{
"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: 2022-10-30 02:23:09.447647\n",
"\n",
"Creating network of 40 cell populations on 1 hosts...\n",
" Number of cells on node 0: 40 \n",
" Done; cell creation time = 4.47 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: 20 \n",
" Done; cell stims creation time = 0.01 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": [
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075537216448\n",
"3.0\n",
"2705.075480267492\n",
"3.0\n",
"2705.0754233185376\n",
"3.0\n",
"2705.0753663695864\n",
"3.0\n",
"2705.0753094206375\n",
"3.0\n",
"2705.075252471691\n",
"3.0\n",
"2705.0751955227465\n",
"3.0\n",
"2705.0751385738045\n",
"3.0\n",
"2705.0750816248647\n",
"3.0\n",
"2705.0750246759276\n",
"3.0\n",
"2705.074967726993\n",
"3.0\n",
"2705.074910778061\n",
"3.0\n",
"2705.074853829131\n",
"3.0\n",
"2705.074796880204\n",
"3.0\n",
"2705.074739931278\n",
"3.0\n",
"2705.0746829823556\n",
"3.0\n",
"2705.074626033435\n",
"3.0\n",
"2705.0745690845174\n",
"3.0\n",
"2705.074512135602\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",
"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] + 2.994438 #dist[i][j]\n",
" rd = rho*d\n",
" s = ((4*2)+(4*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 = (4*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": "a039251a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5339200000.0\n"
]
}
],
"source": [
"print(rho + (1211 * 10000 * Boundary_dist[18][18] ))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5eb858c9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5605620000.0\n"
]
}
],
"source": [
"print(rho + (1211 * 10000 * Boundary_dist[0][0] ))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7808a6c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT_BOUNDARY00.printf() "
]
},
{
"cell_type": "markdown",
"id": "b2a6c256",
"metadata": {},
"source": [
"#### Recordings"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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": 17,
"id": "ca5603a0",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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},
{
"data": {
"text/plain": [
"Vector[1583]"
]
},
"execution_count": 17,
"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": 18,
"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",
"# 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": 19,
"id": "23017f07",
"metadata": {},
"outputs": [],
"source": [
"\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",
"# for i2 in range(12):\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(0,24,2):\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": 20,
"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": 21,
"id": "cd6d9f09",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Running simulation for 6.0 ms...\n",
" Done; run time = 8241.09 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 5.51 s.\n",
"\n",
"Analyzing...\n",
" Cells: 40\n",
" Connections: 0 (0.00 per cell)\n",
" Spikes: 20 (83.33 Hz)\n",
" Simulated time: 0.0 s; 1 workers\n",
" Run time: 8241.09 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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ijE4cwP33l1OPJEmdxhCnSpTRiQNDnCRJAzHEqRJldeKc3CBJUv8McapETyfO4VRJkqphiFMlxjqcOn067LWXnThJkgZiiFMlxjqcGlF04+zESZLUP0OcKjHWThwY4iRJGowhTpUYaycOihmqDqdKktQ/Q5wqYSdOkqRqGeJUibI6cQ8+CNu3l1OTJEmdxBCnSpTVicv00VuSJPXHEKdKlNGJc604SZIGZohTJcroxPU8esvJDZIk7ckQp0rYiZMkqVqGOFWirHviwBAnSVJ/DHGqRBmduOnTYdo0h1MlSeqPIU6V6FkWZCydOHCtOEmSBmKIUyU2biz206aN7ToHHmiIkySpP4Y4VWLjRpg6tZxOnMOpkiTtyRCnSmzYMPYuHDicKknSQAxxqsSGDcXEhLHqefTWtm1jv5YkSZ3EEKdKlBXiepYZ8dFbkiTtzhCnSmzcWN5wKjikKklSX4Y4VaLM4VRwcoMkSX0Z4lSJsodT7cRJkrQ7Q5wqsXGjIU6SpCoZ4lSJDRtgr73Gfp1p04ow6HCqJEm7M8SpElu2wJQp5VzLteIkSdqTIU6V2LoVJk0q51oHHmgnTpKkvtouxEXEfhFxSURsjIjlEfHSumvSnrZsgcmTy7mWnThJkvbUdiEO+BSwFZgDvAz4TEQcVW9J6m3HjmIrqxNniJMkaU9tFeIiYhpwGvCuzNyQmVcC3wVeUW9l6q3nEVlldeIOPBBWr/bRW5Ik9dZWIQ44DNiRmbf2OnYjYCeuhWzZUuzL7MQBPPBAOdeTJKkTtFuImw6s7XNsLTCj94GIOCMilkTEklU+dLPptm4t9mVObACHVCVJ6q3dQtwGYGafYzOB9b0PZOZ5mbk4MxfPnj27acWp0BPiypzYAM5QlSSpt3YLcbcCEyLi0F7HjgGW1lSP+lHVcKqdOEmSdmmrEJeZG4GLgfdGxLSIOAH4C+Cr9Vam3uzESZJUvbYKcQ1nA1OBB4BvAK/NTDtxLaTse+L22gtmzLATJ0lSbxPqLmCkMvMh4AV116GBlT2cCq4VJ0lSX+3YiVOLK3s4FXz0liRJfRniVDo7cZIkVc8Qp9KVfU8cFJ04Q5wkSbsY4lS6KoZT58yBhx7adW1JkrqdIU6lq2o4FXz0liRJPQxxKl1VExvAyQ2SJPUwxKl027YV+wklLmDjUxskSdqdIU6l27Gj2I8fX941DXGSJO3OEKfS7dxZ7KsIcQ6nSpJUMMSpdD2duHEl/umaOhX23ttOnCRJPQxxKl1PJ67MEAc+tUGSpN4McSpdFcOpYIiTJKk3Q5xKV8VwKhQhbuXKcq8pSVK7MsSpdHbiJEmqniFOpauyE7d+PWzcWO51JUlqR4Y4la6qiQ1z5xZ7Z6hKkmSIUwWqHE4Fh1QlSQJDnCpQ5XAqGOIkSQJDnCpgJ06SpOoZ4lS6qu6JmzWruKbLjEiSZIhTBXqGUyPKve748XDAAXbiJEkCQ5wqsHNn+UOpPVwrTpKkgiFOpduxo/yh1B6GOEmSCoY4lc5OnCRJ1TPEqXQ7d1bXiZs7t1jst2fyhCRJ3coQp9JVPZy6bRusWVPN9SVJaheGOJWu6uFUcEhVkiRDnEpXdScOXCtOkiRDnEpnJ06SpOoZ4lS6Kic2GOIkSSoY4lS6KodTZ8yAqVMNcZIkGeJUuiqHUyNcK06SJDDEqQJVduKgWCvOECdJ6naGOJWuyk4c2ImTJAkMcapAlRMbwBAnSRIY4lSBqodTDzwQVq+GrVurew9JklqdIU6la8ZwKhTPUJUkqVsZ4lS6ZnTiwCFVSVJ3M8SpdM3qxBniJEndzBCn0jVjYgMY4iRJ3c0Qp9JVPZw6Z06xN8RJkrqZIU6lq3o4ddIk2H9/Q5wkqbsZ4lS6qjtx4FpxkiQZ4lS6qjtxUIS4e++t9j0kSWplhjiVruqJDQDz5sHKldW+hyRJrcwQp9I1Yzh13ryiE5dZ7ftIktSqDHEqXTOGUw86CLZtgwcfrPZ9JElqVYY4la5ZnTjwvjhJUvcyxKl0zbonDgxxkqTuZYhT6Zo1nAqwYkW17yNJUqsyxKl0zVonDuzESZK6lyFOpWtGJ27SJJg92xAnSepeE4ZzUkScArwaOAqYAawHlgJfzMwfVVad2lIz7omDXcuMSJLUjYYMcRHxBuAtwOeBbwNrgZnAMcCXI+IjmfkvlVapttKM4VQo7ovznjhJUrcaTifuzcAzMvOWPscvjohvAD8FDHH6b80YToWiE3f99dW/jyRJrWg4/ZJpwECDVvcBe5VXjjpBszpx8+bB/ffD9u3Vv5ckSa1mOP/Ufhv4XkScHBGzI2JSRMyKiJOBS4BvVVui2k1m84ZTM+G++6p/L0mSWs1w/qk9C/gV8GXgfmBzY/9l4CrgtZVVp7bUrOeZuuCvJKmbDXlPXGZuBd4GvC0i9gGmAxsy8+G+50bECZn5y7KLVPuJqP49DHGSpG42rCVGejSC28ODnPKfFDNX1cWa1YnreWqDIU6S1I3KvnOpCf0XtYNmdOJmzy5mwbrMiCSpG5Ud4kbVg4mIyRFxfkQsj4j1EXFDRDynzzknR8QtEbEpIn4aEQvKKVlla1Ynbtw4mDvXTpwkqTu1ymO3JgB3AycCewPvAr4ZEQsBImIWcHHj+H7AEuDCWirVsDSjEwfFkKohTpLUjVoixGXmxsw8NzPvzMydmfl94A7gSY1TXggszcyLMvMR4FzgmIg4vKaSNYhmdeKgmNzgcKokqRu15D1xETEHOIzi+axQPLP1xp7XM3MjsKxxvL+fPyMilkTEklWrVpVRkkaoWZ04n58qSepWIwpxEbF/RLwiIt7S+H5eRBzc83pmzhhrQRExEfga8OVej/qaTvHM1t7WAv2+X2ael5mLM3Px7Nmzx1qSRqiZnbiDDoI1a2Dz5ua9pyRJrWDYIS4iTgT+ALyM4t40gEOBzwzjZ6+IiBxgu7LXeeOArwJbgdf1usQG9ly6ZCawfrj1q7maeU8cOKQqSeo+I+nE/TPw4sx8NtDztMqrgScP9YOZeVJmxgDbUwEiIoDzgTnAaZm5rdcllgLH9HwTEdOARewablULyWxeiJs/v9jffXdz3k+SpFYxkhC3MDN/3Pi6Z8BsKyNcMHgQnwGOAJ6fmX0Hxy4BHhcRp0XEFODdwE29hlvVQpo5nGqIkyR1q5GEuN9HxKl9jj0L+O1Yi2is+XYmcCxwX0RsaGwvA8jMVcBpwAeANcDxwOljfV9Vp1mduIMbd2Qa4iRJ3WYkXbQ3At+PiB8AUyPic8Dzgb8YaxGZuZwhZrZm5uWAS4q0gWZ24qZMKZ7cYIiTJHWbYXfiMvMqivvSlgL/RrGO25Mz89qKalMba1YnDooh1bvuat77SZLUCkZ0P1tmrgD+saJa1CGa2YmDIsQtW9bc95QkqW6DhriI+CrDeB5qZr6ytIrUEZrdibviiua9nyRJrWCo4dTbKZ6MsIxicd0XAOOBexo/+xfAw9WVp3ZURydu7VpY76qBkqQuMmgnLjPf0/N1RFwGPC8zf9Hr2FPZtfCv9N+a3YmDYnLDkUc2730lSarTSJYY+RPgqj7Hrgb+tLxy1Anq6MSBM1QlSd1lJCHuBuCDETEVoLH/APCbCupSm2tmJ+5Rjyr2hjhJUjcZSYh7NXACsDYi7qe4R+6pgJMatJtmd+LmzYNx4wxxkqTuMuwlRjLzTuApETEfmAeszExX59IemvnsVIAJE2DuXNeKkyR1l5F04oiIfYFnAM8ETmp8L+2hmSEOivvi7MRJkrrJsENcRPwpxVIjZwFHUzzrdFnjuPTfmj2cCoY4SVL3GUkn7p+BszPzKZn5ksw8AXgt8K+VVKa2Vlcnro4AKUlSHUYS4g4Dvtnn2LeAQ8orR52grk7c5s3w0EPNf29JkuowkhB3G3B6n2MvohhilXZTRycOHFKVJHWPYc9OBc4Bvh8RrweWAwuBQ4E/L78stbO6OnFQhLhjj23++0uS1GwjWWLkVxGxCHgexRIj3wMuzUwHsLSHZnfiehb8dZkRSVK3GEknjsxcA1xQUS3qEHV04ubMgSlT4M47m//ekiTVYdghLiIeTfGYrWOB6b1fy8xHlVuW2l2zO3ERsGCBIU6S1D1G0on7OsUkhjcCm6opR52grmU+Hv1ouOOOet5bkqRmG0mIOwo4ITN3VlWMOkezO3EACxfCtdc2/30lSarDSJYY+TnwhKoKUedo9rNTezz60bB6Naxf3/z3liSp2UbSibsTuCwiLgbu6/1CZr67zKLU3uoaTl24sNjfeSc8/vH11CBJUrOMpBM3jWJZkYnA/F7bwRXUpTZXVycOvC9OktQdRrJO3F8NdU5EvCQzvzG2ktTuWqETJ0lSpxtJJ244Plfy9dSm6ujEzZoF06bZiZMkdYeyQ1wN/3Sr1dTViYsounF24iRJ3aDsEFfTP99qNXV04sC14iRJ3aPsECfV1okDO3GSpO4xZIiLCIOeRqzOTtzatbBmTT3vL0lSswwnoK2IiH+MiMcN49y7xlqQ2l/dnTiwGydJ6nzDCXFnAY8Gro2I6yPi/0bE7P5OzMzhBD11gbo6cY95TLFftqye95ckqVmGDHGZ+R+Z+SJgLsUSIi8C7o6I70bEaRExseoi1V7qeuwWwKJFxd4QJ0nqdMO+3y0zH87Mz2XmU4EjgCXAx4GVVRUnjdSMGTBnDtx+e92VSJJUrRFPWoiIycBxwPHAHOC3ZRel9lZnJw7gkEMMcZKkzjfsEBcRT42I84D7gfcDVwGHZeYzqipO7anOiQ1QhLjbbqu3BkmSqjacJUbOjYhlwPcah56XmYdl5vsyc3m15ald1d2JW7ECNm2qrwZJkqo2YRjn/AnwDuA7mflIxfWoA7RCJw7gj3+ExzlfWpLUoYYMcZn57GYUos5SdycOivviDHGSpE7l0xhUulbpxDm5QZLUyQxxqkSdnbh99oFZswxxkqTOZohT6eruxIEzVCVJnc8Qp0rU2YkD14qTJHU+Q5xK1yqduLvvhkecTy1J6lCGOFWiFTpxmT5DVZLUuQxxKl3dj90COPzwYn/LLfXWIUlSVQxxKl0rDKc+9rHF3hAnSepUhjhVou5O3PTpMH8+3HxzvXVIklQVQ5xK1wqdOIAjjjDESZI6lyFOlai7EwdFiLvlFti5s+5KJEkqnyFOpWuVTtzhh8OmTXDPPXVXIklS+QxxqkSrdOLAyQ2SpM5kiFPpWqkTB94XJ0nqTIY4VaIVOnEHHAD77muIkyR1JkOcStcqnbiIohvncKokqRMZ4lSJVujEgcuMSJI6lyFOpWuFx271OPJIeOABePDBuiuRJKlchjh1tKOPLva//W29dUiSVDZDnErXSp24xz++2N90U711SJJUNkOcStcqExsA5syB2bMNcZKkzmOIUyVapRMXUQypOpwqSeo0LRfiIuLQiHgkIi7oc/zkiLglIjZFxE8jYkFdNaq9HH00/O53sGNH3ZVIklSelgtxwKeAa3sfiIhZwMXAu4D9gCXAhc0vTcPVKp04KO6L27wZli2ruxJJksrTUiEuIk4HHgZ+3OelFwJLM/OizHwEOBc4JiIOb26FGkor3Q/XwxmqkqRO1DIhLiJmAu8F3tjPy0cBN/Z8k5kbgWWN42pBrdSJO/JIGDfOyQ2SpM7SMiEOeB9wfmbe3c9r04G1fY6tBWb0d6GIOCMilkTEklWrVpVcpgbTip24qVPhsMMMcZKkztKUEBcRV0REDrBdGRHHAs8CPj7AJTYAM/scmwms7+/kzDwvMxdn5uLZs2eX9nto+FqpEwfFkOqNNw59niRJ7WJCM94kM08a7PWIOAdYCNwVxb/+04HxEXFkZj4RWAq8qtf504BFjeNqIT2duFYLcU98Inzzm/DQQ7DffnVXI0nS2LXKcOp5FKHs2Mb2WeAHwKmN1y8BHhcRp0XEFODdwE2ZeUvzS1U7Wry42F9/fb11SJJUlpYIcZm5KTPv69kohk8fycxVjddXAacBHwDWAMcDp9dWsAbUyp04gCVL6q1DkqSyNGU4daQy89x+jl0OuKSIRmXffWHRIkOcJKlztEQnTp2jVTtxUAypGuIkSZ3CEKdSteISIz0WL4bly+HBB+uuRJKksTPEqRKt2Il70pOK/XXX1VuHJEllMMSpVK3ciXNygySpkxjiVIlW7MTtvXfx5IZrr627EkmSxs4Qp1K1cicO4Pjj4de/bv06JUkaiiFOlWjFThzACSfAAw/AH/9YdyWSJI2NIU6lauUlRqAIcQC//GW9dUiSNFaGOHWVI48s7o0zxEmS2p0hTqVq9U7cuHHwp38Kv/pV3ZVIkjQ2hjh1nRNOgKVL4eGH665EkqTRM8SpVK3eiQN4ylOKOn/967orkSRp9AxxKlU7LN1x/PEwfrz3xUmS2pshTpVo5U7ctGnFI7iuuKLuSiRJGj1DnErVDp04gJNPhquvhg0b6q5EkqTRMcSpEq3ciQN45jNh+3b4xS/qrkSSpNExxKlU7dKJO+EEmDQJfvzjuiuRJGl0DHGqRKt34qZOLWap/uQndVciSdLoGOJUqnbpxEFxX9xvfgOrV9ddiSRJI2eIUyVavRMHxX1xmfDTn9ZdiSRJI2eIU6naYbHfHscdVzxH9Yc/rLsSSZJGzhCnrjVxIpxyClx6aXsNA0uSBIY4laydOnEAz3serFwJN9xQdyWSJI2MIU5d7TnPKQLnD35QdyWSJI2MIU6lardO3AEHFPfGGeIkSe3GEKdSteO9Zc97HlxzDaxaVXclkiQNnyFOlWiXThzAn/95ET6///26K5EkafgMcSpVO3binvAEWLgQvvWtuiuRJGn4DHGqRDt14iLgL/8SfvQjePjhuquRJGl4DHEqVTt24qAIcdu2wXe/W3clkiQNjyFOlWinThzAk58M8+c7pCpJah+GOJWq3ZYY6dEzpHrZZbB2bd3VSJI0NEOc1PDiF8PWrfDtb9ddiSRJQzPEqVTt2omDYkj1sMPgK1+puxJJkoZmiJMaIuCVr4Sf/QzuvLPuaiRJGpwhTqVq504cwMtfXuwvuKDeOiRJGoohTuplwQJ4xjOKIdV2XS5FktQdDHEqVbt34gBe/Wq47bZiWFWSpFZliFOpOqF79aIXwX77wac/XXclkiQNzBCnSrRzJ27qVHjNa+CSS2DlyrqrkSSpf4Y4laoTOnEAZ50F27fDF75QdyWSJPXPEKdKtHMnDmDRIjj1VPjc54owJ0lSqzHEqVSdMLGhx9lnw4oV8J3v1F2JJEl7MsRJA3je8+CQQ+CjH+2cYWJJUucwxKlUndSJGz8e3vQmuOYauOKKuquRJGl3hjhpEK96FcyZAx/5SN2VSJK0O0OcStVJnTiAKVPgnHPgssvgN7+puxpJknYxxElDOOssmDEDPvjBuiuRJGkXQ5xK1WmdOIB99oHXvx4uushunCSpdRjipGF405uKMPfud9ddiSRJBUOcStWJnTgoAtxb3gLf+x5cdVXd1UiSZIhTyTp5PbXXvx4OOADe8Y7O/j0lSe3BEKdKdFonDmDatCLA/eQncOmldVcjSep2hjiVqtM7VK99LRx+OPzd38HWrXVXI0nqZoY4VaITO3EAEyfCxz4Gt94Kn/xk3dVIkrqZIU6l6tSJDb095znF9t73wgMP1F2NJKlbGeKkUfjYx2DTJnjjG+uuRJLUrQxxKlU3dOKguC/u7W+HCy6AH/6w7mokSd3IECeN0tveBkccAWeeCRs21F2NJKnbGOJUqm7pxAFMngyf/zzcdRe88511VyNJ6jYtFeIi4vSIuDkiNkbEsoh4Wq/XTo6IWyJiU0T8NCIW1FmrBHDCCXD22fCv/wo//3nd1UiSuknLhLiI+DPgI8BfATOApwN/bLw2C7gYeBewH7AEuLCeSjWYburE9fjwh+Exj4FXvAIefrjuaiRJ3aJlQhzwHuC9mXlVZu7MzBWZuaLx2guBpZl5UWY+ApwLHBMRh9dVrPrX6Yv99mfGDPj61+Hee+Gss7rzM5AkNV9LhLiIGA8sBmZHxO0RcU9EfDIipjZOOQq4sef8zNwILGscVwvqpk4cwJOfDO95D1x4IXz1q3VXI0nqBi0R4oA5wETgL4GnAccCTwB6bhefDqzt8zNrKYZd9xARZ0TEkohYsmrVqkoKVv+6uQv11rfC059e3CP3u9/VXY0kqdM1JcRFxBURkQNsVwKbG6d+IjNXZuaDwMeA5zaObwBm9rnsTGB9f++Xmedl5uLMXDx79uwqfiUNods6cQDjx8M3vgHTp8P//J/eHydJqlZTQlxmnpSZMcD21MxcA9wDDNTHWQoc0/NNREwDFjWOq4V048SG3ubNg299C+68s5josHNn3RVJkjpVqwynAnwR+NuIOCAi9gXOAb7feO0S4HERcVpETAHeDdyUmbfUU6o0sKc+FT7+cfj+9+Hcc+uuRpLUqVopxL0PuBa4FbgZuAH4AEBmrgJOa3y/BjgeOL2eMjWYbu/E9fibv4HXvAbe9z744hfrrkaS1Ikm1F1Aj8zcBpzd2Pp7/XLAJUXUFiLgs5+Fu++GM86Agw6CU06puypJUidppU6cOoCduF0mTizujzvySDjtNLjhhrorkiR1EkOcVKGZM+HSS2HffYtOnEuPSJLKYohTqezE7emgg+AnPyk6c896FvzhD3VXJEnqBIY4qQkOOaQIcpnwzGfCrbfWXZEkqd0Z4lQqO3EDO/xwuPxy2LatWIbk+uvrrkiS1M4McSpVNz92azge/3i48kqYOhWe8Qz4+c/rrkiS1K4McaqEnbiBHXZYEeTmzYNTT4XvfKfuiiRJ7cgQp1I5nDo88+fDL34BRx8NL3whfOhDdjElSSNjiJNqMmsWXHEFvOQl8Pa3w8tfDps3112VJKldGOJUKjtxIzN1KlxwAXzwg/D1r8PTngbLltVdlSSpHRjipJpFwNveBt/9Lvzxj/CEJ8CFF9ZdlSSp1RniVCo7caP3/OfDb34Dj3scnH568czVDRvqrkqS1KoMcVILedSj4Gc/g7e+Fb7whSLQXX553VVJklqRIU6lshM3dhMnwoc/XMxenTwZ/uzPiq7c2rV1VyZJaiWGOKlFnXBCMbz6pjfB+efDEUcUkyBcikSSBIY4lcxOXLmmToWPfhSuuqpYW+4VryjC3ZIldVcmSaqbIU5qA8cdB7/+NXzxi8USJE9+chHoXI5EkrqXIU6lshNXnXHj4NWvhltvhTe/Gb79bTj8cDjzTLjnnrqrkyQ1myFOpTLEVW/vveEjHym6cGedVXTnDjkEXve6Yp05SVJ3MMRJbWruXPjEJ+C224pHdp13Hhx6KPyv/wXXXlt3dZKkqhniVCo7cc23YEGxptyddxbDrP/1X8U9c09/OnzjG7BlS90VSpKqYIiTOsS8ecX6cnfdBf/0T7BiBbz0pXDwwfCWt8Dtt9ddoSSpTIY4lcpOXP1mzoS/+7timPWyy4qO3Mc+Vgy1nnhiMey6Zk3dVUqSxsoQJ3WocePglFOKWax33QXvfz888EAxm3XOHHjBC+Cii2Dz5rorlSSNhiFOpbIT15rmzYN3vAN+/3u47rpiJus11xSTIGbNgtNOg698BR56qO5KJUnDZYiTukgEPPGJxfDq3XfD5ZfDX/0VXH01vOpVcMAB8Mxnwr/8S7EenY/4kqTWZYhTqezEtY/x4+Hkk+GTnywC3bXXwt//fTHkes458NjHwqMfDWecAd/6lvfRSVKrmVB3AZLqFwGLFxfb+99fLBr8ox8VEyMuvBA+//niHrvFi+Gkk+BpTyue4brvvnVXLkndy06cSmUnrjM85jHFBIiLL4bVq+GXv4R3vQsmTICPfxye/3zYf384+uji/roLL4R77627aknqLnbiVCrvoeo8EybAU55SbOeeW8xmvfpq+MUviu1LX4JPfao49+CD4bjjisWGjzuu6NztvXed1UtS5zLEqRJ24jrX1KnFkOpJJxXfb98ON9wAV15Z3Fd37bVwySW7zn/sY4tAd9xxcMwxRffOYVhJGjtDnErlcGr3mTBhV0jr8dBDsGTJrlD3k5/ABRfsen3+/CLMHX30rmB36KHFtSRJw+NfmZJKt99+xULDp5yy69jKlXDTTbu2G28sJk5s3168PnkyHHkkHHEEHH74ru3QQ2HKlHp+D0lqZYY4lcpOnAYyd26xnXrqrmNbt8IttxSB7qab4He/g1/9Cr7xjV1/lsaNK5Y66R3sDjsMFi0qrjfO6VmSupQhTlJtJk3aNaza26ZNxWLDt9yya7v55mJx4i1bdp03ZUoxk3bRoj23hQuL60tSpzLEqVR24lSGvfaCY48ttt527IDly+H222HZst23H/+4CH89xo0r7r179KNhwQJ41KN238+fX0zSkKR2ZYiT1DbGjy86b495zJ6vZcL99+8Z7u64owh4994LO3fu/jOzZ+8Kdr1D3sEHF8+bnTMHJk5szu8mSSNliFOp7MSpLhFw4IHFdsIJe76+bRusWAF33VVsy5fv+vrmm+GHP9y9k9dzzTlzikB30EHFvr+v99/fP/OSms8QJ6krTJxY3Ce3cGH/r2cWz4ddvrzo2q1Ysfv+rrvgqqtg1ao9f3bSpCLQzZ0LBxxQBL+BtpkzDXySymGIU6nsxKldRRRLo+y3HzzhCQOft3VrsVxK74DX8/XKlcUQ7q9+BQ8+2P8TTCZPLsLcQGFv9myYNavY9t/f5VUkDcwQJ0kjMGlSce/cggWDn7d9exHkHniguFevv23FCrj++uKcHTv6v8706buHup6v+9v237/YnJUrdQdDnEplJ04qTJiw6x69oezcWTzl4v77i+DXe1u9evfvb7212K9fP/D1Zs7cFer22694zNm++w78dc/3e+3l/3eldmKIU6kMcdLIjRu3q5s2XFu2FMGvb+jrG/7WrCmGeNesKba+M3R7mzhxeGFv331h7713bfvsAzNmFLOHJTWPIU6S2tDkybuegjFcO3cWHbyeQLdmTREE+/t6zRq4775i5u6aNfDww0Nff8aM3cPdSLeZMw2C0kgY4lQqO3FS6xo3bldgGmiW7kB27IC1a3eFvbVrh95WrSoWZu75vvfTNgYyffqewW7GjJFtM2cW1/GRbOp0hjhJ0pDGj981e3fRotFdY8uW4YW/3tuaNcXyLuvX79oGGxLubdq0kYe/nq+nTSuC4LRpuzYXflarMcSpVHbiJA1k8uRiaZUDDhj9NTJh8+bdQ926dbt/P9C2bh3cc8/uxzZvHv57T5q0e7Dr7+uhXu/v3KlT/TtTo2OIkyS1jYhiFu1eexXr6o3V9u2wYcOeYW/jxl3bhg277/t+fd99e563ffvIfqfeHb++HcC99iqCXs/vPdA22DmTJxsUO5EhTqWyEyepnUyYUMyu3Wefcq+7devwQ2B/X2/YUGyrVhWPg+u9Defewr4ixh4E+74+dWqxGHXf/cSJ/hvQLIY4SZJKNmlSse27b/nX3rGjGAbetGnXfrBtqHPWrSu6iX2Pb906uvrGjdsV6gYKekO9NppzuvGeRUOcSmUnTpKqNX58MeQ6fXq177N9exEABwqBmzfDI4+MbL95czFh5b77+j9nJMPQfY0fP3jQmzy5+L73vr9jYzmn2V1IQ5wkSdrDhAm7Zus2y/btgwfAkYbG3j+7bl3xiLstW4rjffcDPfpupMYSBkf6rGRDnEplJ06SNFoTJjSny9if7duLQNez9Rf0ythv2lQsnTPQ6yNhiFMlDHGSpHYyYUKxTZtWbx0j+ffT9axVqp5OnCRJqpYhTqVyOFWSpOYwxEmSJLUhQ5xKZSdOkqTmMMRJkiS1IUOcSmUnTpKk5miZEBcRCyPi0ohYExH3RcQnI2JCr9dPjohbImJTRPw0IhbUWa8kSVKdWibEAZ8GHgDmAscCJwJnA0TELOBi4F3AfsAS4MJaqtSg7MRJktQcrRTiHg18MzMfycz7gB8CRzVeeyGwNDMvysxHgHOBYyLi8HpKlSRJqlcrPbHhX4DTI+IKYF/gORSdNyjC3I09J2bmxohY1jh+y2AXve02OPXUSupVPx54oNjbiZMkqVqtFOJ+BvwfYB0wHvgy8J3Ga9OBVX3OXwv0+1jeiDgDOANg0qSjWbeugmrVrylT4NnPhkMPrbsSSZI6W1NCXKO7duIAL/8SeDpwGfA54CkUoe3fgI8AbwE2ADP7/NxMYH1/F8zM84DzABYvXpy//vXY6pckSWo1TbknLjNPyswYYHsqxWSF+cAnM3NLZq4Gvgg8t3GJpcAxPdeLiGnAosZxSZKkrtMSExsy80HgDuC1ETEhIvYBXsWu++AuAR4XEadFxBTg3cBNmTno/XCSJEmdqiVCXMMLgWdT3Pt2O7AdeANAZq4CTgM+AKwBjgdOr6dMSZKk+rXMxIbM/A1w0iCvXw64pIgkSRKt1YmTJEnSMBniJEmS2pAhTpIkqQ0Z4iRJktqQIU6SJKkNGeIkSZLakCFOkiSpDRniJEmS2pAhTpIkqQ0Z4iRJktqQIU6SJKkNGeIkSZLakCFOkiSpDRniJEmS2lBkZt01VCoi1gN/qLuOLjMLeLDuIrqMn3nz+Zk3n5958/mZN99jM3PGcE6cUHUlLeAPmbm47iK6SUQs8TNvLj/z5vMzbz4/8+bzM2++iFgy3HMdTpUkSWpDhjhJkqQ21A0h7ry6C+hCfubN52fefH7mzedn3nx+5s037M+84yc2SJIkdaJu6MRJkiR1HEOcJElSG+rYEBcR+0XEJRGxMSKWR8RL666p00XE6yJiSURsiYgv1V1PN4iIyRFxfuPP+PqIuCEinlN3XZ0sIi6IiJURsS4ibo2Iv667pm4REYdGxCMRcUHdtXSDiLii8XlvaGyuudoEEXF6RNzcyC/LIuJpA53byevEfQrYCswBjgV+EBE3ZubSWqvqbPcC7wdOBabWXEu3mADcDZwI3AU8F/hmRDw+M++ss7AO9iHgf2fmlog4HLgiIm7IzOvqLqwLfAq4tu4iuszrMvMLdRfRLSLiz4CPAC8GrgHmDnZ+R3biImIacBrwrszckJlXAt8FXlFvZZ0tMy/OzO8Aq+uupVtk5sbMPDcz78zMnZn5feAO4El119apMnNpZm7p+baxLaqxpK4QEacDDwM/rrkUqUrvAd6bmVc1/k5fkZkrBjq5I0MccBiwIzNv7XXsRuComuqRmiIi5lD8+bfjXKGI+HREbAJuAVYCl9ZcUkeLiJnAe4E31l1LF/pQRDwYEb+MiJPqLqaTRcR4YDEwOyJuj4h7IuKTETHgyFanhrjpwNo+x9YCw3oWmdSOImIi8DXgy5l5S931dLLMPJvi75OnARcDWwb/CY3R+4DzM/PuugvpMm8FHgMcRLF22fciwq5zdeYAE4G/pPi75VjgCcA7B/qBTg1xG4CZfY7NBNbXUItUuYgYB3yV4j7Q19VcTlfIzB2NWzUOBl5bdz2dKiKOBZ4FfLzmUrpOZl6dmeszc0tmfhn4JcV9t6rG5sb+E5m5MjMfBD7GIJ95p05suBWYEBGHZuZtjWPH4BCTOlBEBHA+xX/FPTczt9VcUreZgPfEVekkYCFwV/FHnenA+Ig4MjOfWGNd3SiBqLuITpWZayLiHorPeVg6shOXmRsphjjeGxHTIuIE4C8oOhWqSERMiIgpwHiKv2SnRESn/odCK/kMcATw/MzcPNTJGr2IOKAx/X96RIyPiFOBlwA/qbu2DnYeRUg+trF9FvgBxSx4VSQi9omIU3v+Ho+IlwFPBy6ru7YO90Xgbxt/1+wLnAN8f6CTO/kf2LOBfwMeoJgt+VqXF6ncO4F/6PX9yylm2pxbSzVdICIWAGdS3JN1X6NTAXBmZn6ttsI6V1IMnX6W4j+ClwPnZOZ/1FpVB8vMTcCmnu8jYgPwSGauqq+qrjCRYsmow4EdFJN4XpCZrhVXrfcBsyhGFB8Bvgl8YKCTfXaqJElSG+rI4VRJkqROZ4iTJElqQ4Y4SZKkNmSIkyRJakOGOEmSpDZkiJMkSWpDhjhJHS0iljbrwd0RcWRELKnguhdHxLPLvq6k9uY6cZLaWmPx1x57USx8vKPxfVMXPY6IbwMXZea/l3zdJwOfycwnlXldSe3NECepY0TEncBfZ+blNbz3XIrnM8/LzEcquP5twEsys/ROn6T25HCqpI4WEXdGxLMaX58bERdFxAURsT4ifhsRh0XE2yLigYi4OyJO6fWze0fE+RGxMiJWRMT7I2L8AG/1Z8D1vQNc473fHBE3RcTGxrXmRMR/Nt7/8sbzEWk8o/KCiFgdEQ9HxLURMafX9a8Anlf6BySpbRniJHWb5wNfBfYFbqB4oPc44CDgvcDnep37ZWA7cAjwBOAU4K8HuO7jgf6eK3kaRcA7rPHe/wm8neL5iOOA1zfOexWwNzAf2B84C9jc6zo3A8cM+7eU1PEMcZK6zS8y87LM3A5cBMwGPpyZ24B/BxZGxD6NLthzKB5wvzEzHwA+Dpw+wHX3Adb3c/wTmXl/Zq4AfgFcnZk3ZOYW4BKKcAiwjSK8HZKZOzLzusxc1+s66xvvIUkATKi7AElqsvt7fb0ZeDAzd/T6HmA6MA+YCKyMiJ7zxwF3D3DdNcCMYbxf3++nN77+KkUX7t8jYh/gAuAdjXBJ49oPD/RLSeo+duIkqX93U8x0nZWZ+zS2mZl51ADn30QxZDoqmbktM9+TmUcCTwH+HHhlr1OOAG4c7fUldR5DnCT1IzNXAv8F/FNEzIyIcRGxKCJOHOBHfgQ8MSKmjOb9IuIZEfH4xsSJdRTDqzt6nXIixf10kgQY4iRpMK8EJgG/pxgu/RYwt78TM/N+4CfAX4zyvQ5sXH8dxSSGn1EMqRIRxwEbM/OaUV5bUgdynThJKklEHEkxo/XJWeJfro1FhM/PzEvLuqak9meIkyRJakMOp0qSJLUhQ5wkSVIbMsRJkiS1IUOcJElSGzLESZIktSFDnCRJUhsyxEmSJLUhQ5wkSVIb+v8TH2Nzr/qt0AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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RJEkPZ4hTLabaiTvggHKONWuqq0mSpF5iiFMtqujEAdx9dzX1SJLUawxxqkUVnTgwxEmSNBJDnGpRVSfOyQ2SJA3PEKdaTLUT53CqJEmjM8SpFlPtxM2bB3vuaSdOkqSRGOJUi6l24iJKN85OnCRJwzPEqRZT7cSBIU6SpNEY4lSLgU7cVELcAQc4nCpJ0kgMcarFVIdTwU6cJEmjMcSpFlUMpx5wQHn01vbt1dQkSVIvMcSpFlV14jJ99JYkScMxxKkWVU1sAIdUJUkajiFOtaiiEzfw6C0nN0iStDtDnGphJ06SpHoZ4lSLqu6JA0OcJEnDMcSpFlV04ubNg7lzHU6VJGk4hjjVYtu2sp8xY2rnca04SZKGZ4hTLTZuLPs995zaeQ44wBAnSdJwDHGqxcaNMGfO1O6Jg9KJczhVkqTdGeJUiw0byj1tU+VwqiRJwzPEqRZVhbiBR28N3GMnSZIKQ5xqsXFjmVk6VQPLjPjoLUmSHs4Qp1pUOZwKDqlKkjSUIU61qHI4FZzcIEnSUIY41aLq4VQ7cZIkPZwhTrVwOFWSpHoZ4lSLjRunvtAvlG7evHkOp0qSNJQhTrXYsgVmz67mXK4VJ0nS7gxxqsXWrTBrVjXnOuAAO3GSJA3VdSEuIvaNiAsjYmNE3BIRL266Ju1uyxbYY49qzmUnTpKk3XVdiAM+BGwFFgMvAT4SEcc0W5IG27GjbIY4SZLq01UhLiLmAqcDb8nMDZn5I+CrwMuarUyDDTwiq8rh1Pvu89FbkiQN1lUhDjgC2JGZ1w167yrATlwH2bKl7KvsxAHcc08155MkqRd0W4ibB6wb8t46YP7gNyLizIhYGREr1/jQzbbburXsqwpxA09tcEhVkqRdui3EbQAWDHlvAbB+8BuZeV5mrsjMFYsWLWpbcSoGQlxVw6kDnThnqEqStEu3hbjrgBkRcfig944DVjVUj4ZR13CqnThJknbpqhCXmRuBLwNvi4i5EXES8FvAp5utTIPZiZMkqX5dFeJazgHmAPcAnwVelZl24jpI1ffE7bknzJ9vJ06SpMFmNF3ARGXm/cBvN12HRlb1cCq4VpwkSUN1YydOHa7q4VTw0VuSJA1liFPl7MRJklQ/Q5wqV/U9cVA6cYY4SZJ2McSpcnUMpy5eDPffv+vckiT1O0OcKlfXcCr46C1JkgYY4lS5uoZTwckNkiQNMMSpctu2lf3MmdWd06c2SJL0cIY4VW7HjrKfPr26cxriJEl6OEOcKrdzZ9nXEeIcTpUkqTDEqXIDIW5ahf91zZkDe+1lJ06SpAGGOFVuYDi1yhAHPrVBkqTBDHGqXB3DqWCIkyRpMEOcKldnJ2716mrPKUlStzLEqXJ24iRJqp8hTpWrY2IDlBC3fj1s3FjteSVJ6kaGOFWuruHUJUvK3hmqkiQZ4lSDOodTwSFVSZLAEKca1DmxAQxxkiSBIU41sBMnSVL9DHGqXF0TGxYuLOc0xEmSZIhTDQaGUyOqPe/06bD//q4VJ0kSGOJUg507qx9KHeBacZIkFYY4VW7HjuqHUgcY4iRJKgxxqpydOEmS6meIU+V27qyvE7dkSVnsd2DyhCRJ/coQp8rVPZy6bRusXVvP+SVJ6haGOFWu7uFUcEhVkiRDnCpXdycODHGSJBniVLk674kbCHGuFSdJ6neGOFXO4VRJkupniFPl6hxOnT8f5swxxEmSZIhT5ersxEW4VpwkSWCIUw3qvCcOylpxhjhJUr8zxKlydQ6ngp04SZLAEKca1DmcCoY4SZLAEKcatKMTd999sHVrfdeQJKnTGeJUuXZ04qA8Q1WSpH5liFPl6p7Y4FpxkiQZ4lSDdgyngiFOktTfDHGqXLuGUw1xkqR+ZohT5eruxC1eXPaGOElSPzPEqXJ1d+L22AP2288QJ0nqb4Y4Va7uiQ3gWnGSJBniVLm6h1OhhLg776z3GpIkdTJDnCpX93AqwNKlsHp1vdeQJKmTGeJUuXZ04pYuLZ24zHqvI0lSpzLEqXLt6MQdeCBs2wb33lvvdSRJ6lSGOFWuHRMbli4te++LkyT1K0OcKteu4VQwxEmS+pchTpVr18QGMMRJkvqXIU6Va0cnbsmSsr/jjnqvI0lSpzLEqXLt6MTtsQcsWmQnTpLUv2aM56CIeAbwe8AxwHxgPbAK+ERmfru26tSV2jGxAXYtMyJJUj8aM8RFxJ8Cfw78E/AlYB2wADgO+FREvDcz/77WKtVV2jGcCmWZEYdTJUn9ajyduDcAT8nMa4e8/+WI+CzwPcAQp//VjuFUKJ24n/+8/utIktSJxtMvmQuMNGh1F7BndeWoF7SrE7d0Kdx9N2zfXv+1JEnqNOP5p/ZLwNci4mkRsSgi9oiIhRHxNOBC4Iv1lqhukwkR9V/nwAPLte6+u/5rSZLUacYT4s4Gfgx8Crgb2Nzafwq4DHhVbdWpK7UrxA2sFed9cZKkfjTmPXGZuRV4E/CmiNgbmAdsyMwHhh4bESdl5qVVF6nu084Q5wxVSVI/GtcSIwNawe2BUQ75D8rMVfWxzPZc58ADy94QJ0nqR1Xfft6G/ou6QTs6cYsWlVmwDqdKkvpR1SFuUj2YiJgVEedHxC0RsT4iroyIZw055mkRcW1EbIqI70XEIdWUrKq1qxM3bVp5/JadOElSP+qUx27NAG4DTgH2At4CfD4ilgNExELgy6339wVWAp9rpFKNSzs6cVCGVA1xkqR+1BEhLjM3Zua5mXlzZu7MzK8DvwIe3zrkecCqzPxCZj4EnAscFxFHNlSyRtGuThyUyQ0Op0qS+lFH3hMXEYuBIyjPZ4XyzNarBj7PzI3Aja33h/v+MyNiZUSsXLNmTRUlaYLa1Ynz+amSpH41oRAXEftFxMsi4s9br5dGxEEDn2fm/KkWFBEzgX8FPjXoUV/zKM9sHWwdMOz1MvO8zFyRmSsWLVo01ZI0Qe3sxB14IKxdC5s3t++akiR1gnGHuIg4Bfgl8BLKvWkAhwMfGcf3XhIROcL2o0HHTQM+DWwFXj3oFBvYfemSBcD68dav9mrnPXHgkKokqf9MpBP3d8ALM/M0YOBplZcDJ471jZl5ambGCNuTACIigPOBxcDpmblt0ClWAccNvIiIucCh7BpuVQdpZyfuoFYf+Lbb2ndNSZI6wURC3PLM/E7r64F/prcywQWDR/ER4CjguZk5dHDsQuDREXF6RMwG3gpcPWi4VR2kXY/dAjj44LI3xEmS+s1EQtz/RMQzh7z3dOC/plpEa823s4DjgbsiYkNrewlAZq4BTgfeCawFngCcMdXrqj7tCnF24iRJ/WoiXbTXAV+PiG8AcyLiY8Bzgd+aahGZeQtjzGzNzIsBlxTpAu0cTp09uzy5wRAnSeo34+7EZeZllPvSVgH/TFnH7cTMvKKm2tTF2tWJA1i2DG69tX3XkySpE0zofrbMvAN4X021qEe0sxMHJcTdeGN7rylJUtNGDXER8WnG8TzUzHx5ZRWpJ7S7E3fJJe27niRJnWCs4dQbKE9GuJGyuO5vA9OB21vf+1vAA/WVp27URCdu3TpY76qBkqQ+MmonLjP/euDriLgIeE5m/nDQe09i18K/0v9qdycOyuSGo49u33UlSWrSRJYY+TXgsiHvXQ78enXlqBc00YkDZ6hKkvrLRELclcC7ImIOQGv/TuAXNdSlLtdUJ06SpH4xkRD3e8BJwLqIuJtyj9yTACc16GHa3YlbuhSmTTPESZL6y7iXGMnMm4EnRsQyYCmwOjNdnUvDamcnbuZMWLLEteIkSf1lIp04ImIf4CnAU4FTW6+lh2nns1MHLFtmJ06S1F/GHeIi4tcpS42cDRxLedbpja33pf/V7uFUMMRJkvrPRDpxfweck5lPzMwXZeZJwKuAf6ilMnW1pjpxTQRISZKaMJEQdwTw+SHvfRE4rLpy1Aua6sRt3gz339/+a0uS1ISJhLjrgTOGvPcCyhCr9DBNdOLAIVVJUv8Y9+xU4LXA1yPiNcAtwHLgcOA3qy9L3aypThyUEHf88e2/viRJ7TaRJUZ+HBGHAs+hLDHyNeCbmekAlnbT7k7cwQeXvcuMSJL6xUQ6cWTmWuCCmmpRj2iiE7f//jBrFtx8c/uvLUlSE8Yd4iLiEZTHbB0PzBv8WWYeXG1Z6nbt7sRNmwbLlxviJEn9YyKduM9QJjG8DthUTznqBU0t87F8OfzqV81cW5KkdptIiDsGOCkzd9ZVjHpHuztxAI94BKxc2f7rSpLUhIksMfID4LF1FaLe0cRjt6B04u67D9avb/+1JUlqt4l04m4GLoqILwN3Df4gM99aZVHqbk0Npz7iEWV/883wmMc0U4MkSe0ykU7cXMqyIjOBZYO2g2qoS12uqeFU8L44SVJ/mMg6ca8c65iIeFFmfnZqJanbNTmxAZyhKknqDxPpxI3Hxyo+n7pUE524hQth7lw7cZKk/lB1iGvgn251mqY6cRGuFSdJ6h9Vh7iG/vlWp2miEwflvjg7cZKkflB1iJMa68SBnThJUv8YM8RFhEFPE9ZkJ27dOli7tpnrS5LULuMJaHdExPsi4tHjOPbWqRak7td0Jw7sxkmSet94QtzZwCOAKyLi5xHxJxGxaLgDM3M8QU99oMlOHMCNNzZzfUmS2mXMEJeZ/56ZLwCWUJYQeQFwW0R8NSJOj4iZdRep7tJkJ+6ww8reECdJ6nXjvt8tMx/IzI9l5pOAo4CVwAeA1XUVp+7VVCdu/nxYvBhuuKGZ60uS1C4TnrQQEbOAE4AnAIuB/6q6KHW3zOZCHJRunCFOktTrxh3iIuJJEXEecDfwDuAy4IjMfEpdxak7NTmcCoY4SVJ/GM8SI+dGxI3A11pvPSczj8jMt2fmLfWWp27VdCfu9tth06bmapAkqW4zxnHMrwFvBr6SmQ/VXI96QCd04gBuugke7XxpSVKPGjPEZeZp7ShEvaXpThyUIVVDnCSpV/k0BlWu6U7coYeWvffFSZJ6mSFOtWiyE7fPPrDffoY4SVJvM8Spck134gAOPxyuv77pKiRJqo8hTrVoshMHLjMiSep9hjhVrhM6cYcdBrfdBg85n1qS1KMMcapFJ3TiMn2GqiSpdxniVLmmH7sFcOSRZX/ttc3WIUlSXQxx6kmPelTZG+IkSb3KEKfKdUInbt48WLYMrrmm2TokSaqLIU6V64SJDQBHHWWIkyT1LkOcatF0Jw7KfXHXXgs7dzZdiSRJ1TPEqXKd1InbtAluv73pSiRJqp4hTrXohE7cUUeVvZMbJEm9yBCnynVKJ25gmRHvi5Mk9SJDnGrRCZ24/feHffYxxEmSepMhTpXrlE5cxK7JDZIk9RpDnGrRCZ04cJkRSVLvMsSpcp2w2O+Ao4+Ge+6Be+9tuhJJkqpliFNPO/bYsv+v/2q2DkmSqmaIU+U6qRP3mMeU/dVXN1uHJElVM8Spcp0ysQFg8WJYtMgQJ0nqPYY41aJTOnERZUjV4VRJUq/puBAXEYdHxEMRccGQ958WEddGxKaI+F5EHNJUjeouxx4L//3fsGNH05VIklSdjgtxwIeAKwa/ERELgS8DbwH2BVYCn2t/aRqvTunEQbkvbvNmuPHGpiuRJKk6HRXiIuIM4AHgO0M+eh6wKjO/kJkPAecCx0XEke2tUGPppPvhBjhDVZLUizomxEXEAuBtwOuG+fgY4KqBF5m5Ebix9b46UCd14o4+GqZNc3KDJKm3dEyIA94OnJ+Ztw3z2Txg3ZD31gHzhztRRJwZESsjYuWaNWsqLlOj6cRO3Jw5cMQRhjhJUm9pS4iLiEsiIkfYfhQRxwNPBz4wwik2AAuGvLcAWD/cwZl5XmauyMwVixYtquzn0Ph1UicOypDqVVeNfZwkSd1iRjsukpmnjvZ5RLwWWA7cGuVf/3nA9Ig4OjMfB6wCXjHo+LnAoa331UEGOnGdFuIe9zj4/Ofh/vth332brkaSpKnrlOHU8yih7PjW9lHgG8AzW59fCDw6Ik6PiNnAW4GrM/Pa9peqbrRiRdn//OfN1iFJUlU6IsRl5qbMvGtgowyfPpSZa1qfrwFOB94JrAWeAJzRWMEaUSd34gBWrmy2DkmSqtKW4dSJysxzh3nvYsAlRTQp++wDhx5qiJMk9Y6O6MSpd3RqJw7KkKohTpLUKwxxqlQnLjEyYMUKuOUWuPfepiuRJGnqDHGqRSd24h7/+LL/2c+arUOSpCoY4lSpTu7EOblBktRLDHGqRSd24vbaqzy54Yormq5EkqSpM8SpUp3ciQN4whPgJz/p/DolSRqLIU616MROHMBJJ8E998BNNzVdiSRJU2OIU6U6vcN10kllf+mlzdYhSdJUGeJUi07txB19dLk3zhAnSep2hjhVqpMX+wWYNg1+/dfhxz9uuhJJkqbGEKe+c9JJsGoVPPBA05VIkjR5hjhVqtM7cQBPfGKp8yc/aboSSZImzxCnvvOEJ8D06d4XJ0nqboY4VaobOnFz55ZHcF1ySdOVSJI0eYY4VarTlxgZ8LSnweWXw4YNTVciSdLkGOJUi07uxAE89amwfTv88IdNVyJJ0uQY4lSpbunEnXQS7LEHfOc7TVciSdLkGOJUi07vxM2ZU2apfve7TVciSdLkGOJUqW7pxEG5L+4Xv4D77mu6EkmSJs4Qp1p0eicOyn1xmfC97zVdiSRJE2eIU6W6YYmRASecUJ6j+q1vNV2JJEkTZ4hT35o5E57xDPjmN7trGFiSJDDEqWLd1IkDeM5zYPVquPLKpiuRJGliDHHqa896Vgmc3/hG05VIkjQxhjhVqts6cfvvX+6NM8RJkrqNIU6V6sZ7y57zHPjpT2HNmqYrkSRp/AxxqkW3dOIAfvM3S/j8+tebrkSSpPEzxKlS3diJe+xjYfly+OIXm65EkqTxM8SpFt3UiYuA5z8fvv1teOCBpquRJGl8DHGqVDd24qCEuG3b4KtfbboSSZLGxxCnWnRTJw7gxBNh2TKHVCVJ3cMQp0p12xIjAwaGVC+6CNata7oaSZLGZoiTWl74Qti6Fb70paYrkSRpbIY4VapbO3FQhlSPOAL+5V+arkSSpLEZ4qSWCHj5y+H734ebb266GkmSRmeIU6W6uRMH8NKXlv0FFzRbhyRJYzHESYMccgg85SllSLVbl0uRJPUHQ5wq1e2dOIDf+z24/voyrCpJUqcyxKlSvdC9esELYN994cMfbroSSZJGZohTLbq5EzdnDvz+78OFF8Lq1U1XI0nS8AxxqlQvdOIAzj4btm+Hj3+86UokSRqeIU616OZOHMChh8Iznwkf+1gJc5IkdRpDnCrVCxMbBpxzDtxxB3zlK01XIknS7gxx0gie8xw47DD4m7/pnWFiSVLvMMSpUr3UiZs+HV7/evjpT+GSS5quRpKkhzPESaN4xStg8WJ473ubrkSSpIczxKlSvdSJA5g9G177WrjoIvjFL5quRpKkXQxx0hjOPhvmz4d3vavpSiRJ2sUQp0r1WicOYO+94TWvgS98wW6cJKlzGOKkcXj960uYe+tbm65EkqTCEKdK9WInDkqA+/M/h699DS67rOlqJEkyxKlivbye2mteA/vvD29+c2//nJKk7mCIUy16rRMHMHduCXDf/S5885tNVyNJ6neGOFWq1ztUr3oVHHkk/NmfwdatTVcjSepnhjjVohc7cQAzZ8L73w/XXQcf/GDT1UiS+pkhTpXq1YkNgz3rWWV729vgnnuarkaS1K8McdIkvP/9sGkTvO51TVciSepXhjhVqh86cVDui/uLv4ALLoBvfavpaiRJ/cgQJ03Sm94ERx0FZ50FGzY0XY0kqd8Y4lSpfunEAcyaBf/0T3DrrfCXf9l0NZKkftNRIS4izoiIayJiY0TcGBEnD/rsaRFxbURsiojvRcQhTdYqAZx0EpxzDvzDP8APftB0NZKkftIxIS4ifgN4L/BKYD7wZOCm1mcLgS8DbwH2BVYCn2umUo2mnzpxA97zHnjkI+FlL4MHHmi6GklSv+iYEAf8NfC2zLwsM3dm5h2ZeUfrs+cBqzLzC5n5EHAucFxEHNlUsRpery/2O5z58+Ezn4E774Szz+7P34Ekqf06IsRFxHRgBbAoIm6IiNsj4oMRMad1yDHAVQPHZ+ZG4MbW++pA/dSJAzjxRPjrv4bPfQ4+/emmq5Ek9YOOCHHAYmAm8HzgZOB44LHAwO3i84B1Q75nHWXYdTcRcWZErIyIlWvWrKmlYA2vn7tQb3wjPPnJ5R65VauarkaS1OvaEuIi4pKIyBG2HwGbW4f+Y2auzsx7gfcDz269vwFYMOS0C4D1w10vM8/LzBWZuWLRokV1/EgaQ7914gCmT4fPfhbmzYPf+R3vj5Mk1astIS4zT83MGGF7UmauBW4HRurjrAKOG3gREXOBQ1vvq4P048SGwZYuhS9+EX71qzLRYefOpiuSJPWqThlOBfgE8McRsX9E7AO8Fvh667MLgUdHxOkRMRt4K3B1Zl7bTKnSyJ70JPjAB+DrXy/3yUmSVIdOCnFvB64ArgOuAa4E3gmQmWuA01uv1wJPAM5opkyNpt87cQP+6I/gla+Et70NPvnJpquRJPWiGU0XMCAztwHntLbhPr8YcEkRdYUI+OhH4bbb4A//sAyzPuMZTVclSeolndSJUw+wE7fLHnvAl74ERx8Nz38+XHll0xVJknqJIU6q0YIF8M1vwt57l06cS49IkqpiiFOl7MTt7sAD4bvfhZkz4WlPg1/+sumKJEm9wBAntcFhh5UglwlPfSpcd13TFUmSup0hTpWyEzeyI4+Eiy+GbdvKMiQ//3nTFUmSupkhTpXq58dujcdjHgM/+hHMmQNPeQr84AdNVyRJ6laGONXCTtzIjjiiBLmlS+GZz4SvfKXpiiRJ3cgQp0o5nDo+y5bBD38Ixx4Lz3sevPvddjElSRNjiJMasnAhXHIJvOhF8Bd/AS99KWze3HRVkqRuYYhTpezETcycOXDBBfCud8FnPgMnnww33th0VZKkbmCIkxoWAW96E3z1q3DTTfDYx8LnPtd0VZKkTmeIU6XsxE3ec58Lv/gFPPrRcMYZcOaZsGFD01VJkjqVIU7qIAcfDN//PrzxjfDxj5dAd/HFTVclSepEhjhVyk7c1M2cCe95T5m9OmsW/MZvlK7cunVNVyZJ6iSGOKlDnXRSGV59/evh/PPhqKPKJAiXIpEkgSFOFbMTV605c+Bv/gYuuwwOOghe9rIS7laubLoySVLTDHFSFzjhhBLk/vmfyxIkJ55YAp3LkUhS/zLEqVJ24uozbRq88pVw3XVliPWLX4Qjj4SzzoLbb2+6OklSuxniVCnv16rfXnvB+95X1pQ76yz4xCfgsMPg1a8u70mS+oMhTrWwE1e/JUvggx+E668vj+w67zw4/HB44Qu9Z06S+oEhTpVyOLX9DjmkrCl3883whjfARReVe+hOOQU++1nYsqXpCiVJdTDEST1i6dKyvtytt8Lf/m25T+7FLy6zWt/4RidBSFKvMcSpUnbimrdgAfzZn5Vh1osugic/uYS6ww4r3bnzzoO1a5uuUpI0VYY4qUdNmwbPeAZ86UulO/eOd8Ddd5fJEIsXw2//NnzhC7B5c9OVSpImwxCnStmJ60xLl8Kb3wzXXFMmPbz61XD55fC7vwsLF8Lpp8O//Avcf3/TlUqSxssQJ/WRCHj84+H97y/3zH372/B7v1cWEn7FK2D//eGpT4W///uyHp1LxkhS5zLEqVJ24rrH9Onw9KfDhz4Et90GP/1pmQBx993w2tfCox4Fj3gEnHlmWVjY++gkqbPMaLoASc2bNq0sS3LCCfDOd5aZrN/+Nvznf8LnPgf/9E/lmBUr4NRT4eSTyzNc99mn6colqX8Z4lQpO3G94dBDy3b22bB9e+nS/ed/lmD3gQ+UJ0ZEwKMfXWa/nnxy2ZYubbpySeofhjhVynuoes+MGfDEJ5bt3HPLbNbLL4cf/rBsn/xkGZKFsibdCSfAiSeW/YoV5TFhkqTqGeJUCztxvWvOnDKkeuqp5fX27XDllfCjH8EVV5Ttwgt3Hf+oR+0aqj3uODj2WIdhJakKhjhVyuHU/jNjxq6QNuD++8tSJldcUYZiL74YLrhg1+fLlpUwd+yxu4Ld4YeXc0mSxsc/mZIqt+++ZaHhZzyjvM6E1avh6qt3bVddVZ4osX17OWbWLDj6aDjqKDjyyF3b4YfD7NnN/SyS1KkMcaqUnTgNJ6JMeli6FE47bdf7W7bAtdeWQHf11fDf/w2XXgqf+cyuY6ZNK0udDA52RxxRJl4sWVI+l6R+ZIiT1JhZs8pw6nHHPfz9TZvKYsPXXlu2a64p+4svLsFvwOzZ8MhH7ppNO3hbvhz22KOtP44ktZUhTpWyE6cq7LknHH982QbbsQNuuQVuuKGsZTd4+853SvgbMG1auffuEY+AQw6Bgw9++H7ZsjJJQ5K6lSFOUteYPr103h75yN0/yyxPmxga7n71qxLw7rwTdu58+PcsWrQr2A0OeQcdVIZ+Fy+GmTPb87NJ0kQZ4lQpO3FqSgQccEDZTjpp98+3bYM77iidvFtvLdvA19dcA9/61sM7eQPnXLy4BLoDD9x1X9/Qr/fbz//mJbWfIU5SX5g5s9wnt3z58J9nlqVRbrmldO3uuKPsB76+9Va47DJYs2b3791jjxLoliyB/fcvwW+kbcECA5+kahjiVCk7cepWEaWjtt9+8LjHjXzcli1w113DB73Vq8sQ7o9/DPfeO/wTTGbNKmFupLC3aBEsXFi2/fZzeRVJIzPESdIEzJpV7p075JDRj9u+vQS5e+4p9+oNt91xB/z85+WYHTuGP8+8eQ8PdQNfD7cNhFBn5Ur9wRCnStmJk4oZM3bdozeWnTvLUO7dd5fgN3i7776Hv77uurJfv37k8y1YsCvU7btveczZPvuM/PXA6z339P93pW5iiFOlDHHSxE2btqubNl5btpTgNzT0DQ1/a9eWId61a8s2dIbuYDNnji/s7bMP7LXXrm3vvWH+/DJ7WFL7GOIkqQvNmlUmUixZMv7v2bmzdPAGAt3atSUIDvf12rXl3r9rrilfP/DA2OefP//h4W6i24IFBkFpIgxxqpSdOKlzTZu2KzCNNEt3JDt2wLp1u8LeunVjb2vWlIWZ160rIXDr1rGvM2/e7sFu/vyJbQsWlPP4SDb1OkOcJGlM06eXodR99y2PNZuMhx4aX/gbvA0s+7J+/a5tuFm/w5k7d+Lhb+DruXNLEBy8n+G/mOow/iepStmJkzSS2bPLtnjx5M+RWRZlHhzqhm4PPjjyZ7ff/vDXmzeP/9p77LF7sBsu7E30vdmz/ZupyTHESZK6RsSuADSemb9j2b4dNmzYPQBu3Lhr27Dh4fuhX69evftx27ePv4Zp03b9TEND3ty55Rm/e+458jba5wOfzZplUOxFhjhVyk6cpG4yY0aZXbv33tWed+vW8YfA4b4eCJb33FM6j4O3LVsmXk/E+MPfeELhwNezZ+++nznTfwPaxRAnSVLF9tijbPvsU/25d+wow8CbNu3aj7aNdcyDD5aZyEPfH89ElOFMm7Yr1I0U9Mb6bDLHzJxZ7e+5GxjiVCk7cZJUr+nTy5DrvHn1Xmf79hIARwqBmzeXySqj7Yd7b926EhqH+2ykJ5eMx/Tpowe9WbPK69H24zlmtP2MGe39988QJ0mSdjNjxq7Zuu0yEBxHC4HjCYrDHbNuXXkqypYt5f0tW3Z9/dBDoy+EPV4RUw+CE2GIU6XsxEmSJquJ4Dhg+/aHB7yR9uM5ZrTv2bixLJ0z0rETYYhTLQxxkqRuMmNG2ebObbaOifz76XrWqtR4F+GUJElTY4hTpRxOlSSpPQxxkiRJXcgQp0rZiZMkqT0McZIkSV3IEKdK2YmTJKk9OibERcTyiPhmRKyNiLsi4oMRMWPQ50+LiGsjYlNEfC8iDmmyXkmSpCZ1TIgDPgzcAywBjgdOAc4BiIiFwJeBtwD7AiuBzzVSpUZlJ06SpPbopBD3CODzmflQZt4FfAs4pvXZ84BVmfmFzHwIOBc4LiKObKZUSZKkZnXSExv+HjgjIi4B9gGeRem8QQlzVw0cmJkbI+LG1vvXjnbS66+HZz6zlno1jHvuKXs7cZIk1auTQtz3gT8EHgSmA58CvtL6bB6wZsjx64Bhn64WEWcCZwLsscexPPhgDdVqWLNnw2mnweGHN12JJEm9rS0hrtVdO2WEjy8FngxcBHwMeCIltP0z8F7gz4ENwIIh37cAWD/cCTPzPOA8gBUrVuRPfjK1+iVJkjpNW+6Jy8xTMzNG2J5EmaywDPhgZm7JzPuATwDPbp1iFXDcwPkiYi5waOt9SZKkvtMRExsy817gV8CrImJGROwNvIJd98FdCDw6Ik6PiNnAW4GrM3PU++EkSZJ6VUeEuJbnAadR7n27AdgO/ClAZq4BTgfeCawFngCc0UyZkiRJzeuYiQ2Z+Qvg1FE+vxhwSRFJkiQ6qxMnSZKkcTLESZIkdSFDnCRJUhcyxEmSJHUhQ5wkSVIXMsRJkiR1IUOcJElSFzLESZIkdSFDnCRJUhcyxEmSJHUhQ5wkSVIXMsRJkiR1IUOcJElSFzLESZIkdaHIzKZrqFVErAd+2XQdfWYhcG/TRfQZf+ft5++8/fydt5+/8/Z7VGbOH8+BM+qupAP8MjNXNF1EP4mIlf7O28vfefv5O28/f+ft5++8/SJi5XiPdThVkiSpCxniJEmSulA/hLjzmi6gD/k7bz9/5+3n77z9/J23n7/z9hv377znJzZIkiT1on7oxEmSJPUcQ5wkSVIX6tkQFxH7RsSFEbExIm6JiBc3XVOvi4hXR8TKiNgSEZ9sup5+EBGzIuL81n/j6yPiyoh4VtN19bKIuCAiVkfEgxFxXUT8QdM19YuIODwiHoqIC5qupR9ExCWt3/eG1uaaq20QEWdExDWt/HJjRJw80rG9vE7ch4CtwGLgeOAbEXFVZq5qtKredifwDuCZwJyGa+kXM4DbgFOAW4FnA5+PiMdk5s1NFtbD3g38n8zcEhFHApdExJWZ+bOmC+sDHwKuaLqIPvPqzPx400X0i4j4DeC9wAuBnwJLRju+JztxETEXOB14S2ZuyMwfAV8FXtZsZb0tM7+cmV8B7mu6ln6RmRsz89zMvDkzd2bm14FfAY9vurZelZmrMnPLwMvWdmiDJfWFiDgDeAD4TsOlSHX6a+BtmXlZ62/6HZl5x0gH92SIA44AdmTmdYPeuwo4pqF6pLaIiMWU//7tONcoIj4cEZuAa4HVwDcbLqmnRcQC4G3A65qupQ+9OyLujYhLI+LUpovpZRExHVgBLIqIGyLi9oj4YESMOLLVqyFuHrBuyHvrgHE9i0zqRhExE/hX4FOZeW3T9fSyzDyH8vfkZODLwJbRv0NT9Hbg/My8relC+swbgUcCB1LWLvtaRNh1rs9iYCbwfMrfluOBxwJ/OdI39GqI2wAsGPLeAmB9A7VItYuIacCnKfeBvrrhcvpCZu5o3apxEPCqpuvpVRFxPPB04AMNl9J3MvPyzFyfmVsy81PApZT7blWPza39P2bm6sy8F3g/o/zOe3Viw3XAjIg4PDOvb713HA4xqQdFRADnU/5X3LMzc1vDJfWbGXhPXJ1OBZYDt5b/1JkHTI+IozPzcQ3W1Y8SiKaL6FWZuTYibqf8nselJztxmbmRMsTxtoiYGxEnAb9F6VSoJhExIyJmA9Mpf2RnR0Sv/g+FTvIR4CjguZm5eayDNXkRsX9r+v+8iJgeEc8EXgR8t+naeth5lJB8fGv7KPANyix41SQi9o6IZw78HY+IlwBPBi5qurYe9wngj1t/a/YBXgt8faSDe/kf2HOAfwbuocyWfJXLi9TuL4G/GvT6pZSZNuc2Uk0fiIhDgLMo92Td1epUAJyVmf/aWGG9KylDpx+l/I/gW4DXZua/N1pVD8vMTcCmgdcRsQF4KDPXNFdVX5hJWTLqSGAHZRLPb2ema8XV6+3AQsqI4kPA54F3jnSwz06VJEnqQj05nCpJktTrDHGSJEldyBAnSZLUhQxxkiRJXcgQJ0mS1IUMcZIkSV3IECepp0XEqnY9uDsijo6IlTWc98sRcVrV55XU3VwnTlJXay3+OmBPysLHO1qv27rocUR8CfhCZv5bxec9EfhIZj6+yvNK6m6GOEk9IyJuBv4gMy9u4NpLKM9nXpqZD9Vw/uuBF2Vm5Z0+Sd3J4VRJPS0ibo6Ip7e+PjcivhARF0TE+oj4r4g4IiLeFBH3RMRtEfGMQd+7V0ScHxGrI+KOiHhHREwf4VK/Afx8cIBrXfsNEXF1RGxsnWtxRPxH6/oXt56PSOsZlRdExH0R8UBEXBERiwed/xLgOZX/giR1LUOcpH7zXODTwD7AlZQHek8DDgTeBnxs0LGfArYDhwGPBZ4B/MEI530MMNxzJU+nBLwjWtf+D+AvKM9HnAa8pnXcK4C9gGXAfsDZwOZB57kGOG7cP6WknmeIk9RvfpiZF2XmduALwCLgPZm5Dfg3YHlE7N3qgj2L8oD7jZl5D/AB4IwRzrs3sH6Y9/8xM+/OzDuAHwKXZ+aVmbkFuJASDgG2UcLbYZm5IzN/lpkPDjrP+tY1JAmAGU0XIEltdvegrzcD92bmjkGvAeYBS4GZwOqIGDh+GnDbCOddC8wfx/WGvp7X+vrTlC7cv0XE3sAFwJtb4ZLWuR8Y6YeS1H/sxEnS8G6jzHRdmJl7t7YFmXnMCMdfTRkynZTM3JaZf52ZRwNPBH4TePmgQ44Crprs+SX1HkOcJA0jM1cD/wn8bUQsiIhpEXFoRJwywrd8G3hcRMyezPUi4ikR8ZjWxIkHKcOrOwYdcgrlfjpJAgxxkjSalwN7AP9DGS79IrBkuAMz827gu8BvTfJaB7TO/yBlEsP3KUOqRMQJwMbM/Okkzy2pB7lOnCRVJCKOpsxoPTEr/OPaWkT4/Mz8ZlXnlNT9DHGSJEldyOFUSZKkLmSIkyRJ6kKGOEmSpC5kiJMkSepChjhJkqQuZIiTJEnqQoY4SZKkLmSIkyRJ6kL/P1DtLgfxV5ToAAAAAElFTkSuQmCC\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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iVImxDqeCIU6SpN0xxKkSDqdKklQtQ5wqMZZO3D77wIQJhjhJknbHEKdKjKUTN24cPOYxhjhJknbHEKdKjKUTB84VJ0nSUAxxqsRYOnFgiJMkaSiGOFVirJ04h1MlSdo9Q5wqMdZO3GMeAw89BFu3lleTJEndxBCnSoy1Ezd3LmQWQU6SJO3KEKdKlNGJA3jggXLqkSSp2xjiVIkyOnEA999fTj2SJHUbQ5wqMdZOXDPE2YmTJGlghjhVotmJG+twqp04SZIGZohTJcY6nDp9Ouyxh504SZIGY4hTJcY6nArFkKohTpKkgRniVImxduKgGFJ1OFWSpIEZ4lQJO3GSJFXLEKdKlNWJM8RJkjQwQ5wqUVYnzkdvSZI0MEOcKlFGJ6756K0VK8qpSZKkbmKIUyXK6MQ5V5wkSYMzxKkSZXXiwOviJEkaiCFOlSjrmjgwxEmSNBBDnCpRZifO4VRJknZliFMlyujETZ8O06bZiZMkaSCGOFWiOS3IWDpx4FxxkiQNxhCnSmzYUKynTRvbcebOdThVkqSBGOJUiQ0bYOrUsXfifPSWJEkDM8SpEuvXj70LB8Vwqp04SZJ2ZYhTJdavL25MGKu5c2HlSnj00bEfS5KkbmKIUyXKDHHgo7ckSerPEKdKbNhQ3nAqOKQqSVJ/hjhVouxOnDc3SJK0M0OcKlFWiGt24gxxkiTtzBCnSpTdiXM4VZKknRniVIkNG2CPPcZ+nD32KMKgnThJknZmiFMlNm+GKVPKOZZzxUmStCtDnCqxZQtMnlzOsXxqgyRJu+q4EBcRe0fEJRGxISLuioiX1F2TdrV5M0yaVM6xHvMYQ5wkSf11XIgDPg5sAeYCLwU+GRFH1VuS+tq2rVjKCnFz5zqcKklSfx0V4iJiGnA6cE5mrs/MK4BvAS+vtzL11XxEVpnDqatW+egtSZL66qgQBxwKbMvMW/psux6wE9dGNm8u1mUOpwI8+GA5x5MkqRt0WoibDqzpt20NMKPvhog4MyKWRMSSFT50s+W2bCnWZQ6ngkOqkiT11Wkhbj0ws9+2mcC6vhsy8/zMXJyZi+fMmdOy4lRohrgyh1PBmxskSeqr00LcLcCEiDikz7ZjgBtrqkcDqGo41U6cJEk7dFSIy8wNwMXAuyNiWkScCDwfuLDeytSXnThJkqrXUSGu4WxgKvAg8J/AazLTTlwbKbsTN3UqzJxpiJMkqa8JdRcwUpm5CnhB3XVocGXf2ADOFSdJUn+d2IlTmyt7OBV89JYkSf0Z4lS6sodTwUdvSZLUnyFOpauqE+dwqiRJOxjiVLqqrolbvXpHl0+SpF5niFPpqhpOBR+9JUlSkyFOpatqOBW8Lk6SpCZDnEr36KPFekKJE9g0O3GGOEmSCoY4lW7btmI9fnx5x2x24ry5QZKkgiFOpdu+vVgb4iRJqo4hTqVrduLGlfiva8oU2HNPh1MlSWoyxKl0VXTioLguzk6cJEkFQ5xK1wxxZXbiAObNM8RJktRkiFPpqhhOBTtxkiT1ZYhT6RxOlSSpeoY4la7KTty6dbBhQ7nHlSSpExniVLoqO3HgHaqSJIEhThWo6saGZohbvrzc40qS1IkMcSpdlcOp4HVxkiSBIU4VqHo41RAnSZIhThWoqhM3e3ZxTEOcJEmGOFWg2YmLKPe448cXz1A1xEmSZIhTBbZvL38otcm54iRJKhjiVLpt28ofSm0yxEmSVDDEqXRVd+KcYkSSJEOcKlB1J+6BB3ZcdydJUq8yxKl027dXG+K2boVVq6o5viRJncIQp9JVPZwKXhcnSZIhTqWrejgVDHGSJBniVLoqO3Hz5hVrQ5wkqdcZ4lS6qq+JA0OcJEmGOJWuyuHU6dNhjz0McZIkGeJUuiqHUyOcK06SJDDEqQJVduLApzZIkgSGOFWgyk4cGOIkSQJDnCpQ5Y0NYIiTJAkMcapAK4ZTV62CzZurO4ckSe3OEKfSVT2c2pwr7sEHqzuHJEntzhCn0rWiEwcOqUqSepshTqVrxY0NYIiTJPU2Q5xK14obG8C54iRJvc0Qp9JVPZy6777F2k6cJKmXGeJUuqqHUydNgn32McRJknqbIU6lq7oTB84VJ0mSIU6lq7oTBz4/VZIkQ5xKV/WNDQDz58N991V7DkmS2pkhTqVrxXDqfvsVnbjt26s9jyRJ7coQp9K1Yjh1/nx49FFYubLa80iS1K4McSpdKzpx8+cXa4dUJUm9yhCn0rWiE7fffsV62bJqzyNJUrsyxKl0rbqxAezESZJ6lyFOpWvVPHFgiJMk9S5DnErXiuHUSZOKx285nCpJ6lUThrNTRJwK/ClwFDADWAfcCHwuM/+nsurUkVrRiQPnipMk9bYhQ1xEvAl4K/DvwDeANcBM4BjgCxHxwcz810qrVEdpRScODHGSpN42nE7c3wBPz8yb+22/OCL+E7gMMMTp/7TixgYo7lD95S+rP48kSe1oOH9qpwGD9TvuB/Yorxx1g1YOpz7wQDHpryRJvWY4f2q/AXw7Ik6JiDkRMSkiZkfEKcAlwNerLVGdJrN1IS6zCHKSJPWa4fypfTXwM+ALwAPAxsb6C8BVwGsqq04dKbM152lO+Ot1cZKkXjTkNXGZuQV4O/D2iJgFTAfWZ+bD/feNiBMz88qyi1Tniaj+HM0Jf51mRJLUi4Y1xUhTI7g9vJtdvk9x56p6WKs6cT61QZLUy8q+cqkF/Rd1glZ04ubMgQkTDHGSpN5UdogbVQ8mIiZHxAURcVdErIuI6yLi2f32OSUibo6IRyLisog4sJySVbZWdeLGjYN58wxxkqTe1C6P3ZoA3AOcBOwJnAN8LSIWAkTEbODixva9gSXAV2upVMPSik4cFEOqXhMnSepFbRHiMnNDZp6bmXdm5vbM/A5wB/CExi4vBG7MzIsycxNwLnBMRBxeU8najVZ14sCnNkiSeldbXhMXEXOBQymezwrFM1uvb76fmRuApY3tA33/mRGxJCKWrFixooySNEKt6sTtt58hTpLUm0YU4iJin4h4eUS8tfF6fkQsaL6fmTPGWlBETAS+DHyhz6O+plM8s7WvNcCA58vM8zNzcWYunjNnzlhL0gi1uhO3ejVs3Ni6c0qS1A6GHeIi4iTgd8BLKa5NAzgE+OQwvvfyiMhBliv67DcOuBDYAryuzyHWs+vUJTOBdcOtX63Vyk4cwL33tuZ8kiS1i5F04j4C/ElmngZsbWz7BfDEob4xM0/OzBhkeQpARARwATAXOD0z+z4R80bgmOaLiJgGLGLHcKvaSGbrQtz++xfre+5pzfkkSWoXIwlxCzPzR42vmwNmWxjhhMG78UngCOB5mdl/cOwS4HERcXpETAHeCdzQZ7hVbaSVw6mGOElSrxpJiPttRDyr37ZnAr8eaxGNOd/OAo4F7o+I9Y3lpQCZuQI4HXgvsBp4EnDGWM+r6rSqE7egcUWmIU6S1GtG0kV7M/CdiPguMDUiPg08D3j+WIvIzLsY4s7WzLwUcEqRDtDKTtyUKcWTGwxxkqReM+xOXGZeRXFd2o3AZynmcXtiZl5TUW3qYK3qxEExpGqIkyT1mhFdz5aZy4B/rKgWdYlWduIADjgAbrutteeUJKluuw1xEXEhw3geama+orSK1BVa3Ym77LLWnU+SpHYw1HDqbRRPRlhKMbnuC4DxwL2N730+8HB15akTtboTt//+sGYNrF3b2vNKklSn3XbiMvNdza8j4gfAczPzp322PYUdE/9K/6fVnTgoros7asAHsUmS1H1GMsXI7wNX9dv2C+DJ5ZWjblBHJw68uUGS1FtGEuKuA94XEVMBGuv3Ar+qoC51uLo6cZIk9YqRhLg/BU4E1kTEAxTXyD0F8KYG7aTVnbj582HcOEOcJKm3DHuKkcy8EzghIvYH5gPLM/PuqgpT52rls1MBJkyAefMMcZKk3jKSThwRsRfwdOAZwMmN19IuWhniwAl/JUm9Z9ghLiKeTDHVyKuBoymedbq0sV36P60eTgVDnCSp94ykE/cR4OzMPCEzX5yZJwKvAf6tksrU0VrdiTvgALj77noCpCRJdRhJiDsU+Fq/bV8HDi6vHHWDujpxmzbBypWtP7ckSXUYSYi7FTij37YXUQyxSjup45o4cEhVktQ7hn13KvBG4DsR8QbgLmAhcAjwh+WXpU5WVycOihD3e7/X+vNLktRqI5li5GcRsQh4LsUUI98GvpeZq6oqTp2rrk7c3U56I0nqESPpxJGZq4EvVVSLukQdnbi5c2HKFLjrrtafW5KkOgw7xEXEQRSP2ToWmN73vcw8oNyy1Ola3YmLgIUL4Y47WnteSZLqMpJO3H9Q3MTwZuCRaspRN6hrmo+FC+HOO+s5tyRJrTaSEHcUcGJmbq+qGHWPVnfiAA46CK6+uvXnlSSpDiOZYuQngPf9aUitfnZq08KFsGoVrF3b+nNLktRqI+nE3Qn8ICIuBu7v+0ZmvrPMotTZ6hpOPeigYn3nnXD00fXUIElSq4ykEzeNYlqRicD+fZYFFdSlDldXJw68Lk6S1BtGMk/cq4baJyJenJn/ObaS1Onq7sR5h6okqReMpBM3HJ8u+XjqUHV04vbZB6ZNsxMnSeoNZYe4Gv50q93U1YmLKLpxduIkSb2g7BBX059vtZs6OnHgXHGSpN5RdoiTauvEwY5OXJ01SJLUCkOGuIgw6GnE6uzErV0LDz9cz/klSWqV4QS0ZRHxjxHxuGHse/dYC1Lnq7sTB14XJ0nqfsMJca8GDgKuiYhfRsRfRcScgXbMzOEEPfWAujpxzRB3++31nF+SpFYZMsRl5n9l5ouAeRRTiLwIuCcivhURp0fExKqLVGep67FbAIsWFeulS+s5vyRJrTLs690y8+HM/HRmPgU4AlgCfBhYXlVx0kjNmAFz58Ktt9ZdiSRJ1RrxTQsRMRk4HngSMBf4ddlFqbPV2YkDOPhguO22+s4vSVIrDDvERcRTIuJ84AHgPOAq4NDMfHpVxakz1T29hyFOktQLhjPFyLkRsRT4dmPTczPz0Mx8T2beVW156lR1duIOOQSWLYNHHqmvBkmSqjZhGPv8PvAO4JuZuanietQF2qETB8Udqo/zfmlJUpcaMsRl5mmtKETdpe5r4qC4ucEQJ0nqVj6NQaWruxPXnGbE6+IkSd3MEKdK1NmJmzULZs82xEmSupshTqWruxMH3qEqSep+hjhVos5OHBjiJEndzxCn0tU92S8UIe6ee2CT91NLkrqUIU5d6ZBDijDpM1QlSd3KEKfStUMn7vDDi/XNN9dbhyRJVTHEqXTtcGPDYYcV65tuqrcOSZKqYohTJeruxE2bBgccYCdOktS9DHEqXTt04gCOOMJOnCSpexniVIm6O3FQXBd3882wfXvdlUiSVD5DnErXTp24Rx6Be++tuxJJkspniFMl2qUTBw6pSpK6kyFOpWunThx4c4MkqTsZ4lSJdujEzZkDe+1lJ06S1J0McSpdu3TiIrxDVZLUvQxxqkQ7dOKgCHEOp0qSupEhTqVrh8duNR1xBDz4IKxYUXclkiSVyxCnrnb00cX617+utw5JkspmiFPp2qkTZ4iTJHUrQ5xK1y43NgDMnQv77gs33FB3JZIklcsQp0q0SycO4PGPN8RJkrpP24W4iDgkIjZFxJf6bT8lIm6OiEci4rKIOLCuGtVZjj4afvMb2Lat7kokSSpP24U44OPANX03RMRs4GLgHGBvYAnw1daXpuFqp07c0UfDpk2wdGndlUiSVJ62CnERcQbwMPCjfm+9ELgxMy/KzE3AucAxEXF4ayvUUNrperim5s0NDqlKkrpJ24S4iJgJvBt48wBvHwVc33yRmRuApY3takPt1Ik74ggYN84QJ0nqLm0T4oD3ABdk5j0DvDcdWNNv2xpgxkAHiogzI2JJRCxZ4SyvLdWOnbipU+HQQ51mRJLUXVoS4iLi8ojIQZYrIuJY4JnAhwc5xHpgZr9tM4F1A+2cmedn5uLMXDxnzpzSfg4NXzt14qAYUv3Vr+quQpKk8kxoxUky8+TdvR8RbwQWAndH8dd/OjA+Io7MzOOAG4FX9tl/GrCosV1tpNmJa7cQd9xx8LWvwapVsPfedVcjSdLYtctw6vkUoezYxvIp4LvAsxrvXwI8LiJOj4gpwDuBGzLTR5trWBYvLtbXXltvHZIklaUtQlxmPpKZ9zcXiuHTTZm5ovH+CuB04L3AauBJwBm1FaxBtXMnDmDJknrrkCSpLC0ZTh2pzDx3gG2XAk4polHZay84+GA7cZKk7tEWnTh1j3btxAE84Ql24iRJ3cMQp1K14xQjTYsXw113gbPOSJK6gSFOlWjHTpw3N0iSuokhTqVq505c8+YGQ5wkqRsY4lSJduzEzZwJhx0G11xTdyWSJI2dIU6laudOHMCTngQ//3n71ylJ0lAMcapEO3biAE48ER58EJYurbsSSZLGxhCnUrXzFCMAJ5xQrK+8st46JEkaK0OcesqRR8KsWfCzn9VdiSRJY2OIU6navRM3bhw8+cl24iRJnc8Qp55zwglw442wenXdlUiSNHqGOJWq3TtxUNzcAHDVVfXWIUnSWBjiVKpOmLrjiU+E8ePhiivqrkSSpNEzxKkS7dyJmzateATX5ZfXXYkkSaNniFOpOqETB/CMZ8DVV8O6dXVXIknS6BjiVIl27sQBnHIKbN0KP/1p3ZVIkjQ6hjiVqlM6cSecAJMnw//+b92VSJI0OoY4VaLdO3FTpxZB7kc/qrsSSZJGxxCnUnVKJw6K6+J+9StYubLuSiRJGjlDnCrR7p04KK6LA7jssnrrkCRpNAxxKlUnTPbbtHgx7Lkn/Pd/112JJEkjZ4hTz5o4EU49Fb73vc4aBpYkCQxxKlkndeIA/vAPYflyuO66uiuRJGlkDHHqac9+dhE4v/vduiuRJGlkDHEqVad14ubMKZ6laoiTJHUaQ5xK1YnXlj33ucUjuFasqLsSSZKGzxCnSnRKJw6K6+Iy4dvfrrsSSZKGzxCnUnViJ+7YY2HhQvj61+uuRJKk4TPEqRKd1ImLgBe9CC69FFavrrsaSZKGxxCnUnViJw7gj/4IHn0UvvWtuiuRJGl4DHGqRCd14gCOPx4OOMAhVUlS5zDEqVSdNsVIU0TRjfvhD+Hhh+uuRpKkoRnipIY//mPYsgW+8Y26K5EkaWiGOJWqUztxUEz6e+ih8MUv1l2JJElDM8RJDRHwylfCT34Cd9xRdzWSJO2eIU6l6uROHMDLXlasv/SleuuQJGkohjipjwMOgKc/vRhS7dTpUiRJvcEQp1J1eicO4FWvgttug8svr7sSSZIGZ4hTqbqhe/WiF8Hee8MnPlF3JZIkDc4Qp0p0ciduyhT48z+Hb34T7ruv7mokSRqYIU6l6oZOHMBZZ8HWrfCZz9RdiSRJAzPEqRKd3IkDWLQITjsNPv3p4pmqkiS1G0OcStUNNzY0nX12MZz6zW/WXYkkSbsyxEmDeM5z4JBD4J/+qXuGiSVJ3cMQp1J1Uydu/Hh4y1vgmmvgssvqrkaSpJ0Z4qTdeMUrYO5c+OAH665EkqSdGeJUqm7qxEEx3cgb3wg//CFcd13d1UiStIMhThrCq18NM2bA+99fdyWSJO1giFOpuq0TBzBrFvzVX8FFF8GvflV3NZIkFQxxKlW33sX55jcXYe6cc+quRJKkgiFOleimThwUAe6tb4XvfAeuuqruaiRJMsSpZN3aiQN4wxtg333h7/6uu39OSVJnMMSpEt3WiQOYNg3+/u+LOeO++926q5Ek9TpDnErV7R2qV78aDj8c/vqvYcuWuquRJPUyQ5wq0Y2dOICJE+FDH4Jbb4WPfrTuaiRJvcwQp1J14xQj/T372cVzVd/9bnjggbqrkST1KkOcNAof+hBs3FgMq0qSVAdDnErVC504gMMOK+5S/Y//gO9/v+5qJEm9yBAnjdLb3w5HHFHc7LB+fd3VSJJ6jSFOpeqVThzA5Mnwmc/APfcUU49IktRKbRXiIuKMiLgpIjZExNKIeGqf906JiJsj4pGIuCwiDqyzVgnghBPg7LPh3/4NfvKTuquRJPWStglxEfEHwAeBVwEzgKcBtzfemw1cDJwD7A0sAb5aT6XanV7qxDV94AOwaBG87GWwenXd1UiSekXbhDjgXcC7M/OqzNyemcsyc1njvRcCN2bmRZm5CTgXOCYiDq+rWA2s2yf7Hcj06fDlL8Py5cX1cb34GUiSWq8tQlxEjAcWA3Mi4raIuDciPhYRUxu7HAVc39w/MzcASxvb1YZ6qRMH8MQnwrveBV/7Gnzxi3VXI0nqBW0R4oC5wETgj4CnAscCvwc0LxefDqzp9z1rKIZddxERZ0bEkohYsmLFikoK1sB6uQv1t38LJ50Er30t/OY3dVcjSep2LQlxEXF5ROQgyxXAxsauH83M5Zn5EPAh4DmN7euBmf0OOxNYN9D5MvP8zFycmYvnzJlTxY+kIfRaJw5g/Phi3rgZM+D//T94+OG6K5IkdbOWhLjMPDkzY5DlKZm5GrgXGKyPcyNwTPNFREwDFjW2q4304o0Nfc2fDxddBHfeCS9/OWzfXndFkqRu1S7DqQCfA14fEftGxF7AG4HvNN67BHhcRJweEVOAdwI3ZObN9ZQqDe4pT4GPfAS+8x34h3+ouxpJUrdqpxD3HuAa4BbgJuA64L0AmbkCOL3xejXwJOCMesrU7vR6J67p7LPhz/4MzjsPPve5uquRJHWjCXUX0JSZjwJnN5aB3r8UcEoRdYQI+NSniqc5nHkm7LcfnHpq3VVJkrpJO3Xi1AXsxO0wcSJ8/etw1FFw+ulw3XV1VyRJ6iaGOKlCM2fCd78Le+9ddOKcekSSVBZDnEplJ25X++0HP/oRTJoEz3wm/O53dVckSeoGhjipBQ4+uAhymfCMZxjkJEljZ4hTqezEDe7ww4sg9+ij8NSnwi9/WXdFkqROZohTqXr5sVvD8bjHwRVXwNSpcPLJ8OMf112RJKlTGeJUCTtxgzv0ULjySliwAE47DS65pO6KJEmdyBCnUjmcOjwLFsBPfgLHHAMvfCG87312MSVJI2OIk2oyezZcdhm89KXwjncU640b665KktQpDHEqlZ24kZk6FS68sOjE/ed/Fs9dve22uquSJHUCQ5xUswh4+9vhW9+CO+6A446Dr3yl7qokSe3OEKdS2Ykbvec9D371q+IO1he/GP7yL2HdurqrkiS1K0Oc1EYOOKCYduRtb4MLLoDHPx4uvbTuqiRJ7cgQp1LZiRu7iRPh/e8v5pObMgX+4A+KrtyaNXVXJklqJ4Y4qU2dcAJcdx289a3w2c8WT3y48ELYvr3uyiRJ7cAQp1LZiSvX1KnwwQ/CVVcVQ62veEVxB+uSJXVXJkmqmyFO6gDHHw8//zl87nNw++3wxCfCy18OS5fWXZkkqS6GOJXKTlx1xo2DP/1TuOUW+Ju/gW98Aw47DM48E+65p+7qJEmtZohTqQxx1Zs5sxhiXboUXvMa+Pzn4eCD4bWvLbp0kqTeYIiTOtS8efDRj8KttxZDq//+73DIIfDHfwzXXFN3dZKkqhniVCo7ca134IHwmc/AnXcWw6w//GFxzdzTnlY8ymvz5rorlCRVwRAndYn58+EDH4C774Z/+RdYtgxe8hJYsKCYpsRnskpSdzHEqVR24uo3cyb89V8Xw6w/+EHRkfvQh4qh1pNOgvPPh9Wr665SkjRWhjipS40bB6eeWtzFevfdcN558OCDcNZZMHcuvOAFcNFFsHFj3ZVKkkbDEKdS2YlrT/PnwzveAb/9LVx7LbzudXD11cVNELNnw+mnwxe/CKtW1V2pJGm4DHFSD4mA444rhlfvuQcuvRRe9Sr4xS/gla+EffeFZzwD/vVfi/nomqFcktR+DHEqlZ24zjF+PJxyCnzsY0Wgu+YaeNvbiiHXN76xmEj4oIOKyYS//nWvo5OkdjOh7gIk1S8CFi8ulvPOKyYN/p//KW6M+OpXiznoxo0r3j/5ZHjqU+HEE2GvvequXJJ6l504lcpOXHd47GOLGyAuvhhWroQrr4RzzoEJE+DDH4bnPQ/22QeOPrq4vu6rX4X77qu7aknqLXbiVCqvoeo+EybACScUy7nnFnez/uIX8NOfFsvnPw8f/3ix74IFcPzxxWTDxx9fdO723LPO6iWpexniVAk7cd1r6tRiSPXkk4vXW7fCddfBFVcU19Vdcw1ccsmO/Q87rAh0xx8PxxxTdO8chpWksTPEqVQOp/aeCRN2hLSmVatgyZIi0F19dXEX7Je+tOP9/fcvwtzRR+8IdoccUhxLkjQ8/sqUVLq99y4mGj711OJ1JixfDjfcsGO5/vrixomtW4t9Jk+GI4+EI46Aww/fsRxyCEyZUt/PIkntyhCnUtmJ00AiigmH58+H007bsX3zZrj55iLQ3XAD/OY3xU0U//EfO/YZN66Y6qRvsDv0UFi0CObNK96XpF5kiJNUm8mTi+HUY47ZefsjjxSTDd98c7HcdFOxvvTSIvg1TZlS3Em7aNGuy8KFMGlSS38cSWopQ5xKZSdOZdhjDzj22GLpa9s2uOsuuO02WLp05+VHPyrCX9O4ccW1dwcdBAceCAccsPN6//2LmzQkqVMZ4iR1jPHji87bYx+763uZ8MADu4a7O+4oAt5998H27Tt/z5w5O4Jd35C3YEEx9Dt3Lkyc2JqfTZJGyhCnUtmJU10i4DGPKZYTT9z1/UcfhWXLik7e3XcXS/Prm26C//7vnTt5zWPOnVsEuv3223FdX/+v99nHf/OSWs8QJ6knTJxYXCe3cOHA72cWU6PcdVfRtVu2rFg3v777brjqKlixYtfvnTSpCHTz5sG++xbBb7Bl5kwDn6RyGOJUKjtx6lQRRUdtn33guOMG32/zZrj//oGD3vLlxRDuz34GDz008BNMJk8uwtxgYW/OHJg9u1j22cfpVSQNzhAnSSMweXJx7dyBB+5+v61biyD34IPFtXoDLcuWwS9/WeyzbdvAx5k+fedQ1/x6oKUZQr0rV+oNhjiVyk6cVJgwYcc1ekPZvr0Yyn3ggSL49V1Wrtz59S23FOt16wY/3syZO0Ld3nsXjznba6/Bv26+3mMP/78rdRJDnEpliJNGbty4Hd204dq8uQh+/UNf//C3enUxxLt6dbH0v0O3r4kThxf29toL9txzxzJrFsyYUdw9LKl1DHGS1IEmTy5upJg3b/jfs3170cFrBrrVq4sgONDXq1cX1/7ddFPx9cMPD338GTN2DncjXWbONAhKI2GIU6nsxEnta9y4HYFpsLt0B7NtG6xZsyPsrVkz9LJiRTEx85o1RQjcsmXo80yfvmuwmzFjZMvMmcVxfCSbup0hTpI0pPHji6HUvfcuHms2Gps2DS/89V2a076sW7djGeiu34FMmzby8Nf8etq0Igj2XU/wL6bajP8kVSo7cZIGM2VKscydO/pjZBaTMvcNdf2XtWsHf+/ee3d+vXHj8M89adKuwW6gsDfSbVOm+DtTo2OIkyR1jIgdAWg4d/4OZetWWL9+1wC4YcOOZf36ndf9v16+fNf9tm4dfg3jxu34mfqHvGnTiruGp04t1oMtu3t/6tTiGkqDYvcxxKlUduIkdZIJE4q7a2fNKve4W7YMPwQO9HUzWD74YNF57Lts3jzyeiJGFwAHe3/q1GKZMmXX9cSJ/g1oFUOcJEklmzSpWPbaq/xjb9tWDAM3Q13fr3e3DLbfmjVFN7H/fsO5EWUg48btCHWDBb2h3hvNPhMnlvs5dwJDnEplJ06SqjV+fDHkOn16tefZunXw4LdxY7Fs2rT79UDb1qwppq8Z6L3BnlwyHOPH7z7wTZlSDCuPdj2cfVrdhTTESZKkXUyYsONu3VZpBsfdhcDhBMWB9lmzphiK3rRp53Xz67EEyKaIsQXFkT4r2RCnUtmJkySNVh3BsWnr1p1D3WjWw9lnw4Zi6pzB3h8JQ5wqYYiTJHWSCROKZdq0eusYyd9P57NWqYY7CackSRobQ5xK5XCqJEmtYYiTJEnqQIY4lcpOnCRJrWGIkyRJ6kCGOJXKTpwkSa3RNiEuIhZGxPciYnVE3B8RH4uICX3ePyUibo6IRyLisog4sM56JUmS6tQ2IQ74BPAgMA84FjgJOBsgImYDFwPnAHsDS4Cv1lKldstOnCRJrdFOIe4g4GuZuSkz7wf+Gziq8d4LgRsz86LM3AScCxwTEYfXU6okSVK92umJDf8KnBERlwN7Ac+m6LxBEeaub+6YmRsiYmlj+827O+itt8KznlVJvRrAgw8WaztxkiRVq51C3I+BvwTWAuOBLwDfbLw3HVjRb/81wIBPV4uIM4EzASZNOpq1ayuoVgOaMgVOOw0OOaTuSiRJ6m4tCXGN7tpJg7x9JfA04AfAp4ETKELbZ4EPAm8F1gMz+33fTGDdQAfMzPOB8wEWL16cP//52OqXJElqNy25Ji4zT87MGGR5CsXNCvsDH8vMzZm5Evgc8JzGIW4EjmkeLyKmAYsa2yVJknpOW9zYkJkPAXcAr4mICRExC3glO66DuwR4XEScHhFTgHcCN2Tmbq+HkyRJ6lZtEeIaXgicRnHt223AVuBNAJm5AjgdeC+wGngScEY9ZUqSJNWvbW5syMxfASfv5v1LAacUkSRJor06cZIkSRomQ5wkSVIHMsRJkiR1IEOcJElSBzLESZIkdSBDnCRJUgcyxEmSJHUgQ5wkSVIHMsRJkiR1IEOcJElSBzLESZIkdSBDnCRJUgcyxEmSJHUgQ5wkSVIHisysu4ZKRcQ64Hd119FjZgMP1V1Ej/Ezbz0/89bzM289P/PWOywzZwxnxwlVV9IGfpeZi+suopdExBI/89byM289P/PW8zNvPT/z1ouIJcPd1+FUSZKkDmSIkyRJ6kC9EOLOr7uAHuRn3np+5q3nZ956fuat52feesP+zLv+xgZJkqRu1AudOEmSpK5jiJMkSepAXRviImLviLgkIjZExF0R8ZK6a+p2EfG6iFgSEZsj4vN119MLImJyRFzQ+De+LiKui4hn111XN4uIL0XE8ohYGxG3RMRf1F1Tr4iIQyJiU0R8qe5aekFEXN74vNc3FudcbYGIOCMibmrkl6UR8dTB9u3meeI+DmwB5gLHAt+NiOsz88Zaq+pu9wHnAc8CptZcS6+YANwDnATcDTwH+FpEPD4z76yzsC72fuDPM3NzRBwOXB4R12XmtXUX1gM+DlxTdxE95nWZ+Zm6i+gVEfEHwAeBPwGuBubtbv+u7MRFxDTgdOCczFyfmVcA3wJeXm9l3S0zL87MbwIr666lV2Tmhsw8NzPvzMztmfkd4A7gCXXX1q0y88bM3Nx82VgW1VhST4iIM4CHgR/VXIpUpXcB787Mqxq/05dl5rLBdu7KEAccCmzLzFv6bLseOKqmeqSWiIi5FP/+7ThXKCI+ERGPADcDy4Hv1VxSV4uImcC7gTfXXUsPen9EPBQRV0bEyXUX080iYjywGJgTEbdFxL0R8bGIGHRkq1tD3HRgTb9ta4BhPYtM6kQRMRH4MvCFzLy57nq6WWaeTfH75KnAxcDm3X+Hxug9wAWZeU/dhfSYvwUeC+xHMXfZtyPCrnN15gITgT+i+N1yLPB7wN8P9g3dGuLWAzP7bZsJrKuhFqlyETEOuJDiOtDX1VxOT8jMbY1LNRYAr6m7nm4VEccCzwQ+XHMpPSczf5GZ6zJzc2Z+AbiS4rpbVWNjY/3RzFyemQ8BH2I3n3m33thwCzAhIg7JzFsb247BISZ1oYgI4AKK/4p7TmY+WnNJvWYCXhNXpZOBhcDdxT91pgPjI+LIzDyuxrp6UQJRdxHdKjNXR8S9FJ/zsHRlJy4zN1AMcbw7IqZFxInA8yk6FapIREyIiCnAeIpfslMiolv/Q6GdfBI4AnheZm4cameNXkTs27j9f3pEjI+IZwEvBv637tq62PkUIfnYxvIp4LsUd8GrIhExKyKe1fw9HhEvBZ4G/KDu2rrc54DXN37X7AW8EfjOYDt38x/Ys4HPAg9S3C35GqcXqdzfA//Q5/XLKO60ObeWanpARBwInEVxTdb9jU4FwFmZ+eXaCuteSTF0+imK/wi+C3hjZv5XrVV1scx8BHik+Toi1gObMnNFfVX1hIkUU0YdDmyjuInnBZnpXHHVeg8wm2JEcRPwNeC9g+3ss1MlSZI6UFcOp0qSJHU7Q5wkSVIHMsRJkiR1IEOcJElSBzLESZIkdSBDnCRJUgcyxEnqahFxY6se3B0RR0bEkgqOe3FEnFb2cSV1NueJk9TRGpO/Nu1BMfHxtsbrlk56HBHfAC7KzK+UfNwnAp/MzCeUeVxJnc0QJ6lrRMSdwF9k5qU1nHsexfOZ52fmpgqOfyvw4swsvdMnqTM5nCqpq0XEnRHxzMbX50bERRHxpYhYFxG/johDI+LtEfFgRNwTEaf2+d49I+KCiFgeEcsi4ryIGD/Iqf4A+GXfANc4999ExA0RsaFxrLkR8f3G+S9tPB+RxjMqvxQRKyPi4Yi4JiLm9jn+5cBzS/+AJHUsQ5ykXvM84EJgL+A6igd6jwP2A94NfLrPvl8AtgIHA78HnAr8xSDHfTww0HMlT6cIeIc2zv194O8ono84DnhDY79XAnsC+wP7AK8GNvY5zk3AMcP+KSV1PUOcpF7z08z8QWZuBS4C5gAfyMxHga8ACyNiVqML9myKB9xvyMwHgQ8DZwxy3FnAugG2fzQzH8jMZcBPgV9k5nWZuRm4hCIcAjxKEd4OzsxtmXltZq7tc5x1jXNIEgAT6i5AklrsgT5fbwQeysxtfV4DTAfmAxOB5RHR3H8ccM8gx10NzBjG+fq/nt74+kKKLtxXImIW8CXgHY1wSePYDw/2Q0nqPXbiJGlg91Dc6To7M2c1lpmZedQg+99AMWQ6Kpn5aGa+KzOPBE4A/hB4RZ9djgCuH+3xJXUfQ5wkDSAzlwM/BP4lImZGxLiIWBQRJw3yLf8DHBcRU0Zzvoh4ekQ8vnHjxFqK4dVtfXY5ieJ6OkkCDHGStDuvACYBv6UYLv06MG+gHTPzAeB/geeP8lyPaRx/LcVNDD+mGFIlIo4HNmTm1aM8tqQu5DxxklSSiDiS4o7WJ2aJv1wbkwhfkJnfK+uYkjqfIU6SJKkDOZwqSZLUgQxxkiRJHcgQJ0mS1IEMcZIkSR3IECdJktSBDHGSJEkdyBAnSZLUgQxxkiRJHej/B/jtGB54iYeEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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iVIvJdOL23BOmTTPESZK0I4Y41WIynbgpU2DvvQ1xkiTtiCFOtZhMJw6cK06SpNEY4lSLyXTiwBAnSdJoDHGqxWQ7cQ6nSpK0Y4Y41WKynbi994Z77oHNm6urSZKkXmKIUy0m24lbsAAyS5CTJEnbM8SpFlV04gDuvLOaeiRJ6jWGONWiik4cwF13VVOPJEm9xhCnWgx04gxxkiTVwxCnWjicKklSvQxxqsVkh1PnzIGdd7YTJ0nSSAxxqsVkO3FQunGGOEmShmeIUy0m24mDcl+cw6mSJA3PEKdaVNGJW7DATpwkSSMxxKkWVXTi9t7bTpwkSSMxxKkWVXXiVqzw1VuSJA3HEKdaVHVPXCYsX15NTZIk9RJDnGpR1dOp4JCqJEnDMcSpFlV14sCHGyRJGo4hTrWoshNniJMkaXuGONWiyk6cw6mSJG3PEKdaVNGJmz27LHbiJEnaniFOtdi0qaynT5/ceZwrTpKk4RniVIt168p61qzJnce3NkiSNDxDnGpx331lKHQy98SBIU6SpJEY4lSLdetgzpzJn8fhVEmShmeIUy0GOnGTNfDqrQcemPy5JEnqJYY41eK++6rrxIGv3pIkaShDnGqxbl11nThwSFWSpKEMcapFVZ04X70lSdLwDHGqRVWduIHhVDtxkiQ9mCFOtbATJ0lSvQxxqsW6dbDzzpM/z847lzBoiJMk6cEMcarFpk0wc2Y153KuOEmStmeIUy02boSddqrmXL61QZKk7XVdiIuIPSLivIhYFxE3R8QLmq5J29u0qboQt/fehjhJkobquhAHfATYBCwAXgh8LCKObLYkDbZlS1lmzKjmfAsWOJwqSdJQXRXiImI2cCrw9sy8LzMvAr4FvKjZyjTYpk1lXeVw6sqV284rSZK6LMQBhwBbMvMPg7ZdCdiJ6yBVh7iBueLuvrua80mS1Au6LcTNAdYM2bYG2GXwhog4PSKWRMSS5b50s+0GQlyVw6ngfXGSJA3WbSHuPmDukG1zgbWDN2TmWZl5bGYeO3/+/LYVp2LjxrKucjgVDHGSJA3WbSHuD8C0iDh40LajgKsbqkfDqGs41YcbJEnapqtCXGauA84F3hkRsyPiBOBZwOebrUyDOZwqSVL9uirEtZwBzALuBr4MvDIz7cR1kKqHU2fNgrlzDXGSJA02rekCxiszVwLPbroOjazq4VRwrjhJkobqxk6cOlzVw6ngq7ckSRrKEKfK1dGJ23tvO3GSJA1miFPlqr4nDuzESZI0lCFOlatjOHXvvWHVqm0BUZKkfmeIU+XqerABfPWWJEkDDHGqXF3DqeCQqiRJAwxxqtwDD5T19OnVnXPgrQ2GOEmSCkOcKrdlS1lPnVrdOQc6cT6hKklSYYhT5bZuLWtDnCRJ9THEqXIDIW5KhX+6Zs6EXXd1OFWSpAGGOFVuYDi1yhAHTvgrSdJghjhVro7hVICFCw1xkiQNMMSpcnbiJEmqnyFOlavjnjgwxEmSNJghTpWrazh1771h7VpYt67a80qS1I0McapcncOpYDdOkiQwxKkGdXbiwBAnSRIY4lSDOu+JA0OcJElgiFMNHE6VJKl+hjhVrq7h1HnzyjkNcZIkGeJUg4FOXES15506FfbayxAnSRIY4lSDrVur78INcK44SZIKQ5wqt3Vr9ffDDTDESZJUGOJUuS1b6g1xd9xRz7klSeomhjhVru7h1Lvu2vbwhCRJ/coQp8rV3YnbvBlWrqzn/JIkdQtDnCpXdycOvC9OkiRDnCpX54MNCxeWtSFOktTvDHGqXN3DqWCIkyTJEKfKOZwqSVL9DHGqXJ2duDlzYOedDXGSJBniVLk674mLcK44SZLAEKca1DmcCr61QZIkMMSpBnUOp4IhTpIkMMSpBnbiJEmqnyFOlavznjgoIW7lSti4sb5rSJLU6Qxxqlzdw6kDE/7efXd915AkqdMZ4lS5dgyngkOqkqT+ZohT5drxYAM4zYgkqb8Z4lQ5O3GSJNXPEKfK1f1gw157lbUhTpLUzwxxqlzdw6k77QR77mmIkyT1N0OcKlf3cCo4V5wkSYY4Va7uThz4/lRJkgxxqlw7OnH77AO3317vNSRJ6mSGOFWu7gcbABYtKp24rVvrvY4kSZ3KEKfKtWM4ddEieOABWLGi3utIktSpDHGqXDuGUxctKutly+q9jiRJncoQp8q1oxO3zz5l7X1xkqR+ZYhT5dp1TxwY4iRJ/csQp8q1a544MMRJkvqXIU6Va8dw6k47lddveU+cJKlfTRvLQRHxZOClwJHALsBa4GrgM5n5w9qqU1dqRycOypCqnThJUr8aNcRFxOuBNwOfBL4JrAHmAkcB50TEBzLzP2qtUl2lHffEgSFOktTfxtKJexPwhMy8dsj2cyPiy8AFgCFOf9SO4VQoT6hefnn915EkqRON5Z/a2cBI/Y47gZ2rK0e9oJ3DqXffXSb9lSSp34wlxH0T+HZEnBwR8yNip4iYFxEnA+cB36i3RHWbrVshov7rLFoEmXDXXfVfS5KkTjOWEPcK4OfAOcBdwPrW+hzgEuCVtVWnrpTZnhDnhL+SpH426j1xmbkJ+Dvg7yJiN2AOcF9mrh56bESckJkXV12kuk+7OnHgNCOSpP40pilGBrSC2+odHPLflCdX1ccy23Md39ogSepnVT9D2Ib+i7pBOzpx8+fDtGmGOElSf6o6xE2oBxMRMyLi7Ii4OSLWRsQVEfHUIcecHBHXRsT9EXFBRBxQTcmqWrs6cVOmwMKFDqdKkvpTp7x2axpwK3AisCvwduBrEbEYICLmAee2tu8BLAG+2kilGpN2dOLACX8lSf2rI0JcZq7LzDMz86bM3JqZ3wFuBI5pHfIc4OrM/HpmbgDOBI6KiMMaKlk70K5OHJQnVA1xkqR+1JH3xEXEAuAQyvtZobyz9cqB/Zm5Dlja2j7cz58eEUsiYsny5curKEnjZCdOkqR6jSvERcSeEfGiiHhz6/tFEbHvwP7M3GWyBUXEdOCLwDmDXvU1h/LO1sHWAMNeLzPPysxjM/PY+fPnT7YkjVM7O3GLFsGqVbB+ffuuKUlSJxhziIuIE4HfAy+k3JsGcDDwsTH87IURkSMsFw06bgrweWAT8OpBp7iP7acumQusHWv9aq92deIGJvy97bb2XE+SpE4xnk7cvwN/npmnAJtb234JPHq0H8zMkzIzRlgeCxARAZwNLABOzczBb8S8Gjhq4JuImA0cyLbhVnWQdnbi9tuvrG+9tX3XlCSpE4wnxC3OzB+1vh74Z3oT45wweAc+BhwOPDMzhw6OnQc8LCJOjYiZwDuAqwYNt6rDtKsTZ4iTJPWr8YS430XEU4Zs+zPgN5MtojXn28uBo4E7I+K+1vJCgMxcDpwKvAdYBTwGOG2y11U92vXuVIB9W3dkGuIkSf1mPF20NwDfiYjvArMi4hPAM4FnTbaIzLyZUZ5szczzAacU6QLtHE6dObO8ucEQJ0nqN2PuxGXmJZT70q4GPk2Zx+3RmXlZTbWpi7WrEwew//6GOElS/xnX/WyZuQz4p5pqUY9oZycOyn1x11/f3mtKktS0HYa4iPg8Y3gfama+uLKK1BPa2Ynbbz+44IL2XU+SpE4w2nDq9ZQ3IyylTK77bGAqcFvrZ58FrK6vPHWjJjpxa9bAvfe297qSJDVph524zPzHga8j4vvA0zPzZ4O2PZZtE/9Kf9TuThyU++KOHPZFbJIk9Z7xTDHyJ8AlQ7b9EvjT6spRL2iiEwc+3CBJ6i/jCXFXAO+NiFkArfV7gF/XUJe6XFOdOEmS+sV4QtxLgROANRFxF+UeuccCPtSgB2l3J27RIpgyxRAnSeovY55iJDNvAo6PiP2ARcAdmXlLXYWpu7WzEzdtGixcaIiTJPWX8XTiiIjdgScATwROan0vPUi7O3FQhlQNcZKkfjLmEBcRf0qZauQVwCMo7zpd2tou/VE73506wLc2SJL6zXg6cf8OnJGZx2fm8zPzBOCVwP9fS2Xqau0OcfvtB7fc0kwXUJKkJownxB0CfG3Itm8AB1VXjnpBU8OpGzbAihXtv7YkSU0YT4i7DjhtyLbnUYZYpQdpohMHDqlKkvrHmJ9OBV4HfCciXgvcDCwGDgaeUX1Z6mZNdeKghLhHPrL915ckqd3GM8XIzyPiQODplClGvg18LzNX1lWculdTnbhbnPRGktQnxtOJIzNXAV+oqRb1iCY6cQsWwMyZcPPN7b+2JElNGHOIi4iHUF6zdTQwZ/C+zNy/2rLU7drdiYuAxYvhxhvbe11Jkpoynk7clygPMbwBuL+ectQLmprmY/FiuOmmZq4tSVK7jSfEHQmckJlb6ypGvaPdnTiAhzwELr20/deVJKkJ45li5KeAz/1pVE124lauhHvvbeb6kiS103g6cTcB34+Ic4E7B+/IzHdUWZS6X1OdOChDqo94RPuvL0lSO42nEzebMq3IdGC/Qcu+NdSlLtbEu1OhdOLA++IkSf1hPPPEvWy0YyLi+Zn55cmVpG7X1HDqQCfOJ1QlSf1gPJ24sfhExedTl2qiE7fnnjB7tp04SVJ/qDrENfBPtzpNU524iNKNsxMnSeoHVYe4hv75VqdpohMHzhUnSeofVYc4qbFOHGzrxDVZgyRJ7TBqiIsIg57GrclO3L33wurVzVxfkqR2GUtAWxYR/xQRDxvDsbdMtiB1v6Y7ceB9cZKk3jeWEPcK4CHAZRHxq4j4vxExf7gDM3MsQU99oKlO3ECIu+GGZq4vSVK7jBriMvO/MvN5wELKFCLPA26NiG9FxKkRMb3uItVdmuzEHXhgWV9/fXM1SJLUDmO+3y0zV2fmJzLzscDhwBLgg8AddRWn7tVUJ26XXWDBAkOcJKn3jfuhhYiYARwHPAZYAPym6qLU3Zp67daAgw4yxEmSet+YQ1xEPDYizgLuAt4NXAIckplPqKs4aSIOPtgQJ0nqfWOZYuTMiFgKfLu16emZeUhmviszb663PHWjTujELVsG99/fXA2SJNVt2hiO+RPgbcB/ZuaGmutRD2h6ot2DDirrpUvh4Q9vthZJkuoyaojLzFPaUYh6S9OdOChDqoY4SVKv8m0MqlzTnTinGZEk9QNDnGrRZCdut91g3jxDnCSptxniVLmmO3HgNCOSpN5niFMtmuzEgSFOktT7DHGqXKd04m69FTb4PLUkqUcZ4lSLpjtxBx9cwqTdOElSrzLEqXJNT/YLcNhhZf373zdbhyRJdTHEqScdemhZX3NNs3VIklQXQ5wq1wmduNmzYf/9DXGSpN5liFPlOuHBBoDDD4drr226CkmS6mGIUy2a7sRBuS/u2mth69amK5EkqXqGOFWukzpx998Pt93WdCWSJFXPEKdadEonDrwvTpLUmwxxqlwndeLAECdJ6k2GONWiEzpx8+fD7rv7cIMkqTcZ4lS5TunERZRunJ04SVIvMsSpFp3QiQOnGZEk9S5DnCrXKZ04gCOOgLvvhuXLm65EkqRqGeJUi07pxD384WX9m980W4ckSVUzxKlynfDarQGPeERZX3VVs3VIklQ1Q5x62oIFsNdeduIkSb3HEKfKdVInDko3zk6cJKnXdFyIi4iDI2JDRHxhyPaTI+LaiLg/Ii6IiAOaqlHd5eEPh9/+FrZsaboSSZKq03EhDvgIcNngDRExDzgXeDuwB7AE+Gr7S9NYdVonbsMGWLq06UokSapOR4W4iDgNWA38aMiu5wBXZ+bXM3MDcCZwVEQc1t4KNZpOml5kgA83SJJ6UceEuIiYC7wTeMMwu48Erhz4JjPXAUtb29WBOqkTd8QRMGWKIU6S1Fs6JsQB7wLOzsxbh9k3B1gzZNsaYJfhThQRp0fEkohYstxZXtuqEztxM2fCIYcY4iRJvaUtIS4iLoyIHGG5KCKOBv4M+OAIp7gPmDtk21xg7XAHZ+ZZmXlsZh47f/78yn4PjV0ndeKgDKleeeXox0mS1C2mteMimXnSjvZHxOuAxcAtUf71nwNMjYgjMvNRwNXASwYdPxs4sLVdHaQTO3EAxxwDX/sarFwJe+zRdDWSJE1epwynnkUJZUe3lo8D3wWe0tp/HvCwiDg1ImYC7wCuykxfbd6hOq0Td8wxZX355c3WIUlSVToixGXm/Zl558BCGT7dkJnLW/uXA6cC7wFWAY8BTmusYI1ooBPXaSHuUY8q6yVLmq1DkqSqtGU4dbwy88xhtp0POKWIJmT33eGggwxxkqTe0RGdOPWOTu3EARx7rMOpkqTeYYhT3zjmGLj5ZnDWGUlSLzDEqVKd3okDu3GSpN5giFOlOnWKEfDhBklSbzHEqRad2ImbOxcOPdQQJ0nqDYY4VaqTO3EAj3kM/PznnV+nJEmjMcSpFp3YiQM44YTyYMPSpU1XIknS5BjiVKlO73CdcEJZX3xxs3VIkjRZhjjVolM7cYcfDrvtZoiTJHU/Q5wq1clTjABMmQJ/+qflvjhJkrqZIU595/jj4eqrYdWqpiuRJGniDHGqVKd34mDbfXGXXNJsHZIkTYYhTn3n0Y+GqVPhoouarkSSpIkzxKlS3dCJmz27vILrwgubrkSSpIkzxKlSnT7FyIAnPhEuvRTWrm26EkmSJsYQp1p0cicO4OSTYfNm+NnPmq5EkqSJMcSpUt3SiTv+eJgxA370o6YrkSRpYgxxqkWnd+JmzSpB7sc/broSSZImxhCnSnVLJw7KkOqvfw333NN0JZIkjZ8hTrXo9E4clIcbAC64oNk6JEmaCEOcKtUNU4wMOO442HVX+J//aboSSZLGzxCnvjVtGjzlKfC978HWrU1XI0nS+BjiVKlu6sQBPP3pcOedcMUVTVciSdL4GOLU15761BI4v/e9piuRJGl8DHGqVLd14ubPL+9S/e53m65EkqTxMcSp7z396eUVXMuXN12JJEljZ4hTpbqtEwfwjGeUur/97aYrkSRp7AxxqlQ3TfY74OijYfFi+MY3mq5EkqSxM8SpFt3UiYuA5z0Pzj8fVq1quhpJksbGEKdKdWMnDuC5z4UHHoBvfavpSiRJGhtDnGrRTZ04KG9v2H9/h1QlSd3DEKdKdWsnLqJ0437wA1i9uulqJEkanSFOtei2ThzAn/85bNoE3/xm05VIkjQ6Q5wq1Y1TjAw47jg49FD43OearkSSpNEZ4qSWCHjxi+GnP4Ubb2y6GkmSdswQp0p1cycO4C/+oqy/8IVm65AkaTSGOGmQ/feHJzyhDKl260MakqT+YIhTpbq9EwfwspfB9dfDhRc2XYkkSSMzxKlSvdC9et7zYI894KMfbboSSZJGZohTLbq5EzdzJvzlX8J558HttzddjSRJwzPEqVK90IkDePnLYcsW+NSnmq5EkqThGeJUi27uxAEceCCccgp84hPlnaqSJHUaQ5wq1SudOIAzzijDqf/5n01XIknS9gxxqkW3d+IAnvY0OPhg+Od/7q1wKknqDYY4VaoXphgZMHUqvPGNcNllcMEFTVcjSdKDGeKkHXjxi2HBAvjAB5quRJKkBzPEqVK91ImDMt3I618PP/gBXHFF09VIkrSNIU4axSteAXPnwnvf23QlkiRtY4hTpXqtEwew667w2tfCN75hN06S1DkMcdIYvOENsNtu8I53NF2JJEmFIU6V6sVOHJQA9+Y3w3e+A5dc0nQ1kiQZ4lSxXp5P7bWvhb32gre+tbd/T0lSdzDEqRa91okDmD0b/v7vy5xx3/1u09VIkvqdIU6V6vUO1SteAYcdBn/zN7BpU9PVSJL6mSFOtejFThzA9OnwwQ/CddfBhz7UdDWSpH5miFOlevXBhsFOOaW8V/Wd74S77mq6GklSvzLESRPwb/8G69eXYVVJkppgiFOl+qETB3DoofC2t8GXvgTf+17T1UiS+pEhTpqgt7wFjjiiPOywdm3T1UiS+o0hTpXql04cwIwZ8MlPwm23lalHJElqp44KcRFxWkRcExHrImJpRDxu0L6TI+LaiLg/Ii6IiAOarFUCOP54OOOM8qTqT37SdDWSpH7SMSEuIp4EfAB4GbAL8Hjghta+ecC5wNuBPYAlwFebqVQ70k+duAHvfz8ceCC86EWwalXT1UiS+kXHhDjgH4F3ZuYlmbk1M5dl5rLWvucAV2fm1zNzA3AmcFREHNZUsdKAOXPKAw533FHuj+v1CY8lSZ2hI0JcREwFjgXmR8T1EXFbRHw4Ima1DjkSuHLg+MxcByxtbVcH6cdOHMBxx5V54772Nfjc55quRpLUDzoixAELgOnAc4HHAUcDjwQGbhefA6wZ8jNrKMOu24mI0yNiSUQsWb58eS0Fa3j93IV685vhxBPhVa+C3/626WokSb2uLSEuIi6MiBxhuQhY3zr0Q5l5R2beA/wb8LTW9vuAuUNOOxcYdmKHzDwrM4/NzGPnz59fx6+kUfRbJw5g6tQyrLrLLvC//hesXt10RZKkXtaWEJeZJ2VmjLA8NjNXAbcBI/VxrgaOGvgmImYDB7a2q4P063DqgEWL4Otfh5tuKg86bN3adEWSpF7VKcOpAJ8BXhMRe0XE7sDrgO+09p0HPCwiTo2ImcA7gKsy89pmSpVG9tjHwr//O3znO/AP/9B0NZKkXtVJIe5dwGXAH4BrgCuA9wBk5nLg1Nb3q4DHAKc1U6Z2pN87cQPOOAP+z/+Bd78bPv3ppquRJPWiaU0XMCAzHwDOaC3D7T8fcEoRdYUI+PjH4dZb4fTTYZ994ClPaboqSVIv6aROnHqAnbhtpk+Hb3wDHvYweO5z4Ve/aroiSVIvMcRJNZo7F777XdhjD3jyk516RJJUHUOcKmUnbnv77AM/+hHMmAEnnwzX+jiOJKkChjipDQ46qAQ5KEHu979vth5JUvczxKlSduJGdthhJcg98AA87nHeIydJmhxDnCrVz6/dGouHPQwuughmzYKTToKf/KTpiiRJ3coQp1rYiRvZIYfAxRfDvvvCKafAeec1XZEkqRsZ4lQpO3Fjs+++8NOfwlFHwXOeA+99r5+dJGl8DHGqhZ240c2bBxdcAC98IbztbWW9fn3TVUmSuoUhTpXywYbxmTULPv95eN/74CtfKe9dvf76pquSJHUDQ5zUsAh4y1vgW9+CG2+ERz2qBDpJknbEEKdK2YmbuGc8A379a3j4w+H5z4e//mtYu7bpqiRJncoQJ3WQ/feHCy8snbmzzy6B7vzzm65KktSJDHGqlJ24yZs+vdwjd9FFMHMmPOlJpSu3Zk3TlUmSOokhTupQxx8PV1wBb34zfPrT5Y0Pn/88bN3adGWSpE5giFOl7MRVa9Ys+MAH4JJLylDri19cnmBdsqTpyiRJTTPESV3guOPgF7+Az3wGbrgBHv1oeNGLYOnSpiuTJDXFEKdK2Ymrz5Qp8NKXwh/+AG96E3zzm3DooXD66XDrrU1XJ0lqN0OcKuWro+o3d24ZYl26FF75SvjsZ+Ggg+BVrypdOklSfzDEqRZ24uq3cCF86ENw3XXlXrlPfQoOPhj+9/+Gyy5rujpJUt0McaqUw6ntd8AB8MlPlrc9vOlN8IMflHvmHv94+PKXYePGpiuUJNXBECf1iEWL4P3vh1tugX/9V1i2DF7wAth33xLurruu6QolSVUyxKlSduKaN3cu/M3flND2gx+UjtwHPwiHHFK+PussWLmy6SolSZNliJN61JQp5W0P3/xmeXr1Pe+B5cvh5S+HvfeGZz8bvv51WL++6UolSRNhiFOl7MR1poUL4a1vhd/9Di6/HF7zGrj00vIQxLx5cOqp8LnP2aGTpG5iiJP6SAQ86lHlnrlbb4Xzz4eXvQx++Ut4yUtgr73giU+E//iPMh+dU8ZIUucyxKlSduK6x9SpcPLJ8OEPl0B32WXwlrfA3XfD615XJhJ+yEPKZMLf+AasWtV0xZKkwaY1XYCk5kXAsceW5d3vLpMG//CH8P3vw1e/WqYwmTKl7D/pJHjc4+CEE2D33ZuuXJL6l504VcpOXG946EPLAxDnngsrVsDFF8Pb3w7TppUnXZ/5TNhzT3jEI+DVry5B7/bbm65akvqLnThVynuoes+0aXD88WU588zyNOsvfwk/+1lZPvtZ+MhHyrH77gvHHVcmGz7uuNK523XXJquXpN5liFMt7MT1rlmzypDqSSeV7zdvhiuugIsuKvfVXXYZnHfetuMPPbQEuuOOg6OOKt07h2ElafIMcaqUw6n9Z9q0bSFtwMqVsGRJCXSXXlqegv3CF7bt32+/EuYe8Yhtwe7gg8u5JElj41+Zkiq3xx7w5CeXBUq4v+MOuOqqbcuVV5YHJzZvLsfMmAFHHAGHHw6HHbZtOfhgmDmzud9FkjqVIU6VshOn4USUd7suWgSnnLJt+8aNcO21JdBddRX89rflIYovfWnbMVOmlKlOBge7Qw6BAw8skxhP8fEsSX3KECepMTNmlOHUo4568Pb77y+TDV97bVmuuaaszz+/BL8BM2eWJ2kPPHD7ZfFi2Gmntv46ktRWhjhVyk6cqrDzznD00WUZbMsWuPlmuP56WLr0wcuPflTC34ApU8q9dw95CBxwAOy//4PX++1XHtKQpG5liJPUNaZOLZ23hz50+32ZcNdd24e7G28sAe/222Hr1gf/zPz524Ld4JC3775l6HfBApg+vT2/mySNlyFOlbITp6ZEwN57l+WEE7bf/8ADsGwZ3HJLWW6+edvX11wD//M/D+7kDZxzwYIS6PbZZ9t9fUO/3nNP/8xLaj9DnKS+MH16uU9u8eLh92eW98PefHPp2i1b9uD1LbfAJZfA8uXb/+xOO5VAt3Ah7LVXCX4jLXPnGvgkVcMQp0rZiVO3iihTo+yxBzzykSMft2lTmS5lcMAb+PqOO8oQ7s9/DvfcM/wbTGbMKGFupLA3fz7Mm1eWPfd0ehVJIzPESdI47LRTuXfugAN2fNzmzSXI3X13uVdvuGXZMvjVr8oxW7YMf545cx4c6ga+Hm7Zc8+y+FSu1B8McaqUnTipmDZt2z16o9m6tbzl4q67SvAbvKxY8eDv//CHsl67duTzzZ27LdTtsUd5zdnuu4/89cD3O+/s/3elbmKIU6UMcdL4TZmyrZs2Vhs3luA3NPQNDX+rVpUh3lWryjL0Cd3Bpk8fW9jbfXfYdddty267wS67lKeHJbWPIU6SutCMGeVBioULx/4zW7eWDt5AoFu1qgTB4b5etQruvLM8ubtqFaxePfr5d9nlweFuvMvcuQZBaTwMcaqUnTipc02Zsi0wjfSU7ki2bIE1a7aFvTVrRl+WLy8TM69ZU0Lgpk2jX2fOnO2D3S67jG+ZO7ecx1eyqdcZ4iRJo5o6ddvTuwceOLFzbNgwtvA3eFm5skz7snbttmW4p36HM3v2+MPfwNezZ5cgOHg9zX8x1WH8I6lK2YmTNJKZM8uyYMHEz5FZJmUeHOqGLvfeO/K+22578Pfr14/92jvttH2wGy7sjXfbzJn+namJMcRJkrpGxLYANJYnf0ezeTPcd9/2AXDdum3Lffc9eD306zvu2P64zZvHXsOUKdt+p6Ehb/bs8tTwrFllPdKyo/2zZpV7KA2KvccQp0rZiZPUTaZNK0/X7rZbtefdtGnsIXC4rweC5d13l87j4GXjxvHXEzGxADjS/lmzyjJz5vbr6dP9N6BdDHGSJFVsp53Ksvvu1Z97y5YyDDwQ6gZ/vaNlpOPWrCndxKHHjeVBlOFMmbIt1I0U9EbbN5Fjpk+v9nPuBoY4VcpOnCTVa+rUMuQ6Z06919m8eeTgt359WTZs2PF6uG1r1pTpa4bbN9KbS8Zi6tQdB76ZM8uw8kTXYzmm3V1IQ5wkSdrOtGnbntZtl4HguKMQOJagONwxa9aUoegNGx68Hvh6MgFyQMTkguJ435VsiFOl7MRJkiaqieA4YPPmB4e6iazHcsy6dWXqnJH2j4chTrUwxEmSusm0aWWZPbvZOsbz76fzWatSY52EU5IkTY4hTpVyOFWSpPYwxEmSJHUhQ5wqZSdOkqT2MMRJkiR1IUOcKmUnTpKk9uiYEBcRiyPiexGxKiLujIgPR8S0QftPjohrI+L+iLggIg5osl5JkqQmdUyIAz4K3A0sBI4GTgTOAIiIecC5wNuBPYAlwFcbqVI7ZCdOkqT26KQQ9xDga5m5ITPvBP4HOLK17znA1Zn59czcAJwJHBURhzVTqiRJUrM66Y0N/wGcFhEXArsDT6V03qCEuSsHDszMdRGxtLX92h2d9Lrr4ClPqaVeDePuu8vaTpwkSfXqpBD3E+CvgXuBqcA5wH+29s0Blg85fg0w7NvVIuJ04HSAnXZ6BPfeW0O1GtbMmXDKKXDwwU1XIklSb2tLiGt1104cYffFwOOB7wOfAI6nhLZPAx8A3gzcB8wd8nNzgbXDnTAzzwLOAjj22GPzF7+YXP2SJEmdpi33xGXmSZkZIyyPpTyssB/w4czcmJkrgM8AT2ud4mrgqIHzRcRs4MDWdkmSpL7TEQ82ZOY9wI3AKyNiWkTsBryEbffBnQc8LCJOjYiZwDuAqzJzh/fDSZIk9aqOCHEtzwFOodz7dj2wGXg9QGYuB04F3gOsAh4DnNZMmZIkSc3rmAcbMvPXwEk72H8+4JQikiRJdFYnTpIkSWNkiJMkSepChjhJkqQuZIiTJEnqQoY4SZKkLmSIkyRJ6kKGOEmSpC5kiJMkSepChjhJkqQuZIiTJEnqQoY4SZKkLmSIkyRJ6kKGOEmSpC5kiJMkSepCkZlN11CriFgL/L7pOvrMPOCepovoM37m7edn3n5+5u3nZ95+h2bmLmM5cFrdlXSA32fmsU0X0U8iYomfeXv5mbefn3n7+Zm3n595+0XEkrEe63CqJElSFzLESZIkdaF+CHFnNV1AH/Izbz8/8/bzM28/P/P28zNvvzF/5j3/YIMkSVIv6odOnCRJUs8xxEmSJHWhng1xEbFHRJwXEesi4uaIeEHTNfW6iHh1RCyJiI0R8dmm6+kHETEjIs5u/RlfGxFXRMRTm66rl0XEFyLijoi4NyL+EBF/1XRN/SIiDo6IDRHxhaZr6QcRcWHr876vtTjnahtExGkRcU0rvyyNiMeNdGwvzxP3EWATsAA4GvhuRFyZmVc3WlVvux14N/AUYFbDtfSLacCtwInALcDTgK9FxMMz86YmC+th7wP+MjM3RsRhwIURcUVmXt50YX3gI8BlTRfRZ16dmZ9quoh+ERFPAj4A/DlwKbBwR8f3ZCcuImYDpwJvz8z7MvMi4FvAi5qtrLdl5rmZ+Z/AiqZr6ReZuS4zz8zMmzJza2Z+B7gROKbp2npVZl6dmRsHvm0tBzZYUl+IiNOA1cCPGi5FqtM/Au/MzEtaf6cvy8xlIx3ckyEOOATYkpl/GLTtSuDIhuqR2iIiFlD+/NtxrlFEfDQi7geuBe4AvtdwST0tIuYC7wTe0HQtfeh9EXFPRFwcESc1XUwvi4ipwLHA/Ii4PiJui4gPR8SII1u9GuLmAGuGbFsDjOldZFI3iojpwBeBczLz2qbr6WWZeQbl75PHAecCG3f8E5qkdwFnZ+atTRfSZ/4WeCiwD2Xusm9HhF3n+iwApgPPpfzdcjTwSODvR/qBXg1x9wFzh2ybC6xtoBapdhExBfg85T7QVzdcTl/IzC2tWzX2BV7ZdD29KiKOBv4M+GDDpfSdzPxlZq7NzI2ZeQ5wMeW+W9VjfWv9ocy8IzPvAf6NHXzmvfpgwx+AaRFxcGZe19p2FA4xqQdFRABnU/4r7mmZ+UDDJfWbaXhPXJ1OAhYDt5Q/6swBpkbEEZn5qAbr6kcJRNNF9KrMXBURt1E+5zHpyU5cZq6jDHG8MyJmR8QJwLMonQrVJCKmRcRMYCrlL9mZEdGr/6HQST4GHA48MzPXj3awJi4i9mo9/j8nIqZGxFOA5wM/brq2HnYWJSQf3Vo+DnyX8hS8ahIRu0XEUwb+Ho+IFwKPB77fdG097jPAa1p/1+wOvA74zkgH9/I/sGcAnwbupjwt+UqnF6nd3wP/MOj7v6A8aXNmI9X0gYg4AHg55Z6sO1udCoCXZ+YXGyusdyVl6PTjlP8Ivhl4XWb+V6NV9bDMvB+4f+D7iLgP2JCZy5urqi9Mp0wZdRiwhfIQz7Mz07ni6vUuYB5lRHED8DXgPSMd7LtTJUmSulBPDqdKkiT1OkOcJElSFzLESZIkdSFDnCRJUhcyxEmSJHUhQ5wkSVIXMsRJ6mkRcXW7XtwdEUdExJIazntuRJxS9XkldTfniZPU1VqTvw7YmTLx8ZbW922d9Dgivgl8PTO/UvF5Hw18LDOPqfK8krqbIU5Sz4iIm4C/yszzG7j2Qsr7mRdl5oYazn8d8PzMrLzTJ6k7OZwqqadFxE0R8Wetr8+MiK9HxBciYm1E/CYiDomIv4uIuyPi1oh48qCf3TUizo6IOyJiWUS8OyKmjnCpJwG/GhzgWtd+U0RcFRHrWudaEBH/3br++a33I9J6R+UXImJFRKyOiMsiYsGg818IPL3yD0hS1zLESeo3zwQ+D+wOXEF5ofcUYB/gncAnBh17DrAZOAh4JPBk4K9GOO/DgeHeK3kqJeAd0rr2fwNvpbwfcQrw2tZxLwF2BfYD9gReAawfdJ5rgKPG/FtK6nmGOEn95meZ+f3M3Ax8HZgPvD8zHwC+AiyOiN1aXbCnUl5wvy4z7wY+CJw2wnl3A9YOs/1DmXlXZi4Dfgb8MjOvyMyNwHmUcAjwACW8HZSZWzLz8sy8d9B51rauIUkATGu6AElqs7sGfb0euCcztwz6HmAOsAiYDtwREQPHTwFuHeG8q4BdxnC9od/PaX39eUoX7isRsRvwBeBtrXBJ69yrR/qlJPUfO3GSNLxbKU+6zsvM3VrL3Mw8coTjr6IMmU5IZj6Qmf+YmUcAxwPPAF486JDDgSsnen5JvccQJ0nDyMw7gB8A/xoRcyNiSkQcGBEnjvAjPwQeFREzJ3K9iHhCRDy89eDEvZTh1S2DDjmRcj+dJAGGOEnakRcDOwG/owyXfgNYONyBmXkX8GPgWRO81t6t899LeYjhJ5QhVSLiOGBdZl46wXNL6kHOEydJFYmIIyhPtD46K/zLtTWJ8NmZ+b2qzimp+xniJEmSupDDqZIkSV3IECdJktSFDHGSJEldyBAnSZLUhQxxkiRJXcgQJ0mS1IUMcZIkSV3IECdJktSF/h/tBSf6utVBbwAAAABJRU5ErkJggg==\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": {
"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": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting 2D representation of network cell locations and connections...\n"
]
},
{
"data": {
"image/png": 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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 = 3.53 s\n",
"\n",
"Total time = 8256.64 s\n",
"\n",
"End time: 2022-10-30 04:40:46.083769\n"
]
}
],
"source": [
"sim.simulate()\n",
"sim.analyze()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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": 23,
"id": "ddb4904a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Duration: 2:17:48.664795\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": 24,
"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",
"# v_diff_816 = (Abeta0_vext1_node8-Abeta0_vext1_MYSA16)/1000 #volt\n",
"# Longi_Current_node8_MYSA16 = (v_diff_816/xraxia)*1e12 \n",
"\n",
"# v_diff_918 = (Abeta0_vext1_node9-Abeta0_vext1_MYSA18)/1000 #volt\n",
"# Longi_Current_node9_MYSA18 = (v_diff_918/xraxia)*1e12 \n",
"\n",
"# v_diff_1020 = (Abeta0_vext1_node10-Abeta0_vext1_MYSA20)/1000 #volt\n",
"# Longi_Current_node10_MYSA20 = (v_diff_1020/xraxia)*1e12 \n",
"\n",
"# v_diff_1122 = (Abeta0_vext1_node11-Abeta0_vext1_MYSA22)/1000 #volt\n",
"# Longi_Current_node11_MYSA22 = (v_diff_1122/xraxia)*1e12 \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a336588c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 25,
"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": 26,
"id": "890baeb5",
"metadata": {},
"outputs": [],
"source": [
"## saving the data\n",
"\n",
"\n",
"\n",
"\n",
"# ## writing\n",
"\n",
"\n",
"import csv\n",
"\n",
"\n",
" \n",
"with open('BoundarytoGround1000_misaligned(0-180)_20Fibers_v_Abeta0_stimulateALL_edgedist3_.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_misaligned(0-180)_20Fibers_imembrane_Abeta0_stimulateALL_edgedist3_.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",
"# 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 , Abeta_ap08 , Abeta_ap09 , Abeta_ap010 , Abeta_ap011 ))\n",
"\n",
"\n",
"\n",
"\n",
"# with open('Onlynearedge_boundary1_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 , Abeta_ap08 , Abeta_ap09 , Abeta_ap010 , Abeta_ap011 ))\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 , Abeta0_imembrane_node8 , Abeta0_imembrane_node9 , Abeta0_imembrane_node10 , Abeta0_imembrane_node11 ))\n",
"\n",
"\n",
"# with open('mis_ina_Abeta0_stimulateonlyAbeta0_dist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_ina_node0 , Abeta0_ina_node1 , Abeta0_ina_node2 , Abeta0_ina_node3, Abeta0_ina_node4 , Abeta0_ina_node5 , Abeta0_ina_node6 , Abeta0_ina_node7 , Abeta0_ina_node8 , Abeta0_ina_node9 , Abeta0_ina_node10 , Abeta0_ina_node11 ))\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": 27,
"id": "9766ae7e",
"metadata": {},
"outputs": [],
"source": [
"# netParams.cellParams.keys()\n",
"# netParams.cellParams['']['']"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "e19fa77c",
"metadata": {},
"outputs": [],
"source": [
"# pyplot.plot(t, ap1 )\n",
"# #pyplot.xlim((0, 10))\n",
"# pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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