anisotropic_nest-comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allow reload of objects\n",
    "%load_ext autoreload\n",
    "%autoreload\n",
    "from pelenet.utils import Utils\n",
    "from pelenet.experiments.nestcomparison import NestComparison\n",
    "from pelenet.network import ReservoirNetwork\n",
    "\n",
    "# Official modules\n",
    "import numpy as np\n",
    "import scipy\n",
    "from scipy.sparse import csr_matrix\n",
    "import nxsdk.api.n2a as nx\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import os\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initilizes the experiment and utils\n",
    "utils = Utils.instance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt = np.load('data/nest2/one_network_15_locations/weights_ee_0.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 1., 0., 1.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 1., 1.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 1., 0., 0.],\n",
       "       [0., 1., 1., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get inputs\n",
    "inp = []\n",
    "inp.append(np.load('data/nest/left_top/input.npy')[0,0])\n",
    "inp.append(np.load('data/nest/somewhere/input.npy'))\n",
    "\n",
    "# Get weights\n",
    "masks = []\n",
    "\n",
    "exex = csr_matrix(np.load('data/nest/left_top/weights_ee.npy'))\n",
    "exin = csr_matrix(np.load('data/nest/left_top/weights_ei.npy'))\n",
    "inex = csr_matrix(np.load('data/nest/left_top/weights_ii.npy'))\n",
    "inin = csr_matrix(np.load('data/nest/left_top/weights_ie.npy'))\n",
    "masks.append({ 'exex': exex, 'inin': inin, 'inex': inex, 'exin': exin })\n",
    "\n",
    "exex = csr_matrix(np.load('data/nest/somewhere/weights_ee.npy'))\n",
    "exin = csr_matrix(np.load('data/nest/somewhere/weights_ei.npy'))\n",
    "inex = csr_matrix(np.load('data/nest/somewhere/weights_ii.npy'))\n",
    "inin = csr_matrix(np.load('data/nest/somewhere/weights_ie.npy'))\n",
    "masks.append({ 'exex': exex, 'inin': inin, 'inex': inex, 'exin': exin })\n",
    "\n",
    "# Get times\n",
    "times = []\n",
    "times.append(np.load('data/nest/left_top/times.npy'))\n",
    "times.append(np.load('data/nest/somewhere/times.npy'))\n",
    "\n",
    "# Get indices\n",
    "nest = []\n",
    "lastIdc = np.where(times[0] < 500)[0][-1]\n",
    "nest.append(np.array([times[0][:lastIdc], np.load('data/nest/left_top/inds.npy')[:lastIdc]]))\n",
    "lastIdc = np.where(times[1] < 500)[0][-1]\n",
    "nest.append(np.array([times[1][:lastIdc], np.load('data/nest/somewhere/inds.npy')[:lastIdc]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simLoihi(inp, ma):\n",
    "    # Init experiment\n",
    "    exp = NestComparison()\n",
    "\n",
    "    # Build network\n",
    "    exp.net = ReservoirNetwork(exp.p)\n",
    "    \n",
    "    # Define mask\n",
    "    exp.net.initialMasks.exex = ma['exex']\n",
    "    exp.net.initialMasks.exin = ma['exin']\n",
    "    exp.net.initialMasks.inex = ma['inex']\n",
    "    exp.net.initialMasks.inin = ma['inin']\n",
    "\n",
    "    # Set weights\n",
    "    exp.setSparseWeightMatrix()\n",
    "\n",
    "    # Connect\n",
    "    exp.net.connectReservoir()\n",
    "\n",
    "    # Add patch input\n",
    "    exp.net.addRepeatedPatchGenerator(idc = inp-1)\n",
    "\n",
    "    # Build the network structure\n",
    "    exp.net.build()\n",
    "\n",
    "    # Get and print spectral radius\n",
    "    utils.getSpectralRadius(exp.net.initialWeights)\n",
    "\n",
    "    # Run network\n",
    "    exp.run()\n",
    "    \n",
    "    return exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update({'font.size': 13})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  \u001b[33melementType would be deprecated in 0.9 in favor of messageSize, which provides more flexibility\u001b[0m\n",
      "\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  \u001b[33melementType would be deprecated in 0.9 in favor of messageSize, which provides more flexibility\u001b[0m\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  Connecting to 127.0.0.1:33401\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Host server up..............Done 0.07s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Encoding axons/synapses.....Done 14.94s\n",
      "\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  \u001b[33m~/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages ~/05-reward/v0.x\n",
      "Compiling in directory : /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506\n",
      "Include dir is : /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/\n",
      "Compiling -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_0_0.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_0_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_0_0.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_spiking_packed.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_spiking_packed.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_spiking_packed.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset_init.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset_init.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset_init.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/2_reset.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/2_reset.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/2_reset.c\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c: In function ‘reset’:\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c:45:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "         nx_fast_init64(nc->cx_state, neuronsPerCore, *(uint64_t*)&cxs);\n",
      "         ^~~~~~~~~~~~~~\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c:60:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "             nx_fast_init32(nc->cx_meta_state, neuronsPerCore/4, *(uint32_t*)&ms);\n",
      "             ^~~~~~~~~~~~~~\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_0_0.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_0_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_0_0.c\n",
      "OBJFILES :  /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_0_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_spiking_packed.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/2_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_0_0.c.o\n",
      "gcc -Wall -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_0_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_spiking_packed.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/2_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_0_0.c.o -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/launcher.link nxsdk/lib/embedded/libboot.a nxsdk/lib/embedded/libembedded_nx.a nxsdk/lib/embedded/liblmt.a nxsdk/lib/embedded/libnx.a -Wl,--script=nxsdk/utils/linker.scr -Wl,--build-id=none -Wl,-N -nostdlib\n",
      "\u001b[0m\n",
      "\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  \u001b[33m~/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages ~/05-reward/v0.x\n",
      "Compiling in directory : /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506\n",
      "Include dir is : /home/seq/05-reward/v0.x/pelenet/snips/\n",
      "Compiling -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_1_0.c\n",
      "gcc -Wall -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_1_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_1_0.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_reset_init.c\n",
      "gcc -Wall -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_reset_init.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_reset_init.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset.c\n",
      "gcc -Wall -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset.c\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c: In function ‘reset’:\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c:45:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "         nx_fast_init64(nc->cx_state, neuronsPerCore, *(uint64_t*)&cxs);\n",
      "         ^~~~~~~~~~~~~~\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c:60:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "             nx_fast_init32(nc->cx_meta_state, neuronsPerCore/4, *(uint32_t*)&ms);\n",
      "             ^~~~~~~~~~~~~~\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_1_0.c\n",
      "gcc -Wall -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_1_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_1_0.c\n",
      "OBJFILES :  /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_1_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_1_0.c.o\n",
      "gcc -Wall -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/initnetwork_1_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/0_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/1_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/main_1_0.c.o -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/launcher.link nxsdk/lib/embedded/libboot.a nxsdk/lib/embedded/libembedded_nx.a nxsdk/lib/embedded/liblmt.a nxsdk/lib/embedded/libnx.a -Wl,--script=nxsdk/utils/linker.scr -Wl,--build-id=none -Wl,-N -nostdlib\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Compiling Embedded snips....Done 0.59s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  Using Kapoho Bay serial number 308\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  Args chip=0 cpu=0 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/launcher_chip0_lmt0.bin --chips=2 --remote-relay=0 --epoch=0 \n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  Args chip=1 cpu=0 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424362.4117506/launcher_chip1_lmt0.bin --chips=2 --remote-relay=0 --epoch=0 \n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  Lakemont_driver...\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Booting up..................Done 1.56s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Encoding probes.............Done 8.38ms\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Configuring registers.......Done 15.11s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Transferring spikes.........Done 3.26ms\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Transferring probes.........Done 0.02s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Transfered values 20, 535, 30 \n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Transfered values 20, 535, 30 \n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 0 to 30\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 0 to 30\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 30\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 30\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Executing...................Done 34.28s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Processing timeseries.......Done 0.22s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  Executor: 535 timesteps.........Done 49.65s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 halted, status=0x0\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 halted, status=0x0\n"
     ]
    }
   ],
   "source": [
    "exp1 = simLoihi(inp[0], masks[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exp1.net.plot.reservoirSpikeTrain(fr=exp1.p.resetOffset, to=491 exp1.p.resetOffset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:  \u001b[33melementType would be deprecated in 0.9 in favor of messageSize, which provides more flexibility\u001b[0m\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:  \u001b[33melementType would be deprecated in 0.9 in favor of messageSize, which provides more flexibility\u001b[0m\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:  Connecting to 127.0.0.1:38329\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Host server up..............Done 0.07s\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:  \u001b[33m~/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages ~/05-reward/v0.x\n",
      "Compiling in directory : /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065\n",
      "Include dir is : /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/\n",
      "Compiling -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_0_0.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_0_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_0_0.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_spiking_packed.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_spiking_packed.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_spiking_packed.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset_init.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset_init.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset_init.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/2_reset.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/2_reset.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/2_reset.c\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c: In function ‘reset’:\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c:45:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "         nx_fast_init64(nc->cx_state, neuronsPerCore, *(uint64_t*)&cxs);\n",
      "         ^~~~~~~~~~~~~~\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c:60:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "             nx_fast_init32(nc->cx_meta_state, neuronsPerCore/4, *(uint32_t*)&ms);\n",
      "             ^~~~~~~~~~~~~~\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_0_0.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_0_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_0_0.c\n",
      "OBJFILES :  /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_0_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_spiking_packed.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/2_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_0_0.c.o\n",
      "gcc -Wall -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_0_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_spiking_packed.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/2_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_0_0.c.o -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/launcher.link nxsdk/lib/embedded/libboot.a nxsdk/lib/embedded/libembedded_nx.a nxsdk/lib/embedded/liblmt.a nxsdk/lib/embedded/libnx.a -Wl,--script=nxsdk/utils/linker.scr -Wl,--build-id=none -Wl,-N -nostdlib\n",
      "\u001b[0m\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:  \u001b[33m~/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages ~/05-reward/v0.x\n",
      "Compiling in directory : /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065\n",
      "Include dir is : /home/seq/05-reward/v0.x/pelenet/snips/\n",
      "Compiling -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_1_0.c\n",
      "gcc -Wall -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_1_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_1_0.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_reset_init.c\n",
      "gcc -Wall -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_reset_init.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_reset_init.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset.c\n",
      "gcc -Wall -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset.c\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c: In function ‘reset’:\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c:45:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "         nx_fast_init64(nc->cx_state, neuronsPerCore, *(uint64_t*)&cxs);\n",
      "         ^~~~~~~~~~~~~~\n",
      "/home/seq/05-reward/v0.x/pelenet/snips//reset.c:60:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "             nx_fast_init32(nc->cx_meta_state, neuronsPerCore/4, *(uint32_t*)&ms);\n",
      "             ^~~~~~~~~~~~~~\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_1_0.c\n",
      "gcc -Wall -I/home/seq/05-reward/v0.x/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_1_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_1_0.c\n",
      "OBJFILES :  /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_1_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_1_0.c.o\n",
      "gcc -Wall -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/initnetwork_1_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/0_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/1_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/main_1_0.c.o -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/launcher.link nxsdk/lib/embedded/libboot.a nxsdk/lib/embedded/libembedded_nx.a nxsdk/lib/embedded/liblmt.a nxsdk/lib/embedded/libnx.a -Wl,--script=nxsdk/utils/linker.scr -Wl,--build-id=none -Wl,-N -nostdlib\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Compiling Embedded snips....Done 0.69s\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Encoding axons/synapses.....Done 14.86s\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Encoding probes.............Done 3.80ms\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  Using Kapoho Bay serial number 308\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  Args chip=0 cpu=0 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/launcher_chip0_lmt0.bin --chips=2 --remote-relay=0 --epoch=0 \n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  Args chip=1 cpu=0 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1586424555.4144065/launcher_chip1_lmt0.bin --chips=2 --remote-relay=0 --epoch=0 \n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  Lakemont_driver...\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Booting up..................Done 1.70s\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Configuring registers.......Done 15.93s\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Transferring spikes.........Done 5.96ms\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Transferring probes.........Done 0.03s\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  chip=1 cpu=0 Transfered values 20, 535, 30 \n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  chip=0 cpu=0 Transfered values 20, 535, 30 \n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 0 to 30\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 0 to 30\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 30\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 30\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Executing...................Done 34.27s\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:      Processing timeseries.......Done 0.22s\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m\u001b[0m:  Executor: 535 timesteps.........Done 50.47s\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  chip=0 cpu=0 halted, status=0x0\n",
      "\u001b[1;30m\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m\u001b[0m:\u001b[34m\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m\u001b[0m:  chip=1 cpu=0 halted, status=0x0\n"
     ]
    }
   ],
   "source": [
    "exp2 = simLoihi(inp[1], masks[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztvXuYHVWd7/350Ul3GmhIyM2WBCKt6VzIZQQzDHhpcF5lHOYcR8Cj44DOODI6c/R9c aAd3zH48iLiqjH2 hhRMHBAZnRkRGGI6QlcpkQJLcmdExISycn6UCTQEMu3TTr/aP2bqpq12Wt2lW7au9an eppy7rUr/61tp71bqLUgqLxWKxWNLiuLwNsFgsFktrYTMWi8VisaSKzVgsFovFkio2Y7FYLBZLqtiMxWKxWCypYjMWi8VisaRK02csIjJHRH4uIs KyF0iMjdvmywWi6XMNH3GAvw9sBWYC6wDbs7XHIvFYik30swDJEVkHrALmKOUOla5Ngy8SSn1RK7GWSwWS0lp9hLL2cBgNVOpsBk4Kyd7LBaLpfQ0e8YyBzjou3YQeGUOtlgsFosFmJa3AXUiwKTv2iQwo8ajyBXAFQAdHR1n9fb2Mjk5ycTEBAAzZszwnHd0dKCUYnx8fOoc4Ngxp3DU3t6OiEydT58 nba2No4ePeo5/81ToyzsOoFp06Yxffp0jh49ilKKPS8c4dWzZ7Hz6WdYcOLxtLW1MX36dMbHx3nppZc47rjjaG9vZ foQdRLL3HaSSfS0dHhOZ8xYwa/GT3IguOdx917bAI1Ofny dEJUC9xamdH5XwclHr5/Mg4Z8zsYmxsjJFjExw3bRpnzDyJY8eOMTz2AsdNm0bPrJM5evQow2MvIG1tvPqUmfzmqVEAXjXrZKZPn86Op0ZBKRbNPInp06ez8 lnUAHnp5/cxfCRY6gXX2TBCZ2ICHuOjteeu5/B9UzDYy9w3PR2FnUdz8TEROV8Oq866UR2jR5kYdcJU89UfYae2bPY/dzznDqjneGxFzjjlJkMjR3m1BnTXz5//jCndkwHYM/hoyw8oROAJ597fuoZjh075nlv1fe05/nDvHrOKUxMTDA5OYmI0NHR4TmfMWMGExMTvPjii4FpLS7tuZ9JJ 3tOXyUV58y03P mtmzptJeNa1Vn8md9n777Binn9w1lfYWnNDJxMQEXV1dU880/PxhFs dzcTEBLsPPsvCrhM8zzA89gKvmTs78Jmq76mjo4MnDo1xame755l2Pv1M5T0eo2fWyVO/n ozhf2eop7J/XuamJhg6NBznHbSiew5Os5px8/wnJ9 QufUM 09NsGiE48PfE/j4 M89RKetBf2TP73VH0G/3ur55mySnvu/73R0VGefvpp8f 3BqKUatoNeBdwj /aLcBHo8KtXLlSmXD rXfWHJ9/652B18Pc4s7TDqu7V0qpc799c9Tjl469e/fmbUKhsHrUUkZNzjrrLKU0/5ubvfH zcDXlFLLXdfuAX6klPpuWLje3l41ODjYCBObgv7 fvr6 vI2ozBYPbxYPWopoyZnn302Gzdu1CqxNHsby6NAj4icBCAibcBqYEOuVhWEC267K/ZalJ wvcn93WH8x6bnWYet7tf2b/Ccm yjriWN02SvS5jNRXsnRQ27tn9DQ96rmzBbwvzkim7RpqgbcDvwDZz2oo8DD1LpRh22LV  vK4ioZ wqrKoffU4qmpNt5qrXtsHBgYi3aPOW5EoPcqI1aOWMmpiUhWWe8ZQ7wbMAu4EnscZILkwLkxcG4tuRpFHG0uUvUH osJW3fbv3x8Zv258rYKJHmXA6lFLGTUpVcaSZFu8eHGsiDp/yGmXHKKuhd2j3nuff udat26dYniNL13PbYmCZv0flU9ypCJ6rD6M9dOHeuUYE1K7Ukx7aQSV3MQ9TxB H8zZcAkY2n2NpbMuPfSC2Pd/Pu07 e FnaPey 9MFFdsb  2O3mJqqe2X8ed5 4eNMOW9VMN94wTOu/w/xExV1vPH6/UTaats9c37dm6tidDqsah93X7xbkt97zIHvizt37sHQSRtWPvx0uyE/YeSnQzYFaaVu2bJl2Lq1TDZWknaRIYV/3pW8ahdWNN2jfDGzZsiVvEwqF1aOWMmpiq8JitlWrViUSNkn1WNb7OLt0GBkZqSt8q2H18GL1qKWMmtiMJWbr7e1Nrm4TEVUf7j6 7777jMOb3l/Xlrj7pOk3LOx9993XVCWsrAlLH3EEveM0P6h07qfjrtN 6ffzO3/7xVC/rYrNWGI2ncb7NND5kYRVZ7njCPuBhh2b7JWqbYiM 1G2OmVsmI3C6lFLGTUpzcj7pCxbtkw99thjqcTlbyBsRjZt2sTq1avzNqMwWD28WD1qKaMmZRp5n4jp06dr y1DD4/u7u68TciEqHcV1YPKr0eSnlv19PYqWhorcvowHY2u23MuKj4otiZFoJQZS3W2UR38pZG482Zk165dQPIfnUnX1npIOh1GkLu7i6n/He7atSu226ybuK7iumF03NLA9F1U00dUXEH7uK7CUW66Yd3nYV3v4whKN/6u1P543JoU7UOgEOjWmbXSllYbS5q9xOLiz7Ldo4z1xVFYPbxYPWopoyZ2gGQMbW1tsX50vkL8X5b L K4fVAcYfHrfBUnpaurK5V4WgWrhxerRy1Wk2hK2Xi/atUqtXnz5rzNKAwjIyPMnz8/bzMKg9XDi9XDywW33cUtb/yd0mliG 9jqK6O1giC6nvT2gcdJ6mrHhoaCnWLup7U5jRIoz0ozKahoaFA90bVpZu0CyQNr PPnz504qm3bS7ouonupu8oyTu999ILp9KIJQTdOrNW2rJoY0lj4Jfp9ChptLGcf6szCaXOILGyUMb68yisHrWUURPbxhLDccfFP7bOF7i/t1AS3G0u7viC2ley6JF276UX0tnZGTnJZVEw/fINCquz7 zsTFxqjLNb194i0dnZmUo8SZ9bp0dYnL96/AbhTiOmNuveo6nRzYFaaTNd877VKfraEiZTcej4jZvaxa1HXImt6CU63TnlouagC9MjidY6fqNK5GmnhaQU/TeTBXZKl5ht6dKlydWtkPRHFRdfmnMpRVWvuf1u3LhRy2/aNur MaRR5WjCxo0bY Mwec9p/gGmXS2qo181faRxP9N7Z7GPOtalmkbCninJ/ep9hzrXk/wnVbEZS8wW18ai 0cWlXCS/MHnFbbaxmKSsSTNdNxE/djS/vGZ0Kj686hnSZoZ6dzDVMMytif48WsW1C6pq2s9767eOOr5XdmMJWYznd04ja/iNL W0ub   /P24RCYfXwYvWopYya2EkoY7DjWLzYcQperB5erB61lFETO44lhomJiVzvn3SMRVa9SPbt25dJvM2K1cOL1aMWq0k0pcxYJicntf3G/fGbdkN1T cC4dO2ZDmFi59Dhw5lEm zYvXwYvWoxWoSTSkzFpHo0px/rIr/GGrHlvjnAouKr56R9UHx1ovO3Glp3C9pv/9G9/fX1SMO0/EtRSUtPfykMUped1yL7r11w7e1tTX8d9pU6DbGtNKWdM37KibdX6vHRe4VNjIyklqvsCzDxj2H6T7s2L2eeRE6V2SFrobV9BHknsb96 1RqHufuHAmPabsmvfRW 5/8nlsy5Yt0xLSNNPI6w 33rBbtmxJ7U 5FdiyZUveJhQKq0ctZdTEJGOZlneJKQ9efPHFULeoqVrSmMIl7D5B07X47xVXLZfk/N5LL6S/vz wai9qH3Xc7IyOjuZtQqGwetRiNYlBNwdqpS2tSShbhUYMgKtnYFajKdqAQJ2SYVZVU ffeueUHvVUw rez2TvPw6zK8pvUoqWRuJI49ltVVjM1si5wtJoCzCpe05i3/79 1P/cQdd16krr/eZ0tCk1eeBitI86NidPoLCl5FWTyNB2AGSMZx55plq27ZtgW7 aqa466Z ishjjz3GsmXL8jajMFg9vFg9aimjJnaAZAxRAyR1p48Pa4MJci/6tNkHDhzI24RCYfXwYvWoxWoSTSkzljSIK5noNGw3Y kmb3TGCBQlw7ZYykopM5YZM2ZEupuMsL/gtrsCj03P49yi9klwh/3k4N6642gUOoud1ZthL168uK7wrYbVoxarSTSlzFiCpnRx/0kGVXv5q7kaWdrwdz32n5usjljdu7su/0Pf2R73IOKq/lqJsbGxvE0oFFaPWqwm0ZSy8b63t1cNDg7mbUZh6O/vp6 vL28zCoPVw4vVo5YyamIb75uMojfuFwFdLaxmybC6WdKklBlLR0eHcRid9pIwtzjCJqY0mbAy6R6gp6en5lnjjhuNbrVbGtVzfj1ahaj3564a9dOqetRDs2kS18lF53jHwef0b6g74KWVtjPPPLNm8E Wk pF S0CO3fuzNuEQmH18GL1qKWMmpgMkCxliWV8fLzmWhpfulHzamVxv7QYHh7O24RCYfXwYvWoxWoSTSkzlkYQ126SRpfhuHuF UlavWW6vohpL7O017fQDZdk3Y4ikXRNkrB4/Pu1/Rsi3YPSsh1f1FiSvPNM34du0aaVthUrVtQU83Tm9PL7TzpnV5GqwZRSavfu3XmbUCisHl6sHrWUURM7V1gMK1euVFu2bMnbjMIwNDTEokWL8jajMFg9vFg9aimjJra7cQzHjh3LNH7TKqMk8ZpUbcXdb2hoKJE9rUoZ9YhKI2XUIw6rSTSlzFh0qKcbb1Q3YYhvuA/7kessshU1xUlWGZ6l SlSZxJL81PKjKW9vd1zHjXlicl51LQrYVPxm0yVYtJAZxLvwoULI93LRlUPi4PVo5ayaJJ0HIttYwmg0XOB5c3w8HBpfig6WD28FEWPIv0ui6JJI7FtLDHEtbEUJfE2il27duVtQqGwengpih5F l0WRZOiUsqMJY56pkfRjdtisViKRJrTN5WyKixoaeIiFbMbzeDgIL29vXmbURisHl6sHrWUURNbFRZDW1tbzbWyZioAXV1deZtQKKweXqwetVhNoillxnL06FEtf0mnEqk3TKPZsWNH3iYUCquHF6tHLVaTaHLJWETk3SJyjut8joj8XESeFZG7RGSuy61TRG4WkYMi8qCIvNrldpyIfEVEnhaRbSKyJok9cW0m1XN/F2Odvd9/1HE94ZLQDJmexWJpQnTnfkljA6YB7wDGgHNc128HrgXagY8C/ 5yuw74R2AG8G5gAGiruH0YuBc4GTgf C0wM86O5cuXx86LU7T5vLJkYGAgbxMKhdXDi9WjljJqUuRp878HfMx9QUTmAW8BrlZKjSulrgWWicgZItIG/AVwpVLqqFLqFuAg8KZK8A8Cn1ZKPauUWgesx8m4IglqY/HT6DYX3dJIFrPGzp49u67wUeQ102o98WWlR6NLiDqzOeuUvGfPnp3prNxJZp2ux69p2CD3K 7fnKoNeZGZzbo5UJobMESlxAK8Ddjoc78DuBRYBjztc/s6cCVwAvAScKLL7b8D34i7/ LFi2NzZ3 JJawEEzQrcpB70kW/ksapE1/1eN26dbHPUSbWrVuXtwmFwupRSxk1KXKJJYg5OKUQNweBV8a4zQYmlVLPB7hpE/bV5C xRJVggtpg/OFMF/1yxxlkm0736LBpXYLsjZtvzGKxWHSZlrcBgACTvmuTOG0qSd1qbyJyBXAFOFUdIyMjjI6OcvXcGfT393PzeSsZHBxk3759gLOm9fj4 NRKcdUpsquzmi5cuJD29naunuvcbnBwkK6urqneIvPmzWP27Nls376d6j3nz5/P4OAgk5OTzJw5k 7ubnbt2sX4 DhdXV0sWLCAoaEhjhw5wjWnzWJkZIQ9e/Zw9dwZPPDAA/T09LBv3z4OHTrE377iBA4cODD1HABLly5ldHSUAwcOAIQ U/WZq8/0xBNPeJ6pOqq4u7s71Wfq7Oxk0aJF7Nmzh7GxMdrb2z3P1NbWRm9vb QzLV68mLGxMeP3ZPJMAOvXr8/kmdb2b CuD7136pnW9m/g367406lnWtu/gX99/7sZHx/nkptu5/q NZ5nWtu/gR9fdjHt7e38pxtu4fq NaHPdOG3vs/1fWvqfk/Dw8OMjIwU7j3pPNPbbriF685dHfierhr4LV9/3dKpZ/r44F6 9bvLp57pU7/Zx3fOWxX4np544gk sX2Ym95ybuAzhb2nP /fyN/1nhr4TFc9sp07LruYwcFBPnLPg3zv7W8p3O9JG92iTZob3qqwdwH3 NxvwWnEPwfY5XO7BvgW8AqcEovb7S BO Puv2rVqpQKh82Jv6prZGQk1k jyfP QXqUGatHLWXUpNmqwp7CySTczAOeqbjND3EbBXB3TXa5RRI2jiXt7rxFxV/VNTg4GOun0eR5/yA9yozVoxarSTRFyFgeBXpE5CSASk w1cAGYDcwLiJLXf7PBjYopSaArZVzj1vcDZWqncbG32aR5I9Nt dM0TKtyUl/jWK5sXp4sXrUYjWJJveMRSn1DPBvwDUiMg24CtgBbFFKvYTTRfkaEekQkfcAJwF3V4LfAPy/InKSiPQB5wE/irtnVlO6 Bvqw LMuzTgZ bMmQ27V9JVMJPEn5R69ajXhqw PHS7sfv9zZw5s67u8Gn4LRqN/M00I9qTUIrIDODtwAJ8GZJS6guacXwb FPgeOAocFgpNUdEZuEMgnwD8DBwuVJquBKmAycD c/Ab4D3KqW2VtwEZwDl 4ER4ANKqV/G2bFq1Sq1eXNtP/SyMjIywvz5/hrH8mL18GL1qKWMmmQ1CeVPgc8BZwJLXdsS3QiUUh9USp2olDpOKXW8UmpO5fpBpdQfVNzOr2YqFbdjSqk/VUp1KaVeW81UKm5KKfXflFInK6UW62QqADuffqbmC8xdfRXmFnYetfcfR13LC7u2hBerhxerRy1Wk2hMSixjwBlKqaeyNSl7ent7lW18e5n /n76 vryNqMwWD28WD1qKaMmWZVY7gVOSWZSsdCZ0qXIpDHrsjtsV1dX4sXNslgMTdfuoOOwayZtO249wuKPu0ecX12bdN39bmnuo9JHPZiklSy0ivMTFU/ctPmNnpYpiS2m2mSy5r2IfB14D9AP7AemAiql/kr7jgUgro1FZ1R7K1HG uIorB5erB61tIImQf9zUf99WZVYngK AmzCyVhGXFtTMT4 7jn3f5kETaNSpDaRtDEeVdviWD28WD1qaQVNgv7n3NTVU1N3JGUrbTqTUBaFsMkhdSe/1HFf/ZlrQ93KSBoTDMZpXiTibK3qUVT788BOQhkzu4quR VUmf0JzoDG54BtwPtNwhdlW7JkSZ0StxYPPfRQ3iYUiiz10PlzNv0DN5kd2 TjpEqYHmXOaPyaJHmvWaQF3Th0Zmv3k0nGgjN314PAG4HTcdZQeRj4sG4cRdlWrlwZKl6QyLp7nbiC/Of9A92/f3 u9y8aVg8vVo9ayqhJVhnLPuAVvmsLgCd04yjKtnTp0lgR8/6zbyQbN27M24RCkaYe9axzo/MBkuTL0xSbPmopoyZZTULZDhz2XXse6DBr1ckfnXl ytQrbGxsLG8TCkWaetQz/5zOmkA6cSbpnuvej42NGXU3TtJVOy7OotGsv5l6NM6qu/H1wCKcqq89ItIDfBXYppT6WAI7c2PJkiXq8ccfz9uMwvDAAw9w7rnn5m1GYbB6eLF61FJGTbLqbvzfcWYT3iYiL I04m8FPmVuYr50dAQXsrIY/Kc7sCvP7s09PT0Nu1czYPXwYvWoxWoSjUmJ5Syl1COV4zlKqacztSxDli1bph577LG8zSgMmzZtYvXq1XmbURisHl6sHrWUUZOsSiy3icipAM2cqYBdS8HPoUOH8jahUKSpR73TyOuUZNMq3Ybdy6aPWqwm0ZhkLJ8Fvisis7MyplE4s 3Hk/Z8UUXkgtvumpo7rdlszwqTueTi0kW1cV2n6lOncV7Hj26aDJpxIsj2uPSRZK4t3Tji4qlnHqx60P3N1KtNFnO/ueMOOw9yM2m8N luvB54FpgEngQeq266cRRlC1vzvkxdjN2Ucf3uKNLWIy5dmaS7rNJoVNfmkZGRhnRrbibcaURXgzQHvur4j qenmRMnkl3Y5M2ljdFZE5a66AUheXLl6uBgYG8zSgMW7duZcWKFXmbURisHl6sHrWUUZNM2liUUr8M25Kbmg8vvvhi3iZkimm9/uW33 k5jysuR 11bUurKB8Wf5C7ro2jo6Na/sqC1aOWsmiStErRpMRydZibUuqzie6eE0ELfZVtqnw3ZVy0KAqrhxerRy39/f189qmjLfWfEfUfeMFtd/HctZ/SLrGYZCzf8l8CXg/sUUo1lbp2zXsvrbC2RJpYPbxYPWopoyYmVWHTdCNVSn3If01EOoDrDWwrBLa7sZfR0dHS/UiisHp4sXrUYjWJxqS7cQ1KqWPAySnZ0jAmJia0/JWl  2BAwfyNqFQWD28WD1qcWuStMtzK/ /aJdYROSbAZePB9akZ04xqNY1tlL9qcViyYaw/4m4/49W/n/RzlgIXoJ4ArguJVsaxowZMyLdW/mFB7F48eK8TSgUVg8vVo9arCbRmHQ3/tuA7fNKqa1ZGpgFrdDGYjKiN27/jtt buQ/7n5Rdpp2B86juiBqSvR6Rp83K806RXyWNEKTrNJO1O8stXvqjqQETsQpnTwM/LZy7XLgQ7pxFGVrpjXvG0Hc t0my9kGjeDVDZPEPYuR32VczzwKq0ctqz9zbd4mNJysVpD8J ATOAt Haxcmw8M6sZRlM1mLF7sH4eXLPVoxilQbPqopZU1CUujWa0g VbgWqXUOFAd/PI08Mq6i00NJmw9lrJi15bwkqUezdh Z9NHLa2oiX/yUfc1MFtB0iRj2Qx82HftMpzJKZsKpfQGhTYzJm0w4 PjRu0nrdBuEMX4 HjeJhQKq0ctrahJ3MzZi2edpB2XScbyXuA9IrIdOEFE/h34M D9BnEUgqBEUdQ/Th1bgmy/99ILtZ9jeHg4mXGY25d23FlQ1SPpUgkmc5TpjnVI0rHClLA4L7np9kS2xM3vZtpZI ncb1ng/81k1fFEZ 4 k6nvCzdtfnUDfhe4BDjLNGxRNt02Ft2G4rjG57h4de6VZd18K9cXJ8Hq4cXqUUsZNcmqjaWaEf2HUurHqrJMcTOi28ais8iS/7pOHbquH5ORu7qz/wa5LVq0KHFYHZtM48ibqh5hFKVbdKOI06OMWE1i0M2BgEuBQeB54HBlOwIc1o2jKNuKFSvqy7pbjN27d dtQqGwenixeng5/9Y7C6dJVA1Hki7 QX67zniNUhmUWL4GfBs4E1ha2ZZU9k3FsWPHtP228pdolaGhobxNyBX/O/7jG29NFK5VKXv68HPvpRcWTpOo2pW4RnndqWeyaryfAP6nUmpIKfVb92YQR9PRjN1DLWb43/H1fXrT39m0YWll/B9OWXU3/iTwVRFZKSLzRWRedTOIoxC0t7fnbUKhWLhwYd4mFAqrhxerRy3NrIlOL73qRLzuttusSizfBz4EbAL2Afsr2z6DOAqBiNZaNaXBZrReiqBHPV2Y046nCHpkRVJ9wjTR/dOOOk5rH4V/WEI1I6m6 QdJGpfOdRtjWmkr0pQueXYzrlLGrpNR1KuHbmOozvUk6SHtNGPTRy2N0CSogT1urxufbhg3Jt2NtZcmbiWC1rwvM3ZNcy9WDy9Wj1rKqInJ0sR1rSDZrEyfPj1x2KxGY tOqZJmMbl63N3dneuI3rjnTNL7qp5R31U9dMLo3r9R yzuH6RHvT3ikqYfnbD 63FpyjRtAnxs887C/F4aFTbTkfetsK1cuVKr6Je0SqPZ2Lt3b94mFAq/HrpjBLJaHkC3GqTefRhR6SNJ3KYzVZj4axRVTUzGj gsMxH3Ts6/9c6ae0Sd 6/VkxYymTa/lTadNhaTto iJXpT8qxDr7fNwK7Hkj1Wj1paSRPd31AqU7qIyJ0iMlu/7NNaxE3nYjqNS1EIKtqv7d8Q6O6/puOmW1VTJWiKbr N7t4qfne/W9RzRF1vFCZVa1F TcKmiY6uOs9o6l/XPalfU9y/maB7Rf1W4q41mrj/r0Q2huU4wCgwzXX jG5uVfRt fLlWjl0WRgYGMjbhEJRBD3qLYmlWZIrgh5FY2BgIFHVZtIqKZNwUVVhYX7CbHfvU6kKA74J/BS4qrIddR17Nt2bFWXTbWMpC/v378/bhEJh9fBi9ailEZoknf8rjY KejOW0O7G4owifC wBugE3gP8MLjQo/7cvKyUH7a7sZcydp2MwurhxepRSxk1SaW7cSXjulEp9VdKqT8Dvq U rOArakyFUst1fripO0kJqTRnda03j7Olqhrpm0bpv50w6Vha1Hr CFd203badLQsVlIkn6TPK/RAEkROQl4H/Bq4EngRqXU08Z3zZnly5ergYGBvM0oDFu3bmXFihV5m1EYrB5erB61lFGTTAZIisjrgAFgNU7D/u8AW0VkZSIrc6SeAZKtyPz58/M2IRfCvsTq0SPq6zbuKzBs0J7JPqmtUbbNnz8/Mn4Tt2b/ q/aW00jcRqmUUKv19a09pkMkAQeAd7qu3YR8CuDOH4f2I6zWNhtwMmV6z3AeuBZ4EdApyvMHODnFbe7gLkut07gZuAg8CDwah07ent7k7dqtSD33Xdf3iYUCquHF6tHLWXUJKuliZcA/9t37S40F/oSkTnAl4ALgVOBacDXK50EfgT8EzAPeBr4iivo3wNbgbnAukpGUuVzOKWubpyFyH4qIm1xtiiD6r88SNpWkHT/kXseDLxePQ77ysx6r/v8aTM5Odnwe qUdBqhdxBRejQyDRSpFBD2m2m0rUG/T394vx d87j4Y9HNgYBfApf5rr0fuFsz/GXAB1znc3EWD3sT8BvX9RNwSiedOBnNGNDhch8GzgDaKv5Odbn9CrggzpalS5emnZk3NY8  mjeJhQKq4cXq0ctZdTEpMQyzSAP gDwMxF5H/BbYDFOSeH3NcPfDhzvytCeEpFRYBWw2XX9BREZxlkCeS4wqJRyryW8GTgLmAFMKKX2utw2VdzujTLEtrF46e7uztuEQmH18GL1qMVqEo12VZhSagfOn/03gMeArwLLlVJF6nKDAAAgAElEQVS7NcMfVq4eZCIyEzgFmIXTRuLmIPBKnPaVJG6RmKx532oEVbns2rUr1E9cMbjZGl918OtRdqwetVhNojEpsaCUmgD OaV7fwS4qXLsr8SdxCmRSEK3GkTkCuAKgFmzZjEyMsLo6CgHDhwAYPHixYyNjbFvn7MgZk9PD Pj4wwPDwOwaNEiAIaGhgBnadL29vapBNbd3U1XVxc7duwAYN68ecyePZvt27cDMHv2bObPn8/g4CCTk5PMnDmT7u5udu3axfj4OF1dXSxYsIChoSGOHDlCZ2cnixYtYs ePYyNjdHe3k5PTw/79u3j0KFDtLW10dvbO/UcAEuXLo19pu  9jX09/eztn8D//K d/IPr1vCT37yE/767l/x48supr29navnzqC/v5/u7m5uPm8l/f39nme68Fvf5/q NVz1yHbuuOxiXvvZL/HlN57NlQ8PcMfll/C27/6Q617/Wq78j63c8b538rYbbuG6c1fT2dnJ32x5gq  drHnmS76wY/54uuWJ3qmT2wf5qa3nFvznv74xlu5vm N5z2t7d/Aj979n6fe09r DfzwnRdNvae1/Rv4wcV/wMjICOvXr cj9zzI997 lqn39Nd3/4r/9Udvbsh7KlLa27ZtW AzXXTT7XzhLKeJ9VO/2cd3zltl9Ezv/rdfcs3y01nbv4EfX3Yxl939AJ9funDqPf15/0b rvfUqfd0xf2b dxruqfe04f Y4Brek dek//9eHtfGH56Z735E57Qe8padrbtGkTCxcuDHxPQWkvzfcU9Hv6i5/dwzfe8nquGvgtX3/dUv7sJ3fztTf/Hh8f3Mu3fnc5l99 J9f3rYl8T2v7N9A5v5ub3nIul9x0OwD/8r53Tj3TrqJPmw 8AdiA057yMeAGn/uDwH8B3gXc43O7BfgocA6wy d2DfCtuPvbNhYvGzduzNuEQmH18GL1qKUImpjMsK7jN85PVr3CUkFEVgFfBC5SSr0APAW8wudtHvCMhpt/wEHVLRLbxuJlwYIFeZtQKKweXqwetRRBE5MZ1nX8pjlje0MzFhFZANwAvEspdaBy RHgtZVux1Sm6j8N2Ag8CvRURvxT6Uq8Gqe0sxsYFxF3d ezK26RjI Pp/NABSFJd0J3t8a33XCLUdg4N3/8/nBJiYpL95oO1Son0zijugzXg0530jT2YVT1iLKt0fsoW6KO49x0/fl/M3Hh0yTuflHuJv8LfresBki2h1xfqBn RKAfWBXgthFnpuTpwLeAH7ncbsfpMDAN DhONVl1KprrgJ8AHTiTZO7GNbgybNNZ6KtMFG2hL5OV96L86Pr1n7v1MJ1RNquZZevZx9ke53fdunWprpQZdd2ENFefNI3Lr0lYXGm9M9OVI02nyY96hipdZ7xGKd38QtsjPAyc7rv2FmCnZvjLccatPO/bXgcsAh7AGbPyz8AsV7hZwJ0Vv vcGVklQ7m5Eu7XwAodW5YsWWIkcqvz0EMP5W1CobB6eLF61NKsmtTzAZbKtPl ROSPKiWE/6aUukNErsYZw3K5UmpIK5KCsGrVKrV58 Z4jyVhZGSktPOFBWH18GL1qKWMmmQyCaVS6mc4o Q/ISJbcLr1nt9smQrAxMRE3iYUij179uRtQqGwenixetRiNYnGtPG DzgZZ8Gv83EGTDYdecwFVWTGxsbyNqFQmOoR12icRkN79TisIT/LxvKqHo1upM4K3Ub qLB/8bN7POeNnrMrr7Da6NaZ4fTm t/AnMr5EpweXZ/SjaMom53d2Mv999 ftwmFwurhxepRS5wmOh1Omo2sxrEcwJk2/ lKhvQ48HqcCSGbio6OjrxNKBQ9PT2B102 4NLoJhr1hR91nDZhepQVq0ctcZoEjQOpd2xII6n796WbAymnlLISp1vwNZXzNwIrTeIowmZH3nsp40ytUVg9vFg9ammUJml3PTfp7uwnkxKLiPwlztooe4APVi4/j9NTrKmwbSxeDh06lLcJWsQNQNQdFBfnv6pH3oP/8r5vmB6tikkJ/dChQ3W1nfnjDLvuLuWYnN976YVaz1P1l3ZpyqS78f/BGScyKiIHlVKzROQ4YFQpNStVqzJmyZIl6vHHH8/bjMKwfv163vCGN RtRmGwenixetRSRk0y6W6MM3PwnMpxNTfqBUYN4igEM2YEToBcWnp7e/M2oVBYPbxYPWppRk3iSu0mbnGYZCwfA9aJyMeAaSLybpx16/8m8d1zwo5j8TIyMpK3CYUibT10ftBpdUyop tx2LFbj3qrH1uFtNJImtVpcXt39Vh1H1VlVk/1mHZVGICIrMZZ0 Q0nHm5vqGc3mFNRW9vrxocHMzbjMLQ399PX19f3mYUBquHF6tHLWXUJKuqMJRSm5RSf6WUukgp9eFmzFTKgMkXTVQcYQOlkgy4ShpW5xl0vuTCzotK0hJMFl 9a/s3JIo7Lq3VW7oKQ0e7ZkoXOs QtBSjs09EWHcx4Ju6m24XtKJsK1euDO1SVxbc3QrP 84/Bl4Pu9bsA73i2L9/f673N9G33neh877d6SPunrqzHac5A7OpPWmQdxpJCxN9UpndGPiMb7sFZwbiL1a2LwGPA9/XvVlRtuXLl2uLWQYGBgbyNqFQDAwMGE33bpoZm67gl2Zmn SP1qaPWqI0iXuPjVoWIex60sw2q9mN/x24VCn1nOvaAuArSqlLkpeZGo9tY/FSxvriKKweXqwetZRRk6zaWM4FZvquHQXebBCHpQDU2wPJtG2jyPXXFoslfUwyll8At4pIn4icJiLnAT9GYyngolH2cSz ta0XL16cKLx/H3WPZsJUDxNMGp DOjzEdXyIcksa9pODexN3qAjzH3et6NSTRpJ2xkjrfdYTVhvdOjOgE/gfwE7gMPAE8P8BXbpxFGWzbSxeHn/88bxNqMGk0Tbt/e9 5buBtoQt/2qyLGxYmLD7JdlHHSehiOmjXkzbwPzuVU2Svhvd86j0ExZ/VmTSxtJK2DYWL2WsL47C6uHF6lFLGTXJbByLiJwjIpeIyDvdWzIzy00qfcVD4jJp8zCpsjC1x8StnraetKmO29C5n25bUty4CVPNwqrG6tknIcm9wqp3ouIM08s07ev6yRrd yXR1R9/0avCfggcBPqBda7tXt04irKtWLGi3lJhS/Hkk0/mbUKhaJQe9VZnxFXBpbV/8sknE40nMekC22yYphHdd2NSpapT5ZpG2OreZByLSXfjMWCVUuoJ8 yrWKxYsUJt3bo1bzMKw65du xiTi6sHl6sHrWUUZOsqsIewVl/pekZHx/P24S6iCraR1WNhMU1PDysFV cLWlXwZkWwXWrl LucclNt0f6TVptkqZWSavgkuBOH6bVSvVWz6VZbZemTcPDw5HVkWnZako9Va6polu0AT6LM/Hk9cBHcVaSvAq4SjeOomyLFy9WrY5JdcO6devqilu3h02SqhHT8DrVMXF Vn/mWq340xzVHBSHTlVJI6pOqnr4eyvVS5rVdUHPEHa/OPt10pDub6be3nkm7z7rtGDSK8wkY/leyPYPunEUZWtUG0sj6r/TsHH37t2J/oAbSVp/FDqauvXI653497p/DLp/GmH g9i9e7ex7UVF9yMoLvw5X7/Rcx6lv/s478zBJKx/n0kbSyuxcuVKtWXLlrzNKAxDQ0MsWrQobzMKg9XDi9WjljJqkkobS2Vkvfv8tLCtXoMbzbFjx/I2AUivPjauS6fbX9A9h4aGtLsgplE3btJ UK9bkvtW9UgSb5Q9edS5p8HQ0FDg9aRtWEVpJzG11X3s1yTrd5203TM3wooywJjv/AjOiPsjvu2wbvGoKFuj2lhMqpKyqm/WqbJxtykUvUqsEZi0OZlWwZnEmVVVmGnYqh6NqG6JSqdRejY6TZq2S/ox0aJ6nFc1WnWfSRtLK21nnnmmsrzMzp07M/lh6sapk5kFXdP549b5A/LHs3Pnzsg4dUiS0Uc9g6mWOn9W/jBh51U9wtxbAdMPBLcmJlrH2aCTOSR5x7r3j8JmLDFbmQZIxv35nn rdwCczh v/1g3TNxXqI5/XXS/dIOu wcERvkNi7OV/nyrgwF1MvZ699XjNEs7cWk2SeYbNECymd 5Tnq3GUvMlldVWNB13a9w0z9vnTDV86BifVF JFllKlHarFu3LpH2JvakvQ 7ZkJYKcqth84Xte552mGz pIPuqe7ejDMr nHTdR9w95NUNxBfkzPg9xSWUGylbcyjGMxod764lbD6uHF6lFLkTRJMwMNi1MpsxKL0SSUrcL06dPzNqEQVHuPdHd3e8797mWjqofFwepRS5wmjRyNr7M Ur1xmlLKjKWtrS1vE3Lngtvumko0f96/EXASkTvhhyWqtLt06vpJI7xO2K6uLs/1RnRlTWJ3o6jqkZQsniurqWJ0p5IJ0iTsOZt50bvE6BZtWmmzVWFeilSsLwJWDy9Wj1qy0iRJe51u2KR q9iqMIvFhe4Axiru9ViSDIR0X6u3lGN6nkXYoOsmpZCkg2PrHfCaxf2D/Orsdd9FtdYgLKw7Xr/fuLBRfnWea8fB5yL18KCbA7XSZpcm9jIwMKDtN2kPqXp7u5mG07keRlWPovSMyxuT9KFDFo3Lpl2Gk6alKmu /G0j/62ALbHEUIQ2lrgvP5OvnCRfp 6wV9y/WTtskI3VeuOgLyv/F5L72B3Wr0vQvfxtQG7/fnf/dRNmz54deo8k 7i4GlHqqCfsFfdvTjXt1WOj/9z9boPO447d6KSl6v5f/ Tt2v6THOuS5f C382kxFLKSSjtmvdeyrh dxRWDy9Wj1rKqElma95bLEUh7qsz7DirfdRxlN1RYXTdLOliqnsa6ax6bFK6q c Uc TCrp1Zq20LVu2LHE9YyuyZcuWvE0oFGXQQ2cKjyp PUzbE3R6L8X5KVobRjOmkaQzAVTd7XosMaxevVpt2rQpbzMKw4EDB5g3b17eZhQGq4cXq0ctZdTEVoXFcPTo0bxNKBS2vclLnnpEVU2YVNHUe 7Gpo9akmgSVY0WVXWVhDSqdutCt2jTSpsdIOnFDoDzkmQ9cxNMq3mSdIdNE5s akmiiUm3/Czeb71x2u7GMaTd3bieBuOsugaahL3y4QGjsEnu00xhq3rE6RbXxTSMqO7PQfEF3SfMfz2EPcuVDw9klvaaMWxVkyjN4t6jG/c19xxdUe80LB1EuZl0u6 3JFPKNpZVq1apzZs3x3ssCSMjI8yfPz9vMwqD1cOL1aOWMmpi21hiKMqa90Vh165deZvQcKK wMqoB4Rr8rbv/rDh9wxyM/Hrv5ZW2OqxO40EfdXrlmSibEujFJobunVmrbS1ahtL0kWiqgs51bMIUCuFDVroKypenfuZkPQ91rsPskEppVZ/5tpId5Ouq0FxBO3j9I9ya0TY1Z 51jiNNOL9JWmv08WuIBmzLV26NLG4OiR5eVklKp0/gI0bN9aErScBNjsbN27M24RCoaOHTnrRTVNx6bgIlDGNFDZjAX4PeAR4FrgbWFC5Pgf4eeX6XcBcV5hO4GbgIPAg8GqX23HAV4CngW3AGh07Vq5cma7iTc7 /fvzNqFQ5KVH0lJAko8Lkz9qtx5JSyethk4aiXufUe8qDW3TLgkVMmMBTgEGgXMAAT4N3FVxux24FmgHPgr8uyvcdcA/AjOAdwMDQFvF7cPAvcDJwPnAb4GZcbYsWbLE/C21MA899FDeJhQKq4cXq0ctZdSkkN2NlVLPAP9FKfWQUkpVMpM1IjIPeAtwtVJqXCl1LbBMRM4QkTbgL4ArlVJHlVK34JRc3lSJ9oPAp5VSzyql1gHrgXfE2fLSSy l/4AZYtrwGOQW5e/IkSOxfspEVY9WJElDtV PuDjKQCunkTRoaK8wpZR7HpW34lRfnQ0MKqXcXbU2A2cBvcCEUmqvy20TcJaInAAsrfj1uMXZcdxx2T12kp4hcRlA2FTgfreocFH Ojs7Y/2UiaoerUjQOw5LJ9Vjvx5xcZSBVk4jadDw7sYi8mYR TLwMWAtTvvKQZ 3g8ArY9xmA5NKqecD3CJpb29PZrwGQT9M3b3/OC7 tFi0aFHqcTYzVg8vVo9a0tbEpBTYDKXDPMaxLALmAntx2k0EmPT5mazTLZKJiQlTm1uaPXv25G3CFHHVMmHnaVIkPYpAq lhknbC/KatiUkpsBlKh9MafUOl1A3ADSJyHnAH8FfAdJ 3jsr WEK3GkTkCuAKgFmzZjEyMsLo6CgHDhwAYPHixYyNjbFv3z4Aenp6GB8fZ3h4GHj5C2VoaAiAhQsX0t7ePjVQqru7m66uLnbs2AHAvHnzmD17Ntu3bwecVQnnz5/Phd/ AV9 49lc fAAd1x CW/77g 57vWvpauriwULFjA0NMSRI0fo7Oxk0aJF7Nmzh7GxMdrb2 np6WHfvn0cOnSItrY2ent7GRkZ4fLb7 T6vjV86jf7 M55q3jPrXdwfd8aPjm4l3/oO5t33fJTru9bwye2D6NenOCaFWewtn8D//K dwKwfv16xsbGYp/po48O8q9/8vapZ7rqke3ccdnFDA4OMjk5ycyZM nu7q7rmX70pteydetWRkdHuXrujKn3dPXcGfT390 9p p5Fu/pySef5PDhw55n2rVrF Pj43W9p9HRUQCWLl2aS9rzv6ewZ/qbBzbx8/e/e qZtm3bxoIFC0KfaW3/Bu760HsDn6ma9tzP5E57f3zjrVzft4aFCxdy2d0P8PmlCz3P9IffuZnr 9Ywb948rrh/M597TTdr zfwg4v/wPN7mjlzJv/14e18YfnpU8/0f/96B9etPKPmPV09dwYPPPCA1nv6 vLT6O/vr3lPmzZtYt68ebm pzzSnja6rfxZbDjdhC8GBnzX7wE APQAz/vcbgD DidTmcTbNfnTwA/j7tvb25u4Z0QaFK2b5v3335 3CYXC6uHF6lFLGTUpZK8wEVkkItNd59OB4yunPSJyUuV6G7Aa2ADsBsZFZKkrqrOBDUqpCWBr5dzjFmdLR0dowSaUNKteilaU7enpydsEI0ynw4jz73ev6mHa SKtfZB9UX6SThQYF091f9XAb0Pvo9vLLGuNdEnLtp6eHuPOOO73qHtehLD a1ro5kD1bsAbgTuBV FUwX0WZ1xLO07X429Urn8cZyBkdYLM64Cf4FRzvQcns lUL49j Q/gJKAPpwQ0P86WrEfeN4J6BsX53R599FGjuIP8pmGPyZQjppjc69FHH419rqKVOtPG/XzV9JH2MyeZUiVo2pc00pbJ9Cj 30zR00Ja9hVygKRyMoJ3AI8Bh4BfAD2V67Mqmc7zwDpgoStMB87I zHg18AKl5sAX8YZsb8DeJOOHUnmCktzypOoOYiS/NjqDRs0V5jfT5i7KTqjjqOu13Os65bG iNJpiVJ8w8qLr3q6nL rXdO6WH6kZHkAyXMb5L3q3M9KXmvUZNHZmaSsZRy2vwlS5aoxx9/PLP4L7jtrpqxJ0Wr/nKzfv163vCGN RtRmGwenixetRSRk3stPkxzJgR2yO5LvyZSJEzFYDe3l7jMCbdf7Osc68ep1nf3NvbG lHB512k7jr9YQ1jTcqbJL0oRN31m1lWeyrmGhi0l6ko0kzUMqMpRXGsfgbdJM21l1w211cdNPtRmHh5RXuws7dYasZa1xJTqekF7eyXhpcdNPtnue599ILI58v6tz97FGZsfteQbqZhg1y84fTDTsyMhL6fDpahMUdZ5M/bFA6ivPr91/Fn45Mzy 66fbEacHkGer9bev8lk0zVS1068xaactyPZakdZ9pNWS6bdDdV9fbqLduPAv3PMi7/lwp/XeQpJOBqd g9WnC7p/VXsfWqOtpY5pGTNpodd1Mfqdhfkz0sm0sMfT29qrBwcG8zSgM/f399PX15W1GYbB6eLF61FJGTWwbSwxZt7E0G0uXLo33VCKsHl6sHrVYTaIpZcYyOemfYkyfqDrKIL9p1odmFfY//eNPjMImuU9Q2CBbotzrwaQ eXR01Lj ua766DpsDbt3vXGE6aFzXxP7/GF0zxsd1v9eq1Ok6JDmu9DZRx3rukdd10K3zqyVtjTaWJKMoQi7ZuKeBUVoUygSVg8vVo9ayqhJIad0aTXi1rDwH0ddM3G3NA/ L76oL8A0SjqW4hGXBlr1vZcyYyl6G4tOMdXvX7d6Isj/Jwf3avkNir oVUT13Hfx4sWR8QfZFHSc10dC2lVibj2yqpLKch917Mak mjx4sVamURQt2W/e0uiW7RppW358uXJy4M TLv8uf3FdRk2OU/K bfeqR5//PFQG8tIkB5KlVeTMD3KTBk1KexcYUXZshzH4qZZ/ohaub44yXiBVtYjCUXUI /fVhE1yRrbxlIQWraY20REvQP7fpoX  6KTSkzliTrsdSDbjuJf2/STlJPN8pPbB82CpvkPnmFTdLGkvd6LLrvNex569EqyM2tR97vM0nYevZhx 71WNzotLtEuTXC9ji70qCUI 9XrFihtm7dmrcZ2lxwW7YNwbt27Wq6xb6yxOrhxepRSxk1sSPvYxgfH8/bBCOyLvZX17e2OFg9vFg9aimyJlmXRnQoZcZSVNIo6jaqeqkMYZPcJyhskC1 f1ncN62wa/s3FOadNGPYOLe09zqY E2Ebit/K20rVqxI3DMi6Sp6Ovs0Zzc26fq8e/fuhs5Smwcms7 69YgKm9fqhY1m9 7dDb9n0TXLQ5O8sbMbx7By5Uq1ZcuWvM0oDENDQyxatChvMwqD1cOL1aOWMmpi21hiOHbsWN4mFIqhoaG8TUhEWJVDnN84//XqkXk1Q4NppvRRj/a6YS 47a6m0iQPSpmx5ElcvWhR6tWLVEcdFtb9DLqr7QWFTVMbP377dfb1kEU9fTO1sYStuplmWDemaS4OnbC6100 plJHt86slbYzzzxTu17RpK7X3 5hEt6kbSLt uedO3emGl zY/XwYvWopYya2JH3MYjEVxP6v4T914P2Ud2C474Qq2HD9m7813S OqO Xtrb2yO/1uPiMiGtL osSwHt7e1NY2tQWJP3p3Nc1UPXFlN/Jrb73fJ6N7qaZGVD0lJLw9DNgVppa9RcYc2CzrxHaU2Y2Qxhg9Z4Nz1vpbCrP3Nt7u kaGGrv5lmfJ/ 5wnaB9F1xmuUsiWW/Ij7SoiqhzWpZ25kWIsXd5vOvZdeWFOytZSDoHcflBb8NRqm59X7RJ0HhQ1Kn 6w1ev 83opZXfjM888U23bti1vMwrD4OAgvb29eZtRGKweXqwetZRRE9vdOIa2tra8TSgUXV1deZuQKaZ1 lU9TOv7ddyj2jyC/GXZ3qVLM6UPUz10/Af58WuS1bvJ653XSykzlqNHj ZtQqq4q61MGv6q z/8zs2RcQbdI2kVXB5h/V2R/dVYfn127NgR6hZ0H78fk8wgrAolrEoiKu6sqkX/8Ds3F p9RrmZdjfW6abu1qfqtmPHjtj7BnX8MT2Pijfq aKuuff 49TQbYxppS3txvu47r95NNLpNM65G6uTPFerUsZFnKKwetRSRk3sCpIxm nSxEF/xiZ/3P7jojEwMJC3CYXC6uHF6lFLGTWxGUvMtnLlSmNRi5wx1Mv /fuVUvqDMXWuRemVJKOudx91P789 /fvD/1Q0C0hmpD0w0U3rnrvF6SH6XPm1Q03yI569lWqvxkdGpG2o957Wv9ddhLKGERkDBjM244CMQd4Om8jCoTVw4vVo5YyanK6UmqujsdpWVtSUAaVUmfnbURREJGNVo XsXp4sXrUYjWJppS9wiwWi8WSHTZjsVgsFkuqlDVj U7eBhQMq4cXq4cXq0ctVpMIStl4b7FYLJbsKGuJxWKxWCwZUaqMRUTmiMjPReRZEblLRLS6zrUCIvJuETnHdR6qhYh0isjNInJQRB4UkVfnY3X6iMjvi8h2EXleRG4TkZMr13tEZH1Fjx JSKcrTMumGxH5PRF5pPJsd4vIgsr1UqaPMKweZpQqYwH HtgKzAXWAbWTZLUYIjJNRN5BbZ1wlBafw0kb3cDXgJ KSNPP3Ckic4AvARcCp J0t/ 6OCu//Qj4J2AezviEr7iCtmS6EZFTgBuBvwZmAuuB/1VxLl36cCMi7xSRIyLSV7lUaj2M0R1J2ewbzh/GGNDhujYMnJG3bRk/903AhsqznxOnBdAGPAuc6nL7FXBB3s SghaXAR9wnc8FJoA3Ab9xXT hokFnq6cbYLXreBnwTFnTh t5OoG7gceBvrLrkWQrU4nlbJyBkcdc1zYDZ VkT0NQSl2mlFoDjLouR2nRC0wopfa63DbRGjrdDvxL9UQp9RSOLqtwnr96/QWcP44zafF0o5Ta5Dp9K7CN8qaPKlcC/ Y6L7sexpRp5P0c4KDv2kHglTnYkjdRWoxEuDU1SqnDwOHquYjMBE4BZhH zCdHuLUEIvJm4A B9wBvA5ZTwvQBUGlj lOcj40PVS6X8vdSD2UqsQgw6bs2CczIwZa8idKiTDp9BKeqEMqtxyKcasG9xD9zq vxBeDzSqkjrmtl1iMRZcpYjgHTfdc68jCkAERpUQqdROQNwEU4mUup9VBK3aCUugz4MHAHME4J9RCR38NpZ/qBz6nU6SMJZaoKewp4he/aPJzGyrIRpcVTwPwAt/0NsKshiMgq4IvARUqpF0TkKeANPm9VPV6iJOlGKXW/iEziZCxlTB9/BJwG/B noyBzgH/Gqe56yee3DHokpkwllkeBHhE5CaDSHXA1To pshGlxW5gXESWuvyfTYvoVKlDvwF4l1LqQOXyI8BrK92OEZHZOH8wG2nhdCMii0Rkuut8OnB85bR06UMp9Qml1ClKqVcopV4B7AHeAZxHCfWoh9JkLEqpZ3B6elwjItOAq4AdwJZcDcuBKC2UUi8B36u4dYjIe4CTcLpfNjUiciLO IP3Kw5fXa4AAAPYSURBVKWGqtcrPaP2AldW/lw/B9yulDrY4unmNOBfReRVlWf7NE5vuJ9RwvQRRll/L3WRd3/nRm44vX/uBJ7HGeS0MG bGvDM364870s4PaKejtMCp474Zpy  78GVuT9HClpcTnOuJXnfdvrcBqwH6g88z8Ds8qQbnC yB8DDgG/AHrKmj4CtBkC qwe5pudhNJisVgsqVKaqjCLxWKxNAabsVgsFoslVWzGYrFYLJZUsRmLxWKxWFLFZiwWi8ViSRWbsVgsFoslVWzGYrEYUFl50j/9i8VicWHHsVgsFoslVWyJxWIJQURuFJH35W1HFREZEpFFedthscRhMxaLxWKxpIrNWCyWAETkPpwVFb9daVe5uHL9aLXUUCnR/A8R aWIHBaRH4rIShHZKCLPishXXPHNEJHvichzIrJBRJZH3PtLIrJXRJ4SkV IyMki8jzOpJEDFXvmVfy R0T2iMh EfmQK44hEfm0iDwsIi IyLci4u9KVz1L2SnTeiwWizZKqTeKyI1Av1LqxgivlwPn4Ew3/whwOnABzlTrPxeRryqldgOfB7pw1u44D2dG3DX yERkGc7EkEuAFypxjSmlThSRIZxJEYcqflcD11X8PAXcISIPqpfXsT8BOLdi389F5KfAkwHxv2Aoj8USiS2xWCz18ROl1D6l1C5gM850 88ppe7EWexpvogcB1wB/D9KqSNKqV8Ah0TEv0AUwBHgZKAPOEEp9QvlTM0exF8CX1NKPaaUegr4GvBWn20TSqn1OGvQvN0wfoslETZjsVjqw71SoPKdH6vsZ OUaLaJyCEROYRTkpjrj6xSuvkIztrrB0XkK5WMKYjTgE 44vwmcEqI30FgvmH8FksibIKyWOrD318/qP/ 08ABYK5SamZlO1EptS0wQqV qJRaCrwKpwTyf4Xcezfwl644T1ZKfTTE73KcRbxM4rdYEmEzFoslnMPAchGZU2n7SIRyBovdAPxPETlBRE4UkT8RkZl vyLyGhH548pKhc/gtH9M uxZJiJzgG/glFiWiUibiJwlIn2u6C4RkWkichHwfuDGmPgtllSwGYvFEs5PgQ8C9wE9dcb1tzh/4HtwlrU9h5erytyMA5fhlC6eAB4F7qm43Qb8E/AdnBUutwOfwllK Bmc6q1DrrheBAaAvwf Win165j4LZZUsCPvLZYWpNKD7F1KqYfytsVSPmyJxWKxWCypYjMWi8VisaSKrQqzWCwWS6rYEovFYrFYUsVmLBaLxWJJFZuxWCwWiyVVbMZisVgsllSxGYvFYrFYUsVmLBaLxWJJlf8fWXbFyOYDuwcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp2.net.plot.reservoirSpikeTrain(fr=exp2.p.resetOffset, to=491 exp2.p.resetOffset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#plt.figure(figsize=(16, 10))\n",
    "cmap = colors.ListedColormap(['white', 'red'])\n",
    "#plt.title('NEST Reservoir spikes')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('# neuron')\n",
    "plt.imshow(spikes[0], cmap=cmap, aspect='auto')\n",
    "plt.savefig(exp1.net.plot.plotDir   'spikes_raster_nest.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get NEST spikes\n",
    "def getSpikes(nest, exp):\n",
    "    spikes = []\n",
    "\n",
    "    mi, ma = int(np.floor(np.min(nest[0]))), int(np.ceil(np.max(nest[0])))\n",
    "    for t in range(mi, ma):\n",
    "        idcs = []\n",
    "        for i in np.arange(0.0,1.0,0.1):\n",
    "            idcs.extend(np.where(np.isclose(nest[0], t i))[0])\n",
    "        spkVec = np.zeros((exp.p.reservoirExSize))\n",
    "        spkVec[nest[1, idcs].astype(int)-1] = 1\n",
    "        spikes.append(spkVec)\n",
    "        \n",
    "    return np.array(spikes).T\n",
    "\n",
    "spikes = [ getSpikes(nest[0], exp1), getSpikes(nest[1], exp2) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztnX98HWWd7z/fpkkbIFBIaS1tITSQNP29FlmQhbYoWBG68ksBxfWKsu76Yu9lF1RY5bYuupfd5eq6 INVdtEFUVxWRK/UXaUV5MKtgbZp0zY1aQ9NQ5tCaSClPxLS5/4x54SZOTPPr5nJzDnzfb9e8zrnzPPr 3zmmXnO83NICAGGYRiGiYtxaRvAMAzDVBdcsTAMwzCxwhULwzAMEytcsTAMwzCxwhULwzAMEytcsTAMwzCxUvEVCxFNJqJfENHrRLSaiE5N2yaGYZg8U/EVC4D7AWwCcCqANQAeStcchmGYfEOVvECSiKYA6AEwWQhxtHiuF8ASIcSOVI1jGIbJKZXeYjkHQFepUimyEcDilOxhGIbJPZVesUwGcMB37gCA01KwhWEYhgEwPm0DIkIARnznRgBMLPNIdDOAmwFgwoQJi1tbWzEyMoLh4WEAwMSJEz2/J0yYACEEhoaGRn8DwNGjTuOorq4ORDT6u7a2FjU1NThy5Ejg7/Hjx Pw7pdQN20GhBA4/PJunDzrLBw9etTz 7Ud3TjutBkYN24c6urqMDw8jJGRERARhvbsxsTpp ONXQWcMON0DO3ZjfoZZ Ctt94KzMPwnj4cd3oTBgo7cPz0mRja0wcBYMK06XizrxenzDoLADA4OIjhV/px8pnNOPjSTgwfO4bjp8/E0T19OOnMZuzv T2Onz5zNE/7e34PADjpjDNH83Rw9y6ceHoTamtrQ/N06OXdOGXWWco8jRwTmHjaDLzZ14vG5rMxsKPb8/v1nT2jeagdNw4nnHEmDuzsGc1jQ9Ossuv02o5uAMCkplk4tKuA2mL4SU2zcKi3gNp3TB 9TiMjTpE6uHsXxhE5eejpxnHTZ2jlacKECTiwoxv1p83Aob5e1NaMQ/2MM/D6Sztx/PSZGN7bh NmNgWWvdJ1eaOwo w6lfJ0dE8fTmyahdd2dI9ep3EAaqdNHy17pTy92dc7ep2GR47huOkzy8peTU0NjvTtwoTTZmKw9yU0zDwDR1/uxcTpp4/a1dDQ4MlT41ktONDze0w8bYZ22StdJwBleXLfT6PXJeQ6lfJUW1uLI0eOjObBXfZKv/3XqZSH0nXyl8Wg6zRQcHrVG5vPHs3j0NAQGhoayp4Rg4UdqCvaXLqf6tx5KOYp7BnhztNoWQspe0HPCHeeZGWvdF1K18n/3PPfT6/t6Mbg0Ft4fWiI/M/WQIQQFXsAuA7Ar33nHgHwOVm4BQsWCFuevGJZ6G /myoO/2eYH5m/MD 6n0II8dAl71bmQZY3mf0qVPrpxmND2LXs6 sLtMvElih2y669zvWMg7DyoRsu7Lra3C9ZpK vT8tfJefRz LFi4XQfDZX uD9ewB8XQgx13Xu1wB KIT4Tli41tZW0dXVNRYmVgRr167F0qVL0zYjM7AeXliPcvKoyTnnnIP29natFkulj7GsB9BMRCcCABHVAFgEYF2qVlU4q1dcbOymez7IX mc201mgyxsUByyc0HprLvz1kA7dOyT5ddGV103Xb1kBGkTpldYPmWfKlttwsZts04 gLfLiG6 dK5TkB06fnTvKZUfnXS10W3aZPUA8BiAb8AZL7oDwHMoTqMOO bOnRupSaiLqgtAds6kOyQqnZ2dUvttuzXS6AZQpanjXtLDpFvQVhdbe027Jm3SKBFWPvJMHjUx6QpLvWKIegA4GcCTAA7CWSA5UxXGdoxF94ER9TOKXTZh9u7dW3ZOd/zHNl2dT9N04tD3ySuWib1792qFsUlHJ7 6xFXeVNc4SA/dP0UqP3Hk0xSbsSt/WqV7Jg77k/qMGtZPrioWm6OlpSVUPBm6/x51blhZfDr 4mwNrFmzJra4ohCmY9DvJFtDWdEjDuLQKYt6RCkrNvegn5ImNi1KE/86tupUKEHnTP88csWSUMWSJnE8IMIK1D3nL9JOI45/5yq/Ov qTP9xmYTxPzTiSF9FlH ScekcFt895y KLZ86dsed57CwJrb5z6nKiGl6snR1/XKLJeVjzpw5oeLZoFtwdB9Mqn8QQQ8CVaGSuX9zybsC05M9eHRt1LVVJw6d MP8mdjY0dFhHG QnTrp2VxXmXsS5ahUPsLSS/p6BsWl40fnIWpSMbjdOjo6jOM0TdcmXllcUTn7pAYhuGIJPxYuXGivbkLoFOiwB5LKTZVWf3 /lj15IUiPPMN6lJNHTbjFojhaW1vt1a1Cnn766bRNGCWogvR/T5os6REnthpWqh5hrdUocZRwayL7U2gSp4l/0/tE5UcnDm6xKI4sjLHIWhoq/2EtEtum9po1a4ya3Cao8ml6U5r4sY3DrUeYP5NKz/Ram8QVtTWr49c/BhfH9cs6qgd02OB9teohRI5W3tsyZ84csWXLlkhxrF5xMZY/8VTo77BzWWTDhg1YtGhR2mZkBtbDC tRTh41ydPKeytqa2uNw/hXnvorjKAKpBIqFQCYNm1a2iZkCtbDW94rWQ/bHQlU4Ww1ka2St/Vjs7uDTZpG6DZtqungMRYvzz77rBAinvEN3W4hkzBxodvnXtKDcTSKqodKd9Pu4DB/pl10UcYm7r3gnaF dbt/bcdhxmJWWFBcPHivOOIaYzG9CaIUCN1Ca0MWF8ClyVjrEXVMKemKmctHOXnUxKRiyWVXWE1NTaTwpSbi8ieeCt3g0O8mi6vUZSbrOivFp9O9ZtqE3XTX7drhdTapi9KU1/ltiu6mh6XvDQ0N2psRRtmUsfTpLithnzI//rE WZ5lOoQRpIfMVl07dO8P3TR03Ez9hNnT0NAgzU8c3VOmfk3uMd1wOnYFolsDVdMR5X0sJdKaCpsEJvse5QHWw0sW9YjSyjM9H QvSJOoM/2i2BTkN 78c4tFQemNb1Fw/0uMOkhv8q9FFt7285FrLjPyH8U21bmgsDp w LW/Rfo/iwUCsZpqNLTCWPyGfXamFzjQqGg7VcnnTg0Kt1zOvHrpBfWcgwL4y4jQS1HfxiZdqrnh l9GfY76LzbLaiXxfre162BqulIeh2LzSC4yT8wnfUJYXEGufvXsejYWOmtNBlR s9tJi 4z8exfsbWhjC3PI4nqMijJjx4rzhmz55tr24Rk2Zz1nn  efTNiFTsB5eWI9yKkET0xmYqj8tJivvc9kVVldXFzkO3e6vSljL0tTUlLYJmYL18JI1PUwGucPOqbq6VOd33PVXUj m3b2qbl TiQXurjl/XEGLuMPcIqFbA1XT0dbWFlprjwWyfxJxt3B0us3a29sjTX02TVcVR5QwOtO8ZW5PXrEsUA8dkppOHkcYmU2qtNrb20PTMcmrbpkICqNzPu5uRJku7jKik54sfpveD530bLrhZd 5K0xxJPmirzgeECZ bdLzxxe2F5Qsv6YPM52xBVX6qnR14tKJw79XmE78Yf5s4jCNP0hfXVt1bA5690hS11M3vzp ZOmq/MjiFiJ8jMXkfjQtm7L4da6NTVzuT96EUnGYrrw3eeCbuJlWAknBK829sB5eWI9y8qgJb0KpYOHChWLjxo1pm5EZ vv7MXXq1LTNyAyshxfWo5w8asKbUCoYHh5O24RMsWfPnrRNiBXdOfdh/mz1MFnXoRvGZi1P3FRb YgD1kROLiuWkZER4zAmN7LJwyCth4WbgYGBVNL1E1f o87Ys9VDJ12dXbHD3ONclGtCnOVDd9aVTgVs4ycusnLPZBbdPrNqOmx2N9YdpDWduaGbhuk5Ezv /t1/YGyfbMKCiR26flT TPMvOx kh841jpKmyfWy1dmWSn2DpAzTe9NPFjQxHaSPmhbPClMcSb7zXnURdWaqlD7jmHkUFpc7nOr93bYzXGThbSojm/RtZtf09/dr5dnUBpNJIFFnF9mGCfLzyKUXjP620UXHxiQ bfTXjdtdRuKIL4nPuOwpwRWL4pgzZ45UeBU6D8q4b26dCsf2Juvo6DC20fYBI0MnjbhvsiBKemTtgRB3WJ1wT16xTFo dNM08WP6wE7CJhUqTXTu zjtGQu4YlEcUdex2DzYg/yr0hkr8rjvkQzWwwvrUU4eNeHdjRPC/96UsIFY1aduOgzDMJVILiuWiRMnpm2ClKjbyZt 7vnbL4SmJbPPZP8i2XcTv6r9mGRxqvz49YhClC3wZXHI8qxzXXS3XnfT1tYW6jaWZUF3BqVuWJuyUfre1tamFbduuiZ5NL1PwsKq3KLMqCMh8rdAct68eWLz5s1WYWWbtfndYt/YLSG2bNmCOXPmpG1GZmA9vLAe5eRRE14gqcB2gaS7ogj6d2DyOuKwOIL8mfo1Zd  fdZhqxHWwwvrUQ5rIieXLZbW1lbR1dWVthmZYe3atVi6dGnaZmQG1sML61FOHjXhFouCKGMspv23un3QOv3ptumo3FpaWoz7fU1sUcWpIurYj2mabj3ivJ5xfOq6yWwK8icL19LSEuqWVXTyp/Ij08uviem4h nYSMWhO32smo65c caTbMzWZMSFi5sLYZszYk/Ltk0Z930g8J Z9kfGufTdg2Ojh dfJrmVyf 0rlt27ZZ62ljW5g/mzhM4w8rO262bdtWFj7su8pd9j3ITpX/tNaAuDXJC7yORXEk8c57mwKuc1Pp/NY5J7Mv6P0jcdzAlbLwy08e1yjIYD3KyaMmXLGkULEkhW7lE4U83iQysqyH7p8Km/NhZFmPtKhGTVTlgisWxTF//nyFxOWobtIoq 11wurGp2qlBLnv2rXLuLtPN98mOsjSNIlPp5tH1q30/feer/Sjm14cXWA6ccnSlX3q2OAuH373uL4H2RF2TrclrnMf6br5/QSVEZNPWz9BtgQRRa w3/wGScUxb968UHGDRDUtDDqoHggye Kmu7s7sbgrEZkeJtfBtHsxjWuvA5ePcvKoCb9BUgFPN/aSx6mTMlgPL6xHOXnUhKcbVzhxTTmNOsVSZpvKzcZfFFtMtqpQnV93562hfuOa0hw1fttp37p2ut39epheJ1N/cRKlPPvdZZq43U2mEpuWf1MNbe6LWNBt2lTToTvGYtpPLev7tunOsOkntWHnzp2xxhc3Y90VlHU9xhrWo5w8asJdYQoWLFggOjo6rMKuXlEZ 3 ZUCgU0NTUlLYZmYH18MJ6lJNHTbgrTMHRo0etw8ZZqeh00Zg2n20oFAqR46gmWA8vrEc5rImcXFYstpj0Z v0Zfs3tAzqY3VvbBnmrkrXpK/WtLIz3VZEhu6YQBzjEaq0df3Kro3OWFeQv6DPKHmwGaeRfc8DuuNIcYyDVCW6fWbVdMimG vO77edv66ao64znqA7t10n/ievWDY6dVJ3 qssTZkdqjijahZ1ynfps7u723ptgSwd2fk41h2Y2GRCHqfWqsijJjzGosB2jKUax1cAoLe3FzNnzkzbjMzAenhhPcrJoyY8xqLAdoylGisVAOjp6UnbhEzBenhhPcphTeTksmKxIYn e3ffuY0N/vNhv4POy8JE6XO3nTdv6hbHuEpYXO41CqZjSCZjUTZholwD7vtnxgzdPrNqOmy3zfd/DzuX9hYcpuRxC3AZrIcX1qOcPGpiMsaSyxZLTU2NkX93F1hQd5j/XKV1mTU0NKRtQqZgPbywHuWwJnJyWbEcOXIktriqYbrh9u3b0zYhU0TRI87yEGXKa5xw SiHNZGTSsVCRNcT0Xmu35OJ6BdE9DoRrSaiU11u9UT0EBEdIKLniOgsl9s4IvoaEb1KRJuJ6Nyotun2gYfd9LYzx0zGAsLsSOLBE f8fd1xHdN4ZWFMxr50bZLlQ6dFq4tuS7jSWshMDtDtM4vjADAewFUABgGc5zr/GIB7ANQB ByAX7rc7gXwAwATAVwPoBNATdHtFgBPATgJwDIALwGYpLLDdIzFTaWNn jQ2dmZtgmZgvXwwnqUk0dNsjzG8q8APu8 QURTAFwK4C4hxJAQ4h4Ac4hoFhHVAPgkgNuFEEeEEI8AOABgSTH4pwF8UQjxuhBiDYBn4FRcUkzHWNwkvaVLXCufVWHd5xobGyuu y5JGhsb0zZBq0UYdcW87s4A/vKh21qOs4VtOhPStAdAB7f/jbfeHHg rlmEMr8mutv4jwXdGijOA0ABxRYLgMsAtPvcfw7gWgBzALzqc7sPwO0AjgdwDMAJLrfbAHxDlb7tq4lls7/i2n04rpdDydz8tvpfs2qzO4DNLgJx7mhgko7q857zFxmF182biV/blf9JrMavxtfwRiWPmmT 1cS iuVjAP7L5/5vAP47gIsA/N7n9jcAvgrgdADDPrebAPxElb6/YomreyvKw9DmoaXyp4vuTRKlktP1Z/qwtNlqRVUJyvQwrWjDzlVSl2oeH6Iq8qhJ5rd0IaICgOuEEM8T0Z8AuF4Isdzl/iCArQCeB3C/EGK2y20lgKkA/heALiHERJfbxwF8WAjx/oA0bwZwMwA0NjYu7uzsxP79 7Fv3z4AQEtLCwYHB7Fnzx4AQHNzM4aGhtDb2wsAo1tkFwoFrLvzVlz9b/ Ourq60RW406ZNQ/stn8D0O78MAJgyZQoaGxuxdetWFNPE1KlT0dXVhZGREUyaNAnTpk1DT08PhoaG0NDQgBkzZqBQKODw4cOor69HU1MTdu/ejcHBQdTV1aG5uRl79uzBwMAAampq0Nraiv7 fuzfvx8A0NbWZpWnp556CrNmzcLMmTPL8tTQ0DA6A6aS8lTafdYmT/v370dNTU1V5SnKdert7cWll15aVXmKep127NiBSy65xDhPlfqMKBQKuO2227S3dMlCxXIdgE8JId7jcn8EwAYAvwHwsBCi2eX2twAmAVgFoE8IUeNy 1MAHwyqWNwsWrRIbNiwIc4sVTT79u3DlClT0jYjM7AeXliPcvKoSaXtFfYKgHf4zk0B8FrRbWqI234AcE9NdrlJ0V3HYjMgWIl0dXWlbUKmYD28sB7lsCZyslCxrAfQTEQnAkBxJtgiAOsA7AQwRERtLv/nAFgnhBgGsKn42 OmSlC3leZ/10kJm/2o/JjOHkmSkZGRVNJNEpNZUn73kh4ms/bC/OjEo3M TaqxfESFNVGgOxgT5wHX4H3x92MAvgFnncsdAJ7D29109wJ4HMAEAB BU9nUF91uAfD/AJwIYCmAVwFMVaXf1tYWdRyrqli/fn3aJmQK1sNLVD3SnOQRV1z 3/dddI5x3DazLXXi0nU3TcN/LpHBeyKaCOCDAGbA19IRQvydZhzfBvBRAMcBOALgkBBiMhGdDGcR5IUAfgfgY0KI3mKYCQAeAPDHAH4P4E EEJuKblSseG4C0A9nrOY3KjsWLlwoNm7cqGNyLujv78fUqf4ex/zCenhhPcrJoyZJjbH8FMDdAOYBaHMds2WB3AghPi2EOEEIMU4IcZwQYnLx/AEhxPuLbstKlUrR7agQ4qNCiAYhxDtLlUrRTQgh/lIIcZIQokWnUgGA13q6pe5RFi3Zfuqkm8SiLyA775aIo0tRtlhN129PT49RfGF5yEI3ZxxkpXxkCdZEjkmLZRDALCHEK8malDytra2CB9/eZu3atVi6dGnaZmQG1sML61FOHjVJqsXyFIBT7EzKFlG2dMk6Ntts LcAt9mCoppm0EXdEl1nYN52O5Y4tg/RbYWVvm 663ap3zhbiyq/OnYnyVhoEmcviG5 dMK 0a2/o7NJi U OIPnawHsBTAaUAjx59opZgAeY/GSx/5iGayHF9ajnDxqklSL5RUAX4OzcHEvnMHy0lFRDA0NBZ6vpCmgcVJaKcw4sB5eWI9yWBM543U9CiFWJWnIWHLs2LHA83l938Xhw4fTNiFTsB5eWI9yWBM5Ji0WENENRLSeiN4ovljrpqQMS5Jx44yyXfXU19enbUKmyJIeprMDk2hdZ0mPrJC0JpXeS6L9hCWiz8FZkPjfAcwH8JcAPk1EtyRkW2LU1dVZhYtjsCyJ8LLvYWHdlDaaSwpVF2PUwUi3P5M4w9xKeiQ5qKrb7Wr6RsokWtdJl49KJGlNKr6XRHclJYA9AN7hOzcDwA7dOLJy2Ky8j2MF7VhslW6TRnt7ewKWVC6shxfWo5w8apLUGyTrABzynTsIZ6uVisJmnx/3P4ig72HuYXEkhU0ag4ODCVhSubAeXliPclgTOSYVy/cBfI IZgAAETUDeAjOS7kqCmcnGKaEbddgtcJ6eGE9ymFN5JhULLfB2U14MxG9BWdX4k0AvpCEYUkyYYJ5I0tnkZbqXFZpbm5We8oJq1dczHr4YD3KYU3kmFQsi4QQdwkhJsEZazlRCHGHEOKtpIxLiuHhYeMw/i6m1Ssulg6s6nSNZYXS2 MY51qxHl5Yj3JYEzkmFcuPiWg6AAghXk3InjEhjncpVFLFoWJgYCBtEzIF6 GF9SiHNZFjUrF8CcB3iKgxKWPGiqhjLLb7PGWVLO dprt3lon2Kr/tf/2Xof5101Clq4rDtlwlUe6yXD7SImua2OwPpgoXqSzpTh8D8AyA1wGMANgFYEvp0I0jK8fChQu1p9jJXvij 5Ie3Thk8Zi8EMiU/v7 WOOrdFgPL6xHOXnUJKkXfS2RVE5a70HJCnPnzhWdnZ1pm5EZNm3ahPnz56dtRmZgPbywHuXkUZNENqEUQvwm7LA3NR3eeqvi5hskyv79 8c0vax3E461HlmH9SjHRhNZV2gc3etZuq9MtnS5K xI0sCskKWLVunYTnYw3X2arxmTJfzlXrXoWvZdJ/40MekK 5b/FIA/ArBbCLE8bsOShN/H4iWP75aQwXp4YT3KyaMmJl1hJtvm/5n/HBFNAPBVA9syQRzTjauJ/fv35 4mkcF6eGE9ymFN5ETaP14IcRTASTHZMmbYLJCsZvbt2zcm6ej2Mavcdbq okyjHCs9TIlr2rMpWdUjTVgTOSZdYd8MOH0cgAuEEGfHalXCtLa2iq6uLqMw/pX2YSvvK5G1a9di6dKlaZuRGVgPL6xHOXnUJJGuMAS/gngYwL0GcWSCiRMnGoeRDbxVOi0tLWmbkClYDy sRzmsiRyT6carAo6vCCE2JWlgEvAYixfbLcDj7J5ShY2Sbpi/sHjcevDMMt4iPgjWRI7JdOMTiOheIvodEb1UPPcxIiob1M86PMbixXZDPdN30Oi08kynDgdtDlo67w7j78aU2ebWo5paprbwhovlJKlJHH9m4trixdYWkzGWHwHYCOAfAPQLIU4moqkAnhZCtFqlnhI2YyzVTB77i2WwHl5Yj3LyqElSYyzvA3CDEGKEiEq10asATjM1MG1s3sdSzfC7JbywHl5Yj3JYEzkm0403ArjFd 5GOJtTVhS6rbRqQNW0Xb3iYgwNDQW6mcRrct503MXGrqBdj8P8 L X9AgKn8cxF7ceWUP3esS9HUqWNckEurtVAmgC8DsAWwEcBfBLAL8BME03jqwcLS0tWrt5Bu0iHPfOwmlSysuaNWsCz8u B/12n1PFobMbtCycbpw2u0KX9Ijr ld6mfGXDyafmpjsbmz8UAbwhwCuAbDYNGxWDt2KJQzVA1Zn6/so8ek88Ex 5/EmkcF6eGE9ysmjJolsm19NLFiwQHR0dKRtRmYoFApoampK24zMwHp4YT3KyaMmiWybT0TXElEXER0kokPF4zARHbI3lWEYhqk2TAbvvw7g2wDmAWgrHrOLnxXF0aNHI4WvtgHcQqGQtgmZgvXwwnqUw5rIMZluPAzgn4QQuX9LFi aYxiGCcekxfLXAP6RiBYQ0VQimlI6kjIuKerq6tI2IVPMnDkzbRMyBevhhfUohzWRY9Ji V7x07 FiwBQE485YwOR1vhTbuCK1gvr4YX1KIc1kWOyCeW4kKOiKhUg hhLtdHT05O2CZmC9fDCepTDmsiJ9KIvhmEYhvGTy4qltrY2trji3ioiLIzu2xdl25WExTtt2rTQrVB0tkhRYRJXkJ wvLvddLdu0Umn 0ufN7JfJ39RtpiJa5sck6103DZNmzZNabtuXKbnosZhWi5U MuIrBzZviYirnsvqfKqhe5Kymo6FixYYLzqtNK35ZDR19cXW1y6OtnuQhDmFtWf 3xfX19VX29T4iwfNiS1tVKUONLWJA145b0C3jbfSx63AJfBenhhPcrJoyaxrLwnoieJqDE s5hKQvflP6bfGaaSMdnV23QH8CxjanNoi4WI9gOYKooLIonoNSHEKZEtzADz5s0TmzdvTtuMzLBlyxbMmTMnbTMyA vhhfUoJ4 axPWirx8BeIyIni3 Po6IPhvkUQjxd4Y2pkpNTcXNkE6UxkZumLphPbywHuWwJnJks8I A AncN7D0lb02xZwzE7WxPg5cuRI2iZkiq1bt6ZtQqaw1UPnpWqy3zrxms7 ikIpHi4f5bAmCnRH QF8R9dv1o o72OpNpJ8t4TOi71s44r6GeaWx3dtyAjTQ/eFcDphwvyYpmfysjdTO93n7jl/kTKOOEiijAfFL0u7RGKzwojoRAAfB3AWgF0AHhRCvJpEhZckc fOFZ2dnWmbkRk2bdqE fPnp21GZmA9vLAe5eRRk6Tex/IuAJ0AFgHYD APAGwiogVWVqZInAskVVTCDJCpU6embUKmsNHDdvFaUFhdN9u0TeHyUQ5rokC3aQPgBQDv8527HMBvDeJ4L4CtAA4C DGAk4rnmwE8A B1AD8EUO8KMxnAL4puqwGc6nKrB/AQgAMAngNwlo4dra2toc2/PPL000 nbUKmYD28sB7l5FETk64wky1dZgP4L9 51dB80RcRTQbwDwCWA5gOZ0bafeRsNfxDOLPQpgB4FcDXXEHvB7AJwKkA1hQrkhJ3w2l1TYPzIrKfEpFyypfI4aJQGSMjI2mbkCnyroe/BfTc5/7CKlw1E6WMmLRQKxbdGgjAbwDc6Dt3E4D/1Ax/I4BPuX6fCuflYUsA/N51/ng4rZN6OBXNIIAJLvdeALPgbNX/OoDpLrffArhYZUtbW1vclXlFs36zg7oAAAAgAElEQVT9 rRNyBSshxfWo5w8amLSYjF5H8unAPyMiD4O4CUALXBaCu/VDP8YgONcFdorxUWYCwFsdJ1/k4h64bwC VQAXUII9z73GwEsBjARwLAQos/ltqHoJn3F41iOsVQCpU0GGQfWwwvrUQ5rIke7K0wIsR3Ow/4bALYA EcAc4UQOzXDHxKuGWRENAnAKQBOhjNG4uYAgNPgjK/YuEnh97F4kb1bQjVYPJbNeJOddHW/B8Hv2vCSZT1MJjfEsbtyiZ6eHmWZUpU/WTmMa 2SzT0Qx32c2iaURHQXgDPgTFueJoT4tMttLYBvwWmVXC EWO5yexDOBIDnAdwvhJjtclsJZxsa/1suQUQ3A7gZAE4  eTFW7duxf79 7Fv3z4AQEtLCwYHB7Fnzx4AQHNzM4aGhtDb2wsAaGpqAgAUCgUAzqtJ6 rqRm 6adOmoaGhAdu3bwcATJkyBY2NjaMLqRobGzF16lR0dXVhZGQEkyZNwrRp09DT04OhoSE0NDRgxowZKBQKOHz4MOrr69HU1ITdu3djcHAQdXV1aG5uxp49ezAwMICamhq0traiv78f /fvBwC0tbVZ5enxxx/HokWLYs/Tprtux4d /HMUCgX85tZPY8lXv42mpiY8eu3lmP lv0ddXR1eWvVZtN1zX2Ce1t15K/7kJ6vx HUr0Po/79HK00t334El330Ej95wJc79ylex42/uwCUPPurJU/stn8D0O7 MdXfeisu/ zAaGxvxvSuX49yvfBWNjY3o7u7G5MmTPXl6 OrL8M677/VcJ3eexuI6pVX2Nm/ejGuvvbaq8hT1Om3YsAFXX311VeVJdZ1uu 027enGqSxQBHAhgHVwxlM D ABn/tzAD4M4DoAv/a5PQLgcwDOA9Djc/tbAN9Spc9jLF7a29vTNiFTsB5e0tJDtfgwjlcqBLnrLNDMYxlJalZYLBDRQgB/D ByIcSbAF4B8A6ftykAXtNw808mL7lJ4TEWLzNmzEjbhEzBenhJS4/lT0iHSqXubjfTeML8u89zGZEzphULEc0A8ACA64QQ 4qnXwDwzuK0YxS36j8dQDuA9QCaiyv UZxKvAhOa2cngCEick93PqfoJmVoaCieDKWETb y7G1xpaa7Tpomb7WzeZuf7A18Mj qfmFbPapm mcECoWC8T5nJmNatlvRm44ZyMY5TMdhHrnmMqWNJumZlueg9EzDJYpu0wZAXcj5mZrhTwCwFsDCALd2AJ8FUAtnbOWHLrfH4EwYGA/gDjjdZKWxoXsBPA5gAoCPwKls6lW28F5hXnhvLC sh5c49UjqbZBjTdbKyFhomFRX2LNEdIb7BBFdCmfRog5XAbigGM9B1/EuANcA CCcbqypANyD75 Es25lAMClAD4kxOiMgzvhrOJ/FcBfAVghhDisMmTcuDHvAcw09fX1aZuQKVgPL3HqEdTNpOqqyiJZKyNZ09DkCfslAP9FRJcDo7O6vgDNdSxCiO8LIWqFECf4jt8JIQpCiHcLIRqEEFcJIQ64wh0QQry/6HeZEKLX5XZUCPHRYrh3CiE26dhSV1dnkO3qpzTzg3FgPbywHuXErUmUacRZxGQdy8/grJK/k4g64EwFXiaEKCRkW2IMDw nbUKm2L17d9omZArWwwvrUU7cmuhMGNA5nxVM 4SWAjgJwMMAlsFZMFlx5H0vKD Dg4Npm5ApWA8vrEc5rIkck23zHwDwCQBLhBD3APhvAP6FiL6QlHFJUZyAxhTJe9egv1sh73r4YT3KiaJJVrux4lyNb9Ji2Qdn2/xXAUAIsQ3AH8EZWK8oJkyYkLYJmaK5uTnV9GXTM4P8hZ3TCRPkf/kTT3nONzc3Z/bmT4O0y0eahJUDU01sp2eP5ft3dNcF6WD6BskFcLa9P1kIcQcRXQRgQAjRYZRqysyZM0ds2bIlbTMyw4YNG7Bo0aK0zcgMrIcX1qOcPGqS1Bsk/xTOu1F2Ayjt63UQzlqSioLHWLwMDAykbUKmYD28JKlHpbYMuYzIMekK 59wtmH5gevcBjir3SsKHmPxUlOjfDdarmA9vCSpR9ZnN4XBZUSOScUyAmeregAo9Z 1Atgfq0VjwMSJE9M2IVO0trambUKmYD28sB7lsCZyTCqWzwNYQ0SfBzCeiK6H8976v0rEsgThdSxe vv70zYhU7AeXliPclgTOSYLJB8GcBmcDSKfBvBuANcIIX6akG2J8dZbb6VtQqYovauBcWA9vLAe5bAmcowWSAohNggh/lwIcbkQ4pbilGOmitCd0qu7g2uU3Yfj2uXYdgdZXf VOgDtxuZtiLbTYm2m1uqmn8SuwUnt7B3kN kyb2KrjWYlQqcbE9E3dSMRQvy5ccopsnDhQrFx48a0zcgM/f39mDrV/2qb/MJ6eElaj9UrLvYM4vt/xx1/HOSxjMQ13bjfd5wM510obxaPQwAuhvMWyIqCpxt74Wa9F9bDS9J66L5oK67444DLiJzxYQ5CiFXu30T0SwDLhRBvuM59DcDXkjMvGXjw3su ffswZ86ctM3IDKyHF9ajHNZEjskYy7sBTPKdOwLgPfGZw1Qqqv50m7EPkz5eVb9/1G3Jq2EcBVDnK6v5tH2jok5Y0zKj6zeuMh/Fr 53nTy80b29zD0M7S1diOgnAKbBmXa8A8BMAF8GcFQI8T7tFDMAj7F4efnll3HaaaelbUZmYD28sB7l5FGTRLZ0AXADgP8C8F0A2wD8G4Dn4bz9saLgMRYvvAX426xecbFHj2qeCaZLUuXDdNaYLKxJq9bET1iYkiY2LQPb8yq/uq0t3Vl/7u m5d1oE8pqobW1VXR1daVtRmZYu3Ytli5dmrYZmYH18MJ6lJNHTZJqsYCIziOia4joQ 7DzkzGBJ2xCp3wYXEltXYhT//smbEnahnk8qlPUmMspZX3G/H2XmEAIIQQFXV1FixYIDo6Kmqn/0Tp7e3FzJkz0zYjM7AeXliPcvKoiUmLJXS6cQArACwUQuywMys75LH7T8bQ0FDaJmQK1sML61EOayLHpCvsBTjvX6l4qqlQRBkYLH1/7MZrAt114guLUzesDrZdGbbp9vb2WoWrZGTXraSHyVRV02mtUeIwKQM25TgoTG9vr1VXsspvlAkAcWlkGj4QIYTWAeBLAHYC CqAzwH4bOnQjSMrR0tLi2DeZs2aNWmbkClYDy sRzlBmjx5xTLpb5l/lV dMDpxBPlRxVP6vXjxYiE0n7EmLZaZANbCWSQ5G0Bb8ZhtVpWlz1i/896kFWC6aEnXT5jb6hUXo6mpSSucTfxR44oT3X90L919hzScKrwsjC424eOYYhp0zl0 bJAt6IsrTpuwUZBpUop/ RNPaU16Wb0ifC8zv98gf7L0/Om6/YTF6fdjsyVOLqcb8 C9l0KhEPnhUU2wHl5Yj3LyqEks042J6ALf79PDjqgGjzVHjx5N2wQtZP92bD D0igUCli9Inw7bptPVfpJh4nyj/bRG64sSzMsLlX8ti0ZHX86/fGm/flB59x62NgpC2tbfk3KjUm6Yf79fgqFgtSPLF6dchzkN q9rvIb5z0kG1MZ9P0 DGdH48O 45Buv1tWjqyMsaj6O0vfZeeifgrxdn9xkJvKNpN82MStCqdysyGpMQWbfnSd3zZpmfS1 8uHTjpR vJ1v5u4yWxVuQWRx3EnkzGW1B/yaRzz5s2zV7cK6e7uNvJvehPaPPhtBj51Hhw6tpT0MHmAqx6Eug8100rYxDadPypB5x64 LzQdGVxq ySxWMap8yfbQUjs9N9z9jqGhSvaVnQCWN63cLcuGJRHPPnzy8TOi8E3WS7du0K9RNHCyPKDWLTalPFrUrXr4fMvkpB9 EalMddu3YZaWuTlgzTFqxpOJtrWSojUcu SZmyvS9N9Q9L5 yTGoTgiiX8qJSusDA//vO23RGl77ZTJyvt4apLHrs5ZLAe5VSzJmHPFpMWCwmRv1lhvAmllzxuqCeD9fDCepSTR00S24SyWqitrU3bhEwxbdq0tE0wIo51CDKS0COO2WOymX2m6ZlQaeVjLGBN5OSyYqmpqUnbhNQIeuA0NDSkaofp9New36r4db 33/IJdQYMUS0y03nve1gcJn5tSKN8pIFJxe0uIzrhbKeGR7FRN84o/sPgrjAml816GayHF9ajnDxqwl1hFYDs37fuP3GV32omrm6hILd1d94a6G6yAC3sXBL/bm3Kjk6LUOXPZpGdbjxx2i4jyvWwcU8yjIk/mx4DI3RH avpmDt3rv4UiSojaCZXZ2dnoovJbO0KO286vdV0inNnZ2dstqrSV2E7rTbsvMn01xJuPSphJmBSNrrj1blnokx3NsmDSXlXhZfFxetYFMeCBQuUYo8VJoXR5FNnjUfp3A8uvUArbdUDLChdWT5M49BNR5WuKuzevXu1wtnao2tvlLA6ZUFX35IeqrRM7TL1G1QGTa9FHGWwpInp/WJro64f3euhW6798HRjBTzG4iWP/cUyWA8vrEc5edSEx1iYisFkGm5cm uFnTO113ScI8rYmW6cUWYNRenXd7tH0dk2bJTp2ibYXKc4wqSB6Yw1N7lsscydO1d0dnambUZm2LRpE bPn5 2GZmB9fDCepSTR024xaKAF0h6mTp1atomZArWwwvrUQ5rIieXFcuRI0fSNiFT8HiTF9bDC tRDmsiJ5cVSx67/2SMjIyklrbteh6Z36jjCSMjI8q1EnGsL7EZd9D5rYrDVDeVHnHls5JI854ZK6Jct1xWLGlv6aLzMNX5DIvH7RZ2o7vPT5o0SRpvUPo66alsXb2i/B3cpa1IVJqUwrrDqcKU/ASl645j0qRJZfb4bQtLzx fnyB7wjQKCxsUj/t80LUIsj1MB38ckyZNGg2v8850nfyoyqcsLyo3VRw25dZ/zn3PqOwyubeSyqdOXP44w37rkMvB 4ULF4qNGzembUZm6O/v5z5jF6yHF9ajnDxqwoP3CirlnfdjRU9PT9omZArWw0tUPaqxKywPZYS7wgzJYytNxtDQkJY/3f51k2a2CSZNfB1bwuIeGhrS6lbQscvk0 RcVExskpUPnXj8XX5RPqOGDXM3Dfvb2z6jXdZMu6tUturmIQzdsP7r9kb3dmm8HnSX6FfT0dbWFrzXQYrYbNsSFjYo3iD30u/29nat8Drnq4GSHtWKznYq7t9uPar5uptQ7WUkiMxu6UJE5wO4D8BZAP4fgE8IIXYT0WQA3wdwAYDnANwohHilGKYewHcAfADAtqJbd9FtHID/DeCjAPYW41unsoPHWLzksb9YBuvhhfUoJ4 aZHKMhYhOAfAggM8AmATgGQDfLTrfD2ATgFMBrAHwkCvo3UU7pwH4OoCfElFpWtdnACwA0AzgFgA/JqJJUKDb9ZMXCoVC2iZkCtbDC tRDmsiZ8wqFiHEawA LIR4XjjNpMcAnEtEUwBcCuAuIcSQEOIeAHOIaFaxAvkkgNuFEEeEEI8AOABgSTHaTwP4ohDidSHEGjiV1VUqW44dOxZ/BlMgqN/Z5vvhw4eTMrEiyYseumM2edHDBNZEzpgO3gshNrh vg/AZgDnAOgSQrinam0EsBhAK4BhIUSfy20DgMVEdDyAtqJfj5vKjnHjsjlnwXRwNmiNhc33 vp6o3Srnbzoofv64rzoYQJrImf8WCdIRO BM17yEQCXAZgLpxXi5gCA0wD0S9waAYwIIQ763C5Q2VBXV2dle9LE Z5yE5qamlJJN6uwHl5Yj3JYEzlp/HVvgjOW0gdgIgAC4N8fYSSim5Th4WFTm6ua3bt3p21CoqhWEvt/7969uyrXXthSKeXD5JqppgarzpU00elq1vlti669caapQ83KlSvHLDEAWLly5fqVK1f ZNWqVZsB/BzAEwDetXLlyu X/KxaterDcGZ59QC4duXKlV93ub0PTuXxawC3r1y58ksutz8EMHvlypUP 9MloptXrVp1/6pVq24eGRk57eMf/zh27dqFzs5OFAoF1NXV4eWXX8bmzZtRKBRQU1ODV199FR0dHaMDdQMDA9iwYQMKhQJGRkbw5ptvYv369SgUCjh69CiGhobwwgsvoFAo4NChQzh27Bja29tRKBQwODgIIsKLL76InTt3YmBgAESEjo4O9PT0YP/ /aipqcF3378ER2a1or /H7W1tejq6sL27dvx8ssvo66uDt3d3di2bRt6e3tRX1 PHTt24HtXLsezD9yPsy6/Eg9f8R4cbDobj95wJc58/wppnn7zyesx6aL34h vXYGGuQvw1E3XY/LF78P971 K4ebZ NUnrsM7LrkML7zwAh694UqcfP5F NVHr8T mbPw6A1X4sRzL8Caj12NA01n40fXfxDHLz4Pa/7kGgw2z0ZPTw9 9rFr0XTZH4/m6fEbr8aZH/gg/vWyZTh8Zgtefvll/PaT12Nk/jvx/avej2Mtc0fztHXrVhQKBdTX14/myX2d/mXFJRhuno1nPnUDTrhgKTo6OvDoDVdi nuWe67TUzddj/O 84PR6/SrT1yHP/qXH41ep1/ tw9j2fcfQ3t7 2iedu7ciUmXfGA0T0SEf16 BEdntY7mqbOzEz/40BUYP 8P8MxN12FozkKt6zTcPFuZp5qaGjx21XK8fsZZnuv0z5ctw3Dz7MCy98sb/hivzWy2KnvuPHV3d5eVvRdffBFnnHEGuru7R6/Tczd/BGLRu7B161Y8esOVoWUv6DqV7qefXvN DJx lud KpW9sDx989ILMdw825On0nU6 4aPj16n0v3kzpP7OnX8nycw6wMfxPc sAyHzmxBb28vnrv5I2i 5z5P2du1axee eHDGG6ejbq6Ovzojy/BG2echXXr1mH69OnY ONHRq9T369XY9JF78WGDRsw/vwlo/fT vXrMf78JZ776cWfPY6Tz78o8Dp969ILMdTcioGBAc/95M5Tqey1feyTnvuprq4Ozz704Gie3PfT POXjObJ5rn379 8D7d8/o5VWg963XnJSRwAXgVwNYBO3/lfA/gUnNleB31uDwD4MoBaOC2UU11uXwTwsCrd1tZWq3ncY81YrRl49tlnxySdSoH18MJ6lJNHTUzWsYzldOMmIqp1/a4FcFzxZzMRnVg8XwNgEYB1AHYCGCKiNldU5wBYJ4QYhjNF Ry/m8qWCRMmRMmKhySbl2M15tLc3Dwm6cSBTjPfdLW9 /vqFReP6mG6k4BuV4hJ94UOut0sptqVvjc3N2vnU3bexk4b7XX9mHRP d38ZUTl3zR9HdtkuxEEnTPtKvSHy TKewAXAXgSwJlwJg18CUAXgDo4U4 /UTx/B5xFkqXFm/cCeBzABDgD/jsB1BfdboGz0PJEAEvhtICmqmzJ4sr7scbdGlq/fn2oWxLp6brLdhfQddNJ3x9u/fr1FbfC3DTPJn7vu gcqV/bdHS 24Q3idNkFwI3/jJiWn5NbbbNn076umXHpMUy1l1fVwHYAmAAwK8ANBfPn1ysdA7CWSA50xVmApwFk4MAXgQw3 VGcFbevw5gO4AlOna0tLRIL4IpOjeazoMwyrYuQWnpft5z/iJlHlXx29pqkp7MBlkapn7WrFmjZYMs7ig262gVp76qOEvlI4lyG3WrIpv8R7m/omoSxXabiljl1 TeOPukBiE0n/UkRP42ZJw9e7bYtm1b2mZkhmeeeQYXXnhh2mZkBtbDC tRTh41yeSWLlli4kTljORc0dramlratv3ktjvbyuIvffbdfYeWP504VW66O9GG VWNfZjaHBSutbXVahfeKNckyLawtHTjtikLYfGa3DO2ZTbqZ9SwkdBt2lTTMWfOnMAmX1YxaW7rdh 43Ts6Oqzi0Ik/qOltG0fUsLpxuPUwjV WT9suHZM8JKFRR0eH1B7d OOMw7as24b1f35zybsSy58qrK67bnq66WZ2jCUrR9xjLCpML2KQu pBYBrGnc6aNWus4pZ9mroF5T3qd9k5WVi3Hrq2yTDRxkZnWVq25cb9qRpPCLNHR0OdsCqdTezQtUkVXreMyNI3KcMmNqr8muCOhyuWjFUsWSdosLpasLnBqlkPG3T0kD0Yk3jomcZh odD5Wcsy4iu7aYVVZi/sHNcsSiOBQsWhAqdR/bu3Zu2CakQ9sAL0yOpf4VZR6d8VKsWqjJiUibi1CiOilXl5j/PFYvimDt3bqjIlYZpV0lQ2G8vPVc7jE6acXSbmdoQFlb3xnef7 zslLpH/beYte60MI3CyoeJDSZ5itKCiKKzTpx PyVNdOPyuydRnuIKwy0WyyPOrrAo/xyiPmBtHlBByJr11fpPVIZfD5suBp3wMn/u31EeJjZh/X5Lesj8R6kUKpE8dpdyxTKGFUs1kMebRIbpmILsXNKMRZpJl480tIzagpH9 VDFr4Nuy8cmLtM/SqXzmdwrLEtU jqWOOecr15xMfq 8teB8ZqsWfB/6s6PN4lLlgfbsEFxtLS0KNdDLH/iKem6AR0b49CoZIeNRjrX16 Hyq9Oftxhw7R075OnE69p/vy6uf349 gLCt/S0uI5VwoTpo8ME91M9JeFlWnij9d9XhvdGqiajrEeY9H5hyDrqzbpEzftt37yimXiO8v UGlDFj7D7NcNq t327Ztidiqi0kZUKWrE05l87Zt27TjMvnUYaz0Nf00KSNRbFGdk8WlE4/KLjfcFaY4stQVloV 56A 9DB0ugmifNe1I0midv3EmX dSlXlT5cwO/xrNrJ2vdIgj93HXLFUUMWSBfJ4k8hgPbzkSQ dP05CmP0ZGyt0eid0/Ib5M9mEMpdjLHG jyUuooyX6Pazho09uN 3odNXLvOj0z9sMkZkG6/J JD/XO X79SyO 6xJNmnDknpW9Ijqq46nzbXW XHZHzI71bCP/5QumfCxmPiHA9S QmzMci95BY2xhQ0pmL1bNKtgarpmDdvXmhtnUVMujxMul5Kn93d3dI 27zR3d2dtgmZgvUoJ4 amHSFkRD52za/tbVVdHV1pW1GZli7di2WLl2athmZgfXwwnqUk0dNeNv8KkfWbDdttkbp5ona/WHTbWcbh4mfdXfeWuYvSl5M41ARR/dZkmFN8mESNq5upihlcSzS00k3yE/U7jQdN210mzbVdMyfP9 6OajCZlBMZ0qsaVo6lMLv3LlT26/KzcRek2492ziC/KmmYMr00L1WUaaiRpm2G2Xqb5hbkB6qaxPl2tnMdrOJK8rsvQffc56WjdXUxcxdYQoWLFggOjo60jYjMxQKBTQ1NaVtRmZgPbywHuXkURPuClNw9OjRtE3IFIVCIW0TMgXr4SUuPXS7cHW m8Rt81tlR0kTXZuCuplsug5l9urYbRI2ErpNm2o6srCORXexnP 8 zOsae92C vqcH/3v8hJJx1dvya26ubFNH yMEE2Br3YSrf7yfSaBPkz0SLIn65tYen6w4Tti6WrvcoOk2ujcx/YpmeShyBNwvJgY2NQXk1tDIpP1z0oPpN1LKk/5NM44p5urLqwspsijk9dtzCbbadOBsUrOx/mrvqtE2ecRJ1KqpOfIDdZON04bOOXhcvj1FoVedSEN6FUQKTVTVhGbM1EH6UFSUGfJXf3p0mc/nDuc6XvdXV1VrNTwuyR2enOn 7vIFuC7Irrs66uLtTdZvZUkB463RpBC9qC7AjbPFA3vaDf7jj/76dvDExXlueos6R0Z2Allb4qb6UyIvMjm52lm66NbTrXRqe8 N3e6N4emn4ZujVQNR1Z6ArLErpbdug01 NoqcnSNwljk06pm0PXJpt8mNhoo1Hp0zSOsK4Sna6wKHbodNH4/YXZoopL1zZVuLB33uvEZ2KzSb5M0jWxrwS3WDKIyT9tWXjVPzDVIGFQmNK6jbB/gKp/Xm7CWiv Fpj/X7FOvpc/8VRoHDoa sMGhfHbH7Z9u9 uoLCqf9Ru /22qWwPskOFKo6SvipddfKpY0cprPt3kN5B/oLcwspIUD51bQtqGS5/4qnQtU5uotgcZGssW61YYpoWCZG/6cbz5s0TmzdvTtuMzNDV1YXW1ta0zcgMrIcX1qOcPGrC040V1NTUpG1CpmhoaEjbhEzBenhhPcrR0UTnX75qPCSOc7qtHJU/kzGWXFYsR44cSduEWNEZnJR9PnTNBzy/TdJQ THttguKS9YVpxvWJN3t27cbxyvTwTQOG22S1Felh268QXbq2KyyVZVPHY1UeQi6Z1S26tgie1Om6lxY3P5zsok87vhVXZwnnuV9a6YU3cGYajrGYvDednqo2z1sqqjJtFEdsvy DZ0pzarpyaZ Ze/aMInLfy7qwLxuWN1zunmy1cP2HtC5drYD2abXLyz LN8zScEv lIcUV5NbFLQdR88svNjQWdnZyzxyG5K1Q0bteK00TXM7dtLzzUKo2uvbSVpmk7cxFU qok8asIVi JYsGCBvbpVyN69e9M2IVOwHl7i1kO3xS77HhZPlErdJM29e/dqxxn1D4XMv 3UY500/PAmlAqIaBAAv5DlbSYDeDVtIzIE6 GF9Sgnj5qcIYQ4Vcfj KQtyShdQohz0jYiKxBRO vxNqyHF9ajHNZETi5nhTEMwzDJwRULwzAMEyt5rVj OW0DMgbr4YX18MJ6lMOaSMjl4D3DMAyTHHltsTAMwzAJkauKhYgmE9EviOh1IlpNRFpT56oBIrqeiM5z/Q7VgojqieghIjpARM8R0VnpWB0/RPReItpKRAeJ6MdEdFLxfDMRPVPU44dEVO8KU7XlhojOJ6IXinn7TyKaUTyfy/IRButhRq4qFgD3A9gE4FQAawA8lK45yUNE44noKpT3Ccu0uBtO2ZgG4OsAfkpEFb9zJxFNBvAPAJYDmA5nuv195Lz57YcAfgRgCpz1CV9zBa3KckNEpwB4EMBnAEwC8AyA7xadc1c 3BDRh4joMBEtLZ7KtR7G6LVd1SgAAAZFSURBVK6krPQDzgNjEMAE17leALPSti3hfP8bgHXFvJ n0gJADYDXAUx3uf0WwMVp5yUGLW4E8CnX71MBDANYAuD3rvPHFzWor/ZyA2CR6/scAK/ltXy48lMP4D8BbAOwNO962Bx5arGcA2dh5FHXuY0AFqdkz5gghLhRCHEugP2u0zItWgEMCyH6XG4bUB06PQbgJ6UfQohX4OiyEE7 S ffhPPgmIcqLzdCiA2un 8DsBn5LR8lbgfwf1y/866HMXlaeT8ZwAHfuQMATkvBlrSRadEvcatohBCHABwq/SaiSQBOAXAywvN8ksStKiCi9wD4AICPALgMwFzksHwAQHGM6aNw/mz8WfF0Lu XKOSpxUIARnznRgBMTMGWtJFpkSed/gJOVyGQbz2a4HQL9kGd52rX4 8AfEUIcdh1Ls96WJGniuUogFrfuQlpGJIBZFrkQiciuhDA5XAql1zrIYR4QAhxI4BbAPwcwBByqAcRnQ9nnOn7Pqdclw8b8tQV9gqAd/jOTYEzWJk3ZFq8AmBqgNveMbBrTCCihQD HsDlQog3iegVABf6vJX0OIaclBshxLNENAKnYslj bgCwOkAXnYmCmIygP A0911zOc3D3pYk6cWy3oAzUR0IgAUpwMugjNjKm/ItNgJYIiI2lz z0GV6FTsQ38AwHVCiH3F0y8AeGdx2jGIqBHOA6YdVVxuiKiJiGpdv2sBHFf8mbvyIYS4UwhxihDiHUKIdwDYDeAqABcgh3pEITcVixDiNTgzPf6WiMYD CyA7QA6UjUsBWRaCCGOAfjXotsEIvoIgBPhTL saIjoBDjrD24SQhRK54szo/oA3F58uN4N4DEhxIEqLzenA3iCiM4s5u2LcGbD/Qw5LB9h5PV iUTa853H8oAz  dJAAfhLHKambZNY5DnbxfzewzOjKhXVVrA6SN CM7c/RcBzE87HzFp8TE461YO o53wRnA/r/FPP8HgJPzUG7g/CPfAmAAwK8ANOe1fARoUwCwlPUwP3gTSoZhGCZWctMVxjAMw4wNXLEwDMMwscIVC8MwDBMrXLEwDMMwscIVC8MwDBMrXLEwDMMwscIVC8MYUHzzpH/7F4ZhXPA6FoZhGCZWuMXCMCEQ0YNE9PG07ShBRAUiakrbDoZRwRULwzAMEytcsTBMAET0NJw3Kn67OK5ydfH8kVKrodii Rsi g0RHSKih4loARG1E9HrRPQ1V3wTiehfiegNIlpHRHMlaf8DEfUR0StE9CsiOomIDsLZNLKzaM Uot PENFuItpLRH/miqNARF8kot8R0ZtE9C1J/A3xqsfknTy9j4VhtBFCXEREDwJYK4R4UOL1YwDOg7Pd/AsAzgBwMZyt1n9BRP8ohNgJ4CsAGuC8u MCODvinuuPjIjmwNkYcjaAN4txDQohTiCiApxNEQtFv4sA3Fv08wqAnxPRc Lt99gfD DdRft QUQ/BbArIP43DeVhGCncYmGYaDwuhNgjhOgBsBHOdvtvCCGehPOyp6lENA7AzQD hxDisBDiVwAGiMj/gigAOAzgJABLARwvhPiVcLZmD JPAXxdCLFFCPEKgK8DeJ/PtmEhxDNw3kHzQcP4GcYKrlgYJhruNwUK3  jxc9GOC2azUQ0QEQDcFoSp/ojK7Zu/gLOu9cPENHXihVTEKcDuNMV5zcBnBLitwvAVMP4GcYKLlAMEw3/fP2g fuvAtgH4FQhxKTicYIQYnNghEI8LIRoA3AmnBbIJSFp7wTwp644TxJCfC7E71w4L/EyiZ9hrOCKhWHCOQRgLhFNLo59WCGcxWIPAPgnIjqeiE4gohuIaJLfLxGdTURXFt9U Bqc8Y8Rnz1ziGgygG/AabHMIaIaIlpMREtd0V1DROOJ6HIANwF4UBE/w8QCVywME85PAXwawNMAmiPGtQrOA3w3nNfanoe3u8rcDAG4EU7rYgeA9QB XXT7MYAfAfhnOG 43ArgC3BeJfwanO6tAVdcbwHoBHA/gM8IIV5UxM8wscAr7xmmCinOILtOCPF82rYw YNbLAzDMEyscMXCMAzDxAp3hTEMwzCxwi0WhmEYJla4YmEYhmFihSsWhmEYJla4YmEYhmFihSsWhmEYJla4YmEYhmFi5f8D6yJ IQaN/3QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(16, 10))\n",
    "cmap = colors.ListedColormap(['#ffffff', '#b74d41'])\n",
    "#plt.title('NEST Reservoir spikes')\n",
    "plt.xlabel('time steps')\n",
    "plt.ylabel('index of neuron')\n",
    "plt.imshow(spikes[1], cmap=cmap, aspect='auto')\n",
    "plt.savefig(exp2.net.plot.plotDir   'spikes_raster_nest.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "sl1 = exp1.net.exSpikeTrains[:,exp1.p.resetOffset:491 exp1.p.resetOffset]  # spikes from loihi\n",
    "sn1 = spikes[0] # spikes from nest\n",
    "\n",
    "sl2 = exp2.net.exSpikeTrains[:,exp2.p.resetOffset:491 exp2.p.resetOffset]  # spikes from loihi\n",
    "sn2 = spikes[1] # spikes from nest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAACnCAYAAAA7QtpQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnXd4HMX5xz/v3enUe5dlW7Zsy70b2xiDMb33FjABQk0MgZAQAqmQUBMICUkooYSSH6ETSmjGvfcqy7ZsWZLVq9Xr/P7Yu9Odmu kk2Tr5vM89 h2dnZ2vrerfXdm3nlHlFJoNBqNRtNTTANdAY1Go9Gc2GhDotFoNJpeoQ2JRqPRaHqFNiQajUaj6RXakGg0Go2mV2hDotFoNJpe0a GRERiROQLEakUkS9FJLaLfGeKSLqIVIvIeyIS7rQvVURW2sp4R0QC 0 BRqPRaNrT3y2SF4GdQCywFHirfQYRiQH CJwLDAEswPO2fQK8A/wHiANKgD/3R8U1Go1G0znSXxMSRSQOyARilFINtrQc4DSl1EGnfIuAAKXUy7btWCAPiAHGAP9WSo227Qu27UtQStX1ixCNRqPRuNCfLZKZQIbdiNjYDsxol 8D4CP7hlKqGCgFhgOzbMfY99UAOcDEPqqzRqPRaI6BpR/PFQOUt0srB5KcE5RStUCtfVtEIoAo4Ii7ZWg0Go2m/ hPQyJAS7u0FiDgGMfdA7yplCq1jZG4VYaI3A7cDhAQEDBjzJgxtLS00NTUhC3NZdvf3x lFI2NjY5tgIaGBhoaGggNDUVEaGgwGlR fn6YzWbq6 s73bZYLPj5 VFfX49SCrPZjJ fHw0NDS7bjY2NtLa2YjKZsFqtNDU10dLSgojg7 /vsh0QEEBTUxPNzc2davBEE4DVau1WU0tLC8HBwYNKkzvXyV63waTJnetUXV2NxWIZVJrcuU41NTX4 /sPKk3uXKeGhgbCwsK61bRv374SpVSnTlHO9KchaQD82qX5d3eAiMwHLgRO97QMpdRLwEsAM2fOVJs2bfK0vg727NnD PHje3z8iYrW7Vto3b6FO7pF5LA7ZfXnGEkxkNAuLQ4o6yyziEwBngYutI2FeFyGt4iOju7L4o9btG7fQuv2Lbypuz8NyVYgVUTCAETEDEwFNrTPKCLJwCvAtUqpIqddm4Hpti4uRCQaGAb0vLnhBunp6X1Z/HGL1u1baN2 hTd195shUUqVAZ8Dj4uIBXgA2AfscM4nIiEY80t oJTKalfGNoxB95 JiB/we ADpVT7AXiNRqPR9BP9PSHxVmAkUAGcDVytlFIi8pCIvGDLczkwD1htm9lu/8yy7b8SuBSjOyseuKuvK62bvr6F1u1baN29pz8H27G1HM7rJP0xp 9vAG90U0YWcHJf1K8r4uPj /N0xw1at2 hdfsW3tStgza6QUZGxkBXYUDQun0Lrdu38KZubUjcoKWl/dQV30Dr9i20bt/Cm7q1IXGDiIiIga7CgKB1 xZat2/hTd3akLhBYmLiQFdhQNC6fQut27fwpm5tSNwgMzNzoKswIGjdvoXW7Vt4U7c2JG5gj0Pja2jdvoXW7Vt4U7c2JG4QGho60FUYELRu30Lr9i28qVsbEjdITk4e6CoMCFq3b6F1 xbe1K0NiRtkZWUNdBUGBK3bt9C6fQtv6taGxA3q6nxzFV t27fQun0Lb rWhsQNAgMDB7oKA4LW7Vto3b6FN3VrQ IGKSkpA12FAUHr9i20bt/Cm7q1IXGD3Nzcga7CgKB1 xZat2/hTd3akLhBVVXVQFdhQNC6fQut27fwpm5tSNzAarUOdBUGBK3bt9C6fQtv6u5XQyIiMSLyhYhUisiXIhJ7jPzXicic/qpfV6Smpg50FQYErdu30Lp9C2/q7u8WyYvATiAWWIqxpG4HRMQiIpcDL3WyL8NmiCpsn6V9WmMgPz /r09xXKJ1 xZat2/hTd39tkKiiMRhLK/7PaVUI/CkiCwWkZFKqYPtsr8GpHVRlD9wjlJqXR9W14WKior OtVxhdbtW2jdvoU3dffnUrszgQylVINT2nZgBuBiSJRSiwBEJKvfatcNZrN5oKswIGjdvoXW7Vt4U3d/dm3FAOXt0sqBpB6U9UsRKRWRpSLSk M9Ii2tq8bR4Ebr9i20bt/Cm7r7s0UiQPu1HVuAgB6UVQAkAo8CbwJndDiZyO3A7WAscl9YWEhpaSlFRUUAjBkzhqqqKkc/YWpqKo2NjeTk5ABtk3WysrI4ePAgp512Glar1RHDPzExkdDQUPbt2wdAXFwc0dHRpKenAxAdHU18fDwZGRm0tLQQERFBYmIimZmZNDY2EhoaSnJyMllZWdTV1REYGEhKSgq5ublUVVVhtVpJTU0lPz fiooKzGYzaWlpDh0A48aN67EmgKFDh3arqaKigrlz5w4qTe5cp4qKCg4cODCoNLlznZYtW0Z8fPyg0uTOddq4cSMjR44cVJrcuU4HDx7krLPO6laT2yil3PoAIcAzwEbgsC3tRuAuN4 /FljSLu3/gJ93c0wWMKdd2iQgyPZ9CKCAId2de8aMGao3LF26tFfHn6ho3b6F1u1buKMb2KTceL570rX1ClACzAPCbGlfAfe6eXwxkNAuLQ4o86AOKKV2KqVqbZsltr993r2l0Wg0ms7xxJCcAzypDI8rZUsrwf2H FYgVUTCAETEDEwFNrhbAREJEJFwp6Rhtr H3C2jJ4wbN64viz9u0bp9C63bt/Cmbk8MyXbg7nZpi4CV7hyslCoDPgceFxEL8ACwD9jhQR1OA/4jIgkiEgI8AbytlCo5xnG9wt7f6Gto3b6F1u1beFO3J4bk 8D1IpIOBIvIV8DNwA88KONWYCRQgTGn5GqllBKRh0TkBXsmEXlBRKoxWhzfiUgJgFLqK AzYD2Gy3AZcIcH5 8R9oEqX0Pr9i20bvdoUYrGlvZ Qyce3rzebnttKaWygFkichLGA/6QUmqzJydTSpUD53WS/li77TuBO7so43ngeU/Oq9FoNN7it2u2sjqviO uOnegq3Lc4HaLRETuBVBKbVBKva U2iwiU0Xklr6r3vHBmDFjBroKA4LW7Vto3e6xOs94kzecmgzSSyv4KusIrUrxxp4DlNU3dHX4cYM3r7cnXVuPdJKWCTzkpboct gw076F1u1b9FR3va1767H1O/jRd t4cuNOdhSX8dG6Tbz9 uterGHf0K9h5EXk1yLya8Df/t0p7Z9AlNdqc5yig7r5Flq3b GO7saWFs56/yuWZOc50qqbmgH41imtoqGR6798m9Ff/IfW43wcxZvX250Widg zt/tn00YnlQajUYzaCmrb6RFKV7YnuFIq25sIrPC9lavFA 8 RQV771JSH0NAK0N9S5l1JeVsu3pR2murem3evcXxxxsV0r9DkBEUu3ffQ29XoFvoXX7Fu7otntpNbW2OtLSyyr546ZdAFhajNZJwLIvHfvzVy6lpaGBlIuvoLW5mQP/fp2ClUuxBARSV1LEjF/ ntbmFnK//pzhF16G9HPwSG9eb0 8tm702llPMBobGwe6CgOC1u1baN2u5NfUYhETsUEB1DQbhqLZyZB8lXXE8d2/qePg u6/PQNA7IyTWHnX9xGT0QGU 80XABw9lEn 8iUc/vRDAuMTiZ8zD4DCdasIHjKUgJg4LIGBjvKyPnkfk58fw86/pDdyHXjzenvitZUmIitE5KiI1Dp9Bl87rR32gGa htbtW/i67halaHAa17j ixVc8/ky1uYV8aMlxvJHdc1t 3eWtAUz92/s2ktrz5efA6CcjBBATU421TmHAWiuqaauuJCqw4fY tivWfWjm/n2mgtc8u995e/seeG5nkjsFG9eb0 i/75p 9yCMSFwJsackJO8VhuNRqPxEsW19YT7 2E9RpfR8ppmhpQf5dOD2Xx2MJclV56DiDj2P7x6C6E1R/FrbqI8NJJp 7axO3UCiSX5HE5MIaiuhhu/eKPrevz3vU7f2Hc 96Tje21Bnsu249jN6wmIjiU0ZeSxBQ8gnhiS0cDztpnohwGllPq7iPy4j p23GAPrexraN2 xWDSrZTizm/XMC46gkdPnoaIsLu0nPFREQ633caWVh7fsIMNtc2s W4tTa3GvJCapmZUu/Lu tAIvPH5yedz1sZvOXPjtwjw8amXMGfXOqzNTV3WxaTal9aRrP9 0Gn65t/94thie4g3r7cnhmQHRpDFrcA3wH0i8g3GuiAajUbTryilaGptxWo2U1hbR6ifH5uLSvnNmq388 x5lDc0siaviJVHCgmwmHlw5WZunjCaN/YcIDYwgLigAHbYuqfsRiSgoY5dz/ RkKtvIqiuhlaTiXr/tnGK8OpKoM2N9dIVn3Rbx3fPuIohxUeYt2MNAIWRccSXFxEyLAVLcAgV6cZgfUtdbXfFHPd4MiHxJ0CQ7fsTwELgVeBhb1fqeMPjRV4GCVr38U1NUzNfZuW6zLDuDd7WXVbfwKHKvpnkWN/cwn/2ZXHuh9/w6LptXPf5ci78 Ft s2YrAK/vPgBAgNnMq7v2c8Dmpvva7v20KEVBbZ1hRJTi/NWfMyzfGKuYtWcjFcu 4Y1X/sni9//GPe/ 1eW8EdWerXN JHYIq6ecwurJJwOwerIxoG4Nj2DOk3/hzHc ZdgFl3pUZtGGNVTnZlOwZgVfX3EOzXV1NFYdZe r/6C1yf0BdG9eb09aJIftUXZtMbMmea0WGo0GgKLaOnKrapkeH41SytFXr5SipqmZEKufI /z29L5KusIySHBTIyJ9Og8lQ2N7C2rZHZibI/rqpTi/f1ZLEhOJDao40Kni/63grrmlh7HpMqsqGJoaDBWs v77vbiMn66fCMtNgO6NKegw7GrjhQCcOHIZN7ff5hlOW2T7yZER7C71DAIfs1NTDy4m4kHd/PUogcQW5l2d16bUMfXscW5HmloshjXyzAm8zC1trJ3WBo3Xm9ElrIEBRMzfRbZn3/sdplbfv9LLEHBWMMjaG1qomjDGnb//Vla6moJTh7G0LMvOHYhXsaTFsk2EfHsbh0kDB06dKCrMCBo3X1PcW09i5espbSunnX5RVz7 XJ umIjByqOctHH33LOB1/z6zVbufu7dVz8yRKK64xJbi1KUWr7XtXYxL7ySse rKPVHKnu3pny12u28otVm6lpantgeqo7r6aWf2zP4Hfrthlatmzky4sXUlNSRGtzMxN2rMPc0uziMguGEdtWZIQwL66rp87mWvtJZjbfZRsP/MyKKm77ZjX/3pvpctx5H37Nfcs2OIxIZ0QH Du X5xqLFl0oKKKlLAQfj5rEvdOn DYH9DYNmkwoqocZTPcJqc6h9RWO75bqiq7/U1iZrj6Hn18qdMq4CK0ms3897RLCBwzFoBlOQXUjRhD1ORpJMw/vduynWmuraG xIj5teNPf3B0jVUdPED2l586Wqk1ebkcPZTZaRnevM89aZH8FXhWRH6glDq 5/57GavVOtBVGBA6062UIm/ZN8TPme/i4z6Y6M/r/dnBHPaUVfL67gN8fqjtbXfxknU02h5m9rdrgDd2H COyWn8a88BNhUaD O65hbu/HYtAM cNoufLN8IwHdXnYtSijfTM1k4NJHk0GBHOQdtXU51zc0E 1l4bP0OjlZX84Qbk9Q2FBSzsaCEc1OGAMYMb4Dszz4C4N433uX8mFAWbl6KX3MTpfVnEB9k3CvNra1c9t/vABgZHsrByiqmxEby7ILZPLdlDwAz4qNZvSed8KoKCmrq DrrCJsKS6luaqKhpbV9dRz8eNp4ntu6B7MIb543n8NHa0gODebRedPZVVLO98aOJNTqR0tzMw8U7eNNCeHUrSscx9/ 8cuO79MD2zy9fvjhPzo934zfPknk2Al8e 2FjrRpv3iEJx/5HdN3GNcjzOlemhUfw8ZCY mkCz76lmCLxTE/5Y5Fi5m9dzMFK5d2/cO3o7Wp4wB/9hfGmE1QvDF0vfO5J7EEh3DKX/7ZYcKjN 9zTwzJROAi4CIR2Q04zLRS6nyv1eg4JDMz0yffzp111xYW0FBeCq2Knc8 QfnZO5i4 KcDXMO oT vd0yg8fbsbEQAhxFpz eHcimpa2CX0xwG5/kMdiNiJ6 mltd3H Bfuw ggMdOmUF9czMttsHl2qZmCHSNF9WqFGe /xW3TBzNDeMMw1LX3EygxcKfNu1y1HVWQgwAIdWVLPneJSiL8ThRIqzIyeccIKKqgk25 US8 xqW0DBM19zsOI/dmG0vLucHX60CIPJoGStXrab w3e4oL6WA6NH8cTGnd3 hjeMS XLrFzOSozixR1mbp00hiEhwQwJMQznvKQ45iXFOfIXb1gDX33Mom7KHFJVTnkX 8RiQTU3EzNlOmI2E3L2RVR//SkAZquVVdMXOAyJM8F ro9buxEBeHFHBpedcxb561ZhAsp3e7LeX0c2/eYBx/fpD/  01nz3rzPPTEk39o Gh9kxW3fA2D6r/4AQF1xEXteeI7KzP3MfVovD9NTnOcruMv6gmKXbecWizPv7TtEaZ0xUc7eEfTQKtclhJwfZnZqbWmv7trP6YkxvL99N58UHeWZCcOp uBtmHYqfs1N5Hz MRIyhIYjWTRVtw2qz0zfRH6M8UYc2FDHm0tXctOG1QA8HzkSAoMZWpBNSn4WkVXlfDbvQg4drSakporbPvknAKEhEYTXVLJ99TJkxHguWvUpu0ZO5GByKk/Mn0GgxcKPl64n0GLmlomjubj5KCuuu5i3Hv8zUcON1b8PffgOEeMmEjluIpsfeYjA ATG33EPxRs7PuTbU76na M18zdPEJ42zvFwPmXxfVQtuhmLLbZW 5eAi1OHEmA2ExMYwLLcjuM5dg40KR6efi6PT09jxKpvOPTBO7RExWAuM1oxw86/xNHi8ISw1NEeH MpnoRI VdvTyYiMcAbwDxgLbBIKVXcTf7rMBbQWtfTMrxBYuLx7 Gcv I7/KNjiJowudt8Sikq9 8lYsyx12vuTHeDbXlOMZk6vamrsg/RXFtL5NgJHfadKHj7epfU1aMUnQ5IO8 U7imlXax98Q nAINdUdnQ5OJZ9dnBHALsb69K8dGvHiTtcAbDzryGgjefYg6wK3Uio3P2E7x1BbOnzie5yLU1lVJwmJQCwwtq1JFMRh1p66O/ZMUniFIkF7eFFymKjGPdxDkMcUoLrz2KSSkuWPMFU4OtDDmcwYSSI4y94VUiao5SFZMAGK0ngMp9ewHY8It7GX/XvYQOH0HG6y8ZdfjezRRvMh4hUZOmcmTJV8f8XbojMCEJS4Brt25oeITju731Yx8vsY/J2Ov69 17Oy13uc3I7Kxt4tbv384D4akMT9/C Wv/x8S7f0b4mLFkf/EJkeMnUZ6 y8UJoDvsoVna4837XLzlOujWyUQ AA4AvwLuAxYqpc7pJJ8FuBj4F3BWO0PiVhnOzJw5U23atKnH9c7LyyMpKanHx/cHX168EIBzbf3P1bnZBCcls//t16jcv5eEUxYgCJiEXX95mmkPPeqI7QPQ2tLC4f9 QMIpCwiMjaOuuJCdr73EjHt/TnNNNUu/fyUAfqFhNFUdJXbWHIo3Oi4Lp/ztNZqqqlj/4D0u9TgR8fb1XvieEcjvu6vOJftoNaX1DaRGhBJmtfLmngO8ZnNV9YSk4CDyano/9yDS30p5Q0eXUWtjA1d 977jgV8RHE5ETdtAc0FUPAllnbeEekKj2Q9rS9eT gAswcHEzZpL3rJvmfvS23x /2IsoWGkREciJhOl27e4da7A ETqS4tRnbTGAIaedzE5//uvS9q42xcTNWkaud98wdhb7ury4QxGgMfyujrigoI65DtSXcui/63ALNLBYcDPZKKptZVhocE8Mm8at3y1mtbWVq5XVdxwxeX4WywUb15P9OTpmPz8qC0scPQU2ImeNhOT2UzxpvWOtK7 F925z0Vks1JqZreZ8Mxrq1eISBzGOu2/Vko1KqWeBMaLSGdz/18DHuxlGV5j3759fVl8lyzLyXd45nRH 5eB2vwjrPrhTRz49 vkLf2a0u1b2P38n9j1/B8p27kdgKOZbZpUSwsH3n6NjNdeYPvTj3A0cz97//l3ylYtZedzT7Lyh9935G2qOgpAY6Wr98qqH93sMCInOn11vZdk53HTV6u4f/lGfvzdevKqazt9iLvDTRNGuZ03xK/zjofIo2Xc8upjxJQXY21s4NaPX2bSgR1Ympu4YukHLq0GZyMC9MqI EdFO777hYQCHNOIRI6fRHNNDfmrlgGQ crfiaoqJyzvMGU7t7ltRABSLr0Kk7ndb2IyERAXz5AzzmXUtTcSMXa8y 6h51xE6PARjLv1R90aEQCr2Ux8SEin ezXYlREaId99sjC2VU13PSlMWaECG bwnh3XxYAsTNmY/IzXIqD4hMYe9tixt2 2FHGrN89ReL8hY7tEZdd02U9vXmfezJG0ltmAhlKKed2 HZgBnDQOaNSahGAiGT1tIwTnarGJh5Zt520yDD cebJ3eZtqatzfC/auI7GSmOYMPPdtzrktfcPF6xaRun2LaRcehXbn3rEEVCuYu8e1tx3hyO/3YskatJUynZuc6RX7kvvtk5Hln5NYGw8lqBgtj7xG Y /TesTs3/qkOZKKUIG n A/FEZ41tiVaAw1U13PC/FS77I6rKuf3jl9l/zW1cc9ml/HDJOsrqG3hi/gyiA/x5d18WRxua P0p0zlQftTl2Mu/ 4CchKFsHH8SAi4hPmYlxLjMtfAzCU2tikkHdmJubeWWz15z7Dtv7ZeMyDtEclEuJeHRxFSWek3/yX9 CWtEJOt/3vbCETV5GqOuvZF9peWUPv4wJoufy3odfmHhzH7sWVqaGll7352OVkTR tWdnsMSHExzTUfX5 EXX0HcrLlUZKQz7PxLyHjtRZf9Z3/wFWIyOcaspv/yDxStX0PI8BTKd 90PLx7S7i/lYdOmsz0 Giu/PQYHlpOL4g5VZ27c6dcdDlKKdJfahunTDx1Ic11tQw58zzM/eSB2J GJAY6OEKUA570IbhdhojcDtwOEB8fT2FhIaWlpRQVGf/MY8aMoaqqyrFKWGpqKo2NjY6ImPY4NFlZWWRlZZGZmYnVaiUz0 jvTUxMJDQ01GHV4 LiiI6OJj3deMBGR0cTHx9PRkYGLS0tREREkJiYSGZmJo2NjYSGhpKcnExWVhZ1dXUEBgaSkpJCbm4uVVVVlGL0U2dXVrFs2TLMZjNpaWkOHQDjxo2jtLSU/el7sHvOb3m0 5WP7YOiNUcMndue2H2s3xzrzT iJiyCkLAIqlcvc/6Ru yn3fnsE0hwCOHjJlJXkM/SB 9F5eUw6dmXKDhyhJI3XkLMZqY 8nSnmgpzcqClmbSp09y TmD4xntyncJamghubKAoJNxxnUwmE2vWrOlwnWprawkMCGDEyJGO62S1WklNTSU/P5 KigrMZjPNcUm8eyCbcwOgprXt99mbX0hITRWBDXVUhoZz dIP eaksyiNMLyfkooNz6nI7RuonTeXxsYGI0xHXi7W2Bjm1pYZ5WzaSENwCABRtge9fSyiZMocFg2N5d/FVaiGejIbW4lvcO0COz1jCzW1NQQ1dN41NvZwBhvHzaTWP4jTtq3oNI 7 N/9Cxr  jgAZSYLWTt20jxqLBQa17Ok8iiVWdkkJiYS/6cX2Ld6JbzTZtiaRdh0MIuosDBHmnnqLKQoH3NrKw0FeS7nU GRUFND0PmX4V9SSPmGNVjOvojy8dOIShhClTKxfPlyx qFERdfxaHCIlasXOm49xzPiAlTKKyqIj8yjsPLlnnt3rMc2seOQ7B4aAzP55R0 ds5D9ln5hfy2ao1RMQnUFhURHRTPbUmC1NGp1JcWIjl5AVYhqZQVFRk/D8FhXNwzZqOmpyee1lZWeTk5HSryV3cHiMRkb8qpe7uJF2AG4BGpdR/ujn  8B1SqlzndJeB9JtXVSdHZMFXGsfI lJGdD7MZLCwkLi4 N7fPyxKK9vIDLAn08OZKNQXDpqOKuPFPKrNVuJ9Lfy2CkzWJqTT15NHQ/MnMja/GLC/f04KSGW4ppa3v/JYkbkZ3Vatl o8Q9o75LqDDGbUd0sC rcx1q4diVbH/8NAOq XyPPPoJY/FDdBK1zOZctr8lqxS80jNNfe9exr66oADGZCYiJZckNl9F0tJLZTzyHOTCIsBF9s hS 7El6Pp6Z//vv z5x59Z NZHWMPCOy2vvL6BW79e3WmXlVmEu99 BmtLEx ddimXLf YzCEj WChMf40O3svpy3/L7GnnsGMnz7Mn7fsZuxv78YcEMhZ737uUmf/mFgePe9mHnjzKZdzTP73ZySFGJGMMsoquWfpel495xQW/W8FAQ11BNfV8INPXwUgK2G4Y1C8PX 5 m5SczO5YI2xdsbh GGcPHE8uaVlfGMN5 wN3zjyNpvMTP/ba6Q/sNhxn4267vsEDx1O4ikLWHHXjZgsVk75q GRpZSiobyM7C8 JuXiK11 y9r8I6y4o80xN2xUGic/8w HboBpDz1CzPSTaGmopzbvCJUHMkh/8S8AhAwfSfXhg8z6wzMEJSSy95V/MOmeB7AEBeHMV5efg2pu4tQX36TKZOnT/ /u Nu2dD7Yf5hHT55GSngIi/63stN87cey3r/odK78dCkXjkzmJzMm9ujc7jzX3B0j8aRFcpqIBANpwD6llH0eyeMYoeSDRSRaKfX3Lo5vANq3D/07y9gN3ijDY9LT0/vsRttRXMa9yzbwyMnTeG6rMSHr0lHDKaw1uqv8TCbuWtLmrjjBotj41f/Ykjadty84jaL9GZ0aEcuY8cQlJRE9ZQZ5y76hdPsWx0B5yKg0qg 0efQkzD d/GVtnt2Tf/IQiaedwbKvvmTeya7davFz53Pqi2/yzbLlPFdSz71/epXzk PZ88KfyVv6DcfCbnBaGxtpKC jtaUFk81LaPmtxsChc/fE geN4NKT7/sFCacuJP3FvzDsgksJTh6GammhpaGe766/lMn3P0xjRRlVWYeY9OMHKN zi6L1q0m7 Y5OatGR5ro6xwTLPXv2sPXm JKTAAAgAElEQVS26xhxxXUOR4XwU88k8yPD6NUVFbo8/MrrG1j83Trmx0XyXmYuql3f PD8LEQpspJGOMYCbhweTxXQbOurH3HkINZ62zU3mynbvYPLS3PZA7TU19Ha1IjJz0pLg9Gr21BSjKUT4203IvWlJUTnHOaLy8 iNjuL7238mpjMdAKcFmDqzIgsO sqsi0B1PsHcv/J09lrMyRrJ83ljnvuIC/rCNs27mTYhEncPnc6K 5YhOXGO0kakkzUX/7JspuvBgxDYmf 3143Wq82RISAqGjG3PCDDue3v/hEjp9EeNo4hp17kWPfpHsfpKmqkrjZ8xARzFYr1rQw/CMj2wxJ8lCqDx8kMDaOwNh4pj342w7nAEC1Os6XvmnzgBmSWyaOZkhIEHOT4jA5/UbOYVyADi8ma23dpGvzio2OfaChpQV/D1ZZ9OZzzRNDsh7YCxwBEkTkNqXUN8ClwFkYD/hPga4MSTGQ0C4tDijzoA7eKOO44vBRwx6vdeo/ByisNQbZi9oNth996c cWZTLmRuX8OvcTE7aswHnKUW1/oEENtRhuuIGJs dQ6tSHD18kNLtWzg6az41m9exYtRUfnHvA5RkHuA/R0rITRlJ5eE8pocF0bB9E4XDRrH2wGGiAwI7ffMOShxCzbQ5sHMf2TXGA3jyfb8gb k3rq2Tbrq AGhtZf bryAhIdTkt3VRdNbHvePZxwkZlkLOl59Stms7IUOHU7h2JaNvMGIW7Xnhz47jJvzwPjY8fB qpYXmulpyvvwU/ gYZv3uKcTiR/qLf2HKz37pGOgFWPOTO4gcO4Gmqioa8oxB5kMf/B8AZTu20vziXx3xl5qqjqJaWhzzCFbnFZFfU0fKI7/jgpRxfDa/7eEHcM23hgF6alHbJDFlM StJhPWxgau u59xz4xCduffpSGsrbxiYqMdCInTKY2v20A/J9DgtnT/idtbqY2L5fNjzxEXVEBo2/4Ac011STv3UZ37LphMSvqWohPTKLA5g4cbVsD4 uTziI7cTgA8TYX5vhx4wlKSOLcT5Y4ygiIjmHe86 g2s0 P9bgtDOWoGAQITAhibE33 myb8jCszs9xj ybfB wuL7SVp4NkEJ3feYT7n/YTLf 7dxvgEk0GLh0lHDO6TPSYx1MSTtx71W254XpfUNPLVxJ2vyijja2MQ/z57HyPCOA/l9jSeG5CzgNKXUQREZBXwGjMV4sOfa1ikZ0s3xW4FUEQlTSh0VETNGWPqfeFAHb5ThMdHR0cfO1EOCbF4czm8c/9i 12UZT2eC69oeslcs 7DD/qUzTmf3yAmQW8GTH3xNU2sraU0BnBwRw1txo2m62PBpDx02gs0SyNcl2yC/HE65kBHjRnDGjbdw2WbDz/23qfEU1NSSEByEUoo/b9nDwmGJlNY1GDOigaMNjRTW1mEWYeHbH1NXWMDan9xJk8WPDdffzbx/PdOt/kMfvuP2b5W71ZhMV19STE1uNgD73zK6aZyNz87nnnR01eV8acw4bigtYdXiWxx5Vj5wDxPubBv0rT2SS 2RrgPyOQfx2/bUI6jmZiYsvp/4ufNpbGl1tA7GZ6U7DMmonP2Mzt7fVoiTUW3IMMKHDy3M4d7/uK56V7B6OS31ri8QGx66j4CYOEd8JYA9f/x9h3p fbnrw3b/W690qUn8/VG2Fs5N55/NF/9bxW2pQ3nWFqokdFgKC15/j6e 3eA4ZmpcNM dPpvx0RGdlhk6bESX53MHMZkYdv4lxM6c7fYx9oHwsFFj8AsOIW7W3GMek3DKAhJOWQD07f 3p5yWnMDy3AJS2hmDcVHh7Clr85xbl982de5Lp2fFgYqjjAwPZV95JZkVVZw3IrnLc3lTtyeGxATY/5sUbWNBFpsRicUpbEp7lFJlIvI58LhtMayfAfsw1jlxC2 U0RO82ezNqaohOSQIEaGgpo5PDhgPxDKnSWXv2Vz9FiQnsCy3gIdnT YP6w2JrZ283ZWER/P6BTcxprWBvX5tfcF2d8KMqEQyLrrF9Zi6 g7B9PKbWwmfOA62Gef/bWYhZBYyJTaSvWWVNLS08ulB1 U586prue7z5fibTfzv8rNpsXXPNPhZWdNsYp5T3oCYODZFJjJx/3YwmaCLMCBdkf2GMcHMfo72WCMiaawoJ3 Fq998 JhxHbzMGnMPs/WX93t0fjvNNcZtvuNPfyB6ygyarriJ0Nq2SX3nrf6C8rBITt3m2t/9YyeD0VRqDLKG1HVsfbU3InacjYg7RIydgCU4mJLNbYaAwCCm/ QXbPnDr0i94joq0ndTsnUjMSEhvDV/GkkJ8by8cx/VtheFgKhofnvyNIY6xema5GGkYU8Zf4fnbuQLXnu3x62LgerW6oxfzZnCL9VkFHDpqGFMjY1ibFQ4Xx/OczEkXWER4/lgj73WnSHxpm5PDMlPgHW2AfChQKGIfAQoEfk1MAzo Irsyq3Av4EKYCNwtc0IPQQMU0rdCSAiL2AM4AcB34lIrVIqprsyPNDhMRkZGcTFxR074zHYUlTKT5dv5MFZkzg7ZQj3L99Afo3xUMw 2vGB8uBJk7hu7EhGRoTyh/U7SM054OKOqfys/PWyO2gxmWk1m5k3ZQp7d7W9Ad86aQwjwkJ4eHVHH/urP1vGRSNd4 zsK 98QH57cVdRh3Dc3PZgegHRsSSeeR6P 8ehTCYO3f0rPik6yoMj45k/YzpPvfs5E/dvJ2TaLEJDQki58ntcs3oHo7P3OwZ2e0LQyNFMv/8hVv3o5g778kdPJOgY7so9pXT7ZsK3b Ymp3kJkw7u6jSvvwdrRSSdfhb5y5d0WOcbYO4zL7D2J0a3z/CLLufwp8a/XcolV5F67SJqcrJZ98BiUq 5gbrCAhdDkjz/dOJmz2Ph2x/jFxKKam6m1eZSm7l/H0kJ8bx53qmOMCkApya3700 /giIjjl2pi7w1v 3NzCJOMaT7pnWNpfl8lHDyT5azbfZ QSYzY5VHtvT1O5 aW5txdJF16I3dXsSIuV9EVkCjMIYK1EY3VqFwJNAE/DzY5RRjrHOe/v0x9pt3wnc2T5fd2X0JS3deDR5gt33f3/FUc5miMOIAJ3eGFazmdGRYdSXFnNv3m6syz532X/uu5/z7jdrOGzzMZ8ZH0N2VQ3fHM4jKsCf740dSXNrK8PDQrh6TApPb3J9wLVvXewsKWdjQdfuiMfigRUbMYmwIbFtqZodEkCjtYEP62Cu2Uy9NYAjMUlsDYgh5KTT2LAhA/z82Z1qeJ5csOYLWkwmzF20Vp6/8kcsfv9vHdLNDzxKcGIsK297kAXjx/LFkiVU1tVz7tov TY8ics90PHOmdcQXVnKpUkxrN21m8mZRtylOmsAgY2urYXqwGBC6mrwa l8lnR3RIwdT7XFSvOujuMXCaecTsHqFajGtpbqrN//CZQifNQYR9qYG28jaeHZlGxaT o1ixzlnvPxt53O9g4flQaA1TaoLX5 jq4h 30e7m8l3N93Il576/ 7Lwnys3Dm8CS zc5nWlwUE2MieXlnxwmFNe0iAlc3NRPRxbX0pm6P5pHYHuLO4UXtsR1 6LUaHYdERHTeH wp9mBu7Rfq6Yz7Z7TFqtrwi/uwtvOXB8Nt9 Wz53H5f7 juqmZ6EB/fnHSZH42c6JjYM5iMvHaOacAOAzJX06fzVMbd5JbXUtCUCCzE2OZFhfFC9sz PnKnrtJ28OaO5NRbrRYdpVWGKvWifD2eTcYO9sZrZpAo2ti18iJxJ1xDokvPt2hvNrAYN44fxHNJguBDXUklBbQYPVnQmk5O4rLWFvfytoteyAyESLhhSvuws/U5g3zwmV3cOX25aiyEmIrjPP/  zrEKW47pt3aI5NIDtxONmJw5k6YRTL/GMdhuSv19zjcLfdPWI8WYkppB3OcIkl5QlDz7mIIWec43BrdSYwPh6Tn4XWxgam/PSXqNZWoidPc yfsPh yndtx zvT3jqGMJTx7gcbx/gDkoyujZipp9E2i13EDI0pcv6eOs P9E4UXRPi4vm/BHJLBqfSnxQIMtyCthf4dqLUN7QyI7iNt jo42NXRoSb p225CIyDjgx0Ay7UKrDPYw8t4KbtZo6/6xmjq66Nlj7/x27lTmD4l3iQpb24kRmfRjo/FnMZl4cv5MPj US5RtQZ umrJ2wvz9GBoaTG51LUkhQfx4utGEHhISzG3fGDOGTSJ8P3UIuU2tfHO47fz/veQMntm8m2W5BY4xHIApsZFsLy4nPijA4XHmZxKGhARz88TR/GbNVv6TcQiA506fzY XGrGAHjtlOknBQdz01SrqbGtjl0TEMLmTgcD3F14BQHFMkiNOUU6CsXDRjr2HutTb1Kp44bI7aDWZqA4K5dV5FwMwZ daqoNCyI0fCkqxZtJc0sdMdRyXXlZJvdU10OILl91Bq5ioDjYGQw8OGcmIvENcuNq1tVg3bQ5jhyVz JP36YqAONc 6rSb73DMuA6MTcAvJIzmmhoiJ0zu0HUz9OwL3FoJLzA2jqk//w1Rk6Z2OffFzokQnLQvOFF0 5lM/HRm25yRkeEh7K84ykkJMWywvZS9nX6Qt9Pbgny8vGMfI8NDuXlixwjA3tTtSYvkPSAdeB9XT7RBT2ZmZq8Gppbl5FPe0Ei9rd9ZoTqE/k4MDiS3upb4oEAXI1JX3HGAdfSiWxlyRlucynHREYzrwoumM8KsVsw2YzM9vu2BnRoRykkJMUQG BsTH9eu5do5c7hl4mh vHQ9RbX1hFj9uGXiaKbERXH6UMOQjI O4NkFs9lTWkFcUACPrd/BtuIy3rlgAZE24zYxOoJdpRXMSYx1GaydkxhHta053hCXxOa06WQMT PKiEjKMbqTPp1/EYcThjvmZ8QE juMlbscDXF9iA4PC2HdJCfvHhFWTZ3vkmdPaYXL/IfOymkIDGbPyAkoEYbnH bg Bk01NVy1tlnM27sSAqVic/ySzlz4xIC4uKJnTGbgOhY9r/1CsFJhpPj Dt/TG1BHiMuu4bWxkYOffQulsBAZvzmcY4s colNlVPSJh3mlv5enufn6icqLrvmT6ec0ckMzkmkjPe7zyi8eq8og7jJna8qdsTQzIEmK6U6lmUuROYxsbeSX5knREo8cxhhm/7juJy/m va2iwn86cyJ 37GFYWDBN1dXse Nlijetp760GJOfH9bIKEZdeyMJJ5 GOaBjOHJPCPWzcMXo4ZTVN3BxuwH3J a3TWJtbGzEz2QiPiiQl8 a5 h/TQ4Ndqy299gpMxgXZTxc7S6hj5w8jR0l5Q4jAm2tpCtGGz7z148dSbOtVRHi58fd08YxKz6GG22ttri4WJqGpfDhyGlkJbm6lJpE Pf5p/K9L4zwHRYRR1nH4u5p45idEMsfN 3iMDB/SDwru1jP46ht5b 3zrmemsBggixmaptbeOyU6Xx2MJc1eUVcOXo4U OieQhIHzGeZ06bxY6SchYONd72Ftx6JzOyDrJ64xLCR6Ux4a57UUox/MJLHV5Gw86/xHHO1GsWOcY6QpKHkfb929zS5Q16e5 fqJyougMtFqbERgGwaFwqb6YbXaxDQ4NdYnOlRXbeEvWmbk i/74MXOW1M59AhIZ6Z4LP7lLD 2lbcRlKwctntc0anxwbxavnnEKgxULuN1 Q8 Wn1JcUERAdy9xnX2DBP/ P5DPPw9JJaGp3eemsk7lrShpmk4kpsVE8v3AOIdaug9E56w61 pEQHNQhz5zE2A4DsyFWP05OcvUGuXvaOBaNS2VanPF2/YNJY7hjcppj/2WjhrssBRsXGsIpz7/KgaGjHXV/7nRjboFJhITgIKba/onumzGBvy2cQ3JIx/o589HFC7ls1HCSQoIc7q1nDe84cS3er63r8eHZk8mLG0JlaATPnT6Ha9NGMCs hqgAQ3OEv5U5ibGO/DGBAdw4fhQJwW3rVYSmjGTSvQ8y8W5jRUkRGfCJcJ3hrfv8RGMw6Hbuurok1fXlcExU54bEm7o9aZGcDNwnIn8BinDq3lJKje/yqEFAcnLXvtie4OyldfPE0aRGhHXI01Rd7ZinADD7yb8QGOsdF71REWGM6uScXeEt3QAjwkMZ4caM25snjOLNPZmOZUlfOXseVrOZISFBjkmQ9uVfzbZup hAf8ZFR/CPM0 mrrmZqz9bBsBv505lSEgwS3PyGRMZ5mLwnFtX/3fBaWwpLKWqsYnC2jpGWM0kxcSQEh5ChL/VMYcnNSKU1AjD AnGuQMsruNdYf6dG auZmUfT3jzep9IDDbdl40aTmxQIL9ZsxUwupU7w5u6PTEkD3vtrCcYWVlZvepLtAdcu3zUcD48YMQ3SnR u1eKnc89Rcz0WWz/0x9cJuoFxMS2L67f6K3unrBo/CgWjW8LLe9sfIL8LHx3lSNepyM2kT3AbrCfxWVdbPv8h9RO1n5YPHUcz29LJyk4CKvZ5DJxa/369Uyf0NZaWpCcwKjIzg1w w61YC FGx8IBuJ6Hw8MFt2vnD2PuuYWRIT5Q KZFR/DzISYLl25vanbk3kky71yxhOQurrOZ1K7S21zM1ePSeHOKWMdhiQm0Bg/uH/GBOIa6zjy1tMcWfKly3Hz/vpKj9b09ha91d3XLByWyMbCEoaFunYT3TE5DdMxfra5SXHMTeq8pdde96/nTu2QJ9HWdRVtGwc6fWgCS3MKHK2kE5Hj/Xr3FYNFd/sW/5Ondh 015u6uzQkIvJ3pdQPnbZ/3VVepdQjXqvRcUhgYOCxM7WjsqERswg3fbWKhpbWDm qMYHGgPl5Q PZ/PtfdlpG6PDexS3qLT3R3Z ckzKEBUMTOkQ8vSatd7 bO7qvGpNCUkgQ84cYb3QPzZ7Cz2dN7tV5B5rj/Xr3FVp37 muRdL 1erEcLbuA yLvXjCFZ8updXJkyi43XKn0bYWScGalZRudZ0EmHT6WcTNnsdA0xPd/Y0nYbPdxR3dZpPJJXSIWQSz cRtjcCJcb37Aq2793RpSJRSd7VL pVSqufxM05gcnNzPepLbFXKxYgAhFiNn3peUhyr84rws3leVR084HqwCBPvecCxRsdA4qnuwYLW7Vto3b3HEz/SbSLSt2E/j1OqqqqOncmJotqOfY/2rq3fzp3Kf885mewvPqG2sICKjD0EJSUz wkjMqw1POK4MCLgue7BgtbtW2jdvccTr62/As KyA UUsd/lDMvYrV6FsAu2zYZ6KGTJvNtdh4bCkocLRSzyUTOf95wRGwFI4Jr5PhJ EdFYw0/fuL eKp7sKB1 xZad /xxJBMBC4CLhKR3TitPTLYY22lprq/XnhDSwu5NkMyPT4aBWwoKCEy9xCbXn6W6b/8PVWHXeNChY8ZB8DQcy/q8WTDvsAT3YMJrdu30Lp7jyeG5Fvbx fIz893qy9xW1EpP1m kZHhoVhNJiL9rZw1PInZiTFs eH3qS8qZPkPrqWh3HVl4Ig0w5CMuvbGPql/T3FX92BD6/YttO7e48k8kn/19mQiEgO8AcwD1gKLlFLFPc3XX1RUVBwzzycHsnluq7FE6cHKKscqiM21NRR/9hFmf8Pd125Epj30CIHxiRSsXkZg/PHpEOeO7sGI1u1baN29p7t5JA8qpZ5w2u7ydVkp9Yab53sR2AlcCtwHvAWc40k EfkamEPbUr lSqk bZua3Rj8Xm4Lp25nZGE2FXv3ULB2BVkfvdshf9TEqfiFhBA24vhtVrujezCidfsWWnfv6a5FMh94wmn7ui7yKYzWQ7eISBxwNvA9WwThJ0VksYiMVEod9CCfFbhdKfXOsc7pLdLS0o6Zp665hXFR4Vw5JoVH121nzoevsu7DVzusOWGyWkm76Xb8QkL6qrpewx3dgxGt27fQuntPd4bEJCLRSqlSAKVUb5e3nQlkKKUanNK2AzOAgz3I128UFhYec23jvOpaTh WQFpkOJbmtuUu64vaQpSPvPJ7jL7 ZuQEeQNyR/dgROv2LbTu3tOdi9AsoNK ISJl3eR1hxigvF1aOdA jrc7 W4WkSIR2SwifR55uLS04xKydpZk57EsJ5 qpiaGhASTGBzInQmdh20OSx19whgR6F73YEbr9i207t7TXYvkXeADEVlt2w4SkQc6y6iUesqNcwnQfv5JC9B lSZ38rUCKcBNwEciMr793BYRuR24HSA Pp7CwkJKS0spKjJWHBwzZgxVVVXk5 cDhitcY2MjOTk5QFv4gKysLLZt28bQoUOxWq1kZhqLxyQmJkJAIH/YYAyw 4kw2k9Yvnw54Xt30kQnpIxizZo1NDY2EhoaSnJyMllZWdTV1REYGEhKSgq5ublUVVVhtVpJTU0lPz fiooKzGYzaWlpDh0A48aN67EmoFNNoaGh7Nu3z5GvsLCQ9PR0AKKjo4mPjycjI4OWlhYiIiJITEwkMzPzhNEUFxdHdHR0t5rKyspOqOvkjiZ3rlNGRgbAoNLkznXatm3boNPkznXatm3bMTW5i6guVpYTI zs94GTgEDgeuDtTrIqpdQtxzyRyLXAbUqpM5zS/g/YppR60t18IpIGFCqlKkTEAlQBZyqlVtMFM2fOVJs2bepq9zEpLCzs1E3O7u4LRsRZe7DA9Jef5/CnHxI6YhRVh4wQKAtef4 AXi6Z2t90pXuwo3X7Flp314jIZqVU92GE6T7WlgJet30QkWalVG/W/SwGEtqlxQHtu8y6zaeUynCqY7OIVNCxe8yrlJaWdvqDH6xsCzEww2nt89Id24ieMp1Zj/6R7C8/JXx02glnRKBr3YMdrdu30Lp7j9vTqHtpRAC2AqkiEgYgImZgKrDBk3wi4lAuIoFALHCIPsTeLGzPgYoqgixm/njaLMfKg4c epfqwweJmjwdgGHnXkR46pi rF6f0ZXuwY7W7Vto3b2n3 JxKKXKgM Bx21dUg8A 4Ad7uYTkbHAZyKSKiJW4ElgrVKq5/1WPUQpxZaiUmbExzA9rq21UbBqKUGJQ0i55Mr rpJGo9EMCP0d2OlWYCRQgTFX5GqllBKRh0TkhWPlU0rtBZ7GMDQFGF1al/d1pceM6diieCs9k6LaemY6dWmp1laqsw8TO3M25kEQCK4z3b6A1u1baN29x5NYW71GKVUOdJiPopR6zJ18tn3vYniU9RudhVv  EA2AWYzC4a2hTepK8ynpaGe0OEj 7N6fYYOr 1baN2 hTd1Hz hZo9j7O5/dlpaW6loaOTqtBRCrW1L6Nqj oakDOwSud6ivW5fQev2LbTu3qMNSQ oaGhEAVEB/i7pVVmHQISQYSkDUi NRqMZCLQhcYP2cfvL6huBjoak vBBguITsQQE9lvd hK9ToNvoXX7Ft7UrQ2JGzQ2Nrpsl9UbYcA6tkgOEjJ8cHRrQUfdvoLW7Vto3b1HGxI3sIcPsNNmSNo8s2qO5FBzJIfICZP7tW59SXvdvoLW7Vto3b1HG5IekHXUWGU40qlFkr/iOxAhcf6CAaqVRqPRDAzakLiBPZAZQGVDIx/tP8zpQxPwt0XyVUqRt3wJUROnEBAdO0C19D7Oun0Jrdu30Lp7jzYkHpJXXUuzUpwxrC2819ED 6jNyyXptDMHsGYajUYzMGhD4gbOIZWL6 oBiAtqi2qft2IJYvEj/uT5/V21PsXTUNKDBa3bt9C6e482JB5SVGsYktjANkNSsmk9MVOn4xcSOlDV0mg0mgFDGxI3GDp0qON7cV09VpOJMNuM9qbqKmqO5BAxduJAVa/PcNbtS2jdvoXW3Xu0IXEDqy0AY2ldPe/tyyIqwB9j3S o3L8XgIi0cQNWv77COggCT/YErdu30Lp7jzYkbmBfOnNtfjEAoyLDHPtKtmxCLH6Ejx47IHXrS y6fQ2t27fQunuPNiQeUG6biPirOVMAw 23cN0qYqZOxxIUNJBV02g0mgFDGxI3SEw0QsWXNzQS6ueHn8n42RrKSqkrzCd66jGXND4hsev2NbRu30Lr7j39akhEJEZEvhCRShH5UkQ6nb3XXT4RCRSRt0SkXETWisiovqpvSXEx7zz KN9t38mbezL5 EA2EbawKHVFBex742UAwkb2WRUGlNBQ3/RC07p9C6279/R3i RFYCfGOutLgbd6kO/3GPVOBP4CfGJb193rBAQEELF2Kbk7d/Da7v0ABPtZaG1pYe39PyJv6TcAgypQozP79u0b6CoMCFq3b6F1955 MyQiEoexbO6vlVKNSqkngfEiMtLdfDaDcSvwM6VUvVLq/4By4LS qHNIaCiVIRHEVJQAEFNezLCVX1GyZQONleWOfNbQsK6K0Gg0mkFPfy61OxPIUEo1OKVtB2YAB93MFwA0KaWOOO3bZtv3XV9UuiwyhpjKEgLra7nxizewtLawZcsKLMHBzP/7v2htGrwhqOPi4ga6CgOC1u1baN29pz 7tmIwWg/OlANJHuRztwyv0RQ/hNiKEu788AXMrS1YbQPrIy6/Dv/IKALjEvrq1ANOdHT0QFdhQNC6fQutu/f0Z4tEgJZ2aS0YrQx387lbBiJyO3C7bbNaRDJ6UGc7MYDRv/XpUtr Xt LIk8I2nT7Flq3b6F1d81wdwrqT0PSAPi1S/P3MJ 7ZaCUegl4ycM6doqIbFJKDU4f327Qun0Lrdu38Kbu/uzaKgba9wPFAWUe5CsG4t0oQ6PRaDT9RH8akq1AqoiEAdg8sKYCGzzIdwhoFBHnwFYzOylDo9FoNP1EvxkSpVQZ8DnwuIhYgAeAfcAOd/MppVqB12z7/EXkeiAM LqPq  VLrITEK3bt9C6fQuv6RallLfKOvbJRCKBfwPzgY3AjUqpHBF5CBimlLqzu3y2ff7AK8AlwH7g 0qpnf0mQqPRaDQu9Ksh0Wg0Gs3gQwdt7AZ3Y4OdqIjIdSIyx2n7uIhx1peIyJkiki4i1SLynoiE29JTRWSlTfs7IhLodMwJfx IyH3E5dYAAAamSURBVFwR2WzT8LWIJNvSB/017wyt27u6tSHpHndjg51QiIhFRC6nYx/pcRHjrK8QkRjgj8C5wBAM9/fnxVil7B3gPxhegCXAn50OPaHvAxGJAl4HfgREACuBf9p2D prbkdErhaROhFZYEsa1LpFJMNmLCpsH9sEuD7SrZTSn04 GA UKsDfKS0HGDnQdfOCtjcxPN2qgDnH0guYgUpgiNO VcDCgdbioe5FwG1O27FAE0astv1O6cE2vYGD5T4Apjp9H4/hMj/or7mt3oEYDjl7gQW oBvIsv9vO6X1mW7dIuma7mJ ndAopRYppU4CSp2Su9ObRtcxzk4kPgA sm8opYoxfoMpGFrt6TUY/2ATGST3gVJqm9PmOcAufOOaA/wMwxPUjq/obk f6daGpGv6Pa7XAHNcxTjrC5RStUopR0gIEYkAooBIBrl2ABE5Q0SeAR4E7sMHrrltLOgGXLtxB71uG78UkVIRWSoix9LWK93akHSN23G9BgleiXF2gnEPRjcf Ib2FIzuvCMcW9tg0f0U8JhSqs4pzRd0AxRgjHdswLjP 0y3NiRd43Zcr0GCV2KcnSiIyHzgQgxj4hPalVKvKKUWAXcDnwGNDGLdIjIXYzzojXa7fOF6XwTco5RqxBg4Xwg000e6 zNo44mGu7HBBgs9iXFW0A/18joiMgV4GrhQKVUjIsUYk1 dsWtvZZDdB0qp1SLSgmFIBvM1vwgYBuQZjnnEAB8ChRjX1ZnBpBvlOknb3p2bTB9db90i6Rp3Y4MNFnwixpmtz/wV4FqlVJEteTMw3eYGjIhEYzyANjEI7gMRSRERP6dtPyDItjlor7lS6iGlVJRSKkEplQDkApcD8xjEukUkwD4/ysYw29/X6CPd2pB0gXIzNthgoTu9auBinHkVEQnB8Jv/gVIqy55u82g6AvzM9pD9PfCBUqp8kNwHw4D/isgIm4ZfYXilfcogv ad4QP3 mnAf0QkwXbPPwG8bXtx6hvdA 3vfDx/MLx5/gdUY0zeGTrQdfKSrhdsmlqBWqDkWHox kvfwvBD3wJMGmgdPdB9I8a8kep2n1kYA9FrbPo BCIH032A8Sa B6gAvgVSfeGat/sNsoAFvqAbWAwcBoqAl4HgvtStY21pNBqNplfori2NRqPR9AptSDQajUbTK7Qh0Wg0Gk2v0IZEo9FoNL1CGxKNRqPR9AptSDQajUbTK7Qh0WiOgW01xfYhVDQajQ09j0Sj0Wg0vUK3SDQaJ0TkdRG5aaDrYUdEskQkZaDrodF0hzYkGo1Go kV2pBoNDZEZAVwPfCCbVzkClt6vb1VYGuxPCoiy0WkVkTeFpHJIrJJRCpF5M9O5QWIyGsiclRENojIhG7O/UcROSIixSLyrYiEi0g1RsDF3bb6xNnyXi8iuSJSICJ3OZWRJSK/EpGNIlIjIv/opvxQ7/56Gl/m/9u7nxAZ4ziO4 8PblYrdnMjSbF7UUqi5CIXB8pBiotCKbkpubi4KX iKLUXB7nY0rqsEgculFrkwG5SDqs10dr86 Pwe8qYZm2P56D4vGqapvn1nadpmm/fZ575fZJHElGxvUXSEHDX9tBvlu4HNlK2Yn8ErKAEB20GRiSdsz0OnAYWUXIeNlN2V93QWUzSAGVTxTXAdFXro 0eSROUjQYnqrXrgDPVmknglqQH/pnJvhDYVB3fiKRh4HWX tM1356IWWUiiajvpu23tl8CTyhbzn wfZsSBLRM0jzgIHDM9oztUaAlqTM8CGAG6AW2UnZpHXXZ1rubQ8B5289sT1LS77Z3HNtX2/cpuSs7a9aPqC2NJKK 9tQ4dzz XN0vpUwsY5JaklqUSaG/s1g1vRyl5Iu/l3S2akTdLAdOtNW8BCyZZe0LYFnN hG15cMUUV/nNfPdrqF/R8mC6Le9uLr12B7rWtC ZnstsJIyYWyb5bXHgUNtNXttH59l7SAlwKpO/Yja0kgifvUJGJTUV/128Udc/qB1FbggaaGkHkl7JS3uXCtptaRdVWrdFOX3i 8dxzMgqQ 4SJlIBiTNl7Re0ta2crslLZC0AzgADM1RP6KxNJKIXw0Dh4F7wKqGtU5RvrDfUCJNN/Lz1Fe7L8A yvTwipITf6d67gZwHbhCSW18DpykxOROUU5XtdpqfQOeApeBI7Yfz1E/orH8sz3iH1Fd4bXH9sO/fSzxf8lEEhERjaSRREREIzm1FRERjWQiiYiIRtJIIiKikTSSiIhoJI0kIiIaSSOJiIhG0kgiIqKRH/QYG0nT4mGPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAACnCAYAAAA7QtpQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnXd4HMX5xz9z6l2yepclW7KEG 4VdwzE9FBDSUJwIHR gRBCSUISCCGQACF0gunVdAy2cTfu3ZYlS1bvvUt3upvfH3t3upNO0p11kmzdfp5Hj25nZ2fne7u3787MO 8IKSUqKioqKiqnima4K6CioqKicmajGhIVFRUVlQGhGhIVFRUVlQGhGhIVFRUVlQGhGhIVFRUVlQGhGhIVFRUVlQExpIZECBEmhPhGCNEghFgjhAjvJd9SIUSmEKJZCPGRECLIYl KEGKLsYz3hRA Q6dARUVFRaU7Q90ieQk4DIQDG4C3u2cQQoQBTwHnAbGAO/C8cZ8A3gc ACKAauBfQ1FxFRUVFRXbiKGakCiEiABygTApZYcxrQhYIKU8aZHvesBbSvmKcTscKAXCgFTgXSnlWOM P O KCll25AIUVFRUVGxYihbJNOALJMRMXIQmNot3yfAatOGlLIKqAESgenGY0z7WoAiYPwg1VlFRUVFpR/ch/BcYUBdt7Q6IMYyQUrZCrSatoUQwcAooMTeMlRUVFRUho6hNCQC0HdL0wPe/Rx3J/CWlLLGOEZiVxlCiJXASgBvb  pqamp6PV6dDodxjSrbS8vL6SUaLVa8zZAR0cHHR0dBAQEIISgo0NpUHl4eODm5kZ7e7vNbXd3dzw8PGhvb0dKiZubGx4eHnR0dFhta7VaDAYDGo0GT09PdDoder0eIQReXl5W297e3uh0Ojo7O21qcEQTgKenZ5 a9Ho9fn5 I0qTPdfJVLeRpMme69Tc3Iy7u/uI0mTPdWppacHLy2tEabLnOnV0dBAYGNinpuzs7GoppU2nKEuG0pB0AB7d0rz6OkAIMR9YASxytAwp5cvAywDTpk2Te/bscbS Zo4dO0ZGRsYpH3 moup2LVTdroU9uoUQBfaUNZRjJFVAVLe0CKDWVmYhxCTgH8AK41iIw2U4i9DQ0MEs/rRF1e1aqLpdC2fqHkpDsh9IEUIEAggh3IDJwK7uGYUQccBrwNVSykqLXXuBKcYuLoQQoUACcOrNDTvIzMwczOJPW1TdroWq27Vwpu4hMyRSylrga BxIYQ7cD QDRyyzCeE8EeZX3KTlDK/WxkHUAbd7xNCeAB/AT6RUnYfgFdRUVFRGSKGekLir4BkoB44F7hSSimFEA8KIV405rkMmAtsM85sN/1NN 7/KXAJSndWJHDrYFdabfq6Fqpu10LVPXCGcrAdY8vhfBvpf7P4vApY1UcZ cCcwahfb0RGRg7l6U4bVN2uharbtXCmbjVoox1kZWUNdxWGBVW3a6Hqdi2cqVs1JHag13efuuIaqLpdC1W3a FM3aohsYPg4ODhrsKwoOp2LVTdroUzdauGxA6io6OHuwrDgqrbtVB1uxbO1K0aEjvIzc0d7ioMC6pu10LV7Vo4U7dqSOzAFIfG1VB1uxaqbtfCmbpVQ2IHAQEBw12FYUHV7Vqoul0LZ pWDYkdxMXFDXcVhgVVt2uh6nYtnKlbNSR2kJ fP9xVGBZU3a6Fqtu1cKZu1ZDYQVuba67iq p2LVTdroUzdauGxA58fHyGuwrDgqrbtVB1uxbO1K0aEjtISkoa7ioMC6pu10LV7Vo4U7dqSOyguLh4uKswLKi6XQtVt2vhTN2qIbGDpqam4a7CsKDqdi1U3a6FM3WrhsQOPD09h7sKw4Kq27VQdbsWztQ9pIZECBEmhPhGCNEghFgjhAjvJ/81QohZQ1W/3khJSRnuKgwLqm7XQtXtWjhT91C3SF4CDgPhwAaUJXV7IIRwF0JcBrxsY1 W0RDVG/82DGqNgbKyssE xWmJqtu1UHW7Fs7UPWQrJAohIlCW171WSqkF/i6EuF0IkSylPNkt xtAWi9FeQHLpZQ7BrG6VtTX1w/VqU4rVN2uharbtXCm7qFcancakCWl7LBIOwhMBawMiZTyegAhRP6Q1a4P3NzchrsKw4Kq27VQdbsWztQ9lF1bYUBdt7Q6IOYUynpICFEjhNgghDiV4x0iLa23xtHIRtXtWqi6XQtn6h7KFokAuq/tqAe8T6GsciAaeAx4C1jS42RCrARWgrLIfUVFBTU1NVRWVgKQmppKU1OTuZ8wJSUFrVZLUVER0DVZJz8/n5MnT7JgwQI8PT3NMfyjo6MJCAggOzsbgIiICEJDQ8nMzAQgNDSUyMhIsrKy0Ov1BAcHEx0dTW5uLlqtloCAAOLi4sjPz6etrQ0fHx SkpIoLi6mqakJT09PUlJSKCsro76 Hjc3N9LS0sw6ANLT009ZE0B8fHyfmurr65k9e/aI0mTPdaqvrycnJ2dEabLnOm3cuJHIyMgRpcme67R7926Sk5NHlCZ7rtPJkydZtmxZn5rsRkpp1x/gDzwN7AYKjGk3ALfaefzVwPpuae8Bv vjmHxgVre0CYCv8XMsIIHYvs49depUORA2bNgwoOPPVFTdroWq27WwRzewR9rxfHeka s1oBqYCwQa074D7rbz CogqltaBFDrQB2QUh6WUrYaN6uN/we9e0tFRUVFxTaOGJLlwN l4nEljWnV2P8Q3w kCCECAYQQbsBkYJe9FRBCeAshgiySEoz/8 wt41RIT08fzOJPW1TdroWq27Vwpm5HDMlB4I5uadcDW w5WEpZC3wNPC6EcAfuB7KBQw7UYQHwgRAiSgjhDzwBvCOlrO7nuAFh6m90NVTdroWq27Vwpm5HDMmNwM EEJmAnxDiO AXwE0OlPErIBmoR5lTcqWUUgohHhRCvGjKJIR4UQjRjNLi EEIUQ0gpfwO ArYieIyXAv82oHznxKmgSpXQ9XtWqi6XQtn6rbba0tKmQ9MF0LMQHnA50kp9zpyMillHXC jfS/ddu BbillzKeB5535LwqKioqKoOH3S0SIcTdAFLKXVLKj6WUe4UQk4UQvxy86p0epKamDncVhgVVt2uh6nYtnKnbka6tP9tIywUedFJdTlvUMNOuharbtVB1D5x DYkQ4hEhxCOAl mzRdqrwCin1eY0RQ3q5lqoul0LVffAsadFIox/lp9Nf3tQPKlUVFRUVFyUfgfbpZR/AhBCpJg uxrqegWuharbtVB1Dxy7x0iklDc47axnGFqtdrirMCyoul0LVbdr4UzdjnhtpQkhNgshGoUQrRZ/LU6rzWmKKaCZq9Fdt15K8hpG/sCker1dC1X3wHHEa st4CNgCtAGnAX81pim4gKsOprDTd9vo7CpebirAmAK4qmiojLMOGJIxgLPSylzgAJASilfAGYPSs1OI0yhlUcqFa1t/PHH/bTqOvnTjwd47YgS9nq/hx9//HE/x2rq SK3kL2VSkiFqtb24awuANJg4LuLl5D15itOL3ukX /eUHW7Fs7U7ch6JIdQgizuB9YC9wgh1qKsC6JyBvPKoWw2F1fg6 7OpuJyAC5OSeDLUsVwbC6usMrfqNUNeR2701ahuC7mf/4xaTfe3GO/QadF4 HptPNJKcle9QrR8xYRmDLWaeWqqIwEHGmR3Av4Gj8/ASwGXgf 4OxKnW44vMjLMFLb3oHOYOgzj5SS4qYWXjuSjUFKmnWKYVhbUAqARgh t3lPr8f/dechqlrbqW3vGLbupaZ8ZXVmn7Bwq3QpJW2V5Xx/ XkUr19j81h9RwffX76css0/2Nxv63rr29rI  R9dj54z8AqfhpzJt3nzkTVPXAcaZEUmKLsGmNmTXBaLVTs5sfSSsaHhRDg6WFOK21uZXtpJecmxfDTLzdw dhEbpvcFSK6VdeJl7sbbkLQoddz14adZNc1AlDfrmVXuRI8WS8laSGBzI L4tXD2b3WwSAlv1n/IzXtHVwzbjQ3T0gjp76R0UEBuAnR63HOQBoM6NvbzIbEKzSsa59ez3eXLiNwjBL6oWzjeuKWnNejjPaaagw6HVn/e5nocxZb7WvMy6X96T9TFxFKSEbXLd7ZpiyBo 8Y/m49FZXTDUdaJAeEECGDVpPTmPj4 OGuAgD1HVr sG0fD27dy1N7jpg9qB7bcZAXDh7n7WPKEp fnCjgQGWN8nbe2cmKz9bx1x0Hufiz9fxzzxGzEQH4Oq/Y6hxJgf4sjOu /lhPato7APgsp5ATdY2sXLudnWVVzpJqE0NnJyfefYN1V19Ic1GBkmZsTTXkZtNwIguAxhzFCEqDnpbSYgw6nbnlVLZlAw3ZylKn7dWV7PjdnbSUFtNSoniw1B7ap TbutHq3CZDQj tvTOZ0 U H2pU3QPHkRbJc8AzQoibpJTd114f0Xh6Oq vfSDUdyh 30dr6jlaU883ecU8MX8q1W3KW/JXJ7uMwr2bdjMlIpR9xgHyjcaxj3WFPcMinJ8Uy igAF44eJyzwkKI8fdl5cQ0ogxaVhVWkd/YTFpIEFl1DT2ObevUs6FIKXMwB HLt2/mwBN/NG/XG41Ba3kpm1deR2t5aY9jag8fYMstyvSnwOQxJF16FYf  VerPPWZR9j14D101NaQ/us7acg5AUDZpvWMvvQqfMIjAOhsHfFe7qfNfT7UqLoHjiOGZDxwIXChEOIoYPYBlVJe4LQanYbk5uYO61tLW2cnte0dNHT0nED0wJauSP7temv7bjIi/ZESHMhlYxOZFxtJhK83AFenjWbjxo08MX8mB6vqqGptI6uugdsnp2OQko y85kfG8mnOQV8eVJ5m69qa0dvMOCmcaSh2z/6jnYKvvjEKq29UnEA0DU2oGvsaeC603gyp4cRMdFRq3xPmS89a07TNTWy/a6b8Y2NJ/W6ER/gGhj  3y4UHUPHEcMyTrjn8oQs pYLp/nFHLb5HFOLdddCMaGBDIrWhmwjvLz6ZEnwteHZYlK rXpXSEVfpqaRFlLK5/mFNCi6wTg3eMnKWxq4c9zzna4LsogeQW kUq3mr6jnR9uuJz4c1dQ M1n5i6soUTX3ERD1jF2P/xbxt9x35CfX0XlTMGRha3eHOjJhBBhwCpgLvAjcL2UsteOdSHENSgLaO041TKcQXT00Ho4v3o4m7RRQcyPjSSzpp7Pcwpp1 vNb/6nyoyoMOL8/ZgYHsI5doyD9Kc70ren4dlaUsHaglJma3Rsve0XTH30ccKnzuz3XEeefZKS9d8x8f/ QNWenYRPm4m rY38z/uf7xp/3oUkXnQ5W3/z837znipHnvuH fPWO24i45a78IuJo LHLRR99xWznnweNy vfstpKS2mo66WUWdNdHodtU2NeAYEopeSt47lcOmYRIK87O  GOr7/HRhuHXnNTTRrteTPioYgILGZsJ9vPH1cOQ933Gcqdu5fRD98xJwGAgHNgBv28okhHAXQlwGvHyqZTiTgICAwT4FrxzOIrOmnuq2dt49fpJHt 8H4LYfdpi7rCwHye3hoZmTuspfNocHZ07k9rPT7TIi0L9ujRDckNEz8Nvjuw6ZxzDKNq3nzaM5vHAg07y/ctd21ly0mI66WkAZRC9Z/x0Ah/75V8o2rbPqhopdej4L3/jQvO0Xn2h1Pr/4RPzjEkj7xS3En3eh1b6QsyZy7uq1jL70KpsaUq68jphF5xJ9zmJ8Imx/Lxm33GW13VyQx55H7mPLb37OsRf/TVNeLk15uZx493/UHD5gswwTW265gV2/vxuAih1b2fPo76xcqKXBYNX6Muj1SH3/Q5KVu3fww88uofboIfZWVLPqWC7P7c kw45jTZzKfZ7X0MR/Dx7HcAZHGRiK37cleoOBkuauMbebvt/GbeuVd2W9lPziu608tG2fzWPfOpbD4o/WOMXt3pm6h8yQCCEiUNZpf0RKqZVS/h3IEEIk28j BvDAAMtwGtnZvbvCOoMmrY73jufx1ckivrHworpxzRbz55B 3izHBAfw xkTmRmldFPdN208ixO63jiSggIIdHBwzR7dN2aMYfVFi3ukS3fFPbmzvY31Gzbw8YkC876CL1cD0HhSGdg2eWCZiF2yHO wCPO2b3Qs3hZuvv5GQxKUloFPVDTR8xcBMPrSKznrN/fgF5 IZ3AIoyZMJmbRMjRubgSlKt2Cli69vjFxpFx1HRPveYBJv32I f/tanQv/3y9 XPErHk99Bl0OjpbukLF1B49RO77q9j9h3vZ/7dHWHvFBXx32XK23XWzzR 9lJL9f3uE6v270TV1vSAc/c/TfH/5cmqPHARg/TUXsvP3d1O fTPNxYXGY/aw988P0lJSRPH33yClpHr/biX/1m2cbFDq9UNRGed/upajNXV8l1/C9lJljW69wcCrh7PZVlrJ1V9vpKCxmRZdJ4eOZ/WoZ388un0/H2XnU9HaBsCnJwp4y g9eKYw0N 31OspWvMVhs5Om/vbOjt5aNs s/H49/5jXP/tFt7NzOWWddut8tYYHWcOVCkvWaZ7p0mr45u8Yt44mgP0HA89FZz5XBvctpM104AsKWWHRdpBYCpw0jKjlPJ6ACFE/qmWcSZR1qK4lu4qr6a2vUtaUZNy4y2Oj ZXE1K59ptNBHl60GCcWf7S0jkEeLpzw7dbuCFjDPNiI80eVH7dmsXOmt/RXlvDxp9fwaT7HiZ6/iKEEFbdJ7 akMqrh7P56 4jXAJU7tjGtWzj7eU/Q6/VcvLjd81zMar37aZixzaCxqZZnSNm4TIm3PU7tt5xE80FefhGxwCw4NV3Meh05H/ MQCjMiaQ9otf96jj/P 80SMtIEl510j4ySXUHTsMwDkvrrLKo/HompsjhMAnMhrh7oZXSNfabVHzFxGcOo6yLRvNbsQAJRaTHyt2bDV/bsrLpT7rGCHjzrI613cXLzF/bi0rxTMwCIDitd8AsOvBe5j 16fRt7dTf/woB544SmDKWBpzT5jLr9qjvMX6JSRiMEZy1W1ex8fuwRAYgqe2gzHFOXydEM0ao7feD1ecx/ayKt493vVz2V5ayapjOXhLA6VHTpAY6Ed5SxvzYiNJDPTv8V1aojc 6HLqm9AZDDxvbHlm1zWQHhrMteOS0RsMbCmpJDHQj4e27eOfC6bj7 GBv3EulFZvQCKVRY6EwMPJzhqDTcn67zj6wtN0tjYz rKre zfXlrJ9tJKPDQaHp09mTV5JQC8euSEVb62zk6e2HXYvL34ozWEenvxwpLZPLPvKDss3Ouf3Z/JPVPOwtPt9PiuhtKQhAF13dLqgJjBKEMIsRJYCRAZGUlFRQU1NTVUVipvZampqTQ1NZlXCUtJSUGr1ZojYpri0OTn55Ofn09ubi6enp7k5ipvW9HR0QQEBJitekREBKGhoWRmKj k0NBQIiMjycrKQq/XExwcTHR0NLm5uWi1WgICAoiLiyM/P5 99cobpGluRoKHoFDX9RYb31jDiX17eHnOREoqKng v5JYd0Ggto2ashoeDPOi80QmpUJPU7NSVubRo4yWOu5IieF4eSUbN2600gSKH3lfmhobG6moqLDS5NugvCkdevt1CsvK0OTnUl9STMicFdRpPIjVSMLqqjh35/dW18O/rZmtH75H24ddD  CLz8FoGTTequ8xbV1HN64kY5G5U29qr2DrI0bzdep2fjQLCovx8uopV9NJ3LxuuP31IVGMuGZl8nOzGTjxo09r9M1v6BN48b27dsxXP1LAgICzPcMQMc55 KTlIRbQAgaNw98ZszBbd9OGg/vpzcO/vtJQn/xG8o fMvm/h333cboR5 kqNvcld1/uNdquzHX sFjYuf9d5g/j2qq45rv32P99CVcsvlzAD4OCoHQGPxbm7jnxdfIDo zOn5NVi4degMdwNuZuSAlQkq zDrJcl8NY3w8SUtLY/PJAtaU13F1kAdjUtPYVVyGtl15KTB1xZrYVlrJttJKjuTkUqQzUNKpGAoJ3LJmM40G OvEFDrKSniptoNaA ikJMxN8JtRXgP6PbW1teHj40NSUhLFxcU0NTXh6elJSkoKZWVl1NfX4 bmRlpaGhUVFeTn57Nx40bS09NP6RmRc0DRXlGQj3tRkfneq/UPolG4UW0sr61J T119tIt9fTGHzlQZ 1mXtPewVVfb yR97v8Enzqa7hqYnoPTZtOFqBrasJXA1Mz0mmqq7OpKT8/n6Kiol6fe44g7O1rE0I8J6W8w0a6AK4DtFLKD/o4/kbgGinleRZp/wMyjV1Uto7JB642DbafShkA06ZNk3v29B7yoz8qKiqIjIw85eP7QkrJ77bsYU9Fl6vuxSkJfJ5baN5efdFiuwdN/777MN/ll/D0gulMjgg1p s7Ojj0zOOk3XgzvtGxVsc0F bTVJAHYO4mAtu6qw/sZc8j9 GfOJpm4zEAoxYuoyEvlymP/ZPdN1zao15tnt6kXnARRZ992GMfQMj4SdQZu3MWv70az8AgCr76lMyXn2fJu1/g4d/1ZnzinTfI/eAtxlxzI2OuudGu78URbOleY y O  LnmFVWkqK2PvnB0FKAlPGUr5tk3mfxtMTg1aLb0wcraXFPY4dCtbOWEpUTTkTco8AkB0/FikE62YspcWnZ4vjos2fM64giyevvx A8aHBSJT5SwBPL5jOP/YcoaylbUD1WpoQze1np3PJ59bf6Q9X9IxGMJg48vsubW7Fy01DqI 3Oe3kJ  R/eYrJF16JeN cYs5ffFHSis1xMuTug4t5yXFsiI5ntt/2NGjXFtcNiaRdYWlvca2u/PsdJYnxSIQeLu79TgvgL HO6svXmKzR8Ie3UKIvVLKaf3V1ZEWyQIhhB QBmRLKU0dxI8DMwA/IUSoMSKwLToAj25p/bu5OL8Mh8nMzHS6IclraKK1s5PCxhYrI6IRgglhIXyeW8gjsyYzPzbCoXkZt09OJ31UEJPCR1ml1x7eT8X2zXS2tTL9T0 a06WUbL29a56EpSGxpdvUn2 e6W0qf NaAA7fu9JmvXy07b0aEYDImXPMhsTDXxkETPjJpSScfzHCzc0qr8ZduW0HyyXYlu6Fr3 Artn2Wix sfGc81JXa2PDz68wz02JXbycusyjNBf03/NqMjq2cAsKQd/QvTHeNwWRCSRWFLJsl7XXfmqR0rJJK8zmhctuJaC1icV7f2DrxHlMrCxgXIEyVuLd0UZoQw2j9m1i85SuFbXv3bTbKV2l6wrLzBNkk0rzWLBvEx8su4q3tv7I9fOGLqi4rett6OyktbQY/4QkDvzjMcKmTCduyXlc9 1mwNrYmV7Gsw8fpvbulWQ8 iRPZyovWR46LaOKc6iLG8Oa/BLW5JfYXa8IX28 XLGI8z793ub Q1V1PLs/EwHMiArnz3PP7jHo3azrJLe kdSQILt0nyqOdLDtBI4DLwBHhBDLjOmXADcC1wC39XF8FdDdLSYCqHWgDs4oY9jRS8m9G3dxxw87 cce5S3x34tm4ufhTqSvN4vio3h52RwWxkc5ZETaa2vQF ZxUUoCwvhDb8zLpWrPDkwNT0OH0n1We QgBV tpuSH76zKkHo9J95 na133IRsb0fb1EhrWQkbbvwp9VmZZkOib7U2JOY6VDvmiT398X/hveKnxJz7E3OaMGoWQvQwItA1ljGUc0u8w8LN4yz9MeXhLo8z4e5OwgUXAZB0yRVMuNvah8R/8XLO ueL P/fo7z iwfgvj TG2t9noNjJvL 1CXo3Nz5dtZ5bJ0417zvpUtW8s9r7yUrIbVHPRr9Avut662fv8z1a94mtqqUS/dvIP3wTvO 6Ooyrlr7AbOO7sRT28FFKV2T1x6eNYn7po1XNHi486 FM1iWaOxhlhK69XSYnEC6IwwGEsvyuWD7N0TWVfLr1S8R/uQfaNF18tKhLEqau 4zW70nBp2OzFf/Q3tNtVW6XqulvbqK uxMag7uM3u GTo7qTm4T4m31m3AuuLHLZRuWEtreRnF675l652/oiEnm/ItGygwjst1P8fev/yBwv3KpGDPnEwaT bwxurP2FqiTJhdsns9l2/4lND6arw72rhg3wZeXzTdXEaERcsG4ItLlpAaoly3Ud5eVmMgUy16GKArWkV8WQG7Siv45EQBt67/sUc9i5ts/1adiSMtkmXAAinlSSHEGOArYBzKg71YSimFELF9HL8fSBFCBEopG4UQbihh6e/t45jBKMNhQkND 8/kADtKK80D5gB3nZ3BhLAQpkaEohECIQRjgvt/CHRn2x03oWtqZOaTzxGclkFLSRHb71JCrE 463cA6FpayP3oXU689arNMuoyj5D7oeJRrVn1Ij 88CReIaPoqKvl2H fIXLOOUo5vbydA4TPmE3Vrp43tAmtuwdhEyeTdtnVvNGm4ZuQZP7e0PfN3qLrxMtNg7tGQ0CyEsZ9sMK5D/R6B6WkkvbLW8l6/b A4jxQvX83MYvONQ qm9hYWk1rRRPFrYLK1jY 9woka/FPuf8tpdW4beIctk2cC0LwzLVdt3lRVDxTOtuYNC6NzSUVfL7gEvMx/7rqLn6y7WtW3HIbBfffCoDB3Z2MG1dS8uMWmozOBs0 fvi3dfXJe9ZavwTMOrIDaXwhia8sYsKYGHbUVuDb3krE9/nEXXcT/zuaw2 njWdi CjGhgQyKXwUgT98TfPnH/DUz/4Pg0Z5EZgVHc7OcqX8hXFRBHt78llOIdOP7Wbh/q6uQC d0iL79kQeH2TlkVXbwNMLZ7C sJS/7jzEhysWEqjr4Nh//0XGrXdTn3WMgi8 QVtfx6TfPgQoD/j1114MSHMLzyMwiHnPvUbeZx Sv1ppGftERDH7mRfRbFlHfkMVx1/7r7kePlHRYDCQ 4HS0mzKP0nmK89z4eFjbJ00j9JN68lf/QGNJ3N6XP q/DzS/WuZc2g7np3K cejw 3QdsYf34tm3w5A V4mho9iXWFXeB9/Dw9CvZVOlu4D6WdHjDKvCWQioaKIq9d9wPGENLw/eo7yC38JvkqLPrS mvT8TOrKjsOdd/eopzOfa44YEg1g8m TgCl6nbvRiIRjETalO1LKWiHE18DjQoi7gPuAbJTgfv6sAAAgAElEQVR1TuzCGWWcCs7u1vrkRAERPt6EeHuSVdfITOPM8odnTQI7uwyq9u3m8DNPkHLVdYRPn43U682thZ3330HErLlU7thmzn/c FBrLjjJibd672bZZREm3dCslGea79F4MsfmD6c7gcljzYZk2mNPsf9kPt8cy2a5cQD 9Qt/Sau3L  MTeebrzYC8G1eMWNjk0gO73LzLWluJcLXGw Nhgs/W8c5sZH8cc7ZhE2eyvz/volvTFyPczsDZ1xvN2/lTdPdxxd3Hx/G/ 6P1LR34O9p/ZPz1raT09xqjg6Q1W2u0Nk33oy otrssfPysjmsXLudosgELpo8jrvGJpn7xP1/9iuCq8u5Y/YUfM ZQXp8NCbH6nGvfUhSSDA1h/fTBPxwzoXoOjpYvvN7ohcspWyTdfeX3tef MquMZ3LN3yKbsOn3CgEQkryUbo5X1i0jIrP3qFq izyVn I5tB 80Pg/5Ii2V1WSeqB7Zx70SLWfvIRLT5 3L94GpublZZxbJXtrp63du0Hv0DajC613 7ax3nb17Ku/DipbY1UbtuEd1gE/kmjASjPOUHLP/5CTGoaGk8vDNoOq/J0jQ3kfvQO5Vs2mtPaKsvZ cBdtBYVcHy3tRtuW7nS5Va5s s3VPDlp6QD6fnH 3zgnHNgS4 0ee11NJYp3V0tBXksPlHAhqmLuGdqBjH Pqw6lst1xsgRN01Ipa5Dy9RI5bfw LypFDa14G9jguLyYGVS8LhCpTtyTFEOB9LOxr 1iZu fB2ApuaerVVw7nPNEUNyL7DDOAAeD1QIIVYDUgjxCJAAfNpPGb8C3gXqgd3AlUYj9CCQIKW8BUAI8SLKAL4v8IMQolVKGdZXGQ7ocJisrCwiIiL6z2gHG4vKOVBVy62TxrEkIZpjNfXm0CS9dWPVHTuMd1g4x15 jol3/x4Pf39y3nsTbUMdeZ9 QObLz/U4xtKIAFZzFdJ vpKCr1Y73A1liUdAoFWZADGLl MTFk7s4uXkvq94Z4VNmsKEMek83ulL9KgQbpg7k8YspUn 1O4j5mM3FpezcfGVrEiOY9Puw9wyKY3rv93Mhcnx3DxB SFsLulaYMsvdvBiIznjesctWU5HbY15IuQLB4/zRW4Rqy9azNRHH0fb0MDhfz2B1sOTQqOb96L4KLaWVLIsMYbJf3oSbUszCWMTSQkOMBuSWH9f8zmi/Hytzhlz3oUkBwUw3iItfNosGk4cJzlEmTWtMbYQPDw82Bufxq3TJjBm8blmQ Lm44u rZWU81eQ/8n7PXQJi59ayfo1Zrfnwm8 75F3mrsezZvPIPV66n7cwoptXwOwcd2HaKbOYmGbnghsR1Neumsdrd6 JJQXsnr32Sz9cYOyI/cwJh 6popyNEYnFFlaRGNpEY1bbK8xA1D41Wqr7YQLLraqt3dYBO3VXR56UmgQ0oBPZLR5IbV105cQV1mM3seXs4737qnXHd3arzDFgSj89gumdXZyPGkcJ19 junA/LHjSD5rDADJQQG8sKRrjGhmdDgzo8PN0b7vPDud47UNbC tZKy7N5adeuP8vKgtK2Dq8a4YfNVBtlseznyuORIi5WMhxHpgDMpYiUTp1qoA/g7ogN/1U0YdcL6N9L91274FuKV7vr7KGEz0Tpj8k1vfREKgH9/llxDj58tlYxJw02iYF9v3W0F9ViY7H iaWb3 2ovI M09NGQdA7C68Wc 8W/QaDB0dLD74d8y rKrCZ8206qVAcpcisJvvrCr3ss/X8/x1/9LQOJoyrdtonrvLgDmPvcaSMnx1/9L3NLzqdq7k TLr8ErZFSPNTui/Hy4b9p45sYuJtDTk3/H1HH/5t3srqgmzt XMB9v8wQsUwRjU9felyeLmBdr 2bv0Ov5sbSKBXGRCCGQUnLnhp1cNjaRRfGnHv7BGddb4 HJ2Gt/znP7jzEnJoKDVcpA YaiMvzC4nk8t5Z556xgV0xXZICVE9P4/YyJuHd7oYi2iIHm4971k50YZr2qQ7ANz76pj/zNamwh7Re/xtCp46arriS1ppGxGSnm8TSAlCuuJXvVq4SeNZGirz9D337qEZ0bc7LNYxPZq6y7Ug17dzCjj2PHFlu0fE1GpBs1O7ZQY58DlIJGY7UMQOqNK60MyVm33cvePz2Am7c3RzOmUybcWLR3IxXJ4zj3vocorahkX3kb 8ZNBWDruOlk5B1j/sGtPU7lGRSCthfnCGlsZc0qzqboiDKRlDVfkrz4XLAxJmhidFAA7/9kAeE 3lwYr2XtX 6moVtXacKGr0jodtxZZ52FLZxxn5twaB6J8SG 2yLJdLV/47QanYYEBwcP6PjyljZuXruNC0bHcai6lqUJMXYPotu6GY 98AwACSsupfCr1WTcchexS5bj5tU1cDf32Vfxj0 kvca61bHso2 UfJqeXWjJP70Wj4AAIucsYPPN1wLKgHf6TcrljVt6PmsuWYp3WDjeo5S3nMn3PQxA2NldHoIaz56OdOeP7uqGmhAWwkOzJvPm0RxunZRGs66TE/WN5u4dUEJBmPidRYTjb/OKeTszlzvPzuCtzFyO1dTz7KKZjA8LoUmnM4fYH4gh8QkMpLa9g1HejjkENut0fJtXYn5JqG5rZ3VOIatzCs2G8dn9xkmMQrA1MYMwHy q2zqI8vWxGbsMMLuaZoxSHhoXpcQT5edrXtzs7IhR7K s7dVF3NJQ EbHMvWRxwFIjuoy0H6x8bSUlZB44WVICaMmTiFg9BjqM48QfsElxGSM5 BTf lR9px/vUzNgT0UrvnS3B0EikNEwdefmbfbqyvxDApG21Df95fYjYKz57A5Mhm9mxsL9m0isrYC3w7F7bjN0xsfrX2GzicymgWvvENLaQlbbrkeAHcfHyY/8EdO7NjOpMuuJDApmZlPPod3aBgfHcghp66RBr8gcuNS0Ok92aP3BrpcnhsCgtmTPs3KkBxPTGNcQRYT7rpPcQnvgzFHdlttH3n Kc66/bdo3NxoKS2mfOtGDFotQWkZBKelU7rhe1rLSgm4/Bpzb4A9ka/HJY 2mT7Q55oldhsSIUQ6cBcQRzdvr5EeRn4gwc12lVexz jeawp/MjHc/vXB nojTL3uJgJHjyFm8blour3JmDyMvEK7vGUSL7zMbGw8fP3MPwnv8Ajaqyrxi40ndsly8xtswPhJdGfpe19CP8M4wo5xnrkxEcyN6XqQzY N5NMT Tx/4DiAlTOCJSYvt99v7TIuNe0dfJtXjFbvnEWnni9rpCR3Q5/zGZSwNidZlhjDxqJybshI4aWDWXydV4y7RuDn4Y6vReshp952nLSFcVGsSIknqI/wNW5C8Nq5cwk3GpS7p1i/YT42ZwrlrW0Dcsmd/cyLVFZU4ublTcoVyktEYLJiSAIDAqxWorQkMHkMgcljiJw9n82/Vh7QM863lpIfvqP4 6 t8iZf8TMSLriYnQ/cSdLFV3DwH48BSpdoaTfvQYBZTz5Pk96dsmPKmN5HS6/EU9vB3R/8G4DnrrwDhEBj0OOp0 Lf2oy3tp1JTdWU6Qws3aNMdPUMDSN95e0AeEVFsz91MoHGLrXw2fO5tqSVnzV1chOYIxBoRC4IQXaiEnXhtSO2J4PqPDz5Zs75LPDzxFBZxo64dOY99BjhkV3fV8zCpZRuXEdg8hjc/QOoPWS7S6xk/Xc05p5A19JMe1WlzTygdCMGj8vodb JrIRUzp8xjbCzp9vc78ygjY60SD4CMoGPUbq1XIbc3NxTGpjqNBis1gsBuHlCKvNj w6aKKWkuSCPgKRkOnp5g/OLS8Dd15e4ZX338mnc3Fi ei1oNFYP En3P0Lxd1 TeuPNlG/fzMEn/0xgstJHK4Rg4RsfsPfosR7lufvYfmt2BjOjI3jpUBZJgQGcqG8kIzSY0uZW84JevVHY2GyOQWSiWavDx8Pdrodrs1bHFyeLuCptNG5CUGJcoEtKiRCCx3cdYnxoMBG PowbFcTJhib b5PyNvnJiQJ0BgPHauvZa3xheG5/Zq/nMpEaEkh2XSMGCQkBfYchAaVbozd8PdxJ7mO/Pbh7 5BfUkJMYldAzIBE5WVE21BvjnXmERDIgtfeY92VP7E63jtc X34xsQRkjGeoLT0HoYkev4iNO7uzH5KmWp26J9/RRoMZKy8g5gFS9jzqDIB0jssnLhlFxA8LoMwY8ifmyek8srhbLTG1m5dQLDZMcWgcaPdy4d2L XeLI5Uxs W7lnP8cQ05j/0GMFho3g7M5cwHy/WzjwXgB   IGkIOW7f f4Seo6tFyXnkyUny8aO 6bV5bN4Zl9xzjCBC6aMZFz4iJJrKrt4aYbOnkapRvX4RMZTdiU6TYNScYtd6Hx8LSKMt0X9cd7/jZNmDwG3fwDSb3hV73mO9Xnmi0cMSSxwBQpZd /6hGItpdJYv1R0NjlWrkgLor7p4 36uPujYrtmznw9z/hGxOHX4y1R7VPZDQz/vY0Gg/7AzDamovhFxNnjlMVPW8h4VNm4O7bNXjrHRqOznDq7wvpK2/HM8ixlZlj/X355KLFtHXqefHgcW6fnI4Qgke37 f80bE8aRycHxscyAmLN/y1BT1XR7zo8/UkBvjxyrlzcddoqG3v4PFdh7h9cjoJAX58k1dMbbuW5OAAdpdX8UVuEYkBfsy2aCW16/VUtbWztqDUfI5wH2 q2rpaiTpjn7vJiHhoNOgMBtyFoFNKIny8qTTmnxgWwqHqOq5OG83C ChuWfcjKcFDG3m2L7rf5zGLllKXeYTRxrEvgJQrf4a7t/LATljRFcFA4 7O9MeeMkdm1ri5Mfl3j9JcmI9ncAhSr7eKWQYw55mXqM/KxN3X16prdOHrXQEyFsRFIWfCgrhIXjmshO4pv 8vfFCgvLGH yjzrj7Mzu h5 lr7kGvccNQXM7Te4/2mIlf16GlrrJrCto3ecV8k1eMr7sb3nb8ThMD/UkK9Dc7zHi6uTHDYr7MzCefo72ywtx6iF16HoEpiuNIytU3KN2JxYXkfvAWfvGJhE6YTEjGeAq /NRq7MYU8621pP/ICD4RUQSnpQNw Yq O4pO9blmC0cMySvAFcA7Tjv7GcKphlvOtXjYjQ7yt8uIgBLoD6C1tNgqrEbUvIVMvv RU6pLf1gaERMDCTOduOKyUzrO38MDfw8PHp412Zz270XKeiYmQzI1MtRsSNyEoLjZ9hyUgqYW3sk8SbivN0er69hbUcMHWXksio/mn3uPmvOdY3R4KG1pY5XF2ExufROHqqznuloake6kBAXw7OKZ6KXk4 x8Vh3LZXLEKL43GqHRQQEcqq5DIwSpIUF8uGKhec7A6UD36 3m5c3Ee7omUFqGh7EVKiZ00hSr7ai5C2Dugh75zOcbnULA6J7LEFiiEcIqijWAPjiMjvJGEgP8eHLBdMK8vfB2d2PVsVyCvTzNLdhOYwRqyyWo7aG1U09rZ9dA9IyoMJYkxLClpAIBHKyqJTHQH3eNxhyVO9rGonAh484CY1eZ5fe16M2P8QgMQuPmhtTrCZ00hVHGbmS/2HgybrmL9F/faQ7sOfmBRwlKSTWH6enuMRk55xwqtm/GIzCIBa  C8DCNz60iphtC2eGkXfEkMwB7hFCPAtUYtG9JaXsv8PuDCYuzvH5CjqDgczaroEwWx41vdFS1tO3PnTSFCJthDMfTE5F91AQ4q18lxelxDMnJoLv80sZGxLIS4cUX/oIX288NRraOvW8ecy6y tgVS1N3cZfdpUrDpRr8orNrrgAd27YSW/cNSWDFw5kWrXagrw8zS8L0yLDWHUsl/lxUWZDcm16Mtl1DVycovjVhHWb1TzcDPf1nvvsq b5N7Z4ZuEMDFLi7 HBO8dP8tCsyeZxIxMTw0LMbuK 7m5WBuFU eu8qbgJ0TVz34KF8VHopXTIMcOyZSbc3MxGxBIhBF6hYXTUVOMbqRjS8Xfeh8bNneiFS2kuyqd63x6yXv8v0ecspmL7ZqtJwv0ZEXDu9XbEkPzBaWc9w8jPz7e7L/H5/Zk0aLXkNTRxsqGZIE8PfD3crQaWe0Pb1MjW3/wcbUM9vrFxpN1wM/sffxSv0DCmP/bUQGU4jCO6h4Jrxo2mpq2DBXFRfJiVzyVjEkkK9Dd3J3yYlUddh5Y3ls9DIChsaubdzJPmB0taSCBZdY09ujhMazvkNSpT6ZYGebOuoavlMSMqzGxsTFyUHM/nOYXkNzYT4 dLaUsrgZ5dYeDGh4WYg23eO/UstHoD4T7e/GfJ0MWQcpThvt79haCxjB/X3RGiw hokRIcYL7eX126jJVrt5FT33sUBnvoa5wtJTiQlFOIQmEP0x97iup9u82x5 KWdo2HBiSMxj8ukbil53WFCjI45mzizOvtyDySTf3nGpm0tdkX5bS9U8 nOdaLNJ0TF8U9U237cZuo3LWd2iMHCRqTZnaPjJ63iMjZ85n3nzes1skYSuzVPVTcPKFr3ZKPLlzUY/87FyygrbPT3CpIDQnij3PO5uPsfF44eJzr0lN42BjufHlSLDvLqvjXwhlsL6tiTnQ4G4vL aGwjLEaHZbzvEd5e/Hi0tnUtnfg5eaGh9Fx4b7p46lqbaeoqYXXjpywisAKmN1xVyQP3uRJZ3K6XW9HMHURxvr7MTl8FPEBfgA8s3AmncYH7JGaeh62sfJgmJtgdmKceSnraD8f88vGbZPHDUX1beIfl4B/XPdZIV0IjQYP/wCzl6XlYnD24Mzr3ashEUK8IKX8jcV2r53zUso/O61GpyE dnoqHaiyjoMT5evDJWN6vxFMHH3hGXO0WBOmtzP/bsvKDiX26j5d8HZ36/EwB7h8bCIXjI6zmuj326lnmefyJBgXb7ohYww3ZIxh0487oE7pZ185IZXzRsfZ7JpMHxVM ij40hjy/0x3ZTzTrrcll45JINLPh3kxEVZjKpYLvCUHdXnHzY2JYJtxxchIT3fumXoWYT7evHH0BAviong/K49bJ6Vx dikIdNwqgghmPnEs/g42Lpw5vXuq0XSvT3nPKfjMwzTYi 9oTcYWHUsF223puW7P7E90Jj/xSeMGj JwOQxtNfW0FFbY179zkR/g5BDQX 6zxSEEPgaHyh/mTuF AC/PieEpo1OguID/HRsIleP6z/ir7msM3jdcjizr7ebRsP8fqJEmCLtjg0O5LG5U9hUXM6ffjzANGO38/UZKVyfkYJBSubERJxWHnX9EZIxvv9M3XDm9e7VkEgpb 2W9LCUstpm5hFOcXFxn32JO8ureSuz73WqpZQYdDp0TQ0cf/U/SqIQ5ofPWbf9Hznv/Y q3Ttw9/PHN8qRhSMHh/50n4nMsWOsqqSkhLU/XW73GgumIAH2TMQ8nRmJ19sSN42G/yyeRYwxXtk5sZH8bd5U3Eqsu6M1QjA zDHX9TMRZ15vRwbbDwghJhjDpLgUTU19D9bVWayzHujpQaNWh0e3ECQnP3qXE2 /xribb 9KtHiDDRidwtSHrUKODTv96R6pNDU1OTRLfEFcFPsqarlp/OCEtR8qXOF6p4d2hQURQjArOpyNWUf7OGLk4szr7cjCVs8BzxjXAHEpPPsIXwFYeQFNMc5qndvZQlN V7j2knXfAlC6Ya05LXxGlwdP9xAnpwP96R6pOKrbx92dB2dOtFp 9UxEvd6uhTN1O9IiGQ9cCFwohDiKxdojIz3WVkpK7 MV3xeU8O7xLoORHhrMipR4Kn91BdvegaXvf0nJD9 bPa8ac7LMecOnziR28XK8Q22vHjfc9KV7JKPqdi1U3QPHEUOyzvjncpSVldnsS2zW6Xh2n3XMm/gAP6ZEhLLGuJ315isUfWs7ZLuHnz9RxhUHT0d60z3SUXW7FqrugePIPJI3B3oyIUQYsAqYC/wIXC l7LG6kr35hor6etuBE9cWlNLaqefFpbNZfaKA7wpKSQjwo93CldeWEYmav4jO1hbCp80atDo7g950j3RU3a6Fqnvg9DWP5AEp5RMW2zf0lldKucrO870EHAYuAe4B3gaWO5JPCPE9MIuupX5rpJSD2jZ162X84mBVLTF vqSGBJEY6E wlydh7hp uPYKq3zdXXtDJ5xN/HkrBrPKTqE33SMdVbdroeoeOH21SOYDT1hsX9NLPonSeugTIUQEcC5wrTGC8N FELcLIZKllCcdyOcJrJRS9lwHdJBIS0uzmV7b1kGErzLA tPUJC5IjkNbWW7eHzV/ER011cQuOY8jz/0Dn6ho/OMTCZtqe32A043edI90VN2uhap74PRlSDRCiFApZQ2AlHKgy9tOA7KklB0WaQeBqcDJU8g3ZFRUVNhc27i2Xcs446p17hoNgZ6eVFV2rSueet0v8Y2OpfboIUBZTOp0c/Hti950j3RU3a6Fqnvg9OX Ox0wh68VQtT2kdcewoDuc1DqgO4z7 zJ9wshRKUQYq8QYtAjD9fU1PRIk1Ial2O1dqFrM7ZIEi64GB/jpEKfCGUhq9Nhtroj2NLtCqi6XQtV98Dpq0XyIfCJEGKbcdtXCHG/rYxSyiftOJcAusd01gPdne/tyWcAkoCfA6uFEBlSSqtjhBArgZUAkZGRVFRUUFNTQ2WlEl8nNTWVpqYmysqUFdhSUlLQarUUFSmB20zhA/Lz8zlw4ADx8fF4enqSm6vMYA JiKRdr6e tISN9YplDw0NJWvPLtBo6Jy9kKqqKrKystDr9Yy6 U7CJk1h /btaLVaAgICiIuLIz8/n7a2Nnx8fEhKSqK4uJimpiY8PT1JSUmhrKyM vp63NzcSEtLM sASE9PP2VNQA9N0dHRBAQEkJ2dbc5XUVFBZqay4l9oaCiRkZFmTcHBwURHR5Obm3vGaDJdp7401dbWnlHXyR5N9lynrCzFNX0kabLnOh04cGDEabLnOh04cKBfTfYiZC/xgYQS7 FGYAbgA/wM24taSSnlL/s9kRBXAzdLKZdYpL0HHJBS/t3efEKINKBCSlkvhHAHmoClUspt9MK0adPknj17 qtir1RUVPRwkytqauHGNVt4YMYEzk3sWsXw4FN/oT4rkwWvnPnrf9nS7Qqoul0LVXfvCCH2Simn9ZmJvmNtSeB/xj EEJ1Sypsdqqk1VUD3xcojgO5dZn3mk1KaZ/RJKTuFEPX07B5zKjU1NT2 8FzjGgfdFydqq6zAJ2Jk3JS2dLsCqm7XQtU9cOwOkTJAIwKwH0gRQgQCGEOtTAZ2OZJPCGFWLoTwAcKBvAHWrU9MzUJL3s86SZy/LxO6BXdrqywfMYbElm5XQNXtWqi6B44jsbYGhJSyFvgaeNzYJXU/kA0csjefEGIc8JUQIkUI4Qn8HfhRSnnq/VanSElzKzOiwvGwCEdu0GnpqKs1D66rqKiouAJDZkiM/ApIBupR5opcKaWUQogHhRAv9pdPSnkc AeKoSlH6dK6bLArnZqaarXdaTDQouskyMt65cK2qiqQEu8R0iLprttVUHW7FqrugeNIrK0BYwxB32M ipTyb/bkM 77EMWjbMjoHm65UauskRzYLXpm1Z4dAPhGjow1wFwhrLgtVN2uhap74Ax1i SMxOT Z6KhQ1mG1bJFIqUk573/ETJ EsHpjq9WdjrSXberoOp2LVTdA0c1JKeAuUVisY53e3UlnS0tRM9fdFquLaKioqIyWKiGxA66x 03t0gsuraaCxTHsYDE0UNXsUFGXafBtVB1uxbO1K0aEjvQarVW27a6tpoK8gHwT0gaqmoNOt11uwqqbtdC1T1wVENiB6bwASZqjGu0Ww62Nxfm4xUahod/wJDWbTDprttVUHW7FqrugaMaklPgUFUdKUEBeLp1fX3NhXkjqjWioqKiYi qIbEDUyAzgPZOPUeq65gWFWZOk3o9zUUFBCSMnPERsNbtSqi6XQtV98BRDYmDlDS30iklaSGB5rSWshIMWi3 iUnDVzEVFRWVYUI1JHZgGVK5uq0d6ArWqG1s4MizShT94NT0Ia/bYOJoKOmRgqrbtVB1DxzVkDhId0NS8NVq6o8fA8AvLmHY6qWioqIyXKiGxA7i4 PNn6vbFI tUB8vAHRNjQDELjkPoRlZX6elbldC1e1aqLoHzsh68g0SnkY338LGZt48lgNgjvrbVlFGwOgxTLjL5uKRZzSe3WKJuQqqbtdC1T1wVENiB6alM7eXVfXY11pWim/0oK6rNWyYdLsaqm7XQtU9cFRD4gCtuk4AHptzNgAGnY7WinJ8o0amIVFRUVGxB9WQ2EF0tBIWvra9g1HeXsyNVdYbqTm0H9mpI SsicNZvUHDpNvVUHW7FqrugTOkhkQIESaE EYI0SCEWCOECHc0nxDCRwjxthCiTgjxoxBizGDVt7y8jHfuupWPNm3lX/uO8k1eMaO8lX7F9poqct57EzcfH0InTRmsKgwrAQEjJ9yLI6i6XQtV98AZ6hbJS8BhlHXWNwBvn0K v6DUOxp4FvjcuK670wkKCiYkP5uOwjy yFXi0ni6uaFrbmbH/XfQXJDH Nt/i9sIHazLzs4e7ioMC6pu10LVPXCGbIVEIUQEyrK510optcDfhRC3CyGSpZQn7ckHFKAsw5shpWwH3hNC3AYsAH5wdp19fHxoDAgmtL4GgBlHd Jv0JOTu4f2mmpmPfEsweMynH1aFRUVlTOKoVxqdxqQJaXssEg7CEwFTtqZzxvQSSlLLPYdMO5zuiEBqA0OI6yhmsiachbu2wRAwQGInHPOiDciERERw12FYUHV7VqougfOUHZthQF13dLqgO4uT33ls7cMp9EZEUNYQw03frMKg4cHEXPOwS8 kTFXXT9YpzxtCA0NHe4qDAuqbtdC1T1whrJFIgB9tzQ9SivD3nz2loEQYiWw0rjZLITIOoU6mwgDqgH49Hsl5YX/DaC4M4Yu3a6Fqtu1UHX3TqI9BQ2lIekAPLqleTmYz94ykFK DLzsYJ5cPAEAAAeWSURBVB1tIoTYI6Wc5oyyziRU3a6Fqtu1cKbuoezaqgKiuqVFALUO5KsCIu0oQ0VFRUVliBhKQ7IfSBFCBAIYXXYnA7scyJcHaIUQlvHap9koQ0VFRUVliBgyQyKlrAW Bh4XQrgD9wPZwCF780kpDcAbxn1eQoifAYHA94Ncfad0kZ2BqLpdC1W3a E03UJK6ayy j ZECHAu8B8YDdwg5SySAjxIJAgpbylr3zGfV7Aa8DFwAngRinl4SEToaKioqJixZAaEhUVFRWVkYcatLEP7I0NdqYihLhGCDHLYvu0iHE2mAghlgohMoUQzUKIj4QQQcb0FCHEFqP294UQPhbHnPH3gRBithBir1HD90KIOGP6iL/mtlB1O1e3akj6xt7YYGcUQgh3IcRl9OwjPS1inA0WQogw4CngPCAWxf39eSGEAN4HPkDxAqwG/mVx6Bl9HwghRgH/A24DgoEtwKvG3SP6mpsQQlwphGgTQiw0Jo1o3UKILKOxqDf bTDuGhzdUkr1z8YfygOlCfCySCsCkoe7bk7Q9haKp1sTMKs/vYAb0ADEWuzbCiwebi0O6r4euNliOxzQocRqO2GR7mfU6zNS7gNgssXnDBSX RF/zY319kFxyDkOLHQF3UC 6bdtkTZoutUWSe/0FfPrjEZKeb2UcgZQY5Hcl940eo9xdibxCbDatCGlrEL5DiahaDWlt6D8wMYzQu4DKeUBi83lwBFc45oD3IfiCWrCVXR3Z9B0q4akd4Y8rtcwc1rFOBsMpJStUkpzSAghRDAwCghhhGsHEEIsEUI8DTwA3IMLXHPjWNB1WHfjjnjdRh4SQtQIITYIIfrTNiDdqiHpHbvjeo0QnBLj7AzjTpRuPnAN7Uko3Xkl9K9tpOh EviblLLNIs0VdAOUo4x37EK5zwdNt2pIesfuuF4jBKfEODtTEELMB1agGBOX0C6lfE1KeT1wB/AVoGUE6xZCzEYZD1rVbZcrXO8LgTulsqbTs8BioJNB0j2UQRvPNOyNDTZSOJUYZ VDUC nI4SYBPwDWCGlbBFCVKFMfrXEpN3ACLsPpJTbhBB6FEMykq/5hUACUKo45hEGfApUoFxXS0aSbqT1JG1Td24cg3S91RZJ79gbG2yk4BIxzox95q8BV0spK43Je4EpRjdghBChKA gPYyA 0AIkSSE8LDY9gB8jZsj9ppLKR UUo6SUkZJKaOAYuAyYC4jWLcQwts0P8pIgvH/GwySbtWQ9IK0MzbYSKEvvXL4Ypw5FSGEP4rf/E1SynxTutGjqQS4z/iQ/QvwiZSyboTcBwnAF0KI0UYND6N4pX3JCL/mtnCBe30B8IEQIsp4zz8BvGN8cRoc3cPt73w6/6F483wLNKNM3okf7jo5SdeLRk0GoBWo7k8vSn/p2yh 6PuACcOt4xR034Ayb6S52990lIHo7UZ9nwIhI k QHkTPwbUA uAFFe45t2 g3xgoSvoBm4HCoBK4BXg/9u7l9A4yygO4/9HXQhJSdHW7rwggiYggiDFgnQjblwouhCLIghWEcSdILoQwZVCVbyCmE0X4saC1E0ErQvFGwit4kIbpKIQqbGa1ivHxfuVTodJwuenBOzzgzBM8nLmnSHM4XyXc6b y/dtry1J0iAe2pIkDWIikSQNYiKRJA1iIpEkDWIikSQNYiKRJA1iIpHW0U1THG hIqnjfSSSpEGsSKQRwDxw10bv4yRgEbh4o/chrcVEIkkaxEQidYADSXYlebE7L3JL9/tfT1YFXcXyOPAucBzYC1wJfAz8BOwZiXcu8CpwDPgQmFvjtZ8EvgWWgAVgBvglreHioW4/F3RrdwFHgO B 0ZiLAKPAh8BK8ALa8Tf9O9 ejqTOY9E6lTVdcB8kneqan6NpXcm2Z7Wiv2TJBelDQ7akWQ/8HRVHU7yRJJNaXMedqR1V71mPBgwm9ZU8fIkK12sn6tqGlhMazS42K29KslT3ZqlJG8C79epmexTSa7t9rcf2JfkmwnxV3p PNKqrEik/t6oqu q6qskn6W1nD9WVW lDQLaBpyV5J4kD1bViapaSLIMjA8PSpITSWaS7Ezr0rpQra33JLuTPFNVn1fVUtr0uxvG9vZHVb2XNnflpp7xpd5MJFJ/o1Pjauz5b93j WkVy0FgGVhOqxS2jgfrqpcH0uaL/wjs6RLRJBcmeXgk5vNJzltl7ZdJtvWML/XmP5PU3/g185Ouof8hbRbE1qra3P1MV9XBiQGr9lbVFUkuSaswrl/ltQ8n2T0Sc6aqHlpl7VzaAKs 8aXeTCTS6Y4nmQO2dOcu/pFqN2i9kuRZYAqYBm4HNo vBS4Dbu6m1h1NO3/x19h ZoEtSZ5Lq0hmgbOBq4GdI FuBc4Bbkxyd5L5deJLg5lIpNPtS3JvkgNJLh0Y67G0L wjaSNNt fUoa9Rvye5I616 DptTvzb3d9eT/JakpfTpjZ keSRtDG5R9MOVy2PxPozyaEkLyW5v6o XSe NJh3tkv/E90VXrdV1QcbvRedWaxIJEmDmEgkSYN4aEuSNIgViSRpEBOJJGkQE4kkaRATiSRpEBOJJGkQE4kkaZC/AcseoUhnA5G9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sl1m0 = np.mean(sl1, axis=0)\n",
    "sn1m0 = np.mean(sn1, axis=0)\n",
    "sl2m0 = np.mean(sl2, axis=0)\n",
    "sn2m0 = np.mean(sn2, axis=0)\n",
    "\n",
    "twoCols = [ '#41aab7', '#b74d41' ]\n",
    "\n",
    "plt.figure(figsize=(6,2))\n",
    "plt.plot(sl1m0, color=twoCols[0], label=\"Loihi\")\n",
    "plt.plot(sn1m0, color=twoCols[1], label=\"NEST\")\n",
    "plt.xlabel('time steps')\n",
    "plt.ylabel('firing rate')\n",
    "plt.yticks(np.arange(0, 0.21, step=0.05))\n",
    "plt.ylim((0,0.2))\n",
    "plt.savefig(exp1.net.plot.plotDir   'firingrate_exp1.svg')\n",
    "p = plt.show()\n",
    "\n",
    "plt.figure(figsize=(6,2))\n",
    "plt.plot(sl2m0, color=twoCols[0], label=\"Loihi\")\n",
    "plt.plot(sn2m0, color=twoCols[1], label=\"NEST\")\n",
    "plt.xlabel('time steps')\n",
    "plt.ylabel('firing rate')\n",
    "plt.yticks(np.arange(0, 0.21, step=0.05))\n",
    "plt.ylim((0,0.2))\n",
    "plt.savefig(exp2.net.plot.plotDir   'firingrate_exp2.svg')\n",
    "p = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAACwCAYAAAA2RqiHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xt4XVd95vHvq8uRjmX5JtmKYjtVImrj9DJABANN 6SkJSRToB0a0kASkg6JCw2UEkqGhhbcliEktIVShknCMAQSLiWhPFCggbRJubSQ4Ewcp6kjFyXK2I4iW9YlsnSko8tv/thH7oki22fLOke2/H6eZz8 e 211177aMs/rb3XXksRgZmZWblULXYFzMxsaXOgMTOzsnKgMTOzsnKgMTOzsnKgMTOzsnKgMTOzsqpooJHULOlbkoYk3SNp7THyv1HSy2el/aqkXZIOSbpL0sry1trMzI5HpVs0twKPAmuB 4E758okqUbS64HbZqU3A38OXAisB2qATxRtH5M0WLR8tjynYWZmpVKlXtiUtA7oApojYryQtgc4LyKemJX3DmAzsAV4VUT8qJB BVAfEZ8qrK8Fni6UOSQpgNaIeKYiJ2VmZsdUU8FjdQCdM0Gm4BHgHOA5gSYirgCQ1D2rjK8Ay4ryHZB0EPgpYGcZ6mxmZsepkrfOmoGBWWkDwOmlFhARoxHRN7MuaRWwBthXlO0WSf2S/lbSiuOpsJmZHb9KtmgETM1KmwLqj6PM3wPuiIiDRWk7gDcAnwU CrxlzspIW4GtAPX19eds2rSJqakpJiYmKKQ9Z72uro6IIJ/PH14HGB9PGmiZTAZJh9dra2uprq5mbGxszvWamhpqa2sZGxsjIqiurqa2tpbx8fHnrOfzeaanp6mqqiKTyTAxMcHU1BSSqKure856fX09ExMTTE5OznkOPiefk8/J57RQ57R79 6 iDhqh64ZlQw040DtrLS6 RYm6ZeA1wCvLEp KfBwRExJ p/AP0q6JiKmZ 8fEbdR6GzQ0dER27dvn29VzMxOOZKeKjVvJW dHQBOm5W2DuhPW5Ck/wR8BHhNRIzMpEfE9oiYaTX1kQSypvlV18zMFkIlA83DQPvMcxNJ1cCLgAfTFCJpA/Bp4NKI2F UvkZScYvpDGCYJOCYmdkiqVigiYh 4JvAjZJqgOuB3aToLSZpOcm7N2 JiO5Zm98AfErSqsL7Nh8APh6ecMfMbFFV oXNq4GzgEHgAuCSiAhJN0i6ZSaTpFskHSJpldwnaaZV8nrgXOCfCyMDzCwvBf438BSwiyR4/QDYVqkTMzOzuVXshc0TmTsDmJmlI mhiOgoJa8H1TQzs7JyoDEzs7JyoDEzs7JyoDEzs7JyoDEzs7JyoDGzitm2bRuSjrhs27ZtsatoZeDuzbh7s9likYT/Dzo5uXuzmdkicIttbm7R4BaN2WJZ6i2apXx btGYmdkJw4HGzMzKyoHGzMzKyoHGzMzKyoHGzMzKyoHGzMzKqqKBRlKzpG9JGpJ0j6S1x8j/RkkvL7UMSVlJd0oakPRDSS8o17mYmVlpSg40kpZL ktJP5b0VCHtzZLeluJ4twKPAmuB 0mmZZ7rWDWSXg/clrKMD5KcUyvwceBrkqpT1M/MzBZYmhbNp4E kqmUVxTSvg38fik7S1pHMn3z yMiHxE3AWdLOmuO7J8B3pumjEJAuRp4T0SMRcQXgQHgvBTnaCc4v3ltdvKpSZH31cCbImJK0syrrn3A6SXu3wF0RsR4UdojwDnAE8UZI IKAEndKcqoByYiYl/Rth2FbfeVWEc7wW3btu05wWQpv3lttlSkCTSPAO8APlaUdgXw/RL3byZpYRQboPRAdawyehegfDNbwu553flL9pgXfv3E/Xs6TaC5ErhL0u8ADZK TdKKuLTE/QVMzUqbKpRRqqOVkap8SVuBrQAtLS309vZy8OBB9u/fD8CmTZsYHh6mp6cHgPb2dvL5PHv27AGgra0NgO7ubgA2btxIJpOhq6sLgNbWVhobG9m9ezcA69ato6mpiV27dgHQ1NRES0sLnZ2dTE1NsWrVKlpbW nq6iKfz9PY2MiGDRvo7u4ml8uRzWZpa2tj7969DA8Pk8lkaG9vp6enh8HBQaqrq9m8eTMf MAHuOWWW474BV577bVcfPHFJ9U5zfxsALZs2fKcnxNAZ2fnSfdzOto5Lea1d 13fkBNNkvdmmZyB3qZzuepymTIrm1hvL PyVyOqpoasi2tjA/2MzkygqqqWHb6BvJDA0wMDwPQsH4jE4eeJT80BMCy1vVMjeUYH gHINvSSkxOAPDibTdTv7YFgLEDvQDUNzWjmlpyvcl3ULd6DdX1WUZ7khsWmZUrqV2 gpF9yXdS29hIZuVqRp/eS0xPU9PQQN2qNeR6e5ienDx8ThcPDJGfmiZTXUXLsix9uXFyk5PUVFXR2pClf2yckYlJqiQ2LF/GwHie4XxSz42NDTybn2BoPA/A uXLyE1O0T W3GBpbcgyMR305cYAaFmW/Nezsy/5 7c5W09tlegZyQGwpr6ObE01 w6NArCyLsOKTC17hkcAaMzUsrouw95Do0xH0FBbw5r6OnpGckxOT5OtqaE5W0fvaI781DSrH3ig4tdeqVIPqinpZcAZwJMR8VCK/S4FromIXylK yKwo/CsZa59uoFLI JHxyoD C7w YhoL9p2I7AqIo7aYWEpD6q51G8tLfXzq7Tz77qnose7/5KLeOWX/75ix7v jpsrdiyAi/7ufv7 ta syLEq3aIpy6Cakn4fICIejIi7I IhSS S9N9KLOIAcNqstHVAf6l1OEYZB4CW4yzfzMwWWJpeZ386R1oXcEOJ z8MtEtaAVDoJfYi4MEUdThaGU8CeUlbivJ3pCzfzMwW2DGf0Uh6f FjXdHnGT8DrCnlQBHRL mbwI2S3gm8B9gN7Cy1skcrIyJC0mcK234LuJikG/Z3Si3fzMwWXiktGhWW4s8zy3bSvadyNXAWMEjyPswlhQBxg6TDT7Al3SLpEMmzoPsk9R2rjMK2G4BDJN2u3w28LiJyKepnZmYL7Jgtmoj4EwBJ7TOf5ysiBoCL5kj/0Kz1twJvTVNGYds4cPnx1NHMzBZWyd2bI LN5ayILQ1L T0FOLHfVTA7UaXpdbZZ0vckPStptGgZKWcFzczs5Jam19kdwF3AS4AcSUeAPyikmZmZzSlNoPlp4BMR8RPgKSAi4pPAK8pSMzMzWxLSDEGzk SdlYeBe4F3SbqXZEh K1Gl37yu9DGvr9iRzE48d3Y yed3dz8n7aK/u//w58s2tXH55jMrXKvFlybQXAcsK3z MPA94I3A xa6UmZmJ6PLN595SgaSY0kTaJ6KiD443MX458pTJTMzW0rSPKPZIWl12WpiZmZLUppA89fARz01spmZpZHm1tnPAq8FXivpMZKhXgCIiP y0BUzs6XnyS/fSffdn39O2v2X/MdAH20XX8aZl3hwj6UmTaD5h8Jitmjcq fkduYllzuQnILSDEHz2XJWxKwU7tVjdvJJ84zGzMwsNQcaMzMrKwcaMzMrKwcaMzMrq5I7A0j664h4xxzpIplsLB8Rf3OMMpqBzwHnAj8EroiIA/PNZ8/n7qNmdqJJ0735PEkNwGZgd0TMvEdzI/AyoEFSU2FE5yO5FXgU A3gXcCdwKtLzSfpF4FvzMpbBXwpIrZK hRwKTBVtH19RJwyc a4 6iZnWjS3Dp7AHgc CTwr5JeVUj/DeBKkgE2rz3SzpLWARcA74 IfETcBJwt6axS80XEDyJi1cwCrAa C8x0va4F/kdxnlMpyJiZnYjStGheBZwXEU9IegFJy KFwGnA3ogISeuPsn8H0BkR40VpjwDnAE/MIx8kI0rvjoh/TnEeZmZWQWkCTRUwWfgcwPRMGYUgs5aiYWnm0AwMzEobAE6fTz5JpwF/SDIhW7GLJL29UJdrIuL7c1VG0lZgK0BLSwu9vb0cPHiQ/fv3A7Bp0yaGh4fp6ekBoL29nXw z549ewBoa2sDoLu7G4CNGzeSyWTo6uoCoLW1lcbGRnbv3g3AunXraGpqYuCxnQDUNjaSWbma0af3EtPT1DQ0ULdqDbneHqYnJ6nJZqlb00zuQC/T TxVmQzZtS2M9/cxmctRVVNDtqWV8cF JkdGUFUVy07fQH5ogInhYQAa1m9k4tCz5IeGAFjWup6psRzjA/0AZFtaickJxg72AVC/tgWAsQO9yXpTM6qpJdebfAd1q9dQXZ9ltGcfAJmVK6ldvoKRfXsOn9PgeJ69h0aZjqChtoY19XX0jOSYnJ4mW1NDc7aO3tEc alpMtVVtCzL0pcbJzc5SU1VFa0NWfrHxhmZmKRKYsPyZQyM5xnOTyTfc2MDz YnGBrPA7B  TJyk1P0jyV/l7Q2ZJmYDvpyYwC0LKsHoHc0WW/O1lNbJXpGcgCsqa8jW1PNvkOjAKysy7AiU8ue4aQh3JipZXVd5vA5nbZjB62trXR1dZHP52lsbGTDhg10d3eTy XIZrO0tbWxd 9ehoeHyWQytLe309PTw DgINXV1WzevPnw9QawZcuWilx7u3btAqCpqYmWlhY6OzuZmppi1apVh89p4LGdJ 21V8rv0 MDQyfttXes36fVDzxQ8WuvVIqI0jJKFwMfB7qBjUAvsAf4VeAjwBnAaET83hH2vxJ4Y0RcWJR2O7CrcHssbb6PAA0R8buz8v18oU4vJ3m2c2ZEDB3t3Do6OmL79u3H/A4WwmJMfFZJ199x82JXoawu/Pp9i12FsvL1efKq9LUp6aGI6Cglb5ohaO6W9I/AC0ie1QTJbbNe4CZgAvjvRylinOQZSrG6 eSTVEvSGrlgVr5tQC4i oFvSToIvAb4PGZmtijS3DqbmfDsx0VJPyn8 7tzZJ/tAElgKrYO6J9HvlcAAh6aVb/uWfv18fxbc2ZmVkFp3qPZArwT2MCs3molThPwMNAuaUVEPFuY1 ZFJA/00 Z7JfCjiJgs3lHSaRHxTOGzSG7xPVnqOZqZ2cJL06K5C9gF3E1y2yyViOiX9E3gRknvBN4D7AZ2ziNfG7CveD9Jq4D7JF1WyHsdMAb8Xdq6mpnZwkkTaNYDL4mI/HEc72rgC8AgyS24Swo91m4AzoiItx4tX1E564DHiguOiMFCb7NPA 3Ag8AFs7pJm5lZhaUJNJ8C3sBxPFgvPOO5aI70D5WSr2j7rx0h/T7gJfOtn5mZLbw0geYXgHdJ jiwn6LbZxFx9kJXzMzMloY0geZ9ZauFmZktWWneo/luOStiZmZL0xEDjaRPznrr/v1HyhsRf7rQFTMzs6XhaC0azVpvLWdFzMxsaTpioImIt81K uOI6CtzfczMbIlJMx/NDkmry1YTMzNbktIEmr8GPloYEsbMzKwkabo3/yzwWuC1kh6jaO6ZEsc6MzOzU1CaQPMPhcXMzKxkad6j Ww5K2JmZkvT0d6jeW9EfLho/c1HyhsRn1voipmZ2dJwtBbNLwEfLlp/4xHyBeBAY2ZmczpaoKmS1BQRBwEi4oijKZuZmR3J0bo3vxQYmlmRNHvKZTMzs2M6Wovmy8BXJP1zYX2ZpOvnyhgRN5dyMEnNJLfZzgV CFwREQfmm2 O/bIk8 b8GvB4Yb flFI3MzMrj6O1aK4FvkoybfKWQt4tcywvTHG8W4FHgbXA/cCdafNJ o6kZyUNFpauov0 WKhnK/Bx4Gt wdTMbHEdbayzAG4vLEiajIhr5nsgSeuAC4A3FaaDvknS2yWdFRFPpMiXAbZGxJdmlV9NMgX02RExBnxR0rXAecB98623mZkdn5KHoDmeIFPQAXRGxHhR2iPAOfPMN9tmYCIi9hWl7ShhPzMzK6M0IwMcr2ZgYFbaAHD6PPL9dmFK6T0kz2H LUX5AEjaCmwFaGlpobe3l4MHD7J//34ANm3axPDwMD09PQC0t7eTz fZs2cPAG1tbQB0d3cDsHHjRjKZDF1dyZ281tZWGhsb2b17NwDr1q2jqamJgcd2AlDb2Ehm5WpGn95LTE9T09BA3ao15Hp7mJ6cpCabpW5NM7kDvUzn81RlMmTXtjDe38dkLkdVTQ3ZllbGB/uZHBlBVVUsO30D aEBJoaHAWhYv5GJQ8 SH0r6dCxrXc/UWI7xgaRfR7allZicYOxgMih3/doWAMYO9CbrTc2oppZcb/Id1K1eQ3V9ltGeJJZnVq6kdvkKRvbtOXxOg N59h4aZTqChtoa1tTX0TOSY3J6mmxNDc3ZOnpHc SnpslUV9GyLEtfbpzc5CQ1VVW0NmTpHxtnZGKSKokNy5cxMJ5nOD RfM NDTybn2BoPA/A uXLyE1O0T W/F3S2pBlYjroy40B0LKsHoDe0WS9OVtPbZXoGckBsKa jmxNNfsOjQKwsi7Dikwte4ZHAGjM1LK6LnP4nE7bsYPW1la6urrI5/M0NjayYcMGuru7yeVyZLNZ2tra2Lt3L8PDw2QyGdrb2 np6WFwcJDq6mo2b958 HoD2LJlS0WuvV27dgHQ1NRES0sLnZ2dTE1NsWrVqsPnNPDYzpP22ivl9 nxgaGT9to71u/T6gceqPi1Vyold8jKT9KVwBsj4sKitNuBXRFxU6n5JP0TkAN E7gKeCdwNvCLwK0R8cKi/bYBLXNMefAcHR0dsX379uM8w9Kcf9c9FTnOYrn jpL6hZy0Lvz60r4L6 vz5FXpa1PSQxHRUUreNKM3H69xoHZWWt088v0OSSAaBW4DzgBenqJ8MzOroEoGmgPAabPS1gGz3885ar6I6IyIwcLnSWCQ5PbYAaClhPLNzKyCUgUaSadJ6pD0suKlxN0fBtolrSiUVQ28CHgwTT5Jh4NJ4b2ZtcCThSUvaUtRWR1zlG9mZhVUcqCR9OckD9/vBv6maPnS0fabERH9wDeBGyXVANcDu4GdpeaT9ELgG5LaJWWAm4AfRsT2iJgGPlPYr07SZcAK4DulnqOZmS28NC2aa4BzI6ItIs4sWs5KUcbVwFkkt7suAC6JiJB0g6RbjpUvIh4HPkISiJ4huWX2 qL9biCZkK0PeDfwuojIpaifmZktsDTdm7uAx47nYBExADxvcM6I FAp QrbvkwyPM5c28aBy4 njmZmtrDSBJo7gX SdCdJa Jwv jCf/5mZmbPkybQvJbkttRvzEoPjtDCMDMzSzOV8yvLWREzM1uaKvkejZmZnYLSdG9 oaTvFYboHy1eyllBMzM7uaVp0XwOuAt4CclYYz8D/AF PmNmZkeRJtD8NPCJwoyVT5FMWfNJ4BVlqZmZmS0JaQLNTpKhYADuBd4l6TUks1mamZnNKU335uuAZYXPHwa B7wJeN9CV8rMzJaONN2bHyr6PAD8XFlqZGZmS0qaXmfLJf2FpB9LeqqQ9mZJR51UzMzMTm1pntF8GjgInEsyKjLAt4HfX hKmZnZ0pHmGc2rgTdFxJSkmXHO khGUDYzM5tTmhbNI8A7ZqVdAXx/4apjZmZLTZoWzZXAXZJ B2iQ9G2gHri0LDUzM7MloeQWTUR0R8RLgauAy4AbIuK8iOgptQxJzZK JWlI0j2S1qbNJ nFkh6SdEjSvZI2lHp8MzOrvNSDakbEAxFxd3F35xRuBR4F1gL3k8xxU3I SbXA/wHeAqwDOoEvzOwkqbMQnAYLy/3zqKOZmS2gNN2bL5X05KwBNXOlDqopaR3JtMzvj4h8RNwEnC3prBT5Xg58KyJ2RMQoyXTNZ0uaeaenDnh1RKwqLJ7awMxskaV5RvMxktbE7cD4PI7VAXQWplue8QhwDvBEifm Ajw5kxgR45J ApxJ0gIyM7MTTJpAM0bSypic57GagYFZaQM8v3v0EfNFxDSwdyZRUhVJkNlTlPePJL2CZGy2yyLi6XnW18zMFkCaQHM98DFJtwG9JFM4AxAR 0vYX8DUrLQpkp5r88kHcDnwrxHxcFHaMyQDff4ZcAfwK3NWRtoKbAVoaWmht7eXgwcPsn9/ciqbNm1ieHiYnp6kr0N7ezv5fJ49e5KY1tbWBkB3dzcAGzduJJPJ0NXVBUBrayuNjY3s3r0bgHXr1tHU1MTAYzsBqG1sJLNyNaNP7yWmp6lpaKBu1RpyvT1MT05Sk81St6aZ3IFepvN5qjIZsmtbGO/vYzKXo6qmhmxLK OD/UyOjKCqKpadvoH80AATw8MANKzfyMShZ8kPDQGwrHU9U2M5xgf6Aci2tBKTE4wd7AOgfm0LAGMHepP1pmZUU0uuN/kO6lavobo y2jPPgAyK1dSu3wFI/v2HD6nwfE8ew NMh1BQ20Na rr6BnJMTk9TbamhuZsHb2jOfJT02Sqq2hZlqUvN05ucpKaqipaG7L0j40zMjFJlcSG5csYGM8znJ9IvufGBp7NTzA0ngdg/fJl5Can6B9LGsCtDVkmpoO 3BgALcuSy6Z3NFlvztZTWyV6RnIArKmvI1tTzb5DyR3glXUZVmRq2TM8AkBjppbVdZnD53Tajh20trbS1dVFPp nsbGRDRs20N3dTS6XI5vN0tbWxt69exkeHiaTydDe3k5PTw Dg4NUV1ezefPmw9cbwJYtWypy7e3atQuApqYmWlpa6OzsZGpqilWrVh0 p4HHdp60114pv0 PDwydtNfesX6fVj/wQMWvvVIpIo6dC5D0DuBmkucgxSIiqkvY/1Lgmoj4laK0LwI7Cs9h0ubbDNwNvCoinimk/RzQFRGjktaTtH42RMS o9Wto6Mjtm/ffqxTWBDn33VPRY6zWK6/4 bFrkJZXfj1 xa7CmXl6/PkVelrU9JDEdFRSt40vc7 jORWVNWs5ZhBpuAAcNqstHVAf9p8kjaStFZ cybIAETEo4VOApCMWgAeucDMbFGlCTS9wDeO41gPA 2SVgBIqiaZ3 bBNPkK6V8G3hYRu2d2klQvaWVROWcU/n0SMzNbNGkCzV8Ad0v6dUn/WdLLZpZSdo6IfuCbwI2Sakie ewmeWhfUr5C0LkT MAc7/GcB/yNpNMkLSeZM fzEdGHmZktmjSdAf6w8O/HZqUHcBaluZrkBctB4MfAJRERkm4AzoiItx4j37nArwHnSyou98qI IqknwYeALLA1/DI0mZmiy7NxGdnHu/BChOmXTRH odKzPc94IjPhCLiE8AnjreeZma2cFIPQWNmZpaGA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZWVA42ZmZVVRQONpGZJ35I0JOkeSWvT5pOUlXSnpAFJP5T0gqJtVZI JqlP0r WOvunmZmVT6VbNLcCjwJrgftJpmVOm  DJPVuBT4OfK0wxTPAtcDPA 3AO4C7JK1a4HMwM7MUKhZoJK0DLgDeHxH5iLgJOFvSWaXmKwSUq4H3RMRYRHwRGADOK z VuCPI2IoIu4Hvg 8vjJnaGZmc6lki6YD6IyI8aK0R4BzUuTbDExExL6ibTuAcyQ1AFsKeZ zbYHqb2Zm81DJQNNM0vooNgCcniLf0bY1AVMRcegY5ZuZWQXVVPBYAqZmpU0B9SnyzXfb8ysjbQW2FlYPSeo8Rv2tBPcnfwz0LXY9ykZa7BrYcVjS12flr82fKjVjJQPNOFA7K60uZb75bnueiLgNuO1I221 JG2PiI7FrofZXHx9Lo5K3jo7AJw2K20d0J8i3wGg5QjbDgLM6jI9V/lmZlZBlQw0DwPtklYAFHqQvQh4MEW J4G8pC1F TuAByNigqRLdMfsbWU4FzMzK1HFAk1E9APfBG6UVANcD wGdpaaLyKmgc8UttVJugxYAXynsPungW2SVkj6ZeBc4EtlPzkr5tuRdiLz9bkIFBGVO5i0GvgC8EvAj4E3R8QeSTcAZ0TEW4 Wr7CtjiSg/Drw78CVEfFoYZuAvwDeAvQC10TEdyt2gmZm9jwVDTRmZnbq8aCaZmZWVg40JylJt0uakHRo1nJLBevw94XbnickSX 12HU4Vfn6PDJJ10l6WtJBSR8s3PJf0nzr7CQl6XaAiLhqcWty4pG0HHg38N6IyC52fU5Fvj7nJulVwDuB3yIZteQbwM0R8elFrViZuUVjJw1J2yRtKyHrDuBVJKNFmFVEidfnpcC7ImIkIv4duAH43bJXbpFVcmQAs4qIiBdIagMeX SqmM32pxHxVNH6Y8CZi1WZSnGLZgmS9GJJTxSmXKDwXlGnpHML61G4T/xo4b75 4r2PVPS9yQ9K muohdnt0m6SdKfS9pbSLtH0lWFz1cV8t9eKHO7pM2SvippWNK9khqLjvNuSfsldUv6r0XpIenNkvZI6pX0W4X0O0j  ruhUP51Zf8irSxO5etzVpABeAGw57i/1BNdRHg5CRfgdiAPDM5afrGw/Srg2yS3j74AXFe0bwBvB6pJ5uuZJJliQcB2kucbtcAfAR8t7LMNGAJuBhoLafcAVxUdbwL4ZaAR BfgGeBs4AyS4YN u5D3dUAnsAFoA3YB64rq9r ADHAdsLuo3tuAbSV P23A2GL/nE7Vxddnyd/TPwJXLPbPq9yLWzQnty9ExKpZyw8AIuJ2kiF7vgPUR8Rfztr37oiYioi/JXkg evAy0h Cf8ykiF9biZ5aXbGvoi4PiKGj1Cff4uIfypsvxd4ICL LSL H/AP/Mc4dW8D/iQi9kZEN8kMqq8sKue2iMgDXyX5ZbeTk6/Po5D0XuCJiLhjvmWcLPyMZmn7INAN/MIx8nWS/JKdQXK/eKCox2Xx/D7/9xjlPFP0OWatF09kdwZwi6RPFtargPfOUU7g27tL2Sl7fUq6kmRSxkvns//Jxr/ES5SkKuBTJLcgPqFkBtIj RmS 8RPAt d9Rdo8V9sh be/bDZfeWP1Hf SeCiomOsiIhPlrCfLRGn8vUp6QLgcuDyiJg9h9aS5ECzdP0Rya2CW0jul88eTPANkmokvQU4H/hSRGwH6iX9dmFbS Evr4X2V8BHJG2UVCvpPEkvKWG/UeDswsPjF5ehXlY5p T1KelsknO/OJ47Xf2S5kBzcrtMz3/z l/DD7zhAAAArklEQVQknQ 8AXgfQOGvsUZJby/adw3QVcjzhoh4upB KXAJyYPbHwDLF7rSEXEv8DngRySDn76L50/RPZd7SN6P2UFy22FOkh4j6TZaV/hOvnrclbb58PX5fH9A8qxpX9F3sjRn/CzikQFOQZICaI2IZ46Z2azCfH0uPW7RmJlZWTnQmJlZWfnWmZmZlZVbNGZmVlYONGZmVlYONGZmVlYONGZmVlYONGZmVlb/H2o8WSq7Lx0sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = 100\n",
    "\n",
    "plt.figure(figsize=(6,2.5))\n",
    "barWidth = 0.25\n",
    "\n",
    "# Values and errors\n",
    "vals1 = [np.mean(sl2m0[start:]), np.mean(sl1m0[start:])]\n",
    "errs1 = [np.std(sl2m0[start:]), np.std(sl1m0[start:])]\n",
    "vals2 = [np.mean(sn2m0[start:]), np.mean(sn1m0[start:])]\n",
    "errs2 = [np.std(sn2m0[start:]), np.std(sn1m0[start:])]\n",
    "\n",
    "# Bar positions\n",
    "r1 = [0, 0.75]\n",
    "r2 = [0.25, 1]\n",
    "\n",
    "plt.bar(r1, vals1, yerr=errs1, width=barWidth, color=twoCols[0], ecolor='black', label='NEST', error_kw=dict(lw=1, capthick=1, capsize=4))\n",
    "plt.bar(r2, vals2, yerr=errs2, width=barWidth, color=twoCols[1], ecolor='black', label='Loihi', error_kw=dict(lw=1, capthick=1, capsize=4))\n",
    "\n",
    "plt.xticks([0.125, 0.875], ['Experiment 1', 'Experiment 2'])\n",
    "plt.yticks(np.arange(0, 0.2, step=0.025))\n",
    "\n",
    "#plt.legend()\n",
    "plt.grid(axis='x')\n",
    "\n",
    "plt.ylabel('mean firing rate')\n",
    "plt.xlim(-0.25, 1.25)\n",
    "plt.ylim(0,0.125)\n",
    "plt.savefig(exp1.net.plot.plotDir   'meanfiringrate.svg')\n",
    "p = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-1.0126169233955162, pvalue=0.3115571794407685)\n",
      "Ttest_indResult(statistic=-14.110142762635595, pvalue=1.9701559521198946e-40)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.ttest_ind(sl1m0[start:], sn1m0[start:]))\n",
    "print(scipy.stats.ttest_ind(sl2m0[start:], sn2m0[start:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.46079353448868127, 5.998553052185158e-22)\n",
      "(-0.41510776524684, 1.018962830936758e-17)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.pearsonr(sl1m0[start:], sn1m0[start:]))\n",
    "print(scipy.stats.pearsonr(sl2m0[start:], sn2m0[start:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getClusteredFrequencies(sm, binSize, exp):\n",
    "    topologyIndices = np.arange(exp.p.reservoirExSize).reshape((exp.p.topologySize, exp.p.topologySize))\n",
    "    numBins = int(exp.p.topologySize / binSize)\n",
    "\n",
    "    smb = []\n",
    "    for i in range(numBins):\n",
    "        # Define from and to variables for index i\n",
    "        ifr, ito = i*binSize, (i 1)*binSize\n",
    "        for j in range(numBins):\n",
    "            # Define from and to variables for index j\n",
    "            jfr, jto = j*binSize, (j 1)*binSize\n",
    "            # Get topology indices and set connect neurons between output cluster and output neuron\n",
    "            idc = topologyIndices[ifr:ito,jfr:jto]\n",
    "            smb.append(np.mean(sm[idc]))\n",
    "    return np.array(smb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAACnCAYAAADg XByAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAF/hJREFUeJzt3X2UXFWZ7/Hvj053Ou9N0kkTSEKTHhOiozCXgJfJZRwERRAclleZjBjUC0QcfBvvjDPgOKPLGRFH53JdvkBGNAMIKoMOilyQWRoNYRQSgWBubrJs0pA3Om8dSMhLJ81z/zinQ6XS3VWnu6q6qvP7rNUrVbt2nfOc05V6ep 9z96KCMzMzAZywnAHYGZm1c/JwszMCnKyMDOzgpwszMysICcLMzMryMnCzMwKqmiykNQs6UFJL0p6SNLUfuqdK2lVWu nkmZUMk4zMztapVsWtwHPAFOBnwN35VeQNBlYClwPNAHLgW9WLkQzM8unSt2UJ2ka0A40R8TBtGwj8KaIeDav7pkR8VT6 LXAoxExuSKBmpnZMSrZspgPrOtNFKmngbPyK/YmitRFwG/LHJuZmQ1gVAX31Qx05ZV1ASf3VVnSBcDbgSuBS8obmpmZDaSSyUJAT15ZD9DYT/1Wkr6Nzf3VkbQYWAzQ2Nh41pw5c jp6eHQoUOkZUc9Hz16NBFBd3f3kecABw8mjZ2GhgYkHXleX19PXV0dBw4c6PP5qFGjqK v58CBA0QEdXV11NfXc/DgwaOed3d388orr3DCCSfQ0NDAoUOH6OnpQRKjR48 6nljYyOHDh3i8OHDfR6Dj8nH5GPyMZXymNavX78jIvocbJSrksniIFCfVza6v8oRcTtwu6QFwAOSZkdEV16dJcASgPnz58fKlStLHLKZ2cgm6bli6lWyz2I7cFJe2TRg10BviogVJC2Q88sUl5mZFVDJZPEk0CZpIoCkOuBM4PHcSpJaJdXnPK8HxnJsf4eZmVVIxZJFROwCfgLcJGkU8ElgPbA6r os4EeSTkvrfRrYCKyoVKxmZna0SvZZAFwD3A3sBp4AroiIkHQjMCsirouIX0pqJkksJwMrgUsiorvCsZqZWaqiySLtoL64j/LP5z3/AfCDSsVlZmYD80SCZmZWkJOFmZkV5GRhZmYFFZ0s0qnFr5DU7410ZmY2MmVpWXybZJ6m5yUtSe sNjOz40DRySIi7o2IPwHmkqwx8VFJj0r6tKTWMsVnZmZVYDB9Fi8C24D9JIsTtQA3S3pA0h UMjgzM6sORd9nIen3gGuBRcDvSCbwuy4iDqSvzwD Azi9DHGamdkwynJT3n8CdwIXRMTaPl7fSjJ9h5lZzXvzvQ/1 9rP3v22CkZSHbIkizZgT SswyrpZOCliNgbET3AO0odoJmZDb8sfRa3kYyGyvUO4ObShWNmZtUoS7K4APh Xtk9wOWlC8fMzKpRlmQxir5XthtboljMzKxKZUkW9wDfkdQGIGk2SYf33eUIzMzMqkeWZPEx4GlglaTDwFPAb4GPlyMwMzOrHkWPhoqIwySr1n1aUnNE7ChfWGZmVk0yLX4k6SRgBnBCehkKgIh4vP93mZlZrctyB/eXSC5FbQYi56UAZvf5JjMzGxGytCyuBRa4FWFmdvzJ0sHdDqwpVyBmZla9srQs7gKWSboLeIGcS1ERkX znpmZjSBZksVlwF6OvWM7OPbObjMzG0GyDJ09v5yBmJlZ9co6dPYy4G1AU0RcKentwN6I EVZojMzs6pQdAe3pM8AVwPfAS5Oi9uBmzJso1nSg5JelPSQpKn91LtQ0lpJeyXdK2lSsfswM7PSyzIa6nrgyoh4DFBatg54XYZt3AY8A0wFfk7SaX4USc3Al0haMKeQtH6 mmEfZmZWYlkuQ3UBZwCP8epIqDcCHcW8WdI04K3AeyKim2Td7g9Lmh0Rz ZUvRj4WkQ8l75vMbBF0ocj4sUM8ZqZldVAq nByFpRL0uyuB64X9J9wGhJNwAfSH KMR9YFxEHc8qeBs4CcpPFfeRMex4R2yXtBE4FVmeI18zMSqToy1AR8QjwBpIv9m8D3cAfRsSKIjfRTNI6ydUFnJy3n325kxRKagImk0wzYmZmwyDTaKiI2Ap8cZD7EtCTV9YDNBZ430eBOyNi5zEbTC5RLQZoaWmhs7OTnTt3sm3bNgDmzJnDnj172Lp1KwBtbW10d3ezceNGAFpbWwHo6OgAYObMmTQ0NNDe3g7A9OnTmTBhAuvXrwdg2rRpTJkyhbVr1wIwZcoUWlpaWLduHT09PTQ1NTF9 nTa29vp7u5mwoQJzJgxg46ODvbv38 YMWNobW1l06ZN7Nmzh4aGBtra2ti6dSu7d  mrq6OuXPnHjkOgHnz5vmYfEw pmE4pq41q2mc2gLAge2dADROaUaj6lm2bBkA 7Zsoq5xDPu2Jn/LNkyaRP34iby8OTknz5x SlUdU1 /p2IpIgrXAiT9nKMnEDwiIt5cxPsXAtdGxAU5ZfcAT0VEn t4SzoP DJwfkS8PND258 fHytXriwUhplZUQbqj jtixgJfRaSVkXE/EL1srQsbs3fB3Ah/SSQPmwHTsormwbs6quypDOAfwIuLZQozMysvLLcwf29Poq/Kyk/ifTnSaBN0sSIeElSHXAm8In8ipJmALcD74qIbcXGaGZm5ZHlPov nFpMpYjYBfwEuEnSKOCTwHryRjhJGk9y/8XVEdFRgvjMzGyIsix tJZjLzmNJZmBtljXAHcDu4EngCsiIiTdCMyKiOuAdwILgBWSct97fkQ8kWFfQHHXHc3MbGBZ iyu66PsEPCbYjcQEV28OlVIbvnncx7fAdyRIS4zMyuzLH0WnizQzOw4leUy1CeLqRcRg70Pw8zMqlSWy1BnkAyVvR84SNI5fgnJlB29d2YXO4zWzMxqSJZkUQ 8MXeEkqQ24O8jotj5oczMrAZlGTr7ZmBfXtlO uiwNjOzkSVLslgG/Juk0wEkzSFZe/vZgd5kZma1L8tlqA8AtwBPSmogGTb7Q4qfotzMCvB9QVatsgyd3QNcLelakunGd0TEK2WLzMzMqkaWobPjgc8CfwRMi4hTJV0FjIuIb5QrQDOrfW4x1b4sfRa3k3RoLwAmpmUPAx8vdVBmZlZdsvRZXESyfnaPpN77KXaQt9JdLfJfPWY2EpRzfY0sLYungY/klS0Clg9672ZmVhOytCzeD3xf0geBcZIeJlkSdWE5AjMzs qRZTTUBuBsSecAs4ANEbGqbJGZmVnVyDIa6lfAeRHxOPB4 UIyM7Nqk6XPYg3woXIFYmZm1StLn8UzwGckvZVkwaO9vS94WnIzs5Et6xTl96ePZ aUe1pyM7MRrt9kIen/AO NiJ0AnobczOz4NVCfxTnAi71PJO0qfzhmZlaNBroM9T3gPkkr0udj 1ta1X0WZmYj20DJ4nrgfSQtjDEkrZB5fdQ7LvosPCWImR3P k0WERHA0vQHSYcj4trKhGVmZtUkyx3cThQjUDEtJreqzCzLTXlmZnacynKfxZBJagbuIFkT4z BRRGxfYD6f0YyB9WvKhSimWXklufxoaLJAriN5E7wy4G/AO4iWSfjKJJGAe8AlgBvqWSAQ H/NGY2UlUsWUiaBryVZAGlbuBmSR WNDsins2r/m1gbqViMzOzgWVKFpJOAmaQ19eRzkRbyHxgXUQczCl7GjgLOCpZRMSidH8dWeIbKdxCsYH482HDIcsU5V8CPgZs5uh7KwKYXcQmmoGuvLIuRsCyrGZmI12WlsW1wIIiWxF9EdCTV9ZDstre4DYoLQYWA7S0tNDZ2cnOnTvZtm0bAHPmzGHflk0c7EpmKhnTMp04fIgDO3cA0HH26QB0rVkNQOOUZjSqnv2dWwEYfeJktmzZwvr16 las5qGSZOoHz RlzdvBKB wgQaJp3I8uXL6enpYU9HO6ObJrO/cyuvHD7MqDFjGD25mf3bO1m2rJExY8bQ2trKpk2b2LNnDw0NDbS1tbF161Z2795NXV0dc fOZe/zGzi0Zw8A406ZyaG9L9H9YjLzypYFb2DPnj1s3ZrE2NbWRnd3Nxs3JjG1trYmx9bRAcDMmTNpaGigvb0dgOnTpzNhwgTWr18PwMubnuvzmPZt2cSyZY00NTXRvburz2N6pbubX886sahj6v3dAMybN  Y31Mpj2natGksXvF0n8cUr7zCqHHjeOCqd9He3k53dzcTJkxgxowZdHR0sH///qJ/T U4pt7P4ugTJ1PXOIZ9WzcD0DBpEp2dnaxdu5auNav7PKbRTZN57LHHKn5MR/7/TG0B4MD2ziP/nzZu3Eh7eztda1b3eUz14yeybNkyAKZMmUJLSwvr1q2jp6eHpqYmpk fPmy/p641q/s8Jo2qPxLzvi2b jym3s/eM6efUtFj6lqzmrHTT6HnwP4 v/eWTW085rNXLCX33hVRUfoNyeJHL2faw6vvXwhcGxEX5JTdAzwVETf3854OYGExo6Hmz58fK1euPKa8VPcRVPJ hEpeZhip91mUc H6cqrF30ctxlyMoR5Xbr1KGUw8klZFxPxC287SsrgLWCbpLuAFci5FRcT3i3j/duCkvLJpgCcoNDOrclmSxWUkCx5dnlceQDHJ4kmgTdLEiHhJUh1wJvCJDDFYBrX415yZVacs032cP5QdRcQuST8BbpL0MeCvgPXA6qFs12ywqu0Sglk1q/R0H9eQjJzaTXLPxRUREZJulHRrbyVJt0raC8wCfiZpR4XjNDOzHFmGzp5Ockf1mfnvi4ixxWwjIrqAi/so/3ze8 uA64qNzcwsC1 izS5Ln8UdwJ3A/wB TXKT3cUk612YWRXxl2H1GCmXO7NchnoN8NWI B3wHMmSF18Hzi1LZGZmVjWytCxWk1yCehJ4BPgLSY8A08sRmFl/Rspfana0WrxP6XiSJVl8Aujtm/gC8EvgPcCnSh2UmZlVlyxDZ1flPO4CXl WiMzMrOpkGQ01Hvgs8EfAtIg4VdJVwLiI Ea5Ahxp3EQ2s1qU5TLU7SRTii8AOtOyh0kuRzlZHOecBAvzObJaliVZXESycFGPpN55oXbgKcbNrEKccIdPlqGzTwMfyStbBCwvXThmZlaNsrQs3gfcK mDwDhJD5OsRbGwLJHZgPwXlg2VP0OWRZbRUB3A2ZLeCMwENuSOkDIzs/IZ7vuLMq3BDRARvyaZ7sPMzEpguBNBMYrus5C0UNIGSftyfvZL2lfOAM3MbPhlaVncAnwLWAocLEs0ZmZWlbIkiwPA30XE4XIFY1aN3BFslm3o7CeBWyS9QVKLpGm9P UKzszMqkOWlkULcDXw53nlAdSVLCIzM6s6WVoWnwOujIgT8n6cKMzMRrgsyaITeKBcgZiZWfXKchnqy8C/SbodeIHk8hMAEfF4qQMzM7PqkSVZ3JD e0teeQCzSxOOmZlVoyzTfZxWzkDMoDbuZD2eeNiw9co83YfZYDkRmNUuJwuzEvBf4DbSZRkNNWSSmiU9KOlFSQ9JmjqUemZmVhkVTRbAbcAzwFTg58BdQ6xnZmYVULFkkU4L8laS aW6I Jm4LWSZg mnpmZVU4lWxbzgXURkTtj7dPAWYOsZ2ZmFVLJZNEMdOWVdQEnD7KemZlVSCVHQwnoySvrIVnHezD1kLQYWJw PSjpt1kDcp1B1WkGdpRyX6Xc1giqc Q8V0k8I7nO8fyZPrWIt1Y0WRwE6vPKRg hHhGxBFgCIGllRMwfapBWmM91Zfg8V47PdWGVvAy1HTgpr2wasGuQ9czMrEIqmSyeBNokTQSQVAecCeRPQlhsPTMzq5CKJYuI2AX8BLhJ0iiSlffWA6sHU68PS0oetPXH57oyfJ4rx e6AEVE4Vql2pl0InA3cB7wBHBVRGyUdCMwKyKuG6hexQI1M7OjVDRZmJlZbar0dB8l53mkyk/Sn0n6rznPfc5LTNKFktZK2ivpXkmT0vI2ScvTc/1dSWOGO9ZaJulcSavS8/lTSTPScn mC6j5ZIHnkSobSaMkvZNjr f6nJeQpGbgS8DbgFNIhrR/VZKA7wLfIxkRuINjFx zIkmaDCwFrgeagOXAN9OX/ZkuoKYvQ6XzSLUDzb3Tg0jaCLwpIp4d1uBGAEl3AnOBecBbIuJXPuelJ2kR0BgR/5I nwpsAS4EvhkRr0nLx6XlJ0XE/uGKt5ZJOjMinkofvxZ4FDgdf6YLqvWWheeRKqOIWBQR5wA7c4p9zkvvPuCHvU8iYjvJOT D5Nz2lr8MbAR v9IBjhS9iSJ1EfBb/JkuSq0vfuR5pCrP57zEImIfsK/3uaQmYDJwIj7XJSfpAuDtwJXAJcDr8HkuqNZbFkXPI2Ul43Nefh8F7kwf 1yXXitJ38RmknPpz3QRaj1ZFD2PlJWMz3kZSToPuJQkYfhcl0FE3B4Ri4CPAA8A3fg8F1TrycLzSFWez3mZSDoD Cfg0rR/wue6jCJiBUkLohuf54JqPVl4HqnK8zkvg3S8/ 3AwojYlhavAv5LOoQWSVOAWcDK4YmytklqlVSf87weGJs 9We6gJpOFkOYR8oGyee89CSNJxnXf3VEdPSWpyN3NgN/lX6x/QNwX0Tkd8ZacWYBP5J0WvrZ/TTJ6LIf4890QTWdLFLXALOB3SRrd18RtXzzSBWRdKukvST/yX4mqXchHp/z0nonsABYkd7B3ftzNvAu4HKSSyItwIeGMc6aFhG/BP6FJDHsAP4QuCQiuvFnuqCavinPzMwqYyS0LMzMrMycLMzMrCAnCzMzK8jJwszMCnKyMDOzgpwszMysICcLGzbpvQTnDeJ9n5F0azli6md/J0j6uqRdkh6u1H5rjaSlkv5muOOw8qj1KcqthkXE OGOoUjvA2YC04GJwxyL2bBwsjAr7BxgRbo4zvbhDsZsOPgylFlhY4Y7ALPh5mRhw0bSAUmt6eOlkj4n6RdpX8Y9ObOtjpX0w7T8EZI1k3O3c6WkTZJekPShtOwLkr6VU cySask9bmgjaS5kn4paZ kJyWdk5Z/k2RFtc9K2j3AsYSkqyRtlNQp6U9zXjst3fZLku7Nmd30qL6XdC6uz6SP/1jSo5I IGmDpLMljZL0v9K k87c/oF0W1 T9KPe8yRpLP1Iz/dtkh6U9KKk5ekKfRTYz3hJd6TH8ryk9/e3DxtZnCysmlwFLCRZe/pi4Ny0/AbgFOD1wLuBU3vfIOlM4Mskk7 9Hnh/WvYp4NQ0kZwM/DPw7og4kL9TSQ0kM49 i6RP4gbgB5KaIuIa4DvA30dEU4H4zwXagJuBz6XbFnAvcD8whWRt588WeT7eAPwp8KaIeAL4O5LJ7tqAPwAul/TenPrvBf4mfX02yQSEA2lL3/P7JEu49iaFgfbzdZIlYE8C3gLcOJhBClZ7nCysmvx7RGyNiHaSL9UZafl7gBsiYkNE7AZW5Lzng8BXIuL/RsR24CvARRHRQ5J4PgXcB/xlRDzbz34vAzZHxNKIOBwRDwGPAO/PGP SdAbTH bEfg4wAfjniDgEfBEo9st1AnBVRDyfJp2PAR NiK6I2AL8NfCXOfWXpeehE3g0J4b /EdE7IqIjcDfkiSFfvcjqRn478DHI2JfRKwD/hH4n0Uej9UwJwurJi/kPA5e/XyeDLT3855ZJH/d7k4vE30dmAyQJo87SL50fzTAfmcDz WVPQf8XqboX40/N/ZZwGlAVxrfNo5dla0/m3MWQmomOY6NA8TY3/krxjqSKdAH2s9pwPa81tlgzpPVICcLqyb9zZf/AsnQ1V7KebwB GBENKU/kyLirwEknU5yKeYBkksr/XkOeE1e2Rzg SzB03f8G4Bf5MTXFBH9/cWvvOd7cx7vILn80zZAjENZb B1JAlioP08D0yXNG6AGGyEcrKwWvB94B8kTVWy/OilOa99jaRl8VpJdZLOSjuHxwJ3A39OconlLZIu6mf7PwZOlnStpHpJlwIXAP861MAjYiXQmHZUj5LUIul96cuHSJf1lDQa G8DbCfSY/3fkk5Mz8PngVuGEN7FkiZImgd8AVg60H7Sy1v/DnwlHXQwj6R/ZygxWI1wsrBa8I/AQeB3JH0SR9ZGjoi1JMngxySryX2RZLWzrwH3R8RvIuIwsAj4hqRT87ZNROwHLgSuILlMdCPwtvTLsRQWptveTdKX0Hsz4o BBZLuIfnC3VxgO38LPAOsSbdzV0QsGUJcHSSrxj0GPEhybgvt52qSFsxzJInjUxHx0yHEYDXCK WZHYckLQX X0R8YbhjsdrgloWZmRXkZGFmZgX5MpSZmRXkloWZmRXkZGFmZgU5WZiZWUFOFmZmVpCThZmZFeRkYWZmBf1/tDj1s  LRQkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAACnCAYAAADg XByAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGBhJREFUeJzt3X UXGWd5/H3J53udEI6xKSTJpCEkB4ToqPgEHEcluMwoAiC63FnmIwI6uGHOPhrXccZcJxxjjMijsyyHnUlI5pFBJVBB0QWdI60QliFRCDRzSYzTRqSEJJ00gkd0ulKmu/ cW9DpdLdVbe7qrqq83md0ydVTz117/fertS3n e593kUEZiZmY1k0ngHYGZmtc/JwszMinKyMDOzopwszMysKCcLMzMrysnCzMyKqmqykNQq6X5J yQ9IGnOMPXeLGltWu8nkuZXM04zMztStVsWtwDrgTnAQ8DthRUkzQJWAdcCM4GHgW9UL0QzMyukat2UJ2ku0Am0RkR/WrYFeEtEPF1Q9/SIeDJ9/BrgkYiYVZVAzczsKNVsWSwHNg4mitRTwBmFFQcTRep84DcVjs3MzEYwuYr7agV6Csp6gBOHqizpXOAdwKXAhZUNzczMRlLNZCFgoKBsAGgepv4ikrGNbcPVkXQ1cDVAc3PzGUuWLGFgYIBDhw6Rlh3xfMqUKUQEuVzu5ecA/f1JY6epqQlJLz9vbGykoaGBgwcPDvl88uTJNDY2cvDgQSKChoYGGhsb6e/vP J5LpfjpZdeYtKkSTQ1NXHo0CEGBgaQxJQpU4543tzczKFDhzh8 PCQx Bj8jH5mHxM5TymTZs2dUfEkBcb5atmsugHGgvKpgxXOSJuBW6VdBZwn6TFEdFTUGclsBJg fLlsWbNmjKHbGY2sUl6ppR61Ryz2AWcUFA2F9gz0psiYjVJC ScCsVlZmZFVDNZPAG0S5oBIKkBOB14LL SpEWSGvOeNwLTOHq8w8zMqqRqySIi9gA/Bm6QNBn4FLAJWFdQdSFwr6RT0nqfAbYAq6sVq5mZHamaYxYAVwJ3AHuBx4FLIiIkXQ8sjIhrIuIXklpJEsuJwBrgwojIVTlWMzNLVTVZpAPUFwxR/vmC5z8AflCtuMzMCj3wzj8a9rW33/uzKkZSGzyRoJmZFeVkYWZmRTlZmJlZUSUni3Rq8UskDXsjnZmZTUxZWhbfIpmn6VlJK9M7q83M7BhQcrKIiLsi4j8DS0nWmPiopEckfUbSogrFZ2ZmNWA0Yxb7gJ1AH8niRG3AjZLuk/SGcgZnZma1oeT7LCT9DnAVcBnwHyQT F0TEQfT1 cD/wacWoE4zcxsHGW5Ke//AN8Gzo2IDUO8vp1k g4zM5tgsiSLdqA38tZhlXQi8EJE7I IAeCd5Q7QzMzGX5Yxi1tIrobK907gxvKFY2ZmtShLsjgX H5B2Z3Au8oXjpmZ1aIsyWIyQ69sN61MsZiZWY3KkizuBL4jqR1A0mKSAe87KhGYmZnVjizJ4mPAU8BaSYeBJ4HfAB vRGBmZlY7Sr4aKiIOk6xa9xlJrRHRXbmwzMyslmRa/EjSCcB8YFLaDQVARDw2/LvMzKzeZbmD 0skXVHbgMh7KYDFQ77JzMwmhCwti6uAs9yKMDM79mQZ4O4EflupQMzMrHZlaVncDnRIuh14nryuqIgovFmvZnjRdTOzscuSLC4G9nP0HdvB0Xd2m5nZBJLl0tlzKhmImVm9GannAiZW70XWS2cvBt4OzIyISyW9A9gfET vSHRmZlYTSh7glvRZ4ArgO8AFaXEncEOGbbRKul/SPkkPSJozTL3zJG2QtF/SXZKOL3UfZmZWflmuhroWuDQiHgWUlm0EXpthG7cA64E5wEMkg ZHkNQKfImkBXMSSevnKxn2YWZmZZalG6oHOA14lFeuhHoT0FXKmyXNBd4GvCciciTrdn9Y0uKIeDqv6gXAVyPimfR9VwPPSfpwROzLEK Z2TGlkmMoWZLFtcA9ku4Gpki6DvhA lOK5cDGiOjPK3sKOAPITxZ3kzfteUTskrQbOBlYlyFeMzMrk5K7oSLip8DrSb7YvwXkgD IiNUlbqKVpHWSrwc4sWA/B/InKZQ0E5hFMs2ImZmNg0xXQ0XEduCLo9yXgIGCsgGgucj7Pgp8OyJ2H7XBpIvqaoC2tjZ27NjB7t272blzJwBLlixh6/4D7DmYNGbmHTeVQy8F3X0HATi1qwuArvTfBQsW0NTURGdnZ1J/3jxaWlrYtGkTAHPnzmX27Nls2LABgNmzZ9PW1sbGjRsZGBhg5syZzJs3j87OTnK5HC0tLcyfP5 uri76 vqYOnUqixYtYuvWrfT29tLU1ER7ezvbt29n7969NDQ0sHTp0pePA2DZsmVHHVNvby/bt28HoL29nVwux5YtWwBYtGiRj8nH5GMqwzGt6 6hbVry9bTjQPKd0Tq1mcZJoqOjA4Ct w8wdXID2/YfAOD4KU3MaGpkS  LAJy0fn1Vj2lddw8nTZ9G3 GBIb/3mjs6jvo9lUoRUbwWIOkhjpxA8GURMXJHWfL FcBVEXFuXtmdwJMRMeQ63pLOBm4CzomIF0fa/vLly2PNmjVHlfsObjMbjVK O2rtPovRxCNpbUQsL7btLC2LrxfuAziPYRLIEHYBJxSUzQX2DFVZ0mnAPwIXFUsUZmZWWVnu4P7eEMXflVSYRIbzBNAuaUZEvCCpATgd ERhRUnzgVuBP46InaXGaGZmlZHlPovhnFxKpYjYA/wYuEHSZOBTwCYKrnCSNJ3k/osrIqKrDPGZmdkYZVn8aANHdzlNI5mBtlRXAncAe4HHgUsiIiRdDyyMiGuAdwNnAasl5b/3nIh4PMO zMysTLKMWVwzRNkh4NelbiAienhlqpD88s/nPb4NuC1DXGZmVmFZxiw8WaCZ2TEqSzfUp0qpFxGjvQ/DzMrEl4xbuWXphjqN5FLZe4B ksHxC0mm7Bi8M7vUy2jNzKyOZEkWjcCb8q9QktQO/G1ElDo/lJmZ1aEsl87 EXCgoGw3QwxYm5nZxJIlWXQA/yLpVABJS0jW3n56pDeZmVn9y5IsPgD8O/CEpAGSm l2k9wXYWZmE1iWS2d7gSskXUUy3Xh3RLxUscjMzKxmZFmDe7qkm4BfAY9HxEuSLpf0ocqFZ2ZmtSBLN9StJN1OZwEz0rIHgY XOygzM6stWS6dPZ9k/ewBSYP3U3RTsNKdmZlNPFmSxVPAR4Cb88ouAx4ua0RmVhW y9uyyJIs3g98X9IHgeMkPUiyJOqKSgRmZma1I8vVUJuBN0o6E1gIbI6ItRWLzMzMakaWiQR/CZwdEY8Bj1UuJDMbibuPbDxkuRrqt4AvkzUzOwZlGbNYD3xW0ttIFjzaP/iCpyU3M5vYsk5Rfk/6eEFeuaclN7MRueus/g2bLCT9b C9EbEbwNOQm5kdu0ZqWZwJ7Bt8ImlPRMyqfEhmZqPnVkxljJQsvgfcLWl1 nzacEureszCzKrBiWD8jJQsrgXeR9LCmEpy5dSyIep5zMLMbIIbNllERACr0h8kHY6Iq6oTlpmZ1ZIsd3A7UZhVmLtZrFZluSnPzMyOUVVNFpJaJd0vaZ kByTNKVL/zyT9frXiMzOzoVW7ZXELyZ3gc4CHgNuHqiRpsqR3AyurGJuZmQ0jyx3cYyJpLvA2kgWUcsCNkj4saXFEPF1Q/VvA0mrFZmZmI8uULCSdAMynoEWSzkRbzHJgY0T055U9BZwBHJEsIuKydH9dWeIbLQ8qmpmNLMsU5V8CPgZs48h7KwJYXMImWoGegrIevCzrUZy87Fjkz31ty9KyuAo4q8RWxFAEDBSUDZCstje6DUpXA1cDtLW1sWPHDnbv3s3OnTsBWLJkCVv3H2DPwaQxM  4qRx6KejuOwjAqV1dAKzrTnJY69RmGieJ7S/2ATCreQrPPfccmzZtAmDu3LnMnj2bDRs2ADB79mza2trYuHEjAwMDzJw5k3nz5tHZ2Ukul6OlpYX58 fT1dVFX18fU6dOZdGiRWzdupXe3l6amppob29n /bt7N27l4aGBpYuXcrmF/bTmzsEwIKW43ghd4h9/TkAXv/cc/T29rJ9 3YA2tvbyeVybNmyBYBFixYB0JUe24IFC2hqaqKzszM5B/Pm0dLSUvVjGvzdACxbtuyo35OPKTmmwc/irOYpTJ3cwLb9BwA4fkoTO3bsYMOGDazr7qGlqZFXTWli6/4DvBTBcY2TmdU8hUcffZRcLse/7 2ldeoUdhzoIzfwEk0Nk2ibNpXuvn6aOzpoamqipz/HnoP9vHjoMJMk5k fRk9/jt7cIZo7Osr6e1rX3TPkMc1oaqSjowOAzS/sH/KYtr/YR3NHBy0tLfT054Y8pr7Dh5nx6KO0t7fTua93yGMCeMMQ3xHDHdO67h7apiVfTzsOJN8Zg98RgzFv3X9gyGPa0vsiACetX1/Vz9667h5Omj6NvsMDQ37vNXd0HPV7KpWSe 9KqCj9mmTxoxcz7eGV968AroqIc/PK7gSejIgbh3lPF7AiIn5ZbPvLly PNWvWHFVeyl8rtfYXTa3FY9VTrs9rrX3u6/G4xrqvrPsrh9HEI2ltRCwvtu0sLYvbgQ5JtwPPk9cVFRHfL H9u4ATCsrmAnsyxGBmZuMgS7K4mGTBo3cVlAdQSrJ4AmiXNCMiXpDUAJwOfCJDDGZmY bWe3ZZpvs4Zyw7iog9kn4M3CDpY8BfAJuAdWPZrtlQaq17YCLzF  xoWr3WaSuBO4A9gKPA5dEREi6HlgYEdcASPo68F5gGvAzSQciorXKsZqZ1Yzx/gMoy6Wzp5LcUX164fsiYlop24iIHuCCIco/X/D8GuCaUmMzG43x/s9nVk yTPdxG3AX8HtAH/Ba4JOUNl5hZmZ1LEs31KuBr6TdRs QLHnxtXT8wWzCcp 8WbaWxTqSLiiAnwL/VdJFwLyyR2VmZjUlS8viEyQDzgBfAH4BvAf4dLmDsvrjv77NJrYsl86uzXvcA7yuIhGZmVnNKbkbStJ0STdJejwds0DS5ZI VLnwzMysFmQZs7gV2A2cBcxIyx4EPl7uoMzMrLZkGbM4n2ThogFJg/NCdeMpxs3MJrwsyeIp4CPAzXlllwEPlzUiK4kHlM2smrIki/cBd0n6IHCcpAdJ1qJYUZHIrGY4MZlZlquhuoA3SnoTsADYnH FlJmZTVyZJxKMiF8Bv6pALGZmVqOyTCS4ArgBaMsvJpn2o6SJBM1dOuXgCQDNqi9Ly Jm4JvAKqC/ItFY1Tl5mVXWRPnjJkuyOAj8TUQcrlQwVl5OBGb1oR4SSpab8j4F3Czp9ZLaJM0d/KlUcGZmVhuytCzagCuAPy8oD6ChbBHVMf8lb2YTVZZk8Tng0oj4QaWCqWVOBHYs8ufeBmVJFjuA yoViFk9K VL1V 8Vs yJIubgH RdCvwPEn3EwAR8Vi5AzMzs9qRJVlcl/57c0F5AIvLE46ZmdWiLNN9nFLJQGziq4fLA81saFkunTUzs2NU5rmhzCrJrQ z2lTVloWkVkn3S9on6QFJc8ZSz8zMqqPa3VC3AOuBOcBDwO1jrGdmZlVQtWSRTgvyNpL5pXIRcSPwGkmLR1PPzMyqp5oti XAxojIn7H2KeCMUdYzM7MqqWayaAV6Csp6gBNHWc/MzKqkmldDCRgoKBsgWcd7NPWQdDVwdfq0X9JvskUk1xldnVagu6z7Kue2Jk6dV85zbcQzkescy5/pk4u/sbrJoh9oLCibMoZ6RMRKYCWApDURsXysQVpxPtfV4fNcPT7XxVWzG2oXcEJB2VxgzyjrmZlZlVQzWTwBtEuaASCpATgdKJyEsNR6ZmZWJVVLFhGxB/gxcIOkySQr720C1o2m3hBWlj1oG47PdXX4PFePz3URiojitcq1M lVwB3A2cDjwOURsUXS9cDCiLhmpHpVC9TMzI5Q1WRhZmb1qe5nnfU8UpUn6c8k/X7ec5/zMpN0nqQNkvZLukvS8Wl5u6SH03P9XUlTxzvWeibpzZLWpufzJ5Lmp X TBdR98kCzyNVMZImS3o3R/fn pyXkaRW4EvA24GTSC5p/4okAd8FvkdyRWA3Ry8 ZiWSNAtYBVwLzAQeBr6RvuzPdBF13Q2VziPVCbQOTg8iaQvwloh4elyDmwAkfRtYCiwD3hoRv/Q5Lz9JlwHNEfHP6fM5wHPAecA3IuLVaflxafkJEdE3XvHWM0mnR8ST6ePXAI8Ap LPdFH13rLwPFIVFBGXRcSZwO68Yp/z8rsb OHgk4jYRXLOTyM5t4PlLwJbgN tdoATxWCiSJ0P/AZ/pktS74sfeR6p6vM5L7OIOAAcGHwuaSYwC3gVPtdlJ lc4B3ApcCFwGvxeS6q3lsWJc8jZWXjc155HwW nT72uS6/RSRjE9tIzqU/0yWo92RR8jxSVjY 5xUk6WzgIpKE4XNdARFxa0RcBnwEuA/I4fNcVL0nC88jVX0 5xUi6TTgH4GL0vEJn sKiojVJC2IHD7PRdV7svA8UtXnc14B6fX twIrImJnWrwW L30ElokzQYWAmvGJ8r6JmmRpMa8543AtPSpP9NF1HWyGMM8UjZKPuflJ2k6yXX9V0RE12B5euXONuAv0i 2vwfujojCwVgrzULgXkmnpJ/dz5BcXfYj/Jkuqq6TRepKYDGwl2Tt7kuinm8eqSGSvi5pP8l/sp9JGlyIx e8vN4NnAWsTu/gHvx5I/DHwLtIukTagA NY5x1LSJ AfwzSWLoBv4AuDAicvgzXVRd35RnZmbVMRFaFmZmVmFOFmZmVpSThZmZFeVkYWZmRTlZmJlZUU4WZmZWlJOFjZv0XoKzR/G z0r6eiViGmZ/kyR9TdIeSQ9Wa7/1RtIqSX813nFYZdT7FOVWxyJi njHUKL3AQuAecCMcY7FbFw4WZgVdyawOl0cZ9d4B2M2HtwNZVbc1PEOwGy8OVnYuJF0UNKi9PEqSZ T9PN0LOPOvNlWp0n6YVr U5I1k/O3c6mkrZKel/ShtOwLkr6ZV diSWslDbmgjaSlkn4h6YCkJySdmZZ/g2RFtb TtHeEYwlJl0vaImmHpD/Ne 2UdNsvSLorb3bTI8Ze0rm4Pps /kNJj0j6gKTNkt4oabKk/56OnezIHx9It/VVSfcOnidJ0xhGer5vkXS/pH2SHk5X6KPIfqZLui09lmclvX 4fdjE4mRhteRyYAXJ2tMXAG9Oy68DTgJeB/wJcPLgGySdDtxEMvnb64D3p2WfBk5OE8mJwD8BfxIRBwt3KqmJZObRb5KMSVwH/EDSzIi4EvgO8LcRMbNI/G8G2oEbgc l2xZwF3APMJtkbee/K/F8vB74U AtEfE48Dckk921A28A3iXpvXn13wv8Vfr6YpIJCEfSnr7nd0mWcB1MCiPt52skS8CeALwVuH40FylY/XGysFryrxGxPSI6Sb5U56fl7wGui4jNEbEXWJ33ng8CX46I/xsRu4AvA dHxABJ4vk0cDfwyYh4epj9Xgxsi4hVEXE4Ih4Afgq8P2P8K9MZTH YF/uZQAvwTxFxCPgiUOqXawtweUQ8myadjwEfjYieiHgO Evgk3n1O9LzsAN4JC G4fxbROyJiC3AX5MkhWH3I6kV C/AxyPiQERsBP4B G8lHo/VMScLqyXP5z0OXvl8ngh0DvOehSR/3e5Nu4m BswCSJPHbSRfuveOsN/FwDMFZc8Av5Mp lfiz499IXAK0JPGt5OjV2Ubzra8hZBaSY5jywgxDnf SrGRZAr0kfZzCrCroHU2mvNkdcjJwmrJcPPlP09y6eog5T3eDHwwImamP8dHxF8CSDqVpCvmPpKuleE8A7y6oGwJ8GyW4Bk6/s3Az/PimxkRw/3Fr4Ln /Med5N0/7SPEONY1ht4LUmCGGk/zwLzJB03Qgw2QTlZWD34PvD3kuYoWX70orzXvkrSsniNpAZJZ6SDw9OAO4A/J lieauk84fZ/o AEyVdJalR0kXAucD/GmvgEbEGaE4HqidLapP0vvTlQ6TLekqaAvynEbYT6bH D0mvSs/D54GbxxDeBZJaJC0DvgCsGmk/affWvwJfTi86WEYyvjOWGKxOOFlYPfgHoB/4D5IxiZfXRo6IDSTJ4Eckq8l9kWS1s68C90TEryPiMHAZ8D8lnVywbSKiDzgPuISkm h64O3pl2M5rEi3vZdkLGHwZsQfAWdJupPkC3dbke38NbAe G26ndsjYuUY4uoiWTXuUeB knNbbD9XkLRgniFJHJ OiJ MIQarE14pz wYJGkV8P8i4gvjHYvVB7cszMysKCcLMzMryt1QZmZWlFsWZmZWlJOFmZkV5WRhZmZFOVmYmVlRThZmZlaUk4WZmRX1/wG PfR/gCNkaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "binSize = 10\n",
    "ex = exp1\n",
    "numclusters = int(ex.p.reservoirExSize / np.square(binSize))\n",
    "\n",
    "# Get binned spikes\n",
    "slmb = getClusteredFrequencies(np.mean(sl1, axis=1), binSize, ex)\n",
    "snmb = getClusteredFrequencies(np.mean(sn1, axis=1), binSize, ex)\n",
    "\n",
    "# Plot Loihi\n",
    "plt.figure(figsize=(6,2))\n",
    "plt.bar(1 np.arange(numclusters), slmb, color=twoCols[0])\n",
    "#plt.title('Loihi')\n",
    "plt.xlabel('index of neuron pool')\n",
    "plt.ylabel('mean frequency')\n",
    "#plt.tick_params(bottom=False, labelbottom=False, left=False, labelleft=False)\n",
    "plt.xlim((0,numclusters 1))\n",
    "plt.ylim((0,0.3))\n",
    "plt.grid(axis='x')\n",
    "plt.savefig(exp1.net.plot.plotDir   'frequency_loihi.svg')\n",
    "p = plt.show()\n",
    "\n",
    "# Plot Nest\n",
    "plt.figure(figsize=(6,2))\n",
    "plt.bar(1 np.arange(numclusters), snmb, color=twoCols[1])\n",
    "#plt.title('Nest')\n",
    "plt.xlabel('index of neuron pool')\n",
    "plt.ylabel('mean frequency')\n",
    "#plt.tick_params(bottom=False, labelbottom=False, left=False, labelleft=False)\n",
    "plt.xlim((0,numclusters 1))\n",
    "plt.ylim((0,0.3))\n",
    "plt.grid(axis='x')\n",
    "plt.savefig(exp1.net.plot.plotDir   'frequency_nest.svg')\n",
    "p = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/seq/05-reward/v0.x/datalog/2020-04-09_11-23-28/plots/'"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp1.net.plot.plotDir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ks_2sampResult(statistic=0.16666666666666669, pvalue=0.6584781905494747)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.ks_2samp(slmb/np.sum(slmb), snmb/np.sum(snmb)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topl = np.array([binl[:,i:i 60] for i in range(0,3600,60)])\n",
    "topl = np.moveaxis(topl,1,0)\n",
    "\n",
    "topn = np.array([binn[:,i:i 60] for i in range(0,3600,60)])\n",
    "topn = np.moveaxis(topn,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#topl = np.array([sl[i:i 60,:] for i in range(0,3600,60)])\n",
    "#topl = np.moveaxis(topl,1,0)\n",
    "\n",
    "#topn = np.array([sn[i:i 60,:] for i in range(0,3600,60)])\n",
    "#topn = np.moveaxis(topn,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topl[topl>0.2] = 1\n",
    "topl[topl<=0.2] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "cmap = colors.ListedColormap(['white', 'darkred'])\n",
    "p = plt.imshow(topl[:,:,0], cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topn[topn>0.2] = 1\n",
    "topn[topn<=0.2] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "cmap = colors.ListedColormap(['white', 'darkred'])\n",
    "p = plt.imshow(topn[:,:,0], cmap=cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "\n",
    "#cmap = colors.ListedColormap(['white', 'darkred'])\n",
    "\n",
    "frames = []\n",
    "for i in range(topl.shape[0]):\n",
    "    frames.append([plt.imshow(topl[i], animated=True)])\n",
    "\n",
    "ani = animation.ArtistAnimation(fig, frames, interval=100, blit=True, repeat_delay=1000)\n",
    "ani.save('loihi.mp4')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "fig = plt.figure(figsize=(4,4))\n",
    "\n",
    "#cmap = colors.ListedColormap(['white', 'darkred'])\n",
    "\n",
    "frames = []\n",
    "for i in range(topn.shape[0]):\n",
    "    frames.append([plt.imshow(topn[i], animated=True)])\n",
    "\n",
    "ani = animation.ArtistAnimation(fig, frames, interval=100, blit=True, repeat_delay=1000)\n",
    "ani.save('nest.mp4')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}