From 8f8ac4ce66c494c4e5535bd218c1b54e83e8c1ea Mon Sep 17 00:00:00 2001 From: dcyang Date: Sun, 3 May 2026 19:06:53 +0800 Subject: [PATCH 1/6] =?UTF-8?q?m1-m6=20=E4=BD=9C=E6=A5=AD=E7=B9=B3?= =?UTF-8?q?=E4=BA=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- homework/m1_numpy.py | 31 +- homework/m2_pandas_cleaning.py | 45 +- homework/m3_pandas_advanced.py | 53 +- homework/m4_timeseries.py | 54 +- homework/m5_visualization.py | 90 +- homework/m6_plotly_capstone.py | 99 +- homework/test.ipynb | 2985 ++++++++++++++++++++++++++++++++ 7 files changed, 3294 insertions(+), 63 deletions(-) create mode 100644 homework/test.ipynb diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py index 5ff41ab..656fa97 100644 --- a/homework/m1_numpy.py +++ b/homework/m1_numpy.py @@ -19,20 +19,26 @@ def green_mean(): """建立 [10, 20, 30, 40, 50]，回傳所有元素的平均值 (float)""" # TODO: 你的程式碼 - pass + #pass + return np.array([10,20,30,40,50]).mean() def green_double(): """建立 [10, 20, 30, 40, 50]，回傳所有元素乘以 2 的 ndarray""" # TODO: 你的程式碼 - pass + #pass + return np.array([10,20,30,40,50])*2 + def green_filter(): """建立 [10, 20, 30, 40, 50]，回傳大於 25 的元素 (ndarray)""" # TODO: 你的程式碼 - pass - + #pass + arr = np.array([10,20,30,40,50]) + return arr[arr>25] + + # ============================================================ # 🟡 核心題（每題 15 分，共 45 分） @@ -42,7 +48,8 @@ def green_filter(): def yellow_expensive_count(prices): """回傳單價 > 1000 的商品數量 (int)""" # TODO: 你的程式碼 - pass + #pass + return (prices > 1000).sum() def yellow_top3_stock_indices(stocks): @@ -51,7 +58,8 @@ def yellow_top3_stock_indices(stocks): 提示：np.argsort """ # TODO: 你的程式碼 - pass + #pass + return np.argsort(-stocks)[:3] def yellow_restock_cost(prices, stocks): @@ -60,7 +68,8 @@ def yellow_restock_cost(prices, stocks): 提示：布林遮罩 + .sum() """ # TODO: 你的程式碼 - pass + #pass + return (prices[ prices < 500] * 50).sum() # ============================================================ @@ -77,4 +86,10 @@ def red_double11_prices(prices, stocks): 提示：np.where 可以巢狀使用 """ # TODO: 你的程式碼 - pass + #pass + new_prices = np.where(stocks>=100, prices*0.7, np.where( stocks>=20, prices*0.9, prices)) + return new_prices + + +if __name__ == '__main__': + pass \ No newline at end of file diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py index fa36bff..d50e82e 100644 --- a/homework/m2_pandas_cleaning.py +++ b/homework/m2_pandas_cleaning.py @@ -20,7 +20,9 @@ def green_read_csv(): 提示：pd.read_csv() """ # TODO: 你的程式碼 - pass + #pass + df= pd.read_csv("../datasets/ecommerce/orders_raw.csv") + return df def green_shape(df): @@ -29,7 +31,8 @@ def green_shape(df): 提示：df.shape """ # TODO: 你的程式碼 - pass + #pass + return df.shape def green_dtypes(df): @@ -38,24 +41,29 @@ def green_dtypes(df): 提示：df.dtypes """ # TODO: 你的程式碼 - pass + #pass + return df.dtypes + # ============================================================ # 🟡 核心題（每題 15 分，共 45 分） # ============================================================ -def yellow_clean_columns(df): +def yellow_clean_columns(df:pd.DataFrame ): """ 清理欄位名稱：去除前後空白、全部轉小寫 回傳清理後的 DataFrame（不要修改原始 df） 提示：df.columns.str.strip().str.lower() """ # TODO: 你的程式碼 - pass + #pass + df_new = df.copy() + df_new.columns = df.columns.str.strip().str.lower() + return df_new -def yellow_clean_amount(df): +def yellow_clean_amount(df:pd.DataFrame ): """ 清理 amount 欄位：移除 '$' 和 ',' 符號，轉為 float 假設欄位名稱已經是小寫的 'amount' @@ -63,16 +71,20 @@ def yellow_clean_amount(df): 提示：.str.replace() + .astype(float) """ # TODO: 你的程式碼 - pass + df_new = df.copy() + df_new['amount'] = df['amount'].str.strip('$').str.replace(',','').astype(float) + #pass + return df_new -def yellow_drop_duplicates(df): +def yellow_drop_duplicates(df: pd.DataFrame): """ 移除完全重複的列，回傳去重後的 DataFrame 提示：df.drop_duplicates() """ - # TODO: 你的程式碼 - pass + ## TODO: 你的程式碼 + #pass + return df.drop_duplicates() # ============================================================ @@ -93,4 +105,15 @@ def red_clean_orders(path): 提示：pd.to_datetime(errors='coerce') """ # TODO: 你的程式碼 - pass + #pass + df= pd.read_csv("../datasets/ecommerce/orders_raw.csv") + df.columns = df.columns.str.strip().str.lower() + df['amount'] = df['amount'].str.strip('$').str.replace(',','').astype(float) + pd.to_datetime(df['order_date'], errors='coerce') + df = df.dropna(subset=['order_date', 'amount']) + #這個不確定, 先comment + df['qty'] = df['qty'].fillna(df['qty'].median) + + df = df.drop_duplicates() + return df + diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py index 68410e8..32052ff 100644 --- a/homework/m3_pandas_advanced.py +++ b/homework/m3_pandas_advanced.py @@ -24,60 +24,78 @@ def green_load_and_merge(): 提示：pd.merge(how='left') """ # TODO: 你的程式碼 - pass - - -def green_row_count(df): + #pass + order = pd.read_csv("../datasets/ecommerce/orders_clean.csv") + customer = pd.read_csv("../datasets/ecommerce/customers.csv") + prod = pd.read_csv("../datasets/ecommerce/products.csv") + df = ( + order.merge(customer, how='left', on='customer_id') + .merge(prod, how='left', on='product_id') + ) + return df + + + +def green_row_count(df:pd.DataFrame): """回傳 DataFrame 的列數 (int)""" # TODO: 你的程式碼 - pass + #pass + return df.shape[0] -def green_column_list(df): +def green_column_list(df:pd.DataFrame): """回傳 DataFrame 的所有欄位名稱 (list)""" # TODO: 你的程式碼 - pass + #pass + return df.columns.tolist() # ============================================================ # 🟡 核心題（每題 15 分，共 45 分） # ============================================================ -def yellow_top_category(df): +def yellow_top_category(df:pd.DataFrame): """ 哪個商品類別 (category) 的總營收最高？ 回傳該類別名稱 (str) 提示：groupby('category')['amount'].sum() """ # TODO: 你的程式碼 - pass + #pass + return df.groupby('category')['amount'].sum().idxmax() -def yellow_gold_vip_stats(df): +def yellow_gold_vip_stats(df:pd.DataFrame): """ Gold VIP 客戶總共下了幾張訂單？總金額多少？ 回傳 tuple: (訂單數 int, 總金額 float) 提示：df[df['vip_level'] == 'Gold'] """ # TODO: 你的程式碼 - pass + #pass + mask = (df['vip_level'] == 'Gold') + return (mask.count(), df[mask]['amount'].sum()) + -def yellow_region_avg_amount(df): +def yellow_region_avg_amount(df:pd.DataFrame): """ 計算每個地區 (region) 的平均訂單金額 回傳 Series（index=region, values=平均金額） 提示：groupby('region')['amount'].mean() """ # TODO: 你的程式碼 - pass + #pass + region = df.groupby('region')['amount'].mean() + region.name = '平均金額' + return region # ============================================================ # 🔴 挑戰題（25 分） # ============================================================ -def red_rfm_top5(df): +def red_rfm_top5(df:pd.DataFrame): """ RFM 分析：找出最有價值的前 5 位客戶 @@ -94,4 +112,9 @@ def red_rfm_top5(df): 提示：groupby('customer_id').agg(...) """ # TODO: 你的程式碼 - pass + #pass + rfm_h5 = df.groupby('customer_id').agg(R=('order_date','max'), + F=('order_id','count'), + M=('amount','sum')).reset_index().sort_values('M', ascending=False).head(5) + rfm_ans = rfm_h5.merge(df[['customer_name','customer_id']], how='left', on='customer_id').drop_duplicates() + return rfm_ans diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py index 047af6a..6a588df 100644 --- a/homework/m4_timeseries.py +++ b/homework/m4_timeseries.py @@ -8,14 +8,16 @@ """ import pandas as pd +gdf:pd.DataFrame def _load_data(): """輔助函式：讀取並解析日期""" df = pd.read_csv("datasets/ecommerce/orders_enriched.csv", parse_dates=["order_date"]) + global gdf + gdf = df return df - # ============================================================ # 🟢 送分題（每題 10 分，共 30 分） # ============================================================ @@ -27,7 +29,12 @@ def green_avg_by_month(): 提示：df['order_date'].dt.month """ # TODO: 你的程式碼 - pass + #pass + global gdf + gdf['月份'] = gdf['order_date'].dt.month + month_avg = gdf.groupby('月份')['amount'].mean() + month_avg.name = '平均金額' + return month_avg def green_top3_dates(): @@ -37,7 +44,12 @@ def green_top3_dates(): 提示：value_counts().head(3) """ # TODO: 你的程式碼 - pass + #pass + global gdf + date_ord = gdf['order_date'].value_counts() + date_ord = date_ord.rename_axis('日期') + date_ord.name='訂單數' + return date_ord def green_date_range(): @@ -46,7 +58,11 @@ def green_date_range(): 格式為 pandas Timestamp """ # TODO: 你的程式碼 - pass + #pass + global gdf + dmax = gdf['order_date'].max() + dmin = gdf['order_date'].min() + return dmin, dmax # ============================================================ @@ -60,10 +76,13 @@ def yellow_monthly_revenue(): 提示：set_index('order_date').resample('ME')['amount'].sum() """ # TODO: 你的程式碼 - pass + #pass + global gdf + ts = gdf.set_index('order_date').sort_index() + return ts['amount'].resample('ME').sum() -def yellow_rolling_avg(monthly_revenue): +def yellow_rolling_avg(monthly_revenue:pd.Series): """ 計算 3 個月移動平均 接收 yellow_monthly_revenue() 的結果作為輸入 @@ -71,17 +90,21 @@ def yellow_rolling_avg(monthly_revenue): 提示：.rolling(window=3).mean() """ # TODO: 你的程式碼 - pass + #pass + return monthly_revenue.rolling(window=3).mean() -def yellow_category_median(df): +def yellow_category_median(df:pd.DataFrame): """ 計算每個商品類別 (category) 的訂單金額中位數，由高到低排序 回傳 Series（index=category, values=中位數） 提示：groupby + median + sort_values """ # TODO: 你的程式碼 - pass + #pass + cat_med = df.groupby('category')['amount'].median().sort_values + cat_med.name = '中位數' + return cat_med # ============================================================ @@ -101,4 +124,15 @@ def red_monthly_report(): 提示：resample + agg + pct_change """ # TODO: 你的程式碼 - pass + #pass + global gdf + ts = gdf.set_index('order_date').sort_index() + mon_report = ts.resample('ME').agg( + order_count=('order_id','count'), + active_customer=('customer_id','nunique'), + revenue=('amount','sum'), + avg_order_value=('amount','mean') + ) + mon_report['revenue_growth'] = mon_report['revenue'].pct_change() + return mon_report + diff --git a/homework/m5_visualization.py b/homework/m5_visualization.py index 7e7335d..7ed8ae3 100644 --- a/homework/m5_visualization.py +++ b/homework/m5_visualization.py @@ -11,11 +11,16 @@ import pandas as pd import seaborn as sns +gdf:pd.DataFrame +plt.rcParams['axes.unicode_minus'] = False +sns.set_theme(style='whitegrid') def _load_data(): """輔助函式：讀取資料""" - return pd.read_csv("datasets/ecommerce/orders_enriched.csv", + global gdf + gdf = pd.read_csv("datasets/ecommerce/orders_enriched.csv", parse_dates=["order_date"]) + return gdf # ============================================================ @@ -29,7 +34,11 @@ def green_bar_category(): 提示：sns.countplot 或 value_counts().plot.bar() """ # TODO: 你的程式碼 - pass + #pass + global gdf + co = gdf['category'].value_counts() + ax = co.plot(kind='bar',figsize=(10,4),title='Order count in Category',rot=45) + return plt.gcf() def green_hist_amount(): @@ -39,7 +48,11 @@ def green_hist_amount(): 提示：sns.histplot(bins=20) 或 plt.hist() """ # TODO: 你的程式碼 - pass + #pass + global gdf + fig = plt.figure(figsize=(10,4)) + ax = sns.histplot(gdf['amount'], bins=20, stat='percent', kde=True, ylabel='%') + return fig def green_set_labels(): @@ -51,8 +64,18 @@ def green_set_labels(): 回傳 matplotlib Figure 物件 """ # TODO: 你的程式碼 - pass - + #pass + global gdf + reg_order = gdf['region'].value_counts().reset_index() + fig = plt.figure(figsize=(7,5)) + sns.barplot(data=reg_order, x='region', y='count', palette='viridis', hue='region') + plt.xlabel = 'region' + plt.ylabel = 'order count' + plt.title('region vs. order count') + for r,c in enumerate(reg_order['count']): + plt.text(r,c, f'{c:,.0f}', ha='center',va='bottom',fontsize=10) + plt.tight_layout() + return fig # ============================================================ # 🟡 核心題（每題 15 分，共 45 分） @@ -68,7 +91,18 @@ def yellow_line_region_trend(): 提示：分別 groupby 再 plot，或用 sns.lineplot(hue='region') """ # TODO: 你的程式碼 - pass + #pass + global gdf + fig = plt.figure(figsize=(12,4)) + filt = gdf[ gdf['region'].isin(['North','South'])] + filt['month']= filt['order_date'].dt.to_period('M').astype(str) + piv = filt.pivot_table(index='region', columns='month', values='amount', aggfunc='sum') + piv.T + sns.lineplot(data=piv.T, legend=True, markers='o') + plt.xticks(rotation=45) + plt.ylabel('revenue') + plt.title('North vs. South') + return fig def yellow_box_vip(): @@ -78,7 +112,13 @@ def yellow_box_vip(): 提示：sns.boxplot(x='vip_level', y='amount', data=df) """ # TODO: 你的程式碼 - pass + #pass + global gdf + fig = plt.figure(figsize=(7,4)) + my_order = ['Platinum', 'Gold', 'Silver', 'Bronze'] + sns.boxplot(data=gdf, x='vip_level',y='amount', order=my_order, flierprops={'marker': 'x', 'markeredgecolor': 'red'}) # + #紅色是離群值 + return fig def yellow_scatter_price_amount(): @@ -88,7 +128,13 @@ def yellow_scatter_price_amount(): 提示：plt.scatter() 或 sns.scatterplot() """ # TODO: 你的程式碼 - pass + #pass + global gdf + fig = plt.figure(figsize=(7,4)) + sns.scatterplot(data=gdf, x='unit_price', y='amount', hue='region') + return fig + + # ============================================================ @@ -107,4 +153,30 @@ def red_category_dashboard(category="Electronics"): 提示：fig, axes = plt.subplots(2, 2, figsize=(14, 10)) """ # TODO: 你的程式碼 - pass + #pass + global gdf + fig, axes = plt.subplots(2, 2, figsize=(14, 10)) + category="Electronics" + filt = gdf[ gdf['category'] == category] + filt['month'] = filt['order_date'].dt.to_period('M') + m_a = filt.groupby('month')['amount'].agg('sum').reset_index() + m_a['month'] = m_a['month'].astype(str) + sns.lineplot(data=m_a, x='month', y='amount',ax=axes[0,0]) + axes[0,0].tick_params(axis='x', labelrotation=45) + axes[0,0].legend( + loc='upper right', # 放在右上角 + fontsize='small' + ) + + r_a = filt.groupby('region')['amount'].agg('sum').reset_index() + sns.barplot(data=r_a, x='region', y='amount', ax=axes[0,1]) + + p_a = filt.groupby('product_name')['amount'].agg('sum').sort_values(ascending=False).head(5).reset_index() + # p_a['product_name'] = p_a['product_name'].replace("Data Science Handbook", "Data Science\nHandbook") + sns.barplot(data=p_a, x='amount', y='product_name', ax=axes[1,0],orient='h') + axes[1,0].tick_params(axis='x', labelrotation=45) + + filt.head(5) + sns.histplot(filt['amount'], bins=10, stat='percent',ax=axes[1,1]) + axes[1,1].set_ylabel('Percent(%)') + return fig \ No newline at end of file diff --git a/homework/m6_plotly_capstone.py b/homework/m6_plotly_capstone.py index 0e2c32a..c2ff0f0 100644 --- a/homework/m6_plotly_capstone.py +++ b/homework/m6_plotly_capstone.py @@ -11,8 +11,10 @@ import pandas as pd import plotly.express as px import plotly.graph_objects as go +import plotly.io as pio from plotly.subplots import make_subplots +pio.templates.default = 'plotly_white' # 乾淨白底 # ============================================================ # 🟢 送分題（每題 10 分，共 30 分） @@ -26,7 +28,15 @@ def green_plotly_bar(): 提示：px.bar() """ # TODO: 你的程式碼 - pass + #pass + df = pd.read_csv("../datasets/ecommerce/orders_enriched.csv", + parse_dates=["order_date"]) + c_a = df.groupby('category', as_index=False)['amount'].sum() + fig = px.bar(c_a, x='category',y='amount',text='amount', color='category', title='category vs. amount') + fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside') + fig.update_layout(height=500, showlegend=False) + + return fig def green_plotly_line(): @@ -37,7 +47,15 @@ def green_plotly_line(): 提示：先 groupby 月份算總營收，再 px.line() """ # TODO: 你的程式碼 - pass + #pass + df = pd.read_csv("datasets/ecommerce/orders_enriched.csv", + parse_dates=["order_date"]) + ts = df.set_index('order_date').sort_index() + m_a = ts['amount'].resample('ME').sum() + m_a = m_a.reset_index() + #m_a + fig = px.line(m_a, 'order_date', 'amount', markers=True, title='month vs. amount') + return fig def green_plotly_pie(): @@ -48,7 +66,12 @@ def green_plotly_pie(): 提示：px.pie() """ # TODO: 你的程式碼 - pass + #pass + df = pd.read_csv("datasets/ecommerce/orders_enriched.csv", + parse_dates=["order_date"]) + fig = px.pie(df, 'vip_level') + return fig + # ============================================================ @@ -63,10 +86,28 @@ def yellow_clean_and_merge(raw_path, customers_path, products_path): 回傳：合併後的 DataFrame """ # TODO: 你的程式碼 - pass + #pass + order = pd.read_csv(raw_path) + customer = pd.read_csv(customers_path) + product = pd.read_csv(products_path) + order.columns = order.columns.str.strip().str.lower() + order['amount'] = order['amount'].str.replace('$','').str.replace(',','').astype(float) + order['amount'].fillna(order['amount'].median()) + + pd.to_datetime(order['order_date'], errors='coerce') + + order = order.dropna(subset=['order_date']) + order.drop_duplicates() + + df = ( + order + .merge(customer, how='left', on='customer_id') + .merge(product, how='left', on='product_id') + ) + return df -def yellow_kpi_summary(df): +def yellow_kpi_summary(df:pd.DataFrame): """ 計算 4 個核心 KPI，回傳 dict： { @@ -76,11 +117,22 @@ def yellow_kpi_summary(df): "avg_order_value": float, # 平均客單價 } """ - # TODO: 你的程式碼 - pass + # TODO: 你的程式碼 + #pass + t_r = df['amount'].sum() + o_c = df.shape[0] + a_c = df['customer_id'].nunique() + a_o_v = t_r/o_c + + return { + "total_revenue": t_r, # 總營收 + "order_count": o_c, # 訂單數 + "active_customers": a_c, # 不重複客戶數 + "avg_order_value": a_o_v, # 平均客單價 + } -def yellow_plotly_scatter(df): +def yellow_plotly_scatter(df:pd.DataFrame): """ 用 Plotly Express 畫互動散佈圖： - X：商品單價 (unit_price) @@ -91,12 +143,16 @@ def yellow_plotly_scatter(df): 提示：px.scatter(hover_data=['product_name']) """ # TODO: 你的程式碼 - pass + #pass + + fig = px.scatter(df, 'unit_price', 'amount', hover_data=['product_name'], color='category', title='unit price vs. amount') + return fig # ============================================================ # 🔴 挑戰題（25 分） # ============================================================ +from plotly.subplots import make_subplots def red_dashboard(): """ @@ -115,4 +171,27 @@ def red_dashboard(): 提示：from plotly.subplots import make_subplots """ # TODO: 你的程式碼 - pass + #pass + df = yellow_clean_and_merge('../datasets/ecommerce/orders_raw.csv', + '../datasets/ecommerce/customers.csv', + '../datasets/ecommerce/products.csv') + df['month'] = df['order_date'].dt.to_period('M').astype(str) + m_a = df.groupby('month', as_index=False)['amount'].sum() + p_a = df.groupby('product_name', as_index=False)['amount'].sum().sort_values('amount',ascending=False).head(10) + r_a = df.groupby('region', as_index=False)['amount'].sum() + c_a = df.groupby('category', as_index=False)['amount'].sum() + + fig = make_subplots(2,2, subplot_titles= ('Monthly revenue trend', 'Top 10 Products', 'Revenue by region', 'Category share'), + specs=[ [{'type':'xy'}, {'type':'xy'}], [{'type':'xy'}, {'type':'domain'}] ]) + + fig.add_trace(go.Scatter(x=m_a['month'], y=m_a['amount'],mode='lines+markers', name='Monthly' ), row=1, col=1) + fig.add_trace(go.Bar(x=p_a['product_name'], y=p_a['amount']), row=1, col=2) + fig.add_trace(go.Bar(x=r_a['region'], y=r_a['amount']), row=2, col=1) + fig.add_trace(go.Pie(labels=c_a['category'], values=c_a['amount'], name='category', hole=0.4), row=2, col=2) + + fig.update_layout(height=800, title_text="Sales Dashboard", showlegend=False) + # fig.show() + return fig + +if __name__ == '__main__': + pass \ No newline at end of file diff --git a/homework/test.ipynb b/homework/test.ipynb new file mode 100644 index 0000000..e6fc493 --- /dev/null +++ b/homework/test.ipynb @@ -0,0 +1,2985 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "73d0c41a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1000., 24., 3000., 44., 55.])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "np.array([10,20,30,40,50]).mean()\n", + "np.array([10,20,30,40,50])*2\n", + "arr = np.array([1000,20,3000,40,50])\n", + "(arr >= 1000).sum()\n", + "np.argsort(-arr)[:3]\n", + "(arr[ arr < 1000] * 50).sum()\n", + "newarr = np.where(arr<40, arr*1.2, np.where( arr<1000, arr *1.1, arr))\n", + "newarr\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "12bca02f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['order_id', 'customer_id', 'product_id', 'qty', 'order_date', 'amount']\n", + "\n", + "Index: 198 entries, 0 to 209\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 order_id 198 non-null int64 \n", + " 1 customer_id 198 non-null int64 \n", + " 2 product_id 198 non-null int64 \n", + " 3 qty 198 non-null object \n", + " 4 order_date 198 non-null datetime64[us]\n", + " 5 amount 198 non-null float64 \n", + "dtypes: datetime64[us](1), float64(1), int64(3), object(1)\n", + "memory usage: 10.8+ KB\n", + "\n", + "Index: 188 entries, 0 to 208\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 order_id 188 non-null int64 \n", + " 1 customer_id 188 non-null int64 \n", + " 2 product_id 188 non-null int64 \n", + " 3 qty 188 non-null object \n", + " 4 order_date 188 non-null datetime64[us]\n", + " 5 amount 188 non-null float64 \n", + "dtypes: datetime64[us](1), float64(1), int64(3), object(1)\n", + "memory usage: 10.3+ KB\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import datetime\n", + "df= pd.read_csv(\"../datasets/ecommerce/orders_raw.csv\")\n", + "df.columns\n", + "df.shape\n", + "df.dtypes\n", + "type(df)\n", + "# df.info()\n", + "#df.describe()\n", + "df.columns = df.columns.str.strip().str.lower()\n", + "print(df.columns.tolist())\n", + "df['amount'] = df['amount'].str.strip('$').str.replace(',','').astype(float)\n", + "#df['amount']\n", + "df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')\n", + "# df.info()\n", + "\n", + "df = df.dropna(subset=['order_date', 'amount'])\n", + "# df.info()\n", + "df['qty'] = df['qty'].fillna(df['qty'].median)\n", + "df.info() \n", + "df = df.drop_duplicates()\n", + "df.info()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "93e0df80", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "79 285982.0\n" + ] + }, + { + "data": { + "text/html": [ + "
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order_idcustomer_idproduct_idqtyorder_dateamountcustomer_nameregionsignup_datevip_levelproduct_namecategoryunit_pricestock_qty
05064202210264.02025-03-268600.0Victor LinNorth2023-02-27GoldDumbbell 5kgSports215051
15023202610215.02025-01-051355.0Zoe HuangSouth2023-05-16PlatinumThrow PillowHome271150
25123201310132.02025-09-113538.0Mia HuangNorth2023-07-17PlatinumCotton T-ShirtClothing1769174
35118200510281.02025-05-221618.0Emma LiuWest2023-05-18BronzeWater BottleSports1618186
45161201710193.02025-08-201846.0Quinn ChenEast2023-08-11SilverCoffee MugHome1846274
.............................................
1835094202610193.02025-02-135538.0Zoe HuangSouth2023-05-16PlatinumCoffee MugHome1846274
1845041201410015.02025-10-038135.0Nick HuangWest2023-09-28GoldWireless MouseElectronics162712
1855157200510265.02025-01-0210750.0Emma LiuWest2023-05-18BronzeDumbbell 5kgSports215051
1865134201510125.02025-06-039580.0Olivia HuangNorth2023-12-15BronzeClean CodeBooks191681
1875135201010074.02025-09-052436.0Jack LiuSouth2023-03-12PlatinumPython CookbookBooks609258
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188 rows × 14 columns

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" + ], + "text/plain": [ + " order_id customer_id product_id qty order_date amount \\\n", + "0 5064 2022 1026 4.0 2025-03-26 8600.0 \n", + "1 5023 2026 1021 5.0 2025-01-05 1355.0 \n", + "2 5123 2013 1013 2.0 2025-09-11 3538.0 \n", + "3 5118 2005 1028 1.0 2025-05-22 1618.0 \n", + "4 5161 2017 1019 3.0 2025-08-20 1846.0 \n", + ".. ... ... ... ... ... ... \n", + "183 5094 2026 1019 3.0 2025-02-13 5538.0 \n", + "184 5041 2014 1001 5.0 2025-10-03 8135.0 \n", + "185 5157 2005 1026 5.0 2025-01-02 10750.0 \n", + "186 5134 2015 1012 5.0 2025-06-03 9580.0 \n", + "187 5135 2010 1007 4.0 2025-09-05 2436.0 \n", + "\n", + " customer_name region signup_date vip_level product_name category \\\n", + "0 Victor Lin North 2023-02-27 Gold Dumbbell 5kg Sports \n", + "1 Zoe Huang South 2023-05-16 Platinum Throw Pillow Home \n", + "2 Mia Huang North 2023-07-17 Platinum Cotton T-Shirt Clothing \n", + "3 Emma Liu West 2023-05-18 Bronze Water Bottle Sports \n", + "4 Quinn Chen East 2023-08-11 Silver Coffee Mug Home \n", + ".. ... ... ... ... ... ... \n", + "183 Zoe Huang South 2023-05-16 Platinum Coffee Mug Home \n", + "184 Nick Huang West 2023-09-28 Gold Wireless Mouse Electronics \n", + "185 Emma Liu West 2023-05-18 Bronze Dumbbell 5kg Sports \n", + "186 Olivia Huang North 2023-12-15 Bronze Clean Code Books \n", + "187 Jack Liu South 2023-03-12 Platinum Python Cookbook Books \n", + "\n", + " unit_price stock_qty \n", + "0 2150 51 \n", + "1 271 150 \n", + "2 1769 174 \n", + "3 1618 186 \n", + "4 1846 274 \n", + ".. ... ... \n", + "183 1846 274 \n", + "184 1627 12 \n", + "185 2150 51 \n", + "186 1916 81 \n", + "187 609 258 \n", + "\n", + "[188 rows x 14 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "order = pd.read_csv(\"../datasets/ecommerce/orders_clean.csv\")\n", + "customer = pd.read_csv(\"../datasets/ecommerce/customers.csv\")\n", + "prod = pd.read_csv(\"../datasets/ecommerce/products.csv\")\n", + "#print(order.columns)\n", + "#print(customer.columns)\n", + "#print(prod.columns)\n", + "\n", + "df = ( \n", + " order.merge(customer, how='left', on='customer_id')\n", + " .merge(prod, how='left', on='product_id')\n", + ")\n", + "# print(df.columns)\n", + "mask = (df['vip_level'] == 'Gold')\n", + "df[mask]['amount'].sum()\n", + "\n", + "print(mask.sum(), df[mask]['amount'].sum())\n", + "region = df.groupby('region')['amount'].mean()\n", + "region.name = '平均金額'\n", + "region\n", + "df\n", + "# df.groupby('category')['amount'].sum()\n", + "# df.groupby('category')['amount'].sum().idxmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "d59717c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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customer_idRFMcustomer_name
020152025-12-231256165.0Olivia Huang
1220222025-11-271351242.0Victor Lin
2520252025-11-271246739.0Yuki Chen
3720182025-12-261243486.0Rachel Lin
4920212025-12-31940346.0Uma Wang
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" + ], + "text/plain": [ + " customer_id R F M customer_name\n", + "0 2015 2025-12-23 12 56165.0 Olivia Huang\n", + "12 2022 2025-11-27 13 51242.0 Victor Lin\n", + "25 2025 2025-11-27 12 46739.0 Yuki Chen\n", + "37 2018 2025-12-26 12 43486.0 Rachel Lin\n", + "49 2021 2025-12-31 9 40346.0 Uma Wang" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm = df.groupby('customer_id').agg(R=('order_date','max'),\n", + " F=('order_id','count'),\n", + " M=('amount','sum')).reset_index().sort_values('M', ascending=False)\n", + "rfm5 = rfm.head(5)\n", + "rfm5\n", + "rfm2 = rfm5.merge(df[['customer_name','customer_id']], how='left', on='customer_id').drop_duplicates()\n", + "rfm2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c0e291e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Timestamp('2025-12-31 00:00:00'), Timestamp('2025-01-02 00:00:00'))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf = pd.read_csv(\"../datasets/ecommerce/orders_enriched.csv\",\n", + " parse_dates=[\"order_date\"])\n", + "gdf['月份'] = gdf['order_date'].dt.month\n", + "month_avg = gdf.groupby('月份')['amount'].mean()\n", + "month_avg.name = '平均金額'\n", + "month_avg\n", + "\n", + "date_ord = gdf['order_date'].value_counts()\n", + "date_ord = date_ord.rename_axis('日期')\n", + "date_ord.name='訂單數'\n", + "date_ord\n", + "\n", + "dmax = gdf['order_date'].max()\n", + "dmin = gdf['order_date'].min()\n", + "dmax, dmin" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7613a8e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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order_idcustomer_idproduct_idqtyorder_dateamountcustomer_nameregionsignup_datevip_levelproduct_namecategoryunit_pricestock_qty月份
05064202210264.02025-03-268600.0Victor LinNorth2023-02-27GoldDumbbell 5kgSports2150513
15023202610215.02025-01-051355.0Zoe HuangSouth2023-05-16PlatinumThrow PillowHome2711501
25123201310132.02025-09-113538.0Mia HuangNorth2023-07-17PlatinumCotton T-ShirtClothing17691749
35118200510281.02025-05-221618.0Emma LiuWest2023-05-18BronzeWater BottleSports16181865
45161201710193.02025-08-201846.0Quinn ChenEast2023-08-11SilverCoffee MugHome18462748
................................................
1835094202610193.02025-02-135538.0Zoe HuangSouth2023-05-16PlatinumCoffee MugHome18462742
1845041201410015.02025-10-038135.0Nick HuangWest2023-09-28GoldWireless MouseElectronics16271210
1855157200510265.02025-01-0210750.0Emma LiuWest2023-05-18BronzeDumbbell 5kgSports2150511
1865134201510125.02025-06-039580.0Olivia HuangNorth2023-12-15BronzeClean CodeBooks1916816
1875135201010074.02025-09-052436.0Jack LiuSouth2023-03-12PlatinumPython CookbookBooks6092589
\n", + "

188 rows × 15 columns

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" + ], + "text/plain": [ + " order_id customer_id product_id qty order_date amount customer_name \\\n", + "0 5064 2022 1026 4.0 2025-03-26 8600.0 Victor Lin \n", + "1 5023 2026 1021 5.0 2025-01-05 1355.0 Zoe Huang \n", + "2 5123 2013 1013 2.0 2025-09-11 3538.0 Mia Huang \n", + "3 5118 2005 1028 1.0 2025-05-22 1618.0 Emma Liu \n", + "4 5161 2017 1019 3.0 2025-08-20 1846.0 Quinn Chen \n", + ".. ... ... ... ... ... ... ... \n", + "183 5094 2026 1019 3.0 2025-02-13 5538.0 Zoe Huang \n", + "184 5041 2014 1001 5.0 2025-10-03 8135.0 Nick Huang \n", + "185 5157 2005 1026 5.0 2025-01-02 10750.0 Emma Liu \n", + "186 5134 2015 1012 5.0 2025-06-03 9580.0 Olivia Huang \n", + "187 5135 2010 1007 4.0 2025-09-05 2436.0 Jack Liu \n", + "\n", + " region signup_date vip_level product_name category unit_price \\\n", + "0 North 2023-02-27 Gold Dumbbell 5kg Sports 2150 \n", + "1 South 2023-05-16 Platinum Throw Pillow Home 271 \n", + "2 North 2023-07-17 Platinum Cotton T-Shirt Clothing 1769 \n", + "3 West 2023-05-18 Bronze Water Bottle Sports 1618 \n", + "4 East 2023-08-11 Silver Coffee Mug Home 1846 \n", + ".. ... ... ... ... ... ... \n", + "183 South 2023-05-16 Platinum Coffee Mug Home 1846 \n", + "184 West 2023-09-28 Gold Wireless Mouse Electronics 1627 \n", + "185 West 2023-05-18 Bronze Dumbbell 5kg Sports 2150 \n", + "186 North 2023-12-15 Bronze Clean Code Books 1916 \n", + "187 South 2023-03-12 Platinum Python Cookbook Books 609 \n", + "\n", + " stock_qty 月份 \n", + "0 51 3 \n", + "1 150 1 \n", + "2 174 9 \n", + "3 186 5 \n", + "4 274 8 \n", + ".. ... .. \n", + "183 274 2 \n", + "184 12 10 \n", + "185 51 1 \n", + "186 81 6 \n", + "187 258 9 \n", + "\n", + "[188 rows x 15 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts = gdf.set_index('order_date').sort_index()\n", + "monthly_revenue = ts['amount'].resample('ME').sum()\n", + "type(monthly_revenue)\n", + "\n", + "mrr = monthly_revenue.rolling(window=3).mean()\n", + "mrr\n", + "\n", + "cat_med = gdf.groupby('category')['amount'].median().sort_values(ascending=False)\n", + "\n", + "gdf" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "cf89a85f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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occustrevavgpct
order_date
2025-01-31171260062.03533.058824NaN
2025-02-28161172367.04522.9375000.204872
2025-03-31171155920.03289.411765-0.227272
2025-04-30141158506.04179.0000000.046245
2025-05-31161447879.02992.437500-0.181639
2025-06-30161350901.03181.3125000.063117
2025-07-3111940796.03708.727273-0.198523
2025-08-31241662706.02612.7500000.537062
2025-09-3010850745.05074.500000-0.190747
2025-10-31151274376.04958.4000000.465681
2025-11-30181062533.03474.055556-0.159231
2025-12-31141049597.03542.642857-0.206867
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" + ], + "text/plain": [ + " oc cust rev avg pct\n", + "order_date \n", + "2025-01-31 17 12 60062.0 3533.058824 NaN\n", + "2025-02-28 16 11 72367.0 4522.937500 0.204872\n", + "2025-03-31 17 11 55920.0 3289.411765 -0.227272\n", + "2025-04-30 14 11 58506.0 4179.000000 0.046245\n", + "2025-05-31 16 14 47879.0 2992.437500 -0.181639\n", + "2025-06-30 16 13 50901.0 3181.312500 0.063117\n", + "2025-07-31 11 9 40796.0 3708.727273 -0.198523\n", + "2025-08-31 24 16 62706.0 2612.750000 0.537062\n", + "2025-09-30 10 8 50745.0 5074.500000 -0.190747\n", + "2025-10-31 15 12 74376.0 4958.400000 0.465681\n", + "2025-11-30 18 10 62533.0 3474.055556 -0.159231\n", + "2025-12-31 14 10 49597.0 3542.642857 -0.206867" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts = gdf.set_index('order_date').sort_index()\n", + "mo = ts.resample('ME').agg(\n", + " oc=('order_id','count'),\n", + " cust=('customer_id','nunique'),\n", + " rev=('amount','sum'),\n", + " avg=('amount','mean')\n", + " )\n", + "mo['pct'] = mo['rev'].pct_change()\n", + "mo\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b6b93a27", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei'] # 設定為微軟正黑體\n", + "plt.rcParams['axes.unicode_minus'] = False # 解決負號顯示為方塊的問題\n", + "sns.set_theme(style='whitegrid')\n", + "import matplotlib.font_manager as fm\n", + "# 列出所有可用的字體名稱\n", + "# [f.name for f in fm.fontManager.ttflist if 'Chinese' in f.name or 'MS' in f.name]\n", + "# [f.name for f in fm.fontManager.ttflist if 'Jheng' in f.name or 'MS' in f.name]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "82103229", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gdf = pd.read_csv(\"../datasets/ecommerce/orders_enriched.csv\",\n", + " parse_dates=[\"order_date\"])\n", + "# co = gdf['category'].value_counts()\n", + "# ax = co.plot(kind='bar',figsize=(10,4),title='Order count in Category',rot=45)\n", + "# 後續用ax 調整, 這個就是matplotlib 物件\n", + "# plt.show() 是plt module 裡的一個函數,非class or instance level \n", + "# plt.show() 是總管：它會掃描目前程式中所有已經畫好的畫布，然後把它們一起打包裝進一個視窗裡顯示出來\n", + "\n", + "# plt.figure(figsize=(10,4))\n", + "# sns.histplot(gdf['amount'], bins=20, stat='percent', kde=True)\n", + "\n", + "reg_order = gdf['region'].value_counts().reset_index()\n", + "reg_order\n", + "plt.figure(figsize=(7,5))\n", + "sns.barplot(data=reg_order, x='region', y='count', palette='viridis', hue='region')\n", + "plt.xlabel('region')\n", + "plt.ylabel('order count')\n", + "plt.title('region vs. order count')\n", + "for r,c in enumerate(reg_order['count']):\n", + " plt.text(r,c, f'{c:,.0f}', ha='center',va='bottom',fontsize=10)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40e5cc25", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\f5486\\AppData\\Local\\Temp\\ipykernel_19080\\167007142.py:8: UserWarning: \n", + "The markers list has fewer values (1) than needed (2) and will cycle, which may produce an uninterpretable plot.\n", + " sns.lineplot(data=piv.T, legend=True, markers='o')\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'North vs. South')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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2WkyePBkPPvgg1q9fr27l37r44ovb9BxEsviOOW06YsddDEddDXQRZridzjYvymUxb4hPP+q+qMFTYO4/HvbSPDQUSfZA41F0AM7qUoRpdc1BALfTgUOv3ClRK+jjUmBI9GQO6JO6qnN9bHLz5xIRERGFwraAEf2T4Q8sEQYMz05Wh3C63Kq2gKdtoad1odQeOFhYrY7vVuxXnycZAk1bCeS2T7dYlUlA1Bl8+kqrrKxUV/dvuukm9OnTR91366234sILL8TOnTuRk5ODXbt24cYbb0RiYuJPvr6hoQGvvfYa7rrrLkycOFHd9/TTT2P8+PEqkHD++edj1qxZ0Ov1eOihh6DT6VSmwf79+1XgQQIBbXkOoqbMgLq6Ouw9kN+ugjSt0eiNMKb0VEdLUoDQWVPe/LGjpkz9+y5bDeyl+eqo3b6s+fEwnQEpv7gXpu791cf2snyE6YzQWuJYf4CIiIiCRo3Vji17PHvzR/ZLgT+S2gAZadHqOPe0DHVfRXW9p3Whyhoox84D5er/snpboTqEJAf0SI1G3x6xKnNAjuS4CL6Xo+ALBERHR+PJJ59s/risrAyvv/66uiqfmZmJAwcOqIVXz55HL5KabN++HbW1tRgzZkzzfVFRUejXrx9WrVqlFvGrV6/GyJEjVRCgyejRo/F///d/KCkpQX5+/gmfg0KbLS8XtgNbYR4wAdCGw2azdfi/qZUihC1qBOijk9D9jtdVcKApa6Dp1l5yEG5HA3TRR4JlJd/9D9bda9UWBk/NgW7Qqy4GkkHQDVqTpcP/D0RERETetm57kbriLmn2KfGRaq0QCGIsRowekKoOYXe4sDe/sjE4IMUIy1BcbsWe/Ep1fL10n+frzEZVY0A6FEjWgLRMNB6nRSLRyfCb3JP77rtPXb2XVPwXX3xRXW3Nzc1Vj7311ltYuHAhNBoNJkyYgNtvvx0WiwUFBZ60oNRUzw9Uk6SkpObH5LYp26Dl4+Lw4cNteo72kPYjgfCLyWq1HnVLP1Wx4ivYti2GtTgPhrG/8O18acOBlD4wyNF4l9vlgrOyCA36CNgbX3NOKUwo9QdstbAd3KaOliKHnw/L+Ms8n1tTAVdtOXRxaaqzgbfxNcY56wx8nXHO+DrzT/zZ5Hx527JNnhZ+Q3rHq/fagfwa65JgRJeEVEwZ5lmHlFXZkHugErkHK5B7UAICVaioqcfyzQXqaMo26JFqUe0L+3SNVrcJ0eEn9e/KRa3w8M65uBUsrAHyOpM1qNQsC6hAwDXXXIPLLrsM77zzjqobIHv2JRAgi39ZlL/00ksqQ+Cxxx5T2wbeeOON5m/Esfv4jUaj2nYg5AXe2uNCihO25Tnaw263qyKFgWLfPk/UkY7haEBM7grIj1NhRBc4G+fJL+er4MhWAvS7EMg6D9raMmiri6CpLoa2phhaubVVoqjOgUONr0/j/tWI2PYd3FI4MyIOTksCnOYkOC2JcJoT1X3QaNo9LMnGkT82hw4dgsPh8Mb/NGT45evMz3HOOGd8nfkn/mxyvrxBMgGa0ujjw2uPeq8dLK+xaC0wooccFtidZhwua8DBkgYcKmnAweJ61Nhc2J1XpY6vG3eJWkxadE00oGuC50iJNaiOCseS92NJyWkqKztVa4I5woCKykoUFx5mUKCNAuF11tYad34TCJCtAEKq+2/YsAFvv/22Ov/lL3+J2NhY9Zhc2ZdaAZdeeik2bdqkXsxC9vk3nTct8KXQoJD75fGW5HEhWQdteY72kLoETf8nfyaBEHlB9+jR45T+v8HKunUxKp12aGNS0HvMFPVLMtDny1VvhUQ2NAbP+Gvr9qFmnwWwVkNbV6YOFHqycYQ2Lg2J1/xLnbvdLtTv3Qh9QhdoLPE/G3FUHRTC3NDq9HBYa6AzmeF02OFwh6loJR0ffy5PHueMc9YZ+DrjnPE15luyt97akKeK7J05biC0Wk3Q/1wObHEu75+KKyRrwJMxIJkD+wtqUG11YusBqzqEXqdBz7Qo9OkWjT5dPZkDsVHh0OoM+HDeTsxetAy1VjsiTXpcML4nZk7OgtPRwPdnPyNQXmdSX6+tfBoIkJoAy5Ytw9lnn928h18yAGQBXVRUpM6bggBNevfurW4lbb8pnV8+t1u3bs2fIx/37dtXnUu9Afm4paaPk5OTm69Q/txztIcsgrxRTK6zyAs6kMbbWSp3LFW3UYMmqU4TTQvfgJ6vY8YdMf5iJIy7CM7aSjQU74e9+GCLLgYHYYxPb/6/SgvEws89dT3CDCZP/YGkbkfdaiOi1OMuRwMqlnxywpaLdHwB/TrzEc4Z54yvM//En03Olzds3L1X3Up1fovFHJKvMXk/2iM9Hmc1ljez1Tuw82CFqjHg6VJQ7mlneKBCHYCnQ8FDN45RRRY/mHvkYo8EA977boc6v2hSJkwGv7lG7LdMfv46a+u2AOHT77YU67vjjjvwyiuvqCr9TSn1W7duVe3+7r77brUglwKCTSQTQEiwoGvXrjCbzVixYkXzIr6qqkp9fVO7wREjRuD999+H0+lsbju4fPlyVfU9Pj5e1Ro40XNQaHJUlcC6b7M6V4UCg5i6em+OUQcyBjXfLxkAbskgaOSy1qiig9Ly0N1gRX3eDnW01PWW56G1xKJi6WeoWPzhka+11aJi0Sx1HjPmQtUFgYiIiKitVm4t9OtuAb4QbtQhJzNBHU1ZA/kltao7gQQGduwvR3m1TXUg+Ndbq1t9ji8W7cElZxxdU42Cn08DAZLqL8X/Hn74YXXIfhWp5i8L8V/96ldq34+0E3z++edxwQUXYO/evaoNoFTylzaAQhbrTzzxBOLi4lQrwscff1xlAZx11lnqcWkRKIGGv/71r7j++uuxceNGFVh48MEHm/dQnOg5KDRVb1oov04R3r0/9DGeApOhJixMg7AW3QuMaZnoeuPTcDvtsJcebuxcsF9lDkg2gWQV6GSu3G6VCdCaqlVfIXbsxZ34vyAiIqJAV1Bai4OF1apY3pCs0Hxf1taLO+mJZnWcMcJzkbPOZkddvUNlALRG7pfPiTZ7v2g0+S+f53889dRTqoWgdAKorq7G8OHDVcHAtLQ0dTzzzDN4+eWX8d///lddvZ82bRr+8Ic/NH/9bbfdptL77733XrV/WzIAXn31VbVHX8hVfwkESL2BGTNmqBoDkmkg5219Dgo9Ek2t2TRfnVtyJvp6OH4nTKv3bAVI6gb0H9d8v8tejzCNVgUEJAOgNXK/q74W2ojoThwxERERBbKVWz1V8/v3jFc1AqjtIsL10Ou0qiZAa8EAuV8+h0KLzwMBsrh/4IEH1NGac845Rx3HI+n+f/zjH9VxPAMHDsQHH3xwSs9BocaN2Im/RM3mRYjMatyERSekaWw/KPUA5GgtGKAeMx7JMiAiIiI6kVWN2wJG9EvmZLWD0+VShQGbagK0JPfL43q0v0sUBR6fBwKI/DUl3pw1Rh108twuhyoM2FQToCW5Xx4P0/LXDxEREZ2YpK1v3l2izlkfoH3CDTrMnNy7uSZAU9eA88dmqPsNek8tNQodfCdORF4nhQClO0BTTYDmrgHDz2XXACIiIjop63KL4XC6kZ4YibTEo7sFUNvJYl+6A0hhwJq6epjC9Vi3oxgL1x3ClJHdOZUhhoEAomPU7liJqrXfImrYVET2GcH5aSdpESjdAWLHXgRHbRW04ZGwHdrB1oFERER0UlZu8dQHGMFuAV7JDKirq0P+wb3IrzbhuQ83IdZixPghXWBkVkBI4UYQomNUb5wH6571sB3azrk51V8w+nBY6+3YuXc/Drz4O1Qs/RiuhiPtCImIiIh+jtPlxprtbBvobVIgfVS/JCTGmlBeXY+5K/bzhRhiGAggasFZV4W6XWvVuSXndM6Nl1gdbiRc/U+kXfkQNAYT55WIiIjaZOeBclTWNCAyXIfsjDjOmhfpdBpcPMlTN+Cj+btgd7g4vyGEgQCiFmq2LAZcThhSesGQ6Om9Sl76ZWOM4FQSERFRu9oGDstKhk7LpYu3nTmym9oaUFJhxfw1B/nqDCH8aSJqoWbTj+rWMpDZAB3B7bCjbucaOKpK+bojIiKiE2LbwM4pICg+/CEXTiezAkIFAwFEjRqKD6L+8G5Ao4W53zjOSwco/ORJFMx6BDWbF3B+iYiI6GcVldVh3+EqaMKAYdnJnK0OMnV0D0RFGlBQWoeF6/M4zyGCgQCiRtWN2QARvYZCGxnNeekAEZlD1W3NtuWcXyIiIvpZqxq3BWRnxMMSYeBsdZBwow7TT++lzmfNzYXL5eZchwAGAogkZd3tRu3WpWouLAMnck46SGTfUUCYBg0Fu2Gv8FQAJiIiImrNym1N3QKYDdDRzhubgUiTHoeKarBs02G+IEMAAwFEAMLCwpB+7aOIP+s6RGQO45x0EMm0CO/WT53XbmdWABEREbXOWu/Axp0l6nxEvxROUweLCNdj2rie6vyDuTvURTIKbgwEELVYpEaPOBdhOj3npANFZo1Rt7XblnGeiYiIqFXrc4vhcLqQGh+JLklmzlInmDa+J0xGLfbmV2FVYzYGBS8GAijkuRwNcNnrQ34eOktk1ijJwUB9/k44Kos570RERHTc+gAj+iWrzE3qeFIw8NzTMtT5rO9zmRUQ5BgIoJBXu2Ux9j97PcoXzQr5uegMOnMswrtlq/Mabg8gIiKiY0ixuqYr0iO5LaBTXXh6Lxh0Guw4UI4NO3nBJpgxEEAhT7oFuOvrVNtA6rztAYakbtCamOpHRERER9t1qAIV1fUwGXXo1zOe09OJYi3hOHtMD3X+/ve5nPsgxkAAhTR7ZRFs+7eoVHXLgAm+Hk7IiBo+FV1ueBqWgZN8PRQiIiLyMysbtwUM7ZsEvY7Llc520cRM6LQabNlTis27PQUbKfjwJ4tCWs2mheo2vMcA6KITfT2ckBEWxl89RERE1LpVWxu3BfRn20BfSIgxYcrIbup81lxmBQQrvhunkCVtUWo2/ajOLTmn+3o4IcftdsGWl4vKVXN8PRQiIiLyEyUVVuzJq4TUBxyWxUCAr1w8KRMaTRjW5RYj90C5z8ZBHYeBAApZUrXeXnYYYXojIrNG+3o4IcdZXYb81/+M0u9eg6Oaf2CIiIgIzUUCs7rHIdps5JT4SEp8JCYO7aLOmRUQnBgIoJBVvXG+upUggMZg8vVwQo4uKgHGtN6SG4DaHSt8PRwiIiLyAyu3HGkbSL51yRm9VWbGii0F2JtfyW9HkGEggEKWo9wTcTZzW4DPRGaPUbe125f5bhBERETkF2wNDmxsbFnHtoG+1yXJgnGD0tU5swKCDwMBFLJSf/k3dLnxaZi6D/D1UEJW05YM24GtcNYy0kxERBTKNu4sQYPDhaRYE7qlWHw9HGrMChBLNubjYGE15ySIMBBAIc2Q2A1hGq2vhxGy9DHJMKT0Atwubg8gIiIKcU1tAyUbIExy0snnMtKiMap/Ctxu4MMf2EEgmDAQQCHHWVcN64Etqmo9+Z4525MVULt9ua+HQkRERD7s5tTUNnBEvxR+H/zIZWf2UbcL1uXhcEmtr4dDXsJAAIWcms0LcPitv6Hw4yd8PRRqsT3Aum+TCtIQERFR6NmdV4myKhvCDVrkZMb7ejjUQu+usRialQSXy42P5+/k3AQJBgIo5FRvWqBuWRvAP+jj0mDqNQRRw6bC7bT7ejhERETkA03ZAEP6JkGv47ZNf3PZFE9WwA+rDqC43Orr4ZAXMBBAIaWh6AAaCvYAGh3M/cf5ejjUKPUX9yLh7Ougs8RxToiIiEK4PsCIbLYN9Ef9MuKR0ysBDqcbn/zIrIBgwEAAhZTqTfPVbUTmUGgjonw9HCIiIqKQJ1sCdh2sUPMwvB8DAf6eFfDd8v0or7L5ejh0ihgIoJDhdjlRs2mhOrcMnOjr4dAx7OUFKF/yCep2reHcEBERheC2gD7dYhBrCff1cOg4BvZOQN/usarF42cLdnOeAhwDARQyrHs3wllbAY3JojICyL9IkKb8x3dQtfY7Xw+FiIiIOtGqFm0DyX9JS8emrIA5S/eisqbe10OiU8BAAIWM6k0/qlupDRCm1ft6OHSMyMY2gnV71sNlY2saIiKiUNBgd2L9zmJ1zraB/m94djJ6pkfD1uDE7EV7fD0cOgUMBFBIVafXmuNgzuG2AH+kT+gKfXw64HSgltsDiIiIQsLGXSWob3AiITocGWms3xRIWQGzF+9BjZUdnwIVAwEUMuImXIZuv3sJxtRevh4KHecPS2T2GHVeu20Z54iIiCiUugX0S1HvBcj/jR6Qiq7JFtTZHPhqCbMCAhUDARRSwjRa/pHxY5FZnkCAdfc6uOrZo5aIiCiYud3u5kKBI/uzPkCg0GjCcGljVsDnC/bAWu/w9ZCoHRgIoKBnryhC2YL3VVV68m+GpO7Qx6XC7bSjbvdaXw+HiIiIOtC+w1UoqbDCoNciJzOBcx1Axg9KQ2pCJKrrGvDNsn2+Hg61AwMBFPRqNv2IisUfomTOS74eCrVle0BjVkDttqWcLyIiohDYFjCkTyKMeq2vh0MnQavV4JLJvdX5pz/uQr3dyfkLMAwEUNCnnFVvWqDOzQNZJDAQRPYbC3P/8TAPnOTroRAREVEHatoWMKJfMuc5AE0c1hWJsSaUV9dj7or9vh4OnSQGAiio1R/aAUd5AcL04Yjs62lPR/7NmNwDSdP/gMjew309FCIiIuogFdX1yD1Q3tySjgKPXqfBxZM8WQEfzd8Fu8Pl6yHRSWAggIJa9cb5zT3qNYZwXw+HiIiIiACs3lYItxvI7BKN+GgT5yRAnTmyG+KijKrWw7zVB309HDoJDARQ0HLZ65v3mVtyuC0gkLidDlSt+x4Fs/6pvo9EREQUvG0DKXBJoccZExuzAublwulkVkCgYCCAglbdztVw1ddBF5WA8O79fT0cOhkaLSoWf6S+h9Y96zl3REREQcTucGJ9bpE6H8lAQMCbOro7oiINKCitw8L1eb4eDrURAwEUtKo3/qhuzTmnIyyML/WA6x6Q3dg9YPtyXw+HiIiIvGjT7lJY650qpbxnejTnNsCFG3WYfnovdT5rbi5cLrevh0RtwNURBa3oUefDPGACzNwWEJCa2wjmroLbYff1cIiIiMhLVrXYFqDRhHFeg8B5YzMQadLjUFENlm067OvhUBswEEBBKyJjEJIu/D0M8Wm+Hgq1gzG9N7SWOLgbrKjj9gAiIqKgae28sqltILsFBI2IcD2mjeupzj+Yu0N9n8m/MRBARH5JtnNEZnlaPnJ7ABERUXA4UFiNorI6GHQaDOqT6OvhkBddMKEnTEYt9uZXYVVjsIf8FwMBFHTqC/ch742/onrDPF8Phby0PaAudyXcTm4PICIiCnQrt3i2BQzsnYhwg87XwyEvskQYcO5pGeqcWQH+j4EACjo1m35E/aHtqNu1xtdDoVMU3jULWnOs6v5g3buR80lERBTgmq4Uj+yX7OuhUAeYfnqmaimYe6AC63OLOcd+jIEACipulxM1mxepcxYJDI7tAbHjZiLh3FtgTO/j6+EQERHRKaisqceO/WXqfHh2CucyCMVYjKqdoPhgbq6vh0M/g4EACirSc95ZWwFNRBQieg3x9XDIC6KGTUXUkCnQmiycTyIiogC2ZnsRpLNcRloUEmNNvh4OdZAZEzOh02qwZU8pNu8u4Tz7KQYCKKhUb1qgbs39xyNMy31nRERERP7WNnBkP2YDBLOEGBOmjOymzmcxK8BvMRBAQcNpq0XdjpXq3JIz0dfDIS+qP7wHhZ8+hdK5r3NeiYiIApDd4cLaHUXqfATrAwS9iydlQqMJw7rcYuQeKPf1cKgVDARQ0KjdtlRVltcndoUhxVOxlIKD01aN2q1LVMaH1IEgIiKiwLJ1bynqbA7EmI3o3TXW18OhDpYSH4mJQ7uoc2YF+CefBwJKS0vxxz/+EaNHj8aQIUNw4403Yvfu3c2Pb9u2DVdeeSUGDx6MyZMn48033zzq610uF5577jmMHz9efc4NN9yAgwcPHvU53ngO8n+yUGzKBggLC/P1cMiLTN0HQGOywFVXBduBrZxbIiKiALOycVvA8OxkdaWYgt8lZ/SGvCVfsaUAe/MrfT0c8rdAwG9+8xvs378fL7/8Mj766COEh4fjV7/6FaxWK8rLy3HttdeiW7du+Pjjj9XnPvHEE+q8yQsvvIB3330Xf//73/H++++rRf3111+PhoYG9bg3noMCQ/LMe5B4/m9gHnC6r4dCXham0SKy7yh1XrttGeeXiIgogLjdbqza0tg2sD/bBoaKLkkWjB+Urs7ZQcD/+DQQUFlZifT0dDz88MMYOHAgevXqhVtvvRVFRUXYuXMnZs2aBb1ej4ceekg9dvHFF6sggQQNhCzUX3vtNdx2222YOHEisrKy8PTTT6OgoADfffed+hxvPAcFBo3RBMugydBZmG4WjCKzRqvb2h0ruD2AiIgogBwqqsHh0lpVSX5wnyRfD4c60SVTPO2fl27Mx8HCas69H/FpICA6OhpPPvkk+vTxvEDKysrw+uuvIyUlBZmZmVi9ejVGjhwJne5I9XfZQrBv3z6UlJRg+/btqK2txZgxY5ofj4qKQr9+/bBq1Sr1sTeeg/yb2+1ShQIpuJl65EATblbtIW2Htvt6OERERNRGq7Z6sgEGZibAZGRXp1DSIzUKowekwO0GPvwh19fDoRb85ifxvvvuU1fvDQYDXnzxRURERKir8k1BgiZJSZ4o4uHDh9XjIjU19Sef0/SYN56jvSlQdXV18HeyBaPlbSBqOLQDZZ/8C6bssYg+87oO/beCYb46mzfnzNhzCKxbF6Fy0yK4E4K3ICRfZ5wzvs78E382OWd8jbXP8s156nZQZqzX3x/z59L/5+yCcd2wfHMBFqzLw/QJ3ZESF4FAYw2QNYCsQdtaK81vAgHXXHMNLrvsMrzzzjtqH7/s2bfZbCow0JLRaFS39fX1zd+I1j5Hth0IbzxHe9jtdlWkMFBIhkSgitj8FYxOOyrKy5HfSXMeyPPlK96YM114KiwAarYtQ17ycKgKNEGMrzPOGV9n/ok/m5wzvsbarq7ehe37K9R5lLayw94f8+fSv+csM9WIXYfr8cYX63HBqMDdxrsvANYAx65r/T4QIFsBxD/+8Q9s2LABb7/9tioceGzBPlm8C8kYkMeFfE7TedPnmEwmde6N52gPqUvQ9H/yZxIIkRd0jx49Tun/6ytuRwOK5u2AW7I6TjsPhi7ZHfrvBfp8+YI358zdOxO1EWEI7z0CaTHBW2yIrzPOGV9n/ok/m5wzvsZO3pKNh+F256Nrkhmnjcjx+hTKRT/J8pXs3pbv5cm/fpddFZGK+19ZhQ376nDdjKFIiA6s75U1QNYAu3btavPn+jQQIDUBli1bhrPPPrt5D79Go1ELaCkYKLUC5Lalpo+Tk5PhcDia75OuAC0/p2/fvurcG8/RHpKSIYGGQCEv6EAab5OaLWvgbrBBF52E6N5DEBbWOWUvAnW+fMlbcxZ5+qUIFXydcc74OvNP/NnknPE11nbrd5Wr21EDUr3+3sllt8FktMCsD4M+wqyKCWv0gbXADJXfZUOzI5DTKwGbdpfg6+WHcNOMgQhEJj9fA5xMC3WfFguUYn133HGHCga0TKnfunWrqvA/YsQIrFmzBk6ns/nx5cuXIyMjA/Hx8arCv9lsxooVK5ofr6qqUl8vXyu88Rzkv6o3LlC35pwJnRYEIP8pEin7oIiIiMg/OZ0urNnuuQA3sl+KV5/b5WhAxdLPsP+Z63DouevVbcWyz9X95J8uO9NTt+275ftRXmXz9XBCnk9XTlLEb8KECap9oFToz83NxZ/+9Ce1EJcWf9Lqr6amBn/9619VmsMnn3yiugrcdNNNzfsfrrzySjzxxBP44YcfVAeA22+/XWUBnHXWWepzvPEc5J8c1WWw7t2gzi05E309HOpEZfPfwYHnbkR9/k7OOxERkZ/auq8MtVY7oiIN6NM91quZABVLPkHF4g/hauwcJbcVi2ahYumn6nHyP9I1Iqt7LBocLny6YLevhxPyfH4J9amnnlKt+2Txfckll6CiokIVDExLS1NX7F955RXs3bsXM2bMwPPPP4+7775bnTe57bbbMHPmTNx77724/PLLodVq8eqrr6o9+sIbz0H+qWbzQsDtgrFLX+jjju76QMHNXlkEZ005arcfySYiIiIi/2wbODw7GVqN9wr8hml0qFo9p9XHqlZ9pR4n/0xbv+xMz9brr5fuRWWNp24b+YbPf0osFgseeOABdbRm4MCB+OCDD4779bJo/+Mf/6iO4/HGc5D/sZcdVrfMBgg95qwxqN2yGLXbliNu8tUntR+KiIiIOsfKLZ5W3CP6ebfAr1z9b8oEaPWx+lpoI6K9+m+SdwzLSkLP9GjsyavE7EV7cOU5HVvom/w4I4CovRLPuwVdf/MizP3HcxJDjKnXEITpjXBUFqGhYI+vh0NERETHyC+uQV5xjcoEGNo3yavzowmPVMdxHzO2/hj5SVbAFE+tgNmL96DGavf1kEIWAwEU0PQxSdAY/beFB3UMjd6IiMyh6pzbA4iIiPzPysZtAQN6xSMi3Lvbbd0uB6JGnNvqY1HDz4Xb6ekKRv5p9IBUdE22oM7mwFdLeEHHVxgIoIAjv9zrdq1VLWIodEVmn6Zua7YtY/cAIiIiP7Nqa0GHdAsQ0iIweuQ0xIyb2ZwZILfycfTwc1C3a43X/03yHo0mDJc2ZgV8vmAPrPUM3IRkjQCik1W3ex0KP3wUhqTuSL/+Se4PD1ERsj1AZ4CjvAANhftgTMnw9ZCIiIhIsvWsdmzZU6rmYkQHBAJc9noUvPd3xJx2Ebr9/hU4rbXQRZhhLytA/lv3wVFZDENiVxgSu/H74afGD0rDu99ux+GSWnyzbB9mTMz09ZBCDjMCKODUbFqgbk09chgECGEag0nVChB1u9f6ejhERETUaO2OIjhdbnRNNiM1wfv79Ws2L1IthEvn/g9WWwNyD+TDWm+HPiEduugEuB0NKPrsabgcDfye+CmtVoNLz+itzj/5cRfq7cz07WwMBFBAcVqrUbtzlTo350z09XDIx2LHzUTarx5VVwSIiIjIP6xs3BYwItv72QButxtVq79W51HDpiJMo4HNZlMfh4VpkDjtd9BGRqOh6ADKfnjT6/8+ec/EYV2RGGtCRXU9vl+xn1PbyRgIoIBSu3Up4HSobQFMBSdjSk+Ep/dmZggREZGfkEyANdsKO6RtoIcb0WMuRHi3/rAMmvyTR3XmWBUMEBIwqM31XEAi/6PTajBzsicr4ON5O2F3uHw9pJDCQAAFlOrGbQHMBqBjOY/TT5iIiIg6z479Zaius8Ns0iO7R5zXn1+u+lsGTEDaVQ9Ba7Ict45Q9KhpMKb3gSGJdQL82ZQR3RAXZURJpQ3zVh/09XBCCgMBFDAaSvNRn7cDCNPAPGC8r4dDfqK+cB8OvvwH5L/5V18PhYiIKOSt3OLZFjAsK1ntA/f2toC2ipt0BdKu+jv0MR2RlUDeYtBrMWOiJyvgo3m5cDqZFdBZGAigwCsS2HOQSvsiEvroRNhLD8NefBANJYc4KURERD60qnFbwMj+3l+AVyz5GIUfP476w7tP+LlhWj3CtLrm1tPW/Zu9Ph7yjqmjuyMq0oCC0josXJ/Hae0kDARQwNBZ4qCLTYFl4CRfD4X8iPQNjug5SJ3Xblvm6+EQERGFrILSWhwoqFZ94of2TfLqc7udds+e/+3LYS/3ZB20haveivw378Xhdx6ELS/Xq2Mi7wg36jD99F7qfNbcXLhcbc/8oPZjIIACRtSws9H1lucRmTXa10MhP9P0mqjdzkAAERGRr6za6skG6J8RD3OEwavPLQEAZ20FtOZYRPYd1eavCzOEQxebDLhdKPr0abhYU8gvnTc2A5EmPQ4V1WDppnxfDyckMBBAASUsLAxhGq2vh0F+JqLPCECjRUPRflVLgoiIiHzYNrADugVUNrUMHHpWc8p/W987Jk69EbroJDgqi1D89f+dVK0B6hwR4XpcML6nOv/g+1x+jzoBAwHk91wNNpT+8IYqCkfUGqkabOqR03zFgIiIiDpXnc2OzbtL1PnI/ilefe76w3tQf2gHoNHBMuTMdm0jTJpxuyo4Xbt1CWo2zvfq+Mg7po3vCZNRi32Hq5qzS6jjMBBAfq82dyUql3+Bwo8eY3SQjisya4zn9cLtAURERJ1uXW4xHE430hIikZ5o9upzV63xZANEZo9ud8Ho8PQ+iD39F+q85NtXmUHohywRBpx7WoY6/2DuDr7v72AMBJDfq9n4o7q15ExU6V1ErYnsO1JF+h0VhXDWVXOSiIiIOtGqxm0B3s4GkL/pNVsWq/Po4eee0nPFjJmO8O4D4LbbUPTpU3A77F4aJXnL9NMzVUvB3AMVWJ9bzIntQAwEkF9zVJXCunejOjfnTPD1cMiPaSOikHbNP9D9D69CG2Hx9XCIiIhChtPlxurGtoHerg9gPbBZtf8zpPSEMb3PKT2X1JlKuuA2aEwW1YVAig+Sf4mxGFU7QfHBXHZ56Ehtr7RB5AM1mxdKwxiEd82GPta7EWYKPpL2R0RERJ1r58FyVNY0IDJch34Z8V59bnPWGBhv/Q+cdVVeyQzVRcUj9fK/QZ+QDo3e6JUxknfNmJiJOUv3YcueUlV3YkCvBE5xB2BGAPktqehavcmzLcCcM9HXw6EA4qgph9Na4+thEBERhYSmwm5Ds5Kh03p/eaGPSUJ4WqbXns+Y2rM5COB2u+Cqt3rtuenUJcSYMGVkN3U+i1kBHYaBAPJbDQV7YC85hDCdAeZsTyE4ohMp+f5/OPDsDajeOI+TRURE1AlWbumYtoGyPVRS+DuKo6YCBe8/gsJPnlABAfIfMyf3hkYTpopQ5h4o9/VwghIDAeS3qhuLBEqPeGn7QtQW+hh5E+JG7Ta2ESQiIupoReV1qt2bJgwYluW9QEBDySEcfvdBHPjPraqVdEdwWathO7AF1j3rUbnyyw75N6h9kuMiMGlYF3X+wfesFdARGAggv2XOOR2WoWchqh39Yil0RWaNlnJAqM/boYpNEhERUcdvC8jqEYeoSIPXnrdqzTfq1pjSCxpDODqCIbEr4s+8Vp2XzXsH9Yd3d8i/Q+1zyRl9IGUhVm4twJ68Sk6jlzEQQH5L9oIlnnMTTD1yfD0UCiA6SxzCu2ap89rty3w9HCIiotBoG9jPe0WdXfV1qN44X51HjTgHHcky5ExE9B0FuBwo+uxpuBpYL8BfpCeaMX5Qujqf9QOzAryNgQAiCtKsAAkEcHsAERFRR7HVO7BxV4nX6wPI9lB3gw36+DSYegxER5JOBInn3QKtJR72ssMo+fbVDv336ORcMsXTEWrpxnwcLKzm9HkRAwHkd5x11ch77W5ULP8CbpfT18OhABSZ5SkuaTu4HY7qMl8Ph4iIKCit31kMu8OFlPgIdE22eK1rVNWar9V51LBzvNIy8ES0JguSpv8eCNOgZuN81GxZ1OH/JrVNj9QojB6QArcb+JBZAV7FQAD5nZqtS9QerZrNCxGm0fp6OBSApEewMb2vp2jgjhW+Hg4REVGQdwtI8dqC3bpvI+yl+QgzmGAZOAmdxdStP2LGXqzOy5d8wotRfuSyKfKeDliwLg+HS2p9PZygwUAA+Z2aTZ5uAZaBE309FApgkdmjAY0OTmYEEBEReZ3L5cbqbZ5CgSO9uC2gavXXze8DNUYTOlPs+EsQM+4SpF31d16M8iOZXWMwLCtJveY+nr/T18MJGgwEkF+RVjH1+TtValZkv3G+Hg4FsKhBZ6D77a8hbtIVvh4KERFR0Nl1qALl1fUwGbXo3zPBa88rNQH0cWmIGjYVnU0yUeNO/wW0JnOn/9vUtqyAH1YdUC0r6dQxEEB+pWbTAnUb0WsIdOYYXw+HApgmPBLa8EhfD4OIiCio2wYO6ZsEvc57S4roEeeiy83PwZDg6SHvK9K5oOiL52Ddt8mn4yCP7Iw4DMxMgMPpxqfzd3FavOCUfmp3796NN998E0888QQKCwuxevVq1NTUeGNcFILcbheqGwMBZm4LIC9xNdj4R5yIiMjLpLe7t9sGNumMAoEnUrH0U3WBqujzZ+Gsq/L1cAjApY0dBL5dsR/lVTbOiS8CAS6XC/feey/OP/98PPLII3j11VdRUlKCF154AdOnT0dBgecXA9HJsO3bDGd1qbqSG9F7OCePTpmzthL7n74Wh999SJ0TERHRqSuttGJPXiVkvT4syzv1AWo2L0LxnJfQUHwA/kAKB+rj0+GsKUfxl/9R3QzItyQjIKt7rOpU8emC3fx2+CIQIAv+2bNn4+GHH8aSJUuafzD++Mc/qiDB008/farjohDtFiAis8dCozP4ejgUBLSR0eqPONwu1Oau8vVwiIiIgmpbQN9usYixGE/5+WQtUbH8c1Sv+x51u9bCH2gM4UiacQeg1aFu5+rmIobkO5IpctmZnloBXy/di8qaen47OjsQ8PHHH+O2227DxRdfjJiYI/u4s7Oz1f0SHCA6WfFnX4eki+5C9PBzOHnkNZHZY9Rt7fZlnFUiIiIvbguQtoHeUJ+3Aw2FexGmM8Ay6Az4C2NyD8SfcbU6L/vhTdQX7vP1kEKedA/omR4NW4MTsxftCfn56PRAgGwDkEV/a5KTk1FVxX001I4Xo84Ac/YYGJK6cfrIayKzPIEAKfbjtFZzZomIiE6BrcGBDbnF6nxkf+8EAiobr7ab+4+DNsLiV9+fqOHnIiJzGNxOO4o+exouO69C+zwroLFWwOzFe1Bjtft0PCEXCOjevTsWLPAUdTvWypUr1eNEJ8NpZZFJ6hiG+DQYkroDLifquD2AiIjolGzcVYIGhwuJsSZ0Tzn1Rbujuhy12zxZe1F+mBUqC8/E838DrTkW9pJDKPvxXV8PKeSNHpCKbikW1Nkc+GoxswI6NRBwzTXXqG4BDz30EJYuXap+QPbv34/XXntNHb/85S/bPSAKPY7KYux/5joUfPAI3E6Hr4dDQZwVULt9ua+HQkREFBT1AaRbgDeq+0tdAAnWG7tkwZjSE/5acyjpgtsQ3iMHMaMu8PVwQp5GE4ZLz/BkBXy+cDes9Vw/tIeuPV90ySWXoKysDC+++CLee+89VeDjjjvugF6vx/XXX4/LL7+8XYOh0FS9eRHgcqg2b2Hadr0kiU5YJ6B84fuo27MBLlut6kxBREREJ0fe869qrg9w6t0CJN2+au236tzfa0SZMgaqQIA/tDYkYNzgdLz77Xbkl9Ti66X7cNGkTE7LSWr3quumm27CFVdcgbVr16KyshJRUVEYNGjQUcUDidryB6Vm03x1bs45nRNGHcKQ0AX6xG7Q6I1wVJfBwEAAERHRSZOWgaWVNoQbtMjplXDKM2jdvwXO2gpoI2MQmTXK778jTUEAl6MB1Wu/U1sZwjRaXw8rJGk1YbjkjN549oP1+HTBLpw3LgNGPb8XJ+OULr+azWZMmDDhVJ6CQlx9/i7YS/NVlVgpFEjUUdKvfVQFAoiIiKh9VjZuCxjcJxEGLyy6InoORvp1T8BRVYIwrT5gLmIVvPsQbAe3wVVfh9jxl/p6SCFr4rCuePe7HSgut+L7Fftx/jj/3FoSVIGAq6/2tNH4OVJDgOhEajb9qG4js0ZDY4zghFGHYRCAiIjo1BzZFuCdbgHCmJKhjkAhWQGWoWepQED5og9h6pGD8K6td1OjjqXTajBzcm+8+PFGfDxvJ84e3QN6XbtK4IUkTXsjYccetbW12LhxI3bt2oWePRmNoTa8jhx21GxdrM65LYA6g9vlVG0EbXk7OeFEREQnoazKhp0HK9T5iOxTrw/QUHwQbrcrIL8HlgETPO9d3S4UfvYMu1/50JQR3RAXZURJpQ3zVh/05VBCIyPgrbfeavV+qRVwww03MBBAbVK3ay1c1hpozXEqmkrU0SoWf4zyRR8gou8opMy8mxNORETURqu3ebYF9O4ag9io8FOaN6e1Gnmv3Q1ddALSrnpYVeUPNAln3wDboR1wlBegZM6LSLroLhYS9AHZonLRpN545fPN+GheLqaM6AqtllkBbeHVWYqOjsaNN96I119/3ZtPS0GqofQQEKaBOWcCC61Qp4joM0LdWnevg6vBylknIiI6yW0BI/uf+raA6g3z4HY0IExnhCYiKiC/BxqjCcnTbwc0WtWeuHr9XF8PKWSdPao7os0GFJTWYcG6PF8PJ2B0SLiktLS0I56Wgkzs2IvR/fevIHrkNF8PhUKEIbkHdLEp6s2HZKQQERHRiTXYnViXW+yVbQGyTa9q9TfqPGr41IC+im5My0TcxF+q89LvXkNDySFfDykkhRt1uHBCL3X+4Q+5cLrcvh5S8G4NWLVq1U/uczqdKCgowAsvvID+/ft7Y2wUAgIxFYwCl7zZkMKUlcs+Q+32ZTD3G+vrIREREfm9TbtLUN/gRHx0OHqmn9p7NwnEOyqLoDGZYe4/HoEuevQFsO7dCGddFcI0TEn3lfPGZuDj+btwqKgGyzblY9ygdJ+NJagDAVdddVWr0TspGpiamoq//OUv3hgbBTH5IxDevT8ruVOnM2eNUYEAVaPCXs/XIBER0Qms3HKkW8CpXsGvWvO1urUMOiMo/gaHhWmQNON2aPThCNMFRgvEYBQRrscF43vive924IPvczF2YFpAZ5v4bSCgtdaAMtFmsxl9+/aFhtEwOkGV2IIP/qH2hHX77UtB8UeAAochtRd00YlwVBarWgGSIUBEREStkwt9qxoLBY7sd2rbAhpK82Dds0FWDogadnbQTLnWZDlq64O9vACGeF6R7mzTxvfEZwt2Yd/hKqzaWuiVehbBrF2BgJEjR3p/JBQyqjf9qG7D0/syCEA+2h4wBpUrvkDN9mUMBBAREf0MWVQVl1tVdfaBvRNPaa6aagNE9B4GfcyptyD0N87aShR+8iQaig+gyw1PQWeJ8/WQQoolwoBzT/NsEfhg7g6M6JfMrABvBwLEkiVLMH/+fFitVrhcrp+80X7kkUfa+9QUxCRKWrN5oTo3Dzzd18OhEBWZPQa1uSthSOjq66EQERH5NbmyKgb3ToRRrz2l59JFxav6UFHDz0Uw0hgj4Kqvg8tajaIvnkPq5fexM1Ynm356JmYv3ovcAxVYn1uMIX2TOnsIwR0IeO211/DYY4/BaDQiLi7uJ5EW7seg47Hu2wxndRk04WZEZg7nRJFPGNN6o+stz/N3FRER0QmsbGwbKFdXT1XMmOmIHnEeoD21gIK/khoBUi8g79U/wrZvEyqXf46Y0y7y9bBCSozFiKljuuOLhXvwwdxcBgK8HQh4++23MW3aNPzjH/+AwWBoz1NQiKpp3BYg1dpZUIV8hcFKIiKiE6uorkfugXKvBQLU3+AgL6gntQHiz7oOJV+9gLIf30N49wEIT+/j62GFlIsmZmLOkn3YsqcUm3eXYECvBF8PyS+1q8dFSUkJZs6cySAAnRRXvRW1O1aoc/PAiZw98nnxo/rCfShb8D7cDju/G0RERMdYva0QbjfQq0s04qNN7Z4f64GtKJv/DhxVJSExx5ZBkxEpLYrdLhR99jRctlpfDymkyGv1zJHd1LlkBZAXAwH9+vXDzp072/OlFMKkb7vbXg99XJpKzSbyLTcK3n8YFYs/VP1/iYiI6GirtjVuC8g+terrlStmo2LpJ6hY8knIZB4mnHMTdNFJcFQUofibl9UFCOo8F0/uDY0mTNUJ2LG/jFPvrUDAX/7yF1Un4JNPPsHu3buRn5//k6OtKioq8Le//Q0TJkzA0KFDcfnll2P16tXNj1977bWqJWHL46qrrmp+vL6+Hg8++CDGjBmDIUOG4M4770RZ2dHf7GXLluGiiy7CoEGDMHXqVHz11VdHPd6W5yDvFFAxJGeobACmZpM/9P1tah0o3QOIiIjoCLvDiXU7itT5yP7t3xZgryxC3U7Pe/uo4eeEzBRrwyORNP0PQJgG9fm74LLW+HpIISU5LgKThnVR57Pm8gK212oEyGJdOgVIQOB4C7pt27a16bnuuOMOFBcX46mnnkJ8fDzeeustXHfddfj000/Rs2dP7NixAw888ACmTJnS/DV6/ZG9RfKYBA7+/e9/q60K999/P2677TZVx0BIoOKmm25SAYXHH38cP/74I+6++25V5FAW/m15DvIOWXTJIZ0DiPyBtBGsWv016nJXwu20I0wb3PsWiYiI2mrz7lJY652ItRjRKz2m3RNXvfY7lSIf3iMHhsTQ6tYT3qUvkmfeDVP3AdAY27+1gtrnkjP6YN7qg6rg5Z68SvRMj+ZUnmog4OGHH4Y37N+/X7UhfPfddzFs2DB133333YdFixZh9uzZuPLKK1FaWqqu5Ccm/rRvaWFhIT777DO89NJLGD7cU4FeAgpy1X/dunXq6v4bb7yhsghuv/129XivXr2wdetWvPLKKyoQ0JbnIO8K0wRnpVgKPOFds6CNjIGztkJ1tIjoxZ93IiIisWqbp23giH4pKsW6PVyOBlStm6vOo0MoG6ClyD4jms/V9gC3i++FO0l6ohnjB6Vj4fo8zPohF3+6+sj3gtoZCJgxY4ZX5i42NhYvv/wycnJymu+TDAM5qqqqVDaAnGdkZLT69WvWrFG3o0d70nuFfG5ycjJWrVqlFvFypb9lNkHT50vHA/lhbMtztIc8d11dHfyd1Wo96rYjyFzULJkFY8Zg6NP6BPS2gM6Yr2Dj73Nm6DUM1o0/oHLzIiC1L/yBv8+ZP+Kccc74OvNP/NkMzPmS924rNh9W5wN7xbT7Pa11yyK4rNXQWOKB9H4d8t7YX+bsRGQeKr9/FdqYZERNuNynYwmUOfOGaeO6qUDA0o35yN1XhC5J5qCeM7fb3ea1VrsCAaKhoQEfffQRli5dqlL7H3nkEaxcuRL9+/fHwIED2/QcUVFROP3004+679tvv1WZArLtIDc3FxaLBQ899JDKHIiIiFBX6m+99VaVwi9X8yWYYDQaj3qOpKQkFBR4ipvIbUpKyk8el29ieXl5m56jPex2e5u3R/iDffv2ddhzayvyELXqS9Ss/RYVk34P6AK/5WRHzlew8tc50xmTYZFiljtWIj9tNOBHGSv+Omf+jHPGOePrzD/xZzOw5quo0o6iciu0GsDgKMa2baUn/yRuNyzLv1CLjZrUHJTuyA3qOTsRfdFOmHd7LkAWhkXBkdDT10Py+znzlqwu4dh+yIY3Zm/ARafFBf2cGQyGjgsESCG9a665Bnv27FH7+Hft2gWbzab23z/66KN4/fXX23Ulfe3atfjzn/+Ms846CxMnTlTBACnkJ4EF2eMvC+vHHntMFSOUW1nMt/YflUW9fJ2QcR37OU0fSzCjLc/RHlLHIDMzE/5O/v/ygu7RowdMpo7Zu1T5w3JI7MzUZyRScwYhkHXGfAUbf58zt6sPijbPhsZajZ5mwNg929dD8vs580ecM84ZX2f+iT+bgTlfuQv3ynIVOb3iMSinf7uew16wB6VVBYBWjx6TL4XGJGH34J2zE8rORqWjTGUhRm/9GvFX/QPaCN/sWQ+YOfOSa6Kq8OeXVmDzASuum9EdKXERQTtnsi5vq3YFAmQRXltbizlz5iA9PR0DBgxQ9z/33HOq0J/c/u9//zup55w7dy7uuusu1TngiSeeUPdJJsA999yD6GjPD0mfPn3UAlv2+0vBv/DwcLWYP5Ys4Ju+QbKgP/Zzmj6Wz2nLc7SHpGRIBkOgkP9rR4xX+rMX5a5Q5zFDzgioOfHFfAUzf56zqMFnqGq+kfEpMPjRGP15zvwV54xzxteZf+LPZmDN1/pdnu5ZYwamt3sc7oz+MFzxABpKDsEc3/6uA4EyZ20RPvU65B3Ohb34IGq+fxUpv5DC6+1q4hYyc+YNA3pHYFhWEtZsL8JXSw/hd5cODto5O5kt2O165c2fPx+///3v0b1796P+MVl0//rXv8aWLVtO6vmkOv/vfvc7TJo0SRXta0rT1+l0zUGAJr179z4q5V/aDx67kC8qKlJ7/EVqaqr6+NjH5Rso2w7a8hzUfrU7V8Nlq4XWEq8qphL5o/jJVyHxvFtgSOrm66EQERH5VFVtA7bv8wQChme3/72wrBFMPXJCtkhgazR6I5Kn34EwnQHWPetQufLolubUcS6b4qkDNW/1ARSV+38dt87QrkCAXC2PiWm9jYhWq1X749tKOgb8/e9/xxVXXKGq9bdM07/qqqvUVoGWNm3apLICJC1DOg1IG8Omgn9i7969at//iBGeqpDSCUBqF7S0fPlylXmg0Wja9BzUfjWbflS3lpwJrJBKRERE5OfWbC+Eyw30SI1CUmz7rnw666q8Pq5gIRcd4qdco87L5r2N+sN7fD2kkJCdEYeBmQlwON34dH7b0+eDWbsCAVLlXxbwrZG2f01bBU5EFtxSZPDMM8/ETTfdhJKSElV4UI7q6mqcffbZ+Pzzz/Hee+/h4MGDaiuCbEuQ7Qdms1ldsT/vvPNw7733YsWKFdi4cSPuuOMOjBw5EoMHD24OJsj9st1g9+7deO211/DNN9/g+uuvV4+35TmofZy1lajbvU6dm3MmchrJrzlqKlC58kt1EBERhaqVWzzFskf2P7rYdlu5nQ4c+u+dyHvjL7BXHp2VSx6WoWcjos9I6a+I0rmvc1o6yaVT+qjbb1fsR3mVLeTnvV01AmRbwK9+9StceOGFquq/pP58+eWX+Pe//43FixfjlVdeadPzSIcAyR74/vvv1XFsi0IpPCjP/dZbb6mAQWJiovp3b7zxxubPk2wCeey3v/2t+njChAlqUd9yK8ELL7yAxx9/HG+88Qa6dOmizseMGdPm56D2qdmyCHA5YUzNhCGhC6eR/FqDFDX6/n/QRsYgavg5zGAhIqKQ43C6sHaHZ/E+ol/7tgXU5q6Es6ZMqvFCZ4718giDg6xvEs+/FaVzzYiffKWvhxMyJCMgu0cctu0rw6cLduPX09pXCDOkAwGSbi/FAJ988km16Jd+hdIpoF+/fvi///s/jB49uk3Pc/PNN6vj58iWATmOR/b6P/zww+o4HlnYy3Eqz0Enz9RzMKJHXwBDUg9OH/k9U0YONMYIOGsrYDu0A6Zu/Xw9JCIiok61ZU8p6mwORJsN6NO1fYv4qlVz1K1lyBSEafVeHmHw0JosSJr2G18PI+QCMJIV8OAry/H10r24eFImos1Ht5APJe0KBAjZP//++++r9nyVlZUqVT8yMtK7o6OAJlkA8Wd49kAR+Tt5syJpelLXonb7MgYCiIgo5KzaWthcJFCjaXv18Sb1hftgO7gN0GgRNfTsDhhhcHI12FA2/x1Ejzof+hgWK+9I0j2gV5do7D5UiS8W7cFV5/i+bXRA1QiYPn26ygCQPf3Sfk/22TMIQESBLjLLk81Uu3053G6Xr4dDRETUqVZtbawP0K999QGqVn+tbiP7joLOEufVsQWzkm/+i6rVc1D02TOqxgJ1bFbAZY21Ar5cvAc11rYXuQ827QoEpKWlqW0BUh9ACvdJgUDJDCASbpcTeW/8FWULP4Cr3spJoYBh6jkIYQYTnNVlqM/b6evhEBERdZpDRdXIL6mFTqvB4D6JJ/31TmsNajYvVOdSa4faLnbCZWp7Yn1eLsoXzeLUdbBR/VPRLcWitsF8tTh0uza0KxAgxfeWLl2KBx98UNUH+NOf/oTTTjsN99xzj7pf7qPQZd27EfWHtquocJiu3btPiDqdRmdAZO/h6ly2BxAREYXatoCcXvGICD/5vf3VG+bB7WiAIak7wruGbrp1e+hjkpBwrqduWsWST2Ddt8nXQwpqsu3l0jM8WQGfL9wNa31oZmG0KxAgLBYLZs6cqdrxLVy4EHfeeSfy8/Nxww03YOJEtooLZdWbflS35v7jWCSGAk5ktqejSO22ZQxqEhF5gWwjJf+3snFbwIh2bguQIECYPhxRw89V6dd0csz9xsIy6AyZSRR98RycddWcwg40bnA60hIiUV1nx9dL94XkXLc7ENBSaWmpqhdQVVUFp9OJ6OhobzwtBSBXfR3qdqxU5+YcBoQoMLtdRGafhrhJV6rWR0RE1D4uuw0mox69u6WpW/mY/FNNXQO27i07pbaBseNmovttL8Occ/xOXfTz4s/6NfTxaWqLYvFX/+EFiQ6k1YThkjN6q/NPF+xCvd0Zci/PdudtHzx4EF9++SXmzJmDXbt2ISEhAeeffz7+9a9/ISsry7ujpIBRs22pigjrE7rAmNrL18MhOmkavRHJF93JmSMiOgUuRwMqln6mCqC5bLXQhEciasR5iDlthtqGRf5lzfYiuFxutW86Jb79XcDk+0ztpzGEI2n67ch7/c+oy12F6rXfIWoYuy90lInDuuK973agqNyK71fsx/njeiKUtCsQcPHFF2Pr1q0q1evMM89UNQLGjBkDjcYrCQYUwGo2LVC3lpzTmRZGREQUguTKvwQBKhZ/eOQ+Wy0qGougxYy5EBo9twv4Y32AEdknnw1gLzuM2p2rYBk4GVqTuQNGF1qMKT0RP/kq1GxdAlOvwb4eTlDTaTWYObk3Xvh4Iz6etxNnj+4BvS501rPtCgTExMTg0UcfxVlnnQWTyeT9UVFAslcUwnZgqzTmgHnA6b4eDtEpZ7dI9ePY8ZeqP8pERNQ2YRqdygRoTdWqrxA79mJOpR9xOl1Ys90TCBjZ/+TrA1Su+QZVK7+E7eB2pMy8uwNGGHokeyZq2FSEaVl0u6OdMaIb3v8+FyWVNsxbfUAFA0JFu0Ier776Ki688EIVBKiursbu3bvR0NCg6gNQ6KrdulTdmjJyoIuK9/VwiE759SxpeVI0kIiI2k6u/stx3MfqW3+MfGPbvjLVS90SYUDf7nEn9bWuBqvqFiCihpzZQSMMPVJssSkI4HY6ULtjha+HFLQMei0umpSpzj/8YacKjIVK4dN25z6sWLECl1xyCUaOHIlp06Zh586dqnOAZApQaIoefQFSfnEvYsZd4uuhEHmte4Cqe8GWqEREJ7VP/Hh7xdVjRu4j9ycrG7cFDM9OUgXUTkbNpoVw19dBH5cKU89BHTTC0OV22pH/1n0o/Ogx1G5nMKCjnD2qO6LNBhSW1WHBuryQKXzarkDAsmXLcN1116moyF133dX8JlmKBL755pv43//+5+1xUgAI02gR0WsITN36+XooRKcsotdQhOkMcJQXoKFoP2eUiKgN5D2h2+VQqc2tkfvlcfIfq9rZNlC+15VrvlbnKo09LHT2VneWMK0e4V2z1XnxVy/AUVXi6yEFpXCjDhdO8BQ5//CHXDhdnrVty8Kn+5+5Doeeu17dViz7XN0f6Nr1E/vMM8/gjDPOwFtvvYVrrrmmORBw88034/rrr8eHHx4pDkOhwWmt8fUQiLxKYzSpVoKC2wOIiNqmcvnnqFz2heoOEDP+0ubMALmNGTcTMWOms1CgH8kvqcGhohqVCTC0b9JJfa3twBbYiw8iTB8Oy8BJHTbGUBc38XLVictlq0HR58/C7eJW7I5w3tgMmE169fOwbFP+kcKnSz5RhU+btjs1FT6tWPppwGcGtCsQsG3bNtU5oGkPS0tjx45FXt5PUyooeLns9Tj4wq3If/NeOGoqfD0cIq9vD6jdzu0BREQnUpu7CmXz3kb5og9gPbBVdQfo/odX0fW2V9Dttv+qwqtFnz0dFFfSgq1bQP+e8Yg06U/qaytXzWnuFMW2gR2bFSAtBcMM4aootyxMyfsiwvW4YLynOPQH3+eqC90nKnwqj4dcIMBisaC4uLjVxw4fPqwep9BRt3O1io7ZK4uhjYzy9XCIvCYycxig1cFemg97yUHOLBHRccgWqqLPn5GEcViGnoWIjEHqyr+13o7cA/moqyxH8ZyXVBHWul1rOI9+ti3gZLsFSIq6fC9F1PCpHTI2OkJqMCRMvUGdly+apTo0kPedP74nTEYt9h2uwubdpUFf+LRdgQDZFvD0009j06ZNzfdJZkBBQQFeeuklTJw40ZtjJD9XvfFHdWsZMIH7wyioyBUOeTMrarct9/VwiIj8krO2EgWz/gl3gw3hPXKQcNZ1R2WM2mw2aCKikHTh75F86Z9hzvJkW5Fv1VrtarEjRvRLPqmv1UbGIGn6HxA9ahoMid06aITUkiVnIswDJgBul8qscR5ngUonz+1ywnpgC+oXvoEHYz5CjKYWH87LDfrCp+3KZ5DuABs2bMCll16KhIQEdd8dd9yhAgGpqanqnEKDo6Yc1j3r1bl5IANAFHyihp+jagVEZo329VCIiPyO22FHwUePwVFZDF1sCpIvuvO4vc8jGuuukH9Yl1ukiqJ1STIjLcF8Ul8r32Nzv7HqoM4jWQG2QzsQptPDVVcF7XEWqdS2xb/t4HbUbluK2u3L4az1bG82AhgafhDzdkSiqtqqCpxKTYDjFT493u+7QNCukT/xxBN48MEHkZubi+XLl6OiokJtB7jqqqtw0UUXwWQyeX+k5JdqtixSkUljeh8Y4tN9PRwir5NOGJCDiIiOIntoJd2//tB2aIwRSLn0z9CaLG2uJ1CzeaG6qixdh6jzrdzSzm4BTrvat06dT37OUi+/F1pLPDR6WbJSe5T+8IZqfdm0+G/OAu0zEubsMbBs0gCLD+CVObm4/dIZzTUBZDuAfJ4EAaQgqkZnCOhvQLsCAV988QXOOecclREgB4Wumo0LmgvFEBERUehwVBSiNnel9A9G0kV3wZDQpc1bCYo+ewZuuw2VKRmIOe2iDh8rHfM9cLmxelvRSW8LkOBP/pv3QRsRhfgzr1V716lz6ePSWnw/XHDVVUMbGc1vw89e+d+mipVKIEXYywtVEODI4v80mDJymgNcMxKt+GrZIfy45hAuGNcTvcZciNixF8FRVwNdhBlupzPggwDtrhEwZMgQlQlAoa2+cB8aivapYmqRTA2jICZFkYq//A/y33nA10MhIvIb+tgUpP/qn0ic9htE9PTUU2kLWbTEn/VrdV7243uw5eV24CipNTv2l6G6rkF1CujXI67Nk1Sfv1MddXs3NC+qyDekU1fB+w/j8Ht/V1t06Jg9//s3o+Trl3HguRtx+O37PUHLRtLGNOWyv6quJknTfouIzKFHZbnER5tw5khP7Ys3v952VOFTudUYwoNiutuVEdC3b1+89tpr+Pbbb5GVlYWIiKN/EUiBmEceecRbYyQ/rhAcpjOoH562pgISBaIwndFTFNPtgr0s/6hoPFEgCw8Pjjcz1Llc9VZojJ5toJIF0NZMgJYsgybDuncDarcuUYXPulz3BFvQ+aBt4LCsJGi1bb8uWLX6a3Vr7jeOV6F9ze1GfcFeVSugdP7bSDjzWiDUr/wf2IrabctQu2PFMWn/Zrhsdc0fh6f3OeHzXTy5N75bsR/rc4tV4KxrYrgqfBpM2hUI+P7775GUlAS73X5U54AmLSvFUvCS7QCRvYfDGeCtM4hORBthgalHjudN6/blTGOlgOey22Ay6tG7Wxr0Rr36WK54ELXlKmT+63+COWciYidc2u5uQfJeMeGcm1CftxOOiiKUfPNfJF74e76H7CQrm9oGnkR9APne12xb2lxIl3xLZ4lF0vm/RcGsR1C18ktEZAxEhLQ9DlGFHz/e3NKyafEf2XckIiXtv8eAk65rkRwXgUnDumLuqgOYNXcn7rw8B8GmXYGAefPmeX8kFJB+rq0GUTCRrgESCKjZxkAABTaXowEVSz9D1eo5QVf4iDqWpB8XfuzpECDFgmNGTUPYKbwHkIrnUiww/8171fNJhxYLOxB1uILSWhwoqIZGE6YyAtqqev1cwOmAMa03wtMyO3SM1DYRvYep399SyK5o9vPocv1TKkAQ9Ff+929BzbZlMPeTRb5ngW7qMVDVAojs07T4zznliv4zz+iNeasPqMDZwcKMoMuiC9x+B+RT1r0bYUztxSAAhYzIvqPUFauGgt2wVxRCH3NyPZeJ/IFc+ZcgQMXiD4/cZ6ttbo0UM+ZCZgbQCToE7GjuEOCNCwHhXfoidsJlKF/wnvoda8oYFPQLGX/ZFtAvIw7mCEObF19Va79V58wG8C/xk69SC2Op21U8+zmkXH5fuzN1/H3Pf1Pav2yHUPc77c2BAMvgMxA19CyvtvNLTzTjggm90L9nPHqkx6PWaoHeYIStwYFwQ+AvowP/f0CdzmmrRcEHj0heH7rc9Cz0MW2PJhMFKiluFd69P2z7Nqk/RFJohijQhGl0KhOgNXJFKXbsxZ0+JgoMlcs/R82mH0+6Q0BbSDZKfV4uIrPHQGuO8drzUutWNW4LGJHd9m0BUmjNWV0GTUSUqrBO/iNMp0fSjNuR99rd6kJd5fIvguY9ihQSrd4w76jFv9CYLOoCjXnA+CP3dVA7xSumZuGjH3bimffXodZqVwU2LxjfEzMn94ZBH9itTxkIoJNWu22pisDpE7tCF53IGaSQYc4a7QkESJ2AIPkjS6FFrv7LcdzH6muhjWAbKjpabe4qlM17W51Ly7iT6RDQFmEaLZIv/TPrA3SCOpsdm3aXqvOR/due2WYvyVNBoKghZ6qFJ/kXCczFn/lrlMx5EVXrvkf0iPMC8vskV/7d9vrmjhR1u9ehet33Ry3+Vdp/9/5evfJ/PHLl/+P5u/DB3COdTSQY8N53O9T5RZMyAzozIHBHTj5Ts2mBurXkTOQfbQopEX1HAd+8olonOapKoYuK9/WQiNpV16W1YIB6zMiaL/TTVsFFnz0jb9ERNfTsDksLb1loWvb5up2O5pRf8h6pgO5wupCaEKnSntsqdtxMWAZOCsjFZaiQ1Hi33aYKeQbS90ml/e+TtP+lKvNEXmfxZ1ytHjP3G6syUSKlFkD3ASpo2Jm0Gg1mL9rT6mNfLNqDS844cfcBf8ZAAJ0Ue3mB+gMtUWHzgAmcPQopOnMsEs69WbWd0Vra3neZyF+4XQ5VWKqpJkBLUcPPVY93xlUWChyO8kLA5VSL8vizft3hFwDqdq1Fwax/qm4t6VL4jFsFOqQ+gHQLONnvJYPf/k2+n9Ejzz+qroe/dnKTQN9Re/6t1c2PSb2DlpkOiefd4qNRArU2u8oAaPUxq11l2ESbO2ZLQmfgX3s6KdWN2QCmjIHQcSFEIShqyBRfD4Go3aRFoOzHbqoJ0Nw1YPg5iB5+Dmq2LkXUoMmcYWoWmTUKqVc/DH1scqcEiaQWiz6hC+zFB1A8+3mk/OIvQVf4zFdcLjdWb/MEAkb0S25zXai63JWI7DeWXUUCiPxuL/7mZUT0GqIyeP2JdAgp+fbVoxb/UntC7fnPPk39DvAXkeF6VROgtWCA3B8RHjiZF61hIIDaTCKLqlBQ47YAolD/eZCrZLx6SoGiYtlncNnrVYqvdAeIHXsRHHU10EWY0VCSh/y37oNlMANd5Pn91lC0H8bkHmo6OrNVnBT8SlaFz+6Bdc86VK78EjGjLuC3xQt2HixHRU09IsJ16JfRtq1tUqitbO7rqN44H2lXPsTvQ4CokgJ7WxajbudqhKf3hT4u1XdX/vdtUucSlBBac5wKAngW/6Nhzh6jFv+dnfbfFk6XSxUGbKoJ0JLcL4/rEbiBSgYCqM1kS4CjoghhBhMi+o7kzFHIqlr7HSqWfYrokdMQPeJcXw+HqE2Vl8vmvyN7A2BMzkBk35Goq6vD3gP5yMjIgCmpu0q/DO+azdkkVC77DGU/vov4s65D9PCpnT4jhsRuiJ/yK5R88zLK5r0DU7cBMKb25HfmFK1s3BYwtG8S9LoTL17cbheq1nyjzs39xnH+A4i8N5FMDtuBrarGR9o1DyNMq+/UxX/Tnn+XtQaGlF7NgYDwrllIveIBhHfr55eL/5akEKB0B2iqCcCuARTS5IdWH5vaYS06iAKB29GggmLyR46BAPJ3ktpb9OnTKggg6b0RfUY0P2az2dSt7CNtGQSoz98F68FtiBk1zSdjJh93CJCgEdw+/TZYhp6Fur0bULdjBYo+ewrp1z0OjcHk0zEFTdvAfm1rG2jdvR6O8gJVwZ11oQKLLLCTLvw9Dv33TtQf3oWyBe8jfvJVnbr4P6r9clqmKgoo45IjkAqBGvRa1R1ACgPW1NXDHGFUmQCB3jpQMCOA2szUrR9MV/1d/SAThTLZx1b6/f9gO7gdjupy6Cyxvh4S0XFTvEvmvARHZRF0MUlIPOemExaPctSU4/B7D6k9pu7GrQQUgh0Chk31STZAE3mdSpbKofxdsJcdVnuKk6b91mfjCXRF5XXYm18FTRgwLCupTV9TuXqOurUMmgyNIbyDR0jepotKUD9DhR8/rrJ8TD0Ger31ZxPZgiD/TsvFf0TTnv8AuPLflswAyaLLP7hXZdFFRHjaGwa6wN3UQD4T6D/MRKdKF50IY5qkirlVtVsif1W94Qd1hQZydWj67aowYFu6Y0SPvlCdly94D+WLPuyEkZKvOWoqUDjrn6r9mOoQcOa1vh4StCaLuqoZpjOo7QKqNgu1S1ORwL7d49pU5VyCL9bd6+RdnwoKUWCKzBoNy5Cz1HnxF8/BWVt5Ss/ndtpVZ4+i2c+j8LOnm+839RoCXUyyajEqaf/dbvuvCjzL75JgWjfYGrPoggUzAqhNKpZ+AkNSD5h6DgqqH2ii9orMHoP6/J2o3b7Mp1fNiI6noeQQSr99VZ3HnX65anvZVrFjL1ZXZCVFvHzh+5JagNgJl3Kyg5TbYUfhx4/BUVUCfVwaki66y28KoZq690fX37zINoKnaOUWz7aAkf3bti2gqTaALPB8VWiOvCP+zF/BdnAr7PI3Ye7rKrh2sot/696NqNm2TNUdkGwxRaOF86zrVatP2TLc9db/+G27Qmqdf/yWJ78mqc9lP76n9pd2veV5/kEgaoyyl/3wpirEIxF2SYMj8hdy5VSu/kg9C2n3Gj3Gc4X/ZMScdpGkgKFs3lsoX/SBKhwWO+EyvtELxtfKnJdQf2iHyhhJvvTP0JrM8Cc6c8xRAS59bIrfBCoCga3egY27StrcNtDVYFXdAoS0FaXA5unEcQdK572FuElXNt8fHv7z2z3slUUoX/gB6nJXHVn8q7T/GPUeKDL7NGjCj6TIMwgQePhblE6oZstCT6XpLr5rP0Lkb/QxyaoKbkPBbrU9IGqoJ/WOyB/IG7KEc25CyXevIXHabe3uwx4zZronGPDDG6hY/KEnM+D0X/ANX5CRivy1W5cg6aI7YYhPg7+qWv8DSr/5L6JHX4C4ib/09XACxoadxbA7XEiOi0C3ZMuJvyBMg7hJV6gUcFOvwZ0xROpghqTuSP3FvercJdt/jHr07pYGvVGvPtbow9WV/4biQzCmZKjP0+iMqNnkWQM0L/77nYbwLlnMDg4SDATQCa8UVG/8UZ1bciZytohakN63ZQW7Yd27gYEA8jvG1F5Iu/rhU160x4y+QCILqpe4o0L2Gcs+baZ/Bgt5fUSPOE/189ZFta23vK9ojCa1WKlY8onaexxIlcf9oW2gZAO05feBXEGWugCsDRB8XI4GVCz9DFWr56ir/JIFFDX8XESPmoaCdx9CQ2keut/+GjQ6g8p0lG0FhuQeXPwHKQYC6Gc1FO6FvfiA6j0qKUBEdIQ553QY0/uonrhE/sBRVaqqN0vrNXnD761UTWkjKFeKTT0Htzu7gPyvQ4DUBghP9/TI9vcggJAK5NbBG1C9fi6KPn8OXW54EtqIKF8Py6+5XG6s3tZYH6ANbQPlAhBTvIOTXPmXIIDK7mq6z1bb+LEbMWMvRsk3L8NRVgBDUjf1uAQJKXjxrzn9rOpNC9RtRJ/hfrdnkMjXdJY4VciKBTTJH0hr16LPn1Fv5Mp/fNfrzx+ROaz5tS4tBiuWf8Eq7gFKvn8Fs/6Jw2//TRUBCyTSzUAfnw5nTRmKv3yBr8ET2J1XgbKqepiMWgzodeJgj9QWkYrw9nJP8ICCR5hGpzIBWlO1+mtE9BqCbre93BwEoODHQAAdl9vpQM3mherckjOJM0X0M061JQ/RqapY/LEqXhlmCFd9vzuKXEU+/M4Dqm6AHGzpFnipwYUfPQZnVYnqM25I6YlAIv3sk2bcAWh1qNu5qrm6PbVuVeO2gMF9kqDXaU+YUVSzZTFqNs6H217PKQ0ycvW/ZdG/nzzWUMeMrxDDQAAdV92e9XDVVak9QtI2kIh+ylVvRd5rd2P/czfCaa3hFJFPWA9sRXljumfC1Bs7tLBrmE6P6OHnqvPKFbNVOyoGAwKDfJ9KvnoR9Xm5ftshoC2MyT0Qf8bV6rxs7htqmwO1buXWtm8LqFr7nSoMF96tvyouR8FFfublOO5jxtYfo+DFQAAdlzE5Q7WKih51Adv0EB3vl6jRBJfDLpfZ1NUpos7mtFaj6PNn1Rt4c85EWHJO7/B/M2rY2aorgaha+SVKv3+NwYAAULnsU0+mX5jG7zsEnIgUOJPtKpK9KJkw9FOllVbsPlQptT4xPDv5hJk+1eu/b5xbtgwMRm6XA1HH2fMv98vjFFpYLJCO/+KIikfs+Es5Q0Rt6B5QXnwAtduWwTKQ22iok3vAf/mCSvOWLICEs6/vtH9btcwM06BkzkuoWjVHqpIh/uzrWWjMT0mb07L576jzhLOvQ0RGYGf6SUG7xPN/g4biA+wecIJtAX26xSLGYvzZ+azZtlRtcdNa4hHZd6T3v2Hkc9IiMOa0Geq8atVXR7oGjDhP3S+dAii0MBBARHSKIrPGoHzhB6jbu6H5DytRZ6ha8y3qcldKw2ckTb9dZah0pqghU1RrQUk3V3u13W7ET5VgABMO/YkUfpMq+yKY2sKprYuRR1oISnZAmJZvbY8NBEjbwBORYnFNAT4WwA1estiPGXMhYsdeBEddDXQRZridTgYBQhT/UlOrCj56DKVz34CjupwzRHQChsSu0Cd0AZwO1O5czfmiTmNISIfWHIu4yVfCmNrLJzMfNfgMJE77jVyjhS1/F4uM+SFdTBKiR54PU8YgxJ/1awSjmq1LcPDF38FRWezrofiFersT63cWt6k+gPzc1ufvVAUYo4ac2UkjJF9mBljr7cg9kK9upQAnhSaGTeknGkrzUbdjhUr5lPoARHRikVmjUbH4I9RuX9Ype7SJhKlHDrrc+DQ04b4t+CZbYmQM4V2zoDF0blYCnZhkaMRNvFy1mAzGq73y/5LClY7KIlUvI/XKB4Py/3kyNu4sRoPdiYQYE3qkRv3s59r2bVK35uzTVJYFhQabzebrIZCPMSOAfqJm0wJ1K50CdJZYzhBRG7cHCOvu9aqTwKkKD2eEnn7+Cl4TrcniF/vyI/uMUGNpKjxWuWoO3G6Xr4cV0vUjpCZAQ8mh5vuCdXEs/6+k6X9AmMEE28FtqpVmqFvZuC1gZL/kE/5+kP3hXW58BrHjL+mk0RGRP2AggI4ib9pqNv2ozi05Ezk7RG0krZZUyzaNBg1F7W9lZWtwQG8IR2qXnupWPiY6tqhX/v/uUUUC/bFtn4yp8NOnUPrdq54xupy+HlJIqlj6KSqWfoL8t+6Dq74OwU4fm4LExk4W0kpTWmqGKvkZXNXYNnBEG9oGNm9xiwvcLhJEdPK4NYCOIi14HFUlCDNGIKLPCM4OURvJFZfkmfeovbga/c9XZz4eSeP8eP4uzF60B7VWOyJNelwwvidmTu4Ngz44r+TRybFXFKnCfEJSeP0hE+BYMiZz/3Go27kaNRvnqwKCieffGrRXo/21Q0D5j54OAXETfgGNMQKhwDxgPOr2rFcXNGSLQJfrn4TW5NttM76wN78KpZU2GA1aDMxMOO7nSZDOumeDygDlzydR6GFGAB2leuOC5n1i7V3MEIUquaLS3p8bufL/4bydeP+7HSoIIOT2ve924KN5O5kZQKoietFnz6iru8b0PoidcJnfzoq531gkzbhd1ZqRRVnx7OeZGdBJ6gv2HukQMPwcRA07G6FEWmjqYlNUS82SOS/6ZdZMR1vZmA0wuHfizwaR63JXoeCDfyD/jb+G5DwRhToGAqiZq8GG2u1L1bllILcFELWX01qtrtyeDK1GozIBWvPFoj3qcQpt0qKyPm+HythS+6H9vE2aBJSTLroD0GhRs3khir/4N4MBHcxRU46CDx+F225TV3njz7wWoUZaaCZPv1211KzdvlxlOoaatm4LqGxsGShFR/0xu4iIOpbP31lWVFTgb3/7GyZMmIChQ4fi8ssvx+rVR9pvLVu2DBdddBEGDRqEqVOn4quvvjrq6+vr6/Hggw9izJgxGDJkCO68806UlZUd9TneeI5QULd7HdwNNuhikmHskuXr4RAFpKp1c7H/metQNu+tNn2+XIXZsrcU5dW25kyAY8n95VU2vPvtdqzdUQSHkwXYQo113ya151sknnsz9DEn7gvuD8xZY5A8405PMGDLIpWuzZoBHcPlaEDhR4+pK+H6+DQkzbgzZNO9jWmZiD/zV0iacQdM3fsjlMjfitwDFep8RL/j/55oKD4A2/7NKmsn1LJGiMhPAgF33HEH1q1bh6eeegoff/wxsrOzcd1112HPnj3YvXs3brrpJowfPx6ffPIJLrnkEtx9991qYd/kgQcewOLFi/Hvf/8bb7zxhvq62267rflxbzxHKLU/S7v6H6rHMCPDRO1jSO4BuJyo27UWLnv9cT/P6XJj0bo8/OGpBXjkfysRFWFQNQFa/dk06REVacBXS/bi/peX4eoHvsFzH6zDmu2FsDsYFAh2ztpKtYAG3LAMnqLS7gNJZNYoJF90l7pCa9u/BY7qUl8PKShVrfwK9Xm5qo1jyqV/hjY8EqEsevg5Afez4g2rt3m6BWR2jUFc1PG7z1St/kbdSj0oXdTx6wgQUfDyaV7h/v37sWTJErz77rsYNmyYuu++++7DokWLMHv2bJSWlqJv3764/fbb1WO9evXC1q1b8corr6ir94WFhfjss8/w0ksvYfjw4epzJKAgV/0luCBX92Vhf6rPESpk8S89oImo/YypvdSbKim6Ka0EZRF0bEHAeasP4pP5u3C4tFbdF27QIr+kRhUGlJoAx5L7q+saMHZgGpZtOoyKmnp8v/KAOiRIMHpAinpscJ8k6HU+j++SlzWUHoLbXg99QhcVqA1EkX1HIuWSu6GLToI+OsnXwwlK0aOmqd87EtRn9fej1e3ZANuh7Yjz47oa3q4PMPJntgW4bLWobmwVLQETIgpNPg0ExMbG4uWXX0ZOTs5Ri1E5qqqq1BaBKVOmHPU1o0ePxj/+8Q+VTrtmzZrm+5pkZGQgOTkZq1atUot4bzxHKJA/CogIjarCRB1Jfn9FZo9B5YrZqN2+rDkQUGez45tl+/DZgt0or/ZkClgiDJg2vifOH5ehzrskWZprArTWNeDWmYNw00UDsXVPKRZvyMNSCQpU1+OHVQfVERmuw8j+KRg3KB2D+/x8kSgKHKZu/ZF+w5Nw2xsCuohrRKYn4C/k769sFTBnj0GYtvVMGGobmUv13kmrQ8LUGzhtx7CX5aPg/YelRD6MKT0RGcQdkSTQvD63+ITbAqo3zld1JPSJXRHefUAnjpCI/IlPAwFRUVE4/fTTj7rv22+/VZkCf/nLX/Dpp58iJeXoiGZSUhKsVivKy8vV1XwJJhiNxp98TkGBJyIqt6f6HO39w1xX5/99e2UepLVTyfsPokxnQPTUm6BP6OrrYfn3fLW4Jc5Za7Q9hgASCNi5Cvn5xfhm9WF8t+Igam0O9Xh8dDimje2OScPSVTYA4EBdnUO9mZ8+oScuOaM3auoaYI4wqDd2TkcD6uxHKjr3SotAr7TeuOrsTGw/UIEVWwrVUV7dgPlrDqnDZNRiWN9EjB6QjEGZ8UEfFAjGn03JAghrWvjrzYAecHjx74ov56x62SeoXf4pqjYvQsx5v/P7wof++jqzF+1D1Y9vI+acW6G1xMEf+XzOwmMQMeRs1K39GkWzn0fCVY9Aa46FvzqV+Vq/swS2BidiLUakxupbfR/qdrtQsaqxSGDOGX7zWg7o11gA4pwF75w1BYfbwq/+8q5duxZ//vOfcdZZZ2HixImw2WwwGAxHfU7Txw0NDeobcezjQhb1UgBQeOM52sNut2Pbtm0IBNqqArjKD8Op0WFXfglQXOPrIfm9ffv2+XoIASek5szthsVghq6hBv95/gNsrPcE1xKidBjXz4IB3SOg09Zi7+7cVr9cp9Opw+FwqOPnyK/60T2BkRmJOFjcgK0Hrdh6wIpqqxOLNxaow6ALQ5/0cPTrFoHeqeHQ64K3OnTQvM4cDYha9hrsSX1g7X26KrYXTHOmsxtg1mhRv3stDr73CGqHzFA1BAKFP7zOwuprELXsf9DYqnFozn9RN/AC+DOfzllCDixRa6GrKkT+x0+iZsTlqkhesM3X3FXl6rZnsg7bt29v/ZOcDoQn9oHB4cB+TQIQIO9VA+XnMtBwzoJzzlpb27bGb/7qzp07F3fddZfqHPDEE080L8Zlsd5S08cmkwnh4eE/eVzIAl4e99ZztIder0dmZib8nQRCCud8p85NvYcjdWBobIU4lfmSXwA9evQ4pddHKAm1OTtQWIMvFu1DYnVXnG7chhzdftQl9sf08RkYlpUIjSasw+ZMamNPla0+Ljd2HarE8i2FWL6lCKWVNmzeb1WH0aDF0D4JGN0/GUP6JKiPg0Gwvc4qvv0/2GrLoC/ORbep16gCcEE1Z9nZqO/eHeWfPw1D8U5Ydn6HmPNvQ5jOv7cJ+MvrzO1oQNmHj8Buq4Y2NhVdL/wtNH5aHNBf5syRdidK37kP+rL96FqzG+aRFwTVfMlVwOe/WqzOzxjdG9l9E4//yQNy1OenB0nLQH95jQUSzlnwztmuXbva/Ll+EQh4++231Z59KdD3r3/9qzmKkZqaiqKio3txy8cRERGwWCwq5V/aD8pCvmXkQz5H9vh76znaQ1Iy5N/wd9LGyWyvhCsiCjFDpgTEmP2B/ALgXHHOWtq2twwfzdvZXKipp64bxhu3Y2hPCy67ZmK7OnGcyutscFYkBmel4cYZbuw8WI7FG/KxdGM+isqtWLa5UB0SBBielYyxg9IwPDsZJqNf/ElAqP9sVm9eCNvWxeqKZfKMP8AUlxSUcxaRPQrG8D+jcNajqN+7HlVznkfyzD9Co2vblYxQfZ3JAq7485dhL9itAkRpv/gL9HE/s+jzEz7/2YzohbCzr0fxl/9BzdKPEdV7KMLT+yBY5mvf4SqUVNpg0Gkwor9sOwv83+cB9xoLQJyz4Juzk3m/6fPfEtIx4O9//zuuuuoq/PWvfz1q8FLFf+XKlUd9/vLly1XWgEajUZ0GXC6XKvgnHQDE3r171b7/ESNGeO05gpXLboMp3IjUi26HNiLa18MhCjiq4Oj2IhUA2LLH0xJNfoWdNjANMyeOR0b8ZdBG+vZnSzIQ+naPU8evp/XHzoMVWLIhH0s25qOwrE7dyiE1BIZlJanuA1JkKiLcv6/MBit7eQFKvn5ZnceMm6kKBXYkyYrzpYiMQUi57C8o+OARWHevReGHjyH5krsDIhjgKxVLPlaFFmW7SPLFd7FDwEkwD5yEuj3rUbt1CYo+fRpdbnwKGoP/Xtk7Gasag9CD+iQeNwhQvuhDuOrrEDXiHHbvICLfBgJkwf3II4/gzDPPxE033YSSkpKj3pxIcGDGjBlqq4DcLliwAN98841q/Sfkiv15552He++9Vz2PRGjuv/9+jBw5EoMHD1af443nCEYuRwMqln6GqtVzVMcASSmMGnEeYk6bwTdgRCfgdLrU4lkCAHvzq9R9Om0YJg/vhosmZSI90ftp3N4ggdY+3WLV8avz+2H3oUpPIGBDvmplKK0J5ZAWhE1BAelCwKBA53A77Sj69Cm4G6wI75qN2HEzO+zfsjU4oDeEI7VLT+gNRvWxr64gmnrkIOUXf/UEA/ZtQkPBHoR3YSvb1tRuX47yBe+p84Szr1dzRyf3OzDxnJvQULQf0cPPRZjet4Ewb1q5xRMIGHGctoGuBhsqV85W7/lM3QcwEEBEvg0ESIcAKar3/fffq6MlWbQ/+uijeOGFF/D444/jjTfeQJcuXdR505V7IdkEsoD/7W9/qz6eMGGCWtQ36d279yk/RzBmAkgQoGLxh0fus9WiYtEsdR4z5kJoguiPI5G3SAX/H1YfxCfzd6Kg1FONWarznz26B6af3gvx0T+9suR22FG3dwMieg72q8ro8oY4s2uMOq4+N1sFNKQloQQF8ktqsXxzgTp0Wg2G9k1S2wdG9U9RLQ2pY5T9+B7qD3vSvZOm/wFhHVQgUF7HH8/fhdnHaVPpC7IwkWCAy1bHIMDPZCBVrvxSnUcNPxdRQ8/qzG9R0JALH11ueKrDfr58obKmHjsOeAoFjshufVurZJHIez1dTDJMmawHRUQ+DgTcfPPN6vg5siiX43hkj8bDDz+sjo58jmASptGpTIDWVK36CrFjL+70MRH5szqbHXOW7sPnC3ejotrTTSQq0qAWT+eOzYAlwnDcN+6H/ns77GWHkXL5fSoY4I8kKNAzPVodV52TrfaaSkBA6grkFdeougdySNbD4D5JGNcYFJD2huQdjupyVK35Rp0nnn8rdFEJHTK1cuVfggDvf7ej+T4JBrzX+LFktPgsM+CYbRDWvRth7NIXmqYWiiFOfk5TfnEvKlfNUQF7OoW5bBEEsOXvgjY8IqC3WKzeVijNatTv8ISYVgLSbjeqVntaBkYNn4owP++YQESdw38uT1GnkYiwHMd9rL6WNQOIZD9ltU1dNZ2zZC9qbZ42fomxJsw4PRNnjuyG8BMU15M37uE9clQgoHbbMr8NBBw75oy0aHVcMTULBwqqVUBAthAcLKxWbzjlkKDAoN6JavvA6JzU4wZDqG10llikX/so6natRWTfUV6fNrvDqbJYkuMi1Gu6NV8s2oNLzvCP4mk125eh6JOnEN6tH1Iu/TM0htDNUpOtfLJwk4wimYfYsRf5ekhBo0ZqBXz+LAxJ3ZH+q0cQpg3MjKdVWwvVrdR3aY3t4Fa1HSJMb4Rl4OROHh0R+SsGAkI0LU6O1oIB6jGjf7YgIuosBaW1+PTHXZi78gAaHC51X9dkC2ZOzsSEIV1UunxbmbPGoHrtd6jdsQIJ59wYUOmoEhTonhqlDk9QoApLNh7Gkg152F9QrQolyvGfjzaooIAUSRw9IAXRZl7BbQ9DYjd1nFLqeE0DDhVVq0yOQ0WeI6+oBoVlteo1fN+vR6kMgNbI/ZL94g/fP505Vi1abPs3q9oBKZf9OWiKup3s97TkyxfgqClH8kV3QRth8fWQgorUotAYTaouRdn8dxE/5RoEGrvDhbU7PN2xRh6nPkBTNoB5wARoTf5Zw4aIOh8DASHI7XKowoBNNQFakvvlcX/ay0zUWSQl/uN5O7FwfR5cLre6r2+3WMw8o7d6gyUV+E9WePf+0JgscFmrYdu/BaaMgQhU3VKi1HH5WX1VdoC0I5RsAZk3eSMqxwsfh2FgrwRVU2BMTqpfLCr9WcWKLwCXC9GjL2hzuq688ZdglWehf2TRLwv+muMs8oW1wYkYi1HVBGgtGCD3+0thSFmgpf7ybzj83t9hO7AFBe//Q9UQCLVgQMsOAQ0lB2Hq1s/XQwoquqh4JJ73GxR+9C9UrvhC/X6O6BVY++e37CmBtd6BWIsRmV1ifvK4o6oUtdtXqPOoYVN9MEIKBU6nU9V9C2b19fXNt9J5zlf0ej20Wu9cVOJqLwRJIUDpDtBUE4BdAyjUbd1big9/2KlS3ptIgTwpnjagV/xJ9WQ9lmQASKp39fq5Kt05kAMBLcnV5cvO7KsOWYiqloQb8rEnvxLrdxar48WPN2BAi6BArCV007tbY8vLRdm8twGXE/r4dET2GfGTAmAtF/nqtrgah0vrmgNVx5KXamJsBLokmtElyXOkq1uLWijU252qtkVTTYCWzh+bgf2HK1Wwx1dFA1uSHu+pl9/nCQYc3OYJBlz2V3UFNyQ7BDAI0CEi+45UC2Sp0VE8+99Iv/4p6Mw/XVD7q5WN2wKGZye3Gqyu3bFcrgCpbTbG5B4+GCEFe9ZSQUEBKioqEOxcLhd0Oh3y8/N9GggQMTExSElJOaX3p4KBgBAlPZql2JDsNXTU1UAXYYbb6WTrQAqpP16y8JcWgFv3lqn75D2UpLdLAKBXK1dW2isya7QKBNTtWAH32dcH1PaAtpB2iZdO6aOO/BJPUECyBXYdqsTGXSXq+L9PNqJ/zwSMHZiKMQPTEBcV2kEBCcAWffa0CgJoeo7E5vp05M3f2ZzOL0d1XcNxvz7coPUs8BMt6JJsVt8DWfSnJZph/JlFvBQClNd3U02Apq4B08Zl4PxxPfGn/yxWQYB7rhqunssvggG/vB8F7z6oggGH3/87Un9xLzTGCASz+oI9KPriueZMPXYI6FhxZ1zduI/+AIpnP4+UX/wlIArqyd+xE7UNlA4TxpSenighkZc1BQGSkpJU8fVTXZj6e9ZDfX09jEaj167It+dnvq6uDkVFnu1Aqampp/R8DASEeGaAvJj2HshHRkaG+gEmCnZOpwuLNuSrLQCS0i5kz/8ZI7rioomZHbL4kV7f0hLOWVsJ28HtMHU/ujp6MElLMKuCc3JI+nrT9oGdByuwaXeJOv7vs03olxGvCg2eNjC11baLwaamruHIIr+wCt12vo8e1iKUuSLx2JqesK5e1erXSQXwpiv7nqv8FhUAiI8Ob/cbLlnoS3cA+R7V1NXDHGGE0+XCroMVqsaABCD+8PQC/O6SwRg/JB2+Fp6WqYIBh997CPWHdqB2x0pYBk5EMHeQKJj1KNz2eph6Dg7IfeuBRjpTJE2/A3mv3Q3rnnWqTWPMqAvg72SLVmFZHfQ6DQb3STx+0dqu2Z0+Ngp+sjBuCgLEx8cjFP6/Ijw83GeBAGEyed4zSTBA5v5UxsJAAMFms3EWKOhJSvQPqw7gk/m71BsnYTJqcc6YDFwwoWeHLkal5kZEnxGqIJXbcfyrvMEmJT4SF03qrQ6ZcwkKSLaA9LvesqdUHS9/tgnZPeJUS0LJxmit9VUgBZkKy+tapPE3Xd2vVgvsJqMMuzDBvA1OdxjeqJkAp86Enokt0/g9V/jlOFFnivaSzAAJBOcf3NscCJZtHM/dORGPv71aZck89vZqFbi5/sIBPt8qYFTBgAdg3b85qIMALnu92q/urC5V20WSZ9wRdBlE/sqQ2BXxZ16Lkm/+Czg9XWICpVtATmYCTK38rqgv3Kc6IgTzVVrynaaaALyQ2Pma5ly+BwwEEBEdh6Q+z1m6V6VBV1R7Cr1ERRrU4v+80zJg7qS2d4nn3hSwram8QdrWzZiYqY6icgkKeLoPbN9fjm37ytTx3883I6t7rKopIEGBpNgIv31NeRb51Ucq8xfXIL+4Fg6np8tEa+Qqfr/4BkyvXAW4gYacC3DfhJlIiDa1qxBlRwSCJRDzyC1j8c6321XdjK+X7cP2/WW45+oRKjDhS8bUnupoImncWktcUFVBd9ZVwmmthsZk9rRNDGcXn85kGXKmunouQYFAsHJrwXG7BdQf3q0yHIzpfZF29d8ZUKIOw0BT4M45MwKIKCiVV9nU4l+CAHU2z9WdxFiTSv+fMrKbuiLamUI5CHAsWeBPP72XOkoqrJ5MgY35KhgggQE5Xv1iC/p0i8HYgekqMCCBhBORdD1vcbrcKC6vO+rKvudKfzXKGwNKrTHoNGp7Sfox6fxpiZHqil3+/+5BfYUd4T1ykDHtCr98c67VanD1uf0woGcCnnx3DfbmV+H2p3/Eb2YOxulDu8AfyJXOw+/cD110suouECzBAH10EtKvfRT28kLo405t7ye17811yyCAtG2UVpb+qKq2Adv3eerbjMhO/snjlY0tA/WxyX75e4aIfI+BACIKKrIvXdL/5646oNqsiW4pFlw8qTcmDElX9QB8xe12oT5vp6oVIJWqyXMF+oIJvdRRWmnFsk2HVU0B6eSQe6BCHf/7cgsyu8Zg3MA0FRSQLQct2Roc0BvCkdqlJ/QGo/q4rYGeOpvn6n5TOv+hxnO5r+n105q4KCPSpVBfi3R+WfAnxvz81f34s36N0u/+h6QLbvP7N+dDs5IatwqsUds4nnhnjdoqcMP0nJ8tSNhpV0PCNGgo2I3D7z7YGAywIFBJdoM+sYsqUCf/j0D+vwSL6g3zUPLtK0g87xaY+4+Hv1m7vRDSPKRHahSSjgmUOuuqULtlsTqPGn6Oj0ZI5P+uuuoqdfvWW28hFDEQQERBYW9+peoAsHh9nnpzJCTNXAqiHa+tUmez7lqHglmPQGuJR0Sf4QFRlbozSZ0GqVwvR1mVTQUFpKaA9MmWQnZyvP7VVvTqEq0KDZ4+NB0x5nB8PH8XZreogC/t8aQyftO+dmm1J5kHssiXK/ot9/CXVh6/RooEjeRKftOefVnoN53Lv9Me4V2ykHbtowGTSinfk3/cfJpqNzjrh1x8u3w/duwvxz1XD1fz4Suy7zntigeR/879qvbG4XckGHA/tBGBt4CuP7wH+W/+FRGZQ5F4wW2qcB35nr2iSBVsLP76ZRjTekMf23pVfl+3DRzR76fZANXrf4DbaYchpZcaOxG17v777w/pqWEggIgCmlyplACAtAJseSXzksm90b9nvF8tuMIzchBmCFeFwOrzd6nWaNQ6aS943tgMdZRX27C8MVNg8+4S7D5UqY6uyRbsOngAH8zNbf46CQbIolVa7AzLSsaLH29UAYAGu6fab2tizMYWV/UbK/MnmtVVNq0XAkiOqlJY926AeeAk9Xr0p9dkW7cKXHlOtvp5kq0C0m3j9qcX4DczB2HiMN/tpTYkdUPalQ/i8DsPoKFwr7pNvUKCAVEIqA4BH/5TFRF1NdhUYVHyD7HjL4F13ybUH9qOos+eQdrVD/vN90dqkUhGQGv1AdwuJ6rWfKPOo4dPDbjfN0SdKTMzM6Qn3D9+oxERnQRZ5K3aVoiPftip9pULWa+NG5SOiyf3Rs/0aL+cT43OgIjew1XKZu22ZQwEtFGsJRznnJahjsqaepUpsC63CIN7J+KZ99e1+jWzF+9V20FKKq0qCKDThiE1IfLoK/uNe/g7smCkvCkv+vxZ2A5sUVcY407/BQLVkL5JePaOiXjynbVqi8CT767Fxl0luHFGTqfX3GhiSOyG1CsfwuG370dDkadugHQX0Eb65++An3QI+PBROKvLoE/owg4Bfka27iRN/z3y/nsn6vN3onzhB4ibdAX8gWydqrU5EG02oHe3o2sY1O1cDUdVCTQmCyL7j/PZGIm8ZfLkyZgyZQp27NiBdevWYdq0afjjH/+Ip556CnPnzkV1dTWys7Nx++23Y8yYMc1fV1NTg8ceewzff/+9Kow7ceJEDBo0CP/85z/Vc7W2NaC+vh6vvPIKZs+ejby8PKSmpmLmzJm4/vrr0eSaa65B9+7d1fHuu++itLQU/fv3x1/+8hcMHDgQgYSBACIKqPZsi9bnqQyA/QXVzenbUvxvxsReqoe9vzNnneYJBGxfhrgzrubVmpMUbTZi6pge6qiotqkMgNbI/fJG+U9Xj1DV+qXYoFzZ7mwVSz5RQYAwfTgsORMQ6GSrwN9vPg3vf7cDH8zdge9XHkDuAdkqMEJlaPiCIaELUiUzQAUDDqBy5WzETboS/h7MLP7yP6g/vIsdAvy8eGPCebeg6JMnUbH0U5gyBsLUI8dv2gZK1tOxWUtVjUUCo4ZMUcFnomDwzjvv4Nprr8UNN9yAyMhItRgvKSlRi/+kpCR8/PHHarEui/imYMCtt96Kbdu2qc9JS0tTi/Ynn3zyZ38v33zzzVi/fj1++9vfIisrCytWrMAzzzyDgwcP4oEHHmj+3G+//Ra9evXCvffeq77uX//6F373u99h3rx5p9TOr7MxEEBEfq/e7sTcFfvxyYLdKCqrU/dJBfZzT+uhisxJGnmgMPUarBaFjspiNBzerfqjU/tEmgxqr35rwQC5X9pExkcn+Gx6bQe3o3zRLHWeMPUG6OPSEAxk4XHF1CwM6BmPJ95do4Jytz+zALdePBCTh3fzXTDgqodQtWoOYk+/HP6uYvFHqN26BNBokXzxH/1u/zkdYc4+DdbBG1C9fq7K7ulyw1M+336ycktj28D+P90WoE/qjvrCvbAMPctHoyPyPlnI33XXXep81qxZ2L59u7qVK/xiwoQJ6ur+E088oYICy5YtU4v4f//73zjrrLOaP+f888/H7t27W/03Fi5ciKVLl6pMg/POO0/dN3bsWNWR6Nlnn8UVV1yBrl092+EcDgdeffVVmM2eC1C1tbW45557VOBhwIABAfMSYKUqIvJbNVY7Zs3NxXUPf4eXPt2kggCSCnnVOdl47b6z8Kvz+wdUEEBIITApCiZqti/z9XACmtPlUoUBWyP3y+O+4rTWoOizp+WdOcwDJsAycCKCzaA+iXjujokYmJmA+gYnnn5vHZ59f53q2uALhvh0FXBp6sYg3Tmk/Zu/qdm2DOUL31fnMl5T98B50xiqpNuHbN9w1pSjNneVT8ciRU7zS2rVdqchfRKPekxe+wlnXovuv39FZTMQBQtJ/W8ii/zExESVji8LcjmcTicmTZqEzZs3o7KyEsuXL4der1dbCppoNBqce+65x/03Vq5cCZ1Oh6lTpx51/wUXXKBuV69efVRtgaYggEhO9hTttFqtCCTMCCAivyMV479YuBtzlu6Dtd6zqEiKNeGiiZmYMqq7z1uXnarI7DGo3bZU1QmQFGYWc2of2Zcu3QHEFz/TNcAnad9fvaD26epiU5Aw9UYEq9iocDx002mY9f0OvPf9DtW2c8eBcvzp6uHoluK7q6YSBMh/5wHA5UTqFQ9CZ/GfXvC6qARozbGIzD4NUUPO9PVwqI0B3KTpt6uf6cjew/0iG2BArwREhLfevSRM276uJkT+KiLiSIvMiooKFBcXq0BAa+Sx8vJyxMTEqMV/S/Hx8cf9NyorKxEbG/uT1H4JOoiqqqrm+0wm01Gf0/TvuHx4AaI9GAggIr9xuKQWn/y4Cz+sOtDcw717ikUt6sYNTlf1AIJBRK8h0MelwtRrqKoWHsZ2Ye0mi/2LJmWqNpE1dfUwRxhVJoCvggCieu13qNuxQqpDInn67dAYj37DEGxkq8DlZ2ehf694PPH2GhwsrMYdzy7EzTMGqvodvuBqsMJVXwdnVQkOv/03VUNAZ4mDPwhP74306x73eXp5Z5LU2kBnTO6hjpYBP18EcVcdp21g9aYFqiONZfCUkHptUeixWCzo0aOH2gbQmi5duqgr9BIMkIV5y2CAFPY7nujoaPU1kl3QMhhQVFSkbiVIEGyC4101IdT/QFNg25NXicffWo2bH52Lb5btU0GA7B5xuO+6UXjuzkmqRVmwBAGExmBCl5v/jYSzfs2e4V7KDLA32JB/cI+69VUF+ya66ERoIqJUhfFQqgExMDMRz945UXVzkK0Cz36wDk+/txa2xqyeziR77qW1oFx9t5flq2CAtHH0ZYeAms2L1OJR6MyxzVsYgplsE9EbwpHapae69dW2EW+r3bESh9+6T31fO1NNXQO27C39SdtAt9ul6k6UzX8HNVJ7giiIjRw5EocPH1ZX93NycpqPJUuWqGKBsoiXz5EtA1K8r4n8/pUuAz/3vA6HA99842m/2eSLL75Qt0OHerZ1BhNmBISwo/9AG9XHvn4DTaETbJJfyFv2lOLDeTuxdrsn2iqGZyerDADpWR7MuB3A+6Q9kD+QGhBdb3wGmgjfVNH3davHB24cgw9/yMV7327HvNUHsfNgOe65agS6p0Z1ejAg9aq/qyCAveww8t/+W3NwwBcdAqQ4YHTRPsRP9rSrCnbStvPj+bsw24+27XiDLP5LvnlZ1Qso/f5/SDz35k77t9fuKILL5VYdOlLiI5vvt+7dqAJeYQYTLDnBV4+EqKWLLroIb7/9tuoiIFX+pcWfFPn773//iyuvvFLVBhgxYoQq9PfXv/5VdReQYoMfffSRaht4vPdfEyZMwKhRo1QngMLCQtU1QOoGyPPOmDFD1QXwl/cZ3sJVX4gK1j/Q5P/BJoNOi9XbCtVCYft+TyEv6X4kqf/y+stI8//+397SUJqvagVEZo1WVc8psNUX7mtOHQ6EPvYduVXgF2f2VcG8J95ejYOFNY1bBXLUVoHODILpY5JUNwFpLegoL0D+W43BgOiji6x1VoeAiF7Bd0WpNfK7Xt5jSJvJJvJe473Gj2U7T6BeeFD1Ai64DYfffQjV676HKWMQzNlHepd3xraAkcdsC2hqGWgZOCnotyIRSb0AaScorQAff/xxVFdXIz09HXfeeSd+/etfN0/Q008/jUcffVR9nlzpP+OMM3D55Zfjs88+a3USw8LC8H//93947rnn8Prrr6OsrExtM7jjjjtU0KEpoyuYBOZvYTolwfwHmvw72DRtXAamje+F/325BYeKaqDXaTBlRDf1mmt5dSNUlM17E3W5q+B2OhB3+i98PRw6BdIBoujjJxA1/BzEn3UdMz4A5PRKwLN3TMKT767B+txiPDdrPTbtLsEtFw9S7T87i1RPT7vyIeRLMKCiECXf/w8pM+/2QYeAG2Hq3npxq2Cj1WjU7/7WSGFPqekRyEwZAxFz2nRULP0UJXNeRHhaZocHl5xOlwqiixEttgXYKwpRt3ONOo8afnS1c6Jg0DK9v4lsC3jkkUeO+zV5eXlYv3497r//fvzzn/9svv+2225D9+7dmz9+6623jvo6KQIobQDlOJbUDhBvvPHGTwoKSiaBZBsEmuDZdEte+wMtjxOdarBJUv4l2NTU411u3/8+V3UDuPb8/rh4UiZe/euZuHXmoJAMAojILM9VpFq2EQxo9soilHz1ojqXwo/c9nFEjMWIB28Yo1p+SubP/DWHcMczC7Dv8JHqy51BFmlpVz2EiL6jkHjuLZ3yb9Yf3o3iL55T59Ejz0fUkCNtrIKZXDWrrmto/t1/LLm/xtqgUtwDWeyEX8CY1hsuWy2KPn8WbpdnkdBRJINOWupaIvTI6n6kaFnVmm9l1mHqOUi10CQiTxX/P/3pT+pYvHixajn42GOP4bvvvsPVV1/NKWrEFV8IqrXZf/YPtPwBrw+Sgj7kf8GmL5fsxZC+SfjV+f1V67FQptpQaXSwlxxCQ/FBXw+H2kHe/Bd99qxaDMiiIO70yzmPx9BownDplD74xy1jERcVrrKB7nxmAb5dvr9TUy2lNoBkAmgbaze4nfYOKyDoqC5DwYePqq4gpl5DEHdG8L/xlIX9is2H8eAryxFh1KkssNbI/SaDDrc+Nk91mZA6EuXVgbfvNkyrQ9L0P6h9+baD21Cx+ONOaRs4LDsZ2sbiuVKvoHr9D+o8atg5HfrvEwUSqRsge/ulS8Af/vAH3HLLLVixYoXqNCD7/cmD+d8hKDJcr/4QtxYMkPvlD/gtj81Dvx7xOH1oulq0BVPFduoYUul/fW4Rtu0txdljevxssKnOZke02Rjy3wpNeCQieg5C3a41KivAkNg15Ock0JQvnIX6Q9sRZozwLAq0/LN6PNL3/Lk7J+Kpd9eqomfPf7gem3aV4NaZA4/bD72jyHacwk+eQn3BHlUzQAoLeu253W4UfvwEnNVl0Cd0US0kg7lDgMPpwqL1efho3k4cKKhW923cVay2gkkW2LGmjc3Axl0lyCuuUceCdYfU/b26RGNo3yR1ZPWIC4j3HfK6STjnRhR//izKF3+IyH6ndVi9l1XbPIGAkdlHXqvyd8Nlq4EuOkkVKSWiI0aPHq0OOj6+YwlB0mNbCgM21QRoSf5wb91XhuJyKxaUH1J/oC0RBowblIbTh3ZRLd3k6g6Rei05XdiwqwSL1+dh2abDKm0xKtKAS6f0/flgUye/6fdnUiiwKRAQO/5SXw+HToJ1/2ZULPFcBUw85yavLiaDlQQA779+ND6evxNvf7Nd/Y3Zdagc91w9olMLhUoGh730EJxVJc0FBPVxqV55btkaEjvhUpR881+kXPpnFfALRvV2J+au2I9PftyFonKruk9qP5x7Wg/07R6HwX2S1Fx80UpRYnkf8ehvxqmA0Nrthdh1qBK7G48Pf9iJiHAdBvVO9AQGspKQFBsBf2UZMAH1h3aojCB9B6XmHy6pVUU3pRDnkKyk5vvN/cdDY4hQmUnBHGwioo7BQEAIkkKA8odYtPYHWgq4Pfn7CViw9hAWrs9DRXU9vl62Tx0JMSacPiRdBQV6pEZxL2wIcrrc2LqnVF0BWropH5U1Dc2PxVqMGDsoTW0/OV6wSe6XYJSeO5OUiD4jVDXxhqIDaCjN4x7PAOGsq1JbAmRvrmXQZJj7j/P1kAKGLAKlWFy/jHg8/vZq5BXX4s5nF+LG6Tk4e3T3Tvm7Il0dUq/0dBOwl+Y1txbUx6V55fkjeg5G15ufC8rFmQR85yzZiy8W7W7+/R9tNuDCCb1wzmkZMLfYEiCFYOV7XVNXD3OEUf3ub+pMJF0l5JD6EfI+Y12uBAWKVHCgqrZBBZflENIub1iWJ1tAvsbfuhslTL2hQ59/1VZPNoD831vOr7y+IvuO7NB/m4iCFwMBIUr+iP7cH+g+3WLV8etp/VWV5wVrPYu+kgqrqgQvh/xhlq0Dpw/pErLF3kKF7P3csb8cizbkYcmGPJRV1Tc/JhkAYwemYfzgdPTrGa+uWIifCzb525s4X9KaLDD1yIF1z3rUbl8Ow9iLfT0kaoOGov1w1ddBH5+mugTQyZNFzbN3TMQz769T1dD/89EGtVXgN5cM6pSsIZ051hMMeOd+VadDMgNSr3yw3cE46RAApwPmAePVx8EWBCivsuHzhbsxZ+k+WOs9dYSSYk24aGImpozqDmMrv9flwkNdXR3yD+5FRkaGavt1vKKSk4Z1VYf8vdmdV6GCAmu2F2HH/jIcLKxWx2cLdqu/HwMzE1RQQIIDqQmRfnVRwnpgK5xVpc2vA29Y2RgIaNktwFFTAZ05xmv/BhGFHgYCQlhb/kBLQRpJ75PjlosHYtW2QpUpIG/a5I/y219vV4dUsJUsAbkaHGsJ7QJwwUL2ue46VIGF6/KweIMnCNREFvWn5aRi3OB0DMpMaC5cdDLBJjrCkjMR2ogohHfpy2kJEBK86XL943A7HNAY+DvvVLYK3PfrUfj0x1148+ttKgtNfu/IVoGe6R2/VUAWUqq1oAQDig+qDIHUKx446X3eTR0CpDigqv0RRPu1JS1dvj9zVx1QtWBEtxSLCupKALgte/ltNttJZYz07hqrjsvO7IuaugZs2FmCNdsLVbZAaaVNvQdpaqWXEh/RXFtgYO/ETm1NeSzboe3qNSS1QgzJ3WFI7HbKzyk1dTbv9hS1HNkvubkY5YHnb4GpR38kz7wHGj1r7hDRyWMggNr8B1oWcHLlVw5JDVy+KV9lCkhRIGlrI8d/P9+Mwb0TVabA6AGp3AsegIt/aeslaf9yFJTWNT8mb65GDUhRb/yG9ElSW0hOpK1Xg0KdXDny5tUj6jhuhx1hOs/Vam+lkYc6WfhdPLm32irw2NurkV9Si7ueW4jrLxyAc8b06PCrvbJNIO2KB3H43QfUFp2iT59C+vVPICxM0/YOAbOOdAiQNm7BYG9+pSoAKDVgmjr9SdBfgrvDs5M7rV6QOcKgLjLIIX+jpCChZAqs3VGILXvK1N8pyVKQQ6cNU6+jptoCnb2F0ZjeB6YMyfDagKLPnkbatf+CRmc4peeU4IdsyUtPNCMt0azuq1r3PeBywG1vYBCAiNqNgQBqF9mjNmVkd3WUVdnUGwUp+pR7oMJT/GdHEQy6DRjRP0VtHRieLQtHXgn2V5LdIQt/ufovVZybGA1ajOwni/80DMtKbvfV/JO5GkTkr1wNNuT97x5EZo1B7PhLgi7129eyM+LUVoGn31urrva++PFGtVXgd5cO7vCgsqoZcMWDavEWP+XaNgcBpH1b4YePwlkTPB0CtuwpVQGApivuQhbVkgEwoGe8T9Pw5d/unhqlDsk4ky0Ksn3Rs42gUAUFpCOBHK9/tVW1q1S1BbKS1EUKCSp07Pg0SJz2O+S9cqcKKpX98CYSzr7+lJ5z1VbP92FEYzaAtL2sXvudOo8azpaBRNR+DATQKZM/tBdM6KWO/JIatZj8cc0htaBcsiFfHZHhOpw20NN5QFpINe0jJ9+R75Us/hevz1dZAE3kSr9c7Rk/KF298Qj3YZplKHHVW1GzZZHaex4x4QpfD4daUfLtq2ovufTtjh55nqrvQN4lNUdkq4DsBX9zzla1LUkqyd9z9XD06tKx+6Fle07qL+8/6j5nbaUKErRGrk4Xz35ebQvQmCwB3SFA/i+y8JeK/dv2lan75M+0/N2WAEBHz317SaaaBKvlaPq71lRbQIIBcqHi+5UH1CH/H+lmIEEByRjI7BLTIVkNUntCggEF7z+MqtVfw5QxCJFSFLYdJBOgKSDT9H+UWjLO2gpozbGI7DvKq2MnotDCd/jkVWkJZvzizL64bEof7MmrxIJ1cpX5kNrT1/THOC7KiPGDu6jtA/KH2J+K/AS7orI6LN7gSfuXdk1NJJ1S6kBI2v/oASnc0uEDklYs7cbgdsE4+GxfDIF+hgRpajbOk2t+SJr+ewYBOpAszuRqb7+ecXjsrdU4XCpbBRbh+gv649yxGZ32N6N84SxUrvlaBQeMyT1+8njFog9Ru22p6vqRfPEfA7J9pLSAXbQhHx/P29kcEJY9/2eM6KqKADalogfSe5C0cWacP64nGuxObN1b2riNoEhtKZAghxzvfLNdBZ1km5sEBob0TfRqfaOIXkMQPWoaKlfMRvGX/4Hx+iehi4o/6efJ3V+uOihIXR7JmBGVq79Wt1FDz1K1CIjo1E2ePFndfvHFFzCbj/6996c//Ql5eXl4/fXX2/388+fPR9euXZGZmYkVK1bg6quvxg8//IAuXU6uHo238TcIdQh5oyZXEOT41Xn9sGVvqSoyKNkBUnFeKg/LkZ4YqbYOTBjaRe1/I+8rrbSqeZciXFL5v+WbbSn0J4v/MTmpHZ4yST9PrjqGd+sH2/7NsO1cCURkcMr8hL28AMVz/k+dx4y7GKbuA3w9pJCQ1d2zVeCZ99apqukvfboJm3aXqq0CsjDqSC5HA+p2rYarrgqH33lAFRBsCgaEh4fDWV6A8kWz1McJ59wIU/f+CCSySJbif5/M34XCMk8tGJNRi6ljMnDhhJ6IjzYh0MlWtqZix9LXo7jc2rh1sRDrc4vVAlu2NMohenWJbi46mNUjrk1FEH9O3MQrYN2/BQ0Fe1D0xXNI/eXfTnrbyKptnm4Bw/omqfHUH96D+kM7AI0OliFnntL4iPyNrcEBrUajWlBHhutVgWmpNdVZ8vLy8Nhjj+Ghhx7y+vPefPPNePPNN1UgwJ8wEEAdThacOb0S1HHTjIFYt6NIBQWWbylQ/aPf/W6HOjK7xqiggOxHD4Y3Ib4kPZmXbMxXV/7lioi7sdCTXEgb0FMW/2kq5VMqdpP/kL3nEghoOLgV4YOyfT0cUvtxHSj67Bm4G6wwdslC7PhLOS+dyBJhwL2/HonPF+7B619uUb/XpLXcPVeNUH8zOooUeEv55f0oePch1B/ehZKvX1LV2U0mM3p3S4M+wozwW59Hbe4qRA2egkAhFeilqJ4E4uXvhJAr49La9byxGUEdEE6MNeHs0d3V4XC6VGBcBQa2F6oMud2Nh2yPiAjXYVDvxOaig0mxJ1/oVoqKJk2/Hfmv/xkRvYd7/gCfpJVbGtsG9vdkm1St8WQDmLPHqC0IRMFCgpPSmny2D1tOd+3aFR988AGmTp2K0047zatbr/wVAwHUqWT/+cj+KeqQIj8rNh9W2wfkj/GugxXqeG32ZhU0kHoC0qIumN+YeFN1XQOWbjysCjdKJ4emKs8iu0ccxg32dHxgkMV/Rcqbu6g4mHoMRKKtDnqjHi67DRo929P5StmC91Cfv1Pt/ZYtAYFeCC5QM8ymn94L2T1i1VYBKQj3x38vwq+n9cf54zpuq4A2PFJdxS2e85Iq+Fa5eo7a8+2y1arXQ9SI8xBz2gwEAln0f7FoN+Ys2Ytam0PdlxBjwoyJvXDWyO4hVwtGrq737xmvjqvOyVbzsy5XggKebQSSLbBs02F1iK7JFk/Rwb5J6mvaujAxxKeh229fhMZ48oGEonIr9hf8f3vnAR5V1bzxSS8kJKFD6L1GeguhiIgKKIIIIqB+ivVTwYL8FeSzYEUEAelYQenSrEhXqkjvvYQWhBTSk/0/78S7bCAJSSDJ3dz39zz7JHs3WZbJOeeemTPzTrQepuDfVmcCIpau7hQJJKYGYzUhMSXbP59qs8nC1Yfl+1/3268hGPDdv8+x/rvmYJ2H0HVu7gv33nuvbN26Vd544w1ZsmTJdSUCIDIyUj7++GNN9b906ZLUrVtXBg8eLC1apOl1jBs3TlP/S5YsKatXr5bmzZvrzwKUA/z3v//VawCvf/fdd3Ls2DGpVKmSvPrqq9K+fXvJT6y18hPTify0b1JBH5ExCSoKhUwB1O8Zqr9QjUbHAQQFmtUtI17sQZ8OLJQbd59RgUakOkJYyKBGhUBN+0fLpdycZpD8x9XLJ60f+ZLx1zkbN9uCiuSc5KiLErVpmX5fssuz4hFQimYsQGoZpQLf/y0bd5+VKT+gVCBCXujdSDvZ5AWYgyXueUYiNy6Sy+vm2a9jfl7+tzQgsNV9pg3WIe1/4apD8tvG45KYnKrXKpT2k54dauh99WbT3wsLgf5e0qFJBX2kpto068QQHdx//B/trIMHRCwRBAipXkKDAnDQy5YokqXT4RgEgBise9ES2RKV3Lr/gj2Qj8wYULLLM1Ksw8MqTkmIWYMAr41fZxcdvRHISJr+RifNBMiIxWuPqF7J4yN/0wBddsCc+fC/bXIcDHBxcZGRI0dKt27d5MMPP5R33nkn3espKSny7LPP6lcEA4oVK6bp/o8//rjMmjVLQkJC9Oc2b96sTv+iRYskKSlJf6dXr14aJAgNDZVdu3bpz+F3UYZQqlQpGTVqlAwaNEj++OMPKVIk/0RnGQggpgAp6khLxAMbFwgMwrmFeNGGXWf1gcABatlRPnBbjRLiZtENDDIpNu85q/bBJgUpjgZVyhVV57/NbcG6OSHOA07+L//5g1M6G4UViHuVe+Q9iT28VYrUblnQH4f821P+jcea66bxi6W79cQWwrRD+jeVmhXzJlXa1cNTMwEyImrzMgkK7Wm6v83xM1Eyb+VBvU/AsQU1KwbKA7fXlBb1yuSJWn5hAbapUSFIH7071ZKY2ETZfjBC2xMiWwDix1DyN9T8yxT3tWsLhNQoqXuVjIjeuVoilk0U31rNtWTgRk7K1v0R+rX5v20DHbtbEFJYCPL30sNAHGxlBK5HXknUn8tuIOBmCA4Oltdee03efPNN6dy5s7Rp08b+Gpz0vXv3qoNfu3ZtvfbWW2/Jzp07Zfr06TJ27Fj7z77wwgvi758WsDt1Kk2HJCAgIJ2T//rrr9szCZ577jlZvny5HD582B5QyA8YCCCmo3QxX+nVsaY+EAhAUACZAkiTW7HlpD4C/bykzW1p7QhrVQoq9J0HEpJSdNOBmn/0FEYtlQFOd9Dqr03DYE1fJM6Ji6u7RG350amcDSvgVbaqPoh5wHqPdrUQdPvwmy0aPH5t/Fp5rGs96RZW9ZbfDxCQwyPT1xKuiJtvxi0G85t9x/7RGneIKxo0rFlSenWsoSV3hf1emVfBJ2TW4YHTTnQfSOtEcE52H7mopSrQXcADHXjqVilu1xaoXLao3eYexcqKLTVFruz5Q2KqNhT/29JUyjMiISlVdh9NO1FFNiREB5P+CRe/emHi6smAMDEvGO84jc9JaQAO9qAJkFEwANfRpnzUC23zvDTAoHfv3vLLL7/IsGHDZOnSpfbrBw8e1HKBGjVq2K/h32natKmsW7fOfq148eL2IEBWVKlyVRS6aNG0AF98fLzkJwwEEFODm2jlsnW1hm/fsUuq7gtn+HJMgiz946g+EDho2yhYgwKVyhSeSHlScor8vf+C/n+R/h+XcHVRxWk/Tv7xqFTGn5u7QsCNnA30jU6MOKWdBVgmkLdEblqqtbhFm97NuWVikAGAUoHPZv+tmQFTF+3SUoEXUSpwC7VlkMaNR0bzU1/zKtjsKzinOKlGAACOKcAeuHWDctLz9up6sk1uDdj0VypbVB9ocYkMPYy5tDKCcxoUMEobv1y2Rx0Y1RaoXUoa1qgsQe36yKVVsyTil+kqPgoNgYz4J9ZN2/hCOb18KT85+918iTu6XZIvn9fSAELMPk9yojuCbgEQBjQ0ARzBde0ekM86Ju+++66WCLz//vs3FP3DdXf3q58PnWWyg6ura4ELCzIQQJxmUUEPXTyeuK++bD94Ia3zwK4zehqEDRAeCBwgIIDAgDPWxSPNf8fBCFmz7ZRs2HnGLupkKB7j5B/OP9oc8WSncHFDZ8PbT84vGiuSkixF6rQWvwZtxbtCHXGBeBS5ZcSHH5KLv38tkpoiHkGlxbd6E1rXxEAb4P8eaSZL1x1VoVmUkR05vUpeG9DslpUK2FKTVavDKNNxBNfxekH0c4cmzJ/bw2XeioNyJDxSr+FEGnXucFLLl2KGWF6DMoDmdcvoA4RHxNi1BRAM+CcqXn7bdEIfqMaoXbGEPORfVYpGH5HzC0dL8KPva3cBR4fIw9NbWjSqLXeEhsjpCzGSdPG0BgEQnGTLQFIYQYtAdAcwNAEKqmuAI+XKlZOhQ4dqVgC6CZQtW1Zq1qwpMTExmhlglAbAcf/rr7+ybAto5v06AwHE6YC4UZPapfWBmyZS5REUQDQepQTHlu2Rr5btUWXfdo2CTd8mD5u5XYcj9OQfqv9Q/zfAaQJKIMIaBUutioW/BMLKZO1s3KO97F3cPSUlNkqity3Xh3tAKfGrHyZ+DdqJZ/HgAvnchYnUhFg5/8OnGgSAJoBPtcYF/ZFINsC6iJKA2g5dBYaMWyuPdq0r97WtdtPrJrQ5jO4AKNMpaCFPZIuhRA6tts5EpAUOvT3dpHPLyqqujW4ApGAoV8JPyrXxk65tqmoJH9r3ppURnNeSgj3HL8sol8YyJOC0+J07Kr9+Plo8WveVJnVKiY+n+3Xt0zCu/VLX6nv71mgiHoEULCWFEzj7CGCiLBhtTpERg0yAgggCGEDg7+eff9a0fwQCIPRXq1YtVfcfPny4lgB8++23cuDAARkxYoRkhq9v2sEkfg5dBswEAwHE6aOIRoq80T4PmgJI1UOKJB6TF+6URrXSOg9AJCkzIZ/8BOJNUFRFqz/0xb70bz9nAP2D1iFl9f+EWkOKOlmD7DgbaEMVf2KPxOxcLTF710ty5Hm5/Md8rR0t3eOVgv4vODWI6kf8PFWSL51VVW8oxTPw5lwgBX7M4PYybs42XVenL94tuw5flBf7NLKrrucWzD8IdgaF9pDk2Bhx9/UTW0pKvgYBsDn+ef1xWbTmkPwTlXbP8Pf1kG5tqkqXNlVVfZuYBzgwDWuW0sfjInLhUpwGBKAtMP9wmDzitVxqRG+SifP8xbff/do+efbyA/bfRzBg4a87pXmJFQJXCKVKhBT2PT0wDu88pOAzHt/9t0QAuLm5yeeffy6fffaZtgFMTEyU+vXry5dffikNGzbM9D2CgoKkZ8+e8tFHH8nx48elU6dOYhZcbPldjGABoB4JGjRoIGYnNjZWFTDr1Kljj1gVBi5GxqlaMjQFDp9KS5k0BEQQDEBQoFHNUuLh7ppv9sJUO3jysn6uP7aflojIq4Ig2My1alBOwhqWU0GnwtQRobCOsbzsHoBe9emcjQzEoVKTEiT24BYNChRt0tmewg5laohRIUvAt2Yzy+gJ3Ow4i96xUts2Iv223IB3xLt8WtpfYaawzk2stT/+cVSmLd6t5VYoq0JXgdqVit0Smx09elRFnvLLZlDUxikxNHEMMa3iAd7SvV116dyykimC21YcZzcDxuWR+Z+L28FVssW3jdz37H/l0Xd+vU4srY3XPulVZJO4FwuWCk+PZXAyEzjGCsZmELYz1sPs1sU7MykpKfp/xv8VQYGCJCvb58QPNffdg5BcUjzAR+5vX10f6P1rBAWQRonv8YDzHXpbsJYP5NXJOzakaG2FtP9128NVz8DA19tdWtZPO/mHqjP7ORMjM0CdjRPhWTobrh5e4lc3VB+ORG9fIfHHd0nsob/ExctX/KgncEMSL4ZLxM/T9Pugtr0tEQQozCCTAyfktSoXk4++3iJnLl6RoePXyYB76mrq/M2u9fml6nz+UqwsXHVIft14wt4pJrhkEenZoYa0b1Ihx4FsYh5wv6/e40lJPNNRqlWoI5ej4zNQTLdJmHeaeJpXSCcGAQghtxwGAkihBy31Hr6rtvTtXEtP5LXzwN+nNR3/5/XH9FEiwFvaNiqvmQJVyl1t95MR2Yl6Hj8bleb8bzstpy9cFX9DHWfzemXU+Ud7oYKsfSLmJrfORom7BqaVDuxaI8lREQ56AiXFr35bbVnlEZQmbEUQrEuVC4vGiC0pXrwr1bOXZxDnp3r5QBnzUjstFUAg9oulu2XXkQgZ1KexqdPoT5yN0lpxaN9AQwZULx8gD3SsqcFjtzwIWpP8B9laEHwFRXw8JdDHRS7HXU3Srel+Rsq4RUq8zUOCG2beapAQQnILAwHEMsC5h4o0Hv/pVl92HYrQoMCfO8I1TX/BqkP6qFDaT9o1QueB8tqm71o137Llq4qHp5c+N+qZANR94fzjAVEgA093V2lat7Q6/03rlE73O4TcajxLlNf2UkHtH7qqJ7BvgyRHXlA9AdS/G4EAZKxYvQ4eXReK3d5fLv72hZS690UtyyCFBwhOoSwgpPoxbS8IcdkXR6+SIf2aahcaM7H/+D/aAQCdDwxCqpeQXh1ryG01Slp+rhZmYnavlWH+c2VsYnuJ9C4nQf5ecjG6vMyKaSWtawdKVQ/zCh4TQpwXeiTEkuBE5baaJfXxdI8Q7Tiweutp2bTnrJw8FyPf/rxPH1Dq7xJaRVrfVu46NV+0NUGK5qqtJ+XHP49pCYABWjihq0GbhsHSvG5p3YwSkt8Ork+l+voo3vmJND2BXWu19aDBhaUTJDU2Kk1PoEZTLTewIj6VG0jwE5/Q0SqkINh1d+sqUqtSMfnw680SHnFFhn6+Th65p47W2RekICuCcdsOXNAAANrNGbRqUFbbZt2qFojEvGAMJBzcKL4BQfJhj8aamZR4JVo8i/jLhYutpUSJQGYPEkLyBAYCiOVBej6E+vCAk79h1xlNydx+8ILsP3FJHuhYQ+YuP3Cdmu93v+5X9f/qFQI1CIDNJGr9w24LlpYNymp/a0LMQEZ6AhAbvLL3T7FBdNDQE6jdSoMC3hXraCChMJMc/Y/En9xrt4nVMyOsQNXgAPl0cDuZMHe7rNl2Wr5Yukd2Hr4og/o0yvcWs0j537DzjMxbcUAO/StoiwA1ytMQAEBJG7EGWHtKdn1O25ZGbl4mFxd/mq5rjHtJlCsxU4kQcuthIIAQB3DS37FZRX1cio6XTbvPSqOaJWXM939naCeoOH/1ZmfdSCLtP783k4TcTHAg+D8fpdcT2P67PlRPoF6YFG16j7j7F74TSVtqipxfNFZFFZMunZWg0J4F/ZFIPoHsrFf6NZEG1UvIlB92ypa9aaUCr/ZrKvWqFs/zfz8pOVVW/nVSFqw8aNePQTAa6v8QMiwVRFV9K+Li5iaXNy6Ry+vm2a8hGHB57Rz9Hq0rISRLCCG3EgYCCMmEIH9v6dyyslyOSchAzTcNXIdWAAIHhDi3nsDef/UE1qfpCfy5QPxv64CZoD9rS04SF/fCkeVyef0PGgRw8fCWIrVbFfTHIQVwAntXq8pSq1KQlgrAIX994h/S767aWu6VF6UCcQnJ8suGY/LD6sNy8d/WsQg8d21TRbq1qcogssVxcXWXqC0/Zvha1OZlDFYSQvIEBgIIuQFFvD10w5ZRMADXWf9PCoeeQD19FO/8uOoJJJw5JB7FytlP0E9Oel48S1Z0ej2B+FP75NLq7/X7Ep0fF8/iaf9HYj2qlAuQ0YPayefzdqhw7Nc/7pVdRy7KSw81vmWOedSVRFm67og+omPT7iHFinqpNgGyAHj/IMbpPx6ZvpZwRdx8A2gsQkjhDQRMnjxZ1q1bJ99884392rBhw2Tu3Lnpfi44OFhWrFih36empsr48eP1Z6Kjo6VZs2by5ptvSoUKFew/v3fvXhk5cqTs2rVLihUrJo8++qgMGDDA/np23oNYl5TUVBUGhCbAteA6XveQwl1PTaytJ5AQfkizBPC4qifQUvwatHcqPYGU+Cty/ocxiGxo6YNfCDIeiJWBI/7yw43TSgUW7pCt+87LC5+s0k4DN1MqEHE5ThauPiS/bDguCYkpeg1daHp2qC63N60gHu6s+SZXgR4AHhkFA/Q1r6sdjAghpNAFAmbOnCljxoyRpk2bpru+f/9+efrpp6Vfv372a25uV2+gn3/+ucyaNUs++OADKVOmjHz88cfyxBNPyJIlS8TT01MuXbokjz32mNx+++3y1ltvybZt2/RrkSJFpGfPntl6D2Jt0O4P4k1g8TVdA3Ad9Z2EFGa8y9eS8k+NVS0B1ROIvCDR21foA+0I/eq3lcA2D5g6SwDK3BE/TtTP7h5YWkrc/SQFAom9VACn8zUrBsqHX2/RVrCvf75OHr6rjq7xOSkVOHU+WhasPKQ6AMkpaT3hq5YLUNHZ1iHlVBCQkOvWp9RkFQY0NAEcwXW87uJmmi07IaSQUOCryrlz52TEiBGyceNGqVy58nUbt0OHDsmTTz4pJUuWvO53ExMTZcaMGfLKK69I+/bt9dqnn34qYWFh8uuvv0rXrl1lzpw54uHhIW+//ba4u7tLtWrV5Pjx4zJlyhQNBGTnPQiBs9+jQ3Xp1bGmxMQmiJ+vl2YCMAhALKUn0L6vBLXrk15PICpCe2BDZ8AgJS5a3HzMpXoevW25XNm7XsTVTUp1HyyuXhRlI9eXCqCrwOfzt8uqv07JNz/tlV2HI+Slvk0k0P9qkMvb+3rRtoMnL2kLwPU7z4gtzf+X+tWKS6/ba0qjWiUZdCJZAiHAwNb32zUBHLsG4LqrOw+lCMkPFi9eLN9++60cOHBA1+2qVatKr169pE+fPrfs38Ah9fLly/V9Qf/+/TXbHQfSlgsE7N69Wx11GH7ChAly+vRp+2snTpyQ2NhY/SNkxL59++TKlSvSqtVVsaeiRYtK3bp1ZfPmzerEb9myRZo3b65BAIOWLVtqGUJERISEh4ff8D0IMTIDMB7DTx6VKlWqiK8vHQliPTLSE4CGgFEekHjhhJya+rL4VmtkKj0BN5+iurEObN1DvIPTMnwIuRYfL3fVCAipVkImLdwpfx+4IC+OXilvPNZCKpbxFw9Pbylbvqp4eHpJfEKyHDsTJTN/3ifbDl6wv0eLemU0k6B25WI0MMk2cPbRHSAotIckx8aIu6+f2FJSGAQgliE1KV6FM41AGDJh8rNbxrx587SU/I033pAmTZrogfQff/wh7777rvqMzzzzzC35dz766CM5deqUPRBQkBR4IAAp+3hkBKIxAJoBa9asEVdXV2nbtq0MHjxY/P395ezZs/p62bJl0/1eqVKl7K/ha82aNa97HZw5cyZb75EbMHjgNJqduLi4dF/Jje0VHx9Pe3GM5SnONC9dKzfSr8Z6d+XAX1qDDy0B1RPw9BHvGs3Eu04b8SxfK8/0BG5kM5eKDaR4//fFtUiAU6zN+YEzjbP8JrRBSalQqrmMmb1DT4VKF/OV+SsOypJ1R+3lYV1Dq0i3sKoSERmn5QOhDcrIvWGVpWJpP30PjrM0OM5yRnx8rO5PsS/V7JNkrlc3gmOsYGyWkJCgOmspKSn6uBlcbCly+c8ftHvGtRkxNpf8KcGdNWuW9OjRQ+6/Py07B1SqVEn9wa+++kpL1Q0f72b+v7CZ43vg+5y+J34W74O/H746gvfCfcspAgFZgUAAnH845ZMmTdIMAURRDh48qH8QY/BeW8fv5eUlkZGR+j2ctoxeNwZwdt4jNyQlJalIobNw7Nixgv4ITgXtRZtxnGWCd0VxbfOkeIbvEs/w3eIWHylxu9foI8W7qCRWaCzx1Vrn29x0jYmQVL8SDldyH+AtrHA9y5xHOgRI0RKVZMnaIzJ7edrhBEAwwHj+fK8GmikW5OcmV/45KXv/yYc/mhPCcZYzjh49mkd/icILx1j+2wwZ1/CnrsWWdP21dLi5i4trmoPv6WqT6E1L5fK6q+LwCAYYmhn+TbtIos3F7uRKcmKWb+1yE1mIW7dulfPnz2t2uAFS97t06aL/T/iV0Jb76aef5MKFC1rWPnDgQOnYsaP+LDLc//e//+n7GDheQzk8NOgAss9xDY58VFSUDB061C6G36FDB33u4+OT4efEZ0lOTpYjR45k+Hp2Ne5MHQhACkbfvn0lKCitjzVO9qEV8OCDD8rOnTvtdXqo83es2YNxDMPhOl53xBiwSO3OznvkBpQ7VK9eXcwOAiFYBDCQb+b/axVoL9qM4yy7hInNlipJp/dL3N4/JP7AJnGLj5JinqkSUKeO/kRqQpzYUhJvSVusjOZm/KEtcnndVCnS5G7xC+vDOm2uZ7kC5QDvfLElw9eW/nFUeneqJZXLpGUBkOzNTZKztYxkDW1WMDaDr4QSaxyeXqudcnzM1e5sGVGi+2ApUjutLNslNUUzATICmhmBrbqLt2uay5oSGyWnJgzM8r0rDb1edDM7PPHEE/Lyyy/LXXfdpWXlELBv0aKF1K9fX/1PBCFeeuklLU2HQ49sgaVLl8qQIUNk7Nixcscdd6j/Bxzt4XgN3fBwWIwsA/wOruHQGwEAaOKhPAH6ePgc5cuXl+effz7LIEzFihXtB9wG+P3sYupAAAxjBAEMatRIq+2EAY10fkRuYAgDPK9Vq5Z+jy4AeO6I8bx06dIaTbnRe+QGpGQ4Uw05FgFn+rwFDe1Fm3GcZZOaTSSwZhNJTUpQPQGP4sHi9e9aE7l7pVz87UvxqdpQ/EPa3xI9AWNuojvA+d+m4/xA3D3ctVMM4XqWGy7HJGgGQEbgemx8kgT48f6Z3blJsgftlXNos/y1Gfw0PNDNzbGjW3Z/1/id5CuXM2ydCXA9NTZK3AP+FY13vXF5YU4/i8E999wj5cqVk6+//lq1AVCWDhAsee+997QsfdWqVZoRYJS1v/jii5rBPnXqVOncubP+v679DI7XAgMD1eY4sYePaviMISEh6vwD6JCFhoaqjl5m/xdcx/viva4NwmS3LMD0gQBEWOCQf/nll/ZryAQAOG2vUKGC+Pn5accBw4lHasWePXvs7QabNWsm33//vdZSGMbcsGGDGrl48eL6R73RexBCCLk54OD71Q1Ndy3x3DHVE4g7vFUfLl6+4le7pYoMelesm2s9AYgXnl80VlLjY8SrbHXtdkBIbini7aGaABkFA3Dd1zvttIcQQkgalV+dmaUpXNyvrptuRQJUEyCjYACuu/kFXn3uW/SG730zNGzYUB9I18fJ/+rVq7WLANL/0YEONG7cON3vwNccPXr0Tf2713bOCwgISCegn1fkjWrTLQKRlfXr18v48eNVHwB/jNdff12V/NEGENEUOOujRo2S33//Xf9gEBJEhOXOO+/U90CLwJiYGFWARKrEggULNLDw1FNP6evZeQ9CCCG3npJdn5PyT38mgaE9NdpvS4iV6O0r5My3I+Tk+GckeseqXL3vpXXzJP7kXhUqLHX/YHFxo6NGcg9axd4blnH3IlzH64QQQq7i6umd5cPQBwDoDgBhwIzAdQT3HU+7b/TeueHs2bPy1ltv2YXicdqOGn6UqcNvRIe5zEDJgGN3umvJjghgbrMYbhZTZwRAeGHMmDEyZcoUTbnA6X23bt1k0KBB9p954YUXNL0fNRcQcEBUZvr06fZ6DJz6T5s2TdtBQAUSNR7INHBUhLzRexBCCMkbPIsH64l9ULs+6rzH7FwjV/b+KclREek2Ckn/hIurVxE9OcgMpMclnTkol9fN0+cl735KPILSUu8IuZnWsWgHCBavPWLvGoAgAK57ehTMBo4QQgoDaBGI7gCGJsC1XQPQWjOv8fT0lLlz52rZOWr1HTGEA+FDAgj8GeKAAK3qDV04w3fEITQyzjMSZMxJ6r6lAgEffPDBddfuvvtufWQVQXn11Vf1kRmou5g9e/ZNvQchhJC8A2UAPhXr6aN458dVT8C3ehP76xeXf63tCFVPoEE78a3ZzK4ngN7DPl4eUqNiWXH38hX3nq9KfPhB8asfxj8ZuSXA2e/Robr06lhTYmITxM/XSzMBGAQghJCbB85+YKv7JCi0p6QmXNHAvy0lOV+CAKBYsWIqFggBP5z+QzAQjjyyyaEJANFAiAeGhYVpiQB8R4gFLlu2TDPKcXANUFYAR3/cuHHabQAl7QsXLhRHoMmA0veTJ09qmXtBYqpAACGEEIIbv1+dqy0GkRaIev90egKePlK06T0SFNpDLq+/pvdw07slqE0vGpLc8syA2NhYbRUInSEK3xFCyK3NDABGJyEXt/x1UwcNGqS1+nPmzJGZM2dqljjEA3EgbZSU49B64sSJWnIOTTl0tIPT36lTJ30djj1KDCZPniyzZs2SJk2aaCb6a6+9Zv93unfvLr/99puWuv/6669SkDAQQAghxNSgRKDcgHcl8eJpLR2I2bVGkiPPi3dwdbm8fqG9FMDeexjPXVz1dMHYWBByq8DmkBBCSOGje/fu+sis1h8q/dCrGz58eKbv0bt3b31c+74GDRo0sHckAN988022suTzAgYCCCGEOJGewEMS1K63xJ8+KF6lK8uFJeMz/FnUGSLFkBBCCCGEOFnXAEIIISRDPYHytcSWGJ917+GEzFV+CSGEEEKsDAMBhBBCnBLoAeCR6WteGb9GCCGEEGJ1GAgghBDilNy493Byvn8mQgghhBBngBoBhBBCnBIz9B4mhBBCrIzNZivoj2A5bLfI5gwEEEIIKQS9h3tIcmyMuPv6iS0lhUEAQgghJA/x8PDQr2irCjV9kn/A5o5/g9zCQAAhhBCnzwzATfHoiXD2dyeEEELyATc3NwkMDJTz58/rc19fX3FxcSm0tk9JSZGEhAT7/72gMgGw34HNYfub/RwMBBBCCCkUsL87IYQQkn+UKVNGvxrBgMJMamqqJCcni7u7u7i6FqzMHoIAhu1vBgYCCCGEEEIIIYTkCGQAlC1bVkqVKiVJSUmF2npxcXFy5MgRqVixYoGWQqAc4FZlJDAQQAghhBBCCCEkV8AxLah0+fzMCABeXl7i7e0thQG2DySEEEIIIYQQQiwEAwGEEEIIIYQQQoiFYCCAEEIIIYQQQgixEAwEEEIIIYQQQgghFsLFhoaE5JaydetW7fPo6elpesvic0LlEwqUhbn3562C9qLNOM7MCecmbcZxZk44N2kvjjHzwXlZeG2WmJion69x48Y3/Fl2DcgDzDw4MvqszhCwMAu0F23GcWZOODdpM44zc8K5SXtxjJkPzsvCazN8zuz6oswIIIQQQgghhBBCLAQ1AgghhBBCCCGEEAvBQAAhhBBCCCGEEGIhGAgghBBCCCGEEEIsBAMBhBBCCCGEEEKIhWAggBBCCCGEEEIIsRAMBBBCCCGEEEIIIRaCgQBCCCGEEEIIIcRCMBBACCGEEEIIIYRYCAYCCCGEEEIIIYQQC8FAACGEEEIIIYQQYiEYCCCEEEIIIYQQQiwEAwGEEEIIIYQQQoiFYCCAEEKIpbHZbAX9EUghJyUlpaA/AiGEEJIOBgIIyWdSU1Np8xxCR432ygt+++03SU5OFhcXlzx5/8IO17Ls8e2338qmTZv0e65lOYc2o83yA46z7MF1v3DZz72gPwBxLk6ePCkVKlQo6I/hVGzbtk0uX74srq6u0qpVK/Hw8NDTITc3t4L+aKbl0KFDEh0dLSVKlJDg4GC1HcmauXPnire3t3Tr1k0dW2xq6OBmzsiRIzUQEBISIqVLl+bwygarV6+WS5cu6ff33HOPeHp60m43YPz48fq47777dP3nvLwxO3bs0PW/SJEi0qBBA71X0m5Zc/78eYmKipJixYpJYGCg3jPhcPDemTnHjh2TK1eu6PofFBSk44x7s8z55ZdfpHPnzjqmOB8Ljw/AQADJFpj0O3fulAcffFC+/PJLadmyJS2XDUaPHi3Lly/XGzScNNxscDrk5eXFhTQLm61cuVLCw8OlXLlyMmDAAOnVqxftlQnY7P3zzz8ybtw4cXd313HWqVMnBgOy4P3335eFCxfqXMQm0Ngwc3OTOR9++KH8/PPP6vzHxsbKqVOn5L///S+djSx49913ZfHixdKoUSOJj4/Xa3TMsmbUqFF6z8TGuXjx4nL77bfLSy+9xKBmFowZM0Y2bNggR44ckYoVK2oAHXb08/PjmnaDvdmZM2ekSpUqUr58efnoo4/0/sn7wPVg/XrxxRd1L/bOO+9wf1GIfAAes5FsgcEKpwy88sor8ueff9JyN+Cbb76RBQsW6Mnj999/L2+++aZcvHhRN4dmWQDMxowZM/Rke/jw4TJnzhw92Vi1apW+ZtjLbGlVBQ0cC2z8GjZsqONq8uTJsnTpUrvNaK/0fPLJJzq2Zs+eLbVr17bb0LAXYIro9SdBS5YskYkTJ+q6Nm/ePA0CONqONkvPe++9Jz/88IMGnPr37y+7d+/WgB3nY+ZgXsJecMhwH8CatnHjxnT3So6z9MycOVPXssGDB8vXX38tffv2laNHj8oDDzygGZxGdhhJP84wvt544w1dz3r27Kk2u/fee+XEiRO0WSbAB4DdhgwZYr9fcmw5vw/AjACSbTBwy5QpoxGtZ555RiZMmCBt2rShBTMgKSlJ0xsfeeQRPQ0CpUqV0u/37dtnmgXATGMrMTFRtm7dqg5G8+bN9XrHjh3l999/1ywU/Ezv3r3F19fXVIuoWfD399cHSimmTZumNkKZAE8gr7J//36ZNWuWPP3001KtWjW9BscMG+mzZ8+qXgAyUFgqcH3acY0aNdRmSGuEzXCqcfr0aZ2Hjz76qK5vJA2cmCFwAhthPqLUCeMLdkTqNskYOGNdu3bVch1w9913q2MGO2I9w3MEPUkaWK+wz+jevbu0aNFCr1WvXl3vkYMGDZLnn39epk+frpkVLBNIXxKA0qbQ0FB9XqdOHd2bvf3227qW4R6BvS5tdhVkgmHt79Kli+7JkKWD026WITq/D8CMAJJt1qxZo04FUpCxiOIms27dOlowA5CijdR2THjHhbRx48a6sUGaELkKFkWMLaTpHT9+3H4dwSacouHUAxsabBJhV0air2JE5Js2baobGwRS4Mh+8cUXsnbtWpk0aZKeDBGRWrVqaa02ovMRERFqEpygLVq0SNcypPBhU719+/Z0trU6586d03mJjaBhsx9//FH+/vtvWbFihZ6k/fXXX/qa1W2G2nY4/jidRcYJ7IG60Jo1a2ppBZ4zKyBjLly4IOvXr9fvY2Ji5IMPPtB7AhwznK4hsIlgMbD6OAOoMUaJDvYUjnsPOBtwbuPi4tSxRZCdAeH04wylro52rFu3rpaMIdD0n//8RxISEuzlYlbG6HaC9R0lYdhfvPXWW1q+iWAA4H7senCvRKDcGXwABgLIDTE2LVgUUUeFqBYWAJzWYlFgMCDjSD3qznCDQSqQgSF6BJtee4Oxensp2ASOvhE93bNnj9xxxx0aAEC66NSpUzUb5eWXX9afNVNEtaBwzIwoWbKkirnh1Pa5557TWlGk8H322WeaKQDogIim55UtW1aeeOIJTXWH+CnE3HDqiLTHevXqyf/93/9pRJ9jLI327dvr+o95+N133+k8RJDuq6++0jRbnODCZqgjtbrNMNewViEIYKxT2ADifoD5aQQ9re5gZARStCFGiUxDnP6jphZBO8xLjDsIB7722mvq2Fp9nBn7DDiwcCyMAIkRRIHtXn/9dX2ODDGSZi/QoUMHDZIgMOcI5ijKBbBP+/jjjy2debhr1y79agjaoSygcuXKWq4J+40YMUKDAdiPAQYD0o8zrP1Vq1bVwJzZfQAGAkiGQHQGD5y+GpHk+++/XyOB2NTA6cDGD44agwHpbYYoIKKBCJY89thjujE0JjyigFBCLlq0qP0Gg7RkbGzMoiBaEDbDqQbEUx5++GHdABopjtjIIEUPokc44ejXr5+e5CKib1Uc56bjJgUBOtxIMNbgmMEpwwM37y1btujPWPFUyNFeBtjEwBbYIEOMDKdAPj4+Oi+feuoptZtjZorVbQanFgEmaAUg3RGZFQgG4F4Am+EeAPVt1MFbFcf13+imgDFmOB8op0N5ABxaYFUHI6txhqwmOGAI0iGQ+dBDD+ncxD0T6e0IcMKBQ4mPVbl2n4HTa2ShQBwQJ9oIzEEfoEmTJtKuXTsNnlh5Xjo6tXDKAMooMKaQaeKYGQBuu+02dXT37t2rwWArgkwv6DRBC8YAJU7ILsS6D9vdeeedeh9FRhiDAdePM6z9KM9BqSGCJ2b2AagRQDIU00KkFJthRJYxkOHw44aCiCk2NhjouDEjGAAgVAOBHyygVuRam8GhRYoxnFdgtArBIoAFwXDIjNZS2AAZNctWtRkEtRAEMGyGzfS17clQX2ucqFmRjObmXXfdpTZD6jFuOAcPHtSTWmwWUauMFnkQLsO4QzcBq9sLZU1wZLGRgZAPvncEYwtz1UiDtxoZrWUIwEHgCBodcNrQPQYYGxmcPmJzg4cVyWpe4l6JuYdAXbNmzTT1HS24rK4VcK3NMMZQYoKORHggOHxtwBfjDUFhjrM0m6FEB4EAZEwggIJSMIgr4hpKNwH2bKjpNvZtVnRqUfuPdQwBEoC9K/arCDTBbnBkETw31jPcS3/99VdNhbdii1QExQ8fPqzZONi7Yt031nmA9Qw6FLiHAojhIYAOoWKrBjj/zmCcIYh5raaJGX0A660KJEtwQ0H6JxZHnGQj8v7pp5/qVyyaiDDjZmKIqGBBxaktToMQHUSkFQuElcjMZqgTNWxmRPqwqOLkG0BoBQ7b/PnzLRcEyMxmBw4csNsMQB8AqaKGfdAiCdkouFFZjazmZp8+fTR9G84rAiqYl0h7h92QUYEbuqGQb3V74aTnySeflIEDB+pYCwgI0FZlCKIAdKlAtB7XrUZmNkOdIzKccEKEIAAcCwRQsOmBQwKHDvcFzE2rkZ17JjbHGFOYmxCPwqYPwQKrph5nZjMEMTG+kKWDtQynja1bt7aLB2JuYl7iVNJqZDXOkJEDxxYn2NibGXsMAH0YlD9ZcZxl5dQi4wTlYJiHyKaA8wb9GJzMQiMGgTtHO1pRGBDBI4w77PXRNhD7WEcBRez1EdSEngJKxaAlY1WhXZ9Mxtm1gpNm9AEYCCDpQApZWFiYihuB+vXra20QbjgYsNjsQZDMsec2TjYQEcRNyGpBgJzYDOAmgwXj888/11Z5WDRQk2w1smszROUhEgVQb7Vp0ybtIIBTIauRlc1gE8xDbGQgfIegnHFjgdOBbB6rbWoysxduvhA8RecAvAbnY9iwYbqZweYQJ2qwpxEYsBJZ2Qxpx7AT1ixoT0C7Y+zYsTovcXqL0yAEoKxGTtZ/ZAS8+uqrmqED58yqGXRZ2QzrvSHYBkFKjDk4JUhN3rx5s943rRgIyGpujhkzRsspoK0AZwwaFTiIgKYTynkgtmvF0sOsnFojWwLlAf/73/+0TAxBFpTS4UACc9eKBw6GMCDuhZiPEOxEuQmA3WA/R+cWNkImDzLtDC0iK+KZyTi71l7wk8zmA1gzv5Zch1G/gs0wTveNAWsoHmPjFxkZqbUsaL0CHCPMcEKsFgnMic3QFglg84fTNdxkUCuKm7mVyK7NsDii1RZOhnr06KGpolBbxXWjdMAqZMdmSDfDTQU3b0Tm0erN8XetFAS4kb0gNoZTbMw/OLBwKpDJZJRXcIxlbDPYEg4HNjz4isezzz6rKaFY4wp6M+MM90yATTNOITFXrUZ2bYZsJqT/Yy7CwcDcbNiwoc5Zrv/Xz01oBMBZw+k/7AYHF9k5OJiBzbCuWRXDqcV6hQAT7ARHzQB7MgRTUPqEUgvsN/C61caZIzhowR4CQRGML9wjHe1mOLcGcGytHAS40Thz1IpBcM50PoCNEAfmzp1rq1Wrlm3nzp36PCkpyZaamqrfr1271takSRPbjBkz9Llx3epkx2bTp0/X5/v27bN16tRJv1qZ7NhsypQpBfwpncdma9assTVt2tT2xRdf6HPOzZyNseTk5AL6qzqfzaZOnVrAn9J575kG+Bkrk5O5mZKSUqCf1ZlsNm3atHQ2o+1sth9//NH26quvqj2OHDliGzhwoO2BBx6wzZkzp8D+lmbn5MmTtlOnTtmfHz9+PEO7cXzlfJwdOHDAdD4AAwEkHbi5PP3007YOHTroYDauGRN+8uTJto4dO9qio6PpbOTQZpcuXdLnsbGxlh912bVZVFQUbzacm3k+xoxAgNUDKDlZ/7kJpM3yepxxbtJm+eXUMhh8PbgfGnYx7Na7d2/bN998c9N/F6uOMzP6ACwNIOlAPSNqzVCPB9VZKI8bqsdGCQBSgJBqbFXxmdzYzFHp2Kp1Z7kZZ1CptWqHgGvh3My7MWbUz1p9TcvJGOO8pM3yepxxbtJmNwtSsTHOMLYg1Ib0behOIN0dIm0QDARW1VC4Fsde9rgfGgKBsBtaCuLa8uXLtRyRZH+cGVpXZvQBuMMmdoybMPrP4iaNmn/UMm7bts2+OEAVE04tRO9I9m2GTQ1txnGWWzg3aa+8hmOMNssPOM5os/yCTm3GQEwSrXMzshccfwhOQqgTWh6OmgDQU4CgIoRj0QWFZH+codOOWYMnLkgLKOgPQfJ/EYC6M0RRMloE0LINbdogeAGxHrSGgtgMToEgevf1119brhUZbUabcZyZD85L2ozjzJxwbtJmZhhncGoh5NaxY0e7aK6h4g6xWPxM2bJlxSpAFBFBAJxSQ1TyWnudPn1auw/169dPBg0alO53rdrutNCPs4KuTSD5yzvvvGNr1KiRvS7PwKgDQo0LXh83bpz9teXLl6vY3VdffaV1L1aDNqPNOM7MB+clbcZxZk44N2kzs4wziCh++umn1/2uFfVgRo4caWvevLlt7969mda5h4WF2UaMGEENGAuNMwYCLEROFgEKp9BmHGecm2aFaxltxnFmTjg3aTOzjTMKm9psEyZMsIWEhFxnLwjXGULW6DoEp5f2stY4YyDAInARoM04zswJ5ybtxTFmPjgvaTOOM3PCuZlzXnrpJVvbtm1tcXFx+jwhIUGd14ceesjWrVs329ChQ+2v8SDQWuPMvaBLE0j+AMG6wMBAqVy5sj6HcN17770nBw4ckJiYGKlXr56MGDFCVXodhS+sDG1Gm3GcmQ/OS9qM48yccG7SZhxn5sKoUx81apR06dJFBg8eLBMnTpQXXnhB4uLitKYdooBz5sxRoWt8ZQcFa61nFAu0yCKA7A8sApUqVdJF4Omnn9ZFoG3btvZFoFSpUvrV6tBmtBnHmfngvKTNOM7MCecmbcZxZk6SkpLEw8NDv9+yZYsMGTJE1ewhXPfKK6+oAJ7x2ssvv6zXunXrJlYm1Wp+U0GnJJC8JTEx0f795s2bbR06dLA98sgjmgYUERGR7jWkDS1evNjyfxLajOMsP+A4o704xswH5yVtxnFmTjg3s8/MmTNtw4cPt/Xu3ds2e/Zs2+nTpzXlf9KkSbb69evbBg0alK4EICYmxtapUyfbtGnTbFYn0WJ+k2tBByJI3jBr1ix58803pX///hqtCg8Pl0aNGknv3r21xUV8fLymvBjUqVNHW16cP3/esn8S2ow24zgzH5yXtBnHmTnh3KTNOM7Mx+jRo2XSpEmaCVCuXDkZOXKkTJ8+XVP+O3fuLHfeeac89dRT6UoAPD09JTg4WIKCgvS5FTvLz7Ko38RAQCGEiwBtxnFmTjg3aS+OMfPBeUmbcZyZE87NnLFx40b56aefZPLkyTJ8+HC136BBg+S7776Tffv2ab37Bx98ILVr15YjR47I5s2b5cyZMzJ+/Hh9vVmzZvo+Li4uYiVGWzl4UtApCeTWsmHDBtsdd9xh27Nnj/3ajBkzbHXq1LErXxppL4cPH7Zt2rTJFh4ebhs9erStZcuWthMnTljuT0Kb0WYcZ+aD85I24zgzJ5ybtBnHmTlZvny5rWvXrqpqj84ARp/7Fi1a2BYsWKDPcR2lAPi5Bg0a2O6++25b586dbbt377ZZkQ0W95vYNaCQASVLKFhCCAQKl4hYIZKF6ODevXs1Coio1ZUrV+TFF1+U48ePS/ny5VUcA9GvChUqiNWgzWgzjjPzwXlJm3GcmRPOTdqM48ycQMzu4MGDuqfH/h/4+/vrSXZsbKw+d3d319fGjRsnp06d0teqV68uJUuWFCsSY3G/iYGAQgYXAdqM48yccG7SXhxj5oPzkjbjODMnnJs554477pDu3btr67tixYrZOwfAwYUSPjC+IqXdaI1nZeIsHjxhIKCQwUWANuM4Myecm7QXx5j54LykzTjOzAnn5o1ZvHixHD16VL9HX3vYDBoAcPwN4ODCcfXx8bFfmzBhgkRERMhrr72mgndW0wRw5A6LB08YCHByuAjQZhxn5oRzk/biGDMfnJe0GceZOeHczBmffPKJzJ07V5o3by7Hjh2TJUuWyPz581X4D6fXCADAgb148aIq3htO7tixY2XixImyYMECTYm3Ghxn6WEgwInhIkCbcZyZE85N2otjzHxwXtJmHGfmhHMzZxw4cEB+/vlnGTNmjLRs2VLT21euXCkfffSR9O3bV6ZOnSpFixbVn8Vpf0JCggYHcH3atGkaMKhbt65YDY6zDChotUKSO/bv368ql+vXr9fnsbGxtmXLltnatWtne/DBB22RkZH2n923b5+tdu3a+rNTpkyx1a9f37Zr1y7LmZ42o804zswH5yVtxnFmTjg3aTOOM3MC5frQ0FBbRESE/VpSUpLt77//tnXq1MnWp08f+3V0A0DXgPvvv98WEhJi27Fjh82KcD3LGAYCnBQuArQZx5k54dykvTjGzAfnJW3GcWZOODdzzpkzZ2wdOnSwzZkz57rXtmzZYmvbtq1t8ODB+jwlJUVbBDZu3NjeDs+KcJxlTJr6AXE60K4CaT4rVqywX4OqZcOGDeX999+X8PBweemll/Q6Wl+gNggtL2bPni0NGjQQK0Kb0WYcZ+aD85I24zgzJ5ybtBnHmXn47bff5KuvvpJJkybJkSNHpFatWrJmzRrZvXt3up/DHv/5559XJXy8htKAhx56SDUB4A9YFa5nGUONACdbBODgoxYoJCTEvgigzgdqodcuAlgwsAjgdSwCbdu2lUqVKomVoM1oM44z88F5SZtxnJkTzk3ajOPMfIwaNUp++OEH3fdjXw+HvmLFijpfZ8yYoXt+Q80eh4RhYWF6KIhgAPyD/v37ixXhenZjXJAWkI2fIyZeBFq3bp1uEQDnzp2Te+65R4YPH65tMawIbUabcZyZD85L2ozjzJxwbtJmHGfmY9myZTJ69Ght+Ye9P3rbDxgwQKpVq6b7/Oeee046deokjz32mB4SAogDPvLIIzJw4EDp2LEjysAt1yKQ61n2YEaAkywCP/30kyp9Oi4CmOjoF4pFAG1CHBeBwMBAqVGjhvj7++tzqy0CtBltxnFmPjgvaTOOM3PCuUmbcZyZE5QBYD+Pg0D0t/f19ZUnn3xSy3/feOMN7QQwdOhQiYyMlNDQUM0K/v3337Uc2CgFsNL+H3A9yz4MBDgBXARoM44zc8K5SXtxjJkPzkvajOPMnHBuZh/jAO/ChQty8eJF/d7Dw0NfCwgIkOTkZC0XbtWqlWYLzJkzR7799lv9GR8fHy0ZCA4OFivCcZZ9GAgwMVwEaDOOM3PCuUl7cYyZD85L2ozjzJxwbuYc4xQfaf/btm2TkydPquCdEQhwc3OTxMREtW39+vX1ER0dLSkpKfqakRFsJTjOcg4DASaGiwBtxnFmTjg3aS+OMfPBeUmbcZyZE87N3APhP5QGFC9e3H4tJibGfvJvAIFwCAVCHNyqcJzlHAYCnAAuArQZx5k54dykvTjGzAfnJW3GcWZOODdzR5kyZdI9hyA4SgNw6g/nd+zYsTJ58mRZtGjRLfk7OTscZ9nHNQc/Swp4ETBqgzJbBD788ENp2rQp/060GccZ56Zp4VpGm3GcmRPOTdqM48w5gGgg0v/9/PxUHwB6ANAIQOYASYPrWfZgRoCTwkWANuM4Myecm7QXx5j54LykzTjOzAnnZs5r4L28vKRo0aIybNgwWb58uXz//feqEUA4znIKAwFOBhcB2ozjzJxwbtJeHGPmg/OSNuM4Myecm7mvga9cubJ2E1i5cqXMnTtX6tSpc8v/PoUFjrOsYSDAyeAiQJtxnJkTzk3ai2PMfHBe0mYcZ+aEczP3VKlSRR5++GHp27evVKtW7Rb+VQofHGdZ42JDqIQ4HfHx8fLxxx9zEaDNOM5MBucm7cUxZj44L2kzjjNzwrmZ+5IKR+0wwnGWGxgIcGK4CNBmHGfmhHOT9uIYMx+cl7QZx5k54dwkHGcFAwMBhBBCCCGEEEKIhWD7QEIIIYQQQgghxEIwEEAIIYQQQgghhFgIBgIIIYQQQgghhBALwUAAIYQQQgghhBBiIRgIIIQQQgghhBBCLAQDAYQQQghxWmw2W0F/BEIIIcTpYCCAEEIIIU5HVFSUDBkyRLZs2WK/1r9/f30QQgghJGsYCCCEEEKI07F3715ZtGiRpKamFvRHIYQQQpwOBgIIIYQQQgghhBALwUAAIYQQQnLF7bffLuPHj5f33ntPWrRoIY0aNZKXX35Zrly5IlOmTJG2bdtKkyZN5Pnnn5dLly7p76SkpMjMmTOlW7duEhISIu3bt5dRo0ZJQkKC/X2HDh0qjz76qMyfP186d+4s9evXl/vuu0/WrFmjr2/cuFEGDBig3+OrYzkANAOmTp2q74v37927t+zYsYN/YUIIIcQBd8cnhBBCCCE5YcaMGRIaGiqffvqp7Nq1Sz755BPZvXu3lCpVSt555x05deqUjBw5UkqUKCEjRoyQN998U1P6Bw4cKE2bNpU9e/bIhAkTNNV/2rRp4uLiou+L9zp//ry88MIL4ufnJ2PHjtWAAoIB9erV0/d5++239SuCEAZ//fWXJCYmyvDhwyU5OVk++OADeeaZZ2T16tXi7s5tDyGEEMJAACGEEEJuCjjpCALAyW7durUsXLhQzp07J3PnzhV/f3/9mbVr18rWrVvl0KFDMm/ePM0aePLJJ/U1BBEQNIDwH5z8du3a6fXo6GhZsGCBVKxYUZ/7+vpKv379ZMOGDZolUL16db2Or8b3wNPTU7MRAgMD7aKCw4YN03+7du3a/GsTQgghLA0ghBBCyM2A9HvHk3ac/FepUsUeBABwyuHYb9q0SZ936dIl3XvguZubm6b8GxQrVsweBABlypTRr3FxcVl+HgQFjCAAKF++vH7Fv08IIYSQNKgRQAghhJCbygi4FpzeZ0RkZKR+LVmyZLrrCCQEBQWlc9Z9fHzS/YxRMnCjLgHX/tuurmlbHXYXIIQQQq7CQAAhhBBC8oWAgAD9euHChXTXk5KSVEwQwQBCCCGE5D0MBBBCCCEkX2jevLl+XbZsWbrreI5uAugwkF1QSkAIIYSQ3EH5XEIIIYTkC6jfv//+++Wzzz7TWv9mzZpptwC0IITyf1hYWLbfy9AgWLVqlWYaUAiQEEIIyT4MBBBCCCEk30ArwUqVKsn8+fNl6tSp2jFgwIAB8uyzz9rr+bNDjRo1pGvXrjJz5kztSrB06dI8/dyEEEJIYcLFZrPZCvpDEEIIIYQQQgghJH+gRgAhhBBCCCGEEGIhGAgghBBCCCGEEEIsBAMBhBBCCCGEEEKIhWAggBBCCCGEEEIIsRAMBBBCCCGEEEIIIRaCgQBCCCGEEEIIIcRCMBBACCGEEEIIIYRYCAYCCCGEEEIIIYQQC8FAACGEEEIIIYQQYiEYCCCEEEIIIYQQQiwEAwGEEEIIIYQQQohYh/8HaYHS0d04ICUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#ts = gdf.set_index('order_date').sort_index()\n", + "#ts = ts[ ts['region'].isin(['North','South'])]\n", + "fig = plt.figure(figsize=(12,4))\n", + "filt = gdf[ gdf['region'].isin(['North','South'])]\n", + "filt['month']= filt['order_date'].dt.to_period('M').astype(str)\n", + "piv = filt.pivot_table(index='region', columns='month', values='amount', aggfunc='sum')\n", + "piv.T\n", + "sns.lineplot(data=piv.T, legend=True, markers='o')\n", + "plt.xticks(rotation=45)\n", + "plt.ylabel('revenue')\n", + "plt.title('North vs. South')\n", + "\n", + "# sns.lineplot(data=filt,x='month',y='amount',hue='region')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "715ca3ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(7,4))\n", + "my_order = ['Platinum', 'Gold', 'Silver', 'Bronze']\n", + "sns.boxplot(data=gdf, x='vip_level',y='amount', order=my_order, flierprops={'marker': 'x', 'markeredgecolor': 'red'}) #\n", + "#.xticks(['Platinum', 'Gold', 'Silver', 'Bronze'])\n", + "#紅色是離群值\n", + "\n", + "fig = plt.figure(figsize=(7,4))\n", + "sns.scatterplot(data=gdf, x='unit_price', y='amount', hue='region')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "7eba3d9a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\f5486\\AppData\\Local\\Temp\\ipykernel_19080\\94827295.py:9: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + " axes[0,0].legend(\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Percent(%)')" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "category=\"Electronics\"\n", + "filt = gdf[ gdf['category'] == category]\n", + "filt['month'] = filt['order_date'].dt.to_period('M')\n", + "m_a = filt.groupby('month')['amount'].agg('sum').reset_index()\n", + "m_a['month'] = m_a['month'].astype(str)\n", + "sns.lineplot(data=m_a, x='month', y='amount',ax=axes[0,0])\n", + "axes[0,0].tick_params(axis='x', labelrotation=45)\n", + "axes[0,0].legend(\n", + " loc='upper right', # 放在右上角\n", + " fontsize='small'\n", + ")\n", + "\n", + "# gdf['month'] = gdf['order_date'].dt.to_period('M')\n", + "# c_m_r = gdf.groupby(['category','month'])['amount'].agg('sum').reset_index()\n", + "# c_m_r['month'] = c_m_r['month'].astype(str)\n", + "# sns.lineplot(data=c_m_r, x='month', y='amount', hue='category',ax=axes[0,0])\n", + "# axes[0,0].tick_params(axis='x', labelrotation=45)\n", + "# axes[0,0].legend(\n", + "# loc='upper right', # 放在右上角\n", + "# fontsize='small'\n", + "# )\n", + "# #plt.xticks(rotation=45,)\n", + "\n", + "\n", + "r_a = filt.groupby('region')['amount'].agg('sum').reset_index()\n", + "sns.barplot(data=r_a, x='region', y='amount', ax=axes[0,1])\n", + "\n", + "p_a = filt.groupby('product_name')['amount'].agg('sum').sort_values(ascending=False).head(5).reset_index()\n", + "# p_a['product_name'] = p_a['product_name'].replace(\"Data Science Handbook\", \"Data Science\\nHandbook\")\n", + "sns.barplot(data=p_a, x='amount', y='product_name', ax=axes[1,0],orient='h')\n", + "axes[1,0].tick_params(axis='x', labelrotation=45)\n", + "\n", + "filt.head(5)\n", + "sns.histplot(filt['amount'], bins=10, stat='percent',ax=axes[1,1])\n", + "axes[1,1].set_ylabel('Percent(%)')\n", + "\n", + "# axes[1,0].tick_params(axis='y', labelrotation=45)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "d193a4fb", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "from plotly.subplots import make_subplots\n", + "\n", + "pio.templates.default = 'plotly_white' # 乾淨白底" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "088a01e3", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + "Dumbbell 5kg" + ], + [ + "Water Bottle" + ], + [ + "Running Shoes" + ], + [ + "Jump Rope" + ], + [ + "Jump Rope" + ], + [ + "Water Bottle" + ], + [ + "Dumbbell 5kg" + ], + [ + "Running Shoes" + ], + [ + "Jump Rope" + ], + [ + "Dumbbell 5kg" + ], + [ + "Resistance Band" + ], + [ + "Water Bottle" + ], + [ + "Running Shoes" + ], + [ + "Running Shoes" + ], + [ + "Dumbbell 5kg" + ], + [ + "Water Bottle" + ], + [ + "Water Bottle" + ], + [ + "Water Bottle" + ], + [ + "Water Bottle" + ], + [ + "Jump Rope" + ], + [ + "Jump Rope" + ], + [ + "Water Bottle" + ], + [ + "Resistance Band" + ], + [ + "Jump Rope" + ], + [ + "Yoga Mat" + ], + [ + "Water Bottle" + ], + [ + "Yoga Mat" + ], + [ + "Water Bottle" + ], + [ + "Running Shoes" + ], + [ + "Dumbbell 5kg" + ], + [ + "Jump Rope" + ], + [ + "Running Shoes" + ], + [ + "Dumbbell 5kg" + ], + [ + "Yoga Mat" + ], + [ + "Yoga Mat" + ], + [ + "Dumbbell 5kg" + ], + [ + "Water Bottle" + ], + [ + "Resistance Band" + ], + [ + "Water Bottle" + ], + [ + "Yoga Mat" + ], + [ + "Resistance Band" + ], + [ + "Water Bottle" + ], + [ + "Dumbbell 5kg" + ] + ], + "hovertemplate": "category=Sports
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"automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "unit price vs. amount" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "unit_price" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "amount" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.read_csv(\"../datasets/ecommerce/orders_enriched.csv\",\n", + " parse_dates=[\"order_date\"])\n", + "# c_a = df.groupby('category', as_index=False)['amount'].sum()\n", + "# fig = px.bar(c_a, x='category',y='amount',text='amount', color='category', title='category vs. amount')\n", + "# fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')\n", + "# fig.update_layout(height=500, showlegend=False)\n", + "#fig.show()\n", + "\n", + "# ts = df.set_index('order_date').sort_index()\n", + "# m_a = ts['amount'].resample('ME').sum()\n", + "# m_a = m_a.reset_index()\n", + "# fig = px.line(m_a, 'order_date', 'amount', markers=True, title='month vs. amount')\n", + "# fig.show()\n", + "\n", + "\n", + "# fig = px.pie(df, 'vip_level')\n", + "fig = px.scatter(df, 'unit_price', 'amount', hover_data=['product_name'], color='category', title='unit price vs. amount')\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b273622e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " category amount\n", + "0 Books 182244.0\n", + "1 Clothing 133841.0\n", + "2 Electronics 100235.0\n", + "3 Home 93753.0\n", + "4 Sports 176315.0" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['month'] = df['order_date'].dt.to_period('M').astype(str)\n", + "m_a = df.groupby('month', as_index=False)['amount'].sum()\n", + "p_a = df.groupby('product_name', as_index=False)['amount'].sum().sort_values('amount',ascending=False).head(10)\n", + "r_a = df.groupby('region', as_index=False)['amount'].sum()\n", + "c_a = df.groupby('category', as_index=False)['amount'].sum()\n", + "\n", + "fig = make_subplots(2,2, subplot_titles= ('Monthly revenue trend', 'Top 10 Products', 'Revenue by region', 'Category share'), \n", + " specs=[ [{'type':'xy'}, {'type':'xy'}], [{'type':'xy'}, {'type':'domain'}] ])\n", + " \n", + "fig.add_trace(go.Scatter(x=m_a['month'], y=m_a['amount'],mode='line+marker', name='Monthly' ), row=1, col=1)\n", + "fig.add_trace(go.Bar(x=p_a['product_name'], y=p_a['amount']), row=1, col=2)\n", + "fig.add_trace(go.Bar(x=r_a['region'], y=r_a['amount']), row=2, col=1)\n", + "fig.add_trace(go.Pie(x=c_a['category'], y=c_a['amount']), row=2, col=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36ea48d2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41eb8823", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48c585ca", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1d13f2c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e826e934", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f573dc7856e7915694abf115beab67c710fc6a01 Mon Sep 17 00:00:00 2001 From: dcyang Date: Sun, 3 May 2026 22:52:10 +0800 Subject: [PATCH 2/6] fix hw m5 --- homework/m1_numpy.py | 5 ++++- homework/m5_visualization.py | 8 +++++--- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py index 656fa97..9ec5d96 100644 --- a/homework/m1_numpy.py +++ b/homework/m1_numpy.py @@ -92,4 +92,7 @@ def red_double11_prices(prices, stocks): if __name__ == '__main__': - pass \ No newline at end of file + print(green_mean()) + print(green_double()) + print(green_filter()) + print(red_double11_prices(200,100)) diff --git a/homework/m5_visualization.py b/homework/m5_visualization.py index 7ed8ae3..e28dff3 100644 --- a/homework/m5_visualization.py +++ b/homework/m5_visualization.py @@ -22,6 +22,7 @@ def _load_data(): parse_dates=["order_date"]) return gdf +_load_data() # ============================================================ # 🟢 送分題（每題 10 分，共 30 分） @@ -36,6 +37,7 @@ def green_bar_category(): # TODO: 你的程式碼 #pass global gdf + #gdf = _load_data() co = gdf['category'].value_counts() ax = co.plot(kind='bar',figsize=(10,4),title='Order count in Category',rot=45) return plt.gcf() @@ -51,7 +53,7 @@ def green_hist_amount(): #pass global gdf fig = plt.figure(figsize=(10,4)) - ax = sns.histplot(gdf['amount'], bins=20, stat='percent', kde=True, ylabel='%') + ax = sns.histplot(gdf['amount'], bins=20, stat='percent', kde=True) return fig @@ -69,8 +71,8 @@ def green_set_labels(): reg_order = gdf['region'].value_counts().reset_index() fig = plt.figure(figsize=(7,5)) sns.barplot(data=reg_order, x='region', y='count', palette='viridis', hue='region') - plt.xlabel = 'region' - plt.ylabel = 'order count' + plt.xlabel('region') + plt.ylabel('order count') plt.title('region vs. order count') for r,c in enumerate(reg_order['count']): plt.text(r,c, f'{c:,.0f}', ha='center',va='bottom',fontsize=10) From 199f33a089cd73d5dc3eaacab19cc5e021a9e5e2 Mon Sep 17 00:00:00 2001 From: dcyang Date: Sun, 3 May 2026 23:04:25 +0800 Subject: [PATCH 3/6] fix hw m4 --- homework/m4_timeseries.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py index 6a588df..9fda516 100644 --- a/homework/m4_timeseries.py +++ b/homework/m4_timeseries.py @@ -18,6 +18,7 @@ def _load_data(): gdf = df return df +_load_data() # ============================================================ # 🟢 送分題（每題 10 分，共 30 分） # ============================================================ @@ -49,7 +50,7 @@ def green_top3_dates(): date_ord = gdf['order_date'].value_counts() date_ord = date_ord.rename_axis('日期') date_ord.name='訂單數' - return date_ord + return date_ord.head(3) def green_date_range(): @@ -102,7 +103,7 @@ def yellow_category_median(df:pd.DataFrame): """ # TODO: 你的程式碼 #pass - cat_med = df.groupby('category')['amount'].median().sort_values + cat_med = df.groupby('category')['amount'].median().sort_values(ascending=False) cat_med.name = '中位數' return cat_med @@ -129,7 +130,7 @@ def red_monthly_report(): ts = gdf.set_index('order_date').sort_index() mon_report = ts.resample('ME').agg( order_count=('order_id','count'), - active_customer=('customer_id','nunique'), + active_customers=('customer_id','nunique'), revenue=('amount','sum'), avg_order_value=('amount','mean') ) From a24079c68157aedd83b63c6eb13936e3c6693a37 Mon Sep 17 00:00:00 2001 From: dcyang Date: Sun, 3 May 2026 23:09:25 +0800 Subject: [PATCH 4/6] fix hw m2 --- homework/m2_pandas_cleaning.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py index d50e82e..ddd77fe 100644 --- a/homework/m2_pandas_cleaning.py +++ b/homework/m2_pandas_cleaning.py @@ -21,7 +21,7 @@ def green_read_csv(): """ # TODO: 你的程式碼 #pass - df= pd.read_csv("../datasets/ecommerce/orders_raw.csv") + df= pd.read_csv("datasets/ecommerce/orders_raw.csv") return df @@ -106,10 +106,10 @@ def red_clean_orders(path): """ # TODO: 你的程式碼 #pass - df= pd.read_csv("../datasets/ecommerce/orders_raw.csv") + df= pd.read_csv("datasets/ecommerce/orders_raw.csv") df.columns = df.columns.str.strip().str.lower() df['amount'] = df['amount'].str.strip('$').str.replace(',','').astype(float) - pd.to_datetime(df['order_date'], errors='coerce') + df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce') df = df.dropna(subset=['order_date', 'amount']) #這個不確定, 先comment df['qty'] = df['qty'].fillna(df['qty'].median) From 3c9dbc5c2d63d48034b95af93ff949e9422a51c1 Mon Sep 17 00:00:00 2001 From: dcyang Date: Sun, 3 May 2026 23:18:59 +0800 Subject: [PATCH 5/6] fix hw m3 --- homework/m3_pandas_advanced.py | 8 ++++---- homework/m6_plotly_capstone.py | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py index 32052ff..89c12f3 100644 --- a/homework/m3_pandas_advanced.py +++ b/homework/m3_pandas_advanced.py @@ -25,9 +25,9 @@ def green_load_and_merge(): """ # TODO: 你的程式碼 #pass - order = pd.read_csv("../datasets/ecommerce/orders_clean.csv") - customer = pd.read_csv("../datasets/ecommerce/customers.csv") - prod = pd.read_csv("../datasets/ecommerce/products.csv") + order = pd.read_csv("datasets/ecommerce/orders_clean.csv") + customer = pd.read_csv("datasets/ecommerce/customers.csv") + prod = pd.read_csv("datasets/ecommerce/products.csv") df = ( order.merge(customer, how='left', on='customer_id') .merge(prod, how='left', on='product_id') @@ -74,7 +74,7 @@ def yellow_gold_vip_stats(df:pd.DataFrame): # TODO: 你的程式碼 #pass mask = (df['vip_level'] == 'Gold') - return (mask.count(), df[mask]['amount'].sum()) + return (mask.sum(), df[mask]['amount'].sum()) diff --git a/homework/m6_plotly_capstone.py b/homework/m6_plotly_capstone.py index c2ff0f0..a168f6b 100644 --- a/homework/m6_plotly_capstone.py +++ b/homework/m6_plotly_capstone.py @@ -29,7 +29,7 @@ def green_plotly_bar(): """ # TODO: 你的程式碼 #pass - df = pd.read_csv("../datasets/ecommerce/orders_enriched.csv", + df = pd.read_csv("datasets/ecommerce/orders_enriched.csv", parse_dates=["order_date"]) c_a = df.groupby('category', as_index=False)['amount'].sum() fig = px.bar(c_a, x='category',y='amount',text='amount', color='category', title='category vs. amount') @@ -172,9 +172,9 @@ def red_dashboard(): """ # TODO: 你的程式碼 #pass - df = yellow_clean_and_merge('../datasets/ecommerce/orders_raw.csv', - '../datasets/ecommerce/customers.csv', - '../datasets/ecommerce/products.csv') + df = yellow_clean_and_merge('datasets/ecommerce/orders_raw.csv', + 'datasets/ecommerce/customers.csv', + 'datasets/ecommerce/products.csv') df['month'] = df['order_date'].dt.to_period('M').astype(str) m_a = df.groupby('month', as_index=False)['amount'].sum() p_a = df.groupby('product_name', as_index=False)['amount'].sum().sort_values('amount',ascending=False).head(10) From 1a530f5b3824694acefb8d05cc8a330c07714c32 Mon Sep 17 00:00:00 2001 From: dcyang Date: Sun, 3 May 2026 23:33:49 +0800 Subject: [PATCH 6/6] fix hw m6 --- homework/m6_plotly_capstone.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/homework/m6_plotly_capstone.py b/homework/m6_plotly_capstone.py index a168f6b..d767ed7 100644 --- a/homework/m6_plotly_capstone.py +++ b/homework/m6_plotly_capstone.py @@ -94,7 +94,7 @@ def yellow_clean_and_merge(raw_path, customers_path, products_path): order['amount'] = order['amount'].str.replace('$','').str.replace(',','').astype(float) order['amount'].fillna(order['amount'].median()) - pd.to_datetime(order['order_date'], errors='coerce') + order['order_date'] = pd.to_datetime(order['order_date'], errors='coerce') order = order.dropna(subset=['order_date']) order.drop_duplicates() @@ -175,6 +175,8 @@ def red_dashboard(): df = yellow_clean_and_merge('datasets/ecommerce/orders_raw.csv', 'datasets/ecommerce/customers.csv', 'datasets/ecommerce/products.csv') + print(df['order_date']) + print("") df['month'] = df['order_date'].dt.to_period('M').astype(str) m_a = df.groupby('month', as_index=False)['amount'].sum() p_a = df.groupby('product_name', as_index=False)['amount'].sum().sort_values('amount',ascending=False).head(10) @@ -194,4 +196,4 @@ def red_dashboard(): return fig if __name__ == '__main__': - pass \ No newline at end of file + red_dashboard() \ No newline at end of file