{
"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 02:24:54.405095\n",
"\n",
"Creating network of 40 cell populations on 1 hosts...\n",
" Number of cells on node 0: 40 \n",
" Done; cell creation time = 4.84 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": [
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\n",
"3.0\n",
"2705.075594165407\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] = 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] + 2.994438 #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": [
"22.0\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": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Running simulation for 6.0 ms...\n",
" Done; run time = 3170.84 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 8.41 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: 3170.84 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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1GkOcSjHSTpwhTpKkgRniVIqRzBMHeTgVvLlBkqT+GOJUCodTJUkqlyFOpRjpcOr06TBunCFOkqT+GOJUipF24iJyN87hVEmS+maIUylG2omDHOLsxEmS1DdDnEox0k4c5Jsb7MRJktQ3Q5xKYSdOkqRyGeJUiiI6cT56S5Kk/hniVIoiOnEHHJCP8/vfF1OTJEmdxBCnUox0sl9wrjhJkgZiiFMpihpOBUOcJEl9McSpFEUNp4J3qEqS1BdDnEphJ06SpHIZ4lSKIjpx++wD48cb4iRJ6oshTqUoohPno7ckSeqfIU6l2LYtr8eOHdlxnPBXkqS+GeJUio0b83rixJEdx0dvSZLUN0OcSrFhA0yePLJr4sBOnCRJ/THEqRQbN+YQN1KzZ8PKlbtvlJAkSZkhTqUoKsT56C1JkvpmiFMpNmyAKVNGfhznipMkqW+GOJWiyOFUMMRJktSbIU6lqN/YMFI+ekuSpL4Z4lSKjRsdTpUkqUyGOJWiqOHUadNgwgRDnCRJvRniVIqiQpyP3pIkqW+GOJVi69bcQSuCE/5KkrQ3Q5xKsXUrjBtXzLF89JYkSXtruxAXETMi4tqI2BgRSyPi1VXXpL1t21ZciLMTJ0nS3sZUXcAw/BuwDZgNnAhcFxF3pJQWV1qV/mDXLtixA8aPL+Z4PR+9NXp0MceUJKndtVUnLiImA2cD700pbUgp3Qx8E/jzaitTT9u353WRw6m7dsGqVcUcT5KkTtBWIQ44AtiZUrq3x7Y7gGN77hQR50bEoohYtHLlyqYWqHw9HBQ7nAoOqUqS1FO7hbgpwNpe29YCU3tuSCldllJamFJaOGvWrKYVp2zbtrwushMHhjhJknpqtxC3AZjWa9s0YH0Ftagf9RBX5DVx4B2qkiT11G4h7l5gTEQc3mPbAsCbGlpI0Z04h1MlSdpbW4W4lNJG4Brg4oiYHBHPBF4CXFltZeqp6Gvipk6FiRMNcZIk9dRWIa7mAmAisAL4L+B8pxdpLUUPp/roLUmS9tZ288SllFYDL626DvWv6OFUcMJfSZJ6a8dOnFpc0cOp4KO3JEnqzRCnwtmJkySpfIY4Fa7oa+Igh7hVq/KjtyRJkiFOJSijE1d/9JYP4JAkKTPEqXBlXBPnXHGSJO3JEKfCldWJA0OcJEl1hjgVrqwbG8A7VCVJqjPEqXD1mw9Gjy7umA6nSpK0J0OcCrdrV14XGeKmTIFJk+zESZJUZ4hT4eqduFEF/umKcMJfSZJ6MsSpcGV04sAQJ0lST4Y4Fa4e4orsxIEhTpKkngxxKlwZw6kABx5oiJMkqc4Qp8KVOZy6evXuyYQlSepmhjgVrszhVIAVK4o9riRJ7cgQp8KVNZxaD3EOqUqSZIhTCcocTgVYvrzY40qS1I4McSqcnThJkspniFPhyrombv/989oQJ0mSIU4lKGs4ddw42G8/Q5wkSWCIUwnqw6kRxR/bueIkScoMcSrcrl15KLWMEOdTGyRJygxxKlw9xJXBECdJUmaIU+F27iw/xKVUzvElSWoXhjgVbteu4m9qqDvgANi8GdatK+f4kiS1C0OcClf2cCo4pCpJkiFOhSt7OBUMcZIkGeJUuLKHU8EQJ0mSIU6FsxMnSVL5DHEqXJmduBkzYOxYQ5wkSYY4Fa7MGxsinCtOkiQwxKkEZQ6ngiFOkiQwxKkEZQ6nQg5xy5eXd3xJktqBIU6FK3M4FezESZIEhjiVoBnDqStX5vNIktStDHEqXDOGU3ftykFOkqRuZYhT4ZoxnAoOqUqSupshToUrezj1wAPz2hAnSepmhjgVrhnDqWCIkyR1N0OcCld2J2727Lw2xEmSupkhToUruxM3aRJMm+ZccZKk7maIU+HKvrEBnCtOkiRDnApX9nAq+NQGSZIMcSpc2cOpAHPmGOIkSd3NEKfCNWM4dc4cePRRSKnc80iS1KoMcSpcM4ZT58yBTZtg3bpyzyNJUqsyxKlwzRhOPeigvF62rNzzSJLUqgxxKlyzOnGQh1QlSepGhjgVrlnXxIEhTpLUvQxxKlyz7k4Fh1MlSd3LEKfCNWM4ddIkmD7dTpwkqXuNaWSniHge8HrgWGAqsB5YDHw+pfSD0qpTW2pGJw52TzMiSVI3GjTERcSFwN8BnwX+G1gLTAMWAFdExEdSSp8otUq1lWZcEwf5DlWHUyVJ3aqRTtzfAs9OKS3ptf2aiPgv4EbAEKc/aMZwKuRO3JLefyolSeoSjfxVOxnob9DqMWBSceWoEzRzOHX58nw+SZK6TSMh7r+B/4mIMyNiVkSMi4iZEXEmcC3wtXJLVLtp5nDqjh2walX555IkqdU08lftm4H/Ba4AHgc219ZXAD8Dzi+tOrWlZg6ngtfFSZK606B/1aaUtqWU/j6lNBeYAcwD9kspza1t31bfNyKeWWKtahMpNTfEeYeqJKkbNTTFSF1K6QngiQF2+Q75zlV1sZSac57681MNcZKkblR0vyQKPp7aVDThT8Ls2fk8DqdKkrpR0SFuWD2YiBgfEZdHxNKIWB8Rt0fEC3vtc2ZELImITRFxY0TMK6ZkFa1ZnbixY2H//e3ESZK6U6s8dmsM8DBwOrAP8F7gqoiYDxARM4FrattnAIuAr1ZSqRrSjE4c5CFVQ5wkqRsN6Zq4sqSUNgIX9dj0rYh4ADgJeBB4GbA4pXQ1QERcBKyKiKP6mIRYFWtWJw7yzQ2PPNK880mS1Cpa8pq4iJgNHEF+PivkZ7beUX+/Fvrur21XC2pWJ87np0qSutWQQlxE7BcRfx4Rf1d7PSci5tbfTylNHWlBETEW+BJwRY8u2xTyM1t7Wgv0eb6IODciFkXEopUrV460JA1RMztxBx0EK1bA9u3NO6ckSa2g4RAXEacDvwFeQ742DeBw4NIGvvdHEZH6WW7usd8o4EpgG/CWHofYwN5Tl0wD1vd1vpTSZSmlhSmlhbNmzWr0R1SBmtmJA7txkqTuM5RO3L8Ar0wpvQDYUdv2c+Dkwb4xpXRGSin6WU4FiIgALgdmA2enlHr2VhYDC+ovImIycCi7h1vVQlJqXog7+OC89ro4SVK3GUqIm59S+mHt6/qA2TaKuzniUuBo4E9SSpt7vXctcFxEnB0RE4D3AXd6U0NrauZwaj3EPfRQ884pSVIrGEqIuzsint9r23OBX4+0iNqcb+cBJwKPRcSG2vIagJTSSuBs4BJgDXAKcM5Iz6vyNLsT9/DDzTmfJEmtYihdtLeTp/64DpgYEZ8B/gR4yUiLSCktZZA7W1NK1wNHjfRcKl8zO3FTp8I++xjiJEndp+FOXErpZ+Tr0hYD/wE8AJycUrq1pNrUxprViYPcjTPESZK6zZCuZ0spLQM+WlIt6hDN7MSBIU6S1J0GDHERcSUNPA81pfS6wipSR2hmJ+5JT4JFi5p3PkmSWsFgw6m/JT8Z4X7y5LovBUYDj9S+9yXAE+WVp3ZURSdu5UrY3PueZkmSOtiAnbiU0gfqX0fE94AXpZRu6rHtVHZP/Cv9QbOviYM8V9zhhzfvvJIkVWkoU4w8HfhZr20/B/6ouHLUCaroxIHXxUmSustQQtztwIcjYiJAbX0J8KsS6lKbq6ITZ4iTJHWToYS41wPPBNZGxOPka+ROBbypQXto5mO3AObOzWtDnCSpmzQ8xUhK6UHgGRFxMDAHWJ5S8mFH2kuzh1MnTIBZswxxkqTuMpROHBGxL/Bs4DnAGbXX0l6a2YkD54qTJHWfhkNcRPwReaqRNwMnkJ91en9tu/QHze7EQZ4rzhAnSeomQ3liw78AF6SUvlLfEBGvBP4VeFrBdanNVdGJu+GG5p5TkqQqDWU49Qjgql7bvgYcVlw56gRVdOIOPhjWrcuLJEndYCgh7j7gnF7bXkEeYpX2UEUnDhxSlSR1j6EMp74N+FZE/DWwFJgPHA68uPiy1M6q6sRBDnHHHtv880uS1GxDmWLkfyPiUOBF5ClG/gf4dkppdVnFqX1V1Yl7yElvJEldYiidOFJKa4AvllSLOkQVnbg5c2DMGFi6tPnnliSpCg2HuIg4hPyYrROBKT3fSyk9qdiy1O6a3YkbMyZ34x58sLnnlSSpKkPpxH2ZfBPD24FN5ZSjTtDsx27VHXIIPPBA888rSVIVhhLijgWemVLaVVYx6gxVDKcCzJ8P3/lONeeWJKnZhjLFyE+Ap5RViDpLVZ245cth8+bmn1uSpGYbSifuQeB7EXEN8FjPN1JK7yuyKLW3KjtxkO9QPfLIamqQJKlZhhLiJpOnFRkLHNxje0V/ZauVVdWJg3xdnCFOktTphjJP3BsG2yciXpVS+q+RlaR2V3UnzjtUJUndYCjXxDXiMwUfT22qik7cgQfCuHHeoSpJ6g5Fh7gK/upWq6mqEzdqFMybZydOktQdig5xXh8noJpOHDhXnCSpexQd4qTKOnGQr4szxEmSusGgIS4iDHoasio7catWwYYN1ZxfkqRmaSSgLYuIj0bEcQ3s+9BIC1L7q+qxW7B7mhGvi5MkdbpGQtybgUOAWyPilxHxNxExq68dU0qNBD11uKqHUwF+97vqapAkqRkGDXEppW+klF4BHEieQuQVwMMR8c2IODsixpZdpNpPVZ24ww7L6/vvr+b8kiQ1S8PXu6WUnkgpfSaldCpwNLAI+DiwvKzi1J6q7MTNmAHTp8Nvf1tdDZIkNcOQb1qIiPHA04BTgNnAr4suSu2vqk5cBBx+uCFOktT5Gg5xEXFqRFwGPA58CPgZcERK6dllFaf2VGUnDvKQqiFOktTpGpli5KKIuB/4n9qmF6WUjkgpfTCltLTc8tSuqurEQQ5xDz4I27ZVV4MkSWUb08A+TwfeDXw9pbSl5HrUAVqhE7drVw5yRxxRbS2SJJVl0BCXUnpBMwpRZ6m6Ewd5SNUQJ0nqVD6NQYWrcrJf2DPESZLUqQxx6jizZsG0aXDffVVXIklSeQxxKlzVnbgI71CVJHU+Q5wKV/WNDWCIkyR1PkOcSlFlJw52TzOyfXu1dUiSVBZDnArXKp24HTtykJMkqRMZ4lSKqjtxRx2V17/5TbV1SJJUFkOcCtcKnbh6iLvnnmrrkCSpLIY4laLqTty++8Ls2YY4SVLnMsSpcK3QiQM4+mhYsqTqKiRJKochTqWouhMHeUj1nntaJ1RKklQkQ5wKV/Vkv3VHHw1PPAGPP151JZIkFc8Qp4519NF57ZCqJKkTGeJUuFbpxHmHqiSpkxniVLhWuQZt7lyYPNlOnCSpMxniVIpW6MRF7L65QZKkTmOIU+FapRMH+bo4Q5wkqRMZ4lSKVujEARxzDDzyCKxdW3UlkiQVyxCnwrVSJ+744/P6rruqrUOSpKIZ4lSKVunEnXBCXv/619XWIUlS0QxxKlwrdeIOPhj22QfuvLPqSiRJKpYhTqVolU5cRB5SNcRJkjpNy4W4iDg8IrZExBd7bT8zIpZExKaIuDEi5lVVowbWKpP91p1wQh5ObaUOoSRJI9VyIQ74N+DWnhsiYiZwDfBeYAawCPhq80tTOzrhBFi3Dh56qOpKJEkqTkuFuIg4B3gC+GGvt14GLE4pXZ1S2gJcBCyIiKOaW6Ea1WqdOHBIVZLUWVomxEXENOBi4O19vH0scEf9RUppI3B/bbtaSCsOWR53XF4b4iRJnaRlQhzwQeDylNLDfbw3Beg9XetaYGpfB4qIcyNiUUQsWrlyZcFlqhGt1ImbOhUOOcQQJ0nqLE0JcRHxo4hI/Sw3R8SJwHOBj/dziA3AtF7bpgHr+9o5pXRZSmlhSmnhrFmzCvs5NLhW7MRBHlK9447B95MkqV2MacZJUkpnDPR+RLwNmA88FLmFMwUYHRHHpJSeCiwG/qLH/pOBQ2vb1YJaqRMH8NSnwje/CevX586cJEntrlWGUy8jh7ITa8ungeuA59fevxY4LiLOjogJwPuAO1NKS5pfqgbSqp24hQtzbbffXnUlkiQVoyVCXEppU0rpsfpCHj7dklJaWXt/JXA2cAmwBjgFOKeygjWoVuvEnXRSXt92W7V1SJJUlKYMpw5VSumiPrZdDzilSIurd+JaLcTNnp0fwbVoUdWVSJJUjJboxEnNcNJJhjhJUucwxKlQrdqJg3xd3L33wtrek9VIktSGDHHqGgsX5vUvf1ltHZIkFcEQp0K1cieufnODQ6qSpE5giFOhWnWKEYCZM2H+fEOcJKkzGOJUilbsxAGccgr89KdVVyFJ0sgZ4lSoVu7EATzjGfDww3mRJKmdGeJUilbtxD3zmXn9v/9bbR2SJI2UIU6FavVO3IIFMGkS3HJL1ZVIkjQyhjiVolU7cWPG5OviDHGSpHZniFOhWnmKkbpnPhPuuAM2bKi6EkmShs8Qp67zzGfCzp3w859XXYkkScNniFOh2qET9/Sn5/ocUpUktTNDnLrO9Olwwgnw4x9XXYkkScNniFOh2qETB3DmmbkTt3lz1ZVIkjQ8hjgVqtWnGKl7znNg61bni5MktS9DnErR6p24Zz0rTzdyww1VVyJJ0vAY4lSodunETZ0KJ58MP/xh1ZVIkjQ8hjiVotU7cZCHVG+9FdaurboSSZKGzhCnQrVLJw7yzQ27dnmXqiSpPRniVIp26MT90R/l56h+73tVVyJJ0tAZ4lSodpliBGD8eHjuc+G669qrgyhJEhji1OVe9CJYuhTuvrvqSiRJGhpDnArVTp04gLPOyuvrrqu2DkmShsoQp642dy4sWGCIkyS1H0OcCtVunTiAF784P4JrzZqqK5EkqXGGOBWqHW8QePGLYedO+M53qq5EkqTGGeJUinbqxJ18MsyZA1/7WtWVSJLUOEOcCtWOnbhRo+Dss3MnbsOGqquRJKkxhjiVop06cQCveAVs2eINDpKk9mGIU6HasRMH8IxnwAEHwNVXV12JJEmNMcSpFO3WiRs9Og+pfvvbDqlKktqDIU6FascpRur+7M9g82b4xjeqrkSSpMEZ4qSaU0+F+fPhiiuqrkSSpMEZ4lSodu7EjRoFf/7ncP31sGxZ1dVIkjQwQ5zUw+tel4Pol75UdSWSJA3MEKdCtXMnDuCww/Kdqldc0b532kqSuoMhToXqhODzhjfA3XfDT39adSWSJPXPEKdStGsnDuBVr4Jp0+Df/73qSiRJ6p8hToXqhE7c5Mnw+tfniX9XrKi6GkmS+maIUynauRMHcP75sG0b/Md/VF2JJEl9M8SpUO1+Y0PdUUfBc54Dn/407NxZdTWSJO3NECf144ILYOlSuO66qiuRJGlvhjgVqlM6cQAveQnMmwcf/WjVlUiStDdDnNSPMWPg7W+HW26Bm2+uuhpJkvZkiFOhOqkTB/DGN8LMmfCRj1RdiSRJezLESQOYNAne+lb41rfgrruqrkaSpN0McSpUp3XiAP7qr/Lccf/0T1VXIknSboY4FaoTJvvtbb/9cpD78pdh8eKqq5EkKTPEqRSd1IkD+Lu/g6lT4b3vrboSSZIyQ5wK1YmdOMjduLe/Ha69FhYtqroaSZIMcSpJp3XiAN72thzm3v3uqiuRJMkQp4J14o0NddOmwT/8A3z/+/Dd71ZdjSSp2xnipCF4y1vgsMPgwgth+/aqq5EkdTNDnArVyZ04gHHj4GMfgyVL4NJLq65GktTNDHHSEL34xfC858H73w+rVlVdjSSpWxniVKhO78RB/tk+/nHYsAHe8Y6qq5EkdStDnDQMxxwD73wnXHFFvtFBkqRmM8SpUN3Qiat7z3vgyCPhvPNg48aqq5EkdRtDnArVqZP99mXCBPjsZ+HBB+F976u6GklSt2mpEBcR50TEPRGxMSLuj4jTerx3ZkQsiYhNEXFjRMyrslYNrBs6cQCnnQZvfjP8y7/AzTdXXY0kqZu0TIiLiD8GPgK8AZgKPAv4Xe29mcA1wHuBGcAi4KvVVKqBdFMnru6jH4X58+G1r4Unnqi6GklSt2iZEAd8ALg4pfSzlNKulNKylNKy2nsvAxanlK5OKW0BLgIWRMRRVRWrgXVLJw5g6lT48pfhkUfg/PO7M8hKkpqvJUJcRIwGFgKzIuK3EfFIRHwqIibWdjkWuKO+f0ppI3B/bbtaSDfd2NDTKafARRfBV74CV15ZdTWSpG7QEiEOmA2MBV4OnAacCDwFeE/t/SnA2l7fs5Y87LqXiDg3IhZFxKKVK1eWUrDU29//fb5G7oIL4K67qq5GktTpmhLiIuJHEZH6WW4GNtd2/WRKaXlKaRXwMeCs2vYNwLReh50GrO/rfCmly1JKC1NKC2fNmlXGj6R+dGsnDmD06NyJmzIF/vRPvT5OklSupoS4lNIZKaXoZzk1pbQGeATo72qixcCC+ouImAwcWtsutYw5c+BrX8vTjrz2tbBrV9UVSZI6VasMpwJ8HnhrROwfEfsCbwO+VXvvWuC4iDg7IiYA7wPuTCktqaZU9aebO3F1p54Kn/gEXHed88dJksrTSiHug8CtwL3APcDtwCUAKaWVwNm112uAU4BzqilTGtz558Mb3wiXXAKXX151NZKkTjSm6gLqUkrbgQtqS1/vXw84pUiLsxOXRcCll+ZpR847Dw46CF7wgqqrkiR1klbqxKkDOEfabmPHwtVXw/HHw8tfDrfdVnVFkqROYohTKbq9E1c3dWq+Nm7mTHje8+DOO6uuSJLUKQxxKpTDqXubMwduuAEmToTnPhfuvrvqiiRJncAQJzXBk5+cg9zo0XDmmXDPPVVXJElqd4Y4FcpOXP+OOAJ++MP8GZ12GvziF1VXJElqZ4Y4qYmOOQZuuQWmTYPnPCeHOkmShsMQp0LZiRvcoYfCzTfDIYfAWWfBV79adUWSpHZkiJMqMGcO/OQncPLJcM45+ckOPqJLkjQUhjgVyk5c4/bdF66/Ht7wBvjgB+EVr4CNG6uuSpLULgxxUoXGj8+P5frYx+DrX4enP907VyVJjTHEqVB24oYuAi68EL77XXj8cVi4ED7/eZ9+IUkamCFOhTJ4DN8f/zH86ldwyinwl38Jr30trFlTdVWSpFZliFMp7MQNz5w58IMfwMUX57tWjz0WvvnNqquSJLUiQ5wK5XDqyI0eDe99b54MeNYseMlL4DWvgVWrqq5MktRKDHFSi3rqU+HWW+Gii+Cqq+DII+HSS2HnzqorkyS1AkOcCmUnrljjxsH73w+33w4nnAAXXAAnnZTnmJMkdTdDnNQGjjsObrgBrr463+xw+unw0pfCr39ddWWSpKoY4lQoO3HliYCXvzzPI3fxxXDjjbBgAbz61XDvvVVXJ0lqNkOc1GYmTco3PjzwALzrXfCNb8Axx8DrXmdnTpK6iSFOhbIT1zwzZsCHPwy/+x389V/DNdfk6+Ze+MI89OqcfZLU2QxxUpubPTs/tuuhh+BDH8o3QZx5JjzlKfDpT8P69VVXKEkqgyFOhbITV50ZM+Dd74YHH4TPfjZvO//8PIHweefBL39ZaXmSpIIZ4lQoh/CqN2ECvOlNuSP3s5/lmyGuvDJPTbJgAXz0o/Dww1VXKUkaKUOcSmEnrnoR+Tmsn/88LFsGn/wkTJwI73wnzJsHZ5yRO3arV1ddqSRpOAxxKpTDqa1p333hLW/Jnbl7780TCD/6KJx7Luy/PzznOfCv/wpLl1ZdqSSpUYY4qcscfngOcb/5TX6s1zvfCY8/Dn/zNzB/fn7c1wc+kJ/d6iO+JKl1GeJUKDtx7SMCFi6ESy6BxYtzh+6f/znPQ/eBD+Sh2Fmz4M/+LA+72qWTpNYypuoCJLWGww+Hd7wjL6tWwQ9/CN//Pnzve/lxX/V9zjgDTjstL/PmGdglqSqGOBXKTlxnmDkTXvnKvKQES5bkQPeDH8BVV+2ewuTgg3cHutNOg6OPhlH29yWpKQxxkgYUkcPZ0Ufn6+Z27oS77oKbbsrLjTfCl7+c9506NU9l8rSn5eXkk+FJTzLUS1IZDHEqlJ24zjd6dJ5vbsGCfMdrSnD//XDzzflGiVtvhU98ArZty/vPmrU71C1YkB8NdsghduwkaaQMcZJGJAIOOywvr3993rZ1K/z61/kO13qw+853dof8KVPg+ONzoKsHu+OPh2nTKvsxJKntGOJUKDtxAhg/Pt/5unDh7m0bN+a7YO+8My933AFf/Sp85jO795k3Lw/bHnXUnsv++/tnSpJ6M8SpUD52S/2ZPDlfI3fyybu3pQSPPJID3Z135mvtliyBn/wENm3avd++++4Z6o44Ag49FJ785HxcSepGhjiVwq6JGhGR73A9+GB48Yt3b9+1K4e7JUvgnnvyesmSPCT7+c/veYwDDsiBrq9l5kz/LErqXIY4FcrhVBVh1Kh8V+uTngTPe96e7z3xBPz2t/lmip7LDTfAf/7nnvtOnZpvopg3Lx+r9/qAA7zBQlL7MsRJaivTp+99vV3dli3wwAN7hrsHHshPm7jpphwAexo7NncB64GxHu7mzoU5c/Ky337+o0RSazLEqVB24lSlCRN2z2nXl3Xr4KGH8rJ06Z5f33gjLFuWh3J7Gjcuh7mDDtod7Opf99w2ZUr5P58k9WSIk9Q1pk2D447LS1927MhB7tFH+17feSd897uwfn3fxz7wwHwn7ezZAy+TJpX7c0rqDoY4FcpOnNrZmDF5SHXevIH3W79+z4BX/3r5cnj88TxH3vXX7z18WzdlSv9hb9asfENGfdlvv9wNlKTeDHGSNERTp8KRR+ZlIFu3wsqVOdj1t9x3X37axe9/3/8UPdOm7Rnsei/77bfn6xkzciCV1Nn831yFshMn7TZ+fL5JYu7cwffdsSMHvlWr9l5+//vdXz/+eJ40edWqPIFyf/bdN4e7GTPy1/VlsNeTJ/v/r9QuDHGS1ALGjMnX1B14YOPfs3nzngGvd+BbtQrWrIHVq/OdumvW5KX3zRs9jR3bf8jr+fX06bDPPnsvdgCl5vF/NxXKTpzUPBMnNt7pq9u1K1/TVw90q1f3/XX99WOP5QmXV6+GtWsHP/6kSX2Hu74Wg6A0Mv6vokIZ4qTWNmrU7rA0f/7Qvnfnzhzk6oGukeWJJ/IULvXXmzcPfp6eQXDatHwN4kDLQPuMHTucT0lqD4Y4SVJDRo/OQ6ozZgz/GNu25fn6Gg2B69blzuGKFXldX7Zta+x848cPHvT6CoSTJ+e7iCdP3vPrCRP8R6pahyFOhbITJ2kg48btvot2JLZt2x3o6kFvsKW+38qV8Lvf7d6+YUP/dwb3NmrU3sGu59d9bWvk/cmTHUbW0PlHRpLUdsaNy3ff7rffyI+1axds2rRn2Nu4MS8bNuz9dV/b1q7N8wX23LZp09DqGD++73A3aVJeJk7c/fVgS3/7GhQ7i/85VSg7cZLazahROThNmTK0u4MHUw+HgwXAgbZt3pzvMt60ac9l48aB7zLuz9ixjQe+wfabODEPL/e1njgxD7+rXIY4SZJK0DMczp5d7LFTgu3bd4e6zZv3Dnr9Lf3t+/jjfW9vdKi5tzFj9g52A4W+RrY18t6oUcV+1q3MEKdC2YmTpPJF5CHlcePyVC1lSSlff9hfGNyyZeD1QO+tXNn/e8MNjpA/k75C3/jx+fVA60b2aeQYY8Y05+9BQ5wkSepTxO5gsu++zTlnvcs4WAhsJCj23Gfr1vz6iSfyuv6693o4w9S9jRo1/CA4FIY4FcpOnCRpJHp2GffZp/nn37Gj/4BX5Hr9+ny9Y+/tQ2GIkyRJqhkzJi+TJ1dz/qE0Qbro8j81g504SZKawxCnQhniJElqDkOcJElSGzLEqVB24iRJag5DnCRJUhsyxKlQduIkSWqOlglxETE/Ir4dEWsi4rGI+FREjOnx/pkRsSQiNkXEjRExr8p6JUmSqtQyIQ74d2AFcCBwInA6cAFARMwErgHeC8wAFgFfraRKDchOnCRJzdFKIe4Q4KqU0paU0mPAd4Fja++9DFicUro6pbQFuAhYEBFHVVOqJElStVrpiQ2fAM6JiB8B+wIvJHfeIIe5O+o7ppQ2RsT9te1LBjrofffB859fSr3qw4oVeW0nTpKkcrVSiPsx8H+AdcBo4Arg67X3pgAre+2/Fpja14Ei4lzgXIBx405g3boSqlWfJkyAF7wADj+86kokSepsTQlxte7a6f28fQvwLOB7wGeAZ5BD238AHwH+DtgATOv1fdOA9X0dMKV0GXAZwMKFC9NPfzqy+iVJklpNU66JSymdkVKKfpZTyTcrHAx8KqW0NaX0e+DzwFm1QywGFtSPFxGTgUNr2yVJkrpOS9zYkFJaBTwAnB8RYyJiOvAX7L4O7lrguIg4OyImAO8D7kwpDXg9nCRJUqdqiRBX8zLgBeRr334L7AAuBEgprQTOBi4B1gCnAOdUU6YkSVL1WubGhpTSr4AzBnj/esApRSRJkmitTpwkSZIaZIiTJElqQ4Y4SZKkNmSIkyRJakOGOEmSpDZkiJMkSWpDhjhJkqQ2ZIiTJElqQ4Y4SZKkNmSIkyRJakOGOEmSpDZkiJMkSWpDhjhJkqQ2ZIiTJElqQ5FSqrqGUkXEeuA3VdfRZWYCq6ouosv4mTefn3nz+Zk3n5958x2ZUprayI5jyq6kBfwmpbSw6iK6SUQs8jNvLj/z5vMzbz4/8+bzM2++iFjU6L4Op0qSJLUhQ5wkSVIb6oYQd1nVBXQhP/Pm8zNvPj/z5vMzbz4/8+Zr+DPv+BsbJEmSOlE3dOIkSZI6jiFOkiSpDXVsiIuIGRFxbURsjIilEfHqqmvqdBHxlohYFBFbI+ILVdfTDSJifERcXvszvj4ibo+IF1ZdVyeLiC9GxPKIWBcR90bEm6quqVtExOERsSUivlh1Ld0gIn5U+7w31BbnXG2CiDgnIu6p5Zf7I+K0/vbt5Hni/g3YBswGTgSui4g7UkqLK62qsz0KfAh4PjCx4lq6xRjgYeB04CHgLOCqiDg+pfRglYV1sH8E3phS2hoRRwE/iojbU0q3VV1YF/g34Naqi+gyb0kpfa7qIrpFRPwx8BHglcAvgAMH2r8jO3ERMRk4G3hvSmlDSulm4JvAn1dbWWdLKV2TUvo68Puqa+kWKaWNKaWLUkoPppR2pZS+BTwAnFR1bZ0qpbQ4pbS1/rK2HFphSV0hIs4BngB+WHEpUpk+AFycUvpZ7Xf6spTSsv527sgQBxwB7Ewp3dtj2x3AsRXVIzVFRMwm//m341yiiPj3iNgELAGWA9+uuKSOFhHTgIuBt1ddSxf6x4hYFRG3RMQZVRfTySJiNLAQmBURv42IRyLiUxHR78hWp4a4KcDaXtvWAg09i0xqRxExFvgScEVKaUnV9XSylNIF5N8npwHXAFsH/g6N0AeBy1NKD1ddSJd5J/Bk4CDy3GX/ExF2ncszGxgLvJz8u+VE4CnAe/r7hk4NcRuAab22TQPWV1CLVLqIGAVcSb4O9C0Vl9MVUko7a5dqzAXOr7qeThURJwLPBT5ecSldJ6X085TS+pTS1pTSFcAt5OtuVY7NtfUnU0rLU0qrgI8xwGfeqTc23AuMiYjDU0r31bYtwCEmdaCICOBy8r/izkopba+4pG4zBq+JK9MZwHzgofxHnSnA6Ig4JqX01Arr6kYJiKqL6FQppTUR8Qj5c25IR3biUkobyUMcF0fE5Ih4JvAScqdCJYmIMRExARhN/iU7ISI69R8KreRS4GjgT1JKmwfbWcMXEfvXbv+fEhGjI+L5wKuAG6qurYNdRg7JJ9aWTwPXke+CV0kiYnpEPL/+ezwiXgM8C/he1bV1uM8Db639rtkXeBvwrf527uS/YC8A/gNYQb5b8nynFynde4D393j9WvKdNhdVUk0XiIh5wHnka7Ieq3UqAM5LKX2pssI6VyIPnX6a/I/gpcDbUkrfqLSqDpZS2gRsqr+OiA3AlpTSyuqq6gpjyVNGHQXsJN/E89KUknPFleuDwEzyiOIW4Crgkv529tmpkiRJbagjh1MlSZI6nSFOkiSpDRniJEmS2pAhTpIkqQ0Z4iRJktqQIU6SJKkNGeIkdbSIWNysB3dHxDERsaiE414TES8o+riS2pvzxElqa7XJX+smkSc+3ll73dRJjyPiv4GrU0pfKfi4JwOXppROKvK4ktqbIU5Sx4iIB4E3pZSur+DcB5KfzzwnpbSlhOPfB7wqpVR4p09Se3I4VVJHi4gHI+K5ta8vioirI+KLEbE+In4dEUdExN9HxIqIeDgintfje/eJiMsjYnlELIuID0XE6H5O9cfAL3sGuNq5/zYi7oyIjbVjzY6I79TOf33t+YjUnlH5xYj4fUQ8ERG3RsTsHsf/EfCiwj8gSW3LECep2/wJcCWwL3A7+YHeo4CDgIuBz/TY9wpgB3AY8BTgecCb+jnu8UBfz5U8mxzwjqid+zvAP5CfjzgK+Ovafn8B7AMcDOwHvBnY3OM49wALGv4pJXU8Q5ykbnNTSul7KaUdwNXALOCfUkrbga8A8yNieq0L9kLyA+43ppRWAB8HzunnuNOB9X1s/2RK6fGU0jLgJuDnKaXbU0pbgWvJ4RBgOzm8HZZS2plSui2ltK7HcdbXziFJAIypugBJarLHe3y9GViVUtrZ4zXAFGAOMBZYHhH1/UcBD/dz3DXA1AbO1/v1lNrXV5K7cF+JiOnAF4F318IltWM/0d8PJan72ImTpL49TL7TdWZKaXptmZZSOraf/e8kD5kOS0ppe0rpAymlY4BnAC8GXtdjl6OBO4Z7fEmdxxAnSX1IKS0Hvg/834iYFhGjIuLQiDi9n2/5AfDUiJgwnPNFxLMj4vjajRPryMOrO3vscjr5ejpJAgxxkjSQ1wHjgLvJw6VfAw7sa8eU0uPADcBLhnmuA2rHX0e+ieHH5CFVIuJpwMaU0i+GeWxJHch54iSpIBFxDPmO1pNTgb9ca5MIX55S+nZRx5TU/gxxkiRJbcjhVEmSpDZkiJMkSWpDhjhJkqQ2ZIiTJElqQ4Y4SZKkNmSIkyRJakOGOEmSpDZkiJMkSWpD/w8VTCIYcGgfCwAAAABJRU5ErkJggg==\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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4SZIGY4hTKYbbiYuAAw4wxEmSNBhDnEox3E4cOFecJEmNMMSpFMOd7BcMcZIkNcIQp1IMdzgVDHGSJDXCEKdSjHQ49bHHYNu2YmuSJKmTGOJUipF24gAeeaS4eiRJ6jSGOJVipJ04cEhVkqSBGOJUiiI6cYY4SZL6Z4hTKezESZJULkOcSjGSTtz+++dJfw1xkiT1zxCnUoxknrgxY3KQM8RJktQ/Q5xKMZLhVHCuOEmSBmOIUylGMpwKhjhJkgZjiFMpiujEOU+cJEn9M8SpFCPtxM2eDStW7D6OJEnakyFOpRhpJ+6AA2DHDli9uriaJEnqJIY4laKIThzAo48WU48kSZ3GEKdSjLQTZ4iTJGlghjiVYiTzxEEeTgVvbpAkqT+GOJXC4VRJkspliFMpRjqcOn06jBtniJMkqT+GOJVipJ24iNyNczhVkqS+GeJUipF24iCHODtxkiT1zRCnUoy0Ewf55gY7cZIk9c0Qp1LYiZMkqVyGOJWiiE6cj96SJKl/hjiVoohO3AEH5OM89lgxNUmS1EkMcSrFSCf7BeeKkyRpIIY4laKo4VQwxEmS1BdDnEpR1HAqeIeqJEl9McSpFHbiJEkqlyFOpSiiE7fPPjB+vCFOkqS+GOJUiiI6cT56S5Kk/hniVIpt2/J67NiRHccJfyVJ6pshTqXYuDGvJ04c2XF89JYkSX0zxKkUGzbA5MkjuyYO7MRJktQfQ5xKsXFjDnEjNXs2rFy5+0YJSZKUGeJUiqJCnI/ekiSpb4Y4lWLDBpgyZeTHca44SZL6ZohTKYocTgVDnCRJvRniVIr6jQ0j5aO3JEnqmyFOpdi40eFUSZLKZIhTKYoaTp02DSZMMMRJktSbIU6lKCrE+egtSZL6ZohTKbZuzR20IjjhryRJezPEqRRbt8K4ccUcy0dvSZK0t7YLcRExIyKujoiNEbE0Iv6s6pq0t23bigtxduIkSdrbmKoLGIZ/A7YBs4ETgWsi4raU0uJKq9L/2rULduyA8eOLOV7PR2+NHl3MMSVJandt1YmLiMnA2cD7U0obUko3Ad8CXlNtZepp+/a8LnI4ddcuWLWqmONJktQJ2irEAUcAO1NKd/XYdhtwbM+dIuLNEbEoIhatXLmyqQUqXw8HxQ6ngkOqkiT11G4hbgqwtte2tcDUnhtSSpeklBamlBbOmjWracUp27Ytr4vsxIEhTpKkntotxG0ApvXaNg1YX0Et6kc9xBV5TRx4h6okST21W4i7CxgTEYf32LYA8KaGFlJ0J87hVEmS9tZWIS6ltBG4CrgwIiZHxKnAi4HLq61MPRV9TdzUqTBxoiFOkqSe2irE1ZwPTARWAP8FnOf0Iq2l6OFUH70lSdLe2m6euJTSauAlVdeh/hU9nApO+CtJUm/t2IlTiyt6OBV89JYkSb0Z4lQ4O3GSJJXPEKfCFX1NHOQQt2pVfvSWJEkyxKkEZXTi6o/e8gEckiRlhjgVroxr4pwrTpKkPRniVLiyOnFgiJMkqc4Qp8KVdWMDeIeqJEl1hjgVrn7zwejRxR3T4VRJkvZkiFPhdu3K6yJD3JQpMGmSnThJkuoMcSpcvRM3qsDfrggn/JUkqSdDnApXRicODHGSJPVkiFPh6iGuyE4cGOIkSerJEKfClTGcCnDggYY4SZLqDHEqXJnDqatX755MWJKkbmaIU+HKHE4FWLGi2ONKktSODHEqXFnDqfUQ55CqJEmGOJWgzOFUgOXLiz2uJEntyBCnwtmJkySpfIY4Fa6sa+L23z+vDXGSJBniVIKyhlPHjYP99jPESZIEhjiVoD6cGlH8sZ0rTpKkzBCnwu3alYdSywhxPrVBkqTMEKfC1UNcGQxxkiRlhjgVbufO8kNcSuUcX5KkdmGIU+F27Sr+poa6Aw6AzZth3bpyji9JUrswxKlwZQ+ngkOqkiQZ4lS4sodTwRAnSZIhToUrezgVDHGSJBniVDg7cZIklc8Qp8KV2YmbMQPGjjXESZJkiFPhyryxIcK54iRJAkOcSlDmcCoY4iRJAkOcSlDmcCrkELd8eXnHlySpHRjiVLgyh1PBTpwkSWCIUwmaMZy6cmU+jyRJ3coQp8I1Yzh1164c5CRJ6laGOBWuGcOp4JCqJKm7GeJUuLKHUw88MK8NcZKkbmaIU+GaMZwKhjhJUnczxKlwZXfiZs/Oa0OcJKmbGeJUuLI7cZMmwbRpzhUnSepuhjgVruwbG8C54iRJMsSpcGUPp4JPbZAkyRCnwpU9nAowZ44hTpLU3QxxKlwzhlPnzIGHH4aUyj2PJEmtyhCnwjVjOHXOHNi0CdatK/c8kiS1KkOcCteM4dSDDsrrZcvKPY8kSa3KEKfCNasTB3lIVZKkbmSIU+GadU0cGOIkSd3LEKfCNevuVHA4VZLUvQxxKlwzhlMnTYLp0+3ESZK615hGdoqIZwOvB44FpgLrgcXAF1NKPyqtOrWlZnTiYPc0I5IkdaNBQ1xEvAN4N/B54BvAWmAasAC4LCI+llL6VKlVqq0045o4yHeoOpwqSepWjXTi/hr4o5TSkl7br4qI/wJuAAxx+l/NGE6F3Ilb0vu3UpKkLtHIX7WTgf4GrR4BJhVXjjpBM4dTly/P55Mkqds0EuK+AXw7Is6KiFkRMS4iZkbEWcDVwNfLLVHtppnDqTt2wKpV5Z9LkqRW08hftW8B/ge4DHgU2FxbXwb8AjivtOrUlpo5nApeFydJ6k6D/lWbUtqWUvrblNJcYAYwD9gvpTS3tn1bfd+IOLXEWtUmUmpuiPMOVUlSN2poipG6lNLjwOMD7PI98p2r6mIpNec89eenGuIkSd2o6H5JFHw8talowm/C7Nn5PA6nSpK6UdEhblg9mIgYHxGXRsTSiFgfEbdGxPN67XNWRCyJiE0RcUNEzCumZBWtWZ24sWNh//3txEmSulOrPHZrDPAgcAawD/B+4GsRMR8gImYCV9W2zwAWAVdUUqka0oxOHOQhVUOcJKkbDemauLKklDYCF/TY9J2IuA84CbgfeCmwOKV0JUBEXACsioij+piEWBVrVicO8s0NDz3UvPNJktQqWvKauIiYDRxBfj4r5Ge23lZ/vxb67q1tVwtqVifO56dKkrrVkEJcROwXEa+JiHfXXs+JiLn191NKU0daUESMBb4MXNajyzaF/MzWntYCfZ4vIt4cEYsiYtHKlStHWpKGqJmduIMOghUrYPv25p1TkqRW0HCIi4gzgN8DryJfmwZwOHBxA997Y0Skfpabeuw3Crgc2Aa8tcchNrD31CXTgPV9nS+ldElKaWFKaeGsWbMa/RFVoGZ24sBunCSp+wylE/fPwCtSSs8FdtS2/RI4ebBvTCmdmVKKfpbTACIigEuB2cDZKaWevZXFwIL6i4iYDBzK7uFWtZCUmhfiDj44r70uTpLUbYYS4uanlK6rfV0fMNtGcTdHXAwcDbwopbS513tXA8dFxNkRMQH4AHC7NzW0pmYOp9ZD3AMPNO+ckiS1gqGEuDsi4jm9tj0L+O1Ii6jN+XYucCLwSERsqC2vAkgprQTOBi4C1gCnAOeM9LwqT7M7cQ8+2JzzSZLUKobSRXsneeqPa4CJEfE54EXAi0daREppKYPc2ZpSuhY4aqTnUvma2YmbOhX22ccQJ0nqPg134lJKvyBfl7YY+AJwH3BySunmkmpTG2tWJw5yN84QJ0nqNkO6ni2ltAz4eEm1qEM0sxMHhjhJUncaMMRFxOU08DzUlNJrC6tIHaGZnbgnPAEWLWre+SRJagWDDafeQ34ywr3kyXVfAowGHqp974uBx8srT+2oik7cypWwufc9zZIkdbABO3EppQ/Vv46IHwAvSCn9tMe209g98a/0v5p9TRzkueIOP7x555UkqUpDmWLkqcAvem37JfC04spRJ6iiEwdeFydJ6i5DCXG3Ah+NiIkAtfVFwG9KqEttropOnCFOktRNhhLiXg+cCqyNiEfJ18idBnhTg/bQzMduAcydm9eGOElSN2l4ipGU0v3A0yPiYGAOsDyl5MOOtJdmD6dOmACzZhniJEndZSidOCJiX+CPgGcCZ9ZeS3tpZicOnCtOktR9Gg5xEfE08lQjbwFOID/r9N7adul/NbsTB3muOEOcJKmbDOWJDf8MnJ9S+mp9Q0S8AvgX4CkF16U2V0Un7vrrm3tOSZKqNJTh1COAr/Xa9nXgsOLKUSeoohN38MGwbl1eJEnqBkMJcXcD5/Ta9nLyEKu0hyo6ceCQqiSpewxlOPXtwHci4i+BpcB84HDghcWXpXZWVScOcog79tjmn1+SpGYbyhQj/xMRhwIvIE8x8m3guyml1WUVp/ZVVSfuASe9kSR1iaF04kgprQG+VFIt6hBVdOLmzIExY2Dp0uafW5KkKjQc4iLiEPJjtk4EpvR8L6X0hGLLUrtrdiduzJjcjbv//uaeV5KkqgylE/cV8k0M7wQ2lVOOOkGzH7tVd8ghcN99zT+vJElVGEqIOxY4NaW0q6xi1BmqGE4FmD8fvve9as4tSVKzDWWKkZ8ATyqrEHWWqjpxy5fD5s3NP7ckSc02lE7c/cAPIuIq4JGeb6SUPlBkUWpvVXbiIN+heuSR1dQgSVKzDCXETSZPKzIWOLjH9or+ylYrq6oTB/m6OEOcJKnTDWWeuDcMtk9EvDKl9F8jK0ntrupOnHeoSpK6wVCuiWvE5wo+ntpUFZ24Aw+EceO8Q1WS1B2KDnEV/NWtVlNVJ27UKJg3z06cJKk7FB3ivD5OQDWdOHCuOElS9yg6xEmVdeIgXxdniJMkdYNBQ1xEGPQ0ZFV24latgg0bqjm/JEnN0khAWxYRH4+I4xrY94GRFqT2V9Vjt2D3NCNeFydJ6nSNhLi3AIcAN0fEryPiryJiVl87ppQaCXrqcFUPpwL84Q/V1SBJUjMMGuJSSt9MKb0cOJA8hcjLgQcj4lsRcXZEjC27SLWfqjpxhx2W1/feW835JUlqloavd0spPZ5S+lxK6TTgaGAR8ElgeVnFqT1V2YmbMQOmT4d77qmuBkmSmmHINy1ExHjgKcApwGzgt0UXpfZXVScuAg4/3BAnSep8DYe4iDgtIi4BHgU+AvwCOCKl9EdlFaf2VGUnDvKQqiFOktTpGpli5IKIuBf4dm3TC1JKR6SUPpxSWlpueWpXVXXiIIe4+++Hbduqq0GSpLKNaWCfpwLvBf47pbSl5HrUAVqhE7drVw5yRxxRbS2SJJVl0BCXUnpuMwpRZ6m6Ewd5SNUQJ0nqVD6NQYWrcrJf2DPESZLUqQxx6jizZsG0aXD33VVXIklSeQxxKlzVnbgI71CVJHU+Q5wKV/WNDWCIkyR1PkOcSlFlJw52TzOyfXu1dUiSVBZDnArXKp24HTtykJMkqRMZ4lSKqjtxRx2V17//fbV1SJJUFkOcCtcKnbh6iLvzzmrrkCSpLIY4laLqTty++8Ls2YY4SVLnMsSpcK3QiQM4+mhYsqTqKiRJKochTqWouhMHeUj1zjtbJ1RKklQkQ5wKV/Vkv3VHHw2PPw6PPlp1JZIkFc8Qp4519NF57ZCqJKkTGeJUuFbpxHmHqiSpkxniVLhWuQZt7lyYPNlOnCSpMxniVIpW6MRF7L65QZKkTmOIU+FapRMH+bo4Q5wkqRMZ4lSKVujEARxzDDz0EKxdW3UlkiQVyxCnwrVSJ+744/P6d7+rtg5JkopmiFMpWqUTd8IJef3b31ZbhyRJRTPEqXCt1Ik7+GDYZx+4/faqK5EkqViGOJWiVTpxEXlI1RAnSeo0LRfiIuLwiNgSEV/qtf2siFgSEZsi4oaImFdVjRpYq0z2W3fCCXk4tZU6hJIkjVTLhTjg34Cbe26IiJnAVcD7gRnAIuCK5pemdnTCCbBuHTzwQNWVSJJUnJYKcRFxDvA4cF2vt14KLE4pXZlS2gJcACyIiKOaW6Ea1WqdOHBIVZLUWVomxEXENOBC4J19vH0scFv9RUppI3BvbbtaSCsOWR53XF4b4iRJnaRlQhzwYeDSlNKDfbw3Beg9XetaYGpfB4qIN0fEoohYtHLlyoLLVCNaqRM3dSoccoghTpLUWZoS4iLixohI/Sw3RcSJwLOAT/ZziA3AtF7bpgHr+9o5pXRJSmlhSmnhrFmzCvs5NLhW7MRBHlK97bbB95MkqV2MacZJUkpnDvR+RLwdmA88ELmFMwUYHRHHpJSeDCwGXtdj/8nAobXtakGt1IkDePKT4VvfgvXrc2dOkqR21yrDqZeQQ9mJteWzwDXAc2rvXw0cFxFnR8QE4APA7SmlJc0vVQNp1U7cwoW5tltvrboSSZKK0RIhLqW0KaX0SH0hD59uSSmtrL2/EjgbuAhYA5wCnFNZwRpUq3XiTjopr2+5pdo6JEkqSlOGU4cqpXRBH9uuBZxSpMXVO3GtFuJmz86P4Fq0qOpKJEkqRkt04qRmOOkkQ5wkqXMY4lSoVu3EQb4u7q67YG3vyWokSWpDhjh1jYUL8/rXv662DkmSimCIU6FauRNXv7nBIVVJUicwxKlQrTrFCMDMmTB/viFOktQZDHEqRSt24gBOOQV+/vOqq5AkaeQMcSpUK3fiAJ7+dHjwwbxIktTODHEqRat24k49Na//53+qrUOSpJEyxKlQrd6JW7AAJk2Cn/2s6kokSRoZQ5xK0aqduDFj8nVxhjhJUrszxKlQrTzFSN2pp8Jtt8GGDVVXIknS8Bni1HVOPRV27oRf/rLqSiRJGj5DnArVDp24pz411+eQqiSpnRni1HWmT4cTToAf/7jqSiRJGj5DnArVDp04gLPOyp24zZurrkSSpOExxKlQrT7FSN0znwlbtzpfnCSpfRniVIpW78Q94xl5upHrr6+6EkmShscQp0K1Sydu6lQ4+WS47rqqK5EkaXgMcSpFq3fiIA+p3nwzrF1bdSWSJA2dIU6FapdOHOSbG3bt8i5VSVJ7MsSpFO3QiXva0/JzVH/wg6orkSRp6AxxKlS7TDECMH48POtZcM017dVBlCQJDHHqci94ASxdCnfcUXUlkiQNjSFOhWqnThzA85+f19dcU20dkiQNlSFOXW3uXFiwwBAnSWo/hjgVqt06cQAvfGF+BNeaNVVXIklS4wxxKlQ73iDwwhfCzp3wve9VXYkkSY0zxKkU7dSJO/lkmDMHvv71qiuRJKlxhjgVqh07caNGwdln507chg1VVyNJUmMMcSpFO3XiAF7+ctiyxRscJEntwxCnQrVjJw7g6U+HAw6AK6+suhJJkhpjiFMp2q0TN3p0HlL97ncdUpUktQdDnArVjlOM1P3pn8LmzfDNb1ZdiSRJgzPESTWnnQbz58Nll1VdiSRJgzPEqVDt3IkbNQpe8xq49lpYtqzqaiRJGpghTurhta/NQfTLX666EkmSBmaIU6HauRMHcNhh+U7Vyy5r3zttJUndwRCnQnVC8HnDG+COO+DnP6+6EkmS+meIUynatRMH8MpXwrRp8JnPVF2JJEn9M8SpUJ3QiZs8GV7/+jzx74oVVVcjSVLfDHEqRTt34gDOOw+2bYMvfKHqSiRJ6pshToVq9xsb6o46Cp75TPjsZ2HnzqqrkSRpb4Y4qR/nnw9Ll8I111RdiSRJezPEqVCd0okDePGLYd48+PjHq65EkqS9GeKkfowZA+98J/zsZ3DTTVVXI0nSngxxKlQndeIA3vhGmDkTPvaxqiuRJGlPhjhpAJMmwdveBt/5Dvzud1VXI0nSboY4FarTOnEAf/EXee64f/iHqiuRJGk3Q5wK1QmT/fa23345yH3lK7B4cdXVSJKUGeJUik7qxAG8+90wdSq8//1VVyJJUmaIU6E6sRMHuRv3znfC1VfDokVVVyNJkiFOJem0ThzA29+ew9x731t1JZIkGeJUsE68saFu2jT4u7+DH/4Qvv/9qquRJHU7Q5w0BG99Kxx2GLzjHbB9e9XVSJK6mSFOherkThzAuHHwiU/AkiVw8cVVVyNJ6maGOGmIXvhCePaz4YMfhFWrqq5GktStDHEqVKd34iD/bJ/8JGzYAO96V9XVSJK6lSFOGoZjjoH3vAcuuyzf6CBJUrMZ4lSobujE1b3vfXDkkXDuubBxY9XVSJK6jSFOherUyX77MmECfP7zcP/98IEPVF2NJKnbtFSIi4hzIuLOiNgYEfdGxOk93jsrIpZExKaIuCEi5lVZqwbWDZ04gNNPh7e8Bf75n+Gmm6quRpLUTVomxEXEHwMfA94ATAWeAfyh9t5M4Crg/cAMYBFwRTWVaiDd1Imr+/jHYf58ePWr4fHHq65GktQtWibEAR8CLkwp/SKltCultCyltKz23kuBxSmlK1NKW4ALgAURcVRVxWpg3dKJA5g6Fb7yFXjoITjvvO4MspKk5muJEBcRo4GFwKyIuCciHoqIT0fExNouxwK31fdPKW0E7q1tVwvpphsbejrlFLjgAvjqV+Hyy6uuRpLUDVoixAGzgbHAy4DTgROBJwHvq70/BVjb63vWkodd9xIRb46IRRGxaOXKlaUULPX2t3+br5E7/3z43e+qrkaS1OmaEuIi4saISP0sNwGba7v+a0ppeUppFfAJ4Pm17RuAab0OOw1Y39f5UkqXpJQWppQWzpo1q4wfSf3o1k4cwOjRuRM3ZQr8yZ94fZwkqVxNCXEppTNTStHPclpKaQ3wENDf1USLgQX1FxExGTi0tl1qGXPmwNe/nqcdefWrYdeuqiuSJHWqVhlOBfgi8LaI2D8i9gXeDnyn9t7VwHERcXZETAA+ANyeUlpSTanqTzd34upOOw0+9Sm45hrnj5MklaeVQtyHgZuBu4A7gVuBiwBSSiuBs2uv1wCnAOdUU6Y0uPPOgze+ES66CC69tOpqJEmdaEzVBdSllLYD59eWvt6/FnBKkRZnJy6LgIsvztOOnHsuHHQQPPe5VVclSeokrdSJUwdwjrTdxo6FK6+E44+Hl70Mbrml6ookSZ3EEKdSdHsnrm7q1Hxt3MyZ8Oxnw+23V12RJKlTGOJUKIdT9zZnDlx/PUycCM96FtxxR9UVSZI6gSFOaoInPjEHudGj4ayz4M47q65IktTuDHEqlJ24/h1xBFx3Xf6MTj8dfvWrqiuSJLUzQ5zURMccAz/7GUybBs98Zg51kiQNhyFOhbITN7hDD4WbboJDDoHnPx+uuKLqiiRJ7cgQJ1Vgzhz4yU/g5JPhnHPykx18RJckaSgMcSqUnbjG7bsvXHstvOEN8OEPw8tfDhs3Vl2VJKldGOKkCo0fnx/L9YlPwH//Nzz1qd65KklqjCFOhbITN3QR8I53wPe/D48+CgsXwhe/6NMvJEkDM8SpUAaP4fvjP4bf/AZOOQX+/M/h1a+GNWuqrkqS1KoMcSqFnbjhmTMHfvQjuPDCfNfqscfCt75VdVWSpFZkiFOhHE4dudGj4f3vz5MBz5oFL34xvOpVsGpV1ZVJklqJIU5qUU9+Mtx8M1xwAXzta3DkkXDxxbBzZ9WVSZJagSFOhbITV6xx4+CDH4Rbb4UTToDzz4eTTspzzEmSupshTmoDxx0H118PV16Zb3Y44wx4yUvgt7+tujJJUlUMcSqUnbjyRMDLXpbnkbvwQrjhBliwAP7sz+Cuu6quTpLUbIY4qc1MmpRvfLjvPvibv4FvfhOOOQZe+1o7c5LUTQxxKpSduOaZMQM++lH4wx/gL/8SrroqXzf3vOfloVfn7JOkzmaIk9rc7Nn5sV0PPAAf+Ui+CeKss+BJT4LPfhbWr6+6QklSGQxxKpSduOrMmAHvfS/cfz98/vN523nn5QmEzz0Xfv3rSsuTJBXMEKdCOYRXvQkT4E1vyh25X/wi3wxx+eV5apIFC+DjH4cHH6y6SknSSBniVAo7cdWLyM9h/eIXYdky+Nd/hYkT4T3vgXnz4Mwzc8du9eqqK5UkDYchToVyOLU17bsvvPWtuTN31115AuGHH4Y3vxn23x+e+Uz4l3+BpUurrlSS1ChDnNRlDj88h7jf/z4/1us974FHH4W/+iuYPz8/7utDH8rPbvURX5LUugxxKpSduPYRAQsXwkUXweLFuUP3j/+Y56H70IfyUOysWfCnf5qHXe3SSVJrGVN1AZJaw+GHw7velZdVq+C66+CHP4Qf/CA/7qu+z5lnwumn52XePAO7JFXFEKdC2YnrDDNnwitekZeUYMmSHOh+9CP42td2T2Fy8MG7A93pp8PRR8Mo+/uS1BSGOEkDisjh7Oij83VzO3fC734HP/1pXm64Ab7ylbzv1Kl5KpOnPCUvJ58MT3iCoV6SymCIU6HsxHW+0aPzfHMLFuQ7XlOCe++Fm27KN0rcfDN86lOwbVvef9as3aFuwYL8aLBDDrFjJ0kjZYiTNCIRcNhheXn96/O2rVvht7/Nd7jWg933vrc75E+ZAscfnwNdPdgdfzxMm1bZjyFJbccQp0LZiRPA+PH5zteFC3dv27gx3wV7++15ue02uOIK+Nzndu8zb14etj3qqD2X/ff3d0qSejPEqVA+dkv9mTw5XyN38sm7t6UEDz2UA93tt+dr7ZYsgZ/8BDZt2r3fvvvuGeqOOAIOPRSe+MR8XEnqRoY4lcKuiRoRke9wPfhgeOELd2/ftSuHuyVL4M4783rJkjwk+8Uv7nmMAw7Iga6vZeZMfxcldS5DnArlcKqKMGpUvqv1CU+AZz97z/cefxzuuSffTNFzuf56+M//3HPfqVPzTRTz5uVj9V4fcIA3WEhqX4Y4SW1l+vS9r7er27IF7rtvz3B33335aRM//WkOgD2NHZu7gPXAWA93c+fCnDl52W8//1EiqTUZ4lQoO3Gq0oQJu+e068u6dfDAA3lZunTPr2+4AZYty0O5PY0bl8PcQQftDnb1r3tumzKl/J9PknoyxEnqGtOmwXHH5aUvO3bkIPfww32vb78dvv99WL++72MfeGC+k3b27IGXSZPK/TkldQdDnAplJ07tbMyYPKQ6b97A+61fv2fAq3+9fDk8+mieI+/aa/cevq2bMqX/sDdrVr4ho77st1/uBkpSb4Y4SRqiqVPhyCPzMpCtW2Hlyhzs+lvuvjs/7eKxx/qfomfatD2DXe9lv/32fD1jRg6kkjqb/5urUHbipN3Gj883ScydO/i+O3bkwLdq1d7LY4/t/vrRR/OkyatW5QmU+7PvvjnczZiRv64vg72ePNn/f6V2YYiTpBYwZky+pu7AAxv/ns2b9wx4vQPfqlWwZg2sXp3v1F2zJi+9b97oaezY/kNez6+nT4d99tl7sQMoNY//u6lQduKk5pk4sfFOX92uXfmavnqgW72676/rrx95JE+4vHo1rF07+PEnTeo73PW1GASlkfF/FRXKECe1tlGjdoel+fOH9r07d+YgVw90jSyPP56ncKm/3rx58PP0DILTpuVrEAdaBtpn7NjhfEpSezDESZIaMnp0HlKdMWP4x9i2Lc/X12gIXLcudw5XrMjr+rJtW2PnGz9+8KDXVyCcPDnfRTx58p5fT5jgP1LVOgxxKpSdOEkDGTdu9120I7Ft2+5AVw96gy31/VauhD/8Yff2DRv6vzO4t1Gj9g52Pb/ua1sj70+e7DCyhs5fGUlS2xk3Lt99u99+Iz/Wrl2wadOeYW/jxrxs2LD3131tW7s2zxfYc9umTUOrY/z4vsPdpEl5mThx99eDLf3ta1DsLP7nVKHsxElqN6NG5eA0ZcrQ7g4eTD0cDhYAB9q2eXO+y3jTpj2XjRsHvsu4P2PHNh74Bttv4sQ8vNzXeuLEPPyuchniJEkqQc9wOHt2scdOCbZv3x3qNm/eO+j1t/S376OP9r290aHm3saM2TvYDRT6GtnWyHujRhX7WbcyQ5wKZSdOksoXkYeUx43LU7WUJaV8/WF/YXDLloHXA723cmX/7w03OEL+TPoKfePH59cDrRvZp5FjjBnTnL8HDXGSJKlPEbuDyb77Nuec9S7jYCGwkaDYc5+tW/Prxx/P6/rr3uvhDFP3NmrU8IPgUBjiVCg7cZKkkejZZdxnn+aff8eO/gNekev16/P1jr23D4UhTpIkqWbMmLxMnlzN+YfSBOmiy//UDHbiJElqDkOcCmWIkySpOQxxkiRJbcgQp0LZiZMkqTkMcZIkSW3IEKdC2YmTJKk5WibERcT8iPhuRKyJiEci4tMRMabH+2dFxJKI2BQRN0TEvCrrlSRJqlLLhDjgM8AK4EDgROAM4HyAiJgJXAW8H5gBLAKuqKRKDchOnCRJzdFKIe4Q4GsppS0ppUeA7wPH1t57KbA4pXRlSmkLcAGwICKOqqZUSZKkarXSExs+BZwTETcC+wLPI3feIIe52+o7ppQ2RsS9te1LBjro3XfDc55TSr3qw4oVeW0nTpKkcrVSiPsx8H+BdcBo4DLgv2vvTQFW9tp/LTC1rwNFxJuBNwOMG3cC69aVUK36NGECPPe5cPjhVVciSVJna0qIq3XXzujn7Z8BzwB+AHwOeDo5tH0B+BjwbmADMK3X900D1vd1wJTSJcAlAAsXLkw///nI6pckSWo1TbkmLqV0Zkop+llOI9+scDDw6ZTS1pTSY8AXgefXDrEYWFA/XkRMBg6tbZckSeo6LXFjQ0ppFXAfcF5EjImI6cDr2H0d3NXAcRFxdkRMAD4A3J5SGvB6OEmSpE7VEiGu5qXAc8nXvt0D7ADeAZBSWgmcDVwErAFOAc6ppkxJkqTqtcyNDSml3wBnDvD+tYBTikiSJNFanThJkiQ1yBAnSZLUhgxxkiRJbcgQJ0mS1IYMcZIkSW3IECdJktSGDHGSJEltyBAnSZLUhgxxkiRJbcgQJ0mS1IYMcZIkSW3IECdJktSGDHGSJEltyBAnSZLUhiKlVHUNpYqI9cDvq66jy8wEVlVdRJfxM28+P/Pm8zNvPj/z5jsypTS1kR3HlF1JC/h9Smlh1UV0k4hY5GfeXH7mzedn3nx+5s3nZ958EbGo0X0dTpUkSWpDhjhJkqQ21A0h7pKqC+hCfubN52fefH7mzedn3nx+5s3X8Gfe8Tc2SJIkdaJu6MRJkiR1HEOcJElSG+rYEBcRMyLi6ojYGBFLI+LPqq6p00XEWyNiUURsjYj/qLqebhAR4yPi0trv+PqIuDUinld1XZ0sIr4UEcsjYl1E3BURb6q6pm4REYdHxJaI+FLVtXSDiLix9nlvqC3OudoEEXFORNxZyy/3RsTp/e3byfPE/RuwDZgNnAhcExG3pZQWV1pVZ3sY+AjwHGBixbV0izHAg8AZwAPA84GvRcTxKaX7qyysg/098MaU0taIOAq4MSJuTSndUnVhXeDfgJurLqLLvDWl9O9VF9EtIuKPgY8BrwB+BRw40P4d2YmLiMnA2cD7U0obUko3Ad8CXlNtZZ0tpXRVSum/gceqrqVbpJQ2ppQuSCndn1LalVL6DnAfcFLVtXWqlNLilNLW+svacmiFJXWFiDgHeBy4ruJSpDJ9CLgwpfSL2p/py1JKy/rbuSNDHHAEsDOldFePbbcBx1ZUj9QUETGb/Ptvx7lEEfGZiNgELAGWA9+tuKSOFhHTgAuBd1ZdSxf6+4hYFRE/i4gzqy6mk0XEaGAhMCsi7omIhyLi0xHR78hWp4a4KcDaXtvWAg09i0xqRxExFvgycFlKaUnV9XSylNL55D9PTgeuArYO/B0aoQ8Dl6aUHqy6kC7zHuCJwEHkucu+HRF2ncszGxgLvIz8Z8uJwJOA9/X3DZ0a4jYA03ptmwasr6AWqXQRMQq4nHwd6FsrLqcrpJR21i7VmAucV3U9nSoiTgSeBXyy4lK6Tkrplyml9SmlrSmly4Cfka+7VTk219b/mlJanlJaBXyCAT7zTr2x4S5gTEQcnlK6u7ZtAQ4xqQNFRACXkv8V9/yU0vaKS+o2Y/CauDKdCcwHHsi/6kwBRkfEMSmlJ1dYVzdKQFRdRKdKKa2JiIfIn3NDOrITl1LaSB7iuDAiJkfEqcCLyZ0KlSQixkTEBGA0+Q/ZCRHRqf9QaCUXA0cDL0opbR5sZw1fROxfu/1/SkSMjojnAK8Erq+6tg52CTkkn1hbPgtcQ74LXiWJiOkR8Zz6n+MR8SrgGcAPqq6tw30ReFvtz5p9gbcD3+lv507+C/Z84AvACvLdkuc5vUjp3gd8sMfrV5PvtLmgkmq6QETMA84lX5P1SK1TAXBuSunLlRXWuRJ56PSz5H8ELwXenlL6ZqVVdbCU0iZgU/11RGwAtqSUVlZXVVcYS54y6ihgJ/kmnpeklJwrrlwfBmaSRxS3AF8DLupvZ5+dKkmS1IY6cjhVkiSp0xniJEmS2pAhTpIkqQ0Z4iRJktqQIU6SJKkNGeIkSZLakCFOUkeLiMXNenB3RBwTEYtKOO5VEfHcoo8rqb05T5yktlab/LVuEnni4521102d9DgivgFcmVL6asHHPRm4OKV0UpHHldTeDHGSOkZE3A+8KaV0bQXnPpD8fOY5KaUtJRz/buCVKaXCO32S2pPDqZI6WkTcHxHPqn19QURcGRFfioj1EfHbiDgiIv42IlZExIMR8ewe37tPRFwaEcsjYllEfCQiRvdzqj8Gft0zwNXO/dcRcXtEbKwda3ZEfK92/mtrz0ek9ozKL0XEYxHxeETcHBGzexz/RuAFhX9AktqWIU5St3kRcDmwL3Ar+YHeo4CDgAuBz/XY9zJgB3AY8CTg2cCb+jnu8UBfz5U8mxzwjqid+3vA35GfjzgK+Mvafq8D9gEOBvYD3gJs7nGcO4EFDf+UkjqeIU5St/lpSukHKaUdwJXALOAfUkrbga8C8yNieq0L9jzyA+43ppRWAJ8EzunnuNOB9X1s/9eU0qMppWXAT4FfppRuTSltBa4mh0OA7eTwdlhKaWdK6ZaU0roex1lfO4ckATCm6gIkqcke7fH1ZmBVSmlnj9cAU4A5wFhgeUTU9x8FPNjPcdcAUxs4X+/XU2pfX07uwn01IqYDXwLeWwuX1I79eH8/lKTuYydOkvr2IPlO15kppem1ZVpK6dh+9r+dPGQ6LCml7SmlD6WUjgGeDrwQeG2PXY4Gbhvu8SV1HkOcJPUhpbQc+CHwTxExLSJGRcShEXFGP9/yI+DJETFhOOeLiD+KiONrN06sIw+v7uyxyxnk6+kkCTDESdJAXguMA+4gD5d+HTiwrx1TSo8C1wMvHua5Dqgdfx35JoYfk4dUiYinABtTSr8a5rEldSDniZOkgkTEMeQ7Wk9OBf7hWptE+NKU0neLOqak9meIkyRJakMOp0qSJLUhQ5wkSVIbMsRJkiS1IUOcJElSGzLESZIktSFDnCRJUhsyxEmSJLUhQ5wkSVIb+n/INCFJ6LZbUQAAAABJRU5ErkJggg==\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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JO2OIUy0m0okDQ5wkSTtjiFMt7MRJklQvQ5xqYSdOkqR6GeJUi/F24vbYA6ZONcRJkrQzhjjVYryduAjYe29DnCRJO2OIUy3G24kD14qTJGk0DHGqxXgX+wVDnCRJo2GIUy3GO5wKhjhJkkbDEKdaTHQ49eGHYdOmamuSJKmXGOJUi4l24gAeeKC6eiRJ6jWGONViop04cEhVkqQdMcSpFlV04gxxkiSNzBCnWtiJkySpXoY41WIinbi99iqL/hriJEkamSFOtZjIOnFTppQgZ4iTJGlkhjjVYiLDqeBacZIk7YwhTrWYyHAqGOIkSdoZQ5xqUUUnznXiJEkamSFOtZhoJ27BAnjooa3nkSRJ2zLEqRYT7cTtvTc88QSsXFldTZIk9RJDnGpRRScO4MEHq6lHkqReY4hTLSbaiTPESZK0Y4Y41WIi68RBGU4FJzdIkjQSQ5xq4XCqJEn1MsSpFhMdTp03D6ZNM8RJkjQSQ5xqMdFOXETpxjmcKknS8AxxqsVEO3FQQpydOEmShmeIUy0m2omDMrnBTpwkScMzxKkWduIkSaqXIU61qKIT56O3JEkamSFOtaiiE7f33uU8Dz9cTU2SJPUSQ5xqMdHFfsG14iRJ2hFDnGpR1XAqGOIkSRqOIU61qGo4FZyhKknScAxxqoWdOEmS6mWIUy2q6MTtuitMn26IkyRpOIY41aKKTpyP3pIkaWSGONVi06aynTp1YudxwV9JkoZniFMt1q0r2112mdh5fPSWJEnDM8SpFmvXwqxZE7snDuzESZI0EkOcarFuXQlxE7VgASxfvnWihCRJKgxxqkVVIc5Hb0mSNDxDnGqxdi3Mnj3x87hWnCRJwzPEqRZVDqeCIU6SpKEMcarFwMSGifLRW5IkDc8Qp1qsW+dwqiRJdTLEqRZVDafOnQszZhjiJEkayhCnWlQV4nz0liRJwzPEqRYbN5YOWhVc8FeSpO0Z4lSLjRth2rRqzuWjtyRJ2l7XhbiI2D0iLo2IdRFxZ0T8XtM1aXubNlUX4uzESZK0vSlNFzAO/wRsAhYAxwGXRcR1mXlDo1Xpf23ZAk88AdOnV3O+wY/emjy5mnNKktTtuqoTFxGzgNOAd2fm2sy8CvgK8KpmK9Ngjz9etlUOp27ZAitWVHM+SZJ6QVeFOOAQYHNm3jxo33XAkYMPiojXR8TSiFi6fPnythaocj8cVDucCg6pSpI0WLeFuNnA6iH7VgNzBu/IzE9k5pLMXDJ//vy2Fadi06ayrbITB4Y4SZIG67YQtxaYO2TfXGBNA7VoBAMhrsp74sAZqpIkDdZtIe5mYEpEHDxo37GAkxo6SNWdOIdTJUnaXleFuMxcB1wCnBMRsyLiROClwEXNVqbBqr4nbs4c2GUXQ5wkSYN1VYhrOQvYBXgI+HfgTJcX6SxVD6f66C1JkrbXdevEZeZK4DearkMjq3o4FVzwV5KkobqxE6cOV/VwKvjoLUmShjLEqXJ24iRJqp8hTpWr+p44KCFuxYry6C1JkmSIUw3q6MQNPHrLB3BIklQY4lS5Ou6Jc604SZK2ZYhT5erqxIEhTpKkAYY4Va6uiQ3gDFVJkgYY4lS5gckHkydXd06HUyVJ2pYhTpXbsqVsqwxxs2fDzJl24iRJGmCIU+UGOnGTKvzTFeGCv5IkDWaIU+Xq6MSBIU6SpMEMcarcQIirshMHhjhJkgYzxKlydQynAuyzjyFOkqQBhjhVrs7h1JUrty4mLElSPzPEqXJ1DqcCPPRQteeVJKkbGeJUubqGUwdCnEOqkiQZ4lSDOodTAe6/v9rzSpLUjQxxqpydOEmS6meIU+Xquidur73K1hAnSZIhTjWoazh12jTYYw9DnCRJYIhTDQaGUyOqP7drxUmSVBjiVLktW8pQah0hzqc2SJJUGOJUuYEQVwdDnCRJhSFOldu8uf4Ql1nP+SVJ6haGOFVuy5bqJzUM2HtvWL8eHn20nvNLktQtDHGqXN3DqeCQqiRJhjhVru7hVDDESZJkiFPl6h5OBUOcJEmGOFXOTpwkSfUzxKlydXbidt8dpk41xEmSZIhT5eqc2BDhWnGSJIEhTjWoczgVDHGSJIEhTjWoczgVSoi7//76zi9JUjcwxKlydQ6ngp04SZLAEKcatGM4dfnych1JkvqVIU6Va8dw6pYtJchJktSvDHGqXDuGU8EhVUlSfzPEqXJ1D6fus0/ZGuIkSf3MEKfKtWM4FQxxkqT+ZohT5eruxC1YULaGOElSPzPEqXJ1d+JmzoS5c10rTpLU3wxxqlzdExvAteIkSTLEqXJ1D6eCT22QJMkQp8rVPZwKsHChIU6S1N8McapcO4ZTFy6E++6DzHqvI0lSpzLEqXLtGE5duBAeewwefbTe60iS1KkMcapcO4ZT9923bO+9t97rSJLUqQxxqly7OnFQhlQlSepHhjhVrl33xIEhTpLUvwxxqly7ZqeCw6mSpP5liFPl2jGcOnMmzJtnJ06S1L+mjOagiHge8BrgSGAOsAa4AbggM79VW3XqSu3oxMHWZUYkSepHOw1xEfEW4O3AJ4H/AFYDc4FjgQsj4oOZ+ZFaq1RXacc9cVBmqDqcKknqV6PpxP0p8KuZuWzI/ksi4t+BKwFDnP5XO4ZToXTilg39UylJUp8YzX9qZwEjDVo9AMysrhz1gnYOp95/f7meJEn9ZjQh7j+A/4yIUyNifkRMi4g9I+JU4FLgi/WWqG7TzuHUJ56AFSvqv5YkSZ1mNP+pfQPw38CFwIPA+tb2QuBHwJm1Vaeu1M7hVPC+OElSf9rpf2ozc1Nm/nlm7gfsDiwC9sjM/Vr7Nw0cGxEn1lirukRme0OcM1QlSf1oVEuMDMjMR4BHdnDI1ykzV9XHMttznYHnpxriJEn9qOp+SVR8PnWpaMOfhAULynUcTpUk9aOqQ9y4ejARMT0izo+IOyNiTURcGxEvHHLMqRGxLCIei4grI2JRNSWrau3qxE2dCnvtZSdOktSfOuWxW1OAu4FnA7sC7wa+EBGLASJiT+CS1v7dgaXA5xupVKPSjk4clCFVQ5wkqR+N6Z64umTmOuDsQbu+GhG3A8cDdwC/BdyQmRcDRMTZwIqIOGyYRYjVsHZ14qBMbrjnnvZdT5KkTtGR98RFxALgEMrzWaE8s/W6gfdboe+21n51oHZ14nx+qiSpX40pxEXEHhHxqoh4e+v7hRGx38D7mTlnogVFxFTgM8CFg7pssynPbB1sNTDs9SLi9RGxNCKWLl++fKIlaYza2Ynbd1946CF4/PH2XVOSpE4w6hAXEc8GfgG8gnJvGsDBwHmj+NnvRESO8Lpq0HGTgIuATcAbB51iLdsvXTIXWDPc9TLzE5m5JDOXzJ8/f7S/oirUzk4c2I2TJPWfsXTi/h74ncx8AfBEa9+PgRN29oOZeUpmxgivkwAiIoDzgQXAaZk5uLdyA3DswDcRMQs4kK3Dreogme0LcfvvX7beFydJ6jdjCXGLM/Pbra8HBsw2Ud3kiPOAw4Ffz8z1Q967FDgqIk6LiBnAe4DrndTQmdo5nDoQ4u66q33XlCSpE4wlxN0YEc8fsu+5wM8mWkRrzbczgOOAByJibev1CoDMXA6cBpwLrAKeDpw+0euqPu3uxN19d3uuJ0lSpxhLF+2tlKU/LgN2iYiPA78OvHSiRWTmnexkZmtmXg4cNtFrqX7t7MTNmQO77mqIkyT1n1F34jLzR5T70m4APgXcDpyQmVfXVJu6WLs6cVC6cYY4SVK/GdP9bJl5L/ChmmpRj2hnJw4McZKk/rTDEBcRFzGK56Fm5qsrq0g9oZ2duCc9CZYubd/1JEnqBDsbTr2V8mSE2yiL6/4GMBm4p/WzLwUeqa88daMmOnHLl8P6oXOaJUnqYTvsxGXm+wa+johvAi/OzO8P2ncSWxf+lf5Xu++Jg7JW3MEHt++6kiQ1aSxLjDwD+NGQfT8GfqW6ctQLmujEgffFSZL6y1hC3LXAByJiF4DW9lzgf2qoS12uiU6cIU6S1E/GEuJeA5wIrI6IByn3yJ0EOKlB22jnY7cA9tuvbA1xkqR+MuolRjLzDuCZEbE/sBC4PzN92JG20+7h1BkzYP58Q5wkqb+MpRNHROwG/CrwHOCU1vfSdtrZiQPXipMk9Z9Rh7iI+BXKUiNvAI6hPOv0ttZ+6X+1uxMHZa04Q5wkqZ+M5YkNfw+clZmfG9gREb8D/APwtIrrUpdrohN3xRXtvaYkSU0ay3DqIcAXhuz7InBQdeWoFzTRidt/f3j00fKSJKkfjCXE3QKcPmTfyylDrNI2mujEgUOqkqT+MZbh1DcDX42IPwbuBBYDBwMvqb4sdbOmOnFQQtyRR7b/+pIktdtYlhj574g4EHgxZYmR/wS+lpkr6ypO3aupTtxdLnojSeoTY+nEkZmrgE/XVIt6RBOduIULYcoUuPPO9l9bkqQmjDrERcQBlMdsHQfMHvxeZj6p2rLU7drdiZsypXTj7rijvdeVJKkpY+nEfZYyieGtwGP1lKNe0O7Hbg044AC4/fb2X1eSpCaMJcQdCZyYmVvqKka9oYnhVIDFi+HrX2/m2pIktdtYlhj5HvCUugpRb2mqE3f//bB+ffuvLUlSu42lE3cH8M2IuAR4YPAbmfmeKotSd2uyEwdlhuqhhzZTgyRJ7TKWEDeLsqzIVGD/Qfsb+k+2OllTnTgo98UZ4iRJvW4s68S9dmfHRMTvZua/T6wkdbumO3HOUJUk9YOx3BM3Gh+v+HzqUk104vbZB6ZNc4aqJKk/VB3iGvhPtzpNU524SZNg0SI7cZKk/lB1iPP+OAHNdOLAteIkSf2j6hAnNdaJg3JfnCFOktQPdhriIsKgpzFrshO3YgWsXdvM9SVJapfRBLR7I+JDEXHUKI69a6IFqfs19dgt2LrMiPfFSZJ63WhC3BuAA4CrI+KnEfEnETF/uAMzczRBTz2u6eFUgF/+srkaJElqh52GuMz8cma+HNiHsoTIy4G7I+IrEXFaREytu0h1n6Y6cQcdVLa33dbM9SVJapdR3++WmY9k5scz8yTgcGAp8HfA/XUVp+7UZCdu991h3jy49dbmapAkqR3GPGkhIqYDTwOeDiwAflZ1Uep+TXXiIuDggw1xkqTeN+oQFxEnRcQngAeBvwR+BBySmb9aV3HqTk124qAMqRriJEm9bjRLjJwdEbcB/9na9eLMPCQz35+Zd9ZbnrpVU504KCHujjtg06bmapAkqW5TRnHMM4B3Al/KzA0116Me0AmduC1bSpA75JBma5EkqS47DXGZ+YJ2FKLe0nQnDsqQqiFOktSrfBqDKtfkYr+wbYiTJKlXGeLUc+bPh7lz4ZZbmq5EkqT6GOJUuaY7cRHOUJUk9T5DnCrX9MQGMMRJknqfIU61aLITB1uXGXn88WbrkCSpLoY4Va5TOnFPPFGCnCRJvcgQp1o03Yk77LCy/cUvmq1DkqS6GOJUuU7oxA2EuJtuarYOSZLqYohTLZruxO22GyxYYIiTJPUuQ5wq1wmdOIDDD4dly5quQpKkehjiVIumO3FQhlRvuqlzQqUkSVUyxKlyTS/2O+Dww+GRR+DBB5uuRJKk6hni1LMOP7xsHVKVJPUiQ5wq1ymdOGeoSpJ6mSFOleuUe9D22w9mzbITJ0nqTYY41aITOnERWyc3SJLUawxxqlyndOKg3BdniJMk9SJDnGrRCZ04gCOOgHvugdWrm65EkqRqGeJUuU7qxB19dNn+/OfN1iFJUtUMcapFp3TijjmmbH/2s2brkCSpaoY4Va6TOnH77w+77grXX990JZIkVcsQp1p0SicuogypGuIkSb2m40JcRBwcERsi4tND9p8aEcsi4rGIuDIiFjVVo3asUxb7HXDMMWU4tZM6hJIkTVTHhTjgn4CrB++IiD2BS4B3A7sDS4HPt780daNjjoFHH4W77mq6EkmSqtNRIS4iTgceAb495K3fAm7IzIszcwNwNnBsRBzW3go1Wp3WiQOHVCVJvaVjQlxEzAXOAd46zNtHAtcNfJOZ64DbWvvVQTpxyPKoo8rWECdJ6iUdE+KA9wPnZ+bdw7w3Gxi6XOtqYM5wJ4qI10fE0ohYunz58orL1Gh0Uiduzhw44ABDnCSpt7QlxEXEdyIiR3hdFRHHAc8F/m6EU6wF5g7ZNxdYM9zBmfmJzFySmUvmz59f2e+hnevEThyUIdXrrtv5cZIkdYsp7bhIZp6yo/cj4s3AYuCuKC2c2cDkiDgiM58K3AD8/qDjZwEHtvarA3VSJw7gqU+Fr3wF1qwpnTlJkrpdpwynfoISyo5rvT4GXAY8v/X+pcBREXFaRMwA3gNcn5nL2l+qdqRTO3FLlpTarr226UokSapGR4S4zHwsMx8YeFGGTzdk5vLW+8uB04BzgVXA04HTGytYO9Vpnbjjjy/ba65ptg5JkqrSluHUscrMs4fZdzngkiIdbqAT12khbsGC8giupUubrkSSpGp0RCdOaofjjzfESZJ6hyFOlerUThyU++JuvhlWD12sRpKkLmSIU99YsqRsf/rTZuuQJKkKhjhVqpM7cQOTGxxSlST1AkOcKtWpS4wA7LknLF5siJMk9QZDnGrRiZ04gKc/HX74w6arkCRp4gxxqlQnd+IAnvlMuPvu8pIkqZsZ4lSLTu3EnXhi2f73fzdbhyRJE2WIU6U6vRN37LEwcyb84AdNVyJJ0sQY4lSLTu3ETZlS7oszxEmSup0hTpXq5CVGBpx4Ilx3Haxd23QlkiSNnyFOfefEE2HzZvjxj5uuRJKk8TPEqVLd0Il7xjNKfQ6pSpK6mSFOfWfePDjmGPjud5uuRJKk8TPEqVLd0IkDOPXU0olbv77pSiRJGh9DnCrV6UuMDHjOc2DjRteLkyR1L0OcatHpnbhnPassN3LFFU1XIknS+BjiVKlu6cTNmQMnnADf/nbTlUiSND6GONWi0ztxUIZUr74aVq9uuhJJksbOEKdKdUsnDsrkhi1bnKUqSepOhjjVohs6cb/yK+U5qt/8ZtOVSJI0doY4VapblhgBmD4dnvtcuOyy7uogSpIEhjj1uRe/GO68E268selKJEkaG0OcKtVNnTiAF72obC+7rNk6JEkaK0Oc+tp++8GxxxriJEndxxCnSnVbJw7gJS8pj+BatarpSiRJGj1DnCrVjRMEXvIS2LwZvv71piuRJGn0DHGqRTd14k44ARYuhC9+selKJEkaPUOcKtWNnbhJk+C000onbu3apquRJGl0DHGqRTd14gBe/nLYsMEJDpKk7mGIU6W6sRMH8Mxnwt57w8UXN12JJEmjY4hTLbqtEzd5chlS/drXHFKVJHUHQ5wq1Y1LjAz47d+G9evhy19uuhJJknbOECe1nHQSLF4MF17YdCWSJO2cIU6V6uZO3KRJ8KpXweWXw733Nl2NJEk7ZoiTBnn1q0sQ/cxnmq5EkqQdM8SpUt3ciQM46KAyU/XCC7t3pq0kqT8Y4lSpXgg+r30t3Hgj/PCHTVciSdLIDHGqRbd24gB+93dh7lz4539uuhJJkkZmiFOleqETN2sWvOY1ZeHfhx5quhpJkoZniFMturkTB3DmmbBpE3zqU01XIknS8AxxqlS3T2wYcNhh8JznwMc+Bps3N12NJEnbM8RJIzjrLLjzTrjssqYrkSRpe4Y4VapXOnEAL30pLFoEH/pQ05VIkrQ9Q5w0gilT4K1vhR/8AK66qulqJEnaliFOleqlThzAH/4h7LknfPCDTVciSdK2DHHSDsycCW96E3z1q/DznzddjSRJWxniVKle68QB/NEflbXj/vqvm65EkqStDHGqVC8s9jvUHnuUIPfZz8INNzRdjSRJhSFOteilThzA298Oc+bAu9/ddCWSJBWGOFWqFztxULpxb30rXHopLF3adDWSJBniVJNe68QBvPnNJcy9851NVyJJkiFOFevFiQ0D5s6Fv/gL+K//gm98o+lqJEn9zhAnjcEb3wgHHQRveQs8/njT1UiS+pkhTpXq5U4cwLRp8OEPw7JlcN55TVcjSepnhjhpjF7yEnje8+C974UVK5quRpLUrwxxqlSvd+Kg/G5/93ewdi287W1NVyNJ6leGOGkcjjgC3vEOuPDCMtFBkqR2M8SpUv3QiRvwrnfBoYfCGWfAunVNVyNJ6jeGOFWqVxf7Hc6MGfDJT8Idd8B73tN0NZKkftNRIS4iTo+ImyJiXUTcFhEnD3rv1IhYFhGPRcSVEbGoyVq1Y/3QiQM4+WR4wxvg7/8errqq6WokSf2kY0JcRPwa8EHgtcAc4FnAL1vv7QlcArwb2B1YCny+mUq1I/3UiRvwoQ/B4sXwylfCI480XY0kqV90TIgD3geck5k/yswtmXlvZt7beu+3gBsy8+LM3ACcDRwbEYc1Vax2rF86cQBz5sBnPwv33ANnntmfQVaS1H4dEeIiYjKwBJgfEbdGxD0R8dGI2KV1yJHAdQPHZ+Y64LbWfnWQfprYMNjTnw5nnw2f+xxcdFHT1UiS+kFHhDhgATAVeBlwMnAc8BTgXa33ZwOrh/zMasqw63Yi4vURsTQili5fvryWgqWh/vzPyz1yZ50FP/9509VIknpdW0JcRHwnInKE11XA+tah/5iZ92fmCuDDwIta+9cCc4ecdi6wZrjrZeYnMnNJZi6ZP39+Hb+SRtCvnTiAyZNLJ272bPjN3/T+OElSvdoS4jLzlMyMEV4nZeYq4B5gpLuJbgCOHfgmImYBB7b2Sx1j4UL44hfLsiOvfCVs2dJ0RZKkXtUpw6kAFwBvioi9ImI34M3AV1vvXQocFRGnRcQM4D3A9Zm5rJlSNZJ+7sQNOOkk+MhH4LLLXD9OklSfTgpx7weuBm4GbgKuBc4FyMzlwGmt71cBTwdOb6ZMaefOPBP+8A/h3HPh/PObrkaS1IumNF3AgMx8HDir9Rru/csBlxTpcHbiigg477yy7MgZZ8C++8ILXtB0VZKkXtJJnTj1ANdI22rqVLj4Yjj6aHjZy+Caa5quSJLUSwxxqkW/d+IGzJlT7o3bc0943vPg+uubrkiS1CsMcaqUw6nbW7gQrrgCdtkFnvtcuPHGpiuSJPUCQ5zUBk9+cglykyfDqafCTTc1XZEkqdsZ4lQpO3EjO+QQ+Pa3y2d08snwk580XZEkqZsZ4qQ2OuII+MEPYO5ceM5zSqiTJGk8DHGqlJ24nTvwQLjqKjjgAHjRi+Dzn2+6IklSNzLESQ1YuBC+9z044QQ4/fTyZAcf0SVJGgtDnCplJ270dtsNLr8cXvtaeP/74eUvh3Xrmq5KktQtDHFSg6ZPL4/l+vCH4Utfgmc8w5mrkqTRMcSpUnbixi4C3vIW+MY34MEHYckSuOACn34hSdoxQ5wqZfAYv1/7Nfif/4GnPx3+4A/gla+EVauarkqS1KkMcaqFnbjxWbgQvvUtOOecMmv1yCPhK19puipJUicyxKlSDqdO3OTJ8O53l8WA58+Hl74UXvEKWLGi6cokSZ3EECd1qKc+Fa6+Gs4+G77wBTj0UDjvPNi8uenKJEmdwBCnStmJq9a0afDe98K118Ixx8BZZ8Hxx5c15iRJ/c0QJ3WBo46CK66Aiy8ukx2e/Wz4jd+An/2s6cokSU0xxKlSduLqEwEve1lZR+6cc+DKK+HYY+H3fg9uvrnp6iRJ7WaIk7rMzJll4sPtt8Of/Rl8+ctwxBHw6lfbmZOkfmKIU6XsxLXP7rvDBz4Av/wl/PEfwyWXlPvmXvjCMvTqmn2S1NsMcVKXW7CgPLbrrrvgL/+yTII49VR4ylPgYx+DNWuarlCSVAdDnCplJ645u+8O73wn3HEHfPKTZd+ZZ5YFhM84A37600bLkyRVzBCnSjmE17wZM+B1rysduR/9qEyGuOiisjTJscfChz4Ed9/ddJWSpIkyxKkWduKaF1Gew3rBBXDvvfCP/wi77ALveAcsWgSnnFI6ditXNl2pJGk8DHGqlMOpnWm33eCNbyyduZtvLgsI33cfvP71sNde8JznwD/8A9x5Z9OVSpJGyxAn9ZmDDy4h7he/KI/1esc74MEH4U/+BBYvLo/7et/7yrNbfcSXJHUuQ5wqZSeue0TAkiVw7rlwww2lQ/c3f1PWoXvf+8pQ7Pz58Nu/XYZd7dJJUmeZ0nQBkjrDwQfD295WXitWwLe/Df/1X/DNb5bHfQ0cc8opcPLJ5bVokYFdkppiiFOl7MT1hj33hN/5nfLKhGXLSqD71rfgC1/YuoTJ/vtvDXQnnwyHHw6T7O9LUlsY4iTtUEQJZ4cfXu6b27wZfv5z+P73y+vKK+Gzny3HzplTljJ52tPK64QT4ElPMtRLUh0McaqUnbjeN3lyWW/u2GPLjNdMuO02uOqqMlHi6qvhIx+BTZvK8fPnbw11xx5bHg12wAF27CRpogxxkiYkAg46qLxe85qyb+NG+NnPygzXgWD39a9vDfmzZ8PRR5dANxDsjj4a5s5t7NeQpK5jiFOl7MQJYPr0MvN1yZKt+9atK7Ngr7++vK67Dj7/efj4x7ces2hRGbY97LBtX3vt5Z8pSRrKEKdK+dgtjWTWrHKP3AknbN2XCffcUwLd9deXe+2WLYPvfQ8ee2zrcbvttm2oO+QQOPBAePKTy3klqR8Z4lQLuyYajYgyw3X//eElL9m6f8uWEu6WLYObbirbZcvKkOwFF2x7jr33LoFuuNeee/pnUVLvMsSpUg6nqgqTJpVZrU96Ejzvedu+98gjcOutZTLF4NcVV8C//du2x86ZUyZRLFpUzjV0u/feTrCQ1L0McZK6yrx5299vN2DDBrj99m3D3e23l6dNfP/7JQAONnVq6QIOBMaBcLfffrBwYXntsYf/UyKpMxniVCk7cWrSjBlb17QbzqOPwl13ldedd2779ZVXwr33lqHcwaZNK2Fu3323BruBrwfvmz27/t9PkgYzxEnqG3PnwlFHlddwnniiBLn77ht+e/318I1vwJo1w597n33KTNoFC3b8mjmz3t9TUn8wxKlSduLUzaZMKUOqixbt+Lg1a7YNeANf338/PPhgWSPv8su3H74dMHv2yGFv/vwyIWPgtccepRsoSUMZ4iRpjObMgUMPLa8d2bgRli8vwW6k1y23lKddPPzwyEv0zJ27bbAb+tpjj22/3333Ekgl9Tb/NVel7MRJW02fXiZJ7Lffzo994okS+Fas2P718MNbv37wwbJo8ooVZQHlkey2Wwl3u+9evh547ez7WbP891fqFoY4SeoAU6aUe+r22Wf0P7N+/bYBb2jgW7ECVq2ClSvLTN1Vq8pr6OSNwaZOHTnkDf563jzYddftX3YApfbxXzdVyk6c1D677DL6Tt+ALVvKPX0DgW7lyuG/Hvj+gQfKgssrV8Lq1Ts//8yZw4e74V4GQWli/FdFlTLESZ1t0qStYWnx4rH97ObNJcgNBLrRvB55pCzhMvD9+vU7v87gIDh3brkHcUevHR0zdep4PiWpOxjiJEmjMnlyGVLdfffxn2PTprJe32hD4KOPls7hQw+V7cBr06bRXW/69J0HveEC4axZZRbxrFnbfj1jhv+Tqs5hiFOl7MRJ2pFp07bOop2ITZu2BrqBoLez18Bxy5fDL3+5df/atSPPDB5q0qTtg93gr4fbN5r3Z81yGFlj5x8ZSVLXmTatzL7dY4+Jn2vLFnjssW3D3rp15bV27fZfD7dv9eqyXuDgfY89NrY6pk8fPtzNnFleu+yy9eudvUY61qDYW/zHqUrZiZPUbSZNKsFp9uyxzQ7emYFwuLMAuKN969eXWcaPPbbta926Hc8yHsnUqaMPfDs7bpddyvDycNtddinD76qXIU6SpBoMDocLFlR77kx4/PGtoW79+u2D3kivkY598MHh9492qHmoKVO2D3Y7Cn2j2Tea9yZNqvaz7mSGOFXKTpwk1S+iDClPm1aWaqlLZrn/cKQwuGHDjrc7em/58pHfG29whPKZDBf6pk8v3+9oO5pjRnOOKVPa899BQ5wkSRpWxNZgsttu7bnmQJdxZyFwNEFx8DEbN5bvH3mkbAe+H7odzzD1UJMmjT8IjoUhTpWyEydJmojBXcZdd23/9Z94YuSAV+V2zZpyv+PQ/WNhiJMkSWqZMqW8Zs1q5vpjaYL00e1/agc7cZIktYchTpUyxEmS1B6GOEmSpC5kiFOl7MRJktQehjhJkqQuZIhTpezESZLUHh0T4iJicUR8LSJWRcQDEfHRiJgy6P1TI2JZRDwWEVdGxKIm65UkSWpSx4Q44J+Bh4B9gOOAZwNnAUTEnsAlwLuB3YGlwOcbqVI7ZCdOkqT26KQQdwDwhczckJkPAN8Ajmy991vADZl5cWZuAM4Gjo2Iw5opVZIkqVmd9MSGjwCnR8R3gN2AF1I6b1DC3HUDB2bmuoi4rbV/2Y5Oesst8Pzn11KvhvHQQ2VrJ06SpHp1Uoj7LvB/gUeBycCFwJda780Glg85fjUwZ7gTRcTrgdcDTJt2DI8+WkO1GtaMGfCCF8DBBzddiSRJva0tIa7VXXv2CG//AHgW8E3g48AzKaHtU8AHgbcDa4G5Q35uLrBmuBNm5ieATwAsWbIkf/jDidUvSZLUadpyT1xmnpKZMcLrJMpkhf2Bj2bmxsx8GLgAeFHrFDcAxw6cLyJmAQe29kuSJPWdjpjYkJkrgNuBMyNiSkTMA36frffBXQocFRGnRcQM4D3A9Zm5w/vhJEmSelVHhLiW3wJeQLn37VbgCeAtAJm5HDgNOBdYBTwdOL2ZMiVJkprXMRMbMvN/gFN28P7lgEuKSJIk0VmdOEmSJI2SIU6SJKkLGeIkSZK6kCFOkiSpCxniJEmSupAhTpIkqQsZ4iRJkrqQIU6SJKkLGeIkSZK6kCFOkiSpCxniJEmSupAhTpIkqQsZ4iRJkrqQIU6SJKkLRWY2XUOtImIN8Ium6+gzewIrmi6iz/iZt5+fefv5mbefn3n7HZqZc0Zz4JS6K+kAv8jMJU0X0U8iYqmfeXv5mbefn3n7+Zm3n595+0XE0tEe63CqJElSFzLESZIkdaF+CHGfaLqAPuRn3n5+5u3nZ95+fubt52fefqP+zHt+YoMkSVIv6odOnCRJUs8xxEmSJHWhng1xEbF7RFwaEesi4s6I+L2ma+p1EfHGiFgaERsj4l+brqcfRMT0iDi/9Wd8TURcGxEvbLquXhYRn46I+yPi0Yi4OSJe13RN/SIiDo6IDRHx6aZr6QcR8Z3W57229XLN1TaIiNMj4qZWfrktIk4e6dheXifun4BNwALgOOCyiLguM29otKredh/wl8DzgV0arqVfTAHuBp4N3AW8CPhCRBydmXc0WVgP+yvgDzNzY0QcBnwnIq7NzGuaLqwP/BNwddNF9Jk3Zua/NF1Ev4iIXwM+CPwO8BNgnx0d35OduIiYBZwGvDsz12bmVcBXgFc1W1lvy8xLMvNLwMNN19IvMnNdZp6dmXdk5pbM/CpwO3B807X1qsy8ITM3Dnzbeh3YYEl9ISJOBx4Bvt1wKVKd3geck5k/av2dfm9m3jvSwT0Z4oBDgM2ZefOgfdcBRzZUj9QWEbGA8uffjnONIuKfI+IxYBlwP/C1hkvqaRExFzgHeGvTtfShv4qIFRHxg4g4peliellETAaWAPMj4taIuCciPhoRI45s9WqImw2sHrJvNTCqZ5FJ3SgipgKfAS7MzGVN19PLMvMsyt8nJwOXABt3/BOaoPcD52fm3U0X0mfeATwZ2Jeydtl/RoRd5/osAKYCL6P83XIc8BTgXSP9QK+GuLXA3CH75gJrGqhFql1ETAIuotwH+saGy+kLmbm5davGfsCZTdfTqyLiOOC5wN81XErfycwfZ+aazNyYmRcCP6Dcd6t6rG9t/zEz78/MFcCH2cFn3qsTG24GpkTEwZl5S2vfsTjEpB4UEQGcT/m/uBdl5uMNl9RvpuA9cXU6BVgM3FX+qDMbmBwRR2TmUxusqx8lEE0X0asyc1VE3EP5nEelJztxmbmOMsRxTkTMiogTgZdSOhWqSURMiYgZwGTKX7IzIqJX/0ehk5wHHA78emau39nBGr+I2Ks1/X92REyOiOcDvwtc0XRtPewTlJB8XOv1MeAyyix41SQi5kXE8wf+Ho+IVwDPAr7ZdG097gLgTa2/a3YD3gx8daSDe/k/sGcBnwIeosyWPNPlRWr3LuC9g75/JWWmzdmNVNMHImIRcAblnqwHWp0KgDMy8zONFda7kjJ0+jHK/wTfCbw5M7/caFU9LDMfAx4b+D4i1gIbMnN5c1X1hamUJaMOAzZTJvH8Rma6Vly93g/sSRlR3AB8ATh3pIN9dqokSVIX6snhVEmSpF5niJMkSepChjhJkqQuZIiTJEnqQoY4SZKkLmSIkyRJ6kKGOEk9LSJuaNeDuyPiiIhYWsN5L4mIF1R9XkndzXXiJHW11uKvA2ZSFj7e3Pq+rYseR8R/ABdn5ucqPu8JwHmZeXyV55XU3QxxknpGRNwBvC4zL2/g2vtQns+8MDM31HD+W4DfzczKO32SupPDqZJ6WkTcERHPbX19dkRcHBGfjog1EfGziDgkIv48Ih6KiLsj4nmDfnbXiDg/Iu6PiHsj4i8jYvIIl/o14KeDA1zr2n8aEddHxLrWuRZExNdb17+89XxEWs+o/HREPBwRj0TE1RGxYND5vwO8uPIPSFLXMsRJ6je/DlwE7AZcS3mg9yRgX+Ac4OODjr0QeAI4CHgK8DzgdSOc92hguOdKnkYJeIe0rv114C8oz0ecBPxx67jfB3YF9gf2AN4ArB90npuAY0f9W0rqeYY4Sf3m+5n5zcx8ArgYmA/8dWY+DnwOWBwR81pdsBdSHnC/LjMfAv4OOH2E884D1gyz/x8z88HMvBf4PvDjzLw2MzcCl1LCIcDjlPB2UGZuzsxrMvPRQedZ07qGJAEwpekCJKnNHhz09XpgRWZuHvQ9wGxgITAVuD8iBo6fBNw9wnlXAXNGcb2h389ufX0RpQv3uYiYB3waeGcrXNI69yMj/VKS+o+dOEka3t2Uma57Zua81mtuZh45wvHXU4ZMxyUzH8/M92XmEcAzgZcArx50yOHAdeM9v6TeY4iTpGFk5v3AfwF/GxFzI2JSRBwYEc8e4Ue+BTw1ImaM53oR8asRcXRr4sSjlOHVzYMOeTblfjpJAgxxkrQjrwamATdShku/COwz3IGZ+SBwBfDScV5r79b5H6VMYvguZUiViHgasC4zfzLOc0vqQa4TJ0kViYgjKDNaT8gK/3JtLSJ8fmZ+rapzSup+hjhJkqQu5HCqJElSFzLESZIkdSFDnCRJUhcyxEmSJHUhQ5wkSVIXMsRJkiR1IUOcJElSFzLESZIkdaH/H3a6+ftsWBHFAAAAAElFTkSuQmCC\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 = 5.40 s\n",
"\n",
"Total time = 3191.23 s\n",
"\n",
"End time: 2022-10-30 03:18:05.637408\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: 0:53:15.844726\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_stimulateALL_edgedist3_.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": "713a1670",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"\n",
" \n",
" \n",
"with open('BoundarytoGround1000_20Fibers_v_Abeta0_stimulateALL_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 , 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_edgedist3_.csv', 'w', newline='') as f:\n",
" csv.writer(f).writerows(zip( t , Abeta0_imembrane_node0 , Abeta0_imembrane_node1 , Abeta0_imembrane_node2 , Abeta0_imembrane_node3 , Abeta0_imembrane_node4 , Abeta0_imembrane_node5 , Abeta0_imembrane_node6 , Abeta0_imembrane_node7 , Abeta0_imembrane_node8 , Abeta0_imembrane_node9 , Abeta0_imembrane_node10 , Abeta0_imembrane_node11 , Abeta0_imembrane_node12 , Abeta0_imembrane_node13 , Abeta0_imembrane_node14 , Abeta0_imembrane_node15 , Abeta0_imembrane_node16 , Abeta0_imembrane_node17 , Abeta0_imembrane_node18 , Abeta0_imembrane_node19 , Abeta0_imembrane_node20 , Abeta0_imembrane_node21 , Abeta0_imembrane_node22 , Abeta0_imembrane_node23 , Abeta0_imembrane_node24 , Abeta0_imembrane_node25 , Abeta0_imembrane_node26 , Abeta0_imembrane_node27 , Abeta0_imembrane_node28 , Abeta0_imembrane_node29 , Abeta0_imembrane_node30 , Abeta0_imembrane_node31 , Abeta0_imembrane_node32 , Abeta0_imembrane_node33 , Abeta0_imembrane_node34 , Abeta0_imembrane_node35 )) \n",
" \n",
" \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# with open('stimulateonlyAbeta0_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",
" "
]
},
{
"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_stimulateALL_dist3_.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('imembrane_Abeta0_stimulateALL_dist3_.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",
"# with open('ina_Abeta0_stimulateonlyAbeta0_dist3_.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 ))\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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