{
"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(1,2):\n",
" for j in range(2,3):\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(1,3):\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(1,3):\n",
" for j in range(2,3):\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(1,3):\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(1,2):\n",
" for j in range(2,3):\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"
]
}
],
"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",
"# 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",
"\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",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d5ef8f97",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"###################################### Locating each axon in specific (x,y) #################################################\n",
"\n",
"\n",
"\n",
"netParams.popParams[\"Axon0\"] = {\n",
" 'cellType': 'type1', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type1', \n",
" 'xRange':[C[0][0], C[0][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[0][1], C[0][1]]} \n",
"\n",
"netParams.popParams[\"Axon1\"] = {\n",
" 'cellType': 'type2', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type2', \n",
" 'xRange':[C[1][0], C[1][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[1][1], C[1][1]]}\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"########################################### Locating Boundary Cables ########################################################\n",
"\n",
"\n",
"\n",
" \n",
"netParams.popParams[\"Boundary0\"] = {\n",
" 'cellType': 'Boundary', \n",
" 'numCells':1 , \n",
" 'cellModel': 'Boundary', \n",
" 'xRange':[Boundary_coordinates[0][0], Boundary_coordinates[0][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[Boundary_coordinates[0][1], Boundary_coordinates[0][1]]} \n",
"\n",
"\n",
" \n",
" \n",
"netParams.popParams[\"Boundary1\"] = {\n",
" 'cellType': 'Boundary', \n",
" 'numCells':1 , \n",
" 'cellModel': 'Boundary', \n",
" 'xRange':[Boundary_coordinates[1][0], Boundary_coordinates[1][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[Boundary_coordinates[1][1], Boundary_coordinates[1][1]]} \n",
"\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": 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': 0.4}\n",
"netParams.stimSourceParams['Input1'] = {'type': 'VClamp', 'dur': [1, 0.02, 0], 'amp':[-80, 0, 0]}\n",
"\n",
" \n",
"netParams.stimTargetParams['Input1->Stim_1'] = {'source': 'Input1', 'sec':'node_0', 'loc': 0.5, 'conds': {'pop':\"Axon0\"}} \n",
"#netParams.stimTargetParams['Input1->Stim_2'] = {'source': 'Input1', 'sec':'node_0', 'loc': 0.5, 'conds': {'pop':\"Axon1\"}} \n",
"\n",
"\n",
"\n",
"\n",
"XG1 = 1e-9 # 1e-9: disconnect from ground 1e9: Connect to ground\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "90a2f08b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Start time: 2022-12-28 15:42:34.853680\n",
"\n",
"Creating network of 4 cell populations on 1 hosts...\n",
" Number of cells on node 0: 4 \n",
" Done; cell creation time = 0.31 s.\n",
"Making connections...\n",
" Number of connections on node 0: 0 \n",
" Done; cell connection time = 0.00 s.\n",
"Adding stims...\n",
" Number of stims on node 0: 1 \n",
" Done; cell stims creation time = 0.00 s.\n",
"Recording 6 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",
"2705.0750246759276\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] = 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] = 1e9\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\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[0]\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] = XG1 # 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] = 1.18e+03 ###1e6\n",
" seg.xg[1] = XG1\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] = XG1\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] = XG1\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] = XG1\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": "004941aa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[36. 36.]\n"
]
}
],
"source": [
"print(Number_of_nodes)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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[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])*(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])*(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 #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": 12,
"id": "b71ff07f",
"metadata": {
"scrolled": true
},
"outputs": [
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" \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(a2)]\n",
" area = (math.pi)*(parameters[0][1])*(np.ones((len(Boundary_RG),1)))\n",
" area = area * 1e-8 #cm^2\n",
" \n",
"\n",
" SEC = locals()[\"Boundary_to_\"+str(a2)]\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\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": 14,
"id": "2f2f0781",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
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"execution_count": 14,
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],
"source": [
"len(Boundary_neighboring)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7808a6c6",
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"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": "25ca22ac",
"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(36):\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",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "ca5603a0",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Vector[450]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Recording v and vext[0], Abeta\n",
"\n",
"###################################################### Abeta0\n",
"\n",
"\n",
"for i1 in range(36):\n",
"\n",
" locals()[\"Abeta0_v\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
" locals()[\"Abeta0_v_node\"+str(i1)] = h.Vector()\n",
" locals()[\"Abeta0_v_node\"+str(i1)].record(locals()[\"Abeta0_v\"+str(i1)](0.5)._ref_v)\n",
"\n",
"\n",
"# for i2 in range(36):\n",
"\n",
"# locals()[\"Abeta0_vex\"+str(i2)] = sim.net.cells[0].secs[\"node_%s\"%i2][\"hObj\"]\n",
"# locals()[\"Abeta0_vext0_05_node\"+str(i2)] = h.Vector()\n",
"# locals()[\"Abeta0_vext0_05_node\"+str(i2)].record(locals()[\"Abeta0_vex\"+str(i2)](0.5)._ref_vext[0])\n",
"\n",
" \n",
"################################################################################################## \n",
" \n",
"for i2 in range(36):\n",
"\n",
" locals()[\"Abeta0_vex\"+str(i2)] = sim.net.cells[0].secs[\"node_%s\"%i2][\"hObj\"]\n",
" locals()[\"Abeta0_vext0_node05\"+str(i2)] = h.Vector()\n",
" locals()[\"Abeta0_vext0_node05\"+str(i2)].record(locals()[\"Abeta0_vex\"+str(i2)](0.5)._ref_vext[0])\n",
"\n",
"\n",
"for ii2 in range(36):\n",
"\n",
" locals()[\"Abeta0_vex\"+str(ii2)] = sim.net.cells[0].secs[\"node_%s\"%ii2][\"hObj\"]\n",
" locals()[\"Abeta0_vext0_node1\"+str(ii2)] = h.Vector()\n",
" locals()[\"Abeta0_vext0_node1\"+str(ii2)].record(locals()[\"Abeta0_vex\"+str(ii2)](1)._ref_vext[0]) \n",
" \n",
" \n",
"for ij2 in range(36):\n",
"\n",
" locals()[\"Abeta0_vex\"+str(ij2)] = sim.net.cells[0].secs[\"node_%s\"%ij2][\"hObj\"]\n",
" locals()[\"Abeta0_vext0_node0\"+str(ij2)] = h.Vector()\n",
" locals()[\"Abeta0_vext0_node0\"+str(ij2)].record(locals()[\"Abeta0_vex\"+str(ij2)](0)._ref_vext[0]) \n",
" \n",
" \n",
"for i3 in range(36):\n",
"\n",
" locals()[\"Abeta0_vex1\"+str(i3)] = sim.net.cells[0].secs[\"node_%s\"%i3][\"hObj\"]\n",
" locals()[\"Abeta0_vext1_node05\"+str(i3)] = h.Vector()\n",
" locals()[\"Abeta0_vext1_node05\"+str(i3)].record(locals()[\"Abeta0_vex1\"+str(i3)](0.5)._ref_vext[1]) \n",
" \n",
" \n",
" \n",
" \n",
"for i5 in range(36):\n",
"\n",
" locals()[\"Abeta0_vexx\"+str(i5)] = sim.net.cells[0].secs[\"node_%s\"%i5][\"hObj\"]\n",
" locals()[\"Abeta0_vext1_node0\"+str(i5)] = h.Vector()\n",
" locals()[\"Abeta0_vext1_node0\"+str(i5)].record(locals()[\"Abeta0_vexx\"+str(i5)](0)._ref_vext[1])\n",
" \n",
"\n",
" \n",
"for i6 in range(36):\n",
"\n",
" locals()[\"Abeta0_vexg\"+str(i6)] = sim.net.cells[0].secs[\"node_%s\"%i6][\"hObj\"]\n",
" locals()[\"Abeta0_vext1_node1\"+str(i6)] = h.Vector()\n",
" locals()[\"Abeta0_vext1_node1\"+str(i6)].record(locals()[\"Abeta0_vexg\"+str(i6)](1)._ref_vext[1])\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"for i4 in range(36):\n",
"\n",
" locals()[\"Abeta1_vex\"+str(i4)] = sim.net.cells[1].secs[\"node_%s\"%i4][\"hObj\"]\n",
" locals()[\"Abeta1_vext1_node05\"+str(i4)] = h.Vector()\n",
" locals()[\"Abeta1_vext1_node05\"+str(i4)].record(locals()[\"Abeta1_vex\"+str(i4)](0.5)._ref_vext[1])\n",
"\n",
" \n",
"\n",
"i8=1663 \n",
"locals()[\"v1Mext\"+str(i8)] = sim.net.cells[2].secs[\"section_1663\"][\"hObj\"]\n",
"locals()[\"boundary0_vext1_section\"+str(i8)] = h.Vector()\n",
"locals()[\"boundary0_vext1_section\"+str(i8)].record(locals()[\"v1Mext\"+str(i8)](0.5)._ref_vext[1]) \n",
"\n",
" \n",
" \n",
"for ii3 in range(36*2):\n",
"\n",
" locals()[\"Abeta0_vexe\"+str(ii3)] = sim.net.cells[0].secs[\"MYSA_%s\"%ii3][\"hObj\"]\n",
" locals()[\"Abeta0_vext0_MYSA05\"+str(ii3)] = h.Vector()\n",
" locals()[\"Abeta0_vext0_MYSA05\"+str(ii3)].record(locals()[\"Abeta0_vexe\"+str(ii3)](0.5)._ref_vext[0])\n",
" \n",
" \n",
"for ii4 in range(36*2):\n",
"\n",
" locals()[\"Abeta0_vexxx\"+str(ii4)] = sim.net.cells[0].secs[\"MYSA_%s\"%ii4][\"hObj\"]\n",
" locals()[\"Abeta0_vext1_MYSA05\"+str(ii4)] = h.Vector()\n",
" locals()[\"Abeta0_vext1_MYSA05\"+str(ii4)].record(locals()[\"Abeta0_vexxx\"+str(ii4)](0.5)._ref_vext[1])\n",
" \n",
" \n",
" \n",
"# for i3 in range(0,36*2):\n",
" \n",
"# locals()[\"Abeta_v0Mext\"+str(i3)] = sim.net.cells[0].secs[\"MYSA_%s\"%i3][\"hObj\"]\n",
"# locals()[\"Abeta0_vext0_MYSA\"+str(i3)] = h.Vector()\n",
"# locals()[\"Abeta0_vext0_MYSA\"+str(i3)].record(locals()[\"Abeta_v0Mext\"+str(i3)](0.5)._ref_vext[0])\n",
" \n",
" \n",
" \n",
"# for i3 in range(0,36*2):\n",
" \n",
"# locals()[\"Abeta_v1Mext\"+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_v1Mext\"+str(i3)](0.5)._ref_vext[1]) \n",
"\n",
"\n",
"# i3=1663 \n",
"# locals()[\"v1Mext\"+str(i3)] = sim.net.cells[2].secs[\"section_1663\"][\"hObj\"]\n",
"# locals()[\"boundary0_vext1_section\"+str(i3)] = h.Vector()\n",
"# locals()[\"boundary0_vext1_section\"+str(i3)].record(locals()[\"v1Mext\"+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",
" \n",
" \n",
"t = h.Vector()\n",
"t.record(h._ref_t)"
]
},
{
"cell_type": "markdown",
"id": "d83f15db",
"metadata": {},
"source": [
"#### Simulate and Analyze"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "cd6d9f09",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Running simulation for 6.0 ms...\n",
" Done; run time = 97.14 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 0.49 s.\n",
"\n",
"Analyzing...\n",
" Cells: 4\n",
" Connections: 0 (0.00 per cell)\n",
" Spikes: 1 (41.67 Hz)\n",
" Simulated time: 0.0 s; 1 workers\n",
" Run time: 97.14 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": {
"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 = 0.46 s\n",
"\n",
"Total time = 99.48 s\n",
"\n",
"End time: 2022-12-28 15:44:14.333716\n"
]
}
],
"source": [
"sim.simulate()\n",
"sim.analyze()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ceb34061",
"metadata": {},
"outputs": [],
"source": [
"# plotting\n",
"\n",
"#sim.analysis.plotLFP( plots = ['timeSeries', 'locations'] , electrodes=[ 'all'], lineWidth=1000 , fontSize=14, saveFig=True)\n",
"\n",
"# from matplotlib import pyplot\n",
"# %matplotlib inline\n",
"# pyplot.plot(t, ap1 )\n",
"# #pyplot.xlim((0, 10))\n",
"# pyplot.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "ddb4904a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Duration: 0:01:42.511916\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": "eb4751f0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d18ce34b",
"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": 23,
"id": "d833f599",
"metadata": {},
"outputs": [],
"source": [
"# Transverse current: Picoamp/micron^2\n",
"\n",
"\n",
"# v_diff00 = (Abeta0_vext1_node0 - Abeta1_vext1_node0)/1000 #volt\n",
"# Trans_Current_node0_node0 = (v_diff00 * 3.45e+04 )*1e12/1e8 #volt*S/cm2 = Amp/cm2 = PicoAMP/cm2 = PicoAMP/micron^2\n",
"\n",
"# v_diff11 = (Abeta0_vext1_node1 - Abeta1_vext1_node1)/1000 #volt\n",
"# Trans_Current_node1_node1 = v_diff11 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff22 = (Abeta0_vext1_node2 - Abeta1_vext1_node2)/1000 #volt\n",
"# Trans_Current_node2_node2 = v_diff22 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff33 = (Abeta0_vext1_node3 - Abeta1_vext1_node3)/1000 #volt\n",
"# Trans_Current_node3_node3 = v_diff33 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff44 = (Abeta0_vext1_node4 - Abeta1_vext1_node4)/1000 #volt\n",
"# Trans_Current_node4_node4 = v_diff44 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff55 = (Abeta0_vext1_node5 - Abeta1_vext1_node5)/1000 #volt\n",
"# Trans_Current_node5_node5 = v_diff55 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff66 = (Abeta0_vext1_node6 - Abeta1_vext1_node6)/1000 #volt\n",
"# Trans_Current_node6_node6 = v_diff66 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff77 = (Abeta0_vext1_node7 - Abeta1_vext1_node7)/1000 #volt\n",
"# Trans_Current_node7_node7 = v_diff77 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff88 = (Abeta0_vext1_node8 - Abeta1_vext1_node8)/1000 #volt\n",
"# Trans_Current_node8_node8 = v_diff88 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff99 = (Abeta0_vext1_node9 - Abeta1_vext1_node9)/1000 #volt\n",
"# Trans_Current_node9_node9 = v_diff99 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff1010 = (Abeta0_vext1_node10 - Abeta1_vext1_node10)/1000 #volt\n",
"# Trans_Current_node10_node10 = v_diff1010 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff1111 = (Abeta0_vext1_node11 - Abeta1_vext1_node11)/1000 #volt\n",
"# Trans_Current_node11_node11 = v_diff1111 * 3.45e+04 *1e12/1e8 \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "cbe681f7",
"metadata": {},
"outputs": [],
"source": [
"# import csv\n",
"\n",
"# with open('v_diff66_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , v_diff66 ))\n",
" \n",
" \n",
" \n",
"import csv\n",
"\n",
"# with open('misaligned_vext1_node15_MYSA30_stimulateonlyAbeta0_edgedist3_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_vext1_node15 , Abeta0_vext1_MYSA30 )) \n",
" \n",
" \n",
" \n",
" \n",
"# with open('misaligned_vext1_Abeta0_stimulateonlyAbeta0_edgedist3_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_vext1_node0 , Abeta0_vext1_node1 , Abeta0_vext1_node2 , Abeta0_vext1_node3 , Abeta0_vext1_node4 , Abeta0_vext1_node5 , Abeta0_vext1_node6 , Abeta0_vext1_node7 , Abeta0_vext1_node8 , Abeta0_vext1_node9 , Abeta0_vext1_node10 , Abeta0_vext1_node11 )) \n",
" \n",
" \n",
" \n",
"with open('mis_nodexg0xhanged_ALLExtraVoltages_stimulateonlyAbeta0_edgedist3_.csv', 'w', newline='') as f:\n",
" csv.writer(f).writerows(zip( t , Abeta0_vext0_node0515 , Abeta0_vext1_node0515 , Abeta0_vext1_node015 , Abeta0_vext1_node115 , Abeta1_vext1_node0515 , boundary0_vext1_section1663 , Abeta0_vext0_node115 , Abeta0_vext0_MYSA0530 , Abeta0_vext1_MYSA0530 , Abeta0_vext0_node015))\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"id": "8f3b15f1",
"metadata": {},
"source": [
"#### saving the data"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "890baeb5",
"metadata": {},
"outputs": [],
"source": [
"## saving the data\n",
"\n",
"\n",
"import csv\n",
"\n",
"\n",
"\n",
"\n",
" \n",
"with open('misaligned_nodexg0xhanged_v_Abeta0_stimulateBOTH_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('misaligned_imembrane_Abeta0_stimulateBOTH_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",
"# #################################### Connected to ground \n",
"\n",
"# with open('ConnectGround_v_Abeta0_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 )) \n",
"\n",
"\n",
"\n",
"# with open('ConnectGround_LongiCurrent_Abeta0_NodetoMYSA_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Longi_Current_node0_MYSA0 , Longi_Current_node1_MYSA2 , Longi_Current_node2_MYSA4 , Longi_Current_node3_MYSA6 , Longi_Current_node4_MYSA8 , Longi_Current_node5_MYSA10 , Longi_Current_node6_MYSA12 , Longi_Current_node7_MYSA14 , Longi_Current_node8_MYSA16 , Longi_Current_node9_MYSA18 , Longi_Current_node10_MYSA20 , Longi_Current_node11_MYSA22 ))\n",
" \n",
" \n",
"\n",
"# with open('ConnectGround_TransCurrent_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Trans_Current_node0_node0 , Trans_Current_node1_node1 , Trans_Current_node2_node2 , Trans_Current_node3_node3 , Trans_Current_node4_node4 , Trans_Current_node5_node5 , Trans_Current_node6_node6 , Trans_Current_node7_node7 , Trans_Current_node8_node8 , Trans_Current_node9_node9 , Trans_Current_node10_node10 , Trans_Current_node11_node11 ))\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" \n",
"# ##################################### Not connected to ground, Stimulate only one fiber \n",
"\n",
" \n",
"# with open('m_v_Abeta0_stimulateBOTH_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 )) \n",
"\n",
" \n",
" \n",
" \n",
"# with open('m_vext1_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_vext1_node0 , Abeta0_vext1_node1 , Abeta0_vext1_node2 , Abeta0_vext1_node3 , Abeta0_vext1_node4 , Abeta0_vext1_node5 , Abeta0_vext1_node6 , Abeta0_vext1_node7 , Abeta0_vext1_node8 , Abeta0_vext1_node9 , Abeta0_vext1_node10 , Abeta0_vext1_node11 )) \n",
" \n",
" \n",
"\n",
"\n",
"\n",
"# with open('LongiCurrent_Abeta0_NodetoMYSA_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Longi_Current_node0_MYSA0 , Longi_Current_node1_MYSA2 , Longi_Current_node2_MYSA4 , Longi_Current_node3_MYSA6 , Longi_Current_node4_MYSA8 , Longi_Current_node5_MYSA10 , Longi_Current_node6_MYSA12 , Longi_Current_node7_MYSA14 , Longi_Current_node8_MYSA16 , Longi_Current_node9_MYSA18 , Longi_Current_node10_MYSA20 , Longi_Current_node11_MYSA22 ))\n",
" \n",
" \n",
"\n",
"# with open('TransCurrent_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Trans_Current_node0_node0 , Trans_Current_node1_node1 , Trans_Current_node2_node2 , Trans_Current_node3_node3 , Trans_Current_node4_node4 , Trans_Current_node5_node5 , Trans_Current_node6_node6 , Trans_Current_node7_node7 , Trans_Current_node8_node8 , Trans_Current_node9_node9 , Trans_Current_node10_node10 , Trans_Current_node11_node11 ))\n",
" \n",
"\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"# ##################################### Not connected to ground, Stimulate BOTH fibers \n",
"\n",
"\n",
"# with open('v_Abeta0_stimulateBOTH_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 )) \n",
"\n",
"\n",
"\n",
"# with open('LongiCurrent_Abeta0_NodetoMYSA_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Longi_Current_node0_MYSA0 , Longi_Current_node1_MYSA2 , Longi_Current_node2_MYSA4 , Longi_Current_node3_MYSA6 , Longi_Current_node4_MYSA8 , Longi_Current_node5_MYSA10 , Longi_Current_node6_MYSA12 , Longi_Current_node7_MYSA14 , Longi_Current_node8_MYSA16 , Longi_Current_node9_MYSA18 , Longi_Current_node10_MYSA20 , Longi_Current_node11_MYSA22 ))\n",
" \n",
" \n",
"\n",
"# with open('TransCurrent_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Trans_Current_node0_node0 , Trans_Current_node1_node1 , Trans_Current_node2_node2 , Trans_Current_node3_node3 , Trans_Current_node4_node4 , Trans_Current_node5_node5 , Trans_Current_node6_node6 , Trans_Current_node7_node7 , Trans_Current_node8_node8 , Trans_Current_node9_node9 , Trans_Current_node10_node10 , Trans_Current_node11_node11 ))\n",
" \n",
" \n",
" \n",
"\n",
"# with open('Connectground_vext1_Abeta0_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_vext1_node0 , Abeta0_vext1_node1 , Abeta0_vext1_node2 , Abeta0_vext1_node3 , Abeta0_vext1_node4 , Abeta0_vext1_node5 , Abeta0_vext1_node6 , Abeta0_vext1_node7 , Abeta0_vext1_node8 , Abeta0_vext1_node9 , Abeta0_vext1_node10 , Abeta0_vext1_node11 )) \n",
" \n",
" \n",
"# with open('vext1_Abeta1_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta1_vext1_node0 , Abeta1_vext1_node1 , Abeta1_vext1_node2 , Abeta1_vext1_node3 , Abeta1_vext1_node4 , Abeta1_vext1_node5 , Abeta1_vext1_node6 , Abeta1_vext1_node7 , Abeta1_vext1_node8 , Abeta1_vext1_node9 , Abeta1_vext1_node10 , Abeta1_vext1_node11 )) \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "7a4d2e6a",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# with open('STIN220_vext1_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_vext1_STIN220))\n",
" \n",
" \n",
"# with open('STIN220_v_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_STIN220)) \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a594bc51",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 27,
"id": "8e386b67",
"metadata": {},
"outputs": [],
"source": [
" \n",
"# with open('icap_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_icap_node0 , Abeta0_icap_node1 , Abeta0_icap_node2 , Abeta0_icap_node3 , Abeta0_icap_node4 , Abeta0_icap_node5 , Abeta0_icap_node6 , Abeta0_icap_node7 , Abeta0_icap_node8 , Abeta0_icap_node9 , Abeta0_icap_node10 , Abeta0_icap_node11 )) \n",
"\n",
" \n",
" \n",
"# with open('ik_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_ik_node0 , Abeta0_ik_node1 , Abeta0_ik_node2 , Abeta0_ik_node3 , Abeta0_ik_node4 , Abeta0_ik_node5 , Abeta0_ik_node6 , Abeta0_ik_node7 , Abeta0_ik_node8 , Abeta0_ik_node9 , Abeta0_ik_node10 , Abeta0_ik_node11 )) \n",
"\n",
"\n",
" \n",
"# with open('il_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_il_node0 , Abeta0_il_node1 , Abeta0_il_node2 , Abeta0_il_node3 , Abeta0_il_node4 , Abeta0_il_node5 , Abeta0_il_node6 , Abeta0_il_node7 , Abeta0_il_node8 , Abeta0_il_node9 , Abeta0_il_node10 , Abeta0_il_node11 )) \n",
"\n",
"\n",
" \n",
" \n",
"# with open('mis_ina_Abeta0_stimulateonlyAbata0_edgedist3_.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 , Abeta0_ina_node12 , Abeta0_ina_node13 , Abeta0_ina_node14 , Abeta0_ina_node15 , Abeta0_ina_node16 , Abeta0_ina_node17 , Abeta0_ina_node18 , Abeta0_ina_node19 , Abeta0_ina_node20 )) \n",
"\n",
"\n",
" \n",
"# with open('inap_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_inap_node0 , Abeta0_inap_node1 , Abeta0_inap_node2 , Abeta0_inap_node3 , Abeta0_inap_node4 , Abeta0_inap_node5 , Abeta0_inap_node6 , Abeta0_inap_node7 , Abeta0_inap_node8 , Abeta0_inap_node9 , Abeta0_inap_node10 , Abeta0_inap_node11 )) \n",
"\n",
" \n",
" \n",
"# with open('imembrane_Abeta0_stimulateBOTH_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 )) \n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "70b06d8b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2705.0750246759276\n"
]
}
],
"source": [
"print(xr)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d39abd5c",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}