ICs_IL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model discovery from multiple experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ICs variations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION']='.4'\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",
    "\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\n",
    "\n",
    "\n",
    "from time import time\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "key = random.PRNGKey(42)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "noise_level = 0.1\n",
    "number_of_samples = 2000\n",
    "path = 'logs/ICs_IL' # path to log tensorboard files\n",
    "\n",
    "vL=[0.1]\n",
    "vICs=['','_pICs','_sICs']\n",
    "\n",
    "u = []\n",
    "scale_in = []\n",
    "for viscosity in vL:\n",
    "    for ICs in vICs:\n",
    "        filename = '_n_'+str(number_of_samples)+'_100v_'+str(int(viscosity*100))\n",
    "        \n",
    "        x     = np.load('data/burgers'+ICs+'_x'+filename+'.npy')\n",
    "        x = x-x[0]\n",
    "        t     = np.load('data/burgers'+ICs+'_t'+filename+'.npy')\n",
    "        t = t-t[0]\n",
    "        u1     = np.array(np.load('data/burgers'+ICs+'_u'+filename+'.npy'),dtype=np.float32).reshape(len(x),len(t))\n",
    "        \n",
    "        scale_in.append([x.max(),t.max()])\n",
    "        u.append(u1)\n",
    "\n",
    "u = np.array(u)\n",
    "n_out = len(u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,3, figsize=(12,4))\n",
    "\n",
    "for i in range(n_out):\n",
    "    bx = sns.heatmap(u[i].T,ax=ax[i],cmap='Spectral',cbar=False)\n",
    "    bx.invert_yaxis()    \n",
    "    bx.set_xticks([])\n",
    "    bx.set_yticks([])\n",
    "\n",
    "    ax[i].set_xlabel('x')\n",
    "    ax[i].set_ylabel('t');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DeepMod parameters\n",
    "\n",
    "# sparsity estimator\n",
    "randomized = True\n",
    "sparsity_method = 'IL'\n",
    "\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 = 1e5+1\n",
    "\n",
    "# logging\n",
    "write_tensorboard = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input data with some noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "un = []\n",
    "for ui in u:\n",
    "    un.append(ui + noise_level*np.std(ui)*np.random.randn(ui.shape[0],ui.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_grid,  t_grid = jnp.meshgrid(x,  t, indexing=\"ij\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig,ax = plt.subplots(2,3, figsize=(12,5),gridspec_kw={'height_ratios': [0.25, 1]})\n",
    "\n",
    "\n",
    "for i in range(3):\n",
    "    \n",
    "        \n",
    "    ax[0,i].plot(un[i].T[0,:],'.k',linewidth=3)\n",
    "    ax[0,i].set_xticks([])\n",
    "    ax[0,i].set_yticks([])\n",
    "    ax[0,i].spines['top'].set_visible(False)\n",
    "    ax[0,i].spines['right'].set_visible(False)\n",
    "    ax[0,i].spines['bottom'].set_visible(False)\n",
    "    ax[0,i].spines['left'].set_visible(False)\n",
    "    \n",
    "    #ax[0,i].set_xlabel('x')\n",
    "    ax[0,i].set_ylabel('u');\n",
    "\n",
    "    \n",
    "    bx = sns.heatmap(un[i].T,ax=ax[1,i],cmap='Spectral',cbar=False)\n",
    "    bx.invert_yaxis()    \n",
    "    bx.set_xticks([])\n",
    "    bx.set_yticks([])\n",
    "\n",
    "    ax[1,i].set_xlabel('x')\n",
    "    ax[1,i].set_ylabel('t');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = jnp.concatenate([(t_grid/t.max()).reshape(-1, 1), (x_grid/x.max()).reshape(-1, 1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = []\n",
    "scale_from_outputs = []\n",
    "for uni in un:\n",
    "    y_data.append((uni/uni.max()).reshape(-1, 1))#[idx])\n",
    "    scale_from_outputs.append(uni.max())\n",
    "y_data = jnp.array(y_data)[:,:,0].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 2)\n",
      "(2000, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_data.shape)\n",
    "print(y_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.1958143615580346, 0.6168733930375413, 6.748982723014706]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale_from_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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, shuffle= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f80582a2550>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_train[:,0])\n",
    "plt.plot(y_test[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8058207100>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g80GTawQamcDtsqMuxoWJgHkv5VS/oYx5HkUJd+TexTjxfD2z5BeMeepr2OvXLGrs0usHnhmHSkHgchl7vjNjTcPw+KLv8ff3/N8vcN4g2/lgkvfpbrzxn32iPsKCvZNZD3zrvBlUVWrZe6hE2ghFRwxTVZWWliItLY0XnpKSAgCSM46EhARMnToV3bp1Q3h4OLZv346VK1eisLAQq1atQkREhFHZVozcjKHThbUAptX/Eq90Xm0POz3mdDxJNNblFtZLqAD/2yhNuuAYs3OdQbos5EZt3245gQ4tGsFqZY9j9HQ1roQT567gxLkrAX2mWeF+qkv1u94ddWxhHjR1kx8oEXJewUFh9Q9HcfqCwBG9gURBMTObJNcsGmNwZgwUHLW1tQgPD+eFR0a6vavabOKboaZPn876nZubiw4dOuCFF17AV199hYkTJ+qb2QCidODy076z6CfgiNbfzYSO4/vwVOI3WO0YCJq+USB9N+u3Hkc05UANLeQNV+wuYbyqKuHr3+85g9ZNEzBuSAdWuJDgOHj8EmKj+fUqVNBzZK6kLtU5XQiz6qdY0HNiETRLJVWqKgofrDlodJZQWeNAHKde+2qqzCagDVNVRUVFweHg20AzAoMRIEq58847ER0drcic10w4aRoWoXoqW3eVVBRvnNvnrFHkXsBZ4T7bI40SWdyvz9eU2F/wStJKZc+XeRdKbo0DwOWr/IEE03i/2HwE/1d/KNDct3/BY3nyawg2hxPbflO2+1mKSxU1Kpw+BhYlFlK3Pb0mADkJLbhyQ6j6MrNjq2Dj1QZN04L+yn47ehF3Pvstdhfxz90xq+dpwwRHSkqKoDqqtNS9rT81NVVVehaLBWlpaaio0HnHq0aUfs73vipQlp4C23IAyAi7KPh0e51L0hST+yAK0qOYXpEn5dNS6OSQaXtSqie7gGrO6XTH/+ibQny7VUF+fFj6dQFe/miXx/pNbfMrKavGhbJqzHjBjzPGBV73v5uPaEsLwLbfzmHy/LVw1DlR54dFmNn4Zf9ZbD3gv5DXE6buWXQUHBt2/IFpz6/HsTPlrPDf/3DX0YMc/3BHz5SjTGfHmHphmODIzs7GiRMnUFXFHgXv37/fc10NDocD58+fR1JSkm55DA7Kppxi1fXRhO88f1vt7IUwJZuFnBfdfnoscAlmhZ+GeH6Vqs2Yxie1OM7VnQPwq3O8UOa2+Kqs3/l77mIVFn66W/H997z0He59yV3WQkJNK2KnOSrh/a8P4mq1HZcqak23WOvPRrV/frIbCz427sAyLkqsqow4a+bAEbcxxpkSZQvYr322FyfPX6nPj27Z0AXDBEdubi4cDgdWrVrlCbPb7fjyyy/Rs2dPz8L5uXPncOwYe6drWRlfjbJ06VLYbDbccMMNRmVZHX67Z1KaAD8eLXKrXB2vPf077IU/AgDq6lw4cV5+IViPZuPdxyE14+ALCbWL4/e/vBFPvvmT+5n1Yb6P/GnfWVXp+Yu/bX3TrlP49+oDnt9WBeXoD78du4i5b/8ieM1sHRfDpYoa1OjlLVqXVPx4voJCNovmyrDF8W7duiE3Nxd5eXkoLS1Fq1atsHr1apw7dw4LFizwxJszZw527tyJQ4e8PnOGDBmCkSNHIjMzExEREdixYwfWr1+PXr16YdSoUUZlOSAwdcNe58TXPx8T7Bxdtmr0jZRyG6Gt9tRd8Xr/pOCqt8KgMSJ6P/baWrvDeUkrmHHI1GaLAnNcu4A7ErWC4/ylKo8prHekGOzuQDtv/GcfAOCB23IAeIvZ5aIVbaZUyx4BHbsX4fUsLQJFyadZu+0kDv9xGX++Q9rDxIwXNqBVejzefmqo+ueLBsCU+yde/mgnCk9IbzwOFIa6HHn11VfxxhtvID8/HxUVFcjKysK7776LXr16Sd43evRo7N27F+vWrYPD4UDz5s3x8MMP44EHHkBYmDmOSdc8Nadp/DXxK6zfnYO19rY+wd5WVL79a/5tejzbB0Z1G005kBt9AAMjDwFwW7MlWbxTaakn0Ypi+ZjjSvR2QteM3sdhNGIjSKfTxTM9luKHvWfgcDhZKr9gqapEv7TCKrntt/PYf+SibLwl/3WrtOUEB+A+I0MNStRPTBtb5I9bIZ3Z9ps27700Teu+yG5oLxwZGYk5c+Zgzpw5onGWLVvGC3vxxReNzJZh2C6cZP2Oo2qQauWog2gX0qxXcFfsFuzxERxqRsZiMZXqTgH++oQVrvpwIJLyTv37Rx4BHMILdExVtJadgKMsFeHJTQXjnS11zwK4skHOxFCXneMmnHCMfXoNVr86WrGZLNN5tUqPB+AuR6PfS7VgUhj95Y92qs+MzijpQvXoZysqbThTUonObRv7nZbZ1rSIW3UdOfv+bNbvWQlr8eeE9awwi1N4/4rYLIL2/Ev5RhaEezKZo6IEJflvwuXgP5MRHF4XKN4/fOvoxFgpd+TuiBE7l+H0/z0qEc+N1IzDLLrbQFEnYAzAQNO0Z1HfF1+Vn9qOZPNucU8G+T8dw1c/HjOt6ae//LD3jLId1expvd88s2QL5r79C89aKtAiwAiZQwSHCtKt5Yij3D6rlLSxJlZ+Ze2683mR2N6vay85KZmu1KNdDhsubvgArtoqXPzmHVQW/ISTr04WTcNbqbyuSLRy9fRRyetcweErLIUqtz8V3qznGAjx2zG26mbDjj9w57Pf8uKxVVXqnvH6in2i197PL8DSr5WZjYsiU2+2F5zHxL9+wwqjQWNnYbF/z1XAouV78FjeD57flAITWz1EKLPbXMzgQA1ynzvQMxJzLBiECM8kfo1qVziAOwxI3fvhqw/v8gkVsKqSqNYV27/GlV3fICyuEaSqm1dVRfn8Xx12hxPwcQJb+skcxM/7QjS+02UedxWBaGiSj6Dc5Xf73P/xLu35Xdgdj0dw0PyS2/t7CXpmq9sbFUg++t9BQeunfywNzAFbvibVYnXd6aKxveA8+nZONzQvegglbv1V7rBUH8iMQyUxFn1OBOMZdoh83SlxW9AuTPmorO6qe1psiYzh1Sbfn5SPEozLqBjx0ake1NrqMOOF9Th8qtzvtGpsdRg9O1/CQ6xwwQrt0tWbl2T0+Wp3pDMDZRfNV1X9TQfnjL477bmlJuZTTTnmUYNx34H5uWHHH3jpw53YVXTBULWdER255EDIgEESERymQfzjDo6S9yTsQcIvlO+OVUpANUXT7rlMTgR7rUT0USqbABO76GQZLlXosyO2vN5Vydc/s82X5SzPuKPfA0e9psonFexvUcLR0+XiFyWKTmyDGnuNQ3k+lM6uGCMG4TSk7xUr7cp64Sj0SluCdMa3nEyouGqTjbPvUAmuVtux82AxbA4nvth8BIs+M9ACi1P+XMEWYH+gRFVlGnT68JSlXndEu3hJsqbr9fXOsyYO9XpzIWiaxn82HMKgni0ELrr/+fv72/1/kAxyaxzccMb8EwDKrxrv5oGGW4AKItJpaTXHVevOXk9e/HAnXnlkoOAIft22k6zfH6w5iNZN4zG0dytD8yQ3qKAVxHnu3W2ICLPAXufCTX1a4budbuOD2ZOltxoA7tNDY6PCcF0n5Sox7vfee6gEF8qqPGXle33Z2iI0TvSelmrE1yeCQyP+T2W5Qwjpzyu1rsGiXnDQLqfkMNEioKra/fsFVavjQjOO8qs2fLbhEL4TOvSnHqGObMfBYk2uq+XWScSuSt0ViO0jNE3jn58Iu0AR+wK+O/DVCHlNO81p967s6to6tEyLx3kZq6Tj5yrQr0tTnm8n5psqqVarf3AbV/gjONybI2lBU+cjpy+jQ8sk2bwoLS57vWWc2BklYuwuuoDdRRew9NmbkJoUo8xbLydPn288DMBbVr7fmLnGQKyqTITeGlAptY8alRBFuT/pnkK+GsA3FQtc6BFxwrNngwKNMxcq/X4v5hlC5qY0Dfxrpfj6ycOvbvbz6W7+98txj+pJdGQu0ZoCcdqb1CRATFXl+y5qZhxa9sPQoDHjhQ2ebyKXxMrvDvPUhQB/Zms0b67cJ+oR+Ik3fsLPvypzO6NmXKi1urxS75+L+ZY2h5TzSumHBHpWSQSHBhzlF2Bx6bNI7kFV7ZPqddyftOBoiWS8DuEXMCPuZ0yMcauNKABWKyXbYOylpzEpZiso1qnqynLodLk8U3q9EFIp/Hv1b6ixqXVM6E0nIMeESj1DpGCZW2hanfpBS6eipAgqq+2stJev+11USAVqjwh3LxOXs6WVvDrOf1d15eVbBtW1Do+DTTmOcNbA/vfLCTz7zlbBuFv2S68HSX8vsjhuCk6//TA6/fEffROV+PLcJnf8TIV43PoZBwV272IvPYWUA95d+lGUW/AlWbwLokq8hl744lVcH3UUqRbhBWQmhXKB8zWMgFFVVVSqs1CSVlUpb2ijZ+cLbtYTCmM9Q+IRSlyMq5lxFF9S1pGx0pe5XmOrw53z12LHQa/FX63diW+2sK3b9HCPIzST8Qc5IUbT6gSd0+dbPLNki8erMv/B/KBLFTU49Mdlz2/uZkEGsSMTmD1AUvWBqKoCjMtF45NvC3FZYLG08VXt5yoA4B3uJFVRuaqqH3ad4MV5odEqt6v0+jWOaMoO3+Z/eetXsnmyWinIKRUcl87V51c2OR5GjuP99ZDq+z5qR+hCh/M8vuh7yXukGnqdU/ia5x5aXWfw59d+UB7Z+zDB4M83HUad0yU6qi7jWssxqio/5IfYmTbVteLCmSkroXKWXeNQEEfoWQBw/Kz4oE6oATyy8HuW8FXLX5dsAUCsqkzFb0cvYtWmI1i1yT8hoQzlX/7J6P/ywhItNYijaj1WVcOiC1HroxUqr7RD6MBVZtE9jHIhoq4Kzog4RXmgQKs/xtbPyn3b03znj7KPVL/EocsIrfRyjeT1h/6pfj3HN1tGq9PEOqIf9pxBdqsktKz3m8VFbPOiHjMPX5588yccOnVZ9LrLRcNqpXjf8nTxVX5eOJF2FRarOjdFaKCh9CyZKpmZqVIkZxy6PIENmXFI4FTQOK3Q6ZAfHToCCoBL5JMqGUU3PvEdoi4dko3nfpZIehL9g7+dndhIXAvchubPjEMLmo6j9Uw4VC5yaEDoYC0Gu8Q17h4YiveH/5y+cFVSaADeusYtpp9+Pct3Dc+Jo/awLaH6IrhAb+Ayj1TbMsJLAplxSOC7x6F7xEnWNWak3juCrzbSgh4fN4Kqw8HNm9BC4Ksq6bQTz/yCxDPK/OpYlBsIewnCdgKlbk18i0fJgMEXPdd9nRIjVd93MXrG4WsaLSRk1T5ezzISsr6rtdUhKtJb8Z0u2j3DFlJV6ZcVd3omcA5JrKpMBFMfros4hhlxP7OvMTuvdeoNpTo4Csr63NtjdqJFmPBITKxeac2/+z7+vVJnLQTFN5UGVZXaRqhnxyE10ve1qgok3OdRFKV4EKDFHNem4ajefYdLWb+ZbyiUTe738rc89TyX3BDI4nhgYXShcRZjdxInW67C9eljfqfTyCJuPSPWFzYLK9f0LLEZx1c/invI1TIoulguvVYgh9gj128/yfrtj6pKz25DSg0UyDUO1nMl1HqKMXxUzs6jGhf+/g5ozCA4Aj2YIKoqCSgFYjU3er98JBnk1F2dIs76bTWhx6FIvoj1A5ImvRqyIHSkrD8cPVOOWa//yAs/fcG7+1et2vDHvWcQEx2OP3TwceWQel9fvX0AO4qVnJ3ISsy2vWj3vuwPTH0X+pR6L9SbwXd/oL3jEsEhgRIVRJJVvY08F4evb3IRlA1qAmfLbYFLUM0l1aloGdn5q7vlvreSPRJqR/OfbVBmUKAEKQEfrBlHwTH23gKjJxxa1vu4t3jLR4E5rp9FWWvXd3CjN2RxPMAw9atdmLCJob/cGPUbTtU1gYOWFxxKkGqcScX6Htkp7k9J/B4t9dfvmRLnoUpGy8F0CijVifn2hYEc5HIFvpKDkDxxPWscyu/R491cEjMOvSccZlgcD/T6IREcElC0E53Cz6BLhPCuzQxrqWC4UkbXn3uxorK/X+kEAwq0oJCQ0vdqGfnIduIqk1Sijw6m4kHy2T4XhcpSy6KyEgQXx9Vi9IyD89sptTguc69agi03zpZWIlzh+fV6QRbHJQgr+B8eiBffqPVE4lpdnqNEVWU2xKyqpDoVLQ3Ub1UV57cZFjKlkOo0vXsThMeXf170gyF54rrBsFDKR7gU51/D4KqqPDMOdXVUC6eK5b06r9p02LARyYOvbEKNXdxzghGzUzLjkMBy1RgVFRcnrY/81ss0WOmzhJqfZJvUkD1ZXb5KF9nBHh36A9s7Lv+6Wvfe2uHvyJa9Q0XBaxkrcAWZZ8AhtDgu6+RQfz75VsVhbBpYZnD6XMiMwwRo2EoXdG6J+RVW8E1HpXTZWhZ0Zdc4ZC+rX+MIJlJl5OkLaWMWPJViUb6Nw9NLqyp2Hd7N6aJRXevAHc9+y89SCLY3OXxP9+RCrKoCTmAa5zTO5sJQoHWY8EY/vRfH/V6o5tyuRHAE1bpScmOiW1C/n1+AuGghz2OBQc0GQGYfjuEzDpo/yDhVfFVw4MHPSvDNaf3lotRRzMSqKrAEalwSRulz5FwgVVWieZAstMCb43JH8P7ot49KjOr0QuptmTPBA6eSEoai1M0etxeoO1u8olKbS/7l67zqGjUzMhNswwg5iKqKoCsRtLjzPi0yQO+NixY/avyBI/5Z0SnhVLH/mwiNRq3wPVPCPzxJCk0nQdJgebGWqjVmMJ8NJEbIRSI4JAmtoYgZmsPAyg3iF4OgqtLFlDSAvPzRrmBnQRYLpW5E73S6DF9bUrOPweRVQHfIQU4BxgyqHzWYIb+RtLiuVcsmpVMX5E0dpeB2cP51YNdYjyMCRamraXq6wxeD1zlKPFJvJ4fXIkRwSBFiFcoM3ZpUkWlpoKf9FBxc/NvHEWIVwiAope6a63G6XAEf5UuqqlTFDn2Iy5GAE2oVygz51dfJoVydl0uSexa5og5M5KHHJM56v5agKEpVZ+TPCZqjZ+crisebcEjk71pTVRkBmXFIEGpTWDO0B6k9KVr2cfi7xrFx1ynW720H1Fn4+PLTr2f9yktDwaJSVRUsvvrpmGC42de5QgEiOBoQZmjOV6rErao0uRyRETZqu4BfA2AZ1dDRcgKg4XAyRNPAlv3ynpAFbm1wkMXxgBNaNcoMAylpJ30aZhwy94TWF2oYnC2tRFWNhjPTDUSNqsrOcQZJ6pB6yBqHFCE3FDFDfqVUVepTo/XZG+k3wXTxYTaM9rukBe7nkfpa3+1kqy93FRbrnyETYYTLdTLjkCS0OgsTTDhgpaRcexsw4whQh07khtlRITk4FJ4o0zcrZoOoqgJMiHUWFhNkOCtcfPRmhK8qIzp0oSQDeeIegWB2DBUcdrsdCxcuxMCBA5GTk4OJEydi27Ztiu69cOEC/vznP6N3797o2bMnHn74YZw+fdrI7PIg6gl90XSQUxC+QZ2Trx8jdcHc8FVV5HsxhJzLkblz5+Ljjz/Grbfeinnz5sFiseC+++7Dvn37JO+rqqrCtGnTsGfPHjz44IN4/PHHUVhYiGnTpqGiIpC29KFV+cxgVSWFlr43GB320q8P8sKCeZosQT1Eznsxog0ZJjgOHDiAb775Bk8++SSefvppTJo0CR9//DGaNm2KvLw8yXs/++wz/PHHH3j33Xdx7733YsaMGVi6dCkuXLiAjz76yKgs8wmxymd2waGl83XJLI4HbI2DSA5TE0hBcXPfjMA9zKQYJjjWrVuH8PBwTJgwwRMWGRmJ8ePHY8+ePSgpET9db/369ejevTs6derkCWvXrh369++PtWv1Oa61IWKGxXFp9FdVSe0b0ROyxmF22N+nxiZ+lKq/REWG3lHPemOY4CgqKkKbNm0QGxvLCs/JyQFN0ygqEjbpc7lcOHToELp06cK71rVrV5w8eRI1NTWG5JlPaHUWMRZz2dZzMWJx/JklWzTmRh16u3cn6Au3bj37zlbDnhVqJwiG1AbA0tJSpKam8sJTUlIAQHTGUV5eDrvd7onHvZemaZSWBmj3Lxll6oqW0nTK6aoCxN7fA3P+PIEQChgmOGpraxEezj/eMjIyEgBgswmf8sWER0REiN5bWytxTCLBvGgQxJevajsNTm/ylu8JdhYIEgRyiGcGDw1qCKkZR1RUFBwOBy+cEQyMEODChNvtfLULc29UVJRe2ZSBzDj0RIu251Sxvm7VCQSC/xgmOFJSUgTVUYyaSUiNBQCNGjVCRESEoDqqtLQUFEUJqrGMgNju6wwpT4JBkKolTki5HMnOzsaJEydQVVXFCt+/f7/numCGLBZkZmaioKCAd+3AgQPIyMhAdHS0/hkmGA5p2wTjCFztCjm37KGkqsrNzYXD4cCqVas8YXa7HV9++SV69uyJtLQ0AMC5c+dw7Bjbb/7w4cPx66+/orCw0BN2/PhxbN++Hbm5uUZlmQ8ZxugKMWklGEUgq1aIiQ1DMMw7brdu3ZCbm4u8vDyUlpaiVatWWL16Nc6dO4cFCxZ44s2ZMwc7d+7EoUOHPGGTJ0/GqlWrcP/99+Puu++G1WrFRx99hJSUFMyYMcOoLAtAOjq1SK1jvPcVfxZJIIQaZMJhsFv1V199FW+88Qby8/NRUVGBrKwsvPvuu+jVq5fkfXFxcVi2bBlefvllLFmyBC6XC3379sW8efOQlJRkZJYJfhNirYrQIHj7v/uDnQXTEnJnjkdGRmLOnDmYM2eOaJxly5YJhqenp+Nf//qXUVlTBplwqIYUGYHQ8CFu1SUh3aBapM4cJxAIDQMiOKQgi7kEggeLhQwKgNCzqgqpDYCEaxMiahsurZsmBDsLpiDE5IYhEMEhAW2WA69DCKKqariQDjM0CakNgA0BiqiqCAQPFiI5QhOiqgowLuN8+jdUyIyj4ULkhptQW+MwAiI4pCCCQzVkjkZo6ISa2Ai5M8dDHcrlDHYWCAQCgYcaC7eQOnO8QUBmHBoItfEYQSlERWMeRg1sE9TnE8EhAUUEh2qIPUHoM+L61oLhRGzUY4KCUHN8bXSk/g5CiOCQgqiqVGPk4jjZRxAYxg1uH+wsmJpgnzkeZqVUGSo0TtT/GAoiOKQgMw7VGDnh6NKusYGpExiI2a00ehTPhGEdNN/72MTu/mfAT4jgkKAuoVmws0DwgajBAoPVKtwzkjUO/VCzuN2pTbKBOdEGERwSVGWPCnYWCD6Qg6ACgxl9UmVlmOc4BT1Kx6pCCKc3jkVMlO86RfC/DxEcEpB+Sj1GrnGQ7xEYxHT4ZMKhHxHhVlXxuXU/2LM/IjgkIP2UeowVHOSLBAJTCggzfXodCqhTG+XrdTRNs+q+Gb4PERwS0FLnoBICjot8j4ARHxMe7CyYFn877i7tGoNS0fPSNJCRzrYoDLbsIIJDgqpae7CzQPCBTDgCR7BVITxMlB1/s2KhKFWWazQN/O2+fkhOiPTmIcjlQQSHBJU1jmBnIeRwGtjCyeJ4YKAo4X0CpPjr8bOK53Rooio+TdOIj4lA13YpejxeF4jgkMBFjuNQjYs2rkqRNY7AIbRATspfHyYMzVQ34zAwL1ohgkOChthQztclGpq+08Aq1QA/hymhKGFVSDCLvyoIs38xTwX+7hy3WCjZacPNfTM8fzP9kBEHMmmFCA4J9FCNVLsidMiJfrx2ZaSh6buuIXPczFaNDE3/uk5phqYvhbCqKngf4ExJZcCf2dHAjXdyMw7fy7xSpygE206ECA4J1LaTcle0z98xAICK+n+DxQlHCt69OsTz2w6+tcwlZ5xuzyOqKv24d0yXoDyX8vk/AHSr18kbVfypSfK+lLJasTcAPnBbV4RZje2+xLp2PRam5dKorvVxd0Rz/gWwq7DY/0z4AREcEqjtqFZUXe/520EzG3yC29nRAA46WsrG4fJzbZZg3A+u/kkyLbMvjg/vlyEfyUBen/UnvD/vJkVxg+ZMj6Lgu3mcyYdRNTkhVn5W3oEzu2ucGIXwMP/LZ82iMaLX/LUss0rswJdLOzkhyvM3t95T4AiWIEAEhwRq+6la2tsA6uoFh1D9WF7pFjDVrghcyB6vOX9K0Lohb4stUyQ9aVwmX+O4rmPw1D8A0L5FI6QlK5uFBtPkkvLp9Dz58PkAA7qp9+M2pFcLwXAlnzUYjhfFZxz+50UuhcE9W2DSTcJtEHB7yA0mRHBIoHaE6xvdAXGXAn/Uuaf+Rq4HMGjta120aLORvE/rjGNAjnxHpMeMQ6jR39Snlaa0Aqk5e+j2HLz5xGDJOE9P7S35Ww2jBngPCmKEiL96db/c4vt8tqyMJHTrkGJ8+RvYPGVlDwW0a+42ZBHSfFgNVtPJQQSHBGorpu/o3quqko5nNAUyaipAXX7kLJSdPmsc8+7uozhdJTANaHBP4ZGrVh6Z0F3X9IwgPiYC8THSKh22Izzghu7NNT8vt39rz9/MaJ/VgWnqtEV8YCm60xsr7/FBiIkK3s52PVqv3KzFd51JqB8K9l4OIjgkULvG4RubmXFQQV7j+L62k2ycYqfXRNdBuRukuDCRrrK+qqqmjWPlM6gC5nNoUZN4EMi+lC7aLNA0zRul5rRnbyTT7T1oWrBj8xVcWkxDLX70NlrMg0Phu4pB+awzMfWe9lwLvoUhERwSqBcc3orKWBdZOWP05y7fzrnL/8pd4ZKySpFPnwaFIw637r/GEusJK7TzR6xysxPfGYdSVXA3hTtpGVWVWn13SlI0mjRyl5HSO5s2jkWjuEjBa9yRPQC8/hdpowEt+HbeQmqibh1SWL/1Ul9wn9U7OxXNmsTi/rFdcc+tnQFo67jERtlKktKyriB0S3SkOq+0StMND1NX9nJ9i3svDaMipFn3UKCC7kWBCA4J/JlxeEYHnDgVdKwq1VBEmvyh9M+Xj8OZOu3nFdCg8O7VoXipfAwrbwcE1FxyJeJrWSbV2H0X9xrFRYnGYz2bZtKVjpfTvgmemX6d53efTulolR7vydPtQ9rjUT/UU+kCMykllkG+TL5Z2GpNFIEZB3fUr9cIm6bZNTQ+NgL/fuZGZDRNQEqSdvNyf9aUhW7VklzrpnpsgOU/ee706/AsRzXr1/tS0psEg22aTgSHBLToArFIfIEvLauqkht5WORHSHqsmdgRjhKXt1FRoNEsRf3+jgra27FINRxfG3ylDYwZZcmNPps0isb1PovtzVJiMeaGdgDcVk0zRnXGDd1l1F0UZHsm1pdT+QmSEpQJS8FniQTqta+BptlJ+5a3PzWtVVq8bJxBPYTXZfTaye6bTptm/p1hf+ugtuiVnepOF+Ctu0g1beZSy7Q4/HvuMH4+IV3Wwd7RRASHBGqng/ePzfH8zXTmQoKD3ShlElXQq4rlUqlAEbr/zdlDMPZP7XnhaizBpDoy7tSeGT0/OC5HKLo7jkuZqop7edSAtuiZnYo1i8agUbyw+omXhtTFALVa3zwIjTC5IVIzjhm3yK91MVgslOj5D8zfal3cPzG5JxJFVH++r9a5rfA5FVpUVX+7t5/k9X/NHiJ5XQwmK74L9kL58y2hlqJCkxIeoFHeWayQ+XawjxgggkMCtdNBq09n6F3IEk9DL+sqf9PxvZ/JbUQY5fcRolaJ1VClOuEu7bwdia+qSuoENaZBx0aF4cbrWgm+h5KOSM3bG7JZzydJrsPNob1b8ka0UoJabP1jkIDlFVftxi4rbe/pe1dcdDiemNxTMJ6YtZQWb73dM1MF0tH3OyntIVwuGkm+gxbOjQseHsD6TQHIykjG/Hv6YubozrxbiKrKxIh9m8hmHQTDw8O8nVlUhHsBVaiaSnX0u63CDUoakfSUNhJRnYvAKEqh+i4+JsI7MhO4hdXJsYfWrHgLHh7o+dt3cfyLV0YhI114FMc8b/k/RuLxSd0V5VcsDbNA+1g6WS0UZt3Zk1dWUoJedMMYJ5gRyHqpp4RonBiFziIn4GW2aiQ7U1DDPbfq77ZFS91wOF14f95NWLXgFsG0oiPDOOHuC306pXsHST4Lp8RXlYlRK9Xbt/Q6ReuW6bZ4sajUa+wK740vqq6TjXexhX5WPLTID7nptxhd2jXGZ/8Y4TOl59MqXb1+2TPjUFhrrRZKdISpaO+Aig5CbWeiNr6QepPpPFqlx2PxU0MQKTELE5uNcNV+Qh0ZS4hwTEQ1I/H+vQV292vdOT72T+003acHvjm2252ICLd6BpT+FJ/bHFc4hZSkaCTGGe9YlQgOCdSucVCsEZ/77/gI/pY5vucZNj/ZOnr+bjL8XsFntW0mfJRk+h3PSqYtjDdeFGOuKPru9Ws3EdGIyhAezXHVNkKdd4tUr15XLpftWzbCbYPb8xbHjT2lTkHaBqsLfAUB29LJ/deferrVTM9Mvw4Z6QlITY4RXSMQm3FIzVJ61A9+WGscTH40dH3K1IPKZkbuTGjJg5q4SuqA758cKzcfYW1zCPuWYp7AfZacmljsrKCn7uqNN2YNlrxXD4jgkEC0XopcYH185u86oeNnWboZiWtAZDP+AjUrfSk0aKriYyIFQr14FscpCs2mPI9md/8TRx1uXXKJk6068nbw/HQsFIVxg/nvJvTU1//yJ8wc3dkzypJfHNdLoLDTGTWwDdKSY5DWODAejxPjItGsidv018WaCbr/bZEajzWLxqBFqrfcb/FxFeKL2Iyjazu2oBGqmRbWjEO8bP81e7DoNVCcBXeVCrBgOHzkjurjosNZThFp0N69FQLZ813Hs9mdkmlzkXtboftjosLQsU1yQNSshgqOK1euYP78+ejXrx+6d++OadOmoaioSNG9c+fORVZWFu+/iRMnGpllFuIf1xse3thncVGhDkWXcapY3vxcyLTGNXLfaQ1TJJyimrVHsdN9zw+cXepSt1OU19KEoijB15mSm836rXQfh6KGIxNHaITeIzMV78+7SXBh36jGmsNs8qNpeGW2+MPEhKrY4viw61ohXUwQim1Ggv+TLbFXUBOuyRxXZwHkLSJ+ur71RMhCEZD4loLvS/v8zeb5+/rj478NBwA0TozG3aM6i+ZZD/hbYHXC5XLh/vvvx+HDhzFz5kwkJSXhs88+w9SpU/Hll1+iVSt5x3LR0dF4/vnnWWHJycYdrsJFTHCwgsV06PXhycOmo+7KRVzZ9Q0/HQC1cc15YYpQNIv2RjrpTIWVFp4u77a39fyddtsTqDq8E+HJzWAvOc2Ly/jgEnp8+xaNsOWYkLkiBaVvxpRto/hITLqJvUlO685xtXTvkIKHx3fDM0t+4eSN4/vBIO4b2wUHj18CANx6Q1vsPFiMfl2aeq7LCWQhxGYcFEUhOSEKxZeqedeYjoo945DLvQYUlKdes0ihZBLjIlBRydcMqHomxRemEfWCY0BOM9w9mt2RZ6QnYNSANhh9Q1sIISWYKVA8c9y4mHDP+gkAjBvSHh/+76Dy/KvEsBnHunXrsG/fPrz66qt49NFHcdddd2HZsmWgKAqLFy9WlEZYWBjGjBnD+u+GG24wKss8urRLkY/kO8sQmHGEJTZBk5tnit5uS2yFuZcnqc6b+MhJQF0GoPXMBci7MooXu+28L1Do8DoNtMYmIqGH+7yImKw+SOw/lhW/ko7ipc3QsU0SOycS7c6twxXam8C4VeAjpRZgP1eBLl0izvRRndC0ib5+ttTQr0tTPDPdvQu5ZVo8Pv7bcCQlRHm+uZT6m3kv7uFIWtxwCzrXE3J4qADWPJiiDDcmUMOnz4+QVrPVo+aNh/fLwF31M2ZHHX9BwmKh8MC4HM8eDu77Cd3D8jHZUM1x169fj9TUVAwb5t0VmZycjBEjRmDjxo1wOJSdIex0OlFZGfhjIwGge6aYDyXhzVGc1lEfVahz9Ea0WijU0Mo2pSlCoIEN6NYMbVs0Up8URSGxVy4rrIYWt9hgXlXJjEDIUod9nR/Wqd6Es1G89K5rf/sYivMvY/YbqKYqr06RUlW5/+W61pDa4yH2LaS+Jw2+CakUzVL8E8RGq+0jI7T5sGI2lPoaMnRt1wSPTuju2fBY55TzKc3fvOmoc4rEdMM1xw20IDFMcBQVFaFz5868kV3Xrl1RVVWFU6dOyaZRVVWFXr16oVevXujbty8WLFgAm81mVJaVw/pIYkUoXtWZ25Pio3jlo3wzH433rw7GTpvwVFcJdgnX71xsdJjszIi7BiE1qu/ctrGMnpx/74xbOuGducPkD0Lys5fh5tvfGYz650uHS6qqPGdncC18NMw4GFGpcN1BrAzen3cTOrRMErwGANECTiP5zzJOVQVAmbpMIOyhcTl4dEJ3dPI5n5x5Rk77JhjUo7mkNwSGlmnxuH9sV48xRF0dP0PjBreH1UKhc9vGuFOtrzOdMUxwlJaWIjWVv3OTCSspKZG8PyUlBffeey9efvllLFq0CAMHDsRHH32ERx55xJD8akaulQv2jvo0gt8crbC8aiArSd2tT+rfw0lTGNC7A9p7Zi7iK6ZMI/cOothlMHfadeiZ5a0brDxLNGCr1YLmAu4ZHhnfDfNn9vVJTx5FpcQV6txRnpI06okIt6JPp3RFcWVVcRLXBM/OgHDHK3x4ljeed8YhkDeBlxcb9XIFve9mRoB94JTovhsDFujlSE2KxoPjcjD/HnfdEnpcTFQ4hvfLEMx3eJgVT03prUjtSVEURt/QFv944HrcMqANMjP4gja7dTK+WngrGsVHYvzQDujftalASoFB0VzT5XIpVi1FRrqnZ7W1tYiI4Ks1mLDa2lrJdGbPns36PWrUKKSlpWHp0qXYsmULBgwYIHJngKHkOr0A6yINVAY/Pqk7nDVXcer1f7PC++c0Aw4f5mfB04mx04mLlj+ER5G1cX2czFZJLF9AWkan44d2wH83H5HJi/i3pAD0zE7F3t/ZAyKrhYLTRePv9/ZD1/bK3MfLIfV+YuMVf2oFyycTvIJJLM2M9HjUOWmcLVWmYk5W6fCR4bbB7bBqk/A3E0PNwOrWG9rBYqFQfpWr5RBJQ4emnpoco2iGEmwUzTh27dqFnJwcRf+VlZUBAKKiomC38y0VmLCoKPWVZeZM9yLztm3bVN+rK/Wt0hqfDHYlEhqGuf+JbtsNla5IXiz+KFZhxVY03GKnNfuuXlj81BCVz/LVf1OCDS8pjnkvzgY9kRSZzU3+bz726cR8HnbrIPXqu+m3dOJ5S+Vbh7lhFjS553U8f19/fh4NEOTSVlX1qiqX/IxDKD32Gkf99xToJQTn0Qrf1V2P+M8DJOpMfURf0+FpI5U7btQCU08DsS8i1FA042jbti0WLFigKMG4OHejSklJEVRHMWFCaiw5mjRpgvDwcFRUVKi+Vw9eqxiBOXd1A3auAACkT5qHi9/8n3BkZrRd38Sa3vkclryzFfcNTsPK/J2sOHogmBInkDly9Xj9b786bkFDAE4UkddLTnQPGoT2ZSQnRqFPp3SMHyrsD0wJzZrIu4P3R/Vxx02Z6NQmGSfOXcEeZoYh+in132EtvY/D/S93jUNogbtnNr8N+sbzfB+WpR4rI6L56NKusaIZh9IWQHneS+ENKh+o6JyRYB+7ZyIUCY6UlBSMGzdOVcLZ2dnYt28fT5954MABxMTEKNrHwaW4uBgOhyOgezl8+cOZAmvzjnCxHTp5/xbaGesT9o8H3YcceQSHAH7LEhUJqD1vRD49+X0Wi58cIrhOwWChKI9OWQ16vol3psGG+ZRWqwU9slJx4twV2bS8Wz+Udzpi7+J1uSJxr8I1ghUvjkQsZ1G6Y+tkPDK+m+S9Ulo7xgR4/NAOGNCtOdZv/0MioyrDBdqTFpjkuZtLpbw1K/USnd0mGZ3bNsa9Y/R3rGg2DNsAmJubi/Xr12PTpk248cYbAQBlZWVYt24dhg0bhvBwr56bsbBihInNZoPD4fDMXhiWLFkCABg4cCBMg+xWV35F5yy5sn/53ZmrEBya0hW/y6uqcv/2LNT6xMlomsC6w/OXHx2CtjuVL6KId/ryLjQ8kzF5i0x5hGYAHJgyd/JUVex4vutMTHpTRmSzvg/NMXbwTUfIj1tMFNslx0O35yBVYCTvXh9h1EDK6mvLNHdfoNeYv33LRorjKm1RkeFWvPKIifomAzFMcAwfPhzdu3fH008/7dk5vmLFCrhcLjz22GOsuDNmzAAAbN68GYDbIuu2227DqFGj0LZtW7hcLnz//ffYtm0bRo4cieuuk/ceaxQUKB8VC8VqkdZoATffKk1OlTYMJfFsTbKBEvGK7985HkK6nvorFCeKjLVNRtMEFBy76F8WjFhL4PzW0mlZrRbUOZ1w8Gz5paYNwsG0zHXAZ/1IgVUVL30RqzGWKrF+ITuzVRKOn5VWGY+8vo3sM/lrHOyAr/NuRWWNAzW2OsE8qsW7gVHVTf49tAFimDmu1WrFu+++ixEjRmDZsmVYuHAhkpKS8MknnyAjI0Py3oSEBAwePBhbtmzBokWLsGjRIpSVlWHu3LnIy8szKst8BGqXuw75eKgJc1uJNZ3yPCxRsdyIqvFfVQVcdbkbd3mn2wWjbK11rx/USmzmE82QRIvzdjTKRpPdM1Ox6M+DcKuI2wWz4FU7ssN9i0LsVRkvwEo2gcnhVQUqicv+La3ekn6eb2feKj0Bbz05BFNysyVOtZOGvXNcfj0nPiaCZQjhF360L+bJfTq53b4P6dnSv7yEMIbNOAAgMTERL730El566SXJeMxMgyEhIQELFy40Mmv+4VN5U0c/hopd3yCqFdfCQ161w0lKEZQ1HLRTyjTaVyEtvMHvu9ouuD7qCC65lJ8pzmwgs8TEI67rYCT0Gs6PxNHBK2mjma2SWL+1Ck61t6kx99XKin+MwOJV+3HsTIWgCwlRROoErWDKEVO/bsEYIHjvUCJthJ/HtapqXa/Omn9PXxw+dRl/f2+7fNoiKC3jiHB3JmQ3fwaAZilxLJVcsPAti0Av2xsqOBo8FBCW0BiNh00TvAZAtWSQUh+1fOgtVB3ehbJNnyhKq1tmE3TrcIlntljmiseyygEocjRHrsi9Xtz5YdxLUJQFqbc+xonCnv579dfeKPNn9jXO/5PBrUbL2ROAe9Mfs+iqRnCInQMjtCGPS5tmiXjyrl64rhP7MCQtQtA7rxYmPiYCvbLTBBfUtSKWz8S4SPx1Rh/R80YUp68w3qAe/CN1zaawmjayI7768VhQnk3O41AJRVE+wkCqKulfzcKTm3mHfxJngjDWMlERYXjxwQG8Dvu5e/pit70dqmhtG6/E4OrEPfpkAH06p0uqNhi30x1bK+8YGPcMUZFW9U7zFMXhmjGoNCegoElwSKUHyLvo+FPPFryzuxWtcYhMOeTuze3fGrn9W8umL5QfNftc+ndtyjsP3ZflL4xQnJacyuupKb15YWYzxg0PsyKzVaOgPJvMOFTCquaSlZ4ZhQt1GDTvr/N1iWgaJr3Y6H6knAqMQliYBS6h86PquU7E9cU/HuBvYFMj/7jmuGr68s5tG6ue/j8+qQeG9GqJFqnxujh5U5uGb2yxDpBxLlgn47SOnQ/h8MaJUZh+Syfc0J0/GpbDnxmHkW7s+f6utKclJVQYbu6bgT2/l6BNs0TF6ZptpmEGyIxDLazFcT6tn/wUrZ/6VFkD8Imz+OrNSJ80D7SPQjk8Rdvim9bdyt0z1W/KrH+i+x+vrorJCCtYb6Ijw9Cnc3r9o/Rr3pTIIo3696AwtLf7G3oOZFKAmKqKoiiMH9pBk55fS/l4xjwG9JzcIo6QOC9dLxrFReL6nGZYs2gMmjSKlr+hHmbW2Cubfxb6tQqZcUhAWfnFQ4GSnLNaIqM9MQEont9W0tGIad8TwA8AgDmX78DKORoW4HRv5OoT1DLjCDgqOlIxqyq55CjK7ZhO7UzKCEGryapK4mwUvWCeHR5mgd2hfFamhWXPy6/oCRERbsX7825CcoKOxx/oRDCO1AXIjEOSqIwuSB5yl3gERa1RzKS3/qpIJ1FLR8ASpsBclp+6hnv0SZdr9MO85+1DRM5NNxnpjd1rJszZCt4vKPyRcgScFv7fnKF+50NqF7NWlNQK3j4O3/1KhuXHPIOMfz4qvnkvLTkG4WHGz4r0pGd2KrIEvOzqAZlxSEBRFBpdPw5l3y/3CQNULZMJSAZX/e5wwQ2DitMF/jqjDxonRmH2mz9pT0cGVZ0GT1NFmcJsUQiht5p1Z08cOHrRY0wg9+q+psRM1Bap8bwwpTw6oRuS4qM0e4uVQtLJoexeCr1z402U4gwyjBBSSmEOCmsoCDnd1Asy41AJRQHWOLevLMoq4R5cogFcoWOwqqoP0ifNE43TsbW8P67+XZt6NphRPtu20yc9i/geN8Eaq3wBUBcodx6kTpszM7HR4ZJnHGg1y1XzfGbNRm+U9MfctzP0VDmaqwYzTmBMGNaBd5QuwT9Cs4UHmbRxs5Ey+jGEN1KwmCzS+H6xZSMsoTHEZi/3jdXuKC2yaVukjHwwYKO3xL6jEJ7SEl1vHIlbBrTBjFGdA/Jc4+GY4wp8Kq97FZFFDlVP0/973VfvcE/SMkpEq2qkVZVXrcmZeej+JLf79aXP3mxAytcuRFWlkOp69xwURcEam4j4nMEyd8iZzWqE04j5jTrwU/3wRmloef8bAIAHx4XGdF+vvpCC+ez7fbl1UDvcOqgdzl+sUn2vou1KGhExwCNoJcCVkMw4FND6yU/xz5o71N3EsVCVgznGU3n74bQ8gu7w3HQpieR7Sdfc+Ie2jtk4qyqa557GTKVFkIMIDgVYIqNhr5+cKa3elnC36R5llbbEYARLbL2ba3k5I9Ob6T10a6BDQb3VeEKphUrR3XFTFmKiwni7kI20qvJioI6KYBhEVaUWhRU8adAkUNYwBSqt+mR1VJ0YwjXYsBU5QjQ+G7og1fl3btsYK1+6hRcegLVx3tqGmdV+BC9EcKhE6ZTaEhGN5CFTNKSvEObc83qvtRHhVkCPw4K056jBYq23EpOaVQivjZun7LTkhKtO0hPuoV+eA6/qnzltZEfeme5SvP3UEF3zR5CGqKpUoncj8neEFR5mxT8fHeg1NzRRZxXqPDguBxnp8Xj27j649Ya2GNCtmUCsEClvDdnknq+iJ4mxkRjauyWeu6cf8xTW9QnDMnFTX+lzewDg1kHus1xapbtdvd/UR/2R1CFNkKofERwNgE5tGvucixwiHZlBDO8n39koJad9ChY/NRSNE6Nx39iuIbs/BdC4+Gyg/YXFQmHWnT1557GoVY/dN6Yra5Pp45N66JE9ggxEVRVkhvRsgYPHLmFKbkdWuJH65YZK/sJbAz7hCpUJnjbvuIGz3POqqox/VkPipj6tcOiPy0hrHNgDrojgUIne0/aoyDA8OaWX/wlxDeOvQSxKzlQ1CDOtZwjhmz0lXgkA/sFcRiLnFyxQLH32ppBaoR/erzWG92sd8OcSwaEQrhWI0Yg9hznX3BLNOfbV5fYsKukGRRMh1IqCgNbqECw5ExsdjgUPD1AUl3swl6GYRPCmJgX/aNpQgAgOlQSqeotN2eO63ADaYUN8DtuKhK5zn0NOhektOBjM0bBNh0YX8n07p+Mt/XMjCjMjCrdaPFZisig8AVAPZM8nI5gKIjgUErABkcyDKMqChJ58vzu0s859XcOMY+HjNyAu2iiB07DRWi8S4yIx8cZMfL7xsL4ZEkGLKshAjyM8mGeIHWJFMBdEcCiEDui8XT20k5lxqD/DIztDmc6bII7puzsti+MBlRz6PSTv8RuUz6oImiCCQyXBEBvJN06H/fxxyTheVRX5pIFEqD7ERoejqsah/l4DKxezwK1qQO9xfR7AxXEdJHAWGQgZDullVBKMCUejvrfKxvGoqjSdGnjtMm6wPqcT+p5d8casP+HwqcvyNwWwLmkxd3UF0CBkeL8MfPJtERLjSP0NBYjgaCA0nfJ3XN3/AyhLaB1vGUz0OJ1QqFNNbxzrOYZW8l7TGxwY53KEy/ihHXD7kA5BNakmKIcIjiDw0kPXIyZKeDG6X+d0HD1djsaJ6o4PjW7VGdGtGsoBSqGEBhUQc6ePnyaahupvrg0Vi+MBXLihKMqsy4cEAYjgCAI57VNEr00YlokR17dBQiyZsocCGenx+P2Py5pGyswdE4Zlom/ndJ77DT1hTGrVCAMjfVURQhsiOBQTmOGXxUIRoRFCPHdvPxw7U47oSO1NiaJgqNBgnqEa4oyAIAKxWbtGUSyciF29JPExEeieqeDseSE8K9b65UcOVUZVCNwGQEJoQWYc1ygfzL8ZtEtFN0I6D90JpNzwfD0VA4H+XZth3baTiIogBhcENkRwXKNEhpPOINh49y4EQHRoEPwP3tYVk4dnIcoPNRyhYUJUVQRCkMiu91LbqU3jgD1TjYiyWi1Iig+EpRch1CBDCYU0T4nD0TMV15ydORXpPlkwocdNQc5Jw6NbhxR89o8RiI8x3hji2qq1BKMhgkMhf7+vP46cLkdUxLVVZJawCLR55nOAIpNTIwiE0PAlmLYOyQnu2cuQXi2ClwmCLlxbvaAfJMZFonfHtGBnIyiQ3eihj3eJI3iSIzEuEl/+czTCrGT+E+oQwUEgEAJGeBiZuTYEDBMcx48fx3/+8x8cOHAAhYWFsNls2LRpE1q0UD5N3bt3LxYuXIjCwkLExcVhxIgRmD17NqKjo43KNoHQoCHbcgh6YJj4//XXX7Fs2TJUVlaiXbt2qu8vKirCjBkzYLPZMHfuXIwfPx4rV67ErFmzDMgtgdCw8bgcCXI+CA0Dw2YcQ4cOxa5duxAXF4ePPvoIhYWFqu5/7bXX0KhRIyxbtgyxsW5Poy1atMCzzz6Lbdu2oX///kZkm0BokDAqomG9WwY5J4SGgGEzjkaNGiEuLk7TvZWVldi6dSvGjh3rERoAMGbMGMTExGDt2rV6ZZNAuCYIs1rw+cu34P7bcoKdFUIDwJSL44cOHUJdXR26dOnCCo+IiEDHjh1RVFQUpJwRCKGLP44YCQRfTGniUFpaCgBISeG7H09JSUFJSUmgs0QgEAiEehQNQVwuFxwO+TOUASAyMtKvDAFAbW0tAPcMQyh95jqBQCAQAo8iwbFr1y5MmzZNUYLbtm1DcrJ/h8VHRbl3mNrtdt41m83muU4gEAiEwKNIcLRt2xYLFixQlKDWBXFfGBUVo7LypbS0FKmpGs8/IBAIBILfKBIcKSkpGDdunNF58ZCZmYmwsDAUFBTg5ptv9oTb7XYUFRVh9OjRAcsLgUAgENiYYnH82LFjOHfunOd3fHw8+vfvj/z8fFRVVXnC8/PzUV1djdzc3GBkk0AgEAgw0Bz36tWrWLZsGQD3LnIAWL58OeLj49GsWTOMHTvWE3fkyJHo06ePJz4AzJo1C3fccQemTp2KCRMmoLi4GB9++CEGDRqE66+/XnE+nE4nAKC4uNj/lyIQCIRrBKbPZPpQXwwTHBUVFXjzzTdZYR988AEAoE+fPizBIUTnzp3x4YcfIi8vDwsWLEBcXBwmTpyIJ554QlU+mHWSu+66S9V9BAKBQHD3oRkZGawwig7IuZXBo7a2FgUFBUhJSYHVStyDEwgEghKcTidKS0vRpUsXniVrgxccBAKBQNAXUyyOEwgEAiF0IIKDQCAQCKoggoNAIBAIqiCCg0AgEAiqIIKDQCAQCKoggoNAIBAIqiCCg0AgEAiqIIKDQCAQCKoggkMAu92OhQsXYuDAgcjJycHEiROxbdu2YGcrZNmxYweysrIE/zt27Bgr7t69e3HnnXeiW7duGDBgAF588UXU1NTw0iTfiE9JSQny8vIwdepU9OjRA1lZWdixY4dg3E2bNuG2225D165dMXjwYCxevBh1dXW8eFeuXMH8+fPRr18/dO/eHdOmTRM9ullpmg0RpWU/dOhQwXaQl5fHi2vmsieHEAswd+5cbNiwAdOmTUNGRgZWr16N++67D8uWLUOPHj2Cnb2QZfr06ejcuTMrLC0tzfN3UVERZsyYgfbt22Pu3LkoLi7GBx98gDNnzuCdd95h3Ue+EZ8TJ07gvffeQ0ZGBrKysrBv3z7BeD/++CMeeeQR9OvXD/Pnz8fhw4fx9ttv4/Lly5g/f74nnsvlwv3334/Dhw9j5syZSEpKwmeffYapU6fiyy+/RKtWrVSn2VBRWvaA2w/f9OnTWWGZmZms36Yve5rAYv/+/XRmZib94YcfesJqa2vpG2+8kZ48eXLwMhbCbN++nc7MzKS/++47yXj33nsvfcMNN9CVlZWesM8//5zOzMykt27d6gkj30iYq1ev0mVlZTRN0/R3331HZ2Zm0tu3b+fFGzlyJH3bbbfRdXV1nrDXXnuNzs7Opk+cOOEJ++abb3jf7dKlS3Tv3r3pp556SlOaDRWlZT9kyBD6oYcekk3P7GVPVFUc1q1bh/DwcEyYMMETFhkZifHjx2PPnj0oKSkJYu5Cn8rKSsEpdGVlJbZu3YqxY8ciNjbWEz5mzBjExMRg7dq1njDyjYSJi4tDUlKSZJyjR4/i6NGjmDRpEsvp5+TJk+FyubBhwwZP2Pr165Gamophw4Z5wpKTkzFixAhs3LgRDodDdZoNFSVl74vdbhdUwTKYveyJ4OBQVFSENm3asDovAMjJyQFN06I6RoI8Tz31FHr16oVu3bph5syZOHTokOfaoUOHUFdXhy5durDuiYiIQMeOHVnlTr6RdgoLCwGAV85paWlIT0/3XAfc5dy5c2dQFMWK27VrV1RVVeHUqVOq0yQAW7ZsQffu3dG9e3fceOONWLlyJS+O2cuerHFwKC0tZendGZhz0K/V0aw/hIeHY/jw4Rg0aBCSkpJw6NAhfPDBB5g8eTL++9//ok2bNp5zU5hy9iUlJcVzGBhAvpE/yJWzb9mVlpaiX79+vHipqakA3OXcrl07VWle62RmZqJ3795o3bo1Ll++jM8//xzPPfccKioqcP/993vimb3sieDgUFtbi/DwcF54ZGQkAMBmswU6SyFPz5490bNnT8/vYcOGYejQobj99tuxePFiLFq0CLW1tQDcMwwukZGRnusA+Ub+IFfOvuqT2tpawXhMGJOWmjSvdbhGHuPGjcPkyZOxZMkS3HnnnYiPjwdg/rInqioOUVFRHv2hL0xnxHROBP/Izs5G//79sX37dgDwHBRjt9t5cW02G+sgGfKNtKO2nIXiMWFMXDVpEthYrVZMnz4dNTU1LEsss5c9ERwcxKZ3zJSQmSoS/Kdp06aoqKgA4J1qM+XsS2lpKavcyTfSjh7lzIQxcdWkSeCTnp4OAJ62AJi/7Ing4JCdnY0TJ06gqqqKFb5//37PdYI+nD592mOJkpmZibCwMBQUFLDi2O12FBUVoWPHjp4w8o20w5Qjt5wvXLiA4uJiXjkfPHgQNOeQ0AMHDiAmJsazl0BNmgQ+p0+fBuC2mmIwe9kTwcEhNzcXDocDq1at8oTZ7XZ8+eWX6Nmzp+CiLEGasrIyXtju3buxY8cODBw4EAAQHx+P/v37Iz8/nyUQ8vPzUV1djdzcXE8Y+Uba6dChA9q2bYuVK1fC6XR6wlesWAGLxYKbb77ZE5abm4uSkhJs2rTJE1ZWVoZ169Zh2LBhnnUmNWley5SXl8PlcrHCbDYbli5ditjYWHTv3t0TbvayJ4vjHLp164bc3Fzk5eWhtLQUrVq1wurVq3Hu3DksWLAg2NkLSf7yl78gOjoaPXr0QFJSEo4cOYKVK1ciKSkJjz32mCferFmzcMcdd2Dq1KmYMGECiouL8eGHH2LQoEG4/vrrPfHINxJnyZIlAOBx5ZKfn489e/YgISEBU6ZMAQA8/fTTeOihh3DPPfdg5MiROHz4MJYvX45JkyahTZs2nrSGDx+O7t274+mnn/bsXl6xYgVcLhfru6lJsyEjV/abN2/GO++8g+HDh6N58+YoLy/H6tWrcfLkSfz9739nmZebvewpmjsXIsBms+GNN97AmjVrUFFRgaysLDzxxBOszougnE8++QRr1qzBqVOnUFlZieTkZAwcOBCPPfYYmjVrxoq7e/du5OXlobCwEHFxcRg5ciSeeOIJxMTEsOKRbyRMVlaWYHjz5s2xefNmz++NGzdi8eLFOHbsGJKTk3H77bfj4YcfRlgYeyxZUVGBV199FRs3boTNZkPXrl0xd+5cnusYNWk2VOTKvqCgAIsXL0ZhYSHKysoQERGBzp07Y+bMmRgyZAjvPjOXPREcBAKBQFAFWeMgEAgEgiqI4CAQCASCKojgIBAIBIIqiOAgEAgEgiqI4CAQCASCKojgIBAIBIIqiOAgEAgEgiqI4CAQCASCKojgIBAIBIIqiOAgEAgEgir+H/MopO3xwOF5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_train[:,1])\n",
    "plt.plot(y_test[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f80581e0370>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_train[:,2])\n",
    "plt.plot(y_test[:,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model initialization and parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss step 0.0: 0.5957659482955933\n",
      "Loss step 500.0: 0.0026311385445296764\n",
      "Loss step 1000.0: 0.0016453958814963698\n",
      "Loss step 1500.0: 0.0016290920320898294\n",
      "Loss step 2000.0: 0.001617330010049045\n",
      "Loss step 2500.0: 0.0016058182809501886\n",
      "sparsity at  2875.0\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 3000.0: 0.026310894638299942\n",
      "Loss step 3500.0: 0.017495451495051384\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 4000.0: 0.003023250726982951\n",
      "Loss step 4500.0: 0.0025583745446056128\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 5000.0: 0.025668857619166374\n",
      "Loss step 5500.0: 0.017293261364102364\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 6000.0: 0.0026189228519797325\n",
      "Loss step 6500.0: 0.0022514485754072666\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 7000.0: 0.004113590344786644\n",
      "Loss step 7500.0: 0.002480483381077647\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 8000.0: 0.0021711806766688824\n",
      "Loss step 8500.0: 0.0020358003675937653\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 9000.0: 0.004499257542192936\n",
      "Loss step 9500.0: 0.0023567487951368093\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 10000.0: 0.02683054842054844\n",
      "Loss step 10500.0: 0.01356496848165989\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 11000.0: 0.0027102313470095396\n",
      "Loss step 11500.0: 0.001717698760330677\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 12000.0: 0.002589843701571226\n",
      "Loss step 12500.0: 0.0018579012248665094\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 13000.0: 0.019144538789987564\n",
      "Loss step 13500.0: 0.009689778089523315\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 14000.0: 0.002797456458210945\n",
      "Loss step 14500.0: 0.0015986092621460557\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 15000.0: 0.001936158980242908\n",
      "Loss step 15500.0: 0.0018271238077431917\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 16000.0: 0.0017158917617052794\n",
      "Loss step 16500.0: 0.0016511387657374144\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 17000.0: 0.0016136281192302704\n",
      "Loss step 17500.0: 0.001587409758940339\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 18000.0: 0.0015626675449311733\n",
      "Loss step 18500.0: 0.0015400914708152413\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 19000.0: 0.0015168014215305448\n",
      "Loss step 19500.0: 0.0014934190548956394\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 19900.0\n",
      "1289.6721069812775\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",
    "\n",
    "        update_sparsity, optimizer = sparsity_scheduler_group(loss_test, epoch, optimizer)\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": null,
   "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": 22,
   "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": [
    "# Resulting DeepMod data interpolations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gRfaw4Jm9UyRiM0VR8bGDW+6JnJvR3W4r87K9h1FN71sCKldV3yKa4xfGAveOp44unoQ/tYAqOrZLxQKv++Z8CYiLTdIfOBUL3JWPofXYqTGaiUpviQpwuh9w2K4mdzNN+QML6OSCrhBI1fIJbqbhvHBc8wfGR068vnB6bgxwvM889Arw8phgcY+QWGBxeBB/YHmU354QTTgkuHrjLSIiev0/OePgj3/2lNfa/XrZVE0NY4KxUhzie71T/r+pHCVg965uwzAMwzAMwzgn9hJsGIZhGIZh7B0bCYeQZLLhMBxXax+UYr7c3bXse8GVdrtI39yg7CIbueWSoX1glYZlCbV1a9ZoOK52r2WGQVwUbWs0IqKq1sslR8Pu4hlyju5UueSeJEMDeoar1LXrirusq3qZdRd27SOa5Zj8DUaMmhB33gIjarsVQ0l2KSxC/lZ5yKbu+9mJcRlXYa5lmhb+kJt4B0UytJxVLTzCz1fCILTQBKK4SEYqTCGVMKeXTS465+dE7rXRimRE1mj8h804MW40chOkSMaM53/0qCY6e+i29Y3XiYjo4MjNO3ngXisnk/AZKD9X897mtq0lwCH934nid5fO9fZbrWEYhmEYhmHsPhtRgnMV4G3msh2LUOnNFSXUY5sol9z59aStrK81Wu+iF5mWO/JZfV6LNCif7JPhylotlxwXySAYl3asJJcUjO80KIvD9Jp/D/XX2xGFraybU7uERJjepX+VYxYlE15Bzp9U2DOZ0NCLZPhyyZl2lIOBXiQjZYGZSnyTyWKNliiKkVskA2ks0MIiGjgfaSe1YSJcV5uu+VpSqDZ+3lMdi2Rg77mosTJfFGAZlyIZo7FbcM7Lf+rWgupTlwBXXHOWaOOJq5rxwWtu/PD68n3Dn1+ekdq7jkwvEudZVCTDEuMMwzAMwzAMI2SjMcF9BcG+Mb+lhEVtwau+FrOLMTkXtR21ImXqa6lruqIWq9ZoqRhfLRY4VwGG/YoVYSUeE4thlInP7aruiP0Nh43Ah1Zpsgn58g4V4a2nXSwDFbk9Qk6Vtd84NUV43du5BPT4R7RKC+c3fzMsf5XKGV8yxRi6LLFIxsh3y3avYJVuWu1+n7A4S/YIKVZpatELsEaLLNIUBRiLZMSlkQtV4dXIjd1Nxf7mFt2oUAEuu2OBxQJN2s3n4W8wHofHSBz1PnP7jOite0REdPf/vENERIc33cy3v+fWeeP28mMyOsh7fog1mj89oAS4WiQj8z1xC14PDcMwDMMwDONy2YgSPBqDy0Nv1/T9BZVjVeFVSpWmMiWXGVBHX1yteQFJ5TfTJQJR7UPWqxTV8Pldtz6/c4tkoEuEnOM+FvhKxQRD4Jr2e5z3d7pkRVAztA/nQWywto9aoYfU7+/bb59eoZWObeaH4xhPLUMpchCBxzJxbFER9HunVkG5ekUyPINuNSwukjEK2/vll9yjtSIZufdtzQUicn3IjQUO3R80GmU4zx0C8XHAXdMgzl1TkTU3CLwd5LpOpFiwsivuEAK+Ryy4UIW0i1wjWEl+/pab/tTkY1S/8U9ERPTBa4du3g+4GOGDCRfaSLg+iArdKL1abzn/R0rZBSer5LuOrD+rlWEYhmEYhmFcIbaybPJFIypcrsNDVdaX5j2c+OhV0T7Ck+WSe/gD4zzNFUItk4zjuZ6Uub6R51Qaa1Q0o8CsKt8f2JeGdNPnC4gFBr/hrWcVVQz/uOHulPrtoqoLGkAPi/bzaZnpGl7FxPTt7J2TBIh+i62Dvp0ZvTs/UOHTyiZHG1rxWF4BonswHqvU/X8ZufdjdIXIjRFG1OV56B1aul0fotjfyEUi7EmIVdlw3E3r9u9NlV7X2qnKsPYoyo0NVlwhxO0BY4YX8+WK8aduuvGDR/do/o9vu/8+cut6eMe5Qly/4W5CxWDBQ7dsDfuQqm3QuEEkXCF8eyW/KbHc/t0dDMMwDMMwjL1nQ+4QbriYg6qiyCr4In+VKsOtih7zqyyQOFbJOJrBQFUNCk1VuKyKcetSgDVavsGx72+3P7BfVI0FDufvJAl/YH253f2bdX/QzHtRwid4W8n28l2CnOrexIWnZ3stK8dqHfuWRa5yuQ0k7ufeNSJb1WWv77Z/9aouPkp3p+oPjMuBK0Sq16VRhhUXCZieuparljtEqhJcilxld2VXiKp7/0ThFV9giQWWoVSKk3ZHEzf+g7enbgWvvEr1mXuYHV53w/vvudfJg6OEwgtuD2peE84fKudZojc7xQ5czYZhGIZhGIaxXuwl2DAMwzAMw9g7NhIO0bv39ArmN/TtUevbPlUuWbU78+30IhpqALq28XWVTc6eXwWDCLFUSjiVS5hEaJnWbYWWKpLRbFrCI9wwVZ9jK1GsiKJuaV+0RMZ325KqquOeN+wOzU6IiyzTlJviDiYTprt2Q/sotfs5dWy06et+UGz5edkJ3pu1IhlIjt1ZKqkOwySwX1srgyxkW6R1J8BhkQzyrbFd93y5x2vnabtssqCdyximkEqk65sAh/j1QqKbjB8cuNAFCXMQazQpkiHtD48WwXpf5BLIn+Ckt+p7b9Nr/4db15MvuOH0dTfvGtuo6cn6bjgcLbdG8yGfynvGeYtkCDt4dRuGYRiGYRjG+diMElzKV0jCTHkV+5Y1so6yy7miBH4NaYlvqeXi+YkVaHZnuJ5hkSytrFqjnbdssuxDAarbuhJi4LPcJ8qBTBtUDs4skuFLxIIlmqwak0O3lmIQn0x9rao2rGqmzPQRVILcf7shKjTJRJjcktM73O2VKh0btw+H2Qsk2ynn2w4nZPYFe+vquTsmvkjGOm0q8x9y3eMpZThSlN0gWRwDlGE/XVGAU+ejrKWsi2xLNA3t/qFZr2nKsJYQJwlumBiHZZK1YhqiGI/Grv3znOz25OQFIiKa/dMdKufu9RF/Bv8zFbUyvw7mo8Np3JFwMUUyhN270xqGYRiGYRjGOdmIEiwKcAnVMncw/O3S0cOoUopwpjk6Fsno+sqXuOKUNdqayiajAozTV1aEE8pQDdJAXRVJ5ReLZJQ+BphX5S3TQoV46+k68XDfc5VhbJ+zrUskZ/c1q7S+REUyrhByjLDAiEYUT40XWbNiHq5oM7cr19x5kJ49kA4bJTjxHNDuwe2yydoyuUUykJQyHJVLrruHWBxDsVLDIhlIXNI4vua1sseCViRDU3jx1M4FY4GjIhlDUV1DJXjKFmlilSZFNMY8vHFzRkREz3FBjMGbrlQyzSu68dSciIiO77vXyMmEr/dR3s4PMCa4KoLx5p1m+btLMkcptR8rLWUYhmEYhmEYO8xGlOA5xEFK7K8owylFeJ+KZKQU3qh9puqSMlPvap/tCpHrDpEqg4gKcFQ1pQra1bnlk6OUXHCL6CiXLKuNXCDqbmU4KpIB22yKZSzf1a1ELWObitHjL/wdVT3boppcCiXEDSfjjjVXCO+kkbget1DFzCkuQNQows3lFcdcL19RPxshf77lrT2fXXj8aPfoUWaRDCEnFjh1zmpuD1gkI7Ucoxa9AFeISPlFFwmvELtx7XwsOxTgvjG/2vRU7G9mHad4uijCPEQl+OzUvfpJLLAowegK8bEn3LH51C1XJKP+f79JREQPX6vp8Kab9+6rEyIiGo15W4lTS1whpF0NcctqkQzEv4+AK4S5QxiGYRiGYRjGcjbkDuGGA/gA3JaY4F0oy5wKm/QKcubfEKm8muNDG2yTjPXNiwVOKsA4PVdZzHUxUNwi6rqgatHtA4xZuNou7aQvMFG3+pVSgP303fjW1gStrtPm3ILsqsF/a9uBi0MvJdsdI9y7cngVKnm57YnO+YDZIb9guZ/LvavxB+ZHftQ7p9yruxx8+voD4/x4Z7uHyvya3AtE0h2CQoW4mV53TtfieZGussmaipxyk9FcIVJE64Myyfg8Go3CBU6O3XmAMcSiFMvwyQM3/Mg1ZxQ8/893iIjo4QdjeuI5Fy98/Ngdx6ee61Z05WdKlUn2p02uPzDmL63I7lzVhmEYhmEYhrEm7CXYMAzDMAzD2Ds2Eg7Rl3X5xkty0iaLcGDiWm6P0UBJkMs+NlguuW+ox7BIW6PhTuUmxOWGQfQNf0gB5ZGF7nLJbJF2ziIZu0pVl80XM4RBNDZfK3bx9yxiES1+zqIp2mnc1TUp3Z1Dvo617k7f1aoVyVBrxw+CwYUld52D3KSfFNj9HHVva7XJoxXt+MV1GeRao/UhleCcsjpLhUdE1mo8rGXQbY0WWaRFVmrLwyCQdpEMN0wnrqXCIlLWaKuWT26KZUCRDHh/OH48Dtr7dpxAdzBhqzT+CW4Xt4mI6OTtx0REdHh9TNPjMLzIl0FOJOen5kdFMjLP2VXDIkwJNgzDMAzDMPaOrVSC+yq1O1xpdG2kPq5VwF4kKpKRY6WmlT/28zOLaWjr0+avnFQEyymWaE1iHPGwWLlIxq4T2A2lVE0NtAOLpMDLVfQKrwEsVyYataZQp3mLtMS6InoXFtmS7GEiSv1amutbcxxrGM/dcOK86/tASJ2/sJ66rkmp37N98InqewBHiUe+lqTcnq7el6FIhqBkRHlrtFRRDUmIU4peII0y3G2R1oyLZV94DaeKZHSdr9plnLJE08i9LWCRDCmDLOOIWKGJRZoUx0Cl+Np1Vwjj07c4CfFbf0dERPNj1+7wZkn33j5wyw4l+ZKCodBMrzvn+yrnWDxDc0rAd5JzGhjs8WujYRiGYRiGsa9sRAkeeL/ui/mcXtXibJ2K8nnt1dTKkpnFMJqvL4irOc8x1+zTkmWSM2OB1XaRlNTdTqqt+Okgw4Lio8YC+wAujjFrlaBctUgGlku+ElgsZn/UYhlr7s66ZFW9TWw15Ya6IuxQFb5kPDUsl3uP22KbufOC9zZfJANJ9cotQ7NRy7Q6i0gtp8T+YpEM8q2xXagQN7HBGL+Ltmfh9KqO2wip8shRnaaesb/aejRrNFFphbNTdx5IkQxNCZbl/gUXyaj+5ttERHTvHVcY4+hmSaeP3baOjkSZDZ+3oujWvhxy99+UOm1UtCIZUanw5fcDU4INwzAMwzCMvWMrYoK3pUjGJunrw97E16ymZhRaXA2qvH44yM/WzCyO0VsBxunwOezLJ2N7VIC1z/PIDSJ0dmjHBEflk/2X+NVyhRCCeDpNmUtm7w+7p+NyKK/n7mPfeFzZK6V3ZXnsnyhBmduEE6dx1FhTXOslcRHi6crrvOyLCxTJrQYlRV8ko/ve6VlWJEOGueekFuubK/1hueREFDoqw81y3QpxXLAiXF+XK4RG3zLKWpGMvspwqkiGHGpRfo+P2RUCYoZF+cVyy88d3SQiotk/f0hEzW3s9NGQplM3cuu2uEYt39d438N9HIpqrRTf0opkJF0hEvO36w5rGIZhGIZhGJfAVijBhk7u15XECq+qDK+Eqhb0jA2O1puIU1uzApRShquSs5Or2B+4KTvJiyiuEFq5ZCwdvq0EakoUz9rXEWG75XGM88Ps8C56q5nasfPySOaJsQVxrVrWfAnCjhYbnL+hRI/DJVFTtVW+zZ3IDWeo9MohyXtyx3y872e6PETTURmOlGI3QHcI9AdGIv9gRUnWPH1xvO0eoRncaA4Tuf7AGvmKsBvHWODF3M0XVwhBYoF92WRe7r983j3Anjh1v8HJyYKIiI5uunYPPxiT9IgMx5yDBS4QQgGlmBHs1S4S52LKFQJLhReHy19zTQk2DMMwDMMw9o7NuEMoX6W5bhF93R/wozha3yVUkosrw/VTcPrG27RjeYm6Y3zb40l/4GGRdoVQFWCI7dFigXMzlf38KhgkQfWtDJUlrBSHXsBV2YoJXqAi7JZZzMOY4LmMi6rsN7l5Ba83uf7AOD3X1/myfYLlPFR+ihzlN+lx2wSUp1d6RdC8UVO3a8ziT6Kdh6KiewPSYXe7aH3b3UPRh3ru/pZiqNybhVzHnvb81HMyofx6f+DU8ozm94uuEJH7g+ILjP7AQuwK0f13dk3PraKYiv1d1R8Yhz7Glw+BxASLT3CKH3+OXSG+8Y9ERHTygai+zXZH/N+pSnG9XSG0FzUk4Qrht3+4/G82JdgwDMMwDMPYO+wl2DAMwzAMw9g7NpoYl7JG2zJ3oEshVQxj5fLIqyLhFIMiHf4gpBLkcFxtr3S7Reb4ie52ZXodZS1gueQw2aCqilZ5ZMUajRPiFokwiF0rp1wsSwVaNWEpN7HOd2vzeGbYhNat7q301ASY7ulETVepZqsmXbRFSl9Qj9mOZEpeBkp9VeweT7KFJacvHDlph3BvTVmjadO75mtFMnB+qmhGzyIZKXIt0hqbw+71aGWT3TLdIROq3VqUKLd826l98uP8/JFwB425lFOGBLioSAaPf/TIPaDKf3qLiIge33WxDyNOgptPiSYTeTdY8LB72wOlXLLg33kUa7RmRRDKqSEHb8z7d32ytPkevmYahmEYhmEY+85WWKRdZELarpJrdaa6yijlknsXycghabCekWzRtb5V0VS2RFEMLJfcLpLhhh15TmiNxkPIuYsS5oRdOvfr2teC5vEVk7xyE5TwEz1XCeqZYFcqCTFxUov+Wy2bF4Dq95YWxVgHchwHmaXeheTvlyo57b3YEsv3Bay6thlJ7k3eX1J2ll3tU0l2KSu01L5E1mhaAlx3+WShaR/OT/X4aEUyliWvaUl0udZoWnEMVREGBTh61EGCnCTEyXQpl+yVYX7f+MwPPCQioqcmL7rtf3DC7d3800duPfNFTddYYB1KHZZRmFCeroWC1mrdx3D1Ihm84kNTgg3DMAzDMAwjYCNKsMQCr1sF20VRJaX49rVSOzdLlOKkNVrfYhmJsso68tkryk+/ANs6DtAKhl4ZrkMbtHbZZB8b7Atq8CrBMg3LKMdFM7ZfVSIiKorB+i2ktLLLPWN/hZRpftMutEqKFSDZvbTyW+Wqgpp62ZcdsPGK4yRlnJUf5bAmrdG0wiJGjL+Wiu7pI4iPTpWy7yyWAUUyBEUC9NZoWvclLs/kWuZFxTS8Uhxe70KlxvWGMcDLyqdrym2qSIYWM5yLZo3m51fLx7FIxmjkjtV/+7wbjt/+FhERzcbuNzs4mhMR0fF998q4mFfeiRDji0tQlzWa00RigsNzstB6oxNFMvzyYo12MF66Hzv0umgYhmEYhmEY62GrimUgKWW3b9GMeHle/xqThi9KhU65RiTJLZKxjJTymzJe99O7C3MkY4VXVs+6A7BS5ZJ9LHBLEcayyc2ugcoAscC+aAYutyOfoUvdIVJ49XO7/liJEZzzfi2qbmWoHe/bu/RvMr5VzhtUiHfPyUCLsezb4eeVP5GKootGifWnxANjTdR1RdtfN5lJxk32uP9r60u5O6QqPSntsOhFqkhGs1x30RVZTovfLxMKMBbTCOfhusLpmitEqphGvI+8PsUVIqUACxITvGDXiCduu+IYn3rCDeu/fZmIiI5fPeUl+NgsmvWNDsT1Qd7DWAleLD/nfHnkots1IlKAtRjgxHtfqlyyX31WK8MwDMMwDMO4QmxICV4+X/vw7/uBf16l+CJIlRBMLx9+fTVfU+C1h+WSU+tVyipHLhM5qDHDPZXkaL2oDCdigeHzvFF+88oll1Aaua6LDgU43JVK+YTXXCJ2yR0issS4qPWvqXxyKlt8LmpKpSs8ayO35LQGKsSXFA97rh4ABhXhlStHR84auxFPvxGkF27c04w/ujd3KMXa/TrlApHrC8zDmsL7e8oVIlaIu10jhEit9dNRAY6XTblBaGiKcV8q8LDH6RLje3oavuKJWovcuj0jIqJnD53nb/n2AyIiOr7nli855ng2dcuPxgUNeBu+bPLy0O4If5qk/IEF6MX2KPL54Br/7Yl7pSnBhmEYhmEYxt6xFT7BQrZ96A69uuf6/V4Wmj9wRJd/cK4rRCoW2I9rscTKPq2q/ICClKoU1yjARTBeVc2Xtp+XOGn9qsEtomSRo1xVCrhkggpouRegEhQqsX5+6qqqpleCYP2JbHJ0hZhHsYDdy7Wn4ym0si7rb2qKYuez/C83RthfuzWMZ4Dxj1Jdr4Jxjb4+z8ljiDu2B0ROPoI/VmHGfPbvOxjErhCaIpyrDEfL87CWQaY7hNIucoFRr2/MBQiHXcujp3jKFcIvB6d47mPAxwJLBThwhZDKcKIEY8wwukJcu+FcH77vaTd8evJxIiKqH82C5WYnbj3TqVvv9RtDGo1DpV4U4RSNKYgoyInnSV9/YDn3xRViNl++P8vXZhiGYRiGYRhXD3sJNgzDMAzDMPaOzSTGDcNCA0ZDrquMvjwmNCS6ECCkYWm7VEJb34S4aBtKuITWXZ4bNqEsj+WS67nr3ukqkkHk7GG8OTkkxGlILL9PzlyEYRE963xcLS4puUtLoBFrNN00P17XqqWAk1zhrvpy1WOmemNeUoLmLjPmYzdKJAflFjbKQXloqUUycDlJiNOKXijWaFgEA4tkaGiWZ8uKZAipV5dUkQytBHPuK1FTLrl7H33inH9e8TXIIQgSTnHzORf28NJtN3383neIiOjs7hkRER1cc8fy5OGI18eJcQc1jQ7cvKjohQIm8fvTZbgmLZb3rRjDebZY/pA1JdgwDMMwDMPYO7YiMS62+QCrC1D8zpsgtw32VLlKr5ZjkKo8qYJFMqL5S6zR+ibEpex2UsUycLtRuWSwwMIvfy37YB4WzYjLJodf0W3nLu90BZ/seM42ux5+gTfnXKgIbztFUaTTU3IyypaxZos0DVR6tGSW5evoVo9778QVRiuesXIHoHp+KcVYxAJR7kt9t5dZhnsb8YUCUj2A2njUvnWPxodO3yIZqeVAAU7hFeIaleJQQS61Hh8ZKvcFVIyrjuI5qSQ6LJKBaGWUo7zGUhLj8J2oO0FOmy9cu+Es0V687ob1q04JfvDt8NgvZvwcY1F/NK683ZrsU25NFG/TBsmbySIZSjI/9uhGtoCLxdL9MiXYMAzDMAzD2Ds2ogQ3dlE8nlBFUsrtNhbF2DRa8YtkO2EIX2ddx75n+eSVi2WsXC652xoNyyXXcymawZO9JRp/vbfiqkQlni8qnhaeyyXsqnw5j8cQS+ytbrZfVfLAp74o/rX2+2CXjdpOOQaZSlBxid/yJYgYgqhDTTxj4pjgeFQIRqQgCtZbXJCCfBnqpqhhI9iW2iFXJXoGkufdmmzlLil2fZ1EcZFau9xeu6UrSdz3c5VhRo/97S6S0SwHCrBijabH43artasUy8iN/e3bKyKxwDKUHsYSrNKicsoQG3wwcXGyN9ki7SUullG/86FbfuaWn09d++PHrv2In2PDg6qpaj5f/t6FscD+tGDFNlnQSyuSIcBBLA75uhcFOHHPNCXYMAzDMAzD2Ds2owQrCrAUELhkX/iNsqoLRERC8Y3bhV9VUdyN0FZpNXUgVRwjcn1IKcc836thqJKlyiUrapv/LA/LJWtFMjBEta5iVwgsl6w5nkgvxsh71Lu/qdxFd4i+dc9zWbPiFmeZd/82ueWS2+qPpvBEnPcC3+IKQblx0eio0bTvPniFdswu2hUixS512kwkJlgy5BfheOJeHtFeLrdIhrYOLaGFdylye0i4QqBSjKSubz0GGNt1rbt7nqoIQ+XvlGIc7av0RHLM79HRIpiO7bThtetOAX7xumv/0YNPEBHR9DsPiKh55okivODe0qMjUaBb+TGLvPsnFsnIRS2SgQqwtJOYYHGFSNw7t+/OahiGYRiGYRgXzIaUYIlj6Z6/l4pwpoem99iDOBst91n9ilI8eztdIaR9XzeH7KGWwQyKcF+iGOBQERZfYJku/sA+fgqU4aosfAyvnMML9v0tQaTS4tzHI46p8oduN75D67rurwBHil3mBX3B7hAlKD/CKk4PuC7MVN+V33edqFn4idjMAu9h2BWTiin381Plk3cvxrc3o8QxSJWqT92biZIuD5E/sLY8o/n9psB2cVx+eD7Gbg8UTBc0H+Guzj4txje1bLYCzPPF3xdjftXlQAEWbt5ySvAnrvOx+vZ/cOt95zEREY0PXZflwztueDBxy0neVVHUkZ++pgijj3BfJyut99ojB/GAzzuOh5fnfjFafl/fvzu0YRiGYRiGsffYS7BhGIZhGIaxd2wkHGKu2EJddPEK6bJeV5W+PvQNdxioYQ8JtHAGrUiGklDXaY2WXSZZsdnRxnH9EVUwUMHEGc0iDfpjxSKtWgx5vpst3T3thLmS16klxKXKIYtrk3QrieXMLuK7L6Pu6ExWXS5BbjdqLu2uyjhkgsNhtFCKZOlfJWMGN66d2xpry7iN6Rs20nQBL0+Ia1pJhpTyt2JcRbSDUDwjZaV2FdHCIVKJcKniGoNBfG6lKjZh2ETPIhmaNVrKIi15mvBQL5sM7VcokiH0DYPQ2s+m7lhVZaKLH8ojC6OR+6tv3Z4SEdELUiTjn98lIqJHHP4gCXFijdZOiCNyP5m3FPX2a+E+qGWSAR+2kJusL8vBwfO2gEJmGKUpwYZhGIZhGMbesaFiGW64Li/zaP3b6yq0dpqPa+XLMFUkw7eDr64udTe7THJKMVYS6/BTERU9v3xCwUH1DMslz1hJgCIZWMgCvfqrqqUAS3InLgvzY0IFeJeVYBX82/EU7Fs0Q+ipbsblVNev/OEuo22Tp68yuyU3MW9XtkJuaqN2hQqwljinIj04isVd0y6RGHeVlV9ETVrOtEZbdt5pyq9mfZZa3lujdVuhaUSWaVAkQ0tKS6mvOdZo2jxVGa66589ZRRXlVh6/PpGNp8+m7pw+PR2F8wGvAENRDVGOD2865feFG67dZ59yCXLVu/fd/pxxUhkcuzEnxrVzVX1RKRii4O9/7szecD1ZH3p20CLt2pg6MYs0wzAMwzAMwwjZiBIsXLQifJXRPm5SZZAJYn1j6zTNKmcQt8mOBVbM1f1OJ6bLbC3QVouTVMola0Uy8IvWW6R567Q6KpMcxQKjQgxfq3iu74oSXFOlmPCRHt+KX+7rVuIuMO41BdoqoUVa/ork2BTd477d9p8nqRKwzbHia0VpL+pzXc15QS3WH3uKhrIhmH/OBwz0SEVWbttIonR903uX+Fvw3t6+5pQY38gaLaUcM6j8popk+HZ1qABr1miIViRD0OL8q7pQi2Fow2bZcDrG9g6Hded0UYJPjp3aKYqvKMIDyBnyNp+wnpu3XHnkpyeu/UcGz7v1/sMHRERU1+6V8PjeiNfrjiXanJVlEefM1OG+FDD0p6S8g8hDkh/w8hc0scDK/R0PqrdGg+s8s/S3KcGGYRiGYRjG3rHRYhkX7QaxjeAXW/5ybqiKX1rsb8qBgZfzRTXQFaL9NbXuWOBUmc1Vs/xRKcKalWU49KbfYP6NJuDzRRW5QvhNgkjdKMS4a6AMT3cwTjFXmfOKcE8lboPOB8so61jJEVBF8hnuqRLAzQqWt0s5HeSygbhYLcu+UcsgBlCU1tzzTNvQurngIi6XQqpUfapIRtHhDpG6jyfapYpkaK4QcQzxcleIvkUy4uXiPym32IWmCGNsbwkxvSP+m85O3T308cMDIiK6/dRZ5/LCbNZ9z33mI6fB9uvvfN2Nn7hY4XLhXgkf3GNXiGvyrA8dHqpFQbWo0WX43ERQRVYLe0FvteYOge5OxeGws12nw1UHpgQbhmEYhmEYe8dGlOCUArzucsmiyg20snsXQCqpO+X7m/txrXnooQKs+gNr411KQVLxTcUKKwqw6qXKQ+0TM+Gp6mOAuTyyd4OAofgDS2yT+B76WOGW84PmCiHEbhGgGEfTd/g7NFkmWaaf829MdINgHO55HA2IYsVoGVo2eC7eI5PwxEkocn2R9a54by3WcJ7Gxyrxt/muGEy1x5hfJopBL7rb7RMYn5/tzQ7z2+20+7Z2f08oxzXx/RmV3YTyrrlCCJqCi/7Amsev1uvTNQ3Xgev2Pr+8cXGFEMX34CDsShR/X+k5FleIyTHHyT/F6+X1SCyxtJMYYlGKDyZu/R+/6dpd4ze/+o13iIjo4R2nMN98xq3/vdcPePuu3XAUxiDXlfPNl/9uz5O2mitEKuY3UoDxusb2HAus9npbTLBhGIZhGIZhhGzUHSI3VPCyYoebeE3Oclyyf97ZYs0iV264YxRfk6gU55fT4m1Sjg6DQeMo0TsWOKEAp/yB/fxMdwjtUz6qEAfZrRAL3HaFcKuvk64QqPzGuwjuEic7qFJpx9nP53E8F/vKpBcU+9t3N7oUy2w1U0ipmt4gFFXOUAURtWvdd0SMs8wh9zimFODkFrNjgtFZw2yHPNo9OWqnzO9Sf/vGAkfr5KF3VkF3iG5XiJR/sO7qIMMwJrhZDtvr69Eqxmn+wJorhB9W4XDGFdsGrPCescI7npbBdEEUY2mHleJusCvErbFr9+PPuYpx5b+747bLz8CT+859An3shwf83Dpr9rN5XoIrhOIHrJ4m+O4iCrHWc1/CPRMrxUXrNSXYMAzDMAzDMALsJdgwDMMwDMPYOzYaDiFs2ipNK9ohXdvDLbByK5SuBjXhDbsUFLsQtVhGV/fZqglxuWEQzU5xOx6XbDPld/AJEao1WhgGQRCSgFZoFYRHBIlxCWs0BMMghLKU4QXbOq2TZDf08kTFVdfvzfeTPoEXS9skv2seUdMVm+qy9UiIjlzfWrEM7NrXYhE2UGY5fUxwOg7RIk0SG/lYLMpwiCtqJvBgoMxX0EJAlOnFhs6/lUBbS8k61+7d0fId91wl0U0tkoHLKUUy+pY314pk6BZn3WEQWpEMDN8ZtJ69WiJsbrEMzdpMwiMknGExCsMcjhZTbjcI1iPDk8fjzh37yPMnwf589skXiYjo0d/fIyKi+dQlwr33OifSHbp2o4k7piMOo5idNOvE0sqpKtnRu4mCt2vFcw/LJHMYhPoOlHkv3KGr2TAMwzAMwzDWw1YowZtiF4p2aFZq0cc4JsBlFsnwxtT49dSl9i5LmiMiGg3DISp36yqSoSmMaJHmFWCRXatgHJMTfCJcFSbMzSWRrqqT1mh+VxR5rEQx66IN/i+CKOtDOSi5quSqyyVYh62XhpYTGIHncqLEd6R2yh06pab3PWa+mMfyZussERwnE4XrjtTzqCsmUZQFkgjXbo2Wecy2Erwna+QkNef2yCQs0ryCqxXHWLFIhqBZo/lxpWhGTuELLRFOS8qL2oOCi8PpNOz5mZ26Y3aTn0Wns3EwX6zWhpw4V0Ky2LNPOOszmTx85z+79TxyN5jhSPYrTLg/vN7d1VmVhU+Ik3eU4Vieu+H0Zpg4X7DXOmVpq83PTIjzzbNaGYZhGIZhGMYVotdL8Je//GX6xje+oc7/h3/4B/ryl7+c3uiQ/w2KQIUdDs9XKKMqa1WBOw+o8l0GRVF3Wo20Q3OJyCm6rRibYlAEKjCO07AI/+GKcQOtaUVROHu0Af4bdP+TEps43vdfiqoK/y1K92/G//jTvZ5X/K+mel5TuSjgH1G5cF+57p87n5w1mrNHi6fxPz99/QLUqqzreiVSVFV/zJ1nUF2G/859MFLnQ+75sSJihSTKT/u/hfa8toJU8P88zl0+OlfrqnY9Fn5F2AZWvE0nmALusoDHLtU+bgjHRI4hnm94jLI3sFnWeb168B7tpyfGNbquP+3+juuG6TVVQfxvzf/TxnG6LF/XdVQoow3+/GVd8L/wfIwutbrgfzLPjc8r/T6A4/ivqoqsf7PpkGbTIU3533BeuX8L9+/s/pDO7g/p/t1Dun/3kB4/PPAlld0xd7/7aFLTaFLTzQOimwdEn75V0advVVS/9gbVr71Bx/dGdHxvRNduz+na7bl/Jxvyv4NrFR1cq2gwqmkwqqmuC/evFdM8GNY0GNY0HNW+UAaRU4Trqkjepoth4f7hu4r/IeB6lncXOa/wHYjX58/9BL2eIH/xF39Br7/+ujr/zTffpL/8y7/ss0rDMC4Iu14NY3ew69UwLp+1xgSfnJzQaLQ9YcZYMVIQZTeRpLgRBpkxwPGCSoakLA9FMop2rG/nsCPuNxULjG4QEmikldHMLZes/fFKbKovlwyuEH4o8VfeBSLtCuGGdTRtE70E66L39RodmEx1bd0xmivGDC9Ti9osyx7HuNYwMq9rZWBBgsi5CuM+F6DvsRZWjbfPQNuVZcUF2miZ9Rj76demlEuuQVautWO3ZtYZJ92HlZ6v2j0b50ft4V7eVSQjtU21XLIbYPGL5veH3xXbKa4QjftDOIynh7ulFcnQzG6k56d7WR5mFsnwy0HRDInxRQa8wpt3z4iIqObf6eEzR0RENK4Wbnvc7vDIjV9nhfbHnuP5f/2ma3/odvTe2xMiIjo9dQ+2m0+4Z3oT58tx3K1Lqon1DffRm7rM2GniiNtHfwy8c6SKZPjt8jsMFsmQ5XLj35nkFfX222/TW2+95ce/853v0N/93d9F7R48eEB/+qd/Sp/85CezNmwYxvqx69Uwdge7Xg1jsyRfgv/8z/+cfv/3f9/Hg371q1+lr371q1G7uq5pMBjQb//2b1/IjnahKb3bRP8yyCHq36Z9LaG8jf7AMD/LFYKIY4FxnqIyaMrvEE63VLlkJOkKAUNQgIldIUp0g4Cv8C5XCKLlqm9cTnn5n3JRXMr1qmXng3Ti84zxLrPlMa1C7B8a/7fmB+oB9bzxtE7Io/4YDWH8Ysn2N6ZYYUO0PxHVcy2jvllRd0+PLkmvqJ5vgAu/XsUfuIDnQF9HpC6nB80fWFuWwR4Z1R2CtOmhApzr1BK7QHQrxqlruiu2XbsfxJc1ukF0T5+xO4SM1/ycLkfuWB6wC8T0yN1cBwu3oqPjeTA8O3Jxwk87oZeerW4TEdHJq/fd8sdu/gfvuPXd+9Apxc9/zK13xOWS2Z64KZE8inOW5mdu3+R5KuOiNh8MoIdHCjNILzUqu9oPMFYSx3q6QgjJl+Cf+qmfohdffJHquqZf+7Vfoy9+8Yv0uc99LmhTFAVdu3aNfuRHfoSef/75XjtgGMb6sOvVMHYHu14NY7MkX4Jfeukleumll4jIdd184QtfoB/8wR8810bRl/c8jhC7iv+w7nCAWL6c19fcwCu8SkzwQImzURXgJUOMtdFigVOKcPPHRH+fW56HdeKLTvEPbWKASxiycrBw+y2Kb7ngxX28lqi5oaorrhDBvIQbyWX7AF/E9epRPG9rlD1S/o4pUPW85ApdccUo8uMp8xn159Y8b7F7gStBeblU9cTNJBUbnPip+ijDGlrcZLQtiP1Udy0R1Omr7/XZSaK1xEv35cKu19yKcFF75R7u23Wk+mv3efV+D7G9MI5EirD3C+4+kSL11U+HOFylAxErHcrtTMbnrd1Bf+CkIoxKsDxz+NAs5hwTzBXjJEeoYgV4wWrp/MA9w0QJHk9DP98n3z8mIqKTU/dw+9z/6KTc+pVvERHRIxcSTIOhW//77zrleDYN82bkJxFVVxiN499qdur2Sd5RxNt4ejwMtjXi4cArwKAID2TbdTAe1TQYKtPXrQS3+eVf/uVeKzcMY3PY9WoYu4Ndr4Zx+WxxNK1hGIZhGIZhXAwb9TPLDYNYV3lj6boerNBd65ddcR9SlYK19n4cwiY6TaVb07X5UaJcL4u0cybE5R6EZIKc0rUsJWdnYfiDT5CTHuhFt0XaMms0mX4VrNFWoSiKpmNcS1AUpO9vXeXIL6mbWrNGa3evagkvKrLveHKlFkO7r2hn12w7h9vPtJPrQkuU09rJMcSyuPECGDoCYRDSfZo6P7c5k3rd9LVI05bvSIjrvay3RlOs0HzCm4RH1J3Tm/bd1miCPr17d+Myyji/WV5PfIvbEjXhDZgQ59ctjzC2RqskRG/s2kv4gSTGPb7tMt2G/Gwrrw+D8YrvvXefu0ZERC9ce9+t/29edtPfdMs/8dEZr1+OkVv/o4fuQfcEl1WeTzkJsmWLJv9dQ2hHAcl+EkoxuV74Zd2mut9Vau2mmnu+IYl75B7dDQzDMAzDMAzDsREleN2JcGiVdh7Fd9PEuQSoAMN8CCbPLZKhW6OFSW+BtY6WEDfg00gUXxnPTZDLVfrgMzuym5JELRmfhYlyXgEGw/ImQW65NZqUSO7cpYQ1Wll2T78SyO8Bx6bQJBW/XOJ330CiEpGed7UsMQ7VpkLOcaXACBZ6ECI1029gs4UfMDmpi1QRgt4V7fE+oVQoUJWjC6KzhPi2oSm/OD9qD0nOQse9O7JGSxXJYDRFNwUWydDAazGV+JZKahNmYcdiQLY1GhbJgES5uSTEwTmNRbRGkzpY7vCak47H77INKD+jn/yES4h7n9XYJ+9PeXn3Rzz+0D2rP/WDbv4r/3xKREQfcKLc0x8JDQ0luY2ofUqEzz7pLR8dhAfKnwaQCKclxPXuUVzR/m8HrmbDMAzDMAzDWC/bU+M4g3XFBm8jvV2ghvDlHs1HCzWwSsu1Rhu1VN1UW1QEBkosMKoQ+GWdqxQ3gVSuWRQDzKobW6TFyi8F05dZo0WbPqc12mVbp60FrWyyJp1gVYSUmrllyjAqSO1yqYJWclVfmWIDJQpwtJxSGOaSwPjcZGGLDnIVYIwB1W6KSeW3t+R8BcktkqE9P5bZnKUeVsqy/vf1iq4yhPLZuUUyNGs0IVaIwxjgRdWtCAvyeCnr2DZN6G2NVoX7JDHBEtuLj8oFxwT75UdiqeY2dPuhi/E9vuWKYPzQpx8SEdH337xFRERnrznrtHdec/M//gNu+Yf33Ppv3hI7s3D/pOBF1cqf8S6L+Djn6aOx26eDa27CkFXk4pB7mUURltLxQ+XARxZo0Oum2QBmVlMzJdgwDMMwDMPYO3ZKCV6VXSivjGAssN/3VLlkrUhGtisEf3WhgjAadrRVlF5t6NWJMCjc18RIWS5obhBe8YUiGWeiELv5ixmXolRcIeYLVoA7XCFkOu5iShHeB5oYLj44ci7qAbTLx9FRQSnSQRdUZCeOGWyGuX+SV1CjtHGIDdakZbn+fdGMxHnWN2bYH9Pug5gbp0m0PIZ6WbtmehFsM9sdQuO8uSAbikVfK6kiGWpMsBLP257etxgSD2sKEyNyC7GgAiyKcm6vhFb8RosRVkLPfUxw1z1AU4TjMsnhsIQY4QXEBIuaKmWUZzfcDaE45Z2ZuOmLY44NPnZK8IfPXyciov/uebf8jTsfEBHRHS5occrLz8/c+sQN4sln3DN9MnH7MTpYBPsxP+O/b1G0zJ/49+CeVZku6vGY448HXLpZFOCoKAYlSEUA5J7rODu1XcMwDMMwDMO4amxECZaY3lXjIi8qNlgUwMtUjHPDqyKwlKDiudfbFWJp2WR0gYDYX3SH4J0vhhAcCn+U7HFdZCowqK5xTDD6A/d1hRBVd8GK8HyOMcK16gKxF64QmropMV3+i56nj9dzIdV1yeu9XLoyvfUYwFBNiliXuwPGY/dlRZUzxzdYU+ZyXSIav2CICU74A59b+b2KpO7nGlreRo7Hu7osD+H3bWJ/w1jwKDZcFq/D5WTFFfgBoz+w3lOx3BVCK6s8rwrfU+O3hbdGGZbLz01UhiUmWEBXCDpg54VH7ALB99hrHAs8Z2W4fspN//5bXC757/+JiIhOH7pn82jkdvj+h2790nk3HomzAxx7uATruiBSlHx/qnGM75A9jjUF2Pcglsq5hedTwhHLI+tPvCeaEmwYhmEYhmHsHXsRE5wCleVlSvNFxxdjZTg/3X8R5qkehfZ11FsBbsUBo4dkwg/YK8Dn9QdGBVKUX4kJXkCFOG/omOcKsUDFtyMWmCivStxOuj7k0P6zML61BEVYpBRWEwpNNu/rFnHBscAabYUoJS4kq6VpAYcKvnKcdqzOqTCnPG9RjXNqeELdSlTeErTYYXVfSlCOhivehC/Ic3mryH1A4X1eWBLnq/oDK8tGld/gvo+uENF0mJ+0H6fu+agYY0ywdmnKeDsmeKAso7pClKAYowLMscAS+1sMxB2iDtoh4iJxdOx8fR/fPiQiok995j4REX3MhQbT4lsuJvjB+279zzznhu+zH/ATT7pxyYs5oDBvZj4N82fcPvK9yb+zuDaiAIuaHKvKMD4Xpbi7h6fAvCYk5Q9sMcGGYRiGYRiGEWIvwYZhGIZhGMbesZFwCK3LWEsiWneZZb+9RK+aD324gO2nequ0+ZjgFgWHJ4tkZFqjSWlkSYYbjeKEuMgCbcT7poRBpLIAfd1rHkbFGaBspoxDIlw1DYtnlIvQGg2HJa9HwiIiG7TW+ZpbJvlKohRuqLX+yVyPLESzRDsn/txOWDNhYgxOb8/Tkmgi+6dEsQwkWTwD13PBtl5dlmm65ZkbpgoJaAlN2fZs/gYeJtwk7cH2gHMXyfDtlty7cy3SEmEQSBQWAQlziH69Lk+Qk+ntIhjLhvNWMQ2sAxTti2aNJqF5Svlkb5EGG/BhEafcXkoTL9zOH5y6Z97Dp24QEdFLH3lERETPjV8kIqLTdx4TEdG9D91yTz835vXK+t30sxO3vus3OcEOwjfqWt4Tav/z1hAeNRhhQhyE3PB1qz43FDDpPzscysIhDMMwDMMwDCNko4lxufZR0i6lCG+iKIZPojunTU9khcJI0HmyrHJ2kYxEIpwvk9yhJGBCHFqiReWTYToiH/yR/4qiBPlEuDAxrimXzPN5fDHjr27+usYEOe+w5hPjeLegfLLf3X0vjKEo8z4hbh56DBbyxV92q5qi7KhXTqpoxppJWXd1WaTFhvuoZsK5X4EKIitM3T/UxDjcodWOUSpBLodVE+AiFV0tlhH2PMTKECTS5JJ7fvWubb9BNEu0vkUycHww0I+DohqrVmgJazS0UhN0K75Q+W2mh+1S1d5xOVSKl63DK8FQFjk1FGu0ASu7lZRHhoS48dTdY6V88oQV4AFf98UzrPRO+Nh99++JiOjxm9wrWrmXqEcP3HqOrrn1yDPwlJXgAaus8qzsQnusj1gB9glx4/A1E+998nxQr2vs1RZS13u70NcSduiqNgzDMAzDMIz1sNMWaamiGaLcoUqrTVe3U9Y0XFNhDiyHHM9PrEC+hrSvI7+eMGY4VSSjEJldU4yHrZhgjAHj6YUEMmnKMOJDxhJ/dFR6lscTRTI0azSJYfIK8JIyyUSrWaNdqSIZgrcsCr/g4yDQUO0stNLBO4gm3PpDoS2I564WMIvBhppj/xYfw1TxAU1h0+zlfCworFCOYLSUZvu1T+QqwELK7mxZW7VcshtgLG9uzHeN95tEkQwhlaKgxQpr8f2x7Rn5Z5daWEOxRkPQGk04mLDiC8UzCvijRAmeT9wz98VPuNjfT93iZ+I/vk5ERI/uuBvLcx9123n3LVdc4/oNLsc8rYL9lJ7pBRewQOvW9k/unSslfUisMbViSVoQN4K92TJM9cjKelkBLhIhBKYEG4ZhGIZhGHvHRpRgVMkuqgzyLqApwzLdf5hHrg+g9KLiG7lCdBS/IGriZaLY4GE4LAaR8uvdIDTzdC17uHeRjOXFMmqOk6rPFsEw5QoxF8WY19tVJpkoVIjP6wpxJYppwO8RxXRJuyscQ51yOvDqlxY4V8I5nXvv0wqI5MbVn5OqLnK9G1SxW1OAZTxy1mCSLiSTns+PFS/imqpLL+Hdm1QBAWwnaPdsKYBUdKhqijtEpABj8QsoguGHfnqoAGs/v59OOL1bMU4rxeFwXsVqbtL5BGJ+/T5pMcHiCkFQJANii+U5P+CdH3IM8cnNAyIi+sTTD4iI6L/6iHOJmP1vbxMR0Z133PafeCrccXkeTaduOFGuIXSAGI5qtUiGuENEvdRw4H2uCHSr4XNEjf1PntuQ56Q1W74WwzAMwzAMw7h6bDQmOMq+32NF+NygUixo3pmqO4SiDLdjgjHmV3OL0OLM0BVCixn29g2KK8SZxAKHirD3/1VcIbBMcuwO4YZ7XSa5ixp+jyBIjuJYzSjYDtTPHaOs48z0ktWu2CdYczgI46W9GtLznqc6a1ywg0YXmiomaLGV3tSlCpW6CFBsNbcRj1rTerfPv7WgPQc0f2Ck3SOo3belXDLxfRmU3RSoAMu5Hl974XLo0NJMx2HY85BSdZd1aFXYq5HpD4wKsOxc4woh70L8jOKyxSOZDvvx+DlXLnk8cErwteMTIiK6/z1nLPzg3ojX59YzHofPQhk++fQo+LsEua1gSeT2vKhX2z9AlRh9+UGU2OEC8p763iOj3mwFU4INwzAMwzCMvcNegg3DMAzDMIy9Y6vKJuP8dYdFnKeYRum7A2RdvI/nLJIhND1MWqJcmPgWlUtWimSo1mhiH4JlkqOwiFZoAyZJiCVaKgwi6mrjoWaNhub10oUsGZVQJKMJi5DyxxD+MAu7pOaLsAvI5yhBQpzQLqKxxc5UFw+Wr851nU+tD9mhogTxIeDEOOn6xb8l7j/lodj/hKWAo+VSQ2RN4RFaydo2mlVVbE1VB9OzoxPgmNUUFmUxWqQs0bT2y4pkIGoiNM9XEh1TRTJ8u0xrtFJJeIvDGrrDIHRrtO72wyKjSAZYo0UJcjzfW6BJGeRxFbSXcAgpkjGQxGxeT83LPfMRF/7w9CHPeO0VIiJ6dMclzE24eMajh249krOGyeDXb0gIYfdF6e3Pijoq/Yynjk9wk7x5fhepcd1DXE/ROT3bIi1K+l/+jrY7TxvDMAzDMAzDWBObTYyTKquJcsiXRaMAylfY+taNZZHRfBqJlWH8KgLl1y8HSnGk7OJ0KJOMivCwlfQmlmhDKIqhFM9IKnqphDhMxJLhbO5mgyWaDBczt/1SyiYvwuHCW6Px5uTrGhLiVimTfCWLZAhoWSdFSrCcLXzxR0UzNC6z3vk5UY30Rf1KKbBYOnSQsAXK5YJLTPe5JLRjM/MJcTI9VPI8eL6VoKJrRVkQUY5TZbqvImqZZD4KaqELKNLQtsFcljTXolF6Qws0Dc06TbM0E7S16kU0wvMtqQwv2e24bbc1mt8HKJMs4PuBV5IlkW0q5ZH5WcX32AUnlT1z2xW/+FdP8z35LacMH3NC3OE1N13KJUvxL/nbRmN5b+g+yGh/VtdFlNc+5HnDIST7DkABhh9Svddh77YAirBPlEW7Sa0kOG5m6VzDMAzDMAzDuIJsJiYYi2VsWBH2YXhR+N7FxCavBJZLZtJFMjTlF9YXTcf43g6LtNR4HCS07C9MK8BskeYt0aBMMsb+lhCfhWWS52iNtiQWON7V/irxlQGU36RHVrT8xaiUhS/G0j0/FdeqhffnqJ+oMukxwaF6ERUYkX2F6V4pVisFrHY+YqGCaH7HNauVq8VdEjC2cuDH3QSxSENrq2hfsMeh2cDyHYh2cA9iiHOfWZrd2TIbtERBDR/LC+dOqkiGb5ewUotjeDE2GBVgjCUO14PrTaU4DAax2560FYU3LpvcPfTrZPXUF80YhONHs/ClSRTi6aF71l67fkxERD/xEbdj8z97lYiIXv/ulIia8siSwyTvPPJMvPnE8pcvUXmFckFU898wnlTBcDDhRmNY5zzsORxck5wiOMcgnwkV5dx7nS+XbEqwYRiGYRiGYYRsNCb4shAlD50ctOlZ61qTOuw/ogtZbzgefYir7hDKeFQUQ76ysCgGxAQPY3VXdYPQFODoEGWWTcZYQCmSIbHAUiaZ46RkXIpjiCIsMcFYJnmxEEU4zIwVtFjgfRCQllLD7wFKfIeFCk/PCK5rkzLjXzPDRHx+m1QcoS/9m9vr0ffY4PIaXnVz10aRGSOcitvsAyp2EMrbigkOlyvkxlErDVJFWQQM6oxKTmtdBlfwQk/FSWouEJHzwxJlmGkU3joYNvO7j3tukQxBn+6GKbcIIbczpf2aoJZJBlcI3973Rg6CYYSPa+XxGb8XwM7J+OPbTnZ95rrb4DOjjxIR0ckdVyRjOvV7QEREB5PQ/UGefYdHrOJ7hVrejcJYYDkdFovCxw/LvCE7WxSg2DY9t/xOM4Z3FCaKDdZcIjSi5fOeH6YEG4ZhGIZhGHvHRn2CLyrW9jx+wNr6Nu5ggcdKyZj0McJjRenVyiSPNK/fliPEIKEAD7pjwgrx8xRPYk11wMx28QNGf2DvBhGOlwt2heCvbCyT3MQEQywwukKcwx3iSgOBcD7btwTfYCXONVoPsqYLtlhT/n/Oz19CfGsUE4xEElEoKWkxwtmsyxc4s8RtF5oCPJewxmE4Xd8JdIfolvJ8fDVLy0XkobzeGOHzHJtLJ+XkoCnCWi/MYLBkWTfA2HtUhv108AduYoZDRVjzBxY0JTf2py5gejhslis6p/vttU4XcToRZTfyA4YYYIwZ9u1YKRaFV943RqfsCsE7I24QY44RvnbLzf/+m3zM3v4nIiK6/zrHEh/xfk7djh5Mwr9BXCEmk+47Dp4O7b8dXSHkdPDPgbm4NXnPG7fvk4TCq0z3rkNrdhkyJdgwDMMwDMPYO/YiJhhZt1Lch9wwR79vqWoq3FCNEQal18cCj5Qh+gO31d+OOOH2MhXHH8YxYFztppYqdQkFuOSYU4gFbvyBeTsnbtzHAPtKca45VoiTHgiskuOHGbHA5gpBUYxl8stcXV9CEdaqWAHFJX3Lt08PXz0KYwO1Q6EowJF6jsowN096LCOooio9WajCqavznq35XWKq4iY9LyOYzkT3BwFdSeSYzcOunMY3VPmb9uEaTvkDCwl3iMAfGFFcIQTVHQJcIZrpoQKsKbx+Oo+j0qu5QaQrykG7c/gD57pC+L9Jpoviys+ya9Nu4/mR9HocuYfdf/Ek946+9h4RET264/J3BgP3jMSiitIrKhXiJC/Kvxv5WgnwG7ZGxQ9Y4ob98eI45LIcBMuMxnywuJc6qmmQyndSKDRFedAhX3c1WzrXMAzDMAzDMK4g9hJsGIZhGIZh7B1bGQ6BCXPDTSelUdNVPrygZL64TDLMx2IY2vSENZo6DolwUYnkVtlkLQxCC4eQRKWaE+MKtW8WwiFmEhYhCXBgjSYJcd4aLRzOuVsmSoyzhLjzkUhUojG0x27qC9y1iwYdzcqie74GFsloSgFL1x1Mz735aTZgGkrIASa19kkCU7uXVaez0I4p3hkIu4FEzKYZJMaJ99oYdyDTmlEb30W05xX+/lroUVdBDK1NVCY5N9QmDIPQitrE1mZKaEEU/tCdEKcn0i3d3WDZyCJNi8CBMAl1hVImmRPihgs41+H3fPIp95D77FPuPlH+29eJiOi9N93ys6m8t/BmvC2sGxfLNPn5pb3Pm5+ECXvCYFTT8IDXjYU0Sklgc+PNqwXf/5V3GR/+kApvQBtHLfQnE1OCDcMwDMMwjL1jK5TgjduPMY3AIF9Lac1K1MRKPicyj2jjMrP8szMqGahNh1KDquKLlmhLimNEQ5iHCrAMO/4IIupQlbBMchkqvz4x7sx97VacCFcfu2HFti/zqZMeF/Pwa1sS4kQBPk9CXLv9XlJXsUWaVu0gRar9JRXJQMqEclTWRTRtoKhKUdEJJSvHW6LJOMhUPlEO1XW/XlV64h2BoUIfxTf35xalV2yktCIFSeUNNoiJmMUMzks5dn3V8atMqkgGji9T06ICGm6g9R5g0Qy0RkPk2tHKIJdKwlvK6ixVLCNljdasN7ZGw+IXovxikYyUNZq8P0zAGq2S5zu3mx65ht/3UVcu+amDF4mI6PS7D9x87gV99NA9S2/eCl+ypLf94BCOrWJfK0U0pKd6OKy9AizT6hrfUci3JSIqxkpyJibE+eXhnSYXXL8lxhmGYRiGYRhGyFYowcK6i2egFVrf8smi8nZ9FF+UzVoUGwwxgZF9CH5FRWWQwRoNFWFF+Q1igWXI8+QLPlKCqVsJlj+lFskAf2Yf+wcxwHPFGo1tY+ZTiQHmr24eyvhCyiT3jAU+jwIsh7lURHGkymy3FaCyBuql/2JHPx51fQkvopQi7IPO2HJnxWhjrfyq0PXze4s0UDmjOEWt4AOsSEqLevVSYoQ1BXhNRDZW1D2eW1qWSFfaRD07GHS3E7zlXXwhdi7YxATzMeRCAvVEzos9JLdIhmKRFlmjdcUMR+WSMSY47z6ARTKE2BqNFeDE+uKyyd1nQCpGeNn6ZRksfoHWaFpxDEEeeT7aldsdcW+nFMeoBqFi/PAJV/Xis0/yMf/e3xER0em77tyfTNyzesq9pU+O3fiYi2NIr+hk0n2eNO9C0rvixtrxvQWXm5eiVIPWPKKWhZrEDI/lXUKxQvMb7/lSdc73RlOCDcMwDMMwjL1jq5Rg5KJdIXLV3Kqqs1XqCws/w8xJrUhGVAY5VITVohioCA8PwuFg5N0dyppjckEBxpjgxhVCOcCiAJSs+JZQHONs5uZDcYzKF8ngjFiIy0I3iEbxzYsFNpYTORyIEsfz/ZVyUccX1EtfjntNm9NiDKs6djrwJYG18Og6nFHjgjDdOxyIs4FX1TNvhhgDjLHAkfLXfcM6T2nglGqcfVpoKjqW7R5Kz9GQh3wMtSoJ+0SqSAaOp3pfOtwh0FGkmY7FMPoVyRA0ZTddJGN57HDKzUSjqlvXe64rhKIEi7LrlV52gxBXCFGCvSsEtx/ecqM/zO4Q9d++SkREb7186NYzDJ/FUh5ZEEVYqyfRhJBL3C8FQ6LmeTsUBX/sViJNmt5sJZ8JQUXYO14p52SU/5QRz96BKcGGYRiGYRjG3rERJVhU1cEwHN8FRJhZt6MF+gP7Ly6ttGDSHxgU4KiMshx8iAUeTXi9o2hY1U6pFcUXFWHNH1gtg4qxwFV3meQKFeBTN1vKJEsMcLWQeKcwFtjHBF9gLDCixQbLub6TThOpfUa1U1t+1az9lMqZCSpJOL0ZJ27XDOW/Z7DptoNEuBJJI18EDSV+tfFOroIV12M+cVgJuqg7ZMrLVVS+VNw0UVcWfzhMKXz6ivmYoAKsSXvZthOrUdfV9gcaa37xmj8wjnf5AwtYLhlyQXJ7ETR/YO1ci69X6hzPLousXbMJyrojBhhigUucDv7AMl74Fwp2axB/YH6GVdfhmhq5Y/7UM+4h+JknbrvpbzhXiLffnAXtj45c+wn7/Z6euPVevzHk/XXt5BkpdRAG4P0r7ydeOV40+1X7dyJ53nNPjD9NFJcHJZ8p2w1CrYlgSrBhGIZhGIZhLGUrY4L7xgKLqqYpyrkuEcu3wetYuwIM40V3HE3TXr5+QBkeD8Pp4goh8ZJRrDAovaODcDrEBld1SWXllFgtJli+5P02+RvLq0w+4RR8gUEB9q4Q7A8sscD1SVghbn4WukNItZsZZ8SuGgu8TpW2r1vEThBVO+MhVABSFWFtfWumiUFM/N6KQtnlKyqxgKgIq6omZL57BVgayjjH/KJvcDaoqqdUcrnx9NxMjmqWirkUolBfXBHEU8dKLx9b7hGSWGCMWVf3OLcn4ipUjhNylN4uuvyDQwOBWNlN+AOjK0TjJtEd2yukr9dwudzzMccfWIY+/2TRfdy0WGCcjq8T4g8sMcBYIU78gV/6pFN+r1dPEBHRe//+oVsvP2eOH7v/OLomFeHC2GWJEW7yZtxQ3CJQzZXXBHk/qeuieScCv2B/imi91VqlOGQI7zroD47zhZ6RBaYEG4ZhGIZhGHuHvQQbhmEYhmEYe8dmEuO2NCGu6SYP7UMuAkyEU73NsUtBLZKhJcKNYChhEWH4QzEYh9N5KF1XZb3wYRAlJMhh91fB3VCyC1F3NCY2RdZoUiaZu4aOF8E4lkn24RFrSoi7CDAsojn3dyhBDotkYJlagv5tRu2WxgS53FK/ifmr2nul8qnaiXHeIqmQ8e6uW7+PkBgXlU2Ggg91xYUe+oZF4DHRLNJ60u6JzE0kisId5GAN6mC6+icqFmlyjGS8kHsjXuCa59U+lFHGBGnNEg1Qi2S0l5OEOCW8QdCKZqSs0dS8Wh6mrdGoc6idZ32KZMgQrdEwzEEbCmKFJkgi3AEXghJrNL+P/DvOb7rn9Y8/y8fu1f9IRETTY/e73XTREXTnA/csffo596yUEEF/WvDPLEUzBuCcikW7fJim7E9r9+N6KhIegWEQ3Ylxfj3nToiD6b798nueKcGGYRiGYRjG3rFViXEXXRxDQyua4RPuOr5QvIrol1mPqt18TYUKLxpM+/Gx8jUUKb9giYYJcTIEa7SycpYrZT1XyyRrRTJ8+VM8NFGZZLZ1OQuHNZeOrB67cSmT3CTE5RXHmC/ChDgtMe4ybMt0RXiHEYWOR4uU9NIXVH6jJDAZrPf3ixJtqla5VO59qLg06KwK1c1m36BYBlbV8MeOS/6Ww2D6hXGBHRGpXfc2UnV4zLTytt4iDRRgn28rCXKrnne7aFfYF0yAS5RNVpcL/hsS3FJ2e1phlrr7+OeWPxbiYhrhenC9uUUycD/mVev6L7uVXjUxDq3RmDGX+pYiGfOD7peh20+fERHRf/3RE7e+v/4OERG9+aqsP/xjxSLt0cMwUU56R+WZecTvEZLkphXJaN+KfX0t3zZcRnqvUeEt8J0mUm6733n88mpCnFmkGYZhGIZhGEYWGy2WsW4uyiqtKuNlhiv+DVGIVZFQIZRyydFXFligFUNQgGUI5ZF9LDBYokncrx9W89a05UUyBhxXhl/+vmhGyUrvwsX+0oyt0KY8PHZfu6IAS5GM+ZlbbxML7IZTsEaLY4K7Y3/7hgZ22Zzl9l7E52StrnMrKQaNIocqpnfA4/FksCegxQbnLrcisnRchpU6h+2YYFR45nKOgVpVSy+JxARLzK/YecGxu+hCD4iodnJtFjXY3ME1vOyIazGYfjoUDJiLZaEWCwo1quW8axv1t6cXML520Opxm9FUMU3x7asMk25BmLRGi6zU2NJSsUTTrsu4NspySzStSEbupda+9jWlVyuSoVmjSdlksUYTJCZYYoFl/MXnHhMR0ceuP+emv3qfiIjufRguLwqwIM/GJ56UZ6g8E91QLNPQGg1Pn7ou/Hx5dxkMw2EU+6sU/BKyY4GRcxbJ8KtZbeuGYRiGYRiGsbtsRUzwumOBU4rwRayziScNs0ZzvzIKiKuJDoovigEuEVqGZEIBjmKBxQ2CC11gYYwcd4gC/lpRhP3fKBKKxAKXEgPMivCJU4ClKEb1yM2XMskYEzz3QnJYHANjg4VVyyMvU2tlXt9z2JcO38XPUJBWaq+G8DmsudkjsnwNy6WKE0SOB8Hm1ZjEXJbFEMouikn+wDsdhBnuHl+XNFSAfaEHOHY1bhxXqN3TsCR14hhocZh9yC0uIKBqJoVGmoIj3X+bjwWWUrLyJ4oLjS/a0jcWeA9cIpa5PLSHCCrD7XFx/YFzSIv5RbBIhoZ3e0isLzd2OFXGO7X+dj4AKrulEhss+F4QeY+AobhCiPKLRTJk/JPPumfi0dz14L7y79zROWTl94P33DP65i1WfBfhe4sM0S1i6AtehPvti2N0dNINYJnoWYb3KsXhKmqfmk/KBkVxLnA5c4cwDMMwDMMwjICtUIIvG90NQv4rVHU3otZBHA3G03hFGBXfyBWi2w3CxwIPD4JhWTduEDhc8Dx0iRAkrhG/xEQB9sqwuEHMnfLrXSFOnOQrCrAowrNTt5wowRgLHMUAQ3lkTfBZpxtEX0V4JxVgLFcLx3cgEhFm6+eyWHRP976k7KAgty3Fl9SPZ1ofaMpQl3Lk/xti/rCccrMT3UqwDMvS/W3+tIH417WzhhMPdy03Gz+Ko67Coe8IIFCzwVkDY4uRKKtcv9HnAedVoamn2wQeA035lVhw9AdG2rHAqAB7ZReGEPOb6w+sX49hzHCuT3Bu2WSkyxWCSGKCu5dJ+gPDHyX+wOgKUcHvtxi54/+5p/nYvfwfXDu+FsT14fTErefZj4yDcYn5FcQfWGKH49MCf2P4Oxq76JaKLENYGdY0aK9kFbzTVaY7RCIiYAeuZsMwDMMwDMNYL1daCc6NDU65RFRVHc2TMDQ8gKVkWyZSiLUCPtGHOMTRRBXi5KsncoUARXjYHQMsQ/H8RVeIBfsEL+oZlZXMC7N5hfGgO9bLKyfiLlBKMC/HAJ86BbgWN4gHbn752O2TVIjTYoHnqk8wD1esDLeKc0NKEY4+UlfNjL1sWrtZg9QiKoH3PgbFOBl8V6EU2P1DebUqFe+6ZvPbdgEy7w+qxLfqPsFhLLA4HPjZ0tGDgezJnbsgxTjaTHye5vq2InIMRd2a8bGZYzylKIdePed7lI/HDpr1B+OoU/gN7ZB2hA8UjPXFdtqw1U48rVHZTRG5Qnif6NR5FCq8uttD91BzhfDrT/z8sZ9w4RVYjAVW/YHFFQKu7xErwANeuVYprubnxL9+1vkDL/76W0RE9Oi+W+74scT4unZjVn7v33e/1e3brDBX4bNydEviaOVdqTs2GCmKOlKAozb4zqIqwon5vl1C6e2pAPtmWa0MwzAMwzAM4wphL8GGYRiGYRjG3rHRcIjLKpO8Tss0nwQ0DMdXRc1DiMIfEkUyNGs0KIccJ8RJeWQXY7CQoU+Qk2IZC18adsG1orXEAvldfUKcpP2UkBB3Eg4lIU6G01PuLj1NJMRJWWRIiNO4jPLIGBaBFnrn9PfeDHKyQ/d0jd3YvoiGYvuFdn64fryoMg/SRZVNbo/H3Z5unlrwARPjxArJ536JJZKMQyiJcN57l1JyuimSEdoLFYku6jZad7SfD8dM7OVkOK8kQUo5H+R8ksRXKZYxgg2htdKuhBpdBKkiGaqvlbIeHx5BjRVhVPSiDodYJAOs0RpLte4ENyySgURlzRPFMrTlNbqs0WSoWaEJUViEYo0mCXF+mxwiJPOxaMbHrr9IRESP/sN9IiJ6cM89YKRYBibAyTPyYDIIxoXJRN4zwumpXNKuYhlYLjkCrku1SMZQeT5ozw3ZJ7RGw+UUdukRbBiGYRiGYRhrYSNK8GUpwCk0q7TOtpL0o7SV+TQ+5075IhjKV1OkAIeJcZESDOWQMSGuSYCbwvgsGJ6VNU1LUW7CL64hfxGOB+EXvhTP8NZocxfUTzMePg6HPiHuoVijhQlxUy6agQlxcuzXlRC3TlYtprFt1HXdsvmSISe2aIlKmlcWkvsDKck6q5r25xIUy0AFWCzSeDirQhXM7xOUS27Kqbr5xXl3uedJ7lWTFcTzOFEplXjMCrBPKqRgXGphxIVGRAGWhLjQlk6IeiLUHQmVyGipzGNY7ELd5K4iF+2hNENrtFTiHMXKrp+emaGoWaMhzW2kWyn27RKJckLfIhlRIl5L3dUS4aJyyTxEBVgKA434fiCKrwyPjp01KU3c73Pjthsfv/sqERG99fIhb9dNn/IzUSzPoo4k/pln0/CPb5TjfjeCYlD7Yhn+nUgtfqEkZ2qkenBk+dwXOFOCDcMwDMMwDCNkqyzSMGZ33bGbydLH/tMRv46KC4/dlPiayB4ElGFfJMMrwO4TL7ZGCxXfYjRx42P3BVnzp+GicvKqxAD7YUsBJiI6XQxoyrHA+LNwGBNNJK6QxICdbWEKlsd9cQy2RDtmBfi+U4BFCcZY4AUXyZhyPKWPCeZY4LnEBJcwhK/h3PNpFWu0/HWG59hOVW4VJU0KPXibL/5bRCGCOLjkH+m/7OH3yTXyZ1ABPm/5ZKSqOxQfiXOdS5x8WCDAy+KKzZfEt/r7S65FWl97rxVpSqFLgRxdpUlVy8ZjJX+zai8HMeh4zMF5sX9PgxaDLqB6WksP13m7+y6RbC9OZTkY1nWdVICTRTKUcsm55Y8FTRleVvY82I7ys2O7dpEMbTlVGQZrNFGAxRJNxiUWWBjzs+70ujvXPvsjd936/ubviaiJAUbEclNif5vcE+6xEms0VoAHEFfvf+6i+zdql1luOg3k3aU7Zjcq+KWA833vdyIWONo5HLdiGYZhGIZhGIYRslElOOXWIPMvWhHuFRvsHQh4XJYZrRYnphlNR8bRGCscuUDw8EDcH5RYYF8e2cUSeTcIVn7nlVNr2wowEdFpOfCxwPLlPZDsUN4l+XJGBbjgg1XPjl0DiAUu77ltlg/cPsxO3T5KLPDpqfuBZlqZ5Essj3xeUBGuLkB1vgiKoqCKd74+YzVTYjwl7o3beucDn9XPSpDEw8lKc7tXJK59SZziOsBLbQjndVm34lv5ukCz/Dmeg6Ceo8OBLzTCwk7tBeQLOmf9BtxA4lsbl4j0MdWKETTzsUcvvM/OvRLMPTkSI4yxvRCD7mOCvXqemd7f7Mjy9pmoWejbhFYkA8czimO0qamKXSAUV4hmmTBXRIYY6yugK0TfIhmCViSjrysE9mBUZRFd936bKVcIXqkvksHTp9zDK/NFCRb+zSfcDeLsz9507YYSE+zWM5mEvSvCeIz754aTQ7nu8+4zA1CIi0GjIqu3YcWtpVDynrzzVaocMsYEqzsNSrLWbPlaDMMwDMMwDOPqsREleB1+vZeNV48TmYteIU59bgJNhiV8DYEi3PgDj8KhKMDgD4yxwBV/ybXLIbeHU45dEgX48WLI0wuv1MifNh6IIgzeo/xt5WOBxQ3i7DEREdWP3LBiBbi6y+rzMW/rOIwJnrHyOD3jTNo1l0cWLiIWWFehV8vK3SiKMifDSFXQpBrtB8L7AqpTigIQxR4qMYfnpZ0VjkqQdzoQoVVxh1iAeu7jWwuIp14zde22XzRSM29X4l37rzP2dVXaaXHUfI9pYoILXNDtmtLzIOsRNV21BYiTA8L2KdU9N452m8hVehHNP5gPcfu8TrmwaP7AGv58Wr6Hvf2B+7pC4HbwfGvfB/y+YxvFFQLdIYSa732iEI/4Xjs/cM/EH3vO/R7vf5ef47xz4v87GkM5Zq8AiyNLGCM8GoVx9bnpE+2YYX+/RyVX7cXOvH609tryqemJc36HrmrDMAzDMAzDWA/2EmwYhmEYhmHsHVtlkXbZaAlyaGNFVNNQ2mBCHKyrw4K9k2RSxxC6pQ66E+EaazQsk4zFMVw3iliiSQKchEXMuKTx8dytR8IgjrkLcloWHab4HJSPvSKFO61GA7ftenrPzXjswiDooUuQq+66fZGEuOkxJMSdSEIch0FckCXaJuh7vmwFC5ecUZ9wKe15mKhUj6B7WvD9kloYRCJpJxpXllNIddtqHu9IVRa0WIRd+di1L3+qZpFWQQiJ38ca7kE9w6kitC5ACIeQ069Q9JDzFB5RLdIW4bbmEJ3QWsDtA1jySRLhkKOt/J80r2AIVThS9wPsE1aPIe3OZaskuqlFMnC5ljWa0IQ3oAVawhotUSRDS7DEsBss1pJKlOtrjYbrw2u9HQqhFssAazRByiRLkYwKbjaYEPfkJ1zi+lMzF9b4nXc47GruLoInn3HP2gf3Qss0TIiTdxwJ5zw4DJNLy0QYli+RPGqG6u0XimTEiW7huJr8rw3lh8kNr7BwCMMwDMMwDMMI2VBiXDi+CwUD5IMO912mawcyFXCOhtO+KAYkwvmvJSyPLAlx3hqNE+LGR25cEuLY7B6LYczZIu2xogA/9gk/RWSNNgSroNFAprt9GYpCc/rQDR88cvty1ynB5R2nBJ8+cn/TVBLjTtx6Z1gc44Is0S4iIU4DLdH6JlBujJqIZs5Wrz7j0tqsToj6EZ3rZWiN5m2/1MS4zKQdGK/XlFw4VOyCmt0uWqV/w4S4BavisypUqeSgaDZfQj2SJKKeEmOq4IPW3hd+COt591F+UxZVzSa71bIFybGTe0yoIGLZ5AqKs+DQn28iLWvSX98Hzi4lxAmpMsjYLmmR1iS55V5vkTUaq8nYoxhbm4XXkKbQaglxucK/hm6R1iTFYmKc33eePvAJbmGRDFGAB8rOjWf8nObn+3/zL92zs/rGf+R9CJ99N265oSi88jwZKZatohCP/fzlByl22gsT7FwbUGqVxLgiYSoQJf+jJZqG1nWXufwOXt2GYRiGYRiGcT62Oib4smI6tXLK8lWVY+nWCDLni/Us0GBati3lkkdYHhligaOhU4Ln1QkPz3joFOBHTtyjY457ejhzQ4kNPmH1pV0EQKzRJkNRht30A/6P8YBt2aYcA/z4gRved1+15XtOCV7ccxs/e+T2VSzRpDgGKsDrtkTbBrY5bjmgrrxF2mLG58Q0VOJGk+WKsB78mWBNRTIalXPQ+v982tZIs+nQTyNq4gYbERKUXolrbalJRG3XNzh2ooL2LQWs7zwFG0iUAF6m9mFxg3i8e7kSCo2MqArGo2IZUdlkjrtehMfKj0sMcBmeZ3IMi/PeKHZJEdaumZQyHFmruQHG97b/G4tk4LnTTJfj3x3ji0UyNLDohTbU2uvrDYeanWH7PqCVTx/yuYhWaJoCLLHC4ykXxbjunon/5hP3iYho+j+/LGtw/88/z/iw+5weDGV74bgoxCmrV6QpjRxPU991UttIzVeLZCS6brFcssUEG4ZhGIZhGEbIVivB50WL9RwOu6cLWEbZjbPqCH79/qNEKU+Yi/9YUWq3NkUywBXCF8nojgUuyX1ZogJ8wpn+xwt3CjycDYPhY1asTvnvm1fNLl3ns0Y+asf8RShuEJPhdSIiqk9edw3uOiW4uuOU4fIDFwt88tCtSIpknBy79ZyBK4S4QaRigrcxFviiSn9vhEVYtGDBvQZoOCBg6d8aYzTPG5upfOH7srZrOuRtZWjuY4B5yCrmlJVhDpeOVU1eCcYE1xJH750OePq63CE0FaSSYhmr2xykYjv9pjqUNKKWAszHcuYF3PDCrP2x43FfkCRUhGOXiMQFvqoyvPohu3xSscCaMhyVS27HBOcdN3SNSMXqNsYomlIcLq/FDgtR+8RuRzHIcJ52uUP4ZWHe4bT73NOUYXGFkJjhh0+55/cPPPFRIiJ67x/DbUpsbxSzK72z4gYhijErwJWkDo26f4TYICVUgJuyya3lc4tkaD3q4oSltV815jfTPcKUYMMwDMMwDGPv2Iw7BGQyIhetmokCiIqwFhu8CXym5Bh9gKFMMijBogA3scCPeXgWDB/OQwX4HitZDzje8yHHCp9JmF1d0AF//Y3hQ23CgUcHgxtu+sypzfWjO254zynB5dtuX2b33EpPIRb4jP1nVTeILfYDxnMJVebmnJK/5eL3aa3UVVP6l88RiW8V9RIdD5pFuxVhVEW6thkM10zuWtvKkC/1KzHBrE7KOBoTCLXiEzyUXhVRiFM7hceS4y2TdyxRRRLH1Mdv9jjmsb9rOPTtMI4SYoR9yWmU8L07xChcHhRhOaaDyB0CeyCiHVvy11F2+dWtQnF98P7AqfZM1/nQ2x9YKZesKbsamk+wpgCnXEu09j51oQrPM4lp79w3ifEHVwhhMeo+d4a8sTFvpOJ3oyc+7dybDj54i4iI7r3rfrfR2O0kP2JV4phgztfpTgFQkdNBlOP2aRJdDil/YEFVjHG4qisELG8xwYZhGIZhGIYRcqVjglOgWidqXuzwUHslz/sFQ+W4Rq2UdvwVXIXxRBio6GNr0BUCh5EbBLhCiALMQ/EDxlhgjP29zwrWXc70v+8Wo2NWVSTGcVgQVeAtKLHA18fuoPhY4AcfumZ37ru//W1WgtkV4vhe6At8/JiVYYwFnoMivAMKsDZdzjUfI7wr/sBtqvAcn3s1gmM2vV9w97keKXFabDA6Gci4HNPoC1/iW1mBqAfB+KqxwV1Kps8Y5+tCYvnOTt05LTH0cUwwu0OACiqMDtAZoad6ifNTMcGg9GpxnpqK14Ua64k+wXBsZlPuCfIF3iAmuArjqWt00vD7WnTvSN9rTVPBW9XTzhFKfTloKlquL7D34IbfosMdAkF/YCGuOBpO1yq/pSrCCXG7PFeIZj94eXB66HSHUOLcRdkVt4dSUYAlFnjEeRay3Mkt56z0r19ynvr13/4dERE9eujaHbADjzwbq4WiMPNzZgG/0cjHEncfFM0XGGOBi6KOFVxUeGWdmU4UkT9wsxPdCyRjgvO2a0qwYRiGYRiGsXfYS7BhGIZhGIaxd2xVOMSmu7e1hDmilm1awl7tvBQQ1F1AkYzIGk3KJI9Ca7T5whWmmJXOjuwxJ5fcn7mfXMIgPjhz2/uQu7bvc1jEMXf3SpLAeBD3OhyN3MxrI9eFMyG3L/WDd93wPZcYt3jTde0c3+Fuz0ccBvHI/d7HjyUMwo3vQhiEsOycaSPzfViEb7/t/aoNvuADd0tLAqNwHZO7cvshMSzC92eiJ1ZY7KIvWiKN1k5oO7tJEpeEQcjwZOqugVPu2p9rxTJmEk4Rzm8iQBLnQ98SwKmEKKBJcsq/xrREpAq6lwVMkBN7ObnnVNgF7xNkpZtauoK7wyL8clEISSIMJ17B8vm7AFqh5c6HiKZ2iINeDCNMosstkpE6ys151W2Zljr/UkTLKaEOXWAbCW8Qq7NK6ZIflKE1moRHPH7C3Uf++xfd8/v4f3U2ow/uuWvkyafZVvRU7sWSLMrrGyvbU50Su9unaq0MBnGZY5/Ej6GcGphApxXHQPAdqVCWz8SUYMMwDMMwDGPv2IgSjFZXF6XsofqWS9O+nSDH+yyJcFA0o/RfhGFSXa4fe4H2Iv7rKlSCvQI84dLEkBAnCXCzyinAZ6wE35+55T48cz/5nTO33vdZCeb6FfSI7a8k0Ue4dn3u//uQd+X2gTtQ10ZPExFR/eAdN3zPJcaVb7iEuPm7Ljnv9KHb55OHbtsnnBCHRTH0fKntUYBXZRgpwDv0N5WhAie/m78GFlI8I1Pd1pK7FiIJwlBR5go+lgV/0xdrUtejhJ2ySYiRxJcJZ47OT90PO+OE07gEcJjcJb0eBxSqmf5PLOFv1bKANFAVSSTKdZXF7QvWQol2GZMCZ66B3GskGdcnxsExk0RM6YE4zFDqwh1YsUgLUFO1/f038Dv7ngBV+YWEOEXdbf930hotUSQj7nFRlGIlcU5LlOudCAdDrTgG9mC0kSS6a9Pl5ZJlOFyEPUnzCd83XnTXwueecc/K97/rnuv377ln6LUbrp0kkYtNJaIL/8sPTlMcAy3RcHrdPMxEEUbLM0RJnNMt0hJXWcpKzYplGIZhGIZhGEY3WxUTLKxa7nhVVimSIUKNHMDG8kq+FsP26Pak4b+SxvDFjkUypEzygbMlo8k1IiKaze8REdG0PCEiontTt5wowB+yAvzOqdvOu64Z3X3o1nvy2LWTr+ERx/2ORhUNr7s/6okDN+2ZQ44JHtx0f+OH/+yGb7xHRETz11xc06O7vO4HYonmlpOvWSyOIUNh3QrwZZRLFvRzKjxftp6WslT6WE6J1WTFbs4FJLxVGqwjN1YLYoLr0vVCFIkYTa8Ie6u07mObMuVfFlMoatCBmNyzkjM5dft6xorwtARlTWKCWTGa87hYFvn4Vty32Kdt+RCIFMCUdRqzrDTuoAj3CY8XWk01uxgqapOpO2Ynx+7+cMydTV515B6BOcdZn3IcZFNEx/1t8XkG8di8Y/irq4VG8NicUzneCKnfOWWRhvG9HYpwilSRDAGV3mY6DrtjgTWlGXsmNKL1KUUy2j0OeC5LkQzpIRLFt5byxfKOwRuRmGGZPj1yz92Pf5/Ln7n1wBWW+t5993vIu88ZPDPnUDSjb6Ev+TlVp7xC3o062vmCXtDrEMX4LleGI0VXD2DOayfryyxuY0qwYRiGYRiGsXdsJiYYYoEvU5kL9yOlOLbjNsOvQ8nu94qvlD+Fvy27KMJQ+ZrCWGAZSizwgVOAMRZY3CDuTp1i/D4rVG+euPW+5epW0Ad33Hoe3HftJD5PvnAlFvjGrTld59KJzx45hebmmGOB777mhm+4mODFd10s8Ml77mAc33PxTY8fuuV9cQxQdho3iLxj5gtPZLa/yPMs1yVCwDLKOwF3fzQxweEBLaEkMFLglz8SqZscC1zOuueLyCmxwIniGGjWn1KEl+HN7lnROWIZ8/ixuz6nEjfIAyk5LbHAEk8t2dwVFICISk2LmpkqoiGg0jccdc5fFvvZBarAbdQse4yhrMLM+Oqhm/9oHjarp+53n/G9S1Sw0h+jpbsaxx6mjlkm54mbvnRSMeFKaVnNJaTm/xF1nDuJIhkY44sx9017HM9zg9Daa6gKsHchwUJXMdJGil3I/QARhbjwMcFhu8e33XP4xz/ORTL+n39PRE2RjCduc9w8PzvlWSl5NCnk1B8l3viakHElJrhopvv7uby7ZBbFQFeIqEhGUulV2uWWW8bV9WptGIZhGIZhGFeADSnB26UAp9sV/uPCO1vI58MI4t4gNliIYv008OtK/IHHoSuEjwVmJXi6uOuGHAv8IccCf8DK7lsc+/uGCzWi9951vsIf3nHDx/fd+jFbVWKCDyYlPcfi8yduOIXmOrll67e+QURE5Xc+ICKi2ffcRh7fFTcIVnoeStzkcgVYK2ctYNwTjuPv2zfG/CLOS/3jdHdigrF8rfxuosxNp6FSLC4RBWT7quUxhUUZDktwiRBYCiwG7hyXGOCUO8Q6Q8wly1sUngfH7rqci3rE55h43coxkpi+EZccP+rtcJBQhL2kMwqHAh/7ZbG/ueCmRQyTvALMthfVTOKoxzPuGTrje13BKzhxvVrTY7e8qGDSC4c9Dl6lSilSa3KJ2Gq0eEhF+W0kPzfAeN6c8yTlD4w0duKaUsztICZYu35z/YG1GHZRdTV3iGDfFX9gUXjRH9jHAis+wg+ecc/Sn3rRees/+uqbRER05323L888557Pdz8M74GYP4N1DKSnOtUjLcrvcNitADftePp4oLtCDOE+TxS2EyIFONFTCM+Lxh+46G7newYTvVtL5xqGYRiGYRjGFWQr3CE0ZTaV6ZiaL+tN+QWjk4Nff+urSnOQQF9gXCeKDY1LxPKMSZ9x6WOB+aeCWGCJAZ6WLshX/H/fO3Ht3+DY3+89cut952233Dtv3XCr+cAF4j37yKm3omhJ5ZrBM278qcOavv+Wa/vU5GPub3mL3SC+7arazL/lnCkevO/ii08fuH3wCrDiBjFXYoHTvz/xct3zV1V0tfNlWQ9C36zcgf9K3qH4QukFUdwhZmfh/Oh3yY39qryUyONa5bgQ7wohQ/jGz1WANSFxMKy9YiKIolPwBX8SxQSHDgfijCLXwmQirhAJPYKPcV1KbHCeO4Q/thgTjMrwGsAYS1HWMLte4qlFCRY1XVwihqzo1nfdzevxXXdBPnrIPVA3MH/CDf1phNdiSiK8ioowVgpMuUQwXsX1qm48zPUHFiLXEFB2NTSfYCE3RjgFVjZEVwjfrnUeS1v0BxbFtxyFx1V6O6SnVRTj0+vunL/9vMvp+VfPuGfra2+45/y9D10X7nMfde0kl8C7yvgKpPheIsMwdnjse+fCYxCZxyiuEF4ZHg5j5TflAqGN943l1dprbhSmBBuGYRiGYRhGiL0EG4ZhGIZhGHvHRsIhSh8qsLzfYpUiFm1SFloYBrHMIm04hAQ4rPgqSUMQ5lCKaba3QAqtkJAoyFw8TQ65TDKHQdDEhTNMS5cQ92juyiq+d+ravcnJJN/lMIi33nQJO299zy337Fsu/OGJO667RbomJSFOzLuv33AH6Ydu1/RDt906D+67BLj65W8TEdHsHzmY/223LSmKIfYuUhRDumS0MIg4LCUMNRE7Ka0XJLdX86KKrhClrdKw2wrHt5a68v2GFSTGye8oXXUlFn4QS5wxHxSx/VMtrPgHhcS4msvpFhgWwacJlk/2u85dvJo1k9h+aVU7l91+pFvTd3Meu2FTLMPtu9h8SalwCSWRrv1kmJTga7tC8iCe/FpinGKF5RfzhUb66yNoTeW7jCVBTuykeJ8Pj114g9jLffj4CSLyuca0eMfdo+584I6hJMYdTDAxVpJ5pKtWOYarJsT1KBCxtWjFUrBcMoU34Rw7OAyDaK637vaxVaFMD8dTZZHjdt0hAfH2w+EcwnUqHO9KjOM2WCTDz5ekMQnJVCzUTm655/Wnf+CBW9+rrxER0aO77vdo3mHC7cuzUEPeO5qE/e52ellkmF6E02lQ6GWStbCGIT64E2ENGn0t0kpIqMbVLd+aYRiGYRiGYVw9ilqrbWgYhmEYhmEYVxRTgg3DMAzDMIy9w16CDcMwDMMwjL3DXoINwzAMwzCMvcNegg3DMAzDMIy9w16CDcMwDMMwjL3DXoINwzAMwzCMvcNegg3DMAzDMIy9w16CDcMwDMMwjL3DXoL3iMViQT//8z9PP/qjP0rf/va3g3l/9md/Rp/5zGfo937v9za0d4ZhtLHr1TB2C7tmdw+rGLdnvPXWW/QzP/Mz9MILL9DXvvY1Ojg4oFdeeYV+7ud+jn74h3+Y/uiP/oiGw+Gmd9MwDLLr1TB2DbtmdwtTgveMF198kX7rt36LXn75Zfqd3/kdOjs7oy996Us0mUzod3/3d+3iNIwtwq5Xw9gt7JrdLUab3gHj8vnCF75Av/ALv0B/8id/Qt/85jfplVdeoa985Sv0wgsvbHrXDMMA7Ho1jN3CrtndwcIh9pTpdEo//dM/Ta+//jp98YtfpN/4jd/Y9C4ZhqFg16th7BZ2ze4GFg6xp7z88sv0zjvvEBHRK6+8QovFYsN7ZBiGhl2vhrFb2DW7G9hL8B7y+PFj+pVf+RW6ffs2felLX6Kvf/3r9JWvfGXTu2UYRgd2vRrGbmHX7O5gMcF7yK//+q/T22+/TX/4h39In//85+mb3/wm/cEf/AF9/vOfp5/4iZ/Y9O4ZhtHCrlfD2C3smt0dTAneM772ta/RX/3VX9Ev/dIv0ec//3kiIvrN3/xNev755+lXf/VX6d69exveQ8MwBLteDWO3sGt2t7DEuD3i29/+Nv3sz/4svfTSS/THf/zHNBo1HQFf//rX6Rd/8RfpJ3/yJ+mrX/3qBvfSMAwiu14NY9ewa3b3sJdgwzAMwzAMY++wcAjDMAzDMAxj77CXYMMwDMMwDGPvsJdgwzAMwzAMY++wl2DDMAzDMAxj77CXYMMwDMMwDGPvsJdgwzAMwzAMY++wl2DDMAzDMAxj77CXYMMwDMMwDGPvsJdgwzAMwzAMY+/4/wHS7Q7v9l37jQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,3, figsize=(12,4))\n",
    "\n",
    "for i in range(n_out):\n",
    "    bx = sns.heatmap(uu[0][:,i].reshape(nt,nx)*scale_from_outputs[i],ax=ax[i],cmap='Spectral',cbar=False)\n",
    "    bx.invert_yaxis()    \n",
    "    bx.set_xticks([])\n",
    "    bx.set_yticks([])\n",
    "\n",
    "    ax[i].set_xlabel('x')\n",
    "    ax[i].set_ylabel('t');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library and recovered PDEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "xi = coeffs[:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00, -4.39520931e+00],\n",
       "       [ 9.93720954e-02,  1.03463442e-01,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  8.03816342e-05,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [-9.94670536e-01,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "xiS = []\n",
    "xiS2 = []\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",
    "    \n",
    "    \n",
    "    A = uu_train[2][i]\n",
    "    y = uu_train[1][i]\n",
    "\n",
    "    sol = np.linalg.lstsq(A[:,[2,7]], y, rcond=None)\n",
    "    xiS2.append( sol[0].ravel() * scaleUT * scaleX[[2,7]] *  scaleU1[[2,7]] * scaleU2[[2,7]])\n",
    "\n",
    "    \n",
    "    \n",
    "xiS = np.array(xiS).T\n",
    "xiS"
   ]
  }
 ],
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