{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load imports.py\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brian2 as br"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A 1d analog of polar interneurons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analogue of a spherical axon in 1dim is a connectivity within a certain radius $r$ of the interneuron somata. Correspondingly, the analogue of an ellipsoid, that is of an orientation, is a connectivity that has an asymmetry with respect to the somata, that is one branch has length $(1-\\lambda)r$ and the other one $(1+\\lambda)r$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The advantage of going to 1d is that from the Rosenbaum 2014 paper (Balanced networks of spiking neurons with spatially dependent recurrent connections), we know that spatial patterns should delevelop in such a network and can then study how this patterns change, when the interneuron axonal morphology changes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another advantage is that in 1d, there are clear and simple difference between the two morphologies in terms of connectivity. Namely, the profile of the number of common neighbors between two interneurons versus distance changes when making the axons more oriented. Circular (non-oriented) axons have the highest number of common neighbors and this drops up to zero at $2r$. Oriented axons have a lower number of common neighbors at small distances, but can then share neighbors even distances exceeding $2r$. Moreover, the orientation might induce a directional bias, although the length of this bias will decay very fast when orientations are random.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Steps**\n",
    "- connectivity matrix\n",
    "- activity\n",
    "- spatial analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connectivity matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neurons will be placed on a regular grid on a line from 0 to 1. Possibility of perturbing these positions with some noise of spatial width $\\chi$. Then the width of the axonal branch $\\sigma$ has to be chosen and each interneuron gets an ellipsoid index $e\\in[-1,1]$. For the start, we will assume that $e$ is the same for each interneuron, only changes sign in a random fashion. This gives the inhibtory to excitatory connectivity. In the following, we assume **periodic boundary** conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def connect_interneurons_to_excitatory_neurons(in_positions, ex_positions, ellipsoid_indices, sigmas):\n",
    "    N_e = ex_positions.shape[0]\n",
    "    \n",
    "    sigmas_repeated = np.repeat(np.expand_dims(sigmas, axis=1),N_e, axis=1)\n",
    "    ellipsoid_indices_repeated = np.repeat(np.expand_dims(ellipsoid_indices, axis=1),N_e, axis=1)\n",
    "    distance_to_right_end = (1+ellipsoid_indices_repeated)*sigmas_repeated\n",
    "    distance_to_left_end = (1-ellipsoid_indices_repeated)*sigmas_repeated\n",
    "    \n",
    "    rel_position = np.repeat(np.expand_dims(in_positions, axis=1), N_e, axis=1) - np.repeat(np.expand_dims(ex_positions, axis=0), N_i, axis=0)\n",
    "    \n",
    "    connected_via_right_branch = np.logical_or(np.logical_and(rel_position > -distance_to_right_end,rel_position <= 0), np.logical_and(rel_position-1 > -distance_to_right_end, rel_position >= 0))\n",
    "    connected_via_left_branch = np.logical_or(np.logical_and(rel_position < distance_to_left_end,rel_position >= 0), np.logical_and(1+rel_position < distance_to_left_end, rel_position <= 0))\n",
    "    \n",
    "    return np.logical_or(connected_via_right_branch, connected_via_left_branch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_e = 1000\n",
    "N_i = 200\n",
    "chi_e = 0.0/N_e\n",
    "chi_i = 0.0/N_i\n",
    "sigma = 0.1\n",
    "e = 0.25 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex_positions = np.linspace(0,1, num=N_e, endpoint=False)+np.random.normal(loc=0, scale=chi_e, size=N_e)\n",
    "in_positions = np.linspace(0,1, num=N_i, endpoint=False)+np.random.normal(loc=0, scale=chi_i, size=N_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ellipsoid_indices = e*np.random.choice([-1,1],N_i) #random\n",
    "#ellipsoid_indices = e*np.ones((N_i,)) #one-sided\n",
    "\n",
    "ellipsoid_indices_circ = np.zeros((N_i,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigmas = sigma*np.ones((N_i,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_ex_connectivity_ell = connect_interneurons_to_excitatory_neurons(in_positions, ex_positions,ellipsoid_indices, sigmas)\n",
    "in_ex_connectivity_circ = connect_interneurons_to_excitatory_neurons(in_positions, ex_positions,ellipsoid_indices_circ, sigmas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'In')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(in_ex_connectivity_ell, aspect='auto')\n",
    "plt.title('Ellipsoid')\n",
    "plt.xlabel('Ex')\n",
    "plt.ylabel('In')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'In')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(in_ex_connectivity_circ,aspect='auto')\n",
    "plt.title('Circular')\n",
    "plt.xlabel('Ex')\n",
    "plt.ylabel('In')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from brian2.units import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dynamics_dict = {\n",
    "    \"excitatory\": {\n",
    "       \"model\": \"placeholder\",\n",
    "        \"threshold\": \"v>v_threshold\",\n",
    "        \"reset\": \"v=v_reset\",\n",
    "        \"refractory\": \"tau_refractory\"\n",
    "\n",
    "    },\n",
    "    \"ex-in\": {\n",
    "        \"model\": \"w: volt\",\n",
    "        \"on_pre\": \"v+=w\"\n",
    "    }\n",
    "}\n",
    "\n",
    "model_string = \"\"\"\n",
    "    dv/dt = 1.0/tau* (-v + u_ext+stimulus(t,i)) :volt (unless refractory)\n",
    "    tau :second\n",
    "    u_ext : volt\n",
    "    v_threshold: volt\n",
    "    v_reset: volt\n",
    "    tau_refractory: second\n",
    "\"\"\"\n",
    "\n",
    "dynamics_dict[\"excitatory\"][\"model\"] =model_string\n",
    "dynamics_dict.update({\"inhibitory\":dynamics_dict[\"excitatory\"]})\n",
    "\n",
    "dynamics_dict.update({\"in-ex\": dynamics_dict[\"ex-in\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_dict = {\n",
    "  \"duration\": 1000*ms,\n",
    "  \"excitatory\": {\n",
    "    \"tau\": 10*ms,\n",
    "    \"v_threshold\": -40*mV,\n",
    "    \"v_reset\": -75*mV,\n",
    "    \"tau_refractory\": 0.0*ms,\n",
    "    \"u_ext\": -0*mV\n",
    "  },\n",
    "  \"inhibitory\": {\n",
    "    \"tau\": 5*ms,\n",
    "    \"v_threshold\": -40*mV,\n",
    "    \"v_reset\": -75*mV,\n",
    "    \"tau_refractory\": 0.0*ms,\n",
    "    \"u_ext\": -35*mV\n",
    "      \n",
    "  },\n",
    "  \"ex-in\": {\n",
    "    \"w\": 0.4*mV\n",
    "  },\n",
    "  \"in-ex\": {\n",
    "    \"w\": -1*mV\n",
    "  },\n",
    "  \"initial_condition\":{\n",
    "    \"excitatory\": {\n",
    "       \"v\": '-75*mV+35*mV*rand()'   \n",
    "    },\n",
    "    \"inhibitory\": {\n",
    "       \"v\": '-75*mV+35*mV*rand()'   \n",
    "    }  \n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatio_temporal_input = {\n",
    "    \"excitatory\": br.TimedArray(np.zeros((1,N_i))*mV, 100*ms),\n",
    "    \"inhibitory\": br.TimedArray(np.zeros((1,N_i))*mV, 100*ms)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from interneuron_polarity.actions.simulate_network_activity import create_neuronal_populations_and_synapses, run_experiment\n",
    "import brian2 as br\n",
    "\n",
    "def run_1d_network(in_ex_connectivity, dynamics_dict, experiment_dict, spatio_temporal_input = None):\n",
    "    ex_neurons, in_neurons, ie_syn, ei_syn = create_neuronal_populations_and_synapses(dynamics_dict, in_ex_connectivity)\n",
    "    \n",
    "    ex_neurons.v = experiment_dict[\"initial_condition\"][\"excitatory\"][\"v\"]\n",
    "    in_neurons.v = experiment_dict[\"initial_condition\"][\"inhibitory\"][\"v\"]\n",
    "    \n",
    "    ex_spike_recorder = br.SpikeMonitor(source=ex_neurons)\n",
    "    in_spike_recorder = br.SpikeMonitor(source=in_neurons)\n",
    "    \n",
    "    if spatio_temporal_input is not None:\n",
    "        ex_neurons.namespace[\"stimulus\"] =  spatio_temporal_input[\"excitatory\"]\n",
    "        in_neurons.namespace[\"stimulus\"] =  spatio_temporal_input[\"inhibitory\"]\n",
    "        \n",
    "    \n",
    "    \n",
    "    run_experiment(experiment_dict, ex_neurons, in_neurons, ei_syn, ie_syn, [ex_spike_recorder, in_spike_recorder])\n",
    "    \n",
    "    excitatory_spikes = ex_spike_recorder.spike_trains()\n",
    "    inhibitory_spikes = in_spike_recorder.spike_trains()\n",
    "    \n",
    "    return excitatory_spikes, inhibitory_spikes\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO       No numerical integration method specified for group 'neurongroup_1', using method 'exact' (took 0.29s). [brian2.stateupdaters.base.method_choice]\n",
      "INFO       No numerical integration method specified for group 'neurongroup', using method 'exact' (took 0.03s). [brian2.stateupdaters.base.method_choice]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting simulation at t=0. s for a duration of 1. s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    neurongroup's variable 'v' has NaN, very large values, or encountered an error in numerical integration. This is usually a sign that an unstable or invalid integration method was chosen. [brian2.groups.group.invalid_values]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 (100%) simulated in 1s\n"
     ]
    }
   ],
   "source": [
    "circ_ex, circ_in = run_1d_network(in_ex_connectivity_circ, dynamics_dict, experiment_dict, spatio_temporal_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO       No numerical integration method specified for group 'neurongroup_2', using method 'exact' (took 0.05s). [brian2.stateupdaters.base.method_choice]\n",
      "INFO       No numerical integration method specified for group 'neurongroup_3', using method 'exact' (took 0.04s). [brian2.stateupdaters.base.method_choice]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting simulation at t=0. s for a duration of 1. s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    neurongroup_2's variable 'v' has NaN, very large values, or encountered an error in numerical integration. This is usually a sign that an unstable or invalid integration method was chosen. [brian2.groups.group.invalid_values]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 (100%) simulated in 1s\n"
     ]
    }
   ],
   "source": [
    "ell_ex, ell_in = run_1d_network(in_ex_connectivity_ell, dynamics_dict, experiment_dict, spatio_temporal_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Raster plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from interneuron_polarity.actions.plot_network_activity import plot_spiking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Early"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plot_spiking(N_e, N_i, circ_ex, circ_in, t_start=0, t_end=200)\n",
    "_=fig.suptitle('Circular')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oBIkZAABAJUjMAAAAKkFiBgAAUAkSMwAAgEqQmAEAAFSCxAwAAKASJGYAAACVIDEDAACoBIkZAABAJUjMAAAAKkFiBgAAUAkSMwAAgEqQmAEAAFSCxAwAAKASJGYAAACVIDEDAACoBIkZAABAJUjMAAAAKkFiBgAAUAkSMwAAgEqQmAEAAFSCxAwAAKASJGYAAACVIDEDAACoBIkZAABAJUjMAAAAKkFiBgAAUAkSMwAAgEqQmAEAAFSCxAwAAKASJGYAAACVIDEDAACoBIkZAABAJRZKzMzsZ83sg2Z2k5m9zsy+0szOMrN3mtmtZvYHZnZSs+7JzePDzfO7h2gAAADAupg7MTOz0yX9tKSLnHOPkXSCpGdJeoWkVzrnzpZ0t6TnN5s8X9LdzrlHS3plsx4AAAAai97KPFHSV5nZiZIeKOkOSU+U9EfN89dKekbz96XNYzXPP8nMbMH4AAAAa2PuxMw590lJvybp45olZEcl3SjpHufcfc1qRySd3vx9uqRPNNve16y/c974AAAA62aRW5kP1ewq2FmSHinpQZKeFlnVtZtknvPLvczMDpnZoaNHj85bPQAAgJWzyK3M75b0Uefcp51z/yzpjyV9m6RTmlubknSGpNubv49IOlOSmud3SLorLNQ5d6Vz7iLn3EU7duxYoHoAAACrZZHE7OOSHmdmD2zeK/YkSR+SdEDSDzfr7JX0pubv65rHap5/u3Nu2xUzAACATbXIe8zeqdmb+N8j6QNNWVdKeqmkl5jZYc3eQ3ZVs8lVknY2y18i6fIF6g0AALB2TuxeJc05tyVpK1j8EUmPjaz7T5KeuUg8AACAdcY3/wMAAFSCxAwAAKASJGYAAACVIDEDAACoBIkZAABAJUjMAAAAKkFiBgAAUAkSMwAAgEqQmAEAAFSCxAwAAKASJGYAAACVIDEDAACoBIkZAABAJUjMAAAAKkFiBgAAUAkSMwAAgEqQmAEAAFSCxAwAAKASJGYAAACVIDEDAACoBIkZAABAJUjMAAAAKkFiBgAAUAkSMwAAgEqQmAEAAFSCxAwAAKASJGYAAACVIDEDAACoBIkZAABAJUjMAAAAKkFiBgAAUAkSMwAAgEqQmAEAAFSCxAwAAKASJGYAAACVIDEDAACoxEKJmZmdYmZ/ZGYfNrObzezxZvYwM3ubmd3a/H5os66Z2W+Z2WEze7+ZXTBMEwAAANbDolfMflPS/3HOfYOk8yXdLOlySdc7586WdH3zWJKeJuns5ucySa9aMDYAAMBamTsxM7OHSPpOSVdJknPuS865eyRdKunaZrVrJT2j+ftSSa9xM++QdIqZnTZ3zQEAANbMIlfMHiXp05J+z8z+xsxebWYPknSqc+4OSWp+f02z/umSPuFtf6RZBgAAAC2WmJ0o6QJJr3LOfaukL+jYbcsYiyxz21Yyu8zMDpnZoaNHjy5QPQAAgNWySGJ2RNIR59w7m8d/pFmidmd7i7L5/Slv/TO97c+QdHtYqHPuSufcRc65i3bs2LFA9QAAAFbL3ImZc+7vJX3CzM5tFj1J0ockXSdpb7Nsr6Q3NX9fJ+lHm09nPk7S0faWJwAAAGa3IxfxU5Jea2YnSfqIpB/XLNl7g5k9X9LHJT2zWffNki6RdFjSF5t1AQAA0FgoMXPOvVfSRZGnnhRZ10l64SLxAAAA1hnf/A8AAFAJEjMAAIBKkJgBAABUgsQMAACgEiRmAAAAlSAxAwAAqASJGQAAQCVIzAAAACpBYgYAAFAJEjMAAIBKkJgBAABUgsQMAACgEiRmAAAAlSAxAwAAqASJGQAAQCVIzAAAACpBYgYAAFAJEjMAAIBKkJgBAABUgsQMAACgEiRmAAAAlSAxAwAAqASJGQAAQCVIzAAAACpBYgYAAFAJEjMAAIBKkJgBAABUgsQMAACgEiRmAAAAlSAxAwAAqASJGQAAQCVIzAAAACpBYgYAAFAJEjMAAIBKkJgBAABUgsQMAACgEgsnZmZ2gpn9jZn9efP4LDN7p5ndamZ/YGYnNctPbh4fbp7fvWhsAACAdTLEFbMXS7rZe/wKSa90zp0t6W5Jz2+WP1/S3c65R0t6ZbMeAAAAGgslZmZ2hqTvlfTq5rFJeqKkP2pWuVbSM5q/L20eq3n+Sc36AAAA0OJXzH5D0n+Q9C/N452S7nHO3dc8PiLp9Obv0yV9QpKa54826wMAAEALJGZm9n2SPuWcu9FfHFnVFTznl3uZmR0ys0NHjx6dt3oAAAArZ5ErZk+Q9HQzu03S6zW7hfkbkk4xsxObdc6QdHvz9xFJZ0pS8/wOSXeFhTrnrnTOXeScu2jHjh0LVA8AAGC1zJ2YOeeucM6d4ZzbLelZkt7unHuOpAOSfrhZba+kNzV/X9c8VvP8251z266YAQAAbKoxvsfspZJeYmaHNXsP2VXN8qsk7WyWv0TS5SPEBgAAWFkndq/SzTl3UNLB5u+PSHpsZJ1/kvTMIeIBAACsI775HwAAoBIkZgAAAJUgMQMAAKgEiRkAAEAlSMwAAAAqQWIGAABQCRIzAACASpCYAQAAVILEDAAAoBIkZgAAAJUgMQMAAKgEiRkAAEAlSMwAAAAqQWIGAABQCRIzAACASpCYAQAAVILEDAAAoBIkZgAAAJUgMQMAAKgEiRkAAEAlSMwAAAAqQWIGAABQCRIzAACASpCYAQAAVILEDAAAoBIkZgAAAJUgMQMAAKgEiRkAAEAlSMwAAAAqQWIGAABQCRIzAACASpCYAQAAVILEDAAAoBIkZgAAAJUgMQMAAKgEiRkAAEAlSMwAAAAqMXdiZmZnmtkBM7vZzD5oZi9ulj/MzN5mZrc2vx/aLDcz+y0zO2xm7zezC4ZqBAAAwDpY5IrZfZL+vXPuGyU9TtILzew8SZdLut45d7ak65vHkvQ0SWc3P5dJetUCsbfZt+/437nnU+uUxCjd1l+3ZJtwnXniLFJWbh3/ubFile6/0hi5eBdfnF5vnrHR1T+lfZsTq3OqHX3L7iqrdBwvO17XOrEYJeOryymnlK/bFWPesvrEGCNmV78vMp+V2L17se1T24RjZqgx365XMu7nfa5rPkiVVXqclZbfVXbY3/Oej1Plt5Z1bLX9Mk//JDnnBvmR9CZJ3yPpFkmnNctOk3RL8/f/lPRsb/3710v9nHPOOS5nz55jf0vH/3bOua2t+PP+OiXaOKXbbm0dv27JNuE6uW0OHDhwf9tidepTVsk6/nNjxerqK8kd12Z/37YOHDhQFC8XI1V2Tqx/cvunpC6x9cJ6dW3bPp/ql5Ky2rHcJ16J2Lp+n5WW16dv/baEfbJo3efdbt6y+sTw+X0QUzJWYnHCuXaeeSFcL3cc5o7hvnJzS9cx1Ldd884FQxyfqbJy23XNY13jpWvMz3M+7iq/5LlFYqXm4WO/dcgtmk8tWsCsHtot6eOSHiLpnuC5u5vffy7p273l10u6KFfuOeecU3xwxgZneMC1v0sP/ljZpSeLsROzXJ2GSJZik1Oq7K2tYz/znhj89vixYxNDrM1bW/0Ss1yMvsmZf8C225aOmT6Tad/9WpKYpfZbqt9L4sXKiZUbLt8+wc1++y/AwjL79G3blj17jvVJqj45/pgP9U0mcifceaT2gb+fh0rM/D7oOtnOkzTMc9yUjsGw7qnjdZ7EzC+7z4uN3LyTa2+f80zsWC/p59S42bv3o9l4fptic02q3NLxH5YfPjevXbvisWLlVpmYSXqwpBsl/WDzOJWY/UUkMbswUt5lkg5JOnTqqafeP5G2uhKG1E7vOvDCyd+PNXZi1vdg6ZOYlSRLsXipCTJ1sgvbnIvTlZil9quf9MTq0Scxy8Uo3c8l29eemKXaG6v/PIlZ6gSe2ie5BC0Wp7Rv/XHjj5U+J7Wwrrlx3FWfrvXnOans2ZOOUTq2w7ESmxfDMsP6do3VVJmxcnxdCXmu/eHj1DHa/u3fJXGuX2KWOu+k1o9tVxInFSundB4Jk6h5xnusfrH+nafcVPmpFwt9dZ2jUnfjZr8nTswkfYWkt0h6ibds0FuZ/oQddkrq5Brb6akBlupsf1npSda57SeAsJ7huqlyc9uUJGalt9LCeLm+Ta1XOumn6uNPqLn9k4sjzV69xV5ppSbmrhglr9pidY9NRn1fCbdlp+rcta3/fHtSiZ0Qc/0Zxu2qa+4k1XWCSvVdat3Y37lXzGH58yZmseM7FbPkufB48pfnEphU+WGMsL5dx2jpLd7cHNc1Vkv7qG8ZYdwwuXIuPT/HjuHUre/YW2hi5fvPl4yz1L7JvbiOxUrNNe0xWjKP5Pqo9Ip1rH65crvq1Kf8PmWkyvXl8oDt+3jCxEySSXqNpN8Ilv+qpMubvy+X9CvN398r6S+b7R4n6V1dMfzErOtEkTvIUs+3j2NXYboO4JTcCcYvO1en2Db+8lRiVnrbL1Vu7Hf7d+qKR59JP1WfXH1L4+Ti5+It0pZU3XOTRVfdUs/1GSPh82ESEqt/quzSiS7X96njMLVeybpdf8ee83/mTcy6xkfp/gyXdY3RPnXL1ber3HVKzHJ1S805qb7y+6WrjrkxvMgxlNum5L1mXcd6VztS/ZlTOj/GtiuRm2v7Hj+5+Lk6b9/Hiydmi3wq8wmSnivpiWb23ubnEkkvl/Q9ZnarZh8GeHmz/pslfUTSYUm/K+nf9Q2Y+/Tb1lZZGXv2bF+2f//2cv1lpfw6bW2l67R/f/cnUcJtU+v7cdo6l/aFL9YvvlR/lMQq+dTNPHUuLXtsft3DdnS1a952lxi67DHrOm+8oeq0jLatS4x5LKte88TJbbPM/pw31tZWv/PV0H3Ud7u+c+RGWjSzG/MnvGIW/uRuzTh3/KuI1D3t1CukWJxUdh9eqUrdUond/isp1y9DOv6KWSxOuH5X2eH6YVld7/uJ9V/uvYCxVyOpW69+PP8qZuo9EF39Ers6Go6X2CX/rluRYV26PqUWkxs3uXHVJbw6FKtbbjyUxOl6tZ8bP+3j3PsHc49L1gvLz11FzImN9VjM2IdLcnXOvULP6bqtFNa3z6cyS26hpcZU3w9BxJ7rujXdVW7pXOXHSo1D/8MiuXqkxnvsrTgxuX2YWz8W04+bK6fkuA1jhctKx3jXPOBvk/rgUKyesTqnyi55i0rueEyNgWN5whm3Ozfhe8zG/jnnnHOOO0nmJsXUib3rtmHsPRj+erGDOhQrOzZRhQd918kvdVDEEjN/sMzzps2SE2fq+fBTX6m6p+oT9kXspNZ1mzY1LlLxUyfO2InML2PPnnTCnJqkchNrrJ6lHzopKdO5/G271Mk3NQmlpE444X5JvTG3z/stS9+vlzqBtMfQjh3ln/7yy8mNta73aO7YEa9j7njuqk/sb39Z6n1PMeEtu64TZ8l7Ovs8X/pcnzktNxenygjb7h9D/vKTT96+fTgXh8+XtNGf97r2Q1hun7nX74vYe1pT56DcMr/scJ32045dY7FkHgjXC19Qd/VZTmyOzPXr8dte6Jxb88QsnAhTk2LqgOp6JZS6ShIeYH3LDk/quTZ0nRzD9sUSszB2rB6pssPy53m+FXvDbdjOrrrn+CeDdrt2UklNYF2TU1j/cB+VvvGzZJLJvQpM9XFXu7qSi/Z777r2bWpZ7KPjufXD5bExmFo/drIvGROx9XLJd+qqc678dtvcWPPXa7fNT+Lx5allpXFi5fsnsJQ+iVlK14cWSvotpusqZJ9xXTJ/+eu0XwuRO0GXlJ/q05L38KbKa3/n5t5U7Nj5qF2eGrtdy1LPxeb5Pv2UipHqq1Q/+8tLXuTF+id/8WCDErPwNlbugMidOMOTRGzHhtvF1u0qOzVgcvXwpQ6Kra3tJ5VYUlgy8NttY+3tmnC6koHYCSMc4LG+6nMFI3Ygpg7e0lsJYbldMfzluSsIsX7tmvjCsmPlhmXGtOMltX7XSa5k36TqUPKiI1ePXNnh86VjR+qXmMUm5K5xFI6X2EnTfzzPladYnFDuSmRMLjHrum1aKrftkOXG+jY8iebKys31XS8Gur5mJLY89Ts31lLnqdTfsfNO7IVGrtzUF7unXlClzolh+eG2qf3plxl7oZ4rO9XO3IvBsNyUY3E3KDFrG97eSsolDrkDKrZdbNvwwMtdzow6T2crAAAdbElEQVSVHSvXPzC6Bl0qlnPbTyqxsksGfvg7d1UlNRGlxPo0NamVnjRiMfxtzz//7uRJtKs/UuXGDvZwTLTLU7cwUvG7JsPwd2zyyN02aXUlZqGSV8p9rmql9kluvdxXEuS2C6USHv8YKn2RURIzNbZT+7K037rW6TqGSo+xXGIW/t33NnCsHOe6X6CG65SWWzLeY+W2J/uSub7vPJnaD7ljv6v+uauysfNXrl1hO3JX9FPbp/qgZJ3Yurl2+fXtOofFyk2tG5unc/Pf8WVtWGIWXjWLd0r3AdUO5tyVptz2qee6rsTFBmZYVlcs5/KJWfuTSmBjdY/F7DvhOHcsZurWX+mkEMZMCfdprF9idcgJJ7BYv/gTQEn/+WMjtt9yVy3DCSc2xrraVpqYxW4Rpya63EQWKy9cL3UMd51oYnITZuw5aftXzqSudqbip2KWHlf+Pk3JPdf3xU24T1P6JGZd+6WtZxgzN3Zy829bXoq/TtcJOhUvFbNrf5bG7LqCnBo/qbJiL+JT4zGse7s8VkZXvNz4S+0zf52uhDtct2tOCOPn+jk1H4XrObf9rS2p+MfH3bDELNaxsQ6OdWDuhJs7CPznwi/RjB24sXqGsWKX1v338XQNhFRilmpjKLz8G4uZ6oe2nrEJPtbGWN389be2tr+hPoyZOojDdqYSM3+/lbz/JfWBk1jMtrzUlcrcSSnsr/B2V2q/tnIxfX5iVpKApGKmxn74XGqdWNvC9XNj3++jWJxYPVKTcOy7AGP1TX1xbni8xuraPo6dsLtihjFCqfJSUmPIf9654ROzrv0ZKzfcvm+SlRu3pYl7rLx2nV27uvfnEDHD2LFtUvs0Vm5b79QclBsXsfX98lNjPNWW0rEaixnGjZWdak87nsJycuMudqymyp79bMCnMlMDyRcmGqkdGHZybJClJvbYDkz9nRtYuckubGM4gPzJM9UnuVi5eqdO8ql6hgM1jB2ul3vOLyMW0//tJ2lhuanELFZWLqb/07UvU31S0o+pvglfCedOqiUx9+79aHY8hHVLxcz1Z9c4i20XG+O5YyS2Tapeuef8sVLSzljMvvHn6duSmGHf+MIXgOG49tdpt100MeuKmTuBh3/7cXLjLJYEh8dOrvzw75K+ze3PoWK2bQvHQyxmybiLtcW/ApY6XlNzbqqdsfqGbemah1JzXy6RzZ2Hwjqm6hxrZ+yDgamyZz8V/K/MMX9KE7Pc87kDKHbQlw7w3MEXDtpYvK4rDbFJ1p88YwMxFcsvI1bXsN5hO1OPUwd9bH+kDuTUgRGWW9LOWL+E7Uy1PXZQhxNhLK6/fapPfKlxkuu3IWKmJqvc1azY8eHHzcUM1w/bnLqCmuqLXHIaa0PXScG/itjVztRJKxYztn/8debp21zMWF3DdbrihnNLrP6xWKn9m4sZmwtSx2RYRliHWF/kjpnYfszNEf4V7PDfvY0Vs/RYK5kXUuWm6pPq25K4uXGba69f5zBm6nzdrpMbQ6l5IvbCIbd9atzGjs1j621IYpYaKGHnxh73GdTS9lslsUGbOqnMGzN1udgXS8xSB0XqgI3FzLWh9ACPxQ7bEpaXKqNPzPAn9l6qVJKXi9m1b/u0MzVJlcTMjZu+MVMTSmp8pMaq/3xXEpHq89j+jMXN9UVK6dgpScxS4zYXs2t8jxEzFi/1XCyuHzP3L5lS47VPzPAnNn+H8VP9lKtXGMN/PpdIpn53vX91jJj+9iUxwz5LzaWpK86lx2tuHPVNRFPrdB0vsTsK4fa5GLE4se1jYzVW9vHbbUBiFiZKYdbbtQNKB3W4o/14/u+wjL4xYwMqFjOXrIUJSBgrdxUmjBlra6yNfScMvxz/UrD/vpncq8GSmOHPgQMH7o+Vameqz7raEOuXXDu7vh6hJGbuFeOiMVNtcy59myR31TA1UYXPxRJSf/1UO1PPdcVMXeH1v9stN9b7xsxdbfDbP2TMWLzUc7G4/k/7fV2t3FcjpOqQixkb17n3feb6KVavruMlHM+pcsLffROzIWK2v2MvLktjttt33aFp6xaukxsrqedi5+hWqh59Ysba6ccseREaixMr1y8/fM5vz/HPbUBilpucws71l7WdFptMUhNEbOfnYsYOsK6Y4fZ9YzrXfcWsz+2x0jamJodFY8YmotKY4Zv0w2/nzu2nXMxU38bK7mpnrr0lMVMn8kVjhs+VtjP1XGwsdU3CsQ/TLDpuczH9q92pseJvO3TMXDuHiNln3Kbi9YkZ/i6J2bed4TphHbruFqTqE/ZXWGb4OPx6lWXEHKKd4XPhC34/RqxOXWMl9lxY5tAxY+2MxUxdGQy3Lz1WSmOSmHkdFS4Ln4t1tv+VEqUTRVjWsmM6F/8m966Yqef6tLFvO0ti5pKvvjHb94GUTkypmH2u6oS3tsOYudsGU8fMPZeKGXvOLydm0Zi55HTemKnEbGsrPXYXjZlr5xAx/US3K2Ybt8+cEMZMfYo4FzN1/KbEEttUzFS81NUO/7k2ViyGc/FP8Y4dc4h2pj5V3CdmLu6pp/5jct2xYub6tm/M2NW1RWNuZGKWu8XnL2t/5yb30skpFTN1kh8iZhjXXyf2vw+Hjtl1OXjomLFbTvPETP0MEdNfd5Vipv6dzDwxY8+1cfyYsRhDxwzb2Sdm2CfLiDlFO7tiliS9q9LO3JwQxoyV2ScxW0bMKdoZixn7SqB1i+l/ZdOiMTcyMYs9DneAPzGUXAJO7YQpY4Zx/ZhtYrbMmFO0k5jDxPQT+UVjlpzIu44fYhJz6pglMfzH7V2KZcacop25mKm4xAy3W/PEbOfOb+rs3L5vHC39mTpmGNePGfvk1Ngxp2gnMYeJ2Y6XKdrpX0EhJjFriRmeiLtixr7fbeyYU7QzF7M07qbHXPvETLqw+HJkSYeXdHL799Qxwzj+34skZvPGHKqdsTKJOW7M8Nb3ojHDn2W0k5jEHDpmbp4PYw6RmPWNOUU7czH7xN3kmEMkZg9Q5fbtm3+9ra38Y2IuP2YpYhKTmMTsE3Pfvn4xS+Y/YpaXQcwBLZrZjfnT/DPQZBbsP05lxPNkwmPEzL1hM1dOLGZ4xWwZMYdqZ1f/EnP4mLEPi6xjO4m52TH99YaOWXKXYh3aSczFY2qAK2bmnBsp5Vuc2UXOuUP3P7744tnvgwf9dWZd0f7eXsbsd/tc+zjHL2eomO36pTEPHjz2OyzjwIGDurit2JJiDtVOf31iLifmvn2z8bKO7WxfsR48SMxNj9mWM0bMgwePn3OXEXOKdhJz8Zg33GA3Oucuym/ZYdHMbsyf8IpZLEtNZcLtvd+uL7H0M+PYd74MFTOM3RUzLN9fFv4/u2XEHKqdXeUTc/iYXVfMVr2dJfUg5vrHDNcbMmb4SfhlxJyincRcPKY24c3/fgeGneF3rL889rHieAce3+GpcoaIGV4W7YoZW94u8yeJZcUcqp3t38RcXszY16usUzvDMoi5mTH9x0PHTH2p95gxp2jnkDFj269SzHB5eV6xQYlZrLN9sU7q7sD0Dpk6Zrjcfz781vJlxByqnam/iTlezK5/TL3q7QzLIObmxUydZIeKGf4bvGXEnKKdQ8ZMJYOrEjMVuyvmEIlZ9Z/KLJX7lMquXd3rzfMJImISk5irGfPUU/9p6TE3pW+niHnw4LHniVlHzP37iTm3RTO7MX927vymbdltm9nm5DLbWFl+mbGMeoqY4XJf1xWzMWIO1c5wvaFjhvuLmPFP8a5TO/0yiLlYzNS/qlq3dvaNGbtito7tzMVs425KzFh5JTG17rcyzznnnG2dmOvw1EnC/zssK7XO1DHD5b7Y/z4cO+YU7Zwnpn8QE3P2O5aYrVM7/VsQxFwsZviib13b2TdmLjFbp3bmYobxpNm5aNkxl9VOP06fmBuXmIWd43ey31nturFO85flBvzUMcPlxCTmvDHDN/+HMdt/1bTMdhKzzpix/6u6ju3sG7NNzNa9nbmYYTy/X1IxY8sXjdnVzqljblxiFnZCmBXnOi3WweHjXHnLjun/TUxiLhIz/ERZqi4lMdv1F2knMeuNGfv3XevYzr4xc5/KHCJmSV2mjhmrg98vsZixZHDRmP76sa9nmTrmEIlZ1V8we+6557pbbrlF0vH/+mD//lnzc19m6C/z+dvFHvvlTRlz377tX3JHTGLOE/PAgYP6ru+6OBozjN8VM6zbPO0kZr0x2y+vXvd29o3Z9stYMVNtG7OdfWPG6uD3y7JiTtHOPjGlNf+CWf+KmZ/p+r+l7Rly+zuV+ca2C9fpiunc9oybmNPGLHnj8jq0s2/M8HZD15XhXMx220XaScx6Y4b/rHtd29k3ZuxLmoeM6f+9rHb2jRk+3to6/orZsmJO0c4+MbVJtzLbRnddfgx3gv8tveEJzS831+GxmLG/iTltzPCNy+vazr4xc29c9tddVjs3LaZzzu3atRoxw8RsXdvZN2aYmA0d0y97We3sG9O545MT546fW5YVc4p29om5kYlZ7KDxH8d2QtiRsZ2W6/BYzHDHEXP6mKWJ2aq3s29Mf/IsmXDGbuemxYxtU2vMMDFb13b2jZm7YjZEzHbbZbZziJglidk6tLNPTBIzHd95/iXHcP12mV9W+ImM0oPW38FTxGxv2U3RzlhZY8Vsyyht5/nn3730mFO0s2/MdvKMxWzjLrOdy4zplztVTH+b2mN2JWY1tzNW1lAxY//WbOiYqbFZc8w+iVl4/K1SO/vEJDELfodinRi7BNx3J4fb+2UvI2bJJDF0TL+8cP3YJDxvzFQfpto5RMxc2asU098P/vJ28lxmzCnaGYuZKjd8Lnb1ct79GdMnZvhKfaiYuTkhvOq8zJhDtDMsa+i+jf1bs9hFgUVixq7otNrb77mycjFj82pujPvb5GLGrsbH+sP/if0T8HCb0naGn5AMY+auloXzyFAxNy4xy936SXV225H+Mr+s7Z16fIfnYuYG4Zgx/Te5L6udqeWxQbpIzNz6sXaGZcVelc3TzpKYftvHjlmSaIfHQbusT2I2VMx52hk7dheNGWqXn3/+3dmJedE5IRZ3z550Upkqc96Y7fqxOSEX009AlhVznnZ2nVT9ZUPEbN/knorZ/h4ypt9Ov//CF8KxcRvGTNUpNcZLY6a+xyzsj9RPGM+v6zztTMUMt0n1zzAxNywxS3VOrrP9wZVaNzyIS2O2OzI10MeKmTqYY3FS9esTs2t5OJHEYsYGfSymv35YzzBeWGa4fVfMsJ4lr2xzdR8zZmpy8GOG+6FdN0zMSl5NLxoz3D5c13/VnDqOu2KmEsdwWZgodLU1jBm2sSRmOAZSMVNzRvhCwO/b2BgKlezPWMzU1fgxY/ptLI2Z6sdc3EVi+sdQLGbquXb7XbvcNrljJdeOmNj24bjPJWal+9O54/vMf5uEH7NrnMdixo6jWEy/PbH5MxbT3yaMF4uZ2gdlMTcwMfNPfH6HlQwIv3PD7cNtYzFzSVEsXuqgzcVs65ZqZ+p3ONi76ufHCtuZKju1vGvCSsWLxUxtO8/zsXjterkkIqxfakJM7eMxYqb2ZxgjjLe1tT0xi62b2pf+86kkMhYzFiu2bu446Wqnv5+7xn2rJAmN1SXWxtKY/nO5fs+NobC/uuaPkv0Zixm+yT0Vs+tEnptHUu30+7arnbnjPhU3FzPs33Csz5uYxeobrhc7r/RpR277cDz4V21L+ibcn2Gdwn4J1wnPnbGYuePIL6/r/Bu2e54xlHquJOaxeCuYmEl6qqRbJB2WdHlu3TYxS72KDCePWIfGdk5qEo5NOLFX2+E2qcknVSc/VmywpNrZVe8+9QvrlYuZKjeVQHT1t3PxK0vhJBB7HO7r0smy60QaS+zDcnPLYxOin2THYob1ycUMt0+9zyH2tz95xmL6/ZyLmZowwzbGnkvVL3bs5hK2sF6hrjkhPEn424W3psMy279L+iA2HnLjNTXGwhi5MR6uN8/+TH3zf+qYDmPH9l3upBxu37edzm2PGTveumJ29U2YgOSSuNj24bGQitVnf4bl5I7BVJ26+iZsZ1hmV2KWipeaG8Nlqbp2jcXYNqkrdLFyUsdNLuaxeCuWmEk6QdLfSXqUpJMkvU/Sean128Ss5ODsGgixTi3p8NTOS5XXlamnDpQwGYnVoeR317KuenXVs6vP2sfhJ1li8VIHX/i4NPb5599dFK9kLOTqlSsz9VzpGEvF9LdPvXk2tT9jk2dX3/SNWdKuvhNf13Gf6rfcen3GbarMknaV7tNc38ZipOaNVPy++7MkMcslCGE7/W272pnaLtfO3LbOlceMxfVjpo6hcPtUzNycNO/+7KpHuF2sTql9mdvW/90nMcu9OAnXzfV1rG6p+nc9H6tHqi6pmNuvQK5eYvZ4SW/xHl8h6YrU+l2JWeryqv98blIp2cldgyh2UKUGUWy7kjqkbg2V3EqJTTq5KxixmLFt/N+xV4H+49xVmjBmauLqaq9z/f+hbmpZKHVwp2LFnkv1l9+uXMzUGAu3if0dvnE5jFkyVrti5sZtKmYuTuntnVDXnJCqb8nEHFuvJGZq27CNqVvvfnm5W+ux+GG88LkwZiwxy91aT8VO1TfXztx2qVj+49SxURIzdVW+lUrMUvWMzYthvf35LVe/1PaxclLHSqyeuSv24fOpubJPYubXI6y7Hy+2XViH3DwYbpPq267zVvh3bnwdX8fVS8x+WNKrvcfPlfTfU+t3JWa+3CSeu6TvXPyyarhu+3fqJBJuE1s3jBlOCGGMPm3M1c+P2XVyjMXM9V3qAGrFPqEVWy+3Tkl7nTuWgHQlgl39FatTbLk/XlJjIBWnS26iSNUptX74Uf9cLL8dJSfyknGbihnG6XoBEotZEiPWL3v3fjQ7MeeOk3nmoXZZqi25pKxPHWJzWtc6vlhiFouZOh5S65a0M7ddbN1Y7Nh2fWPGys+9uInVs2TMpua3VIxYue1zuQQxjBfbPif11oKtrf6JWWmdUvNpbp6Nxekz36aO+a6Y4Rw4RGK21H9ibmbPlPQU59xPNI+fK+mxzrmf8ta5TNJlzcPHSLpJuvBC6cYbj/2OSa0TW54rL7V9+7d0/LZnPFI6cvv2MiTpzjtmz8Vip/72Y6TaePqjpU8ejrcxVr9TT8uXF2u3v07bjljfxfqrq/659WLrhOv5sf266eGSPtMdL9dfsX4o7cNwuV92GKdLGLOkn5P7M+iXXF1Tz3cdL9v2RYd5juXS53Pr3f93ZKxI84/bVjsflMwtqWW5ctu/+47H4nWafgnnta7xGJYvlfdZKsa87SyNF4uRHFuZY6gkXmos3nlHuo2x7bvKDo9Bf7tUf5aOGSlyznu4dOGu+JxZchzMOza6lvcpO6yTX/+Sumw7n3zeOffV3THTTlxk4zkckXSm9/gMScc13jl3paQrJcnMDrlF/0v7GqJf4uiXOPplO/okjn6Jo1/iZv1yaPfU9aiJmR1atIwHDFGRHt4t6WwzO8vMTpL0LEnXLbkOAAAAVVrqFTPn3H1m9iJJb9HsE5pXO+c+uMw6AAAA1GrZtzLlnHuzpDcXrn7lmHVZYfRLHP0SR79sR5/E0S9x9Esc/bLdwn2y1Df/AwAAIG3Z7zEDAABAQrWJmZk91cxuMbPDZnb51PWZipmdaWYHzOxmM/ugmb24Wb7PzD5pZu9tfi6Zuq7LZGa3mdkHmrYfapY9zMzeZma3Nr8fOnU9l8nMzvXGw3vN7HNm9jObOFbM7Goz+5SZ3eQti44Pm/mtZq55v5ldMF3Nx5Xol181sw83bf8TMzulWb7bzP7RGze/M13Nx5Xol+RxY2ZXNOPlFjN7yjS1HleiT/7A64/bzOy9zfJNGiupc/Jw88uiX4Q2xo96/uumdf6RdJqkC5q/v1rS30o6T9I+ST83df0m7JfbJD08WPYrav7/qqTLJb1i6npO2D8nSPp7Sbs2caxI+k5JF0i6qWt8SLpE0l9KMkmPk/TOqeu/5H55sqQTm79f4fXLbn+9df5J9Ev0uGnm3/dJOlnSWc256oSp27CMPgme/3VJv7CBYyV1Th5sfqn1itljJR12zn3EOfclSa+XdOnEdZqEc+4O59x7mr//QdLNkk6ftlbVulTStc3f10p6xoR1mdqTJP2dc+5jU1dkCs65v5J0V7A4NT4ulfQaN/MOSaeY2WnLqelyxfrFOfdW59x9zcN3aPb9khslMV5SLpX0eufcvc65j0o6rNk5a63k+sTMTNKPSHrdUitVgcw5ebD5pdbE7HRJn/AeHxHJiMxst6RvlfTOZtGLmkujV2/abTtJTtJbzexGm/23CEk61Tl3hzQ7eCR9zWS1m96zdPykucljpZUaH8w3xzxPs1f3rbPM7G/M7AYz+46pKjWh2HHDeJG+Q9KdzrlbvWUbN1aCc/Jg80utiZlFlm30x0fN7MGS3ijpZ5xzn5P0KklfL+lbJN2h2WXlTfIE59wFkp4m6YVm9p1TV6gWNvvy5qdL+sNm0aaPlS7MN5LM7Ocl3Sfptc2iOyR9nXPuWyW9RNLvm9lDpqrfBFLHDeNFeraOf+G3cWMlck5OrhpZlh0vtSZmnf+6aZOY2VdoNgBe65z7Y0lyzt3pnPuyc+5fJP2u1vBSeo5z7vbm96ck/Ylm7b+zvUTc/P7UdDWc1NMkvcc5d6fEWPGkxsfGzzdmtlfS90l6jmveGNPcqvts8/eNmr2X6pzparlcmeNmo8eLmZ0o6Qcl/UG7bNPGSuycrAHnl1oTM/51U6O5l3+VpJudc//VW+7fo/4BSTeF264rM3uQmX11+7dmb16+SbMxsrdZba+kN01Tw8kd92p2k8dKIDU+rpP0o82npx4n6Wh7S2ITmNlTJb1U0tOdc1/0lj/CzE5o/n6UpLMlfWSaWi5f5ri5TtKzzOxkMztLs35517LrN6HvlvRh59yRdsEmjZXUOVlDzi9Tf8Ih88mHSzT7tMPfSfr5qeszYT98u2aXPd8v6b3NzyWS/pekDzTLr5N02tR1XWKfPEqzT0W9T9IH2/Ehaaek6yXd2vx+2NR1naBvHijps5J2eMs2bqxolpjeIemfNXvF+vzU+NDsVsNvN3PNByRdNHX9l9wvhzV7D0w7v/xOs+4PNcfX+yS9R9L3T13/JfdL8riR9PPNeLlF0tOmrv+y+qRZfo2kFwTrbtJYSZ2TB5tf+OZ/AACAStR6KxMAAGDjkJgBAABUgsQMAACgEiRmAAAAlSAxAwAAqMSJU1cAAGLMrP34uSR9raQvS/p08/iLzrlvGyjOMyR9s3PuPy1Yzq9JerNz7u1D1AvAZuLrMgBUz8z2Sfq8c+7XRij7/2n25aqfWbCcXZJ+1zn35GFqBmATcSsTwMoxs883vy9u/mnyG8zsb83s5Wb2HDN7l5l9wMy+vlnvEWb2RjN7d/PzhGb5OZLubZMyM7vGzF5lZgfM7CNmtqf5B9Y3m9k1zTonNOvd1MT4WUlyzn1M0k4z+9oJugTAmuBWJoBVd76kb5R0l2b/BubVzrnHmtmLJf2UpJ+R9JuSXumc+2sz+zpJb2m2eYJm31Tue6ikJ2r2j+D/rFnnJyS928y+RdIJkk53zj1GkszsFG/b9zTrv3GMhgJYfyRmAFbdu13zv+fM7O8kvbVZ/gFJ39X8/d2Szpv9mztJ0kOa/7d6mo69b631Z845Z2YfkHSnc+4DTdkflLRb0g2SHmVm/03SX3jxpNk/Ln7kgG0DsGFIzACsunu9v//Fe/wvOjbHPUDS451z/+hvaGb/KGlHojy/rPvLc87dbWbnS3qKpBdK+hFJz2vW+UpJx8UAgD54jxmATfBWSS9qHzS3JCXpZkmP7lOQmT1c0gOcc2+U9DJJF3hPnyPppsWqCmCTkZgB2AQ/LekiM3u/mX1I0gua5X8l6VvNu8dZ4HRJB83svZKukXSFJJnZV2iW5B0arNYANg5flwFgo5nZb2r2vrL/u2A5PyDpAufcy4apGYBNxBUzAJvulyQ9cIByTpT06wOUA2CDccUMAACgElwxAwAAqASJGQAAQCVIzAAAACpBYgYAAFAJEjMAAIBKkJgBAABU4v8DnaXDnPnxP0MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plot_spiking(N_e, N_i, ell_ex, ell_in, t_start=0, t_end=200)\n",
    "_=fig.suptitle('Ellipsoid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Late"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plot_spiking(N_e, N_i, circ_ex, circ_in, t_start=8000, t_end=8200)\n",
    "_=fig.suptitle('Circular')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plot_spiking(N_e, N_i, ell_ex, ell_in, t_start=8000, t_end=8200)\n",
    "_=fig.suptitle('Ellipsoid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rate distribution in space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.gridspec import GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isi_to_rate(isi):\n",
    "    rate = 1.0/isi\n",
    "    rate[np.where(isi==0)] = 0*hertz\n",
    "    return rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/brian2/units/fundamentalunits.py:227: RuntimeWarning: Mean of empty slice.\n",
      "  return Quantity(func(np.array(x, copy=False), *args, **kwds), dim=x.dim)\n",
      " [py.warnings]\n",
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 500)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "grid = GridSpec(2,2)\n",
    "ax = fig.add_subplot(grid[0,:])\n",
    "ax_ex = fig.add_subplot(grid[1,0])\n",
    "ax_in = fig.add_subplot(grid[1,1])\n",
    "\n",
    "spike_times = circ_ex\n",
    "positions = ex_positions\n",
    "label='circ ex'\n",
    "color='red'\n",
    "\n",
    "\n",
    "isis =  [np.ediff1d(spike_times[neuron_idx]) for neuron_idx in sorted(spike_times.keys())]\n",
    "mean_isi =[np.mean(isi) for isi in isis]\n",
    "ax.plot(positions, mean_isi/ms, '.', color=color, label=label)\n",
    "ax_ex.plot(positions, isi_to_rate(np.array(mean_isi))/hertz,'.', color=color, label=label )\n",
    "\n",
    "spike_times = circ_in\n",
    "positions = in_positions\n",
    "label='circ in'\n",
    "color='blue'\n",
    "\n",
    "isis =  [np.ediff1d(spike_times[neuron_idx]) for neuron_idx in sorted(spike_times.keys())]\n",
    "mean_isi =[np.mean(isi) for isi in isis]\n",
    "ax.plot(positions, mean_isi/ms, '.', color=color, label=label)\n",
    "ax_in.plot(positions, isi_to_rate(np.array(mean_isi))/hertz,'.', color=color, label=label )\n",
    "\n",
    "\n",
    "spike_times = ell_ex\n",
    "positions = ex_positions\n",
    "label='ell ex'\n",
    "color='orange'\n",
    "\n",
    "isis =  [np.ediff1d(spike_times[neuron_idx]) for neuron_idx in sorted(spike_times.keys())]\n",
    "mean_isi =[np.mean(isi) for isi in isis]\n",
    "ax.plot(positions, mean_isi/ms, '.', color=color, label=label)\n",
    "ax_ex.plot(positions, isi_to_rate(np.array(mean_isi))/hertz,'.', color=color, label=label )\n",
    "\n",
    "\n",
    "spike_times = ell_in\n",
    "positions = in_positions\n",
    "label='ell in'\n",
    "color='green'\n",
    "\n",
    "isis =  [np.ediff1d(spike_times[neuron_idx]) for neuron_idx in sorted(spike_times.keys())]\n",
    "mean_isi =[np.mean(isi) for isi in isis]\n",
    "ax.plot(positions, mean_isi/ms, '.', color=color, label=label)\n",
    "ax_in.plot(positions, isi_to_rate(np.array(mean_isi))/hertz,'.', color=color, label=label )\n",
    "\n",
    "\n",
    "ax_ex.set_xlabel('x')\n",
    "ax_ex.set_ylabel('r_ex (Hz)')\n",
    "ax_ex.legend()\n",
    "ax_ex.set_ylim(0,40)\n",
    "ax_in.set_xlabel('x')\n",
    "ax_in.set_ylabel('r_ex (Hz)')\n",
    "ax_in.legend()\n",
    "\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('<ISI> (ms)')\n",
    "\n",
    "ax.set_ylim(0,500)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of inhibitory input in space\n",
    "\n",
    "The ellipsoid axons generate a spatially heterogeneous input, that produce the ups and downs of the excitatory firing rates. The inhibitory axons mimic this, but depending on their orientation, they are a right shifted, respectively left shifted version of the excitatory profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of inhibitory inputs')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "ax= fig.add_subplot(111)\n",
    "\n",
    "ax.plot(ex_positions, np.sum(in_ex_connectivity_circ, axis=0), '.')\n",
    "ax.plot(ex_positions, np.sum(in_ex_connectivity_ell, axis=0), '.')\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"Number of inhibitory inputs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spike counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from interneuron_polarity.analysis.spike_trains import spikecounts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 50*ms\n",
    "t_start = 0*ms\n",
    "t_end = 10000*ms\n",
    "times = np.arange(t_start, t_end+dt, dt)*second -t_start\n",
    "\n",
    "\n",
    "circ_ex_counts = spikecounts(circ_ex, dt, t_start, t_end)\n",
    "circ_in_counts = spikecounts(circ_in, dt, t_start, t_end)\n",
    "\n",
    "ell_ex_counts = spikecounts(ell_ex, dt, t_start, t_end)\n",
    "ell_in_counts = spikecounts(ell_in, dt, t_start, t_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rates in space and time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "\n",
    "grid = GridSpec(2,2)\n",
    "\n",
    "ax_circ_ex = fig.add_subplot(grid[0,0])\n",
    "ax_circ_in = fig.add_subplot(grid[1,0])\n",
    "\n",
    "ax_ell_ex = fig.add_subplot(grid[0,1])\n",
    "ax_ell_in = fig.add_subplot(grid[1,1])\n",
    "\n",
    "min_idx = 0 \n",
    "max_idx = 40\n",
    "im = ax_circ_ex.imshow(circ_ex_counts[:, min_idx:max_idx]/dt/hertz, aspect='auto', extent=(times[min_idx]/ms, times[max_idx]/ms, 0,1))\n",
    "fig.colorbar(im, ax=ax_circ_ex)\n",
    "\n",
    "im = ax_circ_in.imshow(circ_in_counts[:, min_idx:max_idx]/dt/hertz, aspect='auto', extent=(times[min_idx]/ms, times[max_idx]/ms, 0,1))\n",
    "fig.colorbar(im, ax=ax_circ_in)\n",
    "\n",
    "im = ax_ell_ex.imshow(ell_ex_counts[:, min_idx:max_idx]/dt/hertz, aspect='auto', extent=(times[min_idx]/ms, times[max_idx]/ms, 0,1))\n",
    "fig.colorbar(im, ax=ax_ell_ex)\n",
    "\n",
    "im = ax_ell_in.imshow(ell_in_counts[:, min_idx:max_idx]/dt/hertz, aspect='auto', extent=(times[min_idx]/ms, times[max_idx]/ms, 0,1))\n",
    "fig.colorbar(im, ax=ax_ell_in)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax_circ_ex.set_ylabel(\"x\")\n",
    "ax_circ_in.set_ylabel(\"x\")\n",
    "\n",
    "ax_circ_in.set_xlabel(\"t(ms)\")\n",
    "ax_ell_in.set_xlabel(\"t(ms)\")\n",
    "\n",
    "ax_circ_ex.title.set_text('Circular')\n",
    "ax_ell_ex.title.set_text('Ellipsoid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial frequencies\n",
    "\n",
    "#### Spectral analysis with unequally sampled data\n",
    "\n",
    "https://joseph-long.com/writing/recovering-signals-from-unevenly-sampled-data/\n",
    "\n",
    "Here is how to get the phase as well https://stackoverflow.com/questions/49859075/lomb-scargle-phase\n",
    "\n",
    "A better lomb scargle from astropy https://docs.astropy.org/en/stable/timeseries/lombscargle.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.signal as scs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_spatial_periodogram(positions, spikecounts, spatial_frequencies):\n",
    "    powers = [scs.lombscargle(positions, count, 2*np.pi*spatial_frequencies, precenter=True) for count in spikecounts.T]\n",
    "    return np.vstack(powers).T\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_frequencies = np.arange(0.1,40,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "circ_ex_spatial_powers = get_spatial_periodogram(ex_positions, circ_ex_counts, spatial_frequencies)\n",
    "ell_ex_spatial_powers = get_spatial_periodogram(ex_positions, ell_ex_counts, spatial_frequencies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_power = scs.lombscargle(ex_positions, np.random.randint(np.min(circ_ex_counts),np.max(circ_ex_counts), ex_positions.shape[0]), 2*np.pi*spatial_frequencies, precenter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Power (a.u.)')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(spatial_frequencies, np.mean(circ_ex_spatial_powers, axis=1), label='circ')\n",
    "plt.plot(spatial_frequencies, np.mean(ell_ex_spatial_powers, axis=1), label='ell')\n",
    "\n",
    "plt.plot(spatial_frequencies, random_power, label='random')\n",
    "plt.axvline(1.0/(2*sigma), color='k', label=\"1/2*sigma\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "\n",
    "plt.xlabel(\"Spatial frequency\")\n",
    "plt.ylabel(\"Power (a.u.)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "circ_in_spatial_powers = get_spatial_periodogram(in_positions, circ_in_counts, spatial_frequencies)\n",
    "ell_in_spatial_powers = get_spatial_periodogram(in_positions, ell_in_counts, spatial_frequencies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_power = scs.lombscargle(ex_positions, np.random.randint(np.min(circ_in_counts),np.max(circ_in_counts), ex_positions.shape[0]), 2*np.pi*spatial_frequencies, precenter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Power (a.u.)')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(spatial_frequencies, np.mean(circ_in_spatial_powers, axis=1), label='circ')\n",
    "plt.plot(spatial_frequencies, np.mean(ell_in_spatial_powers, axis=1), label='ell')\n",
    "\n",
    "plt.plot(spatial_frequencies, random_power, label='random')\n",
    "plt.axvline(1.0/(2*sigma), color='k', label=\"1/2*sigma\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "\n",
    "plt.xlabel(\"Spatial frequency\")\n",
    "plt.ylabel(\"Power (a.u.)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intermediate summary\n",
    "\n",
    "- Rosenbaum shows that in a spatial network with no recurrent connectivity there is no dynamic balance possible\n",
    "- their results indicate that instead certain fourier modes stabilize so that the system has a spatial output profile\n",
    "- although they have a more complicated connectivity rule, I tried to reproduce the periodicty in the spiking output\n",
    "- my idea was to contrast this periodicity with spatial profiles in the case of ellipsoid axons\n",
    "- my spatial analysis indicates spatial periodicity in the circular case (double check), however it is not stable and moves across the tissue\n",
    "- with the 1d analogue of the ellipsoids this spatial profile dissolves and new spatial heterogeneity emerges, because inhibition is distributed in a non-uniform way\n",
    "- I think the spatial periodicity only comes in when inhibition is strong enough to bring the excitatory neurons below threshold, it requires the non-linearity of the excitatory neurons\n",
    "- in the same way that asymmeric (ellipdoid) connections affect the spatial periodicity, so does noise in the neuronal positions\n",
    "- a network with ellipsoid axons that all show in the same direction preserves the spatial periodicity\n",
    "\n",
    "\n",
    "#### Questions\n",
    "- what about noisy input, mimic Rosenbaum setup with spatially shared input noise\n",
    "- look at correlations where mean firing is substracted\n",
    "- better understanding of the necessary input strengths and synaptic weights\n",
    "- could one study the asymmetry alone by generating two inhibitory populations one with right directionality and with left directionality? how would such a network behave?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Notes on input strength\n",
    "- when total excitatory drive > inhibitory drive (that is E fire more often than I) , then spatial differences stem from the unequal distribution of inhibition\n",
    "experiment_dict = {\n",
    "  \"duration\": 10000*ms,\n",
    "  \"excitatory\": {\n",
    "    \"tau\": 10*ms,\n",
    "    \"v_threshold\": -40*mV,\n",
    "    \"v_reset\": -75*mV,\n",
    "    \"tau_refractory\": 0.0*ms,\n",
    "    \"u_ext\": 0*mV\n",
    "  },\n",
    "  \"inhibitory\": {\n",
    "    \"tau\": 5*ms,\n",
    "    \"v_threshold\": -40*mV,\n",
    "    \"v_reset\": -75*mV,\n",
    "    \"tau_refractory\": 0.0*ms,\n",
    "    \"u_ext\": -41*mV\n",
    "      \n",
    "  },\n",
    "  \"ex-in\": {\n",
    "    \"w\": 0.05*mV\n",
    "  },\n",
    "  \"in-ex\": {\n",
    "    \"w\": -0.4*mV\n",
    "  },\n",
    "  \"initial_condition\":{\n",
    "    \"excitatory\": {\n",
    "       \"v\": '-75*mV+35*mV*rand()'   \n",
    "    },\n",
    "    \"inhibitory\": {\n",
    "       \"v\": '-75*mV+35*mV*rand()'   \n",
    "    }  \n",
    "  }\n",
    "}\n",
    "\n",
    "- but this behaviour persists even when E fire less often than I \n",
    "- more important seems to be, to be in the non-linear regime, where ensembles of excitatory neurons can silence other ensembles\n",
    "experiment_dict = {\n",
    "  \"duration\": 10000*ms,\n",
    "  \"excitatory\": {\n",
    "    \"tau\": 10*ms,\n",
    "    \"v_threshold\": -40*mV,\n",
    "    \"v_reset\": -75*mV,\n",
    "    \"tau_refractory\": 0.0*ms,\n",
    "    \"u_ext\": -0*mV\n",
    "  },\n",
    "  \"inhibitory\": {\n",
    "    \"tau\": 5*ms,\n",
    "    \"v_threshold\": -40*mV,\n",
    "    \"v_reset\": -75*mV,\n",
    "    \"tau_refractory\": 0.0*ms,\n",
    "    \"u_ext\": -35*mV\n",
    "      \n",
    "  },\n",
    "  \"ex-in\": {\n",
    "    \"w\": 0.4*mV\n",
    "  },\n",
    "  \"in-ex\": {\n",
    "    \"w\": -1*mV\n",
    "  },\n",
    "  \"initial_condition\":{\n",
    "    \"excitatory\": {\n",
    "       \"v\": '-75*mV+35*mV*rand()'   \n",
    "    },\n",
    "    \"inhibitory\": {\n",
    "       \"v\": '-75*mV+35*mV*rand()'   \n",
    "    }  \n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions to Farzada\n",
    "- how well do you think this provides an analogue of the 3d case? how well do these findings apply to the 3d case?\n",
    "- how can I characterise the network? what input strengths and synaptic weights have to be tested for? \n",
    "- do you think that this is an interesting network to start with in the circular case?\n",
    "\n",
    "\n",
    "### What is the purpose of the model? \n",
    "\n",
    "The experimental finding of this unusual axonal morphology (and the non-trivial connectivity rule) stands in contrast to the usual belief of the homogeneous blanket of inhibition. So the model should adress the question how the unusual morphology affects current believes about the role of interneurons and the way they shape network activity.\n",
    "\n",
    "So I should read up some Yuste work to see how they connect the blanket of inhibition hypothesis with dynamics of networks and the functional role of interneurons.\n",
    "\n",
    "I still require a starting point, so ideally the network with a spherical axon morphology generating some known and relevant dynamics. Then I could study how deviations in the morphology affect this dynamics. \n",
    "\n",
    "Possible starting points are\n",
    "- head direction tuning by recurrent inhibition\n",
    "- the shepston paper about the subiculum\n",
    "- the yuste paper about the blanket of inhibition hypothesis\n",
    "- the rosenbaum paper about the effect of spatially structured connectivity on network dynamics especially dynamically balanced states\n",
    "\n",
    "The first seemed to be hard to connect, because these networks mostly live in feature space and it is not clear how their connectivity fits to the spatial connectivity rules that we had found. However, rethinking what I observed so far, maybe one could use the directionality of the asymmetric neurons to build structurally similar networks, but this is not possible with the symmetric ones in the circular case.\n",
    "\n",
    "The second and third, I still need to read.\n",
    "\n",
    "The Rosenbaum paper start at least from a very similar perspective, they look at states a network can be in for given spatial connectivity rules. Their setup is slightly more complex, but it seems to be a valid question to see whether their analysis applies to our network as well. What could be the function? Can I go back to a balanced state with inhibitory axons that distribute inhibition more broadly? Probably not, because their is no recurrent excitation. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2019-08-26 Discussion with Farzada\n",
    "\n",
    "- she pointed out that spatial heterogeneity can also be generated by changing the positions of the inhibitory neurons in a random fashion, actually  I's that just change position in a random fashion with a small jump to the left and right generate exactly the same network connectivity, however the distance-connectivity relation would be like in the circular case, in a way this means that for 1d connectivity, at least if propagation time is not included, the ellipsoid connectivity can also be generated from circular connectivities, does this also hold for 2d? another way to break this simple way of mimicking the ellipsoid heterogenity by moving the interneurons is to provide input that has a location, because then in the ellipsoid case, two Is that project to the same E might have very different input, but in the circular-position distorted way will have the same input, this means if I start out with a network that has positional changes and one that has ellipsoids I would see differences in the way that they react to localised input\n",
    "- why are their this travelling waves and how do they move? This did not become entirely clear to me, she suggested using localised pertubations to study this propagation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2019-08-26 Yangfan \n",
    "\n",
    "### Correlation input strength and activity\n",
    "- alle bekommen denselben Inputs, allerdings bei spatially localised input (subselection of synapses or up and downscaling or regulation of excitability)\n",
    "- wie sieht das in 3d aus...?\n",
    "- r_ex gegen number of inhibitory inputs?\n",
    "\n",
    "- unser wichtigere point, pyramiden-pyramiden activity, was passiert wenn man ein ensemble aktiviert...\n",
    "- was ist spatially tuned input, extremely suppressed when the neuron does not fit with the direction\n",
    "- was bringt es, dass sich die Pyramidalneurone nicht gegenseitig erregen, sondern nur hemmen\n",
    "### Rosenbaum\n",
    "- inwiefern kann unser Netzwerk überhaupt filtern, paragraph in den discussions...\n",
    "\n",
    "### Echte Head Direction cells in the presubiculum\n",
    "- very different peak firing rates, from 20 to 100Hz\n",
    "- signal to noise ratio that goes up to 1000\n",
    "- \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2019-08-27 some more thoughts\n",
    "- how does a recurrent connection come about? what synaptic plasticity rule can generate this?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Localised input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- use TimedArray to allow for temporally and spatially modulated input\n",
    "- can this be part of the parameters via the namespaces of the neurongroups, https://brian2.readthedocs.io/en/2.0rc/advanced/namespaces.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_start = 50*ms\n",
    "t_end = 150*ms\n",
    "\n",
    "def get_spatial_stimulus(spatial_shape, neuron_positions, t_start, t_end):\n",
    "    number_of_neurons = len(neuron_positions)\n",
    "    \n",
    "    dt= math.gcd(int(t_start/ms), int((t_end-t_start)/ms))*ms\n",
    "    stimuli = spatial_shape(neuron_positions)\n",
    "    \n",
    "    unit = stimuli[0]/br.asarray(stimuli[0])\n",
    "    before_onset = np.zeros((int(t_start/dt), number_of_neurons))\n",
    "    during_pulse = np.repeat(np.expand_dims(stimuli, 1).T, (t_end-t_start)/dt, axis=0)\n",
    "    \n",
    "    return br.TimedArray(np.vstack([before_onset, during_pulse, before_onset ])*unit, dt=dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_profile = lambda x: 30*np.exp(-(x-0.5)**2/2.0/(0.07)**2)*mV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_stimulus = get_spatial_stimulus(spatial_profile, ex_positions, t_start, t_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatio_temporal_input = {\n",
    "    \"excitatory\": spatial_stimulus,\n",
    "    \"inhibitory\": br.TimedArray(np.zeros((1,N_i))*mV, 100*ms)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting simulation at t=0. s for a duration of 1. s\n",
      "1.0 (100%) simulated in 1s\n"
     ]
    }
   ],
   "source": [
    "circ_ex, circ_in = run_1d_network(in_ex_connectivity_circ, dynamics_dict, experiment_dict, spatio_temporal_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting simulation at t=0. s for a duration of 1. s\n",
      "1.0 (100%) simulated in 1s\n"
     ]
    }
   ],
   "source": [
    "ell_ex, ell_in = run_1d_network(in_ex_connectivity_ell, dynamics_dict, experiment_dict, spatio_temporal_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa3b8e6f7b8>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = [0*ms, 50*ms, 100*ms, 150*ms, 200*ms, 250*ms]\n",
    "for t in ts:\n",
    "    plt.plot(ex_positions, spatial_stimulus(t, np.arange(0,N_e,1)), label=t)\n",
    "    \n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plot_spiking(N_e, N_i, circ_ex, circ_in, t_start=0, t_end=300)\n",
    "_=fig.suptitle('Circular')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gkV66qwF8IrL9AM65VzvnbnDO3XC4tNt92VnHgXYcn1qDXUIIIfvoG5i9CUB7Z+UpAG/0tn9fc3fmkwDc0yx1vgXAd4rIg5s7OL+z2UYIGQMGu3vwgZWE1MWQk8qVLqHmPC7jlwD8VwDXicgdIvICAK8A8B0i8hEA39H8B4A3A/gogNsA/AyAHwQA59xdAP53AO9uPv+s2UamgLMn4zKGPfvcRg4w4AK62Z/v6lsXaxzL2Kcvs7MzzB4rXULNuSvzuc65hzvnHuCcu9o59xrn3J3Ouac6565pvu9q0jrn3Iucc1/nnPtG59w5L5/XOuce3Xx+bspKbR52/P0MtccQ+d3dvcGn7122Q28cWIMvzFWHvkHAGmxcKkNtO1VgN+QRFLwZ6DLsOyrlP/m/ROhMdbHkQHj69LL+smTda3tdSp922t3lgbZkpup7K33iPCmDOgKzVOea+8DHgZiQNFs4eHEsIISMTPmBWc4Z6VyD41gB4NCZhJpm7Ibq2tdWfctdctlzCEN8qiZ/Gpvjx7db/9pmNMfmyJGlNZiHNfj3lVcurcGslB+YlXRGOpYuQ2cSSrJJiqG69rVV33KH6tt36XLo4DnEp6b0p9IPCrffvmx/WtI+a5nR7BtglfaczBR961nT8cLi0qX+shUG4OUHZlNQ+sGC1E2fgXANg6fGWus1FkvZZ01j4JAAawk79L0hobZAshT62G3hx+5sMzDjwYLMRe23+/O5YNOzhI05Bu6xhB1qC4q3OAYs/NidegOz2pybbJNSb/fPhc8FSzP0IZe0MSkZ+ufs1BeYtRes+mc6Sx+8SmTrF/auBZ6AlM9KH3JJJoJ9miSoLzDTLlgt/QGUSzD1hb2l26KvfkPrVZpdStOnNI4du2wjHjDHZ6027fsmD4DLyH04dGhpDWalvsBsSdpBhge78gfcJR+XUZJ/lN5OS3PhwmUb8YA5Pmu1KZf35uULX1hag1mpIzAr6UAH8GBH4tA/LlNa3x2blb5EmZDVUOEYVEdgxgMdIXWy9r5b6/VlDCjrg23WjwrHoDoCM17ITkidbOyJ3dVQa0C5Zdhmm6H8wGxnZ/+F7O1ZQ4XTk7PCsytSAkOe2L0V2FeXg7YnBVJ+YBZOQ7ZnDRVOT97HHHf+lXJ2VcJdjjUE8UvaaSr7LGH3Eu2QKnOpvjpHfUsuY0nbz8nQNthi8LrwMUOcc4sqEOO6665z58+fB0SAMfUcO78VcfbsWZzk0vGsFG3zI0eAixf7yxfa14q2+UpZlc2H9IsZ+8SqbF4JInKrc+6GIXmUP2NGCFmOIUEZyaPm2f8SWMJ+7BdkQhiYEULIkqz1WV9zQfuRlbHNwKyGa44IIYQQsjm2GZhx6YCQeeBJECGEdKKOwIzvsiOkTthnCSGkE3UEZrF32Y018PMAQgghy3HkyNIaENKdCfy2jsAsxlgXfvICUkLIEnC5d4977uknR/uRJenrtxHqD8xqhLNzhJCWkseDobrN8Qytku2nwUCyO7W18UAYmC0BZ+cIITUwdKzyX6dH9thYkDEKGztmFh2YHbrzzqVVIIQQ0gcGIIT0oujA7AoGZoQQUicbm+UgZCyKDsyy4Ho9IYQQQlZC/YHZWNPlDPAIIWQ5TpxYWgNCujOB39YVmE0ZPPF6CEII2c+cJ6xnz85XFqmLkidOJvDbugKztQRPx44trQEhhKQZOuZyrCNjsJZjfyZFB2ZfPHp0aRWm4cKFpTUghBBC5mWNAdbx46NnWXRgdqmmwGyNDlcbbIN+0G7rZ6k2vv32ZcqdGvaZfqzxTt0JfLzowKwadnencTh2/m6M3QZzPLW8BEobLLdi9zkprY1Loa+v0Z7zs6FxgYHZGEwVlA3Jl0HdcBvwqeXD6TOY1mr33d31HjzWWq8lfG2CpS+VtR0D+rbVEHsvZEMGZi2lOfHQYI9BHc9qS6DWIKsPp0+XXd8h/brketXGXMu7HP/2GGLvhWzIwKyFTnwZ2oKQ8hh6wlRTv17LySEhPRgUmInIPxaRD4rIB0Tkl0TkShF5lIi8U0Q+IiK/LCJXNGkPNf9va/YfT+U/2bsy2ekJyWPuvsK+aVNTYAUMe/ZUbXUlZER6B2YichWAfwTgBufcYwHcD8BzAPxrAD/unLsGwN0AXtCIvADA3c65RwP48SZdlMnelclOr1PCQXGIDks+hHDJa3CmbLe5+8ra+2YJfawLQ/Sds6612bUPc9WxTzm54582RtfedhMcd8Q5109wLzB7B4DHAfgzAL8O4N8D+AUAD3PO3SsiTwaw65x7moi8pfn9X0Xk/gD+BMBXu4gCN4i4cz31w+6u3eAiQJivti0Xkb3vvvJT5NmnPiI4e+YMTn7bt41bl446jFb20Ly6yA8o6+zZszg5JLCbsp5z2rBND0zuf4NtrpGju8jeQ1f7PMtwiTGqLbNv2Z5cJ5v38ZuB+s0iN2eZ7Xje1c97Hjsmt0clbSUitzrnbuhX4B69Z8ycc38M4McA/BGATwK4B8CtAC465+5tkt0B4Krm91UAPt7I3tukn+ZBZVM9vmJOSn4FBZmG2s8cNdb6/kOtrXL77BLP9uJ4sj3W2vda1jheNgxZynwwgGcBeBSARwB4EIBnKEnbcFMi+/x8bxSRcyJyrq9uowVlYzR83zzW5HS5Z2xrqnNITt1qOZno0k5rff+h1lYl++/uLoOzsSi5nVtOnlz/nbS1jJc9GHLx/7cD+Jhz7k+dc38B4FcB/A0AR5qlSgC4GsAnmt93AHgkADT7DwO4K8zUOfdq59wNQ6cCR2GMhq9tAJ+C3AFixR1tVXUrtS6xfrXW53Dlsru73iC5Za5x9fRp4PDhecrqy9qDspUzJDD7IwBPEpEvFxEB8FQAHwJwBsB3N2lOAXhj8/tNzX80+98eu75sUpY+cyz1wEb2k/MC5q0F2SUT61dbP1CV/oy1MegzrvY9Fly82E8O4Ivd+9K3rSq095BrzN4J4A0A3gPg/U1erwbwIwBeKiK3Ye8astc0Iq8BcLTZ/lIAL0uV8aUHPrCvenHGPpguHegtxVxPsO7L0HZJXaB98mQZQfaU/je3b9fel2rXv1b62n2JE6s+N34A9K2+bdXX3sBiNh/0HDPn3I5z7uudc491zj3POXfJOfdR59wTnXOPds59j3PuUpP2C83/Rzf7P5rK/88f+cj+ys1p0Kmu3yi9I05xEfOYdZ560C1lBmJoPWM253PMupHSv/Q+PTZz1bd2v8lhC3UsDb6SycA3TK6Rdna6G3ToADJFA87tFCUcNHLqfPw4rxkaE9/mR44spsYm2NrB9ZWvXFqD7VLCeJ5DLXrOSPmBmb9UlLts1Gfwm2rAzHW6EgbsLjospe/u7t5M3VSzVX0Dvtit6VMFkTlt0KbJba977umpTAZrOHlpWeK6lSXKHHrQnNKflqKWQKLveD63n5Vw7CsN51yxn2uvvdY5wN2H/3sOpipvZ2f6sk6c6CV25syZPF32HruXl+mY6dpyc9Jqdk7tz9X1xIl8PRLpzpw5063sVu/csnPT7ux0y7MrY/tLmDYmF+y7z+ZLMLX9NHLbduyyPblONu9T3tzHh5I5dsy5nZ18mw+1XV/5Y8f6ye3s9JedGADn3MDYp/wZszUSeyvBWKz91vgcUjOsfS7cb9ttyevLuuqdO2M35Y0MY/g7l6/7U9tzzHht2jAuXCjjxiSL1u59r1M+fXrYRf2Fw8BsKUruNMQmbLcaDnZzBZGxwGkMfx+xHo9/yUtGy6saagpC5nwmGZkf2j0KAzNSDnNc2zD2Iz7GPICcODHOrNBSB+C+gdMC+h75vd+rK1AhhGyGOgIzDqDb4Pbb89u673vglnhPYS5nz44zKzT12ejYs4RznD1rT2pf6qy9hllWQshi1BGYbWHas6TBegpdcgOuVFvv7Ox9SruGLla/kl8mXNIdzC0p/+tzN+qQJ7WPTV/7lTRGlAjt0x/ariyG3j0w5efaa6/dfzfR3HfdpO7qG0KhdxBl38XT3pWYw0h3L95Xbi6puy61slLlt/tPnNiffygX6plzV2ZO+X4aK21Yx5hd/e1T3rmXq0NO/lYbar9jZRbaB4uk71joyS16J6zFmn2gy12ZI5TViaHH9CmPzQPBJu7KXHIZc8qyaz9D6TJjNWZduyz19Wk/7To3/7q0ti5nz8bzL+WtADs7B2fsrOvYSvd3bTmy9n5UC319g5ehLMectl/DA90LovzAzGdNg/DKHWsfc9d1yNPrtVuw/evSamu33d2DQXQbNC45cPe5CUN7WOmUdeDjOQiZhtrG0ZmpKzBjY66Tsds19rTxJZ6e3oWc69HGerflUtdutm9vGJsO1/JdOHUqfaJXyqwnmY7Sx4O1Q/ur1BWYkXXSBghzdNLSH0qYs0QcC76mDLaGzCD5Ok+lY4fl9QvPfz5P9Ej548Haof1VGJiR6ck9AI7RSWs72E5x7c6QJf9Q1i9nyPV9YwZjvo6WHdZ02QMhZFPI3k0EZXLddde58+fPAyJ791CRyTl79ixO5s6M5LaLyN63ldbP5+TJ+MxHan+qLKvcoenCNKn/3vazZ87k29zPDziYZ6Sc+7a3v3NspclZ+G2j1b8tKyx3SDukZI39F57/fBy/6abu5ZHedBpbyCjQ5vMjIrc6524YkkfRM2aH7rxzaRUIMO8slBV0tTos8fwya4m1ptm5OWaQ2rax7DLn89wSbXP85pvn0YOQIdQ0xpDRKDowu6INzLgssSynTy9/h1q7FDbkjsu+WEustTz4eOzXUKWw7DJnUD3kBfWElEItYwwZlaIDs/sYc8BcOsCw6FrHuetRynsQY3dc5tDXbmMHN131GFL+7bfvH+D9GcAaTnrauk+tawkHwbmD6FLHw5Lp20a0dX1ceeUixdYRmPmcPDnsYD/3LfBjvYoopIZb+Xd3L9drzmWs2AG8j93GeLxDqFNXPbTy+wYqbV47O+MFzql+GdM1dsMBcFnfkh+YORZzv8u1hnFkTIYEvq1P9G2jvraeO1gvlb59coj9Ll3qLzuEoa8OmPJzvfXKnKleGzMFXV83M3a+MZTXWqiv8LBsnvP6opz2OnYsvt8vK/eVSTn7h75SCrhsw9QrmSw9gLzXpoT5d3m9Uah/F9/x08bayS8j1eY5+7X/qVdsaa/I6mqnXB1jdHltmE+fV2INfTXNjOPhaK8H6mtf58Y5dvTNo4/cwONdka/B6sucdh8gi028kqkk1nYNSglLN1M9bLRL+UMo4XqqvoR1T9liyXYC0v5agj8D/WdG+uhfSp3nZGuzfGRzFB2YffHo0aVV2M8WB8GpCW06d/Aba9PSA/Gxg8qx/LuC69YuPu5xS6uwPKX7NyEbpejA7NKYgRkHoWHMdY1YScFv6Xf2+frFZr/mfu1JBX3tva985dIqLE9JfY0Qch9FB2ajPsdszEGoggMPMZg6wBz7YNf35hH/f/i4D76fjhBCiqXowOwKLTCb8+4+iy2eafa5rqPEJa2prv06dmyagH0KX5viWrFjx5Zp76mCzD51GaP9txA088SWkChFB2YqQw+sJQR2ayFly9wBuMQALpdW9wsXugVRQ+vcVX5qG99++zIH3KlegtynLkOD6J2dbbzU+fTpZR4UDdQ3/temLxmFOgKzdpAcY+Cf+46emoOOFGPNPtVwBm214xQvIQfSzwXrWm5fPdfsvym61H2Mh4fW0A9C+uo89EHRfRkyZi3RF4bou9DDUclw6gjM2jPRGpcQaxxs56S0mR+rLK0dp9Tlllv2+3tuWWPrtGX/7VL3rT7CocYxuS+19YWlHo5KBlNHYDYGpZ/5l6zfEN1SsnPN/PTBL0srd+kn0ZfsM0OovV61608IWZTtBGZjHETbAXeKgberfnMO/kNst7u7jgNV7szAnNeEaO2So+eUfjwGS81MjNV2tc2s1MbSfrt0+Vulr90rvE5vO4HZGIx5rdtQxtDh0KHheeTQRdfDhydT4wBjD7A7O+U88T9WN8uPSz3g9Akk+8iU0nZdGHLQWaq9h5a79Pjbt/y+Y1up/XJu+tp9SL+e83jkIXuvdiqTb3rIQ9z7PvMZQGTvjWHhdx92d5fv2AVz9uxZnNQuZB5i8zHILb+rnjnpRfa+u+R78mRknzb+AAAgAElEQVTegCCCs2fOHLR5W2ZuuWP0jdwycval0gLj69mh7qafDykbWLaP9GGov/S1+dLjyRawxhYyKSJyq3PuhiF5FD1jduDJ/2OcOTAoWzcLneEcIPdicMunT5wo70w5ps8Wnr8Vo8T2Kpmt+wshEe6/tAKdYFC1HLUcdC5e7JZ+6Xrt7uoza+22m27Ky2eOesT6X5fnb504Mc7jJUqixmVQYDn/38Lz2pZm6bGN9KauwKyFDjc/SwfFU7V5Tr1OnFjucQi5B7C2HjX0jamCmBrqXhpL92syHdZJHymeopcyTTiYbI8l2/zs2XqWXkrpG0vcCVVK3QkhZACDAjMROSIibxCR3xeRD4vIk0Xkq0TkrSLykeb7wU1aEZGfFJHbROR9IvKE7IJ4JkyWhksv3eCZOiGE9GLojNlPAPjPzrmvB/A4AB8G8DIAb3POXQPgbc1/AHgGgGuaz40AXpVdCs+ECSGEELIBegdmIvKVAL4VwGsAwDn3RefcRQDPAnBzk+xmAM9ufj8LwM+7Pd4B4IiIPDxZEB9vQWqklLtDCZkS+jkhozNkxuxrAfwpgJ8Tkd8VkZ8VkQcBeKhz7pMA0Hx/TZP+KgAf9+TvaLbFOX16W+9jI+ug692hhNQI/ZyQ0RkSmN0fwBMAvMo5980APofLy5Yaomw78IRBEblRRM6JyLl77rlngHqEEEIIIXUxJDC7A8Adzrl3Nv/fgL1A7VPtEmXz/Wkv/SM9+asBfCLM1Dn3aufcDc65Gw5b0+RjLG1yeZSsGfo3mRr62HZh209K78DMOfcnAD4uItc1m54K4EMA3gTgVLPtFIA3Nr/fBOD7mrsznwTgnnbJszNjLG1yeZSsGfo3mRr62HZh20/K0AfM/hCAXxCRKwB8FMD3Yy/Ye72IvADAHwH4nibtmwE8E8BtAP68SUsIIYQQQhoGBWbOufcC0F7W+VQlrQPwos6F+M8w4x2a6+H4cT4bjBBCauXIEd78MRHlP/nfD8bWPH26tYDz9tuX1oAQQkhfeHPeZJQfmC3BEkHSmoNOQsi0HD++tAakZug/lynAFnUGZmO8oimWxxaCJC4Ld2NJW7Gd9rNGewyp0+5ufTPQXeq7xvYeE98+fW3V1X/mfk3iyZPzlVVCX3LOFfu59tpr3T72LlWz2dmJ788lVc4U5fctsyutjoBa5pkzZ+bRYwkbD2HC9knavE/Zc/nTEoxQt9n8PJchdTL68uR0LHOfzXNld3bW7ctj4NsnsFXSz/3jwZByp6aisgCccwNjnzpnzCyWnOna3c0vf87oP6T22cDa9SfbYu2zPVPXb+7+vvb2CuF4WiTrCsyWpIuD33LLdHoswdYGM1I3c/rr2g98a6vfkPocOTKeHqQ/c7TDxGMIAzMynLUNzmTddPVXnniQHHiXYhnM0Q4TH/PWEZhx4ByHrdixgLtuSEUsdeKxlf5IulOjb3DczaauwMy6E2SMgdN39LnvOFmCnZ2D9dzKzFffu276Xhu45DWFXahxsO/C0Dsf5ya3P+7sACdO7N+29rZMUUP9hwQqvm/Ucrwq4W7HSpC9mwjK5LrrrnPnz59PJxTZuy+p7Yx9OmWbR19E9r5z8tDKGlp+LrHHZIjg7JkzONk1kOije9/6jvGYjz5ltzd39NE5Ud7Zs2fjNp/LvnP54FD66tnKdfXzLn3bKrNPuiHt0VX25Eng7NnuZQDZ5ezz89ynxve1/VAf6UNX2SnKCuyaPbbMMV7MZdsxjuUp+UgaEbnVOae9ESmbomfMDt15ZzeBLndGrpGcgKWGM8kYQ/XvO3tVm90OH15ag7Iovf2OHVu2/LlvSMp9lc/SdqmNrq9Imtu+pfdDoAifKzowu6JrYFYL4bLDWGw5KM1lbXfEWvAddvtp+0apBwa+N1anFrvs7tZ5MjS3fWs4RhXgc0UHZqul65LBGpkqOF0jBZzBjc5SAVINB4ZaOHasnuubpub06bpPhvqMMWz7yVhHYDa1g5R6ll0zSwantQU6BZzBjQ4DpDSlH/guXODYuBb6jDGltn3p/SaDdQRmJTx9WrvLkZRJ30CHs3zbZYm+PWRc41i0brbWvl3qW2rA2IH7L63A6PR12KGOvgJn2Ax97kAD+s/ybW0QLZ0+AXZt/bs2fWOstf8Mqdea2jeHjdV3HTNmPn0bcOmGX+vgMxVD2mvuGwCW9q1c1uyDft26BthL2WXOB3KW3Pa19J+urLVeFidOlO1nBbG+GbNa2VonHUL7WBTabFzWbM/d3b2ArM/jUpayy5wP5Fxz25My4E1v2TAwI/XBC8dJH3hgIFNR2w1FpGjWt5RJCCGlUOOzrUh31njnNFmMogOzLx49urQKhBDSn5qfbUUIWYSiA7NLDMwIIYQQsiGKDszIzCz1qJE52dnh88hqpyZ/I8tAHyEVU/TF/51fYj4VtXTyMZ7F1ucC6bnv6OLzf+pnSHC8lTbkCUR/+voIbU5ymNhPig7MinmJeS0Hglr0HMpW6rlmeIdkGtpofmhzksPEfsKlTEJyYUBICCFkYhiYEZILn59GCCFkYuoKzDhjQQghhJAVU1dgxhkLQgghhKyYogOz+x4wy5kyUhJ9/ZF+XC9sO+JDf9g2E7d/0YHZfQ+Y5QurSUn0nbnljG+9sO2IT19/4HFsHUw8HhQdmO2DAyNZmlqeZ0cIKRMex0gG9QRmhCwNz3YJIYRMzLYCMx5YSc0cObK0BoQQQiZmW4FZzdPIWwwqjx9fWoOyuOeeafNf0t5b9O+xqM127Nf92IrdhvjzSmxUR2A2xrU9Sw5eY5Rda1B58mR/2dtvH00NksGS9q7Vv0tgCdsNGdPYr/uxFbsN8eeV2GhwYCYi9xOR3xWR32z+P0pE3ikiHxGRXxaRK5rth5r/tzX7j2cXsrurB2ddBoclB/7aDzpDBuFbbhlNjWymDsIPH54u79pmP1qG6F1rnXPIrVttNugxph2/6ab66mlx7NjSGpTNEu085xg0dfs75wZ9ALwUwC8C+M3m/+sBPKf5/dMA/qfm9w8C+Onm93MA/HIq72uvvdbtA4j/jwF0S5/Dzk5+2UMZW3eDM2fOjFv2ErIz2aoTOzumXvtsHtN96not2c4zt5nq5xaRtlPT+uTK9a1/F9002b70KbNt5yX0LYWZ+1gnP7foY/elxv2usm3dRhx/AJxzA+OqQTNmInI1gL8J4Geb/wLgKQDe0CS5GcCzm9/Pav6j2f/UJn0+Yz+uoGuUHK5f1z4TRuZjC76ykus7DtCl7eZu5yHl1eaTtelbEkNmk9Zs90LrNnQp85UA/hcAf9n8PwrgonPu3ub/HQCuan5fBeDjANDsv6dJPx9hYNe1UVayfk3IJLB/EFImfQKQtSw7pyiwnr0DMxH5WwA+7Zy71d+sJHUZ+/x8bxSRcyJy7p72LrTWcEOj2wIbgBiwrYZDGxJC+lLobNLoFFjPITNm3wLgu0TkAoDXYW8J85UAjojI/Zs0VwP4RPP7DgCPBIBm/2EAd4WZOude7Zy7wTl3w+H2IusxDMenttdFgZ2lOmhDUiIciwmJ0jswc8693Dl3tXPuOPYu5n+7c+57AZwB8N1NslMA3tj8flPzH83+tzcXyvWnSwfn7ME2qL2dSzpozWXLkuo8Nrl129nZs/eQx8sUzr0PetDlehJCTKZ4jtmPAHipiNyGvWvIXtNsfw2Ao832lwJ42eCSunTwKQaDWg8oax0Yd3fLnSXK9ZUl22bINZhDbh8v0R99nXZ2+vf1Lo/LOH263+NlKhmH7v+5z5XZ1rUw5WN6YsztX3PWs9C+I0Mnrabkuuuuc+fPnwdE9m6Ebb/7oMl2za9v+UP0HjOPjLzOnj2Lk+FZ+9h2z5UDurdPS4l+ffw4cOHCgc2qzTXm8tclyhvTvzNI2nyJvt7X5/vOQonsBdSKT2bJdq3n3P5YIsYYkEWP+t/n50Paa3e3u3/Vcsxo5Q4fBi5e7F6mmp3c6py7YUgedTz5fyrmipYLjcqLZ2126zsgExKDT0qvhxrHgFJXITT6zrZN/bq7jmw7MJuLLU/fr22Ji5CS2NIT6LdU1xKo0d59Zr0KrOe2A7OazgRqpcYzREJqYUv9a0t1LYGt2LvAetYVmC29tLV0+Vuir63ZRstB22/HBlupJ+kH/WMQdQVmSy9tLV3+WNTQafrYmrfi72fudh5i+xp8Moeh/jenHYaUxX42PzX1EfrHIOoKzIZQk1NrjKl/105Ti+3aetWib1e61mvo4HjixDD5LpQ2kC/hQ31PLKZ+lMdIXDh1Kp2I2AxpryF9uZbxdEV1rOtxGWOzhtuvRyb70Q1kNGjz+aHNG4Y8vqEjtPn8VGnzGX1yCvi4jJqWDQghpDQqPgCSlUKfrDww40vNCZkO9g9CCJmdOgIzzmwRMj98nAwhhMxOHYEZz9wJIYQQsgHqDcwYrPXnyiuX1oD0YYjPHzkymhqEzAb9dnl4vJidOgIzbUllLcssx4/PX+alS/OXSYYzxOcLexccIVnQb5eHx4vZqSMws5jzOUtTwZcILwtnXokG/WI6aFuyNkb26aIDs0N33hlPcMst8yhC1staZl7JuNAvpoO2JWtjZJ8uOjC7IhWYEUIIIYSsiKIDMwCc9iZkKfiYGkIImZ3yAzNripAHDUKmhSdFZGkOH15agzTsJ/nQVlmUH5hZsIEJIV2p7b2BW+fixaU1SMNr5vKhrbKoNzBbC2u4s7Rmapp5rWH2oGR2d/NvGKrJL2qDtq2LUtqr5MmYkW1UdGD2xaNHl1Zhes6eXVqDbVNyZw+pYfagZLqcrdfkF7VB29ZFKe1V8mzblh6XcSkVmJUSyS8Fn8hcBmyHeaG9l4dtMB60JQkoOjC7DysAKyWSX4q1P5F5ibci9GHt7VAaW7B36a8iWmMbLGXzNdqSDKKOwGx3l0HYFuFbEcZj67PLtcFXEc1PX5uzb5GRqSMwA8peXyakdHhiQ8g0sG9NTy2rJyNRfmC29rMRduplWfLxCXO2/VoeE9HHZn7djx0bS5PyWEsbD2FLB/At1XXI6kmFdio/MIsNxHMNRDkHg74H2SVmAnMf0VFD0DhExy6PTxiTVueubT+krnPWcyq/2d3t11/8ul+40K/cOWSGssZ3B3e141KXP8zd3ru789W1huNAjBoviXHOFfu59tpr3X0A7gDatikAnNvZSafJxc8rJRcrt2/9I3JnzpwZnr9zaXvlkFP+EB2BfvJh3brm0aZvvvfZPEeuKydOzNdXnJuurLa9BtrbuQ42D+QmlVmizL79tEdZnWzet5yh40Etspn94IDN+7T3mP15yDHr0KF5y+yCZ1cA59zA2Kf8GbOQodF7CTNbXfIaWu5Syxtrviawtrr1nUmpcAlgMtZqi9p8uURq8o2a2/thD1taA5uR7VpPYNZeazbUADU7Zh/WuLxB5qHGJYAUfR+JsEZbkHGgb8zDhuxcT2BW+zo3IWR5+BgKUhprv8GNdKbowOzQnXde/rPWwIydsk7GWL5g2/djC++XXfPdo2Q/N920tAblM3efX/gBz0UHZlf4gdmSS5D+AXTsAHFIfls4QJXKGNPqc55srOkF6F3fL7t0kNPnOs8+d4+SOqlpiW6pa5b7vFN6yPg6YGb964Hr+he8R9GB2QGWmjXb3U1f49Zl9mOsmZK+L0DPLb+GGZ0hOtZQv7GYcwlvKrvu7PTLe+iBr0+Z/knTXNd5rtGf11in2vDbYKgvzzmZ0B6rZz4xexDwFYMzGXpb55Sf6/3bXNvbg7s8akJjidvYh3DixKzyvW5p1xjDTnPYuo99w8dPdL0FPahXsY/LOHasX3ml0dbds0NnPx9ii7nHjC70HV/melxGV5Z45MWxY/U8LmPo8WRJX+7aB+dsE8+uTdzCx2Wsmr6zYmPJr50+9gllSvfJvj6wtuW0IX2hpkcidIHjw3Bq6ic1t3fJdh7ZrvUFZj6c5iY1sqbrvbYCHztDSoPHv9XSOzATkUeKyBkR+bCIfFBEfrjZ/lUi8lYR+Ujz/eBmu4jIT4rIbSLyPhF5QqqMLx49evmP5oSlz1QQonHx4tIaEEJqZmeHx78VM2TG7F4A/8Q59w0AngTgRSLyGAAvA/A259w1AN7W/AeAZwC4pvncCOBVqQIuHT162feaH7u7cX9s94XfLSdxJlVsNJ/dE2c69Yc2bd+7b8eSP368ez9ubb272+9mHN92/ieXK/H5XmX65Vh+EKbX0vj/D8h7Jwr+KpdWTup1rzfddPzAdjWfE5d912+PnDKP42PZeh3oMyfzytH2a36TyufkyfxX5GptF2urI4d0n8rxyyuvTKdR5Xr4MXC5z/fpt33GC3+MSJYZnCifPJn2ST9t7vbQL1IcP3x3VlkaR7Bftovdj+Du3nFSL//Y3e3tjydP9l+Z7zsmt99dyg37dFed2/GyT18Ij/Ndyj6Pa7sXGDL0IrX2A+CNAL4DwHkAD2+2PRzA+eb3/wXguV76+9JZn2uvvXb/NXjAvnsA2o92zZ7yerx9/1s561pIX35nZ///3DwsPVo5Tf+u8v53Sl6zWyjrXyzqv5rQqrNZ9s7OgbLbz7FjedfL+3X2bZyyt6+vZj9fXkurlR/uC/Ow0uXIWjJhPa1yLHm/ncM0vk/728K0oe9r5aT0sHQLt7X1TV3XrJWpbdN8rN0XXhStlbdPvhl7chnjdbgxu8fktf6aIx/rCxonTuiylnxrc0u3XP8K5WK2zrlHx+oj7ViVou0jml/nynallUsdA7QbLjLvG1AZcr9d13L9+vXRuR1Thtxb0Y6h3fS+3rmO8VP4GSR8XybAcQB/BOArAVwM9t3dfP8mgP/O2/42ADfE8j0QmDUHey1g8A0aO5ho+32nDhtCO9DHygg7SuzAoenfVd66QdCX9wcOK3BpOXPmzIEDZCzQsRzW10srO8fR/YE4Nej76VJtFP62fCEcGHKCMSvoaAnby7KFdnCNlWnJx/qBVSdNP2tw8vOIHVhDu1r6hL9jwVWoc6iHVfaJE3pgFg0UoAdmqaAq1C2VVtue6iuWLQ8f7tbPYv5iyQGXb0pM+WQYmGl6W7rlylnjvZW/lm8YDKROpLVxzU8XO4lNjYVWYKj17zD/Y8f6BWaxYDTlF76sdgyLyR8+fLCsHNm2nFDv2LGmTRvrv1q7hjrqsgUEZth7ZsetAP5O898KzH5LCcyuV/K7EcA5AOce+tCH7nN0K8DQBn2tE4fyfvqYvPXfOpDFvsNt1iATHgxj8tY+y16xAfjMmTNZdbXktVkPq3ytU2izPDmDfqycUEZrb/+3NYMQy0OrtxVQaf+1gDrHh/zffh6pdrZ8RwuCYwNcrIyY7pY+XfIM88/te8D+A5ZvL6scBz0w09rOKtMvzyxH2W7tt+RTbRYrJ7dMrZxUH10qMEvVKeY3sTEgLCM23sRObGK2zhnrrHxCPx9aZmpfuN+yZ27eObYPJwA0+dxxyZLt3i8WDswAPADAWwC81Ns2yVKmZihrQAgNGR7stAbXAj8rv1QZsW9tNi4WjOTIp/bFbObLOjc8MNM6Uk67+fKpDt2lM1kDZSiTCtotHcKDuqW/tUwXs2/M1uHvWNv76bXZUEsXK6/cpeCY7prfxOqu5Rnmr9nBKrs9YLUDu6WT/yfXX1P1jZajbLf2x/KL9bFYOaENU3Lhp52J0OS1wMyahda2xXzZysOqU2ymWOtH/n/n7JOgmM/526xZuZCYb2hjV5gmNzDLXXoP6x/K+3WJjWkp2TC9JRsbK2J9INW3NN+Opd8vu2BgBkAA/DyAVwbb/y2AlzW/Xwbg3zS//yaA/9TIPQnAu1JljBWYWXmk8o3lFysj9Z3Kyyo3Jp/KO+Vo7feSgZkW5IR5aWX623M6lOUvOfbMXerW9NcG0Nx2zq2DVictD60NUtssPbTtqaV4zW9idQ/Thr9z6uyn84MEyx/3/T982NQlt33UfI06WvrH0oXlaXVKldNVLqe/tWiBWapfW22tfWt5WHXKzTe3b+f0kZTfarTjhrbdKttPkxuYpdpB09vanrJnLM/Y2NKl//n7UmVqdPHrg7LDA7Mhd2V+C4DnAXiKiLy3+TwTwCsAfIeIfAR7NwO8okn/ZgAfBXAbgJ8B8IO5Bfl3R8Qe3ZKbLof2zRFDX5M5VI+xHlXT960Ufd+C08p2yWfM16G2ZeXcVW7p1W735U+fjusZq6Mld+rUhU755KTNkZ/yMUhdbOS/8awrWvv2ySsp4z3iJOZPfe92m1uuZaonLgx9i1XffIa8fSd8vODQtweNZdvTp9fxfOPa3p7nlzn763aHRnZTftoZMy1q1ZYgrDOT9qzDyit19pFKE/7WlgLC/c7FrwWKnWFp8qm6+nfxWEunzh2cMQvTxWZCtGUk65qlcLtli3Z7askjZcvUhfuavfztob+lfC6Wd5g2tHksH8sGKR/S2qX9ry31t2lSZ43hNR6pJX1N/xy9LdlQ71i+ftpwxiylYyo/a7Y3LMMaG7R6xm6q8PMK98eWZy15q61jaH7t+0BIasas1SlWnvade5e25hvaBf1W37b6U2hHa1zWfCBlN2u/ZnPNr86cOaPWz/Jt/zihkeorsTHHOb2tLH8I/1uyMZ0sX46NL/720D/y+kUBF/9P+YkFZppDWh3QcuJYAJObh78/TJu6461vYNbKamXn1iknMLNuPrDqExu4wjSpi8zDwU4rO9zmf/zX11m6h3pqvpZz7Vaqray8231LBGahnH9nXSivLd9a5VrlxdowpnfswNnuj9lWG1gB506d+piqc2wQ1tJZbZtjo1B/i5gNLdkw35zlMG080giD+lzZWGCm3Sij6Rv7tmRy+qiPdaKS8m+tv+deu6r5RUr/2MlA+98fW0KbhNs03Z3bf7djrKww79j1hilZ5/KudQz7ok+sb7XbUo/Lyt2+P82GAjPr8Qu+sSwHDjuJNpi3H+vWb0sPf3+YNjYQxAKoNl04M+Zvt8oO87XuFrQGmLAjpzpi7GJNSy7s+OF2y4Zh2S1hnXMGLK1+uYNgOBj4dtZso7Wzn3bswMw6a4/JWT6Y8oWUzXMGuFh/snQOy/dniVpis1OxvuvLa+Va/d0vL/ci5pifa+VadWnLC/0wFvCEbZ8706a1V45sLDAD0u8Ct3wrJWP16ZhsTkAVyvv9zjrGaPlovqv5l7Y/dVfikMAsNu6k5MNtuc/6DO3ZyuUEUKFObXtoj/9Itf+hQ+nyLIBvvte5jQRm/gAUOwikBj6ts2j5hnmkZrdyznCsvGJ5+3nG6uNc3uyBL6MFaFZg1mXWyk+vdRa//FBWs4tWjj8TEsqEuliDSFi/sC1iuoX1sfK39PPlW5trs4jadyunlaGVaeXh62+1XWpQtmyTGvhS9bR81ypfk9PKD/XUTvics2eYtHSWr1knVrF2CmXDcq26WG2j/bbktfxSS31WPpoNUoFZzA6xk0nLx8J8UysYlpyfNhzXYv0w1jf8E+ZwRjrUL9YmqVUNLTDzfd7PJ1bvsFyrn1ljQK4/xfw31idTOqV8OJZvq3cs/X7ZDcyYWcEGcHCq1HLgWGdpndSavrYCgPZ/aknO355zFmUNsLF0ueVbafw6WIFZiDYwaQOrJq/ZWLNPrrxVX21gCfOMDYZaebFALDZoabZpaW1utVlqEEnp3P4/cSJ+nYald6iP9rgMrU1jOqfqabWlJmvJaeWn7BzqYh3MU77W/tYeehtrp7Cs3Oc05ZYfk7fys3TT+l/MttqT/2N9uN2emo3N6RepOsTkQp/OkQvz0OoaC+r9bVq62ASFn0dqxiz085hPafqF/dySy/WnnH6uyYa/Y/m22/w2ib1lJczLuf2+cFB2A4FZa4zYgKF1BOtMOCevVt56SnvYWdr/Vv6xpdNYJ2tfeWLp76fTdAz1stL4abXALDyr8rdr+Ybpwidf59hfGwxS8lZHzhlgwv2+3uHydqrTavutgcW56QKz2ExNjn6WPpaslYdVpnP2TIjVlmGeWn9s0V7HkrJzjq/k+FosP6svhnrmHHh934y9VSLWdn4e1nhjBajtcpPlA35aKzCzVipC++T0Z6uOqT7rE1t5COXb9Nosc87KTsx3NBnN1jH/yA3MLN3abbmz+SmfT7VFqp/nvqbOytd6jqMl69z+N2jE2uryfwZm9/3OXdNvB6FwOjeVh58u/IQHl/CaAS2tJWt1EMuhYjN21pR1TB8tMLOcUetI1jZf3irfOd12fl5aOdbA4Q+W/n7rzNdaGtVs7hPrtNbSrc9YgVls+SvMO5TV9oc21fQP65ez5G3pFS7vhPppeVp9SZML08d0yfE/rZzU9pyTmVBPq/2tsSKll1WGZYeUvVJt0H5bS5mhfGunlB1idbNO4rT9ll3844KWJkwf/m7/p+oU8zVr3M71j3A8D+uk2TJ2/Ar1075jPm/1SW2f1c81Wav+lk65fSsmb5e7gcAstpQZOlFsoIgNYqk8wgaz9Enpqv3PDcy08v1t2n7twJrS73GPu/tAx9XyytGtyxljmE8YVFny4Xa/XK3OIeG23CA9zEMLzi07heQEZtoSqqZDTDamf659nNPPWmN5a3Kaz4f7tFmI9r8VdGv+qOna3pWp6RLLr/3OeftBbHuL1a6a32l2j83uhOWngj+tTaw+nZLXdMwJzEIbafutoMKfmdfaVCNme+1EQ5OzbOfrquUfs1uunCXf5tGOLbE3j4Rjnr89LFeT88tO+XyqHbU2tFanwjL87bHjqnayHBuDtDpZ/Xnv/wYCM79Bcl6MbX1SZ2App/cboUse7b7cAU1Lo80eWE5l5Z2aUdT2W9vCclpyll0tXWLPGQvlrVk1rdPF2tKXscoL89Kw2jF1p1pbrhWYWbaODdox2bCulv7hvtgsWE7QF/7Wgl9NRusXsYGyy4yZ/0R0q19qddXytdJbbabZT0sT00VLk9IrR77dbvme1bf8sdm6tPI69hIAACAASURBVKINhmMz06GNYr6r+UBOvbU8WlIncppcl/L8NDn9OiWXkvfHFk0/a3ywfNHaFvZPf1v7OxXkpGyq1deytVXn3Hy09DFd98tuLDBLNRZweT3Y6tCpvPxry7ROljPrBFxeMo2dVVh5hGn2N7rtLDl5x+R9G6QGAUu/1GNNrItMrU7it6HfluHvME+rDtasgV92qlP6aAN5Ttl+Pa3ALNTR1yNWD0s2rKufptU5ZzYstL1vc638sL5a+2gyVr/QnlGn5RX+92/06ROYtXnEAkC/vrHZsJhsKx/6TWjjWP/066vZwpKP6RHK5M4mxNo5Z3Yy5tvht6WvRcomOXJWebFHPKQCQG22TrN7TM8TJ+zALDZREequ7bfaPqxfmDbmL35QaPlcTttq8rEVIMs3tbrHfPXy/40HZtYsUPixArNYkBCWlzNrFdPJuXh5WuChOYzWocMOYKXRdPTlU53B2h+itVF4AX0qwNVsHRsIYnXU5EMdtDrF7By7iylmv3C/c/HALPaGC2umTxtUUwNpmIeWr7YtZlsr75QP5fplKq+UzcPgKaxL6kzal9f0GRKYpfTX9LVsGZuZDOub0iPWXy3f6tLO2j5N1pKP6aeRskmOXMoeMb1jdQMunwhY5cT0BPZmKS0fj+UT+kPOOBBu1/pCyl9ydEy1rVWOpXPKN3N8fL8sAzN1f5cluJzgzi9Py8PfFu6POYKmb5hfF8fTdLCWDWIzeaF8rFP4Zfj6afnGgk+rk2h28vNOLRFrn/Bu11gnjPmctS2mS7jfuYOBWWxWRNNLa7twe5ebKlq7au2hlWXpo+VttZe21BfzS8sPLJuE/7VgONZmsdnZmD/E+odWNyvYjv3X2ldLo+2zZgBbUicv/sxOzP+08sNls5hPhrKaXKyvWFh17SIX2lGzgyab8hGtLv4jolJ21XRJ+XxoBz9NbDbdanOt7NDnYj4b/k/NAsfKd27/rHksn3Bfysf3y24oMAudJTUjom1vZzpCQ6c+4bJC2LB+PlaeWmPHBpDY4JDKN5VfKBfO/sTuyvFltA4d6hPTy7Jtbie38s5px5TPhPr5vmPpEx6ktDLDejl3MEjIabdYfjF5y3etPDQf1HSJ5WmdKGl10/pX65NhfaxAX9Mz9K2ugZllF1+XVB6hbSxbavti/2NlpZaMLZ/RykhdM5njfzFS/pOSi/WV3DLbT+5bCEK5WMARysaWM1N1CfuQlr+lk9UvrDRhGb6vW0G11Xes31bdU/lZT/W38s+5RMDS2d+unUhe/r+hwMw3kj/jk/Nyaauhwjy0g0XOc2P8A4SVh5/eyks7sIeOoHUk7Y4bzaFiDmvZzCpT0zH2HeuQVrm5ZcTyyQ3ErQEqZ7mzi15hvXZ2Dj75P6avlp8WoGj20+rsp4/NolozOZY/xdrEsnuYvg2GtbJjB5SW2AHJCsys2a6Uz1r2ji2HxmzZ5X+srLBe4Xa/7WP6aZ8Qy/+sAELTM/yt6Rcr12rT3DItm3SRS718PpTVgsBYv/fzAPTnRKby0PqJdvwKywjz1+oUXq9q6a/5S6iLvy1sl1DO10WbyfX3WbprdQzrZqW9/H+DgVnsmpvYtvYTe/BszrKd1Tksec0BrAOwpYdWVq4eLbE8ff38j3ZAjA1Ise/U4NIl+LOmoWNtkHN9n9YmMV/oWmet/YHL1ztZ+efooPmZVd+YH8T8LcxT+63lY9nbWnLR2tQ5fQnE0iV2MuTc/sAsdkDLsYuvX8q+4f9whtCys/Y/p0+HOlsBtqWf1X5+nVPyrZ7+I0py5No6hgFS7KJ6ra/58uF/q64aYTtbclZbaC/11vzIqotVhlVuztgR2sHqq1abhttC/f28Y6tNmlyoX6wfWb+tfUD8Jeuafa1Z1IPlbTAwizmbNshZn9Do7e/YGnSsA4af1LUbmrNrdcntbJYeseBKc2Ct42qBZCw/60DnnP7Q29ig0Q6kWuASe2yG1SaxtvRnUGO2tuSsOoe2C/MN7xDUfCCUTx3Qtfy0vLX0qTaJ9SEtn5g/W/nF8tds027XAjIt3/BuNT+tP3PQxy6AfudomJcm19YtJ1Cz7NXWof3WbOncwQDDmkHV+kFYl5xgR7sTVitX+23lr82U5PQHTX+rjmFaP03qJNqSD/UJy9Xy9dvSOqnR9NT6UxjsWjIpPw+3af9zTvD9377vhvaw/ls+YQWQsT7j/7bGIp+DbXL1J5zbeGCWuh4qbAgtbepMV8sj3G85ctcBIpTNnZ52Lm/GztJNkw/ltM6s6RsLzMJytQOHtoSgTbN3aa+YX1i6xnwm9j/MRxsc/PKtwMxqx9QFwK1Ph2nCgCO0Qah/mCa1ZJ7jz1balH21PFN9LTa7FgvMnBsemPl+HPYz7X/KP0NbaLIxPWPy/m9tRSEWtMTaPtQv9HNtnIjlYe3rMt5Y+6y+ZNU1Naseltv+j822pdo29ooxrVxLN61+sb4V83NNj1Q5sTpZv7V9lh9o5Wnla/ayjnGpOu0fa3DOuQ0HZqllzdisV+y6shxHDxsklYf2u/0fW5qz9sfKyO1QOR0n1CmWNmY/vy6xKe9UB8ttr5w3QrRpY/b382r1z62zNmuhldEesFLBpC8flh3TTxv8UrNxsYOd1WYxHUObxOyRyj+3r8V8KzVjFu7Lmd0N7RrKauVodQzbU7NTuM0P1rXgOyYf5hX6grYvlLfq69dHOwHR7GHp5f/XZhU1n8uto9XXUnWN+aHVVpY+sfSaX4V1teyQyiMmo/ljrB7Wf+cOjsk5s5ttmdq+VHmxdDl+429L2W5/fhsIzLo87T9MEx5gUjNn2nYrjy76+A0YNmbqt7YttiwUG2Q0/UNyOp2V1i87FWzG7JDqYLFBw9rffnKuD7HK8fNPvQw3dxAB9r+qJuVPYRkxX25tYpUffsJnzWll+b9zX15tHeg1e2g+ZNXRCkI1O4T6PfShn9+nj2XXkDD/mLyPFeyG6bX2SwVWMfnQ5pbOYf6p2brYbJeln3YCYsnm9J9Y3wvTa2ODlZdWTz+fmC6+Hm2wbJUd6qr5TKyPWfL+96lTH4vWz88nZg9rv6ZHLIjMWdXRZvZD+8TK9+/UTPmOVndNPmY7f9zca68NBGY5nSjXcdrv1OAf6+BaQ/qf2KAT65ChU2vltPs1eZ8cG2hyoayvT05arezY87MsO8TKynmqfqxt2/2pgVqri5+nZYNYGqvOWmCm6W5d/B5bsouVn+pLbdocu6R8Tcujj36WLjEdU21g1dXyw1z5cF9Y9656x/LtIq+1U6qsVPtqtgrL1Pzcku3qH6EuqVl5y5etmV9NNjbrrNXFsn/KDlYfC+W1YLlrYKaNO6kVG60euf0r1bbtNl8H7Zl9lh1D+6QeeZTra5r9Ln82GJhZB6wcx7Eaoc3Xul4tNw9NLnR4/7dVTuqaiVgZYf6W/qE+7bffkcM6huQ6ckxXqyxfp9gSX2jL2FJjuE2zU0w2TBuTtwYHX679bu9Wi+WV62eaDbu0kVa/VDtZZfrflr5d9bN0yVli9mcwNJ+OtaF2Fp+Sd86+Ls9K75edGo+sesZs4vuer0PMllrZVl1C/PzmDMx82tf0WWXH9LHK9f/7NrWuA9Pkwzz82dFUXS3baG8KSOXRZRZQq3OoR0zXY8fSqzq5Pm35SGx7Tt1yfC1kf5oNBmaWsXJekRPLo90We7Kwv90KFrTnZoV5+fJaOVZ5LbFrK3zZUM5yQP/bf6aWZpNQPz+fWABl6RrrONq20Naxmz/CoC71Ci6/vqmyQxtbtk1dG2Ll51z8Ork2fSqADW2c006xttHqmDoLDesc6h7qB+xfHojplfsWh/AuybDM2DtetW1Wm2r1jG2L2TblH74drbaI6ZIr18rEnmUVBhShfjs79sywJhuWrc2AaX0tll4rO9Qjd8Ys7Ne57371dYv1Gyu48vt7+x32JX97+0k9EFzrjzl+rNkiZgfLz1tSfVnz3dgqlTY2h8eMnKVmzf72kucGA7OYw2mG9A1n5aHd5eb/t4Ks2McvNxZIpZYK/W9/u2WXMI2Vn6VPG5hZZeXqYdkkp6OnOn9sENDSpM4ItTa20mllpWwb2i/1UOSc8ttAI7WEq+Xry+bWL2fGN9bOmr20Ng7ziw3UsYDY2m+1SUw2ppOPVVZ47V74Hepp+bo1k6WVbclrafxtVoBhBSyhDUJaufaF2lb5mlxYlv8/lkdsLIzZJ0wblp0rm+MX/v+w/a0ZvpRe7e/UWBk7nlllpvpR6sQmJhtrX8vumk7aGG7VM/SR0HaaLik/ufzZYGAWczjLkP7gEOtcOf9Td/v5DmLpk+OcYbqWoctnVr3ab/9hp1beMT20ellpQj3DTpMbAFh1a/flBGYt/lO0Y23c5pN6kGmsbTQ/jb0nM6aLb4vQrla+ms3Du5WtA2TXdrFkwrSh3a0Zz3C2Iyw/9dRxv25hXVOzD1af8MtO2cYvJ9TTkgvrYdUztlQU00krO9b+4bYWaznOD8y0G02sPDT9rSUtS9dc+2j/w30pWcsvYmWlfD9Hr5SOVh5WOr/9U37iXHzmKSZr+UusTs7plwmEtknV00qn6WKNoQd1ZGC2b3vuAT2Vj9ZZYvnEBgmrHGtfS1huKr9Ypw//x/Txb2m38k7VK7WsptkgpmNqW8ymsU5l1a1NEzsgh0FHeJC3bB1botN06OJvWnvH3iWn/dZ01vJPBcNaHrG3bmgnPKl6WvZp9dPShr+1/6n6xexjlWv1Z61OllyurprOWnqtDXKDAWt2QtvW5uPflRlbKQjzaH/Hbn7R5LWTlJR9wv9hgJgT1Gn21vLWfDL1CW2izcZacr5NrMkKqx6xfmS1t7U9lsbfZ83wxvqdVU+tTC2fUL6Ln1y2JwMz03i5A2us4XKXMP3ytE6pOZqfnzWtGuqRqmvoOJqcVk//OwzMYss2KUfPufbIn81pB+xY/n6e4fKaL5e65kNrlzCNljY2QIQ2C+uh1evQITsfP51Wb63NLVnNRu3vQ4cO1tkvS5tBstLH+ocm49xlG1htnuo/MV/UytPspvmIb5ec9g/rpJXrf4eyKbmYrlZ9LT9N+WW4P3adaWyb76vWg5Q1fL+x+lLsReNWe4f5ttepanIpnXZ27OuMLHnrUQ6pPqTNemtlhnKnTn3svn6TMxvv7/PHxZxAJ8w7HFfD8Vo77oV28eXaj/XCcU3eso0lp8lYx1dbfqOBWWwZw+rQWrrUMlTugcBaPgq3p87ifTTn9LeHg5KV3joYh7MX7bc/eFpnemEevn19uTadFUCF5fgdzno1Vpveaqtjx/au0fD1tnQPB4bUxc2a74V0Wdpst/nvEAzzCNtJ82PNptqAkvI7y76afPit2Sclq/mvJqfVw1r20fqYVudY39Hsotk81WfDbX1mmXy59ncsreZnmp/GZnbbvqEdhGN6ay/v9vO1HqQcylvlajLhOGKNd5otw/qkZvFyZXPkNb00P7PaT+tzfp6hzf3yrb4T5hnzm7CscL82U9/KaPXI2RbzQf8YYrW9ZqtWZ+2B82Ga1Jhx2fc2Gpj5vzXn0tJqMjGnDuXC/VpHTOlileeX0aINQKkgNDZw+s4bq6f2PrvcW8CtAMGqXyyN1TnC/VYbtek1u4Rp/O3aAcmqs0Zo75gvtNu05WM/D6uO1iASymr5xeqn6Rnehp/6zu1LVnltmWFbhmljbRWeEKTK1mYFwv0xv0z5mr+txZqNtGYTQvmwDM1GFoB+EqP15TAv64XcYV38PNvrVzX/sMqy+o411sTGgRYrQNZ00mZ9UrKazWOzVlYels01Xa2+144t2l3JsYDOz9OfVbTaw9LLD/Jjdgu3+TazytLy0Oqg2Shms9B3Qr0svS/v22BgpgUXOR06lIkFVjkNmPqf2h47QIb6W3loF0OHpGzn3P7tWmBmpW3/a7pqeWh6WWlittHqoOmq/be2hXlaOsXkrTSWz7X7Y4FZqo4xXVN+aNVPSxtrr1g/SaWJlRcrN5a3lia37Nj+9nfOrJwmG+ph5aGli83WxHzEykOTS7VbrO5WXfyP/xqs3Pbp6r+xNvR/x2YeLblQRytwCGUsudw+YNXBaid/nAnvhNV+W/p3aY+YXlZ7aTYIy7XytfKI9eXYd8zuoV6W3puZMTt69BuTnTDlNGEDpM68Uw2pdaxYA4flAgcvAPfLCPPWytKma2OBWWzWJkybCsxibTHGjJm1FKHVKzWIhHlq+lqDqz9zoemZm6bdH3tOlHYnbCuXqqNlp/aWe8vnrHbUbKHZNPWd25fC8rRlFsu2YZ6p63389I973N33bffbJ9Sl3W/Zy9+Xesl0qIdll1i68Hf7P7WEbslZbRZr05S+mh2B8QIz7TKMmJyVru0X2vaYzcK6WZd0pOSsPtDu18YnS9fw27d5zDb+dyuTc0dzWBdfr9ijM0LbWAFyqr5WHlp5lo1ivu/36VCvMN+Deq88MAOuNxvVGkRyGsSadUs5e6wjWflZ8rkdK+VwqWsbQn209F1mzHI6Tbiv/Y4NMpZ9wrSpMrU2islq9g7tYi3rpOqr1VkrW3t2XKiTtRSnXYMWK0/TXQuoc5ectO9QbyttGPiE+8K2C+uvHUC0uvvbtNe5+Oljsq2uLanZ4jBfX06rk1YH7bd2otFiPZNR063V3w8CUv0qR1+rD4eBmb88HrvEIzYmaP7e/k4FNan35mo20+wRGw9y5KwTfSsP7eTFag8rMPOXVrtcuqH9TvlzrF3CMrU8tHxCtPJ8/7Z0C2V8We2ShlCflv023GhgZh00Up1YcyKtIVMNqMk7F39wqKWfVVZOemtg8LHSWWXHArPUmWpu585Jk9OZtWsXtHKsGbOYPcM8tTI0/aw0ms+2clZgFmsva2ZRa5fQL2MHQc1O1gBlDXixl6HHrg2K9YVQNpbeOlmJncRYvmBdG6PJaemsfhmmt2xo5avloV1HZJ3xx/QP/dY5+wBu+W377cuHgZmlR45/p+qi5d/WR+tTWt1j+Wh9M+a3uXJ+WiuAscaRsGzgYGCWmtW12lP7vbOj+6kfEGl1jLVNWG6oo+/Pqdn00Dbh3emxCRp/eyjf/tb02/u90cBMa3wtOtbkNGPG8siRt7aF+aSmXLW0VvrYgb7FShOm98uIBWaxDtzlrMtKox1YNNlQ3go0YrI5b4yIfceeyRXmp+Xvf9r3k1o2S9k+p+5hHuHMSphvbF94x1Ws7lZgE+tHWl/Q9Iq1j9UntD6r1TVmc0uu/W/N4ljysTqE6aw8rPFCKyulf0xnS1//t9XmfmCmPfJAa2+tTrHlwpS/WX0q3BfK5eST248sudgMcE4dtBOonLct+O2h1T3mV2GZsWOOJt/KaPXW2kazR5hHrI01UisTqb4Q6rfpwCz81hw0tlbu/7fOpHMGy3CgieUTa3Tr4Ylt3qmBpMv+WKfpEpillqlSAZNvk9iBxeqUXWaNfMK2inVM7dtqK8tmYVk57yX065iyvVV3TUcryPHL1fZZt79r9tHqFZ7pWn7q5xPe3JI7Y6a1q1WWv90a3MP9lpwvaz2VPKTLSaCFpa9WR0tWy8sqS/vW8g//586YObffLlq7hLMfWlpL7/ATW0rU+pO27Gv1oxyZmK65ddBsCNiBmdafwjK1fC2bhXqmbKwR6pA6wYn5c8y+Vtn+mBHmFdM31A+4+hPObSwwsw6cmvPkDHYpJ4rJW2v9sXz8TpGaQdM+uQ8JjO2PdewugZlmH80ulu0tHf08NFmt/FQbWwdV/xM+AsD6TpUf5m2lj7Vbl7y05z9peYX7LN2tfeEsR46+qTLD9NpSTqxvtP+tIFQrS3swqe8jKX/X6t7qmyvnExvTtHI0HXLK7doWVln+t7VUqi2ZpQIza2lK0y2nT1p6W32v9XHN7rG2tXSKrXqE/pmqe7g9d4z1AzNrGTdmT21/7tistXUX3/Kx+qyWh/YGixR+28dOBKzy99voeufcigOz9q5M7WzDN0xq2S/2Ti2tk2iDZChvDeKpfDQd/H2pKdmY/tZMRGzZM6yTFpilBoHQJlpdQ91j9rNs42PNtKVmY3xido19t79Ty+axWTUriNfKi9UxNVjFbJiTV0pe09eyq5avlj7Vb8L/qQOg1rbhQ33DgDDlv76cVY+YXGgP6yQu1NuS18rVZkctWS0vq6xYXv7vsPxUYJYqJ+YjYV2sumu+kernsbL7+q2VxtJD257yNWB/YJbSIVX3mA6hnrH+18e3tO05PpjT/6yyAftmIedi7x9eeWDWPscsFuk7pw/MsbNQLSjoGpj5MtpSWE5HTQUX4cdPr9XHCkrCtFo9WrTATNPdagcrvTabY7VreJAKZa3288sObRLKpwKj2JKmZRtN99TgpslqyzGWLf0liVAmLMtPp/22BtjY4BQuiYT1itnd3661hWUnLe9Uvw7tET47Lqx/zN8ttHrnyoW/NRt1LTckNmvc7tdsq+Wh7bf6R0v4gNlQ1nryf6irVc+Y3pZvWv0yJddu6zLe+z5pzQZbevj/UzPJfnoGZun+Z5Xdp+/v/a4wMAPwdADnAdwG4GWxtP4DZlMdyeowqXRdOqolG8sr1MNavszJy9KhJfa/LVvb7jMkMEt1itwOrOUXm0mz8vVlLd1SA0usflZ5mkyuP2p5hGXE7JrSw0pnpYnZySrbkulicyu/VFrn9MsEwnRTBWaxV8LE5MLfOXKarm0/7yqr5ZOjs3VSoMlrj26I6aHlGeszMb19u/gf69V0YT1S41Tu9lA/7aQnt8+Gbd3+9/d3DcxSKxZWHX1SJ71WG8WCfm27daLh/87tC1bbWycSmvzlPCoLzADcD8AfAvhaAFcA+D0Aj7HSdwnMrIAnTOdv1y7Cjs2UaM6sXfRvrXHH9AnTxZZvw9++Q4czIakBLHS4PoFZ6h15Wn5a/a3ZppCww1j5htusci351AxWrLxQVquX5R9hHu1vazD0f4eDVWw2LERb/onZySrbGtBybK7Vy5rljc0U+tu0PKcKzHx9u8qFOqfkfBlr9jlX1i8z52CmpYv1defigVls2V8rU9tvzcr7eeaMN7HgJJytDvPWtmsnxD6W36f6bEzvdntOYBb2JS1/bexKyfp1b9OG2zQs38vx6Rw7xuQ0f8rtw5fzqC8wezKAt3j/Xw7g5Vb6MDCzOqX2bXW80Mg5AZQ105Q66LS/wzuINH1iQVT4nkKrPlan98vW8vCxAjPN9pa9tPRaWZrNrPw0udiySGxQ6yOv5ZFKb8l3aT9LPmbXmGxXGc0/U/l06SMtsZkD5+z+katHuC0VmIWzbtrF7DH5OeRCmZh9c8vu4oux9tLkY4GZL2Pp4W/TTny0cv3xL/TnlpzlWV8n7eQnZxxJBTGhPtZJVqibFejs7Ox/cbxfVmgDSz7UL1WmL5s7robExv0usqkZOE0udnKakr9cXn2B2XcD+Fnv//MA/J9W+jAw8w3lG9M64PlGixk5dFRrcLL0CPO20qT0iQ1GLTkzg1pZsXr4WIFZW3ass2r20gbAUI9Ye2mkbGxty5W3Doqxg1CXzq8dLMI8U/Kpg6Al20UmRz9tmzVDYLV3uD+lcx89wnRaYJazNBKjy7LeGHKhfNvfcv3RudgFzGm5WHtp8n5gFrvLzdIjR7dUv7eCIytfv6/GghHN5imZUK+u5LRx6Oex60UtwnE/l9iz6rrI+fJ9ZIeWmaP3/n40PDAT5xzmQkS+B8DTnHN/v/n/PABPdM79kJfmRgA3Nn8fC1x9F/DQh+/9vfVW4Prr9/+2vts0AHD1I4A7PnF5u78POJg+zK/l6kfs6aKVcf31wKc+aevqE+pp1cdP20VeSxtuM2UeAuAztmwsjzB9TN7Pw69DWD9NPizblw33a7qk5C3drXax8tD01trX1yVlL8v3csuO1c+Si+mX6yP+/5aYX8Z07qPHgTw9P4+R6oulyPnpc/1Rk2/lcn3KLyepe2PzWL+2xvhwf05bZ+kUkc2R8dO1Onelazt3klP8fAldh5Y7pOwxdAby8zh6vXOfkX7l7XH/IcI9uAPAI73/VwP4hJ/AOfdqAK8GABE559zHb5hPPbJnc0ebzwhtPj+0+fzQ5vNDm8+PiJwbmseXjaFIB94N4BoReZSIXAHgOQDeNLMOhBBCCCFFMuuMmXPuXhF5MYC3YO8Ozdc65z44pw6EEEIIIaUy91ImnHNvBvDmzOSvnlIXokKbzw9tPj+0+fzQ5vNDm8/PYJvPevE/IYQQQgixmfsaM0IIIYQQYlBsYCYiTxeR8yJym4i8bGl91oqIXBCR94vIe9u7SUTkq0TkrSLykeb7wUvrWTMi8loR+bSIfMDbptpY9vjJxu/fJyJPWE7zejFsvisif9z4+ntF5Jnevpc3Nj8vIk9bRuu6EZFHisgZEfmwiHxQRH642U5fn4iIzenrEyEiV4rIu0Tk9xqbn262P0pE3tn4+S83NzhCRA41/29r9h9PlVFkYCYi9wPwUwCeAeAxAJ4rIo9ZVqtV823Oucd7t1W/DMDbnHPXAHhb85/05ybsvSPWx7LxMwBc03xuBPCqmXRcGzfhoM0B4McbX398c70rmrHlOQD+WiPzH5oxiHTjXgD/xDn3DQCeBOBFjW3p69Nh2Rygr0/FJQBPcc49DsDjATxdRJ4E4F9jz+bXALgbwAua9C8AcLdz7tEAfrxJF6XIwAzAEwHc5pz7qHPuiwBeB+BZC+u0JZ4F4Obm980Anr2gLtXjnPsdAHcFmy0bPwvAzzcPkX4HgCMi8vB5NF0Phs0tngXgdc65S865jwG4DXtjEOmAc+6Tzrn3NL//G4APA7gK9PXJiNjcgr4+kMZfP9v8fUDzcQCeAuANzfbQz1v/fwOAp4pI9AG0pQZmVwH4uPf/DsSdjfTHAfhtEbm1eesCADzUOfdJYK/jA/iaxbRbL5aN6fvT8uJm2ey13hI9bT4yzXLNNwN4J+jrsxDYHKCvzsvYegAABOBJREFUT4aI3E9E3gvg0wDeCuAPAVx0zt3bJPHtep/Nm/33ADgay7/UwEyLJnn76DR8i3PuCdhbVniRiHzr0gptHPr+dLwKwNdhb/nhkwD+XbOdNh8REfkKAL8C4CXOuT+LJVW20e49UGxOX58Q59yXnHOPx97bi54I4Bu0ZM13Z5uXGpglX91ExsE594nm+9MAfg17Tvapdkmh+f70chquFsvG9P2JcM59qhlQ/xLAz+DyEg5tPhIi8gDsBQi/4Jz71WYzfX1CNJvT1+fBOXcRwFnsXd93RETaZ8P6dr3P5s3+w0hcZlFqYMZXN82AiDxIRP5K+xvAdwL4APZsfapJdgrAG5fRcNVYNn4TgO9r7lh7EoB72mUgMozg+qW/jT1fB/Zs/pzm7qlHYe9i9HfNrV/tNNfNvAbAh51z/4e3i74+EZbN6evTISJfLSJHmt8PBPDt2Lu27wyA726ShX7e+v93A3i7SzxAdvYn/+fAVzfNxkMB/FpzHeL9Afyic+4/i8i7AbxeRF4A4I8AfM+COlaPiPwSgJMAHiIidwDYAfAK6DZ+M4BnYu+i3D8H8P2zK7wCDJufFJHHY28Z4QKAfwgAzrkPisjrAXwIe3e5vcg596Ul9K6cbwHwPADvb66/AYB/Cvr6lFg2fy59fTIeDuDm5m7WLwPweufcb4rIhwC8TkT+OYDfxV7AjOb7/xaR27A3U/acVAF88j8hhBBCSCGUupRJCCGEELI5GJgRQgghhBQCAzNCCCGEkEJgYEYIIYQQUggMzAghhBBCCqHIx2UQQoiIHMXeS68B4GEAvgTgT5v/f+6c+xsjlfNsAN/knPtnA/P5MQBvds69fQy9CCHbhI/LIIQUj4jsAvisc+7HJsj7/wPwXc65zwzM5xiAn3HOfec4mhFCtgiXMgkh1SEin22+T4rILSLyehH5AxF5hYh8r4i8S0TeLyJf16T7ahH5FRF5d/P5lmb7tQAutUGZiNwkIq8SkTMi8lEROdG8BPrDInJTk+Z+TboPNGX8YwBwzt0O4KiIPGwBkxBCVgKXMgkhtfM47L1E+C4AHwXws865J4rIDwP4IQAvAfATAH7cOfdfROSvYu+tIt+AvSenvyfI78EAngLguwD8RpPm7wN4d/M09fsBuMo591gAaF/P0vCeJv2vTFFRQsj6YWBGCKmdd7fvWBSRPwTw28329wP4tub3twN4TPP6MQD4yuY9sQ/H5evWWn7DOedE5P0APuWce3+T9wcBHAdwC4CvFZF/D+C3vPKAvRd0P2LEuhFCNgYDM0JI7Vzyfv+l9/8vcXmM+zIAT3bOfd4XFJHPAzhs5OfndV9+zrm7ReRxAJ4G4EUA/h6AH2jSXAlgXxmEENIFXmNGCNkCvw3gxe2fZkkSAD4M4NFdMhKRhwD4MufcrwD4UQBP8HZfC+ADw1QlhGwZBmaEkC3wjwDcICLvE5EPAXhhs/13AHyzeGucGVwF4KyIvBfATQBeDgAi8gDsBXnnRtOaELI5+LgMQsimEZGfwN51Zf/vwHz+NoAnOOd+dBzNCCFbhDNmhJCt8y8BfPkI+dwfwL8bIR9CyIbhjBkhhBBCSCFwxowQQgghpBAYmBFCCCGEFAIDM0IIIYSQQmBgRgghhBBSCAzMCCGEEEIKgYEZIYQQQkgh/P/tcZy/Ez+zngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plot_spiking(N_e, N_i, ell_ex, ell_in, t_start=0, t_end=300)\n",
    "_=fig.suptitle('Ellipsoid')"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}