PPRL_Multiclass_Block_Lastname_2022.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ffe7aeb",
   "metadata": {},
   "source": [
    "## Record-pair Linkage Predictive Modeling Program\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b633140",
   "metadata": {},
   "source": [
    "### Author: CDC's Data Science Upskilling (DSU) Team named, \"MYABC 4 PPRL\"\n",
    "### Membership: DSU 2021-2022 (Cohort 3)\n",
    "#### Team Project: Privacy Preserving Record Linkage (PPRL)\n",
    "\n",
    "\n",
    "Python Program Description: \n",
    "This NoteBook contains a record-pair matching predictive modeling Python program. It analyzes a Bloom Filter (field level)-encoded comparison output where the blocking variable was \"lastname\". Separate NoteBooks on Blocking and other PPRL previous steps are included in the project folder.Multiclass classification, a supervised machine learning (SML), was carried out with three algorithms for predicting whether each record-pair matches, doesn't match, may/maynot match. The algorithms are logistic regression, OneVersusRest (Estimator= Random Forest), and Multinomial Naive Bayes).\n",
    "\n",
    "Dataset: Synthetic\n",
    "PPRL Data Source: Febrl (Freely Extensible Biomedical Record Linkage)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a805cc7",
   "metadata": {},
   "source": [
    "## CAUTION!\n",
    "Please run the file named, 'PPRL_with_Field-level_bloom_filters_using_double_hashing' before running this program as the output of that file is the input data to be loaded in this file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3703bd78",
   "metadata": {},
   "source": [
    "##### In the next cell is a graphical illustration of the modeling pipeline"
   ]
  },
  {
   "attachments": {
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EOX31xRLT8qBh95n6HBQcohv+cIspxLJz6zdq07Gbv083gAoiKDhEcQktTePEsaPmqi7z3Kp2tu7QVUmD71RO9inNmvqi6QsB705/2ZietstN/ZTQqq1WfPqRKSDcukNXJbRqK0mq9ltlzfy8XFOwWZLuH/2MYho21rqVS00h5Iy0w9qza6upqh4AAIAnGalp+mbVumK3c50GzH1qsbbdO5mqtdlsNnW8saupCtXmtRuMbX4fGKBxU57Tqcws43HXm7qeKr+dPH5CNWvX8vfpMjz5xotGf4J+W5efk2sJI44Y95DpRnLN2rXUtnsnY7s9W3cpPyeXKi+oMvJzclVgL1D2qdP6YOq/LBUzu/S50bT82KSnTNs0bBJr/OzpdZN1LLPYkIin16/D4dDA4UPUot11xnbXJjY3frbZbIqsH23qS+q+A0WG7iSZptceMvJuU+jO0zYjxj9sGUeOHEg1xsVtGzabHotqUM8I3Dl169fTFLqb/8/3ShxK/nrFatNyvz8PNJ3vgMBA9RzQzxSqKS6EGNWgnhGgBlD2gkJDFJ/YzPS6zDiSbtrm3Snm+2htu3fSwOFDlH3qtKY/96qloqezeugNSX9Qi3bXmSp4Op/vHDedVUrzc3It4eCHnh+vBo0babXbFN7ph47o++92FDuWAlUFoTsAAFChxCW01KgxE/mWDFBG6kbXL7I6k6fqb2fyc4sM3Q0adp/Hx8/9eNayLrR6Tcu6hFZtiwzd7duz07R8dbUgncrKLPI4k7dtInQHwOSauGtNoTvXnw/s220Zh4bc82Dh9D3hEXrixWmmSnkZaYflcDhks9nUqHFTNWrcVMnbNplCdwmt2lrGof97f6ZpuXWHrkaors+tQxQeEWkK5RG6AwAAvkg/dMRyY9QT12DdD7tTTI/Via6rk8dPFPn8lZ8sMyqtBAQGGhWXnArsduXn5Pn7dPgkPrGZxwBggb3A47oCt3NTJ7quafn40WOE7nBJc63uWJTg6iFq27WjsRwQGKioBjGmEJ3D4TCNN1EN6pkCIOmHjhQZuvP2+rXZbKpZu5blMdd9ub92j6UdLfaY3F/brqFb5zG7b+N+TKcys7xOQx0UFuJx/HVtI+90rvEe1FfuFarC60QUO85vXPlVkcEZ5xTkAMpP44R40+vX9ef93++1hHrveqTws/matWtp4ozXLNVDnWOH87ptx7ffmcanFu2us4R63auGtu3eyRgbkm4fpFqRtU3XnslbthO6A35D6A4AcMmoXrOWWnfoaiw3cKvehIrnrpFjlJWZocyMdEVERqnd9T2Y/hEoJwf27dbWb9fp2NEjxU5zWJzY+GY+bRcZHePxw8HqNYuudOA+3bR7lSgA8EV4RKTXxxo1bqppHyyVJCPU6xomDgq23kDNOX2yxNcpw0eN1fBRY1Vgt+tMfq5RAc+bfbt3+Pu0AQCAS1RGapppedGcj0xVS3y1c9NWbVz5lfbuSDZVVqnoru/Z3fN5casmI/kWNioqUANUFfGJzfS3Z8Z6/GLm/u/3atXiz8tkrPD2+nXKz8nVp/MWKGXH95YqfCXVtnsny7qgkGDTcpMWCZZt3CvquXKvNLpn6y6fxpnsrFM+Vwt1OByWdcVNR+mLkLDQUrcBoGi1Imt7fSz22ib694qPJZ0PFJs+v/LwBYCSjB1OI8Y/rBHjHza+UOGsgOdNyo7v/X3agAqD0B0A4JLhrDqCyoOqVED5czgcmjPjtSIry5VUYOCVHtcnb9tkWo6Mqu9xu9CwGl7bLq6inTdbNq7V8FFjy+wYAVQdNcIjdCorU8nbN3us2FkWnB+InsnP1c6t3xjrszIzTNu5Vs4DAAAoSxcaRHFOo+pwODTr5Tct1VYqux/PXNj139n8M/7uOuA3bbt3UoceXXTtdS0sX7Z0OByaP/M9j1OyXqirql3t9bFvVq3zqfKnr65pGmdZ17BJrFbq/PG4TmdbUWRnnbqg5+3dkVzk41T0BCqOmrVr6eTxE9q5aesFX78UJyAwUAqV8nPytG33+amxT2QcN21X2oAzcCkhdAcAAABcwhbOm2UJ3MUltFSHLjdJks6eydfHc2aUyb4axMab9pWRnupxu5xs7x8EepqOtu/AoUVWrAIAT9yDwK4VkaXC6dDm/XtqmYaSPTmwb7dmTX3RUsUTAADgQrXt3kkjxj9coue4T33Ytnsnn4Ijvw8MkCStWbLCEriLT2ym63t2V42IcIXVqO7zdJQl4S3cVlxQxFeewjy+TKdYIyK8zI8VqEgeen68IutFWdYXVz1p89oNlsCd+1jxwsNPlKj6nad+SIWhYE+BO+dr+JqmcVqxcEmJAoBXB1Urv5P6m6gG9dT7tlvKtM2wcOsXXPsPG1xkBS0AFYN7NUz3ipsFdrvenTKj3L/4sP/7vZr+3KuVqpIxUBEQugMAAAAuYVu/XWdaDg4N06gxE41vIh9NSy2zfV1dLci0nJF2WA6Hw/Kt57TUA17b8DQdbXhEJJUxAZSYe/C3msuUsaeyMvXqs4+VexDus//O09IFc/19KgAAADxOfdj+hs4+P/+rz740LQdXD9EjLzxpvIdzTnnmyn7uXKn7/cPuFPXo39u0Lj8nt8xuCHsK81yb2JzqTqjyIutFlXh6QskaHgmuHqIR4x4yXlMFdnuZvX4P7bN+vvTQ8+PVvG2isexpCumilEcVyxbtrrOEZUoy/vrC0+dptSJrl/l+AJS9jNQ007LrlNYnj58ocVD5Qiz+4GMtmvORv08FUCld5u8OAAAAACgf+Xm5lkDJA48/b/ogbv+eXWW2vyuvslYIyDl90rLu0P49RbbjXo0q9UDKRTlfAC4dR9NSLVO11m90fpqgVZ9/Yhkf+w4cqtdnLdC0D5Zq6nuLS90Hh8NhCdwFh4bpiUnTNPW9xZr2wVLdP/oZf58qAABQRbhPmViSSnEFdrslsDfyqdGm95aegi0XMvWYL/30FLS5UAG/VfJzdfzosTJrH6hq3MNlt/31TlOI9ciB1DLbV/KW7abltt07mQJ3DodDe7aW7HOvH3aX/2dQ6YeOyOFwlKoN96kepcKKgq4O7t1f7scCoHTSDx22XC81bBJr/Lxi4RJL4K7/sMGavvh9/XvFx/rnsg9L3QeHw2EJ3AVXD9GzM1/VP5d9qH+v+FgPPT/e36cKqLAI3QEAAACXqN//3nrzwH7uR+PnU1mZWr/aOsWG3X6+GkG1IN+/3R/bpJll3YzXn9WBfbslFd6omT3t5WKnckxo1da0vG/3Dp3KyjSt27xhjUbd3lcvjB+p2dNe1uYNa4ps89uvV5b16QVQQeXn5eqtl/5uWd8wNt742b0KaFxCS/W5dYgCAgMlSccz0lRantq498EnVDe6vnGDes+urf4+XQAAoIpwn0o273Suvlllviba//1e3dNzkO7pOUiP/OlezZv+jiQpIDBQwdXN7w1PZWYZP588fkIrFi4pcZ+Wzf/Esq5t146Wfs6c9IYK7HZJ0jer1mn269PL7LwEhYZYgiqrFn9u7E8qvBn99IjHjHPz6riJHiv7ASicOtWVazjs5PETWvS+tZLShVaXqxbsNuOCS7WoArtd82e+Z3lOfm7eRT8nrTq2saxbs2SFKXiXn5NrjDH39BykedPfMT3uWvlKklYvWW4J7l3fs7tpefNXG5R+yPxltMUffGzs4+kRj1n+HwBw8eTn5Oq18c9Z1rt+AWHzVxtMj8UnNlPS7YOMz6+OlbCapyee2hj51GhFNYgxPr9yDzkDOI/pZQEAAIBLVEBgoOISWiolebux7sPZb6lT9946tH+PEX4LDg0zVXxasfgjJQ2+U6HVaxpv4H3dX7deSVqz/HyFqIy0w3p94hhLP4qS2K6zFs6bZfQpI+2wJjxyt1p36KqeSYO1f88ufTxnhvHYmfw8DbnnQVMb7vtbumCuwiMiJUnNE9uX6LgAVA4Oh0OpB1K0dOFcj1XsaoRHSPptOiO3xwcO/Ytp2VMV0IP79xhteJKVmWFaznCbvjs4NEyNGjc1rXMP/wEAAJSXmrVrqW33TqYqVP+aPFXL5n+i3rfdohMZx7V6yXLjsbzTubp5yEBjuXu/XqYqKP+aPFUnMo7rWNpRo83g6iGmaizL5n+ia5rGKSg0WAGBgbqmaZxp/+mHjujVcRPVOCFeHW/sqpq1aykoNERRDeqZqr5sWr1ee3ckKzgkxFjvvq/SuOvh+/T4sFGm/W1avV79hw1Wrcja+nrFalN/GifEX9C0m0BV0KXPjZo37R1j2TluFDVWLPnPArVod50xVviq441dTeOS65jiut51f3u27tL+7/eqdt06CgoNsYTZykNAYKCGjLrbdF7mTXtHS/6zQP3+PNA4B67adO1oqiaa0LqlVn5y/ouzeadzNeXJFxRZL0o9B/RTzdq11KZrR83/53vGseadztXTI0arbfdOatHuOh3cu9/UhnM/AC4uh8Ohg3v3a9H7H3msYue8xvA0HfefRtxpWk7ZudvS/g+7U4q8TnGvlOleaS+4eohir21iWuce/gNwHpXuAAAAgEvYDX+4xbSckXZYH8+ZYQrcPfPqLEVGxxjbbNm4VhMeudvj1LDF+UP/Pyk4NMyy3jUA13fg0CLbsNlsuvfBJyzrt2xcqxfHjzICd04PPP685UPZ5ontLc9/d/orenf6KzqTXzY3ZgD4T/K2Tdq8YY3x77P/ztPfH7pTr08cYwn4BoeGqVfSbcZyQGCgacyTpB1bCj88LLDb9dl/51nGGUk6eya/yD6tW/mZqdJAZHR90+N5OdlG1c5TWZmaPe1lS/jP2QcAAIDyMOT+4ZaKdemHjuhfk6dq0Rzzjd+Hnh9vmhIysVNbS3uL5nxkCtFMfvctte3eydT248NGaduGzZKs1fakwgDMojkfmaZ07NLnRst2eadzjZvCQ0bdrTZdyi4oUrN2LfUfNtjj8f1r8lTT9JTxic3U988DymzfwKWmrYcQl/tYMXHGa+pxS2/j8bzTuaaxwlc1a9eyVNZzjilO46Y8p5FPjTZtM/mRpzTv7dmSzNM4lqdu/Xpaqmrmnc7VvGnvaN60d0zjb/9hgy2Bl7jmTS1t7tm6Sys/WWaMnzabzXKsUmGQ+F+Tp1oCd6NfmmAK9gEoezu+/U7frFpn/Fv8wccaPXSEJj/ylGX66+DqIaZrjIDAQMsYt3X9JkmFnx0t/uBjU5jXqbjqoe6VMt33kXc616joe/L4Cc2c9IbHLzrw+RVQiNAdAAAAcAmLb5aobr2SPD4WGR2jJ16cpoDAQN336NNlsr+g4BA98eI0S6DFub9HJ7yidtf3KLadRo2bauKUdzy24xQcGqa7Ro5RXbdgiyR17tFHrTt0LaezCsDftmxcawRp353+ipYumOsxwBYcGqYHHn/eciOhU/fepuWlC+bqhfEj9ei9A7V0wVxJsowhyxfP15oVnxrBuTadzFP35OVk68E7kzTq9r7avGGN6kbXt4SQJzxyt14YP1ITHrnbCD/HJbQ0bTPzjeeKnTIbAADgQgSFhujVuTNNYRd3wdVDNGTU3WreNtG0PqpBjPoPG2wJ7UmFQbSJM15TQGCg7nrkPo/bSIUBmb+Me1DF6davpym856pt907q1q9nmQdlkm4fpGdnvuq17859jxj3ECEVoAhBoSH6y7gHLSEOqTDYMXHGawoKDdFtI+4s8vXmq7+Me9ASZpMKx7JxU55T7LVNFHttkyLHvYvBZrNp9OQJGjLq7iK36z9ssJJuH2RZHxAYqHFTniv2nMVe20RvfDTL4zlximpQT+OmPGcKVgMoH87Qq/Of+5ccnIKrh+ixSU9ZrjHcv4iwaM5HenrEYxqZdIcRMHa/ZlrynwVauWiZEZzr0KOL6fG807n6a+8/6Z6eg/TNqnWKahBjGVseHzZKT494TI8PG2WEpt3HlTefeZkpqgExvSwAAJo97eUiH28QG6+rqwWpYWx8kVOKAcDF1OWmfkpodb7SQMPYeI/b2Ww2DbpjhJIG3an9e3dp8/rVSmjVVk0SWiko+Pyb6RrhEZo8fZ7+V3D+G2qh1WtKku4aOcbUZrWgoj+UCwoO0ZOTpqvAbtfJrOOyn/tRUfUaGtXonIGV4tQIj9C4599UzumTOrh/j1IPpOhMXq4axMarZq3aim+W6PVmh81m0/BRY9WmU3ed+/GsUg+kKDwiUrHxzYzjAnBp69YrSQOG3OtxnLiufRdLNbuMtMPGz30HDlWfW4eoQWy8sV1eTrY+njNDV1cLUo3wCMU2aabidO7RxwjxedrPoxNeUf1Gcfr7Q3caocGU5O1KSd6uNh27+fsUAgCACmLI/cM1cPgQYzkgMOCC27LZbBoy8m4NvHuIso5lKv3QEZ3IOK5akbV1VbWrde11Lby+z0q6fZCSbh+k9EOHlbJzt8LrRCiueVNT5fGAwEBNnPGaCuwFxrqg0PPTN7a/obNadWyjlJ279eOZs5IKAyB16kWZ+jhi/MPq+6dbjf7Ft2qmhk1ijb616thGL82Z5nEfkkyPeXrck6gGMZry4SydPH5CGUfSlbrvgGpF1pYkXdM0jillcckKC69hec2Ehde44Pba39BZ7W/orPRDh7V1/SbVb9zIMlbYbDZNfvct5efkGeucr9OS9CWqQYxGT56gArtdXy9fLamwqqb76/W2EXeq54B+xrJzHHUfSzyNr8WNN5J1nPY25vTo31vd+vVUdtYp/bA7xRh/nfspanrd2GubaPK7b+nIgVSdysz67fjrKbyO+Z5FUGiIRk+eoPycXB0/ekxHfjgkSbo6qJpqRIRbqui5nmf3cw+g/PW4pbduG3Gnx+uvtl07WqrZuU4H6wzqXtM0ztjOWUXz6qBqqlm7lsdKme669+tlqhLqvp9xU55TwyaxGj10hGm67j1bd6n9DZ39fQoBvyJ0V4VkZRVODxYezk1GAHDlrDLiy+PBoWFKbNdZN/zhlnIP4B1NS9WKxYUXuV1u6qdGjYu/MAZQdTRq3LRE40JAYKASWrZRQss2XrcpDOFZA3UXEvwosNt1Jj9XtSOjLR8YHNy/x+d2bDabaoRHqEZ4xAX1w3m8hFeAS19cQkvVqVtP9RvFFftliaDgED0xaZp2bNmgdSs/MwJvcQkt1Tyxvbr1vFlSYWhu/eplRlAuODRM1WsW3rwJCAzU5OnzNHXSeFOQTpKxTa+k21SvQaxWff6Jadrbbr2S1LFbL6NS5+inX9OER85XPCiqyieAquPE9k+Vtvp8QDisSVc17D22zNo/uOxlZe89/37Xvf3Te9fo0LJXym3/AHwXFBqioDJus3DKshhFNSj5dUdxzyuuvwGBgZZKeiXdT0BgoAJqew+nlCYgV7N2LdWsXcunPgKXApvNVi6h0uLGCm+v4wvpS0BgoHr0917NztsxFjeW+LpNScZpZ18u9Did1fuKExQaoqDQEJ+2LeocAShb8YnNFFkvSg2bxBYb6g8KDdGzM1/V1vWbtHrJciPwFp/YTK06tDHGvW79euqrz740gnLB1UNUIyJcUuG48cZHs/Tq4xNNQTpJxjZ9/zxA9Rs30oqFS0zT3va4pbe69O5hjOVPvvGiHh82ynjcU1VToKohdHeJe2v6TG3c+K3Xx4NDg3RtfFNF1qmt1q2vU93IOpRGB4Ai5OVka83yxVqzfLH6DhyqXkm3ldu4uWHNciPwl9CqLaE7AJXC1MlPWoIlg+4YYSw7HA4tnDfL9Bxv098CgNO0D5aWeZt1o+urbnR99Uq6TTmnT6paUIilqoDNZtOTk6Z7bcNZ2dPhcCjndOEX3VzDfjabzQg8O8PInsKANcIjyuUYgarouyl9Pa5v8cACXX6F95ulZzJ2K2X+GMv6Br3HqHqTbn45lssDyzpiAwAAAAAoL/9e8XGZt+kMMPf98wBlZ51SUGiwx8+vnp35mtc2CsN7r8nhcCg765Qkc8DZZrOpedtENW+bqAK7Xfk5eR7DgDVr1yqXYwQqM0J3VVxeTr4RyluwcJEkaeCA/uqf1K9cw3dH0tI1/okJkqSR9/9VnTq29/epKBcL/7vIOK9vvP4yVQaBSiIyOkaRUfWN5X17dhrVT1wtXTBXP6R8r1FjJpb5mOlwOLRm+WJ/nwoAKLFr4q41he6cY1liu85KSz2g9auXWcbUG/5wi7+7DaAKc1bULO82AgIDi5wqCED5smcdVLVI719kyjuy3d9dBAAAAADAo7KoRulLG75U9wRwHqE7WCxYuEhfrFqlh//2gOIax5bLPrZs+c7fh3lRfLFqlb+7AOAC9Lx5sGUawqNpqZbpxyQpJXm7li+erz63DvHanrOqiSTlZJ9SaFiNYm/Kph5IKXG/T2Vlmvbx+4DA36aKBICLp1fSbZIKg8lOzgqh7iKjY3TXyLHlPl03AABA9r51RYbusnZ+5u8uVjjBjdor4e53jOXLruDGCwAAAAAAgBOhuyomOjpKN/frYyyfPXtWq1avVVpaumm7vJx8TXxuUrlVZ6sKYbQjaenKy8n3dzcAlBHn9GMtWnfUi+NHmR5bumCuevQeYKlckp+Xq88Xfei1Yl3fgUMtz5s97WVjSllX705/Re9Of0WSNHHKO6aAyoF9u/Xh7LeUkXbY8rzg0DDd++ATTE0L4KKx2Wzqc+sQtbu+h+b+e6qp6p1TcGiYBgy5V4ntOpdrdWUAAACnE9sWK7rbCI+PnTuZqp/PZpewxUvf5VcE6vIQgnYAAAAAAACeELqrYqKi6lqmcu15Uw8dSUvX3HkfKjl5t+mxCROf01tvvO71ZmjKvv06efKUMjMzlXHsuFq1bKGaNWvomkYNLc+xFxQoPy9fR49mmMJomZmZyso6KUkKCg5SYECA5Xl796To7I8/6sCBg6pW7WpFREQoOjpK9aKjij1me0GBvvtum9HHxrHXqEmTONWqFW7ZlzdZWSd19GiGTmRl6fjxTDVq1FCNY69R9ephluPMy8tTQcH/tGbNV6b1R49mGD97eh6AyqFudH09OuEVvT5xjGn9xq++ULeeNxvLDodDUyeN9xiEc1q6YK7WrfxMz//jvQseE46mpVr64iovJ1uvTxyjvgOHFlmNDyhPnbr31c39eqlPrxvVND5Ol/N/YJVQIzxCD457wajC6apaUAhTLAIAgIvu3MlUXVmzvmV9fvquC27zpx9z9fOP2Tp3MlWS9PvgWrri6hr6fVBN/e4y3657C3IzdfbYHl0WcLWujmisK64qecXyX39x6H/5J3Xu9BH9UnBWknR1nfgS9cPdzz/Z5fgx11i+7IpAU99++jFXv/xkN5Zd9/XrLw6dPZ6i/+WdKNFxOc+Fs/8BIRGmx1y5PgYAAAAAAHCxEbqDJKledJTGjn5EL786xRS8y8vJ165d36tly+am7Y+kpWv62/+0VMjbuPFbSVJwaJBu//OfTQG/777bpulv/9Oy7wULF2nBwkWSpJH3/9X0nBVfrNR7c+bKm4SEphp1/18VHBxseczhcGjuvPlavuJLj32UCiv//WnwHy3HZxx/Xp6mvf1PSxjRKTg0SC+98Jxp/3M++I9pH06vvPaG8XN5VRDEpeHY8UzVqc0HxxVZo8ZNFRwaZppm9tD+PabQ3dZv15kCd8GhYerco48yM9JNlezycrI1Z8ZrGj5qrCSpQWy8JGnfnp2m9iOjYxQZVV+S9PuAwqCKw+HQu9NfNvUtMjpGrdpeb5kGd+mCuWrRuqPqRtf39+lDFfXpkuVauuxL/eJwEMCrYpg6FpcirtcAVBbnztl15ZVVO+geWDNG9pOF782yf9jgMXSXtWuZ8XO16JY6k7a92HYLcjP1w+JnjbY9adB7jKo36eb18TMZu5Uy3/olqsuvDlOT217z+RhzDm3W4S/+4bVaX50OQxXZvuRfwso78I0OLXvFWA5r0lUNe481ltPWzlT23vPvbxv0HqOwxp11aPlrpvWu/ajT9jaPIcBzJ1O1b+HfPR5D3G2v6OracUp+527T+useWVriYwIAoLR4PwigMsjOyVVgQECVfz8IAOXtMn93ABWHzWbT2NGPKNqtetyHH/2fadnhcHgM3LnKy8nX9Lf/qe3bd15wf46kpRcZuJOk5OTdevzJp+RwOCx9fPnVKZbAnbu0tHS98tobWvjfRR73f/+oh70G7pzHef+oh5Wyb/8FHyfg7o9/vlu9+/9Js96dqwMHU/3dHXjRON4c1t235/x4V2C3G1PBOj3w+PPqc+sQDR81VneNNN9UcQ3hdet5s4aPGmtpv+fNgzV81FgNHzVWQcGF1QHcg32R0TF6ctJ09bl1iJ7/x3tq3aGrqY0dWzb4+7Shivvlt/+vP12yXKMeHqeuNyZp8mtTtTN5t352+78cACoyrtcAVBY39hmov4x8VBu+2axz5+ylb7ASCm/W2/g5e//Xlsd/+jHXFJyLuO6WYts8sf1TJb9zd5GBO0k6tOwVpfzfk/r1F+u1rrfAnST9fDZbe+c/prPHU4rtS9qamTrwyTNFTo97bONcZXwzr+xPrgfpX83yGLhz9sPTMZ3J2K3d74/yegwp88fo2Kb5F6X/AAAUh/eDACqDzVu28X4QAC4CKt3BxGazqWl8E1Ogzj1c9823m03roqOjNPyuOyRJb7z5lmnq2Jn//rfefvMfkqTGsddo5P1/1bbtO0yV4Dp0aKdWLVsY20jng32uevW8Ud26ddGWLd8ZlfGkwuDbosVLNODW/sa6oxnHrFPlPjVe1cPCdM5u1969KaZA34KFi9Snzx9M08267z86OkqPPfKgJGn+xwtMxzD73fc1+cWJkqSbbrxBrVq2sDx/4ID+iogo/PZTUHCQn37DqCzy8vI1+715mv3ePFULqqaB/fuqR/cuatSwvr+7ht8ktGprqVjndCY/1xKsc60wl9iusyWUdyor84KqQbnuJ9JlHzabzdLHzIz0kjQNlCvXAJ6zAl6Xzh10c59eatWiGd/AA1Dhcb0GoLLYvSdFj//9Of3icCg6OlJ/vDVJPbp3UVhoyacwrYxCGrRV2uoZkiT7ycP66cdc0zSn+Ue2GT8H1ozRldXrFdneuZOpRnuuwpp0VWBYlI5tNH+B9Ezadh3bNN9Uae7XXxxeK9yFN++jYxvn6uez2TqxbXGRfTmTsduyTa1WSbq6dpwkmarUHds4V8H1WqpaZNNyO9fHNn9kBBG9VQzM3rfO0ofDK9/y2F509/t0bNN8/Xw223JecenZmbybaugAKg3eDwKoLFzfDzaNj9PwYX/m83cAKEOE7mDRqFFDyzqHwyGbzSZ7QYElTDb8rjsU1zhWkvTWG69r2F1/MR7Ly8lXVtZJhYfXNP5J5ileW7VsYZpSVrIG+4JDgzTsjsIPJ+tFRykiIsLUD/dKc+5BwYED+ht9dLYhSe/Nmavo6ChFRdXV4cNHjG3Wb/jGsv8nxo02ppF9YOQIRdapbYT/0tLSlbJvv+Iaxxr/3M9T5+s7MaUsLsiZ/DN674P5eu+D+cYb+PbtWvNBZAVWIzyiyACdrYx+b206divy8eo1a5mWt2xca0xjC9/sTN6t48dP+LsbpVKt2tVqUL9e6RsqR84A3lfrNurrDZv0i8OhFs0TdPuf/6hWLZr5u3sAUCyu1wBUdM7rrbS0DP1j2r80ZeqMKhPACwiJ0OVXhxlV1LL3faVaLW82Hs9K/sL42bUqnjcHl71sWg6sGaNrkp5WQEjhe8Ba1w3QsfXvmcJwxzbOVXjzvkbYL/fwVku70d3vU3jzPvrdZTaPbXjiHlar1SpJ0d1GGMtB9Vpp58zzYb+jG+Yq7o8vlNu5tp88rMCaMWrYe6yurFlfP/2Ya5mC9sS2xYrqcq8xxey5k6mWioFhTbqq3o0P6vIrAhXevI+y960zBQhxabr/b4VB1Jv79VKfXjdyHQWg0uD9IICKzvl+0D2AN2hAktq0bnVJvx8EgPJG6A4Wzmpzrk6fzlZ4eE1dcfnleuN184eLrkEym82m6OgoU2Dt6NGMEofNmiU0Ne0nIOD3RfbRvard2bNnTcsLFi5SwwYN1LLl+ekae97UQz1v6uFx/9u27zAt35qUZATunDpf38lUce/bbzebgn1AeXB9Ay/xQaQ/ZWVmmJYjo2Ms2xxNS9WOLRv0Q8r3SkneXi79cDgc2vrtOiVv26R9e3aaKu6h9O7/2xjd2KNr6Rvyk0OpqTpw4HDpG7qInB8A7NiZrB07k9WoUYxuuqG7v7sFoAI5cDC1QlcP4HoNQEVXFQN4zupxkpT9wzdG6O7nn+ymamxBUUV/4aMgN9MSEIvp8YARuJOky68IVHS3ETq9b51putT8I9tUvUk3SdLpvatNbVSLbmkKAl5+RaCiutxbZOjOU1/qdLrTtHzFVSEKa9LVCL2dSdtuqfRX1lwDiFdcFaIGvR6zTDd79niKUe3u3MlUSxsNej1mhPJ+d5lN1Zt0U1byFx4r5+HS8+mS5fp0yXJJ56+jYhs1pBoLgEqB94MAKjrXAN5zk1+vshXRAaCsELqDxTm7dU53Z+jNZrOZAnQOh0NZWSdN24aEBCst7fzy2R9/LHEf3ANueXl5pv0cPZpR5PPbt2tjmj5Wkl557Q0lJDRVl87Xq3HsNapePcxrtan8/HzT8r79P+jqq68ucp9HM4ruE1AePH0Q6fj1F393q0pwn6o1Mqq+8bPD4dDCebO0ZvniErZaMqeyMjXj9WeVkVa5QlWVzbN/pzpgaXXq3ten7S6z2fSLw6HIunU06NabdVOPbgoLDdGKL9dIOuTvwwAuCdk5ubJ7uN6vLA6lHtGMWbN1+58G+7srPuN6DShfl0JlYl80alhfV111ZZm3W1wA71IRdk1HI3R3Jm27fv7JrsuvCNSZ9F3GNpdfHaYra9ZXQW6m13bOnT5iWRcY3tDjttUbdzaF5nIObTJCd+5Bs6tqWitT/+4ym6lCn7tffjpnWZd34BvLup/O5pqWf/4xu1xDd64BROdxBNaMMQUE/5d3QvotdHf2eIr5fNaMMQJ37ueI0F3V8+mS5Vq67EtLNXQCeAAqC0/vB3/5hfeDQFnp1L2v1q9e6u9uVFre3g8O6N9PN/Xo5u/uAUClQOgOFu5Ts0rWENyKL1bqv4sXKy8n39dmSywvL0+fLFqi5Su+LPFzg4ODlZDQ1FIBLzl5t2ndwAH9PU776v68jRu/NU2J64n7c6oiX0MVKB9rv9qgWuE1Va2YgChKz+FwaMtG8zf1E1q1NX5et/IzS+AuLqGlOnS5yVh+d3rpp8aZ8MjdlnV9Bw5VeESkJCl52yZLP1EyvGEvf86gHd+mKx8FdrvO5J+/0fr7gEAFBVft81vVz0m/W4eUvhE/a9QoRuu/2eTvblwQrteAslfZKxP76tkXyn9qTdcbLlOmztC69d+odWJLfx96mbiyZn3T8pn0XQpt0Eb5LtO8hjfvU2w7/8s9blq+/OowXX6F5/DP1bXjTMuu4Tf3CnXu2zoFRTe3VIlz8lQhzpcpWM+dTLWcj7IS1sTza/HKmvUtx2ycl3O5lm098XaOUD7OnbNXmGCbazX0Xd/vMaZDGz7szwTwykiB3a78nDxjOSAwQEGV8H15fk6uCuwFxnJQaLACAvn7uBB8zl8+jPeDV/F+8FLmcDiUnXXKtK5m7Vr+7tYljTELAOBPhO5gceDAQdNyQkJT42d7QYGmvPFmuQfMUvbt1xtvvlWqUN/Y0Y9o5ao1lop3rhYsXKQFCxdp4ID+GnBr/3I9pqqCgErpleQNQnBwkAbeerO6d+lkTLNWWBEK5WnaKxMs66rXPP/GeedWc3WB4NAwjXj4KeODvvy8XJXWqSxr9YVuvZLUxyVM4T4FLlBR/O53v9Ovv/7KjZKLYOfWb0wh39Ydumr4qKpdvZFzUrmv15yV+urUjih9Y6Xw5UrfQ+1crwHlrypUJr7QYyxpxWH3SlKXUsXhWq2SjMpz+Ye3KiQm0VSJLuyajsW2cebYHtNyUHRzn/dfVJW2q+vEe1xfrU6819BdRXDFleZQTmBYlMftQhu09fk4qnk5F78Prvw3qytTZc7132xSaEiIro2vWGFH1+nQHv/7c0YAb9CAJJ09W/LZViqSArtdWccylX7IXFEzqkE9hdeJKNfw2LYNm/WvyVON5bbdO2nE+If9fUpKbN7bs7Vp9Xpj+S/jHlT7Gzr7u1uVVmV+31je+Pz+vJNl8P9aWHgNr7NSVUbZWaf0+LBRpnX/XvGxv7t1yWKs8m7Fl2uK/fIWX4gHgNIjdAcTe0GBNm4yV3SrGxlp/Pzdd9ssgbsOHdrptkEDJUlBwUF65tkXPFbLK4nZ775vCdyNeexh1a17vi8PP2r+wNnhcJguzG02m3re1ENdulyvvXtStHNXsjZu+tZjkG/BwkXqn9TP64W9syJecdz7UNVwcXtxOKdd7Nypvd9vOFdFn/13nlKSt5vWRUbHqFHjwoCyw+GwPD703odMH44ePrjP0m5O9inVCPf993lwv/lmT2R0jAYMude07oeU7/19ugCLihq0G3V76b4ROe0D/g9E5VDZr9cKP/ir+B/+cb12cR1NS1VGWqqq16yl0LAaCq1es0q/LysrmzesMS03T2xfIavFVPZxzZ+cN1hu7tdLXTp1qHDXZ2UtzGW61xPbFisoJtH0eGD16GLbcA/B5aft9LptziFzVVZvVeAk6aezpyzTskrWkF9xEu5+p9htLrui7H7H7tXn7NmePw90n0K2KGeO7VGtljdb1v944kCZ9dtfKltlzpVr1iknt/RfGkTRTh4/oQWz55nCYp607d5Jff90q6IaxPi7y37jWpHvUgvpVDRcX5VOVXo/6B4uuxAvzZlGJTjgInK+D3R+caFN61YE7VChfbNqnWm5Vcc2FfLzKVRdhO5gcDgcmvLGm5ZQWrt2bYyft23fYXosODRI995zlwIDAiQVhvZKG7jz1MbAAf3VsuX5bw+v3/CN5XmnT2dbpomVpMCAALVs2VwtWzbXsDuGKC8vT7uSd+uD//zHdKzffLtZnTq2l1QYJHSdTvbMmbMe2wYuFucb9Zt6dOPi1w/y83K1buVSrVv5mfJysi2P3/fo0z63VWC3a9Xnn1jWnz55wgjuebLi04/UpmM3Y/lKt2kIMtLMU/Uc2LfbEv4D/KXHDV30h5tuuORv5AKo2kp6vfazw2EJNV0I1+uDqubAvt2aNfVFy/XZxCnvlOjLDPDMtSqoVHhe+VDz0nFzv17q0+tGNY2P0+VVJLTgHhA7+vVs4+darZL0u8uKPw+/D6ltWv75bLZ+/snucYpZ90Cea1W4wJoxpulW/5d3Qoq0vh8sqjqcp+p4tqtCvE5360/uU8i6cq+Ol713rdTbWtmxpAHEiqoyVebMzsm9qJ9B+VJJ2PUmsfuXuSpj5aidm7bqH3+f5NO2m1av16bV69V/2GAl3T7I3133izefeVl7tu6SREgHFQ+f3wOoyIq6hipvJ4+f0A+7zV/CqRERrthrm/j7tKCScK3ILBVeBwbUrnjve1F1EbqDJCkvL0+fLFpiqWKXkNBUcY1jjWXXIJok3f7nPxuBO0k6cSKrxPs+cOCgEXaTpMOHj1i26Z/Uz7TsHv7zdkw5uYXffKsXff4DvODgYHXq2F7R0VEa/8QEU5vOfgQHBZna2r1nr6V9h8Oh06cLb+5Urx5W7DfrTmdnE9xDifBG3X/enf6K5SajN30HDjXd1LXZbOrWK0lrlp+fpmjurH9owJB7lZWZoaULPE95veLTj9QwNl7VgkIUEBioBrHx2rLx/AfOGWmH9dl/5yk8IlINY+MV26SZpY05M15TQqu2WvHpR5YQntPmDWvUMDaeG9G4qCY+9bi/u1BltenYrUoHcjzhnKAsleZ6zfHTTz5fbxSlqv495+fl6vWJY/zdjUoteftmnfvxrKSq+3dU1dzav4963ti9SgXtXP3uMpuqRbc0pnl1Db2FNfZt+sErq9ezrPsp97gur1nftO7nn+z6+aw5EOwa+ruyZn3T/rOSv1D1Jt0sbZSUt75IqpBhPEkKDI20rPvpx1xdcdX5/1d//cVRZFXByqKyVY6qKJ9FeZv+urJLP3TYa+CubffCGVc8Vb9bNOcj1W/cSM3bJqoqKbDbjcCdNyPGP1wpp8VF5cXn9/CkZu1aTCeLCsXfM8+8+8YMj/+H/3PZh1StreBcA5PlWV3OWcmOMCYqK0J3VUx+fr6ysk7qdHa2Tp48JUn6dMlnXqvT3Xv3XablhISmpmCea1AtZd9+zX73fUsbmZmZRfZp+YovNXjwQCO8FxNj/QDzaMYx1YuOkr2gQJ999rkl/CdJ5+yFHyLaCwp0z733mx57e9obCg4ONq07feq0aTmyzvlvK/f+Q08tX/GlsZyWlq6F/zVPQbto8RItWLjI2KZXzxs17I4hxnJ0dJTpvM5+931NfnFiqX+HuPQ9/eQYyjlXAsGhYRp670NKaNnG8ljHbr1Mobu8nGzTTfXg0DA98eI0zZ7+slGNLiPtsCY8crfuGjlGbTp2U2y8NVTnDOw5t3EP923ZuNYU1Os7cKjaXd9DEx6521jn7AdTYQJFc75+AMATrtf87/NFH1rWte5QOGVftSB+L8UpsNv19qvPGMueQnfO8+n0+4DKH3Coiu4ZPlRdr++omJjoKhm0cxdx3S1G6M6VexU8bwJCIixV6g4ue1kxPR5Qtd8q1RXkZurohvcszw1udP4Lp9WbdDdVsTuTtl2n965RWOPO+t1lNhXkZir1i6kqSkBIhMKadDW1c2TNv1T/pgeNqWp//cWh72ffawoANr1jmq50C+b5k+t5cdr9wShFd7lXYY07K/fwVh3+4h+WECOqBmdVzthGDS+JoJ2rres3WdY99Px4U5ju3rF/08G9+zX5kadM2y3499wqF7pL2bm79I0AZYT3g+e9NGeax/UbvlyrRXM+8mnbsPAa/j4M4JLUpnUrffnZAr9eQxUVmj+4dz8BqwrONTBZXtXl8nNyjUp2bbt38vg34fxCilNAYIBPbQMXC6G7KiY5ebceftS3aQzuHDbUUpltwK39TaG7jRu/VXr6UUkyAmbuwbwFCxcpIiJCjWOvUXh4TUVHR1n2dc+996tDh3ZqHHuNet7Uw9LG+CcmWNa5h9rmzvtQXTpfr+uua6VePW80heYef/IpXRvfVK1atpAknT17Vu/NMVebuvba89N4hIfXtEwxu2DhIn2xapU6tG2noxkZlr4MHXKbqb22ba4z9S8tLV3jnpigqKi6Gnb7ny0hQMCp543d/N0FFCE4NEyJ7ToradCdXr/VUTe6viUQ5/r8J16cpqDgEA0fOVbjRg7x2sagYffp4zkzvPblhj/con27d3isatd34FD1ubWw7eLaAWAVHhF5wRUhD+zbrdMnTxjLzRPbG+PF0bRU7d+zS4f271FEZJTqNYj1GN51lZ+Xq8MH92nPrq06k5erNp26K6ZhYwUFF3646z49pWtw4lRWpg7uPz8VV/WatUxTWbs/7loJMz8vV99985XR1/CISCW261zsNxCdbWZlZigzI904zjp16/l0TvPzcrU3eVuJnu/pnF9+xRXKOX1Sdvs5ZaSlGr+H0pyTU1mZ2rVtk+n3F98sschz4v77S2jVVk0SWvn0+0PFxfWa/53JM09V2LpDVw0fVXmm7PO3/Xt3FbsN5/PScPewIaVv5BJSLcr65aZq0S19mlrWqWHvsdr9/ihj2X7ysFLmj1FYk8KgqqcpYRv0HmOqNBcSYw3LHFr2io5t/khhsdfr2EbPFdLd1e14pyW8l/zO3Qpr0lWBYVHK3v+1KawW3f2+ChW4kwor8LlWIJQKp+09tOwVHVp2/strl18dRvCuiqgq01/vSzZPmdy2eydLkM5msyn22ibqcUtvrfxkmbE+KCxEBXa7x8+lnBVJDu7dr4wjhZ9NN06IV/3GjRTXvGmpK5Tk5+Tq+607S9V+fk6uNq3dYJpqrkOPLh6f76x8smz+J6b1G75cq1qRhV+ib39DYbXS/d/v1anM8zPxXNM0zusUtAV2u7Zt2KwTGcd1LO2oMlLTFFk/Wi3aXaerql3tNdTofJ6Ta0UW53Ft27hZQSHBqhNdVx1v7Mo0uJcg3g+e5+3v2/n69GVb5+vcyVM1JffXnnT+tS9Zp690fW2mHzqslJ27jdfmNU3j1LZrRwV5CE26jyPOvhTY7TpyIFWb125Qyo7vFVk/Wh16dPE4VhTX17IeRwrsdqXs3K3kLduVcSRdkfWilNC6pRo0bqSg0BClHzqs9EPnZxgramzEpaUiBIO/Xr7a62OrFn9O6K4C86XKcFnYtHZDsdtQyRgVHaE7WERHR+mxRx70OBXqNY0aWta5BssSEppq7OhH9M23mzX97X8a66e//U+NvP+vCg+vqbqRdTzu1xlw63lTD/Xu1dMy1a3r8sAB/dU/qZ9efnWKsT45ebeSk3frjddf1i39+5mCcXk5+dq48VuPFfKc7blOoytJw27/s/Lz8037zcvJN4X5nJ4YN9pys7V16+tMlfCc5yotLV23DRpYvr9EACVy18iipya78qqrVaduPYVWr+lzuetBd4zQgCH3KvVAirZ+u07hEZG6rn0XI2QhSUHBIZo8fZ7+V3B+2iDXyizdet6s2PhmykhLVVZmhiQp7tqWiqpXOBbXCI/Qk5OmG8GYrMwMJbbrrKh6DU0fTHTu0UfNWrX192kGqoyvvlhiqjp5/+hnFNOwsV58YpTycqw3CuMSWmrUmIkex5c1Kz61hGadbTur8blPT+ka2jq4f4/p8dYdupoCZu6P3zVyjEKr19TfH7rTY19XfPqRRj/9uscbKQV2u+b9e6rp2N25BoLdORwOLZw3y2Ngubjnu5/ziVPe0bdfrzRN6T1xyjsKCAy8oHNSLShEz4y+1+vvb8TDT3k8Jwf27bZMf+nsZ7deSRp0x4gif3+4tAUEBnqtPDt72sumv+mqEijzdvPYFwlc65TIqs8/8XcXAL+4/IpAS6W6iOtuKVEbV9asr+ju9ylttfkazVPYTpLCmnS1TF/7u8tsHtuwnzysY7/1LbBmjMKb9bZs4yogJMLnvoQ16apaLW++SGe6ZBr2GavdH4zyGqoLrBljCTvi0vP2m69c8kE7V/nZ5i8Q5OfmyeFweHxfeNuIO9VzQD8FhQYXea30zap1RqUQV84bpsHVQzRxxmsegya+8KX9J9940WuYIz8nVzMn/8PjDVznVLpDRt2tHv17G+s97U+SqYKWM8yyavHnpil5/zLuQY998XYc6YeOGM+PalBPf3v2ccvz83PyTM8Nrh6iKR/O0sxJb3idDti9giEAM/fXo6dqSu6vPckcZMs4km56vG33TmrYJFZTnnzBMuZsWr1eS/6zQI9NekpRDWJMj7mPIy/Nmaasc5l6esRo03bO8SI+sZlGT55Qor6W5TiSn5OrCfc9przT5/9P2bN1l1Z+sswY85d++F+fxkagPHz1mfmeenxiM+M1uWn1et31yH3lNmUpSudiVRl2/xsBKiNCd5AkdejQTpF1aqthgwZq1uxar6ESm82mSS9O1Jo1X5nCZ8GhQerQtp1u6V84/Wqnju21bfsOjyE3m82mt6e9oWlv/9MSrHNq1uxajXnsYX340f+ZQn3R0VG6uV8fY0rbUff/VfePetjy/ODgYCP8V9T0udHRUfrT4D+qZcvmHtsY//horfhipaUqnut5S7q5r8eqdfWiozThqfF64823lJeTf7F+lQAuQHkFHGw2mxo1bmoKc7grDOF5/7CzbnR91Y2uX+R+goJD1K2n95snNpvtgit2ASi9kyeOa9Xnn3gMbElSSvJ2pR5IsYwVngJ3rpYumKsfUr4v076mHkhR6oEUr33NSDusjV99YRlzCux2r6E09z6fPZOvQXeMMK13OBxeg37uz8/MSC82fJSTfcoUuCutmW88V+Tvz9M5Sd6+2TR1o7s1yxdbKnUBF+LAvt366oslxnKXm/qpUeOm2rxhjc6eydeh/XuMdU4Oh0Nbv12njV99YUx1L0mR0TFq3LSFbvjDLR6vHfLzcvV/7880lhvExqtbz5tVYLdr/95d2rx+tREWjEtoqb4DhhZ5HXQqK1OrPv/EFLaNjI5Rq7bXK+7alqpVu67pCwtSYSBRkvbt2Wlav+LTj5S8rXCatj/eMcL0PIfDoT27tmrz+tXKSE81qgRHRscoMqq+2nTq7rXqaEnOr7fz46zgufGrL3Qs/bAS23VWYrvOqt8oznjv7fwSxfrVy5SRdliR0THq1L13kdd4BXa7Vi5bqG2bvjZVPo5LaKlr4q5Vj94DTB9eu/bP9ffuel6dfXZd5+R+Xl0lb99c5Pn1VhXU/fw6fxeu58zZ19Yduipp8J1c16LU3INsnqrfFadWy5sV0qCtflj8rCnA5+ryq8PUqN8TxrSzntr42Z7vsard5VeHqfHASco/ss3nvuyd/5jX0FqdDkNVp+1txbblL1dcFaJrh8/SsfXv6fS+dcZxBNaMUVjs9Qpv3le//GQ3Pefyq8P83W2UseYJTUvfSCUS1+JaU+WhPVt3afTQER5DcTabrdiAhLewhqu804XhDE9Bk+Is/uBjy1SRntp/fNgoPTvzVUv76YcO67Xxz5mCIZ7Mm/aOzuafUdLtg8r2hJfgPBX294jXY3E/5p2bthbZ5uzXp2vKh7PK5XgAFIqsZ51ha+l/Fnqt0pR3OlcfznzPEphz98PuFM3/53teH9+zdZf2f7+3VNW68k7n6ptV60o8jhTY7ZbAnXu7E+57TMEh/q92hqrp5PETpmud+MRm6jmgn+l1mbJzd5HB9JWLlpmqWN6Q9AePr7eZk94wLQ+5f7jlesrhcOj773Zo48qvjNdb2+6d1KLddWrTtaMkadbLb5qe41phbf/3e7Vq8eeWvjiraTrDY9d1bq/ETm1N1w/phw5r6/pNWr1kufJO5yo+sZn63zG4yLHD4XBozZIV+mF3ivbuSFbe6VwFVw9RkxYJRp89fc7ifs6c58K9D1EN6imuxbW6bcSdpnacz9+7I9nU7rtvzFBQSLDH85ufk6vVS1YYbbv+zlt1aKNu/Xqa9pGfk6t5b8/+7dyc/xvZuyPZ+F1e0zTO+CKGL79f999xRmqa0XZUg3pFVigt6vfrWoHU+bfbtnsnDRw+hAAzDITuLnHDbv9zkVXVgoKDFBhQsnmv60VHadgdQzR0yG364cBBXXnllarnYcrYB0aO0AMjR3hswxloy8o6KUk6Z7erVq1woy82m00tWzZXy5bNlZeXp4MHU9UkPs7S1+DgYM19/x2P+3CG/zp1bK+8vDzl5OYZ4bvo6CiFhgT7NMVrz5t6qOdNPWQvKNCJE1lKS0tX49hrFBDw+2KfH9c4Vm+98bpOn87WObtdVwYGqnr1MJ8rZQEAgMpv+eL5RmCrW68kbf12nSXAtXThXD047gVj2eFwaPni+Za2IqNj1P+24UawxT00UVquwZe+A4daghzO43EPgaxcttB0TMGhYXrg8ecVGHiljh09YgqfrVm+2BLocT1HTk9MmmY8f+6sfxiPb9m4Vj2TBhcZSJ419UXj57iElqU6TwvnzTL27e2crF+9zHJOFs2fbWkrODRM9z74hFGZr6iqgICvTp88YfpbSmjVVqdPnjBVUUxo1dYIvzkcDk3++988Tk+fkXZYGWmHtWb5Yo+VJf9XYLf83Xbu0UevPvuopb2U5O1KSd6uQcPu8xgc++y/8zyGY519cD7m/nxvrxvn8yQpafCdcn6p4VRWpma8/myRx7tl41rFJbTU0HsetAS6PJ3fcz+e9Xh+PZ2fDl1uslQ6XbN8sdYsX2wcm6eqmBlph/XxnBnKysywBJWdxzXhkbs9ngvnuV+38jM98PjzxnjpqX+ezqvzfLtv63penfLzcjV7+ssex1nX8xsZHaP7Hn262PMryWt1WOe4+cSkacV+KQVVW8Ld7xT5eHjzPgppcL46puu0r5L0+6CapjZsV3m+URkQEqFr75iun37M1c8/ZuvcyVRJhZXwLr8qTFdcVfwNzsj2Q1Sn7W2yn07TuZOp+n1wLQWGNzT6FNyovakvl10R6LUvLf76gX7+ya6fco+b+nJFSG3LMTpFdx2huh3v9Np+cfv3tX/u+/F0Ti+/IlDR3UYoutsI/fRjrn53RYCp36f3mgOIQdHNBVRmCa1bmqaMlc4HJG77652KalBPdepF+fQ58snjJyxhjf7DBqvXH2/W5VdcoVkvv2k8nnc6V0s//G+JpunKz8m1BO76Dxus7v166qqgapo/8z3Tsfxr8lQ9O/M1Y9nhcHgM3A0ZdbfimjfV1vWbTO0vmvORMZ3iS3OmSZIeH2audPnQ8+M9hmyKsv/7vR5DLf2HDVZip7aWalDOY5kw/eUifw//+PskSYU3YOtE17Wcq7zTuUo/dLjEQUegKjh5/ES5tLt3R7Lxeu4/bLCOpR21vL73bN1VbLV1Z0W64Oohuu2vd2r+P9+zjGWb124o9RSZzv2UZBz5evlqS1+c/awREa7pz72qvNO5xYadgfKy4Uvze/2eA/oprrn5SxYL/j23yNDdD7tTTK/dFu2u8/h6c399Dxw+REEuyw6Hw2vly02r1+vrFav1pxF3WtpxvV46lZll6cuVV13psRLmojkfGcH9lYuWad4083vUPVt3ac/WXV6rWHr7skLe6Vyjz8vmf6LRL02whM/cz9nA4UP0vYcqv+mHjij90BFt/mqDXp0707jWcX++a589nd+Tx09YrtPcj3PJfxaYqiEX2As87sN5fE7O0F1xv19nP958+iVTiM/9WDetXu+1mrH77/eapnGqXbeOHh58r6U95++guC9noOogdHeJ8yVUdqFsNptlStaS8jSFradj8FSJrqTnITg42GM40FeBAQGqFx1V4jZsNptPxwkAACoO14pJxSmu6lpeTra69UpS0qA7FRAYqAFD7tXyxfNNYZOU5O2mD/o8VZtzDZ4ktGyjLjf1s4Q0yoJr8KTPrUO0ecMaU7gkLydbR9NSjcBDfl6uJThz74NPGI/XCI/QxCnvmMIhqz7/xAiRFNjtluc/OuEV0/OfeHGaxo08H/5ZsfijIs97Xk52kdO+lkReTrZad+iqIfc8qIDAQPVKuk1bv11nOicZaYeVn5drVIA6mpZqCfgMGnafOvfoY1RBTRp8p1599rFiq/sBJeWc2t6brd+us/x9RkbHWNYtXTBXLVp3LDLctGXjWlULDvEYaHPyFNTdvGGN5XUfHBqmxvHNLQGsj+fM0NXVgi6oMnFRwTR3KcnbNeGRu/X6rAVFjhvJ2zZZqux5s2/PTs3791Svr/OP58xQs1ZtTUFhd2uWL9aAIfdabvTO/bd1SjT332NeTrbeeunvev4f75XLF78K7HbT2FyUjLTDmvDI3Zo45Z0iK9Xl5xeG+IoaGxfM/ZcpqA64Cwgpuhri7y6zFblNcY+7u+KqEF1xVYiurFn/gvr7u8tsurJmfY/Pv/yKQF0e4vu1zOVXBOpyL21563tRldeL27+v/StuP7/+4tD/8k/ql5/O6dzJVIU17qzfXWYet+w5GRd0foGK6trrWpimWHPKO51ruinqrBByfa/uXq9RFsyeZ1rucUtvU6U45w1j543Ekk7n9um8Babl+MRmpvaHjLxb1YKDjJBI+qEjOnn8hHEzc+l/FppuGrtPQxvVIMYSiFmxcImGjLzbawWRyHpRJa4uMv25V03Lbbt3Mp2HEeMf1sDhQ0w3jtMPHdH33+0oMhDgfvO2e7+eevXxiaabvlvXb+KmLHAROStJ/e2ZscZrfODwIXrh4SdM41FxVbakwoCws0pTm64dtWbJClOAZuUnyyyVokrqQsaRJf+xjs2ux/vq3JmWUDRwMa1esty0HNe8qQICA03XP+mHjig/J9dj1bKyNH/me14rX0qF4bDXxj9XojZ3fPtdkZUw/zV5qm5/8C+WwJ2rFQuXWMag/JxcS5DPKbh6iDGGpR86ogn3PWYKzHmy4cu1RVYrzjudq4N7919wePjNp18qsp/Ofbz59EvFfpHhQuXn5HoN/rlzVjOevvj9Iq+Ft23cXOzUt18tW6khI3373BGXtsv83QEAAACgonFW5vHlny/+0P9Pxps4m82mXknW6b3273Uprf/9dsvj7qGVRo2bKi6hZZkf+8ChfzEFItp07KbgUPP0XRlpqcbPhw/uMz0WGR1jmU6yRniEqQ3XinrpRw6atg0ODbM8Pyg4RJHR5z9Y3LJxrQrs5inG3A39LSRXFpIG32n6/XkK/+xNPl99xfX8OHXocpPpQ4Ua4RFqHE+FFpS9NcsXKy8nW8GhYRo07D4NGnafqtd0fpPUbgqMSoVVJZ+cNF0Tp7xjGVNWLP7Ip/0Fh4bprpFjdNfIMZbxwhnUdbXxqy9My3eNHKNJb32g4aPGatoHS3XXSHOgeOG889PoTJzyjse+3j/6GeOx0OqFX3ryFExr3aGrsV3rDl0tjy/+2PsHplLh+OPt/LrLy8nWlo1rFRwapkcnvOJxzJ7wyN1Ge49OeMU01jmlHkgxLSdv32yqLBccGqbXZy3Qk5Om69EJr5h+B3k52UYIM7R6TU2c8o7uH/2MZR/Oc/LHOzxXq/dkXhHn9/VZC9R34FDr+f2o6PPrrNIXGR2jRye8YvlbcG5T3P8BACqPX39xaOs/kpT8zt3a/f4oHVr2io5tMld8Pncy1TINb6hLtUKgMrLZbPrbM2PV45beRW63Z+suzZv2jkYm3aGZk97Q/u/3WrZxr/7Rc0A/yzYt2l1nWt62YbPPfd381QbTcv87Blu2iW9lnqp7x7ffGT+73+ht0iLBEpgbOHyIetzSW38Z96D+Mu5BNWxSui/7uzt5/ISlWkyHHl0s7xlr1q5l+Z1sXPlVkW1f17m96XiCQkPU+7ZbTNu4Bw8AeBcUWjbFRHoO6Gd6jdesXUtNWiSYtinu9S0VVlpyfp5js9nU9rdpKF1lZ50qVV89jSNd+txo2sZ1HCmw2y1j2vU9zeFsm82mLr17lMm5BEpq//d7LdOMOv8+3a9TNq3dUKK2S6rAbvcYPu0/bLD+uexD/WXcg5aQmC82rV5vBHzHTXlOwdXNwcH0Q0c0+ZGnjOP3tI2z4qYr57SrTsHVQ/TPZR/q3ys+1uR331Lb7p2Mx/JO52pzMefPeR3WtnsnjZvynOn5Tq5tDLl/uFFp2NVDz4/XS3Om6aU50xQWXkOS9M2qdaZwcFSDepq++H1N+XCWHnp+vOl40w8d0cG9+yVJYeE19NKcaXro+fGmfcQnNjP2MeT+4T7/LtzPmfN4nW15OuZ3p8woss09W3cp/dARRTWop3FTntOQUdZw3cpPlsnhcPjcT1y6qHQHAAAAlKPg0DCjApqTzWazVCQ69+NZ4+fMjHTT9t7CdXXq1ivzKWZrR0Zb1rlXn0retskInrn222nzhjWWddWCgk2Vi05lZapGeIROnzxh2c7T892dyc/1Gqpr3aFrkZWUSspTW607dPUauszKNFdiCQ4N89jXBrHxTDGLchEZHaNxz79p+fbozq3fmIJmGemppqqSA4f+RS+OP//NUF8qusUltNSoMRONfSW262yZvnbHlg2minnu45Z7aK1Nx246eyZfV1crnCyiYWy8HA6HbDab8XoMCjKPq3Xq1jO9Vg/s223Zz6MTXjGFeoePGqs2nbpbpsD+Q/8/WcZtX86vt/PjrLr54LgX9PH7M03BY/dzOO75NzXtlQmmvqd8v93U7z27thq/x/z8XDVPbG+MMY0aN1XnHn1MlQSdY5Lr+XNX0jEzPy/XMn65n98+tw5Ri9YdTX9TWzauVZeb+lnC1a669Uoyqvs1atxUzRPb65nR95r+D9m/d5fq1K1Xoj4DqJh+d5lNtVol6cS282PjsY1zlb3/a4U3663sH77RmbTtpudcfnWYwhp39nfXgVILCAzUkJF367YRd+r773ZoxcIlRVZhcU5l5ToVmacg+rtvzFBQiDm0kp+bZ1o+kXHc536634Re9P5Hxbb/w+4UY0owd9c0jbOsq1m7VrlWCrGfO2dZ52162oZNYrVS52/Oux+bO/fAoSTViAgv8hwCKOQpYFdWX6D09Bpv0e46j1MaehOfaH19l0dFLk/jSL1rGpiWXceR/BzruORpbA2vU3afjQEl4R4Ecw3auU8xu+Q/C7xeM5SFlJ27LetemjPNCLq2v6Gz2nTtqL/2/lOJ2+4/bLD6/nmAbDabXp07UxNHjrVMb9p/2GCjQvDkd9/SyKQ7LP1zXtfl5+RaxqiJM14zPn8KCAzUiPEPm7ZZNv8Ttb+h6PdGrtOgxl7bRC3aXWeqrOxasTMoNMQydavkucrwjm+/MwJtGalp6n3bLcYY3rxtopq0SDD19cgPhxR7bRPZbDaPFYuDQoJLXMl4//d7Leds3JTnTJX7Rox/WDck/cEIQUqF19VD7h9e5Jju+ruLvbaJ2nbtaJlutjRVAnHpIHQHAAAAuGndoasSWpVN9YzEdp7f9EZG1S9ySkZX18Rd63F9/UZxPj3fV5HRMR5DJAmt2noNh7lPw5uRdthSSaso7gE1X59vt5/z+lhEZFSxz/eVp0pYxe3DPTTpraJddP1GZdZPwNVdI8d6fC236dityGlaAwOvNC37Mv2xexVHm82mTt176+M5578x6ilI7Boqe33iGNMU2pK1umdJuQd6JXkMesU3s04ldPjgPiW0bOO17T8Nf8Dn6TBu+MMtpptGnsbtvgOGmiondOhyk+n8rFv5mfrcen4a10HFVKMLj4g0Lbuf/7LgWt3TKapeQ8u6utH1LSHz0ydPFBm669itl+n8BgQGWsLfm9evVtLgO8v8uAD4R1SXe3V17TgdWnb+GtB+8rDSVlurD9TpMFS1rhtgmX4WqMxsNpuat01U87aJcjgcOnYkXSk7d+uH3SkewyH/+PskY0osT8GLooJ7TsfSjvrUt5PHrddUvrTvlJ9jDZu5B0kuBvcb4JJ8vqlb3PHWa1Tfsi6sRvWLfoxAZVRWATtPPL3G3QOxxWnVwfP7wrbdO5nG54wj6SUOirgqi3HE1wBjSc8BUFIOh8NSWc41aOc+xWze6VylHzrscRr2vTuSTcsX8vebuu+AZZ3769Vms1le177o/tvU0842IutHW645ev3x/OdbAYGBlv0kb9luhO6OHz1mem5w9RCPoTDXNtIPHdHJ4ye8jkHB1UMs57ZVR+vYlp11qsTj2IjxDxf5uHvQuagvZVyoU5lZlnWeKibHXtvEUtHw+607iwwsdrzRfF8gKDTE9LcrFQZMCd2B0B0AAADgJqFV2yKDKSVRFsG4q64OKnUbvoiMqn9R9iNJx44eUY3wiAsOg2SkpZoqZ7lyD5yUh5Lso1pw2X8LGiiKt9eGU35erk4cP6q01AM6tH9Pkds6K8x50zA23rKuZq3apuUtG9dq+KixxvINf7jFUoXu4zkz9PGcGYpLaKnmie0VG9+s2OMoinsg2FvFUJvNZqlc6amCp6uiAmPu3KuxRXo4plq165qW3Sv/eQs/nsrK1LGjR3Tk0H7TWJqRnnrB581Xvlb0lKwh89QDKUX+H1szvLZlHZVBgUvb7y6zqXqTbgpu1F5Hvpyq7L3W13tYk66q2/FOBYRQsQWXNpvNpqgGMYpqEKMe/XtbqnI4HTmQWqobfMVVbyutTavXe70Re+VVV5assYvsqmpXl2j78gwNAShbJQ2yXR1Uzaftfjxz1qftvCmLceTyK64ol3MAlNT33+0wLbft3snyN95zQD9TcGnr+k0eQ3fuldIu5O/X/YsGnqYalQqrRZY0dOceiHNvI7h6iOXY60SbPwNyvSZzD5Dlnc7VPT0HlfiY3c+hu7K+djl5/IQyjqQrdd8B0/nOSE0r0/144pyy1imqQT2vn2O6/z2dzT9TZNvOaXRdNU6IN/3tlvc1NSoHQncAAABABXdo/x6PVZ/cQw+R0TG+Nllm3IMQrTt0NYVriuNeRS8uoaUeHPfCRT+OsuQestu3e4fH7TxV4gJKq7hxYPOGNSWqRplz+mSRU496eqy4aT/jmyV6naI5JXm7EciLjI5Rz5sHK7FdZ58ryzm5B8+8VQz1xHUKbXfBoWEl6of7+XGvJijJMpVtaFiNItt0OByaM+M1v4bQfK3oKVkrg57J8z69WmR0jMcPf6kMClQNl18RqIa9x0q9fb+WBCo7h8Ohn3/6yevNz9hrm+gv4x40TQEmnZ+eKyAwwPIc1ynTSsvTzUbXKcqK46k6S/qhIz4/v6x4CtIV2O0ez7t7RZyoBkxpD1xMnio2/bA7pUza9jTV9KXCU5UqT9VGgfK2YuES03JGappmTnrDtM49qLRozkfGNK3lzdNUzJLvIVsnT+E99zY8Bd5qRdb22uaJjOMXdEw/7E7xeu3Xot11Xvtf0pChuwK7Xe9OmVHqdkoj44jb7BotfP/8r6jKe/GJzTz+PRb1+0PVRegOAAAAqGDcQ1vuVaKcfkj53rR8MSvVOV1dzVyFz1tffeVe/aqicg88ui67Vzf0No2weyUuoCwUNQ58/P5MrVm+2Lx9dIzxnPz83IvyGrTZbBo+aqzadOquRfNne32NOKebrl6zVomqy5VUfr75RkRR1SmLCpddLH9/6E5L9bu4hJYKCirsd0Z6qs/Tl18MJalo6u3vl5AyAOBSsnLRMv2wO0V7dyQr73Su4hObafTkCV63b39DZy2b/4lpujJnZQ5PobbSTnPoytPNxpKG5txv6h7cu98ylVeB3a5tGzab1rXq2KbMKrFE1ouyrMs6lunxOPYlmytBR9aPLpM+APBN9qnTljHMvZLRhfI01XRl5Clw7Sl04z5VJVDeCux2y7Ts6YeO+PTaO7h3f7FVfMsiOJtZiV4XwdVDPAb33JXFtNElvX50OBwad9cDpulapcKwWlBI4XTXGalplXbcdR4D4AtCdwAAAEA5Km4aPU/imyVagjGnsjJNFZNOZWW+BoJZAACAAElEQVRWiICap6kS8/NyLZWbDuzbbVRv+n1AoPG4p6kp3Y+1qOf7S1EhEvepIZ39dw0NFdjt2rdnp1+PAVXP1m/XmZaDQ8P04PhJxuvpaFqqXhw/6qL1J6FlGyW0bKMCu13pRw5q67frtPXbdZZA2Yez39KTk6aXqG33KU3dQ8qu3MfSspgWvLycysr0GLhzrRD62X/nlXvozr16XVHjmftj7s8FAKCqOZt/xhRC27N1l75Ztc4SRHP6ZtU6yw3L+o3PV4GNalDP9HjqvgNq3jbRtP3J4yf0w+4URTWop8Arr1RAYIDHwJ4n7qG5Hd9+Z+lrfk6uvt+6UzUiwhVWo7qpffep1lJ2fC+Hw2EK9LlXSQmuHuL1fEieQzlFCQq13jhN2bnbErpzOByWsIC3ijhO3irmAfCN+xi2ee0GNWwSa4wRBXa7Nn+1wd/dNPH3dIJBoSEKrh5iCrt8vWK12nTtaJw3h8OhVYs/92s/UfV8vXz1BT931eLPiw3deQr+nzxesi/puVdGcyqrcG9peKqiNmL8wxdl3yWdJvvYkXRL4K5t906m/s6b/k65h+4i60WZrt1Sdnj//M+9Ip+3KoBASRG6AwAAANyUtAJZw9h4r9MvFjWNnjexTZpZ1k145G5165WkG/5wi779eqWWLpjr79MkSaobXV+R0TGmgMfUSeN118ixqvtbIO/Avt16feIY4/HI6BiNe/5N2Ww21QiPsDx/xuvP6k/DHzBCakfTUk3PDw4N0/P/eO+iTDngq/CISONnT4Gd1yeOUesOXfXHO0Zo3cqlFeb3h6ojPy/XEta698EnTAHW/Xt2lbTZMhEQGKhGjZuqUeOmGnTHCJ3KytSrzz5m9Dcj7bDHMG5R3Keu9hZSPpWVaVnnKThbURzcb668EhwaphEPP2Vat23T16Zl90p+ZcF1zJOkvJxsjzecC+x2y9+d+3NdEUYGAFQF3fv11KI5H5nW/WvyVH29YrWu79ld1yY2V4G9QNmnTutUZpZlallJauASurv9wb9o8iPnrwecbff6480KCAxUgd2uFx5+wnRj9NmZr/ocuuv7p1tNNymdP9/1yH0KCAyUw+HQq49PNN1UHTflOaP963t115L/LDD2n37oiKY8+YJGjHtIQaEh2v/9XstN0O79epmW3cMli97/qMjqgO4CAgM1ZNTdmjftHWPdvGnv6IfdKRpy/3BdFVRNm9du0Px/vmfZ7/W9uhfZdn5OngJqE7oDLlSXPjeaXpsrP1mmjCPpuuvh+/TD7hSPY6C/HU0t2xDJhYR3u/frZfq/ZM/WXRo9dIRu++udkqT5/3zPEogByttXn31pWo5qUM9rxdj83DxTWGrT6vXGtYU3nqrlFjf9tHv43z1c7+Qe1vLH9PLu+8w7nVthw/0pO3ebloOrh+jesX8zrbsYgemGTWK1UsuM5fRDRyxf7pA8T7d9VbWrL+YpwyWM0B0AAADgZsvGtaawRnHuGjmmRGGQ4gQEBqp1h66WPqxZvthUAS8uoWWFqHb3p+EPmEJxGWmH9eL4UYpLaKn83GxLxaUHx08yvfG979GnNeGRu03Pf33iGEVGxxjLrkY//VqFCty5s9ls6jtwqCVY5/53FRwaZgmjAOXlxPGjlnVR9RoaPzscDu3c+k2596PAbtfOrd8oKzNDmRnp6nJTP8vUsTXCI9Qr6TZ9PGeGsS4n+1SJxllPwbnk7ZuV0LKNad0uDyHrWrXrlvt5uFDuU1tXCwo2ffian5drGTN9+X+ipKHGJgmtLOt2bv3GUtnV09+UpwqnToyJAICqICg0RP2HDbYE7/Zs3eX1RrCrIaPuNgXmYq9tYqkUtWjOR1o05yPLeufzSzI9bFSDGEu1u02r12vT6vWKT2xm6XP/YYNNlWoCAgN121/vNAVn9mzdpYcH3+txf8HVQ9S9X0/TujZdOmrlJ8tMz7+n5yBJ0ktzpvlU9a5bv56m8J/rcbiH+pxu++udFfJGN3Apadu1oyl0JxW+xh8fdr4Ku6cx05+atEiwhIVL40LCu54C3Hmnc01jbXD1EAWHhFTa6R1RueTn5Fr+1ka/NKHIkP/TIx4zPSdl525TtV73wNzmrzao54B+xv/7+Tm5Wjb/kyL7Ve+aBpZ1KxctU7d+PWWz2eRwOLR57QZL3/0xvXx4HevnMkcOpFoqAC7+4GNJ5yvjterYpsTXKyUdw3Z8+5169O9tLLtP01u3fj3T5/Unj58ocfDXl+ClO09T637/3Q5L1efvt1q/5On6JRagNC7zdwcAAKgqCux2ncrKNP7lX0D1q6rcP6CqGXbfY4pLaOn18biElhp6z4P+7qYkqVHjprpr5BjL+pTk7Zbwx/2jn7FMDVsjPMLj8zPSDnt8flkGHMtLr6Tbiv39PfD48/7uJqqQRo2bKjg0zLRu8cfvKT8vV5s3rNHfH7rTYzjLvbJaaaUfOah3p7+ipQvmasvGtZo19UUdTUuVw+EwtjmVlanli+ebnucaEPT1eN1fg2+/+oxmT3tZ+Xm5OrBvt14YP9IU7JOkvgOH+n366qK0aN3RtJyRdljJ2zcrPy9Xn/13nsaNHOLxea7XddWCrMe36vNPjGtAXwQFh6h1h66mde9Of0Wzp71stDN18pN6d/orpm1ad+haKcZwAADKW9LtgzRuynMKrl6y644ho+423fB0euIfLyg+0Vox3f0GctvunTw+vzh3PXKf2nbvZFnvHriLT2ymvn8eYNmuTdeO6j9scLH7Ca4eookzXrPcoO/Su0eJ++zOZrPpyTde9Fi5xtNN4b+Me7DIKW4BlI2g0BCP44uTt3GlqgsKDdG4Kc95fdw5nvojOISqafWSFablqAb1iq2q26XPjablFQuXmJbbdjV/BpJ3OlePDxullYuWaeWiZXp48L3FhkobNom1XG/Nm/aORg8doW9WrdPooSMqTEXNgMBAy/XS5Eee0rzp76jAbleB3a6Vi5YZX65wVkoujy8IuF9XLvnPAp08fkInj5+Qw+FQG7ffzZ6tu7T/+73Kz8nV4g8+NgWnnTatXq8Cu93leAMs2yx4Z56xH1/EXtvE8n/IP/4+STMnvaGTx09o//d79fSIxyy/4x639Pa56jNQHCrdAQBwkezc+o3pxmPrDl01fNRYf3er0vQPqCwaxMaXqEqeNzabTaPGTNTWb9cpedsmbdm4VsGhYUps11nxzRIV3yxRxzPS/H24hjYdu6lhbLwWf/Sex+OPjI4xTRnr6flNElrp/96feUHPr2hsNpseHPeCkrdv1ub1q40pE11/fzmnT/q7m6hiOvfoY6rA6F49Mzg0TEPvfUhvv/qMse7d6a9o4bxZeuLFaWXSh0aNm5qmlM7LydaL40cZ+/dU6SwuoeUFfYA49J4HTVU0paIrmQaHhqlX0m1ldLbLR93o+pbz5Pr7cp6va+KuNf2ux40cosjoGD05aboCAgMt03q7/i1M+2CpT30Zcs+DlnNZXKXYIW5h8SuvYioPAEDVFXttE706d6a+/26HNq78ymvFkagG9XRd5/bGdLGeBAQGavTkCfpm1Trt+PY7S1vB1UN021/vtNwg9VVAYKBGjH9YLdpdV2z7nqqS22w2Jd0+SPGtmmnV4s89HuuQUXcbVWes5yBGL82ZZpkmNz6xmccbtt7UrF1LE6a/rM1rN+jrFastocHg6iFq06Wjbh4ykBuxwEV079i/KSgk2FTRMqpBPXXpc6Ou79W9Qs924E+x1zbRGx/N0vdbd2rHt99p745ktenSUQmtWyqueVMFBAYqI7XifHaIS9vqJctNy+6BOk/cK13u2bpL+Tm5xv/BQaEhHqv2uj7HGbjydh1ls9nU788DLRU13StDVpSKmr3+eLOlHys/WWYaH52Cq4fob8+Uzz28Vh3amK6TnIFHqbDKsDPM6HpdNvmRp0xtxCc2U+OEeNPxjEy6Qz1u6a0hIwsrN7u34Xqs/17xsU99HXL/cMvv31nN2JPg6iG6bcSd5XLeUDURugMAVAr5ebkeK3c8OuGVShPAAFBx+Row8EW3njerW8+bi91u+KixxQZbbTab2nTspjYdu3ncNiMttcjnO597oY+XdLsa4RHGceXn5f4/e/ce39L9/wH8NWGNS9KLVqm2FL2oopjSmmtdVjM2vpvvamPMZi5fY8P47cs2trluzOa2mY2pjX0ZHTrXuoxqUaXVK6pNtWi1TeKSjPD7I3L0NEmb3qTl9Xw8vo9vcs7nnPM5J5GlySvvN/7R6n+51kBma1FgRia3Ldf2llzLyr4mlh7Tz7+zUTtLg5wrxdoWuFne4omoPLo8H2zU9thAbmeP//tyBWRyW7w68j1RBThVYYHw77EyzPz8W6xYPMeosp65wN3E6XPLdZyGTs6Yu3QdVn/9mVHVzOKeC+yJ0Lcn14gvc4q33jV1vSQSCS6knBdd46LXoHhb8PKwkUqxYOUm/LRyUaktbL39/DF6wgyj1/ImTd1BRET0NJNIJGgX0BHtAjpi3KwpUBcqodVohfX2Tg3L9P6ka5/u6Nqnu2hfpbVeNWxT3v2XZY6ebXzg2canXNs7Nm6Epb+tBaBvJ1dP1kC03bhZUzBu1hSLrnnRc7b0OhnmYMkXwJaOI3pSleV1BdD/uwydMAahE8ZAXajEs1Ibo78dSvo3Zem/udLmZenrSEnjSptLZb+OyOxsSzwvtpalxyErPcOoamyABUF/U6G6mMPHRVV5h709At/8d77J7eUOthg74z9Yu+jbEo8TPCQEt9Q3zYbq3pk5Ga18vatF6M5GKsWyLWuxZsE3Rj8OKO7jZV9WSZU7AHh+QG/s/HWr2RaxEokEvQcNMHvNWndsi6lffAwAOH30hOgxVitVwu3h746qcKVBmZ0tFm5YgW8/WVjqa15A724IHT+6Rnz+RzUHQ3dERFQjHD1gOhBzZN9Ohu6I6Imj0+lQmJ8ntHVMOBOD0LcnG/0RHXVkn7Wnapa+PWP5KwNUdHtru5F7DTlXMnHn9i1cvpiCF4b826hlZVJ8rOi+i2tza0+bqgkPz9Yl3i/KwbGRqM1nSWMbOjlj+fpwJMXH4uBf25GSEIfnAnuic7feaNbCS3iOdg8eiNxr2Wje0lvYtoHMFv/8ozVqKWrKszbSEscZKkFeUVzG2VPHcfTAblHgzlDVM6jXADR1a27RNXrWxvSHjA2dnDHz82+RFB+Lk8cioVYrhYDYc4E90UBui45dupt9P1mW61vaeVfWmF79X0Knrj1w+sQRHIuMwE21Ch27dEfHLt3RvKW38MHh6Akz8NeO30SPo0FLL1/MXboO4VvWIzvrMrIVGfD280e7jl2FMcXnYOoay+S2QlXPpPhY5FzJFK6vt58/mjR1L/H6WnI9yvo4EBER1WQyO1vIquG+qmL/Fdm+MqvQVfV1IqKyYZVJy+RdvY4LiSm4pb6JC4kpaOXrbdQ+PO18srWnSU+JrPRMUYvPJm5NLf63POztEYg6cES4f0t9U7S+XUBHfLZmCXb99geSzyZAla9E8MshQkVHiUSCVr7izz1MVcEd/Mar6D2oP9JTLyLhVBwayGVo3aEt3Fs2h41UWuq/l4bOTqJzLH7Myhwjs7PFtAVzcC4mFgmn4pBy9rwQJjNUjzNXAbn4/ho6O5k8n+CXQ0Tht+LjbKRSLAlbg12/bkOO4gpiIo/B1cMd3u3bQGYnF13TmMPHcWT3fqiUSvQeNACtO7RFCx9P4fOp/3z2EY7vP4xGLo2NjtW1T3e08vXG1p82IfuyQnguFT2P4u1jTT2+jo0b4bM1XwnXLDszSwgtBvTuBpmtHJ17BsGzjU+5H9+yjKOnC0N3RERUIxw9sNvk8lNRh00GUajsLK3uRESPR/GWiAAw8r0PhT9WD+3906iyUI9+g6w9bXpoyWcfikJEOVcyMW7KbOG/VwlxJ0VtPQHAr0OAtadN1YSlFTMBfXiqLD9AkEgkJVZgNIx59c1xRsttpFKLKjwaqlaWpqlbczR1a46Br4SWOrYi18iSczanLNfXkvOuzDGlXQOZ3Nbk42hgqE5qjqUVRAGU+/pa+lwx9TjcyL1W5uMRERERERE9CS4kpoiqQ8VEHoN7Kw8hUJJ39TpWzlsi2iagdzeLqnoSlVVZq1sWZaj4WxJXj2YlVqEMHhJiFDo1RWZna/Z4mRfSS9zWUK33cYwpy7Up77UInTCm1DESiQSD33gVAMxef5mdbanHdGzcSNiPufUlPb6WVCCtyDUDLH9cyvL40dODoTsiIqr2rigui4ILLm7NRO2x0pLjy/UlHxFRdSWRSIzaO56KOoxTUYfxXGBPnIo6bLSNt58/K39WI0NDx+LnlYuF+ykJcfhg7DB4+/kjJyvDqI2mt58/g89ERERERERERFSqDkGdIXewFbV+XDB1NgAYLTcsGza67D94I6rptBoN/t4TiQuJKVArVUiKjcfCDStEAVSdTocju/eLtpPZyq09dSKqIRi6IyKiau/sqeOi+/8ePQlfz50u3N+x+acSQ3dajQbnYk8I9x0cGwnBFK1Gg7TkeCTFx+KmSgm/DgHw8etg1AKwuCuKy0hLikf6w9aPgL5CUQvP1mjo5Fym8ys+PwBo17Gr2ep9F1MTkZ93Xbjv8rBKTPExKefjcC07CwDg7OIK7zb+cHVvYXa/N3KvCa0si1+n0s7fsP9GjZuWeu2IyDK9+r8Et+Yt8dtP34mCxsUDd3I7e4wY+z7Dx9VM56BeaOHZGqu//kz0+BWvTii3s8fQ0LHo2KV8vwYlIiIiIiIiIqKni6H148nDx0UV7wAYBe6GjHwNL74+VOieQfQ0sZFKcWT3fqE9KwB8+8lCvDH5HbTw8UROZhZ2/faHaD0A9B/KjjJEZBmG7oiIqFrT6XTYtTVMtKx5S2/0GjBYaMuXrciAWqU0G/a6qVaKqg25uDXDx/NXYvcfm4z2fSrqMOR29hg7+f9MBs50Oh0W/Pc/ogBF0W0B4LnAnmVqeVu7Th1s27RWVPVo/LRPTQZodDqdKHAIAB/MeXRuapUSy+fPMjk/w7maO79LaUmi6/RcYE+jMZbunwEgosrR0ssXMz//FrHRR0X/Pg1eHfkeugcP5Idm1VRDJ2d8PH8lTh4/ZPQ6DwAvDhuB4JChbJFORERERERERERlIpFI0LVPd3QI6oyt6zZBrVSJ1sts5XgpdBhkdvyRPD3d3pj8DlbOWyIEUrPSM4XKkKa8M3MyWzETkcUYuiMiomrt8sUU0f1eAwZDIpGgY5fuQugOAE6fOIJe/V+yaJ/ZigwkxJ00CtwZqAoL8NtP3+Hj+SuN1m3btNZk4KyoU1GHoVYrMXnmFxbNRyKRoHvwQNF8Dv613WRorfj1kNvZC8E4tUqJL/9volGow9T5fT13Ot6aML1MrQy1Go3F+1+15FO8OvI9ix8TIjJPIpGgc1Avth6twfj4ERERERERERFRVbCRShE6YYy1p0FUbXm28cGSsDXY9es27Niwxey4gN7d8NbU9/gDaSIqE4buiIioWjuyb6fovqH9XvOW3qLle8I3lyngtWrJpwAAbz9/yGS2Ru0aTVXPu5F7TRT0A/QV6Zq18MJfO34TrUtJiMPF1ESz7VmL6x78oih0l5IQB61GY/TmPjb6aLHtBgq3T584IgrEubg1w3sffAI7B0cU5uch+u8DomNs27QWHbt0t7hC1rnYE6L9y+3sMe2Tr9BAZot//tHi9Ikj+H3DamH97xtWswIXERERERERERERERERWY1EIsHgN15FUN+euJCYYrS+la83q9sRUbkwdEdERNWWVqMxCsMZwnYSiUTUYlZVWIAristo6tbcon0Xb7Haf/Br+G7hf0WhsuLV81Z//ZloHy8OGyFUo3v1zXG4qVKK5qu4fNHi0J1MbgtvP3+kJMQJy9KS40XV7nQ6nVHor3vwi8Lt9LQk0bohw0ejoZMzAH2Lw4GvhOJadhaysy7DxbU5Gshtce/uXYtDccWr7A0NHSvs30YqRa/+LyH3WjZSE88K+79966bZtr9EREREREREREREREREj4Nj40YM1xFRpWLojoiIqq205HjRfUNrWYPiLWbPnjpuceiue/BAUSCuqVtzDBg8XFSp7VhkhCh0Z2g3q1Yp8Y9WgwYycZjMr0OAKHSXnpZUpup7Lw4dIQrdFW8xezVbIRrv4tZMFGhrUCzctmrJp/hgzmLReY6eOKPcj0f9BjLR/Z9XLoaDYyPR/l99c1y5909ERERERPQ4PbivQ2LYf6DJy4D38MVo4GL8o6k7eZdRq05d4b6NrbO1p20VlyIWoSD5MPzGrLP6NdAqryFhnb6FWqepu6x9aYiIiIiIiIjoKcXQHRERVVs7Nv8kuh/Ua4DofvEWs0cP7MaAwcMtqtzm3cbfaJljo8ai+9mKDJPbyuS20GpskJd7FdmKy8LyhDMxFTrf5i29IbezF6rtFW8xe/bUcdH4IcNHi+4XDyECwNdzp+O5wJ7w8GwNz9Zt0djFrdztXts/FyRqT2vYv7efP9p17Aq35i3h6t7CqCUuERERERFRdZR7bjc0eRmw9+kpCtzdva1EQeoR5MRsxr1bBUbbufV+D07tBuKZWuX724oq5lmZo7WnQERERERERETE0B0REVVPapXSKPSWrbgsCrkB+mpvhnGqwgJcvphiUUvXRo2bGi1r0tS91O0S4k5i1ZJPq+ScJRIJugcPFAXbiraYPXpgt2i8p09b0f3mLb1F18PgVNRhUQW+F4eNQJfng4XWsJZq6tbc5P5TEuJEFfp6DRiMoF4DLK46SERERERE9LhpldegiFyN2vXt4d53smhd6tZZ0OTp/+6x9+kJO48A3NOocTMnCQXJh6GIXI3c+Ah4DZuPOvVsy3P4Gset5zg0DRpVLQJvDDsSERERERERUXXA0F0lUalUiE9IBAB0C+pq7ekQEdV4p08cMVr288rFpW4XG33UotCdTF72L0Z+/2WNUSW5ytY9+EVR6C4pPhZ+/p2hVimFCniAPthWvKKcRCLBzM+/xdEDu0VtcovbtTUMu7aGYfy0T0Xtay1hyf4P7QnHoT3heHXke2Vqr0tERERERPQ4PLivw4XwzwAAzfq9j9p1Hv1tdTM7UQjcFW+l2sj/JdztOQ7n1oRCk5cBxeE1aBEyw9qn81jow4VPR8CQiIiIiIiIiMgSDN1VkhWrvkdCQiImjH/X2lMxkqnIQvifuwAAkyaMK3GsTqfDhYuXEB19Eiq1Gh3828PLsxWcnEz/ilWj1eL06TO4ePESVGo1XJo0Rps2vmjVskW52xca9rv2x58BACPfeB1yudxojCHoeCbuLADAy7MVfHy84e7mana/KpUKJ6JPIjXtAlyaNEYLDw+0aNHc5P6JyLr2hG8u13aH9oRj8KujKr3FqVqlNArcye3sMe2Tr4SKcYf2/lliGM0SMrktvP38hcpxh/aEY2joWCQnnBGN69ilu8ntJRIJevV/CYE9+iEr8xJSzsfh6IHdosCewY7NP5U5dGfYf/fggbh8MQUp5+NwJuZvk61494RvZuiOiIiIiIiqnYLUo0JbWTsP8d9EGQe+AwA0CRwhCtwZ1KlnC+/hi1GQehT1G3sLy7XKa7iVk4Rn5Y1ErWoN8pMPAQDkLbsKIT/DMgefXtAqr0GZHoObOUmoU9cWsmYdYduso8mqbnfyLkOdFY+bOUkAgAZNWuNZ28ZG53LvrgaqiyfwrLwR6jf2xq2rKbh9/SKelTvj/j+3hWObcifvMu7kXRbO52Z2Iv5RXRfNv+gxCtNjAABSe1fI3f1Rv7G3ybnfzE7E7esXRXOXubZFXcfmJufx4L4OBalHUZgeU+pYIiIiIiIiIqLHiaG7SnDs+AkkJCTCz8+3WlW50+l0OHDwENZveFQxqaTQ3YZfNmHP3v2iZVFR0QCACePfNTo3lUqFjz6eDVWhWrR867Yd8PPzxYxpU8sdvFv748/CsYe/OsxofUpqGubOm29yrsOGDsHQV4YYzfXLBUugUGQZ7UtuJ8PSJYsgtbGpgkeBiMrjiuKyyZCYpYq2ZK0sGZdSjZZN+uhzUYvWc7EnKuVYLw4dIWrXevliChLOxAj35Xb2pVbzs5FK0dLLFy29fDHwlVCoVUokJ5wRVQvMVmTgiuJyudrASiQS0f61Gg3SkuMRtvYb4bFTFRbgYmqiRZUHiYiIiIiIHpfchH0AAAef3kbr7D2fR05eBnLP7YZTuxdNto9t4OJrFKy7lZOE9IjFsPfpaTJ0lx6h/1vMb8w61LaVipZpCrORExUmGn/9TDgauPnDa+hcUXjtetyfUESKf+xVkHxYP3efnvAY8KEwXndbKcxJmhknHKNZv/eRse8bAEBdx+YmQ2yXIhZBk5cBt97voYGLL66f3YmC5MOi+ecnHxLOoaicqDA0cPOH97++EJY9uK9D1pG1uH4m3OTc3Xq/h0b+4h9t5ScfguLIWty7VSAa6xEyvVKfD0RERERERERE5cHQXQWpVCqsXPU9AGDsmLesPR3RvAzV99zcXE2GzYrKzc0TAnejRo5A1y76sMqJ6JNYvyEMK1d9j7Z+vkJFOI1WKwTuBvTvi5eHDALwqOJfQkIiDhw8hP79gss892PHTwgBOnNzNQTuDHPVav/BV0uXQ6HIwtZtO+Ds7CwKCW7Y+CsUiiz4+fli6CtD0KyZOzIyMrHtjx1ISEjEli1bMfLNUGs/bET00NlTx0X3vf38MXnmF2bHF68wd/Cv7ZUeukuKjxXdfy6wpyisptPpREG5imje0htyO3shvBYbfRSnog4L67sHDzS5nVqlxD9aDS6lJaGFZ2tRIFAmt0XnoF4AxG1605LiLQ7daTUa3FQrcSktCS5uzUXb2Uil8PPvjEkffY4vZ00Ullva7peIiIiIiOhxuHdXg5uKOABAA9e2RusbdRqKnKgw3LtVgMSNE9Fm9FpRZbeqkBMVBqljMzQLngQbu6ZQHF6DguTDuKmIQ0HqUaEanVZ5TQjcNQkcIYQCC9NPImPfNyhIPgypvStcuoo/41IrzqEg+fDDyn4BkLl3QKMOg3H9TDgKLhw3Ct3dva0UWuw6tOlncs4P7uuEwJ1HyHTYe3XHM7Uk0Cqv4UL4Z7ipiEN+8iFh7gWpR4XAXcuXP0UD17aQSOog99xuKCJXQxG5GvUatRQCizezE4X92/v0RNOgUQCAK8fXmwz6ERERERERERE9brWsPYGabsXDwN2okSPMtmA1UKlUUKlUj2Ve8Q+DbwP698UX8z4pdfzadT8D0FeJ698vGHK5HHK5HP37BcPPz1fYp0FyUgpUhWr4+fli5JuhwvhZH03DqJEjAADrN4RBp9OVad6GEKPcTiYct7ijfx8DAAzo31eYq5OTI76Y9wkG9O8LAPhz525hfG5unhDimzrlP/D28oTUxgbeXp5CULJ4hT8ish6dToddW8W/8O/zwsslbtOpaw/R/ZSEOKhVykqdV/F2rqeiDkOr0QAALqYmYsF//2O0TWrSuTK/DgL6KnJFg3XF29p2D37RaJsrisuYOSEUc6aOwc8rF2PJZx8K8yvq8sUU0X235i0tmpNapcQHY4cJ+/9u4X9NXuPjh/aI7rdu27FiF56IiIiIiKgSaXIvAQCkjs1Mhulq15EKldTu3SrA2e+G4ez3b0BxaA0K00/i3l1NmY5niQZu/vAd8S0auPiiTj1btAiZgSaB+s/Xck5uEcZdOb4eANCow2C4dA0VqvDZeXSG19DPAQC553bjwX3x36H3bhWgUYfBaBEyAw4+vVCnni3svboL44srSD0izMtc4PAfdZ7+etW3FwJ3AGBj6wyvYfPhETIdz8obAdAH9BRH1gLQB+7sPDqjdh0pnqklQSP/l+DW+72H5xdWZA5HAegDdy1CZsDG1hk2ts5oETIDjToMrvTHgIiIiIiIiIiorFjprgJUKhUSHgbRgvv0Eq37buUaREVFY8L4dwEAG3/9VWjD6ubmin+/9i+0bdtGaL+qUqmg1f5j0XEdHOxLbdtav149zP9yLtzdXEvdn06nQ4/uz6NH9+fRqVMHo/U9uj8vnKdBxJ69AICQAf2NxvfvF4w/wsOhKlTjwsVL8PbytPiaGkKMU/4zCfv2HzQ5Zt9B/fKQF8THlkgkGPlmKPbs3Q+FIgsqlQpyuRw2Ns9iwvh34ejY0KiFrLmg5LHjJ4QKhmG/rLN4/kRUccVDYQDg6dO2xG1kclu4uDVDtiJDWHb6xBH06v8SKkvzlt5Gyz4YO0x0XLmdPZq4NhMq3qkKC7Bh9Vfw6xAgVJmzVPfgF43ChwDg4tYMMrlxe6Ombs1Fc1EVFuCDscPwXGBPOLu4ol59GY5FRoiuEQC4urewaD4yuS28/fxF5zZzQqho/+diTxhV+2vWwqvSHgMiIiIiIqKK+kd1HQBMtlQ1sPfqjmfljVCQehT5qUdx71YBrp8JFyq12fv0RKP2g0y2kS0P+1ZdRS1kAaCes/7zNEPFOeBRe1VHvwFG+zCcz71bBfhHnQcbW2fR+uLb1G/sjdr17XHvVgHu5F0WXY/c+AgAQNOgEWbn/KzMUdg+ddscURvcOvVshQp3gD6gZ2gRa6q6oK1HABSRq4UKhACEa23nEWA0vn5j47/PiYiIiIiIiIgeN4buKuBE9EkA+opr5kJwR47+jYSERMjtZBg2dAhiTp6GQpGFxV8tE4XiNmz8tcSWqkUt+3pRqVX1/P3bWXweEolE1IrV1DkA+iCfgSGE17Spi8ltAgO6YM/e/cjLu2Fx6O7Y8RNISEhEYGAXeHt5mgzdabRaIbzo4GBvcj9+fr5ISEhEztVrQgU+c+eXm5tn8XUiosfjyL6dovu9BgyGjbT0Vj79X3pN1Db1WGREpYbuJBIJeg0YbFR1rmjg7v++XIFnn7XBp9PGCq1hT0Udxqmow2UO3RUPuRkMGT7a7DaTZ83H8vmzRMG6om1pi/tgzmKLrq3B6Akz8NPKRaI5lbZ/UwFBIiIiIiKi6uyZWhI0cPFFAxdfuPUaB63yGm7lJKEwPQYFyYeF//mNWWcUbiuPZ20bGy2r6+Au3C5eXS/z0A+oU9/4by1DCO7+3TvG+ysWMnymlgRO7QYiJypM1GK2aGvZksJtz9SSwK3HWKRHLMZNRRxivxmMBm7+cPLrB3nLrqIKeUXnk7l/eYnX4u5tpVDBz9S8AaB+k9bCba3yWqU8BkREREREREREZcXQXQUcjNQHDdq19TM7JiEhEaNGjkBwn16QSCQY+soQoQreqVOnhdCdl2cri49rY/PsYztHjVYrhAZ9Wht/0GYu+NaypWWVkwyKtpUd+/ZbZsepVWrhtrmgo0wmAwCLAn+GVrWGlrgGXp6thCqFRPT4aDUaoxBX8bau5rTrKA7XZisycEVxGU3dmlfa/Aa/Ogqt23bEjs0/icJ2Hbt0xwtD/i0EzEaMfR+rlnwqbOft51+u4/V54WWj0F1JVf9kclvM/PxbHD2w22RVO4MXh41Al+eD0dCpbF9MyOS2mDh9LmKjj2Lvn1vM7r/XgMHo88LLZd4/ERERERFRVdMUZgMApPald4cwMLQ2dfDphXt9J+P8T2Nx71YBrhxfjxYhMyo8p6IBO1N0t5Wi+0UrwplSvHKdOfatgpATFYbcc7vh0jUUwKPWsk0CRxhV3yvOwaeXUBHw+plw3FTECXOz9+kJjwEf4plaEtzJuyxsY6jWZ879uxoAj0J3terUrfD1JSIiIiIiIiKqCgzdVYBCkQXAfLU3g/79gkX3O/i3R1RUNPYdPIihrwwRxhQfVx3s3v0XAGDc228LrVlVKpWwvrQ2t2fizpZYRQ/Qt7c1tJUtepyqptFqse/gQcjtZEbtgZ2cHEutJkhElc9GKsWKjbsqfduGTs4W7be0cTZSKfz8O8PPvzPUKiX+0WpMBsv8/Dub3E/noF5lqnh38lik6L4lVf8kEgl69X8Jvfq/JMyxsOAGpHXrQSqtCzsHR7Ov3ZbMTyKRCOPKun8iIiIiIiJrk9rpP8fTFGSVa/vadaRo1u99XNz+KQqSD0MbNKrUSmsP7utKXH8nP7PEfdjYOov24fvmCotCdaWp69gcUsdm0ORlCEE9Q2tZ+1ZBFu3DUBHQtcdY3LqaIgTwCpIPw8GnN+w8Oosq07WftFVUBa80d2/dMLo2d2/dqPC5ExERERERERFVFEN3VWxA/75GywxV7QxtUqsjnU6HHeE7sXXbDgQGdilTu9qyOnDwEBISEjGgf98qPU5RKpUKH308G6pCNeZ/OZcBESIqM31Vu6prnXpFcdmo6l+fF14u1xyrquJcVe+fiIiIiIjocbqTdxk5J7fgTt5l+I741mylt6KV6QytU2vZ1AcAqBXnjMbfuppS4nFvX0uDnUdn8VzyM0X3i85FnRVfKaE7AHBqGwJF5GoUXDgOANDkZUDq2KzM+y/akvfuHSUKkg/jyt8/GZ2XJvcSGrj4lro/Q6vcf1TXgWLj/1Fdr5RzJyIiIiIiIiKqiFrWnkBNlZubJ9wuqSJaWdusVgcarRaLlizF1m07MGzoEEyaME60Xi6XC7d1upJ/qdvBv32J63Nz87B+QxjkdjKMCB3+WM4vJTUN4ydOAQDM/3Ku0OKXiMjarigu4+TxQ9j9xyZ8t/C/onUubs0YbiMiIiIiIqqgZ+WNAAB3bymN1tWuZw+14hw0eRnIOrLW7D4u71sOAKJwWgPXtgCAe7cKcLdYO9iC1KMlzkmddV5Uye7BfR3UGbEA9G1aDQy3Cy6cMKqed/e2Ein/+xjX4/7Evbsai6+HvVcPAEDuud1C8M6pbUip212P+xPnf5mAwvSTRusatR8kum9j6wypYzMAgCozzmj8zexEpPzvY+QnHxKWOXh1188rYZ/oXO/d1SDn5BaLz4+IiIiIiIiIqKqw0l01sXffAaSmXbBo7Mg3XhcF3ypT0Qpww4YOEdrfmpOfX2AydHgm7qxFx/tqqf5DylcGD0Z+foFonVqtrwR45Uo2AEAml0Eml5W6T8N2jo4NjdZt+2MHtm7bAbmdDAu/mFdl15GIqDy+nDXR7LrJs+Zbe3pEREREREQ1ntRJ/wPZm4o43LurEbU6rVPPVmgde/1MOG7nZcK+VVfIHgbq1FnxKLhwAjcVcQCAJp1fE7atXUeKBm7+uKmIQ+rWWWj6/GjUtqmPjAPf4d4dVYlzuqmIw7m1o+DWYyyelTdCxoHvoMnLACAOsLn3nYyC5MOi8fWbtMad/Exc+fsnaPIyIHNtU6b2rXXq2QrzzokKAwA4tOlX6nb2Xj2giFyNi9s/hVvv92DrEYBadaTQFl7BxZ1fAgCaBU8SxjcLnoSUzdORExWGgrS/0Sx4EurUb4hbOUlQHFmLe7cK0DRohDDe0W8Arp8JF861Wb/3cV97C+kRi1G7vv1jfMYQEREREREREZnG0F05FQ2a5ebmlVjtzhKpaRcQFRVt0djhrw6rknNKSU3Dsm+/g6pQjTmzZ8Hby9PsWDc3VygUWcgvMB26O5+UCMB08K0ohSILALB+QxjWbwgzOWbxV8sAABPGv4tuQV2F5aauu06nQ0KC/thNGjuLlodt2ow9e/fDz88XU6f8B1Ibmyq5jkREle2DOYsftnIlIiIiIiKiiqhdRwqpYzNo8jJwMyveqP2pnUdnuPV+Dzkxm3FTEScE7IqSOjZDs+BJRm1SmwaNwMWdGdDkZeDi9k+F5d7DF4uCdMW1fPlTXNz+KdIjFouWNwkcITpG7TpS+L65Aqnb/ot7twqMxjdw84dL19AyXxPnTi8L59nAzd+i0F6derbCvBWRq6GIXG00l6Jzb+DiK4zX5GUgZfP0Es+1rmNzNAkcgZyoMNy7VSC6nl5DP0fiL+Z/tEZERERERERE9DgwdFdNjHzjdYvDdA4Olf9rTo1Wi7nz9FWUJox/t8TAHQD06d0T6zeEYd/+g2jVsgUkEomwLlORBVWhvtpcq1La604Y/67ZdX/u3A2FIgvDhg6Bs7MzvDxbAQAG9O+LPXv3I+KvvRgROlx07BPR+pYWbm6uoip2O8J3Ys/e/ZDbyRi4I6Jq67nAnkhNOgdVYQHkdvYYMHg4Anv0g43U8ioFREREREREVLImnV9DesRi5CdHGoXuAKCR/0to5P8S7uRdxp28y6J1z8obGYXtDBq4+KL9uxtxJ+8yCi4ch9zdH1KnFqhdR4pmwZPwj+o6JPWMf1BV18Ed7SdthSb3ElSZcZDauaB+k9awsXU2HuvYHO3GrocmX4E7eZehKcwWHacoST1beIRMR2kauLYVxtVv0trkmEbtB8HOI0A0fzuPzuj4frgwl1tXUyBr1hENXNuaDO7ZeXQWzvP29YsAAJlrW0gd3PBMLYnReJeuoWjYOhi3cpJwT6NGvUYtYWPXFHWKnJep60lERERERERE9DgwdFcBhmpvV65kV7jSnbXbnJ4+fQaAPtBWtJqcOV27dMb6DWGIiorG+aREzJoxHU1dmgjV5Az7KhqIM7TQ9fJshf79ggGgxGOdiTsLhSIL3Z/vJrq+vXr1wJ69+7Fn734kJiXjw6mTIZPLsPbHn4VqgX169xTta9/BgwCAuXNmWxS4y83NE9r9WnI9iIgqw+iJM6w9BSIiIiIioieevVd35JzcgoLkwyj06W0yeAfoA251HZuXef+mtmvg4guYCesBD9vTuviaDfQV9UwtiUVzq11HCgefXqXuz5Jx5uZfdC6WHsvS8wQAG1tnk+FDS45FRERERERERFSVGLqrAEO1t3PxCfD3b2ft6YiMeHNMqcsDA7tg0oRxAPRV5QAIYTZzwn5ZB0AfEpwzexbmzpsPVaEas/5vjmicn58vRoQOFy0r2kLXELorD3c3VwwbOgRbt+2AQpGFKR/MgNxOJlTX8/PzRXCfXsL4opX3pnxgPtBS9Hqkpl3AylXfA2DojoiIiIiIiIjoSfJMLQlaDf4ECevGIGPfN2gweq1FLVWJiIiIiIiIiIgMall7AjVZB//2AFBiSK0mUKlUUCiyyrydt5cn5n85F4GBXYRlbg8DcTOmTRVVuatsQ18Zggnj34Wfn/5XsapCNfz8fE0e+9Sp0+U+jtxOVmXnQERERERERERE1mFj64wmgSNw71YBMvcvt/Z0iIiIiIiIiIiohmGluwpwcnKEn58vEhIScez4CVFFtEkTxglV00xtZ6gYV1XKsn+5XF7u+bi7uQrnqtFqS2zdOmnCOJxPSoRLk8YW7bukawjoK9AZrrlOpzMb8hv6yhAMfWVImc6rW1BXHDn6N2Qyhu6IiIiIiIiIiJ5ETQKGI/fcbhQkH8adzq+Vq5UsET09zsXE4pv/zoerhzs+W/MVACDv6nUAgL1Twwr/CD3tfDI2Lv8BANBjYF8EDwmx9ilXubyr1/HRyIkAgB/3/m7t6Zil1WiQefEy7Bs6wLFxo0rf/9v9XwUALNywokr2T0REREREVYOhuwqaOP5djJ84BStXfY+2fr6Qy+XWnpLVlBS4A4CU1DSoCtV47rlOlX7syq6qp9FqkZCQiFEjR1T6XImIiIiIiIiIyPqeqSVB+3c3Wu34nabusvYlICILaTUa/PT1SgDAfz77SFhuCIxVNCyVd/U6FkydLdy/kJjyxIXu1IVKaDXaGhMqy0rPwJGIA0g5ex5Z6Zmida07tkWHwM4I6BkEmZ2ttadKRERERERWwtBdBcnlckwY/y5WrvoeK1Z9j1kfTbP2lKqtvLwbCAzsAnc3V2tPpVQZGZkIDOyCrl06W3sqRERERERERERERGRFPy9dDVW+EqETx1RJaOxCYgoAwNXDHf/57KMaE0wri02rfkJM5DFRRTt7p4ZYuGGFtadmJO18sigEGdC7G2S2cqiVKsREHkNSbDySYuNxJuokpn7xcYWLAhiugb1TQ2ufOhERERERlQFDd5XA0IrUVJtZeqRoO9jqztvLE95entaeBhERERERERERERFZ0bmYWMREHoPcwRa9BvWvkmNcz74KAHBp7vZEBu4AICbymNEyiURS7c63aOBuyMjX8OLrQ0WhunGzpgithpNi47H04y8wbcGcCh2zul0DIiIiIiKyDEN3lWTi+HcRn5Bo7WkQEREREREREREREVElMbSVnTB7mkUVzfKuXseFxBQ0dHZCCx9PXEpOQ9KZeOQoriAwuAfadGov7Eer0eDM8ZM4ffSEsP2Jg0fR0NkJnm18hGXnYmJxOfUichRXAACtfL3h3c4Xrh7NTB67la83ZHZypJxLxOXUi+g9qD+eldrgzPGTwr7TzicL82rfpRM6BHWGjVQKrUaDlHOJSDgVhwZyGYL69jQZCtPpdCjIvYELiSk4G30aMls5/J7zh3c7X9hIpcK4tPPJyLyQLjo/AOjap7tw/ob7RakLlTgfew5no08DAGS2crTw8UTnnkFGj0PRfRbdrolbU/Qe1N/iFrDqQqUQuHtn5mSjORm0C+iImUvnYcHU2foKeIVKo2OoC5VIT70oPG7mHrOi8y/6GBS9LupCJWIOH8eFxJQynxMREREREVUdhu4qiVwurzFV3IiIiIiIiIiIiIiIqGRZ6RlQ5Sshd7AVheBKciExBT8sWI6A3t1w8vBxHNgeIawzVHubuXQePNv4QF2owg8LlovWx0QeQ0DvbvBs4wOdToelH3+BpNh40TEM+xky8jUMfuNVo2OHThyDI7v3Iys9EwAQ1LcntBotfliwHHIHW/QeNAA7NmwR7c/Vwx3/980X+PbTRaLj7diwBQG9u2HcrCmiOWxes150bgCE+6ETxyB4SAgA4GD4X6Iqd4bz1YfJVKL7Bmnnk7Fy3hKo8pXi/SMCm79fj4+XfSkKAhr20dDZSdQWtqT5m3L1Sg4AQO5gi849g0oc69nGR9Qqt6i8q9fx0ciJJh8zuYMtloStEQUHDfNfuGEFbBpLRdfF3DkVf+yJiIiIiOjxq2XtCRARERERERERERFVFw/u66BVXoNWec3ibQzjH9zXWXv6T4V7dzXQKq/h7m1lxXdmgfI8J+jJEHssBgDQuUdQmbeNiTyGA9sjMGTka1gZ/gs+W7MErTu2BQCcPHwcgL6t6I97f0dA724A9NXVftz7uxAQ2/XrNiEAZ9jPwg0rhPE7NmxB2vlko2Pv/HUrVEolgl8OwTszJ8PeqaGwTpWvFEJby7asxfufz4LcwRZZ6ZmY+dYkXLmciXdmTsayLWvxzszJwrloNRphH3lXrwsBu8/WLMGPe3/Hsi1rMWTka8LxDcbNmoKFG1YI93/c+7vZsBqgr/63YOpsIez4zszJWBn+C96ZORlyB1uo8pX49pOFJrddMHU2Anp3w2drlmBl+C8InTjG5PzNMVTk6z1ogEVVDc35edlqAEDwyyFYGf4Lvo/4DTOXzhPmfyk5zeJ9Gc5p4YYVWLZlrXBOOzZsgU7H/+YQEREREVkTQ3dERERERERERET0WNzMTkR+8iHkJx/CvbulByDKOr4y/KPOQ8K6MUhYN8bibQzj/1HnPZY5lsZw3Z7UkJjq4gkkrBsDxeE1j+2YF8I/Q8K6MShMP2nt06fHKDUhCQDg95x/ubafuXQeBr/xKmykUrh6NMO/x40CoK8IV1pgSqvRCNXoiu7HsXEjjJs1RQhfbVz+g9G2qnwlJsyehtAJY9C1T3ejANk7Mydj8BuvQmZni3YBHYVQoSpfiY+XfYmufbpDZmeLrn26w9XDHQCQefGysP3Z6NMI6N0N78ycLLRLldnZCpXXVPlKZKVnlOua7fnfnwAAVw93LAlbg659usNGKkXXPt2xJEz/bz4rPdNk2LB1x7YYN2sKXD2awUYqRfCQEMgd9G1YU84llnrsM1H6f9+NXBqXa+6Avq2szFaOgN7dMGxMKGykUkgkEni28cHwd/WP/45ftli8P0OVPsfGjSCzsxUqCAIoU3iPiIiIiIgqH0N3RERERERERERE9FhcP7sT6RGLkR6xGPnn95U49sF9HS7u/FIYr3tMVc3Kw96nJ+x9eqJWHam1pwLg0XW+lZNk7alUiWfljWDv0xMNmrR+LMd7ppYErQZ/AgDI2PfNYwuAkvUZqsy5uLuWa3v3ls1F952aOAu3C3JvlLitulAl3G7ctInResO+DC1ki2vh42l23618vc2OLdq2FQBcmrsBAG5cyxWWBQ8JwbhZU0QtYQ0MVfjMzas0OYorwnGLhwUlEokQAiw6H4Pn+/c2WmYIFCaciiv12IbHu6Gzk8n1Jw4eNfm/ogFAmZ0txs2agnGzpsBGKv5vgmHuxdsFF1f0MQgM7mH2Gpu6BkRERERE9PjUtvYEiIiIiIiIiIiI6OmTGx+BRv4vmV1/62oK7t0qsPY0LdIiZIa1p/BUaeDiiwYuvo/1mDa2znDr/R4UkauRuX85H3MqVeuObY1CV8XvlyQ7M0u4LbOzNVpfNAioLlSKxgT07lZie9Tiwbqi21lKXahE5M69yFFcQUzksUq7btmXFQCA9l06mVzv0twNWemZOBt92ij0VzxMCOgDhQcQAbVShdK4ergjKz0TN67lwrONj9H6HxYsN3vdio7X6XQ4f/osog4cQfZlRbkDiADg3e7xvtYREREREZHlGLqzAo1Wizq1a5f4R29RhjLzlo6vzGNrtFpIbWzKdAyVSgW5XF7JV8169u47gNS0C+jXtw+8vTwrvkMiIiIiIiIioqdc7fr20ORl4E7eZdR1bG5yzPWzO4WxpYXvHtzXf372TK2Kf35mzr27GtSuYCW7suzjwX0ddLq7FT5meZTnelbG9anoHKp6Xk7tBiI3PgIFyYdR6NMbdh6dK+186ckjs63YZ+S3b94CoA/vmdy/3aP9azVayB7juZ04eFQIoMkdbEVhvYqGzAzbmqs218rXu1JDfkV5t2+DrPRMXM++anL9OzMni+6fjT5tNBetRoMv3/9YOI/WHdsioPmj61PWuZclqElERERERI8XQ3cVoNPpELZpM/bs3Q8ACPtlndmxKpUKJ6JP4mDkYSgU+l+oBQZ2wfNBgfBp7W0y2KbT6XAi+iQ2/vorVIVquLm5wre1D3r16gF3N8vL2Wu0WiQnpSBiz14kJCQCAPz8fNGpYwcE9+llMoBX/Nh+fr5o6uJi8tg6nQ4XLl7C+fOJ2HfwoDDXgM6d0KaNb40PqqWmXUBUVDQ6+Lev8edCREREj8ehvX/i9w2r8VxgT4yeOANajQY31fp2aA1ktqV+aK7T6VCYnwcAeNZGCpncttRjmjPxjRcBAHOXrkNDJ+dKG1sV21eV339Zg0N7wvHqyPfQq/9LFd8hERERVZhTu4HIiQpDwYXjJkN39+5qUJB8GFLHZqjr2BwFyYeNxty9rURB6hEUXDiBm4o4AEADN384+fWDvGVXk0Gre3c1yDm2HnfvKHEn7zLu3VGh5aD/M1s17cF9HXJiNqMg7W9o8jIgdWyGZsGTUL+xtygMdnqp/n2Q35h1sLF1Fi3rNHUX8pMPITdhH24q4lC7vj2aBAyHvVcP1Klna3S83HO7kRsfAU1eBgB96NDBqzuadBtVpQG8B/d1yDqyFvmpR4WQo9SxGeRu7eHaY6xR+O3BfR0KUo+iMD1GeHykjs3g1DYEth4BwnUAAK3yGhLWjQEAtJ+0FTnH1kOlOIu6js3RImQGLkUsQkHyYfiNWYf7d+8g89APwmPaJHAE7FsFiZ4n+cmHkB6xGPY+PYWKc4ZlHiHTIXPvgKsxv+H6mXAA+va/jdoPMvk4X4/7U7jetevbw6ndQDi1exGKw2tQkHwYHiHT4eDTC4A+ANgseBJSNk9Hxr5vYPfuxip7PIgMoTNzrUiLtp8tGsB7HIpWfJu7+itRlb2p/x5boX237tgWSbHxZqvNXUhM0Z9zBUONphiq4kXu3IMXXx9q9N2JqXa6xUN0f++JFAJ3wS+HIHTCGGHdiYNHqywwSEREREREjx9Dd+WUm5uHr5YuFwJ0JdHpdPhywRIoFFmQ28kwbOgQ3Lx5C3v27kdUVDQCA7tg0oRxRttMmvIBVIVqAICbmyuUaiX27N2PPXv3Y9WKZRZVk9NotZg6bYawHz8/X2RmKZCQkCgE8Pr3Cy5xGzc3V2GbqJhoLF2ySBQSPBF9EitXfS/c9/PzRUJCIhSKLGzdtgNzZs9iWI2IiIieGjdyr+H3DasBAKFv638Ffy72BH5euRgA4O3nj8kzvyhxH7HRR4XxhuBeZTKE+uwcHCulmnJ1N/jVUYiNPorfN6xG2w4B1SoQSERE9LSybxWkD5ed240mAcONAl2qiycAAE5tQ3AzJ8nkPgyhqNr17WHv0xN3bylxUxGHm4o4URjL4O5tJRI3ThQCZQ3c/KHJy0DK5uloEjjC5DxSt83BTUUcpI7NIHVsJoxv1GEw3HqNgyVS/vexELZr4OaPm4o4KCJXIydmM9oXCW09uK8Tjgfog2IAUJB8GNfPhCM/9Sh831hhFNSrDPfuanD+p7G4d6tAuJ6GY2vyMqBSnIX3v78Whf5yz+2GInK10VwN59Zu7HqTVequn94mhOGKBy6vHF8vBPjsfXqiIPkwcqLCkBMVhnbjNll07reupkBxZC3u3SoQrndB8mEUJB9Gy5c/FVWnyz6xCTlRYQD04cbadeX6MGja36hd197k/us31revvHerAHdvK6vk8SACAPuGDsLt4u1jgUfhM+DxVkPLu3pduD1z6TzRvNSFSqjylRXavyFMZ6p9LAAkn00AoA/IlYWp1rPFdQjSvz6o8pU4efi4yeOX5kzUSQD68ODwcaNE685Gn67QtSEiIiIiouqFobtyyFRkYdb/zQGgr1YXFRVd4viwTZuhUGRhQP++GPlmqLB8ROhwTJryAaKiojH81WFwcnIU1h04eAiqQjXkdjK88frr6NqlMyQSCfbuO4D1G8Lw5YIl+GLeJ6V+Sbp7919QFaoRGNgFY99+SwjLpaSmYe68+Vi/IQw9ejwvCtGt/fFnoVrd6LfeFAJz361cg6ioaKz98WchJKjT6bDx118BABPGv4tuQV0B6IN7W7ZsxZ69+7Htjx2Y9dE0az9s5dbBv32pjzERERGRQdiP+l/8vzVhuskvPlIS4qDVaEr8UmTvn1uqdI5J8bFYteRTo4p0c5fqKzfbOTiWd9fVko1UihFj38eqJZ9i9defYebn3z4VYUMiIqLqrFadukK1u1tXU4wqkOWc1L8fsvfqYTJ0dzM7UQhntRm9VgiDGSqqFSQfxr2+k4Xld28rcW6N/nO5JoEj0KjTUNSuIxXCZjlRYagtlaGRv7gqriY/A97DFwvzu5mdiJTN03H9TLjJ6m+m3FTEwSNkOuy9uuOZWhJhLvduFYja6xakHhUCd0WPea/vZFzcMQ83FXG4GvObxWG/srh+epsQuCsa7NMGjcLlfctxUxGH66e3waVrqHA9DYG7otX97vWdjJTfPoAmL8Pk4woAOVFh8AiZjvpNWouq4QH60F6jDoOFqn5Fz12deUaoOFfiuZwJRwM3fzQf/hVsbJ2FaoU5UWFQZ8QKobu7t5VC4K7oc+JmdiIyDnwnPBbFPVNL8ijMl3rE6DlDTxZD1bWCG/lwbNzosR7b3qkh5A62UOUrcT72nCgAptPphABX0dauj4ON9NF3Ce4tm4vWxRw+Ltw216JVp9OV+PdYYHAPxEQeQ/LZBKO/nfOuXhdCfZaE6IqqL2tgwblJETpxDDatWCdU8zMXvEs7n4zN3683uy8vv9ai81QXKkVV7kr7XKDodsUDl0REREREVD3UsvYEaiJDxbr5X87F8FeHlTo+MSkZANClS2fRcolEgn59+gAAIv7aKyzXaLVYv0H/gc/SJYvQLair8MdZcJ9emDD+XXw4dXKpx9Votdi6bQcAYPirw0TBOm8vT7g9bBOrVqmF5bm5eYiKiobcToYv5n0iqlA39u23MGH8u6Jz3hG+UwjoGQJ3ACC1scHLQwYBABISEpGbmyc6xrHjJ3Ds+IkyXXedTodMRRb27juAvfsOINNMlUHD/lNS0wDoW/vGxZ3Dtj92mN2m6DXbu+8Ajh0/gUxFFnQ6XZnmSERERE+3k8cPISUhDt5+/ugc1MtovdxOXy0j6sg+s/u4kXsN2YoMYWxVyExPM7m8oZMzGjo5P5GBND//zngusCeyFRk4emC3tadDREREABq21ndfKEg9Klp+J++y0MrVXBUxG7um8AiZDu/hi0XV12xsndHAzR8AkH/+0XuuW9dSAeir27l0DRW2qV1Himb93hcqtRXn1mOsKDhW9LYmX2HRedr79ISDTy8hoFennq0wR3XWo7aRiiNrAQAeIdNFx6ldR4oWA/VV+66fCce9u5pKfywM4TOvoZ+LrrmNrTOaBo0AoK9sZ/BMHRu0fPlTtHz5U1FwrnYdKZzahgAArhwPE5ZLiuzTcD2KB+6Ea95rnOjxsW+l/8wxN8H8e+jiWgycIez/mVoSyN39hev34L7+8z515hn9Merbi54TDVx80fT50SXu38mvHwCYrcJITw4vv9YAgKQz8RXcU9lJJBIMf1dfKe2HBcvxybgPkXY+GScOHsW0EeOEANew0aEVOUyZyexs4erhDgDYum4TstIzcOLgUUz991hsWrEOwS/rXwNOHz2BvKvXodPphAAhABzauRcnDh6FVmP6taxNp/ZC2HDC4DcRvvF3ZKVnYM38Zfho5EQA+qBhVYUgew3qj9Yd2wrXfcnMuQjf+DvOxcTixMGjCN/4O5bMnIsFU2dDla9E645tRY/BkDdfAwBE7tyDtPPJwtynvDYWrTu2Fa7DmeMnoS4svSqgVqOt8seUiIiIiIjKh5XuysHLsxW+W/Y1JBKJKExmjqtrUygUWbhz+45F+z99Wv+Bj5+frygoB+j/0C4abiuJ1MYGG37+Afn5BaIqega+rX2M2uNu/n0rAKBfnz5GX7ZKbWyMjj30lSHoG9wbzxabZ3E2Ns8Kt1PTLgjtaC09l2PHT4ha2BrI7WQY9/bb8PdvZ7T/wMAuyMu7Idpu67YdkNvJMGvGdLg/DB0C+rDd7t1/CSFFAz8/X3Tq2MGiORIREdHTTavRCC1hR08w3Q62e/BA7NoahmOREejV33RFjOi/D4jGFnUj9xoupSXBwbERWnoZVw05efwQAKBdx64mfzGv1WhwLvYEzsT8LRzLydkFLTxbo6GTc4nbX1FcRrbisnDfsI2l1yYv9yrSkuKRnpYEZxdXeLfxR/OW3iYDfjqdDknxsUiKj8VNlRIN5LZo3bYjPH3aGs2r+FgA6NytN5q18IJMLv6S/l9vjsOpqMP4fcNqdOraw2g9ERERPV42ts6QOjbD9TPhQmUzACi4oK+UVFLwqU49W6Hq2b27GuhuPwou1Kmv/2/8zZwkoQrZ7Wv6Hx3Uc3Q32pedR2dRy9GiZO7GnwsZqpwVrVJXEgef3kbL7Ft11bfCLTJHQ9vbWjb1oVVeM7s/3W0lattWXivJ4scqfl/ybD1hfoZ2qrXrSIVr9uC+Dv+oH31Gek+j/4Fv0UpxRYORjdoPMjuXRh0GGy2r16il0f5KUru+vVFYU+rUQrj9jzoPNrbOKEyPAQDI3NoZ7aO+s1elXV+q2Tp2C8CODVsQuXMPBr/x6mM/ftc+3XFLfRObVqxDVnomFkydLayTO9hiwuxpj70CHwAMe3sEvvnvfBzYHoED2yOE5TOXzoNnGx+knD2PrPRMfDRyIhZuWAHHxo0wYfY0LJg6G5tW6CusL9ywwuS+JRIJ5q7+Cks+mous9Ezs2LAFOzY8qgYf0Lsb3pr6XpWdm0QiwbQFc3Di4FH8sGA5kmLjkRRrOnT5zszJRpXwWvh4wtXD3ejxat2xLaZ+8TFOHj6OHxYsxw8LliOgdzeMmzWlyh8vIiIiKrslM+ciKTYe738+C+0COhqtz0rPgLRuXeF+Se/J8q5et2icgbpQKQTv7Z0alvojeUv3r9VooC5Ulek6GI5fdE6WbncpOQ0Lps6Gq4c75qxc9ET+2J+IobtyMBVgK4lcJgMAROzZKwqHAcC+gwcBiKvgXbx4CQDQo/vz0Ol0yM8vQGraBTg6NhRVnrOERCIxOV+dToc9e/cDEAfiDJ57rhN0Oh2uZOdAociCm5urKKQmOj+53OTyDRv1bWfd3FzNjrGERqsVgnMTxr8LL89WyC8owL79BxEVFY01P/6IVd9+Y7Td+aREREVFw8/PF0NfGYI7t+/gty3/g0KRhfA/dwktcgFg6bJvkZCQCAAY0L8vevXqgfA/dyEqKlpYTkRERFSStGT9h/Defv5mw1xOzi54LrAnTkUdxo3ca0ahNZ1OJ1Rha/9ckFHo7lJaEn5euRjPBfY0GbozhP7mLl1nMnR3U60UxgAQ9v/WhOlo6ORsdvuEuJNYteRTo/29NWG6yYp+xc/p02ljoSosEC03HLt4i9srisv4buF/jcYf2hMOuZ09Pl2yVpjbjdxrWPLZh0ZjT0XpW80tWLlJ9FjI5Lbw9vNHSkIckhPOlDp3IiIiqnpNOr+G9IjFuJkVDzuPzvpWoA+rrjVwbVvitvnJh5AesdiSw0CddR4AUL9x2doRmqq0Zwj1Waqug3HQr7ZUJrpfNOh2cfunJe7v/l3LfthrqaL7S/xlYiljNQD0538n7zJSt/1XCAtaqk79hmbXmXp8ShpviqkQXdHQX3FSe+PPPM1VWDR4Vt5IuAb0ZGvi7ipUXcu7et3oS8x3Zuo70sjsHn3+3crXG+/MnIyGzk4m92lqmz6DX0D7Lp1MtkwNHhKCgJ5BSE+9iMupF9HIpTEaOjvBvWVzo7/7Sjq2zE4uHLu4krYzNbd2AR3xfcRvuJSchswL6XBv5YEWPp7CF6n/980Xwhe69k76f8OebXzw/uez4OLuKiy/d/euyTnJ7GwxZ+Ui5GRmISs9E9ezr6KRS2O08vU2+UWyqWta/NzK2o62a5/u6BDUGepCFS4kpghzaOjshMZNm5ht+SqRSPDZmq+gLlQicudeNPdqCQ+vlsL4zj2DhLkYWvWamn9J51TS84WIiIgq7sTBo0iKjUfrjm1FgTutRoO/90TiyO79yErPNNoudOIY9BrU3yhcZqjWCwCfrVkCV49mZo+t0+kw570PocrX/7DM8AMGU3Pc/P16YVxRrh7uGPb2CKOw4JnjJ/HDguVluhaG429a9ZNQadnS7Tzb+CCgdzfERB7DoZ17ETwkpFIeH6LqhKG7x2BE6HA0aFAfW7ftwPj/vI9+ffrg5s1biIqJhqpQjQH9+6JVy0e/tlSp9b8GvXbtGiZN+QCqwkftX+V2MrwyeDB69HjeqApeWcTH6z9oHNC/rygQdz5JHzDLv5GP+YsWi47t5uaKlwYNRNcunc1WJDkRfRIAcOTo30JY7d+v/Us0rltQV4sr3AGPKv8FBnYRtnNycoS3lyfOJyVCVahGSmqaUSBRVajGqJEj0L9fsLCsaVMXTPlgBqKiojF+3FhIJBKoVCphrvO/nCuECydNGId+fftg7rz5FX4OEBER0ZPv4F/bAeirxJWkR79BOBV1GNF/H8DAV8RtgC5fTIGqsAC9BgyGVFoXla2hkzNWbNyFiW+8CMA48GbK7j82CQG5F4eNQHDIUGRlXsJvP32Hn1cuRsKZGIyeOMPs9kcP7IaqsAAubs0wedZ8yOS2uKK4jL3hW0xeh73hW4RrMPjVUbCRSqFWKfHXjt9waE84Nv24XDhe9N8HoCosgLefP0ZPmAGZ3BZajQYHIrZh19Yw/LRyESZOnyt67xrYox9SEuIQdWQfQ3dERETVgLyl/r3TtdPbYefRGbeupgDQVzwrKSilOLQG18+EA9BXnjO0+6zfpDUu71tuVBXt3p2yBcOsqYGbf4nBPt0/t6vs2Oba7BrUeviYFKafFMKBtevbw63HWABAXcfmKLhwXAhO1gTFA5DC8vr2ZgOFhiCgJi/D2tOnKiaRSDD6gwn45r/z8e0nC40qdBSvcgboq4uUVGHE1DaebXzg2cbH7DYyO1u0C+hossqKpce2kUpNHru07czNTSKRmF1nI5XCprHxa3jx+UskErNzkkgkcPVoVuKX0iVdU0vOrTSG8yjP9jI7W5PVESUSidH+TM2/pHMq7flCRERE5acuVArBtHEz3xet+/L9j4WwXUDvbmjfpRMA4Gz0acREHsOmFeuw89et+HjZl2bfP8Qeiynx/c2l5DSTQbqi81uz4BuhEq/cwRY+7f3QxK0pchRXEBN5DFnpmfjmv/OFCsGGH2o0dHZCQO9u4v0pVcK+iq8DHv1IwMDVwx0uzd1KvY6G7ULHjxauTfsunaxSpZmoKjF09xhIJBIMHPgCYk6ehkKRJWphGhjYBSNCh4v+UM/KugJA3wrVzc0VrwweDB8fb5w6dRpbt+3A+g1huHnzJoa+MqRc81GpVFjz44+Q28nw2mvDxOsehuwWf7UMfn6+CHm7P+rWq4vo6JPYs3c/Vq76HvXr1TOq2AcA+fkFolaubm6u+L+Z0ypU5Q4oOaTXprUvoqKiER190mQVwA7+7UX3HRzsRfN1cnLEpUuXhWXFq/mVtbIgERERPb1SEuIAAG07BJQ4rnlLb8jt7HH0wG6j0N2ubfovJzt26Y6cK5moDkxVpGvp5Yt/j56Er+dOx6mowxj53odmS8OnpyXhucCe6D/4NaHqXFO35hj82iicijqMXVvDMGDwo/fDhip1fV54WfgwQCa3xdDQsWje0ht169WHTqeDRCIRqgIG9ugn7NtGKsXAV0Lh5Oxicj4tPFuLHi8iIiKyrtp1pLD36YmC5MO4e1uJgtSjAAB7r+4lbpf/cFzt+vZoOWS2KKCnyTcOQtV1bA5NXgZuXU0R2tJWJ8/KHnWqaBo0Ag1cfCuwt7KpVefRjz2aBo2Cja1zqdvkJ0cKt72Gfi5qs5uXsOexzb0yFG3xa3D3trLECn63cpIAlB5SpCdDu4COrNBBRERE9JRYs0DfYe+dmZNFlW2z0jOEwF3x6nNd+3RH6PjRQoW6bz9ZiM/WfGW0b7mDLXZs2IIXXx9q9vP0g+F/CWNNhe+KBu5Mtb4dN2sKDuyIwKYV64TKdIZ29qaC+3lXrwuV+Cxpex8y/OUSfxxQnMzOFu/MnIwfFizHz8tWY9qCOZX9kBFZVS1rT+BpkJKahrfHjodCkQU/P19M/3AKRo0cAT8/fWBs0pQPkKnIEsYritz+Yt4n6N8vGO5urhj6yhDM/3IugEdtacsqU5GFjz6eDQCY8p9JZqvlye1kmPXRNPj7t4O3lydGvhmK6R9OAaBvk2uKjc2zCAzsAj8/X+E8Pvp4NuLizlX4Gup0Ohw7fgLb/tiB71auEf5nqMxnqA4IAI6ODYVzKN5a19R/vG7d1v862M1M+1zD+RARERFVBolEgu7BA6EqLMDF1Edt7LUaDVIS4iC3s0dLL1/cuX3L2lPFjdxHbc6KV8Rr6eWLFRt3YcXGXWY/IACA0RNnYPTEGWjq1ly0vOj+CvPzhNvPBeq/uFzy2YdQqx59qCCRSNA5qBf8/B9VXe4ePBCAvq1u0bEA0DmoFzoH9TKaWwPZow9KtBqNtS8xERERAWjUfhAAoCD1CK6fCUft+vYlhs60ymtCIMpr6OeiwJ25sJSdh/6HEbfzjH/YcCfvMi5FLILi0BqrXYNnaklQu77+x6KqzDiTY+7eVpZhj5YrGvgzhMmKu3dX/L6pIFn/QwmPkOmiwB3wKBBpjqRe2drzVpUGTfQ/xjDVIlZbeMXa06Nq5q2p70HuYItNK9Yh7+p1a0+HiIiIiKrAuZhYJMXGw9XD3ShYdiTiAAB9NThT1dpkdraYMHsagl8OQcjwl03uv/egAQD01exM0Wo0iIk8BlcPd/i09zM7P0Af/DNXBTl4SAje/3wWADysfGfdCt1d+3SHq4c7kmLjcS4m1qpzIapsDN09Bj/9/AsAYPqHU4QgW/9+wZj10TQMGzoEqkI1Dh06IowPDOwCQN/6tfiXhIZKbKpCtSioZ4ljx09g1v/pk8MLv5hXYhW3VwYPNlrm09obAJCQkAiNVmu0Xi6XY9KEcZj10TSE/bJOOLfFXy0zOd5SmYosTJryAVau+h5bt+1AVFQ0oqKizY53sNd/QNmmddnCcq6uTU0ul8lkZdoPERERPX1KCqeZ0uX5YABAbPSjLySjjuwD8ChIVh1cStN/6ert51+h/VxMTcTvv6zB8gUfY+IbLwr/M2Xwa6MAAKrCAsycEIovZk3Aob1/iq6xQXDIUOG2YezuPzbhiuKy2bkYqucBwE111XxxTURERGVTv7H+MydF5GoAQJOA4SWOf1bmKATUirZa1Sqv4WrMb8L9u7ce/bfe0Mb2piIO+cmH8OC+Tlh3KWIRCpIPC/OwFkOL1oK0v6FVit/73MxOxLk1oUj538eVftxnaknQqIP+s8Cck1uMjn097k+c/W6Y6NgN3PwBAJrCbGHZvbsaZJ/YJAo9Fr3OBroqCg9aylDJT+baVn8OeRm4HvenMNc7eZeRceC7EvdhOG+pvSvo6WAjlWL0BxMAAN9+stDa0yEiIiKiKrB3204AMBmac27aBIA+xKYuNP03jWcbH4ROGGO2ElxQX/0Pzg3V7Io7c/wkAKDHwL4lzs9c8K+odgEd4erhDgDY9dsfVrumBoZrajgHoicFQ3dVTKVSCZXr2rZtY7S++/P6vth79u6HTif+EKpBg/om92kI5SnKELrb9scOrFz1PdzcXLHwi3lmW74a9t3IycloXdGqeBkZpbc7GzJ4EOR2+sDa6dNnyn0NV676Xmh7O/3DKQj7ZR3CflmHSRPGwd219H7hllIXqZZXlKHdLxEREVFlaejkDBe3Zji0J1x4D3gsMgIA0D34xYrsukrIZOWvRvLTikX4eu50HNoTDrWyAM8F9sRzgT3Ra8Bgk+MbOjlj7tJ1eHHYCMjt7JGtyMDvG1ZjztQx+GLWBFFFOxupFAtWbhKN3bU1DF/OmogvZk0oMXxHRERE1ccztSRoEjhCuG/v1aPU8U7t9D9UuLjzS2Sf2IRLEYuQsG4M8lOPwvfNFQD0ATvFoTXQKq+hdh0p3Hq/BwBIj1iMc2tHIfvEJpz9/g1o8jIgdWxWakvbqmbv1R2169tDk5eBhHVjcP6XCchPPoRLEYuQsnk6AH3r2bLIObkFlyIWmf2foXpek26jRMe+FLEI1+P+RMr/PoYicjVq17dHi4EzhP0a5pETFQbFoTX6a/ndMOREhcF7+GJIHZvpr/Wer3AzO7FMc35c6jo2F8KDisjVOLd2FFL+9zESf5mI2nXthXWm5J7bDQCQu/uXfiB6YrQL6Igf9/5uslUYEREREdVsOp1OqCLXpmM7o/W9BvWH3EH/Ofmc9z4sV/Vjx8aN4OrhjpjIYya7sERs3g4ACOgZZHJ7w/z6DH7BouMZgm7ZlxWP92KaYLimSbHx7EBDT5Ta1p7Aky7n6rUS19vYPCvcvnXrFuRyOZ4PCkRUVDRiTp7G0FeGGG1jqPJWv169Uo+v0+mwaMlSJCQkws/PFzOmTS2x/ZdLk8YAgL+PR8HfX/wfk6LV6gzV5L5bqW+7MfilF4UqfAYSiQT9+vTB1m07cPHiJXQL6lrm66fRaoVw4bChQ0Rz0ul0SEiovA/tzO2rLOFGIiIiIkv1f+k1/LxyMZLiY9GshReyFRlwcWsGmbx8AbfiP+CoTNlZl8u13Y3cazgVpW895uLWDDM//1Z4L6pWKXFoT7jJ7Ro6OWPgK6EY+EoobuReQ/yZGPy+YTWyFRn43y9rMHrioy98ZXJb0dhLaUnYtmktshUZ+HnlInw8f2WVXRciIiKqPPatgpATFYYGbv6oY0H7Uad2+h8q5ESFIScqDIC++lqLgTNQp54tmgSOQE5UGK6fCUf9xt6wsXVGI/+XAOgDVvduFSAnKgy169ujSeAIOLV7Ec/UkpR63Kr0TC0J2o1dj6wja3H9TDg0eRlIj1gsnFvToBEltt01RZOXAU2e+VZCTYNGAbBF7TpStBm9Fhd3zMNNRRwKkg8LLWTtfXqiSefXRI9L/cbecOv9HnJiNuP6mXCjOTYLnoSUzdOFfZR13o+L19C5yDqyFvmpR3HvVgHu1ZWjSeAINOo0FBd3zAMAo/a5RdsbW7s6IhERERERVY6C3BvCbZmd8d+kEokEw98dhR8WLIcqX4mPRk6E3MEWnXsEwe85f3i38xV1WTEnZPjL+GHBcqScSxS1h81Kz0BWeiZcPdwhs7M1CsoVDarZN3Sw6JwMle6y0ksvqGSps9GnSx3TytfbqBJf0WuqLlTBpnHp14qoJmDorop5e3nCzc0VCkUWLly8ZNTS9US0vkRoYGAXofqcoY2rQpEFlUolqkqXm5sn3G7a1KXU41+4eAkJCYmQ28kwdcp/SgzcAcBzz3USWriOHzdWNL5odTuZ/FHL1aioaGRlXcEX8z4RjdfpdNi6bQcAIOSF/uW6fv8UCfoNHChObMfHn6/4AwTArUhYsPj1LtrC90zc2XIFB4mIiOjJV7SlrFqltCg4166j/n3Fwb+2C7eHDB9d4jZ16+krIZ+KOiwKngHA1ezK/7VaC8/WAIBsRQZ0Op3Rez1De9x2Hbua/EAh/kwMAEBuZ4/Js+aLtk9OsKwSckMnZ/Tq/xLqN5Dh55WLcSrqMELfnmzyeA2dnNHQyRkOjo3w9dzpyFZk4EbuNdHjU/TDiQYVqOBHRERE5dMiZAYQMsNoeV3H5ug0dZfF29SpZwuXrqFoEjAc/6jz8KzMURSac+kaCpeuoUb7auT/Ehr5v4S7t5W4f1cjtBotysbW2exczM3H1PiS9uHg0wsOPr2Mlj9TSwK3XuPg1muc0Oa1+LlV5DqXpHYdKbz/9QUACMc2dX0M8zRcS63yGiT19ME9gwYuvkbnX9ZramDq8TB1/cxd05KOX/R6P7ivE13nm4o4/XWpZy/a5vqZ7QCAJoEjrB7UJCIiIiKiypGdqc8FtO7Y1uyYrn26o6GzE5LOxCNy5x6o8pU4sD0CB7ZHCNv2HzpIFKYrrkNQZwDA1h/DRONij+k/Sx/2tr6qePGgnLpQJdyW2clhCWndukW2V5oME5ZVTOQxxEQeK3HMOzMnm2x/G9C7G2Iij+FCYkqp7XGJagqG7sqhaPDtypVsk8ttbJ4VwlsvDRqIlau+x08//4J/v/YvIVSXnJSCP8L1vwTt17ePsK3UxgaBgV30wbeJUzBs6BB0f74bzsSdFcb7+fnCyclR2Oa7lWsQFRWNwMAumDRhnLB82x/60NsrgwdDrVJDDdMtVA37cndzhZ+fLxISEjFpygd44/XX4eXZCkf/PiYE6Ab07yu0mu3Xtw/OJyVCocjCoiVLETKgP5o2dcGVK9mI2LMXgD7UVnSux46fwMpV3wMAwn5ZV+K1lsvlQmhx9+6/MHDgC1Cr1Nj8+1ZERUUL684nJUKj1Ypa4FraFtbdzVXYz/iJUzBh/Lto6+eL/QcihXMmIiIistQ/Wg2A0v94tZFK4e3nj5SEOKiV+koZrdt2LHEbT59Hf/AXD/cdP7SnzHMtLLghCqQVZ+fgCLmdPVSFBYiNPorOQb2EdUnxsfh55WK4uDVDxy6mW7HVb6D/oUYDmVw0V51Oh6gj+4zmcTE1Ebu2hUEmszUKFRqCiQZXFJexNewHAMDE6XNFgb7mLb2FeRd3Uy1uT0tEREQ12zO1JGaDYSXRV22r3gH88pyXNY5tzXlWxL27GuSf34ebOUlw6zlOVMnvTt5lAEDt+vZGy6+fCddXSAwYbu1TICIiIiKiSnL75i0AgMy25ECbZxsfeLbxweA3XkXe1eu4kJiCs9GnkXw2AUmx8UiKjcfCDSvMhspspFIhfGYIwul0OuzYsAUA4N3O18x2j3IQ5akUVxmBOwAYMvI1BPXtWcqxLAsFEj0JGLorhykfzCh1edHwW9cunfHnzt1QKLKw+KtlRtsFBnYxqoA3ftxYqNVqJCQkYuu2HaLwl5ubKyaOf7fUeWq0WqFl6voNYVi/Iczs2KLhtxnTpgotaQ3hOAM/P1+89tow4b63lycWfjEPH308GwkJiUYtWt3cXPHh1MkVut7/fu1fWPzVMqPrMGzoEAx9ZQhm/t8cKBRZeHvseCz7epGwvixtYUe/9SbmzpsPAKJz9vPzRaeOHUq8dkREREQAhADdpbSkEoNsRb04dARSEuKQrchArwGDS61KXDSot3z+LAwZPhp169XHbz99h2xFhkXHBIAXh43Arq1h+O2n7/Dv0ZMAAC29jP+Yl0gkGBo6Fj+vXIyfVy5G1JF9GDbiHaQlxWNP+GYAwL9HTzI7b0NQLluRgUN7/0TbDgGI/vsAdm0Ng9zOXjiX2OijsLNviOYtvaFWFiAlIQ4N5LYI6jUAclt7qJQFQsDuxWEjYCOVoqlbcwBASkIctm1ai45dusPVvQVuqpUI37IeqsICPBfY0+ixyLmSKTxeRERERPT0ql1HinsaNQqSD0OtOAevoZ+jVp26uJWTBMWRtQAgCtY9uK9D6rb/AgC8hn7OKndERERERE85x8aN4Ni4Ebr26Q6dTodpI8ZBla/E1p82YdysKWa36zP4BX3FuMPHETwkBJeS0wAAwS+HmP2heNHQnObOHYvmdyExBcCjNrOVoZFLY1apIyqCobtymGBB4M3RsaFwWyKRYMGXc6HRapGclIJz8QkAgC5dOqNZM3dRdbai28yYNhUXLl7C+fOJyM65CrlMhnZt/dC2bRujLzYNFeeK+kertWiulhzbpUljtPDwgL9/O6PxcrkcS5csQnJSCm7dvo0zcWfh0qQxuj/fTVThzsDLsxX8/Hwhk8ksmQ78/dvhx7WrcOTI30hNuwAvz1bo4N9e2PeHUycjNe0CgEdtb0s6b8O6oi1yvb08sWrFMpyIPonUtAvCtfZp7Q21So369euLHlMiIiKi4vq88DJSEuIQdWSfqCJcSZq39BZuB/UaYNE2w0a8g+8W/hfZigysWvKpsPyDOYuxa1sYUhLiSt3HgMHDcfTAbmQrMvD13Ol4LrCnydAdAHQO6oXca9nYtVW/7y9nTRTW9Row2Ox2gD4k2GvAYBzaE47fN6zG7xtWA9C3m/2/L1fg2Wdt8MHYYTi0JxyH9oRjxcZdmDxrPmZOCBWWFSW3s0dwyFDh/rgps/HptLFmxw5+bZTRnA7+tR2AceU8IiIiInr6GFoQ50SFIfGXiaJ1Ddz80cj/JeF+1pG1uHerAI06DEZdx+bWnjpRjaAuVEKr0UJmJ6/ySuMVOVbe1esAAHunhqX+GI6IiIjIFIlEgg/nz8Yn46YhJvIYho0ONRtOa+HjCbmDLXb+uhXBQ0Jw8vBxAEDnnkElHqN1x7ZIio3Hrt/+KDHUZ3A2+jQAwKW5m7UvD9ETi6G7cugWVL4v6KQ2NvD3b2cyuGaKRCKBt5enURU8U5o1c4eqUA0vz1bCMrlcXu65luXYRc/Nkuvj5OSIzCwFAgO6lOna9e8XjP79gk3ur3i4r6Q5mFsnl8tNHkPqZGMyPEhERERUlKH1a0pCnFHr1xaerfHWhOlo4dlatI1EIsEHcxYjP++6ULmt+DYOjuI/zJu6Ncf87zbiiuIyzp46Du82/nB1bwEbqRQvDh2BwB790ED26NhvTZgOAKJlEokEkz76HFJpXQDAszZSs2MBYOAroejyfDAupSUh91o2vNv4o3lLb6MvI0xt/+qb4zD41VFIS45H3vWr8GzdVnSuC1ZuetiSV08mt8WKjbtwI/caLqUl4fLFFDRv6Q0fvw6iawroQ33zv9uIG7nXkHMlE0nxsXBydkHbDgEmqw1qNRohlNipa4/H/AwhIiIiourIpWso6jl74r5W305KU5gN+1ZBomDdvbsa3L2jhL1PTzTpNqqcRyJ6PLLSM5CVrq/w3SGoc5WH3UqyadVPiIk8hndmTkbXPt2r7bG2/rQJMZHHEDpxDIKHhFjtehEREZH11GtQHwCgVqqM1uVdvY6tP21C9mUF5qxcZDakb2tvJ9wuuJFvNnQnkUjQe9AA7NiwBWnnk3FgewTkDrbwbONT4hyf798bSbHxpYb6DHOOiTwGQF9Zj4iqBkN3T4jTp88A0Leyre4yFVlQFarRpQbMlYiIiMhSNlIpXh35Hn7fsBr/+2UNRk+cIaxr6ORstuVsSy9fk9XiStoG0Ifvigf1TO3LXNW94tuWNNaS+ZS0vY1UCj9/0+/99EE6W6PlhuNZUjXQMNbcMQw2/bgcAPDqyPeMAnxERERE9PSy8yj5fWTtOlK0CJlh4d6IrOuHBcuF0N3jCLtVF6189ZXkGzo7lXnbt6a+h+SzCdi0Yh3ad+nElmlERERPIRd3VwBAUmy80TqZnRzJZxOgyldi7aJvzVaZ27TqJwCwKEAX1LcndmzYgo3LfwAADHp9WKlz7NwzCBGbtyMrPRNfTPk/fDh/Nlw9mhmNy0rPwFez5gEAAnp3K3Uuj4shBGh430b0JGDo7gnh6NgQE8a/C7lcbu2plKquVIoJ499Fq5YtrD0VIiIiokrVPXggjkVG4FTUYXTu1rvUEBg9PglxJ3Eq6jBc3Jqhe/BAa0+HiIiIiIio0hWtcgcAEZu3PzWhu+AhIeWuUmcjlWL0BxPwzX/n49tPFpZYwYaIiIieTPZODYXb6kIlZHaPfrRtI5WKWsdmX1agU/euaN2hLerWq4us9Ez8vTdSCOxZEqBzbNwIrh7uwnu3gFJaywL6CnnvzJyMr2bNgypfiU/GTUNA725o36WTMOZs9Gkh3CZ3sEXo+NGVep0MLWtL08rXW/RDBnWhUrgts6v+mRYiSzF094QoSytYazPVDpaIiIjoSSCRSPDeB59gztQxCFv7DT5dstaqrXxIT6vRIGztNwCAtybM4JcnRERERET0RDoScQAA8P7ns/DT1yuRlZ6JrPQMkxVQzNFqNBb/HVuWsdVdu4COaN2xLZJi43Fo5162mSUiInrKSCQS4b3A+dhzRj9ccPVohtCJY7Dz160P32NlYseGLcXGuGPY2yPQplN7i47ZY2BfbFqxDq07thWF/Eri6tEMC37+Dj8vXY2YyGPC/4oLfjkEw8eNqvTPws0dr7h3Zk4Whe7SUy8CAFp3bPvEvH8kAhi6IyIiIiKqVA2dnIU2s5t+XC5qM0vWcSBiG1SFBeg1YLDJtrpEREREREQ1nVajwYHtEQAA73a+GPT6MGxasQ5HIg4gdMIYo/EnDh7FDwuWY8jI19B7UH/8uWkrTh45DlW+Eq07tsWQN19DCx9Poy9q084n4+Th48JYuYMteg8agNYd2pbYuizv6nV8NHIiAGBl+C8mv2w9sCNC+OJ52oI5wvE2Lv9BVMEv+OUQ9AgJFoUJ18xfhpjIY0YtdS3dHgDemvIePho5EZtWrEOvQf35gy0iIqKnTP+hg5AUG2+2WrChsq66UInzsedE6xo6O5l9L/TOzMkmlz8/oDfqyxqYbLdq2MZUVTgbqRTjZk1B6PjRUBYUIis9E9ezr6KRS2O4erjDqYmzxcE2mZ3c7PyK6jP4BVFFvdIUP6etP4bpz7l/b8sfEKIagKE7IiIiIqJK1qv/S+jV/yVrT4MeGvhKKAa+EmrtaRAREREREVWZv/dEAnhUPSSgZxA2rViHA9sjMGxMqNkvXk8fPYHInXuEsB2QiaTYeCTFxhsF2PKuXseCqbMB6Cu5+LT3Q/LZBOzYsAU7NmzBZ2uWmK2qV7SF2pnjJ01+kX1k934A+i+8ASB84+9CBZmA3t0gs5Uj5ex5HNgegQPbI7BwwwpRBZXiStt+2Za1oqoyRed4KTmtxBAhERERPXmKVr49cfCoyfcrACCzszW7zhRzY22kUrPrLNm/zM4WMjvbMlU1LsscivJs41Pu90YnDh5FVnomXD3c0dmCNrpENUkta0+AiIiIiIiIiIiIiIiIym/nr1sBAEPefA2A/ktYfYgOOHP8pNntstIz0bS5OxZuWIFpC+ZgSdgaDBmp38ffeyNFY39etlp/jJGv4bM1X2HcrClYErYGoRP1lfQM7W3NCRn+MgAgYvN2o3XqQqVQjc7Qki1y5x4A+na542ZNQeiEMfhszVdCNZZvP1lY4vEM289cOk+0vWG+Sz6aa7RNp+5dAQAnDx+v6oeMiIiIqqG3prwHAPhhwXJoNRprT6fG02o02Pz9egDAfz77iJWE6YnD0B0REREREREREREREVENlXY+WWj12sLHU1huqBhnKuRW1L/HjRIqxkkkEgT17QkASIqNF75s1ul0+Pe4UXhn5mQM+Nejyu4SiURoNXZge0SJX053COoMQB/0y7t6XbQucudeAPpAn0QigVajgSpfCQCoW7+eaGzXPt2xcMMKfLbmK7PHKrp9ccFDQsxu39yrJQAgOzOrih4tIiIiqs4cGzcSAvo/L11t7enUeD8vXQ1VvhKhE8eUWKGYqKZi6I6IiIiIiIiIiIiIiKiGOhj+FwCg96ABouohhopxWemZyErPMLt98ZZk9k4NhdvqQhUAfbjO1aMZuvbpDhupFOpCJfKuXkfe1evQ3LljNN4UG6kUwS+HAACO7z8sWmeoSmcI/NlIpULFvQVTZ+PEwaOiQF9pX9qWd3sXd1cA+sAhERERPZ16DeoPVw93xEQeQ9r5ZGtPp8ZKO5+MmMhjcPVwR69B/a09HaIqUdvaEyAiIiIiIiIiIiIiIqKy02o0iIk8BkBfpa14BbmA3t0QE3kMRyIOIHTCGKPtA3p3M1pmru2XVqPBz0tXC8crjx4hwTiwPQKRO/dg8BuvAgCy0jOgylfC1cNdFIYb8K+XELlzD1T5SvywYDkAwNXDHT0G9sXzA3rDRiot8VgV3Z6IiIieThKJpMSKumQZzzY++HHv79aeBlGVYuiuGsrNzUNq2gV4ebaCk5NjqeNVKhUuXboMAGjRojnkcnmp22QqsqBQZFl8jNLmCwAODvYm/xjX6XTIzy8ocR82Ns+anHdubh7yCwqQl3cDXp6tjI6h0Wrx9tjxAIBlXy+q8LkQEREREREREREREdUUf++JFG5/89/5Zscd2B6BYWNCyx0002o0mPnWJKFla+jEMagva4CGzk4A9NXkLOHq0QyuHu7ISs9E2vlkeLbxQeyxGABAyPCXRWNtpFIs/W0t0s4n4+Th4zh55Diy0jOxacU6bFqxDp+tWWJUpa8s2y/csIJtzoiIiIiIqNwYuqsmdDoddoTvxL6DB6EqVIvWBQZ2wdi334LUxka0XKVS4aOPZxuNl9vJsPCLeUYhNpVKhf0HIk0eY9TIEejfL7hMc9ZotVi67FskJCQCMB96u3DxEubOm1/ivgIDu2DShHGiua5Y9b2w76LmzJ4Fby9PAIDUxgYTxr+Llau+x9p1P2PWR9Oq7kEiIiIiIiIiIiIiIqomdDoddv66FQAQ/HIIWvh4mhwXsXk7stIzceb4SXTt071cx0o5lygE7oaMfA3BQ0KEdWVtuxYy/GX8sGA5Th4+Ds82PkJr2Q5BnU2O92zjA882PgidMAZZ6Rn4atY8qPKV2PXbHxg3a0qpxzO3/dafNlm0PRERERERkSkM3VUTBw4ewtZtOwAAA/r3RcuWLXDx4iXs2bsfUVHRcGnSGENfGSKMLxq48/PzRciA/vj7eBSioqKhKlTjo49nY+mSRaKg3oaNvyIqKhpyOxmGDR0CZ2dnHDn6NxISErF+Qxg6+Le3uFJcXNw5rPnxR6Pwnil5eTcAAG5urnhp0ECTYxwdGwq3dTodvlywBApFlnA92rX1E443d958TP9wCvz92wEAugV1Fc7j2PET6BbU1doPJxERERERERERERFRlbqUnCYE4YaPG2W2Lewt9U1sWrEOEZu3lzt0l3AqDoC+PauhLaxB0pn4Mu3LEK47sD0CnXsGQZWvRPDLIUZV+NSFSjwrtREtd/Vohg/nz8Yn46YhJvIYQsePhszO1uRxyrO95s4d4TyJiIiIiIhKUsvaEyB9xbj1G8IA6KvFjXwzFN2CumLkm6GYM3sWAGDrth3QaLXCNtt37ISqUI3AwC6Y9dE0+Pu3w6QJ4zD9wymQ28mgKlTjyJG/hfG5uXmIiooGACxdsghDXxmCbkFdMeujaRg1cgQAYPPvWy2ab25uHhZ/tQwAMP3DKRafp6trU3QL6mryf4bKdQAQH39eCNytWrEMI98Mhb9/O3y37Gthrmt+/FG074nj3wUArFz1PVQqlXUfUCIiIiIiIiIiIiKiKrbjly0A9FXuzAXuAOD5Ab0BAFnpmchKzyjXsfye8zdaptPpkHY+WahUBwAFN/JL3ZeNVIrgl/WV8lbOWwIA6BEi7sRzYEcEprw2Ft9+ugg6nU60Lj9X/0N/Vw93s4G78m6flZ4JAHBp7lau60RERERERE8Phu6qgYyMTAQGdsGA/n2NKs15e3lCbicDACQnpQDQ/yG7Z+9+AMDIN14Xjff3b4dZM6YDAA5GHhaWn4k7i8DALhg1coRRm9quXUyXbC+Jn58vFn4xT6g2Z4kO/u0tGncpPV04RtEWuRKJBD16PA8AUBWqkfkwmAcAcrlcCORt37GzYg8IEREREREREREREVE1pi5UIilWX2GueGCtOBupFAG9uwEAjkQcKNfx2nTSf76flZ6JJTPn4sCOCEwbMQ4Lps5G70EDMGTkawCAjct/wIEdEaXuzzBnVb4ScgdbuHo0E60PHhKC1h3bIik2HnMnzED4xt9x4uBRbFq5Dj99vRIA8Mbkd8zuv7zb/703EgDQvkunyny4iIiIiIjoCcTQXTXg7eWJSRPGYeSboUbrcnPzhBauTZu6AADy8wsAAHI7mSiUZtCokRMAQKHIEqq+9e8XjEkTxqF/P+M/vguVZasM5+Bgj1kfTTN5bFNu3bpVpv1n51wFAHTq2MFondTGBm5ursL5FWUID+7Zu9/ol2tERERERERERERERE+KyJ17AeirtRUPrJnSZ/ALAPQtXbUaTZmPJ5FIMHPpPCHItmnFOgDAkJGv4cXXh2LAv14CoA/lGdaVxNWjGeQO+ipzg14fZnLMfz6dgYDe3aBSKrFjwxb8sGA5DmyPgNzWFqETx8CzjU+Jxyjr9lqNRggytuloecEBIiIiIiJ6OtW29gSoZGvX/QwAcHNzFarg5RfoQ3dtWvua3KZoJTut9p9Sj3Ho0BEAgEuTxhbNqaQy9aakpl0QbmcqsnDq1GkAgLOzM9zcXOH+MERnYJjH1avXTO7P1lYOhcJ4uVwuF1rrXrh4SdSyloiIiIiIiIiIiIjoSTH4jVcx+I1XLR7v2cYHP+79XbjftU93dO3T3ez4omOL7mPagjlQFyoBQNSaVSKRGG0zbtYUjJs1xeT+tRoNVPn6/Rja3xZnI5UK26sLldBqtJDZyWEjlRqNNXWssmwPAGeOnwQAtO7Y1mzbWiIiIiIiIgOG7qoZlUqFDRt/RVbWFaGSW2BgFwx/9dEvvfLyblTa8QytauV2MgwZPKhKz23lqu9NLh/Qvy9GhA4XwnzPPdcJW7ftQFRMtFH1P41Wi4SERAD6lrndgrqK1rdp7YuoqGjk5d1g6I6IiIiIiIiIiIiIqJJVRiDt56WrAQABvbuZDcEVP6asgnMuaXt1oRI/LFgOABg38/3KvFxERERERPSEYuiumtFq/0FUVLRw383NFf369hGq3FUmjVaLpcu+BQDMmjG9zBXsymPC+Hfh5dkKAHD072PYum0H9uzdjyvZ2Zj10TQAECrfqQrV2PDLJoS80B9OTo7IVGQh/M9dwr7kMuM/kb08WyEqKhoXL14yCuQREREREREREREREZF1qAuVOB97DmejTyMm8hjkDrYIHT/a2tMCAKxZ8A0A4J2Zk1nljoiIiIiILMLQXTXj4GCP6R9Owa3bt3Ht2jXsO3gQc+fNh9xOhlXfflNpx8nNzcOcufOgKlRjzuxZRi1eK9PIN17H8FeHQSaXiVrfDn1lCFp4eGDxV8uQkJAIjVYrrJ8w/l2sXPU99uzdL1TiUxWqIbeTYUD/vtizdz9atmxhdKz69esDAFRqdZWdDxERERERERERERERlc2mVT8hJvKYcP/D+bOrRcDtxMGjSIqNR+uObUtsuUtERERERFQUQ3fVjEQigb9/O+F+9+e7YcoHM6AqVCMlNQ3eXp5wq2BALiU1TQjyzZk9q8rbsMrlcrPr2rZtI9zOyMgU5tItqCu6dumME9En8efO3QCAfn364LnnOgnV7hwdG1bpvImIiIiIiIiIiIiIqHL0GfwC2nfphFa+3rCR2lSLwB0AnI0+jYDe3apN1T0iIiIiIqoZGLqrJnQ6He7euyeqBAcATk6OQmW3ffsPwtvLE3WlUgBAVFQ0Jk0YZ7QvlUol3HZwsBet27vvANZvCIPcToaFX8wrMRD3OEgkEqGKnal13YK6GrWJzcq6oj83e3uLjkFERERERERERERERNbl2cYHnm18rD0NI+NmTbH2FIiIiIiIqAaqZe0JEPDdyjUY+dY7WPvjzybXN2igb5nq0qQxAH0QT24nA6BvE1tcfEIiAMDPzxcSiURYbo3AnUarxbHjJ7Dtjx3Q6XRG6zMVWULgrlWRdrEarVYUHjRQqVRQKLLg5uZqFCgEgGvXromuFRERERERERERERERERERERERUWVi6K4a8PJsBUBfua54iE6n02Hrth0A9K1mDfr16QMAmDN3HuLizglj9+47gJWrvgcAhAzoL9rP+g1hAIBZM6ZbFLhTqVTIzc0zGX6zlNTGBkeO/o2t23YgbNNmo3M7dOgIAGBA/75CQDBTkYW3x47H+IlToNFqhfGZiix8uWAJAGD0W2+KAoUG2TlXAQDOzs5V8VAREREREREREREREREREREREdFTju1lq4HgPr1wOvYMEhISMeWDGfDz80WP7s/jTNxZREVFA9BXrXNychS26RvcG/sOHoSqUI3FXy1DYGAXqNVqJBSpcte2bRth/IWLlwAAcjsZwv/cZXYu/fr2gbeXJwBgw8ZfERUVjcDALqI2tnv3HUBq2gWjbdeu+xkymUy4b9hm6pT/YOq0Gdizdz+uZGejU8cOuHnzphAmBIAuXToLt93dXBEY2AVRUdH49LMvENC5E7JzrgrXIjCwizDHonQ6nTDGzc3V2g8rERERERERERERERERERERERE9gRi6qwYkEglmTJuKHeE7sXXbDiQkJArhObmdDK8MHowePZ4XbSOXy7Hwi3n46OPZUBWqhbAZoA/czZg2VVQJbt/+gwBgNLa4Dv7tTQbaikpNu2ByH4Y5GxhCd1IbG8yaMR1hm34TnRsADBs6BAMHvgCpjY1o27FvvyWECBWKLGH5qJEjENynl8l5XcnOEa6ZO0N3RERERERERERERERERERERERUBRi6qyYkEgmGvjIEQ18ZgtzcPNzRaHDnzh20atnCZBtVQB+8W/XtN1CpVLh06TLq1quLZs3cjQJsADD81WEY/uqwUuchkz+qVDfyjddxPinRaMzIN163aF9Fubu5YtZH0wDo29Zqtf9AJpeZnCvwMKj3cHxubh6uXMmGT2tvs+MBCK1qDa13iYiIiIiIiIiIiIiIiIiIiIiIKhtDd9VQ0TaylpDL5fD3b1ep+zRQFarxfFCg0fEqoqzbOzk5ljr/TEUW9uzdD7mdDEMGD6rQ/IiIiIiIiIiIiIiIiIiIiIiIiMypZe0JUPW1/0AkAMCntbe1p1IinU6H+YsWAwBmzZhutjIgERERERERERERERERERERERFRRbHSHZllaHdb3YVt2gxVoRoD+veFu5urtadDRERERERERERERERERERERERPMFa6oxpNo9VCpVYjMLALRoQOt/Z0iIiIiIiIiIiIiIiIiIiIiIjoCVO5LQsAAIAASURBVMdKd1SjSW1sMGnCOGtPg4iIiIiIiIiIiIiIiIiIiIiInhKsdEdERERERERERERERERERERERERkIYbuiIiIiIiIiIiIiIiIiIiIiIiIiCzE0B0RERERERERERERERERERERERGRhRi6IyIiIiIiIiIiIiIiIiIiIiIiIrIQQ3dEREREREREREREREREREREREREFmLojoiIiIiIiIiIiIiIiIiIiIiIiMhCDN0RERERERERERERERERERERERERWYihOyIiIiIiIiIiIiIiIiIiIiIiIiILMXRHREREREREREREREREREREREREZCGG7oiIiIiIiIiIiIiIiIiIiIiIiIgsxNAdERERERERERERERERERERERERkYUYuiMiIiIiIiIiIiIiIiIiIiIiIiKyEEN3RFRhGRmZaNjQwdrTICKqNDlXMmHvYG/taRARVZrCght8XSOiJwrfrxFRTXH31g3I7fh6RUTVX3ZmFuwdbK09DSKiUvH1iohqCr5ePfme+tCdTNYAGYpMa0+DqEY7fToW/37tX9aeBj3UoEF9ZGddtvY0iGq0pPhYvDJ4oLWnQUXwdY2oYmKjj6JPr+7WngY9xPdrRBXH92uPzz/5l609BaIarSD1KIJ7831YVWvQoD6yLyusPQ2iGi3hVBxeGTzI2tN44vH1iqji+Hr1ePD1iqji+Hr15HvqQ3edn+uEy+npKCxUWnsqRDVWeHg4ggIDrD0NeqhD+7bIVmRAreLrGlF5HdoTjs6d/K09DXqo83Md+LpGVEGx0UfRl1/2Vht8v0ZUcXy/9nh0fq4Dbl3PwN3bfL0iKq/C1KPo34fvw6pah/ZtkZWeCTU/5ycqtwPbI/j+6jHg6xVRxfH16vHg6xVRxfH16sn31Ifu6tWri65dOmPBwkXQ6XTWng5RjRMZeQgA4OPtbe2p0EN160rRsaM/flrJ1zWi8jh5/BAAwLNlC2tPhR6yt7OFh0cz/LRykbWnQlQjJcSdhKqwgK9r1QjfrxFVDN+vPT72drZwa94Ml3bzfRhReRSmn8Q/t/g+7HGoW1eKjp3aY82Cb/j+iqgcThw8CoDvrx4Hvl4RVQxfrx4fvl4RVQxfr54OT33oDgBWLP8aWYpMLF70FTQajbWnQ1RjhIVtwqJFi7Fo/jzUq1fX2tOhIhZ9PhvXszOwYfVX0PJ1jchiu//YhJ9XLsacj6ejbl2ptadDRXz79Xxcz87A7j82WXsqRDXKyeOHsGrJp3j7rRF8Xatm+H6NqHz4fu3xW7V0PmqpM5B9gu/DiMoiP/kQLm7/FGP4PuyxWTRvDq5ezsTaRd/y/RVRGYRv/B0/LFiOOR9P4+vVY8LXK6Ly4evV48fXK6Ly4evV0+OZBw8ePLD2JKqD/PwCDB46HDdu5KO9vz+CAruiSxe2y6wuEhOTrD0FKiI7Oxs7d+6CpFYtLF+2CB07+Ft7SmRCQaESb749EQX5BfD280e7jl3RtgNf16qLS2l8XatOcq9l4+iB3agFYP68/0M7P19rT4lMuHjpMka+PREA0GvAYLRu2xFNmrpbe1pE1Y5GcwdpSfE4FhmBbEUGVn27mK9r1RTfr1VvfL9WvfD9mnUVfR/WqMNgyJp1RF0Hvg8jKu7+3TtQZ8Uj/3wEbl3n+zBr0L+/Go+CfCVad2yLDoGd0b5LJ2tPix66kJhi7SlQEdezryJy5x5IAHz52X/5evWY8fWqeuPrVfXC1yvr4utV9cbXq+qFr1dPH4buirlyJRuRh49i//5DiD550trToYe6dO4Mh4b21p4GPeRgb4+BIf3g4+3NCnc1QM7Va/j7eAyOHDuB2Ng4a0+HHurY0R92trbWngY9ZG9ni759usOzZQv+4qSau6fTITEpBSdPx+GP8N0oyC+w9pSIqqU+vXuie1AAOj/XAfZ2/O9Ndcf3a9UT369VL3y/Zn2G92HRp+KwbcduqAr5Pqys7uM+arHxyBOvR8+e6P0834dZm/79VTSOHD+B2NNnrT0deqhjp/bV8v2VSqmC3FZu7Wk8dvZ2tujbuwffX1kZX6+qp+r6evW04utV9cDXq+qJr1fVC1+vnj4M3RERERERERERERFVscNH/kaTxo3h5dXK2lMhIqKHbtzIx4T/fIDNm3629lSIiIiIiKiG4U8riYiIiIiIiIiIiKrY2nXrsWffAWtPg4iIitizdz/OxSdApVZbeypERERERFTDMHRHNdbFS5etPQUiIiIiIiIiIqJS3b59B6dOn0HYr79ZeypERFTEpt9+BwAcjDxs7akQEREREVENw9Ad1UgFhUqMfHsi7tzRWHsqREREREREREREJTp56jRqSSRQKtVITb1g7ekQERH0rWUvXkoHwNAdERERERGVHUN3VCNpNPqwXd26UmtPhYiIiIiIiIiIqERr163HfZ0OANhiloiomtgevhPPPKP/mmzf/kjhewciIiIiIiJLMHRHREREREREREREVEUMrWUN2GKWiKh6iDoRjQcP7gv3/9q739pTIiIiIiKiGoShOyIiIiIiIiIiIqIqkpySgloSiXBfqVQjP7/A2tMiInqqqdRqHDseLVrGFrNERERERFQWDN0RERERERERERERVZE/duwUWssa7Nz9l7WnRUT0VDsYeVhoLWvAFrNERERERFQWDN0RERERERERERERVYF79+7hf1u3Gy3fGLbZ2lMjInqqHYw8LGota8AWs0REREREZCmG7oiIiIiIiIiIiIiqwLn4BJPLFVlZbDFLRGQlGo0G+/ZHmlx3+PAxa0+PiIiIiIhqCIbuiIiIiIiIiIiIiKrAHzt2ml3HFrNERNbxx44/za77a+8+tpglIiIiIiKLMHRHREREREREREREVMnMtZY1YItZIiLr+Pt4dInrSwrlERERERERGTB0R0RERERERERERFTJzLWWNWCLWSKix+/evXs4ePBQiWNKC+UREREREREBDN0RERERERERERERVbo/d5bePpYtZomIHq9du/aUOubgwUNsMUtERERERKVi6I6IiIiIiIiIiIiokv225X+ljmGLWSKixysqOsaicWwxS0REREREpWHojoiIiIiIiIiIiKgSpaZesGgcW8wSET1e+yMPWTQu6sRJa0+ViIiIiIiqudrWngARERERERERERHRk8TVtSn2/xUuWtb3hcFGywBAKpVae7pERE+Fe/fuYc13y0TLcnPzMHX6LADAxp9/EJbb2dtZe7pERERERFTNMXRHREREREREREREVInq1auLevXqGi1v2tTF2lMjInpq1a5dG506dRAtu3IlW7hdfB0REREREVFJ2F6WiIiIiIiIiIiIiIiIiIiIiIiIyEKsdEdERERERERERERERERUif55cN/aUyAiIiIiqhFq4RnUfuYZa0+jzBi6IyIiIiIiIiIiIiIiIqqgew8e4Mb9u1DdvwedtSdDRERERFSDSADIa9WG7BkJ6tWSWHs6FmHojoiIiIiIiIiIiIiIiKgCNA/uQ3FPw7AdEREREVE56AAU3L+HAtyD44M6cJTUsfaUSlXL2hMgIiIiIiIiIiIiIiIiqqnuPXiAywzcERERERFVirz7d3H7fvV/d81Kd0REREREREREVLPdvAkUFgK3b1t7JlST1KsHyOX6/6/Nj0mJiKj8bj2o/l8IEhERERHVJNd0/8CjVl1rT6NE/DSJiIiIiIiIiIhqpgcPgCtXGLaj8rl9+9Fzp1kzwMbG2jMiIqIaSnn/nrWnQERERET0RNHiAe49eIDazzxj7amYxfayRERERERERERUMzFwR5UlKwu4x8AEERGVz+0H9609BSIiIiKiJ46mmr/PZuiOiIiIiIiIiIhqnnv3GLijyqPTAfn51p4FERERERERERE9pMMDa0+hRAzdERERERERERFRzaPRWHsG9KRRq609AyIiIiIiIiIiqiEYuiMiIiIiIiIiopqHVe6osul0wN271p4FERERERERERHVALWtPQEiIiIiIiIiIqIy0+msPYNKk5OXj/hL6WjsYI92rVpU2tjqqDLmf0erxdGzCQCA/gGdrH1KRERERJXm7f6viu4v3LACjo0b1Zj91wRZ6RmIPRaD1IQkyGzlAIA+g1+AZxsfa0+NiKhaUBcqodVoAQAyOzlspFJrT4mo2mLojoiIiIiIiIiICMAnP64Xbs9+6w3UlkhKHL908zYU3tS3JP3s7VHlPm78pXR89uNG9A3wLzWIVpax1VFlzL9QfQuf/bgRAEN3RERETzt1oRKbVv1ktLyVrzeCh4SUuO2BHRG4kJhitDx0/GjI7GytfWrV0pr5y0T31UoVkmLj0bpjWyHABgDtu3RCQ2enahdkC9/4O3Zs2GK0vH2XTtVurvT4FX9NsOR1hOhJkXY+GQfD/0Ly2QSo8pVG6wN6d0OfwS+ghY8nJKV8VkL0NGHojoiIiIiIiIiICMD+mDjh9oCAzghq62t2bE5ePv538KhwvyKhO3PW7dwDef16+Ffv7sKyxg726Bvgj7Ytal7g7kmYPxEREVUvWo0WMZHHjJbHRB5Dr0H9SwwG7Px1q8lgwbDRoZBZ+8SqKVPXGgCSYuNNjnP1cMcbk9+pFiGNrPQMk4E7IoMLiSlGz3GG7qg6OxcTi6gDRwCUPzCu1Wjw89LVZl/fDWIijyEm8hjkDrb4cP5suHo0MzkufOPvyFFcAQCMmzXF2peIqMoxdEdERERERERERFTM5gOHSwzdRZw4WaXHv6fT4cc//0LfAH9R6K5dqxY1ssLdkzJ/IiIiqjlyMrPMhgKy0jNMBu6ocmWlZ2LB1NmQO9hi7uqvrFpBMPZYjNGy9z+fBRd3V8js5OXYIxGRdW39MQxZ6ZkAyhcY1+l0+PbTRUbB6ZKo8pX4ZNw0fLZmidF/Y7UajSjczNAdPQ0YuiMiIiIiIiIiIiqipWsTnEpKxR2tFnVtbEyO2XbkWBn3Wja5BRX7ErhAfRMa7T9o4uhg8VgAFo0H9KHA3AIlnOxtjdrw3tFqUai+ZfG+KjJ3IiIiInNij8WYDd2ZCmBR2RVvKwsA2ZcVQgjEQJWvxJoF32DagjlWm6uh8pJBQO9uaBfQ0WrzISKqCHWh0ui1tqxOHj5uFLhr3bEthrz5Gho3bYJnpTbIvHgZB8P/MqqE99ua9Uav6SnnEq19WYgeO4buiIiIiIiIiIiIinjv5UGY/t0P2HU8RlRlzuDchUsoUKrxrz7dRS1mDbqNmwoA+N8Xs42CY3tjTuOzHzeib4C/2Za0n/y4Xmh1uz8mTrh9bM1Sk9sbln3y9hto7GCPJb/+DxezcgDoA4RvDAhGn07+RuE4w3bFvf3SCxgZ0lc03nBOG+ZMR/jfJ0Tn/VxrL3z1n3ehvn0Hn/74C04lpQrHfu/lQQjw9Rb2Ze78C9Q3sWzLVpxOuYgCpVpY/q8+3TH4+a5o2dTFOk8GIiIiqrEid+7B4DdeNbnu9NET5d6vulAJZUEhstIzUa9Bfbi4u8LeqWGZ2qfmXb2OC4kpcPVwNxsMtHQ/BTfyceNaLlw93OHUxBk2UmnVXdRi3pryHhwbNzJannY+GRuX/yAKhCTFxiPv6nWT44tf14bOTmjctEmJlfHUhUpoNVrhfvHHIO/qddg7NURB7g39eKVKtL3MVo68q9f1t+3kJq+bVqOBulCFC4kpAIBWvt5mxxbfRjhOsfFajQb/aLSQ2dlCp9MJ8wMAG6mN6JzVhUpcvZID95bNzR4zKz0DWemZaOXrbdHz0HBMw/PG8Bwu6bxKOyd1oRLnY89Z9LgVZ3gMTJ1/acr6nKmokp5zOp0OOZlZAGD237RWo0HKuUTUrV+vxLkWvSYARP9m1IVKpKdeBAB4eLU0u4+yPA9L2v5CYkqpr3PFj1V8zqVdR3OPu6k5lLTf0s45Kz0DANDE3dXkeeh0OlxKTsOdW7dLPVbx86ms1y7D88jwb9rUv0vD9sf3HxYtz374/Cu+z5JcSk4T3Zc72OI/n84QHdOzjQ882/hArVSJAnpJsfHQajSwkUqF5+zebTtF+yv6XLb0tb+k17vSrp9Wo0Hmxctlfgzzrl5HdmYWbt+89VheS+jJwtAdERERERERERFRER28WgIAwv+OMhm623pYHzgL7uRvMnRXUcN66o+5PyZOCM1ZYtfxGJxKSoW9rQxvv/QCDp05i4tZOfjsx41oULeuqF3uup178OOffwEA7G1leCukH8L/jsLFrBz8+OdfOHvhEr6ZMt7oGMt/34FTSanoG+AvzPFUUiq+/d8OHDgdhwKlWnTs6d/9gA1zppcamlu2Zatwvm+F9IPq1m1sO3IM/zt4FAdOx2HnormVfp2JiIjoyRPQu5tQjUeVr4S6UGn0xXnx6kDBL4fgwPaIUvd9LiYWP3290mxb2tYd22LczPdL/KL+wI4IbFqxzmh58MshGD5uFCyh0+lwaOdek/sBAFcPd0xbOMeqgQHPNj54Y/I7WDB1tmh5wY18oxBEVnoGvpo1z+x1nbl0Hjzb+Bgt37TqJ1HlpYUbVkBz545oXws3rMBHIyea3O+B7RHC4/7OzMno2ufR+/68q9fx7ScLzVaRkjvY4sP5s02Gq84cP4kfFiwX7g8Z+RpefH0o5k6YIewvoHc3jJs1BQW5N0Tzc/Vwx2drvsK5mFhR20hA//z697hRwjHDN/6OyJ17RNdN7mCLj5d9aTZocuLgUWz+fr3Jay13sMXwd0ehc88go7BO8XMyXK8DOyKw89etRvszPJ/NhX7UhUpE7txrNH/DNQgZ/rLo8SiupH+LcgdbTJg9zeRzpqKKP+fe/3wWvNv5Yuu6TUavIcEvhyB0whgA+ufTz8tWG1UUMzw3il+n4s/ZhRtWoOBGPlbOW2J0zuZaNxd/zAyPiannYVFp55NNHsegdce2RmHb3Jxr+GTcNKM5m3seznnvQ9H+i//7UxfqK2Oaa3k6ZORrJgPVpp6nnXsGYfOa9Th55LjomAG9u2HsjP9AIpFAXajEn5u2Gj2GrTu2xdQvPjb7PK7M1y57p4bYvGa9yf8Wvf/5LFFVzuLbG3zz3/kWXf+iioeRfdr7mQ28jZv5Pv7ctBUtfDyFEGTtOnUAwOzrbNHlP+79XbSuvP+OTV0/G6mN0fOqtMcB0L8mRmzebvK1Xu5gi849gtAjJLhC4Xh68tWy9gSIiIiIiIiIiIiqk7o2NvhXn+64mJWDnLx80bo7Wi32x8TB3lYG3yr64LVdqxbo1tYPAODh4oz+AZ3QP6BTqdudSkrF2y+9gO3zP8GYQQPw/+zde3yMd97/8Xc79hYlJtE6E4JWhJAK0aUaWjeyTt0W1aIHh6oelJ+0dLupqrWlmy7V0qpDtWgV7VK6KbdWQmO3QRtCBGWIQ+KYjGiNezt3f3+kc3WuOeRAGNHX8/Hw2Mx1XfO9vtf3ujLpxtvn80Hi8xo36D5J0rr0rcZx+YXnjcDdpOFDtOrVSerftbM+SHxef3t6pDHWgWPHvc5x4HiuPnjpOU0e/ogmD39Ek4YPkSSt/GqzagRX09qkKca5+//yFyep32UWO++d3x80qvnNff5Z9e/aWcN699CqVyepXYvblG8v1M7vD16RtQYAANeXNh1iVL3Gr+GT9NQtXse4b2sQHqYmEbcWO6bT6dTcV2fqjT+/6jdcIRVV/Rk7cIR2pn/rc4zPlqzwG5T7clWyPv/o0xKv76LDoVeefN7vOJJ01Jajl54Yb1R1ChRf63rmxCnT6w/nLNSkUQnFruu0cYn6cnXJocj8M2c1b9qsYscqjS9XJ2vCw08V27bx3Fm7Jo1K0IdzFpY4Xu6RY0pZu75UbSCP2nJ0Ou+k3vjzq17H7/k2U/OmzZLT6dSXq5O1+oPlXtd67qzdb/hl/+7sYtfn3Fm75k2bpY/nvl/iPHd8s10707/Vh7MX+hzvy1XJSlm73s81HtZLT4z3OX/XGsybNksfzlkop9Pptf/DOQuL/V48d9auaeMS9e8r8I+jPP14/gctmvGOz6DUl6uStTP9WzmdTk0d+yefAbLVHyz3u07ujucc1bRxiT6v+dxZu156YrxXdTxPhfZz2pq6xe9z6Pqc83celz3fZmrCw0+ZvicbhDcyfe5KRc+IL0dth73Gv71je+Pr/buzNXbgCL+BO9e6zX11ps/nw93J43lKWbteX65K9jpn+sY0paxdX3Td097weQ/3fJvp9/uhPD+7HBcuaP5rb/oNf7/x51dVWHB5n2v+eLYHT9+Yps+WrPB9bIhVDz05THfc3VmtY9vqljq1ylTh1V1JP1Nd38e+fp56Wv/pWr+BO6noPuzfne21/bMlKzRv2iy/3xPnztr15apkTRqVcFU+T1BxEboDAAAAAAAAPNwTEy1JSv73VtP2z7ekS5Luu6uTV7vWa8GD/93FNC9X1b4N6Rn66Ze/lNi6Z6+xv3tsjOn4jlGRatfiNknSZ197t127JybaVLUuqkm48fWQHvcoNLia8brlL6HElO92FDvnWxvW18qpiVqbNEVVKlc2tleyWNS4bm1J0pfbMwK9tAAAoILo2ruH8fV3/9rqtd99211/6FbieJ9/9KnPqkKxXTupQXiY13ZfAYmD2fu1+oPlXse2aBtljOFrv6d1K9d4BQQeemqYRk4coxZto4xt587a9foLU0oMpVxJPxae99rmvl77d2f7rA42cuIY3XNvvGn7h7MXlhgqmjMlyWd4IrZrJ8V27eQVCmoQHmbsu7l2TWNOvgKNrmM9fbkqucQwxvFDR4oNSXp6c9J0SfKar1QUSPt47vvGeL6OKTrOHLh0Op2aMyXJa11GThzj9Qx/uSrZZ0DF85pcVbX8zWHTPzf4nJevoNI998b7nIdnELUsz8y8abNKfGYuV/LHq4zPBl/r8N7f52j+a28a1+vrGF+fUZ7c19rzOqWi7/dFM98pdozjh46YqsB52pq6pUyfc57fk+6fu5Lv+y9J36ale43vqqx20eHwqo7Zom2URk4co4eeGmba7grNFWfj2nXFfq98OHuhPp77vhHw83XM1k3ewe3y/ux6/YUpxT5Hkjks3iyyuc/PoxZto4zPtMpBlVUavsLRqz9Yrs+WrChT0M91Xn/b3fftTP/W61nr9/BAn+tXmsChe6jS1/eHJK/Pv8ICu9fP3IeeGqZn//KCnv3LC17XMm/aLF10OEq9Hvhtob0sAAAAAAAA4KF1syYKtQbr001pGub2Fwifff0vSVL8He0vdegrpl2L20yhNUm6qbJ3a5jdv/wlnKtFrKc2zZpo2559Kjhf6LWvQ6S5LUvN0F//UsA9gOf++sDR3GLnXaVyZVWpXFk/OZ3KPX1Wttw8nb9wQZKMOfiaCwAAgC9tO8Uaf5m+59tMXXQ4TKEO9wpKbTrE6PusvX7HOp130usv5mO7dtL9jz1ktO7bvztbS2bNMwW+Pnz7PVPbxtWLzWNUr2HVY//vSaNlYEktHf3Nxb1t3h13d9ZnS1YYx5w7a9fB7P1XpM1maWz0EYipG9bA+HrJrHmmfe7tOO+4u7Puir/H1LJy/adrjf2+nDtrN1qktmzbWhcdFxVa82bjPsx9daYp6OHZwtRXMK1BeJiGjBlprOH9jz2kT9770DTOx+++77Mlq4t7K8+HRj+mi46LxQZijtpyjFaguTlHvdpXusI+rmN2b9/h1aLx27R0UzvElLXrTfsbhIcZ63LH3Z314ZyFphDRnu8yi31ujtpyTO1ofyw879Xy8agtx6u9s2foq0F4mJ6ZPMH4XtqZ/q2pRebGteuM9qtOp7PMz8yWDak+25CWF9c6PJmYoCYRt+rzjz41fY+eO2tX+sY0ox1x3bAGmv/am6Z18vyM8ie2ayc9Ou4JVQ4KUp+H7vda7z3fZvpsp+0+V9c4ns/hRYfD696U5nPuzUnTNXnu65Kkrr27m679qC3H53VtXLvO9Pruvj2Nr9etXGPa1yA8zNTeNTauo5ImvGLMYe1Hn+iefr5DVq71l4ras7aMaaOtqVu8rtP13LuO8XUPj9oOG99Pl/Icluazy/2zxtdab/rnBuNa7+kXr3v6xXsF1zzb/pbG7R3bq3oNq9fPntUfLNfqD5YbLVabRNyq2zu299969pfPE885ebYvdjqdeu/vc0zb3NsF33F3Z7WP62gKX25cu77E72P3tsf3D3vIq+XzubN27d+dbXyu2fYd8LqH7s9S69i2CrZWN42xd2eWqc0v4EKlOwAAAAAAAMCH++7qZGptmnv6rA4czVXTBnVV95YagZ6el5Dgm4rd76po5wqwRTVp4vO4BrVukSTZjp/w2hdet47PMSVd8pr85HRq5cbNinsyQf1fnKLn3pqnyQuWaPKCJUbbWQAAgNJyD3ZJRX9R7uvr6jWsuqVOLZ08nud3rC0bUr22jXphrCnYcGvLCA0ZM9J0TPrGNKM6j2fQT5IeePwR01/e39oyQg88/kix1+UZDmzRNsorGNW1d3fTa89wyJVUWGDX6byT2r87W5NGjfcZVnSFZ07nnfSqSvfAKPP1NwhvZKqw9eWq5BIrDT32/57UHXd3VnCItcytDw9m7/cKnjwzeYJpjW+pU0uPjnvCdMy5s3bt3l58ZefYrp006oWxxrz8BaOkXwM8FotFDcIb+Xwu+j080DimdWxbr2O2bzZXrG7TIUYTZ0zRyIljjD/uPKtd7du1p8T1cq21xWJRcIhVo14Y61Wly7061/7d2V73fOTEMabvpdaxbU1juK9t/qkzZX5mVn+w/IpXp3px5l91a8sIWSwWv8GgV955XQ3CG8lisXiFkCTz55IvrpCkK/AUHGL1eg4lafe3O4sdx99z+N0W72p7I55/xutzzvO5cbVDds3JsyKe53Wdzjvp9T3m/ux5fmaMnDjG9D0cHGJVTOc7jNeuIFVxJs6YotaxbWWxWHTH3Z19VmObPDfJOKbvkAHFXselPIel+ez60xtTjc+aW1tG6JnJE7zW+kq0mK0cFKTxryb63e9qsTpv2iw92Xeokia+ov27sy+5imr+qTNez0CvB+8zvb61ZYTpc2D1B8tLPF/CtJeM57VyUJDPkONWt8+jH8//YN63aYvXs/TAqEc0/YPZWrB+hRasX0HgDn5R6Q4AAAAAAADwIf6O9lqw5gt9uT1DrZs1MVrNDulxT6Cndl35IHmDFqz5QlJRtb6JQx4w9i37MkUrS2jZBQAA4M5iseiee+ONCjW7tmUYf1m+/tO1xnGudoi5R475Hctzn3sLV3e+WvRddFxUsKTCgnNe+5pFNi/VNncHs/ebXtcLa1Bi20Jf7VbL04SHnyr1sb0G/dH4+njOUdO+6jWsyj91xus99Ro3NF1DYcE5Va7ju9JSg/CwywpFnDlxymtbaM2bvbZVDgpSi7ZRpiClZ4CjuGsvSat20abXvp6LFrebn0NXe1wXz/t+S51auqVOLVOAsLhnxzMk6kv4bU29tkW0aWWqdPV91l6jepSv9a1Zt7bXthnL5vs8n+cz0yA8zOczExxqlWy/vi7umSkPnpXFYrt2Mq1B9RpWr5BlWZ8f96CZi6+KYz/4aOnszt9zuOOb7abXDcLDfAZW3asnuuSfOWuswV1/6GZqpfyvLzeZvic9z9Pv4YHGeXyF0i78eMHrOa0aXM30+syJU8VWZQxr2tj0um7D+l7HeAa1m7dpafoecn+Or9Rnl+f99FWxzvUzpbw1CG+kd5OXaff2HVr/6dpiv//3fJupPd9mqnoNq1555/ViA8S+eIbH/X0f128cpnNnf51H/qkzfqv4+fu57Pm9WGj/9Wdxy7atTceeO2vXtHGJeuipYWreOlI169ZW5aCgMlcOxG9ThQvd/eR06tSp04GeBgLs5C/PQG7eicscCRVd3Tq1L38QAAAAAAB8qHtLDTVtUFcrv9qsZ/r3U8p3RVUWOrdpVar3/3jR+y8OXK1dA6lR7aJqdZkHD6p/185e+11zDK93df4/t2tdQ63BmjZ6mKlF7nf7vg/0cgEAgAqofVxHI3T35apkoxKRe5igbafYEsfJ3rHL9Pq2Vi18HucroPJ91l6/f2EfHFLda5uvgJc798CA67rcW9/543Q6y1Txrbz5Cmd4hozOnbWXKsDnHvDxVK9xw8uap2eo0V/wSJKCreb7t+Ob7aZWtZ48Qz3FqVeKY0NvrlHsa18uOhxat3KN9u3aU6pQXUl8hW3adIjxai/p4llRsnoNa4ktVd15PjNHbTmX/cxcLl9V0zxDXRE+/r9jvbAGZboHjX0EHF3n9xdy9MXfc+j52dK8TUu/Y3gGBt1Db3f26GoK3aVvTNOI558xvo82/XODaSz3z2Bf4WT3FqP+HMze7/d7z9czVqueuXK7r+/zJhG36kv5/my9Ep9dvkKVrrm5B/eO5xy9Ys+yq2pm69i2Kiywa/e3O3Uwe7+2btris+35ubN2vfTEeCUtnVumny+ewdDSfh8X5/bft/e53fN70f05d1Vm9Awouz+/DcLDFP/AvcW21QWkChS6y807oWUr/qGVn665/MFw3ej/4LDLHwQVWmgNq+7pcpc6tI9RxzvaX/6AAAAAAAC4GdLjHk1esEQfJG/QgaO56n93Z1MozJd2LW7Ttj37dOBYrprWr2ds/8npvCaqtkU0KvoLyQ3pGZo83LtdlSvoFlLtSvw7erMLFy/qwNFcSdIbzz5hWlv3fQAAAGXhWd3IM1Al+a7a5MkzbOAZ2DCPF+azspxnZR/Jd5WqkoIL/sJMJSmuQtCV1KJtlG7/fXt16d3d69qKa+lbnOKqWpVUKbAknsGj4kJ8zSKbl+l+lCWUUlL4UlKZ7+dR22G9/sIUn+GZS+ErbFYSz6qREaX8h0wunlXSSqukSmiXw9cz5/kZ0aZDjNcxxYW6fPEXxPRVta04/p5DzwCgr8qdLp6BU/fQm68qkAez9+vWlhEqLLCbPh8bhIeV6jO4JJ7ft+5K84yVNax7JT67/P1c8ayWV1JFxPISHGLVHXd31h13d9ZDTw7TRYdDnyz80Cvkfe6s3bi/peXr52Fp3+fvcy+sWbjP7Z7r6vmc/+mNqVo04x2/n+VHbTmaN22WpKJWx8UFq/HbViFCdzt3ZWn0M8+pX594vf1mksIaNgjov8gAcG1wOBw6cPCQMnZm6q9/m6H7+vbWsEceCvS0AAAAAADXEVdVO1f707533lHie3p1jNW2Pfv0+ZZ0dW7TSlUqV9aFixf1zqrPS33eOjVCJRUF4yYOGVRi0K8sYiObK9QarHx7oSYteF9jB96v0OBqunDxoj76nxQj6PbEvb2M9zRtUPeKBOCqVK5shBS/23fACCnu/P6gPkn9NaDoLyAIAADgi2f4Y893mTp/rtDYf8+98Zc0rr/2jU6n028r19KGwXy1V3TnWdWqRdso3dm9a4nj+qqqV16e/csLPgNBJYXCPMMQ1WtY9cDjJf+3XnFr6dl28nIVF+a51OBIaVyJvwOfNCrBa9tDTw1T1eBqahbZXDu+2W6q8HQleAYVPatIlsSzil55PDOXq7yfOX/8VUkrrjV2WXgGhosLlnkGlDwDet3v620KN+35LlO3toxQeuoW03F3/aFbifPq9/DAYoPOkndr5SvtSnx2XesqBwXpgVGP6HjOUa/gWs73tjKF7jw/B1zV5ErzPn/8BRo9n2PPNrSVg4I06oWx6jXoj/o2LV3bN//b78/xj999X+3jOpJRgk/XfOjuwMFDGv3Mc3py1DD17X1p/wEM4PoUFBSklpERahkZoejWURo/IVERzW+l4h0AAAAAoNxUqVxZ3WKjtSE9Q6HWYFPlOn/atyj6hfC2PfvUbcxEI1QmyRirJK2bNTGCbgMSpyrfXqi0uTPK5ZoqWSz609BBeu6tedqQnqEN6Rle8xo36D5T0C+8Xu0rVnXugXvitG3PPs1Y9qk279il/MJCHTiaq1BrsFZOTdTI12Yq316oZ2e+reG9e6hmSMgVmQcAAKj43KsuuYc/Nq5dZ6ry1T6uY6nGK237xvxTZ7y2FRcS8NXy1Vd7xeIEW6sHvPJOvbAGl1RF76ZqVU2vz521B/xaPENdxbX/9AweXcuBmtN5J7229Xt4oOk59hcmLU+e1ajOnbX7+T6w66LjooJDqpsqQl4Lz4xnZbmS2gqXF1dwzZNncNH9OfRcr+J4VlTbt2uPz+MKC7wrJXqG3pq3jjS93rh2nfoOGeDVWvbOHubAsK9wcNtOseVSDa88XQvPYXkpLLAr71iuzpw4pYPZ+1W7fl2/7YktFovGTX1Rrzz5vOlZKamlsSfPoOpRW85lr5+/70PPUKpnlUaXBuGN1CC8kfoOGaCLDodyDhzSnu8ytfqD5cYx587atXv7DrWObVtOq4/ryY2BnkBJPvv8Cw0dPJDAHYBitYyM0CuTXtBzL7wc6KkAAAAAAK4z98cV/QL3vrtK10YpNLiaVk5N1PA+PRVqDS4K38VG64OXnjPGKo3po0eoaYO6imneVN1io8v1mjpGReqDl54zxnUF7tq1uE1vP/eM+ne9en9xEBvZXH97eqQRTjxbeF7D+/TU4sTnVfeWGnrj2SfULTZaIcE3XbU5AQCAism9Opl7+MOzrWZx7RPdef4lvb82dL5byBb9AwZfYRJf7W5Lqp7m2abyUtvNXgt8VcfzFQ67mnyFlHzNyVfwqGbd2uU2D1/jXw7P56p6Dat6PXifadv2zf8u13P6Uqd+Xa9tnmFVp9Opl54YrwkPP6Un+w7V8O4D9OXqopaW18IzU1LVtSvF1/256HB4fa65B5r8taT1xTM06i9wmnfM+x9hhd5cw/S6clCQqZLoubN27Uz/1hTUatE2yqvFtq+W2/6qjgXStfAc+uO4cKFMx29cu17TxiVq3rRZ+nJVsj6cvVBHbYf9Hv9j4Xmds5ufuZICx54VXH0df7mfef5+Fnpu9xW6u+hwmM5fOShIt7aMUN8hAzR5bpLp2E8WLL2seeL6dc1Xulv56Rq9Pn1KoKcBoAKIub2NpKIKmU2bNA70dAAAAAAAFYy/SnKtmzXxu8/f9rq31NCw3j30cHxR25xKbhUcPN/TPTZG3WNjfI7xQeLzJR7r7/2uMfzNsWn9epo8/BFNHv6ILly8WGwLW9dxZV2D0lxrJYtFHaMi1TEqUj85naa1cp9nac4JAADgUjkoyKttolTUWra0LeLuir9HX65KNm37bMkK9ejfxwiJ7N+dreSPV5mOadE2SsEhVr/zWDJrnp6ZPMGoErd/d7Y+fvf9YufiK6zw7682m1reHbUdNrUSvefeeD305LCA3QN/Qmve7LUm6z9dq/uHPWSs60WHQxMffdoIFrVoG6VnXn7eZzinPDRvHanqNaymINMn732o+x97yLhPp/NO6pP3PjS9r3oNq1d1r8tx0XFRweV4XZ6VyM6dteun//xHFotFTqdTW1O3+Aw3+apCdzmCQ6xelSPfnDRdCdNfMr5XtqZu8QqSxf5SlTK05s1e9yfQz8zVctSWow/nLNQDox6RxWLRRYdDi2a843Xc7R0vrRNYmw4xXu2FP5yz0LS2R22HtWTWPNMxDcLDFFrzZq/x2sd1NH1uvvf3Oab9/YYO9DmPfg8PNFUXS/54lZpFNjdV05z76kzjGWoQHqaRE8dc1Wp419Jnl+f3w+fL/qFRL4wt9fs7doszrbckvf7CFD32/55U89aRpvledDg0d9obXt+fnhUsPX29bqOpEp6v7+M1H35y2es399WZenTcE6ocFCSn06mP53r/PL0r/h5JRZ9t819703iOqtew6pV3Xjc+h/yp17jh5d4yXKeu6dBdbt4JSSI8A6BULBaLusR1InQHAAAAALhmVCrHv6i6kooL3F1NFWW9AABAxXDXH7p5hUlatYsu9fsbhDfyCgqt/mC5Nq5dp94P3q9N/9zgM7D06NgnTK/vHz5Yb/z5VeP1UVuOJjz8lPo9PFC5R46VqmrdLXVqeYVS5k2bpY/ffV8PPP6ITh7P08a160zv6X5f78AsfAksFouGjBmpaeMSjW1frkrWl6uS1e/hgaoaXE2b/rnBFMy4s3vXKxqeqhwUpAcef0Tzps0ytqVvTFP6xrRi79MDjz9imleD8LBAL6+Jr6qOf332RcV0vsPUdtkzCJOydr3adIi5pPbB/tz/2EOmNTxqy9FLT4xX7wfv1/dZe73W1z28arFY9GRigs9n5qGnioKlV/uZuZq+XJWsrZu2qPeD93t9pklFrbAv9Vp9fba4r62veyNJz0ye4DOY6fnMlbbSaI/+fUxzcH1OxnbtpDYdYvT1+o2mKnzBodar3n72Wvrsan+XOdzoukd1G9Y3BcP9uaVOLbVoG2Va03Nn7cbPqgbhYarXuKHfn08t2kZ53UvPQOLajz7R91l7VbdhffUdMkAWi8Xrc9b92ZYu7fs4fWOasnfsUtfePbzaybvm5XpWPJ/Zc2ftSprwiuIfuFc3166p0JtrKP/MWc2ZYq5051ltFnC5ptvLZu7ao+joKAVdJz8MAVx5DRvU1+492YGeBgAAAAAAAAAACDBXlSx3Za1K9ui4J7yCVOfO2n9pxecduBs5cYxXUKllTBu1aBvldezqD5YbgYZ+D/uu/uSuR/8+Pucyb9osrf5guSlo4Gse15JbW0Yotmsnn2viubaxXTvpjrs7X/E53XF3Z79z8hU88TWvoCpVrsbylZrFYjG1+5SKwkzuz0u/hwfqlXdeNx3z4eyFmvDwU+U6F1e4y53re8lzfavXsOqZl81Vv/09Mx/OXhiwZ+ZqcK2Za608Va9h1UOjH7usc/j6bJHk89645uTv88Visfj9POv38EC/FRQrBwXp2b+84LU9fWOa5k2bZQqHVa9h1bipL5bXEpfJtfLZ1d7Hz7f0jWla/cFyFRacK9UY46a+6PNnk1T0OeEvcFe9hlWjJj7rdS9jOt9hen3urN2Yk4uvz1nXs+25fvfcG1/i+rnGOnfW7vVz0GXImJGm1w+NfkzVa/xa2e6oLUfzps3StHGJmvDwU5o2LtE0Tou2UT7XG5Cu8dCdJIX46K2Ma9u0pJmmPwX2y+vDDZRFvbp1VHCZvd8BAAAAAAAAAEDFFxxiNf3Feou2UWWuOFQ5KEgvzXnNqKblT4PwME3/YLbPgIDFYik23NCibZR6PXifzyCHr7mUFNB76KlhFSJwNOqFsXr2Ly+Y7pGn2K6dNOL5Z66pOVWvYdWzf3mhTK0cA6nPQ/d7Be9cHnpqmPoOGaDgEKtGThxzxefSd8gATZwxpcR7/so7r/v8XnXdn+Jc7WfmSuvYLc7v509pW2OWxPXZUtIz0CA8TJPnJqnvkAHFHte2U2yZtru0jm2r6R/MLrZiZIPwML3yzuvl2v64rK6Fz65bW0aU+HOpJBaLRQnTXtLEGVP8/nzy1O/hgUpaOtfnM1ean2Ou9SvpWbvn3ng9MOqREsdq0yGm2M+UiTOm6NaWEaZtwSFWJS2d6/dz0fN6x019MaDPG65tN/z8888/B3oS/qzfkKLUr7doYsLYQE+lQnA6ncreu1+bvt4i+7mi9PLhnCNqFNZQHdrHqHGjMIU3vvIlVnv2Mf+QXTR/turUvnb/JQ2uLxtTN2v7txmanDgh0FMBAAAAAFxJublSYWGgZ4HrTXi49LvfXZGhW0S1057MbYG+QgCAm2PHjqtbz76SdFmf0dn/+THQl/Kb5XQ6lX/qjPG6clBlryBAYYFdFx0XJUnBIdW9gjzu+yUptObNfv9y/aLDocKCc/o+a69+KDyvqsHVjHZ0pa0qdzrvpL7P2quTx/PU4vYohTVtbMyprHM5lXtCR205xlxuqlZVLWPa+H3P6byTptfFjX8l3u+P6z4ezzmqU7knVDW4miSpZdvWfsNEnmvl695ezvGec5KkmnVrq15Yg2Kv2/OZlOT32fB1rOfYpRmvtOd0Op3avX2HTuWeUPPWkT5bc3re41vq1DKeexdf32eSSn2c+5zdv5duqlZV4bc1LVWAzN8zc3vH9pfcytPzGfGcf2muz/MYX89ZSccM727+u/bpH8w27udR22Ht3ZmlqsHV1CyyeZmerdJ+Rrl/zp08nqda9eoYn3Nl+Z73fJbKMgfX/cg7lquc723G/XVvE+pv3mW5P5d6jOc6X4nPrrIcZ9t3QD+e/0GSin0uSnLR4VDOgUM6c+KUJOlg9n4V2s+pTYcYNYtsXur77/o5J0k3VauqemEN/H4meX4OSMV/H899daapAt/IiWOMkPnpvJPa8c32Er8/POewe/sO/Xj+Bx3M3i+pqAXyTdWqqnnryOumRXVFVtfyX7LeWCnQ0/CL0N11wOl06vPk9Zozd2GJx4aHh2ng/feqa9yV+9cthO4QSITuAAAAAOA3gtAdrgRCdwDwm0LoDgBwrSkudAf81hUXusP16VoP3V27M0OpFNjtmpb0hjIyMkt1vM2Wo+lJs3Q8N0+DBw0o1XsAAAAAAAAAAAAAAAAAAEUI3VVgTqdTLyS+Ipstx2tfeHiYGoU1VIH9nM9A3uKly3XuXKFGP355fb4BAAAAAAAAAAAAAAAA4LeE0F0F9uLLU70Cd9HRUXr5xecV5NZb2ul0Knvvfv3l1STlF9iN7avXJOuxhx8yHetLgd0uh+OiQqzVSzz2WuF0OvWf//yn2Pk6HA4V2M9dVutb1xg1byl9/3pJyjtR1MM+KKiyQqzWUr8PAAAAAAAAAAAAAAAAQGARuqug8k6c9Kpg169PvM/KdRaLRS0jIzQj6a96dMRTpn2frFrjs81s+rZv9enqtT6r5HWJ66QH+v9R4Y0bXdK8PefwxZoVZT5uY+pmTU+aZbx+ctQw9e0dr/Rt3+qrlE1KSS3q4x0dHaWOHdqrV3x3WSwWOZ1Obf9uh9e1DR080Oc6TEuaaYwlSYvmz1ad2rV8rk94eJhenfKS3xBd3omT+sfqtVq9Jtm0PTTEqjZtWqlD+xj9vkN7n0FBp9OpU6fPmLZdTlgQAAAAAAAAAAAAAAAAwKUhdFdBfbkx1WvbYw8/VOx76tSupQkJY/TN1u3q0D5GLSKaewW3Cux2vTPvPVPQzFNKappSUtPUJa6Tnhv3TJkqvF0pWdl7VadObb00+VXT9oyMTGVkZCo4uJq6xnXWshWfavHS5V7vL2273QsXLuiztcmaM3eh1z6bLUeDhozQqhWLvYJzu7OyNX5Cos8x8wvsxpqGhlj15xcS1DIywnTMqdNnShVWBAAAAAAA15b8wvOaufwTSdLk4Y8EejoAAAAAgGvY9A9mm16H1rw50FMCrhkPjX5M9z/2ay4mOKR6oKeE3zhCdxWUZ3CsS1ynUrV+7RrXWV3jOvvdPy3pDZ/V7cLDw7xa2bqCeRMTxgZ6OXQ454hmvDHH735XVTxfgTuXTZu3lBi6m7vgfZ/r4+5f32w1rbHT6fQK3EVHR6lRwwaqXj1Yaz9fZ7T9zS+wa/yERJ/BPQAAAAAAcPlyT59V/xenSJK6xUYXG4SbtOB9bUjP0KThQ9Q9NuaSzue4+L/akJ4hKfChu/Xp2zV5wRKf+5o2qKvbb2umvnfeoab16wV0ngAAAADwW3VLHbqdAf4Eh1gVHOhJAG5uDPQEUD46tL+0X/y6S9/2rVegrEtcJy2aP1tvz3pdi+bPVpe4Tqb9KalpyjtxMtCXL5stR/kFdnWJ66RlS+Z7zVP6NXjXr0+8li2Zr+joKNP+/AK7bIcOF3se1/o8OWqYVq1YrCdHeYf0ln+yyvQ658hR0+vo6ChNfflFjX58mAYPGqCPFnvP91/fbA30kgIAAAAAcN3bkJ6hA8eOX9Fz1Ay1auXURK2cmnj5g11BB47mauVXm/XwK3/Tw1NeC/R0AAAAAAAAgGsale6uE1WrVr3sMTwrxfXrE2+q/Fandi1NTBirAvs5Uzhv0eIPr4lqd0MHD9TgQQMk/Vp9z7NN7oSEMUYVumlTXtLExFdM15K5K0vhjRsVe55F82cbbXn79o5XVKtIjX4mwdhvs+WowG5XiNUqSTp02Fwh8PChHBWeP2/sl6SxTz+hu7vcpbCGDYy1BgAAAAAAV96zb7yjVa9OUiWL5YqMX8liUd1bagT6Mk18Vfi7cPGiPvqfFC1Y84UOHM1V7umz19y8AQAAAAAA8NtR9YYr8/u68kLorgJyOp1e21xhrcsZ09Xi1CWi+a0+j72vX29TUG3Hjl2BXhJJ0j1d40yvIyOae4Xubo9ubXrdqGED07VkZe9V397xfs8RGmL1CsT5Wvt9+w8otl1bn+fML7Br9NPj9ecXEtS0SWMFBQUpKCjION6XOrVr6Ys1KwK9xAAAAAAAXDfatbhN+YWFOnA0V6s2bVH/rp3L9P6fnE4dzjuhA8dydfTkacXdHuWzLeuFixe1+ZffnXSPjdHO7w8q72y+opqE+w21rU/fLkmmYzzP16DWLWpav64a1aldLoHBKpUra1jvHkr5bocOHM1V8r+3aljvHj6vZ/+RY9qWvV8Nat2i9i2aKzS4ms/5+9rnupavtmdIkjq3aaUqlSsb27Nsh5V3Nt+4xuLWyX1tJalOjVDd2rC+MR4AAFdL8A0WFf7svPyBAAAAAEiSLJIq3XBDoKdRLEJ3FZDFxy9S92TvvazqaKdOn/Ha1iKiuc9jPUNmnmG9QPG8/mAfv9R1ry4nFQULV69JLvU52rRp5bXNYrEoPDxMNtuvFe1++OEH0zk99+cX2DV+QlFbmfDwMLVu1VI9u99TYpU9AAAAAABQPkKCb9KYAf308Ct/04xln6pTVMtSV3bbkpml596aZ9q2YM0XkqRJw4eoe2yMsb2g8AdNXrBEUlHoblv2fi1Y84XatbhNb4wd7TX2gWPHjeM3zJomScovPK+XFyzWtj37vI5v1+I2TRs9rNyCZqHBwZJyVb3qTV77Ji14XxvSM7zfYw3WvOfHGuv3+ZZ0bduzT8P79PQZ3EvP2qvJC5aoaYO6xlrlnj6rka/NVL690Ov4/nd31jP9+5nChSs3btaMZZ/6vAbPewAAwJVmvbGSCp2E7gAAAIDyUv3Gaz/SdmOgJ4BL0yWuU7mOl3PkqNe2mrfcXOr35504GdD1CA8PK/GY8lizDu19/8K2UVjDYt836cUJfudos+Vo9ZpkjX4mQaPHjFf6tm+vwooBAAAAAICm9eup/91FFe6mLfm4VO/5yenUXxcvkyQN79NTK6cm6oOXnjPGmbxgiX4q5i/d//jL7ye27dmnCxcveu1P/a6oIn+7FrcZQbpnZ87Rtj37FGoN1gcvPaeVUxP19nPPKNQarG179mnUa2+Uy3rknj5rBPvuaXe7ad/O7w8agbu3n3vGmEPTBnWVby/Usi9TjGOH/xK0+3RTms+1+PjLVElS3zt/L6moYl3/F6co315ousZJw4dIklZ+tVkfJG8w3n/h4kUjcDdp+BCtnJqotUlTjOMnL1ii/MLz5bImAACURrUbLbrlxt8FehoAAADAdcEi6eYK8N/XhO4qqEiPKnTfbN1e6vfuzsr2alHrK2BXeN73LyfPnDnrtS3EWr1cr8/XOYpTUugt0OrUrqW3ZrymCQljFB0d5fc4my1HM96Y47OFMAAAAAAAKH9P3NvLCK+52qIWJz1rrySpaYO6Gta7h+reUkNN69fTuAfuU7sWt5mO8SU0uJqaNqgrSfpu3wGv/Z9uSpP0a3Bt5/cHdeBorkKtwVqc+Lya1q+nurfUUOtmTbTq1UkKtQbrwNFcHTh2vNTXbDt+QuvTt5v+TFrwvvq/OEWSNG7QfV5tYT9J3SypKOTWulkTYw5vjH1SUlEwzhUijAxvpFBrsPLthcqyHTaNc+HiRSPY16tjrCTpo/9JMdbU/Rq7x8Zo5dSiTgEL1nxhjO9aN1elvLq31FBocDV1j43R354eqf53d9aRAP8DUQDAb8/NN1ZS8A2X3/IdAAAA+C276YYb1bRSlWu+taxEe9kKq06d2qbXKalpenToQyW2mN2YulnTk2ZJkoYOHqiOd8QqvHEjValSxevYY8dyvdqxStKBgzavbUFBQZd8LXknTnrNO2Nn5tVbzKvEYrGoa1xndY3rLKfTqZwjR5W5K0tbvtmqjIxfrze/wK7svfvVMjIi0FMGAAAAAOC6V6VyZf1p6CA999Y8zVq5Wp3btCq2VWvHqEitfe0Vn/sa162tbXv2aV36VnWMivQ7Rt87f68Zyz7Vx1+mmo47cOy40V41MryRJGlb9n5JUkzzpl5BuEoWi+6JidbKrzYr9btMNa1fr1TXfOBortHC1tPwPj2NMJy7ycMf0eThj3htd5/Td/sOqGNUpCpZLLrvrk5asOYLfbk9Q62bNTEdI0ndYqONdd7x/UFJ0pAe93hdY91bahgBPtf44XXrGNexcuNm9e/a2XR/ilt7AACulBtuuEH1K1XWzz//rAs//59+/Pn/dPHn/wv0tAAAAIAKodqNFlW9wVIhwnYuhO4qqJjb2yg0xKr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    }
   },
   "cell_type": "markdown",
   "id": "8f159705",
   "metadata": {},
   "source": [
    "![DSU_PPRL_ML_Pipeline_2022.png](attachment:DSU_PPRL_ML_Pipeline_2022.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df91cba6",
   "metadata": {},
   "source": [
    "### Import the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8e0ae37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt  # works with seaborn etc to display plots created by seaborn etc\n",
    "from matplotlib import style # to make the output of matplotlib pretier (all charts then turned red)\n",
    "style.use(\"ggplot\")# used because I prefer blue color for my charts\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import plotly.express as plotExp\n",
    "import plotly.figure_factory as factory_fig\n",
    "import plotly.graph_objs as graphObj\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler # would handle outliers (data standardization)\n",
    "from sklearn.preprocessing import LabelEncoder # for transforming Categorical data to Numerical data\n",
    "\n",
    "#import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a4e27a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## So we could display our plots in a cell\n",
    "%matplotlib inline\n",
    "from pylab import rcParams\n",
    "rcParams ['figure.figsize']=10,8 # intending figure size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e856d1d",
   "metadata": {},
   "source": [
    "***** To be able to print multiple output from a cell, we'll let all output come through from Ipython and we'll utilize Jupyter's interactive shell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10691591",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity=\"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54956254",
   "metadata": {},
   "outputs": [],
   "source": [
    "# By default, Pandas will display only 10 rows/cols\n",
    "\n",
    "# Globally set Max rows displayed in output to 20\n",
    "pd.set_option('display.max_rows', 30)\n",
    "\n",
    "# Globally set display for Pandas to always display all the columns\n",
    "#pd.set_option('display.line_width', 5000)\n",
    "pd.set_option('display.max_columns', None)# Settig to none makes display unlimited, better than putting a number\n",
    "\n",
    "# Globally set Max rows displayed in output to 20\n",
    "pd.set_option('display.max_rows', None)  # Great! No deprecation, many rows printed as wanted\n",
    "\n",
    "# Globally set decimal places Displayed in output to any number of choice, change it prn\n",
    "pd.set_option('display.precision', 2)\n",
    "\n",
    "# Globally set floatto avoid getting such values in\n",
    "#exponent notation, in correlation especially (Worked)\n",
    "pd.options.display.float_format = '{:20,.4f}'.format \n",
    "\n",
    "# Globally set max_colwidth display\n",
    "pd.options.display.max_colwidth = None # This removes any limit to num of col to be shown\n",
    "\n",
    "# Globally set display expand_frame_repr\n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c4c3fb",
   "metadata": {},
   "source": [
    "### Read the pickle file containing the comparison output where the blocking variable was lastname to memory "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e40c4620",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2=pd.read_pickle('comparison_output_ln.pkl')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366f64a9",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec95ea11",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ccbd531",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46dcb84a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b45ce6ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bb81b5c2",
   "metadata": {},
   "source": [
    "#### Rename columns as needed, using Dictionary data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db3a6f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.rename(columns={'dob': 'dateOfBirth',\n",
    "                         'firstname':'firstName','lasttname':'lastName'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16b23217",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c654c07",
   "metadata": {},
   "source": [
    "#### Plot data distribution to see if data follows Gaussian distribution (many ML algorithms will not do well if data is not normally distributed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2de421e",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1, figsize=(15,6))\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.5)\n",
    "sns.distplot(pprl_DF2, bins=10)\n",
    "plt.title('PPRL Data Distribution')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed3775ab",
   "metadata": {},
   "source": [
    "*****The distribution plot above shows the data does not follow Gaussian (normal) distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43c32f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "####Describe the data and transpose result if you so desire\n",
    "pprl_DF2.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08d29d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You may also save the output for reporting purposes\n",
    "\n",
    "pprl_DataDescrp= pprl_DF2.describe().T # Remember describe only works on numerical data\n",
    "\n",
    "# You may also save the output for reporting purposes\n",
    "#pprl_DataDescrp.to_csv('Report: PPRL_DataDescription.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e025fed",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.head()\n",
    "pprl_DF2.isna().sum()# Count the total number of missingness per column\n",
    "pprl_DF2.shape # Shape is total row bumbers multiplied by total column numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66d5b0f",
   "metadata": {},
   "source": [
    "#### In case you want to perform auto-descriptive statistics with Pandas Profiling package "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "708628f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install  https://github.com/pandas-profiling/pandas-profiling/archive/master.zip \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f840d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_profiling import ProfileReport\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4897a78",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Obtain descriptive statistics of the DataFrame (DF) using Pandas Profiling package from Github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0588b111",
   "metadata": {},
   "outputs": [],
   "source": [
    "summaryReport= ProfileReport(pprl_DF2,title='PPRL_UnlabelledData_DescriptiveReport', html={'style':{'full_width':'True'}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7698db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "summaryReport"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2e257d",
   "metadata": {},
   "source": [
    "### Multiclass Classification Target Engineering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9371b09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Remove the blocking variable and the binary target\n",
    "pprl_DF2.drop([\"true_links\", 'lastName'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30438cad",
   "metadata": {},
   "source": [
    "##### Compute record-pair mean (rowwise average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a1d8b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Round to 1 decimal place to permit float comparison down the road\n",
    "pprl_DF2['row_Means'] =pprl_DF2.mean(axis=1).round(1) # Round to 1 to permit comparison, based on exp with working wt mock data\n",
    "pprl_DF2['row_Means'].describe()\n",
    "pprl_DF2['row_Means'].head()\n",
    "print('============================')\n",
    "pprl_DF2.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f5b42e",
   "metadata": {},
   "source": [
    "#### Bin the row means and categorize them as matching classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd6591d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #### View distinct values of matched DF and their frequencies, sort them by frequency as well\n",
    "\n",
    "pprl_DF2['row_Means'].value_counts().sort_values()\n",
    "\n",
    "pprl_DF2['matched']=pprl_DF2['row_Means']\n",
    "\n",
    "\n",
    "pprl_DF2['matched']=pd.cut(pprl_DF2.matched, bins=[\n",
    "    -0.001, 0.45, 0.75,2.0], labels=['no','mayBe','yes'])\n",
    "\n",
    "# ##### Check class distribution of the target\n",
    "pprl_DF2['matched'].value_counts()\n",
    "\n",
    "##### Plot a bar chart to display the class distribution\n",
    "pprl_DF2['matched'].value_counts().plot(kind='bar', color='b',\n",
    "                                        title=\"Matching Distribution Plot by Classes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5491029a",
   "metadata": {},
   "source": [
    "****Both the textual and vizualized distribution results show there is class imbalance in the dataset (against MayBe and Yes). We'll address it at modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9ccf534",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0b9cf9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6095451",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b18ae45",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis completed "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c22a522",
   "metadata": {},
   "source": [
    "#### Start modeling-related operations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e02550cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop firstname based on FImportance, drop row_means as well\n",
    "pprl_DF2.drop('row_Means', axis=1, inplace=True)\n",
    "\n",
    "#pprl_DF2.drop('row_Means', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56595a8b",
   "metadata": {},
   "source": [
    "##### Create feature vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81ec6e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =pprl_DF2.drop(\"matched\", axis=1)\n",
    "y =pprl_DF2.matched\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f71a43c",
   "metadata": {},
   "source": [
    "###### Transform categorical data into numerical data\n",
    "only the target is categorical in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "395497f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encod=LabelEncoder()\n",
    "label_encod.fit(y)\n",
    "y=label_encod.transform(y)\n",
    "classes=label_encod.classes_\n",
    "classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51119cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.shape\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3eb10e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Import the modelling-related classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f01dadb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install imblearn # Required before importing the imblearn pipeline and SMOTE \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5419d34",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from numpy.random import RandomState\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import svm\n",
    "\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report \n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, f1_score\n",
    "from sklearn.model_selection import cross_validate, GridSearchCV, cross_val_score\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from imblearn.pipeline import Pipeline as imbaPipe # The SMOTE class is in the imblearn package.\n",
    "from imblearn.pipeline import make_pipeline # For gridsearching SMOTE inside Grid\n",
    "\n",
    "\n",
    "from imblearn.over_sampling import SMOTE # SMOTE= synthetic minority oversampling technique\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "from sklearn.metrics import recall_score, f1_score, precision_score,roc_curve\n",
    "from sklearn.metrics import roc_auc_score, precision_score, recall_score\n",
    "from sklearn.metrics import precision_recall_fscore_support # For summary table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab58f883",
   "metadata": {},
   "source": [
    "##### Split the dataset (Create train-test data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "240008e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Split Data into training and test (you can split as desired, but 70:30% was used here) \n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y,\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33c373a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confirm the Output Dimensions\n",
    "# We can confirm the dimensions of the data are the same within test and train\n",
    "# The proportion should also be close to the test_size argument.\n",
    "\n",
    "x_train.shape\n",
    "x_test.shape\n",
    "y_train.shape\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bd55a2",
   "metadata": {},
   "source": [
    "##### Try viewing  the ground truth of some record pairs \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d92f3bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test[655:671] # slicing, so, this excludes the pair of records in index 671"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "391f89f9",
   "metadata": {},
   "source": [
    "##### Obtain the frequency distribution of each class per data split\n",
    "**** Remember, these are no longer in DF format but ndarray, so, vlaue_counts() can't work, but we can obtain the same info from the ndarray using np.unique() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76381aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "collections.Counter(y_test) # we could acheve the same goal using numpy\n",
    "print('y_test value frequency=========')\n",
    "\n",
    "unique, counts = np.unique(y_test, return_counts=True)\n",
    "dict(zip(unique, counts))\n",
    "\n",
    "print('x_test value frequency=========')\n",
    "\n",
    "unique, counts = np.unique(x_test, return_counts=True)\n",
    "dict(zip(unique, counts))\n",
    "\n",
    "\n",
    "print('y_train value frequency=========')\n",
    "unique, counts = np.unique(y_train, return_counts=True)\n",
    "dict(zip(unique, counts))\n",
    "\n",
    "print('x_train value frequency=========')\n",
    "\n",
    "unique, counts = np.unique(x_train, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381ba06d",
   "metadata": {},
   "source": [
    "#### Now scale the train and test data \n",
    "***** Do so separately to avoid data leakage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d93585c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()# to normalize the values so they could be between 0 and 1.\n",
    "\n",
    "x_train=scaler.fit_transform(x_train)\n",
    "x_test=scaler.transform(x_test)\n",
    "\n",
    "##### It is expected that scaling put all the training data in a range of 0 and 1\n",
    "\n",
    "x_train[:5]# Show me the first 5 records in the scaled Xtrain\n",
    "x_test[:5] # Show me the first 5 records in the scaled Xtest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c42fdfa",
   "metadata": {},
   "source": [
    "#### Perform ML Experiments "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a7248e",
   "metadata": {},
   "source": [
    "#### Build multiple models\n",
    "***** Steps:\n",
    "\n",
    "1. Build baseline models\n",
    "3. Evaluate the models\n",
    "4. Embark on model performance improvement activities\n",
    "4. Build optimized models\n",
    "5. Evaluate the models\n",
    "6. Identify the most generalizable model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb64dcc8",
   "metadata": {},
   "source": [
    "##### Instantiate the intending classifiers and their Scikit Learn pipelines (for baseline models /(Model Set 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f17af629",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbayesPipe_SK = Pipeline([(\"dataScaler\", scaler), (\"naive\", MultinomialNB())])\n",
    "\n",
    "logregPipe_SK = Pipeline([(\"dataScaler\", scaler), (\"LogReg\", LogisticRegression())]) # Per scikit,newton is as one of thise meant for multic in lo\n",
    "\n",
    "# Any classifier can be used as an estimator, not only random forest\n",
    "ovr_ForestPipe_SK=Pipeline([(\"dataScaler\", scaler),\n",
    "                            ('ovrForest', OneVsRestClassifier(RandomForestClassifier()))])# Notice that it doesn't have to be SVM that we utilize with OVR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d4d5f1",
   "metadata": {},
   "source": [
    "### Baseline Models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e81a59",
   "metadata": {},
   "source": [
    "##### Create a modeling result dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93746866",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics_DF_pipeSK = pd.DataFrame(index =['Accuracy','Precision','Recall','F1'], \n",
    "                          columns=['NBayes', 'OVR','LReg'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb5caa7",
   "metadata": {},
   "source": [
    "#### Naive Bayes Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99b9a653",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pipe1 = nbayesPipe_SK.steps[1][1]\n",
    "\n",
    "NBayesmodel= pipe1.fit(x_train, y_train)\n",
    "\n",
    "y_pred_testBayes= NBayesmodel.predict(x_test)\n",
    "\n",
    "Bayes_pos_probs =NBayesmodel.predict_proba(x_test)[:,2]# index2 holds the predicted probabs for \"yes\"\n",
    "Bayes_pos_probs[10] # Show me the 11th of the \"yes\" probabilities  \n",
    "\n",
    "metrics_DF_pipeSK.loc['Accuracy', 'NBayes']= accuracy_score(y_pred = y_pred_testBayes,\n",
    "                                                            y_true=y_test)\n",
    "metrics_DF_pipeSK.loc['Precision', 'NBayes']= precision_score(y_pred = y_pred_testBayes,\n",
    "                                                              y_true=y_test,average='weighted')\n",
    "metrics_DF_pipeSK.loc['Recall', 'NBayes']= recall_score(y_pred = y_pred_testBayes,\n",
    "                                                        y_true=y_test,average='weighted')\n",
    "metrics_DF_pipeSK.loc['F1', 'NBayes']= f1_score(y_pred = y_pred_testBayes,\n",
    "                                                y_true=y_test,average='weighted')\n",
    "# classification_report\n",
    "train_y_predBayes= NBayesmodel.predict(x_train)\n",
    "print('Bayes train :', classification_report(y_train, train_y_predBayes),flush=True)\n",
    "print('Bayes test :', classification_report(y_test, y_pred_testBayes),flush=True)\n",
    "\n",
    "# Get confusion matrix\n",
    "\n",
    "sns.set(rc = {'figure.figsize': (15, 8)})\n",
    "conf_matrix = confusion_matrix(y_pred_testBayes, y_test)\n",
    "np.set_printoptions(precision = 2)\n",
    "print(\"Print Bayes's crosstab (Conf matrix) :\" '\\n',\"===================\",'\\n',conf_matrix)# Matrix Table, not the graphic one\n",
    "\n",
    "# Now Plot the Conf Matrix\n",
    "\n",
    "#sns.heatmap(conf_matrix, annot=True, fmt='d')\n",
    "\n",
    "group_names = ['T MayB','F MayB','F MayB',\n",
    "               'F No (F-Neg : T2Error)','T No (T-Neg)','F No (F-Neg : T2Error)',\n",
    "               'F Yes (F-Pos : T1Error)','F Yes (F-Pos : T1Error)','T Yes (T-Pos)']\n",
    "\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in conf_matrix.flatten()]\n",
    "group_percentages = ['{0:.2%}'.format(value) for value in \n",
    "                                         conf_matrix.flatten()/np.sum(conf_matrix)]\n",
    "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(3,3)\n",
    "\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=labels, fmt='', cmap=\"Blues\")# or cmap=\"OrRd\", \"OrRd_r\" colors\n",
    "\n",
    "\n",
    "plt.xlabel('Actual Label')\n",
    "plt.ylabel('Predicted Label')\n",
    "plt.title(\"CMatrix for NBayes Baseline: PPRL, Multiclass [noLastname]\")\n",
    "#plt.savefig('')\n",
    "plt.show()  # SAVE AS THEY SHOW\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f6bd7a",
   "metadata": {},
   "source": [
    "#### OVR Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "018f45f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pipe2 = ovr_ForestPipe_SK.steps[1][1]\n",
    "ovrmodel= pipe2.fit(x_train, y_train)\n",
    "\n",
    "Y_pred_testOVR= ovrmodel.predict(x_test)\n",
    "OVR_pos_probs =ovrmodel.predict_proba(x_test)[:,2]# this is  y_score in scikit\n",
    "\n",
    "\n",
    "metrics_DF_pipeSK.loc['Accuracy', 'OVR']= accuracy_score(y_pred = Y_pred_testOVR,\n",
    "                                                            y_true=y_test)\n",
    "metrics_DF_pipeSK.loc['Precision', 'OVR']= precision_score(y_pred = Y_pred_testOVR,\n",
    "                                                              y_true=y_test, average='weighted')\n",
    "metrics_DF_pipeSK.loc['Recall', 'OVR']= recall_score(y_pred = Y_pred_testOVR,\n",
    "                                                        y_true=y_test,average='weighted')\n",
    "metrics_DF_pipeSK.loc['F1', 'OVR']= f1_score(y_pred = Y_pred_testOVR,\n",
    "                                                y_true=y_test,average='weighted')\n",
    "\n",
    "# classification_report\n",
    "train_y_predOVR= ovrmodel.predict(x_train)\n",
    "print('OVR train :', classification_report(y_train, train_y_predOVR),flush=True)\n",
    "print('OVR test :', classification_report(y_test, Y_pred_testOVR),flush=True)\n",
    "\n",
    "# Get confusion matrix\n",
    "\n",
    "sns.set(rc = {'figure.figsize': (15, 8)})\n",
    "conf_matrix = confusion_matrix(Y_pred_testOVR, y_test)\n",
    "np.set_printoptions(precision = 2)\n",
    "print(\"Print OVR's crosstab (Conf matrix) :\" '\\n',\"===================\",'\\n',conf_matrix)# Matrix Table, not the graphic one\n",
    "\n",
    "# Now Plot the Conf Matrix\n",
    "\n",
    "#sns.heatmap(conf_matrix, annot=True, fmt='d')\n",
    "#group_names = ['True MayB','False MayB','False MayB','False No','True No','False No','False Yes','False Yes','True Yes']\n",
    "group_names = ['T MayB','F MayB','F MayB',\n",
    "               'F No (F-Neg : T2Error)','T No (T-Neg)','F No (F-Neg : T2Error)',\n",
    "               'F Yes (F-Pos : T1Error)','F Yes (F-Pos : T1Error)','T Yes (T-Pos)']\n",
    "\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in conf_matrix.flatten()]\n",
    "group_percentages = ['{0:.2%}'.format(value) for value in \n",
    "                                         conf_matrix.flatten()/np.sum(conf_matrix)]\n",
    "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(3,3)\n",
    "    \n",
    "sns.heatmap(conf_matrix, annot=labels, fmt='', cmap=\"Blues\")# cmap=\"OrRd\", \"OrRd_r\" colors\n",
    "plt.xlabel('Actual Label')\n",
    "plt.ylabel('Predicted Label')\n",
    "plt.title(\"CMatrix for OVR-Forest Baseline: PPRL, Multiclass [noLastname]\")\n",
    "#plt.savefig('')# must come before the show function\n",
    "plt.show()  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60b9d51",
   "metadata": {},
   "source": [
    "### LReg Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4aeb55e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pipe4 =  logregPipe_SK.steps[1][1]\n",
    "logregmodel= pipe4.fit(x_train, y_train)\n",
    "\n",
    "y_pred_testlogReg= logregmodel.predict(x_test)\n",
    "lreg_pos_probs =logregmodel.predict_proba(x_test)[:,2]# this is  y_score in scikit\n",
    "\n",
    "\n",
    "metrics_DF_pipeSK.loc['Accuracy', 'LReg']= accuracy_score(y_pred = y_pred_testlogReg,\n",
    "                                                            y_true=y_test)\n",
    "metrics_DF_pipeSK.loc['Precision', 'LReg']= precision_score(y_pred = y_pred_testlogReg,\n",
    "                                                              y_true=y_test, average='weighted')\n",
    "metrics_DF_pipeSK.loc['Recall', 'LReg']= recall_score(y_pred = y_pred_testlogReg,\n",
    "                                                        y_true=y_test,average='weighted')\n",
    "metrics_DF_pipeSK.loc['F1', 'LReg']= f1_score(y_pred = y_pred_testlogReg,\n",
    "                                                y_true=y_test,average='weighted')\n",
    "\n",
    "# classification_report\n",
    "train_y_predlreg= logregmodel.predict(x_train)\n",
    "print('Lreg Sklearn train :', classification_report(y_train, train_y_predlreg),flush=True)\n",
    "print('Lreg Sklearn test :', classification_report(y_test, y_pred_testlogReg),flush=True)\n",
    "\n",
    "# Get confusion matrix\n",
    "\n",
    "sns.set(rc = {'figure.figsize': (15, 8)})\n",
    "conf_matrix = confusion_matrix(y_pred_testlogReg, y_test)\n",
    "np.set_printoptions(precision = 2)\n",
    "print(\"Print LReg Sklearn crosstab (Conf matrix) :\" '\\n',\"===================\",'\\n',conf_matrix)# Matrix Table, not the graphic one\n",
    "\n",
    "# Now Plot the Conf Matrix\n",
    "\n",
    "#sns.heatmap(conf_matrix, annot=True, fmt='d')\n",
    "group_names = ['T MayB','F MayB','F MayB',\n",
    "               'F No (F-Neg : T2Error)','T No (T-Neg)','F No (F-Neg : T2Error)',\n",
    "               'F Yes (F-Pos : T1Error)','F Yes (F-Pos : T1Error)','T Yes (T-Pos)']\n",
    "\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in conf_matrix.flatten()]\n",
    "group_percentages = ['{0:.2%}'.format(value) for value in \n",
    "                                         conf_matrix.flatten()/np.sum(conf_matrix)]\n",
    "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(3,3)\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=labels, fmt='', cmap=\"Blues\")# cmap=\"OrRd\", \"OrRd_r\" colors\n",
    "plt.xlabel('Actual Label')\n",
    "plt.ylabel('Predicted Label')\n",
    "plt.title(\"CMatrix for LReg Baseline: PPRL, Multiclass [noLastname]\")\n",
    "#plt.savefig('')\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b60896",
   "metadata": {},
   "source": [
    "#### Results dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "411ed2ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('== Score Table: PPRL Model Set 1 (Baseline data) ==')\n",
    "\n",
    "100*metrics_DF_pipeSK\n",
    "### STORE THE DATAFRAME OF METRIC RESULT IN A VARIABLE AND SAVE IT AS A CSV FILE\n",
    "\n",
    "lName_MClass_ResultTable=100*metrics_DF_pipeSK\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24139ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('PLOT RESULT VISUALS')\n",
    "fig, ax = plt.subplots(figsize = (8,5))\n",
    "metrics_DF_pipeSK.plot(kind='barh', ax=ax)# wondering why the legend is no longer well positioned\n",
    "ax.grid()\n",
    "\n",
    "plt.title('Subplots of PPRL MultiClass Models And Scores: Baseline noLastname')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b9b49e8",
   "metadata": {},
   "source": [
    "*****The performance of the baseline models is not satisfactory and it's unreliable due to the imbalance nature of the data. Therefore, we need to carry out some model improvement operations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d84bc13",
   "metadata": {},
   "source": [
    "# Pause Here!!!\n",
    "### Go to the file named, 'PPRL_Multiclass_Fselection+ParamTuning_2022' to perform two model performance improvement activities (Feature selection, and Parameter tuning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66b97975",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "883d225f",
   "metadata": {},
   "source": [
    "### Now Use the Optimized Algorithms for Further Modeling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9dff419",
   "metadata": {},
   "outputs": [],
   "source": [
    "pprl_DF2.drop('firstName', axis=1, inplace=True)# based on FImportance result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1bdf678",
   "metadata": {},
   "source": [
    "### Use the output of parameter tuning and K search to create full imblearn pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dabeb3c9",
   "metadata": {},
   "source": [
    "##### Full Imblearn pipeline for NBayes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5de20e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "smNbayes_Pipe= imbaPipe([('smote', SMOTE(sampling_strategy =\"auto\", k_neighbors=9, random_state=40)),\n",
    "                                           (\"dataScaler\", scaler), (\"naive\", MultinomialNB(alpha=2))])# nbayes defined above as NBayes()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b27a869",
   "metadata": {},
   "source": [
    "##### Full Imblearn pipeline for OneVersusRest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e604debf",
   "metadata": {},
   "outputs": [],
   "source": [
    "smOvrRforest_Pipe = imbaPipe([('smote', SMOTE(sampling_strategy =\"auto\", k_neighbors=9, \n",
    "                                              random_state=40)),\n",
    "                                (\"dataScaler\", scaler), \n",
    "                          (\"oneVRestRF\", OneVsRestClassifier(RandomForestClassifier(\n",
    "                              bootstrap =False,max_depth=25, min_samples_split= 2,\n",
    "                             n_estimators = 5,random_state= 42)))])# min_samples_leaf= 2\n",
    "\n",
    "\n",
    "                             \n",
    "                             "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aaa84a4",
   "metadata": {},
   "source": [
    "#### Full Imblearn Pipeline for LReg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9975e979",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "smLReg_Pipe = imbaPipe([('smote', SMOTE(sampling_strategy =\"auto\", k_neighbors=1, random_state=40)),\n",
    "                              (\"dataScaler\", scaler), (\"LReg\", LogisticRegression(C=10, \n",
    "                                                                            penalty='l2',\n",
    "                                                                        solver='newton-cg'))])\n",
    "\n",
    "## LReg will automatically use multi_class='multinomial', penalty='l2' when it discovers it's a multiclass problem\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b1fc28f",
   "metadata": {},
   "source": [
    "### Construct optimized models with the imblearn pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "191d5ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create a Dataframe of Model Eval Metrics, with classifier's name as their columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22e79af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics_DF_pipeSM = pd.DataFrame(index =['Accuracy','Precision','Recall','F1'], \n",
    "                          columns=['NBayes', 'OVR', 'LReg'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53db216e",
   "metadata": {},
   "source": [
    "#### Naive Bayes Optimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e5774d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pipeline1 = smNbayes_Pipe\n",
    "x_train.shape\n",
    "y_train.shape\n",
    "\n",
    "# Augment the training data\n",
    "x_train_oversampled, y_train_oversampled = pipeline1.steps[0][1].fit_resample(x_train, y_train)\n",
    "x_train_oversampled.shape\n",
    "y_train_oversampled.shape\n",
    "\n",
    "NBayesmodel= pipeline1.steps[2][1].fit(x_train_oversampled, y_train_oversampled)\n",
    "\n",
    "y_pred_testNBayes= NBayesmodel.predict(x_test)\n",
    "NBayes_pos_probs =NBayesmodel.predict_proba(x_test)[:,2]\n",
    "\n",
    "\n",
    "metrics_DF_pipeSM.loc['Accuracy', 'NBayes']= accuracy_score(y_pred = y_pred_testNBayes,\n",
    "                                                            y_true=y_test)\n",
    "metrics_DF_pipeSM.loc['Precision', 'NBayes']= precision_score(y_pred = y_pred_testNBayes,\n",
    "                                                              y_true=y_test,average='weighted')\n",
    "metrics_DF_pipeSM.loc['Recall', 'NBayes']= recall_score(y_pred = y_pred_testNBayes,\n",
    "                                                        y_true=y_test,average='weighted')\n",
    "metrics_DF_pipeSM.loc['F1', 'NBayes']= f1_score(y_pred = y_pred_testNBayes,\n",
    "                                                y_true=y_test,average='weighted')\n",
    "# classification_report\n",
    "train_y_predNBayes= NBayesmodel.predict(x_train_oversampled)\n",
    "print('NBayes train :')\n",
    "print(classification_report(y_train_oversampled, train_y_predNBayes))\n",
    "\n",
    "print('NBayes test :')\n",
    "print(classification_report(y_test, y_pred_testNBayes))\n",
    "\n",
    "# Get matrix\n",
    "\n",
    "sns.set(rc = {'figure.figsize': (15, 8)})\n",
    "conf_matrix = confusion_matrix(y_pred_testNBayes, y_test)\n",
    "np.set_printoptions(precision = 2)\n",
    "print(\"Print NBayes's crosstab (Conf matrix) :\" '\\n',\"===================\",'\\n',conf_matrix)# Matrix Table, not the graphic one\n",
    "\n",
    "# Now Plot the Conf Matrix\n",
    "\n",
    "group_names = ['T MayB','F MayB','F MayB',\n",
    "               'F No (F-Neg : T2Error)','T No (T-Neg)','F No (F-Neg : T2Error)',\n",
    "               'F Yes (F-Pos : T1Error)','F Yes (F-Pos : T1Error)','T Yes (T-Pos)']\n",
    "\n",
    "\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in conf_matrix.flatten()]\n",
    "group_percentages = ['{0:.2%}'.format(value) for value in \n",
    "                                         conf_matrix.flatten()/np.sum(conf_matrix)]\n",
    "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(3,3)\n",
    "   \n",
    "sns.heatmap(conf_matrix, annot=labels, fmt='', cmap=\"Blues\")# other colors::cmap=\"OrRd\", \"OrRd_r\" colors\n",
    "plt.xlabel('Actual Label')\n",
    "plt.ylabel('Predicted Label')\n",
    "plt.title(\"CMatrix for NBayes Model: PPRL, AugmMulticlass [noLastname]\")\n",
    "#plt.savefig('')\n",
    "plt.show()  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fbc4541",
   "metadata": {},
   "source": [
    "#### OVR Optimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07ec68d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pipeline2 = smOvrRforest_Pipe\n",
    "\n",
    "x_train_oversampled, y_train_oversampled = pipeline2.steps[0][1].fit_resample(x_train, y_train)\n",
    "\n",
    "\n",
    "print('------Fit the pipeline on the data. I copied the training set to another variable\"')\n",
    "\n",
    "\n",
    "x_train_oversampled.shape\n",
    "y_train_oversampled.shape\n",
    "\n",
    "ovrModel= pipeline2.steps[2][1].fit(x_train_oversampled, y_train_oversampled)\n",
    "\n",
    "y_pred_testOVR= ovrModel.predict(x_test)\n",
    "y_pred_testOVR[5]\n",
    "OVR_pos_probs =ovrModel.predict_proba(x_test)[:,2]# this is  y_score in scikit\n",
    "\n",
    "OVR_pos_probs\n",
    "\n",
    "metrics_DF_pipeSM.loc['Accuracy', 'OVR']= accuracy_score(y_pred = y_pred_testOVR,\n",
    "                                                            y_true=y_test)\n",
    "metrics_DF_pipeSM.loc['Precision', 'OVR']= precision_score(y_pred = y_pred_testOVR,\n",
    "                                                              y_true=y_test, average='weighted')\n",
    "metrics_DF_pipeSM.loc['Recall', 'OVR']= recall_score(y_pred = y_pred_testOVR,\n",
    "                                                        y_true=y_test,average='weighted')\n",
    "metrics_DF_pipeSM.loc['F1', 'OVR']= f1_score(y_pred = y_pred_testOVR,\n",
    "                                                y_true=y_test,average='weighted')\n",
    "\n",
    "# classification_report\n",
    "train_y_predOVR= ovrModel.predict(x_train_oversampled)\n",
    "print('OVR train :', classification_report(y_train_oversampled, train_y_predOVR),flush=True)\n",
    "print('OVR test :', classification_report(y_test, y_pred_testOVR),flush=True)\n",
    "\n",
    "# GET CMATRIX\n",
    "\n",
    "sns.set(rc = {'figure.figsize': (15, 8)})\n",
    "conf_matrix = confusion_matrix(y_pred_testOVR, y_test)\n",
    "np.set_printoptions(precision = 2)\n",
    "print(\"Print OVR's crosstab (Conf matrix) :\" '\\n',\"===================\",'\\n',conf_matrix)# Matrix Table, not the graphic one\n",
    "\n",
    "#pd.crosstab(ovrModel.y_pred_testOVR, ovrModel.y_test)\n",
    "    \n",
    "# Now Plot the Conf Matrix\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d')\n",
    "#group_names = ['True Neg','False Neg','False Pos','True Pos']# This is an array of size 4, but we have a multiclass, so,size9\n",
    "\n",
    "group_names = ['T MayB','F MayB','F MayB',\n",
    "               'F No (F-Neg : T2Error)','T No (T-Neg)','F No (F-Neg : T2Error)',\n",
    "               'F Yes (F-Pos : T1Error)','F Yes (F-Pos : T1Error)','T Yes (T-Pos)']\n",
    "\n",
    "\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in conf_matrix.flatten()]\n",
    "group_percentages = ['{0:.2%}'.format(value) for value in \n",
    "                                         conf_matrix.flatten()/np.sum(conf_matrix)]\n",
    "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(3,3)\n",
    "\n",
    "#labels=classes\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=labels, fmt='', cmap=\"Blues\")# cmap=\"OrRd\", \"OrRd_r\" colors\n",
    "plt.xlabel('Actual Label')\n",
    "plt.ylabel('Predicted Label')\n",
    "plt.title(\"CMatrix for OVR-Forest Model: PPRL, AugmMulticlass [noLastname]\")\n",
    "#plt.savefig('')\n",
    "plt.show()  # SAVE AS THEY SHOW"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f51ad6",
   "metadata": {},
   "source": [
    "#### LReg Optimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc18b238",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pipeline4 = smLReg_Pipe\n",
    "# Augment the training data\n",
    "\n",
    "x_train_oversampled, y_train_oversampled = pipeline4.steps[0][1].fit_resample(x_train, y_train)\n",
    "x_train_oversampled.shape\n",
    "\n",
    "y_train_oversampled.shape\n",
    "LRegModel= pipeline4.steps[2][1].fit(x_train_oversampled, y_train_oversampled)\n",
    "\n",
    "y_pred_testLReg= LRegModel.predict(x_test)\n",
    "LReg_pos_probs =LRegModel.predict_proba(x_test)[:,2]# this is  y_score in scikit\n",
    "\n",
    "\n",
    "metrics_DF_pipeSM.loc['Accuracy', 'LReg']= accuracy_score(y_pred = y_pred_testLReg,\n",
    "                                                            y_true=y_test)\n",
    "metrics_DF_pipeSM.loc['Precision', 'LReg']= precision_score(y_pred = y_pred_testLReg,\n",
    "                                                              y_true=y_test,average='weighted')\n",
    "metrics_DF_pipeSM.loc['Recall', 'LReg']= recall_score(y_pred = y_pred_testLReg,\n",
    "                                                        y_true=y_test,average='weighted')\n",
    "metrics_DF_pipeSM.loc['F1', 'LReg']= f1_score(y_pred = y_pred_testLReg,\n",
    "                                                y_true=y_test,average='weighted')\n",
    "\n",
    "# classification_report\n",
    "train_y_predLReg= LRegModel.predict(x_train_oversampled)\n",
    "print('LReg train :', classification_report(y_train_oversampled, train_y_predLReg),flush=True)\n",
    "print('LReg test :', classification_report(y_test, y_pred_testLReg),flush=True)\n",
    "\n",
    "# Get Confusion Matrix\n",
    "\n",
    "sns.set(rc = {'figure.figsize': (15, 8)})\n",
    "conf_matrix = confusion_matrix(y_pred_testLReg, y_test)\n",
    "np.set_printoptions(precision = 2)\n",
    "print(\"Print LReg's crosstab (Conf matrix) :\" '\\n',\"===================\",'\\n',conf_matrix)# Matrix Table, not the graphic one\n",
    "\n",
    "#Now Plot the Conf Matrix\n",
    "\n",
    "group_names = ['T MayB','F MayB','F MayB',\n",
    "               'F No (F-Neg : T2Error)','T No (T-Neg)','F No (F-Neg : T2Error)',\n",
    "               'F Yes (F-Pos : T1Error)','F Yes (F-Pos : T1Error)','T Yes (T-Pos)']\n",
    "\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in conf_matrix.flatten()]\n",
    "group_percentages = ['{0:.2%}'.format(value) for value in \n",
    "                                         conf_matrix.flatten()/np.sum(conf_matrix)]\n",
    "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(3,3)\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=labels, fmt='', cmap=\"Blues\")# other colors: cmap=\"OrRd\", \"OrRd_r\" colors\n",
    "plt.xlabel('Actual Label')\n",
    "plt.ylabel('Predicted Label')\n",
    "plt.title(\"CMatrix for LReg Model: PPRL, AugmMulticlass [FirstORlastname]\")\n",
    "#plt.savefig('')\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b10b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "### RESULT DATAFRAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c09e2f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('== Score Table: PPRL Model Set 2 (Augmented data) ==')\n",
    "\n",
    "Aug_firstORlast_MClass_ResultTable=100*metrics_DF_pipeSM\n",
    "Aug_firstORlast_MClass_ResultTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08d61faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "### PLOT RESULT VISUALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eb5f0fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,5))\n",
    "metrics_DF_pipeSM.plot(kind='barh', ax=ax)\n",
    "ax.grid()\n",
    "\n",
    "plt.title('Subplots of PPRL MultiClass Models And Scores: Augm_lastname')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f618f48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Firstname data says,\"Awesome!\":::::::::::::::::::::::::')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35d48888",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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