From f14aa279a98aa2fb11fe1a5bb0a6aa72a8ca8168 Mon Sep 17 00:00:00 2001 From: kaeldai Date: Tue, 22 Apr 2025 12:34:42 -0700 Subject: [PATCH 01/19] initial checkin of ssn code --- examples/ssn_l23/build_network.orig.py | 121 ++++ examples/ssn_l23/build_network.py | 93 +++ examples/ssn_l23/config.simulation.json | 83 +++ examples/ssn_l23/demo_params.csv | 29 + examples/ssn_l23/input/bkg_rates.npy | Bin 0 -> 108128 bytes examples/ssn_l23/input/initial_state.csv | 5 + examples/ssn_l23/input/initial_state.npy | Bin 0 -> 160 bytes examples/ssn_l23/input/l4e_rates.npy | Bin 0 -> 108128 bytes examples/ssn_l23/model_data.py | 168 +++++ .../network.orig/bkg_l23_edge_types.csv | 5 + .../ssn_l23/network.orig/bkg_l23_edges.h5 | Bin 0 -> 6328 bytes .../ssn_l23/network.orig/bkg_node_types.csv | 2 + examples/ssn_l23/network.orig/bkg_nodes.h5 | Bin 0 -> 5784 bytes .../network.orig/l23_l23_edge_types.csv | 17 + .../ssn_l23/network.orig/l23_l23_edges.h5 | Bin 0 -> 6328 bytes .../ssn_l23/network.orig/l23_node_types.csv | 5 + examples/ssn_l23/network.orig/l23_nodes.h5 | Bin 0 -> 5784 bytes .../network.orig/l4e_l23_edge_types.csv | 5 + .../ssn_l23/network.orig/l4e_l23_edges.h5 | Bin 0 -> 6328 bytes .../ssn_l23/network.orig/l4e_node_types.csv | 2 + examples/ssn_l23/network.orig/l4e_nodes.h5 | Bin 0 -> 5784 bytes .../ssn_l23/network/bkg_l23_edge_types.csv | 5 + examples/ssn_l23/network/bkg_l23_edges.h5 | Bin 0 -> 43608 bytes examples/ssn_l23/network/bkg_node_types.csv | 2 + examples/ssn_l23/network/bkg_nodes.h5 | Bin 0 -> 15744 bytes .../ssn_l23/network/l23_l23_edge_types.csv | 17 + examples/ssn_l23/network/l23_l23_edges.h5 | Bin 0 -> 43608 bytes examples/ssn_l23/network/l23_node_types.csv | 5 + examples/ssn_l23/network/l23_nodes.h5 | Bin 0 -> 15744 bytes .../ssn_l23/network/l4e_l23_edge_types.csv | 5 + examples/ssn_l23/network/l4e_l23_edges.h5 | Bin 0 -> 43608 bytes examples/ssn_l23/network/l4e_node_types.csv | 2 + examples/ssn_l23/network/l4e_nodes.h5 | Bin 0 -> 15744 bytes examples/ssn_l23/run_ssn.orig.py | 179 +++++ examples/ssn_l23/run_ssn.py | 609 ++++++++++++++++++ 35 files changed, 1359 insertions(+) create mode 100644 examples/ssn_l23/build_network.orig.py create mode 100644 examples/ssn_l23/build_network.py create mode 100644 examples/ssn_l23/config.simulation.json create mode 100644 examples/ssn_l23/demo_params.csv create mode 100644 examples/ssn_l23/input/bkg_rates.npy create mode 100644 examples/ssn_l23/input/initial_state.csv create mode 100644 examples/ssn_l23/input/initial_state.npy create mode 100644 examples/ssn_l23/input/l4e_rates.npy create mode 100644 examples/ssn_l23/model_data.py create mode 100644 examples/ssn_l23/network.orig/bkg_l23_edge_types.csv create mode 100644 examples/ssn_l23/network.orig/bkg_l23_edges.h5 create mode 100644 examples/ssn_l23/network.orig/bkg_node_types.csv create mode 100644 examples/ssn_l23/network.orig/bkg_nodes.h5 create mode 100644 examples/ssn_l23/network.orig/l23_l23_edge_types.csv create mode 100644 examples/ssn_l23/network.orig/l23_l23_edges.h5 create mode 100644 examples/ssn_l23/network.orig/l23_node_types.csv create mode 100644 examples/ssn_l23/network.orig/l23_nodes.h5 create mode 100644 examples/ssn_l23/network.orig/l4e_l23_edge_types.csv create mode 100644 examples/ssn_l23/network.orig/l4e_l23_edges.h5 create mode 100644 examples/ssn_l23/network.orig/l4e_node_types.csv create mode 100644 examples/ssn_l23/network.orig/l4e_nodes.h5 create mode 100644 examples/ssn_l23/network/bkg_l23_edge_types.csv create mode 100644 examples/ssn_l23/network/bkg_l23_edges.h5 create mode 100644 examples/ssn_l23/network/bkg_node_types.csv create mode 100644 examples/ssn_l23/network/bkg_nodes.h5 create mode 100644 examples/ssn_l23/network/l23_l23_edge_types.csv create mode 100644 examples/ssn_l23/network/l23_l23_edges.h5 create mode 100644 examples/ssn_l23/network/l23_node_types.csv create mode 100644 examples/ssn_l23/network/l23_nodes.h5 create mode 100644 examples/ssn_l23/network/l4e_l23_edge_types.csv create mode 100644 examples/ssn_l23/network/l4e_l23_edges.h5 create mode 100644 examples/ssn_l23/network/l4e_node_types.csv create mode 100644 examples/ssn_l23/network/l4e_nodes.h5 create mode 100644 examples/ssn_l23/run_ssn.orig.py create mode 100644 examples/ssn_l23/run_ssn.py diff --git a/examples/ssn_l23/build_network.orig.py b/examples/ssn_l23/build_network.orig.py new file mode 100644 index 00000000..e3951078 --- /dev/null +++ b/examples/ssn_l23/build_network.orig.py @@ -0,0 +1,121 @@ +# %% quick network creation example +import pandas as pd +import h5py as h5 +import numpy as np +import model_data + +# make an example network for a l2/3 simulation. +# in this simulation, each population type only has one population. +# layer 2/3 (4 cell types) and layer 4 excitatory (1 cell type) +pops = ["l23", "l4e", "bkg"] + +# parameters are derived from the pre-run optimization. +# connections are multiplied by the expected influence matrix from the data. +p = pd.read_csv("demo_params.csv", index_col=0, header=None).squeeze("columns") +scale = p["conn_scale"] + +decays = p[["tau_e", "tau_p", "tau_s", "tau_v"]].values * 1000 # s to ms +l4e_to_l23 = ( + p[["stim_e", "stim_p", "stim_s", "stim_v"]].values + * model_data.e4_l23_infl_matrix_mean +) + +# divide by scale to be consistent with the new framework +bkg_to_l23 = p[["input_e", "input_p", "input_s", "input_v"]].values / scale +celltypes = ["e", "p", "s", "v"] +# get the relative value from the fit + + +l23_to_l23 = p[[f"{p1}_to_{p2}" for p1 in celltypes for p2 in celltypes]].values +# then, multiply by the expected influence matrix +print(l23_to_l23) + +l23_to_l23 = l23_to_l23 * np.array(model_data.l23_infl_matrix_mean).flatten() + + +dicts = { + "l23": { + "node_type_id": [0, 1, 2, 3], + "pop_name": ["L23"] * 4, + "cell_types": ["Exc", "PV", "SST", "VIP"], # optional + "model_type": ["rate_population"] * 4, + "model_template": [None] * 4, + "scaling_coef": [scale] * 4, + "input_offset": [0.0] * 4, + "exponent": [2.0] * 4, + "decay_const": decays, + }, + "l4e": { + "node_type_id": [0], + "pop_name": ["L4Exc"], + "model_type": ["virtual"], + "model_template": [None], + }, + "bkg": { + "node_type_id": [0], + "pop_name": ["BKG"], + "model_type": ["virtual"], + "model_template": [None], + }, +} + + +def create_node_h5(popname, node_type_ids): + # create an h5 file with one node per node_type_id + # each node has a unique node_id + f = h5.File(f"network/{popname}_nodes.h5", "w") + f.create_group(f"/nodes/{popname}") + f.create_dataset(f"/nodes/{popname}/node_type_id", data=node_type_ids) + f.create_dataset(f"/nodes/{popname}/node_id", data=node_type_ids) + f.close() + + +# generate and save them as csv files in the network directory +for pname in pops: + # create the node types csv + df = pd.DataFrame(dicts[pname]) + df.to_csv(f"network/{pname}_node_types.csv", index=False, sep=" ") + create_node_h5(pname, dicts[pname]["node_type_id"]) + + +def create_edge_h5(pop1, pop2, edge_type_ids): + # the edges are also defined only per type, so this will only contain trivial + # information. + f = h5.File(f"network/{pop1}_{pop2}_edges.h5", "w") + f.create_group(f"/edges/{pop1}_{pop2}") + edge_ids = np.array(range(len(edge_type_ids))) + source_node_id = np.array(edge_type_ids) // len(dicts[pop2]["node_type_id"]) + target_node_id = np.array(edge_type_ids) % len(dicts[pop2]["node_type_id"]) + f.create_dataset(f"/edges/{pop1}_{pop2}/edge_type_id", data=edge_type_ids) + f.create_dataset(f"/edges/{pop1}_{pop2}/edge_id", data=edge_ids) + f.create_dataset(f"/edges/{pop1}_{pop2}/source_node_id", data=source_node_id) + f.create_dataset(f"/edges/{pop1}_{pop2}/target_node_id", data=target_node_id) + f.close() + + +# target is always l23, so we can loop over the sources +weight_dicts = { + "l23": l23_to_l23, + "l4e": l4e_to_l23, + "bkg": bkg_to_l23, +} + +target_ids = dicts["l23"]["node_type_id"] # types id are the same as node ids +for pname in pops: + source_ids = dicts[pname]["node_type_id"] + n_edges = len(source_ids) * len(target_ids) + count = 0 + type_dict = { + "edge_type_id": [], + "edge_group_id": [0] * n_edges, + "edge_group_index": [0] * n_edges, + "syn_weight": [], + } + for i in source_ids: + for j in target_ids: + type_dict["edge_type_id"].append(count) + type_dict["syn_weight"].append(weight_dicts[pname][j + i * 4]) + count += 1 + df = pd.DataFrame(type_dict) + df.to_csv(f"network/{pname}_l23_edge_types.csv", index=False, sep=" ") + create_edge_h5(pname, "l23", type_dict["edge_type_id"]) diff --git a/examples/ssn_l23/build_network.py b/examples/ssn_l23/build_network.py new file mode 100644 index 00000000..6cf92b43 --- /dev/null +++ b/examples/ssn_l23/build_network.py @@ -0,0 +1,93 @@ +import itertools +import pandas as pd +import model_data + +from bmtk.builder import NetworkBuilder + + +params = pd.read_csv("demo_params.csv", index_col=0, header=None).squeeze("columns") + + +## Create L23 network with recurrent connections +l23_net = NetworkBuilder('l23') +l23_net.add_nodes( + pop_name='Exc', + model_template='ssn:Recurrent', + model_type='rate_population', + scaling_coef=params['conn_scale'], + input_offset=0.0, + exponent=2.0, + decay_const=params['tau_e']*1000.0 +) +l23_net.add_nodes( + pop_name='PV', + model_template='ssn:Recurrent', + model_type='rate_population', + scaling_coef=params['conn_scale'], + input_offset=0.0, + exponent=2.0, + decay_const=params['tau_p']*1000.0 +) + +l23_net.add_nodes( + pop_name='SST', + model_template='ssn:Recurrent', + model_type='rate_population', + scaling_coef=params['conn_scale'], + input_offset=0.0, + exponent=2.0, + decay_const=params['tau_s']*1000.0 +) + +l23_net.add_nodes( + pop_name='VIP', + model_template='ssn:Recurrent', + model_type='rate_population', + scaling_coef=params['conn_scale'], + input_offset=0.0, + exponent=2.0, + decay_const=params['tau_v']*1000.0 +) + +for src, trg in itertools.product(['Exc', 'PV', 'SST', 'VIP'], repeat=2): + lu_key = f'{src[0]}_to_{trg[0]}'.lower() + if lu_key in params: + l23_net.add_edges( + source={'pop_name': src}, target={'pop_name': trg}, + syn_weight=params[lu_key]*model_data.l23_infl_df.loc[lu_key]['mean'] + ) + +l23_net.build() +l23_net.save(output_dir='network') + + +## Create L4 -> L23 Connections +l4e_net = NetworkBuilder('l4e') +l4e_net.add_nodes( + model_template='ssn:External', + model_type='virtual' +) + +for trg, syn_weight in zip(['Exc', 'PV', 'SST', 'VIP'], params[['stim_e', 'stim_p', 'stim_s', 'stim_v']].values*model_data.e4_l23_infl_matrix_mean): + l4e_net.add_edges( + target=l23_net.nodes(pop_name=trg), + syn_weight=syn_weight + ) + +l4e_net.build() +l4e_net.save(output_dir='network') + + +## Create BKG -> L23 Connections +bkg_net = NetworkBuilder('bkg') +bkg_net.add_nodes( + model_template='ssn:External', + model_type='virtual' +) +for trg, syn_weight in zip(['Exc', 'PV', 'SST', 'VIP'], params[['input_e', 'input_p', 'input_s', 'input_v']].values / params["conn_scale"]): + bkg_net.add_edges( + target=l23_net.nodes(pop_name=trg), + syn_weight=syn_weight + ) +bkg_net.build() +bkg_net.save(output_dir='network') diff --git a/examples/ssn_l23/config.simulation.json b/examples/ssn_l23/config.simulation.json new file mode 100644 index 00000000..e14ae2f2 --- /dev/null +++ b/examples/ssn_l23/config.simulation.json @@ -0,0 +1,83 @@ +{ + "manifest": { + "$BASE_DIR": ".", + "$OUTPUT_DIR": "$BASE_DIR/output", + "$INPUT_DIR": "$BASE_DIR/inputs", + "$NETWORK_DIR": "$BASE_DIR/network" + }, + + "target_simulator":"SSN", + + "run": { + "dt": 1.0 + }, + + "inputs": { + "init_states": { + "input_type": "init_states", + "module": "csv", + "file": "$BASE_DIR/input/initial_state.csv", + "strict_mapping": true, + "node_set": { + "population": "l23" + } + }, + "l4_inputs": { + "input_type": "external_rates", + "module": "npy", + "file": "$BASE_DIR/input/l4e_rates.npy", + "node_set": { + "population": "l4e" + } + }, + "bkg_inputs": { + "input_type": "external_rates", + "module": "npy", + "file": "$BASE_DIR/input/bkg_rates.npy", + "node_set": { + "population": "bkg" + } + } + }, + + "output": { + "output_dir": "$OUTPUT_DIR", + "rates_file_csv": "l23_rates.csv", + "rates_file_h5": "l23_rates.h5", + "log_file": "log.txt", + "overwrite_output_dir": true + }, + + "networks": { + "nodes": [ + { + "nodes_file": "$NETWORK_DIR/l23_nodes.h5", + "node_types_file": "$NETWORK_DIR/l23_node_types.csv" + }, + { + "nodes_file": "$NETWORK_DIR/l4e_nodes.h5", + "node_types_file": "$NETWORK_DIR/l4e_node_types.csv" + }, + { + "nodes_file": "$NETWORK_DIR/bkg_nodes.h5", + "node_types_file": "$NETWORK_DIR/bkg_node_types.csv" + } + + ], + + "edges": [ + { + "edges_file": "$NETWORK_DIR/l23_l23_edges.h5", + "edge_types_file": "$NETWORK_DIR/l23_l23_edge_types.csv" + }, + { + "edges_file": "$NETWORK_DIR/l4e_l23_edges.h5", + "edge_types_file": "$NETWORK_DIR/l4e_l23_edge_types.csv" + }, + { + "edges_file": "$NETWORK_DIR/bkg_l23_edges.h5", + "edge_types_file": "$NETWORK_DIR/bkg_l23_edge_types.csv" + } + ] + } + } \ No newline at end of file diff --git a/examples/ssn_l23/demo_params.csv b/examples/ssn_l23/demo_params.csv new file mode 100644 index 00000000..ce561bb7 --- /dev/null +++ b/examples/ssn_l23/demo_params.csv @@ -0,0 +1,29 @@ +conn_scale,22.392148198236782 +tau_e,0.010050042910677964 +tau_p,0.011825739886481192 +tau_s,0.010015948224903206 +tau_v,0.04931170456054022 +input_e,0.19465505917300674 +input_p,0.0029966508877549147 +input_s,0.039722318358674236 +input_v,0.39979221257359865 +stim_e,0.5333175435065735 +stim_p,0.10026347130019181 +stim_s,1.0133911731127814 +stim_v,0.39228751531098527 +e_to_e,1.1282945393356596 +e_to_p,0.10047058990909456 +e_to_s,0.3810285897907353 +e_to_v,0.19299616032022132 +p_to_e,0.9352584843159919 +p_to_p,0.7941286335405499 +p_to_s,1.3762135389793335 +p_to_v,1.0 +s_to_e,0.506969776983961 +s_to_p,1.3573803090100334 +s_to_s,1.0054684169938746 +s_to_v,1.961156370360251 +v_to_e,0.6631046229768919 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diff --git a/examples/ssn_l23/run_ssn.orig.py b/examples/ssn_l23/run_ssn.orig.py new file mode 100644 index 00000000..b4d26228 --- /dev/null +++ b/examples/ssn_l23/run_ssn.orig.py @@ -0,0 +1,179 @@ +# %% test run of the SONATA version of the network + +# This is merely trying to check the validity of the files, and not specifying how +# the files should be actually read. +# In particular, in this example, I'm assuming one node per population, which is not +# generally the case. + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from numba import njit + +# load the input files +l4e_input = np.load("input/l4e_rates.npy") +bkg_input = np.load("input/bkg_rates.npy") +init_state = np.load("input/initial_state.npy") +ext_inputs = np.concatenate([l4e_input, bkg_input], axis=1) + + +# load the nodes +network_dir = "network.orig" +pops = ["l23", "l4e", "bkg"] + +nodes = [] +for pop in pops: + filename = f"{network_dir}/{pop}_node_types.csv" + df = pd.read_csv(filename, sep=" ") + nodes.append(df) + +# concatenate them + +nodes_recurrent = nodes[0] +n_neu_recurrent = len(nodes_recurrent) # number of recurrent neurons + +nodes_all = pd.concat(nodes, ignore_index=True) +n_neu_total = len(nodes_all) # total number of neurons + +# extract necessary elements for simulation +scales = nodes_recurrent["scaling_coef"].values +input_offset = nodes_recurrent["input_offset"].values +exponents = nodes_recurrent["exponent"].values +decay_constants = nodes_recurrent["decay_const"].values +dt = 1.0 # ms this should come from the config file + + +# %% load the edges +edges = [] +for pop in pops: + filename = f"{network_dir}/{pop}_l23_edge_types.csv" + df = pd.read_csv(filename, sep=" ") + edges.append(df) + +# construct the connectivity matrix +edges_all = pd.concat(edges, ignore_index=True) +# transpose to multiply to state vector +mat = edges_all["syn_weight"].values.reshape([6, 4]).T + + +# %% define the simulation +# from numba import njit + + +@njit +def relu(array): # non destructive; a litte slower, but safer + return array * (array > 0) # still faster than np.maximum() + + +@njit +def relu2(array): + # if the element is negative, set it to zero using loop + # destructive method (alters the original array), but faster than the above one. + for i in range(len(array)): + if array[i] < 0: + array[i] = 0 + return array + + +@njit +def step(state, mat, scales, exponents, decay_constants, dt): + """Execute One step of the SSN model + Arguments: + state {np.ndarray} -- current state (firing rates) of the network including the + external inputs + mat {np.ndarray} -- connectivity matrix (N, N + M) + scales {np.ndarray} -- input scaling coefficients + exponents {np.ndarray} -- exponents (alpha) + decay_constants {np.ndarray} -- decay constants (tau) + dt {float} -- time step size + + N and M are the number of recurrent and external neurons, respectively + Returns: + np.ndarray -- updated state of the network (only recurrent neurons) + """ + input = relu2(np.dot(mat, state) * scales) ** exponents + n_neu_recurrent = mat.shape[0] + recurrent_state = state[:n_neu_recurrent] + dr = (-recurrent_state + input) / decay_constants * dt + return relu2(recurrent_state + dr) + + +@njit +def simulate(init_state, ext_inputs, mat, scales, exponents, decay_constants, dt): + """Simulate the network + Arguments: + init_state {np.ndarray} -- initial state of the network (only recurrent neurons) + ext_inputs {np.ndarray} -- external inputs (n_step, n_pops) matrix + mat {np.ndarray} -- connectivity matrix (N, N + M) + scales {np.ndarray} -- input scaling coefficients + exponents {np.ndarray} -- exponents (alpha) + decay_constants {np.ndarray} -- decay constants (tau) + dt {float} -- time step size + Returns: + np.ndarray -- simulation results (n_step, n_neu_recurrent) + """ + # state is initial state + # ext_inputs is profile of external inputs. (n_step, n_pops) matrix + n_steps = ext_inputs.shape[0] + n_neu_recurrent = len(init_state) + n_neu_total = n_neu_recurrent + ext_inputs.shape[1] + + # define the results including the external inputs. + results = np.zeros((n_steps, n_neu_total)) + results[:, n_neu_recurrent:] = ext_inputs + # fill in the first time step with the initial state + results[0, :n_neu_recurrent] = init_state + + + for t in range(n_steps - 1): + results[t + 1, :n_neu_recurrent] = step( + results[t, :], + mat, + scales, + exponents, + decay_constants, + dt, + ) + # state = step(state, mat, scales, exponents, decay_constants, dt) + # results[t + 1, :n_neu_recurrent] = state + + return results[:, :n_neu_recurrent] # return only the recurrent neurons + + +print(f'init_state: {init_state}') +print(f'ext_inputs: {ext_inputs}') +print(f'mat: {mat}') +print(f'scales: {scales}') +print(f'exponents: {exponents}') +print(f'decay_constants: {decay_constants}') +print(f'dt: {dt}') +# , scales, exponents, decay_constants, dt + +# print(scales) +# print(init_state) +# exit() + +# run the simulation +result = simulate(init_state, ext_inputs, mat, scales, exponents, decay_constants, dt) +# %timeit simulate(init_state, ext_inputs, mat, scales, exponents, decay_constants, dt) +# 5.87 ms ± 10.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) + +print(result) + +# %% +fig, ax = plt.subplots(2, 1) +ax[0].plot(result[:, :]) +ax[0].legend(nodes_recurrent["cell_types"].values) +ax[0].title.set_text("Entire simulation") + + +# usually, I only use the latter half of the simulation because the first half is +# a settling period, but showing the whole simulation here for demonstration. + +ax[1].plot(result[6750:, :]) # if you only need the latter half +ax[1].title.set_text("After settling") + + +# save the figure +fig.tight_layout() +fig.savefig("test_run.png", dpi=100) diff --git a/examples/ssn_l23/run_ssn.py b/examples/ssn_l23/run_ssn.py new file mode 100644 index 00000000..b76e6ad9 --- /dev/null +++ b/examples/ssn_l23/run_ssn.py @@ -0,0 +1,609 @@ +from bmtk.simulator import ssn +import numpy as np +from numba import njit +from six import string_types +from pprint import pprint +import pandas as pd +import matplotlib.pyplot as plt +from pathlib import Path +import h5py + +from bmtk.simulator.core.simulation_config import SimulationConfig +from bmtk.simulator.core.simulator_network import SimNetwork +from bmtk.simulator.core import sonata_reader +import bmtk.simulator.utils.simulation_inputs as inputs + + +class RatesRecorderMod: + def __init__(self, name, output_type, module, file_name, **kwargs): + self._name = name + self._output_type = output_type + self._module = module + self._params = kwargs + self.file_name = file_name + self._output_dir = kwargs.get('output_dir', '.') + self._node_set = kwargs.get('cells', None) + + self.file_path = Path(self.file_name) + if not self.file_path.is_absolute(): + self.file_path = (Path(self._output_dir) / self.file_path).absolute() + + if self._output_type not in ['csv', 'h5']: + raise ValueError(f'{self.__name__}: Invalid output_type "{self._output_type}". [Valid options: csv, h5]') + + def initialize(self, sim): + pass + + def finalize(self, sim): + if self._output_type == 'csv': + self._to_csv(sim) + elif self._output_type == 'h5': + self._to_hdf5(sim) + + def _get_nodes(self, sim): + if self._node_set is not None: + node_set = sim.network.get_node_set(self._node_set) + return [sim.network.get_node(n.population_name, n.node_id) for n in node_set.fetch_nodes()] + else: + return list(sim.network._ssn_recurrent_nodes) + + def _get_timestamps(self, sim): + return np.linspace(0.0, sim.tstop, num=sim.nsteps, endpoint=False) + + def _to_csv(self, sim): + output_df = None + times = self._get_timestamps(sim) + ssn_nodes = self._get_nodes(sim) + for node in ssn_nodes: + tmp_df = pd.DataFrame({ + 'population': node.population, + 'node_id': node.node_id, + 'timestamps': times, + 'firing_rates': sim.results[:, node.gid] + }) + + output_df = tmp_df if output_df is None else pd.concat([output_df, tmp_df], ignore_index=True) + # print(node.population, node.node_id, node.gid) + + # exit() + if output_df is not None: + output_df.to_csv(self.file_path, sep=' ', index=False) + + + def _to_hdf5(self, sim): + mode = self._params.get('modd', 'a') + times = self._get_timestamps(sim) + nsteps = len(times) + ssn_nodes = self._get_nodes(sim) + mappings = {} + for n in ssn_nodes: + subpop = mappings.get(n.population, {'node_id': [], 'gid': []}) + subpop['node_id'].append(n.node_id) + subpop['gid'].append(n.gid) + mappings[n.population] = subpop + + with h5py.File(self.file_path, mode) as h5: + ratesgrp = h5['rates'] if 'rates' in h5 else h5.create_group('rates') + for pop_name, pop_data in mappings.items(): + subgrp = ratesgrp.create_group(pop_name) + subgrp.create_dataset('mapping/time', data=times) + subgrp.create_dataset('mapping/node_ids', data=pop_data['node_id']) + + data = subgrp.create_dataset('data', shape=(nsteps, len(pop_data['gid'])), dtype=float) + for col, gid in enumerate(pop_data['gid']): + data[:, col] = sim.results[:, gid] + + + + + + + +class ExternalRatesMod: + def __init__(self, name, input_type, module, **kwargs): + self._name = name + self._input_type = input_type + self._module = module + self._params = kwargs + + def initialize(self, sim): + if self._module == 'npy': + npy_path = self._params['file'] + inputs_arr = np.load(npy_path) + + + if sim.tstop is None: + # WARNING THAT TSTOP IS BEING SET BY INPUT + + sim.tstop = len(inputs_arr)*sim.dt + + if sim.nsteps < len(inputs_arr): + # WARN THAT input is being cut + inputs_arr = inputs_arr[:sim.nsteps] + + elif sim.nsteps > len(inputs_arr): + # GIVE WARNING + inputs_arr = np.append(inputs_arr, np.zeros(sim.nsteps - len(inputs_arr))) + + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + ssn_node.external_inputs = inputs_arr.flatten() + + def finalize(self, sim): + pass + + +class InitStatesMod: + def __init__(self, name, input_type, module, **kwargs): + self._name = name + self._input_type = input_type + self._module = module + self._params = kwargs + + def initialize(self, sim): + # node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + + if self._module == 'csv': + self.from_csv(sim) + elif self._module == 'constant': + self.from_pregenerated(sim, itype='const') + elif self._module == 'random': + self.from_pregenerated(sim, itype='random') + elif self._module == 'list': + self.from_pregenerated(sim, itype='list') + elif self._module == 'function': + raise NotImplementedError() + else: + raise ValueError(f'{self.__name__}: Error in {self._name} input module, no valid module [options: csv, constant, random, function]') + + + def from_csv(self, sim): + csv_path = self._params['file'] + sep = self._params.get('sep', ' ') + index_col = self._params.get('index_col', 'node_id') + value_col = self._params.get('value_col', 'initial_state') + strict_mapping = self._params.get('strict_mapping', False) + + init_df = pd.read_csv(csv_path, sep=sep).set_index(index_col) + # init_df = init_df.set_index(init_df.columns['node_id']) + + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + if ssn_node.node_id not in init_df.index: + if strict_mapping: + raise Exception('COULD NOT FIND APPROPIATE ID IN CSV') + else: + # TODO: warning message + pass + else: + ssn_node.initial_value = init_df.loc[ssn_node.node_id][value_col] + + def from_pregenerated(self, sim, itype): + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + nsize = len(node_set) + + if itype == 'const': + init_states = [self._params['initial_state']]*nsize + + if itype == 'list': + init_states = self._params['initial_states'] + if len(init_states) != nsize: + raise Exception('SIZE OF LIST DOES NOT MATCH NUMBER OF NODES') + + if self._params.get('shuffle', False): + np.random.shuffle(init_states) + + elif itype == 'random': + dist = self._params['distribution'] + if dist == 'uniform': + init_states = np.random.normal( + low=self._params.get('low', 0.0), + high=self._params.get('high', 1.0), + size=nsize + ) + elif dist == 'normal': + init_states = np.random.normal( + loc=self._params.get('mean', 0.0), + scale=self._params.get('std', 1.0), + size=nsize + ) + elif dist == 'poisson': + init_states = np.random.poisson( + lam=self._params.get('lambda', 1.0), + size=nsize + ) + elif dist == 'lognormal': + init_states = np.random.lognormal( + mean=self._params.get('mean', 0.0), + sigma=self._params.get('sigma', 1.0), + size=nsize + ) + else: + raise Exception("AAAA") + + for idx, node in enumerate(node_set.fetch_nodes()): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + ssn_node.initial_value = init_states[idx] + + def finalize(self, sim): + pass + + + +@njit +def relu2(array): + # if the element is negative, set it to zero using loop + # destructive method (alters the original array), but faster than the above one. + for i in range(len(array)): + if array[i] < 0: + array[i] = 0 + return array + + +class Config(SimulationConfig): + pass + + +class SSNNode: + def __init__(self, population, node_id, gid): + self.population = population + self.node_id = node_id + self.gid = gid + + self.type = None + self.input_offset = [] + self.scaling_coef = [] + self.exponent = [] + self.decay_const = [] + self.init_value = 0.0 + self.external_inputs = None + + + def __repr__(self) -> str: + return f'{self.gid} > ({self.population}.{self.node_id})' + + +class SSNNetwork(SimNetwork): + def __init__(self, grouping_key='node_id', **opts): + super(SSNNetwork, self).__init__() + # self.n_neu_total = 6 + self.grouping_key = grouping_key + + # self._pop_index = {} + + self._node_id_map = {} + + self._nnodes_recurrent = 0 + self._nnodes_external = 0 + + self._connectivity_mat = None + self._scales = None + self._exponents = None + self._decay_constants = None + self._initial_states = None + + self._nodeid2grp = {} + + self._recurrent_nodes = {} + self._external_nodes = {} + + + self._nodes_idx = {} + self._ssn_recurrent_nodes = set() + self._ssn_external_nodes = set() + self.gids = 0 + + self._conn_mat = [] + # sefl._connectivity + + + + @property + def n_neu_recurrent(self): + return self._nnodes_recurrent + + @property + def n_neu_total(self): + return self._nnodes_recurrent + self._nnodes_external + + @property + def connectivity_mat(self): + if self._connectivity_mat is None: + self._connectivity_mat = np.zeros((self._nnodes_recurrent, self.n_neu_total), dtype=float) + for r, c, syn_w in self._conn_mat: + # print(r, c, syn_w) + self._connectivity_mat[r, c] = syn_w + + # print(self._connectivity_mat) + + return self._connectivity_mat + + + @property + def scales(self): + if self._scales is None: + self._scales = np.zeros(self._nnodes_recurrent) + for n in self._ssn_recurrent_nodes: + self._scales[n.gid] = np.mean(n.scaling_coef) + + return self._scales + + @property + def initial_states(self): + if self._initial_states is None: + self._initial_states = np.zeros(self._nnodes_recurrent) + for n in self._ssn_recurrent_nodes: + self._initial_states[n.gid] = np.mean(n.initial_value) + + return self._initial_states + + @property + def exponents(self): + if self._exponents is None: + self._exponents = np.zeros(self._nnodes_recurrent) + for n in self._ssn_recurrent_nodes: + self._exponents[n.gid] = np.mean(n.exponent) + + return self._exponents + + @property + def decay_constants(self): + if self._decay_constants is None: + self._decay_constants = np.zeros(self._nnodes_recurrent) + for n in self._ssn_recurrent_nodes: + self._decay_constants[n.gid] = np.mean(n.decay_const) + + # print(self._decay_constants) + + return self._decay_constants + + def build_nodes(self): + for node_pop in self.node_populations: + for node in node_pop.get_nodes(): + model_type = node['model_type'].lower() + + if model_type in ['population', 'rate_population', 'recurrent']: + self.add_recurrent_node( + population_id=node_pop.name, + node_id=node['node_id'], + input_offset=node['input_offset'], + scaling_coef=node['scaling_coef'], + exponent=node['exponent'], + decay_const=node['decay_const'], + initial_value=node.get['initial_value'] if 'initial_value' in node else 0.0 + ) + + elif model_type in ['external', 'virtual']: + self.add_external_node( + population_id=node_pop.name, + node_id=node[self.grouping_key] + ) + + # pprint(self._node_id_map) + # exit() + + # print(node.population_name) + # print(node['model_type']) + + # exit() + + def build_edges(self): + for edge_pop in self._edge_populations: + for edge in edge_pop.get_edges(): + # print(edge.source_population, edge.source_node_id, edge.target_population, edge.target_node_id, edge['syn_weight']) + # print(edge.source_population in self._node_id_map) + # print(list(self._node_id_map[edge.source_population].keys())) + # print(edge.source_node_id in self._node_id_map[edge.source_population]) + # exit() + # print(self.get_ssn_node(edge.source_population, edge.source_node_id).gid) + src_node = self._node_id_map[edge.source_population][int(edge.source_node_id)] + trg_node = self._node_id_map[edge.target_population][int(edge.target_node_id)] + + self._conn_mat.append([trg_node.gid, src_node.gid, edge['syn_weight']]) + # print(src_node, trg_node) + + # exit() + + def get_node(self, population_id, node_id): + return self._node_id_map[population_id][node_id] + + + def get_ssn_node(self, population_id, node_id): + if population_id not in self._node_id_map: + # print('new population') + ssn_node = SSNNode(population_id, node_id, gid=self.gids) + self._node_id_map[population_id] = {int(node_id): ssn_node} + self.gids += 1 + + elif int(node_id) not in self._node_id_map: + # print(f'new node {population_id}, {node_id}') + ssn_node = SSNNode(population_id, node_id, gid=self.gids) + self._node_id_map[population_id][int(node_id)] = ssn_node + self.gids += 1 + + else: + # print('found') + ssn_node = self._node_id_map[population_id][node_id] + + return ssn_node + + + def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, exponent, decay_const, initial_value=0.0): + self._nnodes_recurrent += 1 + + + ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id) + ssn_obj.type='internal' + ssn_obj.input_offset.append(input_offset) + ssn_obj.scaling_coef.append(scaling_coef) + ssn_obj.exponent.append(exponent) + ssn_obj.decay_const.append(decay_const) + self._ssn_recurrent_nodes.add(ssn_obj) + + + def add_external_node(self, population_id, node_id): + self._nnodes_external += 1 + ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id) + ssn_obj.type = 'external' + self._ssn_external_nodes.add(ssn_obj) + + +class SSNSimulator: + def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): + self.dt = dt + self.tstart = tstart + self.tstop = tstop + self.network = network + + self._fr_results = None + + self._mods = [] + + + @property + def nsteps(self): + return int((self.tstop - self.tstart)/self.dt) + + @property + def results(self): + if self._fr_results is None: + self._fr_results = np.zeros((self.nsteps, self.network.n_neu_total), dtype=float) + # print(self.network.initial_states) + self._fr_results[0, :self.network.n_neu_recurrent] = self.network.initial_states + + for ext_node in self.network._ssn_external_nodes: + if ext_node.external_inputs is not None: + self._fr_results[:, ext_node.gid] = ext_node.external_inputs + # print(ext_node.gid, ext_node.external_inputs) + # exit() + + # exit() + + return self._fr_results + + + def step(self, state, mat, scales, exponents, decay_constants, dt): + input = relu2(np.dot(mat, state) * scales) ** exponents + n_neu_recurrent = mat.shape[0] + recurrent_state = state[:n_neu_recurrent] + dr = (-recurrent_state + input) / decay_constants * dt + return relu2(recurrent_state + dr) + + def add_mod(self, mod): + mod.initialize(self) + self._mods.append(mod) + + def run(self): + # print(self.network.connectivity_mat) + # print(self.results) + # exit() + for t in range(self.nsteps-1): + self.results[t+1, :self.network.n_neu_recurrent] = self.step( + self.results[t, :], + self.network.connectivity_mat, + self.network.scales, + self.network.exponents, + self.network.decay_constants, + self.dt, + ) + + for mod in self._mods: + mod.finalize(self) + + + # print(self.results) + # print(self.results.shape) + + # fig, ax = plt.subplots(2, 1) + # ax[0].plot(self.results[:, :]) + # # ax[0].legend(nodes_recurrent["cell_types"].values) + # ax[0].title.set_text("Entire simulation") + # plt.show() + + @classmethod + def from_config(cls, configure, network, **opts): + # load the json file or object + if isinstance(configure, string_types): + config = Config.from_json(configure, validate=True) + elif isinstance(configure, dict): + config = configure + else: + raise Exception('Could not convert {} (type "{}") to json.'.format(configure, type(configure))) + + sim = cls(network, dt=config.dt, tstart=config.tstart, tstop=config.tstop, **opts) + + # if 'output_dir' in config['output']: + # network.output_dir = config['output']['output_dir'] + + network.io.log_info('Building nodes.') + network.build_nodes() + + network.io.log_info('Building recurrent connections') + network.build_edges() + + for sim_input in inputs.from_config(config): + if sim_input.input_type == 'init_states': + mod = InitStatesMod( + name=sim_input.name, + input_type=sim_input.input_type, + module=sim_input.module, + **sim_input.params + ) + sim.add_mod(mod) + + if sim_input.input_type == 'external_rates': + mod = ExternalRatesMod( + name=sim_input.name, + input_type=sim_input.input_type, + module=sim_input.module, + **sim_input.params + ) + sim.add_mod(mod) + + + if 'rates_file' in config.output: + mod = RatesRecorderMod( + name='RecordRatesH5', + module='rates', + output_type='csv', + file_name=config.output['rates_file'], + **config.output + ) + sim.add_mod(mod) + + if 'rates_file_csv' in config.output: + mod = RatesRecorderMod( + name='RecordRatesH5', + module='rates', + output_type='csv', + file_name=config.output['rates_file_csv'], + **config.output + ) + sim.add_mod(mod) + + if 'rates_file_h5' in config.output: + mod = RatesRecorderMod( + name='RecordRatesH5', + module='rates', + output_type='h5', + file_name=config.output['rates_file_h5'], + **config.output + ) + sim.add_mod(mod) + + return sim + + +configure = Config.from_json('config.simulation.json') +configure.build_env() + +# print(configure) + +network = SSNNetwork.from_config(configure) + +sim = SSNSimulator.from_config(configure, network) +sim.run() From 27377aa5dc53af9099a09dba836d8a510c18b801 Mon Sep 17 00:00:00 2001 From: kaeldai Date: Tue, 22 Apr 2025 16:23:07 -0700 Subject: [PATCH 02/19] updating some missing functionality --- examples/ssn_l23/config.simulation.json | 10 +- examples/ssn_l23/input/bkg_rates.csv | 13501 ++++++++++++++++++++++ examples/ssn_l23/input/bkg_rates.h5 | Bin 0 -> 221520 bytes examples/ssn_l23/input/l4e_rates.csv | 13501 ++++++++++++++++++++++ examples/ssn_l23/input/l4e_rates.h5 | Bin 0 -> 221520 bytes examples/ssn_l23/run_ssn.py | 310 +- 6 files changed, 27273 insertions(+), 49 deletions(-) create mode 100644 examples/ssn_l23/input/bkg_rates.csv create mode 100644 examples/ssn_l23/input/bkg_rates.h5 create mode 100644 examples/ssn_l23/input/l4e_rates.csv create mode 100644 examples/ssn_l23/input/l4e_rates.h5 diff --git a/examples/ssn_l23/config.simulation.json b/examples/ssn_l23/config.simulation.json index e14ae2f2..05c8f5a3 100644 --- a/examples/ssn_l23/config.simulation.json +++ b/examples/ssn_l23/config.simulation.json @@ -15,17 +15,16 @@ "inputs": { "init_states": { "input_type": "init_states", - "module": "csv", - "file": "$BASE_DIR/input/initial_state.csv", - "strict_mapping": true, + "module": "function", + "init_function": "set_init_states", "node_set": { "population": "l23" } }, "l4_inputs": { "input_type": "external_rates", - "module": "npy", - "file": "$BASE_DIR/input/l4e_rates.npy", + "module": "h5", + "file": "$BASE_DIR/input/l4e_rates.h5", "node_set": { "population": "l4e" } @@ -44,6 +43,7 @@ "output_dir": "$OUTPUT_DIR", "rates_file_csv": "l23_rates.csv", "rates_file_h5": "l23_rates.h5", + "include_columns": ["pop_name"], "log_file": "log.txt", "overwrite_output_dir": true }, diff --git a/examples/ssn_l23/input/bkg_rates.csv b/examples/ssn_l23/input/bkg_rates.csv new file mode 100644 index 00000000..7800134f --- /dev/null +++ b/examples/ssn_l23/input/bkg_rates.csv @@ -0,0 +1,13501 @@ +population node_id timestamps firing_rates +bkg 0 0.0 1.0 +bkg 0 1.0 1.0 +bkg 0 2.0 1.0 +bkg 0 3.0 1.0 +bkg 0 4.0 1.0 +bkg 0 5.0 1.0 +bkg 0 6.0 1.0 +bkg 0 7.0 1.0 +bkg 0 8.0 1.0 +bkg 0 9.0 1.0 +bkg 0 10.0 1.0 +bkg 0 11.0 1.0 +bkg 0 12.0 1.0 +bkg 0 13.0 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(results[:, :network.n_neu_recurrent])\n", + "\n", + "\n", + "# %% simulation runs the network twice, first half for stabilizing the activity, second half for recording the activity\n", + "duration = 6750\n", + "\n", + "def plot_traces(results):\n", + " plt.plot(results)\n", + " plt.legend([\"Exc\", \"PV\", \"SST\", \"VIP\"])\n", + " plt.xlabel(\"Time (ms)\")\n", + " plt.ylabel(\"Firing Rate (Hz)\")\n", + " \n", + "# plot only the second half of the simulation\n", + "plot_traces(results[duration:duration*2, :network.n_neu_recurrent])\n", + "plt.title(\"Simulated traces\")" + ] + }, + { + "cell_type": "markdown", + "id": "03dceff5", + "metadata": {}, + "source": [ + "Now let's load some target data. The present model is pre-optimized to reproduce this data." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "de0fd37a-26f3-4c73-ba61-1d0c9dc56a52", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Experimental traces')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with open('optim_data/l23_mean_traces.pkl', 'rb') as f:\n", + " l23_mean_traces = pickle.load(f)\n", + "\n", + "l23_mean_traces.keys()\n", + "\n", + "cells_to_read = ['RS_G', 'FS_G', 'SST_G', 'VIP_G']\n", + "mean_traces = np.array([l23_mean_traces[cell][\"mean\"] for cell in cells_to_read]).T\n", + "\n", + "plot_traces(mean_traces[:duration])\n", + "plt.title(\"Experimental traces\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1a03cfd3", + "metadata": {}, + "source": [ + "For a demonstration purpose, let's jitter the parameters of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6fd104bd", + "metadata": {}, + "outputs": [], + "source": [ + "# Jitter parameters by 5%.\n", + "\n", + "original_connectivity_mat = network.connectivity_mat\n", + "original_decay_constants = network.decay_constants" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "029f51de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Jittered traces')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# fix the seed for reproducibility\n", + "np.random.seed(0)\n", + "\n", + "frac_change = 0.01\n", + "\n", + "new_connectivity_mat = original_connectivity_mat * np.random.uniform(1-frac_change, 1+frac_change, original_connectivity_mat.shape)\n", + "new_decay_constants = original_decay_constants * np.random.uniform(1-frac_change, 1+frac_change, original_decay_constants.shape)\n", + "\n", + "# set the new parameters to the network\n", + "network.connectivity_mat = new_connectivity_mat\n", + "network.decay_constants = new_decay_constants\n", + "\n", + "# run simulation\n", + "results_jittered = sim.run(return_output=True)\n", + "\n", + "\n", + "# plot the results\n", + "plot_traces(results_jittered[duration:duration*2, :network.n_neu_recurrent])\n", + "plt.title(\"Jittered traces\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "8b7b8efc", + "metadata": {}, + "source": [ + "We can see that the jittered traces have different SST/VIP balances. Let's see whether we can recover the activity of the original model through optimization. First let's define the loss function simply as the mean square errors between the model and target traces." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9b403940", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + "[ 3.26582520e-02 -9.51552194e-03 -1.20423184e-03 -3.06067351e-04\n", + " 1.25451260e-02 8.71665535e-03 4.03836696e-02 -1.24359888e-02\n", + " -2.73894555e-03 -7.28094768e-04 2.80587844e-02 1.39064582e-04\n", + " 7.55342953e-02 -4.55712809e-03 -1.81240800e-04 -7.35804537e-03\n", + " 3.93736453e-03 2.83286709e-03 5.52239624e-02 2.68534950e-04\n", + " -1.26825971e-02 -1.32681187e-04 1.21353784e-02 1.94150006e-02\n", + " 1.00500429e+01 1.18257399e+01 1.00159482e+01 4.93117046e+01]\n" + ] + } + ], + "source": [ + "def loss_function(results, target):\n", + " # pick up the time window that corresponds to the target data\n", + " # For the simulation, the first half is for stabilizing the activity.\n", + " # The second half should be compared with the target data.\n", + " results = results[duration:duration*2]\n", + " target = target[:duration]\n", + " \n", + " return np.mean((results - target)**2)\n", + "\n", + "def set_parameters(parameters):\n", + " matrix_parameters = parameters[:network.connectivity_mat.size]\n", + " decay_parameters = parameters[network.connectivity_mat.size:]\n", + "\n", + " network.connectivity_mat = matrix_parameters.reshape(network.connectivity_mat.shape)\n", + " network.decay_constants = decay_parameters\n", + " return\n", + "\n", + "# formulate the optimization problem\n", + "def optim_func(parameters):\n", + " set_parameters(parameters)\n", + "\n", + " results = sim.run(return_output=True)\n", + " \n", + " loss = loss_function(results[:, :network.n_neu_recurrent], mean_traces)\n", + " return loss\n", + "\n", + "\n", + "# let's minimize using minimize in iminuit\n", + "init_parameters = np.concatenate([network.connectivity_mat.flatten(), network.decay_constants])\n", + "m = minimize(optim_func, init_parameters)\n", + "\n", + "print(m.message)\n", + "print(m.x)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "79ad7dc3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results of the optimized network.\n", + "set_parameters(m.x)\n", + "\n", + "results = sim.run(return_output=True)\n", + "\n", + "plot_traces(results[duration:duration*2, :network.n_neu_recurrent])\n", + "plt.title(\"Optimized traces\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8dfd0084", + "metadata": {}, + "source": [ + "We successfully recovered the original activity of the network that matches with the experiment. Note that this is a simple demonstration of the optimization using the SSN model. Real-world optimization problem using the SSN model is typically much more difficult and require more elaborated setup. Please refer to the paper [Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics](https://www.sciencedirect.com/science/article/pii/S2211124724011148) for an example of an actual study." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bmtk_ssn", + "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": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/pop_ssn_123/run_popnet.py b/examples/pop_ssn_123/run_popnet.py index c52e5d7b..3699f2b8 100644 --- a/examples/pop_ssn_123/run_popnet.py +++ b/examples/pop_ssn_123/run_popnet.py @@ -36,7 +36,7 @@ def run(configuration_path): network = popnet.PopNetwork.from_config(configure) sim = popnet.PopSimulator.from_config(configure, 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Mon Sep 17 00:00:00 2001 From: shixnya Date: Thu, 22 May 2025 15:46:55 -0700 Subject: [PATCH 14/19] Fixing the error in the notebook --- examples/pop_ssn_123/PopNet_SSN.ipynb | 333 ++++++++++++++++++++++---- 1 file changed, 289 insertions(+), 44 deletions(-) diff --git a/examples/pop_ssn_123/PopNet_SSN.ipynb b/examples/pop_ssn_123/PopNet_SSN.ipynb index e79a883d..254c88ff 100644 --- a/examples/pop_ssn_123/PopNet_SSN.ipynb +++ b/examples/pop_ssn_123/PopNet_SSN.ipynb @@ -179,28 +179,7 @@ "execution_count": 3, "id": "5c9b76fa-0d4d-40e9-b387-a72e5eb0cda6", "metadata": {}, - "outputs": [ - { - "ename": "BlockingIOError", - "evalue": "[Errno 35] Unable to synchronously create file (unable to lock file, errno = 35, error message = 'Resource temporarily unavailable')", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mBlockingIOError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 22\u001b[39m\n\u001b[32m 15\u001b[39m l23_net.add_edges(\n\u001b[32m 16\u001b[39m source={\u001b[33m'\u001b[39m\u001b[33mpop_name\u001b[39m\u001b[33m'\u001b[39m: src}, \n\u001b[32m 17\u001b[39m target={\u001b[33m'\u001b[39m\u001b[33mpop_name\u001b[39m\u001b[33m'\u001b[39m: trg},\n\u001b[32m 18\u001b[39m syn_weight=connections_df.loc[src][trg]\n\u001b[32m 19\u001b[39m )\n\u001b[32m 21\u001b[39m l23_net.build()\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m \u001b[43ml23_net\u001b[49m\u001b[43m.\u001b[49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mnetwork\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/Modeling/bmtk_ssn/bmtk/bmtk/builder/network_builder.py:297\u001b[39m, in \u001b[36mNetworkBuilder.save\u001b[39m\u001b[34m(self, output_dir, force_overwrite, compression)\u001b[39m\n\u001b[32m 286\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msave\u001b[39m(\u001b[38;5;28mself\u001b[39m, output_dir=\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m'\u001b[39m, force_overwrite=\u001b[38;5;28;01mTrue\u001b[39;00m, compression=\u001b[33m'\u001b[39m\u001b[33mgzip\u001b[39m\u001b[33m'\u001b[39m):\n\u001b[32m 287\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Used to save the network files in the appropriate (eg SONATA) format into the output_dir directory. The file\u001b[39;00m\n\u001b[32m 288\u001b[39m \u001b[33;03m names will be automatically generated based on the network names.\u001b[39;00m\n\u001b[32m 289\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 295\u001b[39m \u001b[33;03m you can also specify an integer (1-9) to specify the level of gzip compression.\u001b[39;00m\n\u001b[32m 296\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m297\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43madaptor\u001b[49m\u001b[43m.\u001b[49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforce_overwrite\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_overwrite\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/Modeling/bmtk_ssn/bmtk/bmtk/builder/network_adaptors/network.py:564\u001b[39m, in \u001b[36mNetwork.save\u001b[39m\u001b[34m(self, output_dir, force_overwrite, mode, compression, **opts)\u001b[39m\n\u001b[32m 555\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msave\u001b[39m(\u001b[38;5;28mself\u001b[39m, output_dir=\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m'\u001b[39m, force_overwrite=\u001b[38;5;28;01mTrue\u001b[39;00m, mode=\u001b[33m'\u001b[39m\u001b[33mw\u001b[39m\u001b[33m'\u001b[39m, compression=\u001b[33m'\u001b[39m\u001b[33mgzip\u001b[39m\u001b[33m'\u001b[39m, **opts):\n\u001b[32m 556\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Used to save the network files in the appropriate (eg SONATA) format into the output_dir directory. The file\u001b[39;00m\n\u001b[32m 557\u001b[39m \u001b[33;03m names will be automatically generated based on the network names.\u001b[39;00m\n\u001b[32m 558\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 562\u001b[39m \u001b[33;03m :param force_overwrite: Overwrites existing network files.\u001b[39;00m\n\u001b[32m 563\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m564\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msave_nodes\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforce_overwrite\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_overwrite\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mopts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 565\u001b[39m \u001b[38;5;28mself\u001b[39m.save_edges(output_dir=output_dir, force_overwrite=force_overwrite, mode=mode, compression=compression, **opts)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/Modeling/bmtk_ssn/bmtk/bmtk/builder/network_adaptors/network.py:592\u001b[39m, in \u001b[36mNetwork.save_nodes\u001b[39m\u001b[34m(self, nodes_file_name, node_types_file_name, output_dir, force_overwrite, mode, compression, **opts)\u001b[39m\n\u001b[32m 589\u001b[39m os.makedirs(ntf_dir)\n\u001b[32m 590\u001b[39m barrier()\n\u001b[32m--> \u001b[39m\u001b[32m592\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_save_nodes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnodes_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 593\u001b[39m \u001b[38;5;28mself\u001b[39m._save_node_types(node_types_file)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/Modeling/bmtk_ssn/bmtk/bmtk/builder/network_adaptors/dm_network.py:103\u001b[39m, in \u001b[36mDenseNetwork._save_nodes\u001b[39m\u001b[34m(self, nodes_file_name, mode, compression)\u001b[39m\n\u001b[32m 100\u001b[39m prop_ds.append(node.params[key])\n\u001b[32m 102\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mpi_rank == \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mh5py\u001b[49m\u001b[43m.\u001b[49m\u001b[43mFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnodes_file_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m hf:\n\u001b[32m 104\u001b[39m \u001b[38;5;66;03m# Add magic and version attribute\u001b[39;00m\n\u001b[32m 105\u001b[39m add_hdf5_attrs(hf)\n\u001b[32m 107\u001b[39m pop_grp = hf.create_group(\u001b[33m'\u001b[39m\u001b[33m/nodes/\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m'\u001b[39m.format(\u001b[38;5;28mself\u001b[39m.name))\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/miniconda3/envs/bmtk_ssn/lib/python3.13/site-packages/h5py/_hl/files.py:564\u001b[39m, in \u001b[36mFile.__init__\u001b[39m\u001b[34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, fs_page_size, page_buf_size, min_meta_keep, min_raw_keep, locking, alignment_threshold, alignment_interval, meta_block_size, **kwds)\u001b[39m\n\u001b[32m 555\u001b[39m fapl = make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0,\n\u001b[32m 556\u001b[39m locking, page_buf_size, min_meta_keep, min_raw_keep,\n\u001b[32m 557\u001b[39m alignment_threshold=alignment_threshold,\n\u001b[32m 558\u001b[39m alignment_interval=alignment_interval,\n\u001b[32m 559\u001b[39m meta_block_size=meta_block_size,\n\u001b[32m 560\u001b[39m **kwds)\n\u001b[32m 561\u001b[39m fcpl = make_fcpl(track_order=track_order, fs_strategy=fs_strategy,\n\u001b[32m 562\u001b[39m fs_persist=fs_persist, fs_threshold=fs_threshold,\n\u001b[32m 563\u001b[39m fs_page_size=fs_page_size)\n\u001b[32m--> \u001b[39m\u001b[32m564\u001b[39m fid = \u001b[43mmake_fid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muserblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mswmr\u001b[49m\u001b[43m=\u001b[49m\u001b[43mswmr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 566\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(libver, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[32m 567\u001b[39m \u001b[38;5;28mself\u001b[39m._libver = libver\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/miniconda3/envs/bmtk_ssn/lib/python3.13/site-packages/h5py/_hl/files.py:244\u001b[39m, in \u001b[36mmake_fid\u001b[39m\u001b[34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[39m\n\u001b[32m 242\u001b[39m fid = h5f.create(name, h5f.ACC_EXCL, fapl=fapl, fcpl=fcpl)\n\u001b[32m 243\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m mode == \u001b[33m'\u001b[39m\u001b[33mw\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m fid = \u001b[43mh5f\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh5f\u001b[49m\u001b[43m.\u001b[49m\u001b[43mACC_TRUNC\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 245\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m mode == \u001b[33m'\u001b[39m\u001b[33ma\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 246\u001b[39m \u001b[38;5;66;03m# Open in append mode (read/write).\u001b[39;00m\n\u001b[32m 247\u001b[39m \u001b[38;5;66;03m# If that fails, create a new file only if it won't clobber an\u001b[39;00m\n\u001b[32m 248\u001b[39m \u001b[38;5;66;03m# existing one (ACC_EXCL)\u001b[39;00m\n\u001b[32m 249\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mh5py/_objects.pyx:54\u001b[39m, in \u001b[36mh5py._objects.with_phil.wrapper\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mh5py/_objects.pyx:55\u001b[39m, in \u001b[36mh5py._objects.with_phil.wrapper\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mh5py/h5f.pyx:122\u001b[39m, in \u001b[36mh5py.h5f.create\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[31mBlockingIOError\u001b[39m: [Errno 35] Unable to synchronously create file (unable to lock file, errno = 35, error message = 'Resource temporarily unavailable')" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -807,10 +786,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "c4b3bc43-114c-4498-a48a-c0c26588aea2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-05-22 15:46:06,396 [INFO] Created log file\n", + "2025-05-22 15:46:06,432 [INFO] Building nodes.\n", + "2025-05-22 15:46:06,433 [INFO] Building connections.\n", + "2025-05-22 15:46:06,434 [INFO] Add input module \"init_states\"\n", + "2025-05-22 15:46:06,435 [INFO] Add input module \"l4_inputs\"\n", + "2025-05-22 15:46:06,437 [INFO] Add input module \"bkg_inputs\"\n", + "2025-05-22 15:46:06,438 [INFO] Running Simulation.\n", + "2025-05-22 15:46:07,749 [INFO] Simulation Finished.\n", + "2025-05-22 15:46:07,752 [INFO] RatesRecorderMod: Saving rates to /Users/shinya.ito/projects/Modeling/bmtk_ssn/bmtk/examples/pop_ssn_123/output/l23_rates.csv.\n", + "2025-05-22 15:46:07,817 [INFO] RatesRecorderMod: Saving rates to /Users/shinya.ito/projects/Modeling/bmtk_ssn/bmtk/examples/pop_ssn_123/output/l23_rates.h5.\n" + ] + } + ], "source": [ "from bmtk.simulator import popnet\n", "\n", @@ -829,16 +825,6 @@ "sim.run() " ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c2548cb", - "metadata": {}, - "outputs": [], - "source": [ - "sim.run()" - ] - }, { "cell_type": "markdown", "id": "9e3d7980-4428-432e-bf05-76dc0f33bf81", @@ -849,10 +835,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "de0fd37a-26f3-4c73-ba61-1d0c9dc56a52", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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3177JHic3T4ZpLdOMjUttWe8ydr0TsQQbx8+amprgsMMOYz+XXnopnHfeefCd73wHzj77bPv7TZ4MJ598Mvv54Q9/CHPnzoWrrrqKia4b2eoaDP34xz9m1N1FF11U9nuuvPJK+N73vlfV7xVZZI20EJeyIC/RpL286uWqjxHZOARDDeKQoN61VLiqKdnEHvF15Ya2KjVxrYfzXk1QkG277baBet50Og2bbbYZk7w0utUtGHrxxRfh6quvhpdeeqkidf0ll1wCF198sYsZmj17dpW+ZWTj2cYKRV8LMFQL9imycQiGiEOC93GUFRne1q5dy9ge1PCiRggzwV544QX4yU9+Ascffzzcfffd8Pe//51piLbccku2/t11113w73//m6XYN7rVLRjCFD8s5ITFn4Tl83n4yle+wjLK3nvvPd/3YaEo/IksslqKN6ulGcLPfXHli7Dt5G2hLe2krZq0Wmwe9V5wbbwZZTIbWe+mZjXhscQT5u/nZX3LWJbXNpO3gbFqbW1tsMcee8DPf/5zWLRoEWSzWUYkoKAahdNYJ6ilpYXtwUuXLmX77BZbbMFS7VHO0uhWt2AIT+6hhx7q+htWwsS/f+pTnxq17xVZZLXsWn/nojvhsqcug12m7QI3HHlD44KhiBmq23u3FsxgLRwSwdCmwHyizRG3HsEe7z3xXtiwfUPjn18PqeqZTIbJTPDHzzbddFP4/e9/X/bnBREW9WqjCob6+vpg4cKF8vfFixfDvHnzWIVLZIRQqEUNs8mw1sFWW20F49Ew62P14GqY1RaVGKg3q5Ze4aa3bmKPL616CRqZtRlLoYsP+z6Eqc1TIZVo3OzWsdJKxo8ZquYYC7sWVgUMBY1XtTFogabImI3qCoXxSFSi4w8aan3w+WWXXTaaX6tu7YIHL4DDbz0cXlpZvY0xsvpqBVALUShmllTbxkqY7JXVr7A5+Kn7G5udVrU2Y8WqMQ+ppqpaoD4CJ+OcGcKeJ5Wg4Eaj3Uzbsyvs1My/v/132GX6LqP9dca91SKbLBmr/hSlC3y1BKhx4nc1ssgVw5ZoWCl4zFRPb2DNkArkqsFyUTBUi3NVE2AUYS+PNeaKNN4tupHHTVpvLUADHaNaoI4yQ/WcAl3KGhXEFbNGvh4qcKjG/TtW9FXUIibKa2NvZo8HGxsRh4a3WjBDYwUM0THGEhPRqDZW+up5NENVOBZX1mgDn6vIilsEhhrQqr1BNnJ5/lramGGGyDJQreOg2WSNvKHUKusH52A1xxormiGV4ai2gFrNXqvGGBFrMzoWgaEGNLp5mbbrXr0Odr9pd3h1ddQyoR4E1GORGWrkat212KjWD62HA245AL7x2DdqA+QbmKlTAWO1w2Q1AcMRFhoVi8BQA1o1M3OufulqtqD84NkfVG2MsWLWwNoxAYZo2LUWzFAjMxG1+O53v3s39I70wr3v3VubJsONzNT1Lq+6gLrWLFotAHfEPnktAkMNaLXwThp5w6qZDfeOiTAZXRdrwdo08uZbC6tFqQM6vxt5rhcWPOD6vRosVy3OVQRORt8iMGTIhnJDsLJ/ZU3GqsVmEm1Ypa1AGZVC44KhWoT7qNaimoDrg94Pqvr5yXhtSx3URPzfwEUX1ZhSVcJkY4RFi6y4RWDIkJ1z/zlw6D8Phfe6q18LaawU4mt0o8twtZpdVlMf5gdUquX51iJ76YllT8CR/zoSvvn4N6EW86Ja56oW89slCi40LjNkxRNVP3e11lfVJEzmE1049thj4Ygj7LYjfr1CUZ7xyiuvsEfsFCFq/+Hv4ge7Rnz0ox+Fl19+GRrNIjBkyF5dYwuO/7PkP1Ufqxa0dgSGShtdTho5TObqsVYllqAWG8q1r1zLHquptXGVCKjSNa/JhluDTMhamLoSVsMpqQUz5AJA1ujIK84991x44IEH4IMPPvD8G3al32233aCjo8P3vQ8++CBr5Hr//fezNltHHnkkdHV1QSNZBIYM21gpypZQPK7IvEaXlloUK2zkasS16IVVi3NFx6gFi1a1McZI7Rx1e69KnaEahJHdWGh09EPHHHMMTJ06Fa6//nrX3xHc/OMf/2BgKciQEcK+oQiYrrrqKli5ciU8+6zdMaFRrG671jeqVStcQi1ihsYPM1SL61ALz9cViqtSWKYmrUtoTSYEjlW4PPQa4BjxhHkHa6xUVbZqzKqFnR94vq3BwcB/L2Btt8Eh+3khBYXEAJiwWHOzBPB0nvsBrmQyCWeddRYDQ9/61rfk+xAI5fN5OP3002H9+vUlx2xubmaPIyMj0EgWgSHDVotsnEZevMaSWSRdfMwwQ9UKA9QgLJNM1FbcXIuQCbJoKUhVd4wxVPepGsdigjlFIPT2LrsWfY24s/oB4G0wY1u99CLEWlrKfv0555wDP/3pT+HRRx9lvUNFiOykk06Czs7OkmAIQ2Pf//73oa2tDXbffXdoJBsbMZ06slosLLVIrY9SPSvLJqvWdafMULWuey30PLUIk6Vi5kGDx2LVd0pq0bpkzDBDypSodp2hxs68K21bb7017L333vDHP/6R/b5w4UImni4WIkPD9yAAmjhxIsyfPx9uvvlmmD59OjSSRcxQI4KhGgCVRl4gR8NqIaDGa1KNsFkt9CO10CXVQudWi7YitWiPMmZ6kym/V7vOUNjPx3AVsjRBNpIfgUVdi9jztnQ7zG7fMNQ4fuNW6kyde+65cOGFF8Kvf/1rxgptttlmcMABBxR9D4KfbbfdlmmHJkyYAI1oERhqEKBSa0+uVj2YGtZQA0B+rdYGr26+iSqIVFzZZFUC87Wo4lvr5IWqXXMSGq1FKK6h23HUIExGhwh777LU8yLhqng+CTDcZP+SzkC8gtCW6fX9lFNOgS9+8Ytw0003wZ///Ge44IILSobrZ8+ezUBTI1sEhgxb1UIZUP1mgdQa2VusiRXyrqtQk27vNdgYa5EhVTV9FY1hjZGU9ChMVgfZZLRgaA3CZKPtiLa1tcGpp54Kl1xyCfT09MDZZ58N48EizVCDMEOuBasGc6WRF8iaWCHnTq2v0iJZC/1ILUJYtdh8a12tu2qtGWoAuMaKDqYWzFAtNHX11o/s3HPPZWLpww8/HGbNmgXjwSJmyLDVonfNWGkWKI6lIWszFbKubLJaaIaq1iqjBuLmsVJnqNZlCGqhGWpkx0f95tW4t2rSSqYO6gxR22uvvXwZqk022cT1d/X3RrYG3IXq22rhLdYiTFarBbJh9QrIDNUgm6wWgt2aZJPVoOVHrZmhWrBotWAiGjkkrgKHqpwvq8atUuqMJRovFoGhBtQMjaWu9Q27EOfdYbJabFqNXI24Fht8LTRDtWaBG7k9ymhYtesM1aRrfbXacUQAqKhFYKhBWJta09rVHINuWg1L0auaoRpkYTVyQcRapHLXnBlq4DIEYyZMpnz3qjRqrTGLVjXQUmehuHqzCAwZnpC1KIzX6GDIpYNpVPGmVfAIqKtx7WuRhVXroovV2lAoyK4FQ9vI4HTMhMmU61xtZmis1JEbKzofkxaBIQNWqueL6TFqYdUcz9Xsskp9qqpuVt5zhqoBIGsBgmsSJqvB5luL+jw1YYZgbOiSamGWMieqDVYqmYOVAI5ahMnq1aw6AWYRGGqUDYuyT2OoAnXDeqUWblmxMdEKoBaeby1CP7WoyUStakC+BoLdMSugrnLX+nI+P5Wy28IMDIRrtloLh7qewmQD/DyJ8zZaFqXWN0imTM1T66uJ1mucnVG1MJmi161GF/OaZy81cCq3K/OuSh3la80M1cK5amTNUC2YoUqb2iYSCdaSYtWqVez3lpaWkmUfhrPDUMjax5LL52BoyO5gb9Kw5YcYA7vQV2OMStcdBEJ4nvB84XkbTYvAUKM0TiVjNHqj1loDu1q046iF/qmRN8ZadElX+7iNCQF1xAxVphmqBjsbgvmfMWMGexSAqBygsmZwjdNjbx0YN5x3qwbs75OMJ6Gwrj7W3gkTJsjzNZoWgaEGrErb6MzQmGgSaRVg6nqL+fArJ8VqI96sVpp1DYBKLeZIzcNkDTzXa91WpFpm5Quw9VILFswCyCdiVWFPwwBHZIJmzpwJ06ZNg2w2W/L1b697G3786I/Z80nNk+D6I64H07asbxn8+AF7jBmtM+D3H/09jLalUqlRZ4SERWCoAWvB1KToYhXHGAtNIq3sCPzw+gKk8gDnfjEBw+kqLcR0Y6ySRmWspNZTa+TjqEnG2hiYg2gbP9ULlz+ah7/tH4fb9olV3SGp9FzhRl/OZm8lLVg+spw9H4mPQFMTb9pq0OLDcTlGLBWryhiNbJGAukFCWLX2FmsxRiN7pfmeHmgdBkjnACb11qZJ5JjJXmrkBqc1ABFjpdRBLWyTp/rZ4+mPFRq6ztBYKdnQyBaBoQZhbWo9IWtVBbVRNUOF3j75PMNZ8GyhNB1esZHTVZNMrwYGXGNFz1PrUgeNOgfRktnaNmqtRUZnI8+PRrYIDDVgan0twiW1qKRdqyJm1bB8nwOGkvnqLWI1yVQcI2GyWouCqwYiyP5ek2vewGEy1aqdTVattXesZCk2so0qGHrsscfg2GOPhVmzZjHB2e233y7/DUVn3/jGN2CHHXaA1tZW9pqzzjoLPvzwQxjv2WS1mCy1qODbyJOykB2Rz5OFxvZKax76qUEYoCabVgN78GM1ZFIPdYZ0rRbXvFGd0DELhvr7+2GnnXaCX//6155/w/oDL730Elx66aXs8V//+he8/fbbcNxxx8F4rzNUtQlZg941KshqWDCUc0JiTTmrJpqhWrCOtQgDNHLaey3mYU2u+RhlCaqdxBD1oxu7NqrZZEceeST78bPOzk544IEHXH/71a9+Bbvvvju8//77sNFGG0G9WK09uUZeIFWQ1ai9yQo553tn8lU8FlqgsoEFu7VI368JUKlxGY1aXPNGnYN+Vu0q8LUAKqLPYalCjfWoRWtka6jU+u7ubnaDYJGmIBseHmY/wnp6eqCW1tD9o2hWXJWYIfX8NKyH4geGGrTO0FipeFxrp6QmYctIQF2RVTtUXYv7SlyTRMxs/Z2xqhMbdwJqLB2OGqLTTz8dOjo6Al935ZVXMlZJ/MyePbvq363mLRMamEatRZfp0WKGql3wbayEyarGDNWY4RorJS7G0sZYFYekxuUtqnXdo2yyMQCGUEx9yimnsAl8zTXXFH3tJZdcwhgk8bN06dKqf7+xIniMUuvLNyvnCKgzVdQM1TpkUosxGrl/31gRUI/VjXEszMFqOSXqNa+XbvH1YslGAUJLliyB//73v0VZIbRMJsN+RssaWVRZi9T6sVJ0kTJDSat6dYZq0bW+1qLgWmzwtdDBNHIIa6yGyRpVQK06idVmmasVimtkSzYCEFqwYAE8/PDDMHnyZKhHGzOdxWucxdLIFL2Vd4BPqlDFOkM11to0cmHHmtf7qoFTUouQYqOGqv2s2g5JLYB8tcbx02smIAJDdQGG+vr6YOHChfL3xYsXw7x582DSpEmsyd3//M//sLT6u+++G/L5PKxYsYK9Dv89nU5DvdhYqeBb63pJjeyVWlnnGiQLVQyT1bgGUCNrVGqSsTZGnJIxywxVu85QDUKjtUjGYGNYOUhByvg4jWqjqhl64YUXYO7cuewH7eKLL2bPL7vsMli2bBnceeed8MEHH8DOO+/MwJH4eeqpp6CerCYLS42r0tZMQN2gab1WPudhhmqZyTKQHYCL/nsR/OWNv2iPMWbq89SCRYsqgte11bpr/R0L74DPPvBZWDe0ruGcxLEEghueGTrwwAOLshGNIvAaK4sXnfRRBeriViBgKMmrHdeyxsk9i++Bh5c+zH4+se0njG3wjcyo1LowaS3qJdVifowlMFSVOVjk3v32k99mj7e8fQt8dqfPmpMPVFkIPtbCo+Mmm6zerSaaobHiWVtjRDNEBdREM4QLzhPLnoDF3YurKgpeM7DGGHAdM6GfWpSfqHUiQwNnxY2GiQ1+9cBq+M97/zFybOWcq6W9S+seqNQifb+RLQJDDQpUqsHc1LrYWzXHqbZZWQqGLLmAPbP8GbjgwQvg/AfOrypLQIGR7sJZC11SLRiVsVLYsSY1mWrc1LZWJs7Xhf+9EL7y6Ffgvvfuq9q5omtwKq6nvRkNZmgsXXcTFoGhBk29rMZir1agrgrgGiMT0spTAbVzLO+sf4c9X96/HHpGeqrGdtDng/nBhgrB1kLP09BNbWtdL6lB56CfiWN5fe3r7PGh9x+q2vo+nHc6HSTjSbPrYg1S68cSI2jCIjDUoJWba5F6We3sqEaekFRATZmhgdyA/PvyvuU1WYiHc87zhijyVwP2qZFDWLXuKD+WwiV4b2VJ2QtMNDAKTrk+EK0v2yefx2Px+l97x4gjWi2LwFCDpPTWhEatAfs0VnqTWTkvM4TXvmuoS/59Rb9dCqIaxQrpQjyUH2qotPexUn6ikfVVY1kz1DXszMH+bL/2ZwaFp/tGnDk4kh8xNkbNHNEIDLksAkMNwgyp7TGqHSar1WRp1AlJmaGE5RzL+uH18u/0uWmgQhf5odxQ/Ve5HiMZl7VumNzQmXdVNouwNPT+pfOOAiPToVE6BwdzeqHqWlfSrtYYjWwRGGoQHcFYCWEVOw5kVa6Zdw0s6VkCdW9EM5QIYIa6h7urtmn1jvQaq7o7VlK5a51aXxMwNAr9z1B0fPvC26HujWR00mMxPgcDQqO92erMwVqU6WhkVn5ctuNoFBsrvWtGG3D9/tXfsyKCL656Ea776HVQz1YgYbI4YYaot7h+aH31ii4SbZLuvVDzzbeBG866Qlg+zEQjFnGl5wrH+9qjX2PPd5yyI2w6YVNohPIWwtAhoSHk7pFudkw6mh6VOcX7LBaLufRIuvdbLUqOjJWCt9WyiBlqwCaU1RqnFlVQix3HI0sfYY/PLn8W6t5c2WSOgJrqd0xQ9EE6GBoa013Uat0MtpEzpMZiuI+O1zPsZECuHFgJjZLRaf/BYnOEJhfgsVEW1aTO0egcrIFEIWKGilsEhgzYmEmtr4XATtE+0TF0RYijtRDHSZiMLsTGU+vJuXItxLp1hmrdNywCXKPftd7yvx4UwFej6Wk1maGYxR0SRUOnGyoLWnup42N63tQkmyzSDLksAkMN2tOrFmGyqjBDRaqgNhIYcmmGSJiMLsS6wuZagaGx0lG+1k7JWDwOCoZMi4KNm8IMoXYPgR11SExnW1LwSM+PyTlYtfW9BhnJjWwRGGqQlN7R1vMYG6NI2Xl1stazFXyYITxfFNDpLsLFxPm00KI2M0QYgDEjCm7k1PpR1FdRzZuJGj21DJMJ7Z4K4kxmWwaFyUwLqGvNykcWgSEjNmbCZLUo7DhW+uP4MEO4IFIApFsMsRhrQxdi7es0sG5M1BkaM7WMatzex3Vfkfu37plaJUwmnBK1tpDKFFVq6holnA9XmEzzOlk97gKt1ahXN1YK3lbLIjDUKAv9WBHYFfFOGmlyWvlCSc2QbpuMoHsLx6GeqDZFTzzpRm4CXJPKzTVOra/auaKMLBmDAvi61wwFMEMqGNIN9wWtvUZ1e73LRzV5JbIIDDVkE9VGTq0vFhtvKJbIJ0yGQChoYzEZzlA9Xf1sMghc1F9f8zqsGVwD2kaq9Y6VRq1VAyq1cK5Wv+0aQ9xn9N6q1nWqloA6USVmKMhJpCBLmxmKJwLnNJbomL96vtbnszGidhxFLQJDjQoiajBGLTLWgjaXegdGVq7gCZPR+iamBNR+4QzV09XvWu9/X7286mU47Z7T4MKHLtT6fDbGundrCiJq0u29Btqnqp2r3g99x6TAoe6ZITVMFsAMaWuGAnSORpmhIs7u5x/6PHz83x+H51c8b/SaNxITXwuLwFAVrNYgolpWjQ3FE38nHhDdiOvdKwWykIhFWBWcmthM/FgCdXHXLrpIntNCgncsvIM9vrb2Na3PZ59bC11dDcJLNW/5US3ApRQhFMfSSGBIzSZL5f3BkO5xBOkcqWbIeDYZCYm/uuZV9vy/7/9XawxLuZfq3eGstUVgyIAFCexMWjGKEyf//e/dr+0B1aSJqlK1N2hzqfeF2E8zpC7CJu4DvzCZep1NMkMUnNKNUTvUEIOa6pKqBqZJR3R1fsxbNQ/eWvdWY+iSYuSCBADtup+DpAo8WjKAGTJZhytoHupndPrLB2iIujXVqjeGci9FFajdFoGhRqnPM+QuHEYXyd/M+w189dGvwjXzr2mArvXKhAwQcmbJplOXRsBQrOAfJjOx2PgyQ0rKvrZeAWKBIFuYbhVfVyiOeKj4/IpnroBb3r5F6/PtMWrADHUt8T1XuGmdde9ZcMY9ZxjtYF61MJkKhnz0aHXPzpJmyWipgHmonfYeUGfIaEancq7F560eWC3/ph6XTgkN9d56atlTcMnjl5jRBzaoRWDIgNWiRoQ13Bt4I//5jT+zxz++9sf6z1hTPlMch7pg1b1XmvdqhqoRJitHM6QdBoj5AxW6MOqLUMkY5B54ZvkzcPPbN8P3n/m+9sZfkxAWAYh0jBX9K9g6gNdieb87M6g+yxA0fphMzSZLB8xD03oeKaA2WOuroJxrMQ9XDa4yVyIg5wbpdK5/4/FvwN3v3s0aZY9Xi8CQAasJiFAyk6pTELEGAmrFm5OLsHJ89e+Vepmh/ly/i87GY9DNLPQT7JoOkwXdu7TRrD4Y8mefFnYtNMg+1QJEgO8Y64bWmTuO/lXVZ7gCwmQuMFTn7KyV9WeGxDxsSbZURc9TFQG18n7BPnUNdZmr+6QCLn7N8fi6eOXxeu9HVzdd67u6uuC2226Dxx9/HJYsWQIDAwMwdepUmDt3Lhx++OGw9957w3i0wshA4OTBUMPS3qWw9aSttcawFDagKtlkpEmjOsbra1+Hn73wM/jC3C/A3GlzQ49hKaEjucEroZ9690opGJJZLCMOGBIhJjyHyVhF06xk7Rx1UdTdMINCWCYL8AWJtNcOrZXP8Zx1ZjrDj9G/JvCcLFy/EKY0T4EJTRNCfz4bI+A4aP8r3crNhZVvADQ3eeYg3gs/ePYH7PHbe36bdU4PaypEr0ZV5Wqb6lglC+552JZqg4HcgPlML595qB2qVpxbv3VR2yFRwK34zhS8T2yaCOPVymKGPvzwQzjvvPNg5syZcMUVV8Dg4CDsvPPOcMghh8CGG24IDz/8MBx22GGw7bbbws033wzjzaxVb7h+p5Pvp8//FE6+62S45917tMYoKJtRdcCQ26OlG+P/vfB/8NyK5+A7T31Hb4yAMJk60et+IS54mSHhzeEiXJUmqvzcZS3/RS2sUdgQVOXaJBiiWiraJV0bRJDUYXre3+t+Dz5258fg9HtOB10LYrjopqUKeCsfw38OLutbxkKKt7xzi7YHPzY0Q0qYLO++v1rTrVXpKO8X2td2SAJYeRoS19aiqXtIwQuGsnW+7lbTynJZkfn55Cc/CS+++CIDPH6GAOn222+HX/ziF7B06VL46le/CuPFgrwstFsX3Moeb3rzJjh606PDj1GTTC/FOyGLvahxsbh7sVEPSCxUKhiq+1YAPsyQMJNgiLJ1YvFSwxdGmSFegA9ZBwqGTGaT0e9LF2IR3gg9BviP8cjSR9jjB30fyGMzDRxpmHe4oKntCDhXVIuEYY0ZrTOMgLqgMNlIob7noJV1z4MkAy0xzzzUdkj6HREz/TyjYEhJ3/cTaWs7JIrTJK55z4izxugC+TEPht544w2YPHly0dc0NzfD6aefzn7WrnWo7/FghUSipJ4nlUiZvZGrUoG6+nUogmpdqJqhuq+BQZkhBQzRFFht7xo9Q35/iXOifqb+QuzdGBE0UI/aJDNE7126EGu3TaBjkHuZCl1xjJaUrSXRNVe18Sppbehx0HPVRyp66wIuOg49jnqfg2rRxXTBPRGpdk/HCitedf/uMw9NM0N++kBdhwQCGC7qkAxqzsExHyYrBYR0X9/oFuRlUcskMlpjFIqwNqYsyHMwO4b/pFcnet2Xis9bgWCIbra6FD09W+KzVCpbeyFWNkb8PFWkbRQMBWzwuiCiQENYZL7Qz0UNSTUy70yGl+hxBLJo2qE4/zCZK1283isU5xRmSAFDppghlfuX4WoyD7VD1QFFF126PU2mzsPK8/NC52Cu3kOj9ZRNlkgk4KCDDoJ165zsCbSVK1eyfxuPFiSwM+khBoXJTHpvKuCqSp2hgHoaqoC67r1SH80QBb7JeNJ8qwwRJgvICjExhvjOKhjSDv0EaG1M6hWCABe9t7RbM5QxRrWOg7JBJsOWQdq9up+DKhhSpoFghvQrULtPlpjTJpkhlbURn21UMxSwh7jmYH78aoYqBkNInw8PD8Nuu+0Gr7/+uuffxqMFbfDUC40pE6paWhu9Qaofigtin9SJXv/MULBmqCnZBKl4yvhCXL0wGXjOvemWH+rnV6NLelCVa7qh6IYBglgbev8aBUPkvPdme82xBMrvfutJvc9BVTOUUqo4t6UNaYYUjZkUUBPgoO2QBISwzPY/C2CfGiiDsK7AEIoPb731Vjj22GNhr732gjvuuMP1b+PRgkI/dOHVr4KqoHo+8elmogu4gkJxdKFPx9NVAUPq5lvvXikQSt6XGeLp9CZp56AwmWlmCO9fqrMxPYYrvEQYJ33g6D+GSTAUlBVnctNyheKCWDSDIcUgZqjewRAomiE1TGaszlCstKZOlwxQ9Zp+jLk+4PIHQyaB/LhjhjAcdvXVV8NVV10Fp556Kku3H6+sULHwklGPNGCy0MULw3VaEzLgOOginE6kq1JPo5E1Q2rMwWSYzJVZxAGwumDpF3Z0Gy7yppkhehx4n4p7y7UQG9zgKZgezBoEQz7Xw3Tl5qCsOBomM71pyXlInKt6n4OeMJnilEgBtW5bHJ8yBH7zWud8qVILv8KO2g2ZA9ZeyjJmxzEYCl8NDgA+85nPwBZbbAEnn3wyPPbYYzBeLagBHg2TmU6LDKrPg5MobOZakOdAe+LoCwX9j8PTb6vexZtFssmqphkK0KKZZob8BNTatVqU3/E7x2Pxqnml9Psa1V0EhfsMgqEgfRWdh0ZasPg0hKXzsN7ZWbWIIAVDyMxiuNq0oF2cK7/zr3O+gtZek/3PgjolNFLV8bpihjbeeGOXUBrF1M888wyrLVSpIYDCcNusWbNYiA3rFKkX77LLLmPFHjF1/9BDD4UFCxZAvZnacVgyQ9nqhcmChMc64wQBLpPeu8c7CWjHUe9eabEwGS7CuNGbBip+wk37q2j29PLxfNX7yiSoY2NaBfaZ9PyY7LHmYoZMhqvLSK03GRoN1CXpzsNywmT17pB4utY7VyeTzEAiljDDqKjDWjnf+0iLGQqSWuRN3rtRmMwoGFq8eLEndX7zzTeHl19+Gd59992KPqu/vx922mkn+PWvf+377z/5yU/gl7/8Jfz2t7+FZ599FlpbW1nbj6EhvYwQ0+YJYflohvQb+ZUnPNYCQwGpl/QzcSHQ8oAU4BgUJqt3r7RYaj0yQ2IhNiluDmpqq7XYFwo1CZP56ZI89652mKy0gLoaTJ1pIXjQcdDP1R/DG5ZhyTGNnE1WcM9BUw6JehYwPOp3/nXC1YGsDQ1b6oaqy1h7s+MYDGmFyag1NTUx1qgSO/LII9lP0M2B1ay//e1vw/HHH8/+9uc//xmmT5/OGKTTTjsN6sWCRMG1EFCrm5bOOIUKOsqHrZtUbrivkZghdaenC7E2a0M8+KrUGfJ5Ly6Spq+HXy0uFZhUKwuLhqv1xyjdjkOftQH/ekkGwZBfAT41Q63ewVAxAbVJh0SFQ373rvi78UwvgwJqv+NAi5ihCsHQxIkTy8oWU+sPhTVkoFasWMFCY8I6Ozthjz32gKeffjoQDGHaP/4I6+lxNx+thgWFsIx6pAG6JKOMSsBxqIs7/h4WDKllCGTZ+QarM+TqWu/HDMUTZkCET5E/cS9hZh9uYAWP71rJAHmv54sbo8LamA6T+QGuaoWwjDK0AQJqer5Mhi3pZ9F5aJJ9CmQD69whUStQi671xh0SnxpA4lrgOOI+1hlHHcOv9Y42OxtQZyjSDFUIhpCloazNBRdcAJdffjlMmzYNqmEIhNCQCaKGv4t/87Mrr7wSvve970EtrTAaqfUBjIrWhAzQoqgeo06Nk8DjaGjNkJJNljS3EBcLk+E4IyMj2gC4HE2ESVAnPs8Dsg0KjynoMTkPISi13mQKNHlOP4vOO/2K4F5mqNFC1d52HG7dnnBIqtFEVTRLpmBI67orY/ixwNrV7NUx+ByJmKEKwRA2aqV24YUXwkknnQSbbrop1JNdcsklcPHFF7uYodmzZ1d1zKB+W5SeN60ZqkZ4KShu7QlnaHkP/rokz0KsxLfrzujXU5mhOBFvmhRQKwtkU6IJeqFXUzOUl0XlUICai8V89TzGNUNWFUJxPgJqvKeNMkNBAmoC5vXbowQwQwbDZJ6q+VUIjdYcDBEdH5YAMaYZ8mHMxRqIxVWxvhstF6Gz9tI5iEbnYbWYITqGxY9DnLvxZHV7xDNmzJBtPqjh7+Lf/CyTyUBHR4frp9qm3mRi0zKbxWKVV6xQA0QECcFNpnKr389vQuqOUW1jnc8pM6SCIcoMaYI6v81Xhsl4zSc9QXvek43jlzpsMoQlmQilxYdJwCX1EBhGDAAUJsXNJlPSAwXURkMm7t9xHHUO1jszpPYmo2EydBSMaYZ8wJCYg1jGxMQ4ItQt5iB+Fq4zlA3UrpdURmp9va+94xIMzZkzh4Gehx56yMXyYFYZVr6uJwusnWOylHoAUDEJIoLEzSZDJkH6KtPNR6tqeeX41XYcZCHWZzu8zUepXkF3DIsssElyr6qhUP2MHG9/J9Obrx+I8NRL0tY++TeDpcein1rvL9I22RjUz7lS52C9N+30aIZoar3JbDIfGYQ4V4wZ4nPUCDNEgI8KfvDz9XRJ/mtvo4VH6z6bLIz19fXBwoULXaLpefPmwaRJk2CjjTaCL33pS6y6NRZ2RHB06aWXsppEJ5xwAtSTBTEqLorTyrO/izi2dlpkFRqclgtUdLzSIFDXSGDIUsFQAT06gFzMrHhTDVvKMBnRK/i9rrIxCp7NhGkiVDZQl4nwaQzqVzBUa4wynAWT/c/otTXKDAWItM2CodIarnqeg/5hsiqVt/BhVMS1wOKqOE4WskY0Q2IOMsDlI0cQxUpDDaFmkwU51IU8wDjsuV42GKI6HDQUbv7gBz9gGV7Ufvazn5U9+AsvvMCKNqpjoD7p+uuvh69//eusFhFWuu7q6oJ9990X7rvvPpbGX1cWAFQ8npaVg0TIu6xcAbUOlVpuar2JsIyIjVeDfaq2WVm1J1EMEJbkfBZikyX6ZZgsnzPGDBX4Z5UKk5m+HnifmmaG/NLeqxnuE2Oo7RlM1rUJCpPps2hujYpf7Zx6noPMijFDJFStDxx9NEOEGTIREqfXQx3DNXYhL5tAa9d4E8yQGq626vy6jzYYwqKK1Pbee29PkcVKG7UeeOCBRb1a/DzMWMOferYg+tGPdg6dkm6VKTw2pB8pVthRB3AJZkgswpLtaCSvVNEq4DqWgRj08wXNVDaZ+l6hI6DZZLpjYGq9sESREJZJ4TH7veBN39cGET4NTk2Hfvw0XKbDDBTUBemdtENYeKvGiGDXZ/Ot6znow9AmgzRDmro9v+rQUjMUdzRDJpJXxBz0S2LQHiNAd2qaBR7zYOjhhx+u7jdpYFPrvKj1YISxmy5lRsRXjZT0Wuh5xBjixmtIZkhZhHEdS+P/fMJkJhcv8XkSDJnQDJF7VFL0ZAxc6FmIVysE4D0SP8GuydCPKjY3kgnp41mrVZvF302F4qjjQeeIPuBya1RqwQZWuwI1FVCbzSYLDpO5NEOgvy4Wm4O6IDiocG8jJa+MSwF1I1lQbzKTFL0fS4DmqRqr4QUF9ccxqRkS30/SwcoYYoOvZ69U1Srg7pWw/IsumtQMqZksJs4VZRxR96R6pc3JZn0mwqeWETsOH4Goac2Q0TBZmQUqq1FbyrRmSHwqnYcCKGIxT3XshuhNRjI8TWaT+TmJqmZIe+2VYTJnDHFfiYazpsGQmH/qZ+YjMBRsP/rRj2BgwKmZU8ww2+uee+6B8WRB7ThMpqQHsTYmUX0Q4DJ7HCJM5v4sFQzVcyaLVzPE/m+8UasvM1QwzAxRzRAfjy72RsCQD3hWdTbaYwRobUzeu6wmk0+D02pm3onQKJ4bOj/1xeY8LEPmoaxfxTffegdDxZghFqqOG9IM+WWTkTpDRua6ZMwdcCruK8o064GhAA2iYadkTIOhN954g/Ud+9znPgf33nsvrF69Wv5bLpeDV155BX7zm98wHdGpp54K7e3tMJ4sSDNk8iarSQgroLCjUfpcnfTKcaBHR8euSyMAgpkVgxwBLuhZG6k94gcifMJkprLJksU2Rq2WH867EyQMoC7s1RBQG13okRmKla7WbZIZEp9XrTH8ALCYg/XOEKgMbYIwQ63JVoPNkq2q1xlSmSF6X1Fdkp+DFFqkHVBHLlfHjuioa4awQer8+fPhV7/6FZxxxhms3k8ikWAFDgVjNHfuXDjvvPPg7LPPrr9sryqbVYtihWWmpOsxEQHsk+r56oTJ+GcKj1Rln4QouJ4XYk+YDA0p8oTtW6CGQHqLOmUIVNClMCpm6gw57/VL65WhOB0RKmprOFDBhTgfINjV1SW5BNQBc1BrY2TMkPInvzIEhtPe/cCQbgE+dfOlY0hhfp1XgVe1exnydVvTrQZ7kwXXGUrGkpKB0nEYhC7Jbw6aYp+EI5ogonm0iBmqUEC90047wbXXXgu/+93vGBO0ZMkSGBwchClTpsDOO+/MHserBbaxUG4yvfo8ARSnQe86MCuuCuE+MenF928kit7iWoVsAiDFT0UTPhJxvBFv0ec803owJjRDrgrUfINkae8cAIswme4iLETBuOAM8+/s0SroZLEouqTqsJpeMOTHcOlm46hXk7EEKqjTzZBSrzk2H1VD1boVj2vklIwk7BpDzYQZak+1myt8WkxAnUhBnAdY9PSaViBTR8XgJjShYg4WKwEzHq3ioouIghH84E9klYGIugcqhRqwT0JAzSe9umk1AkUvtArZpAOGNszmYEmTM51MeHJq41zPppU0m03myxKIUJwGPU8ZFREmo8dhTs9ThjhUq49bwdVRXnye+RAWz3snn2e8cS5/pO0fxFrSEKFqtBx3CJMcDJHeZO3pdoPNkhXhcSHnSq03k7UWLKCmYTI99klJXuHZkB6HulDn171KFmWTGbBA4bHRBcyfffJkkxlkIoLYJxMVqKlwk05IE/22qm7EIxX2P9297PG8Hc4zxwyR8x730doYYYZ8KlDTbDIJuAyxNrSkAt1QTLA2FKiIBd0skPcJW1YhJb2c6tDmssnIGAZBdi1MhJHFPMwULJiT7ICJmYmw7eRtjTNDVO/myiYzkTkKPmEywgyJ9H0TCRk0a5R+XsrEPGxgG9V2HGPG1JRFvmgaDWGVKW42mcpd3TpDzlh+Xmk9F/4SWgVkhoQd1D8Ejx5/F0zs3Jj9biTDhJwDXCSHeTdrk2UIXMwQeMWbzYlmA8JjAoZ8QB1ecxxPC2R7Coa6w6/CTJ2rWqXWs8/zqw6tPT9s9olmLwlrFGZIhquTTsPkW6ceDIMHfg060h2won+FmWbJJLSv6t1MMUOStYESzJCRMJnDBtI9qknMw3EKhiJmyICp1GVVGpwqS6TU2hisHhpYy8hoBWpB1brHaag6Qzy1noIhvDyTUm3Sg5PZH1qZXqQgog9QMcMMOfdLoggzpJ1Nxs9LinilJtlAFajkg1hNQ01tqxnCUjPWqlEdWtyWxUKjIq2/3hlaMQ+xLU6qkGdACM1cOw7B2viHyczMdTf7RJslM12SCPnpFHbkj34Zaw3DylfRImbIgKmsjSoKlq8zVByPfpZZ1iagXpLR43DHrT0bvInNt1aaIRImw4UYSDVwEzVO6H0l6HMEKeIzzVSg9skmU1gb3TFonSHfVG4umtc7V/5OgdF718fR8NM+mRI3FwNDuiJXAbjEPMTPs7gAWcxB+7tgHmBlbZZqztCKeYinnVQDN110MeUjNscwmYkQlrjqfs2SsVSHmcbPCjNUUJihZNO4Tq3XZoYwzf7222+HN998E8arBTbUNFnZs8wQlh5rU94YuvoRFYVTatZE7ZxqmzU8Ij1Ssamwr0sYNDOaIQIi+PmgrR/MVKAmdYb4o4sZMpBa73ccftonk+J/BNOsj5vRoosBzNAopNYbY4bI56nXw8Q41TRrxD4nwyn/OWis6KJfE1WS9m6kpliR9ijmCjt6j0Nc83gsLjVD9XzN6woMnXLKKazeEBqm1u+2227sbzvuuCPceuutMB6t3HYcJkJYNJSBJjYtM41BlQ1FiFCrUC+JMkO0v1pDhMmGh9jjSBLAEjNIXYiNLF5ePc9gblD+zUydIWcMKhCV1W8NMHUFy7l/pHDedL2kAKBisoYKZYZkOIOwmqaYCPXdVD9irAI1CmzUdhxKmKzenZICd0qG0sIjQXbWrENiv79ImCxRHaBC2ScMXxkJxfE7i4rmxXEksV6SobDiuAFDjz32GOy3337s+W233cYuTldXF/zyl7+EK664AsajqRuF7MReqF59nmqkpJdb5dpM2XnHhvI2uGiUuHVhyAZvI8mYk8GkhMlM1hnCTDKxSA7lnHOV6VvFX6eT9l6QY8iO2T7ViPUYLso+Ea/UUsCQVs+7gEwvo6UnvE1tqbjZRLjPr6mtn4DaXAVq8K1rI8ep53D1iA18BtOUGSKhalOaIb+CiIK1yecgwbPaTCSvBKXWGwVcRDQvwVDcyYqLwFCZ1t3dDZMmTWLP77vvPjjppJOgpaUFjj76aFiwYAGMRytVENFE40PhndCMH9PFCq0gAbXBCtTiXOHmG/PZ4NMv/VW/rk2tmKGUwwzZC3EWoHclwA3HQfzF6/VZNL7IxgijMpi3mSHUcaTuvFB/jAIdg4g3lSaReple/rWMJDMk2Cct1qZ42rucgwUzY/hl/ZhguPzAUDVS6/2y+yQz9Mad8nV17ZQM2993SGA3MQfRXrwBElfvZLQdh9AMZa2soxl66S8QW/Ga9jgyFEcyvXyLLprQDFnFG87m6ziTt67A0OzZs+Hpp5+G/v5+BoY++tGPsr+vX79+3LXhCOwGrGR6manb4WaGxA3rCTUYrHKtFnbECUP/Hm6MvLP58r8JHQxuxsnBrvpfhIecMFkhbjND7OvicTz3O4DFj8qJZYIZipFFUgBHjO7HLRNjFDzXw7f/mQY4DWKGjFY89gNDNBRnAHBRIXjKJ0xmJCXdp8o13RgxnGGyyrVv1/r1SxoiTCY0Q0NSM8TZ2cH1AHddJOeH9rlSWHkXM9S7AhL8bJqsM0QdEgpUjOiSguolxSJmqCL70pe+BGeeeSZsuOGGMHPmTDjwwANl+GyHHXaA8WilUutNLJKSGSI3MhtD1OcZ7jMwRvEwmYmaM67NV2GGcCEwASKqbRYHQyjcFG0m2BNcvFa96dHGhB6nQMAQ/zznXCF4sfTH4ACEjkE1EU1PX2NUlyRT6/2KR2qxNt7v5xfu0wFcVPvkp7URc1A3wUCtcu0CXCZCccRkIhZNrScAqF43RswkEyUuhjKKbm+NHaEQ80PVQhpNrcc1i9zTocdQdElUt5de8RrEuj8wF4rz0dSliBB8vDJDFafWY+f63XffHZYuXQqHHXaYVOxvuumm41Yz5Meo4A0lJlHGaFqkbWIyignT1L8WIJM2wtp4eqyJjTE3DL2a4k1xHHhG4opmCBeCuNKmox4tv349e+xrxjAZZ4bwa6N4c+1C9ruJ4xBgiGmGFGYIfzfCPgnARc49DVs2rV0EMG2KkWJvgSBCAfhhrOADclzsk3ydoaw4vzH617A5qMs++TFDMmxZyEFftcNkZD2r13mY7+IMMmY0N+MctJxsMjEHBXPK/s2SKfDamiHKqICjtTOTTeasG1IztOQpSLQ0A6TThlp+OOyTSzNk6TNc467OEGaQYfbY4sWLYbPNNoNkMsk0Q+PVPMJjpV9RU9cHAOmUngevlmtXWBuxgJkAXLj5oneqtjTIjPQDpFLmUrktdzYZxuRNhH6qbSPLlrHHnpaYBEO2Vzpsa4YUj1t3g49TzRDPJsP7wGlpogFO+TWOAYq03WNQoGKqICINxUlm6PU7ANpbjabWq7qkpu5l2puJK9xHs34EO8vPkW5bEQFFUFNnKRWPM0N9AKmksTBZyk9fRcBQvYbJsnwODjQD5MRNJcJkvXbl6YRyLwjmQ1fPQzO9qANnghkSc5o6JWnLbEicrk2S4epdCfE17wI0ZcZto9aKw2QDAwNw7rnnMtH0dtttB++//z77+4UXXgg/+tGPYDyaJ+1dKZKWNoC4PeI37CtTyMvPzBjxrvmEVAXUwis1MAYFXGIREaJg3GCcTPX6XIQLg4PQ/+RT7PlrG8WgIAXUMYDsIMBwN/vdDHXu3XzFuUoVCgzAsNdphckcpk6EFVzZfTJrasRsnSGq5+EgQyec4QJcfunJJPsr/Bg0TOYD6sQYiti5wkEkUJHzkGasibVEJ7WeiLT9NEMZMvXqNZus98GH2OOC2ajbA6Lby9qaIcLOmpIolAqT6QFHt7PrZswNsU8+UgvJcGUHXE17x6NVDIYuueQSmD9/PjzyyCMuwfShhx4KN998M4xHk4jbZ4GkIMJEQUS/G5kuxCbqUHjYJy5wNjGGKLroElALZsiyGYp6npBd/7wVrMFBGOmMweIZTpiMfW2e6u7WK2hsvjybLE4oXBkmQ0/XIPvEvrOiS8J7zdksde5dmk3mnBcHRPDsS3682pletIqvwVAcDSO7mtqKmkzSe9er1o1skGsMP8BlqIArXbP8mKF6nIeF/n7o4jXtXtpSLXw6DDDU5WFZTLRCconmRVKJIZ2jky3sfOlBdK749TCxLnqKR9LUessBj1EF6jINq01j0cV9993XFYNFlmjRokUwHq2gpF7SMBl6qWJSGmlj4ZONYzpM5hVpjxhkn7wCapFNVu8C6nxfH6z5zW/Y83UfSQPEYnLjYofSZ4fI0JxMlqyZbDKP2NzW+ehvjBScqmM4HqkJ9glNfB5uKDkZ4g3W/WiF4ij7ZGB+iFIHnnkoGBXOOqlh8/Asmjd93wlb6oXiCn51n+TGiGyHAcenSrb2+ushv3YtpGZMtsFQXElikMyQYybWRQqAJTNExtEDKuDp2SiZIdQlmQiTeapcE80QNIZEoa7A0OrVq2HatGmev2OqfViBWqObCiKoR0o3eD3vRExIb8sE6s1ppUAr2QaqLqmpYGIMZ/MV50WmkJIJqbOhVMu6/vFPJp5Ob7wxdG1nnyWnAnXMAUOt0xzWhlTErdTEhoegR3yeSzNkIptMiLQDdEliRuswQwXeQ0p8ppwj3PMVzJAW+yTaCri0VHkHyBdMMFxevZurcrMBcErZJ7+aMyZAnS3SjgUydTZLwF9aZ/MQw9Trrr+BPZ92+iGQT8YgT2t94Xzj5TkSLVPl+/TWXqVZsksz5ABHE+s7rcxPHR8je4gnukA0QxYmaeiPMa7AEIqn77nnHvm7AEDXXXcd7LXXXjAezaEfweNlsRRoI6yNeiOTZoFsAzDg+aqgrsD7O/HJIXUXBrwTV7o494Dwd7n56uguqmQ9d93FHid96lNQ4KhN1hnCX7lwE9pnOIuX0kYhXGq9twI1bpTiXOm1yqCAy60Zsu9d9+v0N3hwvGt+LAJE6LWrofWrKOAa4GMY0D6JMgQku8+dhVUwyz7RkLg4VxzUibBKKMNu9LJRK7ke/Phc91adgaG+Rx+FQm8vpGbPhvbdt2ZXQYTJZBKDYIY6Z8n36WV6eeUDdO01yQyh0yOK0VKnxEh2qursqmEySx/Ujatssh/+8Idw5JFHwhtvvAG5XA6uvvpq9vypp56CRx99FMajqfFeSp3TFGgj2WQyrps3zz4p2QaiZYKYqCbCZLR2jpjglBkSGxnVmdSD5bu7YYg3I247+CCwbrOTBaRmqEA0Qy2TIDFiZ7tYWpsvYW3436SA2gVUTKW9g1LYkSzCWrVzAhq18vCoEZDtwziyUBxmQLrEzTmjLBrVjzjMkA5z6g33MQE1Pw4T54pmrLnDZG7nKosZpXUmoO5/9ln22HbQgRildoEhlsRABNSJtlkA2XUG7i37xhJgCNfdVCFJ6n2BMaAitJQ5VT5gMkxGQq20REBc+S7jzSpmhlArNG/ePAaEsMjif/7zHxY2w6rUu+66K4xHK1bm3HwmAB9DoWqltsOAgNo1WfLVEWnTcIaLGRKTvs4Kfw288AJbFdNz5kBq2jR5Dpw6QzGA3g/tFzdPhJiBAmbFNUPUW9QPW+JnqRXB3RoufdbGy566Bbvi3tASm1tKuDrnZoZ0yhCIe5KyT3YW1pCbGdI5Dr/MO8YMDbo1QzrZlqSWket6SKcE6pYZGnj2OfbYuvvuNsPF2Fn+j/gLhl45GIp1zDAsoAZP5p1dDsQEYw6BLLCpPUSNYORcOjGo29BoXdcZwtpC1157rflv06Dm18jPL0xmpkIpCZOJCclAhDnv2g9wGcvIIanNCZUZoptvnWU0DDxnL8Ite+zuWlhc2WRD3RIMiaaHBQNaGxpSFNS58aKLRNDu0PPubKPQYwQIj1VxM2oYwpoQX8cUJlbokuS9a6DHGu0Vx8TNMhTHX6cDTn36uLnHMMEMOVCKNu3M8qxOV4mLOmIJcqtXw8i777LEhZbddgN49y47MkY1Q7heiTULtXtrLMhjooOWw2DH793ZZGA2m4wdCXYcdJimIQ7kUasUM9ok2zkXrrXXGt9hsoqZoUQiAatWOSnEwtauXcv+bTya8AT9BI+0QqkRgR3/3RUmoxujln7E/v7S6yUMF4It4RnpLCxiGaYbil9VZbop1IP1U4+UnAOXZkhY80SIy15COoJdR8/j6U1Gw2RagnZvr7hqsU9qmxJVi6YHIrwhLBqKM5laTwXtNohwUqBNXQ9P2F3qqwwwQ6Tlh7txLikkaCAsY9oGnn+ePWa23hoSEyZwUBcjmiGSwINV/1smGwERftnCrnMlXqcFUPm6SM79IGWGTMwRRXdKWeAkAQP1BIDrGgwFnajh4WFIp0X74PFlpXvXmGBtVPbJHSaT64FW2wT3cRQUhsvRj+gUfKNF/lTvhNQZqqMwWW79ehh+6y32vEWAIbEZSWYo5gZDcXNhMhbC8qHOTYQyfHvFkZReMw1nqQ7GmQvi3hIgwtuIIgwz5A/mjeiSCt7rwVgCFajoOAs+qfXMuZKhOBFS1Ni0fEJxtNSBLaAWoKtQhw7JR1xCcFfhU2FNEwCSGTPJKz5NVLM+RRdNjGHXFBPz0BuuNgHqaPr+sEukDeOaGSo7TPbLX/5SZo9h5lhbW5v8t3w+zxq1br311jAeTdCofqn1NBarlS2j6HmoSBsXeiN1bZQxkH2S/XHoxqiTIUWEgrIXltQMkYWljpghphfC87z5ZpCcMoUch1JniIIhLoPUYbhcjVo9AmpHbK7DEojzTNkO2qXb6e+kD7KpToxtKEr1dC2vl4QUXeyTaMdRMNf/LObJinMXJdXT8/iEFEkIS+0bFqbFhF9NJhuc5us6TOaEqvew/yA0QzEf3755IkAiZQjMK86ulYMYr6nhSjLQYeXls5i8f6mTbbSWEbk/h4Z75TW3uHtVT9e8LsHQz3/+c3mifvvb37pCYsgIbbLJJuzv49EKAgzJxcvxFk2J38T969cgknYwpy0DKh5CbILSs1braYB2uwF30UX7bw7gopO+jsCQEiJzFiqlN5mHGcpppos758qbWg8QSzbLoUM3ohRjxHARdi+C7L7KtOmHyajWhjJDfGyT4SW8R13MkMmyEFTv5nJKlIKIBhguNLdGxV341B4Hq5AnjIzhSq03VNfGpGVXroKRxYttvZBI1FHAkOUBQ2mjIIIyQ2KsZKLZ0BjcSUyk5P0rLJVuMxJdEB6bixnK9sm1N1eHbGBdgiFsyop20EEHwb/+9S+YOHFiNb9XQ5nD2pDKnlzwSLMNzBRdFJ/lLuwoQyY6qcOqB6QWFxObjA4zxI8DNUhi0su4NY4RT9VdmKz/6afZY8ueeyrAMe7OJhPWNMERUGtWClYF1K4MwkynPktA0vfVxYDdVxhugLyeuNkl0iZ6HvCCobCgzhmDsKRMpJ1XNEN6KelqiNdmn0aUrLjw4JQCLtc8VFri6GyMgkXzCNqFwDaRMdRvy5wNPGPPwabttoNEJ7/v1WwyP2aI3AumOsqzzxPrcaYD4taQMfYJ1z8KVuwxOiEBPdpj+B3HcLZfrr0Ffr/Wk06srjVDDz/8cM2AEIbfLr30UpgzZw40NzezLLbvf//7dTNBpal1hvI5WewNb2ynXkjOXGq9FRDC0mCGiofiiMDOAPtEN8Zh2QsLIJ5uq6sK1NmVK2EE28zEYtAq6HmyeFkJUmfIV0Ctn5JO2Q43UHGDoVBjFApw6qN5+PKNBUg4/VnlGAkGhnQFu7S2FAVD9mem447WMGx1cz8hOBVpm9Hz0GwyZ46MKNWhdY6DrhGUtRGAS4T7tMCQq1ec+CwChliY1wATYdD6n7LBUCst7KuAIctSwFDcYVn0+im6nURqqaZOI13rO7oAfvb7HHS8g6E9y2cMMKBBFOFq52/DYp+KJYhEoX4c0bpPrf/ggw/gzjvvZB3rR0bcLMHPfvYzU98NfvzjH8M111wDN9xwA+t99sILL8CnPvUp6OzshIsuugjqxVRhGgsvccRNw0t6jIqYkI6HO0JSoJ0bWadjtnsMb9l5PoaBEgHuRq1Oungs04rKmLpZhAeeeYY9Nm2/veORulLr4/6aoXhSGwC7NEPKQsxAdlMHbhNa1DYydSc9ZX9413spgI2cf2Mpvbip5NbqhbDEcZAaQAjkxSdmOOASG0ocM4EqHcPF2jgbeZafF0fPUzCU3ed83yw/PgqGwh4HLVDpJ9ilTVTDbvAuZoh/Bn6+ELAjGIrB2roJmeBxCna2dW8fMBTzT2KgYTIj2WQ+5zvZ1AHx/hXagGufJwA2xFP+kAWJnd3/lmqeAPHh9+3voqUJ5WCIX/dcLAbDvPREMtkEcb5n6exT4woMPfTQQ3DcccfBpptuCm+99RZsv/328N5777EbYZdddjH65bCq9fHHHw9HH300+x11SX/729/gOS6kC8pqwx9hPT02vVhNc1gbvuCSVgOpGEb17YVMS0yrsE9ow0T8Jjdfnc7fAgzx33ERFvVNWNq7CP0YCMXFYnGnUasAQ7EkD5MN1rdHSpkh34V4grweerVznNCPhxnC64FhstxyexzckMNUtqBp78PusA6rRIybSq8mo+LDDInQKFqagaFerQ3FzdqAJ/STSTTx72LiOJzQty0E52AoRhiu0MdBwBB/ZJohwT7FnCU77AZP0/dlRidfr9CSTRMgnrPBUD2w8FhbKLdqFcQyGWime4xIrRfMUJEwmUnNELVUZiLE+/RZm3b79ves8WyM5kkQ7zKQvCLLmsTs8xIj2WTJZgmGrDpshVQLq9h1ueSSS+CrX/0qvPrqq9DU1AS33norLF26FA444AA4+eSTjX65vffem4Gvd955h/0+f/58eOKJJ1g7kCC78sorGXMkfmbPng3VNr9uwDJMFk+SZpc6qB48VO3QSI8jPOZaG7rQ6eqScCPJjvQ67FNSbCj6hb+wQrO6MSbjCQm4dI6jKh7pXnu6/00puijXSRQ1p5plmEyn2zvtKO/RESAAbp6gH5bJ5QIXA3uMidpp706Va4e1cYEhPoZuuE/tscZYG/48k2rWPg7gY7g0Q2wMDrjSLdrH4VeBmjJDTelW/TF82qMMk03W1sHojVENh6R5l7kQz2SUeklKbzJPNpllUGvjNqZ7bJ7gMJE6EgUy+QSYd4Eh8ToNoCIdUSLSlpm8ySYz0YXxBIbefPNNOOuss9jzZDIJg4ODLM3+8ssvZ2Etk/a///u/cNppp7GU/VQqBXPnzoUvfelLcOaZZxYFa93d3fIHgdpoVKDOupgh0GdthJ6HeIaDIzwTIJaAGA/X6GlUuIZDgqE8ZMUYBHBpaZ9EO4540rMQM+AotTajvwhjbSHmkTY3uz1Sl2ZIuqW28c3KYYY0rgcpJKh6i0xfpYSXwphFwFBMOeUpMkZYsBVUdJGCIRomC33dXTWZgGSTcZF2ssVAppdghmLurvVijJQ+UCkEhMlGRLVuMkZo9olsqOI4Rsj1SGY6nTpDdTAPsTkrWts++7j/gX83pwJ1cJjMSCYvqRnHfudhZJm8orMukq/uYYGbUEANxsJktpZK0WsmMhAXe8g4BUMVh8laW1ulTmjmzJmwaNEipudBW7NmjdEvd8stt8CNN94IN910ExsDe6IhGJo1axZ88pOf9H1PJpNhP6OjGXI80pygH+NJiPMpqVeNmAMuzpygDfHmjQiG5BhaTIQb1OHkGR622ad0LM41EHnNDCkRJnMEe8NcHMrCZHUEhnoffliGyFweqS8zJMJl9rlJIBjK6wnBXR3l/ZihDGqGNPUjuWwwM4RMHU/f1+LpCnloGbJgo/XorLuZIdYBHsHQkC4z5ITJqNhUzDibURnRUsC4Wpfw689CWIJ9YuL/9XrgUdQyworv4FOTKd2O9f71gDY5xyke5hVzEC2Zaa+bPlWF/n4YkM1ZD3L/Y7E6Q61TGDNkopkxbZWB4HGEj8fWyaYOZwxDIMIz1zMooI7r604tC+assCCZTkqJwohYexNpsofUT1mTugZDe+65JwtVbbPNNnDUUUfBV77yFRYyw3R7/DeT9rWvfU2yQ2jYGHbJkiUsFBYEhkbDnI7yQMrn89APbvpig9ehOAXg4uwM2pCoEcGABc5QS48ZkmEy529DHAyxMRgQy5sJk8UTkODr0zA/L8gMsTEKo78Io/U9wj3Sgw70/JvaqFXd+2IGmCGaeUdL6KOlUHPFw5Y641g55300y4SNgR5kwtHB4DUJIwrG++WiOwqwy7sWvHd0AmAKeqROG4AYAXVhj4PqkhxRcBby/PKkEURY67WPw6nJRICKDxgKfRx5/4y1LIejqUw7xHJrmFYtfOYdKbrImWYBTpH5iDfVT5gMw9RWNgup2bMhvemm7n8sqNlkhF7p2ICBJcFw6TdqFX3DuNhGgCEGHB2taLgBMK3d+VxV+odhy0RMH6hM/8CC8/6Rh55N4pA4xf7bkCgOnEjLORFlk5VpmC3W12dvwt/73vfY85tvvhm22GILo5lkaAMDA5K6E4bFHoU+oF4MN8aJvRakcj5hsnhS3sh6ExKgadiCeCwJMVaGPuY08kMQwRbOvNaN7IA6Z5EdEJohQ+wTLq4nPVGAWRPjsGZjfr5ECBBj/Agi6gAMYQuOoVdfZc/b9t/f8+8y3CKyyZon25ldmx3Mfk8kUkabqKoTNRlPs9i/tn5khGiG1DAZ3ldJhxELCyJQo4JACG3igjTAju7aUgkDDBdufOmsBRP6sTyD/SdRekIyKsO6mV4FiBUsaBl2wpauMfA4hjWZOqKvkt3FBTMU43qenM3UhRXs4vzddLkF+71egOQ2bqE/24ixyJ/4PjoaKwPW98QT7LHtgAO8dZt4jzUbSChJDBNmA/QsN9TtHXy1e+x5uhXiHMSEBir82gpTi58m020s4UTXod7ydfux472CU/BWdK1PUDAUMUNl1f3BtPodd9xRhsyqWXX62GOPhR/84Aew0UYbsTDZyy+/zADXOeecA/VkUz+04Jq/5eG97VPw92N5dV3RVyaeMnKTta+14I9/zUP3NjFIHIvVQgEGeVok27TYwpjXEh6LSto0a2JQgCE2hn7tnPYPs3DE47gwjcBrF7r/DVkvKTweZTDEqk5bFmvBkZo+3ecVCjO0zfEAm68FOPjb7FeZ3adViM3RwXiYIQRCLAxgd+UOPQ5lhpTLmoqnIa4wQ2HMdS3jPj3WMu3OGCEDWbjB//CGPGy0GuDPn7IAWpwx0DJYk0kTqOD8/eKdBZi7yIJ5J8YAJmAzzUFnjIwzhhFxswjFEfaJhUz6bTAUmhkqFODyv+QhjWHc7gzASc6/JVW2ow6YIbTWvff2/qOqGcJ1dvfzAWZsb2v3EikWYjbWNwxDuuScM6cx1eKs7xphS3ol3YALw+HtRpihOKtRZa9XAuwOiR5rCQKG6qj6f92CIWRlPvrRjzIR9QTsGlxl+3//7/+xoouf+9znYNWqVUwrdP7558Nll10G9WR7PJZnN9emryUBEKiwYoXDDtth4Ebe6jWAZAFg8us5SB4Tt2tESPYpBTE+63WASmKkABfdl4cpU5IAXKs4KOolMfYpoc0MpYac96Y5k+ZiO0T6/mh7pEIv5LcI+wiorfbZAB+7Qv47MnjaWgX5XrvTmQpUgGQqhtbaEM1QTOwqYgwEW6QgYujQDxFpC5d0RGSx4LhNHUwjg2xneM1QlgEhtE3eiwPMcDJlJFDRPA4EdXu/aX//WW8nAea4heAIVExkk+31RgH2fqsAfXtbAJ1Cl8THwLINzGkJf64wTIZACC22Si2ngCerw0gTYF0bXrQIskveB0iloEU0Z/VhTqVmCIH9UT9xXkCypvQ0Q7bFEhmXU8KcRpY5ypn/sOt7AXMcY77roj2GA7gwUzmsJXJewDVsUTCU4F8nCpOVZVhX6N1332VVoatt7e3t8Itf/IL91LOp27bdZVpkSKX1JwsmR9gRMWYyE0B0F2dj2J+d1wghznp7BHZ/g62GkNjbZhwGeYkArBIc4zS0DuDKE3VgWtEC2sBRH0ToWmFkBHofeog97zj8cN/XqF3r1RR6ESbTCo2KbLIYZi8pwDGJYCjBmKGcIWYopkiocYGMkR6EJkTawlOXPdbwM9NtvK2tBogYcm6mQtI9BhMjmwAq+byzZfH7WGif0JJYKZi3NdBh0c67vwDtQwALO1MM1DnMUAzSTVgdWhPU0XVIiKoo25HBXlji+4yeU9Jz333ssW3vvSFBGoOrjKPUDKlrHy26aKBycyyZcTklDFCkMEzGHaLQmiH390an1zVGGsGQ/zoTNmNNNsnm91ASs8liQnc6PpmhigPnV1xxBaszdPfdd8Py5ctZUUP6Mx4tr7jtGH/PiVYZKEKVoR8dxG15YsqDIhSXMDNGc7czC8XmK8IAVPukJbAj7QQySvibxa1lnaHRA0P9TzwJhb4+SE6bBs1z5/q+pqBUoIa8+/s6JQL0U9Lxs1RmKJ3IMGZIu8JulmiGaDsDdl9ljDBDNH1fbLKihQWDjMmMPhPRO+hZ6GWrARDVuvXGsLIEOPIvPJgfJO1ROrSzsJA9RiCE1tYVk7okdEzU1gzhdUnkOJSv6YTJYNQrUPfcey97bD/yCN9/F06Z3ORVRgOzIQWo0yl4yx9jySaXU5KWzFBMj7WxCMjGz82roTgHcGmBuhj5XP4xIr8yiXWGxJoVaYbKM8wgQ8Mq1FTQJhoToq5o3Jmi60PSU/QSSiUzkOcp6TrMkCumzB+HBOBKZCAWG9Cng0k6UapgwXAcF/shoucpsC+ixdoQ0JDMe8FQjG/7Wg01Na3nPr4IH3G4rN/kMgzpiKdCQK3UkJKNWk0UK4zFPNVv8ZpjvRDtjZEyQyoYwgXSgEi74AO4MJSMxgJ96DBYFmR1wmRZB1mn+HDDIsHAUB83zGoKYrhkZhGXZYTX8xANF/+aQ0SXlMLq0JZm2JJuqDG/MFn7qNcZGl6wAEYWLoJYKgXtB9tJCSWZIQ8YIs4CKR1QqYkrifq5pJVVgEqz44iGZeXxOKhOiHwMu6/SrdwRLUCBjK/DDGVEso8YM4nMkM3PapVOGU9gCBu1RuY2dQrYAmonFisyjvQyvRxjWWsJFL+N+IjfzNCozVkL+pKEfWJ6niybPTpjxGk8XBXsIhPBwdBoZbEUhoeh76H/sucdRwRUOqfXMeHPDJkQUMv7hbUucf8bgmy22GtujG4wlPBcD1Fo0xiIUDwHBoYS+toniwAuEX4VzBBrOEuKFZrQPgl2doj0IEStjc1K6YTJvGBI6PYkGNJsPkoBl+LH2QJhDFuOcmp9z712iKx1330h0eGwei4TzJDTpVo65cwYo8rPlVahWH4ykhlIQp9HzyMyvUIzQ4W8K5MzRZkhDoZihtPe1bU3mUBmSIxRX9nadQuGsO1GZIqRGhEOMyTqNyAzpJ+FRbNGm/LuGhG4MQoGI7R3otCoLfjRzQiGHMCVyBW0jyNGwFCShMzY78hw8a+vE17Ssf4nnmCF3pIzZkDzzjv5v4j3REIrCAE1FQkzMGRCCC6YoYS3XxEWQ8QwgG5XbhcYcm+NGexXRJih0KE4Vy0jhX1C350wXOG1T6QTOx/OCZPFIJZMa4u0KeBKcK3NkARcZoTHfmBogFeBZ+MQzZButW7bfJihVPOoC6iFXqgjIERGmaFcXLkPUvyepc6CAVYeC5AmCDHDrnm6RcoHwmuGLEjk/bPJmjjgkhIFrYK3ztO0WkYj2eysWZFmKLKwRrchrEOCNiyAChOm6Wdh0TGauOc7KDMBnHivjmCX6geas27AlcYQlpws4RdIcX7Qkop4k1G1EkSMziLc/+ST7LH90EP9Q2Sivol4mkz4gyEuBM+baAxKmtrSEJYJzRBlhkABKk2pFpm+r7XBuwTU7jGS6MUbaKhJz39CCS+xrdEE4KJgiD+VmjoWMmnS1wyR94lzIoqrsjRr1KjohsmsImEy/N0Vfq39PMwuX86as0Ii4a067XOuaBKkax6S+aHXxsI2BNS0VYaXGQoLhtzMEA2TNeFwLISlrxkKYp/YmCmnn+J4LboYgSED5gphCa9UMENEmBZeYGe5buQMv5GH+E2LG6MjoDYDVETq7aAEdWm7xYQmGKJhMq9mqIkIqEeHGRp46WX22LLbbmXVBZG9yQI0Q1o9vURj0FjcQ+EyZshEV+58MDPEwJCB9P1iuiS2XbHN1zImbhZetuy7xENxcYMFKhM8pjwkwshM++QwEUZKHfBHkdHJmAjSfNSELkmtY2hruBzGcTTEtIMv23OwaeutIdHu1KBSTax1NIHFpRuKx40ch6wzxATU4EmtF2uvTp0hFzNEnrPUgljMQC0j9x7iWXuTyAwZYJ8a2CIwZMLIgiLEmyJlkQGVuK7AznIVxBOAa0iU6E82E9bGTAhLAC7KPhlhhohHkvR4J00Qi/HKzaOgGcr39cHwO++w50FZZMxYBXD+lKee0xAKWkwyQwbqDMUT3qKLLEyGG7ym0JWHPtkwClDJpNrYGNrMEBVQU2EaE+YnGFDRrRTszwxxcTMHXPoMFw1hxd1JDGwMcj1AX18lroZgn9gdZQKcFguT4aZLixWOAhgSDknROUiOw8UMkfOHJqpD58NWbi7gleTnCMGQq+gi/q1ZX89TyLvAUBNZF5s40BKVxkKv74W8i/lPqxKFZMQMRWDIhLlCP/bjEAcRyAyJNGsdGpVOFsz0QhsRVWlJ4S8t1oZOFsEMSVCHVK1ZZkh4125mSIxRezA0OH8+W/xSG2wAqenTgl9Ijl8WXVTCZJJF0wF1FmWG3OcqjRV2mQcPeptvEdamCXttkTFCh+Ioa+YJk9lARW6+oA/q1DCZCMXFjJYI4Gnvwllg4SXnXIUWN/uIzUVGJzsCVkhQv7CjY6qgXYA68drsqDFDLbvMLWt+uJYRdR4aaJXhaIYUZoidq7h+yRFkhnyYf/acX4lE3OwYKjOUShMwVAd9IUfDIjBkwNyxWPtxiN9Q6MHLNGtDNKoAKnJMiuoNsTYCcA2K5q2M4UpWmRlqHlXN0ODL88rySF2aoVTSXzMkepNpgCFxnnFTpA1T0VI8hOV0zDYTJkORsTDWeJRujKE3eK8oWBj2vKOhHxPMkHRIBIjAMZAZ0tYMFSlDgJ+OoA409TxZJwVcMA4yiYGXIdDWVxUJk9nnimjRNHphhbHCwAAMvfVWWfNQ3vN4EEn/eRjTBkN5yQLHUk1uzZBgbXQdUcXZzVDNEF9zTYwRJxrNlFpfKtVKmP/xKaCuOJts7ty53oZ5vJNzU1MTbL755nD22WfDQUWEb2PKCoi4yU3GmI+YBBFUmJY3BIYEUJG/Y7l2AyEsGopzmCG7iWCaheIMMEMEANFjklStbHBqjZpH2lyGR1pKM+QsXgaKLkKM1WCilsJUcbL5mmBU0MXOWBYM8fndhFWbDaTvu4rhMRBBak0h20iFrgZCWJIZEi1xWOhHNDPWKVDpDZMJSzGGywkpmmCGBPskdHtpITbX7JLuarfAqw67mToS7qtxyGTw1dfY/YLZnKmZM8svHplKMiCk1hqSAFirVUYAMySACn/UWt8DmKEmXtqC7SGWhoAamywXY4ZSmLEWMUMV2RFHHMHacWCTVgQ8+NPW1gaLFi2Cj3zkI6wq9aGHHgp33HEHjAtjiNv5NSNAhAxhOUBFpyhXIkcAlwoiWO8afQG16zh4OEsMyxguw+yTGiZj4T6+sNQaCuEiOjjPZoZaymCGZC+hAM2QwwyBgWwyHzDEWRvt3ksEDLGSJpQZwi7sjFHR2+Bp0UVPxWO83myD56810FZEpL1L8b/CdpgoUCmAirAUznHTYnM+P8R2bIdlSGiUM0aVWmFkODhMhseBgl3+91qHyQZffok9Ns/dueRr6XoaSySLaoZ0WHk5CrLjtAI1X6tkan3Ye7cQzPxnOBjS7jCg1jLyYYbiwtkdp5qhipmhNWvWwFe+8hXWQFVt07FkyRL4z3/+A9/5znfg+9//Phx//PEw5g3Fb5TWFMJjXnsIgYoIL4UW0yoCOy8z1EbaP+hohmgNIPe/YZhMLDim2CebRXMzQ5J9qjEcGnrzLVZfKN7aCpkttyz+YqoZCkqtN6kZgphdvJNS9IwZ0s9ecoGhQsy1INjMkL5mKFYkY4151zT0E3YhdjFDbjCU5GEy/TIEhcDjsBkufQ2Xuz2Kcq5UfVVYoELuVcY68v528ly5wku13RgHnnuePbbM3aXkay16jnmYTNUMiVYZoc8VhslEp/dUiy2aVpghUYLDFDNE23E0YaV5MUZeYw9BZsiVsabsIWkEQxEzVJHdcsstcPrpp3v+ftppp7F/Q8N/f/vtt2FcGMZ7c15GRYQacNOKVzH1ko2BjfwMFPmL+WSsOWPgcQjWxjISJlMBl60ZGh0Bdf/jj7HHlr32dDUn9bVC3hFtCmZIFW6KbDJNzdDMtRbM/DBr9yIjls60uzff0Kn1FAy5/6mpaYIRwOUKXXiyyQQzpJcCbZHjSHAQMST0bgxw6WfFUaAiWBthYgzdBqcuAbUyRprrqyQbGFLPY40QRglD4OS7MlBHGBXscF9LvdDA8zYYat13n8q0TwGaIZlNphEmQzZz1wUFiOUTLIwsDPtOoumWNcHq2LTmGi2ImOG9AbVLpyiAy8MMpduMRBfGFTOEuqCnnnqKaYOo4d/w34SwTTwf86bQjyJM5g5h6YrfVM0QeEImJjK93FStV5eEm69uuri7ArX735LJFiniq3U7jr7Hn2CPbfvuV/rFVDOUDNAM8YVSZ1nBRel7N+ahs38dJM+dANCqhMkIiAhfHZp8Q8sdnkwLzZC2uJkwKuq9i+eJaoZChn5cVa4VPQ8DXHGRV2SoDAEDKnRjTBrLWIsFlCFI4RyPkeMIucFTkTaeCtr3jp0rF6NSO2ao/7nnGBhMzZoF6TlzSr6ebtoOGHJ/X6fbe/gab/vOAzjxvwWw1r8MmW28YIiV0bB05qAb1LqYISyuSsT0OuyTWzNk+Qiok3pjjDcwdOGFF8JnP/tZePHFF5lGCO3555+H6667Dr75zW+y3++//37YeefSMd8xYUomgCfTC4GKDJloUJxF0yLbjVCcdtHFWECYrAVyBtgnv8w7Ycl0CxTkuaqd5bu7pV6obb99y6TOldR6VTMkr4cGM1TIwwTelspaHQcg2f5JxgwZCJMpzBD9trFMmw32Dabvqx+BDYBdxSNDgqGYB6h4AZd2NpmndQkFEWIMS0sf6LqPPMeB4b4EySAMC4aczdcqWG5mSIR+dLOwQlg/d0ha99/PN0mnqIBagqGAOkOhhcd52Gs+H+Oh1yCzFQFDnK11WJuQbOAw1XC5mSEBhrSZIUVqkVTvrURastnjNUxWMRj69re/DXPmzIFf/epX8Je//IX9bauttoJrr70WzjjjDPY7gqULLrgAxoVhX5kizBCCIRkyqRLFmTJEcbrYJ+U40uk2KMhwnwYYImtrwmeMEcmo1I4Z6n/6GSZiTG+2GasxVNJc2WQBmiEDAmq6+SYtqlYAiCtp72bAkEuFwVoNQHaQAJWQG6NLFAw+QIWGybLaoTiPMF8BXOGz4qwiYTI8jgTRPuUMtC4B7xhM3Kw5BgVD+YJd2ZpbWmQvSTBUOwF13xOPs8e2/cpgZwOYIU+dIdHtXaPOkCiki+YKk/GkBkfPE5INJEydyto0YXFVNoZ+RrK7jpzCDMWTRorqjiswhHbmmWeynyBrbrYv4LgwBXELzZArTKbLEijiN08IK9Pu3MhV1AyNGBAFU5F2QqnVgmAoPwpgqI/rhdr2LYMVUrPJAgTUCQPHQT8zRrrHM0vhHMON0TLKDGWpR45jFLJOKC5szRkyhocZQtBItU9hN60izJAp1oYehwfU4cZIs8kKI/rZZExH4s1e0mVtaFsRBHiUGZJgqMZhspElSyC75H3WZLVljz0rLD1hp9YXrzMUPguLzmDWOFU8T3DWRrPkiEvDpTBDrVgFnjBDWnWGikQXkjg/Ril5paHBENrIyAisWrXKU+xto402gnFlaphMzfRKpI0IqIuFl+yOwwY0Q0ViyghUBjXpYDRXRoOV8IT7hmocJsNjkfR8mR4pskieMJnCmsj4u86Xo5lenF2Uhtlk+RFtZohquPCki6wi+x/tflviKoVNs3YLqH1ABEnlDs1EUKBi+QEVh7XJhwUqLjDkDTO49FVhgWO2BIvmqp2T1WafrLzlajGBDZnRsKtXLcNkQrPXsssukGgj4rgywmQM8CT8NUMJAepC94XMA5WgUeDYxBwSXgNIBwwp5QBSJGetPW33ZtMuqltEd4rzJRFLOGVNImaoPFuwYAGcc845TDCtbioY580rRa/GW2p9OudF3NoZUsoYntR6DAEYyMJypb2rCzGpQ5HXgCp0jLjlvv3STPskJiTUxIbfWQC5Vasg1tQELR8p0pw1UEDNtyZFM5QQi7POlyMLu6WcK8baIEjW1gxZrjBZU8GCbIJs9Mh2CBARdoN3aYZUel5svvyfQ49BmCGSmWOPoYq0qwG4Mu4imKHDZFQzpI7BwzJyg9cvHqmGycQYNjNkha9rU6H1Py5CZGWys8Qpi5WhGdJpY0ErHFBmqJmHsHQdUTVM1mY5NcVasdaXq7CjhmaIOru0jAq/p2KyOHAEhsoyrC6dTCbh7rvvhpkzZ5YldBvLpqZFelgbLFqXMCugTimLPY5hoo1FUZE2Ybj0U+tj/mGyJOlNViOqtv8J7pHu/hGIZ9zp60ULsYkS/QFhMtmoNWYKDMW9YCg3rB9eomAoX4Aze3rhtxM7YX+xp7jCS2FBBLmWqlZBMhExY0DFrs9DtB3JjBFdUrFwHzYyNiFudjEbKjsrBLvitQayydDroBWPxRj2um7VJExWGBlhmWQVsbMEfLCNPCi1XjcLi5bRYJoh58I3o6bOQKsMa8R9P7ZxBwFtRst0PoaB1PoAGYQAAQmRyRuBofJs3rx5LJNs6623rs43ajRTUL2vME0KqENmG+RzrgJsFNVLMCRqABlihtSsOKTPHe8EjIyRYLefPfGwBgxOxpgJ4XEF1v/sM+yxda+9y38TWViDBNS0lxgukmLBDLv5FmhrbjTe7V23Po9LM5QvwFndPdBRKMBhzRvaf4wJmIIv1WdtWFiGFPnDYp7scHSzl6i42QfIu9qKhAZcVECtjIGAi4ibQ4cUi4nNBVCRndhDniuFxfQDQ7WsMzT0yitgDQ5CYvLk0gVPqXGghuejuIBar1ErDZNlyNrXhKFqUkYjbE0xlRlKxDLwu+VLoTcegxl7zeJj6LE2WJPJtYdQYMSvdUwy/5FmqCzbdtttWRXqyALSIv2YobjhmLLPGCYE1MWqXNvhPrNjxEmfqrTB+jzlGgKYwRdeZM9b99i9gjeSMyDAUECdIR0wRBucWko1YnsQklofcoOn7VEQGLVbFnyipxegwxZu4gbvdP4OqxlyZ6ylaMVjBBE0TBZy04oVETcnk5y1EWOYEIL76PbY2LoMlyekSFhnFTiGBCoqcBdV8wU7y8Zg92s+fEp6BSZYIWRnK4k0UAG106jV/X3l9dDQa9LlLkM0ss1c3CzbLRkKk0E8A3sPDTkZnUSkHZqVVzPWCNMsZP+Jca4ZqniF/vGPfwxf//rX4ZFHHoG1a9dCT0+P62e8meXq8+MTXmIhLM3Qj3Ijp6iHiuI3FIdqx5TLCcXpV6CmG1WcCKhTfLOXoK4G0dfhBQtY1dt4WxtkttoqVIaJLLoY0JtMT9xMCyL6VMWmtXMK+mCIghaxCLNhDIII3Kxos8ukYCJkcTx99smXUTGQku7SO6nOggB1MV2Gi+p5VG0gz17STq13L1K0S7oAXLVsxzH4Mu8JuFuZmj1uQs+E4DAoTOYwQ+HDZHQpaiXMewsWJWXXXG/tVZ1dK0b6EKbteegURDSTsUaTV1hzXjDUQmg8MUPYhBXtkEMOcf19vAqo1ZvMU0iQCqjD0qiKKJAWzMrwxdfxHMxkxamZXq4wmTFmyBkj7RNeEvdUtWzo9dfZY9N225VuweFp3igatQYswvxc6dW1KQRrhtBI9lLodgN0w8UsOYs3Mm/qcIbRDWFRUMeZIQGxVEYlX40wmRC6amesBWufMkI/wn8Pu/m6gApef3QS+BxI8+wlKaDWEGnTWdWU8zJDTvPR6q7nOMfFPGzefvuK3uuvGcr6a4Y0ii7S5W4KYZ4mtkxxa23Cru/KHmLFSBkNDrh09TyePYQ6oiJMFhOhuAgMlWUPP/xwdb5Jg5qVHQ5kbYRKX8aUq4DqM3z5lQ1OdWoZUQ9R2XxdzJAhzZAdJrMtLZohKuElUV+jGjZIwFBFxhADfx7UqFUBddoC6kIMrly1Br49dTL8LMeBSizu9MIKudi7wmRoeA8k3MyQLhOBIIsaK0yadDMqTigubJisGGsjwBAYrJekgqE288DRssPVIrtPsDbiXIW95jSbTNU5pgU41dXalGm5FSsgv24dCzdXxM7a8m75XNQZ8jRq5VfdClvkQlmz52Qt2GQkCxMLeZjYMk1hbcJqhtzf2VVGo6nTDVTCAi5FzpGgYIhf6wRfe3V0p+MKDB1wwAHV+SZjkBkSfbK1PQc13iu3J2SGOMWpm+mFArsAZiiuMFzGmCFyHGl+HEJszr4SFMBRepi3wZdeZo/N21cKhnzacSiMKA2TmegbhpqhY/oH4Mj+AUhswjNuSH2e0OJmBQzhODE8uhHeB8TF2oQNYbnHaMK1P0P0PCSV24yAWtHaCN2Fbp8qAoBQ+4Si/zz/zEyai2nlv4dtOOu+V5pzCIbcx2FfDys026HqaqjOUTJcuun7ZdrASy/Z426xBcQr7WeJIDtevM4QY4awb5hOmIyWHog3wZ3LlrP5H8Mq8K713Qxrg5WfpHGGVruwo8oMsXXVPidJvnfEDJROGfNg6JVXXoHtt98e4vE4e17MdtxxRxjXmiGanq7EYkN7DsqNTFPSWSdrOkZYoIJhsoIKhgouGlVbM2RZLsBFC9cJOpuCiGp6KCMffADDb7/NPNKWvfaq7M1UQC3rDKn0vAHNkKsGEA+H4v/4xmuPo7fBq8yQFccNaRBgktMoU4KIvBlmiAp2M0kRXtIDQ1RADQFgSBvUKfoqrM8jwBDWyGJj8N9NZN6hteYtEErMtMhekjWA9NujqNlkKcFwiTBZ2GrdZVrfQw9V1IKDmlgf8HwU1QxZmgJq+msMufg++04SAFg3W1hZOyDlhKhBaNF0pRbKGImCA4bEHpLga2+tm2Q3FBjCpqsrVqyAadOmsec44f02qvGpGVIQtw8z5GRIhQ2XKBOcARX7s5r4RJRhstBAxV2Hwh7DXgia+RjOhAQjPdZiBNSlFFDHxqliVkPvAw9K0WZy4sTKa4+I5wGaIRMCapdmiOY6uMTNelobDxg66ucAyx4C2OdLVWOGaJX2jBSh2pmFYQXU7qw4JUzGRai6WXFq5l2qEIMhflmasHEuTUkPy0Qo62cLuazNogAfuxcKoa+5GpahzFBLkz0XhNy8mswQ1hfqe+RR9rz9sEMrfz+pQB2kGXL0VeHr81B9lZXEyu9r7V/4faWdZasKqKdsBbB1C8DmzjnRbenkAUNkPWni+1NMjgHj0soCQ4sXL4apU6fK55EFa4aSpChFUkHcYbd2T5iMAZWcq1+RkxYZPhTnqkPBxsi6AJdTTwOMAK4YOVciJi7qDFUbDPXzfmTthxxc+ZspM5TizJBlMS+aNW2sQpjMlU1GmSFZDybs5qv8vtmhAHue7vqT1KiELlyn6Gto40u+wTuAy8BxFCyI4fWQ6fuqgNpANhnLwrKglz9Pp9rNa4YQANGmnWkOhvg0DQ1UFGaIjtHaPImPocmolGGDL77IsjmTU6dCU4XiaU+jVqEZUsCk09MrfJiMrlkWXmex5PMwmW71f49mCPnfE3/v+ptpZogmr2RE4dN4lE1W0jbeeGP2mM1m4Xvf+x5ceumlrHN9ZF7NEA1hZURjRV1U7xEFOmDIg+pDjeBT+ItMlma+sct6GoZ0SbTOvQg3qALqanmkA1wv1FppiAyNfi9+TmSWTjrtpL1zTUlYgEpT69kCKcwnTBZWFBxTNnj1XmOv4WGZ8JleqoCaMkPtRvQ8KsOFG1iOr24Zly4Jb0MzYzSRe1n2qRLhPgMZhEIzJJ+LcyVEwWHZDjUUR8ZoaZnsBkNVTK3vf/ZZe8y99pRORCXm1BlCzVDCF1hIIbhO5Wb6VipuFvoqTebfEybzmYP6objg9T3DK14nRqFJdj1ZRXdgKpWCW2+9tXrfZiyEyQjbkRGNFfnNFr5CqaoZIsJjUadF13MoAuqaeX0TR5cUzqx8FhJEJ0TXp4IAQ7Ryc5WEfKzi7dAQq3ib3nzzyj+ACajdXeuZ0euE1bS1U+upZqh4mCy81qbEwmw600sBQ02iro1MgQ45hppBRoGKuH8Npu+zz6WgTq2XZIINVJkhpQZQ6DCZwp6gLkk+b56itJioHhgaeJa34Nhjj3AfwBcQWzOUKlpnKPQctPKue8nVI1AAYMOlU3znoMj0MiS1oJm8Ga5Lisms5wgMlWUnnHAC3H777VArW7ZsGXz84x+HyZMnQ3NzM+ywww7wwgsvQL2YB3FTMCTpR80buQgYEguktneiCMHjsvKPdwzL0LlyOWtSQJ2suoBaeqQVVrz1601GwZBrIebMkJ6A2p1NJs2vBlDIxd6jGfJjhgy2ykBL08bGfI6YBlwJvzEkUDEDuFzhPrVVhon+Z0oNoBYhNtcFXNlCICBuE7okWWeoSuxsfz8Mvvoqe94SEgzJOkNFNENxA2EyFxiiIUbJZhsOkym/s6E0E2RUZ9cFhhI2qNOucj3eUuu32GILuPzyy+HJJ5+EXXfdFVpbHcoe7aKLLjL25davXw/77LMPHHTQQXDvvfcy3dKCBQtgYqWC1yqah1Fhu6TlG4sNzaioE5xmkwk9BKdRw4fJgsFQs+zBoznG8JD790IM9hochKebm+HwDrsnUZw0KaxWmEzfIyXLBSnW6PK4XV3Sw+pHvNlkzJomOMPIejDhNl9X2DJgIRYgwhgYomyHYG00N1+V4XIxQ5JR0RMeqze+HzPkhPvMh8mkUyJZGzPM0CxSd0hl6qo2BzFMnctBatYsSG/I++BVaLJrPWOG+Dz0S63XDJO5wNDU7QCWzlPG0BNQF5Togp9D4hRdNKQZYtlktjVx7VOsxn0hGx4M/eEPf4AJEyawZq34Qw1vSpNgCFt/zJ49G/70pz/Jv5XSKg0PD7MfYdVuEeKf9p53U+fa4jc1m4xkAoi4tYiZh841V0TacaeDezOnzgWNGh4MuQEXaoYuWbseHm0eglN2PLgmAurC0BAMzuPl/8OCIdKOg2kd8Nzn8+7rhAURxctDAxWavZQDOPR7AAsfBNj2ePlnXo8vdLZMTFl3/Sh6JxRnhn1qIRu8ZFRkPRgDmV4KGFKZIVOgDgXUnlCDbhsLJfOuPYkbVV4BjppjKMzQFkNZuHTNOticbMoOyK5OmGzguWf15qCLGYuR3mRqJXhxHOEbnLrA0LYnAljvAOz6Kfk3wWaHdnbVbDKfOajNDCmf6bhqjm4vHgmoK7NaZpPdeeedcPjhh8PJJ58Mjz76KGywwQbwuc99Dj796U8HvufKK69kIu/R1QxxMMQZlURSaIYMMUMkFNcs6psYDpMlE21OaDzDi4sldEGdEibLW7BpNgdzsr0A7TNJ81GLaYiqQdEjEEI2LzltGqQ32STch9AsFk7RM2+bXifaJd1AyITdZ/t+yf4hpst2iD5euWQMkjnL2zTS1dLATBYW3Vw6eIaUbsaaygzZ7FPMl30ypUuiWUbNkqHV1NoozFDblB0AwAbvHa7UejMlG9CslqlwSu8Hrr85TF11wFD/MwIMVdAgWTHBzxbTDDktJsIdRyE7Agly2a2myQDn2WU5hOnKIIQTNZy0Q6/FNEOmdEkpwgy180ra8XHODFUu4a+hvfvuu3DNNdew0Nz9998PF1xwAWOebrjhhsD3XHLJJdDd3S1/li5dWtXv6EHcVDOkAJXQWhtP9VCHPWkXgkdd1kZhhtx6FU5HizFCZkihaNn1O+pg2mcB4HmaNdf+Izad1W2bUMT6n3ySPbbsuUf4vmeWU2fI3Rcp59s3DIXjYSxGw2Q+1LlbB1P5Ys+y3/gQ2SaRjeOzEGtqVFTWhjZSTfEFWFtArYzRRvttiXC1YbE5BXWtroKIOllx7hmcTNt1mChwlBmEhkJxMOcw+/Ggb/mACPNbY279etmPrHXPPUN/jghFFtMMidT6sI5VXnUOfENYuqyN/ZmD6SLsrK5EQY0u0BYsPOweH+fZZGUxQxdffDF8//vfZ/ogfF7Mfvazn5n6boza3G233eCHP/wh+33u3Lnw2muvwW9/+1v45Cc/6fueTCbDfkZXMyS+iyilrlefx3Mjk67Gba3TlcJfYIa1IYvKFA64aPNR9IJESKD8MdzsExMjnv8Yul8ALZMkiBCfauW9LIWu9T3yCHts21+jrQypM8TOgViIXZohp5FIvjCiX4E6AAzZLEE+FIig9242k4TmvpzvYi8K8JnKwprZOhsAeOE6brIxaNhQnHLj01CcygyZqjNEwZD4bJMNZ9EmJyfISY0tcdhYIrXeUMaatcmBAHufDzDF1u1VW0Dd//jjrCp5ZsstITVjRujPkU4alNYMhQV1WIbDNaYvUNFNLLE/cwiX9YFSYMiQZogB6+Xs+bQ2m5V32KfxaWWBoZdffpnVGEJ76aWXAj1q0x3GZ86cCdtuu63rb9tss01dpffLGzkF0JTFGwrp8iF3n5+E4QrUKRSQv8+eJ0UNIN3Cjsqkh2wOvrTLl+C2hbfBKVud4lsDSCw0YTVD7Ny12cU81SysbCwWvrt4sRYcCxYyjU/bfvvqgSF+r7uYIZdmKCHDZCbq2gSCIR1miFzzXDnMkCHN0H67fA7+nb0Zjt/6VOc1muElVQjezNhTnsggmsFqam1UwBVvQ+Hval9QFz6E5T5Xe0zZFTYZmg+HbkyqEWsWRKT1q0RrEZi2jetvTsXjfPUckoMO0voc2QssFqwZkj0bQzfJVh04nxpAhhq1MjDkw2651nfLkC4p3gQXbHEqvNSzCA6YbTuGuprQcQGGrr76aujosFmOR/iNXAvDTLK3sX8UsXfeeUcWgawHEzcZUpwIhuIxh5VyBNRpoxRnnMwIwdpox619PKBzdziX/QiLkbT3MIt9OQsLZYYKhsNkovR/yy67QKLTCT9UbGQzRS89luLn3lVnSF9A7QJDPiCFDSPDS/nQXi+SmTleSdtXvCnEzZp6HoyKYlhuYnoS3HjUrcaOAxkCMUY2k4DUcB4SaXQY1ik1gBJ6IEJ524adW3jAkGCGQocUFQF1Z6IV7vrYXa6/aQuolTF8N3iZkm52a8T7q+/xJ9jztgP1mn4LgIPhT985SEBd2OajhdxI6XMltZRQxTCZXsaaN30/C5/b+9vuMZLjGwyV5dpjeGrNmjXs+aabbgpr17op7mrZl7/8ZXjmmWdYmGzhwoVw0003we9//3v4/Oc/D/UIhtDixOtqE+XaE5paG5+iXOfveD7sMWMP2G/D/Qyl1pemg0VbkdBgiIfJ5Dv9NniSkh66u3gpj/TAA/U+CAXe/CljhnjVaU8tD11QR7uk+3iL9hjhGRUh/seu6BZPS/MvumgmvJRN8dCLn+5CR7CL2ifxNMPbutBMLwGGwKyA+tiNjoBtJm0DV+53pbksLLXuk9/GaEikXShjDNNhMkypL/T2QmLiRGjWbOrtSq0PmIMJsS6G1gypa68fM5QyBIaKzEFdh5rPOYxg2L8Hh/sKMD6tLGYIU+kxiwwbtb733nvhm95VaB/5yEfgtttuY6JorG2EafW/+MUv4Mwzz4R6MVEjYogTQjESs27jHaATtKoylnfnXle5Jgp9jSQB0jzb4Atzv+B6jWBtdNPekSXA8I5v8T3NGkDW8Iikg1tGgpihuBNeMhgmwyJvA7zYYttBumDI7Z/FM2lf5ktcZSM1gHwWYV0djNg4skmAQjIYqGiHZQRrk45DeqTgX1ROhxkim0cug/NgBGKkdo63g7lm5l0qBsmsBdPTk+GWQ28JOFd6AmrBovm3ZtAUN3OHLZuJQWbY8t0YY1VihhzN3v4QIzW6wpjtkmAeYgziXCeqzkHR5iPscajZwkVBRNh8DL6+D4vltWhqfUjLueUcfutJfJxnk5UFhk466SQ44IADmIYHUTiKmhMBNzJmgJm0Y445hv3Uq3mYIZKpIcCQS2sDBXCktWWOQW5kAYZMew6YQiqBynDx9M7wYMheqAYzHAwFhH6qESbrf/ppNl5qo40grdtXj7XjAMcrTfl7pbIdR6F62WQyZBLiehQGByTIlmBIWfzdY+hphnLpYsyQ0HbogaE8Z4boPPSGlwp6YCidgGQ2F8CoaI7Br/lIOg6Z4ULRMXRDccNpGwz5Ai5NEFFaL6TpkDCfxAFDghlSBc9Oo9awAmofnaNitGp+GC1lYcj+zgPcofabg4KV107fFyJt36y4tP19zEp/xxYYwtDUiSeeyEJVmNqOdX7a2+208fFujvjNrjxNw2SdGVuXEud1hsKHl7JysnQMBug6BDMUVsQuGK4UB0O+sXEKhkJoVHgFagEcWSXnfN7jIepqbfysV4bIDtAX+hdycklimqEgil683IDwOAg46nTlLvT1y/vKEmDIt+iiXuE6ygzZY3iz66SAOgwzJLRPCIbS9vnYumNzeBTmQTvvJu/Wweil1mebktDUHwCGNMKWrjEYUCnC1GlcD6EZGsnEAXoLNdMMjSxZAiNYpy6ZhNZ99tH+PBsMpti9E0sLZihIMxSyOnQ5TVQVxrxiMDRo3789vOWgPyvPmaHQ7BMHQyJM5ruHpOzvw15gyXYj48XKLrp4xBFHsEesOv3FL34xAkPcxOST4rdcDk7f+nR4t+td2GvWXp4wGQsDJMKFl5BRYb8XSb2UgtIKb2ThUcmMhiJUbeiwDGeGxBjsb6j3CAJDhpgh9CD7HrXF0+26eiG0fNadTcYp+oKSLefUtdEPkwUxQ0LcHAZEFPr75L2bkV2/g/Ujed3wUhFmyBkjPDOUY9on+zgOmrk/PNw0AOduf66ZMQoFeT2K1WSSWhtNzRAKwaE378/U6QIu7rCNZIRGxS9saR4MiTnYgpEFA/uH0BS6NEPqHNTUDKlgqLSTWPk4+UH7O/e02A61v17TtGaohPapgPtUxTWZG9oqPlraGiMy/0yAb+7xTddraCf2MIJEa6SMbANlQlasS6K1LgIF1MQDCpEuLsNknEWzx8lhQaYAZsiMgHro9Tcgv3oNxFta2EKsbbQdB6HoPV6pfHnIMBm5VYKLLobX8+R5q5qBTAxSiWJAhW/+YTO9BBjiIaxiOphQ80OAIRLu27BpOtx63K3+oTgN9kkwQ/bfAjLveDhchw3MImsTCLg0gQofY5hdj2xASFwvJb14AoNeFpkw8d0QDDm6vZGAc2Uqy9YvxKuXZSuYoV67iHnANTeTsSYiGL7zPCnCZDHGfo83MFTXFagbwcoBEVQzFEY/IlibQenJ+bE2mqE4MYb4mFzOk/nmYoZCFESkmiH5N7/2D+LfDIEhsQi37ruvBC5aVsi6SPeYWIgDmaGwtXNKa4Z0GmqOLH6PPa5rJ5qhIhqVUIwKBRE8hOWbkaPRNkEwcjnEhQLU+bId4Ss30+rp2Wb/mjYmsskkGCqDRQvPDIkwWSw4LCObj5oBQ/m+Puh//gVz7CxlhqhDos5BmZIeUkCtZvL6nSsNZijf2wu57kH2fNWE4PVdv45c1hUm8xNpx+keklf6SI4Di8CQphX4ooveNbNs1gMiKDMUCkSIMYqxNlTcrJFZNJQi4TV1HF4QMTSo4+E+nJAizORf8VgvvFS1lHoaJiMbUzwwtd5g+wef+8pV5K/CzRf1G+v+/Gf2/M2NYmDJOkPe+1OnkGBhYMAZs0UUpzTLRFh8DATZFq9EXGyMUMfB710EXEKXVFRArakZEixasZozYbPJBBhyAFcx9slMa4b+J59i9zD2AwzdEzAwTJZwBNTK/Suq5oc9joKSlWi65MjKH17J9DkfTgJY217aoQ6dIMNF2n12MfYAwJV2Xl+F6v/1bhEY0jQR7xUUJzPlRkvwOidhQyblaIZcBRFDAS43w8X+pgIV0jfMCtFiQnhtmL0EYtPy83wNMkPZlavsPkixGLTtb9dkMskMubLJlIVY9lgzkVqP5uvBV66DQdH6h5d8E6zBQVg/Kw+P7hCDQqI6YRkcA20kAZAvUmdIpwK1AFyohygIUFdUFByG4RqWZQiELsn4ubIsR5dUBKiIprahxigUJOAaKRa25NlkpsJkxh2SAnI9pM5QpjYCat8yBJT5r+De6v3vf6H7tttY2uk1RyfYvVVKl2SFrFUnwPxAU3naJ8twjbdGsAgMaVrBBwypi6SLRs0Nh56QMoTlxz7F9W5kyQxRMKQu9qzFBGeGQmiGZDgD16dEMTCkx6hQ6779dvbYvNNOkJxiV+vWtryTTcYo+gABtdPGwkxjUP9WGZUzKt133QWDL70E8dZWePPgEXuBlb2diqRZhxFpczCEbGBBbLC+tVoSoTOkJBhK0zBZ1iwY4tcWC1QWDykKAXV47RMbpwgzFNNguGgYaaQp+DhiUnhsGQmR9d5/P3vedrBeCw5pxMGws8mKC6hDt+PwFF0soaUscxz8nBVXXMGeTzh0a3h7wxhjHZnl8+4+hyHH8ANDfU3BodE4v6/YVwixTzW6RWBI0wqD9uTo5WmRvhNGN7w0wAEXyzbgppadpxlrIZihwqCtiejnnoM/M0RaTIRguAr9A04qd7H2D4a61uP3X3/z39nzCac6fbC0DVPryaUIrkAttDbhwBBtu1Jqgy/XI8VNeu1117Hnk88/HwbbeJG/IuElHRBRGOBgCIFKMjg0qsOo4GYrQryW7BNXLCsuxBi9vZKddcBQcImAUNqnfrvUAQ0p+nZJ1zhXBX6u8J1DzUU0XJpaG2rdt9/BAGt6002h5SMfASNGylu4kxhGXI6i7BsWlhlSz78vOK08TNZ9zz2Q+3A5JKZMgc4j7f6b+UTw2usaI0zyilLLCJk1FXDFSUkA062QGsEiMKRp+R57g+9qxZKxAQI4AiKsEDdZvt8GKt1FAJcrmywM4OJgCDO9im0osnZOiA1e1LVh3olfc1O1qJwmM9R9xx32gjNxInQcdSQYMxImi7u80gDNUN5MmMzTTDdEcby+xx6DkYWLIN7WBhNPO1UeR3EwFF4zlF+/TjKn5YSXwoCI3Cq7P1hXGw2/BrNooUAdz7xDZ6GoLkmjo7xwFjCMnC/iLGgBRw7qMFwia0tVsc4QsqVr//AH9nziGWeYa+ZNdHsxotvDTZ4CSKfhrJmeXmpRRzZ+wmHMyzlfCNbW/dHOyp501llIuDuMuRxXlVpQwFX5epLrtZ2SrlYyhqepbdwZI9IMRVaJ5dauhcKAfdOswT62Ac0C0USLiVCsTX8ZNzINk4UJYQ0MOWGyVLIM1qby48j3OkX+nDCZXx0V/p1CMirsvQMDsPrqX7Lnkz/zGVmu31yYLEbCZEFpvWbEtOWEycr2SP91G3uccNKJkGhrlQxXecxQ5Rtj/9PPsMf1rTHCqBRhIsJkxS2xs+LWdhQ/Dp3wkihD0J9BZ6FIeElj8xXMEIbDywnFhWK4urrlHHTClsGd2MM2BhWGIv3c8uWQnDEDJvzPSWDMCjnSHzAuQ9XqPHRS0kOCIeXcWCX6KZZzb2Gpj+F33mFO1MRTT5EhPBkmKyG1qHTtzS5fDiMr1rPnazpjwQ3AY84XyIfQhDa6RWBIw3ofeog9Lp6OvcmCOye7a+dUdpMhlZld0+PcyAHsk1iE7TFCsE88nIGLZExuKD4UvewbVjlQGVm+Wm5awoOHKmmG1v35L5BbtQpSG24IE884HYxaobJssjCp3Oz9hdLMUCXF8fJdXdD38MPseefHPsbqJclmnYKJ8G3HEUKkXSjAyh//BNbxumSvzIlVhYlATZI4prc3iDkCap9zJYFjiOsxsnRpmWUINITgfb0+YMivbUL4EFZ26fvSecvzsGWxjLUwYl1h+e5uWPu737PnU7/0RYg3kRi8EWYo5mFnVfZGZpOZAkM+9xUDQ2JdLOP+FTrG9kMPgURnp7xXrHjMcRI9zFC4TK+hN96A9047ndWqw3v3vWnkHxVHNE7gQJgIRqNbBIZCGk6SdX/4I3v+5Lb2aSwGhmR4qULWZt0Nf4bCUBZ6mwBWTAxmn1hGhaBqK7yRcePKfmgDlXUs1FBEdxHyOAZffRVGlq6AfNyCd2fEirJPAkSEreKL1LxIG5/6xYvMsUJP/xrgnq8CDHYpXev9m0TKrB9D2WR+FL2T9l76XPXcey8735mtt4amrbdmDWcL4lyLa24AqCBwWHTEkRIILd2jAPd+BJmhIkBFblqVbfBdt9wC+XXrob+jYAOuNJ8fQ8PG2Kfc+vXQ9Tdbe7ZgVnm6pDCCXdaqggOu8oTglY2BwKaLb8TsOGShzSICag1maP3fb2YapcyWW0LncceBEVu7CODW8wAW3C9ZTQaGEEQkvPeXbGMRNkxGGm/bn+3PDCX4eSo1D/G79dxzD3veecIJnuso9hCPJjReGRjCa73q6qth8YknQW7lSkjPmgiXnxm3M9ZEaN3DZMdDsU8IejFK0ugWgaGQ1n3HnaxWS7wtDf+Zy0MmClDpe/xx6H/qKfvf+PsqEVAPvvY6rPr5z9nzvx8Yg3wC2aekcfYJN5SR5WtZxs9iClT8NCohxkCwteLy77Pn87cC6GnFhTg4m0zoCsIyQz133wP5desgOWsmdBxpSCv0/HUA938T4PlrAV69RTYzLNYk0jmOsEUX3b/7Vzwuf2NEISta5/HH8w8kW50hzRAuwssu/gpk37cZiOnf+hYs263AMtYc1sYPqIhNq/wNnmkv/noje/7WLjk2PwoZXo9lyGY6c6tXs00ZtTJhur3jGMv/9xL2OX0TC/DwjgTU+WZhhasBhCLw1b/+DXv+5uziwDEsqOv9zwMw8PQzYCUAHpwbl2DIt4O5ttYmC+tvuok9n3TOp2T3eC3rWQ5w48kAr/4D4K4vuhwS9ijT60cMNjjNl8UMyebSJdYs3BPy69dDYuoUaN17b/szCYCKBQBtV7PvMhzR3gcegLXX/JY9b9pxR9j468fDCizqiOcqgMmOYSVv6VCXv753/es2WLDf/rDyRz+GRrYIDIU0FIbiJth2yJYsROYCQ7kcjLz3Hiz9zPnw/nmfhuzKlYQZKv8mW/u73zEPoW3uHHhwJz7dygrF5SoKMwhtze3720AFOJ1t8caq/qGf8sfouesuGHr1VYg3Z+AeUYm/SChOJ0zGNsi//IU9n4SCTb64aNmSpwDu+Yp7nDIE1A4zFDabzH6UYlpfRqW8TWt48WIYnD+fec+dxxzNDyIvQZ1VBGQ7hetKb779TzzJrnWsuRk2/+9DMOkTH5fsQoGPIdJ8/Y6jEkYFx8kuXcrGem9LHmoQ+i3ODK288kew4rvfhdW/uLqi45BjvPa63VMrlYJXD8tCFouSihCvr4YrnNh8/V/+wrQ1Qx0At+0dhwIv7CiaG1MLA+oQqK+66ir2fN1uSVg5EWtLFUuz5vdDyGwy3IyRkcBsqY6jjgIj9qcjANYtkr/SbDK0uFgbSXq9U2cItJghVhstEAwlHPlACaAiHZJjjpVrE3WWgqILcVpHroRDjWvgGg6EJp1zDmxy898h2ZZ2zldAxfywa2/vffcx4Xpq9obQyBaBoZA2+bzzYLMHHoDmfTbyvZEHXnzJ7vxbKMDQa685BfjKBENIPYpCZVOP3xUs7llJz8H3RoaKs7C6/vFP5qmkpk2Ah+fyP/LJUuAbCoadRBaKAHVWBQzX2utvYI+TTzoIeoUIXIZlgsNkYcJLA88/D8NvvQWxpiaY8D//A9qWGwG48yL7+ew9AVL2AUgBdayYgDp8FhZuUHEeB8g3i6KO4ZkhIZxu3XcfSE6dygdxglLFQz/lMxEMwGPm0CmnQGrWLPszLUWX5HPvirBMJeEMEW5oP3A/yLGeSw4YEiCi59//Zo/r//Y3sjFWUJPpzjvZY8dhh0HPZPt9guHyA3WxEKAOxf6ocUNbtlcMRlIxp8o1CfdhuA5ZVqe2VPnnqu/BBxlwxGu/bjf+/csQm4cNL4njmXjqqU6ml47Nuwlg/Xv4xQA2PcjjkAQxQw6o08smK1bw1maGROmU4PU9t24d9PI1XYTIgsJkxWUQxfeQ/scfh+E334RYSwtM/vR57L0oOZDNpQNqMrllECNli7OZkxWLQfuhh0EjWwSGNCw1fZpTO4XeyCNZGF64UP4dWSJHB1MeUOm5735b37HllpDZYKIzRnOTC6hQq7QGEEt7FTVnjtsb8ijgwzGa+KIyNMgW3yVnfhwWHnQwjLz/PqmqXN5xjHzwAZuYyEhMOGQ3GecvFu4T2bdhwktCK4ShoMSECaBtT/8/gLUL7Ocn/Abg47cCbH0MFCZuIkWHUkAd2JssTF0QhxGQYKgYM1Rkg8f3dd1qNy2dcNJJSsNZfl+mi4DsMgW7qBUaeOEFpknA0IgcRmwURcaolLXB+6bn3/ey5x0fPVh+s4K4FkPD7rAlVneusEAlAlIBpjqPP05uqAVxHOQa6eh5WBgPHZLZs2HN5mIMEe7joO7ee2HBXnvDqqv+zwEqFYC6ngcesI/jhOMhn+TAoFgV+Fh4EDH4yiswOG8eWw+xfIO29a8FuP9b9vPNDwU4/W8Ae30BCnP2c4fJRLia3F+iTVFeM0wm+0L6MUOxhOMkFll7u//1L8YmNm2/PTRttaUzBs+axRGCCriyY+GPpZxEIdDG7L3kxImee16s78XGyJcJhnruvY89Nu+yC9sPG9kiMGSqczKbjA6qz61Z46qDUmlrhu67bI+087hjXZup9H586PNYhZleuEFixhWmvXbut70U04ou8gi4hhcsZMwWCiH7Hn2M1M4pb4zeBx5kj9gtPtmWcZbvZJGicrJWS2UgAuPxfQ/aGX4Yngll6H0Jb/i+SwAeutx+/rHfAUzeDGDjvQBOuxGs1ilOmKy52VVt2ZuFFaLUAdHV5Fv8232Uu/n2PfEE01BhyKL94INdx2opIMJvgSx3gxcLY8seu0Nq+nTnWBQQ4TtGhRt89933MB1PYvJkaNtzN6deUpOjGcJjpseayFeWko56v/zatZCYNAla99qLnKtUkRBWZV3r8Z5Z+0c7EWPKZ8+HAk9LyklmyB5j9a9+zR7X/fGPpJxCeRs8nu/+Rx9jz9sPO0yuWY6A2mcOJsIJqHEs1m8LQerRRzssZKUmjm312wA/3RRgcB3A9O0BTrsJINUMcPgPoLDnBa4sqDifh6IFjJs5Dfk1cgozVEIzFMQMUfH6hFNPcf2bmFe4tsaFI+rHnorXFwEqyDL2PszZp2Md0borFCcSPooxQ4VsWdda6MI6jz0GGt0iMKRpgunBG5lSnLk1dnYWWm71KqIZKn2TZZctg8EXXmQUSccxx7jo8FimyV0x+tnnoO+JJ9nzSsbAlP2113JW6NPnQZwyXIQZQiAkbHjRwoqLLqJ2AK390ENd2UtOmKyYgLp8Zqj/6afhg89/gT2fcNqpkNl8c6jYRvoBfvURgF/sCPDAZQDP2IJW2PwwgB1P9V3AmOiwtdXTlFS3HQeee7R8DKQouJiYttgGj4JytM5jjnFrqKhmSIzhx3aUWdgR2Qs0VbQuN98iYbJKGmpiuGjVT37Cnk/65Cchlog74T6iGVIzXDK9ucpqMt11tzweLDchAVdGMEPDsgbResxq6+mpmBnquvVfkF+zBlIbbMAyrsS5Upmh7Icfyvckh6yKABeCOrw3k9OnM1ZCfDenBYuZdhx4fy49/7OMFcI5MfWiCyGUPfNbgB9uAPDvrwP8enfn78f9EoD2z+LfTawXfvNQCKhDl47M2u8cFmHYQM2Q/V3Uqs7CsK4QFjtFZ7bjiCNc/yZa0Ng1ywTzP1SEGQpeT1DfhmAQWcam7beTf3eJtMvQDOXLWLPW/Po3kP3gAyYGN5YtOIpmQF06vk2kUsZcYbJhtsC5mSF8hVUW4u6+x6bmW3bfHVIzZkD2TXIjS89hCLIrVsD7557Letlses89UkhYDvvU/8wzTLCJdS6YtuaVvzgZUoIZGhyCQn+fcxzLV0BsI34cZQAu9NwHX36ZPW8/7FCAFQ87Ir5i2UsVhpfwMz7830vYQtV2yCEw/X//F0LZwoeckNiTV9uPyWbbG1Uq5wpOBc95vKXV007B/jfORIQI9wlQwhqDFssgLEM/MsCvgadJJskmU7U27jFK63lQOyDCocg+uIbhj2KD9w/FpcoGEat/9jMWVspssQVMPvuTAIOrnHCfAHXDQ25mCMPavVmAVHlsB/OwH3xQsrMMyPNbIM/nudiwMIsGQyCD8+ZDfP/KWBvWqBNB3dlns/VDXMeCAFyDg+y6U7Yj3T1c0RjiONAhwawucfyy7pNfbSmptSnfUJc18MwzDJRs+JvfSM1YxfbIDwGy/QDP2fozZif9AWCDXQPnIPvOLS2eeRjXDZNxAbVIkvHVDKGeR669/qwN9gIUDHmivV0ZhDND+DnSEfVhhvAQYsWBSu9/7ZpbHUcc7qr27QqTlRGKs0qEyVAGIhjNGZddJs99I1vEDGmaqIVjb4p8Mg4MQm41AUOrBRgqj34UReSEh01vZFG4DENYjLVBZsWyoP/ppyoSUPfceRd7bD/qSLsOD/aq4f+G4mP2OcNDruNA8CXYjnI8h148DstiqZ0I6ljVWDE/JYtWrB1HvuwWEyxzZfJk2OD/rgpf3G2B3UzSZRc8CZD0CkDFpsWKLra2BDBD4QvwWSMcDCUIGPJlhkTvJX8QgeFaBL24YCMr4DJadFEAFc44uscorbURoBfrFwmdghxGbL6C7eAhQDyeobfftru1S11S8U1r8NXXmOgfbcb3vmvrRAqE4RJjDA55maGebPlZcc8+a3vYs2ax+9cFHHkoTgBWpgXhj5UwQ8OLFsHQ668zlrTj6KNc5zjPG7XihoXCW2rp7pGyQ4rIVPTxDVKAVMESFCtvUWkFaryG62/5B3s+9SsXQ+sehNGptI7QkF0lWxqysjt4kyFkeInPMz9myDkO0AJDRTVDZbDyeN+iNe24g89x2GPgUcQ5M+QngygnI1nMQwzr+o0BrjFGZCQix+8xyQyVkEGwUGguB20HHcSSC8aCRcyQpolwEW7g8Wa+Kfb2sGq/dEMSadalQAS+j6nz0ZM/YH/786hmiAIVMmFGFr0L8dlQ1hjM6xWCShFXtojvJAR2uKFQhmvlyopaZWBWAzuOAw9wwjLiOIqFyaAyENHFF+EJHzshPBBCcLPAPidw8LcBPngB4ICv2zohH3OFyQQzNDDANgUp6NSoM0SZIcmiFQmTBTFDWOwSDZtkYvsN9yCk6KKoz+PL2pRudjnw8jz22Dx3rjKGRcCQO7y0/DvfZczItK99DeI7lOfBr7vBzkzsOPZYaNllF09rBspw5deqzNAIwOTyMqTEvdt6wP72dWRi89KaoeQwF4uXsf1232U7JG377gvJSZPc+ioRyhh0O1bsOBAMtZWXTYZgC1k07EXXsusuLkapWKFNUaywXPYJN+GRRYtYBlPncbyOVRgTc3CjvQGmbQPQPAHg4Et9X0odEvYonVEChkyFyTgYKvjo9tg4JdZFLAOB1rxDMBhi/BJxdlUrVcsInW4MWzHHBwE8HaPgFyYbguEFC+DdEz4GycmTYbMH/lOW0z709jt2/bxEAqZ/65swViwCQ5pmUfEbn4w0vi9o21QhzWZMqb5h7CYrFCCzxeaQmjnTHsN1I5MQFu9AjTby3mJIzBahuOIUJ1KpuGBgq4rmuTvzL+ks9nEKuAgYwnT/ZCHFjqOUgBrp5P6nnmbP2/bb3zNG0SrXFRRdRLYKhdNonTRTqlJbPg+gbyVAqgVg74sAksWrVgu9BmMEuUeK4UoMAYkFTYsZ4hstNm9MlsMMBTARQ9wjbVZZIfaBWAyRP5daGx+PtIwif6gTYePsvLMyhiPSlgzX8DDLUhQhIhRhJn58fMnNFzdtUW5i0plnuI5DGtEMiSaxwpL92bJq5+Am2/fY4+57lzkL/L4USQyYsaaAx5Y1Q8yFL8UM4Rg9XJPEwnDy74IZcuYHJjlQS/QJZqg0UBFzAwv8iTC+/G7FQtUVMkOCres48ggv6K7E3rFF+LDVkQD78JIWAUYdEhczRMJkIpusILJLKjU1TOZXgbpEZX5ca5EFRPOws3QPQceqSIKMrFsWwD4N8DmIGciJtjZlDJ89ZHjYTvXP59k9xmrBiVBwETDU9187SaXtgAMgvWFj1xaiFoXJNE1s2DEfMBTv7JSlz9sGy9PziEW4dV87bVQNe8mMCQWoZFeuKjvtveduLgw95mgnrkyYIemdMGZotWtjax0sL9yHmyMuStgxvmm7bUlYRql14VvlunwwxDbUQoHF4jNz5kAow8VFFFXc/JCSQMgTJmuxr4nHK5UMV+V+qQglYZgMZHjJrx1H8U1r8DXbI23y8UgpUyfDZAhUFEAiQz8BY2BG1BDqhRAMCHAtx3BkvpJRGRlxg+y+vrLYJ2Q50AGId3S4j8fFDDl6npxghvg9nuBgqBQzNLL4PeZhI3ho3XMP7xhNog7XkEsbiJbhIaxSjMrgy/NYeALXDAw1eMTmpDYP8/aJJZHhKpO16X/8CfbYut++njFkiQC/nncVhJfw+vVg4T2Wzq1R2+v12wDetUN6sFXpQo3UIUGT4er+gVDH4Wu5MgTUJVrvYH8wXKNQwJ6a5k0/F6wNS8IhmbyVFtXFe8rXIVHWIAdwjbCIgrCRJe+X1UJINF9uI/fUWLAIDJnSDMUcmja7zAZDqJ1AgTJaG9c/FtvgWXz/CXvxatufgCGfG5kt9moIS2htAjIa2OvWr5djdB7reKS27sJd1p6lJysUfXsZx4HWJxbhffd1SvEj4BIibUkHu9PR2TGWyaiwdNXbbtdjhVa9CfD9KQDLXrR/P+jbZb2NgiE8PgwPeMSbIrU+RJiswItcYkpvMQF1sS7p+B0lM7SDDzNENUMcRPjWS5KZRQHsE9euYRp1UhXNEkZFhLCw1goFEQVkHHP8NUXAkFiEW/fYw+5FRY9DgHrRhw4ZldU2kE9vtqmLGSrFdgj2qeUjuznCUNTUifpXsv8ZaupW++p5So0hHBLU8QgHx13qwLkeIx/YTWJVZqhUNhkLub/yigzFCRP3iix14McGJsqvM8R63g0OslCs30Zc0jCb6vpjAP5xtv37Lp8EmFI6G9QTJpOaIcIMaYbJrCF77g60BPeKK9VuSeqF/Oagay2lAmq/6v/i9dmieiHJ9rvGIJohyvwT9jSL4LyEI4qOjxinZc89YSxZBIY0Tdw0LEzW6maGsOgfMiOUGSqmHxl4/gW2SSCj1LKrkzlBb355IyusDXrMTSOiCmowqu+9/362cWW22QYymxE9DGWGeGFHbIApmA70atAEM1SKfep74nGv90A2X1kviXtA6HGJkIMEQyVABOo6sAcWblgdh38UKjZcTG/5pPP7TmcATNu6rLeKjeiSf70Kq3uHffUKOmEyAYYGMjGwZJZiZQ1OUTiNehEMSWJzVu8g5BoSJkJdiEuFTAQ9jxshzWCxP4y8iwCu7IqVrpclu3gpgWJZcc8/zx5bBFtDjsPRuxFGZdkye9jN7I010TdcHlDhLAdmJvqPwQEXNjhGcToxkelVrNQBKxgpyhAcc4zvuYolknKOZD9Y5pqDid6hsmrnYLkJNeTOhuH3iijZgOuB2OTzff1KFlYZwukb7VozE078mPf6l2PYa+w9e71gtv/XynqbAGovLemCPz25mGSTeQXUYdtxwJA9R3rbymOG/JJXmEg+KFTtCZNxJ7FIKyQ/oMKSEfg4LX6AlDrUvM4Q6yywvsslOYiXCMUNzn+F3St4L6Y3sQvPjhWLwJCmUfGbmIxCPI2skKiC3CoZlWCgIqrddnz0MBlGUieYZFRQIKqwNpN6S4/RzT1S2Z9KHog3TJblHikWFRR9Z9r7uVdZBAxhyG74DTts0rrPPmQMJ9QAzQ77hN7GoqOPYZ3OkVkRC2opwLX2uj+wxwmnnBIutXPduwBr3raf7/xxgCN/VPZbhVe6ePUA/OulDwhF72WGwoAh0f5kgDFDQkBdpFWGzwYvdArpTTaWjKLLWHaf6HmXlF2/VR2MBEMBQIWFAQIyZWhKugB1aNkVbhCR6i0OVPB8CzG4V5ekhPs4E4nVz9EynBlK9JdOSccq2kPIpsTj0HH44b5gSDAqdAxh6a7SgAtBCmvWOXkytO61p7+zgBujmIdL35daELQ4B0OlNEMy5L6PO5whGL48Aad4zbGFxju77Qbr//53GV4qxQv1P/mU3f6mpSV8iOwte01idva/ASbwTJASJo6jbzgP37vrDV/NkOj2HlIxBCCZoYSjC/Rh3iUz5AMixDwU189zHHy9ZmGyIqn1xToM4BgIiDCEnNp4Y+8YruiCk01Gy0/ksTBribV38NVXZKJEKOBbxxaBIV0TaZEsq8i9IVNmqHWwOGuDaJuxNgiGlMaGvpqhQYeiFx7kBK6nDrqRMazGijn6jEHBkBDfDS9+jz0mp0yB5KTJ9nFwp6sYa9N7Py/RvtNOLEvBL2QiJyRWuX7nHdY3CZkMbOfgVKAOHgM3xoHnnmOsx6RPngWhbNF/7cdN9gM44dcATXZIsxxzFpcYvL2iFxLtHey3fFe3F6iECJOJEGgP3lJlCKj9Nni5CG+6WaBWShwF0z5Jtk5lhoprhobffIs9Nm3DtWHU6AafTEnhPNasopbgYChog8eWNoWeHnavN6mbChX/xxMQF3VcONuR5sdfDoigVbTxvvdlhvB6cMAlWBvhvCS7h0oyQyKTE8GWWgRThOLYcSjzULRwEMdRDHChLkvUF6KaJJrdx8pbiND60BCsv/FG9nzVT69yt7EoAh7X/sEu3Drx5P8J1/4G58biR+3n5z4IsAlxnkqY1LbxkyauOyZ6CEsk7OuSZxmBlQXLmGM2Yr+nu8PZKn0Z2gBmCBMFRhYvZs9dTDwdR0ot4k7plGICap/1ZEjOwW18QQrNcqNVrikYyq1bVzJMxhyFgKy4RrcIDGkarR7qC4YmdCrMUD7YW+zuZi0TsNgiNZpNJkAEAiFBbWPxOXuMWNH0TqEVymy7jYs2twdxdBfxDr6Z8JRb3BQSkyYqxxHM2nTzBppYjt9lBYclkP1xhjB12An3Db3zDmljEQwiRK+tjqOO9B5LubaIizU3VYoRlmGyii8uHjGAxGQ7NTq/bq2ncnMl3cVlNtOTz7LnyybHnJpMxcCQz+cIcaTQzHgPgoAI1D6RtN6lF3wOFn70cJs6L1K4DjeMkSVL2POmrbfyGQNBBNei4QYvQj8r3GAoKZkhKJqa3LTttjIrKug41KJ2ghmK99kbTLGUdMnOqs4CY59EyQTnXCGIp3MwMSD0PGVkqlGQohwHtpGId3a45qFkhnoG3aAmIISMoVYMZ7TstqsSiuPzHENx8poPyeuI19S5r2LubD1iIx8sgwHUccViMOmskA7Jhy/bdYUynQCzlLIMFTgkaMLxonWZXALqChlarOaMtr4VoL/N0aj5Cs5lFpZ7XUTJBAJNBMuYvetnwumzBdSOk4idBd7Zcy9Y8/trlTF8RNpvvSnrfPmOIRN9nC4GyD7TsH4ewZDUOQaEyV7h7KwfC9zgFoEhU9lkWD1UBUMTJ0hvqaUEiJDeIobIqDhUBUN88RL0PNKiSZ6h0MKZ1aCCWUJz0abQ5mwM8h78TGrJKZNlHZSWAc5wWdngMMN8HmY40l12nobJZL0kbJtAs+I+WOZobQKAI6ZY997/H/Y8VE0TphU6C+BtG7TBZgdX/hFiI7JijBVMTrZZhNwaAobKYLiC7oXhBe/CSMqCx7eLuaoqq1YMqEhmiGtmPEavB6Hoh995mxX+RD0WNkMVuiQ/EIE1R/B8MvE0ZVICNnjBYrJCkMQSuMELbYePB190ESbhPuaUCDDP7zNRCTk+lIVE3gpmn5YuZSEfVkUb28coxyFiISyExY9jZBkPxfH2L4l+kenlDyDwnGKyA26OLbt/xHMc9FwlOtxMpQBDsVwemoeL66t6hEOCrURcYnNyzVU2kIDMxGBOtoNxacuI9d53r1Mpf4MNoGJ770mA67gua9P9Abhou9I5KEseTOAOCVlPXALqCtri4Bqz+td2O54H5sahgKdQtKXxCWEFMUOiYTfqa9R1XY4ldKcsNOqk1mMTbZRcYMV1nHvFMr2ELCGzzdYlta2izpA6B3MYJivCDGVXrmT3Lp6Hpu2cVh9jxSIwpGkOqo97ajvYzJANhpp5PyG/EBbzFnkjxbaDvBszvTGTfAxcVNnvU6eSjDW+OAQW/uKZRTvvFHgc7FjIZsKOA5mhiW4wFFQdWtQ1QQG4p0kjEWk7lbQHWbsSYdnlH5ZkhjB7iWkuOjud1OdK7KUbAN64w37esSHATO/5KGV5uWHbib1JzgzRqsciTFYJM8QW4Z//gj2fv3MB+lpiTir3gE/mXQBQwd+xCB5lRoqBCDtMZl+Tgeds0IyGokynS7oV6JEi2xg8hviucanrEGBeNLlN9NgeKhvB57oLrULTDu5icvabnFCczQw5YB4BGguf8I0MsyGDGBUxB7GYo1pFm4IIu+q4fRzZJVzPs0V5Im2RWYSblkfHRYpgxnwYLgQcwuHqQHIoSF/FslLtfoXtamIBqdaN96c4/yy0RDKl4mu6S4IIcb6w9UPFNtTjZI+hbV15o08JOHmYbKTNvu7IdoimyTEOhqxYrGSNN2rdd9zB5o/VHId/fwQdngSRKLgrzRfr6VWSnXU51HHSm2xYAim03IcfyhICKlBhWaNvOWEy/zGItjVgDloDA5DJFQnFcXYWgf9YaL+hWgSGNM0pCR+DBNfVCMNu1w4zJECEd0IOv7OAFb1CL9bjLRJwwzLWFG+RhbA4GBLsk18qNyv8xSdX0/Ze7xpj2/J7tytjTEYwZG8OzVL7FMA+ifTnffb2/iOtMxTADLF+aVJr4w8iRIXu5l12cWsuyrXnbNqZFVg84++s0aIOGBrJF5gYlv19LfFK+QZcrI2Fat233840BonOdnhhVx6KkxXBg8GQClSQ8mYbXCwG6aD6S4SJoGGy4Xdp7ZElDjPkxz4JrcLW25QeA//jYD7Hw2SZTXkIq3vAYSIUgSjemzhH2Djb+umSKKMSgwQB8zg/sPQBLXFRKFUxnVd+V8dwAS6FPU1zZig+OFKUfZKViH3mIGOfyBgyTMY1SXjuknw9aR8IzpAaeuNNpq9CEOjRdtCsUdzgBcPFw31y/NV2Ekge56vPeoKgfZA3ccYaXxXbgv8A9PNikh+9AmAHdyf3csxxAOw1ZSDdLLVbwikRmqFSbSxUvdXq//cr9nxkn4kw0BTjzH9z8DwM0PMMv1uCnSWOJZsfYq739bn0PGweBhSjxaxJfD2Gj8V88h6Uwz4llDmIhRNjnBVs486uHzM09NbbwXNwDFhDgaEf/ehH7Kb80pe+BPXYmwzDSdRQyyIWYQkifLzegRd4yvAuu/hm/Ti9a9wLvQRDAnDxKIrvjYxF8bDw17RpkJruV/jL2YASvmGyiS5myC9DCj3SfhQ1Ixjyq0GBm5bwSolWgWqGsEaTwwwFZDRgGI4JtH1YglI22AWw0k5BhS/OB5gRLvZN9Qr9wzkZihl539lU0NMrpX1SNxihD5h8xgkwzG+FcsCQClSGF9qLMOoUAluU0Ow+vO7cY8QS/VS47DBc3o8YeuftYL2QMgYDKgp7KsJL8Z7+QCYCdTlYxwZDbOmNN/IZg6Sko8NAmaGpduhOzENkhvyACgKuAdFMc8+9fMYgrCb+p8wRuglhuDoIcAkA4Vtzhun2+HEwwOU4JYkpk+3zx8PV7YPBmqH+Z56W4SuPs8Cuh9BwxSUrkF3qzoqLrVxvvzwgTIaOFV4TZBmwvlDF9r7tNMEeFwDsfaFk7ioxp5wEB0MjeTkPBXMuwsjl9oVE677tdgYUUG+V3bXVua9EuyW/eSiBinsM7FRflJ0laykLvwrRPDokxBkcxnkoAJeyngy//bYE5B49nV8JmLZ2r9M+ebIE2UHOLia6FMuKa3RrGDD0/PPPw+9+9zvYUem5Mtpm0RsZaW0SF05Omy6BShMHQ3704+BLvFgW7x1UTJfkBSoIhgQzFJyxJtKS/crB2wdCvNJ0ysnIwQ111izJDBU7DqRqsYAeTmjfcUgdlXgTp2aV4pHIYKX51w/SXYhCcs1h7oVlL9jBmIlzANq8oDAMRd83nJObOi5iwmOVDFeZzFDPffezRRzvmYnHHOy0ypBgaED2tMIO4XaDU/86KiPCIy22UamhH35vIasgX9LXBwneb0vdfFkoTiz2XEBclA1kC7EbDKV5hk2sq48wEe6FGJu5sjFwsffTXdBQXCzuymAUoVoxDzGU7Hc1hhcsZIJjDEP5C8HzdkZS0DycPl3OGRaK8yuCmcvJMgS+2TgufVWMaQ6FiU1ezMOOgWDANfDMs0UcEgdCsNCPrGXkZoZi63uIhisfOAexEniQFqaoLbW/I2wUIswdIKBGpyS9uX0/DfPwlJgfaPkymCG8RqjVQZt87jlg8R3SrjTvNOLG+lKrrrpK1rLyC5Ph/BAsq8hoLLW+i3sI11FqmH0ZlOklHB8Rqi06Bn5XpV0K3lMJ7uy2iWxhqwgY4lmNY80aAgz19fXBmWeeCddeey1MVGP5o2wOaxO3UxpJDQpkVBKdXDM0WAi8yQZe5h6paDwZcCOjuM0jbp5KwmRDRYDKa7zwV8kqqPamJeoKoaU33tgBQwMFO5PFj30SIAWL7/mFr0iadbzZnxlix8FrGQWF+0R7AiwcWZHhBvXfK+zns8Mvwux7EC4CwVB69mwmQMW4O8b3XSnpZYKh9X/9K3uceNYnIJ5JOU1UuWbIGhhkdPmHX/s6rPje5XYvoSBmSGoVghdhCjr8Qj/CEt2DvmAIxZSspksyCemNNgoegwCVeLvKDPG0967eQCZChMgCPVJasiEWd927qdn295JhsiF/Pc/gSy9KttH/3nWAI4JcqqvDOYmgwgFcAXWfECgjm9LS4h+6VATtaf7d0USBOwGQUDNUCKrHxOehK4ssIH1fFFgdUZghWM81Qywl3WdjXMBD7pXOQZFBtsL+jrBhyO721PgB9Q7lZBmJ4UX29xMsc7ntfTDTD5lIvJZYN6kg3QxkhkSYbABW/OAHrM7Ziu9f4RqH6jVxXWPFU+NxSM/ZpDzdKb9PVUOHMahnozjWoqE44rSrWjS8p5Jc4iHCZCozhGzYCGfbPKUtxog1BBj6/Oc/D0cffTQcqmZ4+Njw8DD09PS4fqppTo0I+0YVnglS+ugxiQUyM2hBzAdE4E2e+3A5mzBBTAcdw3MjE82QYG38wjIyNdlPq6BQr2xDmWZXuxXCTXEciQLYmSx+3qIIAWy/XWnBrkghRfAgRMecKhdgyA9EYNiGfY+JE70i11L2/HX2Qow2x0cXUoE5nn8M+oZyjJ4WgEDUhalEQI0bJWt2mkjAxJNPdouIBXAcHIT+Z591hQtFtkwgM1SEni+WhcWMgwIBhtQxhEeKYJkWCfWMUSxMJpir7l57fvD3VETPK2NQYCYLLgqtjQARCnMjstVa5gakd7u0NsgMOZuWyKKjiQz5IgkMmIkjW9QUAXV0AxUbnbjn2wf8x0BmkTFc6bRkK4sBLjEPRYkAmb/NwZDNDHlBhKidk950TuVh6r9/3H4+eXOAzhBZaAHMEGNo+fXG/nK0yTB7fRnMUPdt/2KPnSecwECrxct8s0wvKaAehL4HH3K1bnGYIeeqjHBWCMF5PGh+kPXd1yHhrBsCqyBmSLKzfO8pOgbR7QnDeyrJE0BapWZICcXhGIWCzSL5ZY2OAat7MPT3v/8dXnrpJbjyyivLej2+rrOzU/7MRo+9imZxsafwDKZ+4UK2KM641O5xJUJYcYuDCDXeS7Udovu5OgZt5JdKuZT8qenTS4q08z09Ti2YAKBCBdR4LJPO/iSbNJPOPpt5yqg7kZksSNFbRdinoLLzOSclVVbrJlksYrNrRvYpANQNi0W40qasK14DuO8S+/kOJwPsdDromKWEydAyfGMQi6AsulgGGOq+3c5ua9tvPzu0Q/uGEc2Q6HvHxsFWJAFFF4V2qWjJfMrUKRoV3BTFdUyuHwjQJXHGpgT75ArFEb0Cq2zOwz+xfJ5p3hgbFpCeHBiKy424NnhkWJt33ZUV/RQ1uxwwZPmKgktVCS4moE7NmO4DuKzKmuZ6QlhxdrzYbR51flhyg40hwFBANplwSLD9iq9+JI/nimQQclGwYGdlanZX8WwyWUiw0nl4x+cBej4ASLcBnPIX0DEPGBrKSf2SnINkiysVJsO+jb2P2LWFOj/2MRfTw0pPiHNFymew7zE4KMXNNCtXMCml2laI42ClDhQwhPcwG3P1at+SIyy5gB9rsXnoCsWpmqEJGCbjRXWlI6rOD2cOjrXK08JCpOLUzpYuXQpf/OIX4YEHHoCmIBGoYpdccglcfPHF8ndkhqoJiCyuzxFiWUwzpammSJ/joo/eBFvAlAwpJ9ugyI2MExIbwfKbMDlrpvQGktNnQJzXjWDMkGV5QIToWYOAK4hNcfU/i8Whec89Ycunn3ItqPje7MCATdEr4T4s5y+z1QJqUFDPLKlkxeEij8AO67w099nf3w9wCY+vGO3sa/dfAoDHyKpN/zaUYDNoIUZ63v5O9kI8vPhdt2aoRJtIDG2IYn+dxx3rYSJE6xK8hzDr0NVYMW6ziXQELMYpemYFFXpjls+6ixUqWVgIVJCtinX3A0z1YZ8WlfZIVeFxgmRIIXhgWVIdHUyn1IkZUp3ubDKcLyIsim1F/I9j2An9YFZcOg2b3GiHHJ2xnGwy9n1wcxDNSF1lCAKOhYn/OYiAOKRmzXRpA9VQnC8zVCJUrWqfkD2afd21DCSJ0B1uXMIhkaCOsB+yNcp2ARk/uRHXNVc336attmY1a6x1PJuMhcncR4OhbdF/sSKnBIucstYbMYAzbgGYrpeV5FSgth96MVy9zRwJHrCooGiTw15WIkzGar1lswxIimrfTt+wuBRQj7xvO5bCsiTtnYaXhCg9vWHx/cf+XimbfUqnWVanqAKP0YLBl15ixyPHIGsvrgGlijrax0EE1Ajq8H7ixTwxTGaN2I5qaz93RNUkBuFc+bT6GCtW18zQiy++CKtWrYJdsO5HMsl+Hn30UfjlL3/Jnud9esRkMhno6Ohw/VTTxAZPY9OqUS1BUB2KotkGvD5GjF+uBEH2mBkmPj+ZB8hkvRSnEKAWi+9ToCK8KdWzlF7pAIb73Bs8y97I5+303xkz/MfIOUUDmXaEABImBOfC1+b+fGB4KZRHih3pFz8GgJklJ/ym4uJufiY2FAxrDOcKMJIryI1BhPLK1QxhCBM3fGTe2g480LMxCgE1AzmkpxcDQz5duVl153yeLZCeWk/UcoMkewmzIR36G4GQzMTqsjO9rKAwWSlmiAiPBXBAS06zv5so6NnZL5iIvEuXxCr+JpOQCrivIDfsAlwl5yDTJTmACwEm0z4lEsGLPWldgseRIhtccuYMH5G2IjZHD55n6QXOw7wDVIT3zTK+iIbJYYYsGwCq68m7i4uzaPlh1zUXmkZhQhxr8QaefszQCNZWQvF+R4fMbivLnvi5/bjH+RW13QgyhxlzmCEMwyb4fTvy3hLXuuzXN4waFhhF6zj6KH/WRmTeccZHGM5dwdrQ+msjXJRONWx+VsiLBtX2ekHnoagJh3XVUvwGpOu7LOo4Z07RMiPCCWclAuJxOffYeFOnSWZIShSUPUSUXkhtVN1Iy2haXYOhQw45BF599VWYN2+e/Nltt92YmBqfJ8JkMRg20TRPMEN+5lokVfqRM0PFsw3EZIm5UspxIcLQGgtfiToRDHApN7IQ0xaLKRMwFESDUorecxwcALBKqwHvt8gYWLuG6p9YBWMet27iYMhXMxQmTLbA7tEEWx8FMCFA6FuhqbWDWHo93xRzK1cpYbISXdLvsVmh9oMOckKghFEBwooKD42Ns2qVTB0uxJwxhP4DPUVfbYp84aATMkG2gwCB1AazZGXz2LpeT58qlikjmaFg4SYtiMiE+TykxMaYYbMrAgR3oA5GEeyKcB+Cs8DFPjdMihUG3LskTKZu8IIVQhF8oPYpNyzZHlXPI+qu0PR99c4tpy0DgiHG9hQDdROVOkMqUOHzMBMUmiEhRcbUqcwQb+dg9Q9AKmsxICukAN45GDzXPTbSD7DELgQJu38GTJhXM5R13Ve5lSvszV/cs0VS67OrVsEA1+N1HHmUZ57bqfXNDhhU3ivLaPgxQyUiE1aO7yHceaLgid1beN9bFrTx/rNUM1SS0fRJrWdjUE3orJly7W0Z8GflBQCkov6xZnUdJmtvb4ftFf1Ja2srTJ482fP30TKnlHowMHPVOFGAiqSbi3gPhdwIQNoZY/L550O+vx86jznG8fAmdLIu9ixbRglhyZhyMcBF9DwivOM5DpLJoobinPDVnPLYJ/S08DvzFFKWFcerXGcCwBALZwjQVQkYes8upgdzDgBTJkNfJJNlOl9gWMn6MpkhPCbRUNPVvoT0kIphJ3YE/vm8TOVFw1L9ST4+vRqismwpjxTBkIBQ85Z2w2FzN3JlYTlgyE5CYN8HjyWWsDNlMDkBM2WK6pLchR0pa5jk9a4oM2SHsJxNS6R8F91QMExGxvAzVwhLCf2IOZgqNgayT3zjx/mBIeOZP7iCZe21H3CA2+nxyViTgAudhSBQh8wQxxZU+Est6UqtdwNHZA6FBx84P9i5cjRDInwojL0PHatsls3ztSk7hE6/zch7nH3aZE5lqfT4XTtnA0wKUZeojHYcQrvH7qtX7fYRaPjdcyU0Q30PP8IAR9OOO0J6ww08jAo6okIzROcgWn7tWki0emsAifAuZRF9dXv8fh/MAixc1QctLqCCDO1UVoy2o88CaFWZIX5fFQtVk+8lGUcqf5gxA/Ld9hxv4WEyFQyJNSUdMUORldrgBar3M7pIUoEdboSie3eySLNRJxTHNUOTJsGsK65w1RFJUoEoFdixWjALy8g2cDagIG+PZrKo7TgkSAnSdShg6Ip73nRn5GBbEf756QHeF0mZkLjosMaCLExRYqMXhiDvA95eYhNvT7awpla/7R3OSvCAIZd8X7+TTVZEM4TXBhdXzD5s3Wcf/27vpP2D8iVkB3O69bq0Cvg9V71lnwdfZsi2Pz7xHvz8qWUsiwavQ+fxxzH6nI2/VuhHHCYiywX5mGnoVyjUDww9+vYa6G53wioCnIsmt50iQ8qPGSq2COdI6Efk8RcJk7HvQ9iOLJ+DRRv+5oZcYTK0CSedBNO//jXJJglngbGzDDhaPiHFIkCAMVwljoMDx1a87Pj5FNThJp3LMc0J1j3yNSagtu2u+cvhgywBZhjGnDRJriV4HJT99lwTvwKYQfbeE84cNCTAdWp92duY0O4J1kMwtLKNRUARV5oV1n7wwQEgIi6LkqqWW+s0OBVABfVK6KygMXDVtwqgZ7nPm4ckAF66fggO/dmjMPHjZzKGeNKnPsWAswhptduluFxARSTGlJINCKc9mwf4z+sroGUPXlqE65SSfA4yvSbqTsl9hWsZrr0lHYYGt7pmhvzsEX7T1oVhKEMRUBdnhtziZiy3zvQQuLnzjdTPbJo6zm7k+19fAYdv59VOxAn7RAEXxpplW4YiHjwLYSUcGrVUmEzV85Sj5RFjoN307FI4I90iPc7kjJlyM0n353zDS8IjYwXuiqSqevRCqFVqnQowxVx9DKd0n32+ugezkJjVyUALgqHcqpVFG5wK60WPlFU93kPS8L41gCZMcIohJu0QI15boA1O/bQK8260M3g2PQjg47cCdC0BePiHAK/+w/5uM8R9F4N/vvgBfP1HV7LvyzZ84UWuUfUjGRjh1yJFvOhSYZlH3lkDF697DX7z61/BwIsvQecJdpNdUeOkAzVDKmuztAx6njBcgWEyys4yEEHCGUJszsOcgVob/vQ/r6+CgzbIQmdzKnCe2/WS8lKfJkT1xdhZqksqxXDhvzYjICLHITMtkX0KCo/i9eCnaMnaQfjJ+x/CJcQhQcYA5zkyfxi2xPsCGZWUzzzENg6hwJAhUxlXnINoAggKhhbXmCzRXno+Z2gI+p+2q3a3HcQ1e8o8Z2CIg0RhjLFBofbatRDfxM0MCVYIQ8DxTBLglwfZ7UfOutPuhYjz79GfsMy6giidwUFdbOttYcsXX5D3MoI7vNTtvZy1ofeuZJ9K6ZLsczMwXIDP/OVFeOMbH4fcmtXQcdRRLpCdyFss67mAAlQ5xlKn16ZS2mUsWcQM6dhgl6TOe4cteHqRO+VSmKsYG6VRBSuE/ZOK1aHgArveoTyc/5cXYXAkX1wgShYJCSCmTnVvtorlJXMQg0v+9arvBi7CWH6p9Q4zFAy4CjlSxt6KwdBkBwCilkQwT+l+e+J6AJec+BXUJnnnfuMeKZo8O9ytW8e7lcuFmOoIioTJpEcqhNN+rRmwDQtZiJn3zkWWLNNLZYZE2AfT1v/7A/uP7z4McPeXAX45VwIh8vUlqGPP5CLMr8/gEDQNW66wTPaDMjfE3KCcIzjY4wvWQPshhzBGRbQJcZghDuoIEyFZiGIhv8H1UjN0zysrYAi9BsXE+UvlAZLYjJJsKDkuSkdAHnwcTigOwdA1jywKHMNmhtxARczDosfBxM32cfz1maXwygc2CKWGTEGMZ/21DMTc5+o9myUoGrbE6+F8GrwFbd56ScTpYYehaG3K3YCl9a0GWPZSFcCQm5115qB93+ZWc+2eeH2AZqj/mWeYngvZebW0ggyTKXMQLbOVXakc66QJjZcAKnIObrCB7ZBgOQF0Bu+5GOCvJwLcdZH9N9fctT9jde+wC9QLxlmCIb72YgavKIlQ6lpYfA+R5wpSMPO734VWXnoi3tQk2Wc2D6kQnOuFUkGFVceIRWAorCF4WPWG9OSWdQ3D6dc+AwVepCsYDBW8i3AQPY+TPTtEBIz25VrOi+C5xqCNKOkizJvxiYyXUjQqfr2/Pfc+LFk7UES8abmOg1HCyFIEpV6i5ik7BIUhW4hrWwx6psx0aVTEIpzqy/qmpIsaO2lcYEoZnrtHfwrwpN0BHrbhKeuGzBFQKwsx31Byq9dIZiiohxTWNWGFFllzUEXPRMJkGFLMtrrF5tirihkHQ7YOxn5HbhVfIPveBOh16hLBSzd4vkMh2aSiImm4OIoCbRP7RHXovHtzF9figxcBfrc/wD/PAbjnK3Zojr3QCS8FmWifgUyEKm4WrE3gNcfrvHahPMP/fHEZ/OlJG5hTo0kGrIcfZZ9kmCw4W42FFAlw/OOTNgtDzS2gVgCXGKMY4CIhrPfXDsI51/PwrnosXOfDQmWukCIHQ0EZccO9rFs8BUOrm50NXjSGdbJGveFtbFch1hR57YvZusUAv9nD1oFhH8AJ5lKzcyKbWHVIpkyVc5AdF399kGZIOCRtBx7gYRbFGvTeukG46z33mijatuTXrXXCZByoiBIYLJsT1yFhq94AeP9pZYyYh2WmJhystl53DSCxHuK9zfYYZL7uvBDgukPtxxf+KNcEC9dgoq/qGvACwwSfh52Ks4t6JZFUETgHV9g1tBrZIjAU1rCa8Q3HOAu9ENJyEV85Amq5CPulDGcHAa4/GuAH0yGfG3DRqD1DuSK6JAzFEcAlxpheHAwVcDxm9nGs5QuLr3iTAS4yWfjiiKE6j7YFF8Ofbwtw1RZgddFNKgbLN9+JbVAYh0Y9kywRgA0Xs+7jcHmkG2xYusrt/20N8PAVDhDa7kQwaWpa79o+vhDzVHZWMVYWRCwEd0kvFFhdE1F80E9A/e7qAVg44kS0EXCJ0BLwnl6WqE1UKEhvMbncrpILM3e2i9yhbbQXwLdWAHy3G+A7XVCYYWcm4lh+jIrwSif1CqCS916LoR6Av50KsHw+wGu3yrkBIwMeJsLPEjS1nmSToSBYaBWkBoYyltkhgOsOYZsLnYd3zScAUIyMAljOqLTirc5ZAmRA5ebuNw9f+QfAlbMB/vt916Yliuy5jkP0IcwCxLGrLd9Q2Bg8ZFM0FEfCfTjGGn5PqYaJB7KPlIvhEmP4AK5/fx3gyg0B7vyCcxxWDKxYHNoPsws6Tjjpf/hx2J/f4dPrMItjlFO2Ae3lGwF+uTPAwFqARMau72WQnc3K8h7OmoXnms5BNzPk01zasqCPF1rEbE7Pv3PndnCkAL95eW0AM0Q1QwWZYYaWai44Dsk2xzlvxpA1zsHvdkPhtJv4YP5ARWiG2kT9NTHGMq4N3GADG8Q9/n8AL/3Z1kjiIzLBL/zBfg9nhiyxhyiAiyYydPS7nV15LNPIHKTz8MU/Afx2P4DHCOhrQIvAUFgbtJkQdaHvGvAuYKIJHvN86U3GmSHfBfK538tUVPGOAlfYYBq3Zwxa/ZaG4sRCH7QID6wDuP4YKHQvUbwTn+MgHqMrFCfG8BNtPvtbgP7VAMM9CkMQg5VTNoDN/n0PbHLLzax1CW1069fsUrIRpej5R38M0Gd/J9hgN4Dj/p/RRdhPM7SeX3e5ELNeQqLoYkB38ae4TmG/fYsWRGTA0XJEylhHRYSWrG6R9m5vjEy0yYupJVfiIh8DOPlPACf+HmCPzwKccTOu0PxjYy5QN5QtBIOhPncTVRcz9Nzv7GuMtjNvtYC/P34VQP9act0DhPnUI+XHwc6h6FnHdSzw9n0A35sA8IOZdqr2vL/amjB8S1oU14vBSN4ffMY5s9mK4SU+BjbExGKW7HuoYAjn+L1fZ+Er9lrxdysGyYQPk4b3L9fq2HqefOkxhD13LdN2OTM3+H6NUzF43ocFJuULmHUvs6+R8/VdY8y86qcw5/bboPOYowOYIZrd94FT6qBY2QYcE0NBwk66DmCG2QzgrKwzZx8H1vrCzvWiPhbOBdRkOm0svAAAs2DRmUNwJ6qVUxOia3QWetKtvmAIWfGEyOoUzJBwSAbsVjKw69kAx/8KYLdzAM78J8DmhwY6Vl3K2ivC1a08TCaKLrrmIK7jT//KfsNGeztv/u/3mXNYGBFOUyzYoZ4cwAxxITpzSNABueFYex6i44O/owYRjwEBbwNbwwmo68YOvARgq6PAWv0SwPyryaaYhY2dptkuj3NKD8C7PqyNR6uA9OYDl/noU9xZE0Hs07BfKE4dA6l/rAj77iMs9dzi6b7Cc1jPdTuuMfgiyeh5v0VYBVyv3GKDIW6yvQRbOGJsQtKUabtT90TIr1ljs2joVRFz2Igi9Pxwn+2RinomR/7EOBDiR8H+355JQ0/OYdLEQmwzQzZgYYJd9d2WJfuMtfh1Fyfp4niuupvc1aFFNVwLe0htgBucXQ9G0POJ1jQwZ3XLw+1UZvzZ+mjvMKStiB+IwKKeIkwmivyxLEihVZjQAvAfvgifeB3AjicDbLQnYyCYp4ofPX0que7BzBBmW7KbRAAuwaagsBd7yv3jbPsN2QGAF68HeOVm+/ctPgrQOgzQjaGrGGSDwND0qZBf+C5jueQY/N5lNbtolfv+tQA/344xW8IKsgN6DBJx77EwcIDFRLt7oHkw5j+Gmnn34Twb2P37q/wyCNamSEIGKXGBCRwxhaF1MUOYwfRrssnHk1DI4L1ki6PRRmJJaOb1hVxZo/zQXcyQGh4NMmQLBGv15Te0epAFWU6mvcchk4yz4qcYKtsQwSKvsGzreYKLLg48+wx7bJ471339hbFjSDJWvi/t1lyyZBG8XpYFzXiuWDkgJUzW9wYATqHdzwdo6gQ45ufeIfjKGBTCEqxoaw9+dpyE4lY7//7MNczhhGnbAZx9jw3g8bp3vQ/w443B4l0KhEPdO+THDE2UDK0rusDnIWOocA6KUiX3fdNmiNDx6dgQYM8LoJEtYobCGqbSb7ALFETNDFlvxnuTicUJFy8YKZHFguGqB75jP8fP/uZyKBx1lWuyFGOGWrEyLQ3FSepcASp3fMEObTx7jZM2zcYQgMsHDHV2ssPEVyR4LaDAUNzaRQD/+rT9HCfKpWvAOoeLmaV34jMhhS6JFagsuNsyiJpMbXmAx2zWgRkuipitgt7Rq7cADHfb5+6IH1cFCFFx+fQOewFd5xsmC2aGsIgZi8WnUqyXVrGsH3z7sk5ng0OBrEiFtdY7jYhR2yE90hR36/f5YtFjySuhhryieaNhMsE+YSNQDGGhJRb/C2CoC2DyFgDb81DkzmcAJJ2NoxQzxIA8ZxmYOJ9rO6RHOrEV4LqDXcAEHvyeHZZDO+5XrhBWDkNUfuNwp2RSDwn3YZNkvxAZOiRiPAxpfO4ZsGbuWHKMGNfdYEaOBEN8nnsYm+WvAFx7MMD1pOKxCGcWy+rk4HFCnyV1MJgRJXR7Lob2vv8F4KwAnHgtwEXzoLDVUS5wqs514fTgtfAwQzw0w8AQhkMX8oKmaN0f2NoxdLQQrKKdfH1VgBDVDCXjcZjcam/26JSwCstCu7dmDST4ufRrx9H/jO2QtO7JU80VE/pLXBcLSoafyLxDa5bd3vMuvVKyKQew+WFFW484JQICNEN8DmaGCpDGQphijHU8hNzR4jidB37DnkvI/u7rtKUS80M4u6ImE7WEyOpkEQyyh6zie8hLPwd4x67SbX/ICoBbz7Wfb3scAK+G36gWgSHD9WZ6Br03GZatz6XtU51CGkENL334AMCCB+w/vn67jfDRPv1fgHQLFORE4mCIACoVDLGCiOATikOg8uo/Af50NMAfDrdBg7DOjcA678GS7BNmsgxNsD3bpi6yQK70CcWxHkTcjvopmyiFiW7xpN+5Er2X7FCcs+HgJs824EQCkveea9O/93/T/scHL7P1Vb/eA+Deb9h/+8intfuPBRltkCnBUL8aJlvtVIf2YYYGXn5Z9h7yzfIjYlqcpgsnzbYFwPE4A09i4SrwHlLseSHveKTNOYBZuwBsTCjzMqr4qloCUWuICajZG/LM22bfqq0V4s/zRfgAXIR5oQR8xJBAup2BsYLMILLHUJkb3LxECIv13BJgiC/CSSBajS++YhfuE9kxU7YCaJ/u2lCCmKEEByOTex1BqZgfyY4kwCM/svVPuNmIzXyfL9khjWnbQJ5fz2JjCDDUyvQ8fNOSmqSZAF1LAe68COAvJwL8bj+pK2J2zC+gsOkBJcGQYGWmdTtaEOG9Yx9EUWaDhTDe5pvXtG0BtvsYwITZTCdUzCmR/c+4ZoiGl2RG58hCWyh/02kA69/jQum9bND6/3az2YL2mQBb24Vhq2GCGcJWGJPabDC0rn9YSWTw7+klbJDPQ78QGXuPAEN8XbSOtQH/hFNPtccR9XlEsUI+ayWb0pwH2PsLRY9DbbirSi0wiQGvK9rEXmfOCj1douc1e8+Yvj3A1iRRZMdTATbYlVXdt47mLK2oi+azvidpVqcQXiMLLJySbvtcwbFXAxz6Xfebsedjg1sUJtM0leL0Yzsw/DM0sQnaVg5AE6M6cQ0kG9fbfwR4/zqAiXOcLJeDLwVonqg0JHRXWvWLK0/CySImMI4hbuTeVwD+e6H7TTufCbD/VwEmbAKF7nfdk8VnDLTBKc3QvH4Ymtd7maHklEkAK9+wF/g37rT/EVktbINBJ30RFk16pUrnb0nPT2iCWJ/d4wle+bstjkaKGA3reLAXtdrsRJWMMlbT0SuDISdMVmY2mexTRcIT3ro2jtB1baaNNR9FQJjeaCNWo4p9Ni/s5mGGmgoAO55S8lhUZqhrMAsTuZfNPoezDBNRQI2bKOqSeBHGRHPMywoJ2+F/7B/8+g+cL49DLMSTyBjss6ZOgcLadTC5x5I6GDk/cvYGDOc/BoCAGsMA6JFiCICHHahTEgRUBFif3ONUVZas5rpnAR65D+CRKwE2PdBOe26ZDHDQNz0hRZwjKoPmOg7cLHspM0QE2n/5GMDaBeQNGYBz7weYuAmb74WHXnSdq1y+AMmEG9SL9PxpXcgSKGNMnw4xPC89H9rCXQRLHRsAXPCUZEmdLE1//YiaWp+nIXGR0bn6YQCMcCNQEuUbhBPXzdtV7HZuVdmCLF8r4/E4TOLNjL2JDMgMgS8zhJoicY8J/Y/LLEuGvUR4qeeTn4VtDtxbZn+KciNNpKcXZtwJlo6tB5vsX5FDrYbJWC+8adNYgUXU7skw2RrODC1/BGCWcEjIvYI6OnSocYwVIjMxeO1N8rVrUo8Fy/k9wlhg3jSWAbttT7D1T0JEjaHw7U4A2JJUz29Qi8CQpqmZQn4qfbTBic0MDGW6icAOad6YBckM/4z1PF0XPdBdznLGUBYvbEiomkjPZ5ksvGgh8+BxDKSNH7zQzQO2zwI47HKA1im+Y/hNFnYcU1oAFnRBcxehUYW2A7VTr9udue2PirtS2j0smp/2iYg3uykYEuGMZkXYffOZjueLWStY6RU3SJIyXM0w2YyOZimgxrIKYhFG0Ww8Z5UEQ5ktAvp60TAZ2HoIZJFUsWOBL7oSDIkFsilfVk0XlaK3vdJWTyYLW4TZ6/LOGGADMnYfFanArgKuPh8whDVMsm+9AzPXOxqVLAfyKbypUQiPxerQEBAhk4mfyxd/ClSyASEsoWmb2m2xc8WK8QnWppnc76ijE85CMuNzHHHIFSynOCWx+EzOPpFQnGQJlt4NUCBASLCms+Z6rgf14Ck4RRM6u+ldKN0TIUXukLQnAK527hNmuIGR76lecw8bSPqf4YZHmaHs8g+985CyzFjcE2taISOx1+ehmiaYIRom85a4WA3xdhECVtoU8cr8KAbHBq/+veLc8oGeWAo6j3a0dyKRoXlA1AAq2EkMbI2wILHV3iUZamcPifuGydjxcDA0sc+RD8gwWXIQYMPdi7Jw6jX3dag3tpn7WesAPhBjiMKV6QLKzQA+wsNieD/td7HNnFaJga+1RWBI01TWxo9+RBtCEAFroZmDIakXas5DDD0/vJHf5GwKIu82pyCh0yE++EZGYeZQewqaerOQ7uJgSLBCTTlbTIs1ZT7zqA0aMMWa3MTeBdL/OIan2otGKwdDdksRHmoYeAuA9n3EXmDtTujM45H6THpZy2jQgvU0TCY2lAT3Ps+6A+DPJzjycgzLTN7czgBCL7uKRusfiTAZMgV47Ts6O5mWABmcVI+9MJOWq57muYENFkmYDDfGYSXtXXik1uAQZEYSMJyOQSE/BPmVtleeaEnaALGEOUJJhxmiJvQnjHHklZvlIpzJ2yGkrY6saAw/9hSbfuLeO2utDVTcoYaCf50ocv+WEoLTKtZTuxFrDrOqyvLebcnZYaTXb7Nf3DQBYHeueeOWE6wtnyOYudSK1YWJJWfNBAzUIMMlQmCi+F8qyzM29/qCHcJAR6TDXVLB65R4wVBm1oYMKKdzANm1awA2J8zQiLf+Eex8uuvXUk6JrPeVB8hkHXDKyjYI0S6yBJidiDohERLH1PFT/wKw+h27IXLKR5Bs0JA1Y983FoeJLRwMebI6HTBE66+hYV+5on29sFccf1qwEr7nKimZIQGGcH7YTkIiU4DYRv5apKIOiR8YovNQtPzgLHCi2QI46idFQYknHO6zT2V4sU5k5eN8/RcOCbveOCfUcNgYAUJoERjStJyMQ4vJ4s+oDE2xve3mLo64RQZWSx5g6tb2IoJF0ZA2T6YDmoIG14hAG5iYscFQd9a1CDMRH9qn/g0wbesyJ4v/GMNTbDDUwo+j0Ndn9wvDxbOlALDn5wEw8wcLiyFt6zdGsTCZ0D6xasQEDAkRH2phsK0GhjLw89/+ty0SRk8UDTM2qmyUGepoSkFbJskA6tr+YehsSbFQSe7D5ZDq4X3DFM0QE4OL2h1qfSGfTuysWnfOvcHHW1tYPzNreJidq9Vpu6Fvnl9zpikqwtYEsTbdaiYL97CTBcz2irEO23nODCUwFIcZeyVE6nKMIg5DZo6diDBrHfZFyrrBEM4RKV72t5zc6GIsxRpDZSk1vDR9OuTi9rFk166FZsoM4b270+l2LRx2cCnP+fObIx4wxBlazBzF7D58pbjWbEPZ4RSAQ77jmePecyVqivnMkXQG1nTYmqHsh6tcur1kotsOEx99ld2GBTPtsNhhkeNQ5yHqUzDVHNPSmaCdn1sWmhVlGxCgztnfnovdS23gfTimWCPaNNf2ppgJAJxAZkhohmSYjISr5/iDIcGmFZuDIrGkAP5rr2CGMoIZKuQhv269BEMwqXQzWzW1Xp2D7Hi4DEIwQyiYLwzwcg2bznWxi2X1UvSZg/HWVhic0ATNXUPQvM7yap/wPqpKZm59WASGNK0U5SxsiDMqLd2WO+yDC73ISGMpr0XGCPDe5RiTMgDv90GzYIboIoyCYgEYQk4WFzMkjoN71jaNatnhvUO/Yxc+bJ9eorlpjoWW4iRNmab1ulpMyGMpOKLggy6xf0YxNJpJJljIB8EQUvSbTuU9iz5cDgkEQwkvM8T0BCgGx8aYQYXrhrtdYTLc4LEoYlMq4ZQhmDTJ7maNYGgCgqEhyK23vdLklGllHoub2VTFm7gpxidPYnqeaV22YDe30g6VsPDubH/hqV84oxizKXrabbDG1vO4ChXi/TvdvaGXAhG+uqRkioGIGV0AIytW2aBUbIhiHhZhM1RQh9qOmZ1u8bvYWNm5ErokntHJ7l0UsgYAIXuMfOlNKxaHVZ0xmNaNBSPtrCWpfcLjQBYNNXMKMxu0+aossChxgRshay0iwKko29CUt5lmZJdbJtlarlEwmk0mrrVfIgMLZVo+bUXE/RXU1Hao25OFpYJTUagww7NrEXDl+RxkYAgZshLmXd+9Nd6wXZG4r0Ys1O3ZDkksbkF8i71Kj6FUeesLcHb7p7cxMNSyVlSzp2CouEPS6DZ2OK5RMmfxCq4OjTYytcMNIlyLcImOwwqI8Ispow1MtWvPtKxTwVABYPp2FXaA9h8jy4+juc9inqOL4cIsH2SeUGehACHXGHKB8WbGOeJNrEBNw2SrnHEwPj6KRrM/MomUDGM4Imp7IU518crhyvtlHH7yZP+edLjpfjjPU4BP3RjFQtwpxJvoyfIijIlp5aUzlwO0U1ttwR7nrLQY4MqvssFQAvVSXORfCfvkd281bbElA38TBpC16XYLN3Fjayte7dhpgBysu2AgYgLPaFuxxt5Qsjlbt9dildy41HCf3xipTTaSoYZs13rGmiJ7yo4Ds8maaBy5CHAswp7i5r6GE6C5Vfam6DBcGLq0K0qzlHYfdlCuWWUlMjip3C5wiqJ5BEKjaDKbjIAhbyLDaplaT6v/u9YUUVlZteXzXRmdfsBRZHVmZHPpvNtZwDWxQqmFX6uMzJZbyTmIx+EKxU3ZvIIxgrOF0fo25DKFNZb7miNzqjCMY80iMFQjZmhkms36NGPLgaEhx1ukzFCZYwSBocHpnH1am1dKwpcGXM5mUpwZKnS0AnaGiGPbtJUr3YtwCc9BDfeVymShwmMXXSuEtKNkLmYo5SPe5BR9nPeQ81TSFguMaIRKDQXgN50CsPI1TzNYTwq0KJ/Ps35yuUHI99mpxYnplYEhIdj1u7cy29rao01XoJ5nmHnbtEhbuWMUu7cSLS2wnH9c9v3VEsjHUwWIz9qmjDHyJUPJiVgCVnFdfW7lOlmDCzPvYhM3dImlyzkOXzDU2g5rOcE7/P6HMrsvlixAfNqcioFjkHO1ppNv8CttMORySkqGFEuHxJ0+hIQZkvqRwqjPQXquWJgsgBnKr17jVKBWepPJNUVlhnC+zvsbwO0XkFpf/udKzIG0AEMYJlvNnYWWBEDGR5hd4r7CZAm1NU7TdvYcxNBofCAry1uwRAlFd+ZnKhsYCIY24usvL/7u0ohNKz0PG9kiMKRpAkTIbIMARqXQ1gIDGSdN3GGGCqXBkCKq9Ispow1wMNS2TqmCijcy1vwwMFliiSSs7nCOQy7CZQAu4Z2IjbdYwTe2CPPvxETaQjPUmgSY6pMGO1rMUDIZSNEnJDPkBkOu8vbUsJDgVVsALLRrTmWxTg8z/w1eMEMT7F6tUOjtAYtri5JTA7zdUkDbzyvlC/HWSy3II/u0nqfWTy4vFEfFzUGeL9r703lF5KVriEeaZ7Vxyh6jWIkLbAHDmSEEQzIDi927m1Z8HH7nCsdYPolvau8vlXMQe1TFyti0pEi7RFanYIbyq7ugMDhoZzCJ9aREM1TvGD7ZqdQpEe1R6HpSAnDVVDOEAmo+B9fzOZjgcxATGVjFfB/NkHQWeZV1WSz2xxsD3P5ZgNwQFCSz4+/siqzOdC9vLm3lIc+dBawCX1l5FsfUOZJob4feqXZItvP9YUeXhLo9LJ1gIBSH1r+x7ch1ruHrLl3fywj5NbJFYEjTvKJgpZZFwWJ6j3giBatELbQPPiA6gtI3mZ/3TkW5w7k88ySGZ3TKEFa+r9+djdM2vbwxuGc9mM371mtJIBjiXikeh6xwjZtWiSwuv4allHbG87QqZms2MjmABM7Xgp2qamE4A49l4y1HvdIpPfco0lW9UtGZPLHSXrBEsUJhMg7f97rNBKFhXaYnfmY/RxB00h8gS4r8+bJoghni1W9zHKTEEhbEOpSeMCGzydBa9tiDecgbrgUYWbkScj02+kpOm1HeGJKJAFcfN9Xen2bfe7mlCFTIxlvBYi/uX5W1wfnhDi91ue/dcrQdSkhcHQOBC4biPuTRo5H3lzlaN/TgfULHwYCreDhjLQd1hVVdTsFFZJ8mzyiZxaWG4tQNfkX3EFgdNjPUOeAI2rN1ph/xY4ZQh4jrYTydlkBlcpe3kjYCSCx/gZZc9bhT/f/mTzCtELOtjobCrp8ses1l5t1QHhJ5EcLirE27TzFVH5PZwoQx9wMra7ayb6yJizGJYZUTivPRhZVae7EP4eCIt3Ht0IaTWZJBejjG9IjZlTzbsi1ZVki8kS0CQ5qmahXowrKyZwgO+/mj7CdbcPQKI++95xTHw4UYa/5UALiwxkk/v5ExvfSU3z0Du3z/AehJpaCL96scWfKeA4baUiWzrFTPgS7EAyM5+No/5sMX//4y5CwEQ/a/Y3sM2fuMgaE5FemS1PP1lX/Mh/3+3zMw0vr/2/sKMLnKs+1nZkfW3S3uTgyCFEJwKE5KaXBKkQKFUspPKR8VoLSlyEeB9ivSAsUKFAkhEAJBQggJMeLEZSXrOnr+637lyOzuSDKz7LLvzbXsZnd23znnvHI/92PCAsImxnzwIsbGHSBb6bfvtzYyl4hSnFZlaOHXVfTvGp6XkLSD3xsmtZsket/e3eyzs3Mrb66IAnkvzeM/RFbILRtYwUJDSeu6Eb+4fBc99XWjRRkKNMoslgDZoqyzpM9f8UzMAdSYvzUtneTMzqatQljsWL6RguI1SSI2KuIYgkTI65CkEXh+2S66752N7Nr2FwjXT1WzKXATlm9kRcVoH9A1KPjhRVto4v8spGc/30kHxBoM1DYZylBqtITLqmyaD6w3V++jiXcvpHve3qQrQ7691VY3QwR1lr0vWfm3G2XonbX7ad4/ltGCdVXUkCm6pNc1W8t0RJG9JJUhQ8027tXq3Y109P0f0It7+PdK6g0SqFfSZjGI8W26eijr0GlPYlmdsl/cjgPtdPebX1N7KSe4xbUy08u4lzqBTAqSfeHPeH+4t39OVCPqpM17nejC5ykgXafdhCjUNHfS5a9u0tt0IJEBRRp15TTT2tg12jPErAwhwWRjVTMzqg+M5estb0eA/LWi3x3IkDtyBm13ey+yX/n9aqM7X19HSzbXks3lpv2CzHdu3kwBUeGeqWff4UwyQGWTHSJCJxlIiqwa+5s319O2Wn5S7Wn0UIY4n9pXreJBsgjcRCp5BEsuNIBaysFI6V68qZZtYMDuBi+byAhC9WzeQoHGJiOGJcJEDiVcciPGQf/Upzvo5RW8CnCSo43SmDKksWq0sShDRhkCY0G2ePiiX7e3iR0ouMZ9Odk0uK2D8up4xWNLIHhuYmsIRQNDhbBZ3GTr9zXTG6v3kcPrp++RjeyNLZTZlkQBp42TIdEt3r/7G+OA/PQh/gHgsLx8oZ5tZMTB2CwugDV7Gum2/6ylE3xOaw8p+bzZBpkZ5bXwMXLSkqnOYyhD2OhPeOAjVrPnyUun0trBNhq5T6P25Rv5L9o0SsqJUX3SrGRoc3UL/b/X1rKvdze0U1WeOOBrW43mv0wZioYMWceQRAXz6oH3eOfwvy3ZRjZBhrTGNvLuEqQ0WqISskbQlFnid2+vZ6Em/1y6i44Rh4l/b4117kZhwXdx9wlShwy8G174ihWU/GpXI2UN5v0Ek/xB6li3znCRRVFjyycrSneTLPGnhZvYGJ8Fsgh14ytqoXYIN9l+XgXekenUi7V+m/DrbrIklpGKWkMHWj30sxdX0fr9zeRqSyNUIiusEe0rzD3WpIssNci3xntMc+yMh4mGHdetkWhWNWG8fbyljq52pVGup4U3OE0JGEkMWZHjhUINksIMN9W0eHQy9Pv5G+gfn2ynUycU04iR+cywSmvUyLNpEx8jlbfoiXYMc4gCmnGX5/C5+/6GGvrX5zvpulM12pdno4oDGrV/8QWPn7JrUccg9mcoZegQ0ZOiAhfTh5uEC4QdAEFdGWr/8kt9o7dlx+LvNR6XPFCgREg0tBObyGyM5aL8OiZyQSwyaldlaNn2ekNCb/EbyhBin2TROmz2kdx93cjBcrPHQSWxW7h/CmFUB/3W+JEI6lNvwGzJuRxGJsum6hZmwXkcbqpK49eAw4RRWdNGjOaR+j0zA3VaTGnXRqwRfyZQaYD/CGLaKIIzZQC1vgmDDEXIWgp97nlpbksczJtr9jPFAHL6/7yxgTaXCVVz3U69lIItJbqaTqHB+XLufrKF3wfg/fXVVJ9tYxI9eQPUsWaN8cxjiomwW+rNfLCxxuL+aU+xUTtusUbUvpK3vnCAREQThBqyRuQY2w+0UXWz6JVGdn0N+veHxCWlR0GGQjO9TMaCrKwNYqTZ7VQnHnHHl/I6olsfRsyQNUNqw/5m+lg8kx2ZJboypHWK/mdS5YIbuA+oBPJeOZO4TS9dZSBC5msoqhVEwKTo6tcC96UZKCqIgpg9uF8PtHrZGt/f1EGfbOX3qkmuQzQ4RWFK6UbOjnJ9mGK4ZBFXKOZw90HNBOavraL9ATvtERy07QveJ8yRET7oP/Q6zHsvClRCefp0q9H7b3tdp+7mbRdNbHGPbAlqttuXoMjQISLUzQDAut5d3667soD69gBVC2UIGQ66JScWbHRZWDaSJXlwoGAiLzYRrobOIO0TEn37Mj6RnZjIUYzRnfokg1A3VRmd0evbNT1mqHPjRtI6+EnsLEKBHVf0JEIUxIPsjM3lo80ifQGbWTZf8SXVNq4M6ZW0I6tPveom02wsZii0ng2wXdzzkXu5BW8hQw0tRiYKCm6OP4/ojuou/b1CCWptCz+UPv2mzroJy9T6+maTdB4dGZIHfL5wTWLuYi6AnEhsqWmlbSXWw4+P0X1drJ5cP+lup8W6hjIkgQyaYJKdqkRYgmf9hpiUIS0kyUCmWK/dK+I/GMcKkt1m0zPK9HUYrfokxrCHkLpFG4x7xcbOJE7qfH7qWL3G5CaLRRmyxgxtqjLuFfupZmM1k4D2FSsM4pjF+5ZFQ4YctiSL68e8l9QlZ1JTuo2SMLX2NFLQ46FAizjky779NWi5DlE+ICfNGku4PUsQun0+Hs9jatRqccMOO55Xr7/kTaJL37Io9aFrEHsV5u9nW+t4xw3cP1ea3uAUffUCLR2WmL5I8AWMM6Qo060rm8u21bN1YT5Dtoauwyjjksx7bzbUJNHUFoos4kMl6toCtFeQ+c71603nVOT10d+hyNAhojtFBZNsb6Mw1wXq2oN6JotE1Bu9iajIIm/Y7KtbOpmlItHmRSaLtbEpl+djIVwmF1ann8WQGFYvUVMn6cqQrJ+CGBV7bln0Uq1mp+Iso9v7rvp2iy9+VaYkQ8QqLPtlEF+Uga6JhkdU4cW9gjJUkSsCtQTOPayclhfxSt/nfRqkKeuFmwy/giaOraJ+zsxzia5bRnTeP7p1lWohzwTyOTbjXXXcL9boSrf2kKprMA7FKCtxB0U1owKxqbK2Ih4/fb3PIBGYfq1pKPRn/B4bI1oyJJ57XqoxdzGnN4Yc8DbNyMTSx8lwRiR2+Fuh9wruEmBPQ0ePGWUSPG4vijUi1mF+urwOj67aWGC36aTOug5jV2hlPE/ovQKkUSLXISdDZVGTiOxUfvDiMESA+do9puuw2WhXCVdc7DsajRIBUJpLvn111jyvHKIhcqVpHZ42oYT25FdQgzudkjs1+tULAdLaRVoZU+12Gc/loleIfrqCV9SOQpWvafbQzjoRqGdWaNuRYh/EL/EQiJ4KqobAK9yWiOGSyhDcZF/vM4xQub9vLQ1ZH7npMStD8gypb/N1IdlNnZpuUEtEq2r2dygydIjQg0O1JCrLFptkq1ffhAuEjIm9GSTCnJbtTIscPB3qZijNliTCw+R5IM3FLaN2n02X6PUx0v0RM8lCN+Fkp1Bt2o3FkirGaPbaqDGdyM9MRjK5MqIJDjWuo0SQIVhZIEPAyKJ0phjtycylNjd6L9nIs3UL+ffx2A5HGhzm0W0wiQTka7PClZ/upqEF3DoclJdK508rp8XlU2hbYQnr73TBQhxY/D6y+iCaiLkpLI9KtckWfZewCVc1dzKFw5lko/xBpRSw2dkYOa1E2gERMwRLThClaJ97ZrKL3A7+3LdUt7JDGCokrodfKdEW00bM5m6MZCg/PVnPGoSrZ4tQhspzUnSisi8kDMmRlxl9vBtrnMvfL4wEkKQ9Ym6VivmG+lhSGeKDauQA4YoiU0bGV8k1LZWhHYKcynUI5SiU1LlwUIuYsWiuRVdOQ1S0FFmBnGy6USLhTPNHtZ/4xXXgmWMeyWuR63B8GSefO0v4WEm7mi11vmxRqE+9ATmvnCJmZsYQY/KcNaWMRpfl0JPjeFPVcbuIMpYaype/ShRGROHQMDE3hvJv0xVguKvlMx9XmkkHhLu4sFGjlOagcZ9Ss2M2ruTcgkK7tYaTXLm3tPmsa5Bde0Hsrjh9723z6vOqXKzBxo5glzXI9vfveCYZoMjQIUKyeitR8dJeQYYmV/AF0erVyO+wUavpjHJl+KNzk5lSYYtNrH6nWJBTRKEsZHrBFRe0hRCuGMlQeQ4/UGpbPbRZLMiZQ3LZxokxNJuNWkznIBsjirgOs0uxVBBHtgkLK6syN40yU5zo9UzfCDm4Y/0Go7BjblafiFXo9AsVS7Pr/a9+ffpYdo/uOXsCI8WIG7plzg+oOYU31Oz4en1IrEKQbBmFUal1pVmpujK0UxDgipxUys1Op70ikLUSsUn1/GfOLHfUDRSlooJK2lI+X7GTx4gNykvTrW2oNmar1JUeiIFw8eee7jYRrppW5kYG4Zo+WMiZmk2X6HUVIiuyu89Mhkqz+futa/WwWBioXMDkymydRFTlmEhdaoDs2dHFwEhDpkAoQziwoKTtEPP3sEF8HeIvWQ4Uu0aOwmgz74KWMeAaBamTh9bRI/L15yGVIQlHjEaJC9XTZYNTtg75fjJRVCHeXcwVF8dec5mO6Nx9vYFgSMzQ6RNL6IxJpXThjEqaPbqQ7THvV06nhbP5HErezvcyQA9sz06PughmhSAMbB0K4nhYZQ7tzCzW12BKi2Zk3EXpqpZnCALB5fOAIbq1Rjzz4fyZw0suY4YkkrKyYlaGpCoPhVaSupmCSMLR0J5so3ZUZBfgTVoT3/Px24YiQ4cIb6DrAY9JJt1kE8v4JPIFuZXVkGMq2JcZnSXnEfEmkFGlhQtlCCmRwPDCdMpwO8hPDgok2ajFtAZdUIZSI2f9mLPJ5AGIjCLEPgFD8tMpK8VFAdmQNjNU4YomHsJQVCRxhIomLVKoEJkp2NhstEucHd5vtpO/VtTtiPJASTR0S47sejrvsaMK6cWrj6Ajh+ezzQZnqzdgow0VMr6KZzTp1ZsR/xQmI4e7Rvk9Ls1O05/5NvHMK8W92iE24knbNKI6Eb8V4rYLB024yVKcTspO4Rvxlzu4u21YQbpuqeIqzPEKyXneGDLWBOFyOvQgV+laglsgXzTZtCP42KSowFiwpebEMK+IygQZMisdUO4kwcf8NhMVF9ZglPEQktQVZvC/hUcEIiQzfyYJEgFlyHwdIFy29PyY7lVhBl8fIIz7mjr12KFJwrjCEzG7LaFwMZeJiF+J5n65knhfPeCb2ladOI4t4c91X4FwkzV4yLtzZ5+LH5HxVdJNhr59j1w4he49ZwJbl3I//qac30vX/k495EAWLXTk5URn7Go2KhfrCgRVuskmlGfp8YHD9hMV7NcMlS5KAuEVMYiolyQNEijmUhk6YhifO60+Yvt7Qx6/7uTc6NegzCBENlmpSRmSRvu4Uv53PD5RNiJbsxrtUZbq6M9QZCiOylCx8PdiI96D1C4kJ+SnUUayg4KCRHwzlE8yu0tM5rS8GGTUJIuiIi3SwXmprHt2UOObQpNpIruzopvI5urQZgtIkqGK3BTKTHaw9wDUmmIDk7N9RCm5B+m3Ng4tkLCMZCf7+W5Rc6Zj4xYKyOyM4tI+R4a6A9Qi1D3Bz3VSt5vL8np9KViOYVx+ZrWjJDOFKSgIRVi5k2/ig/PSKCvFSasKeN+w05drRB6NqSluUXwzJmXIkcT+HrBCjAGSLQkSDvjNZURJh5dR7qhWSi3wRu0m08lQkkOvFCzjU6CiSTcg1A6ZyaJvwlEcKOZ7VSaII2pxybgnuACkxY1ZtQmZceLQSS/xRGUs8HE4iUh1GSqafB4IfNWJo2YlQ+4srI/YWpdkJrt117QcA39fjoHDeU++MQaURntGdAG7ZmVIdntfJcpzmK+jNTmJ1S2D0Nz26Wd8nCjLEPQGzKSuO0iitz+PPytHe1AvtOiv55+TROuciMHNZNddSSApkgBPLM9iylBjipuSfUQTvuLvxZ0TAxkSbncoQ1DGAYQngAiD1E2q4H+n08f3m5VH+SltbBEVT2uKeg16TOUUdO9Cu5f2iZZBY0oymQEns+YaTNM1OTv6a+nPUGToECEXS5LdsLIg0UvGXZYDEuGkoCARX03SqPj2n1PlnBbeQzE5O6bDVwbYMYnzgFBU8tMoPRnKEB+jukgQLqcmiEos1rXhJmNkSJA6uGVA6uSU2VZpEK7UQm9UhMvIwgIZMojjvsZO/WAE4YICtkuSodVfGy6T4m8/eBrwCDeZLczyYYelZjM1Bq2xyvNMGQpDhkwtIlNcLqZuAF/skC4sKENOenfwTFo4w8iiAUmxpcYonYu2IlnigJeZWCBDkiBBtdHsNrKfXkpFU5q5VylGMuRKMmoyrRPBoSD3kljgfrWm2igwiM+/rKHtUa0PszKEOBhG2k0HPA4xPne5mwzu6uCPJ1HxRbMoZ3hb1FavQRyd+nWs3NWguxRR94vDRrvxaEU8TlqRN2oyJNUOkFPUnDGTUxgp8tpAuJDtFSzk6yijrDPqA0veL4yRK0oqSKUOa1DeK9TRkkaJuRxIn3GTaVY3WSiyxdz1OJKoQQhm3r17Kej1UkAEvzuKSqNS/u22JN3YXS7WIEgjDBW/3UF/PG663lsPSCvyxKzaMGUoxboGsc5l2Qu/xn+2t1KjynmjKSXXFz0ZMp0h8jpwTu0Xe28FM3axZ/EzZOtQ1MGzUUqBlyv/UazD/g5FhuKkDCFNVW6Q1SLQFSjPTmGbpKz2GrQT5Zx5IqVkiij+KDbiTr+o+EtJuiUH99JOESMClQBjIJ4HWDE1SHmXXkTlR9URy56NYiM2ExUsDBkouLuek7ryXGySTt1y2DiMqPiWq6n81OSoF4u5rYhUuBAMvEX4xuFe4gsSVq/1d1lAYoTO5b2vBoYhQyz2yW7UZNrPFSH//j2GMhRGTTMTlWQcjCLlVgbmMzIEkm2z06vHuelP59gpMNRLRVOboq4xZCYRKU6XvhFLDCuA+mQcvuz9d/KDmU2sKAKCza44l8OhKzSoaQOAFEv1ySabrP6olIb/5gzKKPXErAzB3SeJo0GGJJE36vcES1MpZ0YxseLBUW70QfLrY+SK61i5s1FXZ2GQ8OvgcRf2eRMo/5SxlDOsLfoxdKLi1F1lknDx65DEkd+r9gsGUcHc4yh/QnPUpE6OAXJquC3F82AGidNEhqy/68xwRf3cEw353J09KEOSZAeCRrC5b89eCpgz4wpKoiIqDptdfx5yDZqf+YbCQrpzXhKtnRCgwsPaOFGJch1KgxruPqmSSsBVjYxVBM4HBBli76hd1AaKMm5P7lkwauQZ8k1tG9t/YdgUZSaL+8Xv5ZZhRMOff4gGHStqgSllSCH6iWwQFRT9gksDkxgbM1swYvMKmCcyEIX1IFm93US44FpCQTy4T2D5csLFJ7LHRVR4yfe5RQpEsREbQcFJeqo4iJBMedcPFGE5BGw2yjl9NmWUihTTaJQhWedDs7NDUWat4ToAECSpPnlc1mwZHsTXN6wT+TzCKUNM7jYFufpq6kgLBvVWHA7ETPVg0XZ3wBeIA14CSoRUbTDOF6Ps5D+6mcehRasQmPqloa2IHlcjMAzKkDhQ7GL++mXfJqQTRxnMrrvJTNW6JbAJ54gxNNEi3G8LkjPVH/W8spA6h0GGZDq6XB98EFkvxk/UKZpWxagMJZuUIRTalM8DcXtSfWKvH5NFBceVRm2Q8Gvx62SoQBDgNcKlqFvv7I/z5+HLt1P+KRPJ4daiXh+GMmQ8D1lrpiRTGCS8LaCuDEk4cqJrMdGrypCIGQoFYhyBQMC0Ds2FYlksV/gkBp/e/yzJcFEK4JnDjYU9C4Zoc5qNlswOUt5IsUaiXIe+oGFQwzgwLyuos/xaYIjy6/TjBfIMiVIZMlxxRh83CShPziShSsl1bgPxtfG5ix6JfYQAJxKKDB0i9EwA5iYTVXwFgYDkjDLxfCMWFik+tQu2jZ4yomBY+DF8uk85dCKDQGAiszH0iWwzGoCCbIU5dENdP5gSoXVzMCb+PidDYhOW/bY6xIESxUbsM6WkI6vIvLng3zgU+Rj8Xu03WaUse6mPBPHJZx7eTYbnZGMF+FB0UfOjn1At+fbxjdiVH22vOOkyMeoQ4fHigJdkSJbYD8haVzEGNrNfSXLoGy8AFw0ORTPhAvye5pgsUguJMLnJzGRIEi4oETohkEQlBmUIv5/sMowSCbiqzS4s9js4gDpiHUPTyVDoGFgziNvjb0QYPgEPUYdQ0qJVbcggKtJNJjEol8cfWogj9oYYSZ1BTp1dDngoQ3KMoGanXaa4JMBZ2HcUAulKxr0Kpwz5g0ahTdZcWtR+Yop2hFIdZhdW1+ch3LlCBQYs9ayjdZMJowRnCILAEZJgVobkGPK4Zu9IzqtYlSF7Uhf1SRZ6zML35RmCddJWZ8yrPpDFm2goMhRPZShko5d1h8zKUNAyybJiCn7DYsEB5ZBlqEUsD8A2YqHasFe3VsW0QXYIVxz+Bixc3a0gNnqAS/RyDBvv7owNP8pxZFYc7gUWvblIGreIbJYxzK4ylvXTx5QhSM49QVpZQbuN6sWeyKzSalH1uDg/aqKS6nTqbjI5r0CQZLClPrfktIgxdkTGJZnJkKxtYpAhQYIPggzphxbcSyFrpDiLky72OviQpctWKlDRxAyZaqhgXoUSFeaq1t1k4j0d9BgIoHZ1uQ7MZWMMoUJgvkvCFXUAtcjuc7i6EBWsQyOeRzwPNB+NwSAxjwESYT54AWQayetA7bQ9IVwhUvZVnyJtCm4AAFJASURBVIoZEmTIF0DmnaEMIW5Iz/iKQIbQZ5K9FmTItAZlcgzAXfv8eeiRlJBUosjs4+9PKEPCMDavQ6syJA1RKEOiRZIo+BhxDJmxRknMY2He32UMURYbQ+zvZqN9ALjIAEWGDhG6xGnnwc0mnqIXrAO5kNY7W1r6JItu89KtE9GQUMbbAJJQmIObmTLUUhXTGGZXHEiJfO+W60Bws+4mQ96vuA4EXrgyYgriw4K0kiFBHN3GGLtNVmkKMu/6iDIkyalNtDPoDrJEACDdfZ1r15LGek1p5EAX6GhVG6eDCsWGBQwRm3C6O0m34AG92UC0MUMhbrLB+cbzQCkFdh0hbhm/V9RqiXKj578r3UsOptKYgTWjkzpJhvD6mFUbg2RLN5kEc7+6reoTq1GjE5UYXLxCRQt1KSK4WXeTSRWNKadSGcqJKb4KRAWxgGZgHcqYIXkw+oMHoQyZFBXzGpRxe1Ca4cLGOuxw2/Si9O5sH9n6yBoMDczvDnLuBsyxe/v26sqQiylD+VGrNqkuh/6MAfl8uOpo2nvlGoxSTQltKzKqOKMLGWL7iU5UbMYzj3IdykBwXIfZUAeKRDJLBjtDTMau3N/7iBFKA50M3XvvvTR9+nTKyMigwsJCOuuss2iT6Njbl5Qh+K1xwJs3SePQMhaLhUREm3qpkyG+EC1ERRxgZhLBWL10k0W5CcuYIbtYDLLWiLkGhUx752NgsdQY1xFFkT9zrA187WZ3nFQizCraihFEKaMHUVqZj2es9cKiXLhjIb297e2wr+nwinvFom+7h0ytB6RV2vqZSE9Gob+soqhVGyhDo4oyumzC0i2jiRpWzGI8SGUIByPUpj+cO4GOGVlA1x47jI+tjxGy2cdAhowsLKsSgfUCq9RwYdlNrp+mgyAqdkp2IEDUbVEHcJ9CVRurKy4nNuLoclnWIA4RKEXGGJKoxE6GpJss2eGyrEGk2UM9xT1jhSulUWJx90UZ+6STUyeViHpfEkOFWwZ95HSj5PgUcuanU9Hkpl4xSKrbqunva/7OPkelojmtgf/me4Z9Btchszq9e/eRd9u2qN1kkqjIIG0zmZd7bxqMEnGvRJmemNSUUDJ02ZGDWdFIFHKVawNEzKL8S0Sp0PpMMUOAee+V7r4M097bZX8fAOjzZOijjz6i6667jj7//HN67733yOfz0YknnkhtbUZvmG8TXtFzyonCQSbpVAagGi4sEVQJxi2VoSg3llAZ1bwRjyvN6sZNZjO5yaLbhL2CDEnLYZYo9AUcPjSv62LB/1qrD0p9gsIFmK1SaQ0xy0sECra67TT47kup8uhanvWTgJLwm+o30b83/puaPE302b7P6JaPbqFffvxLWrpvaY+/0xFyHd2BE0fRbV7sJW0fLTFqP0WwSI02L9z1A0IqqzfLquZp2CDZax0HRYYssTaizcPc6ZX0z8tn6JtluhhDVyLkGFEGbloDj6GoGIcJgsIdSZwYp5vqZDFFJYaYIeYqYhfkYNcxwuRmGFmYYRxY7M2IdRijMhQapI3gWQmMBzUVGT9Qhm1yHTI3WYzKkHgmOOCxPqQFf8TQPDYGwNWhQ1GGDPUJKpBZzZZuUa4C8+dRNdFOw+88kdKKE2OQ4Pm9tOklWl61nNU7u2nxTfTwVw/THZ/eEeE65L3qXhnibneeySub2mrt7dTx1Sr2tTvPHpHUG3svH+N7Iwv0TEupPMFgkMTRJ+P2DoEMIT7wyUun0+VHGT3guOETsgZjIUN6YUc+htkoGS1Id6YptZ4Z7dK70IfUwEQicmTtt4wFCxZY/v30008zhWjFihV0zDFdG+v1NmRwszPJqR9US0QH9qmiPL85gJpNe6naRLmxeMVmLycyKt0+S7zR4KTyLMNlYsoEMJSh7JiUIak+obQ9ijrCxSDL83OJ2Mgmo9bamBa+LBEAV5wkXNis0Kvq6BEFXZQhLHqtpcpogRtnCwWxKde+fy3VdNTQPcvuoTSnsTE+u+FZOqL0iO6vw+eJSIbMbjJulYYUwoxAhoxSBzyoEpshNkgUEkTLAUAW5etChqJtAwDlQowBVag7pOquOEFU5A9icZPZBBlycrICMo9WMqdNNNKaWWFSnUR4YornYbE57A7zewUjBK1jfAGNDh/Kyxfg+vA9Pdjc30nkbYmdcInrQNYVYnpQjXiaaCeCwzfNhYNRzF9fB5GP1+mKuvCpTaodLuYSf/AHk+ntNfvpuuOGW+6VV94rzBNvrMpQgE3NZCc34K48eij9bck2uviIQfprsGc1yHl1EJl3seCFjS/Q/cvvZ19nODOoxcefy7L9y2hzw2YamTOy298LaszsozSXNX7LDBzwuFeoLeVJI3JLG9qmkasocmCw3Htlxtq1xw1n92b2GMPNzXrSaQe3BtnviDFcST1fB/Z3i/IvEXVcktVNNqqYkygojfKcymQE2BRA3bx/QClDfZ4MhaJJVBDNze2+RovH42EfEs3N1s6/8YY8UFxCGbry6CGszDlcDdJysARQYyI374tRGZKuOKfehBDtPkYXZ+iZARZliBGV2FQbc38cABb7TXOsmxCuw2IByTGiJlyyXhK/DrgW5t9wNKsWbHYpyjHYtbTs56/G5hJF5l00aPG20KOrHmXusEaP2ORZI0RDbVyyZwntbN5JgzKNA8J4Hb8Oh3jm3cFckym0h5Q70xeFPG+4fqQihFYf+JCQbrKgqD8ij+sqCtKvF/6YOvwdVJRWxFSmi8ddTFMKp3RLIrCRyzFCAeUAG6YlrReIsRGs2Z2BlgmLNtTQFUcbli/WSqMcA89BKK7RzC2pzlIQZIhnVj5y4WH02TcH6Kpjhuqv43NLrMMOEYAa5WZvKHU8LglE5a8XHUbvra+m62cPtzwTWS/JLwkE1oo7K8a6T/xeoW+b3rvNdB31ZjLU2RGjMsTnVqqDz9/bTh5NJ4wtYtWUzWMcaBfPI8Zg82jx/s73af72+fTezvf070kiJPH8hufpf2b9T7fEUeNHNmW4e077BnGsFc+jM1Mjd5tNb1Nkz4rcT9Ev9kVU65bK2U+P51XfJcwuLLOb7J9f/5Ne2/oaDckaQu3+dppUMImunHClvo+HkqHQ7/c0Br9qOqgAasS2yjMEzYxhvEtXXIZJcWTX0bJvQMUM9SsyhIalN910Ex155JE0fvz4HmOM7r777l57Tward+qb+m/Psr43fmjJAGoEN8fGuOXBKGVUZzdEBWPolgOZVJsoN0g9fb+Hmh2ANX0frEAqQ9GN0e7jhSiTbMaiD03jNwfxWchQHBfkk+uepOc2PKf/+4RBJ9D2pu2U6cqkn075Kf1j3T/ok72f0OmvnU5jcsfQiYNPpPrOesp2Z9NZw8+idm+nxTXaHXilYP7MQ7tAp+T5iFIjBG5q1sy77gACAxeTrEwricqD21+npfuFm088oq9qvqL3zn/PsuF6RCagFoYMScs3ILYK3SqN0iI1SB2PUQGgNEq10fzc63ziOqDa6Bk56dGTIY0rT8DJ44vZh+U63A69t17QnJos1m50TYaT9HvVHVGBO65DBqHKzDsE00YRU2e+Vz3FwehExWMmQ9ErQ4zUCfUpzcVJBOZQF8IFpa5dpqab4pLi5KrGerptyW2GOklEPxz9Q1q8ezGdNvQ0mlkyk65aeBX9Z8t/6PWtr9OZw8+kiowKqmqrouMqjqPJ+TOI7Px+ZYYhQ9iPq9tFgcosjbL224w1GEGdNZOInjLWDJJtVYa2OJ30xy//yL7e2riVff5076eUn5JP5488P2QMfg/cYZWhkL1XIsbUeunug1JqVhsBcx05tpfINaKUob4HxA6tW7eOPvnkkx5fc/vtt9PNN99sUYYqKioS9p5YkCSbXOElTr3yLVlJBDaFV7e8SseWH0vDc6yTsyur71kZMQdpM1avuwBiU4Ycwk3WHbosloNUhsIpKgjc1NNXMI4M4jOVIUCcT3FaMWVFYW2HYmP9Rvq/tf+n/zvFkUI3T72ZyjPKLWoGyBCwoX4D+5B4ZOUTpDVPJVsWfteatWSG2cpqSrORIy+N/HVtZHNovExAlG4yENyeiArcMnCV+TUXGwkbMY65JbUr2M9PGXIKU3/e3/U+1XXW0fL9y2lW2awuzyOcm0ySiDbdTcbncZszmf6x8mEamTuSThp0kh7PEs69lO6yBuuGzq2AV8RAyW8mZzHn4rPr/8UC2+eNnceIaU9lIXCvUK27JzDXj6wErxMVPncX7FhAHr+Hzhh2RreB8XoAtWYLe68wRrs8GP1CsYmxGCKQ6ux5bjGi0injqzwxueLM5CPdHeZ5gDjKZ24JNudjNHQ20IGOAzQix6qSRIN2Xzv97vPfWd4Lnu0vpv+Cbp95u678TMifQGsPrGX3BXukxIubXqQs/1Fks/E1kp0SXhkKtvHrqC/1U8lGvvekol1GBIPEvA5d4VQbSwA1XwcfEleZS9JKGHl7fuPz7N8Lti/oQoZY3BdzWbmiGkNXZ5PctGT/UlpVs4ouGnMR5aXkRY5ttYVXs0nGDJl/kJLNVPJ7l93LyNxtM26jjCiyh/sb+g0Zuv766+mtt96iJUuWUHm5cXCFwu12s4/egtzsk8Ow+jRXSDaZREoO/b+P/x99uu9TFsS74JwFeuxRuCDt7sDiR0JdGWyMbKaEbKzbSL+Y8Qs2mcPGPoVVhowDni2WGJWhTr8nohzMXYomZSjE3YdN8a7P7mIqzTOnPENDsww3SDiAQCE4GotablLPnfocL5aZbFjFHn+AJhdMo/nnzKcnVj5Hy/atouqmIHk6s8iRtpnsrnqyZS2NeJiYU2GBrDPHUsuiGsovWcXDFFJjixnqCWkuBzUKMuS12agmKYlafG3MArz3qHvZ9d299G56ZfMrjBSZyVCbV7iTNZ4JGW6MJl194t/7W+smenL3avb1n1L/RJWZlVSeXs425VG5o7qqNoxUhSePgWarwgUytHDnQj2eZO2StZTuTLdcA9AqlDrm7nOGuQ63g+pCN/uUbPpi/xd060e36kT47BFnd/ldQ0Vzhr1XcGcEfCHrMCWbtjVuowdXPsjI3OlDT4+oDIWLgwFRCUqlTnftRueKMz+PjDDkFOswEBTuV5A03U2WxcjMhW9fSHtb99IlYy+hn0//OUUD3FscqCAzcHIB9xx1D43LH0dDMg2XKYAmpf885Z/0xta36bl1b9C+pnZqaOFxkc7sr6gx6VOyoe8JM0R7nlco2wBjAdg/1EczOo4hatxOmRX7olKG5DNBgcqekGZ2kwljYatwWv9g9A/o8vGX04/G/ohOffVUWlG9ghHJHFMGoyRDyWGNxBDln4gOJKezYHOcQc98/QwNyx5GpemlNLVoKluHZlIfGtva/b1ydE2UwNfudLp+0fW0o3kH+zcMq4dnPxx2H++P6PPZZLAQQIRee+01+uCDD2jIEOui+bYhJ3JKGEuOu7BMRRcFmh0uRoSAmvYa/etQeIN8I3YnJUcno5q+/7Wvmf6y4i/0zo53WJBwRGUonBxsIipBm81oJZqSzcjGRW9fRHd8cgeLVekOnmhInRN1juysvxO7Fr3sfCY1djbSn778E/snYn2eWP0ERQNY9TcuvlEnQsD1U66ngtQCCxFCN+rv3f8hHfbb9+j0BzbQs+9MoC2r51HzjkuoyPtDmjecW60Sg3N7DpI0F2Jj1zw2h4a98jRlDe7gdZkiuBs8umpjxAxFtkqJ9ojMmrL0Mj1Yck7lHPZ50a5FlhRx6bbEJhuWDLnNfZH4/F3cxtuKANXt1SwTCPERP37vxywzL9RYwPxPc/W8efKWBuLwlUskJZv+u/W/+mugEFz/wfXMujajXQS0h4t94tdhHCiYv2bCJQG3THdoQzA0G8MZcQwZbG5k92XTvV/cy1xAWB8gRpHIEIsRCbMOfUGXNX4kOZM0m42RjQvfupBlRkZS6sIRLuwnARHb50dgd7vhMnlizROMCAHPrH+G9rWK2JIIeHnTy/TCphd0IjQqZxSdPPhkZtBIZRH7/V3/XUfj73qXjvnDR3Tzkw5a8cU5tH/Tj8hX9UM6q/znVJE6XidC5njNSPOq02WjyscfocqLxxCz+aIhQ6JsgwyD6NkQtcYM7QmKBqgZFfrn0bmj2RzGPLCO4YtI6vh8sLrJPktL058nVDYo2FjjMB4Qr9QdCQ7nisswZymavr/G26gTIQCq+TXvXcNI8XcJ9v7gGnv22Wfp+eefZ7WGqqqq2EdHR/cHbm/DJ4hKSpjeLdaCiMb3N/qswd1vbXur29/3BvjCcidFIlwhMipcJi3GxotAxUsXXGrZECU6RZyGKwJRMU8ZuWA0dxbd+emdtObAGnrjmzdYjZBwB3w464S1L3E5yC4L18kif8lZ7G8j+Fniw90fWizdnoD4GWzeyBZbeO5CWjVvFX1/2Pf1n+9r7KAXvthFF/79c9Zgt9Xj11uqzBqWRw9fOIU+uOV7dNuxJ9HgzMH672WFyRjBgYlicLIAnw8WvFTSUvMixpC0++QzCq8MmS1GWKV7HAYZkphRPIPJ2nDJrqxZaRqD3zub5mBxI9Ec8JhbeO67fdxt8tLpL9EzJz/DLPzC1EI2xuOrH9d/F66naBQuWPChhMvnzmSWNPDsqc8ydwPmLpRBM+Fq6eSbso1cPbrrdNePVFTkN1NyaEOd4QZdXbuadjcbRE8fw8PH0IIgQ+GeB9yWpoaaWFvJmfRF1Re6OnLmf8+kz/d/3uV35VxG3ajUMMTRTFR0pTk5m5E6uGPW1a1jSleztznMGI6IhMuIRTPc7p1ONyM1ZpgDoMPhlS2vsM9XjL+Cvpr3Fb18xsv6XtDpC9CybXV0/b+/omeWcqNlX1Onnmhx9feG0pJfHEf3nTuRfjxlrvW9hombgVESIJeh2oDU6uswMhlidZzY3uuMbg2K+bdH7O3mdSiNEtwvljkoxxBkCLWleoK5lpFcHzuEMg232ytnvEKPzH6EzhlxDvve42seZ27MLok+YcmQo6srDll9bTxz+cRBJ9Kjxz/KQguWVS2jf63/F32X0OfJ0GOPPcYyyI499lgqKSnRP1588UXqC/BqXKZG8G1P4JleIdYi4lE8tZYFs3jXYiah9kS4ksMoQ2k9ZBvs8JiawqKQYfUKenjlw11+v1MQrnBjMKIi0nHZOHLh23lsjQSCk83XAdXgg10fUGeAE1i3vecx5EYsm13K+6W5M5jyANwx8w4qSClgGRrykAkHqS6cOexMKkkv0RUTbMDz/rGMZt33Af3y1bUsTRrFDR+76DD67Znj6N2bjqHnrzqcvj+plGXX4aBFIKcEXHU9QdY4kaTOBxVGljtIK2SkDvFLPaFDEJVIqo3VKrVR3cgT2NcgJhI4cEAkgMvfvZzOfeNcRrybPK06iQiHNPPcshFVO3DgB5lMjrTnw4oOY7E2v531Wz1VWhIMsysuHBky17CSB/w2t5s9Y7jGED/yl2P/QsOzh7Pvwe0nUS/iWRxaavjrcCdRULp+xLwKpOSw9G3zOpTzzIxWr6EMhX0eLNbG6u7b5XJZsuoABA9DDTZDt7Q1NDFOCq8MCdePvp+k5LC4KgkQoefWG0kCII8vb35ZV3RwHWGfB7sOoT6Z9qz3a1ayjK/StFIW4wOEKh3dAXMdH5gzl42/jLlxJXFF6YApv3mP5v7tc/Y1vn3rSaPoz+dPogfnTqbPbz+ebj9ljF5zCYeyGeH2LHO9JKbaQLWWcYjphUzR7o40dtc4tyekhuzvgbRCavC1dlmHSNSQysqM52bQfV/cR7tbdpNfEwa1I5Lyb02t3y0MH2S8wjV9bMWxdNcRd9G4vHEsM/bPX/5ZJ126uy9ckLYL+65ROkXStbWtnAxNK55Gx5QfQ786/Ffs3yDe0Rij/QV9ngyxFMpuPi699NJv9X1hsiFwzUv80M8ME1AWar1LbGzbwz4jQwlZS2DvOLi/rPqSyd1Xv3c1nfyfk6nWvyHiYuFF5UwB1AI723nMzYPHPki/PZIfVv9c/88u1q9HkiFH+O7E5rgPuSiXe/gYSN3GdeCw+sfaf9ANH9xAE56ZwA5guKnWt/BNM8URPgMC90vvki5lZ0cSy8rAJnrq0FPZwpcEMhxwCICIAZLIBIMavbpyD819Yil9vOWAruRcOmsw/efaWXTKhBKad8RgS1l8CVhe2Hzgjz+89PCwY0PtkNfh8xtkKJhWwO7J+W+ez9KLu4Ou2qCbUDjVxmXaiKdeSo0lPJMxy2WNH7l47MU6Ycfhf/vHt9P9K3lRuyQKTyLgipONKEG094tNGHFXklgCiOXBc4FrAbEM2ChbBRniwc09bzeWvnfikNwpXH5wo+B+YywE2sqAZwCke23dV/w6bJHIkEFUJInY4XQwQyDVkUo3Tb1Jj0vDvIHCiZg+xHr8ZpkoABh0hX0emLsyRkXO3V1J/IvxeePpkx98wq4HClqom7fZK9UnF6WIGlLd3qtu4ke05Cz6svpL9jXiReQ6n79tPh3+/OF01AtH0W+W/obNO/b6QEpEwqXXfZLftDtp8T6eWHD6sNPp+Mrj9UzF7oy47gwSkHKZ+LD9QBvd/ebXdOMLX1GHj7tvUS7khasOZ5lO504tZyngoeQz1ZlKNx12k/73wqmBrNWLWbUxGSXvtG6j8948j65eeLVFqTHDr/dxCxcTajVImq9YoLsCzetwaPZQPV4Mcw5G4wVvXkCdxPfPnDC9KqHihcbz7Ldr+jqUwDr55YxfMmMSBg/UdPa+hDIUTn2y21Eny2Tsis8723nxRRgiwKlDTmWxp5jDMEYRvgAiHkr4+xv6TQB1XwPq1EiZELK22QLoNgVa3GqdqNjstFXEsMB3jsmFQNc/r/hzj38nPUw6M5QLmfGA2AEsYSydna2ccOEAR7bauzveZZbJRfMvYpsSrBWQmJ2dXLZPDkO42Htwu0SehNyINdrs4YRiYv5EZj389IOfsliCnpDqCJ+JgI24QSoqYrNfGeSWFqweHOrYBGHpwlV2x+F3hAQLepmrZlvTNmb5gWQi6wVEDZseNmApxWNfeerS6ayybLhNVQIxRq+d+RqLiwqnBkqrtFknQ+16VfCq9BzaWM/dP6izMmcQl8/NaBckwh5hiZrdo77kDN19lB0S1A7L8YMLPqBdzbvone3v0FNfP6Vbi84IZCjUKm0WLr7ulLHfHfk7Oue/59C+tn2MsGQlCTeBlsTmaE/gBd+sB/xO8fLBWYZrEs/9btvdTGXAs71u0XUsZglIpvD93ph7SXOyOyrX4RY738AxP3C45yXnsQBRkIfukGQLn5yR5kJwc4g7Q2Q9DcoaxNbcnYffSZe9exm9tPkl5tLCwQWV5dn1/+Z/JOgMSxzN9b6kilaXkkH1HdvZOrjxsBtZwUIYD7d9fFu3f4OTIXuErE6r6weES7pZDy85nAXrYu/a1LCJPt77scX1DKysXsmKl0LxkjFM0iDZXN1C5z72GbV08ntz5PA8+uflM3kF7yjW4RUTrqDzR53PXDbhYJ5X7DrgdhdkaFH91+wznsGelj1Ukdk161i6sMKVOjCvQeCAKDIKt3xoSADcyXg+6+vW02OrH7Oow3lhsgEta1B8r1ncptB1OLlwMl07+Vp2Rj217in2XIy4pPBBzxluN8lABE66bLS3je9bsuYaDFK5/yJGCc/572v/zkjSH475A/VX9HllqK8Ch7KEr3E6ZYTJLOKl+jnjRsYPoKXk0B4hWSMTBxPJ7F+GG+iMoWdY/k5BBB83umlLsEMrOZtahVwrU8evnng1K6zY4GlgQXGYxNcuulb/vbQIRCUj2aUXU5Yb8Xbhm4blAxkVae8S2PxhxQ3LGmZch7sy7BjdScJf+/khLwsHog4JrHlUj/7Z4p/RcS8dx6x4WCcgQrguLNQ9ggxeNvYKemP1Pjrnsc90InTahBJGhI4dVRjVBiwBqT8SEQotb+9D7IzYhKvcBvlYd2Bdt78rY1TsEVxYZj8/4mmavPw+dVd2ADFnOPRvOOwG+t/Z/6vXe0q1lUYxhtExu0WQoYxuWnJg3AvHXMi+fnb9s9TYKVxxWniSbQ44l898h8atWXPhS2ThTCuaxr6GVS+JkBZwU442M+wY/NByWtbhHpFXVplRyZ4rDlkzYCyY76VTC//cLbEdYoy9wiqXwbTI9plZzN8rDsWv676mSxZcQu/tEoHhGm9TEk1WpyRc202xYiAIc0dZ42pwIKLgn0TQUxgxFi1UqatJyWBxKNg/4LYEjqvk7leUq4CBddIrJ7E5jXpAIKqIj0FiCJQSGCNFjon0+7fX0ykPfcyI0OC8VLppzgh67EdTmeIWyzrEGoyU0cTnlSlEoXkvJgs75GtN7jHEO3aHgHBhpbtSo1qDQG1HbY/GAq4P++PsytksW07OA8zfkvSeC7FamqiKe9QiVKvMbmIXoQ5if/ym6RvWWsgX5G7eNGd6RDIkAdK1Jz2XBX1jTuFMCo1/grsaey3QXdmL/gSlDB0kQF7Ajk9+7N+0q7qAslLCH1rprhRqFVksmMItqQZRwQYGRQbpipCxUZL+udOeYyoE4jHu/uw3FPAUUnlGeBIBiVOGlWKTrEvDJq6x4Fmp+MBqwCJEmvXqmtWWgFogP7kk/HWICruaLWhsxG2c1KHSKixTpNv+YfkfGHF56qSnmHsD9+qKt+6kPfvKqbIyfEYg8/O3Wxf+LnGvMIYMBDyy7Ei22X6wm7vB3tz2JrNapDSMOjtI2R2XM53+/GYnrd7DexLhT/7qtLF0han3TyKA67B1CBIBN6To9VNlyjzc37afHRxmAgm0eLn+lkThlQizGwCKmFSGQt1kocC9u3LYn+jhpW9QSdbxEa7DGAPPXCpDmc7uicF5I85jLiDEkT26lluK9gjxPN0Fb+4JtltIhAQOdXOs2NEFc+mdzwZT9uDwVb27c1fLMaSx8KMxP2LW+pvfvMnqqeBQQUzJWa9dQLWdeykjaK3iHQprJXj+vTphy8vDBAfiQ7Mfov9s/g+LGUEGmzmpwe6PfB2hY2xP0izrA3Mfbj6oXE+c8AQzHqCKNrX76PmvvqSkppNZ8daoSLb43q5kKNPNTBGS+wnWNYwPFC2V+PlHP2f3E/sbFPOzh5/NPjfVTKTTHv6MVZwHRhal04s/PoJy0sLvnYcC89xl19EgsqLS8qlKEGnp6kOxx1AEiJOhjDCqvHkNAnUdPE4zksEEgvHonEdp1l/vosaGYspIDpdNhr531nieFqH2dFfzB9+DSx/KHEIUOomHQURWs93611iHe1Lxej9bg2aiihABWQcKQKbc7IrZ1J+hyNBBghW8c6ZSWxMIipdlPEQiKiIvilkoe1JhNdRSYUqhvrEgGBXZTpBWpcVz3sjz6B8Lk2nD3gBlfy+SRO82yBCCadkYDUz6N2NiwUT2AXy29zPa1bKL/rVsM22ubqcho7ov/GjZJFn12yAbo93uoP1iU5G1QnCAQEaH4iVjSqAajdR+Rtvrq/VmkGEPrTarq2GXt6HLwQiVC8HZCEZGlhesIBn8Cov+/mPup0BQo4ufXEar9zQxyRxxQYhFMDfaTBSYVdrejTIUcgbBbYnnbEarCKZ12MIrKvzQkj2kfIabTFiluH70yhtemM6qfbd0+uib2jZqaPfSjn155D1wAhWV5US8DnNfpJbCUUSBmh4Lr8FFh4BqyOj723lsmpMiKY5d03rrRW2f0NpY6Bl37aRrmZthRskMmuSeS/P9mynf1K2+ZxJhTWTYI0i2nFdY178/6vfMbSUVIRwgN41+nG58eSmNKuvanqUnMiQJV524DvM6hAsFLVKAn0z6CXOXNre56eFP3qcc/+yYieN2oaLJNYj3/uqZr7LYRpAXeW3fH3QlPfmWEYwc7l6FxqjsFsozVDQJqD1QnWCAQP2F2owgbXzAMHlizhPMPb94Uw394u3l7HfQquiHMypZZ/ZwwejxAK+dY3KTCTIUSC+0BLBjDYIshipTQeL3Nd2k5nYbeGxDyxooW1q3ytCuunbadqCV9WDECGinhFIeeH91e49mr8lP7/kMwftKd7n1ciZtSS7yCGWop3WIit6IS5LJMUBhWvdtrCQyofyTofzXMxLY1GUNwuiFS+yyBZcxY+GWabfEpOr1RSgydJDYWNVMb67eR3VtfLEUZkTaiJNFmBwkeqLdqRlEnlpL5WMABEsCfvWNVS20aa+NbeJ5aRHIkNuYyLAZ6pIziPwNllo6oUDQK/57efFn5KtviDgG2yQ9RqD27ow8feHLOBUsCnM17Y8211Kbx09r9oi+chGII9wAminGCteyz1PfZSNGHMyi8xexxZjjzqGL37mYyd2wwOcU/IQeeG8zLfy6it1DWFavXDOLRhb1XuVUSyYLDkRRQLLKUsWDGGmA0gjXJdwQOGBaPNxz77SFP7R4DzTDTSZ7reEw3NPQTre8tJqWba9nLogxJRm0cX+LbplLmDvJ93QdOlGZOJdaUJ5/0wsWef7jLbX0xqp9hD+dneqksRVn0bDsldTc4aXqlnbK146O4l5ZD/gGxFnBNebuStaumXwNzR09l3ZUE/3mrQ1RrUEzUcEaBHaLeRW6DiURavf6acnmA7RgXR1p/kzKC3NgsTFcXRtq1onr6KlCML6PwPBFG6rJU51GhaYeYdHWFNsh3CDm+Cpcg7wOZEq+v6GaNV8GctIiGCQWwsW/twslBfxWg0SSR7SxwbqDSnvzh7wDwKVjrqHPNznprztX0ZtreC2iH86spN+fNb7XDk6uDJmKYAoydCAtn/waV7NA2kDe8N6PKjuKZUJCycL1aDZOZHOwl4YtByLWoc1Pte0GGUKyxjNLd9B972wkjz/ISChIlywbIOFKslNRZgTDx20o/w2XvUG06Mcs3gzZlkB9m5f+uXQHbalppbw0FxVnJdMPRl5MS/a9T/ubPORpK6IROUMiG3CddtZcGYZog8vN2iaYi0RKYB6gOG1VSws980k1DTmmM+I19GUoMnSQ+GBjDT26+Bv29ZTK7IhSb7rJFwuLca/IBDDHCUkg0wldpHGIm1EW4dBiJEJDCjhXbeqEO6O7TRiWyttr97P0cjSbXL+f+8/zI5I6jIEUTA8FyEYHhMunKLXI8jos+FdX7qWXvtzNDmMzusvS6lL91rSBIXspoAVZCi02Kbxn1AaCugN3GawWbPZ/PuZRWl27ip5botG1/9piOWiRptubREjK5zpRQQpqOycqVYF2XRFAgCPiRmY+b8S7QBXUK5vbI1lyRiwBWm80izYTn21ppz/P/5jVTJIK0bq9/Gewxoszk6m508dUugumVUSvRLjTqU0c7rBIa5o76Y7X17E51PX3rtEDZCvGFEUxhiDZ4pBv8suyFdlsTeAwx7orzUph2Ubf1LbSXz/kaxBA7FekeB5zDymMUdV5oNt1WNXUSb+fv4ERlHavUaiyLDtyxpo5vgqoF3FcoUaJ1x+kheuraHN1K1U3ddKLX3IVDQdZOLAAar1umVCfRFxS6DpExtbTn26n177aS83iWQCjizOjdsVJhWuXEHFkDNfu+nZ2eGalOpmbF9Xbx2UdxWpPLdq4l/76hp/avUZM3JwxRXTXGWN7VUGwuPswrCBDVSkZRO3E3vfRZUczgwQZkN0BNZkKUiKswxQnNbFx/Hp9H6ctjS5+8gv6ZKtR7weKkATIu8NuY2vksqOGhHVbsmtxJ+tkqF4oVaixBEL00vLdbL7K+mgGxtDYkpnUJPb3goxISrOTtA4Uvg2y+VsvtCjM3U1VLUwAwLmxv6mDzj2snI4ekU/3zN9Iu+rbWUbgPWfzWLL+CEWGDhLDCtLp1AnFZLfZ6PZTx0R8PbMc/ElkswfYJDuQmkvUsZ1a2lLp5AeXsIwzdBBu7PDRf1dxKwoLBYsZi+Xyo4ZE4QZIIvLCOgEZIqrLqyTav4PSHTk0f+1+NpFBTFKcSZZFKZEDi74kwiYpLHhWsgNjiPWLxbJubxN9tauBBX9+uaOB/rOSBy8b6gLRT743jN27cGDtBjQnHwPEURS5w4G1clcDXfHMl9TY7mMk9OIjBtEzn+2kVbsbWSwQ7pkvoLEDHwHSIEAXTCunvAj3LuHKELJYRF2fKqHeINX6gWMfYJXBZf0XWHkylkwLJFOly9p6oltLLsjHgCokK+be/foOVq9m6qAceuCCSdTc4afPt9XRzKG5XZqkRr4Oa1ySrMuybreXHnl1CTW0+9h9/8GMCirJSqEdB9rY4SuJEDBzSPjDJA2uBhHc7LHbqRGuOOpgG/11z26i5dtlB3iibQfa6JaXeSsQdh/LMumimYPoyOH5UR/wCKBuyCxmAfd2stOv/7OTNlevZ+5EzJkXlu+mA61cFQBh7PAGqCQ7mc23iGOYssnaiydQR4AfYTtr7PTWyq30ypd7GDHx+ALUIsiqGUeNiCFmSHyvThBHly2T1epB8VDsKX95b7OuXsvfHVGUTj87wdroudsMWNGnUBKufSJ6qDi1lK57biU7FJGR9tPZI5jy+49PtjP1A2sPRA/AfnLUiHx2aMJF1NtwmrJsGalr4MkTVckpnAylFrPehCgH8va2t/VYHrRfkanivsaZEY1drPVGYSRKMrRgTTPV7TnA7tEdp42ls6eU0fw1+5mShLMjXNHL7sdwd4lLSklKp0ufWs7Ud1maAOUI4A5/Z10Vbatt0w1dkK/K3PDhARnYp1GLy+5j6ml97iCiAwdob10Snf76x2xvlXhu2S72IdVl7Lf9GYoMHSROGlfMPqIFsxh92MAC5Dvxt1Tb8Q0iK+m9tR3UWccVIMS1ANh7fnrccLrsyCFsEUJqxQKKOIZLxvP4yH/mo1TfzNM2X1/RTP/cbw2UBrBBVeamMpKFDWtQXip/nxGIiiHR26g+byhR5zaqbnTS6Y90baB7wtgiZsWD6HXnkw+nDDkE4WoTcUdpjmy67KnluoX71a5G9iGBUiFYrMhQ+etFU2lsaeSMr0QCREVvzSC9/Y4UqhIxBbBK4epDdtFLm15ivYUg06Ndw1OfbafnPmmj8pmG2yOSe0luwij1gKJ61x03jG4+YZReF2dCBPdLT8hC3IUoVohDQlYBf2NlA/nbfezAe/AHky3K2zXHDqNXVuyhkuwUmlyezQhLOGB+ywrrTSUzqH7mLUQLLyUHpTMiBDfnhTMq6dhRBaw21NJv6hhZufzIIXTl0UOimleW4ObKI6h2wmVEH99CTlsmLfi6RidaC4XKhYPl92dPoCkV2byfnHALRT3GiOOpbtK1RAsvYVmB8/4PAfzW38faO2FsITnsdjpiWB5V5KRGvFecqBguRRxP9YKgXv/sZqptMIwQqTRhDV50eCVzx0Rzr/CaZKeTzVrpJqsXauV/ljfRO2u5MdXpC9If391k+V0QIUw5jHnTnJFh6zL1BpBl65FKnbiGqqQkfQ1CXbnv6PvonOHn0KraVXom3ub6bXTB/24hLZAW0bVvVqDkOmxud9KQ/DT6v0um6QbgBdMPvmk4knS0gINsdkN9qm600zfbatlzvfnEkXTlUUP0TMSfzRnJ1MaaZg9NLM9ihlGkGK1MpmaD1BF1zv0XNezgBHHB6ha2t04fnMNcnQDc4vBeDC1Io/vPmxQxDq2vQ5GhXgJbLC243V7yjjqZDiz9Dfu+35tOIwrTae70Clq7t4myU5x08vgStjFKREOE9NThZj6RveVTqW4lr+3R0ZHCFvMJY4ronMNgNfiZBTw4P/YgYq4MiYNx3FlUX1hJtHkbbdzDD/sjhuYxqXZ3QzvddcY4Om+qEYsRrTxuzZaxUYPYuHbV2hkRgiL0p/Mn0eMffkMrdjawQx4VauvaPMyFNrki51vfgI2+SKKjvPheZ0Yhiw0CZAYZgmlRlRduhxe+2ENf7Wqjl1fAmncyv3848EwWvlHraebBFJo5JI9+fuKouLgkuDIk2ib4O+mAcPdhnDtPH0vzDh/UZZMdWpBOvzh5dEzjpDiTCUdsO2IVhObR6eEb7DOXz6Dpg7m6dLAKAy9WyLc8T+5gOiAalXZ0cJcDUrxBsNo9AebKnXfEoJitd3PMkDcll+rEweT3Yq3ZmEKGNQgXb1DT6LDKnLAp7t0BzxTEESuuNXsINc/6DflX38d+VtvoZAQLxsfSbw7Q1MG59PAPJlN2auwZWygH0iaMHhCuBtFva8GaVrLZcunv86axNff8F7spEAzSVUcPZa5KqLQgyAURXO69hVSnW5Ah43tVohaQuWAhC8YvmEqffVNHy7fX0+JNrYwIYT/iil8EhdYbsg4DKfTnH0yKqITHdoa4kG6oB2kH/G46TOyHWHNmgBRBMY15DE3s7xnF+l4V8KXSnDGF9PeLp+l7ytlTem6Y3h+hyFAvgccrcPcS4kFqpRXvz6Ab54yg0yeWxmUM6TJBT6gDQkrV/Omsns6kitjcI5EycjzTLqW67fP5GIF0OmdKGT0wdzL/d5QqULdjhPj567OwYXVSTSPvrv7ABZOZxfXH8ydZfi8ScehtgKjA3cfIkLgX1elw5dSSXXPTUfcspV+cMoa5Nz/YWEtvr+EByBKwtCJJz0wZEk07ZYNcbMLfGxVdEclowMdw6s1dGzq4CjG+uCiu5QlSHZwMgXDJisaaP41On1iiE6FDgVm18fh9unUd9Geww/vG40cc8j1jY8h75e9kqe3sOgLpdPK4Ynp83lSKB5CBisit5rQSqh8yi2g1d6s6bC567dpZLHPwUNagbD7dJuZuh81GnbLprj+N5s0cRHPG8vikudOtJT9QwLQvAVm2DabaUkCVaHH01JIGWrv2C7pk1mBG4qBm7mkwQghgU0WjPLI14rGuwxR7OlMV4wWW1SmMXRmkjbV+55ljuxChg0UGxhDzF1lotaJRdjCQTr8+fVy/zxgLB0WGegmWTJaAl2pEs8Asdx6dOLY4bmNoQiXARN7XUqvH80AmjQcYUTEtluo2sVj8afTj7w3VX3coi4Zb8MI6ySyj+rLJRNWfswPlslmDGRHqDzC7sCQZqkrJJPLVks+byRSIO1+3Fl2EDD1jSC5TDJCCHCmoMhvSeTDEAg+k0JgIAbKxADFmsvgjure3B3hM08iC8AHLsSLNlQLPMXUGPFTXwYPuYZmb1cVDAevjJrY8dLo3kyGoQPHY6HmvOD5327wdemwHDJKTxocPIo8FKYIMdfg7WVsEOcapE0oYEQIO9XrSHcmEO9TuSKH60acSda5lB2WqM4VuPRkxXf0DaUIBNPeFrPJxV29HewYtrqmlxZtqLa7L40YV0PiyLEb4onH/QHnTGqzqW2VOXlzJA4/n4WPsFTXLoM5GSkiJeQxNFCYNePV1OLGklCrzwicP9HcoMtRLwKElFRXUgvGIVNgTRg6PW62NHEjhkqj4O6nRyyfyMUOji6mI2dXg99DORr4oK7MLI2aoHBThOuomqtrzuW6Rnj+t/0izXarfssDNVOYz03zcYhyan8aud9qgXDptYjFNHRSbAoIeVqkhLQmwQY4uid8GibmDAwWOq1ZvG/k1PndH5MdXAUD5CcAb8NCWOj6vkrR0mjUscnfxaMAqwTtczOXTYZq75M9gqk28xgBZCAriuL1BjBFIp9mj4keGUp3JjDhiDda0CVIXR+IIpImMpRZnBtXP/iXR/IsYOT1lfCmf2/0ErKqy39oXcq845IP+LBajhdCB8aVZzBhBXE+s7lHmEgwxSoblxXd9MMIl9pN9omZZuiM95vcasX1JkP89JEr4NO4aPWGkYeh+V6HIUC8BMTtSJagSje9gZZ0wOnxV6ViAQEm5WGAtyoJhRw+L30TmLiwjmLZeuDOOHBw+0DcW8No5hvq0o7FGJ1zDC3s3Pf5QYG7HIevayMBNbMLPXjGTBa4fKvLQl8r0bwelsdT5eCLLncYO33oPVzqAscXxO9yBDLQ88MKN7KHNB/gaKc8siGthPhCuFtH7bUvdfva90oz8uFZBznSnUKNwKW4VpC4/JY+loMdb7fAGPbShRmSfahmWWMNDRYYrhRF3XxBxYobChZin/gRW1sRvuMm8rnRqEIbi9PIh9OKV4WtgRUuGQhXaMUXxXR+sKKPYF+uEqpnHCuvGe89ysq/3tfD1gXNr9qjYYo/6I1Rvsl4CK9YmiMruJkGGAmlxiYWwjCFk1F3Ne3TCNW1QfKxeveiiyU3m1Xj8yNTyiriOIZWhVm87NQg3wGFl/UcVMoLNhYomu6QHeGCwzZ9Nhw2Kz0aWl2YliDnJWXH37eekCpXAJ2J5gk4aXRzfjTjDnaJ32K5u5Zv9kJz4uuIyUESOKadeqm3j82pkQXxTgjNZ2woeOyJd1eVZ8T0Y050puivjG0G4cpPzIrpVY0GGUIZ8moe+qRcKVzCNuXH7E7KgxpoMkuoMPqeggMTLiONkyEqoJ5bEd17BfSf33rYAX4dF6XFeg8k8mwzYUs/rXlEgjUYVfbuZub0BRYZ6VRkSk6yOExWkDcfTWkT1aDnGpgO8/oMtkEGlcQwszmDdrPl73tNUzSquAtMrKuNMuFx6w9KOoAzY7V8WKTLanKKdRoeNL7XNHu5iqsgoj5u8XZRu3aiK0sO31zgY5KZY47RswZSIxQFjRSZqv8BrpXmpycsz1obkFiVkDLiRm318Xo3Ij+8YOSn88IULSwZQD8+L78GYLomj5qE9gnAVs+D8+CFLkCGNgrShlu9ZWa6cuBKu3kC2IKeoX4XymXuyuXEY9OXQERFqU0ULVv3cRIZQ/HZiWXyJPKszJ/ZeifKs+BLTDFOIwrYGXvvMbcuIOqO5P6N/zep+jHy0uRAy6o4mvrGkO+MT1CyRa1KGvmnkrD41KTvuQXyyON7aatH0MOimsuys+JIIO7+Ouo5G0mycQEwt71/KEOBOEmRIbCZ7RT2YSSXxc10WZ2TqfaSAQXFWIbpTn1z2tLirT9nCgg+STy9UOKbw0LMszchBADueR6CDPJJkF8WXqOQI4ggXVpufk7pxRaUJIaf+oJdq2rmKNjinKCGKCrC9ke9Zhanxc8P1FnJTjUyrTpuNtrpE2yB//BJLCjOSSQsaRqc9mCH67cXXTRaqPg3JjS8BTnUl6fu7DOfIcMbfuOqLUGSol4CGfDaNk6GaDu6Lze+m38uhBjfbiU/kmnY+Rk6YvmQHA1gIyGQxb5BuW3xJHZCSxC3fb+pFATnNTqML43/IJxpucR3NKfkUOOIGatO4a+boISPiNkY50qj9BlkZWRDfgxcoSLOm7qY54pPK2x2JAGwOTiJG5cfPxQvkoycgi+dpJ7Lzqs1j40yG8sThi0Bwzc6zliaVxJfIF2dxUhcgL7WIJsaj4/zcc1PRR8tu2U8qsuKrdvQGQBzRRBXoKBxDy1xcxc5PLia3qG5/qIBK6gga+2BqUvwJBBJkJFGRGJ4XXzJks8EQ5edUm1/EJUVoRfJdgSJDvQRWKM3OD0YPS1iFRR9fK4tlyzhFYKWNj1GaEf+aHzJ4s9Fbo/eOijeyhKthv9iEkyidHCL4uD9BZnrtLphBa8ZfDr8lO2BmD48fGUKNHDQRlZhYHL/4LYnBudbNPcvUpDVeGJRrzCNbEg/+z4uzElGUzg8sLzWy1jhAPlrjxBElmZxwBWwdZEvi2TilIk4lXigTY8CJpSUlRkUry0nVXSYtAe6KG57X/wwSVG7Waz9d9CJtaOFEe1hORVyNxLJM497kuuNLUuQYyFQ0oyzObjJzVqdml3M3/tfSF6HIUC8i080tRptNS0hwKJAl/OMSQ3Lia1mbA13JyS3SggRI5zkp/F51agf0Vhz9EenIkBKB4Iu28rYFLi3X0mfoUAGpXwsYLo0h2fF3J04sLbK44vLT4q8GDs3P4K1ETJBd1+M2Rl6eZQ1CSUUvqnhiSJ51rtooiTJd8SWP5dnG37M5GxPiwiplHdY5iQgmcUVzTJzdfb0BZArK+mtt3naq7eQG1mGlw+I6zpRSI25yeG5iXPr5qVajBM2S442cZKvqOyinbxXRTBQUGepF5KZaN8TK7PgzbqRZmzEstzjhYwzKjr+1mC9cDeiezMaMs7uvt1AogptBhr7YvVWX5+MJFNucUWa0vihMjT/JRoVbM+Eqy4w/AcbhS0FjDLc9nZyiyWa8MLrQuubSHJlxj30aXWQ9PEDk4z3GoFwjTkwSu7yUOJOhLDwPl2UdlsdZ4eoNVOQY17GuqpYCdh7UPmtQ/NRZ4OpZM/WvD68YS4lARWZBQo0FoCDNutdW9kPX6MFAkaFeRHmW1WLMdsdf7agIIViJOBiH5FoXZGUCAnbLsqwWUCLcfb0BBDcD7f4O2lzHO2YPy4lf5p3EH064nmYWz6S7jrgrISXzkUGUYQr4r8wqTkyHcZuhbOa440+4hubl6PEjQG6c4/aA8qwMSwG+wpT4r8G8dDfZTcQRHebjrT4hztFO1kxUVLPvj5m8NqEMLVi/jWwOHsc1KCu+6s2wvEL66ZSf0jHlx9Dpw06jRGDGIOM9u+zJrK9hvDEipJhqfopykynEGRNKrZtiQWr8D/hpIRlXiRhjYqk14LQgJf5jTCotS7j61BsoF1l2LZ426hCxYhOK4tfPSwJNX//vpP+j80aeF/e/3R0JTsQzB3JTDAOhLAEqRFKSXS93wMbILExIbEdykqGeViTAWABSHZkW4hhvEoy/ZybAQG4/DKbFdbhEIsMnO79mSprD5k4IsfvxxB/To8c/GndiKjG60DBCitMSo9hMrbDuvYoMKcQdFVnWSVWcGn/renCIapMIq3RwiA85EYtlcqk1NiERcUm9gUE5YsO1e8nu5HEXFZn9q16SRHmGMV/j7ZKRGFlgEIfCtMRswgWmuAtz1/J4otK01osSoM4C5VnGMyhOSww5NdcuSklKI3dS3+hEf7CJDDYX7yhflFrSL5uOFqUWJZykFKdbSaIiQwoJnchAYQKYfajrLRETOdRPnYiDMT+E/OQl908yNKqQP2NbUjvZRaBrWXr/JENDs4zaSIMz49d+xYy8lJyEP/OSdIPUFaUlRrUxZ6jlpyb+0EoUOZ1kMkr6q0EC5KbwPcuezAsJDsmOf8Zlb2Bw1uCEP/OcENdxosbpa1BkqBcBV4YEJNpEWFnmMUCMnEnOhC8W85jxAjJ8XKLwIlCa3v+yWMwHiM3uI7urvl+ToRklM9jn0rTShF1DZYYRT1WRkZgDy0yAEqHOhq4J8zUlah0mYg2GZqj11zVodrMnJXNlqLyfrkGcGYcVHsa+Pr7y+ISMUZpmfc79VQ2MFapRay/CHL+TKJ+y2Q+eKBdAqMWeiCBtSNj4u3taedHF8oz+V30aSHWksmra6LcFOOyOhMRx9QamFk2lF05/gT3/JHtSwtWnYdnxTX3ubrMfmTMyIWMMzx6e8OsYlDko4YTLTLISRU57A2MKSuhDnlHf74nd/x7/v7SxfiNNK5qWkL/vTHJSujOdWn2tNCQr/vGNfRVKGepFIE342PJj2dfnjjg3YeNcO/laxuavnHBlQv4+iIpcJCBfdtF3K94oyyhLuAWfaOBeoWO5+UBJ1P3qDYzLG5cwFQI4vPRwRlAmFkykKYVTEjLGsRXH6kHgiSJDsytns3g9WPG9QbhG5iZmDPPfTRSp6w2EunoqMxNDHnsDqC00vXh6QmOefjb1Z4wQ3TDlBhooUMpQL+N3R/2O1tetZ5M5Ubhm0jWMCMW7RosZP5/2c/rd57+jW6ffmrAxfjTmR7Smdg3NHTU3YUpEbwCEcX8bN0uHZA4cS+tggFThV854JaEb/eTCyfTsqc+yeLpEuJEBuBEXnLcgoWtwVuksOrzkcEauodolAqNyRtGkgkm0r3Ufza6YTf0VoVlwA0nxOBhcMOoCOn/k+f0yyPxgYdM0jVfs+o6iubmZsrKyqKmpiTIzE+OaUkgcvAEvuZLi2x29t/HLj39Jb297m30NknrjYTd+229JQSFq4IjwBX39eh1uadhC57xxju6q/uKHXySMCCv0z/O7/+r1CgMC/XkDlhiTO0b/enpR4hRBBYXE1enp3+vQnIWF9aiIkEIoFBlSUEgwvj/s+zQ+bzzNqZxDM0uMkv0KCgq9A7grb512K3Nf3jLtlm/77Sj0QfQLN9mjjz5Kf/zjH6mqqoomTZpEjzzyCM2YwdN8I0G5yRQUFBQUFPofmpWbzMCLL75IN998M9111120cuVKRoZOOukkqqmp+bbfmoKCgoKCgsJ3AH2eDD3wwAN01VVX0WWXXUZjx46lxx9/nFJTU+nJJ5/8tt+agoKCgoKCwncAfZoMeb1eWrFiBc2ZM0f/nt1uZ/9eunRpt7/j8XiYtGb+UFBQUFBQUFDol2TowIEDFAgEqKjI2j8I/0b8UHe49957mY9RflRU9N+qqQoKCgoKCgoDnAwdDG6//XYWbCU/du/e/W2/JQUFBQUFBYU+jD5dgTo/P5+SkpKoupo315PAv4uLu28J4Ha72YeCgoKCgoKCQr9XhlwuF02dOpUWLVqkfy8YDLJ/H3HEEd/qe1NQUFBQUFD4bqBPK0MA0uovueQSmjZtGqst9OCDD1JbWxvLLlNQUFBQUFBQ+M6Toblz51JtbS39+te/ZkHTkydPpgULFnQJqlZQUFBQUFBQ+M5WoD4UqArUCgoKCgoK/Q/NqgK1goKCgoKCgkLvQJEhBQUFBQUFhQENRYYUFBQUFBQUBjQUGVJQUFBQUFAY0Ojz2WSHChkfrnqUKSgoKCgo9B80i3O7N/K8vvNkqKWlhX1WPcoUFBQUFBT65zmelZWV0DG+86n1qFi9b98+ysjIIJvNFnfWCpKF/mcDOW1f3QcD6l4YUPfCgLoXBtS94FD3IfK9AD0BESotLSW7PbFRPd95ZQg3sLy8PKFj4OEN9MkMqPtgQN0LA+peGFD3woC6FxzqPoS/F4lWhCRUALWCgoKCgoLCgIYiQwoKCgoKCgoDGooMHQLcbjfddddd7PNAhroPBtS9MKDuhQF1Lwyoe8Gh7kPfuhff+QBqBQUFBQUFBYVwUMqQgoKCgoKCwoCGIkMKCgoKCgoKAxqKDCkoKCgoKCgMaCgypKCgoKCgoDCgocjQQeLRRx+lwYMHU3JyMs2cOZO++OIL6s+49957afr06axSd2FhIZ111lm0adMmy2s6Ozvpuuuuo7y8PEpPT6dzzz2XqqurLa/ZtWsXnXbaaZSamsr+zq233kp+v9/ymg8//JAOO+wwljkwfPhwevrpp6mv4r777mOVy2+66aYBeR/27t1LP/rRj9i1pqSk0IQJE+jLL7/Uf478i1//+tdUUlLCfj5nzhzasmWL5W/U19fTRRddxIqpZWdn0xVXXEGtra2W16xZs4aOPvpotp5Qifb++++nvoRAIEB33nknDRkyhF3nsGHD6Le//a2lZ9J39V4sWbKEzjjjDFYFGGvh9ddft/y8N6/75ZdfptGjR7PXYC7Onz+f+sq98Pl8dNttt7H3lZaWxl5z8cUXsw4IA+1ehOInP/kJe82DDz7Yd+8FsskUYsMLL7yguVwu7cknn9S+/vpr7aqrrtKys7O16upqrb/ipJNO0p566ilt3bp12qpVq7RTTz1Vq6ys1FpbW/XX/OQnP9EqKiq0RYsWaV9++aV2+OGHa7NmzdJ/7vf7tfHjx2tz5szRvvrqK23+/Plafn6+dvvtt+uv2bZtm5aamqrdfPPN2vr167VHHnlES0pK0hYsWKD1NXzxxRfa4MGDtYkTJ2o33njjgLsP9fX12qBBg7RLL71UW7ZsGXvP7777rrZ161b9Nffdd5+WlZWlvf7669rq1au173//+9qQIUO0jo4O/TUnn3yyNmnSJO3zzz/XPv74Y2348OHahRdeqP+8qalJKyoq0i666CI2//79739rKSkp2hNPPKH1Ffz+97/X8vLytLfeekvbvn279vLLL2vp6enaQw899J2/F5i/d9xxh/bqq6+C+Wmvvfaa5ee9dd2ffvopWyP3338/WzO/+tWvNKfTqa1du7ZP3IvGxka25l988UVt48aN2tKlS7UZM2ZoU6dOtfyNgXAvzMDPcb2lpaXaX/7ylz57LxQZOghggl933XX6vwOBAHvQ9957r/ZdQU1NDZvgH330kb7QMcFwCEhs2LCBvQaLXi4Ou92uVVVV6a957LHHtMzMTM3j8bB//+IXv9DGjRtnGWvu3LmMjPUltLS0aCNGjNDee+897Xvf+55OhgbSfbjtttu0o446qsefB4NBrbi4WPvjH/+ofw/3x+12s00LwOaEe7N8+XL9Ne+8845ms9m0vXv3sn//9a9/1XJycvR7I8ceNWqU1ldw2mmnaZdffrnle+eccw7bpAfSvQg99Hrzui+44AL2HMyYOXOmdvXVV2vfBsIRALNBhdft3LlzQN6LPXv2aGVlZYzIwLAyk6G+di+UmyxGeL1eWrFiBZOCzf3P8O+lS5fSdwVNTU3sc25uLvuMa4YMbL5uyJKVlZX6deMzJMqioiL9NSeddBJrwvf111/rrzH/Dfmavnbv4AaDmyv0vQ6k+/DGG2/QtGnT6Pzzz2euvilTptDf//53/efbt2+nqqoqy3WgjxDcxuZ7Afkbf0cCr8eaWbZsmf6aY445hlwul+VewE3b0NBAfQGzZs2iRYsW0ebNm9m/V69eTZ988gmdcsopA+5emNGb190f1kx3+yjcQ7j+gXYvgsEgzZs3j4UIjBs3rsvP+9q9UGQoRhw4cIDFD5gPOgD/xqbwXQAmMWJkjjzySBo/fjz7Hq4NE1Iu6u6uG5+7uy/yZ+FeA6LQ0dFBfQEvvPACrVy5ksVRhWIg3Ydt27bRY489RiNGjKB3332XrrnmGrrhhhvomWeesVxLuLWAzyBSZjgcDkayY7lf3zZ++ctf0g9+8ANGfJ1OJyOGWCOIdxho98KM3rzunl7TF++LjC1EDNGFF16oNx8dSPfiD3/4A7s27Bndoa/di+9813qFg1NF1q1bxyzfgYbdu3fTjTfeSO+99x4LxhvIACmG1XbPPfewf4MAYF48/vjjdMkll9BAwksvvUTPPfccPf/888zKXbVqFSNDCB4daPdCITKgHl9wwQUsuBwGxUADFPSHHnqIGZVQxvoDlDIUI/Lz8ykpKalL9hD+XVxcTP0d119/Pb311lu0ePFiKi8v17+Pa4OLsLGxscfrxufu7ov8WbjXwHJCJkpfWMQ1NTUsywtWCj4++ugjevjhh9nXsDgGwn0AkB00duxYy/fGjBnDMuXM1xJuLeAz7qcZyKpDFkks9+vbBqR+qQ7BBQr5/2c/+5muHg6ke2FGb153T6/pa/dFEqGdO3cyo0qqQgPpXnz88cfsOhE+IPdR3I9bbrmFZWH3xXuhyFCMgItk6tSpLH7AbEHj30cccQT1V8CCARF67bXX6IMPPmApxGbgmuEeMF83/LY4GOV14/PatWstE1xuBvJQxWvMf0O+pq/cu+OPP55dAyx/+QF1BO4Q+fVAuA8A3KSh5RUQMzNo0CD2NeYINhzzdcDNB3+/+V6AOIJkSmB+Yc0grkS+Bmm6OETM92LUqFGUk5NDfQHt7e0slsEMGEW4joF2L8zozevuD2tGEiGUFnj//fdZSQozBsq9mDdvHkuJN++jUFFhVMDl3ifvRUzh1gp6aj2yJZ5++mkWEf/jH/+Ypdabs4f6G6655hqWHvvhhx9q+/fv1z/a29stKeVIt//ggw9YSvkRRxzBPkJTyk888USWno808YKCgm5Tym+99VaWhfXoo4/2uZTyUJizyQbSfUAmjMPhYGnlW7Zs0Z577jn2np999llLWjXm/n//+19tzZo12plnntltWvWUKVNYev4nn3zCsvTM6bPIPkL67Lx581jWCdYXxulLqfWXXHIJy4qRqfVIF0a5BGQFftfvBTIrUSICHzgyHnjgAfa1zJDqretGCjXm45/+9Ce2Zu66665eTycPdy+8Xi8rK1BeXs7WvXkfNWdDDYR70R1Cs8n62r1QZOgggbowOBBRbwip9qiT0J+BydzdB2oPSWBzu/baa1mqIybk2WefzRa6GTt27NBOOeUUVgsCh8Utt9yi+Xw+y2sWL16sTZ48md27oUOHWsboD2RoIN2HN998kxE7kP/Ro0drf/vb3yw/R2r1nXfeyTYsvOb444/XNm3aZHlNXV0d2+BQlwflBS677DK2kZqB+jRI48ffAOnAAduX0NzczOYA1nxycjJ7XqixYj7kvqv3AvO0u70BBLG3r/ull17SRo4cydYMSlO8/fbbWl+5FyDJPe2j+L2BdC+iJUN96V7Y8L9DkcMUFBQUFBQUFPozVMyQgoKCgoKCwoCGIkMKCgoKCgoKAxqKDCkoKCgoKCgMaCgypKCgoKCgoDCgociQgoKCgoKCwoCGIkMKCgoKCgoKAxqKDCkoKCgoKCgMaCgypKCgoKCgoDCgociQgoLCIePDDz9k3alDG9j2BaC/GvpntbS0JGwMNHD985//nLC/r6CgkFioCtQKCgox4dhjj6XJkyfTgw8+qH/P6/WybtNFRUWMFPUlnHPOOazR8B133JGwMdatW0fHHHMMbd++nbKyshI2joKCQmKglCEFBYVDhsvlYupLXyNCu3btorfeeosuvfTShI4zfvx4GjZsGD377LMJHUdBQSExUGRIQUEhaoBUfPTRR/TQQw8x4oOPHTt2dHGTPf3005Sdnc2IyKhRoyg1NZXOO+88am9vp2eeeYYGDx5MOTk5dMMNN1AgEND/vsfjoZ///OdUVlZGaWlpNHPmTPa3JXbu3ElnnHEG+138fNy4cTR//vwe3+9LL71EkyZNYn9P4mDf21//+lcaMWIEJScnMwUMv2MG3tcLL7wQt3utoKDQe3D04lgKCgr9HCBBmzdvZkrIb37zG/a9goICRohCAXLx8MMPM4KAeB24q84++2xGREBgtm3bRueeey4deeSRNHfuXPY7119/Pa1fv579TmlpKb322mt08skn09q1axkRue6665hLbsmSJYwM4bXp6ek9vt+PP/6Ypk2bdsjv7csvv2Tk6F//+hfNmjWLuQTxt82YMWMG/f73v2eEzu12x+FuKygo9BYUGVJQUIgaiIeBSwxqCtxi4eDz+eixxx5j7iMASgrIRHV1NSMwY8eOpeOOO44WL17MCAdcWk899RT7DCIEQCVasGAB+/4999zDfgaSMmHCBPbzoUOHhn0PUJK6I0MH895Avk4//XTKyMigQYMG0ZQpUyx/E+8ZRK2qqor9XEFBof9AkSEFBYWEAIRJkg0AriW4oMxKDr5XU1PDvob6A7fUyJEjLX8HSkteXh77GurMNddcQwsXLqQ5c+YwYjRx4sQe30NHRwdzax3qezvhhBMYwQH5glKFDyhJ+DsSKSkpuuqkoKDQv6BihhQUFBICp9Np+Tdiirr7XjAYZF+3trZSUlISrVixglatWqV/bNiwgbnngCuvvJK5sObNm8fIE1SfRx55pMf3kJ+fTw0NDYf83qAGrVy5kv79739TSUkJ/frXv2axSOZSAnCdSbehgoJC/4IiQwoKCjEBbjJ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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from bmtk.analyzer.firing_rates import plot_rates\n", "\n", @@ -885,10 +882,178 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "f974427c-9175-4656-9272-06db661c63e9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "population", + "rawType": "object", + "type": "string" + }, + { + "name": "node_id", + "rawType": "int64", + "type": "integer" + }, + { + "name": "timestamps", + "rawType": "float64", + "type": "float" + }, + { + "name": "firing_rates", + "rawType": "float64", + "type": "float" + }, + { + "name": "pop_name", + "rawType": "object", + "type": "string" + } + ], + "ref": "f8bcb7eb-2e47-4e16-b5ae-43d22a26d87e", + "rows": [ + [ + "0", + "l23", + "0", + "0.0", + "1.38853652", + "Exc" + ], + [ + "1", + "l23", + "0", + "1.0", + "1.3888492328282886", + "Exc" + ], + [ + "2", + "l23", + "0", + "2.0", + "1.3888386429052615", + "Exc" + ], + [ + "3", + "l23", + "0", + "3.0", + "1.3888246577790455", + "Exc" + ], + [ + "4", + "l23", + "0", + "4.0", + "1.388349355469538", + "Exc" + ] + ], + "shape": { + "columns": 5, + "rows": 5 + } + }, + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    populationnode_idtimestampsfiring_ratespop_name
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    " + ], + "text/plain": [ + " population node_id timestamps firing_rates pop_name\n", + "0 l23 0 0.0 1.388537 Exc\n", + "1 l23 0 1.0 1.388849 Exc\n", + "2 l23 0 2.0 1.388839 Exc\n", + "3 l23 0 3.0 1.388825 Exc\n", + "4 l23 0 4.0 1.388349 Exc" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd\n", "\n", @@ -898,10 +1063,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "7ce4c858-bccd-4b2f-9afc-6300ee274bd4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "('population', 'pop_name')", + "rawType": "object", + "type": "unknown" + }, + { + "name": "firing_rates", + "rawType": "float64", + "type": "float" + } + ], + "ref": "ce4b898f-6c01-4704-ba18-02d6552c93ef", + "rows": [ + [ + "('l23', 'Exc')", + "1.7384323192377584" + ], + [ + "('l23', 'PV')", + "4.813857991932312" + ], + [ + "('l23', 'SST')", + "4.094843130180547" + ], + [ + "('l23', 'VIP')", + "4.53581523245813" + ] + ], + "shape": { + "columns": 1, + "rows": 4 + } + }, + "text/plain": [ + "population pop_name\n", + "l23 Exc 1.738432\n", + " PV 4.813858\n", + " SST 4.094843\n", + " VIP 4.535815\n", + "Name: firing_rates, dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Get the firing rates averages\n", "l23_rates_df.groupby(['population', 'pop_name'])['firing_rates'].agg('sum')/sim.tstop" @@ -927,10 +1145,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "ce7ec8af-7e4a-4b0f-9a1e-0ad0d62a2d08", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l23_rates.h5/\n", + "|--*rates/\n", + " |--*l23/\n", + " |-->[data] (13500, 4)\n", + " |--*mapping/\n", + " |-->[node_ids] (4,)\n", + " |-->[pop_name] (4,)\n", + " |-->[time] (13500,)\n" + ] + } + ], "source": [ "import h5py\n", "\n", @@ -953,10 +1186,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "4b8f5ba5-8851-48f8-ae8f-9a9f581bff36", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l23\n", + " Exc: 1.7385611015415763\n", + " PV: 4.8142146004212325\n", + " VIP: 4.536151243661364\n", + " SST: 4.095146474363834\n" + ] + } + ], "source": [ "# How to calcuate firing rate averages from the h5 file\n", "with h5py.File('output/l23_rates.h5', 'r') as h5:\n", From 3b2ac3f320b8115cee7f3400dda77968518a1425 Mon Sep 17 00:00:00 2001 From: shixnya Date: Thu, 22 May 2025 16:36:30 -0700 Subject: [PATCH 15/19] Adding link to original paper, changed the figure color to match with our paper --- examples/pop_ssn_123/PopNet_SSN.ipynb | 53 +++++++++++++------ .../pop_ssn_123/PopNet_SSN_optimization.ipynb | 45 +++++++++++----- 2 files changed, 69 insertions(+), 29 deletions(-) diff --git a/examples/pop_ssn_123/PopNet_SSN.ipynb b/examples/pop_ssn_123/PopNet_SSN.ipynb index 254c88ff..d913d082 100644 --- a/examples/pop_ssn_123/PopNet_SSN.ipynb +++ b/examples/pop_ssn_123/PopNet_SSN.ipynb @@ -7,7 +7,10 @@ "source": [ "# PopNet Simulation with SSN\n", "\n", - "[Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics](https://www.sciencedirect.com/science/article/pii/S2211124724011148)\n", + "\n", + "Original work by Rubin et al.: [The Stabilized Supralinear Network: A Unifying Circuit Motif Underlying Multi-Input Integration in Sensory Cortex](https://doi.org/10.1016/j.neuron.2014.12.026)\n", + "\n", + "Application study by Ito et al.: [Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics](https://doi.org/10.1016/j.celrep.2024.114763)\n", "\n", "$$\n", "\\tau_j \\frac{dr}{dt} = -r_j + (s(\\sum_i J_{ji} r_{i} + J_{E}r_{E}) + b_{j})_+^2 \\tag{1}\n", @@ -23,7 +26,8 @@ { "data": { "text/html": [ - "\n" + "\n", + "\n" ], "text/plain": [ "" @@ -35,7 +39,8 @@ ], "source": [ "%%html\n", - "" + "\n", + "\n" ] }, { @@ -794,16 +799,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-05-22 15:46:06,396 [INFO] Created log file\n", - "2025-05-22 15:46:06,432 [INFO] Building nodes.\n", - "2025-05-22 15:46:06,433 [INFO] Building connections.\n", - "2025-05-22 15:46:06,434 [INFO] Add input module \"init_states\"\n", - "2025-05-22 15:46:06,435 [INFO] Add input module \"l4_inputs\"\n", - "2025-05-22 15:46:06,437 [INFO] Add input module \"bkg_inputs\"\n", - "2025-05-22 15:46:06,438 [INFO] Running Simulation.\n", - "2025-05-22 15:46:07,749 [INFO] Simulation Finished.\n", - "2025-05-22 15:46:07,752 [INFO] RatesRecorderMod: Saving rates to /Users/shinya.ito/projects/Modeling/bmtk_ssn/bmtk/examples/pop_ssn_123/output/l23_rates.csv.\n", - "2025-05-22 15:46:07,817 [INFO] RatesRecorderMod: Saving rates to /Users/shinya.ito/projects/Modeling/bmtk_ssn/bmtk/examples/pop_ssn_123/output/l23_rates.h5.\n" + "2025-05-22 16:26:04,408 [INFO] Created log file\n", + "2025-05-22 16:26:04,438 [INFO] Building nodes.\n", + "2025-05-22 16:26:04,439 [INFO] Building connections.\n", + "2025-05-22 16:26:04,441 [INFO] Add input module \"init_states\"\n", + "2025-05-22 16:26:04,442 [INFO] Add input module \"l4_inputs\"\n", + "2025-05-22 16:26:04,444 [INFO] Add input module \"bkg_inputs\"\n", + "2025-05-22 16:26:04,446 [INFO] Running Simulation.\n", + "2025-05-22 16:26:05,757 [INFO] Simulation Finished.\n", + "2025-05-22 16:26:05,760 [INFO] RatesRecorderMod: Saving rates to /Users/shinya.ito/projects/Modeling/bmtk_ssn/bmtk/examples/pop_ssn_123/output/l23_rates.csv.\n", + "2025-05-22 16:26:05,847 [INFO] RatesRecorderMod: Saving rates to /Users/shinya.ito/projects/Modeling/bmtk_ssn/bmtk/examples/pop_ssn_123/output/l23_rates.h5.\n" ] } ], @@ -841,7 +846,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
    " ] @@ -853,6 +858,22 @@ "source": [ "from bmtk.analyzer.firing_rates import plot_rates\n", "\n", + "import matplotlib as mpl\n", + "from cycler import cycler\n", + "\n", + "\n", + "# For visualization\n", + "line_cols = { # copied form project_colors in util\n", + " \"Sst-IRES-Cre\": (158 / 255, 218 / 255, 229 / 255),\n", + " \"sst\": (158 / 255, 218 / 255, 229 / 255),\n", + " \"Slc17a7-IRES2-Cre\": (255 / 255, 152 / 255, 150 / 255),\n", + " \"slc\": (255 / 255, 152 / 255, 150 / 255),\n", + " \"Vip-IRES-Cre\": (197 / 255, 176 / 255, 213 / 255),\n", + " \"vip\": (197 / 255, 176 / 255, 213 / 255),\n", + "}\n", + "color_list = [line_cols[\"slc\"], \"mediumaquamarine\", line_cols[\"sst\"], line_cols[\"vip\"]]\n", + "mpl.rcParams[\"axes.prop_cycle\"] = cycler(color=color_list)\n", + "\n", "plot_rates('config.simulation.json', label_column='pop_name')" ] }, @@ -921,7 +942,7 @@ "type": "string" } ], - "ref": "f8bcb7eb-2e47-4e16-b5ae-43d22a26d87e", + "ref": "3dceead1-2336-4cef-8307-463d17f810dd", "rows": [ [ "0", @@ -1082,7 +1103,7 @@ "type": "float" } ], - "ref": "ce4b898f-6c01-4704-ba18-02d6552c93ef", + "ref": "f618740d-418a-4d1f-a7c4-ad4dafb34a06", "rows": [ [ "('l23', 'Exc')", diff --git a/examples/pop_ssn_123/PopNet_SSN_optimization.ipynb b/examples/pop_ssn_123/PopNet_SSN_optimization.ipynb index 662ca257..e7904670 100644 --- a/examples/pop_ssn_123/PopNet_SSN_optimization.ipynb +++ b/examples/pop_ssn_123/PopNet_SSN_optimization.ipynb @@ -23,7 +23,9 @@ { "data": { "text/html": [ - "\n" + "\n", + "\n", + "\n" ], "text/plain": [ "" @@ -35,7 +37,9 @@ ], "source": [ "%%html\n", - "" + "\n", + "\n", + "\n" ] }, { @@ -48,9 +52,24 @@ "# Import block\n", "from bmtk.simulator import popnet\n", "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "from cycler import cycler\n", "import pickle\n", "import numpy as np\n", - "from iminuit import minimize" + "from iminuit import minimize\n", + "\n", + "\n", + "# For visualization\n", + "line_cols = { # copied form project_colors in util\n", + " \"Sst-IRES-Cre\": (158 / 255, 218 / 255, 229 / 255),\n", + " \"sst\": (158 / 255, 218 / 255, 229 / 255),\n", + " \"Slc17a7-IRES2-Cre\": (255 / 255, 152 / 255, 150 / 255),\n", + " \"slc\": (255 / 255, 152 / 255, 150 / 255),\n", + " \"Vip-IRES-Cre\": (197 / 255, 176 / 255, 213 / 255),\n", + " \"vip\": (197 / 255, 176 / 255, 213 / 255),\n", + "}\n", + "color_list = [line_cols[\"slc\"], \"mediumaquamarine\", line_cols[\"sst\"], line_cols[\"vip\"]]\n", + "mpl.rcParams[\"axes.prop_cycle\"] = cycler(color=color_list)" ] }, { @@ -73,12 +92,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-05-20 11:57:37,259 [INFO] Created log file\n", - "2025-05-20 11:57:37,299 [INFO] Building nodes.\n", - "2025-05-20 11:57:37,300 [INFO] Building connections.\n", - "2025-05-20 11:57:37,301 [INFO] Add input module \"init_states\"\n", - "2025-05-20 11:57:37,306 [INFO] Add input module \"l4_inputs\"\n", - "2025-05-20 11:57:37,308 [INFO] Add input module \"bkg_inputs\"\n" + "2025-05-22 16:19:08,048 [INFO] Created log file\n", + "2025-05-22 16:19:08,065 [INFO] Building nodes.\n", + "2025-05-22 16:19:08,066 [INFO] Building connections.\n", + "2025-05-22 16:19:08,067 [INFO] Add input module \"init_states\"\n", + "2025-05-22 16:19:08,068 [INFO] Add input module \"l4_inputs\"\n", + "2025-05-22 16:19:08,070 [INFO] Add input module \"bkg_inputs\"\n" ] } ], @@ -166,7 +185,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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", 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    " ] @@ -385,7 +404,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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    " ] From c37d9636710497c8f5fdcf5b1806376f9e87f2bf Mon Sep 17 00:00:00 2001 From: Shinya Ito Date: Mon, 23 Jun 2025 16:33:00 -0700 Subject: [PATCH 16/19] Update bmtk/simulator/popnet/ssn/modules/rates_recorder.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- bmtk/simulator/popnet/ssn/modules/rates_recorder.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bmtk/simulator/popnet/ssn/modules/rates_recorder.py b/bmtk/simulator/popnet/ssn/modules/rates_recorder.py index caaddf75..9168a9a0 100644 --- a/bmtk/simulator/popnet/ssn/modules/rates_recorder.py +++ b/bmtk/simulator/popnet/ssn/modules/rates_recorder.py @@ -71,7 +71,7 @@ def _to_csv(self, sim): def _to_hdf5(self, sim): - mode = self._params.get('modd', 'a') + mode = self._params.get('mode', 'a') times = self._get_timestamps(sim) nsteps = len(times) ssn_nodes = self._get_nodes(sim) From f223b351c98288ffd622f1ac33eb6fb83f66e3ea Mon Sep 17 00:00:00 2001 From: Shinya Ito Date: Mon, 23 Jun 2025 16:33:08 -0700 Subject: [PATCH 17/19] Update bmtk/simulator/popnet/ssn/modules/initial_states.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- bmtk/simulator/popnet/ssn/modules/initial_states.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bmtk/simulator/popnet/ssn/modules/initial_states.py b/bmtk/simulator/popnet/ssn/modules/initial_states.py index c93da7cf..5b196f00 100644 --- a/bmtk/simulator/popnet/ssn/modules/initial_states.py +++ b/bmtk/simulator/popnet/ssn/modules/initial_states.py @@ -52,7 +52,7 @@ def from_csv(self, sim): ssn_node = sim.network.get_node(node.population_name, node.node_id) if ssn_node.node_id not in init_df.index: if strict_mapping: - raise Exception('COULD NOT FIND APPROPIATE ID IN CSV') + raise Exception('COULD NOT FIND APPROPRIATE ID IN CSV') else: # TODO: warning message pass From 3ef1e7573aa3ed4c76594fd7d11cf85387533a4e Mon Sep 17 00:00:00 2001 From: Shinya Ito Date: Mon, 23 Jun 2025 16:34:18 -0700 Subject: [PATCH 18/19] Update bmtk/simulator/popnet/ssn/modules/external_inputs.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- bmtk/simulator/popnet/ssn/modules/external_inputs.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bmtk/simulator/popnet/ssn/modules/external_inputs.py b/bmtk/simulator/popnet/ssn/modules/external_inputs.py index 55b47b73..40b0080a 100644 --- a/bmtk/simulator/popnet/ssn/modules/external_inputs.py +++ b/bmtk/simulator/popnet/ssn/modules/external_inputs.py @@ -84,4 +84,4 @@ def initialize(self, sim): # TODO: WARNING THAT TSTOP IS BEING SET BY INPUT sim.tstop = len(inputs)*sim.dt else: - raise ValueError('Uknown module') + raise ValueError('Unknown module') From bdd5f6c19a3de273f48de5946592fd802744376d Mon Sep 17 00:00:00 2001 From: shixnya Date: Mon, 23 Jun 2025 16:38:37 -0700 Subject: [PATCH 19/19] Correcting uniform distribution for SSN initialization --- bmtk/simulator/popnet/ssn/modules/initial_states.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bmtk/simulator/popnet/ssn/modules/initial_states.py b/bmtk/simulator/popnet/ssn/modules/initial_states.py index 5b196f00..d140b4cf 100644 --- a/bmtk/simulator/popnet/ssn/modules/initial_states.py +++ b/bmtk/simulator/popnet/ssn/modules/initial_states.py @@ -77,7 +77,7 @@ def from_pregenerated(self, sim, itype): elif itype == 'random': dist = self._params['distribution'] if dist == 'uniform': - init_states = np.random.normal( + init_states = np.random.uniform( low=self._params.get('low', 0.0), high=self._params.get('high', 1.0), size=nsize

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z6*OB>vz0VkS+i9%TUE2wG+SM>H8fjOv$Zr^TeEdETUWF7G+SS@4K&+OvyC*{ShGzu z+f=j7G}~OWEi~Iwv#m7STC;65+g7vfG}~UY9W>ifvz;{CS+iX<+f}pOG}~RXJv7@> zv%NIiTeE#M`=4g}YPO$d`)hW9W(R6^kY)#Kc8F$&YIc}rhii6(W=Cpvlx9b3c8q4n zYIdAv$7^W*2I9 zk!BZbc8O+}YId1smuq%~W>;!0GNlx9zB z_KarFYWAFF&ujLAW-n^?l4dV!_KIe&YWA9DuWR;(W^ZctmS%5j_Ks%nYWALH?`!sf zW*=(yk!Bxj_K9YnYWA6CpKJDoW?yRdm1bXS_KjxWYWAIG-)r`RWx8%MKoH5*T} z@im)3vk5huNVADGn?$ooHJePc$u*lovne&3O0%gon?|!~HJeVe={1`{vl%s;Nwb+X zn? 0: + ssn_node.external_inputs = inputs + + if sim.tstop is None: + # WARNING THAT TSTOP IS BEING SET BY INPUT + sim.tstop = len(inputs)*sim.dt + + elif self._module in ['h5', 'sonata']: + with h5py.File(self._params['file'], 'r') as h5: + ratesgrp = h5['/rates'] + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + + node_id_map = ratesgrp[f'{ssn_node.population}/mapping/node_ids'][()] + idx = np.argwhere(node_id_map == ssn_node.node_id)[0][0] + inputs = ratesgrp[f'{ssn_node.population}/data'][:, idx] + + ssn_node.external_inputs = inputs + + if sim.tstop is None: + # WARNING THAT TSTOP IS BEING SET BY INPUT + sim.tstop = len(inputs)*sim.dt + else: + raise ValueError('Uknown module') + + def finalize(self, sim): pass @@ -154,10 +347,19 @@ def initialize(self, sim): elif self._module == 'list': self.from_pregenerated(sim, itype='list') elif self._module == 'function': - raise NotImplementedError() + self.from_user_function(sim) else: raise ValueError(f'{self.__name__}: Error in {self._name} input module, no valid module [options: csv, constant, random, function]') + def from_user_function(self, sim): + fnc_name = self._params['init_function'] + generator_fnc = py_modules.user_function(fnc_name) + + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + init_val = generator_fnc(ssn_node, sim) + ssn_node.initial_value = init_val def from_csv(self, sim): csv_path = self._params['file'] @@ -249,7 +451,7 @@ class Config(SimulationConfig): class SSNNode: - def __init__(self, population, node_id, gid): + def __init__(self, population, node_id, gid, **node_properties): self.population = population self.node_id = node_id self.gid = gid @@ -261,10 +463,28 @@ def __init__(self, population, node_id, gid): self.decay_const = [] self.init_value = 0.0 self.external_inputs = None + self.node_properties = node_properties + self._sonata_node = self.node_properties.get('node', {}) + + def __contains__(self, property): + return property in self.node_properties or property in self._sonata_node + + def __getitem__(self, property): + if property in self.node_properties: + return self.node_properties[property] + elif property in self._sonata_node: + return self._sonata_node[property] + else: + raise KeyError(f'SSNNode does not contain property "{property}"') + def get(self, property, default=None): + if property in self: + return self[property] + else: + return default def __repr__(self) -> str: - return f'{self.gid} > ({self.population}.{self.node_id})' + return f'SSNNode {self.gid} > ({self.population}.{self.node_id})' class SSNNetwork(SimNetwork): @@ -374,22 +594,17 @@ def build_nodes(self): scaling_coef=node['scaling_coef'], exponent=node['exponent'], decay_const=node['decay_const'], - initial_value=node.get['initial_value'] if 'initial_value' in node else 0.0 + initial_value=node.get['initial_value'] if 'initial_value' in node else 0.0, + node=node ) elif model_type in ['external', 'virtual']: self.add_external_node( population_id=node_pop.name, - node_id=node[self.grouping_key] + node_id=node[self.grouping_key], + node=node ) - # pprint(self._node_id_map) - # exit() - - # print(node.population_name) - # print(node['model_type']) - - # exit() def build_edges(self): for edge_pop in self._edge_populations: @@ -398,30 +613,27 @@ def build_edges(self): # print(edge.source_population in self._node_id_map) # print(list(self._node_id_map[edge.source_population].keys())) # print(edge.source_node_id in self._node_id_map[edge.source_population]) - # exit() # print(self.get_ssn_node(edge.source_population, edge.source_node_id).gid) src_node = self._node_id_map[edge.source_population][int(edge.source_node_id)] trg_node = self._node_id_map[edge.target_population][int(edge.target_node_id)] self._conn_mat.append([trg_node.gid, src_node.gid, edge['syn_weight']]) - # print(src_node, trg_node) - # exit() def get_node(self, population_id, node_id): return self._node_id_map[population_id][node_id] - def get_ssn_node(self, population_id, node_id): + def get_ssn_node(self, population_id, node_id, **node_properties): if population_id not in self._node_id_map: # print('new population') - ssn_node = SSNNode(population_id, node_id, gid=self.gids) + ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) self._node_id_map[population_id] = {int(node_id): ssn_node} self.gids += 1 elif int(node_id) not in self._node_id_map: # print(f'new node {population_id}, {node_id}') - ssn_node = SSNNode(population_id, node_id, gid=self.gids) + ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) self._node_id_map[population_id][int(node_id)] = ssn_node self.gids += 1 @@ -432,11 +644,10 @@ def get_ssn_node(self, population_id, node_id): return ssn_node - def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, exponent, decay_const, initial_value=0.0): - self._nnodes_recurrent += 1 + def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, exponent, decay_const, initial_value=0.0, **node_properties): + self._nnodes_recurrent += 1 - - ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id) + ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) ssn_obj.type='internal' ssn_obj.input_offset.append(input_offset) ssn_obj.scaling_coef.append(scaling_coef) @@ -445,9 +656,9 @@ def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, self._ssn_recurrent_nodes.add(ssn_obj) - def add_external_node(self, population_id, node_id): + def add_external_node(self, population_id, node_id, **node_properties): self._nnodes_external += 1 - ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id) + ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) ssn_obj.type = 'external' self._ssn_external_nodes.add(ssn_obj) @@ -463,7 +674,6 @@ def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): self._mods = [] - @property def nsteps(self): return int((self.tstop - self.tstart)/self.dt) @@ -478,10 +688,6 @@ def results(self): for ext_node in self.network._ssn_external_nodes: if ext_node.external_inputs is not None: self._fr_results[:, ext_node.gid] = ext_node.external_inputs - # print(ext_node.gid, ext_node.external_inputs) - # exit() - - # exit() return self._fr_results @@ -498,9 +704,6 @@ def add_mod(self, mod): self._mods.append(mod) def run(self): - # print(self.network.connectivity_mat) - # print(self.results) - # exit() for t in range(self.nsteps-1): self.results[t+1, :self.network.n_neu_recurrent] = self.step( self.results[t, :], @@ -598,6 +801,23 @@ def from_config(cls, configure, network, **opts): return sim +@inputs_generator +def load_bkg_inputs(node, sim, **opts): + # print(node.node_id, node.population) + # print(sim.dt, sim.tstart, sim.tstop, sim.nsteps) + return np.ones(sim.nsteps) + +@init_function +def set_init_states(node, sim, **opts): + if node['pop_name'] == 'Exc': + return 1.38853652 + elif node['pop_name'] == 'PV': + return 3.50304166 + elif node['pop_name'] == 'SST': + return 1.61686557 + else: + return 5.02447877 + configure = Config.from_json('config.simulation.json') configure.build_env() @@ -607,3 +827,5 @@ def from_config(cls, configure, network, **opts): sim = SSNSimulator.from_config(configure, network) sim.run() + +plot_rates(rates_files='output/l23_rates.h5', label_column='pop_name') \ No newline at end of file From c404c52e8a3d0c9b067752fa54d4af9499cf49ab Mon Sep 17 00:00:00 2001 From: Kael Dai Date: Tue, 22 Apr 2025 20:07:29 -0700 Subject: [PATCH 03/19] moving code into its own popnet module --- bmtk/analyzer/firing_rates.py | 86 + bmtk/simulator/popnet/__init__.py | 1 + bmtk/simulator/popnet/ssn/__init__.py | 28 + .../popnet/ssn/default_setters/__init__.py | 1 + .../default_setters/activation_functions.py | 22 + bmtk/simulator/popnet/ssn/modules/__init__.py | 3 + .../popnet/ssn/modules/external_inputs.py | 91 ++ .../popnet/ssn/modules/initial_states.py | 110 ++ .../popnet/ssn/modules/rates_recorder.py | 104 ++ .../popnet/ssn/modules/sim_module.py | 72 + bmtk/simulator/popnet/ssn/popnetwork.py | 179 +++ bmtk/simulator/popnet/ssn/popnode.py | 37 + bmtk/simulator/popnet/ssn/popsimulator.py | 158 ++ bmtk/simulator/popnet/ssn/pyfunction_cache.py | 96 ++ bmtk/simulator/popnet/ssn/utils.py | 8 + examples/ssn_l23/config.simulation.json | 6 +- examples/ssn_l23/run_ssn.py | 1405 +++++++++-------- 17 files changed, 1705 insertions(+), 702 deletions(-) create mode 100644 bmtk/simulator/popnet/ssn/__init__.py create mode 100644 bmtk/simulator/popnet/ssn/default_setters/__init__.py create mode 100644 bmtk/simulator/popnet/ssn/default_setters/activation_functions.py create mode 100644 bmtk/simulator/popnet/ssn/modules/__init__.py create mode 100644 bmtk/simulator/popnet/ssn/modules/external_inputs.py create mode 100644 bmtk/simulator/popnet/ssn/modules/initial_states.py create mode 100644 bmtk/simulator/popnet/ssn/modules/rates_recorder.py create mode 100644 bmtk/simulator/popnet/ssn/modules/sim_module.py create mode 100644 bmtk/simulator/popnet/ssn/popnetwork.py create mode 100644 bmtk/simulator/popnet/ssn/popnode.py create mode 100644 bmtk/simulator/popnet/ssn/popsimulator.py create mode 100644 bmtk/simulator/popnet/ssn/pyfunction_cache.py create mode 100644 bmtk/simulator/popnet/ssn/utils.py diff --git a/bmtk/analyzer/firing_rates.py b/bmtk/analyzer/firing_rates.py index 3c7cdcbc..e4bd2147 100644 --- a/bmtk/analyzer/firing_rates.py +++ b/bmtk/analyzer/firing_rates.py @@ -23,6 +23,10 @@ import pandas as pd import numpy as np import matplotlib.pyplot as plt +from pathlib import Path +import h5py + +from bmtk.utils.sonata.config import SonataConfig def convert_rates(rates_file): @@ -87,3 +91,85 @@ def plot_rates_popnet(cell_models_file, rates_file, model_keys=None, save_as=Non if show_plot: plt.show() + + +def plot_rates(config_file=None, rates_files=None, population=None, times=None, label_column='node_id', title=None, show=True, + save_as=None, group_by=None, group_excludes=None, + nodes_file=None, node_types_file=None, plt_style=None): + + def is_hdf5(file_path): + try: + h5py.File(file_path, 'r') + return True + except Exception: + return False + + def is_csv(file_path): + try: + pd.read_csv(file_path) + return True + except Exception: + return False + + + sonata_config = SonataConfig.from_json(config_file) if config_file else None + + rates_paths = set() + if isinstance(rates_files, (list, tuple, pd.Series)): + rates_paths.update([Path(rf).absolute() for rf in rates_files]) + elif rates_files is not None: + rates_paths.add(Path(rates_files)) + + if sonata_config is not None: + for k in ['rates_file', 'rates_file_csv', 'rates_file_h5']: + if k in sonata_config.output: + rates_paths.add(Path(sonata_config.get('output', {}).get('output_dir', '.')) / Path(sonata_config.output[k])) + break + + fig, ax = plt.subplots(1, 1) + for rpath in rates_paths: + if is_csv(rpath): + rates_df = pd.read_csv(rpath, sep=' ') + for (population, node_id), subdf in rates_df.groupby(['population', 'node_id']): + times = subdf['timestamps'].values + frs = subdf['firing_rates'].values + + if label_column == 'population': + label = population + elif label_column == 'node_id': + label = node_id + elif label_column in subdf.columns: + label = subdf[label_column].iloc[0] + else: + label = '' + + print(label) + + ax.plot(times, frs, label=label) + + elif is_hdf5(rpath): + with h5py.File(rpath, 'r') as h5: + for population, popgrp in h5['/rates'].items(): + times = popgrp['mapping/time'][()] + node_ids = popgrp['mapping/node_ids'][()] + + if label_column == 'population': + labels = [population]*len(node_ids) + elif label_column == 'node_id': + labels = node_ids + elif label_column in popgrp['mapping']: + labels = popgrp['mapping'][label_column][()].astype(str) + else: + labels = ['']*len(node_ids) + + for idx, node_id in enumerate(node_ids): + frs = popgrp['data'][:, idx] + ax.plot(times, frs, label=labels[idx]) + + + ax.legend() + ax.set_ylabel('firing rates (Hz)') + ax.set_xlabel('times (ms)') + + if show: + plt.show() \ No newline at end of file diff --git a/bmtk/simulator/popnet/__init__.py b/bmtk/simulator/popnet/__init__.py index 7b591caa..35dadd8c 100644 --- a/bmtk/simulator/popnet/__init__.py +++ b/bmtk/simulator/popnet/__init__.py @@ -23,3 +23,4 @@ from .popnetwork import PopNetwork from .popsimulator import PopSimulator from .config import Config + diff --git a/bmtk/simulator/popnet/ssn/__init__.py b/bmtk/simulator/popnet/ssn/__init__.py new file mode 100644 index 00000000..2e9931cc --- /dev/null +++ b/bmtk/simulator/popnet/ssn/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2025. Allen Institute. All rights reserved +# +# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the +# following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following +# disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following +# disclaimer in the documentation and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote +# products derived from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, +# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, +# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +from .popnetwork import SSNNetwork +from .popsimulator import SSNSimulator +from .pyfunction_cache import inputs_generator, init_function + +from bmtk.simulator.core.simulation_config import SimulationConfig as Config +# from .config import Config diff --git a/bmtk/simulator/popnet/ssn/default_setters/__init__.py b/bmtk/simulator/popnet/ssn/default_setters/__init__.py new file mode 100644 index 00000000..ddc2fbd9 --- /dev/null +++ b/bmtk/simulator/popnet/ssn/default_setters/__init__.py @@ -0,0 +1 @@ +from . import activation_functions diff --git a/bmtk/simulator/popnet/ssn/default_setters/activation_functions.py b/bmtk/simulator/popnet/ssn/default_setters/activation_functions.py new file mode 100644 index 00000000..69e45afd --- /dev/null +++ b/bmtk/simulator/popnet/ssn/default_setters/activation_functions.py @@ -0,0 +1,22 @@ +from bmtk.simulator.popnet.ssn.pyfunction_cache import add_activation_function + +try: + from numba import njit + +except ImportError as ie: + from bmtk.simulator.popnet.ssn.utils import empty_decorator + njit = empty_decorator + + +@njit +def relu2(array): + # if the element is negative, set it to zero using loop + # destructive method (alters the original array), but faster than the above one. + for i in range(len(array)): + if array[i] < 0: + array[i] = 0 + return array + + +add_activation_function(relu2, name='default', overwrite=False) +add_activation_function(relu2, overwrite=False) diff --git a/bmtk/simulator/popnet/ssn/modules/__init__.py b/bmtk/simulator/popnet/ssn/modules/__init__.py new file mode 100644 index 00000000..12141cce --- /dev/null +++ b/bmtk/simulator/popnet/ssn/modules/__init__.py @@ -0,0 +1,3 @@ +from .initial_states import InitStatesMod +from .rates_recorder import RatesRecorderMod +from .external_inputs import ExternalRatesMod \ No newline at end of file diff --git a/bmtk/simulator/popnet/ssn/modules/external_inputs.py b/bmtk/simulator/popnet/ssn/modules/external_inputs.py new file mode 100644 index 00000000..a9388362 --- /dev/null +++ b/bmtk/simulator/popnet/ssn/modules/external_inputs.py @@ -0,0 +1,91 @@ +import numpy as np +import pandas as pd +import h5py + +from ..pyfunction_cache import py_modules +from .sim_module import SimulatorMod + + +class ExternalRatesMod(SimulatorMod): + def __init__(self, name, input_type, module, **kwargs): + self._name = name + self._input_type = input_type + self._module = module + self._params = kwargs + + def initialize(self, sim): + if self._module == 'npy': + npy_path = self._params['file'] + inputs_arr = np.load(npy_path) + + if sim.tstop is None: + # WARNING THAT TSTOP IS BEING SET BY INPUT + sim.tstop = len(inputs_arr)*sim.dt + + if sim.nsteps < len(inputs_arr): + # WARN THAT input is being cut + inputs_arr = inputs_arr[:sim.nsteps] + + elif sim.nsteps > len(inputs_arr): + # GIVE WARNING + inputs_arr = np.append(inputs_arr, np.zeros(sim.nsteps - len(inputs_arr))) + + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + ssn_node.external_inputs = inputs_arr.flatten() + + + elif self._module == 'function': + fnc_name = self._params['inputs_generator'] + generator_fnc = py_modules.user_function(fnc_name) + + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + external_inputs = generator_fnc(ssn_node, sim) + ssn_node.external_inputs = external_inputs + + if sim.tstop is None: + # WARNING THAT TSTOP IS BEING SET BY INPUT + sim.tstop = len(external_inputs)*sim.dt + + elif self._module == 'csv': + # TODO: Check Timestamps match, line up if needed + rates_df = pd.read_csv(self._params['file'], sep=self._params.get('sep', ' ')) + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + + # TODO: Check that node exists in file + inputs = rates_df[(rates_df['node_id'] == ssn_node.node_id) & (rates_df['population'] == ssn_node.population)]['firing_rates'] + if len(inputs) > 0: + ssn_node.external_inputs = inputs + + if sim.tstop is None: + # WARNING THAT TSTOP IS BEING SET BY INPUT + sim.tstop = len(inputs)*sim.dt + + elif self._module in ['h5', 'sonata']: + with h5py.File(self._params['file'], 'r') as h5: + ratesgrp = h5['/rates'] + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + + node_id_map = ratesgrp[f'{ssn_node.population}/mapping/node_ids'][()] + idx = np.argwhere(node_id_map == ssn_node.node_id)[0][0] + inputs = ratesgrp[f'{ssn_node.population}/data'][:, idx] + + ssn_node.external_inputs = inputs + + if sim.tstop is None: + # WARNING THAT TSTOP IS BEING SET BY INPUT + sim.tstop = len(inputs)*sim.dt + else: + raise ValueError('Uknown module') + + + # def finalize(self, sim): + # pass \ No newline at end of file diff --git a/bmtk/simulator/popnet/ssn/modules/initial_states.py b/bmtk/simulator/popnet/ssn/modules/initial_states.py new file mode 100644 index 00000000..b8583e95 --- /dev/null +++ b/bmtk/simulator/popnet/ssn/modules/initial_states.py @@ -0,0 +1,110 @@ +from ..pyfunction_cache import py_modules +from .sim_module import SimulatorMod + + +class InitStatesMod(SimulatorMod): + def __init__(self, name, input_type, module, **kwargs): + self._name = name + self._input_type = input_type + self._module = module + self._params = kwargs + + def initialize(self, sim): + # node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + + if self._module == 'csv': + self.from_csv(sim) + elif self._module == 'constant': + self.from_pregenerated(sim, itype='const') + elif self._module == 'random': + self.from_pregenerated(sim, itype='random') + elif self._module == 'list': + self.from_pregenerated(sim, itype='list') + elif self._module == 'function': + self.from_user_function(sim) + else: + raise ValueError(f'{self.__name__}: Error in {self._name} input module, no valid module [options: csv, constant, random, function]') + + def from_user_function(self, sim): + fnc_name = self._params['init_function'] + generator_fnc = py_modules.user_function(fnc_name) + + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + init_val = generator_fnc(ssn_node, sim) + ssn_node.initial_value = init_val + + def from_csv(self, sim): + csv_path = self._params['file'] + sep = self._params.get('sep', ' ') + index_col = self._params.get('index_col', 'node_id') + value_col = self._params.get('value_col', 'initial_state') + strict_mapping = self._params.get('strict_mapping', False) + + init_df = pd.read_csv(csv_path, sep=sep).set_index(index_col) + # init_df = init_df.set_index(init_df.columns['node_id']) + + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + + for node in node_set.fetch_nodes(): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + if ssn_node.node_id not in init_df.index: + if strict_mapping: + raise Exception('COULD NOT FIND APPROPIATE ID IN CSV') + else: + # TODO: warning message + pass + else: + ssn_node.initial_value = init_df.loc[ssn_node.node_id][value_col] + + def from_pregenerated(self, sim, itype): + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + nsize = len(node_set) + + if itype == 'const': + init_states = [self._params['initial_state']]*nsize + + if itype == 'list': + init_states = self._params['initial_states'] + if len(init_states) != nsize: + raise Exception('SIZE OF LIST DOES NOT MATCH NUMBER OF NODES') + + if self._params.get('shuffle', False): + np.random.shuffle(init_states) + + elif itype == 'random': + dist = self._params['distribution'] + if dist == 'uniform': + init_states = np.random.normal( + low=self._params.get('low', 0.0), + high=self._params.get('high', 1.0), + size=nsize + ) + elif dist == 'normal': + init_states = np.random.normal( + loc=self._params.get('mean', 0.0), + scale=self._params.get('std', 1.0), + size=nsize + ) + elif dist == 'poisson': + init_states = np.random.poisson( + lam=self._params.get('lambda', 1.0), + size=nsize + ) + elif dist == 'lognormal': + init_states = np.random.lognormal( + mean=self._params.get('mean', 0.0), + sigma=self._params.get('sigma', 1.0), + size=nsize + ) + else: + raise Exception("AAAA") + + for idx, node in enumerate(node_set.fetch_nodes()): + ssn_node = sim.network.get_node(node.population_name, node.node_id) + ssn_node.initial_value = init_states[idx] + + # def finalize(self, sim): + # pass + diff --git a/bmtk/simulator/popnet/ssn/modules/rates_recorder.py b/bmtk/simulator/popnet/ssn/modules/rates_recorder.py new file mode 100644 index 00000000..3b8f97e6 --- /dev/null +++ b/bmtk/simulator/popnet/ssn/modules/rates_recorder.py @@ -0,0 +1,104 @@ +from pathlib import Path +import h5py +import pandas as pd +import numpy as np + +from .sim_module import SimulatorMod +from bmtk.simulator.core.io_tools import io + + +class RatesRecorderMod(SimulatorMod): + def __init__(self, name, output_type, module, file_name, **kwargs): + self._name = name + self._output_type = output_type + self._module = module + self._params = kwargs + self.file_name = file_name + self._output_dir = kwargs.get('output_dir', '.') + self._node_set = kwargs.get('cells', None) + + + self.file_path = Path(self.file_name) + if not self.file_path.is_absolute(): + self.file_path = (Path(self._output_dir) / self.file_path).absolute() + + if self._output_type not in ['csv', 'h5']: + raise ValueError(f'{self.__name__}: Invalid output_type "{self._output_type}". [Valid options: csv, h5]') + + self._include_columns = kwargs.get('include_columns', []) + self._include_columns = [self._include_columns] if not isinstance(self._include_columns, (list, tuple)) else self._include_columns + self._include_columns = [k for k in self._include_columns if k not in ['population', 'node_id', 'firing_rates', 'timestamps']] + + # def initialize(self, sim): + # pass + + def finalize(self, sim): + if self._output_type == 'csv': + self._to_csv(sim) + elif self._output_type == 'h5': + self._to_hdf5(sim) + + def _get_nodes(self, sim): + if self._node_set is not None: + node_set = sim.network.get_node_set(self._node_set) + return [sim.network.get_node(n.population_name, n.node_id) for n in node_set.fetch_nodes()] + else: + return list(sim.network._ssn_recurrent_nodes) + + def _get_timestamps(self, sim): + return np.linspace(0.0, sim.tstop, num=sim.nsteps, endpoint=False) + + def _to_csv(self, sim): + + output_df = None + times = self._get_timestamps(sim) + ssn_nodes = self._get_nodes(sim) + + for node in ssn_nodes: + tmp_df = pd.DataFrame({ + 'population': node.population, + 'node_id': node.node_id, + 'timestamps': times, + 'firing_rates': sim.results[:, node.gid] + }) + + for c in self._include_columns: + tmp_df[c] = node.get(c, None) + + + output_df = tmp_df if output_df is None else pd.concat([output_df, tmp_df], ignore_index=True) + + if output_df is not None: + io.log_info(f'Saving rates to {self.file_path}.') + output_df.to_csv(self.file_path, sep=' ', index=False) + + + def _to_hdf5(self, sim): + mode = self._params.get('modd', 'a') + times = self._get_timestamps(sim) + nsteps = len(times) + ssn_nodes = self._get_nodes(sim) + mappings = {} + for n in ssn_nodes: + subpop = mappings.get(n.population, {k: [] for k in ['node_id', 'gid'] + self._include_columns}) + subpop['node_id'].append(n.node_id) + subpop['gid'].append(n.gid) + for c in self._include_columns: + subpop[c].append(n.get(c, None)) + + mappings[n.population] = subpop + + with h5py.File(self.file_path, mode) as h5: + io.log_info(f'Saving rates to {self.file_path}.') + ratesgrp = h5['rates'] if 'rates' in h5 else h5.create_group('rates') + for pop_name, pop_data in mappings.items(): + subgrp = ratesgrp.create_group(pop_name) + subgrp.create_dataset('mapping/time', data=times) + subgrp.create_dataset('mapping/node_ids', data=pop_data['node_id']) + for c in self._include_columns: + subgrp.create_dataset(f'mapping/{c}', data=pop_data[c]) + + data = subgrp.create_dataset('data', shape=(nsteps, len(pop_data['gid'])), dtype=float) + for col, gid in enumerate(pop_data['gid']): + data[:, col] = sim.results[:, gid] + diff --git a/bmtk/simulator/popnet/ssn/modules/sim_module.py b/bmtk/simulator/popnet/ssn/modules/sim_module.py new file mode 100644 index 00000000..57a8643d --- /dev/null +++ b/bmtk/simulator/popnet/ssn/modules/sim_module.py @@ -0,0 +1,72 @@ +# Copyright 2025. Allen Institute. All rights reserved +# +# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the +# following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following +# disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following +# disclaimer in the documentation and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote +# products derived from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, +# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, +# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +class SimulatorMod(object): + """Class for writing custom bionet functions that will be called during the simulation. To use overwrite one or + more of the following methods in a subclass, and bionet will call the function at the appropiate time. + + To call during a simulation: + ... + sim = Simulation(...) + mymod = MyModule(...) + sim.add_mod(mymod) + sim.run() + + """ + + def initialize(self, sim): + """Will be called once at the beginning of the simulation run, after the network and simulation parameters have + all been finalized. + + :param sim: Simulation object + """ + pass + + def step(self, sim, tstep): + """Called on every single time step (dt). + + The step method is used for anything that should be recorded or changed continously. dt is determined during + the setup, and the sim parameter can be used to access simulation, network and individual cell properties + + :param sim: Simulation object. + :param tstep: The decrete time-step + """ + pass + + def block(self, sim, block_interval): + """This method is called once after every block of time, as specified by the configuration. + + Unlike the step method which is called during every time-step, the block method will typically be called only a + few times over the entire simulation. The block method is preferable for accessing and saving to the disk, + summing up existing data, or similar functionality + + :param sim: Simulation object + :param block_interval: The time interval (tstep_start, tstep_end) for which the block is being called on. + """ + pass + + def finalize(self, sim): + """Call once at the very end of the simulation. + + :param sim: Simulation object + """ + pass diff --git a/bmtk/simulator/popnet/ssn/popnetwork.py b/bmtk/simulator/popnet/ssn/popnetwork.py new file mode 100644 index 00000000..b9b30126 --- /dev/null +++ b/bmtk/simulator/popnet/ssn/popnetwork.py @@ -0,0 +1,179 @@ +import numpy as np + +from bmtk.simulator.core.simulator_network import SimNetwork +from .popnode import SSNNode + +class SSNNetwork(SimNetwork): + def __init__(self, grouping_key='node_id', **opts): + super(SSNNetwork, self).__init__() + # self.n_neu_total = 6 + self.grouping_key = grouping_key + + # self._pop_index = {} + + self._node_id_map = {} + + self._nnodes_recurrent = 0 + self._nnodes_external = 0 + + self._connectivity_mat = None + self._scales = None + self._exponents = None + self._decay_constants = None + self._initial_states = None + + self._nodeid2grp = {} + + # self._recurrent_nodes = {} + # self._external_nodes = {} + + + self._nodes_idx = {} + self._ssn_recurrent_nodes = set() + self._ssn_external_nodes = set() + self.gids = 0 + + self._conn_mat = [] + # sefl._connectivity + + + + @property + def n_neu_recurrent(self): + return self._nnodes_recurrent + + @property + def n_neu_total(self): + return self._nnodes_recurrent + self._nnodes_external + + @property + def connectivity_mat(self): + if self._connectivity_mat is None: + self._connectivity_mat = np.zeros((self._nnodes_recurrent, self.n_neu_total), dtype=float) + for r, c, syn_w in self._conn_mat: + # print(r, c, syn_w) + self._connectivity_mat[r, c] = syn_w + + # print(self._connectivity_mat) + + return self._connectivity_mat + + + @property + def scales(self): + if self._scales is None: + self._scales = np.zeros(self._nnodes_recurrent) + for n in self._ssn_recurrent_nodes: + self._scales[n.gid] = np.mean(n.scaling_coef) + + return self._scales + + @property + def initial_states(self): + if self._initial_states is None: + self._initial_states = np.zeros(self._nnodes_recurrent) + for n in self._ssn_recurrent_nodes: + self._initial_states[n.gid] = np.mean(n.initial_value) + + return self._initial_states + + @property + def exponents(self): + if self._exponents is None: + self._exponents = np.zeros(self._nnodes_recurrent) + for n in self._ssn_recurrent_nodes: + self._exponents[n.gid] = np.mean(n.exponent) + + return self._exponents + + @property + def decay_constants(self): + if self._decay_constants is None: + self._decay_constants = np.zeros(self._nnodes_recurrent) + for n in self._ssn_recurrent_nodes: + self._decay_constants[n.gid] = np.mean(n.decay_const) + + # print(self._decay_constants) + + return self._decay_constants + + def build_nodes(self): + for node_pop in self.node_populations: + for node in node_pop.get_nodes(): + model_type = node['model_type'].lower() + + if model_type in ['population', 'rate_population', 'recurrent']: + self.add_recurrent_node( + population_id=node_pop.name, + node_id=node['node_id'], + input_offset=node['input_offset'], + scaling_coef=node['scaling_coef'], + exponent=node['exponent'], + decay_const=node['decay_const'], + initial_value=node.get['initial_value'] if 'initial_value' in node else 0.0, + node=node + ) + + elif model_type in ['external', 'virtual']: + self.add_external_node( + population_id=node_pop.name, + node_id=node[self.grouping_key], + node=node + ) + + + def build_edges(self): + for edge_pop in self._edge_populations: + for edge in edge_pop.get_edges(): + # print(edge.source_population, edge.source_node_id, edge.target_population, edge.target_node_id, edge['syn_weight']) + # print(edge.source_population in self._node_id_map) + # print(list(self._node_id_map[edge.source_population].keys())) + # print(edge.source_node_id in self._node_id_map[edge.source_population]) + # print(self.get_ssn_node(edge.source_population, edge.source_node_id).gid) + src_node = self._node_id_map[edge.source_population][int(edge.source_node_id)] + trg_node = self._node_id_map[edge.target_population][int(edge.target_node_id)] + + self._conn_mat.append([trg_node.gid, src_node.gid, edge['syn_weight']]) + + + def get_node(self, population_id, node_id): + return self._node_id_map[population_id][node_id] + + + def get_ssn_node(self, population_id, node_id, **node_properties): + if population_id not in self._node_id_map: + # print('new population') + ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) + self._node_id_map[population_id] = {int(node_id): ssn_node} + self.gids += 1 + + elif int(node_id) not in self._node_id_map: + # print(f'new node {population_id}, {node_id}') + ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) + self._node_id_map[population_id][int(node_id)] = ssn_node + self.gids += 1 + + else: + # print('found') + ssn_node = self._node_id_map[population_id][node_id] + + return ssn_node + + + def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, exponent, decay_const, initial_value=0.0, **node_properties): + self._nnodes_recurrent += 1 + + ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) + ssn_obj.type='internal' + ssn_obj.input_offset.append(input_offset) + ssn_obj.scaling_coef.append(scaling_coef) + ssn_obj.exponent.append(exponent) + ssn_obj.decay_const.append(decay_const) + self._ssn_recurrent_nodes.add(ssn_obj) + + + def add_external_node(self, population_id, node_id, **node_properties): + self._nnodes_external += 1 + ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) + ssn_obj.type = 'external' + self._ssn_external_nodes.add(ssn_obj) diff --git a/bmtk/simulator/popnet/ssn/popnode.py b/bmtk/simulator/popnet/ssn/popnode.py new file mode 100644 index 00000000..b911aef8 --- /dev/null +++ b/bmtk/simulator/popnet/ssn/popnode.py @@ -0,0 +1,37 @@ + + +class SSNNode: + def __init__(self, population, node_id, gid, **node_properties): + self.population = population + self.node_id = node_id + self.gid = gid + + self.type = None + self.input_offset = [] + self.scaling_coef = [] + self.exponent = [] + self.decay_const = [] + self.init_value = 0.0 + self.external_inputs = None + self.node_properties = node_properties + self._sonata_node = self.node_properties.get('node', {}) + + def __contains__(self, property): + return property in self.node_properties or property in self._sonata_node + + def __getitem__(self, property): + if property in self.node_properties: + return self.node_properties[property] + elif property in self._sonata_node: + return self._sonata_node[property] + else: + raise KeyError(f'SSNNode does not contain property "{property}"') + + def get(self, property, default=None): + if property in self: + return self[property] + else: + return default + + def __repr__(self) -> str: + return f'SSNNode {self.gid} > ({self.population}.{self.node_id})' \ No newline at end of file diff --git a/bmtk/simulator/popnet/ssn/popsimulator.py b/bmtk/simulator/popnet/ssn/popsimulator.py new file mode 100644 index 00000000..bc492e22 --- /dev/null +++ b/bmtk/simulator/popnet/ssn/popsimulator.py @@ -0,0 +1,158 @@ +from six import string_types +import numpy as np + +import bmtk.simulator.utils.simulation_inputs as inputs +from .modules import InitStatesMod, ExternalRatesMod, RatesRecorderMod +from .pyfunction_cache import py_modules +from bmtk.simulator.core.io_tools import io +from bmtk.simulator.core.simulation_config import SimulationConfig as Config + +# TODO: leave this import, it will initialize some of the default functions for building neurons/synapses/weights. +import bmtk.simulator.popnet.ssn.default_setters + + +class SSNSimulator: + def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): + self.dt = dt + self.tstart = tstart + self.tstop = tstop + self.network = network + + self._fr_results = None + + self._mods = [] + self.activation_function = py_modules.activation_function('default') + + + @property + def nsteps(self): + return int((self.tstop - self.tstart)/self.dt) + + @property + def results(self): + if self._fr_results is None: + self._fr_results = np.zeros((self.nsteps, self.network.n_neu_total), dtype=float) + # print(self.network.initial_states) + self._fr_results[0, :self.network.n_neu_recurrent] = self.network.initial_states + + for ext_node in self.network._ssn_external_nodes: + if ext_node.external_inputs is not None: + self._fr_results[:, ext_node.gid] = ext_node.external_inputs + + return self._fr_results + + + def step(self, state, mat, scales, exponents, decay_constants, dt): + input = self.activation_function(np.dot(mat, state) * scales) ** exponents + n_neu_recurrent = mat.shape[0] + recurrent_state = state[:n_neu_recurrent] + dr = (-recurrent_state + input) / decay_constants * dt + return self.activation_function(recurrent_state + dr) + + def add_mod(self, mod): + mod.initialize(self) + self._mods.append(mod) + + def run(self): + io.log_info('Running Simulation.') + for t in range(self.nsteps-1): + self.results[t+1, :self.network.n_neu_recurrent] = self.step( + self.results[t, :], + self.network.connectivity_mat, + self.network.scales, + self.network.exponents, + self.network.decay_constants, + self.dt, + ) + + io.log_info('Simulation Finished.') + + for mod in self._mods: + mod.finalize(self) + + + # print(self.results) + # print(self.results.shape) + + # fig, ax = plt.subplots(2, 1) + # ax[0].plot(self.results[:, :]) + # # ax[0].legend(nodes_recurrent["cell_types"].values) + # ax[0].title.set_text("Entire simulation") + # plt.show() + + @classmethod + def from_config(cls, configure, network, **opts): + # load the json file or object + if isinstance(configure, string_types): + config = Config.from_json(configure, validate=True) + elif isinstance(configure, dict): + config = configure + else: + raise Exception('Could not convert {} (type "{}") to json.'.format(configure, type(configure))) + + sim = cls(network, dt=config.dt, tstart=config.tstart, tstop=config.tstop, **opts) + + # if 'output_dir' in config['output']: + # network.output_dir = config['output']['output_dir'] + + network.io.log_info('Building nodes.') + network.build_nodes() + + network.io.log_info('Building connections.') + network.build_edges() + + for sim_input in inputs.from_config(config): + if sim_input.input_type == 'init_states': + mod = InitStatesMod( + name=sim_input.name, + input_type=sim_input.input_type, + module=sim_input.module, + **sim_input.params + ) + network.io.log_info(f'Add input module "{sim_input.name}"') + sim.add_mod(mod) + + elif sim_input.input_type == 'external_rates': + mod = ExternalRatesMod( + name=sim_input.name, + input_type=sim_input.input_type, + module=sim_input.module, + **sim_input.params + ) + network.io.log_info(f'Add input module "{sim_input.name}"') + sim.add_mod(mod) + + else: + network.io.log_info(f'Unknown input module "{sim_input.name}" (module={sim_input.module})') + + if 'rates_file' in config.output: + mod = RatesRecorderMod( + name='RecordRatesH5', + module='rates', + output_type='csv', + file_name=config.output['rates_file'], + **config.output + ) + sim.add_mod(mod) + + if 'rates_file_csv' in config.output: + mod = RatesRecorderMod( + name='RecordRatesH5', + module='rates', + output_type='csv', + file_name=config.output['rates_file_csv'], + **config.output + ) + sim.add_mod(mod) + + if 'rates_file_h5' in config.output: + mod = RatesRecorderMod( + name='RecordRatesH5', + module='rates', + output_type='h5', + file_name=config.output['rates_file_h5'], + **config.output + ) + sim.add_mod(mod) + + return sim \ No newline at end of file diff --git a/bmtk/simulator/popnet/ssn/pyfunction_cache.py b/bmtk/simulator/popnet/ssn/pyfunction_cache.py new file mode 100644 index 00000000..701b648b --- /dev/null +++ b/bmtk/simulator/popnet/ssn/pyfunction_cache.py @@ -0,0 +1,96 @@ +from functools import wraps + + +class _PyFunctions(object): + def __init__(self): + self.__user_functions = {} + self.__activation_funcs = {} + + @property + def activation_functions(self): + return self.__activation_funcs.keys() + + def activation_function(self, name): + return self.__activation_funcs[name] + + def add_activation_function(self, name, func, overwrite=True): + if overwrite or name not in self.__activation_funcs: + self.__activation_funcs[name] = func + + @property + def user_functions(self): + return self.__user_functions.keys() + + def user_function(self, name): + return self.__user_functions[name] + + def add_user_functions(self, name, func, overwrite=True): + if overwrite or name not in self.__user_functions: + self.__user_functions[name] = func + + +py_modules = _PyFunctions() + + +def inputs_generator(*wargs, **wkwargs): + if len(wargs) == 1 and callable(wargs[0]): + # for the case without decorator arguments, grab the function object in wargs and create a decorator + func = wargs[0] + py_modules.add_user_functions(func.__name__, func) # add function assigned to its original name + + @wraps(func) + def func_wrapper(*args, **kwargs): + return func(*args, **kwargs) + return func_wrapper + else: + # for the case with decorator arguments + assert(all(k in ['name'] for k in wkwargs.keys())) + + def decorator(func): + # store the function in py_modules but under the name given in the decorator arguments + py_modules.add_user_functions(wkwargs['name'], func) + + @wraps(func) + def func_wrapper(*args, **kwargs): + return func(*args, **kwargs) + return func_wrapper + return decorator + +init_function = inputs_generator + + +def activation_function(*wargs, **wkwargs): + if len(wargs) == 1 and callable(wargs[0]): + # for the case without decorator arguments, grab the function object in wargs and create a decorator + func = wargs[0] + py_modules.add_activation_function(func.__name__, func) # add function assigned to its original name + + @wraps(func) + def func_wrapper(*args, **kwargs): + return func(*args, **kwargs) + return func_wrapper + else: + # for the case with decorator arguments + assert(all(k in ['name'] for k in wkwargs.keys())) + + def decorator(func): + # store the function in py_modules but under the name given in the decorator arguments + py_modules.add_activation_function(wkwargs['name'], func) + + @wraps(func) + def func_wrapper(*args, **kwargs): + return func(*args, **kwargs) + return func_wrapper + return decorator + + +def add_function(func, name=None, overwrite=True): + assert(callable(func)) + func_name = name if name is not None else func.__name__ + py_modules.add_spikes_generator(func_name, func, overwrite) + + +def add_activation_function(func, name=None, overwrite=True): + assert(callable(func)) + func_name = name if name is not None else func.__name__ + py_modules.add_activation_function(func_name, func, overwrite) diff --git a/bmtk/simulator/popnet/ssn/utils.py b/bmtk/simulator/popnet/ssn/utils.py new file mode 100644 index 00000000..2b066565 --- /dev/null +++ b/bmtk/simulator/popnet/ssn/utils.py @@ -0,0 +1,8 @@ +from functools import wraps + + +def empty_decorator(func): + @wraps(func) + def defunct(*args, **kwargs): + return func(*args, **kwargs) + return defunct \ No newline at end of file diff --git a/examples/ssn_l23/config.simulation.json b/examples/ssn_l23/config.simulation.json index 05c8f5a3..0c6fe9c5 100644 --- a/examples/ssn_l23/config.simulation.json +++ b/examples/ssn_l23/config.simulation.json @@ -27,7 +27,8 @@ "file": "$BASE_DIR/input/l4e_rates.h5", "node_set": { "population": "l4e" - } + }, + "enabled": true }, "bkg_inputs": { "input_type": "external_rates", @@ -35,7 +36,8 @@ "file": "$BASE_DIR/input/bkg_rates.npy", "node_set": { "population": "bkg" - } + }, + "enabled": true } }, diff --git a/examples/ssn_l23/run_ssn.py b/examples/ssn_l23/run_ssn.py index d5151911..fd9cb7eb 100644 --- a/examples/ssn_l23/run_ssn.py +++ b/examples/ssn_l23/run_ssn.py @@ -1,813 +1,818 @@ -from bmtk.simulator import ssn import numpy as np -from numba import njit -from six import string_types -from pprint import pprint -import pandas as pd -import matplotlib.pyplot as plt -from pathlib import Path -import h5py -from functools import wraps +# from numba import njit +# from six import string_types +# from pprint import pprint +# import pandas as pd +# import matplotlib.pyplot as plt +# from pathlib import Path +# import h5py +# from functools import wraps -from bmtk.simulator.core.simulation_config import SimulationConfig -from bmtk.simulator.core.simulator_network import SimNetwork -from bmtk.simulator.core import sonata_reader -import bmtk.simulator.utils.simulation_inputs as inputs +# from bmtk.simulator.core.simulation_config import SimulationConfig +# from bmtk.simulator.core.simulator_network import SimNetwork +# from bmtk.simulator.core import sonata_reader +# import bmtk.simulator.utils.simulation_inputs as inputs -from bmtk.utils.sonata.config import SonataConfig +# from bmtk.utils.sonata.config import SonataConfig -class _PyFunctions(object): - def __init__(self): - self.__user_functions = {} +from bmtk.simulator.popnet import ssn +from bmtk.analyzer.firing_rates import plot_rates +# import inputs_generator, init_function +# from bmtk.simulator.popnet.ssn import Config - @property - def user_functions(self): - return self.__user_functions.keys() +# class _PyFunctions(object): +# def __init__(self): +# self.__user_functions = {} + + +# @property +# def user_functions(self): +# return self.__user_functions.keys() - def user_function(self, name): - return self.__user_functions[name] +# def user_function(self, name): +# return self.__user_functions[name] - def add_user_functions(self, name, func, overwrite=True): - if overwrite or name not in self.__user_functions: - self.__user_functions[name] = func +# def add_user_functions(self, name, func, overwrite=True): +# if overwrite or name not in self.__user_functions: +# self.__user_functions[name] = func -py_modules = _PyFunctions() +# py_modules = _PyFunctions() -def inputs_generator(*wargs, **wkwargs): - if len(wargs) == 1 and callable(wargs[0]): - # for the case without decorator arguments, grab the function object in wargs and create a decorator - func = wargs[0] - py_modules.add_user_functions(func.__name__, func) # add function assigned to its original name +# def inputs_generator(*wargs, **wkwargs): +# if len(wargs) == 1 and callable(wargs[0]): +# # for the case without decorator arguments, grab the function object in wargs and create a decorator +# func = wargs[0] +# py_modules.add_user_functions(func.__name__, func) # add function assigned to its original name - @wraps(func) - def func_wrapper(*args, **kwargs): - return func(*args, **kwargs) - return func_wrapper - else: - # for the case with decorator arguments - assert(all(k in ['name'] for k in wkwargs.keys())) +# @wraps(func) +# def func_wrapper(*args, **kwargs): +# return func(*args, **kwargs) +# return func_wrapper +# else: +# # for the case with decorator arguments +# assert(all(k in ['name'] for k in wkwargs.keys())) - def decorator(func): - # store the function in py_modules but under the name given in the decorator arguments - py_modules.add_user_functions(wkwargs['name'], func) +# def decorator(func): +# # store the function in py_modules but under the name given in the decorator arguments +# py_modules.add_user_functions(wkwargs['name'], func) - @wraps(func) - def func_wrapper(*args, **kwargs): - return func(*args, **kwargs) - return func_wrapper - return decorator +# @wraps(func) +# def func_wrapper(*args, **kwargs): +# return func(*args, **kwargs) +# return func_wrapper +# return decorator -init_function = inputs_generator +# init_function = inputs_generator -def add_function(func, name=None, overwrite=True): - assert(callable(func)) - func_name = name if name is not None else func.__name__ - py_modules.add_spikes_generator(func_name, func, overwrite) +# def add_function(func, name=None, overwrite=True): +# assert(callable(func)) +# func_name = name if name is not None else func.__name__ +# py_modules.add_spikes_generator(func_name, func, overwrite) -def plot_rates(config_file=None, rates_files=None, population=None, times=None, label_column='node_id', title=None, show=True, - save_as=None, group_by=None, group_excludes=None, - nodes_file=None, node_types_file=None, plt_style=None): +# def plot_rates(config_file=None, rates_files=None, population=None, times=None, label_column='node_id', title=None, show=True, +# save_as=None, group_by=None, group_excludes=None, +# nodes_file=None, node_types_file=None, plt_style=None): - def is_hdf5(file_path): - try: - h5py.File(file_path, 'r') - return True - except Exception: - return False +# def is_hdf5(file_path): +# try: +# h5py.File(file_path, 'r') +# return True +# except Exception: +# return False - def is_csv(file_path): - try: - pd.read_csv(file_path) - return True - except Exception: - return False +# def is_csv(file_path): +# try: +# pd.read_csv(file_path) +# return True +# except Exception: +# return False - sonata_config = SonataConfig.from_json(config_file) if config_file else None +# sonata_config = SonataConfig.from_json(config_file) if config_file else None - rates_paths = set() - if isinstance(rates_files, (list, tuple, pd.Series)): - rates_paths.update([Path(rf).absolute() for rf in rates_files]) - elif rates_files is not None: - rates_paths.add(Path(rates_files)) +# rates_paths = set() +# if isinstance(rates_files, (list, tuple, pd.Series)): +# rates_paths.update([Path(rf).absolute() for rf in rates_files]) +# elif rates_files is not None: +# rates_paths.add(Path(rates_files)) - if sonata_config is not None: - for k in ['rates_file', 'rates_file_csv', 'rates_file_h5']: - if k in sonata_config.output: - rates_paths.add(Path(sonata_config.get('output', {}).get('output_dir', '.')) / Path(sonata_config.output[k])) - break +# if sonata_config is not None: +# for k in ['rates_file', 'rates_file_csv', 'rates_file_h5']: +# if k in sonata_config.output: +# rates_paths.add(Path(sonata_config.get('output', {}).get('output_dir', '.')) / Path(sonata_config.output[k])) +# break - fig, ax = plt.subplots(1, 1) - for rpath in rates_paths: - if is_csv(rpath): - rates_df = pd.read_csv(rpath, sep=' ') - for (population, node_id), subdf in rates_df.groupby(['population', 'node_id']): - times = subdf['timestamps'].values - frs = subdf['firing_rates'].values +# fig, ax = plt.subplots(1, 1) +# for rpath in rates_paths: +# if is_csv(rpath): +# rates_df = pd.read_csv(rpath, sep=' ') +# for (population, node_id), subdf in rates_df.groupby(['population', 'node_id']): +# times = subdf['timestamps'].values +# frs = subdf['firing_rates'].values - if label_column == 'population': - label = population - elif label_column == 'node_id': - label = node_id - elif label_column in subdf.columns: - label = subdf[label_column].iloc[0] - else: - label = '' - - ax.plot(times, frs, label=label) +# if label_column == 'population': +# label = population +# elif label_column == 'node_id': +# label = node_id +# elif label_column in subdf.columns: +# label = subdf[label_column].iloc[0] +# else: +# label = '' + +# ax.plot(times, frs, label=label) - elif is_hdf5(rpath): - with h5py.File(rpath, 'r') as h5: - for population, popgrp in h5['/rates'].items(): - times = popgrp['mapping/time'][()] - node_ids = popgrp['mapping/node_ids'][()] - - if label_column == 'population': - labels = [population]*len(node_ids) - elif label_column == 'node_id': - labels = node_ids - elif label_column in popgrp['mapping']: - labels = popgrp['mapping'][label_column][()].astype(str) - else: - labels = ['']*len(node_ids) - - for idx, node_id in enumerate(node_ids): - frs = popgrp['data'][:, idx] - ax.plot(times, frs, label=labels[idx]) +# elif is_hdf5(rpath): +# with h5py.File(rpath, 'r') as h5: +# for population, popgrp in h5['/rates'].items(): +# times = popgrp['mapping/time'][()] +# node_ids = popgrp['mapping/node_ids'][()] + +# if label_column == 'population': +# labels = [population]*len(node_ids) +# elif label_column == 'node_id': +# labels = node_ids +# elif label_column in popgrp['mapping']: +# labels = popgrp['mapping'][label_column][()].astype(str) +# else: +# labels = ['']*len(node_ids) + +# for idx, node_id in enumerate(node_ids): +# frs = popgrp['data'][:, idx] +# ax.plot(times, frs, label=labels[idx]) - ax.legend() - ax.set_ylabel('firing rates (Hz)') - ax.set_xlabel('times (ms)') +# ax.legend() +# ax.set_ylabel('firing rates (Hz)') +# ax.set_xlabel('times (ms)') - if show: - plt.show() - - -class RatesRecorderMod: - def __init__(self, name, output_type, module, file_name, **kwargs): - self._name = name - self._output_type = output_type - self._module = module - self._params = kwargs - self.file_name = file_name - self._output_dir = kwargs.get('output_dir', '.') - self._node_set = kwargs.get('cells', None) +# if show: +# plt.show() + + +# class RatesRecorderMod: +# def __init__(self, name, output_type, module, file_name, **kwargs): +# self._name = name +# self._output_type = output_type +# self._module = module +# self._params = kwargs +# self.file_name = file_name +# self._output_dir = kwargs.get('output_dir', '.') +# self._node_set = kwargs.get('cells', None) - self.file_path = Path(self.file_name) - if not self.file_path.is_absolute(): - self.file_path = (Path(self._output_dir) / self.file_path).absolute() +# self.file_path = Path(self.file_name) +# if not self.file_path.is_absolute(): +# self.file_path = (Path(self._output_dir) / self.file_path).absolute() - if self._output_type not in ['csv', 'h5']: - raise ValueError(f'{self.__name__}: Invalid output_type "{self._output_type}". [Valid options: csv, h5]') +# if self._output_type not in ['csv', 'h5']: +# raise ValueError(f'{self.__name__}: Invalid output_type "{self._output_type}". [Valid options: csv, h5]') - self._include_columns = kwargs.get('include_columns', []) - self._include_columns = [self._include_columns] if not isinstance(self._include_columns, (list, tuple)) else self._include_columns - self._include_columns = [k for k in self._include_columns if k not in ['population', 'node_id', 'firing_rates', 'timestamps']] +# self._include_columns = kwargs.get('include_columns', []) +# self._include_columns = [self._include_columns] if not isinstance(self._include_columns, (list, tuple)) else self._include_columns +# self._include_columns = [k for k in self._include_columns if k not in ['population', 'node_id', 'firing_rates', 'timestamps']] - def initialize(self, sim): - pass +# def initialize(self, sim): +# pass - def finalize(self, sim): - if self._output_type == 'csv': - self._to_csv(sim) - elif self._output_type == 'h5': - self._to_hdf5(sim) +# def finalize(self, sim): +# if self._output_type == 'csv': +# self._to_csv(sim) +# elif self._output_type == 'h5': +# self._to_hdf5(sim) - def _get_nodes(self, sim): - if self._node_set is not None: - node_set = sim.network.get_node_set(self._node_set) - return [sim.network.get_node(n.population_name, n.node_id) for n in node_set.fetch_nodes()] - else: - return list(sim.network._ssn_recurrent_nodes) +# def _get_nodes(self, sim): +# if self._node_set is not None: +# node_set = sim.network.get_node_set(self._node_set) +# return [sim.network.get_node(n.population_name, n.node_id) for n in node_set.fetch_nodes()] +# else: +# return list(sim.network._ssn_recurrent_nodes) - def _get_timestamps(self, sim): - return np.linspace(0.0, sim.tstop, num=sim.nsteps, endpoint=False) +# def _get_timestamps(self, sim): +# return np.linspace(0.0, sim.tstop, num=sim.nsteps, endpoint=False) - def _to_csv(self, sim): - output_df = None - times = self._get_timestamps(sim) - ssn_nodes = self._get_nodes(sim) +# def _to_csv(self, sim): +# output_df = None +# times = self._get_timestamps(sim) +# ssn_nodes = self._get_nodes(sim) - for node in ssn_nodes: - tmp_df = pd.DataFrame({ - 'population': node.population, - 'node_id': node.node_id, - 'timestamps': times, - 'firing_rates': sim.results[:, node.gid] - }) +# for node in ssn_nodes: +# tmp_df = pd.DataFrame({ +# 'population': node.population, +# 'node_id': node.node_id, +# 'timestamps': times, +# 'firing_rates': sim.results[:, node.gid] +# }) - for c in self._include_columns: - tmp_df[c] = node.get(c, None) +# for c in self._include_columns: +# tmp_df[c] = node.get(c, None) - output_df = tmp_df if output_df is None else pd.concat([output_df, tmp_df], ignore_index=True) +# output_df = tmp_df if output_df is None else pd.concat([output_df, tmp_df], ignore_index=True) - if output_df is not None: - output_df.to_csv(self.file_path, sep=' ', index=False) +# if output_df is not None: +# output_df.to_csv(self.file_path, sep=' ', index=False) - def _to_hdf5(self, sim): - mode = self._params.get('modd', 'a') - times = self._get_timestamps(sim) - nsteps = len(times) - ssn_nodes = self._get_nodes(sim) - mappings = {} - for n in ssn_nodes: - subpop = mappings.get(n.population, {k: [] for k in ['node_id', 'gid'] + self._include_columns}) - subpop['node_id'].append(n.node_id) - subpop['gid'].append(n.gid) - for c in self._include_columns: - subpop[c].append(n.get(c, None)) +# def _to_hdf5(self, sim): +# mode = self._params.get('modd', 'a') +# times = self._get_timestamps(sim) +# nsteps = len(times) +# ssn_nodes = self._get_nodes(sim) +# mappings = {} +# for n in ssn_nodes: +# subpop = mappings.get(n.population, {k: [] for k in ['node_id', 'gid'] + self._include_columns}) +# subpop['node_id'].append(n.node_id) +# subpop['gid'].append(n.gid) +# for c in self._include_columns: +# subpop[c].append(n.get(c, None)) - mappings[n.population] = subpop - - with h5py.File(self.file_path, mode) as h5: - ratesgrp = h5['rates'] if 'rates' in h5 else h5.create_group('rates') - for pop_name, pop_data in mappings.items(): - subgrp = ratesgrp.create_group(pop_name) - subgrp.create_dataset('mapping/time', data=times) - subgrp.create_dataset('mapping/node_ids', data=pop_data['node_id']) - for c in self._include_columns: - subgrp.create_dataset(f'mapping/{c}', data=pop_data[c]) - - data = subgrp.create_dataset('data', shape=(nsteps, len(pop_data['gid'])), dtype=float) - for col, gid in enumerate(pop_data['gid']): - data[:, col] = sim.results[:, gid] - - -class ExternalRatesMod: - def __init__(self, name, input_type, module, **kwargs): - self._name = name - self._input_type = input_type - self._module = module - self._params = kwargs - - def initialize(self, sim): - if self._module == 'npy': - npy_path = self._params['file'] - inputs_arr = np.load(npy_path) +# mappings[n.population] = subpop + +# with h5py.File(self.file_path, mode) as h5: +# ratesgrp = h5['rates'] if 'rates' in h5 else h5.create_group('rates') +# for pop_name, pop_data in mappings.items(): +# subgrp = ratesgrp.create_group(pop_name) +# subgrp.create_dataset('mapping/time', data=times) +# subgrp.create_dataset('mapping/node_ids', data=pop_data['node_id']) +# for c in self._include_columns: +# subgrp.create_dataset(f'mapping/{c}', data=pop_data[c]) + +# data = subgrp.create_dataset('data', shape=(nsteps, len(pop_data['gid'])), dtype=float) +# for col, gid in enumerate(pop_data['gid']): +# data[:, col] = sim.results[:, gid] + + +# class ExternalRatesMod: +# def __init__(self, name, input_type, module, **kwargs): +# self._name = name +# self._input_type = input_type +# self._module = module +# self._params = kwargs + +# def initialize(self, sim): +# if self._module == 'npy': +# npy_path = self._params['file'] +# inputs_arr = np.load(npy_path) - if sim.tstop is None: - # WARNING THAT TSTOP IS BEING SET BY INPUT - sim.tstop = len(inputs_arr)*sim.dt +# if sim.tstop is None: +# # WARNING THAT TSTOP IS BEING SET BY INPUT +# sim.tstop = len(inputs_arr)*sim.dt - if sim.nsteps < len(inputs_arr): - # WARN THAT input is being cut - inputs_arr = inputs_arr[:sim.nsteps] +# if sim.nsteps < len(inputs_arr): +# # WARN THAT input is being cut +# inputs_arr = inputs_arr[:sim.nsteps] - elif sim.nsteps > len(inputs_arr): - # GIVE WARNING - inputs_arr = np.append(inputs_arr, np.zeros(sim.nsteps - len(inputs_arr))) +# elif sim.nsteps > len(inputs_arr): +# # GIVE WARNING +# inputs_arr = np.append(inputs_arr, np.zeros(sim.nsteps - len(inputs_arr))) - node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) +# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - for node in node_set.fetch_nodes(): - ssn_node = sim.network.get_node(node.population_name, node.node_id) - ssn_node.external_inputs = inputs_arr.flatten() - - - elif self._module == 'function': - fnc_name = self._params['inputs_generator'] - generator_fnc = py_modules.user_function(fnc_name) - - node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - for node in node_set.fetch_nodes(): - ssn_node = sim.network.get_node(node.population_name, node.node_id) - external_inputs = generator_fnc(ssn_node, sim) - ssn_node.external_inputs = external_inputs - - if sim.tstop is None: - # WARNING THAT TSTOP IS BEING SET BY INPUT - sim.tstop = len(external_inputs)*sim.dt - - elif self._module == 'csv': - # TODO: Check Timestamps match, line up if needed - rates_df = pd.read_csv(self._params['file'], sep=self._params.get('sep', ' ')) - node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - for node in node_set.fetch_nodes(): - ssn_node = sim.network.get_node(node.population_name, node.node_id) +# for node in node_set.fetch_nodes(): +# ssn_node = sim.network.get_node(node.population_name, node.node_id) +# ssn_node.external_inputs = inputs_arr.flatten() + + +# elif self._module == 'function': +# fnc_name = self._params['inputs_generator'] +# generator_fnc = py_modules.user_function(fnc_name) + +# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) +# for node in node_set.fetch_nodes(): +# ssn_node = sim.network.get_node(node.population_name, node.node_id) +# external_inputs = generator_fnc(ssn_node, sim) +# ssn_node.external_inputs = external_inputs + +# if sim.tstop is None: +# # WARNING THAT TSTOP IS BEING SET BY INPUT +# sim.tstop = len(external_inputs)*sim.dt + +# elif self._module == 'csv': +# # TODO: Check Timestamps match, line up if needed +# rates_df = pd.read_csv(self._params['file'], sep=self._params.get('sep', ' ')) +# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) +# for node in node_set.fetch_nodes(): +# ssn_node = sim.network.get_node(node.population_name, node.node_id) - # TODO: Check that node exists in file - inputs = rates_df[(rates_df['node_id'] == ssn_node.node_id) & (rates_df['population'] == ssn_node.population)]['firing_rates'] - if len(inputs) > 0: - ssn_node.external_inputs = inputs - - if sim.tstop is None: - # WARNING THAT TSTOP IS BEING SET BY INPUT - sim.tstop = len(inputs)*sim.dt +# # TODO: Check that node exists in file +# inputs = rates_df[(rates_df['node_id'] == ssn_node.node_id) & (rates_df['population'] == ssn_node.population)]['firing_rates'] +# if len(inputs) > 0: +# ssn_node.external_inputs = inputs + +# if sim.tstop is None: +# # WARNING THAT TSTOP IS BEING SET BY INPUT +# sim.tstop = len(inputs)*sim.dt - elif self._module in ['h5', 'sonata']: - with h5py.File(self._params['file'], 'r') as h5: - ratesgrp = h5['/rates'] - node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - for node in node_set.fetch_nodes(): - ssn_node = sim.network.get_node(node.population_name, node.node_id) - - node_id_map = ratesgrp[f'{ssn_node.population}/mapping/node_ids'][()] - idx = np.argwhere(node_id_map == ssn_node.node_id)[0][0] - inputs = ratesgrp[f'{ssn_node.population}/data'][:, idx] - - ssn_node.external_inputs = inputs - - if sim.tstop is None: - # WARNING THAT TSTOP IS BEING SET BY INPUT - sim.tstop = len(inputs)*sim.dt - else: - raise ValueError('Uknown module') - - - def finalize(self, sim): - pass - - -class InitStatesMod: - def __init__(self, name, input_type, module, **kwargs): - self._name = name - self._input_type = input_type - self._module = module - self._params = kwargs - - def initialize(self, sim): - # node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - - if self._module == 'csv': - self.from_csv(sim) - elif self._module == 'constant': - self.from_pregenerated(sim, itype='const') - elif self._module == 'random': - self.from_pregenerated(sim, itype='random') - elif self._module == 'list': - self.from_pregenerated(sim, itype='list') - elif self._module == 'function': - self.from_user_function(sim) - else: - raise ValueError(f'{self.__name__}: Error in {self._name} input module, no valid module [options: csv, constant, random, function]') - - def from_user_function(self, sim): - fnc_name = self._params['init_function'] - generator_fnc = py_modules.user_function(fnc_name) - - node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - for node in node_set.fetch_nodes(): - ssn_node = sim.network.get_node(node.population_name, node.node_id) - init_val = generator_fnc(ssn_node, sim) - ssn_node.initial_value = init_val - - def from_csv(self, sim): - csv_path = self._params['file'] - sep = self._params.get('sep', ' ') - index_col = self._params.get('index_col', 'node_id') - value_col = self._params.get('value_col', 'initial_state') - strict_mapping = self._params.get('strict_mapping', False) - - init_df = pd.read_csv(csv_path, sep=sep).set_index(index_col) - # init_df = init_df.set_index(init_df.columns['node_id']) +# elif self._module in ['h5', 'sonata']: +# with h5py.File(self._params['file'], 'r') as h5: +# ratesgrp = h5['/rates'] +# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) +# for node in node_set.fetch_nodes(): +# ssn_node = sim.network.get_node(node.population_name, node.node_id) + +# node_id_map = ratesgrp[f'{ssn_node.population}/mapping/node_ids'][()] +# idx = np.argwhere(node_id_map == ssn_node.node_id)[0][0] +# inputs = ratesgrp[f'{ssn_node.population}/data'][:, idx] + +# ssn_node.external_inputs = inputs + +# if sim.tstop is None: +# # WARNING THAT TSTOP IS BEING SET BY INPUT +# sim.tstop = len(inputs)*sim.dt +# else: +# raise ValueError('Uknown module') + + +# def finalize(self, sim): +# pass + + +# class InitStatesMod: +# def __init__(self, name, input_type, module, **kwargs): +# self._name = name +# self._input_type = input_type +# self._module = module +# self._params = kwargs + +# def initialize(self, sim): +# # node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + +# if self._module == 'csv': +# self.from_csv(sim) +# elif self._module == 'constant': +# self.from_pregenerated(sim, itype='const') +# elif self._module == 'random': +# self.from_pregenerated(sim, itype='random') +# elif self._module == 'list': +# self.from_pregenerated(sim, itype='list') +# elif self._module == 'function': +# self.from_user_function(sim) +# else: +# raise ValueError(f'{self.__name__}: Error in {self._name} input module, no valid module [options: csv, constant, random, function]') + +# def from_user_function(self, sim): +# fnc_name = self._params['init_function'] +# generator_fnc = py_modules.user_function(fnc_name) + +# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) +# for node in node_set.fetch_nodes(): +# ssn_node = sim.network.get_node(node.population_name, node.node_id) +# init_val = generator_fnc(ssn_node, sim) +# ssn_node.initial_value = init_val + +# def from_csv(self, sim): +# csv_path = self._params['file'] +# sep = self._params.get('sep', ' ') +# index_col = self._params.get('index_col', 'node_id') +# value_col = self._params.get('value_col', 'initial_state') +# strict_mapping = self._params.get('strict_mapping', False) + +# init_df = pd.read_csv(csv_path, sep=sep).set_index(index_col) +# # init_df = init_df.set_index(init_df.columns['node_id']) - node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) +# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - for node in node_set.fetch_nodes(): - ssn_node = sim.network.get_node(node.population_name, node.node_id) - if ssn_node.node_id not in init_df.index: - if strict_mapping: - raise Exception('COULD NOT FIND APPROPIATE ID IN CSV') - else: - # TODO: warning message - pass - else: - ssn_node.initial_value = init_df.loc[ssn_node.node_id][value_col] +# for node in node_set.fetch_nodes(): +# ssn_node = sim.network.get_node(node.population_name, node.node_id) +# if ssn_node.node_id not in init_df.index: +# if strict_mapping: +# raise Exception('COULD NOT FIND APPROPIATE ID IN CSV') +# else: +# # TODO: warning message +# pass +# else: +# ssn_node.initial_value = init_df.loc[ssn_node.node_id][value_col] - def from_pregenerated(self, sim, itype): - node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - nsize = len(node_set) +# def from_pregenerated(self, sim, itype): +# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) +# nsize = len(node_set) - if itype == 'const': - init_states = [self._params['initial_state']]*nsize +# if itype == 'const': +# init_states = [self._params['initial_state']]*nsize - if itype == 'list': - init_states = self._params['initial_states'] - if len(init_states) != nsize: - raise Exception('SIZE OF LIST DOES NOT MATCH NUMBER OF NODES') +# if itype == 'list': +# init_states = self._params['initial_states'] +# if len(init_states) != nsize: +# raise Exception('SIZE OF LIST DOES NOT MATCH NUMBER OF NODES') - if self._params.get('shuffle', False): - np.random.shuffle(init_states) - - elif itype == 'random': - dist = self._params['distribution'] - if dist == 'uniform': - init_states = np.random.normal( - low=self._params.get('low', 0.0), - high=self._params.get('high', 1.0), - size=nsize - ) - elif dist == 'normal': - init_states = np.random.normal( - loc=self._params.get('mean', 0.0), - scale=self._params.get('std', 1.0), - size=nsize - ) - elif dist == 'poisson': - init_states = np.random.poisson( - lam=self._params.get('lambda', 1.0), - size=nsize - ) - elif dist == 'lognormal': - init_states = np.random.lognormal( - mean=self._params.get('mean', 0.0), - sigma=self._params.get('sigma', 1.0), - size=nsize - ) - else: - raise Exception("AAAA") - - for idx, node in enumerate(node_set.fetch_nodes()): - ssn_node = sim.network.get_node(node.population_name, node.node_id) - ssn_node.initial_value = init_states[idx] - - def finalize(self, sim): - pass - - - -@njit -def relu2(array): - # if the element is negative, set it to zero using loop - # destructive method (alters the original array), but faster than the above one. - for i in range(len(array)): - if array[i] < 0: - array[i] = 0 - return array - - -class Config(SimulationConfig): - pass - - -class SSNNode: - def __init__(self, population, node_id, gid, **node_properties): - self.population = population - self.node_id = node_id - self.gid = gid - - self.type = None - self.input_offset = [] - self.scaling_coef = [] - self.exponent = [] - self.decay_const = [] - self.init_value = 0.0 - self.external_inputs = None - self.node_properties = node_properties - self._sonata_node = self.node_properties.get('node', {}) - - def __contains__(self, property): - return property in self.node_properties or property in self._sonata_node - - def __getitem__(self, property): - if property in self.node_properties: - return self.node_properties[property] - elif property in self._sonata_node: - return self._sonata_node[property] - else: - raise KeyError(f'SSNNode does not contain property "{property}"') - - def get(self, property, default=None): - if property in self: - return self[property] - else: - return default - - def __repr__(self) -> str: - return f'SSNNode {self.gid} > ({self.population}.{self.node_id})' - - -class SSNNetwork(SimNetwork): - def __init__(self, grouping_key='node_id', **opts): - super(SSNNetwork, self).__init__() - # self.n_neu_total = 6 - self.grouping_key = grouping_key - - # self._pop_index = {} - - self._node_id_map = {} - - self._nnodes_recurrent = 0 - self._nnodes_external = 0 - - self._connectivity_mat = None - self._scales = None - self._exponents = None - self._decay_constants = None - self._initial_states = None - - self._nodeid2grp = {} - - self._recurrent_nodes = {} - self._external_nodes = {} +# if self._params.get('shuffle', False): +# np.random.shuffle(init_states) + +# elif itype == 'random': +# dist = self._params['distribution'] +# if dist == 'uniform': +# init_states = np.random.normal( +# low=self._params.get('low', 0.0), +# high=self._params.get('high', 1.0), +# size=nsize +# ) +# elif dist == 'normal': +# init_states = np.random.normal( +# loc=self._params.get('mean', 0.0), +# scale=self._params.get('std', 1.0), +# size=nsize +# ) +# elif dist == 'poisson': +# init_states = np.random.poisson( +# lam=self._params.get('lambda', 1.0), +# size=nsize +# ) +# elif dist == 'lognormal': +# init_states = np.random.lognormal( +# mean=self._params.get('mean', 0.0), +# sigma=self._params.get('sigma', 1.0), +# size=nsize +# ) +# else: +# raise Exception("AAAA") + +# for idx, node in enumerate(node_set.fetch_nodes()): +# ssn_node = sim.network.get_node(node.population_name, node.node_id) +# ssn_node.initial_value = init_states[idx] + +# def finalize(self, sim): +# pass + + + +# @njit +# def relu2(array): +# # if the element is negative, set it to zero using loop +# # destructive method (alters the original array), but faster than the above one. +# for i in range(len(array)): +# if array[i] < 0: +# array[i] = 0 +# return array + + +# class Config(SimulationConfig): +# pass + + +# class SSNNode: +# def __init__(self, population, node_id, gid, **node_properties): +# self.population = population +# self.node_id = node_id +# self.gid = gid + +# self.type = None +# self.input_offset = [] +# self.scaling_coef = [] +# self.exponent = [] +# self.decay_const = [] +# self.init_value = 0.0 +# self.external_inputs = None +# self.node_properties = node_properties +# self._sonata_node = self.node_properties.get('node', {}) + +# def __contains__(self, property): +# return property in self.node_properties or property in self._sonata_node + +# def __getitem__(self, property): +# if property in self.node_properties: +# return self.node_properties[property] +# elif property in self._sonata_node: +# return self._sonata_node[property] +# else: +# raise KeyError(f'SSNNode does not contain property "{property}"') + +# def get(self, property, default=None): +# if property in self: +# return self[property] +# else: +# return default + +# def __repr__(self) -> str: +# return f'SSNNode {self.gid} > ({self.population}.{self.node_id})' + + +# class SSNNetwork(SimNetwork): +# def __init__(self, grouping_key='node_id', **opts): +# super(SSNNetwork, self).__init__() +# # self.n_neu_total = 6 +# self.grouping_key = grouping_key + +# # self._pop_index = {} + +# self._node_id_map = {} + +# self._nnodes_recurrent = 0 +# self._nnodes_external = 0 + +# self._connectivity_mat = None +# self._scales = None +# self._exponents = None +# self._decay_constants = None +# self._initial_states = None + +# self._nodeid2grp = {} + +# self._recurrent_nodes = {} +# self._external_nodes = {} - self._nodes_idx = {} - self._ssn_recurrent_nodes = set() - self._ssn_external_nodes = set() - self.gids = 0 +# self._nodes_idx = {} +# self._ssn_recurrent_nodes = set() +# self._ssn_external_nodes = set() +# self.gids = 0 - self._conn_mat = [] - # sefl._connectivity +# self._conn_mat = [] +# # sefl._connectivity - @property - def n_neu_recurrent(self): - return self._nnodes_recurrent +# @property +# def n_neu_recurrent(self): +# return self._nnodes_recurrent - @property - def n_neu_total(self): - return self._nnodes_recurrent + self._nnodes_external +# @property +# def n_neu_total(self): +# return self._nnodes_recurrent + self._nnodes_external - @property - def connectivity_mat(self): - if self._connectivity_mat is None: - self._connectivity_mat = np.zeros((self._nnodes_recurrent, self.n_neu_total), dtype=float) - for r, c, syn_w in self._conn_mat: - # print(r, c, syn_w) - self._connectivity_mat[r, c] = syn_w +# @property +# def connectivity_mat(self): +# if self._connectivity_mat is None: +# self._connectivity_mat = np.zeros((self._nnodes_recurrent, self.n_neu_total), dtype=float) +# for r, c, syn_w in self._conn_mat: +# # print(r, c, syn_w) +# self._connectivity_mat[r, c] = syn_w - # print(self._connectivity_mat) +# # print(self._connectivity_mat) - return self._connectivity_mat +# return self._connectivity_mat - @property - def scales(self): - if self._scales is None: - self._scales = np.zeros(self._nnodes_recurrent) - for n in self._ssn_recurrent_nodes: - self._scales[n.gid] = np.mean(n.scaling_coef) +# @property +# def scales(self): +# if self._scales is None: +# self._scales = np.zeros(self._nnodes_recurrent) +# for n in self._ssn_recurrent_nodes: +# self._scales[n.gid] = np.mean(n.scaling_coef) - return self._scales - - @property - def initial_states(self): - if self._initial_states is None: - self._initial_states = np.zeros(self._nnodes_recurrent) - for n in self._ssn_recurrent_nodes: - self._initial_states[n.gid] = np.mean(n.initial_value) +# return self._scales + +# @property +# def initial_states(self): +# if self._initial_states is None: +# self._initial_states = np.zeros(self._nnodes_recurrent) +# for n in self._ssn_recurrent_nodes: +# self._initial_states[n.gid] = np.mean(n.initial_value) - return self._initial_states - - @property - def exponents(self): - if self._exponents is None: - self._exponents = np.zeros(self._nnodes_recurrent) - for n in self._ssn_recurrent_nodes: - self._exponents[n.gid] = np.mean(n.exponent) +# return self._initial_states + +# @property +# def exponents(self): +# if self._exponents is None: +# self._exponents = np.zeros(self._nnodes_recurrent) +# for n in self._ssn_recurrent_nodes: +# self._exponents[n.gid] = np.mean(n.exponent) - return self._exponents +# return self._exponents - @property - def decay_constants(self): - if self._decay_constants is None: - self._decay_constants = np.zeros(self._nnodes_recurrent) - for n in self._ssn_recurrent_nodes: - self._decay_constants[n.gid] = np.mean(n.decay_const) +# @property +# def decay_constants(self): +# if self._decay_constants is None: +# self._decay_constants = np.zeros(self._nnodes_recurrent) +# for n in self._ssn_recurrent_nodes: +# self._decay_constants[n.gid] = np.mean(n.decay_const) - # print(self._decay_constants) +# # print(self._decay_constants) - return self._decay_constants +# return self._decay_constants - def build_nodes(self): - for node_pop in self.node_populations: - for node in node_pop.get_nodes(): - model_type = node['model_type'].lower() +# def build_nodes(self): +# for node_pop in self.node_populations: +# for node in node_pop.get_nodes(): +# model_type = node['model_type'].lower() - if model_type in ['population', 'rate_population', 'recurrent']: - self.add_recurrent_node( - population_id=node_pop.name, - node_id=node['node_id'], - input_offset=node['input_offset'], - scaling_coef=node['scaling_coef'], - exponent=node['exponent'], - decay_const=node['decay_const'], - initial_value=node.get['initial_value'] if 'initial_value' in node else 0.0, - node=node - ) +# if model_type in ['population', 'rate_population', 'recurrent']: +# self.add_recurrent_node( +# population_id=node_pop.name, +# node_id=node['node_id'], +# input_offset=node['input_offset'], +# scaling_coef=node['scaling_coef'], +# exponent=node['exponent'], +# decay_const=node['decay_const'], +# initial_value=node.get['initial_value'] if 'initial_value' in node else 0.0, +# node=node +# ) - elif model_type in ['external', 'virtual']: - self.add_external_node( - population_id=node_pop.name, - node_id=node[self.grouping_key], - node=node - ) - - - def build_edges(self): - for edge_pop in self._edge_populations: - for edge in edge_pop.get_edges(): - # print(edge.source_population, edge.source_node_id, edge.target_population, edge.target_node_id, edge['syn_weight']) - # print(edge.source_population in self._node_id_map) - # print(list(self._node_id_map[edge.source_population].keys())) - # print(edge.source_node_id in self._node_id_map[edge.source_population]) - # print(self.get_ssn_node(edge.source_population, edge.source_node_id).gid) - src_node = self._node_id_map[edge.source_population][int(edge.source_node_id)] - trg_node = self._node_id_map[edge.target_population][int(edge.target_node_id)] +# elif model_type in ['external', 'virtual']: +# self.add_external_node( +# population_id=node_pop.name, +# node_id=node[self.grouping_key], +# node=node +# ) + + +# def build_edges(self): +# for edge_pop in self._edge_populations: +# for edge in edge_pop.get_edges(): +# # print(edge.source_population, edge.source_node_id, edge.target_population, edge.target_node_id, edge['syn_weight']) +# # print(edge.source_population in self._node_id_map) +# # print(list(self._node_id_map[edge.source_population].keys())) +# # print(edge.source_node_id in self._node_id_map[edge.source_population]) +# # print(self.get_ssn_node(edge.source_population, edge.source_node_id).gid) +# src_node = self._node_id_map[edge.source_population][int(edge.source_node_id)] +# trg_node = self._node_id_map[edge.target_population][int(edge.target_node_id)] - self._conn_mat.append([trg_node.gid, src_node.gid, edge['syn_weight']]) +# self._conn_mat.append([trg_node.gid, src_node.gid, edge['syn_weight']]) - def get_node(self, population_id, node_id): - return self._node_id_map[population_id][node_id] +# def get_node(self, population_id, node_id): +# return self._node_id_map[population_id][node_id] - def get_ssn_node(self, population_id, node_id, **node_properties): - if population_id not in self._node_id_map: - # print('new population') - ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) - self._node_id_map[population_id] = {int(node_id): ssn_node} - self.gids += 1 +# def get_ssn_node(self, population_id, node_id, **node_properties): +# if population_id not in self._node_id_map: +# # print('new population') +# ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) +# self._node_id_map[population_id] = {int(node_id): ssn_node} +# self.gids += 1 - elif int(node_id) not in self._node_id_map: - # print(f'new node {population_id}, {node_id}') - ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) - self._node_id_map[population_id][int(node_id)] = ssn_node - self.gids += 1 +# elif int(node_id) not in self._node_id_map: +# # print(f'new node {population_id}, {node_id}') +# ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) +# self._node_id_map[population_id][int(node_id)] = ssn_node +# self.gids += 1 - else: - # print('found') - ssn_node = self._node_id_map[population_id][node_id] +# else: +# # print('found') +# ssn_node = self._node_id_map[population_id][node_id] - return ssn_node +# return ssn_node - def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, exponent, decay_const, initial_value=0.0, **node_properties): - self._nnodes_recurrent += 1 +# def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, exponent, decay_const, initial_value=0.0, **node_properties): +# self._nnodes_recurrent += 1 - ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) - ssn_obj.type='internal' - ssn_obj.input_offset.append(input_offset) - ssn_obj.scaling_coef.append(scaling_coef) - ssn_obj.exponent.append(exponent) - ssn_obj.decay_const.append(decay_const) - self._ssn_recurrent_nodes.add(ssn_obj) +# ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) +# ssn_obj.type='internal' +# ssn_obj.input_offset.append(input_offset) +# ssn_obj.scaling_coef.append(scaling_coef) +# ssn_obj.exponent.append(exponent) +# ssn_obj.decay_const.append(decay_const) +# self._ssn_recurrent_nodes.add(ssn_obj) - def add_external_node(self, population_id, node_id, **node_properties): - self._nnodes_external += 1 - ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) - ssn_obj.type = 'external' - self._ssn_external_nodes.add(ssn_obj) +# def add_external_node(self, population_id, node_id, **node_properties): +# self._nnodes_external += 1 +# ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) +# ssn_obj.type = 'external' +# self._ssn_external_nodes.add(ssn_obj) -class SSNSimulator: - def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): - self.dt = dt - self.tstart = tstart - self.tstop = tstop - self.network = network +# class SSNSimulator: +# def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): +# self.dt = dt +# self.tstart = tstart +# self.tstop = tstop +# self.network = network - self._fr_results = None +# self._fr_results = None - self._mods = [] +# self._mods = [] - @property - def nsteps(self): - return int((self.tstop - self.tstart)/self.dt) +# @property +# def nsteps(self): +# return int((self.tstop - self.tstart)/self.dt) - @property - def results(self): - if self._fr_results is None: - self._fr_results = np.zeros((self.nsteps, self.network.n_neu_total), dtype=float) - # print(self.network.initial_states) - self._fr_results[0, :self.network.n_neu_recurrent] = self.network.initial_states +# @property +# def results(self): +# if self._fr_results is None: +# self._fr_results = np.zeros((self.nsteps, self.network.n_neu_total), dtype=float) +# # print(self.network.initial_states) +# self._fr_results[0, :self.network.n_neu_recurrent] = self.network.initial_states - for ext_node in self.network._ssn_external_nodes: - if ext_node.external_inputs is not None: - self._fr_results[:, ext_node.gid] = ext_node.external_inputs - - return self._fr_results - - - def step(self, state, mat, scales, exponents, decay_constants, dt): - input = relu2(np.dot(mat, state) * scales) ** exponents - n_neu_recurrent = mat.shape[0] - recurrent_state = state[:n_neu_recurrent] - dr = (-recurrent_state + input) / decay_constants * dt - return relu2(recurrent_state + dr) - - def add_mod(self, mod): - mod.initialize(self) - self._mods.append(mod) - - def run(self): - for t in range(self.nsteps-1): - self.results[t+1, :self.network.n_neu_recurrent] = self.step( - self.results[t, :], - self.network.connectivity_mat, - self.network.scales, - self.network.exponents, - self.network.decay_constants, - self.dt, - ) - - for mod in self._mods: - mod.finalize(self) - - - # print(self.results) - # print(self.results.shape) - - # fig, ax = plt.subplots(2, 1) - # ax[0].plot(self.results[:, :]) - # # ax[0].legend(nodes_recurrent["cell_types"].values) - # ax[0].title.set_text("Entire simulation") - # plt.show() - - @classmethod - def from_config(cls, configure, network, **opts): - # load the json file or object - if isinstance(configure, string_types): - config = Config.from_json(configure, validate=True) - elif isinstance(configure, dict): - config = configure - else: - raise Exception('Could not convert {} (type "{}") to json.'.format(configure, type(configure))) - - sim = cls(network, dt=config.dt, tstart=config.tstart, tstop=config.tstop, **opts) +# for ext_node in self.network._ssn_external_nodes: +# if ext_node.external_inputs is not None: +# self._fr_results[:, ext_node.gid] = ext_node.external_inputs + +# return self._fr_results + + +# def step(self, state, mat, scales, exponents, decay_constants, dt): +# input = relu2(np.dot(mat, state) * scales) ** exponents +# n_neu_recurrent = mat.shape[0] +# recurrent_state = state[:n_neu_recurrent] +# dr = (-recurrent_state + input) / decay_constants * dt +# return relu2(recurrent_state + dr) + +# def add_mod(self, mod): +# mod.initialize(self) +# self._mods.append(mod) + +# def run(self): +# for t in range(self.nsteps-1): +# self.results[t+1, :self.network.n_neu_recurrent] = self.step( +# self.results[t, :], +# self.network.connectivity_mat, +# self.network.scales, +# self.network.exponents, +# self.network.decay_constants, +# self.dt, +# ) + +# for mod in self._mods: +# mod.finalize(self) + + +# # print(self.results) +# # print(self.results.shape) + +# # fig, ax = plt.subplots(2, 1) +# # ax[0].plot(self.results[:, :]) +# # # ax[0].legend(nodes_recurrent["cell_types"].values) +# # ax[0].title.set_text("Entire simulation") +# # plt.show() + +# @classmethod +# def from_config(cls, configure, network, **opts): +# # load the json file or object +# if isinstance(configure, string_types): +# config = Config.from_json(configure, validate=True) +# elif isinstance(configure, dict): +# config = configure +# else: +# raise Exception('Could not convert {} (type "{}") to json.'.format(configure, type(configure))) + +# sim = cls(network, dt=config.dt, tstart=config.tstart, tstop=config.tstop, **opts) - # if 'output_dir' in config['output']: - # network.output_dir = config['output']['output_dir'] - - network.io.log_info('Building nodes.') - network.build_nodes() - - network.io.log_info('Building recurrent connections') - network.build_edges() - - for sim_input in inputs.from_config(config): - if sim_input.input_type == 'init_states': - mod = InitStatesMod( - name=sim_input.name, - input_type=sim_input.input_type, - module=sim_input.module, - **sim_input.params - ) - sim.add_mod(mod) +# # if 'output_dir' in config['output']: +# # network.output_dir = config['output']['output_dir'] + +# network.io.log_info('Building nodes.') +# network.build_nodes() + +# network.io.log_info('Building recurrent connections') +# network.build_edges() + +# for sim_input in inputs.from_config(config): +# if sim_input.input_type == 'init_states': +# mod = InitStatesMod( +# name=sim_input.name, +# input_type=sim_input.input_type, +# module=sim_input.module, +# **sim_input.params +# ) +# sim.add_mod(mod) - if sim_input.input_type == 'external_rates': - mod = ExternalRatesMod( - name=sim_input.name, - input_type=sim_input.input_type, - module=sim_input.module, - **sim_input.params - ) - sim.add_mod(mod) +# if sim_input.input_type == 'external_rates': +# mod = ExternalRatesMod( +# name=sim_input.name, +# input_type=sim_input.input_type, +# module=sim_input.module, +# **sim_input.params +# ) +# sim.add_mod(mod) - if 'rates_file' in config.output: - mod = RatesRecorderMod( - name='RecordRatesH5', - module='rates', - output_type='csv', - file_name=config.output['rates_file'], - **config.output - ) - sim.add_mod(mod) - - if 'rates_file_csv' in config.output: - mod = RatesRecorderMod( - name='RecordRatesH5', - module='rates', - output_type='csv', - file_name=config.output['rates_file_csv'], - **config.output - ) - sim.add_mod(mod) - - if 'rates_file_h5' in config.output: - mod = RatesRecorderMod( - name='RecordRatesH5', - module='rates', - output_type='h5', - file_name=config.output['rates_file_h5'], - **config.output - ) - sim.add_mod(mod) - - return sim - - -@inputs_generator +# if 'rates_file' in config.output: +# mod = RatesRecorderMod( +# name='RecordRatesH5', +# module='rates', +# output_type='csv', +# file_name=config.output['rates_file'], +# **config.output +# ) +# sim.add_mod(mod) + +# if 'rates_file_csv' in config.output: +# mod = RatesRecorderMod( +# name='RecordRatesH5', +# module='rates', +# output_type='csv', +# file_name=config.output['rates_file_csv'], +# **config.output +# ) +# sim.add_mod(mod) + +# if 'rates_file_h5' in config.output: +# mod = RatesRecorderMod( +# name='RecordRatesH5', +# module='rates', +# output_type='h5', +# file_name=config.output['rates_file_h5'], +# **config.output +# ) +# sim.add_mod(mod) + +# return sim + + +@ssn.inputs_generator def load_bkg_inputs(node, sim, **opts): # print(node.node_id, node.population) # print(sim.dt, sim.tstart, sim.tstop, sim.nsteps) return np.ones(sim.nsteps) -@init_function +@ssn.init_function def set_init_states(node, sim, **opts): if node['pop_name'] == 'Exc': return 1.38853652 @@ -818,14 +823,14 @@ def set_init_states(node, sim, **opts): else: return 5.02447877 -configure = Config.from_json('config.simulation.json') +configure = ssn.Config.from_json('config.simulation.json') configure.build_env() # print(configure) -network = SSNNetwork.from_config(configure) +network = ssn.SSNNetwork.from_config(configure) -sim = SSNSimulator.from_config(configure, network) +sim = ssn.SSNSimulator.from_config(configure, network) sim.run() plot_rates(rates_files='output/l23_rates.h5', label_column='pop_name') \ No newline at end of file From 7e24e31cbf4858583daf965eafaba3e78d938685 Mon Sep 17 00:00:00 2001 From: Kael Dai Date: Wed, 23 Apr 2025 10:18:54 -0700 Subject: [PATCH 04/19] updating structure of popnet to support multiple backends --- bmtk/analyzer/firing_rates.py | 2 - bmtk/simulator/popnet/__init__.py | 44 ++++++++++++++++++- bmtk/simulator/popnet/config.py | 2 +- bmtk/simulator/popnet/dipde/__init__.py | 25 +++++++++++ bmtk/simulator/popnet/{ => dipde}/popedge.py | 0 .../popnet/{ => dipde}/popnetwork.py | 8 +++- bmtk/simulator/popnet/{ => dipde}/popnode.py | 0 .../popnet/{ => dipde}/popsimulator.py | 10 ++++- .../popnet/{ => dipde}/sonata_adaptors.py | 0 bmtk/simulator/popnet/{ => dipde}/utils.py | 0 bmtk/simulator/popnet/ssn/__init__.py | 4 +- .../popnet/ssn/modules/rates_recorder.py | 9 ++-- bmtk/simulator/popnet/ssn/popnetwork.py | 8 ++-- bmtk/simulator/popnet/ssn/popsimulator.py | 3 +- bmtk/utils/sonata/config/sonata_config.py | 1 + examples/pop_2pops/config.json | 3 +- examples/pop_2pops/config.simulation.json | 3 +- examples/ssn_l23/run_ssn.py | 28 +++++++----- 18 files changed, 117 insertions(+), 33 deletions(-) create mode 100644 bmtk/simulator/popnet/dipde/__init__.py rename bmtk/simulator/popnet/{ => dipde}/popedge.py (100%) rename bmtk/simulator/popnet/{ => dipde}/popnetwork.py (98%) rename bmtk/simulator/popnet/{ => dipde}/popnode.py (100%) rename bmtk/simulator/popnet/{ => dipde}/popsimulator.py (97%) rename bmtk/simulator/popnet/{ => dipde}/sonata_adaptors.py (100%) rename bmtk/simulator/popnet/{ => dipde}/utils.py (100%) diff --git a/bmtk/analyzer/firing_rates.py b/bmtk/analyzer/firing_rates.py index e4bd2147..2437241e 100644 --- a/bmtk/analyzer/firing_rates.py +++ b/bmtk/analyzer/firing_rates.py @@ -143,8 +143,6 @@ def is_csv(file_path): else: label = '' - print(label) - ax.plot(times, frs, label=label) elif is_hdf5(rpath): diff --git a/bmtk/simulator/popnet/__init__.py b/bmtk/simulator/popnet/__init__.py index 35dadd8c..a47049a0 100644 --- a/bmtk/simulator/popnet/__init__.py +++ b/bmtk/simulator/popnet/__init__.py @@ -20,7 +20,47 @@ # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # -from .popnetwork import PopNetwork -from .popsimulator import PopSimulator +from six import string_types + +from bmtk.simulator.core.simulation_config import SimulationConfig +from bmtk.simulator.core.io_tools import io from .config import Config +from .ssn import inputs_generator, init_function + + +class PopNetwork: + @staticmethod + def from_config(conf, **properties): + if isinstance(conf, SimulationConfig): + config = conf + else: + try: + config = SimulationConfig.load(conf) + except Exception as e: + io.log_exception('Could not convert {} (type "{}") to json.'.format(conf, type(conf))) + + if config.target_simulator is None: + io.log_debug('Unspecified PopNet "target_simulator", defaulting to SSN (Options: SSN, DiPDE)') + elif config.target_simulator.lower() == 'dipde': + from .dipde import PopNetwork + return PopNetwork.from_config(config, **properties) + elif config.target_simulator.lower() == 'ssn': + from .ssn import PopNetwork + return PopNetwork.from_config(config, **properties) + else: + io.log_exception(f'Unrecognized PopNet target_simulator "{config.target_simulator}') + +class PopSimulator: + @staticmethod + def from_config(configure, network, **properties): + if network.target_simulator is None: + io.log_debug('Unspecified PopNet "target_simulator", defaulting to SSN (Options: SSN, DiPDE)') + elif network.target_simulator.lower() == 'dipde': + from .dipde import PopSimulator + return PopSimulator.from_config(configure, network, **properties) + elif network.target_simulator.lower() == 'ssn': + from .ssn import PopSimulator + return PopSimulator.from_config(configure, network, **properties) + else: + io.log_exception(f'Unrecognized PopNet target_simulator "{config.target_simulator}') diff --git a/bmtk/simulator/popnet/config.py b/bmtk/simulator/popnet/config.py index 8822bc63..b9d470d2 100644 --- a/bmtk/simulator/popnet/config.py +++ b/bmtk/simulator/popnet/config.py @@ -30,4 +30,4 @@ def from_json(config_file, validate=False): class Config(SimulationConfig): - pass \ No newline at end of file + pass diff --git a/bmtk/simulator/popnet/dipde/__init__.py b/bmtk/simulator/popnet/dipde/__init__.py new file mode 100644 index 00000000..dafcad7d --- /dev/null +++ b/bmtk/simulator/popnet/dipde/__init__.py @@ -0,0 +1,25 @@ +# Copyright 2017. Allen Institute. All rights reserved +# +# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the +# following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following +# disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following +# disclaimer in the documentation and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote +# products derived from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, +# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, +# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +from .popnetwork import PopNetwork +from .popsimulator import PopSimulator +from ..config import Config diff --git a/bmtk/simulator/popnet/popedge.py b/bmtk/simulator/popnet/dipde/popedge.py similarity index 100% rename from bmtk/simulator/popnet/popedge.py rename to bmtk/simulator/popnet/dipde/popedge.py diff --git a/bmtk/simulator/popnet/popnetwork.py b/bmtk/simulator/popnet/dipde/popnetwork.py similarity index 98% rename from bmtk/simulator/popnet/popnetwork.py rename to bmtk/simulator/popnet/dipde/popnetwork.py index 7ee57b52..e062144b 100644 --- a/bmtk/simulator/popnet/popnetwork.py +++ b/bmtk/simulator/popnet/dipde/popnetwork.py @@ -25,8 +25,8 @@ import numpy as np from bmtk.simulator.core.simulator_network import SimNetwork -from bmtk.simulator.popnet import utils as poputils -from bmtk.simulator.popnet.sonata_adaptors import PopEdgeAdaptor +from bmtk.simulator.popnet.dipde import utils as poputils +from bmtk.simulator.popnet.dipde.sonata_adaptors import PopEdgeAdaptor from dipde.internals.internalpopulation import InternalPopulation from dipde.internals.externalpopulation import ExternalPopulation @@ -162,6 +162,10 @@ def __init__(self, group_by='node_type_id', **properties): self._external_connections = {} self._all_connections = [] + @property + def target_simulator(self): + return 'DiPDE' + @property def populations(self): return self._all_populations diff --git a/bmtk/simulator/popnet/popnode.py b/bmtk/simulator/popnet/dipde/popnode.py similarity index 100% rename from bmtk/simulator/popnet/popnode.py rename to bmtk/simulator/popnet/dipde/popnode.py diff --git a/bmtk/simulator/popnet/popsimulator.py b/bmtk/simulator/popnet/dipde/popsimulator.py similarity index 97% rename from bmtk/simulator/popnet/popsimulator.py rename to bmtk/simulator/popnet/dipde/popsimulator.py index 8792beaf..6875c9f2 100644 --- a/bmtk/simulator/popnet/popsimulator.py +++ b/bmtk/simulator/popnet/dipde/popsimulator.py @@ -30,13 +30,21 @@ import dipde from bmtk.simulator.core.simulator import Simulator -from . import config as cfg +# from bmtk. import config as cfg +from bmtk.simulator.core.simulation_config import SimulationConfig +from bmtk.simulator.core.io_tools import io from . import utils as poputils import bmtk.simulator.utils.simulation_inputs as inputs from bmtk.utils.reports.spike_trains import SpikeTrains from bmtk.utils.io import firing_rates +def from_json(config_file, validate=False): + conf_dict = SimulationConfig.from_json(config_file) + conf_dict.io = io + return conf_dict + + class PopSimulator(Simulator): def __init__(self, graph, dt=0.0001, tstop=0.0, overwrite=True): self._graph = graph diff --git a/bmtk/simulator/popnet/sonata_adaptors.py b/bmtk/simulator/popnet/dipde/sonata_adaptors.py similarity index 100% rename from bmtk/simulator/popnet/sonata_adaptors.py rename to bmtk/simulator/popnet/dipde/sonata_adaptors.py diff --git a/bmtk/simulator/popnet/utils.py b/bmtk/simulator/popnet/dipde/utils.py similarity index 100% rename from bmtk/simulator/popnet/utils.py rename to bmtk/simulator/popnet/dipde/utils.py diff --git a/bmtk/simulator/popnet/ssn/__init__.py b/bmtk/simulator/popnet/ssn/__init__.py index 2e9931cc..2c10b35a 100644 --- a/bmtk/simulator/popnet/ssn/__init__.py +++ b/bmtk/simulator/popnet/ssn/__init__.py @@ -20,8 +20,8 @@ # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # -from .popnetwork import SSNNetwork -from .popsimulator import SSNSimulator +from .popnetwork import PopNetwork +from .popsimulator import PopSimulator from .pyfunction_cache import inputs_generator, init_function from bmtk.simulator.core.simulation_config import SimulationConfig as Config diff --git a/bmtk/simulator/popnet/ssn/modules/rates_recorder.py b/bmtk/simulator/popnet/ssn/modules/rates_recorder.py index 3b8f97e6..caaddf75 100644 --- a/bmtk/simulator/popnet/ssn/modules/rates_recorder.py +++ b/bmtk/simulator/popnet/ssn/modules/rates_recorder.py @@ -23,15 +23,12 @@ def __init__(self, name, output_type, module, file_name, **kwargs): self.file_path = (Path(self._output_dir) / self.file_path).absolute() if self._output_type not in ['csv', 'h5']: - raise ValueError(f'{self.__name__}: Invalid output_type "{self._output_type}". [Valid options: csv, h5]') + raise ValueError(f'{self.__class__.__name__}: Invalid output_type "{self._output_type}". [Valid options: csv, h5]') self._include_columns = kwargs.get('include_columns', []) self._include_columns = [self._include_columns] if not isinstance(self._include_columns, (list, tuple)) else self._include_columns self._include_columns = [k for k in self._include_columns if k not in ['population', 'node_id', 'firing_rates', 'timestamps']] - # def initialize(self, sim): - # pass - def finalize(self, sim): if self._output_type == 'csv': self._to_csv(sim) @@ -69,7 +66,7 @@ def _to_csv(self, sim): output_df = tmp_df if output_df is None else pd.concat([output_df, tmp_df], ignore_index=True) if output_df is not None: - io.log_info(f'Saving rates to {self.file_path}.') + io.log_info(f'{self.__class__.__name__}: Saving rates to {self.file_path}.') output_df.to_csv(self.file_path, sep=' ', index=False) @@ -89,7 +86,7 @@ def _to_hdf5(self, sim): mappings[n.population] = subpop with h5py.File(self.file_path, mode) as h5: - io.log_info(f'Saving rates to {self.file_path}.') + io.log_info(f'{self.__class__.__name__}: Saving rates to {self.file_path}.') ratesgrp = h5['rates'] if 'rates' in h5 else h5.create_group('rates') for pop_name, pop_data in mappings.items(): subgrp = ratesgrp.create_group(pop_name) diff --git a/bmtk/simulator/popnet/ssn/popnetwork.py b/bmtk/simulator/popnet/ssn/popnetwork.py index b9b30126..cd46aa8b 100644 --- a/bmtk/simulator/popnet/ssn/popnetwork.py +++ b/bmtk/simulator/popnet/ssn/popnetwork.py @@ -3,9 +3,9 @@ from bmtk.simulator.core.simulator_network import SimNetwork from .popnode import SSNNode -class SSNNetwork(SimNetwork): +class PopNetwork(SimNetwork): def __init__(self, grouping_key='node_id', **opts): - super(SSNNetwork, self).__init__() + super(PopNetwork, self).__init__() # self.n_neu_total = 6 self.grouping_key = grouping_key @@ -36,7 +36,9 @@ def __init__(self, grouping_key='node_id', **opts): self._conn_mat = [] # sefl._connectivity - + @property + def target_simulator(self): + return 'SSN' @property def n_neu_recurrent(self): diff --git a/bmtk/simulator/popnet/ssn/popsimulator.py b/bmtk/simulator/popnet/ssn/popsimulator.py index bc492e22..7d9a68a2 100644 --- a/bmtk/simulator/popnet/ssn/popsimulator.py +++ b/bmtk/simulator/popnet/ssn/popsimulator.py @@ -11,7 +11,7 @@ import bmtk.simulator.popnet.ssn.default_setters -class SSNSimulator: +class PopSimulator: def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): self.dt = dt self.tstart = tstart @@ -23,7 +23,6 @@ def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): self._mods = [] self.activation_function = py_modules.activation_function('default') - @property def nsteps(self): return int((self.tstop - self.tstart)/self.dt) diff --git a/bmtk/utils/sonata/config/sonata_config.py b/bmtk/utils/sonata/config/sonata_config.py index ecccc22d..f8c0d9fb 100644 --- a/bmtk/utils/sonata/config/sonata_config.py +++ b/bmtk/utils/sonata/config/sonata_config.py @@ -203,6 +203,7 @@ def _set_class_props(self): self.gid_mappings = self.get('gid_mapping_file', None) # TODO: Remove self.node_sets = self.get('node_sets', {}) + self.target_simulator = self.get('target_simulator', None) def copy_config(conf): diff --git a/examples/pop_2pops/config.json b/examples/pop_2pops/config.json index 66329680..fb882f22 100644 --- a/examples/pop_2pops/config.json +++ b/examples/pop_2pops/config.json @@ -25,7 +25,8 @@ "output_dir": "$OUTPUT_DIR", "rates_file": "spike_rates.csv", "log_file": "log.txt", - "overwrite_output_dir": true + "overwrite_output_dir": true, + "log_level": "DEBUG" }, "target_simulator":"DiPDE", diff --git a/examples/pop_2pops/config.simulation.json b/examples/pop_2pops/config.simulation.json index adeb8eda..7eb33644 100644 --- a/examples/pop_2pops/config.simulation.json +++ b/examples/pop_2pops/config.simulation.json @@ -25,7 +25,8 @@ "output_dir": "$OUTPUT_DIR", "rates_file": "spike_rates.csv", "log_file": "log.txt", - "overwrite_output_dir": true + "overwrite_output_dir": true, + "log_level": "DEBUG" }, "network": "config.circuit.json" diff --git a/examples/ssn_l23/run_ssn.py b/examples/ssn_l23/run_ssn.py index fd9cb7eb..1e6257bf 100644 --- a/examples/ssn_l23/run_ssn.py +++ b/examples/ssn_l23/run_ssn.py @@ -1,4 +1,6 @@ import numpy as np +import argparse + # from numba import njit # from six import string_types # from pprint import pprint @@ -16,7 +18,7 @@ # from bmtk.utils.sonata.config import SonataConfig -from bmtk.simulator.popnet import ssn +from bmtk.simulator import popnet from bmtk.analyzer.firing_rates import plot_rates # import inputs_generator, init_function # from bmtk.simulator.popnet.ssn import Config @@ -806,13 +808,13 @@ # return sim -@ssn.inputs_generator +@popnet.inputs_generator def load_bkg_inputs(node, sim, **opts): # print(node.node_id, node.population) # print(sim.dt, sim.tstart, sim.tstop, sim.nsteps) return np.ones(sim.nsteps) -@ssn.init_function +@popnet.init_function def set_init_states(node, sim, **opts): if node['pop_name'] == 'Exc': return 1.38853652 @@ -823,14 +825,20 @@ def set_init_states(node, sim, **opts): else: return 5.02447877 -configure = ssn.Config.from_json('config.simulation.json') -configure.build_env() -# print(configure) +def run(configuration_path): + configure = popnet.Config.from_json(configuration_path) + configure.build_env() + + network = popnet.PopNetwork.from_config(configure) + sim = popnet.PopSimulator.from_config(configure, network) + sim.run() -network = ssn.SSNNetwork.from_config(configure) +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('config', nargs='?', default='config.simulation.json', help='SONATA configuration json file.') -sim = ssn.SSNSimulator.from_config(configure, network) -sim.run() + args = parser.parse_args() + run(args.config) -plot_rates(rates_files='output/l23_rates.h5', label_column='pop_name') \ No newline at end of file + plot_rates(args.config, label_column='pop_name') From 7a33802ffaaa2b9d72b5c758b18deb5efc21ad6c Mon Sep 17 00:00:00 2001 From: Kael Dai Date: Wed, 23 Apr 2025 12:24:41 -0700 Subject: [PATCH 05/19] Cleaning up code --- .../popnet/ssn/modules/external_inputs.py | 14 ++--- .../popnet/ssn/modules/initial_states.py | 7 +-- bmtk/simulator/popnet/ssn/popnetwork.py | 53 +++++++++---------- bmtk/simulator/popnet/ssn/popnode.py | 8 ++- bmtk/simulator/popnet/ssn/popsimulator.py | 18 +------ bmtk/simulator/popnet/ssn/utils.py | 2 +- 6 files changed, 41 insertions(+), 61 deletions(-) diff --git a/bmtk/simulator/popnet/ssn/modules/external_inputs.py b/bmtk/simulator/popnet/ssn/modules/external_inputs.py index a9388362..55b47b73 100644 --- a/bmtk/simulator/popnet/ssn/modules/external_inputs.py +++ b/bmtk/simulator/popnet/ssn/modules/external_inputs.py @@ -19,11 +19,11 @@ def initialize(self, sim): inputs_arr = np.load(npy_path) if sim.tstop is None: - # WARNING THAT TSTOP IS BEING SET BY INPUT + # TODO: WARNING THAT TSTOP IS BEING SET BY INPUT sim.tstop = len(inputs_arr)*sim.dt if sim.nsteps < len(inputs_arr): - # WARN THAT input is being cut + # TODO: WARN THAT input is being cut inputs_arr = inputs_arr[:sim.nsteps] elif sim.nsteps > len(inputs_arr): @@ -48,7 +48,7 @@ def initialize(self, sim): ssn_node.external_inputs = external_inputs if sim.tstop is None: - # WARNING THAT TSTOP IS BEING SET BY INPUT + # TODO: WARNING THAT TSTOP IS BEING SET BY INPUT sim.tstop = len(external_inputs)*sim.dt elif self._module == 'csv': @@ -64,7 +64,7 @@ def initialize(self, sim): ssn_node.external_inputs = inputs if sim.tstop is None: - # WARNING THAT TSTOP IS BEING SET BY INPUT + # TODO: WARNING THAT TSTOP IS BEING SET BY INPUT sim.tstop = len(inputs)*sim.dt elif self._module in ['h5', 'sonata']: @@ -81,11 +81,7 @@ def initialize(self, sim): ssn_node.external_inputs = inputs if sim.tstop is None: - # WARNING THAT TSTOP IS BEING SET BY INPUT + # TODO: WARNING THAT TSTOP IS BEING SET BY INPUT sim.tstop = len(inputs)*sim.dt else: raise ValueError('Uknown module') - - - # def finalize(self, sim): - # pass \ No newline at end of file diff --git a/bmtk/simulator/popnet/ssn/modules/initial_states.py b/bmtk/simulator/popnet/ssn/modules/initial_states.py index b8583e95..c93da7cf 100644 --- a/bmtk/simulator/popnet/ssn/modules/initial_states.py +++ b/bmtk/simulator/popnet/ssn/modules/initial_states.py @@ -1,3 +1,6 @@ +import pandas as pd +import numpy as np + from ..pyfunction_cache import py_modules from .sim_module import SimulatorMod @@ -43,10 +46,8 @@ def from_csv(self, sim): strict_mapping = self._params.get('strict_mapping', False) init_df = pd.read_csv(csv_path, sep=sep).set_index(index_col) - # init_df = init_df.set_index(init_df.columns['node_id']) - - node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) + node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) for node in node_set.fetch_nodes(): ssn_node = sim.network.get_node(node.population_name, node.node_id) if ssn_node.node_id not in init_df.index: diff --git a/bmtk/simulator/popnet/ssn/popnetwork.py b/bmtk/simulator/popnet/ssn/popnetwork.py index cd46aa8b..1f9736eb 100644 --- a/bmtk/simulator/popnet/ssn/popnetwork.py +++ b/bmtk/simulator/popnet/ssn/popnetwork.py @@ -16,25 +16,30 @@ def __init__(self, grouping_key='node_id', **opts): self._nnodes_recurrent = 0 self._nnodes_external = 0 - self._connectivity_mat = None + self._scales = None self._exponents = None self._decay_constants = None self._initial_states = None self._nodeid2grp = {} - - # self._recurrent_nodes = {} - # self._external_nodes = {} - - self._nodes_idx = {} self._ssn_recurrent_nodes = set() self._ssn_external_nodes = set() self.gids = 0 - self._conn_mat = [] - # sefl._connectivity + # Not the actual connection matrix used during sim, used for storing connections while building network + self._conn_mat = [] + + # The actual N x (N+M) connection matrix used during simulation, created before simulation begins + self._connectivity_mat = None + + # Keeps track if connection matrix needs to be rebuilt (mainly if new nodes/edges are added after a simulation) + self._conn_finalized = False + + @property + def network_finalized(self): + return self._conn_finalized @property def target_simulator(self): @@ -50,20 +55,18 @@ def n_neu_total(self): @property def connectivity_mat(self): - if self._connectivity_mat is None: + if self._connectivity_mat is None or not self._conn_finalized: + self._conn_finalized = True self._connectivity_mat = np.zeros((self._nnodes_recurrent, self.n_neu_total), dtype=float) for r, c, syn_w in self._conn_mat: - # print(r, c, syn_w) self._connectivity_mat[r, c] = syn_w - # print(self._connectivity_mat) - return self._connectivity_mat @property def scales(self): - if self._scales is None: + if self._scales is None or not self._conn_finalized: self._scales = np.zeros(self._nnodes_recurrent) for n in self._ssn_recurrent_nodes: self._scales[n.gid] = np.mean(n.scaling_coef) @@ -72,7 +75,7 @@ def scales(self): @property def initial_states(self): - if self._initial_states is None: + if self._initial_states is None or not self._conn_finalized: self._initial_states = np.zeros(self._nnodes_recurrent) for n in self._ssn_recurrent_nodes: self._initial_states[n.gid] = np.mean(n.initial_value) @@ -81,7 +84,7 @@ def initial_states(self): @property def exponents(self): - if self._exponents is None: + if self._exponents is None or not self._conn_finalized: self._exponents = np.zeros(self._nnodes_recurrent) for n in self._ssn_recurrent_nodes: self._exponents[n.gid] = np.mean(n.exponent) @@ -90,12 +93,10 @@ def exponents(self): @property def decay_constants(self): - if self._decay_constants is None: + if self._decay_constants is None or self._conn_finalized: self._decay_constants = np.zeros(self._nnodes_recurrent) for n in self._ssn_recurrent_nodes: self._decay_constants[n.gid] = np.mean(n.decay_const) - - # print(self._decay_constants) return self._decay_constants @@ -125,44 +126,37 @@ def build_nodes(self): def build_edges(self): + self._conn_finalized = False for edge_pop in self._edge_populations: for edge in edge_pop.get_edges(): - # print(edge.source_population, edge.source_node_id, edge.target_population, edge.target_node_id, edge['syn_weight']) - # print(edge.source_population in self._node_id_map) - # print(list(self._node_id_map[edge.source_population].keys())) - # print(edge.source_node_id in self._node_id_map[edge.source_population]) - # print(self.get_ssn_node(edge.source_population, edge.source_node_id).gid) src_node = self._node_id_map[edge.source_population][int(edge.source_node_id)] trg_node = self._node_id_map[edge.target_population][int(edge.target_node_id)] + # TODO: Move to add_edge function self._conn_mat.append([trg_node.gid, src_node.gid, edge['syn_weight']]) def get_node(self, population_id, node_id): return self._node_id_map[population_id][node_id] - def get_ssn_node(self, population_id, node_id, **node_properties): if population_id not in self._node_id_map: - # print('new population') ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) self._node_id_map[population_id] = {int(node_id): ssn_node} self.gids += 1 elif int(node_id) not in self._node_id_map: - # print(f'new node {population_id}, {node_id}') ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) self._node_id_map[population_id][int(node_id)] = ssn_node self.gids += 1 else: - # print('found') ssn_node = self._node_id_map[population_id][node_id] return ssn_node - def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, exponent, decay_const, initial_value=0.0, **node_properties): + self._conn_finalized = False self._nnodes_recurrent += 1 ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) @@ -173,9 +167,10 @@ def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, ssn_obj.decay_const.append(decay_const) self._ssn_recurrent_nodes.add(ssn_obj) - def add_external_node(self, population_id, node_id, **node_properties): + self._conn_finalized = False self._nnodes_external += 1 + ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) ssn_obj.type = 'external' self._ssn_external_nodes.add(ssn_obj) diff --git a/bmtk/simulator/popnet/ssn/popnode.py b/bmtk/simulator/popnet/ssn/popnode.py index b911aef8..b2851e2e 100644 --- a/bmtk/simulator/popnet/ssn/popnode.py +++ b/bmtk/simulator/popnet/ssn/popnode.py @@ -24,8 +24,12 @@ def __getitem__(self, property): return self.node_properties[property] elif property in self._sonata_node: return self._sonata_node[property] + elif property in ['node_id', 'node_ids']: + return self.node_id + elif property in ['population', 'population_name']: + return self.population else: - raise KeyError(f'SSNNode does not contain property "{property}"') + raise KeyError(f'{self.__class__.__name__} does not contain property "{property}"') def get(self, property, default=None): if property in self: @@ -34,4 +38,4 @@ def get(self, property, default=None): return default def __repr__(self) -> str: - return f'SSNNode {self.gid} > ({self.population}.{self.node_id})' \ No newline at end of file + return f'{self.__class__.__name__} {self.gid} > ({self.population}.{self.node_id})' \ No newline at end of file diff --git a/bmtk/simulator/popnet/ssn/popsimulator.py b/bmtk/simulator/popnet/ssn/popsimulator.py index 7d9a68a2..9a9d1e24 100644 --- a/bmtk/simulator/popnet/ssn/popsimulator.py +++ b/bmtk/simulator/popnet/ssn/popsimulator.py @@ -17,9 +17,7 @@ def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): self.tstart = tstart self.tstop = tstop self.network = network - self._fr_results = None - self._mods = [] self.activation_function = py_modules.activation_function('default') @@ -31,7 +29,6 @@ def nsteps(self): def results(self): if self._fr_results is None: self._fr_results = np.zeros((self.nsteps, self.network.n_neu_total), dtype=float) - # print(self.network.initial_states) self._fr_results[0, :self.network.n_neu_recurrent] = self.network.initial_states for ext_node in self.network._ssn_external_nodes: @@ -69,16 +66,6 @@ def run(self): for mod in self._mods: mod.finalize(self) - - # print(self.results) - # print(self.results.shape) - - # fig, ax = plt.subplots(2, 1) - # ax[0].plot(self.results[:, :]) - # # ax[0].legend(nodes_recurrent["cell_types"].values) - # ax[0].title.set_text("Entire simulation") - # plt.show() - @classmethod def from_config(cls, configure, network, **opts): # load the json file or object @@ -91,9 +78,6 @@ def from_config(cls, configure, network, **opts): sim = cls(network, dt=config.dt, tstart=config.tstart, tstop=config.tstop, **opts) - # if 'output_dir' in config['output']: - # network.output_dir = config['output']['output_dir'] - network.io.log_info('Building nodes.') network.build_nodes() @@ -154,4 +138,4 @@ def from_config(cls, configure, network, **opts): ) sim.add_mod(mod) - return sim \ No newline at end of file + return sim diff --git a/bmtk/simulator/popnet/ssn/utils.py b/bmtk/simulator/popnet/ssn/utils.py index 2b066565..3becf1b6 100644 --- a/bmtk/simulator/popnet/ssn/utils.py +++ b/bmtk/simulator/popnet/ssn/utils.py @@ -5,4 +5,4 @@ def empty_decorator(func): @wraps(func) def defunct(*args, **kwargs): return func(*args, **kwargs) - return defunct \ No newline at end of file + return defunct From 78741beadc46d68d11f4fed2b275789240955245 Mon Sep 17 00:00:00 2001 From: kaeldai Date: Thu, 24 Apr 2025 15:30:35 -0700 Subject: [PATCH 06/19] adding first documentation --- bmtk/simulator/core/node_sets.py | 3 + bmtk/simulator/popnet/ssn/popnetwork.py | 5 +- examples/ssn_l23/PopNet_SSN.ipynb | 864 ++++++++++++++++++ examples/ssn_l23/build_network.py | 4 + examples/ssn_l23/config.simulation.json | 9 +- .../ssn_l23/network/bkg_l23_edge_types.csv | 8 +- examples/ssn_l23/network/bkg_l23_edges.h5 | Bin 43608 -> 43608 bytes examples/ssn_l23/network/bkg_nodes.h5 | Bin 15744 -> 15744 bytes .../ssn_l23/network/l23_l23_edge_types.csv | 18 +- examples/ssn_l23/network/l23_l23_edges.h5 | Bin 43608 -> 43608 bytes examples/ssn_l23/network/l23_node_types.csv | 10 +- examples/ssn_l23/network/l23_nodes.h5 | Bin 15744 -> 15744 bytes .../ssn_l23/network/l4e_l23_edge_types.csv | 8 +- examples/ssn_l23/network/l4e_l23_edges.h5 | Bin 43608 -> 43608 bytes examples/ssn_l23/network/l4e_nodes.h5 | Bin 15744 -> 15744 bytes 15 files changed, 901 insertions(+), 28 deletions(-) create mode 100644 examples/ssn_l23/PopNet_SSN.ipynb diff --git a/bmtk/simulator/core/node_sets.py b/bmtk/simulator/core/node_sets.py index 2c6140aa..a2ef6e1a 100644 --- a/bmtk/simulator/core/node_sets.py +++ b/bmtk/simulator/core/node_sets.py @@ -57,6 +57,9 @@ def fetch_nodes(self): for node in pop.filter(self._filter): yield node + def __len__(self) -> int: + return len(self.node_ids) + class NodeSetAll(NodeSet): def __init__(self, network): diff --git a/bmtk/simulator/popnet/ssn/popnetwork.py b/bmtk/simulator/popnet/ssn/popnetwork.py index 1f9736eb..af8d6d9e 100644 --- a/bmtk/simulator/popnet/ssn/popnetwork.py +++ b/bmtk/simulator/popnet/ssn/popnetwork.py @@ -109,7 +109,6 @@ def build_nodes(self): self.add_recurrent_node( population_id=node_pop.name, node_id=node['node_id'], - input_offset=node['input_offset'], scaling_coef=node['scaling_coef'], exponent=node['exponent'], decay_const=node['decay_const'], @@ -155,13 +154,13 @@ def get_ssn_node(self, population_id, node_id, **node_properties): return ssn_node - def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, exponent, decay_const, initial_value=0.0, **node_properties): + def add_recurrent_node(self, population_id, node_id, scaling_coef, exponent, decay_const, initial_value=0.0, **node_properties): self._conn_finalized = False self._nnodes_recurrent += 1 ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) ssn_obj.type='internal' - ssn_obj.input_offset.append(input_offset) + # ssn_obj.input_offset.append(input_offset) ssn_obj.scaling_coef.append(scaling_coef) ssn_obj.exponent.append(exponent) ssn_obj.decay_const.append(decay_const) diff --git a/examples/ssn_l23/PopNet_SSN.ipynb b/examples/ssn_l23/PopNet_SSN.ipynb new file mode 100644 index 00000000..ce50f69a --- /dev/null +++ b/examples/ssn_l23/PopNet_SSN.ipynb @@ -0,0 +1,864 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4a48c45c-27e9-458e-8ed5-0729be661d34", + "metadata": {}, + "source": [ + "# PopNet Simulation with SSN\n", + "\n", + "[Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics](https://www.biorxiv.org/content/10.1101/2023.10.21.563440v1.full)\n", + "\n", + "$$\n", + "\\tau_j \\frac{dr}{dt} = -r_j + (s(\\sum_i J_{ji} r_{i} + J_{E}r_{E}) + b_{j})^2 \\tag{1}\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "313c08fa-97eb-41b0-a135-864d64c17fa1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "747d7113-a5f0-4aa5-bec3-38534725e04e", + "metadata": {}, + "source": [ + "## Building and Saving the network\n", + "\n", + "First step is to either build a new, or download an existing, network of nodes and edges to simulate. We will use the BMTK [NetworkBuilder](https://alleninstitute.github.io/bmtk/tutorials/NetworkBuilder_Intro.html) to create a standard network and save it to the SONATA network file format. Unlike simulators like BioNet or PointNet, which deals with networks of individual spiking cells - PopNet simulates the firing-rate dynamics between subpopulations/cell-types. As such when we build each sub-population using ```add_nodes``` method we only have 1 cell in a population (eg. We let the ```N``` parameter default to value **1**).\n", + "\n", + "It is also possible to use PopNet with an existing network containing individual cells, as long as we specify during simulation how to group individual cells into populations, which we will demonstrate below. (**NOTE**: If using a BioNet or PointNet network with PopNet you will also need to update and map parameters, which at the moment needs to be preformed manually)." + ] + }, + { + "cell_type": "markdown", + "id": "3c511cb5-48b9-4d5f-b92f-c85923340e5d", + "metadata": {}, + "source": [ + "### Building Recurrent Networks\n", + "\n", + "We will first build and save a model representing the major cell types found in the mouse cortex L2/3. We start by instantiating a network called \"l23\" and addng the four major cell populations whose firing-rates dynamics will be calculated during simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9d0a1961-dea7-4ca2-a89b-affd484fe338", + "metadata": {}, + "outputs": [], + "source": [ + "from bmtk.builder import NetworkBuilder\n", + "\n", + "\n", + "l23_net = NetworkBuilder('l23')\n", + "l23_net.add_nodes(\n", + " pop_name='Exc',\n", + " ei='e',\n", + " model_template='ssn:Recurrent',\n", + " model_type='rate_population',\n", + " scaling_coef=22.392148198236782,\n", + " exponent=2.0,\n", + " decay_const=10.0500429106779\n", + ")\n", + "l23_net.add_nodes(\n", + " pop_name='PV',\n", + " ei='i',\n", + " model_template='ssn:Recurrent',\n", + " model_type='rate_population',\n", + " scaling_coef=22.392148198236782,\n", + " exponent=2.0,\n", + " decay_const=11.8257398864811\n", + ")\n", + "\n", + "l23_net.add_nodes(\n", + " pop_name='SST',\n", + " ei='i',\n", + " model_template='ssn:Recurrent',\n", + " model_type='rate_population',\n", + " scaling_coef=22.392148198236782,\n", + " exponent=2.0,\n", + " decay_const=10.0159482249032\n", + ")\n", + "\n", + "l23_net.add_nodes(\n", + " pop_name='VIP',\n", + " ei='i',\n", + " model_template='ssn:Recurrent',\n", + " model_type='rate_population',\n", + " scaling_coef=22.392148198236782,\n", + " exponent=2.0,\n", + " decay_const=49.3117045605402\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cc6f0a35-8359-4bcc-a0e4-9e7c215103f6", + "metadata": {}, + "source": [ + "Each of the four node-populations are set using a combination of optional and required attributes which will be used to instantate the cells in the network.\n", + "\n", + "* **pop_name** and **ei** are *optional* attributes we assign to each population, will be useful during analysis and \n", + "* We must set **model_type** to ```rate_population``` and **model_template** to ```ssn:Recurrent``` to indicate to PopNet how to instantiate each sub-population.\n", + "* **scaling_coeff** is the connectivity scaling factor of the network: variable $s$ in eq (1)\n", + "* **decay_const** is the decay rate of the popuatlion: $\\tau$ in eq (1)\n", + "* **exponent** is the exponent calcuated at each time step (defaul 2)\n", + "* **init_state** is the *optional* default initial state ($r_0$ in eq (1)). Although this value can be overwritten at the start of each simulation (see below)." + ] + }, + { + "cell_type": "markdown", + "id": "1ed7c9a9-6a62-456b-8840-7c5008a58350", + "metadata": {}, + "source": [ + "---\n", + "\n", + "#### [OPTIONAL] Dynamic Parameters File\n", + "\n", + "The above network will save the relevant PopNet simulation parameters - **scaling_coeff**, **decay_const**, **exponent**, and **init_state** inside the SONATA network file. It is also possible to use the ```dynamic_params``` keyword to store and load parameters from an external json file. For example you can also use:\n", + "\n", + "```python\n", + "\n", + "l23_net.add_nodes(\n", + " pop_name='Exc',\n", + " ei='e',\n", + " model_template='ssn:Recurrent',\n", + " model_type='rate_population',\n", + " dynamics_params='Exc_ssn_params.json'\n", + ")\n", + "```\n", + "\n", + "Where **Exc_ssn_params.json** is a json file\n", + "\n", + "```json\n", + "{\n", + " scaling_coef=22.392148198236782,\n", + " exponent=2.0,\n", + " decay_const=49.3117045605402,\n", + " init_state=0.0\n", + "}\n", + "```\n", + "\n", + "Both options will work with PopNet and produce the same results. Using ```dynamics_params``` makes it easier to adjust and swap out parameters on the fly, but can add complexity to the simulation and could potentially lead to issues with reproducability.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "52fc5ab5-ceb3-4628-9da1-a503ab9dc6e6", + "metadata": {}, + "source": [ + "Now we want to add connections between populations using the ```add_edges``` method. For each population we only need to indicate the **source** sub-population, the **target** population, and the **connection-strength** ($J_{ij}$).\n", + "\n", + "After we set the connections we can build and save the network to the *network/* directory.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5c9b76fa-0d4d-40e9-b387-a72e5eb0cda6", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import itertools\n", + "\n", + "matrix = [\n", + " # Connection weights have been pre-calculated\n", + " [ 0.032540014514440, 0.04043037008531871, 0.07409100928480848, 0.054303329629300665],\n", + " [-0.009474168446121, -0.01234075896522014, -0.00496813087571539, 0.0],\n", + " [-0.001201518371451, -0.00271476061802006, -0.00061333573436626, -0.014630226522887473],\n", + " [-0.000305028126569, -0.00073158484138907, -0.00777536182340318, -0.0001246269351861529]\n", + "]\n", + "connections_df = pd.DataFrame(np.array(matrix), columns=['Exc', 'PV', 'SST', 'VIP'], index=['Exc', 'PV', 'SST', 'VIP'])\n", + "\n", + "for src, trg in itertools.product(['Exc', 'PV', 'SST', 'VIP'], repeat=2):\n", + " l23_net.add_edges(\n", + " source={'pop_name': src}, \n", + " target={'pop_name': trg},\n", + " syn_weight=connections_df.loc[src][trg]\n", + " )\n", + "\n", + "l23_net.build()\n", + "l23_net.save(output_dir='network')" + ] + }, + { + "cell_type": "markdown", + "id": "00173cfd-0c1e-4494-b51d-b7447b8f8f24", + "metadata": {}, + "source": [ + "### Building External Inputs\n", + "\n", + "Now we also want to create inputs that can be used to drive our network ($b_j$). In our model we will have inputs from two separate networks; subcortical inputs (**bkg**) and inputs from the local L4 cells (**l4**). We create these two networks like we do with the recurrent - but the caveat that we must set ```model_type=virtual``` which will indicate to PopNet that these aren't populations to simulate, but rather virutal \"placeholders\" that will be used for input stimulus (which we can define at run-time).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2f355141-8c74-4dee-9e25-243ebd8418e7", + "metadata": {}, + "outputs": [], + "source": [ + "# Create L4 --> L2/3 connections\n", + "l4e_net = NetworkBuilder('l4e')\n", + "l4e_net.add_nodes(\n", + " model_template='ssn:External',\n", + " model_type='virtual'\n", + ")\n", + "\n", + "for trg, syn_weight in zip(['Exc', 'PV', 'SST', 'VIP'], [0.01256496, 0.0278943, 0.0034658, 0.01172155]):\n", + " l4e_net.add_edges(\n", + " target=l23_net.nodes(pop_name=trg),\n", + " syn_weight=syn_weight\n", + " )\n", + "\n", + "l4e_net.build()\n", + "l4e_net.save(output_dir='network')\n", + "\n", + "\n", + "# Create BKG --> L2/3 connections\n", + "bkg_net = NetworkBuilder('bkg')\n", + "bkg_net.add_nodes(\n", + " model_template='ssn:External',\n", + " model_type='virtual'\n", + ")\n", + "for trg, syn_weight in zip(['Exc', 'PV', 'SST', 'VIP'], [0.00869301, 0.00013383, 0.00177394, 0.01785412]):\n", + " bkg_net.add_edges(\n", + " target=l23_net.nodes(pop_name=trg),\n", + " syn_weight=syn_weight\n", + " )\n", + "bkg_net.build()\n", + "bkg_net.save(output_dir='network')" + ] + }, + { + "cell_type": "markdown", + "id": "660fcb60-76ec-44c8-a52b-16a625165b46", + "metadata": {}, + "source": [ + "## Setting up and running a simulation" + ] + }, + { + "cell_type": "markdown", + "id": "b1a15845-b96b-42f6-b9a3-20853e5bf680", + "metadata": {}, + "source": [ + "Once we have built or imported a set of network file(s) we can go ahead and start the simulation. We must tell BMTK where find the network files for simulation, as well as what inputs/stimuli to use, and how to record and save the results of the simulation. The easiest way to do so is through a SONATA configuration file; in this case **config.simulation.json** which we can be easily edited with the text editor of your choice (you may also multiple configuration files corresponding to different simulations). \n", + "\n", + "We will go through the **config.simulation.json** file and explain the different sections and options available when running a simulation" + ] + }, + { + "cell_type": "markdown", + "id": "b89f9f14-8c3a-4783-a9a2-ddfa9a42315f", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### \"run\" and \"target_simulator\"" + ] + }, + { + "cell_type": "markdown", + "id": "a256b83e-0163-4997-abd1-a3e8b043a5ab", + "metadata": {}, + "source": [ + "We specify to **target_simulator** parameter to indicate to use the **SSN** simulator backend (available options: **SSN**, **DiPDE**) and the **run** section to indicate overall run-time and grouping parameters \n", + "\n", + "\n", + "```json\n", + "{\n", + "\n", + " \"target_simulator\": \"SSN\",\n", + " \n", + " \"run\": {\n", + " \"tstop\": 13500.0\n", + " \"dt\": 1.0\n", + " },\n", + "\n", + " // ...\n", + "\n", + "}\n", + "```\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
AttributeunitsDescriptionDefault
tstopmsThe amount of time (in ms) to run the simulation. If not explcity specified, BMTK will attempt to infer tstop from external input(s)
dtmsThe time step in ms. Is required1.0
nsteps_blockstepsUsed by modules for block processingtstop/dt
groupbystringUsed to specify which attribute/column for when grouping individual cells into populationsnode_id
" + ] + }, + { + "cell_type": "markdown", + "id": "d64dfd77-ec22-4abd-a35d-157c5db1d8bc", + "metadata": {}, + "source": [ + "### \"inputs\"" + ] + }, + { + "cell_type": "markdown", + "id": "5adf877c-e9df-47d3-b24e-7f58718b53c9", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "#### initial states" + ] + }, + { + "cell_type": "markdown", + "id": "2fafc522-85c6-4fd4-a64a-6ca9a706e8c2", + "metadata": {}, + "source": [ + "\n", + "We use the following block to define the initial states of the recurrent populations, eg. $r_0$. (**Note**: Any ```init_state``` attribute saved in the SONATA network file or dynamics_params json will be overwritten). \n", + "\n", + "```json\n", + "\"inputs\": {\n", + " \"init_states\": {\n", + " \"input_type\": \"init_states\",\n", + " \"module\": \"csv\",\n", + " \"node_set\": {\n", + " \"population\": \"l23\"\n", + " },\n", + " \"file\": \"$BASE_DIR/input/initial_state.csv\"\n", + " }\n", + " // ...\n", + "}\n", + "\n", + "```\n", + "\n", + "We must set ```input_type``` to **init_states**, and use ```node_set``` to indicate which nodes to target (in this case all nodes in the \"l23\" network). While the ```module``` is used to indicate how to actually assign initial states (randomly, using a pregenerated list, or through custom functions).\n", + "\n", + "In the above case the init_states are saved in a csv file *inputs/initial_states.csv* which contains assignements of each inital_state based on the id of each subpopulation:\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
node_idpopulationinitial_statepop_name
0l231.38853652Exc
0l233.50304166PV
0l231.61686557SST
0l235.02447877VIP
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "1743de79-fddd-447d-b649-ce3c7cdf57ef", + "metadata": {}, + "source": [ + "We could aslo pass it in as a list directly specified in the json file:\n", + "\n", + "```json\n", + "\"inputs\": {\n", + " \"init_states\": {\n", + " \"input_type\": \"init_states\",\n", + " \"node_set\": {\n", + " \"population\": \"l23\",\n", + " \"node_id\": [0, 1, 2, 3]\n", + " }\n", + " \"module\": \"list\",\n", + " \"initial_states\": [1.38853652, 3.50304166, 1.61686557, 5.02447877]\n", + " }\n", + " // ...\n", + "}\n", + "\n", + "```\n", + "\n", + "Options for **init_states** inputs. \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
moduleDescriptionOptions
constantAssigns all populations the same constant valueinitial_state: initial firing rate value (float)
randomAssign initial states randomlydistribution: name of distribution for sampling (\"uniform\", \"normal\", \"poisson\", \"lognormal\")
\n", + " seed: integer random number generator seed (int, default: None)
\n", + " low, high: parameters used in \"uniform\" random sampling
\n", + " mean, std: parameters using in \"normal\", \"poisson\", and \"lognormal\" distribution
\n", + "
listAssign population initial values from a list or npy array, by default assignment is done in orderinitial_states: A list of initial firing rates (list) or path to npy file (string)
\n", + " shuffle: shuffle list before assigning initial values (default: false)\n", + "
csvAssign populations init_values from a csv file\n", + " file: path to csv file containg inital rates
\n", + " initial_states: A list of initial firing rates (list) or path to npy file (string)
\n", + " shuffle: shuffle list before assigning initial values (default: false)\n", + "
functionWill call a user-defined function to fetch initial states for each node\n", + " init_function: name of function to execute
\n", + "
\n", + "\n", + "\n", + "**Using custom function**\n", + "\n", + "The ```input_type=\"function\"``` options allows users to write special functions that will be called before each simulation that returns a init_value according to the rules specified by the user. In the json we must tell BMTK the name of the function to call using the ```init_function``` attribute. Then in the main python script (or any imported python script) we use the ```@init_function``` decorator to register our function with BMTK.\n", + "\n", + "\n", + "For example we create a function named **set_init_states** by adding the following to our run_popnet.py script\n", + "\n", + "\n", + "```python\n", + "\n", + "from bmtk.simulator import popnet\n", + "\n", + "@popnet.init_function\n", + "def set_init_states(node, **opts):\n", + " if node['ie'] == 'e':\n", + " return 1.5\n", + " else:\n", + " return np.random.uniform(0.0, 5.0)\n", + "\n", + "```\n", + "\n", + "The function will be called for each node that requires init_state value to be assinged. The **node** parameter is a dictionary like object that user can get attributes from each node. The **\\*\\*opts** contain additional options including simulation, network, and input parameters if the user needs them. The function is expected to return a value firing rate value (a positive floating point number).\n" + ] + }, + { + "cell_type": "markdown", + "id": "56123954-ff29-4e0b-9061-e58b0931a1bd", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "#### external inputs" + ] + }, + { + "cell_type": "markdown", + "id": "e12d0b86-2385-40f6-a833-3a56519d8f7c", + "metadata": {}, + "source": [ + "We also use the \"inputs\" section of the json file to indicate how our virtual/external nodes will calculate firing-rate inputs into our recurrent network. \n", + "\n", + "```python\n", + "{\n", + " \"inputs\": {\n", + " // ...\n", + " \n", + " \"l4_inputs\": {\n", + " \"input_type\": \"external_rates\",\n", + " \"module\": \"h5\",\n", + " \"file\": \"$BASE_DIR/input/l4e_rates.h5\",\n", + " \"node_set\": {\n", + " \"population\": \"l4e\"\n", + " }\n", + " },\n", + " \"bkg_inputs\": {\n", + " \"input_type\": \"external_rates\",\n", + " \"node_set\": {\n", + " \"population\": \"bkg\"\n", + " },\n", + " \"module\": \"npy\",\n", + " \"file\": \"$BASE_DIR/input/bkg_rates.npy\"\n", + " }\n", + " }\n", + "}\n", + "```\n", + "\n", + "We must set ```input_type``` to value **external_rates**, and use the ```node_set``` attribute to indicate which nodes to target. Because bkg and l4e uses different stimuli inputs we have two independent **external_rates** section. Like with init_states the ```module``` tells us the modality of the inputs\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
moduleDescriptionOptions
npyRead in array containing firing rate dynamics to assign to external inputfile: initial firing rate value (float)
csvRead in a csv file containing firing rate dynamics to assign to external input (See below for rates format)distribution: name of distribution for sampling (\"uniform\", \"normal\", \"poisson\", \"lognormal\")
\n", + " low, high
\n", + " mean, std
\n", + "
h5Read in a hdf5 file containing firing rate dynamics to assign to external input (See below for rates format)initial_states: A list of initial firing rates (list) or path to npy file (string)
\n", + " shuffle: shuffle list before assigning initial values (default: false)\n", + "
fuctionHave popnet call a user-defined function for each node that will return an array of firing ratesinit_function: name of function to call before simulation starts
\n", + "\n", + "**custom function**\n", + "\n", + "When ```module``` is set to value **function** then PopNet will expect find a user defined function with name ```inputs_generator``` that will be called before the simulation. In the main python script (or any imported python script) we use the ```@inputs_generator``` decorator to register our function with BMTK. For example:\n", + "\n", + "\n", + "```python\n", + "\n", + "from bmtk.simulator import popnet\n", + "\n", + "\n", + "@popnet.inputs_generator\n", + "def load_bkg_inputs(node, sim, **opts):\n", + " # print(node.node_id, node.population)\n", + " # print(sim.dt, sim.tstart, sim.tstop, sim.nsteps)\n", + " return np.ones(sim.nsteps)\n", + "```\n", + "\n", + "\n", + "**WARNING: simulation time and step size**\n", + "\n", + "The SSN simulation engine requires all input arrays have the same time signature (eg. same start, stop, and dt) as specified in the \"run\" section. To make things easier, if ```tstop``` and ```dt``` are not defined the \"run\" section PopNet will try to infer it from the external_inputs array (in the case the user doesn't know the time signature in the numpy/csv/hdf5 file).\n", + "\n", + "However if the ```tstop``` and ```dt``` are defined in the \"run\" section, issues may arise if the time signature of any of the inputs don't match up. By default BMTK will compalain and stop the simulation. But users have the option of using ```\"strict\": false``` option to allow PopNet to attempt to run the simulation anyways. If the simulation ends before the input array PopNet will remove the tail of the input. If the input array is too short it will append the end with zeros. It will also try to align timestamps if the sampling rates are different. But if the inputs can't align with the run time BMTK will throw an error and exit the simulation.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7f084275-8568-4579-841b-fc5a77ea463b", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### \"output\"" + ] + }, + { + "cell_type": "markdown", + "id": "72ab6979-8d81-4942-8506-e7ebf429ffca", + "metadata": {}, + "source": [ + "The \"output\" section of the configuration json is used to indicate where and how to save simulated firing-rates\n", + "\n", + "```json \n", + " \"output\": {\n", + " \"output_dir\": \"$BASE_DIR/output/\",\n", + " \"rates_file_csv\": \"l23_rates.csv\",\n", + " \"rates_file_h5\": \"l23_rates.h5\",\n", + " \"include_columns\": [\"pop_name\"],\n", + " \"log_file\": \"log.txt\",\n", + " \"overwrite_output_dir\": true\n", + " }\n", + "```\n", + "\n", + "In the above example, when the simulation is finished it will save the L2/3 firing rate dynamics in two different file formats, a csv file (under ./ouput/l23_rates.csv) and a hdf5 file (user ./output/l23_rates.h5). See below for more information about how rates are saved to file.\n", + "\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
moduleDescription
output_dirDirectory where any output files will be written to (assuming they aren't using relative paths)
log_filePath to file where simulation log will be written.
log_levelLevel of logging: DEBUG, INFO, WARNING, ERROR. (default: INFO)node_id
rates_file_csvPath to csv where firing rates will be written to (see below for rates format)
rates_file_h5Path to csv where firing rates will be written to (see below for rates format)
include_columns(Optional) Include other columns/attributes in output rates file(s)
" + ] + }, + { + "cell_type": "markdown", + "id": "e4d64ba4-7ca2-482f-b705-611505c74bb3", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### \"networks\"" + ] + }, + { + "cell_type": "markdown", + "id": "e34b97c8-e7bf-4353-bab0-5c6005611cc4", + "metadata": {}, + "source": [ + "The \"networks\" section allows us to define which networks to use during the simulation. If we want to run a simulation with different inputs, or add recurrent connections, we can do so here:\n", + "\n", + "```json\n", + "{\n", + " \"networks\": {\n", + " \"nodes\": [\n", + " {\n", + " \"nodes_file\": \"$NETWORK_DIR/l23_nodes.h5\",\n", + " \"node_types_file\": \"$NETWORK_DIR/l23_node_types.csv\"\n", + " },\n", + " {\n", + " \"nodes_file\": \"$NETWORK_DIR/l4e_nodes.h5\",\n", + " \"node_types_file\": \"$NETWORK_DIR/l4e_node_types.csv\"\n", + " },\n", + " {\n", + " \"nodes_file\": \"$NETWORK_DIR/bkg_nodes.h5\",\n", + " \"node_types_file\": \"$NETWORK_DIR/bkg_node_types.csv\"\n", + " }\n", + " ],\n", + " \"edges\": [\n", + " {\n", + " \"edges_file\": \"$NETWORK_DIR/l23_l23_edges.h5\",\n", + " \"edge_types_file\": \"$NETWORK_DIR/l23_l23_edge_types.csv\"\n", + " },\n", + " {\n", + " \"edges_file\": \"$NETWORK_DIR/l4e_l23_edges.h5\",\n", + " \"edge_types_file\": \"$NETWORK_DIR/l4e_l23_edge_types.csv\"\n", + " },\n", + " {\n", + " \"edges_file\": \"$NETWORK_DIR/bkg_l23_edges.h5\",\n", + " \"edge_types_file\": \"$NETWORK_DIR/bkg_l23_edge_types.csv\"\n", + " }\n", + " ]\n", + " }\n", + "}\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3ec9640-2434-4bc8-be59-3b09e48b3b1a", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### [OPTIONAL] activation_function" + ] + }, + { + "cell_type": "markdown", + "id": "bc59da9a-f7f7-41a8-b7bc-070ed0cd183e", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "7dc99302-689a-4be6-8876-ed6b6affaeec", + "metadata": {}, + "source": [ + "## Running the simulation" + ] + }, + { + "cell_type": "markdown", + "id": "2a084018-9650-4ce5-acd9-b09983dacf7a", + "metadata": {}, + "source": [ + "Now that we have created our network, and defined our simulation parameters in the json configuration, we can go ahead and run the simulation. In the terminal we could run the script \n", + "\n", + "```bash\n", + " $ python run_popnet.py config.simulation.json\n", + "```\n", + "\n", + "Or in jupyter lab cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c4b3bc43-114c-4498-a48a-c0c26588aea2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-04-24 14:49:22,022 [INFO] Created log file\n", + "2025-04-24 14:49:22,081 [INFO] Building nodes.\n", + "2025-04-24 14:49:22,083 [INFO] Building connections.\n", + "2025-04-24 14:49:22,086 [INFO] Add input module \"init_states\"\n", + "2025-04-24 14:49:22,089 [INFO] Add input module \"l4_inputs\"\n", + "2025-04-24 14:49:22,091 [INFO] Add input module \"bkg_inputs\"\n", + "2025-04-24 14:49:22,092 [INFO] Running Simulation.\n", + "2025-04-24 14:49:23,142 [INFO] Simulation Finished.\n", + "2025-04-24 14:49:23,150 [INFO] RatesRecorderMod: Saving rates to /local1/workspace/bmtk/examples/ssn_l23/output/l23_rates.csv.\n", + "2025-04-24 14:49:23,325 [INFO] RatesRecorderMod: Saving rates to /local1/workspace/bmtk/examples/ssn_l23/output/l23_rates.h5.\n" + ] + } + ], + "source": [ + "from bmtk.simulator import popnet\n", + "\n", + "\n", + "@popnet.inputs_generator\n", + "def load_bkg_inputs(node, sim, **opts):\n", + " # print(node.node_id, node.population)\n", + " # print(sim.dt, sim.tstart, sim.tstop, sim.nsteps)\n", + " return np.ones(sim.nsteps)\n", + "\n", + "\n", + "configure = popnet.Config.from_json('config.simulation.json')\n", + "configure.build_env()\n", + "\n", + "network = popnet.PopNetwork.from_config(configure)\n", + "sim = popnet.PopSimulator.from_config(configure, network)\n", + "sim.run() " + ] + }, + { + "cell_type": "markdown", + "id": "9e3d7980-4428-432e-bf05-76dc0f33bf81", + "metadata": {}, + "source": [ + "We use BMTK built in ```analyzer``` functions to plot the recorded firing rates." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "de0fd37a-26f3-4c73-ba61-1d0c9dc56a52", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bmtk.analyzer.firing_rates import plot_rates\n", + "\n", + "plot_rates('config.simulation.json', label_column='pop_name')" + ] + }, + { + "cell_type": "markdown", + "id": "7f2095ab-7e4b-43ad-92da-f71e4d76693e", + "metadata": {}, + "source": [ + "## Firing Rates File" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a15b82e-cc9e-41ae-ba40-9049da8bde7a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/ssn_l23/build_network.py b/examples/ssn_l23/build_network.py index 6cf92b43..4eee2c06 100644 --- a/examples/ssn_l23/build_network.py +++ b/examples/ssn_l23/build_network.py @@ -49,6 +49,7 @@ decay_const=params['tau_v']*1000.0 ) + for src, trg in itertools.product(['Exc', 'PV', 'SST', 'VIP'], repeat=2): lu_key = f'{src[0]}_to_{trg[0]}'.lower() if lu_key in params: @@ -56,6 +57,8 @@ source={'pop_name': src}, target={'pop_name': trg}, syn_weight=params[lu_key]*model_data.l23_infl_df.loc[lu_key]['mean'] ) + # print(src, trg, params[lu_key]*model_data.l23_infl_df.loc[lu_key]['mean']) + l23_net.build() l23_net.save(output_dir='network') @@ -74,6 +77,7 @@ syn_weight=syn_weight ) + l4e_net.build() l4e_net.save(output_dir='network') diff --git a/examples/ssn_l23/config.simulation.json b/examples/ssn_l23/config.simulation.json index 0c6fe9c5..ea5300ec 100644 --- a/examples/ssn_l23/config.simulation.json +++ b/examples/ssn_l23/config.simulation.json @@ -15,11 +15,14 @@ "inputs": { "init_states": { "input_type": "init_states", - "module": "function", - "init_function": "set_init_states", + "module": "csv", "node_set": { "population": "l23" - } + }, + "file": "$BASE_DIR/input/initial_state.csv", + "sep": " ", + "index_col": "node_id", + "value_col": "initial_state" }, "l4_inputs": { "input_type": "external_rates", diff --git a/examples/ssn_l23/network/bkg_l23_edge_types.csv b/examples/ssn_l23/network/bkg_l23_edge_types.csv index 2b084186..e17af2ba 100644 --- a/examples/ssn_l23/network/bkg_l23_edge_types.csv +++ b/examples/ssn_l23/network/bkg_l23_edge_types.csv @@ -1,5 +1,5 @@ edge_type_id target_query source_query syn_weight -100 pop_name=='Exc' * 0.008693005130625849 -101 pop_name=='PV' * 0.0001338259670856798 -102 pop_name=='SST' * 0.0017739395973541316 -103 pop_name=='VIP' * 0.01785412498319743 +100 pop_name=='Exc' * 0.00869301 +101 pop_name=='PV' * 0.00013383 +102 pop_name=='SST' * 0.00177394 +103 pop_name=='VIP' * 0.01785412 diff --git a/examples/ssn_l23/network/bkg_l23_edges.h5 b/examples/ssn_l23/network/bkg_l23_edges.h5 index c63d3a3dd4137beacbaff0c1ea98150d55475747..d3560bdc33b3112b826f13d864ab94ac5c381b1f 100644 GIT binary patch delta 101 zcmca{h3Uo>rVUa&j0~HldBTMl8752fSWI3mcOA;wEe~aFKCRHs2oc=et(4CU)bLx? 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i-w8;0XY`8zsoOQ|d_Z~c42#Xu-QhqHFmv_%wOjx~#UHl- delta 101 zcmca{h3Uo>rVUa&j7*!QdBTMlnI=o~SWI3mcOA;wEe~aFKCRHs2oc=et(4CU)bLx? i-w8;0XY`8zsoOQ|d_Z~c42#Xu-QhqHFmv_%wOjy3u^-C- diff --git a/examples/ssn_l23/network/l4e_nodes.h5 b/examples/ssn_l23/network/l4e_nodes.h5 index 8623d6bf17bac84192396efe3f67d4650a884a25..3536ffbd8b07c342c24db54d8be90f337df1f371 100644 GIT binary patch delta 47 qcmZpuZm8ZM#ly(3S(>L^h>>BkG>^sR+j8ujP^Pp7oEdGX$^!r^;|ojx delta 47 qcmZpuZm8ZM#ly(7S(>L^h>>ZsG>^sR+j8ujP^Pp7oEdGX$^!r_uM1QF From e95c08b1935fc1bb4799be33e0f4ee982fe214d4 Mon Sep 17 00:00:00 2001 From: kaeldai Date: Fri, 25 Apr 2025 09:39:41 -0700 Subject: [PATCH 07/19] updating notebook and run script --- examples/ssn_l23/PopNet_SSN.ipynb | 234 ++++++++- examples/ssn_l23/run_ssn.py | 805 +----------------------------- 2 files changed, 234 insertions(+), 805 deletions(-) diff --git a/examples/ssn_l23/PopNet_SSN.ipynb b/examples/ssn_l23/PopNet_SSN.ipynb index ce50f69a..d653787d 100644 --- a/examples/ssn_l23/PopNet_SSN.ipynb +++ b/examples/ssn_l23/PopNet_SSN.ipynb @@ -831,10 +831,242 @@ "## Firing Rates File" ] }, + { + "cell_type": "markdown", + "id": "e8fc7c67-5e35-49d6-9dbb-2ccf2f229557", + "metadata": {}, + "source": [ + "### CSV File" + ] + }, + { + "cell_type": "markdown", + "id": "1761d5c4-2655-4cdd-bb10-9bd89bf3c0a1", + "metadata": {}, + "source": [ + "When writing or reading a firing-rates file PopNet is expecting a space-separated csv file with at least columns \"node_id\" (integer) and \"firing_rates\" (float). The format may also include other optional columns that can be used to identify and cluster populations" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f974427c-9175-4656-9272-06db661c63e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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populationnode_idtimestampsfiring_ratespop_name
0l2330.05.024479VIP
1l2331.05.028511VIP
2l2332.05.032359VIP
3l2333.05.036106VIP
4l2334.05.039591VIP
\n", + "" + ], + "text/plain": [ + " population node_id timestamps firing_rates pop_name\n", + "0 l23 3 0.0 5.024479 VIP\n", + "1 l23 3 1.0 5.028511 VIP\n", + "2 l23 3 2.0 5.032359 VIP\n", + "3 l23 3 3.0 5.036106 VIP\n", + "4 l23 3 4.0 5.039591 VIP" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "l23_rates_df = pd.read_csv('output/l23_rates.csv', sep=' ')\n", + "l23_rates_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7ce4c858-bccd-4b2f-9afc-6300ee274bd4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "population pop_name\n", + "l23 Exc 1.738432\n", + " PV 4.813858\n", + " SST 4.094843\n", + " VIP 4.535815\n", + "Name: firing_rates, dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the firing rates averages\n", + "l23_rates_df.groupby(['population', 'pop_name'])['firing_rates'].agg('sum')/sim.tstop" + ] + }, + { + "cell_type": "markdown", + "id": "33411777-c2c6-4604-8f8b-4cc027fb6a9e", + "metadata": {}, + "source": [ + "### HDF5 File" + ] + }, + { + "cell_type": "markdown", + "id": "bc16227c-a7c3-48b6-9cc7-98768ad28845", + "metadata": {}, + "source": [ + "Another option is to save and load rates from a SONATA formated HDF5 file.\n", + "\n", + "Rates will be saved by **population** (if there are multiple recurrent networks in the simualtion there will be multiple subgroups) under the group **/rates/{population}/**. The **data** dataset contains a $T \\times N$ (where $T$ is the number of timesteps, $N$ the number of nodes) matrix where each column represents the recording firing-rate dynamics for each node. And the **mapping** group contains ways of mapping individual times and keys/attributes to the rows and columns, respectivly." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ce7ec8af-7e4a-4b0f-9a1e-0ad0d62a2d08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l23_rates.h5/\n", + "|--*rates/\n", + " |--*l23/\n", + " |-->[data] (13500, 4)\n", + " |--*mapping/\n", + " |-->[node_ids] (4,)\n", + " |-->[pop_name] (4,)\n", + " |-->[time] (13500,)\n" + ] + } + ], + "source": [ + "import h5py\n", + "\n", + "# Will parse and show the rates hdf5 tree\n", + "def tree_recurse(h5_obj, depth):\n", + " indent = ' '*(depth-1) + '|--'\n", + "\n", + " for key, node in h5_obj.items():\n", + " if isinstance(node, h5py.Group):\n", + " print(f'{indent}*{key}/')\n", + " _show_tree_helper(node, depth=depth+1)\n", + " elif isinstance(node, h5py.Dataset):\n", + " print(f'{indent}>[{key}] {node.shape}')\n", + "\n", + "\n", + "h5_root = h5py.File('output/l23_rates.h5', 'r')\n", + "print('l23_rates.h5/')\n", + "tree_recurse(h5_root, depth=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4b8f5ba5-8851-48f8-ae8f-9a9f581bff36", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l23\n", + " VIP: 4.536151243661407\n", + " SST: 4.095146474363789\n", + " PV: 4.814214600421233\n", + " Exc: 1.7385611015415747\n" + ] + } + ], + "source": [ + "# How to calcuate firing rate averages from the h5 file\n", + "with h5py.File('output/l23_rates.h5', 'r') as h5:\n", + " for pname, pgrp in h5['/rates/'].items():\n", + " node_ids = pgrp['mapping/pop_name'][()].astype(str)\n", + " time = pgrp['mapping/time'][-1]\n", + " print(pname)\n", + " for idx, nid in enumerate(node_ids):\n", + " fr_avg = np.sum(pgrp['data'][:, idx])/time\n", + " print(f' {nid}: {fr_avg}')" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "9a15b82e-cc9e-41ae-ba40-9049da8bde7a", + "id": "ce58dadc-b4b2-46a2-865c-4688edd02aee", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/ssn_l23/run_ssn.py b/examples/ssn_l23/run_ssn.py index 1e6257bf..33363290 100644 --- a/examples/ssn_l23/run_ssn.py +++ b/examples/ssn_l23/run_ssn.py @@ -1,811 +1,8 @@ import numpy as np import argparse -# from numba import njit -# from six import string_types -# from pprint import pprint -# import pandas as pd -# import matplotlib.pyplot as plt -# from pathlib import Path -# import h5py -# from functools import wraps - -# from bmtk.simulator.core.simulation_config import SimulationConfig -# from bmtk.simulator.core.simulator_network import SimNetwork -# from bmtk.simulator.core import sonata_reader -# import bmtk.simulator.utils.simulation_inputs as inputs - -# from bmtk.utils.sonata.config import SonataConfig - - from bmtk.simulator import popnet from bmtk.analyzer.firing_rates import plot_rates -# import inputs_generator, init_function -# from bmtk.simulator.popnet.ssn import Config - - -# class _PyFunctions(object): -# def __init__(self): -# self.__user_functions = {} - - -# @property -# def user_functions(self): -# return self.__user_functions.keys() - -# def user_function(self, name): -# return self.__user_functions[name] - -# def add_user_functions(self, name, func, overwrite=True): -# if overwrite or name not in self.__user_functions: -# self.__user_functions[name] = func - -# py_modules = _PyFunctions() - - -# def inputs_generator(*wargs, **wkwargs): -# if len(wargs) == 1 and callable(wargs[0]): -# # for the case without decorator arguments, grab the function object in wargs and create a decorator -# func = wargs[0] -# py_modules.add_user_functions(func.__name__, func) # add function assigned to its original name - -# @wraps(func) -# def func_wrapper(*args, **kwargs): -# return func(*args, **kwargs) -# return func_wrapper -# else: -# # for the case with decorator arguments -# assert(all(k in ['name'] for k in wkwargs.keys())) - -# def decorator(func): -# # store the function in py_modules but under the name given in the decorator arguments -# py_modules.add_user_functions(wkwargs['name'], func) - -# @wraps(func) -# def func_wrapper(*args, **kwargs): -# return func(*args, **kwargs) -# return func_wrapper -# return decorator - -# init_function = inputs_generator - - -# def add_function(func, name=None, overwrite=True): -# assert(callable(func)) -# func_name = name if name is not None else func.__name__ -# py_modules.add_spikes_generator(func_name, func, overwrite) - - - -# def plot_rates(config_file=None, rates_files=None, population=None, times=None, label_column='node_id', title=None, show=True, -# save_as=None, group_by=None, group_excludes=None, -# nodes_file=None, node_types_file=None, plt_style=None): - -# def is_hdf5(file_path): -# try: -# h5py.File(file_path, 'r') -# return True -# except Exception: -# return False - -# def is_csv(file_path): -# try: -# pd.read_csv(file_path) -# return True -# except Exception: -# return False - - -# sonata_config = SonataConfig.from_json(config_file) if config_file else None - -# rates_paths = set() -# if isinstance(rates_files, (list, tuple, pd.Series)): -# rates_paths.update([Path(rf).absolute() for rf in rates_files]) -# elif rates_files is not None: -# rates_paths.add(Path(rates_files)) - -# if sonata_config is not None: -# for k in ['rates_file', 'rates_file_csv', 'rates_file_h5']: -# if k in sonata_config.output: -# rates_paths.add(Path(sonata_config.get('output', {}).get('output_dir', '.')) / Path(sonata_config.output[k])) -# break - -# fig, ax = plt.subplots(1, 1) -# for rpath in rates_paths: -# if is_csv(rpath): -# rates_df = pd.read_csv(rpath, sep=' ') -# for (population, node_id), subdf in rates_df.groupby(['population', 'node_id']): -# times = subdf['timestamps'].values -# frs = subdf['firing_rates'].values - -# if label_column == 'population': -# label = population -# elif label_column == 'node_id': -# label = node_id -# elif label_column in subdf.columns: -# label = subdf[label_column].iloc[0] -# else: -# label = '' - -# ax.plot(times, frs, label=label) - -# elif is_hdf5(rpath): -# with h5py.File(rpath, 'r') as h5: -# for population, popgrp in h5['/rates'].items(): -# times = popgrp['mapping/time'][()] -# node_ids = popgrp['mapping/node_ids'][()] - -# if label_column == 'population': -# labels = [population]*len(node_ids) -# elif label_column == 'node_id': -# labels = node_ids -# elif label_column in popgrp['mapping']: -# labels = popgrp['mapping'][label_column][()].astype(str) -# else: -# labels = ['']*len(node_ids) - -# for idx, node_id in enumerate(node_ids): -# frs = popgrp['data'][:, idx] -# ax.plot(times, frs, label=labels[idx]) - - -# ax.legend() -# ax.set_ylabel('firing rates (Hz)') -# ax.set_xlabel('times (ms)') - -# if show: -# plt.show() - - -# class RatesRecorderMod: -# def __init__(self, name, output_type, module, file_name, **kwargs): -# self._name = name -# self._output_type = output_type -# self._module = module -# self._params = kwargs -# self.file_name = file_name -# self._output_dir = kwargs.get('output_dir', '.') -# self._node_set = kwargs.get('cells', None) - - -# self.file_path = Path(self.file_name) -# if not self.file_path.is_absolute(): -# self.file_path = (Path(self._output_dir) / self.file_path).absolute() - -# if self._output_type not in ['csv', 'h5']: -# raise ValueError(f'{self.__name__}: Invalid output_type "{self._output_type}". [Valid options: csv, h5]') - -# self._include_columns = kwargs.get('include_columns', []) -# self._include_columns = [self._include_columns] if not isinstance(self._include_columns, (list, tuple)) else self._include_columns -# self._include_columns = [k for k in self._include_columns if k not in ['population', 'node_id', 'firing_rates', 'timestamps']] - -# def initialize(self, sim): -# pass - -# def finalize(self, sim): -# if self._output_type == 'csv': -# self._to_csv(sim) -# elif self._output_type == 'h5': -# self._to_hdf5(sim) - -# def _get_nodes(self, sim): -# if self._node_set is not None: -# node_set = sim.network.get_node_set(self._node_set) -# return [sim.network.get_node(n.population_name, n.node_id) for n in node_set.fetch_nodes()] -# else: -# return list(sim.network._ssn_recurrent_nodes) - -# def _get_timestamps(self, sim): -# return np.linspace(0.0, sim.tstop, num=sim.nsteps, endpoint=False) - -# def _to_csv(self, sim): -# output_df = None -# times = self._get_timestamps(sim) -# ssn_nodes = self._get_nodes(sim) - -# for node in ssn_nodes: -# tmp_df = pd.DataFrame({ -# 'population': node.population, -# 'node_id': node.node_id, -# 'timestamps': times, -# 'firing_rates': sim.results[:, node.gid] -# }) - -# for c in self._include_columns: -# tmp_df[c] = node.get(c, None) - - -# output_df = tmp_df if output_df is None else pd.concat([output_df, tmp_df], ignore_index=True) - -# if output_df is not None: -# output_df.to_csv(self.file_path, sep=' ', index=False) - - -# def _to_hdf5(self, sim): -# mode = self._params.get('modd', 'a') -# times = self._get_timestamps(sim) -# nsteps = len(times) -# ssn_nodes = self._get_nodes(sim) -# mappings = {} -# for n in ssn_nodes: -# subpop = mappings.get(n.population, {k: [] for k in ['node_id', 'gid'] + self._include_columns}) -# subpop['node_id'].append(n.node_id) -# subpop['gid'].append(n.gid) -# for c in self._include_columns: -# subpop[c].append(n.get(c, None)) - -# mappings[n.population] = subpop - -# with h5py.File(self.file_path, mode) as h5: -# ratesgrp = h5['rates'] if 'rates' in h5 else h5.create_group('rates') -# for pop_name, pop_data in mappings.items(): -# subgrp = ratesgrp.create_group(pop_name) -# subgrp.create_dataset('mapping/time', data=times) -# subgrp.create_dataset('mapping/node_ids', data=pop_data['node_id']) -# for c in self._include_columns: -# subgrp.create_dataset(f'mapping/{c}', data=pop_data[c]) - -# data = subgrp.create_dataset('data', shape=(nsteps, len(pop_data['gid'])), dtype=float) -# for col, gid in enumerate(pop_data['gid']): -# data[:, col] = sim.results[:, gid] - - -# class ExternalRatesMod: -# def __init__(self, name, input_type, module, **kwargs): -# self._name = name -# self._input_type = input_type -# self._module = module -# self._params = kwargs - -# def initialize(self, sim): -# if self._module == 'npy': -# npy_path = self._params['file'] -# inputs_arr = np.load(npy_path) - -# if sim.tstop is None: -# # WARNING THAT TSTOP IS BEING SET BY INPUT -# sim.tstop = len(inputs_arr)*sim.dt - -# if sim.nsteps < len(inputs_arr): -# # WARN THAT input is being cut -# inputs_arr = inputs_arr[:sim.nsteps] - -# elif sim.nsteps > len(inputs_arr): -# # GIVE WARNING -# inputs_arr = np.append(inputs_arr, np.zeros(sim.nsteps - len(inputs_arr))) - -# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - -# for node in node_set.fetch_nodes(): -# ssn_node = sim.network.get_node(node.population_name, node.node_id) -# ssn_node.external_inputs = inputs_arr.flatten() - - -# elif self._module == 'function': -# fnc_name = self._params['inputs_generator'] -# generator_fnc = py_modules.user_function(fnc_name) - -# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) -# for node in node_set.fetch_nodes(): -# ssn_node = sim.network.get_node(node.population_name, node.node_id) -# external_inputs = generator_fnc(ssn_node, sim) -# ssn_node.external_inputs = external_inputs - -# if sim.tstop is None: -# # WARNING THAT TSTOP IS BEING SET BY INPUT -# sim.tstop = len(external_inputs)*sim.dt - -# elif self._module == 'csv': -# # TODO: Check Timestamps match, line up if needed -# rates_df = pd.read_csv(self._params['file'], sep=self._params.get('sep', ' ')) -# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) -# for node in node_set.fetch_nodes(): -# ssn_node = sim.network.get_node(node.population_name, node.node_id) - -# # TODO: Check that node exists in file -# inputs = rates_df[(rates_df['node_id'] == ssn_node.node_id) & (rates_df['population'] == ssn_node.population)]['firing_rates'] -# if len(inputs) > 0: -# ssn_node.external_inputs = inputs - -# if sim.tstop is None: -# # WARNING THAT TSTOP IS BEING SET BY INPUT -# sim.tstop = len(inputs)*sim.dt - -# elif self._module in ['h5', 'sonata']: -# with h5py.File(self._params['file'], 'r') as h5: -# ratesgrp = h5['/rates'] -# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) -# for node in node_set.fetch_nodes(): -# ssn_node = sim.network.get_node(node.population_name, node.node_id) - -# node_id_map = ratesgrp[f'{ssn_node.population}/mapping/node_ids'][()] -# idx = np.argwhere(node_id_map == ssn_node.node_id)[0][0] -# inputs = ratesgrp[f'{ssn_node.population}/data'][:, idx] - -# ssn_node.external_inputs = inputs - -# if sim.tstop is None: -# # WARNING THAT TSTOP IS BEING SET BY INPUT -# sim.tstop = len(inputs)*sim.dt -# else: -# raise ValueError('Uknown module') - - -# def finalize(self, sim): -# pass - - -# class InitStatesMod: -# def __init__(self, name, input_type, module, **kwargs): -# self._name = name -# self._input_type = input_type -# self._module = module -# self._params = kwargs - -# def initialize(self, sim): -# # node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - -# if self._module == 'csv': -# self.from_csv(sim) -# elif self._module == 'constant': -# self.from_pregenerated(sim, itype='const') -# elif self._module == 'random': -# self.from_pregenerated(sim, itype='random') -# elif self._module == 'list': -# self.from_pregenerated(sim, itype='list') -# elif self._module == 'function': -# self.from_user_function(sim) -# else: -# raise ValueError(f'{self.__name__}: Error in {self._name} input module, no valid module [options: csv, constant, random, function]') - -# def from_user_function(self, sim): -# fnc_name = self._params['init_function'] -# generator_fnc = py_modules.user_function(fnc_name) - -# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) -# for node in node_set.fetch_nodes(): -# ssn_node = sim.network.get_node(node.population_name, node.node_id) -# init_val = generator_fnc(ssn_node, sim) -# ssn_node.initial_value = init_val - -# def from_csv(self, sim): -# csv_path = self._params['file'] -# sep = self._params.get('sep', ' ') -# index_col = self._params.get('index_col', 'node_id') -# value_col = self._params.get('value_col', 'initial_state') -# strict_mapping = self._params.get('strict_mapping', False) - -# init_df = pd.read_csv(csv_path, sep=sep).set_index(index_col) -# # init_df = init_df.set_index(init_df.columns['node_id']) - -# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) - -# for node in node_set.fetch_nodes(): -# ssn_node = sim.network.get_node(node.population_name, node.node_id) -# if ssn_node.node_id not in init_df.index: -# if strict_mapping: -# raise Exception('COULD NOT FIND APPROPIATE ID IN CSV') -# else: -# # TODO: warning message -# pass -# else: -# ssn_node.initial_value = init_df.loc[ssn_node.node_id][value_col] - -# def from_pregenerated(self, sim, itype): -# node_set = sim.network.get_node_set(self._params.get('node_set', 'all')) -# nsize = len(node_set) - -# if itype == 'const': -# init_states = [self._params['initial_state']]*nsize - -# if itype == 'list': -# init_states = self._params['initial_states'] -# if len(init_states) != nsize: -# raise Exception('SIZE OF LIST DOES NOT MATCH NUMBER OF NODES') - -# if self._params.get('shuffle', False): -# np.random.shuffle(init_states) - -# elif itype == 'random': -# dist = self._params['distribution'] -# if dist == 'uniform': -# init_states = np.random.normal( -# low=self._params.get('low', 0.0), -# high=self._params.get('high', 1.0), -# size=nsize -# ) -# elif dist == 'normal': -# init_states = np.random.normal( -# loc=self._params.get('mean', 0.0), -# scale=self._params.get('std', 1.0), -# size=nsize -# ) -# elif dist == 'poisson': -# init_states = np.random.poisson( -# lam=self._params.get('lambda', 1.0), -# size=nsize -# ) -# elif dist == 'lognormal': -# init_states = np.random.lognormal( -# mean=self._params.get('mean', 0.0), -# sigma=self._params.get('sigma', 1.0), -# size=nsize -# ) -# else: -# raise Exception("AAAA") - -# for idx, node in enumerate(node_set.fetch_nodes()): -# ssn_node = sim.network.get_node(node.population_name, node.node_id) -# ssn_node.initial_value = init_states[idx] - -# def finalize(self, sim): -# pass - - - -# @njit -# def relu2(array): -# # if the element is negative, set it to zero using loop -# # destructive method (alters the original array), but faster than the above one. -# for i in range(len(array)): -# if array[i] < 0: -# array[i] = 0 -# return array - - -# class Config(SimulationConfig): -# pass - - -# class SSNNode: -# def __init__(self, population, node_id, gid, **node_properties): -# self.population = population -# self.node_id = node_id -# self.gid = gid - -# self.type = None -# self.input_offset = [] -# self.scaling_coef = [] -# self.exponent = [] -# self.decay_const = [] -# self.init_value = 0.0 -# self.external_inputs = None -# self.node_properties = node_properties -# self._sonata_node = self.node_properties.get('node', {}) - -# def __contains__(self, property): -# return property in self.node_properties or property in self._sonata_node - -# def __getitem__(self, property): -# if property in self.node_properties: -# return self.node_properties[property] -# elif property in self._sonata_node: -# return self._sonata_node[property] -# else: -# raise KeyError(f'SSNNode does not contain property "{property}"') - -# def get(self, property, default=None): -# if property in self: -# return self[property] -# else: -# return default - -# def __repr__(self) -> str: -# return f'SSNNode {self.gid} > ({self.population}.{self.node_id})' - - -# class SSNNetwork(SimNetwork): -# def __init__(self, grouping_key='node_id', **opts): -# super(SSNNetwork, self).__init__() -# # self.n_neu_total = 6 -# self.grouping_key = grouping_key - -# # self._pop_index = {} - -# self._node_id_map = {} - -# self._nnodes_recurrent = 0 -# self._nnodes_external = 0 - -# self._connectivity_mat = None -# self._scales = None -# self._exponents = None -# self._decay_constants = None -# self._initial_states = None - -# self._nodeid2grp = {} - -# self._recurrent_nodes = {} -# self._external_nodes = {} - - -# self._nodes_idx = {} -# self._ssn_recurrent_nodes = set() -# self._ssn_external_nodes = set() -# self.gids = 0 - -# self._conn_mat = [] -# # sefl._connectivity - - - -# @property -# def n_neu_recurrent(self): -# return self._nnodes_recurrent - -# @property -# def n_neu_total(self): -# return self._nnodes_recurrent + self._nnodes_external - -# @property -# def connectivity_mat(self): -# if self._connectivity_mat is None: -# self._connectivity_mat = np.zeros((self._nnodes_recurrent, self.n_neu_total), dtype=float) -# for r, c, syn_w in self._conn_mat: -# # print(r, c, syn_w) -# self._connectivity_mat[r, c] = syn_w - -# # print(self._connectivity_mat) - -# return self._connectivity_mat - - -# @property -# def scales(self): -# if self._scales is None: -# self._scales = np.zeros(self._nnodes_recurrent) -# for n in self._ssn_recurrent_nodes: -# self._scales[n.gid] = np.mean(n.scaling_coef) - -# return self._scales - -# @property -# def initial_states(self): -# if self._initial_states is None: -# self._initial_states = np.zeros(self._nnodes_recurrent) -# for n in self._ssn_recurrent_nodes: -# self._initial_states[n.gid] = np.mean(n.initial_value) - -# return self._initial_states - -# @property -# def exponents(self): -# if self._exponents is None: -# self._exponents = np.zeros(self._nnodes_recurrent) -# for n in self._ssn_recurrent_nodes: -# self._exponents[n.gid] = np.mean(n.exponent) - -# return self._exponents - -# @property -# def decay_constants(self): -# if self._decay_constants is None: -# self._decay_constants = np.zeros(self._nnodes_recurrent) -# for n in self._ssn_recurrent_nodes: -# self._decay_constants[n.gid] = np.mean(n.decay_const) - -# # print(self._decay_constants) - -# return self._decay_constants - -# def build_nodes(self): -# for node_pop in self.node_populations: -# for node in node_pop.get_nodes(): -# model_type = node['model_type'].lower() - -# if model_type in ['population', 'rate_population', 'recurrent']: -# self.add_recurrent_node( -# population_id=node_pop.name, -# node_id=node['node_id'], -# input_offset=node['input_offset'], -# scaling_coef=node['scaling_coef'], -# exponent=node['exponent'], -# decay_const=node['decay_const'], -# initial_value=node.get['initial_value'] if 'initial_value' in node else 0.0, -# node=node -# ) - -# elif model_type in ['external', 'virtual']: -# self.add_external_node( -# population_id=node_pop.name, -# node_id=node[self.grouping_key], -# node=node -# ) - - -# def build_edges(self): -# for edge_pop in self._edge_populations: -# for edge in edge_pop.get_edges(): -# # print(edge.source_population, edge.source_node_id, edge.target_population, edge.target_node_id, edge['syn_weight']) -# # print(edge.source_population in self._node_id_map) -# # print(list(self._node_id_map[edge.source_population].keys())) -# # print(edge.source_node_id in self._node_id_map[edge.source_population]) -# # print(self.get_ssn_node(edge.source_population, edge.source_node_id).gid) -# src_node = self._node_id_map[edge.source_population][int(edge.source_node_id)] -# trg_node = self._node_id_map[edge.target_population][int(edge.target_node_id)] - -# self._conn_mat.append([trg_node.gid, src_node.gid, edge['syn_weight']]) - - -# def get_node(self, population_id, node_id): -# return self._node_id_map[population_id][node_id] - - -# def get_ssn_node(self, population_id, node_id, **node_properties): -# if population_id not in self._node_id_map: -# # print('new population') -# ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) -# self._node_id_map[population_id] = {int(node_id): ssn_node} -# self.gids += 1 - -# elif int(node_id) not in self._node_id_map: -# # print(f'new node {population_id}, {node_id}') -# ssn_node = SSNNode(population_id, node_id, gid=self.gids, **node_properties) -# self._node_id_map[population_id][int(node_id)] = ssn_node -# self.gids += 1 - -# else: -# # print('found') -# ssn_node = self._node_id_map[population_id][node_id] - -# return ssn_node - - -# def add_recurrent_node(self, population_id, node_id, input_offset, scaling_coef, exponent, decay_const, initial_value=0.0, **node_properties): -# self._nnodes_recurrent += 1 - -# ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) -# ssn_obj.type='internal' -# ssn_obj.input_offset.append(input_offset) -# ssn_obj.scaling_coef.append(scaling_coef) -# ssn_obj.exponent.append(exponent) -# ssn_obj.decay_const.append(decay_const) -# self._ssn_recurrent_nodes.add(ssn_obj) - - -# def add_external_node(self, population_id, node_id, **node_properties): -# self._nnodes_external += 1 -# ssn_obj = self.get_ssn_node(population_id=population_id, node_id=node_id, **node_properties) -# ssn_obj.type = 'external' -# self._ssn_external_nodes.add(ssn_obj) - - -# class SSNSimulator: -# def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): -# self.dt = dt -# self.tstart = tstart -# self.tstop = tstop -# self.network = network - -# self._fr_results = None - -# self._mods = [] - -# @property -# def nsteps(self): -# return int((self.tstop - self.tstart)/self.dt) - -# @property -# def results(self): -# if self._fr_results is None: -# self._fr_results = np.zeros((self.nsteps, self.network.n_neu_total), dtype=float) -# # print(self.network.initial_states) -# self._fr_results[0, :self.network.n_neu_recurrent] = self.network.initial_states - -# for ext_node in self.network._ssn_external_nodes: -# if ext_node.external_inputs is not None: -# self._fr_results[:, ext_node.gid] = ext_node.external_inputs - -# return self._fr_results - - -# def step(self, state, mat, scales, exponents, decay_constants, dt): -# input = relu2(np.dot(mat, state) * scales) ** exponents -# n_neu_recurrent = mat.shape[0] -# recurrent_state = state[:n_neu_recurrent] -# dr = (-recurrent_state + input) / decay_constants * dt -# return relu2(recurrent_state + dr) - -# def add_mod(self, mod): -# mod.initialize(self) -# self._mods.append(mod) - -# def run(self): -# for t in range(self.nsteps-1): -# self.results[t+1, :self.network.n_neu_recurrent] = self.step( -# self.results[t, :], -# self.network.connectivity_mat, -# self.network.scales, -# self.network.exponents, -# self.network.decay_constants, -# self.dt, -# ) - -# for mod in self._mods: -# mod.finalize(self) - - -# # print(self.results) -# # print(self.results.shape) - -# # fig, ax = plt.subplots(2, 1) -# # ax[0].plot(self.results[:, :]) -# # # ax[0].legend(nodes_recurrent["cell_types"].values) -# # ax[0].title.set_text("Entire simulation") -# # plt.show() - -# @classmethod -# def from_config(cls, configure, network, **opts): -# # load the json file or object -# if isinstance(configure, string_types): -# config = Config.from_json(configure, validate=True) -# elif isinstance(configure, dict): -# config = configure -# else: -# raise Exception('Could not convert {} (type "{}") to json.'.format(configure, type(configure))) - -# sim = cls(network, dt=config.dt, tstart=config.tstart, tstop=config.tstop, **opts) - -# # if 'output_dir' in config['output']: -# # network.output_dir = config['output']['output_dir'] - -# network.io.log_info('Building nodes.') -# network.build_nodes() - -# network.io.log_info('Building recurrent connections') -# network.build_edges() - -# for sim_input in inputs.from_config(config): -# if sim_input.input_type == 'init_states': -# mod = InitStatesMod( -# name=sim_input.name, -# input_type=sim_input.input_type, -# module=sim_input.module, -# **sim_input.params -# ) -# sim.add_mod(mod) - -# if sim_input.input_type == 'external_rates': -# mod = ExternalRatesMod( -# name=sim_input.name, -# input_type=sim_input.input_type, -# module=sim_input.module, -# **sim_input.params -# ) -# sim.add_mod(mod) - - -# if 'rates_file' in config.output: -# mod = RatesRecorderMod( -# name='RecordRatesH5', -# module='rates', -# output_type='csv', -# file_name=config.output['rates_file'], -# **config.output -# ) -# sim.add_mod(mod) - -# if 'rates_file_csv' in config.output: -# mod = RatesRecorderMod( -# name='RecordRatesH5', -# module='rates', -# output_type='csv', -# file_name=config.output['rates_file_csv'], -# **config.output -# ) -# sim.add_mod(mod) - -# if 'rates_file_h5' in config.output: -# mod = RatesRecorderMod( -# name='RecordRatesH5', -# module='rates', -# output_type='h5', -# file_name=config.output['rates_file_h5'], -# **config.output -# ) -# sim.add_mod(mod) - -# return sim @popnet.inputs_generator @@ -814,6 +11,7 @@ def load_bkg_inputs(node, sim, **opts): # print(sim.dt, sim.tstart, sim.tstop, sim.nsteps) return np.ones(sim.nsteps) + @popnet.init_function def set_init_states(node, sim, **opts): if node['pop_name'] == 'Exc': @@ -837,7 +35,6 @@ def run(configuration_path): if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('config', nargs='?', default='config.simulation.json', help='SONATA configuration json file.') - args = parser.parse_args() run(args.config) From 1d30d9cfe225c502bec5e4c68d39055706d216ba Mon Sep 17 00:00:00 2001 From: kaeldai Date: Fri, 25 Apr 2025 09:41:04 -0700 Subject: [PATCH 08/19] updating example directory --- examples/{ssn_l23 => pop_ssn_123}/PopNet_SSN.ipynb | 0 .../{ssn_l23 => pop_ssn_123}/build_network.orig.py | 0 examples/{ssn_l23 => pop_ssn_123}/build_network.py | 0 .../{ssn_l23 => pop_ssn_123}/config.simulation.json | 0 examples/{ssn_l23 => pop_ssn_123}/demo_params.csv | 0 .../{ssn_l23 => pop_ssn_123}/input/bkg_rates.csv | 0 .../{ssn_l23 => pop_ssn_123}/input/bkg_rates.h5 | Bin .../{ssn_l23 => pop_ssn_123}/input/bkg_rates.npy | Bin .../input/initial_state.csv | 0 .../input/initial_state.npy | Bin .../{ssn_l23 => pop_ssn_123}/input/l4e_rates.csv | 0 .../{ssn_l23 => pop_ssn_123}/input/l4e_rates.h5 | Bin .../{ssn_l23 => pop_ssn_123}/input/l4e_rates.npy | Bin examples/{ssn_l23 => pop_ssn_123}/model_data.py | 0 .../network.orig/bkg_l23_edge_types.csv | 0 .../network.orig/bkg_l23_edges.h5 | Bin .../network.orig/bkg_node_types.csv | 0 .../network.orig/bkg_nodes.h5 | Bin .../network.orig/l23_l23_edge_types.csv | 0 .../network.orig/l23_l23_edges.h5 | Bin .../network.orig/l23_node_types.csv | 0 .../network.orig/l23_nodes.h5 | Bin .../network.orig/l4e_l23_edge_types.csv | 0 .../network.orig/l4e_l23_edges.h5 | Bin .../network.orig/l4e_node_types.csv | 0 .../network.orig/l4e_nodes.h5 | Bin .../network/bkg_l23_edge_types.csv | 0 .../network/bkg_l23_edges.h5 | Bin .../network/bkg_node_types.csv | 0 .../{ssn_l23 => pop_ssn_123}/network/bkg_nodes.h5 | Bin .../network/l23_l23_edge_types.csv | 0 .../network/l23_l23_edges.h5 | Bin .../network/l23_node_types.csv | 0 .../{ssn_l23 => pop_ssn_123}/network/l23_nodes.h5 | Bin .../network/l4e_l23_edge_types.csv | 0 .../network/l4e_l23_edges.h5 | Bin .../network/l4e_node_types.csv | 0 .../{ssn_l23 => pop_ssn_123}/network/l4e_nodes.h5 | Bin .../run_ssn.py => pop_ssn_123/run_popnet.py} | 0 examples/{ssn_l23 => pop_ssn_123}/run_ssn.orig.py | 0 40 files changed, 0 insertions(+), 0 deletions(-) rename examples/{ssn_l23 => pop_ssn_123}/PopNet_SSN.ipynb (100%) rename examples/{ssn_l23 => pop_ssn_123}/build_network.orig.py (100%) rename examples/{ssn_l23 => pop_ssn_123}/build_network.py (100%) rename examples/{ssn_l23 => pop_ssn_123}/config.simulation.json (100%) rename examples/{ssn_l23 => pop_ssn_123}/demo_params.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/input/bkg_rates.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/input/bkg_rates.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/input/bkg_rates.npy (100%) rename examples/{ssn_l23 => pop_ssn_123}/input/initial_state.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/input/initial_state.npy (100%) rename examples/{ssn_l23 => pop_ssn_123}/input/l4e_rates.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/input/l4e_rates.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/input/l4e_rates.npy (100%) rename examples/{ssn_l23 => pop_ssn_123}/model_data.py (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/bkg_l23_edge_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/bkg_l23_edges.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/bkg_node_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/bkg_nodes.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/l23_l23_edge_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/l23_l23_edges.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/l23_node_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/l23_nodes.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/l4e_l23_edge_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/l4e_l23_edges.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/l4e_node_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network.orig/l4e_nodes.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/bkg_l23_edge_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/bkg_l23_edges.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/bkg_node_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/bkg_nodes.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/l23_l23_edge_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/l23_l23_edges.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/l23_node_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/l23_nodes.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/l4e_l23_edge_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/l4e_l23_edges.h5 (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/l4e_node_types.csv (100%) rename examples/{ssn_l23 => pop_ssn_123}/network/l4e_nodes.h5 (100%) rename examples/{ssn_l23/run_ssn.py => pop_ssn_123/run_popnet.py} (100%) rename examples/{ssn_l23 => pop_ssn_123}/run_ssn.orig.py (100%) diff --git a/examples/ssn_l23/PopNet_SSN.ipynb b/examples/pop_ssn_123/PopNet_SSN.ipynb similarity index 100% rename from examples/ssn_l23/PopNet_SSN.ipynb rename to examples/pop_ssn_123/PopNet_SSN.ipynb diff --git a/examples/ssn_l23/build_network.orig.py b/examples/pop_ssn_123/build_network.orig.py similarity index 100% rename from examples/ssn_l23/build_network.orig.py rename to examples/pop_ssn_123/build_network.orig.py diff --git a/examples/ssn_l23/build_network.py b/examples/pop_ssn_123/build_network.py similarity index 100% rename from examples/ssn_l23/build_network.py rename to examples/pop_ssn_123/build_network.py diff --git a/examples/ssn_l23/config.simulation.json b/examples/pop_ssn_123/config.simulation.json similarity index 100% rename from examples/ssn_l23/config.simulation.json rename to examples/pop_ssn_123/config.simulation.json diff --git a/examples/ssn_l23/demo_params.csv b/examples/pop_ssn_123/demo_params.csv similarity index 100% rename from examples/ssn_l23/demo_params.csv rename to examples/pop_ssn_123/demo_params.csv diff --git a/examples/ssn_l23/input/bkg_rates.csv b/examples/pop_ssn_123/input/bkg_rates.csv similarity index 100% rename from examples/ssn_l23/input/bkg_rates.csv rename to examples/pop_ssn_123/input/bkg_rates.csv diff --git a/examples/ssn_l23/input/bkg_rates.h5 b/examples/pop_ssn_123/input/bkg_rates.h5 similarity index 100% rename from examples/ssn_l23/input/bkg_rates.h5 rename to examples/pop_ssn_123/input/bkg_rates.h5 diff --git a/examples/ssn_l23/input/bkg_rates.npy b/examples/pop_ssn_123/input/bkg_rates.npy similarity index 100% rename from examples/ssn_l23/input/bkg_rates.npy rename to examples/pop_ssn_123/input/bkg_rates.npy diff --git a/examples/ssn_l23/input/initial_state.csv b/examples/pop_ssn_123/input/initial_state.csv similarity index 100% rename from examples/ssn_l23/input/initial_state.csv rename to examples/pop_ssn_123/input/initial_state.csv diff --git a/examples/ssn_l23/input/initial_state.npy b/examples/pop_ssn_123/input/initial_state.npy similarity index 100% rename from examples/ssn_l23/input/initial_state.npy rename to examples/pop_ssn_123/input/initial_state.npy diff --git a/examples/ssn_l23/input/l4e_rates.csv b/examples/pop_ssn_123/input/l4e_rates.csv similarity index 100% rename from examples/ssn_l23/input/l4e_rates.csv rename to examples/pop_ssn_123/input/l4e_rates.csv diff --git a/examples/ssn_l23/input/l4e_rates.h5 b/examples/pop_ssn_123/input/l4e_rates.h5 similarity index 100% rename from examples/ssn_l23/input/l4e_rates.h5 rename to examples/pop_ssn_123/input/l4e_rates.h5 diff --git a/examples/ssn_l23/input/l4e_rates.npy b/examples/pop_ssn_123/input/l4e_rates.npy similarity index 100% rename from examples/ssn_l23/input/l4e_rates.npy rename to examples/pop_ssn_123/input/l4e_rates.npy diff --git a/examples/ssn_l23/model_data.py b/examples/pop_ssn_123/model_data.py similarity index 100% rename from examples/ssn_l23/model_data.py rename to examples/pop_ssn_123/model_data.py diff --git a/examples/ssn_l23/network.orig/bkg_l23_edge_types.csv b/examples/pop_ssn_123/network.orig/bkg_l23_edge_types.csv similarity index 100% rename from examples/ssn_l23/network.orig/bkg_l23_edge_types.csv rename to examples/pop_ssn_123/network.orig/bkg_l23_edge_types.csv diff --git a/examples/ssn_l23/network.orig/bkg_l23_edges.h5 b/examples/pop_ssn_123/network.orig/bkg_l23_edges.h5 similarity index 100% rename from examples/ssn_l23/network.orig/bkg_l23_edges.h5 rename to examples/pop_ssn_123/network.orig/bkg_l23_edges.h5 diff --git a/examples/ssn_l23/network.orig/bkg_node_types.csv b/examples/pop_ssn_123/network.orig/bkg_node_types.csv similarity index 100% rename from examples/ssn_l23/network.orig/bkg_node_types.csv rename to examples/pop_ssn_123/network.orig/bkg_node_types.csv diff --git a/examples/ssn_l23/network.orig/bkg_nodes.h5 b/examples/pop_ssn_123/network.orig/bkg_nodes.h5 similarity index 100% rename from examples/ssn_l23/network.orig/bkg_nodes.h5 rename to examples/pop_ssn_123/network.orig/bkg_nodes.h5 diff --git a/examples/ssn_l23/network.orig/l23_l23_edge_types.csv b/examples/pop_ssn_123/network.orig/l23_l23_edge_types.csv similarity index 100% rename from examples/ssn_l23/network.orig/l23_l23_edge_types.csv rename to examples/pop_ssn_123/network.orig/l23_l23_edge_types.csv diff --git a/examples/ssn_l23/network.orig/l23_l23_edges.h5 b/examples/pop_ssn_123/network.orig/l23_l23_edges.h5 similarity index 100% rename from examples/ssn_l23/network.orig/l23_l23_edges.h5 rename to examples/pop_ssn_123/network.orig/l23_l23_edges.h5 diff --git a/examples/ssn_l23/network.orig/l23_node_types.csv b/examples/pop_ssn_123/network.orig/l23_node_types.csv similarity index 100% rename from examples/ssn_l23/network.orig/l23_node_types.csv rename to examples/pop_ssn_123/network.orig/l23_node_types.csv diff --git a/examples/ssn_l23/network.orig/l23_nodes.h5 b/examples/pop_ssn_123/network.orig/l23_nodes.h5 similarity index 100% rename from examples/ssn_l23/network.orig/l23_nodes.h5 rename to examples/pop_ssn_123/network.orig/l23_nodes.h5 diff --git a/examples/ssn_l23/network.orig/l4e_l23_edge_types.csv b/examples/pop_ssn_123/network.orig/l4e_l23_edge_types.csv similarity index 100% rename from examples/ssn_l23/network.orig/l4e_l23_edge_types.csv rename to examples/pop_ssn_123/network.orig/l4e_l23_edge_types.csv diff --git a/examples/ssn_l23/network.orig/l4e_l23_edges.h5 b/examples/pop_ssn_123/network.orig/l4e_l23_edges.h5 similarity index 100% rename from examples/ssn_l23/network.orig/l4e_l23_edges.h5 rename to examples/pop_ssn_123/network.orig/l4e_l23_edges.h5 diff --git a/examples/ssn_l23/network.orig/l4e_node_types.csv b/examples/pop_ssn_123/network.orig/l4e_node_types.csv similarity index 100% rename from examples/ssn_l23/network.orig/l4e_node_types.csv rename to examples/pop_ssn_123/network.orig/l4e_node_types.csv diff --git a/examples/ssn_l23/network.orig/l4e_nodes.h5 b/examples/pop_ssn_123/network.orig/l4e_nodes.h5 similarity index 100% rename from examples/ssn_l23/network.orig/l4e_nodes.h5 rename to examples/pop_ssn_123/network.orig/l4e_nodes.h5 diff --git a/examples/ssn_l23/network/bkg_l23_edge_types.csv b/examples/pop_ssn_123/network/bkg_l23_edge_types.csv similarity index 100% rename from examples/ssn_l23/network/bkg_l23_edge_types.csv rename to examples/pop_ssn_123/network/bkg_l23_edge_types.csv diff --git a/examples/ssn_l23/network/bkg_l23_edges.h5 b/examples/pop_ssn_123/network/bkg_l23_edges.h5 similarity index 100% rename from examples/ssn_l23/network/bkg_l23_edges.h5 rename to examples/pop_ssn_123/network/bkg_l23_edges.h5 diff --git a/examples/ssn_l23/network/bkg_node_types.csv b/examples/pop_ssn_123/network/bkg_node_types.csv similarity index 100% rename from examples/ssn_l23/network/bkg_node_types.csv rename to examples/pop_ssn_123/network/bkg_node_types.csv diff --git a/examples/ssn_l23/network/bkg_nodes.h5 b/examples/pop_ssn_123/network/bkg_nodes.h5 similarity index 100% rename from examples/ssn_l23/network/bkg_nodes.h5 rename to examples/pop_ssn_123/network/bkg_nodes.h5 diff --git a/examples/ssn_l23/network/l23_l23_edge_types.csv b/examples/pop_ssn_123/network/l23_l23_edge_types.csv similarity index 100% rename from examples/ssn_l23/network/l23_l23_edge_types.csv rename to examples/pop_ssn_123/network/l23_l23_edge_types.csv diff --git a/examples/ssn_l23/network/l23_l23_edges.h5 b/examples/pop_ssn_123/network/l23_l23_edges.h5 similarity index 100% rename from examples/ssn_l23/network/l23_l23_edges.h5 rename to examples/pop_ssn_123/network/l23_l23_edges.h5 diff --git a/examples/ssn_l23/network/l23_node_types.csv b/examples/pop_ssn_123/network/l23_node_types.csv similarity index 100% rename from examples/ssn_l23/network/l23_node_types.csv rename to examples/pop_ssn_123/network/l23_node_types.csv diff --git a/examples/ssn_l23/network/l23_nodes.h5 b/examples/pop_ssn_123/network/l23_nodes.h5 similarity index 100% rename from examples/ssn_l23/network/l23_nodes.h5 rename to examples/pop_ssn_123/network/l23_nodes.h5 diff --git a/examples/ssn_l23/network/l4e_l23_edge_types.csv b/examples/pop_ssn_123/network/l4e_l23_edge_types.csv similarity index 100% rename from examples/ssn_l23/network/l4e_l23_edge_types.csv rename to examples/pop_ssn_123/network/l4e_l23_edge_types.csv diff --git a/examples/ssn_l23/network/l4e_l23_edges.h5 b/examples/pop_ssn_123/network/l4e_l23_edges.h5 similarity index 100% rename from examples/ssn_l23/network/l4e_l23_edges.h5 rename to examples/pop_ssn_123/network/l4e_l23_edges.h5 diff --git a/examples/ssn_l23/network/l4e_node_types.csv b/examples/pop_ssn_123/network/l4e_node_types.csv similarity index 100% rename from examples/ssn_l23/network/l4e_node_types.csv rename to examples/pop_ssn_123/network/l4e_node_types.csv diff --git a/examples/ssn_l23/network/l4e_nodes.h5 b/examples/pop_ssn_123/network/l4e_nodes.h5 similarity index 100% rename from examples/ssn_l23/network/l4e_nodes.h5 rename to examples/pop_ssn_123/network/l4e_nodes.h5 diff --git a/examples/ssn_l23/run_ssn.py b/examples/pop_ssn_123/run_popnet.py similarity index 100% rename from examples/ssn_l23/run_ssn.py rename to examples/pop_ssn_123/run_popnet.py diff --git a/examples/ssn_l23/run_ssn.orig.py b/examples/pop_ssn_123/run_ssn.orig.py similarity index 100% rename from examples/ssn_l23/run_ssn.orig.py rename to examples/pop_ssn_123/run_ssn.orig.py From 6692a62b70319f1ea15eb92d44d7da718dac806a Mon Sep 17 00:00:00 2001 From: kaeldai Date: Fri, 25 Apr 2025 11:11:20 -0700 Subject: [PATCH 09/19] fixing documentation --- examples/pop_ssn_123/PopNet_SSN.ipynb | 28 ++++++++++++--------------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/examples/pop_ssn_123/PopNet_SSN.ipynb b/examples/pop_ssn_123/PopNet_SSN.ipynb index d653787d..9cb41d15 100644 --- a/examples/pop_ssn_123/PopNet_SSN.ipynb +++ b/examples/pop_ssn_123/PopNet_SSN.ipynb @@ -275,9 +275,7 @@ { "cell_type": "markdown", "id": "b89f9f14-8c3a-4783-a9a2-ddfa9a42315f", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "### \"run\" and \"target_simulator\"" ] @@ -344,9 +342,7 @@ { "cell_type": "markdown", "id": "5adf877c-e9df-47d3-b24e-7f58718b53c9", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "#### initial states" ] @@ -502,9 +498,7 @@ { "cell_type": "markdown", "id": "56123954-ff29-4e0b-9061-e58b0931a1bd", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "#### external inputs" ] @@ -548,27 +542,29 @@ " \n", " npy\n", " Read in array containing firing rate dynamics to assign to external input\n", - " file: initial firing rate value (float)\n", + " file: path to file containing array of firing-rate dynamics to use during simulation\n", " \n", " \n", " csv\n", " Read in a csv file containing firing rate dynamics to assign to external input (See below for rates format)\n", - " distribution: name of distribution for sampling (\"uniform\", \"normal\", \"poisson\", \"lognormal\")
\n", - " low, high
\n", - " mean, std
\n", + " file: path to file containing array of firing-rate dynamics to use during simulation
\n", + " sep: column separator to use (default: \" \")
\n", + " index_col: column to use to identify and map nodes (default: \"node_id\")
\n", + " firing_rates_col: column to use to get firing rates array (default: \"node_id\")
\n", + " ignore_population: do not use population (network name) for mapping nodes from network to csv (default: false)\n", " \n", " \n", " \n", " h5\n", " Read in a hdf5 file containing firing rate dynamics to assign to external input (See below for rates format)\n", - " initial_states: A list of initial firing rates (list) or path to npy file (string)
\n", - " shuffle: shuffle list before assigning initial values (default: false)\n", + " file: path to file containing array of firing-rate dynamics to use during simulation
\n", + " ignore_population: do not use population (network name) for mapping nodes from network to csv (default: false)\n", " \n", " \n", " \n", " fuction\n", " Have popnet call a user-defined function for each node that will return an array of firing rates\n", - " init_function: name of function to call before simulation starts
\n", + " inputs_generator: name of function to call before simulation starts
\n", " \n", "\n", "\n", From 42bd88a751b91d8e3f175ca23e757f8006ef054e Mon Sep 17 00:00:00 2001 From: kaeldai Date: Sun, 27 Apr 2025 10:37:49 -0700 Subject: [PATCH 10/19] Adding ability to replace activation function --- bmtk/simulator/popnet/__init__.py | 2 +- bmtk/simulator/popnet/ssn/__init__.py | 2 +- bmtk/simulator/popnet/ssn/popsimulator.py | 12 +++++ examples/pop_ssn_123/PopNet_SSN.ipynb | 65 ++++++++++++++++++----- examples/pop_ssn_123/run_popnet.py | 6 +++ 5 files changed, 71 insertions(+), 16 deletions(-) diff --git a/bmtk/simulator/popnet/__init__.py b/bmtk/simulator/popnet/__init__.py index a47049a0..428c0b57 100644 --- a/bmtk/simulator/popnet/__init__.py +++ b/bmtk/simulator/popnet/__init__.py @@ -25,7 +25,7 @@ from bmtk.simulator.core.simulation_config import SimulationConfig from bmtk.simulator.core.io_tools import io from .config import Config -from .ssn import inputs_generator, init_function +from .ssn import inputs_generator, init_function, activation_function class PopNetwork: diff --git a/bmtk/simulator/popnet/ssn/__init__.py b/bmtk/simulator/popnet/ssn/__init__.py index 2c10b35a..9e7f7646 100644 --- a/bmtk/simulator/popnet/ssn/__init__.py +++ b/bmtk/simulator/popnet/ssn/__init__.py @@ -22,7 +22,7 @@ # from .popnetwork import PopNetwork from .popsimulator import PopSimulator -from .pyfunction_cache import inputs_generator, init_function +from .pyfunction_cache import inputs_generator, init_function, activation_function, add_activation_function from bmtk.simulator.core.simulation_config import SimulationConfig as Config # from .config import Config diff --git a/bmtk/simulator/popnet/ssn/popsimulator.py b/bmtk/simulator/popnet/ssn/popsimulator.py index 9a9d1e24..fb47f569 100644 --- a/bmtk/simulator/popnet/ssn/popsimulator.py +++ b/bmtk/simulator/popnet/ssn/popsimulator.py @@ -66,6 +66,9 @@ def run(self): for mod in self._mods: mod.finalize(self) + def set_activation_function(self, fnc): + self.activation_function = fnc + @classmethod def from_config(cls, configure, network, **opts): # load the json file or object @@ -78,6 +81,15 @@ def from_config(cls, configure, network, **opts): sim = cls(network, dt=config.dt, tstart=config.tstart, tstop=config.tstop, **opts) + act_fnc_sig = config.run.get('activation_function', None) + if act_fnc_sig: + if act_fnc_sig not in py_modules.activation_functions: + io.log_error(f'Could not find activation fucntion with signature {act_fnc_sig} registered to PopNet') + else: + io.log_debug(f'Setting simulation activation function to {act_fnc_sig}') + sim.set_activation_function(py_modules.activation_function(act_fnc_sig)) + + network.io.log_info('Building nodes.') network.build_nodes() diff --git a/examples/pop_ssn_123/PopNet_SSN.ipynb b/examples/pop_ssn_123/PopNet_SSN.ipynb index 9cb41d15..c4f5ba31 100644 --- a/examples/pop_ssn_123/PopNet_SSN.ipynb +++ b/examples/pop_ssn_123/PopNet_SSN.ipynb @@ -328,6 +328,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", "
Used to specify which attribute/column for when grouping individual cells into populationsnode_id
activation_functionstringname of function to call at each step (see below).relu
" ] }, @@ -715,14 +721,46 @@ "jp-MarkdownHeadingCollapsed": true }, "source": [ - "### [OPTIONAL] activation_function" + "### [OPTIONAL] \"run/activation_function\"" ] }, { "cell_type": "markdown", "id": "bc59da9a-f7f7-41a8-b7bc-070ed0cd183e", "metadata": {}, - "source": [] + "source": [ + "By default the SSN use a regular [relu function](https://en.wikipedia.org/wiki/Relu) at the each time step, however it is possible to overwrite it with a function of your own. We can replace it with our own function by first creating a function that takes an array of size $N$ (number of recurrent nodes), does some processing, then returns an array of size $N$. To make sure that PopNet can see the function we need to register it with the ```@activation_function``` decorator and make sure it's somewhere in our run script.\n", + "\n", + "For example we could have\n", + "\n", + "```python\n", + "from bmtk.simulator import popnet\n", + "\n", + "\n", + "@popnet.inputs_generator\n", + "def custom_activation_fn(state_arr):\n", + " return np.tanh(state_arr)\n", + "\n", + "\n", + "configure = popnet.Config.from_json('config.simulation.json')\n", + "configure.build_env()\n", + "//... rest of run script\n", + "```\n", + "\n", + "Then in the **\"run\"** section of the configuration json\n", + "\n", + "```json\n", + "{\n", + " \"run\" {\n", + " \"tstop\": 13500.0,\n", + " \"dt\": 1.0,\n", + " \"activation_function\": \"custon_activation_fn\"\n", + " }\n", + "}\n", + "```\n", + "\n", + "And next time we run the popnet script like usual it replace the normal relu function with out custom one.\n" + ] }, { "cell_type": "markdown", @@ -748,7 +786,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "c4b3bc43-114c-4498-a48a-c0c26588aea2", "metadata": {}, "outputs": [ @@ -756,23 +794,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-04-24 14:49:22,022 [INFO] Created log file\n", - "2025-04-24 14:49:22,081 [INFO] Building nodes.\n", - "2025-04-24 14:49:22,083 [INFO] Building connections.\n", - "2025-04-24 14:49:22,086 [INFO] Add input module \"init_states\"\n", - "2025-04-24 14:49:22,089 [INFO] Add input module \"l4_inputs\"\n", - "2025-04-24 14:49:22,091 [INFO] Add input module \"bkg_inputs\"\n", - "2025-04-24 14:49:22,092 [INFO] Running Simulation.\n", - "2025-04-24 14:49:23,142 [INFO] Simulation Finished.\n", - "2025-04-24 14:49:23,150 [INFO] RatesRecorderMod: Saving rates to /local1/workspace/bmtk/examples/ssn_l23/output/l23_rates.csv.\n", - "2025-04-24 14:49:23,325 [INFO] RatesRecorderMod: Saving rates to /local1/workspace/bmtk/examples/ssn_l23/output/l23_rates.h5.\n" + "2025-04-27 10:31:43,728 [INFO] Created log file\n", + "2025-04-27 10:31:43,781 [INFO] Building nodes.\n", + "2025-04-27 10:31:43,783 [INFO] Building connections.\n", + "2025-04-27 10:31:43,786 [INFO] Add input module \"init_states\"\n", + "2025-04-27 10:31:43,789 [INFO] Add input module \"l4_inputs\"\n", + "2025-04-27 10:31:43,792 [INFO] Add input module \"bkg_inputs\"\n", + "2025-04-27 10:31:43,793 [INFO] Running Simulation.\n", + "2025-04-27 10:31:44,741 [INFO] Simulation Finished.\n", + "2025-04-27 10:31:44,750 [INFO] RatesRecorderMod: Saving rates to /local1/workspace/bmtk/examples/pop_ssn_123/output/l23_rates.csv.\n", + "2025-04-27 10:31:44,897 [INFO] RatesRecorderMod: Saving rates to /local1/workspace/bmtk/examples/pop_ssn_123/output/l23_rates.h5.\n" ] } ], "source": [ "from bmtk.simulator import popnet\n", "\n", - "\n", "@popnet.inputs_generator\n", "def load_bkg_inputs(node, sim, **opts):\n", " # print(node.node_id, node.population)\n", diff --git a/examples/pop_ssn_123/run_popnet.py b/examples/pop_ssn_123/run_popnet.py index 33363290..c52e5d7b 100644 --- a/examples/pop_ssn_123/run_popnet.py +++ b/examples/pop_ssn_123/run_popnet.py @@ -24,12 +24,18 @@ def set_init_states(node, sim, **opts): return 5.02447877 +@popnet.activation_function +def tanh(state_arr): + return np.tanh(state_arr) + + def run(configuration_path): configure = popnet.Config.from_json(configuration_path) configure.build_env() network = popnet.PopNetwork.from_config(configure) sim = popnet.PopSimulator.from_config(configure, network) + # sim.set_activation_function(tanh) sim.run() if __name__ == '__main__': From 8e93c03d99209274587411e074e5b2b989c845cb Mon Sep 17 00:00:00 2001 From: kaeldai Date: Mon, 28 Apr 2025 09:38:19 -0700 Subject: [PATCH 11/19] fixing ssn issue with dynamics_params json --- .../core/sonata_reader/node_adaptor.py | 2 +- bmtk/simulator/popnet/ssn/popnetwork.py | 14 ++++++++------ examples/pop_ssn_123/build_network.py | 12 ++++++++---- .../components/pop_models/exc_ssn.json | 6 ++++++ .../components/pop_models/pv_ssn.json | 6 ++++++ .../components/pop_models/sst_ssn.json | 6 ++++++ .../components/pop_models/vip_ssn.json | 6 ++++++ examples/pop_ssn_123/config.simulation.json | 6 +++++- .../network/bkg_l23_edge_types.csv | 8 ++++---- examples/pop_ssn_123/network/bkg_l23_edges.h5 | Bin 43608 -> 43608 bytes examples/pop_ssn_123/network/bkg_nodes.h5 | Bin 15744 -> 15744 bytes .../network/l23_l23_edge_types.csv | 18 +++++++++--------- examples/pop_ssn_123/network/l23_l23_edges.h5 | Bin 43608 -> 43608 bytes .../pop_ssn_123/network/l23_node_types.csv | 10 +++++----- examples/pop_ssn_123/network/l23_nodes.h5 | Bin 15744 -> 15744 bytes .../network/l4e_l23_edge_types.csv | 8 ++++---- examples/pop_ssn_123/network/l4e_l23_edges.h5 | Bin 43608 -> 43608 bytes examples/pop_ssn_123/network/l4e_nodes.h5 | Bin 15744 -> 15744 bytes 18 files changed, 68 insertions(+), 34 deletions(-) create mode 100644 examples/pop_ssn_123/components/pop_models/exc_ssn.json create mode 100644 examples/pop_ssn_123/components/pop_models/pv_ssn.json create mode 100644 examples/pop_ssn_123/components/pop_models/sst_ssn.json create mode 100644 examples/pop_ssn_123/components/pop_models/vip_ssn.json diff --git a/bmtk/simulator/core/sonata_reader/node_adaptor.py b/bmtk/simulator/core/sonata_reader/node_adaptor.py index 52428e79..864ebaea 100644 --- a/bmtk/simulator/core/sonata_reader/node_adaptor.py +++ b/bmtk/simulator/core/sonata_reader/node_adaptor.py @@ -145,7 +145,7 @@ def preprocess_node_types(network, node_population): params_dir = network.get_component('point_neuron_models_dir') elif model_type == 'point_soma': params_dir = network.get_component('point_neuron_models_dir') - elif model_type == 'population': + elif model_type in ['population', 'rate_population']: params_dir = network.get_component('population_models_dir') elif model_type == 'lgnmodel' or model_type == 'virtual': params_dir = network.get_component('filter_models_dir') diff --git a/bmtk/simulator/popnet/ssn/popnetwork.py b/bmtk/simulator/popnet/ssn/popnetwork.py index af8d6d9e..80e52a75 100644 --- a/bmtk/simulator/popnet/ssn/popnetwork.py +++ b/bmtk/simulator/popnet/ssn/popnetwork.py @@ -106,14 +106,17 @@ def build_nodes(self): model_type = node['model_type'].lower() if model_type in ['population', 'rate_population', 'recurrent']: + params = node.dynamics_params if node.dynamics_params is not None else {} + ssn_attrs = ['scaling_coef', 'initial_value', 'exponent', 'decay_const'] + for attr_name in ssn_attrs: + if attr_name in node: + params[attr_name] = node[attr_name] + + params['node'] = node self.add_recurrent_node( population_id=node_pop.name, node_id=node['node_id'], - scaling_coef=node['scaling_coef'], - exponent=node['exponent'], - decay_const=node['decay_const'], - initial_value=node.get['initial_value'] if 'initial_value' in node else 0.0, - node=node + **params ) elif model_type in ['external', 'virtual']: @@ -123,7 +126,6 @@ def build_nodes(self): node=node ) - def build_edges(self): self._conn_finalized = False for edge_pop in self._edge_populations: diff --git a/examples/pop_ssn_123/build_network.py b/examples/pop_ssn_123/build_network.py index 4eee2c06..7a0c804f 100644 --- a/examples/pop_ssn_123/build_network.py +++ b/examples/pop_ssn_123/build_network.py @@ -17,7 +17,8 @@ scaling_coef=params['conn_scale'], input_offset=0.0, exponent=2.0, - decay_const=params['tau_e']*1000.0 + decay_const=params['tau_e']*1000.0, + # dynamics_params='exc_ssn.json' ) l23_net.add_nodes( pop_name='PV', @@ -26,7 +27,8 @@ scaling_coef=params['conn_scale'], input_offset=0.0, exponent=2.0, - decay_const=params['tau_p']*1000.0 + decay_const=params['tau_p']*1000.0, + # dynamics_params='pv_ssn.json' ) l23_net.add_nodes( @@ -36,7 +38,8 @@ scaling_coef=params['conn_scale'], input_offset=0.0, exponent=2.0, - decay_const=params['tau_s']*1000.0 + decay_const=params['tau_s']*1000.0, + # dynamics_params='sst_ssn.json' ) l23_net.add_nodes( @@ -46,7 +49,8 @@ scaling_coef=params['conn_scale'], input_offset=0.0, exponent=2.0, - decay_const=params['tau_v']*1000.0 + decay_const=params['tau_v']*1000.0, + # dynamics_params='vip_ssn.json' ) diff --git a/examples/pop_ssn_123/components/pop_models/exc_ssn.json b/examples/pop_ssn_123/components/pop_models/exc_ssn.json new file mode 100644 index 00000000..fa4eb17e --- /dev/null +++ b/examples/pop_ssn_123/components/pop_models/exc_ssn.json @@ -0,0 +1,6 @@ +{ + "exponent": 2.0, + "decay_const": 10.0500429106779, + "scaling_coef": 22.392148198236782, + "init_rates": 0.0 +} \ No newline at end of file diff --git a/examples/pop_ssn_123/components/pop_models/pv_ssn.json b/examples/pop_ssn_123/components/pop_models/pv_ssn.json new file mode 100644 index 00000000..38e4a72c --- /dev/null +++ b/examples/pop_ssn_123/components/pop_models/pv_ssn.json @@ -0,0 +1,6 @@ +{ + "exponent": 2.0, + "decay_const": 10.0500429106779, + "scaling_coef": 11.8257398864811, + "init_rates": 0.0 +} \ No newline at end of file diff --git a/examples/pop_ssn_123/components/pop_models/sst_ssn.json b/examples/pop_ssn_123/components/pop_models/sst_ssn.json new file mode 100644 index 00000000..dc255a48 --- /dev/null +++ b/examples/pop_ssn_123/components/pop_models/sst_ssn.json @@ -0,0 +1,6 @@ +{ + "exponent": 2.0, + "decay_const": 10.0159482249032, + "scaling_coef": 22.392148198236782, + "init_rates": 0.0 +} \ No newline at end of file diff --git a/examples/pop_ssn_123/components/pop_models/vip_ssn.json b/examples/pop_ssn_123/components/pop_models/vip_ssn.json new file mode 100644 index 00000000..2d2d2436 --- /dev/null +++ b/examples/pop_ssn_123/components/pop_models/vip_ssn.json @@ -0,0 +1,6 @@ +{ + "exponent": 2.0, + "decay_const": 49.3117045605402, + "scaling_coef": 22.392148198236782, + "init_rates": 0.0 +} \ No newline at end of file diff --git a/examples/pop_ssn_123/config.simulation.json b/examples/pop_ssn_123/config.simulation.json index ea5300ec..c5108448 100644 --- a/examples/pop_ssn_123/config.simulation.json +++ b/examples/pop_ssn_123/config.simulation.json @@ -52,7 +52,11 @@ "log_file": "log.txt", "overwrite_output_dir": true }, - + + "components": { + "population_models_dir": "$BASE_DIR/components/pop_models" + }, + "networks": { "nodes": [ { diff --git a/examples/pop_ssn_123/network/bkg_l23_edge_types.csv b/examples/pop_ssn_123/network/bkg_l23_edge_types.csv index e17af2ba..2b084186 100644 --- a/examples/pop_ssn_123/network/bkg_l23_edge_types.csv +++ b/examples/pop_ssn_123/network/bkg_l23_edge_types.csv @@ -1,5 +1,5 @@ edge_type_id target_query source_query syn_weight -100 pop_name=='Exc' * 0.00869301 -101 pop_name=='PV' * 0.00013383 -102 pop_name=='SST' * 0.00177394 -103 pop_name=='VIP' * 0.01785412 +100 pop_name=='Exc' * 0.008693005130625849 +101 pop_name=='PV' * 0.0001338259670856798 +102 pop_name=='SST' * 0.0017739395973541316 +103 pop_name=='VIP' * 0.01785412498319743 diff --git a/examples/pop_ssn_123/network/bkg_l23_edges.h5 b/examples/pop_ssn_123/network/bkg_l23_edges.h5 index d3560bdc33b3112b826f13d864ab94ac5c381b1f..c63d3a3dd4137beacbaff0c1ea98150d55475747 100644 GIT binary patch delta 101 zcmca{h3Uo>rVUa&j7*!QdBTMlnI=o~SWI3mcOA;wEe~aFKCRHs2oc=et(4CU)bLx? i-w8;0XY`8zsoOQ|d_Z~c42#Xu-QhqHFmv_%wOjy3u^-C- delta 101 zcmca{h3Uo>rVUa&j0~HldBTMl8752fSWI3mcOA;wEe~aFKCRHs2oc=et(4CU)bLx? i-w8;0XY`8zsoOQ|d_Z~c42#Xu-QhqHFmv_%wOjx~#UHl- diff --git a/examples/pop_ssn_123/network/bkg_nodes.h5 b/examples/pop_ssn_123/network/bkg_nodes.h5 index 505c3e64e52476f9141357e4028ef88f15030e5b..af8971aab1f5a694d242912d4526bd5c0d7a5460 100644 GIT binary patch delta 47 qcmZpuZm8ZM#ly(7S(>L^h>>ZsG>^sR+j8ujP^Pp7oEdGX$^!r_uM1QF delta 47 qcmZpuZm8ZM#ly(3S(>L^h>>BkG>^sR+j8ujP^Pp7oEdGX$^!r^;|ojx diff --git a/examples/pop_ssn_123/network/l23_l23_edge_types.csv b/examples/pop_ssn_123/network/l23_l23_edge_types.csv index e7c766d1..e752a70f 100644 --- a/examples/pop_ssn_123/network/l23_l23_edge_types.csv +++ b/examples/pop_ssn_123/network/l23_l23_edge_types.csv @@ -1,17 +1,17 @@ edge_type_id target_query source_query syn_weight -100 pop_name=='Exc' pop_name=='Exc' 0.03254001451444 +100 pop_name=='Exc' pop_name=='Exc' 0.03254001451444043 101 pop_name=='PV' pop_name=='Exc' 0.04043037008531871 102 pop_name=='SST' pop_name=='Exc' 0.07409100928480848 103 pop_name=='VIP' pop_name=='Exc' 0.054303329629300665 104 pop_name=='Exc' pop_name=='PV' -0.009474168446121 -105 pop_name=='PV' pop_name=='PV' -0.01234075896522014 -106 pop_name=='SST' pop_name=='PV' -0.00496813087571539 +105 pop_name=='PV' pop_name=='PV' -0.012340758965220145 +106 pop_name=='SST' pop_name=='PV' -0.004968130875715394 107 pop_name=='VIP' pop_name=='PV' 0.0 -108 pop_name=='Exc' pop_name=='SST' -0.001201518371451 -109 pop_name=='PV' pop_name=='SST' -0.00271476061802006 -110 pop_name=='SST' pop_name=='SST' -0.00061333573436626 +108 pop_name=='Exc' pop_name=='SST' -0.0012015183714519876 +109 pop_name=='PV' pop_name=='SST' -0.0027147606180200666 +110 pop_name=='SST' pop_name=='SST' -0.0006133357343662634 111 pop_name=='VIP' pop_name=='SST' -0.014630226522887473 -112 pop_name=='Exc' pop_name=='VIP' -0.000305028126569 -113 pop_name=='PV' pop_name=='VIP' -0.00073158484138907 -114 pop_name=='SST' pop_name=='VIP' -0.00777536182340318 +112 pop_name=='Exc' pop_name=='VIP' -0.00030502812656937027 +113 pop_name=='PV' pop_name=='VIP' -0.0007315848413890771 +114 pop_name=='SST' pop_name=='VIP' -0.007775361823403184 115 pop_name=='VIP' pop_name=='VIP' -0.0001246269351861529 diff --git a/examples/pop_ssn_123/network/l23_l23_edges.h5 b/examples/pop_ssn_123/network/l23_l23_edges.h5 index 82c994aea30372d4b18aa20a1af9af4c1486efe8..0d5ee4cb2a3635d3100b887bb26336ac1a9f6d94 100644 GIT binary patch delta 352 zcmca{h3Uo>rVUa&j7*!QdBTMlnI=o~SWI3mcOA;wEe~aFKCRHs2oc=et(4CU)bLwX zmWfe$;zse!1{$0k5@1NY`g3V0^i`XX{SQsz{Z%(w(=U`0VJkbG2P1tg_Y=s5Xfk3J*c zX2sr*oS~)XJoyeN@US>?BzYhC^#6L%g5MJHUYksk+Y8i_jrVUa&j0~HldBTMl8752fSWI3mcOA;wEe~aFKCRHs2oc=et(4CU)bLwX zmWfe(;zse!1{$0kJSfih^07z}$tQe`u546EMqhAC_-L5&x zD=5GK0ZdR@VDiQ;L!g7aGb}i7^gsj!Cii62Y+leEz{uz{`D2ehqrhgx-jAGNrsu4M z8Wb3g1US?wb$z$jWj$%O{?wVa0x`qngMH?HH5c_3#=7oqER(+#n{oZZJICFvW&gH) z-adE#+qydi2N)SE;*t{@7{p9wG50=mT+F(_gn_Mr!EtjT-wO800`rp~u9&=f{#vG> L>6@#v{&4~TqltOA diff --git a/examples/pop_ssn_123/network/l23_node_types.csv b/examples/pop_ssn_123/network/l23_node_types.csv index 763e64ad..b2a99118 100644 --- a/examples/pop_ssn_123/network/l23_node_types.csv +++ b/examples/pop_ssn_123/network/l23_node_types.csv @@ -1,5 +1,5 @@ -node_type_id model_template ei exponent pop_name decay_const model_type scaling_coef -100 ssn:Recurrent e 2.0 Exc 10.0500429106779 rate_population 22.392148198236782 -101 ssn:Recurrent i 2.0 PV 11.8257398864811 rate_population 22.392148198236782 -102 ssn:Recurrent i 2.0 SST 10.0159482249032 rate_population 22.392148198236782 -103 ssn:Recurrent i 2.0 VIP 49.3117045605402 rate_population 22.392148198236782 +node_type_id model_template model_type input_offset exponent decay_const scaling_coef pop_name +100 ssn:Recurrent rate_population 0.0 2.0 10.0500429106779 22.392148198236782 Exc +101 ssn:Recurrent rate_population 0.0 2.0 11.8257398864811 22.392148198236782 PV +102 ssn:Recurrent rate_population 0.0 2.0 10.0159482249032 22.392148198236782 SST +103 ssn:Recurrent rate_population 0.0 2.0 49.3117045605402 22.392148198236782 VIP diff --git a/examples/pop_ssn_123/network/l23_nodes.h5 b/examples/pop_ssn_123/network/l23_nodes.h5 index 9cd6f8349d2ff1836674d098d7bbd3b219f698d8..f4c6095768bae8c43cf5ad777b566f5dd4fc5bce 100644 GIT binary patch delta 47 qcmZpuZm8ZM#ly(7S(>L^h>>ZsG>^sR+j8ujP^Pp7oEdGX$^!r_uM1QF delta 47 qcmZpuZm8ZM#ly(3S(>L^h>>BkG>^sR+j8ujP^Pp7oEdGX$^!r^;|ojx diff --git a/examples/pop_ssn_123/network/l4e_l23_edge_types.csv b/examples/pop_ssn_123/network/l4e_l23_edge_types.csv index 8a2015b0..36a3e1b7 100644 --- a/examples/pop_ssn_123/network/l4e_l23_edge_types.csv +++ b/examples/pop_ssn_123/network/l4e_l23_edge_types.csv @@ -1,5 +1,5 @@ edge_type_id target_query source_query syn_weight -100 pop_name=='Exc' * 0.01256496 -101 pop_name=='PV' * 0.0278943 -102 pop_name=='SST' * 0.0034658 -103 pop_name=='VIP' * 0.01172155 +100 pop_name=='Exc' * 0.01256496132501487 +101 pop_name=='PV' * 0.027894300350426362 +102 pop_name=='SST' * 0.003465797812045712 +103 pop_name=='VIP' * 0.011721550957492239 diff --git a/examples/pop_ssn_123/network/l4e_l23_edges.h5 b/examples/pop_ssn_123/network/l4e_l23_edges.h5 index 7e579bac6c83a59578ce37c2423863d3836853b9..1b72013309201a09d73c215668c4dd4a1bc12359 100644 GIT binary patch delta 101 zcmca{h3Uo>rVUa&j7*!QdBTMlnI=o~SWI3mcOA;wEe~aFKCRHs2oc=et(4CU)bLx? i-w8;0XY`8zsoOQ|d_Z~c42#Xu-QhqHFmv_%wOjy3u^-C- delta 101 zcmca{h3Uo>rVUa&j0~HldBTMl8752fSWI3mcOA;wEe~aFKCRHs2oc=et(4CU)bLx? i-w8;0XY`8zsoOQ|d_Z~c42#Xu-QhqHFmv_%wOjx~#UHl- diff --git a/examples/pop_ssn_123/network/l4e_nodes.h5 b/examples/pop_ssn_123/network/l4e_nodes.h5 index 3536ffbd8b07c342c24db54d8be90f337df1f371..8623d6bf17bac84192396efe3f67d4650a884a25 100644 GIT binary patch delta 47 qcmZpuZm8ZM#ly(7S(>L^h>>ZsG>^sR+j8ujP^Pp7oEdGX$^!r_uM1QF delta 47 qcmZpuZm8ZM#ly(3S(>L^h>>BkG>^sR+j8ujP^Pp7oEdGX$^!r^;|ojx From d98c518da3b6c66af3373f66734d60ca157a24be Mon Sep 17 00:00:00 2001 From: shixnya Date: Tue, 20 May 2025 12:09:30 -0700 Subject: [PATCH 12/19] Sped up (~60x) SSN using numba. Prepared setter of some variables. Included optimization notebook. --- bmtk/simulator/popnet/ssn/popnetwork.py | 18 +- bmtk/simulator/popnet/ssn/popsimulator.py | 77 ++- examples/pop_ssn_123/PopNet_SSN.ipynb | 245 +++------- .../pop_ssn_123/PopNet_SSN_optimization.ipynb | 438 ++++++++++++++++++ examples/pop_ssn_123/run_popnet.py | 2 +- 5 files changed, 569 insertions(+), 211 deletions(-) create mode 100644 examples/pop_ssn_123/PopNet_SSN_optimization.ipynb diff --git a/bmtk/simulator/popnet/ssn/popnetwork.py b/bmtk/simulator/popnet/ssn/popnetwork.py index 80e52a75..3b620057 100644 --- a/bmtk/simulator/popnet/ssn/popnetwork.py +++ b/bmtk/simulator/popnet/ssn/popnetwork.py @@ -62,7 +62,11 @@ def connectivity_mat(self): self._connectivity_mat[r, c] = syn_w return self._connectivity_mat - + + @connectivity_mat.setter + def connectivity_mat(self, value): + self._connectivity_mat = value + self._conn_finalized = True @property def scales(self): @@ -72,6 +76,10 @@ def scales(self): self._scales[n.gid] = np.mean(n.scaling_coef) return self._scales + + @scales.setter + def scales(self, value): + self._scales = value @property def initial_states(self): @@ -90,6 +98,10 @@ def exponents(self): self._exponents[n.gid] = np.mean(n.exponent) return self._exponents + + @exponents.setter + def exponents(self, value): + self._exponents = value @property def decay_constants(self): @@ -99,6 +111,10 @@ def decay_constants(self): self._decay_constants[n.gid] = np.mean(n.decay_const) return self._decay_constants + + @decay_constants.setter + def decay_constants(self, value): + self._decay_constants = value def build_nodes(self): for node_pop in self.node_populations: diff --git a/bmtk/simulator/popnet/ssn/popsimulator.py b/bmtk/simulator/popnet/ssn/popsimulator.py index fb47f569..d2d4f240 100644 --- a/bmtk/simulator/popnet/ssn/popsimulator.py +++ b/bmtk/simulator/popnet/ssn/popsimulator.py @@ -10,6 +10,42 @@ # TODO: leave this import, it will initialize some of the default functions for building neurons/synapses/weights. import bmtk.simulator.popnet.ssn.default_setters +try: + from numba import njit +except ImportError as ie: + from bmtk.simulator.popnet.ssn.utils import empty_decorator + njit = empty_decorator + +@njit +def step(activation_function, state, mat, scales, exponents, decay_constants, dt): + """ A function to compute one step of the network. + This function needs to be here so that run_njit can be compiled. + """ + + input = activation_function(np.dot(mat, state) * scales) ** exponents + n_neu_recurrent = mat.shape[0] + recurrent_state = state[:n_neu_recurrent] + dr = (-recurrent_state + input) / decay_constants * dt + return activation_function(recurrent_state + dr) + +@njit +def run_ssn(activation_function, results, connectivity_mat, scales, exponents, decay_constants, dt, nsteps, n_neu_recurrent): + """ + A function to run the whole simulation. + The results will be modified in place. + """ + for t in range(nsteps-1): + results[t+1, :n_neu_recurrent] = step( + activation_function, + results[t, :], + connectivity_mat, + scales, + exponents, + decay_constants, + dt, + ) + return + class PopSimulator: def __init__(self, network, dt=1.0, tstart=0.0, tstop=None, **opts): @@ -38,29 +74,32 @@ def results(self): return self._fr_results - def step(self, state, mat, scales, exponents, decay_constants, dt): - input = self.activation_function(np.dot(mat, state) * scales) ** exponents - n_neu_recurrent = mat.shape[0] - recurrent_state = state[:n_neu_recurrent] - dr = (-recurrent_state + input) / decay_constants * dt - return self.activation_function(recurrent_state + dr) - def add_mod(self, mod): mod.initialize(self) self._mods.append(mod) + - def run(self): - io.log_info('Running Simulation.') - for t in range(self.nsteps-1): - self.results[t+1, :self.network.n_neu_recurrent] = self.step( - self.results[t, :], - self.network.connectivity_mat, - self.network.scales, - self.network.exponents, - self.network.decay_constants, - self.dt, - ) - + def run(self, return_output=False): + + activation_function = self.activation_function + if not return_output: + io.log_info('Running Simulation.') + + # self.results will be modified in place + run_ssn( + activation_function, + self.results, + self.network.connectivity_mat, + self.network.scales, + self.network.exponents, + self.network.decay_constants, + self.dt, + self.nsteps, + self.network.n_neu_recurrent, + ) + + if return_output: + return self.results io.log_info('Simulation Finished.') for mod in self._mods: diff --git a/examples/pop_ssn_123/PopNet_SSN.ipynb b/examples/pop_ssn_123/PopNet_SSN.ipynb index c4f5ba31..e79a883d 100644 --- a/examples/pop_ssn_123/PopNet_SSN.ipynb +++ b/examples/pop_ssn_123/PopNet_SSN.ipynb @@ -7,16 +7,16 @@ "source": [ "# PopNet Simulation with SSN\n", "\n", - "[Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics](https://www.biorxiv.org/content/10.1101/2023.10.21.563440v1.full)\n", + "[Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics](https://www.sciencedirect.com/science/article/pii/S2211124724011148)\n", "\n", "$$\n", - "\\tau_j \\frac{dr}{dt} = -r_j + (s(\\sum_i J_{ji} r_{i} + J_{E}r_{E}) + b_{j})^2 \\tag{1}\n", + "\\tau_j \\frac{dr}{dt} = -r_j + (s(\\sum_i J_{ji} r_{i} + J_{E}r_{E}) + b_{j})_+^2 \\tag{1}\n", "$$\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "313c08fa-97eb-41b0-a135-864d64c17fa1", "metadata": {}, "outputs": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "9d0a1961-dea7-4ca2-a89b-affd484fe338", "metadata": {}, "outputs": [], @@ -176,10 +176,31 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "5c9b76fa-0d4d-40e9-b387-a72e5eb0cda6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "BlockingIOError", + "evalue": "[Errno 35] Unable to synchronously create file (unable to lock file, errno = 35, error message = 'Resource temporarily unavailable')", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mBlockingIOError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 22\u001b[39m\n\u001b[32m 15\u001b[39m l23_net.add_edges(\n\u001b[32m 16\u001b[39m source={\u001b[33m'\u001b[39m\u001b[33mpop_name\u001b[39m\u001b[33m'\u001b[39m: src}, \n\u001b[32m 17\u001b[39m target={\u001b[33m'\u001b[39m\u001b[33mpop_name\u001b[39m\u001b[33m'\u001b[39m: trg},\n\u001b[32m 18\u001b[39m syn_weight=connections_df.loc[src][trg]\n\u001b[32m 19\u001b[39m )\n\u001b[32m 21\u001b[39m l23_net.build()\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m \u001b[43ml23_net\u001b[49m\u001b[43m.\u001b[49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mnetwork\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/Modeling/bmtk_ssn/bmtk/bmtk/builder/network_builder.py:297\u001b[39m, in \u001b[36mNetworkBuilder.save\u001b[39m\u001b[34m(self, output_dir, force_overwrite, compression)\u001b[39m\n\u001b[32m 286\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msave\u001b[39m(\u001b[38;5;28mself\u001b[39m, output_dir=\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m'\u001b[39m, force_overwrite=\u001b[38;5;28;01mTrue\u001b[39;00m, compression=\u001b[33m'\u001b[39m\u001b[33mgzip\u001b[39m\u001b[33m'\u001b[39m):\n\u001b[32m 287\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Used to save the network files in the appropriate (eg SONATA) format into the output_dir directory. The file\u001b[39;00m\n\u001b[32m 288\u001b[39m \u001b[33;03m names will be automatically generated based on the network names.\u001b[39;00m\n\u001b[32m 289\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 295\u001b[39m \u001b[33;03m you can also specify an integer (1-9) to specify the level of gzip compression.\u001b[39;00m\n\u001b[32m 296\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m297\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43madaptor\u001b[49m\u001b[43m.\u001b[49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforce_overwrite\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_overwrite\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/Modeling/bmtk_ssn/bmtk/bmtk/builder/network_adaptors/network.py:564\u001b[39m, in \u001b[36mNetwork.save\u001b[39m\u001b[34m(self, output_dir, force_overwrite, mode, compression, **opts)\u001b[39m\n\u001b[32m 555\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msave\u001b[39m(\u001b[38;5;28mself\u001b[39m, output_dir=\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m'\u001b[39m, force_overwrite=\u001b[38;5;28;01mTrue\u001b[39;00m, mode=\u001b[33m'\u001b[39m\u001b[33mw\u001b[39m\u001b[33m'\u001b[39m, compression=\u001b[33m'\u001b[39m\u001b[33mgzip\u001b[39m\u001b[33m'\u001b[39m, **opts):\n\u001b[32m 556\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Used to save the network files in the appropriate (eg SONATA) format into the output_dir directory. The file\u001b[39;00m\n\u001b[32m 557\u001b[39m \u001b[33;03m names will be automatically generated based on the network names.\u001b[39;00m\n\u001b[32m 558\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 562\u001b[39m \u001b[33;03m :param force_overwrite: Overwrites existing network files.\u001b[39;00m\n\u001b[32m 563\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m564\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msave_nodes\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforce_overwrite\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_overwrite\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mopts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 565\u001b[39m \u001b[38;5;28mself\u001b[39m.save_edges(output_dir=output_dir, force_overwrite=force_overwrite, mode=mode, compression=compression, **opts)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/Modeling/bmtk_ssn/bmtk/bmtk/builder/network_adaptors/network.py:592\u001b[39m, in \u001b[36mNetwork.save_nodes\u001b[39m\u001b[34m(self, nodes_file_name, node_types_file_name, output_dir, force_overwrite, mode, compression, **opts)\u001b[39m\n\u001b[32m 589\u001b[39m os.makedirs(ntf_dir)\n\u001b[32m 590\u001b[39m barrier()\n\u001b[32m--> \u001b[39m\u001b[32m592\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_save_nodes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnodes_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 593\u001b[39m \u001b[38;5;28mself\u001b[39m._save_node_types(node_types_file)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/Modeling/bmtk_ssn/bmtk/bmtk/builder/network_adaptors/dm_network.py:103\u001b[39m, in \u001b[36mDenseNetwork._save_nodes\u001b[39m\u001b[34m(self, nodes_file_name, mode, compression)\u001b[39m\n\u001b[32m 100\u001b[39m prop_ds.append(node.params[key])\n\u001b[32m 102\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mpi_rank == \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mh5py\u001b[49m\u001b[43m.\u001b[49m\u001b[43mFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnodes_file_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m hf:\n\u001b[32m 104\u001b[39m \u001b[38;5;66;03m# Add magic and version attribute\u001b[39;00m\n\u001b[32m 105\u001b[39m add_hdf5_attrs(hf)\n\u001b[32m 107\u001b[39m pop_grp = hf.create_group(\u001b[33m'\u001b[39m\u001b[33m/nodes/\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m'\u001b[39m.format(\u001b[38;5;28mself\u001b[39m.name))\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/miniconda3/envs/bmtk_ssn/lib/python3.13/site-packages/h5py/_hl/files.py:564\u001b[39m, in \u001b[36mFile.__init__\u001b[39m\u001b[34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, fs_page_size, page_buf_size, min_meta_keep, min_raw_keep, locking, alignment_threshold, alignment_interval, meta_block_size, **kwds)\u001b[39m\n\u001b[32m 555\u001b[39m fapl = make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0,\n\u001b[32m 556\u001b[39m locking, page_buf_size, min_meta_keep, min_raw_keep,\n\u001b[32m 557\u001b[39m alignment_threshold=alignment_threshold,\n\u001b[32m 558\u001b[39m alignment_interval=alignment_interval,\n\u001b[32m 559\u001b[39m meta_block_size=meta_block_size,\n\u001b[32m 560\u001b[39m **kwds)\n\u001b[32m 561\u001b[39m fcpl = make_fcpl(track_order=track_order, fs_strategy=fs_strategy,\n\u001b[32m 562\u001b[39m fs_persist=fs_persist, fs_threshold=fs_threshold,\n\u001b[32m 563\u001b[39m fs_page_size=fs_page_size)\n\u001b[32m--> \u001b[39m\u001b[32m564\u001b[39m fid = \u001b[43mmake_fid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muserblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mswmr\u001b[49m\u001b[43m=\u001b[49m\u001b[43mswmr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 566\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(libver, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[32m 567\u001b[39m \u001b[38;5;28mself\u001b[39m._libver = libver\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/miniconda3/envs/bmtk_ssn/lib/python3.13/site-packages/h5py/_hl/files.py:244\u001b[39m, in \u001b[36mmake_fid\u001b[39m\u001b[34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[39m\n\u001b[32m 242\u001b[39m fid = h5f.create(name, h5f.ACC_EXCL, fapl=fapl, fcpl=fcpl)\n\u001b[32m 243\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m mode == \u001b[33m'\u001b[39m\u001b[33mw\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m fid = \u001b[43mh5f\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh5f\u001b[49m\u001b[43m.\u001b[49m\u001b[43mACC_TRUNC\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 245\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m mode == \u001b[33m'\u001b[39m\u001b[33ma\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m 246\u001b[39m \u001b[38;5;66;03m# Open in append mode (read/write).\u001b[39;00m\n\u001b[32m 247\u001b[39m \u001b[38;5;66;03m# If that fails, create a new file only if it won't clobber an\u001b[39;00m\n\u001b[32m 248\u001b[39m \u001b[38;5;66;03m# existing one (ACC_EXCL)\u001b[39;00m\n\u001b[32m 249\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mh5py/_objects.pyx:54\u001b[39m, in \u001b[36mh5py._objects.with_phil.wrapper\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mh5py/_objects.pyx:55\u001b[39m, in \u001b[36mh5py._objects.with_phil.wrapper\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mh5py/h5f.pyx:122\u001b[39m, in \u001b[36mh5py.h5f.create\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[31mBlockingIOError\u001b[39m: [Errno 35] Unable to synchronously create file (unable to lock file, errno = 35, error message = 'Resource temporarily unavailable')" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -217,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "2f355141-8c74-4dee-9e25-243ebd8418e7", "metadata": {}, "outputs": [], @@ -729,7 +750,7 @@ "id": "bc59da9a-f7f7-41a8-b7bc-070ed0cd183e", "metadata": {}, "source": [ - "By default the SSN use a regular [relu function](https://en.wikipedia.org/wiki/Relu) at the each time step, however it is possible to overwrite it with a function of your own. We can replace it with our own function by first creating a function that takes an array of size $N$ (number of recurrent nodes), does some processing, then returns an array of size $N$. To make sure that PopNet can see the function we need to register it with the ```@activation_function``` decorator and make sure it's somewhere in our run script.\n", + "By default, the SSN applies a standard [ReLU function](https://en.wikipedia.org/wiki/Relu) to the input at the each time step before raising it to an exponent, however it is possible to overwrite it with a function of your own. We can replace it with our own function by first creating a function that takes an array of size $N$ (number of recurrent nodes), does some processing, then returns an array of size $N$. To make sure that PopNet can see the function we need to register it with the ```@activation_function``` decorator and make sure it's somewhere in our run script.\n", "\n", "For example we could have\n", "\n", @@ -759,7 +780,7 @@ "}\n", "```\n", "\n", - "And next time we run the popnet script like usual it replace the normal relu function with out custom one.\n" + "And next time we run the popnet script like usual it replace the normal relu function with the custom one.\n" ] }, { @@ -786,27 +807,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "c4b3bc43-114c-4498-a48a-c0c26588aea2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-04-27 10:31:43,728 [INFO] Created log file\n", - "2025-04-27 10:31:43,781 [INFO] Building nodes.\n", - "2025-04-27 10:31:43,783 [INFO] Building connections.\n", - "2025-04-27 10:31:43,786 [INFO] Add input module \"init_states\"\n", - "2025-04-27 10:31:43,789 [INFO] Add input module \"l4_inputs\"\n", - "2025-04-27 10:31:43,792 [INFO] Add input module \"bkg_inputs\"\n", - "2025-04-27 10:31:43,793 [INFO] Running Simulation.\n", - "2025-04-27 10:31:44,741 [INFO] Simulation Finished.\n", - "2025-04-27 10:31:44,750 [INFO] RatesRecorderMod: Saving rates to /local1/workspace/bmtk/examples/pop_ssn_123/output/l23_rates.csv.\n", - "2025-04-27 10:31:44,897 [INFO] RatesRecorderMod: Saving rates to /local1/workspace/bmtk/examples/pop_ssn_123/output/l23_rates.h5.\n" - ] - } - ], + "outputs": [], "source": [ "from bmtk.simulator import popnet\n", "\n", @@ -825,6 +829,16 @@ "sim.run() " ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c2548cb", + "metadata": {}, + "outputs": [], + "source": [ + "sim.run()" + ] + }, { "cell_type": "markdown", "id": "9e3d7980-4428-432e-bf05-76dc0f33bf81", @@ -835,21 +849,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "de0fd37a-26f3-4c73-ba61-1d0c9dc56a52", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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TJsX8SeLHiCpDs2fPxnXXXYfPfvazoc9ZtWoVLr30UvzhD39AJpNJ8OhSpKgOn0TfwLvS4VAwCrGPYVomfj3/13h5zcuxj5VieLDEoWw2LxlKu9bXh4HyANYNrvOp2ixOPfVUfPTRR3jggQdw/PHH46mnnsJ+++2HO+64AwDwwx/+EGvWrMEtt9yCPffcE7fccgt22203vPXWWwl+injQ0IFiy7Jw7rnn4pvf/Cb23HPPml5TLBZ9El9vb29ch5diC8doSetVEL9H4bmPnsPNb9yMm9+4GW+d3/wTZ7ODVYOaWRlqliSGFr0FL579YtXnvLfxPQDA+JbxmNg6Udq4LFb1rULZKkNTNIxvGR/4mnw+j2OOOQbHHHMMrr76alx88cX4r//6L1xwwQX28Y0fj9NOOw2nnXYafvSjH2HGjBn46U9/ijvvvFPKMY8UGpoM3XDDDdB1HZdddlnNr7n++utxzTXXxHhUKVLYaJaJeDiE7RJl4vV1r8c+RoraMWrIUJNsSBRFGTZcldfzAIDWTGvNoa16Qb/rolnpCQrDHnvsEernzWaz2HHHHTEwMCDj8EYUDUuGXn31Vfz85z/Ha6+9Vpe7/sorr8QVV3i+h97eXkybNi2OQ0yxhWO0SPRJkKEk1KcUtWPUkCFmQ2IRK82KFMDGjRtx2mmn4cILL8T06dPR0dGBV155BT/5yU9w8skn46GHHsKf/vQnnHnmmdhll11ACMGDDz6Iv//977j99ttH+vCF0bBkaO7cuVi3bh222WYb9zHTNPH1r38dN910E5YtWxb4ulwuh1wul9BRptiSwZo34/IMmZaJV9e+ij3G74H2bPvwL4iAJBaPRi+4tqWBVTKb2e/GZzWZlglVk389r+pfhd5iL3Yfv7v0924UtLe348ADD8TPfvYzLF68GOVyGdOmTcMXvvAFXHXVVVi9ejVaW1vx9a9/HStWrEAul8POO++MW2+9Feeee+5IH74wGpYMnXvuuTj66KN9jx133HE499xz8fnPf36EjipFCg9JdK1/YPED+N6872G/ifvhztnxxOQTIUOpMtRQYK/dJJTBuMBuSABboc1AfqLN8fcdDwB45JRHsHXH1tLfvxFS1XO5HK6//npcf/31gX/fYYcd8Nvf/rbm9wsTLBoVI0qG+vv7sWjRIvf3pUuXYv78+Rg3bhy22WYbjB/vN3hlMhlMnjwZu+66a9KH2hAom2WsL6zH1Pa0xECjIS6/wt3v3w0AeG3da7G8P5CMajOaQhcf9X+ErVq2QkZr3uzW0dJKJkgZinOMRd2LYiFDYePFNgZGnnw1GkZ0hnrllVcwY8YMzJgxAwBwxRVXYMaMGfje9743kofVsLjkn5fguPuOw2tr41sYU9SOJFoBJGEK1RQt9jFGS5jszfVv4rj7jsPnH2tudZr32owWxHEfsp6quEh9Sk5GHiOqDB111FF1seBmk91k48U1dmrmnz74E/abtN8IH02KJLLJdCX+W5Sd4OMyoKqIf4wk8MDiBwAAb6x/Y4SPRAy+6ulN7BniiVwcKhdLhpI4V4kQo5R7VaA5Z6QtHemF3BBIIq03CdLAjhEXqWOVoUZOgR4OzUriqqGZvw+eOMRx/Y4WfxWLVImqxOi7s7cEjI6IQ9MjCWVotJAhdozRpEQ0K0ZLX70Kz1AMn8WXNdrE5ypFdaRkqAkR9wLZzOX5k8SoUYaYaSCuz8FmkzXzgpJU1k/ZLMc61mjxDPEKR9wGaj57LY4xUtVmZJCSoSaEGuPXdutbt+KAuw/AW+vTlgnDIQkD9WhUhpq5WncSC9Xmoc048t4j8e1nvh3bGD4i38RKHU8Y4w6TJUKGUy40IkjJUBMizsycn7/2cxiWgR+++MPYxhgtIIMb3Z+bmQyxYdcklKFmViKSOPaHljyEvlIfHln2SGxj+JoMN7NS17fa93scBuqkVbQkCHeqPlUiJUNNiCR2J828YCWGYp/7YzOHydh5MQnVppkX3ySQRKkD9v5u5nvdWvi47/c4VK4kzlVKTkYeKRmShCFjCGsH1iYyVhKLSbpgDQ+LVVRiCjUkQYaSCPexXos4CdfKvpWxvr+uJlvqIC74zP9NXHSRjynFEiYbJSpaiupIyZAkXPjYhTj6L0djWc+y2McaLYX4mh3sNBxXs8s4/WEULFGJa+ebRPbSs6uexey/zsZVc6+K5f0B/30R17lK4v72mYKt5lWGiOqfp+I4d0n7qxIJkwVEF0488UQcf/zxgc+fO3cuFEXBm2++CUVRMH/+fAB27T9FUdx/48ePx7HHHovXX389zsOPBSkZkoS3NtiG438s/0fsYyUha6dkaHiw00kzh8l8PdZiUgmSWFB+9+bvACBWr42vREBM33kiC24CmZBJgJ8J49iUJKEM+QhQTFxoOHvFRRddhMcffxwrV66s+Nvtt9+OmTNnorOzM/C1//znP7F69Wo89thj6O/vx+zZs9Hd3S3jsBNDSoYkY7QUZdPUlAwNB3ZqSaJYYVxIohpxEr2wkjhX7BhJqGixjTFKaufwy3ssdYYSCCP7udDI+IdOOOEEbLXVVrjjjjt8j/f39+PPf/4zLrrootDXjh8/HpMnT8bMmTPx05/+FGvXrsWLL74Y8xHLRcN2rW9WxBUuYZEqQ42BJJShJL6HJHa+vlBcTGGZRFqXgCseGcPXw34HpmVC1eRvsEZLVeUKMhSzqhb1/iCEgBQKoX+3zDJQGLJ/tjKwtMFI4/BQWlpcAs/e50GES9d1nHfeebjjjjvwne98x33dn//8Z5imibPOOgubN28edsyWlhYAQKlUkvEREkNKhiQjiWycZp68RhMIky4+apShuMIACYRldC1Zc3MSIRODGMggE+8Yo6juUxyfRYZySgoFfLDfx6s+h15ZAwA+iDRKJXZ97VUora01P//CCy/Ef//3f+Ppp5/GUUcdBcAOkZ166qno6uoalgx1d3fjBz/4Adrb23HAAQeIHHriGB0xnQZCEhNLEqn1aarn8GCzyeL63lllKK7vPQk/TxJhsowinzRUgPnO49qUJNG6ZNQoQ9wtEXedoebOvBseu+22Gw455BDcdtttAIBFixZh7ty5VUNkAHDIIYegvb0dY8eOxRtvvIF77rkHkyZNSuKQpSFVhiQjETKUAFFp5glyJJCEgdoiVixhsyT8I0n4kpLwuSXRViSJ9iijpjcZ93vcdYaivr/S0oJdX3s19O8ls4TF3YsBAO3ZDkzr2DrSOEHjUtS6mbroootw6aWX4le/+hVuv/127LjjjjjyyCOrvuaee+7BHnvsgfHjx2PMmDEihzxiSMmQZMRFVJLeySXVg6lpQfzfdFwLPL/4ajGYVHzZZDGR+SSq+CadvBDbd67ET7h8PrFmbseRQJiMHSLqtasoStVwlWrqQDFv/5LNQa0jtBUF1eb3008/HV/96ldx991346677sIll1wybLh+2rRp2HHHHWUfZqJIyZBkxBbKYBeTmJoFsmjm3WIisEzft5BIt/cEFsYkMqRi81chfn9V0inpaZisOhLJJkP8IV4WI70RbW9vxxlnnIErr7wSvb29uOCCC0b0eJJC6hmSjLiUId+ElcC90swTZCKwDH9qfUyTZBL+kSRCWEksvklX646tNUMChGu0+GCSUIaS8NQ1Wj+yiy66CJs3b8Zxxx2HqVOnxnhUjYNUGZKMJHrXjJZmgYD9WZqyNpNV9mWTJeEZiq1VRgLm5tFSZyjpMgRJeIaaeePDH3kc11YirWQaoM4Qi4MPPjhQodpuu+18j/O/NzOacBVqbCSxW0wiTJbUBNm0fgXLAEkgmywJw24i2WQJtPxIWhlKQkVLQolo5pA4TxxiOV/MEIm0SmkwlWhLQUqGJCMJz9Bo6lrftBOx6Q+TJbFoNXM14iQW+CQ8QyySUIGbuT3KSCDuOkOJdK2Pqx1HSoCqIiVDEpCEapO0rB3nGOyi1bQSPe8ZSiALq5kLIiaRyp24MtTEZQhGTZiMO/ZYGrUmrKLFRloaLBTXaEjJkASwN2QShfGanQz5fDDNat4kVoWBOo7vPoksrKSLLsa1oLAkOwmFtpnJ6agJk3Hfc9zK0GipIzdafD4ykZIhCRiu54vsMZJAnOP5ml3G1KcqdhCz4gzFQSCTIMGJhMkSWHyTqM+TiDKE0eFLSgKEuyfiJiv13IP1EI4kwmSNikYhZikZkoBEFixWfRpFFaibdldKLFicR6VZWwEksfNNIvSTRE0mFrER+QQMu6NGGeIN1DF3ra/l/TMZuy3M4GC0ZqtJbKgbKUxGzxM9byOFNLVeApLIlEk8tT5Otp5wdkYsIJYvmwyIp4t54tlLTZzK7cu8i6mjfNLKUBKbq2b2DCWhDNXb1FbTNIwZMwbr1q0DALS2tg5b9qFYLsIq25/FMA0MDQ0JHHEwSmbJHcM0zVjGqAeEEAwODmLdunUYM2YMNC3+djrVkJIhCUikcSozRrM3ak2a2MUCUnmG4vY/NfPCmESXdL6PWxxI3ECdKkNVUeEZikOdjaD8T548GQBcQjQcSmYJGwobADg99jbVeZA1wLAMrBu0j0dXdVibGmPuHTNmjHu+RhIpGZKApKvSNrsyNCqaRBILW20msACsHWfv+mI3b8aVZp0AUUniHkk8TNbE93rSbUXiAjEt7LaCYOFUwNSUWNTTKMRRURRMmTIFEydORLlcHvb5H2z6ADc8fQMAYFzLONxx/B2RjrUaVvWvwg2P22NMbpuM3x77W+lj1ItMJjPiihBFSoYkIOlaMIkUXYxxjNHQJJKUS/jRHRYyJnDRVzUUszFNxOzCGJNHZbSk1rNo5s+RSMbaKLgHAWDbeX249mkTfzxCxf2HKrFvSOo9V5qm1bTYE51gdWk1AKCklpDP5+s7yBqgFlV3DCWjxDJGMyM1UEtAEiGspHeLSYwBNO+u1OztRVsRyBrAuD7nsZibRI6a7KVmbnCaAIkYLaUOksB28wYAAGc943hhmrTO0Ggp2dDMSMmQBCSh2iR9QyZVBbVZPUNWX7/7c85RwcvW8HJ43WBOVyKZXk1MuEaLnyfpUgfNeg8CgF5OtlFrEhmdzXx/NDNSMiQBSafWJxEuSaKSNpBMEbM4YPZ7ZEh35pU4JrFEMhVHSZgsaVNwbCSCWd8T+c6bOEzGI+5ssrjm3tGSpdjMGFEy9Mwzz+DEE0/E1KlToSgK5syZ4/6tXC7j29/+Nvbee2+0tbVh6tSpOO+88/DRRx+N3AGHIOlssiRuliQq+ALNe1Na5ZL7s+58hGbdlSYe+kkgDJDIotXEO/jRGjJphDpDokjiO2/WTWicGFEyNDAwgH322Qe/+tWvKv42ODiI1157DVdffTVee+01/PWvf8UHH3yAk046aQSOtDqSrjMU2w2ZQO8anmQ1LRkyvJBY3rA/U9yeoSRUxyTCAM2c9p7EfZjIdz5KVYK4kxjSfnSjFyOaTTZ79mzMnj078G9dXV14/PHHfY/98pe/xAEHHIAPP/wQ22yzTRKHWBOS3sk18wTJk6xm7U1mGd5x55yvPJbPwhaobGLDbhLp+4kQlYTLaCTxnTfrPRiEuKvAJ0FUaJ/D4Qo11j1GAl60ZkZTpdb39PRAURSMGTMm9DnFYhHFYtH9vbe3N4Ej89DU/aPYrLiYlCH+/DTtDiWIDDVpnaHRUvE46U1JImHL1EBdF+IOVSdxXQH2d6IpcuvvjFafmCw0jYF6aGgI3/72t3HWWWehs7Mz9HnXX389urq63H/Tpk2L/dgSb5nQxDJqEl2mk0CQMhR3wbfREiaLTRlKWOEaLSUuRtPCGMuGJOHyFkA833uaTVYdTUGGyuUyTj/9dBBCcPPNN1d97pVXXomenh7334oVK2I/vtFieExT62sHMTwDdS5Gz1DSIZMkxmjm/n2jxUA9WhfG0XAPAvFsSvjvvFG6xTcKGj5MRonQ8uXL8a9//auqKgQAuVwOuVwuoaOrRDObKpNIrR8tRRdZZUh3PlIcdYaS6FqftCk4iQU+CR9MM4ewRmuYrFkN1PwmMW6VGYgnFNfMaGgyRInQwoUL8eSTT2L8+PEjfUiBGDWdxRPOYgGaV6Inpkd8Ms5HiqXOUMJem2Yu7Jh4va8ENiVJhBSbNVQdhLg3JEkQ+bjGCfJrakjJEMWIkqH+/n4sWrTI/X3p0qWYP38+xo0bhylTpuDf/u3f8Nprr+Ghhx6CaZpYs2YNAGDcuHHIZrMjddgVGC0VfJOulwQ0766UlL3vQLdiDJMlXAOomT0qiWSsjZJNyahVhuKuM5RAaBSIPxkDsNXTDDLSx2lWjKhn6JVXXsGMGTMwY8YMAMAVV1yBGTNm4Hvf+x5WrVqFBx54ACtXrsS+++6LKVOmuP/mzZs3koddgUQmloSr0iZmoG7StF5iesdNlaEkM1kGy4O47F+X4ffv/l54jFFTnycJFS2tCN7QSLpr/d8W/Q1ffvzL2DS0SWyMEdgkjiYSLAMjqgwdddRRVdWIZjF4jZbJi73p0wrU1WExZEh3qh0nWePk4aUP48kVT+LJFU/i3D3OFRqDXeCbWVFJujBpEvWSkrg/RhMZiuUerHLtfve57wIA7v3gXnx5ny9HHqPCPhCzERwYXeFRGWiKbLJGRyKeodGys+Zj483qGWIN1IxniBCCZ1c9i6U9S6WME2YK3jC4wTsWQeI6akI/SZSfSDqRoYmz4kYCdIFfP7ge/1j2DymfrZZztaJPLGs5CaKSRPp+MyMlQxIwEkQlDuUm6WJvcY4TN0iZJUP2ZzIsAy+sfgGX/PMSfOnxL8kZJ0QlYImR6MSZhC8pCUVltBR2TKQmU8JNbZMCPV+X/utSfP3pr+PRZY8Kv2fYuWLn4Iwq5r0ZCWVoNH3vMpCSIQkYidTLOCZ7vgJ1LIRrlNyQxGQN1Pb/JjGxYPMCAMDqgdXoLYlXPw9TO9ifC2ZBaIykQ7BJ+Hmauqlt0vWSmvQeDAL9LO9sfAcA8MSHTwi/Z9j8XjS9Tge6KuY4qZgXE0itH02KoAykZEgCRqJycxKpl3FnRwHNe0MSM1gZGjQG3cdX968WH6eGibhoFCGCxIv8JaA+NXMIK+mO8qMpXGJaJspM2YvB8mCVZ9cGHzm1vHPVX+53f1YVsaU0kbl3lGxE40JKhiQgiZTeRGTUBNSn0dKbjBiVypBhGege6nYfXzOwRnicsGKF7EQ8ZA4JjZF02vtoKT/RzP6q0ewZ6i52u78PlAeE3zMsPN1f8u7BklmCCEZkI5qSIR9SMiQBSShDfHuMuMNkQDI3S7PekKwypDmnzSQmNhc3u4+zP0ceJ4SosJP8kCFIhpKocj1KMi6Tbpjc1Jl3MYNYlXOgQQzffccSo8jjhIRG2XuwYIiFqnnEXUk7rjGaGSkZkoAkfASjJYRV7XN0D3Xj5vk3Y3nvcunjSgfjGdJClKGeYo/wMGGLVl+pz/1ZtOruaEnlTjq1PhEyNAL9zx5d9ijmLJoTy7hSYVQSd5OY8u/BkNBoXzmeexCIv0wH0LyqfFxo6HYczYLR0rtmpAnXb9/6LX7/7u/x6rpXceuxt0ofWyYsJkymMsoQu1vcPCRZGWKLLjLeJNFrIfHFt4kbzvpCWAHKhAwkXcSVPVcWsfDNp78JAJg+YTp2GLNDPONLAAkgQ4Zl+ELIPaUeWMQS8vTwyikhBIqi+PxIotdbEiVHRkvB27iQKkMSkHQTyrjGSaIKarXP8dSKpwAAL65+Ufq40uHLJvMM1Kx/R4ZEH+aDYUNjopNa0s1gmzlDajSG+9jxeoteBuTawbWxjC0LbEan/QCBaZm+5AKLWD4VNQrCfI5S78EELAqpMlQdKRmSgFGTWp+EwY7zPrFjiJoQkwQ7EatMmIydiKWn1pMQMiRaZyjpvmEp4aqKRLrWk+DvgyXwcTQ9lQleGVKIsyHhPHSiobKwuZfd+Mi+bxLJJks9Qz6kZEgCRqKnVxJhsliUoSpVUJuJDPk8Q0yYjJ2IRY3NQDJkaLR0lE96UzIaPwdLhmSbgqWDU4Y0yyZ27IYEkJttCXjkkT0/Mu9BIKb5PYGM5GZGSoYkIImU3pH280gbo0rZef5mbWRYAcqQaZk+Qic6CQPh5ny20KKwMsQoAKPGFNzMqfUj6K9iPW8yavTECT5MRr17PImTmW0JBIfJZBuok1blU6RkSApGTZgsicKOo6U/ToAyVLbKPgIkWgwRCFdt2IlY+Hsa9DpuN3OdoVFTyyjh9j6+64q5fhteqeXCZHRTwtcW4pWiesHPUXTz4QuTCX5PpNdfoDWOenWjpeBtXEjJkAQkMtGPFoNdld1JM92cxPTOTZhnSLRNBhB8bRmW4duJCkv0zE66mZsAJ1K5OeHU+tjOFavIMmOwBL7hPUMhyhBPhkTDfWFzr1TfXp+fDCWdvJIiJUNSkHQTVaB5U+urxcabSiUKCJMVzWLowhIVQeEMfqcrnk3GvBc3qb+z4R1sKGwQen8AAFOtd7Q0ao2NqCSxuVr/gW8Mep2x11Zc35Ms8AZqLSZlKGyTWJC4iSCq5vudvac3D23GG+vfEHp/IG3HMRxSMiQBI0IiEhgjiYy1sMWl0YkRMbzjo2Eytr4JIMdAHRTO4He64l3rPbDX1evrXseZD5+JS5+4VOj9AYBsWuL+nASJSKTbewLep9jOVd9HgWOyxKHhlSE+TBaiDAl7hkJ8jlKVoSqb3X9/4t/xub9/Di+veVlsDO47byYlPgmkZCgGJE0i4kIcC0pF/J3ZAbELcaPvSsFMJHQS5g2nMhaTIJWAn9yFiy4yP7OFBP+26G8AgLc3vi30/gBgJeGrSyC8lHjLj7gIF1eEkH6WZiJDfDZZxvmVJ0OinyPM58h6hqRnkzEh8bc2vAUA+NeH/xIag3DXUqNvOJNGSoYkIMxgJxPVJM6B8gAeW/aY8A4okSaqVvgY7Gdq9Ik4yDPET8IyroOgMBn/PctUhlhyyi6MwqEGxfs5CV9SbGSa6YjO3x/z183H+5veFx8jCV+Sovh+DyLaDX8PGv5zo4coQzLrcAHB96F4RmewfYANUbdl2sTG4K6ltAK1HykZkoBE6vMM+QuHsZPkr+f/Gt94+hu4+Y2bhcZIpms9d0OGGDnLZmNPxGDIkOL8yIfJZEw2gcoQl7Iv7FeAtzDyJJtCtIqvLxTH7FBNy8R1L1yHez+4V+j97TESUIa6vb557LnaUNiA8x45D2c/fLbUDuaxhcl4MhTgR2t4ddb0H18m5D4UTnsPqTMkNaOTO9f0/dYPrncf4z9XvSDceWCvrXmr5uHKuVfK8Qc2KVIyJAFJ1IggRf9ixF7Id717FwDgtrdvExsjiYw1EizV8hNWw+9KGTKkxRgmq8UzJBwGYFUby7/AU4ibUJkxmGvghdUv4J4P7sEPXviB8MKfSAiLIYjsGGsG1oCAoGyVsXpgddBLa0YyZQiaP0zGZ5NlQ+5D2X4e10AtsdaXxZ1reh+uK6xzHxMuEWD4STp7r3977rfx0JKHcPN8sQ11MyMlQxKQCIngMpPiKYiYgIHaDN4B8ZlXjb8rrVSGBgx7oaRytmEZwpmFQYZd2WEyFuy1yzaaFSdDwerTou5F7s/i6lMSJMIDO8amIa9Wk/DnGPAWwNgUrpAwmY8MNbg6S8rByhC9D1v1VgDy/TyxGKi511P1qXuo231MuO4TT7ic79willt5vNH70cWJurrWd3d34/7778fcuXOxfPlyDA4OYquttsKMGTNw3HHH4ZBDDonrOBsaVsm/E2FvnoHyAFb0rcBu43YTGoNwakAs2WRFfy8tdox3Nr6DG1+5Ef8x4z8wY+KMyGMQLnTkLvBc6KfRd6UsGXKzWEoeGaIhJpOY0JW6bjMfgmrn8JOi6IIZFsKSWYAvzKS9cWij+/NAeQBdua7oYwx4ShZ/ThZtXoQJLRMwJj8m8vsD4Z+D7X8lWrnZWvsu0JIH4L8HCSH44Ys/BCEE3z3ou1A4QlMPeIoeR1XluMFvrHRKhpz7sD3TjkFjUH6mV8B9KByq5ja3QfOi8IbEDCZDLHkfmx8rNEYzoyZl6KOPPsLFF1+MKVOm4LrrrkOhUMC+++6LT37yk9h6663x5JNP4phjjsEee+yBe+65J+5jbjiQde/6fmdvvv9++b9x2oOn4eElDwuNYXGLUTxkyL+jZRfG/3nlf/DSmpfwX/P+S2yMkDAZf6M3/ERsVSpDdDfXnml3/ya1iapz7sokeFKLCpY2hFW5lkmGWC8V2yVdmEQwqcPseV/WswyffeCzOOvhs4TeHwhXuNhFizfw1j+GB/YeXNW/Cvd8cA/uXXCv8A5+dHiGuDCZ8yu9vtqybb7fo4JXd4NC+8IbkhBVng2JC3vR+DXEqiRDjT7vxomatqwzZszA+eefj1dffRV77LFH4HMKhQLmzJmDm266CStWrMA3vvENqQfayAjbZQHAfQvvAwDc/d7d+PQOn44+RiKZXtzuhJnsaY2LpT1LhYbgd0B0ouLJUMO3AghQhihkkiFWraOTFx++kKoMOQX4FEXxkSGZ2WTs8bITMQ1vRB6D+Zkd46kVTwEAVvavdD9bVIQRRzbMW7QEvR0h54r1InUXuzG5bXLkMVhSBwSHyUpWY9+DpOy/D3RCAOZz0ftQeEMysN73O30/qWSIS98PMmkLb0hI8PzeW/LmGFEi38yoiQy9++67GD9+fNXntLS04KyzzsJZZ52FjRs3Vn3uaIOl+auHBvl5MlpGaIyKCzmWCtTx16EIq3XBe4YavgYGqwxxZIhNgRXeXRsFwLm+6Dnh31N8Iq5cGAkhvh21TGWIvXbZiVi4bQI7BnMts0bXglFAa6ZVaBwKX7XxmLw27Odgz1V/SSyzyOL4YBAZavR7kC+6mLX8NyLr3ROBteYt/+8B96FsZSjIHyi6IUGIwsVuSETvwWZGTWGy4YiQ6PObHWG7LBY5LSc0hlVFtZGFsJ2D3DGCb3r+Rm/4UvGmN7HwZIhdbEUlevZs0ffipWzhiZhbGC1iVZi0pZKhkAVelERYbAiLuV/Y9x00BENxIZl3MsNL7OcIVdGEQ3HBYTJfunijVyg2OGWII0OylCFe+3fD1cx9KByqDim66PPtCSp1Faq8c17Ye7DhQ6Mxou5sMk3TMGvWLGzatMn3+Nq1a6FxCsmWgjCDncwdYliYTObujSdcsdQZCqmnwRuoG35XGuAZoshpOeiqLbpKbZVhVU7CgIw6Q34YllFBhoRDPyFeG5l+hTDCxV5bwq0Zahgjrs/BqkEyw5ZAsHev4e9BngxxtwFVhsQrUPtPFr2nZSpDvGpD31uqZyhkDfHdgw2eQRgn6iZDhBAUi0XMnDkT77zzTsXftkSELfDsLlRBdJ8CULvXRmyQ+ENxYeoTf6M3vjIU7hnK63lkVDssKnMiji9M5odJTOktP/j3p5DZJT2syjW7oIiGAcJUG/b6lUqGmPPeV/YWLWGVgPs9aD5p9HuQ9wxluCrO7VlJniHOY+YaqE15ylCYSVtu/7MQ9amJMgjjRN1kSFEU3HfffTjxxBNx8MEH429/+5vvb1siwkI/7MQrXgWVY/VWpddGlHCFheLYiT6rZoXGCCND/OLb6LtSMJJ8oDKkyFGGWISFyWQrQ6Zl+nw2ssfwhZcseWTIZ2624iFDYVlxMhctK4TUydzB82pHkDLU6GQInGeID5NJqzMUUJOJ99QBYmIA79cMUszFCVcwGZJJ5JsZkZQhTdPw85//HD/96U9xxhln4LrrrttiVSEgPLwkdUcacrOwkxcBEbshQz4HOwlnNUEyFFJPo5k9Q3zMQWaYzJdZZAVX6xYv7OiHQSrDZKLKEPs5CIh7bfkmYokLPEumC2WJZCjg+wDkVm4Oy4pjw2SyFy33PjSahwxVhMm4TYlroBZtixNQhiDovhY5X7zVIqiwo3BD5pC5l1UZt2QyFL0aHIAvfvGL2HnnnXHaaafhmWeekXVMTYewBnhsmEx2WmRYfR7DMiJnroXtHNieOOJGweDPUdFvq9HNm1WyyWLzDIV40WQrQ0EGauFaLdzvJjGhKmpsu1L2eKX6LpiffeE+iWQozF/F3odSWrAENIRl78NGV2f5IoIsGdIVHXndLlwp09AO2Ocq6PyLnK+wuVdm/7OwTgnNVHU8TtStDG277bY+o/SsWbPwwgsvYMWKFXUP/swzz+DEE0/E1KlToSgK5syZ4/s7IQTf+973MGXKFLS0tODoo4/GwoUL6x4nbvAdh11lqBxfmCzMeCwyThjhkrl7r9idhLTjaPRdabUwWV7PQ1XsW0smUQkybtqHItjTK2Dny19XMkkdYB+zYRm+8yOzx5pPGZIZrmbHC0mtlxkaDfUlid6HtYTJGn1DUtG13vt2cnoOmmKvU8KKCj8sMQKvIyFlKMxqYcq8dtMwWTXUTYaWLl1akTq/00474fXXX8eSJUvqeq+BgQHss88++NWvfhX495/85Cf4xS9+gVtuuQUvvvgi2tracNxxx2FoSCwjRDYqQlgBniHxRn61GY+FyFBI6iX7ngYxxHZAVvBNzytcjb4rrZZan9O8iVimuTmsqa3QZG9ZiYTJgnxJFdeucJiMef8QA3UcSh0g1wge9jnY9xUfozIsQwhp7mwy5nBzWk7ahoQ/C5ZlBZ5/kXB1qGpjyCOnVg1z75ZMhoTCZCzy+Ty23Xbbul4ze/ZszJ49O/BvhBDcdNNN+O53v4uTTz4ZAHDXXXdh0qRJmDNnDs4880zhY5aFMFNwEgZqftESGScshBXUUT5q3aRaw33NpAzxKz07EQurNswOPpY6QwGvNS1T+vcRVIuLJyZxZWGx4WrxMYZvxyGu2njw1UuSSIaCCvDxGWqNToaqGahlbkh4OhR07dLHoyI000uigTrocwCpMkRRMxkaO3ZsTdlifP2hqFi6dCnWrFmDo48+2n2sq6sLBx54IJ5//vlQMlQsFlEsehN5b29v4PNkIiyEJXVHGuJLkqqohHwOfnIvm9HJEF+GwC0732R1hnxd64OUIdWR6EVJRECRP3otZdUsSlYJVsXetZ4BzMqdL7EqVBvZYbIgwhVXCEuqQhtioGbPl8ywJfte7H0oU30CQtTABt+Q8BWoMyHKkPD3EVADiH4XOS3nXsci4/BjBLXeEVZnQ+oMpZ4hGzWToZtuusn9mRCCSy65BNdeey0mTpwYx3FhzZo1AIBJkyb5Hp80aZL7tyBcf/31uOaaa2I5pjBUVA9NIrU+RFERuiFDvCj8jlGkxkno52hqzxCXTabLm4irhclyeg6lUkmYANfiiZBJ6uj7VZBsicZjlvTIvA9Z+FLrZaZAMz+z78Xed+IVwSuVoWYLVVe24/B+zut5d0MSRxNV2iyZJUNC3zs3RpAKLFzNnh/DuUdSZchGzWTo/PPP9/1+6aWX4tRTT8UOO+wg/aBEcOWVV+KKK65wf+/t7cW0adNiHTOs3xYrz8v2DMURXgqLW1eEM4R2D8G+pIqJ2Grsidi3teaVIZUxb8o0UHMTZF7Low99gp4h0y0qpxMCQ1EC/TzSPUMkhlBcgIGaECJXGWJ/Zg3UhjyvDQlThiSGySqq5scQGo0bFWSI8fFltaw8z1CAYk7nwIyagQLFVy4iCujcy96DgJ+oxKUMsWPQz0HP3ZaEhv3EkyfbHZnXrl3re3zt2rXu34KQy+XQ2dnp+xc3+IuMLlpys1iCiUpFsUIBEhFmBJeZys0fX9ANKTpG3CCEQGGVIZ4MscqQIKkLWnzdMJlT80nM0O6dZ5qNE5Q6LDOEBThKBNfiQybhcv0QVimUUERBmLlZZkp6qIFaasjE/7tJKglwoytDfG8yNkyW1/LyPEMBZIjegxktI2UcGuqm96BFLBBCfGqgcL2kGlLr2ce3NDQsGdp+++0xefJkPPHEE+5jvb29ePHFF3HwwQeP4JFVIrR2jsxS6iFERSaJCDM3ywyZhPmrZDcfjRUm9/n5dhzMRCyudlQ2H2X9CqJjEGaCpTKxYRkVoVDxjJzK/k6yF98gElFRL0nY+xTcDJb9LOKp9cEmbZmNQYM2V/w92OhNOys8Q8RvoI5NGbK8c5VRM66XVooy5PxuEKOC/FjEEvQlBc+9zRYejQvSssmioL+/H4sWLXJ/X7p0KebPn49x48Zhm222weWXX47rrrsOO++8M7bffntcffXVmDp1Kj7zmc+M3EEHIExR8UmcxIRpmW4cu+4x+LTIGBqc1kpURHalYaSumcgQ4cmQBegEMJw1TJZ5kw9bumEy4idDYim93vFlmF1phRooqkQENAYNKhgqNAbzc9hmQWb/M/a7laoMhZi05ZIhP4I8XI18DwJBYTLvZ6nlLQIUFfpd6KoOTdFQRlmKZ4jeg6ZV+X3QsaOGsCoy1sI21JYJbIE912smQ6wPBwBKpRJ++MMfoqury/f4jTfeWPPgr7zyCmbNmlUxxvnnn4877rgD3/rWtzAwMIAvfvGL6O7uxmGHHYZHH30U+Xy+5jESQQhRqdhpEQNaxKusVgO1iJRaa2q9jLAMjY3HoT7FDVLmexIpyAGgj7ITscwS/W6YzDTccUTHsExGGaoSJpP9fRhEvjIUlPYeZ7iPjsG3Z5BZ1yYsTCauovk9KkG1cxr5HgRQkVqf4Youyit8GuAZYpQhGSFx9vvgx/CNbZluE+h6UVHjjSpDfLi60b/3mFAzGXr99dd9vx9yyCEVRRbrbdR61FFHVd3VKoqCa6+9Ftdee21d75s0wuTHINk5cko6n1ofZjyW5B8Bwgs7ihAuqgzRSdhVO5ppV8p5FUCAHBQMOBOarGwy/rXUR8Bmk4mOAea6ojQ9KIQl03gM2AuHbJ9YUINT2aGfIA+X7DADCemx5sssEg1hEQAKY9gNWHwb+h5EpULLFl30eYYEfXtB1aFdz5CakbPxcdZBeg8GJTEIjxHiO5WtAjcraiZDTz75ZJzH0dTg67zw9WAoymYZiEbqK0x8caSkJ+HnoWPQC68plSFuEiYEyBLiWj1k+RX4yYu+n0uGZHiGmGs0E7Ar1RTNDvEKhQAqP0mQYVdm6Ic3m1MIZUIG7Kz5qs30cRGwoTh248HeI+KEy+9RSUINlA2+AjVroJabTRYeJvN5hgTqfRE+TBZwDwJiJDiscG8zJa/EiYY1UDcTwnqTyZTog1QCoLLmj8guKKw/jkzPED0+nYmNs2PQBb6Rd6W8VwFEgcbMl2zRRZmeIcCfySLjXLGKo+4Mx+5KW/QWAIJKREAtI5OYgQZREQR5hqSGyWosUBlHbSlArmeIvqse4FHJquJZiomA703GZHjKzCYL2iTyniFAcO51w2TeGPS6og1nAblkiN5//HumZKgKfvzjH2NwcHD4JwJ48cUX8fDDDwsdVLMhrB2HzJT0MNVGJqsPI1xyPwcNk/nfiydDjZzJUukZAtglTFaj1kBlyJKsDLGeIVTuSqWQoQDyzPtshMdAsNdG5rULywxscBpn5h0NjRqWvyeguNncCcsw96Fbv8pZfBudDFVThnJ6DqoqyTMUlE3G1BmScq+7irlHTul1xSrNYmQoxIMoeVPSrKiJDL377rvYdttt8ZWvfAWPPPII1q9f7/7NMAy8+eab+PWvf41DDjkEZ5xxBjo6OmI74EZEmGdI5kWWSAgrpLCjVPmcv+m5z5HXmmAiNiuVIYMhLlk1K6f2SBCJCAiTycom06stjEItP7xXa0wYgJ/Y4zBQS53oiQmu2HhgtW6ZyhB9v7jGCCLA9B5sdIWAV2g1Rhlq09skNkuuDJPJrjPEK0PsdcX6koI2SLWiwqQdUkeukTeicaImz9Bdd92FN954A7/85S9x9tlno7e3F5qmIZfLuYrRjBkzcPHFF+OCCy5ovGyvmBGaLi6zWGGNKeliSkSI+sTvfEXCZM570h0prz5RU3AjT8QVYTLA9pNo9t5CURRvtyhShoAnXfArKnLqDHmvDUrrdUNxIiZU4ikqOiEwQwy7or4kn4E65B4UWhgtM5ioyFSfUBsZEi3Axy++7BiuMb/Bq8Dz3r0cc7ht2TaJvcnC6wzpiu4qUCIbBupLCroHZalPdCOqMaZ5IFWGKGo2UO+zzz743e9+h9/85jd48803sXz5chQKBUyYMAH77rsvJkyYEOdxNjRC21hwF5lYfZ4QiVPi7jo0Ky6GcB+96enxN5NETxyvQlkDMs6pyJvwmeOl7BYDzjNbD0aGZ8hXgdpZIA3iFV2kYTLRSZiagnUARdjHXOFVEMli4XxJ8aialWQoSOESzcbhv02DGJWkTjRDyvnf/c4tozJULVrxOGbQTUlJs2sMtTDKUEemQ17h02oGai0DFeKp9V7RxUqljjWDy/CE0nuwWgmYLRF1F11UVRX77rsv9t133xgOpzlRK4loeKLCp9bHoT5RAzW8An/sGM0g0VOvQln3yNDWZQPL897tJGMnxzfOBbhFS4KKxmaTBaoENBQnIM+ziopGKhdfCnE/j4dQc6hQHzfL11Gevp/8EJaT9868n/TGuc7/bPsHOpc0RagaAAxnQ6g7ZIjpTdaR7ZDYLJkzHluGL7VeTtZauIGaDZOJqU9c8oqTDVmxoW5wRTAupNlkEhBqPJY6gQWrTxXZZBKViDD1SUYFata4yd6QMvptxQ5mR0rxbz19AICL974YgCRliDnvaoDXRooyFFCBms0mcwmXJNWGLanALiiAuGrDEhU6ocsl8gFhyxhS0mupDi0vm4wZQyLJTgI0jEzvw5xFsL3eibG5sdhj/B7SlSEtIO1dV3U5maMICJMxyhBN35eRkMFmjbLvJ+U+bGKMaDuOUQM+ZdEK3pXKNNPGUh06JNwXT50hb6ygXWkjF/6iXoUyc/fMGhjC0yc/iLFd2wKQowyxSl2GEBSdbtYyyxD4lCHnf9a82aK1CI/hI0MBpC6v5VG2ymIku6JgqD/8SiHrXLHvF2dqPYDg6tDC94etPrHZSxTNogy54WrnwlUIcN9Wn0DhqG+iM9uJNQNrAIgrHfQ8ZAL8brKUIVe1cX4PVYakhMk8NZBdo9z7cAslQ6kyJAG8dBlLg1NuinS9NhKrh4bWMpJagZpKtf5xmqrOkJNaz5IhEGBcpt3dwbnZH0KZXkxBROd/lqjIUYa860WrogwJZ5M55yXD7EplqoE8UTHDVE0hv1swGZKtDPEZa3FUh6aXZbXQKE3rb1gY/vuQEAUZy0RnthOAnA0JwKo2zrBcmEzOve5Xn9hmyRmNafkhUtjR+T8oYw1oElU+RqTKkATwqg1vCnafJ6k4HvteclWbkHpJUj+HP24NcAu8jMU3ZrieISZMRogCMNXAZdQ4sThlCLCJKX1PORWoA7LJONVGdAwEmLSD0vfFzlXwpkDqtRuw0QjyPskyN1MEkSFRkyslXPQ+NIgBYnntZLxjIVBQX5ulpOAqtPQ+tAAw1cBlF13MBJjNdVWXEsICFyZj1fKsmpXU+JlThixOGXLuwy01tV5YGert7cWcOXPw3nvvyTiepkRoQ02ZlT1rDGGJqTa1jSHqHwH8LJyVZmXUzokbpGjv2Mq6t6gQAoBR0OR4hhgS4ZwPtvWDnArUTJ0h53+fMiQhtT7ocwR5n2Sa/y04fdykFl0MUYZGILVemjLEvB//fcgYJ06Qkn1Oio5syt+D0oouBjVRZdLepdQUq9IeRV5hx8rPQb9zVVFdz1Ajf+dxom4ydPrpp+OXv/wlAKBQKGDmzJk4/fTTMX36dNx3333SD7AZUGs7DhkhLDaUAXghLDmNQbkFhZpQY6iXxCpDbH+1pgiTFYcAACUdIPQO4idiKZNXpZ+nYBTcx+TUGfLGYA2ibvVbCUqdRbzrxzXOy66XFEJUZNZQYZUhjVFU2B5SomMAlan1rH+EQrgCtVJZgI8PkwGNvSmxnE3JUJbuSBTAkLshsV9fJUymxUNUWPUpq2XlhOLgtyiwZEhXdGlhxWZF3WTomWeeweGHHw4AuP/++0EIQXd3N37xi1/guuuuk36AzQB+oTBCzJsy6/PEkZJea5VrOWXnPQyZQ+7PzRC3toZs8lbSFS+DiQuTyawzpBLiTpJDhneucv3rnOeJpL1b7hhux+yAasRiCherPjG7UsKRIaGedyGZXlJLT1Q2tWXNzTLCfUFNbYMM1PIqUNvg69q44zRyuLpkE5+Cc7i2MsSEqmV5hoIKIlLVxjSgOVltMpJXwlLrpRIuxjTvkiEmKy4lQzWip6cH48aNAwA8+uijOPXUU9Ha2opPf/rTWLhwofQDbAYMVxBRRuNDy72QbfCES0axwoquxtRALbECNT1XKiFQAhb47Gv/Zz9PpK5NzHCVoYynDNkTcRnoWwvceRLUV+8AIKiiOZOsAk9RKZgF5zEFmQcuFR/DYsdgzJtck0ixTK/gWkauMqRLCPcNk/bu3oMC4T52jKCsHxkKVxAZiiO1Pii7z1WG3n3AfV5Db0qK9vEOUe5G70EAePVOaD/fx36epHYc1DNUJmXPM/Ta76GseVt4HDcUx2R6BRZdlOEZCiinwDacbeRM3jhRNxmaNm0ann/+eQwMDODRRx/FscceCwDYvHnzFteGg6KiGzCX6SWnbodfGaIXbEWoQWKVa76wo67qvsejjWG/VgFcJYL6YDRCoBe6ATT4JDzkhcks1VaGCDVvvvQbYOnT7o0lQxlS4E2SlDhmAKgMsYg+huWOEaQMySi6GKYMSa14HESG2FCcBMKFALM5GyaTkpIeUOWaXRh1xbkHJVW5Duxav3m5dzgNHCajnqEh1zPkqLOFzcCDl7n3h/C5IpVzr6sM9a2B5pxNmXWG2A0JS1Sk+JLC6iVJqsvUrKibDF1++eU455xzsPXWW2PKlCk46qijANjhs7333lv28TUFhkutlzFJusoQcyEDHlHJF/sljFE9TCaj5oxv8eWUoQwhUkhE3CAOGSpm4LaZAFFsz9A6O5FAkzARU1OwAm83550rQAuoEVP3GMRThtyUW8YTkX/+ZnsMSb6kDLMrrSgeKaTaVB5fULhPhHCx3qcgrw29B0UTDPgq1z7CJSMUx8BNxGJT6xkC1KgLIzFNt8TFELU4Ud/eBjtCobkqS4yp9YR4pEtCCIu9P6gin13zNpSelQAkheKc31lPHWsE31KVobpT67/yla/ggAMOwIoVK3DMMce4jv0ddthhi/UMBSkqpmW6N1FOalqkDXoz0hsmP7ARyGWlqDYUbo81ujAaRfRBzLxJP4cKj4lTz1CGACrXpqMRYW7eDADobwEIVYYIbPPmxkUA5HwOSoZUQiqUIR1yiKNLuJhzz4Yt8xsXAxMnSCn2BoSQCI7gR4EVQHJ86pP7PElZcUFjDGwAcllh9SlIGXLDlpaBfsQcJmPms0a9D83ubgC2wtXbogAgXjYZvQepcgoCQoibAl8vKjxDrKIC4iOUUWEFhMlcz9DyedBaW4Cs2PwOThliSZ2u6tCIuMLVzIhUZ2jmzJmYPn06li5dih133BG6ruPTn/607GNrGlQYj7l+RfnulUA2I7aD58u1c6oNncBkEC6VEFiKUtHSIFcaADIZeanczmRFs8kyIFJCP3GjtGoVAKC3VXHJEKh5s28tAMiZIJ0FXgXjGXKyyTKEMC1NBMipRZU64n4fbMZaXsZ1xZBnNhTnKkPv/A3oaJOaWg/4fUn5nlXCi4kv3Mdm/VB11jlHom1FKBVRCAHhKh7nhvqBjC4tTEaLefr8VQwZatQwWdm5BwdbAINeVDRM1mdXnmbKgMEilqt81Avez8NmerEbOBnKkMacbropyRK5IXF2bnIVrr61UDcsAfK5LbZRa91hssHBQVx00UVobW3FnnvuiQ8//BAAcOmll+LHP/6x9ANsBlSkvXNF0rISGHeF+c2y1Sf6njkpu2vnhnR/dwzUdFcqYQyWcNFJhJqCdSZM1qgGaqtQwMBz8wAAb2+jwHIN1ApQLgDFHgCQJJ1XLr70XGUsC4pzjoKIQL1jqPDCCr7sPjdrqlTx2prHCKozxPp5LHp/yDFpBzWD9T6HCKljw2T2/z5SR8fgzM51DuISFfc+ZDPW6FwiklrPmLSDPEM55tZr1Gyyvn8+AQBYOA3ePWjBNlAXbOVWZeYQGRaF4cJkYsTRv9kFWMVckvoUYLVwFa7yoK9p75aIusnQlVdeiTfeeANPPfWUzzB99NFH45577pF6cM0Cl3EHTJCARyJkFEQMupABbyKWUYeiQn1yDM4yxqBFF30GaqoMEbgLfKPekN1/uQ+kUECpS8HSyfArQ06qO8D6FQQWX9NThqiE64bJiCVVfQK869cbgzCLpci1y2aTeefFIxFO9qUp4OexgseQGYpjw8i+pra0JpO7exer1k3c1iXeMVcQLkkFXNk5K0gZasT70BoYQLdT0+61XcAVPi0CQ90A/CqLjFZIPtM8TSqR5HP0soW9gy6U7Y1PlhAp82JF8Ug2tZ5RuNIK1DVizpw5+OUvf4nDDjvMF4Pdc889sXjxYqkH1yyg8V6aesmGyTQmnCGljUVANg4gN0xWadKmk70M9anSQE2zyRrdQG3292PDr38NANi0fxZQFHfhIgRA/1r3uV4mS3SVwJdNVmE2t30+gOjCyJJTfgxvRypDfQI8AmwQA4blVyKCfD81jxEUirOCSITAPciQNd99SBUVR3Xiw+b1wK+iwR2DV2fFvnPTyyZj6z65CyOBKmPjExM23nEHzI0bkZk83iZDrpzsJDG4ypAHGfNiJoBEZJhxxIiKDbZno6sMQc4a4oX7bJiE8Qw1iUUhTtRNhtavX4+JEydWPD4wMBDZoNbs4EkEuyNlF3ix3Qm9IZ33svxVaeluTigFmvA3i9+XlLdkjOEtvvS8uCmkYCXnxrshu//8F5ibNyO77bbo3tM+S4SdiCkZapvoqTZG9PASXfAU4i3wPs+QjGwyatJGsC+J3tEiypBlVioqFrFQdna+VBkSUp9oWwGfl8r0iLwlQ+Gq9Lv5KjdLIKes+hRUc0YGqbNN2vY3G6TU2SoBxMeJAVahgE133AkAmHjWJ2HqCky21pdRApzyHFrrVu7rxOZeG26zZJ9nyCOOMuZ3tjI/u/GRsoZURBcsX7iPn/e3NNRNhmbOnImHH37Y/Z0SoFtvvRUHH3ywvCNrInjyo/07u8vKEE8BkVOhlF7ITLNAQqSMwTdRtSynv5Nzc2RlfA7nXPnSxU0aloG3+Ir4LmJC74MPAgDGff7zsBzWZrHZZI5xEx2TvcnLFPfa2OZmv2qjwyMqYq0yWMLljMFk92nSF3gbpmXCcD5LjiEWUWExKhoboiyXB50x6DUt8H3QMgRMdp8/C0vcG+hTn9iQOD1XFv1sYqFqWhKCJXU0jO+7thqMDPU//TSsvj5kpk1DxwG7gcALk7lJDFQZ6prqvk4s06vSPsDOvTKVIQ1wi9GymxIp2an8ZpcPk0kgdc2MurPJfvSjH2H27Nl49913YRgGfv7zn+Pdd9/FvHnz8PTTT8dxjA0PPt7LSudsCrSUbDI3rmvKV5+4bAPaMoHeqDLCZGztHHqDs8oQXchIg2U0mD09GHKaEbd/YhbI/XaygOsZsuB5hlrHQSvZ2S5EaPFlVBvnMddA7SMqstLe7f+9wo7MJCxUOyfAQE1MGE54VArJDlAcDWLAKA0AYM3NIupTpYrG+kc8ZUhEOa0M91mWhbLzOWScKzZjzR8m82+uyorScAbqgRdfBAC0zzoKigIfGSJE8RmotfapQHkTANFrC4DikaGyVUbGspfODKlMOIk2hrdJ1AAY4OwDMsNkTKiVLRHgitwNGBpNAnUrQ4cddhjmz58PwzCw99574x//+AcmTpyI559/Hh//+MfjOMaGR7Uy5/IzAZwxOKnW9XZIMFD7bhYzHpM2G87wKUP0pm+wwl+Dr7wCEILs9tsjM3Giew68OkMK0PeR/eSWsVAkFDCr7hlid4viYUuVqZcS7OESV20AXj31G3aJwMLrms0JF642/MqQSBkCy6pUn+wsLKpwOcqQyOcIyrwjJsqOSuB6hkSyLZlaRr7vw92UoGGVocEXXwIAtB1wgK1wgfUMwc7odMiQ0jnZfZ0cA7XzXkzmnV0ORIZibiNIBZa1hvARDIPwBmrxMZoZkeoM7bjjjvjd734n+1iaFkGN/ILCZHIqlDJhMnpDEk9alZu+b/jrJUlRhlgzLacMsYtvg2U0DL5kT8KtBx4AwJtYfNlkQ3ZaPVrGuk0PRT4H9dqwIUUqnUsvugjvOvXkeX+2UeQxQozHvLnZlJAVp3BKLPUl5Zl7MyqoL4ntFWcSEyU3FOc8T4ScBvRx848hQxmyKpUhy0TZyer0lbhoIJXAWL8epSVLAEVB68yZwJIH7cgY6xmyyvY/wPbubSAwFUXsOyHEpwzZaqD9N2nZZLDlJzbLdsgh8hkw9gEJyj+bseabe7fwMFndypCmaVi3bl3F4xs3boSmRStq1eygO8EgwyNboVSKwc753RcmYxdGIf+Iffwas2DRMRRC3J2RyMRCp2F2QQmqqkwajAwNsDtSeOfA5xmiaBkL1e0lJGLY9fw8Fb3J2DCZkKG9sldcXOoT4G9TwnvRxEhEZQiLDcXJTK1nDe02ifBSoAE53wfAhd1df5UEZYhp+eFvnMsUEpQQlpGNwZdfBgDkdtsN2pgxDqlTGM8Qk8CjqEDreCkkIihb2Heu6POECKozLzLnvsAqQzLuEc53CngqsA40JAFOEnWTobATVSwWkc1mA/822jF87xoZqg2vPvnDZO58INQ2wf85LE7h8vwjIgXf2CJ/NrzdCVNnqIHCZMbmzSi+/z4AoPUAvzIEVxliJuKWsVBVeWEyFd5kyBIVGaGMwF5xJusZkjEG64Px7gV6bVESUdmIonZYTI+1IDIvxZdkVX4fBjEqiYrIZiEgtd4iFhOKoyFFgUUrIBTHljqwDdSUdDUOGfI2JPvbDzhGcF/hU4r8GEDPyUle4esMWQbKAUUXZYxh1xSj92FluFoGqWPT94s+k7b4GM2MmsNkv/jFLwDY2WO33nor2tvb3b+ZpolnnnkGu+22m/wjbAJQGTUotZ6NxQply3B+HtaknWVuSKG6NtwYBpO+71sYRTKkGKOg2wvL9Ir8uRNLAylDg6+8AgDI7rQj9AkTANDPwdUZomgZC9WxQYooXL5Grc5jnoHaM5uLqAT0PLNqB9ul2+vvJE6yWZ+YYRkoc9XThXa9Jhvusx+z096ddhyWd99EBUu4/Flx/qKkYn6egJAiE8Li+4ZFaTERVJPJJqf2uWnUMJkXqj7QfoB6hlwOxOztW8YCWkYSmec2u8SA4tTU8CUZiKjy7k+Ke/2ym2yptYyY63Oo2Gc/RoibQ9hI33mSqJkM/exnPwNgn6hbbrnFFxLLZrPYbrvtcMstt8g/wiaARcmQO3l5u0VZ5jd6/QY1iGQ7mBOhIn/OIujurPl6GvbzRNoN+Isu2o95hIu96RuIDHEhMoBOVFxvMgpXGTIE08W9c1WZWg8oeos7dORGlHQMRXF30RQZAmi5dudYREJYlaqN3THbHltmeEklnDIksywE63fzbUq4gogSFC6A96j4C5/a41jQUD8ZChrDl1rPbuAaRCUor12H0tKltl+IJupwZIhUkKGsVBLBKkN0LF1rkTSGs0nUMu71S5HJtkuJLtAdm08ZKvfbYwAwJJC6ZkbNZGjp0qUAgFmzZuGvf/0rxo4dG9tBNRs81cb+3SQGjDI1v8ktyuUZqK3AMJlQ6jC/A+KLi9FFRkQZcj6HwigbbtyaEKiqXdqskcJkA88/DwBoPegg9zGbOKr+bDKK/BjPQC1YKRjwG6h9GYS5LvepkVUCJn2fnwwyhEDJjwFgipmbrQDVhpgwUEmGopI6bwzi6wtH1Y48c99ERkCpA1t9su8HLysuOjllCZfvPjSDlaEoIGawL4mSU13LSeq3JQ+DL9j3YH7PPaF1Odc9n00WpAwx10JU8B3lAYDW8M7kOqESe4MiQ31S1YyPrNhjdEFDr/AYQZ+jWLZLNuiEuD6yRvKJJYm6PUNPPvlkYkTINE1cffXV2H777dHS0oIdd9wRP/jBDxrmBnVB/BeZaRpusTedDT+IZBZxrJ4t0e8LYQkoQ9VDcYzBToL6xC6MRcNLrVez7b7njTTKa9eitHgxoChoo/I8vMmLaEydIQqfgVo8JZ1VOyhsouInQ5HGsCyc8bSJr/3Bgjbk/1uGEGj5MfbzhAy7bG0pGzYZst8zq3pew6jVzYOM4KxJW46fh80m8+6RElcdGoj+Odg5glVtKOGi4T5AgAyRyow1iyVDLWOlFPmTiYF5NhlqYwv7cmSIEI4MqZ7KItZP0b9JZJHJd0npWt/ZDdz4WwOdCzK+BrPeGDbEPIg0XO09VqTrlKIxFoXG2YgmiUip9StXrsQDDzyADz/8EKWSXyW48cYbpRwYANxwww24+eabceedd2LPPffEK6+8gs9//vPo6urCZZddJm0cUfDGNJMYKDuMmw0viSkq9Ib0drglJgXau5BFOmb7x6gsO++MIaFEgL9Rq5curuTaABQaZhIefOEFAEB+r728HSnY1Hp7mqrwDKn2rSVCgH2eIW4i1gmg5TsB2NdZVGmbwMKp8+w3716WAbbx/pYBoLSMBYyNYiEs+jmIR7RLZsl9x5xDuAB7QVGVuvdoXIFKbyEvw+9LEvM+sdl93vGWLT/hoo9H+Rxsgcogwy7bRDXqAu9Thpz3KFuGa2DXW8ZCwUYAjREyIYS46mzbIQFkiCpwXBIDGyaTkk0WcL71fCfUgTXucUbFoc8CW28E8ASBtq//b5mWMVCLH9rHIuQJdcgQ7O/dUBQUndITup6H6qxZIutUM6NuMvTEE0/gpJNOwg477ID3338fe+21F5YtWwZCCPbbbz+pBzdv3jycfPLJ+PSnPw0A2G677fDHP/4RLzlGuiAUi0UUi0X3997eXqnHFARPtXEmXKbVQEbRoMGeyITMtJz6BABFxvzmLr4inb8pGXJ+NyzDrW+ig7gZUjJCcYqieo1aKRlSdCdM1jhkKHBHCkYZCpyIx7jfh1jtHC/0U6EMgUDNdQHGanscy0QE+4jrGQIAregP6+iEQGsZC/QJKioByhANjQJANj8GgH0tR11Q/KqN/Rgb+slpeedYZHwOL/RtG8EdMqQwClfkz8GQIed/g/Hu5RRvyo66wLPp+25Gp+HJgnp+DFRjo/PckVfhS0uWwFi3DkouhxZ2jaGp9VQZqhImk+kZYpHJjYVq226EVJuOPu9nnb/XW8ZB7bZ/FkpeccuaKPZ5UZhsMr3FJUOkAVshJYG6ty5XXnklvvGNb+Ctt95CPp/HfffdhxUrVuDII4/EaaedJvXgDjnkEDzxxBNYsGABAOCNN97As88+i9mzZ4e+5vrrr0dXV5f7b9q0aVKPKQhB3YDdMJmqM80uRVi9DVaqHSrZRC8DuF4bIqLacL4kg1gol/rccVWdLijihb8URatYGHVVcwmXyOeQBd+O9OCD/H9zlSEum0xvATItbphMpNs721G+wkdACNSWMRXHUy8swyO2/GRgj2GHxEXS3r0q16jwiQFAtsULu4uE+wB/jzWTmCg7P+cyttlc5HPAqiwLYY/hEK5sq/fUqCGsoLR3RhnKZ9vExwhoj1JkFlnbByM2hkzQDUnLfjOg5nLeH4gFS4G/NxmFm00mHu6zuPmdQiEEWssYT4kUsSgwN5/G3+st45hMXhGLgkOGGJO2m8mr5+VEF5oYdZOh9957D+eddx4AQNd1FAoFtLe349prr8UNN9wg9eD+8z//E2eeeSZ22203ZDIZzJgxA5dffjnOOeec0NdceeWV6Onpcf+tWLFC6jEFIagCddmnDNkQUm2on4fZGRZK/e4YihOuEfOoOB4OlwyZKJe8bAPP3Cz+OVRVr5iIdVV321g0wiRcfP99e0fa0uLfkYL1DLnbUhvOYuUpQwLfB1NIkN8t6gRQufBSFBCGDCncKc8wY0QlW4Df/+X6xBgyxIbJIn/vvppM9kN2Nplj0tZtoiKW6UWVIcXftZ6OkREnKlZImKxEq3UzY0RWn5gFlX6OEvN96Lkur85QA9yH/U7Py/ZDD/X/wTk2rwJ1eJhMSiYv8RRBwMnobBnrJa+IzIvMoVeowPkuxncqHiazvVQ2XL+mloNK15AtlAzVHSZra2tzfUJTpkzB4sWLseeeewIANmzYIPXg7r33XvzhD3/A3XffjT333BPz58/H5ZdfjqlTp+L8888PfE0ul0OO3T0kAM8z5O1IDSo/qjpUUKIiIdNL9WIhQ07zxoyieWMIKRF+UkdAUCza6lNWUR0PhCmYIUXDZJ5hr+iYQ3VFZ4zHIz8J9z35JAA7RKZy11SlMkTDZfa50VQdMMWM4L6O8kHKUK7TO56o/hHDm/gqlCFVg6pTRUUAlonWIYJtNgNaxq8MKYRAz48BnChNdGXIC5OxZlN6x9mKSknIAeNrXQK6YTA89SnbDmCz/dyopIvWMiLEF652azJlOwDHzxOZaDPnOOOEeYtMQ2E919EwfaqsgQEMus1ZZ/n/WK3OUNsEQMtIaWbMtsrQCVByxssQAuQ7vTEkkYiKez3X5c7vQr5TQrD9GgI9q7sWhRKde7Uss4Y0TlmTJFE3GTrooIPw7LPPYvfdd8enPvUpfP3rX8dbb72Fv/71rzjooIOGf4M68M1vftNVhwBg7733xvLly3H99deHkqGRgNdR3oZdPt8J/Siat8CLSJyUcKkZ97GhsqcMqUQBQMSUITdM5j025JChjEJDWGJkyA2TqRo0Z34qOudFV3V7DGvkJ2EA6H/K2ZHOOqrib3yjVn7tUyQoQ8QXJuN2i4oKzQlbioxDDO91Kj8JqxlomueDsYgVyRRsEROX/c3CfksIln1aAyYARcNrA6AwpC7q52B9SZ4puAzT+Xqy2Q6AbBb+HACtyWQ/ZljBZCjy5zCDM9bKDh3N5DqgGBtAFEUg844puugozZScqoRAzTdOmGzg+edBymVkpk1Ddocd/H+0+GwyRl7p/BhALFfhEm/UqjAk2B4nQwiQ62Aq80clpwSW4r0vb/3L5DqhKeJEZdJKgov/bKJ3OxXa6fZjQ7Q4sJZ174k0m6xG3Hjjjejvtxfha665Bv39/bjnnnuw8847S80kA4DBwUFXuqPQNM31BzQKCCEY20eQMQLCZKruXshiNySQLxKoig6FWCCK4jXyU3VHvjWFLmSP1HmT7CD1DElSnyxi4dRnLUwdq2LDts75oiFALWOHlxqADBmbN2PorbcAAO1HHFHxdzfcQrPJWsYDGAB2/AQAQNOckKKkJqr8jaqrWSiaR4wj+0dKjGeID5OpOlTdU8SikghCTOy3xD5fYxdmgen+2lKaBIULxEK2TDBmAKCZ+iVmF53LdgBOJCh6ppcFxSJoLXphS98YuU53jMhKHeOv0lnCRSy7WWiuE6phK3VRDbuWZWKH1QSHv2NB391v9NcAINvuldEQ8VhJQP+zzwIA2o88srJuk9NjzSYS8CcxjJkG9K6W1O3dBu/d0wmAbBtUh8REJirOd0vBFz/Vs+1QKFER2FDv8o79f+cyyyt4S7vW+8hQqgwNC9M0sXLlSkyfPh2AHTKLs+r0iSeeiB/+8IfYZpttsOeee+L111/HjTfeiAsvvDC2MaNgq48Ibv6jiWV7ZfCnE+0bjzaIzKgZKRdZx0aC2/7PRM/uCrQTAQNAocyE4iwTgClkPKaVtNmsiQIlQ6oO1RKvndPxURnHz7UAlPD2pf6/6WrGMx6PMBkafPElgBBkd9oRmUmTAp7BKUO7nwzstBH4xHcBeJ4hsUJsng+mQhnSMk4YwO7KHXkcVhnivtaMmoXKKUNR4PsuHcnB12Mt1+GNETGQZVkmfnSniW3WA3d9ngCt3hgAkMt3CRMVyzLw1QcszFhMMP8UBRgDFJxwOADkct4YUszNNBTHqE+ZXBfUAZsMRVaGLAvX/t5E1gRITw441fubzqsdDaAMAUDbIYdU/pH3DCkqcMCXgMl72d49LQPKk6T0DSPE593LgACZVm9+Fwhbst+kn3ARqLkOKcqQannqEyW7Q7THGkuGGqj6f5KoiwxpmoZjjz0W7733HsaMGRPTIXn4f//v/+Hqq6/GV77yFaxbtw5Tp07Fl770JXzve9+Lfex6cOAzJlQAO7ytAyc6xmO689XkkKFd3wZ0Cxj/jgH9BNWuEeGqTxkoCp28ohMVrWThskdNTJigA45XsUDrJak6VOKQIQFlKDPkvTZrcDsgNQuFpu+P9I6U+oWCJmFUGqhJxzTgs9e5f1cVcTLkvVatlM7VLMBkKkb22jCeIcX0qyUZLQONKYgYOfTDmLTplrRkMm1F8p1QCAERIHWWVcY26+2ft1umApO9TBnAISoOoqekWzjkPfv4p36gA9v7jeAZriJ4pDEsEwe/a+GQ9y30H0KALupLcsZoGWOrwALnihADWecUKOv4cgoAcp1SmgCLorh4McrLPwQyGbTS5qwMqHLqeoYME/jUT7wnMFlTYp4hG4qW821KMoQ4maOO8h91frdMEEYaYudFewyPcJkCREVjXkoJV5GwZIjaOdIwWU3Ya6+9sGTJEmy//fZxHI8PHR0duOmmm3DTTTfFPpYI+GXb7jJNM6Sy4jcLgJZB72c3E4B2F1ezUBXDGSP6TT/1gxIOeJcAyEE7xFYcCk6JgKyahUJoufboN4vJuAOznBfQJo7iJEIUVqmEvieeAAB0Hndc4HP4rvV8Cj0NkwmFRmk2maJUKEO6ngVUDZpTPE2GMqSAJ0NZKEwPQhkmbbpTd3usEQJk2522tgIkYsi7mCzdP4ZCiByiYprekuVcx9T7BAB6vguq09ZAREW7+DELHUPAoq4MMJlVhhRk82OFCwn66p2ZfjKUAQFy7Q3RjqP30UcBAO2HHAKNaQxOQRVH1zPEz31s0UUJlZsVPefblOgEQKbNtQ9ELqPBXSs686sdimuFqgTPM/WAzVhzm2Q715Cu5ZwxyBarDNUdOL/uuuvwjW98Aw899BBWr16N3t5e378tESa3bTeJBYO2ylAzbs8osdo53qREY8oFGorT5IzR0uPdhXTxLTBZcZoMgx3TTiDHhb91LcvUGRo5MjTw7HOw+vuhT5yIlhkzAp9juWEy5xYy/cfrlQiIvpiwNZl4ZSir5QBVF6+wW2Y8Q4QnQzkpyhCbvk8XWdrCIgMAek5ciejzwlV0ondbDYBW6xYbg5QZ4ugccMGkVeAJlHyncBaWZRnocASt9m57kJJZgukMyLZmiO5LYj4Hd5hemMw5nhGsQN37yCMAgI7Zxwf+nW7K3EWeVzRUL2NVqOCt87+i532bkqyrDNkHEFm1ISZrGULW5ENxHuESInUK877O29D8Sl3Pe8pQ6hmqDZ/61KcAACeddJLP0EYbE5pbosTG+fpMELeXUEbPwXRS0kWUIV9M2fl/iBIuLQdFsSd+ITmYSSfKWARFFSjQcIaagapYABFUbRjSoHOXiq5loTjLvlBDTUH0PupMwscf59Zv8oF4bg23HQdXQ0qTEO5zzbSKUlH9NqPlADUjvjCyyhBPhvQ8VAkmbSuAcBl0RwrFrntCCMoiYbKyx6wzznBFmmAgqY8bKYcrXG5mkWPLiO7nYTxcFh3DI3qZ/Bjh5qO+zYwSFCbrGPE6Q8WFC1FatBhKJoOOT3wi8DkVylAFGWI2C5ZI5Wbn7bQsdMJcZ7DJkLsRjarKE8s3wbPKUIYQINvmbEQtWESgsCPzVedosg8dU885ZMgQK53SxKibDD3peClSeOBvAdtA7cVioYorKuzUmjEIoAFDlHD5zG9yZNSWMkG/zqhPahaKWgZMsTFUo3J34v6u5UA1kJHKYrGKRfQ/8S8AQOfxIZXO2e9RC1aGZBio3etFUStrj+iOMiS4MPrJkF9/ymg5t9AmIIlEcDsHHQqgiXufCEO4aPiVKkMZQqAxxQpleJ+oOjvE9CBErtNRpUTCZJVkiPr2AIcMUVUyKgFmrl9uH2cbhLPtUurziKD3ETtE1nbYYdA6O4OfRJUhr0u1uykHACia16hVqFCsczL0HHT0u49TP48i6uexTF8mZ4ZVhhwypEhOe+fnXl3LM97WxsrWTgp1k6EjjzwyjuNobjA1IgCqDNH6DTmYEjqYs1mjeedtaI2IjJ5zFYzIuxP4ZdTWMoAWoGB6hEsznJ2YwOdQGDKkW/7JXNdyrmwvEl4SwcCzz8IaGIA+eTJa9t0n+ElOTyQAsKiB2vBPhKoUIzhVhrTKfkV6ix0GEFwYfZ4h4l8ac3qLTxmKHIrz1TLi1CeoPoUruveJ6cTuDOeFyRQoelbYpM0SLs3x2gy5hAtSjMdBZGiwxCzAjGdItFq3jQBlKNMy4gZq6hfqDAmRAZ4yZLCXlGEAGeeaZTcLElR5VW+BxggzGcfPQ+0D0T1DBBrzUjabLO8QLk3CZpedQrJ8GQ29xZuzUs9QiqhglyHFWeCLlKhoOSYWK0cZyjs734KbCeDFe0UMu6x/oMW56SnhympMppfABKkwBEjnzJu6nmNIxMhMwgPPPQcA6Dj66OAQGWDXN6E/6o6SxZMhhdZviU6GvCJ/akXtkYyel+IZYpUhcEQln2l10/ft44m4wPsM1P4xdEWR0lCTPf+0mCcNL2UAOYSLJUPOj66nDgTI5MU9Q8zr6DmhxVV1QqBmWsTDZOzr+DCZonDh1+Tvw/Lq1SgtWQJoWmXVaQb0XLFJkL77kLk/xNpY2FD0rK9VRqUyFJUM+ZUhNkyWJwD0HJOEI6DKh6hPAKAz/RS31KKLKRmSAF8Ii+5KqTKks0QlqsGO+C7knHMhDzkXbUbPMwZqOUSFpt4WXFKXtVtMQIwMsWGySs9QnjFQj4wyNPja6wCA1pkzw5/E1AVxe5OFeIaEenrRxqCKWiHhZvQWOV25zXBlKJ9plZK+X82XpLvKkCDhYszNdJft9l1yQnGiRIUtUKk5MeUhGkaG4lMipJQ6cP4vsOoT03xUhi+Jr2Noe7g8xXEkzLSF1+17ML/bbtA6OkKfR+c6NoHF5xtSVSmfw60zpOd9qg1NrddElX9i+ZUh5uccFEBRJNQy8q8hFXOv3uL1JttCPUMpGZIBZkKh5k2aspjR897CGNlgR3wF8SjhGqIl+vUWRrWRE8KihItVn6QoQyZLhvjdSR6K4lRuHgHPkNnfj+KCBQAQmkUGwCZDtAuHk3rOhlAAQFH8lX2jwD3PqlZZdFFvcXa+gkZXg1Ui/NNBLtMOqLq4MsQaqC1OiVA1QNOFKwUHK0OOudkhXOIKFxvCss+Vm8QA1f99RFQ2WX8V/Tao+qQDcshptTCZovqLFY4AGaIbkqr3ILzP4VOGyn4FiFaHNqNWbrYsWPQc6Xmu6CIAvUXcz2OZPjKUZ+bFvEO0aKWxyPO7ZfqU/yxvUdBTZSglQzJgVaodQw6J0PW8m2YtIqOyN0vGGa9Eq9Iyhb+EVBv2ZqHKkEvqclJqALHKkMYvjFqeMR4nT4YKb7wBWBYyH/sYMpMmhj+R+fwkxDPkqmgipI6wypD/XGWzbc4OHs448lWbfLbdN0bkUByrmlWEyWyi4i6+UcOjDKnjw2Q0FOdmYEspEeCkvdPNgqL4zlVkc3OA2ZxmdGagOIUEbYgUdvTAG9opqaPPTb6DOVWGWverTobo/eGbRvj7UEKrDM8zxClDUAFVFS85Qiz3mgW8jSgA5JxvQhNNwuHG4JWhTJYhQw3QF3IkkJIhCfDHYu3/h5wLKqO3eGnWkmTULH8h63IuZFa1oYSrQJu36nkpYbLqylDLiHqGCq/PBzD8jtTnGcrY56TCM6SJK1z0PCtQfA1TASDjhLC8jtlywmQKs4jnsu3+hTHyAl9pCqbIKJov9CNDGXI3JJREKJqtDDl/j+4ZqlKGAPYYooTLKnsp4FRxcJMYnDIEwv6qKmEy+1wxXjSBXlhRYA0OYuj99wEMfx+617yiAHrwfagIkyHTVYGVTN7vGaKqjehGlNvs5ljPkDPnyhhDZTyaGb6+VKaNUf63TAN13dlkM2bMqGyYB7uTcz6fx0477YQLLrgAs6oY30YVLMuncNjNWhWXRLDGtOjKkBWoDLm/Z1qZTAAB1SaAcBWcJoJZvcXtxC42BqMMBcWt3QanI6AMOTvSlhp2pMN5hrzJS8AzxJAhvYIMtfkWXxmKCiwFOUIw5Nzf+VyXlPR9XzE8YtdLodAV3W90lRDCcpUh2hJHUW3PENMFPhJ8pI4jQ4rqCynKUIao+kR9e1lqNhfsku5rt+BUHabQFS7cl3DIpPDW24BpQp88GZkpU6o+11c8MqODGEZFrSGXAAu1yrBRoQxRoqLSkLh8ZSjvlLZQFQ0gAgZqYlX1DGUyrYz3KVWGasLxxx+PJUuWoK2tDbNmzcKsWbPQ3t6OxYsXY//998fq1atx9NFH429/+1scx9t4IKaPROQoiXBDWB5RESnKpRkM4eJJRKZVSoNT3+dwwll02IzeIuVmYZUhPkyWybS4E0vSVIiYJgrz5wMAWmtQhtxeQiGeIU8Zig4vmyyADDmqjXDvJYYMEeJU1XWQy3U6iorYAs8WXayoeKzqzgLvPFdCWxGa9u6a/zm1Q0aBSpUzm2dUTb7Z3Lk/6HJsh2WY0KgZrZCgVSoyvwV4uBTF9dokHSYrvP4aAKBlxr7DPpedTxXNmTdCPEMiqrw7ip73ZXVmnbnKTa2Peu1a4cp/ziFDwh0G+FpGAcqQa1HYQj1DdStDGzZswNe//nVcffXVvsevu+46LF++HP/4xz/wX//1X/jBD36Ak08+WdqBNiws08fqqfmt4NQe0jOtbngpspnWCvYMub9n2pn2DyKeIbYGkP9vGT3vTjiy1KcM36iVVZ8SpkND770Pa2AAalsbcrvsUv3JrGcoLLVepmcIil28k5XoM21Sspd8ZMhSfBOCrQyJe4aUKhlrGVX3h36iTsQ+ZchPhnQnTCZehoBZfLnPYStc4h4uf3sU7lzx/qqoRIW5VhXYfe8MRw3UnXnECy8luzAOvvQyAKB1xn7DPpew59gJk/GeIdoqI/K5Il4TVTXTigzzJ6oM0RIcspQhth1HXst5Y5gCawixfHMvb1HIZNukRBeaGXUrQ/feey/OOuusisfPPPNM3HvvvQCAs846Cx988IH40TUDiOnrBkwVFRpqyGTaGD+P/NRLAMhkW6UU+VMCMta8MdpcA7VIujgbJuMJl84oQ0mHyQbmPgMAaD34IF9z0kBYpmfa1ILJkEazyQQ9Q1M2Ekz5qGz3ImOQzXX4F9/IqfUsGfL/KZ8fI4Vw+UIXFdlkOhf6iRbOIMzn0BwSMUT9bqouJSsOPoUriNTpwg1OfQZqboys469y1cCIfh5SYhQlxa8G6s516ypDCfpHrMFBDL5sk6G2ww4d9vk+71OIZ8jNJhMIkykW8PGFFhRTQ47tWl+h2kT1cBm+mmtsQcSc0xtQuHQKR7gqlKFsu5ToQjOjbmUon89j3rx52GmnnXyPz5s3D/l8HoBtbKM/j3pw8mMuMIQlan7jPUP+P2ey7VIyvfxSbaUvCap43zB/BWr/33S91TXxJd2Oo3/uswCA9sMOH/7JrGdID/EMqeJhMkIsXPMHE10Dm6BfNAbwOkrYYTKGRESvDs0cIfGHJ7PUMyRsbmYUFf7aVTN+z1DE0A8C0t4pdNVWbYSrKrNlCCy/1ybjkDoZGWv0PfgyBBlFBxTmc0Rc4FmTNrHg63unu4bd5JWhgZdeAimXkZk6Fdnttx/2+eyi7ZEh//F63d6j13g7bD5wyr8skM2vI7d7JRlSFN3280S+B/2k1qcM6XlnDHH1ye8Z4pNX2lxVXqRwbzOjbjJ06aWX4stf/jJeffVV7L///gCAl19+GbfeeiuuuuoqAMBjjz2GfffdV+qBNiy4TICKTK9sOxMyEZA4q6ZFdkiROO2ii45cXhEma4UhQX0Kyryj0LOtsNxzlRzMnh7XL9R++GHDv4B4eo+bWs97htzvQ0AZskyMcdpSkfUqwGT767kOOWEyThlij1bJtdtknx6PhPR9/i10NeMvHhmRDCkVRMUDJVzC2WQVrUtYEkHHcPxVEf2Bvuuo4nNogKoxGYRRyZC3+BKL+JUhGvoRzcKKgAFnQ9J2xOGBSTo8fAZqlwyF1BmKbDw2cfAbzhhPvI3crgwZctRaT7WJqAYWi77fWWWIkiFhZYizWuj8taVlXTV7Sw2T1U2Gvvvd72L77bfHL3/5S/z+978HAOy666743e9+h7PPPhsA8OUvfxmXXHKJ3CNtVBDCZQL4/5zJtnshk5gkzowkiVOrEibLZtthKeILvMrMrXw2WTbbjpKafNHFgedfACwL2R13ROZjHxv+Bb5sshDPkAQDNbv46iTj+5PKpb3LIUPEf7yZVqBcYIhKxIXRZwr2/8kmKmyYLGLohwnFVRjzOcIVPSvOuyYrw2QZO2wpSFT8rUv8f8uoGcfcLDgGS4ZMy65s7SBLs5cg6LWJgP5n5wIA2g+vQZ1FsDJUUWeIdnsXqDOUYW0QbJjMSWrw/DwR1cCyfwOg+5ShFmcM8Yxkfx05ThlSdSlFdZsZdZMhADjnnHNwzjnnhP69paUl8gE1HTjGneNNwb6094gLPGd+qwhh5Tq8CzlGz1BJgimYNWlrXK2WbLYd5giQoX7HL9R+WA2qEODPJgsxUGsSPgf7norqJ0PItABQxCtQc8pQmd2RZ1oAq+yF4qLWnGHGqFCGtIzf+xR10aqiDMlSbdjPUUHqtKw/m8yKpnD5sslMv/qUlaTasG1FYPqVIZcMJRwmKy1fjvLyD4FMBq0HHlTTa7zSE3ZqPVCtzlD0LCz2Ds4z5yqvOaqNYMkRn4cLfmWoLdNujyGaIMP3P+NVeVWXUyi2iRGJDAFAqVTCunXrKoq9bbPNNsIH1VTgw2R8ppeWlWKgrhZesjsOS/AMVYkpZ7PtKAjKwYA/m0wnfqNyJtuBoYTDZIQQT56vcUcKq7LOEOFUEzf+LnJwbKaXwt2qmTbALAkrQ6yHCxbcrCL7j4ovvBQ1zdpvoPb/LaNlfanckZUIlqjwioqW9ak2ZlSi4iNDlWEGn78qKnEsD6Oiga2dE/H7MFhliPhaTGQdtUMFrXicTJiMevZa99sPWnvbMM+2QcNkitN7Dqj0DGmU1EXuC2mCtaCxxDGfsTf9ogVv+XIAGSZnrSPbIWWMar5Thdhkyy1rkipDtWHhwoW48MILMW/ePN/jhBAoigLTTGYn0TDgUuuz3D2nq7p4hhQ3RkVqvZaRkoWlhtwsgJMV5y7wclLrVeK//LLZDuaGjDxEXSguWAhj3Too+Txa96/SnJWFz0DtLE2cZ0ijk7PIwTETOyE8GWqxSbKwZ8g7QmIR5C2CssYs9KrukovIypDPM8TL83Txdf4ceQxGGTKDlSHhMaoSrpy/CGbkMBnrGeLHcMIy7gIvXjySD5PRMVSnGGPkujZ1YmAuDZHVqM7C25QpGN4zJNLGgq1wwCpDLU4IS3QjyofJ2olXU6wt1+kbQ6g8C7vZZcuowL6mvLZRKRmqCRdccAF0XcdDDz2EKVOm1GR0G83g0yIrVBs14y6MsgzUGbPSryCjjUVVkzajcImn1tvHXxEm0/Ny6vPUgYFnnR3pAftDzeWGebYDYrneViUkTOY2ahW5PXxkiKuCkWkBjKJ4eIklQ6aFc3r7cMvYLhxB1xRfeCkqiSDBPwNuMUl30ZJAVFTO3JzRc1J8SdXCfRktL8Xc7FM2eHWWGnbpcyVkk4EQX8VjOobikKEkwmRWqYSBl14CUIc6C498KIoSnlovmoXFltEAkGOIQkum1TdG9DCZ/3psVz0yNLl1kjOGhNT6EBsEJQFuQ/GUDNWG+fPn49VXX8Vuu+0Wx/E0HzhWH2hMcw3UEbMNTMNXgE3nlSE149UAkqQM8VlxWS3L7E4iD+EbQ4MOuqpohEBTNSgyjMd1YODFFwAAbQcfUvuLmIk1zEDN9hKziOVOmHWBWXwtk3u90+1dtD6PzzNkWjivpxedloVjWra2H1RUL108cpE/dgziK/KXcbJlVNHsJdbcHEDkfW1FIhMu1kDNjaHnfObmyCHFamZzSlTcTuwRzxWnYgaRoSTrDA29+SZIoQBt/PjhC56ysLwwWXUDtcB1RSxfmCzHzH35jB3Oo2U0otYU45UhTcnhN6tXoE9VMPngqc4YYqoNsUz/GsISI+e79kL7qWeoJuyxxx7YsGFDHMfSlKhIiwxShgRd+hUx5YAxZBioq1W5tsN9csdQmT5VlDrIqM9TK4hhoPDKqwCAtgMPqOOFzBmgZCikzhAQnQyxDU4JCZCY2NT6iAu84lNtLHQQgnN7+4DOducJCtP5O6pnyJ+xlmErHut08YUzRrRFS6libtZ1R7WhY8gwggf49gDGsCstpMiozjxxjEhUeOKeZ8kQHUNRAZjRU9LrAFWFWg/Yv65IA2ugRkidIff7EPBrstNdzmKVIfseUQUVFZ4MQc3hkCG7yTAc9YmamyOr8nzGGqM0U9u/toV7huqeoW+44QZ861vfwlNPPYWNGzeit7fX929LAyn5yVBFeEnNiId+uAs5w+5QiS1viseUawnFiVegZhcqlTFQZwjdnTiEK4Hoa3HhQliDg1Db25HbddfaX8hkmLhFF0N6kwEi5ma2IGJAVWy2dk5EoqJwYTIXziQMSPDzsGTIMH3NLnWqRLjF8cTVp0BFRUJKus/vxG8WdP/niK5wsX4e3hvoZC/RQ4icWu+fpNgu6ZRwJdmOo/D6fABA68waPXsOqJ9JRXiYzFOGoofJ2KmojVHeW3Nd9viCpVP4zS5RmD6EWfs+VATndz5jjU1e0Z1zlLRFodFQtzJ09NFHAwA++clP+h7fUg3U/EVWUUiQNVBHlVE5UyBbMCuncKw+0gioyIrjM718YTJpypA3Br392fASvabiwtA77wAA8nvuOXwLDhbEguU2ag2ZhFXv1ope14YhEbxnCPBlL0VuN8AuuJYFQpxG5vlObxjREBZL6hxliFIsXlGR8TkqwmTU6CqcsRbufcpR/4jze9TF10dUTAsgivOFAFkne0kRJFxslWsAyBuVypDXfDTe+ZwQ4t6HLXvtVddrgz1DnIGaeoYEii6y090ERnka2zoBAOO1iTq/c2sIUZgyGg7hEvXzVKwh7EaUhslcA3VKhmrCk08+GcdxNC1I2a8MsaoNdem7MeUYWH3OmX4V0Wwy3qTNLb4+ZUiSZ0hlxsjSZohceInW14gDBYYM1QVCvOhFWKNWjtRFAmugthRcv24DvrvVeNxoOERFUb1eWBEne4U3gFmwpQdGGRJVIsCV38iZcGceqqhogoTL9zkqVBtKhmzIqZfEkyEnZCKTOBI7XE2z+6hqQ89V1O8cnDKU9YXJKOFKJrXeWLMG5qZNgKbVp84CvkattM5QRaNW51snUYtccHP29mWC7UpljLVMjG21S8J7qk1Uz5D/mH1lNPI2GXKJSlTCxdk5NJYMOd81rY0m4jttZtRNho488sg4jqNpUU0Zon2yhXcOfLwXDBmiEqdoppdlhtYAUuFXuKQpQ8znyDqfQ2EmAgsWNMRIhl57HQDQsle9ZCigHQeniLJhMhl9wwhRcMLAIGYPDELbzsm4YerzRDY3c2SIEAUKCFAacB9TRMfgCpHmDQBO4p7uelRo9pIMZYjz2lDfhWifKoYAEYtAIwSm8565rGOmdf8eteGs/1ppMQjKzi1AP4f9fZDIagfvq2F9jq7CJZq+XyMGX3vNHnfnnaHW28/SsgC1ep0hVVEBIqAMWaavjEJGzeOBVatBAChZpyCiO7/LUW0Iuyw7Cq1wYUdeGYIGanzTnbXDM1BvmZ6hmsjQm2++ib322guqquLNN9+s+tzp06dLObBmQYVniE1P52KxkXcO3IXMpqRnnQtZmKgQrg4F0UBtzBlXtRH0DBHiI1xs4ToqZ7MkIs4dSmnlShQ/+ADQNLQefHB9L2YN1G6dIV6el+AZ8tUAcsKhAJD1itKJNqLklSGi5gEUgHFeo0yXRETNXuKUIdawm9NpeEmMDCk+c3MwGRImdZy/KkPgkqGsUxyPhrBkZN4BQJtJQJ2YWZq95NYAEm+PAvizyTJU4aJhsqjVumtE/xNPAKi9BQcLOj+oVVLrNYcMCRmo2V+VHFT021cSJcCi2cLc3IGMF6IG9aKJWi24MTTLI0N0DdGcuTfpJtmNgprI0L777os1a9Zg4sSJ2HfffaEoSuBCtWV6hjjGHaAMqaKtGfgbnGiggey8cyMqouY3rg6FxpChFmcM74aMCK7HmsKQugxH6gCxatrDoe/xfwKwTZv62LH1vdhi9k4hniEZBmqfZ4jNdfCZm8W8NhVk6FM/A1Y9ARx6ufccycoQW6U955pQ7czCqAZqpYqfR3dMqKJZcXzmXcZSMOR8LfmcUylYNITFzZ+tzNfaQgvwwT5XUb9zPizDKkOtefteoHbzOJUhq1RC/1NPAwA6jjm6/tczFajDPEOevyp6fR7WX0X0NsDcaP/iXFfCWba8gXrCrsBurcBO3jkRbelUQYaY+STvrE9eb7JIQzQ9aiJDS5cuxVZbbeX+nMID7xnSmaIUOse4oy7tFWEyogGwJzTar8hLi4weivPVoSAaAPsGyrtkiNbTiAiOcCnMuaIxcUUGiagBA04/so5PfqL+F7PKUMZRhggBsSy7aSPkh8l82WSsMuTWg4m6+HK/73g0cNBZvoc00TF4fw3b+NJZ4D3CJeFzWAQKISBu+j5voJaQTQYgZxH0OT9nM34yJMUzBKCFbdqZdciQQg8n6nXFheKYMdpaxjljCCoqNaDw6quwBgehb7UV8nWapwG/mdj1DHFkUhNthcT1niSZDoBO+VmaWi/m16zwDEEDTvmt7zHZyhCbvJKjhU/TbLLhse222wIAyuUyrrnmGlx99dXYfvvth3nVlgHeM8SGsHK0saIoq68wBXpkqILVRxqhknCxBrsWZ2FXBb1PvC+JrXNPww28gToOWKUSBh2/UFu9ITLAL52r3nkihgEl6xinVd31lEQlqGxqPUEIGaJhmah1hrgFnr/WAK8acfRML95AzSpDNokQ9fPwCpdmAoYzu+V8viTAihju48fIM9ey26eKhvskZBACtmfI/ZmeKwiGsPhQHDNGa+t4AAwZijG1fuDFF+0xDz7I3UTUA6/OkOLV++KIhSJYHZpXs8Gam6m/SlD5rwiTBdyD4qG48Pk951S8ltFcuplR1xWYyWRw3333xXUsTYmKMJnFkiGnsaJzsUWvUMp7hhjjsSYpplyF1LU49U08X1I0ELMMjfEJsfOTRckQW7k5JiPf0JtvggwNQRs/Htmddqr/DYhZ0bUegF/uVjVXXo+eWs96hqqHyaJ7bfy/V0zMkJzpBT8ZytO6Nm4KdMQx+Awylqho/mKFUsoQgPM+8fWSZKiB4JQhrgZQ5DAZp560+ZQhO13cazERHxkafNFpwXHggdHewJlAbM+Q43cJqTMU+R4kpu9a8vUIzHC9ySSVTgm8B1VBPw+/oWY37Y4vSXGznlMyVBM+85nPYM6cOTEcSjBWrVqFz33ucxg/fjxaWlqw995745VXXkls/OFQwbhZMuTKj4IXchUyRCdI4d0JZwRX4ZESfoyotwp/rnybNddA7U02cRmo3R1pnRVvXTC9yVgy5JuIHWUIEDFQ+7PJXATVAIo42Vd4hoKUIYmtMgAgyzY2du4R2YRLCxrDJSpyCJcv3Me3ypDR/wz+GkCtuj/TKzLhKvvHYAlxO/UluXWGYlJnBwZQeOstAEBrRDJkMcpQeJ0h8TCZjwyxIUZXzZYcJisH3IOCCTL8ZtdHhjSb1AlXuW5y1J1av/POO+Paa6/Fc889h49//ONoa2vz/f2yyy6TdnCbN2/GoYceilmzZuGRRx7BVltthYULF2JsvYbXGFGhqFhek0g+FhtZUeFvcDabjPohFDFzM+99YslQi9uDR3CM4pD/d0vBwYUCnm9pwXGduzhj+Ht6xQHxHSkzXTDFGn07bl+X9Kj+kcpsMgBAfow3jFsPJtriq1YoQ5XHqgkW+asgQ6zaobHtH6IvvrzC5VOGXEVFzHjMX/hBypAX7pMfJsvr/nMVuR0HpwxNZeoO8UpdbPfga68DhoHM1KnIbr11pPdwu9YritswmfdDiTZRBbH8ZGirPYEV87kxxAzUFhddCNqQeEUXJXmGLGZD7Xifku4L2Wiomwz97//+L8aMGYNXX30Vr776qu9viqJIJUM33HADpk2bhttvv919bDivUrFYRJEpMBV3i5DgtHf77snJCmFxCxQbwsrTuDWNmUcaAQBf2FH1Ori3ONK5Itg3jC/8BaLgyo2b8XTLEE6fbhuZ4zZQW0NDKMyfDyD6jpRtx6Goqk2ITNP/PSmqR4YiExU2e8kAjr4GWPRPYI+T3Yc1aqaN6B9RuHk3SKL3QnFy1KdWo5JEKG49GAmZXvCTIV4ZkkXqcmxWnO5XhiJ7bbjMuw69HXQ+8Yij4BicMrTzUBlXb9iEnZhFWRVtYzEMBl9y1Nmo9yBYZUxhepPxleDp54je4NRHhvY4BSALgI9/3n2MqtmRN7t8NlnAPSisDHHvqTNBIerbSw3UdSLJbLIHHngAxx13HE477TQ8/fTT+NjHPoavfOUr+MIXvhD6muuvvx7XXHNNYscY7BlyyJCjqGg69QxFHINXhphQXAutbyI5TKZr7e7PmZxTXEwTJXVcmMwk2KFsYPtyH9AxxX5Q1aESAktRYpHoC/Png5RK0CdORHa77aK9CZvF4kj0xDQB9ntiu6RLCJmQUhk47HL7HwNRtYP28TJ0BbpBKptGgm1pICcLi11cOp0MKdGMNV4ZyjK1hnj1SZYvic0yanEVWkGvDacMtU/YG8B8AECnL7VeTskGACCtW+H0vpW+x9SY23EMvEDJUB0NkjlQfbaaZ8hrMRHtc1jlEjTmayf58cDF//Q9R9QGQTdRRd0OvVbzDMnyJWUYZajDqaStbuHKUP0W/gSxZMkS3Hzzzdh5553x2GOP4ZJLLsFll12GO++8M/Q1V155JXp6etx/K1asiPUYKxg36xniiEpkr01F9VBPPemghkdR1YZThvx+FUeOpmNEzJAiQ1yYjChAx1Qg2wFMnWE/qHoVMCK3TaiCgeeeAwC0HnRg9L5nxKszpIQVfGP6hpGomV5smCxAOgdYH0z9kz0xDLe6bjlPs3ECJmJBjwqv2rCNVDPOBCxsoObGaGf7bdFwtWSzOUvq2nwFEUWy4vx3sJ7tcn/udFPrRcsQcLPE9sfY/8/6jnccLomQvzQamze7/cjaDjoo8vvQUGQ1zxBNrY+6sTL5zUFgCEtUtbHfs5Clvweos6IWBT66wLZgccLuwvXwmhw1KUNXXHEFfvCDH6CtrQ1XXHFF1efeeOONUg4MsKXNmTNn4kc/+hEAYMaMGXj77bdxyy234Pzzzw98TS6XQy6XC/xbHAj2DNFjoaXUxerzVFzITFfj9rZJANjCX1HH4FQbZlKZ0EIbEjLmZhA3JFD7GH71iRgm8KVnAKsMtNr1TaDq7rsSs1KlEEX/U08BANqPODL6mzB1hhRWovd5hrxGIqYV7XP4KlCHkCFbJTAjkQj22i3ndLT0G4GTPS3AJysLa0rbNAAbfY+5jUGjhuK4C58NxfHKkKw6QywZou8ts+EsAIzXx7g3te7cf26/LUkZa2S7o4BDvgRM2MV9LE4D9cDcuYBlIbfLLshMnhz5fdxNGob3DEUldRbfRDWQqIgmltjvOZQFMDgcGZLkGcp2AlgNAJjYbqvymuCGutlRExl6/fXXUXZO5muvvRa6o5bdYXzKlCnYY489fI/tvvvuDZXe717IGSBfBjS1BYCtgLh9fjRBxs3HwTNjAXwIANBpDSDRwo7cTY+ygcv3uxz3L7ofp+96uj0GVwOITjQ1j8F5hki5DLRv5X+Sk4VVVpTo3cVDUFq5EsWFiwBNQ/vhh0V/I2K5Bf18ypDPM6S5YTIZdW1CyZCIMsR850YtypAkz9Dh+30Ffy/fg5N3O8N7jmB4iTeCtyADN5FB95ubI3ufuJtLbd8awHrfY5qoYZc7VwdO+Di2G3oDR2/LVCMWLIio8GTItICJu/se8yoeyw+TuRuSWbOE3sftBaaEe4ZUQWWItw8E3YfC7ZaceWOIKkNGABmi83vEJaTCl6TmccnOZ+C13sU4cpq9MRT1hDY7aiJDP//5z9HZaascTzkXchI49NBD8cEHH/geW7BggVsEshFAL7JC1iZDquKpUp6B2r7KZUmcKnNHTODCZJHj1gE7oIv2vggX7X2R+5jCpL1HmexrmVhYZciSHCajpf9b99sPWlfXMM+uAmYxVaFCyTjn3ldnSNxA7SNDASQFYH0w9S9adNdrKYCRCSdDrrlZ0M9DFEAhwNjsOPzhU/4NjcjnIIS4Y5RzGjJFE1p2LIBNANgaQJoYieBetnXXzuDJkCLqfeIM1F1aGx787IO+x4QN1NwYgQu8m5Iud2kk5TL65z4LAGg/SkCdhUdwVCjB9yA8Uhe1+ahl8Ip5wLnS5ISwqofJxDLWKtP3y/jKId/1j6Fv2WSopq39jBkzsGHDBgDADjvsgI0bNw7zCjn42te+hhdeeAE/+tGPsGjRItx999347W9/i3//939PZPxawJIhAFCZXVc7LdeuCXptAopyfWn6l3Dg5ANx+NZ2g0Px1Prh5WBNMNOLhsncVwYt8ExKeuTu4iFwd6RHHSX2RoT4PUNO1emKWh7O/5FJHdslPWC3aI8RXVGh5v+yBhAnLS246KKc8FI544RegnwXIoZdw3AJtJFz2rqwmV6UDAmG+3gD9YnbHI/dx+2O6w+/3n1MOAuLr/sUtDBKMmnTa7jaGLLDZIOvvQ6rrw/a2LFoEWzq7UutD7kHNTovRvYM8XNvkDIk2m6JkqEq96Dohtq554Yy9PfwcN+WSoZqUobGjBmDpUuXYuLEiVi2bFn0pnd1Yv/998f999+PK6+8Etdeey2233573HTTTTjnnHMSGb8W0BoRQ44gpDAx6/YMzcLy186hpr5aQQt9lXQg62Qb/MeM//A9RxHdnTghLEsBVBK8YCmCNYBI0Z6ohrJAaylMGVK98JLEMJk1MIBBp9hi+6yjxN6M+Pdnao5OxH7li37LUmoABUzCgJgPhi4cZR2w9HCiIhyWoapNVkW2ZAUXlRNRhpjFw8jpAEpQypXv43UwF8y8yyjQywSTsuNx79H3+p6jio7hEBWqogW3ZhA0NzsbtnJOQa5IAhdGJSZlyPPsHQFFq28e5GFvSTQoUKA6PlH+HqRtPqJ+Dj5buCqJiJqP4czvRTq9Vk2tjwjDb+cImk+29GyymsjQqaeeiiOPPBJTpkyBoiiYOXMmtJALecmSJVIP8IQTTsAJJ5wg9T1lokIZYjI1KBnyeW1gQUO9ZMi7kCkZ4iG6c7DKDFEpho0hqAw5hKuQc8hQSOgnjjDZwPPPg5TLyGyzDbKiffUIU2dIUaBkgnelbjuOqGGyGrLJ3JBJhO/DKgwCsEm2S4ZKQSqBnCrXRraaMkS9HWJkyHSUIZXPmAIbXopYk4mSoawGvWyEKCqCYzjfeSmrIle0qo4hGoorZm0yFEi4BElEGDy/0FHC72X3ZrPJEFWGeMOzJkjqLD60H/R9cPaBer2U1pB9zIPOhjroHqSqvHD6PjVpB2bFOedQrvW3aVATGfrtb3+LU045BYsWLcJll12GL3zhC+jo6Ij72JoCnvnNrjzNhsm6crYvRdUFFRXnBhzMAZ2FEF8HVYaimthLHuFqLYbFxlkyFMGj4lSgpsQRhICYZsUOUdRrE4Q+N0R2pLjR3zLcKUmFGi7R06dLMB6HEUeRrtxW/wAA+7oilAwFFl0UK1zHKkP2GJXZdYpAeImedwuAmbXPx26dO+FpzEdHxpunPB+MWGp9Oa8jPxBChgTClr4xsgpyYfehIvZ9UM9QKacCfVZinqHS8uUoLV0K6DraDj1U+P1sMpiBoqhQslQZCvMMRawOXUsTVU4xr5sMFezrt9dpORisyjvKUGT1ySFDNEwWuIYwyhAhbruRLQU1F108/vjjAQCvvvoqvvrVr6ZkyAG9+Vzzm2HgrN3OwpLuJTh4qt0RnQ2TmZaJOoUhN7xUoDuHKqmXgGMorfNCpjuqoSomPoUZI1JYxlGGhrLMY4YRToYkKUPEstD/tG2e7hD1CwGAWfZnkzkSvVXkW5pQlUA8TBamDFFzcxQSYQ30A7Cv3ZwWbqD20pMFw0tVlCFvjOjKkKEBxPkcs6YcgSfzg7hoLy8BQGgMy3K/j2o1mVTBEgHUM1TOaUCfGazUiRIuZ8NWylGPSlDYUj4Zovdg68yZ0CSsH9RT6PMM8fegoGeIJ0PDbxLrH8cs2Mfc22pvqIP9mrI9Q8N4nywT0GqmB6MCdX9atjVGiuBMgKsOvMr3HLYTexRDIinVkG3A3ZB1+5LYWhchY/i8TxHSxd0wWdbr30bKBsDVhfKUITkG6qF33oW5fgPU1la0zpwp/oZsOw6w5k1uV+o+PWKYjLlUwosuRvfzmE6rmsGcgoxWjag4i3/UTC9KhnK0FkyVMQSUU4MJ922dn4T7TvJnrGkin4OtyZR30rgDiIqrcEVcthSXDFVR6kSJijNGMacBKIeExMVS0oPQz6izMkCPTVEUxrfHJTG450pWlm1QiFcsy5YqQ30tdIyg71xOxhqNYATe5zoNkymAZWxxZKihK1A3A2ohEaxnKIp/hKo2hVx4toGwuZmOQd/GMCoy33zKUISCiKxnyH0sqP0D/ZskMkQn4bbDDnOJixCssk90V3Ihu1LBPlVqDXWGRBpqlpYuAwBs6mA8Q1U8KpEUFZZEZKmiUs0UHCX8ap93QwUIJXWBakf0ys1s9fRyS3BNG0A8m8wlQzWoaNGVIRomU0LHcJUhSXZas78fAy+/AkCSOgtGGUIVZUgV+xwVmbxB50pAGTL7+mD0FAAA68aEz+/ideTs96RhsiCTti/cZxYr/j7akZIhQVjOpDvoTCwolytIhOpTVCKQCDpGNdWGNTcLZBYNZZjwGj+OUxARiEjqnHBfMQM3zBRc8VgsvMRDWko9hemRIVVRoYam1kts/xBwXQFMkb86F9/S8uXYdNddAID3tlFA3DpDldenSCFBa3DQG7OVFqeUq0QQZ4xCDiB6leKRanRFhV67hur5kqoaqAU9Q1RFq1ZzJmo2GSVDHuGqpj7Jac0w8Nw8oFxGdrvtovcE5OCFyTTPQM1dv7RqftTPYXFZibJLjqz90fUAIfhoHLCxI3wM0bR3atLuz1cZQ3CdanakZEgQNN5LJU4AFSRC0zwpJErIpBbPkK8gYiTC5Ve4gIBdENM3jERoMUF3bSUdAF20gna+7hjiylB57Tq7D5KioP2Iw4XfD4BPGfJlk5V5MmQjajYZX1U5kDhG8MEQ08RHV14FUihg81QTT++twNLiCcuQgr3rLWmAWaXOkEgFakq4hjKAlalyXQmYzWnKdln3fEnSzxUhni+pClGhTW0jjWFZLuEqVQtbqnLrDEnfkFiWW4CQ9e3FbaAOLEPAKv91XFt9//oXeu6/H1CAmz+toazTMcJ9SSRirTpK5gcpGRrG+yS7xlszICVDgrACyBA/SfpkVKN++ZHekG4IK0h9UsUuZMIZqIGAyV7RoFJlKIJnyA1naAC0amRITFFh0TNnDgCgZZ99oE+YIPx+AADT8HuGQgzUXhsLOY1Bg1tl1K+o9Dz4IAqvvQa1rQ3vfaJkT7D68AtjJJO2Q4aKGcBSqygRVLWJkCHlkqEsGyarRlQikCHnuy1rw4UUqYE6uvcJcAzUIWMoAgoXG0Yq5cM/h6KIKSoszP5+9D32GACg/RNiLThcMBsMO5usuoE6cjuOiqKLw3gpaxyHlMtYc911AIAxR++GD7ZWYHgdqv19DiOOwYKSof58eGiU3oMAYEZYp5odKRkShFWwb46+Vu+xihtGNLw06BCu1vAQFitxmhGUIatgeyIG8t5jlcqQKmQKtgbsRWswByYsEzARO/+Ldq0nhoHN9/wJADDmjDOGeXYdsAwQ5qsIr0BNvTbRyJDKVTyutsDXuiMlloWNt94KABj/pS+h0O4U+asSXhIhEdagQ4ayANHDQ6MiiorZb2fFDWUUED08FCfStNPs6wNgq7MeGQovERDJ+zQw4P5MQ4qBXdIFzpXlnCsLwFBLFQ+XoNeGRc+cv8EaHER2hx3Quv/+wu8HwFfewp/EUPJtFFXBjvIWf/4DyWn9YbKehx+G8dFqaBMmoGu23X/TZHJe+LnXN0aU5BWulhEsq4JwsSUBZLdCagakZEgQZq+9wHe3KW5dhkoy5JEIEuEiMwdsotJThXD5ssmiEK4CrQFUfUFxa+dEWOBpXZv+PNNYMdBMK6aoUPT87W/2hDN2LDo/NVvovXyw/J4hb1ca4hmKOLHwYbKKZrqovzhe/zPPoLRoMdT2dow98wz3c1QnQ9E9Q+Zmuz9YX0tt4aUoJMJYtx4A0N0OJvwarqJFInVO5t1AfhhfkkBHebpZKOmAWWWzIEQcHVI3mIdXWyrGOkNWsYiN//u/AICxZ58tr5m3yYaqPd8eLMtHIL2Gs3J6evFFHQFA0TzFvJbzRQjBptvsrOxx550HmvhrsGSowmrBEq765xOjz96UdLcxY1Q0tWXJUOoZSlEHjI0bYQ3aF82GTgAhzQIBuC0mIqk2AzVcyGyYLEoIa9AmQ0NZAJlwMuSpNvV/DrPPK/LnhcmC6qg4xxRRUQHs0Mn6n/8CADD+i190y/VLgWmAOGfCDpOFpfXKMdNSVAuT1bwj/ev9AIAxp54Crb3NVbhqU4bqXxgHnn8BALC5TWEUlSpKRJSsuOXLAAAbO6t/DpHwEi1DMJBTqhaoVAQWX6oMFbK1heIiKVzdPQDse9ALW4Z3Yo/aGJRi0113wVi9GvrkyRjzb6cKvZcPlsH0B1TdUDXgvw+9lPSIZIg7N4HFT5l+irVcW0PvvIviggVQslmMPeN0N4RnMKtxNatFvXNvefVqlNZsBgBs6PLIaEUDcIYMmRE8oc2OlAwJoO+JJwAASycBQ7nwzskAWzunvouMmCbKG+yJeENXuPqkMPHeKMqQ6YQzBnOAoofL55rbN6x+olJabe/gN3bC3cEHhkwkeIY23fV7GOvWIbP11hh79lmR3ycQVn3ZZFFSuQFA5ebVIGWonuJ4Znc3+p98EgDQ9dnPApbXy9sNWwa244hg0rYsrL3hJ9jk1CV7c3slFiXCKhTcz/TBxxTPQB1wrlziGOH7KK1YAaCWMgQCRvB+JxTnI0NBbROih7DKKz4EYG/eTL1KKreb3RedDJk9Pdj4m98CALa6/KtQ8/lhXlHPm5fdDQmrzgJ+9UYTJHUVZCjguoKqe/0Ua7h+qY+x4+hPQuvqcq8VoireJrFCGYqW6TX07rtYduZZICUDmzqAZROZP3IbUZWhA1EiGM2OlAxFBDEMbPrf2wAAz+1hn8ZqZMgNL9Wp2my68y5YQ2X05YE1YxGqPimKAoVKtXVeyMSyUP7IJiqb2sGEsKqQujo/R+Gtt1BasQamSrBkslJVfaIkImoVX6tYdNPGt/rqZfJUoed/BTz8DaDQzXWtD24SqUmsQA0ES/RaHYtv7yOPgJTLyO22G/K77QYQCxY913q12jn1EZXSihVYfPxslwitONDCI/srsPQqRMVdtOpb4LvvvRfmps0Y6LRswpV17o+hSgNoVPXJ2LwZ3X+0vWcLp9bmS4pi2C0tXQrAJly1GcHrG4MQgm5nIV44VfHGCGzUKq4Mbf7TPbD6+5HbZRd0nXRS5PfxYeNi4L6LgYWPuaqmqqh2FXut8vpy21hEDZMZ/vsqaLMAVYcGGiarfh+SUgm9Dz8MAOj6zGec13jfI11DKjyhan1kiBCCdT//OZaeciqMtWuRnToW156j2hlrNLReoWQzylAdhMvs6YGxcWPNz29UpGQoInr+9gBKy5dDbc/iHzOckAlHVPrnzsXAvHn235zX1WOgLrz9Dtb97GcAgD8dpcDUFChVSYSNetWn7nvvRWn1RhQzwFKWqAR5VCKMQSwLa679AQDgjV2B3jbF848EplmLKUO9Dz0Mc9Mm6FOnoHO2JK/Qy7cCj10FvPw74K173WaG1ZpEep8jatFF/+/BFY9rXxh75vwNANB18snOGzJLnSTPECEEq674Osof2grEpO98B6tmWiAKq9oEERW6aNW+wBNCsOn//gAAeH8/A6amwMo59ViGbKXTWL8em/90D8y+vkjd3gkhWP2fV8JYvx79Yy08OZ0hdYFZWNFqAJn9/Vj/q18DAN6bVp04RiV1ff94HIPPvwCiAf+cobpkKLCDubDXpozNd98NABh34efd7vFC6F0N/OE04K0/Aw9+1bchAcCk1zPKkHCDU54MBStDbnPpYeas/rlzYW7eDG2rCWg75BD7PRkCpYQQbbVOA3Xf449j4823AADy06dj22+djDVjnMhCiJKtKIrnfaqDDHX/9X4sPPwIrP3xDTW/phGRkqGIMDdvgpLNov2Tu2Aox5Ehw0Bp2TKs+OKX8OHFX0B57VpGGar9Itv4m98A5TLaZ2yPf+7j3G41heJqJxFWoeB6a+YcYRMVOHI2KQ5VPN8L/dQ+Ru+DD2LorbegtuTwMK3EXyUUJxImI4Rg0+9/DwAYd/bZ7uQihOXzgIe/7h/H+b+agVoTDPfRMJlZLfRT46JVXLoUhTfeADQNXSd82n6QmC6pI1VItle4bvjFd+DZ5zD01ltQWlqw07+ewLhzP+eqC5YzhlUM/xz1KCpDb72F8ooVUFpasGwXJ9RA/VuOMrT2+h9jzfe/j/U3/byuz+GO8fY7dk+tTAZvHVNGOaN4Id5AD1c0s/nm3/8exurVGOoE7j9EheUUdrSC7sEIhMsqlbDupz8FAGyaqWPtWAWWVi3N2rkeImaT9T3+OIy1a6FNmIDOT30q0ntU4PbjgU2L3V/ZbDIAUOncyKTXe3WGooEqQyVaAyiQDGmefWAYouJuSE440Z2b2M1SWHRBZevIDbOhJoRgg0OExl14Iba750/Q27Pe+QqpmA9Em3v7Hn0UsCxkpm1d82saESkZiojxF1+MHR9/HC2HbuM+xl7Ig6++BhACWBaG3n7bK8BXIxkye3rcQmVbnfxxEGdn5e4cAi9kG/VkYXX/+S8wN29GZuIYPDnDedC5WSxnQbGKRTcLhZI6UofCtfGOOwEA40+dhT5qAnfDMuFhsijhpcGXX0bx/feh5PMY82//VvfrK2CUgAcus3+edhCQsT+Aa6BWqhmoo2dhEcOA6sQBzBZa1DG6MkSN022HHQp9q62cQbygVPXQT+1KxMbf/AYAMPb005GZOtV+T2cWdn1JAdcuDcvUE86g4YaOow6HkXUWdnrtOiSi9+9/BwBs/uMfmYWxjppMDzwAAOg85hj0jrdfRxWuIFKnRCB11uAgNt1lE/hVBysoZRSvyjUT7jM2bwaxLKa2VO3nqv+f/0R5xQroW22FTTOd46/BbB41vEQ/z9gzzvAyvUQw/25g8zJAUYEd7FpF7IYECFaGPFInlk1WreCtrQzR0inh87uxaRP6nDmdhsiA4DBZdRtE9TVkYO5cFN97D0prK8Z/4WIoigJimV5z6ZCaTABrg6htnSqvXm1vshQFHUcfU9NrGhUpGRJAZtJEr3YKmAu5VEZx0SL38dKyZYwPpjai0vvoY7a/Y5ddkPvYWG+MFlu1sYI8Ec7/tXqGrGLRqzlz0iEwVedmyTuTylABxLKw/JzPYdGsT6D04YdMVeXaPkdp5UoU33sP0DSM+eRMN85fLdxHs2+jhJeoV6jr5JOhjRlT9+sr8Pz/AzYutH/+zK+Bz90H7HYCrLHbAbBNh2pYwTcRhYvpheWSoWrKUJUFnpRK6L7Pblo65lQmo8cyXVJHslVIdo2G3dKKFRh85RVAVTHuws97w9CFosoY9ao2pFxG798fAQB0HvsJ98isrKcM+cKWhNTdDJYYhkumuk4+yV1QLfo5hsJVm3oI8OY/3WNvSKZNw4ad6Bg03OeQukcewcKDD8G6n/6PR1TqIHW9jz9uf47PnAxTd4hBtSrwSnQSUXjzTRTmz4eSyWDsmRLqew1sBB77jv3zTkcDZ/0ROPg/YG1vV5RXuEWeLX5K2xRFrTNEw2RuX8ggZUjRvE1ilbm3569/Bcpl5PfaC/ldd/HGILTHGkILuAKM73SYTSI1aI/5t1Ohjx3rjMEQrvzwY9S6ae995FEAQMt++yEzaeIwz25spGRIEG7nZABK1mP1xoYN7nOMdevrbs3Q86C9I+066UTfYurufgLkc6XOTK/u++6DsW4d9MmT0XX4Xq6ZlnaRt4aKKC5chKG334bV34/+p59haufUNkbf4/8EALTOnAm9PedN3zR8FVRUzq3VUh+J6J87F/3/tDP8xp37ubpe64IQT8p49ErgiWvtnz/7G2D8jsC2BwNn/gGkza5mrSoqlBa7/DittkzhZWFFKHXA+GrM1uB2H/YYwy++/c8+C3PTJmgTJqDjE5/w/kC8pc4KWEjqGQPwJsbWAw9AZtIk77NwJCJwjDoX+J6HHoaxfj208ePRftBMr15S3vMMmZs2eS8gBJpZX0r6wLx5MDduhDZuHNoOPpg5V84YgSGs+rrWW4UCNt5mJ2JM+PKXYDlpSYarDNljrP/lrwAAm267jSmnUNsCbxWLGHj6GQBAxzHHuHOWZ6AOuAe1aAZqq1i0+20B6Pz0pz0Vsl7Qz7b+A+C/dwAKm4BJewFn3g1kWoDjfgjroEsAeFlQqnMfEuY+9JTTiIdhcMrQMJ6hMGWINa+POeN039/ofaVCgUo3okHqKX1+FaJiDQ6i78mnAABdJ3qmdV8oLhs+hme1GH6dsopF1xfWdeIJwz6/0ZGSIUFQpUeFP7Xe2LDefY6xfh3jGRr+IiuvWoXCK68CioLOE07wyeFKzlGGaMXoF19C/7PPAUBdYxDTxMbfOarQFy6GyipcjDI09Pbb7uPFxYvqLrrY5+xIO44+Gmz2EqpkL0UxHg88/zxW/vt/AADGnHkGcjvtVPNrXZQGgF/uD9w0HXj8e8ALtqEVOx0DTPfvcOkEpigK1DY7dMY2JQXE2nEQxwBsKnBNwdXMtNUW+N6HnOyVE07we6hYzxAdI0jtqLGwY+8jjlLDmdbdxbdKmKyehprG5s1Y95OfAADGnX8+FE31wn2MZ4jPcMn1GTV9DoqeBx8CYH8eRdc8wpWjypD9OczeXmy+916Yvb11K0Pd9/0V5oYNyHzsY+g66ST3XPHKUPmjj9zX6EMOuayRcA3MmwdrcBD6pEnI77WXe2xeCxY57ThIqYQVX/oyCvPnQ21rw1aXXVrza3144RbgRx8D/v4t4FcHeI+f9AuA7Z9FvN5kAALvQ02w2zvK9iuL2SrKkKq5HQb4qs4UxQULUFq0GEouh87jj/f9jbagsWuWUeW/8j70lKHw+aT/6adBCgVkpk1Dfq893cd9Ju0aPEO1KP8bfvVrlFeuhLbVBHnZgiMICe7SLRs0lVIBGyYrwqxQhhQApCbG3fOwLc23HnAAMpMno/wecyHnPWWovGYNPrzoIsA0scPDD7tGwlrUp4EXXoCxejW0ri7bW/Pm770MKaoMFYZgDfR7n2P1GijbOJ+jBsJlrF+PwuuvAwA6jjkaWPOkZ+Krlr1UZ3iJlMv46D+vBCmV0P7JT2LSf/5nTa+rwKInvJDYcz+3/9db7N0oVzmXaioKFKitziTMtFOw/+YoERHCfZSUlPXq5ma1Bv/IoPMdVDTJZLLJeK+Nf4zh/Tzl1avdcGjHMX7vAK+oBIfinEWrBhKx/sYbYW7ejNzOO2P8BecDhXVeuI+SuuKQXxkCkOkrA5na1A5rcBB9/7RVza6TTrSJvHMJmBk/UVn74xvQ89e/ojD/DahH1Kfa9Nxve7nGXXABlEzG/R4tSrgKBZBy2ad2ZHuKdY1BP0fH0UdDUVX387t1n4JqS7lem9qx+Y9/xOALL0Bta8PWv/616xmrG0/9CCgPAC/9xnvs1P8FPvZx39PYexAA1Fa7RD97H6qiYTLHQE2TZAI9Q4rCzL3Bqk3htdcA2Aq51tHBDeIoQ4rCbEQDlCECQKlOVPr+Zdfc6jz+OF+1b1+YrIZQHBkmTFZctMhVNCd/73vuuW9mpMqQIGgtHHtRdG7GwQKM9QwZWr++LlMwLSJHd9jshUwLl1lDRVu1MQyAEAw8P68uA3XvAw8CADo+Nduuw2N5S4TCZJOxn6O8Zo2rdtSyc+h78kmAEOSnT0dm8mTAMtwFxcuKq9aOozYS0f/MM3bmyvjx+Nj//DR6cbeFj1U+dslzgF5pAKWLlqqoUNvo984rQ9EL8JGSQ4Y0hgwFKkO091IwiTA2bICxejWgKMjvtZf/j2zRRUpUCkHK0PBeG0p687vt5voU3GHo4kvVDicESEolDH3wgd2t3fUlVV+0Cm+9je4//wUAMPma79s+EYtRuOgYhaFKZajXvvdqyop78UV7hz11KvLTp/uJY96vovX89a/u//UoQ8XFizH0zjuArqPz03bGFT3HZo6atIswOFKX7bGvg1pCisQ00e8skJSkUpWgWnmLeitQE0Kw+d4/AwC2+voVaDvwgGFeEYKNi4GhHv9j088A9q5MhnDDS859FqQMeZ8jGigZquoZwvCqfOEtW2HPT9+74m90flABqLnwTN5aMpLpfdh28MGBY8A3hv0+5VWr3GvMVYaGsUGs/dH1gGGgfdYsdHKbn2ZFqgwJgoaLVEWB2uIsin29MLu73ecYGza4adbDkQizu9t25wNoP/II+/1YzxBLVJgbprR4CdRpqGkMa3DQDV+5cWXC7J3ynjLk8z6tXVtXq4yBuXPtz3HUke4Ybm2QamEy1Eciup1JeMxnPxOdCBECLLTPCT7xXWDlK8CR37J9QgHwhclavUmYEOIZOgXqDLHKkFJDzZkwZajw1lsAgOwOO0Brb/P/kS26SOvzBKo2wze7HHx9PgCgZcYM/x8IYciQP7y0+r++j57778fEb34T6t617eA33WlnJnaeeCJa99vPfpBpzcAqXOZGXhkqAeNry5Ci127bkUfY36Nl1uQZ0ouOWbyG5bfnQXtD0n7YYdDHjbPfk/qraCij4N9YAUCmpwS0V1cDKYbeeQfm5s1Q29vR+nH7fFFFqVqhTaWOsCVgL8KlxYuhtLai66STa3pNIOg9uM0hwMTdgZYxwCeuDnwquyEBGGWIJUOywmQOGbICfHvA8C2Ehpz7sGXvcDKkQHHn96AEmeFqGRnr16O8cqW98Zk+3T+GFRQmG0Jx4UIs+cxnoY8fjx0f/0dNm/ahDxbY9fM0DZO+c1Xo85oNKRkSBGHNb87NyMb3AVu2zVhZQMWwfcMG5s0DLAu5nXdCZsoUewzfhcyEsPq9EFZp2VJo02gorrrE2fevJ2ENDiKz9dZombGvc5DeZK+yhIshQ2ZPD3QrA6jDG6hJuYyBec8DANoPP6JijKpVrusoulheswb9zsLVxWZK1YvV84H+tUCmFTjkMkCvXrWa+jUUeJ4hmCZIsehOaELKkLPQGhqg16IMhSgRQ86OtIVXhQBb7aAzrOu1CdiR1lDkrzB/vj3OvvtyY3gmbVfhKhZBLMsNEW2++25oN5zsjBG++BLDcMtNjDvnbN/ncMF4hmiTWAp9wL7WhqudQwhB/zMOkafXLvEy76ycF8rgyWPrhiFAG14ZIoSg1/EkdZ10IvM4VYa8+8NYt873Wq2fKkPDExV6b7QdcogbxnePrVqouk5liKp1nbOPryTd9WCBbcLHrrOBQy+r+lR2QwIwyhATJqPZZJZS2+eoAB8mC6pAjeqV+a3BQRQX2/WRKtRZMGuIorjV8oOVoeqNnwedezC3yy7Q2tu5MQLWkGLRTvU3TRjr1tm14GgouAoZ6v+XnaTSfuSRyG7d3LWFWKRhMkHQBVsJIENqV5db+ry9UJufh07CbYcd7j7Ghr3cjAmOqJTXrqs57b33IccYesKnvbgyowy5u5PCkM8IDkLQVqgt3FeYPx/WwAC0sWOR33MP+0HLdJUIJaQKKlCfZ6jn/vsBy0LrzJnIbb/9sM8PhFn2iiru9MlhiRDAhclaW9zHfbtS1GY8DgINJZU1AG54KagdR/VFq/C2vSPNB+xIWaWOMH4eXnFwQz8hY1iFAobeew8A0ErJtTuGZ/O1mM/hI9n9/TWpT0PvvAOrvx9qZ6f/8/iUIc/PY1BlyLnGNYcMDacMlZYuQ3nlSiiZDNoOOrByjDytwzXk8wYCQM4JYQ2nqBRen4/yqlVQW1vRPmuW+7hrNmdq85RXrvS9Vu+rbQwAGJj7LACg7fDDKsZwSwQE9byrI7xk9vej91GbxAjV9nrnfmCJHdLDrsMXamQ3JAC8cPWAdw/W8zkCYdRgoEb11jtD774LWBb0SZOQmViZfk5VGxWKR1SqlU4JmRcLVJ3lNyTwz0Ee4SqhtHiJ+3hp+Yc1tRCizZfbmWtqNCAlQ4JwPUOKJ9OWV9lkSB87FlpXFwCg3fE/VlvgiWmi/1l78mo/giFDAReyNRQQwqJem5CMBsDOxKFjdJ3o7Uht34W/rL01VIDJSfQdNXwOAOink/Bhh3ml+Inp1Rly5eBCxWtrbXZJCEH3/XPszxJVFVr3HvCDCcCqV+3fZ323ppexZEhRVShB5k0luoHacopcFnLVDdTVuqQTQjxlaO8AZYj1DOWYLB2+XpKbWRSiPjneNX2rraDzpllGUaEhLJTLPhJh9fRAN5znVCFDdBJuO/BAuxcV+zkoqad96MplGOttIp/dcQcAnjI0nNpB1afW/Wd6xlDLU9FcI/jQkDsGBfXzDDcG3ZB0HHOMu8Hxlzrwvo/SyhW+11JlaLhsMrO7G4U33wRgh+Io6LXiljoIUgO12usM9T7yCEihgOwOOwQuxMPCsoA7TgD+fIH9+37nAxOGzwatCJO5niFGGRIMk5Eh+94dbA3vFQdUb7fk+oWC7kGwcylroA6q/k+fH+JLcvxCLfyGBP4wmU/5Z9TT8qpVw25ErULBHaf1oIMCn9OsSMmQICwmtZ7uTKgypI0ZA80xk7a7ikr4wjj48iswN2yA2tWF1o97mRPsxe9eyJxqY/X3I1+iVVDDWX3fY48BhoHc7rsjtyPjh2GVIaewo7lps6t06E7dmDZX4RqGDD1LwwzM7oFZfJWcP2uClEpuyMElQ8OQiIG5c1H+8EOora3oPO7Yqs8NBCHAved7v+9zNjBxt5peSheiK//6Ftb3FQP9CiJhMkqGBnMKiJulWF+DU2P1apibNwO6jtxuAZ+L/Q4ZJYKfiIcLmQwyITKFy7rz9T9jCFd5zVrf0/Rup5RAtay4l18GALRStYbCMhi/G6OorFplD7ujvbBq/cWqn4OCqhztn/xkyBgO4bIslFev9r2WZnpVK3VAymWvDMEJTH0W5lwpmu7eI+WV9ueg96DWZ38/w9XOGXj++YqQO+CFCWnJBhiGu8ib/TaR8LKwqoMQgs1/sGvNjDnls5Xffy1468/Asrne70d8s6aXUaL22vJu3P7cUiabrNJAHbUdB4bse6SvvTZlKCh5ZeiddwCEhKrBh8mcTWKVVkhBRIWUSu44rUGElN1QZ70wmbm52328vGYNU0cuhHC98SZIuQx90iRkt9su8DnNipQMCYI1v9GbkZqnta4utwpym6uohBMVWu2289hj3DAS4L/BXEWlOFSh2ozrG36MHmdH6vancj9IZZis7OxIlZYWt+9Mx4Czq6xChspr16H4rh02aTv0UGYML9SAFk99sgoFLP70CVh8/GxYAwPuhDoc4dp46/8CAMacfnq01M5NS4ANH9g/7/s5YPaPa34p3ZUuXT+Iv762kpHoK5WhKGTIdMkQvPo8QSnQVUJY1KeQ3W5bV1H0wTI8RSWju12/eR+MOoyZdujddwEEZ8qwKemU1AFAeY2fRGT6qhMVQohrBq/0JXHhPkeJLDnhpZyjDGkDw6ekl1aswNCbbwKqis7jjvP+wJAhqqiwY1Bku4cnXAPPP2836xw/Hm0HM7trdrOgKO7Gp7zCbnqb28WuWqw6ZGg4z5Abcj/UH86wXF8S0/yzWMSmu36PBTNnYvOf/uSGl4bThQaem2e3v2ltjR4ie/8h7+cL/g6MmVbTy+jn6C+auObBdwM9Q7Tbe0THEOAqQ44SaZqByrurDAWQCHof0u+PB52vVVRPra/WYaC4eDFIqQS1sxOZbbetHMMXXfCyydjyE+amTcNmCxfespXGlhkzohHfBkZKhkRB0yIVpWJBZpWhtkJ11YaUy7ZqA1Q0Ngz0DBU8iZ7uIMc4fuqwC9nYsMEu5hgwBkuGqPmuuHQZAECfMAH6uPH253A2XdVUm77HnBLt++wDffx45oN4IRP3hhwqorhgAcorVsBYvRqDr7zCVKAOH6Pw1lsYfOklQNcx7vzzQp9XFYv/Zf+/3eHAZ34F5Ltqfqk3uSj4YE0ftI5OAIDZ7aUFu0QlQpiMhkB7W2ETFVQ3UAct8O4kvENwRhzMsrv4qorqeQkqlKHqnqHie+8DAPK771H5R3aB1zOucd5Yvcb3NM0hQ2ELfGnZMli9vVByOeT5RYU1/6saVFrHxVE7ss7nr4VEsFW09QkTmDEYZSiju4SLqjZ086L3OGNUUYZoJmfnccdVFMGkoThV1aBy9yFt4UA/RzXCZZVKbn0h1pPEZvchk3H9VGRoCJv/8AcAwLr//qm/jUUV8rjxf+3CrWNP+7do7W8sE1j6tP3zRf8Etju0+vMZuN4256TR793s8e5BTbO/F1NR7HBcPYc2MAClZL+mp9NbKgMV2hBliFgWSkuXAoBfiWfHca0Wqlc6pZqBOmA+GXLvwd0DSQqb5cZWuWbJkLFp07BhsiEn7BqUFdfsSMmQINjqoYFkaIy9wHrKUPDCOPD88zB7eqBNmIDWA/w1OthsMkoijPXrXWk7t/POzhjOhRyS3km9Qrk9dvfJ5vYgnu9C7XQWEyflVp8wAdo4Suro5whXbXqcBpqdn+bVJ08lcPvjDBV8vouhBQuYNhbhJIL22ur81OzKz1IrFjtmzR2OqvulbhVfKIACaOPt1Ghzk1fbhlZurqe7OOBkMz33IgBg1XjFq8lUjQwFvA81R1LPTOWHYEiEovrSeldc8hUsOvY4WzqvUrjOGhhAaflyAEB+t10DxjC8praq5hKu8ho/GdJdZSgYNDU5v8ceblZU2Ofgi9pRZUjttxeYainprjrLbxYIY/5nzlV5ha2e0ntQG6R+nmD4MtVYksJ9DkXRoHbZBJveh64y1Fvwk5oADMydC6uvD/qkSWidyRQrJJa3IdF05jsfcr9Ha2CAua4Uf7Yeg9LKVRh8/gVAUTDuvIgbko9et+sK5bqAqTOGfz4DdkMCwN14sXWZfAbqOhXa/qdtkra5DRho9zxqgYZzNwvLPy+WP/oIZGgISjaLTEjmFXHrDHkVqMlQEf3PPocFBx2MDb/9HTdGgEn7fVuJzweFw+GRNAVeFwOzr88X1jc3bWJ8jiFhsjcddTZIBW5ypGRIEG42maK4JloKbewYd7fUOgyJcHeLxx7jN4eCI0PO5EXlebWzE7qTodDqKKthBbOo56Kdk80B+FL+1c5O39/0CePdOiitg47CRYJvltKKFRh6wwkzzPaXnWfDZG69pKGiPytu5SrPaxNCHIlhoO+xfwBAtJomhAD3ngd8YJM27PiJ6s8Pegt4u1JVUaCPt1UEYwNDhmpQuILQ9/jjKC5cglKGYO6eiq+qMo9qRMVVhnYMMaOy3wcj0RcXfID+J59E+cMP0fv3R1xfUhCJGPpgAUCIbZ5mlRR3DP8CT1VMg/PaaL2OZwgI3MFXnYSZcJ8CxSPzsK8zWglZHSpDM0m4+rRiBYrvv29X0T766IrPQWMhCpMCXVrlhOKc9i/aAM30CiYQ5Q8/hLF2LZRsFq0H7F/xOdhzpXX6lUpKhhTDREuxur+ql25IZs/mzObMd86rgQzJ1Ar2fGAq8HvLGPQ9avueWg84AJmPfSz0WEKx7DngVseXtcMRgFZfpRd6D7olD8Y4GxJmPvEZqOtoi0MMA+t/ZbfjeXyGCkuDqwYGZXqFKUO0YXd2u+0q5nV3LOo7VdjeZEPYeOutMLu7sf7GG0EIqZrpRW0Jud2DyRDrbaV1hvh70Ni0iRmj8lyV166FsXYtoKrI77lnxd+bHSkZEoTH6tWK2g62MjQGANAyRMNkAeY3QtDvNFJsn1W5MLMXpu6MUf7Q9hHoW23FZKw5k0No4S8ns2jffUI/BwDfYgIA2oQJ0Mb6yVBYdWha16T14x+vbNJIKmsZWUMFGOs8Zai8+qNhlaGht9+2PRddXV7qcz147U7g3b/ZP3duDUypPB/DwbRYGgHojjLEVj2mYbJ6lCFiGFj/s5sAAG/sa6G/VfFSuQcDMu9CiAohBCWXDIUpQx6JsMNk9ncy+NLL7lOG3nmH6ZIeQIacHWluj92rjEGPVXV9HZTM0ya3Wq+9QyVA4A6eehXye0+v+BvrGbKVIY/M6xMm2OETZyHrKISHl+g92LrffhVVtFkSYVcdtz9Hebnj59m5NpM2zSzK7b5bpY+LKYKpBChcmY99zN1wdRbCM73srFS7X2EHn1jAVOtWFc09/2ZPjxtWBAB1Q4/zORBKIuj56jz+uMC/V8VQr5c9BgC71d/o0yWcTpis1G5/79bgoNs0WXHIEFGUYWu8sej5299QWrwYpEXF3/dXoCoaY1EYrHh+WE+vYdVZsBtqlelNVnSJFAAYH33klhDgiQohBEPve2Gy4DEYb2vIPUgGB5EzqoTiHHU2t9NOo6L9Bo+UDAnCYjIBtHHjfX/Txo1jlCFKIipvyOKChTDWrYOSz1fuFuGRGxUKVG63qE+Y4JIhqj4FpXJbg4PuzZXfq3J3TZjduNbBjTF+gut9aimEkzoAtmwOoO3QQyr/yNYZClGGjNWrva7cITF+WqG7Zb/9/J6LWvGSLTsj0wqc/SdADd6xVQNLhkqmBc2R6M2NzK5UpcpQ7WSoZ84clJYuhdbVgVc+7oTi3Irg4WSIJyrmpk32AqcoyIbVX2KUCDZMVlzC1h5Z7ilDQeoT9SrsFk6G3DGguj4YwwmT5XZwQlg99gJjKrDrPjEgloXiArtnXH6PIF8Sq6go0Bgyr0+YAEVVfSUuwr4Nt2K6U/mdH8NHuDj1NOsoQ2qhVFV9cisRB9yDYBVgRfXCZLA9SWp7O3RnPukYDM+QGnr3PVi9vVA7Oiq9HYQLxVGFa4U/fV9d3w0AMKH4jst9G8NAwWni3DpzZsiRVMHCfwADTjHJY68D9j69+vMD4G0A7DllMNvierfopoR6hgBUbWPBwiqVsP7//RIAUDp0LAbziqP826Qh8D4M8fMUlwyjzsLbWKpQXWXI6u/3+XlKy5e75maeDJVXrYLV3w8lk3Hvp8oP5alPGncPZrfe2g09tw+GJ8gMvW8nmwTeg6MATUWGfvzjH0NRFFx++eUjfSgu2N5k+gQ/GcpMmeJOwi6JCNj1Dr7ipAzvt19g1o/FxJQ1TrXRJ0zwCJcTRQm8kN97D7As6BMnIjMpqPCXtwDxE70dJrPJEFWGgjKkiGli4KWXAABtQTUoLMMziDJeBdYzVF7FKkMhGQ1vOCa+fQJUguFQ6AbW2imo+OobwORosW/WrzBQNNxQTOlDb1FRavA+sSCG4foDxp/9GRSdS6EWMsQTleIiexLObL11eIsSNrsPgObsGIsLF7qPlZYtYxSuyrcYWuBMkEF+IW4MhZmIKWh4Se21M4CClIjyihUghQKUXA7ZbbcJGINJSYcClVWGtrJDd/Q+7CgEK1zEsjBIm2kedHDF332+JKgVoWR2EWothhMuSiACa84QT7VRFNUXJtMmjLfPnxOu7iiEe4YGXrArv7cecEDlZsEyvA2JqrqqQHmFPytOWbvZfnpImKy4aBFIoQC1rQ3ZsAW4Gj60N0048BLgkEtd5a4eWEyQFwAGS6Z7H1LlnIaRgeDwUhB67p8DY80a6JMmofzxNmcEpt1S0H3oEhX/GKVFw6iz8OZSRVE80/ySJb5wcXHZMo9wcfNJ8QP7HszutFOln46OwZaAaeeU/3Hj3M1chyN6BW12iwsW2J8lJCuu2dE0ZOjll1/Gb37zG0yfHmEBjBGEvZA7Otz0ZADQJ05yiUq+QBl3QAjgNadYltM7iAfrS6okKhNck3ZrlYw1mpYcVA7e/iDMrjSb8TJyAGSmTnWVoWqfY+j992H19EBtbw8eh6mjouYdaZYrHmkNDiLrHH6Y74IWkmuJci2segUAAcZuD7RXksJawUr0/UXDXdSLS5a4O1atjqadAND76GMof/ghtDFjMPaET3itMlwyZM9UPQ8+iM1//KPT4DS4jkqJ7kirLVR86Me5tqzeXu8p/f3QnH5b/OJLCPEme8dAXDkGowbCm+wpsk6GjdLd73wOpWLxHXIm+9xOOwX7LthQnKL6MhhpqJbeh+2F4GYcxYWLYPX1QWltDTGCm3ZGEkLuw0mT3Humo4DgIpiG4ZYhCMzG8fmrFGhjx7h/oos8vQ87B8MJ1+ALtvk+eENiMd85Y2jnCjsqm+1rwHSOiwe9B/N77x3qhamKFfYxYpsIYW4HvIF6oGggu5N9PRWd8BS9PwDArEEZIoaBjbfaGXLjL7oQxFkh7UrzXiPu8urVWPfTn7q1rILCZIQQV2XNhmV0wj+/02vIYjLiADv7MizTq+jeg+Hqk9cpAVC5dina2LFugkw7zRYO2Ii6ZGjXlAyNGPr7+3HOOefgd7/7HcbysfwRhsV4hhRFAZgaFPqE8dC6xgDA/2fvu+PlqOr2n5md2d5u7ze9k4QUEkKTQOhFRIoYqRZUUBAE5PWnvDbArvgiKAqigDRBaQIBQocACWmkk36T2/vdOjPn98cpM7P9JvfeJLDP55NPkt3ZPTvlnPN8n2+DJ8oKnWV4yCIfMot0dm4y5ICUHtxcYXGTxXIQlbWs8FfeKqh00+J1hQDAOWqUSYYiBs1kyaQ+cZJy+OGZ3VeWNGvZk1kZAgDvACd1md19vD2BK4t/PCsIAV75Kf13w74vwoCVGFAy5GxoAFQVJBKBxtuxFNDTy4quBx4AAJRccjFkl2o2UWUxQyQSRWLHDuy54UY0/+jHtJdQNmVIxCpkX4StpCOT64fD0RNNOWcKraWF1nRRFDgbMyg2fAwLUZEDqcoQS3vvpnWVMikR3EWW1SK1lmyQZNuzqzbQ3yXcZLHM8TzRFbTkhGfmjCzPrkkcHZLDFlcnB4OQXS4L4co8RnzrVqqmeL2ZXZcpAe3OBvOa8gJ3nCAFs7j7CCGCqNiyyMQB9vR9XmA1kaIMoYvFDEnp5BSg5BHIHqOSE3s+BJrpb0T9Pna3t4KdUF9ME2Uk4h/T38dVZiCzYp6K/tffQHLXLjjCYYTPOw+GMDMkETNkRCNo/tnP0PGXv6L5Jz+1jWON19Ta2mjxVFmGc8zo7D/fGncaylzeQ2tvz9qzkZ9rTlecxWhPjUVzlIRF6RTuJktVhoxoFAmmtqWVtviE4JAgQ1dddRXOOOMMLErN8MiAeDyO3t5e25/hhFkjgj6o3DKRXC5IDodYIF1RAikDidDa26Ht2QvIclalwzpG2oNsiRly53DFidTkTLEKsEuvsiRDrawS/1fr6sR5OAzQTJZM1iJ3ARyWJdPAGrDLU0gjETPomEnlnAxlIhGJ7dvp7ygpSQ9yzYf3/0IXYgAYkyEuZBAwLPEK/TENkqoKQsDrwgwmgDq+dSttdupwoOT88+1BxJw4RqMYWLZMvBxdtVpky2RVhnLI87mysACImkCcDKWOwS1S56hRtiKhaWOwf2Z0k3HlqqePzg/2Gds4+eT5lDGsxEwUXOSxNpxEpCg3PFvNOytLejdJUW0sLiyeRWdNZMjkGOUJDO5p08wWNWljmKTOuoHyjY4/84FI5jGSO3dShcvpFGqlDSmEi89DXiJA5G8zMqSzz6SC185xjs0Sj5YN0W7g4S/Rf5eNB0L7kIXGkKoM9cc1cb8TbA46LPGAhcQM9Tz5BAAgdM45kL1eEFbmW5YkSwB1FP0vvUzHZK1bTGXIvCsJpgqpDfWQs80PmOt7RoOEqW5aW1tWZUios+NzqU+WuKSUOaiUlIgEEF8ks0Ed3/IxYBhURcqUNfoJwEFPhh5++GGsWLECt912W0HH33bbbQiFQuJPQ0Nh1Uz3FYQFe3LLoOLqb8ERCqH6B/8PAIQLSyaMRKT6e62xHT67fCnGsDbyU1VbJL9aVZU3SFvv7TVrwWQhKtYAalmSUXrZpZD9fpRedhkkRYHsdpuZLJHMMUNCfcpWdl4zU1JFtW5LFgvf7DwRViE3wxhxvggPtilr81rg+Zvpv6efD8y8aHCfTwFJcZMBgIttDHwRlAfhJuv5N81u8x97LHXtWPuGWWKGeN87AEjs3Jm16CKPXcpZMt9IycKyBudLkriPShfP9EqNS2KKTR71yeaKs8QrSB6PcP9Iug5vDFQNy5KenNUVpyVsG7x39mx45syBZ+ZMUbPLJEMkY1BwvirBuQKo1eqqlDGyqE+5muYCKS4sGa4JE+A76igolZUInnwSHYOToSzZZNwgcU2enDl+RE8IxdHaZJirsyI1uzt3NpkoJDjYefifq4De3YDTD1zwj8F9NgVpZCimifglMQctW1w+N5nW1YW+V2ltodDnPgfAVHokmAHU1vIZAJ2XPLjZmpXLlZR8bSv4eUhSeiyaZybNdNXa2jKWHCGGIVxxueahzRWXGjMULhHJPz5hiKbOD3MOftIqT3PsQyrOyGHXrl245pprsGTJErizBYGm4Oabb8Z1110n/t/b2zushIiw+BweLBs89RRbqqnsckHyeECiUbqApWRImdkGOR5kQl0NXCZVamuENaBUVUNmdSPcUVotNpVE8J41an19VjXF1v9MkuE58khMfOdt24KqlJQgGYlQiT7F3WfEzVTQbDUorJaZkpIV5ygpgVpVhfiGDfD06+y808kQt/hyyc4Z8cLNgJFk1abv3qeATSusC3Ef61/kHEMX4vg2ujiJmKE8bSIJIaLYX+jss9iLljBf1rqERKPQWlvF55JNTZDlGWwMy/clk6JnVrZCbwAAPWkvVpiShaXW1iK6ciWkngGgIoP69HF+izQ18NhhyZByhMM0SyoYhNHbi1AE0EOwZZMRwxBuUefoUVnOI266fiQZktOJ0Q8+YDuEGyX+KEvfNzRR18ZehiDLuRgaCFc1IUOtNQt9KkxFtbriMipDeVzVqbFPkiyj4S/3AIYhXHeOsBkzJEidRf0QrVGmZcn40RK2e566+bonTUZ83XqQzm4AvHKz/WyMWEz0XxyUUfLxUtZ6QwK++ChQtX9ZSWYFavpXX1yDcwr9PVpbG/S+PtEmB8icyWtF35IlQDIJ1+TJotq32TdMFgHUiZ07bJ9LWtLere4lHpTurM+9/9DfpVL1yemE5HaLKvCeGTMQXbECWlubOYZl7U02NeUt6kjPwxJA7fVQ1ZcV83SUhEES1FD1DRhp5wEASW5cZWj18UnBQa0MLV++HK2trZg9ezYURYGiKHjttddwxx13QFEU6Bl6xLhcLgSDQduf4QTf4K2+6VRYYwmy1aHImW2gc+uEjuGwMHu1qlJ8v6IDrmS6xMkDUHP5961EhVtTqZalwyLRp6a9J3fuBHSdpv9WV2ceQzOLBsoBv42QKOXlIqPBM0B/fyb30j5ZpE3LgW2vA7IKnPPHQRd3ywQi/pYQ1wwkNENsDNyVV2jMUGzNGiR374bk9cJ//PH0RcvGyAOoSTJp6+mVbGqCnKErd7K5GdB1SE5neq0nK7SoJXtJshVNVGtrzUys7gHbOXMIN1k+ZcgSeKxY3K9KJf1tvKBnaIArEebzq7W00Iq/igI1y3MFLW4jXJlgm4MSKDHmH29tpbFPDkf2xd7SukSSJKiWDU6pqU4ZIz3TixiGyNLLOg/1hM0VB7CML0sMk6kMEUoAU9eTrWx+ZFPR9LjtnvOYRg4eHEtYA89MylBix06AEMjBoMhuKwhv/pb+Pf/KQbXdyAZiidsDqDLk8PvhYM9tYvsO27qcqW+YFb3Psea5Z5jVx22qDc+8Y4oPR3L3bqHaWOuvJVhQujWGLRMMnTeopuuFdR7ymnB6VxdU9gBa13dR1HHMmJxlRrgRLkkSJFkWcw8AlIpKoQyJEIWUPYSXXlAbh9fTciBxUJOhE088EWvWrMHKlSvFn7lz52Lx4sVYuXIlHPuSxTDE4E3zpILIEEmXH5kylDvbgE8WOul5SrmjtBSyz0fdV7xORDQ9nkcU/srlU7aQoWwyqFWiTzsPRgCco0dn/TyxjOGQFVv8k1JRIfzW7gGuDGUnQ4OySDe/RP+efDoQzhLoO0ik1g4aiGtQ2aaotVD1xnSTZa8UDAC9z1JVKLBwoekCtSgqsKiiSUvqvtbaKlKHDckcg8d/qPX1mWNTxIFR02UC2dbgUa2rFZXNpU4a3GztU0UIMV1LmWJTOCwFESVIwqUEAGo1VVc4CQ5GCFMizM2Xu/vU2trsi70WtxQrzPLsWtxkqRs8V4WcDQ3ZY5+0uFB7UuN5eN0Va/p+6pNbSFsG6Amq9iAHqWMB1KLOUCpRYfPQlc01Y3EpykiPUeHtHMhABGqSwJAkEQogxhBzMPtcT0NiANhBC0Fi3tcK+0wepMcM0d/JnyutpZlu/vyZzZFan2xtRYTF4wVPM8kQn+eSJYA6sWNn2mdFGY1MylAezwTR2B7CjCcreXJPnUpVHELgZ/1njQzPbk5XteUzPO7IFhNaWyPWXm8ksyrPCaA1qP+ThoPaTRYIBHBYSvyJz+dDWVlZ2usHCmYp9ezEzFbjJIWoCLk5h/VgaAnAaY5RduWV0AcGEDqTVm2VJAmOcAh6WzvNlklxYQmfci7CZYnncWQ5F2smS6orznRfZScpNvVJkiGHQ6KpolJhVrl2ZSFDhBCx2A+KDG2nxfQw5jOFfyYPhOvLkslSxRYYraUFQGHKECFENNS0tS+x9JCSnCoNpNR1kcoLAHp3NxQ2vvVu8Mqy+SxSJKOCcK3c1YOTZpkLndrQaCFDNAmB8D5VkoNmyvT20kyZnHFJ9sKOVtVQYfWurMoQdWGZmxZP+c65oehx2xiZYHNhpbh++BxUc42hxYWbzCE5oJSUoOZnP0X8460IfIY+V8LoyZCxJgjX6NHZSZ2eEO4+R5ZCoIottd5OHEkyKSz4rPNDj9uCtLn7kMM5Zgw1rJJJBKNAh0pd6NZfk9jO1KfRg5iDu5bR3xpqAEr3oS5RBqS24+Cxe0pVJbCGto8AAAcADbljhvqXvgoQAveMGXDWm0HdXFGRLUUXrXMQAPSODjh86TWAuHtXzeUmM3QRohBNAlta++G1EZVaKBUV0PbuRbCfAL5UZYg9V7lc1ZbfJRRHa/hDdTX0HjrHvQOG7XgOvqY4i8pQEdnAN3gpRxVj6yJpDbAjhIju3UqOZqOmK47FDJWWovanP7XVEVGsAaKGfYwED0DNmW1gbkDZrD1rJktqOw5BUrLFdcBOhn767Hp7Rk5FhVCenBG6qKUSLr2jgzYWlKTcsTBWaHFgN2svMfqYwj5TAFKr3/bFk4I8GAMD0PsHzGyyHDFDiS1bkGxqguRywXe0xXVg7fZuaf+Q8iNEB3Pr1muLVSAEaN1Ar0MqklExxr1vbsdv325C6Jxz4CgpQeizZ0OpYGSoo5ueh8Utk2QB+WpdXcZCoeZ5mGTotY3t6AmYbhVOznmT2xDPkMqkDOVahDWL6we5lSERM2RRO5JsDuZs+KvFbG4yAAh//vOouvEGoSZxY8EfZfE8FkXQdCnmIAJa3KaiZTwPRhx9MdDvt5K6piZA0yC53VCqqjJ+ngZQUzy9ai92Jy3ETJKglJaKtcTP+ymmKkP8nmQqgJkN22mTaIw+xsxY20+Ytb7oNsZj91RhlFCFVrSxyFLEFTCzwgIn2NshmSRCFkVJU6F1mA1OOVHR+/qgd3cDACVX/a1A794MH44JAryrK4ZFv3kNJV9aDNnrRenll0NSFOHSCvTbfxMAkRiTL2yAG+1JHXjxo2Z457PSIixOiStDnn6dxp1aniu9fwA6y/jNaTAc4jiolaFMeJU9tAcFDD0tgDoTTGXIHtysd3bSeAhJglqZvQAglallJHXghY+accq09NgJ2aI+WQmX3tVltmXIYcETPQE4TBk143lY3GSp8TyFxPLwMQDgoWW78EWnV1icSnWN2EycA3TiprqXuEWmVFXlTFW1oWk5oMUAXwVQPnT1MQjsEn1PNAlHbQiyzwdjYABaa0vOBqccfUtfBQB4j5wvZHgANkJALfiwWQxRoS5GvasLsDY4ZbDFKqx8kGbwjF0IfOlfQPcOYOmtwJrH6G+r5s+dhMeX78aNt98GQgjd8LkV2d5Nf5L4XS4k2L1QLVZ0RljcMq9uasd1nWvxxzv/D5HlKxA657P0dFi8QnAArK6NZYPfVYA8b1G4srrJrOosIbbrK4LNa7LEJAE01ob988WPWrGwLomQJyWmzjLPab0kXcSn8aD6XOqsNS4pn8IlA/DEYDsPkWk5enR296iWEMRxR0cUv9i5ByzHEkpFBSRVhaOkBFpbG4IRAkCCridgPVM+D52FGiSAnQwNEVIV154oXYs5EdQsylASZuxl2vfEYhh4h1bt9i883vYen+cSm4NWKBUVNFC7owPyaLsyxFUhR1kZZJcC3LGQth+55CnaC3HNY8BrvwB6d8PgpTMYqZMmT8XE5R+IZ1mtrEIMQKCPqTbWZ1eoT/nikui1icQNfO0fy7Hupi9Ba29D8HTqEuQk26ETeOKAoVgVLrqeOMLhtNIunyQUlaH9QbRbSOd9cYJ3Pu7IeJitGJtVRuWqUHl59lgFmAF2fTEdV/5jOaKJ9MBxW4CoZZEQBKKiwr7ZpkAXyoGEm59Yk3ED526sTKn1CUvMUNbz0Cxl7ImEWJlJANXqKqE8OQfoxE0jXGLiD6I2yaYX6N9DaJECFiWGmXWdrFu5WIitcQQ53GTCIuWB0+J7dZvaYV2IldJSEWQp9aQHNwu3T20t8MrP6ItblwLPfAe4Y5YgQpafD1hIsLkIs/sTjcEdJza3THJ3gRuiFhVzBETCG5vbETjxRFTdeINoE2IqQ4zUWZQIoULkcvlFu0TM0LOrmxFLZp8fqg4omt29pLGgdKU6lzJkuuJe/KgVd736cdYxqDIEO+Hi5DHXeehxEWz+wLu7sHp3d9ohkqJAYll/3ohkv1bbqUqQ022pRS3PioQNMGvOiHpJFqMHsKvGQOEbsEB/G9BEC8sOLRmyq7PmHKTPrdbGYvf48VlihgbefRckFoNSU5NWWkG4yVLmIAC4JtFK5VpHh4jx4kRFzMG6OmqQ9O4G9ATw7HXAA+cCT3+bvgbr3KXf0dYXt5F6rjgLMsTWXiMeFyUR8t0LopvrOwB0QkXN//4vfKz0hOx2C/U5FEmpl8TihdRshVU/ISiSoX2FFgda1wlLrqk7jovueReGkYFE2MiQuTGKRTibPE8IkIxZAhjp7drbk94bx9aI0roIN3M3XA6rF6aMSgzgn+/txI6O9M7MZvAmsZ2H3tdHVQpkSb3UEkAyBiPWZ3lRQm+5ed5qQ6NYhNV+er6pKem8xo6zrgAyRAjw2i+Bt35H/z/lrPyfGQRISvCmWIjZhqK1tQtlKFsPKa2rixZaBOD/TEo8k8VN9tNn1yPpswebO3gfPEaGaBwM/YTWyhbI/vVAn1mXCCvuT/sNhsKCs0k6UZR9PlGgraQfptoBy+bO78Xu5cCfjgMevwJ49nrqmgPofc949iYUSwB1anAzV22y3nNCgI4t4go/vrwJ9721Pe0wa5KBN2aeBx2Du8myZ6shaWbeARLufWtb2mH2AOoUwsXHyEW4LC6snR1RXPG39zMeJrE4H1+KMsRTvrNmxMX7gFivjQy1ecLif7wxrJk1Sl+3ureJpok1RS1kHnZuA/44n8aBVU8HwkOXmq3xbOJUg6ScupW0tnYA5iaXLWaIGyT+4z+TpizyNWh7ZxRPb7evibxti97ZYbrJGFHhJTCUigq6DnG0rgN2vpMyBh/TVJmt4AaWv89eA4ivh5LXS/cYXQOe+hbwl0X07w/uFWsC0ei58/iq7kg6MeSJDKEUY1fjymldbdpn6JcSoHlN5vcOIRTJ0L7i/b8A959pLvQ8kDaeLsVmC6AWi3CmlOFkFPjbGcDPqqBrdBJyGbU3lmEMEZdEbEqEWISrcpMhI8kJFj2PjoH0hUMEb0btQXy8+7EcCqXHtnRuA347FfjVBJDu7ZY3JOwdPxNQVagNDXCNH2eWCEjoUJMkTVERFmldHos02g38ejKwlLXemHIWMO3c3J8ZJFLTejv62UJcwRfiNktBxMx0YOCNNwDDgGvyZFF80BzADKDe2hbBloTp0VbKy4VrCaynFwFobSLDENaisvdlekzN4bTIHQA0LgC+3wz8bw9wSzeM6hns81JGRYVbpaV9nKjY3QBqXT0Q6wX+eSGwdxWw9l9ibiARSVMiMsFhTa23ZJORZFLEKogYGKtimYwBfzkR2PmObR4+vcpCAPnIkiQUFV8UIkibEGJu7pnm4erHgNsagFd+Ytu0HBlURtGHMAnIOhFVxAkhIpg3pysuab9W7f2ZN2+Z10yKIEXh4mNkIFzP3QjcVg88dbV5HkQCkWQETqIFHcOfP4+dB/3+YIZeh8nmlsLKNgDAhw8CdxwORDoAh4vW9xpCdTZp2A2SjoEECCG2OQhYlaH055sQgn5WaDGwcGH6+8y4jSYM/PFDu/JvKkPWmCH6m5KMDKkewzRIppxtfvhL/6Jz8H97YHzhITZYZqLCY4b8vP4aH6OJxQbW1VES98avgRV/pzGSK/5OleAP/ko/w5QhwveQaDoZ4okMwQG7sSvOpdIyB63zcPl9wN3HAq9bSN8hiCIZ2ldEqRKSutB3R9IXMN4EL5iiqPCaMRkXyPf+LFJR+ScMFmEzkIlwWavfWl1xzXms3kgn8LczYfTssJ1HTzTDeVgsRpsrjo+RKWhz2d3AQBsQ701RCCS0lNdh3HPPYvSjj0ByOGyNbjM1uxRqRD55/rWfA/30N6FuLnD2H4Z0EQbSY4a6IilkqL3dTK3P1l38bRancGwG14GlICIgYS8xg5QdFeXCtUR6eNo7AEOjQZusmJrS8hr9feffB5z7Z2D+14EvPgKozF0qSTZSF0umkzZBhvrtTVRtytB7f6L3GAAOZ60WBtqAN34FDHRY7nuWwHyrRcrOAzA3M7A4Fmx8HvhRGPhZDU3VXvkAjQkDYDh5cT0JCT0z+ZSZsumLmOdh9PSAsC7kafWxol3Af28E2EZiJVyKI4OSFgiI2lkei/qUcwyO9+4B/nOVJfYr+/MqW4PB9QwqcHXKPOxpovfI/Pm2MWp+9UuM+feTCJ15BoBMypA1u4+R4Nra3GUbepqoK4jj838Bqoc2Azgp6szR80hoBiIJXdTH0ru7QRIJSxuLdAKQ2LYdWnMzJKdTVCu3ggddE0joddoNPU6G9K4uOHhWJ1eGuEESoa1kMOcy4LP/B8y9Alj8ODDebC2Valh1p6y93F3t6+OV+TPMwUgn8M7/0Q80HmV++JWfANFuGAluNNExMhrU2ZQhFoiuVFVRA+T+s+g8XPsv+v+ltwIglPAewjjkAqgPGhx/MzDpdJC2FcCq38PcFJMYVWY/lFuc5b3A1gyqTVqswgf3Akt+KP5rxqfYsyassKpP8UyuuNQxtDitCLv1VWD7GyAKD/alY3QNZJBR2SLpi4FKsgxZXXGrH6VkiIH/KkIkABJ6Y5otZZp26i6B3t5OVTRPNmUohzwf76cWKUDrmZz2iyEnQhT0rgRcTvRqppLGF2KqDFHComfqYE6I6DPmzdRd3JIuDkjocVvcZOXlohou6eoB6gDC6sFwed7hc0KSAUw8haYyl44FJp+RPoylrUgmEqGy+IuSfogif4QQM1Yh7AVeZIvwuX8BZpwPNB4JPHU1tVQBkKoKds65lSF/DPQh4YSLqykVFZD2fAg8dhn9QDICLP8bsPoR+v8JJwO+ONCzDYCEZDYyVFUBfctWlPaZAdT82XWUlooYJgDAQAfw22mAJc7NEB3QJTjk9HORZBkI+IGeXniiUuYxUjPv9qykxO6579JrJOKrciRkWEpcECMpaJMmDB/LXO/dC9xp2eRlBYYrAPr8MhIhKfCw+kKAJWuUnbpNGUp1j2bD8vtM1eo76/arB1k2aCLtXYZLkRHXDHQOJFAfDosKyzSehyJT0cXIsncBAJ5Zs+z3n8PQACgwiIx+pz3m0jVmDF1bCIEnCkAxk1eEm6x/HVAJYN6VgDsEnPnb9CHYypjNhcVVUV+vDkC2uOLazPffvQuI9wKV04DLnqUE/s55QPdO4OejQFiXAm5Q98UyKUP0vocG7MYuD0RXKivoHOSlSp7/H6oQDbQBwXrgyG+kX79DCEVlaF8hO4C62TB4zQxRbyb9IeOLUzAKIJEniyUZBZbcQv9dOhb4n70wTv8VHYJNllzKkC9qT3tPNmeR5/9zNXVtLLsLAEubBkDACVcGMhQKgUj0oXEMWH3KGVxxHR8DT3yV/jtYD/ygHeQKFswsrJMME5LHJUXtKhoxDLMmk18HXqeqAwDqF9/+JrWO1jwKxHvotTv158NChKzB5VVBuoB2ZnSTZVeGkjt3Ul+8qsI7e3b6IJasHxCgKWRucM7Ro0UqLOkyGxEbesK0SFVm1h99Tc5z0VNcDXpKzJvVTcbVJ6OvD4T1k3NsewKIdQNlE4DDmCvy8C8Cirlx5FOGHKGQUFSCETNGRVikJT7gLyfYiAle+hF1ywHA2f9nc2FpemYlzsGMktJemO6+PWwOpio2H9xrjvelfwHffBekZkbeMSQWd+OJwyRDe7lBkqLY7F0N3HMC8DdLxWPuzsyV1cnIY7ifiDgYIxYTcXs2hfb57wFMFcC59wDfXgljEh2PZFmzHJZaRkCKMtRkMUje/wuw5SXzgz27aeyYFqdkFQDO/9uwECHAjBlSZBllPrrZdwwkaIVlHrvX3g4Hu5aZ2nEMvEsNEt+R8zOOweMvCWQYKRl+PPMOADyi2ztXhmi8kuLWgPEn5Ww9YlgMEiBDzBCbg66YAWeSCHef1slcyEGvaXQefxOdS6oHOMZsS8XnBzd2+zPtIaWW2D2rd6GV7SErfgts+q/5gf5m4F9fpv+eejbgyNAL7xBCkQztJ1LrzfRG0x8yORiE5qSXWu1NV1TUPUuAzUvoix/9mzJ8APjqK4DTC0NMJEaGEtnJUDBqr2sjXHFV1cCax4H7zgD+egolDRyhRpCvsEUth/okKQpiYWrZurstC2RLBlfchmfMf5/+S8ChwiixB09mula89xJ1xZkbjtbWRjdghwPKf79M5d8X/oe++dIPaXzVnfOB/95EXzviq/vdfywbrA0yBRkaSHWTtZnVoTMoQ5EPPwRAew9lzPKzBNMCMraUNtAAYFmGd/ZssXAZrIcUQGMihEXq0YDa2cCoo5ALqVV8U2MJeK2hkn6ImCGNxfHIfh/k99ki/JmbzB5ZsoO6BJwB4OhrYIgMIjpGqnIjybJwYQUjZqCrxhZhBZZYjWtW08J9PDumfBIQqLJtKNmUIQcjI2V9ZkApnx9KUAFevZ3GPxm6uZkffS11aVROgc7uZ64xOBnyRSAIl1BsqmuA7l3AU98G/nEu8KdjRVwRAODM38EYywPps5MhrspU9pixINx6lzweUWYDyRiwkW1elVOBaZ8Dwg0gYlPPbJSI/mcsZsjqXhIZnYktNFD+oS8AXdtZoPQCSlr/MJeqBYEaYPKZWc9jf8GVIYcko9RPyVDnAL0eZiJD5p5eHFE2DzO5yACzojQnEeQsSvjDF15Ix+H1eXixQjZrhZri0YGjrs55HqkNd1NDLWS/HxJbI0r6zDnL4+kcvWvpnlF1GDDZkigy40Kgbg4QbgQ5g6m0vC5ahvVdsWZ18sBrQkyjpIdeK5z1e2DR/9o/PPrYnOd4KKDoJttPpEqcmdQOSZIQK3HD3xKBu5cFVeqWjWvjvcDOvwAlY8wslxN+AHjoomQ2JKRjZGL1qrDgLdaMrpsPct9q4JVv2T90+GLguO8C4dEwerbaziNTIDgARMs98HTF4elKV4aU8lKgZR1d4Nc9Rd88/Ve0DQYskz6HiuawBmlbFgkhz4fdkPppjyesfpgGR79L1S0MsCamqo+qE8MEq2JVFfQCiJlusgKzyUSfKot7wgY9bgt07XD5MfrBB0CSSTgbG6F3dtLvZoXdgBRlyG0AMy7Iey6pylB3NIkSn1nmgUv0JX2EWsaGBp0VYXR4pHRViGP6efQPALLkSnEeAF2ISy1jADQOyujoRFkvEXEwYn5odAPGla8DJaOoG+BfX6YuAOZ2sBol2YgKJ+tlvRBVlYWq2bkMePV54NXbgLHH07Rnbxmw8H/Ma0XMuZ6qoNnOA0C4DxZlyBKg/Y/PAR2bLR9wAV9+ASgZDXhKYLy83HatNN2A4rCTep6eX9lNzFRuoc5WQereCfTuoYG7ehwI1gHfeFuopEZKvFtq/Ehqar1udYnzjM62pUADaCA6L9/Ajbge1q5i7peHVS1IsrVSlmWUsmbG6YkM7aKWWaoypHd3i2eMx//YQIhwe3H3Uu+lX8eU448S2Z+83Ijb0tOLaJpQ6ZTycmD0cTnPI9WgTnWTSawOXWLHDpT2WzLW2pkytPdVoBbMILE8K04vNagBkGaemZh97eVrV2kvwV72jBh9faJprOLRgann0PgnHkT9xq+BaecAE09N+75DDUUytJ9IzRTKFKUPANESD/wtEbh6LAF2ug5IBIqLfUcXS9eVVWD2JeYYKYtXfyZWz1xx7iQgs6KFWkcHHUOWobz0LbsOGKgFTvox4CvPOEamyQIA0XIvsLkbnm6LjMpjO1b9HvjoI/NgSbaltKepaJlinyzBmz1WMsTdGZ6UwO5HFtO/K6fSrJX+VrpBWlKGhxpWN1l1kFpsXZEEDMPMZDF6eiBrbIHMQYZcE7L09bK6yUDjITwzZoi3ebCjwRZdgJEhvkC69YJquqRK9NQqNQNFeSZLKVeGiG6OAUrIcNKPbZ3TU5FKuPozkCG1sRHJDZtQ02XGqCQZkVfdSRoIXzOTHlwyCvjKS9Q9KttrORFISGZxYfGYtooeAkNP0GJ8XLXxWJ73ra/Svw9fDChmjI95HjI0g5jFKS2Qa5j6ZHHFCZVg1zOAsdl2PE7/JVA7S/zXIHbjqi+m2cgpYLYmqeoGdI27FJlBEnAAv59hOx5Tz7G5i1PveZoaaOl/BkJsylByL6ufY52HVpV57EJa06puDrDgKgwnuDJkdZOll7hogxzgLmD7esObnKq1tXD4/UiDnhAB7Tx8oFdSETrDjL3jiQyeCK8BZNAkBkIAEDgmHZVXoTb3EHpcqpsMoK6yxI4dKOk3wweEm0yJAvXzcqpwqfc8o0HNSjLUdgK7+Ri8cKXTgKwAOIK5xSQJOPY6qpwOkwI/0iiSof1EqmqTSX4EgFi5F0AHPIwMiXghjw7JIdMHeT1TU+ZcBvjNgoRG6maS4UGWXS7EAircfUk4uxkZ4qqQW6PBtIob+NprlDQ0LrA9xOkLZObziFfQRcPXbaYN8zoUSmQDELQcPOYzQMB0naVZpBkmvcMSM9RldZPxDcXBrM9L/gP8/RyI8PLP/h9QNp5mAJWMzvjbhwrW+kfcTaYbBH0xDcFQCJKqgiSTUHvpwqxnIEO8eW7WBosWNxmBhHhK2ju3SEk0BlfCgbhTgqHHoLdQq9zhVShBzAM9xU3WnXJPePxJaR9z9xmauQi7dOpCmnTaoMbIpJ46x4xGBEBtB7HEDHFXg5G5TpTl+c0XCA6YVawregAtEYcKs4aK4tWoG+mjJ+nB7jAw76u2z2tctWVzJJLQ4XPZl1CltgZxAGW9Zmo9L/6nJlnG5oKrqQvDVw4E7SUV0o2SdDLkqq2HIQFODUh2tAPjLcpQIr3+EQ6/yPbffEaJqPelA66kSU6JYZhBux6dZif27DZd4lPOBi78B9C2iTZEVjMEJA8hNN10k5V4GRlKy+o0yZCRSobyNbDWE2LmGoQpQynXShHKECdDGjSm2jpcBqTGzLFIVqQZJJnIkHUe8pYfTAV2eAhw+i9ykpI0d3iGfYo39w1GATnKkxi4212ncyLVHfYJIUJAkQztNzThh+aTJbOiEiun1ranmzFunoHl1YGKyXQRifdR2VyxL35mU9DsNSIAIFLiomSoh77PF2HFzX7j5c8BlZndMumTJfMY8XJKhrzsPIz+ftovDIDqNYAjrwL2fEgLi33mpsxj5HKT8diniJ1E8PgR1aPRthpjj6ffv/E5GiRcN4ce6A5l/N1DCasyFHSr8LsU9Mc1dAzEEfKqcFSUQ9uzF2pvLO14gAWD89odqfWFODS7myym2Td42eeF5HKBxOMIRoA2J2BoCejsnjtKy3KqNRypqk1PaiYLs7AVA/DHJBAtCZ0pQw63QTP28gSpizFyGAyuMTQRobaTiM1XkCGvDojg5czQxEYnIaEZSOoG1FT3UlUVNJmeS7KjAx5YlCGvAcy8iNbCAah7J+X6ZZojaWSIKbTlvQDRaaZXstWyoUy/ADjxlrQ5zmFeK15TLMMccbrQHqQxQ8k99Lt53J7i6KFu4jN+RduwTDiZFjvMcR6p81DyeCA5nSCJBA1oZ9dW7+w0yzZ4DGDMcXQu9uyixPuUW+kXVAxd25tc4ATYIcso4zFDwk1mcVePyUyGuJqWaw7yxBIDmddergy5uDJk6NA7qVrrcBlA6Zi855GaWp86BwEziJorQ0YsBiPCyjWMnWVTFzOOkdpLMcMclH0+RMNueLpj8HTS422xT9XThykz9+BAkQztJ/JJzhwxpqh4e+hDJtw+Xt3s4uwKZPxs6uKVyXIAgFipC9jZDw9XhqyL8BFfNQlDBhQyWQCLMsTPg1nWVEYl1L236BZa+DBgz55Jb26qwTAIZEuasjWt10ohzA3FMIOCF95M/4wwrK5Rl+JAqc+J/riGzoEExlZQq1TbsxeO3hjgSFeG9K4uIJmkjTGzFa6L99jcZAnNQCypw63SDVqSJDhKS2k36wjQFgYMLQati1qlSnn2Xnf2c7Erm6nBm5LTCbmsFEZHJyq7acCu1kJdJYrLABoyB55aoRWgbPKednXtNJ7HVqjQowNV09M+Y0UqicgYl6SoaA8C1d1AormVklK+IfJ5mEPNSCV13ZEkakL24He+sVZ2W+KSmi0K1xm/zkqE6Bj22jkZNy1JRmtIQmUPQbKZZi2J2CevTlW0w7+YpsxypG6+qSowL3GhtbTQ1iKcnPKyDW6dKs2NCwBvKY3lOgCwZpOVprrJLMqQJEkASa8zJJ6vbE1tYz1pWVip5JQXKnQN8IKIGvQuUxlCuDHveaSv7+k13lQW/F/ZDSSIJoKnJZlAnrAg/xgpVd76sxi7A1V+eLpj8HYwo73VSoZyGySHOj45GtcBgrl4Za8ODQCJCpZlwkmEbRHObT2kkohMPmUAiFTQ2jPezlQyZABV03KOkd4BOvMYSXYenn4CkkjYFa5QA1WeFFcaEbKNIRaY9Mw4M3iT2LPJuFzr1al//ADCmv3hcqjCjdGR0g5A7WaVw1M+L/zwZWWZe9IZBrBnZVoBvtSNURGVm1lskhaHzoowOioLS2cuhGirkyYAAMa0EBhaDHorJUOOoEcE+edCqvqU6dlyT5gIQwLCESDZ0WMP3KyoAPy5qx2bDZCzx13IkozWMMtoa26nG0pSo3F7XpJ340p192UaQx1NvyMYBZLdXTAiERj9NLVdqa4B3MG0z1ihpalomRMy2pkAqrV2sPOxrCfjaUVphOoyqoN6irsvdyKDmcptI6dlEygROoAQ2WQWMpSeyNAmUuv1lArUYk2pzEKG9q6yZXQC6cSRZ3W6RHNp3W4shBqQD6mhFplaZbgm0gDvMS20o7zNFVeeJe4w4xjZs4UBoL8+DAAItPN9it1zr5GmMH7SUCRD+4lClaFEJVV9PAO0JoiwFq3KUIFjZCND0SqmPnWwxUuUhM9PuMzNJLcyZAR9SCiATOhEsS3CeSyHVHcfkDuTxRp4bJNreSDtAYJNGVIzBG8yiV5mPeTSKmnzBaYyg3rT3wo8dAHQsjatGWxaCjQvn8+yfjQtCr2fphY7qgZHhkiODd41lcYejW0mVBlqZ3EjpfmJkHWMXM+Ww+vFXvZ1yZ1tgsjLqgG5dkoBY/ANPrsr2SE50Bqm/9ZaOkUNLsVtQCqptwVLF3IeGcmQL4AOJvDGd+4R2X2SYkCuzO8ySSWO2Yyr9hDb4FsoGbIZJXldivld4g5LELXptrQYVwd4DgLmtXJkCqBmypDe1m5WoE7pTSbWlFRliBBg5T+Bf3/DUusr87Xic8DJyZChQ29jxoLXAbgyBGanIPW5ijMV2Ar3NDoHK3sAOZIU5S0Ut54Wd5YJqWpgVjLUyNZf+tjaY8Qq88/DQxlFMrSf4CRCZBtkUVQMvxcRttYmm5osypCRnwylBFVm8ikDQISRIX8nr1BqcZMFcjSIROGTRXIoaAua5yEW4QIIF7dO+MZLx8lc8C1giRkihJgxQz4FqMiQBjuCsClDipJVoncIZchOhmzl7a146UfAryYAW2jNqaSTu00zb/BcGQrTXq0w+npBWGyRUpHF2k1BGtHOZJWyhXjyLgJdi0Pv6qbnV1aYK05PDRDN8vzurKLvJ3a1WyxSHQjnt64LCdKWIKGFKUNaS6eZgeXJb5BkOo9M10qChL2lbFPbuUvMQdVjQCpg09LS3GSZrxVXhvS2bhjRKM1gAltP8jRDTR8jQ3aq1SgxMrjd8xCukYCIGZJkoc52sTnoYHOQJJO0Yj7SY4aEsVhleY47PgZ+Pgr499cBLQZDKDuZjV2e1enso68bRIfOjAWHL7s71AqzPIuJ1DniCATQV0FdsqGdcTMuyW3Q0gn5xijAFQcAA6OoIRdqZ+uudX0vwOV3KKNIhvYT6UHBKbUsDIKEZkB2qGjltdB277bEEeR/yDJZ79ag3LimI5bUEa+mA3j6CfT+AXs2jj/35ijGYJZ1NKlnrNficChoY1Zpcvdus8K1V8+bxZWpYalVdk5oBlolGrPh0gBHAoBBU1VJkgVujpp4wCudWq+96ki3SnlnckcLXbAMANYu6cIP3/8RVYIAWpfpzd/QfzsDwOf/iqSlyB+QQUXjyhCrfqsxkiI5CKRgSk+YLMiXTQYA3vnzYUhAfQeQaGmB1kvZl1KZu/mvGMOw3/euDP37AGBnJX32tF2dFhVCH9Riz5/fVNUmltRT3Evd9me3kNiOFJd46hh9sSRkScYe5j1K7GwyY93cekbXcSr0NHd1ZqOkg5E6o7XbLLioGJDLqvNmcaW64lI3+OaeGEgwDAAIRcyA9uRBFj+SSRnqi2uIazpkp1MQlbJuery1krYRjcLo6QEAKK2svUQyCjxyMRCjr2PSGTDmXMo+kfmei8y7mA6Hzl1YTLUJZCimmgFGSrwbkJmstE+iD1bJtiT0dvZcuYyMcWFpY6SsvbGkgWhCTzsuVl8GTQaccQna3r1ItrBsS79SkEv8UEaRDO0nUmMVrAtLS28MJ/32NZz029eQNMx4hcT27WZxPK9Oa/7kQCrh0gyCAfYga7qBC/70Lmb/ZAl6VRXdrF9lYsd2kwz51bxZVqmWA2AuxJGEhhseW4VrHv4QGlHQxkndnj1m7zOvTotGFnQeFjeZ5Xpd/9gqHPuHd5HwMQuoG7SuDY+xcemQag+831qzWJge1a4MvfhRM/7ZSvMSHNvptTEkABaJPtm0CwCgxrbQ5oq9e4BHL6Zv1s4Crl8PTD/PoqSlL8SPvL8T933UDcBUhvRunsWiQyqwzpKe4l6yBlC39MbQ2heDGg5jCxMWo+9vgMGOcZTn6VrOxzDs6iknjQDw0LKduP2/G9ATTWJvBXP9NPdaAjeNgtwARoqb10qy73h5M2b874t44N0daGdzUG/rMZUhb6GEy65sWjesp1ftwYwfvYhbn90olKFkU4vdzZBHnQUslX8zKEP/XbMXF/91GZ5f24yuIL2WRkevvUxHAdlLWsr9sJLsVbu6cewvXsEju+lrNZ0mCRSVtD0GrXZ8gMHnoSo7EHSrol/c9vYIfvT0R4jUUoJb3cYzvcxrKQikw4D84ndof7hnvwu0sjppF/8buOgh6Nx1miFEobU3hiue2CjadAQjACG6qZwGzXpduZC6hwCmMmQYBBuae6EbBO1T6Xwr265Da2P97lwG4MqfQZtp7e1g1bq3tw/gB/9ei9c3tUFyurCXkfnYpk3QWYV7taryE51JBhSzyfYbqQ/ZQEIXVWN//PQ6bG2jO9Xu7jgCYXpoZOVKGiQrESjhcF5LLjWAGqBysN+lYOnGNqza1Q0A2NWVwN5SGoQa37QZejezfCrK8z7IqYQLoAtxqc+J+97ajseW0yrADmUAvpAEgCDZtGdQypBZhsCckH1xOunXNvXg6VV7AEjYUxLG6IEoyjpoxWNbIHhp7jFGAqYKIdncZOv29OKpVXugJDR8BhLk7j4EBxzQVYmSIdYtXtv1MQC2Qb71e/oHoJvlFS+KbCMzDobeE+4CWL27Gzf9aw1OSlLlSPSQ4vfbZQCu3IG65rnQMUp8bnTETWWotTeGk37zGhK6gXsvm4M1oyVM3EMQeX8D/aBE4CgZpPpE7GRoU0sf/ufJNQCAXV0RNJexDb6t32z+6yksJiJ1HnKisrapB79ZsgkA8OfXt0JiZIh0DyCxk5HSQolKyhzpsrgyfvrsOhAC/P2dnTiObSZaU6v92S3Agk9z9zFS1x/X8O2HP0RSJ/hwZzdCo2k/QYdmILp2LT0Pr1FQja0kryidIVniVy9uRFIneFsP4XQADW1EFCvU9tIq8EpQFcVaDyQ04SZzQJYllHidaO+P4zuPrMS6vb1wDvhwDoDKVnqctVGrcJF5Dbo03mp5xs66Axi3EEC6kWhVNa9/bBXe2NyBK50+lMb7aINTj24mMYTyxwsBdoOkMuBCa19ckKGfPbcef31zG06fXo0JE8thSICvmyC+cSMdw6sWVOvHDOew7iFJ1JfQZ/el9a34x7s7cNXpBHvKJDS0E0Tee4/GT8mk4BjEQxlFZWg/kU1RSeoGXt3YKl7rHDCEMhT54AMAdKGXwoPx95q3i28oL37ULF7rigB7ytgY77Py6zKBo2IwMmq6MrRsW6d4rblPM5WhpiazCaUnfxppJjmYL/Z/fn2reG0Xc/9UtgEwNHv8SB71aSRgteScipnJsrGlD7pBEFdcaPbRc2hoYxFGloVYa2dNHD0pbsNTbrWlXRspcVytfTT44V+MmHaz4EweQC0WYZeRN2tJjMHue5mPWsA8Dubp1XvRG9MQSxr436fWY1MdUzXX7qBjOA1InsJqOqUG5/Nn983N7eKYl9a1oDMsQZMBJHREV68GwO75oGIiGOFi9WZe2WDOweaeGCIeCREnAAJEViwHACjewtQnkjJH+Bjb2gfQ0hsX4/M5qO1NiUvyF0CGUjO9LMYCr6zdH9dAZBkd7BZHP+DnUdj80FIzYNkcXL+3F2+we7I9SMlhTSdAYqz/GVe5qmsOCpWAXyvVQW167ipbt5cWZuXnUNXGiIBF0RXn4k5xFY0+lhbEZEh1v7b3J6AbBHt7onhzC71WPXweRggMwzDdyOEC54clhosXce2NJhHXdDzwLp1vz61pxl5dxm7GQQfeo33ClEDuoP/U87CuvZ2sav5bWzrEa9s6YsLNG2FNbBW3DmmYmu0eTCiSof1EqpsBoNb1rs6IcGUBQGdER0uYfYZ1NFa9BhDMb5GaWVgSeEmezgH6IC+1EK6umIE9TKKPLKMPsurWIRUwRib1iQehbmzutZwHETFDsQ0bQKJ0J1arKnLWTwFSSITDdP3oBsFrm9rEcevCdMbXtEhUGRKVtPOrTyMB4SYjElSHnFbPBgC2sWs+sYla8DYy1EVJi1JaQgtuHnYe8P2WtP5eqQS1rY9uSm99TBcvsQjz1PpOep8cg1CG+AZfzlyT3Swe7aV1LeKYza392Fpj3/zoGJnrYqWCu378Lqpkcet6U0ufOCauGTAcMppZWEJ83XoAhStDJCXJgKdYr2nqEcckdAOyJImMMjEPC1Wf2BhyCql7eX2L7biOICipS2qIrlptnseglCF7zNDG5j7bcTKR0M5ucWQ5JUOqVwdC9XnH4GRIkWjaPXf9WNeSDncQPX4JDgJgdzeMeBx6H9vk60bnHWMkIM6DlQ8o8dljCbeFGKHbk6TxPMRKhixu2HEn0ur1lz4NXPaMTalPnYO6QdAVSeDtLR3gS2aPk7rDQhGA6Br0Prom8pi+fEjq5h5SFaTkpjuawLKtnYhbiq12RnRsSZ2HBcYlWdfesJdep86BOHZ1RRC1ZK51DOhoYmQ+tm4dAL5P5Z8fhzqKZGg/kUlR6RyIo6k7ajuuI2KITBaOghd6C1HhRd46BhJo6Yuhvd+UbQcSkvD38samVJ4fDOGyuLBiGrojCYvVC/TEIJQhXj/F4dIhl+a3HIRUS2RUh8xu7zs7IzZf/MogJ0MAicehtVjikg6CjIa4ZroZnIqMhlKv7f3Pz67H+1W00vd5bxmYtU4SMUNE06D3s/o58z8PXLUMOO+vGV2lJOWetPbFoRsEOzuoX6zbScmQ6CHVQWOGVK9ecCVug1UzqmCLqm4Q9MU1fLTHJBGEAP0+WSQAiDEKJUPsvpd5zWeXEIINKRu8RMxMLDFOQM1L7AghadeqnZUY2N1ln4fWjDIOGrdXwBxh87Dcz8+DjrHWQrgAALIkSJ19Hg5eoeXxPKnXCoAwSvg8pGQo/zzkJCLspRtvNEkTMNbstpyHJGFnDVVc5O3dZokAmcBRc+DVWcB8rhTWELnRMg/PmF6D3eUN6HL54Y4R/L+HdZBITLyv7aVtaxSPASx+HPjWclpROwWZVPnW3jh2dAyI/wuFNgI4BwzAIDQEIltB1RQkmNuSQBbKUHckiY/29NqOG0hI2FKbMj9KC3PFWZUhvod0DiTTSHZPjAiDmqNQVfNQR5EM7SdEcChxoC7MFsn+hFiEK5iM2R+nJMKalq368gdPA3Y3Q22Yk4g4trXTCelzUssokpSERC/G8Gt5M8kA+yLsVplqEzEni5eN0ZuQ0O0HNIflPLx6QQqXVQ6uYWSoK0LJEABMrPLD6ZCxO1iKARfg1CTEt2yGtofGdig+CfAVtsAMJ+KaXeEq97swtoJah6PKvDh/bj2W1s/C1soaqDpwwYuA0U+vo9bRQXNoJQJHZW4rnqs2YdZ3qbU3jubeGBK6AdUhoXxULXRJhqoDJf0AaWcxQ14DcA5ukQy6nXAp9L5vbulHb0yDLNHzoWcKbLYsxKpv8GSo3E/veUIz0B/XsJkpQ/UldN5IkLAnJQxJKQsWHu8GoDpIf297PyVcu9mzVcueN5lAKEN0UAIloBaUKcPjq/ic5srQdkZO+TyUkU7qnKVeETNWyLkI5TRFRfPwCuSQhFHCofq0gtYTjZ1H0O2E6jBVLj4PD6uj5HNHDR3LsbPXVudLKkB9Ggnw50plMTPzxpgPzzmz6jC5rgT3TqNNVaftBALvmMqX1swKIwY9OWNudEvcHleAW/ti4p5Pqw2inbmLK7sJPL2stIVHh+QNF3QeVuOKP1vd0SS2tFKSy9eWgaR9DgKAWjF4V5xYewcS4rnic7A7aqTNQdWrf+IzyYAiGdpvcFZvJyoJNDEydHhDGADQnyDQFAn9lj3KGdAKc5NZUmGrLax+B5uQs1ihLI0oaAkDhpRCuAZJhupL6IbS1h/HJjYh548pheqQoBEFRJLQZ9kHVV9hcR1Wl2JtmJ9HAjuZldVY6kPQo4LAgY+ZHBxdt94s7FgaOihiFWIaU7GILPpf/fDMqZg/phS3fm466sIexBUXrl/0BfR6aEPN6EdUcjZjFQxIgdx1erhaVxui96O1L44djAA3lHhRGvajiQWyNrYRkE76nhpyFdxAkSsqLocq5PPlO2iM2Kgyn7C2JWK3Sp1+fRCEi953v8tCuFr7MZDQIUvAEaOZnEkkIdEDTIUI5Xf3WclQbZj+3o7+OHqjGvpY64/DG8P0OyGhucRC6rw65HBhMTDckKnwmy5F3SDYzp7f2aNK2BiwbygygVJZaOadYRujrS8OQojYtI6dQO+3RCShDHEogzRKnA7VbHA6kBCK4wxWhXhXNVVclKYBe9HTAhSukYCREjN05owanDWzFhfNa8QJkytRG/bgpcYj8OIJ9Blyb+sXnxWB7eHcz7C1CGYDIwytfXHsYMRxdmMJdgTp9WhsI/D00WdE9RTuquZ7iENyiPvRE0liSyu75+PpPe+LQ8QMcThCBSrAFmWIq/IdAwlB6uYzItmfACJuCRGvuYfQJq3D3/PxQKNIhvYTCT19g+8YSAg32Yw6+hAlDWpldZVYCvYFC7Pk4izehEAWFm7nQBzb2cY4vtKPgEuBBgW6Q0KfZQ46/RrgzZ/1Y80m4xtga28Mu9ikH1PuR8jjhM4b0gZTFa5CiA7UbgAAZW1JREFU4iFMRYUTx45+0yIdVeZF0KMAkLCT7R2Jj7dBa2N1OwrcUIYbwpKDLNJ5j59UiUeuXICjx5ejOuSGJAEJXcL6Bh5fRTOaRPVmt54zI4e6Ruk1rg1Ty7BzII6t7J43smu1nS3EM7cSoIPFb6W47XKBMDeZR1UR9tCF+IPt1N02rsIvLFUJsMUruMsSg8hYY4RLVUSQK3ctVQXdKGdNNmXINoneGdAgefNbpOZzBdQxMmRVOsr9LkHwQezqkzOoFRwPwUldZYB+FyHA9o4Bkfkzk5EIGZLtPFSvDslfWPYVv1aVATo/BhI69vTEROzQTGZcAZLNbQmJUJeJM386N79eTodDqB0ft/UL4ji1ht7XPRXMTdYVR2LHDnYuB0/8CI+v4m4yt+rAHy6ahdvOnQ6HLIn1+ON6ei2de2Mi5IAXLVTKcj9fwtglEurZvGrrM91k0+tDIj5w3F6gYi8jQz6tYAKRYDGIDtmM5+mKJIQytGAcfXb6k4DukNBVRs/bXVr4HEwKV5wk9pAui9E+rZZ+TzzJykaEzfXdGdCAAkt1HMookqH9hFUZqg6aytDuLroQjy73IeBWYDAS8fFY+pDJTvYw+/ITFVNGddgUFW6Rji7zwudSYBC6KPRYHmRXqLAH2Vod2moBcTLUUOpB0K0AhJ5HmyU20B1OAp78wYKZ/dbmptVY6kXArQJExi5Wcya6YTN0np1RfXAswlYylAmqQ0bQrQKQTVK3i8ryor6Ux8jp8rOqHTVBD2SJhiKs2EEX8dFlPoQ8KlZW0L5hZ75PgDiBJBO4qgu34oQypDgQ8nBliI4xvtIvCJIMCZvqAMeRdSid1A9vRaJgN5kgQw5FVArm8Sl1YY9wA0pEEpksAFuEC9hQrNeqjhFHzSAi7qm+xCMsbgnAxjoJYJuOvyZekLFAx6Ekwus0VTR+P6qCLpM4EjsZcoWSBbsZTLelS7im+RgVAXMMEAm7y80xFLcBOVBYwK5VGeLd3ley8hzW8+h3O9DtBSQCDLz1Nh2nwDIEIwErqcsETvT2ltF7pUQMUWhR66R/Oypyk1QR3AxZuJK2tPYLAjyjPoQdwWp0e1xwJ4HpH9Lf4ioZBBlibneH5ECQzcGNzX0YSOhwyBJmNtDviSXperPiGA2+qVWonttT8ByMW8opCO9CJIE9rGXQlJogJMnMmuuyPK7ucOHnciijSIb2E3yyOGTTyurojwvGXVfiQdCtwmAk4sOZBNU3fxeNi/poD0V3OO8Y1s2XB9h1DCSwvZ0pKuU++N0KNEa4WqoY4VIJIyqDsa5NN1lrH802AKhbJuBWwB+ZrY0m4fJWJgoiXGYWliz81p0DCezppoGNdWFKuAhk7ORkaBUtgibJBI7qAx88DQBx5iaTckyfsFcFiGRpDEplec1akTgXGbK0iPQ4nSj30w3qve3cheVF0KPihdHz8eI8M4vGW5GA5B2kdA7aViTENnieiTW+0i8IkgwZRJYgn1mLqlm91Ks0SDLkdJg1mday4NDasEcQCxAJ/V4J+ij6/IXGRgqaH1ZlKOh2UtIOc4OvL/GwZ5e6yTRFgvG1mahefBRKxg8UbPWaxFEV57FiJyUqo8p88Lt42TYJuyoAsHgcX1WiYDLE1Q6X4kAlIyWcnDaUeMS5SURCj1+CUUnnUaAuVvCGxa+XS3GglJVU4EpdXdi8VoRIwiixlgM5aNxkxO4mS0WYPbtxxYEuJpglmppgJBLQWfC7UpXbwOLKvyw5hLH7PpuDFQEXaoIeaLKCXy48QvTWAwBfVXzQqo1DlsVv5nNwVJlXlL3QCH2vqZGg8eLJ8JQmCydDlj2En0dHfxx72dpLjV1VGLtbxhqAJMFTkaDKfwHz8FBHkQztJ7gypEgmGWphga4AUB/2wO9SRLVXQwZKPnsyPEEWxV/AQhzTWMVfOIQl19GfwI5OrgzRhVhjytDyOQbKLluM+mM6IDlQ0EJsJSoNpVwZimFXJyV19aUeBNyqsBw2jAOqr78S9ae7C54s1rYiXOFK6AY2M994dcjNJqSU5htXPDqkPJ3LRwqmGpiDDHlUEMhmTaa9VBHS9tIaQYrHyKmmWYmKW3GgkqXc8sD8UWVeSrIlGU8sdOFX58rQxyZQNaen4BpDVhLhUZ1iIeYYV+FDyGNuvgCgxejGDMlRUEAwYLrinIoiFJr1rBZMTcgt1CeJN1n9Ui3G//gsBGrjg1aGPKoqiKNJhrxig+f1e4xaL0rmVUOSUfBCb0ATY5Sy81ixg44xuswLvyAqNO5Cvng6yk+bipJxA4WPIYiKKlxlnHDR8+DEkV6ryAWjUHHhQpRP7y2Y1BlCUbG6Ldn9CLNNEZwM2T+rBpwF3/fhBr/vahZliJNs3TCDzZO7m6BbM+MqcqtcSbG+y+J+8DlovefrKyvxg4sdWDNdR+XsAUpUCpyH3KBWZEWopBzjKvxwKjI8qgM6I0MaAERYbaAC4/b4miVDFnvIx20DSOi06GRV0M2uF72Wm8cB4x/6PUYdz2qBFZWhIvLBfJBNorJuby8MAjgVmmnkdyti8dIB80EGCrIeOKuXLYRrZ2cEsaQBWaKWLyVc9EGOO4HKS8+mFilQ0EJsBgU7RKr4rs6oSHkXGwqzHHRJQsmZJyBQy1JMC1GGiEm4SrxOkbUWS7JAYWGVyog77dkyNIgv/xgjAX4/cilDQQ8ldaKPW2sHiGGIVhxK2A1ksWiB9A2+wm8vrjaKuckAAETCe5NkaMf20ji0QhUCw0qGFDOuhmFcpV+oRTJ7fjXet8nlLziYXbjJLNW6OaqCbpSwMQhrEa5JBlQve1YKeK5spE4xyRBPR+fzgw7C68VoQKy74DEAUxlyW5ShjSyweVSZDwGXqT4BAJkSQsXC2oINEnou9LxdiooKRoBXM5eisN4BsZ4ky2WUnzYDiosUPD9MZci8H7zWTE3QLcYwDAhliEMpKazFxEhAKENy5nkUYiRb1y3z0Foo1qtD8udOYkiK/mcO00XJMKrMB4csIeCmhmivT8LrJxgom8jmSIHzMGmYBnUNizfkGF/pZ+eigjBjV5Mkcw8pUBkyXXFmHzeOMp8LqoOpUnyeS4AakOizK6sHDQEeThTJ0H5CZALIpuTMCURd2ANZlthCzCxSCUCEsW1XCJAzWzX2Mej3OSRH2oNcG/ZAdch0DPEgS2YDUFcw56bLwV0/QHrdnDKfE36XwsgQW4Ql0No50W56UAELcdKSku5SZNvi4lJklHhVNga9VnstVqnTrx80QXz8nud2kzkBSOgI0qKLRNOhtbUhuYcuxM7yQnvFcZeJWYdIYgSYkyFeYl/nta4GGdgM0HgevvACQGXAhaBbtREuANDirPZJgRYp/X2MRDgykyFOuAgnKsRCVAahDBEiwe00jRKOOisZ4vPQ0CzPbqFjUNewW1HTxmgopXF79Icww0ePA1GmpBWq2sAkKpWpm2+pz3RhceKoJwdN6kxyqqZt8DUWN5lBZOwst5MhtfLgUQgMmCQ7E7gypBlmoc3k7t2i9pPqy+2qBuwurPT7wdy5TAUGmLHLUaibzDBDLdyqAw0Wo2RchUmG+HatAeZzNVhlSHakqU+80GPI6zT3EEjAACNcnvBBkcU73CiSof2ETRlKWeh53SGrMmTYHrLCFpa4hXAF3SoU2Xww+cTxuUzVRgOAftamo8AFMspccSAOBDjx4WOwSU8lej6GRLs76/GCx+FZcSAy3KrDViSNWkSSbQyrq8wZ1A46ZUjO4yYDkWHIEjrZmphsakKyhVU9rs4duGklKl5VFW4ygD5XLsUMthTPFn8sBhk7AtC4JCsZ4rVNQiljJPeBDIlNS1XTyFB1yGVRIthCbGhm5/BCYoYsNVTcajoZqg97hDuDl/ky9nkMwOt0pp1HY6nXMgZTIfSkSbgKDqBm2X2KM42oNJR6LfE87H4YyUEZJNYxXIpi23gBWo/JL8ZwYHcKV8iXfTWSyBszxMhQUpfQalGGEoIMaXnJkKYzV5ws2+YgQJNjADDXPr0fIpJSchSU2Ud/H1OGmGFsnYd2ZYgbohIQYS2SXIXNQ64+OeCAU5Ft6zuPIaJjsPXdarR/ClxkQJEM7TeExCnT4GYLTxEF6wIuRVjvBmB5yMKFjWGpQyFbUkYBs+qqNbhZkySgr3lQY1hdcZIkid9uOw+3IiaLLgEYYOchyYAzv1xrDeJzKnIKGWLE0WWOsctilXpKCwvSHglwcipJ2VU9XiIAMCt2x9asAYknARAoVXlqDFlVG1VBZdBUhsawRdjvcrBjLRYjUHjMUIqbbHS5eT/GlLOquiluGS3BarUUuNDTz3L3koK6ErvcXhV0m6SOkyFi7INqA0Gyy1NcirVhDwIuu/pkGLqFqITzjqFZWjm4HOkuxYYSj+km4yqanrAoQ4WRCGJRhkaX2a/xqDIzZohvjJqxD8qQRVFpTFGBq0NuqA4ZblUGIQ5EXZIoSu8KJyEdJHMQsAfmZwJ/dnVr7N6eJqEMOX25y1sAdtXG61TEPQYg7g9VHS1rL0DnYIFqSmpbkUnV5lrKyVDQYzF2Jcm85wXOw4Ql0QcwDXUAqGLJLHQPsRi7A4Pbpw51HPRk6LbbbsMRRxyBQCCAyspKnHPOOdjIOvYeDODKkCorcCqybZE0Ny1zsthIRKGpl4IM0YloIypsA7OSCE2C6SYrcBHmMUMymwy81ghg1qAIuK0+ZQkYaDXPo4Aif9ZYG4cs2dxxXImwqmjLJwCeyaPgq0vSjLURmJQvbn8Rz259Nucx0QS7VlKOmCG3KWtzq7T/bZae7NUhh3IXwrSqNl5VxaQqc4HkizB3yxBWwyopFuLBK0MuRYFLceDnn5+O4yZW4JvHj6NjizFSFvtBkCFi2XytSoRToZktpguLjaEnTdVmUERFhluRUWYhQ2GvCp9LSVNt7K64ArItDbuKZp2DATd1/5ljcKIyeDLE3WRuxWmbg14njSdxKtTFLGL3bO6+cEFjEGLGPtWE7W1gxjK3jN9lqgS7TvRALfej6vCeETFIWgZacM/qe9Ay0JLzOKGiqWrG971OBxyyBEIcIqsz0bQHia1bARTmJtNEYUdGIixknq+9PpdD3I/kINVZ6xicDF1+9GicMLkSPzxzqpgbXmeK8s9RoEKbtMQMAbCtvaOsBnW29f1TgIOeDL322mu46qqr8O6772LJkiVIJpM4+eSTMTAwkP/DI4AE6zmlylQ259IpQANQAe7CYkGVkExlqMCFJVVGtS7E02pDljEsrF64yQpbhBOMDHHL4ahxpsV05Fhah8U2WQCgny1Wg1SfHExRsVql3BoKuBSABQr2u2SM/tFlaDy2jWb9DENJ+I2dG/HPDf9ET7wHb+95G9e/dj2+98b38M6ed7J+JppyHpkQYFlxAERxvIHXXgfAaj/lsUjNNi/U9TOtNiiqN/Oq5j6nwo6lfw+WDNlibVibhwuPaMTfr5gnFku/0+6WEWSowMBNwBp4rIhaLQBQ4XdBcVBi7LfUydL0xKBihpIGc78aCtyqAxMsboaJlfR3+piKJubhIJWh1CDtURbVZkKlH5IkwaM6IEuAxOehnhy8MsTuiUdV0VjqFRb8grFlkNi1D1iI9r4pQ6b6pDpkm5rN3aJUBab3o3mGjPE/OBm+6uExSJJGEo9ufBTvN78PQgiuXXot7vjwDnz/re/nOQ9+rTIrQ9TtTjN5eVNbEokg+uFKAICrTM5L6s21l47xmYmUPI2r8AnlyWsxRJMY3BwE0slQZcCNey87AlccM0Yc48ukPgGFkyFR2JGOYTVKJjPSbU2t1yWY3oWDSA0cTuSPrD3AeP75523//9vf/obKykosX74cxx2X3lhvpMGDm1UHnRiHN4TxOuvAPoeV57cGUOuAqdoUuLAkDDtRmVkfxgPYyf4dYmM4bJkApjJU2BhcGeLq05kzarC9YwC1YY8oz08lYjObDP2s03yBE5+XCJAlk3AF3AoSmoFjJ9BFxp9inZC+ZrMF7hBbKJqh4ZsvfROt0VbcuuxW+FRzYXxg/QNYULsg83kkaZxULjJkdZNRqzSlEGYeMmSWOqBBlT6XgnsvOwIf7enBWTNpbRRelC+NDBXaBsAw48RcSuZz8QpXHCMq/I3BuMkkRoZUSlZGlXmxoyOCM2aYac0BtyJqcWl6fFDxPElRoZ1eq3GVfqgOCUmd4MixtHyBS3FAdUhmsLkWAxKsvMVgCBc7j5qgGxUBF9r64pjL2olIkgSfUzGJYzIKJGmdroILn0pc7XBCliX87guH49nVe3HVwvHiuIBbQYJfK0MDEt3sh+Ufg46jAxLgVqkB95Vjx+LPr2/FJQtGiWP8LgVd/Lnah8y7weDhDQ/jF+//AgAQUAPoS9L7smzvMmzq2oSJJRMzfs4gGiQAPqcz4/sA3eATxAFNkRD3AS5uQ0sEzqpwXlcWX3t5xto3F46H36XghCmmm9vndAgDbrBzEDCfLacj+3n4XQ678s9RcFyS3U02qZqSKKcii30q6LY8u5CAXprs8WlRhg56MpSKHlZBtLQ0c42WeDyOeNzsst7b25vxuKEC31CcMl9YxqBrIIHjJlYIy8EWQC0B6KXViAtXhrgrjn7fObPq0NQdxeTqgMgMsClDkjRo1cbaHwcAFIeMaxfZFyG/O8UC4mMUTLh4vSR6HqU+J5779rHQDGJzKfIxAEDr20uPdgULyrwrBH2JPty58k48u/VZdMe7xesDSVNtfH3369jRuwOjgqPSPj+QpOehyNkXL2tNptQeUq5gsgB53nT9cEXo6PHlOHq8SaK4m8xg9Uf4dt0MAz988WuIalFU+aqgGzoumXYJZlXOso2RFAHtihgjFaqDxnfZ0nqBQTeCBUx3xh8umoWX17fiy8ealm/QraKbj5EcoJmKQEHPFldnYTjgVmlm5R8umo23P27HV48bK46jzxabh9FO8wsKWOxNpY7GJcmyhD8uno0l61pw9QkmUfG5FFEvSeMEAhLNHM03hq3uE71WR4wuNXu3Wc6j00qGYlH2oXDeMQCzXpJXoc/vTadOxklTqzCj3vyNfpeC9ohiGaNwclooXtrxEp7b9hyW7FgiXuNEiOOh9Q/hf4/637TPEkJA6JaNgCt72nfAraCN3Y9YkMA1QJ9fp1+DHMrfT1Fj66KTGbshj4pvnTjBdozVhWV1k/39o7/jyS1PYkxoDCJaBDMrZuIr078i1nGOpCBcmd19qWNosKwngwygVlg4wzmz6tDen8DhDWHhirMqjkkJQB/bpz4lMUOHFBkyDAPXXnstjj76aBx22GEZj7ntttvwox/9aMR+k8nq6YMcdKv4yTn23+azptZDAvoGx7g1w+4mUzMQFZ81ZggW1abABVKk72ep2QEgJX0fwABXhgobI5KkhSgdkjnpU9P4rUF8gIUMDeGEvHftvXhw/YPi/yeNOgnberYh6AziW7O+hb+u/SvebHoTZz55JqaUTsHJo09GZ6wTYVcY54w/B5EEPQ81BxmilYLpPU/tAu0pSwLePIGbxJ55lwkuhbqYeGVaTlR+t+3feGcvc/OxW/Rh64dYcv4S24IbZ5mAJAcZAqjlq4OTIfZigRapSepojApAG4FytZEj4FbQkWTnodHrSzNy8i/2ggwRRVyrUw+rxqmH2Ssl+1yK6K1nWFOTHdk3IQ6zybBDXKtMRMXnciDKg1B55p07WFBMnfVaZYuDARhRiVvJUDcbJ5z/PAxdqE8+JyURDllKJ1xuBUaEp6Zrg86Ky4fOWCduev0mU50E8MXJX8TSXUtxxtgzML9mPr764lfxr83/wr+3/BufHf9ZNAQa0DzQjIUNC3F4+TxAptcrmIMMBd0qWiKsQGWIILSXPsCesmRedRYwSUS2jDWAk2y7MrRZVfHLD34JANjSvQUA8FbTWyj3lOP8ieenjEGvgSunMpSy9nIMMrWeu/tcisOmNgKw1ZHTJMl08RaVoYMPV111FdauXYs333wz6zE333wzrrvuOvH/3t5eNDQ0DNtv0viDrOSWOEXlW8BGIjpjnXhi8xM4vv54jC8Zn/HzyRQylHkM06eclGBxAYQLOg+zknb2RyJtsuyjMpRLUfG7zCBtANB4EJ+lDMHGzo2o9lUjVIC1nYoNnRvwlzV/Ef/3KB5cN+c61AfqxWsGMfBmE33G1neux/rO9eK9P6z4E0jvHEghwKPYs5assFpZPT4JSpkPWscAJIXQMgEFusmIZfNNhSRJ8Dod0IgTMuhCrAN4vW05AOC0MachqSfx0s6X0BHrwPt738dRdUeJz8e0/G4ygJKIAeEmo8/xgOrGX1fcgYmlE3HKqFNEPEsqrO4lv9Od8RiAPlt6gsVA8RfdIRAAD6z7B17c/iIunnoxTh59ctpneVkIQhxw5zgPv0tBF68EL4hKGADw/PbnEdfiOGvcWRkD40UANZFyXiu/S0GEb4xa1DZGPtiD5rM/W363AiPG46vig3LFWcmH35XjfrgU6Pyek3TC1RXrQnu0HRNKJmT+ghyIJCP46bs/tf2Wi6dejBuPuBE3z78ZAFV+ppdPx5r2NdCJjic2PyGOfWTjIwhpx0CS6BwJe3IrQ8YAPY/OWg01G+ja462K5zVIAHMeOnOpNrYAajoPXgVVmWt8NVjYsBAPbXgIAPD8tufTyJDGDeoce4h1DKHOOlx4fe87WNm6EounLEaZJ3uPPRHbKuVWs0XMkPUNTxg7enfgtmW3odxTjpvm3YRAAdnDhxoOGTJ09dVX45lnnsHrr7+O+vr6rMe5XC64XNkXkqEGX+zdOVg9DXS1ZJNxeErwP2/8D97a8xb+ueGfeP7c50XskRWpQdqZ4LX4rW0Bdp4w7l17LzZ0bMCN825EuSfzAiBin3IqQ+YGrwODVoZiWpyNkcPqtRAuANBS3H1PbH4Ct7x9C8KuMO4/7X6MDY3N8C3p2Ni5Ede/dj129O4AQBepB09/kBbLdJtWcVzTcXjFXDx37nP404oHsWzPSrT0GIjHQlB8myA7OyGFqOqSazOxpsICQOizU9H3civKa1bSMIU8C3FqzFA2+JwKuhkZSkgSWh0O9CUHoMgKbjvmNjhkB370zo/w+KbH8dLOl2xkaCDB3MmEZkLmGqNHqE/0tT/3b8S9u1YBAH7l/RUag42o99dj8ZTFmFQ6SXxWqDYAfDnmZcCtQu+1K1xwh/DijhdFPMma19fAr/pt5wAA/UypA1HgUnOch0tBR+pi7wnjvb3v4YbXbgBAifDnJnwu7bOmiqbmvFZepwI9mTIPPWFs7d6K3634HU4efTLOHHtmxs9alaFccTABlwKDK3XCtVuYK856PwI5yKnfrUA3mPuV6BY3WQiRZAQXPXsRmvqbcOnUS/HdI76bd1yAXtvblt2GRzY+AsJi6G495lZMK5+GMcExtmMHEjr+ftrf8dSWZ/Hg2qewpyeCrj4aF6mGP0S34y1IEv0OVw6jJOhRoRHWsHVsEvOixwHd2xBs2FOQMsTviUvJvmb5rG4yZixsYU7rL0z+Aq447Ap8aeqXcPoTp2N5y3J0xbpQYslg5GTIndNITFH+AbS7/bh26bVIGknc/9H9GBceh1p/LeZUzcHiKYttpD41tjUTgh4lPVECgOby4+qXr8b23u0AgI5YB+444Y6c6/ihiIM+m4wQgquvvhpPPvkkXnnlFYwZMyb/h0YQ/EH25LDkfNZYBYu/t1dx4q09bwEAWiOt4t+pSBh0IXY5cixeVhnV8vpHyV78dvlv8d/t/8Wty27N+nmhDOWSgy1ExZAks5WoJ4yNnRux+NnF+P6b30eUW8QpiBdC6lQHJEkGW+egibLzQXTHuvGrD34FAOiOd+NPq/6U9Xus0A0d1yy9RhAhALh61tWo8FbYiNCW1n585hevYvZPluDM36zHA/+djs2rLkbv9ktRlfgiLh5/s+17R5dmD5K0FmIDAHVqCcY9/jeERkdpXaY87oa4UG3knC4sb0pa726WWVPnrxPBkosaFwEAXt75si1FnLstCXHkJkMua18k+vwuHdgl3m+JtOD95vfx5JYn8bUlX0NPvEe8x40FQmT4nNkXT9rSQBXnAQDwhPGfLf8Rx+hEx9WvXI3nt9mTKiJJk9TldPdZNhQjhXBx/GvzvzJ+diDJnmmi5h3DSA2mdYdx23u3Yemupfj+m9/H1u6tGT9rJUNeZ+55mDToHBLxI+4giCThtmW34aJnLsLbe97O+FmrUpeLcPldCnQW26cRAkRMl8mfVv8JTf20Vs/96+7Hnv49Wb/Hisc2PoaHNz4siNCkkkk4dfSpGBsaK5RFQghu+c9aHHbLCzju56/hunsVLH/vXOzd+CUkm7+Ic+q/iwbvYYIIAWa8ZiZYn6uYU0Lj3X9A4yVTICsojAwRe8xQJlBD1B4ztNtgDVADDeLvyaWToRMdS3ctTRmD3pNcpM5rMai5QfK2zyfuZ8JIYH3nery882X84v1f4O8f/d32eU6Cc7nibFmKltdXJ7oFEQKAN5vexDeWfAMRrkh+QnDQk6GrrroKDzzwAB566CEEAgE0NzejubkZ0WjmDXekkWRExZOjd4u9IKL5+oakPbj7ma3PZPx8QqcTy+XIR7hSZFQAr/eZC++SHUtw2fOX2RZEjhiL08i1sHhVB6yPDJ8wxBXCD976AVa3r8ZTHz+Fe1bfk/HzfIPPZZ3IsgS/U4HMC9fxIn/uEJ76+Cn0cfcfgFd3vWqzdLPhnb3voKm/CT7Vhxc//yJWXrwSZ487W7y/pzuKh9/biYvueRfNvTH0xzXRUuWocWW446JZeOX6z+Cm40/B6OBo8blQjowRlyLD6VBEAb5kcsBU0rxleWNIIkl+j3IrQ1aLMQkJuxWTDHHMq56HgDOAzlgnVrSusIxBr51EFDis+dUpsG7wmiRBA7Ar2Q0AePTMR3H/qffj1mNuRaW3Ep2xTty96m7x2bjGiUru8wh61DTClXQFsbyFuvweOP0BLGxYiKSRxC1v32IjXH0xuihLcGZ11wHM9cMVFf6ipwTrO0w36Kq2VdjVuyvts31xOgYx1DxuMocZw8Vei7mDeK/5PQBUHfnsfz6Ld/e+m/ZZ/iwTwwFvDuJoJSpCaXaH8eKOF/HQhoewtmMtbnjtBvQm0pNHzDGUvIRLs6qBbN7FVBce2/iY7VhrAHQuPL75cQDAlw/7Mj68+EM8dtZjYi2IJXUs29qBq//5Ie5/hxote3romlTqc+LKz4zF6zcuxO2fn4GvzbrQ/ltzxM0E3Sp00DUtCQlIRi3zMD8Z0rkylGPNss1B9vztZmu7dR5yo2TJjiU0c5CPwciQO4ebzFrLiM+P7UyZPn/i+Xj8rMfxhxP+gHMnnAsAuHv13WiPtovPi0SfnGRISXfFAVg2QDOXTx51Mu488U54FA+WNS/DP9b9I+t3HYo46MnQXXfdhZ6eHhx//PGoqakRfx555JED/dMAAAlCZeqgM/vG6LPUzklaHrL1cTop+YRZunMpunhXcAs44XLnUIZ8WbINtsc7bMctb1mOO1bckfb5GCNcucaQZQl+1ZxMfMLslmGLq3lw/YO283i/+X28svMVxHRKYF1y9jEAuhDzZpf8ehFXAE9ueRIA8P3530eFpwIRLSI2mVzg6sJnx30WNf4aoZjEkjou/usyHHX7K/jeE2vQ1hfHpKoA7lo8Gz/57DS8cO1xeOirR+LsmbVQHDIkScJnx39WfG/YFc46Jq9xIgsyFDPLHfgq0Zfow4bODVk/H02acTC53TL2eIWOiScBACq9Zuqv6lCxsGEhAOCKF67A55/6PJ7Z+gx64pRoSsi+QAIpz5YEtCgOaMSAKquYWDIRs6tm46xxZ+EnR/0EAE2V5gTD6orLRYasNaz4Br/V5UJEi8Cv+jG9fDp+e/xvMT48HhEtgsc3PS4+28niWRTiTf1a+3m4HDC464c9V7qnBJu6NgEw5yF/zqzoT5jKUM774VJMUsfG2Ol02rLqAOCm129Ca6TV9pqwtIkzNwF2K0gy149YTzwleHG7qXD1Jnrx4DozSaAn3oPHNj0mFB0QNff9cCnQ2RjWjfGl1hXoS/ah1leLG4+4EQDSlI5M2NC5ARs6N0CVVVx+2OVQZEUQ12dX78WsHy/BhX9+F8+u3gtJAm44ZRJ+ff5M/O7Cw/HuzSfi5tOmiJpLJ4+yx43lWrOs9ZKSEgAtahYT9FdiY+fGjKSRw9o4Nxu8Keu77qtEV5LOLes8PGkUnZtvNr2JeQ/Ow+3v3Y5dfbugEWZQK/mUf3tq/S5m+IwKjsKk0kk4vuF43LLgFkwrm4aB5AB+/cGvBekS7r5cQdpOBZKldAqna2v6KRmaWz0Xx9Ufh/935P8DADy04aGCjNFDBQc9GSKEZPxz2WWXHdDfNZAcwMrWlUiAbvrBHAFlqdY7x4aB3QCAc8afgymlU5AwEvjPlv/gg+YPcNuy23Dlkitx6r9ORZtGN5Zck4UWlbMEUDPsiNCYm98d/zv85Gi6Wf193d/TrN84J0NK7u7E1rgPPinfj9MxZlXOwpTSKYhoEfx1zV/x7Ve+jen3T8cVL1yBa5Zeg3V9S9l55M6A8LsUs0s6l50VB7Z0b4EiKzh97Ok4vuF4AJRA5kJPvAev7HwFAASRMQyCJ1bsxoV/egdvbKbWk0uRcdlRo/Gvbx6F06bX4OIFo21l8TnOnXAuRgVHQZZkHFl7ZM6xgx5VnEdSM8mQ4avAFS9cgfOfPh8v7Xgp42eFagNHbtXGaVmI51yG7hqayRhy2uNHLpl6iSDsm7o24eY3bsYvVtCidg7kJhFel0M0otQgYS9bhGt8JrEEgKPqjsLxDcdDIxquXXotEnoC/Qkea+OAOweJsPW9Y5vkDubyGxsaC1mS4ZAduHjqxQBowDNAA3nXdHxIz0PKR4ZMosJJxHZVQUyPwat4ce2cawHQuLSeeA/uWX0P/ueN/8HpT5yOHy9jBQANZ8774XcpIkaFP7s7HfQfh5Udhje/8CbGhsaiM9aZ5ubtTXD1yQmPMzdRSY0fIe4QPmj5AACweMpiAHSeP7f1ORz50JE45uFj8ON3fowrXriCHq978hIuUfeJvyirWLqHJhacOe5MnNh4IgCaqZjJiLOCGyQLGxaKxIdt7QP40dMf4ZqHP0Q0Sd23k6sDePirR+KqhePx+Tn1OGdWXRr59KpeXDv7WvF9udTAoEe1GQuwGCX/7d+K854+D1e+eKVNqbFCE33ccsWE2g2S3i8/L1yB1nk4NjxWxIvF9BgeXP8gLnj6AsRA18+SHL0qvc70eJ69Mh2jxmfW65IlGd+b9z1IkPDM1mfw1MdP0d/FlKFc6pMsSzbXKb/vOyK0+OL4ME3wOX3M6Sj3lKMz1on3mt9Dd6wbL25/MY3wH2o4ZAKoDzbcufJOIRMSw2GzAFLhUmQ4YLFOAECSsYXFsEwqmYRyTzl+9M6P8Ovlv876Pf4c6cyKQxYZD4RlFckAdvRTwjUqOArjS8bjhe0v4M2mN7H4ucUIuUI4adRJmFU5CztiVLZ35yBcAOB3OcFDNulCTLApTgnFjPIZmFs9F9965Vu4f939Wb/Dq+TORPC7FXRxRYUt9isMamlNK5uGoDOIhQ0L8dimx/Dqrlfx/SO/nxIsmMDdq+7G1p6t2Ni5EQkjgQklEzCldAoIIfjR0x8JKV6SgPsuOwKfmViRc1HlKHWX4snPPomoFs2pBgLUKu0VZCgiqoI3+0uwoZO6f57b9hwWjVqU9tkIIxFynilqdY8m3QHhPgqnBLVPKp2EVy54BTt7d+K/2/6L+z66T1iLah4ylGqV9jIXXyZl7KdH/xTn/udc7BnYg+e3P4+Qg7kJiAOKI1f7kvQNfgc7fHRotDhuYcNC/Ej6ETZ0bsDGzo246uWr0MIIvxu5+735GRlSYM7DzTJdwCeUTMCJjSeizF2GjlgHjnn4mIzf4ZByJ2f4nAoMpLgzWNbTqNAohFwh/ODIH+DyFy7Ho5sexdqOtZAg4cYjbsQD6/5Jv8RQcxJHa70vrqJ1eALojG6DLMm4ZvY1WLZ3GbZ0b8FNb9yU8TsoGcoxhiuFRIASLu5mPbLmSNT6azGpZBI2dm3EG01v2FzPALCiZQUeWP8AIsmIiGHiBsmmlj58/q630Rej1+bo8WX4+xXzaQXvAubhl6d/GedPOh+ePMab9blKShKQ6Bdk6OXOjwAAazvWYnffbjQE07OOuQsrV6kD6xwEgHZWZNSn+tJCAm495lZcM/sarOtYh7tW3WVTh8tyZAPa5iB7rZddptR5eHjl4fjm4d/EnSvvxH1r78PZ4862xCXlDnoOuFzggQiUdEloGqDrFq+5psiKWH9f3vkyVrSswD1r7sHpY07Hz4/7ec7vP5hx0CtDByumlU0T/052H4FAjswiWqqfMu4EX1g8JdjNJOvGYCNOH3O6zb9c4anAWWPPsn1PRR4ft9fK6iWg1x1GP5Nreer4lTOuhENyoCvehe2923HPmnvwzZe/KT7ny0NUAm6nKKbMF+JtzDc9NjwWx9Ufh2qfWd8l5Arh2tnXYlxonHkersacY2SShD/S6CbPCwfOr5kPr+JFa7QV31n6HSx8dCH+543/gUEM3L3qbtyz5h68vPNl7GZk8PKpX8ZTq/bg3LveFkTojOk1uO+yI3D8pMqCFmAOVVbzEiHAXt4+qcXFItzsMsnH2va1GT/LY1TkPC4sq58/aSTRk6DXKVPZAZfDhQklE/Dt2d/G/53wf6Lek1eqLWAMrjpK6GNkKJChJUfIFcJFUy4CADyw7gF0x5grjuQm2UFb3zv62nZCrVlr4csSdwnmVs0FAJz39HmCCBHdhRIyP+cYdNOi58zn4W6WV9YYaIQqq/jy9C/bPnPSqJNs11Ilue+7L0MKdBOzynkw7ZyqOZhfTX/ruo51+KjjI1z6/KVYspMFhhNXTuJozerkhGubJVbMo3hw4SR7XM3Z487GV6Z/RfzfiFfmjUVLVepaPQG0R9vhkByYXj4dALCwkbpf/7LmL1j83GKc8vgpWNu+Fs0Dzbjq5auwZMcSvLXnLRAQTCmdgiplBn727Dqc9vs30BfTMLrMi2sXTcBdX5oDhywNah4GncG8GU30ubKEKPQ2AUQHIKHN4h5b3b464+d15sLyO7MbDIGUDNi2KA1/yGQsSJKEal81Tmg8AX8/7e/iOSC6CzX+7IVYbU1U2TXqY6pVMEPs4uIpi+FVvPi452O8s+cdJA3q5vWpuVX5gFX5B7DbXwqd6PAoHlR4zN/H458e3/Q47llDY0Qzlb04lFBUhvYRp485HQsbFuLUu/6JnS0VCHlyb1p+pwf9oFksOoA+r0lU6vx1cCtu3HHCHbjihSsQUAN48IwHUeouxeyq2fjR2z+GHq9EfSA3ifA5neBhpRokdPhCAAgCzoBQfA6vPBx/P+3veGnnS1jVusoWUAsA5e4a5IKfVdglkmEuxAOU1I0JjYEsybh06qX4+fs/x6zKWbjvlPvgkB1Y2LAQX37mB9i9px6NjbkzAgNuBSRin/g72bUaE6KfdTqcOLruaCzZsQSv7KJusKe3Pg1FVoQ0fNqY0zAmOAbTSo7Ar5+OYdXulQCoGvT/zpiKLx+T+3fsLwJuBVKUkQg9Jnr9NFsyD/cO7EXzQLONQAJAX4Lqbw7kViKsboCEnhDKUKqbLBVH1x2Nr4z7Fe545ynUhE7Mcx4qrPFoXBkKqpmJwXkTzsOfVv0J6zvX48411FKU88TzZAre3G1QQshJBMdXpn/FFit2bMWF+O/boxEenbuqdyZ3NR+DGwtfmvIlbOjcgKc/fho3zbsJi6csRm+iF+c8eQHaYk0IGLNyjuHLQOQ7mC3PNxNJkvD7E36Pf236F3b17cK/Nv/LltQga/nPI3WMbQ5qofD5cdqY03DP6nvQEevAn076E+bXzAchBD2RJB768AM4ek6FmoNw2Ug2e22n2wegF7X+WrGeLGxYiLtX3Y1tPdvEZ7/72ndRH6hHf7Ifld5KfG7851DprURP6wycccfb0Az6WydW+fHI1xagxJd77dwfWJ/dJAB0badv+MrRHDEbwX7Y+iHOGHtG2ud1UDIUyKHKW+cgAHREaZxmPoPJo3hw56I7cdQfb0F3VzUC7lzZZA7RxojH8/QxtSdTzZ+AM4BzJ5yLB9Y/gGuWXoMYYgX9Jutv0CQJu71BABoaAg02onpk7ZGiDhQATC6djBMaTsj53Qc7imRoHyFJEryqFwM9jQASKM0zoX1OJ1heFJKShN3eMIA2VHoqxcIysWQiXvz8i1AdqrB4zpt4Hv76ohvrm3SEP5NPoneZZEiS0OENA+hCmdtejGtGxQzMqJgBAHi76W3s7NuJfyzbhE0tEYyZlLnwI0fArQBxBwADmiQhIivYyxYVXitk8ZTFOLLmSDQGG0VMydjwWEwk38G2zhbRDDIb/C4FZMDuatiZoDEJ1o3xyhlX4v3m99GX6MPo4Gh83POxCH49adRJ+MVxv4BuEFxy7zKs2t2DoFvBZUeNxufn1NsabQ4Xgm4ViGRQhlL2oDeb3sR5E8+zvdbPgmkVKbeiQjctZvkaSdNNxqxS3SB4fVMbxlf60VDqRV8siY/bBtAVSWD7njIk2k9CVV3uNP+gW4W1L1Jf5SRAb81aeC3sDuOscWfhsU2PYW+ExqapyKc4pqf1drLaPqm1sRbULsA3Z34Td626C/Nq5mGm60I8p21CuT/3/PBnSGTYzUg2f64kScLPjvkZbjziRqEIBZ1BXDv5blzz2DuYVJfensUKKxnihKuDnYd1HvpUHy6ZdgkA4Oszv47ntj2H3gEX7njzJZRouTeVTMRxG1PR+BwMuUJ44rNPYCA5gFp/rTi3s0d9Bfc+YwYjZ4O1dQkfYxdTnhstRtmU0ik4e9zZeOrjpzAuNA7be7ejqb8JTf1NUGQFf1r0J4wvGY+lG1tx47PvAwCOm1iBL85rxAmTK3MGow8FaO0ci5uMkSHdX2kLYH+z6U0QQtKUKQP0uvpd2cm836lAkmQQIkGSSEZlaGdHBFvb+3HshApIAJq6o9jS2o+gR0FH07EAgHJ/9j1EkiT4nS5RzmTA4UScKUPZ5uEXJ38RD65/UCTHAEClL3MbK46g2/wNugR0On0AetLmoCzJ+PlxP8flz1+O3kQvrp97/aBUvYMRRTK0j9jQ3IunV+1BxwCdLJWBfAuxG9wOSUjALm8AiLfZKh8DNDiQY1NLHzY092FjkwQQBWW+PGTIZT7ISQAd7gCgddlq6aTiqLqjcBSOwmNL30aysyvvGH6XAhI3A7V3BegCH3aFRZyKJEm2atqvbWrDQFzD6t10o85HHP0uFcQSY5UEsCdOe0lZF+JJpZPw8vkvozfRixJXCS757yVY3b4aFZ4KLKr4On6zZBNe/KgZG5r74HU68Pg3jsLEqpGrnGrLZNHjomJ3s62KB/DYpsdw+pjT0RWnVX2nlE5BX5x67lUp96YVsBCVpJEUvdZCrhB2d0Vw/aOrsGxbJxyyhCk1AWzY2ycscw5rJ/ls5yGIyowL0ecOARsftsnzb2xuw1Mr98AgQNirYmrDORgXXoHeaAItfRGUk2PzjpFaGqJLo4SwxJVO1r5x+Ddw4eQLsb0F+PEzNMEg3xy0EpUEz8hhz1XqPOREKJLQ8Pqmdjy/tgNEC6Isx4YF0GDa1IaaHew8slUILvOU4eKpF+Pl9S2It/hQWZ9b1ctUU2w7c4NY46tCrpA4j7a+OF5a34LtHVRxLPHlMUjc6erTTsUBaHaDhJPHb836Fio8FXhl1yu47lXaAeCyKd/AuxtV/HHHSjy9mtYi+uL8RvzsnMNGbOMMWNxkmoUMtfvKoRGqZimygqb+Jryy6xUcU3cM1nesR6W3EhWeChCJEtkSd/Z1g5YDYfNQ0tAWMcmQYRDc/8523P7fDYhrBurCHhBCRNkADqdDRlUwj+HjMpX/rsufAl7+GiRI8DPXV+dAAn9/Zzs2t/ajzOdEdciNL0y8BK/veQl7e+KID1RhQkluNTzoVoGYDDDlv8vpAgzYikRyNAQa8Ny5z6G5rw/3v9mCMcfF8p7DwYwiGdpHvLKhFXcu/RgAMKsxnFfq9Vt8sUlIaGKZANY4IY4nVuzGn1/fig3NfbbX6/JsWn4XbQ4qSVS16WDujEyL8M6OCJ5dsxexpI4l61qwbi/1n5fnJXUqCFEgIQ4dEtqZy6fKW2U7jhCCJ1Y04dEPdmHZtk7be5mytGxjuC2tACSavaQTA26HG5XeSsSSOvZ0RzGqzAenw4lyTzna+uL49XF3YlXbSjz4OsE3/7FZfF/AreDX588cUSIEUPlcEBU9AUS6AQDNOt0cvz7z67hv7X1Y17EO8x8y411UWTUrm8v5LDkzliCpJ9HL2ky8vTmCXz/3BvrjdLvUDYK1TfQ9pyKjOuhGbyyJkEfFBXNzt6uxKREuPwbY5h5wBtDaG8P3/70WS9a1ZPjcN0SAbMOUqrT308dgJBt0k+/ReNmKMJ5YsRsvrW/BKxtaURvy4KqF4/FxWz/++OrH4juOn5Q7gNrncth6SGkAmmM03i11Hjb3xPCz59bj5fUtiCTMQpV14fwZa9b4KgDoZHFcqUZJQjPw4rpmbGrpR0tPDI98QFW0snxriVsxs/vYGB0sLil1Hm5rH8Df3tqGJz9sQm/MJOGTq3O7S/wZFK6dTMThMVy7OiMIulWEvCqqfdWIazqmhY7Bo2c+ipc3NOGPT2mIJMyYuEVTqnDLWVNHVEGwufskCDLU7AkAEaDaV41j647FY5sew7VLr834HcRQUOHJMw89KnqIA4Am6vuokg+X3Pse3txi1vtp6jbr41UGXFBkCX0xDZcfMyan2xIAAi63IEOdTKnyO/2QIOHR93fhZ8+tF/XRTEzB1Jr56GHre0Ugn9KsgkQdkCQDSUlCJ9OiSt2l2Njch6dX7cGza/Zib08Un59dj2MnlOPW5zZgZ2cE0aSOWz83Pef3H8wokqF9xLgKP06fXg1ZknDz6VPyHu93qiCaA5KsIylJaPeWAtFt6Bvw4tTfvQ6XIuPwhjC6o0n8ZyW1ohSZ1qrpi2m44pgxBbgBHEDCQVm9BHSUNQJ7t8OvlOC5NXvx9Ko9WLatEx7VYZuUHCVeFVNr8iySzIKXQC3GDjZ/S92lWNvUgw93dkFxyPhgexf+tWK3+BxVF4Cvf2YcxlXkC+JTYBCVjgEJTazIXZ2/Dit2duHL93+A7kgSsxrDuGTBKNz/9g6s3NUNSaLXLKkTOBUZZ0yvwcSqAC6YW4+yPNduOGBThhL9AKvr08zUm8PKDsNvjv8Nbl12q6j/4lf9IpaM6G40Oo9K/2ILgm4VMOgY3fFuUTH3R//eDhAn5owqwW8umIneqIZ3t3Zg/tjStCap+c/DHpfE67Ks3ZXAH554HV2RJBRZwhfmNaAm5MH29gE8+WGTIEIAMH9M7s3E51QgseDmuCyju3ISgCgkSLjqgY14f1u3OHZr+wCuf2yV+P9hdUEsnj8KR4/PnWDgtylDErqC1TCIARkyfvivHdjUsg7jK/2YWBXAw+/vQns/VQVCHhXRhI6asBuXLMjtJrMFHkNCpHo6ojrdwna0ynhmxRY8/sFu9MY0xJM6+uJa2nccM2EQMUPstQ5GHJ1SEM+u3ovm3hhciozfLtkk1Gv+2QlVfnznpImpX2uDS5HhkOxtRfaw6KFqby2uenAFnl2zF25VxrdOmICBuIa/vrkNcc2AU5GR0OgmOrUmiGMmlOPYCeU4Ns95DQdUS5ZtEhLQRZMnmt0eSoa81bhuznWIaBE8u/VZADSWJ67HRap4snt+XmM34FbQzYxEToaeX92Ljt3tcKsyvn/GVHxuVh2eW70Xsizh9OnVOYteZh7DXMN4XJLH4cdl972P1zZRNWpydQDnzKpDXyyJ/65txta2AWHoVgZcaCzNHR4QcCuAoQJyEgkJ6CwdBbS3o6nDgTP//QaSuqkqP7hsJx5cRmsQ1Zd4cMb03PGmBzuKZGgfccq0apwyrTr/gQw+lwIkHQB0JE/+CdqiHwMdwJI1UcQ6qAK0irmRJAn41sLxuPzoMSjxOWEYBHKO2iZiDCeP50lC++yd6OylaZv/Xt6Lv+9dkXb8sRPK0VjqRbnfhWMmlGNUmZf+zhwI2CR6CZ1lY4HYVrR0qzjzD2+mHX/S1CpctXA8Dm8IZ/TJZwJXhhRQwjXA4o58ShiX3/e+sHA/3NmND3d2i88RAiR1gtFlXvxx8RxMrc2f8TWcCLpVszUD9/YrHjSzmIJqXzUmlU7CnKo5eHTjoxgXHodj6o7B1u6tuO/tbXjwzQHUzx+dcwyre4kvwsRwAETFVQvH4bqTJom6ONPzuF+yIeRRQFixwrgeF1XAn1rRBS2SxNSaIH73hcNtyts3jh+Hx5fvRk3Yg8PrwzisLve9kGVJVFjvqZmHzvnXAy9eBgV+vL+tG16nAxfNa8TxkyrwxuZ2vPNxB9r747ji6DH4yrFjCnqubMHNjQvQNv1y4I3roUpBPP8RjR/Z2j6AF5nKNbk6gJ99bjpmNYTBvz7fOLYxJpyIjpnfBF68FDKcuPgvKwHYP1/ud+GkqZVQZBkLxpWhocSb91pRomK6FAmATkZQr35gE9q6dtuOL/M5cdXC8Vh8ZCOcrHhoPkiSBLeqwoDpJutkauW/3u/Bf9dQYyqWNPDLFzbaPpvQDMgScNXC8bh20cScdZlGAl6nE3EwpY6dQ7ODXr9qXzX8Tj9uP/Z2nDv+XKxsWyky8TZ1bsUF/7cZRPflde1bFSg+D3sjKsaU+/CXS+cKA/CCI/a9aXjI4wTRFUiyqT61dMv4eGsbnA4Z1508EV85ZozIRPzOool45INdaO2NY0Z9CHNGleSN0aJqtgIJQOzCf6BrOyWIz6/qQ1InOGJ0Cb44n4YqPLVyDzY092FshQ+/OG9m3ji0gx1FMjRCCLgVoE8BkEBi0qlof+fHAAAt4ceESj8uPKIBa5p6EPaoOPWwGiwYZ7q2CiFCAEsd7qUPcqJ+DjpW0Noe0agHpT4nTppShXNn16EvpmF8pR+jywcfREyVIbYxTjsHnZWNwKat2LCbbvYLxpahJ5rErq4IbjlrGs6bY8ZiFCqP27NlJHSxhWtnm4zemIZZjWH86vyZuPvVj7F8Rxem14dw82lT0DEQRyyp4/CGkgO+AAO8LxLrKM9eiwUq0RWnweA8g8yn+nD5YZdjV2cED7+3Gx/uHMBjyxMAVFSH8qSke1SAFfkTaeaGB/PHlOG7J08aEpcEVYZY2wQthnbm7iOGBz84cyouPnJU2iI7tsKPG0+dPKhxPKobUQARSOhimkcsThfY+6+YhyNGU3VpXxUGWqyQLnnx0tFoZ41Ko1Hqcrh20QS098cRieuYVB3AxQtGDdp6t8YMJTyl6GAbk5bwAZAwf0wpzp1dh1FlPhiEYHZjSc4U90yQJEocDQD94THoPerH0FbdDgBo61ZR7nfh8IYw3vm4HXNGl+KOLxyOsHfwGVseldYU00AJVxfrt/X86n5IUinuuXguOgbieOi9XdANA189diyOn1SJlbu6MbUmiIo8LveRgld1MTJkvtbMagFZCxbOq5mHmRVz8PbHHXh/WyeWbuwH0X3wuxSm+GVH0K0CiZR5qHvw6y/MzKuEFwq6hzgBWRNB2rrmwmy2Ho5NGUdxyFg8P7eSmXEMvr4HqsVapSe9WDSlEvdcMlesKZ+blb1h+qGIIhkaIdB4BepeShpJtHErXgvgmkUTcOaM3LVeCh2Du0ziWhztTEolmh/3XXYEZjaE93sMa0ZOfO5l6Nj2HB1D9+PcWXX4zYWH0/8XqAJlHCPFz98ZqgEQQ2s37a7+mwsOx5hyH355/kzb5/IRh5FG0KPCIColQ+xatPjLAbRBJi4cc+s7uPG0KfC7HHhlQxueXU0DkDnqwvml54BbAWFNO3mDXKJ78JlJhRWRLAR0DLpARpIxdEWpCnFYddWQlifwKpQMxbSYqGhMNB/OnFEjiND+wKraxLWksK4NLYCpNUFcc+KE/b5mPhdzMwCIaDF0xNgc1P04dVo17r54zn59P4dbcSMCoNdXg84xRwGrqFtVkZx48ptHoaHUu19zEKDNpwdAn92oJCHGm+5qPlw8fxQWTaXxSRceYS/58ZmJI+8OywWf04UumLWlAKCZtTi67/UurFnzHi49ajRW7urG48t3Y3eXGUIgSyhIeQy4FZC4fR56ZD9mDcGayxF0q8LY5UHaRPfgB5+dmkaE9hUBtyrmekyPoY01yjZ0P3545rRDPmMsF4pkaIRgy2TRE2hlzQJDrjKcPLVwd1u+MQhTCWJ6DHv66Bil7lLM2EcXSSr8bnOxj+kxtAywyaL58LXPjBXH7c+kCViK48WDdeisOxxoeRdE9+Pyo0ZjzD4oWgcCVhcWJ0PNniCQbEMyEUQkruMH/7YXXTxidAnmjSnF7MYSHDexIm9QZdjjBDFSLHDdgyl5AmQHA4/qEMUfB5JRRHQa0zSxInfA8mDhc3rQASCmx9ERpUH3RPfZ1MX9gdfpAF/yIsm4jQxdvGDUkCz0tFccfXYHElER20E0P045LHcQ+WDgYWQoqsXQGesUY5w+vQYNpVTp2t/z8StutAOIKB50Tj4diK0BMVR4VQ9uOHXSfp7ByMHHFEBrX8jmJHX1RiMBLG1tw9KNbeK9cr8LCydV4LC6EBZNrSrI/RP2OkG67OpbY0nZkJIHGs9Dx2hiNcuI4cmbkDLoMXhhUj0h5uGMmlo0luVOHjjUUSRDI4SwxykUlZ54D+IsFfakieOHrNZGiddpEhUthu4EfZCPG1tYTEUhsLkatDh2dNNJ2RiuzJuhUihshOuYa9G8m7YKIZoP5889dKTZtOq3AJrdXiAJkGQYADC23Ae/W8HcUaU4Y0Y15owanALicTrgTWlJQAwPJtcM3QIpSRJ8Tjc0AP2JAWiEPrsTyodWAfCzKu4JPY7NHfS5chA/jhqXOzC6UEiSBI/iBAEQtTy70AI4dRDxf/nG8KoeGKDEcVsXG0P344RJQ0eGvKobHaBzsHWAkbohJI4A4GMZS31qAJ0nfA94bjGI7sNph9XSZ/sQQcDlAjR7X8gmtskbWgiH1QXRF9NwWG0IR4wuwQVHNAzaPVoRcAEpRsm4sqGdH2GvU6y9e1jNMr/iH/RvzYWgWwFh3oXeRC+ShLpGT5o4NtfHPhEokqERQqnPKVSC5ghn9SpOmpy7qvRgUOYzJ0tnrFMUDDt23NA9yH6rT1mPo5O5M44ePXrIxqC1c0z1aXs3nfiN4UqMrxzZ9Pj9gbUdB69rwwM3DS2EB748H8dM2P+NvswTgLV4gQIfqoe43kfI5UMHgM54h3htavXQbe4AEHB6gQSQNOLY1E7nSH2wYkgL8/ldbvSB9n7b3LEXAFAbKB/SKshBlwfdoC7FLYzUlXvKEPIOHYHgakfCiGN9K8s+JQFbrOH+IuD0AEkgacTQHjEVrnNnp5cDOZjhZ2SIu8kSTj+6mKF4RP0YPPKV3DWwCkFFwJWm0E6pGtr5Ue53irW3g6maZd7wkI4RtMQH7umj84MQB06YNLjYo0MRxd5kI4Qyv6kM7ephZEj3DUkshG0MJqPu7KUZJcRQMXfU0Fi9ACu6aHGTJQiNH5lTv+9ZEpnG4MpQfyKCLuYGmF136KhCAA82Zyoa75Ku08BgSQtj9qjwkIxT5rMTxBJ3aMh9+yVephIkWSyPoWJydXhIxwi4qMKVNBJo6aeL/ZiSoXXFBZx0w4ppCbQN0OdqYsXQpgQH3dSNG9WiwlVdHxrajdGv0muV0BP4mBGuUndZXrfqYBBgylCSxPFxJ1O4DB/m5SmTcLAh5KbnwQ2SlgB9poihDJkRR8mQnVDPqBna56rc7xJr74BO52GVPzykY1BDlK5Zmztp3SvoPkyqOrCZuSOBIhkaIZRaVJvNHZSoKPAPqbVY5nOJMTa20/oPkh5A7RAGFgdcpuWwu6cFYB25j2gYOoWLEi66sPTFI4gaPGD30LJIHbIElbXTiEp0qm2KUxdTQ6B+yOTtKn8w5f+522vsC0o99jgtyfDkLQ44WATddIPXSAI9iW4AwJjSoSURfIyYFkNvkj5XE8qHdowSD91841pcBFCPLxvajdHPiSOJYzcjXNX+oXEncoQYGSIwsL6NrlkhZ8mQEq6RQJiR07gsQwewO0yNQyNZggV5alMVisqASxiiAECIjBl1Q0vky/0usfZy1IeGlpgGLCEKW7to7TOXFCg4o/lQxqH1VB/CKPeZPuXtPXRh8atDE9TMUWpRhj7upqze6wgPeRAfL463pmU7fdFwoS48dOfikCWoMj2Pjmg3iEQJxJz6Q0sZAgCXg5Ehtpg0sXowM2uGznVZHQiKPlIAMGqIVQggXX1yyr4hV5/CzII3kBSFCqdU7n+WpRUlHkoco3oUcU6yq4aWqJQw4pgw4hjQugEA06qG9jw4OdWMBFojVEUbXTK0950rKgCwrZuuWZXeoXPDjRRKvWamVUySsMUZBgBI2tAlllQG3CCGaXTKRoD12xs6lPudaerTmNKhJcBep0Os7zycI6AOvXF1MKJIhkYIQY8CiVAy1BqlvtjyDP1e9gcBlwIZ9EFujdAxSnL0JdsXyLIED2ssyxdIlzS0pA4APA5q+X7cyQrIERmTK4d+kx9uuNh59HrKoS/4NgYIdc0cO2bCkI1RX+oF0UyyMrFiaDdeAKjw2VN3fcrQpPJaUWJRnySlGwAwqXzoXLwAUO6l1ymSjAAyrdo8dYjJUBnbfBN6HESmWUsza4aWyFeHKKnTkUAfa2I8eYjve6nXI0g2X08aQkOrdowESj0+EEKJe7RyCpY5qYpd7q6GSxlcjadsKPM5oRjmOuh1DD2BKPE6BVHhGF82tGRIkiSoMt2nBjQWl5SnFcknBUUyNEKQJAkumW6McdCHrDowtFaWJEnwqCywUqJj1AaGvuYHD97sTtDA5iCztIYSIeZq2MsWYQf8UBxDs3CNJHim166KeVh92BWApIMQGSeMHzoyNLUmCKKZrrIZ1UMXv8UxutS+uIdcQx9DMKo0LP4tOWjwf9kQKxFVfrphJdANSaY9x8q9Q7vY1wQp4dKlKCQHzcapDQwtiagLcvJLQBzDo6LVlXiFy6RPp6648WWHnkES8phZtpHFj2B9XzcAYFzJ0M0TWZZQFzSvTalraEkKH8Or2jNH64bYTQaYWZ1E5s/u0J/LwYgiGRpBBF3UYpQkWllvqINDASDktsd2jCkZWssaMANdoVKLtGIYpPMSD71WMUJJnU8JD/kYIwG/k5K6/kQEL2+hbQucpNTWZ2h/MaM+BKKbLo0x4aF3J86orbK54sp9Q68Gji0P0FYiFvCu60M2Rhl9VvkclKHCowxtG4ExZWHb/yU4EHQOLXmsD5vfJ6ndAIbehVUb9oisTsNBFc0pQ+zuGwmU+Jyi/tpAIoK2GDWwZteOG9JxZtWacZPjS4fHpV/utRslAefQZ9eWuO2q76iSg6uI5nChSIZGEKVe+4LYGB56xl3msU+OcaVDT4ZSxxgVHnprsdzLiSMtm182xO6+kUIlC27uT0Tw3q4tAKg8P5TwuRTMqzNbX1R6h55kj63w2whXXXDoCXBt2AMY5hgu2Q9VHtq4i8mV9jnnU4JDHvs0ucq+efiUoY3bA4BRpWacGCd2ZZ4hJkMhj4hB5POwfogVrpFAQ4l5Hmub26DLNKj9qFFDp84CwJVHzRf/PrJh6pB+N0dD0P5sDbWxAAAVPvta23gIukb3BUUyNIKoD4Vt/w+7whmP2x80pBCs4dgYx5TaJ2TjMATs1oXsFtBwuPtGAtUBSoYiWhSbOmjH7HElQ5d5x/Hzk67G/Or5uGXBLcNSMl91yAhYAv4bQ0NPslWHDKdkKpslrqEnXGPLSkT8CACUDnHcHgDUhwK2AnyVnqGfg2V+F2QLcXRIypCrT0GPAhn2TNTSQ9AoKfU5ITFl6Pl1WyEpNI5rVGho1ZtxZZX41qxv4bj643DmuDOG9Ls55o0yf7NTdsOnDn01/tRiquWeopusiCHG9Fr7oljhHfoNfm5KxtVwjDGj1h5wWuEZ+jFm1trT6IdDfRoJ1LMsu774AKIsVmx61dD18+Ko9lXjL6f8BedNPG/Iv5vDSoKH454DQKknLP5dNwwqhMMhi3IHAFAXHPoxZFmC22Gqpw3DYCwAgFcxyU+Ja2hbPwA0BjGQkvFaeggG00qSBCdLZHhzx0eQJAJFcg0LsfvajK/hzhPvHHJiyjG50jRCqn3Do9jMabCvvUUyVMSQoyFkf6iqvUNvXY9OUW2GwyodXTL8lsPhtfbYhOGISxoJjCphC66cgKzSuIuG4KFVL4mjPmA+r0PtkuGYWGESh0rf8CzCFZa4C2vX8qFEo2WuVw2DOgsA9SHzHlT7hoecWmsXeRw+uBwHRyf6wYInMkhO2lG+yltzSDYdrfKa82O4SEq1304Si2SoiCGH9UEGgMphYPaprrfheJBT/dTDsTGWp5CfMvehSYYmVdJ7LDkikFmga53/0CRDY0NmbaTRwdHDMkaZxyQqw3XPa/wmqavyDY9qY81QK/cO/6Y1XOR0psUoOVQNEgAo9dA1S3bTQoJjwkOfcTkSGB0aLf49XPe8JMV1PFzjHGwokqERRLXPXIRL3aXDYmVZxwi7wlAdQ99QMXWyWMccKngUD5yyWWCs1n/oZbEA5gYiyUnITqoMHapkaF7NPABAra922M6hMWDGUzUEhmfDshKg4VBnAfucsJ7TUMI6D4djDgL2DLVDdQ4Cppvd4abKUP0hOgddDhdmV84GAJzYeOKwjFHrs9/nQ1UNHCyKjVpHENb4neHyKVv94MPlAki12IcjSFuSJFR6K7G7nxZdrA8cetWnAcCreKHKTiQNWjdHkZVhieMaCcypmoOHz3wYZe4yOOThqflkVZ/GhYc29ZnDuthPLJk4LGOMD48X/x6u8xgVNJtnDhfhspKs4SKnI4EpFTV4da/5/0OZ2P3fif+HDZ0bMLdq7rB8v+pQ4Vf96E/2Y0xo6OMbD1YUlaERhCqrOL7+eADA5yd8ftjG+ebh34TL4cJXpn9lWL5fkiQxSUrdpZCl4XmM6gKm9TZcFvxwQ5IklFtk5oZAw7Bdr5HAtLJpw6ZCAMCRtUdiYslEzKiYgVmVs4ZljOMbjgdAg8CHiwyd0HgCKj2VmF05e0QI18TS4RnD+r3DRepGAqmunsbg8JDHkUDAGcAR1UcMa8zTd+Z8B37Vj2/P+vawjXGwoagMjTB+esxPsa5jHY6oPmLYxvjGzG/gK9O/MuQ1Wqz47tzv4qfv/hQ3HHHDsI3xpSlfwuq21bhw0oXDpkSMBErdpdg7QM3SMcFPj6W1L/CpPjx+1uPDutAfXnk4Hjj9AZR7yofFjQxQV+jz5z0/rHPwqNqjcGTNkZAlGXOq5gzLGJNKJmFmxUzs6d+DExpOGJYxRgKpWXCfJsVjX3DBpAtw/sTzD8kg832FRAghB/pHDCd6e3sRCoXQ09ODYHB4XFNFDB8SegJOx9B2Rx9pfO+N7+HZrc8CAL4y/Su4ZvY1B/gXFVFE4SCEIGkkD+l5uLlrM8596lwA1FX93hffGzYiXMTQYST370NXry/iU4FDeQHmmFI6Rfz7iKrhUwSLKGI4QOv0HNrz0JqFNaV0SpEIFZGGIhkqoohhxtnjzsZhZYdhUeMizK+Zn/8DRRRRxJBClVXcMPcG1PnrcP3c6w/0zyniIMQh4Sa788478ctf/hLNzc2YOXMm/vCHP2DevHkFfbboJiuiiCKKKKKIQw9FN5kFjzzyCK677jrccsstWLFiBWbOnIlTTjkFra2tB/qnFVFEEUUUUUQRnwAc9GToN7/5Db761a/i8ssvx9SpU3H33XfD6/Xi3nvvPdA/rYgiiiiiiCKK+ATgoCZDiUQCy5cvx6JFi8Rrsixj0aJFeOeddzJ+Jh6Po7e31/aniCKKKKKIIoooIhsOajLU3t4OXddRVWXvH1RVVYXm5uaMn7ntttsQCoXEn4aGQ7dqahFFFFFEEUUUMfw4qMnQvuDmm29GT0+P+LNr164D/ZOKKKKIIoooooiDGAd1Bery8nI4HA60tLTYXm9paUF1deaWAC6XCy7Xp6OxXBFFFFFEEUUUsf84qJUhp9OJOXPm4OWXXxavGYaBl19+GQsWLDiAv6yIIooooogiivik4KBWhgDguuuuw6WXXoq5c+di3rx5+N3vfoeBgQFcfvnlB/qnFVFEEUUUUUQRnwAc9GTowgsvRFtbG374wx+iubkZhx9+OJ5//vm0oOoiiiiiiCKKKKKIfcEhUYF6f1CsQF1EEUUUUUQRhx6KFaiLKKKIIooooogiRghFMlREEUUUUUQRRXyqUSRDRRRRRBFFFFHEpxpFMlREEUUUUUQRRXyqcdBnk+0veHx4sUdZEUUUUUQRRRw64Pv2SOR5feLJUF9fHwAUe5QVUUQRRRRRxCGIvr4+hEKhYR3jE59abxgG9uzZg0AgAEmShvS7e3t70dDQgF27dn2q0/aL18FE8VqYKF4LE8VrYaJ4LSiK18FEtmtBCEFfXx9qa2shy8Mb1fOJV4ZkWUZ9ff2wjhEMBj/1DzNQvA5WFK+FieK1MFG8FiaK14KieB1MZLoWw60IcRQDqIsooogiiiiiiE81imSoiCKKKKKIIor4VKNIhvYDLpcLt9xyC1wu14H+KQcUxetgongtTBSvhYnitTBRvBYUxetg4mC4Fp/4AOoiiiiiiCKKKKKIXCgqQ0UUUUQRRRRRxKcaRTJURBFFFFFEEUV8qlEkQ0UUUUQRRRRRxKcaRTJURBFFFFFEEUV8qlEkQ/uIO++8E6NHj4bb7cb8+fPx3nvvHeiftF+47bbbcMQRRyAQCKCyshLnnHMONm7caDsmFovhqquuQllZGfx+Pz7/+c+jpaXFdszOnTtxxhlnwOv1orKyEjfccAM0TbMd8+qrr2L27NlwuVwYP348/va3vw336e0zbr/9dkiShGuvvVa89mm6Dk1NTfjSl76EsrIyeDweTJ8+HR988IF4nxCCH/7wh6ipqYHH48GiRYuwefNm23d0dnZi8eLFCAaDCIfD+PKXv4z+/n7bMatXr8axxx4Lt9uNhoYG/OIXvxiR8ysUuq7jBz/4AcaMGQOPx4Nx48bhJz/5ia1n0if1Wrz++us466yzUFtbC0mS8O9//9v2/kie92OPPYbJkyfD7XZj+vTpeO6554b8fHMh17VIJpO46aabMH36dPh8PtTW1uKSSy7Bnj17bN/xabgWqfj6178OSZLwu9/9zvb6QXUtSBGDxsMPP0ycTie59957yUcffUS++tWvknA4TFpaWg70T9tnnHLKKeS+++4ja9euJStXriSnn346aWxsJP39/eKYr3/966ShoYG8/PLL5IMPPiBHHnkkOeqoo8T7mqaRww47jCxatIh8+OGH5LnnniPl5eXk5ptvFsds3bqVeL1ect1115F169aRP/zhD8ThcJDnn39+RM+3ELz33ntk9OjRZMaMGeSaa64Rr39arkNnZycZNWoUueyyy8iyZcvI1q1byQsvvEC2bNkijrn99ttJKBQi//73v8mqVavI2WefTcaMGUOi0ag45tRTTyUzZ84k7777LnnjjTfI+PHjyUUXXSTe7+npIVVVVWTx4sVk7dq15J///CfxeDzkT3/604ieby787Gc/I2VlZeSZZ54h27ZtI4899hjx+/3k97//vTjmk3otnnvuOfL973+fPPHEEwQAefLJJ23vj9R5v/XWW8ThcJBf/OIXZN26deT//b//R1RVJWvWrBn2a8CR61p0d3eTRYsWkUceeYRs2LCBvPPOO2TevHlkzpw5tu/4NFwLK5544gkyc+ZMUltbS37729/a3juYrkWRDO0D5s2bR6666irxf13XSW1tLbntttsO4K8aWrS2thIA5LXXXiOE0Imuqip57LHHxDHr168nAMg777xDCKGTQ5Zl0tzcLI656667SDAYJPF4nBBCyI033kimTZtmG+vCCy8kp5xyynCf0qDQ19dHJkyYQJYsWUI+85nPCDL0aboON910EznmmGOyvm8YBqmuria//OUvxWvd3d3E5XKRf/7zn4QQQtatW0cAkPfff18c89///pdIkkSampoIIYT88Y9/JCUlJeLa8LEnTZo01Ke0zzjjjDPIFVdcYXvt3HPPJYsXLyaEfHquReqmN5LnfcEFF5AzzjjD9nvmz59PrrzyyiE9x0KRiwBwvPfeewQA2bFjByHk03ctdu/eTerq6sjatWvJqFGjbGToYLsWRTfZIJFIJLB8+XIsWrRIvCbLMhYtWoR33nnnAP6yoUVPTw8AoLS0FACwfPlyJJNJ23lPnjwZjY2N4rzfeecdTJ8+HVVVVeKYU045Bb29vfjoo4/EMdbv4MccbNfuqquuwhlnnJH2Wz9N1+Gpp57C3Llzcf7556OyshKzZs3CPffcI97ftm0bmpubbecRCoUwf/5827UIh8OYO3euOGbRokWQZRnLli0Txxx33HFwOp3imFNOOQUbN25EV1fXcJ9mQTjqqKPw8ssvY9OmTQCAVatW4c0338Rpp50G4NN1LawYyfM+FOZMKnp6eiBJEsLhMIBP17UwDAMXX3wxbrjhBkybNi3t/YPtWhTJ0CDR3t4OXddtGx0AVFVVobm5+QD9qqGFYRi49tprcfTRR+Owww4DADQ3N8PpdIpJzWE97+bm5ozXhb+X65je3l5Eo9HhOJ1B4+GHH8aKFStw2223pb33aboOW7duxV133YUJEybghRdewDe+8Q18+9vfxv333w/APJdcc6G5uRmVlZW29xVFQWlp6aCu14HG9773PXzhC1/A5MmToaoqZs2ahWuvvRaLFy8G8Om6FlaM5HlnO+ZgvC4AjS286aabcNFFF4nmo5+ma/Hzn/8ciqLg29/+dsb3D7Zr8YnvWl/E4HHVVVdh7dq1ePPNNw/0Txlx7Nq1C9dccw2WLFkCt9t9oH/OAYVhGJg7dy5uvfVWAMCsWbOwdu1a3H333bj00ksP8K8bWTz66KN48MEH8dBDD2HatGlYuXIlrr32WtTW1n7qrkUR+ZFMJnHBBReAEIK77rrrQP+cEcfy5cvx+9//HitWrIAkSQf65xSEojI0SJSXl8PhcKRlD7W0tKC6uvoA/aqhw9VXX41nnnkGS5cuRX19vXi9uroaiUQC3d3dtuOt511dXZ3xuvD3ch0TDAbh8XiG+nQGjeXLl6O1tRWzZ8+GoihQFAWvvfYa7rjjDiiKgqqqqk/FdQCAmpoaTJ061fbalClTsHPnTgDmueSaC9XV1WhtbbW9r2kaOjs7B3W9DjRuuOEGoQ5Nnz4dF198Mb7zne8I9fDTdC2sGMnzznbMwXZdOBHasWMHlixZIlQh4NNzLd544w20traisbFRrKM7duzA9ddfj9GjRwM4+K5FkQwNEk6nE3PmzMHLL78sXjMMAy+//DIWLFhwAH/Z/oEQgquvvhpPPvkkXnnlFYwZM8b2/pw5c6Cqqu28N27ciJ07d4rzXrBgAdasWWN7wPliwDfVBQsW2L6DH3OwXLsTTzwRa9aswcqVK8WfuXPnYvHixeLfn4brAABHH310WnmFTZs2YdSoUQCAMWPGoLq62nYevb29WLZsme1adHd3Y/ny5eKYV155BYZhYP78+eKY119/HclkUhyzZMkSTJo0CSUlJcN2foNBJBKBLNuXS4fDAcMwAHy6roUVI3neh8Kc4URo8+bNeOmll1BWVmZ7/9NyLS6++GKsXr3ato7W1tbihhtuwAsvvADgILwWgwq3LoIQQlPrXS4X+dvf/kbWrVtHvva1r5FwOGzLHjrU8I1vfIOEQiHy6quvkr1794o/kUhEHPP1r3+dNDY2kldeeYV88MEHZMGCBWTBggXifZ5SfvLJJ5OVK1eS559/nlRUVGRMKb/hhhvI+vXryZ133nnQpZSnwppNRsin5zq89957RFEU8rOf/Yxs3ryZPPjgg8Tr9ZIHHnhAHHP77beTcDhM/vOf/5DVq1eTz372sxnTqmfNmkWWLVtG3nzzTTJhwgRb+mx3dzepqqoiF198MVm7di15+OGHidfrPahS6y+99FJSV1cnUuufeOIJUl5eTm688UZxzCf1WvT19ZEPP/yQfPjhhwQA+c1vfkM+/PBDkSE1Uuf91ltvEUVRyK9+9Suyfv16csstt4x4Onmua5FIJMjZZ59N6uvrycqVK23rqDUb6tNwLTIhNZuMkIPrWhTJ0D7iD3/4A2lsbCROp5PMmzePvPvuuwf6J+0XAGT8c99994ljotEo+eY3v0lKSkqI1+sln/vc58jevXtt37N9+3Zy2mmnEY/HQ8rLy8n1119Pksmk7ZilS5eSww8/nDidTjJ27FjbGAcjUsnQp+k6PP300+Swww4jLpeLTJ48mfz5z3+2vW8YBvnBD35AqqqqiMvlIieeeCLZuHGj7ZiOjg5y0UUXEb/fT4LBILn88stJX1+f7ZhVq1aRY445hrhcLlJXV0duv/32YT+3waC3t5dcc801pLGxkbjdbjJ27Fjy/e9/37bJfVKvxdKlSzOuDZdeeikhZGTP+9FHHyUTJ04kTqeTTJs2jTz77LPDdt6ZkOtabNu2Les6unTpUvEdn4Zr8f/bu7uQJvswjuPf4Uu2TdRStrRyaE58SZOkoEAqrCYl5AsYROKBUWFIoGdBB0EdBEIr0qADCwOlA40QKam0PIpeiCZGHay5ozKYwcLSmM9z8MBI0qjm0p7794Ed7N7NdV3cRz/+/z/35jNfGFpOz8L0zz/fvEJVRERExGB0ZkhEREQMTWFIREREDE1hSERERAxNYUhEREQMTWFIREREDE1hSERERAxNYUhEREQMTWFIREREDE1hSEQiNjw8jMlk+u4PbJeD169fY7fbCQaDUetx8OBB2traolZfRKJLb6AWkV+yY8cONm3axIULF8LXZmZmCAQC2Gw2TCbT0g03j+rqajZv3sypU6ei1mN0dJSysjLevn1LUlJS1PqISHRoZUhEIhYfH4/dbl92Qcjv99Pf309DQ0NU+xQWFpKdnc2NGzei2kdEokNhSER+WkNDAw8fPsTtdmMymTCZTPh8vu+2ya5du0ZycjL9/f3k5uZiNpupra1lamqK69ev43A4SElJobm5mVAoFK4/PT1Na2srGRkZWCwWtm7dyvDwcPj38fFxKisrSUlJwWKxUFBQwMDAwILz3rx5k+LiYjIyMsLXfne29vZ2cnJySEhIwGazUVtbO6dXZWUlPT09ET5hEVkKsUs9gIj8PdxuN2/evKGwsJAzZ84AkJaWhs/n++7eqakpLl68SE9PD8FgkOrqaqqqqkhOTmZgYACv10tNTQ3bt2+nrq4OgBMnTjA2NkZPTw/p6en09fXhcrnweDzk5OTQ1NTEzMwMjx49wmKxMDY2htVqXXDekZERSktLI57t6dOnNDc309XVxbZt2wgEAoyMjMypuWXLFs6ePcv09DQrVqyI4CmLyJ+mMCQiPy0pKYn4+HjMZjN2u/2H9379+pWOjg6ys7MBqK2tpauri/fv32O1WsnPz2fnzp0MDQ1RV1eH3++ns7MTv99Peno6AK2trdy5c4fOzk7OnTuH3++npqaGjRs3ApCVlfXDGcbHx+cNQ78zm8ViYf/+/SQmJpKZmUlJScmcmunp6czMzPDu3TsyMzN/7oGKyLKgMCQiUWE2m8NhA8Bms+FwOOas5NhsNiYmJgDweDyEQiGcTuecOtPT06xevRqA5uZmjh8/zuDgIOXl5dTU1FBUVLTgDJ8/fyYhISHi2Xbv3k1mZiZZWVm4XC5cLhdVVVWYzebw/StXrgT+W3USkb+LzgyJSFTExcXN+W4ymea9Njs7C8CnT5+IiYnh2bNnvHjxIvx59eoVbrcbgMbGRrxeL4cPH8bj8VBaWsqlS5cWnCE1NZXJycmIZ0tMTOT58+d0d3ezZs0aTp8+TXFx8ZxXCQQCAeC/bUMR+bsoDInIL4mPj59zsHixlJSUEAqFmJiYYMOGDXM+327JrVu3jmPHjtHb20tLSwtXr179Yc2xsbFFmS82Npby8nLOnz/Py5cv8fl8PHjwIPz76Ogoa9euJTU1dVH6icifo20yEfklDoeDx48f4/P5sFqtrFq1alHqOp1ODh06RH19PW1tbZSUlPDhwwfu379PUVER+/bt4+TJk1RUVOB0OpmcnGRoaIi8vLwFa+7du5fGxkZCoRAxMTG/PVt/fz9er5eysjJSUlIYGBhgdnaW3Nzc8D0jIyPs2bPnt3uIyNLRypCI/JLW1lZiYmLIz88nLS0Nv9+/aLU7Ozupr6+npaWF3NxcDhw4wJMnT1i/fj0AoVCIpqYm8vLycLlcOJ1O2tvbF6xXUVFBbGws9+7di2iu5ORkent72bVrF3l5eVy5coXu7m4KCgoA+PLlC7du3eLIkSMR9RGRpaE3UIvI/9rly5e5ffs2d+/ejVqPjo4O+vr6GBwcjFoPEYkebZOJyP/a0aNH+fjxI8FgkMTExKj0iIuL++FBbhFZ3rQyJCIiIoamM0MiIiJiaApDIiIiYmgKQyIiImJoCkMiIiJiaApDIiIiYmgKQyIiImJoCkMiIiJiaApDIiIiYmgKQyIiImJo/wL46hM/HuNNswAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from bmtk.analyzer.firing_rates import plot_rates\n", "\n", @@ -882,97 +885,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "f974427c-9175-4656-9272-06db661c63e9", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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populationnode_idtimestampsfiring_ratespop_name
0l2330.05.024479VIP
1l2331.05.028511VIP
2l2332.05.032359VIP
3l2333.05.036106VIP
4l2334.05.039591VIP
\n", - "" - ], - "text/plain": [ - " population node_id timestamps firing_rates pop_name\n", - "0 l23 3 0.0 5.024479 VIP\n", - "1 l23 3 1.0 5.028511 VIP\n", - "2 l23 3 2.0 5.032359 VIP\n", - "3 l23 3 3.0 5.036106 VIP\n", - "4 l23 3 4.0 5.039591 VIP" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -982,26 +898,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "7ce4c858-bccd-4b2f-9afc-6300ee274bd4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "population pop_name\n", - "l23 Exc 1.738432\n", - " PV 4.813858\n", - " SST 4.094843\n", - " VIP 4.535815\n", - "Name: firing_rates, dtype: float64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Get the firing rates averages\n", "l23_rates_df.groupby(['population', 'pop_name'])['firing_rates'].agg('sum')/sim.tstop" @@ -1027,25 +927,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "ce7ec8af-7e4a-4b0f-9a1e-0ad0d62a2d08", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "l23_rates.h5/\n", - "|--*rates/\n", - " |--*l23/\n", - " |-->[data] (13500, 4)\n", - " |--*mapping/\n", - " |-->[node_ids] (4,)\n", - " |-->[pop_name] (4,)\n", - " |-->[time] (13500,)\n" - ] - } - ], + "outputs": [], "source": [ "import h5py\n", "\n", @@ -1056,7 +941,7 @@ " for key, node in h5_obj.items():\n", " if isinstance(node, h5py.Group):\n", " print(f'{indent}*{key}/')\n", - " _show_tree_helper(node, depth=depth+1)\n", + " tree_recurse(node, depth=depth+1)\n", " elif isinstance(node, h5py.Dataset):\n", " print(f'{indent}>[{key}] {node.shape}')\n", "\n", @@ -1068,22 +953,10 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "4b8f5ba5-8851-48f8-ae8f-9a9f581bff36", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "l23\n", - " VIP: 4.536151243661407\n", - " SST: 4.095146474363789\n", - " PV: 4.814214600421233\n", - " Exc: 1.7385611015415747\n" - ] - } - ], + "outputs": [], "source": [ "# How to calcuate firing rate averages from the h5 file\n", "with h5py.File('output/l23_rates.h5', 'r') as h5:\n", @@ -1095,19 +968,11 @@ " fr_avg = np.sum(pgrp['data'][:, idx])/time\n", " print(f' {nid}: {fr_avg}')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce58dadc-b4b2-46a2-865c-4688edd02aee", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "bmtk_ssn", "language": "python", "name": "python3" }, @@ -1121,7 +986,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.13.3" } }, "nbformat": 4, diff --git a/examples/pop_ssn_123/PopNet_SSN_optimization.ipynb b/examples/pop_ssn_123/PopNet_SSN_optimization.ipynb new file mode 100644 index 00000000..662ca257 --- /dev/null +++ b/examples/pop_ssn_123/PopNet_SSN_optimization.ipynb @@ -0,0 +1,438 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4a48c45c-27e9-458e-8ed5-0729be661d34", + "metadata": {}, + "source": [ + "# PopNet optimization\n", + "\n", + "This tutorial demonstrates a method for optimizing Stabilized Supralinear Network (SSN) model parameters using an optimizer.\n", + "\n", + "This example requires the 'numba' and 'iminuit' packages. Please ensure they are installed to run the notebook. While Numba is not strictly required for running the SSN model, its inclusion accelerates the simulation by approximately 50-100 times, making it more practical for optimization tasks.\n", + "\n", + "Reference: [Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics](https://www.sciencedirect.com/science/article/pii/S2211124724011148)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "313c08fa-97eb-41b0-a135-864d64c17fa1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "401c8d30", + "metadata": {}, + "outputs": [], + "source": [ + "# Import block\n", + "from bmtk.simulator import popnet\n", + "import matplotlib.pyplot as plt\n", + "import pickle\n", + "import numpy as np\n", + "from iminuit import minimize" + ] + }, + { + "cell_type": "markdown", + "id": "7dc99302-689a-4be6-8876-ed6b6affaeec", + "metadata": {}, + "source": [ + "## Optimizing network parameters\n", + "\n", + "In this example, the SSN PopNet is used in an interactive mode and used for parameter optimization. First, load the network and instantiate the simulator." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c4b3bc43-114c-4498-a48a-c0c26588aea2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-05-20 11:57:37,259 [INFO] Created log file\n", + "2025-05-20 11:57:37,299 [INFO] Building nodes.\n", + "2025-05-20 11:57:37,300 [INFO] Building connections.\n", + "2025-05-20 11:57:37,301 [INFO] Add input module \"init_states\"\n", + "2025-05-20 11:57:37,306 [INFO] Add input module \"l4_inputs\"\n", + "2025-05-20 11:57:37,308 [INFO] Add input module \"bkg_inputs\"\n" + ] + } + ], + "source": [ + "@popnet.inputs_generator\n", + "def load_bkg_inputs(node, sim, **opts):\n", + " # print(node.node_id, node.population)\n", + " # print(sim.dt, sim.tstart, sim.tstop, sim.nsteps)\n", + " return np.ones(sim.nsteps)\n", + "\n", + "\n", + "configure = popnet.Config.from_json('config.simulation.json')\n", + "configure.build_env()\n", + "\n", + "network = popnet.PopNetwork.from_config(configure)\n", + "sim = popnet.PopSimulator.from_config(configure, network)" + ] + }, + { + "cell_type": "markdown", + "id": "3f1342e2", + "metadata": {}, + "source": [ + "When the simulator is run with 'return_output' option, the results are returned a variable instead of being written to a file.\n", + "This mechanism facilitates the formulation of an optimization problem." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2c2548cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13500, 6)\n" + ] + } + ], + "source": [ + "results = sim.run(return_output=True)\n", + "\n", + "print(results.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "0c916d47", + "metadata": {}, + "source": [ + "The result will have a shape of (time steps, number of nodes) including the external units.\n", + "To retrieve the activity of only the recurrent units, one can do:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5c9c25bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1.38853652 3.50304166 1.61686557 5.02447877]\n", + " [1.38884923 3.50402647 1.6179342 5.02851101]\n", + " [1.38883864 3.50404038 1.61873297 5.03235881]\n", + " ...\n", + " [1.38709075 3.49855563 1.61244919 5.01181986]\n", + " [1.38767691 3.50036444 1.61408329 5.01612201]\n", + " [1.38833074 3.50237521 1.61559399 5.02040123]]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Simulated traces')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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