{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "functionsTF loaded!\n",
      "********************************************************************************\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from fns import *\n",
    "from fns.functionsTF import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- init with symmetric gap junctions\n",
      "-- init with symmetric gap junctions\n",
      "-- symmetric plasticity change\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2000/2000 [00:06<00:00, 333.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- init with symmetric gap junctions\n",
      "-- init with symmetric gap junctions\n",
      "-- symmetric plasticity change\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2000/2000 [00:05<00:00, 344.14it/s]\n"
     ]
    }
   ],
   "source": [
    "params = []\n",
    "res = []\n",
    "config = load_config()\n",
    "glist = [1,5]\n",
    "    \n",
    "for g in glist:\n",
    "    # number of iterations to run (dt = 0.1ms, T=2000 -> d=200 ms)\n",
    "    T = 2000\n",
    "    \n",
    "    # initialize the model\n",
    "    gpu = TfConnEvolveNet(config=config, T=T)\n",
    "   \n",
    "    # number of excitatory neurons\n",
    "    gpu.NE1=800\n",
    "    # number of inhibitory neurons\n",
    "    gpu.NI1=200\n",
    "    # mean external drive\n",
    "    gpu.nu = 120\n",
    "    # choose on which hardware to run the simulation\n",
    "    gpu.device = '/cpu:0' #'/gpu:0'\n",
    "\n",
    "    # mean gap junction coupling\n",
    "    gpu.g1 = g\n",
    "    # when to start plasticity \n",
    "    gpu.stabTime = np.inf # static network\n",
    "    # when to stop plasticity\n",
    "    gpu.stopTime = np.inf\n",
    "    \n",
    "    # save the spikes\n",
    "    gpu.spikeMonitor = True\n",
    "    # save the individual voltages, currents, etc.\n",
    "    gpu.monitor_single = True\n",
    "\n",
    "    # run the simulation\n",
    "    gpu.runTFSimul()\n",
    "    res.append(gpu)\n",
    "    \n",
    "    # release memory\n",
    "    del gpu  \n",
    "    gc.collect()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raster Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B/Md/0GftNsCTT9ZLlylWrKCv587R174++p4QgD170hXIAAC33kpf222AN9+k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\nWTBkZGR0IQuGjIyMLmTBkJGR0YX/A0Nug2/NZf3MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcf8a0a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,4))\n",
    "\n",
    "glist = [1,5]\n",
    "for i in range(2):\n",
    "    ax = fig.add_subplot(2, 1, i+1)\n",
    "    raster = res[i].raster\n",
    "    x,y = np.where(raster[-1000:,700:800]!=0)\n",
    "    ax.scatter(x+100,y, marker='.', s=10, color='r')\n",
    "    x,y = np.where(raster[-1000:,800:900]!=0)\n",
    "    ax.scatter(x+100,y+110, marker='.', s=10, color='b')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.text(20, 150, 'I', fontweight='bold', fontsize=20 )\n",
    "    ax.text(20, 50, 'E', fontweight='bold', fontsize=20 )\n",
    "plt.xlabel('Time [100 ms]')\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(left=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voltage Traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fjhewr8ODMrMSnYN+RHq9nKawolEHjVKW0t1FIYYHLwDdQ37sOuaGLyjOdGSU\neFKBUUvDbFDgaJ8PKgUFl4+DSSfHkjotfAEONaVqVBWLFbBmM0gLggCnl8Ohbi8UcgpHen0IMjwM\nGhr15WqsXmhA95AfOw44YdDQKC9Uoii8PXZVkx4FevkE0Y8UdvcGuGjfSkTs4M/zAkKsALVS/E2D\nDI+jfT68f9gFGSUejKlUyODxcxgYCwIATDo51i01omPAj4YKNdw+DlZ7COMuBq6Ys8sKDXJ4/OJv\ndNZK07zvm9PLYtzJoKpEBUEQj3aqL1fD4WFRoJen9MgiIrhxiD0dleUEyCixM/UMB7C7zYMQw8Og\npeEP11gtNimwvEGX8MEIMaJYBRkBe9rc0KhkGLEzCDI8mqs0EACMOxmEWAFKOYXFtVoIAIIhHvXl\nahzs9sLuZjE0HoTLx6HQIIcgAHqNeKBfmVmJECvAqKWhVdPgeQGvfGRDnzUIWkZBKacgpymUFCjA\n8YBGJXZ0j59DU6UGZoM8KsgcL0w5OWDMyeC5HaNQ0BRomkJtqQoODwulQgaNUgZvgIPbx8GkF4WE\n48UBwBfgYPewMGppBBkBC6o0KNDL4Q1w8Ad5ODwseF5A/2gQcppCkOFh1IkPU4gRBb2hQoM9bW7o\nNTTsHharm/UYtoVgdTAoMSkwYg+BF4ACvTxarnHMyYACMGQLQUYBDRUa9I4EwHECBAA1JSoU6OXw\nBfnofQWA1c161JSqpzxgHC+A5cR7Q1FUdJ99RFTGnAzMejlomkL3UACdg34Mh7eblhcq0VSpwdI6\nLShqqnja3QycXg4VRUr0DgfQMxyAVk2jvd8XHchWN+tRUqCEN8DB6WFRX66eUI6Q5wVQFMCwAv7y\n6ggEQYBRJ0eZWYnVzfq4x+6wnHjdvgCHEXsIFEWhc9APAFhUo42+z/ECVjbqcKDLizOWm7CsXnzP\n6mBQZFRATotn7B3o8qK1x4sFVRq4vCycXg5BhodOTUMhp+D0sCgpUODMlQUoMson/A4cL1pvyZwf\nNzQexKiDAS8Ah7rFyP20JQZUl6imjdYjMucN8BgaD8Lp5VBSoIDFGsThHm80cKFlVPQZMOloLK3X\nJdz+OxeI4Mbg8XPY3+nBoW4vljfowLACjll80ffLzEosqNIgFBaGxgr1nLcICoKAY5bjD6bZIEZ6\nAHCg0xPtAAYNjRAroKJIiapiFRor1NDOEGlE/n6QEf+KUk5hcDwEizUAvYbGuIuF1R5CqVkJf5BD\nnzU44b9xUFNIAAAgAElEQVRtqhSFcdgWwrAtBKWcwqoFejCsgMW12pQfQRMbGUUYczIYGg+ifzSI\nujI1PH4O/hAPrUoGjYrG0jptNOqNRGGTxSzI8Bi2hTDqYFBXJk7d44kexwtos/jQOxJE70gAi2u1\nqChUwull4Q3wE/pAZZESg+MhyGkKLDf10TFoaKxs0qO0QIGSAsWcLSRvgEMwxIPhBHx01A27m4FC\nLkMgJJYMpGVAkVEBjUqGrqEANEoZBIiR4+nLjHM6bofjBfSNBDBsC8GgFQewrvBsprpEhVc+ssGk\nEwdMlUIcaAFAo5ShukSFxkoNRuwhFBkV0cGvfzQIlhNQZlbAbEhdmcMIe9vdONLrE22IcN+QUeI5\ngGaDHL3DATCcAEEQnwO3X2x75Jj3ujIVTl9mgjocOCjlVFptFiK4YQRBwPM7x6HXiD6aSiH+8I0V\nalAUYNDKJfNEIxF2ZCo+OSJIx/d5AzyUcgrDthCcXhYePwejTpxOmXTSXXumsbsZHOjyIhi2TFRK\nGUpMChSZFPAHedjdDEoKlHB4WJgNcihoCkqFbMKsKN2wnACHh8WYkwHHi/Va+0eDqCpWocw8d5Gf\nCY4Xv1enpqFWyhBiRPFXKWUZOc06FrePjUavgHg8vNPLorpEhRKTAiFWHJgNWjo6U8kERHAJBAJB\nIkhaGIFAIEgEEVwCgUCQCCK4BAKBIBFEcAkEAkEiiOASCASCRBDBJRAIBIkggksgEAgSQQSXQCAQ\nJIIILoFAIEgEEVwCgUCQCCK4BAKBIBFEcAkEAkEiiOASCASCRBDBJRAIBIkggksgEAgSQQSXQCAQ\nJIIILoFAIEgEEVwCgUCQCCK4BAKBIBFEcAkEAkEiiOASCASCRBDBJRAIBIkggksgEAgSQQSXQCAQ\nJIIILoFAIEgEEVwCgUCQCCK4BAKBIBFEcAkEAkEiiOASCASCRBDBJRAIBIkggksgEAgSQQSXQCAQ\nJIIILoFAIEgEEVwCgUCQCCK4BAKBIBFEcLMMh4fNdBMIBEKaIIKbZVzwnf3oGvRnuhkEQl7j8LC4\n649dkn8vEdwsJBDiM90EAgGAKEwsJ2S6GSnncI8Xr++2S/69RHCzEJqmMt0EAgGAOON69KWhTDcj\n5fAZGkOI4GYhtIwILiF7GHMymW5CyuEypLhEcLMQOYlwCVlEJAD49bMWPPOWNcOtSQ08EVxCpBOQ\nAJeQTUT64xOvW/HE6yOZbUyK4DK0TEIEN4uILE7k3xIFIZeJXVPIF7uLRLiE44JLFDdn6BsJQMjz\nGxYrsvlid8Xqbcshp2SZQURwswgmKrj5/QDnE1f9oBXv7HdmuhlpJTaozZcMmthFs1t+24EXdo5J\n8r1EcLMIhhU7QaZSVtKFy8sixORvbnE+XxswMcLNF0uBm5RbLFXkTgQ3i8hXS+G8W/dj8zOWTDcj\nbeRL1JcIWYxK0HmiGJODGqnuYZ78fPkBm8eWwqg9f3I5T7l59wTPL198zUTko4c7OQ9XqsidCG4W\nEZma5qHe5s2DGsEf5KL/P9+ubTKyWEshT651cpYCsRROAJ7bMYoPjrii/45ETfng4W7/0IbXdtmi\n/84XUYrmSuehr5mIWBtBnifXOjkPVyqrhAhuBvnZE3148O/90X8H82jx5e5Hu/HDrT3Rf8vl+fGg\nxvPZFXlybYnIR0thcoRLLIUThNgOHGTCWQr5EOJi4rXly4MaYo/77NwJsjMwNpqX5cnFTq6ARhbN\nThBihShiKeSH3Oan4DLscduHYfMzq2QysfdOqciT+zhpgZp4uCcIsVOZYJ4tmtF5KbjHRTZWfPOR\nyExrguDK80My2PB9jHi5UvVOuUTfQ0hArP8XjCya5ckTrIh5UBV5IrgRS4HnBQTDmW75mMYHHL/W\nWPLFr45YCpH/leqRI4KbYSZ6uMRSyHZio9pgODUsT8bHKUSvNeYClXkiuBFLIWrjSTRoZvX8wOVy\nYcuWLbBYLHC5XLBYLLj//vvR0tKS6aaljNhptzeQX5ZCrMiqlFnd1ZImdtHMF8yv+zWZyCKugOOC\nlC+WQmQwiQQ5Ug2aWf3rOZ1ObN68GRdccAHWrl2Lq666CitWrMD69esz3bSUEZvX6A2IEVO+TFFp\nmopei0qR1V0taWLrXfiD+WUBTSZ2I07kumX5cRujEW4wJO2gmfWWwtatW7F8+XI4nU7U1NRkujkp\nJzYK9AXya4oqp6loRJgn2UQxU9Dju83y5X5NJjaalzoSTDeRASR6XRJdWNYLLgAYjUYYjcZMNyMt\nxG4IyDdLQSGn8mr3HBCzG5CPtRTy5OImESuyAYkjwXQTyVKQun/myQQhd1HHTLW9/vyyFFRyWXTa\nnS/XFPuA+vPcw421FI4HA/lxsZH7KHX/TEpwt23bhrVr12Lt2rW4//77p7y/ffv2lDcsgsViwfbt\n27F9+3Zs27Ytrd+VCWITye0eFrQsfx5gpYLKu2n3hAg3zyygycSKUb5dqy/cL6WOcGe0FLZs2YKW\nlhbcdtttcLlc2LZtG1asWIFLLrkEgCjG11xzzbR/45ZbboHb7U66Uffeey9qampgMpkAIPpdkb81\n+bVcJLrqGxPh2t0MCo2KvOnUKqUMbl8kas9wY1JE7ACS75ZC5PomXmsmW5Q6fOGIXerrmlZwLRYL\nHA4Htm7dGn3tmmuuwd133x0VPJfLleg/j/LQQw/NqXFGo3GKmF977bX4wQ9+kPOCG28xyeFhUVao\nzFCLUo9KIYMrLLj5spIfm7eZb9H7ZGJ9W0+e2V3e8L1z+1gAWWIptLa24vbbb5/wmtFoxKZNm+By\nubB9+3bJha+mpiaal5vL+IMTpzIsJ8Dj51Cgl+e8OB3P2aTg8kY6dCZblDoikVE+Rn2T8cWkKXrz\nzFKIPH/RgCAbFs0Sien69evR0tICi8WS1lStLVu2THktYjNYLLl9ZEs0OgrfaYeHhUErh1xG5fxO\ns9jTEI536Fy/KhGX7/gAEvU180WFJuGP8TftbhYqBZU3g0vk3kltec0pLcxoNKKlpSXp6HYuHi4A\nbN68GZdccskEUXc6xRNScz0nd/LI6vCwMBvkAJX7EZM75tqcXnEhMF80ye0/LrJ2jyhC+XJtkzke\n4QJOD4tCoyJvLAVfkActO24pZH0ebktLS1QYZ2KuHm5k8Wzy9y5btizn83Inj6wDY0FUFCkho6ic\n79SumGtz+ziYdPKcH0QixA4mdheLYlP+iNBkXD4OGpUMgiAOLoUGeV4MLgzLg+cFKBWy4wNoNlgK\n0yHF9lqTyTTBOohkSfz4xz9O+3enm4i3GRlZLdYgakvVoJD70WBkgYXjBTi9bF740hGOD5QCbG4G\nhYb8ySqZjMvLwqQTRdbhYVFoUOTFwOkL8tCpacgowOPjYNTSkg2ac4pwW1tbJRHcSy65JJqD63A4\n4Ha78eCDD+a8nQBMtRR6RwJortJgcDyY8506dqHM4Ratkly/pghuHwuNShb1NRdUafLm2ibj8nEw\n6mhAEFMW68vV0RoEuYwvIEbu3gAPl4+DQUtnTx5uPA4dOoSNGzemui1xyfX0r0S4fRH/T7zTvcMB\nnL/ajA+OuHJ+iurycdFrG7GHsKROmzeLZhGLxB/kwXIC9BrpoiOpcfvCWTNhS+FkgwIj9lCmmzVv\nvAExwvUFebh9bPgeSvPdc7IUWltbc95DzTSucGcWeNFWOGbxYVGNJuzhZrp188PjD/u2PDBiD6Gi\nSJU30263n4NJR2PMycBskEMmy99FM5cvbCnwwIgthPJCZc73TUC0Rwr0csio4/8/K/JwE+FwOFLd\njhMOt4+DKRw9DI6HoNfQMBsUAJX7Hq7Ly8Kkl8PlY8ELCHtkmW7V/AmEeHCcAJ2GhsUaQFWxCrI8\nyCpJRCSaH3My0Kpp6DV0XsxUbC5xsATEaLfUrMzeRTOXy4VNmzaloy0nFPbIggsP7G13Y3mDDkB+\nlDF0elkU6OQYtoVQWqAQo8BcH0UAjDpCKClQgKYoWEaDqCpRQUblx7XFQ1w0o2EZDaK6WAUqTwYX\nm1tMcYss7mpVsuy1FIxGY976qlJitTMoL1SCFwS0tLqwfrm4oYPKgwd4zMmg1KzAwFgI1SX5EwVa\nHQxKCpSgZIBlJIjqEhWoPMoxjiWSg6tRhaP5PLqPNheDQoMcHA+YdLSkgyYpz5ghrA7RE/OHePy3\n1YUzooKb+w/wqINBqVmsCVFXpg77nDl+UQCsdjHClVEUekYCqAlHuPm4aGYN30MZBYy7WDRUqMVg\nIA+u1e5mRfsOQJFJIekzRwQ3A3C8gHEXi9ICBT484sbiWi2KTWIHyAdLYdTJoCwsuPXl6rwYRICw\nCBUoIKPEvfgLa7R5c22TGXMyKAmLEQA0VWryxlIYdzMoMooebmmBEjKZdIMmEdwMYHMxMOloKBUy\nePwcLlhjjr6X65YCzwsYczIoM4sDSGOlJm+mov2jQVQWq+AJT7drS/Nnmj2ZSDRPhRW3qVKdN9c6\nPC5mzgDiPSQRbp5jsQZRWSQ+rLQMOH91QfQ9isrtY9JtbhY6NR399+Jabd5MRbsG/Wiq1OBApxcA\nIJNROT9AJmLUyaC0QIn+0SAAiH51HtxHQRAwMBZEVXHY8ioXd3dm7aIZYf50DQXQWKmGTkNj3VJj\n1E8CAFpGgcvh3Tw9wwHUlamikZFaKRMPk2Ry95oA8UHtHAygqVINk47GTRvLAQAKmppQHS1fGBoP\noaxQCaWcQmk40qVlYhnRXMbp5UDLKBi0oqWwqkkPo06Ox7cP4//+NZD268uJQyTzje4hPxoqNDhj\nuQnrlk7cQKJTy6K1R3ORnuEA6svV2LDChA/+sBoAUFemwrY3Axlu2fxo7/fDpBNzpV/dvCrqtS+t\n1+Kl922ZbVwa6LMGcNZKE645twTf/GQ1AKDIqMC4k8lwy+ZHJH8aAD74w2rQMgoLqjQ4ZZEB//f8\nAB7YZsGd19Wm7ftJhJsBOgb8aKpUAxAj2liMOjlc3twV3MhgAhy/tqZKDbqG/DkdHb22y45zThKt\nHzpsJQDAmoUG7O3w5PS1xaNvJIjaMnU4shWvtaxQiRF7bgtuW78fzTUT+6ecprC8QYf//Xw9XvnI\nBi6NFhERXInheQFH+3xYUqeL+75JJ4czXPwlF9nf6Y1u4oigVdMoNSthseZmlOv2sfjHu6P45Nkl\nU94rNCpQZlagzeLLQMvSQyDEw+4W88RjMevl8Aa4nLZQjvb6sLhGG/e9kgIlCo0KtPf70/b9RHAl\npn80CL2GRoE+vptj1NLRSmK5hsvLom8kgGX1Uzv0gioN2tLYkdPJv/87jnVLjagpVcd9f2GNFh0D\nuXlt8Tja50NTlQZyeuLsSyYT/dxRR+4WsDna58Pi2viCCwAnLdDjYJcnbd9PBFdiWlpdWLs4ceEf\no04ePcYl13hrnwOnLTFCIZ/arerL1LBYgxlo1fwQBAHP7RiLG91GaKrUoHsoN6P3eBzs8mBFQ/wZ\nWGlB7toKvgCH7uEAFiaIcAExMEjn4EkEV2J27Hfg7FWmhO8btXTOergvf2DDxtMK475XWazEwFju\nCe6uY27IZGLkk4iGCjU6h/Inwj3YPdUWilBsUmDclZuC+9Y+B05ZZIBamVj2mqs1aCeCmx+4vCxa\ne7xTMhNiERfNci/CHbaF0N7vwxkr4g8mlcUqDOag4D720jCuO78sukgWj8YKdV5FuK3dXqxojD/A\nFJlyN1PhpffHcem6+AFBhIZyNXqH03cvieBKyM5DTqxuNkCjohN+xqSjc3LR7JWPbDjvZDNUivhd\nqqpYhcHx3PL+9rS5MTgexMdOL5r2c5XFKthcTLTgSy4zYg8hyAjRjQGTKTYpMJaDgjswFsTRPh/O\nXFkw7efMBjmCjBCtJJZqiOBKBMcL+PMrI7j8jOkfXoNWDo+fy6ndSwzL47l3Rqe9tvJCJcacTM6k\nTwUZHpufseBLH6ucsng0GVpGoa5cje40RkZScShsJySK6IuM8py0FB5/eRgfP6N4WjsBELfWVxYp\nMTSentkYEVyJeP69MRi1dDSXMxFymoJaKYMvmDupN8/tGEN9hTrhNBQQr6vIqMCwLTei3D88P4iq\nYtWMU9AIDeX5YSsc6krs3wLi5odci3APdHqw85ATN4Z3B85EZbEKA2Pp6adEcCXA4+fwyIuDuPXq\nmmm9wAjGHMrF9QY4bH15CF+/omrGz1YWKXNCcPe0ubH9Qxu+f0NdUvcLEKuipdP7k4qPjrmxZmHi\ngVNcNMuNvgkAIYbH5m0WfPWKquh23pmoLFKmbb2BCK4EbH15CGesME3J/2NYPm5ZODFTITc69d9e\nG8GpS4xxU21e2DkWLX4CAGXm7Bdcb4DD//65B9+/oTZhrjQAjLsYdA4eX81uqNDkvKUw7mJgsQai\nMxVBELDzkBMOz/G+WJRDHi7PC/jh4z2oLFZhU4LsmXhUpnG9gQhumhl3MfjXe2P48mWVE153eFg8\n9vIwjvZN3aEk7jbL/gWYcReDZ96y4iuXV055z2INYMgWmpDTWF6Y/YL7238MYHWzYdrFFUEQ8Pd3\nRvH6bnvUa68vV6MnxwX3pffHcd7J5qhn3dbvx6FuLw73eKOfKdDL4faxOeHFP/SPAYw5GPzvjfWQ\nJSg0/f5hF17YOTYh8KksUmKQeLi5yV9eGcYlpxZGT0CIcKTXiwK9HK09vilRrkFLw50Dmx/+9O8h\nbDqtCJXhYiCxHO3zYc1CA3pHAggxoh8t7sXPXsHd2+7GO/sduPXq6mk/F7uC/ULLOACgplRMe8sF\nIYrH0T4f/vzKCD59fikAsSrY3nYPTlqgR2uPF/6geM20jILZkP25uE+9MYKdh5zY/NWmhJkzDMvj\nSK8XQ7bQhO28YgojiXBzjjEngxdbxvH5Syaa9YIgoL3fj0tOFac5rT0To1xjDkS4e9vdeHOvHV+4\ntGLKe0L4JOLFtVo0lKux65gbgBjhjmRZhOvxc+gc9MPtY3HvX3px56drZ/T6RuwMGsrVuOHCMthc\nYuaFSiFDqVk5wULJFUbsIXzn/zpw13W1UWuofzQIrUqGkxbowQti9kKEIqM8q3Nx39hjx19fHcFD\n31gAky7xvWzr94OixGphb+1zwO4Wr6mqWPRw03EKBCnPmCYGxoL4xZN9+PiGYpQUTIxunV4OcpqC\nSSfHSQv06BjwT1gZNmX55gePn8MPt/bg7s/UxfU5bW4WKoUMeg2NhTVa7DzkBJA9loI3wOHdA06E\nGB5PvD4Cb4DHmJPB5WcUzZhFAgB9IwGUmpXQqWkwnIDtH9rwsdOLwpkKftSXx6+5ICUPPtePd/Y7\noFbIcP9XmqIlCSfj8XP49u86cPW5pRNOHhl3MSgrVEKlkGHtIgOYmMg9W3NxWU7AE6+P4G+vjeC3\ntzRHT3VIhN3NYs1CPZY36NA9FMCwLQSzQQGDVg6apuD0ctP6+HOBCG4CWE4Aw/JQKWQJ/Z949I8G\n8cAzFhzo9ODyM4rx1Y9P9TcHxoKoKBJFuLpEhR37HWA5IeqdGbU0bO7MCa4gCOgfDaLMrIRy0nTM\n5mLw7d934KxVBQl9znEng5ICsah6SYEC/iAPp5dFmVlMCxMEIenV/1Tz8gfjuP9pC1Y16aHT0PjE\nWSX41DlinYRk2jRiD6FnOIA1iwwAgGX1OrSGPc5s8HEZlscjLw5hx34HfvqFBnx41I3v/rELj925\neEo+cceAH3c83IkNK0z47EVlE94bdzJorBTLGBp1cnTFLBAWZeH2Xobl8a3fdUAA8PhdixMOMLHY\n3Uz4zD0KaxbqJywOVhaJsxUiuGmG5QTc+UgndhxwQimnYNTJ8f0b6rBhhQkcL+CpN6zY2+7GmSsL\n8PFwoj/DCrB7WLy2y46/vjqMq88txX1fbEi4o2xPmxvnnixGE2qlDGaDHGPO4+XwjDp5xla8e4YD\nuO+JXhzr84HlBKxs1OOT55TAH+RRZJTj50/2YdNpRfify6ZaCRFcPi46laNlFMoLlbDaQ1hQJR5E\n6PFzSafopAqbi8HWl4fx7kEnHvnOQjRXJy5gMh0OD4v6cnX0+pbVa6M2Qn2FGrvb3Clr80wIgoB3\n9jvRNxIAywnoHg5g50EnVjXp8afbF8FsUGBxrRYfHnHh5geO4cyVBVDKKew44MSRXi9YTsD3bqjD\npeumblgZdTI4dYm4Bb3MrMAbe+zR4vLFxuxJDbPaQ3js5WEc7vGizKzEz29unFJjOh5Bhse4i40e\n3lpVrMI/3xvDmoUGKBUy1JaqYbEGps1JngtEcGMQBAG/fLoPDCfgv79fDTlN4aOjLvz4L714bZce\n/aNBKOUyXHFmMR59aQgv7BzDsC0Eu4eFRinDOScX4NdfW4Cl9YlvEsPyU7ZORtKlooKrpeGWqETj\nmJPB3nY3WE70lZ/fOYYvfawSf/j2QoQYHq/vtuPpN6wwG+UYtTP48uWV2Hja9Lvlxl0MmsLREQCU\nFigwYmfQXK2NXqsUgvveQScee3kIA6NBhFgB65cZ8ZfvLoZxGl9vJtw+bsJ/r1HR0fqw9eVqPLdj\ndN7tToTLy+Ldg058cMSFrsEAKIg7GE9dYgQtE4+k/+YnqqMiAohR+6++tgD/bXVh9zE3vEEO15xb\ngpObG6FTy+JWdvP4OXCcAJNODBgMWjmW1eswMBZEfbkaRSZFxjd52MLZP0+8PoIrzyzBFy6twIYV\npqTEFgAGx4IoLVBEd56VmpUoLVDA5mZRXqhETakKfWmobkcEN4wgCPjVM/3Y3+HBo3ccn36tXWzE\n1rsW48Hn+rFpXRGu3FAMmYzC2asK8PpuOxZUaaatrzkZX5CHTi2bMH0tMikwELPYku4CNiwn4P3D\nLuw44MAbu+1Y0aiHVi1DoUGBJ+5eGhV+jYrGZeuLcdn64qT/tiAIGHUwWL/seIGeUrMSHQOij1sV\n3sUz1wgzGThewO/+OYC39jrw9SursKJRhxKTYlbWUCKcXhY1JcenqyoFBTlNYdgWim5+SLVlwnIC\nXnp/HA8+14+VjXqcvsyIT55VAoeHxfrlphm3HqsUMpxzUkFS/jQgFiIqK1ROuIbaUhUOhhfOiowK\n7DoqXSQfiyAI2HHAiZ/8tRfrlhix9c7FqC2bvWc+MBZCVclE26HQKFol5YVK1Jap8f5hV6qaHYUI\nbgyNlWrcuLEces1EK6DIqMC9NzZMeE2tlM1Y1CQedjc7JcIqNMhxsOv4KnA6C9h4Axy+u6UL404G\nF55SiG0/XDYhIpov/iAPjhcm/IbFJgXsHhYcL6C2TJU2n9Pj5/Cv98bw0vvjMGhpPH7X4pR7cONO\nBisbj89gKIrCkjqxAPmGFSaoFDKMOpgpaYCzZWg8iB8+3oM2ix8KOYWGcjV+/62Fsxrc50rsbCtC\noVEBe3hdIZ2LZuKpuiHsOuZG95AfgRAPhhMwamfQ1u+DWimuqdz/5aZpS2bOxLAthIXVEyvbxZ7Z\nVluqwjNvp76fEsENQ1EUrjwzcZHpVDE4FkRl0cTOXKAXt/LyvACZjAoXIU+dpeDysrC5WVisAWz5\nzxAWVWvxq68umDEymgs2NwuTTj4hOpLTFAwaGg4Pi7oydTRSSiXDthC+96cuFBkV+MrHK7FhhSnl\nC3P+IAdfkEeRceIAVWZWYm+7GPHVV4hFbOYquFZ7CH96aQgvtozjy5dX4mdfaoQ/yKO6ZOZFoFQx\nbAuheZIY6dQyMCyPQIhPW01ch4fFXX/sQveQHysa9VjRqINKIQNFAaculuOe5jq4fBwaKtRJWwfx\nCDHiIm7RpECjyCiPbtSpLVOjbyQYfSZTBRFciRkcD2HDpJqxCrkMaqUMHr/oD6Zqa68/yOHeP/di\n5yEnDFoajRUaXLy2ENedX5q2LIH2ft+U6Ag4Xke1tkyN/7w/nrLv8/g5PPj3fry5146rzy3Fly6t\nSOkDEsuYk0FxHGui0CDHuIuFIAhYUKlBm8WH05YkrnkcD6s9hJ/+rRcHurzYdFohXtu8aspMSwpC\nDA+Xl50y66EoccODzcWgyCgu8s7HOgkyPI72+eALcDhtiRHeAIevP9iOtYsM+N03mxMGA6XmuC/P\nCqtDvI+TRbvQKF4fzwso0Mth0tHoHg5MWI+YL0RwJSQQEjtzJGUqFpNODrtHtBvUShl4Qfz8TOXk\nYgkxPHYcEPe+r27W40eP96CpUoNXN6+a1d+ZD1YHgwvWTJ3qFYX9seZqLXpH5r8YIQgCdh1z44Fn\n+rGkVoun7lk672n8TEQe1MloVGIU5gvyWNmkxxt77LP6u0PjQXz5V23YeFoRfvrFxowIbYREYgSI\nA4vNzaKyWAW1Ugabm50S7SeDx8/hs/cdgVZNgxcE/OyJPnj8HC4/oxi3fKIq7SmDVnsIZXH6ikoh\ng0Ylg8PDotCowOqFBuw65iaCm6tEFiPidebI7pa68NHUxvD2XrVyZhEZHAvi7++M4j/vj6OxQgOV\nUoYt/x7Ep84pxRc2lUuW8xpkeHj9HMxxfNNikwL7OoJYZ5RDEARY7aFZC6QgCHj3gBNH+nx45i0r\nikwKfPaicly6rlCSaxx1MGiunvrwUZRYetLuZnHSAj02b7NMyKuejp7hAL7+YDuuO78U111QNuPn\n0008/zaC6OOKVsKCag3a+/0oWjo7wRUEAT/9ay9OXWLEXdfVQhAEHLP4UVKgmJN4z4URewiLpjm5\nd8zJoNCowKXrivDmLAfPmSCCKyHx/NsIJp0cVod/wr+dXg4lMywstxxy4keP9+CitYV4+NaFaKhI\n3Wg8W8acDIoSZAMUhnONAfF8sL0dHly8NrkKTiwnYF+HB8+/N4bWXi/OO9mMh25pxrJp0u/SwZhz\nYvZFLJH6F0vqdKgtU+G9g85pswJsLgY7Djjxh+cH8LUrqnD5GclngqSTYVsIKxrj/64Fenl0wXNx\njRZH+3zTHhcVy7hL3Oa+v8ODEXsIW29cDEAcrKRYCIzA8QJG7AzOXBn/OTRqabjDtTJOWWTAKeEN\nLtADxv0AABQeSURBVKmCCK5ECIKAPmsQ550c/yE06uQTcm8ri1WwWANYUBVfQHuGA3js5SHsOurG\nL+e5Ypsqdh9zoyHBtlaNSrQ0AiEeJzcbsLd9ZsFt7/fhqTeteGuvA9XFKpx9UgHu+HSN5JsmAHFB\nRxAEGLTxp/sGzfHc6c9eVI7f/2sAZywXTzB2eVn85rl+eP0cgoyAwbEghm0hnLHchB/f1BDdYJBp\nOF4IT7fjG6V6DR0t3HPKIgO2vjw8pU4IIPb1zsEA9ra70TUYwIgjhH3tHpy/2ozz15hxzkkFCQvK\npJv+0SDMBnlC20avoTGaxm3LRHAlYtgWAi1DXP8WEG+028dGFyKaqzVo6/dHd6QB4iLYG3sceGe/\nA/s7PLj63FLccW1tRj2/CA4PC5ePSxgdURSFAr0cDg+LtYsN2PaWdcq0W9x84cPuNg92HnSiZySA\njacW4i/fXYya0szWJzjQ6cGiGm1C60KvoWEJ51KfvcqE598bw6+e7ceyeh2eftOKRTUaXLDGDIVc\nhqpiJSqLVdCpM3/fYrG5GBi0dEK/X6eWwRvgIAgC1i834TfP9eOZt6z41Dkl0d/FH+TwyItDePZt\nKy5YY8aSOh1Obtbj+zfUSWYZTEfPcCBhUACIM5V07vIkgisRXUPiameiB1alEF8PMgLUSgoLqzV4\n6QNb9P1hWwh3PtIJnZrGWasKcO+N9dMeRik1vSMB1JaqpvVSI4K7pE6HcrMSr3xkw6XrivD2Pgf+\n8e4ojvX5oJDLcNoSAz5xdgnOWmmaUsshEwiCgN6RwLTTfkPM7kCKovD9z9ThV89a8PY+B649rxSb\nTitMW/ZEqhi2xV9MiqCQy0DLqHAfleGhbzTjW7/rwNv7Hdh0WhE+OupCS6sLJzXp8eJ9K1CYBQIb\nC88L6B0O4OQNie+jXpPeXZ5EcCUgyPDoHPBP+8BSFBU9QFKtlOHUJUbc90Qffv2sBS4fhx37Hbj+\ngjLcuFG6RbDZ0DscmNHWMGrpaH7xN66qwrd/34FXPrThmMWHz1xUjluuqk5ooWSSrqEADFr5tKX+\nJu8OLDYpcN8XG6VoXsoYGAuhqXL6mUTEVlArZaguUeHpHyzFcztG8cYeO1Y363HjxoqsqJYWjyFb\nCBqVbNqt3ZHrS1eBJSK4ErCnzY3KYtWMu54itkKxSQGTTo4/fmch3tnvRE2pGl+6tCJuoe9sYNQR\ngsvHzdg+g5aOpoStbNLjqXuW4p39Dtzx6VpJE/tny6FuL1Y1Tb9Ap1XJwHICggyfMX9yPngDHIZt\noYRrDBEitkIkPU5OU7jm3FJcc26pFM2cMzwv4IPDrhmDAoVcBoWcgj/IQ5sGy4cIbppp7/ehZziQ\n1DZgw6SiNc3V2rTWHEgFgiDgv60urFmonzENSozgjxdbLzUr8alzsvtBHXcxcPs41M2wX5+iqGiU\nO7n+cS5wrM+Hxgr1jBaOTk3D68/u4vjxONTthUxGJTWDikS56RDc3BuKcwiOF5PzzzmpIKmV9dgp\nd67QMxxAkOET5jXGYsihwzEj7G33YHmDLin/VayBkVv3DxAtr9YeL5YlUYpQq6bhDeTWNY7YQ9jT\n7sE5JxUkZRMYYrIxUg0R3DRytM8Hg1Y+Y+X5CBEPN1cYdzF4c68Dpy8zJSVIWpUMIVbImXO/9nd6\nYHMxWFaf3CzDlEPH28eyt92DmlJ1UlkEeg0Nb4CXoFWpIRDi8douO85eZUq6kJFeczwXN9UQwU0T\n7f0+tBxy4vQEifLxiHi4uYDDw+LVj2zYsMKUtP9KUVTOXGP/aBAHOj3YtK4obs3YeEzOpc4Fuof8\n6Bjw49TFySX4RzzcXMAXEKvHNVWqZ7UhyKBNX4RLPNw00DcSwM5DLlx1Vsmscg8jHm4mj6BJBo+f\nwysf2bCiUZ+UlRBL5BrNhuxKGYogCAKO9Prw0TE3LlxjnlWOs04ty6kZypFeL3a3eXDhKeak/Upt\njni4NheD7R/asLBGizULZ7cpSK+hMZCmU3uJ4KYQQRBwqNuLfR0eXHJq4awTvSPnpwVCfFbl2Ebg\neQE9wwG0tLqwvEE3p+NHYrdOZhuxxX8+fkbxrGvp6nLE3xQEAbvbPDja58PHTi+a1XXmwjX2jwbx\n5h471i83zSnNUJ9GD5cIboqwuxl8eNQNt4/DFRuK57z9NOKRZZvgOjws3txjBy8A551cMOcUNYM2\nO6fdY04Gr++2o6pYiSs2FM+pVrBOI0Z/2TxDcXpZtBxyIsgIuOrM4lmvxKsUFHhBHJyyYVNKLCwn\nLlK39/tw0drChEV4ZiKdaylEcOeJw8Nid5sbA6NBrGzS4/zVunkV9o6Mrqk8hWE+MCyPfR0etPb4\ncMoiA5bVJ97emgx6DY1RR+aPSo/gD3I42OXFkT4fzphjRBRBKRd/lxArRHcOZgtuH4vWHh+OWXxY\n1SQeDT6Xfhr14f0cirJEcBmWR8eAH/s6PCg1K/HJs0vmFbCoFBQ4XkjLoEIEd464vCz2tHvQOxLA\nigYdzlyRmm2o6ZzOzIZAiMcxiw8Hu7yoKBI7cSpqNkzONc4UVnsIrT1e9I4E0VChxifOmv/1URQV\njXKzYfODL8ChzxpE95A/fIinJiXXGUmbynRtBJeXxeFecRApMytx9qq5z7xiiQwqHj+HQiK4mSNy\nQOLhXnEzw/IGHa49rzSlD1emBdfuZnCo24vOQbE2wsVrzSlN5M+kh8tyAjoH/Tjc40UgxGNJnQ6n\nLzOltDi7aAlxGasjEGR4dA360dbvh93NoqpYiaZKDS48pTBlRyplso9Gzjw71O2N1rW9ckPxvE5i\njochHMWn+j4SwU2CQIhHe78Pxyx+MKyY5H/teaVpOUVBr6HTdkBfIrjwYtiRXh/sbgZL6nT41Dkl\naalmpVaKW2Cl9AAdHhZH+3xos/hQbBIr+deUqNJSTEZcVJI2T5XjBfSPBtHe74fFGkBVsQqrmvSo\nKVXN6+yvRGRilhJkeLRZfDjc64OMApY36HD+6oKkU/ZmS7oGFSK4CYiMpEf7fOgfDaKmVIXTlxlR\nWaRM64KISScetigFsUJkNsixpE437wP6ZoKiqGj0kE4PkOUig4gXDg+LhTVafHxD8bQFaFKBVNGf\nIIiFtNv7fegcDMBskGNBlQYbVqQ2Yo+HXkNj3DX/Y5JmYvKMsrpEhbNWmlBemN5nEEhfLi4R3El4\n/ByOWXw41ueDSinDohqtJJ04QmS3UrpWugMhcYGhY8APt49Fc7UWl88hBWo+RCKk/2/vfmKbuPI4\ngH9j/CeO47EDiSmE4U+hpCVOt9uK7RL2T5fDxuFU7WF9A3KA3uDEMRGK2pNzSdVTg9SWU0daReop\nlopUrXZjLhDtbjI0JCkteeZvgNhj4v/27MGMFdd2GBvP2I5/nwtk/OyMR/HXb37vzZta1wCVD+hS\nMIafHsTQ7TCh/6ANB97Q9ktkM1u7AY/XtTtDiUTTWArGsByMoa0NOLrPWrP6ulq/XhOj1pKp3N/o\n7XtRpNK50s/fP+rRZG2DcjqtO2py771fo8B9SZZlfH9zHQ+eJXGk14q/Ht9Zl5kCZpMBZmMbNuLZ\nmn2Isi9PORdXo3jwLAneZcH7b3Wit0ebU85XqfW0m414BsvBGJZYFFk5F0J/+2P1U/NeR5fdhMXV\n2oZRMpXF3YdxLAejeB5J4/BeK0791okep6ku08+06MUrX5aLq1HcfRjH3l1m/P4Yh95u7XuzpVBJ\nQWNtbW1493AnTr1vqtngQrWUhbpfN3CljXSut85isLUb8Pb+Dnz0nrPu8ydrsYiNUjJYYlE8CaVw\naE87/vQbJ3Z31SeEFF12I9Yjr3+Gks3KePAsiSUWxeqTBN7YaYb7kA28q73uf58dFgPiyazqG2WW\nI8syHj1P4u7DOH55FMcOQ27hfa3GDyqh1VxcCtxNqp0oXWsOWy5wq1kjNp2R8fPDGBZXc72ht3qt\nGP6w8qvetGS37sCj55XPxZVlGU9CKSyxXN2yx2nC0X25s5F6h5DCYjLAYjZAimaqqhevR1L5kkGH\nxYCjfAdO9HMNdSGMwZC7XdJzKVXVrelDL9JYYlEs34/BbGzD4b1WnP5wJ5ydxoa5YKTDYkAyla35\n+sYUuA2o22HCk5D6OqAsy3gaTuEOy9VmXU4Tjh204cDu+veGSuFsRoQrGBh8Ectg5X4Md1gUsgz0\n8frXLSuxizPhuZRSHbhKXX2JRRFNZHGkNxdAjXaLms16nCashdUHrlIWWVyNQtpI4619VgzVqWyn\nhsHQhm6HCWuhVE0Xx6fAbUCuLjMWft54ZTvlg7q4GkUylUXf/o6GDiLFTrsRG/EM4sls2cFIZara\nndVcyeDNPe34cwOUDNTocZrweD215QpVmayM1cdxLAVjePA0gf2723H8bTt6u7WZrlZrPSo7BU/D\nKfx4Lzeve88uM947ot10tVpzdZnxeD1Jgbvd7bQbEU9mEd5IF/WSSk1Xq+fgQjUMhjb0OM14+CxR\nFEqJVBY/3oti4ecNODuN6OMbq2SgRm+3Bf+eD5d8LPQi/fLCkhi6Oo04ynfgLw1QV6+Uq8uM/90t\n3SlQylq370URiWbwzoGOhqjLVurQnnas3I/V9DUpcBuQwZC7Tfr83Q38YcABIDcAtvRyJN5syg2A\n6TldrdaO9Fpxh8Xygatcprm4GsV+lwWe3zXu6earuJwmpDO5Huz+3e35WSLiLxtYC6XwzgFtro7S\n0y7OiExWxnMplS99KOs1LLEodnImvPtmrqzVDD32UnZ3mbe8i3E12mRZbo7l91tMPJnFP/65hj27\nzAi/SCMSy+DwXive3t/RtEG0WSqdxfS/nsJhMyIazyASy+DoPivch2x1mc5Va/efJvD9zXU4bLk5\nxzbrDvQftOFIr7Wpeutb+c9Kbi2RPr4Dq4/jePAsiT7eincO2HSd191MKHAb2ItYBuxJHM5OI1xd\n5qaoe1UinsyCPYmj07pjW76/RCqLZ+EUOJux4evq1chmZfz3pxdYj6Sxt9uCw3vbNbvUdrugwCWE\nEJ3Q1xEhhOiEApcQQnRCgUsIITqhwCWEEJ1Q4BJCiE4ocAkhRCcUuIQQohMKXEII0QkFLiGE6IQC\nlxBCdEKBSwghOqHAJYQQndAaaqTljI2NYWFhAQDw9ddfg+O4Ou9Rffh8Pty4cQOSJGF6erplj4Oe\nKHBJy2GMtXTQKi5fvgwAuHjxYp33pHVQ4JIt9fX1geM4uN1u2O12OJ1OBAIBMMYwNDQEp9OJUCiE\nYDAIURTh9XoxPj6ef/7IyAjOnz+PwcHBOr4L9QRBgN/vx1dffVXycUmSIAgCeJ4HkAvv8+fPV92O\ntBYKXFIWYwz9/f1FvcGpqSlMTEzg888/L2gvCAJmZ2eLXoMxpsv+vo6xsTHY7XZEIhGEw6XvRwYA\nly5dwvj4eEGQjoyMFAW02naktdCgGSlLkiRcuHBB9am31+uF0+ks2Hb9+nV4vV4tdq+mxsfHcfny\n5XxAliIIAux2e0Ebnudht9vh9/srbkdaDwUuKSscDqO/v7+i52wVWM1OEAScPHmyaPvAwAAEQai4\nHWk9FLikLEmSKg7Q7Ry4oiiWfH88z+dnPVTSjrQequGSsjweT9XPkSQJ586dgyiKmJychMfjAWMM\nly5dAmMMk5OT+XbhcBizs7P5U3pBEOBwOMAYQygUyo+mb8YYw7fffouBgQGEw2FIkqTpoJQkSQAA\nh8NR9BjHcfnH1bZ7lVodq0AgAEmSwHEcGGNwOByYn58veUyJ9ihwiSY4jsP09DSOHz+e38bzfH6b\nKIrweDwFPcGxsTF4PJ6Cmu/IyAgEQSjYJooiRkdHMT09nd/m8/ng8/nqGiRqw1QJwK3U4lgxxiCK\nYsEXEWOMyhp1RCUFoqlyp9aMsYLH3G43AoFAUftjx44VzXwYHR3FhQsXCrZ98sknuHr1qurQq9RW\nMxc2t1HbTq3XOVaBQKDk6zXLFL3tiAKX1MWvB+OUHp/b7S7Y7nQ6EYlE8j8rvbZSz+c4TvMa6VZh\nubmMoLadGtUeq8HBQUxMTGBqaqpgal4zzBrZrqikQOqiXOi86lRbFMX8v8r/FcPDwxWHmVrK627V\ng96872rbVfK7K30dnucxOTmJiYkJTExMgOd5DA0NUf22jihwSVMqNaBXzSCfWkoPulTPlTGWDz+1\n7fTi8Xjg8XggiiIWFhYwNTWF27dv0wUYdUIlBdJUlNPrely95na7S/ZcJUnCiRMnKm6nNUEQ8vvR\n398Pr9eL69evgzGmWa2bbI0ClzQVZdCn1BVbSn1XKx6PB/Pz80XbA4EATp8+XXE7rUmSVHLgbHBw\nsKKBO1I7FLhEU+V6euU+8Gp6XuPj4xAEoaiX6/f7K74yrhJerxfBYLDg94qiiHA4XFDOUNtOjdc9\nVl9++WXRtlAotK0vUGlkO65cuXKl3jtBmoPP58PVq1cxMzODRCKBH374AbOzs3A4HEUfYMYYRkdH\nMTc3B1EUYbFY4HA48ttWVlaQSCTwwQcfwO/347PPPsPa2hpEUYTL5QLP8/D5fLh27RpWVlawvLyM\nkydP5l/n448/xhdffIGVlRWsra3h1q1bOHPmjKr38d1332F4eBgWiyW/bWxsDNeuXcPMzAyCwSDm\n5uYwNzcHl8sFl8uVbzc8PIxvvvkmvzrazZs3ixbxqaRdOZuPX7XHSpIknDlzBn6/H8FgELdu3cKN\nGzdw9uzZgoG4mZkZnDp1quB4EG20ybIs13snCNHTyMgIJicnW349XMXFixfx6aef0vHQAZUUCCFE\nJxS4hBCiEwpcQgjRCQUuIYTohK40Iy2H53mcO3cOQGvftVe5ezFdBKEfmqVACCE6oZICIYTohAKX\nEEJ0QoFLCCE6ocAlhBCdUOASQohOKHAJIUQn/webnVq7Nje4pgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3f75f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Icolor = '#3366cc'\n",
    "Ecolor = '#FF6868'\n",
    "fig = plt.figure(figsize=(5,4))\n",
    "for i in range(2):\n",
    "    gpu = res[i]\n",
    "    ax2 = fig.add_subplot(111)\n",
    "    # get voltage from the 40th inhibitory neuron\n",
    "    ax2.plot(gpu.vAll[840][-1000:] - i*200, c=Icolor)\n",
    "    # get the mean voltage\n",
    "    ax2.plot(gpu.vmI1[-1000:]/200 - 40 - i*200, c=Icolor, alpha=0.5)\n",
    "    ax2.set_xticks([])\n",
    "    ax2.set_yticks([])\n",
    "plt.yticks([-20,-220], [r'$\\gamma = $%d' % glist[0], r'$\\gamma = $%d' % glist[1]])\n",
    "plt.xlabel('Time [100 ms]')\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'vAll' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-f47a61f25ccc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvAll\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'vAll' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(vAll[0][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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