{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2ee1d062",
"metadata": {},
"outputs": [],
"source": [
"# This code is written by Nooshin Abdollahi\n",
"# Information about this code:\n",
"# - Motor axons are not included\n",
"# - there are not transverse connections between Boundary and Boundary"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "af4c646e",
"metadata": {},
"outputs": [],
"source": [
"# show the time of execution\n",
"from datetime import datetime\n",
"start_time = datetime.now()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "493e7e8a",
"metadata": {},
"outputs": [],
"source": [
"from neuron import h\n",
"import netpyne \n",
"from netpyne import specs, sim \n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from typing import Tuple, List\n",
"import math\n",
"import sys\n",
"\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d05a8722",
"metadata": {},
"outputs": [],
"source": [
"# Import nesseccery files from Matlab\n",
"\n",
"R = np.loadtxt(\"R.txt\") # All axons with different radius\n",
"G = np.loadtxt(\"G.txt\") # Axon's groups\n",
"C = np.loadtxt(\"C.txt\") # Coordinates of each axon (x,y)\n",
"neighboringAxon = np.loadtxt(\"neighboringAxon.txt\")\n",
"dist = np.loadtxt(\"dist.txt\") \n",
"dist_edge = np.loadtxt(\"Distance_edge.txt\") \n",
"AVE_area_around_axon = np.loadtxt(\"Ave_area_around_axon.txt\")\n",
"\n",
"unique_radius = np.loadtxt(\"unique_radius.txt\") # including different types\n",
"Number_of_nodes = np.loadtxt(\"Number_of_nodes.txt\") # Number of nodes for the specified axon total length\n",
"\n",
"parameters = np.loadtxt(\"parameters.txt\") \n",
"\n",
"# importing all the connections\n",
"import scipy.io as io\n",
"\n",
"for i in range(1,21):\n",
" for j in range(1,21):\n",
" if j>i:\n",
" l = [i, j]\n",
" z = ''.join([str(n) for n in l])\n",
" Input = io.loadmat('Connect_types_{}.mat'.format(z) , squeeze_me=True) \n",
" I = Input['SAVE']; \n",
" locals()[\"Connect_types_\"+str(z)]=[]\n",
" for v in range(len(I)):\n",
" D = I[v].strip() \n",
" locals()[\"Connect_types_\"+str(z)].append(D) \n",
"\n",
"\n",
"# Boundary connections\n",
"for i in range(1,2):\n",
" Input = io.loadmat('Boundary_to_{}.mat'.format(i) , squeeze_me=True) \n",
" I = Input['SAVE']; \n",
" locals()[\"Boundary_to_\"+str(i)]=[]\n",
" for v in range(len(I)):\n",
" D = I[v].strip() \n",
" locals()[\"Boundary_to_\"+str(i)].append(D) \n",
" \n",
"\n",
"\n",
"#\n",
"Boundary_coordinates = np.loadtxt(\"Boundary_coordinates.txt\")\n",
"Boundary_neighboring = np.loadtxt(\"Boundary_neighboring.txt\")\n",
"Boundary_dist = np.loadtxt(\"Boundary_dist.txt\") \n",
"\n",
"\n",
"############## importing files related to transverse resistance (Rg) and Areas\n",
"\n",
"for i in range(1,21):\n",
" for j in range(1,21):\n",
" if j>i:\n",
" l = [i, j]\n",
" z = ''.join([str(n) for n in l])\n",
" Input = np.loadtxt('Rg_{}.txt'.format(z) ) \n",
" locals()[\"Rg_\"+str(z)]=Input\n",
" \n",
"\n",
"\n",
" \n",
"for i in range(1,2):\n",
" Input = np.loadtxt('Boundary_Rg_{}.txt'.format(i) ) \n",
" locals()[\"Boundary_Rg_\"+str(i)]=Input\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"for i in range(1,21):\n",
" for j in range(1,21):\n",
" if j>i:\n",
" l = [i, j]\n",
" z = ''.join([str(n) for n in l])\n",
" Input = np.loadtxt('Areas_{}.txt'.format(z) ) \n",
" locals()[\"Areas_\"+str(z)]=Input\n",
" \n",
" \n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cf1c9f69",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n",
"\t1 \n"
]
}
],
"source": [
"# Network parameters\n",
"netParams = specs.NetParams()\n",
"\n",
"netParams.sizeX=3000\n",
"netParams.sizeY=3000\n",
"netParams.sizeZ=3000\n",
"\n",
"\n",
"################################# Importing Axons(including C fibers and the others) and Boundary ####################################\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='Boundary', \n",
" conds={'cellType': 'Boundary', 'cellModel': 'Boundary'},\n",
" fileName='Boundarycable.hoc', \n",
" cellName='Boundary', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# Myelinated axons have different types (i.e. diameters)\n",
"# How many types... do I have? print(len(unique_radius)-1), -1 because the first eleman is for C fiber\n",
"# each type is a specific diameter\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type1', \n",
" conds={'cellType': 'type1', 'cellModel': 'type1'},\n",
" fileName='type1.hoc', \n",
" cellName='type1', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type2', \n",
" conds={'cellType': 'type2', 'cellModel': 'type2'},\n",
" fileName='type2.hoc', \n",
" cellName='type2', \n",
" importSynMechs=True) ;\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type3', \n",
" conds={'cellType': 'type3', 'cellModel': 'type3'},\n",
" fileName='type3.hoc', \n",
" cellName='type3', \n",
" importSynMechs=True) ;\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type4', \n",
" conds={'cellType': 'type4', 'cellModel': 'type4'},\n",
" fileName='type4.hoc', \n",
" cellName='type4', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type5', \n",
" conds={'cellType': 'type5', 'cellModel': 'type5'},\n",
" fileName='type5.hoc', \n",
" cellName='type5', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type6', \n",
" conds={'cellType': 'type6', 'cellModel': 'type6'},\n",
" fileName='type6.hoc', \n",
" cellName='type6', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type7', \n",
" conds={'cellType': 'type7', 'cellModel': 'type7'},\n",
" fileName='type7.hoc', \n",
" cellName='type7', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type8', \n",
" conds={'cellType': 'type8', 'cellModel': 'type8'},\n",
" fileName='type8.hoc', \n",
" cellName='type8', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type9', \n",
" conds={'cellType': 'type9', 'cellModel': 'type9'},\n",
" fileName='type9.hoc', \n",
" cellName='type9', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type10', \n",
" conds={'cellType': 'type10', 'cellModel': 'type10'},\n",
" fileName='type10.hoc', \n",
" cellName='type10', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type11', \n",
" conds={'cellType': 'type11', 'cellModel': 'type11'},\n",
" fileName='type11.hoc', \n",
" cellName='type11', \n",
" importSynMechs=True) ;\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type12', \n",
" conds={'cellType': 'type12', 'cellModel': 'type12'},\n",
" fileName='type12.hoc', \n",
" cellName='type12', \n",
" importSynMechs=True) ;\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type13', \n",
" conds={'cellType': 'type13', 'cellModel': 'type13'},\n",
" fileName='type13.hoc', \n",
" cellName='type13', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type14', \n",
" conds={'cellType': 'type14', 'cellModel': 'type14'},\n",
" fileName='type14.hoc', \n",
" cellName='type14', \n",
" importSynMechs=True) ;\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type15', \n",
" conds={'cellType': 'type15', 'cellModel': 'type15'},\n",
" fileName='type15.hoc', \n",
" cellName='type15', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type16', \n",
" conds={'cellType': 'type16', 'cellModel': 'type16'},\n",
" fileName='type16.hoc', \n",
" cellName='type16', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type17', \n",
" conds={'cellType': 'type17', 'cellModel': 'type17'},\n",
" fileName='type17.hoc', \n",
" cellName='type17', \n",
" importSynMechs=True) ;\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type18', \n",
" conds={'cellType': 'type18', 'cellModel': 'type18'},\n",
" fileName='type18.hoc', \n",
" cellName='type18', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type19', \n",
" conds={'cellType': 'type19', 'cellModel': 'type19'},\n",
" fileName='type19.hoc', \n",
" cellName='type19', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type20', \n",
" conds={'cellType': 'type20', 'cellModel': 'type20'},\n",
" fileName='type20.hoc', \n",
" cellName='type20', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d5ef8f97",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"40\n"
]
}
],
"source": [
"###################################### Locating each axon in specific (x,y) #################################################\n",
"\n",
"\n",
"for i in range(len(R)):\n",
" x = np.where(unique_radius == R[i])\n",
" \n",
" if x[0]==0:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type1', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type1', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
"\n",
" \n",
" if x[0]==1:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type2', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type2', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==2:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type3', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type3', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==3:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type4', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type4', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==4:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type5', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type5', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==5:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type6', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type6', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" \n",
" if x[0]==6:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type7', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type7', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==7:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type8', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type8', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" \n",
" if x[0]==8:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type9', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type9', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==9:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type10', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type10', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" \n",
" if x[0]==10:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type11', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type11', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==11:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type12', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type12', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==12:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type13', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type13', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==13:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type14', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type14', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==14:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type15', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type15', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==15:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type16', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type16', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==16:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type17', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type17', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==17:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type18', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type18', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
" if x[0]==18:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type19', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type19', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" if x[0]==19:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type20', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type20', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
" \n",
" \n",
"########################################### Locating Boundary Cables ########################################################\n",
"\n",
"\n",
"for i in range(len(Boundary_coordinates)):\n",
" \n",
" netParams.popParams[\"Boundary%s\" %i] = {\n",
" 'cellType': 'Boundary', \n",
" 'numCells':1 , \n",
" 'cellModel': 'Boundary', \n",
" 'xRange':[Boundary_coordinates[i][0], Boundary_coordinates[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[Boundary_coordinates[i][1], Boundary_coordinates[i][1]]} \n",
"\n",
"\n",
"\n",
"# in Total, how many Cells does Netpyne generate? Length(R)+len(Boundary_coordinates)\n",
"print(len(R)+len(Boundary_coordinates))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "03c9154d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4adc83be",
"metadata": {},
"outputs": [],
"source": [
"################################################### Stimulation ############################################################\n",
"# Which group of axons do you want to stimulate?\n",
"# Group1: motor axons Group2: C fibers Group3: Adelta Group4: Abeta\n",
"\n",
"\n",
"#netParams.stimSourceParams['Input1'] = {'type': 'IClamp', 'del': 1, 'dur': 0.1, 'amp': 0.37}\n",
"\n",
"netParams.stimSourceParams['Input1'] = {'type': 'VClamp', 'dur': [1, 0.02,0], 'amp':[-80, 0, 0] } \n",
"\n",
"\n",
"# for i in range(len(R)): \n",
"# if G[i]==4: # Group 4\n",
"# netParams.stimTargetParams['Input1->\"Stim_%s\"' %i] = {'source': 'Input1', 'sec':'node_0', 'loc': 0.5, 'conds': {'pop':\"Axon%s\" %i}} \n",
"\n",
"\n",
" \n",
"netParams.stimTargetParams['Input1->Stim_0'] = {'source': 'Input1', 'sec':'node_0', 'loc': 0.5, 'conds': {'pop':\"Axon0\"}} \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "90a2f08b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Start time: 2022-10-29 09:48:51.026624\n",
"\n",
"Creating network of 40 cell populations on 1 hosts...\n",
" Number of cells on node 0: 40 \n",
" Done; cell creation time = 6.55 s.\n",
"Making connections...\n",
" Number of connections on node 0: 0 \n",
" Done; cell connection time = 0.00 s.\n",
"Adding stims...\n",
" Number of stims on node 0: 1 \n",
" Done; cell stims creation time = 0.00 s.\n",
"Recording 60 traces of 2 types on node 0\n"
]
}
],
"source": [
"simConfig = specs.SimConfig()\n",
"simConfig.hParams = {'celsius': 37 }\n",
"\n",
"simConfig.dt = 0.005 # Internal integration timestep to use default is 0.025\n",
"simConfig.duration = 6\n",
"simConfig.recordStim = True\n",
"simConfig.recordStep = 0.005 # Step size in ms to save data (e.g. V traces, LFP, etc) default is 0.1\n",
"#simConfig.cache_efficient = True\n",
"#simConfig.cvode_active = True\n",
"# simConfig.cvode_atol=0.0001\n",
"# simConfig.cvode_rtol=0.0001\n",
"\n",
"\n",
"simConfig.recordTraces = {'V_node_0' :{'sec':'node_0','loc':0.5,'var':'v'}}\n",
"simConfig.analysis['plotTraces'] = {'include': ['allCells']} # ['Axon0','Axon1']\n",
"\n",
"simConfig.analysis['plot2Dnet'] = True\n",
"simConfig.analysis['plot2Dnet'] = {'include': ['allCells'], 'view': 'xz'}\n",
"\n",
"\n",
"\n",
"#simConfig.recordLFP = [[56.39,-4000,51.74]] # Determine the location of the LFP electrode\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"sim.create(netParams, simConfig)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "9045099d",
"metadata": {},
"source": [
"### xraxial and transverese conductances"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "41af5705",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.1\n",
"63978.96633503253\n",
"0.1\n",
"63978.9652731409\n",
"0.1\n",
"63978.96421124932\n",
"0.1\n",
"63978.963149358504\n",
"0.1\n",
"63978.962087466985\n",
"0.1\n",
"63978.961025575496\n",
"0.1\n",
"63978.95996368406\n",
"0.1\n",
"63978.958901792634\n",
"0.1\n",
"63978.95783990201\n",
"0.1\n",
"63978.95677800991\n",
"0.1\n",
"63978.9557161186\n",
"0.1\n",
"63978.954654227324\n",
"0.1\n",
"63978.95359233684\n",
"0.1\n",
"63978.95253044488\n",
"0.1\n",
"63978.95146855446\n",
"0.1\n",
"63978.95040666333\n",
"0.1\n",
"63978.94934477223\n",
"0.1\n",
"63978.94828288116\n",
"0.1\n",
"63978.94722099013\n",
"0.1\n",
"63978.94615909989\n"
]
}
],
"source": [
"# Since by default Netpyne does not insert the parameters of the extracellular mechanism, I insert them in this section\n",
"# this section includes \"longitudinal\" resistivities (i.e. xraxial)\n",
"\n",
"#Total_Length=10000\n",
"\n",
"number_boundary = 4000 #Total_Length/Section_Length \n",
"number_boundary = int(number_boundary)\n",
"\n",
"\n",
"\n",
"\n",
"rhoa=0.7e6 \n",
"mycm=0.1 \n",
"mygm=0.001 \n",
"\n",
"space_p1=0.002 \n",
"space_p2=0.004\n",
"space_i=0.004\n",
"\n",
"\n",
"\n",
"\n",
"############################# For Boundary Cables #################################################\n",
"\n",
"# soma section is just for LFP recording, LFP in Netpyne does not work if at least one section is not called soma \n",
"\n",
"\n",
"for j in range(len(R),len(R)+len(Boundary_coordinates)):\n",
" \n",
" S = sim.net.cells[j].secs[\"soma\"][\"hObj\"] \n",
" for seg in S:\n",
" seg.xraxial[0] = 1e9\n",
" seg.xraxial[1] = 1e9\n",
" seg.xg[0] = 1e9\n",
" seg.xg[1] = 1000 #1e9\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\n",
"\n",
"\n",
" for i in range(number_boundary): \n",
" S = sim.net.cells[j].secs[\"section_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = 1e9\n",
" seg.xraxial[1] = 1e9\n",
" seg.xg[0] = 1e9\n",
" seg.xg[1] = 1000 #1e9\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"############################# For C fibers #######################################################\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" \n",
" \n",
" \n",
"\n",
" \n",
"############################## For myelinated sensory axons ##################################### \n",
"\n",
"\n",
"rho2 = 1211 * 1e-6 # Mohm-cm\n",
"\n",
"\n",
"\n",
"\n",
"for j in range(len(R)):\n",
" if G[j]!=2: # if it is not a C fiber \n",
" x = np.where(unique_radius == R[j]) \n",
" x = int(x[0])\n",
" nodes = Number_of_nodes[x]\n",
" nodes=int(nodes)\n",
" \n",
" \n",
" nl = parameters[x][4]\n",
" nodeD = parameters[x][1]\n",
" paraD1 = nodeD\n",
" axonD = parameters[x][0]\n",
" paraD2 = axonD\n",
" \n",
" Rpn0 = (rhoa*.01)/((math.pi)*((((nodeD/2)+space_p1)**2)-((nodeD/2)**2)))\n",
" Rpn1 = (rhoa*.01)/((math.pi)*((((paraD1/2)+space_p1)**2)-((paraD1/2)**2)))\n",
" Rpn2 = (rhoa*.01)/((math.pi)*((((paraD2/2)+space_p2)**2)-((paraD2/2)**2)))\n",
" Rpx = (rhoa*.01)/((math.pi)*((((axonD/2)+space_i)**2)-((axonD/2)**2)))\n",
" \n",
" \n",
" ################### xraxial[1]\n",
" \n",
" radi = R[j]\n",
" \n",
" AVE = (AVE_area_around_axon[j]+0) /2\n",
" \n",
" xr = rho2 / ((math.pi)*(((radi+AVE)**2) - (radi**2)) * 1e-8) # Mohm/cm\n",
" \n",
" xr = xr /1\n",
" \n",
" print(AVE_area_around_axon[j]+0)\n",
" print(xr)\n",
" \n",
" ##################\n",
" \n",
" \n",
" \n",
"\n",
" S = sim.net.cells[j].secs[\"soma\"][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn1\n",
" seg.xraxial[1] = xr \n",
" seg.xg[0] = mygm/(nl*2)\n",
" seg.xg[1] = 1e-9 # disconnect from ground\n",
" seg.xc[0] = mycm/(nl*2)\n",
" seg.xc[1] = 0\n",
"\n",
" \n",
" for i in range(nodes):\n",
" S = sim.net.cells[j].secs[\"node_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn0\n",
" seg.xraxial[1] = xr\n",
" seg.xg[0] = 1e6\n",
" seg.xg[1] = 1e-9\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\n",
"\n",
"\n",
" for i in range(2*nodes):\n",
" S = sim.net.cells[j].secs[\"MYSA_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn1\n",
" seg.xraxial[1] = xr\n",
" seg.xg[0] = mygm/(nl*2)\n",
" seg.xg[1] = 1e-9\n",
" seg.xc[0] = mycm/(nl*2)\n",
" seg.xc[1] = 0\n",
"\n",
"\n",
" for i in range(10*nodes):\n",
" S = sim.net.cells[j].secs[\"FLUT_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn2\n",
" seg.xraxial[1] = xr\n",
" seg.xg[0] = mygm/(nl*2)\n",
" seg.xg[1] = 1e-9\n",
" seg.xc[0] = mycm/(nl*2)\n",
" seg.xc[1] = 0 \n",
"\n",
"\n",
" for i in range(40*nodes):\n",
" S = sim.net.cells[j].secs[\"STIN_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpx\n",
" seg.xraxial[1] = xr\n",
" seg.xg[0] = mygm/(nl*2)\n",
" seg.xg[1] = 1e-9\n",
" seg.xc[0] = mycm/(nl*2)\n",
" seg.xc[1] = 0\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "afaf323f",
"metadata": {},
"outputs": [],
"source": [
"\n",
"##############################This section is about transverse connections between axons #####################################\n",
"# *** If you do not want to include ephaptic interaction, do not run this section\n",
"# To model ephaptic effect, \"LinearMechanism\" in NEURON is used.\n",
"\n",
"\n",
"\n",
"rho = 1211 * 10000 # ohm-micron\n",
"\n",
"count = 0\n",
"\n",
"for i in range(len(R)): \n",
"\n",
" \n",
" for j in range(len(R)): \n",
" \n",
" if neighboringAxon[i][j]==1:\n",
" \n",
"\n",
" a1 = np.where(unique_radius == R[i]) # find type of R[i]\n",
" a1 = a1[0][0]+1\n",
" a2 = np.where(unique_radius == R[j]) # find type of R[j]\n",
" a2 = a2[0][0]+1\n",
"\n",
"\n",
" NSEG = 0\n",
"\n",
"\n",
"\n",
" if a1==a2:\n",
" SEC = locals()[\"Connect_types_\"+str(a1)+str(a1)]\n",
" RG = locals()[\"Rg_\"+str(a1)+str(a1)]\n",
" area = (math.pi)*(parameters[a1-1][1])*(np.ones((len(RG),1))) # micron^2\n",
" area = area * 1e-8 #cm^2\n",
" b1=i\n",
" b2=j\n",
" if a1==0:\n",
" area = (math.pi)*0.8*10*(np.ones((len(RG),1))) # micron^2\n",
" area = area * 1e-8 #cm^2\n",
" \n",
" \n",
"\n",
" if a1<a2:\n",
" SEC = locals()[\"Connect_types_\"+str(a1)+str(a2)]\n",
" RG = locals()[\"Rg_\"+str(a1)+str(a2)]\n",
" b1=i\n",
" b2=j\n",
" if a1==0:\n",
" area = (math.pi)*(parameters[a2-1][1])*(np.ones((len(RG),1)))\n",
" area = area * 1e-8 #cm^2\n",
" b1=j\n",
" b2=i\n",
" \n",
" else:\n",
" area = locals()[\"Areas_\"+str(a1)+str(a2)]\n",
" area = area[ : , np.newaxis]\n",
" area = area * 1e-8\n",
" \n",
" \n",
"\n",
" if a1>a2:\n",
" SEC = locals()[\"Connect_types_\"+str(a2)+str(a1)]\n",
" RG = locals()[\"Rg_\"+str(a2)+str(a1)]\n",
" b1=j\n",
" b2=i\n",
" if a2==0:\n",
" area = (math.pi)*(parameters[a1-1][1])*(np.ones((len(RG),1)))\n",
" area = area * 1e-8 #cm^2\n",
" b1=i\n",
" b2=j\n",
" \n",
" else:\n",
" area = locals()[\"Areas_\"+str(a2)+str(a1)]\n",
" area = area[ : , np.newaxis]\n",
" area = area * 1e-8\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
" locals()[\"sl\"+str(count)] = h.SectionList()\n",
"\n",
" for z1 in range(int(len(SEC)/2)): \n",
"\n",
" S = sim.net.cells[b1].secs[SEC[z1]][\"hObj\"]\n",
" NSEG=NSEG+S.nseg\n",
" locals()[\"sl\"+str(count)].append(S)\n",
"\n",
" for z2 in range(int(len(SEC)/2),int(len(SEC))):\n",
"\n",
" S = sim.net.cells[b2].secs[SEC[z2]][\"hObj\"]\n",
" locals()[\"sl\"+str(count)].append(S) \n",
" \n",
" \n",
"\n",
" nsegs=int(NSEG)\n",
"\n",
" locals()[\"gmat\"+str(count)] =h.Matrix(2*nsegs, 2*nsegs)\n",
" locals()[\"cmat\"+str(count)] =h.Matrix(2*nsegs, 2*nsegs)\n",
" locals()[\"bvec\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"xl\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"layer\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"layer\"+str(count)].fill(2) # connect layer 2\n",
" locals()[\"e\"+str(count)] = h.Vector(2*nsegs)\n",
"\n",
" for z3 in range(2*nsegs):\n",
" locals()[\"xl\"+str(count)][z3] = 0.5\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" d = dist_edge[i][j] + 0.091374 #dist[i][j]\n",
" rd = rho*d\n",
" s = ((6*2)+(6*2))/2\n",
" locals()[\"RG\"+str(count)] = np.array(RG)*s\n",
" locals()[\"Resistance\"+str(count)] = rd/locals()[\"RG\"+str(count)]\n",
" locals()[\"Conductance\"+str(count)]=[]\n",
" for z4 in range(len(locals()[\"Resistance\"+str(count)])):\n",
" locals()[\"Conductance\"+str(count)].append(1/(locals()[\"Resistance\"+str(count)][z4]*area[z4]))\n",
" \n",
"\n",
" \n",
" for z5 in range(0,nsegs,1):\n",
"\n",
" locals()[\"gmat\"+str(count)].setval(z5, z5, locals()[\"Conductance\"+str(count)][z5][0] )\n",
" locals()[\"gmat\"+str(count)].setval(z5, nsegs+z5, -locals()[\"Conductance\"+str(count)][z5][0])\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, z5, -locals()[\"Conductance\"+str(count)][z5][0])\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, nsegs+z5, locals()[\"Conductance\"+str(count)][z5][0])\n",
" \n",
" \n",
" locals()[\"GMAT\"+str(i)+str(j)] = locals()[\"gmat\"+str(count)]\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# geA= 1000\n",
" \n",
"# for z5 in range(0,nsegs,1):\n",
"# locals()[\"gmat\"+str(count)].setval(z5, z5, geA)\n",
"# locals()[\"gmat\"+str(count)].setval(z5, nsegs+z5, -geA)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z5, z5, -geA)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z5, nsegs+z5, geA)\n",
"\n",
"\n",
"\n",
"\n",
" locals()[\"lm\"+str(count)] = h.LinearMechanism(locals()[\"cmat\"+str(count)], locals()[\"gmat\"+str(count)], locals()[\"e\"+str(count)], locals()[\"bvec\"+str(count)], locals()[\"sl\"+str(count)], locals()[\"xl\"+str(count)], locals()[\"layer\"+str(count)])\n",
"\n",
" count=count+1\n",
" \n",
" SEC.clear\n",
" del RG\n",
" del area\n",
" \n",
" \n",
"\n",
" \n",
"#print(count) \n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b71ff07f",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#GMAT116.printf() "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9f7204b0",
"metadata": {},
"outputs": [],
"source": [
" \n",
" \n",
" \n",
"############################### Transverse connections between Boundary cables and Axons ######################################\n",
"\n",
"\n",
"rho = 1.136e5 * 10000 * 4.7e-4 * 10000 # ohm-micron^2\n",
"\n",
"\n",
"\n",
"rows = len(Boundary_neighboring)\n",
"\n",
"for i in range(rows):\n",
" \n",
" for j in range(len(R)):\n",
" \n",
" if Boundary_neighboring[i][j]==1:\n",
" \n",
" NSEG = 0\n",
"\n",
" a2 = np.where(unique_radius == R[j]) # find type \n",
" a2 = a2[0][0]+1\n",
" \n",
" Boundary_RG = locals()[\"Boundary_Rg_\"+str(1)]\n",
" area = (math.pi)*(parameters[a2-1][1])*(np.ones((len(Boundary_RG),1)))\n",
" area = area * 1e-8 #cm^2\n",
" \n",
"\n",
" SEC = locals()[\"Boundary_to_\"+str(1)]\n",
"\n",
"\n",
" locals()[\"sl\"+str(count)] = h.SectionList()\n",
"\n",
" for z1 in range(int(len(SEC)/2)): \n",
"\n",
" S = sim.net.cells[j].secs[SEC[z1]][\"hObj\"]\n",
" NSEG=NSEG+S.nseg\n",
" locals()[\"sl\"+str(count)].append(S)\n",
"\n",
" for z2 in range(int(len(SEC)/2),int(len(SEC))):\n",
"\n",
" S = sim.net.cells[len(R)+i].secs[SEC[z2]][\"hObj\"]\n",
" locals()[\"sl\"+str(count)].append(S) \n",
"\n",
"\n",
"\n",
"\n",
" nsegs=int(NSEG)\n",
"\n",
" locals()[\"gmat\"+str(count)] =h.Matrix(2*nsegs, 2*nsegs)\n",
" locals()[\"cmat\"+str(count)] =h.Matrix(2*nsegs, 2*nsegs)\n",
" locals()[\"bvec\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"xl\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"layer\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"layer\"+str(count)].fill(2) # connect layer 2\n",
" locals()[\"e\"+str(count)] = h.Vector(2*nsegs)\n",
"\n",
" for z3 in range(2*nsegs):\n",
" locals()[\"xl\"+str(count)][z3] = 0.5\n",
"\n",
"\n",
" \n",
" \n",
" rd = rho + (1211 * 10000 * Boundary_dist[i][j] )\n",
" s = (6*2)\n",
" locals()[\"Boundary_RG\"+str(count)] = np.array(Boundary_RG)*s\n",
" locals()[\"Resistance\"+str(count)] = rd/locals()[\"Boundary_RG\"+str(count)]\n",
" locals()[\"Conductance\"+str(count)]=[]\n",
" for z4 in range(len(locals()[\"Resistance\"+str(count)])):\n",
" locals()[\"Conductance\"+str(count)].append(1/(locals()[\"Resistance\"+str(count)][z4]*area[z4]))\n",
"\n",
" \n",
" for z5 in range(0,nsegs,1):\n",
"\n",
" locals()[\"gmat\"+str(count)].setval(z5, z5, locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" locals()[\"gmat\"+str(count)].setval(z5, nsegs+z5, - locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, z5, - locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, nsegs+z5, locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" \n",
" \n",
" \n",
" locals()[\"GMAT_BOUNDARY\"+str(i)+str(j)] = locals()[\"gmat\"+str(count)]\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n",
" \n",
"# geB= 1\n",
" \n",
"# for z6 in range(0,nsegs,1):\n",
"\n",
"# locals()[\"gmat\"+str(count)].setval(z6, z6, geB)\n",
"# locals()[\"gmat\"+str(count)].setval(z6, nsegs+z6, -geB)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z6, z6, -geB)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z6, nsegs+z6, geB)\n",
"\n",
"\n",
"\n",
"\n",
" locals()[\"lm\"+str(count)] = h.LinearMechanism(locals()[\"cmat\"+str(count)], locals()[\"gmat\"+str(count)], locals()[\"e\"+str(count)], locals()[\"bvec\"+str(count)], locals()[\"sl\"+str(count)], locals()[\"xl\"+str(count)], locals()[\"layer\"+str(count)])\n",
"\n",
" count=count+1\n",
" \n",
" \n",
" SEC.clear\n",
" del Boundary_RG\n",
" del area\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"#print(count) \n",
" \n",
" \n",
" \n",
"# from IPython.display import clear_output\n",
"\n",
"# clear_output(wait=True)\n",
"\n",
"\n",
" \n",
"#gmat0.printf() \n",
"\n",
"# for sec in sl0:\n",
"# print(sec)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7808a6c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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},
{
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT_BOUNDARY11.printf() "
]
},
{
"cell_type": "markdown",
"id": "b2a6c256",
"metadata": {},
"source": [
"#### Recordings"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d1494f97",
"metadata": {},
"outputs": [],
"source": [
"## Recording vext\n",
"\n",
"\n",
"# v1 = sim.net.cells[45].secs[\"node_0\"][\"hObj\"]\n",
"# ap1 = h.Vector()\n",
"# t = h.Vector()\n",
"# ap1.record(v1(0.5)._ref_v)\n",
"\n",
"# t.record(h._ref_t)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ca5603a0",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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},
{
"data": {
"text/plain": [
"Vector[1583]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Recording v and vext[0], Abeta\n",
"\n",
"\n",
"\n",
"for i1 in range(len(R)): \n",
" if G[i1]==4: \n",
" print(i1)\n",
" F = np.where(unique_radius == R[i1]) \n",
" #nodes = int (Number_of_nodes[F]-1)\n",
" for i3 in range(int(Number_of_nodes[F])):\n",
"\n",
" locals()[\"Abeta_v\"+str(i1)+str(i3)] = sim.net.cells[i1].secs[\"node_%s\"%i3][\"hObj\"]\n",
" locals()[\"Abeta_ap\"+str(i1)+str(i3)] = h.Vector()\n",
" locals()[\"Abeta_ap\"+str(i1)+str(i3)].record(locals()[\"Abeta_v\"+str(i1)+str(i3)](0.5)._ref_v)\n",
"# locals()[\"Abeta_v_ext\"+str(i1)] = sim.net.cells[i1].secs[\"node_%s\"%nodes][\"hObj\"]\n",
"# locals()[\"Abeta_ap_ext\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta_ap_ext\"+str(i1)].record(locals()[\"Abeta_v_ext\"+str(i1)](0.5)._ref_vext[0])\n",
" \n",
" print(i3)\n",
"# print(nodes)\n",
" \n",
"\n",
" \n",
" \n",
"t = h.Vector()\n",
"t.record(h._ref_t)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e3f90783",
"metadata": {},
"outputs": [],
"source": [
"for i1 in range(36):\n",
"\n",
" locals()[\"Abeta0_imembrane\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
" locals()[\"Abeta0_imembrane_node\"+str(i1)] = h.Vector()\n",
" locals()[\"Abeta0_imembrane_node\"+str(i1)].record(locals()[\"Abeta0_imembrane\"+str(i1)](0.5)._ref_i_membrane)\n",
" \n",
"\n",
" \n",
" \n",
"for i1 in range(36):\n",
"\n",
" locals()[\"Abeta0_v\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
" locals()[\"Abeta0_v_node\"+str(i1)] = h.Vector()\n",
" locals()[\"Abeta0_v_node\"+str(i1)].record(locals()[\"Abeta0_v\"+str(i1)](0.5)._ref_v)\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_icap\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_icap_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_icap_node\"+str(i1)].record(locals()[\"Abeta0_icap\"+str(i1)](0.5)._ref_i_cap) \n",
" \n",
"\n",
" \n",
" \n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_ik\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_ik_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_ik_node\"+str(i1)].record(locals()[\"Abeta0_ik\"+str(i1)](0.5)._ref_ik_axnode) \n",
" \n",
" \n",
" \n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_il\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_il_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_il_node\"+str(i1)].record(locals()[\"Abeta0_il\"+str(i1)](0.5)._ref_il_axnode) \n",
" \n",
" \n",
"\n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_ina\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_ina_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_ina_node\"+str(i1)].record(locals()[\"Abeta0_ina\"+str(i1)](0.5)._ref_ina_axnode) \n",
" \n",
" \n",
" \n",
" \n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_inap\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_inap_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_inap_node\"+str(i1)].record(locals()[\"Abeta0_inap\"+str(i1)](0.5)._ref_inap_axnode) \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "23017f07",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_v\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_v_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_v_node\"+str(i1)].record(locals()[\"Abeta0_v\"+str(i1)](0.5)._ref_i_membrane)\n",
"\n",
"\n",
"\n",
"# for i2 in range(8):\n",
"\n",
"# locals()[\"Abeta0_vex\"+str(i2)] = sim.net.cells[0].secs[\"node_%s\"%i2][\"hObj\"]\n",
"# locals()[\"Abeta0_vext1_node\"+str(i2)] = h.Vector()\n",
"# locals()[\"Abeta0_vext1_node\"+str(i2)].record(locals()[\"Abeta0_vex\"+str(i2)](0.5)._ref_vext[1])\n",
"\n",
" \n",
" \n",
"# for i3 in range(0,16,2):\n",
" \n",
"# locals()[\"Abeta_vMext\"+str(i3)] = sim.net.cells[0].secs[\"MYSA_%s\"%i3][\"hObj\"]\n",
"# locals()[\"Abeta0_vext1_MYSA\"+str(i3)] = h.Vector()\n",
"# locals()[\"Abeta0_vext1_MYSA\"+str(i3)].record(locals()[\"Abeta_vMext\"+str(i3)](0.5)._ref_vext[1])\n",
"\n",
"\n",
" \n",
"# for i4 in range(12):\n",
"\n",
"# locals()[\"Abeta1_vext1\"+str(i4)] = sim.net.cells[1].secs[\"node_%s\"%i4][\"hObj\"]\n",
"# locals()[\"Abeta1_vext1_node\"+str(i4)] = h.Vector()\n",
"# locals()[\"Abeta1_vext1_node\"+str(i4)].record(locals()[\"Abeta1_vext1\"+str(i4)](0.5)._ref_vext[1]) \n",
" \n",
" \n",
" \n",
"# locals()[\"Abeta_vSext\"+str(220)] = sim.net.cells[0].secs[\"STIN_220\"][\"hObj\"]\n",
"# locals()[\"Abeta0_vext1_STIN\"+str(220)] = h.Vector()\n",
"# locals()[\"Abeta0_vext1_STIN\"+str(220)].record(locals()[\"Abeta_vSext\"+str(220)](0.5)._ref_vext[1]) \n",
" \n",
"# locals()[\"Abeta_v\"+str(220)] = sim.net.cells[0].secs[\"STIN_220\"][\"hObj\"]\n",
"# locals()[\"Abeta0_v_STIN\"+str(220)] = h.Vector()\n",
"# locals()[\"Abeta0_v_STIN\"+str(220)].record(locals()[\"Abeta_v\"+str(220)](0.5)._ref_v) \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "4b9344bb",
"metadata": {},
"outputs": [],
"source": [
"## Recording v and vext[0], Adelta\n",
"\n",
"\n",
"\n",
"# for i2 in range(len(R)): \n",
"# if G[i2]==3: \n",
"# F = np.where(unique_radius == R[i2]) \n",
"# nodes = int (Number_of_nodes[F]-1)\n",
"# locals()[\"Adelta_v\"+str(i2)] = sim.net.cells[i2].secs[\"node_%s\"%nodes][\"hObj\"]\n",
"# locals()[\"Adelta_ap\"+str(i2)] = h.Vector()\n",
"# locals()[\"Adelta_ap\"+str(i2)].record(locals()[\"Adelta_v\"+str(i2)](0.5)._ref_v)\n",
"# locals()[\"Adelta_v_ext\"+str(i2)] = sim.net.cells[i2].secs[\"node_%s\"%nodes][\"hObj\"]\n",
"# locals()[\"Adelta_ap_ext\"+str(i2)] = h.Vector()\n",
"# locals()[\"Adelta_ap_ext\"+str(i2)].record(locals()[\"Adelta_v_ext\"+str(i2)](0.5)._ref_vext[0])\n",
"# print(i2)\n",
" \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "d83f15db",
"metadata": {},
"source": [
"#### Simulate and Analyze"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "cd6d9f09",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Running simulation for 6.0 ms...\n",
" Done; run time = 8041.81 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 5.78 s.\n",
"\n",
"Analyzing...\n",
" Cells: 40\n",
" Connections: 0 (0.00 per cell)\n",
" Spikes: 1 (4.17 Hz)\n",
" Simulated time: 0.0 s; 1 workers\n",
" Run time: 8041.81 s\n",
" Done; saving time = 0.00 s.\n",
"Plotting recorded cell traces ... cell\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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gpBdJkgZjIIGxqvavqoyyvbCr+GGs2rvYruO0qtq5qp5EExxnANePcr1rq2q/qtqiql4O7AD8sH08yXbAN4ETq+q81TWfkYe5NckMipIkDcaUGpJO8gJgG1qzozv2b5RkjzS2pZnl/LGqum+UevZsnbNJkncDWwFnt45tA3wbOK2qzhjh3AOTbNa61nOAt9PMutYUZXCUJKm/plRgpJnscklVLenavxHwWWApTU/hfwLHtQ8meW+SyzrKHwr8juZexhcDL62q5a1jb6XpcXx/51qLHeceDNxEM+R9LnByVT2ux1OTz6AoSdJgzJjsBnSqqiNH2X8/sGeP807qen8sq05k6Tz2AeADPeoadYKLJEnSumiq9TBKY+akF0mSBsPAqKFlUJQkaTAMjBp6BkdJkvrLwKihZVCUJGkwDIySJEnqycCooeWkF0mSBsPAqKFlUJQkaTAMjBp6BkdJkvrLwKihZVCUJGkwDIySJEnqycCooeWkF0mSBsPAqKFlUJQkaTAMjJIkSerJwKih5ZC0JEmDYWCUJElSTwZGDS17FiVJGgwDo4aWgVGSpMEwMEqSJKknA6OGlpNeJEkaDAOjJEmSejIwamjZwyhJ0mAYGDW0DIqSJA2GgVGSJEk9GRg1tBySliRpMAyMkiRJ6snAqKFlD6MkSYNhYNTQMihKkjQYBkZJkiT1ZGDU0HJIWpKkwTAwSpIkqScDo4aWPYySJA2GgVFDy6AoSdJgGBglSZLUk4FRQ8shaUmSBsPAKEmSpJ4MjBpa9jBKkjQYBkYNLQOjJEmDYWDU0DMwSpLUXwZGDS2DoiRJg2Fg1NAzOEqS1F8GRg0t72GUJGkwDIwaWgZGSZIGw8AoSZKkngyMGlr2MEqSNBgGRg09A6MkSf01ZQJjkqVd24okH+84/tYkN7WOfS3J1j3q2i3Jt5Msbp3z3zuObZ+kuq51XMfxJDk5yb2t7SNJ0r9PrjVlD6MkSYMxZQJjVc1qb8CWwEPARQBJ9gNOAg4ENgduAc4fqZ4kM4AvAF9ulT0C+HSSXbqKbtpxzRM79h8BvBbYC9gTeA1w5IR8SE0og6IkSYMxZQJjl9cDdwNXtN4fAFxUVQur6hHgRGDfJDuOcO6uwNbAR6tqRVV9G7gKOHSM1z4cOKWqbq+qO4BTgLes+UdRvxkcJUnqr6kaGA8Hzq36ryiQ1kbHe4A9Rjh3pOHjjFD2tiS3JzkrydyO/bsD13S8v6a1b0RJjkiyIMmCRYsWjVZMfeCQtCRJgzHlAmOSbYH9gHM6dn8V+NMkeybZGDgeKGCTEaq4gaZ38tgkGyR5Wau+dtl7gGcD2wHPAmYDn+k4fxawuOP9YmDWaPcxVtWZVTW/qubPmzdvfB9WE8LAKElSfw0kMCa5vDXRZKTtyq7ihwFXVtUt7R1V9S3g/cDFwG3ArcAS4Pbua1XVozT3IL4auBN4F3Bhu2xVLa2qBVX1WFXdBbwNeFmSOa0qlgJzOqqcAyzt6O3UFOH/IpIkDcZAAmNV7V9VGWV7YVfxw1i1d7Fdx2lVtXNVPYkmOM4Arh/letdW1X5VtUVVvRzYAfjhaM1r/Wz3IC6kmfDStldrn6YYh6QlSRqMKTUkneQFwDa0Zkd37N8oyR6tJW+2Bc4EPlZV941Sz56tczZJ8m5gK+Ds1rHnJnl6kvWSbAH8C3B5VbWHoc8F3plkm9bSPe9qn6upycAoSVJ/TanASDPZ5ZKqWtK1fyPgszTDxT8E/hPoXDvxvUku6yh/KPA7mnsZXwy8tKqWt47tAHyNZkj7emA58KaOcz8JfAm4rnX8K619mmLsYZQkaTBmTHYDOlXViOsdVtX9NGsijnbeSV3vjwWOHaXs+YyyhmPreAHvaW2SJEnrvKnWwyiNmT2MkiQNhoFRQ8vAKEnSYBgYNfQMjJIk9ZeBUUPLoChJ0mAYGDX0DI6SJPWXgVFDy3sYJUkaDAOjhpaBUZKkwTAwSpIkqScDo4aWPYySJA2GgVFDz8AoSVJ/GRg1tOxhlCRpMAyMGloGRUmSBsPAqKFncJQkqb8MjBpaDklLkjQYBkYNPQOjJEn9ZWDU0LKHUZKkwTAwamgZFCVJGgwDo4aewVGSpP4yMGpoOSQtSdJgGBg19AyMkiT1l4FRQ8ugKEnSYBgYNbQckpYkaTAMjBp6BkZJkvrLwKihZQ+jJEmDYWCUJElSTwZGDS17GCVJGgwDo4aWgVGSpMGYMdkNkNaWgVGSpJGtWAEPPdRsDz646s/xMDBqaBkUJUnD6NFHRw9xY9031vKPPDIxbTYwaugZHCVJa6sKli9vgtayZc3PkV6vTXhr/3zssTVr4xOeABtvDJts0vzsfD137uP3jVSuc9/LXz72axsYNbS8h1GS1g1VTU9ZO7itLtSN9Hos5dbk70mvQPbEJ44tuI1l30YbwfrrT/x3O1ZjCoxJXga8BdgdmA0sARYCZ1XVN/rWOqkHA6MkTQ0rVjShq731I9StWDG+NiUwc2YTuNpb+/3cubDddo/f3/16pPftrR3ikv58p1PNagNjkmOA9wD/ClwMLAbmAHsB5yQ5uao+1tdWSpKktVIFDz/chLClS1eGu4l4/fDD42/PaIFs883hKU/pHeR6hbr26yc8Yd0Jc4Mwlh7GY4EXVdUNXfsvSXI+8B3AwKiBs4dR0nT02GMTH+ja2x/+MPZ2zJgBs2Y1AWzmzJWvt9ii6Z3r3t9+3Q5tvULdxhsb5obNWALjTOC3oxy7E9hk4pojjZ2BUdJkW7GiCWVLl8KSJav+HGnfaD/b27Jl45/VOlpwmzdv5P1jfb3hhv35zjScxhIYLwa+lOSDwLWsOiT998Dn+tc8afUMjJLGoj0ku7bhrvPneNay22QTmD27CWSzZjWvt9gCtt9+1aA2nlC38cawno/g0ACMJTD+JfAB4Bxga6D95/l3wHnA+/vTNKk3g6I0/a1Y0QSzBx5Y+XO010uWrD7kjXXixPrrN4GuHfDaP+fOXfX9WH/OnDm5M1yltbXawFhVjwB/B/xdkk2BWcDSqrq/u2ySfarqqolupNSLwVGaWqqaodXOUDeWwNf9+oEHmtmxY7HJJisDWme423778Ye72bOb4VjvsZNWGtc6jK2QeH+PIpfRDFdLfec9jNLEavfmLV4M99/f/BxLqOs+tmTJ2CZXbLABzJmzcps9G570JNhpp+Z197HRXs+e3UzQkNQ/E/1/Mf97TANjYJRW+sMfmsC2ePHjt3b4W922ZMnqr5OMHNy22WbsAa/9+glP6PvXImmCTHRg9E+3JI1Tr7A31gA4lrC34Yaw6abN0yfa25OfvOr7kbbOILjJJg7VSusiO/E1tOxh1FSyfHkT5O67r9nar3vta/9cvHj1/4433PDxQW7LLR8fAHttG23U5y9B0rRlYNTQMzBqIlQ1vXRjDXvd+1a3vMrGG8NmmzXbpps2Q7i7775yXzvUjRYADXuSJpP3MGpo2cOokTz2WBPgfv/7Zrv33pWvu9/fe++q4a/XRI2kCXObbroy9O2228rXI/1sv950U+/XkzTcxhUYk2wBvArYqqo+kmRrYL2quh2gqmavaUOSLO3atTHwiar669bxtwJ/CzwZuBL4s6oa8Qk0SXYDTgOeBSwCjq2qS1vHDgE+2VF8vda15lfV1UlOAN4HLO8os2dV/WpNP5v6w8A4vXUGv16hr/v9/fePXmfShLjNN2+2uXNhl116h732zzlzXCBZ0rprzIExyX40T31ZAOwDfATYGXg3cMDaNqSqZnVcayZwF3BRx7VPAl4E/JLm2dXnA/uN0M4ZwBeAM4CXtsp8KckfV9WNVfUZ4DMd5d8CHAf8uKOaC6rqf6ztZ5LUeOyxJtTdc0+zLVq08nXn+84QON7g9/Snr3y/xRYrX3e+f+ITXTxZktbEeHoY/xl4Y1V9K8l9rX0/AJ4z4a2C1wN3A1e03h8AXFRVCwGSnAjckWTHqrq569xdaZ5I89GqKuDbSa4CDqUJht0OB85tldUQsYdxclQ1M3pHC30jvb/vvtHrmzOnCXxbbGHwk6SpajyBcfuq+lbrdftP9CPjrGOsukNcWPX+yPbrPYDuwDjSfZRplV11Z7IdsC/wZ12HDkjye5rHH55aVaeP1tAkRwBHAGy77bajFVMfGRjX3vLlcPfdcOedcNddj9/uvnvVAPjYYyPXs+GGMG9eE/zmzYPttmtet7f2sc5tww0H+1klSeM3nrD3syQvr6qvd+x7CXDdRDYoybY0w8h/3rH7q8AFSc6gGZI+nia0bjJCFTfQ9E4em+SjNMPY+wHfGaHsYcAVVXVLx74LgTNphsSfC1yc5P6qOn+k9lbVma3yzJ8/3+gyQPYw9vbwwyOHv5FC4WjDv3PmNEu3bLkl7LwzPP/5o4e/efOa5+W6Rp8kTT/jCYzvAr6c5CvAxkk+STNUfODqTkxyOSPcb9hyVVW9sOP9YcCVnSGuNQz+fpp7KJ8IfBRYAtzeXVlVPZrktcDHgf9Fc8/lhaw6iaXzWid1nf+zjrffS/IxmiHyEQOjJs+6GBQfemj14a+9LV48ch3t9fue/GTYc8+VgbC9r/36SU9qloKRJGnMgbGqvp9kL+AQ4P8CvwGe054hvZpz9x9Hmw4DPjxCHafRzHwmyS7A3wPXj3K9a+kIqEm+B5zTWSbJPjT3On5uNe0pXC5oShvm4Pjoo03v3t13r7otWrTq+3YIHO1pHptuujLw7b137xDoen6SpPEa1/2HVXUHzezovkjyAmAbWrOjO/ZvBOwELASeSjME/LGqGvFW+iR7AjfSLJlzNLAVcHZXscOBi6tqSde5BwL/AdwPPBt4O/DetfhY6pPJHJKuanrw2oHuvvtg2TJYunTl1vl+8eKVj37r/DnaYs/rrdcM8z7pSc32rGf1DoGu8SdJ6qeegTHJeYzh+dBVddgEtedw4JLuEAdsBHwW2JFmKPosOmY8J3kv8CdV9crWrkOBtwIb0My0fmlVLe8ovxHwp8BBI7ThYJoe1CfQDHmfXFXnjFBOU0Q/A+Njj8FPfwo/+AHccAP84hdw443w2982vYO9zJgBs2c39/W1n8c7dy7suGPzuvMpHu1g2N4239xZwJKkqWN1PYw3dbyeSxPovgTcBmxLcw/jhIWpqjpylP33A3v2OK/7PsRjgWN7lH8Y2HSUY28aQ1M1BfQrKN55J3z+83DppXDllfDgg83+2bObJV9e8ALYdttVA95mm8GsWSu3mTOd/StJmj56Bsaq+kD7dZKvA6+uqis69r2Qkdc2lPqqClasWPl6bT32GHz5y3DaafCtbzV17rwz/NmfwT77NCHxqU91BrAkad00nnsYnwd8v2vfD4DnT1xzpJUefBB+9atmu+WWx29LWw+TXJvAuHw5fPKTcMop8OtfN6Hw+OPh9a+H3Xc3IEqSBOMLjD8BTkpyfFU9lGRj4APAT/vSMq0TFi+Gm2+Gm25qts7Xv+16UvjMmfC0pzXb/vs3P7/1LfjGN8Z/3RUr4Oyz4QMfgN/8BvbdF/75n+GAA5p7DyVJ0krj+dP4FpqJJ4tbjwbcjGaNwzf3oV2aRpYsaSaL3HDD44PhPfesWnarrZpJIS97Gey0U/N6xx2bcLjFFo/v8bv77vEHxmuvhbe+FX70I3juc+Gss+DFL167zyhJ0nQ2nnUYbwVekOSpNOsX/q6qft2vhmm4VDU9gj//eRMMO7c77lhZLmkmjOy4I7zudStD4U47wQ47NBNG1uTaYy33j/8I739/M0nl/PPhjW902FmSpNUZ1+Bbks1oHrW3DXBHki+NthaipqeqZgj32mvhuuvgZz9bGQzb9xRCs2zMrrs2PXe77tpsT396Ew4ncs3AZGyB8cEHmwksF1wABx8Mp57a9FhKkqTVG3NgTPJ84Cs0z2q+DXgN8M9JXl1V/9mn9mkSPfBAEwqvu25lQLzuulUfOfeUp8Buu8H//J9NKNxtt+bnk588mJ67sQTGpUub4PqjH8FHPgLvfre9ipIkjcd4ehj/GTi6qv69vSPJG4F/oXkiiobYPffA1VfDggXN9pOfwG23rTw+Z07z3OE3v7n5+YxnwB57NItOT6bVBb8//AHe8pbmM33uc80wuCRJGp/xBMZdgAu79n0OOGPimqNBWLwYfvjDleHw6qtXDYe77ALPex4ceWQTDPfcc2qvQdirh/Gf/gkuvrj5aViUJGnNjCcw/pLmsXmf7dj3BuDmCW2RJlQV3HorXHXVyu3661eGrB13bMLh297WPK/4mc+c/F7D8eg1JH3ttfDe98Ib3gDvfOdg2yVJ0nQynsD4DuDLSd5Ocw/j9sDONPcyagq55Rb45jebNQqvuGLleoazZzfh8KCDmieXzJ/fzBYeZr0C42mnNY/nO+OMqds7KknSMBjPsjrfS7Ij8GqaZXW+BHy1qn7fr8ZpbO6/vwmI7e3mVp/vVls1C1zvs0+zPeMZsP76k9nSiTdaEFy2rFk25w1vgM03H2ybJEmabsa1rE5rCZ1P96ktGoff/Aa+8IVmu/zy5lnIs2fDi14Ef/M38JKXNLOV19WetQsvbBYM//M/n+yWSJI0/MazrM7TgA8BewOrLK9cVdtObLM0kjvvbHrNPvOZZqIKNGsbvutdzSPtnvvcde+xdu1AXLVqOD7zzCYw/8mfTE67JEmaTsYTLz5LM8HlXcCD/WmOuj36KFxySfPc4//3/5plYubPhw9/GA48sAlF67KRAuPNN8P3v9/MjF5Xe1glSZpI4wmMuwP7VNUf+tUYrXTXXU0v2RlnNJNWnvpU+Nu/hUMPNSR2GikQ/uQnzc/99x9oUyRJmrbGExj/A/hj4Oo+tUU04fDDH27C4vLl8PKXw7/+K7ziFbDeepPduqmrc6b0woVNkNxtt8lrjyRJ08l4AuOtwNeTXALc2Xmgqo6fyEati+6/H048ET7xiWYY+i1vgWOPbe5R1Og6h6TbfvEL2G472GSTyWmTJEnTzXgC40yapXQ2AJ7asX81T/JVL1Vw7rnwnvfAokVw+OFw3HGwww6T3bLhMFJgvP32ZghfkiRNjPGsw/g/V1cmyZuq6vy1a9K648474c/+DC67rFlQ+7LLmietaOxGuofxjjvgOc8ZfFskSZquJvquuE9OcH3T1mWXNQtpf+c78PGPN4/sMyyuuXYPY1UTGLfZZnLbI0nSdDLRgdFFTFajCk45BV796ibU/PjHzXOcndCyZrqHpO+7r5ksZGCUJGniTHRM8X7GHqqap7C8+93N85yvusqZvGurOzDecUfz08AoSdLEsV9rQP7wB/irv2qGn485Bi64AGbOnOxWDT8DoyRJ/bfawJjEUDkB3vc+OP30Zjb0Kac4BD1Ruie9GBglSZp4Y4ktdyT5SJI9xlD212vboOno7LObxbiPOKL56ePqJl53D+PWW09eWyRJmm7GEhj/Enga8KMkP07yN0nmjVSwqsYSKtcpP/pRExRf8hI49VTD4kQbaUh63jzYcMPJa5MkSdPNagNjVX2hqt4AbEWzbM4bgN8k+WKSg5Js0O9GDqulS+GQQ2DLLeHCC2EDv6kJN1JgdDhakqSJNeY76arq/qr6ZFW9ENgNWAB8FPhdvxo37N73PrjpJjjvPNhss8luzfQ00j2MBkZJkibWuKdeJHkC8GzgucCWwHUT3ajp4Prr4bTT4C//Evbff7JbM/119jB6/6IkSRNrzIExyQuTnAncBfwD8H1gl6p6Ub8aN8yOOQbmzIETT5zslkxvnUPSjz7aPI/bwChJ0sRa7bOkk5wAHApsDlwEvLqqrupzu4ba974H3/xms3zOFltMdmumt87AuGRJ89rhf0mSJtZqAyPwPOB9wOer6uE+t2da+MhHYPPN4cgjJ7sl01/nPYztwDh79uS0RZKk6Wq1gbGqXjGIhkwXd90FX/pSs0C3T3IZnM4eRgOjJEkTy+eNTLCLLmoeA3jIIZPdknVD55D00qXN61mzJq89kiRNRwbGCXbhhbDHHs2m/hvpHkZ7GCVJmlgGxglUBd//PrzqVZPdknVH5z2M7R5GA6MkSRPLwDiBli1rlnZ5wQsmuyXrns4eRoekJUmaWAbGCdTu4Xr+8ye3HesSh6QlSeo/A+MEWrYMdtoJnvSkyW7JumOkSS8GRkmSJpaBcQI99BDstddkt2Ld0nkP40MPNT832mhy2iJJ0nRlYJxAy5fD058+2a1YN1XBI4/AhhuuGiIlSdLaMzBOsJ13nuwWrFs6h6TbgVGSJE0sA+MEe9rTJrsF6xYDoyRJ/TdlAmOS7ZN8Ncl9Se5McmqSGR3HX5zkhiQPJvlOku161LV5kkuTLEtyW5I3dx0fta40Tk5yb2v7SDL2Qc5tthnvJ9fa6PxfxsAoSVJ/TJnACHwCuBvYCtgb2A84GiDJXOAS4Dhgc2ABcEGPuk4DHgG2BA4BTk+y+xjrOgJ4LbAXsCfwGuDIsX4IA+PkaPcwbrDBZLdEkqTpZyoFxqcBF1bVw1V1J/A1YPfWsdcBC6vqoqp6GDgB2CvJrt2VJJkJHAQcV1VLq+pK4IvAoWOs63DglKq6varuAE4B3jKWD7D++rDxxuP92FobDklLktR/Uykwfgw4OMkmSbYBXkkTGqEJjte0C1bVMuBmVgbKTrsAK6rqxo5913SUXV1dqxzvOvdxkhyRZEGSBeut99hqP6QmloFRkqT+m0qB8bs0wewB4HaaoeLPt47NAhZ3lV8MjLRE8+rKjvf4YmDWaPcxVtWZVTW/qubvuOOMkYqoj7yHUZKk/htIYExyeZIaZbsyyXrA12nuLZwJzAU2A05uVbEUmNNV7RxgyQiXW13Z8R6fAyytqlrd55w5c3Ul1C/2MEqS1D8DCYxVtX9VZZTthTSTT54KnFpVy6vqXuAs4FWtKhbSTEIB/us+xR1b+7vdCMxI0rki4l4dZVdX1yrHu87VFNM5JP3oowZGSZL6YUoMSVfVPcAtwFFJZiTZlGbySftewkuBPZIclGQj4Hjg2qq6YYS6ltH0VH4wycwk+wAHAueNsa5zgXcm2SbJ1sC7gLMn/lNrIngPoyRJ/TclAmPL64BXAIuAm4DHgGMAqmoRzcznDwH3Ac8FDm6fmOS9SS7rqOtoYGOaZXrOB46qqoVjqQv4JPAl4DrgeuArrX2aggyMkiT135SZpVFVPwX273H8m8DjltFpHTup6/3vadZSXJO6CnhPa9MU56QXSZL6byr1MEprzIW7JUnqHwOjhppD0pIk9Z+BUUPNwChJUv8ZGDXUvIdRkqT+MzBqWrCHUZKk/jEwaqg5JC1JUv8ZGDXUfNKLJEn9Z2DUUPMeRkmS+s/AqGlhxYpmMzBKkjTxDIwaau0exuXLm58u3C1J0sQzMGqodQdGexglSZp4BkYNtXZgfOSR5qeBUZKkiWdg1LRgD6MkSf1jYNRQc0hakqT+MzBqqBkYJUnqPwOjhpr3MEqS1H8GRk0LBkZJkvrHwKih5pC0JEn9Z2DUUDMwSpLUfwZGDbXuexh90oskSRPPwKhpwR5GSZL6x8CooeaQtCRJ/Wdg1FAzMEqS1H8GRg0112GUJKn/DIyaFuxhlCSpfwyMGmr2MEqS1H8GRg0172GUJKn/DIwaavYwSpLUfwZGTQvtHkYX7pYkaeIZGDXUuoekDYySJE08A6OGWmdgXH/9ZpMkSRPLwKih1hkYvX9RkqT+MDBqqBkYJUnqPwOjhpqBUZKk/jMwaqgZGCVJ6j8Do4aagVGSpP4zMGqodS7cbWCUJKk/DIwaap09jK7BKElSfxgYNdQckpYkqf8MjBpqBkZJkvrPwKih5j2MkiT1n4FRQ60dGMHAKElSvxgYNdQMjJIk9Z+BUUPNwChJUv9NmcCYZPskX01yX5I7k5yaZEbH8RcnuSHJg0m+k2S7HnVtnuTSJMuS3JbkzR3HnpfkG0l+n2RRkouSbNVx/IQkjyZZ2rHt0L9PrrVhYJQkqf+mTGAEPgHcDWwF7A3sBxwNkGQucAlwHLA5sAC4oEddpwGPAFsChwCnJ9m9dWwz4Exge2A7YAlwVtf5F1TVrI7tV2v74dQfBkZJkvpvxuqLDMzTgFOr6mHgziRfA9oh73XAwqq6CJpeQOCeJLtW1Q2dlSSZCRwE7FFVS4Erk3wROBT426q6rKv8qcB3+/i51EcGRkmS+m8q9TB+DDg4ySZJtgFeCXytdWx34Jp2wapaBtzMykDZaRdgRVXd2LHvmlHKAuwLLOzad0BryHphkqPG/1E0KJ2B0Se9SJLUH1MpMH6XJtQ9ANxOM+z8+daxWcDirvKLgdkj1DPmskn2BI4Hju3YfSGwGzAP+Avg+CRvGq3RSY5IsiDJgkWLFo1WTH1iD6MkSf03kMCY5PIkNcp2ZZL1gK/T3Kc4E5hLc6/hya0qlgJzuqqdQ3P/YbcxlU2yE3AZ8DdVdUV7f1X9rKp+W1Urqup7ND2frx/ts1XVmVU1v6rmz5s3r/cXoQlnYJQkqf8GEhirav+qyijbC2kmsjyV5h7G5VV1L81ElFe1qlgI7NWur3Wf4o48figZ4EZgRpKdO/bt1Vm2NcP6m8CJVXXe6poPZDVlNEkMjJIk9d+UGJKuqnuAW4CjksxIsilwOCvvW7wU2CPJQUk2ohlGvrZ7wkurrmU0PZUfTDIzyT7AgcB5AK37I78NnFZVZ3Sfn+TAJJul8Rzg7cAXJvgja4IYGCVJ6r8pERhbXge8AlgE3AQ8BhwDUFWLaGY+fwi4D3gucHD7xCTvTdI5+/loYGOaZXrOB46qqnYP41uBHYD3d6612HHuwa3rLwHOBU6uqnMm+LNqghgYJUnqvymzrE5V/RTYv8fxbwK7jnLspK73vwdeO0rZDwAf6HGdUSe4aOoxMEqS1H9TqYdRGrf1Ov4FGxglSeoPA6OGmj2MkiT1n4FRQ82FuyVJ6j8Do4aaPYySJPWfgVFDzcAoSVL/GRg11AyMkiT1n4FRQ83AKElS/xkYNdQMjJIk9Z+BUUPNwChJUv8ZGDXUDIySJPWfgVFDzcAoSVL/GRg11Fy4W5Kk/jMwaqgZGCVJ6j8Do4ZaZ2CcMWPy2iFJ0nRmYNRQ6wyM6/mvWZKkvvBPrIZaZ2DsfC1JkiaOgVFDzR5GSZL6zz+xGmr2MEqS1H8GRg01exglSeo//8RqqNnDKElS/xkYNdTsYZQkqf/8E6uhZg+jJEn9Z2DUULOHUZKk/vNPrIZaZ0i0h1GSpP4wMGqo2cMoSVL/+SdWQ817GCVJ6j8Do4aaPYySJPWff2I11OxhlCSp/wyMGmr2MEqS1H/+idVQs4dRkqT+MzBqqNnDKElS//knVkPNHkZJkvrPwKihZg+jJEn9559YDTV7GCVJ6j8Do4aaPYySJPWff2I11OxhlCSp/wyMGmr2MEqS1H/+idVQs4dRkqT+MzBqqBkYJUnqPwOjhpohUZKk/jMwaqgZGCVJ6j8Do4aagVGSpP4zMGqoGRglSeo/A6OGmoFRkqT+mzKBMcn2Sb6a5L4kdyY5NcmMjuMvTnJDkgeTfCfJdj3q2jzJpUmWJbktyZu7rlNJlnZsx3UcT5KTk9zb2j6SGEumKv+XkSSp/6ZMYAQ+AdwNbAXsDewHHA2QZC5wCXAcsDmwALigR12nAY8AWwKHAKcn2b2rzKZVNau1ndix/wjgtcBewJ7Aa4Aj1+aDqX8MjJIk9d9UCoxPAy6sqoer6k7ga0A75L0OWFhVF1XVw8AJwF5Jdu2uJMlM4CDguKpaWlVXAl8EDh1jOw4HTqmq26vqDuAU4C1r8bnURz7dRZKk/ptKf24/BhycZJMk2wCvpAmN0ATHa9oFq2oZcDMrA2WnXYAVVXVjx75rRih7W5Lbk5zV6sFsW+Vao5yrKcIeRkmS+m8qBcbv0gSzB4DbaYadP986NgtY3FV+MTB7hHpWV/Ye4NnAdsCzWvs/0+P8xcCs0e5jTHJEkgVJFixatGi0zyZJkjS0BhIYk1zemmgy0nZlkvWAr9PcpzgTmAtsBpzcqmIpMKer2jnAkhEu17Nsa5h6QVU9VlV3AW8DXpZkzijnzwGWVlWN9Nmq6syqml9V8+fNm7f6L0OSJGnIDCQwVtX+VZVRthfSTGR5KnBqVS2vqnuBs4BXtapYSDMJBfiv+xR3bO3vdiMwI8nOHfv2GqUsQDsItnsQV7nWas6VJEma9qbEkHRV3QPcAhyVZEaSTWkmn7TvJbwU2CPJQUk2Ao4Hrq2qG0aoaxlNT+UHk8xMsg9wIHAeQJLnJnl6kvWSbAH8C3B5VbWHoc8F3plkmyRbA+8Czu7PJ5ckSZr6pkRgbHkd8ApgEXAT8BhwDEBVLaKZ+fwh4D7gucDB7ROTvDfJZR11HQ1sTLNMz/nAUVXV7iXcgWYyzRLgemA58KaOcz8JfAm4rnX8K619kiRJ66SMcmue1sD8+fNrwYIFk92MdU57OpL/lCVJGrskV1fV/LGUnUo9jJIkSZqCDIySJEnqycAoSZKkngyMkiRJ6snAKEmSpJ4MjJIkSerJwChJkqSeDIySJEnqycAoSZKkngyMkiRJ6snAKEmSpJ4MjJIkSerJwChJkqSeDIySJEnqycAoSZKkngyMkiRJ6snAKEmSpJ4MjJIkSerJwChJkqSeDIySJEnqycAoSZKkngyMkiRJ6snAKEmSpJ4MjJIkSerJwChJkqSeDIySJEnqycAoSZKkngyMkiRJ6snAKEmSpJ4MjJIkSerJwChJkqSeDIySJEnqycAoSZKkngyMkiRJ6snAKEmSpJ4MjJIkSerJwChJkqSeDIySJEnqacZkN0BaWyefDE9+8mS3QpKk6cvAqKH3nvdMdgskSZreHJKWJElSTwZGSZIk9WRglCRJUk8GRkmSJPU0ZQJjku2TfDXJfUnuTHJqkhkdx1+c5IYkDyb5TpLtetS1eZJLkyxLcluSN3ccOyTJ0o7twSSV5Fmt4yckebSrzA79/fSSJElT15QJjMAngLuBrYC9gf2AowGSzAUuAY4DNgcWABf0qOs04BFgS+AQ4PQkuwNU1WeqalZ7a13jV8CPO86/oLNMVf1q4j6mJEnScJlKgfFpwIVV9XBV3Ql8Ddi9dex1wMKquqiqHgZOAPZKsmt3JUlmAgcBx1XV0qq6EvgicOgo1z0cOLeqamI/jiRJ0vQwlQLjx4CDk2ySZBvglTShEZrgeE27YFUtA25mZaDstAuwoqpu7Nh3zUhlW8Pa+wLndh06IMnvkyxMctSafiBJkqTpYCoFxu/ShLoHgNtphp0/3zo2C1jcVX4xMHuEesZT9jDgiqq6pWPfhcBuwDzgL4Djk7xptEYnOSLJgiQLFi1aNFoxSZKkoTWQwJjk8tbEkpG2K5OsB3yd5j7FmcBcYDPg5FYVS4E5XdXOAZaMcLnxlD0MOKdzR1X9rKp+W1Urqup7ND2frx/ts1XVmVU1v6rmz5s3b7RikiRJQ2sggbGq9q+qjLK9kGYiy1OBU6tqeVXdC5wFvKpVxUJgr3Z9rfsUd2zt73YjMCPJzh379uoum2QfYGvgc6trPpAxf1hJkqRpZkoMSVfVPcAtwFFJZiTZlGYySvu+xUuBPZIclGQj4Hjg2qq6YYS6ltH0VH4wycxWMDwQOK+r6OHAxVW1Ss9jkgOTbJbGc4C3A1+YsA8rSZI0ZKZEYGx5HfAKYBFwE/AYcAxAVS2imfn8IeA+4LnAwe0Tk7w3yWUddR0NbEyzTM/5wFFVtbCj/EbAn9I1HN1ycOv6S2gmw5xcVSOVkyRJWifE1WQmzvz582vBggWT3QxJkqTVSnJ1Vc0fS9mp1MMoSZKkKcjAKEmSpJ4MjJIkSerJwChJkqSenPQygZIsAX4x2e1Yx8wF7pnsRqxj/M4Hz+988PzOB8/vfPCeXlUjPQnvcWb0uyXrmF+MdbaRJkaSBX7ng+V3Pnh+54Pndz54fueDl2TMS7s4JC1JkqSeDIySJEnqycA4sc6c7Aasg/zOB8/vfPD8zgfP73zw/M4Hb8zfuZNeJEmS1JM9jJIkSerJwChJkqSeDIwTIMnmSS5NsizJbUnePNltmu6SvC3JgiTLk5w92e2Z7pI8IcmnWv++lyT5SZJXTna7prskn07yuyQPJLkxyVsnu03riiQ7J3k4yacnuy3TXZLLW9/10tbmesYDkOTgJD9vZZebk/xJr/KuwzgxTgMeAbYE9ga+kuSaqlo4qa2a3n4L/APwcmDjSW7LumAG8BtgP+DXwKuAC5M8o6puncyGTXP/CPx5VS1PsitweZKfVNXVk92wdcBpwI8muxHrkLdV1b9NdiPWFUleCpwMvBH4IbDV6s6xh3EtJZkJHAQcV1VLq+pK4IvAoZPbsumtqi6pqs8D9052W9YFVbWsqk6oqlur6g9V9WXgFuBZk9226ayqFlbV8vbb1rbjJDZpnZDkYOB+4FuT3BSpXz4AfLCqvt/6nX5HVd3R6wQD49rbBVhRVTd27LsG2H2S2iP1XZItaf7t24veZ0k+keRB4Abgd8BXJ7lJ01qSOcAHgXdNdlvWMf+Y5J4kVyXZf7IbM50lWR+YD8xLclOS25OcmqTnaJ2Bce3NAhZ37VsMjOnZjNKwSbIB8BngnKq6YbLbM91V1dE0v0/+BLgEWN77DK2lE4FPVdVvJrsh65D/BewAbEOzLuCXktiT3j9bAhsAr6f5vbI38MfA3/c6ycC49pYCc7r2zQGWTEJbpL5Ksh5wHs09u2+b5OasM6pqRet2l6cAR012e6arJHsDLwE+OslNWadU1Q+qaklVLa+qc4CraO6TVn881Pr58ar6XVXdA/wfVvOdO+ll7d0IzEiyc1X9srVvLxyq0zSTJMCnaP7r9FVV9egkN2ldNAPvYeyn/YHtgV83/9yZBayf5I+q6pmT2K51TQGZ7EZMV1V1X5Lbab7nMbOHcS1V1TKaYaIPJpmZZB/gQJpeGPVJkhlJNgLWp/mFvlES/wOov04HdgMOqKqHVldYayfJk1rLXsxKsn6SlwNvAr492W2bxs6kCeR7t7YzgK/QrMagPkiyaZKXt3+HJzkE2Bf4+mS3bZo7C/jr1u+ZzYB3AF/udYJ/YCfG0cD/Be6mmbV7lEvq9N3fA+/veP8/aGZ9nTAprZnmkmwHHElz/9ydrd4XgCOr6jOT1rDprWiGn8+g+Y/724B3VNUXJrVV01hVPQg82H6fZCnwcFUtmrxWTXsb0CyRtiuwgmZy12uryrUY++tEYC7NKOnDwIXAh3qd4LOkJUmS1JND0pIkSerJwChJkqSeDIySJEnqycAoSZKkngyMkiRJ6snAKEmSpJ4MjJI0QZIsTLL/gK71R0kW9KHeS5K8YqLrlTTcXIdRksaotZBz2yY0C5mvaL0f6CLmSS4GLqqqf5/gep8DnF5Vz5rIeiUNNwOjJK2BJLcCb62qb07CtbeieV791lX1cB/q/yXwpqqa8B5MScPJIWlJmiBJbk3yktbrE5JclOTTSZYkuS7JLkn+LsndSX6T5GUd5z4xyaeS/C7JHUn+Icn6o1zqpcCPO8Ni69rHJrk2ybJWXVsmuax1/W+2nhlL67m9n05yb5L7k/woyZYd9V8OvHrCvyBJQ8vAKEn9cwBwHrAZ8BPg6zS/d7cBPgh8sqPsOcBjwE7AHwMvA946Sr3PAEZ61u5BNGFyl9a1LwPeS/PM2PWAt7fKHQ48EXgqsAXwl8BDHfX8HNhrzJ9S0rRnYJSk/rmiqr5eVY8BFwHzgA9X1aPAvwPbJ9m01bv3SuAdVbWsqu4GPgocPEq9mwJLRtj/8aq6q6ruAK4AflBVP6mq5cClNEEU4FGaoLhTVa2oqqur6oGOepa0riFJAMyY7AZI0jR2V8frh4B7qmpFx3uAWcDWwAbA75K0y68H/GaUeu8DZo/het3vZ7Ven0fTu/jvSTYFPg28rxVkadV9/2gfStK6xx5GSZp8v6GZcT23qjZtbXOqavdRyl9LM+y8Rqrq0ar6QFX9EfAC4DXAYR1FdgOuWdP6JU0/BkZJmmRV9Tvg/wGnJJmTZL0kOybZb5RTvgE8M8lGa3K9JC9K8ozWpJoHaIaoV3QU2Y/m/kdJAgyMkjRVHAZsCPyMZsj5c8BWIxWsqruAbwMHruG1ntyq/wGaCS7fpRmWJsmzgWVV9cM1rFvSNOQ6jJI0hJL8Ec3M6ufUBP4iby0I/qmq+upE1Slp+BkYJUmS1JND0pIkSerJwChJkqSeDIySJEnqycAoSZKkngyMkiRJ6snAKEmSpJ4MjJIkSerJwChJkqSe/n+onm4BJMnCzQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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vc0dm/gD4OHA5cD9wH+Xewznt18rMZcCbgAOAB4FjKSFwlbIRsT1wDfDBzLyp5fzfZeZfGsPZPwbOpAyR9ykzz83MmZk5c/r06YP9SiRJkmqjK4ExM/fLzOhn27ut+KGs2rvYrOPszNwhM59NCY7jgd/2c73fZOa+mblxZr4G2Ba4tXk8IrYGvg+cnJlfGaj59D3MrR7zSS+SJHXHiBqSjoiXA1vQmB3dsn9CROwaxVaUWc5nZubj/dSzW+OciRHxYWAz4PzGsS2A64GzM/OcPs49MCKmNa71UuADlFnXGmEMipIkdceICoyUyS5XZOaCtv0TgK8CCyk9hT8BTmgejIjjI+KalvKHAA9Q7mXcH3hVZi5pHDuc0uP48da1FlvOPRi4mzLkfSFwamau1uOpkcPgKElStcb3ugGtMvPIfvY/AezW4bxT2t4fx6oTWVqPfQL4RIe6+p3gopHFoChJUneMtB5GSZIkjTAGRtWWk14kSeoOA6Nqy6AoSVJ3GBglSZLUkYFRteWQtCRJ3WFglCRJUkcGRtWWPYySJHWHgVG1ZVCUJKk7DIySJEnqyMCo2nJIWpKk7jAwSpIkqSMDo2rLHkZJkrrDwKjaMihKktQdBkZJkiR1ZGBUbTkkLUlSdxgYJUmS1JGBUbVlD6MkSd1hYFRtGRQlSeoOA6MkSZI6MjCqthySliSpOwyMkiRJ6sjAqNqyh1GSpO4wMKq2DIqSJHWHgVGSJEkdGRhVWw5JS5LUHQZGSZIkdWRgVG3ZwyhJUncYGFVbBkVJkrrDwChJkqSODIyqLYekJUnqDgOjasugKElSdxgYVXsGR0mSqmVgVG0ZFCVJ6g4DoyRJkjoyMKq2nPQiSVJ3GBhVWwZFSZK6w8Co2jM4SpJULQOjasugKElSdxgYJUmS1JGBUbXlpBdJkrrDwKjaMjBKktQdBkbVXkSvWyBJ0uhmYFRt2cMoSVJ3GBhVewZGSZKqZWBUbdnDKElSdxgYVVsGRUmSusPAqNozOEqSVK0RExgjYmHbtiIiPtdy/PCIuLtx7NqI2LxDXTtHxPURMb9xzptbjm0TEdl2rRNajkdEnBoRjza20yKchzsSOSQtSVJ3jJjAmJmTmxswA1gMXAYQEfsCpwAHAhsB9wKX9FVPRIwHvgV8p1H2COCiiNixreiGLdc8uWX/EcCbgN2B3YA3AEcOy4dUJQyMkiRVa8QExjZvBR4Gbmq8fyNwWWbOzsylwMnAPhGxXR/n7gRsDpyRmSsy83rgFuCQQV77MOD0zJyTmXOB04F3rflHUVUMipIkdcdIDYyHARdm/jUSRGOj5T3Arn2c29fwcfRR9v6ImBMR50XEJi37dwFua3l/W2OfRhiHpCVJ6o4RFxgjYitgX+CClt1XA/8QEbtFxPrAiUACE/uo4g5K7+RxEbFuRLy6UV+z7CPAS4CtgRcDU4CLW86fDMxveT8fmNzffYwRcUREzIqIWfPmzRvah9WwMDBKklStrgTGiLixMdGkr+3mtuKHAjdn5r3NHZn5A+DjwOXA/cB9wAJgTvu1MnMZ5R7EA4AHgWOBS5tlM3NhZs7KzOWZ+RDwPuDVETG1UcVCYGpLlVOBhS29ne3XOzczZ2bmzOnTpw/la9FasodRkqTu6EpgzMz9MjP62fZuK34oq/YuNus4OzN3yMxnU4LjeOC3/VzvN5m5b2ZunJmvAbYFbu2veY3XZg/ibMqEl6bdG/skSZLGpBE1JB0RLwe2oDE7umX/hIjYtbHkzVbAucCZmfl4P/Xs1jhnYkR8GNgMOL9x7GUR8byIGBcRGwP/BdyYmc1h6AuBf4mILRpL9xzbPFcjiz2MkiR1x4gKjJTJLldk5oK2/ROAr1KGi28FfgK0rp14fERc01L+EOAByr2M+wOvyswljWPbAtdShrR/CywB3tZy7heBq4DbG8e/29inEcbAKElSd4zvdQNaZWaf6x1m5hOUNRH7O++UtvfHAcf1U/YS+lnDsXE8gY80NtWAgVGSpGqNtB5GadAMipIkdYeBUbVncJQkqVoGRtWW9zBKktQdBkbVloFRkqTuMDBKkiSpIwOjasseRkmSusPAqNozMEqSVC0Do2rLHkZJkrrDwKjaMihKktQdBkbVnsFRkqRqGRhVWw5JS5LUHQZG1Z6BUZKkahkYVVsGRUmSusPAqNpySFqSpO4wMKr2DIySJFXLwKjasodRkqTuMDBKkiSpIwOjasseRkmSusPAqNoyMEqS1B0GRtWegVGSpGoZGFVbBkVJkrrDwKjaMzhKklQtA6Nqy3sYJUnqDgOjasvAKElSdxgYJUmS1JGBUbVlD6MkSd1hYFTtGRglSaqWgVG1ZQ+jJEndYWBUbRkYJUnqDgOjJEmSOhrf6wZIa8oeRknSWJAJS5bAU0/1vS1e3P+xTttQGBhVewZGSVIvZMKyZWsf3AZz7pr8rZswASZO7HvbeGP49a8HX5eBUbVlD6MkqZPMEsQWLeq8PfVU3/sGs61YMfR2rbde/0Fu0037P9bftv76fe8bN8CNhxGDb7OBUbVlUJSk+lu2rHNwG2zA66/cUETApEllmzhx5ev668MmmwxfkBtfw/RVwyZLqzI4SlK1VqwoAWzhwr63tQl4y5YNrS3rrbcy1LWGu2nT4DnPWf1YX2X7OzZhwtB63caSQQXGiHg18C5gF2AKsACYDZyXmd+rrHVSBw5JS9KqMmHp0v6D3VC3ZkhcvHjwbYjoP5RNmzZwaBso3NWxd240GPBrj4hjgI8A/w1cDswHpgK7AxdExKmZeWalrZQ6MDBKqqNnnik9b2sb5tq35csH34aJE2Hy5FW3qVNh881X3z9p0ur7Wve3Dt/aSzf6DCanHwe8MjPvaNt/RURcAtwAGBjVdQZFSd22ZAksWFC2J59c+XNf20DHFy4c/HXHjYMpU1YPazNmwHbb9R/mOm0TJ8I661T3XWl0GUxgnAT8pZ9jDwITh6850uA5JC1pIM88U4LZmgS6vo4P9n67CRNKwJs6tbxOmVLC3fbbr3zf3AYT7p71LHvt1FuDCYyXA1dFxCeB37DqkPS/Ad+ornnSwAyM0uizdGkJbE8+CfPnr/ra376+At+iRYO7XrMHr32bMWP14Ne+9XXM++w02gzmn/R7gU8AFwCbA80/zw8AXwE+Xk3TpM7sYZRGnhUrVg91Qwl9zZ+ffnrga40fDxtsUAJbM7Q9+9l99+J1CndTppThWXvwpP4NGBgzcynwr8C/RsSGwGRgYWY+0V42IvbKzFuGu5GSpOotWVIC2xNPlK35c3uw6xT6BtOjN25cCW6tYW/GDNhhh1X3tx7va59LoEjdM6RO80ZIfKJDkWsow9VS5exhlFZqPtGiPez1976vfYPp1ZsyZdXwNm0abL31wOGu9edJkwx6Ut0M910W/gpQ1xgYNZpklvvuBhP2+gt/A03IWG892HDDVbctt1x93wYbrPpzc5s8eeBHjUkanYY7MPqnW11nYNRIkVnW1Xv88bI99tjQfn7mmc71T5y4arCbPr0M4/YV9PraN2FChR9e0qjmPC7VlkFRVVmyZGWIG0zYa923dGn/9Y4bV4Zwp02DjTYqr9tuu/LnDTdc+doe/jbYANZdtysfX5JWY2BU7Rkc1Z/ly0uIe/RReOSR8trf1hr8nnqqc73Ne/eawW/XXVcNgc3X9p+nTHFIV1I9jZh7GCOifc379YHPZ+b7G8cPBz4KbArcDPxzZva5oHhE7AycDbwYmAccl5lXNo69A/hiS/FxjWvNzMxfRMRJwMeAJS1ldsvMP67pZ1M1vIdxbHnqqZXhbqDw19yeeKL/+tZdFzbeeOW23XYwc+bAwW+DDVxjT9LYM6RfexGxMfB6YLPMPC0iNgfGZeYcgMycsqYNyczJLdeZBDwEXNZ4vy9wCvBK4C7KowgvAfbto43jgW8B5wCvapS5KiJemJl3ZubFwMUt5d8FnAD8sqWar2fmO9f0s6g7DIz1tXRpCX3z5vW99RX+Os3gnTJl9fDX+n7jjWGTTVZ9P3myM3UlabAGHRgboe1yYBawF3AasAPwYeCNw9yutwIPAzc13r8RuCwzZzfacjIwNyK2y8x72s7dibLA+BmZmcD1EXELcAglGLY7DLiwUVbSGnj66f7DX+v28MPldf78vusZN6705DXD3TbbwItfvHr4aw2BG21UZv9KkqozlB7GzwL/mJk/iIjHG/t+Brx02Fu1eogLVh3ubv68K9AeGPvqM4hG2VV3RmwN7AP8c9uhN0bEY5Sn2ZyVmV8YWvPVDfYwViezDOc++ODK7YEH4KGH+g6CCxb0Xc/48SXUTZ9ethe/uLw++9kr97Vu06bBOut09aNKkgZhKIFxm8z8QePn5p/opUOsY0ARsRVlGPndLbuvBr4eEedQhqRPbLRhYh9V3EHpnTwuIs6gDGPvC9zQR9lDgZsy896WfZcC51KGxF8GXB4RT2TmJf209wjgCICtttpqsB9Tw8jAOHhPP11CX3sQbH3f3JYsWf389dZbNeBtt13/4W/69DK712FfSaq/oYS930XEazLzupZ9fwfcPtCJEXEjfdxv2HBLZu7d8v5Q4ObWENfo1fw4ZUh8A+AMYAEwp72yzFwWEW8CPgf8X8oQ+qWsOoml9VqntJ3/u5a3P46IMylD5H0Gxsw8lxIwmTlzptGli+xhLJ55pszubQ9+fQXBxx9f/fyI0gu42Waw6abwvOeV1003XbmvuW2wgQFQksaioQTGY4HvRMR3gfUj4ouUewsPHOjEzNxvCNc5FPhMH3WcTZn5TETsCPwb8Nt+rvcbWgJqRPwYuKC1TETsRbnX8RsDtCfxCTYj0mgOis2nfjz88Oo9gu1h8KGHyvIx7SZOXBn4dtkF9t9/1fDXPDZ9uuv7SZI6G3RgzMyfRsTuwDuA/wH+DLy0OUN6OETEy4EtaMyObtk/AdgemA1sSenROzMz++gvgYjYDbiTsmTO0cBmwPltxQ4DLs/MBW3nHgj8iPLM7JcAHwCOX4uPpYqN5OC4YkUJfk8+2f/2yCMrg+FDD638ua9ZwePGwYwZK0Pf7ruvGgJbg+DkyaufL0nSmhjS/YeZOZcyO7oqhwFXtIc4YALwVWA7ylD0ebTMeI6I44FXZObrGrsOAQ4H1qXMtH5VZi5pKT8B+AfgoD7acDAlED+LMuR9amZe0Ec59VgvhqTnzYN774U5c2Du3LI9/vjqz/dthsFFiwauc511yn2AM2aUbaedVv48Y0Y51gyDm2zipBBJUvd1DIwR8RUG8XzozDx0OBqTmUf2s/8JYLcO57Xfh3gccFyH8k8DG/Zz7G2DaKpGkKoC4+LF8JOfwPXXw89+BrffXnr+Wq23XlnWZYMNVj7Kbautys9Tpw68TZlSXn36hyRpJBuoh/Hulp83ofQAXgXcD2xFuYfR3jf1RBVBcfFi+M534OKL4dpry0zhddYpQ7+vex284AWw/faw5ZbwnOeUHj8ngUiSRruOgTEzP9H8OSKuAw7IzJta9u1N34thS5VZvBjuu68MD8PwBMc5c+DMM+G//7sMLW+2GRx5JLzmNfCKV5SeQEmSxqqh3MO4J/DTtn0/A/5m+JojFY8/DnfeCffcA3/8Y3lt/jx37qpl1yYw/ulP8PGPw0UXlXre+lY4/HB45Su9V1CSpKahBMZfAadExImZuTgi1gc+Afy6kpZp1Fu0CO6+G+66q4TD5nbXXWXmcKvNNy+LRP/d35XXbbctr1/7Gpx99tCvvXAhfPrTcMYZZUj56KPhmGPKo+gkSdKqhhIY30WZqTy/8WjAaZRFsd9eQbs0SjzzDNx/P/zud/CHP6wMhHfeWYaBW22+Oey4I7zlLeV1hx3K/YLPfS6sv37f9V911dDb9MMfwj/9U5ntfMgh8KlPlYkqkiSpb0NZh/E+4OURsSVlwesHMvNPVTVM9fLMM+W+wtmzSzhsvv7+9/DUUyvLTZtWniTyyleWUNjctt9+zdcNHOyQ9DPPwPHHw6mnlt7JH/2o3J8oSZI6G9I6jBExjfJs5i2AuRFxVX+LZ2t0yixPF/n1r+G221YNhosXryy3xRbw/OfDEUeU1+c/v6wvuPHGw9ueiMEFxsWL4dBD4RvfgPe8pwxFT5o0vG2RJGm0GnRgjIi/Ab4L3EFZVucNwGcj4oDM/ElF7VMPLV9eho5vu60ExOb28MMryzznOeWxc/vuW0LhLrvAzjuX9Qi7YTCB8emn4dWvhltugdNPL/cquhSOJEmDN5Qexs8CR2fm15o7IuIfgf+iPEJPNfbMM3DHHXDrrWWbNassVN18PN1665UweMABsMceZdttt+4Fw/4MJvi9731w883w1a/C21yWXZKkIRtKYNwRuLRt3zeAc4avOeqWuXNXhsNbb4Wf/7w88xjKmoMzZ5aZw81wuNNOsO66vWxx/zr1MF5wAXz5y/CxjxkWJUlaU0MJjHdRnrP81ZZ9fw/cM6wt0rDLLMvX/OhHK7f77ivHxo8vTzF55zvhZS+Dl760TEqpy6PqOvUw/vnP8P73l+HyT3yi/3KSJKmzoQTGDwHfiYgPUO5h3AbYgXIvo0aYe+6B//1fuPHGEhAffLDsnz69zAz+wAdgzz3hhS+ECRN62tS10gyMmauHx3POKWs9nneei3BLkrQ2hrKszo8jYjvgAMqyOlcBV2fmY1U1ToO3cGEJh9deC9ddV3oUoUxK2X9/2Gefsj3veaNrwkd/n2XFCrjwwvJov+c+t7ttkiRptBnSsjqNJXQuqqgtGqJHHoFvfhMuvxyuvx6WLoWJE8sahx/4QAlLO+wwugJif9p7GG+4oSwM/p//2bs2SZI0WgxlWZ3nAp8G9gBWWWI5M31ORpc89hhceilcdll5YsmKFeUxee9/P7zudbD33vCsZ/W6ld3TOiTd6vzzywzuAw/sdoskSRp9htLD+FXKBJdjgacGKKthtGIF/OAH8D//A1deWXoSn/c8+OhH4a1vLZNWxkIvYl/6CoxPPglXXAHvele978+UJGmkGEpg3AXYKzOfqaoxWtWCBWVJmDPPLLOap02DI48sz0HeY4+xGxJb9fUd/OIX5cku9i5KkjQ8hhIYfwS8EPhFRW1RwyOPlHvvzjkH5s8vw8yf+UwJQPaY9a21h/F3vyuvu+7am7ZIkjTaDCUw3gdcFxFXAA+2HsjME4ezUWPVwoXlGcf/8R9lOZiDDoJjjy3rI6pvfQ1J//73ZfHxzTfvTZskSRpthhIYJ1GW0lkX2LJl/wBP8tVAMssklg9+sKyX+OY3w6c+VZ7NrM76Coz33VcmAjlkL0nS8BjKOoz/NFCZiHhbZl6ydk0aW+bOhfe8B665Bl70ojKpZc89e92q+ugrFM6dW9aflCRJw2O4HwD3xWGub1S7+uoyeeWHP4TPfhZ+9jPD4ppq7WGcOxe22KJ3bZEkabQZ7sDoIOAgZMIJJ8ABB5T77H75yzIcPX5Iy6gLVh+SXrIE5s2zh1GSpOE03BHF+xkHsGQJvPvdcPHF8M//DGef7czntdEeGB94oLw64UWSpOFjn1YXLV1aFtr+znfglFPKwttOzFg77d/fX/5SXg2MkiQNnwEDY0SMc7Hutbd8Obz97SUsfv7zcNRRvW7R6NLew7jZZr1riyRJo81g7mGcGxGnRcRglkH+09o2aLQ69li4/PKyzqJhcfj0NyRtYJQkafgMJjC+F3gu8POI+GVEfDAipvdVMDN9tkYfzj8f/uu/4Jhj4EMf6nVrRpe+AuM668D0Pv+FSpKkNTFgYMzMb2Xm3wObUZbN+XvgzxHx7Yg4KCLWrbqRdXb77fDe98Lf/i2cdlqvWzP69BUYN90Uxg33/H9JksawQf9ZzcwnMvOLmbk3sDMwCzgDeKCqxtXdsmVw2GGwwQbwta+5bE4V+pr04nC0JEnDa8gRJiKeBbwEeBkwA/jxcDdqtPiP/4Bf/QquuMIh0qq19jBuvXVv2yJJ0mgz6B7GiNg7Is4FHgI+BfwU2DEzX1lV4+rswQfL0jlvfnPZVI2+hqTtYZQkaXgNZlmdk4BDgI2Ay4ADMvOWittVeyedVBbpPvXUXrdkdGsNjMuXwyOPGBglSRpugxmS3hP4GPDNzHy64vaMCnPmwJe/DEceCTvs0OvWjG6t9zAuWFCC47RpvWuPJEmj0YCBMTNf242GjCZnnw3PPAMf/nCvWzJ2ZMKiReXnyZN72xZJkkYbFx8ZZsuXw5e+BG96E2yzTa9bM/q1DkkvXFh+NjBKkjS8DIzD7IYbyn1073xnr1syNrQGxgULys8GRkmShpeBcZhddlkJLK91IL8rWu9hbPYwTpnSm7ZIkjRaGRiH2XXXwWteA+uv3+uWjC0OSUuSVB0D4zBatgz+9CfYa69et2Ts8B5GSZKqZ2AcRs1Zunvu2dt2jCUGRkmSqmdgHEaLFsG668ILX9jrlowd3sMoSVL1DIzDaNEi2G03mDCh1y0Ze1p7GCdO7G1bJEkabQyMw+jpp+H5z+91K8aW1iHppUthnXXKJkmSho+BcRgtWwbbb9/rVowt7YFxvfV62x5JkkYjA+Mw2267XrdgbGm9h9HAKElSNQyMw2zrrXvdgrEps/TwGhglSRp+IyYwRsQ2EXF1RDweEQ9GxFkRMb7l+P4RcUdEPBURN0REv9EsIjaKiCsjYlFE3B8Rb2873m9dUZwaEY82ttMiWvuxOtt886F+cq0Nh6QlSareiAmMwOeBh4HNgD2AfYGjASJiE+AK4ARgI2AW8PUOdZ0NLAVmAO8AvhARuwyyriOANwG7A7sBbwCOHOyH2GyzwZbUcGgPjOuu29v2SJI0Go2kwPhc4NLMfDozHwSuBXZpHHsLMDszL8vMp4GTgN0jYqf2SiJiEnAQcEJmLszMm4FvA4cMsq7DgNMzc05mzgVOB941mA+wzjo+ErDbvIdRkqTqjaTAeCZwcERMjIgtgNdRQiOU4Hhbs2BmLgLuYWWgbLUjsCIz72zZd1tL2YHqWuV427kd2bvVOw5JS5JUnZEUGH9ICWZPAnMoQ8XfbBybDMxvKz8f6OuZHgOVHerx+cDk/u5jjIgjImJWRMyaOrW9WlWtdUjaSS+SJFWjK4ExIm6MiOxnuzkixgHXUe4tnARsAkwDTm1UsRCY2lbtVGBBH5cbqOxQj08FFmZm9vXZMvPczJyZmTO33HKDvoqoQk56kSSpel0JjJm5X2ZGP9velMknWwJnZeaSzHwUOA94faOK2ZRJKMBf71PcrrG/3Z3A+IjYoWXf7i1lB6prleNt52qE8R5GSZKqNyKGpDPzEeBe4KiIGB8RG1ImnzTvJbwS2DUiDoqICcCJwG8y844+6lpE6an8ZERMioi9gAOBrwyyrguBf4mILSJic+BY4Pzh/9QaTs6SliSpOiMiMDa8BXgtMA+4G1gOHAOQmfMoM58/DTwOvAw4uHliRBwfEde01HU0sD5lmZ5LgKMyc/Zg6gK+CFwF3A78FvhuY59GIIekJUmq3viBi3RHZv4a2K/D8e8Dqy2j0zh2Stv7xyhrKa5JXQl8pLFphDMwSpJUvZHUwygNWes9jM6SliSpGgZGjQr2MEqSVB0Do2rNRwNKklQ9A6NqzXsYJUmqnoFRteY6jJIkVc/AqFHBHkZJkqpjYFSt+SxpSZKqZ2BUrTnpRZKk6hkYVWvNwLh8eQmN9jBKkjT8DIwaFZYsKa8GRkmShp+BUbXW7GFcurS8GhglSRp+BkbVWjMw2sMoSVJ1DIyqNXsYJUmqnoFRo0Kzh9FZ0pIkDT8Do2rNIWlJkqpnYFStGRglSaqegVG15j2MkiRVz8CoUcHAKElSdQyMqrX2IWknvUiSNPwMjKo172GUJKl6BkbVmvcwSpJUPQOjRgV7GCVJqo6BUbVmD6MkSdUzMKrWnPQiSVL1DIyqNSe9SJJUPQOjRgWHpCVJqo6BUbVmD6MkSdUzMKrWDIySJFXPwKhac5a0JEnVMzCq1pwlLUlS9QyMqrXWwLjOOjDOf9GSJA07/7yq1loDo8PRkiRVw8CoWjMwSpJUPQOjaq110ouBUZKkahgYVWutPYxOeJEkqRoGRtWaQ9KSJFXPwKhaMzBKklQ9A6NqzcAoSVL1DIyqNSe9SJJUPQOjas1JL5IkVc/AqFprBsYVK+xhlCSpKgZG1VozMIKBUZKkqhgYVWsGRkmSqmdgVK0ZGCVJqp6BUbVmYJQkqXoGRtVaa2B0lrQkSdUwMKrW7GGUJKl6IyYwRsQ2EXF1RDweEQ9GxFkRMb7l+P4RcUdEPBURN0TE1h3q2igiroyIRRFxf0S8veXYnhHxvYh4LCLmRcRlEbFZy/GTImJZRCxs2bat7pNrbRgYJUmq3ogJjMDngYeBzYA9gH2BowEiYhPgCuAEYCNgFvD1DnWdDSwFZgDvAL4QEbs0jk0DzgW2AbYGFgDntZ3/9cyc3LL9cW0/nKphYJQkqXrjBy7SNc8FzsrMp4EHI+JaoBny3gLMzszLoPQCAo9ExE6ZeUdrJRExCTgI2DUzFwI3R8S3gUOAj2bmNW3lzwJ+WOHnUoUMjJIkVW8k9TCeCRwcERMjYgvgdcC1jWO7ALc1C2bmIuAeVgbKVjsCKzLzzpZ9t/VTFmAfYHbbvjc2hqxnR8RRnRodEUdExKyImDVv3rxORVUBJ71IklS9kRQYf0gJdU8CcyjDzt9sHJsMzG8rPx+Y0kc9gy4bEbsBJwLHtey+FNgZmA68BzgxIt7WX6Mz89zMnJmZM6dPn95fMVXEHkZJkqrXlcAYETdGRPaz3RwR44DrKPcpTgI2odxreGqjioXA1LZqp1LuP2w3qLIRsT1wDfDBzLypuT8zf5eZf8nMFZn5Y0rP51vX5HOregZGSZKq15XAmJn7ZWb0s+1NmciyJeUexiWZ+ShlIsrrG1XMBnZv1te4T3E7Vh9KBrgTGB8RO7Ts2721bGOG9feBkzPzKwM1H4gByqhHDIySJFVvRAxJZ+YjwL3AURExPiI2BA5j5X2LVwK7RsRBETGBMoz8m/YJL426FlF6Kj8ZEZMiYi/gQOArAI37I68Hzs7Mc9rPj4gDI2JaFC8FPgB8a5g/soaJgVGSpOqNiMDY8BbgtcA84G5gOXAMQGbOo8x8/jTwOPAy4ODmiRFxfES0zn4+GlifskzPJcBRmdnsYTwc2Bb4eOtaiy3nHty4/gLgQuDUzLxgmD+rhomTXiRJqt6IWVYnM38N7Nfh+PeBnfo5dkrb+8eAN/VT9hPAJzpcp98JLhp5xrX8J489jJIkVWMk9TBKQ+aQtCRJ1TMwqtYMjJIkVc/AqFozMEqSVD0Do2rNwChJUvUMjKo1Z0lLklQ9A6NqzR5GSZKqZ2BUrRkYJUmqnoFRtWZglCSpegZG1ZqBUZKk6hkYVWtOepEkqXoGRtVaa2AcP2IedClJ0uhiYFSttQbGddbpXTskSRrNDIyqtdbAOM5/zZIkVcI/saq11sDY+rMkSRo+BkbVmj2MkiRVzz+xqjV7GCVJqp6BUbVmD6MkSdXzT6xqzR5GSZKqZ2BUrdnDKElS9fwTq1qzh1GSpOoZGFVr9jBKklQ9/8Sq1gyMkiRVzz+xqjWHpCVJqp6BUbVmD6MkSdXzT6xqzR5GSZKqZ2BUrdnDKElS9fwTq1qzh1GSpOoZGFVr9jBKklQ9/8Sq1uxhlCSpegZG1Zo9jJIkVc8/sao1A6MkSdXzT6xqzSFpSZKqZ2BUrdnDKElS9fwTq1qzh1GSpOoZGFVrBkZJkqpnYFStGRglSaqegVG1ZkiUJKl6BkbVmoFRkqTqGRhVawZGSZKqZ2BUrRkYJUmqnoFRtWZglCSpegZG1ZqBUZKk6hkYVWsGRkmSqmdglCRJUkcGRkmSJHVkYJQkSVJHBkZJkiR1NGICY0RsExFXR8TjEfFgRJwVEeNbju8fEXdExFMRcUNEbN2hro0i4sqIWBQR90fE29uukxGxsGU7oeV4RMSpEfFoYzstwqkVkiRp7BoxgRH4PPAwsBmwB7AvcDRARGwCXAGcAGwEzAK+3qGus4GlwAzgHcAXImKXtjIbZubkxnZyy/4jgDcBuwO7AW8AjlybDyZJklRnIykwPhe4NDOfzswHgWuBZsh7CzA7My/LzKeBk4DdI2Kn9koiYhJwEHBCZi7MzJuBbwOHDLIdhwGnZ+aczJwLnA68ay0+lyRJUq2NpMB4JnBwREyMiC2A11FCI5TgeFuzYGYuAu5hZaBstSOwIjPvbNl3Wx9l74+IORFxXqMHs2mVa/Vz7l9FxBERMSsiZs2bN6/zJ5QkSaqhkRQYf0gJZk8CcyjDzt9sHJsMzG8rPx+Y0kc9A5V9BHgJsDXw4sb+izucPx+Y3N99jJl5bmbOzMyZ06dP7++zSZIk1VZXAmNE3NiYaNLXdnNEjAOuo9ynOAnYBJgGnNqoYiEwta3aqcCCPi7XsWxjmHpWZi7PzIeA9wGvjoip/Zw/FViYmbkmn12SJKnuuhIYM3O/zIx+tr0pE1m2BM7KzCWZ+ShwHvD6RhWzKZNQgL/ep7hdY3+7O4HxEbFDy77d+ykL0AyCzR7EVa41wLmSJEmj3ogYks7MR4B7gaMiYnxEbEiZfNK8l/BKYNeIOCgiJgAnAr/JzDv6qGsRpafykxExKSL2Ag4EvgIQES+LiOdFxLiI2Bj4L+DGzGwOQ18I/EtEbBERmwPHAudX88klSZJGvhERGBveArwWmAfcDSwHjgHIzHmUmc+fBh4HXgYc3DwxIo6PiGta6joaWJ+yTM8lwFGZ2ewl3JYymWYB8FtgCfC2lnO/CFwF3N44/t3GPkmSpDEpvDVv+MycOTNnzZrV62aMOc3pSP5TliRp8CLiF5k5czBlR1IPoyRJkkYgA6MkSZI6MjBKkiSpIwOjJEmSOjIwSpIkqSMDoyRJkjoyMEqSJKkjA6MkSZI6MjBKkiSpIwOjJEmSOjIwSpIkqSMDoyRJkjoyMEqSJKkjA6MkSZI6MjBKkiSpIwOjJEmSOjIwSpIkqSMDoyRJkjoyMEqSJKkjA6MkSZI6MjBKkiSpIwOjJEmSOjIwSpIkqSMDoyRJkjoyMEqSJKkjA6MkSZI6MjBKkiSpIwOjJEmSOjIwSpIkqSMDoyRJkjoa3+sGSGvr1FNh00173QpJkkYvA6Nq7yMf6XULJEka3RySliRJUkcGRkmSJHVkYJQkSVJHBkZJkiR1ZGCUJElSRwZGSZIkdWRglCRJUkcGRkmSJHVkYJQkSVJHBkZJkiR1ZGCUJElSRwZGSZIkdWRglCRJUkcGRkmSJHVkYJQkSVJHBkZJkiR1ZGCUJElSRwZGSZIkdRSZ2es2jBoRsQD4Q6/bMcZsAjzS60aMMX7n3ed33n1+593nd959z8vMKYMpOL7qlowxf8jMmb1uxFgSEbP8zrvL77z7/M67z++8+/zOuy8iZg22rEPSkiRJ6sjAKEmSpI4MjMPr3F43YAzyO+8+v/Pu8zvvPr/z7vM7775Bf+dOepEkSVJH9jBKkiSpIwOjJEmSOjIwDoOI2CgiroyIRRFxf0S8vddtGu0i4n0RMSsilkTE+b1uz2gXEc+KiC83/n0viIhfRcTret2u0S4iLoqIByLiyYi4MyIO73WbxoqI2CEino6Ii3rdltEuIm5sfNcLG5vrGXdBRBwcEb9vZJd7IuIVncq7DuPwOBtYCswA9gC+GxG3ZebsnrZqdPsL8CngNcD6PW7LWDAe+DOwL/An4PXApRHxgsy8r5cNG+X+HXh3Zi6JiJ2AGyPiV5n5i143bAw4G/h5rxsxhrwvM7/U60aMFRHxKuBU4B+BW4HNBjrHHsa1FBGTgIOAEzJzYWbeDHwbOKS3LRvdMvOKzPwm8Giv2zIWZOaizDwpM+/LzGcy8zvAvcCLe9220SwzZ2fmkubbxrZdD5s0JkTEwcATwA963BSpKp8APpmZP238Tp+bmXM7nWBgXHs7Aisy886WfbcBu/SoPVLlImIG5d++vegVi4jPR8RTwB3AA8DVPW7SqBYRU4FPAsf2ui1jzL9HxCMRcUtE7NfrxoxmEbEOMBOYHhF3R8SciDgrIjqO1hkY195kYH7bvvnAoJ7NKNVNRKwLXAxckJl39Lo9o11mHk35ffIK4ApgSecztJZOBr6cmX/udUPGkP8LbAtsQVkX8KqIsCe9OjOAdYG3Un6v7AG8EPi3TicZGNfeQmBq276pwIIetEWqVESMA75CuWf3fT1uzpiRmSsat7s8Bziq1+0ZrSJiD+DvgDN63JQxJTN/lpkLMnNJZl4A3EK5T1rVWNx4/VxmPpCZjwD/jwG+cye9rL07gfERsUNm3tXYtzsO1WmUiYgAvkz5r9PXZ+ayHjdpLBqP9zBWaT9gG+BP5Z87k4F1IuL5mfmiHrZrrEkget2I0SozH4+IOZTvedDsYVxLmbmIMkz0yYiYFBF7AQdSemFUkYgYHxETgHUov9AnRIT/AVStLwA7A2/MzMUDFdbaiYhnN5a9mBwR60TEa4C3Adf3um2j2LmUQL5HYzsH+C5lNQZVICI2jIjXNH+HR8Q7gH2A63rdtlHuPOD9jd8z04APAd/pdIJ/YIfH0cD/AA9TZu0e5ZI6lfs34OMt799JmfV1Uk9aM8pFxNbAkZT75x5s9L4AHJmZF/esYaNbUoafz6H8x/39wIcy81s9bdUolplPAU8130fEQuDpzJzXu1aNeutSlkjbCVhBmdz1psx0LcZqnQxsQhklfRq4FPh0pxN8lrQkSZI6ckhakiRJHRkYJUmS1JGBUZIkSR0ZGCVJktSRgVGSJEkdGRglSZLUkYFRkoZJRMyOiP26dK3nR8SsCuq9IiJeO9z1Sqo312GUpEFqLOTcNJGykPmKxvuuLmIeEZcDl2Xm14a53pcCX8jMFw9nvZLqzcAoSWsgIu4DDs/M7/fg2ptRnle/eWY+XUH9dwFvy8xh78GUVE8OSUvSMImI+yLi7xo/nxQRl0XERRGxICJuj4gdI+JfI+LhiPhzRLy65dwNIuLLEfFARMyNiE9FxDr9XOpVwC9bw2Lj2sdFxG8iYlGjrhkRcU3j+t9vPDOWxnN7L4qIRyPiiYj4eUTMaKn/RuCAYf+CJNWWgVGSqvNG4CvANOBXwHWU37tbAJ8EvthS9gJgObA98ELg1cDh/dT7AqCvZ+0eRAmTOzaufQ1wPOWZseOADzTKHQZsAGwJbAy8F1jcUs/vgd0H/SkljXoGRkmqzk2ZeV1mLgcuA6YDn8nMZcDXgG0iYsNG797rgA9l5qLMfBg4Azi4n3o3BBb0sf9zmflQZs4FbgJ+lpm/yswlwJWUIAqwjBIUt8/MFZn5i8x8sqWeBY1rSBIA43vdAEkaxR5q+Xkx8Ehmrmh5DzAZ2BxYF3ggIprlxwF/7qfex4Epg7he+/vJjZ+/Quld/FpEbAhcBHysEWRp1P1Efx9K0thjD6Mk9d6fKTOuN8nMDRvb1MzcpZ/yv6EMO6+RzFyWmZ/IzOcDLwfeABzaUmRn4LY1rV/S6GNglKQey8wHgP8FTo+IqRExLiK2i4h9+znle8CLImLCmlwvIl4ZES9oTKp5kjJEvaKlyL6U+x8lCTAwStJIcSiwHvA7ypDzN4DN+iqYmQ8B1wMHruG1Nm3U/yRlgssPKcPSRMRLgEWZeesa1i1pFHIdRkmqoYh4PmVm9UtzGH+RNxYE/3JmXj1cdUqqPwOjJEmSOnJIWpIkSR0ZGCVJktSRgVGSJEkdGRglSZLUkYFRkiRJHRkYJUmS1JGBUZIkSR0ZGCVJktTR/w8nqIx0VDnhqgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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z2eWSqlres3828HGa4eJvA9+kWRYHgCQnJbm8q/xRwB00zzI+D3hBVa0CqKqlwBHAO4D7gGcBR3ad+0Hgs8APaHowP9/Zp2nGIWlJkgZj1lQ3oFtVjbreYVXdT7Mm4ljnnd7z+UTgxD7lv0yz/M5oxwp4c2fTEHBoWpKkdk23HkZp3OxhlCRpMAyMkiRJ6svAqKHnkLQkSe0yMGpoOSQtSdJgGBg19OxhlCSpXQZGDS17GCVJGgwDo4aWPYuSJA2GgVGSJEl9GRg1tBySliRpMAyMGnoOTUuS1C4Do4aWPYySJA2GgVGSJEl9GRg19BySliSpXQZGDS2DoiRJg2Fg1NAzOEqS1C4Do4aWQVGSpMEwMEqSJKkvA6OGlj2MkiQNhoFRQ8vAKEnSYBgYNfQMjpIktcvAqKFlUJQkaTAMjJIkSerLwKihNdLDaE+jJEntMjBqaBkUJUkaDAOjhp7BUZKkdhkYNbQMipIkDYaBUZIkSX0ZGDW07GGUJGkwDIwaWgZGSZIGw8CooWdwlCSpXQZGDS2DoiRJg2FglCRJUl8GRg0t3/QiSdJgGBg1tAyKkiQNhoFRQ8/gKElSuwyMGloGRUmSBsPAKEmSpL4MjBpa9jBKkjQYBkYNLQOjJEmDYWDU0DM4SpLUrmkTGJOs6NnWJHlv1/Fjk9zUOfaFJDv1qWufJFckWdY557e7jv1aki8luTfJ0iQXJdmx6/ipSR7pacse7d25NpRBUZKkwZg2gbGq5o1swPbAQ8BFAEkOBk4HDgO2BW4GLhytniSzgE8Dn+uUPQ74aJK9O0W2Ac4Bdgd2A5YD5/ZU84nu9lTVTybtRiVJkobMtAmMPV4J3AVc1fl8KHBRVS2pqtXA24HnJtlzlHOfAuwEvLuq1lTVFcDXgaMAquryqrqoqh6oqgeBs4DntHw/aoFvepEkaTCma2A8Bji/6n+jQDobXZ8B9hvl3Iyxb7SyAM8FlvTsO7QzZL0kyfHjbLMGzKAoSdJgTLvAmGRX4GDgvK7dlwGvSrJ/kjnAKUABc0ep4nqa3skTk2yR5IWd+h5TNsn+nbpO7Nr9SWAfYCHwx8ApSV7dp73HJVmcZPHSpUsncKeaLAZHSZLaNZDAmOTKJDXGdnVP8aOBq6vq5pEdVfUV4K3AxcCtwC00zx7e1nutqnoEeAXwMuBO4I00IXCdskmeBFwO/FlVXdV1/g+r6med4exvAGfSDJGPqqrOqapFVbVo4cKF4/1KNAkMipIkDcZAAmNVHVJVGWM7qKf40azbuzhSx9lVtVdV/RJNcJwFXDfG9a6tqoOraruqehGwB/DtkeNJdgO+DLy9qi5YX/MZfZhbkiRpkzCthqSTPBvYmc7s6K79s5Psl8auNLOcz6yq+8aoZ//OOXOTvAnYEfhI59jOwBXA2VX1gVHOPSzJNp1rPRN4A82sa00z9jBKkjQY0yow0kx2uaSqlvfsnw18HFhB01P4TeDkkYNJTkpyeVf5o4A7aJ5lfB7wgqpa1Tl2LE2P41u711rsOvdI4CaaIe/zgTOq6jE9npp6BkZJkgZj1lQ3oFtVvW6M/fcD+/c57/Sezyey7kSW7mNvA97Wp64xJ7hoejI4SpLUrunWwyiNm0FRkqTBMDBKkiSpLwOjhpZvepEkaTAMjBpaBkVJkgbDwKihZ3CUJKldBkYNLYOiJEmDYWCUJElSXwZGDS0nvUiSNBgGRg0tg6IkSYNhYJQkSVJfBkYNLXsYJUkaDAOjJEmS+jIwamg56UWSpMEwMGpoGRQlSRoMA6OGnsFRkqR2GRg1tAyKkiQNhoFRkiRJfRkYNbSc9CJJ0mAYGDW0DIqSJA2GgVGSJEl9GRg1tOxhlCRpMAyMkiRJ6svAqKHlpBdJkgbDwKihZVCUJGkwDIwaegZHSZLaZWDU0HJIWpKkwTAwaugZGCVJapeBUUPLHkZJkgbDwKihZVCUJGkwDIwaegZHSZLaZWDU0HJIWpKkwTAwaugZGCVJapeBUUPLHkZJkgbDwKihZWCUJGkwDIwaegZGSZLaZWDU0LKHUZKkwTAwaugZGCVJapeBUUPLHkZJkgbDwChJkqS+pk1gTLKiZ1uT5L1dx49NclPn2BeS7NSnrn2SXJFkWeec3+46tnuS6rnWyV3Hk+SMJPd0tncmSXt3rg1lD6MkSYMxbQJjVc0b2YDtgYeAiwCSHAycDhwGbAvcDFw4Wj1JZgGfBj7XKXsc8NEke/cU3brrmm/v2n8c8ArgAGB/4OXA6yblJjWpDIySJA3GtAmMPV4J3AVc1fl8KHBRVS2pqtXA24HnJtlzlHOfAuwEvLuq1lTVFcDXgaPGee1jgHdV1W1VdTvwLuC1G34rapuBUZKkdk3XwHgMcH7V/0aBdDa6PgPsN8q5ow0fZ5Sytya5Lcm5SRZ07d8XuKbr8zWdfZpm7GGUJGkwpl1gTLIrcDBwXtfuy4BXJdk/yRzgFKCAuaNUcT1N7+SJSbZI8sJOfSNl7wZ+FdgNeAawFfCxrvPnAcu6Pi8D5o31HGOS45IsTrJ46dKlE7tZTQoDoyRJ7RpIYExyZWeiyWjb1T3FjwaurqqbR3ZU1VeAtwIXA7cCtwDLgdt6r1VVj9A8g/gy4E7gjcAnR8pW1YqqWlxVj1bVz4HXAy9MMr9TxQpgfleV84EVXb2dvdc7p6oWVdWihQsXTuRr0Uayh1GSpMEYSGCsqkOqKmNsB/UUP5p1exdH6ji7qvaqql+iCY6zgOvGuN61VXVwVW1XVS8C9gC+PVbzOj9HehCX0Ex4GXFAZ5+mGQOjJEmDMa2GpJM8G9iZzuzorv2zk+zXWfJmV+Ac4Myqum+MevbvnDM3yZuAHYGPdI49K8mTk2yWZDvgn4Arq2pkGPp84C+T7NxZuueNI+dqejIwSpLUrmkVGGkmu1xSVct79s8GPk4zXPxt4JtA99qJJyW5vKv8UcAdNM8yPg94QVWt6hzbA/gCzZD2dcAq4NVd534Q+Czwg87xz3f2aZqxh1GSpMGYNdUN6FZVo653WFX306yJONZ5p/d8PhE4cYyyFzLGGo6d4wW8ubNpCBgYJUlq13TrYZTGzaAoSdJgGBg1tBySliRpMAyMGnoGRkmS2mVg1NCyh1GSpMEwMGroGRglSWqXgVFDyx5GSZIGw8CooWVglCRpMAyMGnoGRkmS2mVg1NCyh1GSpMEwMGroGRglSWqXgVFDy6AoSdJgGBg1tBySliRpMAyMGnoGRkmS2mVg1NCyh1GSpMEwMGroGRglSWqXgVFDyx5GSZIGw8CooWVglCRpMAyMGnoGRkmS2mVg1NCyh1GSpMEwMGroGRglSWqXgVFDy6AoSdJgGBg1tBySliRpMAyMGnoGRkmS2mVg1NCyh1GSpMEwMGroGRglSWrXrPEUSvJC4LXAvsBWwHJgCXBuVX2ptdZJfdjDKEma6arg0UfhoYfg4YfX/Tnavon8nIj1BsYkfwG8Gfhn4GJgGTAfOAA4L8kZVXXmhL8BaSMZGCVJg/boo/Dgg2sD20MPje/zWMFtPOHuF7/Y8PbOmgWzZ8OcOY/9OaF6xlHmROA3q+r6nv2XJLkQ+CpgYNSUMTBK0qbrF79YG6z6BbcNCXmjfX700Q1r5+abrxvWegPcdtuNHexG2zfesrP6JL1k/O0fT2DcEvjZGMfuBOaO/3LS5LGHUZKmpypYtaoJWSPbypWP/TwZIW/Vqg1rY7I2YM2du/b3kW3HHdf93Ftmop+32GJyv+NBG09gvBj4bJLTgGtZd0j6LcC/tdc8af0MjJI0fr1hbrQg1+/zeMtsyDDq4x8/dvjadlvYeefJCW9z58LjHjexHrZN3XgC458AbwPOA3YCRv55vgO4AHhrO02T+rOHUdJM9OijTQBbuRJWrFgbxjYkuI31eaJ/byaw5ZZN0Jo7d93fFyyA3XZb+7n3+Fifu4Pc3LnN8Olmrt0yba03MFbVauBvgL9JsjUwD1hRVff3lk3ynKr6+mQ3UhqNQVHSVPnFL5oh0e5Q1+/3iZSb6BBrMnYoW7hw9NA2nkDX/fnxj7c3blM3rmV1RnRC4v19ilxOM1wtDYzBUdJoqmD16g0Pbv3KPfjgxNqyxRZNCJs3r/k58vvChbD77qMfG/m9exst1BnmNAgTCozj4B9ZDYxD0tLM8otfNGFsxQpYvvyxv2/Iz4nMaB0Zdu0NblttBTvs8NgQN1bAGy3sPe5x7X1v0iBMdmD0n24NnIFRGryRiROTGe4m0ms3e/baMDfyc+utYZdd1t0/sq0v1M2b19RpT500uskOjNLA2MMoTdzq1fDAA2u35cvX/Tzavu7Py5evDXnj7b3bbLPHhrt58x4b7sb7c8sth3+JEmnYGBg1tAyM2lSMDNVuTNAb2VavXv/1kiaczZ+/9uf8+bDTTs3PfmFutH1z5thzJw07n2HU0DMwajpbvRqWLYP7729+dm+9++6/f/Sgt2LF+K41Z866AW/+/KYXr/tz7/HRPm+5pcubSFrXhAJjku2AlwI7VtU7k+wEbFZVtwFU1VYttFEalT2Matujjz425I0V9MYq8/DD67/OVlvBE57QbPPnNwsU7777Y8Ncv8C31VYO00pqz7gDY5KDad76shh4DvBOYC/gTcChrbROGgcDo8ayZk0T2u67r9nuv3/t7937ugNf9+8rV67/GnPnNkFv662bn9ts04S9kc/dW+++rbdugt7mm7f2FUjSpJhID+N7gN+tqq8kua+z7z+BZ05GQ5L0DrrMAd5XVX/aOX4s8NfADsDVwB9W1ajvuE6yD3A28AxgKXBiVV3aOfYa4INdxTfrXGtRVX0nyanA3wLdS6fuX1U/2bg71GSzh3HT8MgjYwe93n29x5Yt61/3Fls0Aa87yO200+jBbrTwN3++vXqSNg0TCYy7V9VXOr+P/BO9eoJ1jKmq5o38nmRL4OfARZ3PBwOnA78J3AicCVwIHNxbT5JZwKeBDwAv6JT5bJJfqaobqupjwMe6yr8WOBn4blc1n6iq35+M+1J7DIrD5ZFH4N574e6712733LP297HC3/p6+ebMacLbNts02y//MjztaWs/dx/r3ZyMIUnjM5Gw98MkL6qqL3btez7wg0luE8ArgbuAqzqfDwUuqqolAEneDtyeZM+q+nHPuU+heef1u6uqgCuSfB04iiYY9joGOL9TVkPI/3KDt2ZNE+bGCn+jbfffP3Z9W27ZPLc3Eu722OOx4W604Lf11s3aeZKkdk0kML4R+FySzwNzknyQJsgd1kK7ekNcWHcG9sjv+wG9gXG0/oJ0yq67M9kNeC7whz2HDk1yL3AHcFZVvX9izdcgOCQ9OX7xi2bodiLh7957x/7eZ89uXne2YEGzPfGJa39fsAC22+6xnw19kjS9jTswVtW3khwAvAb4MPBT4JkjM6QnS5JdaYaR/6hr92XAJ5J8gGZI+hSaYfG5o1RxPU3v5IlJ3k0zjH0w8NVRyh4NXFVVN3ft+yRwDs2Q+LOAi5PcX1UXjtHe44DjAHbdddfx3qYmkYFxrapmSZbxBr+RMmvWjF7f4x63brg74ICxg9/INne0/1VKkobahJ4/rKrbaWZHT0iSKxnlecOOr1fVQV2fjwau7g5xnYk2b6WZpf0E4N3AcuAxYbWqHknyCuC9wF/RzOr+JOtOYum+1uk95/+w6+M3kpxJM0Q+amCsqnNoAiaLFi0yugzQTO9hrGpelTaR8Hf33c2zgqPZfPN1g90++6w//M2b5zN+kqT1BMYkFzCO90NX1dHrOX7IBNp0NPAPo9RxNs3MZ5LsDbwFuG6M611LV0BN8g3gvO4ySZ5D86zjv62nPYULkk9L0zUwPvooPPRQsz344Nqt9/Py5c3Q7r33Ns8D9v5+zz1jr+GXrBvy9twTnvWssYPfggXNzF7DnyRpQ6yvh/Gmrt8X0Dxb+FngVmBXmmcYzxvlvA2S5NnAznRmR3ftnw08CVgC7ELTo3dmVd33mEqa8vsDN9AsmXMCsCPwkZ5ixwAXV9XynnMPA/4DuB/4VeANwEkbcVtqWZuBcflyuOmmtdvPfra2p++ee5oZvCPh8OGHm5/jfb/uiDlzmgkf22zT/NxzT1i0qH/423pr1+6TJA1O38BYVW8b+T3JF4GXVdVVXfsOYvSZxxvqGOCS3hAHzAY+DuxJMxR9bvd1k5wE/EZVvaSz6yjgWGALmpnWL6iqVV3lZwOvAo4YpQ1H0jyj+XiaIe8zqmrSQrEmTxs9jPfcA5dfDl/7GnzrW7Bkybr1P+EJzYSO7baDHXZY+57c3m327OZZvpFtzpzH/j5vXhMQnfAhSZruMt7VZJIsAxZU1SNd+7YA7qmq+S21b6gsWrSoFi9ePNXN2GS88pVw8cVNT9tEe/W63X8/fPzj8IlPwNVXN7OGt94afu3Xmm2//eBJT2qWetnKl19KkmaIJN+pqkXjKTuRSS/fA05PckpVPZRkDvA24Psb0EZp3NasgTvugP/5H7j11rU/v/Wt5viG9jB+97vwnvfARRc1w8n77QcnnQS/9VvwjGfAZptN2i1IkjTUJhIYX0szLLys82rAbWhmIP9eC+3SJmTNmubZwJtvhp/8pNluuWVtOLzttsf2IG67Ley6azOEvHr1xK73n/8Jp54KX/hC82q3P/gDOPZYePrTJ+uOJEmaWSayDuMtwLOT7EIzu/iOqvqfthqmmeX++9eGwZFgOPLz1lvXDX2bbda83m233eA5z2l+7rprs+22G+yyy9qh4ZNPhne8Y3xtuOce+Ou/hn/5l+Y5xL//ezj++Oa5REmSNLYJrcOYZBuahbB3pnk132fHmqmsTc+yZXDjjXDDDetuP/7xY18Lt+22zTOBBx4Ihx/evA1kjz2abZddmgWjxyMZ35D0pz8Nf/RHTTve9CZ461ubSSeSJGn9xh0Yk/w68HmaN6ncCrwceE+Sl1XVN1tqn6aZ1aub5WV6Q+ENN8DPf762XAK77w577dWsDzgSBp/4xGabrF698awrePbZ8Kd/2gw5n3suPO1pk3NtSZI2FRPpYXwPcEJV/evIjiS/C/wTzXqFmkEeeaQJhkuWwHXXNT+XLGmCYfdr5HbYoQmFL3857L332m2PPQazXMxIYKx6bHisgre8BU4/vZnIcuGFvrZOkqQNMZHAuDfNK/a6/RvwgclrjqbCHXc0M4a///214fD669e+Yi5pFpPed99m+HiffeDJT26C4fwpXlCpX2A877wmLB53HLzvfS50LUnShppIYLyRZlHrj3ft+x3gx5PaIrWmqpl9/L3vNQFxZOseSt599yYYvuQlzTIz++4LT3nK9O2Z6w6M3VasaHoXFy2CD3zAV+JJkrQxJhIY/xz4XJI30DzDuDuwF82zjJqGli+Hb38bvvEN+OY3m3UL7+tMUdp8c3jqU+HFL4Zf+ZXm+b4DDpj6HsOJGiswnnYa3H57s8aiYVGSpI0zkWV1vpFkT+BlNMvqfBa4rKrubatxmpg774QrrmjeVvKNb8APftC8tSRpwuERRzQ9bk9/etN7OGfOVLd4440WGG+7Dd79bvjDP4Rf//WpaZckSTPJhJbV6Syh89GW2qIJWr4crrwSvvKVZrvuumb/Vls1r7R7y1vg2c9uZilvvfVUtrQ9owXGr32tWej7DW+YmjZJkjTTTGRZnScC7wAOBNZZwa6qdp3cZmkst90Gn/lMs67gV7/aTEyZPRsOOghe8xp43vOaHsRNZYLHaIHxm99sQvN++01NmyRJmmkm0sP4cZoJLm8EHmynORrNz3/eLAnzsY/B4sXNvr32gj/7M3jpS5th10EsYTMdjfZ84g9/2EzW2VRCsyRJbZtIYNwXeE5V/aKtxmitRx+FT30KPvxh+Pd/b9Y+fPrT4R/+AQ47rJm5rLW6exhvvBF+8zenri2SJM00EwmM/wH8CvCdltoimvcd//M/N28nue225jV5b34z/P7vNxNXtK7eIekHH2y+t732mro2SZI000wkMN4CfDHJJcCd3Qeq6pTJbNSm6L774B//Ec48E1auhP/zf5rQ+LKXObTaT29g/MlPmp9PetLUtEeSpJloIoFxS5qldLYAdunaX6MX13g88gi85z3NG0nuvx+OPBJOOsn3HY9Xb2C88cbmpz2MkiRNnomsw/gH6yuT5NVVdeHGNWnT8fWvw+te17yK72Uvg3e8o1k8W+M3VmC0h1GSpMmz2STX98FJrm9GevTRZo3E3/gNeOCBZomcz33OsLghRguMCxbM3HUnJUmaChNauHscfAnbetx5J/zu78J//Af8wR/AP/0TzJu3/vM0ut7AeNNNDkdLkjTZJjsw+jxjHz/6UfPu5rvugvPPh6OOmuoWDb/Rehif97ypa48kSTPRZAdGjeH734fnPx8226x5dd2iRVPdopmhOzCuWgW33w577jm1bZIkaaZZb2BMspmLdW+cG2+EF70I5s6FK65wQsZk6n7Ty333NT8XLJiatkiSNFONZ9LL7UnemWQ8b+b9n41t0Ezz85/DC17Q9IB96UuGxbZUrQ2M22wztW2RJGmmGU9g/BPgicB/Jflukj9LsnC0glU1nlC5yXj0UXj1q5tnFi+/HJ785Klu0czTPSRtYJQkqR3rDYxV9emq+h1gR5plc34H+GmSzyQ5IskWbTdyWP3d38FXvwof+AA84xlT3ZqZycAoSVL7xr0OY1XdX1UfrKqDgH2AxcC7gTvaatww+8EPmoW4f//34eijp7o1M1d3YLz//uZ3A6MkSZNrwgt3J3k88KvAs4DtgR9MdqOGXRUcd1yzePS73z3VrZnZ7GGUJKl9415WJ8lBwNHAq4C7gAuAE6rq1pbaNrQ+/Wn41rfgQx9yxm7bRuth9C0vkiRNrvEsq3MqcBSwLXAR8LKq+nrL7RpaVXDaac3bRhyKbl93YHzoIZg1C7bwqVpJkibVeHoYfw34W+BTVfVwy+0ZeosXw/e+B+9/fxNe1K7uwPjwwzB79tS2R5KkmWi9kaaqXjyIhswUH/4wzJnTLKej9hkYJUlq34Qnvai/Sy+F3/oteMITprolm4buN72sWmVglCSpDQbGSfTww82bXZ7//KluyabHHkZJktpjYJxEy5c3Pw8+eGrbsSlxSFqSpPYZGCfR8uWw006+L3qQDIySJLXPwDiJHnwQnvnMdZ+rU7t6A+PjHz+17ZEkaSYyME6iVatgn32muhWbFnsYJUlqn4FxkhkYB8vAKElS+6ZNYEyye5LLktyX5M4kZyWZ1XX8eUmuT/Jgkq8m2a1PXdsmuTTJyiS3Jvm9nuNj1pXGGUnu6WzvTMY/yLznnhO9c20MA6MkSe2bNoEReB/NO6p3BA4EDgZOAEiyALgEOJnmFYWLgU/0qetsYDWwPfAa4P1J9h1nXccBrwAOAPYHXg68brw3scsu4y2pydAdGF2HUZKkdkynwPhE4JNV9XBV3Ql8Adi3c+xwYElVXdR5PeGpwAFJntJbSZItgSOAk6tqRVVdDXyG5n3Y46nrGOBdVXVbVd0OvAt47XhvYocdJnLL2lj2MEqS1L7pFBjPBI5MMjfJzsBLaEIjNMHxmpGCVbUS+DFrA2W3vYE1VXVD175rusqur651jvec29cWWzSbBsfAKElS+6ZTYPwaTTB7ALiNZqj4U51j84BlPeWXAVuNUs/6yk70+DJg3ljPMSY5LsniJItnzVo1WhG1qPu/isvqSJLUjoEExiRXJqkxtquTbAZ8kebZwi2BBcA2wBmdKlYA83uqnQ8sH+Vy6ys70ePzgRVVVaPdW1WdU1WLqmrRU59qWpkqVbB6tYFRkqQ2DCQwVtUhVZUxtoNoJp/sApxVVauq6h7gXOClnSqW0ExCAf73OcU9O/t73QDMSrJX174Dusqur651jvecq2mme0h69WofCZAkqQ3TYki6qu4GbgaOTzIrydY0k09GniW8FNgvyRFJZgOnANdW1fWj1LWSpqfytCRbJnkOcBhwwTjrOh/4yyQ7J9kJeCPwkcm/a02GkcD4yCPNz8c9buraIknSTDUtAmPH4cCLgaXATcCjwF8AVNVSmpnP7wDuA54FHDlyYpKTklzeVdcJwByaZXouBI6vqiXjqQv4IPBZ4AfAdcDnO/s0DY0ExlWdx0ftYZQkafLNWn+Rwaiq7wOH9Dn+ZeAxy+h0jp3e8/lemrUUN6SuAt7c2TTNjQTG1aubn/YwSpI0+aZTD6M0Yb09jAZGSZImn4FRQ80haUmS2mdg1FBzSFqSpPYZGDXUHJKWJKl9BkYNtd4eRoekJUmafAZGzQgOSUuS1B4Do4aak14kSWqfgVFDzUkvkiS1z8CooeakF0mS2mdg1FBzSFqSpPYZGDXUHJKWJKl9BkYNNYekJUlqn4FRQ80haUmS2mdg1FBzSFqSpPYZGDXUfNOLJEntMzBqRvAZRkmS2mNg1FBzSFqSpPYZGDXUnPQiSVL7DIwaavYwSpLUPgOjhlpvD+OsWVPXFkmSZioDo4Zad2DcYou1nyVJ0uQxMGqodQ9JOxwtSVI7DIwaar09jJIkafIZGDXUugOjPYySJLXDwKih5pC0JEntMzBqRli92iFpSZLaYmDUUHNIWpKk9hkYNdQckpYkqX0GRg01Z0lLktQ+A6OGmkPSkiS1z8CooTYSGNessYdRkqS2GBg11LpfBWgPoyRJ7TAwaqgZGCVJap+BUUOtOzA6JC1JUjsMjBpq9jBKktQ+A6OGmoFRkqT2GRg1YzgkLUlSOwyMGmr2MEqS1D4Do4aak14kSWqfgVFDzR5GSZLaN20CY5Ldk1yW5L4kdyY5K8msruPPS3J9kgeTfDXJbn3q2jbJpUlWJrk1ye91Hfu1JF9Kcm+SpUkuSrJj1/FTkzySZEXXtkd7d66NYWCUJKl90yYwAu8D7gJ2BA4EDgZOAEiyALgEOBnYFlgMfKJPXWcDq4HtgdcA70+yb+fYNsA5wO7AbsBy4Nye8z9RVfO6tp9s7M2pHQ5JS5LUvlnrLzIwTwTOqqqHgTuTfAEYCXmHA0uq6iJoegGBu5M8paqu764kyZbAEcB+VbUCuDrJZ4CjgL+uqst7yp8FfK3F+1KL7GGUJKl906mH8UzgyCRzk+wMvAT4QufYvsA1IwWraiXwY9YGym57A2uq6oaufdeMURbgucCSnn2HdoaslyQ5fuK3okGxh1GSpPZNp8D4NZpQ9wBwG82w86c6x+YBy3rKLwO2GqWecZdNsj9wCnBi1+5PAvsAC4E/Bk5J8uqxGp3kuCSLkyxeunTpWMXUEnsYJUlq30ACY5Irk9QY29VJNgO+SPOc4pbAAppnDc/oVLECmN9T7Xya5w97jatskicBlwN/VlVXjeyvqh9W1c+qak1VfYOm5/OVY91bVZ1TVYuqatHChQv7fxGadAZGSZLaN5DAWFWHVFXG2A6imciyC80zjKuq6h6aiSgv7VSxBDhgpL7Oc4p78tihZIAbgFlJ9urad0B32c4M6y8Db6+qC9bXfCDrKaNpwCFpSZLaMS2GpKvqbuBm4Pgks5JsDRzD2ucWLwX2S3JEktk0w8jX9k546dS1kqan8rQkWyZ5DnAYcAFA5/nIK4Czq+oDvecnOSzJNmk8E3gD8OlJvmVNEnsYJUlq37QIjB2HAy8GlgI3AY8CfwFQVUtpZj6/A7gPeBZw5MiJSU5K0j37+QRgDs0yPRcCx1fVSA/jscAewFu711rsOvfIzvWXA+cDZ1TVeZN8r5okBkZJkto3bZbVqarvA4f0Of5l4CljHDu95/O9wCvGKPs24G19rjPmBBdNP86SliSpfdOph1GaMHsYJUlqn4FRQ80eRkmS2mdg1FCzh1GSpPYZGDXUDIySJLXPwKih5pC0JEntMzBqqNnDKElS+wyMmjEMjJIktcPAqKHmkLQkSe0zMGqodQfGWdNmGXpJkmYWA6OGWndg3Mw/zZIktcJ/YjXUugNj9++SJGnyGBg11AyMkiS1z8CooeaQtCRJ7fOfWA01exglSWqfgVFDzcAoSVL7DIwaag5JS5LUPv+J1VCzh1GSpPYZGDVjGBglSWqHgVFDzSFpSZLa5z+xGmoOSUuS1D4Do4aagVGSpPYZGDXUHJKWJKl9/hOroWYPoyRJ7TMwaqgZGCVJap+BUUPNwChJUvsMjBpqPsMoSVL7/CdWQ80eRkmS2mdg1IxhYJQkqR0GRg01h6QlSWqf/8RqqDkkLUlS+wyMGmoGRkmS2mdg1FBzSFqSpPb5T6yGmj2MkiS1z8CooWZglCSpfQZGDTWHpCVJap//xGqo2cMoSVL7DIwaagZGSZLaZ2DUjGFglCSpHQZGDTVDoiRJ7Zs2gTHJ7kkuS3JfkjuTnJVkVtfx5yW5PsmDSb6aZLc+dW2b5NIkK5PcmuT3eq5TSVZ0bSd3HU+SM5Lc09nemRhLpiv/y0iS1L5pExiB9wF3ATsCBwIHAycAJFkAXAKcDGwLLAY+0aeus4HVwPbAa4D3J9m3p8zWVTWvs729a/9xwCuAA4D9gZcDr9uYG1N7DIySJLVvOgXGJwKfrKqHq+pO4AvASMg7HFhSVRdV1cPAqcABSZ7SW0mSLYEjgJOrakVVXQ18BjhqnO04BnhXVd1WVbcD7wJeuxH3pRYZGCVJat90CoxnAkcmmZtkZ+AlNKERmuB4zUjBqloJ/Ji1gbLb3sCaqrqha981o5S9NcltSc7t9GCOWOdaY5yracLAKElS+6ZTYPwaTTB7ALiNZtj5U51j84BlPeWXAVuNUs/6yt4N/CqwG/CMzv6P9Tl/GTBvrOcYkxyXZHGSxUuXLh3r3tQSA6MkSe0bSGBMcmVnoslo29VJNgO+SPOc4pbAAmAb4IxOFSuA+T3VzgeWj3K5vmU7w9SLq+rRqvo58HrghUnmj3H+fGBFVdVo91ZV51TVoqpatHDhwvV/GZpUBkZJkto3kMBYVYdUVcbYDqKZyLILcFZVraqqe4BzgZd2qlhCMwkF+N/nFPfs7O91AzAryV5d+w4YoyzASBAciR7rXGs952qKGRglSWrftBiSrqq7gZuB45PMSrI1zeSTkWcJLwX2S3JEktnAKcC1VXX9KHWtpOmpPC3JlkmeAxwGXACQ5FlJnpxksyTbAf8EXFlVI8PQ5wN/mWTnJDsBbwQ+0s6da2MZGCVJat+0CIwdhwMvBpYCNwGPAn8BUFVLaWY+vwO4D3gWcOTIiUlOSnJ5V10nAHNolum5EDi+qkZ6CfegmUyzHLgOWAW8uuvcDwKfBX7QOf75zj5NQwZGSZLalzEezdMGWLRoUS1evHiqm7HJGQmN/lGWJGn8knynqhaNp+x06mGUJEnSNGRglCRJUl8GRkmSJPVlYJQkSVJfBkZJkiT1ZWCUJElSXwZGSZIk9WVglCRJUl8GRkmSJPVlYJQkSVJfBkZJkiT1ZWCUJElSXwZGSZIk9WVglCRJUl8GRkmSJPVlYJQkSVJfBkZJkiT1ZWCUJElSXwZGSZIk9WVglCRJUl8GRkmSJPVlYJQkSVJfBkZJkiT1ZWCUJElSXwZGSZIk9WVglCRJUl8GRkmSJPVlYJQkSVJfBkZJkiT1ZWCUJElSXwZGSZIk9WVglCRJUl8GRkmSJPVlYJQkSVJfBkZJkiT1ZWCUJElSXwZGSZIk9WVglCRJUl8GRkmSJPVlYJQkSVJf0yYwJtk9yWVJ7ktyZ5KzkszqOv68JNcneTDJV5Ps1qeubZNcmmRlkluT/F7XsdckWdG1PZikkjyjc/zUJI/0lNmj3buXJEmavqZNYATeB9wF7AgcCBwMnACQZAFwCXAysC2wGPhEn7rOBlYD2wOvAd6fZF+AqvpYVc0b2TrX+Anw3a7zP9Fdpqp+Mnm3KUmSNFymU2B8IvDJqnq4qu4EvgDs2zl2OLCkqi6qqoeBU4EDkjylt5IkWwJHACdX1Yqquhr4DHDUGNc9Bji/qmpyb0eSJGlmmE6B8UzgyCRzk+wMvIQmNEITHK8ZKVhVK4EfszZQdtsbWFNVN3Ttu2a0sp1h7ecC5/ccOjTJvUmWJDl+Q29IkiRpJphOgfFrNKHuAeA2mmHnT3WOzQOW9ZRfBmw1Sj0TKXs0cFVV3dy175PAPsBC4I+BU5K8eqxGJzkuyeIki5cuXTpWMUmSpKE1kMCY5MrOxJLRtquTbAZ8keY5xS2BBcA2wBmdKlYA83uqnQ8sH+VyEyl7NHBe946q+mFV/ayq1lTVN2h6Pl851r1V1TlVtaiqFi1cuHCsYpIkSUNrIIGxqg6pqoyxHUQzkWUX4KyqWlVV9wDnAi/tVLEEOGCkvs5zint29ve6AZiVZK+ufQf0lk3yHGAn4N/W13wg475ZSZKkGWZaDElX1d3AzcDxSWYl2ZpmMsrIc4uXAvslOSLJbOAU4Nqqun6UulbS9FSelmTLTjA8DLigp+gxwMVVtU7PY5LDkmyTxjOBNwCfnrSblSRJGjLTIjB2HA68GFgK3AQ8CvwFQFUtpZn5/A7gPuBZwJEjJyY5KcnlXXWdAMyhWabnQuD4qlrSVX428Cp6hqM7juxcfznNZJgzqmq0cpIkSZuEuJrM5Fm0aFEtXrx4qpuxyUnngQH/KEuSNH5JvlNVi8ZTdtb6i0jT2xlnwA47THUrJEmauQyMGnpvfvNUt0CSpJltOj3DKEmSpGnIwChJkqS+DIySJEnqy8AoSZKkvgyMkiRJ6svAKEmSpL4MjJIkSerLwChJkqS+DIySJEnqy8AoSZKkvgyMkiRJ6svAKEmSpL4MjJIkSerLwChJkqS+DIySJEnqy8AoSZKkvgyMkiRJ6svAKEmSpL5SVVPdhhkjyXLgR1Pdjk3MAuDuqW7EJsbvfPD8zgfP73zw/M4H78lVtdV4Cs5quyWbmB9V1aKpbsSmJMliv/PB8jsfPL/zwfM7Hzy/88FLsni8ZR2SliRJUl8GRkmSJPVlYJxc50x1AzZBfueD53c+eH7ng+d3Pnh+54M37u/cSS+SJEnqyx5GSZIk9WVglCRJUl8GxkmQZNsklyZZmeTWJL831W2a6ZK8PsniJKuSfGSq2zPTJXl8kg91/nwvT/K9JC+Z6nbNdEk+muSOJA8kuSHJsVPdpk1Fkr2SPJzko1PdlpkuyZWd73pFZ3M94wFIcmSS/+5klx8n+Y1+5V2HcXKcDawGtgcOBD6f5JqqWjKlrZrZfgb8HfAiYM4Ut2VTMAv4KXAw8D/AS4FPJnlaVd0ylQ2b4f4e+KOqWpXkKcCVSb5XVd+Z6oZtAs4G/muqG7EJeX1V/ctUN2JTkeQFwBnA7wLfBnZc3zn2MG6kJFsCRwAnV9WKqroa+Axw1NS2bGarqkuq6lPAPVPdlk1BVa2sqlOr6paq+kVVfQ64GXjGVLdtJquqJVW1auRjZ9tzCpu0SUhyJHA/8JUpborUlrcBp1XVtzp/p99eVbf3O8HAuPH2BtZU1Q1d+64B9p2i9kitS7I9zZ99e9FbluR9SR4ErgfuAC6b4ibNaEnmA6cBb5zqtmxi/j7J3Um+nuSQqW7MTJZkc2ARsDDJTUluS3JWkr6jdQbGjTcPWNazbxkwrnczSsMmyRbAx4Dzqur6qW7PTFdVJ9D8ffIbwCXAqv5naCO9HfhQVf10qhuyCfkrYA9gZ5p1AT+bxJ709mwPbAG8kubvlQOBXwHe0u8kA+PGWwHM79k3H1g+BW2RWpVkM+ACmmd2Xz/FzdlkVNWazuMuvwwcP9XtmamSHAg8H3j3FDdlk1JV/1lVy6tqVVWdB3yd5jlpteOhzs/3VtUdVXU38P9Yz3fupJeNdwMwK8leVXVjZ98BOFSnGSZJgA/R/L/Tl1bVI1PcpE3RLHyGsU2HALsD/9P8cWcesHmSp1bV06ewXZuaAjLVjZipquq+JLfRfM/jZg/jRqqqlTTDRKcl2TLJc4DDaHph1JIks5LMBjan+Qt9dhL/D1C73g/sAxxaVQ+tr7A2TpJf6ix7MS/J5kleBLwauGKq2zaDnUMTyA/sbB8APk+zGoNakGTrJC8a+Ts8yWuA5wJfnOq2zXDnAn/a+XtmG+DPgc/1O8F/YCfHCcCHgbtoZu0e75I6rXsL8Nauz79PM+vr1ClpzQyXZDfgdTTPz93Z6X0BeF1VfWzKGjazFc3w8wdo/s/9rcCfV9Wnp7RVM1hVPQg8OPI5yQrg4apaOnWtmvG2oFki7SnAGprJXa+oKtdibNfbgQU0o6QPA58E3tHvBN8lLUmSpL4ckpYkSVJfBkZJkiT1ZWCUJElSXwZGSZIk9WVglCRJUl8GRkmSJPVlYJSkSZJkSZJDBnStpyZZ3EK9lyR58WTXK2m4uQ6jJI1TZyHnEXNpFjJf0/k80EXMk1wMXFRV/zrJ9T4TeH9VPWMy65U03AyMkrQBktwCHFtVX56Ca+9I8776narq4RbqvxF4dVVNeg+mpOHkkLQkTZIktyR5fuf3U5NclOSjSZYn+UGSvZP8TZK7kvw0yQu7zn1Ckg8luSPJ7Un+LsnmY1zqBcB3u8Ni59onJrk2ycpOXdsnubxz/S933hlL5729H01yT5L7k/xXku276r8SeNmkf0GShpaBUZLacyhwAbAN8D3gizR/7+4MnAZ8sKvsecCjwJOAXwFeCBw7Rr1PA0Z71+4RNGFy7861LwdOonln7GbAGzrljgGeAOwCbAf8CfBQVz3/DRww7ruUNOMZGCWpPVdV1Rer6lHgImAh8A9V9Qjwr8DuSbbu9O69BPjzqlpZVXcB7waOHKPerYHlo+x/b1X9vKpuB64C/rOqvldVq4BLaYIowCM0QfFJVbWmqr5TVQ901bO8cw1JAmDWVDdAkmawn3f9/hBwd1Wt6foMMA/YCdgCuCPJSPnNgJ+OUe99wFbjuF7v53md3y+g6V381yRbAx8F/rYTZOnUff9YNyVp02MPoyRNvZ/SzLheUFVbd7b5VbXvGOWvpRl23iBV9UhVva2qngo8G3g5cHRXkX2Aaza0fkkzj4FRkqZYVd0B/DvwriTzk2yWZM8kB49xypeApyeZvSHXS/KbSZ7WmVTzAM0Q9ZquIgfTPP8oSYCBUZKmi6OBxwE/pBly/jdgx9EKVtXPgSuAwzbwWjt06n+AZoLL12iGpUnyq8DKqvr2BtYtaQZyHUZJGkJJnkozs/qZNYl/kXcWBP9QVV02WXVKGn4GRkmSJPXlkLQkSZL6MjBKkiSpLwOjJEmS+jIwSpIkqS8DoyRJkvoyMEqSJKkvA6MkSZL6MjBKkiSpr/8fgwfIujNL6twAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
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{
"data": {
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"data": {
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"data": {
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{
"data": {
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"metadata": {},
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{
"data": {
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"metadata": {},
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{
"data": {
"text/plain": [
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{
"data": {
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{
"data": {
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{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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},
"metadata": {},
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{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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},
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{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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},
"metadata": {},
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{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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},
"metadata": {},
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},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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},
"metadata": {},
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{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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},
"metadata": {},
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},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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},
"metadata": {},
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{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
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},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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},
"metadata": {},
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},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
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},
"metadata": {},
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{
"data": {
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"<Figure size 720x576 with 0 Axes>"
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},
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting 2D representation of network cell locations and connections...\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Done; plotting time = 3.81 s\n",
"\n",
"Total time = 8060.33 s\n",
"\n",
"End time: 2022-10-29 12:03:11.354731\n"
]
}
],
"source": [
"sim.simulate()\n",
"sim.analyze()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ceb34061",
"metadata": {},
"outputs": [],
"source": [
"# plotting\n",
"\n",
"#sim.analysis.plotLFP( plots = ['timeSeries', 'locations'] , electrodes=[ 'all'], lineWidth=1000 , fontSize=14, saveFig=True)\n",
"\n",
"# from matplotlib import pyplot\n",
"# %matplotlib inline\n",
"# pyplot.plot(t, ap1 )\n",
"# #pyplot.xlim((0, 10))\n",
"# pyplot.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "ddb4904a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Duration: 2:14:40.474044\n"
]
}
],
"source": [
"# show the execution time\n",
"\n",
"end_time = datetime.now()\n",
"print('Duration: {}'.format(end_time - start_time))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ce6eb39",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b23076f1",
"metadata": {},
"outputs": [],
"source": [
"# Longitudinal Current: picoamp\n",
"\n",
"\n",
"\n",
"# xraxia = xr*1e6 #ohm/cm\n",
"# xraxia = xraxia*2*1e-4 # ohm, length between node to MYSA is 2 micron\n",
"\n",
"\n",
"# v_diff_00 = (Abeta0_vext1_node0-Abeta0_vext1_MYSA0)/1000 #volt\n",
"# Longi_Current_node0_MYSA0 = v_diff_00/xraxia #amp\n",
"# Longi_Current_node0_MYSA0 = Longi_Current_node0_MYSA0*1e12 #picoamp\n",
"\n",
"# v_diff_12 = (Abeta0_vext1_node1-Abeta0_vext1_MYSA2)/1000 #volt\n",
"# Longi_Current_node1_MYSA2 = v_diff_12/xraxia \n",
"# Longi_Current_node1_MYSA2 = Longi_Current_node1_MYSA2*1e12 \n",
"\n",
"# v_diff_24 = (Abeta0_vext1_node2-Abeta0_vext1_MYSA4)/1000 #volt\n",
"# Longi_Current_node2_MYSA4 = v_diff_24/xraxia \n",
"# Longi_Current_node2_MYSA4 = Longi_Current_node2_MYSA4*1e12 \n",
"\n",
"# v_diff_36 = (Abeta0_vext1_node3-Abeta0_vext1_MYSA6)/1000 #volt\n",
"# Longi_Current_node3_MYSA6 = v_diff_36/xraxia \n",
"# Longi_Current_node3_MYSA6 = Longi_Current_node3_MYSA6*1e12 \n",
"\n",
"# v_diff_48 = (Abeta0_vext1_node4-Abeta0_vext1_MYSA8)/1000 #volt\n",
"# Longi_Current_node4_MYSA8 = v_diff_48/xraxia \n",
"# Longi_Current_node4_MYSA8 = Longi_Current_node4_MYSA8*1e12 \n",
"\n",
"# v_diff_510 = (Abeta0_vext1_node5-Abeta0_vext1_MYSA10)/1000 #volt\n",
"# Longi_Current_node5_MYSA10 = (v_diff_510/xraxia)*1e12 \n",
"\n",
"# v_diff_612 = (Abeta0_vext1_node6-Abeta0_vext1_MYSA12)/1000 #volt\n",
"# Longi_Current_node6_MYSA12 = (v_diff_612/xraxia)*1e12 \n",
"\n",
"# v_diff_714 = (Abeta0_vext1_node7-Abeta0_vext1_MYSA14)/1000 #volt\n",
"# Longi_Current_node7_MYSA14 = (v_diff_714/xraxia)*1e12 \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a336588c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 23,
"id": "e600ae81",
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"\n",
"# with open('mis_LongTranVoltageDifference_stimulateALL_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , v_diff_36 ))\n",
" "
]
},
{
"cell_type": "markdown",
"id": "8f3b15f1",
"metadata": {},
"source": [
"#### saving the data"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "890baeb5",
"metadata": {},
"outputs": [],
"source": [
"## saving the data\n",
"\n",
"\n",
"\n",
"\n",
"# ## writing\n",
"\n",
"\n",
"import csv\n",
"\n",
"\n",
"\n",
" \n",
"with open('BoundarytoGround1000_radius6_20Fibers_misaligned_v_Abeta0_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
" csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 , Abeta0_v_node12 , Abeta0_v_node13 , Abeta0_v_node14 , Abeta0_v_node15 , Abeta0_v_node16 , Abeta0_v_node17 , Abeta0_v_node18 , Abeta0_v_node19 , Abeta0_v_node20 , Abeta0_v_node21 , Abeta0_v_node22 , Abeta0_v_node23 , Abeta0_v_node24 , Abeta0_v_node25 , Abeta0_v_node26 , Abeta0_v_node27 , Abeta0_v_node28 , Abeta0_v_node29 , Abeta0_v_node30 , Abeta0_v_node31 , Abeta0_v_node32 , Abeta0_v_node33 , Abeta0_v_node34 , Abeta0_v_node35 )) \n",
"\n",
"\n",
" \n",
" \n",
"with open('BoundarytoGround1000_radius6_20Fibers_misaligned_imembrane_Abeta0_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
" csv.writer(f).writerows(zip( t , Abeta0_imembrane_node0 , Abeta0_imembrane_node1 , Abeta0_imembrane_node2 , Abeta0_imembrane_node3 , Abeta0_imembrane_node4 , Abeta0_imembrane_node5 , Abeta0_imembrane_node6 , Abeta0_imembrane_node7 , Abeta0_imembrane_node8 , Abeta0_imembrane_node9 , Abeta0_imembrane_node10 , Abeta0_imembrane_node11 , Abeta0_imembrane_node12 , Abeta0_imembrane_node13 , Abeta0_imembrane_node14 , Abeta0_imembrane_node15 , Abeta0_imembrane_node16 , Abeta0_imembrane_node17 , Abeta0_imembrane_node18 , Abeta0_imembrane_node19 , Abeta0_imembrane_node20 , Abeta0_imembrane_node21 , Abeta0_imembrane_node22 , Abeta0_imembrane_node23 , Abeta0_imembrane_node24 , Abeta0_imembrane_node25 , Abeta0_imembrane_node26 , Abeta0_imembrane_node27 , Abeta0_imembrane_node28 , Abeta0_imembrane_node29 , Abeta0_imembrane_node30 , Abeta0_imembrane_node31 , Abeta0_imembrane_node32 , Abeta0_imembrane_node33 , Abeta0_imembrane_node34 , Abeta0_imembrane_node35 )) \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# with open('mis_v_Abeta0_stimulateALL_dist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap00 , Abeta_ap01 , Abeta_ap02 , Abeta_ap03, Abeta_ap04 , Abeta_ap05 , Abeta_ap06 , Abeta_ap07 ))\n",
"\n",
"\n",
"\n",
"# with open('mis_imembrane_Abeta0_stimulateALL_dist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_imembrane_node0 , Abeta0_imembrane_node1 , Abeta0_imembrane_node2 , Abeta0_imembrane_node3, Abeta0_imembrane_node4 , Abeta0_imembrane_node5 , Abeta0_imembrane_node6 , Abeta0_imembrane_node7 ))\n",
"\n",
"\n",
" \n",
"# with open('stimulateAbeta4_v_Abeta4_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap40 , Abeta_ap41 , Abeta_ap42 , Abeta_ap43, Abeta_ap44 , Abeta_ap45 , Abeta_ap46 , Abeta_ap47 , Abeta_ap48 , Abeta_ap49 , Abeta_ap410 , Abeta_ap411 ))\n",
"\n",
" \n",
"# with open('stimulateAbeta5_v_Abeta5_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap50 , Abeta_ap51 , Abeta_ap52 , Abeta_ap53, Abeta_ap54 , Abeta_ap55 , Abeta_ap56 , Abeta_ap57 , Abeta_ap58 , Abeta_ap59 , Abeta_ap510 , Abeta_ap511 ))\n",
" \n",
"\n",
"# with open('stimulateAbeta7_v_Abeta7_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap70 , Abeta_ap71 , Abeta_ap72 , Abeta_ap73, Abeta_ap74 , Abeta_ap75 , Abeta_ap76 , Abeta_ap77 , Abeta_ap78 , Abeta_ap79 , Abeta_ap710 , Abeta_ap711 ))\n",
"\n",
"\n",
"# with open('stimulateAbeta9_v_Abeta9_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap90 , Abeta_ap91 , Abeta_ap92 , Abeta_ap93, Abeta_ap94 , Abeta_ap95 , Abeta_ap96 , Abeta_ap97 , Abeta_ap98 , Abeta_ap99 , Abeta_ap910 , Abeta_ap911 ))\n",
" \n",
"\n",
"# with open('stimulateAbeta12_v_Abeta12_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap120 , Abeta_ap121 , Abeta_ap122 , Abeta_ap123, Abeta_ap124 , Abeta_ap125 , Abeta_ap126 , Abeta_ap127 , Abeta_ap128 , Abeta_ap129 , Abeta_ap1210 , Abeta_ap1211 ))\n",
" \n",
"\n",
"# with open('stimulateAbeta15_v_Abeta15_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap150 , Abeta_ap151 , Abeta_ap152 , Abeta_ap153, Abeta_ap154 , Abeta_ap155 , Abeta_ap156 , Abeta_ap157 , Abeta_ap158 , Abeta_ap159 , Abeta_ap1510 , Abeta_ap1511 ))\n",
" \n",
" \n",
"\n",
"# with open('stimulateAbeta17_v_Abeta17_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap170 , Abeta_ap171 , Abeta_ap172 , Abeta_ap173, Abeta_ap174 , Abeta_ap175 , Abeta_ap176 , Abeta_ap177 , Abeta_ap178 , Abeta_ap179 , Abeta_ap1710 , Abeta_ap1711 ))\n",
" \n",
"\n",
"# with open('stimulateAbeta18_v_Abeta18_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap180 , Abeta_ap181 , Abeta_ap182 , Abeta_ap183, Abeta_ap184 , Abeta_ap185 , Abeta_ap186 , Abeta_ap187 , Abeta_ap188 , Abeta_ap189 , Abeta_ap1810 , Abeta_ap1811 ))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16d8bddc",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 25,
"id": "9766ae7e",
"metadata": {},
"outputs": [],
"source": [
"# netParams.cellParams.keys()\n",
"# netParams.cellParams['']['']"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "e19fa77c",
"metadata": {},
"outputs": [],
"source": [
"# pyplot.plot(t, ap1 )\n",
"# #pyplot.xlim((0, 10))\n",
"# pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "94e4f559",
"metadata": {},
"outputs": [],
"source": [
"#(1211 * 1e-6 ) / (0.1225 * 1e-8)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aca60f88",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}