{
"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 15:40:23.132298\n",
"\n",
"Creating network of 4 cell populations on 1 hosts...\n",
" Number of cells on node 0: 4 \n",
" Done; cell creation time = 0.35 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": [
"2.0\n",
"4283.03635742856\n",
"2.0\n",
"4283.035405642914\n"
]
}
],
"source": [
"# Since by default Netpyne does not insert the parameters of the extracellular mechanism, I insert them in this section\n",
"# this section includes \"longitudinal\" resistivities (i.e. xraxial)\n",
"\n",
"#Total_Length=10000\n",
"\n",
"number_boundary = 4000 #Total_Length/Section_Length \n",
"number_boundary = int(number_boundary)\n",
"\n",
"\n",
"\n",
"rhoa=0.7e6 \n",
"mycm=0.1 \n",
"mygm=0.001 \n",
"\n",
"space_p1=0.002 \n",
"space_p2=0.004\n",
"space_i=0.004\n",
"\n",
"\n",
"\n",
"\n",
"############################# For Boundary Cables #################################################\n",
"\n",
"# soma section is just for LFP recording, LFP in Netpyne does not work if at least one section is not called soma \n",
"\n",
"\n",
"for j in range(len(R),len(R)+len(Boundary_coordinates)):\n",
" \n",
" S = sim.net.cells[j].secs[\"soma\"][\"hObj\"] \n",
" for seg in S:\n",
" seg.xraxial[0] = 1e9\n",
" seg.xraxial[1] = 1e9\n",
" seg.xg[0] = 1e9\n",
" seg.xg[1] = 1e9\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\n",
"\n",
"\n",
" for i in range(number_boundary): \n",
" S = sim.net.cells[j].secs[\"section_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = 1e9\n",
" seg.xraxial[1] = 1e9\n",
" seg.xg[0] = 1e9\n",
" seg.xg[1] = 1e9\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
"\n",
" \n",
"############################## For myelinated sensory axons ##################################### \n",
"\n",
"\n",
"rho2 = 1211 * 1e-6 # Mohm-cm\n",
"\n",
"\n",
"\n",
"\n",
"for j in range(len(R)):\n",
" if G[j]!=2: # if it is not a C fiber \n",
" x = np.where(unique_radius == R[j]) \n",
" x = int(x[0])\n",
" nodes = Number_of_nodes[0]\n",
" nodes=int(nodes)\n",
" \n",
" \n",
" nl = parameters[x][4]\n",
" nodeD = parameters[x][1]\n",
" paraD1 = nodeD\n",
" axonD = parameters[x][0]\n",
" paraD2 = axonD\n",
" \n",
" Rpn0 = (rhoa*.01)/((math.pi)*((((nodeD/2)+space_p1)**2)-((nodeD/2)**2)))\n",
" Rpn1 = (rhoa*.01)/((math.pi)*((((paraD1/2)+space_p1)**2)-((paraD1/2)**2)))\n",
" Rpn2 = (rhoa*.01)/((math.pi)*((((paraD2/2)+space_p2)**2)-((paraD2/2)**2)))\n",
" Rpx = (rhoa*.01)/((math.pi)*((((axonD/2)+space_i)**2)-((axonD/2)**2)))\n",
" \n",
" \n",
" ################### xraxial[1]\n",
" \n",
" radi = R[j]\n",
" \n",
" AVE = (AVE_area_around_axon[j]+0) /2\n",
" \n",
" xr = rho2 / ((math.pi)*(((radi+AVE)**2) - (radi**2)) * 1e-8) # Mohm/cm\n",
" \n",
" xr = xr /1\n",
" \n",
" print(AVE_area_around_axon[j]+0)\n",
" print(xr)\n",
" \n",
" ##################\n",
" \n",
" \n",
" \n",
"\n",
" S = sim.net.cells[j].secs[\"soma\"][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn1\n",
" seg.xraxial[1] = xr \n",
" seg.xg[0] = mygm/(nl*2)\n",
" seg.xg[1] = XG1 # disconnect from ground\n",
" seg.xc[0] = mycm/(nl*2)\n",
" seg.xc[1] = 0\n",
"\n",
" \n",
" for i in range(nodes):\n",
" S = sim.net.cells[j].secs[\"node_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn0\n",
" seg.xraxial[1] = xr\n",
" seg.xg[0] = 1.77e+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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},
{
"data": {
"text/plain": [
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]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT01.printf() "
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},
{
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"id": "9f7204b0",
"metadata": {},
"outputs": [],
"source": [
" \n",
" \n",
" \n",
"############################### Transverse connections between Boundary cables and Axons ######################################\n",
"\n",
"\n",
"rho = 1.136e5 * 10000 * 4.7e-4 * 10000 # ohm-micron^2\n",
"\n",
"\n",
"\n",
"rows = len(Boundary_neighboring)\n",
"\n",
"for i in range(rows):\n",
" \n",
" for j in range(len(R)):\n",
" \n",
" if Boundary_neighboring[i][j]==1:\n",
" \n",
" NSEG = 0\n",
"\n",
" a2 = np.where(unique_radius == R[j]) # find type \n",
" a2 = a2[0][0]+1\n",
" \n",
" Boundary_RG = locals()[\"Boundary_Rg_\"+str(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",
"text": [
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT_BOUNDARY00.printf() "
]
},
{
"cell_type": "markdown",
"id": "b2a6c256",
"metadata": {},
"source": [
"#### Recordings"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d1494f97",
"metadata": {},
"outputs": [],
"source": [
"## Recording vext\n",
"\n",
"\n",
"# v1 = sim.net.cells[45].secs[\"node_0\"][\"hObj\"]\n",
"# ap1 = h.Vector()\n",
"# t = h.Vector()\n",
"# ap1.record(v1(0.5)._ref_v)\n",
"\n",
"# t.record(h._ref_t)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "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 = 109.64 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 0.50 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: 109.64 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 = 112.03 s\n",
"\n",
"End time: 2022-12-28 15:42:15.166956\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: 0:01:55.582634\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_edgedist2_.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_edgedist2_.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('mis_nodexg0xhanged_ALLExtraVoltages_stimulateonlyAbeta0_edgedist2_.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_edgedist2_.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_edgedist2_.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('mis_imembrane_Abeta0_stimulateonlyAbata0_edgedist2_.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": [
"4283.035405642914\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
}