{
"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 02:23:58.283126\n",
"\n",
"Creating network of 40 cell populations on 1 hosts...\n",
" Number of cells on node 0: 40 \n",
" Done; cell creation time = 3.93 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 = 0.00 s.\n",
"Recording 60 traces of 2 types on node 0\n"
]
}
],
"source": [
"simConfig = specs.SimConfig()\n",
"simConfig.hParams = {'celsius': 37 }\n",
"\n",
"simConfig.dt = 0.005 # Internal integration timestep to use default is 0.025\n",
"simConfig.duration = 6\n",
"simConfig.recordStim = True\n",
"simConfig.recordStep = 0.005 # Step size in ms to save data (e.g. V traces, LFP, etc) default is 0.1\n",
"#simConfig.cache_efficient = True\n",
"#simConfig.cvode_active = True\n",
"# simConfig.cvode_atol=0.0001\n",
"# simConfig.cvode_rtol=0.0001\n",
"\n",
"\n",
"simConfig.recordTraces = {'V_node_0' :{'sec':'node_0','loc':0.5,'var':'v'}}\n",
"simConfig.analysis['plotTraces'] = {'include': ['allCells']} # ['Axon0','Axon1']\n",
"\n",
"simConfig.analysis['plot2Dnet'] = True\n",
"simConfig.analysis['plot2Dnet'] = {'include': ['allCells'], 'view': 'xz'}\n",
"\n",
"\n",
"\n",
"#simConfig.recordLFP = [[56.39,-4000,51.74]] # Determine the location of the LFP electrode\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"sim.create(netParams, simConfig)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "9045099d",
"metadata": {},
"source": [
"### xraxial and transverese conductances"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "41af5705",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.7546811423\n",
"0.1\n",
"95769.75230176943\n",
"0.1\n",
"95769.74992239669\n",
"0.1\n",
"95769.74754302404\n",
"0.1\n",
"95769.74516365153\n",
"0.1\n",
"95769.74278427915\n",
"0.1\n",
"95769.74040490772\n",
"0.1\n",
"95769.73802553555\n",
"0.1\n",
"95769.73564616352\n",
"0.1\n",
"95769.7332667916\n",
"0.1\n",
"95769.73088742064\n",
"0.1\n",
"95769.72850804898\n",
"0.1\n",
"95769.72612867825\n",
"0.1\n",
"95769.7237493068\n",
"0.1\n",
"95769.72136993548\n",
"0.1\n",
"95769.7189905651\n",
"0.1\n",
"95769.71661119316\n",
"0.1\n",
"95769.71423182305\n",
"0.1\n",
"95769.71185245218\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] + 0.094438 #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": [
"5535382000.0\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",
"output_type": "stream",
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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 = 8002.99 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 5.72 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: 8002.99 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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\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": {
"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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Qp1pMphMHPrVBkqSxGOJUi8l04sAhVUmSxmKIUy0m24kzxEmStHOGONViMrcYAUOcJEljMcSpFrs6nLrnnjBrliFOkqSxGOJUi10dTo3wNiOSJI2HIU612NVOHBjiJEkaD0OcarGrnTgwxEmSNB6GONViMp24ffctIS6z2pokSeonhjjVYjKduEWLYNMmWL++2pokSeonhjjVYjKduEWLyvrOO6urR5KkfmOIUy129T5xsD3E3XVXdfVIktRvDHGqxWSGUxcuLGs7cZIkjc4Qp1o4nCpJUr0McarFZDpxD3tYCX+GOEmSRmeIUy0m04mbOhUWLPCaOEmSdsYQp1pMphMH5bo4O3GSJI3OEKdaTKYTB+W6OEOcJEmjM8SpFpO5xQgY4iRJGoshTrWY7HDqokXlmjgfvSVJ0sgMcarFZIdTFy6ELVtgzZrqapIkqZ8Y4lSLKjpx4JCqJEmjMcSpFlVMbABDnCRJozHEqRZVdeK8V5wkSSMzxKkWVVwTB3biJEkajSFOtZjsLUb23ht2280QJ0nSaAxxqsVkh1Mjtt9mRJIk7cgQp1pMdjgVvOGvJEk7Y4hTLSbbiQOfnypJ0s4Y4lQLO3GSJNXLEKdaVNGJW7QIVq3afixJkrSdIU61qKoTt20b3HNPNTVJktRPDHGqxWRvMQLeK06SpJ0xxKkWVQ2ngiFOkqSRGOJUi6qGU8F7xUmSNBJDnGpR1S1GwE6cJEkjMcSpFlV04mbPhlmzDHGSJI3EEKdaPPBAWU+fPrnjeK84SZJGZohTLdavL88/3X33yR3H56dKkjQyQ5xqsX59GQqdzDVx4KO3JEkajSFOtdiwoVzTNlkOp0qSNDJDnGrR7sRN1qJFsHr19mvsJElSYYhTLdavr64TB3D33ZM/liRJ/cQQp1pUFeLa94q7447JH0uSpH5iiFMtqu7EOUNVkqSHMsSpFhs2VHdNHBjiJEkazhCnWlTViVuwoKydoSpJ0kMZ4lSLjRthjz0mf5yZM2HvvQ1xkiQNZ4hTLe6/H2bMqOZYCxc6nCpJ0nCGONXigQeqC3He8FeSpB31XIiLiLkR8fmI2BARt0TEi5quSQ+VWTpxu+1WzfF8fqokSTua1nQBu+BfgAeAhcBxwFci4srMXN5oVfo/W7eWIFdViPP5qZIk7ainOnERMQs4HXhbZq7PzO8DXwRe2mxlGqr9iKwqh1Pvuw82barmeJIk9YOeCnHA4cDWzLx2yLYrgaMbqkcjuP/+sq5yOBUcUpUkaaheC3GzgbXDtq0F9hy6ISLOjIhlEbFs1apVHStORdWduPajtxxSlSRpu14LceuBOcO2zQHWDd2Qmedl5kmZedL8+fM7VpwKO3GSJNWv10LctcC0iFgyZNuxgJMauki7E1flxAawEydJ0lA9FeIycwNwMXBORMyKiMcBvw1c0GxlGqrq4VQfvSVJ0o56KsS1vA7YHbgb+BTwWm8v0l2qHk6dPh3mzXM4VZKkoXruPnGZuRp4btN1aHRVd+LAe8VJkjRcL3bi1OWq7sSBj96SJGk4Q5wqV/XEBvDRW5IkDWeIU+UcTpUkqX6GOFWuruHUjRth/frqjilJUi8zxKlydXXiwG6cJElthjhVrh3ipk+v7pjtpzYY4iRJKgxxqtyDD5Z1HSHOyQ2SJBWGOFVu69aynlLhny6HUyVJeihDnCq3bVtZT51a3THnzSuh0BAnSVJhiFPl6ujETZ0K8+c7nCpJUpshTpVrd+KqDHHgUxskSRrKEKfK1TGcCj61QZKkoQxxqlwdw6ngUxskSRrKEKfK1dmJu/NOyKz2uJIk9SJDnCpXVydu0aJyI+G1a6s9riRJvcgQp8rVNbHBe8VJkrSdIU6Vq3M4FQxxkiSBIU41qHNiAzhDVZIkMMSpBnbiJEmqnyFOlWt34iKqPe4++8D06XbiJEkCQ5xqsG1bCXBVh7gpU2DBAjtxkiSBIU412Lat+qHUNh+9JUlSYYhT5bZurX5SQ5uP3pIkqTDEqXJ1duJ89JYkSYUhTpXrRCeuPQNWkqRBZYhT5bZtqy/ELVxYQuLq1fUcX5KkXmGIU+XqntgADqlKkmSIU+XqHk4FJzdIkmSIU+XqntgAduIkSTLEqXKd6MQZ4iRJg84Qp8rVObFhzhyYOdPhVEmSDHGqXJ3DqRHeK06SJDDEqQZ1DqeCj96SJAkMcapBnZ04KJ04h1MlSYPOEKfK2YmTJKl+hjhVru5O3KJFcM89JSxKkjSoDHGqXJ2zU6EMp27bBqtW1XcOSZK6nSFOlevEcCo4pCpJGmyGOFWuE8OpYIiTJA02Q5wqV3cnbt99y9oQJ0kaZIY4Va7uTlw7xN1+e33nkCSp2xniVLm6JzbMnAn77GOIkyQNNkOcKlf3cCrAfvsZ4iRJg80Qp8rVPZwKhjhJkgxxqpydOEmS6meIU+U61Ym7445yLkmSBpEhTpWre2IDlBD34IPl8VuSJA0iQ5wq16nhVHBIVZI0uAxxqlynhlOhDKlKkjSIDHGqnJ04SZLqZ4hT5TrRiWs/P9UQJ0kaVNPGs1NEPBV4BXA0sCewDlgOfCwzv1lbdepJnZjYsNtuMH++IU6SNLjGDHER8cfAm4F/BT4HrAXmAMcCn4iI92TmP9VapXpKJ4ZTwXvFSZIG23g6cW8CnpSZK4ZtvzgiPgVcChji9H86MZwKsO++hjhJ0uAaT79kFjDar8o7gT2qK0f9wE6cJEn1G8+v2s8BX4qIJ0fE/IjYLSLmRcSTgc8D/1lvieo1nbgmDkqIu/POEholSRo04/lV+xrgf4BPAHcBm1rrTwA/Al5bW3XqWRH1n2O//UpgvPvu+s8lSVK3GTPEZeYDmfmWzDwAmAscBDwsMw9obX+gvW9EPK7GWtUjMjtzHu8VJ0kaZOO6xUhbZt4L3LuTXb5GmbmqAdepThz41AZJ0mCq+sqlDvzqVrezEydJUv2qDnG79Os7ImZExPkRcUtErIuIKyLiGcP2eXJErIiIjRFxaUQcVE3JqlpmZzpxCxeW8xjiJEmDqFseuzUNuBV4ArAX8DbgoohYDBAR84CLW9vnAsuAzzRSqcalEyFu2rQS5FaurP9ckiR1mwldE1eXzNwAnD1k05cj4ibgROBm4PnA8sz8LEBEnA3cExFLR7gJsRrWqeFUgP33N8RJkgZTV14TFxELgcMpz2eF8szWK9vvt0LfDa3t6kKd6MQBHHgg3HprZ84lSVI3mVCIi4iHRcRLI+LNrdf7RcQB7fczc8/JFhQR04ELgU8M6bLNpjyzdai1wIjni4gzI2JZRCxbtWrVZEvSBHWyE3fAAXDbbZ07nyRJ3WLcIS4ingBcA7yYcm0awBLgQ+P43ssiIkdZvj9kvynABcADwOuHHGI9O966ZA6wbqTzZeZ5mXlSZp40f/788f6IqlAnO3H33gvr13fmfJIkdYuJdOL+EXhBZj4deLC17cfAo8b6xsx8YmbGKMvjASIigPOBhcDpmbllyCGWA8e2X0TELOBQtg+3qot0uhMHDqlKkgbPRELc4sz8duvr9q/pB6hucsSHgCOB0zJz07D3Pg8cExGnR8RM4O3AVU5q6F6d7MSBQ6qSpMEzkRD3y4h42rBtvwX8fLJFtO75dhZwHHBnRKxvLS8GyMxVwOnAucAa4GTgjMmeV/WwEydJUv0m0kV7I+XWH18Bdo+IjwCnAb892SIy8xbGmNmamd8Clk72XOqMTnXi9t+/nMtOnCRp0Iy7E5eZP6Jcl7Yc+ChwE/CozLy8ptrUozrZidttt3LDXztxkqRBM6Hr2TJzJfC3NdWiPtKpThx4mxFJ0mDaaYiLiAsYx/NQM/NllVWkntepZ6e2HXggXHtt584nSVI3GGs49XrKkxFuoNxc97nAVOC21vf+NnBvfeWpF3VyOBVKJ87hVEnSoNlpJy4z39H+OiK+DjwrM783ZNvj2X7jX+n/dLoTd999ZZkz/JbQkiT1qYncYuTRwI+Gbfsx8JjqylE/aKITB14XJ0kaLBMJcVcA74qI3QFa63OBn9VQl3pcpztxYIiTJA2WiYS4VwCPA9ZGxF2Ua+QeDzipQQ/RVCfO6+IkSYNk3LcYycybgcdGxIHAfsAdmfmrugpTb+tkJ659w19DnCRpkEykE0dE7AM8CfhN4Imt19JDdLoTN306LFrkcKokabCMO8RFxGMotxp5DfAblGed3tDaLj1EJztx4G1GJEmDZyJPbPhH4HWZ+en2hoh4AfB+4JEV16Ue1ulOHJTJDStWdP68kiQ1ZSLDqYcDFw3b9p/AYdWVo37RVCeuiQApSVITJhLirgPOGLbtdylDrNL/6fRjtwAWL4Z16+Deezt7XkmSmjKR4dQ/Ar4cEX8A3AIsBpYAz66+LPWyJrphBx1U1jffDPs43UaSNADG3YnLzP8BDgU+APwE+GfgsNZ26SGa6MRBCXGSJA2CiXTiyMw1wCdrqkV9oolOnCFOkjRoxh3iIuJgymO2jgNmD30vMx9ebVnqdZ3uxO2zD+y5pyFOkjQ4JtKJ+w/KJIY3AhvrKUf9oIlOXETpxt1yS+fPLUlSEyYS4o4GHpeZ2+oqRv2j0504KCHOTpwkaVBM5BYj3wWOr6sQ9Y+m7tVmiJMkDZKJdOJuBr4eERcDdw59IzPfXmVR6n1NdOIOOgjWri33itt7786fX5KkTppIJ24W8CVgOnDgkOWAGupSD2uyEwd24yRJg2HcnbjMfOVY+0TECzPzU5MrSf2gqWvioIS4447r/PklSeqkiXTixuMjFR9PPaiJx27B9hDnDFVJ0iCoOsQ18KtbKubOhdmzHU6VJA2GqkNcQ1dDqZs01Ylr3yvOECdJGgRVhzipsYkNUELcTTc1d35JkjplzBAXEQY9TVgTnTiAQw6BG29sNkhKktQJ4wloKyPibyPimHHs+6vJFqTe12SAOvRQWLcOVq1qrgZJkjphPCHuNcDBwOUR8dOI+MOImD/Sjpk5nqCnAdBUJ+7QQ8v6hhuaOb8kSZ0yZojLzP/KzN8F9qXcQuR3gVsj4osRcXpETK+7SPWWJjtxhx1W1tdf31wNkiR1wrivd8vMezPzI5n5eOBIYBnwPuCOuopT72qqE7d4cTm3nThJUr+b8KSFiJgBPBI4GVgI/LzqotTbmuzEzZgBBx5oiJMk9b9xh7iIeHxEnAfcBfw18CPg8Mx8Ul3FqXc11YmDMqTqcKokqd+N5xYjZ0fEDcCXWpuelZmHZ+Y7M9MHHGkHTd/e49BD7cRJkvrftHHs82jgL4EvZObmmutRn2i6E7dqFdx3H8yZ01wdkiTVacwQl5lP70Qh6h9NPXarbehtRo4/vrk6JEmqk09jUOW6YTgVHFKVJPU3Q5xq0Q2dOCc3SJL6mSFOlWu6E7fnnrBggZ04SVJ/M8SpFk124gCWLIFrr222BkmS6mSIU+Wa7sQBHHEEXHNN01VIklQfQ5xq0XQnbulSuOsuWLOm2TokSaqLIU6V64ZO3NKlZW03TpLUrwxxqkXTnbgjjihrQ5wkqV8Z4lS5bujEHXwwTJ8OK1Y0XYkkSfUwxKkWTXfipk8vj98yxEmS+pUhTpVr+rFbbc5QlST1M0Oc+tbSpeWpDVu2NF2JJEnVM8Spct3SiVu6tAS4m25quhJJkqpniFPlumFiAzhDVZLU3wxxqkU3dOLaIe7qq5utQ5KkOhjiVLlu6cTtsw8sWgS//GXTlUiSVD1DnGrRDZ04gEc8An7xi6arkCSpeoY4Va5bOnEAxxwDy5fD1q1NVyJJUrUMcapFN3XiNm+GG29suhJJkqpliFPluqkT94hHlPXPf95sHZIkVc0Qp1p0SyfuqKNKLYY4SVK/McSpct1ys1+APfaAQw91coMkqf90XYiLiCURsTkiPjls+5MjYkVEbIyISyPioKZqVG95xCPsxEmS+k/XhTjgX4DLh26IiHnAxcDbgLnAMuAznS9N49UtnTgoIe6662DTpqYrkSSpOl0V4iLiDOBe4NvD3no+sDwzP5uZm4GzgWMjYmlnK9RYumlSQ9sxx8C2bbBiRdOVSJJUna4JcRExBzgHeOMIbx8NXNl+kZkbgBta29WFuq0TB3DVVc3WIUlSlbomxAHvBM7PzFtHeG82sHbYtrXAniMdKCLOjIhlEbFs1apVFZepnenGTtySJWWCwxVXNF2JJEnV6UiIi4jLIiJHWb4fEccBvwW8b5RDrAfmDNs2B1g30s6ZeV5mnpSZJ82fP7+yn0Pj102duKlT4bjj4Kc/bboSSZKqM60TJ8nMJ+7s/Yj4I2Ax8Ksov/1nA1Mj4qjMPAFYDrx8yP6zgENb29VFurETB3DiifCxj5Vr46Z0U/9ZkqRd1C2/zs6jhLLjWsuHga8AT2u9/3ngmIg4PSJmAm8HrspML1XvUt3UiQM44QRYv77MUpUkqR90RYjLzI2ZeWd7oQyfbs7MVa33VwGnA+cCa4CTgTMaK1ij6uZOHMBPftJsHZIkVaUjw6kTlZlnj7DtW4C3FOkR3daJO/JImDmzhLgXvajpaiRJmryu6MSpf7Q7cd0W4qZNg2OPdXKDJKl/GOI0ME48sYS4bduarkSSpMkzxKlS3dqJgzK54b774MYbm65EkqTJM8RpYJx0Uln/+MfN1iFJUhUMcapUN3fijjkGZs+GH/6w6UokSZo8Q5wq1a23GIHy5IaTTzbESZL6gyFOtejGThzAYx4DV14JGzY0XYkkSZNjiFOlurkTByXEbd0Kl1/edCWSJE2OIU616NZO3KMfXdYOqUqSep0hTpXq9k7c3LmwdKkhTpLU+wxxqkW3duKgDKn+8IfdHzglSdoZQ5wq1c23GGl77GPhnnvgmmuarkSSpF1niNPAecITyvqyyxotQ5KkSTHEqVK90Ik77DA44AC49NKmK5EkadcZ4jRwIuBJTyqdOK+LkyT1KkOcKtULnTgoIe7uu+GXv2y6EkmSdo0hTpXqlc7Wk55U1g6pSpJ6lSFOtej2TtzixWUxxEmSepUhTpXqlU4cbL8ubuvWpiuRJGniDHGqRbd34gCe+lRYvRqWLWu6EkmSJs4Qp0r1UifuqU+FKVPgq19tuhJJkibOEKda9EInbu5cOPlk+NrXmq5EkqSJM8SpUr1yi5G2Zz4TLr8c7rqr6UokSZoYQ5wG2jOeUdZf/3qzdUiSNFGGOFWq1zpxxx8PCxc6pCpJ6j2GOA20KVNKN+5rX4MHHmi6GkmSxs8Qp0r1WicO4PnPh7Vr4ZJLmq5EkqTxM8Rp4D3lKTB7Nnzuc01XIknS+BniVKle7MTNnAnPfjZ84Qs+vUGS1DsMcapUL93sd6jTT4d77oHvfa/pSiRJGh9DnGrRS504KJMbdt/dIVVJUu8wxKlSvdqJmzULnvUsuOgiePDBpquRJGlshjjVotc6cQAveQncfTd84xtNVyJJ0tgMcapUL05saHvGM8rzVD/5yaYrkSRpbIY4qWW33eAFLyizVNeta7oaSZJ2zhCnSvVyJw7KkOqmTU5wkCR1P0OcNMRjHgOHHQbnn990JZIk7ZwhTpXq9U5cBJx1Fnz/+/CLXzRdjSRJozPEScO84hUwYwZ8+MNNVyJJ0ugMcapUr3fiAObNg9/9Xfj3f4f165uuRpKkkRniVKlevdnvcK95TZmh+ulPN12JJEkjM8SpFr3ciQN47GPhmGPggx/sn2AqSeovhjhVql8CTwS84Q1wxRVw2WVNVyNJ0o4McapFr3fiAF72MliwAP72b5uuRJKkHRniVKl+6cQBzJwJf/AH8N//DVdd1XQ1kiQ9lCFOteiHThzAa18Ls2bBe9/bdCWSJD2UIU6V6odbjAw1dy68+tXwqU/BLbc0XY0kSdsZ4qQx/MmfwJQpcO65TVciSdJ2hjhVqt86cQAHHlgexfXRj8L11zddjSRJhSFOGoe3vAWmT4dzzmm6EkmSCkOcKtWPnTiAffeF178eLrwQrr666WokSTLESeP25jfDHnuUrpwkSU0zxKlS/dqJA5g/H/7iL+C//gsuuaTpaiRJg84Qp0r1081+R/LHfwwHHVTWW7c2XY0kaZAZ4lSLfuzEQXmKw9/9XXmCw/nnN12NJGmQGeJUqX7vxAH8zu/AKaeUodV77mm6GknSoDLEqRb92omD8rN98IOwdi288Y1NVyNJGlSGOFWqnyc2DHXMMfBnfwb//u/wzW82XY0kaRAZ4qRd9Na3wpIl8JrXwMaNTVcjSRo0hjhValA6cVAmOZx3Htx4I7z97U1XI0kaNF0V4iLijIi4OiI2RMQNEXHKkPeeHBErImJjRFwaEQc1WasE8MQnlk7cP/wDXHpp09VIkgZJ14S4iHgK8B7glcCewKnAja335gEXA28D5gLLgM80U6l2ZpA6cW3vfW8ZVn3pS2H16qarkSQNiq4JccA7gHMy80eZuS0zV2bmytZ7zweWZ+ZnM3MzcDZwbEQsbapYqW3WLPiP/4C77ipduUG4zYokqXldEeIiYipwEjA/Iq6PiNsi4gMRsXtrl6OBK9v7Z+YG4IbWdnWRQezEAZx4IrzznfDZz8JHP9p0NZKkQdAVIQ5YCEwHfgc4BTgOOB54a+v92cDaYd+zljLsuoOIODMilkXEslWrVtVSsEY2yF2oN70JnvIU+P3fh2XLmq5GktTvOhLiIuKyiMhRlu8Dm1q7/nNm3pGZ9wD/ADyztX09MGfYYecA60Y6X2ael5knZeZJ8+fPr+NH0hgGrRMHMHVqGVZduBCe/3zw3w+SpDp1JMRl5hMzM0ZZHp+Za4DbgNH6OMuBY9svImIWcGhru7rIIHfiAObNg4svhrvvhjPOgAcfbLoiSVK/6pbhVICPAW+IiAURsQ/wR8CXW+99HjgmIk6PiJnA24GrMnNFM6VqLIPYiWs78UT48IfhkkvgDW8w2EqS6jGt6QKGeCcwD7gW2AxcBJwLkJmrIuJ04APAJ4EfA2c0VKd2YlAnNgz3ilfAihXwnvfAwQfDm9/cdEWSpH7TNSEuM7cAr2stI73/LcBbiqhnvOtdcMst5RmrD394GV6VJKkqXRPi1B/sxG03ZQp87GOwciW8/OWw997w9Kc3XZUkqV900zVxUt+ZORP+67/gqKPgec/z0VySpOoY4lQpO3E72mcf+MY34JBD4LTT4Ac/aLoiSVI/MMRJHTB/Pnz727D//mVI9ZJLmq5IktTrDHGqlJ240S1aBJddBosXwzOeAV/4QsMFSZJ6miFO6qB994XvfAdOOAFOPx0+/vGmK5Ik9SpDnCplJ25sc+fCN78JT34yvPKV8Fd/Bdu2NV2VJKnXGOJUKZ9OMD6zZ8OXvlRC3DnnwAteABs3Nl2VJKmXGOJUCztxY5sxA84/H977Xvjc5+CUU8rNgSVJGg9DnCrlcOrERMAb31i6ctddB8cfX+4rJ0nSWAxxUhd41rPgpz8tz1l97nPhj/8YHnig6aokSd3MEKdK2YnbdYcdBv/zP/CGN8A//iM86lHws581XZUkqVsZ4qQuMmMGvP/9ZUj1rrvgkY8ss1ftykmShjPEqVJ24qrxnOfA8uVwxhll9uojHwk//GHTVUmSuokhTupSc+fCBRfAF78I99wDj30svPzlcOedTVcmSeoGhjhVyk5c9U47Da65Bv78z+FTn4LDDy+3Jdm8uenKJElNMsRJPWD2bHj3u8sQ6ymnwJveBEuWwL/+K2zZ0nR1kqQmGOJUKTtx9VqyBL7yFbjkEjjwQDjzTDjySLjwQti6tenqJEmdZIhTpXzsVmc86Unwgx+UmwTPng0veUkZZv3gB318lyQNCkOcamEnrn4R8Oxnl5sEX3wxLFgAv//7cNBBZUbr3Xc3XaEkqU6GOFXK4dTOmzIFnve8cqPg734XHv3ocm+5Aw6AF74QvvMdO6SS1I8McVKfiCiTHr70Jbj66tKV++//hic+EY4+ujwFwu6cJPUPQ5wqZSeuOyxdCu97H6xcCR/7GMyZU57Hut9+8MxnlokQGzY0XaUkaTIMcVIf22MPeMUr4Ec/gl/8At785nKbkpe8BBYuLOuLLzbQSVIvMsSpUnbiutfRR8O73gU33VSuk3vxi+GrX4XTT4f58+G5z4WPfxx+/eumK5UkjYchThowU6bAqafCRz4Cd90F3/42vOpVZZbrK19ZOnSnngrnnguXXw7btjVdsSRpJIY4VcpOXG+ZPh1+8zfh/e+HW26BZcvgLW8pw6tvfSs86lHl1iUvfGG5tu7WW5uuWJLUNq3pAiR1hwg48cSyvPOdsGoVfPOb8PWvwze+AZ/+dNlv8eIyC/bUU8uyZImhXZKaYIhTpezE9Y/58+FFLypLJlx1FVx2GXzve+XWJRdcUPZbsKCEusc9Dk4+GY4/HnbfvdHSJWkgGOIkjSkCjj22LH/4hyXUXXttubnw975X1p/7XNl36lR4xCPKUGx7Oeqosl2SVB1DnCplJ24wRMARR5Tl1a8u2+64o0yE+N//LctnPgPnnVfe22OPEuzaQfDYY+E3fgP23LO5n0GSep0hTpUyxA2uffeF5zynLFBmtV5/fQl0l18OV14JF120PdgBHHLI9lB3zDFw5JFw2GGw227N/AyS1EsMcZJqMWUKHH54WV7ykrIts8xwvfLKco3dlVeW5Qtf2P4PgGnT4NBDS6AbuhxxhJ07SRrKEKdK2YnTzkTAwx9eltNO2759wwa45pryzNf28stfwpe/DA8+uH2/Aw4os2EPO6wEvfb60EMNeJIGjyFOUuNmzYITTijLUFu2wA03PDTcXX996dytWvXQfRcs2DHcHXwwHHQQLFrkxApJ/ccQp0rZiVOVpk+HpUvL8rznPfS9++4rAe/66x+6vvTS7bc/GXqcAw4ogW740u4MzpjRuZ9LkqpgiJPUk+bMKfekO/74Hd/btKk8I/aWW3ZcvvUtuP327f/gaFu0qIS5/fffvuy330NfO2QrqZsY4lQpO3HqBrvvXu5Nd9RRI7+/ZQvcdtv2YPerX21fX3tt6ebde++O3zd79ughb9Gi8tzZhQvLfpJUN0OcpIEzfXq5Xu7gg0ffZ8OG0rFbuXL7ur3cfnu5wfHtt5dAONysWdsD3dBlaNAbGvj8R4+kXWGIU6XsxKlfzJpVZsIuWTL6Ptu2wT33lGB3113blzvv3P719dfDD35Q9hs+hAvlRsjtQDdvXnnc2bx5Iy/z58Nee5Xbt0iSIU6SdtGUKWVW7IIFY+/74INlRu3QsDd8Wbmy3Ddv1SrYvHnk40ydCg972I7hbnjgmzsX9tmnrPfe29m5Uj8yxKlSduKkkU2bVp5qse++Y++bCRs3lu5de1m16qGv28uKFfD978Ovfw1bt45+zL322h7qxloP/XrWLP9/lrqVIU6VMsRJkxdRwtOsWeU2KOOxbRusXbs98K1ZA6tXj75euXL765Gu62ubNm3Hrt5ee21fxnq9554O/0p1McRJUh+YMqUErX322fl1fMNllkkcY4W+9nrVqnKd39q1ZQbvAw/s/PgR5XYw4w197dd77lm+b889yzLN31bSDvzfQpWyEyf1logyQ3b2bDjwwIl//+bNJdC1Q1376529XrmyPFat/Xpnw8BtM2c+NNSN5+vR3vPGzuoXhjhJ0i6bObMsCxfu2ve3r/8bHvTWrSvLffdt/3r46zvuKPf1a2/fuHF855w+ffSwN2tWCbTD1yNtG7qeOdN/vKrzDHGqlJ04SRMx9Pq//fab3LG2boX163ce/Eb7evVquPnmMrS8YUM5zlhDxUNNmTJ20JtIKNxjj+2LQ8kajX80JEl9YerU7dfWVWHLloeGuvXrt3+9s21D31uzBm699aHvjXb7mNFMn75jsBvPMpHvmTHDf3z3IkOcKmUnTlK/mD69TLTYe+9qj/vggw8NhyOFv02byvDwaMuGDWW9Zs2O723aNPGaIsYX/HbfvSwzZ4789Xjfmz7d3xNVMMRJktRB06ZV2zEcbtu20u0bLfhNZNmwodyD8NZbtwfE9jKR4ebhpkzZtfA30X1nzNh+3eaMGeWz76fwaIhTpezESVKzpkzZ3jmrUzssbtq0fd1ehr7elfdWrx79vZEeXzdeU6bsGOyGrkfaNtH9x7OtqieoGOIkSdKEdSosDpVZOoDjDYb3319et9dDvx5t29q15TF4I+23efPkQmTbtGmjh70JHWfypUjb2YmTJNUlogSeGTPqG47emcxyTeN4wuCubpsIQ5wkSdI4RJRJGdOnl/sK1nWO8fKJdqqUnThJkjrDEKdKGeIkSeoMQ5wkSVIPMsSpUnbiJEnqDEOcJElSDzLEqVJ24iRJ6oyuCXERsTgivhoRayLizoj4QERMG/L+kyNiRURsjIhLI+KgJuuVJElqUteEOOCDwN3AvsBxwBOA1wFExDzgYuBtwFxgGfCZRqrUTtmJkySpM7opxB0MXJSZmzPzTuC/gaNb7z0fWJ6Zn83MzcDZwLERsbSZUiVJkprVTU9s+CfgjIi4DNgHeAal8wYlzF3Z3jEzN0TEDa3tK3Z20Ouug6c9rZZ6NYK77y5rO3GSJNWrm0Lcd4BXA/cBU4FPAF9ovTcbWDVs/7XAiA+9iIgzgTMBdtvtN7jvvhqq1YhmzoSnPx2WLGm6EkmS+ltHQlyru/aEUd7+AXAq8HXgI8BjKaHto8B7gDcD64E5w75vDrBupANm5nnAeQAnnXRS/vCHk6tfkiSp23TkmrjMfGJmxijL4ymTFQ4EPpCZ92fmr4GPAc9sHWI5cGz7eBExCzi0tV2SJGngdMXEhsy8B7gJeG1ETIuIvYGXs/06uM8Dx0TE6RExE3g7cFVm7vR6OEmSpH7VFSGu5fnA0ynXvl0PPAj8MUBmrgJOB84F1gAnA2c0U6YkSVLzumZiQ2b+DHjiTt7/FuAtRSRJkuiuTpwkSZLGyRAnSZLUgwxxkiRJPcgQJ0mS1IMMcZIkST3IECdJktSDDHGSJEk9yBAnSZLUgwxxkiRJPcgQJ0mS1IMMcZIkST3IECdJktSDDHGSJEk9yBAnSZLUgyIzm66hVhGxDrim6ToGzDzgnqaLGDB+5p3nZ955fuad52feeUdk5p7j2XFa3ZV0gWsy86SmixgkEbHMz7yz/Mw7z8+88/zMO8/PvPMiYtl493U4VZIkqQcZ4iRJknrQIIS485ouYAD5mXeen3nn+Zl3np955/mZd964P/O+n9ggSZLUjwahEydJktR3DHGSJEk9qG9DXETMjYjPR8SGiLglIl7UdE39LiJeHxHLIuL+iPh40/UMgoiYERHnt/6Mr4uIKyLiGU3X1c8i4pMRcUdE3BcR10bEq5quaVBExJKI2BwRn2y6lkEQEZe1Pu/1rcV7rnZARJwREVe38ssNEXHKaPv2833i/gV4AFgIHAd8JSKuzMzljVbV324H/hp4GrB7w7UMimnArcATgF8BzwQuiohHZObNTRbWx94N/F5m3h8RS4HLIuKKzPxJ04UNgH8BLm+6iAHz+sz8t6aLGBQR8RTgPcALgP8F9t3Z/n3ZiYuIWcDpwNsyc31mfh/4IvDSZivrb5l5cWZ+Afh107UMiszckJlnZ+bNmbktM78M3ASc2HRt/Sozl2fm/e2XreXQBksaCBFxBnAv8O2GS5Hq9A7gnMz8Uevv9JWZuXK0nfsyxAGHA1sz89oh264Ejm6oHqkjImIh5c+/HecaRcQHI2IjsAK4A/hqwyX1tYiYA5wDvLHpWgbQuyPinoj4QUQ8seli+llETAVOAuZHxPURcVtEfCAiRh3Z6tcQNxtYO2zbWmBczyKTelFETAcuBD6RmSuarqefZebrKH+fnAJcDNy/8+/QJL0TOD8zb226kAHzZ8AhwP6Ue5d9KSLsOtdnITAd+B3K3y3HAccDbx3tG/o1xK0H5gzbNgdY10AtUu0iYgpwAeU60Nc3XM5AyMytrUs1DgBe23Q9/SoijgN+C3hfw6UMnMz8cWauy8z7M/MTwA8o192qHpta63/OzDsy8x7gH9jJZ96vExuuBaZFxJLMvK617VgcYlIfiogAzqf8K+6Zmbml4ZIGzTS8Jq5OTwQWA78qf9SZDUyNiKMy84QG6xpECUTTRfSrzFwTEbdRPudx6ctOXGZuoAxxnBMRsyLiccBvUzoVqklETIuImcBUyl+yMyOiX/+h0E0+BBwJnJaZm8baWbsuIha0pv/PjoipEfE04IXAJU3X1sfOo4Tk41rLh4GvUGbBqyYRsXdEPK3993hEvBg4Ffh607X1uY8Bb2j9XbMP8EfAl0fbuZ9/wb4O+ChwN2W25Gu9vUjt3gr81ZDXL6HMtDm7kWoGQEQcBJxFuSbrzlanAuCszLywscL6V1KGTj9M+UfwLcAfZeZ/NVpVH8vMjcDG9uuIWA9szsxVzVU1EKZTbhm1FNhKmcTz3Mz0XnH1eicwjzKiuBm4CDh3tJ19dqokSVIP6svhVEmSpH5niJMkSepBhjhJkqQeZIiTJEnqQYY4SZKkHmSIkyRJ6kGGOEl9LSKWd+rB3RFxVEQsq+G4F0fE06s+rqTe5n3iJPW01s1f2/ag3Ph4a+t1R296HBGfAz6bmZ+u+LiPAj6UmSdWeVxJvc0QJ6lvRMTNwKsy81sNnHtfyvOZ98vMzTUc/zrghZlZeadPUm9yOFVSX4uImyPit1pfnx0Rn42IT0bEuoj4eUQcHhFviYi7I+LWiHjqkO/dKyLOj4g7ImJlRPx1REwd5VRPAX46NMC1zv2miLgqIja0jrUwIr7WOv+3Ws9HpPWMyk9GxK8j4t6IuDwiFg45/mXAsyr/gCT1LEOcpEFzGnABsA9wBeWB3lOA/YFzgI8M2fcTwIPAYcDxwFOBV41y3EcAIz1X8nRKwDu8de6vAX9BeT7iFOAPWvu9HNgLOBB4GPAaYNOQ41wNHDvun1JS3zPESRo038vMr2fmg8BngfnA32TmFuDTwOKI2LvVBXsG5QH3GzLzbuB9wBmjHHdvYN0I2/85M+/KzJXA94AfZ+YVmXk/8HlKOATYQglvh2Xm1sz8SWbeN+Q461rnkCQApjVdgCR12F1Dvt4E3JOZW4e8BpgN7AdMB+6IiPb+U4BbRznuGmDPcZxv+OvZra8voHThPh0RewOfBP6yFS5pHfve0X4oSYPHTpwkjexWykzXeZm5d2uZk5lHj7L/VZQh012SmVsy8x2ZeRTwWODZwMuG7HIkcOWuHl9S/zHESdIIMvMO4BvA30fEnIiYEhGHRsQTRvmWbwInRMTMXTlfRDwpIh7RmjhxH2V4deuQXZ5AuZ5OkgBDnCTtzMuA3YBfUoZL/xPYd6QdM/Mu4BLgt3fxXItax7+PMonhO5QhVSLikcCGzPzfXTy2pD7kfeIkqSIRcRRlRuujssK/XFs3ET4/M79a1TEl9T5DnCRJUg9yOFWSJKkHGeIkSZJ6kCFOkiSpBxniJEmSepAhTpIkqQcZ4iRJknqQIU6SJKkHGeIkSZJ60P8D4mhKMXtAxDAAAAAASUVORK5CYII=\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 = 3.79 s\n",
"\n",
"Total time = 8018.16 s\n",
"\n",
"End time: 2022-10-30 04:37:36.440962\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: 2:13:49.691058\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_edgedist0.1_.csv', 'w', newline='') as f:\n",
" csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 , Abeta0_v_node12 , Abeta0_v_node13 , Abeta0_v_node14 , Abeta0_v_node15 , Abeta0_v_node16 , Abeta0_v_node17 , Abeta0_v_node18 , Abeta0_v_node19 , Abeta0_v_node20 , Abeta0_v_node21 , Abeta0_v_node22 , Abeta0_v_node23 , Abeta0_v_node24 , Abeta0_v_node25 , Abeta0_v_node26 , Abeta0_v_node27 , Abeta0_v_node28 , Abeta0_v_node29 , Abeta0_v_node30 , Abeta0_v_node31 , Abeta0_v_node32 , Abeta0_v_node33 , Abeta0_v_node34 , Abeta0_v_node35 )) \n",
"\n",
"\n",
" \n",
" \n",
"with open('BoundarytoGround1000_misaligned(0-180)_20Fibers_imembrane_Abeta0_stimulateALL_edgedist0.1_.csv', 'w', newline='') as f:\n",
" csv.writer(f).writerows(zip( t , Abeta0_imembrane_node0 , Abeta0_imembrane_node1 , Abeta0_imembrane_node2 , Abeta0_imembrane_node3 , Abeta0_imembrane_node4 , Abeta0_imembrane_node5 , Abeta0_imembrane_node6 , Abeta0_imembrane_node7 , Abeta0_imembrane_node8 , Abeta0_imembrane_node9 , Abeta0_imembrane_node10 , Abeta0_imembrane_node11 , Abeta0_imembrane_node12 , Abeta0_imembrane_node13 , Abeta0_imembrane_node14 , Abeta0_imembrane_node15 , Abeta0_imembrane_node16 , Abeta0_imembrane_node17 , Abeta0_imembrane_node18 , Abeta0_imembrane_node19 , Abeta0_imembrane_node20 , Abeta0_imembrane_node21 , Abeta0_imembrane_node22 , Abeta0_imembrane_node23 , Abeta0_imembrane_node24 , Abeta0_imembrane_node25 , Abeta0_imembrane_node26 , Abeta0_imembrane_node27 , Abeta0_imembrane_node28 , Abeta0_imembrane_node29 , Abeta0_imembrane_node30 , Abeta0_imembrane_node31 , Abeta0_imembrane_node32 , Abeta0_imembrane_node33 , Abeta0_imembrane_node34 , Abeta0_imembrane_node35 )) \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"# 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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