{
"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 15:38:10.606386\n",
"\n",
"Creating network of 4 cell populations on 1 hosts...\n",
" Number of cells on node 0: 4 \n",
" Done; cell creation time = 0.36 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": [
"1.0\n",
"9069.959345142834\n",
"1.0\n",
"9069.957211035253\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] = 3.54e+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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"############################### Transverse connections between Boundary cables and Axons ######################################\n",
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"\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",
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"outputs": [
{
"data": {
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"source": [
"len(Boundary_neighboring)"
]
},
{
"cell_type": "code",
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"id": "7808a6c6",
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}
],
"source": [
"GMAT_BOUNDARY00.printf() "
]
},
{
"cell_type": "markdown",
"id": "b2a6c256",
"metadata": {},
"source": [
"#### Recordings"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d1494f97",
"metadata": {},
"outputs": [],
"source": [
"## Recording vext\n",
"\n",
"\n",
"# v1 = sim.net.cells[45].secs[\"node_0\"][\"hObj\"]\n",
"# ap1 = h.Vector()\n",
"# t = h.Vector()\n",
"# ap1.record(v1(0.5)._ref_v)\n",
"\n",
"# t.record(h._ref_t)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "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 = 110.54 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 0.51 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: 110.54 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 = 0.45 s\n",
"\n",
"Total time = 113.10 s\n",
"\n",
"End time: 2022-12-28 15:40:03.701965\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: 0:01:56.123670\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_edgedist1_.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_edgedist1_.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",
" \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_edgedist1_.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_edgedist1_.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",
"# with open('mis_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": [
"9069.957211035253\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"
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"nbformat": 4,
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}