{
"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",
"\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"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d5ef8f97",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"40\n"
]
}
],
"source": [
"###################################### Locating each axon in specific (x,y) #################################################\n",
"\n",
"\n",
"for i in range(len(R)):\n",
" x = np.where(unique_radius == R[i])\n",
" \n",
" if x[0]==0:\n",
" netParams.popParams[\"Axon%s\" %i] = {\n",
" 'cellType': 'type1', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type1', \n",
" 'xRange':[C[i][0], C[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[i][1], C[i][1]]} \n",
"\n",
" \n",
" \n",
" \n",
" \n",
"########################################### Locating Boundary Cables ########################################################\n",
"\n",
"\n",
"for i in range(len(Boundary_coordinates)):\n",
" \n",
" netParams.popParams[\"Boundary%s\" %i] = {\n",
" 'cellType': 'Boundary', \n",
" 'numCells':1 , \n",
" 'cellModel': 'Boundary', \n",
" 'xRange':[Boundary_coordinates[i][0], Boundary_coordinates[i][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[Boundary_coordinates[i][1], Boundary_coordinates[i][1]]} \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": null,
"id": "03c9154d",
"metadata": {},
"outputs": [],
"source": []
},
{
"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",
"for i in range(len(R)): \n",
" if G[i]==4: # Group 4\n",
" netParams.stimTargetParams['Input1->\"Stim_%s\"' %i] = {'source': 'Input1', 'sec':'node_0', 'loc': 0.5, 'conds': {'pop':\"Axon%s\" %i}} \n",
" \n",
"\n",
" \n",
"#netParams.stimTargetParams['Input1->Stim_1'] = {'source': 'Input1', 'sec':'node_0', 'loc': 0.5, 'conds': {'pop':\"Axon0\"}} \n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "90a2f08b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Start time: 2022-10-30 02:25:31.838772\n",
"\n",
"Creating network of 40 cell populations on 1 hosts...\n",
" Number of cells on node 0: 40 \n",
" Done; cell creation time = 5.41 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: 20 \n",
" Done; cell stims creation time = 0.00 s.\n",
"Recording 60 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": [
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\n",
"0.1\n",
"95769.75706051444\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",
"\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] = 1000 #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] = 1000 #1e9\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"############################# For C fibers #######################################################\n",
"\n",
"\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] = 1e-9 # 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\n",
" seg.xg[1] = 1e-9\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] = 1e-9\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] = 1e-9\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] = 1e-9\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] + 0.094438 #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": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT1516.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(1)]\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(1)]\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 + (1211 * 10000 * Boundary_dist[i][j] )\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": "7808a6c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT_BOUNDARY55.printf() "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5eb4dcc1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"16.2\n"
]
}
],
"source": [
"print(Boundary_dist[0][0])"
]
},
{
"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": "ca5603a0",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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},
{
"data": {
"text/plain": [
"Vector[1583]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Recording v and vext[0], Abeta\n",
"\n",
"\n",
"\n",
"for i1 in range(len(R)): \n",
" if G[i1]==4: \n",
" print(i1)\n",
" F = np.where(unique_radius == R[i1]) \n",
" #nodes = int (Number_of_nodes[F]-1)\n",
" for i3 in range(int(Number_of_nodes)):\n",
"\n",
" locals()[\"Abeta_v\"+str(i1)+str(i3)] = sim.net.cells[i1].secs[\"node_%s\"%i3][\"hObj\"]\n",
" locals()[\"Abeta_ap\"+str(i1)+str(i3)] = h.Vector()\n",
" locals()[\"Abeta_ap\"+str(i1)+str(i3)].record(locals()[\"Abeta_v\"+str(i1)+str(i3)](0.5)._ref_v)\n",
"# locals()[\"Abeta_v_ext\"+str(i1)] = sim.net.cells[i1].secs[\"node_%s\"%nodes][\"hObj\"]\n",
"# locals()[\"Abeta_ap_ext\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta_ap_ext\"+str(i1)].record(locals()[\"Abeta_v_ext\"+str(i1)](0.5)._ref_vext[0])\n",
" \n",
" print(i3)\n",
"# print(nodes)\n",
" \n",
"\n",
" \n",
" \n",
"t = h.Vector()\n",
"t.record(h._ref_t)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e3f90783",
"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(12):\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",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "23017f07",
"metadata": {},
"outputs": [],
"source": [
"\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",
"\n",
"# for i2 in range(12):\n",
"\n",
"# locals()[\"Abeta0_vex\"+str(i2)] = sim.net.cells[0].secs[\"node_%s\"%i2][\"hObj\"]\n",
"# locals()[\"Abeta0_vext1_node\"+str(i2)] = h.Vector()\n",
"# locals()[\"Abeta0_vext1_node\"+str(i2)].record(locals()[\"Abeta0_vex\"+str(i2)](0.5)._ref_vext[1])\n",
"\n",
" \n",
" \n",
"# for i3 in range(0,24,2):\n",
" \n",
"# locals()[\"Abeta_vMext\"+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_vMext\"+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",
" "
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "4b9344bb",
"metadata": {},
"outputs": [],
"source": [
"## Recording v and vext[0], Adelta\n",
"\n",
"\n",
"\n",
"# for i2 in range(len(R)): \n",
"# if G[i2]==3: \n",
"# F = np.where(unique_radius == R[i2]) \n",
"# nodes = int (Number_of_nodes[F]-1)\n",
"# locals()[\"Adelta_v\"+str(i2)] = sim.net.cells[i2].secs[\"node_%s\"%nodes][\"hObj\"]\n",
"# locals()[\"Adelta_ap\"+str(i2)] = h.Vector()\n",
"# locals()[\"Adelta_ap\"+str(i2)].record(locals()[\"Adelta_v\"+str(i2)](0.5)._ref_v)\n",
"# locals()[\"Adelta_v_ext\"+str(i2)] = sim.net.cells[i2].secs[\"node_%s\"%nodes][\"hObj\"]\n",
"# locals()[\"Adelta_ap_ext\"+str(i2)] = h.Vector()\n",
"# locals()[\"Adelta_ap_ext\"+str(i2)].record(locals()[\"Adelta_v_ext\"+str(i2)](0.5)._ref_vext[0])\n",
"# print(i2)\n",
" \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "d83f15db",
"metadata": {},
"source": [
"#### Simulate and Analyze"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "cd6d9f09",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Running simulation for 6.0 ms...\n",
" Done; run time = 3178.69 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 6.92 s.\n",
"\n",
"Analyzing...\n",
" Cells: 40\n",
" Connections: 0 (0.00 per cell)\n",
" Spikes: 20 (83.33 Hz)\n",
" Simulated time: 0.0 s; 1 workers\n",
" Run time: 3178.69 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": {
"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": {
"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": {
"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": {
"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": {
"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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S0AxxqsR4hlP33BOmTbMTJ0nScAxxqsR4hlMjiiFVQ5wkSUMzxKkS4xlOhWJI1eFUSZKGZohTJcbTiQPf2iBJ0u4Y4lSJ8dwTBw6nSpK0O4Y4VaKMTtxDD8GWLeXVJElSJzHEqRJl3BMHcP/95dQjSVKnMcSpEmUMp4JDqpIkDcUQp0qUMZwKPqEqSdJQDHGqRFnDqXbiJEkanCFOlRjvcOq++xaT/hriJEkanCFOlRjvcOqkSTBnjsOpkiQNxRCnSox3OBWc8FeSpOEY4lSJ8Q6nQvGEqp04SZIGZ4hTJcY7nApFJ8554iRJGpwhTpUoYzh13rxiODWznJokSeokhjhVoqxO3JYtsHZtOTVJktRJDHGqRBn3xPnqLUmShmaIUyXK6MTNm1esfUJVkqRHM8SpEmVNMQKGOEmSBmOIUyUcTpUkqVqGOFWijOHUvfeGiRPtxEmSNBhDnCpRxnBqT0/fNCOSJGlXhjhVYvPmYj116vjO44S/kiQNzhCnSmzYUNwPN2XK+M5jJ06SpMEZ4lSJ9eth+nSIGN959tvPECdJ0mAMcarE+vUwY8b4z7PffrBqVd+DEpIkqWCIUyXKCnHz5sH27fDQQ+M/lyRJncQQp0ps2FAMp46XE/5KkjQ4Q5wqUeZwKviEqiRJAxniVIkyh1PBTpwkSQMZ4lSJsjtxhjhJknZliFMlyronbubMYsJgh1MlSdqVIU6V2LgRpk0b/3kinPBXkqTBGOJUia1bx/+2hl5O+CtJ0qMZ4lSJskOcw6mSJO2q7UJcRMyOiMsiYkNE3B0Rf1Z3TdpVJmzZApMnl3M+h1MlSXq0iXUXMAafAbYC84DjgCsi4ubMXFZrVfqD7duLdZmduNWri/NObMf/YiVJqkBbdeIiYjpwJnBOZq7PzGuAbwKvrLcy9bdlS7EuqxO3335Fd++BB8o5nyRJnaCtQhxwBLAjM2/vt+1m4Nj+B0XE6yJiaUQsXb16dVMLVHE/HJQ7nAoOqUqS1F+7hbgZwNoB29YCe/bfkJnnZebizFw8d+7cphWnQm8nrszhVDDESZLUX7uFuPXAzAHbZgLraqhFQyi7E2eIkyTp0dotxN0OTIyIw/ttWwT4UEML6Q1xZXXieodTnWZEkqQ+bRXiMnMDcCnw/oiYHhEnAWcAX6q3MvVX9oMN06cX72G1EydJUp+2CnENZwPTgFXAV4E3Or1Iaym7Ewe+tUGSpIHabtatzHwIeGHddWhoZXfiwLc2SJI0UDt24tTiyn6wAXxrgyRJAxniVDqHUyVJqp4hTqWrajh1zZq+c0uS1O0McSpdFZ243mlGVq0q75ySJLUzQ5xKV1UnDhxSlSSplyFOpevtxE2aVN45e0OcT6hKklQwxKl0O3YU64klTmDTO5xqJ06SpIIhTqXbubNYT5hQ3jkNcZIk7coQp9L1duJ6Svyva8oU2Htvh1MlSepliFPpqujEgRP+SpLUnyFOpauiEwdO+CtJUn+GOJWuqk6c70+VJKmPIU6l6w1xZXfiHE6VJKmPIU6lq3I4dd062Lix3PNKktSODHEqXZXDqeCQqiRJYIhTBarqxDlXnCRJfQxxKl3VnThDnCRJhjhVoKoHGxxOlSSpjyFOpatqOHXuXIiwEydJEhjiVIGqOnETJ8KcOYY4SZLAEKcK7NhRfoDr5VsbJEkqGOJUup07y3+oode8ed4TJ0kSGOJUATtxkiRVzxCn0lXZiesNcZnVnF+SpHZhiFPpdu6sthO3eXPx+i1JkrqZIU6lq3I41bc2SJJUMMSpdFUPp4IhTpIkQ5xKV/WDDeATqpIkGeJUuqqnGAE7cZIkGeJUuio7cfvsUwREQ5wkqdsZ4lS6Kp9O7elxwl9JksAQpwpUOZwKRYizEydJ6naGOJWuyuFU8K0NkiSBIU4VqLoTt99+DqdKkmSIU+mq7sT13hO3c2d115AkqdUZ4lS6ZnTitm2DNWuqu4YkSa3OEKfSVfl0KjjhryRJYIhTBZoxnAo+3CBJ6m6GOJWuGcOpYIiTJHU3Q5xK14wpRsDhVElSdzPEqXRVd+L22gsmT7YTJ0nqboY4la7qTlyEE/5KkmSIU+mqfjoVnPBXkiRDnEpX9XAqFCHu97+v9hqSJLUyQ5xKV/VwKsABBxjiJEndzRCn0jWjEzd/Pjz4IGzeXO11JElqVYY4la5ZnTiA++6r9jqSJLUqQ5xK14xOXG+IW7Gi2utIktSqJo7koIj4E+AvgGOBPYF1wDLg3zLzysqqU1tqxtOp8+cXa++LkyR1q92GuIj4P8Dbgc8D3wDWAjOBRcCFEfGRzPxEpVWqrTRzONVOnCSpW42kE/c24OmZeduA7ZdGxFeBHwCGOP1BM4ZTZ82CqVPtxEmSutdI+iXTgaH+qlwJ7FFeOeoEzejERRTdODtxkqRuNZK/ar8BXB4Rp0bE3IiYHBFzIuJU4DLg36stUe2mGZ04KO6LM8RJkrrVSELcG4BrgQuB+4FNjfWFwHXAGyurTm2pGZ04cMJfSVJ32+09cZm5FXgH8I6ImAXMANZn5sMDj42IkzLzJ2UXqfbSjKdToa8Tl1kMr0qS1E1G9VdtZj6cmfcOFuAavjP+ktTuMpvXidu0Cdaurf5akiS1mrL/qrUfIjKbcx2nGZEkdbOyQ1yT/vpWq2vG8KYT/kqSupmv3VLp7MRJklS9lghxETElIs6PiLsjYl1E3BQRzxlwzKkRcVtEbIyIH0TEgrrq1e41oxO3//7F2k6cJKkbtco9cROB5cDTgL2Ac4CvR8QhABExB7i0sX02sBT42niLVTWa1YmbNg1mz7YTJ0nqTqMKcRGxT0S8MiLe3vh+fkQc2Ls/M/ccSxGZuSEzl2TmXZm5MzO/BfwOOL5xyIuBZZl5SWZuBpYAiyLiqLFcT9Vr1pQf8+fbiZMkdacRh7iIeBrwa+AVFB0xgMOBz5ZdVETMA44AljU2HQvc3Ls/MzcAdza2q8U0qxMHvnpLktS9RtOJ+zjwp5n5bGB7Y9vPgBPKLCgiJgFfBi7MzNsam2cAA2cDWwsM2vmLiNdFxNKIWLp69eoyy9MINHPyXUOcJKlbjSbEHZKZ32t83dtr2coI3voQEVdHRA6xXNPvuB7gS43zvqnfKdYDMwecdiawbrDrZeZ5mbk4MxfPnTt3ZL+dStXM4dSVK4tXfUmS1E1GE+JuiYhnDdh2GvCr3f1gZp6SmTHEcjJARARwPjAPODMzt/U7xTJgUe83ETEdWEjfcKtaSLOHU3fuhPvvb941JUlqBaMJcW8FvhwRFwLTIuJzwAXA20qq5bPA0cDpmblpwL7LgMdGxJkRMRV4D/DLfsOtajHN6sQddFCxXr68OdeTJKlVjDjEZeZ1FN2wZcAXKZ4ePSEzbxhvEY05314PHAesjIj1jeUVjWuvBs4EzgXWAE8GzhrvdVWNZnbiDj64WN9zT/OuKUlSK9jt/Wz9ZeYK4KNlF5GZd7ObOeYy8yrAKUXahJ04SZKqNWyIi4gvMYL3oWbmq0qrSG2vmZ24vfaCPfe0EydJ6j67G079DcV8bHdSTOnxQmACcG/jZ88AHq6uPLWrZnXiIopunCFOktRthu3EZeb7er+OiP8CnpeZP+637WT6Jv6VgOZ24qC4L87hVElStxnN06lPAa4bsO1nwB+VV446RbM6cWAnTpLUnUYT4m4CPhQR0wAa63OBX1RQl9pYM9/YAEUnbtUq2Ly5edeUJKluowlxfwGcBKyNiPsp7pE7GfChBu2i2cOpvU+o3ntvc68rSVKdRjzFSGbeBZwYEQcB84H7MtNBLA2q2Z04KO6LO+yw5l1XkqQ6jaYTR0TsDTwdeAZwSuN7aRd1deK8L06S1E1GHOIi4o8ophp5A/B4ijcs3NnYLu2imZ24Aw8s1oY4SVI3Gc0bGz4OnJ2ZF/duiIg/BT4JPKnkutTGmt2JmzoV9t3XaUYkSd1lNMOpRwBfH7Dt3wHvQtKjNLMTB8V9cXbiJEndZDQh7g4e/dL5l1IMsUp/0OxOHBT3xdmJkyR1k9EMp74F+FZE/DVwN3AIcDjw/PLLUruroxN35ZXNn6NOkqS6jGaKkWsjYiHwPIopRi4Hvp2ZD1VVnNpTHZ24gw+G9eth7VqYNav515ckqdlG04kjM9cAF1VUizpIs7th/acZMcRJkrrBaKYYeUxEfCUibomIe/ovVRao9lPHkGb/CX8lSeoGo+nEfYXiIYa3AhurKUedoK4HGwDuvrv515YkqQ6jCXHHAidl5s6qilHnaHYnbr/9YMoUuOuu5l5XkqS6jGaKkR8BT6iqEHWOOjpxPT2wYAH87nfNv7YkSXUYTSfuLuC/IuJSYGX/HZn5njKLUvurY5qPxzzGECdJ6h6jCXHTKaYVmQQc1G97DX0XtbI6OnEAhx4K119fz7UlSWq20cwT95rdHRMRL8/Mr46vJHWCujpxa9YUc8XttVfzry9JUjON5p64kfhcyedTG6qrE/eYxxRrh1QlSd2g7BDnC48E1NeJA0OcJKk7lB3ivD9Otb2/1BAnSeomZYc4qbbh1L33hpkzDXGSpO6w2xAXEQY9jVodnbiI4glVQ5wkqRuMJKCtiIiPRsRjR3Cs71FVbZ04cK44SVL3GEmIewPwGOCGiPh5RPzviJg72IGZOZKgpy5QRycO+kJcnUFSkqRm2G2Iy8z/zMyXAvtTTCHyUmB5RHwzIs6MiElVF6n2UncnbtMmuP/++mqQJKkZRny/W2Y+nJmfy8yTgaOBpcDHgPuqKk7tq85OHDikKknqfKN+aCEipgBPAp4MzAN+VXZRam91duIWLizWd95ZXw2SJDXDiENcRJwcEecB9wMfBK4DjsjMp1dVnNpXXZ24Qw+Fnh644456ri9JUrPs9t2pEbEEeCUwG7gEeF5m/qTiutTG6uzETZ4MCxYY4iRJnW+3IQ54CvAu4D8yc3PF9ahD1NWJAzj8cEOcJKnzjeTp1Gdn5sUGOI1UXa/d6nXYYUWIc5oRSVIn820MKl3d4enww2HtWnjggXrrkCSpSoY4VaLu4VRwSFWS1NkMcSpdK3TiwBAnSepshjhVos5O3GMeAxMmGOIkSZ3NEKfS1d2JmzQJDjnEECdJ6myGOFWizk4cFEOqt99ebw2SJFXJEKfS1d2Jg7654lqhFkmSqmCIUyVaoRO3YQOsXFlvHZIkVcUQp9LVPdkvwFFHFevbbqu3DkmSqmKIU0c65phifeut9dYhSVJVDHEqXSt04ubPhz33hFtuqbcOSZKqYohT6VrhYYKIohtnJ06S1KkMcapE3Z04KEKcnThJUqcyxKl0rdCJAzj66OLp1DVr6q5EkqTyGeJUiVbpxIFDqpKkzmSIU+lapRPXG+IcUpUkdSJDnCrRCp24BQtg2jRDnCSpMxniVLpW6cT19BST/jqcKknqRIY4VaIVOnHgE6qSpM5liFPpWmGy317HHAP33AOPPFJ3JZIklcsQp472+McX61/9qt46JEkqmyFOpWulTtxxxxXrX/yiziokSSqfIU6la5UHGwAOOABmz4abb667EkmSytVyIS4iDo+IzRFx0YDtp0bEbRGxMSJ+EBEL6qpRu9cqnbgIWLTIECdJ6jwtF+KAzwA39N8QEXOAS4FzgNnAUuBrzS9N7WjRouKeuB076q5EkqTytFSIi4izgIeB7w3Y9WJgWWZekpmbgSXAoog4qrkVaqRapRMHRYjbtAnuuKPuSiRJKk/LhLiImAm8H3jrILuPBf4wIJaZG4A7G9vVQlrpfrheixYVa4dUJUmdpGVCHPAB4PzMXD7IvhnA2gHb1gJ7DnaiiHhdRCyNiKWrV68uuUyNRCt14o45BiZONMRJkjpLU0JcRFwdETnEck1EHAecBnxsiFOsB2YO2DYTWDfYwZl5XmYuzszFc+fOLe330O71duJaKcRNmQJHH22IkyR1lonNuEhmnjLc/oh4C3AIcE8Uf/vPACZExDGZ+URgGfDqfsdPBxY2tku7ddxxcNVVdVchSVJ5WmU49TyKUHZcY/lX4ArgWY39lwGPjYgzI2Iq8B7gl5l5W/NL1XBasRMHsHgx3HcfrFhRdyWSJJWjJUJcZm7MzJW9C8Xw6ebMXN3Yvxo4EzgXWAM8GTirtoI1pFZ8sAHghBOK9fXX11uHJEllacpw6mhl5pJBtl0FOKVIm2i1TtxxxxUPN9xwA7zoRXVXI0nS+LVEJ06do1U7cVOnwuMfbydOktQ5DHGqRKt14gCe9CRYuhR27qy7EkmSxs8Qp1K1aicOivvi1q71zQ2SpM5giFMlWrUTB8V9cZIktTtDnErVyp24Y46B6dO9L06S1BkMcapEK3biJkyA44+Hn/607kokSRo/Q5xK1aqT/fY6+WS46SZYv77uSiRJGh9DnLrKU58KO3bAddfVXYkkSeNjiFOpWr0Td+KJ0NMDP/5x3ZVIkjQ+hjiVqpUfbACYORMWLTLESZLanyFOlWjVThwUQ6rXXQdbt9ZdiSRJY2eIU6lavRMHRYjbtAl+/vO6K5EkaewMcapEq3fiwCFVSVJ7M8SpVO3QiZs3D448En7wg7orkSRp7AxxqkQrd+IAnvlM+OEPYcuWuiuRJGlsDHEqVatPMdLrmc+EjRt9e4MkqX0Z4tSVTjmleA3XlVfWXYkkSWNjiFOp2qUTN3MmPOUphjhJUvsyxKlrPfOZsHQpPPhg3ZVIkjR6hjiVql06cVCEuEz4/vfrrkSSpNEzxKlU7TDFSK8TToC994Yrrqi7EkmSRs8Qp0q0Qydu4kR47nPhW9+CHTvqrkaSpNExxKlU7dSJAzjjjOKeuGuvrbsSSZJGxxCnSrRDJw7gWc+CSZPgm9+suxJJkkbHEKdStVsnbuZMePrT4T//s/1qlyR1N0OcKtEunTgohlTvuANuu63uSiRJGjlDnErVTlOM9HrBC4r1v/97vXVIkjQahjh1vQMPhKc+Fb76VYdUJUntwxCnUrVjJw7grLPg1lvhf/6n7kokSRoZQ5wEvOQlMGECXHxx3ZVIkjQyhjiVql07cfvuC6eeWoQ4h1QlSe3AEKdStXMAOuss+O1v4brr6q5EkqTdM8SpEu3WiYNiSHX6dPjiF+uuRJKk3TPEqVTt3Inbc0942cuKIdX16+uuRpKk4RniVIl27MQB/OVfFgHOOeMkSa3OEKdSteuDDb1OPBGOPBLOP7/uSiRJGp4hTuonoujGXXMN/OpXdVcjSdLQDHEqVbt34qAIcdOmwSc/WXclkiQNzRAnDTB7NrzylXDRRfDAA3VXI0nS4AxxKlUndOIA/vqvYfNm+Pzn665EkqTBGeJUqnaeYqS/Y4+F006Dz3wGtm2ruxpJkh7NEKdKtHsnDopu3IoVcMkldVciSdKjGeJUqk7pxAE873lwzDFw7rmwc2fd1UiStCtDnCrRCZ24nh5497vhllvg0kvrrkaSpF0Z4lSqTnmwodfLXgZHHAEf/GBndRklSe3PECcNY8IEeNe74Oab4fLL665GkqQ+hjiVqtM6cQB/9mewcCGccw7s2FF3NZIkFQxx0m5MnFgMp/7yl/DlL9ddjSRJBUOcStWJnTgo7o1bvLh40GHTprqrkSTJECeNSE8P/MM/wPLl8KlP1V2NJEmGOJWsUztxAKecAs9/PnzoQ3D//XVXI0nqdoY4larTp+H4x3+EjRvh7W+vuxJJUrczxKkSndiJAzjySHjb2+D//T/40Y/qrkaS1M0McSpVp3fioJg3bsECOPts2Lat7mokSd3KEKdKdGonDmCPPeATn4Bly+CjH627GklStzLEqVSd/GBDf2ecUUw78r73FW9zkCSp2Qxx0hh95jOw997w6lfD1q11VyNJ6jaGOJWqWzpxAHPmwHnnFZ24D36w7mokSd3GECeNwxlnwJ//eTF33LXX1l2NJKmbGOJUqm7qxPX61KeKp1XPOgsefLDuaiRJ3aKlQlxEnBURt0bEhoi4MyKe2m/fqRFxW0RsjIgfRMSCOmuVes2aBV//evEWh1e9CnburLsiSVI3aJkQFxHPBD4CvAbYE/hj4LeNfXOAS4FzgNnAUuBr9VSq4XRjJw7g+OPhn/8Zvv3t4h2rkiRVrWVCHPA+4P2ZeV1m7szMFZm5orHvxcCyzLwkMzcDS4BFEXFUXcVqcN0w2e9Qzj4bXvpSeOc74bvfrbsaSVKna4kQFxETgMXA3Ij4TUTcGxGfjohpjUOOBf4wG1dmbgDubGxXC+q2ThwUv/MXvwiPexz86Z/CLbfUXZEkqZO1RIgD5gGTgJcATwWOA54AvLuxfwawdsDPrKUYdn2UiHhdRCyNiKWrV6+upGANrluHU3vNmAHf/CZMmwannw4PPFB3RZKkTtWUEBcRV0dEDrFcA2xqHPqpzLwvMx8A/hl4bmP7emDmgNPOBNYNdr3MPC8zF2fm4rlz51bxK0lDOvhg+I//gBUr4IUvhI0b665IktSJmhLiMvOUzIwhlpMzcw1wLzDUHVXLgEW930TEdGBhY7taSLd34no95Slw0UXF3HEvexls21Z3RZKkTtMqw6kA/wa8OSL2jYi9gbcA32rsuwx4bEScGRFTgfcAv8zM2+opVdq9l7wEPvtZuOIKeM1rnHpEklSuiXUX0M8HgDnA7cBm4OvAuQCZuToizgQ+DVwE/Aw4q6Y6NQw7cbt6/euLCYDf9S6YPr0IdT2t9E8nSVLbapkQl5nbgLMby2D7rwKcUkRt5x3vgPXr4cMfhq1b4QtfgAkT6q5KktTuWibEqTPYiXu0CDj3XJg8Gd73viLIXXghTPT/fZKkcfCvEakJImDJkiLIvetdsHlz8eDDtGm7/VFJkgbl3TkqlZ244b3znfCxj8Gll8JppzmPnCRp7AxxKlU3v3ZrpN7yFrjkErjxRjjxRLjzzrorkiS1I0OcKmEnbngveQl873vw0EPw5CfDVVfVXZEkqd0Y4lQqh1NH7qST4Kc/hXnz4FnPgr//ezuZkqSRM8RJNTr8cPjZz+ClLy2mInnxi2HNmrqrkiS1A0OcSmUnbvRmzICvfrV44OHyy2HRIrj66rqrkiS1OkOc1AIiigcerr0Wpk6FZzwD3v522LKl7sokSa3KEKdS2YkbnxNOgJtugr/6K/iHf4AnPhF+8pO6q5IktSJDnNRipk+Hz30OrriieF3XySfDG94ADz9cd2WSpFZiiFOp7MSV57nPhWXL4G/+Bj7/eTj6aLjgAti5s+7KJEmtwBCnUjlFRrlmzIB/+ie4/no4+GB4zWtg8WL4wQ/qrkySVDdDnCphJ65cxx9fzCn3la/Agw8WDz684AXwi1/UXZkkqS6GOJXK4dTq9PTAy18Ot90GH/oQ/OhH8IQnwIteZJiTpG5kiJPazLRpxcTAd90F73tfMbTaG+auvdYhbUnqFoY4lcpOXPPMmgXveU8R5pYsKSYIPukkeMpT4OKLYdu2euuTJFXLECe1uVmz4L3vhXvvhc98pnht18tfDgsXFu9jXbmy7golSVUwxKlUduLqM306nH12cc/c5ZfDYYcVw64HHggvfCF861uwfXvdVUqSymKIkzpMTw88//nw/e/Dr38Nb30rXHcdnH46LFgAb3sb/Pzn3jsnSe3OEKdS2YlrLUccAR/5CCxfDpddVrzG6+MfL6YsOfLI4p66W2+tu0pJ0lgY4qQuMGlSMaR6+eVw//3FGyAOOgg++EE45phi+bu/K55u3bGj7molSSNhiFOp7MS1vtmz4bWvhe99D1asgE9+EubPL94McdJJsP/+xZshLr3U97VKUiszxKlU3mfVXvbfH978ZrjqKli9Gr76VTjttGLo9cwzYZ99iilLzjkHfvhD2Lq17oolSb0McaqEnbj2M2sWnHVW8Wqv1auLeefe+c7if8sPfQhOOQX23hue+1z46EeLodctW2ouWpK62MS6C1BncTi1M0yaBE97WrF84APFsOoPfwhXXll07b7zneK4KVPgSU+Ck08ulhNPLIKeJKl6hjhJuzVrFpxxRrFA8XDEtdfCNdfAT34C//iPxcTCUDz1+qQnweLFxXLcccUcdpKkchniVCo7cd1h3rziXa0velHx/caNcMMNRai74Ybifa4XXVTs6+kpnn7tH+oe9ziYObO28iWpIxjiJI3bHnv0Db/2+v3v4cYbYenSYrniCrjggr79CxYUYe7xjy/Wj3tcMa/dpElNL1+S2pIhTqWyE6de8+cXy+mnF99nFu93vflm+NWviuWXv4TvfrfvdWCTJ8PRRxfLkUfCUUcV6yOOcEhWkgYyxElqiohiguGDDipeC9Zry5bifa+9we5Xv4Kf/Qy+9rVdp6w56KC+UNe7Pvzw4t2wEyY0//eRpLoZ4lQqO3EarSlTYNGiYulv0yb4zW+KgPfrX/etL7gA1q/vO27SpGJoduFCOPTQvqX3+z33bOqvI0lNY4iT1JKmTeu7V66/TLjvviLU3Xkn/Pa3fevrr4c1a3Y9fs6cvmC3YEHR0Tv44L5l1iz/0SGpPRniVCo7capaRN/9ds94xqP3r1kDv/tdX7DrXa6/vniV2MC3TsyY8ehg1/v9QQcV19ljj+b8bpI0GoY4lcoQp7rtvXexPPGJj963cyesWgX33NO3LF/e9/VNNxX7B5o1qy84zp9fvK6s//e926ZMqfzXk6Q/MMRJ6ho9PbDffsVywgmDH7N5c/EUbW+wu+++YrqU3uXqq4tt27Y9+mf32WfXkLfffrDvvsW8ev3Xc+b4MIak8TPEqVR24tTupk6Fww4rlqHs3AkPPbRruOtdekPfsmVFV2+wsBdRBLnBAt5g2xzOlTQYQ5wkjVJPTxHC5swpJiseSmbx3tn77y8C3apVfV/3Xy9dWqzXrRv8PNOmFdfaZ59d18Nt22MP/zEldTpDnEplJ07qE9F3j95RR+3++E2b+sJe/6D3wAPw4IN967vvLr4e+CRuf1OnDh329tmnr66By7Rp5f3+kqpliJOkFjFtWjENyoIFIzt++/YiyPUGvIFhr/+2X/yiWD/00K6TKA80Zcqjg93s2UOHPgOgVB9DnEplJ05qnokTYe7cYhmpHTtg7doi/D30ULEeblmxAv7nf4qvH3lk+HP3BsBZs2Cvvca2+O5caeQMcZLURSZMKDprs2cXb7UYje3b+wJg7zJYEFy7tlgefrgY+u39ftOm3V9j2rTBw93MmUMHvz333HWZMcOnf9UdDHEqlZ04qXNNnNh3T91YbN1adPN6Q91Il+XL+77euHFk15o+/dHhrv8yc+bI90+Z4p9pak2GOElSU0ye3PegxVht27ZrEHzkkeKp3sGWgfvuvXfX70fSGYQivA4V8mbMKALjaNc+PawyGOJUKjtxkqo0adL4uoH9bd8+dAAcLAT2X3o7hBs2wPr1xXrLlpFfO6IIcv3D3VgDYe+6d3EouXsY4lQqQ5ykdjFxYt+TtWXYvn3XUNf/65Gu162DlSt33T7SIeRekycXAXG0S2+HcCSLQ8ytwRAnSVIJJk7se9iiTDt3FkFuJAFx48bhl4cfHnz7aPV2Eke7TJv26GXq1N1/b2AcnCFOpbITJ0nl6unpG26dN6/882cW9wfuLgDubtmwoVj3Tk0zcN+OHWOvccqU4YPeSMLgaI/p6SnvM66KIU6SpC7Wv6tWpa1bi7C4eXOx7r8M3DaaY9atK95sMtgx4wmOkyfvGuqmTu1bpkzZ9fvhltEcO3Xq6Go0xKlUduIkSYOZPLlYyh5uHs727eUExi1bim39lzVr+r4euH80D7mMhyFOkiR1pP7TwzTTzp1F53Fg8BssDA5c3vKWkV/HEKdS2YmTJHW7np6xDY/C6EJcG9y2J0mSpIEMcSqVnThJkprDEKdSGeIkSWoOQ5wkSVIbMsSpVHbiJElqDkOcJElSGzLEqVR24iRJao6WCXERcUhEfDsi1kTEyoj4dERM7Lf/1Ii4LSI2RsQPImJBnfVKkiTVqWVCHPAvwCpgf+A44GnA2QARMQe4FDgHmA0sBb5WS5Ualp04SZKao5VC3GOAr2fm5sxcCXwXOLax78XAssy8JDM3A0uARRFxVD2lSpIk1auVXrv1CeCsiLga2Bt4DkXnDYowd3PvgZm5ISLubGy/bbiT3nEHPOtZldSrQaxaVaztxEmSVK1WCnE/BP4KeASYAFwI/Edj3wxg9YDj1wKDvtI2Il4HvA5g8uTH88gjFVSrQU2dCs9+Nhx+eN2VSJLU2ZoS4hrdtacNsfsnwB8D/wV8DjiRIrR9EfgI8HZgPTBzwM/NBNYNdsLMPA84D2Dx4sX505+Or35JkqRW05R74jLzlMyMIZaTKR5WOAj4dGZuycwHgX8Dnts4xTJgUe/5ImI6sLCxXZIkqeu0xIMNmfkA8DvgjRExMSJmAa+m7z64y4DHRsSZETEVeA/wy8wc9n44SZKkTtUSIa7hxcCzKe59+w2wHfg/AJm5GjgTOBdYAzwZOKueMiVJkurXMg82ZOYvgFOG2X8V4JQikiRJtFYnTpIkSSNkiJMkSWpDhjhJkqQ2ZIiTJElqQ4Y4SZKkNmSIkyRJakOGOEmSpDZkiJMkSWpDhjhJkqQ2ZIiTJElqQ4Y4SZKkNmSIkyRJakOGOEmSpDZkiJMkSWpDkZl111CpiFgH/LruOrrMHOCBuovoMn7mzedn3nx+5s3nZ958R2bmniM5cGLVlbSAX2fm4rqL6CYRsdTPvLn8zJvPz7z5/Mybz8+8+SJi6UiPdThVkiSpDRniJEmS2lA3hLjz6i6gC/mZN5+fefP5mTefn3nz+Zk334g/845/sEGSJKkTdUMnTpIkqeMY4iRJktpQx4a4iJgdEZdFxIaIuDsi/qzumjpdRLwpIpZGxJaIuKDuerpBREyJiPMb/42vi4ibIuI5ddfVySLiooi4LyIeiYjbI+K1ddfULSLi8IjYHBEX1V1LN4iIqxuf9/rG4pyrTRARZ0XErY38cmdEPHWoYzt5nrjPAFuBecBxwBURcXNmLqu1qs72e+CDwLOAaTXX0i0mAsuBpwH3AM8Fvh4Rj8vMu+osrIN9GPjLzNwSEUcBV0fETZl5Y92FdYHPADfUXUSXeVNmfqHuIrpFRDwT+Ajwp8D1wP7DHd+RnbiImA6cCZyTmesz8xrgm8Ar662ss2XmpZn5H8CDddfSLTJzQ2Yuycy7MnNnZn4L+B1wfN21darMXJaZW3q/bSwLayypK0TEWcDDwPdqLkWq0vuA92fmdY0/01dk5oqhDu7IEAccAezIzNv7bbsZOLameqSmiIh5FP/923GuUET8S0RsBG4D7gO+XXNJHS0iZgLvB95ady1d6MMR8UBE/CQiTqm7mE4WEROAxcDciPhNRNwbEZ+OiCFHtjo1xM0A1g7YthYY0bvIpHYUEZOALwMXZuZtddfTyTLzbIo/T54KXApsGf4nNE4fAM7PzOV1F9Jl/hY4FDiAYu6yyyPCrnN15gGTgJdQ/NlyHPAE4N1D/UCnhrj1wMwB22YC62qoRapcRPQAX6K4D/RNNZfTFTJzR+NWjQOBN9ZdT6eKiOOA04CP1VxK18nMn2XmuszckpkXAj+huO9W1djUWH8qM+/LzAeAf2aYz7xTH2y4HZgYEYdn5h2NbYtwiEkdKCICOJ/iX3HPzcxtNZfUbSbiPXFVOgU4BLin+E+dGcCEiDgmM59YY13dKIGou4hOlZlrIuJeis95RDqyE5eZGyiGON4fEdMj4iTgDIpOhSoSERMjYiowgeIP2akR0an/UGglnwWOBk7PzE27O1hjFxH7Nh7/nxEREyLiWcDLge/XXVsHO48iJB/XWP4VuILiKXhVJCJmRcSzev8cj4hXAH8M/FfdtXW4fwPe3PizZm/gLcC3hjq4k/+CPRv4IrCK4mnJNzq9SOXeDby33/d/TvGkzZJaqukCEbEAeD3FPVkrG50KgNdn5pdrK6xzJcXQ6b9S/CP4buAtmfmftVbVwTJzI7Cx9/uIWA9szszV9VXVFSZRTBl1FLCD4iGeF2amc8VV6wPAHIoRxc3A14FzhzrYd6dKkiS1oY4cTpUkSep0hjhJkqQ2ZIiTJElqQ4Y4SZKkNmSIkyRJakOGOEmSpDZkiJPU0SJiWbNe3B0Rx0TE0grOe2lEPLvs80pqb84TJ6mtNSZ/7bUHxcTHOxrfN3XS44j4BnBJZl5c8nlPAD6bmceXeV5J7c0QJ6ljRMRdwGsz86oarr0/xfuZ52fm5grOfwfw8swsvdMnqT05nCqpo0XEXRFxWuPrJRFxSURcFBHrIuJXEXFERLwjIlZFxPKI+JN+P7tXRJwfEfdFxIqI+GBETBjiUs8Eft4/wDWu/baI+GVEbGica15EfKdx/asa70ek8Y7KiyLiwYh4OCJuiIh5/c5/NfC80j8gSW3LECep25wOfAnYG7iJ4oXePcABwPuBz/U79kJgO3AY8ATgT4DXDnHexwGDvVfyTIqAd0Tj2t8B3knxfsQe4K8bx70a2As4CNgHeAOwqd95bgUWjfi3lNTxDHGSus2PM/O/MnM7cAkwF/j7zNwGXAwcEhGzGl2w51C84H5DZq4CPgacNcR5ZwHrBtn+qcy8PzNXAD8GfpaZN2XmFuAyinAIsI0ivB2WmTsy88bMfKTfedY1riFJAEysuwBJarL7+329CXggM3f0+x5gBjAfmATcFxG9x/cAy4c47xpgzxFcb+D3Mxpff4miC3dxRMwCLgLe1QiXNM798FC/lKTuYydOkga3nOJJ1zmZOauxzMzMY4c4/pcUQ6ZjkpnbMvN9mXkMcCLwfOBV/Q45Grh5rOeX1HkMcZI0iMy8D/hv4J8iYmZE9ETEwoh42hA/ciXwxIiYOpbrRcTTI+JxjQcnHqEYXt3R75CnUdxPJ0mAIU6ShvMqYDJwC8Vw6b8D+w92YGbeD3wfOGOM19qvcf5HKB5i+CHFkCoR8SRgQ2ZeP8ZzS+pAzhMnSSWJiGMonmg9IUv8w7UxifD5mfntss4pqf0Z4iRJktqQw6mSJEltyBAnSZLUhgxxkiRJbcgQJ0mS1IYMcZIkSW3IECdJktSGDHGSJEltyBAnSZLUhv5/fo8RRqD5sjwAAAAASUVORK5CYII=\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": {
"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": {
"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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DnCox1uHUvfYq1g89VG49kiR1GkOcKjHW4dS99y7WDzxQbj2SJHUaQ5wqMd5O3IMPlluPJEmdxhCnSoz1njhDnCRJI2OIUyXG2onbc0+IcDhVkqSdMcSpEmO9J27iRJg7106cJEk7Y4hTJcY6nArFkKohTpKk4RniVImxDqdC8YSqw6mSJA3PEKdKjHU4FezESZI0EoY4VaKM4dTMcmuSJKmTGOJUifEOp27ZUrx+S5IkDc4Qp0qMdzgVHFKVJGk4hjhVYrzDqWCIkyRpOIY4VWK8w6lgiJMkaTiGOFWijOFUpxmRJGlohjhVYjzDqbvuCtOm2YmTJGk4hjhVYjzDqRHFkKohTpKkoRniVInxDKdCMaTqcKokSUMzxKkS4+nEgW9tkCRpZwxxqsR47okDh1MlSdoZQ5wqUUYn7tFHYdOm8mqSJKmTGOJUiTLuiQN46KFy6pEkqdMY4lSJMoZTwSFVSZKGYohTJcoYTgWfUJUkaSiGOFWirOFUO3GSJA3OEKdKjHc4dc89i0l/DXGSJA3OEKdKjHc4ddIkmDPH4VRJkoZiiFMlxjucCk74K0nScAxxqsR4h1OheELVTpwkSYMzxKkS4x1OhaIT5zxxkiQNzhCnSpQxnDpvXjGcmllOTZIkdRJDnCpRVidu0yZYvbqcmiRJ6iSGOFWijHvifPWWJElDM8SpEmV04ubNK9Y+oSpJ0hMZ4lSJsqYYAUOcJEmDMcSpEg6nSpJULUOcKlHGcOruu8PEiXbiJEkajCFOlShjOLWnp2+aEUmStCNDnCqxcWOxnjp1fOdxwl9JkgZniFMl1q0r7oebMmV857ETJ0nS4AxxqsTatTB9OkSM7zx77WWIkyRpMIY4VWLtWpgxY/zn2WsvWLGi70EJSZJUMMSpEmWFuHnzYOtWePTR8Z9LkqROYohTJdatK4ZTx8sJfyVJGpwhTpUoczgVfEJVkqSBDHGqRJnDqWAnTpKkgQxxqkTZnThDnCRJOzLEqRJl3RM3c2YxYbDDqZIk7cgQp0qsXw/Tpo3/PBFO+CtJ0mAMcarE5s3jf1tDLyf8lSTpiQxxqkTZIc7hVEmSdtR2IS4iZkfEpRGxLiLujYjX1V2TdpQJmzbB5MnlnM/hVEmSnmhi3QWMwWeBzcA8YBFweUTcnJlLa61Kv7N1a7EusxO3cmVx3ont+CdWkqQKtFUnLiKmA6cBH8jMtZl5NfAt4PX1Vqb+Nm0q1mV14vbaq+juPfxwOeeTJKkTtFWIAw4FtmXmnf223Qwc1f+giHhLRCyJiCUrV65saoEq7oeDcodTwSFVSZL6a7cQNwNYPWDbamDX/hsy89zMXJyZi+fOndu04lTo7cSVOZwKhjhJkvprtxC3Fpg5YNtMYE0NtWgIZXfiDHGSJD1Ru4W4O4GJEXFIv20LAR9qaCG9Ia6sTlzvcKrTjEiS1KetQlxmrgMuAT4UEdMj4jjgVOAr9Vam/sp+sGH69OI9rHbiJEnq01YhruFMYBqwAvga8DanF2ktZXfiwLc2SJI0UNvNupWZjwIvq7sODa3sThz41gZJkgZqx06cWlzZDzaAb22QJGkgQ5xK53CqJEnVM8SpdFUNp65a1XduSZK6nSFOpauiE9c7zciKFeWdU5KkdmaIU+mq6sSBQ6qSJPUyxKl0vZ24SZPKO2dviPMJVUmSCoY4lW7btmI9scQJbHqHU+3ESZJUMMSpdNu3F+sJE8o7pyFOkqQdGeJUut5OXE+Jf7qmTIHdd3c4VZKkXoY4la6KThw44a8kSf0Z4lS6Kjpx4IS/kiT1Z4hT6arqxPn+VEmS+hjiVLreEFd2J87hVEmS+hjiVLoqh1PXrIH168s9ryRJ7cgQp9JVOZwKDqlKkgSGOFWgqk6cc8VJktTHEKfSVd2JM8RJkmSIUwWqerDB4VRJkvoY4lS6qoZT586FCDtxkiSBIU4VqKoTN3EizJljiJMkCQxxqsC2beUHuF6+tUGSpIIhTqXbvr38hxp6zZvnPXGSJIEhThWwEydJUvUMcSpdlZ243hCXWc35JUlqF4Y4lW779mo7cRs3Fq/fkiSpmxniVLoqh1N9a4MkSQVDnEpX9XAqGOIkSTLEqXRVP9gAPqEqSZIhTqWreooRsBMnSZIhTqWrshO3xx5FQDTESZK6nSFOpavy6dSeHif8lSQJDHGqQJXDqVCEODtxkqRuZ4hT6aocTgXf2iBJEhjiVIGqO3F77eVwqiRJhjiVrupOXO89cdu3V3cNSZJanSFOpWtGJ27LFli1qrprSJLU6gxxKl2VT6eCE/5KkgSGOFWgGcOp4MMNkqTuZohT6ZoxnAqGOElSdzPEqXTNmGIEHE6VJHU3Q5xKV3UnbrfdYPJkO3GSpO5miFPpqu7ERTjhryRJhjiVruqnU8EJfyVJMsSpdFUPp0IR4n7722qvIUlSKzPEqXRVD6cC7LOPIU6S1N0McSpdMzpx8+fDI4/Axo3VXkeSpFZliFPpmtWJA3jggWqvI0lSqzLEqXTN6MT1hrjly6u9jiRJrWriSA6KiD8A/hg4CtgVWAMsBf4tM79XWXVqS814OnX+/GLtfXGSpG610xAXEf8PeA/wBeCbwGpgJrAQOD8iPp6Z/1xplWorzRxOtRMnSepWI+nEvRt4bmbeMWD7JRHxNeCHgCFOv9OM4dRZs2DqVDtxkqTuNZJ+yXRgqH8qHwR2Ka8cdYJmdOIiim6cnThJUrcayT+13wQui4gTI2JuREyOiDkRcSJwKfAf1ZaodtOMThwU98UZ4iRJ3WokIe6twDXA+cBDwIbG+nzgWuBtlVWnttSMThw44a8kqbvt9J64zNwMvBd4b0TMAmYAazPzsYHHRsRxmfmTsotUe2nG06nQ14nLLIZXJUnqJqP6pzYzH8vM+wcLcA3fHX9JaneZzevEbdgAq1dXfy1JklpN2f/U2g8Rmc25jtOMSJK6Wdkhrkn/fKvVNWN40wl/JUndzNduqXR24iRJql5LhLiImBIR50XEvRGxJiJuiogXDjjmxIi4IyLWR8QPI2JBXfVq55rRidt772JtJ06S1I1a5Z64icAy4DnAbsAHgG9ExAEAETEHuKSxfTawBPj6eItVNZrViZs2DWbPthMnSepOowpxEbFHRLw+It7T+H5+ROzbuz8zdx1LEZm5LjPPysx7MnN7Zn4b+A1wdOOQVwBLM/PizNwInAUsjIjDx3I9Va9ZU37Mn28nTpLUnUYc4iLiOcAvgT+k6IgBHAJ8ruyiImIecCiwtLHpKODm3v2ZuQ64u7FdLaZZnTjw1VuSpO41mk7cPwGvycwXAFsb264DjimzoIiYBFwInJ+ZdzQ2zwAGzga2Ghi08xcRb4mIJRGxZOXKlWWWpxFo5uS7hjhJUrcaTYg7IDO/3/i6t9eymRG89SEiroyIHGK5ut9xPcBXGud9e79TrAVmDjjtTGDNYNfLzHMzc3FmLp47d+7IfjuVqpnDqQ8+WLzqS5KkbjKaEHdbRJw8YNtJwK07+8HMPCEzY4jleICICOA8YB5wWmZu6XeKpcDC3m8iYjpwEH3DrWohzR5O3b4dHnqoedeUJKkVjCbEvQu4MCLOB6ZFxOeBLwPvLqmWzwFHAC/JzA0D9l0KPDkiTouIqcAHgVv6DbeqxTSrE7fffsV62bLmXE+SpFYx4hCXmddSdMOWAl+ieHr0mMy8YbxFNOZ8OwNYBDwYEWsbyx82rr0SOA04B1gFPBM4fbzXVTWa2Ynbf/9ifd99zbumJEmtYKf3s/WXmcuBT5RdRGbey07mmMvMKwCnFGkTduIkSarWsCEuIr7CCN6HmplvKK0itb1mduJ22w123dVOnCSp++xsOPVXFPOx3U0xpcfLgAnA/Y2fPRV4rLry1K6a1YmLKLpxhjhJUrcZthOXmWf3fh0R/wO8KDN/3G/b8fRN/CsBze3EQXFfnMOpkqRuM5qnU58FXDtg23XA75VXjjpFszpxYCdOktSdRhPibgI+GhHTABrrc4CfV1CX2lgz39gARSduxQrYuLF515QkqW6jCXF/DBwHrI6IhyjukTse8KEG7aDZw6m9T6jef39zrytJUp1GPMVIZt4DHBsR+wHzgQcy00EsDarZnTgo7os7+ODmXVeSpDqNphNHROwOPBd4HnBC43tpB3V14rwvTpLUTUYc4iLi9yimGnkr8FSKNyzc3dgu7aCZnbh99y3WhjhJUjcZzRsb/gk4MzMv6t0QEa8BPgU8o+S61Maa3YmbOhX23NNpRiRJ3WU0w6mHAt8YsO0/AO9C0hM0sxMHxX1xduIkSd1kNCHuLp740vlXUQyxSr/T7E4cFPfF2YmTJHWT0QynvhP4dkT8GXAvcABwCPDi8stSu6ujE/e97zV/jjpJkuoymilGromIg4AXUUwxchnwncx8tKri1J7q6MTtvz+sXQurV8OsWc2/viRJzTaaThyZuQq4oKJa1EGa3Q3rP82IIU6S1A1GM8XIkyLiqxFxW0Tc13+pskC1nzqGNPtP+CtJUjcYTSfuqxQPMbwLWF9NOeoEdT3YAHDvvc2/tiRJdRhNiDsKOC4zt1dVjDpHsztxe+0FU6bAPfc097qSJNVlNFOM/Ah4WlWFqHPU0Ynr6YEFC+A3v2n+tSVJqsNoOnH3AP8TEZcAD/bfkZkfLLMotb86pvl40pMMcZKk7jGaEDedYlqRScB+/bbX0HdRK6ujEwdw4IFw/fX1XFuSpGYbzTxxb9rZMRHx2sz82vhKUieoqxO3alUxV9xuuzX/+pIkNdNo7okbic+XfD61obo6cU96UrF2SFWS1A3KDnG+8EhAfZ04MMRJkrpD2SHO++NU2/tLDXGSpG5SdoiTahtO3X13mDnTECdJ6g47DXERYdDTqNXRiYsonlA1xEmSusFIAtryiPhERDx5BMf6HlXV1okD54qTJHWPkYS4twJPAm6IiJ9FxP+NiLmDHZiZIwl66gJ1dOKgL8TVGSQlSWqGnYa4zPyvzHwVsDfFFCKvApZFxLci4rSImFR1kWovdXfiNmyAhx6qrwZJkpphxPe7ZeZjmfn5zDweOAJYAnwSeKCq4tS+6uzEgUOqkqTON+qHFiJiCvAM4JnAPODWsotSe6uzE3fQQcX67rvrq0GSpGYYcYiLiOMj4lzgIeAjwLXAoZn53KqKU/uqqxN34IHQ0wN33VXP9SVJapadvjs1Is4CXg/MBi4GXpSZP6m4LrWxOjtxkyfDggWGOElS59tpiAOeBbwf+M/M3FhxPeoQdXXiAA45xBAnSep8I3k69QWZeZEBTiNV12u3eh18cBHinGZEktTJfBuDSld3eDrkEFi9Gh5+uN46JEmqkiFOlah7OBUcUpUkdTZDnErXCp04MMRJkjqbIU6VqLMT96QnwYQJhjhJUmczxKl0dXfiJk2CAw4wxEmSOpshTpWosxMHxZDqnXfWW4MkSVUyxKl0dXfioG+uuFaoRZKkKhjiVIlW6MStWwcPPlhvHZIkVcUQp9LVPdkvwOGHF+s77qi3DkmSqmKIU0c68shiffvt9dYhSVJVDHEqXSt04ubPh113hdtuq7cOSZKqYohT6VrhYYKIohtnJ06S1KkMcapE3Z04KEKcnThJUqcyxKl0rdCJAzjiiOLp1FWr6q5EkqTyGeJUiVbpxIFDqpKkzmSIU+lapRPXG+IcUpUkdSJDnCrRCp24BQtg2jRDnCSpMxniVLpW6cT19BST/jqcKknqRIY4VaIVOnHgE6qSpM5liFPpWmGy315HHgn33QePP153JZIklcsQp4721KcW61tvrbcOSZLKZohT6VqpE7doUbH++c/rrEKSpPIZ4lS6VnmwAWCffWD2bLj55rorkSSpXC0X4iLikIjYGBEXDNh+YkTcERHrI+KHEbGgrhq1c63SiYuAhQsNcZKkztNyIQ74LHBD/w0RMQe4BPgAMBtYAny9+aWpHS1cWNwTt21b3ZVIklSelgpxEXE68Bjw/QG7XgEszcyLM3MjcBawMCIOb26FGqlW6cRBEeI2bIC77qq7EkmSytMyIS4iZgIfAt41yO6jgN8NiGXmOuDuxna1kFa6H67XwoXF2iFVSVInaZkQB3wYOC8zlw2ybwawesC21cCug50oIt4SEUsiYsnKlStLLlMj0UqduCOPhIkTDXGSpM7SlBAXEVdGRA6xXB0Ri4CTgE8OcYq1wMwB22YCawY7ODPPzczFmbl47ty5pf0e2rneTlwrhbgpU+CIIwxxkqTOMrEZF8nME4bbHxHvBA4A7oviX/8ZwISIODIznw4sBd7Y7/jpwEGN7dJOLVoEV1xRdxWSJJWnVYZTz6UIZYsay78ClwMnN/ZfCjw5Ik6LiKnAB4FbMvOO5peq4bRiJw5g8WJ44AFYvrzuSiRJKkdLhLjMXJ+ZD/YuFMOnGzNzZWP/SuA04BxgFfBM4PTaCtaQWvHBBoBjjinW119fbx2SJJWlKcOpo5WZZw2y7QrAKUXaRKt14hYtKh5uuOEGePnL665GkqTxa4lOnDpHq3bipk6Fpz7VTpwkqXMY4lSJVuvEATzjGbBkCWzfXnclkiSNnyFOpWrVThwU98WtXu2bGyRJncEQp0q0aicOivviJElqd4Y4laqVO3FHHgnTp3tfnCSpMxjiVIlW7MRNmABHHw0//WndlUiSNH6GOJWqVSf77XX88XDTTbB2bd2VSJI0PoY4dZVnPxu2bYNrr627EkmSxscQp1K1eifu2GOhpwd+/OO6K5EkaXwMcSpVKz/YADBzJixcaIiTJLU/Q5wq0aqdOCiGVK+9FjZvrrsSSZLGzhCnUrV6Jw6KELdhA/zsZ3VXIknS2BniVIlW78SBQ6qSpPZmiFOp2qETN28eHHYY/PCHdVciSdLYGeJUiVbuxAE8//lw1VWwaVPdlUiSNDaGOJWq1acY6fX858P69b69QZLUvgxx6konnFC8hut736u7EkmSxsYQp1K1Sydu5kx41rMMcZKk9mWIU9d6/vNhyRJ45JG6K5EkafQMcSpVu3TioAhxmfCDH9RdiSRJo2eIU6naYYqRXsccA7vvDpdfXnclkiSNniFOlWiHTtzEiXDKKfDtb8O2bXVXI0nS6BjiVKp26sQBnHpqcU/cNdfUXYkkSaNjiFMl2qETB3DyyTBpEnzrW3VXIknS6BjiVKp268TNnAnPfS7813+1X+2SpO5miFMl2qUTB8WQ6l13wR131F2JJEkjZ4hTqdppipFeL31psf6P/6i3DkmSRsMQp663777w7GfD177mkKokqX0Y4lSqduzEAZx+Otx+O/ziF3VXIknSyBjiJOCVr4QJE+Cii+quRJKkkTHEqVTt2onbc0848cQixDmkKklqB4Y4laqdA9Dpp8Ovfw3XXlt3JZIk7ZwhTpVot04cFEOq06fDl75UdyWSJO2cIU6laudO3K67wqtfXQyprl1bdzWSJA3PEKdKtGMnDuBP/qQIcM4ZJ0lqdYY4lapdH2zodeyxcNhhcN55dVciSdLwDHFSPxFFN+7qq+HWW+uuRpKkoRniVKp278RBEeKmTYNPfaruSiRJGpohThpg9mx4/evhggvg4YfrrkaSpMEZ4lSqTujEAfzZn8HGjfCFL9RdiSRJgzPEqVTtPMVIf0cdBSedBJ/9LGzZUnc1kiQ9kSFOlWj3ThwU3bjly+Hii+uuRJKkJzLEqVSd0okDeNGL4Mgj4ZxzYPv2uquRJGlHhjhVohM6cT098Nd/DbfdBpdcUnc1kiTtyBCnUnXKgw29Xv1qOPRQ+MhHOqvLKElqf4Y4aRgTJsD73w833wyXXVZ3NZIk9THEqVSd1okDeN3r4KCD4AMfgG3b6q5GkqSCIU7aiYkTi+HUW26BCy+suxpJkgqGOJWqEztxUNwbt3hx8aDDhg11VyNJkiFOGpGeHvi7v4Nly+DTn667GkmSDHEqWad24gBOOAFe/GL46EfhoYfqrkaS1O0McSpVp0/D8fd/D+vXw3veU3clkqRuZ4hTJTqxEwdw2GHw7nfDv/87/OhHdVcjSepmhjiVqtM7cVDMG7dgAZx5JmzZUnc1kqRuZYhTJTq1Ewewyy7wz/8MS5fCJz5RdzWSpG5liFOpOvnBhv5OPbWYduTss4u3OUiS1GyGOGmMPvtZ2H13eOMbYfPmuquRJHUbQ5xK1S2dOIA5c+Dcc4tO3Ec+Unc1kqRuY4iTxuHUU+GP/qiYO+6aa+quRpLUTQxxKlU3deJ6ffrTxdOqp58OjzxSdzWSpG7RUiEuIk6PiNsjYl1E3B0Rz+6378SIuCMi1kfEDyNiQZ21Sr1mzYJvfKN4i8Mb3gDbt9ddkSSpG7RMiIuI5wMfB94E7Ar8PvDrxr45wCXAB4DZwBLg6/VUquF0YycO4Oij4R//Eb7zneIdq5IkVa1lQhxwNvChzLw2M7dn5vLMXN7Y9wpgaWZenJkbgbOAhRFxeF3FanDdMNnvUM48E171Knjf++C//7vuaiRJna4lQlxETAAWA3Mj4lcRcX9EfCYipjUOOQr43WxcmbkOuLuxXS2o2zpxUPzOX/oSPOUp8JrXwG231V2RJKmTtUSIA+YBk4BXAs8GFgFPA/66sX8GsHrAz6ymGHZ9goh4S0QsiYglK1eurKRgDa5bh1N7zZgB3/oWTJsGL3kJPPxw3RVJkjpVU0JcRFwZETnEcjWwoXHopzPzgcx8GPhH4JTG9rXAzAGnnQmsGex6mXluZi7OzMVz586t4leShrT//vCf/wnLl8PLXgbr19ddkSSpEzUlxGXmCZkZQyzHZ+Yq4H5gqDuqlgILe7+JiOnAQY3taiHd3onr9axnwQUXFHPHvfrVsGVL3RVJkjpNqwynAvwb8I6I2DMidgfeCXy7se9S4MkRcVpETAU+CNySmXfUU6q0c698JXzuc3D55fCmNzn1iCSpXBPrLqCfDwNzgDuBjcA3gHMAMnNlRJwGfAa4ALgOOL2mOjUMO3E7OuOMYgLg978fpk8vQl1PK/2nkySpbbVMiMvMLcCZjWWw/VcATimitvPe98LatfCxj8HmzfDFL8KECXVXJUlqdy0T4tQZ7MQ9UQSccw5Mngxnn10EufPPh4n+v0+SNA7+MyI1QQScdVYR5N7/fti4sXjwYdq0nf6oJEmD8u4clcpO3PDe9z745CfhkkvgpJOcR06SNHaGOJWqm1+7NVLvfCdcfDHceCMceyzcfXfdFUmS2pEhTpWwEze8V74Svv99ePRReOYz4Yor6q5IktRuDHEqlcOpI3fccfDTn8K8eXDyyfC3f2snU5I0coY4qUaHHALXXQevelUxFckrXgGrVtVdlSSpHRjiVCo7caM3YwZ87WvFAw+XXQYLF8KVV9ZdlSSp1RnipBYQUTzwcM01MHUqPO958J73wKZNdVcmSWpVhjiVyk7c+BxzDNx0E/zpn8Lf/R08/enwk5/UXZUkqRUZ4qQWM306fP7zcPnlxeu6jj8e3vpWeOyxuiuTJLUSQ5xKZSeuPKecAkuXwp//OXzhC3DEEfDlL8P27XVXJklqBYY4lcopMso1Ywb8wz/A9dfD/vvDm94EixfDD39Yd2WSpLoZ4lQJO3HlOvroYk65r34VHnmkePDhpS+Fn/+87sokSXUxxKlUDqdWp6cHXvtauOMO+OhH4Uc/gqc9DV7+csOcJHUjQ5zUZqZNKyYGvuceOPvsYmi1N8xdc41D2pLULQxxKpWduOaZNQs++MEizJ11VjFB8HHHwbOeBRddBFu21FufJKlahjipzc2aBX/zN3D//fDZzxav7Xrta+Ggg4r3sT74YN0VSpKqYIhTqezE1Wf6dDjzzOKeucsug4MPLoZd990XXvYy+Pa3YevWuquUJJXFECd1mJ4eePGL4Qc/gF/+Et71Lrj2WnjJS2DBAnj3u+FnP/PeOUlqd4Y4lcpOXGs59FD4+Mdh2TK49NLiNV7/9E/FlCWHHVbcU3f77XVXKUkaC0Oc1AUmTSqGVC+7DB56qHgDxH77wUc+AkceWSx/9VfF063bttVdrSRpJAxxKpWduNY3eza8+c3w/e/D8uXwqU/B/PnFmyGOOw723rt4M8Qll/i+VklqZYY4lcr7rNrL3nvDO94BV1wBK1fC174GJ51UDL2edhrssUcxZckHPgBXXQWbN9ddsSSplyFOlbAT135mzYLTTy9e7bVyZTHv3PveV/xv+dGPwgknwO67wymnwCc+UQy9btpUc9GS1MUm1l2AOovDqZ1h0iR4znOK5cMfLoZVr7oKvve9omv33e8Wx02ZAs94Bhx/fLEce2wR9CRJ1TPESdqpWbPg1FOLBYqHI665Bq6+Gn7yE/j7vy8mFobiqddnPAMWLy6WRYuKOewkSeUyxKlUduK6w7x5xbtaX/7y4vv16+GGG4pQd8MNxftcL7ig2NfTUzz92j/UPeUpMHNmbeVLUkcwxEkat1126Rt+7fXb38KNN8KSJcVy+eXw5S/37V+woAhzT31qsX7KU4p57SZNanr5ktSWDHEqlZ049Zo/v1he8pLi+8zi/a433wy33lost9wC//3ffa8DmzwZjjiiWA47DA4/vFgfeqhDspI0kCFOUlNEFBMM77df8VqwXps2Fe977Q12t94K110HX//6jlPW7LdfX6jrXR9ySPFu2AkTmv/7SFLdDHEqlZ04jdaUKbBwYbH0t2ED/OpXRcD75S/71l/+Mqxd23fcpEnF0OxBB8GBB/Ytvd/vumtTfx1JahpDnKSWNG1a371y/WXCAw8Uoe7uu+HXv+5bX389rFq14/Fz5vQFuwULio7e/vv3LbNm+R8dktqTIU6lshOnqkX03W/3vOc9cf+qVfCb3/QFu97l+uuLV4kNfOvEjBlPDHa93++3X3GdXXZpzu8mSaNhiFOpDHGq2+67F8vTn/7Efdu3w4oVcN99fcuyZX1f33RTsX+gWbP6guP8+cXryvp/37ttypTKfz1J+h1DnKSu0dMDe+1VLMccM/gxGzcWT9H2BrsHHiimS+ldrryy2LZlyxN/do89dgx5e+0Fe+5ZzKvXfz1njg9jSBo/Q5xKZSdO7W7qVDj44GIZyvbt8OijO4a73qU39C1dWnT1Bgt7EUWQGyzgDbbN4VxJgzHESdIo9fQUIWzOnGKy4qFkFu+dfeihItCtWNH3df/1kiXFes2awc8zbVpxrT322HE93LZddvE/pqROZ4hTqezESX0i+u7RO/zwnR+/YUNf2Osf9B5+GB55pG99773F1wOfxO1v6tShw94ee/TVNXCZNq28319StQxxktQipk0rpkFZsGBkx2/dWgS53oA3MOz13/bznxfrRx/dcRLlgaZMeWKwmz176NBnAJTqY4hTqezESc0zcSLMnVssI7VtG6xeXYS/Rx8t1sMty5fDL35RfP3448OfuzcAzpoFu+02tsV350ojZ4iTpC4yYULRWZs9u3irxWhs3doXAHuXwYLg6tXF8thjxdBv7/cbNuz8GtOmDR7uZs4cOvjtuuuOy4wZPv2r7mCIU6nsxEmda+LEvnvqxmLz5qKb1xvqRrosW9b39fr1I7vW9OlPDHf9l5kzR75/yhT/TlNrMsRJkppi8uS+By3GasuWHYPg448XT/UOtgzcd//9O34/ks4gFOF1qJA3Y0YRGEe79ulhlcEQp1LZiZNUpUmTxtcN7G/r1qED4GAhsP/S2yFctw7Wri3WmzaN/NoRRZDrH+7GGgh7172LQ8ndwxCnUhniJLWLiRP7nqwtw9atO4a6/l+PdL1mDTz44I7bRzqE3Gvy5CIgjnbp7RCOZHGIuTUY4iRJKsHEiX0PW5Rp+/YiyI0kIK5fP/zy2GODbx+t3k7iaJdp0564TJ268+8NjIMzxKlUduIkqVw9PX3DrfPmlX/+zOL+wJ0FwJ0t69YV696paQbu27Zt7DVOmTJ80BtJGBztMT095X3GVTHESZLUxfp31aq0eXMRFjduLNb9l4HbRnPMmjXFm00GO2Y8wXHy5B1D3dSpfcuUKTt+P9wymmOnTh1djYY4lcpOnCRpMJMnF0vZw83D2bq1nMC4aVOxrf+yalXf1wP3j+Yhl/EwxEmSpI7Uf3qYZtq+veg8Dgx+g4XBgcs73zny6xjiVCo7cZKkbtfTM7bhURhdiGuD2/YkSZI0kCFOpbITJ0lScxjiVCpDnCRJzWGIkyRJakOGOJXKTpwkSc1hiJMkSWpDhjiVyk6cJEnN0TIhLiIOiIjvRMSqiHgwIj4TERP77T8xIu6IiPUR8cOIWFBnvZIkSXVqmRAH/AuwAtgbWAQ8BzgTICLmAJcAHwBmA0uAr9dSpYZlJ06SpOZopRD3JOAbmbkxMx8E/hs4qrHvFcDSzLw4MzcCZwELI+LwekqVJEmqVyu9duufgdMj4kpgd+CFFJ03KMLczb0HZua6iLi7sf2O4U56111w8smV1KtBrFhRrO3ESZJUrVYKcVcBfwo8DkwAzgf+s7FvBrBywPGrgUFfaRsRbwHeAjB58lN5/PEKqtWgpk6FF7wADjmk7kokSepsTQlxje7ac4bY/RPg94H/AT4PHEsR2r4EfBx4D7AWmDng52YCawY7YWaeC5wLsHjx4vzpT8dXvyRJUqtpyj1xmXlCZsYQy/EUDyvsB3wmMzdl5iPAvwGnNE6xFFjYe76ImA4c1NguSZLUdVriwYbMfBj4DfC2iJgYEbOAN9J3H9ylwJMj4rSImAp8ELglM4e9H06SJKlTtUSIa3gF8AKKe99+BWwF/h9AZq4ETgPOAVYBzwROr6dMSZKk+rXMgw2Z+XPghGH2XwE4pYgkSRKt1YmTJEnSCBniJEmS2pAhTpIkqQ0Z4iRJktqQIU6SJKkNGeIkSZLakCFOkiSpDRniJEmS2pAhTpIkqQ0Z4iRJktqQIU6SJKkNGeIkSZLakCFOkiSpDRniJEmS2lBkZt01VCoi1gC/rLuOLjMHeLjuIrqMn3nz+Zk3n5958/mZN99hmbnrSA6cWHUlLeCXmbm47iK6SUQs8TNvLj/z5vMzbz4/8+bzM2++iFgy0mMdTpUkSWpDhjhJkqQ21A0h7ty6C+hCfubN52fefH7mzedn3nx+5s034s+84x9skCRJ6kTd0ImTJEnqOIY4SZKkNtSxIS4iZkfEpRGxLiLujYjX1V1Tp4uIt0fEkojYFBFfrruebhARUyLivMaf8TURcVNEvLDuujpZRFwQEQ9ExOMRcWdEvLnumrpFRBwSERsj4oK6a+kGEXFl4/Ne21icc7UJIuL0iLi9kV/ujohnD3VsJ88T91lgMzAPWARcHhE3Z+bSWqvqbL8FPgKcDEyruZZuMRFYBjwHuA84BfhGRDwlM++ps7AO9jHgTzJzU0QcDlwZETdl5o11F9YFPgvcUHcRXebtmfnFuovoFhHxfODjwGuA64G9hzu+IztxETEdOA34QGauzcyrgW8Br6+3ss6WmZdk5n8Cj9RdS7fIzHWZeVZm3pOZ2zPz28BvgKPrrq1TZebSzNzU+21jOajGkrpCRJwOPAZ8v+ZSpCqdDXwoM69t/J2+PDOXD3VwR4Y44FBgW2be2W/bzcBRNdUjNUVEzKP482/HuUIR8S8RsR64A3gA+E7NJXW0iJgJfAh4V921dKGPRcTDEfGTiDih7mI6WURMABYDcyPiVxFxf0R8JiKGHNnq1BA3A1g9YNtqYETvIpPaUURMAi4Ezs/MO+qup5Nl5pkUf588G7gE2DT8T2icPgycl5nL6i6ky/wlcCCwD8XcZZdFhF3n6swDJgGvpPi7ZRHwNOCvh/qBTg1xa4GZA7bNBNbUUItUuYjoAb5CcR/o22supytk5rbGrRr7Am+ru55OFRGLgJOAT9ZcStfJzOsyc01mbsrM84GfUNx3q2psaKw/nZkPZObDwD8yzGfeqQ823AlMjIhDMvOuxraFOMSkDhQRAZxH8V9xp2TmlppL6jYT8Z64Kp0AHADcV/xRZwYwISKOzMyn11hXN0og6i6iU2Xmqoi4n+JzHpGO7MRl5jqKIY4PRcT0iDgOOJWiU6GKRMTEiJgKTKD4S3ZqRHTqfyi0ks8BRwAvycwNOztYYxcRezYe/58RERMi4mTgtcAP6q6tg51LEZIXNZZ/BS6neApeFYmIWRFxcu/f4xHxh8DvA/9Td20d7t+AdzT+rtkdeCfw7aEO7uR/YM8EvgSsoHha8m1OL1K5vwb+pt/3f0TxpM1ZtVTTBSJiAXAGxT1ZDzY6FQBnZOaFtRXWuZJi6PRfKf4j+F7gnZn5X7VW1cEycz2wvvf7iFgLbMzMlfVV1RUmUUwZdTiwjeIhnpdlpnPFVevDwByKEcWNwDeAc4Y62HenSpIktaGOHE6VJEnqdIY4SZKkNmSIkyRJakOGOEmSpDZkiJMkSWpDhjhJkqQ2ZIiT1NEiYmmzXtwdEUdGxJIKzntJRLyg7PNKam/OEyeprTUmf+21C8XEx9sa3zd10uOI+CZwcWZeVPJ5jwE+l5lHl3leSe3NECepY0TEPcCbM/OKGq69N8X7medn5sYKzn8X8NrMLL3TJ6k9OZwqqaNFxD0RcVLj67Mi4uKIuCAi1kTErRFxaES8NyJWRMSyiPiDfj+7W0ScFxEPRMTyiPhIREwY4lLPB37WP8A1rv3uiLglItY1zjUvIr7buP4Vjfcj0nhH5QUR8UhEPBYRN0TEvH7nvxJ4UekfkKS2ZYiT1G1eAnwF2B24ieKF3j3APsCHgM/3O/Z8YCtwMPA04A+ANw9x3qcAg71X8jSKgHdo49rfBd5H8X7EHuDPGse9EdgN2A/YA3grsKHfeW4HFo74t5TU8QxxkrrNjzPzfzJzK3AxMBf428zcAlwEHBARsxpdsBdSvOB+XWauAD4JnD7EeWcBawbZ/unMfCgzlwM/Bq7LzJsycxNwKUU4BNhCEd4OzsxtmXljZj7e7zxrGteQJAAm1l2AJDXZQ/2+3gA8nJnb+n0PMAOYD0wCHoiI3uN7gGVDnHcVsOsIrjfw+xmNr79C0YW7KCJmARcA72+ESxrnfmyoX0pS97ETJ0mDW0bxpOuczJzVWGZm5lFDHH8LxZDpmGTmlsw8OzOPBI4FXgy8od8hRwA3j/X8kjqPIU6SBpGZDwD/C/xDRMyMiJ6IOCginjPEj3wPeHpETB3L9SLiuRHxlMaDE49TDK9u63fIcyjup5MkwBAnScN5AzAZuI1iuPQ/gL0HOzAzHwJ+AJw6xmvt1Tj/4xQPMVxFMaRKRDwDWJeZ14/x3JI6kPPESVJJIuJIiidaj8kS/3JtTCJ8XmZ+p6xzSmp/hjhJkqQ25HCqJElSGzLESZIktSFDnCRJUhsyxEmSJLUhQ5wkSVIbMsRJkiS1IUOcJElSGzLESZIktaH/H7kwaDl0GYbjAAAAAElFTkSuQmCC\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": {
"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"
},
{
"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"
},
{
"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"
},
{
"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"
},
{
"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"
},
{
"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"
},
{
"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"
},
{
"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"
},
{
"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"
},
{
"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 = 4.58 s\n",
"\n",
"Total time = 3197.56 s\n",
"\n",
"End time: 2022-10-30 03:18:49.398102\n"
]
}
],
"source": [
"sim.simulate()\n",
"sim.analyze()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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": 22,
"id": "ddb4904a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Duration: 0:53:21.623656\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": "3ce6eb39",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b23076f1",
"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": null,
"id": "a336588c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 24,
"id": "e600ae81",
"metadata": {},
"outputs": [],
"source": [
"# import csv\n",
"\n",
"# with open('LongTranVoltageDifference_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , v_diff_36 ))\n",
" "
]
},
{
"cell_type": "markdown",
"id": "8f3b15f1",
"metadata": {},
"source": [
"#### saving the data"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "bc5f9cde",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import csv\n",
"\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"with open('BoundarytoGround1000_20Fibers_v_Abeta0_stimulateALL_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 , 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('BoundarytoGround1000_20Fibers_imembrane_Abeta0_stimulateALL_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 , 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",
"# with open('boundary1_stimulateALL_Abeta0_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap00 , Abeta_ap01 , Abeta_ap02 , Abeta_ap03, Abeta_ap04 , Abeta_ap05 , Abeta_ap06 , Abeta_ap07 , Abeta_ap08 , Abeta_ap09 , Abeta_ap010 , Abeta_ap011 ))\n",
"\n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap10 , Abeta_ap11 , Abeta_ap12 , Abeta_ap13, Abeta_ap14 , Abeta_ap15 , Abeta_ap16 , Abeta_ap17 , Abeta_ap18 , Abeta_ap19 , Abeta_ap110 , Abeta_ap111 ))\n",
"\n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta2_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap20 , Abeta_ap21 , Abeta_ap22 , Abeta_ap23, Abeta_ap24 , Abeta_ap25 , Abeta_ap26 , Abeta_ap27 , Abeta_ap28 , Abeta_ap29 , Abeta_ap210 , Abeta_ap211 ))\n",
" \n",
"\n",
"# with open('stimulateonlyAbeta0_Abeta3_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap30 , Abeta_ap31 , Abeta_ap32 , Abeta_ap33, Abeta_ap34 , Abeta_ap35 , Abeta_ap36 , Abeta_ap37 , Abeta_ap38 , Abeta_ap39 , Abeta_ap310 , Abeta_ap311 ))\n",
"\n",
"\n",
"# with open('stimulateonlyAbeta0_Abeta4_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap40 , Abeta_ap41 , Abeta_ap42 , Abeta_ap43, Abeta_ap44 , Abeta_ap45 , Abeta_ap46 , Abeta_ap47 , Abeta_ap48 , Abeta_ap49 , Abeta_ap410 , Abeta_ap411 ))\n",
" \n",
"\n",
"# with open('stimulateonlyAbeta0_Abeta5_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap50 , Abeta_ap51 , Abeta_ap52 , Abeta_ap53, Abeta_ap54 , Abeta_ap55 , Abeta_ap56 , Abeta_ap57 , Abeta_ap58 , Abeta_ap59 , Abeta_ap510 , Abeta_ap511 ))\n",
" \n",
"\n",
"# with open('stimulateonlyAbeta0_Abeta6_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap60 , Abeta_ap61 , Abeta_ap62 , Abeta_ap63, Abeta_ap64 , Abeta_ap65 , Abeta_ap66 , Abeta_ap67 , Abeta_ap68 , Abeta_ap69 , Abeta_ap610 , Abeta_ap611 ))\n",
" \n",
" \n",
"\n",
"# with open('stimulateonlyAbeta0_Abeta7_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap70 , Abeta_ap71 , Abeta_ap72 , Abeta_ap73, Abeta_ap74 , Abeta_ap75 , Abeta_ap76 , Abeta_ap77 , Abeta_ap78 , Abeta_ap79 , Abeta_ap710 , Abeta_ap711 ))\n",
" \n",
"\n",
"# with open('stimulateonlyAbeta0_Abeta8_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap80 , Abeta_ap81 , Abeta_ap82 , Abeta_ap83, Abeta_ap84 , Abeta_ap85 , Abeta_ap86 , Abeta_ap87 , Abeta_ap88 , Abeta_ap89 , Abeta_ap810 , Abeta_ap811 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta9_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap90 , Abeta_ap91 , Abeta_ap92 , Abeta_ap93, Abeta_ap94 , Abeta_ap95 , Abeta_ap96 , Abeta_ap97 , Abeta_ap98 , Abeta_ap99 , Abeta_ap910 , Abeta_ap911 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta10_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap100 , Abeta_ap101 , Abeta_ap102 , Abeta_ap103, Abeta_ap104 , Abeta_ap105 , Abeta_ap106 , Abeta_ap107 , Abeta_ap108 , Abeta_ap109 , Abeta_ap1010 , Abeta_ap1011 ))\n",
" \n",
"\n",
"# with open('stimulateonlyAbeta0_Abeta11_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap110 , Abeta_ap111 , Abeta_ap112 , Abeta_ap113, Abeta_ap114 , Abeta_ap115 , Abeta_ap116 , Abeta_ap117 , Abeta_ap118 , Abeta_ap119 , Abeta_ap1110 , Abeta_ap1111 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta12_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap120 , Abeta_ap121 , Abeta_ap122 , Abeta_ap123, Abeta_ap124 , Abeta_ap125 , Abeta_ap126 , Abeta_ap127 , Abeta_ap128 , Abeta_ap129 , Abeta_ap1210 , Abeta_ap1211 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta13_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap130 , Abeta_ap131 , Abeta_ap132 , Abeta_ap133, Abeta_ap134 , Abeta_ap135 , Abeta_ap136 , Abeta_ap137 , Abeta_ap138 , Abeta_ap139 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta14_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap140 , Abeta_ap141 , Abeta_ap142 , Abeta_ap143, Abeta_ap144 , Abeta_ap145 , Abeta_ap146 , Abeta_ap147 , Abeta_ap148 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta15_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap150 , Abeta_ap151 , Abeta_ap152 , Abeta_ap153, Abeta_ap154 , Abeta_ap155 , Abeta_ap156 , Abeta_ap157 , Abeta_ap158 , Abeta_ap159 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta16_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap160 , Abeta_ap161 , Abeta_ap162 , Abeta_ap163, Abeta_ap164 , Abeta_ap165 , Abeta_ap166 , Abeta_ap167 , Abeta_ap168 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta17_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap170 , Abeta_ap171 , Abeta_ap172 , Abeta_ap173, Abeta_ap174 , Abeta_ap175 , Abeta_ap176 , Abeta_ap177 , Abeta_ap178 , Abeta_ap179 , Abeta_ap1710 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta18_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap180 , Abeta_ap181 , Abeta_ap182 , Abeta_ap183, Abeta_ap184 , Abeta_ap185 , Abeta_ap186 , Abeta_ap187 , Abeta_ap188 ))\n",
" \n",
" \n",
"# with open('stimulateonlyAbeta0_Abeta19_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap190 , Abeta_ap191 , Abeta_ap192 , Abeta_ap193, Abeta_ap194 , Abeta_ap195 , Abeta_ap196 , Abeta_ap197 , Abeta_ap198 , Abeta_ap199 , Abeta_ap1910 ))\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "890baeb5",
"metadata": {},
"outputs": [],
"source": [
"## saving the data\n",
"\n",
"\n",
"\n",
"\n",
"# ## writing\n",
"\n",
"\n",
"import csv\n",
"\n",
"# with open('v_Abeta0_stimulateonlyAbeta0_dist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap00 , Abeta_ap01 , Abeta_ap02 , Abeta_ap03, Abeta_ap04 , Abeta_ap05 , Abeta_ap06 , Abeta_ap07 , Abeta_ap08 , Abeta_ap09 , Abeta_ap010 , Abeta_ap011 ))\n",
"\n",
"\n",
"\n",
"# with open('imembrane_Abeta0_stimulateALL_dist0.1_.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",
"\n",
"\n",
"\n",
"\n",
"\n",
" \n",
"# with open('stimulateAbeta4_v_Abeta4_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap40 , Abeta_ap41 , Abeta_ap42 , Abeta_ap43, Abeta_ap44 , Abeta_ap45 , Abeta_ap46 , Abeta_ap47 , Abeta_ap48 , Abeta_ap49 , Abeta_ap410 , Abeta_ap411 ))\n",
"\n",
" \n",
"# with open('stimulateAbeta5_v_Abeta5_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap50 , Abeta_ap51 , Abeta_ap52 , Abeta_ap53, Abeta_ap54 , Abeta_ap55 , Abeta_ap56 , Abeta_ap57 , Abeta_ap58 , Abeta_ap59 , Abeta_ap510 , Abeta_ap511 ))\n",
" \n",
"\n",
"# with open('stimulateAbeta7_v_Abeta7_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap70 , Abeta_ap71 , Abeta_ap72 , Abeta_ap73, Abeta_ap74 , Abeta_ap75 , Abeta_ap76 , Abeta_ap77 , Abeta_ap78 , Abeta_ap79 , Abeta_ap710 , Abeta_ap711 ))\n",
"\n",
"\n",
"# with open('stimulateAbeta9_v_Abeta9_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap90 , Abeta_ap91 , Abeta_ap92 , Abeta_ap93, Abeta_ap94 , Abeta_ap95 , Abeta_ap96 , Abeta_ap97 , Abeta_ap98 , Abeta_ap99 , Abeta_ap910 , Abeta_ap911 ))\n",
" \n",
"\n",
"# with open('stimulateAbeta12_v_Abeta12_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap120 , Abeta_ap121 , Abeta_ap122 , Abeta_ap123, Abeta_ap124 , Abeta_ap125 , Abeta_ap126 , Abeta_ap127 , Abeta_ap128 , Abeta_ap129 , Abeta_ap1210 , Abeta_ap1211 ))\n",
" \n",
"\n",
"# with open('stimulateAbeta15_v_Abeta15_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap150 , Abeta_ap151 , Abeta_ap152 , Abeta_ap153, Abeta_ap154 , Abeta_ap155 , Abeta_ap156 , Abeta_ap157 , Abeta_ap158 , Abeta_ap159 , Abeta_ap1510 , Abeta_ap1511 ))\n",
" \n",
" \n",
"\n",
"# with open('stimulateAbeta17_v_Abeta17_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap170 , Abeta_ap171 , Abeta_ap172 , Abeta_ap173, Abeta_ap174 , Abeta_ap175 , Abeta_ap176 , Abeta_ap177 , Abeta_ap178 , Abeta_ap179 , Abeta_ap1710 , Abeta_ap1711 ))\n",
" \n",
"\n",
"# with open('stimulateAbeta18_v_Abeta18_boundary17.1_dist0.2_dt0.005_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta_ap180 , Abeta_ap181 , Abeta_ap182 , Abeta_ap183, Abeta_ap184 , Abeta_ap185 , Abeta_ap186 , Abeta_ap187 , Abeta_ap188 , Abeta_ap189 , Abeta_ap1810 , Abeta_ap1811 ))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16d8bddc",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 27,
"id": "9766ae7e",
"metadata": {},
"outputs": [],
"source": [
"# netParams.cellParams.keys()\n",
"# netParams.cellParams['']['']"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "e19fa77c",
"metadata": {},
"outputs": [],
"source": [
"# pyplot.plot(t, ap1 )\n",
"# #pyplot.xlim((0, 10))\n",
"# pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "94e4f559",
"metadata": {},
"outputs": [],
"source": [
"#(1211 * 1e-6 ) / (0.1225 * 1e-8)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aca60f88",
"metadata": {},
"outputs": [],
"source": []
}
],
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"file_extension": ".py",
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