv0nyPOctb3kLv/xrb6NZr/LYxz+Zr6xW2EwUjh8ws2eR6647jOsrnnnDAqVihO2s0u/3aDWnaMztYqFZZZhZjFQ08i1ir85WZukmOd3MEDmCOLccCmLuOXWe6V178dyAeqCYXb2duHWY2CvhOD4/esvT+MO3/w5NmaJ1jswS9KUzOEceiN1aJi9NUZRbqJXjuNU6xdRu1HCDDW8WYy2d1FBxFa6EQGqitIPyfWSeoa3BMQbjlcDkKJ1hL94Lro9ULsZxEVEdM9jEuGVMWEWsnUJJCdUWshiDMZh4gNh5JYy66OYO0sIwFGUCVyOEwlhJXBhyA0luWB4kLNYCrLH0swJzv09t6CiWBwkiHaI2LyAlzO45zFhFuNKCtaRWELqSUCkCR1B2Bb10ovOd01vk0uPE2CNSGpKYNBnTG40hjRnHCUWWYoxGKoXvefiuwvN8PMfBlRblh/SNy3QAXtxFWIvjSERYB6X49O3HOHXvnfzEDz2JzK9QWIFwfQapQWHRduKPG+eWcVagbIHRmnGSIU2BzjP6gz5/n2krpMT1Ahw/IMbBC0JwQ/A9hPKZ8h3qPriOoCQFgQPrH/tLnve23+X3n/8jVFoRP/wLv81Hf/21uDO7+dit97KyvMwLfuwlOBtnGe+4Cj/p4CRdTLfD0dJB9nlDTDhF3wiqniLFQaVjyp7CKIU72qLn1egXHhn2/psny7gwjAqB1hqdDGlUy1SGq7jSMgpahGTo+76JG/qMhgPe8Jvv4Gx7wEtueQqPu2Y/+aDPn3zgb/idT32RH7nlWfzYi19MmxK1ckhgUpLRmF57jaWNDoUuWPAMCzt3EDVmuGTLWOBXP3uGhx6Y5vqFOoGyLASGBIfN2CIkrA4yImfydOH4xpBBWnCpPabQlsXpEkeXesw3Au66b4PRIMEKSbURkY0z1pdWSXptxpsrmKT/z64dO5olnv6AKh9431+gteaNb3wjz3nOc7ZDKrbZZpt/YPsWeJt/k7iuyy/+4i/ypje9ide97nW87Xffwcz0FFdc/XBuX3E5s7ZEXhgiVbCvWeJRh1tkfoXZ+Zyiu86p++7hnFLUWnPMzs3RUWVGmSV0xGSymVvSXNAqOdz59S/xnBe9jBuuPMJUvc7Dvuu7edx1+5jzlvBbexnLgB969vN4wcteyZt/6mVcdfXVsHIMW2lisGjX56JqsttxEX5A4VVwdIrUOfVsi8ILsZ6PFBaNYFAIZNjEl5bC+qxnhmlR4LglXJMirIbZg6jhOiaoQFBGeyVs1GDkViYxzKVpajZGxQNsu00xux+5chq6q5COiVsHCF1NZmA9FuyMBO20oOpJ+vfrags7kTEMs8mEVd4/Pe2nmtxYDsw0cWen6Kwtc+m+OwkbLdLpnZQ8D18KlBAk2qCNZZwLOr0+NRuzPuoxGo0YjMZYY/CDiCCK8MOQcGqaphdQKwVEoY/jeBOrraxH5kU42RhZJKw408TjgqmqIkoiGHZQOsG4OWl1hr/6yw/y6uc8mWV/jtvPbvID1+4gti6Rb4CJ84ZmsmTXciT9tKAVStZjy85Qc+zsEv1+jwOXX0utGjJMNMPxmCxNcOKY4bCHLNbJ0xghJJtRiTUnoGs8ds9Oc2ShzubCIsL1+WKc8/7f+q/83CtfjHfogegs4Rff9nbe/we/NfECyRP8zXOoYkxSnmY0M8t47LCiIpquyyjWCCMJlaC8fhRVn0F6IQKox+u41mfDm0IgWRrk5GaiKW+nmr891uPHbiqT13YipcFFklsX74qbcNrnqTWm+aVXvIThzCKvfvVr+b4H/QxUZ/EfJKgfX+NC6vLIxz+Vx3zf9/PSZz+Dk5tb9AdDmtUSh+YbtBwNfkjR3EMnh0EqCCQ8+ap5Qs+h7kFFD8ipsR5bar5kY5RzsRejteGOpT7jbKL/jnNDLXA5udInS2KOnY1ZO7+GtCluqcZae5X++hpOqcG4vYzJ/3nLbQLLq3/gIH/xzj/hXcdi3vCGN/CCF7wAz/O+/QVqm222+XfN9uR3m38XJEnCa1/7Wn7n7X+AG1TImlchansQVvOq17+Yi+tDZman2DMd4bsO+5oh5WLIuL3OoNvGjSq0ZufZOTuN07nIllNl7Jb51Cc+wUff+Uf84etfxt6d81xc7/LJE2t85QufZXl1ncc99rv5gac/i3qjydGTp/nd//KbDDttXvzsZ3LDI75nEvIgXLZSw5EaDAuLES41kSHGPcTaKSjVKMqzpKVpMgRpmtNwMlKnzFIv5cIg43AzpBa4hCIFY8mtIsj7WC+i0JBLF20tUkpKUpMaQdmmqK0L6LVzZFc+GrKctEhRSBK/RsUVxAWsJwVVd7Kotr/moYVAWcP5XkE5cEi1ZivWVDyFNhprJYXRGGB31SU1ks5gwODSWZLhgOnFvbjlJvF4QD7ukw576HiIQFApl1BRlWatSu5FtGplCiQlV5JpgycMAkVhNb4UWASZhZIZkwoPPxszkD4bmaRCRj1Q+GkPxn2kVIxK01gv5Ju3384vvvENPOUNv42SLj/6gFkuFSWavmBkYJhoHEcgAFdKtuKcAzVJL5eotM/Xb72N1swMV+3dgfI8upRItCXTlqonWBpk1HyHC90Rnsnx84TuoE+n1yPUCVKACkqsrG3gUXD95QfZ3arjlKqQxvz5X36Mv/zg+/iDt76F1vpxktgS3HAz3cpOujmMCoMnFLNl2IwVZU/gSCgVfRwBbh4jjEHmY9KLp4kvewQD63OyPaDs+9RCRZxbuqOUB8z5ZNIHDKkRNEgQQiCLSQxwcvY4YmEvr3vTm5lZ2MGjn/Vi+oXgF3/yR3n6M5+FTAbs3rVIrVLGq82we+c8077EYNAatrTElZLb1sbsrAXUPAehY5TrM0WKyEecMnVW+xm9OONCPyFOCxwpKYeKL59sU2SGzEISZwz6CesX1wimqvSXNzE6JRkMGa6eB5NNZD//g03Z/wqB5fU/dD0fevcfsbq6yutf/3pe9KIXEQTBv1Q52mabbf6ds938bvPvivF4zCte8Sr+6M/+BOFVkHPXUd99JblQNOd3MDXdZG7XPDdftYP90yUWKh6BsMS9DcZb66TxmNZUg4WSojozy+t+8df53Gc/y39+6y9y800PoGd8xtYFYTFpzuf+7pN84qMfJoxKvORn30IRlOm1N/jGx9/HV770JZ74vJfxxMc+hnqgKJOyrj2wlhk7mNitjYdIKUDnJNP7sTpHDTeQacJ47jJODiztRHOoGU3CIRyBwLIZG3xlyfTkyXygoJtZ5kqKUWGZswNcYVCdC9jxkGzxWvqqhCkKHMdBCIEvoZtOUrRibXEkzPuGnnGpOZaz/YLZsoe1mjg3WCFxJCSFxVoYZTkl18VVgm5aMI4T0o2LFL3NSYiAcnDKdYJyjahaZapSYdZLWS0inPulB1KAryShA6MMqp5mmCsMk3+PthPHCBdDbiUztsvpogLAvvgsjueTl1qkVqKlRFtF57+9n/mnPpNP/80n+ewXvsAvvO5VeEFIJxeUoojcGNaGmqqvWOqnTIcuniuoSkti4I5vfAVjLTfd9CB8m5KqgLF2MFbgCDtpgo3FVYJhUmCMZVRoIkexNkzYGBfsLwu63S4qHULcx+iCUrXOjpkmrXoVNww4detXeM3P/zIvf/rj+K5HPHKiVy1NkQkP7TioPIZKk3NjhSsE2kLZA09KpmSM011BGM3Ib0C1RS+zOOfv4o+Wanz31fPsqzh4UhAqQ6wFKuuTBQ1KNkElA7JSi36SkxhLP7ZUI4fXv+LFPPZpz2Km2WS0cYlxnPDZz3yau48e4/1/9QlOdzNuWggQRmHE5CZsY1yQaMMnjq7xfZfNsKfiEK6fIJ07QDeFo6tDPnZsk6TQTFd9Sr5Db5QRKEk/1Rw7s8n6WhdrcwadEcP2Okmvg+sHZEmCjgdIx8Fk8T+rFggsb7zlgXzsg/+VM2fO8JrXvIaXvOQlRFH0L1B5ttlmm/9IbDe/2/y7ZDgc8qKXvJK/+It3IoI6cu46ZGUB6flEjSYPeORDmJ0qUy2FXLu/yWXTJearPsNhn2Rrg62NNbTRtBp1PD/gD/7ojxmMx7z8Db9MqRQx5VkiPaIry1R8hy987Tbe+vNv4glPeSrPfMqTqVRK3HFhi99+228Sr5zlJ172Eh7x0JtYzjySXLM/zPHSHnghav00JqphVs+hGjNkp+5BNZqw+2q2nCqJdag7lpHwKLuSIBuypENibSm0ZWucsTqImSr57Kj4dJKcK+oevh0jx338bIApT5E4ZVxhyIVL4YY4wPJYkxSGiq8QQNURbOWWiisYZRZfCqSCikjYKHwanqQda2JtCV1Bf5SRdDcYba2Tjvq4pSqpWyEQBVlnHac2jTO9k1opwhWWZugSW9gcZVR8l0wbyp7EF4LcChbdEeumzJRvWYtBIsiMoROnzFZ8Wow4NvLZ0wjwkz6BKEj8KtZYcgtq5RQfevar+P5PfRQFvPFnfoIbrr+OH77lhxFFAsrDoOmaErm1LPVTmqFLPVRYBCePHmXQXuXQNQ+gUaugBGwlhqo/sYHzHYG1BmMFvhLEheHYxgilFJ6A+bLHpWFKPymohy7ToUfZkwyGQ7JBl3F3nV6vT7VaZb7V5APv+jNuuvIgN+9pITwfwjLCL2NLFbqZwFbnyIsC1/WwWcylVDBbcpmSOe7GSQgqbJR2US+6tN0GtfZpfuuY4scetpuqHiCxFG4JWcSoUZukvojKUopRl1V/lqVOTGYNcXcLO+zgpT26vT73HD/Jbd+6g9aeQ/zgj/0k++shmdYk2rKv7iGlJNcTecyZTkzgKqqOoBk6hKKgu36JdX+eE1sj7lkZsrQV0yg5zFYD1vsp3zi6wtbFZSqtKS4cPc24s0Y2GoDWYPX/0bUvBbzxRx7EJ//yXRw9epRXv/rVvOxlL6NcLn87Sss222zzHcB287vNv2v6/T63PO+lfOy/fQARTiHnrkeWZ/GrDfyoRLnZ5Kprj3D9gRlu2DvD+QvneO0tj+PK627gux/9aI7s243n+RRFzq3f/Cbf/f1P5rqrr6SuuzjDTdJSEx3NsBrnoDUff88f81d/+SH2HrmSP/uzP+VHX/0WnvZ9j+FvP/4Rvv61r/K93/9EHv/0ZxGGAdM+eEWK119iVN9N6dw3keUGxcYlnHoD29hJP5pFYXCZHF9Zi9A5Y+UzMD6dDMa54asXu7TKHlXfoZMU3Lijgi0sU4GgnHYQElylkNIBW5AEU+RG0MsNkSuY/LIIa0nu7z2kEIxzKLkQEbOSeVQ8xSDV5KM+nfVl+lsbBKUK1eYsY6/KdKXE6c4YEJRkjtlcIu1vUZ3ZQVKdpRX5WKAdF0yX3Im0QQgkkw38WScnkT4NkbChA+LCMs4NHoZGpHCwjKyg4SuE1lghSA1U0i7EXUgy2l/9PPVHfh/D0jRh4PHmN74ZXxje+KY34C8fxSiHvL6Two1Ilc9mrJku+Wysb3Luvrsozy5yYP8BXCXRxuBMfDXQGBylCJUlLgQWSzc2XBzEBEqhrWW+EpAXOa959c/ww09+Ijv27GX3wgxeWJlM+K3B6Iy1U8c4euYiYRAwO9NiVy2k0ZpmnFscCnTQIJMOTpFQHV7CRk1kNqJvXUpBgJP20WfvQe46xGZtP61kjU5pAaxhkE7S8Iwx+GmXgT9NaDJ0kbOhPU5ujeiPU05dXGenF1MzY7R0ODVymZ6fo6M9Mmu59Z1v4+Rdt7Gwaze752Z51k+9Cd8RLJQdfCVRecwAj8+e67Kj6nOhPca3mgNzNUaFIXAkf3ffBgWCwBXcfb6LxLLRyzh5x3HWj9+OG1XIxwMw/2cN7+T9Cm957kP5mw+/izvuuIOf/umf5uUvfznVavX/+NjbbLPNdxbbze82/yHodrv80HN+jE9+7MOIUmvSBJdagCJoNKhONbnyhitplgO+8q63ctUDHsS111xFvRji6ZiHPOwRbA3GxL1NpBBMNRosNEq0IocsbGKliwS8/gXEaMBX1zVfuO88C9WAD77vvVRKEY9+yrPIty7xgfe+l0c94anc8kM/hKcELScnEw6lznmEBG3FxNosrNJTNaw1eNLgZSM8LOgU3V5CxzFJYxeD6hwnNxPGhWaUGY6tDXjskRlCR1HxHUrSMMw1LR/CYoTE0HOnUFJghMAXltRKlLCE403yUpOVscaREqzAVYJAWM73U6JiwPrSecajMbXWHNXmHDKIJo2iLnAdh0uDiaa04TsMs4LhoAebS+RpQnPHbtz6DN0kp1nyEQgKY0gLTSVwWSy7jLUlEgVSOmzGhlhbyp6kKlLCrEfhVXFHbURYJtVANsZvn6dYOoFc2E/3M5+kcng/Yv/1OKUa48Yu/vI9f877PvIxfvGVP8reg4eJkh6yyKDeIt5cI53ex6133YvwI6689kaqnkSRIUd9RFQldxx8U5DioFEMC0gyzbDQNHyH1BgEIJF87pN/xcc/+UkefMVBbr/3Pu69+y5e8hOvYHXpAsdOnGTQ7VAvhzzq8U/hkY/+Hvoby7TXVvDCiMrsLpyoRtl3qXiSeucUniex7TWkq8g3VzFugOu6xKePERy4Ar3rSmxYZSt3yO3EjaMhM2LpE+RDtmyEUrDRHbG8tk5/axMvHzG2DkO3SjjVZHlkudTPuGKuQj10iVzF8W9+mY/8+R/iRmVe97rXU57bw0zkMBXA2EicfMSpkcPnzmxx/FKPlY0haxfXuGaxwrMedz1ppvnyuQ6hK1laH/KNe1ZIh13y0Zjlk8cgG39brm0lBW95zs18+mN/wde+9jVe+cpX8spXvpJ6vf5tOf4222zzncd287vNfyja7TZP++EX8rm//StEeQ41dx0imgbAqdSRWOb37cJe+hayc5ZXvPYNTO0+SKvk08ss/ThjT2gwoy3yfociz5iqV2nOzLHDbuGsn0eEFZLFq+ngo9yAjVHOaP0if/zHf8I9d9/Frv0H+dxff5R3/fXnuHzvDqyxlGWGRVE1Q7QbkYtJQIRGgp38qbVmutgCA/pbn8ICsjZNPn+QUXmOVDic6RYMM83+qRBXSlwFZVdxaZixUBJoDY4wLI0FWWHY3/DwbEFsHUpkeHGbLGqSaDBW09E+QkjyeMjFU8fRaUxzYZGZ+R0kVhEqQ6JBCYEVEiksgc05OzA0Sz7tUYojBaEjSftttpbOIqTEm97J3OwsSimWBylJbtjTCJgruWyOM4QV1APFWBvuWOpx864q5ZW7EOUajAaYYQf8MjIIMIMOZAnW8cjO3MN4ZYP6DQ9A+j5WeWQHbsKxmq+eWuMNr3wp7/zgR8jGCZEd0XI08tStfDmbIikse695ALtlH2EFjknAWNL6/OT1SQdhCobWpTCWfgaFmThieMriKUWexjzjqT/A97729/jZAzH3mib/7cMfYt/iAtPzOzl86CC1xhSeNBTaTnTUwqKKhFtPLZNsXCKISlQW9tFq1EjHQxZCg7tyHBtE6I1LxN/6JvUnPAvbWUX5HrY2Q1FdQI8GFMYwCppMOQmbuUcx7HBxrU066JCMx7ilKrLcYEuEaCdAYhkXllGSM1P2uKxV4uKp4/zMj7+YQnm86tffwdzcHDcuVOgmOXFmmQ4FZ7oF96336Iw13STnb79ylnQck/S22LV3ATHskKOIIofNnmbtwkXSJCYdDv5ZIRT/K3bONviD1zyR3/y1X+ULX/gCL3/5y/npn/5ppqamvi3H32abbb5z2W5+t/kPycbGBk/74R/lC3/314iwiWwenLhDqInXp3JdmtN1iqVvUOQZNz31BdjaTqabZQ4t1NnVLLF/KmDayWlvbDDqbTEeDqlFPjNll9rMIro8BdJFpD3cUp1uasmTMfeePk8lDCjPLFANHc63x7TKLiXPZWcEUjkkRuAJTW4kvtIMc4fcGFq6i1ICtlaQoza2s0ayuUXwXc/ECMvJOAIpqHiSsidQFhxHME41NTsidqtgck73NBpYKHv4yjKKcxazZYwXIo1lEE0TFgM2qHHpwjnaq0tUWgtMzS+CcgkciSuZuDQYGKQai8V1FGWhubudcWAqJNEaVwjGucFaSLIUOdhgY3kJPwiYXdxL7lfINDRLLlVHsJ5oCmNpRRNN8Pu+cZ6fOlIg+huYhSPY4SY2TVHZAFWdQmuBdB2s45Ld/jlkGKGvfAROkWCMxdbmyY1hNXX5lTe/hrXVVX7mLW8lAR60b4GT93yLja0u+y+/msXpGv76CfTaBZwD18Ngk3T2EFq5OMZgxUS6kBrJWFu0gbLSxGsXeO/H/oaPf/yvef7LXkn98A18X7ROt7EXT49xPI9cBigMmZUEaKSABBdPGBJtOdPL8AXEG0tsrCxRm91JML2ThYqLyOPJ1DkeU+uco7T/Spy0j9AFjDrYeIAo14mN5EIeMehu0el0sFhsWKPSaLKcB+xvVRhkmvVhirAWg6WfFuydiih7irKr+It3v5P3/dkf8cgXvJpHPuoRhFIQKsHx1QG3H19lYa7B7UfPcfKzH8ad2sn8Fddy9ot/xdpXP0S48xoue9pPcvff/S1IhZACa8QkWfDbxN75Oj/x+EO8/73v4bbbbuNlL3sZr3rVq2i1Wt+2c2yzzTbf2Ww3v9v8h2Zzc5O3ve1t/MKv/S5kA0R936QRjloIIUBIbNKBznFMnlLddwMHH/EDPOSGg1TLPpfNlVmoRexv+BSjLoPhkM2NTQaDPlYowmqdvY2QaGYPiVBMOZq1RFCVMQke/RzOdmLacc618zXq3mSrv+ZKlNBoOXFbGOeQGYMuNM2SizIaP+/jXbybIjO4B69jKEPaxmOcWUoeTAeSbgot32CzEY41jLw6pazHug0RSmHynFw4DJKUy80qg9ouqskGW+E8Tj7i3qPHSfKc2X1HUH7E+V7KVTMRGomSUPUkeWGJ84n3b24lWZHTTjS76yHCGiTQSTV5oVnMNzlJg/lKwNqlC/TWlgiiEn5rkbmZ6UkKWzYilQFToUuqDTZPmR9fnMQwBzWsdBjlmqoeYI1GeBGiSDFOBMM1KHI26wew1uILS1UkJMqnkzk0Pc37PvCX/P473kGjXuPJT3gcu/bs44tf/Trv/8AHeOHzn8vXvvA5zp89w4++6EW88Nm3EPtVHOUi0yEOBajJ5FdLh29+6w7e/cd/wPJdd/J9z38JP/TDt7CZwmzkUDUjUhxcCozrY5SHk8cUwkUASlhGuGBhXBhyCyU5ida26ZClk8fwXJcDl19F4Dn0MxhmBZEDFV8RSiiSAePzx+kkBZsZxHFKpVplamqKcqWGG0VsZZAZuNhL2DdV5tOnNlnpxhw9uYVXVriuy7W7ajQihy9/9P0cu+1rXP2Dr+TsWsrOhTrd9oj21hbttS38/lFWT9zOuL1Gsn6a5o1PQo3WGIk644t3o/Y8EuFGYIpv63VqreX7rp9nRq7zvve9j7179/KCF7yAW265henp6W/rubbZZptttpvfbb4jsNZy2223ccuP/iQn7r4V3BDZPIhsHEC44eRrihTTPQuDi8xc+XBa138Prekprjkww+WLU1y2UGeu5HDrpR4KgU6HlIoRftYnHo0JSmUajQZRpc6OIEdUZ9lMDdJYzvcT5io+mRa045xGOFkqKjmS1FhGhaWsBKvjnMWqT2EhlIaqGYHjgnBYSRXGWjwp8F2JJ2F5mFP3JL04ZUc1YFBIZuSIvLuOLNcQ1pD6VVw7SeDKrYPUKUoY7rzjTtwgYnrPEc72UmqB4lQ75oE7qwSOojCTpaZRpkkKSyN0iAvojhNiDYs1n+Zolc9uOhyYb9BJC/ZGFq0CCgRGa4w2rF66wGhzmSAqM7drD1P3fYP4xsdgkbR8CzanFLex1mKlhKBCXIAjYEiI40lsYRkVBldJlDDExsGThsjElLMuWWmakZ4k6HkKjBZkww5fuu1OkvGIjcRw9N6jHFmc5bnPfDJv+8M/56orr+S7HvkoCqvxXBdx4lZcVyAqU1zsZ7z4Z17HZZdfzkt/6Cm4f/0Bbnviq3ncwRpZmhBKC36JjRRmdYcsqKKFS24KChSh+nsnC4sFEIJRxiTi2RpqrmUztVw6eYw0HnPgquux0iHXOemgz6DXYTzoMx728TyPcq1GqdqgVqszFTmQJ3jpEONH9FWNcQHdJMNRig/etcLZ1T5nVwZobZibLSEFdG/9GKdv/zr7nvQa7rvtdqb2HWJ6KuDk5z5Kcv42hFfmMU/5AfTMFXzxXb+LN7VA9/jXMOW9JOe+itr7aITjf3uvyzzmWQ+d4dYvforl5WWe9axn8YIXvIDrr79+cnO6zTbbbPMvwHbzu813HHEc85d/+Zf8yItfhR2tIao7kVMHEdWdCCEnVldbx7H9S/g7r6V12c3UmzUe+8gbmJmKWOonlAKXw7NVXCV50I4qwzhh2OuQD7uM+l10nlGqVKlU60xNNZgOJRJLKkMuxIpRVjBf8UiMJVSQGsGMm9POHFxHUXc0K7Gg7kNV5vS1x0asEQJakQIESWFJjWV9mJIbuHqmhERTSbc48dZf4fCLn4MpMuzUTkRQZiQjCkDFA44dO4oKQmb2XUFh4dOn23TGGTfsrLPaT2hVPIyxLFQCRplBC5iJPKQUtIcJvusQuVDtLfHR0wmPvfEgpzopi/WAqUDRSQyBsmiryLTGFjmDzRU6q0s4UtFY3I9Xb7K35iLSETLu4483KUpNbKkOVuIM1hiF0xRORC8rGOQw5TtUXYOwGtdqUiEhGeO5LkhFagUoDz8b8IVbv4WQDo+4fBeFV8IqjyDrYb2AD37ic5y+eIkXvfTHEcJScyVxv4efdLBC8eu//4e0x5pfevPrcLtLJN0BJ6ev5gq/TxFUMGmMLTUx1lLunier7WCrUEgBICh7ktxY0kLTMAMKv0yew8goAmWRwjLSgtAUHLv3TuIkxfNcRsMhrh+gogrVWgMnqnKyX4AVFFg2egkLZYdGCDYvaNZquJ7HyjDhhrkSp7opX73Qox5Ivn66g+9J7ji9SR4XoBPuescrmH7MqxFuxL7pMSc++edc+9incf3Dv5s7zrS5cOunOfOlvyIPF8nOfH5iy2YtzuJD/0E7/3+KtYaW6nN5c8wXPvcZHv7wh/P85z+fJz/5yYRh+G05xzbbbLPN/4rt5neb72jOnj3Lq372LXzowx8Ca5BTByaNcFDD6gyzdQrbv4iq7aR26MG0DlyGMgXN3buZa1a5en+LxxycLJDNlV1WBznr45TdJbDxiGTQZdjrUuQZlSikVilRlKfpEXBgpg5GU3Mt64lkjh5t1cBzBdP5JhdMAyMsrUBxvp+TFQVJbmmWfTwlsAaSwuAqMBamI48qMc5wg97tt1GpGPTGGs7iPuTcXtLWfjCwdt+tnN0Y8eDL9pBV58hkxH1bIwapYVc95GIvIbdwqRdz1WyZUVpgEZRch1qo2EoKdlZ9NkcFSmiyJCeRirQwzEQujlK4jsKVFldIXEfiK8CCMQXnLlyku3oJx3WZ3bmL6ekZCmOZkwOWizLTvsXL+1hjiN06m4XL+jChVvJo+grHGkJZTMzbdE6sBZHIsFIh4y5JMM3tt91KkuXcePUVTDkaihSsASHpRzOcu7DC6173szz56c+gP0p40XNvwVpBnBVcvLTMT7zo+fzFRz5O5Bry3CIdh16hOOCN6YkSX7/Y5WH7W1idUR63yUo1xsZHCDtJaFOKkQZlDDYf4vkRWMNmP2Ew7NPtdklHA9J4TBCG5FlGY7rFwo5FMrfE6a0RSsC5TsKn711jY6VHGHl045xqJSD0FLbQHNlTpxH6XDZf5rJmxDdXRpzrjrlsOuLe1QGX1kd85rZLFIMOK3/zn3ErLZyoxuDiCR746O/myu96PFGpznt/7c1sXrqACWegthdMgb74VdT89Qi/8m251mzS5bLGmI2zd+K6Ls973vN47nOfy969e78tx99mm222+d9lu/ndZhtAa82nP/1pnnTLS0ja5xFRc9IE1/eCdLCDZczancipA4jGPrxKA9cPuezBD+LRD9rPVNlnruyDELSTnJmSz3w1wBHgO4rdssdaP6XbHzAY9EnHQxzXwytVmG7UccMyThQhVEDgQiAKDIp2YgmVZHWcsjXK0BaOTAWMjcRzBFobmp5kI9VMhy4+GZHQxFlB6cIdmHN34iwewe6+ClNuIpbu5cunN9jnxOzcvZNiejcGh66q0I4NU4HiQj8hN5a7VodcM19mY5TjSUE7KZgr+8yVXDxHkhaGeuBwsZsyzAoCVzLODQLLjlpInBtCR1ALPTwFLhP5SWoEjjBsrS2zcukiRaGpz+7gyt1zrGmfKRFTMSOSqEU/hUFhcW1BagULFZduaim0JlQCD0MhACtBQkkn3HPsGO1unwcd3IFTn0F5AW7SxetcwswfZsNG9DLLPV//IusbG3z0Yx/nsiNHuOwBD6ZIYs6dv8if//av8oqf+hl+6Pk/RqYNzeD/au9OYyQ97vuOf6ue+3n6np5jZ2d2Z++DS3JJipQYyzIlkivJgS1LiGFBCmCLBhIkyJs4AQI4SILEsIPEiZIAiW0ZNmDHCWTEViwfsWTJ0uqktTwlcu9rdu6rp8+n+7mfJy96tSIlWaRO02J9gEZ3z6BnetBVg19X/+tfkmFa0NQSOqlguZ9wesZDa6+hBbv4tYMshxpHSpDpJqNUEsYR3W6fkd8l83v0/CEIgVuq4HklqrUapltBM03a25tsrNxi+sT9aALO7wz57NVtpJBcutXB7wfolsGhPWVMSyfLodUPMHSJLODIXA1XCC4vt8gNg6mKw4WlNlvLO4zClPTSR+gtnqd28s2YU8dpbfY49sB9DPsDNr/ySQZLF9D2PvRN54bh1khG3e9oXhVZghWscsDtce3KRd797nfzxBNP8La3vQ1N077j+aooivLdUOFXUb5Ou93mgx/8IL/8wV+HqI+oHUA2joAzAb1b5J2b41PlJo5hTx+gOT1NY/8Bpmeb7G26+GFKvWLzI4cmSPKcmZLDsYZJK8wxJAzCDF0UBKMBRTCEaEjo90mTGMt28cplvFKJRslBuFVSzWSjF9IaxcyXTfaZMSuZg6FBxTQ4v+NTMnTqrokfRuwpWwQZNERAZe0rFPvuYkCZkvCJbp7nCxsRjx+bRveqdJ1pvNxnLa/iGpIoK1gbhBjAtU7AdMkiTjIsXbAVZDQcg7IpaA1TZis2SZqSF3CkKtB0kxd3ArphzoGGQ5rmRFnOsYY9bq0GFIzbppka4+ONc9jY2mZzbYV0NKA0McXs3DwzrkFqOrRGKRKJbRSs+ykLZZNeUnCtNWDSs6jaOlVLI87AMyUXL1ygvb3B/sNHmJmcZHkERSE4UJK44Q7CqRBnOWEKnpHja2V+8V//O5JhD602he14NKemefvjj7IwMzkuVckLpvSYVLcJUygTEmJiG5J82EUTcKmTsrjV4ZCbMhiO8IdDsjTFcj1q1QoTJRu91MBzXGLNIEwzHF2jF2YkWYFGxo3nvsjsqQe50o5Z6Y14frGL5xp0hjFzdYdBmNEsmwzCFMfQaPVG3NgYEAxialWH3daQZORz8OQ8liG5eH6T1q3raLrB/W+5D0PmrO8mXP7Ux0iDIXZjlnDQJu+vk13/GPqp9yKEfMlM0IAM6TXIh+1XPX+KoqAYbnPQ67F+/cscPXqUJ554gve9732qTZmiKK8JKvwqyrfw/PPP8/f/4S9w8bknQXeQjSPIxiFIRmS7VyAJkBNH0adPYldq1GemcRqTTDVdjuxt0JgoM19zODldYrUXUrY0ru+MaJYtDCk50rQpckEhwCVjOPTR4hHxaIDvD4ijCN20CKWFbrm4rkul5BHqLkJKZsoWS90RhtRouAZL3YCapVMydVxDoCVDyl6ZTgxzVsxWz+fi+fO85f4TmKaFL8sIAb1UMGUL+olg04/pxRl5nlO1dXb8GIDNYUzN1hmlObt+xImpMuuDiKZrcnrKRZewE6bc3A2wDA1NCkqmzoGqxdYwwdAlFVOS5QVxJqhakOQF/TDjcjtgUk9pra8ghx2cUompmb2IygSWpqHrku1hgqtBmBVs9GNqjk7TNZh0dZIcdtYWWV5cpDG3wPF9MywONTxDI8lTpj0LmSXoRYyZJwgyRAGFYZONBuROhWFh0h4lGKaBEIKaJdE0iUxjvN4yUXmGQZSRDbu0gpRgfZXItBiOAqQQ4w2PrkHV83DqTUy3RF6AJQssARuxjiYKCgSygLjI2R6mFMCkZ3Lp3GdJZo7x9GaIFLA9iKnYGjt+wluONrF1DT9M2BnG/NX5TfpRhmtqCA16/YR+N2RhX5kjc3WevbrNrRdvMthYxGlMc/Lh06xeXyIOE7avnCePhwDk/hbZ9T+/M961hbciawvf0VwpkhETxRZOsEyv2+H9738/TzzxBPfdd993Ow0VRVG+p/S/6SegKK9l9913HxeeOksYhnz0ox/lff/gn5FuPoco7x2vBntTFJ3rxOf/kKQyz3D9EHp1mhVNcm1ykvljR2g2yizON9mNcyq2TsU1QECc5WgSrm8PmZ9wcHSNODe5d6pO0zTIKZjYPs8vfWqVn374IEGSMehsE20HZFlGoVv0LBthOrQLnbRawTQsWqOcTpBQtjR0zSJPBGUDEmFSWGXyPKeLh4NBhsQrYpqmJEsFaaFRtjUKCvJCIhAs1GyGScozq31qezwcTdLyE55NepiaoO4YrAwibF1HiPFxzLtRimfolC2NQZyx6Y+7Uaz2QgoEk55JkAh2whSRF3SClD1TJfzaPPcfPcrG+gary7fI0mtolSYzs3NIzWR1kDFKMuYqFpYu0KVgEBcYImV5cZFSc4aFvbOYZCT5uGWYXox7EA8KnbphkGUBehqRGC6mBGnbFHmMLVMqaYvIaBD02nQHAyLDIxiNGA56DONlBALXNig5DsOLlzn8Ez+BaRo0bUFqlTCiIYXlkBo2vVjQyPoIaZHJ8ZuB1jDGNsZdKa53QhxDY8I2iKOQosi51knYGSTESYpna2R5wck9FchgZxTw2WdvkekWrV6EaWtMNxw2exGlikkcZ6ys9AlHIdtrPlmeUZk7RNjrcOPFG2xdfAaEwCw1SLKMPPHJ21cR3jSyeWK84fN2H+xXqyhyzHCDw+UBly48w71vfStPPPErvOtd78K27e/TrFQURfnuqJVfRfk2LS0t8W/+7S/zu//7w5Cn43ZpjUMQDci7N6EokPUDiMo+jPIEpm3RmJ1n79EFOrsDDh6cwjUNCl1y/4EJLq71sXXB3fMNhMhZqLscbXjIIiYf9fmp//I5nvoXD7OmT+MnGQeTdZa0aZ65tYlIQqw8xshjrDyBNKJAkGoGuungei4TZY8KEVptkl4qWb1+hclmk4m9++mHCXULqqZGL9VwDUFRwKXWiP1Vm7QoqFoaozjh7K0exyY8bF1ydrHNMMnQBUyXbcq2hi4EeVawMQgRuk7N0giSnIWGw7Yfc7DmsDmMaDomVVtSIOiFKa4uOL8TUDE11v2Ie2fKXNvqU/FMmiIibG9huiWy6vTtkgnJTMkiKwo0TRDEGSVdstbpkUiLk1XBeiixTZ2mZzCMMwSCJAfPABn06IUZcZJSxCFZEjEMAmK/zyhJyfMCWxPYusCtTeAaGmXLoORaOG4JKSEXOn6rTaliU6QRRamJyBJk2CetzZELWOxlHHBSDJGznZgEmWClHxInOZqh4WgS25A0bY3exhKrO7t8rFNnf8OhM4wZRCmjOCMZhayv7DLCZGutRX3vJPXbZSVZWhAlKWmQEkYxnZU1gt1VDK9KPByQhCOkblAUOdmwC0A+alH0blGMdpHNk8jq/Lc9B4qwywGvR3flPJ7r8oEPfICf/dmfZf/+/d/j2aYoivK9p8KvonyH8jzn7Nmz/Nw/+uesXn8R4TSQE0ehNEsxWKXor4A0keU9iPpBnMYUSZTg1RvkFMwePc6ePXXSQmM0injb/fMUUvITh0qkheCjz23wlrv38B/+5Dy//dOHyMqT1CyNziiimwh2RxG32gE3WyPumq3gSEGjpLO206duQk1L6fR9pu0CwiFhlpPE8Z3n73glMs0iFTqubeI6NpnQmCo7hLmkUbLJNBPJ+GP7XpggdB0/zvj0YhsBfHmly12zVaa8cRu0TpSx3h5ypFlC1wWdIGWuarM5CFmoOaz2Q07PlPnK5oCSZXC44VA2NJ7a9BlGCe1hzKGGy2/93yd5/0+9iUf2V8kyje0gxjMkNXvc5i3NCpCgS4kfZfSCmNVBzF3TJbQiZW2Yc1fTpkCwunidYb9HGEWkcQwU6IaJblpohknZdajYGqVsRL+yD8t1mRJDUqkjNQt9sEnsTSE1iR12yIVG6tQhCbGCXfpmA90uYWTjjYIBJkLCxZ2AhaqFngVc7AlqjsGN3QFP3egyXXf4sYMNLu8MmXNSopXLXGOGp7ZibCDNC7aCjLWra7SXbzBz4hR5LknSnGrTY7LhsrHep1I1iaKMtWvr+Ntr+FtL48JqMtyp/WiyIApGxL0WRdAm2z6PkDpy8iRY1W+rl26Rxdw7nSF7N3nxxRd4z3vew8///M/zyCOPIKV85R+gKIryGqHKHhTlOySl5NFHH2Xl6vN0Oh0+9KEP8S9/6T+Rr34JUZpGlGcRTpMiGZG98HsMAP3en6MfDAC4trvDklvC9MqIQuLv7DB//AB/d0/OP/6vH+fIGx/iZ1zJtS+d4zeOTPLAEYf7Jl2udBOmHAM/StnxE3QJji4JswLQmJ+sEWUF62HKpzcj3nv/LHvKFhNSI89TRJ7SWV0k8AdUKw02/JBiNCIe+iRJRL/IyNKExTwHBPJ26zJN19F0nTCXTIc5uqFRrhVYaYo70kkL2KtLChFRTjP0QmMUxrywuUXdM7iwm5PlORsyQAYxk5aDHsZ0+xn1JMJMUnx/iGm5vOuBPZSiLp2gRNXWmHR1PEMnzXNSBKMswywEUZIyaeYMU8mDe8vEac5mkLG/atOPc6TUcMtVypUqmm7QMHO6WoUcSZrlSE2jYmq4hEgpGYUSUxMMRBkhJWkOkT6JVhiUhUCaFbqZhp4KksJkwqlzuSeZJaUsc7YiHT+OGMQZf3l1h3tnyqwPQqqWwa6p89xqj3NXtvBsk+cvb2G5kserXVaHGn90dYv25jZFBpXpSRA5SQq5VqIyUWbUD4iCmDiM6LYTll44j6UXGLU9dJauEI98KLI749Op1nnPTz7EJ//sL7j22V8dj9nmCbS5N72q8V0UBUWwy8kpSdMc8FdPPomwTvGBJ57gve99L/V6/Xs8oxRFUX4w1MqvonyPXb58mU984hP84i//N4atZZA6wpmgGKxBaQZ931sQpvfyBwkd3SkxcfAwf+fefTz74nV0JAcX9vH0i7c4fP9x3nB4kocOTTBTsVnuhjy9tEuSwZ6KxdGmx3JnxNHpKpOexlMrPZIc/ChlqmRyeraCLiWmFHjm7frTxSv4gz7u/DF6hUFe5MyWbGZKJmVtXFubpDmbgwhHpsgixygyBlHC7jDCkpCkCUmSkeY5osgxJQRJSpblJGmGqQlGcYoUIIUgywtcUyNOczxTQ9ckaQF25NPWSsRZgWPplCwDaTpsW5MsVB2aJZOKIRnEGVIK4rSgbApG6fiQECEEWQ7dIGaYwuGGzZqfMF/RSVLBME2pGpJqf4ldb4EL7RGagP1VhyDJ0ARoskCXGrthwoRrEibjQ0X8JMfTJXGW4+oSKSW6FPhxji5yvrDcY3/N5tqOD0iyomBrEPHMzV32T5Zo+xEbi1sEgz5JnuMnLrZjMdfQeP/xnOV+zu89FxP4Q0SR403WSPw+u4s30QyToN+hOjPLqLuL1B1Mx2a4u4lulQh217An5on9NnkcQfG1Y4eNUpk3veEwF8/+AZuXX0BW9yNq+yEJKJLh+FOKIqcY7VIMt8i3z0MWIWoLlMSILPIpioLHHnuMxx9/nMcff5zjx4//YCeToijK94EKv4ryfZQkCefOnePDH/59fu13/g+MWqCZIA2EXUXUDyKr+xCaOX6A1LFKZY6/4QG+8tnPIC0Hp9rEdMvYluD0G+7hn/z4Cf7XuWV6YUK/H+JYOpMlm0GScWjSJYkz2nGGpkn21h2GccbessXhSY/1QcSBusvBuoulwdXrNwha6xSNvazlZX5koY5nGqR5gSEFQZqhC0GYF1QsjbqRkeTjrhCaFDikpIxDbTcuuN4OOdhwuLIz4MXNAYcmPC5sDkizgrmaw1pnxEzVIchyTk6XmHBMhkmKs3iRp+x9zFdtXFOjamt0wph+kNEPEh5eqJMUMOOarHcG7G9WCZKcMB23/TKkYGeUYsoC09CJdlZo77aZbE7gVRukpkPJ1LBin83UohPlmAL8NGNzEDFdsshycExJEGdMlSwMKVgbROO/LUzwTA3X0DhQc2laBZc7MZ+4vElWCGxT4/Pnt6CAzO+QWR4bK12OnJwlilO+/PmvEA/a1Of3EXQGPHZ6ip885XH2yoA/+PwthOVhlcrkaUaRRrQXz0OegWZAlnyTkSXQKk2y/s74nm5SpOOSlry7RN5bgjREr0wxdd9jbDz7aQp/AwyPon3tm45V07JJk5gHHniAd77znZw5c4aHHnoIw/j2NsEpiqK81qnwqyg/QP1+n8985jP8+//8a3zp3F9BNADdHofhyl5k8wTSrmLYDkkYfMPjveYsb/7R+7i0sosUgkKvoGkFtucxN+2xeGOdOJXsOzqLa0qyHBZmKoyimLpnEmY5b1yos6dko0swdY181Gf31lVGucSeWeDQzCTbQUTd0smROLpGWc9ICw1TF2wMY2ZKNpYmcLMRkdCxNQgzyTNbITMlA01KLu74dIP09oqwoBVkIHOCKGcY55zcU2LKtdgYhDx5bYfZpsuRRgnP0uhGKYMwpWzp+FHCg3M1NCHYWr5B6ne498E34SeCS9t9qrbGMExplGwmbZ1cQM8fwahH6vcYDTpoUlKpNdC9CiuRwcHpGsu9iBzB7ihGCoEhoekarPdDZioO90zadKOC5V7IrW7I0WaJnVHE0YaLq+WcvdnjI0+vcmDK5dZKj61WQG9jlf7mLUzbJc0lBx96kN7mJp2NbWK/y93H9vD3TteolSw+9LHL3Ngekoz6ZKMeum0T5fadEFvkGcVwi8LfguEa8tCPf10f3m9UhD3SxU+hHX4nDNbIu4uIIqcwPITUyf3N8ZijQDNMijTi0KFDvP3tb+fxxx/nkUceoVKpfD+GvqIoymuGCr+K8jdoZWWFT33qU/zCv/pVOhs3IE9BtxBmGVHbj6gfQermyx4jDZM8zRBSolkemmHiNRrEow5Bd0TtwCmac02G7QGTC3tYaFj4Bazd2uHIsVkO7ylRd00sTXJ8usyzyx12hiGNcJdT7ojY8NjS6/hYHN9T4eLqgBPTHnMNl0GcsTtMWajbxFnGhK1Rs3S0ImPEuA55e5QwUzK51Bpia3J8/HIOK72QpmexNYjwo5QkLzg+XeLq9pC/eH6NI3srTFdt3nG0QStIWenGNDyDpmPiaDmDlasEYYjfOMhUtYRnapzfGuLHKbqA41MlFltDmhUbDYGlCcI0Z65qI8Ie3XabYNBHxCPQNBLDI7NKJLrLM5sRuqFzqOnQDVOOTZS4f8alm8LuKGGtH+LpGjd3fYoCWr2QWy2fnUHEytWbdNa2cWqT7Fx+Bqmb5FmM7pSpzy0QbFxjv9nmJ99yDycO7+ePP36WP/rzvyROBUZtljRJKII2xe4VhNtElPdSBG3IIorYh2RE/Q3vwU+r33Is5d1b5DsXkRPHyDafh3gAmnXnWGc0A72IqVWrnDkzLmN47LHHmJub+34OcUVRlNccFX4V5TUiz3POnz/Phz/8Yf7j//if5P7W7dBiIdwmsnkUUZp9hR36EmmXMR0Lb/ogkwt7Ge520XTJcDBk7/5JDh6YYrJqM1W2uL7pc225w6H9NYIgY75hMCf67NOGDDLJel5iizJVz2bCMRjGKY2Kja1BxTEp6xJDl+gCyrbBIEy40go4NuUx4ehsDlIGUUrN1gjTgglH42o7oO7oZHnBbNniyxs+F9Z7FAIe2lfnLfs8vrDqszWIMHQNMx7SHK6RGQ7X5RT7J8oEScZiy8fQdQZhzFTZpB8kPL3Y49FT0+z0A3rDBCEFb5ivsTWMCOKcUZxzfMZj1OuQj3zcPGBCixEUjIRFIC36hcVEvU6l5PLsUpeJqkPLD7l8Y4vWICdJQlav3CDPc7KiYPf6ZYj7kAbkQRtinyIe4lqSN7/pQX78nY8xUavwyWeX+PgLXdpbayANyL9WzlCkEXn7GsJwEVaFvLdCvvVltP0/hqjuR8hXPgo4vfInYDgUaQLpCPIETeRI4JFHfuzO6u7dd9/9bXV5UBRF+WGjwq+ivEZFUcSTTz7Jb/7W7/L7f/RnEOyOV/IMF1mZQ0wcR1qlv/bxmuXhNmdIw4gsT6jPHaS/sczMsZPM7W1y+nCDPzl7hUGrzeyRBYJBRKXuYsiC9vYOb1yo8MY5jUmnYCU06cgSG5FJzbMJs5xDUx4b3YD7FyZIkgzP1nF0wRdudtg/4XG4YbMzjNnoRZyaKZEzPpTiqeUud+0pU7MNqpbOtfaIlW6IbQge3FPBNiQXt4dc3vFxB1vMix5XojJXEg9ZwL3zdZ6/1WapNaTh6mxtjXBLJpoGy7e2OXpiju4gprPrU615lD0d0pzmhIehCdK8oO3HmJaGBriOhgwj7pk2KaIRbh4yoSdo5Oz0A3b9mLXdIdcX19nc7bO+skR7e50i8imS0ddCrNAxtZT77znFj555Fw8c28PK9oDPXdrl00/fJIqC8ZuZIn/F1z4frJOtfgn9+Lu/aVAtioKqlTHpCaYrEj3u0tvd5NbiTXZbO5w+fZp3vOMdnDlzhocffhjLsr7TYagoivJDR4VfRflbot1uc/bsWX71v/8m5774RUhG4w1RRY6270cR1X3fsiZUOGWKNMVwStiVKo6p097aJIsivMk9mJVJ3HKF9q1LRIGPbrmUGhM0tCFvfcMh7p93qVqwHZtc9zVamcWVZZ9/+p67+cyFTdKiYLpiUS457Kk4nN/ooUtBnOXE6fjwjqubfWIB983V2FezmfQcelGMo0v6cUbDkjQ8m61Wm83FK8RJxm9/ach6P6Y6WcXyHLQkYWN5E2F5hH4ff2eL+vwR/M1bDFobTB85SSEdwm6byvQkSZIwarcoNxrIdITw6hi2iyQmG/YpTTVprXc4sKdEpDmsLa+T+bsEZz/IgTf9FHsnq8w2POZmJ5lp1mlUSyRpxk6rxc7WJqNRgOPYNJtNZmZm6PkBX77R5ukrGyxt9QnjlOh254y/jiYFlqFhGhqWoWHFLUr+ZSq1JrX5uyi7JjXPoFmvMFGv0O92uHjxAl869zTLS4vMz89z5swZzpw5w1vf+lYajcb3YwgqiqL8UFDhV1H+llpcXOSTn/wkv/LBX2fp2oVxj1fNGvcYtuvjbhJWDazKq/rYXLo1TNslbK9/w/eEbmKVKpx+8H4O13JOTlssNDRGcUFqODy1GvHCtW2S0iRzsw0EsLK6xX33HMD3fRZbMXqWESGZn/FoVjzmKgZzEx4tP8ISkr1NjzCKmE1a9FtbXAldfuOPn6e9G+JUJ4mHLSYPH6d18wb+1hLCtMmCIWQxVm2WqHv7eWsGXnOOoL+LU20SDrpkwzbSqZIHPTBd3HKNyO+RRQGaY5OGIUJkGFISdVYp/G2KuI+cfzMMtynCDqQBuiY5uH+OBx56mBP7J1mYqWJbBt3+gMHAH7dq8ypUXBPb1LHNr7VSz/OCLB8H4Dv/dAuQUqBrX3vTkmY5YZzS6vS4tbjE0tIya+trbG/vEA57dHe3cF2XRx999E7d7oEDB77DUaQoivL6o8KvovwQyLKMK1eucPnyZS5dusQf/ukneOHFC+Rhb9wyyywh7NrtQFwFu4awqgj92/s4XHPKZFGE0DQs2+TIbI27D9Q5MF3h8EwJ29TZGGTc2vJppTY7fszSdsAQnd7GBtPHTmLIHFFkpKOA+UOzXDj3FQ7fd4qfubvEZN5hJGyW5QQf+dwi5z/5/wBBZd9JajOTtJaXCdpbmF6JqLP1yk9Y6uNNhN9EkWcQD0iv/hlIDVGZQ+g2GC7CqlE1E+aqBfsPnWD/vnkOzE2xb6ZOFAZcW2lxZSPg8nKbq6vtv3ZVV4hxRw3L1LANHSnFy74HkEYBo/4u4aBNMuyQh11E3KOIBjiOw4kTJzhx4gTHjx+/c33s2DE07ZXf0CiKoijfSIVfRfkhluc5a2trXLp0icuXL/ORP/0LvnjuObKgB2kAujMOwfZLArFdBcP7tjdFCQFTtRIH5xocmPLYN9tkb8Nmpu4SRCmbnYCdYc7Wbp9uCOsb29jVJocaBW87Pc/OsOCzmzovXNvGrda4+eXn7gRcqzqFbpkMt1cB0JwaWdB91c+tKApIA4qwQxF0kVGLiucwMbWXxuQ0k3sPMlUvMVX3mKq77KnbWKbJTmfA8laPlZ0htza63FjfZXN1iWzjWQjaGKc/8Op/fzKkCHsUURfC3u3b49ehVKlx96m7uOfuU5w8efJOyJ2bm1Ob0xRFUb7HVPhVlNepTqdzZ6X402c/z0c+9hnCwS7E/ngl1KrC7dIJYVcRdg3M8qsqoXgpU5dMNzz2NEpMN1z2TJQ4tKfGzISHY+p3wp0fJPhhSn8YMYwzRqOAIEoJ45Q4ycd1s1lGlhfkty9fJYRA0wS6lOiavFM/a5sadt7H1VLcUoVKtUal5FHyHAB6fZ/d7oDOMGW7n7DdHbHdHbHe6rO5tkLc36GIB+ONbeNfhNBsMD1kZR5hlV/2t351NbkIuy8PulEPioxGc5r77r2H0/fezV133cXx48c5fvy4OipYURTlB0iFX0VRXiYMQ65fv86lS5c4d+4cH/3457hx8waEvXGnAqv8kkBcBauK0J3x5jtpvCwcm7pksuYy2yxxZG+dY/MNjs43WN0ZcO7SOmefX6Y3jKh4FhXXpOxalF0T19JxLQPX1l+yEUxH1wSalOiaGB/yAYz/gxWkWUGa5WR5QZSkxElGGGeMooTRoIe/u8rQH9AbpQzCgl6vQxoMyAfrEPXQT73vFctAijwbd3fIEoo0gOj2Cm7YHQfceICm6Rw4cIh77znF3S9ZyT18+DC2bX9fXztFURTllanwqyjKq5LnOcvLy3dWi89+9knOPnkOv7MDacSdbVy3D1SolDxc18WybXTDIhc6cSbpBjlBIkAzENK4E5pfdl8zx/W6QnvVH/sXRTGu780iyGKKLB6flpbH45Xb25evruIKwwPTRcjbh4jkCUUW3wm34/vJy+5/tU2ZlJJKtcaxY8e45+5Td+pyT5w4wfz8PFJ+65PYFEVRlL85KvwqivJdK4qCMAzp9/sMBoM71y+9/dXrW7dWuLK4wsb2Lv1BnzAISOOILPlq8Ix5WZB+aUCWGgTd8fcN56VP4Ks3xreLfHzJ06/9LMaBWzdsvFIJx3UplUo0ajXm904zM9Wk0WhQLpepVCovu/76r9m2rWpxFUVR/pZS4VdRlNeUoiiIougVQ/RgMGA4HOJ53jcNqF//NcuyVGBVFEVRVPhVFEVRFEVRXj9UYZqiKIqiKIryuqHCr6IoiqIoivK6ocKvoiiKoiiK8rqhwq+iKIqiKIryuqHCr6IoiqIoivK6ocKvoiiKoiiK8rqhwq+iKIqiKIryuqHCr6IoiqIoivK68f8Bl6uwdrG0iuAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
+    "import numpy as np\n",
+    "\n",
+    "vmin, vmax = np.nanpercentile(val, [2, 98])\n",
+    "\n",
+    "# mask fill values\n",
+    "fill = ds.UVAerosolIndex.attrs[\"MissingValue\"]\n",
+    "uvai = ds.UVAerosolIndex.where(ds.UVAerosolIndex != fill)\n",
+    "\n",
+    "lat = ds.Latitude.values\n",
+    "lon = ds.Longitude.values\n",
+    "val = uvai.values\n",
+    "\n",
+    "# flatten for scatter\n",
+    "lat = lat.flatten()\n",
+    "lon = lon.flatten()\n",
+    "val = val.flatten()\n",
+    "\n",
+    "# projection center (mean location of valid pixels)\n",
+    "lat2d = ds.Latitude.values\n",
+    "lon2d = ds.Longitude.values\n",
+    "\n",
+    "iy = lat2d.shape[0] // 2\n",
+    "ix = lat2d.shape[1] // 2\n",
+    "\n",
+    "lat_m = float(lat2d[iy, ix])\n",
+    "lon_m = float(lon2d[iy, ix])\n",
+    "\n",
+    "proj = ccrs.Orthographic(central_longitude=lon_m, central_latitude=lat_m)\n",
+    "\n",
+    "fig = plt.figure(figsize=(8,8))\n",
+    "ax = plt.axes(projection=proj)\n",
+    "\n",
+    "# coastlines\n",
+    "ax.coastlines(linewidth=0.5)\n",
+    "\n",
+    "# gridlines\n",
+    "grid = ax.gridlines(draw_labels=False)\n",
+    "grid.xformatter = LONGITUDE_FORMATTER\n",
+    "grid.yformatter = LATITUDE_FORMATTER\n",
+    "\n",
+    "# plot swath\n",
+    "im = ax.scatter(\n",
+    "    lon,\n",
+    "    lat,\n",
+    "    c=val,\n",
+    "    s=1,\n",
+    "    cmap=\"RdBu_r\",\n",
+    "    vmin=vmin,\n",
+    "    vmax=vmax,\n",
+    "    transform=ccrs.PlateCarree()\n",
+    ")\n",
+    "\n",
+    "plt.colorbar(im, fraction=0.03, pad=0.02, label=\"UV Aerosol Index\")\n",
+    "\n",
+    "plt.title(\"DSCOVR EPIC UV Aerosol Index\")\n",
+    "plt.show()"
    ]
   },
   {

From ab244f766ecb1e451351762e562e9f075c71ea25 Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Tue, 17 Mar 2026 01:04:11 +0000
Subject: [PATCH 09/11] finish epic

---
 examples/docs_4_discovr_epic.ipynb | 5272 ++++++++++++----------------
 1 file changed, 2177 insertions(+), 3095 deletions(-)

diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_4_discovr_epic.ipynb
index 12416a0..03013aa 100644
--- a/examples/docs_4_discovr_epic.ipynb
+++ b/examples/docs_4_discovr_epic.ipynb
@@ -8,6 +8,8 @@
     "# DISCOVR EPIC L2\n",
     "<br>\n",
     "\n",
+    "**The matchups require xarray version > 2026.02**\n",
+    "\n",
     "The DSCOVR Earth Polychromatic Imaging Camera (EPIC) observes the sunlit Earth from the Sun–Earth L1 point and captures full-disk images of the planet in 10 UV–visible–near-IR spectral bands roughly every 1–2 hours.\n",
     "\n",
     "Level-2 products are geophysical retrievals derived from EPIC radiances. \n",
@@ -22,7 +24,9 @@
     "* DSCOVR_EPIC_L2_AER, Version 03, aerosols\n",
     "* DSCOVR_EPIC_L2_TO3 Version 03, total column ozone (TO₃)\n",
     "\n",
-    "*Note: In a virtual machine in AWS us-west-2, where NASA cloud data is, the point matchups are fast. In Colab, say, your compute is not in the same data region nor provider, and the same matchups might take 10x longer.*\n",
+    "These granules are big and the grid is not reqular. A KD tree is used for level 2 data and this can be slow.\n",
+    "\n",
+    "*Note: In a virtual machine in AWS us-west-2, where NASA cloud data is, the point matchups are faster. In Colab, say, your compute is not in the same data region nor provider, and the same matchups might take 10x longer.*\n",
     "\n",
     "## Prerequisites\n",
     "\n",
@@ -62,6 +66,36 @@
     "earthaccess.login()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "fff599ae-eb2f-4cf3-b579-70246c39d2af",
+   "metadata": {},
+   "source": [
+    "#### Check the xarray version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "36dcad28-dd96-4396-83ae-ba03694d8ff0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2026.2.0'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "xr.__version__"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cd008cf7-3bcb-4bf4-8535-7f0e142170bb",
@@ -186,7 +220,9 @@
    "id": "85f3095e-a159-4537-b267-3c2b4124341f",
    "metadata": {},
    "source": [
-    "### Create the plan"
+    "### Create the plan\n",
+    "\n",
+    "We look for granules within 1 hour of our points. There are 14 points that have matches and some have multiple matches (granules)."
    ]
   },
   {
@@ -199,8 +235,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.46 s, sys: 275 ms, total: 1.73 s\n",
-      "Wall time: 13.6 s\n"
+      "CPU times: user 1.55 s, sys: 136 ms, total: 1.68 s\n",
+      "Wall time: 7.36 s\n"
      ]
     }
    ],
@@ -215,13 +251,13 @@
     "        \"short_name\": short_name,\n",
     "        \"version\": \"03\"\n",
     "    },\n",
-    "    time_buffer=\"6h\"\n",
+    "    time_buffer=\"1h\"\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "7c827935-f077-4416-8d10-11858f71db7e",
    "metadata": {},
    "outputs": [
@@ -229,457 +265,40 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Plan: 31 points → 90 unique granule(s)\n",
-      "  Points with 0 matches : 9\n",
-      "  Points with >1 matches: 18\n",
-      "  Time buffer: 0 days 06:00:00\n",
-      "\n",
-      "First 2 point(s):\n",
-      "  [35] lat=9.1007, lon=11.2167, time=2024-07-21 00:00:00: 0 match(es)\n",
-      "  [43] lat=-67.2099, lon=-62.7344, time=2024-04-16 00:00:00: 0 match(es)\n"
-     ]
-    }
-   ],
-   "source": [
-    "plan.summary(n=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "91a014bb-ab2e-4800-8685-b2b911b55cb7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "import earthaccess\n",
-    "results = earthaccess.search_data(\n",
-    "    short_name=\"DSCOVR_EPIC_L2_AER\",\n",
-    "    temporal = (\"2024-01-01\", \"2024-12-31\"),\n",
-    "    version = \"03\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "e21299f0-46d0-4e49-9501-c48b672d366b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_12837/585827809.py:2: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
-      "  ds = xr.open_dataset(f[0], engine=\"h5netcdf\", chunks={}, group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\")\n"
+      "Plan: 31 points → 17 unique granule(s)\n",
+      "  Points with 0 matches : 20\n",
+      "  Points with >1 matches: 6\n",
+      "  Time buffer: 0 days 01:00:00\n"
      ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "823e6847191245ff99d46a089c044ef3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d8c2bdbb61e248f2ab692759f748c5d4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5107661e25a14e2799dbc08e394ab35c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "60fedfc76b594713a51271598449002c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "74982de8f71f4dbf93c506f405480b26",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "090e19ff9b5041b2bf510de9db9ca526",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "      shape=(2048, 2048), dtype=float32)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f = earthaccess.open(results[100:101], pqdm_kwargs={\"disable\": True})\n",
-    "ds = xr.open_dataset(f[0], engine=\"h5netcdf\", chunks={}, group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\")\n",
-    "ds.Latitude.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "4d1e0b9c-9d2e-49da-842a-861e287a1949",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1ac1e89b100a4d1ebe384852d009d949",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "56f4eccafdbb4ca6bc7ad60af622d32a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "44186b330f344a86a7a38734a34ff28f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[PosixPath('/home/jovyan/point-collocation/examples/data/2026-03-16-b2ebee/DSCOVR_EPIC_L2_AER_03_20230201001752_03.he5')]"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "earthaccess.download(results[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "c30e1de2-57ab-4298-b675-fd9be8c9d174",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import h5py\n",
-    "import numpy as np\n",
-    "import json\n",
-    "\n",
-    "paths = {\n",
-    "    \"lat\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields/Latitude\",\n",
-    "    \"lon\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields/Longitude\",\n",
-    "    \"uvai\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields/UVAerosolIndex\",\n",
-    "    \"aod\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields/FinalAerosolOpticalDepth\",\n",
-    "    \"wavelength\": \"/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields/Wavelength\",\n",
-    "}\n",
-    "\n",
-    "with h5py.File(f[0], \"r\") as h:\n",
-    "    lat = h[paths[\"lat\"]][500:700, 800:1000]\n",
-    "    lon = h[paths[\"lon\"]][500:700, 800:1000]\n",
-    "    uvai = h[paths[\"uvai\"]][500:700, 800:1000]\n",
-    "\n",
-    "np.savez(\n",
-    "    \"epic_aer_subset.npz\",\n",
-    "    lat=lat,\n",
-    "    lon=lon,\n",
-    "    uvai=uvai,\n",
-    ")"
+    "plan.summary(n=0)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "77857ef4-72f7-4f0a-a0c6-b8e6975480e4",
+   "cell_type": "markdown",
+   "id": "bde99fb2-5f41-4aee-a2d2-df20bc31cf23",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "import json\n",
-    "\n",
-    "sample = {\n",
-    "    \"lat\": lat[500:510, 800:810].tolist(),\n",
-    "    \"lon\": lon[500:510, 800:810].tolist(),\n",
-    "    \"uvai\": uvai[500:510, 800:810].tolist(),\n",
-    "}\n",
+    "### Look at the variables\n",
     "\n",
-    "with open(\"epic_debug_sample.json\", \"w\") as f:\n",
-    "    json.dump(sample, f, indent=2)"
+    "We will open a file with datatree and see what groups it has."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "db2d2f92-150d-4a43-9310-3a7cd52a234f",
+   "execution_count": 5,
+   "id": "71522438-3b18-4000-b413-03d0f5fe9b62",
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_12837/2386113382.py:1: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
-      "  dt = xr.open_datatree(f[0])\n"
+      "open_method: {'xarray_open': 'datatree', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n"
      ]
     },
-    {
-     "data": {
-      "text/plain": [
-       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "      shape=(2048, 2048), dtype=float32)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dt = xr.open_datatree(f[0])\n",
-    "dt[\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\"].Latitude.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "e5f1e060-0840-4c45-b416-b00af857c4f3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "short_name = \"DSCOVR_EPIC_L2_AER\"\n",
-    "version = \"03\"\n",
-    "\n",
-    "results = earthaccess.search_data(\n",
-    "    short_name=short_name,\n",
-    "    version=version,\n",
-    "    temporal=(\"2023-02-01T00:00:00\", \"2023-02-01T04:59:59\"),\n",
-    ")\n",
-    "len(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "54ed4aed-102a-44da-9a64-a6fdd62d4544",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cf56c7382e97477caa0e9d9d153ad4a4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d2f6c0e0102f4b4582b116b1a1c331b6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "76f3ba01ac334ae3873dd3d9c2a42fe0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_12837/1323344285.py:3: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
-      "  ds = xr.open_dataset(f[0], group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "import xarray as xr\n",
-    "f = earthaccess.open(results)\n",
-    "ds = xr.open_dataset(f[0], group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "83e8fa6c-2b40-4145-9e9c-3bb9660c1546",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_12837/1882213165.py:1: UserWarning: The 'phony_dims' kwarg now defaults to 'access'. Previously 'phony_dims=None' would raise an error. For full netcdf equivalence please use phony_dims='sort'.\n",
-      "  ds = xr.open_dataset(\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds = xr.open_dataset(\n",
-    "    f[0],\n",
-    "    group=\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\",\n",
-    "    mask_and_scale=False,\n",
-    "    decode_cf=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "f08e47db-1a6c-4de2-876d-a16614e270ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-1.2676506e+30 65.678856\n",
-      "[-1.2676506e+30]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import h5py\n",
-    "import numpy as np\n",
-    "\n",
-    "with h5py.File(f[0], \"r\") as h:\n",
-    "    lat = h[\"/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields/Latitude\"][:]\n",
-    "    print(np.nanmin(lat), np.nanmax(lat))\n",
-    "    print(np.unique(lat[:10, :10]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "0bb168a4-a130-40b7-9a22-3f4114591930",
-   "metadata": {},
-   "outputs": [
     {
      "data": {
       "text/html": [
@@ -1161,55 +780,157 @@
        "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
        "  stroke-width: 0.8px;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Latitude&#x27; ()&gt; Size: 1B\n",
-       "array(True)\n",
-       "Attributes:\n",
-       "    MissingValue:  -1.2676506e+30\n",
-       "    Title:         Geodetic Latitude (deg)\n",
-       "    Units:         deg\n",
-       "    _FillValue:    -1.2676506e+30</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;Latitude&#x27;</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b431aaa4-6f57-446d-a7ac-03db26765e98' class='xr-array-in' type='checkbox' checked><label for='section-b431aaa4-6f57-446d-a7ac-03db26765e98' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>True</span></div><div class='xr-array-data'><pre>array(True)</pre></div></div></li><li class='xr-section-item'><input id='section-058d4ef9-e9e2-45a2-9008-17d7f574bb8a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-058d4ef9-e9e2-45a2-9008-17d7f574bb8a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd><dt><span>_FillValue :</span></dt><dd>-1.2676506e+30</dd></dl></div></li></ul></div></div>"
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree&gt;\n",
+       "Group: /\n",
+       "├── Group: /HDFEOS\n",
+       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
+       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
+       "│   │           Attributes: (12/22)\n",
+       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
+       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
+       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
+       "│   │               AuthorAffiliation:   NASA/GSFC\n",
+       "│   │               AuthorName:          Omar Torres\n",
+       "│   │               EarthSunDistance:    0.9862366\n",
+       "│   │               ...                  ...\n",
+       "│   │               ProcessingCenter:    TLCF                                                ...\n",
+       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
+       "│   │               ProductionDateTime:  2024-02-11T01:29:46.0Z\n",
+       "│   │               ShortName:           EPICAERUV\n",
+       "│   │               Time_Band340nm:      2024-02-08 01:57:26\n",
+       "│   │               Time_Band388nm:      2024-02-08 01:56:59\n",
+       "│   └── Group: /HDFEOS/SWATHS\n",
+       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
+       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
+       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
+       "│           │                                            phony_dim_4: 2)\n",
+       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "│           │                                       phony_dim_3, phony_dim_4\n",
+       "│           │       Data variables: (12/29)\n",
+       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
+       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
+       "│           │           ...                              ...\n",
+       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
+       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;\n",
+       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
+       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
+       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
+       "│                   Data variables:\n",
+       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
+       "└── Group: /HDFEOS INFORMATION\n",
+       "        Dimensions:           ()\n",
+       "        Data variables:\n",
+       "            StructMetadata.0  |S32000 32kB ...\n",
+       "        Attributes:\n",
+       "            HDFEOSVersion:  HDFEOS_5.1.15</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a9be9e65-de23-4e51-84ff-6fbcacde34c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9be9e65-de23-4e51-84ff-6fbcacde34c8' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-32e84fd5-215e-4865-842a-f98bc28ba1b3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32e84fd5-215e-4865-842a-f98bc28ba1b3' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-35cc9a9d-0c04-47bc-8327-307e85b12ae3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35cc9a9d-0c04-47bc-8327-307e85b12ae3' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL/FILE_ATTRIBUTES</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-360c4a3d-7030-46cf-b7a2-1f2e564a3e0d' class='xr-section-summary-in' type='checkbox'  ><label for='section-360c4a3d-7030-46cf-b7a2-1f2e564a3e0d' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ACF Filename :</span></dt><dd>acf_epic_sm_ffxt_aug2019.txt</dd><dt><span>AIRSCO_filename1 :</span></dt><dd>AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G16130181606.hdf</dd><dt><span>AIRSCO_filename2 :</span></dt><dd>AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G16129174722.hdf</dd><dt><span>AuthorAffiliation :</span></dt><dd>NASA/GSFC</dd><dt><span>AuthorName :</span></dt><dd>Omar Torres</dd><dt><span>EarthSunDistance :</span></dt><dd>0.9862366</dd><dt><span>GranuleDay :</span></dt><dd>8</dd><dt><span>GranuleMonth :</span></dt><dd>2</dd><dt><span>GranuleYear :</span></dt><dd>2024</dd><dt><span>InputPointer :</span></dt><dd>epic_1b_20240208015633_03.h5                                                                        </dd><dt><span>InstrumentName :</span></dt><dd>EPIC</dd><dt><span>LocalGranuleID :</span></dt><dd>EPIC-DSCOVR_L2-EPICAERUV_2024m0208t015633_v01-00-2024m0211t012946.he5</dd><dt><span>LongName :</span></dt><dd>EPIC/DSCOVR Near UV Aerosol Index L2 Image 20x20km</dd><dt><span>PGEVersion :</span></dt><dd>0.5.0               </dd><dt><span>Platform :</span></dt><dd>DSCOVR</dd><dt><span>ProcessLevel :</span></dt><dd>L2</dd><dt><span>ProcessingCenter :</span></dt><dd>TLCF                                                                                                                                                                                                    </dd><dt><span>ProcessingHost :</span></dt><dd>Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP                                                                                                                                                    </dd><dt><span>ProductionDateTime :</span></dt><dd>2024-02-11T01:29:46.0Z</dd><dt><span>ShortName :</span></dt><dd>EPICAERUV</dd><dt><span>Time_Band340nm :</span></dt><dd>2024-02-08 01:57:26</dd><dt><span>Time_Band388nm :</span></dt><dd>2024-02-08 01:56:59</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7e27dcff-53f7-4d58-b7ab-d387b1e7ca26' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e27dcff-53f7-4d58-b7ab-d387b1e7ca26' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5d181b05-9b73-4839-93cc-6a35fee8b47a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d181b05-9b73-4839-93cc-6a35fee8b47a' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b7d16d39-1e9e-45b6-93bc-d559d0c06caa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b7d16d39-1e9e-45b6-93bc-d559d0c06caa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0765a943-c714-477e-b2bb-39d7890f7b0b' class='xr-section-summary-in' type='checkbox'  ><label for='section-0765a943-c714-477e-b2bb-39d7890f7b0b' class='xr-section-summary' >Data variables: <span>(29)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4cc86686-5af9-493c-916a-e07604ef01f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cc86686-5af9-493c-916a-e07604ef01f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c76d50eb-74b0-4aa1-addb-3f4530a74cda' class='xr-var-data-in' type='checkbox'><label for='data-c76d50eb-74b0-4aa1-addb-3f4530a74cda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-797980e0-b252-40e0-b5f4-9473eb9cdbcc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-797980e0-b252-40e0-b5f4-9473eb9cdbcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3c12cec-1375-4ab8-95f4-5c04dec76cda' class='xr-var-data-in' type='checkbox'><label for='data-d3c12cec-1375-4ab8-95f4-5c04dec76cda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-f9a9e0e3-6075-47e9-9757-4b7325c0b416' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9a9e0e3-6075-47e9-9757-4b7325c0b416' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08551179-a30a-4683-a23a-cfedccb5bec2' class='xr-var-data-in' type='checkbox'><label for='data-08551179-a30a-4683-a23a-cfedccb5bec2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c44b7cd6-5c6e-4ade-a3b0-b3c2d5633ea4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c44b7cd6-5c6e-4ade-a3b0-b3c2d5633ea4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebd6a649-b2b1-4f0c-9eda-8bd3c91f1f67' class='xr-var-data-in' type='checkbox'><label for='data-ebd6a649-b2b1-4f0c-9eda-8bd3c91f1f67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-0705606a-a6dd-4cb5-aef7-03624fbea3d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0705606a-a6dd-4cb5-aef7-03624fbea3d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b8964f4-7d14-494f-9713-333528da5ee0' class='xr-var-data-in' type='checkbox'><label for='data-3b8964f4-7d14-494f-9713-333528da5ee0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-ba6e6387-004c-4825-ba30-443edc60e848' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba6e6387-004c-4825-ba30-443edc60e848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9038b601-0f92-4fd2-a20f-fe4a62d9fa45' class='xr-var-data-in' type='checkbox'><label for='data-9038b601-0f92-4fd2-a20f-fe4a62d9fa45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-4cb13fd9-bb58-4cc9-93a2-a16dcf94526b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cb13fd9-bb58-4cc9-93a2-a16dcf94526b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adbee55f-3b92-4a73-9373-4da49ca0b16e' class='xr-var-data-in' type='checkbox'><label for='data-adbee55f-3b92-4a73-9373-4da49ca0b16e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-f06f4025-9b1f-4a6b-adb6-09af0630cdd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f06f4025-9b1f-4a6b-adb6-09af0630cdd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3b2ba8b-d990-44a3-bb06-803584b3483d' class='xr-var-data-in' type='checkbox'><label for='data-b3b2ba8b-d990-44a3-bb06-803584b3483d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-711f2f3d-2390-4ea0-9c64-7382737cf558' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-711f2f3d-2390-4ea0-9c64-7382737cf558' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f2642f9-5a5d-4693-94b1-3fb348a6cfe2' class='xr-var-data-in' type='checkbox'><label for='data-1f2642f9-5a5d-4693-94b1-3fb348a6cfe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 80.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"25\" y1=\"0\" x2=\"95\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"95\" y1=\"70\" x2=\"95\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c738c132-f32d-41e1-9706-4d698341f0b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c738c132-f32d-41e1-9706-4d698341f0b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4d3f150-2bf8-4052-9885-e9dc9d04849f' class='xr-var-data-in' type='checkbox'><label for='data-d4d3f150-2bf8-4052-9885-e9dc9d04849f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-374dfabe-d29e-46fa-a53f-b99b72f951d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-374dfabe-d29e-46fa-a53f-b99b72f951d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8147c33a-dc74-409d-bf0a-764cc0395fc0' class='xr-var-data-in' type='checkbox'><label for='data-8147c33a-dc74-409d-bf0a-764cc0395fc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-03044103-2e61-4f67-a933-1a161e555b26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03044103-2e61-4f67-a933-1a161e555b26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19c902c3-381b-4515-aa90-1fb96e12583b' class='xr-var-data-in' type='checkbox'><label for='data-19c902c3-381b-4515-aa90-1fb96e12583b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-0b9a81b2-3889-4ae4-9d79-c854795791a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b9a81b2-3889-4ae4-9d79-c854795791a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-514bdded-fd00-4a99-b100-8789dcd6af05' class='xr-var-data-in' type='checkbox'><label for='data-514bdded-fd00-4a99-b100-8789dcd6af05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9409e898-96f7-4b3d-8e42-e406eb645e3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9409e898-96f7-4b3d-8e42-e406eb645e3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bafa02c-06c8-4b23-9e54-c7a50f58be59' class='xr-var-data-in' type='checkbox'><label for='data-9bafa02c-06c8-4b23-9e54-c7a50f58be59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-b2b33a1f-2455-4e17-be90-6da92d904562' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2b33a1f-2455-4e17-be90-6da92d904562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff9917bb-c2a3-4d6a-8a0a-3a84de508f5d' class='xr-var-data-in' type='checkbox'><label for='data-ff9917bb-c2a3-4d6a-8a0a-3a84de508f5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-1fbaf775-de0c-473c-b2ab-a9e014adac2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fbaf775-de0c-473c-b2ab-a9e014adac2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adda6258-4c3b-4c2b-ad66-c3b90af4e370' class='xr-var-data-in' type='checkbox'><label for='data-adda6258-4c3b-4c2b-ad66-c3b90af4e370' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6ba1852b-7058-4bfc-b118-f1a63e85d1cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ba1852b-7058-4bfc-b118-f1a63e85d1cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebe242ab-23f8-4d41-b9b2-3a4bbb6ab679' class='xr-var-data-in' type='checkbox'><label for='data-ebe242ab-23f8-4d41-b9b2-3a4bbb6ab679' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d7ab7974-a905-4e11-ad34-920ba79d86f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7ab7974-a905-4e11-ad34-920ba79d86f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b88c7f38-3740-4870-af22-7d8fd4e43a3a' class='xr-var-data-in' type='checkbox'><label for='data-b88c7f38-3740-4870-af22-7d8fd4e43a3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-8b3c8aea-8fba-49ce-ae44-7a1ed1604700' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b3c8aea-8fba-49ce-ae44-7a1ed1604700' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8e60d82-13a3-403d-8d14-36605bfb7afb' class='xr-var-data-in' type='checkbox'><label for='data-a8e60d82-13a3-403d-8d14-36605bfb7afb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-242aa070-6f39-4e05-901c-b9f2672aa947' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-242aa070-6f39-4e05-901c-b9f2672aa947' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bef70550-e841-4c1d-b193-989ddc0f4631' class='xr-var-data-in' type='checkbox'><label for='data-bef70550-e841-4c1d-b193-989ddc0f4631' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-5793d4a5-cd4f-425f-b448-d5de17f90376' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5793d4a5-cd4f-425f-b448-d5de17f90376' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eedd690-9730-46f4-87f6-4191df7d2914' class='xr-var-data-in' type='checkbox'><label for='data-5eedd690-9730-46f4-87f6-4191df7d2914' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-b8f84df9-90b4-4914-9f69-aae7ae8bb899' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8f84df9-90b4-4914-9f69-aae7ae8bb899' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85aa7fad-883c-4110-afd3-5e4606138633' class='xr-var-data-in' type='checkbox'><label for='data-85aa7fad-883c-4110-afd3-5e4606138633' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-9f128971-6482-45fa-897d-e213b8bdc351' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f128971-6482-45fa-897d-e213b8bdc351' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8ca0455-f776-4760-b367-bdcc9057bd5b' class='xr-var-data-in' type='checkbox'><label for='data-e8ca0455-f776-4760-b367-bdcc9057bd5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-6fe0f4fc-fb89-4c56-98b6-b97aa02b344d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fe0f4fc-fb89-4c56-98b6-b97aa02b344d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-963dd245-2146-4af2-96a3-4180effdd8a3' class='xr-var-data-in' type='checkbox'><label for='data-963dd245-2146-4af2-96a3-4180effdd8a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-65724dab-c49f-4d67-9fb8-a2b85fb7747f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65724dab-c49f-4d67-9fb8-a2b85fb7747f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-988facf7-8ab0-4109-a524-bf1af4f13b91' class='xr-var-data-in' type='checkbox'><label for='data-988facf7-8ab0-4109-a524-bf1af4f13b91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-b551ece2-bddd-4ba2-8ab5-574e225438ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b551ece2-bddd-4ba2-8ab5-574e225438ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0136e835-5529-4073-a5ee-ffc32118d83e' class='xr-var-data-in' type='checkbox'><label for='data-0136e835-5529-4073-a5ee-ffc32118d83e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-ce41b3cb-72eb-4215-a5d0-9934ea9d8483' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce41b3cb-72eb-4215-a5d0-9934ea9d8483' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99a6fe0a-3636-4bfa-afdc-5e02b504711b' class='xr-var-data-in' type='checkbox'><label for='data-99a6fe0a-3636-4bfa-afdc-5e02b504711b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-bb8d1187-279e-41c5-a947-abc4b03aadf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb8d1187-279e-41c5-a947-abc4b03aadf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86fc240f-b523-48b3-9aef-836bc73a2237' class='xr-var-data-in' type='checkbox'><label for='data-86fc240f-b523-48b3-9aef-836bc73a2237' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-cf9a3774-0f15-4e0b-a14c-9146066317fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf9a3774-0f15-4e0b-a14c-9146066317fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18a58047-2db9-481c-94b5-256a25df9da9' class='xr-var-data-in' type='checkbox'><label for='data-18a58047-2db9-481c-94b5-256a25df9da9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12 B </td><td> 12 B </td></tr><tr><th> Shape </th><td> (3,) </td><td> (3,) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"93\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"43\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,43.88583450710766 0.0,43.88583450710766\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"63.885835\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"140.000000\" y=\"21.942917\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.942917)\">1</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-52a9bf51-ea4d-42ff-928e-25876878ce43' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-52a9bf51-ea4d-42ff-928e-25876878ce43' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_5</span>: 2048</li><li><span>phony_dim_6</span>: 2048</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8873f186-2698-482d-ba2d-f3a7db4a128d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8873f186-2698-482d-ba2d-f3a7db4a128d' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6da6326e-06a9-4db6-8914-a4158e62a1f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6da6326e-06a9-4db6-8914-a4158e62a1f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef411b2f-ee22-4194-a29b-9840bdd66fd6' class='xr-var-data-in' type='checkbox'><label for='data-ef411b2f-ee22-4194-a29b-9840bdd66fd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-200b499f-0c02-4e4c-8411-31e789a23fb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-200b499f-0c02-4e4c-8411-31e789a23fb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e73d084b-d8d8-4197-b7f1-d445539b92da' class='xr-var-data-in' type='checkbox'><label for='data-e73d084b-d8d8-4197-b7f1-d445539b92da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-ec626210-fc32-4a6e-9135-f71929a0d210' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec626210-fc32-4a6e-9135-f71929a0d210' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0647c43a-6f8c-417a-8c61-61ec866462ea' class='xr-var-data-in' type='checkbox'><label for='data-0647c43a-6f8c-417a-8c61-61ec866462ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-69ea8c33-d9e8-41be-b21a-08a233697b4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69ea8c33-d9e8-41be-b21a-08a233697b4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be403faf-8177-4817-8722-21ffcff90be9' class='xr-var-data-in' type='checkbox'><label for='data-be403faf-8177-4817-8722-21ffcff90be9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c6d8c0f2-813c-451d-a077-937054046531' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6d8c0f2-813c-451d-a077-937054046531' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-584fe9fa-6e10-44af-a769-a3393418e764' class='xr-var-data-in' type='checkbox'><label for='data-584fe9fa-6e10-44af-a769-a3393418e764' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-579785e2-ce75-4fd7-8525-6a3f82199d95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-579785e2-ce75-4fd7-8525-6a3f82199d95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a35006c-3a04-42b0-afe7-b772502fb1b9' class='xr-var-data-in' type='checkbox'><label for='data-2a35006c-3a04-42b0-afe7-b772502fb1b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-befdf61f-fc01-4840-b7c0-1195e8a6fa2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-befdf61f-fc01-4840-b7c0-1195e8a6fa2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c017ebbe-da56-4042-8a7d-6eaf5a626c72' class='xr-var-data-in' type='checkbox'><label for='data-c017ebbe-da56-4042-8a7d-6eaf5a626c72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS INFORMATION</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b48207a5-f45a-480d-b556-0e623c5d2990' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b48207a5-f45a-480d-b556-0e623c5d2990' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dc5e02b7-a9ac-4b42-b463-f99f51e2fcc1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc5e02b7-a9ac-4b42-b463-f99f51e2fcc1' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>StructMetadata.0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S32000</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-14c9ba34-9bb3-4bdd-a9b1-a18b3b4d8d7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14c9ba34-9bb3-4bdd-a9b1-a18b3b4d8d7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1fa7761-288f-4bcb-a5dc-ada9a43dec55' class='xr-var-data-in' type='checkbox'><label for='data-f1fa7761-288f-4bcb-a5dc-ada9a43dec55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S32000]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92a52bd6-d2b6-414e-a29d-519f11c16fd3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92a52bd6-d2b6-414e-a29d-519f11c16fd3' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>HDFEOSVersion :</span></dt><dd>HDFEOS_5.1.15</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'Latitude' ()> Size: 1B\n",
-       "array(True)\n",
-       "Attributes:\n",
-       "    MissingValue:  -1.2676506e+30\n",
-       "    Title:         Geodetic Latitude (deg)\n",
-       "    Units:         deg\n",
-       "    _FillValue:    -1.2676506e+30"
+       "<xarray.DataTree>\n",
+       "Group: /\n",
+       "├── Group: /HDFEOS\n",
+       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
+       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
+       "│   │           Attributes: (12/22)\n",
+       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
+       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
+       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
+       "│   │               AuthorAffiliation:   NASA/GSFC\n",
+       "│   │               AuthorName:          Omar Torres\n",
+       "│   │               EarthSunDistance:    0.9862366\n",
+       "│   │               ...                  ...\n",
+       "│   │               ProcessingCenter:    TLCF                                                ...\n",
+       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
+       "│   │               ProductionDateTime:  2024-02-11T01:29:46.0Z\n",
+       "│   │               ShortName:           EPICAERUV\n",
+       "│   │               Time_Band340nm:      2024-02-08 01:57:26\n",
+       "│   │               Time_Band388nm:      2024-02-08 01:56:59\n",
+       "│   └── Group: /HDFEOS/SWATHS\n",
+       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
+       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
+       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
+       "│           │                                            phony_dim_4: 2)\n",
+       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "│           │                                       phony_dim_3, phony_dim_4\n",
+       "│           │       Data variables: (12/29)\n",
+       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
+       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
+       "│           │           ...                              ...\n",
+       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
+       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>\n",
+       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
+       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
+       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
+       "│                   Data variables:\n",
+       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
+       "└── Group: /HDFEOS INFORMATION\n",
+       "        Dimensions:           ()\n",
+       "        Data variables:\n",
+       "            StructMetadata.0  |S32000 32kB ...\n",
+       "        Attributes:\n",
+       "            HDFEOSVersion:  HDFEOS_5.1.15"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "fill = ds.Latitude.attrs.get(\"_FillValue\")\n",
-    "(ds.Latitude != fill).any().compute()"
+    "ds = plan.open_dataset(1, open_method=\"datatree\")\n",
+    "ds"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bde99fb2-5f41-4aee-a2d2-df20bc31cf23",
+   "id": "64d26059-d6cc-4703-b9f6-805d2f081772",
    "metadata": {},
    "source": [
-    "### Look at the variables\n",
-    "\n",
-    "We will open a file with datatree and see what groups it has."
+    "The data are in `/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields` and lat/lon are in `/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields`. We create a profile. We open a dataset with the profile to make sure it looks good."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "71522438-3b18-4000-b413-03d0f5fe9b62",
+   "execution_count": 4,
+   "id": "bee93b40-db7f-4185-b07b-46a1ed843fd6",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "open_method: {'xarray_open': 'datatree', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n"
+      "open_method: {'xarray_open': 'dataset', 'merge': ['/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields', '/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields'], 'open_kwargs': {'phony_dims': 'access', 'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n",
+      "CPU times: user 803 ms, sys: 99.6 ms, total: 903 ms\n",
+      "Wall time: 4.35 s\n"
      ]
     },
     {
@@ -1310,6 +1031,7 @@
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -1320,8 +1042,11 @@
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-header > div,\n",
@@ -1332,20 +1057,15 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-obj-name,\n",
-       ".xr-group-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-group-name::before {\n",
-       "  content: \"📁\";\n",
-       "  padding-right: 0.3em;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name,\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
@@ -1360,28 +1080,39 @@
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
        "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
        "\n",
@@ -1389,11 +1120,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
        "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
        "}\n",
        "\n",
        ".xr-section-summary > span {\n",
        "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:disabled + label {\n",
@@ -1421,7 +1166,8 @@
        "}\n",
        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
        "  padding-top: 4px;\n",
        "}\n",
        "\n",
@@ -1430,20 +1176,29 @@
        "}\n",
        "\n",
        ".xr-section-details {\n",
-       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
        "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-section-summary-in:checked ~ .xr-section-details {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
        ".xr-group-box {\n",
        "  display: inline-grid;\n",
-       "  grid-template-columns: 0px 20px auto;\n",
-       "  width: 100%;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
        "}\n",
        "\n",
        ".xr-group-box-vline {\n",
@@ -1457,647 +1212,43 @@
        "  grid-column-start: 2;\n",
        "  grid-row-start: 1;\n",
        "  height: 1em;\n",
-       "  width: 20px;\n",
-       "  border-bottom: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-contents {\n",
-       "  grid-column-start: 3;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree&gt;\n",
-       "Group: /\n",
-       "├── Group: /HDFEOS\n",
-       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
-       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
-       "│   │           Attributes: (12/22)\n",
-       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
-       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
-       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
-       "│   │               AuthorAffiliation:   NASA/GSFC\n",
-       "│   │               AuthorName:          Omar Torres\n",
-       "│   │               EarthSunDistance:    0.9862366\n",
-       "│   │               ...                  ...\n",
-       "│   │               ProcessingCenter:    TLCF                                                ...\n",
-       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
-       "│   │               ProductionDateTime:  2024-02-11T01:29:46.0Z\n",
-       "│   │               ShortName:           EPICAERUV\n",
-       "│   │               Time_Band340nm:      2024-02-08 01:57:26\n",
-       "│   │               Time_Band388nm:      2024-02-08 01:56:59\n",
-       "│   └── Group: /HDFEOS/SWATHS\n",
-       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
-       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
-       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
-       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
-       "│           │                                            phony_dim_4: 2)\n",
-       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
-       "│           │                                       phony_dim_3, phony_dim_4\n",
-       "│           │       Data variables: (12/29)\n",
-       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
-       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
-       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;\n",
-       "│           │           ...                              ...\n",
-       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
-       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
-       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;\n",
-       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
-       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
-       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
-       "│                   Data variables:\n",
-       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "└── Group: /HDFEOS INFORMATION\n",
-       "        Dimensions:           ()\n",
-       "        Data variables:\n",
-       "            StructMetadata.0  |S32000 32kB ...\n",
-       "        Attributes:\n",
-       "            HDFEOSVersion:  HDFEOS_5.1.15</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-00f9a390-1252-4999-b704-0db1de96ee0f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00f9a390-1252-4999-b704-0db1de96ee0f' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5d594e45-0f7b-4db9-ab06-d5774f9e5b51' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d594e45-0f7b-4db9-ab06-d5774f9e5b51' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9f941c95-b34d-450d-8f5f-61048ae8fb06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9f941c95-b34d-450d-8f5f-61048ae8fb06' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/ADDITIONAL/FILE_ATTRIBUTES</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-49b0db5b-a90b-47f5-ad7d-d71a0d2ea308' class='xr-section-summary-in' type='checkbox'  ><label for='section-49b0db5b-a90b-47f5-ad7d-d71a0d2ea308' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ACF Filename :</span></dt><dd>acf_epic_sm_ffxt_aug2019.txt</dd><dt><span>AIRSCO_filename1 :</span></dt><dd>AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G16130181606.hdf</dd><dt><span>AIRSCO_filename2 :</span></dt><dd>AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G16129174722.hdf</dd><dt><span>AuthorAffiliation :</span></dt><dd>NASA/GSFC</dd><dt><span>AuthorName :</span></dt><dd>Omar Torres</dd><dt><span>EarthSunDistance :</span></dt><dd>0.9862366</dd><dt><span>GranuleDay :</span></dt><dd>8</dd><dt><span>GranuleMonth :</span></dt><dd>2</dd><dt><span>GranuleYear :</span></dt><dd>2024</dd><dt><span>InputPointer :</span></dt><dd>epic_1b_20240208015633_03.h5                                                                        </dd><dt><span>InstrumentName :</span></dt><dd>EPIC</dd><dt><span>LocalGranuleID :</span></dt><dd>EPIC-DSCOVR_L2-EPICAERUV_2024m0208t015633_v01-00-2024m0211t012946.he5</dd><dt><span>LongName :</span></dt><dd>EPIC/DSCOVR Near UV Aerosol Index L2 Image 20x20km</dd><dt><span>PGEVersion :</span></dt><dd>0.5.0               </dd><dt><span>Platform :</span></dt><dd>DSCOVR</dd><dt><span>ProcessLevel :</span></dt><dd>L2</dd><dt><span>ProcessingCenter :</span></dt><dd>TLCF                                                                                                                                                                                                    </dd><dt><span>ProcessingHost :</span></dt><dd>Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP                                                                                                                                                    </dd><dt><span>ProductionDateTime :</span></dt><dd>2024-02-11T01:29:46.0Z</dd><dt><span>ShortName :</span></dt><dd>EPICAERUV</dd><dt><span>Time_Band340nm :</span></dt><dd>2024-02-08 01:57:26</dd><dt><span>Time_Band388nm :</span></dt><dd>2024-02-08 01:56:59</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c36d42d7-91f4-4169-a034-b964e0d9bf06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c36d42d7-91f4-4169-a034-b964e0d9bf06' class='xr-section-summary' >Groups: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2a1035c6-c61c-48eb-b3d3-53de2b3ac606' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a1035c6-c61c-48eb-b3d3-53de2b3ac606' class='xr-section-summary' >Groups: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2b8e4c9e-bcba-4a0b-ad61-182e0948cc86' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b8e4c9e-bcba-4a0b-ad61-182e0948cc86' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2121d946-35b1-4545-a49f-364adef14a6f' class='xr-section-summary-in' type='checkbox'  ><label for='section-2121d946-35b1-4545-a49f-364adef14a6f' class='xr-section-summary' >Data variables: <span>(29)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-344cca44-6a0d-4bea-a611-50daeb85cd3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-344cca44-6a0d-4bea-a611-50daeb85cd3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4557071-5725-404b-8bb2-8ec529c25ec0' class='xr-var-data-in' type='checkbox'><label for='data-c4557071-5725-404b-8bb2-8ec529c25ec0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d8a74ca4-9564-4336-b782-3e5cde5ca1b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8a74ca4-9564-4336-b782-3e5cde5ca1b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cbca9be-fc2e-4302-9274-bfb91bf7ba53' class='xr-var-data-in' type='checkbox'><label for='data-6cbca9be-fc2e-4302-9274-bfb91bf7ba53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-cfc3c750-9083-4819-ac22-b76eaa8259c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfc3c750-9083-4819-ac22-b76eaa8259c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bac451e-ee00-4123-aa05-11bdfa725180' class='xr-var-data-in' type='checkbox'><label for='data-1bac451e-ee00-4123-aa05-11bdfa725180' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6bd91939-2319-4568-a0a4-735400c6b6e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bd91939-2319-4568-a0a4-735400c6b6e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-798892a5-f086-4329-b0fb-1fb10f7d40f4' class='xr-var-data-in' type='checkbox'><label for='data-798892a5-f086-4329-b0fb-1fb10f7d40f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-f9d57173-14ca-4273-8bc9-165ee3f96c7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9d57173-14ca-4273-8bc9-165ee3f96c7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd8168cb-9c43-4e8b-a1ef-35307b09ab4f' class='xr-var-data-in' type='checkbox'><label for='data-fd8168cb-9c43-4e8b-a1ef-35307b09ab4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-e075b246-287b-499f-984d-0b9ea33aa1e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e075b246-287b-499f-984d-0b9ea33aa1e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b67464b9-6846-40b3-b454-5193a634485f' class='xr-var-data-in' type='checkbox'><label for='data-b67464b9-6846-40b3-b454-5193a634485f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-f8914832-2462-4085-94e3-411a13e7b412' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8914832-2462-4085-94e3-411a13e7b412' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db36c802-0b6c-44fa-8fe8-d377122a653d' class='xr-var-data-in' type='checkbox'><label for='data-db36c802-0b6c-44fa-8fe8-d377122a653d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-29dd0408-9660-4bc1-a37b-cc0c48a34846' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29dd0408-9660-4bc1-a37b-cc0c48a34846' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89c4acc4-8318-4991-b424-59a62b0d2a18' class='xr-var-data-in' type='checkbox'><label for='data-89c4acc4-8318-4991-b424-59a62b0d2a18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-71425967-63a5-4286-abb1-cb87791389af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71425967-63a5-4286-abb1-cb87791389af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-780d9620-0b7d-40c9-97c3-65636b50ae4a' class='xr-var-data-in' type='checkbox'><label for='data-780d9620-0b7d-40c9-97c3-65636b50ae4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 80.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"25\" y1=\"0\" x2=\"95\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"95\" y1=\"70\" x2=\"95\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a9e969a6-9b07-4b97-9bc4-6470bdedf8e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9e969a6-9b07-4b97-9bc4-6470bdedf8e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a55ed79-6d35-403f-90dd-cd356efdba7b' class='xr-var-data-in' type='checkbox'><label for='data-5a55ed79-6d35-403f-90dd-cd356efdba7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-ce9c78d9-2467-4e17-8246-5383d2ca4e45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce9c78d9-2467-4e17-8246-5383d2ca4e45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b317472c-b7a3-4794-a73a-a520cfb021b5' class='xr-var-data-in' type='checkbox'><label for='data-b317472c-b7a3-4794-a73a-a520cfb021b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-fbb15d52-d9ad-402c-b012-710435798404' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbb15d52-d9ad-402c-b012-710435798404' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eabd9d33-93bc-4685-a18d-d8f7bffd1620' class='xr-var-data-in' type='checkbox'><label for='data-eabd9d33-93bc-4685-a18d-d8f7bffd1620' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-74c3b0b4-18b2-4f2c-be2d-20152db2f629' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74c3b0b4-18b2-4f2c-be2d-20152db2f629' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fd4ae29-a4af-4ea5-b6e1-c551ed022b6c' class='xr-var-data-in' type='checkbox'><label for='data-6fd4ae29-a4af-4ea5-b6e1-c551ed022b6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-f83ffcb7-cd0c-4817-a380-711a7f9d09d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f83ffcb7-cd0c-4817-a380-711a7f9d09d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3aa3db7-3c55-4730-a6a0-99b92872380a' class='xr-var-data-in' type='checkbox'><label for='data-f3aa3db7-3c55-4730-a6a0-99b92872380a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-81c3d50d-8c2d-431a-8bd3-f1978d019080' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81c3d50d-8c2d-431a-8bd3-f1978d019080' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96416707-2ec9-4f9f-af91-0a5d743d0adf' class='xr-var-data-in' type='checkbox'><label for='data-96416707-2ec9-4f9f-af91-0a5d743d0adf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-a21f73ae-8e06-429c-9aea-342c5ce7db68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a21f73ae-8e06-429c-9aea-342c5ce7db68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ee99fd4-1146-4114-8ff2-1cbd716ada17' class='xr-var-data-in' type='checkbox'><label for='data-9ee99fd4-1146-4114-8ff2-1cbd716ada17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 3.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4c6bd9fc-50d7-410f-84b4-d3b205eea6d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c6bd9fc-50d7-410f-84b4-d3b205eea6d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab80560d-fb32-4be8-9461-5867bd818bc8' class='xr-var-data-in' type='checkbox'><label for='data-ab80560d-fb32-4be8-9461-5867bd818bc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-df7dbaa5-2f99-477e-80a0-a61289505017' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df7dbaa5-2f99-477e-80a0-a61289505017' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-699494e9-ae8d-4f08-84fd-2f7d69530057' class='xr-var-data-in' type='checkbox'><label for='data-699494e9-ae8d-4f08-84fd-2f7d69530057' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-70699725-8353-4917-9b99-b5fb28317f73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70699725-8353-4917-9b99-b5fb28317f73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7187566e-e179-4be9-9d3b-a5a973663ebf' class='xr-var-data-in' type='checkbox'><label for='data-7187566e-e179-4be9-9d3b-a5a973663ebf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a34f4b57-3734-45f3-aa34-dda8d1f8ed52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a34f4b57-3734-45f3-aa34-dda8d1f8ed52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61ebc82e-e8f8-4349-9856-bd71f2ef555c' class='xr-var-data-in' type='checkbox'><label for='data-61ebc82e-e8f8-4349-9856-bd71f2ef555c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-f2c49263-40df-4899-8cc3-c4f3e6618e38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2c49263-40df-4899-8cc3-c4f3e6618e38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe267491-6411-49b5-bd79-04552fccbbad' class='xr-var-data-in' type='checkbox'><label for='data-fe267491-6411-49b5-bd79-04552fccbbad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 240.00 MiB </td><td> 15.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 5, 3) </td><td> (256, 1024, 5, 3) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"526\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" /><line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" /><line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" /><line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" /><line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" /><line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"190\" y1=\"25\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"225\" y2=\"60\" /><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 260.5882352941177,70.58823529411765 260.5882352941177,96.00085180870013 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"225\" y1=\"35\" x2=\"250\" y2=\"35\" /><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"190\" y1=\"0\" x2=\"260\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"215\" y1=\"0\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"190.0,0.0 215.41261651458248,0.0 286.00085180870013,70.58823529411765 260.5882352941177,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"260\" y1=\"70\" x2=\"286\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"260\" y1=\"96\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"260\" y1=\"70\" x2=\"260\" y2=\"96\" style=\"stroke-width:2\" /><line x1=\"286\" y1=\"70\" x2=\"286\" y2=\"96\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"260.5882352941177,70.58823529411765 286.0008518087002,70.58823529411765 286.0008518087002,96.00085180870013 260.5882352941177,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"273.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"306.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,306.000852,83.294544)\">5</text><text x=\"215.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,215.294118,80.706734)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-d2537db1-b5e4-45a2-becb-ee9733720412' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2537db1-b5e4-45a2-becb-ee9733720412' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba97d160-717c-44ae-820d-0e49d4f32850' class='xr-var-data-in' type='checkbox'><label for='data-ba97d160-717c-44ae-820d-0e49d4f32850' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 3) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 32 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"26\" y1=\"0\" x2=\"97\" y2=\"70\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"97\" y1=\"70\" x2=\"97\" y2=\"190\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-213d1cab-3330-41ba-af9a-eede329f10a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-213d1cab-3330-41ba-af9a-eede329f10a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-174702d0-5dc0-4213-966e-fc4bf9a2f9c6' class='xr-var-data-in' type='checkbox'><label for='data-174702d0-5dc0-4213-966e-fc4bf9a2f9c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-0afdc9d4-672a-4ee0-86f7-aec8ecfbf954' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0afdc9d4-672a-4ee0-86f7-aec8ecfbf954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca12d348-c198-49b6-a901-70d3ca01b7e9' class='xr-var-data-in' type='checkbox'><label for='data-ca12d348-c198-49b6-a901-70d3ca01b7e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-01fad95b-2887-47f4-ad78-69b6aea9841e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01fad95b-2887-47f4-ad78-69b6aea9841e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80f935f9-e0b5-4ed6-8366-1b0fff129f85' class='xr-var-data-in' type='checkbox'><label for='data-80f935f9-e0b5-4ed6-8366-1b0fff129f85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-f864b073-a891-46f3-8001-393c8721129e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f864b073-a891-46f3-8001-393c8721129e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a07811f8-ce77-492b-9038-5da4e3976fc4' class='xr-var-data-in' type='checkbox'><label for='data-a07811f8-ce77-492b-9038-5da4e3976fc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 32.00 MiB </td><td> 2.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048, 2) </td><td> (256, 1024, 2) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" /><line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" /><line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" /><line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" /><line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" /><line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" /><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" /><line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" /><line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" /><line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" /><line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" /><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" /><line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" /><line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" /><line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text><text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text><text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b45c7fc3-9218-47c4-8b8e-62a559379b91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b45c7fc3-9218-47c4-8b8e-62a559379b91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c85d8ebb-9464-41ac-ad83-c04bd8214acf' class='xr-var-data-in' type='checkbox'><label for='data-c85d8ebb-9464-41ac-ad83-c04bd8214acf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d1f8faaf-7b63-4331-98be-b9f105023328' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1f8faaf-7b63-4331-98be-b9f105023328' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ba9bb8-e5ac-41d5-8bf6-15bfd1cac23d' class='xr-var-data-in' type='checkbox'><label for='data-57ba9bb8-e5ac-41d5-8bf6-15bfd1cac23d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-d7e5b143-cca3-41e1-b2b0-11a44ae37fdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7e5b143-cca3-41e1-b2b0-11a44ae37fdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e8eca97-29b3-4e69-bdf5-80cc99949c53' class='xr-var-data-in' type='checkbox'><label for='data-3e8eca97-29b3-4e69-bdf5-80cc99949c53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12 B </td><td> 12 B </td></tr><tr><th> Shape </th><td> (3,) </td><td> (3,) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"93\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"43\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,43.88583450710766 0.0,43.88583450710766\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"63.885835\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text><text x=\"140.000000\" y=\"21.942917\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.942917)\">1</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e5f50b63-2288-49f7-81c9-86a94143ae8a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5f50b63-2288-49f7-81c9-86a94143ae8a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_5</span>: 2048</li><li><span>phony_dim_6</span>: 2048</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6aa7d461-0a3c-4b80-813f-27a5c6371ace' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6aa7d461-0a3c-4b80-813f-27a5c6371ace' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-6548e3ec-c146-4fd5-ba94-b545f3f3c960' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6548e3ec-c146-4fd5-ba94-b545f3f3c960' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1efb1f1-5145-4e55-b92c-519f7e3ca878' class='xr-var-data-in' type='checkbox'><label for='data-e1efb1f1-5145-4e55-b92c-519f7e3ca878' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-bf67c52a-91ab-4586-ab56-62a2cb1e41e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf67c52a-91ab-4586-ab56-62a2cb1e41e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99d8ce32-edab-4329-83cf-24af83168af6' class='xr-var-data-in' type='checkbox'><label for='data-99d8ce32-edab-4329-83cf-24af83168af6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-8b2adb00-0bdc-47f4-9336-1f7ee1c5c415' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b2adb00-0bdc-47f4-9336-1f7ee1c5c415' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b23f65f4-5455-40d1-a3d0-163f0092764b' class='xr-var-data-in' type='checkbox'><label for='data-b23f65f4-5455-40d1-a3d0-163f0092764b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b1df4134-c2a6-49e4-a92e-19f56a68edfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1df4134-c2a6-49e4-a92e-19f56a68edfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f93112bb-5801-4368-884a-6fd9c7632eb6' class='xr-var-data-in' type='checkbox'><label for='data-f93112bb-5801-4368-884a-6fd9c7632eb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-0d199d71-bed0-4ca1-8f92-73b1d89169e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d199d71-bed0-4ca1-8f92-73b1d89169e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fdfaaa6-ffeb-449d-86af-16f035e75df2' class='xr-var-data-in' type='checkbox'><label for='data-3fdfaaa6-ffeb-449d-86af-16f035e75df2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-92cf6635-3a2f-4605-b74e-2cf405ab6784' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92cf6635-3a2f-4605-b74e-2cf405ab6784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06011614-9c33-4f1a-a80a-b08bece7d2e4' class='xr-var-data-in' type='checkbox'><label for='data-06011614-9c33-4f1a-a80a-b08bece7d2e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_5, phony_dim_6)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-85007e5d-7cb4-46c0-9405-ea00ff0f9cfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85007e5d-7cb4-46c0-9405-ea00ff0f9cfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b90c212-85be-472e-b8f3-91b9a8d4f0c6' class='xr-var-data-in' type='checkbox'><label for='data-4b90c212-85be-472e-b8f3-91b9a8d4f0c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 16.00 MiB </td><td> 1.00 MiB </td></tr><tr><th> Shape </th><td> (2048, 2048) </td><td> (256, 1024) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 16 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" /><line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" /><line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" /><line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" /><line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" /><line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" /><line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" /><line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div></div></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/HDFEOS INFORMATION</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3fb7197a-c704-4df1-9c07-139fdc8779c3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3fb7197a-c704-4df1-9c07-139fdc8779c3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1afe8b2b-da21-4c28-bdf6-670322e97aea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1afe8b2b-da21-4c28-bdf6-670322e97aea' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>StructMetadata.0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S32000</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31d7ee7a-d7f4-477d-96cf-3486a33c7c13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31d7ee7a-d7f4-477d-96cf-3486a33c7c13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf89095b-b7aa-4519-aa31-3aff7abc70b3' class='xr-var-data-in' type='checkbox'><label for='data-bf89095b-b7aa-4519-aa31-3aff7abc70b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S32000]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-51591833-277e-4892-afa1-0904b05aa21f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51591833-277e-4892-afa1-0904b05aa21f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>HDFEOSVersion :</span></dt><dd>HDFEOS_5.1.15</dd></dl></div></li></ul></div></div></div></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataTree>\n",
-       "Group: /\n",
-       "├── Group: /HDFEOS\n",
-       "│   ├── Group: /HDFEOS/ADDITIONAL\n",
-       "│   │   └── Group: /HDFEOS/ADDITIONAL/FILE_ATTRIBUTES\n",
-       "│   │           Attributes: (12/22)\n",
-       "│   │               ACF Filename:        acf_epic_sm_ffxt_aug2019.txt\n",
-       "│   │               AIRSCO_filename1:    AIRS.2016.05.08.L3.RetStd_IR001.v6.0.31.0.G161301816...\n",
-       "│   │               AIRSCO_filename2:    AIRS.2016.05.07.L3.RetStd_IR001.v6.0.31.0.G161291747...\n",
-       "│   │               AuthorAffiliation:   NASA/GSFC\n",
-       "│   │               AuthorName:          Omar Torres\n",
-       "│   │               EarthSunDistance:    0.9862366\n",
-       "│   │               ...                  ...\n",
-       "│   │               ProcessingCenter:    TLCF                                                ...\n",
-       "│   │               ProcessingHost:      Linux ominate.gsfc.nasa.gov 2.6.18-410.el5PAE #1 SMP...\n",
-       "│   │               ProductionDateTime:  2024-02-11T01:29:46.0Z\n",
-       "│   │               ShortName:           EPICAERUV\n",
-       "│   │               Time_Band340nm:      2024-02-08 01:57:26\n",
-       "│   │               Time_Band388nm:      2024-02-08 01:56:59\n",
-       "│   └── Group: /HDFEOS/SWATHS\n",
-       "│       └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath\n",
-       "│           ├── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields\n",
-       "│           │       Dimensions:                         (phony_dim_0: 2048, phony_dim_1: 2048,\n",
-       "│           │                                            phony_dim_2: 5, phony_dim_3: 3,\n",
-       "│           │                                            phony_dim_4: 2)\n",
-       "│           │       Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
-       "│           │                                       phony_dim_3, phony_dim_4\n",
-       "│           │       Data variables: (12/29)\n",
-       "│           │           AIRSCO_Flags                    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           AIRSL3COvalue                   (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           AerosolAbsOpticalDepthVsHeight  (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
-       "│           │           AerosolCorrCloudOpticalDepth    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           AerosolOpticalDepthOverCloud    (phony_dim_0, phony_dim_1, phony_dim_3) float32 50MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
-       "│           │           AerosolOpticalDepthVsHeight     (phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3) float32 252MB dask.array<chunksize=(256, 1024, 5, 3), meta=np.ndarray>\n",
-       "│           │           ...                              ...\n",
-       "│           │           Reflectivity                    (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
-       "│           │           Residue                         (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array<chunksize=(256, 1024, 2), meta=np.ndarray>\n",
-       "│           │           SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│           │           Wavelength                      (phony_dim_3) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>\n",
-       "│           └── Group: /HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields\n",
-       "│                   Dimensions:               (phony_dim_5: 2048, phony_dim_6: 2048)\n",
-       "│                   Dimensions without coordinates: phony_dim_5, phony_dim_6\n",
-       "│                   Data variables:\n",
-       "│                       Latitude              (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       Longitude             (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       RelativeAzimuthAngle  (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       SnowIce_fraction      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       SolarZenithAngle      (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       TerrainPressure       (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "│                       ViewingZenithAngle    (phony_dim_5, phony_dim_6) float32 17MB dask.array<chunksize=(256, 1024), meta=np.ndarray>\n",
-       "└── Group: /HDFEOS INFORMATION\n",
-       "        Dimensions:           ()\n",
-       "        Data variables:\n",
-       "            StructMetadata.0  |S32000 32kB ...\n",
-       "        Attributes:\n",
-       "            HDFEOSVersion:  HDFEOS_5.1.15"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds = plan.open_dataset(1, open_method=\"datatree\")\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64d26059-d6cc-4703-b9f6-805d2f081772",
-   "metadata": {},
-   "source": [
-    " It does have groups; the latitude, longitude is at the base level: `/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields` and the product is in `/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields`. We want `UVAerosolIndex`. We create a profile. We open a dataset with the profile to make sure it looks good."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "bee93b40-db7f-4185-b07b-46a1ed843fd6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "open_method: {'xarray_open': 'dataset', 'merge': ['/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields', '/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields'], 'open_kwargs': {'phony_dims': 'access', 'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n",
-      "CPU times: user 335 ms, sys: 592 μs, total: 336 ms\n",
-      "Wall time: 3.89 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, white)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       "html[theme=\"dark\"],\n",
-       "html[data-theme=\"dark\"],\n",
-       "body[data-theme=\"dark\"],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "  line-height: 1.6;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-obj-name,\n",
-       ".xr-group-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name::before {\n",
-       "  content: \"📁\";\n",
-       "  padding-right: 0.3em;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name,\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "  margin-block-start: 0;\n",
-       "  margin-block-end: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-top: 4px;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box {\n",
-       "  display: inline-grid;\n",
-       "  grid-template-columns: 0px 20px auto;\n",
-       "  width: 100%;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-vline {\n",
-       "  grid-column-start: 1;\n",
-       "  border-right: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "  width: 0px;\n",
-       "}\n",
-       "\n",
-       ".xr-group-box-hline {\n",
-       "  grid-column-start: 2;\n",
-       "  grid-row-start: 1;\n",
-       "  height: 1em;\n",
-       "  width: 20px;\n",
+       "  width: 26px;\n",
        "  border-bottom: 0.2em solid;\n",
        "  border-color: var(--xr-border-color);\n",
        "}\n",
        "\n",
        ".xr-group-box-contents {\n",
        "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
        "}\n",
        "\n",
        ".xr-array-wrap {\n",
@@ -2349,7 +1500,7 @@
        "    SurfaceAlbedo                   (phony_dim_0, phony_dim_1, phony_dim_4) float32 34MB dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;\n",
        "    SurfaceType                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
        "    UVAerosolIndex                  (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;\n",
-       "    Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ad388002-f32e-4f68-83a6-b63936feef12' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ad388002-f32e-4f68-83a6-b63936feef12' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5724ae2b-5327-4c3a-ab4d-0f9deeb57fb0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5724ae2b-5327-4c3a-ab4d-0f9deeb57fb0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-07a0cbda-251d-42e9-8de9-3ba9b8194162' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07a0cbda-251d-42e9-8de9-3ba9b8194162' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dbe1cee-dfff-4220-b2cc-eeb415d5857b' class='xr-var-data-in' type='checkbox'><label for='data-7dbe1cee-dfff-4220-b2cc-eeb415d5857b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    Wavelength                      (phony_dim_3) float32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5fd4e399-1a0b-4ac3-a4ef-eb513584dd76' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-5fd4e399-1a0b-4ac3-a4ef-eb513584dd76' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 5</li><li><span>phony_dim_3</span>: 3</li><li><span>phony_dim_4</span>: 2</li></ul></div></li><li class='xr-section-item'><input id='section-5bfaac28-37ad-48ff-ae1b-3518c61a9190' class='xr-section-summary-in' type='checkbox' checked /><label for='section-5bfaac28-37ad-48ff-ae1b-3518c61a9190' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b06ad325-2f45-4e8a-b00f-a0eed83cdaab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b06ad325-2f45-4e8a-b00f-a0eed83cdaab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20de7968-5755-4729-b416-5367bddbda34' class='xr-var-data-in' type='checkbox'><label for='data-20de7968-5755-4729-b416-5367bddbda34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Latitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2412,7 +1563,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-f215e591-059b-49d6-900a-07f044e0cae7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f215e591-059b-49d6-900a-07f044e0cae7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ada8af48-25e4-4d97-bf96-d282907442d0' class='xr-var-data-in' type='checkbox'><label for='data-ada8af48-25e4-4d97-bf96-d282907442d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b619d6a3-bd2b-4da9-b8b4-4fb1425f67b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b619d6a3-bd2b-4da9-b8b4-4fb1425f67b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ab98bfe-0ff9-4245-b7eb-6f27a7ba9a67' class='xr-var-data-in' type='checkbox'><label for='data-4ab98bfe-0ff9-4245-b7eb-6f27a7ba9a67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Geodetic Longitude (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2475,7 +1626,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8df3e6e9-2f68-47e1-864c-bc2353c706ad' class='xr-section-summary-in' type='checkbox'  ><label for='section-8df3e6e9-2f68-47e1-864c-bc2353c706ad' class='xr-section-summary' >Data variables: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-be95afe6-8526-46b2-99e3-5252b327ed37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be95afe6-8526-46b2-99e3-5252b327ed37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dd34263-31f3-48d4-b431-0193d405b4e6' class='xr-var-data-in' type='checkbox'><label for='data-8dd34263-31f3-48d4-b431-0193d405b4e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4b7d185-342d-4d26-98cf-dde8b382c2a0' class='xr-section-summary-in' type='checkbox' /><label for='section-f4b7d185-342d-4d26-98cf-dde8b382c2a0' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RelativeAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-32573132-0636-472c-9aee-d1032619fbca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32573132-0636-472c-9aee-d1032619fbca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be876936-3ffa-443c-a54a-84207b8a578a' class='xr-var-data-in' type='checkbox'><label for='data-be876936-3ffa-443c-a54a-84207b8a578a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Relative (sun + 180 - view) Azimuth Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg(EastofNorth)</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2538,7 +1689,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-5f9eda47-3c8e-4604-a026-37ee5c14b1e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f9eda47-3c8e-4604-a026-37ee5c14b1e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58673807-c923-4fcb-8f38-f8b113ea4942' class='xr-var-data-in' type='checkbox'><label for='data-58673807-c923-4fcb-8f38-f8b113ea4942' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SnowIce_fraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-414b01ff-f0f3-4ea7-b6c3-164c93ffdbe3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-414b01ff-f0f3-4ea7-b6c3-164c93ffdbe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaf94154-2b57-4adf-87e9-adb8375b142b' class='xr-var-data-in' type='checkbox'><label for='data-aaf94154-2b57-4adf-87e9-adb8375b142b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>SnowIce_fraction)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2601,7 +1752,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-bec00f0b-cbd3-413b-9f35-fafea78bb709' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bec00f0b-cbd3-413b-9f35-fafea78bb709' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c72ad49-85fc-4d6b-9f55-6c26cab91f2d' class='xr-var-data-in' type='checkbox'><label for='data-4c72ad49-85fc-4d6b-9f55-6c26cab91f2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-88f14a03-6d0d-4642-aafb-645ae42dea7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88f14a03-6d0d-4642-aafb-645ae42dea7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61e214f9-6db2-45d5-917e-d4f74cf77d3c' class='xr-var-data-in' type='checkbox'><label for='data-61e214f9-6db2-45d5-917e-d4f74cf77d3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Solar Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2664,7 +1815,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-22cfd2ee-d06d-496f-afac-a529a36ff609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22cfd2ee-d06d-496f-afac-a529a36ff609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c304f7b4-8b56-4ed9-9c34-c26297e86704' class='xr-var-data-in' type='checkbox'><label for='data-c304f7b4-8b56-4ed9-9c34-c26297e86704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-e594d793-d30a-41e4-a522-92b686d84b86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e594d793-d30a-41e4-a522-92b686d84b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e397b668-9c85-466c-af8c-43105c9ea850' class='xr-var-data-in' type='checkbox'><label for='data-e397b668-9c85-466c-af8c-43105c9ea850' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Terrain Pressure (mbar)</dd><dt><span>Units :</span></dt><dd>mbar</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2727,7 +1878,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-8626be37-db7a-4ef9-a0c9-9dfca77c06ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8626be37-db7a-4ef9-a0c9-9dfca77c06ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1282f234-81b9-4530-a5e0-95cbf13b8aa5' class='xr-var-data-in' type='checkbox'><label for='data-1282f234-81b9-4530-a5e0-95cbf13b8aa5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ViewingZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-8eee0019-be2c-43fc-8ab3-5a6eca59a90b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8eee0019-be2c-43fc-8ab3-5a6eca59a90b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d17b35ae-2852-4781-8e93-ddcaede2d109' class='xr-var-data-in' type='checkbox'><label for='data-d17b35ae-2852-4781-8e93-ddcaede2d109' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Viewing Zenith Angle (deg)</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2790,7 +1941,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-b7381166-28d6-4f35-a638-e0fcaac5ed1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7381166-28d6-4f35-a638-e0fcaac5ed1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e02c7c-84d5-4ada-82d3-07e69723edc6' class='xr-var-data-in' type='checkbox'><label for='data-f3e02c7c-84d5-4ada-82d3-07e69723edc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSCO_Flags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-86f34091-4a75-4885-878e-da82fedfc53d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86f34091-4a75-4885-878e-da82fedfc53d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a2daba6-089c-466d-80ed-e842c71a6b1f' class='xr-var-data-in' type='checkbox'><label for='data-2a2daba6-089c-466d-80ed-e842c71a6b1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>AIRSCO_Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2853,7 +2004,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-aa379eee-7b9c-4376-bbac-58508e183385' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa379eee-7b9c-4376-bbac-58508e183385' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2769e51-797a-4633-b983-306d082574c1' class='xr-var-data-in' type='checkbox'><label for='data-a2769e51-797a-4633-b983-306d082574c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AIRSL3COvalue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d6029c03-7a80-4c80-baae-3e083e94d3da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6029c03-7a80-4c80-baae-3e083e94d3da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f16c1f8-1b10-4169-9dd3-e9148664f8fc' class='xr-var-data-in' type='checkbox'><label for='data-1f16c1f8-1b10-4169-9dd3-e9148664f8fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-9999.0</dd><dt><span>Title :</span></dt><dd>AIRS CO L3 data</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2916,7 +2067,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-01ac85c9-1fd6-4342-9a81-1f6a6defa19a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01ac85c9-1fd6-4342-9a81-1f6a6defa19a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e5eaafa-d42f-4926-90de-0eb4d32121f2' class='xr-var-data-in' type='checkbox'><label for='data-5e5eaafa-d42f-4926-90de-0eb4d32121f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolAbsOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-56f043ef-5381-4856-9351-2b0084a7bc86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56f043ef-5381-4856-9351-2b0084a7bc86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfc56d2c-3645-41b3-8821-739dd4e9e3a9' class='xr-var-data-in' type='checkbox'><label for='data-cfc56d2c-3645-41b3-8821-739dd4e9e3a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Absorption Optical Depth (tau_abs) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3019,7 +2170,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-7583f85b-8660-45e5-8e81-8ec1b4288145' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7583f85b-8660-45e5-8e81-8ec1b4288145' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05e52e57-ad36-4251-95b0-acd625c7b944' class='xr-var-data-in' type='checkbox'><label for='data-05e52e57-ad36-4251-95b0-acd625c7b944' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolCorrCloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d8d546a3-d92a-4400-85cf-d9af0b79b506' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8d546a3-d92a-4400-85cf-d9af0b79b506' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1275c1ee-ad26-4662-bb65-3e3cbce4c06b' class='xr-var-data-in' type='checkbox'><label for='data-1275c1ee-ad26-4662-bb65-3e3cbce4c06b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3082,7 +2233,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-52d7baba-2e56-4e8e-adf5-ebde3942434d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52d7baba-2e56-4e8e-adf5-ebde3942434d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91f77969-a2ed-4d50-9c82-1638e376e027' class='xr-var-data-in' type='checkbox'><label for='data-91f77969-a2ed-4d50-9c82-1638e376e027' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthOverCloud</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-4356986b-a4c3-49c3-a92e-c16ad34e52f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4356986b-a4c3-49c3-a92e-c16ad34e52f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de935d63-0d69-48f1-901c-d5aac3a004e2' class='xr-var-data-in' type='checkbox'><label for='data-de935d63-0d69-48f1-901c-d5aac3a004e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth Over Cloud</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3178,7 +2329,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-bed55f0d-73a0-4709-936a-6cb1fabf2cc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bed55f0d-73a0-4709-936a-6cb1fabf2cc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0813567d-8bb5-4638-9dd1-1035c94b2482' class='xr-var-data-in' type='checkbox'><label for='data-0813567d-8bb5-4638-9dd1-1035c94b2482' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolOpticalDepthVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-d48ec2b2-097b-4305-9c0c-33a0228f66e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d48ec2b2-097b-4305-9c0c-33a0228f66e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1f5086e-1f34-45c3-838d-5d5c50397e34' class='xr-var-data-in' type='checkbox'><label for='data-b1f5086e-1f34-45c3-838d-5d5c50397e34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Optical Depth (tau) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3281,7 +2432,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-bed429a1-0457-42d9-8381-20a014e9c96c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bed429a1-0457-42d9-8381-20a014e9c96c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4da2c3d-d19f-4e4d-9a8d-81584f120e94' class='xr-var-data-in' type='checkbox'><label for='data-b4da2c3d-d19f-4e4d-9a8d-81584f120e94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolSingleScattAlbVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-42cb4a0b-d487-49d9-ba91-cbc8b68f4820' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42cb4a0b-d487-49d9-ba91-cbc8b68f4820' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3e7b8e3-e3b9-47dd-b1e4-72a698a3a203' class='xr-var-data-in' type='checkbox'><label for='data-e3e7b8e3-e3b9-47dd-b1e4-72a698a3a203' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Aerosol Single Scattering Albedo (omega0) at 5 levels</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3384,7 +2535,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9142b48f-4c01-4d31-919f-a06bfc5cb21a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9142b48f-4c01-4d31-919f-a06bfc5cb21a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04db58b2-fa6a-4bae-bde5-1290d491037a' class='xr-var-data-in' type='checkbox'><label for='data-04db58b2-fa6a-4bae-bde5-1290d491037a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AerosolType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-e4d79da4-9f45-4953-9848-5c436569ad6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4d79da4-9f45-4953-9848-5c436569ad6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58210d14-ffbd-4e42-bbad-24cff2d32ed5' class='xr-var-data-in' type='checkbox'><label for='data-58210d14-ffbd-4e42-bbad-24cff2d32ed5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Aerosol Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3447,7 +2598,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-9d7ab331-b10d-4a6f-87a1-f9d9ac0f5f21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d7ab331-b10d-4a6f-87a1-f9d9ac0f5f21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbc04636-4405-4815-ac19-dff3768d7628' class='xr-var-data-in' type='checkbox'><label for='data-cbc04636-4405-4815-ac19-dff3768d7628' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlagsVsHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-b2cda88c-9a1e-4cb7-925d-c0ef94f99db9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2cda88c-9a1e-4cb7-925d-c0ef94f99db9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74d50c90-6907-4cf4-8708-fb85714c6121' class='xr-var-data-in' type='checkbox'><label for='data-74d50c90-6907-4cf4-8708-fb85714c6121' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags Vs Height</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3543,7 +2694,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9fca4638-9e3b-4191-81b3-82d9594bdeb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fca4638-9e3b-4191-81b3-82d9594bdeb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0841cc0-f9bc-40c4-a3c0-249a876b743e' class='xr-var-data-in' type='checkbox'><label for='data-d0841cc0-f9bc-40c4-a3c0-249a876b743e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlags_AerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-0d3dda62-4b13-4ccc-91be-b77bdb925f05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d3dda62-4b13-4ccc-91be-b77bdb925f05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4a3fa0b-2eae-44ae-a02e-2789ec3b48fa' class='xr-var-data-in' type='checkbox'><label for='data-a4a3fa0b-2eae-44ae-a02e-2789ec3b48fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Algorithm Flags for AerosolIndex</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3606,7 +2757,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d29a0952-96c4-491f-9b09-2c7d466777f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d29a0952-96c4-491f-9b09-2c7d466777f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9462352b-f4c6-43e2-ad02-f0894b650e43' class='xr-var-data-in' type='checkbox'><label for='data-9462352b-f4c6-43e2-ad02-f0894b650e43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-86d56ad6-1f26-4381-afc5-cdaabedb1fec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86d56ad6-1f26-4381-afc5-cdaabedb1fec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f95f9845-e2a5-463d-b65d-d17ae3b0ba39' class='xr-var-data-in' type='checkbox'><label for='data-f95f9845-e2a5-463d-b65d-d17ae3b0ba39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Fraction</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3669,7 +2820,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-4417013c-8184-46e1-8caa-a723e319506e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4417013c-8184-46e1-8caa-a723e319506e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8f363c0-837a-4de9-871c-027db025bd08' class='xr-var-data-in' type='checkbox'><label for='data-c8f363c0-837a-4de9-871c-027db025bd08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-94726a89-45b7-4c00-be54-426e04f1a05e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94726a89-45b7-4c00-be54-426e04f1a05e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5a9066d-26e6-4663-a063-ed5214741907' class='xr-var-data-in' type='checkbox'><label for='data-e5a9066d-26e6-4663-a063-ed5214741907' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Cloud Optical Depth</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3732,7 +2883,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-13346b05-152b-4b21-aeea-fa04a012075d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13346b05-152b-4b21-aeea-fa04a012075d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d4af539-8822-4886-b0c6-6d33afc2a0e2' class='xr-var-data-in' type='checkbox'><label for='data-3d4af539-8822-4886-b0c6-6d33afc2a0e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolAbsOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-23410117-09ad-42b7-9f18-b7c28987ecf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23410117-09ad-42b7-9f18-b7c28987ecf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fda0c54-0ca4-4b7c-a655-ff650b963439' class='xr-var-data-in' type='checkbox'><label for='data-3fda0c54-0ca4-4b7c-a655-ff650b963439' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Absorption Optical Depth (tau_abs)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3826,7 +2977,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-a94cc12f-2111-4d98-a967-e11836e0058c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a94cc12f-2111-4d98-a967-e11836e0058c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42f51b7d-9d97-4857-9cf3-ad2e9f31b10f' class='xr-var-data-in' type='checkbox'><label for='data-42f51b7d-9d97-4857-9cf3-ad2e9f31b10f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolLayerHeight</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-f2d23b69-f84c-4dc0-a71e-a7116667338a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2d23b69-f84c-4dc0-a71e-a7116667338a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bc45eb0-4201-4e99-b6e6-a53162229b06' class='xr-var-data-in' type='checkbox'><label for='data-1bc45eb0-4201-4e99-b6e6-a53162229b06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Final Aerosol Layer Height (km)</dd><dt><span>Units :</span></dt><dd>km</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3889,7 +3040,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-f6c18e0d-3b1a-4327-ae73-11d8748bbcdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6c18e0d-3b1a-4327-ae73-11d8748bbcdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae245b8d-296e-492e-99c5-33254bee3f15' class='xr-var-data-in' type='checkbox'><label for='data-ae245b8d-296e-492e-99c5-33254bee3f15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolOpticalDepth</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-1b84b0e2-c0c1-4c07-af10-f07cbfe20585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b84b0e2-c0c1-4c07-af10-f07cbfe20585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44eff538-ca55-4f16-9b9b-ac56a170a357' class='xr-var-data-in' type='checkbox'><label for='data-44eff538-ca55-4f16-9b9b-ac56a170a357' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Optical Depth (tau)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3983,7 +3134,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-4ea1c80a-fc8c-4f66-a2a5-5a35049b5bc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ea1c80a-fc8c-4f66-a2a5-5a35049b5bc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bef827a-5480-4d86-adac-bc118d954c9a' class='xr-var-data-in' type='checkbox'><label for='data-2bef827a-5480-4d86-adac-bc118d954c9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAerosolSingleScattAlb</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 3), meta=np.ndarray&gt;</div><input id='attrs-296fc0fc-c70b-4c78-9a04-4bdd4830a2a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-296fc0fc-c70b-4c78-9a04-4bdd4830a2a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06e4f2ba-a738-4518-94ae-4b776b09518e' class='xr-var-data-in' type='checkbox'><label for='data-06e4f2ba-a738-4518-94ae-4b776b09518e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Best Aerosol Single Scattering Albedo (omega0)</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4077,7 +3228,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-099ef20c-1c7a-49d8-abb5-151da9741261' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-099ef20c-1c7a-49d8-abb5-151da9741261' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbd86854-c628-4113-a92a-34dc38df5896' class='xr-var-data-in' type='checkbox'><label for='data-cbd86854-c628-4113-a92a-34dc38df5896' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-72fc59c3-f440-427e-8475-2ef772205f52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72fc59c3-f440-427e-8475-2ef772205f52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe6aa57-b0b4-46db-b061-9b041870ce4b' class='xr-var-data-in' type='checkbox'><label for='data-dfe6aa57-b0b4-46db-b061-9b041870ce4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4140,7 +3291,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-9742efad-4315-48bd-8c51-7fd57d04de3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9742efad-4315-48bd-8c51-7fd57d04de3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81397121-bdd4-49e6-aa35-5a9036f0e40f' class='xr-var-data-in' type='checkbox'><label for='data-81397121-bdd4-49e6-aa35-5a9036f0e40f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalAlgorithmFlagsEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-13f6f0f4-3cc8-4cff-9e7e-1ea60504b397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13f6f0f4-3cc8-4cff-9e7e-1ea60504b397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73bf0adf-18cb-4b16-8496-67695ef2f190' class='xr-var-data-in' type='checkbox'><label for='data-73bf0adf-18cb-4b16-8496-67695ef2f190' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Final Algorithm Flags EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4203,7 +3354,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-ca15c775-576d-42aa-b10e-2f53fb66bd97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca15c775-576d-42aa-b10e-2f53fb66bd97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1139394-eb18-4336-a423-4802eab7085d' class='xr-var-data-in' type='checkbox'><label for='data-b1139394-eb18-4336-a423-4802eab7085d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FinalImRefIdx</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-5a8ee16f-1b7a-4fac-a800-ba3f04696f12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a8ee16f-1b7a-4fac-a800-ba3f04696f12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-381d68e4-4f83-4f0d-a1a5-80d87b081c1d' class='xr-var-data-in' type='checkbox'><label for='data-381d68e4-4f83-4f0d-a1a5-80d87b081c1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>FinalImRefIdx</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4297,7 +3448,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2a43b360-35d9-4984-ba73-d680961791e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a43b360-35d9-4984-ba73-d680961791e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f893811a-f4d3-4936-b4cb-00fc6969fb90' class='xr-var-data-in' type='checkbox'><label for='data-f893811a-f4d3-4936-b4cb-00fc6969fb90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HeightFlags</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-c401b662-6950-4a6c-9682-44ff41e0df18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c401b662-6950-4a6c-9682-44ff41e0df18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faa153b2-3912-4332-b4c7-7b0df5d0f5b8' class='xr-var-data-in' type='checkbox'><label for='data-faa153b2-3912-4332-b4c7-7b0df5d0f5b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>255</dd><dt><span>Title :</span></dt><dd>Height Flags</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4360,7 +3511,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-36631f8d-5d8a-49f7-a448-ee03c445e209' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36631f8d-5d8a-49f7-a448-ee03c445e209' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e850d8fb-2490-486b-8752-5b036f771d96' class='xr-var-data-in' type='checkbox'><label for='data-e850d8fb-2490-486b-8752-5b036f771d96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ImaRefractiveIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 5, 3), meta=np.ndarray&gt;</div><input id='attrs-85615fe8-8465-4cf6-aae2-6ffe88a6adca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85615fe8-8465-4cf6-aae2-6ffe88a6adca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8a7d654-b000-46ab-9b5f-41cfa33f09d3' class='xr-var-data-in' type='checkbox'><label for='data-d8a7d654-b000-46ab-9b5f-41cfa33f09d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Imaginary refractive Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4463,7 +3614,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-ff86bd48-ca82-410e-9e2f-e4538caf38c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff86bd48-ca82-410e-9e2f-e4538caf38c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f8f1edc-dda3-4a44-8286-6445373d9b68' class='xr-var-data-in' type='checkbox'><label for='data-6f8f1edc-dda3-4a44-8286-6445373d9b68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>InputSSAEPICAERAC</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-3730f742-b26b-4cd5-abad-4d0a4f25238a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3730f742-b26b-4cd5-abad-4d0a4f25238a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d95c80f-66ca-4a34-bf5a-b38a1a988089' class='xr-var-data-in' type='checkbox'><label for='data-9d95c80f-66ca-4a34-bf5a-b38a1a988089' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Input SSA for EPICAERAC</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4559,7 +3710,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-cd8ff24a-a1d3-4339-98f8-2bbc79e05345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd8ff24a-a1d3-4339-98f8-2bbc79e05345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-334017a6-167f-45cd-9dbf-5eb419d9c7d8' class='xr-var-data-in' type='checkbox'><label for='data-334017a6-167f-45cd-9dbf-5eb419d9c7d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NormRadiance</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-08ea28c9-a95a-42a3-9863-e946dfd1fb1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08ea28c9-a95a-42a3-9863-e946dfd1fb1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17b60e37-5f93-4f6c-a054-bcd3132f7cc6' class='xr-var-data-in' type='checkbox'><label for='data-17b60e37-5f93-4f6c-a054-bcd3132f7cc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Normalized Radiance</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4653,7 +3804,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-437db2dc-7bf7-403a-a08c-d82a39c2a2e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-437db2dc-7bf7-403a-a08c-d82a39c2a2e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09997e5e-baa2-4147-ad66-83d5acb9f24f' class='xr-var-data-in' type='checkbox'><label for='data-09997e5e-baa2-4147-ad66-83d5acb9f24f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-4ce6e00b-f9ab-4085-a324-c54c05d81ab9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ce6e00b-f9ab-4085-a324-c54c05d81ab9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94386ea4-f346-4804-87a4-0b95bbf31899' class='xr-var-data-in' type='checkbox'><label for='data-94386ea4-f346-4804-87a4-0b95bbf31899' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Lambert Equivalent Reflectivity</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4747,7 +3898,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-2876b59b-f776-40a8-a688-e524085ed666' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2876b59b-f776-40a8-a688-e524085ed666' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23ea04a2-c0e6-4f20-9a60-e61a763a3824' class='xr-var-data-in' type='checkbox'><label for='data-23ea04a2-c0e6-4f20-9a60-e61a763a3824' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-3f57fd2c-88ff-4065-b33e-2ea3103b9d6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f57fd2c-88ff-4065-b33e-2ea3103b9d6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de0a4962-370b-40d8-b955-726d6d0f606e' class='xr-var-data-in' type='checkbox'><label for='data-de0a4962-370b-40d8-b955-726d6d0f606e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Residue</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4810,7 +3961,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-8c65c97a-a7aa-488f-b473-1720f06ccca1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c65c97a-a7aa-488f-b473-1720f06ccca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72ad6fbf-5026-4734-90b6-583caf864ef1' class='xr-var-data-in' type='checkbox'><label for='data-72ad6fbf-5026-4734-90b6-583caf864ef1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceAlbedo</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_4)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024, 2), meta=np.ndarray&gt;</div><input id='attrs-71cadf53-ec1c-434b-ac81-3204458e1ace' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71cadf53-ec1c-434b-ac81-3204458e1ace' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-043ac238-bfff-4823-a4cb-6e6de9657a2d' class='xr-var-data-in' type='checkbox'><label for='data-043ac238-bfff-4823-a4cb-6e6de9657a2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Surface Albedo</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4904,7 +4055,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-0ee95ec0-1512-4d22-a5e0-9348475acdec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ee95ec0-1512-4d22-a5e0-9348475acdec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f543a021-b264-41cc-b725-c6ef89ae393e' class='xr-var-data-in' type='checkbox'><label for='data-f543a021-b264-41cc-b725-c6ef89ae393e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SurfaceType</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-e06373fe-e0f1-4e7c-91a1-d182f40a722f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e06373fe-e0f1-4e7c-91a1-d182f40a722f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9fb9656-098d-4a8c-b963-8c5a316f8d90' class='xr-var-data-in' type='checkbox'><label for='data-e9fb9656-098d-4a8c-b963-8c5a316f8d90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>65535</dd><dt><span>Title :</span></dt><dd>Surface Type</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4967,7 +4118,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-537a310d-0c3c-48aa-a471-c110710dc6d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-537a310d-0c3c-48aa-a471-c110710dc6d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7486216-2561-4c0c-81ac-7c719825b736' class='xr-var-data-in' type='checkbox'><label for='data-b7486216-2561-4c0c-81ac-7c719825b736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVAerosolIndex</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 1024), meta=np.ndarray&gt;</div><input id='attrs-d8c8f993-03b9-4e24-bcff-e1dc2d70a60e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8c8f993-03b9-4e24-bcff-e1dc2d70a60e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1ff0eab-9d4e-4cd3-be28-8a91b1bdb0b1' class='xr-var-data-in' type='checkbox'><label for='data-a1ff0eab-9d4e-4cd3-be28-8a91b1bdb0b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>UV Aerosol Index</dd><dt><span>Units :</span></dt><dd>NoUnits</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5030,7 +4181,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-fe3a27da-c528-4cb1-94ba-85280c98547c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe3a27da-c528-4cb1-94ba-85280c98547c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09bd018b-00f1-49b8-a8e5-4dbba27ef438' class='xr-var-data-in' type='checkbox'><label for='data-09bd018b-00f1-49b8-a8e5-4dbba27ef438' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_3)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-298a8623-30fa-4d07-a4e8-8e8fe7c6312d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-298a8623-30fa-4d07-a4e8-8e8fe7c6312d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86358f94-db1c-4221-9b95-d9074406523d' class='xr-var-data-in' type='checkbox'><label for='data-86358f94-db1c-4221-9b95-d9074406523d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>MissingValue :</span></dt><dd>-1.2676506e+30</dd><dt><span>Title :</span></dt><dd>Wavelength</dd><dt><span>Units :</span></dt><dd>nm</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5117,344 +4268,51 @@
      "metadata": {},
      "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "%%time\n",
-    "discovr_epic_aer = {\n",
-    "    'xarray_open': 'dataset',\n",
-    "    'merge': ['/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields', '/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields'],\n",
-    "    'open_kwargs': {'phony_dims':'access'}\n",
-    "}\n",
-    "ds = plan.open_dataset(1, open_method=discovr_epic_aer)\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c92f60a5-a648-41a5-89bb-4a6a8177b16e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f4286fe6120>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds.UVAerosolIndex.plot(robust=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "b6aae09c-a92b-4ecf-b2c5-ae49e8c08b3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
-    "import numpy as np\n",
-    "\n",
-    "vmin, vmax = np.nanpercentile(val, [2, 98])\n",
-    "\n",
-    "# mask fill values\n",
-    "fill = ds.UVAerosolIndex.attrs[\"MissingValue\"]\n",
-    "uvai = ds.UVAerosolIndex.where(ds.UVAerosolIndex != fill)\n",
-    "\n",
-    "lat = ds.Latitude.values\n",
-    "lon = ds.Longitude.values\n",
-    "val = uvai.values\n",
-    "\n",
-    "# flatten for scatter\n",
-    "lat = lat.flatten()\n",
-    "lon = lon.flatten()\n",
-    "val = val.flatten()\n",
-    "\n",
-    "# projection center (mean location of valid pixels)\n",
-    "lat2d = ds.Latitude.values\n",
-    "lon2d = ds.Longitude.values\n",
-    "\n",
-    "iy = lat2d.shape[0] // 2\n",
-    "ix = lat2d.shape[1] // 2\n",
-    "\n",
-    "lat_m = float(lat2d[iy, ix])\n",
-    "lon_m = float(lon2d[iy, ix])\n",
-    "\n",
-    "proj = ccrs.Orthographic(central_longitude=lon_m, central_latitude=lat_m)\n",
-    "\n",
-    "fig = plt.figure(figsize=(8,8))\n",
-    "ax = plt.axes(projection=proj)\n",
-    "\n",
-    "# coastlines\n",
-    "ax.coastlines(linewidth=0.5)\n",
-    "\n",
-    "# gridlines\n",
-    "grid = ax.gridlines(draw_labels=False)\n",
-    "grid.xformatter = LONGITUDE_FORMATTER\n",
-    "grid.yformatter = LATITUDE_FORMATTER\n",
-    "\n",
-    "# plot swath\n",
-    "im = ax.scatter(\n",
-    "    lon,\n",
-    "    lat,\n",
-    "    c=val,\n",
-    "    s=1,\n",
-    "    cmap=\"RdBu_r\",\n",
-    "    vmin=vmin,\n",
-    "    vmax=vmax,\n",
-    "    transform=ccrs.PlateCarree()\n",
-    ")\n",
-    "\n",
-    "plt.colorbar(im, fraction=0.03, pad=0.02, label=\"UV Aerosol Index\")\n",
-    "\n",
-    "plt.title(\"DSCOVR EPIC UV Aerosol Index\")\n",
-    "plt.show()"
+   ],
+   "source": [
+    "%%time\n",
+    "discovr_epic_aer = {\n",
+    "    'xarray_open': 'dataset',\n",
+    "    'merge': ['/HDFEOS/SWATHS/Aerosol NearUV Swath/Geolocation Fields', '/HDFEOS/SWATHS/Aerosol NearUV Swath/Data Fields'],\n",
+    "    'open_kwargs': {'phony_dims':'access'}\n",
+    "}\n",
+    "ds = plan.open_dataset(1, open_method=discovr_epic_aer)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e019a133-eb41-435a-ab04-a801f95d3f34",
+   "metadata": {},
+   "source": [
+    "## Let's get the matchups"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "id": "6cb81767-0d32-4328-8dcc-eaf1a64fab9d",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f37d9b361e74c589a3b9c3f90370aad",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "532793261c9447c49c934d4628fa29f3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9450a7e5c93040f69919f476baf0dde6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "39085e638fa24d18b4b09b2299380168",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c869f8c89b4247fc8d7e69636586e4c0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "086cc97a2ada400b8f495e452c9f68c5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "41d86561f00e447593f9122c6cd51c38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "211b76a6c0d845c68bc90911a352e367",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2b1a9ff89f77417cb4202d009e763a8d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0456206ceeaf451daa92ec8d75d6712a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d3d20ec538894743931b71514259a94c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "70ae2cf13e66479882158bbb33755263",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.18 s, sys: 223 ms, total: 1.4 s\n",
-      "Wall time: 13.9 s\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "data must be finite, check for nan or inf values",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mres = pc.matchup(plan, \u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m                 variables = [\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mUVAerosolIndex\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m], \u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m                 open_method=discovr_epic_aer)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2572\u001b[39m, in \u001b[36mInteractiveShell.run_cell_magic\u001b[39m\u001b[34m(self, magic_name, line, cell)\u001b[39m\n\u001b[32m   2570\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.builtin_trap:\n\u001b[32m   2571\u001b[39m     args = (magic_arg_s, cell)\n\u001b[32m-> \u001b[39m\u001b[32m2572\u001b[39m     result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2574\u001b[39m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[32m   2575\u001b[39m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[32m   2576\u001b[39m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[32m   2577\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1447\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1445\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupt_occured:\n\u001b[32m   1446\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m exit_on_interrupt \u001b[38;5;129;01mand\u001b[39;00m captured_exception:\n\u001b[32m-> \u001b[39m\u001b[32m1447\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m captured_exception\n\u001b[32m   1448\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m   1449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/IPython/core/magics/execution.py:1411\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m   1409\u001b[39m st = clock2()\n\u001b[32m   1410\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1411\u001b[39m     \u001b[43mexec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1412\u001b[39m     out = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1413\u001b[39m     \u001b[38;5;66;03m# multi-line %%time case\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m<timed exec>:1\u001b[39m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/engine.py:283\u001b[39m, in \u001b[36mmatchup\u001b[39m\u001b[34m(plan, open_method, variables, spatial_method, open_dataset_kwargs, silent, batch_size, save_dir, granule_range)\u001b[39m\n\u001b[32m    280\u001b[39m spec = _normalize_open_method(effective_open_method, open_dataset_kwargs)\n\u001b[32m    282\u001b[39m effective_vars: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m] = variables \u001b[38;5;28;01mif\u001b[39;00m variables \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m plan.variables\n\u001b[32m--> \u001b[39m\u001b[32m283\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_execute_plan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    284\u001b[39m \u001b[43m    \u001b[49m\u001b[43mplan\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    285\u001b[39m \u001b[43m    \u001b[49m\u001b[43mspec\u001b[49m\u001b[43m=\u001b[49m\u001b[43mspec\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    286\u001b[39m \u001b[43m    \u001b[49m\u001b[43mspatial_method\u001b[49m\u001b[43m=\u001b[49m\u001b[43mspatial_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    287\u001b[39m \u001b[43m    \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m=\u001b[49m\u001b[43meffective_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    288\u001b[39m \u001b[43m    \u001b[49m\u001b[43msilent\u001b[49m\u001b[43m=\u001b[49m\u001b[43msilent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    289\u001b[39m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    290\u001b[39m \u001b[43m    \u001b[49m\u001b[43msave_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43msave_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    291\u001b[39m \u001b[43m    \u001b[49m\u001b[43mgranule_range\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgranule_range\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    292\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/engine.py:693\u001b[39m, in \u001b[36m_execute_plan\u001b[39m\u001b[34m(plan, spec, spatial_method, variables, silent, batch_size, save_dir, granule_range)\u001b[39m\n\u001b[32m    691\u001b[39m     _extract_xoak_batch(ds, rows_for_granule, variables, lon_name, lat_name, time_dim)\n\u001b[32m    692\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m693\u001b[39m     \u001b[43m_extract_ndpoint_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrows_for_granule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlon_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlat_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_dim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    694\u001b[39m output_rows.extend(rows_for_granule)\n\u001b[32m    695\u001b[39m batch_rows.extend(rows_for_granule)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/point-collocation/src/point_collocation/core/engine.py:1263\u001b[39m, in \u001b[36m_extract_ndpoint_batch\u001b[39m\u001b[34m(ds, rows, variables, lon_name, lat_name, time_dim)\u001b[39m\n\u001b[32m   1258\u001b[39m     ds_work[lon_name] = xr.DataArray(lon_2d, dims=lat_dims)\n\u001b[32m   1260\u001b[39m \u001b[38;5;66;03m# Build the NDPointIndex once for all query points using the built-in\u001b[39;00m\n\u001b[32m   1261\u001b[39m \u001b[38;5;66;03m# scipy adapter (ScipyKDTreeAdapter).  No tree_adapter_cls argument is\u001b[39;00m\n\u001b[32m   1262\u001b[39m \u001b[38;5;66;03m# passed so xarray's default applies.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1263\u001b[39m indexed_ds = \u001b[43mds_work\u001b[49m\u001b[43m.\u001b[49m\u001b[43mset_xindex\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1264\u001b[39m \u001b[43m    \u001b[49m\u001b[43m[\u001b[49m\u001b[43mlat_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlon_name\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1265\u001b[39m \u001b[43m    \u001b[49m\u001b[43mxr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mindexes\u001b[49m\u001b[43m.\u001b[49m\u001b[43mNDPointIndex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1266\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1268\u001b[39m \u001b[38;5;66;03m# Build the target dataset with all query points at once.\u001b[39;00m\n\u001b[32m   1269\u001b[39m lats = [row[\u001b[33m\"\u001b[39m\u001b[33mlat\u001b[39m\u001b[33m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m rows]\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/core/dataset.py:5019\u001b[39m, in \u001b[36mDataset.set_xindex\u001b[39m\u001b[34m(self, coord_names, index_cls, **options)\u001b[39m\n\u001b[32m   5013\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m   5014\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mthose coordinates already have an index: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mindexed_coords\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m   5015\u001b[39m     )\n\u001b[32m   5017\u001b[39m coord_vars = {name: \u001b[38;5;28mself\u001b[39m._variables[name] \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m coord_names}\n\u001b[32m-> \u001b[39m\u001b[32m5019\u001b[39m index = \u001b[43mindex_cls\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoord_vars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   5021\u001b[39m new_coord_vars = index.create_variables(coord_vars)\n\u001b[32m   5023\u001b[39m \u001b[38;5;66;03m# special case for setting a pandas multi-index from level coordinates\u001b[39;00m\n\u001b[32m   5024\u001b[39m \u001b[38;5;66;03m# TODO: remove it once we depreciate pandas multi-index dimension (tuple\u001b[39;00m\n\u001b[32m   5025\u001b[39m \u001b[38;5;66;03m# elements) coordinate\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/indexes/nd_point_index.py:276\u001b[39m, in \u001b[36mNDPointIndex.from_variables\u001b[39m\u001b[34m(cls, variables, options)\u001b[39m\n\u001b[32m    271\u001b[39m     tree_adapter_cls = ScipyKDTreeAdapter\n\u001b[32m    273\u001b[39m points = get_points(variables.values())\n\u001b[32m    275\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(\n\u001b[32m--> \u001b[39m\u001b[32m276\u001b[39m     \u001b[43mtree_adapter_cls\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mopts\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m    277\u001b[39m     coord_names=\u001b[38;5;28mtuple\u001b[39m(variables),\n\u001b[32m    278\u001b[39m     dims=var0.dims,\n\u001b[32m    279\u001b[39m     shape=var0.shape,\n\u001b[32m    280\u001b[39m )\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/indexes/nd_point_index.py:80\u001b[39m, in \u001b[36mScipyKDTreeAdapter.__init__\u001b[39m\u001b[34m(self, points, options)\u001b[39m\n\u001b[32m     77\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, points: np.ndarray, options: Mapping[\u001b[38;5;28mstr\u001b[39m, Any]):\n\u001b[32m     78\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mspatial\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m KDTree\n\u001b[32m---> \u001b[39m\u001b[32m80\u001b[39m     \u001b[38;5;28mself\u001b[39m._kdtree = \u001b[43mKDTree\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/srv/conda/envs/notebook/lib/python3.12/site-packages/scipy/spatial/_kdtree.py:360\u001b[39m, in \u001b[36mKDTree.__init__\u001b[39m\u001b[34m(self, data, leafsize, compact_nodes, copy_data, balanced_tree, boxsize)\u001b[39m\n\u001b[32m    357\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mKDTree does not work with complex data\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m    359\u001b[39m \u001b[38;5;66;03m# Note KDTree has different default leafsize from cKDTree\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m360\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleafsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompact_nodes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    361\u001b[39m \u001b[43m                 \u001b[49m\u001b[43mbalanced_tree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mboxsize\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32mscipy/spatial/_ckdtree.pyx:560\u001b[39m, in \u001b[36mscipy.spatial._ckdtree.cKDTree.__init__\u001b[39m\u001b[34m()\u001b[39m\n",
-      "\u001b[31mValueError\u001b[39m: data must be finite, check for nan or inf values"
+      "CPU times: user 49.5 s, sys: 497 ms, total: 50 s\n",
+      "Wall time: 1min 11s\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
-    "res = pc.matchup(plan, \n",
+    "res = pc.matchup(plan[0:10], \n",
     "                 variables = [\"UVAerosolIndex\"], \n",
     "                 open_method=discovr_epic_aer)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "id": "e0319b52-da04-4c17-8357-c61256741f27",
    "metadata": {},
    "outputs": [
@@ -5482,126 +4340,95 @@
        "      <th>lat</th>\n",
        "      <th>lon</th>\n",
        "      <th>time</th>\n",
-       "      <th>vertical_column_troposphere</th>\n",
+       "      <th>pc_id</th>\n",
+       "      <th>UVAerosolIndex</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>44.355987</td>\n",
-       "      <td>-95.126999</td>\n",
-       "      <td>2024-01-14 16:55:19</td>\n",
-       "      <td>3.574082e+14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>44.355987</td>\n",
-       "      <td>-95.126999</td>\n",
-       "      <td>2024-01-14 16:55:19</td>\n",
-       "      <td>4.644599e+14</td>\n",
+       "      <th>5</th>\n",
+       "      <td>6.631233</td>\n",
+       "      <td>-138.494233</td>\n",
+       "      <td>2024-05-08</td>\n",
+       "      <td>245</td>\n",
+       "      <td>1.105056</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>44.355987</td>\n",
-       "      <td>-95.126999</td>\n",
-       "      <td>2024-01-14 16:55:19</td>\n",
-       "      <td>2.405348e+14</td>\n",
+       "      <th>6</th>\n",
+       "      <td>-20.064168</td>\n",
+       "      <td>-173.807753</td>\n",
+       "      <td>2024-02-16</td>\n",
+       "      <td>273</td>\n",
+       "      <td>0.151248</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>42.919232</td>\n",
-       "      <td>-107.118997</td>\n",
-       "      <td>2024-02-07 19:04:58</td>\n",
-       "      <td>8.827827e+14</td>\n",
+       "      <th>8</th>\n",
+       "      <td>34.247526</td>\n",
+       "      <td>-166.904639</td>\n",
+       "      <td>2024-08-21</td>\n",
+       "      <td>342</td>\n",
+       "      <td>0.469726</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>42.919232</td>\n",
-       "      <td>-107.118997</td>\n",
-       "      <td>2024-02-07 19:04:58</td>\n",
-       "      <td>6.472115e+14</td>\n",
+       "      <th>9</th>\n",
+       "      <td>34.247526</td>\n",
+       "      <td>-166.904639</td>\n",
+       "      <td>2024-08-21</td>\n",
+       "      <td>342</td>\n",
+       "      <td>0.536712</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          lat         lon                time  vertical_column_troposphere\n",
-       "17  44.355987  -95.126999 2024-01-14 16:55:19                 3.574082e+14\n",
-       "18  44.355987  -95.126999 2024-01-14 16:55:19                 4.644599e+14\n",
-       "19  44.355987  -95.126999 2024-01-14 16:55:19                 2.405348e+14\n",
-       "20  42.919232 -107.118997 2024-02-07 19:04:58                 8.827827e+14\n",
-       "21  42.919232 -107.118997 2024-02-07 19:04:58                 6.472115e+14"
+       "         lat         lon       time  pc_id  UVAerosolIndex\n",
+       "5   6.631233 -138.494233 2024-05-08    245        1.105056\n",
+       "6 -20.064168 -173.807753 2024-02-16    273        0.151248\n",
+       "8  34.247526 -166.904639 2024-08-21    342        0.469726\n",
+       "9  34.247526 -166.904639 2024-08-21    342        0.536712"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# full res shows more info about matchups, like the granule lat lon\n",
-    "res[['lat', 'lon', 'time', 'vertical_column_troposphere']].dropna(subset=['vertical_column_troposphere']).head()"
+    "var = 'UVAerosolIndex'\n",
+    "res[['lat', 'lon', 'time', 'pc_id', var]].dropna(subset=[var]).head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "accb1ce1-e75a-4030-bcbc-81597ba345ef",
-   "metadata": {},
-   "source": [
-    "### Plot our points with data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "386a0026-746c-427e-a542-29aca71d0016",
+   "id": "487f2283-1cfa-4789-ae4e-17cbeac0aa49",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "open_method: {'xarray_open': 'dataset', 'merge': ['/', '/product'], 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n"
-     ]
-    }
-   ],
    "source": [
-    "ds = plan.open_dataset(2, open_method=tempo)"
+    "This takes about 25 seconds per point/granule. We will want to use parallel processing to speed this up so we can make many granule I/O requests at once."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "253e9418-1dad-441c-96b2-47b84beb31b5",
+   "cell_type": "markdown",
+   "id": "8cf432cc-292a-4cee-9ce8-0013e211f1f9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'molecules/cm^2'"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "ds[\"vertical_column_troposphere\"].units"
+    "## Let's make a plot\n",
+    "\n",
+    "We could do `ds.UVAerosolIndex.plot(robust=True)` which is fast but also ugly plot. The following makes a prettier plot."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "407c5482-1dc5-4a33-91ef-e51073ab8acc",
+   "id": "b6aae09c-a92b-4ecf-b2c5-ae49e8c08b3a",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
+       "<Figure size 800x800 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -5611,233 +4438,137 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "\n",
-    "fig = plt.figure(figsize=(10,8))\n",
-    "\n",
-    "ds_plot = ds.weight.coarsen(latitude=10, longitude=10, boundary=\"trim\").mean()\n",
-    "\n",
-    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
-    "\n",
-    "# plot xarray field\n",
-    "ds_plot.plot(\n",
-    "    ax=ax,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    "    cmap=\"viridis\",\n",
-    "    add_colorbar=True\n",
-    ")\n",
-    "\n",
-    "# coastlines\n",
-    "ax.coastlines(resolution=\"50m\", linewidth=1)\n",
-    "\n",
-    "# optional: borders\n",
-    "ax.add_feature(cfeature.BORDERS, linewidth=0.5)\n",
-    "\n",
-    "# plot dataframe points\n",
-    "ax.scatter(\n",
-    "    df[\"lon\"],\n",
-    "    df[\"lat\"],\n",
-    "    color=\"red\",\n",
-    "    s=10,\n",
-    "    transform=ccrs.PlateCarree(),\n",
-    "    label=\"points\"\n",
-    ")\n",
+    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
+    "import numpy as np\n",
     "\n",
-    "# zoom to North America\n",
-    "ax.set_extent([-170, -50, 10, 80], crs=ccrs.PlateCarree())\n",
+    "# mask fill values\n",
+    "fill = ds.UVAerosolIndex.attrs[\"MissingValue\"]\n",
+    "uvai = ds.UVAerosolIndex.where(ds.UVAerosolIndex != fill)\n",
     "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8155c497-801f-48f4-babf-46c2cb3c7669",
-   "metadata": {},
-   "source": [
-    "## TEMPO_O3TOT_L2\n",
+    "lat = ds.Latitude.values\n",
+    "lon = ds.Longitude.values\n",
+    "val = uvai.values\n",
+    "vmin, vmax = np.nanpercentile(val, [2, 98])\n",
     "\n",
-    "This is level 2 swath data. Let's take a look at how to get some matchups. The granule planning takes awhile so let's start with a few points."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "5be7b2ec-4379-4a27-a094-d61909be5d39",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "      <th>time</th>\n",
-       "      <th>land</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>32.033815</td>\n",
-       "      <td>-87.504205</td>\n",
-       "      <td>2024-03-12 19:18:24</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>127</th>\n",
-       "      <td>36.649735</td>\n",
-       "      <td>-80.935069</td>\n",
-       "      <td>2024-04-24 22:48:36</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>29.592103</td>\n",
-       "      <td>-95.889008</td>\n",
-       "      <td>2024-03-30 03:06:23</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>159</th>\n",
-       "      <td>37.789200</td>\n",
-       "      <td>-98.045855</td>\n",
-       "      <td>2024-01-26 18:04:05</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>161</th>\n",
-       "      <td>44.349757</td>\n",
-       "      <td>-72.301856</td>\n",
-       "      <td>2024-06-13 10:50:15</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           lat        lon                time  land\n",
-       "22   32.033815 -87.504205 2024-03-12 19:18:24  True\n",
-       "127  36.649735 -80.935069 2024-04-24 22:48:36  True\n",
-       "138  29.592103 -95.889008 2024-03-30 03:06:23  True\n",
-       "159  37.789200 -98.045855 2024-01-26 18:04:05  True\n",
-       "161  44.349757 -72.301856 2024-06-13 10:50:15  True"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "df_points = pd.read_csv(\n",
-    "    \"fixtures/points_1000_usa.csv\",\n",
-    "    parse_dates=[\"time\"]\n",
+    "# flatten for scatter\n",
+    "lat = lat.flatten()\n",
+    "lon = lon.flatten()\n",
+    "val = val.flatten()\n",
+    "\n",
+    "# projection center (mean location of valid pixels)\n",
+    "lat2d = ds.Latitude.values\n",
+    "lon2d = ds.Longitude.values\n",
+    "\n",
+    "iy = lat2d.shape[0] // 2\n",
+    "ix = lat2d.shape[1] // 2\n",
+    "\n",
+    "lat_m = float(lat2d[iy, ix])\n",
+    "lon_m = float(lon2d[iy, ix])\n",
+    "\n",
+    "proj = ccrs.Orthographic(central_longitude=lon_m, central_latitude=lat_m)\n",
+    "\n",
+    "fig = plt.figure(figsize=(8,8))\n",
+    "ax = plt.axes(projection=proj)\n",
+    "\n",
+    "# coastlines\n",
+    "ax.coastlines(linewidth=0.5)\n",
+    "\n",
+    "# gridlines\n",
+    "grid = ax.gridlines(draw_labels=False)\n",
+    "grid.xformatter = LONGITUDE_FORMATTER\n",
+    "grid.yformatter = LATITUDE_FORMATTER\n",
+    "\n",
+    "# plot swath\n",
+    "im = ax.scatter(\n",
+    "    lon,\n",
+    "    lat,\n",
+    "    c=val,\n",
+    "    s=1,\n",
+    "    cmap=\"RdBu_r\",\n",
+    "    vmin=vmin,\n",
+    "    vmax=vmax,\n",
+    "    transform=ccrs.PlateCarree()\n",
     ")\n",
-    "df = df_points[\n",
-    "    (df_points[\"time\"].dt.year == 2024) &\n",
-    "    (df_points[\"land\"] == True) &\n",
-    "    (df_points[\"lon\"] < -60) &\n",
-    "    (df_points[\"lon\"] > -100)\n",
-    "    \n",
-    "]\n",
-    "print(len(df))\n",
-    "df.head()"
+    "\n",
+    "# points\n",
+    "ax.scatter(\n",
+    "    df[\"lon\"],\n",
+    "    df[\"lat\"],\n",
+    "    color=\"black\",\n",
+    "    s=20,\n",
+    "    edgecolor=\"white\",\n",
+    "    linewidth=0.5,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    zorder=10\n",
+    ")\n",
+    "\n",
+    "plt.colorbar(im, fraction=0.03, pad=0.02, label=\"UV Aerosol Index\")\n",
+    "\n",
+    "plt.title(\"DSCOVR EPIC UV Aerosol Index\")\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5852aa2a-07cf-4539-ba0f-dc57643322c2",
+   "id": "1da88a23-def7-4cb9-9c70-e8c393cf3d3e",
    "metadata": {},
    "source": [
-    "### Get the plan"
+    "## DSCOVR_EPIC_L2_TO3\n",
+    "\n",
+    "Total ozone."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "642aa215-2dcb-47f6-bdc4-5cecb59b6841",
+   "execution_count": 18,
+   "id": "fe057ae9-27e7-4cb0-80c8-7eb55bd555cd",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Plan: 15 points → 12 unique granule(s)\n",
-      "  Points with 0 matches : 9\n",
-      "  Points with >1 matches: 4\n",
-      "  Time buffer: 0 days 01:00:00\n",
-      "\n",
-      "First 1 point(s):\n",
-      "  [22] lat=32.0338, lon=-87.5042, time=2024-03-12 19:18:24: 1 match(es)\n",
-      "    → https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_O3TOT_L2_V03/2024.03.12/TEMPO_O3TOT_L2_V03_20240312T190833Z_S010G04.nc\n",
-      "CPU times: user 2.7 s, sys: 265 ms, total: 2.96 s\n",
-      "Wall time: 39.3 s\n"
+      "CPU times: user 816 ms, sys: 69.6 ms, total: 886 ms\n",
+      "Wall time: 11.9 s\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
     "import point_collocation as pc\n",
-    "short_name=\"TEMPO_O3TOT_L2\"\n",
+    "short_name=\"DSCOVR_EPIC_L2_TO3\"\n",
     "plan = pc.plan(\n",
     "    df,\n",
     "    data_source=\"earthaccess\",\n",
     "    source_kwargs={\n",
     "        \"short_name\": short_name,\n",
-    "        \"version\": \"V03\"\n",
+    "        \"version\": \"03\"\n",
     "    },\n",
     "    time_buffer=\"1h\"\n",
-    ")\n",
-    "plan.summary(n=1)"
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9b7b5461-18d8-49f3-827a-cf4e34d7e592",
+   "id": "ad52e615-3054-492f-a4ed-04915549a47c",
    "metadata": {},
    "source": [
-    "### Take a look at the granules\n",
+    "### Look at variables\n",
     "\n",
-    "These are grouped netcdfs and we need to figure out what groups we need and variable names."
+    "All the data is in the base group so we do not need to pass in `open_method`; the default `open_dataset` will work fine. Our variable of interest is \"Ozone\"."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "dfe6c0fe-714f-4aab-b1b8-d3954ea0ceec",
+   "execution_count": 22,
+   "id": "241d5dbe-5a86-444a-8b52-0f6b8ed8ea84",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "open_method: {'xarray_open': 'datatree', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n"
+      "open_method: {'xarray_open': 'dataset', 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'merge': None, 'coords': 'auto', 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None}\n"
      ]
     },
     {
@@ -5938,6 +4669,7 @@
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -5948,8 +4680,11 @@
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-header > div,\n",
@@ -5960,20 +4695,15 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-obj-name,\n",
-       ".xr-group-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-group-name::before {\n",
-       "  content: \"📁\";\n",
-       "  padding-right: 0.3em;\n",
-       "}\n",
-       "\n",
-       ".xr-group-name,\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
@@ -5988,28 +4718,39 @@
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
        "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
        "\n",
@@ -6017,11 +4758,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
        "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
        "}\n",
        "\n",
        ".xr-section-summary > span {\n",
        "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:disabled + label {\n",
@@ -6049,7 +4804,8 @@
        "}\n",
        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
        "  padding-top: 4px;\n",
        "}\n",
        "\n",
@@ -6058,20 +4814,29 @@
        "}\n",
        "\n",
        ".xr-section-details {\n",
-       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
        "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-section-summary-in:checked ~ .xr-section-details {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
        ".xr-group-box {\n",
        "  display: inline-grid;\n",
-       "  grid-template-columns: 0px 20px auto;\n",
-       "  width: 100%;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
        "}\n",
        "\n",
        ".xr-group-box-vline {\n",
@@ -6085,1055 +4850,1208 @@
        "  grid-column-start: 2;\n",
        "  grid-row-start: 1;\n",
        "  height: 1em;\n",
-       "  width: 20px;\n",
+       "  width: 26px;\n",
        "  border-bottom: 0.2em solid;\n",
        "  border-color: var(--xr-border-color);\n",
        "}\n",
        "\n",
        ".xr-group-box-contents {\n",
        "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
        "}\n",
        "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
        "}\n",
        "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
        "}\n",
        "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
        "}\n",
        "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
        "}\n",
        "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
        "}\n",
        "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
        "}\n",
        "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
+       ".xr-array-wrap > label {\n",
        "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
        "}\n",
        "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
        "}\n",
        "\n",
-       ".xr-attrs dd {\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
        "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
        "}\n",
        "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
        "}\n",
        "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree&gt;\n",
-       "Group: /\n",
-       "│   Dimensions:      (xtrack: 2048, mirror_step: 131)\n",
-       "│   Coordinates:\n",
-       "│     * xtrack       (xtrack) int32 8kB 0 1 2 3 4 5 ... 2043 2044 2045 2046 2047\n",
-       "│     * mirror_step  (mirror_step) int32 524B 132 133 134 135 ... 259 260 261 262\n",
-       "│   Attributes: (12/34)\n",
-       "│       time_reference:                   1980-01-06T00:00:00Z\n",
-       "│       scan_num:                         1\n",
-       "│       granule_num:                      2\n",
-       "│       time_coverage_start:              2024-01-03T12:59:34Z\n",
-       "│       time_coverage_end:                2024-01-03T13:06:11Z\n",
-       "│       time_coverage_start_since_epoch:  1388321992.512726\n",
-       "│       ...                               ...\n",
-       "│       title:                            TEMPO Level 2 total ozone product\n",
-       "│       collection_shortname:             TEMPO_O3TOT_L2\n",
-       "│       collection_version:               1\n",
-       "│       keywords:                         EARTH SCIENCE&gt;ATMOSPHERE&gt;ATMOSPHERIC CH...\n",
-       "│       summary:                          Total ozone Level 2 files provide ozone...\n",
-       "│       coremetadata:                     \\nGROUP                  = INVENTORYMET...\n",
-       "├── Group: /product\n",
-       "│       Dimensions:               (mirror_step: 131, xtrack: 2048)\n",
-       "│       Data variables:\n",
-       "│           column_amount_o3      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           radiative_cloud_frac  (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           fc                    (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           quality_flag          (mirror_step, xtrack) int32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           o3_below_cloud        (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           so2_index             (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           uv_aerosol_index      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "├── Group: /geolocation\n",
-       "│       Dimensions:                 (mirror_step: 131, xtrack: 2048, corner: 4)\n",
-       "│       Coordinates:\n",
-       "│           time                    (mirror_step) datetime64[ns] 1kB dask.array&lt;chunksize=(131,), meta=np.ndarray&gt;\n",
-       "│           latitude                (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           longitude               (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│       Dimensions without coordinates: corner\n",
-       "│       Data variables:\n",
-       "│           latitude_bounds         (mirror_step, xtrack, corner) float32 4MB dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;\n",
-       "│           longitude_bounds        (mirror_step, xtrack, corner) float32 4MB dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;\n",
-       "│           solar_zenith_angle      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           solar_azimuth_angle     (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           viewing_zenith_angle    (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           viewing_azimuth_angle   (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           relative_azimuth_angle  (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "├── Group: /support_data\n",
-       "│       Dimensions:                        (mirror_step: 131, xtrack: 2048,\n",
-       "│                                           wavelength: 12, layer: 11)\n",
-       "│       Dimensions without coordinates: wavelength, layer\n",
-       "│       Data variables: (12/21)\n",
-       "│           ground_pixel_quality_flag      (mirror_step, xtrack) int32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           lut_wavelength                 (wavelength) float32 48B dask.array&lt;chunksize=(12,), meta=np.ndarray&gt;\n",
-       "│           cloud_pressure                 (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           terrain_pressure               (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           terrain_height                 (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           algorithm_flags                (mirror_step, xtrack) int32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           ...                             ...\n",
-       "│           step_2_N_value_residual        (mirror_step, xtrack, wavelength) float32 13MB dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;\n",
-       "│           ozone_sensitivity_ratio        (mirror_step, xtrack, wavelength) float32 13MB dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;\n",
-       "│           temp_sensitivity_ratio         (mirror_step, xtrack, wavelength) float32 13MB dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;\n",
-       "│           step1_o3                       (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           step2_o3                       (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "│           cal_adjustment                 (xtrack, wavelength) float32 98kB dask.array&lt;chunksize=(2048, 12), meta=np.ndarray&gt;\n",
-       "└── Group: /qa_statistics</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-832acc53-21d8-4132-89ee-561fa4bb9a25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-832acc53-21d8-4132-89ee-561fa4bb9a25' class='xr-section-summary' >Groups: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><div style='display: inline-grid; grid-template-columns: 100%; grid-column: 1 / -1'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/product</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b3964da8-09d8-4482-89ad-667781ef9cf5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3964da8-09d8-4482-89ad-667781ef9cf5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>xtrack</span>: 2048</li><li><span class='xr-has-index'>mirror_step</span>: 131</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3afabbb9-5430-4313-816f-f9288125745e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3afabbb9-5430-4313-816f-f9288125745e' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>column_amount_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f4c97298-359a-4357-869f-4a62b8caf387' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4c97298-359a-4357-869f-4a62b8caf387' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372624f1-e685-499a-8bf1-499cdd827ffb' class='xr-var-data-in' type='checkbox'><label for='data-372624f1-e685-499a-8bf1-499cdd827ffb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>best total ozone solution</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>50.0</dd><dt><span>valid_max :</span></dt><dd>700.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radiative_cloud_frac</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-affed888-eac0-450c-8e34-fb7dc42d2b88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-affed888-eac0-450c-8e34-fb7dc42d2b88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b023b2b-7ad7-4d25-9287-a8520e6acb21' class='xr-var-data-in' type='checkbox'><label for='data-9b023b2b-7ad7-4d25-9287-a8520e6acb21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>cloud radiance fraction = fc*Ic331/Im331</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fc</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-1ca58e7a-0d19-4cb5-93f8-4c5368951449' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ca58e7a-0d19-4cb5-93f8-4c5368951449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90ed82c6-97c2-4e75-8abc-51e3ac185f77' class='xr-var-data-in' type='checkbox'><label for='data-90ed82c6-97c2-4e75-8abc-51e3ac185f77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective cloud fraction (mixed LER model)</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quality_flag</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-54f2234d-6221-41da-b182-6f3536de86a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54f2234d-6221-41da-b182-6f3536de86a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4bbd666-c0a3-4c3b-9dbf-5c33e5588b19' class='xr-var-data-in' type='checkbox'><label for='data-b4bbd666-c0a3-4c3b-9dbf-5c33e5588b19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>quality flags</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>65534</dd><dt><span>flag_meanings :</span></dt><dd>good_sample glint_contamination SZA&gt;84 360_residual&gt;threshold residual_at_unused_ozone_wavelenth&gt;4sigma SOI&gt;4sigma non-convergence abs(residual)&gt;16.0(fatal) row_anomaly_error missing_TEMPO_cloud_pressure geolocation_error SZA&gt;88 missing_input_radiance error_input_radiance warning_input_radiance missing_input_irradiance error_input_irradiance warning_input_irradiance</dd><dt><span>flag_values :</span></dt><dd>[    0     1     2     3     4     5     6     7     8   128   256   5121024  2048  4096  8192 16384 32768]</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> int32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3_below_cloud</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-7292a891-d58e-4ca7-b62a-8e9f4a360678' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7292a891-d58e-4ca7-b62a-8e9f4a360678' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe9dcf6f-796d-4c6f-8984-e8cfea5a8ab0' class='xr-var-data-in' type='checkbox'><label for='data-fe9dcf6f-796d-4c6f-8984-e8cfea5a8ab0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ozone below fractional cloud</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>100.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>so2_index</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-70960714-2037-457d-8565-f6830532b341' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70960714-2037-457d-8565-f6830532b341' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92b227ea-16e9-4251-92ef-ca27b0b74309' class='xr-var-data-in' type='checkbox'><label for='data-92b227ea-16e9-4251-92ef-ca27b0b74309' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>SO2 index</dd><dt><span>valid_min :</span></dt><dd>-300.0</dd><dt><span>valid_max :</span></dt><dd>300.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uv_aerosol_index</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-08513761-1e83-486d-8c67-2766ef462704' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08513761-1e83-486d-8c67-2766ef462704' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcf86f09-31e6-4380-ab37-4bbb3bdbbdc1' class='xr-var-data-in' type='checkbox'><label for='data-bcf86f09-31e6-4380-ab37-4bbb3bdbbdc1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>UV aerosol index</dd><dt><span>valid_min :</span></dt><dd>-30.0</dd><dt><span>valid_max :</span></dt><dd>30.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/geolocation</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b7d7286a-b6e6-4a08-9835-9d3969e90954' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b7d7286a-b6e6-4a08-9835-9d3969e90954' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>xtrack</span>: 2048</li><li><span class='xr-has-index'>mirror_step</span>: 131</li><li><span>corner</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5045796b-8dd1-422e-8631-3b7e1b6d3f04' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5045796b-8dd1-422e-8631-3b7e1b6d3f04' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(mirror_step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131,), meta=np.ndarray&gt;</div><input id='attrs-241ae507-d5d2-4a2b-8680-04c75a463bcf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-241ae507-d5d2-4a2b-8680-04c75a463bcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-808367e7-91e6-42d8-ac2c-0b77c27a7fc5' class='xr-var-data-in' type='checkbox'><label for='data-808367e7-91e6-42d8-ac2c-0b77c27a7fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>exposure start time</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10000000000.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 kiB </td><td> 1.02 kiB </td></tr><tr><th> Shape </th><td> (131,) </td><td> (131,) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> datetime64[ns] numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >131</text><text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-84e25c40-9b09-4676-bb1b-8e3518e24c51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84e25c40-9b09-4676-bb1b-8e3518e24c51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2912a7b4-6ead-40ff-a8ca-552e051f6f8f' class='xr-var-data-in' type='checkbox'><label for='data-2912a7b4-6ead-40ff-a8ca-552e051f6f8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>comment :</span></dt><dd>latitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>bounds :</span></dt><dd>latitude_bounds</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-aa0c873a-1979-4fbc-be69-1811a572be7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa0c873a-1979-4fbc-be69-1811a572be7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-444b9a3c-a758-400d-baea-2e33646c4161' class='xr-var-data-in' type='checkbox'><label for='data-444b9a3c-a758-400d-baea-2e33646c4161' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>comment :</span></dt><dd>longitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>bounds :</span></dt><dd>longitude_bounds</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6cdc291e-d6b5-465a-bc5d-e3dc1cf4370c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6cdc291e-d6b5-465a-bc5d-e3dc1cf4370c' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>latitude_bounds</span></div><div class='xr-var-dims'>(mirror_step, xtrack, corner)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;</div><input id='attrs-571a42db-9f34-4d3c-9501-534a2ed6179f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-571a42db-9f34-4d3c-9501-534a2ed6179f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d89a65b4-0224-4b89-87e1-56d2b388eaa2' class='xr-var-data-in' type='checkbox'><label for='data-d89a65b4-0224-4b89-87e1-56d2b388eaa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>latitude at pixel corners (sw,se,ne,nw)</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 4.09 MiB </td><td> 4.09 MiB </td></tr><tr><th> Shape </th><td> (131, 2048, 4) </td><td> (131, 2048, 4) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_bounds</span></div><div class='xr-var-dims'>(mirror_step, xtrack, corner)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;</div><input id='attrs-68d90b8f-b4e4-446c-88af-8480817f608b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68d90b8f-b4e4-446c-88af-8480817f608b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27a89457-fdfb-4402-868a-0b8c9e4ad727' class='xr-var-data-in' type='checkbox'><label for='data-27a89457-fdfb-4402-868a-0b8c9e4ad727' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>longitude at pixel corners (sw,se,ne,nw)</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 4.09 MiB </td><td> 4.09 MiB </td></tr><tr><th> Shape </th><td> (131, 2048, 4) </td><td> (131, 2048, 4) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_zenith_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f4d1cb73-97db-4b9c-8e62-84dc777a11c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4d1cb73-97db-4b9c-8e62-84dc777a11c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2893211-d46f-402a-b9e9-befabbe48ed5' class='xr-var-data-in' type='checkbox'><label for='data-a2893211-d46f-402a-b9e9-befabbe48ed5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>solar zenith angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-78de3838-f903-452f-8fe7-87672433efc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78de3838-f903-452f-8fe7-87672433efc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a122d1a-aa2a-4e07-b6fa-2a80defb4d10' class='xr-var-data-in' type='checkbox'><label for='data-7a122d1a-aa2a-4e07-b6fa-2a80defb4d10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>solar azimuth angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_zenith_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-1341a0de-f0f3-472a-a790-5e2446bae017' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1341a0de-f0f3-472a-a790-5e2446bae017' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9abb0fb8-7874-496f-a9ef-dd34834b74b2' class='xr-var-data-in' type='checkbox'><label for='data-9abb0fb8-7874-496f-a9ef-dd34834b74b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>viewing zenith angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-14347bbb-1a32-45f0-89cf-268d21310683' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14347bbb-1a32-45f0-89cf-268d21310683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-992f8304-64c2-429e-9796-a349106df96b' class='xr-var-data-in' type='checkbox'><label for='data-992f8304-64c2-429e-9796-a349106df96b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>viewing azimuth angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-67814c9a-2d51-43f1-80c1-376f91e6578f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67814c9a-2d51-43f1-80c1-376f91e6578f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bdf8211-fe64-4945-a258-037f9b59f37c' class='xr-var-data-in' type='checkbox'><label for='data-2bdf8211-fe64-4945-a258-037f9b59f37c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>relative azimuth angle (sun + 180 - view)</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/support_data</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a306ffd2-4386-4589-8671-55382e912c0a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a306ffd2-4386-4589-8671-55382e912c0a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>xtrack</span>: 2048</li><li><span class='xr-has-index'>mirror_step</span>: 131</li><li><span>wavelength</span>: 12</li><li><span>layer</span>: 11</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d775754a-6ddd-4d71-a383-0b4a2d9e21ae' class='xr-section-summary-in' type='checkbox'  ><label for='section-d775754a-6ddd-4d71-a383-0b4a2d9e21ae' class='xr-section-summary' >Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ground_pixel_quality_flag</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-d3471317-5e7c-42f4-8ce3-c6593f9eb13a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3471317-5e7c-42f4-8ce3-c6593f9eb13a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fa998b3-0558-4a30-ad84-46ff04b8bc8f' class='xr-var-data-in' type='checkbox'><label for='data-3fa998b3-0558-4a30-ad84-46ff04b8bc8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ground pixel quality flag</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>65534</dd><dt><span>flag_meanings :</span></dt><dd>shallow_ocean land shallow_inland_water shoreline intermittent_water deep_inland_water continental_shelf_ocean deep_ocean land_water_error sun_glint_possibility solar_eclipse_possibility goelocation_error</dd><dt><span>flag_values :</span></dt><dd>[ 0  1  2  3  4  5  6  7 15 16 32 64]</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> int32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lut_wavelength</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12,), meta=np.ndarray&gt;</div><input id='attrs-eecb0384-ecd8-4aa4-9b2b-cbe49eb10992' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eecb0384-ecd8-4aa4-9b2b-cbe49eb10992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e7788c5-5e8e-4c3c-a593-fe4a0fc86e03' class='xr-var-data-in' type='checkbox'><label for='data-9e7788c5-5e8e-4c3c-a593-fe4a0fc86e03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>lookup table wavelength</dd><dt><span>units :</span></dt><dd>nm</dd><dt><span>valid_min :</span></dt><dd>300.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 48 B </td><td> 48 B </td></tr><tr><th> Shape </th><td> (12,) </td><td> (12,) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"87\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"37\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"37\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,37.56358655585696 0.0,37.56358655585696\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"57.563587\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"140.000000\" y=\"18.781793\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,18.781793)\">1</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cloud_pressure</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-269968d4-0991-4e03-9050-546648b18f60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-269968d4-0991-4e03-9050-546648b18f60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc312388-bc02-431c-8e68-35aeac87cb53' class='xr-var-data-in' type='checkbox'><label for='data-bc312388-bc02-431c-8e68-35aeac87cb53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective cloud pressure</dd><dt><span>units :</span></dt><dd>hPA</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1013.25</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>terrain_pressure</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-571e892c-6318-42be-a3ed-0f42df966cd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-571e892c-6318-42be-a3ed-0f42df966cd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-509edc13-634f-421b-bd3d-90220dbe3019' class='xr-var-data-in' type='checkbox'><label for='data-509edc13-634f-421b-bd3d-90220dbe3019' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>terrain pressure</dd><dt><span>units :</span></dt><dd>hPA</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1013.25</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>terrain_height</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f2e3990c-ebea-4876-bb7e-342d756409f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2e3990c-ebea-4876-bb7e-342d756409f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cab51cf2-b80e-42c7-86f0-09bbcf07d6b9' class='xr-var-data-in' type='checkbox'><label for='data-cab51cf2-b80e-42c7-86f0-09bbcf07d6b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>terrain height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-200</dd><dt><span>valid_max :</span></dt><dd>10000</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>algorithm_flags</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-835ab3ed-6fd9-419d-89ce-b7345bdfb137' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-835ab3ed-6fd9-419d-89ce-b7345bdfb137' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-138145a1-23f9-4daa-9741-bb0c6e359a66' class='xr-var-data-in' type='checkbox'><label for='data-138145a1-23f9-4daa-9741-bb0c6e359a66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>algorithm flags</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>13</dd><dt><span>flag_meanings :</span></dt><dd>skipped standard adjusted_for_profile_shape based_on_C-pair(331_and_360 nm)</dd><dt><span>flag_values :</span></dt><dd>[ 0  1  2  3 10]</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> int32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_priori_layer_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack, layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 11), meta=np.ndarray&gt;</div><input id='attrs-04193c8c-8be7-4a76-8f7d-eb210b5bb399' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04193c8c-8be7-4a76-8f7d-eb210b5bb399' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02ef0393-9a99-4836-84b0-f31f7e8c0e5b' class='xr-var-data-in' type='checkbox'><label for='data-02ef0393-9a99-4836-84b0-f31f7e8c0e5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>a priori ozone profile</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>125.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 11.26 MiB </td><td> 5.50 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 11) </td><td> (1, 128, 11) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >11</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radiance_bpix_flag_accepted</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f65eb6c4-1e9c-4282-a1a9-0facb52c867a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f65eb6c4-1e9c-4282-a1a9-0facb52c867a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-190b58ef-0a5c-4419-afd4-5491f7148729' class='xr-var-data-in' type='checkbox'><label for='data-190b58ef-0a5c-4419-afd4-5491f7148729' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>radiance bad pixel flag accepted</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>65534</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> int32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layer_efficiency</span></div><div class='xr-var-dims'>(mirror_step, xtrack, layer)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 11), meta=np.ndarray&gt;</div><input id='attrs-026e0b6f-1aac-414a-a2a2-e8206d3f2eba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-026e0b6f-1aac-414a-a2a2-e8206d3f2eba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde64a96-b58b-497c-9d74-dacf6919557c' class='xr-var-data-in' type='checkbox'><label for='data-dde64a96-b58b-497c-9d74-dacf6919557c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>algorithmic layer efficiency</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 11.26 MiB </td><td> 5.50 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 11) </td><td> (1, 128, 11) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >11</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dNdR</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-526b5929-89f7-4635-a34f-4669a2c687cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-526b5929-89f7-4635-a34f-4669a2c687cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d69ac6f7-cb94-44c2-8516-1647c221b49d' class='xr-var-data-in' type='checkbox'><label for='data-d69ac6f7-cb94-44c2-8516-1647c221b49d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>reflectivity sensitivity ratio</dd><dt><span>valid_min :</span></dt><dd>-200.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_value</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-f2259922-db3d-44b2-811d-e3847be1db86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2259922-db3d-44b2-811d-e3847be1db86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35660612-d5b4-41b3-ac28-2190e9e2a6ba' class='xr-var-data-in' type='checkbox'><label for='data-35660612-d5b4-41b3-ac28-2190e9e2a6ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>measured N-value</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>600.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_reflectivity_at_331nm</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-f52ddb03-a381-4c94-b42d-5523e231c929' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f52ddb03-a381-4c94-b42d-5523e231c929' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6d681b7-cc95-4b1b-9418-fe92cfb5bb1d' class='xr-var-data-in' type='checkbox'><label for='data-c6d681b7-cc95-4b1b-9418-fe92cfb5bb1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective surface reflectivity at 331 nm</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>-15.0</dd><dt><span>valid_max :</span></dt><dd>115.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_reflectivity_at_360nm</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-140e0c70-27ba-4072-936e-19678c61fbda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-140e0c70-27ba-4072-936e-19678c61fbda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c59565e-94b6-4dfb-9620-a7603a73bfab' class='xr-var-data-in' type='checkbox'><label for='data-4c59565e-94b6-4dfb-9620-a7603a73bfab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective surface reflectivity at 360 nm</dd><dt><span>units :</span></dt><dd>percent</dd><dt><span>valid_min :</span></dt><dd>-15.0</dd><dt><span>valid_max :</span></dt><dd>115.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>N_value_residual</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-88fbf7f5-8f75-4f78-8326-b0403a2d33fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88fbf7f5-8f75-4f78-8326-b0403a2d33fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcddd858-e625-47e4-bee1-6dd88a10b88e' class='xr-var-data-in' type='checkbox'><label for='data-dcddd858-e625-47e4-bee1-6dd88a10b88e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>N-value residual</dd><dt><span>valid_min :</span></dt><dd>-32.0</dd><dt><span>valid_max :</span></dt><dd>32.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_1_N_value_residual</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-f293121f-6477-4d2f-81a1-13135a8f6ca9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f293121f-6477-4d2f-81a1-13135a8f6ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93b902da-2516-454c-a154-6cb2c20aeae1' class='xr-var-data-in' type='checkbox'><label for='data-93b902da-2516-454c-a154-6cb2c20aeae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>step 1 N-value residual</dd><dt><span>valid_min :</span></dt><dd>-32.0</dd><dt><span>valid_max :</span></dt><dd>32.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_2_N_value_residual</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-d2ff2930-7a42-4689-9de7-2ff1f33f977c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2ff2930-7a42-4689-9de7-2ff1f33f977c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4fbf506-e096-4b57-9cd6-9d29dc058806' class='xr-var-data-in' type='checkbox'><label for='data-e4fbf506-e096-4b57-9cd6-9d29dc058806' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>step 2 N-value residual</dd><dt><span>valid_min :</span></dt><dd>-32.0</dd><dt><span>valid_max :</span></dt><dd>32.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ozone_sensitivity_ratio</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-5a258cad-8749-4abb-b219-2230528cd101' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a258cad-8749-4abb-b219-2230528cd101' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc9d05d4-f4ab-4c87-b875-8f947ced008c' class='xr-var-data-in' type='checkbox'><label for='data-dc9d05d4-f4ab-4c87-b875-8f947ced008c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ozone sensitivity ratio, dN/dOmega</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_sensitivity_ratio</span></div><div class='xr-var-dims'>(mirror_step, xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 128, 12), meta=np.ndarray&gt;</div><input id='attrs-9b132247-9268-431c-b962-0d77f37f0eaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b132247-9268-431c-b962-0d77f37f0eaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3cb390f-fbb1-416e-ace5-9e759c1083d2' class='xr-var-data-in' type='checkbox'><label for='data-c3cb390f-fbb1-416e-ace5-9e759c1083d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ozone weighted temperature sensitivity ratio, dN/dT</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 12.28 MiB </td><td> 6.00 kiB </td></tr><tr><th> Shape </th><td> (131, 2048, 12) </td><td> (1, 128, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 2096 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" /><line x1=\"10\" y1=\"15\" x2=\"31\" y2=\"36\" /><line x1=\"10\" y1=\"22\" x2=\"31\" y2=\"43\" /><line x1=\"10\" y1=\"30\" x2=\"31\" y2=\"51\" /><line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" /><line x1=\"10\" y1=\"45\" x2=\"31\" y2=\"66\" /><line x1=\"10\" y1=\"52\" x2=\"31\" y2=\"73\" /><line x1=\"10\" y1=\"60\" x2=\"31\" y2=\"81\" /><line x1=\"10\" y1=\"67\" x2=\"31\" y2=\"88\" /><line x1=\"10\" y1=\"75\" x2=\"31\" y2=\"96\" /><line x1=\"10\" y1=\"82\" x2=\"31\" y2=\"103\" /><line x1=\"10\" y1=\"90\" x2=\"31\" y2=\"111\" /><line x1=\"10\" y1=\"97\" x2=\"31\" y2=\"118\" /><line x1=\"10\" y1=\"105\" x2=\"31\" y2=\"126\" /><line x1=\"10\" y1=\"112\" x2=\"31\" y2=\"133\" /><line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" /><line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"122\" /><line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" /><line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"124\" /><line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" /><line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"126\" /><line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" /><line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"130\" /><line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"132\" /><line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" /><line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" /><line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" /><line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" /><line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" /><line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" /><line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"140\" /><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" /><line x1=\"12\" y1=\"2\" x2=\"37\" y2=\"2\" /><line x1=\"13\" y1=\"3\" x2=\"38\" y2=\"3\" /><line x1=\"14\" y1=\"4\" x2=\"39\" y2=\"4\" /><line x1=\"15\" y1=\"5\" x2=\"40\" y2=\"5\" /><line x1=\"16\" y1=\"6\" x2=\"42\" y2=\"6\" /><line x1=\"17\" y1=\"7\" x2=\"43\" y2=\"7\" /><line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" /><line x1=\"20\" y1=\"10\" x2=\"45\" y2=\"10\" /><line x1=\"20\" y1=\"10\" x2=\"46\" y2=\"10\" /><line x1=\"22\" y1=\"12\" x2=\"47\" y2=\"12\" /><line x1=\"23\" y1=\"13\" x2=\"48\" y2=\"13\" /><line x1=\"24\" y1=\"14\" x2=\"49\" y2=\"14\" /><line x1=\"25\" y1=\"15\" x2=\"50\" y2=\"15\" /><line x1=\"26\" y1=\"16\" x2=\"52\" y2=\"16\" /><line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" /><line x1=\"28\" y1=\"18\" x2=\"54\" y2=\"18\" /><line x1=\"30\" y1=\"20\" x2=\"55\" y2=\"20\" /><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#8B4903A0;stroke-width:0\"/><!-- Horizontal lines --><line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" /><line x1=\"31\" y1=\"28\" x2=\"56\" y2=\"28\" /><line x1=\"31\" y1=\"36\" x2=\"56\" y2=\"36\" /><line x1=\"31\" y1=\"43\" x2=\"56\" y2=\"43\" /><line x1=\"31\" y1=\"51\" x2=\"56\" y2=\"51\" /><line x1=\"31\" y1=\"58\" x2=\"56\" y2=\"58\" /><line x1=\"31\" y1=\"66\" x2=\"56\" y2=\"66\" /><line x1=\"31\" y1=\"73\" x2=\"56\" y2=\"73\" /><line x1=\"31\" y1=\"81\" x2=\"56\" y2=\"81\" /><line x1=\"31\" y1=\"88\" x2=\"56\" y2=\"88\" /><line x1=\"31\" y1=\"96\" x2=\"56\" y2=\"96\" /><line x1=\"31\" y1=\"103\" x2=\"56\" y2=\"103\" /><line x1=\"31\" y1=\"111\" x2=\"56\" y2=\"111\" /><line x1=\"31\" y1=\"118\" x2=\"56\" y2=\"118\" /><line x1=\"31\" y1=\"126\" x2=\"56\" y2=\"126\" /><line x1=\"31\" y1=\"133\" x2=\"56\" y2=\"133\" /><line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" /><line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text><text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step1_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-495f177e-f5b6-477b-b160-65f5e5c11184' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-495f177e-f5b6-477b-b160-65f5e5c11184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10d57ea8-70ed-4dac-9585-44549de7923e' class='xr-var-data-in' type='checkbox'><label for='data-10d57ea8-70ed-4dac-9585-44549de7923e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>step 1 ozone solution</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>50.0</dd><dt><span>valid_max :</span></dt><dd>700.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step2_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-af1fa7d0-47d7-4341-81fa-e43736cd14ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af1fa7d0-47d7-4341-81fa-e43736cd14ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07cf6150-815a-46ec-8e73-d648f2455300' class='xr-var-data-in' type='checkbox'><label for='data-07cf6150-815a-46ec-8e73-d648f2455300' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>step 2 ozone solution</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>50.0</dd><dt><span>valid_max :</span></dt><dd>700.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 1.02 MiB </td><td> 1.02 MiB </td></tr><tr><th> Shape </th><td> (131, 2048) </td><td> (131, 2048) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" /><line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text><text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text></svg></td></tr></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cal_adjustment</span></div><div class='xr-var-dims'>(xtrack, wavelength)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2048, 12), meta=np.ndarray&gt;</div><input id='attrs-65d57f4f-28a8-4bff-aec3-2afe72f238e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65d57f4f-28a8-4bff-aec3-2afe72f238e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b92213de-158b-4a6b-9eea-639263641944' class='xr-var-data-in' type='checkbox'><label for='data-b92213de-158b-4a6b-9eea-639263641944' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>calibration adjustment</dd><dt><span>valid_min :</span></dt><dd>-10.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd></dl></div><div class='xr-var-data'><table><tr><td><table style=\"border-collapse: collapse;\"><thead><tr><td> </td><th> Array </th><th> Chunk </th></tr></thead><tbody><tr><th> Bytes </th><td> 96.00 kiB </td><td> 96.00 kiB </td></tr><tr><th> Shape </th><td> (2048, 12) </td><td> (2048, 12) </td></tr><tr><th> Dask graph </th><td colspan=\"2\"> 1 chunks in 2 graph layers </td></tr><tr><th> Data type </th><td colspan=\"2\"> float32 numpy.ndarray </td></tr></tbody></table></td><td><svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" ><!-- Horizontal lines --><line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" /><line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" /><!-- Vertical lines --><line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" /><line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" /><!-- Colored Rectangle --><polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/><!-- Text --><text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >12</text><text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">2048</text></svg></td></tr></table></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><div class='xr-header'><div class='xr-group-name'>/qa_statistics</div></div><ul class='xr-sections'></ul></div></div></div></div></li><li class='xr-section-item'><input id='section-1192019d-018f-4c6a-b6c4-56eb5d62ab1c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1192019d-018f-4c6a-b6c4-56eb5d62ab1c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>xtrack</span>: 2048</li><li><span class='xr-has-index'>mirror_step</span>: 131</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3981dd64-7d29-4081-a3ee-f65263b260ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3981dd64-7d29-4081-a3ee-f65263b260ab' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xtrack</span></div><div class='xr-var-dims'>(xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2044 2045 2046 2047</div><input id='attrs-db1061be-6518-4271-877a-5f51706af7f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db1061be-6518-4271-877a-5f51706af7f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35bf2a81-7c04-4fe7-a694-10da111b8191' class='xr-var-data-in' type='checkbox'><label for='data-35bf2a81-7c04-4fe7-a694-10da111b8191' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>pixel index along slit</dd></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2045, 2046, 2047], shape=(2048,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mirror_step</span></div><div class='xr-var-dims'>(mirror_step)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>132 133 134 135 ... 259 260 261 262</div><input id='attrs-49f7dcef-8715-4d7a-bf09-04b73c4c8255' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49f7dcef-8715-4d7a-bf09-04b73c4c8255' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a00a3dca-25ce-4594-aea3-b85c8ee9eb68' class='xr-var-data-in' type='checkbox'><label for='data-a00a3dca-25ce-4594-aea3-b85c8ee9eb68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>scan mirror position index</dd></dl></div><div class='xr-var-data'><pre>array([132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,\n",
-       "       146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
-       "       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173,\n",
-       "       174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187,\n",
-       "       188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201,\n",
-       "       202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215,\n",
-       "       216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,\n",
-       "       230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243,\n",
-       "       244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,\n",
-       "       258, 259, 260, 261, 262], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd7104cd-60a1-4eb0-954b-a6934592c388' class='xr-section-summary-in' type='checkbox'  ><label for='section-bd7104cd-60a1-4eb0-954b-a6934592c388' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>time_reference :</span></dt><dd>1980-01-06T00:00:00Z</dd><dt><span>scan_num :</span></dt><dd>1</dd><dt><span>granule_num :</span></dt><dd>2</dd><dt><span>time_coverage_start :</span></dt><dd>2024-01-03T12:59:34Z</dd><dt><span>time_coverage_end :</span></dt><dd>2024-01-03T13:06:11Z</dd><dt><span>time_coverage_start_since_epoch :</span></dt><dd>1388321992.512726</dd><dt><span>time_coverage_end_since_epoch :</span></dt><dd>1388322389.4111006</dd><dt><span>product_type :</span></dt><dd>O3TOT</dd><dt><span>processing_level :</span></dt><dd>2</dd><dt><span>processing_version :</span></dt><dd>3</dd><dt><span>sdpc_version :</span></dt><dd>TEMPO_SDPC_v4.4.2</dd><dt><span>geospatial_bounds :</span></dt><dd>POLYGON((58.7460 -63.7219,54.9606 -66.7865,50.7340 -69.3919,46.0275 -71.6021,41.2245 -73.3336,35.7360 -74.8531,29.8413 -76.0882,23.4120 -77.0890,17.2992 -77.7607,17.3834 -71.6776,21.3196 -71.0525,25.0933 -70.3146,28.6897 -69.4649,32.2082 -68.4707,35.5012 -67.3664,38.6396 -66.1259,41.6019 -64.7509,44.3806 -63.2414,46.9641 -61.6037,49.3638 -59.8332,51.7108 -57.8152,53.7180 -55.8028,55.6668 -53.5377,57.4343 -51.1466,59.1001 -48.5181,60.5875 -45.7741,59.5539 -55.2095,58.7763 -63.6414,58.7460 -63.7219))</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lon_min :</span></dt><dd>-77.76071</dd><dt><span>geospatial_lon_max :</span></dt><dd>-45.774143</dd><dt><span>geospatial_lat_min :</span></dt><dd>17.299183</dd><dt><span>geospatial_lat_max :</span></dt><dd>60.587494</dd><dt><span>input_files :</span></dt><dd>TEMPO_RAD_L1_V03_20240103T125934Z_S001G02.nc, TEMPO_IRR_L1_V03_20231228T053116Z.nc, TEMPO_CLDO4_L2_V03_20240103T125934Z_S001G02.nc</dd><dt><span>local_granule_id :</span></dt><dd>TEMPO_O3TOT_L2_V03_20240103T125934Z_S001G02.nc</dd><dt><span>version_id :</span></dt><dd>3</dd><dt><span>production_date_time :</span></dt><dd>2024-08-07T08:06:32.318 UTC-0400</dd><dt><span>day_of_year :</span></dt><dd>3</dd><dt><span>project :</span></dt><dd>TEMPO</dd><dt><span>platform :</span></dt><dd>Intelsat 40e</dd><dt><span>source :</span></dt><dd>UV-VIS hyperspectral imaging</dd><dt><span>institution :</span></dt><dd>Smithsonian Astrophysical Observatory</dd><dt><span>creator_url :</span></dt><dd>http://tempo.si.edu</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>title :</span></dt><dd>TEMPO Level 2 total ozone product</dd><dt><span>collection_shortname :</span></dt><dd>TEMPO_O3TOT_L2</dd><dt><span>collection_version :</span></dt><dd>1</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE&gt;ATMOSPHERE&gt;ATMOSPHERIC CHEMISTRY&gt;OXYGEN COMPOUNDS&gt;OZONE</dd><dt><span>summary :</span></dt><dd>Total ozone Level 2 files provide ozone information at TEMPO’s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on total column ozone and some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The retrieval is based on the OMI TOMS V8.5 algorithm adapted for TEMPO. For further details, please refer to the ATBD.</dd><dt><span>coremetadata :</span></dt><dd>\n",
-       "GROUP                  = INVENTORYMETADATA\n",
-       "  GROUPTYPE            = MASTERGROUP\n",
-       "\n",
-       "  GROUP                  = ECSDATAGRANULE\n",
-       "\n",
-       "    OBJECT                 = LOCALGRANULEID\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;TEMPO_O3TOT_L2_V03_20240103T125934Z_S001G02.nc&quot;\n",
-       "    END_OBJECT             = LOCALGRANULEID\n",
-       "\n",
-       "    OBJECT                 = LOCALVERSIONID\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = (&quot;RFC1321 MD5 = not yet calculated&quot;)\n",
-       "    END_OBJECT             = LOCALVERSIONID\n",
-       "\n",
-       "    OBJECT                 = PRODUCTIONDATETIME\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;2024-08-07T12:06:32.000Z&quot;\n",
-       "    END_OBJECT             = PRODUCTIONDATETIME\n",
-       "\n",
-       "  END_GROUP              = ECSDATAGRANULE\n",
-       "\n",
-       "  GROUP                  = COLLECTIONDESCRIPTIONCLASS\n",
-       "\n",
-       "    OBJECT                 = SHORTNAME\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;TEMPO_O3TOT_L2&quot;\n",
-       "    END_OBJECT             = SHORTNAME\n",
-       "\n",
-       "    OBJECT                 = VERSIONID\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = (3)\n",
-       "    END_OBJECT             = VERSIONID\n",
-       "\n",
-       "  END_GROUP              = COLLECTIONDESCRIPTIONCLASS\n",
-       "\n",
-       "  GROUP                  = INPUTGRANULE\n",
-       "\n",
-       "    OBJECT                 = INPUTPOINTER\n",
-       "      NUM_VAL              = 3\n",
-       "      VALUE                = (&quot;TEMPO_RAD_L1_V03_20240103T125934Z_S001G02.nc&quot;, &quot;TEMPO_IRR_L1_V03_20231228T053116Z.nc&quot;, &quot;TEMPO_CLDO4_L2_V03_20240103T125934Z_S001G02.nc&quot;)\n",
-       "    END_OBJECT             = INPUTPOINTER\n",
-       "\n",
-       "  END_GROUP              = INPUTGRANULE\n",
-       "\n",
-       "  GROUP                  = SPATIALDOMAINCONTAINER\n",
-       "\n",
-       "    GROUP                  = HORIZONTALSPATIALDOMAINCONTAINER\n",
-       "\n",
-       "      GROUP                  = GPOLYGON\n",
-       "\n",
-       "        OBJECT                 = GPOLYGONCONTAINER\n",
-       "          CLASS                = &quot;1&quot;\n",
-       "\n",
-       "          GROUP                  = GRINGPOINT\n",
-       "            CLASS                = &quot;1&quot;\n",
-       "\n",
-       "            OBJECT                 = GRINGPOINTLONGITUDE\n",
-       "              NUM_VAL              = 29\n",
-       "              CLASS                = &quot;1&quot;\n",
-       "              VALUE                = (-63.7218704223633, -66.7865447998047, -69.3919067382812, -71.6020584106445, -73.3336029052734, -74.8530654907227, -76.0881729125977, -77.0889892578125, -77.7607116699219, -71.6775817871094, -71.0524597167969, -70.3145980834961, -69.4649047851562, -68.470703125, \n",
-       "                                      -67.3664245605469, -66.1259002685547, -64.7509384155273, -63.2413864135742, -61.6036949157715, -59.8332366943359, -57.8151702880859, -55.8027954101562, -53.5377426147461, -51.1465682983398, -48.5181427001953, -45.7741432189941, -55.2095375061035, \n",
-       "                                      -63.6414031982422, -63.7218704223633)\n",
-       "            END_OBJECT             = GRINGPOINTLONGITUDE\n",
-       "\n",
-       "            OBJECT                 = GRINGPOINTLATITUDE\n",
-       "              NUM_VAL              = 29\n",
-       "              CLASS                = &quot;1&quot;\n",
-       "              VALUE                = (58.7460250854492, 54.9606132507324, 50.733959197998, 46.0275497436523, 41.2245101928711, 35.7360458374023, 29.8412837982178, 23.4120216369629, 17.2991828918457, 17.3833675384521, 21.3195819854736, 25.0932579040527, 28.6896781921387, 32.2081680297852, \n",
-       "                                      35.5011901855469, 38.6395874023438, 41.6018981933594, 44.3806228637695, 46.9640960693359, 49.3638229370117, 51.710807800293, 53.7180252075195, 55.6668243408203, 57.4343490600586, 59.1000556945801, 60.5874938964844, 59.5538940429688, 58.7763061523438, \n",
-       "                                      58.7460250854492)\n",
-       "            END_OBJECT             = GRINGPOINTLATITUDE\n",
-       "\n",
-       "            OBJECT                 = GRINGPOINTSEQUENCENO\n",
-       "              NUM_VAL              = 29\n",
-       "              CLASS                = &quot;1&quot;\n",
-       "              VALUE                = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29)\n",
-       "            END_OBJECT             = GRINGPOINTSEQUENCENO\n",
-       "\n",
-       "          END_GROUP              = GRINGPOINT\n",
-       "\n",
-       "          GROUP                  = GRING\n",
-       "            CLASS                = &quot;1&quot;\n",
        "\n",
-       "            OBJECT                 = EXCLUSIONGRINGFLAG\n",
-       "              NUM_VAL              = 1\n",
-       "              VALUE                = &quot;N&quot;\n",
-       "              CLASS                = &quot;1&quot;\n",
-       "            END_OBJECT             = EXCLUSIONGRINGFLAG\n",
-       "\n",
-       "          END_GROUP              = GRING\n",
-       "\n",
-       "        END_OBJECT             = GPOLYGONCONTAINER\n",
-       "\n",
-       "      END_GROUP              = GPOLYGON\n",
-       "\n",
-       "    END_GROUP              = HORIZONTALSPATIALDOMAINCONTAINER\n",
-       "\n",
-       "  END_GROUP              = SPATIALDOMAINCONTAINER\n",
-       "\n",
-       "  GROUP                  = RANGEDATETIME\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
        "\n",
-       "    OBJECT                 = RANGEENDINGDATE\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;2024-01-03&quot;\n",
-       "    END_OBJECT             = RANGEENDINGDATE\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
        "\n",
-       "    OBJECT                 = RANGEENDINGTIME\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;13:06:11&quot;\n",
-       "    END_OBJECT             = RANGEENDINGTIME\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
        "\n",
-       "    OBJECT                 = RANGEBEGINNINGDATE\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;2024-01-03&quot;\n",
-       "    END_OBJECT             = RANGEBEGINNINGDATE\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
        "\n",
-       "    OBJECT                 = RANGEBEGINNINGTIME\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = &quot;12:59:34&quot;\n",
-       "    END_OBJECT             = RANGEBEGINNINGTIME\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
        "\n",
-       "  END_GROUP              = RANGEDATETIME\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
        "\n",
-       "  GROUP                  = PGEVERSIONCLASS\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
        "\n",
-       "    OBJECT                 = PGEVERSION\n",
-       "      NUM_VAL              = 1\n",
-       "      VALUE                = (&quot;2.2.1&quot;)\n",
-       "    END_OBJECT             = PGEVERSION\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
        "\n",
-       "  END_GROUP              = PGEVERSIONCLASS\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
        "\n",
-       "END_GROUP              = INVENTORYMETADATA\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
        "\n",
-       "END\n",
-       "</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataTree>\n",
-       "Group: /\n",
-       "│   Dimensions:      (xtrack: 2048, mirror_step: 131)\n",
-       "│   Coordinates:\n",
-       "│     * xtrack       (xtrack) int32 8kB 0 1 2 3 4 5 ... 2043 2044 2045 2046 2047\n",
-       "│     * mirror_step  (mirror_step) int32 524B 132 133 134 135 ... 259 260 261 262\n",
-       "│   Attributes: (12/34)\n",
-       "│       time_reference:                   1980-01-06T00:00:00Z\n",
-       "│       scan_num:                         1\n",
-       "│       granule_num:                      2\n",
-       "│       time_coverage_start:              2024-01-03T12:59:34Z\n",
-       "│       time_coverage_end:                2024-01-03T13:06:11Z\n",
-       "│       time_coverage_start_since_epoch:  1388321992.512726\n",
-       "│       ...                               ...\n",
-       "│       title:                            TEMPO Level 2 total ozone product\n",
-       "│       collection_shortname:             TEMPO_O3TOT_L2\n",
-       "│       collection_version:               1\n",
-       "│       keywords:                         EARTH SCIENCE>ATMOSPHERE>ATMOSPHERIC CH...\n",
-       "│       summary:                          Total ozone Level 2 files provide ozone...\n",
-       "│       coremetadata:                     \\nGROUP                  = INVENTORYMET...\n",
-       "├── Group: /product\n",
-       "│       Dimensions:               (mirror_step: 131, xtrack: 2048)\n",
-       "│       Data variables:\n",
-       "│           column_amount_o3      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           radiative_cloud_frac  (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           fc                    (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           quality_flag          (mirror_step, xtrack) int32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           o3_below_cloud        (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           so2_index             (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           uv_aerosol_index      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "├── Group: /geolocation\n",
-       "│       Dimensions:                 (mirror_step: 131, xtrack: 2048, corner: 4)\n",
-       "│       Coordinates:\n",
-       "│           time                    (mirror_step) datetime64[ns] 1kB dask.array<chunksize=(131,), meta=np.ndarray>\n",
-       "│           latitude                (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           longitude               (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│       Dimensions without coordinates: corner\n",
-       "│       Data variables:\n",
-       "│           latitude_bounds         (mirror_step, xtrack, corner) float32 4MB dask.array<chunksize=(131, 2048, 4), meta=np.ndarray>\n",
-       "│           longitude_bounds        (mirror_step, xtrack, corner) float32 4MB dask.array<chunksize=(131, 2048, 4), meta=np.ndarray>\n",
-       "│           solar_zenith_angle      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           solar_azimuth_angle     (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           viewing_zenith_angle    (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           viewing_azimuth_angle   (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           relative_azimuth_angle  (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "├── Group: /support_data\n",
-       "│       Dimensions:                        (mirror_step: 131, xtrack: 2048,\n",
-       "│                                           wavelength: 12, layer: 11)\n",
-       "│       Dimensions without coordinates: wavelength, layer\n",
-       "│       Data variables: (12/21)\n",
-       "│           ground_pixel_quality_flag      (mirror_step, xtrack) int32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           lut_wavelength                 (wavelength) float32 48B dask.array<chunksize=(12,), meta=np.ndarray>\n",
-       "│           cloud_pressure                 (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           terrain_pressure               (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           terrain_height                 (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           algorithm_flags                (mirror_step, xtrack) int32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           ...                             ...\n",
-       "│           step_2_N_value_residual        (mirror_step, xtrack, wavelength) float32 13MB dask.array<chunksize=(1, 128, 12), meta=np.ndarray>\n",
-       "│           ozone_sensitivity_ratio        (mirror_step, xtrack, wavelength) float32 13MB dask.array<chunksize=(1, 128, 12), meta=np.ndarray>\n",
-       "│           temp_sensitivity_ratio         (mirror_step, xtrack, wavelength) float32 13MB dask.array<chunksize=(1, 128, 12), meta=np.ndarray>\n",
-       "│           step1_o3                       (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           step2_o3                       (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "│           cal_adjustment                 (xtrack, wavelength) float32 98kB dask.array<chunksize=(2048, 12), meta=np.ndarray>\n",
-       "└── Group: /qa_statistics"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds = plan.open_dataset(0, open_method=\"datatree\")\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fca64cc-cf4f-4d14-8975-db386ff7844e",
-   "metadata": {},
-   "source": [
-    "### Ok now we know the groups\n",
-    "\n",
-    "We will make a open_method dictionary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "284f0557-d4cd-4820-a56e-bd653928b40a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "open_method: {'xarray_open': 'dataset', 'merge': ['/product', '/geolocation'], 'coords': {'lat': 'latitude', 'lon': 'longitude'}, 'open_kwargs': {'chunks': {}, 'engine': 'h5netcdf', 'decode_timedelta': False}, 'set_coords': True, 'dim_renames': None, 'auto_align_phony_dims': None, 'merge_kwargs': {}}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
        "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, white)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
        "}\n",
        "\n",
-       "html[theme=\"dark\"],\n",
-       "html[data-theme=\"dark\"],\n",
-       "body[data-theme=\"dark\"],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "  line-height: 1.6;\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
        "}\n",
        "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
        "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
        "}\n",
        "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
        "}\n",
        "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
        "}\n",
        "\n",
-       ".xr-obj-type,\n",
-       ".xr-obj-name,\n",
-       ".xr-group-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
        "}\n",
        "\n",
-       ".xr-group-name::before {\n",
-       "  content: \"📁\";\n",
-       "  padding-right: 0.3em;\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-group-name,\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
        "}\n",
        "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "  margin-block-start: 0;\n",
-       "  margin-block-end: 0;\n",
+       "  grid-template-columns: 125px auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
        "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "  margin: 0;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
        "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 549MB\n",
+       "Dimensions:                      (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "                                  phony_dim_2: 4, phony_dim_3: 11,\n",
+       "                                  phony_dim_4: 26, phony_dim_5: 3,\n",
+       "                                  phony_dim_6: 1, phony_dim_7: 8)\n",
+       "Coordinates:\n",
+       "    Latitude                     (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "    Longitude                    (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "                                phony_dim_3, phony_dim_4, phony_dim_5,\n",
+       "                                phony_dim_6, phony_dim_7\n",
+       "Data variables: (12/27)\n",
+       "    AlgorithmFlag                (phony_dim_0, phony_dim_1) int16 8MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "    CalibrationCoef              (phony_dim_2) float32 16B dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
+       "    CloudPressure                (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "    ColumnWeightFunctionPercent  (phony_dim_0, phony_dim_1, phony_dim_3) uint8 46MB dask.array&lt;chunksize=(256, 256, 11), meta=np.ndarray&gt;\n",
+       "    ControlFileContents          (phony_dim_4) object 208B dask.array&lt;chunksize=(26,), meta=np.ndarray&gt;\n",
+       "    DNDomega                     (phony_dim_0, phony_dim_1, phony_dim_5) float32 50MB dask.array&lt;chunksize=(256, 256, 3), meta=np.ndarray&gt;\n",
+       "    ...                           ...\n",
+       "    SlopeOfReflectivity          (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "    SolarAzimuthAngle            (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "    SolarZenithAngle             (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "    TerrainPressure              (phony_dim_0, phony_dim_1) float32 17MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "    Wavelength                   (phony_dim_2) float32 16B dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
+       "    YearDaySeconds               (phony_dim_5) int32 12B dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5742e5e7-ef8e-4b96-9ec0-aafb5ca8ac53' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-5742e5e7-ef8e-4b96-9ec0-aafb5ca8ac53' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>phony_dim_0</span>: 2048</li><li><span>phony_dim_1</span>: 2048</li><li><span>phony_dim_2</span>: 4</li><li><span>phony_dim_3</span>: 11</li><li><span>phony_dim_4</span>: 26</li><li><span>phony_dim_5</span>: 3</li><li><span>phony_dim_6</span>: 1</li><li><span>phony_dim_7</span>: 8</li></ul></div></li><li class='xr-section-item'><input id='section-a0d91aaf-2668-4b33-903f-f8ce23b2b05d' class='xr-section-summary-in' type='checkbox' checked /><label for='section-a0d91aaf-2668-4b33-903f-f8ce23b2b05d' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Latitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-98b8e822-8de7-46f2-a4ce-497d880db15a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98b8e822-8de7-46f2-a4ce-497d880db15a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f75be803-714b-4483-a72e-440919018244' class='xr-var-data-in' type='checkbox'><label for='data-f75be803-714b-4483-a72e-440919018244' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-fa3af10c-3231-4ce6-9376-489229899af1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa3af10c-3231-4ce6-9376-489229899af1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eac24577-37d2-4aab-9043-63cc77fd8a61' class='xr-var-data-in' type='checkbox'><label for='data-eac24577-37d2-4aab-9043-63cc77fd8a61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-131cbd42-6b15-4198-a653-607eed32a831' class='xr-section-summary-in' type='checkbox' /><label for='section-131cbd42-6b15-4198-a653-607eed32a831' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(27)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AlgorithmFlag</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-7728c592-4c6a-414c-a50e-2f737736d97c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7728c592-4c6a-414c-a50e-2f737736d97c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70c189fd-9b62-417b-88fd-255bef0f6651' class='xr-var-data-in' type='checkbox'><label for='data-70c189fd-9b62-417b-88fd-255bef0f6651' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 MiB </td>\n",
+       "                        <td> 128.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CalibrationCoef</span></div><div class='xr-var-dims'>(phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-82bc9402-0b98-4813-ac71-dc606b68ef25' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82bc9402-0b98-4813-ac71-dc606b68ef25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6cea2f9-b8e7-4d8b-8330-53c4ddb902e0' class='xr-var-data-in' type='checkbox'><label for='data-c6cea2f9-b8e7-4d8b-8330-53c4ddb902e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16 B </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.89879552186203 0.0,42.89879552186203\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"62.898796\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"140.000000\" y=\"21.449398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.449398)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CloudPressure</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-eb1c7fc6-f1f8-48fe-9a43-df2c87bade98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb1c7fc6-f1f8-48fe-9a43-df2c87bade98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73e9025f-feb0-4df6-807a-7c693ac2512e' class='xr-var-data-in' type='checkbox'><label for='data-73e9025f-feb0-4df6-807a-7c693ac2512e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-top: 4px;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ColumnWeightFunctionPercent</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_3)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256, 11), meta=np.ndarray&gt;</div><input id='attrs-022997ab-168a-4c40-a764-baaef3b58592' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-022997ab-168a-4c40-a764-baaef3b58592' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6722e869-9c85-4024-a336-dd4148eb0e83' class='xr-var-data-in' type='checkbox'><label for='data-6722e869-9c85-4024-a336-dd4148eb0e83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 44.00 MiB </td>\n",
+       "                        <td> 704.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 11) </td>\n",
+       "                        <td> (256, 256, 11) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"15\" x2=\"80\" y2=\"85\" />\n",
+       "  <line x1=\"10\" y1=\"30\" x2=\"80\" y2=\"100\" />\n",
+       "  <line x1=\"10\" y1=\"45\" x2=\"80\" y2=\"115\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"75\" x2=\"80\" y2=\"145\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"80\" y2=\"160\" />\n",
+       "  <line x1=\"10\" y1=\"105\" x2=\"80\" y2=\"175\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-group-box {\n",
-       "  display: inline-grid;\n",
-       "  grid-template-columns: 0px 20px auto;\n",
-       "  width: 100%;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-group-box-vline {\n",
-       "  grid-column-start: 1;\n",
-       "  border-right: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "  width: 0px;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-group-box-hline {\n",
-       "  grid-column-start: 2;\n",
-       "  grid-row-start: 1;\n",
-       "  height: 1em;\n",
-       "  width: 20px;\n",
-       "  border-bottom: 0.2em solid;\n",
-       "  border-color: var(--xr-border-color);\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-group-box-contents {\n",
-       "  grid-column-start: 3;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"85\" x2=\"106\" y2=\"85\" />\n",
+       "  <line x1=\"80\" y1=\"100\" x2=\"106\" y2=\"100\" />\n",
+       "  <line x1=\"80\" y1=\"115\" x2=\"106\" y2=\"115\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"145\" x2=\"106\" y2=\"145\" />\n",
+       "  <line x1=\"80\" y1=\"160\" x2=\"106\" y2=\"160\" />\n",
+       "  <line x1=\"80\" y1=\"175\" x2=\"106\" y2=\"175\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >11</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ControlFileContents</span></div><div class='xr-var-dims'>(phony_dim_4)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(26,), meta=np.ndarray&gt;</div><input id='attrs-7bc531de-9cbd-4b72-bb9b-aff11ae86e07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bc531de-9cbd-4b72-bb9b-aff11ae86e07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e600d579-5bff-45a1-892f-4b67c121c06e' class='xr-var-data-in' type='checkbox'><label for='data-e600d579-5bff-45a1-892f-4b67c121c06e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 208 B </td>\n",
+       "                        <td> 208 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (26,) </td>\n",
+       "                        <td> (26,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"82\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"32\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"32\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,32.89421872575591 0.0,32.89421872575591\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"52.894219\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >26</text>\n",
+       "  <text x=\"140.000000\" y=\"16.447109\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,16.447109)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DNDomega</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256, 3), meta=np.ndarray&gt;</div><input id='attrs-9dfd31e6-e584-4c28-ae05-da2883bab292' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9dfd31e6-e584-4c28-ae05-da2883bab292' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6ef16b0-2d8a-4d06-865d-9fa22c9142d7' class='xr-var-data-in' type='checkbox'><label for='data-e6ef16b0-2d8a-4d06-865d-9fa22c9142d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 48.00 MiB </td>\n",
+       "                        <td> 768.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 3) </td>\n",
+       "                        <td> (256, 256, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"15\" x2=\"80\" y2=\"85\" />\n",
+       "  <line x1=\"10\" y1=\"30\" x2=\"80\" y2=\"100\" />\n",
+       "  <line x1=\"10\" y1=\"45\" x2=\"80\" y2=\"115\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"75\" x2=\"80\" y2=\"145\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"80\" y2=\"160\" />\n",
+       "  <line x1=\"10\" y1=\"105\" x2=\"80\" y2=\"175\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"85\" x2=\"106\" y2=\"85\" />\n",
+       "  <line x1=\"80\" y1=\"100\" x2=\"106\" y2=\"100\" />\n",
+       "  <line x1=\"80\" y1=\"115\" x2=\"106\" y2=\"115\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"145\" x2=\"106\" y2=\"145\" />\n",
+       "  <line x1=\"80\" y1=\"160\" x2=\"106\" y2=\"160\" />\n",
+       "  <line x1=\"80\" y1=\"175\" x2=\"106\" y2=\"175\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DNDreflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256, 4), meta=np.ndarray&gt;</div><input id='attrs-01010df6-8f8c-43c9-b59f-07a7af51f676' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01010df6-8f8c-43c9-b59f-07a7af51f676' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32d38d4b-46bc-4f25-9b29-1ddf8fc2ad69' class='xr-var-data-in' type='checkbox'><label for='data-32d38d4b-46bc-4f25-9b29-1ddf8fc2ad69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 64.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048, 4) </td>\n",
+       "                        <td> (256, 256, 4) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"15\" x2=\"80\" y2=\"85\" />\n",
+       "  <line x1=\"10\" y1=\"30\" x2=\"80\" y2=\"100\" />\n",
+       "  <line x1=\"10\" y1=\"45\" x2=\"80\" y2=\"115\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"75\" x2=\"80\" y2=\"145\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"80\" y2=\"160\" />\n",
+       "  <line x1=\"10\" y1=\"105\" x2=\"80\" y2=\"175\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"85\" x2=\"106\" y2=\"85\" />\n",
+       "  <line x1=\"80\" y1=\"100\" x2=\"106\" y2=\"100\" />\n",
+       "  <line x1=\"80\" y1=\"115\" x2=\"106\" y2=\"115\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"145\" x2=\"106\" y2=\"145\" />\n",
+       "  <line x1=\"80\" y1=\"160\" x2=\"106\" y2=\"160\" />\n",
+       "  <line x1=\"80\" y1=\"175\" x2=\"106\" y2=\"175\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ErrorFlag</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-7dcb7e6f-1b7e-4deb-909d-b4e41a4dedee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7dcb7e6f-1b7e-4deb-909d-b4e41a4dedee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a27418a-bd14-41fc-843a-41771ed3e7fd' class='xr-var-data-in' type='checkbox'><label for='data-3a27418a-bd14-41fc-843a-41771ed3e7fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 MiB </td>\n",
+       "                        <td> 128.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ErythemalUV</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-5ab6f72b-7138-4fb3-a986-f8c47d872141' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ab6f72b-7138-4fb3-a986-f8c47d872141' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4360c681-5e66-44db-b03b-51a8cfb37488' class='xr-var-data-in' type='checkbox'><label for='data-4360c681-5e66-44db-b03b-51a8cfb37488' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LERTerrPres</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-200871ce-f2d9-4ec7-9aa4-6384669a2e81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-200871ce-f2d9-4ec7-9aa4-6384669a2e81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15ebf310-e1bb-411b-9c1e-0131b3918792' class='xr-var-data-in' type='checkbox'><label for='data-15ebf310-e1bb-411b-9c1e-0131b3918792' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 24MB\n",
-       "Dimensions:                 (mirror_step: 131, xtrack: 2048, corner: 4)\n",
-       "Coordinates:\n",
-       "    time                    (mirror_step) datetime64[ns] 1kB dask.array&lt;chunksize=(131,), meta=np.ndarray&gt;\n",
-       "    latitude                (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    longitude               (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "Dimensions without coordinates: mirror_step, xtrack, corner\n",
-       "Data variables: (12/14)\n",
-       "    column_amount_o3        (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    radiative_cloud_frac    (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    fc                      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    quality_flag            (mirror_step, xtrack) int32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    o3_below_cloud          (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    so2_index               (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    ...                      ...\n",
-       "    longitude_bounds        (mirror_step, xtrack, corner) float32 4MB dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;\n",
-       "    solar_zenith_angle      (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    solar_azimuth_angle     (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    viewing_zenith_angle    (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    viewing_azimuth_angle   (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;\n",
-       "    relative_azimuth_angle  (mirror_step, xtrack) float32 1MB dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0a448c48-abee-4677-85c2-806aa1e7c574' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a448c48-abee-4677-85c2-806aa1e7c574' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>mirror_step</span>: 131</li><li><span>xtrack</span>: 2048</li><li><span>corner</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a00c09c9-2a55-415a-9f5e-698faea00109' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a00c09c9-2a55-415a-9f5e-698faea00109' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(mirror_step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131,), meta=np.ndarray&gt;</div><input id='attrs-c4a9f7e1-fe67-49f0-b919-29613c849e9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4a9f7e1-fe67-49f0-b919-29613c849e9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f317f95c-a5a8-4ecf-bdf9-45529e80599a' class='xr-var-data-in' type='checkbox'><label for='data-f317f95c-a5a8-4ecf-bdf9-45529e80599a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>exposure start time</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NValue</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256, 4), meta=np.ndarray&gt;</div><input id='attrs-02a663bc-3f34-496f-87ac-7510a067330a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02a663bc-3f34-496f-87ac-7510a067330a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d7a704f-54cd-47c3-ba24-3705e4a7ec06' class='xr-var-data-in' type='checkbox'><label for='data-2d7a704f-54cd-47c3-ba24-3705e4a7ec06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7148,47 +6066,98 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 kiB </td>\n",
-       "                        <td> 1.02 kiB </td>\n",
+       "                        <td> 64.00 MiB </td>\n",
+       "                        <td> 1.00 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131,) </td>\n",
-       "                        <td> (131,) </td>\n",
+       "                        <td> (2048, 2048, 4) </td>\n",
+       "                        <td> (256, 256, 4) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
        "                    </tr>\n",
        "                </tbody>\n",
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"15\" x2=\"80\" y2=\"85\" />\n",
+       "  <line x1=\"10\" y1=\"30\" x2=\"80\" y2=\"100\" />\n",
+       "  <line x1=\"10\" y1=\"45\" x2=\"80\" y2=\"115\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"75\" x2=\"80\" y2=\"145\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"80\" y2=\"160\" />\n",
+       "  <line x1=\"10\" y1=\"105\" x2=\"80\" y2=\"175\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"85\" x2=\"106\" y2=\"85\" />\n",
+       "  <line x1=\"80\" y1=\"100\" x2=\"106\" y2=\"100\" />\n",
+       "  <line x1=\"80\" y1=\"115\" x2=\"106\" y2=\"115\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"145\" x2=\"106\" y2=\"145\" />\n",
+       "  <line x1=\"80\" y1=\"160\" x2=\"106\" y2=\"160\" />\n",
+       "  <line x1=\"80\" y1=\"175\" x2=\"106\" y2=\"175\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >131</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-1940561c-8259-484b-83b0-273279db409f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1940561c-8259-484b-83b0-273279db409f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e3552a7-bf0a-47f9-babd-73b629628c51' class='xr-var-data-in' type='checkbox'><label for='data-1e3552a7-bf0a-47f9-babd-73b629628c51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>comment :</span></dt><dd>latitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>bounds :</span></dt><dd>latitude_bounds</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NoABandCloud</span></div><div class='xr-var-dims'>(phony_dim_6)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-8f1a73de-8d4f-48ed-8a0f-88c16016f68e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f1a73de-8d4f-48ed-8a0f-88c16016f68e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e37c1468-4df9-4cd0-ba77-49c2362916b1' class='xr-var-data-in' type='checkbox'><label for='data-e37c1468-4df9-4cd0-ba77-49c2362916b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7203,14 +6172,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -7218,32 +6187,32 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
        "                    </tr>\n",
        "                </tbody>\n",
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-8471be32-ef1e-4122-ae51-c24974b65727' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8471be32-ef1e-4122-ae51-c24974b65727' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09bde79b-9834-47fb-9c1c-83a676dbcc38' class='xr-var-data-in' type='checkbox'><label for='data-09bde79b-9834-47fb-9c1c-83a676dbcc38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>comment :</span></dt><dd>longitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>bounds :</span></dt><dd>longitude_bounds</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NvalueAdjust</span></div><div class='xr-var-dims'>(phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-822017a9-5814-4675-a89a-7df3ae6b18c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-822017a9-5814-4675-a89a-7df3ae6b18c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d80cfb3f-6993-4fa5-9897-73a6d41ce2de' class='xr-var-data-in' type='checkbox'><label for='data-d80cfb3f-6993-4fa5-9897-73a6d41ce2de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7258,14 +6227,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                        <td> 16 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -7279,26 +6248,26 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.89879552186203 0.0,42.89879552186203\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"62.898796\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"140.000000\" y=\"21.449398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.449398)\">1</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f127462a-ea38-4dff-a756-167b180380c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f127462a-ea38-4dff-a756-167b180380c6' class='xr-section-summary' >Data variables: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>column_amount_o3</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-ed1c451c-7ee1-4049-873a-601230b735a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed1c451c-7ee1-4049-873a-601230b735a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48594ef4-6f8b-42ae-a011-8fc9303fcff6' class='xr-var-data-in' type='checkbox'><label for='data-48594ef4-6f8b-42ae-a011-8fc9303fcff6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>best total ozone solution</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>50.0</dd><dt><span>valid_max :</span></dt><dd>700.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ozone</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-983c0b58-5908-455b-87b5-425a2ecd73a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-983c0b58-5908-455b-87b5-425a2ecd73a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2fb3ef4-38f5-4057-9e80-d420f9d6fd2a' class='xr-var-data-in' type='checkbox'><label for='data-a2fb3ef4-38f5-4057-9e80-d420f9d6fd2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7313,18 +6282,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7334,26 +6303,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radiative_cloud_frac</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-fb43d5c3-4a6c-4e93-95c0-e67c670e2625' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb43d5c3-4a6c-4e93-95c0-e67c670e2625' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72f9d397-9f96-4261-804f-9a55af1db418' class='xr-var-data-in' type='checkbox'><label for='data-72f9d397-9f96-4261-804f-9a55af1db418' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>cloud radiance fraction = fc*Ic331/Im331</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OzoneStep1</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-339c2dc7-fee5-4f59-b594-2744f3b0346f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-339c2dc7-fee5-4f59-b594-2744f3b0346f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-993e454b-9579-4f5e-b153-2df4433cd5e8' class='xr-var-data-in' type='checkbox'><label for='data-993e454b-9579-4f5e-b153-2df4433cd5e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7368,18 +6351,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7389,26 +6372,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fc</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-4ae9e2f5-ebcd-475f-8c4b-c4c508e9f591' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ae9e2f5-ebcd-475f-8c4b-c4c508e9f591' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ab253c4-9c2e-437e-9d57-665e9658d710' class='xr-var-data-in' type='checkbox'><label for='data-4ab253c4-9c2e-437e-9d57-665e9658d710' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>effective cloud fraction (mixed LER model)</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RadiativeCloudFraction</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-aebaf188-2031-444c-b706-82745db62cba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aebaf188-2031-444c-b706-82745db62cba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56b72455-a984-4310-9ef0-b03ad956e8b4' class='xr-var-data-in' type='checkbox'><label for='data-56b72455-a984-4310-9ef0-b03ad956e8b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7423,18 +6420,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7444,27 +6441,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quality_flag</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-8d86d060-0275-4fb2-b96d-7ff2afef2334' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d86d060-0275-4fb2-b96d-7ff2afef2334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c50eb24-c5a4-41d0-a340-1bf75c504b20' class='xr-var-data-in' type='checkbox'><label for='data-8c50eb24-c5a4-41d0-a340-1bf75c504b20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>quality flags</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>65534</dd><dt><span>flag_meanings :</span></dt><dd>good_sample glint_contamination SZA&gt;84 360_residual&gt;threshold residual_at_unused_ozone_wavelenth&gt;4sigma SOI&gt;4sigma non-convergence abs(residual)&gt;16.0(fatal) row_anomaly_error missing_TEMPO_cloud_pressure geolocation_error SZA&gt;88 missing_input_radiance error_input_radiance warning_input_radiance missing_input_irradiance error_input_irradiance warning_input_irradiance</dd><dt><span>flag_values :</span></dt><dd>[    0     1     2     3     4     5     6     7     8   128   256   512\n",
-       "  1024  2048  4096  8192 16384 32768]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RamanCorrection</span></div><div class='xr-var-dims'>(phony_dim_7, phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(8, 4), meta=np.ndarray&gt;</div><input id='attrs-69356098-cbf7-4724-a59c-f9d9a565ec9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69356098-cbf7-4724-a59c-f9d9a565ec9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bcfea71-d5f2-4032-b9af-bc92f6ed08da' class='xr-var-data-in' type='checkbox'><label for='data-2bcfea71-d5f2-4032-b9af-bc92f6ed08da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7479,14 +6489,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 128 B </td>\n",
+       "                        <td> 128 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (8, 4) </td>\n",
+       "                        <td> (8, 4) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -7494,32 +6504,32 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
        "                    </tr>\n",
        "                </tbody>\n",
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"110\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"60\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"60\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 60.0,0.0 60.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"30.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"80.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,80.000000,60.000000)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3_below_cloud</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-4d2f396a-cc38-4d28-b2cc-b55f3617bed0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d2f396a-cc38-4d28-b2cc-b55f3617bed0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a218ad8-7bfe-415d-b79d-7b97f1870deb' class='xr-var-data-in' type='checkbox'><label for='data-8a218ad8-7bfe-415d-b79d-7b97f1870deb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>ozone below fractional cloud</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>100.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-77d5cc24-bda1-4cd5-8ac1-1c33f3f8e934' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77d5cc24-bda1-4cd5-8ac1-1c33f3f8e934' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01edd121-e09c-4f1a-a17d-41afdb49a38d' class='xr-var-data-in' type='checkbox'><label for='data-01edd121-e09c-4f1a-a17d-41afdb49a38d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7534,18 +6544,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7555,26 +6565,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>so2_index</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-631f975f-c8ba-4c2f-9750-6d23a993acc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-631f975f-c8ba-4c2f-9750-6d23a993acc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0848da3-c8ea-4363-ad58-32304495ee0a' class='xr-var-data-in' type='checkbox'><label for='data-a0848da3-c8ea-4363-ad58-32304495ee0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>SO2 index</dd><dt><span>valid_min :</span></dt><dd>-300.0</dd><dt><span>valid_max :</span></dt><dd>300.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Residual</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1, phony_dim_5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256, 3), meta=np.ndarray&gt;</div><input id='attrs-4a166284-077f-4d95-8594-63bd98fad93b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a166284-077f-4d95-8594-63bd98fad93b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1679ff1b-b566-4e7a-aae3-55573a4e8711' class='xr-var-data-in' type='checkbox'><label for='data-1679ff1b-b566-4e7a-aae3-55573a4e8711' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7589,18 +6613,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 48.00 MiB </td>\n",
+       "                        <td> 768.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (2048, 2048, 3) </td>\n",
+       "                        <td> (256, 256, 3) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7610,26 +6634,77 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"156\" height=\"240\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"15\" x2=\"80\" y2=\"85\" />\n",
+       "  <line x1=\"10\" y1=\"30\" x2=\"80\" y2=\"100\" />\n",
+       "  <line x1=\"10\" y1=\"45\" x2=\"80\" y2=\"115\" />\n",
+       "  <line x1=\"10\" y1=\"60\" x2=\"80\" y2=\"130\" />\n",
+       "  <line x1=\"10\" y1=\"75\" x2=\"80\" y2=\"145\" />\n",
+       "  <line x1=\"10\" y1=\"90\" x2=\"80\" y2=\"160\" />\n",
+       "  <line x1=\"10\" y1=\"105\" x2=\"80\" y2=\"175\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"128\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"146\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"45\" y2=\"155\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"164\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"172\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"71\" y2=\"181\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,190.58823529411765 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"44\" y2=\"8\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"53\" y2=\"17\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
+       "  <line x1=\"45\" y1=\"35\" x2=\"70\" y2=\"35\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"88\" y2=\"52\" />\n",
+       "  <line x1=\"71\" y1=\"61\" x2=\"97\" y2=\"61\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"85\" x2=\"106\" y2=\"85\" />\n",
+       "  <line x1=\"80\" y1=\"100\" x2=\"106\" y2=\"100\" />\n",
+       "  <line x1=\"80\" y1=\"115\" x2=\"106\" y2=\"115\" />\n",
+       "  <line x1=\"80\" y1=\"130\" x2=\"106\" y2=\"130\" />\n",
+       "  <line x1=\"80\" y1=\"145\" x2=\"106\" y2=\"145\" />\n",
+       "  <line x1=\"80\" y1=\"160\" x2=\"106\" y2=\"160\" />\n",
+       "  <line x1=\"80\" y1=\"175\" x2=\"106\" y2=\"175\" />\n",
+       "  <line x1=\"80\" y1=\"190\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"190\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"190\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,190.58823529411765 80.58823529411765,190.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"93.294544\" y=\"210.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"126.000852\" y=\"130.588235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,130.588235)\">2048</text>\n",
+       "  <text x=\"35.294118\" y=\"175.294118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,175.294118)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uv_aerosol_index</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-30ef9f33-fa02-4900-9273-e47017c80103' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30ef9f33-fa02-4900-9273-e47017c80103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f973f43c-e21a-4f0d-bd39-159bda81eb6d' class='xr-var-data-in' type='checkbox'><label for='data-f973f43c-e21a-4f0d-bd39-159bda81eb6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>UV aerosol index</dd><dt><span>valid_min :</span></dt><dd>-30.0</dd><dt><span>valid_max :</span></dt><dd>30.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SatelliteAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-b89e0ae9-a838-4f2e-bb65-27c59ad13667' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b89e0ae9-a838-4f2e-bb65-27c59ad13667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-801acdb5-5522-4a01-89e0-ac4647b31939' class='xr-var-data-in' type='checkbox'><label for='data-801acdb5-5522-4a01-89e0-ac4647b31939' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7644,18 +6719,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7665,26 +6740,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_bounds</span></div><div class='xr-var-dims'>(mirror_step, xtrack, corner)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;</div><input id='attrs-7a949263-1b9e-46b5-b59c-46ba47eb2b67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a949263-1b9e-46b5-b59c-46ba47eb2b67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dae8cd09-5f38-4605-a05b-5d86abd07567' class='xr-var-data-in' type='checkbox'><label for='data-dae8cd09-5f38-4605-a05b-5d86abd07567' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>latitude at pixel corners (sw,se,ne,nw)</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SatelliteZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-a7cbcd53-6a7c-4702-8af7-012714882af6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7cbcd53-6a7c-4702-8af7-012714882af6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9be51270-ea22-4e5e-839b-dcac59481cc8' class='xr-var-data-in' type='checkbox'><label for='data-9be51270-ea22-4e5e-839b-dcac59481cc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7699,18 +6788,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 4.09 MiB </td>\n",
-       "                        <td> 4.09 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048, 4) </td>\n",
-       "                        <td> (131, 2048, 4) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7720,49 +6809,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
-       "  <text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text>\n",
-       "  <text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_bounds</span></div><div class='xr-var-dims'>(mirror_step, xtrack, corner)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048, 4), meta=np.ndarray&gt;</div><input id='attrs-29308863-8a65-4a2d-b994-6960f8f93c95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29308863-8a65-4a2d-b994-6960f8f93c95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-854162c9-709b-42e0-b65b-72da41384ccb' class='xr-var-data-in' type='checkbox'><label for='data-854162c9-709b-42e0-b65b-72da41384ccb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>longitude at pixel corners (sw,se,ne,nw)</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SlopeOfReflectivity</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-9af2ac60-2548-4db5-90ec-2a6f712ff7a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9af2ac60-2548-4db5-90ec-2a6f712ff7a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8bff2ac-faf5-4590-8a1e-54f3a978749b' class='xr-var-data-in' type='checkbox'><label for='data-b8bff2ac-faf5-4590-8a1e-54f3a978749b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7777,18 +6857,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 4.09 MiB </td>\n",
-       "                        <td> 4.09 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048, 4) </td>\n",
-       "                        <td> (131, 2048, 4) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7798,49 +6878,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"106\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"120\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 31.176025753615814,21.176025753615814 31.176025753615814,141.17602575361582 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"35\" y1=\"0\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 56.5886422681983,21.176025753615814 31.176025753615814,21.176025753615814\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"56\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"31\" y1=\"141\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"56\" y1=\"21\" x2=\"56\" y2=\"141\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"31.176025753615814,21.176025753615814 56.5886422681983,21.176025753615814 56.5886422681983,141.17602575361582 31.176025753615814,141.17602575361582\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"43.882334\" y=\"161.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
-       "  <text x=\"76.588642\" y=\"81.176026\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,76.588642,81.176026)\">2048</text>\n",
-       "  <text x=\"10.588013\" y=\"150.588013\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.588013,150.588013)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_zenith_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-c4f78054-77ac-447d-b578-b1f24dcdf3f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4f78054-77ac-447d-b578-b1f24dcdf3f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb1777b6-e6bf-4525-8f34-729f5a4cba21' class='xr-var-data-in' type='checkbox'><label for='data-eb1777b6-e6bf-4525-8f34-729f5a4cba21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>solar zenith angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarAzimuthAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-0098b223-76f8-4f92-9398-6147bf8fe166' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0098b223-76f8-4f92-9398-6147bf8fe166' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f54a353-ea23-4d11-9673-ecb33d14706d' class='xr-var-data-in' type='checkbox'><label for='data-1f54a353-ea23-4d11-9673-ecb33d14706d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7855,18 +6926,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7876,26 +6947,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-8ab8b869-1015-4b4a-863b-cefb075fbe32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ab8b869-1015-4b4a-863b-cefb075fbe32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5976b6eb-7935-45b9-8da6-7ffd29607067' class='xr-var-data-in' type='checkbox'><label for='data-5976b6eb-7935-45b9-8da6-7ffd29607067' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>solar azimuth angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenithAngle</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-7e2880f5-f5bf-4387-8f74-e5891918b3f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e2880f5-f5bf-4387-8f74-e5891918b3f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4387609e-890d-4236-8694-3da18975b5ba' class='xr-var-data-in' type='checkbox'><label for='data-4387609e-890d-4236-8694-3da18975b5ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7910,18 +6995,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7931,26 +7016,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_zenith_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-1c8e1ef7-f96e-43c5-91af-d8b73112788f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c8e1ef7-f96e-43c5-91af-d8b73112788f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ef69357-d9c4-45d9-abca-b88c52b39a29' class='xr-var-data-in' type='checkbox'><label for='data-7ef69357-d9c4-45d9-abca-b88c52b39a29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>viewing zenith angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TerrainPressure</span></div><div class='xr-var-dims'>(phony_dim_0, phony_dim_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-b60fb6fb-36be-4704-9147-a07d0badb8ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b60fb6fb-36be-4704-9147-a07d0badb8ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-992ec22f-386c-4dea-a554-6700cb8d17a1' class='xr-var-data-in' type='checkbox'><label for='data-992ec22f-386c-4dea-a554-6700cb8d17a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7965,18 +7064,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16.00 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (2048, 2048) </td>\n",
+       "                        <td> (256, 256) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7986,26 +7085,40 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"105\" x2=\"120\" y2=\"105\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"120\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"120\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"120\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"120\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">2048</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-a77e5178-54ca-4fa4-b4b7-20b99c4e072c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a77e5178-54ca-4fa4-b4b7-20b99c4e072c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a0d142c-df54-4a73-8a72-6d49a66c0c73' class='xr-var-data-in' type='checkbox'><label for='data-9a0d142c-df54-4a73-8a72-6d49a66c0c73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>viewing azimuth angle at pixel center</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>(phony_dim_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-ae7de45e-4019-4f16-9435-fac3f9dd7c95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae7de45e-4019-4f16-9435-fac3f9dd7c95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-582fd8ff-8c98-42fd-bc68-289afdce77d4' class='xr-var-data-in' type='checkbox'><label for='data-582fd8ff-8c98-42fd-bc68-289afdce77d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8020,14 +7133,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                        <td> 16 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -8041,26 +7154,26 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.89879552186203 0.0,42.89879552186203\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"62.898796\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"140.000000\" y=\"21.449398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.449398)\">1</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_azimuth_angle</span></div><div class='xr-var-dims'>(mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131, 2048), meta=np.ndarray&gt;</div><input id='attrs-7d994732-1300-4a2a-a905-02e9cd15fa11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d994732-1300-4a2a-a905-02e9cd15fa11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-593a529c-ff68-460a-a283-dc01f13f9a33' class='xr-var-data-in' type='checkbox'><label for='data-593a529c-ff68-460a-a283-dc01f13f9a33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>relative azimuth angle (sun + 180 - view)</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YearDaySeconds</span></div><div class='xr-var-dims'>(phony_dim_5)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3,), meta=np.ndarray&gt;</div><input id='attrs-5ed07e79-87ae-43fb-b65b-b047a5b588ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ed07e79-87ae-43fb-b65b-b047a5b588ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce20ca9c-225f-4713-adca-4974e162f75e' class='xr-var-data-in' type='checkbox'><label for='data-ce20ca9c-225f-4713-adca-4974e162f75e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8075,14 +7188,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.02 MiB </td>\n",
-       "                        <td> 1.02 MiB </td>\n",
+       "                        <td> 12 B </td>\n",
+       "                        <td> 12 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (131, 2048) </td>\n",
-       "                        <td> (131, 2048) </td>\n",
+       "                        <td> (3,) </td>\n",
+       "                        <td> (3,) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -8090,108 +7203,95 @@
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
        "                    </tr>\n",
        "                </tbody>\n",
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"85\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"93\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"43\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,35.99924378114688 0.0,35.99924378114688\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.88583450710766 0.0,43.88583450710766\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"55.999244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
-       "  <text x=\"140.000000\" y=\"17.999622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,17.999622)\">131</text>\n",
+       "  <text x=\"60.000000\" y=\"63.885835\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
+       "  <text x=\"140.000000\" y=\"21.942917\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.942917)\">1</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
        "</table></div></li></ul></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 24MB\n",
-       "Dimensions:                 (mirror_step: 131, xtrack: 2048, corner: 4)\n",
+       "<xarray.Dataset> Size: 549MB\n",
+       "Dimensions:                      (phony_dim_0: 2048, phony_dim_1: 2048,\n",
+       "                                  phony_dim_2: 4, phony_dim_3: 11,\n",
+       "                                  phony_dim_4: 26, phony_dim_5: 3,\n",
+       "                                  phony_dim_6: 1, phony_dim_7: 8)\n",
        "Coordinates:\n",
-       "    time                    (mirror_step) datetime64[ns] 1kB dask.array<chunksize=(131,), meta=np.ndarray>\n",
-       "    latitude                (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    longitude               (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "Dimensions without coordinates: mirror_step, xtrack, corner\n",
-       "Data variables: (12/14)\n",
-       "    column_amount_o3        (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    radiative_cloud_frac    (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    fc                      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    quality_flag            (mirror_step, xtrack) int32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    o3_below_cloud          (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    so2_index               (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    ...                      ...\n",
-       "    longitude_bounds        (mirror_step, xtrack, corner) float32 4MB dask.array<chunksize=(131, 2048, 4), meta=np.ndarray>\n",
-       "    solar_zenith_angle      (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    solar_azimuth_angle     (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    viewing_zenith_angle    (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    viewing_azimuth_angle   (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>\n",
-       "    relative_azimuth_angle  (mirror_step, xtrack) float32 1MB dask.array<chunksize=(131, 2048), meta=np.ndarray>"
+       "    Latitude                     (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "    Longitude                    (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "Dimensions without coordinates: phony_dim_0, phony_dim_1, phony_dim_2,\n",
+       "                                phony_dim_3, phony_dim_4, phony_dim_5,\n",
+       "                                phony_dim_6, phony_dim_7\n",
+       "Data variables: (12/27)\n",
+       "    AlgorithmFlag                (phony_dim_0, phony_dim_1) int16 8MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "    CalibrationCoef              (phony_dim_2) float32 16B dask.array<chunksize=(4,), meta=np.ndarray>\n",
+       "    CloudPressure                (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "    ColumnWeightFunctionPercent  (phony_dim_0, phony_dim_1, phony_dim_3) uint8 46MB dask.array<chunksize=(256, 256, 11), meta=np.ndarray>\n",
+       "    ControlFileContents          (phony_dim_4) object 208B dask.array<chunksize=(26,), meta=np.ndarray>\n",
+       "    DNDomega                     (phony_dim_0, phony_dim_1, phony_dim_5) float32 50MB dask.array<chunksize=(256, 256, 3), meta=np.ndarray>\n",
+       "    ...                           ...\n",
+       "    SlopeOfReflectivity          (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "    SolarAzimuthAngle            (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "    SolarZenithAngle             (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "    TerrainPressure              (phony_dim_0, phony_dim_1) float32 17MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "    Wavelength                   (phony_dim_2) float32 16B dask.array<chunksize=(4,), meta=np.ndarray>\n",
+       "    YearDaySeconds               (phony_dim_5) int32 12B dask.array<chunksize=(3,), meta=np.ndarray>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tempo_l2 = {\n",
-    "    'xarray_open': 'dataset',\n",
-    "    'merge': ['/product', '/geolocation'],\n",
-    "    'coords': {'lat': 'latitude', 'lon': 'longitude'},\n",
-    "}\n",
-    "ds = plan.open_dataset(0, open_method=tempo_l2)\n",
+    "ds = plan.open_dataset(1, open_method=\"dataset\")\n",
     "ds"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "378dc03b-5bfb-458f-b914-4505720b7928",
-   "metadata": {},
-   "source": [
-    "### Now we get the matchups using this open_method dict\n",
-    "\n",
-    "It is roughly 2 seconds per point. So roughly 5 hrs for 10,000 points."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "0b17d129-7642-4650-a492-85388e0fa90c",
+   "execution_count": 23,
+   "id": "24212141-de67-41af-90d5-13d2033e35bf",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 13.2 s, sys: 754 ms, total: 13.9 s\n",
-      "Wall time: 31.4 s\n"
+      "CPU times: user 35.8 s, sys: 942 ms, total: 36.8 s\n",
+      "Wall time: 1min 34s\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
-    "res = pc.matchup(plan, \n",
-    "                 variables = [\"uv_aerosol_index\"], \n",
-    "                 open_method=tempo_l2)"
+    "res = pc.matchup(plan[0:10], variables = [\"Ozone\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "096a16b2-88be-43c2-8a57-910d1b390651",
+   "execution_count": 24,
+   "id": "1be9adf4-6277-49b7-b430-1e7f4c09a2ba",
    "metadata": {},
    "outputs": [
     {
@@ -8218,139 +7318,84 @@
        "      <th>lat</th>\n",
        "      <th>lon</th>\n",
        "      <th>time</th>\n",
-       "      <th>uv_aerosol_index</th>\n",
+       "      <th>pc_id</th>\n",
+       "      <th>Ozone</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>32.033815</td>\n",
-       "      <td>-87.504205</td>\n",
-       "      <td>2024-03-12 19:18:24</td>\n",
-       "      <td>-2.219414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>36.649735</td>\n",
-       "      <td>-80.935069</td>\n",
-       "      <td>2024-04-24 22:48:36</td>\n",
-       "      <td>-2.591015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>44.349757</td>\n",
-       "      <td>-72.301856</td>\n",
-       "      <td>2024-06-13 10:50:15</td>\n",
-       "      <td>-1.152082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>44.349757</td>\n",
-       "      <td>-72.301856</td>\n",
-       "      <td>2024-06-13 10:50:15</td>\n",
-       "      <td>-1.889225</td>\n",
+       "      <td>6.631233</td>\n",
+       "      <td>-138.494233</td>\n",
+       "      <td>2024-05-08</td>\n",
+       "      <td>245</td>\n",
+       "      <td>249.927444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>44.355987</td>\n",
-       "      <td>-95.126999</td>\n",
-       "      <td>2024-01-14 16:55:19</td>\n",
-       "      <td>-2.514838</td>\n",
+       "      <td>-20.064168</td>\n",
+       "      <td>-173.807753</td>\n",
+       "      <td>2024-02-16</td>\n",
+       "      <td>273</td>\n",
+       "      <td>233.636459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>44.355987</td>\n",
-       "      <td>-95.126999</td>\n",
-       "      <td>2024-01-14 16:55:19</td>\n",
-       "      <td>-2.738163</td>\n",
+       "      <th>8</th>\n",
+       "      <td>34.247526</td>\n",
+       "      <td>-166.904639</td>\n",
+       "      <td>2024-08-21</td>\n",
+       "      <td>342</td>\n",
+       "      <td>316.187988</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>34.035328</td>\n",
-       "      <td>-90.559024</td>\n",
-       "      <td>2024-07-31 23:17:20</td>\n",
-       "      <td>-2.717468</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>34.035328</td>\n",
-       "      <td>-90.559024</td>\n",
-       "      <td>2024-07-31 23:17:20</td>\n",
-       "      <td>-2.282551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>34.035328</td>\n",
-       "      <td>-90.559024</td>\n",
-       "      <td>2024-07-31 23:17:20</td>\n",
-       "      <td>-1.936824</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>45.961109</td>\n",
-       "      <td>-97.761663</td>\n",
-       "      <td>2024-07-01 12:06:54</td>\n",
-       "      <td>-1.305435</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>45.961109</td>\n",
-       "      <td>-97.761663</td>\n",
-       "      <td>2024-07-01 12:06:54</td>\n",
-       "      <td>-1.612602</td>\n",
+       "      <td>34.247526</td>\n",
+       "      <td>-166.904639</td>\n",
+       "      <td>2024-08-21</td>\n",
+       "      <td>342</td>\n",
+       "      <td>314.785095</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          lat        lon                time  uv_aerosol_index\n",
-       "0   32.033815 -87.504205 2024-03-12 19:18:24         -2.219414\n",
-       "1   36.649735 -80.935069 2024-04-24 22:48:36         -2.591015\n",
-       "4   44.349757 -72.301856 2024-06-13 10:50:15         -1.152082\n",
-       "5   44.349757 -72.301856 2024-06-13 10:50:15         -1.889225\n",
-       "6   44.355987 -95.126999 2024-01-14 16:55:19         -2.514838\n",
-       "7   44.355987 -95.126999 2024-01-14 16:55:19         -2.738163\n",
-       "9   34.035328 -90.559024 2024-07-31 23:17:20         -2.717468\n",
-       "10  34.035328 -90.559024 2024-07-31 23:17:20         -2.282551\n",
-       "11  34.035328 -90.559024 2024-07-31 23:17:20         -1.936824\n",
-       "17  45.961109 -97.761663 2024-07-01 12:06:54         -1.305435\n",
-       "18  45.961109 -97.761663 2024-07-01 12:06:54         -1.612602"
+       "         lat         lon       time  pc_id       Ozone\n",
+       "5   6.631233 -138.494233 2024-05-08    245  249.927444\n",
+       "6 -20.064168 -173.807753 2024-02-16    273  233.636459\n",
+       "8  34.247526 -166.904639 2024-08-21    342  316.187988\n",
+       "9  34.247526 -166.904639 2024-08-21    342  314.785095"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "(\n",
-    "    res[['lat', 'lon', 'time', 'uv_aerosol_index']]\n",
-    "        .dropna(subset=['uv_aerosol_index'])\n",
-    ")"
+    "var = 'Ozone'\n",
+    "res[['lat', 'lon', 'time', 'pc_id', var]].dropna(subset=[var]).head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9ce4c95b-aabe-4a8f-85be-cc3e1040f8d8",
+   "id": "8562dabe-34f8-40bd-80ed-aef318c186ff",
    "metadata": {},
    "source": [
-    "### Here is a plot showing points and swaths\n",
-    "\n",
-    "This is just one swath. The matchup routine finds the granules matching each point."
+    "## Make a plot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "30c6b96d-1dce-4331-b375-0bc79dbcb769",
+   "execution_count": 30,
+   "id": "d6d0398a-70f8-4bc2-aa24-7d006f51d1f4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
+       "<Figure size 800x800 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -8360,43 +7405,80 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
+    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
+    "import numpy as np\n",
+    "\n",
+    "var = 'Ozone'\n",
+    "uvai = ds[var]\n",
+    "\n",
+    "lat = ds.Latitude.values\n",
+    "lon = ds.Longitude.values\n",
+    "val = uvai.values\n",
+    "vmin, vmax = np.nanpercentile(val, [2, 98])\n",
+    "vmin=0\n",
+    "\n",
+    "# flatten for scatter\n",
+    "lat = lat.flatten()\n",
+    "lon = lon.flatten()\n",
+    "val = val.flatten()\n",
     "\n",
-    "var = ds[\"column_amount_o3\"]\n",
+    "# projection center (mean location of valid pixels)\n",
+    "lat2d = ds.Latitude.values\n",
+    "lon2d = ds.Longitude.values\n",
+    "\n",
+    "iy = lat2d.shape[0] // 2\n",
+    "ix = lat2d.shape[1] // 2\n",
+    "\n",
+    "lat_m = float(lat2d[iy, ix])\n",
+    "lon_m = float(lon2d[iy, ix])\n",
+    "\n",
+    "proj = ccrs.Orthographic(central_longitude=lon_m, central_latitude=lat_m)\n",
+    "\n",
+    "fig = plt.figure(figsize=(8,8))\n",
+    "ax = plt.axes(projection=proj)\n",
+    "\n",
+    "# coastlines\n",
+    "ax.coastlines(linewidth=0.5)\n",
     "\n",
-    "fig = plt.figure(figsize=(12, 6))\n",
-    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "# gridlines\n",
+    "grid = ax.gridlines(draw_labels=False)\n",
+    "grid.xformatter = LONGITUDE_FORMATTER\n",
+    "grid.yformatter = LATITUDE_FORMATTER\n",
     "\n",
-    "pcm = ax.pcolormesh(\n",
-    "    ds[\"longitude\"],\n",
-    "    ds[\"latitude\"],\n",
-    "    var,\n",
-    "    shading=\"auto\",\n",
+    "# plot swath\n",
+    "im = ax.scatter(\n",
+    "    lon,\n",
+    "    lat,\n",
+    "    c=val,\n",
+    "    s=1,\n",
+    "    cmap=\"RdBu_r\",\n",
+    "    vmin=vmin,\n",
+    "    vmax=vmax,\n",
     "    transform=ccrs.PlateCarree()\n",
     ")\n",
     "\n",
-    "# plot dataframe points\n",
+    "# points\n",
     "ax.scatter(\n",
     "    df[\"lon\"],\n",
     "    df[\"lat\"],\n",
-    "    color=\"red\",\n",
-    "    s=10,\n",
+    "    color=\"black\",\n",
+    "    s=20,\n",
+    "    edgecolor=\"white\",\n",
+    "    linewidth=0.5,\n",
     "    transform=ccrs.PlateCarree(),\n",
-    "    label=\"points\"\n",
+    "    zorder=10\n",
     ")\n",
     "\n",
-    "ax.coastlines()\n",
-    "ax.add_feature(cfeature.BORDERS, linewidth=0.5)\n",
-    "ax.set_xlabel(\"Longitude\")\n",
-    "ax.set_ylabel(\"Latitude\")\n",
-    "plt.colorbar(pcm, ax=ax, label=var.name)\n",
+    "plt.colorbar(im, fraction=0.03, pad=0.02, label=\"Ozone\")\n",
+    "\n",
+    "plt.title(\"DSCOVR EPIC Ozone\")\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1da88a23-def7-4cb9-9c70-e8c393cf3d3e",
+   "id": "a3d281d1-1f5a-4022-b6f6-a4716d7c26f5",
    "metadata": {},
    "outputs": [],
    "source": []

From 63a82c1bb2a2b26399f467e327e5595785618bd4 Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Tue, 17 Mar 2026 01:09:58 +0000
Subject: [PATCH 10/11] re order

---
 examples/{docs_4_ecco.ipynb => docs_1_ecco_l3.ipynb}              | 0
 examples/{docs_4_mur.ipynb => docs_1_mur_l4.ipynb}                | 0
 examples/{docs_3_many_points.ipynb => docs_2_many_points.ipynb}   | 0
 .../{docs_4_discovr_epic.ipynb => docs_3_discovr_epic_l2.ipynb}   | 0
 examples/{docs_4_icesat2.ipynb => docs_3_icesat2_l3.ipynb}        | 0
 examples/{docs_2_pace_l2.ipynb => docs_3_pace_l2.ipynb}           | 0
 examples/{docs_4_tempo.ipynb => docs_3_tempo.ipynb}               | 0
 7 files changed, 0 insertions(+), 0 deletions(-)
 rename examples/{docs_4_ecco.ipynb => docs_1_ecco_l3.ipynb} (100%)
 rename examples/{docs_4_mur.ipynb => docs_1_mur_l4.ipynb} (100%)
 rename examples/{docs_3_many_points.ipynb => docs_2_many_points.ipynb} (100%)
 rename examples/{docs_4_discovr_epic.ipynb => docs_3_discovr_epic_l2.ipynb} (100%)
 rename examples/{docs_4_icesat2.ipynb => docs_3_icesat2_l3.ipynb} (100%)
 rename examples/{docs_2_pace_l2.ipynb => docs_3_pace_l2.ipynb} (100%)
 rename examples/{docs_4_tempo.ipynb => docs_3_tempo.ipynb} (100%)

diff --git a/examples/docs_4_ecco.ipynb b/examples/docs_1_ecco_l3.ipynb
similarity index 100%
rename from examples/docs_4_ecco.ipynb
rename to examples/docs_1_ecco_l3.ipynb
diff --git a/examples/docs_4_mur.ipynb b/examples/docs_1_mur_l4.ipynb
similarity index 100%
rename from examples/docs_4_mur.ipynb
rename to examples/docs_1_mur_l4.ipynb
diff --git a/examples/docs_3_many_points.ipynb b/examples/docs_2_many_points.ipynb
similarity index 100%
rename from examples/docs_3_many_points.ipynb
rename to examples/docs_2_many_points.ipynb
diff --git a/examples/docs_4_discovr_epic.ipynb b/examples/docs_3_discovr_epic_l2.ipynb
similarity index 100%
rename from examples/docs_4_discovr_epic.ipynb
rename to examples/docs_3_discovr_epic_l2.ipynb
diff --git a/examples/docs_4_icesat2.ipynb b/examples/docs_3_icesat2_l3.ipynb
similarity index 100%
rename from examples/docs_4_icesat2.ipynb
rename to examples/docs_3_icesat2_l3.ipynb
diff --git a/examples/docs_2_pace_l2.ipynb b/examples/docs_3_pace_l2.ipynb
similarity index 100%
rename from examples/docs_2_pace_l2.ipynb
rename to examples/docs_3_pace_l2.ipynb
diff --git a/examples/docs_4_tempo.ipynb b/examples/docs_3_tempo.ipynb
similarity index 100%
rename from examples/docs_4_tempo.ipynb
rename to examples/docs_3_tempo.ipynb

From 53f95c38daed688010ec38bdd9771254bdd4baa4 Mon Sep 17 00:00:00 2001
From: Eli Holmes <eeholmes@uw.edu>
Date: Tue, 17 Mar 2026 01:11:18 +0000
Subject: [PATCH 11/11] clean up

---
 examples/testing_more_products.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/testing_more_products.ipynb b/examples/testing_more_products.ipynb
index 2012cac..4e609af 100644
--- a/examples/testing_more_products.ipynb
+++ b/examples/testing_more_products.ipynb
@@ -19,9 +19,9 @@
     "* `concept_id = \"C1962643459-LARC_ASDC\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_explore_aerosol_data_from_EPIC_and_EPA-AQS.html)\n",
     "* WONTFIX (line) `short_name=\"ATL03\"` level 2, icesat-2. [notebook](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/tutorials/Harmony.html)\n",
     "* `short_name = \"PREFIRE_SAT2_2B-CLD\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/PREFIRE/plot_PREFIRE_L2_CLD_with_earthaccess_and_Harmony.html)\n",
-    "* `short_name = \"DSCOVR_EPIC_L2_AER\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_compare_TEMPO_with_DSCOVR_and_AERONET_uvai.html)\n",
+    "* DONE `short_name = \"DSCOVR_EPIC_L2_AER\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_compare_TEMPO_with_DSCOVR_and_AERONET_uvai.html)\n",
     "* DONE `short_name = \"TEMPO_O3TOT_L2\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_compare_spatially_TEMPO_with_DSCOVR_uvai.html) [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_compare_TEMPO_with_DSCOVR_and_AERONET_uvai.html)\n",
-    "* `short_name = \"DSCOVR_EPIC_L2_TO3\", version = \"03\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_plot_ozone_product_parameters.html)\n",
+    "* DONE `short_name = \"DSCOVR_EPIC_L2_TO3\", version = \"03\"` level 2 [notebook](https://nasa.github.io/ASDC_Data_and_User_Services/DSCOVR/how_to_plot_ozone_product_parameters.html)\n",
     "\n",
     "\n",
     "### Special cases\n",