{
"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 15:35:04.522836\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": [
"0.5\n",
"18689.613196051898\n",
"0.5\n",
"18689.608665237658\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] = 7.08e+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": [
{
"name": "stdout",
"output_type": "stream",
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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"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(Boundary_neighboring)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7808a6c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
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],
"source": [
"GMAT_BOUNDARY00.printf() "
]
},
{
"cell_type": "markdown",
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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 = 102.12 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 0.48 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: 102.12 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 = 0.44 s\n",
"\n",
"Total time = 104.59 s\n",
"\n",
"End time: 2022-12-28 15:36:49.116241\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: 0:01:47.579535\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_edgedist0.5_.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_edgedist0.5_.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",
"import csv\n",
"\n",
"with open('mis_nodexg0xhanged_ALLExtraVoltages_stimulateonlyAbeta0_edgedist0.5_.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_stimulateonlyAbeta0_edgedist0.5_.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_edgedist0.5_.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",
"# with open('imembrane_Abeta0_stimulateonlyAbata0_edgedist1_.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 )) \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": [
"18689.608665237658\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
}