FigS9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f23492d6-3993-475d-8ab9-c6fcb4df34d4",
   "metadata": {},
   "source": [
    "# Path to Zenodo files folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c7f711d3-0798-447d-aa69-2f992c3e299d",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'PATH/TO/ZENODO/FILES/FOLDER'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f50fa44-a7cc-4d86-ba28-02a2f1a0132b",
   "metadata": {},
   "source": [
    "# Figure S9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b251d96c-0192-4f3e-b681-9f96e0d3db1a",
   "metadata": {},
   "source": [
    "## Panel a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9134bbc6-36e7-48df-9b67-13931e5c757d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "weights_2ndor_spreg = pd.read_csv(path+'Fig6/FYN_suppressor_mochi_order2_sparse_regularized/task_4/weights/weights_Folding.txt', sep='\\t')\n",
    "\n",
    "weights_2ndor_spreg.rename(columns={'mean_kcal/mol':'mean_kcal_mol', 'std_kcal/mol':'std_kcal_mol', 'ci95_kcal/mol':'ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in weights_2ndor_spreg.iterrows():\n",
    "    if row.id_ref == 'WT':\n",
    "        weights_2ndor_spreg.at[index,'order']=0\n",
    "    else:\n",
    "        weights_2ndor_spreg.at[index,'order']=len(row.id.split('_'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b1ac0d0-6365-4b3b-ad19-02c4fb7de5ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/32/dtq6y36s6mjg1zcf2_9dskdm0000gn/T/ipykernel_9184/3456112449.py:54: UserWarning: Setting the 'color' property will override the edgecolor or facecolor properties.\n",
      "  ax.add_patch(Rectangle((col, row), 1, 1, fill=True,edgecolor='black', lw=0.0001, color='black'))\n"
     ]
    },
    {
     "data": {
      "image/png": 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/jz/++CPu27fPaRlELqGXmjAriZvc3FycN2+e0H5aLBYcP348jhkzxuH3lexrnmXLlmHLli3xo48+wh9++AEzMjKcbl9iYiI+9dRTOGPGDEREvHv3LsbGxtqcoNqXs1gsmJOTg926dRO2Jy0tDR88eIAmk8lhnc6ePYsNGzYUjq0uXbrgqlWrHC5fDjQ8QwdQp7Fht2/fBgB5Y2xTU1OF8U5ykbOewMBAqF27Njz33HPw9NNPQ5s2bWDEiBGwZ88e+PPPPwt8v2TJkjBv3jz4+eefoUaNGnD+/HlYvXq1MAbQ0W3TJ598EmbMmAHt27eH3NxcKFGiBFSuXNnlrfMnnngC1q5dCx06dIDk5GTo1KkTFClSBFatWgVfffWVw7Ft/HYHBATAq6++CkOHDoWRI0fCkiVLHG4LT926dWH8+PGwefNmKFu2LLRq1QqmTZsGLVu2hA4dOkBmZqZw6+jixYvwzTffwLx58yAqKgoePHgAI0aMAADXt5cQEcLDw6FChQpw79492L17N4wbNw6GDh0K8+bNc1gmPj4eGjZsCC+99BK8/fbb0KtXL5gzZw5s3769wD623w8hISGwY8cO4XdTp06Ft99+G8aNGyfcQuTLFy1aFGbPng0HDx6EkJAQmDBhAly9etWm7tbLLlKkCLz55pvQvHlzYZ+/9NJLkJSUJHzP0a36e/fuwXfffQf169eH4sWLQ1xcHIwdOxamT58Oa9asKbC/AMTjqXLlyjBy5EhYsGABDBkyBL799lvhlrg1mZmZsGzZMnj66aehcePGYDKZ4IcffoDvv/8eTp48aTOkYdGiRfDvv//CgQMHoHTp0pCeng6rV6+Gjh07QpUqVWDq1KnQokUL2Lx5s8P9vHXrVnj11Vdh5MiRkJ2dDf7+7pvtkydPwnvvvQfdu3eHhg0bQkJCAixevBhOnDjhdLhFsWLF4PTp0zB//nxITEyEFStWwNChQ6Fjx47w22+/2ewvAIDixYtDrVq1JMc0AMCjR4/gu+++gytXroDFYoGOHTsCADfcwNnt1jt37sCAAQOgevXqUKNGDejQoQP89ddfkJ2dXWA7cnNzoUiRIvD666/Diy++CBkZGfDrr7/C559/DlOmTIEaNWpAmzZthDHOAJxL69vdiAixsbEwadIkuHHjBvTv31+4te/n5wcxMTHwwQcfwOrVq2HAgAFw4cIFeP311+HBgwdw6tQp2L17NwAANGzYEIKDgwsMv7lx4wbMnj0bqlatCq1atYLOnTvDkSNHAMB2XKifnx/4+/tD06ZNYdeuXfDHH3/Ae++9B4mJifDNN9/AqFGjICMjw+E+AwCoUKECHDx4EI4cOQJFixaFChUqQHh4OBw9etTh8I6AgABZbTQAN/xh+/btMG7cODhw4AD88ssvTodfISKULFkSoqOjJbfRiAhPPvkkrF69WnIbzQ/n453ev38fdu/eDYsWLYKjR49Cx44d4caNGwXWA8Dt/927d8PatWth2LBhEBISAsnJybBp0yabds66TO3atWH27Nnw9ddfQ4sWLaBs2bJw8eJFWLduHWzbts3hdq1fvx6GDh0K/fr1gyNHjsDdu3cdfo8nMzNTVtzcv38fihYtCu+99x40bdoUEBH8/Pzg1VdftRmmYl02Ly8PnnzySVizZo2kfW3dFrRv3x66d+8Ow4cPh7Nnz8Lnn38OH3/8scOhF0899RRMmTIFrl+/DitWrIAePXrA999/D4MHD4Zhw4YBQME+zs/PDx577DGIiIiA+vXrQ1ZWFnTp0gX69u0L48ePL9CuAwAEBQXBihUroHnz5gAA0LVrVxg/fjxcu3bNsyEaitNtH8Td2ceBAwfw5s2bspZ58OBB3LVrl+y6pKeny/r+xo0bMSQkBA8ePIiI0s6kNmzYgE2aNME2bdrg+PHjJa3n9OnTePDgQVn7ga/L5MmTsX79+sLViPT0dFy5ciWOGzcOc3Nz0WKx2Nw6tP7/ypUrccSIEfjLL78gImJsbKzwt3v37gn/tz+zHz9+vHCFbvfu3cJVJusy/Nnzo0ePhDPsnJwcfPfdd3H69OkFtoXfnr179+KXX36JiIhDhw7F4sWL47x582y2+eDBg7hv3z5hW3JycvDAgQO4ZMkSm+V+9NFHmJaWJvycmZmJ165dE35OSkrC999/Hw8fPozuGDJkCLZt2xbLly+PX375JZrNZtyzZw++/fbbeP78+QLf379/Pw4aNAh3796NiIjR0dHYq1cvrFatGp46dcrmu//99x+OGDECf/vtN7x48SJev34d69WrhwsXLhS+Ex8fb3PLlb9KaX9Mjhw5Et99913hKvDVq1cRkbtK2rNnT5wxYwZu3rzZpszFixexatWqiIi4ePFiHDFihLBce/fZ2dnYv39/nDVrFl65cgWXLFmC/fv3x5SUFKEMXzf+tn1sbCx27twZEbljp0iRIkJs2Nf/4sWL+Nlnn2GPHj2wQYMG+Omnn+K4cePw6aefxiNHjiAi57V9+/Y4ZswYfPfddzE2Nhbj4+Px7bffxp49ewrfe+ONNxzeCl+/fj02aNAAY2NjcfTo0Thp0iRJV5oXL16MH330Ec6fPx8rVaqEQ4cOxejoaAwPD7e5Ms77XLRoESYkJODFixexU6dOOGDAAHzzzTcxLy8P//33X5w4caJQxrptmjp1KtarV89lTCNyVzF5rO96dOzYETt06CD8fPfuXeH/1mXu37+PiFysHjt2DF9//XXhbw8ePEBE7tbtO++8gxMnThSuKCFyVw+t6d27t7C8LVu2YIcOHXDgwIH4ww8/CN+ZNGkSxsTEYNmyZbF79+44evRoPHr0KB47dgyrVasmtOnjxo3DI0eOYE5ODqampuKkSZOwR48e2K9fP6xYsSKeOXNGWKb9FTHe4+zZs7Fjx442f7t48SImJibi/fv38cGDB/jjjz/iRx99JMRVVlYWtm7dusBQjZMnT+KOHTuEK9ffffcd1qpVC9977z185ZVXMCMjAzt06CBcOUdEm7Z8/Pjxbttoa3JycoRtjIuLw7Zt2+KcOXMK9F87duzAFStWOBxi5qyN3rlzJ65YscLmOHDXRh8/fhyDgoLw5MmTwu8yMjLwu+++w5MnT+ILL7yArVu3xhEjRgh3w/j18O3vH3/8ge+++y727dsXEbm24csvv7TpI/kyvNOdO3fivHnzhDs1eXl5OHXqVIdDG86cOYOVKlXCY8eO4cKFC7F37944bdo0h8MarIex8ccsouu42bBhA7Zo0cLm+zwHDx7Ehg0bIiI3VGzGjBlosVhw586dOHPmTBvHrva1fV6TnJyMLVq0QETEn376CQMDA7Fv3742x/zBgweFPiY1NRV/+eUXLFmyJM6ZMwcRuWOtZs2aBdp86/3y5ZdfYqtWrfDjjz/GpUuXYnx8PC5btgyHDBmCycnJaLFY8MCBAzZ3LvLz84Vt+vzzz/H3339HRGnDWxzhM0kzf+suMTFRcpldu3bhn3/+KQSsKzIyMjAsLAx79OghacwOIncrrUqVKnj06FHJdULkbt9NmjTJJhDcradZs2bYvXt37Ny5s9CJuOLo0aNYp04dPHToEF6/fh27du2KWVlZLsts2bIFIyIiMCoqCl988UVctmyZyyETBw4cKLDt/BhOPsG5ceMGtm/fHu/evYsbNmzAsWPHCskTou2t5JUrV+LkyZOxa9euGBISgrdv38Z//vkHX3nlFRuH/G1NRC5IFi1ahKtXr8aKFSvijRs3cP369di2bVv8448/hDLWAcQH2Ny5c/Grr74SApHfFj4IT506hSNGjMD//vsPK1asiIMGDcKaNWsK40d37tyJfn5++O677+Lu3buFxDkvL89m/N/SpUuxcePGmJqa6jQhMpvN2L9/f/z6668l7ed79+7h1KlThYQhJycH33//feFkIzk5WXCQnp6OEydOxA8++ABfffVVfPXVVxGRG6rAn6Qgcsdl5cqVcebMmTh69Gj8/PPPEZE7lurWrYvff/89njhxAv/3v/9hvXr18OHDh7hlyxZs27atcIvZ/tgcOXIkDhw4EL/88kts3bo1/v3331ilShX85ZdfcO7cudinTx+8ePGi8P3MzEz84IMPcMmSJdioUSNhPPmWLVtw8ODBOHToULx48aLg0Hp9N2/exDfffFMYc83XbcCAAThnzhzhWB4/fjyuW7cOq1atip9++ik+9dRTQmN/4MAB3LhxI8bExCAil+i//fbbNl5mzpyJX3zxBS5YsADffPNNROQSpilTpuCCBQsQEfHKlSs4YcIEHDJkCHbo0AF79+7t0PkHH3wgDBNZvXo1Dhw4UDh27BMYvg28d+8eZmVl4c8//4yTJ0/G9957T/jODz/8gMOHD3fp02QyYX5+vnAs/vTTT9i3b1/Mzs7GTZs24QcffICffPKJMD5w2LBhTmMakRvD+sEHH+Dw4cOFkzbr479jx47YtWtXXLp0KY4ZMwYfPXoklPnkk08KnOglJyfjG2+8gYiIS5YswS+//BLXr1+P1atXx6VLl+K8efNw9OjRNmX4bVm6dCk2adIEU1NTccuWLVi9enVct24dbtu2DT/88EPMzs7G3Nxc7N69O4aHh+PWrVsRkTs52LJlC3788cdC+3/r1i0MCQnBXr16YYsWLXDNmjXC9v/zzz82/YT1PuCHM1m3Oe3btxdOwGNiYrB69eo4fPhwfPnll4UT/KNHj+KoUaOE43fWrFk4c+ZMYRkxMTFYuXJlHDVqFD722GPC+k+dOoV79+4V2vcpU6YIydDBgwexevXqOHfuXGE5Q4cOxY4dOzr1icglwfy+ycvLE/ZvXFwctmnTRkiEtm7diklJSdikSRNs164dLl68WDhp4o+BTz/91KaN5vtwR2V4+H1n3UYjchcySpQogQ0bNhTaxDt37mDDhg0xODhYiKV+/foJJwv8en7//Xc0mUyYkpKCkydPxtKlSwv76ddff8V+/foJQw6aNm0q1I1vYxISErBjx47CSekvv/yCvXv3xqysLJtYXb9+Pb777rvCz9HR0dimTZsC7WNMTAzWrVvX5uKJ9XMNjuImJiYGa9Wq5fRCXXx8PPbq1QtXr16N9erVwwsXLmBMTAzWrl3bJtG0ri/vlt/XW7ZscZjX/Pzzzzhz5kysUKECfvPNN9ijRw/85ZdfMDc3V8iF+IsEiNxJAD9Mhvc5cuRIm7rHxMRg+/btbZ4ZGTduHJYrV05of5KTk7FLly6YlJTkMOey3pb58+fbnGwowSeS5vXr12P9+vWxU6dOOHToULdjERG5K7M1a9bEmTNnYps2bXDRokXC3+w7I/5s6Z133sEmTZrggAEDnD7gwXPo0CF88skncceOHTbL4M/gnSVHe/bsweeff154EMEd/JWoPXv2YF5eHvbt21c483d1JnXw4EHhgST+obbhw4c7veJ84cIF7NevH+7cuRMRuQ78mWeewTlz5giNuDVbt27FwMBArFKlilCGr9Mnn3yC9erVw5MnT+LChQuxcePGuHv3bnziiSewWbNm+MMPP9g0FNb76v3338cXXngBz5w5g1evXsUyZcrgwIED8dNPP7UZ58g3Lr/99hvWrFkTW7dujbGxsbhnzx6sV6+ewzLW/Prrr8K4Q2fbgsg1voGBgbh69WpERFy4cCFeu3YNc3NzcdmyZfjrr7/i7NmzhSu51lfP+e9UrVoVz549i6tWrcJmzZoJx4r9MXLixAmsWLGiTeO2bds2p3WLjo7Gtm3boslkwlWrVmHDhg0xMTER16xZg1WrVsU1a9YInVFWVhbeunULT506JXRm48ePFzrl1NRUfOutt4TjMi4uDl988UVhTOfZs2cxKioK+/fvj82aNcMzZ87gzp07sXz58rhnzx78+++/ceTIkcL3rZOmhg0b4jPPPIP79u3DIUOGCJ3xrVu3sHv37gU6gOeeew4rVaqEZ8+eRUQu+a1YsSJu3boV+/Xrh3369BH2nXUsr127Fps3b47Jycku6zZmzBj08/MTnK5atQovX74stBmfffYZvv7668JVF/vjf8aMGRgVFYUHDx60ueL1559/4quvviocA5mZmZiZmWnTwNs7tx4jmpaWhg0bNhSSK2us28CPP/5Y6IyuXr1qE9Nz587FYcOGOfV54sQJ4bs5OTm4fPlyLF26NMbFxeHmzZuFh6T69u2Lffr0Eb47atQorFOnjk1MJycnFyjTv3//AnVHRIyIiMAnn3wST58+LTyI5KzMnTt3sEuXLjh9+nSsUaMGHjhwwKaz3bp1K7Zs2RJXrVolJLIZGRn466+/YrVq1fDs2bNoNptx2rRpwoNIhw4dwrp16+K8efNw1apVmJGRYfOcQn5+Ph44cABDQkKE43PBggXCSeWBAwfw6aeftvHN4257ELk4Hjx4MO7YsQMrVaokxPLQoUMxMTERMzMz8eHDh9i3b1+cNGkSTpw4EStXriwk4KdOnRIe+EJEHD16NMbExBS4qPHjjz9itWrVhCuYR48exRdffBHfe+89nDZtmvC94cOHY+3atQv4ROTuFLRo0QIfe+wxYV/k5+cLMc3frenUqZMwXnvq1Kn4wQcf4JQpU/C3335DRLF/+u2337BWrVpCG80nzY7KWGPdRlszc+ZM/P7777F8+fLCicOVK1eEE1++vvbrmTx5Mv7vf/9DRMTbt2/j8uXLsW7dujhq1Ch84YUX8OzZs0KibV23RYsWCXcgFyxYgC1atMBhw4ZhhQoVhDYKEYWTlps3b2Lnzp3x119/RUTEiRMn4oABA7Bfv35Cv7d582Zs2LChcHw6SmIRbeMmJSUFX3rpJWEc+71793Dx4sW4ZcsW4bh89OgRlipVCmvVqoVxcXEYExODoaGhQp5y7949vH//foHjht/XK1eutPk+31fl5eXhunXr8IUXXhAeNDx8+DDeuHGjQC7EX9Hn9we/PX/88QfWq1dPuGi2fv16bNq0aYGHBW/duoWvvfYavvzyy8KdkBdffLHAttjnXDzNmzfH6OhoVIrXJ80nTpzAGjVq4JkzZ9BkMuGMGTMK3FK35/Lly9ikSRMhiObNm4e///67TfDxB+mFCxeEHbxy5UrcsWMHjho1CocMGYJr1qwRrjhZl8vPzxfOJrds2YLx8fHYrVs3/Pjjj7FLly5Cg2UdCPz/p0+fLly1unv3Lp48edLm7MyamzdvYrVq1WwG0Pft2xe7d+/udr9duHABe/Xqhf369cPKlSvjl19+iefPn8eXXnoJP/30U5vv3rlzB/v06YMfffQRzp49W/j9O++8g++//36Bp7/z8/Nx6tSp+L///Q83bdqEDRo0EA5knmnTpuHo0aOxTZs2eOrUKbxw4QJu374d4+LisHfv3vjtt9/aJM5ZWVmYnJyMVatWtemYdu7ciYmJibh48WIcOnRogSR48+bNGB4ejufOncMtW7ZgnTp18I8//sDr1687LJOeno6HDh3CF198EWNjYx1uC98xWSwWPHbsGB4/ftxm+3nS0tKEjuSrr77CwYMH43///WdzxXnx4sV48eJFvHTpEjZo0ADr1KmD7dq1K5A48/+OHz8eZ8+ejXl5efjo0SOcMmUKLl68uEDdeD766CNs0aIFNm7cGM+cOYN37tzB9u3b49tvv43Dhg3DDRs22AwL4Zk7dy5Wq1YNL1y4IAxhOHLkCCYnJwt1+eSTT3Dx4sVCGeuru/n5+fjtt9/i0qVLhb8PGjTI5tiyWCx4/vx5rFy5Mi5fvhz/++8/4ZYnv6xJkybh1KlThTL5+fk4ZswY4epzdnY29urVy2Y9bdu2FTo/fj9//fXXWKdOHcGpo7pZX528cOGCzX6/cuUKNmrUSIi1I0eOYL9+/QpcRVq6dCk+99xz2K9fP5w/f77NMIFjx44JV0gRscCVGn45+/btw5UrVwpJO6J4knH8+HF85513bNoqR23gV199hYhcx9G8eXPhJLF+/frCSZcjn9bDhvLy8nDz5s14+fJlTE9Px+7duwu3Ts+dO4evv/66cHcBEYUr1m3btsVTp045LfPDDz/g0aNHhRO2PXv2YIUKFTAuLk64Dcy3KdZljhw5gvfv38f8/HysWLEi1qpVC1euXIk7d+4UZqRJTU3Fli1b4oABA4S7EfPnz8fc3FycPXu24JXfN4hcnL755ps4bNgw3LVrF5YvX17Yf4hcO8x3vMuWLcM33ngD4+PjC8zEMWjQoAIJnKvtsd4Hly9fxrZt2+Lq1auFYyw+Ph5DQkJw2LBh2LFjR0xMTMSTJ0/inDlzcMCAAXj+/HmbYUa8h4SEBCxevDj26dMHGzduLBxHd+/exQ4dOuCZM2eEut+9exd79+6Ne/fuxY8++gh//vlnITGcMWMGjhkzRvBpza+//oqDBg3CsLAwm2Oc5/PPP8eQkBCsUqUKZmZm4v79+3HChAm4YsUKHDt2LI4bN06I640bNwpt9ObNm7FOnTouy5hMJty/f7/QRh8+fBhjYmLwwoULmJ2djd27d8f4+Hhcv349li5dGosVK2bzELXFYnG6nqioKBw/frzg/8qVK3jkyBFMSEjAW7du4fvvv4+I3BVt+7rNmDEDk5KScPPmzfj999/bzNSwZcsWjIqKEn63Zs0abNmyJXbq1Em4szd58mQcP348pqenY8OGDbFnz56IyA3NW7BgAX799dd45coVIdncu3evEDf83YglS5bg0KFD8ddff8WaNWvioEGD8MMPP8QBAwbg/v37MT09Hd955x28ePEi3r59G1977TV8/fXXcdiwYRgfH49t27bFXr16Ybly5YTjkN/XmzZtwgULFuDAgQNx48aNQl4zcOBA7NatGyYnJwt9G383VmoutG7dOqxfv77QPmdmZuIzzzwj3CW7efMm/vnnn/jTTz9hVlYW5uXl4RdffIH9+/fHl19+GTds2OCwbvbrQeSGAMkdZmuN1yfNhw8fFm55InIdyEsvvYQpKSkOr+ampaVhVlaWcAZ47949fOaZZ/Ddd9/Fzp0729w2QeSuWlSsWBFv3bqF69evx48//hgREd9880308/MTxsfw8Gdo2dnZuHHjRuzbty8++eST+MMPP2BsbCx+9913wtU/R/z555/4448/YkZGBtavXx8/+ugj7NChAw4ePLjAd69cuYINGjSwGT+Wnp6O7dq1w7Vr1xb4fnx8PJ46dQpjY2MxNzcXb9y4gTt27MB33nlHuBV369YtfOWVV2wSqfPnz2ODBg1wwYIF+MUXX+CIESNw5MiR2KtXL1y5ciU2aNBAuK0UFxeHt27dwszMTKGhWrFiBTZo0AD//fffAnWyngKIr8PZs2fxo48+wm+++UY46+THwqWnpzscRnL//n1csmQJDhkyRBh2wScE9+7dw5iYGKxUqZLNmGBHZe7fv48WiwUfPHgge1ssFgvGx8fjyZMn8cyZMzZj8RC5E4VBgwbhhQsXcOHChbh+/Xrhb9euXRPukAwYMABbtGjh8IpzTEwM3rx5Ezdu3IjffPMN3rhxQ7htytfN/i5Famqq8KQxIgrDR/jxdNaJM99gjR07Fk+dOoUJCQk4dOhQ/Oeff4SOlr9CNGbMGOGq544dO4STh507d+LXX3+NJpMJExMThfpv2bLFZnaB3NxczMjIwD/++APr1KmDixYtEhJJPhn49ttvhRkI1q9fjydPnrS5FXzz5k28c+cOJicnC/WbOHGize1mRK6Tj4uLc1k36xizHgLx8OFDTE9Pt0lib968KYw15r934sQJLFOmDLZo0QLnz5+PU6ZMwfLlywtPid+8eRO7dOmCiIjdu3e3ORng2bx5M1aqVAm//fZbDAsLw2+//dbm77dv38b+/fsLV1ARnbeB/BXwlJQUnDdvHs6YMQN37Ngh2SffRm7cuBFnz55t8zxA2bJlMSoqCidOnIgjRoywqQ/f9rkqM2bMGGFc7f79+/H69esYGxuLKSkpQsfrqAz/5Py4cePwxx9/xDp16uCvv/4q3BK/c+eOzZ2YtWvXCuNK8/LybOKTj6979+7ZJIXHjh3Dvn37Ck7r1q2LY8eOxfv372NeXh6OGzcOFy5ciDk5OcJx+ueff2K9evVsbiNL2Z6//voL8/PzMTU11aZdS09Px6lTp+KsWbMwNzcX58yZg+XLlxdimD9u7dvC3NxcXLt2rTA+e926dViuXDmbk8xNmzbhxIkThZj/9NNPcefOnXju3Dls3bo1RkRE2CR81nfH+O39/fffcePGjcIV9qlTpwqJ5q1bt7BDhw5YtmxZIfGKjY0VksDJkydjsWLFcNSoUUJixbfRVapUcVuG58GDB7hlyxasV68e/vLLL8LzPMuXL8dDhw7htWvX8Omnn8bnn3/e5s6wu/U89thjOGrUqAJ3ai9evIgNGjTAe/fu4ZUrVwqUsR8SZM0XX3yBDRs2xJkzZwrPcGRkZNgkwXPmzME+ffrg3Llz8fjx41izZk0cM2YMNm/eHKdNm4bdu3fHoUOHCu33gQMH8Pr160IbvWHDBkTkTtybNm0qPF9z+/ZtHDlypDCEkV/fhQsXsEmTJhgfH4/9+/dHPz8/od1ctGgRPv/883j37l3MysrChw8f4qZNm7BcuXI4Y8YM/OKLLzA0NFTIa77++mts164dPnz4UDgxkZILtWvXDjMzM/HMmTPCUEL+eLt+/TpWq1YNR40aJTx31apVK+zbt6+QD2RnZ2NmZqbLuvE5F3+8ezJzBqKXJs3WD2MhigPkc3Nz8e7du/jqq68KB4Z1I7Z+/Xps1KgRpqenCwPeN2/ejCtXrhS+07BhQ5uxroiIUVFROGDAADSbzfjNN9/gkSNHsGzZsvjaa6/hRx99JKwjNjYWixcvjt9++62QVG3cuBGXL18uLOvevXvC2Ch+W6zZtm0bNmzYEKdNmyYMGUlNTcVXX33V4dyRI0aMEG6R5eTkYH5+Ps6YMUPoqPnlb9y4ERs2bIjt2rXD1q1bY9OmTYVbV59//rmQTK5cuRJbtmyJGRkZNusZO3Ysrly5Evfs2YNfffUVTp06VUgsBg0ahGazGTdt2oT169d3+LDZ8uXLsUGDBnju3DncvXs3/vfff8L++u6774QrMTynTp3Cjz76CBctWoRTp07FLl26YE5ODsbExGDnzp1x0qRJNkkMIpckL168GMeOHYvdu3fHunXrYkZGBm7duhWffvppfO+992yuXrsqI2Vbzp49i3v27BFuH1nv47Zt22Ljxo0LjLH/6aefsFGjRvjcc8/ZPCSEaPug0MCBA20SZ+tOjJ+H2f44ReQaTL5uu3btEm5Zb9iwARs1aoRms9nmJItPnPkEnq8Tf9zk5eUJDdGGDRtshlUsXLgQFy9ejFu3bhUaX0RubJv1rV6e3bt3Y9u2bRGRuxW3YMECYcot+wcceU/btm3DmTNnYkxMDDZs2FDocHg/1lcOeX799VdhCqgNGzbg0aNHhWNZSt3+/PNPnD9/vnCVpFGjRpiVlSV0AvyyPvzwQ+GBq/Pnz+PGjRuxVatWNss9ceIEhoeH4/z58zErKwurV68uNPz2xMfHY8OGDYW248KFC9isWTNMTEwsMC6vWrVqmJ2dLfzeVRto/UAQ73PUqFG4fv16m6TL3mdiYqLNsWadRFjf3ZoxYwZ+8sknws9Sy/BTeyE692k9NdiMGTOEEyh+eI39cWM/bd+3336LvXv3Fqah4+OzXbt22LhxY6ENtG7r5s+fj++88w7m5ORgTk4OdujQAWvXro2dO3fGK1eu4C+//II9evQQTqQXLlyI1atXF8bXy9meESNG2PQbfOKDaPuwMiJ318h6KNDWrVuFttD6pMV+Pu++ffsK+379+vVYs2ZN/Ouvv4TkZPr06bhjxw48duwYPvvss9iqVSubO4qOkowrV64Iw0yio6OxaNGi+NlnnyGieHfPvr2dMGEC/vjjj1i2bFkcNWoUjh49GpcvX44Wi8VpG+2sTH5+vtNj4Pvvv8fmzZtj2bJl8b///sPFixcLDzdKXc+YMWOE9VgfGyNHjhTikz9xs67bsmXLhKus1nnKH3/8ga+99hpOmTIFp0+fjrdu3bLxNHv2bCxTpgxWrFhReCA4Pj4eq1WrZvO8xMCBA/GLL76w2V4+pkeMGCHc+T5//rxN7H322WfCSY31ekeOHInbt2/HjIwMm6k9EbkHZq2fL0LkHsTr3r07Ll++3OZOnX1eIzUX4p+V4Nm2bRu+++67OHXqVDx+/DjeunULS5YsaXOB4Y033nB4wUFq3TzFK5Nm/mBwNJl6bm4uvv7665iXl4dLlizBfv36oclkwi1btmDz5s2FW4X8gcMvg1/m559/jitWrLDZwSdPnhRuh7Vp0waLFy8uHJwjRowQLvXHxsZi1apVcfz48di1a1ccNGgQxsbG2owPWrZsGb744os2T4Pbb8usWbOwRIkSwsMUiNzDNtu3b8f9+/fjyJEj8ZNPPsHY2Fj84IMP8LvvvrPZB6dOnbJ5eGnLli3YqFEjIbl78OABjho1Cp966ilMTk7G33//HTt27Ijdu3fHevXq4ZkzZwqs5/3337d5upxvDBYtWoT169fHP//8UxhXhMhd0bd/evfff//FJ554Ap944gm8fPmyzf7q3r27MKcwn9Tdu3cPa9Wqhc8//zyeOnUKt2zZgg0bNsS5c+fihAkTsG/fvjYPifG8//77GBkZiSdPnsTt27dj+fLlcdmyZThz5kycOHGiw3l1e/ToIZTZsmWL7G1xtY+tE+dFixZhaGgoxsXFOXRv3UAPHDgQX331Vfzhhx/wnXfewYcPH+LNmzexZcuWwnjL3NxczM7OxocPHwqNT0xMjFC3S5cuOT32eRYuXIiffPIJDhgwAMuXL4937twpcJVl0qRJ2LFjR9y0aZOQaK1duxafeOIJbNSoEZ45c0bYjjlz5jicC/T06dPCQyi1atXCixcv4owZMwpcFR43bhy2a9cOt2zZgmfPnkV/f3+sV6+ecFXXnZ/ffvsNx48fj2vXrsXKlSvbvHhBat2s99umTZts9ht/7Hft2hXPnj2Ly5YtwyZNmuAff/whPD2fm5sr7MO9e/fie++9h6mpqVilShXhQTxr3/x3rU+AMzIysG3btsIxbp288MmT1Dbw9OnTwglHXl4e/vzzzzh06FBcv3690NbZ+3R0rGVmZqLZbLbp6H777TccMGAAZmdnKyqzefPmAj6tZ1zht5svk5WVhdHR0QWOm7Fjx2K7du2EE8AlS5ZgnTp1hKFZ7uIzKysLFy9ejHXq1MGdO3cK++vs2bO4YMECnDBhAkZFReHvv/+OVatWFW4db9u2zSa5cHR8utqeEydO2LSDvXv3xkuXLtn0QcuXL8eGDRsKibqjttDRCeSyZcuwRo0amJSUVMANf+zs3bsXmzVrhuXLl8cVK1bgmTNncMCAAULSzh+b1sf/vXv3cNiwYbhq1SqsVq0aTp48GYsWLYrTpk0r0N7y82V/+eWXWLFiReHhur/++gtv377tsI12VwYRHbYdkyZNwq5du2K5cuWEITuI3Imj3PXcuXPHYX/LX5ByVobHug198OABTp06FQ8ePIiTJ0/Gfv364QcffCAMzxk0aBA2bNhQcMO7T0hIsDkO+OMwPz8fr169WiCmBw0ahBs3brSJtaVLl2LNmjXx0qVLBbanR48eBe5m8cdN7dq18cKFCzbrP378OA4fPly4WMC3X8uWLcOXXnpJaIfl5EL87/hj+ocffsBx48bhBx98gAkJCcLVa+vZZviHJpXUzVO8LmlOSUnBypUrF0g6ebKysrBr1644ePBgrFOnDp49exZTU1PRz89PGLJw7do1HDNmTIEncv/880+sXbs2NmnSBCdMmCB0lvn5+ditWzeMiopCs9nsMOniGTZsGHbv3h0zMjJw0qRJ2K1bN+zbt6+QnDZs2FC47Wm/LdYH+owZM7BmzZq4ZcsWnD17NtauXRuvXLmCV69exS1btuDQoUNxwoQJWLFiRXz11VeFW+M8ixcvxqtXrwrbzl/BsE6YRowYge3bt0dE7ooYf5sUER2up0OHDjbjMGNiYvD555/Hffv22ezfy5cvY/PmzQu88GPZsmX47LPP2jwcYb2/+Jkx+vTpg9euXcPDhw9jyZIlMS4uTtgOvkNMSkrCDz74oMAk9fwJA3/F9OLFi8J3+BdJTJo0yebqBH818MyZMwWOFSnb4mofjxo1Clu2bInp6elosVhwxYoVwkMbzo5j6//XqlULn3rqKeGWXGpqKr722mtCY/bVV19h27ZtsW3btjh58mRERPz7778L1M3+2E9LS7NZT+fOnfH555/HM2fOCHXjO+i7d+9i06ZN8Y033sDPPvsM//nnH8zOzsZ///0Xw8PD8fLly5iSkoKVKlVCk8mEf/zxh81DYjwPHjzA8PBwm2ENAwYMsJnO7K+//sK2bdvismXLsGPHjjh37lysV6+eMCZNip89e/Zg6dKlsWXLlhgXFye7btbHm6P9xjfGAwcOxNdffx2bNGmCZ8+exbNnz2Lt2rWF28SIXExfv34dW7RogVlZWTZj+/nlpKSkYJUqVYT2yDo5/vDDD4Ux/Pw+k9sG8vH57LPP4k8//STcpp01axZOnToV16xZgzk5Obh161bBJ7+vHR1r7dq1w8mTJ2NWVhb+8ssvWLduXeEqq9wyUuNt3rx5WLduXbfHzfLly4Wx1l26dLFZh6v4zMzMxLi4OOzfvz8eOXIE+/fvjwMHDhQSoujoaNy9e7cwZrVLly5Oh+fJ2R5+vzlqB/v27YtHjhwRTv74dlNKW5iVlYV///23zQOzjty0adMG69Wrh35+fsLV6kePHgm3v+2PNet9N2HCBCxevLhwp/bff//FmJiYAu3txIkT8fz585iRkeHwzp2jNtpVGT4+HB0D/AOgnTp1wgsXLgjftVgssteD6LwfvHTpktMyjvbb7du38ZVXXkFE7mQpKCgIP/zwQ+GEzd7NtGnTsHXr1kLcIHIXXPicJiUlxWVMr127FnNycoQXV/HHmbPt4fOHvLw84bg5cOAAtmrVyiYXysvLw+7du9s8zPq///0Pa9SoYdOvI8rLheyP6cTEROzWrVuB8fL8Pjh48KBHdfMEr0uaEcU3cfHju/Ly8mzO4N98803hISaezZs3Y7NmzfD06dP48ssv25yhPnr0SBgPGhsbi5cuXcL//e9/GBERgZMnT8YTJ06gyWTCbt26CWev9kNE+P8/fPgQe/fujfn5+bhjxw585plnsFevXlixYkUsX758gdfZ2m+L9e3SNWvW4M8//4z9+vVzKv306dM4ffp0nDx5stMHBjdv3iyMxUIUk/MbN25gy5YtJU2h52w9/FV26/3btm3bAmeveXl5OHHiRCF4Xe2v3r17Y+3atXHhwoU2b3LbvHkz1qtXTxiy8Oqrr+JLL72Ew4cPx1mzZglJh6PZPPgE5dKlS0LibH0CYH1bV+62uNvHbdu2LTAsBNH5ccxz5swZrFmzps0wDovFgrNmzcKOHTtiREQEfvjhhzh37lz8888/sXv37rhnzx6cNGmSzTzWro59i8WCt27dwvr169s8YLlx40asX78+xsXFYevWrYUxsz/88AOOHj1aeKOX9b7esGED1q9fHxctWoRz5szBtLQ0mw749u3b2KZNG5uHpf79919s37690GhbjxP94osv8MiRIwXeRufOz9WrV7F8+fI2fpTUzdV+Q+SeoK9UqZLQcebn52N0dDSOHDmywCwKXbp0wdmzZ9t815qNGzdi7dq1hWOBv4Lyxhtv4P79+/GPP/7AKlWqCMeX3DZwx44d6O/vj9HR0divXz/s1q0bjho1Crt164b9+/cXrnBb+3R1rPEdWqdOnSQfn87KuPN5/fp1fOONN2zKuDtuDh06ZPNchqv4bNOmjTC0iL/LdevWLVy6dClWqFABFy9ejNu2bcPWrVsLx1R8fDx27drVYVzL2R5X7WCfPn2E2/P2b7R01RbOnj0b79y5g3v37rVp2525Wbt2Lb7xxhvCsWI/HMP+WOOvUD98+FCol/3xbN/ejh8/3iYmHA35kFvG1TEwZsyYArMueFI3Hr4fHD9+vM1MM47K8PuNT5wXLFiA48ePx7Jly+I333yDX3zxBc6ePRuzsrIwPz/fZdz88ccfwkOcPK5iesCAAcKdDkdv87TfnsmTJwv94d69e4Xj2lku9P777+POnTsxKysLZ82aZXPHV2kuZH9M9+jRA3/66SdE5NyePHkSW7ZsKewDJXVTA69MmhG5q5xVq1YVApk/Az537hxOnTrVYZK5detW9PPzE2bDsA702NhYm/HP/LK++OILfPfdd7F9+/bYq1cvm7FejsjIyMDhw4djr169sGrVqkJycezYMaevybXfFj5xPn36tMPXEVufPSNyB8+kSZNwzJgxTl/nbL8OfqxWhw4dCoxbkrIeRwm6/f7lr4Dt2rULL1++7LBhcba/du7c6XDbY2JisEaNGjh06FB85ZVXcPXq1Thv3jxs0KAB9unTp8DDd464dOkSTp48GceMGeP0ZSFKtkXJPnZ2HB87dsxmon9rzGYz7t+/v8DLAt5+++0Cs2c42x7rCd8RC754wVEZRK7xmjFjBn722WcOX1Tw77//op+fHxYtWhR79uyJ9erVw7p162LHjh3xk08+KVDGbDYLx5S1i7///htffPFFp/OuO/Pz33//4fnz5x2+zldu3dztt3379hVImm7evInjxo3Dbt264e+//46XLl3CTp06YfXq1Z0OJ+KxPxYQuecp3nzzTWzZsqXNiZCj77trA3fu3Im1atXCrKwsjI2NxV9//RWbNWuGxYoVwwYNGjjcZ86OtXfeeQe3bt3q9HXMcss487lnzx48c+ZMgQfeXB03L730ksPjxlV8Wj8zYM3Ro0exWbNm+OOPP2LPnj3xnXfeER5YcjWlp9ztcdYO7tq1y2E7yG+Po7awfv36OHjwYIez4rhyw6/T2bqctVOOXgFuDd/efvrppwXuiHpSxtUx0KJFC0kXg6Ssx1k/KGV7+IcO09LS8N9//8U6deoIV0aPHDlic5LqzE3Xrl1x/fr1NneieVzFdMOGDR3GtLPtGT16dIHnbHgc5UL8dKTOps9VmgvVqFEDhwwZgp07d7YZfsFPu6hG3TzBa5NmRDGQ+TO5H374AStWrOi0kUHkOs7atWsXmGDdGVlZWfjo0SMcM2YMVqlSBZ999lnMyMhweTYaFxeHTz31lM20RXK3Ze7cuVihQgXJU6OcO3cOp0+f7vKs0n4dv//+u82YIjXWw+9fvnH97bffsFGjRi5fOiN3f23fvh39/PxsJtvPz893ue1ytwNR2bYo2cfO3Du6Yu6MlStXYp06dRxe+bLfHv7Ytx/bLKUMIrevXY0P27t3L5YsWRLv3LmDaWlpePToUbx06ZLTxOTGjRs4efJkbN26NX722Wc4YcIEm9vK7uomx4/cujnaB+7ajNTUVOFh0b59+9qMYXaHfXLy5ZdfYlhYmNPbwHLbwM2bN9tcNbxz5w5eunSpwJVMV/z9999ujzUlZeT6VHLcKInP+Ph4nD17Ng4YMAD9/PyEh9fcPYUvd3uU9BtqtIV82+Eu+bXfdz/++CNWqFDBaQJkjZT2VkkZpW2HHnXjiYmJEaa3c/cCMXv4uHHlRo2Y5rfHVe7kLBdylZh6ckzzdZHyAJ+SuinFq5NmRBSeqp81axZWqVLF5paJqzI1a9aUlSwicuPTpDQQiNzDVd98802BeVzd1ct6WxxNlO8Kd505v44GDRrg/PnzsUmTJk7PLD1Zj/U6mjdvbnOb3Bly9xf/tiSpb010hNz9JXVblOxjpe5v376Nc+bMwerVqxe4EulsPXKPfSVl+KmgXDXC1mRkZOCePXtw7NixOHfuXIcPNTmrm1w/cuvGr0fuPrDvIKU24PzVlvv372NKSkqBO2COvi+nDeSXbz87gzvkHmtKysj1qeS4URKfjx49wjt37uD7778v63av3O1R2m8oaQuV+PSkj5LS3iopo7Tt0KNuPPw7Avj2w51bJXGjJKatkbM9euRC9erVU9S/y6mbErw+aUbkpvfx8/MrMAG7KzZs2ICNGzeWdMVAybx+Z8+exTfeeMPh7RFXKNkWuWzatAmLFSsmKcHwZB2PPfaY5HUo2V/WDrVE7rbwZeTuYyXuMzMzcePGjTaTt7tDzrHvqIzc9dgPBVEbJX6U1E3ufnP0zINU1q9fj82aNZNcTu6xo8SnkmNNSRklPuWiRxtovS6p26O039DLJ6I+fZQvIqf98LRd1wo9cyFP2lst8UNEBB8gMzMTSpQoIatMRkYGBAYGalQjZXXypJy3r4NFhzxK6qZXGSUo2W96lVGCXseOXtsDAJCeng5BQUGSvy93H+i5LXJhsX3Sa11K66WnTz33nS+htSNWY9objmmp+EzSTBAEQRAEQRBa4W90BQiCIAiCIAiCdShpJgiCIAiCIAg3UNJMEARBEARBEG6gpJkgCIIgCIIg3OBTSXN2djZMnjwZsrOzmSrDar2ojO+VYbVeVMb3yrBaLyrje2VYrReV8c0yLtFlYjudSEtLQwBw+PpQI8uwWi8q43tlWK0XlfG9MqzWi8r4XhlW60VlfLOMK3zqSjNBEARBEARBaAElzQRBEARBEAThhqJGV0AuFosFbt26BcHBweDn52fzN5PJZPOvFPQow2q9qIzvlWG1XlTG98qwWi8q43tlWK0XlfHeMogIZrMZSpcuDf7+0q8fe90bAW/cuAERERFGV4MgCIIgCILwYpKSkuD555+X/H2vu9IcHBwMAADrdp6DwKBgyeXKlsyGsLAwxevNzMyExMRECAgIgMjISChSpIguZa3Jz8+HxMREyM7OhsjISFnvcvekrD0pKSmQkpICYWFhDvdpaGio7GWmpaXJ+r6SfXrn0hnZ9arcsLnsMpeP7JJdJrxyXdllHu1dKbvM48272vzsziUAwO3LcfLXk5kqu8yDkEjZ8aHkWLt6eIes7z9VpYGs70vZp1qUtcZVfCRfPCl7eegnfyRfbpEA2WWq12ssu8zZE4dll1HC8+WryC4j9/h01A4Wtv5Dq7LWZGZmgunGFdnlnqlUi3zILHvi8gPZdapX8UnJ31Xqw2QyQUREhJBTSsXrkmZ+SEZgUDAEBoVILvfoUSJkZWVBeHi47HVmZmbC/fv34amnnoIyZcrIDpKQkBAICQmB+Ph4ePDggaJl5OfnQ0JCAjz22GNQsWJFRUESEhICCQkJcP/+fQgJCVG0jOTkZHj06BGUK1dO0b50VTepKPWRERSktHqyCFawnqJFi8r28VigfH+P2+3nkJAQCA4OhuTkZKfxka5ge0r4Z8kuk/7YY4rjQw5y/cg5Nj2NDyk+3OEuPh4p8KlX0qwEJfGmBKU+5ODoWKP+w7YOasXHkwqOG94F+RDr4M5HUFC+ouXK+a4nPuyH+bqj0DwIGB4eDsnJyZCcnCyrXGZmJsTHx0Px4sU96sxLlCgBL7zwAmRlZUFCQgLk50s/kPgAy8rKghdeeEHxWWWRIkWgTJkyULx4cYiPj4fMzExZ5fn9Fx4ernnn4Qy1fLCGEh9qwftUEh9qojQ+WEGt+PDEh6/GBwsYGR/Uf4ioFR+eQD5EWOg/PPEhl0KVNMsVq3YHpESsWgHGozTQKGHWFiU+1KSwNXxqo3Z8sNBeEbYo8aEm1H+IqBEfnkI+RApT/1FokmYAeWK16oDkiFU7wHjkBpoeCbNRPlhBiQ+18baGT6+k2qj4YKG9IkSU+FAb6j9EWIgP8iFSWPoP3ZLmP/74Q/j//v37bf72448/Oi2XnZ0NJpPJ5uMJUsRq3QFJEatVgPFIDTS9rjAb6YMFlPjQAm9p+Pj40AMj44OF9ooQketDC6j/EGEhPsiHiDf1H4mJiYqWr1vS/O233wr/HzZsmM3ffvvtN6floqOjITQ0VPioMd2cK7F6dUCuxGodYDzuAk3PIRlG+2ABVnyw3vBZx4cesOyjMMUHK8jxoRXUf4iwEB/kQ8Rb+o/s7GxFy9YtabaeDtp+amhXU0VHRUVBWlqa8ElKSlKlPo7E6t0BORKrV4DxOAs0vccws+CDBVj0YRRS4kMPWPNRmOODFVjwQf2HCPngYNGHUbjzERkZqWi5uk05Zz2th/0UH66m/AgICICAAG2mLuIPnuRkbtqVjIwM3TsgXmx8fDxcv34dAABycnJ0CTAePtASEhIgPj4eAgMDwWw26/7QHws+WIA1H3kP7ui2TnsoPkQoPtiCBR8UHyLkg4M1H4nyp2lWDVc+8vLyFC1TtyvNZ86cgfDwcAgLC4PY2FhBYFhYGMTFyX95glqEh4dDcHAwmM1msFgshnRAJUqUgMjISMjKyhLOgPQKMB4+0CwWC5jNZggODjZklgwWfLAASz6MhuJDhOKDLVjwQfEhQj44WPJhNGr70C1pzsvLg+TkZEhJSYHc3FzhdkFKSgrk5OToVY0CZGZmQkZGhvBzaqr8N5l5Sn5+vs1tjOTkZEOm27Le9oyMDEPmDWbBByuw4sNoKD5EKD7YggUfFB8i5EOEFR9Go7aPQjXlnD3WY56qVq1qyOB16zE25cqVg3LlyhkyT631mKeqVasqnjDdE1jwwQos+TASig8Rig+2YMEHxYcI+RBhyYeRaOGj0CbNjh4S0PupT0cPCRjxggf7hwQ8fdOQEljwwQqs+TAKKfGhB6z5KOzxwQIs+KD+Q4R8iLDmwyjc+VA65Zwfupq6gkFMJhOEhobCzmMJEBQk/f3kFTNPyF5XznM1NBuT4+6pWr2e/HX1VK1eT/6621Y9nvwtLD7SD62XXSaoyZuS66gWWvrI2vSTrLpklKkl6/sAABZ/+c9Yh1dvLLsMInq9DzlQe8VBPkT09JF4+bzsMmUqVZNdRmlaprWPtBPbZZcJrddWch3VQkp8xMXFQePGjSEtLQ1CQqTnkoX2SrMUtDpDlXLw6nGG6u7g1eMMVUrjrvUVA/Ihj8Liw1soLD5YiA9qr0TIB1uw4EMKrMSH0innKGl2gRZi5ZztadnwST3b0zLQ5FwN0SrQyIcyfN2Ht+HrPliID2qvRMgHW7DgQw6sxIcSKGl2gdpildwe0aLhk3t7RItAU3L7kHxw+GrDx4oPb8VXfbAQH9ReiZAPtmDBhxJYiA8lUNLsBrXEeiJUzYZP6XgiNQPNk/F25IPD1xo+Vnx4O77mg4X4oPZKhHywBQs+PIGF+JALJc0S8FSsGkLVaPg8HYCvRqCp8YAK+eDwlYaPFR++gq/4YCE+qL0SIR9swYIPNWAhPuRASbNElIpVU6gnDZ9aT6x6EmhqPtFNPji8veFjxYev4e0+WIgPaq9EyAdbsOBDTViID6kwnzRnZ2eDyWSy+RiFXLFaCFXS8KkVYDxKAk2LKZDIB4e3Nnys+PBVvNUHC/FB7ZUI+eBgpS1hwYcWsBAfUmA+aY6OjobQ0FDhExERYWh9pIrVUqichk/tAOORE2hazhlKPji8reFjxYev420+WIgPaq9EyAcH78NoWPChJSzEhzt0S5rDwsKEHWL94X/vjKioKEhLSxM+SUlJelXZKe7E6iFUSsOnVYDxSAk0PSbZJx8c3tLwseKjsOAtPliID2qvRMgHh7UPvWDVhx6wEB+u0C1pTklJEXaC9Yf/vTMCAgIgJCTE5sMCzsTqKdRVw6d1gPG4CjS93koFQD543PnQC9Z9FDZY98FKfFB7xUE+OOx96AWrPvSChfhwBvPDM1jGXqwRQh01fHoFGI+jQNOzweMhHxyufOgJqz4KK6z6YCk+qL0iH0b64GHVh56w5MMaP1T6knODMJlMEBoaCjuPJUBQkPSrzhUzT8he1xN1W0v6nvXZkL+/vyFC+YPaYrEAAOgWYNbwBzV/RlqiRAndGjxryAeHIx/hyXGylxPU5E2P6sGajwoJ+2SVzShTS/b6LP5FZZcJr95YdhklzTdrPgDYiQ9qr8iHKx+Jl8/LXl6ZStVkl8nIyGDSR6mH12QvJ7ReW4/qoVV88LlkWlqarBEMdKVZBUqVKiX8PzAw0JAzoBIlSkBgYKDDOulFkSJFbAI7PDxc9wYPgHzwOPJhBCz6KMyw6IOV+KD2qmCd9IJ8iLDqwwhY8GENJc0ewp+N+fv7Q3BwMJjNZknTpahNcnIymM1mCA4OBn9/f0NuRWdmZkJiYiIUL14cihcvDomJibo/TEA+RBz50BsWfRRmWPTBUnxQe0U+yAcH9R+O8drhGQdPXIIgGR1gjQrPqF4XR2NslIw/Sj53TPa6w6s1EMvbrdOI8WDW6yyemy67fOnK8m+B26OWD09hzUeZMmUAAHQdD+bn5ye7jFZNkb0PuXVTUq+bl2Jll3muUk3ZZeTAx0dw1j3ZZTOLhcouU6ZCZYe/p/jgUKu9ir9ySfa6H7Nkyy6j5Pi8dfGM7DJPV6gOAN7rw1N8LT6UDGkpkf1QdpmnajSV9X0anqEzzgalS51nUC0cBbXeDz85CjC9IR8ijhpZVqYT0hsjOj0WMWLaLEdQfHCw0l6xSGH2QfHBPpQ0K8DdU5x6BZqrhECvQDPiLNge8iHiyodeDR8rM1R4W8KspQ8jps2yh+KDg5X2inUKmw+KD++AkmaZSJ32ROtAk5IQaB1o3pAw85APDq0bPl9/c5aWaOnD6GmaKD44WGmvlGDEyXBh8UHx4T1Q0iwDuR2QloEmNSHQKtC8KWHm0cqHnASNBR9aNXwsDwHwBrT0wXrCzFNY4sPo/kMJRs1z7us+KD68i0KTNHsqVmkHpFWgyUkI1A40b0yYedT2oSRBY8GH2g2fGkMAjPKhNkr3pZY+vCFh5ikM8WF0/6EEI18Q5Ks+KD7UQ6/40C1pNpvNTv926tQpp3/Lzs4Gk8lk81GCJ2I97YC0aPjkJgRqBZo3J8w8avnwJEFjwYdaDZ9aCZqRPtTCkzdnsebDUyg+OFhprzxFz4fRHOFrPig+1EWv+NAtaW7dujXcv3+/wO+3bt0KnTp1clouOjoaQkNDhU9ERISi9SsVq1YHxELD52mg+ULCzOOpDzUSNBZ8eNrwqZmgGe3DU6x9KIUlH55C8cFOe6UGLLyS3ld8UP+hPnrFh25J87Bhw6BVq1Zw584d4Xe///47DBkyBDZv3uy0XFRUFKSlpQmfpKQkRetXIlbtDsibGz5fSph5lPpQM0FjwYfShs8XfShFzWkXWfHhKRQfbMSHmnhz4syKD+o/tEGv+NAtae7ZsydMmjQJWrduDdevX4dp06bBnDlzYM+ePVC9enWn5QICAiAkJMTmowS5YrXqgLyx4dMjYfYWH1okaCz48Nb40CNhLsw+PIHiw/j40AJvTJxZ8UH9h7boER+6PgjYpUsXmDNnDjRp0gR2794N+/btg9KlS+u2fqlite6AvKnh0+sKszf40DJBY8GHt8WHXleYC7sPFqD4EPGm/kNLvM0H9R/6INVHSkqKouXrljSHhYVBeHg49OjRA9LS0uD48eNQrlw54fd64U6sXh2QtVijcBdoeg7JYMmHIyd6JGgs+GAxPozywUM+2IDiQ8Rb+g+tYdEH9R/sJ87JycnsJ80pKSlCRbOysuD+/fvCz3oHvjOxendARo7B5HEWaHqPYWbJh32g6ZmgseCDtfgw0geA41kDCqMPFqD4EPGG/kMPWPNB/YetD6Nw5yMsLEzRcgvNPM322ItNT083pAMyutEDKBho6enpuj/0x5IP60Az4iEz8iFCPjhY8cEC5EOE9f5DL1jyQe2VrQ8jceWDkmYF8GKLFSsmnKEWtg6Ihw80/oy0WLFius+SwYoP+0Az4ooO+RAhHxys+GAB8sEWznzoCSs+qL3i4H0Yjdo+iqpYN115zC8XHvPL1XQd92P3yS5TsuZLssuEV2tgcwuBhdtunlC6ci3N1/Fo51LZZR5v/aEGNfF9imEuFAvgGtt7NxMklUFELaskYD7qfLpKZ+hRt+cVxIBe+6x4ZsH58t2R9ViQBjXxfe5cOCnr+89UqSt7HS9UqMRs/6FHX0CwTY7fY5CVlQUAAOXKldPsJObGpTiXf3+i2P/X52Ey3HiYDOb0dEXrKdRXmvkxTzk5OcIZqRGD1+3PgIx4Kpo/I+XPUHNycnR/Kpp8iLDkw2jIBwcr8cEC5EOE4oODfIiQD+0otEmz/UMCQUFBhjz1aR9gRgSa/UMCQUFBuk8nRD5EWPNhJOSDg5X4YAHyIULxwUE+RMiHthTKpNnZU7V6P/XpbIyNnoHm7KlaPefhlOpD60AjHxz2PoxCig+tYdGHUfHBAuRDhNorDvIhQj60p9Alze6modHrqU93g9L1CDR309DoEWhyfGgZaOSDg5VpzKT60BJv8OErHZEUvNGHVlB7xcFKfJAPDlZ8aIluSfPKlSuhadOmULJkSShZsiS0b98e9u7dCwAAaWlputRBakKg9VOfUp/i1DLQpM7bqGWgyfWhVaCRDw5vSZh5tEyavcmHL3RE7vBWH1pA7RUHK/FBPjhY8aE1uiTNM2fOhNmzZ8OMGTMgPj4eEhISYOzYsTB27FhYsmQJtGjRwmnZ7OxsMJlMNh8lyE0ItJqaRe60J1oEmtyJzrUINCU+tLiCQz44vC1h1hJv9MFiR1SY40OrCy/UXnGwEh/kg4MVH3qgS9L8yy+/wLZt2+Dll1+GkJAQCA4OhpYtW8KmTZtg8ODBMHbsWKdlo6OjITQ0VPhEREQoqoM3JwRqBprSNwOpGWhKEzS1r+CQDw5KmEXU8uEJasQHCx1RYY8PtS+8UHvFwUp8kA8OVnzohW7DM4KCCs7zGRISAqVLl4bu3bs7LRcVFQVpaWnCJykpSdH6vT0hUCPQPH2VphqB5mmCptYVHPLBQQmziJo+lKJWfLDQEVF8qAe1VxysxAf54GDJh17okjSXK1cOVq9eXeD3q1atgrJly7osGxAQACEhITYfJXhzg8fjSaCp9e55TwJNrQ7I0ys45IPDVxICNVDbhxLUjA8WEmeKD3Wg9oqDlfggHxys+dALXZLmn376Cb766ito06YNjBkzBsaMGQOtWrWCr776Cn7++Wc9quD1DR6PkkBTK8B4lASar3VAPOTDMVIbPl9KmHmU7ks1fXhz4lwY4kMq1F5xqO1DaXyQDw4WfeiFLklzmTJl4MSJExAVFQWRkZEQGRkJY8eOhRMnTii+KuMNaJUQyAk0tQOMR06g+WoHxEM+CiKl4fPFhNkT1PbhjYlzYYkPKVB7xaGVD7nxQT44fN2HO3Sdp7lt27bwySefwCeffALt2rUDPz8/PVevK1oLlRJoWicEUgKNlQ4IAMgH6OfDXcNHCXNBtPDhTYlzYYoPd1D/waG1D6nxQT44CosPVxS6l5vogV5CXQWaXgmBq0BjpQPiIR/6+XDV8FHC7BitfOj9plNnUHxIg/oPDr18uEvUyAdHYfPhDD9ERN3X6gEmkwlCQ0Nh25EbEBgk/aHAZlWDZa8r/sol2WVKhDyhu1D7g8iIhMB+nQD6T/PnKKit983TTz8te5lKwkMvHynnjsgvA0HM+KD48Dw+7p/ZK7vMnaKlyIcXtFe+4EOvu8lK2mklddMjXVLq4+7547LW83TV+k7/5iw+nr5zStY6AABMJcvJLvNslTqy4+PWxTOy1mFOT4cqDV6EtLQ0WRNMFJW1FsItRpwB8etKTk6GR48eQUZGhu5X0Pgz1Pj4eLh+/ToAAOTk5Oh6xYY/Q01ISID4+HgIDAwEs9lcKH04g3wU3vjg4a/gkA9qrwDY8EGIsODDWXzoCSvxYQ8Nz1CZ4OBgQ4SGh4dDcHAwmM1msFgshjR4JUqUgMjISMjKyoKsrCyIjIzUPSHgA81isYDZbC7UPhxBPgp3fAAA+fh/WPBB8UE4ggUfjuJDb1iJD2soaVaZjIwMQx62yczMhIyMDOHn1NRU3euQn59vMxYrOTlZ1XfbS8V62wuzD0eQj4J10guKDxHyIUI+CHtY8OEoPoyAhfiwhpJmlTHiKXXr8UdVq1aVPe+jGliPPypXrhyUK1dO1XfbS8V6zFPVqlULrQ9nkI/CHR8AQD6AHR8UH4Q9LPhwFh96w0J82KNL0vzyyy/rsRom0Ht6J0cPCciZ91ENHA3YV/Pd9lKxf0jAiFkDWPDhCpZ8UHzoGx885IMNHxQfhD0s+HAVH3rCQnw4Qpek2WQy6bEaJtBTrKunavUKNFfT0OjZETl7qtbah9aw4MMdLPmg+DAmcSYfxvug+PBOfN2Hu/jQCxbiwxm6zJ5x7949mD9/vtO/Dx482OnfsrOzITs7W/jZGxJw+6c+tXgiW8o0NNZP4Vr/rBZS5m20fgo3ISFBkwca3E3TxPvQEhZ8SIElHxQf+vhwB/kQofgQYaG9Yg1f9sHKPOUArmch0yM+XKHLlea8vDy4d+8epKSkOPy4Ijo6GkJDQ4VPRESEHlX2GC3PiOTMo6nVGaqcANPyCo7UeU21TERY8CEHVnxQfHAYOVSDh3yIUHyIsNBesYSv+mApYQZw/yIyI6846/Jyk3r16sGJEycUlXV0pTkiIkL2y00aVyohO3FS8nKTFypUsvlZ7YNR6cTzak6ar3Sb1H6Jgdxt0mIyeyN9KHm5SVi1RsL/jfYBQPFhjZK6K3m5SclazZ3+jXyIUHyIKPXhay83ycvLY9aH0pebyDnGMg6skbUOAOUvN5GCdd2fKJInax1KX26iy5VmT/LygIAACAkJsfkowReu4HjSiKt1hupJI67mFRw1O1WlsODDE1jwQfEh4mtXnMmHCMUHWyjdBl/zwdoVZrno+cwSjy5J8/Hj8s6AtMDbOyI1rnp4GmhqBJgaHZG3J8w8LHRELPig+BDxlcSZfIhQfLCFp/X3FR/enjDz6PHMkjW6JM3+/sZPB+3NHZGatwmVBpqaAeZJR+QrCTOPJz7UggUfFB8i3p44kw8Rig+2sPbhCSz6kIsvJMw8ej48bXw2qxPe2hGpPa4OQH7Dp8UZqRIfvpYw8yj1oSYs+KD4EKH2SsRbfVB86Af5kL89vpIw602hSZoB5DV8WjUGcgJNiwDjkdrwaXkLR64PX0yYeZT4UBsWfFB8iHhb4kw+RCg+9IV8yN8mSpiVocvsGWpiMpkgNDQUzp44DMFBQZLLhV09IHtdj7/aX3aZc13ayi5TZuk/BQ5eLQPMGleNidIO6H7sPtn1uFOkpNNt1SNhdretWvtQexYEZ5hO/Cu7TEi9djY/S/Hx8NQu2et5ok5L4f9a+sja/IuseuU8LX+8nCn4Odll/NEiu0yJR/dll7Hez1JR4iP53DHZ6wmv1sDl37Vor+Ti7tjzpL26feGU7PqERlbyqf5DLkp9KOmnStZ8yeHvXW3rrYtnZK/n8awHsss8WftlzX2oMQOVVv250plamJw9g3CN/RmqXg0egPMrBno/JODsCo5eV5hdXTHQ0weLkA+CFR8stFeurjgbcUeMfBjvg4U31QGw4cMeo+NDbShpZgDrQDMiIbAPNCMCzFHDp3eAOWr4KEED8kEAADs+qL2yhXyw4cNZfOgNCz6sMTo+1EaX12gTruFfCXnt2jUA4BoAvRMC/iDmD2x/f3/dA8z6Fbbnz58X6qVngFm/otNIHyyRlZVFPggAYMcHtVci5IODBR+O4qPkY7qtXoAFHzxG9h9aQFeaGaBIkSI2B1J4eLghCUGpUqWE/wcGBhoSYCVKlIDAwECHddILVnywAvkgrGHFB7VXHORDhEUfRsGCD37dPEb4UBtdkuZffvkFUlNT9ViVV5KZmQmJiYlQvHhxKF68OCQmJup+W4e/hePv7w/BwcFgNpsNeSo6OTkZzGYzBAcHg7+/vyGzBrDggyXIB2ENCz6ovRIhHyIs+jACVnwAGN9/qI0uSfOpU6egdu3a8Morr8Bvv/0GDx5IfzI0OzsbTCaTzcfX4MeglS1bFsqWLav7wwT2Y57KlCljyHRC1mOeypQpY8h0W9ZjAo3ywRrkg+BhwQe1V7aQDw4WfDiKD71hxQeP0fGhNrokzfPnz4fExET44osv4OjRo1CzZk147bXXYPHixZCWluaybHR0NISGhgqfiIgIPaqsK9YPbej9FK6zhwT0nofT0UMCes9T6+ghGlaeijYS8kEAsOGD2quCkA82fDiLDz1hxYc1RseH2ug2ptnf3x9atWoFCxYsgMTERBg5ciTMmjULnn76aZfloqKiIC0tTfgkJSXpVGP9sH9oQ6+Gz91TtXoGmrOnavUKNFdPnVOiJkI+Cics+GClvXI1C4ARiQH5MN4HC7P6sOLDFb6QOOv+IODevXthxIgR0KdPH3jmmWdg/vz5Lr8fEBAAISEhNh9fw1GAad3wSZ2GRq9Ac/VUrdaBJqXBYzFR89WGz1t9+Cos+GClvZIybZbeiQH5MNYHCwkzADDhQwrenjjrkjTv27cPhg0bBhERETBlyhSoUaMGnDx5ErZu3Qp9+vTRowpeiVYNn9x5G/UINHdPG2sVaHIaPC19KMEXGz4WfBAinvhQC1baKznzzLKQGPh6/8GCD1YSZgAw3IccWIgPpeiSNI8fPx6qVasGx48fh+3bt8OAAQN8YuoRPVC74VM60bkvBpqSBk8rH0ogH5Q4a4mnPtSAlfZKyYsZWEgMfLX/UMOHp7CUMANAoe/P9UKXpHnXrl0waNAgJuYu9EbUavg8fTOQLwWaJw2eFj6U4Es+AMBwH4SIGvHhKay0V568yYyFxMDX+g+1fHgCawkzABT6/lwv6OUmXoKnDZ9ar9L0hUBTo8FT24dSfMEHj5E+CBG14sMTWGmv1Hj1ry8kzr7oQyksJsxK8YX+g/ehF5Q0exFKGz613z3vzYGmZoNHPkTUuPVplA9ChIWEgJX4UCNB4/HmxNlXfSiBhfhQGxb7DyX9uV74ISLqtjYVMJlMEBoaCodPXICg4GDJ5R5Z5O/U+pVKFvidnKDRKsDy8/OhaNGisstlZGTIbiz8/Pxkr8fRIaVmg2cNKz6kdipqd0DWyNnH5EP+d+WQfO6Y/DKW4uSD4fhQ4rT4I3lvwg2p/4rwf/Kh7LtykBsf1q+Eloq7FMsoH5707az4sP7u3StnZa3LnJ4OVRq8CGlpabJmZaMrzTKRekak5Rmp0mUZ9e55rQIMgB0fUq7gaNkBAUi/YkA+OLT2IRfy4fvxIRXyIcJSe6UFrPiQCks+jLjiT0mzAtyJ9cVbOErRowNiwYe7hk+vBs9dR0Q+OFjpgKwhH4UjPtxBPkRYa6+0ghUf7mDNhxH5FSXNCnEm1mihLKFnB8SCD2cNn94NnrOOiHwY44MFyIcIC/HhDPLBdnulJaz4cAVrPozIr3RJmvv27QupqfLGdnkD9mLT09MNF8oCfGOndwfEgg/7hi89Pd2QBs++IyIfxvpgAfIhwkJ8OIJ8UHvFgg9nFDYfjtAlaW7atCk0a9YM5s2b53ZQvLfBi+XPgIoVK1aoE2YAY6/YsOCDb/iKFSsmXDEwosGz74jIh7E+WIB8iLAQH/aQD2qvWPHhiMLowx75UzAooF+/ftC1a1eYPHkyNG7cGHr27An+/mK+PnjwYKdls7OzITs7W/jZZDJpWleCIAiCIAiCsEe3Mc3FixeHJ598EtLT0yElJcXm44ro6GgIDQ0VPhERETrVWBr8GRB/RpSTk+NVr4TUAiPnfWTBBz8GLScnR7hC4OqpaK2wv2JDPoz1wQLkQ4SF+LCHfFB7xYoPRxRGH/bokjSvXbsW6tSpA3l5eXDixAmYMmUKTJo0Sfi4IioqCtLS0oRPUlKSHlWWhP2g9KCgIEnTpfg6fKOnd8PHgg/7hzaCgoIMeeGGfQdEPoz1wQLkQ4SF+HAE+aD2igUfzihsPhyhS9K8cOFC2LhxI0ydOlX2tC0BAQEQEhJi82EBZ09xsvDmJ1bQs+FjwYezp5z1flOdszGB5MMYHyxAPkRYiA9nkA+22ystYcWHK1jzYUR+pUvSvHnzZihfvrweq9IFd9OesCCWFfRo+Fjw4W5aIL0aPncP0ZAPDpY6Ih7yUTjiwx3kQ4S19korWPHhDtZ8GJFf0TzNMpE6T6CWYpUuyxcDjRUfUqYF0rrhk/rUOfngYKUj4iEfvh8fUiEfIiy1V1rAig+psOTDiMSZkmYZyJ1YWwuxfIApwdcCjSUfUufR1KrhkztNE/ngYKUjAlD/1if5EGEhPuRCPkRYaa/UhhUfcmHFh15DZ6yhpFkiSt9Eo2bDZx1gSvClQGPNh5QGj0fthk/pvKbkg0MLH0ogHxy+GB9KIR8iLMSHmrDiQyks+NBj6Iw9fuhlbxsxmUwQGhoKaWlpkh8KTE5OhqDNC2Svq0RvbmYPNQLM02Wo8SpNNZahxsTzni6DfIiQDw7yIUI+RLzNR9qJ7bKXH1qvrdvv2O/LnEtHZK8nuGbzQudDq2WwEB83L8XKXudzlWoW+J23+lCSSwIUkivNnrzFRq0zUk+uGKj17nlPz1DVaPAAPDtDJR8i5IODfIiQDxFf8uEJ5EOE4kN9vN2HXApF0qwUtRs8JWLVCjAepYGmVoPHoyTQyIcI+eAgHyLkQ8QXfSiBfIhQfIioPdzIW30ogZJmF2jR4MkRq3aA8cgNNLUbPB45gaZVB0Q+RMgHB/kQIR8iLPiQA/kQofgQ0WqMvjf6UAIlzS7QqsGTIlarAOORGmhaNXg8UgJN6w6IfIiQDw7yIUI+RFjwYQ/5oPhQ4kMLvMlHYmKiouXrkjSnpaVBfHx8gd/Hx8eDyWTSowqK0LLBcyVW6wDjcRdoWjd4PK4CTa8OiHyIkA8O8iFCPkRY8GEN+TDeB8WHiLf4yM7OVrRsXZLmoUOHwpUrVwr8Pi4uDoYNG6ZHFRShdYPnSKxeAcbjLND0CjAeR4GmdwdEPkTIBwf5ECEfIiz44CEfbPig+BDxBh+RkZGKlqvLlHM1atSAuLg4h3+rWbMmxMY6n/okOzvb5ozAZDJBRESE7GlCMn+fIr3C/w8/5ZzW8AdTsWLFAAAgJydHlwCzxvpgCgwMBLPZrFuAWcMHWXBwMGRkZBgyJpB8iJAPDvIhQj5E1PahZMq5x6o0k+3j4aldstfzRJ2WTv/mqz6U4C3xodaUc65g2UdeXh67U865mnja3SXy6OhoCA0NFT4RERFqV89wSpQoAZGRkZCVlSWcAekZYADiGarFYgGz2QzBwcG6N3gA3BlqcHAwmM1msFgshjxEQz5EyAcH+RAhHyLkg4N8iJAPEV/0oUvSXLNmTVi+fHmB369YsQKqVq3qsmxUVBSkpaUJn6SkJK2qaRj5+fk2Y3+Sk5N1fZc6T2pqqvD/jIwMQ+Z9zMzMhIyMDId10gvyIUI+RMgHB/kQIR8i5IODfIj4oo+ialTKHXPnzoW33noLfvvtN6hTpw74+fnBiRMnwGQywbp161yWDQgIgICAAD2qaQjWt1HKlSsHANwDkgkJCbqelVmPeSpVqhQkJCRAfHy8rreV7Mc8paamCge7XmfJ5EOEfIiQDw7yIcKCDwAgH/8PCz4oPkRY9/Hkk08qWqYuV5qff/55OHr0KIwdOxZeeOEFiIyMhLFjx8KxY8fg+eef16MKTOLoIQE932zDY/+QgBFvGnL0kICceR/VgHyIkA8R8sFBPkRY8MFDPtjwQfEh4g0+mJ5yjqdNmzYwbNgw+OSTT6Bt27Z6rloRWop19VStnoHm7KlaPQPN1VO1egUa+RAhHyLkg4N8iLDgwxryYbwPig8Rb/HB9JRz3opWYqVMQ6NHoLmbhkaPQJMyDY3WgUY+RMiHCPngIB8iLPiwh3xQfADI96EF3uRD6ZRzlDS7QAuxcuZt1DLQpM7bqGXDJ2feRq0CjXyIkA8R8sFBPkRY8CEH8iFC8cFh7UNtvNGHEjyap/n06dPw119/wfTp05UuQjYmkwlCQ0Nll3u07X+yyxRv11vywSgFZ0L9/PxkL8uT6bWVbJPak7QrnehcDx/uUHuS9uTkZHj66adll7t69arsuqfGHZC9nlI1mjn9m6/6sN6mG5cczzHviqKWXNlliuVmuP+SHaVqNZddRmrbwaoPKWjRXqUlXpJdrqglR9b3w6o1cvo3NX3cizsoq14AAGE1nbcDzkg9s1d2mZI1X3L7HW/qP1LOHpa97LDqjSV/V8/4uHPhpOR68TxTpa7TvxnZf/C5pObzNMfFxcGECROgSpUqMGTIEChVqpTcRXgVap0R6f1mIGcoPUjVvGLgSafKgg81rxh4sh16P9zhCF/1oUYj7s14uw8t2isjYcUHC3h7/6EmLMSHJ3ijD0lJ8/nz52HKlClQvXp1+Pjjj+Gpp56CzMxM2LdvH4wePVqzyrGCp2K1EKqk4fM0IVAj0NS4CsWCDzU6ImsfSvCVho9FH5Qwe78Ptdsro9DCh7fiK/2HGrAQH2rgbT4kJc3Vq1eH7du3w7p162Dfvn0wfPhwKFpUlymemUGpWK2Eyu2I1EoIPAk0NW/bsuDDk8SABR9qQj58A1/zoWZ7ZQRa+fBGfK3/8AQW4kNNvMmHpKR527ZtUK1aNWjXrh28++67sGbNGrBYLFrXjTnkitVSqJyOSO2EQEmgqT3OEYANH0oSAxZ8aAH58G581Qcr7ZVctPThbfhq/6EEFuJDC7zFh6SkuW3btvDzzz/D1atXoU+fPrBp0ybIzMyEt99+G5YuXap1HZlCqlithUrtiLRKCOQEmpYdEAs+5CQGLPjQEvLhnfi6D1baK6lo7cOb8PX+Qw4sxIeWeIMPWQ8CFilSBNq3bw+LFi2CW7duQd++fWHnzp0eV2L16tVO/5adnQ0mk8nmYzTuxOohVEpHpHVCICXQ9OiAyAeHtzR8hcWHt1BYfLDSXrmDlQSNBQpL/+FonfawEB96wKIPaxTP02yxWCAnJ0fxW1WsGTlypNO/RUdHQ2hoqPCJiIjweH1q4EysnkJddUR6JQSuAk3PDoh8cLjzoRfkg10Ksw9W2itnGJ0QsERh6z+sYTU+9II1H9bISppzc3Nh48aN8OGHH8Kzzz4Lq1atgi5dunhcCVfzhkZFRUFaWprwSUpK8nh9amEv1gihjjoivRMCR4FmRAdEPjhc+dAT8sEmhd0HK+2VI4xOCFigMPcfPKzGh56w5MMaSS83iYmJgb/++gt27NgBLVq0gK5du8Inn3yi2tQ1kZGRkJiYKOm7er/cRArWZ0P+/v6yharxchP+oOYf0DQiIeAPav6MtESJEoZ0QJ76UAO5PrR4wY0jH6GZd2Svx9XLTaTgjT6s8bWXm9y9e9erfaiF3Pbq9oVTstch9+UmKRCkiw+WX25yy+8JAPCd/kPJy00Cy9ZkMj5K5KTJXo6rl5tIQav+Q9OXm3Ts2BGuXLkC27Ztg2XLlkGnTp1kH8hhYWGCeOtPWFgY3L59W9ayWMP6BS+BgYGGnAGVKFECAgMDHdZJL4oUKWIT2OHh4YZcsSEfHI58GAH5YAvywcFKe2WNUT5YhPoP9uLDCFjwYY2kpPnKlSvQsWNHeO+996BJkyYwa9YsyMvLk7WilJQU4YzB+pOSkgK5ufKvyrACfzbm7+8PwcHBYDabZc0zqBbJyclgNpshODgY/P39DXnzU2ZmJiQmJkLx4sWhePHikJiYqPvDBORDxJEPvSEf7EE+OFhor+wxygdLUP/BbnzoDSs+rJGUNJcrVw6ioqLg5MmTsGTJEsjIyIDQ0FCoX78+fPnll1rXkVnsx9iUKVNG0QTdnmI95qlMmTKy50VVA+sxaGXLloWyZcvq/hQu+RBx5kNPyAebkA822itHGOGDNQp7/wEAzMaHnrDkwxrZs2dUqlQJJkyYALGxsbB06dJC2ekAOH+KU+4E3Z7i6CEBNV5hKwdHD23oPX0N+RBx5UMvyAe7FHYfLLRXztDbB4sU5v6Dh9X40AvWfFijeMo5AIBq1arB5MmTVaqK9+DuKU69xLp6qlavQHP1lLNeDR/5EHHnQw/IB9sUZh8stFfuYCExYIHC1n/Yr9MaVuJDD1j0YY2k2TNYgn/i8f79+/Dkk086/I5aU5PcuXBSdhnrJ0W1nCpG6rK1nLpH6rK1mCpGyWwT/KwB5EO7qXv4ZZc0yR//9kSdlqrVw2gfSmYnSPYP1cyHteuHJ+W/kMoc9IzsMhEVqwn/N9qHnGWrGR9yj4OnajQV/i91n2nZFnrqI+nyOdl1sz5uALRrr1LjDsgucxtCqP+Q6ePu+eOy65KKj7tdtv0+Mx+LkbUOU3oGPN/qHW1mz2ARVweLr18xkJOMa3WGKid4yYeIL/uwbkyNhAUfStDSh5HzmrLgg4X4kIuWV9SM9iEHVnwAaDPmnOKjIFLaK6OuOMtOmm/cuAH79++HPXv2CB8WYSXQ1Bar5Oq12oGm5GyXfIj4og/7BE0JvuRDCVr68IaEmccX40MpWiUGRvpQghbtlRKo/+DQOj6ktlfWPvRCVtI8evRoaN26NcycORPmzp0Lc+fOhR9//FGrunmMrzV8ngz3UCvQPLk9pJYPTxsJ8sGhpg81EjRf8qEE1nx4Cks+jIwPT9EicTbKhyeoHR9Kof6Dg7X40AtZSfPGjRvh7NmzsGbNGli5ciWsXLkS/v77b63qpgqsiVUaaGqMj/Y00NQYT+WpD08bPB7ywaGWDzUSNF/yoRSWfHgKSz6Mig+1MPrhJwDfSJzVGkJG/QcHS/GhF7KS5mrVqoHZbNaqLprBklglgabmA4VKA03NBxCU+lB7zCz54FDDhxoJmi/58ARWfHgKSz6Mig81ocSZQ6348BRfaq+MiA9vRVbSnJGRAVWqVIFOnTpBt27dhI8nWCwWWLlypdO/Z2dng8lksvkoQa5YrRoDuYGmxQwccgNNiyd2lfhQs8HjIR8cnvpQK0EjHxys+PAE8uH5HTF7KHHmYCU+qL3iKEyJs6ykedy4cbBy5UoYNWoUDBkyRPhIwWQyQXR0NAwZMgS2bNkCFosF5s6dC+XKlYOlS5c6LRcdHQ2hoaHCJyIiQk6VbZAqVqsGj0dqoGkRYDxSA03LKW7k+tAqISAfHORDhHywhbf60AJKnDlYiQ9qrzgKS+IsK2l++eWXoXLlymAymcBsNkOVKlXg5ZdfllT2ww8/hPPnz0OtWrVg0aJF0KZNG/j7779h9erVsH79eqfloqKiIC0tTfgkJSXJqXIB3InVa9osd4GmZYDxuAs0LQOMR44PLRMC8sFBPkTIB1t4ow+tMGLWAHuk+NAaVuKD2iuOwpA4y0qaV6xYAS+99BJs2rQJ1q9fD82bN4c///xTUtmrV6/CkiVLYODAgfDXX39BXFwc/Pvvv1C/fn2X5QICAiAkJMTm4ynOxOrV4PE4CzQ9AozHWaDpEWA8Un1onRCQDw7yIUI+2IJ8iOg9a4Aj3PnQA9Z8UHtl68PXKCrny19//TUcPXpUeBPfgwcPoGXLlvDuu++6LVusWDHh/0WKFIGIiAiPnjL3FF5sQkICxMfHQ2RkJCQnJ9sEmLLR0/Lgg8g+yPRsEPlAi4+Ph4SEBAgPD4fExERdAoxHig89IB8c5EOEfLAF+RAxOmkGcO0D8vV50RFrPqi9En1A9iPN16cnsq40WywWCAoKEn4OCgoCi8UiqeyZM2eEAycsLAxiY2OF/xsV+LzYYsWKCWeoRnRA9meoRlxB4AONPyMtVqyYbgHGQz5EyIcI+eBgxQcLkA+2cOZDT1jxQe0VB+/D15B1pfmDDz6AZs2awdtvvw0AAGvWrIGePXtKKpuXlye/dgRBEARBEATBALKuNH/++efw008/weOPPw6PP/44LFiwAD799FOt6qY5/JinnJwc4YzUiMHr9mekRjwVzZ+R8meoOTk5uj8VTT5EyIcI+eBgxQcLkA+2cOZDT1jxQe0Vh9azkBmFHyKiuy9lZ2dDQECA04NPz9sfJpMJQkNDIS0tzaOHAh09JGDEgzXJyckA9xJllwuv1kC1Ojh6SEDPBwcA2PJhf0tN79ts5EOEBR/J547JLqMkPjP3r5Zd5s6zdQudD4oPEfLBwbKPrH9+lL2c4p2GKq5DYfWRePm8rO+b09OhRr1GsnNJSVea+eEY1atXhxo1aggf/mdvw5k8vadLMXKeTR5nwaTnPJys+bDvbPS8YkA+RFjxwTKF0QfFBwf54GDdh56QD+2RlDRv3LgRAACuX78O165dEz78z96Eu7MdvcRaB5hRuDv71CPQWPThyIkeHRH5EGHJB8sURh8UH+SDx1t86AH50AdZY5qbNm0q6XesIvX2gNZivSHAeLQMNG/zoWVHRD5EWPPBOoXNB8UH+QDwPh9aQj70Q1LSfO/ePTh79iykpaXB+fPn4dy5c3Du3Dk4dOgQPHjwQOs6qoLc8TRaifWmAOPRItC81YcWHRH5EGHRhzdQmHxQfJAPb/WhBeRDXyQlzZs2bYKhQ4dCUlISDB48GIYMGQJDhgyB6dOnw/Tp07Wuo8coHYCutlhvDDAeNQPN232o2RGRDxFv9mGPnk+p85APEYoPEfLBwYoPNSEf+iNp9gyedevWQefOnRWtaOrUqS7/PnHiREnLkTt7hhpPbKqxDFcBptvT+SokBJ4uwxt86LUM8iHCsg8l8ZlevKRsH0pmzyjx4tsFfufrPvRcBsWHCPkQkbMvtZo9g3yIJCcnQ1ZaqqwySmfPkJU0T5kyBfz8/Ar8XkrCW7RoUahRowZ06dIFnnrqKbBf7ZAhQxyWy87OhuzsbOFnk8kEERERkjZUzSlOPFmWuwBT0inDU5GyGj41p5xRuixv8SEHpcsiHyKs+1ASn/eghGwfaiXNAL7tQy4UH54vi3yIyN2HWiTN5EOE9xESIG8faDrlHM9TTz0FpUqVglKlSkFQUBDs2rULkpKSJJW9ceMG9OvXD3bv3g0bNmyAxx9/HN5//31hqIczoqOjITQ0VPhERERIWp/acwIqvZWg1S0cObfa1J6jUcmtHV/1oeTWJ/kQ8UUfAKDIh5qQDxGKDxHywcGKDyWQDxFrH3oh60qzPbm5ufDaa6/Btm3bZJW7ffs2/Pnnn/D111/D119/Db169XL6XWdXmu/fvw9PPvmkwzJaTqItZ9lSA0zplWYpy9ZyUnOpy/Y2H0qQumzyIeItPpQOn5Lr45nbJ2Wvx9mVZvtl+5IPpVB8yF82+RBR6kPNK83kQ8TeB1MvN3HGnTt3ZM3TjIiwa9cumDp1Kixbtgzee+89ePHFF12WCQgIgJCQEJsPAEBiYqLDMyKt3zoj9YxI6zNSKVcMtH4LkJQzVPIhQj5EyAeHtQ8tIB8iFB8i5IODFR9SIB8iRvqQlTSHhYUJlQwPD4c2bdrAV199Jans0KFDoWHDhvDPP/9Az5494fjx4zB79myoUKGCoopnZ2cXEKvXazPdidVLqKuGT6/XZroKNPIhQj5EyAeHvQ+tIB8iFB8i5IODFR/267SGfIgYfQLj0fAMOfj7+0PJkiWFBwn5fxER/Pz8JI+v4mfPuH37Nty/f99mLlU9hFrj6CBSItTT2TPs16lXgFnjaG5bb/XhKeSDw1d8qDG7jRQfaj4I6Ahf8aEGFB8i5INDKx9KhmdcKd+SfMj0odfwDFlJ89tvvw2rV692+zstsZ5yrmjRohAfHw/FihUDAICcnBzdhPJYiw0MDASz2Sw7wNTolPmDKTg4GDIyMnQNMB4+0LzdhxqQDw5f8KHWlJDufGidNAP4hg+1oPgQIR8cWvhQkjSbmnYjHyDPB1NjmvPy8iAzMxOuXr0Kjx49gszMTMjMzIQ7d+7AuXPnZFVUTUqUKAGRkZGQlZUFWVlZEBkZqatQAPFWgsViAbPZDMHBwYbcMggPD4fg4GAwm81gsVh0DzAA8mEN+eAgHyLkQ4R8cJAPEfIhQj44WPFhTVEpX5o3bx7MmTMHbt26BdWrVxfmWA4JCYHBgwdrWkFX5Ofn2wzrSE5Ohscff1z3gys1VZxUOyMjAzIzM2UdXEpeVHIv7mCB3wX+/wcA4MH5IwX+/lSNpqqsxz3BAOC9PtQgMzMTMjIybOqkd7CrFR+O5mZ3h/UNLG/3oSQ+HeHOh9yrxkrxdh9q4Sv9hxqQDxG1fUh5UYk95ENEqo/IilVlLddkMimqj6zhGfPnzzc0SQYQh2fcv38fHjx4YPMQjd7jfgBsx4OVKlVKt3E/SpJZvZLmEuVqA0Dh8mGN/Xiw1NRU3ccpOnrITKkPT5Jm8sGhpg9PIB8c5EOEfIiQD47C4EPu26V5JA3P2L9/PwBwE/Zv3ry5wMcIEhMTbXaemu9Sl4r9oHRvfZe62hRmH44e2pAyvZOaOHqAgnyQD/LBQT5EyIcI+eAgH66RlDTzLy9ZuXJlgc+qVas0raAzsrOzC5xt6CnW2VOcrIhlgcLmw9VTzno1fK6mBSIf5IN8kA8e8iFCPjjIh3tkDc84e/YsVK9e3e3vtMR6yrlnnnnG4Xe0nqJFyjQ0Ws9pyPLwDPv1FAYfUrdRyymlpG6jXB9KhmfExsaSD418yIXig4N8iJAPEfLBUdh8aDo8g+fDDz+U9Ds9cLWTtDwjknrQanlGpHRZetzacURh8CG1EdHqioGcRkSPKwbkgw0fFB8c5EOEfIiQDw7yIR1JV5qPHTsGhw8fhq+//hq++OIL4fcmkwn++OMPOH36tNsVLVmyxOXfe/bs6fD32dnZkJ2dbbPOiIgISWcHap8RKTnLU/sMld+mcEua7LJ3IFj2GaqaV7R92YfcbVLzioHSbZJad09nz3AF+RCh+BAhHxzkQ4R8iJAPDk98aHqlOSMjA+7duwe5ubmQkpIifIoVKyZ5THNsbKzw+eyzz2x+jouLc1ouOjoaQkNDhU9ERIS0LQN1z4iUHqRqnhFZH6RK0PNhAkf4sg+5jYZaVww8aTSMeLjDHvIhQvEhQj44yIcI+RAhHxxGXHGWNab5xo0b8PzzzwMAdwU4NTUVSpcuLXuldevWhZMnT0r6ridXmnk8PSNS46zO0zNU+21wNA+zO56q0VT2tmgxdtoXfSg9y/ZkW9S66uFuW7S80sxDPkQoPkTIBwf5ECEfIuSDQ8m26DKmediwYWA2myEtLQ2qVq0Kbdq0gYkTJ8pZBADI64QDAgIgJCTE5iMXT86I1LoN4skZkZq3QfR6CtcV5ENEqQ81bxN6+xVn8iFC8cFBPkTIhwj54CAfypGVNMfHx0NwcDCsWrUK3nvvPTh37hysW7dOo6qpixKxaj+pqkSsFk+qemviTD44tHiS21sTZ/IhQvHBQT5EyIcI+eAgH54hK2nOzs6Gq1evwooVK+DNN9+UdcU4LCwMwsPDISwszGY6Kv73eiBHrNpCeeSI1XJqF29LnMkHh5ZTH3lb4kw+RCg+OMiHCPkQIR8c5ENEaVItK2meOHEivPXWW1CpUiVo3LgxXLt2DSpUqCCpbEpKCiQnJ0NKSgrk5uYKO43/vV5IEauVUB4pYrWeCxHAfaDpkTSRDxEpPrR+rau3JM7kQ4Tig4N8iJAPEfLBQT5EMjMzITExUdHyZT0IyAJKB2/b4+wg1lqoNc4OYikBpuYDeo62ma9bSOZd1dbjCm/3oSaufGjZ4Fljvc3ly5eXXV6NZoV8iFB8iJAPDvIhQj5EyAeHOx+5ublQp04dbR8EvHLlCrz22mtQunRpKF26NLzxxhtw9epVeVvCCI7OiPQUCuD4jEjvAAMoeIZqfbDpBfkQceVDjwYPwNaHUZAPEYoPEfLBQT5EyIcI+eBw5yMyMlLRcmVdaW7YsCGMHTsW3nrrLQAAWLduHXz11Vdw9OhRRStXglpXmnn4nWixWADA/ZvM3KFkeq68vDxISEgQbiOUKFFCkwAzH4uRXSa4QQdV6+AMufvtxsUzsteBIN+NEp6vVEOV5VjfZvP399etwbNG7fhQAt/oax0f7iAfHORDpDD1H+7wRR9KUCM+ki6fk73eiIrVbH5mxUdgYKDscmoOgHDmIyMjQ/sp54oWLSokzAAAnTt3hqJFi8pZBHOUKFHCRmqpUqV0r0ORIkVsAjs8PFz3Bo9gD+tjMTAwUPcGD4DiwxrywUE+RMiHCPngIB8iRqzTHrV9SEqaz507B+fOnYPGjRvDiBEj4NChQ3D48GEYNWoUtG7dWvHKWSA5ORnMZjMEBweDv7+/IQ8/8YPSixcvDsWLF4fExERd3mxDsAt/duzv7w/BwcFgNpsNmeWE4oODfIiQDxHywUE+RMiHiFEzc1mjtg9Jl4mHDBli8/Pp06eF/yu5ncQK9mNs+Fs7CQkJut7esh7zBACQkJAA8fHxhtxOsYffP4R+OBqDZn2rTS8fLMSH/ZhAAP3jg3yIkA8RFnwAGN9/kA8Rig8Rd9Pf6YErHyVLllS0TENnz0hNTYXVq1fDgAEDJJdRa0yzs0Hpng7cV3IScfXqVZt1aTVwX8mY5oTiz+syLozGNHO4cq/ngxRaxYccnK1LzwdbyIcI+RApjP2HI3zdhxzUjA+lY5pZ9PH000/LLq9GWurOx71796Bx48ayc0lZSfPUqVMd/l7Oq7QfPHgAa9euhb/++gsuXboEr7/+OsydO1dyeTWSZncHjyeBpvRBDvt1aNHwKUmaH0XW0zzQkpOTZQeWLybNUpzr0fBpGR9ScbcOPRID8iF9HeRDxFf7D3sKgw+pqB0fSpLm0uUqM+lDyTHtadIsxUdcXJz2U86VKlVK+AQFBcGuXbsgKSnJbTmTyQSLFy+Gjh07QoMGDSA2Nhbi4uLg+vXrbhPm7OxsMJlMNh9PkHLQ6P2CB0dBrOe71F1hP32N2rBwC4cFpDaqevkwMj6kdHJaxwf5ECEfIiz4sId8UHwAgNf40BqpPnSZcs6e3NxceO2112Dbtm0uv1e8eHFo2LAhfP3119CsWTMAAChbtixcv37d7TomT54MU6ZMKfB7JVea5QpVcoaq9lmVmlcMPJlyTotg8OQWji9daVbiWGsfWsWH2svU4ooa+VC+TPIh4mv9hyfL9FYfai9T6r5TcqXZnAtM+tDzSrMcH0pHLci60mzPnTt34Nq1a26/9+OPP0KJEiWgd+/eEBUVBSdOnJC8I6OioiAtLU34SLmy7QglB4m3vFJYD9Q+Q2XhjJQFlHZqLPhQOz6UdGpqxwf5ECEfIiz4UAL5EPHF+LDG23yojV7j12UlzWFhYRAeHi7826ZNG/jqq6/cluvXrx9s3boVDh48CGXLloVPP/0Ubt++DaNHj4a9e/e6LBsQEAAhISE2H7l4ItQXGz6lqBVoLAQYC3h6FYgFH2rFhycNnlrxQT5EyIcICz48gXyI+FJ82OONPtRCzwc+ZSXNKSkpkJycLPx76dIl6N69u+TypUqVggEDBsCOHTsgISEBypcvL+shQiWoIdQXGj616uxpoLEQYCyg1m1TFnx4Gh9qNHhqxAf54CAfIiz4UAPyIeIL8aEWLPjwFL1fVS4paU5ISIC0tDTh5+3bt8Mnn3wCs2bNguzsbEUrDg8Ph8GDB8N///2nqLwU1BTqzQ0f3+CphdJAYyHAWEDtcYYs+FAaH2o2eJ7GB/kgH9aw4ENNyIeIN8eH2rDgwx4jfEhFUtLctWtXyMjIAACAY8eOQbdu3SAyMhJiY2Nh4MCBmlZQKVoI9caGz7rBUxO5gUYJM4dWU0Gx4ENufGjR4HkSH+SDfPCw4EMLyIeIN8aHVrDgwxqjfEhBUtKclZUFpUuXBgCAZcuWwYABA2DMmDHw22+/wfHjxzWtoDNSUlKc/k1Lod7U8Nk3eGojNdAKW8Is1Yfac6ey4ENqfGjZ4CmND/JBPgDY8KEl5EPEm+JDa1jwwWOkD3dImnKuZs2acObMGfDz84Pq1avDDz/8AG3atBH+Fhsbq3lFefhpQg4ePAjlypUrIE2vBM2VNL2EumrUHP0tNe6A7HWUqtHM7Xdc7XOtfNy5cFJ2GX+L/A4qvFoDyd8V9rmluOz1VKtQWnYZZ6jt4+55+SfGwWWqyo4P0/GtstcTUv8Vp3+TGx9aYUR82GO9z8uXLy+7vJIpoFLOHpZdJucx+R78FNStWG6G7DJP1Wgqu4wzjO4/bimYsrN05Vqq1wNAnfi4eUl+DvJcpZrC/432AeB5eyV3ajdnMc1aeyXVx/0zrieVsMeUngFlX3xVmynnunXrBu3atYOuXbtCkSJFoFWrVgAAcO3aNQgODpZVUbUICwsrcEak5xVNZ2eoep4BOTtD1TMhAHB+hlrYrjDzPoyGBR8UHyKs+SCMh4X4YAXW4oPaK/LhiqJSvjRhwgRo27Yt3LlzB9q1awf+/lyunZeXBz/++KOmFXRGWFgYZGVlFZCqZ4LGi42Pj4eEhAQIDw+HxMREXYXygZaQkADx8fEQGRkJycnJugUYD7/PjfTBAqx0ciz4oPgQYckHwQYsxAcrsBQf1F6RD1dISpoBAJo2LXhrqlKlSqpWRi72Yo1I0Hix165dM+wMiA+069evQ3x8PAAAlCtXTrcA42HBByHCgg+KDxFWfBDswEJ8sAIr8WG0D2qvRFjwYY9HbwQkCIIgCIIgiMKAVyfN9mNs5M4zqAb8GBv+jCgnJ0f3p6L5MU85OTnCLRwjnsJlwQchwoIPig8RVnwQ7MBCfLACK/FhtA9qr0RY8GGP1ybN/FsJeaFGiLUflB4UFKT7dEL2DwkEBQUZMn2NfYAV1sSZlc6OBR8UHyIs+SDYgIX4YAWW4oPaK/LhCsljmj1lyZIlLv/es2dPh7/Pzs62eeugyWQCAC5ptp9yztEYHK1w9hSn/eB1I6aosX+YwKgptfT0wQLimxflTzmnJiz4oPgQYc0HYTwsxAcrsBYf1F6RD1fodqU5Nja2wOfUqVMwfvx46Nu3r9Ny0dHREBoaKnwiIiIAgJs9w5E0Pc6I3E17oscE9u6modFrwnR309AUlivOWr15US6s+KD44GDBh317RRgLC/0HK7AYH9RekQ9X6JY0f/vtt8Lnq6++gnLlysHmzZuhXbt2cPbsWafloqKiIC0tTfgkJSUBAJc0O0NLsVLnCdRSrNR5G+0DTW2kztvo64mz1m9elApLPrwxPtTuiFjwwcK8poQIK/2HEgpzfFB7JeLrPtyh65jm9PR0+Oabb6BatWpw6dIl2L59OyxatMjl1HUBAQEQEhJi85GCFmLldkBaiJU70bl1oKmJ1ADj8dXEWe+J553Bmg8l8aEGnsSHmh0RCz4oYWYLlvoPJRT2+GCtPycf6vcfUtAtaZ44cSLUrl0b0tPT4fDhwzB79mx4/vnnNV2nmmKVdkBqBprSBE3tN9XJDTAeX0ucvTVh5tHSh5L48BRP40OtjogFH5QwswVr/YcSfCk+AFwPIXMGS/25L/lQIz70Qrekedq0aZCSkgILFiyAqlWrCjvc2dhktVBDrKcdkBqB5mmCplanqTTAeNTyYTRqJ8xKt4kFH56ixpvq1IgPNToiFnywkDD74jhcX+o/lOAr8cHDkg9qrzyPD73QLWm2WCxgMpmEqeL4D/+zlngiVq0OyJNA8/Yrmvao4cNItPChpOFjwYdaeLIP1fLhaUfEgg+tEmYl7ZWvUdj7D1+ID2t8zYdcWPChZnzohdfO0ywXJWLV7oCUBJqvJcw8nvowCq18yO2IWPDBAmr7UJoYsOBDyyvMStorX8NoHyz0H94cH/b4mg+5GO2DhTtiSvBDRDS6EnIwmUwQGhoKaWlpkh8KtEZq8GopVOqyVR8CsPtP2WVKvPyu6g2eNfyyn8tPlVXuydovq1YHo3woXTa/z8rfi5W9nsdbvufy72rFR9bmX2TXrXjHAZKWzcOaDy3jozC2V1KXnXzumOzlhVdroKge5EP+sik+OMiHiLt95ufnJ3vdD4//K+v7pvQMiHy5s+xcstBcaeaRckak9RmQlDNUX73CbI9Wy5UDCz6kXsGx9qEFFB8cSnxoGR/kQ595at1BPjgoPkTIhwgLPrSm0CXNAK7F6iXUVaCxkjADqHsLxxlGJ80AbPhw1/Bp3eDxUHxwkA8Rb/ChF+SDg+JDhHyIsOBDSwpl0gzgWKzeQh0FGksJMwAbV4L1ggUfzho+vRo8HooPDvIhwrIPvSEfHBQfIuRDhAUfWlHU6AoYCX/wJCcnw6NHjyAjI0N3odbvUr9+/ToAAOTk5DCRMAOwcRVYT1jwwTd8CQkJEB8fD4GBgWA2m3U/gaH44CAfIiz6eEqXtRaEfHBQfIiQDxEWfGhBob3SzBMeHg7BwcFgNpvBYrEYIrREiRIQGRkJWVlZkJWVBZGRkUwkzIUVFnzwDZ/FYgGz2QzBwcGGnMBQfHCQDxHWfBgJ+eCg+BAhHyIs+FAb3ZLmrKwsmDNnDgwdOhR+/vlnyMvLk1QuOzsbTCaTzUdNMjMzISMjQ/g5NVXeLA5qkJ+fbzP2Jzk52SdfDuAtsOLD+ljMyMgw5FY0xYcI+eBg0YdRkA8Rig8O8iHCgg+10S1p7tWrFxw7dgxq1qwJW7ZsgdGjR0sqFx0dDaGhocInIiJCtTpZj7Hh31Ko9zy11mOeypUrB+XKlVP13faewsLb9/SEFR/WY9CqVq1qyBhOig8R8sHBog+jIB8iFB8c5EOEBR9aoFvSfO7cOVi2bBkMHDgQVq1aBXv37pVULioqCtLS0oRPUlKSKvVxNChdyQTdnuDoIQE1322vBkY+bKM3rPiwf2jDiIefKD5EyAcHqz6MgHyIUHxwkA8RFnxohW5J82OPPSb8v2hR6c8fBgQEQEhIiM3HU1w9xamXWFdP1bKUOOsRaCwk5az4cJYQ6NnwUXyIkA8O1n3oCfkQofjgIB8iLPjQEt2S5jNnzgg7LCwsDGJjY4X/69n4SZn2RGuxUqahYSVx1jrQeB9GwooPdwmBfcOnBRQfInJ9aBkf5IP9hJmHfHBQfIiQDxFvT5x1S5rz8vKEnZSSkgK5ubnC//XacXLmCdRKrJx5G1lInLUMNGsfRsGKD6kJgbUPtaH4EFHiQ8v4IB/ekTDzkA8Oig8R8iHizYlzoZlyTsnE2mqLVTLRua8mzvY+5OJLPuQmBLwPNaH4EFHqQ8v4IB/ekzDzkA8Oig8R8iHirYlzoUiaPXkTjVpiPXkzkK8lzmq8GchXfChNCNSc65KF+AAAr/fBQnz4WnvlrQkzD/ngoPgQIR8i3pg4+3zSrEaC5qlYNV6l6SuJs1qv0vQFH96eEPCo1fB5uw9W4sNX2iuKDw5f8UHxIUI+RLwtcfZDRDS6EnIwmUwQGhoKaWlpbmfSUPtd50oOUrXfPa90m5KTk+HxxBOy1xfcoEOB37nbpuRzx2SvJ7xaA9ll5Po4f/Wm7HX4gfzwKGExyy4TWbGq7DJ+fn6yy1iHu9Rj6dbFM7LXU7pyLUnfYyk+1ErQlG4Ty+2VkmNNCXcUtB1PV63v8u++6EMp/DZVr15ddln+4X058aFm26G2j5Szh2WXKVmlgS7tlZJ4S7h0Ttb3+T7HV+IjDOX1u+b0DCjfpI2kXNIan73SrLZQAPlnRGo3eADKzlDVPoMzYt5HR3jbGSpLaBEf1nhjfKh1RVNJfPhqe8UC5EOEjw8lePsVfzVgob1SG1+JD73w2qTZlVgtA0yqWC07IDmBZp0QqIm3Jc5GvySGJfTogLwxPtSMETnx4evtFQuQDxGlyyrsCTOAZ0PInOFtibOvx4c7vDZpTkxMdChWjwBzJ1YPoVICTesxgd6SOPM+CP06IIoPafFRWNorFiAf3gdLCTMA6NZeGQHFhzS8NmkOCAgoIFbPAHMmVk+hrhIDvR6iYT1xtvZBAMXH/8NCfBS29ooFyIf3wFrCDKD8Kr3UZfPtlVFQfLhHl6Q5ISEB0tLShJ+3b98On3zyCcycOROys7MVLTMyMtJGrBEBZi/WCKGOEgO9nzrX4011UnDngwCKDwbiozC3VyxAPtjGKB8s4MmYc7Wg+HBNUT1W0rVrV1i3bh2EhobCsWPHoFu3bjB27FiIi4uDgQMHwu+//+60bHZ2tk1ibTKZAEAUm5CQANeuXQMA7oDTO8D4TpcX6+/vr7tQPtDi4+Ph/PnzQr30HINm7QOyjLnaDODOxwPD6sUKFB/Gx0dhb69YgHywi5E+WIAF9xQfztHlSnNWVhaULl0aAACWLVsGAwYMgDFjxsBvv/0Gx48fd1k2OjoaQkNDhU9ERITwtyJFith0fOHh4YYEWKlSpYT/BwYGGiK0RIkSEBgY6LBOemHvwyhY8MEqFB8F66QX1F6xBflgG6N8EBwUH47RJWlGRGF+2H///RfatWsHANLmIoyKioK0tDThk5SUJPwtMzMTEhMToXjx4lC8eHGnDwdqCX/LwN/fH4KDg8FsNhsy/VlycjKYzWYIDg4Gf39/Q57C5X0YCSs+WIXiw/j4oPaKDcgHmxjpgxCh+HCMLsMzunXrBu3atYMnn3wSihQpAq1atQIA7jZMcHCwy7IBAQEQEBBQ4PeZmZlw//59YYwNADd2Oj4+XrfL947G2FgPXNfrqqv9GE1+/FFCQoJut1OsxzwZNTzDnQ8CKD4YiI/C3l6xAPlgl7JlywKA/j5YgYX+iuLDObpcaZ4wYQJ8+eWX8P7778OBAwfA359bbV5eHvz444+KlsmfAfGdnt6zODgblK73CzccPdSk97yPjgJMb6T4IIDiA4yPj8LcXrEA+WAbI3ywAgveKT5co9uUc02bNoW33noLgoKChN9VqlQJ6tWrp2h5AQEBBa4S6SXW3VOceol1NQuAXokBC085S/VBgG4NH8UHh6v4KGztFQuQD++BxcRZr/bKKCg+3OO18zRHRkY6TNC0Fit12hOtxUqZNkvrCdO9IWHmoaSZQ6+GzxvjQ+3EWUp8FJb2igXIh2ew/qY6PTCyvdIaig9peG3S7CpB00qs3HkCtRIrJ8C0mjDdmxJmwhY9OiJvjA81E2c58eHr7RULkA8Rpcvyhlc8a43R7ZVWUHxIxw/5aS28BJPJBKGhoZCWlgYhISEuv2stoXr16rLXZb1rPEnQ1AwKpctSmuQmnzsmu47h1Ro4/L2aSW5h9eHpsqTMWGOPlCbCl30o2Wdnz56V7Tb90HrZ6wlq8qbD3/uyD7koXRa1VyLkg+PGpTjZ6yrx91zZZUqO/9nl39XwkRp3QHaZUjWaCf9nwQeAeEyXKpona53m9HSo3LC5pFzSGq+90iwF6zMiT/D04FDrjMiTxlPvh58codYZKvkQ8YUr/r7kg4d8sOHDk/ig9kqEfLAF9eciel+h9+mkGUAUqxS1AszThk+Nqw2+EGjkQ8QXEmYeX/BhDfkw3oca8UHtlQj5YAvqz0UoaVYZpZ2X2gGmtOFT8/acNwca+RDxpYSZx5t92EM+fCc+qL0SIR9s4YkPtShsPgpF0qwULYTKbfi0eEjAGxNnrQLMG334YsLMw6IPb6Aw+TAiPqi9EiEfbKHUh5oUJh+UNLtAK6FSGz4tn6r1psRZ6wDzJh++nDDzsOaDdQqbD6Pig9orEfLBFkp8qE1h8UFJswu0FOqu4dNjGhp3gabH1D7uAk2vAPMWH76eMPOw5INlCqMPI+OD2isR8sEWcn1oQWHwoVvSnJcnbzoQnuzsbDCZTDYfvdBaqLOGT48Gj8dZoGlxC8cZzgJN7wDzBh9GJMyF3QfLFEYfRscHtVci5IMtyIf26JY0N2rUSPj/4MGDJZeLjo6G0NBQ4RMREaFF9QzDvuHTs8HjsQ+09PR0zW7hOMM+0NLT0w0JMNZ9GHGFubD7YJnC6IOF+KD2SoR8sAX50BbdkmbrFyQcOnRIcrmoqChIS0sTPklJSVpUz1DsGz4jEgI+0Pgz0mLFiml2C8cZfKAVK1ZMOEM1IsBY9mHEkAzywS6F1QcL8UHtlQj5YAvyoR1F9VqRkrdqAQAEBARAQECAyrUhCIIgCIIgCOnodqX5zJkzEB4eDmFhYRAbGyucDYeFhRX6q0b2VwjkzsOpBvwZKX+GmpOTo/t0W/yYp5ycHOGMVMmE6Z7Csg8jZjkhH+xSWH2wEB/UXomQD7YgH9qh64OAycnJkJKSArm5uUJgp6SkFOoO0L7BM6Lhs39IICgoSPfptuwfEggKClLlFZ1yYd2HEYlzYffBMoXRBwvxQe2VCPlgC/KhLTTlnAu0FutsDJqeDZ+zp2r1nG7L2VO1ar3bXire4MOIxLmw+2CZwujD6Pig9kqEfLAF+dAeSppdoKVYdw9t6NHwuZuGRo8B++6modEr0LzFh5FXnAEKpw+WKYw+jIwPaq9EyAdbyPWhBYXBhx9aT2vhBZhMJggNDYW0tDQICQmRVVbOPIFaziko5ylnrZ6IljNvo1ZzPFrv42fy78sqW7LmS7LXd+7KLdllqlUobfOzFB/3z+yVvZ7iFerL9vFsbors9TxZ+2WnfzM6PpQ8LHz37t1CER9q+jAfi5Fdl4TizzPbXik5bh6c2CG7THCtlzWPj7vnj8uu19NV6zv9mzf2H492LpVdnxJtesouo1bqo0V/nnLuiOx6pECQT7ZXUnl4apes75vSM6BM89dl55KF5kqzXElanRHJbcS0uGIgN2i0uGJg70MuRp2hanUFR4kPNWElPuRSWOLDaB/e1F5pBUs+pMKCDxbukGkFC/05D7VX+lAokmalZzVqi1V61q9moCntgNQMNDXOMn2tI1LiQy1YiQ+lUHxwaOnD29orLWDJhxxY8MFi4uwL/bk11F7pg88nzZ4maGqJ9fQ2mRqB5mkHpEagqXVbxuhAU7vhMyohYCk+PIHig4OVjogFH2pDPoyPDzXx9v7cnsLcXuk5k5JPJ81qJWieilVrXJkngaZWB+RJoKk5jslXOiIeb0yYedSKD0+h+ODwhUSNtYQZQPndHfIhwlLi7M39uVr4Qnul9/7TJWk+duwY/P3338LPAwcOhG7dukG3bt3g4MGDmqxT7YHmSsWq/SCGkkBTuwNSEmis+FAbFho+JbDiwzo+1IDig8Ob44PFhNlTyIcIK4mzt/bnauPN7ZXa/YcUdEmax44dC1WqVBF+3rdvHwwZMgQ++ugjmDZtmsuy2dnZYDKZbD4A4FKsVjNfyBWr1ZPLcgJNqw5ITqCx4kMrWGj45MCKD4oPDlZ8aAULPliAfIiwkDh7Y3ulFd7YXmnlwx26JM0PHjyAWrVqCT9Xq1YNXn75ZXj11Vfh4cOHLstGR0dDaGio8ImIiAAAgMTERIditZwqDkC6WK2FSgk0rTsgKYHGkg8tsfbBMiz5oPhgx4fWsOCDBciHiDclziy0V1rjTe2VUQkzgI5JszUrV64U/n/nzh2XZaOioiAtLU34JCUlAQB3BdperNZCedyJ1Uuoq0DTqwNyFWis+dAaIwJYLiz5oPgwJj6MggUfLOANiTML8aEXLLZXRsFie2WUD2fokjTXqVMHFi9eXOD3S5cuhdq1a7ssGxAQACEhITYfAIDIyEgbsXoJ5XEmVm+hjho+vTsgR4HGog89YD1pZskHxYcx8WEkLPhgAZYTZ6PjwwhYa6+MhLX2ykgfjiiqx0q+//576Ny5MyxZsgTq1KkDfn5+cPLkSTCZTLBu3TpFyyxRogSEhIRAfHw8XL9+HQAAcnJydBHKw4tNSEiA+Ph4CAwMBLPZrLtQfl3Jycnw6NEjyMjI0L0D4gONZR/3TPLfCOhrsORDLyg+OHgfmfcv6bI+Z7DggwUcxYcRpzQs+LCOD6Ngqb1KuRev2/ocwVJ7ZbQPe3S50vzcc8/B0aNHISoqCl544QWIjIyEqKgoOHr0KDz33HOKl1uiRAnhinNWVhZERkbqJpSHF2uxWMBsNkNwcLAhQsPDwyE4OBjMZjNYLBZDOiDywT6F1QfFBwcrSSkLPljAPj6MggUffHwYCSvtFQuw0l6x5kOXK808bdu2hbZt26q2vPz8fJvxP8nJyfD444/rHuypqanC/zMyMiAzM1P3gyszMxMyMjJs6qT3wUU+2Cc/P79Q+qD4YAsWfLCCdXwYBQs++Ph4Vte1FoSF9ooFWGmvWPPhtS83sR5jU65cOShXrpwhDxNYj7GpWrWqIWPUrMegVa1a1ZCncMmHd1AYfVB8iLAwuwsLPljBPj6MgAUf1vFhJKy0V0bDUntltA97vDZpTkxMtBmUbsRTuPaD0o14uMPRQxt6T1/j6CEBFn3oAQsNnitY8kHxYUx8GAkLPljBUXzoDQs+7OPDKFhqr4yEtfbKSB+O8ENENGTNCjGZTBAaGgqHDx+GGjVqFLhMr9eTv66e4tTrSVN322pfRz8/P9nrcHd4uNtWrXwkXj4vu4wfyD/UIypWk/xdflurV68uez1qhaFRPuzRKz4uX5X/tH3F8mXc1lEtfMWHknhDP/nXZLL85B8LlctHSP4uv63ly5eXvR41u0pv7D+UcPf8cdllQm+dlV2meJuesstYU1h8uMNX2isp8LlkWlqaMCubFLz2SrOzQel6nBG5O3j1OCOScvBqfcVAysHLwjycemDtwyhY8cFCfEihsMSHt/jQGhaHABTm/oMVyAcHtVfS8Nqk2dXZhZZipZ7taSlWztmeVhOmyznb8/XE2d6HEbDig4X4kINWHRH5YAtWhwA4g8X+wxcTZ/LBQe2VdLw2aXaHFmLl3h7RQqyS2yNq385RcnvEVxNnFl7MwIoPFuJDCWp3ROSDLfR+MYMzWPDhSf/ha4kz+eCg9koePps0A6grVul4IjXFepKgqZU0e9IB+Vri7K0JM4+vxYcnqNURkQ+2UDNh9nYfnvYfvpQ4kw8Oaq/k49NJM4A6Yj0dgK+GWG9P0HjU8mE0avtQsk0s+TA6PtTA046IfLCF2leYvdmHGu2VryTO5IOD2itl6Jo0//rrr1C3bl0IDAyEwMBAqF+/Pvz+++8uy2RnZ4PJZLL5yMUTsWo9seqJWF9JmHnU8GEkWviQu12s+TAyPtREaUfk6z68DS2GZLDkw6j+w9sTZ/LB4evtlZb9h25J86+//goLFiyA2bNnw+3bt+HWrVswc+ZMmDdvHvzvf/9zWi46OhpCQ0OFT0SE9OmFrFEiVu0pXpSI9bWEmcdTH0ahlQ85DR+LPoyKDy2Q2xEVBh/ehFZjmFnyYWT/wWLiTD6ovdKr/9Ataf75559h3bp10LJlSwgJCYHQ0FBo2bIlrFmzBubPn++0XFRUFKSlpQmfpKQkxXWQI1ZtoTxyxPpqwszDgg85aOlDasPn6z7kxodWkA8O3oe3UFh8GN1/sJY4kw9qrwD06T90S5ozMzMdXiWOiIhwWfmAgAAICQmx+XiCFLFaJ2hSxLKQMAOA5k+ds+BDCnr4cNfw6TELAAs+5MSHlpAPDqPaHrkUJh8s9B9S4kMvyAe1VzxSfSQmJipavm5Js6tbfAEBAXpVAwBci9UrQXMllpWEGQB0maaJBR+u0NOHs4ZPz2mzWPAhNT60hnx4B4XNByv9h7v40AvywUHtFYcUH0rzTt2S5tOnTws7yvoTFhYGcXFxelVDwJFYvTsgR2JZSpgBQPMA42HBhyOM8GHf8BkxzywLPqTEhx6QD7YprD5Y6T9cxYdekA8Raq843PmIjIxUtFw/RESV66opSt8X7gx+J1osFgBQ/0UgUuAPav5sqESJEpoEmJ+fn+wyeh8eUn0kXj4ve9l+IH9b0rItsn2otZ+trxb4+/vr1uBZw3p8XL4q/2pWxfLKku3C7ENJvKGf/GsyWX7y92fuozTZPtSKUdbjQ08cxYc5Qf5xE3rrrOwyxdv0BADyYU1hbq+sceYjIyNDUS7p8/M0u6NEiRIQGBgo/FyqVCnd61CkSBGbAyk8PNzwK8xGwYIPe4zyYb3tgYGBhrzJjAUfrMQH+WCXwuyD4kOEfIiQDw61fRT6K8382VhwcDBkZGQYMiyCPxsrVqwYAADk5OToflZofQsnMDAQzGazIWeF5IODfIiQDxHywUE+RDY9Vll2mRentZNdpnjZF+SX6TZGdpmMheNllwnsPw0A2PBB8SGipw/zsRhZ3zelZ8Dzrd6hK81ysB5jU6ZMGdnzDKqB9RibsmXLQtmyZXWfp9Z+zFOZMmUMmU6IfHCQDxHyIUI+OMiHCOtvayyMPig+OFjwoQWFNml2NChdzXepS8HRQwJ6v+DB2UMCes/DST44yIcI+RAhHxzkQ0SPaRc9pTD6oPhgw4dWFMqk2dVTnHqJdfVUrV6B5u6pWr0CjXxwkA8R8iFCPjjIh4ie0y56gl6JGks+KD6Mjw8tKXRJs5RpT7QWK2UaGq0DTeo0NFoHGvngIB8i5EOEfHCQDxEjpl1Uil6JGks+KD6MjQ+tKVRJs5x5ArUSK2feRq0CTe68jVoFGvngIB8i5EOEfHCQDxHW5vGXgh6Jszf4oPgQ8ebEudAkzXKE8qgtVkmDp3agKZ3oXO1AIx8c5EOEfIiQDw7yIeKNCTOP3mNqHcGCD4oPEW9NnA1LmvPy8iR9Lzs7G0wmk81HLkqE8qgl1pMGT61A8/TNQGoFGvngIB8i5EOEfHCQDxFvTph5fClxpvgQYSU+9MKwpLlRo0aSvhcdHQ2hoaHCJyIiQtZ6PBHK46lYNRo8TwNNrVdpehpo5IODfIiQDxHywUE+RHwhYebxhcSZ4kOEpfjQC8OSZqnvVImKioK0tDThk5SUJHkdagjlUSpWzQZPaaCp/e55pYFGPjjIhwj5ECEfHORDxJcSZh5vTpwpPkRYiw+9MCxp7tOnj6TvBQQEQEhIiM1HCmoK5ZErVosGT26gqR1gPHIDjXxwkA8R8iFCPjjIh4gvJsw83pg4U3yIsBgfemFY0jxs2DCPyqekpDj9mxZCeaSK1bLBkxpoWgUYj9RAIx8c5EOEfIiQDw7yIeLLCTOPNyXOFB8ihT0+vHb2jJSUFIditRTK406sHkLdBZrWAcbjLtDIBwf5ECEfIuSDg3yIFIaEmccbEmeKDxGKDy9OmsPCwgqI1UMojzOxegp1Fmh6BRiPs0AjH+SDfJAPHvIhwoIPVmA5cab4oPiwxw+lPpHHCCaTCUJDQyEtLQ2ysrIEiQDOX6WpJdYSw8PDITExUXeh1kEVGRkJycnJugWYNdZBxf9MPsgH+eAgHxzkQ0QNH5seqyx7vS9Oaye7TPGyL8gv022M5O/yPsoe/FP2egL7T5NdxhEUHyLeFh/mYzGylm1Kz4DnW70DaWlpkp+VA/DypDkkJMTmbEhvoTyZmZlw7do1AAAoXrw4lC1bVvczoPz8fLh+/TpkZWUBAEC5cuV0DTAe8sFBPkTIhwj54PBGH35+frKXL6V7JR8iFB8c5ENEqo+754/LWq45PR0qNmopO2n22uEZBEEQBEEQBKEXXp0024+xMWJMFH/7gB+Dk5OTo/srIfnbOTk5OcItHDnzPqoF+eAgHyLkQ4R8cJAPEfIhQj44yIcICz7s0SVpjouLk/wyE6nws2fwQo0Qaz8oPSgoSNY8g2pg/5BAUFCQqu+2l4p9gJEP8kE+RMgHB/kQIR8i5IODfIiw4MMRuiTN/fr1g6effhrat28PkyZNgpiYGHjw4IFHy0xJSSkwxkZPsc6e4lTrXepScPZUrVrvtpeKs6dqyQf5IB/kg4d8iJAPEfLBQT5EWPDhDF2S5kOHDsGNGzdg6tSp8OSTT8Lvv/8O9evXh2rVqkHfvn0VLTMsLMzhoHQ9xLqb9kQPse6modEr0NxNQ0M+OMiHCPkQIR8c5EOEfIiQDw7yIWJ04qzbmOZixYpBkyZN4J133oEuXbpA586dwWKxQGxsrMty2dnZYDKZbD4AXNLsDC3FSp0nUEuxUudt1DrQ3AUYD/ngIB8i5EOEfHCQDxHyIUI+OMiHiJGJsy5Tzs2ePRsOHjwIt27dgoiICGjcuDE0atQI6tevDwEBAS7LTp48GaZMmVLg91KmCZF6EEhFycTaak/GrWSicy0mR1eyb8mH8jLuIB8c5EOEfIio4cPTKefIhwjFBwf5EPHUR2C2vCG/Sqec0yVprlKlCgQGBsKbb74JTZs2hUaNGsETTzwhqWx2djZkZ2cLP5tMJoiIiJC8oWqJ9SRY1Ao0T4JFzUDzZJ+SD8/L2kM+OMiHCPkQUcvH008/LXvdfPdKPkQoPjjIh4gaPkr5PZK1TqaTZgCAhw8fwqFDh+DQoUNw5MgRePDgAVSuXBmaNGkCH3/8seTl2L/cRAqeilUjSDxdhhpBosYy1AgS8qHeMsgHB/kQIR8iavqoWbOm7LKISD6soPjgIB8iavkwJ5yXVYb5pJknLS0NDh06BAcPHoSlS5fCzZs3hbfeSEFJ0gygXKyat2OULkvNs0pPlqXm7Rjy4fmyyAcH+RAhHyJq+1BypTkjI4N8/D8UHxzkQ0RNHz71RsCFCxdCnz59oGrVqlCjRg1YuHAhBAYGwuLFi+Hhw4d6VEHR4HW1xy8pGbyu9vglpQ8TqD1+iXxwkA8R8iFCPjhY8qEE8sFB8cFBPkTU9qEXulxpHjlyJDRp0gSaNm0KkZGRHi1L6ZVmHqkHi5ZCpS5biwH/SpatdoApWTb5ECEfHORDhHyIaOVDyYOAV69eJR8UHwBAPqzRwodeV5p1H57hKZ4mzQDuxepxBuRuHVoGmJx1aBlgUtdBPkTIBwf5ECEfIlr6UJI05+XlkQ+KD/JhhVY+KGl2QlpaGjzxxBOQlJSkOGkG4N4omJKSAmFhYTZzPmdmZkJiYiIEBARAZGSkprcMnK0rPz8fEhMTITs7GyIjIzUJMB5X63K2j7SAfIDbdZEP8kE+jPMRGhoqu0xaWprq9eAp7D6sofgAt+vydR/JF0/K+r45PQPqtX4NHj58KCu2i8qtmNGYzWYAAIiIiDC4JgRBEAThHCWJNkEQ+mE2m2XFqdddabZYLHDr1i0IDg5WdLuMIAiCIAiCKLwgIpjNZihdujT4+0ufE8PrkmaCIAiCIAiC0BuvG55x7949WL58OQQHB7t9BTdBEARBEARBWJOdnQ1msxl69OgBTz31lORyXpc0L1++HEaMGGF0NQiCIAiCIAgvZ/jw4ZK/63VJc3BwMAAAPF2mE4SUrAF+wI1r9vO3Gt/sJ45P4Yc9W49/Fv7v7nsOlmk9ipov7mezHGFBBZZtvUw/cFAf/4JlbL5nNezGT1y5g22w/p6D/eNg3TbLtq+X1UKtf+df8M+O62O9Xda1EDfSapkFf+fIsZ+DzbEeleRouxyV9bfZRgd1FHwVLO/noKzN363KCFV39D3Hi7HapwV/5+/ue47qK3HZfoAFv2f1VdtHCbDA70RNjkd+8XW3PbwKLsdxHbDA/22K+BX8nePtKlg322W7Wrd12YL7ysY7/3e/gmUcrc/2/wW3FZyWKbgeP3Swf8BS8HfoYJ+ixeF6xDIWm+8X+D9fHbBYlcEC6xPLWn0PHGyDze6zOK23s2POYRlH9RC22+pv1t+zOFgPFvTpaFtt/m5xUE9H67Y42Gc29Xbxd6m/s16OVb3QxbLRYu0LCvzddp8V/K4wKtTioA7O/u5g2ehwu6yLFPSOwjZY18jqB4ujfcH/R/we/v/fbXePg/paHNTXelstruuDQn2sFg1o+zeb+jjZFw6OJXFfWP+Or4Kjeov/tzhyY13H//+7xWH8ON5uR9sg/C7f6ndW75Hh/2/zd349Vr+z5KPN98/jI9iMaUJOKRWvS5r5IRkhJWtA+HPtwO//B3DbJlRWSSyf9FgN9Pbnf+fme9bL9PdzsB4Xy7EeWO4oEZe+bKvv2fzd3+mybb7nIGn2d1Mfvoz75YD4d4fb5TrZ9Xe0jY6SeIf1Kbhu2ySeL1Pwd9bLttkXDpJYsY7WZWzX6/TvNstxUG8/R/Uu+H/bbQCnZRwnrtZ/xwJ/d7g+68TL4d+tfuciafZzshx/B2X477pbts02uPi7dYIi1MdJ0iyux3rZ0r7naJk263ZRxmnC6WA5bsvwybDDpNn6dw6SXZsE2VHSzP9OxnKEdRdMmt2tDxytxyYRsBQoI9TDQR1sfu9g2Y5/52Q5loJlwEUdlSzHYcLpZP+4XI7F0e+cLMdBGXSxHHS2HAf1Eb4rcdncf/kkzOp36OB3loLebRNJ5+u2/p6jZaLEdTtcn7PlOEpcLQXr43g5rssISaGj9Slajuv6WBwkqTbfy3f+PZv15LtZNr+cPKvf5Vot5/9/7/h34rItfnYJuQVgM6bJHuary2u0CYIgCIIgCMKboaSZIAiCIAiCINxASTNBEARBEARBuIGSZoIgCIIgCIJwg9c9CJidnQ0AAKb7cQAANHsGzZ4hfJdmz3C9HKnLptkzHG8rzZ5hXYZmz7D/O82e4WQ9NHuGg787eojOcX1o9gz+gUHrhwOhwP8lz57x/+s7j48AQMwppeJ1SbPZbAYAgLsJ/8DdhH8Mrg1BEARBEAThjfA5pVS8Lmnu0aMHAAAzbwR89OgR9O/fHxYuXAiPP/640dVxCtVTXaie6kL1VBeqp7pQPdWF6qkuVE/5WL8RUA5+aHMtn5CLyWSC0NBQSEtLg5CQEKOr4xSqp7pQPdWF6qkuVE91oXqqC9VTXaie+kEPAhIEQRAEQRCEGyhpJgiCIAiCIAg3UNLsIQEBATBp0iQmxle7guqpLlRPdaF6qgvVU12onupC9VQXqqd+0JhmgiAIgiAIgnADXWkmCIIgCIIgCDdQ0kwQBEEQBEEQbqCkmSAIgiAIgiDcQEmzG8xmMzRu3BiCgoIgLi6uwN/Xr18PLVu2hJYtW0KZMmXg+++/BwDu5Sv872NjY5mq865duyAiIgJatmwJbdq0YapumzdvhmbNmsFLL70EQ4cOFX7P8v7My8uD3r17Q/PmzWH48OG61g0AYMyYMdC8eXPo0aMH5OTk2PyNxePTXZ2NPD7d1Y3F4xPAeZ2NPjZd1Y3VfQngvM5GH5uu6sZqrLtqP40+Pl3VjdXj01WdjT4+XdWN1eNTNki4JDc3F5OTk7FXr14YGxvr8rtt27bF69evIyJi/fr1daidY9zV+b///sPRo0cbUDP3dUtISMDc3FxERHzvvffwyJEjiMj2/lyzZg2OGzcOERH79euH+/fv161uJ06cwB49eiAi4rRp03D58uVOv8vK8emuzkYen+7qxuLx6arORh6b7urG4r5EdF1nI49NROnxzkqsI7puP40+Pl3VjdXj01WdjT4+peZLLB2fcqErzW4oWrQohIWFuf3evXv3ICMjA1544QUAALh69Sq0aNECBg0aBFlZWRrX0hYpdV69ejU0b95cONvTC3d1i4yMhKJFube7P/bYY8L/Wd6fBw8ehPbt2wMAQIcOHeDAgQN6VU3yulk6PqXU2ajj013dWDw+XdXZyGPT3fpZ3Jfu6gxg3LEppW4AbMU6gOv20+jj01XdWD0+3fVHRh6fUnIP1o5PuVDSrBJr1qyBt956S/j5ypUrsGfPHnj22Wdh/vz5BtasIA0aNICLFy/Cjh07ICYmBo4fP250lQpw/PhxuHfvHtStWxcA2N6fDx8+FF4JGhoaCvfv32du3Swdn+7qbOTxKXV/snR8uqqzkcem1PWztC8BXNfZ6LZTyv5kKdbdYfTxKQXWjk9XGH18SsGbjk9HUNLshFmzZkHLli1h1qxZkr6/atUqeOedd4SfS5UqBQAAXbt2hVOnTmlRxQJIrXNQUBAUK1YMihUrBm+++Sac/r/27j6mxv+NA/j7VMY4M4XkOV82pXU6JdRROySjPOQPC51mnlY21PIUM5sxLBuzaZZIRqZlpGVDa9aEWMwhCtEf1qSozMHoyPX747vun6hzn/DtHHm/tjb3w+dz3ven6xzX7u64f99psgFAbW0tkpOTceLECWWfM6+nu7s73r17B+DfvwQ8PDy6LZuI2PXazlSfapkdWZ/2rKez1GcbW/XniNq0NxvgfGsJ2M7siNq0N1sbZ3iv28vR9anGGevTFkfXpz3+pPrsCJvmTqxfvx4lJSVYv3696rlv3ryBxWLBmDFjAAAfPnxAa2srAODatWsYN27cf5q1jb2Z2z6kAKC0tLRb8tmb7f3794iLi0NGRobyYx5nX8+QkBAUFRUBAK5cuYKpU6d2W7bo6GjV13a2+lTL7Mj6VMvmTPXZxlb9OaI27c3mjGupltkRtWlvNsB53uv2cnR92uKs9WmLo+tTzZ9Wnx1y8DPVf4SoqCgZOnSohISESHZ2toiIJCQkKMczMzMlLS1N2b53754EBgZKeHi4zJ8/X96+fdvdkW1mPnr0qEyaNElCQ0Nl48aNTpVtz549MmzYMDEajWI0GqWkpMTp19NqtcrSpUslLCxM1q1b1+3ZNmzYIGFhYRIXFyefP39ul03EOevTVmZH16etbM5an99ndpbatJXNWdfSVmZH16atbCLO+V4X+fHz05nqs7NszlyfnWV2hvrsLJuI89ZnV/C/0SYiIiIiUsHHM4iIiIiIVLBpJiIiIiJSwaaZiIiIiEgFm2YiIiIiIhVsmomIiIiIVLBpJiIiIiJSwaaZiIiIiEgFm2YiIiIiIhVsmomIiIiIVLBpJiKiHqe5uRkrV67EyJEjHR2FiHoINs1E9Mfz9vbGw4cPVc8rKCiAr68v9Ho9KioquiHZ/2k0Grx//77DYxaLBVqtFqtWrery8erqaphMJkycOBHBwcEICgrC9u3b8fXr11/K1FW/c67fMb+7uzuysrIwfvz4/ywTEf1d2DQT0V8jIyMDO3fuhNlshr+/v93jvnz5YnP7V+Xm5sLPzw9nz57tsDHs7Pjdu3cxffp0xMbG4u7du7hz5w6KiorQ2NgIF5ee/fFeX1+PyMjIdl+bN292dCwi6sF69qcqEf11NBoN0tLSMGXKFIwZMwbZ2dkAgKSkJJSWliI1NRUGgwEAUF5ejoiICOUO7blz59rNs3//fkybNg1bt279YVtt/Pnz5+Hj44PQ0FDs2rXLZuasrCxs2bIFISEhyMvLs+t4a2sr4uPjsWPHDsTExCjnDho0CIcPH+7SmokIUlNTERMTg48fP6KsrAzh4eEICAiATqdDQUEBACA+Ph7BwcHQ6XSYO3cuGhoaVOfWaDTYu3cvJk+ejH/++QfFxcXYunUrAgMD4efnh0ePHinnXr58GUFBQdDpdDAajaisrOx03iFDhqC4uLjd1759+7p03UREXSJERH+40aNHS0VFhYiIAJCDBw+KiEhlZaVotVqxWq0iImI0GqWwsFBERJqbmyUwMFBevnwpIiKvX7+WUaNGSV1dnTLP7t27ldf4ftvW+Pr6evHw8JDHjx+LiEhaWpoAEIvF8kP2hw8fyqBBg6SlpUXOnDkjBoPBruPFxcXi6empXNvPACCvX7+W2NhYWbt2rbS2tkpjY6MMGTJEbty4ISKi7Gu7xjZ79+6VNWvWtJuro+sDIOnp6SIikpeXJ3379pWLFy8q67JkyRIREamvr5eBAwfKgwcPREQkJydH/Pz8VOe3JTExUYYPHy6JiYny7NmzLo0lIvoe7zQTUY9jMpkAAL6+vnBzc8OrV69+OOfmzZuoqalBVFQU9Ho9IiMjISJ48uSJcs6KFSvajfl229b4W7duISgoSHmeNiEhodOsWVlZMJlM6NWrFxYsWICqqipUVVWpHjebzdDpdHBzcwMA3L9/H3q9HuPGjUNSUpLdazV79mzo9XocOnQILi4uKCsrw4QJE5S78S4uLvDw8AAAnD59GsHBwfD398exY8dgNpvteo1FixYBAIKCguDi4oI5c+YAACZOnIiamhoAwO3bt6HX65XHZkwmE2pra1FXV2f3tXwvIyMDtbW1yMjIwNixY396HiIigI9nEFEP1KdPH+XPrq6uHT6DLCLQ6XQwm83K14sXL2A0GpVztFptuzHfbtsaLyJ25bRarcjJycGyZcuU3IsXL8bx48dVj/fr16/dXAEBATCbzZg0aVK7X347e/YsVq9ejTNnznSYYcaMGSgqKoLFYrGZ9fr160hPT8elS5dQUVGBAwcO4NOnT3ZdZ9v3w9XVFb1791b2f/u9ERFoNJofxna0j4jIEdg0E9FfyWAwoLq6GlevXlX2mc1mtLS0/PL40NBQ3Lt3D0+fPgUAHDt2rMM5CgoKMGzYMOj1emXf8uXLcfLkSVitVpvHZ8yYgfLycpSWlirHmpqacOPGDYSEhAD49+7ztm3b4OXlhVmzZnWYYfv27Zg/fz5mzpyJ5uZmGAwGVFVV4ebNmwCAr1+/oqmpCc3Nzejfvz88PDzQ0tKCI0eO2LVO9goNDYXZbFbusufm5mLEiBHw8vL6ra9DRPSz3BwdgIjIEdzd3VFYWIhNmzYhJSUFVqsVo0aNwoULF355vKenJzIzMzFv3jwMHDgQCxcu7HCOrKwsPH/+HN7e3u32NzQ0oLCw0ObxiooK5OfnIzU1Fe/evYNWq0Xv3r2RnJyMgIAAAIC/vz98fX2xY8cOm9eSkpICrVaLiIgIXL58Gfn5+diwYQMsFgs0Gg127dqF6Oho5OTkwMfHByNGjIDBYMCVK1fsWit7DB48GKdOnYLJZEJraysGDBjQ4S9FEhE5ikbs/TkiERH9UaqqqpCdnc1/VYKI6Dfg4xlERD1UeXk5goODHR2DiKhH4J1mIiIiIiIVvNNMRERERKSCTTMRERERkQo2zUREREREKtg0ExERERGpYNNMRERERKSCTTMRERERkQo2zUREREREKtg0ExERERGpYNNMRERERKSCTTMRERERkQo2zUREREREKtg0ExERERGp+B8oRgi8qdS40QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 850x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Rectangle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "firstorderterms=weights_2ndor_spreg.loc[weights_2ndor_spreg['order']==1].copy()\n",
    "\n",
    "for index, row in firstorderterms.iterrows():\n",
    "    firstorderterms.at[index,'mut_from']=row.id[0:-1]\n",
    "    firstorderterms.at[index,'mut_to']=row.id[-1]\n",
    "\n",
    "mutated_positions = []\n",
    "\n",
    "for index, row in firstorderterms.iterrows():\n",
    "    if row.id !='WT' and row.mut_from not in mutated_positions:\n",
    "        mutated_positions.append(row.mut_from)\n",
    "\n",
    "coefs_hm=pd.DataFrame(columns=mutated_positions)\n",
    "\n",
    "for index, row in firstorderterms.iterrows():\n",
    "    if row.id !='WT':\n",
    "        coefs_hm.at[row.mut_to,row.mut_from]=row.mean_kcal_mol\n",
    "\n",
    "singles_boundary=abs(coefs_hm).fillna(0).to_numpy().max()\n",
    "fig, ax = plt.subplots(figsize=(8.5,4))\n",
    "cbar_ax=fig.add_axes([0.125,0.08,0.775,0.02])\n",
    "#ax=sns.heatmap(coefs_hm.fillna(0), cmap='coolwarm', vmin=-1*max(coefs_hm.fillna(0).max()), vmax=max(coefs_hm.fillna(0).max()),cbar_kws={\"orientation\": \"horizontal\", \"pad\": 0.02, 'location':'bottom'}, xticklabels=True, yticklabels=True)\n",
    "#ax=sns.heatmap(coefs_hm.fillna(np.nan), mask=coefs_hm.fillna(np.nan).isnull(), cmap=shifted_cmap,cbar_kws={\"orientation\": \"horizontal\", \"pad\": 0.02, 'location':'bottom', 'shrink':0.5}, xticklabels=True, yticklabels=True)\n",
    "ax=sns.heatmap(coefs_hm.fillna(np.nan), mask=coefs_hm.fillna(np.nan).isnull(), cmap='coolwarm', xticklabels=True, yticklabels=True,cbar_kws={\"orientation\": \"horizontal\", \"pad\": 0.02, 'ticks':list(np.arange(-2,3,1))}, cbar_ax=cbar_ax,ax=ax,\n",
    "              vmax=singles_boundary,vmin=-1*singles_boundary)\n",
    "ax.set_facecolor('white')\n",
    "ax.patch.set(hatch='x', edgecolor='#D3D3D3')\n",
    "#ax.figure.axes[-1].set_xticks(ticks=np.arange(-2,3,1),labels=np.arange(-2,3,1), fontsize=7)\n",
    "ax.figure.axes[-1].set_xlabel(r'Inferred ${ΔΔG_{f}}$ \\ kcal mol$^-$$^1$', rotation=0, labelpad=5, fontsize=7)\n",
    "ax.tick_params(axis='both', which='both', labelsize=7, labelbottom = False, bottom=False, top = True, labeltop=True)\n",
    "ax.tick_params(axis='x', which='both', rotation=45)\n",
    "ax.set_xlabel('WT residue',  fontsize=7)\n",
    "ax.xaxis.set_label_position('top') \n",
    "ax.set_ylabel('Substitution AA', fontsize=7)\n",
    "#plt.title('Background-averaged\\nmutational effects',fontsize=18)\n",
    "\n",
    "\n",
    "cbar_ax.set_xlabel(r'Inferred ${ΔΔG_{f}}$ kcal mol$^-$$^1$', rotation=0, fontsize=8)\n",
    "cbar_ax.xaxis.set_label_position('bottom')\n",
    "cbar_ax.set_xticks(np.arange(-1.75,2,0.25), [str(round(i,2)) for i in np.arange(-1.75,2,0.25)], fontsize=6)\n",
    "for spine in cbar_ax.spines.values():\n",
    "    spine.set(visible=True,lw=1,edgecolor='black')\n",
    "\n",
    "for i in mutated_positions:\n",
    "    for j in mutated_positions:\n",
    "        if i[0]==j[0]:\n",
    "            col=coefs_hm.columns.get_loc(i)\n",
    "            row=coefs_hm.index.get_loc(j[0])\n",
    "            ax.add_patch(Rectangle((col, row), 1, 1, fill=True,edgecolor='black', lw=0.0001, color='black'))\n",
    "            \n",
    "ax.spines[['left','bottom','top','right']].set_visible(True)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('Isofolds_2ndordersprsreg_1storderterms.png', dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d26f146-eb98-45ea-bcbc-bf6d9e6c5d81",
   "metadata": {},
   "source": [
    "## Panel b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1075cfc-a5a4-4d09-b42f-e9f03cbf1058",
   "metadata": {},
   "source": [
    "### Core mutagenesis 1st order energy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ca3e2a0-f6c9-4dfb-aee3-abf9e97cd71a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "weights_core_mut = pd.read_csv(path+'Fig3/FYN-SH3/FYN_mochi_order1/task_1/weights/weights_Folding.txt', sep='\\t')\n",
    "\n",
    "weights_core_mut.rename(columns={'mean_kcal/mol':'core_mut_mean_kcal_mol', 'std_kcal/mol':'core_mut_std_kcal_mol', 'ci95_kcal/mol':'core_mut_ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in weights_core_mut.iterrows():\n",
    "    weights_core_mut.at[index,'order']=len(row.id.split('_'))\n",
    "\n",
    "weights_merger = weights_core_mut[['id','core_mut_mean_kcal_mol','core_mut_std_kcal_mol','core_mut_ci95_kcal_mol']].merge(weights_2ndor_spreg[['id','mean_kcal_mol','std_kcal_mol','ci95_kcal_mol']], left_on='id',right_on='id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1ffa4b1-a7bd-4eab-853e-628a4a777eb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 120x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "plt.rcParams[\"font.family\"] = \"Arial\"\n",
    "\n",
    "df = weights_merger.sort_values('mean_kcal_mol').copy()\n",
    "\n",
    "g = sns.FacetGrid(data=df, aspect=2)\n",
    "g.map(plt.errorbar, 'mean_kcal_mol', 'core_mut_mean_kcal_mol', 'core_mut_std_kcal_mol','std_kcal_mol', fmt='o', elinewidth=0.5, capsize=2, capthick=0.5, markersize=2, color='k')\n",
    "#g.add_legend(bbox_to_anchor=(1.3,0.7), title='', fontsize=6)\n",
    "g.fig.set_size_inches(1.2,1.2)\n",
    "g.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "x = df[['mean_kcal_mol']].to_numpy()\n",
    "y = df[['core_mut_mean_kcal_mol']].to_numpy()\n",
    "\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r2 = r2_score(y,y_pred)\n",
    "r,p = pearsonr(y.ravel(),x.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.xlabel('Permissivity model\\n'+r'inferred ${ΔΔG_{f}}$ kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.ylabel('Core mutagenesis model\\n'+r'inferred ${ΔΔG_{f}}$ kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.title('R = '+str(round(r,2))+'\\np-val = {:0.2e}'.format(p), fontsize=7, color='k')\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('Permi_vs_1stordercore_model_1storderterms.svg', dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8488c45-4882-441c-b30a-e1a2e3e6df90",
   "metadata": {},
   "source": [
    "### Core mutagenesis 2nd order energy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84d86bc5-3e13-441f-959e-ac9bee5989a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "weights_core_mut = pd.read_csv(path+'Fig3/FYN-SH3/FYN_mochi_order2/task_1/weights/weights_Folding.txt', sep='\\t')\n",
    "\n",
    "weights_core_mut.rename(columns={'mean_kcal/mol':'core_mut_mean_kcal_mol', 'std_kcal/mol':'core_mut_std_kcal_mol', 'ci95_kcal/mol':'core_mut_ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in weights_core_mut.iterrows():\n",
    "    weights_core_mut.at[index,'order']=len(row.id.split('_'))\n",
    "\n",
    "weights_merger = weights_core_mut[['id','core_mut_mean_kcal_mol','core_mut_std_kcal_mol','core_mut_ci95_kcal_mol','order']].merge(weights_2ndor_spreg[['id','mean_kcal_mol','std_kcal_mol','ci95_kcal_mol']], left_on='id',right_on='id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bad86f3f-1d16-4242-afb5-3ad4fa7d7def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 120x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "df = weights_merger.sort_values('mean_kcal_mol').copy()\n",
    "\n",
    "g = sns.FacetGrid(data=df.loc[df['order']==1], aspect=2)\n",
    "g.map(plt.errorbar, 'mean_kcal_mol', 'core_mut_mean_kcal_mol', 'core_mut_std_kcal_mol','std_kcal_mol', fmt='o', elinewidth=0.5, capsize=2, capthick=0.5, markersize=2, color='k')\n",
    "#g.add_legend(bbox_to_anchor=(1.3,0.7), title='', fontsize=6)\n",
    "g.fig.set_size_inches(1.2,1.2)\n",
    "g.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "x = df.loc[df['order']==1][['mean_kcal_mol']].to_numpy()\n",
    "y = df.loc[df['order']==1][['core_mut_mean_kcal_mol']].to_numpy()\n",
    "\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r2 = r2_score(y,y_pred)\n",
    "r,p = pearsonr(y.ravel(),x.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.xlabel('Permissivity model\\n'+r'inferred ${ΔΔG_{f}}$ kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.ylabel('Core mutagenesis model\\n'+r'inferred ${ΔΔG_{f}}$ kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.title('R = '+str(round(r,2))+'\\np-val = {:0.2e}'.format(p), fontsize=7, color='k')\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('Permi_vs_2ndordercore_model_1storderterms.svg', dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cafa87-373b-4e99-84d4-6579a9078cf9",
   "metadata": {},
   "source": [
    "### Core mutagenesis 2nd order sparse regularized energy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7188dbf-02da-4997-b3e1-da107b922bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "weights_core_mut = pd.read_csv(path+'Fig3/FYN-SH3/FYN_mochi_order2_sparse_regularized/task_4/weights/weights_Folding.txt', sep='\\t')\n",
    "\n",
    "weights_core_mut.rename(columns={'mean_kcal/mol':'core_mut_mean_kcal_mol', 'std_kcal/mol':'core_mut_std_kcal_mol', 'ci95_kcal/mol':'core_mut_ci95_kcal_mol'}, inplace=True)\n",
    "for index, row in weights_core_mut.iterrows():\n",
    "    weights_core_mut.at[index,'order']=len(row.id.split('_'))\n",
    "\n",
    "weights_merger = weights_core_mut[['id','core_mut_mean_kcal_mol','core_mut_std_kcal_mol','core_mut_ci95_kcal_mol','order']].merge(weights_2ndor_spreg[['id','mean_kcal_mol','std_kcal_mol','ci95_kcal_mol']], left_on='id',right_on='id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "087e10fd-28cd-4c88-b855-6433b95c52da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 120x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "df = weights_merger.sort_values('mean_kcal_mol').copy()\n",
    "\n",
    "g = sns.FacetGrid(data=df.loc[df['order']==1], aspect=2)\n",
    "g.map(plt.errorbar, 'mean_kcal_mol', 'core_mut_mean_kcal_mol', 'core_mut_std_kcal_mol','std_kcal_mol', fmt='o', elinewidth=0.5, capsize=2, capthick=0.5, markersize=2, color='k')\n",
    "#g.add_legend(bbox_to_anchor=(1.3,0.7), title='', fontsize=6)\n",
    "g.fig.set_size_inches(1.2,1.2)\n",
    "g.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "x = df.loc[df['order']==1][['mean_kcal_mol']].to_numpy()\n",
    "y = df.loc[df['order']==1][['core_mut_mean_kcal_mol']].to_numpy()\n",
    "\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r2 = r2_score(y,y_pred)\n",
    "r,p = pearsonr(y.ravel(),x.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.xlabel('Permissivity model\\n'+r'inferred ${ΔΔG_{f}}$ kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.ylabel('Core mutagenesis model\\n'+r'inferred ${ΔΔG_{f}}$ kcal mol$^-$$^1$', fontsize=7)\n",
    "plt.title('R = '+str(round(r,2))+'\\np-val = {:0.2e}'.format(p), fontsize=7, color='k')\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('Permi_vs_2ndordersprsregcore_model_1storderterms.svg', dpi=300, transparent=True)"
   ]
  }
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