{
"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(1,2):\n",
" if j>=i:\n",
" l = [i, j]\n",
" z = ''.join([str(n) for n in l])\n",
" Input = io.loadmat('Connect_types_{}.mat'.format(z) , squeeze_me=True) \n",
" I = Input['SAVE']; \n",
" locals()[\"Connect_types_\"+str(z)]=[]\n",
" for v in range(len(I)):\n",
" D = I[v].strip() \n",
" locals()[\"Connect_types_\"+str(z)].append(D) \n",
"\n",
"\n",
"# Boundary connections\n",
"for i in range(1,2):\n",
" Input = io.loadmat('Boundary_to_{}.mat'.format(i) , squeeze_me=True) \n",
" I = Input['SAVE']; \n",
" locals()[\"Boundary_to_\"+str(i)]=[]\n",
" for v in range(len(I)):\n",
" D = I[v].strip() \n",
" locals()[\"Boundary_to_\"+str(i)].append(D) \n",
" \n",
"\n",
"\n",
"#\n",
"Boundary_coordinates = np.loadtxt(\"Boundary_coordinates.txt\")\n",
"Boundary_neighboring = np.loadtxt(\"Boundary_neighboring.txt\")\n",
"Boundary_dist = np.loadtxt(\"Boundary_dist.txt\") \n",
"\n",
"\n",
"############## importing files related to transverse resistance (Rg) and Areas\n",
"\n",
"for i in range(1,2):\n",
" for j in range(1,2):\n",
" if j>=i:\n",
" l = [i, j]\n",
" z = ''.join([str(n) for n in l])\n",
" Input = np.loadtxt('Rg_{}.txt'.format(z) ) \n",
" locals()[\"Rg_\"+str(z)]=Input\n",
" \n",
"\n",
"\n",
" \n",
"for i in range(1,2):\n",
" Input = np.loadtxt('Boundary_Rg_{}.txt'.format(i) ) \n",
" locals()[\"Boundary_Rg_\"+str(i)]=Input\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"for i in range(1,2):\n",
" for j in range(1,2):\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"
]
}
],
"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"
]
},
{
"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': 'type1', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type1', \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 12:37:03.660952\n",
"\n",
"Creating network of 4 cell populations on 1 hosts...\n",
" Number of cells on node 0: 4 \n",
" Done; cell creation time = 0.30 s.\n",
"Making connections...\n",
" Number of connections on node 0: 0 \n",
" Done; cell connection time = 0.00 s.\n",
"Adding stims...\n",
" Number of stims on node 0: 1 \n",
" Done; cell stims creation time = 0.00 s.\n",
"Recording 6 traces of 2 types on node 0\n"
]
}
],
"source": [
"simConfig = specs.SimConfig()\n",
"simConfig.hParams = {'celsius': 37 }\n",
"\n",
"simConfig.dt = 0.005 # Internal integration timestep to use default is 0.025\n",
"simConfig.duration = 6\n",
"simConfig.recordStim = True\n",
"simConfig.recordStep = 0.005 # Step size in ms to save data (e.g. V traces, LFP, etc) default is 0.1\n",
"#simConfig.cache_efficient = True\n",
"#simConfig.cvode_active = True\n",
"# simConfig.cvode_atol=0.0001\n",
"# simConfig.cvode_rtol=0.0001\n",
"\n",
"\n",
"simConfig.recordTraces = {'V_node_0' :{'sec':'node_0','loc':0.5,'var':'v'}}\n",
"simConfig.analysis['plotTraces'] = {'include': ['allCells']} # ['Axon0','Axon1']\n",
"\n",
"simConfig.analysis['plot2Dnet'] = True\n",
"simConfig.analysis['plot2Dnet'] = {'include': ['allCells'], 'view': 'xz'}\n",
"\n",
"\n",
"\n",
"#simConfig.recordLFP = [[56.39,-4000,51.74]] # Determine the location of the LFP electrode\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"sim.create(netParams, simConfig)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "9045099d",
"metadata": {},
"source": [
"### xraxial and transverese conductances"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "41af5705",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5\n",
"18689.613196051898\n",
"0.5\n",
"18689.613196051898\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\n",
" nodes=int(nodes)\n",
" \n",
" \n",
" nl = parameters[4]\n",
" nodeD = parameters[1]\n",
" paraD1 = nodeD\n",
" axonD = parameters[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] = 1e6 ####1.42e+04 #########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": "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 = ((unique_radius*2)+(unique_radius*2))/2\n",
" locals()[\"RG\"+str(count)] = np.array(RG)*s\n",
" locals()[\"Resistance\"+str(count)] = rd/locals()[\"RG\"+str(count)]\n",
" locals()[\"Conductance\"+str(count)]=[]\n",
" for z4 in range(len(locals()[\"Resistance\"+str(count)])):\n",
" locals()[\"Conductance\"+str(count)].append(1/(locals()[\"Resistance\"+str(count)][z4]*area[z4]))\n",
" \n",
"\n",
" \n",
" for z5 in range(0,nsegs,1):\n",
"\n",
" locals()[\"gmat\"+str(count)].setval(z5, z5, locals()[\"Conductance\"+str(count)][z5][0] )\n",
" locals()[\"gmat\"+str(count)].setval(z5, nsegs+z5, -locals()[\"Conductance\"+str(count)][z5][0])\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, z5, -locals()[\"Conductance\"+str(count)][z5][0])\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, nsegs+z5, locals()[\"Conductance\"+str(count)][z5][0])\n",
" \n",
" \n",
" locals()[\"GMAT\"+str(i)+str(j)] = locals()[\"gmat\"+str(count)]\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# geA= 1000\n",
" \n",
"# for z5 in range(0,nsegs,1):\n",
"# locals()[\"gmat\"+str(count)].setval(z5, z5, geA)\n",
"# locals()[\"gmat\"+str(count)].setval(z5, nsegs+z5, -geA)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z5, z5, -geA)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z5, nsegs+z5, geA)\n",
"\n",
"\n",
"\n",
"\n",
" locals()[\"lm\"+str(count)] = h.LinearMechanism(locals()[\"cmat\"+str(count)], locals()[\"gmat\"+str(count)], locals()[\"e\"+str(count)], locals()[\"bvec\"+str(count)], locals()[\"sl\"+str(count)], locals()[\"xl\"+str(count)], locals()[\"layer\"+str(count)])\n",
"\n",
" count=count+1\n",
" \n",
" SEC.clear\n",
" del RG\n",
" del area\n",
" \n",
" \n",
"\n",
" \n",
"#print(count) \n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b71ff07f",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT01.printf() "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9f7204b0",
"metadata": {},
"outputs": [],
"source": [
" \n",
" \n",
" \n",
"############################### Transverse connections between Boundary cables and Axons ######################################\n",
"\n",
"\n",
"rho = 1.136e5 * 10000 * 4.7e-4 * 10000 # ohm-micron^2\n",
"\n",
"\n",
"\n",
"rows = len(Boundary_neighboring)\n",
"\n",
"for i in range(rows):\n",
" \n",
" for j in range(len(R)):\n",
" \n",
" if Boundary_neighboring[i][j]==1:\n",
" \n",
" NSEG = 0\n",
"\n",
" a2 = np.where(unique_radius == R[j]) # find type \n",
" a2 = a2[0][0]+1\n",
" \n",
" Boundary_RG = locals()[\"Boundary_Rg_\"+str(a2)]\n",
" area = (math.pi)*(parameters[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 = (unique_radius*2)\n",
" locals()[\"Boundary_RG\"+str(count)] = np.array(Boundary_RG)*s\n",
" locals()[\"Resistance\"+str(count)] = rd/locals()[\"Boundary_RG\"+str(count)]\n",
" locals()[\"Conductance\"+str(count)]=[]\n",
" for z4 in range(len(locals()[\"Resistance\"+str(count)])):\n",
" locals()[\"Conductance\"+str(count)].append(1/(locals()[\"Resistance\"+str(count)][z4]*area[z4]))\n",
"\n",
" \n",
" for z5 in range(0,nsegs,1):\n",
"\n",
" locals()[\"gmat\"+str(count)].setval(z5, z5, locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" locals()[\"gmat\"+str(count)].setval(z5, nsegs+z5, - locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, z5, - locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, nsegs+z5, locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" \n",
" \n",
" \n",
" locals()[\"GMAT_BOUNDARY\"+str(i)+str(j)] = locals()[\"gmat\"+str(count)]\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n",
" \n",
"# geB= 1\n",
" \n",
"# for z6 in range(0,nsegs,1):\n",
"\n",
"# locals()[\"gmat\"+str(count)].setval(z6, z6, geB)\n",
"# locals()[\"gmat\"+str(count)].setval(z6, nsegs+z6, -geB)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z6, z6, -geB)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z6, nsegs+z6, geB)\n",
"\n",
"\n",
"\n",
"\n",
" locals()[\"lm\"+str(count)] = h.LinearMechanism(locals()[\"cmat\"+str(count)], locals()[\"gmat\"+str(count)], locals()[\"e\"+str(count)], locals()[\"bvec\"+str(count)], locals()[\"sl\"+str(count)], locals()[\"xl\"+str(count)], locals()[\"layer\"+str(count)])\n",
"\n",
" count=count+1\n",
" \n",
" \n",
" SEC.clear\n",
" del Boundary_RG\n",
" del area\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"#print(count) \n",
" \n",
" \n",
" \n",
"# from IPython.display import clear_output\n",
"\n",
"# clear_output(wait=True)\n",
"\n",
"\n",
" \n",
"#gmat0.printf() \n",
"\n",
"# for sec in sl0:\n",
"# print(sec)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2f2f0781",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(Boundary_neighboring)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7808a6c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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},
{
"name": "stdout",
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT_BOUNDARY11.printf() "
]
},
{
"cell_type": "markdown",
"id": "b2a6c256",
"metadata": {},
"source": [
"#### Recordings"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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": 16,
"id": "5dbd4f4b",
"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": 17,
"id": "ca5603a0",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Vector[450]"
]
},
"execution_count": 17,
"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": 18,
"id": "cd6d9f09",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Running simulation for 6.0 ms...\n",
" Done; run time = 93.19 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 0.48 s.\n",
"\n",
"Analyzing...\n",
" Cells: 4\n",
" Connections: 0 (0.00 per cell)\n",
" Spikes: 1 (41.67 Hz)\n",
" Simulated time: 0.0 s; 1 workers\n",
" Run time: 93.19 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 = 0.43 s\n",
"\n",
"Total time = 95.05 s\n",
"\n",
"End time: 2022-12-28 12:38:38.707886\n"
]
}
],
"source": [
"sim.simulate()\n",
"sim.analyze()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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": 20,
"id": "ddb4904a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Duration: 0:01:37.507443\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": 21,
"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": 22,
"id": "09bf554d",
"metadata": {},
"outputs": [],
"source": [
"# print(xraxia)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d833f599",
"metadata": {},
"outputs": [],
"source": [
"# Transverse current: Picoamp/micron^2\n",
"\n",
"\n",
"# TC = 2.3e+03\n",
"\n",
"\n",
"# v_diff00 = (Abeta0_vext1_node0 - Abeta1_vext1_node0)/1000 #volt\n",
"# Trans_Current_node0_node0 = (v_diff00 * TC )*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 * TC *1e12/1e8 \n",
"\n",
"# v_diff22 = (Abeta0_vext1_node2 - Abeta1_vext1_node2)/1000 #volt\n",
"# Trans_Current_node2_node2 = v_diff22 * TC *1e12/1e8 \n",
"\n",
"# v_diff33 = (Abeta0_vext1_node3 - Abeta1_vext1_node3)/1000 #volt\n",
"# Trans_Current_node3_node3 = v_diff33 * TC *1e12/1e8 \n",
"\n",
"# v_diff44 = (Abeta0_vext1_node4 - Abeta1_vext1_node4)/1000 #volt\n",
"# Trans_Current_node4_node4 = v_diff44 * TC *1e12/1e8 \n",
"\n",
"# v_diff55 = (Abeta0_vext1_node5 - Abeta1_vext1_node5)/1000 #volt\n",
"# Trans_Current_node5_node5 = v_diff55 * TC *1e12/1e8 \n",
"\n",
"# v_diff66 = (Abeta0_vext1_node6 - Abeta1_vext1_node6)/1000 #volt\n",
"# Trans_Current_node6_node6 = v_diff66 * TC *1e12/1e8 \n",
"\n",
"# v_diff77 = (Abeta0_vext1_node7 - Abeta1_vext1_node7)/1000 #volt\n",
"# Trans_Current_node7_node7 = v_diff77 * TC *1e12/1e8 \n",
"\n",
"# v_diff88 = (Abeta0_vext1_node8 - Abeta1_vext1_node8)/1000 #volt\n",
"# Trans_Current_node8_node8 = v_diff88 * TC *1e12/1e8 \n",
"\n",
"# v_diff99 = (Abeta0_vext1_node9 - Abeta1_vext1_node9)/1000 #volt\n",
"# Trans_Current_node9_node9 = v_diff99 * TC *1e12/1e8 \n",
"\n",
"# v_diff1010 = (Abeta0_vext1_node10 - Abeta1_vext1_node10)/1000 #volt\n",
"# Trans_Current_node10_node10 = v_diff1010 * TC *1e12/1e8 \n",
"\n",
"# v_diff1111 = (Abeta0_vext1_node11 - Abeta1_vext1_node11)/1000 #volt\n",
"# Trans_Current_node11_node11 = v_diff1111 * TC *1e12/1e8 \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "b812647a",
"metadata": {},
"outputs": [],
"source": [
"# import csv\n",
"\n",
"# with open('v_diff66_edgedist3_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , v_diff66 ))\n",
" \n",
" \n",
" \n",
" \n",
"import csv\n",
"\n",
"\n",
"\n",
"with open('ALLExtraVoltages_stimulateonlyAbeta0_edgedist0.5_.csv', 'w', newline='') as f:\n",
" csv.writer(f).writerows(zip( t , Abeta0_vext0_node0515 , Abeta0_vext1_node0515 , Abeta0_vext1_node015 , Abeta0_vext1_node115 , Abeta1_vext1_node0515 , boundary0_vext1_section1663 , Abeta0_vext0_node115 , Abeta0_vext0_MYSA0530 , Abeta0_vext1_MYSA0530 , Abeta0_vext0_node015))\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"id": "8f3b15f1",
"metadata": {},
"source": [
"#### saving the data"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "890baeb5",
"metadata": {},
"outputs": [],
"source": [
"## saving the data\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" \n",
"# with open('nodexg0changed_v_Abeta0_stimulateonlyAbeta0_edgedist0.5_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 , Abeta0_v_node12 , Abeta0_v_node13 , Abeta0_v_node14 , Abeta0_v_node15 , Abeta0_v_node16 , Abeta0_v_node17 , Abeta0_v_node18 , Abeta0_v_node19 , Abeta0_v_node20 , Abeta0_v_node21 , Abeta0_v_node22 , Abeta0_v_node23 , Abeta0_v_node24 , Abeta0_v_node25 , Abeta0_v_node26 , Abeta0_v_node27 , Abeta0_v_node28 , Abeta0_v_node29 , Abeta0_v_node30 , Abeta0_v_node31 , Abeta0_v_node32 , Abeta0_v_node33 , Abeta0_v_node34 , Abeta0_v_node35 )) \n",
"\n",
"\n",
" \n",
" \n",
"# with open('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",
"# #################################### Connected to ground \n",
"\n",
"# with open('ConnectGround_v_Abeta0_stimulateonlyAbeta0_edgedist3_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 )) \n",
"\n",
"\n",
"# with open('NotConnectGround_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",
"\n",
"# with open('ConnectGround_LongiCurrent_Abeta0_NodetoMYSA_stimulateonlyAbeta0_edgedist3_.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_edgedist3_.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('v_Abeta0_stimulateonlyAbeta0_edgedist3_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 )) \n",
"\n",
"\n",
" \n",
"# with open('current_Abeta0_stimulateBOTH_edgedist3_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 )) \n",
"\n",
"\n",
"\n",
"# with open('LongiCurrent_Abeta0_NodetoMYSA_stimulateonlyAbeta0_edgedist3_.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_edgedist3_.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_edgedist3_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 )) \n",
"\n",
"\n",
"\n",
"# with open('LongiCurrent_Abeta0_NodetoMYSA_stimulateBOTH_edgedist3_.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_edgedist3_.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"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "7a4d2e6a",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# with open('i_vext1_Abeta1_stimulateonlyAbeta0_edgedist3_.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"
]
},
{
"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_edgedist3_.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_edgedist3_.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_edgedist3_.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",
"# with open('ina_Abeta0_stimulateonlyAbata0_edgedist0.5_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_ina_node0 , Abeta0_ina_node1 , Abeta0_ina_node2 , Abeta0_ina_node3 , Abeta0_ina_node4 , Abeta0_ina_node5 , Abeta0_ina_node6 , Abeta0_ina_node7 , Abeta0_ina_node8 , Abeta0_ina_node9 , Abeta0_ina_node10 , Abeta0_ina_node11 , Abeta0_ina_node12 , Abeta0_ina_node13 , Abeta0_ina_node14 , Abeta0_ina_node15 , Abeta0_ina_node16 , Abeta0_ina_node17 , Abeta0_ina_node18 , Abeta0_ina_node19 , Abeta0_ina_node20 )) \n",
"\n",
"# with open('imembrane_Abeta0_stimulateonlyAbata0_edgedist0.5_.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_edgedist3_.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_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 )) \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "5bcb410c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"18689.613196051898\n"
]
}
],
"source": [
"print(xr)"
]
}
],
"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",
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