2022-11-29_SPADE_rate_modulation_Mat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "92f04e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import quantities as pq\n",
    "import neo\n",
    "import elephant\n",
    "import viziphant\n",
    "import pickle\n",
    "np.random.seed(4542)\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d4c4024",
   "metadata": {},
   "outputs": [],
   "source": [
    "# time step for simulation: 1ms\n",
    "dt = 0.001\n",
    "\n",
    "# number of inputs\n",
    "m = 20\n",
    "\n",
    "# number of time points\n",
    "T = 1.0\n",
    "n_T = int(T/dt) # 1 seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182d727c",
   "metadata": {},
   "source": [
    "Let's start with synchronized activity with jitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8b25584b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcb39047f70>]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalized kernel for modulation of firing rate\n",
    "tau = 0.005 # time constant\n",
    "size_kernel = int(tau*5/dt)\n",
    "kernel = np.exp(-np.arange(size_kernel)*dt/tau)\n",
    "kernel /= kernel.sum() * dt\n",
    "\n",
    "print(kernel.sum())\n",
    "\n",
    "# plot\n",
    "plt.plot(kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "609f0fa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[358 387 709]\n"
     ]
    }
   ],
   "source": [
    "# mean firing rate\n",
    "mean_rate = 5.0\n",
    "# correlated event rate\n",
    "event_rate = 2.0\n",
    "\n",
    "# instantaneous firing rate of neurons\n",
    "rho = np.ones([n_T,m]) * mean_rate\n",
    "# determine events at random times\n",
    "bool_events = np.random.rand(n_T)<event_rate*dt\n",
    "ts = np.arange(n_T)[bool_events]\n",
    "\n",
    "print(ts)\n",
    "\n",
    "\n",
    "# modulate firing rate for affected neurons (n)\n",
    "n = 10\n",
    "nrn_pat = np.arange(n)\n",
    "# modulate firing rate at event times\n",
    "for i in nrn_pat:\n",
    "    # subtract event rate from neurons involved in pattern\n",
    "    rho[:,i] -= event_rate\n",
    "    # loop over events\n",
    "    for t in ts:\n",
    "        # check the \n",
    "        t_end = min(t+size_kernel,n_T) - t\n",
    "        # add kernel \n",
    "        rho[t:t+t_end,i] += kernel[:t_end]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9761e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pattern definition\n",
    "class pattern:\n",
    "    def __init__(self, n_neuron, neuron_list, latencies):\n",
    "        self.n_neuron = n_neuron\n",
    "        self.neuron_list = neuron_list\n",
    "        latencies = latencies\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "05bdc783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integral of firing rate over time: [6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5.]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGiCAYAAADEJZ3cAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABbOklEQVR4nO3deXxU9b3/8deZmcwkhCysWSCyya6CoiKgotUrotdKtXVpq7jftthby7VVtK61pdf22k2rrbeKrbVYW0Wv+kMRxA1QAYMgi+whK1lnkkkymZlzfn9MMkkkC8FM5iR5Px+PPOjMnM/MZ6Ynzjvf8z3fY1iWZSEiIiJiY454NyAiIiLSGQUWERERsT0FFhEREbE9BRYRERGxPQUWERERsT0FFhEREbE9BRYRERGxPQUWERERsT0FFhEREbE9BRYRERGxvS4FlqVLl3LaaaeRkpLC8OHDWbBgAbt27Wq1TX19PYsWLWLIkCEMHDiQyy+/nJKSkg6f17Is7r33XrKyskhKSuL8889n9+7dXX83IiIi0id1KbC88847LFq0iA0bNrBq1SqCwSAXXHABfr8/us0Pf/hD/u///o8XXniBd955h8LCQi677LIOn/fhhx/md7/7HU888QQffvghycnJzJs3j/r6+mN7VyIiItKnGF/m4oelpaUMHz6cd955h7PPPhuv18uwYcN47rnn+PrXvw7Azp07mTx5MuvXr+eMM8444jksyyI7O5v/+q//4vbbbwfA6/WSkZHBsmXLuOqqq461PREREekjXF+m2Ov1AjB48GAANm3aRDAY5Pzzz49uM2nSJI477rh2A8v+/fspLi5uVZOWlsbMmTNZv359m4ElEAgQCASit03TpKKigiFDhmAYxpd5SyIiItJDLMuiurqa7OxsHI6OD/occ2AxTZPbbruNOXPmcMIJJwBQXFyM2+0mPT291bYZGRkUFxe3+TxN92dkZBx1zdKlS3nggQeOtXURERGxkUOHDjFy5MgOtznmwLJo0SK2bdvG+++/f6xPccyWLFnC4sWLo7e9Xi/HHXcchw4dIjU1tcf7ERERka7z+Xzk5OSQkpLS6bbHFFhuvfVWXn31Vd59991WiSgzM5OGhgaqqqpajbKUlJSQmZnZ5nM13V9SUkJWVlarmunTp7dZ4/F48Hg8R9yfmpqqwCIiItLLHM10ji6dJWRZFrfeeisvvfQSa9asYcyYMa0enzFjBgkJCaxevTp6365du8jLy2PWrFltPueYMWPIzMxsVePz+fjwww/brREREZH+pUuBZdGiRTz77LM899xzpKSkUFxcTHFxMXV1dUBksuyNN97I4sWLefvtt9m0aRPXX389s2bNajXhdtKkSbz00ktAJFXddtttPPTQQ7zyyits3bqVa6+9luzsbBYsWNB971RERER6rS4dEnr88ccBOOecc1rd//TTT3PdddcB8Otf/xqHw8Hll19OIBBg3rx5/OEPf2i1/a5du6JnGAH8+Mc/xu/3c8stt1BVVcWZZ57JypUrSUxMPIa3JCIiIn3Nl1qHxS58Ph9paWl4vV7NYREREekluvL9rWsJiYiIiO0psIiIiIjtKbCIiIiI7SmwiIiIiO0psIiIiIjtKbCIiIiI7SmwiIiIiO0d88UP+wPLsqg1zXi3ISIiYgsDHI6juu5PLCiwdKDWNBn37tZ4tyEiImILe88+kWSnMy6vrUNCIiIiYnsaYenAAIeDvWefGO82REREbGGAI37jHAosHTAMI25DXyIiItJMh4RERETE9hRYRERExPYUWERERMT2FFhERETE9hRYRERExPYUWERERMT2FFhERETE9hRYRERExPYUWERERMT2FFhERETE9hRYRERExPYUWERERMT2FFhERETE9nS15g5YloVp1sW7DREREVtwOJIwDCMur63A0gHTrGPtOyfGuw0RERFbOGfuVpzOAXF5bR0SEhEREdvTCEsHHI4kzpm7Nd5tiIiI2ILDkRS311Zg6YBhGHEb+hIREZFmOiQkIiIitqfAIiIiIranwCIiIiK21+XA8u6773LJJZeQnZ2NYRisWLGi1eOGYbT588tf/rLd57z//vuP2H7SpEldfjMiIiLSN3U5sPj9fqZNm8Zjjz3W5uNFRUWtfp566ikMw+Dyyy/v8HmnTp3aqu7999/vamsiIiLSR3X5LKH58+czf/78dh/PzMxsdfvll1/m3HPPZezYsR034nIdUSsiIiICMZ7DUlJSwmuvvcaNN97Y6ba7d+8mOzubsWPH8q1vfYu8vLx2tw0EAvh8vlY/IiIi0nfFNLA888wzpKSkcNlll3W43cyZM1m2bBkrV67k8ccfZ//+/Zx11llUV1e3uf3SpUtJS0uL/uTk5MSifREREbEJw7Is65iLDYOXXnqJBQsWtPn4pEmT+Ld/+zd+//vfd+l5q6qqGDVqFI888kibozOBQIBAIBC97fP5yMnJwev1kpqa2qXXEhERkfjw+XykpaUd1fd3zFa6fe+999i1axfPP/98l2vT09OZMGECe/bsafNxj8eDx+P5si2KiIhILxGzQ0J//vOfmTFjBtOmTetybU1NDXv37iUrKysGnYmIiEhv0+XAUlNTQ25uLrm5uQDs37+f3NzcVpNkfT4fL7zwAjfddFObz3Heeefx6KOPRm/ffvvtvPPOOxw4cIB169bxta99DafTydVXX93V9kRERKQP6vIhoY0bN3LuuedGby9evBiAhQsXsmzZMgCWL1+OZVntBo69e/dSVlYWvZ2fn8/VV19NeXk5w4YN48wzz2TDhg0MGzasq+2JiIhIH/SlJt3aRVcm7XSFZVkEg8Fuez4REZHeLCEhAcMwuu35bDHpti8IBoP8/Oc/j3cbIiIitnDXXXfhdrvj8tq6+KGIiIjYng4JdUCHhERERJrpkJBNGYYRt6EvERERaaZDQiIiImJ7CiwiIiJiewosIiIiYnsKLCIiImJ7CiwiIiJiewosIiIiYnsKLCIiImJ7CiwiIiJiewosIiIiYnsKLCIiImJ7CiwiIiJiewosIiIiYnu6+GEHLMvCCprxbkNERMQWjARHt16tuSsUWDpgBU0K710X7zZERERsIfvB2RhuZ1xeW4eERERExPY0wtIBI8FB9oOz492GiIiILRgJ8RvnUGDpgGEYcRv6EhERkWY6JCQiIiK2p8AiIiIitqfAIiIiIranwCIiIiK2p8AiIiIitqfAIiIiIranwCIiIiK2p8AiIiIitqfAIiIiIranwCIiIiK2p8AiIiIitqfAIiIiIrbX5cDy7rvvcskll5CdnY1hGKxYsaLV49ddd13kooEtfi688MJOn/exxx5j9OjRJCYmMnPmTD766KOutiYiIiJ9VJev1uz3+5k2bRo33HADl112WZvbXHjhhTz99NPR2x6Pp8PnfP7551m8eDFPPPEEM2fO5De/+Q3z5s1j165dDB8+vKstdhvLsggFAnF7fRERETtxeTwYhhGf1+5qwfz585k/f36H23g8HjIzM4/6OR955BFuvvlmrr/+egCeeOIJXnvtNZ566inuvPPOrrbYbUKBAL9b+PW4vb6IiIid/Ocz/yQhMTEurx2TOSxr165l+PDhTJw4ke9+97uUl5e3u21DQwObNm3i/PPPb27K4eD8889n/fr1bdYEAgF8Pl+rHxEREem7ujzC0pkLL7yQyy67jDFjxrB3717uuusu5s+fz/r163E6nUdsX1ZWRjgcJiMjo9X9GRkZ7Ny5s83XWLp0KQ888EB3t34El8fDfz7zz5i/joiISG/g6mSKR0xfu7uf8Kqrror+7xNPPJGTTjqJcePGsXbtWs4777xueY0lS5awePHi6G2fz0dOTk63PHdLhmHEbehLREREmsX8tOaxY8cydOhQ9uzZ0+bjQ4cOxel0UlJS0ur+kpKSdufBeDweUlNTW/2IiIhI3xXzwJKfn095eTlZWVltPu52u5kxYwarV6+O3meaJqtXr2bWrFmxbk9ERER6gS4HlpqaGnJzc8nNzQVg//795ObmkpeXR01NDT/60Y/YsGEDBw4cYPXq1Vx66aUcf/zxzJs3L/oc5513Ho8++mj09uLFi3nyySd55pln2LFjB9/97nfx+/3Rs4ZERESkf+vyHJaNGzdy7rnnRm83zSVZuHAhjz/+OJ9++inPPPMMVVVVZGdnc8EFF/DTn/601Vose/fupaysLHr7yiuvpLS0lHvvvZfi4mKmT5/OypUrj5iIKyIiIv2TYVmWFe8mviyfz0daWhper1fzWURERHqJrnx/61pCIiIiYnsKLCIiImJ7CiwiIiJiewosIiIiYnsKLCIiImJ7CiwiIiJiewosIiIiYnsKLCIiImJ73X615r7EsixCDWa82xAREbEFl9uBYRjxee24vGovEWow+dMP3ol3GyIiIrZwy2/nkuBxxuW1dUhIREREbE8jLB1wuR3c8tu58W5DRETEFlzu+I1zKLB0wDCMuA19iYiISDMdEhIRERHbU2ARERER21NgEREREdtTYBERERHbU2ARERER21NgEREREdtTYBERERHbU2ARERER21NgEREREdtTYBERERHbU2ARERER21NgEREREdvTxQ87YFkWVl1dvNsQERGxBSMpCcMw4vLaCiwdsOrq2HXKjHi3ISIiYgsTN2/CGDAgLq+tQ0IiIiJiexph6YCRlMTEzZvi3YaIiIgtGElJcXttBZYOGIYRt6EvERERaaZDQiIiImJ7CiwiIiJiewosIiIiYntdDizvvvsul1xyCdnZ2RiGwYoVK6KPBYNB7rjjDk488USSk5PJzs7m2muvpbCwsMPnvP/++yPzRVr8TJo0qctvRkRERPqmLgcWv9/PtGnTeOyxx454rLa2ls2bN3PPPfewefNmXnzxRXbt2sVXv/rVTp936tSpFBUVRX/ef//9rrYmIiIifVSXzxKaP38+8+fPb/OxtLQ0Vq1a1eq+Rx99lNNPP528vDyOO+649htxucjMzOxqOyIiItIPxHwOi9frxTAM0tPTO9xu9+7dZGdnM3bsWL71rW+Rl5fX7raBQACfz9fqR0RERPqumAaW+vp67rjjDq6++mpSU1Pb3W7mzJksW7aMlStX8vjjj7N//37OOussqqur29x+6dKlpKWlRX9ycnJi9RZERETEBgzLsqxjLjYMXnrpJRYsWHDEY8FgkMsvv5z8/HzWrl3bYWD5oqqqKkaNGsUjjzzCjTfeeMTjgUCAQCAQve3z+cjJycHr9XbpdURERCR+fD4faWlpR/X9HZOVboPBIFdccQUHDx5kzZo1XQ4R6enpTJgwgT179rT5uMfjwePxdEerIiIi0gt0+yGhprCye/du3nrrLYYMGdLl56ipqWHv3r1kZWV1d3siIiLSC3U5sNTU1JCbm0tubi4A+/fvJzc3l7y8PILBIF//+tfZuHEjf/vb3wiHwxQXF1NcXExDQ0P0Oc477zweffTR6O3bb7+dd955hwMHDrBu3Tq+9rWv4XQ6ufrqq7/8OxQREZFer8uHhDZu3Mi5554bvb148WIAFi5cyP33388rr7wCwPTp01vVvf3225xzzjkA7N27l7Kysuhj+fn5XH311ZSXlzNs2DDOPPNMNmzYwLBhw7ranoiIiPRBX2rSrV10ZdJOV1iWRV2ortueT0REpDdLciVhGEa3PV/cJ932FXWhOmY+NzPebYiIiNjCh9/8kAEJA+Ly2rr4oYiIiNieRlg6kORK4sNvfhjvNkRERGwhyZUUt9dWYOmAYRhxG/oSERGRZjokJCIiIranwCIiIiK2p8AiIiIitqfAIiIiIranwCIiIiK2p8AiIiIitqfAIiIiIranwCIiIiK2p8AiIiIitqfAIiIiIranwCIiIiK2p8AiIiIitqfAIiIiIranqzV3xLIgWBvvLkREROwhYQAYRlxeWoGlI8Fa+Hl2vLsQERGxh7sKwZ0cl5fWISERERGxPY2wdCRhQCRNioiISOR7MU4UWDpiGHEb+hJpcqi+gZ/szuc/Rg5n9qCBR1VTEQxx+85DXJk1mHlD02LcofRVD+8vwh82eeD4EfFuRUSHhETs7s5d+bxR5uOy3D1HXfPbAyW8XuZl4db9MexM+rKwZfHIgRL+eKiUfbWBeLcjosAiYndlwWCXa3zhcAw6kf4kZFnR/324oev7oEh3U2ARsblBrq4fuU13OWPQifQnLQNLVVABWOJPgUXE5tITuh4+BiU0h5ygaXWwpUjbWu42laFQ/BoRaaTAImJz6ccQPgY6m3+1K4L6spGu0wiL2I0Ci4jNpbYIH5VHGT5axpoyBRY5Bi0Di0Kv2IECi4jNHUv4CLf4silr0JeNdF3LwbxyBRaxAQUWEZsLHUP4COnLRr6klvud9iGxAwUWEZtr+ZfusY2w6JRU6TqN0ondKLCI2Fyrv3SP8otDXzbyZYVbBmXtQ2IDCiwiNtcysJQe5WhJyy+bUg3nyzFotd9pHxIbUGARsTmzxf8uDBxdYGn5ZVNUr0NC0nXhFtO9a8MmXoUWibMuL6H57rvv8stf/pJNmzZRVFTESy+9xIIFC6KPW5bFfffdx5NPPklVVRVz5szh8ccfZ/z48R0+72OPPcYvf/lLiouLmTZtGr///e85/fTTu/yGupNlWdRp/QGJs/qGMIQiseVATT21RzE8Hwh2vUakJX+geR8C+NxXx9SUpDh2JHaQlODEMIy4vHaXA4vf72fatGnccMMNXHbZZUc8/vDDD/O73/2OZ555hjFjxnDPPfcwb948tm/fTmJiYpvP+fzzz7N48WKeeOIJZs6cyW9+8xvmzZvHrl27GD58eNffVTepC4aZcu8bcXt9kSZNvznbgCkvHt1FEJtqioEprx7o/qakz2v5X+xvrC6KWx9iH9sfnMcAd9cvF9IdDMuyjnndbsMwWo2wWJZFdnY2//Vf/8Xtt98OgNfrJSMjg2XLlnHVVVe1+TwzZ87ktNNO49FHHwXANE1ycnL4/ve/z5133nnE9oFAgECg+eqhPp+PnJwcvF4vqampx/p2jlDbEFJgERERadTdgcXn85GWlnZU39/dGpP2799PcXEx559/fvS+tLQ0Zs6cyfr169sMLA0NDWzatIklS5ZE73M4HJx//vmsX7++zddZunQpDzzwQHe23qakBCfbH5wX89cR6ch/bs/j1dKq6O3Vp01izAB3hzUP7ilgWUF59PY/po3j1PTkWLUofdBmby1fz20ezVuYPYT7xo+IY0diB0nHcG2z7tKtgaW4uBiAjIyMVvdnZGREH/uisrIywuFwmzU7d+5ss2bJkiUsXrw4ertphKW7GYYRt6EvkSiXAa7m+fFlZpipneyXhsvZqqbUDGtfli5xJTha7UNFYe1DEl+9cu/zeDx4PJ54tyHSI+oC5bT8Vc2rDwApHdbUN1S0up1X3xCDzqQvC33hQpvahyTeuvW05szMTABKSkpa3V9SUhJ97IuGDh2K0+nsUo1If+Kr+RyARKsOgF3++k5rqqp3dLlGpKWQFTmzLNGqBWBfbeCorxYuEgvdGljGjBlDZmYmq1evjt7n8/n48MMPmTVrVps1brebGTNmtKoxTZPVq1e3WyPSn5iOyHyVMewFYFt1XedFhqdVzWc1R1Ej0kLIjJzSnEkxAx0WDZbF7loFX4mfLgeWmpoacnNzyc3NBSITbXNzc8nLy8MwDG677TYeeughXnnlFbZu3cq1115LdnZ2q7VazjvvvOgZQQCLFy/mySef5JlnnmHHjh1897vfxe/3c/3113/pNyjS+0UCy9gW4aOzk/usxsDSVLOnNoA/rDWF5OiFrcj+4iDMxMTI4aBtCr4SR12ew7Jx40bOPffc6O2mya8LFy5k2bJl/PjHP8bv93PLLbdQVVXFmWeeycqVK1utwbJ3717Kysqit6+88kpKS0u59957KS4uZvr06axcufKIibgi/ZHpSABgJHm4DYPqsElefQOjktqfx9U0KjOIcoa7nRxuCLOzpp4ZaTpTSI5O0IwEFidhJrhr2FTr4bPqOtCReomTLgeWc845p8O/7gzD4MEHH+TBBx9sd5sDBw4ccd+tt97Krbfe2tV2RPo8s3GExUOACcluttUE2FJd13FgIXLqoROTyUkODjeEya2uVWCRo9Y8wmIyzlUJDGFLdW18m5J+TdcSErE5y4iEDwcmMwZGlsR+r7K6w5qmix86MDk5OXhUNSIthazIHBYHJlNd+QBs8tVSE9KhRYkPBRYRmwtFw0eYMwdGVnheU+7rcKQz3KJm1gAvAO9V1tBgmu3WiLTUNOnWSZjh5iFGJ7kJWhbvV9bEuTPprxRYRGzObDFaMt1TgcdhUBAIsrODU5Wb/gZ2YDLOcZihCS78YZMPq/yxb1j6hJaHhBqC5XxlcGTZ9LfKffFsS/oxBRYRmwsTOQzkxMQZKuecwZFF454rKm+/pjHkOAkTCpZz4dA0AP7WQY1IS00jLA7CNDSUM79xH1pxuFKHhSQuFFhEbC7UYoQlGCznuuyhACwvqsAbDLVZE248XBSpqWDhiCEAvFpaRYFWLJWj0HIOSzBYwZxBAzl+gIeasMny4opOqkW6nwKLiM2ZjSMsDsIEGsqYOziF8QM8VIdNfr6vqM2asNWyppQTUwYwKz2ZkAX37inodB0XkbDVNIfFpKGhDAOLm0YOA+BX+4spbQjGsz3phxRYRGyuKXw4CVNfX4DDMFg6YSQAzxSWs6yg7Miaxn+dmNTXFwDws/EjcRrwWqmX/zlQotAiHQq2OCRkWSECDYf5dtYQThiYRFUozPVb97c7wicSCwosIjbXcgJtfV3k9NIzB6Xwn8cNB+DOz/O5bus+3q+sJtD4JdPytOammikDk7h/3AgAfnWgmCu27OWtch+1YZ05JEcKtzgkBFBfl4/LYfDolONIdznZ6KvlKx/v4s/5pZQENNoisdcrr9bcU0KmxU/3Fsa7DennvGZkgTgHYerqi6P3Lxmbhdvh4JEDxaws87GyzIfHYTB+QCLbGtKjNYGGEkwzgMPh4eacYTgMeGBPIe9V1vBeZQ0uA8YNSOT0tGQGOPQ3jERsrmpe6Ragrj6fdE5lUnIS/5g+jpu3HeBgfQN37y7g7t0FZHsSmJGaTLYnIZ5tSww5DYN7j8+O2+srsHTAxOKP+aXxbkP6vchKt8nUEgr5CAZ9JCSkYhgGt4/J5N+Hp/G/h8pYWealLBhqdb2XZCKnMdfXFzJgwBgAbhw5jPOHpPJkfimvHvZS3BBkl79eV3SWNg1o2ocaR+oATkoZwJrTJ/J8UQV/L6pgW00dhYEghaVVcepSeoLHocBiWw4Mvt847C4SL6Wlq0irfZdMIhNs6+vzSUiYEn18UnISv5qUwy+tkRysb2BtRTVbC9/GXf0249kFQF1dfjSwAIxK8vDQ+JH89PgRFASCvFtZzf7aQM++MbG1+kAxFcX/4BxWA5ERlpaSnU5uGDmMG0YOwxsMsdlXy4def/QMNel7XIYR39eP66vbnMthcPe4+KVJEYAtNRsoq10bvV1fn09KypQjtjMMg9FJHq4b4WFH9acUVr/RqqYthmEwMtHNN7OGdHvf0rtVVuazufiF6O329iGAtAQX5w5J5dwhqT3RmvRTOmAtYnNlZSWtbtfW5XVeU9q6pu4oakRaMs3WE2m1D0m8KbCI2FyNP3LRwvr6yJWWa2p2dl5T0/UakZbCZuSU5aZ9qL6+gFBIF9CU+FFgEbE5pzNy3LimOnLYpqZmR+c1rtY11TXbY9Sd9FVmODLCEgx6cDojc/mqqzvf90RiRYFFxOaaAkt1zWAA/P49mGbHE2RdTSHHPwhw0NBQRiCgM97k6JlWZITFsgycztEA1Cj4ShwpsIjYnLPxtzRQPxCXKw3LClHj391hjaMxsIRDCSQljgKguuazmPYpfYtpNgUWB5YZWVm5ulqBReJHgUXE5hzOyL+W5SA5eTIAPm9uhzWGYUZrPJ7xR1Uj0lLTISEsg1Aocrak15cbv4ak31NgEbG9yLoWlmWQlHQyAOUV73VcYYUbKw3c7mlHVSPSkmk270N1dSMxDCe1tXupqyuIc2fSXymwiNhe4xeHZZDgmg5AZeU6TLOh3QrLMqM1MBUAn28LwWBlTDuVvqPptGbLcuD3W6SmNoXld+LZlvRjCiwittcUWBwEg5m43UMJh2upqPig/ZLGERYsg/r6RAYmTwQsSktXx75d6ROsFpNu/X4/Q4acDUBp6ZvxbEv6MQUWEZuLjpZgUFtbR0bGJQDkF/yt/RqaJ0z6/X4yMr7aWPNXLC2dLkchekioMbBkDP93wKCi4j1qaw/EtTfpnxRYRGyv+fCO3+9n5IhvAVBevrb9szas1jXZ2d/A4XBTXb2NisoORmZEGrU+JORnwIBRDBkyF4CDB/8Yz9akn1JgEbG9psM7DqqrqxkwYAzDh18EWOzYeRfh8JFrslgt5r1UV1fjdg8hO/sqAHbtulcrlkqnmkZYsAzq6uoIBoOMGvUdAAqL/kFF5fo4dif9kQKLiO01j5ZUVVUBMGH8T3C5Uqiu3srWrd85cjKt1Xxac1PNuLGL8Xgyqas7SG7u9dQHinvqDUgvZFpNIyyRNX28Xi+D0k8jO+sKAD799DuUl78bt/6k/1FgEbG9IwOLx5PBSSc+gcPhobziXT5YN5edu+6h5PD/o7b2QPMIC801LlcK0056EpcrBa/vE9av/wrbt/+Y4uJX8Pv3aG6LtGK1OK0ZaA7LE+4lPf10wuEacrdcz6bNV3Po0DN4vZsJh+vj1a70A654N2BnptnAuvXnxrsN6ecMIzJ60jRaYpomDoeDQYPOYMaMf7Bjxx3U1OykoOA5Cgqea6yhscagvr6euro6kpKSSEmZwqkzXmTHjh/j9X1CUfG/KCr+FwAuVxpOZ1Jc3qPYTzAYOWxoWZG/a5sCi9OZxPRpT7Nn739TUPAcVVUfUVX1EQAOh5uEhMFx6Vdiz2G4mT377bi9vgJLJwIaNpc4MwwIh53U1ycTDoepqakhNTUVgNSUEzj9tFepqPyAstK38Po2N16gzsSyDOpqI9tVVVWRlBQJI8nJY5kx4wW83k0cLl2J17uZ6urthEJeQiFvvN6m2FStPw1oDiwATmciEyfcx6hR/0Fx0Qqqqj7EV72VYLBS/83swxwOd1xfX4GlA4bh4rTTXo53G9LPrXpzFTt2lBAKJQKRL46mwAJgGAZDBp/JkMFnAhBoKGPN6n+xefM+gsGkaE1WVlarmvT0U0lPPxWAYLCSunqtYCrNioqKWPHS69TVRfa1ysojFx1M9GQyevR3gO9gmsHIocWmSeLS5xiNhwfjRYGlA4bhIDXlhHi3If1csPbTViMfVVVVHHfcce1u73EPJRTIIhgsalXTkYSEQSQkDPrSvUrfUVE+kLq6tOjtzvYhhyOBlJTJMe5K+jNNuhWxuUBh61OQy8rKOq/J93W5RqSlcKj1SEl5ebkmZktcKbCI2Fw4FDlLaLA5EICSkpJOa8xjqBFpyQxHAku6mYzDcFBfX4/XqzlOEj8KLCJ254z8M9SMzCUoLu58UqP1hZqSkhJM04xJe9I3meHI/uLCwZDUyOHCo9n3RGKl2wPL6NGjMQzjiJ9Fixa1uf2yZcuO2DYxMbG72xLptczG39KhVgoQWcCrtra2wxrLGZkcl24NICEhgWAwSHl5eUz7lL6lKbA4MBieMgRQYJH46vbA8vHHH1NUVBT9WbVqFQDf+MY32q1JTU1tVXPw4MHubkuk92r8LfVYCaSnpwOdf3FYDqux1MHwocOPqkakJavxkJCBwbCkyNoq2ocknro9sAwbNozMzMzoz6uvvsq4ceOYO3duuzWGYbSqycjI6O62RHot02gKHwbZGZFTkzsL9abZXJM1JPL7dODAgdg1KX1OuHGExcAgwx05JHTw4EEdWpS4iekcloaGBp599lluuOEGDKP987dramoYNWoUOTk5XHrppXz22WcdPm8gEMDn87X6Eemrms7McGAwJnMUAHv27Omkpnk4f8zQkdEaneUhRyt6SMgyGEYaHo+Huro6CgsL49yZ9FcxDSwrVqygqqqK6667rt1tJk6cyFNPPcXLL7/Ms88+i2mazJ49m/z8/HZrli5dSlpaWvQnJycnBt2L2INpNf+lO3rwCAAKCgrw+/3t15jNNSOSM3A6nXi9Xp3eLEfNbDHCQm2YcePGAbB79+54tiX9WEwDy5///Gfmz59PdnZ2u9vMmjWLa6+9lunTpzN37lxefPFFhg0bxh//+Md2a5YsWYLX643+HDp0KBbti9iC2TgqYmCQbHnIzMwEYNu2bZ3XWAbOQGQyPMDWrVtj26z0GU2nNTswMP1Bxo8fD0T2Ox0WkniIWWA5ePAgb731FjfddFOX6hISEjj55JM7HPL2eDykpqa2+hHpq6xoYIFwTZBTTjkFgI8++qjdLw4rOsICZouaTZs2EQqFYt6z9H4tR+nCNUGmTJmCx+OhvLycffv2xbk76Y9iFliefvpphg8fzsUXX9ylunA4zNatW1td90SkPzNbzEcxa4JMmzYNt9tNeXk5W7Zsaaemed5LuKaBSZMmkZKSgt/vZ/369T3Wu/ReLQ8JmTUNeDwepk+fDsCaNWs0yiI9LiaBxTRNnn76aRYuXIjL1fpyRddeey1LliyJ3n7wwQd588032bdvH5s3b+bb3/42Bw8e7PLIjEhfFf1L1zIIewN4PB7OPvtsAN544w0OHz58ZE2LeS9hbwCn08l5550HwDvvvKPDqNIp02w+JGQ1mJh1Ic4880w8Hg+FhYW8/fbbce5Q+puYBJa33nqLvLw8brjhhiMey8vLo6io+aJslZWV3HzzzUyePJmLLroIn8/HunXrmDJlSixaE+l1LJpHS0KV9UBk7tfIkSOpr69n2bJlbNmyhXC4+dovLc8sClcGAJg2bRrjx48nFArx17/+lY8//phgMNjD70Z6CzPcPHcKIFRRT0pKCvPnzwfgvffe4+WXX9Zy/dJjDKsPnOfo8/lIS0vD6/V263wW0zQpKCjotucTORZ/f+Zv1Ibq+WrgVLJShpF110wAamtr+etf/xr9AyA5OZlRo0aRmZnJmjVrAPi3hpMYZQxnxE/nYDgMGhoaeO6556JrsiQmJjJ69GgyMzPJycnB7XbH5T2K/WzZ8AkbP9vM8eFMzglOZci3J5N0wlAAPvjgg+iioIZhkJOTQ3Z2Njk5OZpT2Md191m5Xfn+VmDpQCgU4qGHHuq25xP5MhYETmMoqZHw4YoMjoZCIT744AM++uijNk9zntcwjRxzKFlLTseZ5gEi88Q++ugj1q9frzWMpFPjQ1nMDU0h7eIxpJw1Mnr/gQMHWLt2rRYk7EecTif33HNPtz5nV76/XR0+KgwaNCjeLUg/Z9YESa1PZLAVufJyqCpAwtAkAFwuF3PnzmXOnDnk5+eTn5/Pnj17qCwqZ0CtiwwzPVJTWR8NLE6nk1mzZnH66adTWFjIoUOH2Ldvn641JK1YIRPDG+J4M3IafdOhxSajR4/muuuuo6KigoMHD3Lo0CGthNvHOZ3OuL6+RlhEbK78bzuo29q84NvQG08gcXzHQbry5T341zfPFRt85UQGnDw8Zj1K3xPY76X0j59GbydOHszQhVPj2JH0RV35/o7pwnEi8uXVVla1uh2qqO+0ps7b+lDP0dSItGSFW4+UaB+SeFNgEbG5soMHAAiakSH5UEltpzWH9+9rVRM83HmNSEtmKHLWWXS/K6s7IsSI9CQFFhGbc7oSAKgIFAPQUFTTaY3rCzXBws5rRFpqCic1wUqsBCBsETxcF9+mpF9TYBGxOaczMje+Mho+/J1edbkpsDTVhMrqMBvCHZWItNJ0LSETE7NxaoGCr8STAouIzTmdkfDhDZaB08AKhAl3Mp+gqaYuXINjYAJYECxq/+rOIl/UdEjIsiwaBjQACiwSXwosIjbnaBxhCZlBXMMipzM3FHT8xWE4Ir/apmViDI2El2AnNSItNR0SsjCpT4jMgepsvxOJJQUWEZtzGJFfUwsLIyuyEm1gT1XHRY1zIy1MzKGRpdXrO6sRacEKRXYi0zLxOioAaDhUjRnQ1b4lPhRYROzOavrHJNy4lEr955Udz2OJ1lgE0iPD+YE9VdEvIZHONI+wWPjqy3ANSYSwRWCPrh0k8aHAImJ3TaMllkl9ch24DMJVgY5PbzataI3f4cMxMAGrIUzggL5s5Og0Tbq1LJNabxWeCZHFCut3VcSzLenHFFhE7K7FaEmt3xtd5db/UXH7NS0OCdX6vCRNGdJ5jUgLLeew1Pq8JE2N7EO1W0p1WEjiQoFFxOaMpsBiRb44Bs7KBsC/qQSzrp0vDqtphMWizucleWYWAHXbyglVBdquEWkhGlga9yHP2HRcw5KwAmH8G0vi3J30RwosInbXGFhMTGq9lXiOT8c1PPLF4V25v+0as7nGX1WJe8RA3GPSwLSo+r+9na7jItIUWCL7XRVgMXBOJCz73sojXNMQv+akX1JgEeklLMvEe/gwhsMg/dLjAfB/WEzNhsI2Nm6u8ZWVApD+1XHggPrPyqlenafQIh1qHmExMcNhaqoqSD4ti4SsZKy6EOV/2d7+CJ9IDCiwiNicYUVOS7aw8JUeBiBxXDop5+YAULViL2XPfEb9nsrms4BazHvxHY7MW3FnJZN28Vgg8hdy2f9upW5nhVbAlTZZ4cbDio07k/dwCYbTYPBVEzGSXDTkVVPym03UfFBA2KfRFok9V7wbsDMrbOF9fV+825B+zhmO/Jpalom3tHnSbOoFozCcBr7VedTvqKB+RwU4DRKGDyCxNhGIrKFRU1lBKBjElZBAypwRGA6Dqlf3EdjrJbDXCw4D19AkPKNTMRL0N4xEhPdFzkKzrEgI9pUehklTSchIZthNJ1L+tx2EK+qp+r99VP3fPpypbtyjUnGmuuPZtsSS0yD9orFxe3kFlo5YFjUftDHcLtKDnDgBaDADBPx+6v01JCYPxDAMUs8fRdKJQ6n5oJC67eWYNcFWS/AHzcgS/r7SwwzOHgHAwFnZJE4YRM0HhdRuK8P0NRA6XEtIV3SWNjQ07kPew81h2T1iIBm3nULtphL8HxcTLPIT9jVQt7UsXm1KT3ApsNiXYZByzsh4dyH93O6PN3DwQC41oUogEj4SkwdGH0/ISGbQZeNJ/9rxhCvqqd9dyb53P2Tf55soCxQ01pREAwuAa0gS6V8dR9olYwl7AwR2VxEq15V4pVl1eRlb313FvupPAaKHI5s43E4Gzspm4KxszPoQDXnVBPZ7o2eoSR/kMOL68gosHTCcBmkXjol3G9LPFWx7jgM1n0Vve0tLGD76yL9yDMPANSSJgUOSOLytiL3VudHHvvhl06omPRHXaZnd3rf0btXb/Wx/ZV30tq+0/VOZHYkuEicMIrFxcTmRWNABaxGbq/jCFXK9xUWd1pQXVre6XVXSeY1IS+FQ6zOAqkq06KDElwKLiM3V+iJzCHCkAFCad6DTGr+36zUiLYUbl+Zv2od8pYep9+tqzRI/CiwiNmc4InMCHM4MAEoPdH7mmrPxN7up5vD+vbFpTvqscCgSWAxjAAPSIsvylx08EMeOpL9TYBGxOUc0fETmmZQXHCIUDHZc42z6dziG4aDWW4W/qjKWbUofY0YPCRmkDI2s+XP4gIKvxI8Ci4jNNY2wGM5UPMkDMcNhyg8dPKoaDDfpmZHrCGmURboi3LQIIQ4GpEfOMDt8oJ1LQYj0AAUWEZuLhg8cDDsucnZQ0e5dnVQ1f9kMyh4NQGGnNSLNzHDjCIthkDgwcg2hot0749iR9HcKLCJ2F13XwiBj3BQADny6ucMSs/E6MBgGQ4+bDMDBLR3XiLTUNMJi4MCTPArD4aCiML/dU+RFYk2BRcTmLLPpWj8Ohh53AgB5W7cQDrU/j6VpOXVwMCgrEliK9n5OXbUvhp1KX9I8h8VBMOAie8IkAPbnbopfU9KvKbCI2JxpNo2WOEhMySI5fRDBQD0HP81tvybcHHIgmaHHjQbLYs/GDTHuVvqK6GnNGNRVNzB62gwAdn+0rv0ikRhSYBGxu+hoiUF9TYiJs88GIPfN1zooaTwlFYPa6iCT5swFYMubr2Np6XQ5CtHQazioq25g0uyzwTA4+OknVBbrGmvS8xRYRGzOMpsP79RVNzD9gouAyND84XbWZLFajMrUVTdw4lcuwJmQQMm+PRzcmhv7pqXXM0PNQbmuOkh6ZhZjpkdGWT5++Z/xa0z6LQUWEZtrmo9iYOCvCjAoawQTZp0FlsWbf/w9oYaGI2qih5EaawakpnHSeRcCsPrPfyBQ6z+iRqSllocV6/1BQsEwpy/4BgBb17xJ3rYt8WtO+qVuDyz3338/hmG0+pk0aVKHNS+88AKTJk0iMTGRE088kddff7272xLptVqOllSXR5bcP3fhzXgGJFOybzcv/+ohan3eLxY1/g8H1RWRmjlXXsPAwUOoKi7iX0vvo7q8rIfegfRGTSvdNn1N1FQEGDlpKid+5QIAXv7VzzQBV3pUTEZYpk6dSlFRUfTn/fffb3fbdevWcfXVV3PjjTfyySefsGDBAhYsWMC2bdti0ZpIr2NZzZMffY2BZeCgwVx6+924Etwc2LKZ//3+Tax68lF2rX+fyqICTLNFTVmkxjNgAF+74z48A5Ip+nwnf/7Bzaz8w6/Z8f5ayvPzNLdFWmmew2IA4CurA+Dc625h5OQTaKir5cWl97H8vjvY/P9eoWDXDoINgXi1K/2AYXXzf6Xuv/9+VqxYQW5u7lFtf+WVV+L3+3n11Vej951xxhlMnz6dJ5544qiew+fzkZaWhtfrJTU19VjablM4FOR/v39Ttz2fyLGoqagALNwp38SVmMV3fncOhiPyJVKybw9vPP6bdi9umDBwAc6Esdz0yFl4BiQAUFFYwBuP/4bCz3e02jYxeSAujyeWb0V6kYC/lmCgDod7Cu7kC5n7zYmccHZkxdtgQ4D3nlvGljdfb3HoCJwJCSSldN9/g8VenAkJ3PS7/+3W5+zK97erW1+50e7du8nOziYxMZFZs2axdOlSjjvuuDa3Xb9+PYsXL25137x581ixYkW7zx8IBAgEmpO8zxe7tSVqKspj9twiR8+F4UjDDFnU+hpITo8Ei4yxx3PNw78nb+sW9mzcQNHunRzev69x3ouB4RgEgK+8nmGNgWVw9giuevBhCnftYPdHH1C4aycl+/dGrsSrq/HKFzickQsfNh2OBEhwe/jKdf/B6V/9Otvfe5v87Vsp2rub+mqf/pvZhzkTEuL6+t0eWGbOnMmyZcuYOHEiRUVFPPDAA5x11lls27aNlJSUI7YvLi4mIyOj1X0ZGRkUFxe3+xpLly7lgQce6O7Wj+BwOPn2L34b89cR6ci6F/dQsCuE4UgCIkPzTYEFwDAMRp00nVEnTQfAX1XJu8s/Ye9mP4YjGYDqsnqG5aS0qhkxaQojJkVWzq2rqdYKptJK6aFq3v7rnhaht+6IbQYOHsLpl36d0y/9OuFQiIqCQy0mfEtfYzQeHoyXbg8s8+fPj/7vk046iZkzZzJq1Cj+8Y9/cOONN3bLayxZsqTVqIzP5yMnJ6dbnrslw+EgY8y4bn9eka5wO8sxHM0jH77yerKOb3/75PRBJHqGYrSYodbWl01LSQNTSBp45B8U0n+FgpU4nBXR2y1HWNridLkYNmpMrNuSfiwmh4RaSk9PZ8KECezZs6fNxzMzMykpKWl1X0lJCZmZme0+p8fjwaNj7dJP1O3YCcbI6O2qktrOaz7bDgzvUo1IS2Yw3Op2VUktlmXF/a9s6b9ivg5LTU0Ne/fuJSsrq83HZ82axerVq1vdt2rVKmbNmhXr1kR6haYvjoE1+QCU5Xc+z8QMhrpcI9JS02nNyf4iDAMCtSFqKnUWkMRPtweW22+/nXfeeYcDBw6wbt06vva1r+F0Orn66qsBuPbaa1myZEl0+x/84AesXLmS//mf/2Hnzp3cf//9bNy4kVtvvbW7WxPpnVyRiW4p1QcBKMuv7rwmwdOqprygBtPUacty9KzG0OsIN5CeGtl3yg4dxb4nEiPdHljy8/O5+uqrmThxIldccQVDhgxhw4YNDBs2DIC8vDyKioqi28+ePZvnnnuOP/3pT0ybNo1//vOfrFixghNOOKG7WxPpnVxuAFKqDwGRBbzqa9q/UjMACZGa5NoSXG4HoQYT72EdFpKjZ4YbV1i2wgwaEFlNufSQRuokfrp9Dsvy5cs7fHzt2rVH3PeNb3yDb3zjG93dikjf4HJBAyQE/aQOScRXXk9pfjU5kwa3W2I5I7/ahhlm8HAPh/PrKD1UzaDM5J7qWno5K9h0AU2LtAQ/4NEIi8SVriUkYnPR8GGZDM2KHOop3F3VcU3j0R/DCjN0aOTXvPDzjmtEWmqaw2JYYdLNyGUcCvdU6dCixI0Ci4jNmVbkrAzDMhmRHfnfeZ9VdFSC1filYlgmWYMD0Rotvy9Hy4wGFovUugLcSS4C/hCHD8ZuoU6RjiiwiNhcdLQEk6zUyHoqhw/6qKs58irNTaLXS7RMhiV4cboiF0GsLNY8Fjk6Vos5LFZFOTmTIwvI5W3TSrYSHwosIjbXfHjHxF1XydCcgWDB7o9LOqhpHmExfBWMmJAOwOcftb+CtEhLZqgpsJiEKyoYdUJkif7PPy6JjuCJ9CQFFhGbaxlYwpUVTJmTDcDWtQXtfnGYLUZlQuUVTG6s2f5+IeGglk6XzjVd1NCwTEIVFYw7ZTjuRCfew3Uc2tHxIUmRWFBgEbE5s8UE2lBZORPPyCQh0UlVSS07NxS1WWO1OCQUKi9jzPShJKd7qKsOkrs6r4c6l96s5QhLqLwcd6KLSbMiC4BueHmfJt9Kj1NgEbE5q3HSLZZFsLgId6KLUy8aDcAH/9xDRaG/jZrIv4ZlEioqxul0cMalYwH4+LUDFO/z9kTr0otZjZNusUys2lrCXi+nXDgKd5KL0rxqPnxlX3wblH5HgUXE5prCh8MKEywoBGDaeTlkjEklUBvipf/ZzK4NRYTDzYd6Wk66DRYUADDxjExGnTiEcNDkld/msu2dfEJfuF6MSJPmheMi/wYLCkhO83DWleMB2LzyIKv/soPqio4viijSXWJ+8cPezDItivfrFD6Jr5DlBBrDR37k2kBOp4N/XzSNV36XS2leNW8t28EH/9pD9vh0ho5Mobw+ubEmTLCwEMs0MRwO5t10Aq/94VMKdlXyzt8/Z8PL+xprBpI1Lh2Xxxm39yn24quMrKbcFFgaCgpInDKFSWdkUecLsu6lPexcV8SuDcVkjkll+KhUMsamMnBQYjzblhgyDMgcmxa311dg6YBpWrz4y03xbkP6vchicYZlEjp8GLOhAYfbTeLABC7/0Qw+WXWQT9/Op646yN7NpezdXBqtNCwTKxgkVFpKQkYGCR4nX/3BdLa+nU/u6jxqKgLs31LG/i1l8XpzYnOGFRmFC+YXRO87+YLjyBiTwkf/t5+Cz6so2uulaK8X1sSrS+kJTpeD7zx6TtxeX4GlE2nDkuLdgvRzobIyEkv3kewvAMsiVFiIe/RoAJwJDk69aAwnXzCKkv0+ivd5ydteQdXeQtyleaR5I/MMggUFJGRkAOBwGEw7L4cTzxnB4YPVFO/zcmhHpa41JK2YgQBm4SEySz4GiB5abJI9fhALFg/CW1pH4e5Kivd6Kdzj1SnPfZjDFd9ZJAosHXC6HHz7p7Pi3Yb0c/k/uI3qbW9EbzcUFEQDSxOny0H2+HSyx6dzyrxRFD/4KpVvPRd9PJifD6ec0qrG4XSQOTaNzLFpTD//uJi+B+l9ajdu5OC3fx693XQ48ovShiWRNiyJybOze6o16ac06VbE5sprDre6HTzU9hdHS5V1rVcjbWjny0akPWYo1Op2sED7kMSXAouIze2p+ByAOnfkdmDv3k5rdpVub1XTsKfzGpGWwqHIpNvapn3owEGshvYvByESawosIjbnbjxLaG9mZD2WwI4dndeQ0Kqm/ihqRFoyGwNL8SCwkpOwgkEC+/fHuSvpzxRYRGzObUSmmu2LLDJK/c6dWGbHy+t7aF3TcOAAZq0m1crRC4cjgcV0QGBsZH5K/XYFX4kfBRYRm0toHGHJG2ZgJCRg1tQcccbGFzUFloqBBs6hQ8CyqN+1K+a9St8RDkUO/4QdUDN6GACBnQosEj8KLCI257Yiv6YNLkg4fhwA9Z991mGNo/HM0rADmDC2sWZ7zHqUvscMRibdmgZU5EQWC6vrZL8TiSUFFhGbczReS8h0ANMmA+Bft77josZl1U0HNEyNhBz/+k5qRFpoOiQUdhjkj00BoG7Lp4Rrjrx2lUhPUGARsTmjMXyEHVA3YxIANe+/h2V1sEBXOLI6qekA78mREZba9et1loccNSvUPIclPy1EwqjjIBik9sMNce5M+isFFhG7a5xgaxpQMSUbw+MhVFhEYPfu9muaAosBh0cMwDlkCGZtLbWbdKkJOTpN67CYBlTUVzDwzLMAqFn7Tjzbkn5MgUXE5owWh3fKzWqSzzwTgKoX/tl+kdk8KlPeUEnKeec11rwQ22alz2hahyXsiASWlH87HwDf66/rsJDEhQKLiN21HGGpr2DQVVcB4H3xRcK+dq4mHm6uqayvZNDVkRrfm6sIFhXFvmfp9axw4whLY2AZMHMm7jFjMP1+vC++GOfupD9SYBGxOaPFaElZXRnJc2bjPn4cpt/P4Uceabuo8ZBQ2AGldaUkTp7MgNNOg1CIkp//vOP5LyK0OCTkgIq6CiwsBl97DQCljz1GqExX+JaepcAiYnPNk24NivxFGA4HmffcC0DV8uepeO65I4vM5sNIxf5iADJ+cje4XFSveouyRx9TaJEOmS0OCYWsEKW1paR/4xt4pkzG9HrJX3Rr+yN8IjGgwCJid2YkWJgGFNYUApA883SGfOc/ACh58Kcc+t4i/OvXYwYCkZoW814KqiOLzCVOnEjGj38MQNljj5F3/Q1Ur12LWVfXk+9Gegkz3DzpFqCgpgDD5WLEww/jSEujbssW9n31Uir++izBksMdPJNI93DFuwE7s0IhSh5+ON5tSD/nqY6EENMB+TXNV8wd9oMfYLjdlD36GDVr1lCzZg1GQgLusWMZtLsEgLABh+sO0xBuwO10R4b0nQ4O/+K/qd2wgdoNG8Dlwj16FANOPhkjKSku71Hsx9qyEYPGxQeJBJZTMk7Bc/zxjHr6KfJv+yHBvDxKfvYzSn72M1zDh5M0fTquzIy49i2xYzhdZNzx47i9vgJLR0yTyr/8Nd5dSD/XeLFc/IlQ3VCNr8FHqjsVwzAY9r3vkXrBBVT85a9Ur1lDuKyMQIsl+P2JkT+PC2sKGZ02GoDB3/oWA+fOpeIvf6H6jTcJlZTQsGevrugsrTQOrFDrifxbUNN8OYjEKVMY+/IKvCtWUPXCP6nfsYPQ4cNUv/lmzzcqPcZwuxVYbMvhYMgtt8S7C+nnVuet5j3XfooHN4eP1MGp0cc9xx9P1oMPkPnA/QQLCvC//z7vbnqRdxo+4/ORRGuaAguAe+RIMu+6i4wlSwgVF+Nft56Ggwd78m2JzZXUFvOvg6+yZlrzfteSIymJQVdfzaCrryZcU0Pdli3UbtwYPRwpfY/hcsb19RVYOmC4XAxf/MN4tyH93MdrDvDeoeYwUVBTwKTBk47YzjAM3CNH4r7qKraO2s2qz5svVFfgb/tiiYZhkJCVRfrll3V/49Kr5RVv5F9vvB69/cXA0pJz4EAGzpnDwDlzeqI16ac06VbE5kKlO1vdzq/Ob2fLZuHS1lfVPVR9qFt7kr6v6VpCTbQPSbwpsIjYnOmPnIGR1bguxueVn3deU1PS5RqRlsJmJLA07UOF/kJ8DTqNWeJHgUXE5kKOyJHbqYHIhQt3VuzsaPNIjTOhdU155zUiLYXDkX1nUDhMlnsQAJ9XKPhK/HR7YFm6dCmnnXYaKSkpDB8+nAULFrCrxVkLbVm2bBmGYbT6SUxM7O7WRHqlsLN1YNlXtY+GcMdXXTYbA8ukhgYchoPy+nLK6rQyqRy9phEWpwWTkoYBRxeWRWKl2wPLO++8w6JFi9iwYQOrVq0iGAxywQUX4Pd3fLGs1NRUioqKoj8HdcaCCABm4wjLiFCIVHcqISvEnqo9HdaEHZHAkmxajBoYOVVoR/mOjkpEWokGFmCSKw1QYJH46vazhFauXNnq9rJlyxg+fDibNm3i7LPPbrfOMAwyMzO7ux2RXi/kiJxK6LQsJqeP58PDm/i09FOmDJnSbk0Yq7kmZRT7q/P4tOxTzhp5Vo/0LL1f06Rbp2Ux2YiMeH9a9mk8W5J+LuZzWLxeLwCDBw/ucLuamhpGjRpFTk4Ol156KZ999lm72wYCAXw+X6sfkb6qaVULJ3DGoMjpzB8UftBhTdgKN9cMHA3AuoJ1sWlQ+iSzxQjLDNOF03Cy37u/w9ObRWIppoHFNE1uu+025syZwwknnNDudhMnTuSpp57i5Zdf5tlnn8U0TWbPnk1+ftunby5dupS0tLToT05OTqzegkjcha1IZHFaMKcxfHxY9CHBL5x22naNxeykLAC2lm2lqr4qpr1K3xFqGmHBIrXOy7Rh0wB4v+D9eLYl/VhMA8uiRYvYtm0by5cv73C7WbNmce211zJ9+nTmzp3Liy++yLBhw/jjH//Y5vZLlizB6/VGfw4d0voA0neFmsIHFpOMJIYmDaUuVMf6ovXt1rQcYckIBhg/aDwWFm8fersnWpY+oGkOi8MC/GXMGRFZFG513uo4diX9WcwCy6233sqrr77K22+/zciRI7tUm5CQwMknn8yePW1PLPR4PKSmprb6EemrTJoCCxi1ZcwfMx+A5Tvb/0Mg3CLk4C/j4jEXA/D3nX/HsqzYNix9gmlG1l9xAfhLmT9mPgYG6wrXcdCnkyKk53V7YLEsi1tvvZWXXnqJNWvWMGbMmC4/RzgcZuvWrWRlZXV3eyK9TthqnkCLv4wrJ14JRIbm2zvzp+VhJPylXDb+MtwONzsqdrC+sP2RGZEmocbAEtnvSslJyeHMEWcC8NS2p+LZmvRT3R5YFi1axLPPPstzzz1HSkoKxcXFFBcXU1dXF93m2muvZcmSJdHbDz74IG+++Sb79u1j8+bNfPvb3+bgwYPcdNNN3d2eSK/TFD4cANWFjEodxbzR87CwuH/9/QTCgY5rfEUMShzE1yd8HYCHPnyI6obqnmleei0z3DzplroKCNZz04mR/ya/uPtFNhRtiF9z0i91e2B5/PHH8Xq9nHPOOWRlZUV/nn/++eg2eXl5FBUVRW9XVlZy8803M3nyZC666CJ8Ph/r1q1jypT2T9sU6S+aTlF2WRZU5QFwx2l3kOJOYXv5dn6w5gdU1le2qjEbA4vLssAbqfn+yd9n+IDhHKo+xHfe+g7F/uIefBfS24TM5tOaAfDmc0rGKVw+/nIAbnv7Nt7Lfy9e7Uk/1O3rsBzN8fG1a9e2uv3rX/+aX//6193dikif0Gq0pDGwDBswjN+e+1u++9Z3+aDwA+b9ax4Xj72YmVkzmTx4cnSirgOgMlIz0D2Qx857jBtW3sCnpZ9y8YsXc+GYC5mVPYtJgyYxLn0chmH0/BsUWwpbkUNC0b9qqw7A0OO58/Q7Oeg7yMaSjXxv9fc4Zfgp/Nuof+OEoScwafAkEl1apVxiw7D6wAw8n89HWloaXq+3WyfgBsNBLvzXhd32fCLHoqzuMCawvKCYqWHg7hJwRL5GdpTv4J4P7mFXZduXv3is+DBn19XDHQchKR2AA94D3LvuXj45/EmrbVPcKSQ5k2L4TqQ38Tf48Ifr+Wp1DT8rq4B//zWcegMA9aF6frv5tyzfuZxQY7ABcDvcpHvS49SxxFqCM4GVl6/sfMMu6Mr3d7ePsPQ1h+sOx7sFEZJMk+xQCEwTakogNTIhffKQybxwyQt8WPwhb+e9zZbSLeyo2IFpmbgsi9HBxi+TqrxoYBmdNppnLnyGLaVbWHVwFbmluWwv3051QzXVaG6LtDYu2LjeT+PoHkCiK5E7Tr+D60+4nlf3vcrG4o1sK9tGZaBS/83sw9wOd1xfXyMsHTAtk88rdXVSibM37yHz87dINxvXvL3hDTjujHY3L68rp3TVXQzN/QdDm2qufBYmX9JujTfgpchf1O7j0g8Vforn5e8yOhjCAJh6GXzj6XY3D5pB9nv3R+dPSd9jYDBx8MRufU6NsHQTh+Fg0uBJ8W5D+rnaoIsBZosvgaq8DgPLkKQhJJtJJH6xpgNpnjTSPGlftlXpS3xlEGw+3NPZPpTgSGDCoAkxbkr6s5hfS0hEvpydha3PAKJsd6c1n+VXdLlGpKVwKNT6jvI90PsH5KUXU2ARsblwKDKHYLs5KnJHybau1xRvjUlv0neFww0AfG6OwDRcUF8F3rav7ybSExRYRGzO44z8+6nZuGr0UYSPxC/WlHwGZjgG3UlfZYYj+0sdHmpSj4/cqeArcaTAImJzHkdkLso2qzF8eA9BbUUHFZDojAzd77WysRIGQKguMqQvcpSaVroN46A8pXGiZfGncexI+jsFFhGbS3BEwkeVNRBr0OjInZ18cSQ0hpwgLkJDG1eMLtKXjRw9q3GEJYyDoqTGybTahySOFFhEbC7BiISPEE4Cw6dF7jy4rsMao/HwTwgnviEnNdZ8ELMepe8JhyOTbsM42eduDCx563RoUeJGgUXE5hxW81+6FZlnRe7cvarjohY1RUNnR+7b85bO8pCjZjUdErIcfMbx4EmDukoo/KSTSpHYUGARsTmjcSGuMA4ODZkTubPwE/CXtV9jhqI1+5Ong9MTmftS2vYS/iJfZLY4JFRaG4Zx50Qe6Cwsi8SIAouIzRlW89B8sZkGmScBFmz7V/tFTSHHcnI44ILRZ0bu3/pCjLuVvsJsPCQUwkm5vwHGXxB5YOsLkUtEiPQwBRYRm2s5wlJe0wCnXBt54KM/tfvF0XKEpbwmADMWRh7YtAxCgVi3LH2A1WofaoApC8CTChV7Yd+a+DYn/ZICi4jNNY+wOCirCcC0q8CdEjlNectz7dSYrWsmXgypI6C2DNY/1mO9S+9ltph0W1YTAM9AmP6tyIOrf6rJt9LjFFhEbM7R+MUQthwUeevBkwJzfxR58I274PDOI2pahpwibz04XfCVeyIPvvPfcOijHuldeq/mERaD2oYw3tognPlDSEyDolxY81B8G5R+R4FFxOYMmk9rzq+sjdx5xiIYeTrUe+Hp+bBlOTSe1QFE//oN4yS/si5y37SrYMKFEKqHv34NPnoSgvU9+VakF2lehyWybPKhylpIyYD5D0c2eP8RWLFIy/VLj9HVmjtimpCvv0QlvtxmJHCEcVBY0Rg+nC745vOR4FGUCy/9B7xxN4yaDZknMbImssBXCAcFlXWYpoXDYcDXn4K/XwX734XXb4/8lTxqDmSeADkzwZ0cp3cpdpPg3Q9EgjJAfmUtJ4xIiwRffym8eQ/kPgtb/g4jT4XsUyDntMihR+mjDDhuZtxeXYGlI2YInpoX7y6kn0tt/DeEk5LqegKhMB6XEwYMhhvfhHW/gw//BP7DsOOVyE+jME4awiaHqwNkpiVGAsk1KyKjK+sfjZzqvOu1yI9IC0Ma/w1bkYH46EgdwOzvw4gZ8PbP4cB7cOjDyM+Hj/d8o9JznB6453DcXl6BpTODx8a7A+nnDlcH2FKfwS4rBwsorKpnzNDGkRCXB87+Ecz+ARRsjMxN2bua8sJ97KxNY6MZWaE0v7I2ElgAHE444ztw2k2R9VwOfQj71kbO/hBpVB802e8N85IZOSW+VWCByGjeda9Cxf7IysuHPoz8a2kybp/ldMf15RVYOuJyw39qVUeJr/v/tonXtxZHbx+qqG0OLE1c7sgXyKjZcOZt/Pblbfxl/cHmmspaTh09uHWN0xUZws85DWbfGsu3IL3Qp/sruOKP66O3D1XUtr3h4DGRn5O/1UOdSX+lSbciNhcKt15O/1BlO18cLYTNL9RU1LWzpUjbQl9Y4+do9juRWFJgEbE5s/H6PwM9kQHR3SU1ndY0BZZozeHOa0RaasorTfvQgbJaGkJa4VbiR4FFxOZCjeHjxBFpAGwv8nVaE/5CzWeF3hh1J31V0wjLqCEDSEl00RA22Vuq4Cvxo8AiYnNN4eOkkZHwsaPQh2l2fNXlL9bsL/NT2xCKYZfS1zSN7LkcBlOyIueqfVbYeVgWiRUFFhGbawofEzJScDsdVAdCR56x8QVNozLDUxMZluLBsmBHUXXMe5W+o2nulNNhMCU7Eli2K7BIHCmwiNhcU/hITHAyIXMgAJ8WVHVYE27x1/HUxi+brfkd14i01BSUnQ6DqdmRkbqtnex3IrGkwCJic2b0iwNOHRU5NfmDPWUd1oQb/zp2OAxOazyd+f095THsUvqaptDrdBicNnoQAJ/kVVET0KFFiQ8FFhGbC0UDi4O5E4cB8M6uUiyr/XksLUdY5k6I1KzbW6azPOSoNY2wuBwORg1JZvSQAYRMi3WdhGWRWFFgEbE502oeYZk1dggel4NCbz27StqfkxIdzjciEyaHDnRT2xDmo/0VPdKz9H5N+5DDYQBEg++anfFbml36NwUWEZtrnvzoIDHBydmNXxzLPzrUbk3L+QcOh8G/TcmM1HycF+Nupa8Imc2jdADzpkb2oVc/LdJhIYkLBRYRm2s5WgJwzRmjAPjnpny8dcGOaxq/bL59xnEArNxWTEGVVr2VzkVHWBr3u1njhjB2WDI1gRAvbGw/LIvEigKLiM21nPwIcObxQxk/fCA1gRD/vXJnmzVNi3411UzNTuOMsYMJmRYPvPJZh/NfRKDlHJbIPmQYBjfMGQPAr1d9Tml1IG69Sf8Us8Dy2GOPMXr0aBITE5k5cyYfffRRh9u/8MILTJo0icTERE488URef/31WLUm0qtEvzickS8Oh8PgwUtPAOC5D/P4y/oDR9Q0Lave9GUDcP9Xp+JyGLy5vYRfv7VboUU6FB2lczbvQ1edlsMJI1Lx1Ye4+S8b8da2PcInEgsxCSzPP/88ixcv5r777mPz5s1MmzaNefPmcfhw25O11q1bx9VXX82NN97IJ598woIFC1iwYAHbtm2LRXsivcoXh+YhMjx/67nHA3Dvy59x0zMf8/7uMuqDYaB5hMXRIrBMykzlJxdPBuB3q3fzzSc/ZPWOEq2AK2364qFIAJfTwa+vmE76gARyD1Vx4W/f5ekP9lPsrY9Xm9KPGFYM/syaOXMmp512Go8++igApmmSk5PD97//fe68884jtr/yyivx+/28+uqr0fvOOOMMpk+fzhNPPNHp6/l8PtLS0vB6vaSmpnbb+wiFTX72+o5uez6RY/HCxnxqAiFeXjSHaTnp0fsty+L3a/bwm7c+p2mlfrfTwdhhyewsjpxB9OeFp3Le5IxWz/fX9Qf46as7aAhHQo3LYTB6aDKnjhpEktvZI+9J7O+zAh8fHajgspNH8MiV01s/Vujle3/bzMHy5is4D0/xcMpxg8hKT+zhTqWnuBwGd188pVufsyvf365ufWWgoaGBTZs2sWTJkuh9DoeD888/n/Xr17dZs379ehYvXtzqvnnz5rFixYo2tw8EAgQCzcdPfb7YLBdtWvD0Bwdi8twiXZWalNDqtmEY/Od547noxEye+uAAq7aXUFodiIaVtmoArpk1mnMmDufpDw7w/7YVUeStZ8/hGvbois7Shrb2oanZaaz8wdn8a3M+z398iM8KvRyuDrDys+I4dCg9xe1ydHtg6YpuDyxlZWWEw2EyMlr/VZeRkcHOnW1PECwuLm5z++Litnf+pUuX8sADD3RPwx1wGLDo3HExfx2Rzhw/fCBjhia381gKP//aifxswQnkV9bx7u5SCqvqyE5PYsZxg9qsyRk8gHsvmcK9l0yhyFvH+7vLOFDuj+VbkF4o0eXkitNy2nwsye3k22eM4ttnjKK6PsiWQ14+2l8enSQufY/TEd/zdLo9sPSEJUuWtBqR8fl85OS0/Uv1ZbicDn40b1K3P69ILBiGQc7gAXxr5qgu1WWlJfGNU7v/90f6j5TEBM4cP5Qzxw+NdyvSh3V7YBk6dChOp5OSkpJW95eUlJCZmdlmTWZmZpe293g8eDye7mlYREREbK/bx3fcbjczZsxg9erV0ftM02T16tXMmjWrzZpZs2a12h5g1apV7W4vIiIi/UtMDgktXryYhQsXcuqpp3L66afzm9/8Br/fz/XXXw/Atddey4gRI1i6dCkAP/jBD5g7dy7/8z//w8UXX8zy5cvZuHEjf/rTn2LRnoiIiPQyMQksV155JaWlpdx7770UFxczffp0Vq5cGZ1Ym5eXh6PF5J3Zs2fz3HPP8ZOf/IS77rqL8ePHs2LFCk444YRYtCciIiK9TEzWYelpsVqHRURERGKnK9/fupaQiIiI2J4Ci4iIiNieAouIiIjYngKLiIiI2J4Ci4iIiNieAouIiIjYngKLiIiI2J4Ci4iIiNher7xa8xc1rX3n8/ni3ImIiIgcrabv7aNZw7ZPBJbq6moAcnJy4tyJiIiIdFV1dTVpaWkdbtMnluY3TZPCwkJSUlIwDKNbn9vn85GTk8OhQ4e07H8M6XPuOfqse4Y+556hz7lnxOpztiyL6upqsrOzW11jsC19YoTF4XAwcuTImL5Gamqqfhl6gD7nnqPPumfoc+4Z+px7Riw+585GVppo0q2IiIjYngKLiIiI2J4CSyc8Hg/33XcfHo8n3q30afqce44+656hz7ln6HPuGXb4nPvEpFsRERHp2zTCIiIiIranwCIiIiK2p8AiIiIitqfAIiIiIranwCIiIiK2p8ACPPbYY4wePZrExERmzpzJRx991OH2L7zwApMmTSIxMZETTzyR119/vYc67d268jk/+eSTnHXWWQwaNIhBgwZx/vnnd/r/i0R0dX9usnz5cgzDYMGCBbFtsA/p6mddVVXFokWLyMrKwuPxMGHCBP334yh09XP+zW9+w8SJE0lKSiInJ4cf/vCH1NfX91C3vdO7777LJZdcQnZ2NoZhsGLFik5r1q5dyymnnILH4+H4449n2bJlsW3S6ueWL19uud1u66mnnrI+++wz6+abb7bS09OtkpKSNrf/4IMPLKfTaT388MPW9u3brZ/85CdWQkKCtXXr1h7uvHfp6uf8zW9+03rsscesTz75xNqxY4d13XXXWWlpaVZ+fn4Pd967dPVzbrJ//35rxIgR1llnnWVdeumlPdNsL9fVzzoQCFinnnqqddFFF1nvv/++tX//fmvt2rVWbm5uD3feu3T1c/7b3/5meTwe629/+5u1f/9+64033rCysrKsH/7whz3cee/y+uuvW3fffbf14osvWoD10ksvdbj9vn37rAEDBliLFy+2tm/fbv3+97+3nE6ntXLlypj12O8Dy+mnn24tWrQoejscDlvZ2dnW0qVL29z+iiuusC6++OJW982cOdP6j//4j5j22dt19XP+olAoZKWkpFjPPPNMrFrsE47lcw6FQtbs2bOt//3f/7UWLlyowHKUuvpZP/7449bYsWOthoaGnmqxT+jq57xo0SLrK1/5Sqv7Fi9ebM2ZMyemffYlRxNYfvzjH1tTp05tdd+VV15pzZs3L2Z99etDQg0NDWzatInzzz8/ep/D4eD8889n/fr1bdasX7++1fYA8+bNa3d7ObbP+Ytqa2sJBoMMHjw4Vm32esf6OT/44IMMHz6cG2+8sSfa7BOO5bN+5ZVXmDVrFosWLSIjI4MTTjiBn//854TD4Z5qu9c5ls959uzZbNq0KXrYaN++fbz++utcdNFFPdJzfxGP78I+cbXmY1VWVkY4HCYjI6PV/RkZGezcubPNmuLi4ja3Ly4ujlmfvd2xfM5fdMcdd5CdnX3EL4g0O5bP+f333+fPf/4zubm5PdBh33Esn/W+fftYs2YN3/rWt3j99dfZs2cP3/ve9wgGg9x333090Xavcyyf8ze/+U3Kyso488wzsSyLUCjEd77zHe66666eaLnfaO+70OfzUVdXR1JSUre/Zr8eYZHe4Re/+AXLly/npZdeIjExMd7t9BnV1dVcc801PPnkkwwdOjTe7fR5pmkyfPhw/vSnPzFjxgyuvPJK7r77bp544ol4t9anrF27lp///Of84Q9/YPPmzbz44ou89tpr/PSnP413a/Il9esRlqFDh+J0OikpKWl1f0lJCZmZmW3WZGZmdml7ObbPucmvfvUrfvGLX/DWW29x0kknxbLNXq+rn/PevXs5cOAAl1xySfQ+0zQBcLlc7Nq1i3HjxsW26V7qWPbprKwsEhIScDqd0fsmT55McXExDQ0NuN3umPbcGx3L53zPPfdwzTXXcNNNNwFw4okn4vf7ueWWW7j77rtxOPR3endo77swNTU1JqMr0M9HWNxuNzNmzGD16tXR+0zTZPXq1cyaNavNmlmzZrXaHmDVqlXtbi/H9jkDPPzww/z0pz9l5cqVnHrqqT3Raq/W1c950qRJbN26ldzc3OjPV7/6Vc4991xyc3PJycnpyfZ7lWPZp+fMmcOePXuioRDg888/JysrS2GlHcfyOdfW1h4RSppCoqVr/XabuHwXxmw6by+xfPlyy+PxWMuWLbO2b99u3XLLLVZ6erpVXFxsWZZlXXPNNdadd94Z3f6DDz6wXC6X9atf/crasWOHdd999+m05qPQ1c/5F7/4heV2u61//vOfVlFRUfSnuro6Xm+hV+jq5/xFOkvo6HX1s87Ly7NSUlKsW2+91dq1a5f16quvWsOHD7ceeuiheL2FXqGrn/N9991npaSkWH//+9+tffv2WW+++aY1btw464orrojXW+gVqqurrU8++cT65JNPLMB65JFHrE8++cQ6ePCgZVmWdeedd1rXXHNNdPum05p/9KMfWTt27LAee+wxndbcE37/+99bxx13nOV2u63TTz/d2rBhQ/SxuXPnWgsXLmy1/T/+8Q9rwoQJltvttqZOnWq99tprPdxx79SVz3nUqFEWcMTPfffd1/ON9zJd3Z9bUmDpmq5+1uvWrbNmzpxpeTwea+zYsdbPfvYzKxQK9XDXvU9XPudgMGjdf//91rhx46zExEQrJyfH+t73vmdVVlb2fOO9yNtvv93mf3ObPtuFCxdac+fOPaJm+vTpltvttsaOHWs9/fTTMe3RsCyNkYmIiIi99es5LCIiItI7KLCIiIiI7SmwiIiIiO0psIiIiIjtKbCIiIiI7SmwiIiIiO0psIiIiIjtKbCIiIiI7SmwiIiIiO0psIiIiIjtKbCIiIiI7f1/WxtM5Wz9sRgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scal_aff = 10.0\n",
    "\n",
    "print('integral of firing rate over time:', rho.sum(0)*dt)\n",
    "\n",
    "plt.figure()\n",
    "for i in range(m):\n",
    "    plt.plot(np.arange(n_T)*dt, rho[:,i]*dt*scal_aff+i)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5ca6fdb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bool_spikes = np.random.rand(n_T,m)<rho*dt\n",
    "\n",
    "spike_times = []\n",
    "time_stamps = np.arange(n_T) * dt\n",
    "\n",
    "for i in range(m):\n",
    "    # get spike times\n",
    "    spike_times_tmp = time_stamps[bool_spikes[:,i]]\n",
    "    # append to list\n",
    "    spike_times.append(spike_times_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "126460ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'neuron index')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot raster\n",
    "\n",
    "for i in range(m):\n",
    "    n_spikes = spike_times[i].size\n",
    "    plt.scatter(spike_times[i], [i]*n_spikes)\n",
    "plt.xlabel('times (s)')\n",
    "plt.ylabel('neuron index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7251f19",
   "metadata": {},
   "source": [
    "Test with SPADE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "49d43a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "spiketrains = []\n",
    "for i in range(m):\n",
    "    spiketrains.append(neo.SpikeTrain(spike_times[i]*pq.s,\n",
    "                                      t_start=0.0*pq.s,\n",
    "                                      t_stop=1.0*pq.s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e2400b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.eventplot([spiketrains[i].magnitude for i in range(len(spiketrains))], linelengths=0.75, color='black')\n",
    "plt.xlabel('Time (ms)', fontsize=16)\n",
    "plt.ylabel('Neuron')\n",
    "plt.title(\"Raster plot\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e831a810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for data mining: 0.04159259796142578\n",
      "Time for pvalue spectrum computation: 4.113203287124634\n"
     ]
    }
   ],
   "source": [
    "patterns = elephant.spade.spade(\n",
    "                            spiketrains=spiketrains, binsize=5*pq.ms, winlen=1, min_spikes=3,\n",
    "                            n_surr=100,dither=25*pq.ms,\n",
    "                            psr_param=[0,0,0],\n",
    "                            output_format='patterns')['patterns']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e312df7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'itemset': (0, 1, 6), 'windows_ids': (72, 77, 142), 'neurons': [0, 1, 6], 'lags': array([0., 0.]) * ms, 'times': array([360., 385., 710.]) * ms, 'signature': (3, 3), 'pvalue': 0.0}]\n"
     ]
    }
   ],
   "source": [
    "print(patterns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "69a59f1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.358 0.387 0.709] s\n"
     ]
    }
   ],
   "source": [
    "print(ts*dt*pq.s)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}