chaos_IL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model discovery from multiple experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chaotic and pre-chaotic regimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION']='.6'\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"white\")\n",
    "from time import time\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils.random import sample_without_replacement\n",
    "from sklearn.utils import shuffle,resample\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "import jax\n",
    "from jax import random, numpy as jnp\n",
    "from flax import optim\n",
    "from flax.core import freeze, unfreeze\n",
    "\n",
    "import sys\n",
    "sys.path.append('pdeX')\n",
    "from sparsity_estimators import ir_cond,ir_condAL, get_mask_multi\n",
    "from DeepModx import pdeX, create_update_state, loss_fn_pinn_multi2, mse_test,mask_scheduler, Logger, Convergence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "key = random.PRNGKey(21)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "noise_level = 0.2\n",
    "number_of_samples = 2000\n",
    "path = 'logs/chaos_IL' # path to log tensorboard files\n",
    "\n",
    "x     = np.load('data/KS_x.npy')\n",
    "t     = np.load('data/KS_t.npy')\n",
    "u     = np.array(np.load('data/KS_u.npy'),dtype=np.float32)\n",
    "\n",
    "\n",
    "scale_in=[[x.max(),t.max()],[x.max(),t.max()]]\n",
    "\n",
    "\n",
    "# DeepMod parameters\n",
    "# neural net architecture\n",
    "nn_shared_features = []\n",
    "nn_specific_features = [65,65,65,65,1]\n",
    "\n",
    "# learning parameters\n",
    "deltaSP = 1e-5 # target MSE on test set before triggering sparsity for the first time\n",
    "deltaC = 1e-2  # converge criterion\n",
    "learning_rate = 5e-5\n",
    "max_epochs = 1.5e5+1\n",
    "\n",
    "# logging\n",
    "write_tensorboard = True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparsity_method = 'IL'\n",
    "randomized = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input data with some noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_l = noise_level*np.std(u)*np.random.randn(u.shape[0],u.shape[1])\n",
    "u_noisy = u + noise_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_grid,  t_grid = jnp.meshgrid(x,  t, indexing=\"ij\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1, figsize=(4,4))\n",
    "\n",
    "bx = sns.heatmap(u_noisy.T,ax=ax,vmin=-1,vmax=1,cmap='Spectral',cbar=False)\n",
    "bx.invert_yaxis()    \n",
    "bx.set_xticks([])\n",
    "bx.set_yticks([])\n",
    "\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('t');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6720"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_g1 = np.logical_and(t_grid>45,t_grid<50)\n",
    "idx_g2 = np.logical_and(idx_g1,x_grid>45)\n",
    "idx_g3 = np.logical_and(idx_g2,x_grid<100)\n",
    "\n",
    "mask0 = np.zeros_like(idx_g3)\n",
    "mask0[idx_g3] = 1\n",
    "\n",
    "fig,ax = plt.subplots(1,1, figsize=(4,4))\n",
    "\n",
    "bx = sns.heatmap((u_noisy*mask0).T,ax=ax,vmin=-1,vmax=1,cmap='Spectral',cbar=False)\n",
    "bx.invert_yaxis()    \n",
    "bx.set_xticks([])\n",
    "bx.set_yticks([])\n",
    "\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('t');\n",
    "idx_g3.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6720"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAD9CAYAAAC4PhIWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVVUlEQVR4nO3deXSV5YHH8e/73veuSW5uErITVg1BQDYVUKkigrhQbUUK1WPHsTLtWNujY89ILdZa7Njj0lGwdVpLqdChio5aEUcEUQqiCIZ9CwETyL4nN8nd3/kj79jxWJeOQJzp7/NXct57cp/3nuf95nne3JNr2LZtIyJ/88z+HoCIfDEoBiICKAYi4lAMRARQDETEYfXP027sn6cV+Zs37WOPaGUgIoBiICIOxUBEAMVARByKgYgAioGIOBQDEQEUAxFxKAYiAigGIuJQDEQEUAxExKEYiAigGIiIQzEQEUAxEBGHYiAigGIgIg7FQEQAxUBEHIqBiACKgYg4FAMRARQDEXEoBiICKAYi4lAMRARQDETEoRiICKAYiIhDMRARQDEQEYdiICKAYiAiDsVARADFQEQcioGIAIqBiDgUAxEBFAMRcSgGIgIoBiLiUAxEBFAMRMShGIgIoBiIiEMxEBFAMRARh2IgIoBiICIOxUBEAMVARByKgYgAioGIOBQDEQEUAxFxKAYiAigGIuJQDEQEUAxExKEYiAigGIiIQzEQEUAxEBGHYiAigGIgIg7FQEQAxUBEHIqBiACKgYg4FAMRARQDEXEoBiICKAYi4lAMRARQDETEoRiICKAYiIhDMRARQDEQEYdiICKAYiAiDsVARADFQEQcioGIAIqBiDgUAxEBFAMRcSgGIgIoBiLiUAxEBFAMRMShGIgIoBiIiEMxEBFAMRARh2IgIoBiICIOxUBEAMVARByKgYgAioGIOBQDEQEUAxFxKAYiAigGIuL4q2KwcOFCdu3a9bHHd+/ezcKFCz/3oETk9PurYvD8889TXV39scdPnDjBCy+88HnHJCL94KRuE3p6erAs62T+SBE5TT71yq2traWmpuaD748ePcq77777kcd1dHSwatUqBg8efHJHKCKnhWHbtv1JD1i6dClLly7FMIxP/EG2bWOaJj/96U+55pprPuVpN/6VwxSRk2Paxx751JXBpZdeSnFxMbZt84Mf/IC5c+cyfvz4Dz3GMAwCgQBjxoyhsLDw849XRE67T41BWVkZZWVlQN+WYebMmZSWlp7ygYnI6fWp24RTQ9sEkf7x8dsEvelIRADFQEQcioGIAIqBiDgUAxEBFAMRcSgGIgIoBiLiUAxEBFAMRMShGIgIoBiIiEMxEBFAMRARh2IgIoBiICIOxUBEAMVARByKgYgAioGIOBQDEQE+w79KPxVqug9TGBhOPBWlK94MgNv0ApDpyWd361FGZBZQ11NDyjao6PDSk+jrVm2PQWfcYOHZ+VR0HaWiw8c7TS4m5qRYtcfP0Z1BsgZF8WyJYVzioq3Vx9B1jaRMg5avZPLjWc34XSlWH/Nz3dBe6nrcXJpusi8VJZYyaOq1yPUnePaoj+FBm+HBBIc7LLI88MsN2ZSsbablukwCa3vwd8c/cm7/9lyIlvm/ZN2z0Y89/68/mInny+fS/egGAnPPxkgLkCivxDANzCEDMEaWkSo4ExMX25urODc9GywPuDwkVvwWsziL5t9VUL7J4KIFHlYtCnPjymK23NfCOV9OYmZ6qd+WpHh2Bq6BIaJbqjEDbjoPRsiakYvrsouwKyswR50DloeGW54g72eXk9q8AyM3E2P0aEilMLJKSO14g9j6g3ivHEty7/t0bmoiNL8UY3ARidd3YWZ46NjQQOiWcRglxZCWjX1wD23jRpHjKzkFs0dOlX5ZGZgG/MvORjpiDaypsrBMD787nCQc7+BAWyV3vljAhppWMtw+Ql4/hzpcPL4hG5cB88+wubgwxn07GzjQ5uec3Dj7q32822Ry4bAIRrpBc52frMZuzHUxIu0uYjcHOHBeIeEDFj9el0NNt5t7A0cpe2ED4wfYlCdiFAQsHtyaydicKI++l872fZn8anUez7/vZmuNxR/KA/TWuQDwvhb5iyFI3OLn5apOuprdn3j+Le9GqLpzC++84oHmNtqf2IartJBDy3tIHmqA9la6420YhkF33KTJaCX10jOkDBsz00uysolgmZfpP8rCNcDP/HsDVDzZQV6RhR1P4p05iryRSawLRlG3rAo7nsI9Jo/0gSaG2wSXB+qbafcmSL35n2RNDJDc+C7mhRMxsjKxN2+FWA92VwOR53ZhDczAmDAd17QJZN0zA2P0KIhESdSFiR1uJeveyzFKitk//3WiGZkk91aTve/wKZk7cur0y+cmvHr8KWaGq1lmDOXvRxRS31tBpiefVUe6OT+/l9ueK2L937nY3XmCs0IDufhHNhvuTZKyE5iGhcf0Y2Bwx9t1TC1IEU0a7Gg2aA67SC2N0XJdJqXDwrR0WRw7kknutg5+vrSdr/9sEA/cWk8iZRD0JBlvWhz3ROhNmIzw5WO3VrGgIofZg5NUdBokbbiiJEJNt5tnKjzEliaIpLnpTfeQUxf+i+d23v0mZV/+PUNKXWx6tecjxwuKPHR1JrnyZhtXlo83f5Ng5ntfY3nuCsaem8aErd8jseK3RHc24P/aBBLbKrHOGYpx5ggOWlD6yiaMdA/Jxm5a32gn+4Ig6+/vpL42xgXT08jMj7P9dZuysW4Kx8SxBqbjvqCMoz/cRcmab+N6+g9E3q0jsPA67PfKoScC2Zk0TRpNlqcIHvsN7hsuB9OC948QfXkXqXAc36XDSVb2reJcYwdhpAUgEsWYcBF2VxNGRi72/rchmIZd14RROhJjwNBTOo/kf+PjPzeh32JwWXExhJvZnei7qNyGTVFaOsGqY3QNHk5DbwcALgPy/Nk8sifKmKwUBYEEpZkmae4snjrcSSQJ1w0Lc++ODIYHbTwmbDniJz8vwu5dOeTm9fDVsX0X5Y5mg4kDbMbkRChJ85GVDLCmKcy5eVESqSQvV6dT32tQ2WzxpUFxuuKwbmeQ4kFhwp0epp0RYcvC1MeeV+2wEEvvaaB84NP09nz0cRlBF12dSUaM8tPclGDMuRbetBRH95ic9+0AvXvbCH7/Upru3Ujug9fQ+68vEvjxd8A0SW19DaZeAVteIfLHfQTu/gaJF17GjiSwZk7gufHrKSrxUF8b56yxPlxum/KtEeasHERsZwOecfmQnUmqsgFr7nVElyzH8/ezif7ieSpeNRn1o4EQTMcoyqd50TqCZwfw3vI1En9cg5mdhnHeebSmW2S392C/9Q7xYx3UvB5nyN1lGGecSWz5K3hmT6R2UC7FiSC4fRAInfzJI5/TF+xDVCblu7l/VwN1VjtBd5LWiEVxWpC2aCfrfMXM+FkWNd0e2mMWTx5MIz1uMipk81qNybKDXjbVmSw/1Mnk/Ag7at3csWEAQzJg349SbHzUh8+fBODMsjaqjgR5fG0eVw42+e7oMOurLMbE4iTtBPcd7OWKQYW80+DjvvcyOD8/QtBtE+5088wvMyhflOLiszsxTZtgKMpT63Npy0sj7nV95Jxqh4U4a04X0ZTB1d9zc+GMNABy8v68ZUilYMKkdHKLTMaca1H+VpxIl4uKA700vtFNZ6OH8MPrSc9LkfrT21j5AZIvPEP13McxsoKY4TZobMV//SSiv3iaxIkurHFDWD78VfwBk67OJFk5Ft60JJtf62bUuADk5ZDqSbBmwQmMoYOxrv0KdmMFhs8F0TAte5OMXnkBPdMuIbK6HJIJBjz4VbzfvA4ME9f0KUQ2VdG+8A947lhB7Xf/iFFSiHv8QLIHRjHHX4i9ew+uAX7sQ8coNgtIvbOZ6NKnTs9kkpOmX2IQTXbTHoVHdmdQEChka6ObV4/H+adXczm/wM/im+p4/n03m+o83Dqqi16PycTcbg7uz2LBWREGZ8SYmBvj4e0Z/HhKOzXH09mwPUTTtSGWLe3GfCJCV7fFhPwko96uZcaFTTy8y6Y9ZhHucnPP8SC/Puhm0ZAEm+tryA/EGZQOLqNvkWS6bHxTbA5dVEiaBcf/EADAWxUjHPJy5Nz8j5xTW16A8p05vF7jwzfvPBpO9K0MWhr/fG+hO5wkMy/J8NsKKPlKkK6OJIZpM222n+1vJsg7141nSDrW4CDmoAJSXTHMs4ZS8vgsjDPGk9ryOhTlQSyO56pxeGeN4f3Fe7n+sRzOvzLFRTdbFBS5aWs0+dqdbrZt7sLIzMA7MZ/zL7dpv3cNqd1v0/TD9bgK00mu30bezExi//E23Tc9iX/eOcRfeAdjwHASz71E78O/g9ZmfLNGEFpwDmkzB1H081lguUjsryfzuxcSX/7vdF/0JVyzzqd2ZT32we2k6jrw3vmd0zCT5GTql23C0c5f0xSxGJph83qNl4eeLeDKGQ2kUjAlP8n04mx+urODSbl9v+E317uYnJdiS4NJyAtHWk3G5CYZlmEzJBjlrpfzcLtT+PxJTryXxtB9zYy822DjjhA+f4KMYJxVswJEzb4LdMGGbpZcDJvro0zKsznckWLp9nSmDo3yzPIQj91Vz323pZF0m6RMAyueIniTTeKxOE3Xhuhs9zJ8Q8NfPLcnng3iazxO5PG1vLQkQXc4+cGx6Vf72fBiLwBTLk5n6xthMrMsOtoSAMz4qo+ORjdtLQmikRRTvxfg+CtRBk6z8P7jDUQeWY530e2kVi0HoPetWtJu61v2GQXDaLj1KXJvHEbVkirq3oeiYZCMGewrjzP7xXEkK2rY/mA7xcNTFM3J4Y1FzUz7bRm0dhCvbMMz82zwuEntrsC88LwP/oJBIoK9fz/G6LOxN2/BKCmEASH+84INTD32TdymDwDP7rdJ7j6Ga9oEUpt34vr6TSd76sjn9gW7Z9AeW8WOphiX0EFDlp+Hd6dx17gEHpefb651cfagCN8s66Gm22R7k5fJ+RG+tWIQ/zS7kZ0tBqOybF6rdnFGdoqeONR2mezansfwEe18Y3w3ASvFvY/lkii2yN4TZtXj8FZDmMMdLp7bks23p7cS9CSZku/jaFeYoRkBZjw0gFBhlMIXWwE4cF4hI7fV0VKYTv3QTAbvbyZhuQg1991/aB8Q+ODr/1Y7LMS6u+roWfwM7fUeskoSPP3zP8cgM8visuuTPLPUZsQoP4f29XL5PA+b1yaZdImb9S9EGF7qY+SkJGtWxJn9DYusKwqJ/OkEgVunYZdOIvrDR/BePhJaO6hZXkfJ8hsJ/+DXmF6Ttb+BK26GWBjqDgUYdoWBNThIqjWCa2Qh9EQwxpRhl+/FGFNKcssezOw0krUdrFzQyPmXpHPmXYNI1bThmjEZe1s5djRB7EAL7qEh4sfa8X3rKuz3dpKq78R19UxioVx4+Enco/tWS8blc7Ffe4749CvwugKnaUbJZ/cFu2dQHe67iB5pyaEgcAazSuKsrXbxz+/EmTO6l7HZSe7akkHSNijNTFDd5WHp9cdZV2PwysYCNtUb3H1OF5cURemMg9+b4mfXH2d6aS+PbczizpUl3HpLG6bLJnaBh29tSLK5waIxYvDLuTWYBlwW8vPAzgRv1HrZ0xph+JgOPNtiVEwrAGDktjoAcurCBENR0tujhJp7CN+QTtIyiXtd1A4Lfei8zvxqF0bemaTd/w+sezbKxlUffnn9fpPGYz5ufLqE8df2rRR8GUm6OpPkDO3bTlQejmCn4KbwdwhOzIR4Cv81Z0EsjtnVgnfxHcT/VIkx7iyKFwzi6PyVpN13I1U7AkSjNv6F83AHbOpr45jZPhJVndhJm55n91L/VDV21XGMMaXYNQ24zizGKM7HunwK83+STum/X4nd0olrxmQSL23GvPoGUu1RfLfNwVU2kPBd15JY8yaJo63EbpyL/e47uF98Fs9lZ0Ow7x4J9QcxSgrxHN19CmeQnAr9EoOXqvwMD0aZlBdnZ8sxarstZg8xuXpwgrYYnJUV4R/H9nAi7Kam2+JQh0l5s5dwp4c136kkacO8pUMpb/Zwc1kvDU+4eOxPISo6DOZN7iCrvpuB6XG+cUkz4U4PdSfSmDeshzTLZn2Nn1klfq5b72JIhs2RToO9rW7ycqIEW3vx1cSouSoH4IMbhQuvaCJlGuybXMTi6S3k3GGQW9NF0vrwy5dmQVe8Gbv+CF+6LEBj/Yffi5CR6aJgnE31U81UbXJRcs94nv9138rhf64gmqp8dN/9C1y5fuLHOoh86RJ6R44j8fxLrKmqx1WUjr19D5E33idUEKP7nqcoe3g0sWiK5B/X09VgcdEjxVjTxoHLYOeyXpqqfES6XNDZTeTJN+ha2Xex2jUNhPMK8d4wlc57VmNOuwBMC9fwXOhqxJowDHvrW3Qse4+c7XuJH2jBKsvHf+wAxoihmFfOwW5qJbr+EGRnQnsr23KKaV785qmaPnKK9Ms2ATae/qcUEb5w2wQR+eJRDEQEUAxExKEYiAigGIiIQzEQEUAxEBGHYiAigGIgIg7FQEQAxUBEHIqBiACKgYg4FAMRARQDEXEoBiICKAYi4lAMRARQDETEoRiICKAYiIhDMRARQDEQEYdiICKAYiAiDsVARADFQEQcioGIAIqBiDgUAxEBFAMRcSgGIgIoBiLiUAxEBFAMRMShGIgIoBiIiEMxEBFAMRARh2IgIoBiICIOxUBEAMVARByKgYgAioGIOBQDEQEUAxFxKAYiAigGIuJQDEQEUAxExKEYiAigGIiIQzEQEUAxEBGHYiAigGIgIg7FQEQAxUBEHIqBiACKgYg4FAMRARQDEXEoBiICKAYi4lAMRARQDETEoRiICKAYiIhDMRARQDEQEYdh27bd34MQkf6nlYGIAIqBiDgUAxEBFAMRcSgGIgIoBiLiUAxEBFAMRMShGMgnSiQSzJs3j3HjxlFZWfmhY08//TQjRozg0Ucf7afRycmkdyDKp6qpqeGaa66hqKiI1atX4/F4qKioYM6cOYwaNYoVK1bgcrn6e5jyOWllIJ+quLiY+++/n4MHD/LAAw8QiUS4/fbb8Xq9PPTQQwrB/xNWfw9A/m+YOXMm8+fP5/e//z379++noqKCJUuWUFRU1N9Dk5NE2wT5zKLRKFdddRXV1dXMnTuXn/zkJ/09JDmJtE2Qz+zgwYPU1dUBUFFRQSKR6OcRycmkGMhnEg6HueOOOwiFQtx+++2Ul5ezZMmS/h6WnES6ZyCfyaJFi6itrWXZsmVMmTKF/fv386tf/YopU6YwefLk/h6enARaGcinWr16NWvXrmXBggVMmTIFgMWLF1NYWMj3v/992tra+nmEcjLoBqJ8osrKSq699lrKyspYuXIllvXnxWR5eTk33HADU6dO5YknnujHUcrJoBiICKBtgog4FAMRARQDEXEoBiICKAYi4lAMRARQDETEoRiICKAYiIjjvwDD3jTMvK6OvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_g1 = np.logical_and(t_grid>33,t_grid<38)\n",
    "idx_g2 = np.logical_and(idx_g1,x_grid>0)\n",
    "idx_g3 = np.logical_and(idx_g2,x_grid<55)\n",
    "\n",
    "mask1 = np.zeros_like(idx_g3)\n",
    "mask1[idx_g3] = 1\n",
    "\n",
    "fig,ax = plt.subplots(1,1, figsize=(4,4))\n",
    "\n",
    "bx = sns.heatmap((u_noisy*mask1).T,ax=ax,vmin=-1,vmax=1,cmap='Spectral',cbar=False)\n",
    "bx.invert_yaxis()    \n",
    "bx.set_xticks([])\n",
    "bx.set_yticks([])\n",
    "\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('t');\n",
    "idx_g3.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 559\n",
      "83 94\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xini = np.where(mask1>0)[0][0]\n",
    "xfin = np.where(mask1>0)[0][-1]\n",
    "tini = np.where(mask1>0)[1][0]\n",
    "tfin = np.where(mask1>0)[1][-1]\n",
    "print(xini,xfin)\n",
    "print(tini,tfin)\n",
    "\n",
    "nxx = np.arange(xini,xfin).shape[0]+1\n",
    "ntt = np.arange(tini,tfin).shape[0]+1\n",
    "\n",
    "fig,ax = plt.subplots(1,2, figsize=(8,4))\n",
    "\n",
    "bx = sns.heatmap((u_noisy[mask0].reshape(nxx,ntt).T), ax=ax[0],vmin=-1,vmax=1,cmap='Spectral',cbar=False)\n",
    "bx.invert_yaxis()    \n",
    "bx.set_xticks([])\n",
    "bx.set_yticks([])\n",
    "\n",
    "bx = sns.heatmap((u_noisy[mask1].reshape(nxx,ntt).T), ax=ax[1],vmin=-1,vmax=1,cmap='Spectral',cbar=False)\n",
    "bx.invert_yaxis()    \n",
    "bx.set_xticks([])\n",
    "bx.set_yticks([])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd6b069f580>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rdFKSn1qip3PV4SaLd5rCE55Ha8kCxBr32cav/1aLw2bkY9euSmwz6LV8/s6NBMNRfvL0oZTPxFvEvtaqIyfLxOYVhSnHLCjOonPEXgsRlvmHMNxp6H/pETxvP0nPH79HuLd1xq4b6qwHjS6p209DhxdddkwZKerpwW4x4LQbae3JrMfd0OGlVOtGk1uRtF3S6vDpHFhCmVmHP9rsxqDTsLDkxHp1pL8DNRrGUBg33CZkRcXrC6ExWZF0hlOSG+11+2ns9HLe6uKUfaoiE2w7gi97McdaBpAn6AEuaXVozHaiPlESNptRwgE8bz9F1+++Re8zP4y1az1FDjf2c+B4HzdtXYLJmJxuVF5o5wNXVPPWgU7eqUmuIgm0HAajlZfrZa7YUpFIiBxNWYGddm9s6UaIsMw/hOE+ieiQm6H9L2Fb/R4krY6hAy/N2LXDXQ0YCiqQdHogtl7b1OklqygmlRnxxLLJywvttPdkNvzV3NRJjtaHozJ1/TZizsUqe1EyoFl+pKmfqgonet2Jn/bJms652SYgVhImSRK6rNxT6qK0a2QNcvPK1GSyUFdjrKxvwSoCIZmmrokfnlqbQ3jcs5hwbwvtP7mPgVd+RXSwD//x3XT+8ssM7vnbKZ3vN387isNuHDMJ7cZLl7CgOIsf/fEgvsCJ8spgSw09hnI0Gg1XjfHZ0gIbnkjsOSJEWOYfwnCfhK/2LVAVHOffiKliBf4Z6B4FIwknXQ1J69s9A378wSiFlTFpzuhIAlVhjoUet39GxjUWA82xcF12eZrEK1suTmkYry80o2MKhqI0dHhZsTAnaXu4uwlJq09ohedmx3pixxPUTlVudOeRHkryrJQVpJZ6+et3g6ShcmMso/xI08Tn11odYo17lhLsqKPj0a+iqgolH/oW5R//byru/TGWJRvoe+5h/A37pnS+moY+Dtb3cdOlVZgM6Yt7dFoNn7l1HZ6hII/8JSZqFPH0EvX28k5fNhevL0v8lk+mrMCGT40tYYlQ+fxDGO6T8NfvQZ9fjiGvDPOidUT62mekp23U3Y0S8mMsSV7fBlhQWYhktCSMS2GOBc9QKKMKahFXM0Ai/DwagyMfqyZMn2tmw77HWt0oipqikx7uaYpFMkZqzZ32mMc9MJL4o7PnTDlUHgxFOVjfl9bbBvDX78VUVk1BcQH5TjNHmib+DWmt2cLjnoVEPL30/OE7aM02Su/+NqbyZQBojGYKbrgfQ0FFrClQcPLaBb99/hhOu5Grzl8w7nFLK5y8/6LF/PXtZg7UuRKORF24kFsvH1uNryzfhl+JlUMqQZGcNt8QhnsUqhwl2H4M84j4iaksNjFCM6AhHeocUUIa5XE3dnjRaCQqirKSukflO2OlH64Med2BUBRroJuwzpaoNx+NJS9mzNzdnTM6rtrmASQJli044XGrqkqopynpBcNhj3ki8YzdWKjcjaqOvw49miNNA0RlhfXVBSn7ooP9hLsbsSzZAMQevvVtngnPKTzu2Ycc9NH9+2+hylGKbvsKuqzkCgGNwUT+NZ9G9nlxv/67SZ3z0Ii3ffPWKox67YTH37ltGWUFNv71Z++w57m/4JLtXH7FeZSmSYiM47Ab0ZtMKEgoU3ihEMwNhOEeRai7ETUSwlSxAjihWhbunf6a5FBXPZLOgCH/RFlRY6eXsgIbRr0WnT0XecTzL3DGwmO9GarlbuzwUqJ1Q05qCRRAdmFMiMTnOj0pUYgZ3qEDL9P1u2/i2fH0uMb1cEM/lUVZ2Mz6xDZ5sA8lMJxkuPU6DVlWAwNDsVC+1pYDShTZN/kknoP1LnRaiRULclL2+ev3AGBZcg4Qq6vtGfAz5B8/u1xnzUYNB1HCqSVAgplHlaP0PvmfRAa6KLzpn8bsIGcsXox9/eUM7nl+UmWFv/3bMXKyjFw5jsDKaExGHd/59IVcuT6XCrUdaeEmbtw6voSuJEmU5NsJSyZk4XHPO4ThHkWw5TAA5hHDrTFa0GUXzJDhbsBQuDARzoWYgYzXbWtHedwFGfa4G1r7KdZ6sJUtTrvfURQz3CH36UuzDu19HtefHiLc3cjAy48x8FL6rnPBcJTDTf2srcpP2h7qbgLAWLwoaXtOlinJ4wamFC4/WN/H0gpnSjYwgO/oDnTOIvQjL2FVI4IYE3ndopY780SiCkea+vn980d54cFvEmg6wBOB87jrJy3c8+8v8N3HdrPjUFdK4qXzglj3N89bT457/kP1fRxq6OOmSXrbcbJtRm5f4kGDytorrk7qejcWBTkW/KpBeNzzECF5Oopg+zH0OSVJ0peGgooRnevpQ1VkQt2N2NddltjmHQ7R7w2yaKSsSWfPRR72oCoyudkmNBopYx53b0sjqyQFR7rENEBndyKjQRk6vZIw2T9E/0uPYF60jqLbvkzf336G991nsS4/H9NJTTsO1fcRiSpsXJYcug51N4KkwTDSezuO027EPTSSnGaLec3RoYGkGvqx8AUiNLR7uPXy1Nal0WEPgeYaHOffkHi4Li6LfYf17Z60ofU4Jwy3V8iezhCyolLf5uZgfR+H6vs40jxAKCxzhekgmyyHOGw/H+fKy7lKI9HnCXCw3sUb+ztYWJLF39+4luUjiZC67Hzs67YyuP8lHBfclHgZHI2qqjz6lyPkZJlSRFMmQpWjeHf+CWNpdUKPYCIKnRaGmvTIQoBl3iEM9yhC3Y0JbzuO3llEoOkgqqpO6i33VIj0taNGQklSp/HEtLjHrbPngKogD3vQZeWSl22idyAzHnewK+bFGkbVm49G0mjxaWzoAqeX1De492+okRC5l38YSaMld+td+I6+g+etJyi69UtJx+6u7cFo0LJq8UmJad2N6PNKk0RiAJxZJtp6Yw80nT328JWHJ5dMd7ixH0WFNUtSFdF8R98BVcG24sLENpvFQHGulfp2z7jnFR73zCArKkea+nlzfwdvH+zCMxxbMqkosnPFpgo262rJPrQf2+r3cM37P5M072VZ4c0DnTz6lyP88/++yZ3blnHz1iokScJx3g0M7XsRz7vPkHfF36Vcd+fhbo62uLn3lrUYpuBtA3jffYaop4e893500p8pcJrxKwYiPpFVPt8QhnsE2edFHurHcFJIVecsQo2GYwbT7hzj06dHsPM4kJyY1tSZbLi19hNeoS4rl3ynhd4MhMqD4ShmXyeKWYc+t2TM40L6LIyhU39gqIrM4O6/Yl68PpFroDFayNrwXjxv/pFwfyeGkevLssKOQ11sqC5Ar0t+IIa6mxItUkfjtBvxDAVRVXUkwiIRnaThPlDvwqDTpOihA/iOvIk+rwxDQbIwzZJyB8daxn+RER3Cpg9FUaltHuDNAx28fbCTgcEQBr2WTSsKOW9VMWuq8nDYjHjf2c7Ay49jqTqH/Pf9fcrLular4T0byjhneSEPPXGAR/9SS3e/n7+/aQ16RwG2VRcztO8FnBfclFDDA4hEZX755yOU5lu5fFPFycNLQlVV1GgYNRJGjYbwHdvJwKu/xbrsXCxVGyf9NxfkWGhWDUSFxz3vEIZ7hFCie1Sy4dY7YnKCUU/PtBnuUGcDGpMVfc6J8GhDh5d8pxm7JVbSobPH12FjD//cLBPHJ5GpfKaJJ6bJ2SVJ6/EnIxuzMQfaTvk6wdYjyD4PWesuT9qetXEbnrefZmjf8+RefjcA++pcuIdCXLoxOXkoOuxGHh5I+U4htsYdlVWG/BGyrAY0FvukPe4jTQNUV+akeE3RwX6CbUdxXnxbymeWlDl4Y38H3uEQ2TZjyn4Ybbg9kxqHYHziDWfeONDBWwc66fcG0es0nLO8kIvWlnLOikLMIzkKStCH65kfMFzzOtbl51Nw3eeQtGM/Hq1mPf9050aK86w8/mId/d4AX7jrHBzn38Dwodfw7vozOe+5PXH8H146TnvvMP/ysXPTKp0BBJoP4XnnGYLtR1FDyS/l5kXryH//vVP6+wucFmpVA0rQNaXPCWY/wnCPEE9iOrkuOdE9yt2dqN8849fuPI6xeAmSdGJCN3V6E+vbcCKBKp6g5swyJdZoZ5LjrW4Wagewlpw3/oEWB3bvMYLh6JgCE+PhO/oOks6AefH6pO06mxPLkvUMH36TnK13IWm0vLCzBbvFwDnLk9eFw/HvNM2aoDMrVsvtHgySZTWgszknZbhDEZmmDi83XJK6vu87ugNQsa64IGXfkvLYd9nQ7mXDsvTr3HHZU3nYM+E4BOmRFZW6FjdvH+rkrYOduNwBdFoNG5cVcPc1pWxeUYjFdKLqQAkHGDr4Kp43n0D2eXG+53YcF9yYNBfHQpIk7rpqOfkOMz968iBf+OEbfO2j52Kp3szg7r/iOPc6NEYL+4718vsXjvGe9TFP/WRURab/hV8yuPsvaO052FddjNaei8ZgRNIZ0DuLMFWumvJSXb7TjF81ookEpnWpTzDzCMM9Qri7AZ2zCK3JmrQ90T1qRG5UUVT+9GYju470sGJRLrdeVjXmG/RkUCIhwr0tOM67PrEtGI7S0TvMBWtKE9s0FjtotAnj4rQbCYZl/MFI0oNoumlrame1JoR9jIzyODp7DsbuKJ4BL0VFqYk646GqKr5j72JevD5lbRrAtupi/HW7CLYcpte8gB2Hurh5a1WSzCnEMvVBSkidjsY5Uss9MBiksjgLrS1nUoa7sd2LrKhpw+TDh9/EULgwEcIfzeJSBwDH291jGm6IyZ5Ghcc9aSJRhc6+YY61uKltGmB3bQ+e4RA6rcS6pQXcuW05W1YWYR0pEVTCAUKdLYR6mgg0HcDfeAA15MdYVk3RbV+eVHLiyWw7bwGFORb+49Fd3P/91/jERedTFnwX797nqTFv4sHf76O80M6nb1mb8llVVXA9+z8M17xO1uZryLn0DjQ6Q5qrTB2LSY+sMyOhoIaDSMb0KmuCuYcw3COEuhvTTlpJp0drzU54uj9+6iB/fbuZwhwL+4+78A6H+OSNa075uuGeZlCVpMS0lq5BFPXE+jaAJGli44gb7hGP0TMUmlHD7YsLxUyQ2WpyxBK3PL3dUzbcEVcb8rAby5LYel5Du4e6VjcOu4m1VXlYlmxEMlrwHnyNh1q9WEx6rrs49bsLth9Fn1+OxmhJ2ZcT97gTmeVOwq6Jy/6OtcaWKk5uGxrx9BDqPE7O1rvSfs5q1lOab+V4q2fc8+uECAvH//Y4/uYapLAf5ChRtETREkZPECMBVY9PNjAQlHD5JfyKgYBqQG80srXcxopyC4sLjOhC3UQHjjD8l348g/1Eh/oTS00QqyawLTsX+7rLMZYuPS2PdH11AQ987mK+/7t9fO+5fu7NLqHopSf4L7fCwvJcvvaRLYmw/Gjcr/6G4ZrXcb7ndpwX3nzK1x8LvdUG4Zh6mkYY7nmDMNyAHBgm6ukla/0VafdrbTlEhwZ4+2Anf327mevfs5iPvH8lD2+v4dk3Grl8UwVLyh2ndO1QPDFtdEZ5Z0wIZLThBpLCuTlZJzzGdC0lp4PhQASzrwsspJRXnYwlJ2a4h/umvr4WaIm1MjRVrubhpw/xzBsnlOviYc8rs1dgr3mb+oES7rtzS8q6sarIBDvqsK28kHScCJXHMop1Nkei3G68tfujLW4KciyJz8fxHXkbAOvy88f8bFWFk4PHxy+R09qcRNqPjXvMfOfovkNkh7oIqAaiaDBIYQwaBZMUIUsKYySMnihIgPWkD/fG/sWldCSDGV1WLjp7Dua8NegdRRjyy9HnV6DPKT6j4eOyAjvfvfcidh7ppnkvVLU9ypfOD7Lh+ovQpYnKDe59Hs/bT2Ff/14cI3XgZxqTLQsGYgpwuuz8iT8gmBMIw01MyxrAkCaJCWLry2GPix8/eZBFJdl8+H0rkCSJO7ct4+VdrTz9WgOfv3Py2Z6jCXbUobXnJiW+NXZ4sZr1CYW0OFqbk6g39uCP623HDc9MUN/mplQ7gGzNS+vFjiYrL59hIOSZei13oPkQOkchfz4wyDNvNHLNBQu54dIl9PT7eaemizcPdOL2O/l0VpgvbjWwZX2qolXY1YYa8mMqS5+XYDbqMBu1DIzyuFEVZP9QWhnXOHWtbpZVpqqlDR95C2NpNXrH2GHwqnIHr+5pp98bGLM5REyv3HNWr0lecf+/EAhGMRq0GPXatEtRajSCEvIjB30oQR9KyIcaCaExWtAYzEhGCzqbY8Lf6ZlGo5E4d1UxW1ZeS9djuyhofQEpdA2MyjCHmJZ933MPY1mykbxtH5u279qalR0z3KLRyLxCKKcxdkZ5HK09h6A7lrn89zetSbw9W0x6Lj2nnLcOdjI8gZxlOlRVJdhai6liedL2po5YYlpKKYrNiexLDpXPZILa0RY3pboBLMXp79NoHAWxRLHw0NQajaiqQrD1CJqS5fzqr7VsWVnEx29YTYHTwuoledxz/Wp+8bX38q9fuxutzUml/0ja8wTbagEwlS9Pux9iLz/xFx/tyIvTeOvc/d4ALncgZX073N9BuKcJ24qxvW2IaZZDzPiPhdbqQI2EUEfJnsqKyi+ePTxhHfh8wWTQ4cwyYTHpx8wfiS9hGXJLMJVWYVm0Dmv1FswLVmMsWYIht2TGjXbS+CSJvG0fRwn6cf3pf1GVEw2Bgm1H6XnqPzEUVFJwwz+MG+E5XexOBwDDHlFiOJ+YVsMdDof53ve+x4UXXsiaNWu49dZb2bFjx3Re8pQIdzehtecm1V2ORjU70EX9bF6Wm9TAAuA9G8qIygq7j05d3jPq6UEeHsBcfkL0RVZUmroGU8LkEDfcg6iKjN2iR6eVEh2uZoJj9V3ka4ewlExsuA0WKyFVj+qbmuGO9HeiBH0cHHQQlRU+cu3KlBcYjUbCbjNhW3EB/oa9aZWhAg370DkKxw0POrNMJzqE2SYWYakbWZ8+eX3bd/QdYPwwOcCikmy0GilxnnRoR7x9edR9e/aNRp58tT4h0Zop5sp8ni0YCirIvfzD+I/vovep/ybYUYdnx9N0/ebf0NlyKLrty2gM07vunJ0b+60Oume2U59geplWw/3P//zPPPLII1x77bV85StfQaPRcM8997Bv39R61043oZ6mcZOtDnbG3pZvvyC1lGNpuROHzcjOw91Tvm6wNeYtmkaptXW6hglHZBaVpr5E6KwOQEUe9sSUmuwm3EMzEyqPygre9kYkxlZMOxmfxooU8EzpOqGOOgD+Uidx4bpSSvLGXr+3rbwI5Ci+Y+8kbVciIQLNh7BUnTNuCDIuwgIjoXIYV4SlocODRoKFJ71U+Y/vwVi8JKHANhYGvZaFJVkTeNzxcXiAWLnar56r5ZzlhWlLiWaSuTKfZxPZm64mZ+td+Op20vnLLzHw8mOYF6ym5EPfnPD3cibIzYslhvqExz2vmDbDffDgQf785z/z+c9/ni984QvcdtttPPLIIxQXF/PAAw9M12WnjBIJEenvTNtXGmDYH+bV2phHV2qLpOzXaCQ2rShkz9EeovLk20ICBFqPoLFkoR/VdahhROp0YUl6jxtISlCbihcm+wfx7vwTwfajUxonxDK7C9TYevVktZJDOjv6yNTW1oKdx1F0JlqDtgn1nA3Fi9HnFDN88NWk7YGmg6jR8IQqUzlZJgbiofK4pzuO4W7qGKS0wJ7UHEL2eQl11CUy4CeiqsLJ8TZPSpOKOLqEx+0B4PGX6ohEFe65fup1vGeSuTKfZyOO866n4tM/ovDmL1D60QcovPVLSf0QppO8AieKKhEYEoZ7PjFthvu5555Dr9dzyy23JLYZjUZuvvlm9uzZQ2/v6XeOOhOEe1ti5VhjGKOnX2+gOxjLWB6re9TGZYX4g1EaprgGGWw9jKlsWdIDuaHdg0GnoaLQnnJ8wisceag7p+Bxy/5B2v/vn+h/4Rd0PvIVhg68PKWx7qtzUaYbQDLZ0NonV94VNdgxy1OTWwx1HKdXU4gzy8yKReNfR5Ik7BuuJNhWS7DjeGL78KHX0JhtKbrzJ+PMMhEIRQmGomh0BjQm27iGu7HTy+KTve2GfYA6aSnKpeVOAqEoHa709yWhVz7sodft57kdLVy+qWLcyMNMMFfm82xFl5WLtXoLxqKFM/oClm0zEcBAWOiVzyumzXDX1taycOFCrNbkeo01a9agqiq1tbXTdekpER5JTEunruUdDvHM6w0sXxlb040OpdeaXrEoFvI63Dj5tpCRgU6inl7MC5NrwBvavSwoyUqblKM7KYFqKuppAy//Ctnnoej2/4d5wWr6nnuY6ODkx7v7SA9LzB5MU3nwmB1YVR+KMrlIRFyMpmYwmwvXlaDVTHydrHWXozFa8Lz1RwAinl58dTuxr9mKpB2/vj0hwpIIlzvGDJUP+sL0eQIpkRB//R60Nmfa30864oltR5rS/5Y0FjtIGmSfh9+/EFs2uO2KpZM693QyV+azIBlJkghLRmTR2nNeMW2G2+VyUVCQWhqTnx9LFpotb+ih7iY0Jhu6rNQkpidePk4oLHPLtrVIOsOYUpROu4nSfCuHGyffDSvmqYFllKSnoqg0dHgSKlsnk9CyjofK7Ua8w+EJQ/TRwX6GDr1K1vorsCxaS977PoWqKrjfemJSY3UPBWlqc5Gn9ifVm0+E1u7EIMn4vIMTH8zIS5Sq0BTJ5Zxlk1vP1RjNOM6/Af/xXXjefYa+v/wISasje/P7JvxsSi23fWz1tKZEt7YTuQeqqhBoqcG8cM2kJDIBygpsOOxGDtWnL5OLCe04GOpz8eKuVradV5nov55J5sp8FqQS1ZkhJAz3fGLaDHcwGESvT/V4jMaYlxMKzVz98XiEe5owpPEi+zwB/vxWE5eeU05FUVYso3ucMOrKRXkcbuofc+3yZPwN+9DnFCf1Xe4Z8OMPRhP9m09G0urRmO1p1dPGY/jwG6DIZG+KGTO9owD7qosZPvQ6yiTexN+t6aZM249GVTCWTt77M4zoq3t6J5e4F9eL7yaPlROEyUeTveVazIvWM/DiIwSaDpH73o+gy0ptuXky6dTTxvqOGztTcw8irnYU/yDmylWTHqskSaxZksfBeheqmv63orVm097aiU6r4dbLMu9tw9yZz4I06C1oIoFMj0JwBpk2w20ymYhEUpO54hM8PuEziSpHCfe2ptWy/sWzhwG4/b0xAY+JNKSXL8jBF4jQ2Tfxmq4SCRFsOZzSQKOhI3b+xWWOMT872rg4RkK9ExruI29hLKlCn1Oc2Ja18SrUSJChQ69OON4Xd7Wy1hFbI5uKx21xxryxwUmqp4V7mvFjomJhxZT6FUtaHUW3fYnCW/6Z0o/8R0pHsbEYrVcOIwI3w+60BrWx00tetilJoS3QUgOAaQqGG2DNknzcQyHae9P/VsJ6G5EhN++/cGGKQlummAvzWZAejcmGQQ0SicoTH3yGUCIhhg+/iXfXXwj3tc/Ydc8Wpk05LT8/P234zOWKPcTThd1mmkh/B6ocSVmfPFDn4vX9Hdz+3moKc2JhSq3VQaS/Y8xzVY1Inh5v81BWkJpYNppA86FY1vPiDUnbG9q96LQSlUVjfz4me+oBThie8da5o8Mewt2NOC+5I2m7sXgRhqLFDB18LeGJp6Ole5BjLW4+uHQIXSR/XFWxk7Hl5aEAgYHJGe5AdxPtEQerlkzsLZ+MpNFiXbppSp+xWwxoNdIo2VMnyFGUwDBaS/J30NjhTSkDC7TUoMvOH1ctLR3rl8ZeaHYe7qY8TRJi4wDkafzctHXyL0nTzVyYz4L06C02DFKYPk+Q4ryTNWLPPKGuBnqefCDRmAlJE9Niv+DGab/2bEQO+gi1HSXU20zY1Yo87EafU0L+1Z885XNOm8e9bNkympqa8PmSQ7EHDhxI7M80wbhO+KjmIsP+MD94fB/FudakB+dog5mOskI7RoOW+kn0yPYdeQuNyYZ5QbKnVt/uoaIoC71ubG8z2eOeOFQeaD4IgGVhaiMU28oLCXc3EBnoHPPzT7x0HKNBS06oa0reNkBOYWwZIDQ48dq/qshEXG10RHMSL0HTjUYj4bQbkzxuSC0JC0Vk2nuHk9qsxhXepuptAxTkWFha4eCNA6kvgkebB6jrl7BrgtiMs0fYcC7MZ0F6jPYszFIYl3v617nD/Z10/e6boCgU3f41Ku79Mdbl5+F+9dcM7n1+2q8/G1BVlVBPM+63/kjno1+l5b/upvvxf8f96m/wNh6hp2+Yxr6plQ6fzLQ9GbZt20YkEuEPf/hDYls4HObJJ59kw4YNFBZmVkwCINReh8ZkQ58Ta8OoKCoP/n4f/d4g/3jHhqR6Xa3NiRIcRo2mhgsBtBqJxaXZHJ/AcCuREL66nVirtyRlPauqSkN7arlRynVGQvaqqiZC5eOVhAWaDqAx29NmPdtWXABIdO56hQ7XcEqI+GC9i1f3tnPTphyUoT5MZdXjju1k7Nl2AqoeZRLtMiP9nUhKhA7ZOe5SwZnGmWVKvPiMJcLS2j2IoqhJanaR/k6UwNC4kqrjcdG6UhravUllYaqq8vNnDxPWZyOhjlnFkAnmwnwWpMeanY1OUujrm95a7uiQm+7ffgOA4jv+Bcuideiy8ym47nOYF66l/4VfEO4f20mYy6iqQrDtKN3P/ZzGH/w9HT/7R9yv/oaOrgHeVNfxg8H38oWBD/CPHdfwrx2X8EL01HpbxJm2UPnatWvZtm0bDzzwAC6Xi4qKCp566ik6Ozv59re/fUavNewPs6u2h4Ul2VQU2tFMoowIINhxDGNpFZIkISsqP/rjAd6p6eae61ZRfVIjibhAR9TnRp+dPiy4pNzBcztakGVlTI3lQMM+1HAQ68oLkrZ39fkY8ofT9nkezehwrtFix2rS4Rke23AHW45grlyVVg95IGqmQ1OM9t2X+PbzVnKyzJy3upjNK4vwDIV4+OlDlORZubIygKd26mu5kiThwwr+iQ13uLcZgJCtGLvlzPQjngw5WSZ6BvzAyL0F5OFkg9mYyCg/YbhDnfUAmEpPLZx90bpSHvnzEZ59ozHRFvalXW3UNg/w/q3LYf8bRAf7phyGny5mcj4Lziy2bAcewD0wfbKnStBH9+++iewfpOTOf0s4QxBbxsp//2do+8lnGXjxlxTd9uVpG8dYqKqKEhhG9rmRfV7kYQ9KOIAqR1GVaCzx12BGYzSjMVnRmGxoTDaiegthRUswLBMMRwnF/3d4iKCrg0h3A4b+epz+ZsxqgKiqoS5SzMHIudQpFWTlF1BeaufcQjvlBXbKCm2U5NnQ607PZ57W7mDf/e53+f73v8/27dvxer1UV1fz05/+lI0bT+9t42Tq2jz812/2AmAz61la6WRRSTaLSmP/inOtKcZcDvqI9LVjW3EhvQN+/ucP+9lX5+KWy6q4Nk1vZ501/lD3jGm4q8qdPPN6I609Q2mVzyCWKKaxZKVkIh9tiRmLdJ2nRjM6nKu12HHYx1ZPkwNDRL29ZG28MmWfPxjhqz9+m+WhBVxneJt/vKqQt9u1vPBuC39+K5bdvaA4i69+ZAvRN/8PjSULQ0HFuGNLR0BnxxieuBws1NOMjIbsssnVQ58pnFmmxL0fK1Te2OHFYtIllWWFOo8jGczoc0tP6bq52Wa2nlPB395pZus55Wg1Ej99+iCrFueyaVMlnfshOjj1zmrTyUzNZ8GZxWCL5VEMTZPhVqJhup/4D8J9bRTd9mWMJUtSjhlUzTTmXsjC+uf56Y/+wKKNm7l0Y/mYDs6pIvuHCHXWEepuIuLuIerpJuLpjS1zKtFTOmdE1eBXjQRVPQaiGKUIuZoTkVePaqNFX8mgsxp95TrKSvO5u8hOYY51UloUp8K0Gm6j0cgXv/hFvvjFL07nZdhQXcDDX76cI0391DT009Du5enj9UTlWOjXZNDGvPEiOwVOC7nZJsz9xygCnqhR+PMzL6LTafj0zWvZdt6CtNc4IYnpGXMc8bXZ+jZPWsOthAP4j+/GvubSFA+4ttmNxaRLm6yUbhzRYTeGgopx9crD3fF2panG8LfPH6N7wMdn/+5G2P4OqzX1XHL3XQRCUY61DGDU61ha4UCjkWhtPoS5cuWka5VHEzFk4wg0T3icv7OJ7mg2i8snXwZ2JnCOqoXXGUxIBnNKqLypc5CFJdlJL3+hzuMYixefVmenD79vBXuP9vBPP3wDVJWcbDP3374Rw0j+kDzLDPdMzWfBmUVjiv2ghr1nPlSuRMP0PPFdgi2Hyb/uc1gWrUs55khTP//+y50E/AX8q9PC4oE3+f7vDTz9WgNf/NCmCZ95E44hEmLowCsMH36d0Khe9lpbDnpnIebKVejsOWit2WhtTrxRI3tbg7x91E1Ttx8FDXqNQmm2lqJsLfkWFZs2jFUTxqSGMBHEoASwK2E0eiNaoxnZnoc5v5TchUtYlFs8zuimh3nTj7so10pRrpWt58S8wkhUpq1nmMYOL42dXho7vLxT04V3ONZ+c5vpAFeaYbfLwnUXL+KaCxeR7xy7U89Y3thoinOtWEw6jrd5uGJLZcp+//E9qNFwSpgcYklJ1RXOCcP88SYU8e5RDruR5s70Hm2iXWlhcjcv91CQv7zVxKUby1m1ciHdNesZrnmdnEs+iNmoY93SgqRzyEP9mBeuG3dcY6FYnFj8h1EVeVwjF+pppkPOZfkMrm/DiVp473CI3GwzOntyLbeiqDR3DXLZOeUntkXDhHpayN5yzWldO8tq4IHPXcxTrzag00pce/HiRG25xmxL9F4XCE4HrSkmlxscmpwQ0mSJeHpwPfNDgm215F31CeyrLk45pqV7kK//7B0cdiPf/tSF2BslrC8/xv+7vogfvOjm/u+/xv0f3MB5q0vSXGF8VFVlaP+LuF/7HbLPg6GgEufFH8BUsRxj8eKkzmvuoSCv7+/ktXfaOdYSSwpdVunkpquqWb4gh6oKZ1JO02xn3hjuk9HrtIlQ+WiC4SjuwRChZ3chhSv5ySfeP6nzxVp+SuPWcms0EkvKHBwfQ7N8+MibaG05KQlN/mCElu5Bzl89cfLXiXXY2DWcdiP7xygHC/U0ocvKSyltemV3G+Gows0jWfO2NZfgf3JPrKPWSW/MvqPvgqSZcqlVHK09B22/StA7gNmZvsWm7POiCXrpkKu4ZgzxmekiZ1Qtd262eSRr35PY3+v2EwhFWVByQjEt3NMMShRTyemLo+Rmm/nYdam5Azp73qwLlQvmJhpzzHBHfEOoqjplrXQlFGC45jVC3U2oigKoyEP9BJprkPQGCq6/L9ap7yQiUZnvPbYbvV7L1z9xPgVOC4rjvXje+iMVva/z/fvv5d9/uZN//+UuPnT1cm7eWjXpsclBH71P/zeBhn2YypdTcOP9mCtWJh0z5A/zzqEuXt/XwcF6F4oaW/770NXLuXh9WaLUdy4ybw33WJgMOgqzZZp7jpN9ztWT/pyk1aGx2Mf1uCEWLt/+egORqJxU1qUEffgb9pG1cVtKyLmu1Y2qQvWCidv8aYxmJL3phHqa3YQvGCUckVNES8LdjSlhclVVeXFXG8sX5CRCVJaqc9CYbAwdfCXJcKuqiu/YO5gqVpxyNyPDSD9sT3fXmIY71NMMQNBSjG0GE9MgVfZUa3MSGtWwpCmNYlooXkaYZi3vTKHLziPqnVz9u0AwHnGP26AGGfSFk0SEJiLc30HXb76OPNiHxmxH0hlAktCa7WRvuYbsTe8bU6XwqVcbaOke4v99dEsiP0RjtJC1cRuet5+i7JIP8u+fupAf/H4fj/6llraeIT5z67pxy2EhlvvR9btvEunvIvfKj408U2MG3zsc4t3D3bx1oJMDx13IikpRroWbL1vKxetLqSxKbZc8FznrDDdAsK0W5GhKg4+JmKiWG2IJalFZpalzkKUVJzLEfcd3gRzFtvz8lM/UNruRJKiuGD+jPI7W5kirnlYw6g1SCQeI9HelvAk3dnhp6xni0zevTWzT6AzYVl3E4L4XiA72oxuRKg111BHpax9XoGUirLlx9bQexloJimeUW0sXjXHE9OEcqYWP13LrbE78I+ppkiTR1DmIRoKKUaI4oY7jaO05ifs0HeiyCwi01JyShyQQjEYyWlAlDVYphMsTmLThViIheh7/DsgRSj78LYyl1ZP+LfqDEZ56tZ7NK4rYtKIoaV/Wpvfh3fknvDu2k3/Np/j8HRupKLTzq+eO0t3v58t3b048104mOuSm87H/hxwYovj2r6IrW8Ghhj72HXOxv66Xhg4vqgqFORauf89izl9TQlW5Y97NoXlhuIMdx3G/8TgF19+H1jSxMlCg6QBodFOuwdXaHIk+yWORSFBr9yQb7todaLPy0mp9H27so7IoC6t5/G5WcXSjRFhGq6eNNtzhnhZAxVCUbAx31fYgSXDe6mQzmr3lWgb3Po9nx1PkXfkxADzvbEcyWrCtSg2DTZas/NikHU89bbijEY9ipqJy5pM8Tq6F19qcqNEwasiPZLLS1OmlOM+GyXBiqgQ7j09ZjGaq6HNLUMNB5GE3OvvEkRiBYCwkSQKjFUsghMsdYMkk80i87z5LZKCT4jv+FVPZ1AR2nn2jkeFAhNuvTF3+09kc2NduZXDfizgvvg1dVi63XVFNWYGd//rtXj77n69w+5XLuHRDGSbjiXknB310/e4bRIY91K+8h0deGKam8a+EwjJajcSyBTl88MplnLOskMVl2fPOWI9mXhhuSacn0Lif/ucepuD6+8Y9Nhb+3Yl5wUo0hqnpQGttTsJ9Y8ueAuQ7zWTbDBxv9cCIc60Effgb9yeFdOKEIzK1TQNcdf7ky6C0NseIYT7hMZ6cWX4iMS35vLtre6gqd6S8desdBdjXXMrgnr9hW3EBcmAY/7F3cV58W1KSx1Rx5ufiUrVExkm0CnQ20RHNYckMKaaNRq/TkGU1JErqdLaYkYwOuzGYrDR1DiYpucmBIaLubrLWXTa948qNJetE+juE4RacNlqzDas3hMvjn9TxSjSM991nsVSdg3nB6ildyxeI8PRrDWxZWTTmS0L2uTFHwbvrT+Re9mEALlhbQlGuhZ88dYj/feIAP9tew5KybJxZJuRIhAt7f0+x3MlPh7Zy7JVBSvNlrthUwfrqAlYtzsVimpzjMx+YF4bbWLgA58W34X7tt1iWbhpRBEtPuLeFqLsbx7nXTfk6WqsDedgzbvhSkkYS1NpOrIX7ju8eCZOfl3L80ZYBwlGFNVWT1+fW2pxEG/YDYzcaCXU3xsofRj30vcMh6lrd3H5F+iS4nMs+RKD5EJ2PfhUAQ+FCsk/hPo3GYTdRr1hgOL0KmBqNIA110SmvYOsY7Uynm5ws0yjZ09gY5GE3flshPQN+rthyon491DXyQlQ8fevbAIbcMiCm0DbVB6dAcDJ6axY27SDt7sl1CfPX7UIJDpO1afJ5QHH+9GbM2/7Ae8dOttU7CrGtuIDBvc/jOP8mtCMJdIvLHPzHvRdyuLGftw520tQ5SHOHl228RrnSzpHyG7lh/aUsq3SSm33qDsVcZ14YbmCkJ/Nu+p57GFPFyjGbYfhqd8SypKu3TPkaOpsTlPRNKEZTVe5k37FegqEoJqMO39EdaO05acPkB473odFIrJpCG0udzYkaDqCEgwnP+WSPO9zdhKEwuV3pkaYBVBXWLk2fJKY1WSm9+9t4dz4LGi3Zm65Boz+9rk9ajcSwZCM7kD6pL9zXhkZV8FuKJ71UcKZx2o1JrT0h5nG3dcXKZ5IS07oaAFKWIM40WnsOksFM2NU6rdcRnB1oTTaydP0JlcDxCISi1PxtO0bVyuOvBfhkXmjMNeeTmYy3Hcdx/g0MH34Tz1tPkHv53YntkiSxanEeqxbHnBnvzj/R/8IRHOffyDWX3jHG2c4uZk8Xg9MkJqt3L2o4SN9zP03bmlGVIwztfxHzorWnlCWdqOX2TZBZXuFAUaGhw4sSDRNoPBDTJk8jYHLwuIuqcseUwjyja8r1Og12ix7PqJIwNRoh3NeG8STjUtfqjmmqj9c21JpNzqV3kvOe28d9OZkKIX0WhjHU08IjGeWmNCIxM4Uzy8TA6A5hxO5t00h9/MLiE7+VcHcDOmdRwkOYLiRJwli8OCGtKhCcDhqzHZsmTKdr/LbDqqryP7/eQa6vkQ77SnYd7ePLP3qLQGhyqmPPTsLbjmMoqMS+7rJxW3/6j++h/8VHsFRvwXnJ7ZMaw9nAvDHcAIa8MpwX34b/2Lv4at9O2e87+i6yz0P2OVed0vm1VgcwvnoaQNWIYTze5iHYcjhtC0+AQV+YujYP68bwgCcaR3RUl7DRHnfY1QqKnOIVHmtxs7A0e8aFBhSzE7MyjKqk9gMeam8grGopWpg5w53vMDPgDRCJKkhGC5LeOGK4vdjMevIcJ3IhQl0NSd3kphNTaRWhniaUyPj91gWCidCabRgJ0tnnQ1ZSnZo4u2t7cNftRyupXHb9NXztI5tp7x3iJ08dnPAaU/G24+Rc8kE0BhOuZ36AEg0n7Qs0HaTnyQcwFC6g4NrPnpJy43xl3t2J7HOvxVhSRd9zDydJV6pyFPcbj6PPLcG8eP0pnXu03Oh4OLNM5GWbON7qxt+wD0lnwFS5MuW4PUd7UBSVzSeVS0w8jrjn74ldz25MWuNOJKaN8mJlReV4m3vSJWdnEimrAC0KkTR1yUMdDXTKTpaUZy4BqyTfiqJCz4APSZJiOQTDbo63eVhSdqKURPZ5iXpdM2a4jaXVoMgE244iB4bSRpEEgsmgMdvRKWGUaASXO324XFVVHvnzETbYXUgGE6aypaxbWsCtly3lpV1t7DrSPe41nn2zEV8gwu2T8LbjaK3Z5F9zb6yH9xPfRfYPocoRvLv+TNfvv4U+p4ji27825UTi+c68M9ySRkv+NZ9GjYTo/t23kP2DqKpK/8uPEenvIOeyD5/ym5vuJIM5HisW5VLT2B9T9qlYkXat+N3D3Tjtxkm/naaMY1Qt98mGW2O0oHOcaLXY2j1IMCxP2H1sOjCOaPl6O9uStquqijTQTkfUyeIZVkwbTUl+LOzd2RfrV6yzOYkMumnuGqSqwpE4LvFCNEOG27xoLRqTFc9bT9D60KcY2v/SjFxXMP/QjIiwWKQw7b3pw+VHmgZo6R5itbkn1lFwpO3wbVdUU1Fk56EnDuALpG9rPOgL89Sr9WxZWTTltrzW6s3kXfUJAk0HafnBx2j+r7vpf/7nmCtXU3zH10dUKwWjmXeGG8CQX07hzV8g3NdG248/Q8fP/pHBnX8i65yrsVadc8rnlQxmJJ1hQvU0gNWL82C4j8hAJ5Y0Hn4kqrD3aC+bVxZNug1pHI3FDhrtqFpuUyK5CtInptW1xo7NhMdtL45lZXs6kxOt5ME+dHIAn7koo6UcJXkjhtsVM9xam4Ogpw9FUZPuVzwx7eTcgelCozNgX7uVYOuRmGDQgqm1VRUI4sTzVSxSaEzD/eLOVnKNEfSBviSNC71Ow+duW497MMgv/nQ47Wf/8FIdwVCUu64+tf70WRveS9lHHyB78zXY11xK0Qe+StEHvnLG8mzmG/Mmq/xkLIvXU/p3/4Hn7SeRhz3kbbsH+4bU9pZTIR5GnWiNG2D1kjze1fUC6ftYHzjuIhCKTjlMHhuHBq3VMUr21EgwLOMPRjAbNIR7W8ja8N6kzxxrcWO36CnOm1ig5kyTX1xEUNUhuzqTtsc9WENh5ta3Idbsw27R0947BBCLVBzdiYSSJKIT6mpAn1uCxjhzGsc5l9yB3lmMoWgReufUfysCAZzwuPPNcuJ3Php/MMKbBzq4fqkGOlPn5NIKJ9devJinX2vg4vWlrFlyIi+nvXeIP7/VxKXnlJ+WpKihoILcrXed8ufPJuat4YZYfXfhDfef0XNqbY5xG43EKcmzsswyQEQyYMgvT9n/yp42bGY966unlpgWRzdK9jQ3O7b+0+8NUii5UaPh1MS0VjdLK5wZURMqyLGwV7aT5UleI/M21RJVNeQuOv1mHafLwpJsGjpiuuT6nGI0qswSp5zQMoeY4TanyVWYTiSdPm0/dYFgKmjNMc+1MkdLfZpugm8e6CQYltlUGILO5PyYOHdsW8a7h7v54eP7+eE/XorJqCMSlfn+b/dhMmj50NUrpv3vEMSYl6Hy6SQmwjJxqFySJJaaB2iJ5hI5KZnaH4zwTk03F60rnVBQf8xxZOUl2j7mjwj4uzwBQj2piWn+YIS2nqGMhMkBrGY9A2Sj8/UmbR9qOUq7nMPiilN7eTmTLClz0Nw5SCSqoHXGVMs2lpxIBosO9iMP9c/Y+rZAcCaJdwgrd0g0dnqJRJWk/S/ubKWswEZ2uButPTfturLJoOOzt66ju9/Ptx/Zxe7aHr7xf+9yrNXNp29el2hJK5h+hOGeIpNpNAKghINkhXtpCOexvy45m/qV3W2EIzJbN6V64pNF7ywk6ulFVRXyHDEFoT5PgFB3E5LOgD63NHHs8TYPqgpLM5CYBrGXmIAxD1PYnSj5UBUZzUAzrXL+lJPzpoMlZQ6iskJzl5fOUGw5YVn2iYS/YNsRAEzlwqsQzD3iugPF9lh+TWv3Ca+7rWeI2uYBrthcQbinOa23HWfV4jzuvWUtNQ19/NvP3qGmsZ97b1nLBWun3k9bcOrM61D5dKC1OVGCwyjRMBrd2C0oQ131SKpCj6aI1/a1s3llbH1SVlS2v95IdaXztDxgvaMIVY4gDw2Qm52DJIHLHSDsasRQUImkOeHJH2uJRQiWZsjjBlByKtC4dhPuacZUupRwTwtaJYLPVpHUSCBTrF6Sh0aCnYd7CIYibFaMFGk9if2B1iNIBjOGwsrMDVIgOEUkgxk0WvLNMU+7prE/kf39/LstaDUSl6wtwLOjE+uyVGnm0Vx57gLOXVVMc+cgC0qyptQmVHBmEB73FEmIsEywzh1srwOgcs163jrQSe9I7eRLu1rp6vdxwyVLTmu9WTeSqBRx96DTanDaTbjcw4S6GlP6RNe1uinNt2Kf4V7XozGXxrppDbceA8DXuB8AU8Xs8GAddiMrFuXy3DvNvLCrjUFzMaqrKbE/2FaLqWxZ0guRQDBXkCQJrdmGiSCl+Vb2HostW4UjMi/tauXc1cVYAj2gKuN63HGybUbWLs0XRjtDCMM9RU7UUHvGPS7UcQx9Tgnvu2w1Wq2GHz6+n73Hevn5MzWsWJjD+atPr4Wl3hmr0Y64u4BYVzK5vx01EkxqOamqKsda3FRXZrbDVFF5GR7FjKehBgDP0d20R51ULqqY4JMzx7UXLcIzFCIYilJYtZKwqxUlEiI62E/E1TbjiWkCwZlEY7Yj+wfZuLyQQ/V9eIdDvLirlSF/hKvOXUA4XuWRQflhweQQhnuKjO4eNRaqqhLsqMNYtpQCp4WPX7+K/XUu/uWnOzAadPzjBzeedna3LjsfSWcgMqLxm+cwYxqM1UmbSk8Y7l53AM9wKKNhcoAFJVkcCZeith8i3NeO2l1HTaScVYsn31xlujlvdQnfvfci/vsf3kPxijWgKgTbj+I79i4AlqWbMjxCgeDU0VqzkX1ertxSSSSq8PDTNfz6uaMsX5DDmqo8Qt1NaEw2dFmZTxYVjE/mFxfnGNpJeNxRTw+KfxBTaUz678pzF7CgOIu2niHOXVWM7QyErCWNFkN+OeHeWF/ufIcZS0MnmiwbOucJb76uJXPCK6MpzrXSoKvifLmert98HRVotKyhKHfm68rHY/nCWGRCiaxGMpjwHX4rVr+dX44hryzDoxMITh2tzUmos56Koiyu2FzBCztbsZr1fObWdUiSRLinCUPRwoyUjAqmhjDcUyRWJiGNW8sdbI+t445u41ldmXPGw9WGgkr89XsAKHBayNX2oilYlDTxjrYOYNBpWFCSWdlASZIwLlhDS9t+Koe6eDO0gqr1s7e0SqM3Yl12LkMHYjKj+dd+NsMjEghOD53VgX/E4fj0Let4z4Yyygps5GabUeUo4d5Wsk6xAZNgZpk2w71jxw6eeeYZ9u7dS3d3N/n5+Zx33nl89rOfJT9/7oZiJK0OjcU+bqg81FEXy0BOI7xyJjEULmTowMtEvS7KrGGytV58OcliJnUtbhaXOdBpM78qsmVVMQ8e3MpFi/S82ijz/U2zZ307HTmX3IniH0LnKMS28sJMD0cgOC20NidqJIgSDqA1mFlbdeI5HOnvQJUjYn17jjBthvt73/seXq+Xbdu2sWDBAtra2vjVr37FK6+8wvbt28nNnT1rm1NlolruYPsxTCVLpj0D2TwipepvOkC+L0AY6DIsJK4WHIrI1Ld7uPai2eHZXri2lEf+fIRXG4OsWZI/5WYEM43O7qToti9nehgCwRlhdFtiTY45aV+oO1ZBYcyw/LBgckyb4f7Sl77Exo0b0WhOeHoXXXQRd955J7/5zW/4zGc+M12Xnna0trHV05RwkHBvC47zb5z2cejzy9Hac/Af3wODfXTLDpr8J9aM61rdRGWVlYtmx0uSXqfhG584n11Herh88+z2tgWC+UYisdbnQZ+TXNUS6okLNwkhlbnAtBnuTZtSM3A3bdqEw+GgoaFhui47I2itTsJ9HWn3hbrqQVUwlU2//rYkSdhWXoT3ne0AHDa+h45RnX+ONPUDJxKuZgPlhXbKC0XHH4Fgpol73NE00cJwd1OKcJNg9jKjyWk+nw+fz4fTmdkM59Ml5nF7Yv2kT8rAjAuvGEtmpnGG88KbiQ72oTXb8bvXc7zWlRjXkcYBKovsGRVeEQgEs4MTGhTJ0UJVVQn3NGEVeRxzhhnNWHrkkUeIRCJcddXczlzU2ZygRFECqe3x4sIrM9VHVmO0UHjD/eRtu4eqylwGfWF6BvwEQ1FqGvpYUzV3EwEFAsGZQ2O2gaRJUX2MenpQQn6xvj2HmJTHrSgKkUhkUic0GtNL4O3atYuHHnqIa665hs2bN09+hLMQXXbMGEY9vUlddOLCK5YlGzIyrnit9pGmfsxGPeGowpZT6PctEAjmH5JGGxNhOcnjDvXEEtNObgUsmL1MynDv2rWLD33oQ5M64Y4dO8jJSV5TbWho4N5776W6uppvfOMbUx/lLEOf0AnvTtIFj7q7k4RXZpqFJdnkOcy8vi+2/p5tM7ByFimTCQSCzKKz5xAd6k/aFu5uAkmDoUAkjM4VJmW4Fy1axLe//e1JndBmsyX9d1dXFx/96Eex2+389Kc/xWKxTH2UswydI64T3p20PdiRKrwyk2g0ElvPKefxF2Pr7HdetWxW1G8LBILZgc5RSGhEkzxOqLsJfV7ZuN0OBbOLSRnu/Px8brxx6uVNbrebj3zkI4TDYR555BHy8vKmfI7ZiMZgQmvLSTXcbceQjJZpF14ZjxsvWUJr9yAGnZbrLp4d9dsCgWB2oHMU4Du2E1WRkTTaWGJadyPmRWszPTTBFJi2rHK/38/HP/5xenp6ePTRR6msnF99jPU5RUQGOpO2BduOZLz1o9Ws5yt/tyVj1xcIBLMXvaMQlCjy0AC67HzkwT5knyepo6Bg9jNthvvzn/88Bw8e5KabbqKhoSGpdjsvL48LLrhgui49IxgKKhk6+AqqqiBJGmSfl0hfO/bVl2R6aAKBQJAWXbwdsKcHXXY+wY7Yslqm8nIEp8a0Ge6jR48C8Mc//pE//vGPSfs2b9489w134QLUcJCouwd9TjHBtloATBUrMjwygUAgSI8htxSAsKsNc+WqWF8FnUEkps0xps1wv/zyy9N16llBvOYx1N2IPqcYf+N+JIMJY7EoqRDML+Zrw6CzEa09F40li/BIglqw4zjG4sVIWtEoci4hvq1TxFBQgWQwEWipwbr8PPx1u7AsXo+k1Wd6aALBGWU+Nww625AkCWPRIkJdjciBIUKdx3Gcd32mhyWYIsJwnyKSVo954Vr89XsJNB5A9nmwVoukMMH8Yz43DDobMZYswfPWkwzXvA6qgkU8t+Ycosj3NLBWb0Ye7KPnyQfQ2nKwLjs300MSCM44mzZtSjLa8W3zoWHQ2Yi1ahOoCv3P/xxddoFY3puDCMN9GthWXIipfDlqNELuFXeLMLngrGG+NAw6GzGWLMG67DzQaMm57ENIkjADcw0RKj8NJK2O4ru+jhqNoNGn12gXCOYj86Vh0NlK4U2fR4mExHNrjiIM92kiSRok8eMXzBFEwyBBHGG05y7CcAsEZxGiYZBAMPeZ9YZblmUAuru7JzhSIDi7ic+R+JxJR6YbBon5LBBMzERzedYbbpfLBcAdd9yR4ZEIBHMDl8s1Zm+ATDcMEvNZIJg8Y81lSVVVNQPjmTTBYJCamhry8/PRajPXvEMgmO3IsozL5WLVqlWYTKYzdl6/38+HP/xhGhoaePTRR1m1atUpn0vMZ4FgYiaay7PecAsEgszyqU99ipdeeombbrqJLVuSxTrmQ8MggWCuIQy3QCAYl61bt9LR0ZF23+bNm3nsscdmeEQCwdmNMNwCgUAgEMwhhGSOQCAQCARzCGG4BQKBQCCYQwjDLRAIBALBHEIYboFAIBAI5hDCcAsEAoFAMIcQhlsgEAgEgjnEnDbc4XCY733ve1x44YWsWbOGW2+9lR07dmR6WGeEgwcP8m//9m9cffXVrFu3jksuuYR/+Id/oKWlJeXYvXv3cvvtt7N27VouuOACvvnNbxIIBFKOmw/36+GHH6a6uprrrrsuZd/ZdB/mI/P1exFzeWzEfD415nQd9/3338/zzz/Phz70ISorK3nqqaeoqanhscceY/369Zke3mnx2c9+lr1797Jt2zaqq6txuVz8+te/xu/388QTT7B48WIAamtrue2221iyZAm33HIL3d3d/PznP+eCCy7gxz/+cdI55/r9crlcXHnllaiqSkVFBdu3b0/sO5vuw3xlvn4vYi6nR8zn00Cdoxw4cEBdunSp+otf/CKxLRgMqpdffrn6wQ9+MHMDO0Ps2bNHDYVCSduamprUVatWqV/84hcT2z72sY+pF110kTo8PJzY9vjjj6tLly5V33777cS2+XC/vvjFL6p33XWXeuedd6rXXntt0r6z6T7MR+bz9yLmcnrEfD515myo/LnnnkOv13PLLbckthmNRm6++Wb27NlDb29vBkd3+mzYsAGDwZC0bcGCBVRVVdHQ0ADA8PAwb7/9Ntdffz1WqzVx3HXXXYfFYuGvf/1rYttcv18HDx7kmWee4Utf+lLKvrPpPsxX5vP3IuZyKmI+nx5z1nDX1taycOHCpC8WYM2aNaiqSm1tbYZGNn2oqkpfXx9OpxOAY8eOEY1GU7o1GQwGli9fnnQP5vL9UlWVb3zjG1x//fUsX748Zf/Zch/mM2fb93K2zmUQ8/lMMGcNt8vloqCgIGV7fn4+wLx803rmmWfo6enhqquuAk70No7/zaPJz89Pugdz+X49/fTT1NfXc99996Xdf7bch/nM2fa9nK1zGcR8PhPMWcMdDAbR6/Up241GIwChUGimhzStNDQ08PWvf52NGzcmMjCDwSBAShgOYvchvj9+7Fy8X8PDw/znf/4nH//4x9NOUDg77sN852z6Xs7WuQxiPp8p5qzhNplMRCKRlO3xLyr+xc0HXC4Xn/jEJ8jOzubBBx9Eo4l9bfEG6+FwOOUzoVAoqQH7XL1fP/rRj9Dr9fzd3/3dmMecDfdhvnO2fC9n81wGMZ/PFLpMD+BUOTlkEiceZhnrbW6uMTQ0xD333MPQ0BC//e1vk8JH8f8f/5tHc3IIaS7er97eXh555BE+97nP0dfXl9geCoWIRCK0t7djt9vn/X04GzgbvpezeS6DmM9nkjnrcS9btoympiZ8Pl/S9gMHDiT2z3VCoRCf/OQnaW5u5ic/+QmLFi1K2r906VJ0Oh01NTVJ28PhMLW1tUmJH3PxfvX39xOJRHjggQe47LLLEv8OHDhAQ0MDl112GQ8//PC8vw9nA/P9eznb5zKI+XwmmbOGe9u2bUQiEf7whz8ktoXDYZ588kk2bNhAYWFhBkd3+siyzH333cf+/ft58MEHWbduXcoxdrud8847j+3btyf9cLdv347f72fbtm2JbXPxfpWVlfHQQw+l/KuqqqK0tJSHHnqI66+/ft7fh7OB+fy9iLkcQ8znM8ecVk773Oc+x0svvcSHP/xhKioqEoo5jzzyCBs3bsz08E6Lb33rWzz66KNceumliczTOFarlcsvvxyAw4cP84EPfICqqqqEwtAvfvELtmzZwsMPP5z0uflyv+666y4GBweTlJbOxvsw35iv34uYy+Mj5vPUmdOGOxQK8f3vf59nn30Wr9dLdXU1999/P+eff36mh3ba3HXXXezcuTPtvtLSUl5++eXEf+/evZsHHniAI0eOYLPZuPrqq7n//vuxWCxJn5sv9yvdRIez7z7MN+br9yLm8viI+Tx15rThFggEAoHgbGPOrnELBAKBQHA2Igy3QCAQCARzCGG4BQKBQCCYQwjDLRAIBALBHEIYboFAIBAI5hDCcAsEAoFAMIcQhlsgEAgEgjmEMNwCgUAgEMwhhOEWCAQCgWAOIQy3QCAQCARziP8Pmzp929gZIj0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x144 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2, figsize=(8,2))\n",
    "\n",
    "ax[0].plot((u[mask0].reshape(nxx,ntt).T)[0,:])\n",
    "ax[0].plot((u[mask0].reshape(nxx,ntt).T)[-1,:])\n",
    "\n",
    "ax[1].plot((u[mask1].reshape(nxx,ntt).T)[0,:])\n",
    "ax[1].plot((u[mask1].reshape(nxx,ntt).T)[-1,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7fd6b0622c10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ie44uMZ3T2LM/fpBfmnKYO5XBO1mD5Rqyt5pINNYiA2Gm8UKPfiASnUHgRy9pTj9EOgEELoamnIGtqxXQCwVNV2e6bXCsJqg9Z8X+BqiSYPDpj234/IZ7Hdzth/jLE/Fh5olbxgk//xHmZ9dvIW64NYs4fJDHZ6L397XRLEZ1NvKYbqiBlkpvjJiKyz3cdPJKNz/j4i/2oN4Et0tKF1gJo7NbsKAbP2FsW0OY+iD8NpCEiTixb8pWcwZCx9Qkph7vDADanhZb3gbA1MG28cI+K32D+a6gMrdxQbzS0r7GyuLGvJWZUjdpGN98JHRVnEFQiQ8ZO0+vcEMFVm7dmOz4k9M63/2DG73hqk19xiU9c4Z33bUx+/7/O5rjzn+WT7yuF57q0jrVw79Qv7xV0ATCNFUiykzhBBp9P97LhhJkDPBIoc188F10YWJrcm0F9ENBzdE52xIcO51nx4nkzO8v3VNj6g/X/002p5P9vmt4ZH6ZpU/EP6cfe88i8396acPnW6/tILbeELs9WT6UR546HnlMM1RIlM1uHKDpjkfTW+LAoXiH0GoGdOomstpBuh0MzUJP2CYYWaneUdxxSyJSA2egGYQyIJDib55EDFRUQOgjhNompHQw7PgTtzwdOxN/XOgCzNTAGWhUl1KxuR6AqYMVZG2aTnvjhLfSARIZGaFtZlfFGXxqaonveiB6YDz0YUFlcYV/+cDiBiDHY7+qU/njf8rNr4mGKD/ypQ6nf/Tr/NSWgJ2/oK/bLqz8WsAX3vIAP/y78QCcXjfkcw8GzH6lj/fiPFQbartg6JBKg52j6enU498TbgheP/qxNuo+YcOBbgtLS5MzA0xN0vMFMx2DxxY0Pv3QGCMfqcf/APBv/6jP9l//KF/73OXJsHNvinf/wRbO3nKY//orRUZmWpHftf6ZwT+U5/nCX62vUOzcmyL3U/fyldmLXPNI9Or//jcusvI7GzENALouwe2yf0dXZfqvsMDQeLkGt/xgcivMzGkT9/gK1GZJ6Tky0Ql6ACYm26r6EGN2NkDLrQLFDALp44cC39MSsQtJ5ntyrTyM765t9QqmJJePHxTzXZPiWHx5UWRMRKZM063yUk0weyw+2Q1w33vryNNnNpTo0xmN4h4NL+z/3xMZfGNeY3RXdIjXqPnIl09z+6jH2XvXVwbSHY8nFuc49I/iH9ajX+kgv/owP7y3w+IDpXXH/stnRjF/9McSr833JGdfCulPdQiq/Ssw86o85QQCx02Al4YDpGGEOf1VHLrCoGtiFXqqsdSHC3Mpdry4MQF6pU3vH2JvtcbX/3T9SnPbmxy0N97PH5/KxjoCgJ+9oYP3+ec3fH7dnQFi9008tRQ/A2+oeJw/Gn08CATS7bA9JzdUg1olm7anYd4ymXRr1JZ9/BVXJRE1i1RCZJ0reMhWfBObYcpBAtEilAGhDPBCQRh+a+jDVQsH/SWE6h3qwiSlg2XHb+maroadjT8uUvpa8nC5oyXiC+rDGV4z4kO3R7G8/gHtPZjG2JrHC/v4/jc/ta+KM3jhaBn7xgSI7FOXGLW28V13LlEbXT/xvzBtob/rLWgJqeb5v1pgnzXK999ZX/f5ri8u8gcnZnnvTyYPiIvnHVamUwQLXTXg/EABQlADynXjR2lSAjEMB9sE11trSPFDQcMVXGwLzp8sJoaHAP/0gzWav/alNSgwwNYdNpn33cCC1eMLX4p/riduGeeAPUL9ufXVgMM3ZynfN0yVGs+dj8+r7C9qkR2CAL4roNdkbyFg/PArooNRDScQiH07uOG2eEfeagaqaanbxxAWliZjYb75rK9QizFmWKGaZJqKCpQzAN/XvuUEYhhKwkCoXoLAR0NHEzopHewkZ+CBFdNvp2mqBIqZou3rNBsWmVY0rgTAu9fiuqEeuB7lkcvPJp3R2HbQQ99VwQm6kXiPzeyqOIPcMz20g/GrxNILIfLcM7x9h0v6AdaF+ycupVjJSN79E/Hnf+zzPuE3vsj37JKcf9P6yfH7fzlC5pfeH4mCvNLOnwhwpruE1T7S80CGSBmuhZpxlpRAhEHZ0VdlqVBC1xfM9QTT8yl2HUuOCs6+YYx3lHU+96frP7/1fh/tzrfwV+cyXPNUPPT4A9+3SPjiYzQWL+cTyhWDa+9z0V93Pc8vByw8Hb+vr9jxuZ5+y4DlRa4p97l1b4/Ze4aY3V1i6mCFsYkOoRSI4V3suyV+Ne+0QwXM6vU3zRkUTAjb8c5ANyXCNkBfzReEarsfE7W9WpOrHauBvxYdqMggPixveSDy8TkcVbVK0fE0Om0zFtAGcM/hFmV7DEJJYcTjwKE0O/emuO2eFKWb0og921nqeTTqCb8Xdxnf9Df+Fqy82EHs2hV7/OVnQ4LHjnHz8Ajvu77L8tuLLE3mqY1maTUtnlrUyP6ju2NXmVYzoPXRU5Sbfd752mXapcuZ9f3PL/DFDnz/r5cSr/HieYfanE2w0IG+M2gsUFl/P2Gyh3IAO44zL1iDKzuBRsfTmenA1NnNo4Lf/qFper/5Z/SuANocvjlL8d17eKlxkY8/Fo8raP5gnvfu0QhenCIMBOWKwc69Ke7+bkHqXYcJdtzAFy+Z7Ho53iHJ+ejkIUBr2UROzzOe3sp9WxzuvW2F0j0e5ZtdJiZ6aEIS5Epk7t0S25buexLfE4MehZAYHA+gINxhK37SGFa4BuRZbYn2Qv5WtgmrDp1QdaCamkzeJngCvRhf3RHm5S2o6+ixOY2T905w56hHhjRogvxYwO7DAYdeEzB5J5h370NMXsvZps3iXHLeIcquWqOSGDsQe2x22mXlSytYF17i/q0277ujQfb+kOrhHIYRcqqhwfX3cvgfxuPdjz1qI7/xKO/Z3cF54/rQ999/dALrp//Jptc4ew76M47KWntq4IWSTSODuGoCDAZSGCIJ6foaVUcwN5dm4kQ9+Vp2lzhcW+ah311furv23j7aXd/FRy+kGX04+hyerfOTdzcothz8Sy3sbMD1t1vc9HqPyvfvRLv9fp5dnuXhbyT3u8vz52OPXTjj4D4/h7l0kcNDZb57u8sbDvS4aW+XXXmJLqDj1dCu3cW2G3qxIJy1BF0Y3/YMKjKQ3XhnoOkDKPIgMvBC1fj0N3UGMhTgBmtJRE0oSHJSwq67WWQAa1vQuL1+L2ty6+2LXFsOkM0F0DSMHQXKh23K95ax7j+IduhWVqjx9LLO+PloQpgku2rOoO7Hw2wBHv1ciPvxJ8nNTfOOnZJ3Xdfj9luXqYz0WekLpjtnMN7+2tiqxMljPRqfm2G7OcnbblqfUNv38Dy/dWyW93wweWCcPNajPm8RLneh76zt871Ntwnx51zNJ4QyoONrzPUEU+eKa+2mcfZTP92g9isPrWtVHh03Sb39WpatPg89NYTdi/5h8wMGd48PIU8fRzoBpXGXLa+Bwjt3o93zBo40L/Enp+3YKsKqhefiCVLmZ10WnwmQzzxHenmeGyolXjve59bhgMkM2HpIP2jDxHbs2ye44faN722t0/BVWN6EMDFnoJyB0O21TtMBVijWVrECSR2WMhwAj3wfAl+ViPXNIgPQNnEGq6Zp0R2QF28d5s5RSdEag14dLBN9Iod50wTGbfvRDt/Oitnj0XmNx48UN8UpRP72N/2NvyU7We9z+2vj6/6Nms+D/6LB8+/5KoW/+Dw/NGbw8zd5vGufy3hG8uRCltmiyc7/+V28+b3RIdin/zjE/b0/4icOjpP6f9avRE/9m5BHf+79/MAvxzMpAXztM306T6m+BS0I0ETy6uKFAj+h2iBDAYZOP2hzpmHx1KlMJH3alfbB3/V54x/8OZ/40PrJ/qZfKNK48zZ+6VmTHZ/fiDYMDI3sz+r8z9cL7CNPEZ6ZxdxfpvCeA9g//f3U7ruL/3TG4+f+3TCtX09mFMr/C51gOr5KAfCVT/T4yk9dovZvPkbqoS9xnVngdRMZbhruMZQKaHsB/tAWtLtuYudbde59a2ZdA8/wqIGVDtWKrhl4YTyeYzzj06lFlyrzBV1NvpQNhkUgfbxBS3RSzqBdVBFkqRyffJNSXOYfCNy1dvSCCd2YCV9dTiFGopmqwlC1MKumJ0km661j7XLSBlNvGeGdr1/k/q097E4b2i1EMY927W60O19H79BNfKHe46e/kuF//dc8E5+I73JNsqviDM5fq0AtB/5+fJi/ai8+3WH6w3XCZx5lWJa4odJjR06594WeiZg8zMj9xcj8ge9J2s83kOef4YFt4Yb69y9/fJzUzyaXGsNQ0liwkcstcLuYmozEfa9aUjUBUCufZeKFfWa6MH0uGQjlpA1u91p84X+vdwQHDqUx7jrI0arPy8ei+R5m3zTE27YH5IOUgvnaOvrOYcSBPTQzBp+f1vnkp0cSu+NW7e/tc9DHMomrJigk57MP6fQfOoe8cIwcGSopk5LlowmJF/YhO4S5q8jQVoftu2yyOR3L1iiWDFWCs0ykEPQSJm7OVMnGKDNMgbB1hH4ZcBQOtm6aLjfA1Vdts8jAMMUa9p9QImWwhkJM6fHNSp32oNEtxqTjI6RqVssXPNpFG8/W6WVN5ncU2bG7yd68JK0XkF5PJRwLORgaxs8PMdVa5guXDKqfN1/Vu4yzq+IM3vreGkO2j/6O+/n+nzUTy4QAX/1Mj97HX0a+/DST2S3sKzqUbLXH6tLDuGlPbJZ65ZKNPH6W14z1uXD7yLpje76ywF9fqvOGdyZDZRenNfy5NrLfwtalAtjE2GY5A6EBlokTdLnYFmw5l7y3u/lfhXR/7ZMbugv33ewj9t3Ic8sG/oWN32sOpbjnlho3D/eRLdXjICoFxJ5tiG038MSCy4efzyZyI4LKVdz0yxo3VcbQDk7yPT9jJHIagnIIl541CV8+j1w6S84comjpg87MAJHKw3CZ1KTN2FbBlm0W41tMShVtQC+XwpeqISzOska41jb9SlsHRb6iY1EXyRRlq4tFXLvxGpXa6nhd7U9YgyRHRyrdjolIx5dsZT8Ar0/ODMgVXPpDFivjOZYn84TbdbaXfcYyPpY+GKeWCYUCojhBzZ3lueU0Tzy1uVNf3PZt2ML8jw56VFJpmoUMmf/0M/y9r9+U2GIM8OLDKdyvvoxVnWNrdoitWdUG7AQd2Lad9F3j3HrnxpB/ZV7ivbzEaGo3N9y6MU/xax+ZYNsv3pT42+dO9fGnmtBvYuthYmQwgK3HmjA1sG1qjmBmJpPIcQfwzw8P86n/s965FMsGpTuKtM2QY1WN0vJGR9i6K8tdoz4FawTcLiKfRUyOIbYcYq57ik9MGRifiq9nAyx8T5nf+aV5/umhcczqLGLvPtI//QBv+KODfP/PJsADgacf6eE8NYe8MEXK18gYRcUxiVC0bsU8+pYchVGP0S0ao+Mm2ZKPXlaMUn7okJAfJGcGiuIrwgxTgKWtYznShEpiGkYY20fgrkUG0c7csjXVg6GLNYcghIaG6omKizjaTQsyCc6gqzpZs0bIUEoyNNKjNpahP2kxMdlhMivV4ilUQhQ7h8iN4KXSnGkIHl8U7HwsuZ259C91/vXP1xP/5qo4g0rToWCOcLbZ5WOXVnj54EFu+eh38fq3x6/QJ4/1mHnIQx49QrYfMJHNMppSs07kRtD2bmHbdT127l3/0DvtAO9iC5bP8doxucE7HnpshuVrkjn6Ou1A1eY7LbJGuCnuWybkFETKACvDimMwczF5i3D2DWNon/7rdaVEgEM3pdBv2M5ct8ryQnpDO/WJW8a5/lCd3QWHlJ5THHzDFcT4bmrU+dpsjhe/VEksZWZ/VuejP+Sz86VThJ/+c1ieR47sZnkoy8rth8n88k/xA78Uv81znZC5l22Cly8hq1PkjCFSeg5Ds9UkzaTQR7Nkh0MKIx7F0YBs2UcfSkEugxv2aMY4A8/WyRjGBh6HVbPtAZBnwD4kEGsT1jBDPCt6BV9tQ7Zjtgm2LdAMqaIOTVuLOkxNYmlyzZm80lItFzLxi13YcpFOi4IVUElJKsN9ShMOYxMdRob7jKUlOTNAIBBGGpEpQ3GchrvAiysmR16sJDa1bf83Or9xxxD3yOScz1VxBvKZ5zCcPrMdkwdfyPALX67wZW2YHf8zvkUZ4Btf6OF85QzyzBFK+jAFawRLS4OZQowMkTlUYO91auVctTCEXsuAxVkOVxycwxuTPP/5eXvTla4xb0G1QdoIsax4ZxBskrEWtg6pHAtdk8pUcsb333/vHGf+x8ZoZnxvF7FzBxfbFp2OuQ6ltzSZZ+f1La4fkpRtCw0dkSkjKjvo53I8t+zzmTNWImnK2cMj/I+7R7D++K+Y/W8v0/zMRWgqBqbPX7T4zSM2H5utYf/ke3jb34t/bsee9+g/twwXL66RzpqarR6QbUMhh74lR37YIzfkkaoItJEMpAu4YU+tqBHWy5pYeppGLXoCGKa4PGFhXUNRKu1vyB2tWrBJZJBKa5h2uNYAtcq6rA3amOMig3zdATs+Ue10dHDa2HqW0RQMl11GRnuMTnSZzCoWrJQukEhI5SA3TNNd4nwr5Ol5nfJz8eOo/w+y/IsbDPRPf5j+734+9u/gKomo9L9yjvQNUzhBheZXDcanGvzpg4L/84FtPLjwA/zh2J9Hfi8MJX/xnx3eePI5tvzwLOnD+xEjO0EEkC9j3L6H8cxF7snXmD+To1EPyBd0wsBFnr/ENQdv4YE7qhz57PrzrvxaQO3PvofXHf00X/9CdInvia+32Hd6hsJNN1AsusQF94EktsceQB/JIAoTPHtqIXGPt//f6dz78Bf5k1fwExbLBvl37CIY38fZkwogtArI6g7bHLpxmft3+Byu9DG1LL2giZ8xuNRe4RtTaX7/M1sTS4jzO4o88m8DTr3pt9e4EW9/bZ6ipnGh5fLJYyXcz0ieTxuc+ECHf/37P8Oho78dCVNeWfT4woM6bzWeIf2WOuLwQbTCAMtgWIhKCfNAF72cwmy7aENptL1bEEM7mGnMMDOdYwcbt0Ar4zlKVopLU9HbnHxBV04XwHexjDQlu8NoOmA8Y/DCcDpSmEWVBw30mMhveNREHzMRlQIUCmBlCEIVvqT0QVt1hI1ONxGVeJBdfd6iPDdPceIA15QvARYrOYe8CeNpyVjaQ9cMnKBDn5Cu0+DpRZtPXbRpfkhQakWP2bOHR/jq2wXBb/4OX/mffXq9kHf8UuxlXEV25DDE1tfv36pfNplOh7z1fck12Se+7NH98hTy6CnkwjlkZwUCH5FNo28pkttlUNnmMbZVI1cK1ct1PWyRYmduQIzxCvutY3l2/aNkZqFgoYOlpRMbaEIJQQL2XWRMfHzmmsnY8R+/xmPqz+obPt+6w0KMDNEP2nghqmV4VIOdGjv2NNhbCRjP+OSMED906fh1LrUbPLaQ5ksn0ptiCd7wD1uEX/oET3798gBb4xIcmN3zKC92eOpUhhn3IocfSIDiNgNmT2bwXpxHzswh20tId+DgLBMKObThDPpYFm04hygX8DVY6ev0lqKfUbdkQ3fjs1m7XlOAPhjag+5CQ1gDmjmJjKEgW80FxZEp22kGFHgpMNW/VbJTXUAYExkA9MP41btV1ZCNJrrvM5LyGU2HjKdhJCUp2QH2IGHthX06Xp0LLZ3nVnSOvTCcuNW7+21V9KkXqT3bU/oUMRRxa/efePTvyPSxLJgp0kZIe/TyHn90usnHL2QZ/cnrEr/faga8/Fia3kNTyJPnYWUR+m1FRFLIYm4vUJr0KW9xyFV8UqsdY26XkbQfyaL89U8Nob3pbYm/Gyz1MLVUojMAYoU9AETGwA17VJeTOfEr1VZklFIZA0qqM00XqntvZKzL5LY2Oyf67MxJCpaiUev4fea7Ps8tp/ny6RSdLye/7jM3jPKjByXdz59bF91kSz5YJlkjZGSkT2soTagJ5mZyPDafI/W9Nyee9+gzDvVjDnJqHqpV9a6kwhOIbAYKOUQ5hygXIV+kH7RZ6hkUatHde6WhvkLhxZhuhoPZGULgYmopLD1N3gzIm5DJJWQmE8xKh2g5S11zKreGYQilQBfJnYsdr0a5Eh2IV1d85EoTunXylsFI2mc4FVC2AwpWgK2HSClxwx7zPZ0zDYsXz6UZe7aeeL0/tK+HfP44jUULXSf291ftqjgDc/8QIlMmY0i27WyyNKkSaZ6t8+KijnbXd2+KDjz2QpczT2dxnpxBTs9BtalKPek02ngBY0eB7Bad3JCnMtSGDr5LwQpoDW2ciHuOLnGRZEIRtxZgajaphGeqcgYJkYGtnEEtQSDj9OvHCT7/WOSxdDGAVBqJJGvAUMFjdKLLjhGXnXnJaFoNHifQWOiZnKyn+Pq8YPlhK7G1GeBH3rdMudGlPn+5dHjgUJrSuAOaYCilc6gs6VyTYmkyT9iDZ5YF4qb7EqO5Rs1n6mUD96Vl5FIVmk0F0xwkEkU+hyjmoZRHZCv0gibzPSgvRG/GRsZ6agGIsVXWJfwA6fUQgYelZShYAWULMhEELJuZpgnlDEo25DIIO48b9PClizdIGCcllpf7LkMxk3Fp3lMdsp0V0nqBiu0zlAoo2QFZYwB0kwF1By62LI5UBQsvpmNZs0H1olxf3o3z/CJuD7Zstdh9MHkVuzq0Zwd2QX4UQ5Mc2N4jvN3gzA2jTO8fot8zONc8Q/affBdbtiVvF557ss3ccxresQXkwrKiwdIEFLKK4nxrHmMihz6SVmg0IK2HWPnol/bxC1nufkN81tfp6OD1SSXgDCA5MsAyFalqPf7FvOWeJeY/G40/sNKBkl8DMgaMZ2B7DrbnJFsyktxgQDY9nQtNi6eWBM8+OsaWc/XEaz79+nHet8dEvvwyQpMcujHDDbdl2XNDgLVHoecK5ii3jrjcdOMK4qCGloaLizbHa2cZ/Yd7N1RyrrRjL3RpnA0Jp6vIVkf1eghNbRVyaSjloVCGTIlqP2CmE09HPzHsKCbrGNOty84Arw/9NraeIW2YFKyAdAQ1G7DWFxCFUrRTAjMVQi4D6SykVJLTD138Aa7EshL0OVoWI+PRydZOOyCYa0OtSkrPUbAsKrZPwVRbBIGGG0rqrs65luDUVCaR33J2d4kP3tVEvvQ1Opd8rDRsPyCYOJzsBK+OM9hxiJa/jKlJrh+S3HtDkxveuML+e+qMDbmcbti4+2/ivp8vbhraPPeoQ/NIF//MMrLVVu3BptqL6mMZ9C059OGMeom6OldcOPeVl3KJgitOR0UXSdx8m0UGq84g1Y5/MW/e6vDiYzGZ8sFA14VByfaZSMtBkklSti9HBUs9g5fqghdPFDj0RHKe4MQt4/z7d8+Rr9eRS3VKkx7X3NHn4L0u5TvyGHvK4AfYjss1pSxv3hpw03V1hoZ7tJomX5pJo933Lu76B8kVmdmzJv5UQzFI9Z3BVsGGdBpyeUSmjK+riGa+Hn+uPQWJfyp+Mui6hECq1nO3i+w3lEPQsmTNkFQ6huZ+4ASicCKGITDtUOULBo7ADXt4YR8nUHRqSZHB8bpOcTx+f99f9JErdYwgJKXnyJk6GQNsXTE793z1Tk81BPPHk5G79/xwi/snt+I/chyno5Mt+YxdF5K6Lz6JCVfJGTQNj5W+oqzaX3K4f9LhfXs83r3P5ZZhSdfXONecw/zBd/PAfyglnqvVDJg6ZuGcaCCXGorI1FDCqZTyaCM5xFBW7fPM1FpIF2XtZ3Tsu+OFWn1XDJRy469nU449TXl5ux/fzXRNKRupnbhmoY+ppSjbAcOpkLF0SNkOyK5GBa7GpY7GyxdTiGeSew7qwxn+3Y/M88bJbchzZxG6IHXXJNl37Sf1jkOYt+1GTIyC6yGXzlJwBK8Zldw7ETAx2cH3NZ6csnl65SLW339TInjszPE+3SmXcKkNfVft6XVFEY6dg0yJXtBkoWswdyn+PAdLPs7L8ZRnmjFQKeo70GxDt47s1rD1LAUriF0MVhslo/goNF2gmyEim0GkVV7DCTrUHZ2erylnkNC5eHRJpzQZ/84bi5Yi4u2skNJzWHoaQ7PQhE4oFXP2Qk/j4lSObafiew/O3DDKB6/14Kkv0nqmTugLiqMu9t3bETfeEPs9uEqlxQfPeGgiyw0Vl8NDZTI9D1mfATuF2HGAc80zvLiS5jHH5bp3v4W///060x/4K77yiWjI8dHnOizMmrw2nCV7u4e2fwKRz0E2rdL7lgmVUfrCZblvxNavt56pIX7+TmCjXBkMBskmMl3hJqXF1b+JA4nM7i6Rr8cTlPRbOsVqlczEIbblamQNBy8U6EKJlS71DF5YMXjsVIbMgx0yCVFK6v/R+fhry8gvPYT/5Rra1jLi1sOEWw9RdWeRUlK2t2BUZ5FHXiB46jQi9QJDt+7ngWtfixPM8WDVY/ErNv/42BA//NY5fuZTb6Hw/s9vkGwDFQ6feibD4ew86bE8IpOCbAlh5yE7RFu2mWo1+dp8mvxj8cjM102M8bmP6hChhVAsG+iGJGy7aNUGsu9Aq4Oo9LDzI4ykfLLpgKjsSarjATad9sZ3Y5gCMytgqIhvp1huL3Kpk6LtaXQ8jZYHphnSLtmRe/nlB01SP3oT/OYzkff0wmMeo3tnSO85jbFTJ18Ypuc3ccMeXd9ntmPy1JJA+5ofu306/fpxPvnjsxR+/zOc+LDL/KzBtbeHZN+0He0t7+Fk8zzxxAFXKTJ4dknjbFPgBJpqvpg/g3z2KPKpZ5Dnn2Y0PUTGCDlWE/zlWZvnQ59tP3891xyOD48W5z1WplP4l1qKf2CV5jyXgVwOkS7TD9rUHB2rkUROGS/yqqCoqpsuyWIElZT5igQ1FrpatJWqU4z1WrrSdejWsbUsRUuh0wxN6QI0XZ3zLcH0qXwivdfSu0v8ym0+8gt/Tf8Lp/EuNhHFPGLiGk7UL/HVGYsnFixqziwiVYR2F+9UFefZecKXzsLSGbbnXTI5n9JSl4NPzfHxRytUyzl2vjOZkr5dNVV+x/dV3sBMEeqqk3OuazI9nU3kATSqs5E04XBZx1P2A4JqH9noQr0FzQ702xiaFbvNswYO2nU2PrcwkIrS3Ezhhj3qrs5KX6fmaKw40B9sMeI0F0vLXcTe+KlYW/GpzxjIpSqyW0MPwdAsBEKpMnsaywvpRPj6e+9bpFLvcelzPZ55vM3KooeVDhATI1S9OZ7ZRJjl6nQtni5yadmi5wuE20NOz9F/bIbew1PI54+TCyzGMj61js5zLw7xv46lad5wPbf+TDJ8d+p0SG/aUQNgQHOOZasyUKZE32+z0BORWP41M+KTlqYdgm6tvfgoU9RaCZGB62FqZjwKztTWpMmjrF3VodpAdi+Hk6kBP5gTCJb6gunpLDuOJ1dG/v1blhk6e4GVPz/P4hlLNcuMD1OXdR6ZT/HFSxpPLGrMdgE7g2y7NOd0lqZSStZr+iJbsy6ZrEeq46H7IVservKpKR3z7bfH/m6rGdCpGYR1JbSK0EC3cIIONcflYltn5kI8Ws+zdeTLL8UeNw1B4AvClktY7RMsdAirHWSrjfR6Ct8f992BM3D6G735GqrUuCyBttQXzPdgxRE0XcVzEafGDOCURxK7PmuzFuFMDepV6DXRhYkQGk6oUXNhZTl5Mn//njbBlx/l8Yf6+J6k0w5VS/j4MBfbLi+sJFforooz6E9pLMxl6PoauF3C2RorZ3RmX07Tf3QGuXCSiYx6+uFFyenPFfnQyQDjB38gUQjlwpk+9XmLYKmnkkeaWnWEnccTAYt9jYvtTQQqvARhjmwAVoYEirrNre9gavY6KrYrzXSCdQCfV9r8rKcyz60alrAxNRtdMwiloO1pzHQFc+eyiWCUpXeXuM1K0f39b3DkEY3Wso5I6YihHZxr9nj0osXxIxVOXkyz0DUUdVjLpblksjgtaE5J5Mw8GaNIybrMOWA6AR97Poez9UBi7qC+LAgbjnpHg8jACbss9EzONgWTZ+qx3z1z2xjuV0/HHjdMQegLgppDsNwjWOoRLnUHOhj9NYKaKNMHis++HxMZAGgGvnRpusoZLPYEiz2o9hVLUVzEB3CivsQtd8ZHt2dP9fHO15ErNWS/gSFUZOAEgroLzkL8uc/cMMoEoyx+egV3IDkXhhKtaENumPNNi3OXvg0jg8pch/qyTdcXEPqES12WZnQunPLVKnX+EgVzhGw6INty2XFihQe/MM7jjRVe8yPJGeu5C6jJ0u0P9BItsHP0gxYLXZOZenKaRHbiV9RM0ceTTmwDzauxsOWpRNZQ9GQtVPuQi+cyvDSlqMtYVorFppZCQ8cLBR1PZ7YtmNikLfqfvb6G/5FP8eznLC6ed3AciT6Uws8WOFa1OfnSEPlTPWam8zRdJU8Wtl2qi5L5WY/Ggkkw1yatF3glAjf4WsjzyyvsuzU++lpZ9BVL0WCbEApJ329zqW1yfsFOZH2697ULTD+avMJ5jkavZeDUQtwlV20X2i747prce5QZg/1fVM5nzUEIjUB6tD3lBBZbOtWmSbNub5Cjf6V9bjrFjjvjB8/KoqfUqOdWoFtHhIo41wsFLTc5ot1/T53whcd48ZH1167lLUS2wmxXgcSS7Ko4g0K1R6bp0vGVgGXY9Vma97g05TB/QRCcWSAtLUbTIAarzjWPzPK/X8xgvf8+xrfEh/Lnzzj0FxWvPGGoOvasDD2/pULQi8kPhIWNSkOrZkxk6QctOjGkGqumJdD6htUeKT2ngDMRNjrdRAxFa1GuWueSv7a3NITKOPtSKD7FS9nEyTTztgpvnNzG1F+3OHtKRUGmKdAncjS9JV6qCYbPtxieaWEteXR8QIaELZdG3ae27FNfkQqarafJGusHX2WuzecvWeReH6+6XFsZiMm4HkI38UKH5b7G2Zbg/JloRqBV+77dDk98Nf7+wlAqZ9A0aK+Y9JoGYcNRzmfAR+fG5HR0X20TvAhn4HlSkdmiAEBND+ptg3rVZmkhTa2qnIFIyNN84Ykh7LcfTry/lekU/vkG1BrgdtGEjhMIai7kq/HO4P4dPu7DpzZI5omMCXaO+a5AW06uLF0VZ6CFkkKtT90FodtIL1gj7zh+tEvj0SryzLPcOhywMn5FuPnbDr/XrPDmD18fe27XCbn0cnpNIk2YaXpBk1MNja9esBj9Uj32u0vvLtH9/W9EHitXDKw7dzDbaTAzHe9QTE1NrtjrO9tArFzk9fviX+xXF2ZjCTYAjj5q0H9kGmamMAKl+bfS1zleFzjPJq+aH/uRDq1/8ivroM6VSR/tur08uxTy9ItlRmZaaKEk1xhkxQO1/66t+IShpLbiEyz3oNckHxGoPfWHWfR3fG/sNXTaAf36gAXZytD2Vnh+OcWXnhziwNfiKykAt0+fTazW9Lohsxd9Lp6C6dOCpSmL1rKpnE+4ynAd/d3EyMCTa+raoQyou4K5mSwzZ3L0j2v4xyE/3cPuxa/8Oz6/xJF9Sfl8eOyhNjNf8wmPnUc25jA1JelXq1uJFOrft7vEN35/43Etb+GEXRb7JCZl4So2KmmBVDV/w0KY+jq2o27dRK7U2JL1sCvr39xHnyqiveYtieeen/VUaOh5oCv470LXZPpCPrHv+y03NbnwbPRed8/+FGJilKW+QbMez/SzyqYTZ72WAa1l9hSCDQIxq/aZiyZ3vCH+Ny6c6dOekchaA5wuAo2er1Hti0TI8fHbJ8jNTfP8Q+sjq2zZg/Iw812dZvXyMakJRVfuu2of/spHJ8NIbYPR6SYtw13XSv5K85zBQzIs+kGP822BeSp5/+V+IEPvL6NLc2t/40gaNZ/lRY/lJZ9GPVBaDIOJLJGx7Narq3pcC3p4RWnZDaHfM8jVHYrLPYrLXXL1fuL4AvjQyRRv/5Hk7cTU6VCR6fSa6EL9bT8hGm2XbKxWLbqLUxevWo7+6nUtMgDoGBZaxljXQ95va1BtMJb2qAyvX0HzX+5wpHaOe98an4iZn1UrmZJVV85gugP6mWT67ffu7vLUI9GTaWy3A8PjLHRNRDP+4aqGlYTutZYOy3V25h0Wr4mmoXr4q2NMvj25clKdsZFLdaTbQQhVpq1X7cTBeP/9i/hffHpte7BqdkVHZCss9iHTvBxmOmmDjCGVMwhBH4xHwxDqRuPa+4DZToOde+Md2hqwx0yx1Dc5u2xsCpn+ubsavPC55InkOCGtpoo0a8s+jZqP7w5UkEIlhPOt0qWHIWrLNNgxdNsmubpDodqjUO2Tbbpr5ck4e/GTJSo/eVvi31w406d3oQvVJoZQzjlJLm12VwlmzsUeF2joIp5vYdWumjPwbF05A91C5K11PPqBpxE2HdJGgdLQem+Xabl8/EKarffH5w18Tw6gwwHoBh3P43xLsPVMfP1+ZSLHmBudbc3mdAr7TER5G/M9EjP1m20T+h0duVJjJJ1j3zXR13PoiVmMt90bew6A86dcgkuKpFWg0fWhkRCxAHzf7j6LX9yYXDTGMpAdou4ITPfyYO5lTXJmiAwcNO2yk8vmdLSMOZAui/6t0w2bkcn4iSFDwDDwQoelnsH0hWTnd+KWcW400pw8llAWZlWIZXWFV+U13xXr9gZxzkAOotO4yO5KbksvBN0JSHXctWqK7odrFYk423Fihc71N8WKB69abdZSkd+AhDdIKGcXd7rIqRjIeSDRhE7WiG7dv9KuijNw0gZO2kAXIIVAK9rr9Ot9TyJbLraWYTi3cUA99NQQ5v3JiRi3pw2cgUXdNZhdSG4Z3v5DPcIvfCny2KEbM1jXKwTjYj++gQYU9Z6dUMHptwXhcpuCOcLhCXetY/OVtlyyOXRjcvTjXVShpCZ0egGxyEpQyMbrhyY58tjG2auNZPAYJAuvMFnQVONT4CM0STankc5o5Iu6Ujg2U7GkpedaGoXRZIUoDB0n6DDf1QnPJaM2f/YHF/H++pOJfxMlzNLrhgSehhx4LcnfUJN9sE0IJJhuEKlVsdmk+9PTfQ7/i/iKEaiqmFxsDCpGydc8MdkhmIpeWGTfxxAWeSuefGXVrk5vwlCaXtZEF+BLF5FPU6poa+gxTVdhnaVniOg2pvJEE3Ht3YmYA7enQxgSCslKX2f6QjIz7M9c12X2L6Olxcb29BA7ttD2qqz0SUwSpQyl8xdnnXZIuNRF6zS4rhzSPRx9D398yuLGv5f88loLBjQ76MJUMOhO/OoxdK+HfunYhmyzZWtopTRe2FfMzoPVsZu3KA31yZpK3UiYgnxBp1wxSOcDtKEUgQg3OJBVm24LrPEESTENSNk4QYdLXRHJPHSlvWfPFs79RXQVIZ3R2Lpj/YKyamE4iBRehdhqqF8ef5tZIFXCMQrl2S3YLG4rxALL/vALY2jf8/7E81887yhG7p6i2kti5J4oBKrcHGGy6yECj4otyZW+DbsWm0MpREmQGlBnk8uQLfkMVQzSGU115gUSHYOytfEh5OoO884FDt4WH4b6rnqhbthjvmtQPpfMQryfFF/++MZsq6YJ8tdlYXyCmuPS6hiJ+3JTk6rVNcZ6vVAlN1sL7Cv2OXAouunkzz87jvlDb0685tqsjWy0MDSLQIIVo6gE8Pp9PeRzxzZ8ni/qAzZi5SSctIFn69RHMlSG+xRMpTgsbINi2aAyYiqOiLEsTtCh5UZPnOW2rshNY8wwQ4StkofTbWLVoED15oef/yseeygaLLb/UJqtOyyKZT1SUDcMWcOIJ+EMwk22CeoE6mAgowVeAkOjXbRZ2pZn6mAl8hQHvjbHZ6cvJXJA9LqhmuC9puLdTCBOqaQk7kr0RA9bLvSaijlpLFm166o4g5HDfXbsblKyFJWTGBumsjdg/2GLvQfTZEu+qo8GLhWbSGrr3zyaZfjHD8X+RuALyKRoecs8uiAS+Qbf+hsaZ77/ryKPvfm9Jtb9hwnH93GuadOsW2slqCgrWZApxA9s1wnxV1y4eJFd+e28d7fHyXsnNvzdoSdm+TcXcomZ5ye+3sI/cgm918bUkifU39uX5sLvbSQEGaoYiHyOkICCCeaE5OL+IeS1OtducSlYGQh9jLEMIzv6bDvoUbwxgziwi8XeIqdmoyd8vWqjDcXvlzJFH4YrnG6kOPFyfD/I2cMjfOw9RT76vrMbjuULOt/1QJp9t/TYcrDD+HYRWcEIQqkk0QaTN47qPhj0FcTlfFYFVAKppNqi8gOtUgp2ahy+eYlr76ky/d3DkUpLH/o3FuMP/svEreDiGRs5NctYWm7InV1pFRt6zehx0jrrIefPcH2ly827vg1Li6MTXSpDDlkDVfbIZNHHMuQrHsUKpPI+ImuC0NAE+BFZ0BPns4g9e2J/Q9MkpGzqjs9MQjsswPfuKvPIlzZGDqPjJsU9GoyO0fObtD0dz9PWwskos7TLnAPR1yUIAoHsdDGCkF15lz3765F/+9hHS1R+IlnTwb/YRHZrZDfpP836OudPRHTjGYPVEJ28JSmWHBjVGBnrMZKSis1YhirJW/bJjwWYu4owPM5c12JpIXrCb0YnrxctSBeoOTrt5fg99p0PVJHPfCmSFn3LNov8sIc5nsKqmKTyAdls8pAWQot1BqviKlHRBQwihisqKGFEL3s/a1IsOQznArbkQ8YmupF5oUzL5bnl8xx+a/yK367q0FSsySU7eZujxWwj2lXV/DScyrA3v8k5Eo/+Hdn2HExkIGNIpJRgKULM3JBHtuyrPWnOUujBGJs/nUEMx5M1aIZEpFMs9MxEfYKlyTzG01+LPLbnoI15YAiRG1HMtIEizljl148yUyOWXRfUgJIhquzZrVOy0xyYcJndXdrwtztOrODcenfsuQBaswI6VTJGfCfk0mQeuXSWi+c3ri6aDoQhujAomFAachgZ6zJacRhPy7XSlpa30McUgxRbRhGlSaZaJs5M9G9msj4yIdGqDamekaoj1pUzX2nv3OniPhwtBV8sG6RLIfpIBm0oRSobqOTmKyap/goFpLiJc7maEBMZaFLBp2PmVKgJOgWLXN5jKAUjaUm57MT2oXxuOoX91muiT4Zq6gprfSwtHZk7W7WOD1YmesytzGnImSXy5jDb88kJ3aviDHbmFDtP1gxUZGCm0IayGKNp8kMe+nAachkC/FjxzbHpFj0Rf3NWOoRSnrmOyehU/Bbh8Pu7tP7wuchjlW0O2tYKZIfwQodQqojDjZHRgs0jAzulqcXFD5BOi7SeZ39B4u2PXh0/daHJHa+Ld2b1eQsWq5TtIDZhtTyZQ85G04T5nkQGAYZmU7FhtOAzPNZjMquYeXWhIjSRT2NMZDF2lRBbxugJl1PN+O3XaGGAUowwy9bQyylI5Wh5JFZnrhuyWXkuOkQ27RAtYyIyJlrRxs4qavx8cb2zVsJK4or/j34/STkDTRPrYBVaRN2+W7Do5y1yBZeypTgqs8ZgLEbYUyeziIPxW91OW8HALS1NxZaxSlCLfYE9HP3uL005+GfqGN02O3LJOJur4gy25XzG0uEaX58w0lDIom/JYY6n0MeyiHxWUUP70UQg5cUOHb8emUGGwQvIljjTSkbl/fR1LR75ZPQ5crsMRKWEj48bykFHdIgbM+lADRKR0MaaSmuXQ2jfxdazbM157NgdPan+4JEi+94fv6+8eNZDLlUZSvkE2Zj7GPZUY1OEOY5c66Qs2QEjacl4RjKRhpLto2vKGZDLoI0XYHwYUd5Gw13k9KIZC3HdU1D9C1E2Om6iDWcIDZOOLxITsiVjlKWpaEcpBjJJIqUjUgZa3sJKB2Rzl5+/YYp11YFVAE6UJVUTNJ0NQKtQE+scQi9rkcl5FEylo5DSVaQY53zmT2egsjPmzlV+SXY9DKkxkpKxiNX5Lhi7ons6ais+3XNdZHVKSe0l2FVxBluzLiNpn6wRKuErY6C9N5pFH1P/KCn22ZYXL8vd9pwNq8CqZYo+IjfCuUb8/v7s4RG29O1IirGde1OY2wtQKuFLVxEmaUqRJ6mOrIkBCUaMpTMamj2Q5gp8tFAykvLZWfEjPf/IJ+ro73xT7PkuTTkEUzVGUj6FUpyoiEu4Ep1J7ndDaHfRfI9KymcyA5NZSSUVUrICBYcVmqL7qpQQEyP42QJnmiZnT5Zir+vasq+ilgjbss2CoSJe6ND3iW3uqY1mkc05lmOITMJArPHMCVNDZEzsbEi+cLmqYK/pI14e6nG0dauRgR4bGVz+oi4U98SqpFpgaHTzFrm8S8ZUTgAGojox6MHSco+2v8JoDFFqECqcwCrF/9JkNFBp5mIOff/GJPSqLV1IIS/OkJHJoLSrlDPYzr6iyWhaqP5yOwfDo4gdk+iHJpVS8MReFnoNjtcTOAs9fS0B9kqzrh+haXi89NXo8g7Agz93kekf/XDksWtvD9EOTiIKYwShh62rsHm04NMdTu5NEAkJxnzFV86ukFUOwe0ykk5x23DAxdduFHHRQsmvTlv88O+Oxp5z5RtNRlI72XOgHnncsgPCmI63+VkX73QVuXyenXmLQ0MO1w157Cv2KVh51elnpmB8FPbsR+y4hSPVGR48Y7Hv4WgF517W5HWjW3j26xvD43xBZ8f1bcSObXT9OoFUSbcoq45lwO1GMg8BLM0FuEvuWm5Cy5vkKh4jWzTGt1gUywbFsq4k0cxVqTURy3S0ljOIeH+mKZSTH+whLA2crEm7lKJdslmZyOFPGoyM9SgMcCYdX7U4Z+ZitjlugBc6sazJ2ayGDCWyvcS+os9Nd0bTw+sPe3DPd0eCrgAe/UqH3keOIF+IbsJbtaviDIx+V6ny6gNVXgFYGShklZDG0BBkh1jomSzHZKtB1fT7vej9mL6lSMurseV8Pfb722WZhz8VHebmhjxFoqpbKgMtdNJGSN761kU4QFGda3kLYdtrCVJDWCpEj6kDf+LhUYwH7ok9Z23WxugrXb4o63UNYmNjIJhtQ71KxigylvZUlGEJlTyU4WXl3/wYTXeJ4zWbUydKsedbvKeEvPBcJDXZvmvSino9oyIuXUAnBhmnBRJh5ymWowf58qJHv20Qdn0IlCCqYYbYGRUdZHMa6bSmCFJXVZO/xSG/mke4rK+onKyTNuhlLTp5tUWw7ABTU1XMrg/NukUxpqW8XbSxtHQstX6+qKtoxOuT0nPsK4WRsuql5S6XOue49Z74RWrpnE14bib5HhOP/h2ZrE2TEhksLY0mdKSUCCsLaRWWi9wIffrMdcxEQoaCFVCPEd8UI0Ocb0WTUwJc2lsmfPShSFpzy9bQhlKQskA30IS+Tp4rFcO7v3Z/CbRnhq3KdGsSXQNHkzPD2Fryga/N0azEw1cvnHGQ9Rl25aJ/t9mwVXUmxtqLGnKpSooUGaNI2dawNMXOiwwRuo3IlPFMnapT50hVIF6O3+e/+Y4q4ZMbAU4A43tcjD1lRKZMEPpkTHCLMStjy4X8KMPj0cO0tuIrCrWGo8JpTaBlDAw7JJvTyOZ0LFvDMOW6PM6rABhuME0X6yI+S1tVdNbpZ036OZN02idjKun3XgA1V7CynI7Nq7gTJlmjHKsmXShpqsTuu6T0PPsKSsAmyr42m2Xyvvjt6/njEvelaITt2j0mHv27svlFaC1i61l0Ya5VFES6CNkhyI/S9qqcb0N6Oh5skTYKaxRPV9rouAnjo5xvxj+c7W/p0Pr4mchjQxUDvWgr+jEZoqFjaBZpPVRc9glosEASC301TDHIgBuQshFmWuVLENhaSDkbf96vzfqx8Ov5WRfm5tkWky0O56RK/sVYfd5Czq0gWwtkjJLiVtTSa2VFDAtSBdreChdaNqemMrEdhp6t8707uzQjthBbd9gU9xuKej1VIJAhWUMyNBK9hclXe7SCKsPb48dAq6op8pKuD6Ga9KYdYqVVsjadGUQGurYJtPBVIhBRUYGmKdGU1T4bLQ2pdIClqTHQ9aHuEKsXCbBrbwMas2tcHq+0bMlXDWGBj61n2JF3OXCoGhkdfPGchfWWG2M7Zi+c6dM4+22otShn5pHNBfQBMYdErnHni2yFXthmqedxckVPbGstmmORn09ssxDlbZxKSB6+Y4/H8cejJ1e+qCsEpKZBGGJoq8KdgSoVJTiDUIpYemRNE6rsmLIhpaTk0S0kqlKR1Fn2iXMG190dH5HImXm259zIJOTEhQZiIj6TPH3OxztTh8Y8pheQNgpYematrIiZwpUODbfDqYbB7Ml4EFf1uwscyO/g7PMbn+3e6zSs64ZhfBQ37BFIQcFUcmlRKL10x2O+Wyd7bfzvzc+6uA2VdZeBBFPDsEJMOySd0bBsgW7Idcm/vw3ThYoMAlvHSRuk0v66LULHF9TbBpX5eBj8dZMuzF2IpGbPF3RyFU9FkaGEfpvJbMiNwyHO4Y3P6tzjBeS1d/G6t8ZHgAvnvw0blfyzNVhegV5ThaKA1HRI5fB0Qduvcr5lc/5MMbaSMLu7BCsXIo8NbwnxMznOLcbf/N3jLsePRu/lUmltnXCnJgWGZmNqJikdDCN+GwAo6GuE6fqg7GiZayq+IcGakq+pg5OOdgYvf2OIwuvjk4j+hTqVVJ6FiFUj03JhWzxA6+J5h/aZPnJmAdmcwwwFprARUoFsMCycsMOljsWJumDHiXieyPe8poF87mGOPrd+EuQLOqN7HcSOSURxAi9UoXPRkoyXPOZ2RpfGjqxkMG+JF7ZZnPfoNgyFwQ9DhCYwTIlphxiWRDdDBTLSxVreIM4vrI61OHKT1S/qQm0FjEHPgGGrraNlBarKIKHpKkj2UEx5FeDOUU8JB0fY9t02qVEDrTDIA7hdcsYQh8o+B6+tbXCee19c5IWVi0y8Mx7avVn791VxBu7ZhuLw6zdUEwwCX7q4QY+e32SuG3K8rqGfiF8J9RsE8lQ0Mm1osk/TW0qkJxu143kGtdXF1fWQvgOBi6FZ6MLA1GRs3RgGO4QkqnRTV85At8BQDUKB9NeUnuKotvc/v4B+Qzz82jtTp2CO4u2OdiZueSyRSm15KoW8sADVBehUwWlDoEBdfujS81tcaptMzaQTQULv2OnifO7ohs8PHEpjHx6BLVsgP4onHXShJOG25SSpHdEz8IlFgTh4MPb3fE9ekTdQK6xmSDRDYpjhICq4jP14taw/cSal+g1Tk5hmiGEOHIIRruFH+gM245WldCK/weEhgfN0NM3b6Dap0J6FHGgC6XbImmV2FxxurEimrtlYJfvY+RT6G++I/b2oCORKuyrO4NTTGdynL8H8JegpsI0TdGh4C5xsdPjUVJqPPDyauEX4f985z/lfPhF5LPO2fTy7FFJ4LnrlP/uGMeSLX409d7cdKqakuhIroVtHDwbbBU3GwllB0WH5MZ18a0SblomwsnjSoR+06fkdqo7BSh/S7QTI6HWviz106eUsXHiGt94VvWr/2osNvucX453j419tM//xZfxvvIS8cALZnFPS6ZpBP2hzviV4eE7Qezg+3C79S53KQ0/yl/9h/Qq0c2+Kvff56K+/kXB8H3VvHjfooQuN7TmXm4c97r6pzvlrN5ZWX/qzLFNZjfvfFY/HPXfCUxT51b6iN9NVbsZMScyUxLDCdXx0cXDiNdqzOGe+2ugkVhmtQlJpn1TaJ53x0YV6/yt9VfsvnIjvElz4njJD0zM88uDGY9cczjB+u45+eKva3ukGOG3k8nm2ZffywPY2r3/D4gYI+4X/HPIZr8j3/2wyn0KcXRVncGnKxZvpKjXewerjhX1ars/FlsXLsxZjL9YTz3EgNbaO1HPVtmyzEDsmmeno5BrRWdyRsa5SJYqxTjtQK02vD/0e0usNNBZVyBCl0rtqoYw/vq6ENKDc9qVL29NpuhrtvjaQ+Iq2ftCiMhr9ohvVELmwwuFytPf/9ENjWO+5M/bcAFPHNdxjy0rRutdU9z3gnFjpGyzOZRLRnO/e5dH65NkNFZot2w3MvWUob6Hr13GDHqEMFAOPKRhJ+WzPSsKJjfmO0ekmTyxkGb4+/pkvL/r0W7rKG3jhZSCSkOqfxiomWfXCxNjqNiEq5bPmIMJwDcWoDaLE1UgxkIoBqeOrCk4SS/XN1zQJXzgZyVtYGRcYW/OqzJ7JKmcQ+OC0EZ0aBWuI/UVJa9v6srsWSj5y2iL1tmtjfzfJroozWFn0aFVNaLaRvoNEKvqrvsGppuDk0XIik+v8jiLhlz8VeWz/9TqMbWeuFw9zTaWDhI2hKln5NU8Jb3T74LuXVTmJp80CNSBkzPHwijZaNI1QqnxBx9douIrDMEkSLZTxXXmddgiLKxwo9Znev7EMec0jszQmt8WeG9SecumcTXCxqqIiv4+UcqAgpLEwF5/Im91d4raRUmRSNj/soU0UBw1fXfqBj0Qljy0tTdGSjKXDWJzF1+cF9q3x1OuuE9Jtaordygs2yGEKIdeVBeMWfi1IjgyuLBlH5R3CUOCG0O2Y9Ot6Isz6jtGQ/mPRdf98xUMbyUE+p9TANEPhPfo9ZK9GxiixNRswNrExH3H2oQLazXezc28ys1eUXTUOxPaKgWz1IPSRMqTr+yx0TU4sGux/PrqpZtXe8A9bzP5h9IMcu8ZHlLeRxAodhgIy8WCmXjek2zQI6z0VHQSXOfdDubkzSPAzatkJw0H3W4AfqshgxYFaNfkFGlo8qMT3JGGtz1i6SP7m6FzLH54ME4lkAU4fCfFO1ZArNcW8LASB9FhxwJqLj1pK93iklmYik7L5IQ9RKeNr4ARKwhxQ+A3NwtLTlOyAQsmNrCq8dKSCuCY+XwLKgbs9naAnCXyxjq/wb8MS3ykqGvQ9jZ6j0W0b5DehJb9xuMv8iQjHWdBJ5QOFUE3lBhWnQS+M60G/je77jGV8hkc3JgR3vbzMstHm0GuS8wNRdtWcQX1F8RwqttmAumtwoS04fby8Kd30Tx7K8qWPbnzYh2/OYt8yRsev0Qvi2WBbTRMxFg9TBlRSqtpTYiy+u8Z9F0qB723iDCIkvVdNeuHayAplgBMImq7GiiMS+/oBbGnEIi4NU+kL5s1hbtnfiWyb/cuPjrDz5/Yl/salKYfaBQ251IBuB+RlnYBigqLPd+3vIZ99IfraKpbqNQl7OIGGH17hDISFqdkUrIB81o+spuSO9RBbDiaudvV6gNPR8RyNwNOiHYLQkITxkUFCNWEtqpMhQohXHFPSaq6j0+8ZtFpWLNgNVBQ1lp7kzMsbnXY6o2HlUOhXK6Oa+FZb+X0fXAf6bYZsn+Fs9Fh4bF6n/NpS7O/H2VVzBrUVn7CtykG+dFnqGZxtCkaOxrcbg5IRT339ochje27qo+3fTi9Q+9pWKXrwLC8kcyGA0gMMqv3LMm1XEGEmRQahJHFVkiFrW4WQACdQW4SlHpQX4veYvayJrE5RjQOo5FSILNor3DocULtrY9vz/ucXaN9xRyK7DsD0SR3/XENtFQLVpNVylRJWnN0z0af/8IXIY1pJ4Sr80MULBYEUCDQ0dDRhoAsTW5Ok9WhWq5GZFj0Ttu6KB/A0aj5OV8Nz1L/QFwS+ehevfB9xzuCybkIEnVlAZDIhDFVE4Hka/Z5Ov6fTbZvk6/GRQW+Xjd3rMzu9MVls2RoiZagks26rqEBTzWKEUlW4vB4p3SCGJoEvzmgYtyc7/Si7as5gfsYjbHlgWHT9Bo8vGjz1peHEpMuJW8b5X/IZ/uj+jVDXN32fTfEDNyGuvYOVvkclBUuHo1F31zwyy3G/wV33xWfXjx/p0ZnyCOZb0O0hQ2+wfxb0EjT1AnmFJkCE+Z6mPPwgImp6OjNduHA+n0gKOvFPQvxPPhyrJrRld4jImMjaNPdMpPinb1rh+Gs37rN/8GdMrnkymYzz+NEuM48EBCcuQbeOJqDa1RLzGdebBb74xxvvO5vTlfinlSHkcllO1xTMWxO6IhwRydiNl2pVJm+Ir7T4nmRpNqS9YtCpGXQbOm5Px+srIdYr2ZHjbmNTRaVBZABqXvq+oN8z6HZM6tUUK8tplhYzGDN+YknxlluWkceeTrzf1YQnmoHQTYUCXZV7D31MzaZsyUiQ2swfmPQO35J8/qif/Ka/8bdkq95XmGnansP5qp4IZgH4tQ/MMvXrpzZ8fvjmLMOvSSG2b8M3DUIJFVsyMRmvtvzYQpptt8YPLtcJ6bcMZNdTkUF4WUln8wRi/D2EvliLDKQMlcKuIxJVmgDevDWg+0w0trwyapIfdtHyFngeOaPCdUM9br9zgfrw+iigMtfmPz7n8b5/nfx7StWngew3FPtywj3XhzPI2VORsNorob0CoerzmkSgIRBK1ESqCMkNo7krAJZ6BvpYckTTagb0OzpuT0UHbk855leG/UkwkDgLQ7nhi76n4XsaYU9R83fbJt2mkairATCeQWkiRJjrhGor6XoqaS1VfkmBvwYYlSs4FWREJrNQ7TPfXYqtPMXZVXMGMGCfsTIs9ZX0WRKh58l7J7jX9PjqZzaGqrtvcDBvmESUJnGCDpqA0RRMTPS4tDcakfXYrE7qvt2J1+d0NGTHV84ABVgJJbHyXK/GwpA1xyJRkUbHBy+pVbtkc/toyNyp6Mmwa4+NvSWFKKnjot9iOFXkzlFJ546NidITv2eT/lf/IPE6L5zp051yoVNfQ4nG2eK2fKyIh2ImlhD4ajugh9h6uFamlUgC6dMLVGk1joa+42kqwkiwTjuk1QhoNyWddqCYqD1t4IAHzzzBU4uE0uKaXbFd9DyVJ7B6PqmOh9nyyTRd0p1kZ1C2pNp+RlivGxK0A+g7CvC2er26MUCumgjDHlSi4rkgTjdstu5Ifm+vtKvqDBRbSIGFroF7Ljn7+xs/cInef/3Ihs+3bLPI3VRE7NqKTBfxpIOpSSqpgO05iRPzQI69OIw4fIhyJT7kd3s6YddDBipnoAbuq8gZJByX4WBghiGSEC8UdD2hOvRibOWOAsNimBNHNw6gdEajPOEP0GpZhVZzWmTNMocrfW66rr6BuLO03OWztT7v+8UEtReUqg/VJqZmJxKc2mMhwfnoqM5xQqTjg9cfVA4sTM1U2wOhEUofX7p0PI12y4pdVd0QRAJpDKiJ1GkHtJoBrUZApx3S64XrSFAg+f0l2ZV+ZJW0xOz5pDse2aZLpuWS7niJixoo4hMZw7C9Gt3IXh/8vqpiDbYLWKk1GLsvXfoBsXJuZ5o6peFvLgS6qs5Ay5i4YY9LHY2tZ6NpuVbtbhO++AcbH+D+63WMa8ehPIETdghlQNoI11h7Jra2IzPrY8cahOP7OHRz/GrT64VIZz0mIXgVkUHiNiG4vE2AQQ9Kz0gcQDffUEVePBIZhm/ZalEcddFGc4h8Tq0gvovuumzJGNxYkZw7vLFJ6Vc/PUrqn74v8T7mLkrkSg1by2In9GOUh/r4c9FbMt+TKrrq9zC1FKaWwtAsNAYMQdIf4Bh0atX4d5Ex5CZLtgqxW03lDDqdkG47wO0PSo0J9Parpq1WeV7FPsILBa6jsATptkeq45FpumRazqbVsECCiOlBgQHtebMNXn8NlIdmKEdgqSpDzw/p+CI2NzHTEYqO/puwq+sMija9oMnFdjIPHh+0cX/345GTYWRHHzExisiPrAmBGJpFyQoYTQcMDfc37JtBrY5LvQtsORCfsOx3wzXBTlAhprdJzmAzkyFrCURQHPy+pyUKoNw5KmMbWkbGTczxFAwVIZdRA0aG4HbJGiX2Fl3K+zeWuXZ9cZGnXI9918RHB+fP9AnnGljCppCw/cxkfYLl+Ox50HCg3UUPQkxhYwhrTV04kB79wGepL6guxV9L2Q5U9WkT6/eUE1gXGfhiXTInCUGaZKsRn5RyLTKw+z6pjku25ZDuuJtGBQAtD9VzEGOdmkG43FUgI1+9O2HYYChG6UDXaHo6CdVLlnoilog1zq6aM7BsDVFI0Q/aTFeTEx0/f3uTZx/c+JCzOR1zVwHKw4SGSSgDBIqhJ2sKRVOWUowyUXaibmFdH9/a63tXJI2E9qq2CZtZsJpAfEVW2nLiB9E15T7e0WjKq1TeRxtKI/JZSGdV1lloyMDB1rNMZDx27oxetf/b40Vu//H42r3vScWC1K0n8vZrerzMOYBbCxT0vNfE0jOYmr2WMwikT9PVWeoLCgk4hsmsS7CU3HUH4PQlvW6I66gtQ78b4nsDFeakkO2bMEmIF4Dr6NiDbYLpKN3FzaICgMWeQIxt7MNYtZWlAbN0u7suMhCWAiI5QYe6Y7DSStjiOrqidv8m7Ko5gy3bLMS2CU7WTY6/EM/ic+yOLex76NHI9svDN2cwD40hCmN4oXKTppYipedI6wXG0h7jGUl6JPoFffGSiXFnPG/9WmORoco7fqj2ab7zN3AGnoZ0fKTvoAtzTVQzLhEEcKA0ycJT0c4iUwwwtmQVQ1S2gkgVlUMIQzTfo2SVuKkiIwkxUh/q0PixBxKvt3bUQS6dZV8h/vpWllKYdvxEmz+dxj9yCbl4DrPfI63lMDXlhJygw0zH5OVZi22noqXmFrcV2JfbycKR5OdumGIDRqC64uN0dEV+4geKwi6h0SzJVnUTvEHSVzTDDdgLLYzXr1i1Z4+WkAfvYOuO6EXq9PEenZPdtR4RAOwMMlumIzss9xd5btng+JF44Fwq7dOp/V9STRiqGFDIstLXE/sQ3nTfAiufiG7ztLOBghUPEFqrdWtdmGvtxklU1QttDcrxjkjTVXuaEAJ0i1AGuKFAfxX7zzgLw0HyKHAH1zsQVolpsm+XbMxAtelGX6NUJSfDUm3RZkrRlOnmOnGUVgxC5TNTFnd+V3zI2m0YUG9RSQWxYiDV5bQieY2xmSlP6QYu15DdGjhtRZwCOIGg7SXnC3qHbOTCSeYvJBDNFvRIQlDXCVU0NtjuJWstqumQKLw6EFFZLYNGRQK9nBkJq167pnOCFWeaPdfGJ0S7DUNVHNxBdUW3cMMevaDFQs/kYltQno7nShjPQKe+iczWK+yqOYNcKYR0gaojEqGbb9nm8dLj0Q/NsOQaEEMBVwaOYJCtVv8ffw3NhqLzijPbFiqDbZmgGwSDnEES+GYzC30BrhIz1dCxdYllB2vSXq+0bt6GfptGDNcjcBmgohuX/2nGWpHfTlCB+sKpFNtvjX/+vZaObHUo2T7NGO3E/HRPSa7F2MXzDu0ZiVyqQqeKdFqIUG3pvFD1/tcT+jKuO1xFnjnHhbPRi0a5YigpvJIeSfvlrNbu/UAhH7/J8HnVxEBezQsFXqjYjV9pnq3TLqaojmVpxsggbTlf53hNY/xg/HPvNnTChqPK2kIjFBI37NJ0XeY6JtPzqUSQ2t68ZPbi/yUJxHQxQKSL1F1IJdRlrxvSuXAmehCsCmEiw3URwSrMFZJ563tdQyVmYsyyNYQ9cAaGhRNoeEG04Oaqaet1NjbYWmY7cNGFga2HWFaAF6PS5KQNpNejF9OTIFdxC9qg/KQPIgRDydNJBgCnGK948ZkcqbsmY6+30w6g3aVkBTSGo53BlnN1tAPxbEQA1Rkb/0Idlusq9PVdhNDWcBayHj9B7xyV+Ecu0etGP4Mt2yxGtxiUh41IAhffk2tYAyHEt0SICqxxIoSDpG+UAK+TNmgOpWiNpyM1FkEJ5D63bGDfHq910G4yALz5oGkE0scJuiz0TM61BFNn450vwOGKEztv4uyqOYNsyQM7Rz8QsbVSgLIV37oqpVCe03fRhTlgIzLXhYJeGF8K9H0tUc8xndEUIWXKBt1S5aTwcqtrnCUlbmSIqrsHPoZmkzZC0nYYK40WagJCPxaG7HvaurZoNG0AXbUG0YyHFwpEzDXvOLGC2JHUHqzw8GnDpDAa77TF3niZMICzJxz883XVDdlugNtFQ8eTgo4vYvEFnq1zQ6VP92g98nhl1GR4AnJDPvkSkQpb4WpBKAwHC0X0Na5u1aJEVECB5IRmrnWuRkWI3byNPgTDY12MSSIrWQDPz5loB3fGEpi2GwHhKvpVM/DCPm3PYbZjcqYpGD8VHxUA7C0kO4souyrOQNME1oja37oJ2+9u3kK04yHKXl8opJbXQ4RK/UdICaE/gLpKvFBBRWMtjA+lzFVac8smFBJnEB4aCZGBvklkEAaDyMDz0IVBWg/JGuCkop2B7oeJJwxcoZzLFW3RUtPXKNX80KXrx8u1m04Aw/GJqDCQSMdXen8j8StNM63HJsRAcRXWpg3CSzWoNpBuZ62i4IXx1ze7q8S2XJbFc9Eh9+iYSW7II1fxyBR98oWNW4UwlGsLhxDx8mpJpmlCeQmh4UlBGIoNSV/P1unkLUpDfaViPdalNhrtDE4fL8PWXezaG/3Mer2QtcmhK2ew1De50IYLF3KUF+PzBYvbCmRb8cdj7/Gb/sbfgt35+gzW9SN4QkUEcftlANmLByPNXZRqcLUWka0FRVHWVyGoRNIPJIs9QXkxGktQLLnITryzKY55KlOfHcIJOjRdnY6fvE0wtWQ58sAXSgGo3cUIoWQbbM9J2BonwdVF5EZiNSXbVVNRtF3BV+iEHXpBk45fo+o4nG+LxP0lqfgEomUr7u+0XuCWnT2mDkY7jl94xucNf7A3/jeAb3zWo/6NFYJjF6G+gBGCKSReANlm9P555DV9Cl2fR78SPbjHtkvyewwyB/OUd4aMbZeMb1mfRQ/DQa7GDzbNI8WZpjPgOzPwB52Kr7SV8RzGTtizq82hMY99k32MndHn2/fwPO1Clr03RjvBViNQ5VBDJzAMGm6T55ctvvJigdynkyf6z/6rOr1fjeBT28SuijMY3u4gRkr4g3JgXCkm03LV/jfGFuc9goWuarXtNZH9FtLtrK32TqDR9CAX005aKDnQjHc2djaAXAZhpvGlix+KTTEGm0UGMGBPdj3w+phaipIFuRi57FzdIbTsWE3JVnMQTg7IV9S2oD/gDuhSdQzmu8lKx3jxK76mq+s1tRTbs2DEzPfHHxtDu+et8b+BggvXZm1VWai3wO1i6zJRp6A05EAzGmOhDZiQtbyFyFvo5ZTSWoxQYb7SvtXIIEk2D5QyVL7gUklBwQQVVAYb4OBrf+/Vk1GCumLS9sI+Vcdgui2onbM3BTbdOzHOxWeS+zii7Ko4g+zeNGJsGDfsKa+d8JBFOn7v06j5eDNttQ9t1lRU4HYHkUGIEwpaHSO2dDmekciVeuQxwxRYRW0N1bfKYLwZek0XKKHPGAsDBWSSQaDw+sKiaIUUS/H78a7fiB00tRWfsO6Apya7uk4HJ+hSc0IWuiYL88k9CDTqsYc0TVU/hNtjR95j/8F6ZNvs3qcXON6eSSxTAsxNBTizfWStgew1SBshKT1+DOQtCa1o0NRa56ttrDmE1ECW/coyo3bFZPxW5dUun+Dy99crMJtKjj3vUTAleRPSAzxDXPWp4Qq0XDQWQNMFIqVDysYJOiz1DC419OQIDzj+2i3oxx7n2cc3RsNxWoxrv5l49O/IzEPDMDyOE3Tp+MkJuSCdPLhqszbhbE1N6lYDnO4alVrP12jW4yOLbTmJnFmKPFYsG2hFG5FWjSFB6K3l6eI8PaxGBkloPbEKO0T6DqaWomCFsQrKAC1vmdxQgjNoOCrSkCGB9HCCDjUnZK6rMs/TF+LLpwByLnrlhUEmvusjOyvszCua7ou3bkTPmU7ARy9k2PvD8XgDgKnzDs1FC7nShH4TS0tTMCWeFZ0zMTViO/zgMpGMMDW0vIWdDdZk2VXkoP5d+U6+FT0VfXWbsHoOXeKbGoGhEWqC/kCOPVdwyZusibsmRZIdX1sn+3al2bZQknh2DifostjTmZvJJmJyAH70gXncz70YqTR2w+3Ji8JVcQZi307E0A4We5JaR09kBG64yXyIM2c1vJNV5PSicgjNJtJ3CGVAz9cSeQV35QP8M/XIY6WSjj6UgkwKqekDsZMBNj1hNGmCRITbWsgaqkSnLgxyZkDJildTutCyycXkFHrdkF5LXwOn+KFiMp7rmpxtGhyvCyZORN/jqgWn451Bpx0QLHehtUjJGuemYZfDNy9toOkG+NoLRfS3fZeSXI8x35PUF4yB2Gtd8R9aCqgTZaEkkYDQ64u10qpIKSUsKx2SyemkM0p41TTFpls3SG5hDl5R8NI0hTRclVfr5UwyWY+sASldjYNg0IQWZ4Fc37R2pWVy+oAhKkfNCZnpQnUmmSNzaTLP9+2WLDy+cT7t3Jti8mByruHqOIPJfTSCFc41beZmsokgngubEEUcP9qlelLiHl9BziwpCvRAJRDbnka3Gv8y9hQcOmei8e6rkQHpNIH0CAfiGZouEyMDSNbqE5q8vMKEPoZmkTVCKilJO4am7WTdxNxTij1nr2FA31mjkFvuG1xoGRypCo4frSSyRwE4R+MFOZcWPPozLnJqlrQbsqdgcsdYiHnTxr+VR0NapRJ3PZC8p12c9/AXFF19Ss9TtMJYxF7TA4zkNvM1XsmB8KqVUcKrqYyGldIwTIGuS9C0NU6KKNu0a3HQU6IJpabkmxqubeCkTXpZk+zAGawGf24o6LbjIcGmJpUSVITlCzoin0aky4O8j0ikxQOYeHefcifk7LGN17/vekjdNp74/aviDPrZDOdbXZ5e0uieTN7HHEuAqa7aiWeh+nKoVvnlGnh9JCFtT0tkqR1ND7F8Mfr8mYJE5FT5c9URgFoRkqofm28T5OUM1oDXYDUyiGuoOlIVaHvjX2S3aaj+d6Dnh8x0VERw9KUyI08l7zEv7S2zNBW/4vS6IYvnB70FMycYMie4adjh4N7Whhr62MUmx2pdsu9IriqsLPk4KwFUG5hSp5IKCErR46BeTUEuPrytrfiELXetRVmYOroZkk5rZLMqMjAsuU6WPc42JURd/Tsh1xSY3bRBP2vipg0sO2SVcsENFW9kLmH8FcxAwY4jLF8SUMoTWjZLPYOlhhkr7b5q797rIU8c3aDFUBk1Gdobol+bDAy7Ks7gcxcb/PoTBV7+LYNdLyfLRD+yIHj7jyRjrC+ed/jihx1OfFLS/9pFWF4hlAHzPdh6Jrpa4KQNsoszsSWr4qir2oLt3DruvqwBbgxACDavJpipUAGZDF0hkAKXlJ5mPB1fXnzqs0Not78+9pwXz3qq/x242Lb5/FmLZ/66wLbPLidCvQF+4h83WJlLHgaPPdTm0f/eofe/HyZ85DNcPzTJA9s9enevdyK6H/L5SxbaG96RmEjstAPq8xb+uSpy5Ty7Cz47dkcT4S49aiO2xYuCnDzWo33eJ5htI7v+mppSKicplg2KZR0rHShKuJRNEHqx2BZ9Mw5ELwTfJa2HFEzQC6rno12ysfJKXUkXSkRlpgtn51KJVH5bsuOcfCZ6LA1NOohtk9TdeU43BTMXc4kVoeOv3cKbto7iPnxqw/XfeKdB6k37EDfeE/t9uErO4IlFjakncpsOVFD7r6Efie8svNJeeLpDbdpAttoINDoJxKSzu0rI81Oxx61MiDBN1fCDykKvNj5hfetdi7qhkl1C1weMtwpKnTZCMtnol739VJWOHj8QWs1AAY90g6Wewenj5Vgn+Ep7w1ZJZWLzxqsLZ/pcOpLCP3IJo11ne85lbKLLysT6Sd9ywdFCxvcltxv77oBSrt/G1rOUhqJX0MkzNVw7OTpsVw2C5d66CEE3JLYtsG0N05YDAIjK/cT1mV2ODKIjO+kFyNBbGweptI9n6Xi2voYtcUPFV7DUEywtJCfsUh6cPRl935miD+kCbtCj4ysilSS75voVDKe/YdsxvsUiP+wiJkbp8G0ovPriiQJ7jkZn8a+0Y3dsYX9Rot39Zr73xza/1DCUzJ0zoNpAE3oiutHaLQlORPP2gUIfXkk+KYTA1iUpIxlUtJlpulTiq8blRiIhNGxdkk7HowSX+suxpaFOO1CbVMNiuqNYnF6NHfr3GkNemvHrw1elwPPCk306L9aRK1MMpzJsG/JYGb9cPfBsnUCqtuT0zmTyUlCTC8dR6MaY+a6FkhVnOvE8K3Ma/UVVVZFdDylV9cAwBZYtMKwQzTaUM5ABXkx5eE1eLQYdLz3F5WhqahxYdkBg64SGan4KQ0HXg5ormK+bODObMGItnIx1PNqQIjLpBwqUtZntGfaRvQaEch0Cc2zSJDVpwfA4dTd5zl0VZyCfS55MJ++dYPRf6fzce+Y5POQz416k+Js/yfv/MlkeDFRCMZhpbUriuW1nC/dY/BZFmNrlyTrg+Dc0ibUJwnAzM6xQ1Y8Nfa2zcJUq3ErgBDhZT8USXPqe2g97ocP5ltg0YXhpb5mf+J2Af3l4HDl9HPsNe3ndL1V4zwcF1xyOn8SddsD86Qxyapa8OczOvCQ76a9pHTSH0lia0mbUt+VjnZdhqgkKDNqsTRI6fnl+OZPIVXnxvENj0SJY6BC23DXhW6EpSXbdlINnrtrQ43pV1uTVYiaoIqbxMTSdlC6x7QDLDta6IF1Hp+nBfBfmLmUTMQHNoRTy+NnY41reGlyvuhfLDmLBeb2syfasBL+PMDVFD4BCjw6Nh5jbC4jSJKcbyQ7/qjiDpIcU/kSKj/zYWX71NSO8a1eFAyWPZ5ey/MVMm5m33sWPPHJz4irmexL/UgtTSyUizfZWAmrTCSPQ1NcSTqvt0LYektIlhpEAN5bJhDqaLi+3RWvGWmehPshQx9nJhs7Y1gRwlqnjBl0uzSaHpjNvq/Dnv7zIHRdPEfzFHyFnFhDXXYf+3h8i9+s/xWv+zw2JEmxHn+sSnJzH6DTZkQuY2NphaTJPu2RTH06TtxQ9nFZKxzqvoYqBlQ4RWXPNGaYTyrGPL+gcOBR/X42az/KMRmvZpNswFJGtv+oQ1JZBpAwwLMW5GNPFelmFOfp3wkCsNcWldPW+Vh2B72t0Owb1aorFuQy9aT1xGzx9sIL71KXY48JWwimakGRMyGS92BbyxW0Fhmyptpw5i8qoQbFssGXbgB9za5mucDgWw4mxaleXHfkVduqmMX7zu7pseek84mMPYpx+loI5womG4H9+boQf/etJnt57Lff+8XWJ5+nMBujhALASY9uzkpWZTcgfQokMvbX2aFPITXsPIJkdWTPkWmQgDFs5g4H3SMInnGoI8sMJkGLLxAm7zM0kg7Q+/P4GlU9+jTP/9iWm/7KqwD8je3m6NstH5tqc37eDXf/hhshWYFDRgXN0GVmdYmfeZWfFo7Utzcp4jnBYp2BKhFDIzeHx6HNURkwyRV+Vbm2bQKr7iivZPn8qx0QCVyXAuVN96vMW7apBv6Vk1mQo0A0VHQhbB91Ijgw2yRmsRgaa0EnpSpLdMEPCUNDvGjTqNksLaWYu5pncJGdz6MZlVl6K9jpriEnfJaULypakXHGox7SQuxMmBStQjjVvUaoIxreYbNlukN5mIybHaLgLvLCcPN2/OSqUv0M7e3iEL/zrOu6PP8gf/pGPZWt87z9fIP2LN/L0uTR7vqLAR7/9CQP3A7fzZ59u8EdvOx15rpkTKSqNOcoxhB4AB8suLzwdD8KQjq9q924XPQRLT5M162SN5BXcCyFIgCwbllTMuJksWBl86eKFDl1fSXPFrckvHR0iv1MHYhxCIctCt8/kM9HUYQD/7S8k5+75c1684r5/4PY6zf4F/ssjJTJ/1OFjRob9P38tv/AVnz+8M1r159FPmdx36GkO/OOf4Lu3T9F8zSKzCykKxSbbsqju0WKe4e0Oe/anOHvqcpLswKE0W/eFZK/Nox/cghjZQ9NbYqaTWavzv9LSn+9R/Mmb4ffiVYg67YDHv9qmWDZIpxXGIJvTsNKDZ563EHaert+i2zH45hQFlLk9HbodLG2SgtlfcwSpKyjS7Z7HtleRGP/Jmzo8/sXoHFEYSoK5NrI2TXnLPq4bWqLpwco1PvOd4rrI+tLeMrv2NhhK+SAtjK15JvbVKI3r5CZ8rPsPI669h78+NsvMX6Xh3vhr+raJDG7/7ir56fN8+a8HWVknxF3xQNNYWlg/Raw/6KLdF8/dt7zogdsllZCAzRlB7AoA4PdAOq4SXR2EhpYmBvwWCfDpTbYJQshB8tBY478PpEfP1+h1431zu2mpkmTceW2bpqfH8gKcfv04Q0vVdY4AUPtsGazJret+yJc/XCa8/Q3c/YZoaPHstEv3xSpyWZUFrylJto332ZIPKViqMxDLxC6ElCuXCUfKFYOhEZ1s2UMfyyJGhghSGea6GvP1+Huzez5s2xp7/Epr1HzmZ11WFj1ajWBN6k7oYsALIPC+RRGcqM5H39PINl1KS10qc+1XVSED2JknlrAGoL/ow+IytjTYkvGYSENlpE9zKLXGoOSkDVpDKYpFF1uTqlSVy2COp8hNCsz9ZVWe9OZ5ft5gZKaVeE3fNs7gh/f1cf7isXVsNmEgVF9+hPr6RX+O218bzSTTqAfgtMkmxD2b4dOdjq4UmJ0ueH3FoCT0xK0HbK61uPbjA3oyJcmuxFfbrfj1St+MdTdl0XTjvd89dyzhf+HJDZ+3qya6ZqwrXe16eZlvzM2y4y3xD3D2RBamzlG2tnCw5LOnINmWk8oZoLZBWtEmNxRQGTEV8KVikMoH2GUNMZyHYoWWt8zFlsXcpfiehuZQGpGtfFNyYa1mQG3FH2wXWKOGC6WIzRlsZkrzIhwkfNX/uz2NQrW3qaTalVYfzpDWC2uJvihrLFrIuUVkfYaiZSva/4KHPqSeR304Q2M4Q27Yo2QpDIzQTMhlMLbmMXcV0feOIUb2cKHV4+hz8Szgq/Zt4wxuruzi5KfXf2baIf2wzdj0RkDKN+ayjO2Jrps2aj50+2QT9va9IPnWu00D2ehCt4/0ewOGHLUi6Al7exUZbOYMNNAHySzp0vJ06i6J0OnA1Ahj6NSzOR0y6URncMdoSPWr9Q2fNxZMUnoOy17vbD70UhrrHbfFnu+5JzsEL57DbFU5UOqztxCwNRtSuiIy0IdSZIoBlVGDoYpKaqXzAXo5hSgXEbkR6k6Lcy1Bbzr+2hevKYDQ2H/tN9eW22oGqrdiVV5N015V52mchSFrBCmhBNfVlHjKN+EIAOYOlhAI9ibcz/KMpmjiagtkjCIjaV+1Rpcc2kWb1lCK+nCa8lCfSkpi66GiU89n0bYU0XdVELu20tL7PLVos//Z+U2v69vCGUzvHyJ88vM89+T6VlVjIqs69iJCr2eXBdmtCUt/u6uSKjG20k8GcXRqBmG1r/j+B8o2a1z/CYMplK9CpEPT1qICL+xTc/RNseeZQrxQSb6gQzqrxDlibE/RYfrExpV1+pxPzqhQfgXoZ+VjJv1d8cg/1wlpP7kM509Ssbexr+iwNedStCSGpqTEtZxFKqfoyMoVg0xBYmcD1YBTUKQxlzqWovFKqDDdc+cisrMS27mZZI1aoOjp/waM1lHmhopDM/8qtwVX2vBYl17QYvtr4l/YxfMDmriFZdJGgZIVULIk+YKHWZI0h1LoQ1AoqS5JXWhKQCefQ4wNI3ZsQYwd4HyryaPT5qvSc/i2cAaH39Wg9+CzGz4395a4ELPNmZ5Pqa7CGJO9PgUrnljiQksx6sbZ7LSLP9tWMN8BR4JgczLNV7tNEJqJHzr0fI/6oBElaU83ua1NYzF6G1GuGGDnqCcsUCMpn4vnNw7ci+cdqF1i27b1uYTKXJsvXWpx82viqxNnnk3hP3sGs1VlND3EREYjbeQxhBJy0Uo2ZskgU/TJFH3S+QArp5S0yOXph23mOiYzM5nYvfbJeyf4vt0OXLp0GZvwTVhtxcftaZebmf4WbFVZq92yyFeTUX1RZtsBF9sB9tsPc9d90c+3UfNpzumwWIVunZypUx6Q4BRKDoUhl9JQn3I2oGwpUBxmCrJDMDaGGN9NS+/z2HyKM4++Oj7Ebwtn8MFruzzxmY0DXdu/nSMr0ROgUbchSYiz3aVi+9Ri+PyPVAX7DsXv0S9NOfTnPeRyC7oqOliNDP4mkuyr/fdoBr50abo6MwOQShKd2vXbHIWujLChUYHIlGl6STwLpto+Rdn0WW6qbNz6fOipHNe8Nz76Ovpch9rXanD6ZXJhiqI5RlovIFYL9Sl7sFVQjiCVV+VEUUhBqkA/aDPXYy15GWU/+MYFbh3ZSvDyRXxXY8s2ax1ZyWbWaQ+ETDtK4tz8FmnS12yAYuz4KqmbyCD1CvNsnaXJPI6j8/yyjXbrfex+d/yC1lwyCRa6yG4NW89QtCTFtCLCKZYcyhWHSgrSg+pWKKQS0hnagZcvc7rR4isn0+x9Mb5F/Uq76s7glv+osfvPv7Rh1Xrr+yza113P731uLPJ7YSAIE7yyf67KaHoH5uujB86l3zXY+wvJ3XUnn7Jxnp1HziwgOytqxSOeeh1UadFzEjgdV5NZZoqe3+Jc0+bJKRv94eRB9c8PFzn6XHQpdNvtPh0j4Hw13jn2g/gQu/N7j/LD+0qcumn9sy482OL8P3gr978rfsB+5s88pn/lCOHnPop55nnM2jyyNq2o5wbJrPQ2m8JWSXaLrtSih8uIwhh1p8WxmmDoRDST0c5f0PnpnSnC//37nP2TFpdOa+zcZ/HOD2j8wC9l+IFfzvE9/0hnz/5kZN3yjIY/10b2FbPSK/Mjq7aKpIxjLDZMqZiHwi4zHcHoVHRz1apd2luGD9rs/AWd7f9GR/6wTXVvjvZxg9/73Bj//cQy/j/6QX7gl6Ojg7PHPdzjK1BbIGuUGct4bM9Jto44bN/aZWfFY3tWUrBCQhnQ9Jaoa23OufN88sIK/+pTY2T+6NUTo151Z/DBa4c58afr96uWrTH85govrnTY/kw0ZDiV9glqCWKfCx1Mx+GandH78FzdQbvrzYnXdvZUX4VqA76+tcggISewWWSwZoalGlr6gpnpXCKE+PjtE1jnjkQe27rDxjo0TMevU09o964nJBenX8phLU5xy/0bn/V/eS7H1n93a8KNwNc+26fzxYvIY2dg7iK0lhVGQxOIjIlWstGK6p+q91tgpGh7Ou2mFclR6aQNXjfuIY89zco3mhx/sc/8rIsQktSkjXXzBNa9eyk8sJ3r7/XYviv+3lvNANlywetjaQI7xhmsbin1mEdlWCGYSmav6cWTuALM7yiy9U1d3rXP5Y2TLreNhFSGHDRNUplvM3G0xsMvZ3mpVsW6c0ckyKtR9wdVLSVnnzZC8iaU7NV/koyhWqp7vkbHazLX6fDsUobPXtAZ/Uw99vqi7Ko6g/r7Cmif/CueeXz9yvCG7zHQ77+dL0zHh2H5gofTjL/8/ryHrE1z20i4YcVbtefqM7zhnfEwV9cJqc8rzDu9HrowNy1JBlIkVhNkKNYSiHXXYKYr0M4lJ3d+5J0LOH/1ROSxgzcJtGt2cKkDK8vx91J3ogVGAJ55vI18+jl+aK/LsTvWaygsP2iysG9rYn7F9yTHHk3R++o08vwl5MIKtAdRm6Gj5Sy0vKl4CtODVmJ8eoGC8EYRfM7vKHJ4KMB7+hznjurUVnxqKz6Bp3IRYmwYsXcf4sZryd+/hevujA//u+0AryPB7aNrRiyCdBX7bxgxjUy6agbzQo9OL5mhK3dPwFt3e9w51uOaksbWnKva3x0lJ1io9qk9Z/HovIU4sDfSmfW6oaL57zvrWqfzplzjWVzd9jQ9nUsdixP1FF+eFZz+cjFx2xl5f9/UX/8t2x+8q87Z/7W+k2rrDpvRH95Be3SCrz0dz+dfKDn0O/GrXWPRQl6c5paRLvvvrEf+zW8dybD1H+9IvMapsy7+XBuanYFsW/KeM3w1kYGh44cudUdnuiU2ha6+f7/FSx+L/t3R20zYsY+zDRsuxf9w3dEplqL3/74naX12ijtGt/PDD8yvS7qWlrv8wtM53vr7OxOv8fjRLiefzOA8OYucmkc2moqKTdMQKVVZEANOAVIWQTgAWsXQgvXHLErWON2j9XWiu62mgt1SyCLK2xAT16DdeIDy7dnYnhU1qRQ1nCEsrJhE5CppjWFGOwPDkmCmcAKNbseMZeia3V3irv1d7hjrMJzaTsEapTSobKWuKEPuOLHC83MmorKLylj0WPb62hr4LWOEpI1VhwBpXUUFTiBY6eucaVg8tqBx5KHhTXlCouzqOYMP2gw98gyPfOnynsayNV77IxraG9/JF6ddtnwjHlpbsZWASJzNXxCEJ2fYktnB/Tu9SN275QdN5BvflXiZs9MuzpyLbLTQMf7G1QShSYSu44V9Vvo6c5eSad96WZPcS0ci8wWHb85i3r0bt1jh+RURicdYta4vKJbjnefxx1PwzJf5kf3Aj61fpRb+zGblvtdw+OZkstMXn+4w+5yO8/wicr6uiExD1SQiUobqD0hZg4YhTwncxPTpl4b6WL6kemn9tTRqgRJRtUwCy6Kv+YiJ/Rg372BvTMuK40j+v/b+O1qW7DrvBH/nhEt7b17/vKt6rxwKBe8IgAIIghCNCIoURLJJqXu6yZG0OC1RbppUa61Ry4/YM0sDDhekljQaghIJgZ4UJIIUBBqgCrZQKKC8ff69+65NG+6c+ePEiYzMjMx7y+E+zbr7rbvS54uIjLNj729/+9tpLGCQMUmnpAlpNrfTm+IMHE+B49NLJNEMXMh5QPD21Zi16hruoIenBIGjGKRMlCJffG6O2PeozZVjOkkszIi1JMKVTq4mXXVM743Sgm7scLXr8pVbgi98dYkLD8/WDZ1mB+IMnnrjGv/6A/M8/7+/kD+3sOTyvX8RKj/5Ib60fZ1feaQ6Ux++FWRSVlPs2acGhF+5jnPzed5ztEv3PZOs/9atHr/+3E3e8s7ZzT27N330VhuSAe4rRKOFBFyXWIVc6cH1S7MX2ImfUuz+i/IU4fxb+oj7Xs/FzlW++Vx9JvnFEcxsA3780R79T36V1vVr/F/ftsv6D7by1xZudvnbf1Ljzb/8zpnbCvD5z/S58U2H6KlN1EYP3c+2yZOIwEV43rCVWE1nA9brMbqzzq2xAdw7W4kZr640iYroJ7vEtTri9EmWLqjStukk1iSRhMhMsZrmDKxc+7RqhetrhBsQKUE4xYn16x73Xtjl/HxCNXXNkJ54QKoFnYGcwEeSK9BLtvFr5dGKpUAb8VyPwNEEjs6ZsL1EsD4QPL4t+NqjC5z6zEuPCKwdiDP43Nt/g8/f+//ij36vh5SC7/yzFb7/D96B89G/xD+6scj/8k9X9kRBWz4sv306ihyFis/+qkv0i7/H6U7C//qhW1y6sDjxvt/9G4r7/8v/NPP/+tqDCdHXbqJ3rjHnp1NPJjCRwSyA0fUUzNXZjrb56qWA+x6aLrAC8L8fv86v/8vJE+W931Vj7m+8n6echP/no3VavzIb2b5rYcDiidkEmU/8XMw3fvSPuftTn+VT/12NH/2o5pvZUFb18xHf9qm38KHOR3j3d053YEms+ex/7POlX9Fsf3ad6Ju3zMSnWJmejGyitVWbnnas/EBBd5PnnhpdPO3dlP4LfdjcQSCy+RBX4cTrqXzvvbzrg97EYpZONtYuDHFlwNwU+MNOqvaDyW2q1owUO16FXiJLnZiSgs6H6vzZsxFrwVn0rWfNkFkhuTVwefG5uQmHfeKZLfpJ20QdJaaUYU/a7lnjCMzYwI3QlMj/4OkKX/gPRuZuFk7w7HeUY2fWDsQZPPlPruWdbO98X41jP7rK4L438RvPD/iN3z+yb8ku742zm1duXo/ZebSPfvoZ7lsQNN9UHmk8uLPBkWPTOQcbN2PTONLfpeYqvBk05714LcITEARsR0YHf5b1/mKd8N/+19LXjt4bIk6+ji+vV/nG16djK9ZO1iv41b1ZaF9+sMPlT26hPvtbfOeJY3zku67x/L1mTsL5/3qdn/rUKnf+0p9hfmF2w+uzTw248WyV/qUYtTkwU58KB8cOM5nWtp0kArQiHJQMN90x0naerCCEINUxg7SNWFtm4VjIqXOjqYXnCdMgpvTM/hKLGZSlCa4rTFwuXZQWpc1q18/Mc/JMmxP1yIz6SyITCvo1tkKndOy8F6ZGsXlKamn/H2ErWTpLDRLYCAUvrvs8/c3Wnmum0wr4nZ+8DZWOHvojw7RbPeJx5vt85Hd8kN+/3OYTX2py3+dLupJKLFYg7r13z+aVG88FpN+8wlJwktff0S1lJH7skRrf/mOz/7/2LR86HaqOmtnCDLN7E0Tggl9jc+DSvzabEv1X37nL139z8vk7LlQI3naUtjPgaxuC6pOzr/hPfvtRFvxjRsptH/bQfwlp/4fHEV/+DH/hfI/Xf89Wftwav9ThEztVPvw7r9/ze776hQ7rL1aIr3RMdBBlU4WzZh9PTheKiUKjBFXWWdrZ9GC7jUhCXGGmY8cqhOYC7uk5jp0adVR+IHORWoGYylWz8zBkCTDkesKI0mTqV1JOSua3T1Y504SmVzFj/pLUdKaSsDlwmS+Z+SkzBzVVY8HNulyFzJiPwgCGoeBSW/DsUy3u/vLefQd/85/2mPvt35/5ngOtJrzxvRLv+9/BNW+XTz4RIP9wf9zzTitgkIJau4P3/9Dsz1x8PjQzFdaf5k3Liufvm5wGtPEbHtX/4d0zv2fnpgO7nYy08vJpraLigF9jY+CydH16KtSve7x79STf/NrkCXTqLoG88yTXe9s8c7G258itH/7ADfT60yYq2Yf1e4rHH6ww+O1HOBI3+PHzIU+/ZyjV/tGPL/PY3XfPJCOBSRke/XKfrauBaZXeDtFhaPJfafLfaSlXe9eE5GW2cSMludqF/m6maKVJdYyozuOeaNJcjkewgyAQOb40awqzXdxlcy+kIxByKH3teYqwoJLdr3usHe1yqq4JnLoZhCsF+DXCtMuVHizeKP+9XeETdmcQnfwMZyElTAVbEVzrwaUXmpz86saeJcQbP7DAO64/z8P/dDogDy/RGfzMz/wMjzzyyNTXv/71r/MzP/Mz+/quM3dWWPszy/SP38nvvNjguQfnZo6ZthZWXS5dWCRSgs3wMrUfet1MSayb12O2r/tw4yp3twbMXZisDS9d69C/476ZxJXrV2P0TpuqK2diBnuZDFyEX2d9YP7faeb9qIv6k/9Y+trKuRBOnuKFdrCnstHzH1zlz5zuwbUrCM/Ih+2Hzvv4oz0uPeSgHvwj3rpyku/+9pv5oJM7H7nJ3/ydNU78mx+ZyT8Ak+O/+KRg+4prhuT2BhD2cIUh0dSnKELvbgeI6nwpG/D61Zjkchvd3cB3qrjSQaMhaCAWGswtRyOTmGsNx8y/tKSiPZ3B5Bs8V+QflMJENIP6MLXcONpg9WiP5UqKL6umvuz6iEqTftrmYkeUArxx4BA4dQbt8rTLrylD0vIqRKmhr1/vCy5dqhN9s/w7i7a1WueTP9rn8t/78lQGq7WXpHT0G7/xG7zrXe/igQceKH398uXL/OZv/ib/+B//45nf4weSB96TIN//Lr68vsN/faYxU18+dSWba3UGdY/d5SqrR7soDVe6muX738AbPvgYN6+7bG2URwm9HRd9Y4O1C3dy5mybskPymStt3vc9ERd/vnwbbl6P0TtdPFnBD1JeWtPq0ETdhaDGzcHs0P6/f6BH//9ezjr0Lywi5o9z8flt3M3pkdHz9y7z/e++xanGKfTGowhPcuwcNOfqbG0ktHdTbl6fTpx5+psRJz7zPNWzX+CHlha5eMZn5fMGqDz2i7f4rabg/X/vHL/1l58imVEeffrxPs25BnPLHdxzHUSnh+hoal+/yYXrXZJBijvWVeg8FtK/XKPekBNzA3e2Eno3XKrbm/it86Rf7ZDqDtGpKt58E3e1ysoRh+3tlEHPTFdyvAStBN2vXKf1xCbH2iVX0xsx4ZXZ+Mvg2S3c529x9mKPnb5HM+4TBS4rMdz7wiaLg5ioF+AtSsINiK90We9dR36tybH25G/eFT7O+jE21id/Rz+QBPWUKHSJvrrBVnub3mafeEtSfyTm3idnX+kHrs9P/G8ptd/8LL/6m7NnNMKrLHvW6/VwZ4zCsvaOP1Wh9f1r9JZW+eyjA57+Zov7wnJUfeNog+un52gth9SqCQvBDo05cwJvDBz0seNUvuMcb710kU//avnCGHQEemOXhrvIHXNblC2xTz4Z8N0feQB+/mul3xGFCrU5wJMBgRu/fGcQGIWjm3s0u737yDyP/kkVGE0TmnMOzukFYs/h5kDMLL/e8f5d3ncsxG1vojo98CRzyzGVesrCUUkcuhxfD3jieUV7YNSgtRAgBKC52td8+Y8F77j7cYIvHOEv/cdn8KsJjcUI6WrUPxU8vxFw78k6X+yYur8QAvNpAI3WGidJ+dIzKbvOHG8+1aFyvM2LP/P79H/rWT7iKuZWQvx6QuV4DekJ4s2I7jc1j32ogmjU6Tke2skiBK3xooRvPq55980Nrv2HP+T6//ENANZX69z/ixdwVmrUWl3c+SrC12hP4gUDNj7b5uK//DW+LUiZWw7N4JOjVTQwuNKn+yWPx79ngWtdn3ClSuJIhAY3SdiRit6O4tkPfgI31fwwmupcguunxAOHwRc9+DWziU8Ar//Md/P4j36OdDdGSM1fWh1QXVRUjleRgYNONOH1AdtPS576gUWe3QzordXRQiC0xotTKkGK0+jz9N95jsHzZh/vdRVvWwrxqymVNQ9/pYJcauAcWQHpoHs94qevsPt4j42nqyx84jy/8y/6xK15nN5sHck9V+7Vq1e5cmUI6j333HN86UuTWnQ7Ozv88i//MqdPz2b0AZz7Bw8weOCtfPypDp/+3Aonnxr1cHHgcOPkHOmdLkePd7mwfIOmb6K0NJtO1vINH3sjvMTyO97FUeC75EUefVBz9dJwqUopkI4gXe/jhyFvWEr4g5MtVscJOr8QsvErrwO+NnW74+d3qDhNFvxNZgtITTe5WKGX7HDlSo25Kd/yzXcdJ/jtj5fiBfe/pYq44yS70U22Q7dUPvvFu5c4/f4OP3l/j9ctHEG/8CgkKc5yjZoXUgOEFOg4pXVzwENXVshFAZOCc5EOj10W8K97rAZm4Mz8ux0at0K6KqAmIiqLCdcfa1ALPG4KaJMQo5AIPASelATSoZE6fPNFh91fDvlO7wUGT1UNCemHlmg99gy7zSXqrzettk43xdMvEoeScHOO1HPZFdlwFC2YC3wevyI59u8v4oQtAOrvOEr3oWukuopzegHR3OFiz7wWbyfc33LZWc9GsH1Hjflru3SPHKF2Z8Y/8Tep1ra5+KjHVtogdjUdCQKYw2Vbuzz7yADSBOfPneYz50/w3/+noRrPz//p7+eda5qTD12k+8svsvPQLulujPuBNTqLCWeefob0wnGqq8NU1Fvt43KNF77p03cbdEhJAYmg5XrsADeDOuHVPs6FJl/58Qd4++/+CYvRgH6zReP1CwjXhfvegPCHKYsjY6S6Sn8r5sF/dpm2dqDusbg6W/lxT2fw67/+6/z8z/+88fhC8LGPfYyPfexjE+/TWiOl5B/9o3+011ci7nojFzvrPL5dI2qPnsz9usfNk3NERz1OH99ldSlkpWpm3ctsoKUUUHcNLztVCaK+CsfXWLj7Mufbgn5P5SnDwrI7RNGTiDlf0W4FrJbM5fjSTZcH3lqf0Am0pjqGzjqrc9qoa00PmYVnqMjt3QbTBqW/7V3X6f9auYx2tZlmw2DNBOfenE+v6ee54/rxJo37E15/JOZUwwwo0UkCvoesDX9u4Qh0qql5Du9++zZ/8oV5ap7mSNBh/UpIFCo0oI+vcGXd597vHNB5Cjh1HG5t88eL9/L47in+Kr9F/VTMuRdDNlTAC/R5JLlFMxW0Hc33tqs8t9xiMYy50JdcosHFBzdpXmjSe0qh71+Fx57h30fv4Kd5HACn5tIXDn4tpbKpuEGM6vU5v9njS8fmqCaKC26d574O7/oJn83PCLoPXUP4Eme+AhsVls+5XHi2x6UbAa+/s4usuahtcBYrpHWXiyzzuPc6PsxzAHzDPcPrxdcMPpjC9bjPtex4vf7WLpXlVXPuAf5bFti+cJx/f+3b+Y6nvsbvvO2dhG85gn8ipnEUur/8Ii/+7J/grlTZ/pm3cOTv/w5PiuPctVjji+vL/H+euZe/98aHcBONwCMUkjSKeLJqztMgSVnaSUhb81T9gK2wQ/+v3If37C2ORVv8kvs+fuCuG6wPBF/x38R3+4rNtmCxqQlfvIzc7dG7NGC93aCtwL+5Tnp0hebSbLB9T2fwgQ98gOPHj6O15md/9mf5yEc+whvfODqCVwhBrVbj/vvv5+jRo3t9JZ0Ant4I2Nz1EKkmrHrsLho66O5iheS4y9GjHZYWTb923dVUnCHw4wjDzQ5sjdqvweIC3p0LrNy8zl3dOpdfjEhizdKqkdoSFTPbMHAUgymjzx+84fDTrwt5ZIoIbxxKE8LtAbvOHP/te6YuPmNU93uPaK4/UWE8RQAMV8A1Hr7iQqWVsnG0Qb8RmdHgZwPedu4W5+c0NbdhhFmkhEqAaEZI3zHhFSBShQLufH1Kzd/kM59f4LKa49iJDlE7QqWavpPSDX2qfpe5N62y+R9u0f4L9/Chzz/Mi/4qD+m7eXvrSfpXXe4IXbZkjUjM87hjKhwvuBF3bfd5YaHGznqHRlrlCw/P833/Y8iNVFPbNGDsiegml7t1TtS7OEKzUVmiMb9D45qikbjcdCUCWOlGXJmrQCfk8rUAX7Y5/Xfu5dL/4ymWfuBOhOeiKwFuK+Ctb9jl3u2+UVdqNlGhRtZcUqVRyBE26ThmqAuPFeaB1ub9ziCh6Ws+/7YH+Mz9b2JxecBdQWqowvcvUr1/mehym+WPvoXnrvS4f2eHpxfvpOpu8aVbawig5Yf0bw2IuoJ2LIlVCtn/s9iP0ZUKzUZK/zmNbPlcPL7Ad/zSf+EbnOLMKcVyZcDPfeNN/NifMdu02NRsvbBO8/pFwseuc/OFGs+kNdydHZhvkCI5/z2tGSfmPpzB3Xffzd133w2YlOGDH/wgFy5c2OtjM+1mf5PL3RqDvkMaOHTmA3pNn7Dq4izCylqP1mLIgj90BNVspomTcT8aXkrdUwghUKSI6hzy2ALB+R7L3S6O55PGEr+aUl9IDHtMusgU3KD8yv3Yi1UaDzSBcgJHEklQCf6MK/9evQtWHGPW7LwHlvq88NQUTf3MAUrh0PI1K6s9rsUNdsMqtUbM2XM7XJjXHK3H+HLehP2u6QkQzSrCGaBTQ8AhlaacFPscvUPxAbb4oy+2eGHQZKkZM++GdEMHR2vinuD4hxwe/1qXaidg49gSP3rjD/kX/od4S/g08n6HY1+NOacqbPsNvE7M170ec6ngeC/kZj3guYUqb9zape0scPFrHaqnA+L/fIvNk0vctX6ZR7bu4UTdRGW7tRbL/VvUhaKGoOc5aGC5F3N5vkpHRTTSKi/8YYcLfzNg6c9+L7JSQ3fWEY6DaPo4cy5BnOI3NKLmkQ4UsuGh0z4REl+mpErgSI0z9pumgNAaLYSpMmhNCjiA7JnR67V6jOsp5qumeciTZkDOmd/+HpIk5HrYofYLXydBMr/s0ktcnthZZLXSw5cK3WvTbUu62qFHlH07LPVj1OI8J88rtj+3DR8+xfJ/ehgvTfiT2gP8z0ce43M3jvJsu8UTlyJW5lOee2KL8/1niJ65ya2nPK4kPqlK8V1Bv1bnrjeknP3x2fNGXlJp8ad+6qdesSMAuNz12cqajGqNmMGiz2DNJ1hTrKz1WFwesFhT1DwzeNWXxgFUHKi52pSkPEXVMUNLlU4Rfh2xZEgnjbMuy6dCFo6FzB+JqKxIxHwtkyYvZ48BvPjcPO6F6SqyKjWsuL0W/KxhKEiJIkXNiNiO1uulEmW5JRGeDFitwNqRPsdPtTl9boezd+5wfjXmVEOxFCRGi1ArcFxEtQq1KtQqWY9AdlB9x3QVNn1WT2g+9N5Nzh0N2Qw9nus0accup1dCtALfiVh63wLdX7nM7o++A8fV/Dk+x9eCc6zoXahKznox51OXam2e/2mrztt6HhI4u91j4DrcdBQiivnql6ssPCAIv75JcnaNO+JrPHZzKM8VVup4KKpezJwQpFISOpJGnFKNU64EAtKUJx/x0NfWEf0NQ/TRyrRNN31k08dvaNMxWfdIOwo55yGTlARJ4KT0UpdYSVzH8hCyeYtS4GURlGVMpwqzYnYVdRfmq4pmI6bpm0jVlRqNItEhfd3m4o7Lha89zVPyBPctbvLw5gqJlpxuGKxIdyPCrksHSTvDRPxE0RQuCMGRFR8dKm6+eY37v/YkD4m7efepW8Ra8luX7gDgE3/k8Yuf3OF8/wnCK7vsXkoIvSqXwoCKjgnn5lmYi/m2v3EcceSu6ecUB0Q62g4dBin4vqLRjGgtDVhc7rO03Ke1GDJfNY6gmjVkDP+MCmzDU9Rc8+cIz9SYXd+IbK4u4p1tUTvtG3WdEy7e6TnEghmv3k/kVLbX/M0e4ujq1O3Wimxq8vR9mzVXQUoBUhh8ZUoprtMKaHqTxChrKhX5sNKVasLJBpw5OuD08T53LCfc0dQcqcU0PAdHZIGf65vIILAtxAH4HsISaTyJqDjIhkdjQfPWB3b502/f4m0X2rz73l3uOtNHSNC9mCN/uopOFHO/f4PP/9gHOBZv8rbwaXyV8vyfPkUjTlhBcxyXx5cbxNnBWhgkNMOEK80KTrtNL3TYuDUwef51hQRO9q/w6FZW2qtmqVAjoYmiiWQ3cPNUYSdw0f0BV9d92t/YgI0tCDuQJqZtOnCNhkLDOAVZc0naCXLexUlSEhxqbkw/dQlTB2/MgacCgjQb0S4FAk2agDPnwW5K3SV3AnOeiV4lmKhP9bk5kIRfuMx8v8fG8jGqbsoX1w1x647mNnECaTeiF/kMUs2NbCbG0U6IrlapeAnxswn+fS0qT7yAQvCl4G7evHSDP75xnHZsjs/pepv/7uwTdHdSwotbbDxTIZxTIARdt4bvK77zxzWXfrHDpX/78PQTlwOaqLQTGWHRhVZEJZs87HrG29ZdckdQc0164EsIHEXgaBqe6elu+QlVV+ZSZPg1mD+CcFycahV5Muund810H87eyXZ8ned2PRo3y2uuize6cGz6cFd7xZ8VGVQcjeOXL3TbJy+EQE0Zovni3Us47emdZ+0Nj9VL16gev5vXLylqbsRuJEm1oOYqlisJazVJ1ZnDEV7OjacZmUUiJdodRh2C4YxBmWkPVJoxwULC0tFuzpl3MkZf4PdZfP8i2594kXf/5XN8M/gQd/zbzxArh//0ljfxk5+/ybm+YmdLsuG4PLbS4HU32zgajrZDnlqu09EJ1Tjm4S9X+bZ7Y3Z/bxP/fcs8cPN5fu3i+7l/YYNHdtY4RpNgMWJ+I6WlHLYrLqu9iLVuxMVWlVskrGj46icHvGfuWZz7U6hVDA24VsFZNnoKsulDq0m6cxNnwcXZSImFy5yb0EtcJHroDGwUIAWVMKUdQCIlaE0y0LjzHnorZbGScLTqMkhNO33DS0m0YDvUdBPNl9cr3Pf7j7NFnZOrMbcGFZ7ebQFw9/wW4W5MHHrsxBIZRtzV7nGtEXC0G6KOtLjwgKD/pQ63/sp9vOGhB/ki57l3ZRcp4A+vm+ax5aDHX77760QxOI9fYvNijbnXB+B1caRmrpHwXX+tjlg8ztbPPwqfvs7Jn5h+7h5IZJBqc6W3odZ8VdHyRx2BTQ+kMOGXl/25UhNI4xgc4RlVWDCa8V4VqnOwOIdYWTRqOGtLsLaMmD/KbtTmep/ZU28cf6oikOvrcq5q8eMz5ipYOS3BdBZjpZ6i1XQiUHsb9MYuevcGDXeRU42Q4/WYE42Io7WY5YrCkxVc6SPI6LPSDB3F9zJqqxlPbiZBS4Q0vHthI4Sah2wYZSJnzsWZcxE1F7IKxJHvX0T1E+JfucnK25f47f/lR/m5n/gR1GrACz9yL85WnyPz8Drh0vYcHltukApY6kegNduBh9Pp0osd0iAmbceo5QXOhOvQDfmph/4UD2+u8oQ4QbUa05IpS1qyVfHQQCVVtPoxV+suYhDy7OUq/Se30RtbhuFoy6OeRNbMtmvfJ9kKkQsebpwQ4VJzEnqJRz918SxdOTvOqRD4SiGVJsnKWEki8Fou6WZIzdXmfHWHMz1jJegmkus9j90nN7j7+mUertzFPa0tHlw/ikawHPRZrfbxtnbobzhsKRcZhqz2Yh642UFUqoBgMfCg5hDKHp5O+SPxOu6e3+JSt8FGWGU56PHX7v0ankhRj12mV2vSvuWy9nZYWk74wQ+u8+G/pJl/y3FufCJrUDo2eyjvgTgDR5gFX/fIw626x0Rq4AjjEBwxdAqBo3ClzicjS+wKMyKjwq9DvQWLi7C8BCtHEK3jhFJxredxpSuYmyKkGgUmUKpNGSXueGqPUkG23VMiA0tzlcLJI6Jx24vqvHUrIb3cho2bVKkw5y9ypOayVtUsVhwCp4YvqyYqsJJL0jqEbOCr42Qj3jJv60njCKzmQMVB1LMQu+Zmf54hTAHVow5z71pi++NPsSY1dy4JTq1Ijswn3PxTp9BHqpw94qOUwxsi2K64fG1tjr7n4GpN7Ahkvw9K8cwLLsGay+ARTeI4vMl5hlSbY/y4OIUrFLVqxAIa6Th0sjbjI52Q0HXoRQMSJfjGl3zUjTa620PHxpkKz8lEVVxUZIg+etHHs87AjeklrnEGTnasMm+QCHCUxrXOQGviROLOu6S3BtlE7qHICECYCnYjh2s9yes+8yh94eEfaaA0fP6mqbK9ftEsTL3Vo7vrsYODzNioGqBZY3khoveVNoP3HefC15/laY6yLlo0/YhB6vDGxZv87fu/QuAkXHm8S5oqNh9xWXjHHP68IFj1qJ+v451fordTY/fzppFJz5g9CgeUJkhhwMBUayIpcIQZSe5kz1tH4GXnqhTDyECSobY45sqXmUYjXB90DSFdCLLF5tfQlSbt8JIZ43UrwE/KmVidVgBqh6Cknx3ArWIW1QxzhBkBXvp5b29n4HoK4UzvkVi/EZNcTnCvrcPaFRpLZ/BkhURF+Xe7wjd4QdYyLISDHnMIwz8XkaSm09J3siPqglTgKcjwldxZOAKdphz580d46q9+k+RTW5z60CLduMLNAXhSEv/Zs4hfeIy73tLkya9UeWOvz3rF49rqAovAktCkTeOMrtyq8Pr3DLj+2Q30D5/gfU9+Ay0EbWoE2ZDZykJMq51yWrlcXZ6nFiUoIVhSkn6tQhV46oUab3xxB38+MKmCUkaQteJAJSDuZeXBpQp+HPNm/Qx19xRry+bC0M3pyea3W8Oj33SYQ9DwJNr1SOIIr+XSfmSHqqMyiXIzNj3Vgm4s6cQO61d7fPj5pwB479oVpIC/eNLwXu9Y6hJ2E8Iu7CYeiZA4dUN80p6LcjzuOJuQfiXixhuWuOdTj7DMDt+lvkJ7W3H3sR3Oz+2w05VcfnzAucFVLt5/L+lD11j5zibSSREnmjgrVcTRVdb/zS1E1cV70zzh5dkU+ANxBsXqgBV0tM7Ak8P0wCk4ggx7GzoF4SCERGStnVoohGOGd9g6PI5PQkIvvsXVXsLzHZdrV+qcLqnfA+ys1iCJpkply5q3Z2QgxfRqgjQIExKHeqM8FXBdBX6Nak2OzJ201u8p2jd8/IsbOCevIfw6lfoiyq2aqoqQZhYgDuiMiSlkli5kf1l6kEcGrpO7VU12cXQEpBLh6eGO+ZkDUYrGfVXcpYBrf/tz8HcEZzScyb5DJOYzb/gfThPvPs0LTwccQ9p1BlWPVGtIEhYbKbV6H/BJfmOL/imX99UezQ9zEgvCXZfVu6rMPZkyL8wY93xjPSBNcVXI4EqIc6yHs4zZTsidnqyYc6L7Nx/GPe4ztzBAXr5F2jKhs3erS3vXJY5dIOGYLrD1XA+ShJOrIdVTPulOSPf+32WtRDh1ATgpNO3jPvW5CPf6JmqhxoWMQq+7KckLW2xfCpAVDbuKdD6jnynFseWQY29f5tJXrnH2n3yFzimfSj3mu7yH4QVB1G2gkxR/o8fJSLK1XSH9xavIqkv97gpio2r2eXEeVlaQDYXuJ0Sf20C9czo4DgcYGfgyoxYXzjWbFtjXXVl0BBpHaKQwj0X2z5rSqTmLpQDpGVkt3WOQdtiN2lxsV3l2VxBfm47+HTnegXhAtTrFGTR8U7mYYb6cHhl4hcig0Ywpazz1PAVBgxOnA55+fMosyRsec8/vIM+tQ7UKSYisziO9SraSNZAYZN2mCmMOQQiRRQv2T4PUCE8ah5BqkAqdgYsWU7CYiUgV933y27nxqzeJSeglfXqJmWXoSc3SkSrz7z3PB4INrv6ri3zu0zHdngJMyI3WhnxTCyBJufufnePasy5/fEuy/TmFR4pGkGqH9pEK/+eP3cXK3/p3/Jf/mOS9EyiNUAqhNRGwdbVK5WoHIYUZ4WYdn+8RHK1zz6d/lCc/8UU+9UKV4Jsh3kaKJ02EFiqPbrXCX/j5k1z9id/g1noyxIey/+PE+3xWvmsB5857uPHYc2xHDmEqUFqgNQxS2I3h8vUKc8/2YaDx1xMCJ8EVJv2LlcP6nXfykR+RXPtXX8K/1s72x5y6H/wrdYL/0wNcueM4D/1+n97zhv4t0LgiRV7TKC3ouy7RHRXe/OYBJxsurbM+0u+jW01EtQKL84j6Esf+/nmSI4LORo/2+2aLAR2IMzBXd3MA0sK6sZGBxQiciWhgEsk3UYFG6RSNRmuFIiVREZHqsxv1snl+kisXGyxdm97GefxUB6KQemPSGcwvuIim6SmfZY6YTkd2PQFK4QiXuVrIdtl7XEWU9jl6SvD04+X/x82risXLCd6Lt0yIqRVaJQYvcQ25ytQC1ahUs7B5VyFCyK70kPkRq0YkNdoZulsDMsqRBeI24cT/fD9JrcFOdIN2PKATO3hS06pWkCygj6+xdM9FHtis8uBnOzDmTKNQ0287tBb6nP3ZD/P1F27xicrCRBfr43Mhb/7wAvK3xkQRC3bjomDx+ABR98B3DP1aZoCp5xGcnSf9qfv4/U8vsbSzM/H53cUKf+37llGpRmgN6ehvnSYCopil7z9H/N0+t3Z8upE0MwsSuNYXPHXL4dnPzHE2nF4Reu/flRzd/jqf/l/DzHebY3Lh3ir+fcuIpdNcf2edTyXz4KWlre6Pv/sY73vPDVYv9Dg5fwL31kVYv46oZGlScwFqLWI1YO4n7iDsx+hw9nI/EGdggEBwhMidgV34MsMPXGkiATerIHhS42aRgZXMMhSPlESF+eNUx0SqTztK2AhdXtit80xb8OVnqoRfERyboplw6cIi/5cTW9Dps3h8MoS/740V3FNzRMqUJZUUpVwBKTJBirL9do0z8J0ad8x1+NV7JyWtl9f69NIBJ97n4n92sn0XTEtwpVrnDvcm9XaEe6aFOLqKbjWh1gS/hnCDPKXRKh4DE7PUQCm07TK1DkIphKvMtGEwbEVML4OpRGThd5JC1EP3t3Bdn3l/jcDpMOf30VrhyQDcGhw5QfC+Tc7WLlJpVnnya+lII5kfmLHmyTMbeG96kfce8/j092xz63owUvX5e7+3zK//2J/ngV/4P6b2jjz+aI/mfJOjcZd6pGCthly2gKlPoiP6iZxKBbdt1Elc/vvFoZEtF7GZv2C0CI3YyPW+4MlrPk8+usB9j03XtXzqjWv8f091ufy/fmPk+fkFlwfeN0C87Y1sqg2e2jFy7N5Yb9HlOxcQd0u+9003+La1mCO1Kp4M0H4NllYzjkUTai0iqRkkHWI1INZ7L/UDqibowlV/FCgcdwROIRpwCjMLbBSgtc7ENc148zgbZroRutzouVzswgttM8+vtT69hdO/T3Miy+PrC8mIUs6xkz7N5RgaNZROR6KZyX2b/pp0TInKES5LAchTIhcMAdOuvRSYlMe7s8Xr3jCp6Gxt/XpM+5ZPcrlNer2N3toxk58GJtXRcR+tYuMI7NW+GCUUMYPxSKGAK5j0wBnDGWxCn5pUJB7gCp/AqRPImhEcsfwPr4KYn8M9PcfC0ZDTd/icubPCwpLL0qpHveHguKDDBLrb1N0FLsxrLt+xMLK/vW86PLp5i7vfN3sM3dVLEZ1Nj3QnRHUiQ7uGXDZM6dmzMmeZJZ0Z2TZpxG+1oB3DdgjrN6usXJkuWAPwzg/eQj/6BS4+OboNd1yo4N3ZQjTX6Cdd+qnA9RT9usf2co3dxQpXz7WIz3mcPNPmbEOzVElxpTnOwq1CpYGozENlLmPbRiidEmf7uydz9mUdlVdoxQqBzK72RR6BI4YpgSuHOIE1jcr+jCMwCzQ2HlAN2I4cNgcON/qSK13BrRtV9C09VRUmrLrceecua1VDUgqO+tx1X5WFJTOp+fQdPvOrEaLZIFGRGZTyEoZ/jlimgb9cSTl6vMvFC4tsL9fYWq1z60KTIzXj5DiyzLEL09HfzY2E9oZL74Yivdox3IOdXej0THNSPDCCnGli5h7aWYvjiq1loOKIQxAFh1HYZ7OqII3QaQhRD08GeLKSSZGZ0qaQHjSqiOUmwckaC8cijp92OHbSZ3XNY37BMSXbVEOnR9WZ41xTs3JuFC9ZvbTLgzd8Kt99z8zDe/VSRHvDJdpKUe3YOBllSsJ2YcyalTnLirLlNoEyOIFgo+2yeaPC8oxJ2qkr+fCZmOSrz3P5xdFzcflkjHumBfVFdiMHpcH3U3qtgK3VGuvHm3RPVzh5us2phZTVasqclyJxTAOV65s00a+BXyNWIamOSXWSi6juZQeSJoA9r3LsOl/w1hEUMYKydae1cQX21DY6/EYWajt0uTUw8lDrOx7rN2vMbU5XennmgVX+1MJ16m4FKj7uiSZH7tim2jCo9cKx0AwMnatnaciQtfdSTKXanEzxgMVKyqnVkN75Hjca87iu4vz5LY5WDcAollo0LwTcdZ87MlHIWhQqNm7GNJc8gusDZKuLG7joWhXhZ12ZonAVH8cPoHClz0j39hbMmVHcx2Ikke9QNuY8MXMMcVw8p2IaxxBDB1QJzBDWo/V8hLnruySRwKsk+FVlfugoQUR9jtVjjp/qsFloza52Yx6+7iPe8iZOnH6Cyy+WO0qlNDevxyydcPDbEXqQojOwkmyXZknZzzIrW24tUYJBCu0Idrd9Fm72Zg7EufrBRd643KH3yCbdzvB7mnMOrSMRrC4RExMqadr0G4mZHBaYWQ9Lyx2OLUUcr5lhq/n0ZVIcr2J+X+mSkpCokFQnZo3swxHAgVUTdBayZKgwlDqC4igze19pgdIaKQxQKDKnkKqETizpJpLdSLI+gJt92FivEG5I1kqGe4KZ6XfX/VucbaZ4sgJBDedEi/kLHSoNc+L6RwLc03PQnCdWIbGqIPfSRC8xpUAPTK69FCScbfrEp7u0FkP8IOVc1m0oRQUaTbw7W5y+Z51b6x4bNyfD4/ZuSm8nIKi7uOt9RM3DqXUg8MmEFcDJwEQbHYydrHlVAWVrnwwDxsL9Mo+stPlelRgB0HhgBEela05M6wyki6hWkItVnLU6DdVBCE0SSxxX41dTA04mCUQ9VioJZ5oB3zwzzx2PDuW9r1xq0Pv2kDe8V3H549OP8/r1mNM7AfXdAXqQRQZaZZiSeOVpQv5dJjLY6Uu2Nius3tie+fnveOsWzb7DxRsBMHTwp84FuKc9xMJcFt0KfKmp1WMWFk0k4wcpKysDjtQ0SwHUvXQ4CFinSOkjhJtfFE1EYKPm/UVCBxoZpEONytwRSFEsJQ5LikVLtUBqi/SaHQ5TQT912I0ctkLJxkCwueuxux1Qb0el8mBXz7XQ9zrce6zD0XqMK+cM+La2jP/6CGelg061GfR5dAkqc8SqzyCZrgqc6umzE6JQoQcJOuoy1/Q52zQ/aLseU3HgZF2zVo1xxZwR9zy6xOL5de7aDHjuCcH1q6OhZbej2N1WBHWH+tYA2RzgLPbQtQrCXsmLrEmdXcnHr142esirIOMOgUm8wEYFyix6ncYIOyfAyU4rleRdk/geNGo4y31INXW6JP3UzKCdc3N2o05DmhWXEzWNf1LDo8PN3N0OuNq7ypn3LMDHb079DbqdlH7bIexLgl4mz273ZgZmkE9UmpJcay1Gjl2YmipCr+vR2fY4PWMQK8D7j0XoK5fpt0eZgCvHNe7RBjTnSXVGtHJgoZ4iZYhSgmol5UgNVisw56cEGddGYyppKXFWbTUps3UENkXYTyB7IM7AyVKEokiIHAEVdf5c8YKUakGcpZdm50wIFKYO/VSyMXC52Xd4viN4YcMzcwyvpTQ3B7mcdOpK1o832TlT474HbnH/SsrbV2OO1gxZR7SOg1+HI0dxB5282YnmAnrhGJfXL3G9N/3KonR20pRYezclvdHDuXWdxeV38KaVS9zVGqC0oOoqAqdO1Vml6s6BF8KZEwTfkXBm/hJLJxXXnmpw+cWIm9djlNKEoWLjZoLreXhBwLzqIDyJKyU6SRFJamYb2rQB8rkFwwMvTVVByiw1EKBkttCz+8MfaegMIEsR0mxYSA+tFUIlw9KmMk4CIU3r9HwTeUIhmj6yFeANjIMWFRdnrW62M4moOS3uXtjhDfdv8Se3jnL8mS3cWKH68NCNGnf+wHfx5772S3zyF2aE5JciavMe9c0B7sDIs+9lSVYpmTZ4tWhKp/QTl42B4Ma12p7Aof/TLu9cO0H6Xz5D1DepgesJ7rhQYe2+CHnHMURjhVQnBI7iSNUwdAe1bGCrYxzBSjVhIUhpeCmCjE+jQpRIho5Bx1l0EBOr/TkCOEDSkYkKhltZLC2a94w6AqUFCJ3tmHEKYPK2firpJZJOLGnHJofrdVx6XY9qFOImKakrSTxJp1Vh43iDs2e2uXNRca6pWa7EuKJmQj9H4jRXEX7NlM6SMC/X9JJdtkOHXsfDZfrJNW0Kc7+nSHdC2O0gwz4Nd5FAmtzXlb4B30Rg8m8wSPzCPO7ZXRqDhIV2Qpx4xImmvZORWBLNIBvdHfYlXjtC7YRI30P73pCWVXZFL9gwXYDSyKD4HUVTahgBqASdyiGbUaejJU3fM4pLSYpU2qRMqUJU3Gwgq1mMjvSY81KO1DSLxwes95tUuzGyClsRiMXTVN56hHrjJt1OOe+j31fEAzebYJxO7G+ZpXai0pSR7EXTKGJl0oRB36XZn16p6tc93ruWws511E6IXyWrpEhaawnuWg0atYw5O8ATRrNjwZf00yFN37Tua3w5XBsGN0tzFSYbEVjHoPaZIsABpglW3NTeH08Nhu+bTBGsKGqqBYkWJlyLJZ1YsBsbFli/7xL1Jc3Ma0SBw6Dus71c5cjxDsfWBpxtaNZqMU3PDNcw6UYXKRwc38MNVgwQphUJCf3oOluhS6ft0aKcHTir7AiQ7ibodhc661QXTlLxmqbzMk0gGkBiBDq0zuTNWk3E2iJuL6F18zpJXKHXMT/boDesi8ehQ9R3UJ0Y3YvR3QGi4qOzrsQc/LNVgGmXizKHMG0hKV34vgRSg1No+z2qAFqKIRuQim+Yg14WVrumf4CM8+AIj4aXcrQKK2t9et05drarNOpmcnMv3aFy/ij3v6mTT+cat2igSGOJjtPR7Zj122Rt5fuJDLQ28w7tJGk/nH5xeO6BVe5duIju7KLjFL+asrziUptTNJZinLUlQxRyjDNwpabmKkJPUMlSlsDRWfSost6cIddGaw1iWGYvpgipNutkP3ZgzgCGdc8yRzCOV9mIwP6kSZYHxUrQSwS9jAXWTQSDvsug7+LEWQuq6xD7rhnLvipZO9rjVENzpJawGCR5u2+qYxIdmdIe5qSUwjREJTpiK4zYCF3auz6tGfs1a7zaoOtQ22oj2rfQrm9Kb1qZCMSGspY6LCQENRNer/XxjneY6/dZ2qmiUp0zGFMFSSRII0HaSXHaEaLmIepGw0CDmX48bmXRgZ6CJ9j7Ez+MGjoEqYb7oEsYkK6D8Dx0JftM3j/g5oIrCJnNQ/RYCBRHWjG91R47fpD3cwzSDtXja6yc/Tr1rzql0YENfnScVXAK2zFNfMbOWpzmDMQIz0WRaohTkdGiy7+zX/c4f88WZ5ohXDFkqaCuENKI21ZWXUSrBn7FKFIpkS98MzbObIsnNTV3SMDLt0MrlAChRR4NaFt012LflQQ4wGoCiFzpePL14X3r1Zw8RRg+n2Rz5/qJpJeYYZS92IRtUejgJgolBaknCasuncUKx1farDUUx2uwWEloeuAINz+QsRqQKDMh2JCjDI8/USk3+gHXe4LO7mzJ6VnqyIO2Q3qjh7y1bRZqEXm3C8TNRotZHYK5OvTncI61qWwNqG8mRKFLHGuSrClIpZokFiSxwO3FyF4MgzDP8zVmsU+E+nulC6pEw8Fe9ZUCnOGVVyUZWJmMgpbmoGSpgmswioqCZNhMZEa1O5A1WjnCZc5XHKlJdtf6uJ6mUk3wJMRqAI1lGndWuOs+M9Nx3IYV1eHJZLkB05yBncI8rWs171HL7hQnbk/jnbx49xIfOrZOzZ2H6AbCc6jNJ3iBpDaf4Ky1TIqQNdkJZKboZRyCl22/bd8PMt6NEMPIwFQ3hkQ8pU3aYNeLIgPdxfTzEg7IGYjHt5HdNM9KRRYViBkbmzKSvZIogdaCNAWdOIgYggiaAzhyq0KwuQu7mkrPXk08Tm/fZKXa44JKWWukBPWYxPXQsofSilTF9FNFPxXEaXaVkBohNGEq2e57yHW458kBx9rbgEaijSQWEhDUn3Dznvji3thTpbfrkt7o4q5vmudcZ5jT2ry6VoG6pWa64FcQzTpyqYazVqN6o0fYk9R7Dv2+wrFrMxEkkUT3ElQvRvQSpBvn4bd2nZwHb477XpS0rNQ4C4EqshuVApFgWqALrxdxA9cFX49QoYUQ4LtmX6VZEI70mPP7LAUuR2rgiEHe6ap0iqit4N3ZYuX0OtVHJzs8/YqckK23TWbTukrjwI5kL8+zcydSjDIcc0WLAneC1BZWXY6c73FhTlNxmobiHTj48xKvluIs15GLVUQ9wwuyC48UDoFUxI4ogOlkWMEoXqC1GNLOs74cS8qzF9z92sE0Kv3Dh5FP7Lzkzzkl9yvAPJrzixGur3BcjXQUwjHORWDOf6UEaSwJey7dLQ8QtGHqMBS7TGwAWm/ELDUSXl9JqZ6t4r9tHufUUdMhJiV6EJI8eZGb/9s2n+s26R2r4BQWm9IapRUPPqu51I9YubnLHd+eUlu0ZbgsKqhVzP/te8PIwPFz4s7Aq/Hs9ZSNdZ/ewCHMePTuLYG3o6mupzQuptSWJcfujVm7V5pBG2AWpmUXQh4BjDuFPDrIm5YyrGEa69JGCUINU4OyFN2yGTPsIA+tLZbguGb0uDCRQdVRLAaabmJ+EV/CnJf5pqCGWJln/uhVztxR4fFHRwE8O2xVyOz/LbSeT40Mgn1GBhi1Kqt36Xoq/2zRrp5t8boTWxypJaZXQwqj2rxcRccKZ7mKbFUz8HQYGUjhEDgJqVYkekjKsyzdcRzNprUWP7BRwUtJEeCAnIH62TeQ9ExuWYwOYJLxqrXBCWybqAVEUgWRkoQpHP/qY6w8ZsQk+q5PzwkYaJ8ocxmuUNRExNKgDYQ89TMfoLXs0vA8HOGQ6pQwjdmJHTb6Dp1Oysrjz9Fa38RLYoIwZK7TRQO7rUXqr2vB2fOIpWVUYsJ7x/eQcw1Wth7kyU9LtqRiR8dotJkuJCRV6RAlguRKhWcuSr74ez1aiwrXhfqc5s7Xa06/vQILA4RS0FJGxk1KqFQRC/N845FdHn2siesqqp5COBKy4xJHgnYoubIekD4jkF/UfOSnQpqnQFST7KrsFejFBtSb5hRG0wM1+VwOIOqRWr75ssL77JXUcc3kJkt1LjqDIMgXBBpc4dMK4EQjInBclrLFtlJNTDVKgjx9nMo7driwe53WUoNLz8f0+4ogEBw55lObyyTyK+a7VcZNmTYGvraYAA71VvnrdpKzTmOcwKXuGufUWhywcaQOgBemRFWXjdMNzt+zxbetac7Ph8g0NdWdpbl80Yn5uhHqnZszvQWCLEXyqLkJUoz2FViH4OSRgEaJ4XE3TiAlzUa327VyWzMQ9X0LI2IaYJzBuCMA4wySrFaaapMemBn10iDL7Yh3/uoLPHfnSf7V932I3W6Vrc2Azq5vcANPUavHrB3p8n/7tV/C8R3kdxynWkuoekEODnYG0B14tC8NeNP/+3eZ29zl2uoSO40qg8V5njx2kk8vP8DbXrjGT/FVHt1e5qgr+Lu/2MR1NB/9y32II3TGZ3hSdxE727yv4/GCn/LbrZg/lbZ4oVbhp44m1NZSLm/4dHoOaSy4+qLDc0+63H8p4h0/3IVGzbSj+hGQLRLf4+w7ajz66Yi3vq3PUb9PZ9MjDi3wpQjqitp8Qk9JfvcPFvnGg/CO1dRclezBdZ3hL29z6j20HWe+nmMHOgMRlfHyZUIwVjsBzHssKOm4Q34CZKPTPepuTOhnHBEtqLmGbKN0iqxUEQsNKiuSZjvmSOjR7ShcT1Cb02aCcS0rWUo3C52npwm1egwEhh496xBohcQhcDQ1T1Cvx3RaPjtJFSdWDBoeq0e7rC2aHpSK4xpMKANKRbNqvqxRMymh6+dfLjIA1YCocR6UwTCwKjNLz7fAIfw3EhmM2ziwkQMfWRnRVg3srfkzNd6TX3iCYBDxx+9+Mwluzi5zPfOD+kFKvRFz9+5lFm9t88QPv5e6aw6qOXiQqJRO4tPuae7/158m6A74hY98mMdap0gSgUoNJXRjvcod6WP0E4etrsMdx1NAc2bNDHPRvQ4qazmu4fB8tg397CRqRCk7dcG1XcXdy3D38UGW1miE1Hz92TqPPlSjtRpxzw8MoFqBWkQuteY6rL2uxsKRbZ5+psKZt3aI+uaKmSYiX3tKQasec+ZczJOPeLzh/SlVT4IzdvV2yUhFWZiZiZ5AobJQTBdmmcU8YJgmjPsCq7HgOkMnBEPKdAF0lBip96obUlfDTtEiDRevAnMN3LUaje1d4lDiVx2E0FTqpoQna26eguSYwZQ0oZpJ0XmVyf11PTHEtLRCCo/ASam7glojoTEXsasCIuUw14xYWetzpKZpBcPOQjwPUa2grX5EtZJHLUg3r+QIzChDEwGokaqbQI5EcPYzBj8Y7tdLdQRwmziDMhs6glGHECuRRQcQRyl3/fGjXD59lMvH1lDdIVrsugrXVfhBSqWa8s4vP0HiubTfeob5jOJsw6peYngKi7/3dZau3uLjP/BBvlY9Q3fTI4klKut0i9qSNdFlfVDlcl/y7ZWUowuat5xPSVONbO+Sds2C84XDuqfZdhTXPIWnoBUprgDrXc2ZtotWKUKavNb1NK8702O36/LQf/Y49bYu9VoFMahQ1F2Urstd3+by0K9BJ3bwqylaZeIjBcBMK8Hr7unzwnM+jz+oeeMHsxPQMqmlIOdNuS5kvQla6/ISY74Be0QISgyjgqKDsGYXfBGZL0YFY5FB4EiqjgLfnAtVRyHJyqSOb3oeVmr4mwMaYZxrSQT1bJJS00cEATh+VnsX09OEhjkgfnWyVBkE0qQJGafCEQ2qbsKcB81GTBQ6uZOZb0WsNlNWK1B3jZgNGrPoK4HBSqTIyqlBXkkAq+AlTTeiGC5081r5sbevm6ak2XjvLNs/PelVtLIGJGBiRyynWmmRRQbGEUQKIgUnvvI09d0uX3jXG0bIPhbU8YOUajWhWRlwzxPPceP+MzjZsAqTS6VEStNNHKL1Puf+4Gs8dtdZHly6h53tgO3NCrvbAbvbAb1dF7+fUBEJkXJ45DmHKIb/8UMR33Zvys2rXZIoJembH8YVkhoOvzsf83gl5c7QIQsUiFLDkAu7DoO2y6Dt0Nsx9998d5s0gS/+WmTakQeRofsWEPk73xUgpOaF63W8isKrKFxP4zh6JDJfbCUcPxHzzS85xO0EHSt0rIY5fpEjAPntTK7BuOWMxrHP6NHvzM1uYJFLUeyuzD5rQ2VDQDLTs+puagbnSPMbCjeAio9sVXFWqtQWNfWFmNp8QrWZIOeDDDPwEW6QA22epyamVyspcpFaL1C5krW1Sk0O5ewytaqqq2h60PJhrhWysBiytDJgaTFktapZDHQ+6Mccx4xpWcsigixFEI6NXIbHSmSVBTPiXuaOYBzXyR3B2G9mL6D7iOlyOxBnsJcVo4E0TxGMQ4gy1lcSK+75rw9z89gyz547mTsDKTWepwiClCCLCu67fpHKIOLmm+7M0VilIVJmrHsnlqz+7sMIpfj1t38bO9s+25sVnO0Ufycm6MZ4YYpMNZtJleVKn90efPwzHmsLmhvbgu4LVwkHCpWVJB0Nbx9U2HE0rVTwrq7LICO1VLUmiTX9vsqbjTq70G87yFhw19keTz+s2Xymh+4PTH9EnImUSEFt0efYOcGLLwa4vsINjENwfG3Sjrysp7n/gQGDvuCJr5gR7MYhpJkz0KOLuYSmXGZTS5LF7ykSjsocgv0rpggFhyAQmTNwM0DNTNBypZHHz2dCZFUWuVhFLlYJFiSVlsZZCHDmA0Q9GAKIWW3I9RRhdTQo3l2s0swk7mXDY2F59PVqVQ6xBpXgSI+qo2h6mlagWaxoFloRi3MxSxUzWGXON8NYBRlg62ZVg4o/rCAUZeoKxzePEIQzdAozHIGNCmYBhntJcBxg1+JkLGN3oggUqux+mBpHMEgzrOBLT9HcbPOZP/8ulCUwZRGBjYRdz8zCe8MXnicOPDr3HGNRQKwFJJJYC7qxw9aLfe764pN8/v7X8fDGSeJrgpXNNn4/IfUk/bpHWHWJA4cvxMd4t3+Fu+a2+NJTi3z1GYdUaf7ZW3ZId6OchOIiqOLxl9aDjIEgeKHu42vF6ZbA9QTRwDiEXsagq1Qlq32P44sJz7iaL/3qgO86tWUIQ9XKSInv7Bslf/JJ6FChWR8Qu9po9DFEvUkVa0sxR48nPPJ5hwv3x/j2AFf0qOYhDNOF/EeS5fcnf7jse+TwtgC2DVODYm2uECFkDkGIrOypFVL4uMJHyTRLGTKqsvQMY9SqYAcNxMI8Uik8T6K2Q8MIDBwjiro4D/UmBA3S8Bap9qhUU66v1EZ4AdfOzfPe+g1zGI7VOXtHPNI2vrDk4lUGWP0GX1ZZqiS0Y4cw9Wj5mkHWR9Dy4UgtYbmSmBkW0gMtEV7VoBZJ9v+6vhn8kxHMNImJCLRR/ZY4IxXd4tW/LCJI9RBoNxfUl4YbHGhkoDIvpgrezO6IeZ1CiYScC66jhAc+8xVuHF/lmfOn81Zo6WRYQZYi+H6KJzTnnrnEjfMnzDhyMkejRR4VHPv0wygh+a0Lb6ez7VFrR9TaIUE/xsl6G2LfIa07/F5wjs20wnefeMFsrxLU3YSGl0AvyjsWaxL6nr2GCTqew2bV5z4/ZWHRpVqVOe01DDXdjqK9m7KznZL2JedP9XnxaYdbTw8MkzDKWnGzaUGn7jeL48qNABmYq5bjmj8h9AjZ5o1vHjDoCR77sjAHMFWQ6iFvfyQyGEsbpjQ27f3jFiKDaTaeKljTCvQQWXeEiyM9HOkhcXKKODBsj66YKEC2ApMetEwYLqqVwmIzYJzrGjkxyw1IXUmlZeYnAoiaR21O5b+PH0iqtSwyyJ2Vgyt96tk08AUfFnzjCJqeoQ4H2WDgfF+lO+SOjEcFYsgXAIsdDCOEWWajgldqt1WaYEP9YoOFxQls63Kq4c4HH6Ox0+XBD7wNCqGTlHoEPAx8xdGdHRrtLrcuHM+/O1bGEfQTSXyzx6kvP8UXXncPF6MlGtshiztd3uNe4qjuIJXJL3VV0piLkEuCf6/u5cL8Nmcbhjg172WePkzyakbF0UTO8PBemqvgas23rQnDSW8mVGsSR0Kaai4ryZU+7GwltLc1JxcjPFfx8H9OoDdAh8YhaG3yp8aCYH5Zc+2aB56D62tcz1QmrK4J2f+/spJw8kzC17/g0m/rUexgpNnoZSz6cdvPdxQXf1m0kFlxapZJF9zMEWQ5tP2eigESadTM2Pm5isnHbenOq2QNPOZ7/SBlUPfozFeIA4ftlRpz8yFNm9o3fSrNlOUVDz+QtBYc/Koy4/XsPqYJrvBpuIo53ziEeV8z79vHBt8QorCtjmvwgcwRCGmwglz/wR6GwggASzseryKUHvqXQTQq2m1TTRg6gmEVYegUCpFBqnnrf3oIgHf93kO8zZEkUpJKSSIkShqaskThp4oTV0zot3HuKF7mCNwMg+glkqU/ehypNKvXN/lzW39EuiP4wfPrNCspSsG/e2SNdEPQ6vRpugNSJNtpnfio4G/c9xWu7lRoVU04GYRRPp6llri4vuCFNXOIpedyL4prXcm1gZkmrGNBrFxuVHxu+qb8lIYRvQ3JjTggTiTPPwbda33qroOuFE5G4MgpzZNfdflPn57HlQqRMdTBELQUGWVbCzY3HbQSPP5VwZu+XUOq0LFAeNYJCFNmlGRgYEklYJaNVw6UNt9V7E0Yt3Ew0X5PFlUIITJUXeWEFOsgijM2i9FB/h2ua6oIlQDhBCQ6ycNmT5qZlp1WgHIEuwsVTra61Oy8xapHtZmysGR+k+a8g1dRRo3J7lsS4Uqfqtuh5qoM08pGB7qmh8DJQv18H6VrKiC2mUsOqygm79d5dGAWv0ZrIyFXNq/DYgUw2Zn4ciKFg2lU+vE/hCd2RsKS4oZMjIfUow+2jlYIKin13R0TEgsy9pYe8oi1ISyFStIZeJz98T8YiSLs/1Otx/TXXM6E17nTu2rKfDdaqOUG6WaXH+o+h+6ASo1clkAjXY3uN2G5znF3ADEk7YRoM6J7q8raHS5XnhOcwwPXG+6DhuvbCt/LAMxIkiqB68EZu4++x0YCO9uaRjXhyPEUN/KypqPsiGWVgHvfBmHbjOpKIsOH0JjdtD1J0gHP1Zw4lYAWHDmm0LbTLjVRgpDpkE+Q1/8z/sGeZKMCPqCU+Q+Lr08rLU57nP/MCpEt/OJCsBOj8s9aYK4SIKJ4SGJznDwqsOxDk39rqg7UGjE7TZ/ElQwWfebnt6jZkmMlIJhTtJYk4FKtSfxqbHQX8uOU4DgGv6i7dkCK2a6qa/QJHenlYODI9tqz3UYFQo5UEkYOT+biZ9n4wi9GB/tVOYIDcgb6+06h32EEPCz+YSUrU531EmhBoi0D0ZQSbYTwnB6+TzN635rA4Ai+A152W3VM96MQ5jNhKugksNmTbN6q4OwktLZ7uDcUQmgUgnazSrTkMr8Y0whM22q7Ixlckaw82sER5keMtMvOXXfzHX9hwP2nt2n865s89Lk+mSYLrgNvfGuNU/d18VeyUV+XE25dCnj6sZj1m7b1V1OtCs7eUeHIGc3K6QFO3EL3vaGUWZKgU83imub93xejeonRMBhKQBnGYWEajZ20jC9NBJBKwxZM5fBqXIwOrOX9CVN4A+P2cqKKMtMKIdy85j78XYUJnbPdNHMkzZAUKoE5RkoNI4XcGUQoLbBzPuv1mF7LY1D1aS0NaPlQtcBrxeAOtfkUcHA8jRcoRJBRubM0wfEruMKn6prW5FCZ7D5wFL4UWdXDRj/ZMdSu6eGAEW6FniGWs1+zUfXLtQNxBumfO2duC6lBEQW1xCJbQegmpoIQK4gK+IFSw+8YPwj57EbH5O9110x8tmyufgrrA8G1Hjz3XJPdpzyOvrBDrTZEmHtNn4sXFrn77VvctxbTrEGYwNNbggc/u8o9Xxyd7nPp3ct85w9dJfiTB+nsKkSa5oGK40pqjZTgWAX3XMsszsoOWoVsr3tsb8T58I5BT7OxHlOtBVSbHs2rXUg1cjHNQ1WdoalaaYQUaE+Wt04X9Py00ohIZeIj2QnpqMxZjBFtpBr2L9gFYBucMmJSbuMRgX3ORheOzNKFsbRAFh6P8QxsF2ReWsMyIzPnoAsLyioD14FKYg+40bMMmuBVSGOTwHnSlAKX1/q4nkKlgtZiyKmGppUN6xXNOu6JJgs3bxHUXYTQ1BZTZKuRd4CSRvhygUSG1N0UX4akWe+DIySu9HGlnwOIWmtEcdKV3XYhCzNAijwDkWkUZLhBdvx0Vh4dKhsNqwjjNh5r7BUhHDhmMO4ILJ6V5s8x8lxOVR7b01kDIox2oin92OrcIIVuDN2+w6DvEvQTvGh0QYRVD2cOWo2EpQAWAoUjJJUpk60Hfdd0mSXpiBS22V5DCBJ1DzlnVH2c5YjKlR71RkC1KmnHw/+/31P0e4qoJ9G9ENVLEJUYMgRcxyqfdgSGgTjttxbjBWbrSZXIKguFFMHqH46E+4X61rToYFZEYMuLSuWg5kyz7dBSmQWkZUYf1zlVF61HKxGuP1rGLCD2VvkHTJpQcaDpa5KmwXrmq4Y8FFhn6nvIpo8z51LJ8nvZ8I1oa3YcdBqbpiLp4WjPbFsmZmorIAboFDkIONr2KAtRgRUk2d9lvcg4LDPrIF6qHZgzKJYQR58flhqL4KF9rfgY9p4SY4e7RtYZZ9W1QWok0gZ9o5W42A/xwnFn4FKrGxLJYqCZ81MSJahM0ciL+tJ0Wg7SXJLMmr3qC0/m7DPZ6uMueAQ1TbUmae8O//8wNByEOJREfUnQjhAVB5HqbCy6hjg1JcLMQ04s+omDoUwPZaY5p+WopxVSjzqF8U7FfaUJYzjCK7G8xChAm+0RdppzgZEpHA9dFGKFoTPIaMh24VhNhLoLaSY22gpMOTBXEHIdRN2QluzVR9Y8gxnYyCATc3GEZ8RxROGqnjkAOykczMIVouBgc2dQwvh8mTZtTe3XboPIoDwqKN6H2dqCs8R7lAIT+A/zqVjBIIF+KBn0HZJQTDgCMM5gYS6i5WsanqLhKbqxiQ5EyQYFfYNY61iVSnFpRZa3ewbpblaR8wF+LaYyNvk5ChVRqIhDl3gg8XsxuuuaRWw9YAYAko6HSWMHZPz1VKMdIzBCKgsHfCw6oOAQkHtT2Iq2H8dRZnaRj9zK3CGY27HPWKTeLXzO8RFOAI6bRwUwjAxqtowooeUP9QWBHG+QrcA42lQh6i6i4oweA5UgXcOQ1AUSnU1liiVC8wKMpkqFRqOCJsHLsVfiBKwdDGZQ8GCj3ILyqKCYMoybPd8K6yPHuvL1qmBQODcHyVDVtt938cIUd6wfX0lBv+5xvN5jzjPKtIFU+bngJpMhWtBPjAJTnNLvT75uGYJGANRw1GXTx6+GVGuTCyeJNUkkSGJJHEpEL0EobRwKmCt94aAIKXJHIKQYvubISYeQ76jOUwUtC2SlqelAdtJmEuvAJF4wy4qhvNnQydeKt3rojHJHMD5ZWmTRQeE7LN/fdAMaGXGrs1lxYM4bzvGc80wFwOoE4JgeAtH0kVn0JWpelibInHiEUjkJKscGsujFRgUThCHrEESh7Th3BJMOQQhpxHFfphUxhb0cxoFGBsVWZfu42JNQjBCsjTYkmdtiqmBVl4sRRdEpqCxl6MWCKJJEoYOTKOTYlT6suuiqpFJNqLsGIbZzH1NNaSRhIgMgVqWTfPOxXrn8l4eoeXiBolJzkVKgCjubKghDRRJKM4qsF5sd8NTkVbpwEGy6YG+10pMOIdUgNbYBUKvhCLIcM9hvdGA5BRPPT0kZig5hBE8ocSoj7y35Pwq5t3A8U5cXjukEzGTUdDpccFZQtO6KXJXb/L7aTIKCjCbsmfbnyEcrjawZjUZhtzFLEww/0Mnp0hoxxAoQI9FBDrwWDuG0SGA8YjDvLbnAzCgd/jelZzBKLhqNClRhMZvb0R3bs/wNI6zF4neFmYJyFDokicSN0wl12zhwqNQSqoEicIYToZUWxCkTYCOAX4gMSvc3EUPQTwqE4yAqhtBSrRrKazG9UKlGZYOO01gShxqPTJIchumANCegBsQ+LtJaGdwByMuRoixVeCklwllYwX6Bw9GNHH6ujNass21Ls9tsPoAoNkA5biYDZj5vR/gFjqLiOKbaJKGedUXmrdHSzXEdkWpErEyK4HvDyMBuQ0ZNtgCgpaObrsNsmzLSkGBKf8FLAA+LNs0JpFpMgRZn28HInhXCFusIEiUmooJpi7low3Htk2xF29iklMh1Cez9Qd+h2/EY9FwaKskHaIBJEXrNgFo9pu6aq4mETGHJAI+NktmNzW0zJ093y2vGO1sJuo0hEJnOKkTg4tXMvMHm3FD2W0qBdISZiTCQRFmffhIJ038gQYjU3PcEKMeAk1IYkNBOrU31MGUoLshx3EAaRiIULlyua3oh7LQls2XYSCFPFcanLNn3Fdf/eHowzfJKAmNpwhSzeMF4yc4xbD87agyMI6g6inrWS55qIzhq8KAUR9ay/c7ajOcaRj8ysSpFWbdhsbKSRjhukB03kcuQTVQSmLyyF0uKNl3Qe+3vlO8qXlhHnx+utb04CAfkDEav+GXKRsUNn4YXFG38/cNWZ0mSyDFnIIgLj8E4gNSVOIkRt4wDh5qb5s4m0QKUma8XJuWAo5OYhhE9ZWPDUKPCZNhwlJF9hDTinX5guhmTWON6Il+7RgVaZJiDzLgFOqe0O2iEVEauW+lhdLBfBooyFQlRTB2KnYiS2aXDMp5B/vwYJoAsvz/NUVjHALOdyTi12aYIhd/CyoYFjiZRmlTrwnASPSQ3STcHebVSEAlDb/ZKIgOl8nZrjUbqYQl0luWOYEZEUHxt5P5Y9WFaOvBSKcm3DWYwHhXMAg2nWV4+t9FBJlc2dAhm8cfZ41FH4BAFDq4jiAOHxJU4jrnC5+3UwkQaUegQ9KMp2zA9TRj0FLqnTQdilKDTNE8bHBc8TxhFHamNuq8U+RozKarI79urveMaxSZPZuKoqTZXeRsd7HXA7OJPC/hBmomk2MVcBAqLOMJ4LwIlTmGaoygxk+8PCTYTTqIILFqTcghpFJufLN+/sODsTAI7lwCyeQRySG7KPy9dM+Mh8YxgrMV5stkOI9ukbUOSMNhFYZ/KnMK4A7CPh7fTo4Px16aRjcYdxH7KjgdeTRgHEe3ro7d7AyFFRqJNE5LYOIIoGkYGaeYgxqOCxJN5qpC4Tn7ffJdJD8AMahn0XSoqLN0OpZl6Re73FXqQovuRESuJ4pxFCEZnz89kul3PRAkyBwEFKvMxWk6uCeUIZGwYiroYHTA9Uslfz1IJUoV2hMFPyvQJxoHCMtDvpdCRZ2EJRbzA+p/xnbaPx6nSObNv1CmbmQQKN8MNwA4n0ciMJGTe56Bt81OSIpLse63MfBH8zG6FdDP9gYwhWIgORgaeZGadVF5JmJEe7Dd1mEY2uq3Vka2lY4DhCMNwwiEUypCF3308PbApQq5bmDkCGw2Yz4sc2ZdSk3qS1JXEvouS6Qh+YIVX+9lnewkM+k4uoDJuSoup4Uy/Z0hEQS9B9PqQpOhBku+ElAIv66EPAqN3MBIZKACRU9t14deTrkBkrclmtqKJDkYPePbBsgVYPOBSZ2VGRqODkv4ErfUkboD9nFP47mxx7wUm7hdbsFZ0CPZxoYY/UqZDIoXKyUUKwzo0mFCBF5DzFtzhBGsr715ME+z2WnJU3mmoJhzBfm2/QOL4wp9O4Nv//33AAOLwMYymB+b5Gd9RglvZiEDpYVRgHUEUOcPSHuRRgXR0Hhk41llIgZImnbC9EdYZ7MaCft+dPWtxSmQQhYqo56HaEXIQGX2CMEUlotAPJPA8Exl4nsBzRSZWMrxIMhZ6CmlSCO1k9GKVRQcUFt4UnoFW2WJ2nImIYqTEOH47DhC+FOZh2YIvYgjT3jfNUZTQfItVBLDovgEMk0IPh00bimzBvBrhOuTzIGH4uFSMRSGkRBQmHE3d/bErfVlaUPrcPpmKZUDifroXDxBAHOoa2tp/MToodikWd2KcXViMHOxVPIxkVjqU9PtujhnkDiATQbFCKIOqS5hYhDxFSYGTKAZ9l522x/VqnG/D9R5sb1Q4OmXfUs1ML9bbdWne6CJrLjpWpOs9Bl2HeCABnanrCKo1SaUq8QKGQpxkiz47BkoKZIGOnD2LQwKpY+pmZVRlS0uGvOJgCUc2tBaQvSc1Z0k2NS2/BWy7s5aFPHkW6DjVCTDKNSgQjYY7y5DsM60NeiQqyMLwoshoPq0oxc2Ohy8FjvTzIbtAXonAq5jvizP5MykY1yss9lGYHgpnRGdgAh8YmX6kKOIFw5RB5+8t1TEo7FOxSjBeUixWEcZB+TI7sDShjEg0HhkUbTwdGH+t6ECSRJIkQ2zAgojAyAANKa0ykDZqRllEACBTTRQ6pgQZx+xm50l/4CBK2IWjGzT99Xgg0b0BqhOhY4XqxMShzGYzqGy7zJ/nieH5XbiaWaeGMs+r1DiINBE4HkiVTQXOGYiCCaZi8eDZrkQl8lqttt/hiEmsIN+QwvNlodpIT8MYtjDlKp+DiPZze/ETSr9jsm6fC4gJB0doZJZrSWFyfatCnH+nBREdNeqwxmc8FPenZJ+mCZOMbu/wfJkGHpZpHo5cJCfWxOwUoswOCEAcpgVFjCAt2bmRnRqLBIrvtZGFTQ1UmlUNxkqIQK5vn0cHniItOgKlcRNFJ/Tp91zasbmCgAEPywhH+973WBB3NbIdo+OUtJOShG4mTlJ0VCITJzFX7HHsLL9gJgIhjRiqdDLHECtgbIoSjFI1i01LIxtoqhE4zjBiIHMUlohUJCTNWvTjuIG0G14E4PaRVxRZitNk0gpRwbS1Z8RGdVYGtDm9yKXURt+bgYi2AQqG4OEscFQPG6uG8w5GHcJ49WDP3S9536zFPu1Ce1tWE4aEo8nOxLLmpLJIwL4XhhFBnKkR5dGAjQzi0RRh/FZKTZJdgexV1UkUom9ShW7PxZOmzDjoOwT9PYQoHAmUO4ywD3Eo8doRxClhXxIPBP2eMjwEpfOSonQEUqryi5CtxigQiakmKFdkTkHgSg1xisaZKG5pRqMDy0i0VYUh5yCLCqzDmMUncMbAwnEnMPH+PfCB8ednAYtjzxcJPCMyYvZfoenJRAXD1/LvK/IVittR/MuPiSqNDAwrdMqVfqwfYeL1PdIDayMXRUYX/H8zdOTyKGDy9TxyUOVOY6SHwfIKkoxUlBaYh6mYmLFXNnNPKI2bKSJ7UUqv69HruviBWdz9vpu//nLM9hroXmyakELHtCmHKUk8dAbjZsvp49GBFkPnYNIEo3uokiyqsA4hD/dLIgFL37R5fjFVyJ1E4fWSsuNEVWG/TmCaY4CXXlUQ5SBbEdkXaITWuQKeVVqeQP2LtObiIi+WFsus0GU5vi17lQ/LdQ4nP1MGSRUjhXEiX5HtO8sOrJpgbsujgPEUYTwyKHMElmA04gDsXzq6AIpYwcRrSuMkplch6Cfs9D36PRffV0jH4AiV5OVLVJlORNOFmESCqO/Q6+osMjA/vOvpnFNgtpdszl/xap7dapGpcGmkY/ZVJaYCIVNzelmHQOEbzLdlV/AibuDYx3ahj2EKZfJne7Ur7/X6CJnopZXi9mu247EYAdj+gVIRkuKf4w6R66JTeI1sGvhYfA32vvJPRtSz3//a7dEeNoqCDhf1tBRh/D3jjsAKlhYJRSPEIlV+IGx0IFKTGjixwgtTgn5M0E8I+gmDvsug7xQam6Z7eEcwefUtWBIbB2SiApMiDHqKQV8RhWbSko0Q9jKtxLDMXbivFLlTwFYbYjNBaUQnkSEhKb9N9eTzZeWcXDdRlTyvR58r3h/XKxi34mf2Q7YpSxGmNP6Mi44UQcWJyCATTRmmCyXgoRhziCW9FHvNPDDbXPg9ppQPJynIk/fVWERgX9svk/cAGYjDcuA46cgs9IIcWuE90dj7i2XDKHSIQkkYZgs3SxVyh5CKkT22UUOSSLwowQ9Tqt0oxwSk0mgp2Gg2zLjzICUKnYkOx3GbAOUKFmeRQdR3iPqS3q5kZytieyslyiIDpaDW0IShwq+KqakDGCeggDQxOICQMrvImtxdqqw0makaCc/JowJtAUb73dJ8Rsc2300ROEMSki0zjgOJqiRVGOcmwCSIWNbKPG62N6FItLCP8wM+I0UopgaY/SsqDuVpAgXVZSnNPtmoYOJL5fA7psm8TUsViiVEpjcmlVUY7HeVgYf23eW9PmJfBKQDiQzGO6hmRQP2thg1DJ1EATCMh70HxWhAKWERy9FtSAvvSQVuFhEUG5Dyx5EmiSVpOjvM2q/lV/BEEIZm0VtHAMZhRKHKo4hZF9HiBddGB2ki8scqa3DSKjuQqRpGCBinobMDqosHeWR7C8cvxwyYvPJPRAklnyk7GONP6XTy+X1ScqeZBQ7tAJaRvxnApBDOZNqwF26wDyvrTygnH5VHBOP8gmmvjT8/yw7IGQxvy9OByahg+NrQERTBQbvw03T08cT/rUYjhSSRqMR0HDpJiiw4DYsfOLEaIS29EpM5OGcWaRJronD0B7eOIIn1cFFPRqC5aUv+yYDEIqColciesw5CD3kQk8Xp7HlVCNGGyK0uP8PKF3pZqlB8PCuFeI2sOJVoOOG48G8aZlCkIMtCVPAKHcI0y0uP++AfzFroFjgsvm+WHRhmMJ73vxTMoOgIxjsSc8zAXsVLjkKx/KhSgROrPDIYNy8cOghbhtR7dANOoyODAc6LkbFSmrhEFSnM8IM0ESNX+uGCH92GYqnROhqLH2gFaZp9xkYANjrIFvxe2EHh4E1Z0PYEnhEF5Bs7Y/GXkSleoo2H3iPjyjKHUIwKpkYGWQRQTAnELPWYV2F7Z9GQZ3UzwmSKUHze3M7ejgNxBtN4A2XlRPt4xGkUrvwqu2rmUUFJFcEu5uLzRXaimyi8KMUvcQY5m88+lhq1lyTzDJOOyEhEdpuGyslFi0KVpxA2pdBqFAg1Ski6sDaLgGJWaUhGHYMFFW26QJ4mqFEPXfwhrLMoApDjQKK9X7QycPGArDiJKe9OHKssjH5gNEoQwhmNCF6jqGA/NgESjr1eVpkrDjeeZgfmDMZRzmI5sXhOjp+f44pFxTTBXunLTGYnc+5IshKkVUb2wgSnROS0mDY4js4bm2bt26yYTMrRc8ks5sn3F3GDNJakiRhZ7OPraqSqkEUOaVJMEexxsu8rRAClFQSVv5ZjCSNpghr98aYt/PEfeNz2qi68RJukIY8xC0vSgqJzMHfk5O042Sj/wikciVdo07QNxq/yZXiBfVxWwp9lB6p0ZP9sRSFSk1iBlS8bVy0yV3ZG2pNHUofsvlQqlzV3Uo1WgkgZDy8TRaUfU+2avzKzI9mL051npQmzNBDN9wgcR+XSZVO/JzZj2msdMxo8UDIH6ScdiKk26KyKkCZgGQVaDXkzsvBrm5pCapSWVTZzEcs/kHllIZ+9KKdoHeR9EkO6ci6HllcbCvwE+5zIKgPFaUsF2rGWDIVO7PtepuXfU9I3MME+tGZ1FYviKSMfLuAIL9NGG5XUVNl0+1rpgscChqPDiIYX3KG26G1JOoJyjKCslFjECSZSg4l0YQw4VIZWW7y6m9POOAg3UaVVhHHT0tCCLVFpr/3SJWG/NVkQyjHkp9llyChURAOzgDx32Oqcjvyy5nmRpREiwyIMVpa9JgvRg10bGbnIlhjztmcpjD4iZCVFChTlkn4FuyaK5cQJ0RNVTmUet1m0431ambz4UEFp9Lk9eQAi279xh1DmCF6F1GGvfoXZgOEkWFiGx02zA+MZTJKIxMRrI+8rhPfFvD8uqSgAeVQglR7J+6UU2BmXTmywAid5+Y1HpfuWTO+WscxCIfb4ZRhWFaJQIR05wkpUhf0pXjRHMIOstyBPLTB4BRlDUZjLCkJlTMWMbWgXvoZc62D4/YVuRrMhTIxzn3AOJc+N9xzs1a+wT4pysSnIOoWyHoEiRlAKItqUYFbYX0Y+2odDGC8jzqIhzwIPx1MEc1/kt+kYTnDbOoPxxy85KigDDAtOwR4h6whkMQTBlg1NZPBSeg3MlXf6UVV6iOyXf16/pLQyDDWVWCNDnU/2GjVt1MJT8lRhGCVk6YMypCTH1QZAdDVSC9LEyH7l1OMMTBQO5jgV5NSFfV/mGEb6FYY7l6cDucaBdQQ2RSg6jDLy0atkQ3nyUecw+h45mR5MfJEN4/Z6/dWH36aRjcaBQXM7CcRb2+905gNnIJZFBcX741FBkoi8PXkcSARGqggiHTqC4gQku5htilAGHBbNvt9uxyznkWpy4dIyUxnKn5cC9/iVhjwEhfLESFphzj9hpqEVnIBtXrIL30JkxXTBdubaKkHOSMwWvUYO1ZKzlGBEQbkoF14cxzbCOFQvbZG8hj0K49FBWXowomewl8d+DaoJs3oSrE2TNxu+To4PjKcG/21hBvuJCsYETUdwg8L9PEXIHIHI0gU5Fhk4STpzcdvP2JboJJYz0wq7H9MsLnAH9kNiimNNnOhhup3hEdIx6YHrZcetJDqQ6Bx/E7a7MXMWueNQopAS6InoQDt6lF49TTTVgoRgfzgTReSvOaPvK0sLimY/Px41vAJHYR3CSGlRzIgOysDD4va8RIdQpCLnz42lA9OsNBUoPD9eOhwHDseLQWV2G1QTRjd2vNKQlw0LkUBa8lwxerDAoZOoPE1wY5VHABZQtGSjvSIDqTRJ7BhNxNCZCTbuFRlEoUIlrtE9TEUp4WjkWCmd8xAsZiAdE7GrwrkopZiIDhRWPNUQpVQymgobMpLBCgQFMLEkOhjKojH23MjGjjgFrYsj20pwA3u/rKJQtFcYKYykClO+Z3JI6pTo4FWMCGZiBSWlxaJDGI8Ghs9PRgC3NYBYpneYzbfMSoxlpUQxUUacHI5i8l4LDLqxIugnOTZQpBvLLIUoG6A6btVuzHo3AAL8nellSMgEWWf0MOxspfR2fZQSdNuawR4Sakls2ps9TxDJIZ3ZyqIpJTN2cXHBZGmSLiktRua+6xf+D6ThUDAsLwIjZ9Vo2XEYHeg4HS4j28RULDmKYclxEmgcAxGtZY9HJNCKVmxemmF2jkFRrXjkdVtWnNGbUFpJmHjPPkDDWVoGU/UShw5hHC8wUcEQJMyfz56zsz6KU8luS2cAw6igLD2I1Wh6YMlERbrxRKtyFhFYyTJ7xZdZVOBFSe4AcoeQOY69zLQ0Jwykx3y/t+f7Z4X//Z6i31e4nsOgl9Dv7f3/G8pyht0lOpdEA/JSp5dNYpKOSRdAZN2KYiRl0AVAEbKoIFunxeoCxYqBTRfUDOwAhunBtJIjxcfFKoEcPr/XHIWXYSJzCOPPvWJ7Bd+x3zkJo5qHo6nAeOowdAij31e88M6yA00TytKD3BGkRSdAeXpQLDcmw8VtF7x1CLbZyEYBxYnLcq8jBLm2gZJib8mzvfZdaQY9QySyUmd7fiYFGNKOpRxW+6weg003ZAyONNdxmd0qGylkFYYR/MCyGsu4B+PpgjdaWRjBDsowAyXGlJN1eYpgc5tilFCWGkxLIUrIRJQ4gFdk+ykzljiHfcugj0UHRpfBPDcZ9o86hGJUMHzPZMp9W0YGZVGBTQ8sTpCrFiUFcdNChDDiLAqOYNQJqBwXcLP7YN6rpNiXIwAzar3SjTNnMD1F2K/1+wrpiEzQZH+RgbmYiqxcOAQpZaxxpEknHEmOQ3iFmY2g0cLyH4zZ0r5IZ0cHORHJk5ZrDdi5DJiowfZajDsFs/HDVCGPFMacwiybVXYc4SGUvyUvMZZ8x55lRWv2gJdtx7QUofDV+52nOMsmKwOTPAIYBQ5HS5B7/x+3RWlxP5yC0r+CI7BMQydzBG7uBNLcKYy3J+/XnEQR9GO0LB+4+lKt31NIKeh2XtqVyxKNrHOAjMUoC7eOSRXimBGHkC3hUVJSITpIk8KFeSw6sKBhXlmwKYStQgw3EChUDYoOoHSHCrgBTAcK9+IhZK+PqxLvx/ZMF/ZTZhx/vxgCqLNsqrBJUfmogBdA+aIeX/hFpzFerp9lByaVPh4VzGIaFlmGOXhY6D2wC92mArZsKOxzY47g5VjQT0hdp7Sz8aVaFGqko/aFF8yyJNYTTkDG5rHrTTqEIiEptUxIaaIDwwEy2kbCqiLF6ag46lhlISchFa/0xaoBkBOQcgchyq+m42zDnJW4v3kLZWaxgrLZBaNtzYWZCfmTRQcFI+GYteJIt33YdNFTnb8+/lyZZcFbaYWgGAmMc3ZuS8xg2JhU3ohktAZHqwdWfzB3BAk4cZqH/0VGoReZLkQL/L1SRwDkWMFeZUgYgnrTrN9T2dX9lW9XFKphBGXVm1Izms31Ms5FBiK6SuSVhfyHLwyTTiKJ42pTXVAa6SozREQKcIZOsFhxyO9LYWTRiiYlOQ/BpgpFjAEKuckYiDiLX7CPUuO4A5hWTRh9XOhaLF617eNZVYOXwTkoezzuEOyiH4b+o7eWRzCsHphKgsXf7Nqy92fZwaUJqjw9KFMvKj43dARqhEzkFCoIVtTURgmvlr1a32WrA6+WFSOEMm0E15skJdlqgpSgbGVB6DxyyNdonHU2ZqlD3sQ0XlkoVh+GOzqFc6BGUwjbky3UxKKaOmFpRnWhyDYsqyS8qrbPvoTxCczD58vBxCKACKMlRRi9ypeF/+W8ndkYycFUE9ToLMVUj+oUWJwgBw3VUNGoyC4skomKt26SjgCG3/r9m33Qp4mbvhJLYo3jMEJiko7IGYtFczGNSrbEaKsLw8nUk9UFO8yVsnTBoxxILHIOZPH+HmpB4xWF16J3Idcy2Edvwsj2jL32CmycWDRN1XncithAkVtQBhyOY3Kz7MABxLKowKYHE/TjVCCzRT7uAIrEIieeBAxfDZslavJSbD8g+ks1G20UUxQptbkAOwKVQoLGyVIGYfs35Bh+kEUNQuqR6gKFUe/j0cEERdk6ArNh5KBicerSXiDjfhxAsZOxACLafHu8kpBPV3q5i7joEKZFBGPgYRkFuXRXprxndJEPqwfFyeXm8fRyYrF6N8sOvIU5Hht6UlQ4TgrlxDw9SIccAjkRGaR5GfHVdgRAPqp91nfvRUeGYmTw6m5jMV0w/48BDm204DK8X0wXhBzSlcGkC7YMKeWM6CDjHZRGCzAJKo6nB9aGjRL7cwDTuAYjpY1J+5YRjTIgZb8cg4n3aD0iZjJu6ZhDGIKGYvJCWwIoTrMDJx1NVA/G0oMi21AqNQEYDh1COoIZvBampcg1AqZZrPauRI0D5K+m2XQBhoSkoknH4AdJPEwXVKa/kJORrMKyMhFHHh0UCEdIMeQdpHJIQrL/UUmqMGxrHnutGCpZ/GAEGyhpWNrDhk1J06cgj2siTr5hRopQfE/xdoaV4QbFqoGpNoz+PyNrhdEqwniKYK08MpgshozbgUYGE4NSC1FBsYw4kh5YAlERMCw4glejjDjNlCNQjsCZQUJMNXvOV3gtMANrRXBSluAFRUBcOgKRZmPflW1asjiCHkknhoNcGY5pj7MOQEtOGuZ9o4udKZOZ8+f0yFtyG+9ReA0xhG+llWEFZbYX4AeTzqEMOIzVUFd0lh2IMxikw/TAlgyjcLKMaKciOVllwGoP2PKhk4GFXpjuq/vwlZqtUsyyWAnSaPaPGBeu3q+F2ZkLaZrxDDxBUHCynifwA4lKFUEg0EqilcLJogFXAUgcbRa56yuSCFxLbHbE6OnrpMMSo2OvtmSgoQDb8m2GRk6WGKUcRgSG8LA/otG0DsQMNyhGBzBcdPtmHo586RjvoAwzmFVN2CNctOCh1rpQSZjEC2Jlbm0p0T6fKDFRRjT3Rd4IuFfAfHAA4oguwSiIWOQTyGQ0NSimAq7tOfgWOAJgXx2OsHc1IQpVRhN+ba1YZhwvkdsUwo5+n4gQxvADc2HWhYgAkypk6YKWGpFVFbAVhlwA1d4vISAVKw5ibIFbpzAmljqSQkjGwng5FTco5RrsRwNxnHMw8/3kCxomiUblYqej51WRXzCeIsAoq1Dtcd+mB7ctz2A0FRhrTS4Ahqhh45FNDYrlw2LK8K2w/VCRpdibdATlfIBX25SyKklyND2QYqyxySBvwwtcgbac4QcqgVQIHDQioyoXGDFmgcciqzSIIb6g7BXfKaBZY6lD+cZP4gbTbEYUUaZ/WPo+MeYo9ktD3ie3wDyeXPTD+3v/X8XKAgwxAwscjvf4lI0dmGUH4gySsYUfF0qKRcDQidOJikGxfGhTh9vJfKlx/Nd+ob8US8bSEuusTIPT8LHFMlQiSLNzPC04CJkDi1kjk60ypDKvJFCKHahCBKAKFOYxfMFhWFkoW2RFMG8vLYOSoaevqb0EDGN6T8IQQJxWUhwHDsuwgtGy/W1ORx5WDxghFeVpwlh6UAYWHiSpaJZVHPCrt9d2KaUJB0DFPM5ThEIJ0j72JHlaYNmJUpqKg5ImbRBxFh3EwmAEsQJHmOggqyyQSpMqwPCszPgGI23NRdtni/LM52GixDgtOtjPuHTzxilRwvj/PyPzmwYSlvYqZHjBuBUXs9KTj8d5BTZCsPT/eA9g+8CcgcUFiulB0REUKwcjOEHh/mtVNXglNudBbf6VaR68FjbiEAYKKjIH9fNOxwSkMxzpbtmJ1jkopRGpEVbRsUI40pxpUuTRga0sjJQiixGCTQ/KcAOc4aIbAxEnKgr7sFEC0t6CqC/J9klDHrcyObMi89Bu7zS8YJgSiIkUYSJCUAVHoJipwAUHVU3omypBGDojlYModHDCNJcsy2/DFC9K8mEnt6MTANharXO62aZ2fwvYOOjNmTClNP1e1tKsIIkFQWDux/Z+CkFqKgwyyaoJWkNBxwAUUoFLArhZI1N2cmeVBdMIYaoOQmYLPkkLnX4lVYURPUSGKcM0EFEru1mjNgM/eNm2785EWw3QOXg45BSUg4eWX1B0BGWNSaaSYJqS7MIvVg/GKwj2b5CSr7NZdiDF2qJYSVnloIgRFB3Ba8kheDXs1n1NWkGCe6zOmTsrB705Uy2JzeTn4m3xL05MBUGlIlNyJh/gap/LiUlxio6VGSmXmjFtOjVOJI8Q8jQBbIdaPq3Z/oF5ny48Ltr4c3sBbtlpMgEMltjU9+yLbWid216bMx08LL6nqF8wTjQqzj8oiwqUNiMKTUowdBQjGqEz7ECcwUjzkWUbZp2IZRjBt7J8+Ers/D1btPwUlhe4477XvnT4SiwK1dAhJAXHkAydgl38Kskk5wqj4a1zKMan1iHkqFVUeJz/FSoKxQU+y8kXF/5+EP7XskuxaBMdlntfqEY7Fqe/v4j8j/ckFIFDpUeb/pQeFRe2TYDF2SLT7ECcwUg3op1FYJ3AmCZBDhbe5o7g0oVFLqwkVBwXMd9k6UzI0qq39wcP0OzY92igjENIJiOGPCIoRAX5NGedOQgbHeROQeWPtSqOcS9xBGXPj0sxZTY+P3H89Ql7CdHBK7JZwGGBZzALLyj2Iwz5BGVVhNEoIXcAahgVFNOH8b6fWXZgkUEeFZSkB+Ng4e1WPhy3Tiugcr/ijqbGlT7M1fEvLHLPA/63hFz0SiwZiwasgxhxCGXpgv1TjJyR+fj28ZrWSHRgc+ix9GG/VuYASpxH0aY5hD0dxSwZ9bJNYxQneLk2yiUwz40ChyW9BwVHMCQcWSKfuD0xg6Kika0cjFCNwzTXLrzdHcH2co0X713m3Lk2R2oJrvAhqOEcb7JyZsB9b6jRnHsNucev0Ewvw6gTsE5hHD8opgsjUYKNDoqpQnZ25lHDSLpQhiMUXtOFCAFGF7kucR4vxZF8C22/zMOimElxsdu9UoXKwWjPwejCt1iBfS0Zw+XiPTCDgyEd2QakBLyCI/CzaoHtN7idKwdgqgdX72xx9vw2Z5uaVpDiCBe8CizOUzlVZW0rBiq88EzIztbtV3KEIRuyVFw1YyhKKUhFVmIscBFyTkKqzJnoSeMQHAFeVlqciHHtwi8QkqDgIMYam/bLRCxaSVuzjQLycuN+04dpOgYMv/ulEJxmMQ9HKcWT+EAZ49AEZSJ3BBZDGEbgQ8B+lh1MabHnQqQJwiSfeBT0E5zYqBD7+xiGelAWVl12lmtsHK3TOhryxnM3ON9SvH4x5Ug1xZEewq/DyiL+A2ssy5vUF3q0lgO2bwVcuxSxuZF8S+jIL8VMRJCakybWxInMMATzOFWSQEmT2yqFyqYkaTstCYmrFJIYKq5ZwGFWerT6iVKYMiOQeR10miJkoexoHYQtKRb/xgVTpy3QPRiKLwtDKPu+sa8p1y8Y7VMYfb4ML8gakgrahrZBafiXNftlkUE/FXkJsZ9CmIi8VD/om7GAg75LFM12pAdDOsoiAqeACTixwg+T2xos7Nc9dpZrbC9XmVuNWFntc6SmWa1Aw1M40s3Gj7lQCaBRQ65UCToRjV4MeCSxh+sJbt1M9jUz4VttZpsk0jFzGKQ0t9FAT0YIdtSbY9iJqRAIbxivGmUkEy0gszPXGy8pOiW32rx/3LQqJx+N8w0mnAZ7lv5eiY1c6Uv4BfZ5czupWWCfL5YUgZHHQ0cxmhrk3IJ0GCUMq3STJfxZdiDOIHcCGanIzRyBqSDcnhhBWHVpL1bZXq4SLCmWVgYstCJWqpp5X1P3UiRePs0H10dUK8j5AGe5Sr29g9aCJPKQ0kOlsLlxezoEixnA8KovHU2YPRdIO2dBmPGKrsjZiTpWCGnSBO0VoO5cAyFzEgWHYDoZ89h4uCG5Oqua3q+wX3u1HcLL+K5xDCEnG40JnwIT+MB409HQKYwpIRe6gUfUxuPb1Bl4UYofjrILX0uFoldqvaZPdy5gd7FCsKRYWBww1wpZqhj6ccNTVB2FFI65akkJjg+VAFGvIOcj5HxAJYqIBxJwSGIXpTTtXV7x/IRX24YCKQopJVJqwsHwvusZyrJtaJKJQDqGpmwwhCwCsAvfU6aj0dOMYgfjGMIYniDGjsu4inKxhXncvsUCKNM6I8dblA1DcTRFHJdEt5PJx4HDIlZQ7DsYBw1tN3Bc4PFYLs8sOxBn4BYig9udZtyve3RaFbpNn7TlsDDfY64VsVjRLPiapgdVV+FJjRRZTiakGX3se1CrIJoDZCvAC1MqYYJWMB97mFFpAtjfANZvpRVBRVBIR2ZzIVXW02DER226kEiJkEY01c60wMvoy7ECKRGpMj0M42VGJUruZ12MZZgB7NGoVIIvwKsXHRS+YzxFmGYTmgUFvABGS4YKpgCHwxShyDa0zqG48PM5IxZEDPeeBnZgkYFxAMmrNvHotTAbEXSbPr1WwGKrT3MuptmImfOh5hpHEDiawDHKOrmAhpDgOuB7iKqPbPjoQUql10UlhgDSjI3zsE6h27m9UiTjEIZpgsUPzFxHjSw4ACEhzQBCIUFadMtJEZ5xADpW4JszWrhFZ2CASTF+Duy3Vp+XG5keKXyLrIgX7KVnMPr80MNMRgJiIhoosg1zglG28K1qmE0PotDBifcG5Q/EGVQ7MV5kKgm3I0bQr3v05gI68wG9OZ9KK+XIfIeFpZDFuZjVKhypahYDzUKQMuelOMIbjQykC0GAqNfQSYoTxbkkWCPoI90Y19NUGg6Vmk9nJ6W9K9neSm8rHGFYZcj4B4ERRokqmkpVUlUyW4u2skDWzAQeMShzqueTrx2BaW5KhtJoYBynUqPNTLJQWShUGLTOmqEscDhtVHuZ/oFdny8nQhivHuRy6NN/r9w5FCXNxvACWzWI1bCKYCoI5vkwNX+2ijBIoZsIBgl0k+y5vmsqBqGk33ezSoJL1Jf5FPHbNDIYpga3m/XrHoO6T7/u0a97uHVNrR5TbyTU6jFzHtRdTcWBwNG4UucpgkAYzCDHDdw8OqASICoJsuaiBx5BPyKNlaH0pg7gkCpIFfQ6tx+OYEFFixtIR+O6BlQ0EQKAQroixxOUI5BxioglWqYQS0SshhUGpYeLP0kz0ZNCO7MlII3/jfMNykqMe2oo8tIcwp6NSONRgC51EmUlxdGqQbFDcXhbrBxEGaegSDku8glsWTEKnTz6tmn5LDsYZ3CbOoKw6hpH0PDoNX1EQ1CrR9TrMZVqQt2FmmcETIwzUASOQgo9dASZCeGgs1RBOA7adYwjqLjImofTi/HjFK0giQQ1ZZWgsxwy5baKEIpDWqSjkaHKLsjSpAxuxlCMTeogHWEcQ9angCfNJOcsvhUTDUwqSxUskJgtrqJQasG0ThFaTjqAcfXkWepI+3UIJe8piwrGW5WLz88aowZFjYJxfHX0uSLtuFg9yLGCaIgVONk6s2n5XgD9gTmD2w0j6Nc9wqpHv2EigrjqMlcPqVQTKtXU3DpQdXTuCDwJnjCRgRAFvEDKYaogh9gBroOoeYg4RfQ8vDAkTUSujFRPHFTmI1VqpMpupwjBYggycwIWXHQ9gRtJ85ybIoQZ9uq4GSfBRgexyic7a6uO5I46BCwhqMhDgNFKQtHGxU6UmlR/nWV7OYSXmE5MG6hq7o+mCNbsoochkDiOFQz/RlWQbVQQhUYfJFccDwVBkuaVOy9M8cPZ5YQDcQa3myOwEUFYdbM0waPWMNFAtZbkUUF9JCrQWVRgUtsJ5V2bKrjO8M/3EJUYMXARFRcZxHiJMulCFiFUa+ZEtnT72y1CSGJNmDkBGxm4nqkwSClJoiwySARJZABF28VInKJjaRiJsTTAok0TXLfAORirMOzHAZThBvutQBRPR8HeKYGevuBH3zf7dxtlHZJrF6TZc0k2UdlWEfK/dDQqiCKZlxEtaGinkBcdwV5pgtDfUtXIQzu0Q7td7eBqMId2aId2W9mhMzi0Qzs04NAZHNqhHVpmh87g0A7t0IBDZ3Boh3ZomR06g0M7tEMDDp3BoR3aoWV26AwO7dAODTh0Boe2hyVJwg//8A/zhje8gWeffXbktU984hPcdddd/PN//s8PaOsO7dW0Qwbioe1pV65c4cMf/jDHjh3jk5/8JL7v8/TTT/NDP/RD3HfffXz84x/H2a9q8aHdtnYYGRzannb8+HH+4T/8hzzxxBP8k3/yTxgMBvz0T/80QRDwcz/3c4eO4P9P7EAalQ7tvz374Ac/yI/8yI/w7/7dv+Oxxx7j6aef5qMf/SjHjh076E07tFfJDtOEQ9u3hWHI937v93Lx4kU+8pGP8Pf//t8/6E06tFfRDtOEQ9u3PfHEE1y7dg2Ap59+miS5PSdEHdrLs0NncGj7sk6nw1//63+dVqvFT//0T/Pwww/z0Y9+9KA369BeRTvEDA5tX/Z3/+7f5erVq/ybf/NveOc738ljjz3Gv/yX/5J3vvOdvOMd7zjozTu0V8EOI4ND29M++clP8qlPfYqf/Mmf5J3vfCcA/+Af/AOOHj3K3/pbf4utra0D3sJDezXsEEA8tJn27LPP8oM/+IPcfffd/NIv/RKuOwwmH374YX7sx36M97znPXzsYx87wK08tFfDDp3BoR3aoQGHacKhHdqhZXboDA7t0A4NOHQGh3Zoh5bZoTM4tEM7NODQGRzaoR1aZofO4NAO7dCAQ2dwaId2aJkdOoNDO7RDAw6dwaEd2qFl9v8DzAXuLVPcwrEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1, figsize=(4,4))\n",
    "\n",
    "\n",
    "\n",
    "bx = sns.heatmap(u.T,ax=ax,vmin=-1,vmax=1,cmap='Spectral',cbar=False)\n",
    "bx.invert_yaxis()    \n",
    "bx.set_xticks([])\n",
    "bx.set_yticks([])\n",
    "\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('t');\n",
    "\n",
    "\n",
    "ax.contour((u*mask0).T)\n",
    "ax.contour((u*mask1).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = resample(np.arange(u_noisy[mask0].shape[0]),replace=False,random_state=0,n_samples=number_of_samples)\n",
    "\n",
    "\n",
    "u1_noisy_shuffled = (u_noisy[mask0])[idx]\n",
    "u2_noisy_shuffled = (u_noisy[mask1])[idx]\n",
    "\n",
    "xs = ((x_grid[mask0])[idx]).reshape(-1,1)\n",
    "ts = ((t_grid[mask0])[idx]).reshape(-1,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "scale_from_outputs = []\n",
    "u_d = []\n",
    "\n",
    "# scaling and subsampling\n",
    "u_sampled1 = u1_noisy_shuffled.reshape(-1,1)\n",
    "u_max1 = u_sampled1.max()\n",
    "scale_from_outputs.append(u_max1)\n",
    "u_d.append(u_sampled1/u_max1)\n",
    "\n",
    "\n",
    "u_sampled2 = u2_noisy_shuffled.reshape(-1,1)\n",
    "u_max2 = u_sampled2.max()\n",
    "scale_from_outputs.append(u_max2)\n",
    "u_d.append(u_sampled2/u_max2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = jnp.concatenate([ts/t.max(),xs/x.max()],axis=1)\n",
    "y_data = jnp.concatenate([np.array(u_d[0]),np.array(u_d[1])],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 2)\n",
      "(2000, 2)\n"
     ]
    }
   ],
   "source": [
    "print(X_data.shape)\n",
    "print(y_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_out = y_data.shape[1]\n",
    "n_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model initialization and parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiating model and optimizers\n",
    "model = pdeX(nn_shared_features, nn_specific_features, n_out)\n",
    "optimizer = optim.Adam(learning_rate=learning_rate, beta1=0.99, beta2=0.99)\n",
    "logger = Logger(path)\n",
    "converged = Convergence(delta=deltaC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparsity_scheduler = [mask_scheduler(delta=deltaSP) for i in range(n_out)]\n",
    "sparsity_scheduler_group = mask_scheduler(delta=deltaSP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializing\n",
    "variables = model.init(key, X_train)\n",
    "state, params = variables.pop('params')\n",
    "y, updated_state = model.apply(variables, X_train, mutable=['coeffs_mask'])\n",
    "optimizer = optimizer.create(params)\n",
    "# compiling train step\n",
    "update = create_update_state(loss_fn_pinn_multi2, model=model, x=X_train, y=y_train)\n",
    "_ = update(optimizer, state)  # triggering compilation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 0.0: 0.24307982623577118\n",
      "Loss step 500.0: 0.03499162197113037\n",
      "Loss step 1000.0: 0.026603106409311295\n",
      "Loss step 1500.0: 0.021631591022014618\n",
      "Loss step 2000.0: 0.019312314689159393\n",
      "Loss step 2500.0: 0.018082622438669205\n",
      "Loss step 3000.0: 0.017412319779396057\n",
      "Loss step 3500.0: 0.0170064065605402\n",
      "Loss step 4000.0: 0.016725802794098854\n",
      "Loss step 4500.0: 0.016520382836461067\n",
      "Loss step 5000.0: 0.016358759254217148\n",
      "Loss step 5500.0: 0.016221821308135986\n",
      "Loss step 6000.0: 0.01611865498125553\n",
      "Loss step 6500.0: 0.016020895913243294\n",
      "Loss step 7000.0: 0.015943562611937523\n",
      "Loss step 7500.0: 0.015871811658143997\n",
      "Loss step 8000.0: 0.01581309363245964\n",
      "Loss step 8500.0: 0.015745818614959717\n",
      "Loss step 9000.0: 0.01569184474647045\n",
      "Loss step 9500.0: 0.0156356543302536\n",
      "Loss step 10000.0: 0.015597192570567131\n",
      "Loss step 10500.0: 0.015552770346403122\n",
      "Loss step 11000.0: 0.015515277162194252\n",
      "Loss step 11500.0: 0.015476370230317116\n",
      "Loss step 12000.0: 0.015439918264746666\n",
      "Loss step 12500.0: 0.01540856622159481\n",
      "Loss step 13000.0: 0.015383634716272354\n",
      "Loss step 13500.0: 0.015354486182332039\n",
      "Loss step 14000.0: 0.01532391645014286\n",
      "Loss step 14500.0: 0.015308065339922905\n",
      "Loss step 15000.0: 0.015279397368431091\n",
      "Loss step 15500.0: 0.015254835598170757\n",
      "Loss step 16000.0: 0.015232203528285027\n",
      "Loss step 16500.0: 0.01520797610282898\n",
      "Loss step 17000.0: 0.015192459337413311\n",
      "Loss step 17500.0: 0.015176545828580856\n",
      "Loss step 18000.0: 0.01516246423125267\n",
      "Loss step 18500.0: 0.015144165605306625\n",
      "Loss step 19000.0: 0.015135684050619602\n",
      "Loss step 19500.0: 0.015115858055651188\n",
      "Loss step 20000.0: 0.015103558078408241\n",
      "Loss step 20500.0: 0.015092849731445312\n",
      "Loss step 21000.0: 0.015071852132678032\n",
      "Loss step 21500.0: 0.01506594568490982\n",
      "Loss step 22000.0: 0.015056117437779903\n",
      "Loss step 22500.0: 0.015041611157357693\n",
      "Loss step 23000.0: 0.015022548846900463\n",
      "Loss step 23500.0: 0.01501842774450779\n",
      "Loss step 24000.0: 0.014996427111327648\n",
      "sparsity at  24050.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 24500.0: 0.06434295326471329\n",
      "Loss step 25000.0: 0.03263642638921738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 25500.0: 0.02497282437980175\n",
      "Loss step 26000.0: 0.021227575838565826\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 26500.0: 0.01967553421854973\n",
      "Loss step 27000.0: 0.01881936378777027\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 27500.0: 0.018265120685100555\n",
      "Loss step 28000.0: 0.017877837643027306\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 28500.0: 0.017583517357707024\n",
      "Loss step 29000.0: 0.017357971519231796\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 29500.0: 0.017141161486506462\n",
      "Loss step 30000.0: 0.01694731041789055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 30500.0: 0.0168134868144989\n",
      "Loss step 31000.0: 0.01667034812271595\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 31500.0: 0.016472382470965385\n",
      "Loss step 32000.0: 0.016336023807525635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 32500.0: 0.01622789353132248\n",
      "Loss step 33000.0: 0.01614154502749443\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 33500.0: 0.016063444316387177\n",
      "Loss step 34000.0: 0.015999460592865944\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 34500.0: 0.01593458279967308\n",
      "Loss step 35000.0: 0.015873312950134277\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 35500.0: 0.015827208757400513\n",
      "Loss step 36000.0: 0.01576802507042885\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 36500.0: 0.01570749282836914\n",
      "Loss step 37000.0: 0.01565271057188511\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 37500.0: 0.015615908429026604\n",
      "Loss step 38000.0: 0.015584181994199753\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 38500.0: 0.015549683943390846\n",
      "Loss step 39000.0: 0.01552317850291729\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 39500.0: 0.015492882579565048\n",
      "Loss step 40000.0: 0.015463851392269135\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 40500.0: 0.015439700335264206\n",
      "Loss step 41000.0: 0.015415247529745102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 41500.0: 0.01538892649114132\n",
      "Loss step 42000.0: 0.015365258790552616\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 42500.0: 0.015346892178058624\n",
      "Loss step 43000.0: 0.015325132757425308\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 43500.0: 0.015309665352106094\n",
      "Loss step 44000.0: 0.015289178118109703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 44500.0: 0.015273761004209518\n",
      "Loss step 45000.0: 0.015261837281286716\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 45500.0: 0.01524124201387167\n",
      "Loss step 46000.0: 0.015222105197608471\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 46500.0: 0.01520470529794693\n",
      "Loss step 47000.0: 0.015187890268862247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 47500.0: 0.015172665938735008\n",
      "Loss step 48000.0: 0.015150008723139763\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 48500.0: 0.015128230676054955\n",
      "Loss step 49000.0: 0.015109819360077381\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 49500.0: 0.01509317196905613\n",
      "Loss step 50000.0: 0.015077359043061733\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 50500.0: 0.015054812654852867\n",
      "Loss step 51000.0: 0.015037077479064465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 51500.0: 0.015020141378045082\n",
      "Loss step 52000.0: 0.015005173161625862\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 52500.0: 0.01498963963240385\n",
      "Loss step 53000.0: 0.014982164837419987\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 53500.0: 0.014964945614337921\n",
      "Loss step 54000.0: 0.014954453334212303\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 54500.0: 0.014939627610147\n",
      "Loss step 55000.0: 0.014924122020602226\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 55500.0: 0.014915268868207932\n",
      "Loss step 56000.0: 0.014908455312252045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 56500.0: 0.014893345534801483\n",
      "Loss step 57000.0: 0.014884493313729763\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 57500.0: 0.014867572113871574\n",
      "Loss step 58000.0: 0.014849800616502762\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 58500.0: 0.014828776940703392\n",
      "Loss step 59000.0: 0.0148030836135149\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 59500.0: 0.014784421771764755\n",
      "Loss step 60000.0: 0.014770627953112125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 60500.0: 0.014760324731469154\n",
      "Loss step 61000.0: 0.0147485863417387\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 61500.0: 0.014740832149982452\n",
      "Loss step 62000.0: 0.01472496334463358\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 62500.0: 0.014713907614350319\n",
      "Loss step 63000.0: 0.014702045358717442\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 63500.0: 0.014689533039927483\n",
      "Loss step 64000.0: 0.014681769534945488\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 64500.0: 0.01467102114111185\n",
      "Loss step 65000.0: 0.01466227974742651\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 65500.0: 0.014654258266091347\n",
      "Loss step 66000.0: 0.014646574854850769\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 66500.0: 0.014636077918112278\n",
      "Loss step 67000.0: 0.014631430618464947\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 67500.0: 0.014619403518736362\n",
      "Loss step 68000.0: 0.01461469940841198\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 68500.0: 0.014600912109017372\n",
      "Loss step 69000.0: 0.014592427760362625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 69500.0: 0.014585128054022789\n",
      "Loss step 70000.0: 0.014573848806321621\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged at epoch 70100.0\n",
      "3915.2351863384247\n"
     ]
    }
   ],
   "source": [
    "\n",
    "t_start = time()\n",
    "counter = 0\n",
    "for epoch in jnp.arange(max_epochs):\n",
    "\n",
    "    # updating the model\n",
    "    (optimizer, state), metrics, dt, theta, coeffs, coeffs_mask = update(optimizer, state)\n",
    "    if epoch % 500 == 0:\n",
    "        print(f\"Loss step {epoch}: {metrics['mse']}\")\n",
    "    \n",
    "    if epoch % 25 == 0:   \n",
    "\n",
    "        # computing losses on test set, masks are set individually\n",
    "        loss_test, loss_test_exp_wise = mse_test(model, optimizer.target, state, X_test, y_test)\n",
    "        loss_test_metric = {'loss_test':loss_test_exp_wise}\n",
    "        \n",
    "        \n",
    "        if write_tensorboard:\n",
    "            metrics = {**metrics, **loss_test_metric}\n",
    "            logger.write(metrics, epoch)\n",
    "\n",
    "        update_sparsity, optimizer = sparsity_scheduler_group(loss_test, epoch, optimizer)\n",
    "        \n",
    "        # Loss on test set has increased, triggering the update of the mask\n",
    "        if update_sparsity:\n",
    "            ############################################################################################\n",
    "            ############################################################################################\n",
    "            if counter == 0:\n",
    "                np.save(path +'/theta_IN',theta)\n",
    "                np.save(path +'/dt_IN',dt)\n",
    "                counter = 1\n",
    "                print('sparsity at ', epoch)\n",
    "\n",
    "            # computing new state to feed model\n",
    "            mask,maxP_selec = get_mask_multi(theta,dt,method=sparsity_method,randomized=randomized)    \n",
    "            more_metrics = {'prob_selec':maxP_selec}\n",
    "            logger.write(more_metrics, epoch)\n",
    "\n",
    "            ############################################################################################\n",
    "            ############################################################################################\n",
    "\n",
    "            state = freeze({'coeffs_mask': {'maskC': jnp.array(mask)}})\n",
    "        \n",
    "        if converged(epoch, coeffs):\n",
    "            print(f'Converged at epoch {epoch}')\n",
    "\n",
    "            np.save(path +'/theta_FIN',theta)\n",
    "            np.save(path +'/dt_FIN',dt)\n",
    "\n",
    "            break\n",
    "\n",
    "logger.close()\n",
    "t_end = time()\n",
    "print(t_end - t_start)\n",
    "\n",
    "############################################################################################\n",
    "############################################################################################                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_params = optimizer.target\n",
    "new_variables = freeze({'params': new_params.unfreeze(), **state.unfreeze()})\n",
    "uu_train = model.apply(new_variables,X_train,mutable=['coeffs_mask'])[0]\n",
    "coeffs = uu_train[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def X_new(nx,nt):\n",
    "    x1 = jnp.linspace(x.min(), x.max(), nx)\n",
    "    t1 = jnp.linspace(t.min(), t.max(), nt)    \n",
    "    t_grid, x_grid = jnp.meshgrid(t1, x1, indexing=\"ij\")\n",
    "    Xnn = jnp.concatenate([(t_grid/t.max()).reshape(-1, 1), (x_grid/x.max()).reshape(-1, 1)], axis=1)\n",
    "\n",
    "    return Xnn\n",
    "\n",
    "nx = 100\n",
    "nt = 100\n",
    "uu = model.apply(new_variables, X_new(nx,nt), mutable=list(state.keys()))[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library and recovered PDEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_feats = ['1',\n",
    "r'$u_x$',\n",
    "r'$u_{xx}$',\n",
    "r'$u_{xxx}$',\n",
    "r'$u_{xxxx}$',\n",
    "r'$u_{xxxxx}$',\n",
    "r'$u$',\n",
    "r'$uu_x$',\n",
    "r'$uu_{xx}$',\n",
    "r'$uu_{xxx}$',\n",
    "r'$uu_{xxxx}$',\n",
    "r'$uu_{xxxxx}$',\n",
    "r'$u^2$',\n",
    "r'$u^2u_x$',\n",
    "r'$u^2u_{xx}$',\n",
    "r'$u^2u_{xxx}$',\n",
    "r'$u^2u_{xxxx}$',\n",
    "r'$u^2u_{xxxxx}$',\n",
    "r'$u^3$',\n",
    "r'$u^3u_x$',\n",
    "r'$u^3u_{xx}$',\n",
    "r'$u^3u_{xxx}$',\n",
    "r'$u^3u_{xxxx}$',\n",
    "r'$u^3u_{xxxxx}$',\n",
    "r'$u^4$',\n",
    "r'$u^4u_x$',\n",
    "r'$u^4u_{xx}$',\n",
    "r'$u^4u_{xxx}$',\n",
    "r'$u^4u_{xxxx}$',\n",
    "r'$u^4u_{xxxxx}$',\n",
    "r'$u^5$',\n",
    "r'$u^5u_x$',\n",
    "r'$u^5u_{xx}$',\n",
    "r'$u^5u_{xxx}$',\n",
    "r'$u^5u_{xxxx}$',\n",
    "r'$u^5u_{xxxxx}$']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "xi = coeffs[:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [-8.07370211e-01,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [-7.99634663e-01,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [-8.35063769e-01,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00, -6.94064863e-06],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  1.96356866e-06],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "xiS = []\n",
    "\n",
    "for i, vCoeff in enumerate(xi):\n",
    "\n",
    "\n",
    "    xmax = scale_in[i][0]\n",
    "    tmax = scale_in[i][1]\n",
    "    \n",
    "    scaleX = np.tile(np.array([1,xmax,xmax**2,xmax**3,xmax**4,xmax**5]),6)\n",
    "    scaleUT = scale_from_outputs[i]/tmax\n",
    "    \n",
    "    \n",
    "    scaleU1 = np.tile(np.array(1/scale_from_outputs[i]),36)\n",
    "    scaleU1[0] = 1\n",
    "    \n",
    "    scaleU2 = np.repeat(np.array([1,1/scale_from_outputs[i],1/scale_from_outputs[i]**2,1/scale_from_outputs[i]**3,1/scale_from_outputs[i]**4,1/scale_from_outputs[i]**5]),6)\n",
    "    \n",
    "    xiS.append( vCoeff * scaleUT * scaleX *  scaleU1 * scaleU2)\n",
    "\n",
    "xiS = np.array(xiS).T\n",
    "xiS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(path +'/scale_from_outputs',scale_from_outputs)\n",
    "\n",
    "import pickle\n",
    "a_file = open(path + \"/new_params.pkl\", \"wb\")\n",
    "pickle.dump(new_params.unfreeze(), a_file)\n",
    "a_file.close()\n",
    "\n",
    "a_file = open(path + \"/state.pkl\", \"wb\")\n",
    "pickle.dump(state.unfreeze(), a_file)\n",
    "a_file.close()\n",
    "\n",
    "np.save(path +'/X_train',X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['$u_{xx}$', '$u_{xxxx}$', '$uu_x$'], dtype='<U14')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(name_feats)[np.abs(xiS[:,0])>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['$u^2u_{xxx}$', '$u^4u_{xxx}$'], dtype='<U14')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(name_feats)[np.abs(xiS[:,1])>0]"
   ]
  }
 ],
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