eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "483b5002-00ab-4e69-a36c-a50877290bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cERERuvnmm0scUbx///5ycXGRp6enbrzxRh0/flwrV65U3bp1S9zPIUOG6NChQ1q1apXefPNNubq66s477yy29uOPP1aXLl1kt9udjmfPnj0l/fnanOlCP+8uLi6qW7euDhw4UGL/F6qs3yPZ2dnatm2bbrvtNrm6ujrVRUZGlmlb119/vX755RfFx8fr8ccfV8eOHbVu3ToNGjRIffr0cZz5XrVqldzd3Uv8rim0Y8cO9enTR/Xq1VPNmjXl4uKiQYMGKT8/v8QrlqTz/64CUDUQugH87QQHB6tdu3ZFHj4+Pudcdty4cZo+fboSEhLUs2dP1atXT926dSvx55nOdOzYMUkq9rJTu93umF/4v/Xr1y9SV9y0ktYZFxenBx54QO3bt9f777+vhIQEJSYmqkePHjp58mSR+ssuu8zpeeE/XkuanpOTU2wvZ+5DSftaUFCg9PT0Epcvq5iYGH300UeKi4uTh4eH7rjjjmLrDh06JGOM6tevLxcXF6dHQkJCkaB59j5Lf+73uY5Fenq6jDEl7rf0v2NTqLhaNzc3DR8+XG+99ZYyMjJ05MgRvfPOO7rvvvvk5uZW0uGQJMcAeoWX3Z/t9OnTcnFxcTyPiorS/PnztXfvXt1+++3y9/dX+/bttWbNGkdNWd9Lx44dK9P7tvAS2bFjxxZ5PUaMGCFJpf6MVOE23d3di51f3tdPkmJjY/XUU0/ptttu00cffaStW7cqMTFRrVu3LvZz07t3b9WvX185OTmKjY1VzZo1i+3p+eefV2JiojZu3KgnnnhChw4d0m233abc3NwS9zMwMFDdunXT/PnzNX/+fN19993y9PQstvbQoUP66KOPihzPq6++WlLR41kRn3d3d/dij0lFKev3SOHn7ny+M4vj4uKi7t2767nnntOnn36qffv2KSwsTB9//LFWrVol6c/xDex2e7G3NRRKSUnRzTffrAMHDmjmzJn64osvlJiY6PgjWmnH7Hy/qwBUDVVjaFsAuETUqlVLsbGxio2NVUZGhtauXavHH39c3bt31759+0r8B7Ek1atXT5KUmpqqRo0aOc07ePCg/Pz8nOqKu3cvLS2t2LPdxQ0UtHjxYoWFhenll192mn4xBj46c1/PdvDgQdWoUUO+vr4XvJ1+/fpp5MiRmjJlioYNGyYPD49i6/z8/GSz2fTFF18UG1rPFWTLytfXVzVq1Chxvwt7OVNJgzw98MADmjJliubPn6+cnBydPn1a999//zl7KAwZJZ2BPHDgQJEgMmTIEA0ZMkTZ2dn6/PPPNX78eEVEROinn35SYGBgmd9L9erV01dffVVkm2lpaU7PC4/BuHHj1K9fv2L7DAoKKnEfC5cvvNe8Ii1evFiDBg3SpEmTnKYfPXq02LPS999/v06cOKGrr75ao0aN0s0331zse/sf//iHY/C0zp07y8PDQ08++aReeukljR07tsR+hg4dqoEDB6qgoKDI8T+Tn5+frrnmGj333HPFzj/zDH9FSU9PL/J+rkhl/R4xxshms5X4nXkh2x89erQ2bNig5ORk9erVS5dffrk2bdqkgoKCEoP38uXLlZ2drQ8++ECBgYGO6UlJSefc5sX6rgJwcXGmGwDKqW7durrjjjs0cuRI/f7779qzZ4+k//2j6OyzGV27dpX05z/qz5SYmKidO3eqW7dukqT27dvLzc2tyKBbCQkJTpfBnovNZivyD7Rvv/222FF/K1pQUJAaNmyot956y2lAouzsbL3//vuOkYgvlIeHh/7zn/8oMjJSDzzwQIl1ERERMsbowIEDxV7l0KpVqwvuRZK8vLzUvn17ffDBB06vf0FBgRYvXqxGjRqd8zfICzVo0EB33nmn5syZo7lz5yoyMlJNmjQ553IdOnRQ7dq1ix207YcfftD333+vW265pcT+e/bsqSeeeEKnTp3S999/L6ns76XQ0FCdOHHCcVaw0NKlS52eBwUFqXnz5vrmm2+KfT3atWtX6m9RBwYGysPDQ7/++mvJB6KcitvXlStXFvtHjNdee02LFy/WrFmz9OGHHyojI6PEgfzO9uijj+rKK6/UlClTSv1DWN++fdW3b18NHTq01J/cioiIUHJysq644opij2dFh+6DBw8qJydHLVu2rND1nqms3yNeXl5q166dli9frlOnTjnqsrKy9PHHH59zO3l5eUWuQClUeEtB4fHr2bOncnJySh3sr/APaWe+j4wxZfqZt4v1XQXg4uJMNwCch8jISIWEhKhdu3a6/PLLtXfvXr344osKDAx0jChc+I+imTNnavDgwXJxcVFQUJCCgoL0r3/9Sy+99JJq1Kihnj17as+ePXrqqafUuHFjPfLII5L+vLwzNjZWkydPlq+vr/r27av9+/drwoQJatCgQamXNZ4pIiJCzzzzjMaPH6/Q0FDt2rVLEydOVLNmzZx+QsgKNWrU0NSpU3XvvfcqIiJCw4cPV25urqZNm6aMjAxNmTKlwrZVeOVBaW688Ub961//0pAhQ7Rt2zZ17txZXl5eSk1N1aZNm9SqVatSQ/v5mDx5ssLDw9WlSxeNHTtWrq6umjNnjpKTk7VkyZLz+g3uhx9+WO3bt5ckx09enYu3t7cmTJigMWPGqKCgQP3795evr6++++47x8/QjRo1ylFfeIXAjTfeqAYNGigtLU2TJ0+Wj4+PY5T0sr6XBg8erBdeeEEDBw7Us88+qyuvvFKrVq3Sp59+Ksn5p5deeeUV9ezZU927d1d0dLQaNmyo33//XTt37tTXX3+td999t8R9dHV1VceOHZWQkFDmY1lWERERWrhwoVq0aKFrrrlG27dv17Rp04pcnfLdd99p1KhRGjx4sCNoz5s3T3fccYdefPFFjR49utTtuLi4aNKkSbrrrrs0c+ZMPfnkk8XWubu767333jtn3xMnTtSaNWvUqVMnjRo1SkFBQcrJydGePXv0ySefaO7cuUX24UIUHvsuXbpc8Lo++uijYv/Icscdd5T5e2TixInq3bu3unfvrocfflj5+fmaNm2aateufc4rIjIzM9W0aVPdeeeduuWWW9S4cWNlZWVpw4YNmjlzpoKDgx1XZNxzzz1asGCB7r//fu3atUtdunRRQUGBtm7dquDgYN19990KDw+Xq6ur7rnnHj366KPKycnRyy+/XKZbai7mdxWAi6iSBnADgIvufEd1Nqbo6OUzZswwnTp1Mn5+fsbV1dU0adLExMTEmD179jgtN27cOGO3202NGjWcRu7Nz883zz//vLnqqquMi4uL8fPzMwMHDjT79u1zWr6goMA8++yzplGjRsbV1dVcc8015uOPPzatW7d2Gi26tJG/c3NzzdixY03Dhg2Nu7u7adOmjVm+fHmR0XILRzOeNm2a0/Ilrftcx/FMy5cvN+3btzfu7u7Gy8vLdOvWzXz55Zdl2k5xylpb3GtpjDHz58837du3N15eXsbDw8NcccUVZtCgQU4jXYeGhpqrr766yLKBgYGmd+/eRaZLMiNHjnSa9sUXX5iuXbs6ttOhQwfz0UcfOdWU9Tg2bdrUBAcHl1pTnHfeecfcdNNNxtvb29SqVcs0adLEPPDAAyYtLc2p7vXXXzddunQx9evXN66ursZut5u77rrLfPvtt46asr6XjDEmJSXF9OvXz9SuXdt4e3ub22+/3XzyySdGklmxYoVT7TfffGPuuusu4+/vb1xcXExAQIDp2rWrmTt37jn3b968eaZmzZrm4MGDTtMv9PVLT083MTExxt/f33h6epqbbrrJfPHFFyY0NNTxywFZWVmmRYsWpmXLliY7O9tpfSNHjjQuLi5m69atxphzv2fbt29vfH19TUZGhjGmbCNRFzd6eeH0UaNGmWbNmhkXFxdz2WWXmbZt25onnnjCZGVlGWMq7vMeFRVlWrVqVWqfZ663tNHLS3oUKsv3iDHGLFu2zLRq1crx3TxlyhQzatQo4+vrW2qPubm5Zvr06aZnz56mSZMmxs3Nzbi7u5vg4GDz6KOPmmPHjjnVnzx50vznP/8xzZs3N66urqZevXqma9euZvPmzY6ajz76yLRu3dq4u7ubhg0bmn//+99m1apVRY5FcZ8hY8r2XXXmOhi9HLi02Ywpww/TAgAq3e7du9WiRQuNHz9ejz/+eGW3g4vg22+/VevWrTV79mzHAGNV0aRJk/Tkk08qJSWlws625uTkqEmTJhozZowee+yxClknyub48eOy2+164YUXiv0N8DNt2LBBXbp00dq1axUaGuoY6O9iyMvL07XXXquGDRtq9erVF227F0tBQYEKCgoUExOj999/X1lZWZXdEoAScHk5AFyCvvnmGy1ZskSdOnVSnTp1tGvXLk2dOlV16tRRTExMZbcHi/3666/au3evHn/8cTVo0KDIz8xdymbNmiVJatGihfLy8vTZZ5/pv//9rwYOHFihlze7u7trwoQJevrpp/Xggw/Ky8urwtaN0r3wwgtq0qRJme9fl+QYRyAxMdExoFxFi4mJUXh4uOM2iblz52rnzp2aOXOmJdurbLGxsY594/0PXNoI3QBwCfLy8tK2bds0b948ZWRkyMfHR2FhYXruuefO6ydwUDU988wzeuONNxQcHKx33323Qgadu1g8PT31wgsvaM+ePcrNzVWTJk302GOPlXjP8oX417/+pYyMDP32228MMHUR1alTRwsXLizTWeu2bdsqMTHR8dzKgddOnDihsWPH6siRI3JxcVGbNm30ySeflDhwYFU3ZswYDRw4UJJK/Kk6AJcGLi8HAAAAAMAi/GQYAAAAAAAWIXQDAAAAAGARQjcAAAAAABZhILWLrKCgQAcPHpS3t7dsNltltwMAAAAAKAdjjE6cOCG73a4aNUo+n03ovsgOHjyoxo0bV3YbAAAAAIAKsG/fvlJ/FpPQfZF5e3tL+vOFqVOnTiV3AwAAAAAoj+PHj6tx48aOjFcSQvdFVnhJeZ06dQjdAAAAAFDFneu2YQZSAwAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsUquyGwCA6iwlJUVHjx6t7DbKxM/PT02aNKnsNgAAAKoVQjcAWCQlJUVBQcHKyfmjslspE3d3T+3atZPgDQAAUIEI3QBgkaNHj/4VuBdLCq7sds5hp3JyBuro0aOEbgAAgApE6AYAywVLalPZTQAAAKASMJAaAAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWqdTQPXnyZF1//fXy9vaWv7+/brvtNu3atcupxhijp59+Wna7XR4eHgoLC9P333/vVJObm6uHHnpIfn5+8vLyUp8+fbR//36nmvT0dEVFRcnHx0c+Pj6KiopSRkaGU01KSooiIyPl5eUlPz8/jRo1SqdOnXKq+e677xQaGioPDw81bNhQEydOlDGmAo8KAAAAAKC6qNTQvXHjRo0cOVIJCQlas2aNTp8+rVtvvVXZ2dmOmqlTpyouLk6zZs1SYmKiAgICFB4erhMnTjhqRo8erbSLiesAACAASURBVGXLlmnp0qXatGmTsrKyFBERofz8fEfNgAEDlJSUpPj4eMXHxyspKUlRUVGO+fn5+erdu7eys7O1adMmLV26VO+//77GjBnjqDl+/LjCw8Nlt9uVmJiol156SdOnT1dcXJzFRwoAAAAAUCWZS8jhw4eNJLNx40ZjjDEFBQUmICDATJkyxVGTk5NjfHx8zNy5c40xxmRkZBgXFxezdOlSR82BAwdMjRo1THx8vDHGmB9++MFIMgkJCY6aLVu2GEnmxx9/NMYY88knn5gaNWqYAwcOOGqWLFli3NzcTGZmpjHGmDlz5hgfHx+Tk5PjqJk8ebKx2+2moKCgTPuYmZlpJDnWCaD62r59u5FkpO1GMpf4489et2/fXtmHDQAAoEooa7a7pO7pzszMlCRddtllkqTdu3crLS1Nt956q6PGzc1NoaGh2rx5syRp+/btysvLc6qx2+0KCQlx1GzZskU+Pj5q3769o6ZDhw7y8fFxqgkJCZHdbnfUdO/eXbm5udq+fbujJjQ0VG5ubk41Bw8e1J49eyryUAAAAAAAqoFLJnQbYxQbG6ubbrpJISEhkqS0tDRJUv369Z1q69ev75iXlpYmV1dX+fr6llrj7+9fZJv+/v5ONWdvx9fXV66urqXWFD4vrDlbbm6ujh8/7vQAAAAAAPw9XDKh+8EHH9S3336rJUuWFJlns9mcnhtjikw729k1xdVXRI35axC1kvqZPHmyY/A2Hx8fNW7cuNS+AQAAAADVxyURuh966CF9+OGHWr9+vRo1auSYHhAQIKnoWeTDhw87zjAHBATo1KlTSk9PL7Xm0KFDRbZ75MgRp5qzt5Oenq68vLxSaw4fPiyp6Nn4QuPGjVNmZqbjsW/fvlKOBAAAAACgOqnU0G2M0YMPPqgPPvhAn332mZo1a+Y0v1mzZgoICNCaNWsc006dOqWNGzeqU6dOkqS2bdvKxcXFqSY1NVXJycmOmo4dOyozM1NfffWVo2br1q3KzMx0qklOTlZqaqqjZvXq1XJzc1Pbtm0dNZ9//rnTz4itXr1adrtdTZs2LXYf3dzcVKdOHacHAAAAAODvoVJD98iRI7V48WK99dZb8vb2VlpamtLS0nTy5ElJf16yPXr0aE2aNEnLli1TcnKyoqOj5enpqQEDBkiSfHx8FBMTozFjxmjdunXasWOHBg4cqFatWumWW26RJAUHB6tHjx4aNmyYEhISlJCQoGHDhikiIkJBQUGSpFtvvVUtW7ZUVFSUduzYoXXr1mns2LEaNmyYIygPGDBAbm5uio6OVnJyspYtW6ZJkyYpNjb2nJe7AwAAAAD+fmpV5sZffvllSVJYWJjT9AULFig6OlqS9Oijj+rkyZMaMWKE0tPT1b59e61evVre3t6O+hdeeEG1atXSXXfdpZMnT6pbt25auHChatas6ah58803NWrUKMco53369NGsWbMc82vWrKmVK1dqxIgRuvHGG+Xh4aEBAwZo+vTpjhofHx+tWbNGI0eOVLt27eTr66vY2FjFxsZW9KEBAAAAAFQDNlM4EhguiuPHj8vHx0eZmZlcag5Uc19//fVft6dsl9Smsts5h68ltdX27dvVps2l3isAAEDlK2u2uyQGUgMAAAAAoDoidAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFqlV2Q0AAACURUpKio4ePVrZbZSJn5+fmjRpUtltAAAuAYRuAABwyUtJSVFQULBycv6o7FbKxN3dU7t27SR4AwAI3QAA4NJ39OjRvwL3YknBld3OOexUTs5AHT16lNANACB0AwCAqiRYUpvKbgIAgDJjIDUAAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsUqmh+/PPP1dkZKTsdrtsNpuWL1/uND86Olo2m83p0aFDB6ea3NxcPfTQQ/Lz85OXl5f69Omj/fv3O9Wkp6crKipKPj4+8vHxUVRUlDIyMpxqUlJSFBkZKS8vL/n5+WnUqFE6deqUU813332n0NBQeXh4qGHDhpo4caKMMRV4RAAAAAAA1Umlhu7s7Gy1bt1as2bNKrGmR48eSk1NdTw++eQTp/mjR4/WsmXLtHTpUm3atElZWVmKiIhQfn6+o2bAgAFKSkpSfHy84uPjlZSUpKioKMf8/Px89e7dW9nZ2dq0aZOWLl2q999/X2PGjHHUHD9+XOHh4bLb7UpMTNRLL72k6dOnKy4urgKPCAAAAACgOqlVmRvv2bOnevbsWWqNm5ubAgICip2XmZmpefPm6Y033tAtt9wiSVq8eLEaN26stWvXqnv37tq5c6fi4+OVkJCg9u3bS5JeffVVdezYUbt27VJQUJBWr16tH374Qfv27ZPdbpckzZgxQ9HR0XruuedUp04dvfnmm8rJydHChQvl5uamkJAQ/fTTT4qLi1NsbKxsNlsFHhkAAAAAQHVwyd/TvWHDBvn7++uqq67SsGHDdPjwYce87du3Ky8vT7feeqtjmt1uV0hIiDZv3ixJ2rJli3x8fByBW5I6dOggHx8fp5qQkBBH4Jak7t27Kzc3V9u3b3fUhIaGys3Nzanm4MGD2rNnjyX7DgAAAACo2i7p0N2zZ0+9+eab+uyzzzRjxgwlJiaqa9euys3NlSSlpaXJ1dVVvr6+TsvVr19faWlpjhp/f/8i6/b393eqqV+/vtN8X19fubq6llpT+Lywpji5ubk6fvy40wMAAAAA8PdQqZeXn0v//v0d/z8kJETt2rVTYGCgVq5cqX79+pW4nDHG6XLv4i79roiawkHUSru0fPLkyZowYUKJ8wEAAAAA1dclfab7bA0aNFBgYKB+/vlnSVJAQIBOnTql9PR0p7rDhw87zkIHBATo0KFDRdZ15MgRp5qzz1anp6crLy+v1JrCS93PPgN+pnHjxikzM9Px2Ldv3/nsMgAAAACgCqtSofvYsWPat2+fGjRoIElq27atXFxctGbNGkdNamqqkpOT1alTJ0lSx44dlZmZqa+++spRs3XrVmVmZjrVJCcnKzU11VGzevVqubm5qW3bto6azz//3OlnxFavXi273a6mTZuW2LObm5vq1Knj9AAAAAAA/D1UaujOyspSUlKSkpKSJEm7d+9WUlKSUlJSlJWVpbFjx2rLli3as2ePNmzYoMjISPn5+alv376SJB8fH8XExGjMmDFat26dduzYoYEDB6pVq1aO0cyDg4PVo0cPDRs2TAkJCUpISNCwYcMUERGhoKAgSdKtt96qli1bKioqSjt27NC6des0duxYDRs2zBGSBwwYIDc3N0VHRys5OVnLli3TpEmTGLkcAAAAAFCiSr2ne9u2berSpYvjeWxsrCRp8ODBevnll/Xdd99p0aJFysjIUIMGDdSlSxe9/fbb8vb2dizzwgsvqFatWrrrrrt08uRJdevWTQsXLlTNmjUdNW+++aZGjRrlGOW8T58+Tr8NXrNmTa1cuVIjRozQjTfeKA8PDw0YMEDTp0931Pj4+GjNmjUaOXKk2rVrJ19fX8XGxjp6BgAAAADgbDZTOBoYLorjx4/Lx8dHmZmZXGoOVHNff/31X7eobJfUprLbOYevJbXV9u3b1abNpd4r/o74PAEALjVlzXZV6p5uAAAAAACqEkI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYJFald0AAODSsXPnzspu4Zz8/PzUpEmTym4DAACgTAjdAABJqZJqaODAgZXdyDm5u3tq166dBG8AAFAlELoBAJIyJBVIWiwpuJJ7Kc1O5eQM1NGjRwndAACgSiB0AwDOECypTWU3AQAAUG0wkBoAAAAAABYhdAMAAAAAYBFCNwAAAAAAFilX6N69e3dF9wEAAAAAQLVTrtB95ZVXqkuXLlq8eLFycnIquicAAAAAAKqFcoXub775Rtddd53GjBmjgIAADR8+XF999VVF9wYAAAAAQJVWrtAdEhKiuLg4HThwQAsWLFBaWppuuukmXX311YqLi9ORI0cquk8AAAAAAKqcCxpIrVatWurbt6/eeecdPf/88/r11181duxYNWrUSIMGDVJqampF9QkAAAAAQJVzQaF727ZtGjFihBo0aKC4uDiNHTtWv/76qz777DMdOHBA//znPyuqTwAAAAAAqpxa5VkoLi5OCxYs0K5du9SrVy8tWrRIvXr1Uo0af2b4Zs2a6ZVXXlGLFi0qtFkAAAAAAKqScoXul19+WUOHDtWQIUMUEBBQbE2TJk00b968C2oOAAAAAICqrFyh++effz5njaurqwYPHlye1QMAAAAAUC2U657uBQsW6N133y0y/d1339Xrr79+wU0BAAAAAFAdlCt0T5kyRX5+fkWm+/v7a9KkSRfcFAAAAAAA1UG5Li/fu3evmjVrVmR6YGCgUlJSLrgpAJUjJSVFR48erew2ysTPz09NmjSp7DYAAACAUpUrdPv7++vbb79V06ZNnaZ/8803qlevXkX0BeAiS0lJUVBQsHJy/qjsVsrE3d1Tu3btJHgDAADgklau0H333Xdr1KhR8vb2VufOnSVJGzdu1MMPP6y77767QhsEcHEcPXr0r8C9WFJwZbdzDjuVkzNQR48eJXQDAADgklau0P3ss89q79696tatm2rV+nMVBQUFGjRoEPd0A1VesKQ2ld0EAAAAUC2UK3S7urrq7bff1jPPPKNvvvlGHh4eatWqlQIDAyu6PwAAAAAAqqxyhe5CV111la666qqK6gUAAAAAgGqlXKE7Pz9fCxcu1Lp163T48GEVFBQ4zf/ss88qpDkAAAAAAKqycoXuhx9+WAsXLlTv3r0VEhIim81W0X0BAAAAAFDllSt0L126VO+884569epV0f0AAAAAAFBt1CjPQq6urrryyisruhcAAAAAAKqVcoXuMWPGaObMmTLGVHQ/AAAAAABUG+W6vHzTpk1av369Vq1apauvvlouLi5O8z/44IMKaQ4AAAAAgKqsXKG7bt266tu3b0X3AgAAAABAtVKu0L1gwYKK7gMAAAAAgGqnXPd0S9Lp06e1du1avfLKKzpx4oQk6eDBg8rKyqqw5gAAAAAAqMrKdaZ779696tGjh1JSUpSbm6vw8HB5e3tr6tSpysnJ0dy5cyu6TwAAAAAAqpxynel++OGH1a5dO6Wnp8vDw8MxvW/fvlq3bl2FNQcAAAAAQFVW7tHLv/zyS7m6ujpNDwwM1IEDByqkMQAAAAAAqrpynekuKChQfn5+ken79++Xt7f3BTcFAAAAAEB1UK7QHR4erhdffNHx3GazKSsrS+PHj1evXr0qrDkAAAAAAKqycl1e/sILL6hLly5q2bKlcnJyNGDAAP3888/y8/PTkiVLKrpHAAAAAACqpHKFbrvdrqSkJC1ZskRff/21CgoKFBMTo3vvvddpYDUAAAAAAP7OyhW6JcnDw0NDhw7V0KFDK7IfAAAAAACqjXKF7kWLFpU6f9CgQeVqBgAAAACA6qRcofvhhx92ep6Xl6c//vhDrq6u8vT0JHQDAAAAAKByjl6enp7u9MjKytKuXbt00003MZAaAAAAAAB/KVfoLk7z5s01ZcqUImfBAQAAAAD4u6qw0C1JNWvW1MGDBytylQAAAAAAVFnluqf7ww8/dHpujFFqaqpmzZqlG2+8sUIaAwAAAACgqitX6L7tttucnttsNl1++eXq2rWrZsyYUSGNAQAAAABQ1ZUrdBcUFFR0HwAAAAAAVDsVek83AAAAAAD4n3Kd6Y6NjS1zbVxcXHk2AQAAAABAlVeu0L1jxw59/fXXOn36tIKCgiRJP/30k2rWrKk2bdo46mw2W8V0CQAAAABAFVSu0B0ZGSlvb2+9/vrr8vX1lSSlp6dryJAhuvnmmzVmzJgKbRIAAAAAgKqoXPd0z5gxQ5MnT3YEbkny9fXVs88+y+jlAAAAAAD8pVyh+/jx4zp06FCR6YcPH9aJEycuuCkAAAAAAKqDcoXuvn37asiQIXrvvfe0f/9+7d+/X++9955iYmLUr1+/iu4RAAAAAIAqqVz3dM+dO1djx47VwIEDlZeX9+eKatVSTEyMpk2bVqENAgAAAABQVZUrdHt6emrOnDmaNm2afv31VxljdOWVV8rLy6ui+wMAAAAAoMoq1+XlhVJTU5WamqqrrrpKXl5eMsZUVF8AAAAAAFR55Qrdx44dU7du3XTVVVepV69eSk1NlSTdd999/FwYAAAAAAB/KVfofuSRR+Ti4qKUlBR5eno6pvfv31/x8fEV1hwAAAAAAFVZue7pXr16tT799FM1atTIaXrz5s21d+/eCmkMAAAAAICqrlxnurOzs53OcBc6evSo3NzcLrgpAAAAAACqg3KF7s6dO2vRokWO5zabTQUFBZo2bZq6dOlSYc0BAAAAAFCVlevy8mnTpiksLEzbtm3TqVOn9Oijj+r777/X77//ri+//LKiewQAAAAAoEoq15nuli1b6ttvv9UNN9yg8PBwZWdnq1+/ftqxY4euuOKKiu4RAAAAAIAq6bzPdOfl5enWW2/VK6+8ogkTJljREwAAAAAA1cJ5n+l2cXFRcnKybDabFf0AAAAAAFBtlOvy8kGDBmnevHkV3QsAAAAAANVKuQZSO3XqlF577TWtWbNG7dq1k5eXl9P8uLi4CmkOAAAAAICq7LxC92+//aamTZsqOTlZbdq0kST99NNPTjVcdg4AAAAAwJ/OK3Q3b95cqampWr9+vSSpf//++u9//6v69etb0hwAAAAAAFXZed3TbYxxer5q1SplZ2dXaEMAAAAAAFQX5RpIrdDZIfx8ff7554qMjJTdbpfNZtPy5cuLrP/pp5+W3W6Xh4eHwsLC9P333zvV5Obm6qGHHpKfn5+8vLzUp08f7d+/36kmPT1dUVFR8vHxkY+Pj6KiopSRkeFUk5KSosjISHl5ecnPz0+jRo3SqVOnnGq+++47hYaGysPDQw0bNtTEiRMv+BgAAAAAAKqv8wrdNputyD3bF3IPd3Z2tlq3bq1Zs2YVO3/q1KmKi4vTrFmzlJiYqICAAIWHh+vEiROOmtGjR2vZsmVaunSpNm3apKysLEVERCg/P99RM2DAACUlJSk+Pl7x8fFKSkpSVFSUY35+fr569+6t7Oxsbdq0SUuXLtX777+vMWPGOGqOHz+u8PBw2e12JSYm6qWXXtL06dMZNA4AAAAAUKLzuqfbGKPo6Gi5ublJknJycnT//fcXGb38gw8+KNP6evbsqZ49e5a4rRdffFFPPPGE+vXrJ0l6/fXXVb9+fb311lsaPny4MjMzNW/ePL3xxhu65ZZbJEmLFy9W48aNtXbtWnXv3l07d+5UfHy8EhIS1L59e0nSq6++qo4dO2rXrl0KCgrS6tWr9cMPP2jfvn2y2+2SpBkzZig6OlrPPfec6tSpozfffFM5OTlauHCh3NzcFBISop9++klxcXGKjY1lADkAAAAAQBHndaZ78ODB8vf3d1ymPXDgQNntdsfzwkdF2L17t9LS0nTrrbc6prm5uSk0NFSbN2+WJG3fvl15eXlONXa7XSEhIY6aLVu2yMfHxxG4JalDhw7y8fFxqgkJCXEEbknq3r27cnNztX37dkdNaGio4w8OhTUHDx7Unj17KmSfAQAAAADVy3md6V6wYIFVfRSRlpYmSUVGRq9fv7727t3rqHF1dZWvr2+RmsLl09LS5O/vX2T9/v7+TjVnb8fX11eurq5ONU2bNi2yncJ5zZo1K3Y/cnNzlZub63h+/PjxkncaAAAAAFCtXNBAahfD2ZdtG2POeSn32TXF1VdETeEgaqX1M3nyZKerABo3blxq7wAAAACA6uOSDd0BAQGS/nfGu9Dhw4cdZ5gDAgJ06tQppaenl1pz6NChIus/cuSIU83Z20lPT1deXl6pNYcPH5ZU9Gz8mcaNG6fMzEzHY9++faXvOAAAAACg2rhkQ3ezZs0UEBCgNWvWOKadOnVKGzduVKdOnSRJbdu2lYuLi1NNamqqkpOTHTUdO3ZUZmamvvrqK0fN1q1blZmZ6VSTnJys1NRUR83q1avl5uamtm3bOmo+//xzp58RW716tex2e5HLzs/k5uamOnXqOD0AAAAAAH8PlRq6s7KylJSUpKSkJEl/Dp6WlJSklJQU2Ww2jR49WpMmTdKyZcuUnJys6OhoeXp6asCAAZIkHx8fxcTEaMyYMVq3bp127NihgQMHqlWrVo7RzIODg9WjRw8NGzZMCQkJSkhI0LBhwxQREaH/3969R1VV5/8ffx1BuSkokCAqZukQeCu8a5amQWWSXcYpB5I0zbHGZWqWOc6XaUpLLZ2F6aTTmDeUKbOZlY6ojeaFMjVtUskpo/ACKl5AVEBl//4oz68jiiB8OBzO87EWa3n2/py93+e81/acF3vvDxEREZKkmJgYRUVFKSEhQbt27dInn3yi8ePHa/jw4faQPHjwYHl5eSkxMVF79uzRypUrNWXKFGYuBwAAAABcU4UmUqtqO3bsUJ8+feyPx44dK+mnWdLfe+89TZgwQefPn9eoUaN06tQpde3aVWvXrlWDBg3sz5k5c6Y8PT01aNAgnT9/Xn379tV7770nDw8P+5ilS5dq9OjR9lnO4+LiHP42uIeHh1atWqVRo0apZ8+e8vHx0eDBgzVjxgz7mICAAK1bt07PPvusOnXqpEaNGmns2LH2mgEAAAAAuJJTQ3fv3r3tk5Fdjc1mU1JSkpKSkq45xtvbW8nJyUpOTr7mmMDAQC1ZsqTMWsLDw/Xxxx+XOaZdu3batGlTmWMAAAAAALisxt7TDQAAAACAqyN0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIZ4OrsAwB1kZWUpNzfX2WWUKSMjw9klAAAAALUOoRswLCsrSxERkSosPOfsUgAAAABUM0I3YFhubu7PgXuJpEhnl1OG1ZImO7sIAAAAoFYhdAPVJlJStLOLKAOXlwMAAABVjYnUAAAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADDE09kFAABQW2VlZSk3N9fZZVxXcHCwwsPDnV0GAAC1EqEbAAADsrKyFBERqcLCc84u5bq8vX21f38GwRsAAAMI3QAAGJCbm/tz4F4iKdLZ5ZQhQ4WF8crNzSV0AwBgAKEbAACjIiVFO7sIAADgJEykBgAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbzHVCmwAAHzNJREFUAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQT2cXAAAAnC8jI8PZJZSpptcHAMC1ELoBAHBr2ZLqKD4+3tmFAABQKxG6AQBwa6cllUhaIinSybWUZbWkyc4uAgCACiN0AwAA/RS4o51dRBm4vBwA4JqYSA0AAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADKnRoTspKUk2m83hJzQ01L7esiwlJSUpLCxMPj4+6t27t/bu3euwjaKiIv3+979XcHCw/Pz8FBcXp0OHDjmMOXXqlBISEhQQEKCAgAAlJCTo9OnTDmOysrI0YMAA+fn5KTg4WKNHj1ZxcbG5Fw8AAAAAcHk1OnRLUps2bZSdnW3/+frrr+3rpk2bprfeekuzZ8/W9u3bFRoaqnvvvVdnzpyxjxkzZoxWrlyp5cuXa8uWLSooKNCDDz6oS5cu2ccMHjxYu3fv1po1a7RmzRrt3r1bCQkJ9vWXLl1S//79dfbsWW3ZskXLly/XihUrNG7cuOp5EwAAAAAALsnT2QVcj6enp8PZ7cssy9KsWbM0adIkPfLII5KkhQsXKiQkRCkpKXrmmWeUl5end999V4sXL1a/fv0kSUuWLFHz5s21fv16xcbGKiMjQ2vWrNHnn3+url27SpLmz5+v7t27a//+/YqIiNDatWu1b98+HTx4UGFhYZKkN998U4mJiXrttdfk7+9fTe8GAAAAAMCV1Pgz3d9++63CwsLUsmVLPf744/r+++8lSZmZmcrJyVFMTIx9rJeXl+6++26lp6dLknbu3KkLFy44jAkLC1Pbtm3tYz777DMFBATYA7ckdevWTQEBAQ5j2rZtaw/ckhQbG6uioiLt3LmzzPqLioqUn5/v8AMAAAAAcA81OnR37dpVixYtUlpamubPn6+cnBz16NFDJ06cUE5OjiQpJCTE4TkhISH2dTk5OapXr54aNWpU5pjGjRuX2nfjxo0dxly5n0aNGqlevXr2MdcydepU+73iAQEBat68eQXeAQAAAACAK6vRofv+++/Xo48+qnbt2qlfv35atWqVpJ8uI7/MZrM5PMeyrFLLrnTlmKuNv5ExVzNx4kTl5eXZfw4ePFjmeAAAAABA7VGjQ/eV/Pz81K5dO3377bf2+7yvPNN87Ngx+1np0NBQFRcX69SpU2WOOXr0aKl9HT9+3GHMlfs5deqULly4UOoM+JW8vLzk7+/v8AMAAAAAcA8uFbqLioqUkZGhJk2aqGXLlgoNDdW6devs64uLi/Xpp5+qR48ekqSOHTuqbt26DmOys7O1Z88e+5ju3bsrLy9PX3zxhX3Mtm3blJeX5zBmz549ys7Oto9Zu3atvLy81LFjR6OvGQAAAADgumr07OXjx4/XgAEDFB4ermPHjunVV19Vfn6+hgwZIpvNpjFjxmjKlClq3bq1WrdurSlTpsjX11eDBw+WJAUEBGjYsGEaN26cgoKCFBgYqPHjx9svV5ekyMhI3XfffRo+fLjeeecdSdKIESP04IMPKiIiQpIUExOjqKgoJSQkaPr06Tp58qTGjx+v4cOHc+YaAAAAAHBNNTp0Hzp0SE888YRyc3N10003qVu3bvr888/VokULSdKECRN0/vx5jRo1SqdOnVLXrl21du1aNWjQwL6NmTNnytPTU4MGDdL58+fVt29fvffee/Lw8LCPWbp0qUaPHm2f5TwuLk6zZ8+2r/fw8NCqVas0atQo9ezZUz4+Pho8eLBmzJhRTe8EgKvJyMhwdgllqun1AQAAwLwaHbqXL19e5nqbzaakpCQlJSVdc4y3t7eSk5OVnJx8zTGBgYFasmRJmfsKDw/Xxx9/XOYYANUlW1IdxcfHO7sQAAAAoEw1OnQDwNWdllQiaYmkSCfXUpbVkiY7uwgAAAA4EaEbgAuLlBTt7CLKwOXlAAAA7s6lZi8HAAAAAMCVELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABD+JNhAAAABmRk1Pw/GxgcHKzw8HBnlwEAtRqhGwAAoEplS6qj+Ph4ZxdyXd7evtq/P4PgDQAGEboBAACq1GlJJZKWSIp0ci1lyVBhYbxyc3MJ3QBgEKEbAADAiEhJ0c4uAgDgZEykBgAAAACAIZzpBgC4HFeYoMoVagQAAOYRugEALsR1JqgCAACQCN0AAJfiKhNUSdJqSZOdXQQAAHAyQjcAwAW5wgRVXF4OAACYSA0AAAAAAGMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGeDq7AAAAADhPRkaGs0sol+DgYIWHhzu7DACoMEI3AACAW8qWVEfx8fHOLqRcvL19tX9/BsEbgMshdAMAALil05JKJC2RFOnkWq4nQ4WF8crNzSV0A3A5hG4AAAC3Fikp2tlFAECtxURqAAAAAAAYQugGAAAAAMAQQjcAAAAAAIZwTzdcVlZWlnJzc51dxnW5yp9iAQAAAFD1CN1wSVlZWYqIiFRh4TlnlwIAAAAA10TohkvKzc39OXC7wp85WS1psrOLAAAAAOAEhG64OFf4MydcXg4AAAC4KyZSAwAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhng6uwAAAACgPDIyMpxdwnUFBwcrPDzc2WUAqEEI3QAAAKjhsiXVUXx8vLMLuS5vb1/t359B8AZgR+hGKVlZWcrNzXV2GWVyhd90AwCAqnJaUomkJZIinVxLWTJUWBiv3NxcQjcAO0I3HGRlZSkiIlKFheecXQoAAMAVIiVFO7sIAKgQQjcc5Obm/hy4a/pvkldLmuzsIgAAAACgTIRuXENN/00yl5cDAAAAqPkI3Tdgzpw5mj59urKzs9WmTRvNmjVLvXr1cnZZAAAAqAFcZe4ZZloHqgehu4JSU1M1ZswYzZkzRz179tQ777yj+++/X/v27eM/LQAAALfmOrOsS8y0DlQXQncFvfXWWxo2bJiefvppSdKsWbOUlpamuXPnaurUqU6uDgAAAM7jKrOsS8y0DlQfQncFFBcXa+fOnXrppZcclsfExCg9Pf2qzykqKlJRUZH9cV5eniQpPz/fXKGVUFBQ8PO/dkoqKGuok12+bKum1ym5Tq2uUqfkOrW6Sp2S69TqKnVKrlMrdVY9V6nVVeqUXKfWy3WeU82uU/qpRmnnzp2/+P5XM9WpU0clJSXOLqNcXKVWV6lTkkJDQxUaGursMq7qcqazLKvMcTbreiNgd+TIETVt2lRbt25Vjx497MunTJmihQsXav/+/aWek5SUpD/96U/VWSYAAAAAoJocPHhQzZo1u+Z6znTfAJvN5vDYsqxSyy6bOHGixo4da39cUlKikydPKigoSF26dNH27dvL3Ffnzp0rNeZa6662PD8/X82bN9fBgwfl7+9f5j6rS3lef3Vvt6LPLe/4642rzX2W3KfXHNPu0efyjKvNfZbM9Lqy2+SYrnoc0+Vb7+p9ljimyzumvL12pz5Xdrs1+Zi2LEtnzpxRWFhYmfsidFdAcHCwPDw8lJOT47D82LFjCgkJuepzvLy85OXl5bCsYcOGkiQPD4/rHmSVHXOtdWU9x9/fv8Yc/OV5/dW93Yo+t7zjrzeuNvdZcp9ec0y7R5/LM64291ky0+vKbpNjuupxTJdvvav3WeKYLu+YivbaHfpc2e3W9GM6ICDg+vtKSkpKuu4oSPrpzV21apXOnz+v/v3725ePGzdO9957r/r161fhbXbp0sX4mGutu3J5UVGRXn/9dU2cOLHULwqcqTyvv7q3W9Hnlnf89cbV5j5L7tNrjmn36HN5xtXmPktmel3ZbXJMVz2O6fKtd/U+SxzT5R1Tnl67W58ru11XOqavhnu6Kyg1NVUJCQn661//qu7du2vevHmaP3++9u7dqxYtWji7vErJz89XQECA8vLyatRv3FC16LP7oNfugT67D3rtHuize6DP7oUz3RXUtm1bBQUFacqUKZoxY4bOnz+vxYsXq0OHDs4urUp4eHiod+/e8vTkzoPajD67D3rtHuiz+6DX7oE+uwf67D440w0AAAAAgCF1nF0AAAAAAAC1FaEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNyrszJkz6ty5s26//Xa1a9dO8+fPd3ZJMODgwYPq3bu3oqKi1L59e73//vvOLgkGPfzww2rUqJEee+wxZ5eCKvTxxx8rIiJCrVu31t/+9jdnlwODOIZrPz6X3QfftWsfZi9HhV26dElFRUXy9fXVuXPn1LZtW23fvl1BQUHOLg1VKDs7W0ePHtXtt9+uY8eOKTo6Wvv375efn5+zS4MBGzZsUEFBgRYuXKgPPvjA2eWgCly8eFFRUVHasGGD/P39FR0drW3btikwMNDZpcEAjuHaj89l98F37dqHM92oMA8PD/n6+kqSCgsLdenSJfG7m9qnSZMmuv322yVJjRs3VmBgoE6ePOnkqmBKnz591KBBA2eXgSr0xRdfqE2bNmratKkaNGigBx54QGlpac4uC4ZwDNd+fC67D75r1z6E7lpo06ZNGjBggMLCwmSz2fTRRx+VGjNnzhy1bNlS3t7e6tixozZv3lyhfZw+fVodOnRQs2bNNGHCBAUHB1dV+Sin6ujzZTt27FBJSYmaN29e2bJxA6qz16g5Ktv3I0eOqGnTpvbHzZo10+HDh6uldlQMx7h7qMo+87lcs1VFr/muXbsQumuhs2fPqkOHDpo9e/ZV16empmrMmDGaNGmSdu3apV69eun+++9XVlaWfUzHjh3Vtm3bUj9HjhyRJDVs2FBfffWVMjMzlZKSoqNHj1bLa8P/Vx19lqQTJ07oySef1Lx584y/JlxddfUaNUtl+361syI2m81ozbgxVXGMo+arqj7zuVzzVUWv+a5dy1io1SRZK1eudFjWpUsXa+TIkQ7LbrvtNuull166oX2MHDnS+sc//nHDNaLyTPW5sLDQ6tWrl7Vo0aIqqROVZ/KY3rBhg/Xoo49WukZUvRvp+9atW62BAwfa140ePdpaunSp+WJRKZU5xjmGXceN9pnPZddTFZ/bfNd2fZzpdjPFxcXauXOnYmJiHJbHxMQoPT29XNs4evSo8vPzJUn5+fnatGmTIiIiqrxW3Liq6LNlWUpMTNQ999yjhIQEE2WiClRFr+F6ytP3Ll26aM+ePTp8+LDOnDmj1atXKzY21hnlohI4xt1DefrM53LtUJ5e81279vF0dgGoXrm5ubp06ZJCQkIcloeEhCgnJ6dc2zh06JCGDRsmy7JkWZaee+45tW/f3kS5uEFV0eetW7cqNTVV7du3t9+LtHjxYrVr167K68WNq4peS1JsbKy+/PJLnT17Vs2aNdPKlSvVuXPnqi4XVaQ8fff09NSbb76pPn36qKSkRBMmTGDmWxdU3mOcY9i1lafPfC7XDuXpNd+1ax9Ct5u68r4+y7LKfa9fx44dtXv3bhNloYpVps933nmnSkpKTJQFAyrTa0nMau2irtf3uLg4xcXFVXdZMOB6veYYrh3K6jOfy7VLWb3mu3btw+XlbiY4OFgeHh6lzoAdO3as1G/c4Lros/ug1+6JvrsPeu0e6LP7oNfuidDtZurVq6eOHTtq3bp1DsvXrVunHj16OKkqVDX67D7otXui7+6DXrsH+uw+6LV74vLyWqigoEDfffed/XFmZqZ2796twMBAhYeHa+zYsUpISFCnTp3UvXt3zZs3T1lZWRo5cqQTq0ZF0Wf3Qa/dE313H/TaPdBn90GvUYpT5kyHURs2bLAklfoZMmSIfczbb79ttWjRwqpXr54VHR1tffrpp84rGDeEPrsPeu2e6Lv7oNfugT67D3qNK9ksy7IM53oAAAAAANwS93QDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAC4pXfffVcxMTHGtv/111+rWbNmOnv2rLF9AABqPkI3AACVlJiYKJvNppEjR5ZaN2rUKNlsNiUmJlZ/YQbdfPPNmjVrVqnls2bN0s0331z9BVVQUVGR/vjHP2ry5MkOy/Pz8zV58mS1adNGPj4+CgoKUufOnTVt2jSdOnXKPq53796y2Wyy2WyqU6eOQkJC9Otf/1o//vijfUy7du3UpUsXzZw5s9peFwCg5iF0AwBQBZo3b67ly5fr/Pnz9mWFhYVatmyZwsPDnVjZtVmWpYsXLzq7DKdYsWKF6tevr169etmXnTx5Ut26ddOCBQs0fvx4bdu2TVu3btX//d//affu3UpJSXHYxvDhw5Wdna3Dhw/rn//8pw4ePKj4+HiHMU899ZTmzp2rS5cuVcvrAgDUPIRuAACqQHR0tMLDw/Xhhx/al3344Ydq3ry57rjjDoexlmVp2rRpuuWWW+Tj46MOHTrogw8+sK/fuHGjbDab0tLSdMcdd8jHx0f33HOPjh07pn//+9+KjIyUv7+/nnjiCZ07d87+vKKiIo0ePVqNGzeWt7e37rzzTm3fvv2q2+3UqZO8vLy0ePFi1alTRzt27HCoMTk5WS1atJBlWZV6XzZu3KguXbrIz89PDRs2VM+ePe1ngw8cOKCHHnpIISEhql+/vjp37qz169c7PD87O1v9+/eXj4+PWrZsqZSUlFJn2fPy8jRixAg1btxY/v7+uueee/TVV1+VWdfy5csVFxfnsOzll19WVlaWtm3bpqeeekrt27fXbbfdpgcffFApKSkaNWqUw3hfX1+FhoaqSZMm6tatm5599ll9+eWXDmNiY2N14sQJffrppxV+7wAAtQOhGwCAKvLUU09pwYIF9sd///vfNXTo0FLj/vCHP2jBggWaO3eu9u7dq+eff17x8fGlgllSUpJmz56t9PR0HTx4UIMGDdKsWbOUkpKiVatWad26dUpOTraPnzBhglasWKGFCxfqyy+/VKtWrRQbG6uTJ086bHfChAmaOnWqMjIyFBcXp379+jnULUkLFiywXzZ/oy5evKiBAwfq7rvv1n//+1999tlnGjFihH2bBQUFeuCBB7R+/Xrt2rVLsbGxGjBggLKysuzbePLJJ3XkyBFt3LhRK1as0Lx583Ts2DH7esuy1L9/f+Xk5Gj16tXauXOnoqOj1bdv31Kv+5c2b96sTp062R+XlJQoNTVV8fHxatq06VWfU9Z7cfLkSb3//vvq2rWrw/J69eqpQ4cO2rx5c9lvFgCg9rIAAEClDBkyxHrooYes48ePW15eXlZmZqb1ww8/WN7e3tbx48ethx56yBoyZIhlWZZVUFBgeXt7W+np6Q7bGDZsmPXEE09YlmVZGzZssCRZ69evt6+fOnWqJck6cOCAfdkzzzxjxcbG2rdbt25da+nSpfb1xcXFVlhYmDVt2jSH7X700UcO+05NTbUaNWpkFRYWWpZlWbt377ZsNpuVmZl5zdfcokULa+bMmaWWz5w502rRooVlWZZ14sQJS5K1cePGst4+B1FRUVZycrJlWZaVkZFhSbK2b99uX//tt99akuz7/uSTTyx/f3977Zfdeuut1jvvvHPVfZw6dcqSZG3atMm+LCcnx5JkvfXWWw5jo6OjLT8/P8vPz896/PHH7cvvvvtuq27dupafn5/l6+trSbJ+9atfXfU9e/jhh63ExMRyvwcAgNqFM90AAFSR4OBg9e/fXwsXLtSCBQvUv39/BQcHO4zZt2+fCgsLde+996p+/fr2n0WLFunAgQMOY9u3b2//d0hIiHx9fXXLLbc4LLt81vfAgQO6cOGCevbsaV9ft25ddenSRRkZGQ7b/eUZXkkaOHCgPD09tXLlSkk/naHv06dPpSdECwwMVGJiov0M9l/+8hdlZ2fb1589e1YTJkxQVFSUGjZsqPr16+ubb76xn+nev3+/PD09FR0dbX9Oq1at1KhRI/vjnTt3qqCgQEFBQQ7vZ2ZmZqn387LL9917e3uXWnfl2eyVK1dq9+7dio2NdbhfX5J++9vfavfu3frqq6+0ZcsWtWrVSjExMTpz5ozDOB8fH4fbAAAA7sXT2QUAAFCbDB06VM8995wk6e233y61vqSkRJK0atWqUpcxe3l5OTyuW7eu/d82m83h8eVll7dn/Xzv9ZWh0bKsUsv8/PwcHterV08JCQlasGCBHnnkEaWkpFx1ZvJf8vf3V15eXqnlp0+fVkBAgP3xggULNHr0aK1Zs0apqan6wx/+oHXr1qlbt2564YUXlJaWphkzZqhVq1by8fHRY489puLiYofXdKVfLi8pKVGTJk20cePGUuMaNmx41ecHBQXJZrM5zEZ+0003qWHDhvrmm28cxl6eBK9BgwY6ffq0w7qAgAC1atVK0k+/DHj33XfVpEkTpaam6umnn7aPO3nypG699dar1gIAqP040w0AQBW67777VFxcrOLiYsXGxpZaHxUVJS8vL2VlZalVq1YOP82bN7/h/bZq1Ur16tXTli1b7MsuXLigHTt2KDIy8rrPf/rpp7V+/XrNmTNHFy5c0COPPFLm+Ntuu81hkrbLtm/froiICIdld9xxhyZOnKj09HS1bdvWPgv45s2blZiYqIcffljt2rVTaGiofvjhB4d9XLx4Ubt27bIv++677xzCb3R0tHJycuTp6Vnq/bzyKoPL6tWrp6ioKO3bt8++rE6dOho0aJCWLFmiw4cPl/nar8XDw0OSSp0R37NnT6nJ9AAA7oMz3QAAVCEPDw/75dyXQ9gvNWjQQOPHj9fzzz+vkpIS3XnnncrPz1d6errq16+vIUOG3NB+/fz89Lvf/U4vvPCCAgMDFR4ermnTpuncuXMaNmzYdZ8fGRmpbt266cUXX9TQoUPl4+NT5vixY8eqZ8+eeuWVV/TYY49J+unPcK1Zs0bp6emSpMzMTM2bN09xcXEKCwvT/v379b///U9PPvmkpJ9+UfDhhx9qwIABstlsmjx5sv3MvfRT6O7Xr59GjBihuXPnqm7duho3bpx8fHzsZ+/79eun7t27a+DAgXrjjTcUERGhI0eOaPXq1Ro4cGCpS+kvi42N1ZYtWzRmzBj7silTpmjjxo3q2rWrXnnlFXXq1El+fn72SeDatm3rsI1z584pJydHknT06FG9+uqr8vb2VkxMjH3MDz/8oMOHD6tfv37X7QEAoHYidAMAUMX8/f3LXP/nP/9ZjRs31tSpU/X999+rYcOGio6O1ssvv1yp/b7++usqKSlRQkKCzpw5o06dOiktLc3hHuiyDBs2TOnp6Vedcf1K3bp1U1paml555RX7peht2rRRWlqafQZvX19fffPNN1q4cKFOnDihJk2a6LnnntMzzzwjSZo5c6aGDh2qHj16KDg4WC+++KLy8/Md9rNo0SINGzZMd911l0JDQzV16lTt3bvXfj+2zWbT6tWrNWnSJA0dOlTHjx9XaGio7rrrLoWEhFyz/uHDhys6Olp5eXn2y+GDgoL0xRdf6I033tD06dOVmZmpOnXqqHXr1vrNb37jENAlaf78+Zo/f74kqVGjRmrfvr1Wr17tcKZ/2bJliomJUYsWLa77ngIAaiebda0bpgAAgFt57bXXtHz5cn399dfOLuWaDh06pObNm2v9+vXq27dvpbY1aNAg+6XvJhQVFal169ZatmyZwwR3AAD3wj3dAAC4uYKCAm3fvl3JyckaPXq0s8tx8J///Ef/+te/lJmZqfT0dD3++OO6+eabddddd1V629OnT1f9+vWroMqr+/HHHzVp0iQCNwC4Oc50AwDg5hITE7Vs2TINHDhQKSkpV70X3VnS0tI0btw4ff/992rQoIF69OihWbNmcbk2AMBlELoBAAAAADCEy8sBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAw5P8BWEr3D1YonGUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_usage(collection, start_time, end_time):\n",
    "    # Query to match the submitTime and endTime criteria\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    # Extract maxRMem from documents that match the query\n",
    "    memory_usage = []\n",
    "    cursor = collection.find(query, {\"maxRMem\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "    \n",
    "    for document in cursor:\n",
    "        if 'maxRMem' in document:\n",
    "            # Convert maxRMem from KB to GB\n",
    "            memory_in_gb = document['maxRMem'] / 1_000_000\n",
    "            memory_usage.append(memory_in_gb)  # Add the memory usage to the list\n",
    "    \n",
    "    return memory_usage\n",
    "\n",
    "def plot_memory_usage_histogram(memory_usage):\n",
    "    # Filter out zero values if necessary\n",
    "    memory_usage = [x for x in memory_usage if x > 0]\n",
    "\n",
    "    # Plot the histogram of memory usage\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.hist(memory_usage, bins=np.logspace(np.log10(min(memory_usage)), np.log10(max(memory_usage)), 20), color='blue', edgecolor='black')\n",
    "    plt.xscale('log')  # Set x-axis to log scale\n",
    "    \n",
    "    plt.title('Histogram of Memory Usage (maxRMem) [Log Scale]')\n",
    "    plt.xlabel('Memory Usage (GB)')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.tight_layout()  # Adjust layout to prevent overlap\n",
    "\n",
    "    plt.savefig('mem_usage.png', format = 'png')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Define the start and end times as UNIX timestamps\n",
    "    start_time = 1724043600\n",
    "    end_time = 1724648400\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract memory usage from the documents within the time range\n",
    "    memory_usage = extract_memory_usage(collection, start_time, end_time)\n",
    "    \n",
    "    # Plot the histogram of memory usage\n",
    "    plot_memory_usage_histogram(memory_usage)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7e3370e2-b097-4021-a342-ec0d684db0c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_usage_by_jstatus(collection, start_time, end_time):\n",
    "    # Query to match the submitTime and endTime criteria\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    # Extract memory usage (maxRMem) and group by jStatus\n",
    "    success_memory_usage = []\n",
    "    failure_memory_usage = []\n",
    "    \n",
    "    # Fetch maxRMem and jStatus fields\n",
    "    cursor = collection.find(query, {\"maxRMem\": 1, \"jStatus\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "    \n",
    "    for document in cursor:\n",
    "        if 'maxRMem' in document and 'jStatus' in document:\n",
    "            # Convert maxRMem from KB to GB\n",
    "            memory_in_gb = document['maxRMem'] / 1_000_000\n",
    "            if document['jStatus'] == 'done':\n",
    "                success_memory_usage.append(memory_in_gb)  # Success job memory usage\n",
    "            else:\n",
    "                failure_memory_usage.append(memory_in_gb)  # Failed job memory usage\n",
    "    \n",
    "    return success_memory_usage, failure_memory_usage\n",
    "\n",
    "def plot_memory_usage_histograms(success_memory_usage, failure_memory_usage):\n",
    "    plt.figure(figsize=(14, 7))\n",
    "\n",
    "    # Filter out zero values\n",
    "    success_memory_usage = [x for x in success_memory_usage if x > 0]\n",
    "    failure_memory_usage = [x for x in failure_memory_usage if x > 0]\n",
    "\n",
    "    if success_memory_usage:\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.hist(success_memory_usage, bins=np.logspace(np.log10(min(success_memory_usage)), np.log10(max(success_memory_usage)), 20), color='green', edgecolor='black')\n",
    "        plt.xscale('log')  # Set x-axis to log scale\n",
    "        plt.title('Memory Usage for Successful Jobs [Log Scale]')\n",
    "        plt.xlabel('Memory Usage (GB)')\n",
    "        plt.ylabel('Frequency')\n",
    "\n",
    "    if failure_memory_usage:\n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.hist(failure_memory_usage, bins=np.logspace(np.log10(min(failure_memory_usage)), np.log10(max(failure_memory_usage)), 20), color='red', edgecolor='black')\n",
    "        plt.xscale('log')  # Set x-axis to log scale\n",
    "        plt.title('Memory Usage for Failed Jobs [Log Scale]')\n",
    "        plt.xlabel('Memory Usage (GB)')\n",
    "        plt.ylabel('Frequency')\n",
    "\n",
    "    plt.tight_layout()  # Adjust layout to prevent overlap\n",
    "\n",
    "    plt.savefig('mem_usage_by_jobStatus.png', format = 'png')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400  # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract memory usage grouped by jStatus\n",
    "    success_memory_usage, failure_memory_usage = extract_memory_usage_by_jstatus(collection, start_time, end_time)\n",
    "    \n",
    "    # Plot the histograms\n",
    "    plot_memory_usage_histograms(success_memory_usage, failure_memory_usage)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "61d0f4f2-2989-4708-9cf5-895018699b58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_ratios(collection, start_time, end_time):\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    success_ratios = []\n",
    "    failure_ratios = []\n",
    "    \n",
    "    mem_pattern = re.compile(r\"mem=(\\d+\\.?\\d*)\")\n",
    "\n",
    "    cursor = collection.find(query, {\"maxRMem\": 1, \"resReq\": 1, \"numProcessors\": 1, \"jStatus\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'maxRMem' in document and 'resReq' in document and 'numProcessors' in document:\n",
    "            max_rmem = document['maxRMem']\n",
    "            res_req = document['resReq']\n",
    "            num_processors = document['numProcessors']\n",
    "            \n",
    "            match = mem_pattern.search(res_req)\n",
    "            if match:\n",
    "                requested_mem = float(match.group(1))\n",
    "                \n",
    "                if requested_mem > 0:\n",
    "                    requested_mem_adjusted = requested_mem * num_processors\n",
    "                    requested_mem_adjusted_kb = requested_mem_adjusted * 1024\n",
    "                    \n",
    "                    memory_ratio = max_rmem / requested_mem_adjusted_kb\n",
    "                    constrained_memory_ratio = min(memory_ratio, 1)\n",
    "\n",
    "                    if 'jStatus' in document:\n",
    "                        if document['jStatus'] == 'done':\n",
    "                            success_ratios.append(constrained_memory_ratio)\n",
    "                        else:\n",
    "                            failure_ratios.append(constrained_memory_ratio)\n",
    "\n",
    "    return success_ratios, failure_ratios\n",
    "\n",
    "def plot_memory_ratio_histograms(success_ratios, failure_ratios):\n",
    "    plt.figure(figsize=(14, 7))\n",
    "\n",
    "    if success_ratios:\n",
    "        plt.subplot(1, 2, 1)\n",
    "        bins = np.linspace(0, 1, 21)\n",
    "        plt.hist(success_ratios, bins=bins, color='green', edgecolor='black')\n",
    "        plt.title('MaxRMem / (Requested Memory * numProcessors) for Successful Jobs')\n",
    "        plt.xlabel('MaxRMem / (Requested Memory * numProcessors)')\n",
    "        plt.ylabel('Frequency')\n",
    "        plt.grid(True)\n",
    "\n",
    "    if failure_ratios:\n",
    "        plt.subplot(1, 2, 2)\n",
    "        bins = np.linspace(0, 1, 21)\n",
    "        plt.hist(failure_ratios, bins=bins, color='red', edgecolor='black')\n",
    "        plt.title('MaxRMem / (Requested Memory * numProcessors) for Failed Jobs ')\n",
    "        plt.xlabel('MaxRMem / (Requested Memory * numProcessors)')\n",
    "        plt.ylabel('Frequency')\n",
    "        plt.grid(True)\n",
    "        \n",
    "        # Calculate the percentage of failed jobs with a memory ratio of 1\n",
    "        num_failed_jobs = len(failure_ratios)\n",
    "        num_failed_jobs_with_ratio_1 = sum(1 for ratio in failure_ratios if ratio == 1)\n",
    "        percentage = (num_failed_jobs_with_ratio_1 / num_failed_jobs) * 100\n",
    "        \n",
    "        # Annotate the bar corresponding to ratio=1\n",
    "        plt.annotate(f'{percentage:.2f}% of failed jobs with ratio=1',\n",
    "                     xy=(1, num_failed_jobs_with_ratio_1),\n",
    "                     xytext=(0.7, num_failed_jobs_with_ratio_1 + 5),\n",
    "                     arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "                     fontsize=12, color='black')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('mem_efficiency_by_jobStatus.png', format='png')\n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    success_ratios, failure_ratios = extract_memory_ratios(collection, start_time, end_time)\n",
    "    \n",
    "    plot_memory_ratio_histograms(success_ratios, failure_ratios)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0d592b45-82fe-4617-836f-bb4720edeee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_cpu_utilization_by_status(collection, start_time, end_time):\n",
    "    # Query to match the submitTime and endTime criteria\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    success_utilizations = []\n",
    "    failure_utilizations = []\n",
    "\n",
    "    # Fetch cpuTime, numProcessors, runTime, and jStatus fields\n",
    "    cursor = collection.find(query, {\"cpuTime\": 1, \"numProcessors\": 1, \"runTime\": 1, \"jStatus\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'cpuTime' in document and 'numProcessors' in document and 'runTime' in document and 'jStatus' in document:\n",
    "            cpu_time = document['cpuTime']\n",
    "            num_processors = document['numProcessors']\n",
    "            run_time = document['runTime']\n",
    "            j_status = document['jStatus']\n",
    "\n",
    "            if num_processors > 1 and run_time > 0:  # Avoid division by zero and exclude jobs with exactly 1 processor\n",
    "                cpu_utilization = cpu_time / (num_processors * run_time)\n",
    "                \n",
    "                if j_status == 'done':\n",
    "                    success_utilizations.append(cpu_utilization)\n",
    "                else:\n",
    "                    failure_utilizations.append(cpu_utilization)\n",
    "\n",
    "    return success_utilizations, failure_utilizations\n",
    "\n",
    "def plot_cpu_utilization_histograms(success_utilizations, failure_utilizations):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "\n",
    "    if success_utilizations:  # Check if list is not empty\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.hist(success_utilizations, bins=30, color='green', edgecolor='black')\n",
    "        plt.title('CPU Utilization for Successful Jobs')\n",
    "        plt.xlabel('CPU Utilization')\n",
    "        plt.ylabel('Frequency')\n",
    "        plt.xlim(0, 3.2)\n",
    "        plt.yscale('log')  # Use logarithmic scale for better visualization if needed\n",
    "\n",
    "    if failure_utilizations:  # Check if list is not empty\n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.hist(failure_utilizations, bins=30, color='red', edgecolor='black')\n",
    "        plt.title('CPU Utilization for Failed Jobs')\n",
    "        plt.xlabel('CPU Utilization')\n",
    "        plt.ylabel('Frequency')\n",
    "        plt.xlim(0, 3.2)\n",
    "        plt.yscale('log')  # Use logarithmic scale for better visualization if needed\n",
    "\n",
    "    plt.tight_layout()  # Adjust layout to prevent overlap\n",
    "\n",
    "    plt.savefig('cpu_utilization_by_jobStatus.png', format = 'png')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400  # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract CPU utilization for each job and separate by status\n",
    "    success_utilizations, failure_utilizations = extract_cpu_utilization_by_status(collection, start_time, end_time)\n",
    "    \n",
    "    # Plot the histograms\n",
    "    plot_cpu_utilization_histograms(success_utilizations, failure_utilizations)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5d12a729-cf23-4397-ab45-498be3792f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_cpu_utilization_by_status(collection, start_time, end_time):\n",
    "    # Query to match the submitTime and endTime criteria\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    success_data = []\n",
    "    failure_data = []\n",
    "\n",
    "    cursor = collection.find(query, {\"cpuTime\": 1, \"numProcessors\": 1, \"runTime\": 1, \"exitStatus\": 1, \"_id\": 0})\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'cpuTime' in document and 'numProcessors' in document and 'runTime' in document and 'exitStatus' in document:\n",
    "            cpu_time = document['cpuTime']\n",
    "            num_processors = document['numProcessors']\n",
    "            run_time = document['runTime']\n",
    "            exit_status = document['exitStatus']\n",
    "\n",
    "            if num_processors > 0 and run_time > 0:  # Avoid division by zero\n",
    "                cpu_utilization = cpu_time / (num_processors * run_time)\n",
    "                \n",
    "                if exit_status == 0:\n",
    "                    success_data.append((num_processors, cpu_utilization))\n",
    "                else:\n",
    "                    failure_data.append((num_processors, cpu_utilization))\n",
    "\n",
    "    return success_data, failure_data\n",
    "\n",
    "def plot_cpu_utilization_by_processors(success_data, failure_data):\n",
    "    # Convert to DataFrame for easier manipulation\n",
    "    success_df = pd.DataFrame(success_data, columns=['numProcessors', 'cpuUtilization'])\n",
    "    failure_df = pd.DataFrame(failure_data, columns=['numProcessors', 'cpuUtilization'])\n",
    "\n",
    "    # Group by number of processors and calculate average CPU utilization\n",
    "    success_grouped = success_df.groupby('numProcessors').mean().reset_index()\n",
    "    failure_grouped = failure_df.groupby('numProcessors').mean().reset_index()\n",
    "\n",
    "    # Plot\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    \n",
    "    # Plot for successful jobs\n",
    "    plt.plot(success_grouped['numProcessors'], success_grouped['cpuUtilization'], marker='o', linestyle='-', color='green', label='Success')\n",
    "    \n",
    "    # Plot for failed jobs\n",
    "    plt.plot(failure_grouped['numProcessors'], failure_grouped['cpuUtilization'], marker='o', linestyle='-', color='red', label='Failure')\n",
    "    \n",
    "    plt.title('Average CPU Utilization by Number of Processors')\n",
    "    plt.xlabel('Number of Processors')\n",
    "    plt.ylabel('Average CPU Utilization')\n",
    "    plt.yscale('log')  # Use logarithmic scale for better visualization if needed\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Save the plot to the current folder\n",
    "    plt.savefig('cpu_utilization_by_processors.png', format='png')\n",
    "\n",
    "    # Show the plot (optional)\n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400  # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    \n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract CPU utilization data separated by job status\n",
    "    success_data, failure_data = extract_cpu_utilization_by_status(collection, start_time, end_time)\n",
    "    \n",
    "    # Plot CPU utilization by number of processors\n",
    "    plot_cpu_utilization_by_processors(success_data, failure_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ddfdec75-6ed5-4b9d-b0fe-b30824d56d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_data_by_time(collection, start_time, end_time):\n",
    "    # Corrected query structure with both $gte and $lte under 'submitTime'\n",
    "    query = {\n",
    "        \"submitTime\": { \n",
    "            \"$gte\": start_time,\n",
    "            \"$lte\": end_time\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    data = []\n",
    "    cursor = collection.find(query, {\"submitTime\": 1, \"numProcessors\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'submitTime' in document and 'numProcessors' in document:\n",
    "            data.append({\n",
    "                'submitTime': document['submitTime'],\n",
    "                'numProcessors': document['numProcessors']\n",
    "            })\n",
    "\n",
    "    return data\n",
    "\n",
    "def process_and_plot_data(data):\n",
    "    # Convert list of dicts to DataFrame\n",
    "    df = pd.DataFrame(data)\n",
    "\n",
    "    # Convert 'submitTime' from epoch to datetime in CST\n",
    "    df['submitTime'] = pd.to_datetime(df['submitTime'], unit='s')\n",
    "    df['submitTime'] = df['submitTime'].dt.tz_localize('UTC').dt.tz_convert('America/Chicago')\n",
    "\n",
    "    # Set 'submitTime' as the index\n",
    "    df.set_index('submitTime', inplace=True)\n",
    "\n",
    "    # Resample data into 3-hour intervals and calculate the mean number of processors used\n",
    "    df_resampled = df.resample('2H').mean()\n",
    "\n",
    "    # Plot the average number of processors used over time\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(df_resampled.index, df_resampled['numProcessors'], marker='o', linestyle='-')\n",
    "    plt.title('Average Number of Processors Used Over Time (2-Hour Bins)')\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Average Number of Processors')\n",
    "    plt.grid(True)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout\n",
    "\n",
    "    plt.savefig('processors_over_time.png', format='png')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract data from MongoDB\n",
    "    data = extract_data_by_time(collection, start_time, end_time)\n",
    "    \n",
    "    # Process and plot the data\n",
    "    process_and_plot_data(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c1bf4613-af61-4e21-bdda-1eef3c6b2ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_data_by_time(collection, start_time, end_time):\n",
    "    # Correct query structure with both $gte and $lte under 'submitTime'\n",
    "    query = {\n",
    "        \"submitTime\": { \n",
    "            \"$gte\": start_time,\n",
    "            \"$lte\": end_time\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    # Extract cpuTime, numProcessors, and runTime\n",
    "    data = []\n",
    "    cursor = collection.find(query, {\"submitTime\": 1, \"cpuTime\": 1, \"numProcessors\": 1, \"runTime\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'submitTime' in document and 'cpuTime' in document and 'numProcessors' in document and 'runTime' in document:\n",
    "            # Avoid division by zero for runtime and processors\n",
    "            if document['runTime'] > 0 and document['numProcessors'] > 0:\n",
    "                cpu_utilization = document['cpuTime'] / (document['numProcessors'] * document['runTime'])\n",
    "                data.append({\n",
    "                    'submitTime': document['submitTime'],\n",
    "                    'cpuUtilization': cpu_utilization\n",
    "                })\n",
    "\n",
    "    return data\n",
    "\n",
    "def process_and_plot_data(data):\n",
    "    # Convert list of dicts to DataFrame\n",
    "    df = pd.DataFrame(data)\n",
    "\n",
    "    # Convert 'submitTime' from epoch to datetime in CST\n",
    "    df['submitTime'] = pd.to_datetime(df['submitTime'], unit='s')\n",
    "    df['submitTime'] = df['submitTime'].dt.tz_localize('UTC').dt.tz_convert('America/Chicago')\n",
    "\n",
    "    # Set 'submitTime' as the index\n",
    "    df.set_index('submitTime', inplace=True)\n",
    "\n",
    "    # Resample data into 3-hour intervals and calculate the mean CPU utilization\n",
    "    df_resampled = df.resample('2H').mean()\n",
    "\n",
    "    # Plot the average CPU utilization over time\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(df_resampled.index, df_resampled['cpuUtilization'], marker='o', linestyle='-')\n",
    "    plt.title('Average CPU Utilization Over Time (2-Hour Bins)')\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Average CPU Utilization')\n",
    "    plt.grid(True)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig('cpu_utilization_over_time.png', format='png')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract data from MongoDB\n",
    "    data = extract_data_by_time(collection, start_time, end_time)\n",
    "    \n",
    "    # Process and plot the data\n",
    "    process_and_plot_data(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "60b1d0c8-c20a-4227-85f0-542a6155970d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import re\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_efficiency_by_time(collection, start_time, end_time):\n",
    "    # Query to match the submitTime and endTime criteria\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gte\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    # Extract maxRMem, resReq, and numProcessors for memory efficiency calculation\n",
    "    data = []\n",
    "    \n",
    "    # Regular expression to extract the memory value from the resReq field\n",
    "    mem_pattern = re.compile(r\"mem=(\\d+\\.?\\d*)\")\n",
    "\n",
    "    # Fetch maxRMem, resReq, and numProcessors fields\n",
    "    cursor = collection.find(query, {\"submitTime\": 1, \"maxRMem\": 1, \"resReq\": 1, \"numProcessors\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'maxRMem' in document and 'resReq' in document and 'numProcessors' in document:\n",
    "            max_rmem = document['maxRMem']\n",
    "            res_req = document['resReq']\n",
    "            num_processors = document['numProcessors']\n",
    "\n",
    "            # Parse the requested memory from the resReq field\n",
    "            match = mem_pattern.search(res_req)\n",
    "            if match:\n",
    "                requested_mem = float(match.group(1))  # Requested memory is in MB\n",
    "\n",
    "                if requested_mem > 0:  # Avoid division by zero\n",
    "                    # Adjust requested memory for the number of processors\n",
    "                    requested_mem_adjusted = requested_mem * num_processors\n",
    "\n",
    "                    memory_efficiency = max_rmem / (requested_mem_adjusted * 1024)  # Convert requested memory to KB\n",
    "                    \n",
    "                    # Constrain memory efficiency values to a maximum of 1\n",
    "                    constrained_memory_efficiency = min(memory_efficiency, 1)\n",
    "\n",
    "                    data.append({\n",
    "                        'submitTime': document['submitTime'],\n",
    "                        'memoryEfficiency': constrained_memory_efficiency\n",
    "                    })\n",
    "\n",
    "    return data\n",
    "\n",
    "def process_and_plot_memory_efficiency(data):\n",
    "    # Convert list of dicts to DataFrame\n",
    "    df = pd.DataFrame(data)\n",
    "\n",
    "    # Convert 'submitTime' from epoch to datetime in CST\n",
    "    df['submitTime'] = pd.to_datetime(df['submitTime'], unit='s')\n",
    "    df['submitTime'] = df['submitTime'].dt.tz_localize('UTC').dt.tz_convert('America/Chicago')\n",
    "\n",
    "    # Set 'submitTime' as the index\n",
    "    df.set_index('submitTime', inplace=True)\n",
    "\n",
    "    # Resample data into 2-hour intervals and calculate the mean memory efficiency\n",
    "    df_resampled = df.resample('2H').mean()\n",
    "\n",
    "    # Plot the average memory efficiency over time\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(df_resampled.index, df_resampled['memoryEfficiency'], marker='o', linestyle='-')\n",
    "    plt.title('Average Memory Efficiency Over Time (2-Hour Bins)')\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Average Memory Efficiency (maxRMem / (Requested Memory * numProcessors))')\n",
    "    plt.grid(True)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig('mem_efficiency_over_time.png', format='png')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract the memory efficiency data\n",
    "    data = extract_memory_efficiency_by_time(collection, start_time, end_time)\n",
    "    \n",
    "    # Process and plot the data\n",
    "    process_and_plot_memory_efficiency(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9a89d466-45ae-48ca-b44d-fbfe60f19cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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gQYOc2s6fP99IMvPmzcu3L9nZ2SYjI8N8/vnnRpLZsmWLtc/VZ+I4x+7du40k8/7771v7JkyYYCSZ33//3emYPXv2GB8fH/P44487bT958qSJiooyPXr0sN5rTExMgd/v1atXL7RvxuR8j9pstjzPsH379iY4ONicPn3aGGPMr7/+ary8vMzkyZOtNmfOnDHh4eHm/vvvL/I6uceYq8/ZGGMSEhJcei9ZWVkmIyPDjB492oSHhzvdk/r165vWrVvnOcYx1nM/g9yfz2+//dZIMsuWLSuyHwBwKSNDCgBKMV9fX73wwgv69ttv80x1K47cqyDFxcXJZrPplltusbb5+PjoiiuuyHelv8TERKdpDtWrV1eLFi302WefSZJ++eUX/fjjj7r33nslOWeDdOrUSfv27dPOnTudznnHHXe41Pc1a9aoXr16atasmdP23r17yxhz0bLJ1qxZo6CgIHXv3j3PdSTlyZi4+eabFRkZab329vbWXXfdpV9++cWagtGsWTOtXLlSw4YN09q1awvN1jhf5n9TShzP5d5775Wfn59TNto777yjs2fP6v7775eUkwX26aef6vbbb1dgYGCe55SWlqavv/7a6TquPqfistlsVhaX9Pd4jI6OVuPGja3tYWFhioiIcBqnH374odq2bauYmBin9+QY359//nmh1967d69LmV/PPvus5s+fr8mTJ6tp06bW9uzsbKfrZmVlOR0XEBCgjRs35vsTEBDg1LYkx/v5Psu3337b6ufKlSuVlJSkRx99VK+++qpLx3ft2tXp9VVXXSVJ1rO7+uqr5evrq4ceekhvvfWWU8Zlbo7n89dffxV6TT8/Py1dulTbt2/X5MmTdffdd+vQoUN68cUXFRcXZ30PrVu3TkeOHFFSUpLTs8vOzlbHjh21cePGPNPsHN9vDj169JCPj4/1PShJv/32mxITExUVFSVvb2/Z7Xa1bt1aUs6qcLnl90wOHjyo/v37q2rVqvLx8ZHdblf16tULPEduH3/8sTIzM3Xfffc5vTd/f3+1bt3amka2c+dO7d27t8Dvd1fVr18/T93DxMREnThxQps2bZIk1apVS507d9a0adOs764FCxbo8OHDF5T55+pzLsqaNWvUrl07hYSEWM/rueee0+HDh3Xw4MHz7ldBrrjiCoWGhmro0KGaMWOGU5YaAFxOCEgBQCl39913q0mTJhoxYoTTVJDiCAsLc3rt6+urwMBA+fv759nuKEh9rqioqHy3OaaeHThwQJI0ZMgQ2e12p59HHnlEkpzqn0hyeTrd4cOH820bExNj7b8YDh8+rKioqDw1ZiIiIuTj45PnOgXdk3P79Morr2jo0KFatmyZ2rZtq7CwMN122236+eefC+1LtWrV9PvvvxfaxlGzpGrVqpJynnHXrl319ttvWwGROXPmqFmzZtb0ssOHDyszM1NTp07N85wcwaALfU7FVdB4zD12HdvPHacHDhzQBx98kOc9Od537veUm6OofmFGjRqlF154QS+++GKeX6BHjx7tdN1zC7xLkpeXl6655pp8f7y8nP/XrqTGe2Bg4HmvLhcXF2f1s2PHjpo5c6bi4+P1j3/8o8ipc5IUHh7u9NrPz0+SrMBs7dq19cknnygiIkKPPvqoVRz/5ZdfznMux/NxNagbFxengQMHat68edqzZ48mTZqkw4cP69lnn5X093dW9+7d84ybcePGyRijI0eOOJ0z92fex8dH4eHh1jM5deqUWrVqpQ0bNuiFF17Q2rVrtXHjRmsadO6+5/dMsrOzFR8fryVLlugf//iHPv30U33zzTdWoNiV9+94b9dee22e9/buu+9anwdHvwv7LnOFK9+FkvTEE0/o559/VkpKiqScRQOaN29uTTO8EEU958J88803io+PlyS9/vrr+s9//qONGzdqxIgRklwfa64ICQnR559/rquvvlpPP/206tevr5iYGI0cOfKi/T0PAO7AKnsAUMrZbDaNGzdO7du317/+9a88+x2/mOUutH2xAjP52b9/f77bHL9wVqxYUVJOvaH86i1JUmxsrNNrV1fUCw8P1759+/JsdxRHdly7uMLDw7VhwwYZY5z6dvDgQWVmZua5TkH3xHEuSQoKCtKoUaM0atQoHThwwMqW6tKli3788ccC+9K+fXu99tpr+vrrr/OtI5WamqqUlBQ1aNDA6ZfB+++/X//+97+VkpKiatWqaePGjZo+fbq1PzQ0VN7e3urVq5ceffTRfK9ds2ZNp9fFXfnQHSpWrKirrrrKKvKemyOYU9jxuYMP5xo1apSSk5OVnJysp59+Os/+hx56yCkL0RF4uRCujveCvgcKCr5drOd41VVX6eOPP9ZPP/2UJ4vrQrRq1UqtWrVSVlaWvv32W02dOlUDBw5UZGSk0wqSjudzIZ93m82mQYMGafTo0dq6davTeaZOnVpgrbZzMyClnM935cqVrdeZmZk6fPiw9Xlfs2aN9u7dq7Vr11pZUZIKDN7l90y2bt2qLVu2aM6cOUpKSrK2n89iAo735qhtVxBHvwv7LnOFK9+FknTTTTepQYMGevXVV1WuXDlt2rRJ8+bNc/k6RcnvORdm4cKFstvt+vDDD50C0suWLXP5mufzOWzYsKEWLlwoY4x++OEHzZkzR6NHj1ZAQICGDRvm8jUBwJMISAFAGdCuXTu1b99eo0ePtjJgHCIjI+Xv768ffvjBafv7779fYv155513NHjwYOsXqN27d2vdunW67777JOUEm+rUqaMtW7ZozJgxF/XaN998s8aOHatNmzY5/Uv622+/LZvNprZt21606yxatEjLli3T7bff7nQdx/5zffrppzpw4ID1S2tWVpbeffdd1a5dW1WqVMlz/sjISPXu3VtbtmzRlClTCl3ifdCgQZo1a5Yef/xxrV27Ns9Ke0OGDNHRo0edgk2SFB8fr8qVK2v27NmqVq2a/P39dc8991j7AwMD1bZtW33//fe66qqr5Ovrex536NLVuXNnffTRR6pdu7ZCQ0PP+/i6devq119/zXff888/r+TkZD3zzDMaOXJkvm1iYmKKDHq5ytXx7ljl64cfflCHDh2sdueuplgSNm/eLEmqVKnSRT2vt7e3rrvuOtWtW1fz58/Xpk2bnAJSjul8RRWe37dvX74ZZnv37tWJEyesqZYtW7ZUhQoVtH37dpenjM2fP99pquaiRYuUmZmpNm3aSPo7wJQ7IDlz5kyXzn++58idcebQoUMH+fj46Ndffy10mmZsbKyio6ML/H53dUxv27ZNW7ZscZq2t2DBApUvXz5P9tOAAQPUv39/HT9+XJGRkbrzzjtdukZurj5nKec+5ZftZLPZ5OPjI29vb2vbmTNnNHfu3DxtCzrHuZ/Dc//RpbDPoc1mU6NGjTR58mTNmTPHmtYIAJcDAlIAUEaMGzdOTZs21cGDB51W9LLZbOrZs6dmzZql2rVrq1GjRvrmm2+0YMGCEuvLwYMHdfvtt6tv3746fvy4Ro4cKX9/fw0fPtxqM3PmTN1yyy3q0KGDevfurcqVK+vIkSPasWOHNm3apH//+98XdO1Bgwbp7bffVkJCgkaPHq3q1atrxYoVmjZtmh5++GFdeeWVF/y+zs1QuO+++/Taa68pKSlJu3btUsOGDfXVV19pzJgx6tSpk9q1a+d0bMWKFXXTTTfp2WefVVBQkKZNm6Yff/xRCxcutNpcd9116ty5s6666iqFhoZqx44dmjt3rpo3b15gMErKmco0d+5c3Xvvvbr22ms1ePBgxcbG6sCBA5o1a5ZWrlypIUOG6K677nI6ztvbW/fdd58mTZqk4OBgdevWTSEhIU5tXn75Zd1www1q1aqVHn74YdWoUUMnT57UL7/8og8++OCir/DoDqNHj1ZKSopatGihAQMGKDY2Vmlpadq1a5c++ugjzZgxI98goUObNm00evToPEHCiRMn6rnnnlPHjh2VkJCQp75WQZk1xeHqeI+KilK7du00duxYhYaGqnr16vr000/zXSXzQm3dutVaCfDw4cNasmSJUlJSdPvtt+fJpLsQM2bM0Jo1a5SQkKBq1aopLS1Ns2bNkqQ8n7evv/5a4eHhatiwYaHnfOihh3Ts2DHdcccdatCggby9vfXjjz9q8uTJ8vLy0tChQyXlrPw2depUJSUl6ciRI+revbsiIiJ06NAhbdmyRYcOHcoT8F2yZIl8fHzUvn17bdu2Tc8++6waNWqkHj16SJJatGih0NBQ9e/fXyNHjpTdbtf8+fO1ZcsWl+9J3bp1Vbt2bQ0bNkzGGIWFhemDDz6wprmdy3EvXn75ZSUlJclutys2NlY1atTQ6NGjNWLECP3222/q2LGjQkNDdeDAAX3zzTdW5qaXl5eef/55Pfjgg9b3+7Fjx5ScnHxeU/ZiYmLUtWtXJScnKzo6WvPmzVNKSorGjRuX53uuZ8+eGj58uL744gs988wzFxwUd/U5O+7TkiVLNH36dDVt2tSaQpuQkKBJkyYpMTFRDz30kA4fPqyXXnop3wxHR3bTu+++q1q1asnf318NGzbUtddeq9jYWA0ZMkSZmZkKDQ3V0qVL9dVXXzkd/+GHH2ratGm67bbbVKtWLRljtGTJEh07dkzt27e/oHsAAB7hsXLqAIASce4qe7klJiYaSU6r7BljzPHjx82DDz5oIiMjTVBQkOnSpYvZtWtXgavsHTp0yOn4pKQkExQUlOd6uVf0c6wUNHfuXDNgwABTqVIl4+fnZ1q1apXvKmNbtmwxPXr0MBEREcZut5uoqChz0003mRkzZrj0fguye/duk5iYaMLDw43dbjexsbFmwoQJ1sp9Duezyl6lSpXMHXfc4bTt8OHDpn///iY6Otr4+PiY6tWrm+HDh5u0tDSndpLMo48+aqZNm2Zq165t7Ha7qVu3rpk/f75Tu2HDhplrrrnGhIaGGj8/P1OrVi0zaNAg89///telPm7bts0kJSWZKlWqGLvdbsLCwkzHjh3NihUrCjzmp59+slaeSklJybfN77//bvr06WMqV65s7Ha7qVSpkmnRooV54YUXrDaFrZRXlAtZZc+V8eiQ33M+dOiQGTBggKlZs6Z1r5o2bWpGjBhhTp06VWh/f/nlF2Oz2fKssNi6detCV8dzRUHvzSEoKMhplT1jXB/v+/btM927dzdhYWEmJCTE9OzZ01rNK/cqe4X1Ibf8VtkLCQkxV199tZk0aVK+n4f8VtnL/RnP/ezXr19vbr/9dlO9enXj5+dnwsPDTevWrc3y5cudjsvOzjbVq1fPs2pcfj7++GPTp08fU69ePRMSEmJ8fHxMdHS06datm1m/fn2e9p9//rlJSEgwYWFhxm63m8qVK5uEhASnsev4Hv3uu+9Mly5dTLly5Uz58uXNPffcYw4cOOB0vnXr1pnmzZubwMBAU6lSJfPggw+aTZs2ndcz2b59u2nfvr0pX768CQ0NNXfeeafZs2dPnvtsjDHDhw83MTExxsvLK8/natmyZaZt27YmODjY+Pn5merVq5vu3bubTz75xOkcb7zxhqlTp47x9fU1V155pZk1a5ZJSkpyeZW9hIQEs3jxYlO/fn3j6+tratSoYSZNmlTgMb179zY+Pj7mzz//LPL8DpJMcnKy9fp8nvORI0dM9+7dTYUKFYzNZnP67M6aNcvExsZa389jx441b775Zp6V83bt2mXi4+NN+fLljSSne/PTTz+Z+Ph4ExwcbCpVqmQef/xxs2LFCqfn8eOPP5p77rnH1K5d2wQEBJiQkBDTrFkzM2fOHJfvAQBcCmzG/G9pCgAAcEEOHz6siIgIPfnkkxo/frynu4NLQJcuXZSZmamVK1d6uivI5dNPP1V8fLy2bdumunXruv36ycnJGjVqlA4dOnTRataVVenp6apRo4ZuuOEGl1eSPX78uCpUqKCpU6de0Ip8AICLhyl7AABcoEOHDmnTpk3WsvXn1qhB2TZ27Fg1btxYGzdu1LXXXuvp7uAcL7zwgvr06eORYBQujkOHDmnnzp2aPXu2Dhw44HIR76+//lrvvvuuJKl58+Yl2UUAgAsISAEAcIFWrFihvn37KjY2VgsWLCjWcuMoXRo0aKDZs2ef1+piKHlHjx5V69at9cgjj0CIhM0AACAASURBVHi6KyiGFStW6P7771d0dLSmTZvm8ndvYmKisrKyNHHiRKdC5QAAz2DKHgAAAAAAANzKy9MdAAAAAAAAQNlCQAoAAAAAAABuRUAKAAAAAAAAbkVR84soOztbe/fuVfny5WWz2TzdHQAAAAAAALcyxujkyZOKiYmRl1fBeVAEpC6ivXv3qmrVqp7uBgAAAAAAgEf98ccfqlKlSoH7CUhdROXLl5eUc9ODg4M93JsLk5GRodWrVys+Pl52u93T3cFlhvGD4mD8oDgYPygOxg+Kg/GD4mD8oDgu1fFz4sQJVa1a1YqRFISA1EXkmKYXHBx8WQekAgMDFRwcfEkNaFweGD8oDsYPioPxg+Jg/KA4GD8oDsYPiuNSHz9FlTKiqDkAAAAAAADcioAUAAAAAAAA3IqAFAAAAAAAANyKGlIAAAAAAKBMyMrKUkZGhqe7cVFkZGTIx8dHaWlpysrKctt17Xa7vL29i30eAlIAAAAAAKBUM8Zo//79OnbsmKe7ctEYYxQVFaU//vijyALiF1uFChUUFRVVrOsSkAIAAAAAAKWaIxgVERGhwMBAtwdwSkJ2drZOnTqlcuXKycvLPRWZjDFKTU3VwYMHJUnR0dEXfC4CUgAAAAAAoNTKysqyglHh4eGe7s5Fk52drfT0dPn7+7stICVJAQEBkqSDBw8qIiLigqfvUdQcAAAAAACUWo6aUYGBgR7uSenhuJfFqcdFQAoAAAAAAJR6pWGa3qXiYtxLAlIAAAAAAABwKwJSAAAAAAAAl4k2bdpo4MCBnu5GsVHUHAAAAAAAoAhZ2Vn6cs+X2ndyn6LLR6tVtVby9rqwgt4gIAUAAAAAAFCoJTuW6IlVT+jPE39a26oEV9HLHV9Wt7huHuzZ5YspewAAAAA8Iis7S2t3rdU7//eO1u5aq6zsLE93CQDyWLJjibov6u4UjJKkv078pe6LumvJjiUldu3Tp0/rvvvuU7ly5RQdHa2JEyc67T927JiSkpIUGhqqwMBA3XLLLfr555+t/XPmzFGFChX08ccfKy4uTuXKlVPHjh21b98+p/PMnj1bcXFx8vf3V926dTVt2rQSe08OZEgBAAAAcDuyDQB4kjFGqRmpRbbLys7SgJUDZGTynkNGNtn0xMon1K5mO5em7wXaA89rhbqnnnpKn332mZYuXaqoqCg9/fTT+u6773T11VdLkh555BHt2rVLy5cvV3BwsIYOHapOnTpp+/btstvtkqTU1FS99NJLmjt3rry8vNSzZ08NGTJE8+fPlyS9/vrrGjlypF599VU1btxY33//vfr27augoCAlJSW53NfzRUAKAAAAgFs5sg1y/4LnyDZY3GMxQSkAJSo1I1XlxpYr9nmMjP48+adCxoW41P7U8FMK8g1yre2pU3rzzTf19ttvq3379pKkt956S1WqVJEk/fzzz1q5cqW+/PJL3XDDDZKk+fPnq2rVqlq2bJnuvPNOSVJGRoZmzJih2rVrS5Iee+wxjR492rrO888/r4kTJ6pbt5zv3Zo1a2r79u2aOXMmASkAAAAApUNWdpaeWPVEodkGA1cN1K2xt1IsGECZ9uuvvyo9PV3Nmze3toWFhSk2NlaStGPHDvn4+Oi6666z9oeHhys2NlY7duywtgUGBlrBKEmKjo7WwYMHJUmHDh3SH3/8oQceeEB9+/a12mRmZiokxLUg24UiIAUAAADAbb7c82WeOiznMjL648Qf+nLPl2pTo437OgagTAm0B+rU8FNFtvti9xfqtKBTke0+SvxIN1a/0aXrusqYvIF7V/YbY5ymBTqm7jnYbDbr2OzsbEk50/bODWxJkrd3yf6jAAEpAAAAAG6z7+S+ohudRzsAuBA2m82lqXPxteNVJbiK/jrxV76ZnTbZVCW4iuJrx1/0rM4rrrhCdrtdX3/9tapVqyZJOnr0qH766Se1bt1a9erVU2ZmpjZs2GBN2Tt8+LB++uknxcXFuXSNyMhIVa5cWb/99pvuvffei9r/ohCQAgAAAOA20eWjL2o7AChJ3l7eernjy+q+qLtssjkFpWzKyUKa0nFKiUwxLleunB544AE99dRTCg8PV2RkpEaMGCEvLy9JUp06ddSpUyf169dPM2fOVPny5TVs2DBVrlxZt956q8vXSU5O1oABAxQcHKxbbrlFZ8+e1bfffqujR49q8ODBF/19OXiV2JkBAAAAIJdW1VqpSnAV6xe53GyyqWpwVbWq1srNPQOA/HWL66bFPRarcnBlp+1VgquU+CIMEyZM0I033qiuXbuqXbt2uuGGG9S0aVNr/2uvvaYmTZqoc+fOat68uYwx+uijj/JM0yvMgw8+qDfeeENz5sxRw4YN1bp1a82ZM0c1a9YsibdkIUMKAAAAgNt4MtsAAC5Ut7huujX2Vn2550vtO7lP0eWj1apaqxL/ripXrpzmzp2ruXPnWtueeuopSTn1nypUqKC33nrLyprKrXfv3urdu7fTtttuuy1P/anExEQlJiZe3M4XgYAUAAAAALdyZBv0/7C/DqUesrZXCa6iKR2nlGi2AQBcKG8vbxZbuIgISAEAAABwu25x3RTgE2CtXjW5w2Q93uxxMqMAoIyghhQAAAAAj0jLTLP+XLdiXYJRAFCGEJACAAAA4BGnM05bfz6TccaDPQEAuBsBKQAAAAAecTr974DUudlSAIDSj4AUAAAAAI9wypDKJEMKAMoSAlIAAAAAPOLcDCmm7AFA2UJACgAAAIBHkCEFAGUXASkAAAAAHkENKQAouwhIAQAAAPAIVtkDgJKzdu1a2Ww2HTt2zNNdyRcBKQAAAAAewZQ9AJeVrCxp7VrpnXdy/puV5ekeFapFixbat2+fQkJCJElz5sxRhQoVPNyrv/l4ugMAAAAAyiaKmgO4bCxZIj3xhPTnn39vq1JFevllqVs3z/WrEL6+voqKivJ0NwpEhhQAAAAAjzg3QyotixpSAC5RS5ZI3bs7B6Mk6a+/crYvWVJilzbGaPz48apVq5YCAgLUqFEjLV68WMYYxcfHq3v37jLGSJKOHTumatWqacSIEZKcp+ytXbtW999/v44fPy6bzSabzabk5OQS67cryJACAAAA4BFkSAHwGGOk1NSi22VlSQMG5LTP7xw2W07mVLt2krd30ecLDMw5xkXPPPOMlixZounTp6tOnTr64osv1LNnT1WqVEmzZ8/WVVddpalTp2rgwIHq37+/IiMj8w00tWjRQlOmTNFzzz2nnTt3SpLKlSvncj9KAgEpAAAAAB5BDSkAHpOaKl2MgIwxOZlT/6vTVKRTp6SgIJeanj59WpMmTdKaNWvUvHlzSVKtWrX01VdfaebMmZo3b54mT56s/v376+DBg/rggw/0/fffy2635zmXr6+vQkJCZLPZLplpfASkAAAAAHgEGVIAULDt27crLS1N7du3d9qenp6uxo0bS5Juu+02ffzxxxo7dqymT5+uK6+80hNdvSAEpAAAAAB4BBlSADwmMDAnW6koX3whdepUdLuPPpJuvNG167ooOztbkrRixQpVrlzZaZ+fn58kKTU1VZs2bZK3t7d+/vlnl899KSAgBQAAAMAjzs2QSsukqDkAN7LZXJs6Fx+fs5reX3/lX0fKZsvZHx/vWg2p81CvXj35+flpz549at26dZ792dnZeuaZZ+Tl5aWVK1eqU6dOSkhI0E033ZTv+Xx9fZWVlXVR+1gcBKQAAAAAuF22yXbKimLKHoBLkre39PLLOavp2WzOQSlHcfIpUy56MEqSypcvryFDhmjQoEHKzs7WDTfcoBMnTmjdunUqV66cwsLCNH/+fP3nP//RNddco2HDhikpKUk//PCDQkND85yvRo0aOnXqlD799FM1atRIgYGBCjyPjK2LzctjVwYAAABQZqVmOK9uxZQ9AJesbt2kxYulXNPmVKVKzvZu3Urs0s8//7yee+45jR07VnFxcerQoYM++OAD1ahRQ3379tXQoUPVpEkTSdLIkSMVExOj/v3753uuFi1aqH///rrrrrtUqVIljR8/vsT67QoypAAAAAC43bnT9SQypABc4rp1k269VfryS2nfPik6WmrVqkQyo85ls9k0YMAADRgwIM++vXv36sSJE9ZrHx8fbdiwwXrdpk0bmVzTDKdPn67p06eXXIfPAwEpAAAAAG53bkFziRpSAC4D3t5Smzae7kWpwZQ9AAAAAG6XJ0OKKXsAUKYQkAIAAADgdo4MqSB7zipXaZlpeaaWAABKLwJSAAAAANzOkSFVMbCitY1pewBQdhCQAgAAAOB2jgypcwNSTNsDgLKDgBQAAAAAt3NkSIX4h8jblrNKFRlSAEpSdna2p7tQalyMe8kqewAAAADc7lT6KUk5NaQC7AE6lX5KZzLIkAJw8fn6+srLy0t79+5VpUqV5OvrK5vN5uluFVt2drbS09OVlpYmLy/35BsZY5Senq5Dhw7Jy8tLvr6+F3wuAlIAAAAA3M4qau4bJH8f/5yAFFP2AJQALy8v1axZU/v27dPevXs93Z2LxhijM2fOKCAgwO0BtsDAQFWrVq1YgTACUgAAAADczjFlL8gepACfAEkiQwpAifH19VW1atWUmZmprKwsT3fnosjIyNAXX3yhG2+8UXa73W3X9fb2lo+PT7GDYASkAAAAALidlSH1vyl7EjWkAJQsm80mu93u1uBNSfL29lZmZqb8/f0vy/dEUXMAAAAAbmdlSPmekyHFlD0AKDMISAEAAABwu3MzpPx9/CUxZQ8AyhICUgAAAADc7tyi5o4pe2RIAUDZQUAKAAAAgNvlV9ScGlIAUHYQkAIAAADgdvlmSDFlDwDKDAJSAAAAANzu3Awpq4YUU/YAoMwgIAUAAADA7ZwypHzIkAKAsoaAFAAAAAC3y6+GFBlSAFB2EJACAAAA4Hb51ZCiqDkAlB0EpAAAAAC4Xb4ZUkzZA4Ayg4AUAAAAALcyxig1I1VSToYURc0BoOwhIAUAAADArc5knpGRkfS/DCk7NaQAoKwhIAUAAADArRzT9SQp0B5oTdmjhhQAlB0EpAAAAAC4laOgub+Pv7y9vP/OkKKGFACUGQSkAAAAALjVuQXNJVFDCgDKIAJSAAAAANzKkSEV5JsTkGKVPQAoewhIAQAAAHCr3BlSFDUHgLKHgBQAAAAAtyooQ4qi5gBQdng0IJWZmalnnnlGNWvWVEBAgGrVqqXRo0crOzvbamOMUXJysmJiYhQQEKA2bdpo27ZtTuc5e/asHn/8cVWsWFFBQUHq2rWr/vzzT6c2R48eVa9evRQSEqKQkBD16tVLx44dc2qzZ88edenSRUFBQapYsaIGDBig9PT0krsBAAAAQBlUYIYUU/YAoMzwaEBq3LhxmjFjhl599VXt2LFD48eP14QJEzR16lSrzfjx4zVp0iS9+uqr2rhxo6KiotS+fXudPHnSajNw4EAtXbpUCxcu1FdffaVTp06pc+fOysrKstokJiZq8+bNWrVqlVatWqXNmzerV69e1v6srCwlJCTo9OnT+uqrr7Rw4UK99957evLJJ91zMwAAAIAyIneGFEXNAaDs8fHkxdevX69bb71VCQkJkqQaNWronXfe0bfffispJztqypQpGjFihLp16yZJeuuttxQZGakFCxaoX79+On78uN58803NnTtX7dq1kyTNmzdPVatW1SeffKIOHTpox44dWrVqlb7++mtdd911kqTXX39dzZs3186dOxUbG6vVq1dr+/bt+uOPPxQTEyNJmjhxonr37q0XX3xRwcHB7r49AAAAQKmUJ0OKouYAUOZ4NCB1ww03aMaMGfrpp5905ZVXasuWLfrqq680ZcoUSdLvv/+u/fv3Kz4+3jrGz89PrVu31rp169SvXz999913ysjIcGoTExOjBg0aaN26derQoYPWr1+vkJAQKxglSddff71CQkK0bt06xcbGav369WrQoIEVjJKkDh066OzZs/ruu+/Utm3bPP0/e/aszp49a70+ceKEJCkjI0MZGRkX70a5kaPfl2v/4VmMHxQH4wfFwfhBcTB+3O9EWs7/Nwf4BCgjI0M+//u1JC0zTenp6bLZbJ7s3nlh/KA4GD8ojkt1/LjaH48GpIYOHarjx4+rbt268vb2VlZWll588UXdc889kqT9+/dLkiIjI52Oi4yM1O7du602vr6+Cg0NzdPGcfz+/fsVERGR5/oRERFObXJfJzQ0VL6+vlab3MaOHatRo0bl2b569WoFBgYW+f4vZSkpKZ7uAi5jjB8UB+MHxcH4QXEwftxny74tkqT/7v2vPvroI53OysmYMjJavmK57F52T3bvgjB+UByMHxTHpTZ+UlNTXWrn0YDUu+++q3nz5mnBggWqX7++Nm/erIEDByomJkZJSUlWu9z/QmKMKfJfTXK3ya/9hbQ51/DhwzV48GDr9YkTJ1S1alXFx8dftlP8MjIylJKSovbt28tuv/z+RwCexfhBcTB+UByMHxQH48f9PvvkM+mAVK9OPXVq20lnM89K/5ezr3W71qrgX8GzHTwPjB8UB+MHxXGpjh/H7LGieDQg9dRTT2nYsGG6++67JUkNGzbU7t27NXbsWCUlJSkqKkpSTvZSdHS0ddzBgwetbKaoqCilp6fr6NGjTllSBw8eVIsWLaw2Bw4cyHP9Q4cOOZ1nw4YNTvuPHj2qjIyMPJlTDn5+fvLz88uz3W63X1KD4UKUhvcAz2H8oDgYPygOxg+Kg/HjPo7i5cF+wbLb7fLx8ZFNNhkZZSrzsnwOjB8UB+MHxXGpjR9X++LRVfZSU1Pl5eXcBW9vb2VnZ0uSatasqaioKKf0s/T0dH3++edWsKlp06ay2+1Obfbt26etW7dabZo3b67jx4/rm2++sdps2LBBx48fd2qzdetW7du3z2qzevVq+fn5qWnTphf5nQMAAABlV+5V9mw2mwLsOYXN0zLTPNYvAID7eDRDqkuXLnrxxRdVrVo11a9fX99//70mTZqkPn36SMr5i2ngwIEaM2aM6tSpozp16mjMmDEKDAxUYmKiJCkkJEQPPPCAnnzySYWHhyssLExDhgxRw4YNrVX34uLi1LFjR/Xt21czZ86UJD300EPq3LmzYmNjJUnx8fGqV6+eevXqpQkTJujIkSMaMmSI+vbte9lOvwMAAAAuRVZA6n+r7Ek5Bc5TM1Kt7CkAQOnm0YDU1KlT9eyzz+qRRx7RwYMHFRMTo379+um5556z2vzjH//QmTNn9Mgjj+jo0aO67rrrtHr1apUvX95qM3nyZPn4+KhHjx46c+aMbr75Zs2ZM0fe3t5Wm/nz52vAgAHWanxdu3bVq6++au339vbWihUr9Mgjj6hly5YKCAhQYmKiXnrpJTfcCQAAAKDsOJ3unCElKSdD6ox0JoOAFACUBR4NSJUvX15TpkzRlClTCmxjs9mUnJys5OTkAtv4+/tr6tSpmjp1aoFtwsLCNG/evEL7U61aNX344YdF9hsAAADAhcsvQ8rfx1+SyJACgDLCozWkAAAAAJQ9+WZI+eTUkCJDCgDKBgJSAAAAANwq3xpSFDUHgDKFgBQAAAAAtzqVfkpSARlSTNkDgDKBgBQAAAAAt7Km7OVXQ4opewBQJhCQAgAAAOA2xpi/p+zlXmVPZEgBQFlBQAoAAACA25zNOqtsky0pVw0pH2pIAUBZQkAKAAAAgNs4putJrLIHAGUZASkAAAAAbuOYrufr7SsfLx9ru1VDiil7AFAmEJACAAAA4Db5FTSXzqkhRYYUAJQJBKQAAAAAuE1+Bc2lc6bskSEFAGUCASkAAAAAblNUhhRFzQGgbCAgBQAAAMBtyJACAEgEpAAAAAC4UUEZUlZRc2pIAUCZQEAKAAAAgNsUmCFlJ0MKAMoSAlIAAAAA3KbAGlI+1JACgLKEgBQAAAAAtykyQ4opewBQJhCQAgAAAOA2RdaQYsoeAJQJBKQAAAAAuI2VIVXAlD0ypACgbCAgBQAAAMBtrAypAqbsUUMKAMoGAlIAAAAA3KbIDCmm7AFAmUBACgAAAIDbUNQcACARkAIAAADgRq4UNTfGuL1fAAD3IiAFAAAAwG0KzJD635S9bJOtjOwMt/cLAOBeBKQAAAAAuE1BGVKOKXsShc0BoCwgIAUAAADAbQrKkPLz9rP+TB0pACj9CEgBAAAAcJuCMqRsNptTHSkAQOlGQAoAAACA2xSUISX9XUeKDCkAKP0ISAEAAABwm4IypKS/60hRQwoASj8CUgAAAADcIiMrw1pBr9AMKabsAUCpR0AKAAAAgFs4putJhWdIMWUPAEo/AlIAAAAA3MIxXc/b5i1fb988+ylqDgBlBwEpAAAAAG5xbkFzm82WZz9FzQGg7CAgBQAAAMAtHBlS5XzL5bufouYAUHYQkAIAAADgFlaGVD71oySKmgNAWUJACgAAAIBbODKk8lthTzqnhhRT9gCg1CMgBQAAAMAtisyQspMhBQBlBQEpAAAAAG5RVIaUY8oeNaQAoPQjIAUAAADALU6ln5LkQg0ppuwBQKlHQAoAAACAW1hT9oqqIcWUPQAo9QhIAQAAAHALa8peUTWkyJACgFKPgBQAAAAAtyiyqLmjhlQWNaQAoLQjIAUAAADALYosak6GFACUGQSkAAAAALiFqxlS1JACgNKPgBQAAAAAt3C5qDkZUgBQ6hGQAgAAAOAWLhc1J0MKAEo9AlIAAAAA3KKoDCmrqHkmRc0BoLQjIAUAAADALVzOkGLKHgCUegSkAAAAALiFyzWkmLIHAKUeASkAAAAAblFkhpQPGVIAUFYQkAIAAADgFkXWkLJTQwoAygoCUgAAAADcwuUMKabsAUCpR0AKAAAAQInLys7S2ayzkorOkMrMzlRmdqbb+gYAcD8CUgAAAABKnGO6nlRwhpSjqLlEHSkAKO0ISAEAAAAocY7pejbZnAJP53IKSDFtDwBKNQJSAAAAAErcuQXNbTZbvm28bF7y8/aTRGFzACjtCEgBAAAAKHFFFTR3cNSRYsoeAJRuBKQAAAAAlLhzM6QK45i2x5Q9ACjdCEgBAAAAKHEuZ0j5kCEFAGUBASkAAAAAJc7VDCnHlD1qSAFA6UZACgAAAECJO+8MKabsAUCpRkAKAAAAQIk77xpSTNkDgFKNgBQAAACAEnfeq+yRIQUApRoBKQAAAAAlzsqQoqg5AEAEpAAAAAC4gZUhRVFzAIAISAEAAABwg/POkGLKHgCUagSkAAAAAJQ4VzOkKGoOAGUDASkAAAAAJY4MKQDAuQhIAQAAAChxVkCKGlIAABGQAgAAAOAG1pQ9VtkDAIiAFAAAAAA3cGRIlfMtV2g7q4YUU/YAoFQjIAUAAACgxLla1NwxZY+AFACUbgSkAAAAAJS48y1qTg0pACjdCEgBAAAAKHGn0k9JOo8MKWpIAUCpRkAKAAAAQIk776LmTNkDgFKNgBQAAACAEpVtsq0AU1EZUlZRczKkAKBUIyAFAAAAoESlZqRafy4yQ4qi5gBQJhCQAgAAAFCiHNP1pL8DTgWhqDkAlA0eD0j99ddf6tmzp8LDwxUYGKirr75a3333nbXfGKPk5GTFxMQoICBAbdq00bZt25zOcfbsWT3++OOqWLGigoKC1LVrV/35559ObY4ePapevXopJCREISEh6tWrl44dO+bUZs+ePerSpYuCgoJUsWJFDRgwQOnp6SX35gEAAIAywLHCXqA9UF62wn8Foag5AJQNHg1IHT16VC1btpTdbtfKlSu1fft2TZw4URUqVLDajB8/XpMmTdKrr76qjRs3KioqSu3bt9fJkyetNgMHDtTSpUu1cOFCffXVVzp16pQ6d+6srKwsq01iYqI2b96sVatWadWqVdq8ebN69epl7c/KylJCQoJOnz6tr776SgsXLtR7772nJ5980j03AwAAACilXC1oLp1TQ4opewBQqvl48uLjxo1T1apVNXv2bGtbjRo1rD8bYzRlyhSNGDFC3bp1kyS99dZbioyM1IIFC9SvXz8dP35cb775pubOnat27dpJkubNm6eqVavqk08+UYcOHbRjxw6tWrVKX3/9ta677jpJ0uuvv67mzZtr586dio2N1erVq7V9+3b98ccfiomJkSRNnDhRvXv31osvvqjg4GA33RUAAACgdHFkSBVV0Fw6Z5U9MqQAoFTzaIbU8uXLdc011+jOO+9URESEGjdurNdff93a//vvv2v//v2Kj4+3tvn5+al169Zat26dJOm7775TRkaGU5uYmBg1aNDAarN+/XqFhIRYwShJuv766xUSEuLUpkGDBlYwSpI6dOigs2fPOk0hBAAAAHB+zidDyjFlLyM7Q1nZWUW0BgBcrjyaIfXbb79p+vTpGjx4sJ5++ml98803GjBggPz8/HTfffdp//79kqTIyEin4yIjI7V7925J0v79++Xr66vQ0NA8bRzH79+/XxEREXmuHxER4dQm93VCQ0Pl6+trtcnt7NmzOnv2rPX6xIkTkqSMjAxlZGS4fB8uJY5+X679h2cxflAcjB8UB+MHxcH4KXnHzxyXlFNDqqj77GP+/hXl5JmTLmVVeRLjB8XB+EFxXKrjx9X+eDQglZ2drWuuuUZjxoyRJDVu3Fjbtm3T9OnTdd9991ntbDab03HGmDzbcsvdJr/2F9LmXGPHjtWoUaPybF+9erUCAwML7d+lLiUlxdNdwGWM8YPiYPygOBg/KA7GT8n5z9H/SJLSTqTpo48+KrRtlvk7K+qDVR8o2OfyKJ3B+EFxMH5QHJfa+ElNTXWpnUcDUtHR0apXr57Ttri4OL333nuSpKioKEk52UvR0dFWm4MHD1rZTFFRUUpPT9fRo0edsqQOHjyoFi1aWG0OHDiQ5/qHDh1yOs+GDRuc9h89elQZGRl5Mqcchg8frsGDB1uvT5w4oapVqyo+Pv6yrTmVkZGhlJQUtW/fXna73dPdwWWG8YPiYPygOBg/dQOBDgAAIABJREFUKA7GT8nbv3m/tFuqGlVVnTp1KrK9/f/sysjOUMs2LVU1uKobenjhGD8oDsYPiuNSHT+O2WNF8WhAqmXLltq5c6fTtp9++knVq1eXJNWsWVNRUVFKSUlR48aNJUnp6en6/PPPNW7cOElS06ZNZbfblZKSoh49ekiS9u3bp61bt2r8+PGSpObNm+v48eP65ptv1KxZM0nShg0bdPz4cSto1bx5c7344ovat2+fFfxavXq1/Pz81LRp03z77+fnJz8/vzzb7Xb7JTUYLkRpeA/wHMYPioPxg+Jg/KA4GD8lJy0rTZJU3q+8S/c4wB6gjLMZylTmZfNMGD8oDsYPiuNSGz+u9sWjAalBgwapRYsWGjNmjHr06KFvvvlG//rXv/Svf/1LUs4UuoEDB2rMmDGqU6eO6tSpozFjxigwMFCJiYmSpJCQED3wwAN68sknFR4errCwMA0ZMkQNGza0Vt2Li4tTx44d1bdvX82cOVOS9NBDD6lz586KjY2VJMXHx6tevXrq1auXJkyYoCNHjmjIkCHq27fvZZvtBAAAAFwKrFX2XChqLuWstHfi7AmlZaaVZLcAAB7k0YDUtddeq6VLl2r48OEaPfr/2bvz8Kjqe3/g7zNLJvsK2UhCwp4QNjdATIWyiBSxv9wU21hcqnhbrCRKq/X2uRbbX7FVVCJYf/eiV+hFpRjjvRU1BihgKAqyRAGjbAlJIAuQfZ1k5vz+GM9JJutMcmY78349D89DzpyZ+SYckslnPp/39/dISkrCpk2bcO+998rnPPnkk2hra8OaNWtQV1eH2bNno6CgAEFBQfI5L7/8MnQ6HVauXIm2tjYsXLgQ27Ztg1arlc956623sHbtWnk3vhUrVmDLli3y7VqtFh9++CHWrFmDefPmwc/PD5mZmdi4caMTvhJEREREROol77JnY0C5tNNeW2ebw9ZERESu5dKCFAAsX74cy5cvH/B2QRCwfv16rF+/fsBzfH19sXnzZmzevHnAc8LDw7Fjx45B15KQkIDdu3cPuWYiIiIiIrKdvR1SvjpfAEBbFwtSRERqpXH1AoiIiIiISN3s7pDSsUOKiEjtWJAiIiIiIiKHsjtD6ruRPWZIERGpl90Fqfz8fBw6dEj++NVXX8XMmTORmZmJuro6RRdHRERERESeTy5I2dshxZE9IiLVsrsg9etf/xqNjY0AgFOnTmHdunVYtmwZLl68iCeeeELxBRIRERERkWeTR/bszZDiyB4RkWrZHWpeUlKClJQUAMB7772H5cuXY8OGDThx4gSWLVum+AKJiIiIiMiz2d0hpWeHFBGR2tndIeXj44PW1lYAwN69e7FkyRIAll3spM4pIiIiIiIiib0dUtLIHjOkiIjUy+4Oqdtuuw1PPPEE5s2bh6NHj+Jvf/sbAODs2bOIi4tTfIFEREREROTZhp0hxZE9IiLVsrtDasuWLdDpdMjNzcVrr72GMWPGAAA+/vhjLF26VPEFEhERERGRZ7O7Q4oje0REqmd3h1RCQgJ2797d5/jLL7+syIKIiIiIiEhd7O2QYqg5EZH62V2QAgCTyYT3338fxcXFEAQBU6ZMwQ9/+EPodMN6OCIiIiIiUilRFIedIcUOKSIi9bK7gnT69GmsWLEC1dXVmDx5MgBLftTo0aPx97//HdOmTVN8kURERERE5Jnau9ohQgRg/y57DDUnIlIvuzOkHn74YaSmpqKiogInTpzAiRMnUF5ejunTp+ORRx5xxBqJiIiIiMhDSeN6ADukiIiom90dUl9++SWOHTuGsLAw+VhYWBj++Mc/4uabb1Z0cURERERE5NmkcT2D1gCtRmvTfZghRUSkfnZ3SE2ePBnV1dV9jtfU1GDChAmKLIqIiIiIiNTB3kBzgLvsERF5A5sKUo2NjfKfDRs2YO3atcjNzUVFRQUqKiqQm5uL7Oxs/PnPf3b0eomIiIiIyIPYG2gOdI/sMUOKiEi9bBrZCw0NhSAI8seiKGLlypXyMVG0hBTeddddMJlMDlgmERERERF5ohF1SHFkj4hItWwqSO3fv9/R6yAiIiIiIhUaSYcUR/aIiNTLpoLU7bff7uh1EBERERGRCg2nQ4qh5kRE6mf3LnsAUF9fjzfeeAPFxcUQBAEpKSn42c9+hpCQEKXXR0REREREHqzZ2AzAzg4phpoTEame3bvsHTt2DOPHj8fLL7+M2tpaXLt2DS+99BLGjx+PEydOOGKNRERERETkoaSRvUCfQJvvw1BzIiL1s7tD6vHHH8eKFSuwdetW6HSWu3d1deHhhx9GdnY2Pv30U8UXSUREREREnomh5kRE1B+7C1LHjh2zKkYBgE6nw5NPPombbrpJ0cUREREREZFnG06ouZQh1WHqgFk0QyPYPdhBRERuzu7v7MHBwSgrK+tzvLy8HEFBQYosioiIiIiI1EHukBrGLnsAx/aIiNTK7oLUPffcg4ceegh/+9vfUF5ejoqKCuzcuRMPP/wwfvKTnzhijURERERE5KHkDqlhjOwBLEgREamV3SN7GzduhCAIuO+++9DV1QUA0Ov1+MUvfoE//elPii+QiIiIiIg813A6pHQaHXQaHbrMXZYcKb+h70NERJ7F7oKUj48PcnJy8Nxzz+HChQsQRRETJkyAv7+/I9ZHREREHs5kNqGwrBCVTZWICYpBWkIatBqtq5dFRE4ynFBzwDK212RsQlsXg82JiNTI7oKUxN/fH9OmTVNyLURERKQyecV5yMrPQkVjhXwsLjgOOUtzkJ6c7sKVEZGzDCfUHLAEmzcZm7jTHhGRStlckEpPH/pFo06nQ3R0NBYvXoy77rprRAsjIiIiz5ZXnIeMXRkQIVodv9x4GRm7MpC7MpdFKSIvMOwOqe9ypJghRUSkTjaHmoeEhAz5x8/PD+fOncM999yDZ555xpHrJiIiIjdmMpuQlZ/VpxgFQD6WnZ8Nk9nk7KURkZMNt0NK2mmPI3tEROpkc4fUm2++afODfvjhh/jFL36B3//+98NaFBEREXm2wrJCqzG93kSIKG8sR2FZIeYnznfewojI6UbaIcWRPSIidbK5Q8oe8+bNw0033eSIhyYiIiIPUNlUqeh5ROS5RpIhBbBDiohIrRxSkAoNDUVeXp4jHpqIiIg8QExQjKLnEZHnGskuewA7pIiI1MohBSkiIiLybmkJaYgLjoMAod/bBQiID45HWkKak1dGRM4kiuLwM6QYak5EpGosSBEREZHitBotcpbm9HubVKTatHQTtBqtM5dFRE5mNBlhEi2bFwy7Q4oje0REqmRTQeqGG25AXV0dAOD3v/89WltbHbooIiIi8nzpyenIXZkr/1IpiQuOQ+7KXKQnp7toZUTkLNK4HjCCDCmO7BERqZJNBani4mK0tFh+mDz77LNobm526KKIiIhIHdKT0xEbFCt/fFPsTSjJKmExishLSON6eo0eeq3ervuyQ4qISN10tpw0c+ZMPPjgg7jtttsgiiI2btyIwMDAfs995plnFF0gERERea6mjiZcqLsgf9ze1c4xPSIvMtxAc4AZUkREamdTQWrbtm343e9+h927d0MQBHz88cfQ6freVRAEFqSIiIhI9mX1lwAAjaCBWTSjsqnSxSsiImcabqA5wF32iIjUzqaC1OTJk7Fz504AgEajwb59+xAZGenQhREREZHnO1l5EgAwe8xsfFbxGa63XUdHVwcMOoOLV0ZEzqBEhxRH9oiI1MnuXfbMZjOLUURERGSToqoiAMD3k74PH60PAKCqucqVSyIiJxpJh5Qcas6CFBGRKtldkAKACxcu4LHHHsOiRYuwePFirF27FhcuXBj6jkRERORVTlZZOqRuiLkBMYExAIDKZo7tEXmLEXVIcWSPiEjV7C5IffLJJ0hJScHRo0cxffp0pKam4siRI5g6dSr27NnjiDUSERGRBzKajDhz9QwAYFb0LMQEfVeQYo4UkdcYUYYUQ82JiFTNpgypnn7zm9/g8ccfx5/+9Kc+x5966iksXrxYscURERGR5yq+WgyjyYgQQwgSQxMRGxQLALjSdMXFKyMiZ1GkQ4oje0REqmR3h1RxcTEeeuihPsd/9rOf4euvv1ZkUUREROT5pPyomdEzIQgCR/aIvJAiGVIc2SMiUiW7C1KjR49GUVFRn+NFRUUMOyciIiKZlB81M3omAHQXpDiyR+Q15A6pEYzssUOKiEid7B7ZW716NR555BFcvHgRt956KwRBwKFDh/DnP/8Z69atc8QaiYiIyANJHVKzomcBQHeGFDukiLyG3CE1gpE9ZkgREamT3QWpf//3f0dQUBBefPFFPP300wCA2NhYrF+/HmvXrlV8gUREROR5RFHsLkjFWApSzJAi8j6KdEhxZI+ISJXsLkgJgoDHH38cjz/+OJqamgAAQUFBii+MiIiIPFdJfQkaOhrgo/VB8qhkAGCGFJEXYqg5ERENxO6CVE8sRBEREVF/pO6o1MhU6LV6AN0je1dbrqLT1CkfJyL1Yqg5ERENxO5QcyIiIqKhnKz8LtA8aqZ8bJT/KOg0OogQUd1S7aqlEZETjahDSs8MKSIiNWNBioiIiBRXVG2dHwUAGkGD6MBoANxpj8hbjKRDqufIniiKiq6LiIhcjwUpIiIiUpzcIRU90+o4c6SIvIsSHVIA0GHqUGxNRETkHuwqSHV2dmLBggU4e/aso9ZDREREHu5qy1VcbroMAQJmRM2wuk3KkeJOe0TeQYkMKYA5UkREamRXQUqv1+P06dMQBMFR6yEiIiIPJwWaTwifgCCD9QYosYGxADiyR+Qtmo3NAIbXIaXX6KERLL+ucKc9IiL1sXtk77777sMbb7zhiLUQERGRCpys6n9cD+jukOLIHpF3kEf2htEhJQiCnCPFYHMiIvXR2XsHo9GI119/HXv27MFNN92EgADrHy4vvfSSYosjIiIizyN1SM2KntXnNilDiiN7RN5BHtkbRocUYMmRauls4cgeEZEK2V2QOn36NG644QYA6JMlxVE+IiIiGqxDKjbou5E9dkgRqV6nqROd5k4Aw+uQArpzpDiyR0SkPnYXpPbv3++IdRAREZEKtBhb8O21bwEAs2L66ZCSRvaYIUWketK4HgAE+gQO6zGkkT12SBERqY/dGVKS8+fP45NPPkFbm+WHgyiKii2KiIiIPNOpmlMQISIqIArRgdF9bpdG9qpbqmEym5y9PCJyImlcTyto4aP1GdZj+OmZIUVEpFZ2F6SuX7+OhQsXYtKkSVi2bBkqKy3vcD788MNYt26d4gskIiIizyHnR/XTHQUAkQGR0AgamEUzalpqnLk0InIyOdDcJ2DY0R5yhxRH9oiIVMfugtTjjz8OvV6PsrIy+Pv7y8fvuece5OfnK7o4IiIi8iwnKy35Uf0FmgOAVqNFVEAUAOZIEamdHGg+zPwooLtDiiN7RETqY3eGVEFBAT755BPExcVZHZ84cSIuXbqk2MKIiIjI8wwWaC6JCYpBZXOlJUcqxlkrIyJn69khNVwMNSciUi+7O6RaWlqsOqMk165dg8FgUGRRRERE5Hm6zF04VXMKwMAdUkB3jtSVpitOWRcRuYYiHVIMNSciUi27C1Lf+9738Ne//lX+WBAEmM1mvPDCC1iwYIGiiyMiIiLP8e21b9He1Y5An0CMDx8/4HmxQbEAOLJHpHZKdEgx1JyISL3sHtl74YUXMH/+fBw7dgxGoxFPPvkkzpw5g9raWvzzn/90xBqJiIjIA0iB5jOiZkAjDPyel9QhVdnEgpQzmMwmFJYVorKpEjFBMUhLSINWo3X1ssgLKNohxZE9IiLVsbsglZKSgq+++gqvvfYatFotWlpakJ6ejkcffRQxMQyCICIi8la25EcBlgwpALjSzJE9R8srzkNWfhYqGivkY3HBcchZmoP05HQXroy8gaIZUhzZIyJSHbsLUgAQHR2NZ599Vum1EBERkQeTOqQGy48CeozssUPKofKK85CxKwMiRKvjlxsvI2NXBnJX5rIoRQ7FDikiIhrMsApSdXV1eOONN1BcXAxBEJCcnIwHH3wQ4eHhSq+PiIiIPIAoinKH1KyYwQtS8sgeM6QcxmQ2ISs/q08xCgBEiBAgIDs/G3dPvpvje+QwcofUSApSzJAiIlItu0PNDx48iKSkJLzyyiuoq6tDbW0tXnnlFSQlJeHgwYOOWCMRERG5uYrGCtS21UKn0WHq6KmDniuN7FU1V8Esmp2xPK9TWFZoNabXmwgR5Y3lKCwrdOKqyNvIHVIjCTXnLntERKpld4fUo48+ipUrV8oZUgBgMpmwZs0aPProozh9+rTiiyQiIiL3JnVHpYxOgUFnGPTcqIAoCBDQZe7CtdZriAyIdMYSvYqt45AcmyRHUrJDiiN7RETqY3eH1IULF7Bu3Tq5GAUAWq0WTzzxBC5cuKDo4oiIiMgznKy0LdAcAPRaPUYHjAbAgoijSF1oSp1HNByKhpqzIEVEpDp2F6RuuOEGFBcX9zleXFyMmTOHfhFKRERE6lNUbVuguYQ5Uo6VlpCGuOA4CBD6vV2AgPjgeKQlpDl5ZeRNlAw1Z4YUEZH62DSy99VXX8l/X7t2LbKysnD+/HnMmTMHAPD555/j1VdfxZ/+9CfHrJKIiIjcmtQhZXNBKigGX1Z/iStNVxy5LK+l1WiRszQHGbsyBjxn09JNDDQnh1KiQ0oe2WOGFBGR6thUkJo5cyYEQYAodu/U8uSTT/Y5LzMzE/fcc49yqyMiIiK3V9dWh0sNlwAAM6Jn2HSf2MBYABzZc6T05HTkrszFj979kVV4vFbQ4m8Zf0N6croLV0feQMkOKY7sERGpj00FqZKSEkevg4iIiDxUUZVlXC8pNAmhvqE23UfKLuLInmPNHjMbZtEMAQL+suwveLzgcbR3tSMqMMrVSyMvoGiGFDukiIhUx6aC1NixYx29DiIiIvJQUkHKlkBziZQhxZE9xzpy+QgAYHrUdPz85p/jyJUj2Fa0DduKtuG2hNtcvDpSO0U6pLjLHhGRatlUkOrt8uXL+Oc//4mamhqYzWar29auXavIwoiIiMgznKyyLz8KAGKDvhvZY4eUQx2psBSkZo+ZDQB4YMYD2Fa0DbvO7MIrd74Cf72/K5dHKqdIhhRDzYmIVMvugtSbb76Jn//85/Dx8UFERAQEoXv3FkEQWJAiIiLyMlJByq4OKWlkjxlSDiV1SM2OsxSk0samISk0CSX1JXi/+H3cO/1eVy6PVE7RDimO7BERqY7G3js888wzeOaZZ9DQ0IDS0lKUlJTIfy5evDjshTz33HMQBAHZ2dnyMVEUsX79esTGxsLPzw/z58/HmTNnrO7X0dGBxx57DKNGjUJAQABWrFiBiooKq3Pq6uqwatUqhISEICQkBKtWrUJ9fb3VOWVlZbjrrrsQEBCAUaNGYe3atTAajcP+fIiIiLxBe1c7iq8WAwBmxdjeISWN7FU2V1ptmkLKMZlNOHblGIDuDimNoMH9M+4HAGz7cpurlkZeQskOKY7sERGpj90FqdbWVvz4xz+GRmP3XQf0xRdf4D//8z8xffp0q+PPP/88XnrpJWzZsgVffPEFoqOjsXjxYjQ1NcnnZGdn4/3338fOnTtx6NAhNDc3Y/ny5TCZTPI5mZmZKCoqQn5+PvLz81FUVIRVq1bJt5tMJvzgBz9AS0sLDh06hJ07d+K9997DunXrFPsciYiI1Oh0zWmYRBNG+Y/CmKAxNt8vOjAaAGA0GVHbVuuo5Xm1M1fPoKWzBUE+QZgyaop8/L4Z9wEA9l3ch7KGMlctj1TOZDbJY3Yj6ZDqGWrO4jURkbrYXVV66KGH8O677yq2gObmZtx7773YunUrwsLC5OOiKGLTpk347W9/i/T0dKSmpmL79u1obW3F22+/DQBoaGjAG2+8gRdffBGLFi3CrFmzsGPHDpw6dQp79+4FABQXFyM/Px+vv/465s6di7lz52Lr1q3YvXs3vv32WwBAQUEBvv76a+zYsQOzZs3CokWL8OKLL2Lr1q1obGxU7HMlIiJSm56B5j3H+Idi0BkQ4RcBgDlSjiLlR9085mZoNVr5eFJYEm4feztEiPjvL//bVcsjlWvtbJX/PqIOqe9G9kSI6DR3jnhdRETkPuwuSD333HM4ePAg5s+fj8ceewxPPPGE1R97Pfroo/jBD36ARYsWWR0vKSlBVVUVlixZIh8zGAy4/fbbcfjwYQDA8ePH0dnZaXVObGwsUlNT5XM+++wzhISEYPbs2fI5c+bMQUhIiNU5qampiI2Nlc+544470NHRgePHj9v9OREREXmLk5X2B5pLpBwp7rTnGJ9XfA6ge1yvpwdmPgAA2P7ldnadkENI43oCBHnsbjh63pc5UkRE6mJ3qPmGDRvwySefYPLkyQDQJ9TcHjt37sSJEyfwxRdf9LmtqqoKABAVFWV1PCoqCpcuXZLP8fHxseqsks6R7l9VVYXIyMg+jx8ZGWl1Tu/nCQsLg4+Pj3xOfzo6OtDR0SF/LHVTdXZ2orPTM9/Bkdbtqesn1+L1QyPB68czSYHmqaNT7f63iw6IxmmcRnl9+Yj/3Xn99CUVpG6MvrHP1+XuiXcjQB+Ac7XnUFhaiLlxc12xRLfB60d59a2WvFZ/vT+6urqG/TiCKECAABEiGtsa4a91v50hef3QSPD6oZFw1+vH1vXYXZB66aWX8F//9V944IEH7L2rlfLycmRlZaGgoAC+vr4Dnte7yCWK4pCFr97n9Hf+cM7p7bnnnsOzzz7b53hBQQH8/d3vh6U99uzZ4+olkAfj9UMjwevHc5hEE05esRSkGr9txEeXPrLv/vWWvMeDxw9iVMUoRdbE68ei1dSK4muWsPmm4iZ8dL7vv80tgbdgf91+/N8P/y8ejX/U2Ut0S7x+lFPaVgoA0Ik6fPSRfd8betMLehhFIz7e8zGiDFFD38FFeP3QSPD6oZFwt+untbV16JMwjIKUwWDAvHnz7F5Qb8ePH0dNTQ1uvPFG+ZjJZMKnn36KLVu2yPlOVVVViImJkc+pqamRu5mio6NhNBpRV1dn1SVVU1ODW2+9VT6nurq6z/NfvXrV6nGOHDlidXtdXR06Ozv7dE719PTTT1uNKTY2NiI+Ph5LlixBcHCwzV8Ld9LZ2Yk9e/Zg8eLF0Ov1rl4OeRhePzQSvH48z7fXv0X7l+3w0/nh4R8+bJVTZIt/7v8n9n+2HyFjQrBsybIRrYXXj7X9pfshnhKREJyAe+++t99zAi4FYP9b+3Gk+QjeXfwu/PWe/WbaSPD6Ud7nFZ8D3wLhgeFYtmxk/78Dvw1EbVstZt82GymjUxRaoXJ4/dBI8PqhkXDX68fWLG67C1JZWVnYvHkzXnnlFbsX1dPChQtx6tQpq2MPPvggpkyZgqeeegrjxo1DdHQ09uzZg1mzLLkURqMRBw8exJ///GcAwI033gi9Xo89e/Zg5cqVAIDKykqcPn0azz//PABg7ty5aGhowNGjR3HLLbcAAI4cOYKGhga5aDV37lz88Y9/RGVlpVz8KigogMFgsCqY9WYwGGAwGPoc1+v1bnUxDIcaPgdyHV4/NBK8fjzHmWtnAADTo6bD1zBwt/NA4kLiAABVrVWK/Zvz+rE4XmXJwJwdN3vAr8f3x38fiaGJKK0vxYcXPkTmtExnLtEt8fpRTodoibUI8AkY8ddUypEyCSa3/vfh9UMjweuHRsLdrh9b12J3Qero0aP4xz/+gd27d2Pq1Kl9nigvL8+mxwkKCkJqaqrVsYCAAERERMjHs7OzsWHDBkycOBETJ07Ehg0b4O/vj8xMywumkJAQPPTQQ1i3bh0iIiIQHh6OX/3qV5g2bZockp6cnIylS5di9erV+I//+A8AwCOPPILly5fLOVhLlixBSkoKVq1ahRdeeAG1tbX41a9+hdWrV3tspxMREZGjSflRwwk0B4DYIMtmIpVN3GVPaUcuWzq/+ws0l2gEDe6bfh9+/+nvsa1oGwtSpKgWoyXUPEA//B32JNJOeww1JyJSF7sLUqGhoUhPT3fEWvp48skn0dbWhjVr1qCurg6zZ89GQUEBgoKC5HNefvll6HQ6rFy5Em1tbVi4cCG2bdsGrbZ7bOCtt97C2rVr5d34VqxYgS1btsi3a7VafPjhh1izZg3mzZsHPz8/ZGZmYuPGjU75PImIiDxRUVURAGBm9Mxh3T8m0NKVXNnMgpSSRFGUC1Jz4uYMeu59MywFqb0X96K8oRzxIfHOWCJ5AWmXvQCfkRekfHWWDsy2LhakiIjUxO6C1JtvvumIdQAADhw4YPWxIAhYv3491q9fP+B9fH19sXnzZmzevHnAc8LDw7Fjx45BnzshIQG7d++2Z7lEREReSxTF7g6pmOF1SMUEWQpSV5qu2LRpCdmmvLEcVc1V0Gl0uCHmhkHPHR8+Ht8b+z18eulT/PdX/41/S/s3J62S1K7Z2AxAoQ4pHTukiIjUSOPqBRAREZFnMZlNyCvOQ01LDQQISB6VPKzHkTqk2rva0dDRoOQSvdqRCkt31PSo6fKo02AemPEAAGD7l9shiqIjl0ZeRB7ZU6BDSrqO27vaR/xYRETkPuwuSCUlJWHcuHED/iEiIiL1yivOQ2JOIjLezQAAiBCR8pcU5BXbliHZk5/eD6G+oQCYI6UkW/KjespIyYC/3h9nr5+17IxGpAB5ZE/JDimO7BERqYrdI3vZ2dlWH3d2duLkyZPIz8/Hr3/9a8UWRkRERO4lrzgPGbsyIMK6i+Zy42Vk7MpA7spcpCfblzMZExiD+vZ6XGm6guTRw+u0Imv2FqSCDEHISMnAX7/8K7YVbcPc+LmOXB55CYaaExHRUOwuSGVlZfV7/NVXX8WxY8dGvCAiIiJyPyazCVn5WX2KUYClS0qAgOz8bNw9+W5oNdp+HqF/MUExKL5WzGBzhXSaOnH8ynEAwOw42wpSAHD/jPvx1y//ip0S44WNAAAgAElEQVRndmLT0k02jfoRDYah5kRENBTFMqTuvPNOvPfee0o9HBEREbmRwrJCVDRWDHi7CBHljeUoLCu063Fjg2IBcGRPKadqTqGtqw0hhhBMiphk8/3mJ85HQkgCGjsa8T/f/I8DV0jeQtEOKR0zpIiI1EixglRubi7Cw8OVejgiIiJyI7YWjOwtLEnB5learti9JupLCjS/Zcwt0Ai2v8zTCBrcP+N+AMC2L7c5YmnkZZTskOIue0RE6mT3yN6sWbOstmUWRRFVVVW4evUq/vKXvyi6OCIiInIPMUExip4nn/9dQYoje8qwNz+qp/tn3I8/fPoH7L24F5cbL2NM8Bill0deRCpIBfoEjvix5AwpjuwREamK3QWpH/7wh1YfazQajB49GvPnz8eUKVMUWxgRERG5j7SENMQFx+Fy4+V+c6QECIgLjkNaQppdjyuP7LEgpQi5IGVHfpRkfPh4pCWkobCsEM8efBYLEhcgJigGaQlpduWCEQHKjuzJGVLskCIiUhW7C1K/+93vHLEOIiIicmNajRY5S3OQsSujz20CLJ3Tm5ZusrtwIXVUMUNq5Orb6/HNtW8ADK9DCgCmRU5DYVkhtp7Yiq0ntgIA4oLjkLM0x+4dFMm7OWRkjx1SRESqoliGFBEREalbenI6clfmyr8cSuKC45C7MndYBQtmSCnni8tfAACSQpMwOmC03ffPK87Da8de63P8cuNlZOzKQF5x3ojXSN5D0VBzPUPNiYjUyOaClEajgVarHfSPTmd3wxURERF5kPTkdEwMnwgAWDd3Hfbfvx8lWSXD7p6ROqRaOlvQ1NGk2Dq90UjG9UxmE7Lys/odx5SOZednw2Q2jWyR5DXYIUVEREOxuYL0/vvvD3jb4cOHsXnzZohi3xcxREREpB4mswlna88CAH5+088xIXzCiB4v0CcQQT5BaDI2obK5EkGGICWW6ZWkgtScMXPsvm9hWSEqGisGvF2EiPLGchSWFWJ+4vzhLpG8iCM6pJghRUSkLjYXpO6+++4+x7755hs8/fTT+OCDD3DvvffiD3/4g6KLIyIiIvdS1lCG9q52GLQGJIUmKfKYMUExaLrehCtNVzApYpIij+ltRFHEkYrhd0jZmuHFrC+ylZIdUnKoOTukiIhUZVgZUleuXMHq1asxffp0dHV1oaioCNu3b0dCQoLS6yMiIiI3UnytGAAwKWKSYjuvSTlSLHYMX2l9Ka62XoVeo8fM6Jl2318anVTqPPJuZtGM1s5WAAp1SOmYIUVEpEZ2FaQaGhrw1FNPYcKECThz5gz27duHDz74AKmpqY5aHxEREbmR4quWgtSUUVMUe8zYoFgAQGUzC1LDJY3rzYyeKXeT2CMtIQ1xwXHyjom9CRAQHxyPtIS0Ea2TvEPP0TpFMqQ4skdEpEo2F6Sef/55jBs3Drt378Y777yDw4cPIy2NL0qIiIi8idQhlTwqWbHH5E57IyeP642xf1wPALQaLXKW5gBAn6KU9PGmpZsU64ojdZPG9QDAX+8/4sdjqDkRkTrZnCH1m9/8Bn5+fpgwYQK2b9+O7du393teXh63BCYiIlIruSA1WsGC1HdjYOyQGr7PL38OYHj5UZL05HTkrsxFVn6WVcB5XHAcNi3dNOydFMn7SIHmfjo/aIRhJYRYkTOk2CFFRKQqNhek7rvvPghC/23cREREpH6iKMoje0p2SMkje8yQGhajyYiTlScBDL9DSpKenI67J9+N3+z9DTZ+thG3xN6Cww8dZmcU2UXJQHOgx8geO6SIiFTF5oLUtm3bHLgMIiIicndXW6+irr0OAgRFd8PjyN7IfFn1JTpMHQj3C8eE8AkjfjytRovvJ30fGz/biA5TB4tRZDepQ0qJQHOAoeZERGo18h5aIiIi8gpSd1RiaKLcsaAEjuyNjBRofsuYWxTrZh8TPAYAcLnpsiKPR97FYR1SHNkjIlIVFqSIiIjIJo7IjwK6R/YaOxrlreLJdlJBaqTjej2NCbIUpK61XkNHV4dij0vewVEdUibRhE5TpyKPSURErseCFBEREdnkm2vfAFA2PwoAgnyC5J24mCNlv5HusNefcL9wGLQGAOxcI/sp3SElhZoDzJEiIlITFqSIiIjIJnKHlMIFKUEQmCM1TLVttThXew6AZWRPKYIgyJ1rlxs5tkf2UbpDqmdBijlSRETqwYIUERER2UTKkJoyaorij80cqeE5evkoAGBC+ARE+Eco+tjMkaLhUrpDShAEuSjFHCkiIvVgQYqIiIiG1GxsRnljOQDlM6SA7hwpjuzZRxrXmxM3R/HHlv5N2LVG9lK6QwrozpHiyB4RkXqwIEVERERDkvKjIgMiEe4Xrvjjc2RveBwRaC6Rgs05skf2kjukFCxIsUOKiEh9WJAiIiKiITkq0FwiFaQ4smc7URTlkT2HFqQ4skd2kjukFBrZAwA/PTukiIjURufqBRApxWQ2obCsEJVNlYgJikFaQhq0Gq2rl0VEpAqOzI8CeozssSBlswt1F3C97ToMWgNmRM9Q/PHlUHMWpMhOjuiQkkb2GGpORKQeLEiRKuQV5yErPwsVjRXysbjgOOQszUF6croLV0ZEpA6O2mFPIoWac2TPdlJ+1KyYWfDR+ij++FKoOf9NyF5Kh5oDPTqkOLJHRKQaHNkjj5dXnIeMXRlWxSjAknmRsSsDecV5LloZEZF6yAUpBwSaAz1G9hhqbrPPKz4H4JhxPcA6Q0oURYc8B6mTI0LN5QwpjuwREakGC1Lk0UxmE7LysyCi7wtl6Vh2fjZMZpOzl0ZEpBqdpk6crz0PwPEdUnXtdRzJsZEjA82B7pG9tq421LfXO+Q5SJ0c0iGlY4cUEZHasCBFHq2wrLBPZ1RPIkSUN5ajsKzQiasiIlKXC3UX0GXuQoA+AHHBcQ55jjDfMBi0BgDskhqKyWxCwYUCnKg8AQC4KfYmhzyPn94PYb5hADi2R/ZxRIeUNLLHgjURkXqwIEUezdZfWvjLDRHR8PUMNBcEwSHPIQiC3CXFYPOB5RXnITEnEXfsuAMm0dL9+/2/ft9h4+lSjhSDzclWJrMJ1S3VAIDztecV61KXO6TsHNkzmU04UHoA75x6BwdKD7BrnojIjbAgRR5N+uVFqfOIiKgvR+dHSZgjNThXZCb2zJEiGopUML1YdxEA8OTeJ5GYk6jItTmcUHNpPQu2L0BmXiYWbF+g2HqIiGjkWJAij5aWkIa44DgI6P8dewEC4oPjkZaQ5uSVERGpxzfXvgHguPwoiZRZxA6pvlyVmSj9m3Bkj4bi6IKpr9a+UHNuekNE5P5YkCKPptVokbM0BwD6FKWkjzct3QStRuv0tRERqYXUITVl1BSHPo/UIcXiR1+uykyUO6Q4skeDcEbB1J4MKW56Q0TkGViQIo+XnpyO3JW58ru4krjgOOSuzEV6crqLVkZE5PlEUXRahxQzpAbmqsxEZkiRLZxRMLVnlz1uekNE5Bl0rl4AkRLSk9MxI2oGJmyeAADw1fmiJKuEnVFERCNU0ViBZmMzdBodJoRPcOhzySN7zJDqw1WZiRzZI1s4o2AqZ0jZMLLHTW+IiDwDO6RINa61XpP/3t7VjtbOVheuhohIHaRxvQnhE6DX6h36XBzZG5irMhMZak62cEbB1Fdne4YUN70hIvIMLEiRalQ1V1l9zJEPIqKRk8b1HJ0fBXBkbzA9MxN7c2RmojSyV91SjS5zl6KPTerhjIKpPSN73PSGiMgzsCBFqlHdUm31Md9hJyIaueKrlg4pR+dHAd0dUtdar8FoMjr8+TyNlJlo0BqsjjsyM3G0/2hoBS3MohnVzdVD38ELmMwmHCg9gHdOvYMDpQcYjA3nFEztCTV3VQGXiIjsw4IUqUbvF8rMBSAiGjlpZM8ZBakI/wjoNZaxwN5dr2SRnpyOCL8IAMD629dj//37UZJV4rANPLQardy5xmBzIK84D4k5iViwfQEy8zKxYPsCJOYkIq84z9VLc7n05HQ8v/j5PseVKpjKHVI2jOxJ68ldmQsfrY9D1kNERCPHUHNSDY7sEREpTy5IjXZ8QUojaBAdGI3yxnJUNlUiISTB4c/paerb63Gl2dIBnD0nGyG+IQ5/zjFBY1DRWGHJkRrj8KdzW3nFecjYlQERotXxy42XkbErg0UOQO4WmxM3B2tvWYuYoBikJaQp0okkh5rbMLIn+T9T/g/8df5yx+XCpIX45KefsDOKiMhNsEOKVEMa2ZPeQePIHhHRyNS21aKmpQaAczKkAOZIDeVMzRkAli4PZxSjAO60B1gKLVn5WX2KUQDkY9n52V4/vldwsQAAkJmaiZ9M+wnmJ85XrPhjT6i5pKq5CvUd9fLHRpORxSgiIjfCghSphlSQmh41HQB/mSEiGikp0DwuOA6BPoFOeU4WPwZ35qqlIJUameq055R32vPikb3CskJUNFYMeLsIEeWN5SgsK3TiqtxLa2crDpUdAgAsHr9Y8ceX3nC0JUNKIv1/kXKjyhrKFF8XERENHwtSpBrSyN6s6FkA+MsMEdFIOTPQXCIFmzMHsH+na04DAKaOnuq055R22vPmgpSt16M3X7eFlwphNBkRHxyPyRGTFX/84YzsSR2FN8XeBMByDXt7FxsRkTthQYpUQwo1nxVjKUh584tCIiIlODPQXCIXpNjl2i9XdEixa617lFSp89So4IJlXG/xuMUQBEHxx7c31Bzo/v+yMGkhtIIWXeYubphARORGWJAiVWjtbEWTsQkAO6SIiJQijew5I9BcIhU/WJDqn0s6pKSRvUbv7ZBKS0hDXHCcPPrVmwAB8cHxSEtIc/LK3Meei3sAAEvGL3HI48sZUvZ0SH1XkJoeNV3u9CtvLFd+cURENCwsSJEqSN1RvjpfTB5laRNvMjah2djsymUREXk0qUPKWYHmQHeHCd9U6Otqy1U5ZN6ZRUKO7AFajRY5S3P6vU0qUm1auslrA7MrmypxquYUBAhYOG6hQ55DHtmzsUNKFEV5ZG9q5FTEB8cDAMobWJAiInIXLEiRKkiB5lEBUQg2BCNAHwCAY3tERMPV1tmGkroSAMyQchdSt0dSaJLTQuaB7g6pxo5Gr36jJz05HbkrcxHkE2R1PC44Drkrc5GenO6ilbne3ot7AQA3xNyAUf6jHPIc0shel7kLXeauIc+/0nQFDR0N0ApaTI6YjISQBAAMNicicicsSJEqSHkAUYFRADjyQUQ0Umevn4UIEWG+YYgMiHTa80odUjUtNTb90ulNenZ7OFOQIUgugHl751p6crrVSNqk8EkoySrx6mIUABRc7M6PchSpQwqwbac9qYA7IXwCDDpDd4cUR/aIiNwGC1KkCtLIXnRgNACOfBARjZQcaD462SEBxQMZ7T8aWkELEaL8vZ0spPyo1NHOCzSXMEeqW0l9ifz3a23XvHZMTyKKIvZccGx+FNCdIQXYliMlFXBTRqcAAOJDWJAiInI3LEiRKvQc2QN6dEhx5IM8lMlswoHSA3jn1Ds4UHqA21ST00mB5lMinJcfBViyeqRuV3a5WpM6PpzdIQUwR6onaZQVAGrbalHfXu/C1bjeqZpTqG6phr/eH7fG3+qw59EIGvhofQDYliMl/3/5bgMAjuwREbkfnasXQKQEaWRP7pAKZIcUea684jxk5WehorFCPhYXHIecpTlePxZCztOzQ8rZYgJjcKXpCt9U6EEUxe4OqUjnd0hJb/R4+8/V+vZ61LXXAQCCfILQZGxCSV0JZsXMcvHKXEfqjrp97O0w6AwOfS4/nR+MJqNNI3tfX/0aQHcBl6HmRETuhx1SpAoDdkjx3XXyMHnFecjYlWFVjAIsYzIZuzKQV5znopWRtym++l1ByomB5hIWP/qqaq5CXXsdNILGqbseSjiyZyF1R0UGRMqjYBfrLrpySS7njPwoibzT3hAje6IoDtghVd1SjY6uDgeukoiIbMWCFKmClDMijXmwQ4o8kclsQlZ+FkSIfW6TjmXnZ3N8jxzOZDbh7PWzAFzXIQXwTYWeegY098zScRa5IOXlI3tS8Wlc2DiMCxsHwDpTytu0d7Xj00ufAnBsfpRE2mlvqJG9y02X0djRCK2gxaSISQCAcL9w+f693/QhIiLXYEGKVKH3yB47pMgTFZYVDvoiWYSI8sZyFJYVOnFV5I1K60vRYeqAQWvA2JCxTn9+aWMKjux1k8b1pG4PZ2PXmoVUkEoKTZILUt7cIXWo7BDau9oRGxQrd4w5klSMHapDSgo0nxgxUR4jFASBweZERG6GBSlShd4je9xljzyRrb9885d0cjQpP2ryqMku2UFMLn4083u4RPoF2xX5UQBDzSVSN9S4sHFICk0C4N0FKSk/avG4xU7ZjVMa2RsqQ6r3uJ6EweZERO6FBSnyeC3GFjQbmwF0j+xJv8w0djSixdjisrUR2UMqpCp1HtFwuTI/CgAi/SMBWIow3GXS4vRV13ZISSN7V5quwCyaXbIGd8AOKWvOzI8CbB/Zkwq4vf+/MNiciMi9sCBFHk/qjvLT+SHIJwiAZecbf70/AI7tkedIS0hDXHAcBPT/LrMAAfHB8UhLSHPyysjbyDvsuaAglVech3/98F8BWLpRFmxfgMScRK8O9BdF0eUdUtGB0RAgoMvchWut11yyBnfQs0NKKkhdarjklUXTmpYaFFUVAQAWjVvklOe0NdRc7pCK7L8gxQ4pIiL3wIIUebyegeZSu7ggCN05UhxvIg+h1WiRszSn39ukItWmpZtcMkJF3kUqSDl7Nzdpl8malhqr496+y2R5YzmajE3QaXSYGDHRJWvQa/WIDLB0rnnrTnsmswml9aUALAWpuOA46DQ6GE1Gr4wI2HtxLwBgRtQMuUPd0eQMqUE6pERRxNdXvwYw8MgeM6SIiNwDC1Lk8XoHmku40x55ovTkdOSuzEWEX4TV8digWOSuzEV6crqLVkbeQhRFfHPtGwDO3WGPu0wOTOqOmhwxGT5aH5etw9tzpK40XYHRZIROo0NccBy0Gq0c+u+NY3t7Llryo5yxu55EHtkbpENqsAIuQ82JiNwLC1Lk8XoHmku40x55qvTkdPxb2r9ZHXtv5XssRpFTVLdUo769HhpBI2+X7gzcZXJg8g57ka7Jj5J4+0570rje2JCxcqeqt+ZIiaKIggvOzY8CbAs1l3fYC5/Yp4DLUHMiIvfCghR5PHlkr1dBih1S5Ml6/2J+oe6Ci1ZC3kYKNE8KTZLHY5yBu0wOTMrDSR3tmvwoiRRs7q0je1LRSSpC9fy7VKzyFsXXinGl6Qp8db64LeE2pz2vLaHm8rhePwVcKUOqsaMRjR2NDlghERHZgwUp8ngDjux9txMZO6TIE0njBFrB8i782etnXbkc8iJyoLkTx/UA7jI5GHfpkJILUl46stdzhz2Jt3ZISd1RaQlpcteSM9gysicHmvezI2WATwDCfMMAcKc9IiJ3wIIUeTx5ZC+w/5E9dkiRJ5JeKN885mYALEiR80j5UVMinBtozl0m+2cWzXLHh6t22JN4+8/VnjvsSaTilLcVpFyRHwXYFmo+WEEK4NgeEZE7YUGKPN5AGVLSyJ43jneQ55NeKC9KsmylzYIUOYurOqR67jI5UFHKG3eZLKkrQVtXGwxaA8aHjXfpWrw91FzukArr2yHlTSN7HV0dOFB6AIBz86OAoTOkrHbYG6CjkMHmRETugwUp8ngDjex5+zu55LmMJqN8XS8ctxCApSAlin13HyNSmpQhlTzKuQUpoHuXSanwIREg4O1/edsrg/2lbo/k0ckuL8Z5e4ZUSV3fDinp71XNVWjtbHXJupzts4rP0NrZiqiAKEyLmubU5x4qQ6qsoQzNxmboNXpMDJ/Y7zkJweyQIiJyFyxIkceTQ817jexJOSMNHQ1e8yKR1OFy42WIEGHQGjAnbg4ECGgyNsndgESO0tjRKHe/TBnl3JE9SXpyOkqzSrH//v3Ykb4Do/1HQ4QIvUbvkvW4mpwfNcD4kTNJhcLrbdfR0dXh4tU4V2tnq5xJ2bMgFeYXhlDfUADdBSu1k/KjFo1bBI3g3F8lpA6pgTKkpALupIhJ0Gv7/57BDikiIvfBghR5tGZjM1o6WwD0HdkLMYTI76RxbM9+JrMJB0oP4J1T7+BA6QGYzCZXL8lrSC+S40Pi4avzRWJoIgDg3PVzLlwVeYNvr30LwPL9NMwvzGXr0Gq0mJ84H/dOuxc/m/UzAMBbp95y2XpcSd5hz8X5UQAQ5hsGg9YAwPu6j0vrSwEAwYZgORRb4m05Uq7KjwKGzpA6U/NdftQgGwBIO+0x1JyIyPVYkCKPJnVH+ev9EegTaHWbIAjcaW+Y8orzkJiTiAXbFyAzLxMLti9AYk4i8orzXL00ryC9SJZeNE+KmASAOVLkeK7KjxrMvdPuBQB8eO5D1LXVuXg1zudOHVKCIHhtjlTPcT1BsM4486Ycqeut13H8ynEAlg4pZxtql72hAs0BhpoTEbkTFqTIo/UMNO/9AhFgjtRw5BXnIWNXBioaK6yOX268jIxdGSxKOYH0Ill60cyCFDmLK/OjBjItahpSI1NhNBnxXvF7rl6OU3WZu+RdD92hQwrozpHytp+rUvdTz3E9iXTMGzqk9pXsgwgRqZGp8mssZxoq1NyWgpQ0slfRWAGzaFZ4hUREZA8WpMijDRRoLuFOe/YxmU3Iys+CiL7h2dKx7Pxsju85mDyy912HlBTMeraWBSlyLLlDyo0KUkB3l5S3je2drz0Po8kIf70/xoaOdfVyAHS/0eNtwebyDnuhSX1u86aClJQf5ezd9SSDhZqbRbO8w17K6JQBH2NM0BgIENBh6sDVlquOWSgREdmEBSnyaAMFmkukF84c2bNNYVlhn86onkSIKG8sR2FZoRNX5X2kDinpXVx2SJGzSAUpVwWaD+QnqT8BABwsPTjo9yi1kfJwUkanOD08eiDyTnveNrJX33eHPYm3ZEiJoujS/Chg8FDzS/WX0NrZCr1GjwnhEwZ8DL1WL0c6MNiciMi13OPVDdEw9RzZ64/UIeVtowXDZWsnGTvOHEt6gdx7ZO987Xl2p5HDGE1GXKi9AMC9MqQAYGzoWKQlpEGEiJ2nd7p6OU4j5Ue5y7geAK/NkLKlQ6qkvgSi2LfDWC3OXj+LsoYy+Gh98L2x33PJGgYLNZe6oyaPmjzgDnsSBpsTEbkHFqTIow01sscOKftI7xgqdR4NT+9Q84SQBPhofWA0GRnCSg5hMpvw9qm3YRJN8NP5ITqg/++pruSNY3u25OE4mzdmM4qiOGiH1NjQsRAgoLWzFTUtNc5entNI3VG3JdwGf72/S9Ygjez1lyFlz/8XBpsTEbkHFqTIow3ZIRXEDil7pCWkIS44DgL6BsQDgAAB8cHxSEtIc/LKvEezsRl17ZadxKSRPa1GK48fcGyPlCbtqvng/z4IwNJ5kPRKktttYPCjqT+CXqNHUVWR3Amhdm7ZISWN7HlRhtS11mtoNjZDgNBvlpeP1gdxwXEA1D225+r8KGDwkT17ClJyhxRH9oiIXIoFKfJoNmdIccTMJlqNFjlLc/q9TSpSbVq6CVqN1pnL8ipSd1SIIQTBhmD5OHOkyBE8aVfNcL9w3DnxTgDAW1+pv0vKaDLiXO05AO7VIdVzZE/N42k9SUWm2KBYeWSsNzUHm5vMJuy9sFcuSC1MWuiytQwWai5lrk2NtKEgFcKCFBGRO3BpQeq5557DzTffjKCgIERGRuKHP/whvv32W6tzRFHE+vXrERsbCz8/P8yfPx9nzpyxOqejowOPPfYYRo0ahYCAAKxYsQIVFdYvruvq6rBq1SqEhIQgJCQEq1atQn19vdU5ZWVluOuuuxAQEIBRo0Zh7dq1MBqNjvnkSRG27rJX117X77tp1Fd6cjreSu/7y96Y4DHIXZmL9OR0F6zKe/QONJdMCmdBipTlibtqSmN7b59+W/XFkLPXz6LL3IVgQ7DcfeMOpJ+r7V3tqG+vH+JsdRhsXE/SM0dKTaQOysU7FqPD1AEASN+V7rJitVQQNJqMVt+bzKJZ3pSBI3tERJ7DpQWpgwcP4tFHH8Xnn3+OPXv2oKurC0uWLEFLS4t8zvPPP4+XXnoJW7ZswRdffIHo6GgsXrwYTU1N8jnZ2dl4//33sXPnThw6dAjNzc1Yvnw5TKbuH1SZmZkoKipCfn4+8vPzUVRUhFWrVsm3m0wm/OAHP0BLSwsOHTqEnTt34r333sO6deuc88Ugu4miOOTIXqhvqPziRSpe0dACfQIBAJEBkTBoDQCAgp8WsBjlBL0DzSVyh1QtC1KkDE/cVfOuSXch0CcQpfWlOFx+2NXLcShpXG/q6KkQhP7HqF3BT++HcL9wAN4TbC51PdlSkFJTh5Q7dlBKI3sA5AIZAJTWl6K1sxU+Wh+MDx8/5OMw1JyIyD24tCCVn5+PBx54AFOnTsWMGTPw5ptvoqysDMePHwdgKThs2rQJv/3tb5Geno7U1FRs374dra2tePvttwEADQ0NeOONN/Diiy9i0aJFmDVrFnbs2IFTp05h7969AIDi4mLk5+fj9ddfx9y5czF37lxs3boVu3fvljuyCgoK8PXXX2PHjh2YNWsWFi1ahBdffBFbt25FY2Oja75ANKhmYzNaO1sBDDyyJwgCd9obhg/OfgAA+FHKj+TdttT0Itud9Q40l0gFqXPXzzl9TaROnrirpp/eTy6Mqz3cXBo/cqf8KIm35UgNtsOeRLpNLT8r3bWDUhrZA6xzpKT/L1NGTYFOoxvycaQu5CtNV9Bp6lR4lUREZKuhv2M7UUNDAwAgPNzyzltJSQmqqqqwZMkS+RyDwYDbb78dhw8fxr/+67/i+PHj6OzstDonNjYWqampOHz4MO644w589tlnCAkJwezZs+Vz5syZg5CQEBw+fBiTJ0/GZ599htTUVMTGxsrn3HHHHejo6MDx4xuU7isAACAASURBVMexYMGCPuvt6OhAR0f3uzNS4aqzsxOdnZ75w01atyesv6Le8o5dgD4ABsEw4JpjAmNQUl+C8vpydMa4/+flambRjN1ndwMA7hx/J6qaqlBUVYTiq8VYkrRk0Pt60vXjrkrrSwEAYwLHWH0dk4KT5Nub25ph0BlcsTyH4vXjXKP9Rtt8njv9m9yTfA/++uVfsevMLmxcuFHe3l1t189X1V8BAKaET3G7zykmMAanak5Zfq662dqGa7Dr52KtpciUEJww4OebEGTpar1Yd1EVX5ODlw7a1EG5/+J+3D72dieuDNBr9Og0d6KxrRHBekvW4ldVlv8vyRHJNn39w3zC5N1rL9VdwtiQvmH19lDb9x9yLl4/NBLuev3Yuh63KUiJoognnngCt912G1JTLe8GVlVZRqyioqy7X6KionDp0iX5HB8fH4SFhfU5R7p/VVUVIiMj+zxnZGSk1Tm9nycsLAw+Pj7yOb0999xzePbZZ/scLygogL+/a7bDVcqePXtcvYQhFTdbsgIChUB89NFHA5/43XTnvqP74FfiN/B5BAA433oelc2V8NX4ou3rNmjqLI2U+07uw8RrE216DE+4ftxV0cUiAMD1kuv4qKH7uhZFEf4af7SaW7Ht79sQ7xs/0EN4PF4/zmESTYjQR+B65/UBzxmlH4XG04346Mwg32OdzCSaEKoLxfW269iwawNuDrnZ6na1XD/HLh0DADSXNOOja+7z9QcAU72lK2b/8f0Yfdm2wqan6O/6OVNp6b6pKq7CR+X9/1vUd1rytCoaK/C/u/8Xeo3ecYt0gk/rPrXpvI8PfYyWMy1Dn6ggHXToRCfy9+Uj1mB5I3nPJcu/m65WN/hrwh7CtGGoNlXj3U/eRUpgiiJrU8v3H3INXj80Eu52/bS2ttp0ntsUpH75y1/iq6++wqFDh/rc1js7QRTFIfMUep/T3/nDOaenp59+Gk888YT8cWNjI+Lj47FkyRIEBwf3ex9319nZiT179mDx4sXQ6937xVT7N+3AeWBc5DgsW7ZswPP2FuzF4WOHEZYQhmULBj6PLJ799FngLLB04lLcvfxuNHzVgHd3vwtjkHHQrzPgWdePu/r1//s1AGB52vI+7zpPqZ6CE1UnED01Gssmq+9a5vXjfC+OfREP/P2BPselXTVfvetV3DXlLievamirfFZh8xebcd7vPH637HcA1HX9tHW2obLIMir54A8eHHDjDlc5evAo9v1zHwJjArHsTsd/LzKZTThUfgiVzZWICYzBbfG3Kb7b60DXT6epE9e/tBRtM+/MlHfv7U0URaw5uwatna1IuTUFE8NtewPHXQVcCsBLl14a8rw7b7vT6R1SQWeD0Nbahlvm3YLpkdMBAOvfWA8AuHve3Tb/fJxSOwXVZdUYM3UMlk0d2XWspu8/5Hy8fmgk3PX6sTX2yC0KUo899hj+/ve/49NPP0VcXPdOMtHRlhdgVVVViImJkY/X1NTI3UzR0dEwGo2oq6uz6pKqqanBrbfeKp9TXV3d53mvXr1q9ThHjhyxur2urg6dnZ19OqckBoMBBkPfsRm9Xu9WF8NweMLncL3d8gIxOjB60LWOCbFkXVS3Vrv95+QOPjpveWfx7il3Q6/XIyXS8q7hudpzNn/9POH6cUeiKMqh5uMixvX5Gk4eNRknqk7gYsNFVX99ef04z7k6SyaZTqNDl7lLPh4XHIdNSze57UYGq2ZYClIfnPsAHWKHvBEDoI7r5/S10xAhIsIvAnGhcW4Vag4A8aGWDs2q1iqHf63zivOQlZ9lNT4WFxyHnKU5Drk+e18/5c3lMIkm+Op8ER8WD40wcPxqUmgSzlw9g/KmcqREKdNx4yoLxi1AiCEEDR0N/d4uQEBccBwWjFugeHFwKFKwuQkm6PV6mMwmfHP9GwDAjJgZNl+TCaEJQBlQ2Vyp2HWshu8/5Dq8fmgk3O36sXUtLg01F0URv/zlL5GXl4d//OMfSEqyDotMSkpCdHS0VfuZ0WjEwYMH5WLTjTfeCL1eb3VOZWUlTp8+LZ8zd+5cNDQ04OjRo/I5R44cQUNDg9U5p0+fRmVld3hrQUEBDAYDbrzxRuU/eRqx6ubBd9iTSO9mulMwr7uqaKzAyaqTECBg2UTLu4UTIyzv8pY3llsFiJLyrrddR3tXO4Du0OCe5J32rnOnPRq5y42X8dJnlg6Id9Lfwf779+Pt9Lex//79KMkqcdtiFADcFHsTJoZPRGtnK/7nm/9x9XIUJ++wF+leO+xJnBVq7g67vEkh5YmhiYMWowB17bR37MoxNBmb+r1N6qDctHST04tRQHdBSnpNUlpfivaudhi0BowPG3qHPYm0eUhZQ5nyiyQiIpu4tCD16KOPYseOHXj77bcRFBSEqqoqVFVVoa3N8gNGEARkZ2djw4YNeP/993H69Gk88MAD8Pf3R2ZmJgAgJCQEDz30ENatW4d9+/bh5MmT+OlPf4pp06Zh0aJFAIDk5GQsXboUq1evxueff47PP/8cq1evxvLlyzF58mQAwJIlS5CSkoJVq1bh5MmT2LdvH371q19h9erVHjt+p3ZVzZZsr6FGGbjLnu2kMPM5cXMQGWDJXYvwi0CYr6X78HzteZetzRtIL4qjAqL6DS1nQYqU9Mz+Z9DW1YZ58fPwLyn/gvmJ8/GTaT/B/MT5Lvkl0x6CIODeafcCUOdue2eufrfD3mj322EPAMYEf1eQanJcQcpddnkrqSsB0F1sGox0Tkl9iUPX5Gj17fX48Xs/hlk0Y178PMQFx1ndHhcch9yVuS4rWks77bV1WX5fkP6/TBk1xa7vXQkhliB6qTOZiIicz6Uje6+99hoAYP78+VbH33zzTTzwwAMAgCeffBJtbW1Ys2YN6urqMHv2bBQUFCAoKEg+/+WXX4ZOp8PKlSvR1taGhQsXYtu2bdBqu38ovfXWW1i7dq28G9+KFSuwZcsW+XatVosPP/wQa9aswbx58+Dn54fMzExs3LjRQZ89jVR1y3cdUoE2dkg1s0NqKB+c/QAAcNek7swYQRAwMWIijl4+inO15zAtapqrlqd65Q2WF8XSi+TeWJAipZyqPoU3i94EALyw+AW37MIZSua0TKw/uB57LuxBTUsNwnzChr6Th+jZIeWOpJ+r1c3V6DJ3QadR/uVkYVmhTbu8FZYVYn7ifMWfXyJ1O40Ltb0g5ckdUqIo4pEPHkFpfSmSQpPwYeaHCPQJRGFZISqbKhETFIO0hDSXFq19db4AujukztRYClL2/n+JD7F0SLEgRUTkOi4tSIli33e9ehMEAevXr8f69esHPMfX1xebN2/G5s2bBzwnPDwcO3bsGPS5EhISsHv37iHXRO5BLkgNMbIXE2TpkKptq0V7V7v8QoastRhbsO/iPgDAXZOtQ4wnRUzC0ctHWQhxMOlFsfQiuTcpJLe6pRoN7Q0I8Q1x2tpIXZ7a+xREiMhIycDc+LmuXs6wTIyYiJtjb8YXV77A307/DT+/4eeuXpJi5A6pSPfskIoMiJRzx6qaq/p00CjB1jF7R4/jS91OSWFJQ5xpyZACPLsg9fqJ1/Hu1+9Cp9FhZ8ZO+eeMI4t+9pJG9qQRd+n/y9TR9hWkpDd/OLJHROQ6Lh3ZIxoJW0f2wnzDYNAarO5Dfe29uBcdpg4khib2eVEnFULOXT/niqV5DelFcUJw/x1SIb4hcgH2XC3/LWh49l3ch4/PfwydRofnFj7n6uWMyHDH9kxmEw6UHsA7p97BgdIDDh/7skezsRml9aUA7P8F21k0gkYeh3dUjpT0ZpJS5w2X3CFlx8iepxakztScwdr8tQCADd/fgFvG3OLiFfVvoJE9e/+/SBlStW21aO20bXtyIiJSFgtS5JFEUewONR9iZE8QBPkFK3OkBtZzXK/3+I48KlbLDilHGqpDCuDYHo2MWTTj13t+DQD4xU2/wITwCS5e0cjck3oPNIIGRy4fsTnjLq84D4k5iViwfQEy8zKxYPsCJOYkOiUg2xZfX/0agOXNlgj//8/eecdHUedv/JnddNITQkuDUEQpCtIJIShVBYmAVAFF7ILo3dlOPc+f7TgEsaAeCEiVEBEUEYQAQToIhF5DCSEJ6Qlpuzu/P775zpZsmZmdbcn3fa97bdydnR1Sdmee7/M8nwgXH41laI+Uoz5XE2MTER0cLRRom8KBQ0xwDBJjEx3y+hQqLlH3kzWoi6qkugRFlUUOPS6lqaytxOOpj6NKU4WhCUPxat9XXX1IFjEsNdfqtDh7m0zYkxrZC/ELQZAPqQChkXkGg8FgOBcmSDE8krKaMmFlzFZkD2CT9myh43VCoblhfxSFiiDMIeVYqEOKrtqag/0sGPaw8sRK/HXrLwT7BuOdpHdcfTh20zywOR5sQwaYrDm1xub27jC1zRZCf5SbuqMo9HPVUcXmapUaC4YtMFtq7qwpb6XVpSioLAAgLrIX4B0guLY9zSX1yu+v4FT+KTQPbI7lo5fbnCjoSoQOKU0lLhddFuoYxIiGprDYHoPBYLgW9/20YTCsQN1RgT6BaOLTxOb2bNKedQ7fPIzcilwE+QQhKT6p3uOG3UWl1aXOPrxGg61Sc4C51RjyqdJU4a0dbwEA3uj/BiIDIl18RMpAY3tLji3B7sLd2HV1l9kInrtMbbMFLWh21/4oSqugukl7DorsAcDIDiPN/p6G+Yc5ZcobnbAXGRCJYF9xE5c9sUdq3al1+ObIN+DA4YfRPwhTdt0VIbJXWynE9TpGdpQlTrJicwaDwXAtTJBieCRiC80pVJBik/bMs+kciesNbTsUPmqfeo8H+QYJq77MmeMYNDqNIJiyyB7DEXx+4HNcL72O6OBozOo1y9WHoxhqjlyE3ii7gXnX5mHwysH1Ing6XoeVmStFT21zJSfzPcMhRQWpm+WOW+jZcHYDbt+5jQj/CPw++XfBwTu87XCHi1GAtLgexRN6pAw71NacXIMZG2cAAF7v/7rgOHRnqCBVpamSPWGPQh3JzCHFYDAYrsGlU/YYDLmILTSn0GgBc0iZx7A/yhLtwtvhVvktnC84j+4tuzvr0BoNOWU50PJaeKm8rAqt1K12vuA8eJ6v1/fFYJij4E4BPsz4EADwQfIHQgeLp5N2Jg1TfppS7/7s0mw89uNjGHP3GJRWl+LAjQMoqS4RtU9XR7s9xSElRPYc6JBacGABANJ3NiRhCHzUPth0fhO2Xd4GHa9zeKyMTtgTU2hOodvS57obaWfSMGvLrHribPuI9vjXwH+56KikIXRIaSqF7/PdkXfL2hd1JLMOKQaDwXANzCHF8EjEFppTaKk5c0jV51rJNRzPPQ4Vp8KIdiMsbid0F7Hpbg6BxgWig6Otxg4SwhPAgUNpdSnyKvKcdXgMD+eD3R+gpLoEXZt1xeQuk119OIogJoKXejoVWy9tRUl1CXxU9d2f5nD01DZrFFcVC51MdzeVd4HtLGipuaM6pA7fPIw91/bAW+WN53o8BwDoG9MXgT6ByKvIw7FbxxzyuoY0NIeUpQ41gLif6eKUu2MusmevQ4pF9hgMBsM1MEGK4ZFIjewxh5RlaJl5n+g+VjtlDJ05DOURU2gOkDLXuNA4AOxnwRDHpcJL+PLQlwCA/wz+j0NLoJ1JxrUMqxE8yuxes3Fk5hGUvF7iFlPbrEHdUdHB0QjxC3HZcYhBiOw56HOVuqPG3TNO+Az3UfvggdYPAAC2XNzikNc1RI5Dyl07pKwJuBR36FATAy01L68tx7nb5wDIj7iyUnMGg8FwLUyQYngkUiN7QocUm7JXDzFxPYA5pByNmEJzCuuRYtjCsCNmxqYZqNXVYkjCEAxOGOzqQ1MMse/nPVv1RLcW3eDn7YcFw4jIYUmUcvTUNlvQCXvuHtcD9As9pdWlKK8pV3TfOWU5WHtyLQBgdu/ZRo8NazsMgHMEKSoqyYnsXS256lbiji0B11061MRAI3un8k6hWlsNfy9/UVMQzWFYas7zlsU6BoPBYDgGJkgxPBK5DqmCygJUa6oddlyeRnlNOXZc2QEAeKSDdUGqXYRxdxFDWWhcwJZDCgDahzNBimGZtDNpiF8Qj+RlyZiYNhE7s3YCAIYmDHXtgSmM2Gid4XYpHVOQOi5ViJsZkhSX5JSibGsI8SM3LzQHyLCLIJ8gAMr3SH19+GvU6mrRN6Yv7m95v9Fj9Pd47/W9KKkS1wsmBx2vE6bsSRE7Wga1hI/aBxqdRpSDz1mIFXA9YeGORvYy8zIBAB2bdpTdJxYdHA0AuFN7B4WVhcocIIPBYDBEwwQphkcitUMq3D9cmB5H3VUMYNulbajR1qBNWBt0jOxodduEMNJdVFxVjILKAicdYeNBiOxZmbBHERxShUyQYhhjrSPmta2vGU2e83QSYxNlRfBSOqYga1YW0qemY1XKKnw94msAwO5ru10+RZQKUp7gkAIc0yNVpanCosOLAJC4pSmtw1qjQ0QHaHkttl/ZrtjrmnKr/BaqtdVQc2pRCwUUtUqN+NB4AO4V25Mj4Lor1CGl0WkA2Cfg+nn5IapJFADWI8VgMBiugAlSDI9EamSP4zghtsd6pPQYxvVsTWvz9/YXxBLmzFEeeiLMInsMuTSkjhgxqFVqixE8+t+WInhqlRoD4wdiQucJeLbHs3i4/cPQ8Tr8e/e/HX/gVqCRPU9wSAGO6Wdcnbka+XfyERMcg9EdR5vdxhmxPSomxYbEwlvtLem57tgjRQVcS7hDh5pYaIcUxd6/F6HYnE3aYzAYDKfDBCmGx8HzvOTIHsAm7Zmi43X49cKvAGz3R1GYEOI46ImwqMhe3c/hUuGlBiMuMOynIXXEiMVSBC86OBqp41JFR/DeTXoXALAyc6VQkuxs8ivyhcmZ7j5hj0KLzZWK7PE8j/kH5gMAXuz5IrxUXma3MxSkHBUhFybsyegmcsdJe4YCrim2BFx3g0b2KHIn7FFYsTmDwWC4DiZIMTyO0upSVGmqAIiP7AFs0p4pB7MPIq8iDyG+IRgQN0DUc+ikPVfHWhoalbWVyL+TD0BcZC82JBY+ah9Ua6tZxIAh0JA6YqRAI3jbJm3DnLg52DZpG67MuiKpD+r+lvfjkfaPQMfr8EHGBw48WvNodVqszFwJAGjepHk9B4i7IghSCkX2dl3dhRO5JxDgHYAZ3WZY3C4pLgl+Xn64XnodZ26fUeS1TaH9UW1CxReaU6ggRaf0uQttw9uavV+qgOtqaGSPophDin2eMhgMhtNhghTD46DuqCCfIAR4B4h+Hpu0Z8ymcySuN6ztMNFxBNZd5Bioq6WJdxOE+YXZ3F6tUgsXFsytxqA0pI4YqahVaiTFJWFA2AAkxSXJcnm8N/A9AMCqzFVOdUnREvpXfn8FAHCr4hbiF8R7RN+X0gs98/cTd9QTXZ5AuH+4xe38vf2RFJcEwHGxvcvF8h1S7hjZA4DP9n8GABhz9xihQy19arpkAdfVGDqkArwDEBcaZ9f+DCftMRgMBsO5MEGK4XFILTSnCCfO5cwhBQAbz28EID6uBzCHlKMwLDS31eVFYfFJhilyS74ZhG4tumFkh5HQ8Tq8v/t9p7ympRL67NJsjPlxjNuLUkqWml8uuoyN58jn0su9Xra5vaN7pASHVJh8h5Q7CVI5ZTlYeYK48F7r85rQoTYwfqBHxPQMoUNqACA6KNru2CaL7DEYDIbrYIIUw+OQWmhOYQ4pPVnFWTiZdxJqTo3h7YaLfh4VQS4UXnBYb0djREqhOYWKg0yQYlAaUkeMq3gv6T0ApFj77O2zDn0tayX09D53L6FXskNq4YGF4MFjaMJQdGxqfeoroBekdl/djTu1d+x+fVOomGSPIJV/Jx/lNeWKHpdcvjz0JWp1tegX0w+9onu5+nBkk3YmDQ+velj47/OF5+12FLJScwaDwXAdTJBieBxyCs0BvUOKlZrr43r9YvtZjUWYEh8aDzWnxp3aO6yLS0GkFJpTmEOKYY6UjilYMmpJvfs9rSPGVdzX4j48etej4MHj/V2OdUk1hBJ6w89VHa+TvZ/S6lIs/msxAGB279mintMhogPiQuJQra3Grqxdsl/bHFWaKsH1ReN3UgjxCxE+W6nTypXcqb2Drw9/DQCY02eOi49GPtRReKviltH99joKaWTvRukNtxaAGQwGoyHCBCmGxyFE9iQKUrQ3hQkpwKbzRJCSEtcDAG+1t7Dyy4QQ5RAie0yQYihAsG8wACA+JN5jO2JcCZ24t+bkGpzOP+2w12kIJfTNA5uDAweNToP8inzZ+1l6bCnKasrQIaIDhiQMEfUcjuMcFtu7WnwVABDoE4jIgEhZ+3CnHqnlx5ejsLIQrUNbY1SHUa4+HFk40lHYIrAF1JwaWl4ruPAZDAaD4RyYIMXwOORG9uhK7u07t1GjrVH8uDyF0upS7MzaCUC6IAUA7SLqeqQKWY+UUsiJ7FFBKqs4C9WaaoccF8MzoX/fD7V/yGM7YlzJvc3vxei7RjvcJSX2M8ydS+i91d5Cn6PcHimtTovPD3wOAJjVaxZUnPhTU0GQuqSsIGUY1xPb62eKu/RI6XidUGY+u/dsj30vcKSjUK1SC31orNicwWAwnAsTpBgehxDZk1hqHuEfAW8VmSbXGFfAtDotdmbtxDvp76BWV4u2YW3RIbKD5P20D2fOHKUxLDUXS7MmzRDkEwQePC4VXXLUoTE8ECpIDYwf6NLj8GSoS+rHUz/iVN4pxffP8zx+u/ib1W08pYSe9kjJdR9vvrAZl4ouIdQvFE90fULScwe1HgQvlRfOF5xXVPi5UkxidnLiehR3EaR+Pf8rzhecR4hvCKbfO92lx2IPjnYUsmJzBoPBcA1MkGJ4HFRMkhrZ4zhOWGl25wiEI6BjxZOXJWPBAVJ6fKvilqy+BeaQUhae52U5pDiOY7E9Rj1u37mNzLxMAMCAuAEuPhrPpWvzrkjpmEJcUgpP3NPxOry4+UX8Z+9/hPtMJyN6Ugk9dR/LLTaff2A+AODpbk+jiU8TSc8N9g1Gv5h+AIDfL/4u6/XNYU+hOYU+l4pbrmLe/nkAgGe6P4Mg3yCXHos9iHUKynUUsmJzBoPBcA1MkGJ4HNQhJTWyB+gn7TWmHilLY8XLa8pllYAyEURZSqpLhClM0cHRkp7LfhYMU3Zf3Q0AuKfpPYhqEuXio/FsqEtq3al1OJl3UpF9anQaPPnzk/jq8FfgwOGbh7/B+nHrhbgQxZNK6IVJexIie9Sx+8meT7Djyg6ooMKLPV+U9fqOiO1RQcoeh5Q7dEgdzTmKnVk74aXywku9XnLZcShBYmwiooOj64m3FHsdhVSQYg4pBoPBcC5MkGJ4FDzP60vNJUb2gMY3ac9aCShFaglou3DikLpUeIlNo1EAevIb4R+BAO8A/QNaLbBzJ7B6NbnV1v9eU0HqQgFzqzEILK6nHF2adcGYu8co1iVVo63BhPUTsOz4Mqg5NX4Y/QNmdp+JlI4pyJqVhfSp6R5ZQk/FNLELPYaO3de3vw4A8PXyxeGbh2W9/tCEoQCA7Ze3K9YPSV1NSjmk7JlAaA+0O2rcPeMkL3i4G2qVGguGEYe3IxyF1KHMOqQYDAbDuTBBiuFRlFSXoFpLCpylRvaAxueQckQJaExIDHzVvqjV1eJqyVUlDrNRQ+MBRnG9tDQgPh5ITgYmTiS38fHkfgMEh1Qhc0gxCFSQSo5Pdu2BNBDeGfAOAGDd6XVYcnQJVmeuxs6snTbFeOoAotuXV5dj9NrRSD2dCh+1D9aNXYdJXSYJ26tVagyMH+iRJfRCZE+EQ8qSY7dSUynLsQuQeGWzJs1QUVuBP6/9Kfn5pvA8r0hkLzYkFipOhSpNlUt6K7NLs7Hm5BoAwCu9X3H66zuClI4pSB2X6hBHIe1wZIIUg8FgOBcvVx8AgyEF6o4K9g2Gv7e/5OcLDqlG0iHliBJQFadC2/C2OJV/ChcKLth1ws4wU2ielgaMGQPwJq627Gxyf2oqkEJOullkj2FIfkU+649SmM7NOqNPdB/su7EPT216Srg/OjgaC4YtMHsBnHYmDbO2zDISXXzVvqjWVsPfyx8bxm/AkIQhTjl+ZyBE9mx0SIl17I7qMEqSIKfiVBjadiiWH1+OLRe3ILm1fWJsYWUhSqtLAQDxofGy9+Ot9kZsSCyyirNwpeiKcP7hLBYeXAiNToMBcQNwf8v7nfrajiSlYwpGdRiFjGsZyCnLQYugFkiMTbRbxGWl5gwGg+EamEOK4VEIE/ZkuKMAfdnlzfLG4ZByVAkoE0KUg67GxgTHkFjerFn1xShAf9/s2UJ8j8Ynb5XfEi6gGI0X2h/VKaoTmjZp6uKjaRiknUnD/hv7692fXZpt1tFjyQFEnb1vJr7ZoMQoQHxkzxGOXcqwBOV6pLJKsgAQR7WchS9DXNUjVV5Tjm+OfAMAmNN7jlNf2xk4wlFIO6TyKvJQram2e38MBoPBEAcTpBgeBbW9yyk0BxqfQ8pRJaBUCGGT9uzHaMJeRgZww/IFG3geuH6dbAcgxC9EEGdZjxRD6I+KG+jS42goWHP08HX/e+7X57D32l4cvnkYh7MP4/lfn7fqAPr2yLcNrnuPfq4WVBagSlNlcTtHOHYpgxMGgwOHE7kn7I7kKxHXo9B9OFuQWnpsKYqritE2vC0e6fCIU1/bUwn3D4e/FxEgrQmnDAajYWEasZcayW9on+mugAlSDI/CnkJzoPF1SBmWgJpiTwkoc0gphxDZC44BckReiBls1y6CiINSfxbsA7XhsfPqTgCs0FwpbDl6AOKm6Pd9P/T4rgd6/K+H4OK1hFwHkDsT5hcGPy8/ANY/Wx3l2AWAyIBI9GjVAwDw+8XfJT/fEOqQah0mf8IeRRCkip0nSGl1WszfPx8A6Y5ScexUXwwcx7HYHoPRyDAcsjExbSKSlyUjfkG8xT5DqdszxME+pRgehb2RPbqSm38nH7XafeGd4gAAIABJREFUWsWOy52hJaA+ah+j++0pAaUiCHNI2Y9RqXkLkRdiBtu1D5cuDrIP1IZHfkU+TuadBMD6o5RCrFMnMiASMcExCPULVXS/ngLHcUKPlDVBqry63Pp+ZDp2KUrF9q4U1U3YC1XOIUX36Qw2ntuIS0WXEOYXhqldpzrtdRsCrNicwWg8WIrYS43kW9qeIR4mSDE8CnsjexEBEfBSeRntqzEwot0I8HUdRPOGzrN7rDh1SGUVZ7GuBTvQ8Trhgy0mJAZITASiowHOfMQSHAfE1G1Xh9RJe+wDtWHC+qOUR6xTZ93Ydbj2yjX89PhPiu7XkxAm7VkoNk87k4aUH/WfN6Yxcnscu5RhbYkgte3SNmh0Gln7AIArxUQ8UsIh5YoOqXn75wEAnr3/WTTxaeK0120I0B4p5pBiMBo2tiL5ABmyUVFTgfyKfFwsuGgxkm+4PUsbyIMJUgyPwl6HlIpTCbG9nPKGtUptjYPZB1Grq0XzwOaY3Wu2uBJQrRbYuRNYvZrcavVvss2aNEOQTxB0vM7p3RgNidzyXNTqaqHiVOSCTq0GFpiPWArMn0+2q0NKfFLsBzD7QPU8aH9Ucrx9E8YYeqR28Dmqs88ToMXm2WX1BakVJ1Zg3LpxqNXV4vF7HsfaMWuF7Sn2OHYpPVr1QJhfGIqqinAo+5Ds/dDInpIdUtll2Vb7teyFRrD/vfvf2HNtD7w4L7zY80WHvV5DhUb2qHOZwWA0TMQO2Qj8KBBRc6PQ7ot2ViP59gzlYDBBiuFhUFeT3A4pwGDSXiPpkQL07okBcQPAWXLfGJKWBsTHA8nJwMSJ5DY+ntwPEtFgsT37obGAlkEtBeceUlKA1FTA29t445AQcn+K8QWboSDFm5vOZ4Ajp1wxXEt6VjoA1h+lJIYdfGIcPVK3b0jQyJ6pQ+rbI9/iiZ+egJbXYtq907AyZSXG3TMOWbOykD41HatSVtnt2KV4qbwwOGEwAGDLRXmxPS2vxdWSqwCUEaQiAyIR6BMIgDiKRR+HhI4/wwj2O+nvAAB8vHzMTodkWIc6pFhkj8Fo2EiNzpvWnii1XwaBCVIMj4KWmsuN7AH6YvPG9KYhCFKxIrpl0tKAMWPqT3vLzib314lSrNjcfowKzQ0ZMgTQ1EVORowgt/371xOjACAhPAEcOJRWlyL/Tr7V13PklCuG68iryMOp/FMAWH+U0tAOPrGOHqnbNxToZ/K+G/sEAeWzfZ/hmV+eAQ8eL/R4AYtHLjYS7wbGD8SEzhPEOXZFYm+P1O2a29DoNPBR+wgxRHvgOE5yj5SUjj9LEezK2koWwZYBKzVnMBoHYqPzmyZsguafGvw+WdywjIYYyXcGXq4+AAZDLDzP2x3ZA/RdF43FIaXRabD3+l4AIi5WtVpg1izAnNOG50mH0ezZwKhRaBde55AqYA4pudBYAC1SFTh4kHy/Y2OB998HNm8G9uwhPx+18YWbn5cf4kLjkFWchfMF5xHVJMri6zlyyhXDdVDBuXNUZ0QGRLr4aBoeKR1TMKrDKGRcy0BOWQ5aBLVAYmyiRRFF6vaeTtqZNHy05yMARJBKXpaMYN9glFaXAgD+3vfv+PjBj8W5c+1kaNuhAIBD2YdQcKcAEQERkp6fV5MHAIgPjVdsOl3r0NY4kXtCVLydCkymsWra8WcoatqKYHPgMHvLbIzqMKrB/u4pDSs1ZzAaB4mxiWgV1MpszBwgrubo4GgMbzscapVaiORnl2abfc+l2zfESL4zYA4phsdQXFWMGm0NADsje42sQ+qvnL9QUVuBML8w3BN1j/WNMzLqO6MM4Xng+nUgI0NymTajPvSkNzY41viBvURARN++QNeuQFAQUFICZGaa3Y9Yt1pibKLVVf+G3HHTkKH9USyu5zikOnoc5QByN6iAUlhZaHQ/FaPG3zPeaWIUQBacujTrAh48tl3eJvn5t2pILYAScT0K3ZctQcqWwMSDx7QN0zApbRIeWP4AWi9ozSLYCkPdyqXVpSipKnHx0TAYDEehVqnRO7q32cdYJN/5MEGK4TFQd1SIbwj8vPxk76exOaSoe6J/bH/bK745IkW6nBzmkFIAIbJn6pAyFKS8vIB+/ch/79pldj/tw8UJUipOhbiQOKvbsA9Uz8MjBCkrQxIYnok1AYXy5/U/oeN1Tjwqg9iejB4p6pCi0/GUQBCkiq0LUrY6/gCgrKYMqzJXYceVHaJdPCyCLZ4mPk0Q7h8OgLmkGIyGzNGco9hwdgMAIMLf2EkrNZLfLLBZg47kOwMmSDE8BtofZY87CtDHkRqLQ2r3NX2huU1aiIxqtWghlJpnl2WjoqZC7uE1agSHVIiBQ0qnA/btI1/37Utuk5LI7e7dZvcj1iE1f/987LuxD2pOXS/aF+wbzD5Q7UBKCbGSeER/lI0hCQzPRIyA4gqHzrC2RJDaeG4jVmaulPT3eKvacQ4pWx1SYoWjCZ0mYMXoFZg3ZJ6o7VkEWxpCsTmbtMdgNEhqtDWY/vN0aHktxt0zDrmv5YoespHSMUUYytEpqhMA4B/9/sHOne2ECVIMj4FO2LOn0BxoXA4pHa9DxlVyMSDqYjUxEYiOJl1R5uA4ICYGSExEuH+4sKpwsfCiUofcqDBban7uHFBcDPj7A126kPsG1P3sdu822+8lRpDac20P/rbtbwCABcMW4Oacm0ifmo6Z3WYCADpGdmQfqDKRUkKsNNQB2aVZF/fsjxI5JIHheTh1SIIEh13+nXxw4FBUVYTJaZMl/T3m1pCFLyUdUnRfl4suW52EKlY4mtl9JiZ1mYSXe72M6ODoevERCotgy4M6llmxOYPRMPl4z8c4kXsCEf4RWDh8oexI/uTOkwHopxwz5MMEKYbHoEShOaDvkMqvyEetttbu43JnTuWdQlFVEZp4N8F9ze+z/QS1GliwwHypOWX+fKFYm03ak0+NtkZw/RlF9mhcr2dPwNubfH3//USgun0bOHOm3r6oW+1i4UWzToDc8lyMWzcOWl6LCZ0m4PkezwsfqG8NeAsAcOjmIdaZIQNLU65oCbGjRan0K+REaGDcQIe+jixsDUkAyJAEFt/zSJw2JEGCwy7tTBrGp463WApu6++RClJKOqTiQ+MBkLhdQWWB2W14nhfEZUuYCkys08Qx0E5HFtljMBoembmZ+GD3BwCAhcMXWh0EZIsH2jwAgNQmaHQaRY6vscIEKYbHIET27BSkmjZpCjWnBg/91L6GCo1K9InpA2+1t7gnpaQQp5Qp/v5Aaip5vA4qhFwoZD1SUqGTOnzVvmga0FT/gGF/FMXHB+jTh3xtJrYXFxIHb5U3qrXV9U6iNToNxq8fj5zyHNzd9G58+8i3RgXDsSGxaB/RHjpeJ3QRMcRhq4QYAGZvme3Q+N7OqzsBuGl/lIQhCQzPg04d4sBBpQOSrgDjM8mtSqeQQ0eCw87ev8fymnKUaIgor6Qg5e/tLzizzRWb12hr8OTGJ/HuzneF+8QKTJY6TSx1oDBswybtMRgNE41Ogyc3PolaXS1GdRiF8Z3G27W/+5rfh1C/UJRWl+LwzcMKHWXjhAlSDI9BqcieilMJ+2joZZ90xXVArIRumeJi4NAh8vVXXwHvvUe+9vcHHn3UaFOxZdqM+hgWmhtNoDInSAHGsT0T1Co12oa3BVD/Z/H2jrexM2snAn0CsX7cegT6BNZ7/oOtHwQA/HH5Dzn/lEaLrQ4dR0+5yqvIw+n80wDctD9KwpAEhudBHTqjT/PImg/sXAasXk9us+YDo0/z9jl0JDrs7P17vFJMOp7C/MIQ4hci75gtYKlHqriqGMNXDsfSY0uh4lT4asRXWD9uvSSBybDTREwHCsM6tNORRfYYjIbFf/f+F4dvHkaoXyi+fuhru6e/qlVqJMcnAwC2X96uxCE2WpggxfAYhMiepVJzCR0TjaFHyjACIOlidfVqoKoK6NQJePZZ4M03gcBAoLAQyMw02pQ5pORDV1+N+qMKCoCzZ8nXvU3G0dJi8127RPdIbTi7AZ/8+QkAYMnIJbgr8i6zx/JgmzpB6goTpKTg1A4dM+zKIlMXuzTrgoiACBtbuwAJQxIYnknKGSB1HdCq1Pj+VqXk/pT6CWPxSHTY2fv3mFWcBUDZ/igKnW7605mfhJL1rOIs9FvSDzuu7ECgTyB+mfALnuvxnCyBSWoHCsMyrNScwWh4nL19VnChzh86X7FhDw+0JrG97VeYIGUPXq4+AAZDLFYdUmlpZCXV8OQ1Opr0IaXUP4lrDJP2LhVdQk55DnzUPujZqqf4Jy5eTG6ffJKUmHt7E3fO5s3Ajh1A167CpqxDSj509dVowt7+/eS2Qwcg0qSgulcvEt27eRO4fBlISDB62PRncbHwIqZumAoAmN1rNsbeM9bisSS3ToaKU+Hs7bO4UXoD0cHR9vzTGg1O69CxAI1YumV/FKAfkpCdbd7lwnHkcXMRYYayaLVEuMnJIQJgYqLQBWjXPmfNAsejXq02We3kiINp1Ch5ryXRYSf27+z7Y9+ja/OuuLvp3cJ9Wp0W2y5vAwA08W4CrU6rmKiTdiYNm85tAgCsPb0Wa0+vRVSTKFRrqlFSXYJWQa3wy8RfcG/ze4XnUIGJ4XwMI3s6XgcVx9buGQxPRqvT4smfn0S1thrD2w7HE12fUGzfdEF37/W9qKythL+3v2L7bkywd1mGx2Cx1FzGFKeWgQ3fIUWn6/Vo2UP8G+Tx48CRI0SEmjxZf/+gQeR2u/EKAI2J3b5zG0WVRXYfc2OCrr4aOaQsxfUAEpnsWScs7tpV72H6s9h1dRe2XNyClLUpKK0uRb+Yfvh08KdWjyXULxQ9WvYAwGzHUjDs0DGHo6dcuXV/FKAfkmANgyEJDAchoRRcEo7uCLsp8vO5zmFn6++Rsu3yNtzz1T0YvXY0DmYfFKZkLjq6CACQcT1DsSmZdOhBaY2xhSyvIg8l1SWIC4nD/hn7jcQohmtpFdQKHDjUaGuQX5Hv6sNhMBh2svDgQuy7sQ9BPkH45uFv7I7qGdI+oj1aBbVCtbYaf17/U7H9NjaYIMXwCHie15eaG0b2ZE5xEhxSDbhDavc1GXG9JUvI7ciRQFODom0qSO3aBdTqJxMG+gQK8UcW25MGjewZOaSsCVKAxR6ptDNpeHvH2wCAE7knMHzlcGTmZSLYNxhrx6wVVWjPYnvSMZxyZQlHTbmi/VEcOCTFJym+f8VISQFeecX8Y08/bdbBylAQGQs2orGnI8xaxD4nhwhnr71mfb8cB8TECA47W1PnOHD45MFPkNIxBRw4bDi7Ab3+1wuP/fiYQ6ZkWitZp2h0GmHyr1sjoRLBLZFw/N5qb+EckRWbMxiezcXCi3hz+5sAgLlD5hpPtVYAjuOEaXtsQVc+TJBieARFVUWo1REhxMghJXOFVuiQKm+4DinJ/VHV1cCKFeTrp54yfqxrVyAsDCgvJw4qA9qF1/VIFTBBSgqGpeYAAI0GOHiQfE0n6pliRpCiK/D5d+qv5JZWl+JA9gFRxyMIUpf/AG9O4GWYJaVjCt5JesfsY6/1fc1hxcKG/VHh/uEOeQ3FyMsjt48/DqxaBbz4IvnvjAzziwkMZZC5YCMasd1fv/8O3Lql/29Ljq1164DPPyeR5dWrAZUKGD6cCE+WVrRNHHa2ps79vd/fsX7cepx6/hSmdJli8ZCVmJJpq2QdALLLsh029EAxHOWwcxYyjp8VmzMYnotWp8XOrJ1YmbkSY34cg0pNJQa1HoSnuz3tkNdjPVL2wwQphkdA3VGhfqHw9fLVPyBzhZauSDZUh9SN0hu4XHQZKk6FvjEW3Dam/PwzKS5v1QoYMsT4MZWKnMQBpEfKANYjJY96peYnTgB37gAhIUDHjuaf1Lcvufi6cgW4ft3mCjwHTvQFVZ/oPvD38set8lvC5DaGOEqqyKj4oQlDsSplFSZ1mgQAOHTzkMNeMz0rHYAbx/UoWi3w22/k6+efByZMAP7v/4CgIODMGWDbNtceX0PG0ZE62hFmi2XLgNhYYMoU4NNPzTu2btwAxo0jAlpZGdCjBxHoN28GUlPJ55IhwcHkfjMOOzGl4B2bdsST9z1p9bCtTuUT4bhx9dADRXCkw84ZyDz+xlZsTi/gV2euFkr3GQxPhEawk5clY3LaZBzPPQ4OHMZ0HKNoVM8QKkgdvnmY1ZfIhAlSDI/AYn+UzClO1CHVUEvNaX/Uvc3vRbBvsLgn0TLzadPMd7o8QN5wTQUpwSHFInuiKasuQ3FVMQADhxSN6/XpQwRAcwQFAd26ka9377Z7zLkhvl6+gpvuj8ssticWnuex6TwpLJ7ZfSYmdJ6Ajwd/DDWnxs6sncjMzbSxB3kIhebuLkgdOECmR4aG6qOowcFkaAJgu2OKIR97InViUKuBf/zD/GPU1TRnDvm519YSB+4//mHdFcdxwFdfAfv2Ad27k/tSUoCsLCA9nYiaABG4rMQ9xUydky0YiXTcuHrogVXERNgc7bBzNHYcf6sgIoBuvbS1wQs0hhfwE9MmInlZsmIdagyGM6GJAdPzYh48Xtj8gsN+p1sFt0KHiA7gwQvnZgxpMEGK4RFYnLBHV2gtqd4mHRMUegKYW54LjU6j+PG6GipADIgVGde7dk3vVHjSwqox7ZH680+gqkq4mzmkpEPdUSG+IXrB0FZ/FCWpri9o1y7FV+BZj5R0zt4+i0tFl+Cj9sHgNoMBkHjQ6I6jAQBfHPxC8dfMLc/FmdtnwIGT1hHnCn75hdwOGwZ4GQz2fekl8v68eTNw7pxrjq2h09zMRFpziF3YMYXnibMWAHx9jR+LjiYOpv/+l3xmHDwIDB4sbp8dO9ZfFFGrgYEDgX//m3x98iRxitqBLMFIguPG1UMPLCJGUON5skjlSIedo5HpEEw7k4alx5YCADZf3NygBRpLF/BKdKjZg1THFnN4McR09tkTwbYFi+3ZBxOkGB6B2UJzQPYUp6YBTaHm1ODBI68iT8lDdQsk90ctXUpOzpKTgTZtzG/ToQO5cKmqIqvXdbSLIA6p8wXnWfeQSGgMQFKhOcWgR0rpFXgqSO3M2olaba2NrRkA8Mt5IrgMjB+IIN8g4f6Xer4EAFiRuUJxC/euqx7UH/Xrr+T2oYeM709IAB5+mHy9cKFzj6kxUFhI+pisYWHBRjSrVwN//AH4+ZHIcXo66QhLTydikaGDqUcPYPp0cfu15tgKD9e/B1IxTCaSBSOJjhtbJeuA44YeWMSWoPbGG8DMmURQfOYZcfuU67BzNDIcglSgKa4uNtrElkCj1Wmx6+ou7C7ajV1Xd3mEIGLtAl6JDjW5SHVsuaPDiwlkzkfJxIAchGJzJkjJgglSDI/AYmQPICe9Y8eaf+I335i19atVakHculnWsIrNb9+5jVP5pwAA/WP7236CTgd8/z352pI7CiAXL9QlZRDbSwhLAAcOZTVlDVLccwT1Cs2zs4GrV0lUr2dP60/u35/8LM6dQ6Jve0VX4Ls064LIgEiU15SLLkNv7NC43iPtHzG6PzE2EZ2jOuNO7R18f+x7RV/TY+J6164RoUKlIg4pU2bNIrdLlwLFxfUfZ8gjIwO4915gwwb9Yow5FzHPA3Pnmo9o26KoSD898e23gfbtiYNpwgRya26fMiP29Rg5ktzaKUhJFoxkOG5slaw7auiBWWwJajwPfPwx8N13wM2bRGgUg1yHnaOR+PsmV6ChgsjglYMx7+o8DF452OWCiBjsuYB3lOAi1bHljg4vdxTIGgOu7uxLjk8GBw5nb59Fdmm2Q16jIcMEKYZHYDGyBxBB5UDdxfObb5IV2k6dyH+Xl1vcp9Aj5c6FojLYc20PAKBjZEc0bdLU9hPS00k/R0gI8Nhj1rc1I0j5evkiLjQOAOuREku9QnPqOOvShfREWSMsjGwHQL3nT0VX4FWcSrAdsx4p2xRWFuLP638CAB5u/7DRYxzHCS6pLw99qegKqccIUps3k9s+fYDIyPqPDxpE3qsrKoAlS5x7bJ6OuQ4grRZ4/30iCF2/DrRtSz4b16+vXwpOBapjx+S9/htvkOmJHTsCf/ubuOfIjNjXY9QocpuRQZxgdiBJMJLZySWmZN0p2BLUKKNHk8mIBQXK/LxchcTfN7ECzdy9c3Gz7CZ4nndLQUQsh28eFrXd4qOLcbnosvDfcgQXMQKWVEHQHR1envz74Om4urMvzD8M3VuS3sMdV3bY2JphChOkGB6BVYdUejpxl4SEkJXaCROAZ58lj61ZY3GfdNJeQ3NI0UJz0XE9WmY+cSLg7299W1psfvAgmYRUB+2Rulh0UdKxNlaoICVE9sTG9SgGsT2lV+CFHikmSNnktwu/Qcfr0CmqE+JD4+s9PqnLJIT5heFy0WX8dvE3RV7TI/ujTON6FI4DXn6ZfL1wofuWI7sb5jqAYmKIUP3uu2SRZsoU4OhRUgxuWApOI3WrV5N9ffyx9EmH+/YR9zEALFoE+PiIe55hxN5UJKD/bSZiX4/WrYHOncnvC42E2gEVjLZN2oY5cXOwbdI284KRHQ4vMSXrDkesoDZ2LJm0GxBg+ecFEEeVmJ+Xq6C/b9aqBAyOX+zi5OvbX0erea3Q9D9NMXH9RLcSRCjWBKD9N/bj0TWP4m/bxAnJKzJXIOHzBHT5ugvGrhuLx358TJLgIlbAEisIJi9LxmM/PoaBSwe6NKJlij0CmSdGPt0NGsG2hDM6+1iPlHyYIMXwCKw6pGjcbPx4vaAyZgyJiRw8CFy6ZHafDXXS3u5rEvqjior0RabW4nqUuDjSMaXRGMUS6KS984Ws2FwMQmSPOqQMJ+yJgRab7yY/ayVX4Kkgtf/GfpRWl0p+fmPilwtEcDGN61ECvAPw1H1PAQAWHlSmJ4m6o7o27+re/VF37gDb607KHn7Y8naTJpFeoKwsYNMmpxyaR2OpAygnBzh9msSsli8n/zd0W9JScBqpe/xx0hHE80S8ys0V9/q1tfpuoenT9eK4WFJSSNm5qWOLlqBbmZxnBHVJ2Rnbo6hVaiTFJWFA2AAkxSWZF4yUcni5CjmCmqWfF0B+18QuoriKlBQSJzXF17fe75tY50RscCxUnAoFlQWo1lZb3M7ZggjFkgD01o63kLQ0CX0W98HP58jfjZ+X5VgmBw6hfqEYGDcQak6NzLxMpJ5ONbutJcHFlmNowf4FWH58OV7a/BKe3vS0qH9fxrUMpJ1Jw57re0Rt76wUhNwIpKdGPt0NtUqNeUPmmX3MWZ19hgkD1qkrDSZIMTwCi6XmJSUkjgAYCyrNmunjZWvXmt1nQ3RIlVWX4WjOUQAQtwqwahVQXU1W1umIbVuYie0JDqlC5pASAy01jwmJISXxR8nPTPTJPb3gycwUIitKrcDHh8YjISwBWl4rlOMz6lOrrcVvF4jryTSuZ8jzPZ4HBw5bL23Fudv2T5MT4npxA+3el0NJTye/27Gx+gi1OQIC9ALH/PnOOTZPxVoHECUsjLimxPDZZ8A99xAxaupU4qyyxfz55H0nIgL49FNxr2OKOceWaQm6LaggtWWL0dRXhyLRceN2yBXUTH9e27cD999Pvu///KfDD9suTp8Gzp8nC5Tr1+vfY6qr653ziC25vzzrMsrfKMcHyR+IOgT6eW+Io6bIWRKAbpTewIcZH2L31d3wVnnjyXufxJkXzmBlykpwdf8z/bcCwOKRi5E+LR15f8vDG/3esHqMVHBpvaA1kpclY3zqeEzdMNWiY4gHj9m/z8bUDVPxxaEvRJ8/zu41G1+N+Aqze80Wtb0SES1b33+e5/HntT9F7euN7W/go4yPsPnCZnx35DsW8VMQKrCqTOQNZ3X29YvtBx+1D7LLstnkcYl42d6EwXAtOl4nlGXXi+ytWUNOiu6+m0zxMWT8eDIBaM0a0i1lglSHlFanRca1DOSU5aBFUAskxia6xnZvhb3X90LH69A6tLW+MNsaNK731FOWT1JNGTQI+N//jAQp6pC6UHgBMLOQytDD87xxZO/IEeI6aNaMRFHEEBUF3HUXcPYssGePvuRXIR5s8yAuHbmEPy7/YVVsaczsubYHJdUliAyIRK9WvSxu1zqsNR5u/zA2nd+ELw99ic+H25h8ZgH6/rPx/EYAIgVnV2IY17P13vL880Tc2LWLdBrde6/jj88TEdMBlJNDths40Pb+/P3Jgk2PHqQz6L//td4HdfUq8N575Ou5c833gomFOrbk0r07ce1kZ5PPohEj5O9LCiNHAqGh5kv4v/9emqjmbKigZq4r0lZk0vTnNX8+GbCxeDHwwgvu+zdLz3EeeUT/s9m4kfzOLFsGvPOOsCktuR/z4xhw4IyEFFOHhb/KH/1i+4k6hJe3vIwzt8/g6W5Po3VYa6SdScOsLbOMRIjo4GgsGLbA7AWz2O3FjL0P8glC5nOZQu/nXZF3IXVcqtn9zx82X9h/uH84OjfrLOrfe730unCOI4ZOUZ0wuM1gdGvRDX/b9jfkluea/Tdw4BAdHI25Q+ZCrVJDq9Mi9Uwqskuzrf6bV2euRqeoTogM0L9fSTmft/b97xDRAWtOrsGaU2tEC2r7b+zH/hv7rW7DgwcHDrO3zMaoDqPc7lrDXVl0ZBEAYHbv2XikwyNOv14L8A5A35i+2Jm1E9uvbEeHyA4Of82GAnNIMdyeosoi1OrICPqoJlHGDxpOhzO96ElJAby9yWruqVP19ktXTcQ4pDxlagZ1tCTGibhY/esv8n8fHxKbEUtyMrk9doyUnkLvkLpUdAk6XsQqeyPm9p3bqNKQFf1WQa2M+6PEioKAPra3a5fCR8h6pMTwy3kiuIxoN8LmiQ4tN196bCnKqsusbmsOw/cf+n710paX3O79R4Dn9d0+1uJ6lOhoEkMDgM/lCXaNApml2la55x69a+TNN0nM3Rw8D7z4IoliJiURR5Ur4TjFpu1J4tdfiRgVGQls3QqsXAkkJJDHioqcdxwBMGxpAAAgAElEQVRyGTHC/OAMqZHJfv1I7JPngTlzrLvGxGCupN9eampIdBUAZszQ3z99OrldurSeK1BKJ6MtRxVABoUUVxXjoz0fIeHzBNz3zX2SOphsRd4WH12MzRc244PdH9jsVAKAspoyXCm+Uu/fLCbyL9Zp9NnQz7AqZRUmdRJ3Xvlm/zcxb+g8TO4yGV+O+BKAuCEtYqZkAsC3R79Fu4Xt8MXBL6DRaSSdz1tznD3242Po9HUnfJDxAS4WXoSf2g/+XpZ7WDlwaBrQFB8O+hATO09EfEi81e+LqyKfnkpWcZbgWn/2/mdd1tnHeqTkwQQphttDC83D/MLg6+Wrf+D0aTJBSK0GJk+u/8SwMP2ocTOxPbFT9jxpagb94BoQK6LXg061evRREr8QS/PmxJHG84IYEhcaBy+VF6o0VSioLZB62I0KunLYPLA5+X2WWmhOMSg2Vxo6vvZU/qkGN4VSKTadJ31HlvqjDHmwzYO4K/IulNWUYdnxZZJex9L7T05Zjtu9/whkZpIpb/7+egHbFrNmkdtVq4D8fMcdmydjR6m2VZ5+mhRZazTEWVxSUn+bn34irjdvb1JkLkU8dxQ0trdxo7i4oRJ89x25nTYNGDyYxCPnzNE/5u69IevWkYEkrVoRB7ncyCQAfPIJ6WJKTyc/A7mYK+mPj9f3W8pl0ybg9m3y90DPBQHy7wwOJv9mMws6YgUaW4IIBw6rH1uN1LGpGNxmMHjwOHbL/FRLGmF7/tfnse/6PhzMPoj91/fjuV+fsxp5m7FpBh5a9RD+mf5PuzqVxET+xUYaX+r5EiZ0noAZ3WeY3c4UQ6FL6pAWa9uvH7ceu6btQtdmXVFcVYyXfnsJbRa0ES0IanVavPzby1bdVwA5B1iVsgr5f8/HipQVViOQix5ehDcS38DKlJX48IEPbXxnCOwcTBzfHfkOPHg82OZBtIto57LjoAu66VfSWTm9BJggxXB7LPZHUXfUQw+RuJM5xo8nt2vW1DtRjAogbqtb5bew/fJ2RcbQupIqTRUOZB8AIKLQvKqKrOwCJK4nFTptr6602EvlhYQwskp8s7rhdHJZQ2oHBMWo0Jzn7Rekjh41mnioBBEBEejWohsAtspjjvMF53Gh8AK8Vd4YkjDE5vYcx+HFHi8CAL44+IVoF6Envf8YQeN6Dzxge3InpXdvEh2rrtZPcGMYk5hovlyaIrdUm+OAb78lQyuuXNGXnVNKS/XTEP/xDxIXdgcGDiRun1u3gEOHHP96168Dv9VNy3zaoIB50iTye376NJlA6M58SRwoeO458vdJS+7l9F7FxQGvvkq+fu014kiSiqWS/uxscr89otT//kdup00DvAwaSgIC9OeG9DzSBLGdjLYElHH3jMNjdz+GrVO2YsXoFTYPObciF32X9EWv//VCnyV9hLoKa8SFxGFyl8l4/v7nbW4LyO9UEuNIMnQwiRWwTOPnUoe0WNt+QNwAHJl5BF8/9DXC/MIsRgkFgW/jDLyy5RU8tOohxH4Wi+yybJvflzl95mBC5wkI9AmUJKiJ/Tko0YHV0KnR1mDxXySe+2z3Z116LPe3vB/BvsEoqiqyKEAz6sMEKYbbY3bCXm2t3optbTrcyJHkRPHCBRJPqyPtTBr6LCYTzXjwePCHB+0aQ+sOltqD2QdRo61B88DmaBve1vrGGzaQeEFMjF5ckoKVYvPGIEjZE+E0KjS/fBnIyyOxyW7dpB1EdDSZeKjTAX+KK9OUAovtWWbTOeKOSopPQrBvsKjnPNH1CQT5BOFcwTnR31NPev8xQkpcj8JxepfUV1/Ju7ht6KjVQMeO5h+z1QFki9BQsnCjVhNH8f/+p49QTZ9OBIKEBLN9jC7D1xcYPpx87YzY3pIl5P02Kcl4cltICImvAUTYc1cOHyauch8fY0HNHl5/nbimL14EvvhC2nOtlfTT+2bPlhffu36d9KIB5s8RaWwvNdW8I1ACVBDZNmkb5sTNwbZJ28wKKCpO3CVXpH8k4kLiEOEvzrn+0QMf4YfRP+Dz4Z/LEoCkIEVwkSpgGSJ1SIu17dUqNZ69/1ksfXSpzX9fUVUR5h+Yj80XNuNmubhzWVMHk1hBTa5gx6jPz2d/Rm5FLpoHNsfIDsp2qkrFS+WFpDhSqcEWdMXDBCmG20Mje0aF5r/9Ri7ko6Ksl5kGBuovilavBmAQgSkzb9n9bN9nWHR4EZ746Qk8nvq4qGN0B0ut0B8VmwjOVpyCFn1Ony7v4iUpiVwAnT0L3CQf2rTYPKfa9d8LR2JvhJM6pGKDY/XuqO7dyQhtqTgwtmcoSLHxtcb8coE4gMTE9ShBvkGYfi+5CPrioLgLN7HvK+7w/iNw+7beJSK1aHrsWBKvyckhF4oMYzZuJDErAGja1PgxqR1A5ujdG/i//yNfz5ypj1BRlwp1ArkThrE9R6LV6j83zYk59L4ffzRfeO4OUHfU2LHk3EkJgoKAD+qmzb3/Pvn7F4utkn6eJ8JShgzBfelS8vyBA4G2ZhboevUi4m5lJfmZ2YlapUZSXBIGhA1AUlySWQFFrNNl3bh1yJqdhdRx4t4D6X7tEYCkIMXBJDWC50gqaipEbTcsYRgWPbQInw39TNT25n6uYgQ1az8vgCw4fTr4U1ZoLgJaZj7jvhnwVnu7+GhYj5QcmCDFcHuEyJ6hIEVt1lOmkE4La0yYQG7XroVWU2s1AsODx5ytc/Dcr8/hhxM/iLJLA+5hqaWClMW4Hi0N/fxz/UUNXSWUSliY3tGTng4AgisrszwTu67ucr8YkQIoEaGilvGYkBi9INWnj7wDcmCxeb+YfvBV+yK7LBvnCs4pvn9PpaiyCBlXyQWS1AmEL/R8AQApRL9cdNnm9mJPrNzh/UdgyxZyIdi1K3FgSsHHh0SJAOL0YUKontxcfTHzq68S0S493b4OIHPQgm5z/Pvf9vf6KM3w4SSOdeoUcOmS415n61YijoSFmZ9S16cPKYivrCQ/E3fj9m1hUQ4vvqjsvqdNI1P2SkqAd98V/zxHlPQDxMVGOzJnWOgx4jj9+Y+F2J7SSHXEyHHQOEsAkuJgkhrBcxRiPyf/0f8feOb+Z/BSz5dc5jijbrqjOUdl77uxcO72Oey4sgMqToWnu9ctDDhiSIIEHmhDBKmMqxmo1lQ79bU9FSZIMdyeWxUmkb28PH1HiRhBZfhwsop3/TqOb1hkcwoJAHRr3g3/HPBP/DrhV7QMaml1ioq/lz+6t+hu+zgciEanwd7rRNwwK0gZlobSWIyvL+kfkotBbC/tTBre3UlORK9UXsHglYPdcgqhvSgRoRIEqeAYvZNEan8UhTqkDh0i068UxN/bH/1j+wNgsT1DtlzcAi2vxd1N70absDaSnts+oj2GJgwFDx5fHfrK6rYZVzPw4mbrF45uaemn781S4nqGPPMMEaYOHSLRPRedULoVPE8urPPzgc6diYtJrSbuD3s6gEzRaoFXXrG+jdwIlaMIC9O/DzoytkejeE88Yd7NynF6l9S337qfmLpkCeln69aNuIOURK0GPqtzk3zzDenSEoNYN5XUkv4dO4CsLBKltCbSTplCjn3fPuDMGWmvIQOpDia5jid3EYAMkRrBcwRSBT5nO84MI5+pY4k7bu7eucICGMM83x4h780j2o1AbEis44YkSOCepvegWZNmqNRUYt8N6b2CcjtqPRkmSDHcnnql5itWkGlAPXuSFUlb+PkBo0cDAALTfhH1mq/1fQ3vJ7+PEe1HYOHwhQDMW2oBoFJTicE/DBbtpnIEf+X8hYraCoT6haJTVCfjBy2VhlZX21caWtc9VbFlE8asfQz5d4wnY7njFEJ7USJCRSN7rVURZBoZIN8h1bo1ierU1pJuEIVhPVL1kRPXM+Slni8BABb/tdhshIDneSw8sBCDlg9CbkUuYoJjrE7tUeKEWDFqa4lDCiDDJuQQFUVGygPEyeGiE0q34rvviNDn40OGUfj62n6OHBwZoXIkNLbnKEEqJ4dMbAOsdy9NmUJ+NsePk74md0GrJeIuQP6mHDEhceBAcp6l1eqLzi2h0wEffUTETWvILemnZea2IqbNm+tjxUuXSnsNmSg5Rc6a48kdBCB3Q47A5EzHmWHkc3TH0Zh+73Tw4DHt52korylX5HUaGpW1lVh6fCmAujJzRw5JkADHcRjUmizab78sLbZnT0etJ8MEKYbbY1RqzvN6K7aUuFndRJX4rYegFiE0ixlDGxMcgw+SP0C4fzgOZB9An8V9cL7gvPhjUhAa1+sf29+4ONNaaShF7op3//7gvbzQ5GY+4ovqP+zWU8BkItbyHdXEfD+HRqfBzTLSuRV/IY+cmMfHAy1byjsgjtO7A6TG9kRYmoXxtVnp0Og08o6xAaHRafDbBTJlS2pcjzK83XAkhCWguKoY76S/Y7QCdqf2Dp7Y8ARe3vIyNDoNxncajzMvnHGbDg6b7N1LYjuRkWTBQA5paeT30RQnn1C6DRcu6F1LH35IHFKOwlERKkdDBak9e6R1GIll6VLy/ti3r/VFsPBw8jsKEBHRXdi8Gbh6lRwfnS7nCD79lFQobNmin0ZoSl4eEYHefJN8/g0YQD7HzIlkPC+9pL+gAPjpJ/K1pbieIfQ8cvlystDpBJScIseQhhyByVXf//nD5iM2JBaXiy7jta2vOfS1PJXU06korCxEbEgshrUe7LghCTKQ0yNlb0etJ+NlexMGw7UYlZofPky6Ivz8pJ1YPfggEBEBn4ICjMmPxI/NC8z2AHHgEB0cbXYM7agOo5BxLQM5ZTloEdQCibGJUKvUGHP3GAxfORyXiy6jz+I++Hn8z0LUyVnsvlbXHxVrEteTsuI9cKC0F23SBCX3dkTo4UwMugIsDjeze4MI28B4ift3Q7pEdYGP2gc1WusTwN5JfwdxoXH1ph3mlOVAx+vgrfJG+F91vUxy43qUAQNIZ4mUYvO0NPLBbfi7ER0NLFhgFHG4r/l9CPMLQ1FVEQ7fPIze0b3tO1YPZ+/1vSiqKkK4fzj6RMtztak4FRJjE3Gp6BLm7Z8n3N88sDl81b64WnIVak6N/wz+D2b3ng2O46y+/7gVNK43YoS8CJmtqVscR04oR41SJqLm7mg0xHVz5w5xidmK09mL2GiU1AiVo4mLI51lx4+TCY9Tpyq3b51OLy6JmUz39NPExbZqFfDf/5K6AFdDp9899ZRjS+nbtiV/v3PnAnPmEHEqP5/8viQmEsFwwgQiaPr7k+OaPp0ISKafR5TYWGnHsHIlmdB5333k/7Z46CEyIODWLSKkyY0aS4Q6mBy1PcMycj5PXfH9D/YNxtJRSzFo+SB8c+QbjOowCsPbDXfqMbg7tMx8ZreZUP+513HXOzKgC7oHsw+itLrU5kRmWx21HDjM3jIbozqMcr9zPwVgDimGW6PjdUIUrllgM707KiWFjKkWi7e3sHL5cX5XAMqNoe0Q2QH7Z+xHz1Y9UVhZiAeXP4h1p9Y5PgNc53DRrVoJPj0dKp1JfxTPA9tFKvMyV7yvdyeT9QZdsbF7d5oCJpOiyiIMWznMohhFf3/8vPyw98ZedF3UFV8f+lqYUKfVabHh7AYAQLh/ODhaaG6vIEWLzfftIyfitpBgaVar1ILtmMX2gE3nSGxnRLsRsk8I0s6kYdnxZfXuv1V+C1dLriLYNxh/PPEHXunzitG0TI+IYPz6K7mVG9dzZmTMxaWnovi//yNR3JAQYNkyQOXgU7bERCJMW4p0yY1QOQNHxfZ27CCF8SEhwLhxtrcfMABo3x6oqADWrFH2WORw/jwpZOc4/cAAR/L220SEO3sWGDxYH7kNDye3OTlkut3Bg8CTT5LjSkkhnU+GJf10GM0zz4j/2+R5fVzvqafEPcfHB5g8mXztpHJzhuvxiM9TAMmtkzGrF+l9fWrjUyisLHTxEbkPJ3JPYO/1vfBSeeHJ+550O4dvXGgcEsISoOW1QorFGkp01HoyTJBiuDWFlYVCVChKFaSfEiNnOhyN7W0/gvWPrlY0AhPVJArpU9Px6F2PolpbjXGp4xDxaYTjMsAGpX2qSZOx8dsyXJ3Pofv+q2TKz+LFZOoNHcdsC5kr3pqB5MJk0BXAjKiv3707TQGTwe07tzFo+SAcunkIEf4RmDt4LqKDo422iQ6Oxvpx63HmhTMYGD8Qd2rv4PnNz2PoiqH47sh3iF8Qj5e3vAwAyCvLRdnubeSJ9gpSHTqQ3p2qKlIEbQ1bDhSgnqWZ9Ujpsbc/ytoKGCXQO9C9SsrFcukSKQb28gKGDJG3D2edULpB6alNDhwgU+0A0v8jdWKhHNRq4pIE6otS9L+lRqicBRWkfv+dfAYqBXVHTZoEBATY3t6w3NwdYnu0O+qhh0jnoKPZvh0oK6t/f2kp+YwZOJB8TnUy6bo0Len/7DMiAh49qv832OLwYdLL6OdH/q7FQs8nN24kji4Gw4346IGPcFfkXcgpz8ELm19w9eG4Dd8c/gYA8Ohdj5JrDDd0+AqxPRE9Ukp01HoyTJBiuDW00DzcPxw+G38l/SSxsfoJb1JITCRvRMXFGH29ieKZ8ADvAKSOTcWItqQks6S6xOhxWxlg0Y4qCw6XlqU8vMY+TsSJGTOAEyeILb5JE4eteHcZNROV3hyaVwB3WziPc7spYBLJLc9F8rJkHLt1DFFNorBz2k682vdVi78/8aHx2P7EdswfOh9+Xn7YdnkbZv4y02jl4+58ILiKR7k38JPXRfsO0LBHylZsT4YDhQpSe6/vNVvC3Vi4WHgRZ2+fhZfKC0MThsrah60VMAC4WX7TM1fAqDuqf39p7lVDnHFC6Salp/UwdGz99htxbWi1ZCFFysW1vaSkAKmpQCvjBRtER5P7rU0tcyX33Uc+y+7cEe8MtkV+vr6PSExcjzJ1KnFlHzoEHDumzLHIobxcX9b9ghMuZOmChzUuXTI/pdCUZs2Ajz8mX7/1FnDzpu3nLF5Mbh97jExfFEvnzsD995OI7MqV4p/HYDgBf29/LH90OdScGmtOrsHak2tdfUgup7ymHD+c+AFAXZk5QD6jbLmInezwfaANEaT+uGJ7QVfswr2nL/BbgglSDLdFq9Pi90u/AyCuAZ7G9aZNkxddUKv1lvs1axxm2T2Rd8Ls/dZKvkVPVbDicBG+I+XlpFNj7lxykbV8ObnfASveav8AlPboAgB4wEJsL+WuFLe1Q9sipywHA5cNxMm8k2gR2AK7pu0Sphha+/1RcSrM6j0LR54+Ah+VT7399r1Obg+2Amb98ar9cU6xxeYyHCgJYQmIC4lDra7WM4UShaBxvQFxAxDiFyJrH4YrWyodkHQFGJ9JblU689t5DFSQsqeDxVZkDLDvhFKGQ9ApmDq2RowALl4EIiLEu0OUxFyE6soV9xWjAPI7M3Ik+Vqp2N6yZWRyZI8exHEslqZNgUcfJV+70iW1ciVZxGvbVr5rUQq2FjwAaZHbmTOBXr2I48pWf1pFBfldBcSVmZtCXVJLllgfAsNguIAerXrgrcS3AADPb34eN0puOLYSxM1ZnbkaZTVlaBfeDsmtk8m1zpAhpPMPsHwO8cYbTnX4JscnAwBO5p0UDBbmqKytxKrMVVb3xYHz+AV+azBBiuGWUIHm1a1144OvXQO/o27Vc9o0+TumReg//0xWUhXG0AFh7oKTZoA/2/eZ4DaRNFVBzAkfQFYKX32VrBI6eMW72SPkezriuvGqJy3w++boNziac9Su13AGpg61q8VXkbQ0CWdvn0V0cDR2TduFuyLvkrTPvDt5qNHV73XqU/cj3BsDZTLhVJD680/Lk4J4nvR6iMHAgcJxHIvtwf64HqBf2Rp9GsiaD+xcBqxeT26z5pP7DbfzGMrL9ZPx7BGkrEXGKI89Jv+E0pkdVWKx5NgCyMSw9HTnHYshphEqd4zpmUJje5s26S9M5MLz0srMTZk5k9yuWOGQcw2b8Dzw5Zfk6+efd3z/GKB85FalAhYtIrc//khKxy2RmkqEq4QEfa+iFCZMAHx9SeTvqPufrzAaH28PeBvdWnRDYWUh2n3RznGVIG4Oz/P4+vDXAIBnuj8D1e0C0ld35Qr5+//uu/rXOz51C8Nff03OV5xE0yZN0bUZ6S3ecWWH2W3O3T6H3ot747uj+sULqR3HDQEmSDHcDnMCzdRjgIoHdsQDaVV/yd95r15kJbq8nIxCVhjqbLB1wfm3P/6G0E9C0eu7Xpi6YarFqQqAiaNK7IlcXp7xfztyxbsuPjn0hh+2jd+COXFzsG3SNuS/lo+H2j2EKk0VHl3zqFBO746knUlDm8/i8N57ydj4r4l4771ktJvfBhcKLyA+NB67p+1Gu4h2xk8SUYpsyelCHVJ7Y6xvJ5rOnUnfRnk58Mkn9Y/n9GkyafL9923vq2nTeg6Uxi5IlVSVCKWUD7eXL7gkxiZiRlYEUn8EWpUaP9aqFEj9EXg6K0KZFTBnlnb/8Qcp1E9IIIXO9mBJQA8MJLdffSVfMHKz0lOrji1AP1XQHQvX3ZGkJCA4GMjNJR1c9rB7NykEb9JE2kRfyqBBpLOptJSIKc5mzx4irgQE2LeIJwVHRG7vvVcfA3zhBcv9YLTMnBalSyUsDBg9mnzNys0Zboi32htTukwBAFRpqowes1UJ0pA4fPMw/rr1F3zVvpgW/ygwdCjpr4yOJuciM2bUv965cIHEgDMziRvSiS5I2iO14sSKeo62FSdWoPu33XEi9wSimkRh6+StWD9uvaIdx54CE6QYboVh6S91GE04ATxb19X8/X3mI2+i4Tj9ySUtSFeQFkEtMPo0rF5wjj5NStA1Og0O3jyI8hrLaj11VL3+x+tYlbkKx47/Luo4tM2i6t/pqBXvbt2A4GBwxcVIKg7FgLABSIpLgo+XD1akrED7iPa4XnodY9eNRa22VpnXVJC0M2lY+d5j2PN+tpGAeOkzHUafBl7v/zpah5mUwYosRTbndImoADoUkK/3R1veThIbNgDV1eTrt9/WH8+KFcQp17UrmRbl50diqxxn+aS9uFjvdqmDTto7nnsciw4vanQW8d8v/Q6NToO7Iu9C2/C2svej5oEFv5GvTT986X/P30K2swtnl3b/QtxjePhheReDppgT0AsLyf01NcQJc+6c9P26W+mpOzq2PBkfHxJ3BOyP7VF31MSJZGqcVFQqfXRMTmzPXkH5iy/I7aRJ0vqU7MFRUxr/9S8iUF++DHz4Yf3Hz50jApxKZZ/4RmN7K1eSISGNCU+YOtrI0eq0+O++/5p9zFoliKzXqq3BsTXzsfeTl3BszXxoa0VMcHYSiw4vAgBMThiNiLFPAH/9RRZS//iDnOcA9a93YmPJ+Y+3N1nw+ugjpx2vnzdJj2y+uFlwtMXNj8MDyx7AlJ+moKK2AsnxyTj2zDEMThiMlI4pinccewJMkGK4FTTyZugwWpUGtKwAdAA0nAIRJypI/forWb0Ui4gP7MRWffHF70TosXTBuXCrGjdnXUfWrCw8d79+DLO1Tpnv/5iLmimTcO8nZFy8petVHYBrwUBGnPh/lt14eZE3fACqHcaW1FC/UGx4fAOCfIKw++puzPl9jhMPzDZanRa/fToT66wIiEe+eMv4A15CKXJibCKig6ON7Lc0rnc6EigOUCATTo/H9AT6xg1gyhRg3jwS4xs1ijil1q61HOHs3p10pjz8MPlwr2PPtT3wVnkDAJ779blGZxHfdJ70R9kT1wMAZGQgILfA4gevCkDArQL7BAhnl3brdMr0R5liekLp7U0E1t69gaIiYPjw+k5QW9TW2hbMQkJIMbszcDfHVkOAxvbsEaQKC8l7JCAvrkeZPp38Hu/dC5w6Jf559grKN2/qt3VGmTnFUVMag4KAzz8nX3/ySf3oOe0XHTECaNlS2r4NeeABIpgVFyvXQ+YJeMLUUYbNoSh0AdveCoj9n/8duU0DcO+EV9D39S9w74RXkNs0APs//7td+1WC4qpirD65Gr61wNwvL5D31tBQYNs2MnHaGn376mPMb7+tP29xIGln0vBRRn3xK7ssGzuyyPXSe0nvYduUbUYL047qOHZnmCDFcCtult206DDiAKxMIw4juyJOXboAd91FHCViTzpEfmCrd+5CyxKt1QvOVsVaqPf8ibjQOIy7h5SsW4v4TT8KXPrKC9OOAzoO2JJABCnThgz637OHATl3nByPq4vtcSbOGgDo2LQjVqSsAAB8cegLLPlriTOPzCoZV3binTRiV7IkIL6zvgD7jv9CLoLz84GXXhJdiqxWqbFgGDlBp6IUjevtq4vr2ZUJtxX5AYhguGkTcVHRsd/mHChZWaSD6pFHiLj1yCPAtm1ChLZWZ+xuaywWcY1Og80XSLzXnrgeAPI9FoNcAcKe0m65K+R//QXcukUidbTLzFH4+5P37DZtSOT4kUfE9fPwPLBwIRGx6PfBkjBVUkIm3Dmj90fs99iJY6o9nmHDyHve2bMkcicD1cqV5Pyga1cyfU0uLVqQ31FAvEtKCUH522/JIkT//uTf4Ewc1Vk5ejTw0ENEVH7uOf3fcW2tfpLgU0/JPmwARCibOpV83Vhie+46dZRRD7HXPfZcH+3//O/oOes/aF5i/NnUvESLnrP+4zJRijq2Vr85En3OVWLzz4EI/fMIiVT/9pv497mnn9a/f0ycKM9pLfaYDRI/lmga0BRvD3i7UQhOtmCCFMNpmJZGm9pKD2YfxNyMT7CgrrfS9JeTXj7M3wK0CDATSROLYWxvzRrb29v6wF69mlwkPfGEfrKOLcaOBV55BYlXgZlXwi1G/Nb/CCzZCIRUaIAuXXBs/ZcYPgUYMw7IDjbe/kYwuf+nu11QikwFqT17wNXWj+WN7DAS/xr4LwDEYXPghp39HgpR9sdmxJRafiNUAYguA/p3exQIDweioqyPnzYTsUnpmILUcamICWyJpCvAqLrF3bNtQ+3PhIspuddo9KXhaCIAACAASURBVP07hpiLcPr6AuvWCaIUP3Ik1i94RnzHmQG2/t6djdzj2X9jPworCxHmF4a+MX3lvTjPE2fa30WezH35Jek8MESMYCQ3AiZnhZweD7W+Dx6sLw51JFFR5AQ0PBw4eJCIR9aEnepqEp16+WWy3eTJRIQ1vWCOiQGeeYaIGWvXkov5a9cc82/QaIjTw9Y0MLkRp8ZMaKjg2JXlcuF5qBYvJl/PnGl/BJU6rH74wXYMTIkpkDU1wDffkK9ffFH68SqBIzorOY7EEP39yfvO8uXk9vXXiVMyKooIVvZCI39bt5L3yoaMu04dBViE0Axiz+ubBjSVtX9tbQ1i35kHwPICbcy785we3zN0bD33WQa2/wAMOlkOLV1s7d1b2g7nzyef76WlxFFbUuKQ47blaAOA/Dv5jXp6tRE8QzFKSkp4AHxJSYmrD0UWmppq/vCKufwPz4zgD/9/e3ceF1W99wH8MzOAKyCiAgKSmAsuodJyuy6ZaaVZLpWZmpZP3qxbarcyq6uhTze7T6XXJa1cyl65pjez3NKk3DK5pSLipUVRBDdUwNhm+z5/TIyiLGfmx8wc5PN+veZVDr85fJnzmd+c851zznz6jljNJVWO379ilux+61nZv2JWpePXpq2VFu9Eyh2jIcMehNwxGtLinUhZm7ZWfj3/qwz9bKgg0XG/oOqb9Zttan/sf//rWJbRKPLBByJJSSJWazl/pFUkKqryegwGTTVXdLMbDWKv7OeAyD//KWI2i9VmlaiZUWJINIhxKso8n8apEEOiQaJnRovVVs7f4kk2m0iTJiKAHHnkEbFs3XrN82mz22TQykGCREjzd5tL5vkMzfkRcS1vVY3/veR3eeO7N2TiAH+ldVfh7S9/Eblw4XIxa9eK/aoc2cPCRNaudf25vtLy5drqWb7cteWWlIg88IAIIEUmSN+RKDdvSHTcko4llXl4Za/3ivhq/qlq+f/31y5yx2jIo6uGVlqPWK2OeWT58rLzyYEDIj17Xl4XJpO2debnJzJxosj5846cXD0PRUVdm5+ZM7Ut+6GHRDZvvrzs8uYwg8FxKy+j5dUTEqKeZ1fs3ClSp47jd0+cKGK1imXrVkn+298uzz/Z2SK33355rn/3XRG73fH4itbXjh0iTZs6HtO0qePflY2vSEXjf/pJpEuXy8/bTTddfq61Pv9UublzHc9hx44urS/L1q2SNmyY47F164rk5qrXYrWKREc7lvnaa5XXk5Sk7fWblFT+70lKEnnuOceYsDDHPH69eeuty6/nK5+TwMDqe63ccYdjmWPGuJyfMvNPFeOrZT5RGV8defNE/Vrf77xVj5fGV5Wfqrb/S7fH2s9rL9t+K7uPpGV7af+KWZrysH/FrGtrq8bt8yt9P/slsQFiK2e/yA7I97NfqvT3VOj06csZGzBAxGyu9vW7PGW5c51Utr6Wp5Szfe5qfkTEbDbLunXrxGw2V/33e5HW3gi8VE+N8d5778kNN9wgderUka5du8qO0o1RDWpyQ+r72S9JVnDZHaWsYFOFL3ZXxq9NWytDhkJOBJWdUE4EQQYPhZimmQSJEMPrkDXDu2iaEF3ewb6mqLUi/lc1I8p7w9P6ht2smWOn6NtvHcupqEllMDh+vmaNyGOPiTRo4PIGwdq0tWJINIgh0eCc0JAI532V7WR7zNq1IvXqVfl85hfnS/v32svgoZDMq/JQXXmrbPyuWX+TeXvnysCnQ2RVe4hVy3MPiHXLZscb1rZt2tYX4MjXgAEi48e7vsOvlcoGZVVKSiTzzgQRQEqMkLP1y3/9IhFy+6LbZc7eOZKclSyrUldV+HofMhTl5tNX848r9ZwMNlS88VPeBnTz5iL33HN5x6lePZFp0xxzV2UNiNmzRfr3v3x/RXNE6fh33xWZOtWx8601m1feKmuQGQyOnekrN4bcaWB5ysqVl39/o0bXzsmNG1/+2ZYt2pebkSHSufPl1/FTT7m2g1ReHiIjRQYPvvx8h4SIfPSRo0FW3vjoaDaj3PX++9fm09X1Vb9+9T3/Q4dWXY/VKpKYqO01O3Pm5cZqRfUHBV2f+Vm1qvL5sDr+5mefVc+Pnsfn54ssXar9PWPAAJG9ex0fPHq6/ur6gETPz7/C+Kr2pwLfDHTuEzy46kHJuJhR5fZSibVENh76XJb2i9SUh7WvPSj2K+YfT22/Wc0lkhVsuqYZVXqzAXKykanK5leFkpMdHzwAjoZ2Na+vpGNJgkTHeqlofZX3ga5bDVlhQ+q6snLlSvH395eFCxdKWlqaTJgwQRo0aCDHjx/X9Pia2pCqqANdet/Vk4Qr4602qzz5eGil41+/A/JFn2gpiWim7c0RcG8Hu1RVb3izZzt2Gl9+2fHptZZ6li27dvlaPvH+5BNty7+qAbc2ba1EzYwq05CKnhntu2aUCxsQG976H4/lrarxdkCOBpe93+rvV+kbXkF46OUd8tIj5iprOAYHi3TooG29lrfD7wot9Sgs/9v0r2VvpGNZVx/FV/ocl76plt4GD0Wl62vs46FljuDz9fyjWk+F+b/yNnSoyJXvI1oaEFu2aM9R6c1kunzUUEW3Ro1EHn1UpHVr7ct94gmRxYtFNm50HHnhqTy7Y+TIymuPjBT55RfXl/v77+U3Eq78W8vbQdKSh4cfFjl1quzj3PhElMrh6g5tZeurOhocVdUzaZLI449fPipP661FC8cRPBMn6qdB7GlVHbFeHfNPdeVHj+MBkR49rv3wUOstKkqkX7/qmQ/LG2+1Oj7IcWX91qTnv5rG2w0VbN8aIJeWL5XnNj4nxmlGQSLk4WGmSrdn/t0nWr7o6C/5AdpzcKEO5LNbG8qyqUNk0+sjqnf7bdaLYk9Lkwvv/0vSe2rb/inviC3Nxo/3WJ7d2f5U+cCPDanryK233irjxo0rc1+7du1k8uTJmh5fExtS2jrQRtmavlk2/rxRPju4Uk4GGyodfyLYIM988ZSMXT9W+i29W04EXftiLL1dc6pagwaVv1lW1w68xolX8+3qBpnWT7wVjnCx2qySdCxJlqcsl6RjSd4/TU/E5Q1EVz/xcHW8paS40vGlN3OAn1ifHCNy8GCVb/AVviFV1XA8fFhk1Ci3169mrjRAXWQ1l0h2sLHCU0ptgGQHGeRfKybI6Fl3yE0T60p2w4pf7zZATgZC+r3TVcZ9+qhMWvO0nAyqfD7JDDbKZwdXyopDK+Sj5EWa55+/rP+L3Lf03krnHxsgx4Mgz61/Rhb/tFhWHVguJ4ONlY4/HWgU6w97HadyffmlSGho5eu2WbOKTweuqgGh9Yi8bt0cze0LF1zLw4cfVv9cqJpnV2iZzyMj3X+/sFgcR5pUtOzSI17z80Xy8kTOnBGJiKi8niZN2GzyFFcbFp5ucLi6vREUVHWzoE4dx+m8WpbniwaxJ3nyiGARbesrLMxxZEVKisihQyLh4VXPD0VFjqOLtOQtKsrRDL90SeTcucrnE4PBMb9dvOj4HcXF2vPWpo3jiN3w8MobsqGhjsZ8w4baltu0qcju3Y5tqyNHqp4PGzZ0XB4gPl77GQOtWjke89e/Oj4A9NTr3WLRtr4KChynxxYWOtZHVeOLix3Lrmp9KdSfcjpF7lzc06X9r9yQevJ7QOXjKzp1rrzxrm7Pl7d8Lbfdbz3rudd7s2Yi333nyPTOnY5/V/b8h4eLpKeLHDsmcuyYFDYNqfwD77BQR24sFkeGFN6PanpDyiAi4rsrWOmH2WxG/fr18dlnn2Hw4MHO+ydMmIADBw7gu+++u+YxJSUlKCkpcf47Pz8f0dHRyMnJQVBQ0DXj9Shl9RwkjHyxynHFRsBuBIx2oO7VX+9WjkI/wGoEjAI0vPYa19c42T0e4S9Og/TuDcOmTTD9cdFxwxXxlD8uLmpbuRJyxTpyheG77+DXt2+V4+xxcZAePYBOnWCcPh3IySlTS5maIiNh/eWXa7/K2GaDYdcuxzdmRURAuncvd4zfjTcC2dmuL18HtD6f4u8P+PnBbrPCZK46EHkBgNUE+NmAYA3XT/zd35E3P7u2vB14fzo6jJns/Lfh889h+tvfYMjKulxzVBRs775bbta0jjesXAm/UaOqrMf6ySeQ0gvtu8HV+jUvV+P69bTf/QGbETBpXL+leTDZgUAXxmvNj6usW7dC7rjD5ce5mx/N+dQ6H957LyACw+HDMFR1Ef1y6vEUrfW7/fx7KP/u1kOV0/x+FBDguHi93Q5DVRcah+fzYxs8GDJuHKR7dxi++qrq7Z+774Zh924Yli6F6bPPPFa/3nj6/dTT73eCy1/O40u2efNgHzsWMBgc7xVatreLi2GcPRumKVN8UrMKMRgAo+Oy3AYNF0cXo9HxfqeT3WMxGh3b/yIwWK1Vj2/QAPDzg81SAr/Cque31Hu6ovXUOTAm3Ix981/F7X+79sLmpbt9e9+eiJvi7kTmigUI3fANmuVWvcFkvmL/MUDD/mOxEfgxCrgYUhcDDlZd/4+fvoObho6vesFX0cv2rasqms8tFgu2bt2Kvn37wt/f3weVlS8/Px9NmjRBXl5epb0RPy/WpGs5OTmw2WwICwsrc39YWBhOnz5d7mNmzJiBadOmXXP/119/jfr163ukzup2aed2JGgYV9eOyzOSBvWrnjPLSL6pjWPy274dqFMHEZMmodOiRah3/rxzTFFoKFL/539wqk4dYONG137BHyJ37ICWL3H+qV8/ZP3xFeYRY8bgln/+85qNCQEAESSPGIFTW7ZUvLCgIKCgAKhgTMTIkWrL9yGtz6fBYgEsFmhtqWlpQl3J1SZC6v69OH5lhurUAebMQWhaGupevIjikBCcb9/esRFQXtY0jg89fhzdNdSz9/hxnHcz027Vr5HW9Ws3mWD384PYrfC3VP+34bi6fj09vrB+HdgbhcJgtaLB2bNVjj+waROyCgpc+yVQyI/WPNhsuDs0FHXPny93R0kAFDVpgq1jxwImE0IPHUJ3DTskynnWSGs+3X3+tS7fVe7WQ5XT/H5kNju+jU4jT+dnf8uWyCoqArZu1bb988cHpJHR0R7Nv954+v1U6/oyN2gAu58fTCUl8NfQ0Cylh2YUAOw/ehRZmzY5/uHC9nbkxYuanp/ioCDAaIRfYSH8NLzOjvfujew//xl18vPRdc6cKscfHjkS1gYN0OynnxCRnFzleIOIS9/SZ7C7sLPjBQa7HXChJsMfr3WtO/gHbgjHb+dygM2bgdieWPbsUfT+ZD0i8y//zuxgI5IeewBBrXvhG6sAD4/DpYZGjPyg6tdZgIv7jx+N7oam90+E0S7IGjMMEfn2cr8J2/5HXcfrtsBJD77eixs1grVuXfgVFaGuhm/ks/r7AwYDjFYrjB7IUlXz+datW6v9d6ooLCzUNtArx2vVAFlZWQJA9uzZU+b+N954Q9q2bVvuY4qLiyUvL895y8zMFACSk5MjZrO5Rtz+8+k7mg6JTJn1qph//llSZr2qbfw7k8V85IiUfLRE0/iSLVuura+oSCxbt4rlk0/EsnWrmIuKlP9ey9atmuqxbN1a9nGrVon9qkNx7VFRYlm1qlrWg6eX76mb5ufz008d+Zn5iqbxO6ePlaO7NsrOaWM1jd/zj2fk1L7v5Ic3Kzgf/Krbfz59xzvPUVGR2CMjxV7BIfF2g0HsUVHVkm2frt8/Xi8lW7Zoe71v3iTmS5fkp4X/0DifvCzmw4cl5Z2XNY0/9PbLYk5NlZIli7XVs2SxmFNTNS+/ND/uzid6yo9l1SrHcq76HaX3lZmDdJZnTz//mpf/xRdizs8Xi8b8u50H3qpnfX3yiZj/+1+xLF2qj/yUt3wN2z8en3/0dvPw/OPq86l5/L//LebsbLFUdEH2q8evWyfm3FyxbNqkbfxXX4k5J0csa9fqKm8u59PF9at5+atWiTkjQywav5XYsnKlmE+cEMvq1drX17lzYlm3Ttv4tWvFfPq09vW1YoWYf/tNLJ9+qm38kiViTk2VQ2+7tj1z5a2o4Hf5z6fvyI43n5b/fPqOFBX8fs0YrfuP3854Rn79cbt8O+MZl+vZNfNvlV6DadfMv3n+/cLNPGse//nnruWhgvm8oKBA1q1bJwUFBb6fq6+45eTkCMBrSGlWUlIiJpNJ/v3vf5e5f/z48dKzZ09Ny7h+ryHl/jV9xGqVgrBQ7ReN9vgfrHARaE9fdLYmXtTWxefT03nz+LdyuMOD13jyOFdfLy6+3vU2/7izfE9eVF5EvJMfV77lTU959vTz70b+PZ4Hqpje1ldNX74eeXL+8XR+9DZeb8+PiGvrV2/Pp87Ge3p72Fvb5+V+63Gjir/FT/sfoK/1pfr6NZtr9jWk4KV6aoRbb71Vnn766TL3xcXFXdcXNRfx7LdciYjrF432ND3tUF0PXHw+PZ03l/PpDTX5a91dfb24+HrX2/zj9rfs6aVh5C5XGuJ6yrOnn3838s/3Fx/S2/qq6cvXI0/OP57Oj97G6+35KX2Mpz4gqWXjPb097K3tc6u5RPavmCW733pW9q+YVX0fKutsfam8ftmQuo6sXLlS/P39ZfHixZKWliYTJ06UBg0aSEZGhqbH19SGlIjrHWiXO9Zr14r9qjcYe3SU7zaW9LRDdT1w8fn0dN489omKipp4BFwpV18vLr7e9Tb/uLN8XTWMvEFP9Xj6+Xcj/3x/8SG9ra+avnw98uT84+n86G28q7xRjyc/IKll4z29PVzjt891tr7cff3W9IaUQUTEI1exqqHmz5+P//u//8OpU6fQsWNHzJo1Cz3/uLh1VfLz8xEcHFzlleT1ymYx48DquTiyczvievRG56HPweQfUOn4Q2vno/D4b6gf0wqdHnym0vGw2YCdO53fOocePXz7zXF6q6ems9lgTUrCgU2b0LlfP/jdeWelz6er+fH0eKqCq68XF8frbf6p8fNbbePi/OPO8j2Zf6pmbqwvXeVHb8uvbTydH0/PJ3rLW02vxwvjPZkfT28P1/jtcx3mwdXXi8ViwcaNG9G/f3/dfcuelt4IG1LVqKY3pAD9BppqBuaHVDA/pIL5IRXMD6lgfkgF80Mq9Jofrb2R8r5JkYiIiIiIiIiIyGPYkCIiIiIiIiIiIq9iQ4qIiIiIiIiIiLyKDSkiIiIiIiIiIvIqNqSIiIiIiIiIiMir2JAiIiIiIiIiIiKvYkOKiIiIiIiIiIi8ig0pIiIiIiIiIiLyKjakiIiIiIiIiIjIq9iQIiIiIiIiIiIir2JDioiIiIiIiIiIvIoNKSIiIiIiIiIi8io2pIiIiIiIiIiIyKv8fF3A9UREAAD5+fk+rsR9FosFhYWFyM/Ph7+/v6/LoRqG+SEVzA+pYH5IBfNDKpgfUsH8kAq95qe0J1LaI6kIG1LV6NKlSwCA6OhoH1dCREREREREROQ7ly5dQnBwcIU/N0hVLSvSzG63Izs7G4GBgTAYDL4uxy35+fmIjo5GZmYmgoKCfF0O1TDMD6lgfkgF80MqmB9SwfyQCuaHVOg1PyKCS5cuoXnz5jAaK75SFI+QqkZGoxFRUVG+LqNaBAUF6SrQVLMwP6SC+SEVzA+pYH5IBfNDKpgfUqHH/FR2ZFQpXtSciIiIiIiIiIi8ig0pIiIiIiIiIiLyKlNiYmKir4sgfTGZTOjVqxf8/HhGJ7mO+SEVzA+pYH5IBfNDKpgfUsH8kIqanB9e1JyIiIiIiIiIiLyKp+wREREREREREZFXsSFFRERERERERERexYYUERERERERERF5FRtSRERERERERETkVWxIkU/wWvqkgvkhIl/g3EMqmB9SwfyQCuaH3GG1Wj3+O9iQIq8pLi52htpgMPi4GqppioqKUFxcDID5ISLvuXJjjHMPuaqwsBAXL15ESUkJ80Muy8zMxK5duwA45h82FcgVhw4dwqRJkwDw/Ytcd+TIEbzwwgvIzMz06NzDhhR5RWpqKgYNGoQ+ffqga9eu+PDDD3HixAlfl0U1RGpqKvr374+ePXuiY8eOWLBgAY4ePerrsqiGOHr0KJKSknxdBtVA6enpePLJJ3H33Xejf//+zvctu93u48qoJkhLS8OgQYNw1113oUOHDvjmm28A8EgF0ub06dPo0qULpk6dim3btgFgU4G0O3jwIG699VbUr1+/zP2cf0iLQ4cOoWfPnigsLITFYvHo3MOGFHncL7/8gl69euHGG2/E+PHjcdttt2HSpEmYMGECDh8+7OvySOeOHj2Knj17om3btnjuuefw5z//GQsWLMCLL76I/fv3+7o80rmff/4ZcXFxuOuuu7Bp0yZfl0M1SGpqKrp37w4/Pz906dIFubm5uPPOO2E2m2E0cvOJKpeamooePXqgTZs2mDp1Kjp16oQxY8Y4N+y5U0hVOXv2LOx2OzIzM/HPf/4T27dvh81mA8CmOFXu4MGD6NatG5599lkkJiaW+RmbmlSV8+fPY9SoURg+fDgWLlyI2NhY5OXl4cKFCzCbzdX++0yJV6eUqBrZ7XYkJiYiJiYGCxcuRFxcHAYMGIDk5GR8+eWX+PXXX9G1a1c0bdrU16WSTi1ZsgSFhYVYvXo14uPjcf/99yM4OBg//PADvvnmG3Tu3BlhYWG+LpN0KDc3F88++yzi4+PRpUsXTJs2DfHx8WjTpo2vSyOdO336NIYNG4aBAwdi3rx56Nu3LwYOHIjFixejSZMmiI+P93WJpGNZWVl46KGH8NBDD+Ff//oX2rVrhw4dOuDIkSPo3bu3c4fQz8/Px5WSnjVq1AjHjh3D22+/jTVr1mDv3r1o3bo1WrRogWPHjiEkJMTXJZIOHT9+HLfddhuGDBmC+fPnw2q14q233sLHH3+Mjz76CI0bN0ZISAjq1avn61JJp3JycrBhwwbMnz8f/v7+GD58OBYsWIA333wTv/76KyIiItC8efNq+338iI88ymg04syZM2jUqBEA4PfffwcAJCQk4K677sKlS5ewfPlyWK1WflpI5TKbzTh58iTy8vKc9w0fPhxPP/00LBYLFixYgNzcXB9WSHp19uxZtG7dGsOGDcPSpUsxcuRIPPLII9iwYYOvSyOdO3DgAKxWK8aOHeu8LzQ0FI0bN8a5c+d8WBnVBEeOHEGvXr0wceJE530rVqzA9u3b0bt3b3Tp0gWvv/46Tp065cMqSe/q1KmDH3/8EQUFBVi1ahVyc3Px5ptvonfv3nj44YdhNpu57UzX2LdvHyIiIhAQEID09HT0798fW7ZsQX5+PvLy8jBmzBh8+OGHzn0yoqudPHkSqampKCgowGOPPYbc3FxMnjwZzz33HLKysvD8888jLS2t2n4fG1LkcQ0bNsQ333wDs9mMhg0b4syZM5g7dy7+8pe/4J577sGiRYtQUFDAQ0ipXFFRUbh06RJ+++03AJcvMDxkyBAMGjQIa9eu5Q4ilatNmzYYM2YM7r//fgDA+++/72xKffXVV85xdrsd+fn5viqTdKhv3754+umn0bZtWwBwnmYVERGBkpKSMmO5Q0hX69OnD55++mlERkYCABYsWIC33noLM2fOxJo1azB27FisWLECBw8e9HGlpFelp+bdcsstOHjwIFq2bIlvv/0W+/btw969ezF27FgEBATw9E+6xsMPP4yXX34Z6enpuP322+Hn54fPPvsMq1evRlJSEh577DG88847OH36tK9LJZ2KjY1Fhw4dsH79ehQWFmLmzJm4//778corr+D555+H3W5HcnJytf0+NqTIY0rfTGfMmAGDwYAWLVqgf//+aNWqFQYMGIAhQ4bghRdegN1uR0pKio+rJb167LHHEBsbiyeeeAK5ubnw8/NzNqXGjx8Pf39/rF+/3sdVkl61a9cOwOXrbbz//vsYMWIEhg0bhg0bNsBms2HKlCmYM2eOV77almoGk8nkPDrKbrfD398fgOOo3wsXLjjHvffee9i5c6dPaiR9K21mms1mNG/eHDt27MCTTz6JDh06YNKkSfDz83NeqJroaiaTCQAQFxeHQ4cOAQCef/55mEwmxMTEYP369di8eTMAXhOIrvX4449j9OjR6NevH6ZPn45mzZo5G5czZsxASUkJtmzZ4uMqSa/Cw8PRsmVLTJw4ETt27Chz3cw+ffogICAA27dvr7bfx5PXqVrl5OSguLgYUVFRMJlMEBGEh4dj9+7dePvttxEQEIBHHnkEo0ePBuC4gn9wcDAiIiJ8XDnpQUZGBjZu3IijR4/innvuQbdu3VC/fn0sXboUAwYMQN++fbF+/XpnXgoKChAZGVmt5zFTzXV1fu644w4EBAQAQJk30w8++AAGgwEjR47Erbfeiq1bt+LgwYO8nkstVlV27Ha787+l90+dOhVvvPEGUlNTfVk66UBl+QkICMD999/vnINsNhtyc3PRqlUrJCQk+LJs0onK8hMVFYWkpCSMHTsWGzduxL59++Dv749u3brhgw8+QM+ePa/5FjWqXa7OT/fu3VGvXj2MGTMGCQkJzg/mjEYjRAS//fYbWrZsifbt2/u4ctKDivLzySefoKCgAJ9//jnWrVuHyMhIBAUFAXDMS6W5qhZCVE0OHz4sjRs3ljFjxkh2drbzfpvNVuFjJk+eLDfffLPk5OR4o0TSsZSUFGnRooX06tVLunbtKkajUT744AMRcWQoJSVF4uPjJTY2VubPny+ff/65vPzyyxIaGiq//vqrj6snX6sqP1crKSmRli1bSmhoqBw4cMDb5ZKOaMmO2WwWEZGBAwfKrFmzZObMmVKvXj358ccffVY36UNl+bFarSIiYrfbyzxm6tSp0rZtW8nIyPB6vaQvleVHRCQrK0vCw8MlJiZGfvrpJ+f9J06ckKNHj/qiZNKRqvJTnr///e8SHx8vWVlZXqqS9Kqq/OTm5kq/fv0kODhYnnrqKZk/f75MmDBBQkJC5MiRI9VWBxtSVC1OnTolf/rTn6Rbt25St25defLJJyttSu3bt0+effZZeAP9tgAAFPJJREFUadiwoezfv9/b5ZLOZGRkSKtWrWTy5MlSUlIiIiJvv/22NG3aVE6fPu0cV1BQIKNGjZL4+Hhp2bKl3HbbbWU20Kh2qiw/Z86cuWa8xWKRZ555RoxGoxw6dMjb5ZKOuJqdESNGiNFolAYNGkhycrK3yyWdcTU/GzdulBdeeEEaNWrEbR/SlJ/i4mJZvXp1mZ2/0kYn1W6uzj9ffvmlPP/88xIUFMT5h1zKz5QpU6Rfv37Svn176d+/f7V/kMtrSJEyEcGhQ4cQFRWFTz75BBs2bMDHH3+MqVOnOr9B5srTZQDH17EbDAZ8//336Ny5sy/KJp2w2WxYvnw5OnfujEmTJiEgIAAiggEDBiAwMBCFhYUAHDkrPX1v8+bN+P7777F582Z06dLFx38B+VJV+SkoKLjmMWfPngUAJCcno2PHjt4umXTCneyUnoq+b98+3HzzzT6omvTC1fzY7Xb89NNP2LdvH3bu3Mltn1pOa37q1KmDhx56qMzpMaXXl6Lay9X5x2azYd++fdixYwd27drF+aeW05qf0utBT58+HZ9//jm+//57rFmzBvHx8dVaDy+YQcoMBgM6deqE8ePHo2XLloiNjcWmTZvQr18/AMC0adOc1/ix2WwwmUzo27ev8xxVqt1MJhPatWuHvLw8hISEAHBkKiYmBsXFxcjKykJMTEyZpmZ4eLivyiWdcSc/zZs3x7vvvou6dev6qmzSAXeyM2vWLEyfPh0xMTG+Kpt0wtX8GI1GvPbaa3jmmWec46n2ciU/vGg5Xc3V+cdkMmH69OmYMGECQkNDfVk66YDW/FzZ/K5Tpw7q1KnjkXrYkKJqER4e7mwSWCwW9OnTB5s3b8a9994LAM5veFiwYAHatWuHPn36sBlFToMHD8bgwYPL3CciCAgIgNFodL6hJiUl4cYbb0R0dLQvyiSdciU/sbGxiImJYTOKAGjPzvbt23HjjTeiRYsWaNy4sS9KJR1yJT+xsbG44YYb2IwiJ277kAp35h82o6iUnuYfnrJH1c7f3x82mw133XUXtmzZ4jx9b/To0XjllVf4yTJVSP74Slqr1QqbzYa6desiMDAQADB58mQMGTKE34RGFdKSn9JvLiK6UlXZefDBB+Hv7+/LEknHtOTHU58sU83HbR9SwfmHVOhh/uHsRtWi9FS8UqVfj927d29s2LAB9957L4KDg/Hdd9+hdevWPqyU9Kj0K9VLD0v38/ODwWBAUVERrFYrXn/9dcybNw9JSUmIiIjwcbWkN8wPuYvZIRXMD6lgfkgF80Mq9JQfg5S2xYhcVNqEKv1vdnY2fvjhB9x3333OoxCKi4vx6quvYsmSJfj+++8RFxfn46pJL6rKT2FhIf70pz8hJCQEe/fuxZ49e5CQkODrskknmB9yF7NDKpgfUsH8kArmh1ToNT88ZY80O3v2LFJSUrBnzx4AjguiWSwWmEwmHD9+HB07dkRKSkqZU2JSU1OxZs0afP3112xG1XKu5EdEcOnSJfz888/48ccfkZyczDfUWo75IXcxO6SC+SEVzA+pYH5IRY3JjxBpkJKSIjfddJO0adNGwsPD5YknnnD+LCcnR4KDg+Wpp54Su91e5nFFRUVy8eJFb5dLOuNufj788EM5cOCAt8slnWF+yF3MDqlgfkgF80MqmB9SUZPyw4YUVennn3+WJk2ayJQpU2Tfvn2yaNEiiYuLk4yMDBEROXXqlCxatEhsNpuPKyU9cic/zBKVYn7IXcwOqWB+SAXzQyqYH1JR0/JjSkxMTPTOsVhUE4kI5s2bh4YNG2L+/PmIjIxEREQENm/ejFtuuQXp6eno2LEjEhISnBdFIyrlbn6YJQKYH3Ifs0MqmB9SwfyQCuaHVNTE/PBb9qhSBoMBx44dQ2ZmpvO+pUuXIjk5GU899RTy8vLQpEkT7NixA4GBgc4r9hMBzA+pYX7IXcwOqWB+SAXzQyqYH1JRE/PDb9mjCpVegX/NmjWYNGkSYmNjERMTg2XLlmHt2rXo1KkT/P390atXL9x+++34+OOPfV0y6QjzQyqYH3IXs0MqmB9SwfyQCuaHVNTU/PCUPbqG3W6HwWBw3ho3bowOHTrAZDIhLy8P9957L8aNG4egoCAEBgYiNTUVZ8+exfDhw31dOukA80MqmB9yF7NDKpgfUsH8kArmh1TU9PzwlD0qIz09HYsWLcLFixcRHR2Np556CuHh4Rg0aBAGDRqEoUOH4syZMwAun2taUFCA8PBwZ1eWai/mh1QwP+QuZodUMD+kgvkhFcwPqbge8sMjpMgpLS0Nt99+O5o1a4bs7GwcPnwY//jHP3DTTTehZcuWMBqNyM7Oxt69e2E0GhEQEIB3330Xn332GRYuXIhmzZr5+k8gH2J+SAXzQ+5idkgF80MqmB9SwfyQiusmP776ej/SF6vVKsOGDZNHH31URETsdrucPn1axowZI/Xr15c1a9aIiMiBAwdkyJAh0rRpU2nTpo106tRJ9u/f78vSSQeYH1LB/JC7mB1SwfyQCuaHVDA/pOJ6yg9P2SMAjkP4zp07h+7duzvvCwsLw+LFi1G3bl2MHj0aN9xwAxISEjB37lycOnUKZrMZrVq10k93lXyG+SEVzA+5i9khFcwPqWB+SAXzQyqup/zwW/bIacSIEUhPT0dycjIMBoPzvFK73Y4HH3wQGRkZ2LVrFxo0aODrUkmHmB9SwfyQu5gdUsH8kArmh1QwP6TiesmP0dcFkO+V9iRHjBgBu92ON954AxaLBSaTCVarFUajEWPHjkVubi5Onjzp42pJb5gfUsH8kLuYHVLB/JAK5odUMD+k4nrLDxtS5Lzifu/evdG9e3d8+eWXmDNnDoqLi+Hn5zirMyYmBgBQUlLiszpJn5gfUsH8kLuYHVLB/JAK5odUMD+k4nrLDxtSBAAwm82oW7cuZsyYgYSEBKxevRrjx49HXl4esrOzsXz5cgQEBCAiIsLXpZIOMT+kgvkhdzE7pIL5IRXMD6lgfkjF9ZQfXtScYLPZEBAQgOPHjyM5ORmzZ8/GzJkzsWrVKoSGhqJ9+/bIycnBV199haZNm/q6XNIZ5odUMD/kLmaHVDA/pIL5IRXMD6m43vLDi5rXIpmZmSgqKkKbNm2c99ntdhiNRhw/fhzdunXDo48+irfffhs2mw1FRUXYtm0bmjRpgpiYGERHR/uwevI15odUMD/kLmaHVDA/pIL5IRXMD6moNfkRqhUyMzPFaDRKXFycHDlypMzPTp06JWFhYTJu3Dix2+0+qpD0jPkhFcwPuYvZIRXMD6lgfkgF80MqalN+eA2pWsJgMKBDhw4wm8247777cOTIkTI/e/nllzF37lznRdKIrsT8kArmh9zF7JAK5odUMD+kgvkhFbUpP2xI1QI2mw0mkwlhYWH46quvEBsbiwceeABHjx4FAOTm5uL55593XpWf6ErMD6lgfshdzA6pYH5IBfNDKpgfUlHb8nN9/BVUKZPJhPDwcAQHB+PcuXNYuXIlBg4ciPvuuw9t27aFzWbDsmXLEBQU5OtSSYeYH1LB/JC7mB1SwfyQCuaHVDA/pKK25YdHSNUC8sd16+12O7Zv347Q0FDs2rULubm5WL9+PZ544onrJtBU/ZgfUsH8kLuYHVLB/JAK5odUMD+korblhw2pWsButwMA+vTp47xv1KhRAID4+HhMmTIFqampPqmN9I/5IRXMD7mL2SEVzA+pYH5IBfNDKmpbfgxS2oKj60JGRga++OILXLx4ETfeeCNGjhzp/Nm6devw/vvvIzAwEDt37sS2bdvQsmVL3Hbbbahbty727NmDgIAAH1ZPvsb8kArmh9zF7JAK5odUMD+kgvkhFcwPryF1XTl06BD69euHuLg45OXlISUlBceOHcOUKVMAALGxsUhPT0e9evWwceNGdOzYEQCwe/duXLx48boINLmP+SEVzA+5i9khFcwPqWB+SAXzQyqYnz8IXRcyMjKkVatWMmnSJLHb7ZKfny8ffPCBtG/fXo4ePeoc99FHH0laWpoPKyU9Yn5IBfND7mJ2SAXzQyqYH1LB/JAK5ucyHiF1HbDb7Vi1ahVat26N1157DQaDAYGBgUhISMC5c+dQXFzsHPv444/7rlDSJeaHVDA/5C5mh1QwP6SC+SEVzA+pYH7KYkPqOmA0GnHzzTfDbrc7r7gvIrjpppsQGBiIixcv+rhC0jPmh1QwP+QuZodUMD+kgvkhFcwPqWB+yuK37F0nevTogcmTJwNwBNpgMMDf3x8GgwFFRUXOcdu2bXNeuZ+oFPNDKpgfchezQyqYH1LB/JAK5odUMD+XsSFVQ504cQIbNmzAwoULcerUKZjNZgCAzWaDwWCA1WpFQUEBrFYr6tWrBwD4+9//jrvvvhunT5/2ZemkA8wPqWB+yF3MDqlgfkgF80MqmB9SwfxUwvuXrSJVBw8elLCwMOnSpYs0atRIoqOj5cUXX3ReAM1ut4vFYpGCggKJiYmR/fv3y5tvvikNGzaU5ORkH1dPvsb8kArmh9zF7JAK5odUMD+kgvkhFcxP5diQqmEuXrwoCQkJ8tJLL8mFCxdERGTatGnSo0cPeeCBB+SXX34pM75r165yyy23SEBAQK0INFWO+SEVzA+5i9khFcwPqWB+SAXzQyqYn6qxIVXDHD9+XGJiYmTLli1l7l+6dKn07NlThg8fLqdOnRIRkQsXLkhwcLD4+flJSkqKL8olnWF+SAXzQ+5idkgF80MqmB9SwfyQCuanaryGVA1jMplQr149ZGdnAwCsVisAYNSoURgxYgRSU1Px9ddfAwBCQkLw3nvv4dChQ+jUqZPPaib9YH5IBfND7mJ2SAXzQyqYH1LB/JAK5qdqBhERXxdBrnnggQeQmZmJpKQkNGrUCFarFX5+fgCAhx9+GFlZWdizZw8AwG63w2hk35EuY35IBfND7mJ2SAXzQyqYH1LB/JAK5qdyteuvrYEKCgpw6dIl5OfnO+9bsmQJ8vLyMHToUJjNZmegAeCee+6BiKCkpAQAal2gqSzmh1QwP+QuZodUMD+kgvkhFcwPqWB+XFf7/uIaJC0tDUOGDMEdd9yBuLg4LFu2DHa7HU2aNMHy5cvx3//+F3fffTfS09NRXFwMANi3bx8CAwN9XDnpAfNDKpgfchezQyqYH1LB/JAK5odUMD/u4Sl7OpWWloaePXti1KhRuOWWW/Cf//wHc+fOxQ8//IAuXboAAFJTUzF8+HAUFhYiJCQEERER+Pbbb7Fz507Ex8f7+C8gX2J+SAXzQ+5idkgF80MqmB9SwfyQCubHfWxI6dCFCxfw6KOPol27dpg9e7bz/t69e6NTp06YPXs2RAQGgwEA8N577+HkyZOoV68eHnnkEbRt29ZXpZMOMD+kgvkhdzE7pIL5IRXMD6lgfkgF86PGr+oh5G0WiwW5ubl46KGHAFy+uFlsbCzOnz8PADAYDLDZbDCZTPjrX//qy3JJZ5gfUsH8kLuYHVLB/JAK5odUMD+kgvlRw2tI6VBYWBg+/fRT9OjRAwBgs9kAAJGRkWUudGYymXDp0iXnv3mwGwHMD6lhfshdzA6pYH5IBfNDKpgfUsH8qGFDSqdat24NwNFh9ff3B+AI95kzZ5xjZsyYgYULF8JqtQKA8zBAIuaHVDA/5C5mh1QwP6SC+SEVzA+pYH7cx1P2dM5oNDrPOTUYDDCZTACAqVOn4o033sD+/fvLfHUk0ZWYH1LB/JC7mB1SwfyQCuaHVDA/pIL5cZ0pMTEx0ddFUOVKQ71jxw4YjUakp6fjf//3f8tctZ+oIswPqWB+yF3MDqlgfkgF80MqmB9Swfy4hu25GqD03FN/f38sXLgQQUFB2LVrF7p27erjyqgmYH5IBfND7mJ2SAXzQyqYH1LB/JAK5sc1vIZUDXLPPfcAAPbs2YObb77Zx9VQTcP8kArmh9zF7JAK5odUMD+kgvkhFcyPNgbh5d1rlIKCAjRo0MDXZVANxfyQCuaH3MXskArmh1QwP6SC+SEVzE/V2JAiIiIiIiIiIiKv4il7RERERERERETkVWxIERERERERERGRV7EhRUREREREREREXsWGFBEREREREREReRUbUkRERERERERE5FVsSBERERERERERkVexIUVERERUgyQmJqJz586+LoOIiIhIiUFExNdFEBERERFgMBgq/fno0aMxb948lJSUIDQ01EtVEREREVU/NqSIiIiIdOL06dPO/1+1ahWmTp2K9PR053316tVDcHCwL0ojIiIiqlY8ZY+IiIhIJ8LDw5234OBgGAyGa+67+pS9xx9/HIMGDcKbb76JsLAwNGrUCNOmTYPVasVLL72Exo0bIyoqCkuWLCnzu7KysvDII48gJCQEoaGhGDhwIDIyMrz8FxMREVFtxYYUERERUQ23fft2ZGdnY8eOHZg5cyYSExMxYMAAhISE4IcffsC4ceMwbtw4ZGZmAgAKCwtx5513omHDhtixYwd27dqFhg0b4t5774XZbPbxX0NERES1ARtSRERERDVc48aNMWfOHLRt2xZjxoxB27ZtUVhYiFdffRWtW7fGK6+8goCAAOzevRsAsHLlShiNRixatAidOnVCXFwcPvroI5w4cQLffvutb/8YIiIiqhX8fF0AEREREanp0KEDjMbLnzOGhYWhY8eOzn+bTCaEhobi7NmzAIAff/wRv/76KwIDA8ssp7i4GL/99pt3iiYiIqJajQ0pIiIiohrO39+/zL8NBkO599ntdgCA3W5HQkICli1bds2ymjZt6rlCiYiIiP7AhhQRERFRLdO1a1esWrUKzZo1Q1BQkK/LISIiolqI15AiIiIiqmVGjBiBJk2aYODAgdi5cyeOHTuG7777DhMmTMDJkyd9XR4RERHVAmxIEREREdUy9evXx44dO9CiRQsMGTIEcXFxGDNmDIqKinjEFBEREXmFQUTE10UQEREREREREVHtwSOkiIiIiIiIiIjIq9iQIiIiIiIiIiIir2JDioiIiIiIiIiIvIoNKSIiIiIiIiIi8io2pIiIiIiIiIiIyKvYkCIiIiIiIiIiIq9iQ4qIiIiIiIiIiLyKDSkiIiIiIiIiIvIqNqSIiIiIiIiIiMir2JAiIiIiIiIiIiKvYkOKiIiIiIiIiIi8ig0pIiIiIiIiIiLyqv8HEQv9j9l0lgcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_job_counts_by_time(collection, start_time, end_time):\n",
    "    # Query to match the submitTime and endTime criteria\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gte\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    # Extract submitTime and jStatus fields\n",
    "    data = []\n",
    "    cursor = collection.find(query, {\"submitTime\": 1, \"jStatus\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'submitTime' in document and 'jStatus' in document:\n",
    "            data.append({\n",
    "                'submitTime': document['submitTime'],\n",
    "                'jStatus': document['jStatus']\n",
    "            })\n",
    "\n",
    "    return data\n",
    "\n",
    "def process_and_plot_job_counts(data):\n",
    "    # Convert list of dicts to DataFrame\n",
    "    df = pd.DataFrame(data)\n",
    "\n",
    "    # Convert 'submitTime' from epoch to datetime in CST\n",
    "    df['submitTime'] = pd.to_datetime(df['submitTime'], unit='s')\n",
    "    df['submitTime'] = df['submitTime'].dt.tz_localize('UTC').dt.tz_convert('America/Chicago')\n",
    "\n",
    "    # Set 'submitTime' as the index\n",
    "    df.set_index('submitTime', inplace=True)\n",
    "\n",
    "    # Separate data by 'jStatus'\n",
    "    df_done = df[df['jStatus'] == 'done']\n",
    "    df_exit = df[df['jStatus'] == 'exit']\n",
    "\n",
    "    # Resample data into 3-hour intervals and count the number of jobs\n",
    "    df_done_resampled = df_done.resample('2H').count()\n",
    "    df_exit_resampled = df_exit.resample('2H').count()\n",
    "\n",
    "    # Plot the number of jobs over time for 'done' and 'exit' statuses\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(df_done_resampled.index, df_done_resampled['jStatus'], marker='o', linestyle='-', label='done', color='green')\n",
    "    plt.plot(df_exit_resampled.index, df_exit_resampled['jStatus'], marker='o', linestyle='-', label='exit', color='red')\n",
    "\n",
    "    # Add plot title and labels\n",
    "    plt.title('Number of Jobs Over Time (2-Hour Bins) Separated by jStatus')\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Number of Jobs')\n",
    "    plt.grid(True)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig('job_counts_over_time.png', format='png')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400  # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract the job count data by jStatus\n",
    "    data = extract_job_counts_by_time(collection, start_time, end_time)\n",
    "    \n",
    "    # Process and plot the data\n",
    "    process_and_plot_job_counts(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "99dea8f0-a3b6-49ab-97e4-e437f9077ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:\n",
      "High CPU Utilization   False   True \n",
      "Job Status                          \n",
      "done                  514802  409000\n",
      "failed                 29526  110937\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fisher's Exact Test Odds Ratio: 4.729211308354147, p-value: 0.0\n",
      "Chi-squared statistic: 58772.89880733256\n",
      "p-value: 0.0\n",
      "Degrees of freedom: 1\n",
      "Expected frequencies:\n",
      "[[472486.92295246 451315.07704754]\n",
      " [ 71841.07704754  68621.92295246]]\n"
     ]
    }
   ],
   "source": [
    "import pymongo\n",
    "import pandas as pd\n",
    "from scipy.stats import fisher_exact, chi2_contingency\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_cpu_utilization_by_status(collection, start_time, end_time):\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gte\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    data = []\n",
    "\n",
    "    cursor = collection.find(query, {\"cpuTime\": 1, \"numProcessors\": 1, \"runTime\": 1, \"exitStatus\": 1, \"_id\": 0})\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'cpuTime' in document and 'numProcessors' in document and 'runTime' in document and 'exitStatus' in document:\n",
    "            cpu_time = document['cpuTime']\n",
    "            num_processors = document['numProcessors']\n",
    "            run_time = document['runTime']\n",
    "            exit_status = document['exitStatus']\n",
    "\n",
    "            if num_processors > 0 and run_time > 0:  # Avoid division by zero\n",
    "                cpu_utilization = cpu_time / (num_processors * run_time)\n",
    "                status = 'done' if exit_status == 0 else 'failed'\n",
    "                data.append({'cpuUtilization': cpu_utilization, 'jStatus': status})\n",
    "\n",
    "    return data\n",
    "\n",
    "def create_contingency_table(data, cutoff=0.7):\n",
    "    # Convert to DataFrame\n",
    "    df = pd.DataFrame(data)\n",
    "    \n",
    "    # Ensure there is data\n",
    "    if df.empty:\n",
    "        print(\"No data available for the given time range.\")\n",
    "        return None\n",
    "    \n",
    "    # Categorize CPU Utilization\n",
    "    df['cpuUtilizationCategory'] = df['cpuUtilization'] >= cutoff\n",
    "    \n",
    "    # Create a contingency table\n",
    "    contingency_table = pd.crosstab(df['jStatus'], df['cpuUtilizationCategory'], rownames=['Job Status'], colnames=['High CPU Utilization'])\n",
    "    \n",
    "    return contingency_table\n",
    "\n",
    "def perform_fishers_exact_test(table):\n",
    "    if table.shape == (2, 2):  # Ensure 2x2 table for Fisher's test\n",
    "        oddsratio, p_value = fisher_exact(table)\n",
    "        return oddsratio, p_value\n",
    "    else:\n",
    "        print(\"Fisher's Exact test requires a 2x2 contingency table.\")\n",
    "        return None, None\n",
    "\n",
    "def perform_chi_squared_test(table):\n",
    "    chi2, p_value, dof, expected = chi2_contingency(table)\n",
    "    return chi2, p_value, dof, expected\n",
    "\n",
    "def plot_contingency_table(table):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(table, annot=True, fmt='d', cmap='YlGnBu', cbar=False)\n",
    "    plt.title('Contingency Table: Job Status vs. CPU Utilization (cutoff at 0.7)')\n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400  # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract CPU utilization data separated by job status\n",
    "    data = extract_cpu_utilization_by_status(collection, start_time, end_time)\n",
    "    \n",
    "    if data:\n",
    "        # Create and plot the contingency table\n",
    "        contingency_table = create_contingency_table(data)\n",
    "        \n",
    "        if contingency_table is not None:\n",
    "            print(\"Contingency Table:\")\n",
    "            print(contingency_table)\n",
    "            plot_contingency_table(contingency_table)\n",
    "            \n",
    "            # Perform Fisher's Exact test if it's a 2x2 table\n",
    "            oddsratio, fisher_p_value = perform_fishers_exact_test(contingency_table)\n",
    "            if fisher_p_value is not None:\n",
    "                print(f\"Fisher's Exact Test Odds Ratio: {oddsratio}, p-value: {fisher_p_value}\")\n",
    "            \n",
    "            # Perform Chi-squared test\n",
    "            chi2, p_value, dof, expected = perform_chi_squared_test(contingency_table)\n",
    "            print(f'Chi-squared statistic: {chi2}')\n",
    "            print(f'p-value: {p_value}')\n",
    "            print(f'Degrees of freedom: {dof}')\n",
    "            print('Expected frequencies:')\n",
    "            print(expected)\n",
    "    else:\n",
    "        print(\"No data found for the specified query.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "beb0f3bf-01a4-4104-ba8f-4b194950c3ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fisher's Exact Test p-value: 0.0\n",
      "Chi-squared statistic: 261413.9993046451\n",
      "p-value: 0.0\n",
      "Degrees of freedom: 1\n",
      "Expected frequencies:\n",
      "[[108243.40186745 815995.59813255]\n",
      " [ 17687.59813255 133338.40186745]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import fisher_exact, chi2_contingency\n",
    "import re\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_efficiency_by_status(collection, start_time, end_time):\n",
    "    # Query to match the submitTime and endTime criteria\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    # Extract the ratio of maxRMem to (requestedMemory * numProcessors) and group by jStatus\n",
    "    success_data = []\n",
    "    failure_data = []\n",
    "\n",
    "    # Regular expression to extract the memory value from the resReq field\n",
    "    mem_pattern = re.compile(r\"mem=(\\d+\\.?\\d*)\")\n",
    "\n",
    "    # Fetch maxRMem, resReq, numProcessors, and jStatus fields\n",
    "    cursor = collection.find(query, {\"maxRMem\": 1, \"resReq\": 1, \"numProcessors\": 1, \"jStatus\": 1, \"_id\": 0})\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'maxRMem' in document and 'resReq' in document and 'numProcessors' in document:\n",
    "            max_rmem = document['maxRMem']\n",
    "            res_req = document['resReq']\n",
    "            num_processors = document['numProcessors']\n",
    "\n",
    "            # Parse the requested memory from the resReq field\n",
    "            match = mem_pattern.search(res_req)\n",
    "            if match:\n",
    "                requested_mem = float(match.group(1))  # Requested memory is in MB\n",
    "\n",
    "                if requested_mem > 0:  # Avoid division by zero\n",
    "                    # Adjust requested memory for the number of processors\n",
    "                    requested_mem_adjusted = requested_mem * num_processors\n",
    "                    \n",
    "                    # Calculate memory efficiency\n",
    "                    memory_efficiency = max_rmem / (requested_mem_adjusted * 1024)  # Convert requested memory to KB\n",
    "\n",
    "                    if 'jStatus' in document:\n",
    "                        if document['jStatus'] == 'done':\n",
    "                            success_data.append(memory_efficiency)  # Success job memory efficiency\n",
    "                        else:\n",
    "                            failure_data.append(memory_efficiency)  # Failed job memory efficiency\n",
    "\n",
    "    return success_data, failure_data\n",
    "\n",
    "def perform_fishers_exact_test(success_data, failure_data):\n",
    "    # Define cutoff for high/low memory efficiency\n",
    "    cutoff = 0.8\n",
    "    \n",
    "    # Create a 2x2 contingency table\n",
    "    success_high = sum(efficiency > cutoff for efficiency in success_data)\n",
    "    success_low = len(success_data) - success_high\n",
    "    failure_high = sum(efficiency > cutoff for efficiency in failure_data)\n",
    "    failure_low = len(failure_data) - failure_high\n",
    "    \n",
    "    table = np.array([[success_high, success_low],\n",
    "                      [failure_high, failure_low]])\n",
    "    \n",
    "    # Perform Fisher's Exact Test\n",
    "    _, p_value = fisher_exact(table, alternative='two-sided')\n",
    "    \n",
    "    return table, p_value\n",
    "\n",
    "def perform_chi_squared_test(contingency_table):\n",
    "    # Perform Chi-squared Test\n",
    "    chi2, p_value, dof, expected = chi2_contingency(contingency_table)\n",
    "    \n",
    "    return chi2, p_value, dof, expected\n",
    "\n",
    "def plot_contingency_table(contingency_table):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(contingency_table, annot=True, fmt='d', cmap='Blues', cbar=False, \n",
    "                xticklabels=['High Efficiency', 'Low Efficiency'], \n",
    "                yticklabels=['Success', 'Failure'])\n",
    "    plt.title('Contingency Table: jStatus vs Memory Efficiency')\n",
    "    plt.xlabel('Memory Efficiency')\n",
    "    plt.ylabel('Job Status')\n",
    "    plt.show()\n",
    "\n",
    "def main():\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract memory efficiency data separated by job status\n",
    "    success_data, failure_data = extract_memory_efficiency_by_status(collection, start_time, end_time)\n",
    "\n",
    "    # Perform Fisher's Exact test\n",
    "    contingency_table, fisher_p_value = perform_fishers_exact_test(success_data, failure_data)\n",
    "    \n",
    "    print(f'Fisher\\'s Exact Test p-value: {fisher_p_value}')\n",
    "    \n",
    "    # Perform Chi-squared test\n",
    "    chi2, chi2_p_value, dof, expected = perform_chi_squared_test(contingency_table)\n",
    "    \n",
    "    print(f'Chi-squared statistic: {chi2}')\n",
    "    print(f'p-value: {chi2_p_value}')\n",
    "    print(f'Degrees of freedom: {dof}')\n",
    "    print('Expected frequencies:')\n",
    "    print(expected)\n",
    "\n",
    "    # Plot the contingency table\n",
    "    plot_contingency_table(contingency_table)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "eb60f62d-d8aa-4eea-9d92-688fc2eecce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-squared statistic: 5.486412472266921\n",
      "p-value: 0.01916482936008161\n",
      "Degrees of freedom: 1\n",
      "Expected frequencies:\n",
      "[[ 72030.94156138 543007.05843862]\n",
      " [ 53900.05843862 406326.94156138]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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+Pub48ePWthkzZtidILjbPkrpx9PT06Zvaq+tfPnymfr169u0pXQ8VK9e3WTNmtWcOHEi1XpSCqvR0dGmTJky1n9ekjVo0MDkypXLuu/Sehzt3bvXeHp62gX8O7Vr186EhoaahIQEa9uwYcOMh4eHzUkBpA3DAGC1dOlSSbL7iLV8+fIqWrSo3ce/OXPmVPny5W3aSpYsqQMHDjxQHaVLl1bevHmtt/38/FS4cGGb5S5btkzVq1dXjhw5rG0eHh5q3ry5zbIWL16smzdvqm3btrp586b1x8/PT3FxcXYfF1ksFjVs2PCu27Rw4UL5+fml+hFgci0vvfSS5s+fr4MHD0qS9uzZo0WLFqlr164PPAvCuHHjVLZsWfn5+cnLy0ve3t766aeftG3bNru+TZs2lZ+fn/V2pkyZ1LBhQ/36669KTExMcfkrV67UmTNn1K5dO5v9lpSUpDp16mjdunW6fPmytf/NmzfTNDwge/bsWr58udatW6ehQ4eqcePG2rlzp9544w2VKFFCp06duo+9ccudY+EqVaqkfPnyWV/XyXLlyqVHHnnEejs4OFihoaEqXbq0wsPDre1FixaVpLu+ngsWLKhs2bKpd+/eGjdunLZu3WrXJ3koTPPmzTVz5kz9/fff99yWy5cva82aNXrqqacUFBRkbff09FSbNm10+PBh7dixw+Yxtw/nkW69bu9VvzPUq1fPZghH8n67c6hLcnvy8eDosXmnVq1aydfX1+aj4mnTpikhIUEdOnSwtpUvX16ff/65Bg8erNWrV+vGjRsOb2NcXJyioqI0ceJE/fnnn1q3bl2qx//8+fOVNWtWNWzY0Ga7SpcurZw5c9ptV+nSpZU7d27r7eT9VLVqVZsxpXe+Hnfs2KEjR46oTZs2Nvs/KChI//nPf7R69WpduXLFZl3/+c9/7Ort0qWLpFsX4iYbM2aMSpQooccff/ye+0aSJk+erHXr1tn8rFmzJk2PTYsrV65o2bJlat68uUNjxnfv3q3t27db3xtufz7q1auno0ePOnwcLVmyRImJiXrxxRfvuu7u3bvrxIkTmjVrlqRbw7HGjh2r+vXrp2k2F9girD7EcuTIoYCAAO3bty9N/U+fPi1JKV7pHB4ebr0/Wfbs2e36+fr66urVq/dRrWPLPX36tMLCwuz63dl2/PhxSbcCg7e3t83PjBkz7MJRQECATbBLXvft44tOnjyp8PBwuzGWd+rYsaP8/f01btw4SdJHH30kf3//u4bctBgxYoS6dOmiChUq6JtvvtHq1au1bt061alTJ8V9nzNnzhTbrl+/rkuXLqW4juT99tRTT9ntt2HDhskY80BTnsXGxqp3796aNWuWjhw5oldeeUX79+9/oIusUtvOO1+3yVch387Hx8eu3cfHR5LuOrYsS5YsWrZsmUqXLq0333xTxYoVU3h4uOLj462h6PHHH9ecOXOswSxPnjwqXry4zRjIO509e1bGmFSPRUn3PB6Tx6g/6PF4L6ntt3vtT0ePzZTW26hRI02ePNn6T9fnn3+u8uXLq1ixYtZ+M2bMULt27fTZZ5+pYsWKCg4OVtu2bXXs2LE0b6PFYlGHDh305Zdfaty4cSpcuLCqVKmSYt/jx4/r3Llz8vHxsduuY8eO2W3X/e6/e71fJyUl6ezZszbtKfUNCwtTixYt9MknnygxMVGbN2/W8uXL9dJLL6W6P+5UtGhRxcbG2vzc/g/hgzp79qwSExMdviA3+TX22muv2T0XXbt2lSS75+Nex1HyuON71VKmTBlVqVJFH330kaRb/8Ts37/fof2K/2E2gIeYp6enatSooYULF+rw4cP3PLiSD9KjR4/a9T1y5IjNWUxXy549u/WN6HZ3/gFKrvnrr79Wvnz5nLLukJAQrVixQklJSXcNrFmyZLH+kXzttdc0adIkPfPMM8qaNesDrf/LL79U1apVNXbsWJv2ixcvptg/pT/Kx44dk4+Pj81Zu9sl77fRo0enenVtSv8s3A9vb2/Fx8frgw8+0JYtW6ztvr6+dhe2SfYhLVlq21mwYEGn1JmaEiVKaPr06TLGaPPmzfr88881aNAg+fv7q0+fPpKkxo0bq3HjxkpISNDq1as1ZMgQPfPMM4qMjFTFihXtlpktWzZ5eHjo6NGjdvclX+zhTsfj/XDGsdmhQwfNmjVLS5YsUd68ebVu3Tq74yJHjhwaOXKkRo4cqYMHD2ru3Lnq06ePTpw44dBMKO3bt1f//v01btw4vfPOO3fdruzZs6e6bGdNdXX7+/Wdjhw5Ig8PD2XLls2mPbVPdLp3764pU6bou+++06JFi5Q1a1a7TypcKTg4WJ6ennYX+t5L8mvsjTfeUNOmTVPsU6RIEYeWmXxm9/Dhw4qIiLhr327duqlZs2basGGDxowZo8KFC6tmzZoOrQ+3cGb1IffGG2/IGKPnnntO169ft7v/xo0bmjdvniSpevXqkm6FodutW7dO27ZtU40aNRxef3qd3YmLi9PPP/9s819xUlKS9SOXZLVr15aXl5f27Nlj959/8o+j6tatq2vXrqVpwupu3brp1KlTeuqpp3Tu3Dmn/FdtsVjsZnbYvHlzilf0StLs2bNtzg5evHhR8+bNU5UqVeTp6ZniYypXrqysWbNq69atqe635DM9jkjpD6sk6/CF2z+Gj4yM1ObNm236/fzzz6meDb5zbtCVK1fqwIED/9iE7RaLRaVKldIHH3ygrFmzasOGDXZ9fH19FRcXp2HDhklSqle9BwYGqkKFCpo9e7bNsZOUlKQvv/xSefLkUeHChdNnQ/4hzjg2a9Wqpdy5c2vSpEmaNGmS/Pz8rFfvpyRv3rx66aWXVLNmzRSfn7vJnTu3evXqpYYNG6pdu3ap9mvQoIFOnz6txMTEFLfJ0XCUmiJFiih37tz66quvbGYduXz5sr755hvrDAFp8cgjj6hSpUoaNmyYpk6dqvbt2yswMNApdTqDv7+/4uLiNGvWLIeGChUpUkSFChXSH3/8keprzNF/HmrVqiVPT0+7f4pSkvzlJz179tSPP/7olCFgGRVnVh9yFStW1NixY9W1a1c98sgj6tKli4oVK6YbN25o48aNGj9+vIoXL66GDRuqSJEi6ty5s0aPHi0PDw/VrVtX+/fvV79+/RQREaFXXnnF4fVHRUXJ399fU6dOVdGiRRUUFKTw8HCbUHI/+vbtq3nz5qlGjRrq27ev9eP25HGUyWc8IyMjNWjQIPXt21d79+5VnTp1lC1bNh0/flxr165VYGCgBg4c6NC6n376aU2aNEkvvPCCduzYoWrVqikpKUlr1qxR0aJF1bJlS2vfwoULq06dOlq4cKEee+wxlSpV6oG2W7r1x/Dtt99WfHy84uLitGPHDg0aNEj58+fXzZs37fp7enqqZs2aevXVV5WUlKRhw4bpwoULd93uoKAgjR49Wu3atdOZM2f01FNPKTQ0VCdPntQff/yhkydP2rxZe3l5KS4u7p7jVmvXrq08efKoYcOGio6OVlJSkjZt2qT3339fQUFB6t69u7VvmzZt1K9fP/Xv319xcXHaunWrxowZoyxZsqS47PXr1+vZZ59Vs2bNdOjQIfXt21e5c+e2ftyXHubPn6+PP/5YTz75pAoUKCBjjGbPnq1z585Zz6D0799fhw8fVo0aNZQnTx6dO3dOH374oby9vRUXF5fqsocMGaKaNWuqWrVqeu211+Tj46OPP/5YW7Zs0bRp0/71f/SccWx6enqqbdu2GjFihDJnzqymTZvavD7Onz+vatWq6ZlnnlF0dLQyZcqkdevWadGiRameabuboUOH3rNPy5YtNXXqVNWrV0/du3dX+fLl5e3trcOHD2vp0qVq3LixmjRp4vC67+Th4aHhw4erVatWatCggZ5//nklJCTov//9r86dO5emWm/XvXt3tWjRQhaLxeFjZsuWLSm+90RFRTltXuIRI0boscceU4UKFdSnTx8VLFhQx48f19y5c/XJJ5+kGjo/+eQT1a1bV7Vr11b79u2VO3dunTlzRtu2bdOGDRvsTnDcS2RkpN588029/fbbunr1qp5++mllyZJFW7du1alTp2xes56ennrxxRfVu3dvBQYGpjjlHtKGsJoBPPfccypfvrw++OADDRs2TMeOHZO3t7cKFy6sZ555xuZs39ixYxUVFaUJEyboo48+UpYsWVSnTh0NGTIkxbGk9xIQEKCJEydq4MCBqlWrlm7cuKH4+PgU5890RKlSpbRkyRK99tpratu2rbJly6Y2bdooLi5OvXv3tvmD9cYbbygmJkYffvih9QKMnDlzqly5cnrhhRccXreXl5e+//57DRkyRNOmTdPIkSOVKVMmlSpVKsVv5GrRooUWLlzotLFKffv21ZUrVzRhwgQNHz5cMTExGjdunL799tsUL0p56aWXdO3aNXXr1k0nTpxQsWLFtGDBAlWuXPmu62ndurXy5s2r4cOH6/nnn9fFixetFyLd+aabmJiY6sVat3vrrbf03Xff6YMPPtDRo0eVkJCgXLly6YknntAbb7xhvYhEknr16qULFy7o888/13vvvafy5ctr5syZaty4cYrLnjBhgqZMmaKWLVsqISFB1apV04cffpjiGFVnKVSokLJmzarhw4fryJEj8vHxUZEiRfT5559bz75VqFBB69evV+/evXXy5EllzZpVsbGx+vnnn23GVt4p+dOD+Ph4tW/fXklJSSpVqpTmzp2rBg0apNs2/ZOccWx26NBBQ4YM0cmTJ20urJJuXZxZoUIFTZkyRfv379eNGzeUN29e9e7dW6+//np6bJI8PT01d+5cffjhh5oyZYqGDBkiLy8v5cmTR3FxcSpRooTT1vXMM88oMDBQQ4YMUYsWLeTp6alHH31US5cuVaVKlRxa1pNPPilfX19Vq1ZNhQoVcuixd+73ZJ9++qmeffZZh5aVmlKlSmnt2rWKj4/XG2+8oYsXLypnzpyqXr36XT/lqVatmtauXat33nlHPXr00NmzZ5U9e3bFxMTYXZCbVoMGDVKhQoU0evRotWrVSl5eXipUqJC6detm17dFixbq3bu32rRpk+o/2rg3i7n98wPgX65WrVrav3+/du7c6epSrJKvzN2/f7+8vb1dXQ4A2Jk3b54aNWqkBQsWqF69eq4u56ExevRodevWTVu2bLnrP6e4O86s4l/r1VdfVZkyZRQREaEzZ85o6tSpWrJkSYrfavNPS0hI0IYNG7R27Vp9++23GjFiBEEVgNvZunWrDhw4oJ49e6p06dKqW7euq0t6KGzcuFH79u3ToEGD1LhxY4LqAyKs4l8rMTFR/fv317Fjx2SxWBQTE6MpU6aodevWri5NR48eVaVKlZQ5c2Y9//zzevnll11dEgDY6dq1q3777TeVLVtWX3zxxb9+LLS7aNKkiY4dO6YqVapYpy/E/WMYAAAAANwWU1cBAADAbRFWAQAA4LYIqwAAAHBbhFUAAAC4rYdyNoDzV5NcXQIAOFXOSvYTjgPAv9nVjWPS1I8zqwAAAHBbhFUAAAC4LcIqAAAA3BZhFQAAAG6LsAoAAAC3RVgFAACA2yKsAgAAwG0RVgEAAOC2CKsAAABwW4RVAAAAuC3CKgAAANwWYRUAAABui7AKAAAAt0VYBQAAgNsirAIAAMBtEVYBAADgtgirAAAAcFuEVQAAALgtwioAAADcFmEVAAAAbouwCgAAALdFWAUAAIDbIqwCAADAbRFWAQAA4LYIqwAAAHBbhFUAAAC4LcIqAAAA3BZhFQAAAG6LsAoAAAC3RVgFAACA2yKsAgAAwG0RVgEAAOC2CKsAAABwW4RVAAAAuC3CKgAAANwWYRUAAABui7AKAAAAt0VYBQAAgNsirAIAAMBtEVYBAADgtgirAAAAcFuEVQAAALgtwioAAADcFmEVAAAAbouwCgAAALdFWAUAAIDbIqwCAADAbRFWAQAA4LYIqwAAAHBbhFUAAAC4LcIqAAAA3BZhFQAAAG6LsAoAAAC3RVgFAACA2yKsAgAAwG0RVgEAAOC2CKsAAABwW4RVAAAAuC3CKgAAANwWYRUAAABui7AKAAAAt0VYBQAAgNsirAIAAMBtEVYBAADgtgirAAAAcFuEVQAAALgtwh7Uwo0AACAASURBVCoAAADcFmEVAAAAbouwCgAAALdFWAUAAIDbIqwCAADAbRFWAQAA4LYIqwAAAHBbhFUAAAC4LcIqAAAA3JaXq1Y8efLkNPVr27ZtOlcCAAAAd2UxxhhXrNjDw0NBQUHy8vJSaiVYLBadOXPG4WWfv5r0oOUBgFvJWambq0sAAKe6unFMmvq57Mxq0aJFdfz4cbVu3VodO3ZUyZIlXVUKAAAA3JTLxqz+9ddfWrBgga5evarHH39csbGxGjt2rC5cuOCqkgAAAOBmXHqBVYUKFfTJJ5/o6NGj6tatm2bOnKlcuXKpVatWSkhIcGVpAAAAcANuMRuAv7+/2rZtq4EDB6p8+fKaPn26rly54uqyAAAA4GIuD6t///233n33XRUqVEgtW7ZUuXLl9NdffylbtmyuLg0AAAAu5rILrGbOnKlJkyZp2bJlql27tt5//33Vr19fnp6erioJAAAAbsalU1flzZtXrVq1UlhYWKr9unVzfLoWpq4C8LBh6ioAD5u0Tl3lsrAaGRkpi8Vy1z4Wi0V79+51eNmEVQAPG8IqgIeN28+zun//fletGgAAAP8SLr3AyhijXbt2aevWrbp586YrSwEAAIAbcllY3b9/v0qXLq3o6GiVKFFCBQsW1O+//+6qcgAAAOCGXBZWe/furWvXrmnKlCmaNWuWcuXKpS5duriqHAAAALghl41ZXb58uaZNm6a4uDhJUvny5ZUvXz5dvXpV/v7+rioLAAAAbsRlZ1aPHTum6Oho6+08efLI399fx48fd1VJAAAAcDMuC6sWi0UeHrar9/DwkItm0gIAAIAbctkwAGOMChcubDPX6qVLl1SmTBmbEHvmzBlXlAcAAAA34LKwOmnSJFetGgAAAP8SLgur7dq1c9WqAQAA8C/h0i8FAAAAAO6GsAoAAAC3RVgFAACA2yKsAgAAwG0RVgEAAOC2XDYbwJ3Onj2rL774Qrt27VKuXLnUrl07RUREuLosAAAAuJDLzqyGh4fr9OnTkqR9+/YpJiZGw4YN065du/TJJ5+oRIkS2r59u6vKAwAAgBtwWVg9duyYEhMTJUlvvvmmoqOjtWfPHv3www/avXu3qlSpon79+rmqPAAAALgBtxizumbNGvXr108BAQGSJF9fX7311ltavXq1iysDAACAK7k0rFosFklSQkKCwsLCbO4LCwvTyZMnXVEWAAAA3IRLL7CqUaOGvLy8dOHCBe3cuVPFihWz3nfw4EHlyJHDhdUBAADA1VwWVuPj421uJw8BSDZv3jxVqVLlnywJAAAAbsZijDGuLsLZzl9NcnUJAOBUOSt1c3UJAOBUVzeOSVM/t7jACgAAAEiJ23wpAJDeGtetoaNHj9i1P9X8ab3+Zn8t/ekHzf56prZv+0vnz53Tl9Nnq3B0Ubv+m//YqLFjPtRff26Wl5eXCheJ1siPxsvPz8+m3/Xr19WhdQvt2rk91WWdO3dWrZs30YkTx/XTr2uUKXNm520wgIdO3+fr6a0X6tm0HTt1QflrvmnXd3Tflnr2qcfU679fa8xXv9i0V69QRLlCsujS1QSt/mOf3vrwO+3cf9zaZ9bI51WqcG6FBGfS2QtXtHTNDr016jsdPXne2qdq+cKK79pAxQqG69KVBH01f63iP5qnxET7TzcLROTQ6ml9lJiUpFyPv/7gOwIZCmEVGcbnU2cpMSnRenvv7l166YVOqlGzjiTp6tWrKlW6jGrUrK13B/VPcRmb/9io7i92VvuOnfVa777y9vbWrp075OFh/yHF6A/eU0hIiHbtTP3LLQYP6KeChQrrxInjqfYBgNv9tfuI6r8w2no7Mcl+NF/DqiVVrkSkjpw4Z3ffxm2HNH3hOh06elbBWQLU94X6mv/xi4puEK+k/1/Wr+t26r8TFuvYqfMKD82qIa800Vf/7aRq7UdIkooXCtec0V00bMJideo3WeGhWTX6zZby9PTQGx98a7M+Ly8PTR7SQb9t3KNHS+V35q5ABkFYRYaRLTjY5vbkiZ8qT0RelY0tJ0mq16CxJOnI33+nuoyR7w1Vi6dbq13H56xtefNF2vVbueJXrVn9m4a+96FW/rY8xWV9PXOaLl28oE7Pd021DwDc6WZiko6fvpjq/eEhWfRBn2Zq2PUjfTu6i939E2f/Zv394NEzGvjRPK2b+abyhWfXvsOnJEmjpy69rc9ZvTdpiWaOeE5eXh66eTNJzWo/oi27jmjI+EWSpL2HTqn/6Ln6Ykh7vfPJ97p0JcH6+AFdG2rHvuNaunYHYRX3hTGryJBu3Liuhd/PU8PGTa3z/d7LmTOnteXPzcoWnF2d2j6tOtUf0/Od2mjTxt9t+p0+fUrvDuqvAYOHyc/PP8Vl7d2zWxPGf6wBg4fKw8JhCCDtCuYN0d4f3tG2+QM0eWgHRebObr3PYrFowuC2+uCLn7Rt77F7LivAz0dtGz2qfYdP6fCxsyn2yZY5QC3rxmr1H/t08+atj/h9fbx0LeGGTb+rCTfk7+ejMkXzWtviyhVW05pl1GPozPvZVECSm4TVKVOmqHLlygoPD9eBAwckSSNHjtR33313z8cmJCTowoULNj8JCQn3fBwytl9+/kmXLl5Ug0ZN0vyYvw8fkiR9Om6MnmzaTB9+PF5FomP0YucOOnhgvyTJGKNB/d9Uk2YtFFOseIrLuX79ut564zV1e6WXcuYKf+BtAZBxrNuyX8/2m6KGXT9S17enKSx7Zi39vKeCswRKknp2qKmbiUn6aNovd11O52ZVdPK393V61QjVrBSj+l3G6MbNRJs+g7s11qmV7+vIsuGKyBWsZq+Mt963ZOU2PVqqgJrXeUQeHhaFh2RRn2drS5Jyhdwaex+cJVCfDmyt5+Kn6OLla07cC8hoXB5Wx44dq1dffVX16tXTuXPnlJh462DJmjWrRo4cec/HDxkyRFmyZLH5GfHfoeldNv7l5s75RhUrV1FIaGiaH2P+fyxX0/+0UMMnm6pIdIxe7fWG8kXm17zvZkuSZk77UpcvXVL7jp1TXc5Ho0Yof/4Cqlu/0YNtBIAM54fftmrOT5v01+4jWrpmh5q8PFaS1LphBZUpGqEXn66qzvFf3nM50xeu06NPD9UTnT7Q7kMn9eWwjvL1sR0Z+MHkH/Voy2Gq/8IYJSYm6bO321jv+2n1dr05co5GvdlS59eM1Obv+mvRir8kyXqB1cf9ntaMRev124Y9ztp8ZFAun2c1JiZG7777rp588kllypRJf/zxhwoUKKAtW7aoatWqOnXq1F0fn5CQYHcm9VqSt3x9fdOzbPyLHT3yt5o0qKVh749SXLUadvcf+ftvPVn/Cbsr+P/++7Ca1K+pge8Mswmab77+ijw9vfT2kP/qtR4vacWvS22GFiQmJsrT01O16zbQgMFD1ap5E+3ZvdPaxxijpKQkeXp6qkOn59W568vpuPX4t2KeVaRm/tiXtOfQSe3af1zDeja1XiQlSV5enkpMTNLh42cVXT8+xcd7e3nq6K/D1XXQV5q56PcU++QOzardiwerarv3tWbzPpv7coVk0dkLV5QvPFibZvfTY62G6/etB3X01+EK8v/f32KLxSJPTw/dvJmoFwdP0+TvVjth6/FvltZ5Vl1+gdW+fftUpkwZu3ZfX19dvnz5no/39fW1C6aGLwXAXcz77ltlCw5W5SpxDj0uPDy3QkJCdWC/7Rv1wQMHVKnyrW9be633m+ry0v9CxckTJ9Wt67N6Z9gIFStRUpI07P0PlZDwv4/Etm7ZorcH9NUnE6coT0ReAUBa+Xh7KTp/mH7buFtfLVinn9fssLl/3scv6qsFa+8ZDC2yyMc79UiQ/P93Sn2Sp7NqXidWh46e0cbtt4ZMVW33vjxvmymlQdWS6tn+CVVrPyLFWQqA1Lg8rObPn1+bNm1Svnz5bNoXLlyomJgYF1WFh1VSUpLmz52t+g2flJeX7cv//PlzOn70qE6ePCFJOnDgVigNzpFDOXKEyGKxqHW7jho/bowKFY5W4SLRWjBvjg7s36uh790asnLnGFR//1vjyPLkiVBYWM5bv98RSM+dvfWmnT9/FPOsArirIa800YJf/9Sho2cVGhyk3s/WUaZAP02dt0Znzl/WmfO2J3lu3EzU8VMXtOvArfe1yNzZ9VTtR/TTqm06dfaSwkOzqmf7J3Q14YYW///H+LHF8im2eD6t3LhH5y5eUWTuHOrfpb72HDxpc1b1lbY19MPKbUpKSlLjGqX1Woeaav36ROuZ3R37bKfkKxuTV0nGaOueo+m5i/AQcnlY7dWrl1588UVdu3ZNxhitXbtW06ZN05AhQ/TZZ5+5ujw8ZNauXqVjR4+q4ZNN7e5b/stSDYr/38TafXv3lCQ9+/yL6tzlJUnS063b6fr16/rgvaG6cP68ChUuotHjJnBGFMA/IndYVk0e0kHZswbq1NlLWvvnfsW1e18Hj6Z8Jf+dEq7fVOUyUXrpmarKljlAJ05f1IoNu1Wt/fs6efaSpFtX9TeuXkpvvVBfgf4+OnbqvH5YuU1t+0zS9Rs3rcuqVTlGrz9bW77eXvpz599q9sp4/fDb1nTZbmRsLh+zKkmffvqpBg8erEOHbn10kDt3bg0YMECdOnW6r+WdZxgAgIcMY1YBPGz+FWNWb968qalTp6phw4Z67rnndOrUKSUlJSnUgSu0AQAA8PBy6dRVXl5e6tKli/Vq/hw5chBUAQAAYOXyeVYrVKigjRs3uroMAAAAuCGXX2DVtWtX9ezZU4cPH9YjjzyiwMBAm/tLlizposoAAADgai6/wMrDw/7krsVikTFGFovF+o1WjuACKwAPGy6wAvCw+VdcYCXd+lIAAAAAICUuD6t3fhkAAAAAkMzlYVWS9uzZo5EjR2rbtm2yWCwqWrSounfvrqioKFeXBgAAABdy+WwAixcvVkxMjNauXauSJUuqePHiWrNmjYoVK6YlS5a4ujwAAAC4kMsvsCpTpoxq166toUOH2rT36dNHP/zwgzZs2ODwMrnACsDDhgusADxs0nqBlcvPrG7bti3Fr1Xt2LGjtm7lO4YBAAAyMpeH1ZCQEG3atMmufdOmTXybFQAAQAbn8gusnnvuOXXu3Fl79+5VpUqVZLFYtGLFCg0bNkw9e/Z0dXkAAABwIZePWTXGaOTIkXr//fd15MgRSVJ4eLh69eqlbt26yWKxOLxMxqwCeNgwZhXAwyatY1ZdElbnzp2runXrytvb26b94sWLkqRMmTI90PIJqwAeNoRVAA8bt77AqkmTJjp37pwkydPTUydOnJB0K6Q+aFAFAADAw8MlYTUkJESrV6+WdGsYwP181A8AAICHn0susHrhhRfUuHFjWSwWWSwW5cyZM9W+iYmJ/2BlAAAAcCcuCasDBgxQy5YttXv3bjVq1EiTJk1S1qxZXVEKAAAA3JjLpq6Kjo5WdHS04uPj1axZMwUEBLiqFAAAALgpl38pQHx8vHx8fPTjjz/qk08+sc4IcOTIEV26dMnF1QEAAMCVXP6lAAcOHFCdOnV08OBBJSQkqGbNmsqUKZOGDx+ua9euady4ca4uEQAAAC7i8jOr3bt3V2xsrM6ePSt/f39re5MmTfTTTz+5sDIAAAC4msvPrK5YsUK//fabfHx8bNrz5cunv//+20VVAQAAwB24/MxqUlJSitNTHT58mC8IAAAAyOBcHlZr1qypkSNHWm9bLBZdunRJ8fHxqlevngsrAwAAgKtZjDHGlQUcOXJE1apVk6enp3bt2qXY2Fjt2rVLOXLk0K+//qrQ0FCHl3n+alI6VAoArpOzUjdXlwAATnV145g09XP5mNXw8HBt2rRJ06dP1++//66kpCR16tRJrVq1srngCgAAABmPy8+spgfOrAJ42HBmFcDD5l9zZvX06dPKnj27JOnQoUP69NNPdfXqVTVs2FCPP/64i6sDAACAK7nsAqs///xTkZGRCg0NVXR0tDZt2qRy5crpgw8+0Pjx41W9enXNmTPHVeUBAADADbgsrL7++usqUaKEli1bpqpVq6pBgwaqV6+ezp8/r7Nnz+r555/X0KFDXVUeAAAA3IDLxqzmyJFDP//8s0qWLKlLly4pc+bMWrt2rWJjYyVJ27dv16OPPqpz5845vGzGrAJ42DBmFcDDJq1jVl12ZvXMmTPKmTOnJCkoKEiBgYEKDg623p8tWzZdvHjRVeUBAADADbj0SwEsFstdbwMAACBjc+lsAO3bt5evr68k6dq1a3rhhRcUGBgoSUpISHBlaQAAAHADLgur7dq1s7ndunVruz5t27b9p8oBAACAG3JZWJ00aZKrVg0AAIB/CZeOWQUAAADuhrAKAAAAt0VYBQAAgNsirAIAAMBtEVYBAADgtgirAAAAcFuEVQAAALgtwioAAADcFmEVAAAAbouwCgAAALdFWAUAAIDbIqwCAADAbRFWAQAA4LYIqwAAAHBbhFUAAAC4rfsKq1OmTFHlypUVHh6uAwcOSJJGjhyp7777zqnFAQAAIGNzOKyOHTtWr776qurVq6dz584pMTFRkpQ1a1aNHDnS6QUCAAAg43I4rI4ePVqffvqp+vbtK09PT2t7bGys/vzzT6cWBwAAgIzN4bC6b98+lSlTxq7d19dXly9fdkpRAAAAgHQfYTV//vzatGmTXfvChQsVExPjlKIAAAAASfJy9AG9evXSiy++qGvXrskYo7Vr12ratGkaMmSIPvvss/SoEQAAABmUw2G1Q4cOunnzpl5//XVduXJFzzzzjHLnzq0PP/xQLVu2TI8aAQAAkEFZjDHmfh986tQpJSUlKTQ01Jk1PbDzV5NcXQIAOFXOSt1cXQIAONXVjWPS1M/hM6u3y5Ejx4M8HAAAALgrh8Pq6dOn1b9/fy1dulQnTpxQUpLtWcwzZ844rTgAAABkbA6H1datW2vPnj3q1KmTwsLCZLFY0qMuAAAAwPGwumLFCq1YsUKlSpVKj3oAAAAAK4fnWY2OjtbVq1fToxYAAADAhsNh9eOPP1bfvn21bNkynT59WhcuXLD5AQAAAJzF4WEAWbNm1fnz51W9enWbdmOMLBaLEhMTnVYcAAAAMjaHw2qrVq3k4+Ojr776igusAAAAkK4cDqtbtmzRxo0bVaRIkfSoBwAAALByeMxqbGysDh06lB61AAAAADYcPrP68ssvq3v37urVq5dKlCghb29vm/tLlizptOIAAACQsVmMMcaRB3h42J+MtVgsbnWB1fmrSffuBAD/IjkrdXN1CQDgVFc3jklTP4fPrO7bt8/hYgAAAID74XBYzZcvX3rUAQAAANhJU1idO3eu6tatK29vb82dO/eufRs1auSUwgAAAIA0jVn18PDQsWPHFBoamuKYVevCGLMKAOmCMasAHjZpHbOapqmrkpKSdO3aNRljlJSUlOqPOwRVAAAAPDzSPM9q/vz5dfLkyfSsBQAAALCR5rDq4AxXAAAAwANz+BusAAAAgH+KQ1NXffbZZwoKCrprn27duAgAAAAAzpHmb7Dy8PBQnjx55OnpmfrCLBbt3bvXacXdL2YDAPCwYTYAAA+bdPkGq/Xr1ys0NPS+CgIAAAAcleYxqxaLJT3rAAAAAOwwGwAAAADcVprDanx8/D0vrgIAAACcKc1jVuPj49OzDgAAAMAO86wCAADAbRFWAQAA4LYIqwAAAHBbhFUAAAC4LYfD6vHjx9WmTRuFh4fLy8tLnp6eNj8AAACAszj0DVaS1L59ex08eFD9+vVTrly5+LIAAAAApBuHw+qKFSu0fPlylS5dOj3qAQAAAKwcHgYQERHBt1kBAADgH+FwWB05cqT69Omj/fv3p0M5AAAAwP84PAygRYsWunLliqKiohQQECBvb2+b+8+cOeO04gAAAJCxORxWR44cmR51AAAAAHYcDqvt2rVLjzoAAAAAOw6HVUlKTEzUnDlztG3bNlksFsXExKhRo0bMswoAAACncjis7t69W/Xq1dPff/+tIkWKyBijnTt3KiIiQgsWLFBUVFR61AkAAIAMyOHZALp166aoqCgdOnRIGzZs0MaNG3Xw4EHlz59f3bp1S48aAQAAkEE5fGZ12bJlWr16tYKDg61t2bNn19ChQ1W5cmWnFgcAAICMzeEzq76+vrp48aJd+6VLl+Tj4+OUogAAAADpPsJqgwYN1LlzZ61Zs0bGGBljtHr1ar3wwgtq1KhRetQIAACADMrhsDpq1ChFRUWpYsWK8vPzk5+fnypXrqyCBQvqww8/TI8aAQAAkEE5PGY1a9as+u6777Rr1y5t375dxhjFxMSoYMGC6VEfAAAAMrD7mmdVkgoVKqRChQo5sxYAAADARprC6quvvqq3335bgYGBevXVV+/ad8SIEU4p7EH4ejs8ugEA3Fr+ulwTACBjSlNY3bhxo27cuGH9PTUWi8U5VQEAAABKY1hdunRpir8DAAAA6emBPy+/cOGC5syZo+3btzujHgAAAMDK4bDavHlzjRkzRpJ09epVxcbGqnnz5ipRooS++eYbpxcIAACAjMvhsPrrr7+qSpUqkqRvv/1WxhidO3dOo0aN0uDBg51eIAAAADIuh8Pq+fPnFRwcLElatGiR/vOf/yggIED169fXrl27nF4gAAAAMi6Hw2pERIRWrVqly5cva9GiRapVq5Yk6ezZs/Lz83N6gQAAAMi4HP5SgB49eqhVq1YKCgpSvnz5VLVqVUm3hgeUKFHC2fUBAAAgA3M4rHbt2lXly5fXoUOHVLNmTXl43Do5W6BAAcasAgAAwKnu6+tWY2NjFRsbK0lKTEzUn3/+qUqVKilbtmxOLQ4AAAAZm8NjVnv06KEJEyZIuhVU4+LiVLZsWUVEROiXX35xdn0AAADIwBwOq19//bVKlSolSZo3b5727dun7du3q0ePHurbt6/TCwQAAEDG5XBYPXXqlHLmzClJ+v7779WsWTMVLlxYnTp10p9//un0AgEAAJBxORxWw8LCtHXrViUmJmrRokV64oknJElXrlyRp6en0wsEAABAxuXwBVYdOnRQ8+bNlStXLlksFtWsWVOStGbNGkVHRzu9QAAAAGRcDofVAQMGqHjx4jp06JCaNWsmX19fSZKnp6f69Onj9AIBAACQcVmMMcbVRTjbtZuurgAAnKts/x9cXQIAONXWd2ulqV+azqyOGjVKnTt3lp+fn0aNGnXXvt26dUvTigEAAIB7SdOZ1fz582v9+vXKnj278ufPn/rCLBbt3bvXqQXeD86sAnjYcGYVwMPGqWdW9+3bl+LvAAAAQHpyeOoqAAAA4J/i8GwAxhh9/fXXWrp0qU6cOKGkpCSb+2fPnu204gAAAJCxORxWu3fvrvHjx6tatWoKCwuTxWJJj7oAAAAAx8Pql19+qdmzZ6tevXrpUQ8AAABg5fCY1SxZsqhAgQLpUQsAAABgw+GwOmDAAA0cOFBXr15Nj3oAAAAAK4eHATRr1kzTpk1TaGioIiMj5e3tbXP/hg0bnFYcAAAAMjaHw2r79u31+++/q3Xr1lxgBQAAgHTlcFhdsGCBFi9erMceeyw96gEAAACsHB6zGhERocyZM6dHLQAAAIANh8Pq+++/r9dff1379+9Ph3IAAACA/3F4GEDr1q115coVRUVFKSAgwO4CqzNnzjitOAAAAGRsDofVkSNHpkcdAAAAgB2Hw2q7du3Sow4AAADAjsNhVZISExP17bffatu2bbJYLCpatKgaN24sL6/7WhwAAACQIofT5ZYtW9S4cWMdO3ZMRYoUkSTt3LlTISEhmjt3rkqUKOH0IgEAAJAxOTwbwLPPPqtixYrp8OHD2rBhgzZs2KBDhw6pZMmS6ty5c3rUCAAAgAzK4TOrf/zxh9avX69s2bJZ27Jly6Z33nlH5cqVc2pxAAAAyNgcPrNapEgRHT9+3K79xIkTKliwoFOKAgAAAKQ0htULFy5Yf959911169ZNX3/9tQ4fPqzDhw/r66+/Vo8ePTRs2LD0rhcAAAAZiMUYY+7VycPDQxaLxXo7+SHJbbffTkxMTI86HXLtpqsrAADnKtv/B1eXAABOtfXdWmnql6Yxq0uXLn2gYgAAAID7kaawGhcXl951AAAAAHYcng3g119/vev9jz/++H0XAwAAANzO4bBatWpVu7bbx7O6w5hVAAAAPBwcnrrq7NmzNj8nTpzQokWLVK5cOf3wAxcAAAAAwHkcPrOaJUsWu7aaNWvK19dXr7zyin7//XenFAYAAAA4fGY1NSEhIdqxY4ezFgcAAAA4fmZ18+bNNreNMTp69KiGDh2qUqVKOa0wAAAAwOGwWrp0aVksFt35XQKPPvqoJk6c6LTCAAAAAIfD6r59+2xue3h4KCQkRH5+fk4rCgAAAJDuI6zmy5cvPeoAAAAA7KT5Aqs1a9Zo4cKFNm2TJ09W/vz5FRoaqs6dOyshIcHpBQIAACDjSnNYHTBggM3FVX/++ac6deqkJ554Qn369NG8efM0ZMiQdCkSAAAAGVOaw+qmTZtUo0YN6+3p06erQoUK+vTTT/Xqq69q1KhRmjlzZroUCQAAgIwpzWH17NmzCgsLs95etmyZ6tSpY71drlw5HTp0yLnVAQAAIENLc1gNCwuzzgRw/fp1bdiwQRUrVrTef/HiRXl7ezu/QgAAAGRYaQ6rderUUZ8+fbR8+XK98cYbCggIUJUqVaz3b968WVFRUelSJAAAADKmNE9dNXjwYDVt2lRxcXEKCgrSF198IR8fH+v9EydOVK1atdKlSAAAAGRMaQ6rISEhWr58uc6fP6+goCB5enra3D9r1iwFBQU5vUAAAABkXA5/KUCWLFlSbA8ODn7gYgAAAIDbpXnMKgAAAPBPI6wCAADAbRFWAQAA4LYIqwAAAHBbhFUAAAC4LcIqAAAA3BZhFQAAAG6LsAoAAAC3RVgFAACA2yKsAgAAwG0RVgEAAOC2CKsAAABwW4RVAAAAuC3CKgAA6lsK8QAAIABJREFUANwWYRUAAABui7AKAAAAt0VYBQAAgNsirAIAAMBtEVYBAADgtgirAAAAcFuEVQAAALgtL1et+Ndff01Tv8cffzydKwEAAIC7cllYrVq1qiwWiyTJGJNiH4vFosTExH+yLAAAALgRl4XVbNmyKVOmTGrfvr3atGmjHDlyuKoUAAAAuCmXjVk9evSohg0bplWrVqlEiRLq1KmTVq5cqcyZMytLlizWHwD4v/buPL6Ge//j+Ps42WQnKYIQ+57arq1LuKVRlFy9La6SiK3WaotKtbiWUrd6tWnppSTqV9WFql5atUVraWsL2obaFdHYiZDtzO8P16nTBLG0M9HX8/E4j0fOd74z85nhMd6+850JAODPy7Sw6uHhoU6dOmnZsmXatWuXwsPDNXDgQIWGhmrkyJHKyckxqzQAAABYhCXeBhAaGqpRo0ZpxYoVqlq1qiZNmqRz586ZXRYAAABMZnpYzczM1Lx589SyZUvVrl1bwcHBWrJkiYoXL252aQAAADCZaQ9Yfffdd0pISND8+fNVoUIFxcTE6MMPPySkAgAAwMm0sNqkSROVK1dOgwcPVoMGDSRJa9euzdOvffv2f3RpAAAAsAjTwqokHTp0SOPGjbvmct6zCgAA8OdmWlh1OBxm7RoAAACFhCUesLpw4YLZZQAAAMCCTAurJ06cUNu2beXr6yt/f381a9ZM+/btM6scAAAAWJBpYTUuLk6bN2/WP//5T/3rX//SiRMn1LdvX7PKAQAAgAWZNmd12bJlmj17ttq0aSNJatOmjWrXrq3s7Gy5u7ubVRYAAAAsxLSR1aNHj6pevXrO79WrV5eHh4eOHj1qVkkAAACwGNPCqmEYcnNzHdh1c3PjLQEAAABwMm0agGEYeuihh1wCa0ZGhh599FF5eHg427Zs2WJGeQAAALAA08Lq6NGj87R16NDBhEoAAABgVZYKqwAAAMDVTP+lAAAAAMC1EFYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlmfY2gN/Kzs7WkiVLtHv3boWEhOhvf/ubfHx8zC4LAAAAJjJtZLVZs2Y6c+aMJOn48eNq0KCBOnXqpJkzZ6p3796qWbOmjhw5YlZ5AAAAsADTwuo333yjrKwsSdLIkSNlt9t18OBB/fTTTzp8+LDKli2rUaNGmVUeAAAALMAS0wDWrFmj1157TaVKlZIkBQUFacKECerRo4fJleFuMv2teL097U2XtqCgYK36al2evmPHjNKCjz7QsOfj9GT3GEnS2TNnNO2teG1Yv1a/HDumwMBiavFQSw0Y9LT8/Pyc6x44sF//fnWykrduUXZ2tqpUqaoBg4eoUeMmkqRPP1moUS/G5Vvjqq/WKygo6A4dMYC7We+ICnomsoreXXdQk5bscrYPeKiSHv9LGfkXddf2n89q/OIU7Um74Fye2KuhGlUs7rKtpdtTNXT+Duf3GqX99FxkVdUu6y+HYejL79M0eekuZWTl5qkjoKi7PhncVKUCvNR47Cqdv5TjXFalpK9ebF9ddcoG6GxGtj7ceFjTV+27k6cBfwKmhlWbzSZJOnPmjCpUqOCyrEKFCkpNTTWjLNzFKlWuohnvJDi/F7Hb8/RZtXKFvt++TfeUKOHSnnY8TcfT0vTs0OdVqVJlHT16ROPHjtHxtDRNmfqGs9+gfn1VPixMM2fPkaeXl957d44GDXhKSz5fruB77lHkI2103/0PuGz7pZEjlJWVRVAFUCC1y/jr8b+U1c7U8y7tPR8MU/R95fXCgu914ESGnmpRQe/ENlCb19a5BM0PvzusN1fscX6/lO1w/nyPn6dmxzbU5zuOafxnKfL1dNOIdtU04e+19cy8bXlqGf9YLf107LxKBXi5tPt42jUrtoG+3XdKT0z7VmHB3nr5sdq6mJWrxLUH79SpwJ+AqW8DiImJUceOHZWdna2DB13/4qampiowMNCkynC3crPbFXzPPc5P8eKuowu//PKLJk4Yq5cnvyp3N3eXZVWqVNVrr8ereYu/KrRcOTVu0lSDnh6iNUmrlJNzeSTh9OlTOnTooGJ79VHVatVVvnyYnn72OV26eFF7917+h8HLy8ulhiJ2u7779ltFdXzsjzkJAAo1bw+7Jneqo9Gf/KBzF7NdlnVvVl7/SdqnFT+kac8v6Yr76Ht5udvVrm6IS79L2bk6kZ7l/KRn/joa2rx6sLIdDo1bnKIDJzL0/ZFzGr94pyJrl1S54kVdttOpcVn5ebkp4eu84bNd3RB5uBXRCx9/rz2/pGvFD2makbRP0feVv4NnA38GpoXV6OholShRQgEBAerQoYPS09Ndli9YsEB169Y1qTrcrQ4eOqiWze/XIw//VcOHPqPDP//sXOZwODRyxDDF9OipypWrFGh76efT5evrKze3yzcpAgOLqWLFSvrs00XKyMhQTk6OPv7wAwUFBatGzVr5buOzxYtUtKiXWj3c+vYPEMBd78X2NbRm5wlt2HvKpb1ssaK6x99T63efdLZl5xratP+06pZzHfxpVzdE60Y21+Knm2nYI1Xl7fHrXSYPtyLKzjFkGL/2v5R9eVS2flgxZ1ulEj7q36KS4j76Xo6rO/9P3dBAbdp/Wtm5vy5bt/ukSgZ4qUyxonn6A9di2jSAhISE6y4fM2aM7Pncov2tzMxMZWZmurQZdk95enreVn24+9QJD9eEl19R+bAwnTx5UjP/M13du3bWwsX/VWBgMSXMmim7m5v+8WT3Am3vzJnTmvH2NP398U7ONpvNprffSdCQQf3UrFF9FSlSRMWDgjTtP+/I398/3+0sWrhAj7RpJy8vr3yXA8AVj4SXUs3Sfnpi2rd5lgX7eUiSTqRnubSfSM9S6cBfry//TU7VkdMXdTw9S1VK+uqZh6uoWik/9UrYLEn6du8pDW9TTbEPhGnu+oMq6m7XkIcv/wf+Hr/L/7a62236V6dwvfrFT0o9e0lli+cNn8F+Hjpy+lKeWiQp2NdDR05fvNXTgD8ZSzxglZ+CvmN14sSJ+uc//+nSNvKl0Xpx1JjfoSoUZvc/EOH8uYqk8Hvrql3rVlq8aJEa/uUvem/uu5r/8ULnXOrrSU9P18B+fVWxUiX17T/Q2W4Yhl4eN0bFiwcp4d335OXlpYUff6RBA/pq3gcf6557XOfBbkveqn1792jCxFfu2HECuDuVCvBUXLtq6j17s7JyHNfsZ8h1lNNmk0vLx5t+fS3knl/SdfDEBX08sKlqlPZTytHz2pN2QS98/L2eb1NNQx6uLIch/d/6QzpxPlO5jstbeiayivYdT9dnyTd6tiRvLXlbgeuzbFgtqLi4OD377LMubYadUVXcmLe3t6pUrapDhw6oSBGbTp06qdYtWziX5+bmasq/XtF7c9/V58tXOdsvXEhX/7695O3trX+/8Zbc3X+d2/rdt9/oqzVJ+nrDRvn6+kqSRo6qpW82rNfiRYvUs3cflxoWLvhI1arXUM1atX/nowVQ2NUq7a9gX099NKCJs83NXkQNw4rpH01C1fbfl99sco+vp06c/3V0NcjHQyd/M9p6tR+Pnld2jkPlg7yVcvTyA1tLth3Tkm3HFOTroYtZuTIMKfr+8s7R0CYVi6tKKT89PK6kpF8fmF43srlmJO3Xmyv36sT5LAX7uv57HORzefT3evUAv1Xow6qnZ95b/le9NQO4pqysLO3bt1f16jdQu/Yd1LhpM5fl/fr0VLtHOyjqbx2dbenp6erXp6c8PDz0+pvT8/zdu3jx8oW8yG9GZ21FbDIM15GQjAsX9OUXn2vwkOfu5GEBuEtt2HtK7V9f79I24bFa2n/8gt756oB+PnVRx89lqmnlIKX87y0B7nabGlYopteW7b7mdiuX9JW7WxEdP583QF4JlR0blFZmjkPr91yeD/v0vG3ydP91ql6dMv6a8Pfa6jZjo34+dfk6mPzzGQ15uIrc7TbnvNVmVYL0y9lLTAHATSn0YRUoqCn/ekURzVuoVEiITp06pZlvT9eF9HS1j/qbAgOLKTCwmEt/dzd3BQcHK6xCRUmXR1Sf6h2rS5cu6uVJ/9KF9HRd+N+DgcWKF5fdbte9devK399fL74wQn37DZCnl6cWfvyhjhw+ogcebO6y/S++WKrc3Fy1bffoH3L8AAq3jKxc7fnF9WHki1m5OpOR7Wx/d/1B9WleQQdPZujgyQz1aV5Bl7Jz9d//3a4PLV5U7eqG6KtdJ3T6QpYql/DVsDZV9eORc9p68LRzu/9oEqqth84oIzNXzaoEaWjrqvr3st3Od6heCaRXFPO+fIdp3/ELzj5Lko9pwF8racLfa2tG0n6VD/JWn+YVeM8qbpqpYTUnJ0cTJkxQbGysQkNDzSwFfwK//HJMI4Y9q9Onz6hY8WIKD6+rufM+VOnSZQq0/o8//KAd2y+/Y7DdI61cli39cqXKlCmrYsWKa9p/3lH861PVOzZaOTnZqlS5il5/8y1Vq17dZZ1FCxfooZat5B8QcGcOEMCf3qyvDsjL3a5R7WvIv6ibth8+q14JW5zvWM3OdahJpeLq1qycvD3cdOzsJa3ZdVzTVu6V46qJpHXKBmhgy0ry9nDTvuMXNGbRjwWYn+oqPTNHPWdv1kvta+ij/o117mKO5qw9yDtWcdNshpHP+yb+QL6+vvr+++8VFhZ2x7bJNAAAd5v6o740uwQAuKN+fPnhAvUz9ZcCSFLLli2VlJRkdhkAAACwINPnrD7yyCOKi4vT999/rwYNGuR5ZVX79u1NqgwAAABmM30aQJEi1x7ctdlsys3Nvebya2EaAIC7DdMAANxtCjoNwPSRVYfj2i82BgAAwJ+b6XNWr3bp0qUbdwIAAMCfhulhNTc3V+PGjVOZMmXk6+urffsuv3/tpZde0qxZs0yuDgAAAGYyPaxOmDBBiYmJmjx5sjw8PJztderU0TvvvGNiZQAAADCb6WH13Xff1YwZM9S1a1fZ7b/+6rbw8HDt3LnTxMoAAABgNtPD6pEjR1S5cuU87Q6HQ9nZ2SZUBAAAAKswPazWqlVLX3/9dZ72jz76SPXq1TOhIgAAAFiF6a+uGj16tLp166YjR47I4XBo4cKF2rVrl959913997//Nbs8AAAAmMj0kdVHH31UH3zwgZYuXSqbzaZRo0YpJSVFn332mVq1amV2eQAAADCR6SOrkhQZGanIyEizywAAAIDFmD6yWrFiRZ08eTJP+5kzZ1SxYkUTKgIAAIBVmB5WDxw4oNzc3DztmZmZOnLkiAkVAQAAwCpMmwawePFi58/Lli1TQECA83tubq5WrlypsLAwEyoDAACAVZgWVqOiopw/R0dHuyxzd3dXWFiYpkyZ8keXBQAAAAsxJaxu375d2dnZstvtqlChgjZu3Kjg4GAzSgEAAICFmTJntV69ejp16pQkyWazyWazmVEGAAAALM6UsBoYGKh9+/ZJkg4ePCiHw2FGGQAAALA4U6YBPPbYY4qIiFBISIgkqWHDhrLb7fn2vRJqAQAA8OdjSlidMWOGOnbsqD179mjw4MHq3bu3/Pz8zCgFAAAAFmba2wBat24tSdq8ebOefvrpfMNqcnLyH10WAAAALMT0XwqQkJDgElTPnj2radOmqX79+mrQoIGJlQEAAMBspofVK1atWqUnn3xSISEhio+PV5s2bbRp0yazywIAAICJTJsGIEmHDx9WYmKiZs+erQsXLuiJJ55Qdna2FixYoJo1a5pZGgAAACzAtJHVNm3aqGbNmvrxxx8VHx+vo0ePKj4+3qxyAAAAYEGmjax++eWXGjx4sPr166cqVaqYVQYAAAAszLSR1a+//lrnz59Xw4YN1bhxY7355ps6fvy4WeUAAADAgkwLq02bNtXMmTOVmpqqvn37av78+SpTpowcDoeWL1+u8+fPm1UaAAAALML0twF4e3srNjZWa9eu1Y4dO/Tcc89p0qRJKlGihNq3b292eQAAADCR6WH1atWqVdPkyZN1+PBhvf/++2aXAwAAAJNZKqxeYbfbFRUVpcWLF5tdCgAAAExkybAKAAAASIRVAAAAWBhhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBAABgWTbDMAyziwAKo8zMTE2cOFFxcXHy9PQ0uxwAuG1c12BFhFXgFp07d04BAQE6e/as/P39zS4HAG4b1zVYEdMAAAAAYFmEVQAAAFgWYRUAAACWRVgFbpGnp6dGjx7NQwgA7hpc12BFPGAFAAAAy2JkFQAAAJZFWAUAAIBlEVYBAABgWYRVWFpiYqICAwNvap2YmBhFRUXd8VoMw1CfPn1UvHhx2Ww2JScn59vWvHlzDRkypEDbTEpKks1m05kzZ+54vQBwLevWrVOdOnXk7u7uvF7+tu1mr083c+0DbgYPWMEUMTExOnPmjBYtWuTSnpSUpBYtWuj06dMKDAzUxYsXdf78eZUoUeK2t309iYmJ6tGjR552T09PXbp0SZL0+eefq0OHDkpKSlLFihUVHBys5cuX52k7d+6c3N3d5efnd8P9ZmVl6dSpUypZsqRsNluB6wVgXbdyDbqTwsLCdPDgwTztEydO1IgRIyRJjRs3VtWqVTVx4kT5+voqMDAwT5u3t/dNXZ9OnTpV4GsfcDPczC4AuJ6iRYuqaNGif8i+/P39tWvXLpe2qy/Qe/fuVUhIiJo1a3bdtuLFixd4nx4eHipVqtRtVA0AeY0dO1a9e/d2abs6RO7du1dPPfWUypYte922m7k+3cy1D7gZTAOApeU3DWD8+PEqUaKE/Pz81KtXL40YMUJ169bNs+6rr76qkJAQBQUFacCAAcrOzr7uvmw2m0qVKuXyKVmypKTLIyWDBg3SoUOHZLPZFBYWlm+blPdWWGZmpoYPH67Q0FB5enqqSpUqmjVrlqT8pwGsX79eDz74oIoWLarQ0FANHjxYFy5ccC4PCwvTyy+/rNjYWPn5+alcuXKaMWOGy7EcPnxYnTt3VvHixeXj46OGDRvq22+/1YEDB1SkSBFt2rTJpX98fLzKly8vbrQAv781a9aoUaNG8vT0VEhIiEaMGKGcnBxJ0meffabAwEA5HA5JUnJysmw2m4YNG+Zcv2/fvurSpct19+Hn55fneubj46MDBw7IZrPp5MmTio2Nlc1mU2JiYr5t+V2f1q1bp4iICHl7e6tYsWKKjIzU6dOnJeW99mVlZWn48OEqU6aMfHx81LhxYyUlJTmXX7m+L1u2TDVq1JCvr69at26t1NRUl2OZPXu2atWq5TxfAwcOlCTFxsaqXbt2Ln1zcnJUqlQpzZ49u6B/HCgECKsoVN577z1NmDBBr7zyijZv3qxy5cpp+vTpefqtXr1ae/fu1erVqzVnzhwlJiYqMTHxlvf7+uuva+zYsSpbtqxSU1O1cePGfNvy0717d82fP19vvPGGUlJS9Pbbb8vX1zffvjt27FBkZKQ6duyo7du364MPPtDatWudF+crpkyZooYNG2rr1q3q37+/+vXrp507d0qS0tPTFRERoaNHj2rx4sXatm2bhg8fLofDobCwMLVs2VIJCQku20tISFBMTAxTEYDf2ZEjR9SmTRv95S9/0bZt2zR9+nTNmjVL48ePlyQ9+OCDOn/+vLZu3SrpcrANDg7WmjVrnNtISkpSRETELe0/NDRUqamp8vf319SpU5WamqrHH388T1unTp3yrJucnKyHHnpItWrV0oYNG7R27Vo9+uijys3NzXdfPXr00Lp16zR//nxt375djz/+uFq3bq3du3c7+2RkZOjVV1/V3Llz9dVXX+nQoUMaOnSoc/n06dM1YMAA9enTRzt27NDixYtVuXJlSVKvXr30xRdfuITbpUuXKj09XU888cQtnR9YlAGYIDo62rDb7YaPj4/Lx8vLy5BknD592jAMw0hISDACAgKc6zVu3NgYMGCAy7buu+8+495773XZdvny5Y2cnBxn2+OPP2506tTpmvUkJCQYkvLU06pVK2eff//730b58uVd1suvLSIiwnj66acNwzCMXbt2GZKM5cuX57vf1atXuxxvt27djD59+rj0+frrr40iRYoYFy9eNAzDMMqXL288+eSTzuUOh8MoUaKEMX36dMMwDOM///mP4efnZ5w8eTLffX7wwQdGsWLFjEuXLhmGYRjJycmGzWYz9u/ff83zA6DgoqOjjQ4dOuS77IUXXjCqVatmOBwOZ9tbb71l+Pr6Grm5uYZhGEb9+vWNV1991TAMw4iKijImTJhgeHh4GOfOnTNSU1MNSUZKSso191++fHnDw8Mjz/Vs9erVzj4BAQFGQkKCy3q/bfvt9alLly7Gfffdd839Xn3t27Nnj2Gz2YwjR4649HnooYeMuLg4wzB+ve7u2bPH5VyULFnS+b106dLGyJEjr7nPmjVrGq+88orze1RUlBETE3PN/iicGFmFaVq0aKHk5GSXzzvvvHPddXbt2qVGjRq5tP32uyTVqlVLdrvd+T0kJERpaWnX3bafn1+een47AnmzkpOTZbfbCzwKsnnzZiUmJsrX19f5iYyMlMPh0P79+539wsPDnT9fmb5w5fiSk5NVr169a84fi4qKkpubmz755BNJl2+xtWjRwjmNAcDvJyUlRU2bNnW5i3HfffcpPT1dhw8flnT5dnpSUpIMw9DXX3+tDh06qHbt2lq7dq1Wr16tkiVLqnr16tfdz7Bhw/Jczxo3bnxbtV8ZWS2ILVu2yDAMVa1a1eV6tmbNGu3du9fZz9vbW5UqVXJ+v/panZaWpqNHj153n7169XJep9PS0rRkyRLFxsbeyuHBwnjACqbx8fFx3s654srF+np+e6vayGeepbu7e551rswBu5YiRYrkqed23ezDYQ6HQ3379tXgwYPzLCtXrpzz5+sd34326eHhoW7duikhIUEdO3bUvHnzNHXq1JuqE8CtMQzjmtewK+3NmzfXrFmztG3bNhUpUkQ1a9ZURESE1qxZo9OnTxfoP7/BwcGmXs8cDofsdrs2b97sMnAgyWUaVH7XsivnoyD76969u0aMGKENGzZow4YNCgsL0wMPPFDgOlE4MLKKQqVatWr67rvvXNp++7CQldSpU0cOh8Nlvtn11K9fXz/88IMqV66c5+Ph4VGgbYSHhys5OVmnTp26Zp9evXppxYoVmjZtmrKzs9WxY8cCbRvA7alZs6bWr1/v8p/s9evXy8/PT2XKlJH067zVqVOnKiIiQjabTREREUpKSrqt+aq3Kzw8XCtXrixQ33r16ik3N1dpaWl5rmUFfcOAn5+fwsLCrrvPoKAgRUVFKSEhQQkJCfm+ghCFH2EVhcqgQYM0a9YszZkzR7t379b48eO1ffv2O/JgkGEYOnbsWJ7PjUZkrycsLEzR0dGKjY3VokWLtH//fiUlJenDDz/Mt//zzz+vDRs2aMCAAUpOTtbu3bu1ePFiDRo0qMD77NKli0qVKqWoqCitW7dO+/bt04IFC7RhwwZnnxo1aqhJkyZ6/vnn1aVLlz/s9WDAn8XZs2fz3IY/dOiQ+vfvr59//lmDBg3Szp079emnn2r06NF69tlnVaTI5X+SAwICVLduXf3f//2fmjdvLulygN2yZYt++uknZ9v1nD9/Ps+17Ny5c7d1THFxcdq4caP69++v7du3a+fOnZo+fbpOnDiRp2/VqlXVtWtXde/eXQsXLtT+/fu1ceNGvfLKK1q6dGmB9zlmzBhNmTJFb7zxhnbv3q0tW7YoPj7epU+vXr00Z84cpaSkKDo6+raOEdZEWEWh0rVrV8XFxWno0KGqX7++9u/fr5iYGHl5ed32ts+dO6eQkJA8nxvNdb2R6dOn6+9//7v69++v6tWrq3fv3i6vorpaeHi41qxZo927d+uBBx5QvXr19NJLLykkJKTA+/Pw8NCXX36pEiVKqE2bNqpTp44mTZqU51Zcz549lZWVxfwu4HeQlJSkevXquXxGjRqlMmXKaOnSpfruu+9077336qmnnlLPnj314osvuqzfokUL5ebmOoNpsWLFVLNmTd1zzz2qUaPGDfc/atSoPNey4cOH39YxVa1aVV9++aW2bdumRo0aqWnTpvr000/l5pb/jMKEhAR1795dzz33nKpVq6b27dvr22+/VWhoaIH3GR0dralTp2ratGmqVauW2rVr5/I2AUlq2bKlQkJCFBkZqdKlS9/WMcKa+A1WKPRatWqlUqVKae7cuWaXUqhMmDBB8+fP144dO8wuBQBuWUZGhkqXLq3Zs2czpekuxQNWKFQyMjL09ttvKzIyUna7Xe+//75WrFih5cuXm11aoZGenq6UlBTFx8dr3LhxZpcDALfE4XDo2LFjmjJligI/rqeZAAAIp0lEQVQCAtS+fXuzS8LvhLCKQsVms2np0qUaP368MjMzVa1aNS1YsEAtW7Y0u7RCY+DAgXr//fcVFRXFFAAAhdahQ4dUoUIFlS1bVomJidecjoDCj2kAAAAAsCwesAIAAIBlEVYBAABgWYRVAAAAWBZhFQAAAJZFWAUAAIBlEVYBoJDbuXOnmjRpIi8vL9WtWzfftgMHDshmsyk5OblA24yJiVFUVNTvWTYAFAhhFUChERMTI5vNpqeeeirPsv79+8tmsykmJuaPL+x31Lx5c9lstjyfq8/B6NGj5ePjo127dmnlypX5toWGhio1NVW1a9cu0H5ff/11JSYm/h6HBAA3hbAKoFAJDQ3V/PnzdfHiRWfbpUuX9P7776tcuXImVnZthmEoJyfnltfv3bu3UlNTXT6TJ092Lt+7d6/uv/9+lS9fXkFBQfm22e12lSpVqsAvTg8ICFBgYOAt1wwAdwphFUChUr9+fZUrV04LFy50ti1cuFChoaGqV6+eS1/DMDR58mRVrFhRRYsW1b333quPP/7YuTwpKUk2m03Lli1TvXr1VLRoUf31r39VWlqaPv/8c9WoUUP+/v7q0qWLMjIynOtlZmZq8ODBKlGihLy8vHT//fdr48aN+W63YcOG8vT01Ny5c1WkSBFt2rTJpcb4+HiVL19e1/v9LN7e3ipVqpTLx9/fX9Ll3+q2efNmjR07VjabTWPGjMm3Lb9pAD/88IPatm0rf39/+fn56YEHHtDevXsl5Z0GUNBzuXLlSjVs2FDe3t5q1qyZdu3a5XIsixcvVsOGDeXl5aXg4GDn73IfO3as6tSpk+fYGzRooFGjRl3z3AC4+xFWARQ6PXr0UEJCgvP77Nmz8/3VsS+++KISEhI0ffp0/fDDD3rmmWf05JNPas2aNS79xowZozfffFPr16/Xzz//rCeeeEJTp07VvHnztGTJEi1fvlzx8fHO/sOHD9eCBQs0Z84cbdmyRZUrV1ZkZKROnTrlst3hw4dr4sSJSklJUfv27dWyZUuXuiUpISHBOb3hVqSmpqpWrVp67rnnlJqaqqFDh+bb9ltHjhzRgw8+KC8vL61atUqbN29WbGzsNUeAC3ouR44cqSlTpmjTpk1yc3Nz+XNZsmSJOnbsqLZt22rr1q3OYCtJsbGx+vHHH11C//bt27V169a7bmoHgJtkAEAhER0dbXTo0ME4fvy44enpaezfv984cOCA4eXlZRw/ftzo0KGDER0dbRiGYaSnpxteXl7G+vXrXbbRs2dPo0uXLoZhGMbq1asNScaKFSucyydOnGhIMvbu3ets69u3rxEZGencrru7u/Hee+85l2dlZRmlS5c2Jk+e7LLdRYsWuez7gw8+MIoVK2ZcunTJMAzDSE5ONmw2m7F///5rHnNERITh7u5u+Pj4uHwSExOdfe69915j9OjRLuv9tm3//v2GJGPr1q2GYRhGXFycUaFCBSMrKyvf/V4511eO+VbO5ZIlSwxJxsWLFw3DMIymTZsaXbt2veaxPvLII0a/fv2c34cMGWI0b978mv0B/DkUbPISAFhIcHCw2rZtqzlz5sgwDLVt21bBwcEufX788UddunRJrVq1cmnPysrKM10gPDzc+XPJkiXl7e2tihUrurR99913ki7PBc3OztZ9993nXO7u7q5GjRopJSXFZbtXRg2viIqK0sCBA/XJJ5+oc+fOmj17tlq0aKGwsLDrHm/Xrl01cuRIl7YSJUpcd50bSU5O1gMPPCB3d/cb9r3VcxkSEiJJSktLU7ly5ZScnKzevXtfcz+9e/dWbGysXnvtNdntdr333nuaMmXKzRwWgLsQYRVAoRQbG6uBAwdKkt566608yx0Oh6TLt57LlCnjsszT09Pl+9WBzWaz5QlwNpvNuT3jf3NLf3vb3jCMPG0+Pj4u3z08PNStWzclJCSoY8eOmjdvnqZOnXr9A9Xlh50qV658w343o2jRogXuezvn8ur1b7TPRx99VJ6envrkk0/k6empzMxMPfbYYwWuE8DdibAKoFBq3bq1srKyJEmRkZF5ltesWVOenp46dOiQIiIi7th+K1euLA8PD61du1b/+Mc/JEnZ2dnatGmThgwZcsP1e/Xqpdq1a2vatGnKzs52PmD0RwsPD9ecOXOUnZ19w9HVO3Uuw8PDtXLlSvXo0SPf5W5uboqOjlZCQoI8PT3VuXNneXt73/L+ANwdCKsACiW73e687W632/Ms9/Pz09ChQ/XMM8/I4XDo/vvv17lz57R+/Xr5+voqOjr6lvbr4+Ojfv36adiwYSpevLjKlSunyZMnKyMjQz179rzh+jVq1FCTJk30/PPPKzY2tkAjnBkZGTp27JhLm6enp4oVK3ZLxyBJAwcOVHx8vDp37qy4uDgFBATom2++UaNGjVStWjWXvnfqXI4ePVoPPfSQKlWqpM6dOysnJ0eff/65hg8f7uzTq1cv1ahRQ5K0bt26Wz4+AHcPwiqAQuvK65uuZdy4cSpRooQmTpyoffv2KTAwUPXr19cLL7xwW/udNGmSHA6HunXrpvPnz6thw4ZatmxZgcNjz549tX79+nzfYJCfmTNnaubMmS5tkZGR+uKLL2669iuCgoK0atUqDRs2TBEREbLb7apbt67LXNyr3Ylz2bx5c3300UcaN26cJk2aJH9/fz344IMufapUqaJmzZrp5MmTaty48S0fH4C7h80wrvNyPwDAHTdhwgTNnz9fO3bsMLsUyzEMQ9WrV1ffvn317LPPml0OAAtgZBUA/iDp6elKSUlRfHy8xo0bZ3Y5lpOWlqa5c+fqyJEj15zXCuDPh7AKAH+QgQMH6v3331dUVFSBpwD8mZQsWVLBwcGaMWPGbc3HBXB3YRoAAAAALItftwoAAADLIqwCAADAsgirAAAAsCzCKgAAACyLsAoAAADLIqwCAADAsgirAAAAsCzCKgAAACzr/wEphUMbWl1uhgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pymongo\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import chi2_contingency\n",
    "import re\n",
    "from datetime import datetime, timedelta\n",
    "import pytz\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_efficiency_by_time(collection, start_time, end_time):\n",
    "    # Query to match the submitTime and endTime criteria\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    # Extract the ratio of maxRMem to (requestedMemory * numProcessors) and group by submission time\n",
    "    before_5pm_data = []\n",
    "    after_5pm_data = []\n",
    "\n",
    "    # Regular expression to extract the memory value from the resReq field\n",
    "    mem_pattern = re.compile(r\"mem=(\\d+\\.?\\d*)\")\n",
    "\n",
    "    # Fetch maxRMem, resReq, numProcessors, submitTime fields\n",
    "    cursor = collection.find(query, {\"maxRMem\": 1, \"resReq\": 1, \"numProcessors\": 1, \"submitTime\": 1, \"_id\": 0})\n",
    "\n",
    "    for document in cursor:\n",
    "        if 'maxRMem' in document and 'resReq' in document and 'numProcessors' in document and 'submitTime' in document:\n",
    "            max_rmem = document['maxRMem']\n",
    "            res_req = document['resReq']\n",
    "            num_processors = document['numProcessors']\n",
    "            submit_time = document['submitTime']\n",
    "\n",
    "            # Parse the requested memory from the resReq field\n",
    "            match = mem_pattern.search(res_req)\n",
    "            if match:\n",
    "                requested_mem = float(match.group(1))  # Requested memory is in MB\n",
    "\n",
    "                if requested_mem > 0:  # Avoid division by zero\n",
    "                    # Adjust requested memory for the number of processors\n",
    "                    requested_mem_adjusted = requested_mem * num_processors\n",
    "                    \n",
    "                    # Calculate memory efficiency\n",
    "                    memory_efficiency = max_rmem / (requested_mem_adjusted * 1024)  # Convert requested memory to KB\n",
    "\n",
    "                    # Convert submitTime from epoch to datetime and localize to UTC\n",
    "                    submit_time = datetime.fromtimestamp(submit_time, tz=pytz.UTC)\n",
    "\n",
    "                    # Determine if the submission is before or after 5 PM\n",
    "                    if submit_time.hour < 17:\n",
    "                        before_5pm_data.append(memory_efficiency)\n",
    "                    else:\n",
    "                        after_5pm_data.append(memory_efficiency)\n",
    "\n",
    "    return before_5pm_data, after_5pm_data\n",
    "\n",
    "def perform_chi_squared_test(before_5pm_data, after_5pm_data):\n",
    "    # Define cutoff for high/low memory efficiency\n",
    "    cutoff = 0.8\n",
    "    \n",
    "    # Create a 2x2 contingency table\n",
    "    before_5pm_high = sum(efficiency > cutoff for efficiency in before_5pm_data)\n",
    "    before_5pm_low = len(before_5pm_data) - before_5pm_high\n",
    "    after_5pm_high = sum(efficiency > cutoff for efficiency in after_5pm_data)\n",
    "    after_5pm_low = len(after_5pm_data) - after_5pm_high\n",
    "    \n",
    "    table = np.array([[before_5pm_high, before_5pm_low],\n",
    "                      [after_5pm_high, after_5pm_low]])\n",
    "    \n",
    "    # Perform Chi-squared Test\n",
    "    chi2, p_value, dof, expected = chi2_contingency(table)\n",
    "    \n",
    "    return table, chi2, p_value, dof, expected\n",
    "\n",
    "def plot_contingency_table(contingency_table):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(contingency_table, annot=True, fmt='d', cmap='Blues', cbar=False, \n",
    "            xticklabels=['High Efficiency', 'Low Efficiency'],  # Swapped the labels\n",
    "            yticklabels=['Before 5 PM', 'After 5 PM'])\n",
    "    plt.title('Contingency Table: Submission Time vs Memory Efficiency')\n",
    "    plt.xlabel('Memory Efficiency')\n",
    "    plt.ylabel('Submission Time')\n",
    "    plt.show()\n",
    "\n",
    "def main():\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract memory efficiency data separated by submission time\n",
    "    before_5pm_data, after_5pm_data = extract_memory_efficiency_by_time(collection, start_time, end_time)\n",
    "\n",
    "    # Perform Chi-squared test\n",
    "    contingency_table, chi2, p_value, dof, expected = perform_chi_squared_test(before_5pm_data, after_5pm_data)\n",
    "    \n",
    "    # Print results\n",
    "    print(f'Chi-squared statistic: {chi2}')\n",
    "    print(f'p-value: {p_value}')\n",
    "    print(f'Degrees of freedom: {dof}')\n",
    "    print('Expected frequencies:')\n",
    "    print(expected)\n",
    "    \n",
    "    # Plot the contingency table\n",
    "    plot_contingency_table(contingency_table)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "7d3d48a6-9e05-49a1-9689-a4ef4bef8365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.999th percentile memory utilization threshold: 8.8669\n",
      "JobID: 233798823, UserID: 1015853, Memory Utilization: 9.7518, maxRMem: 9975808, resReq: rusage[mem=999] span[hosts=1], numProcessors: 1, runTime: 23, CPU Utilization: 0.0000, jStatus: exit\n",
      "JobID: 233798823, UserID: 1015853, Memory Utilization: 9.4254, maxRMem: 9641984, resReq: rusage[mem=999] span[hosts=1], numProcessors: 1, runTime: 30, CPU Utilization: 0.0000, jStatus: exit\n",
      "JobID: 234043316, UserID: 103327, Memory Utilization: 9.8359, maxRMem: 1289216, resReq: rusage[mem=128], numProcessors: 1, runTime: 191, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234043307, UserID: 103327, Memory Utilization: 8.9375, maxRMem: 1171456, resReq: rusage[mem=128], numProcessors: 1, runTime: 206, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234043311, UserID: 103327, Memory Utilization: 9.0156, maxRMem: 1181696, resReq: rusage[mem=128], numProcessors: 1, runTime: 211, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234043313, UserID: 103327, Memory Utilization: 9.0469, maxRMem: 1185792, resReq: rusage[mem=128], numProcessors: 1, runTime: 214, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234043314, UserID: 103327, Memory Utilization: 9.0000, maxRMem: 1179648, resReq: rusage[mem=128], numProcessors: 1, runTime: 230, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234044660, UserID: 103327, Memory Utilization: 8.9375, maxRMem: 1171456, resReq: rusage[mem=128], numProcessors: 1, runTime: 214, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234044663, UserID: 103327, Memory Utilization: 9.0156, maxRMem: 1181696, resReq: rusage[mem=128], numProcessors: 1, runTime: 226, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234044666, UserID: 103327, Memory Utilization: 9.7734, maxRMem: 1281024, resReq: rusage[mem=128], numProcessors: 1, runTime: 258, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234044665, UserID: 103327, Memory Utilization: 9.0000, maxRMem: 1179648, resReq: rusage[mem=128], numProcessors: 1, runTime: 259, CPU Utilization: 0.0000, jStatus: done\n"
     ]
    }
   ],
   "source": [
    "import pymongo\n",
    "import re\n",
    "import numpy as np\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_utilization(collection, start_time, end_time):\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    memory_utilization_stats = []\n",
    "    \n",
    "    mem_pattern = re.compile(r\"mem=(\\d+\\.?\\d*)\")\n",
    "    \n",
    "    cursor = collection.find(query, {\"maxRMem\": 1, \"resReq\": 1, \"numProcessors\": 1, \"jobId\": 1, \"userId\": 1, \"jStatus\": 1, \"runTime\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "    \n",
    "    for document in cursor:\n",
    "        max_rmem = document.get('maxRMem', 0)  # Default to 0 if key is missing\n",
    "        res_req = document.get('resReq', '')\n",
    "        num_processors = document.get('numProcessors', 1)  # Default to 1 if key is missing\n",
    "        run_time = document.get('runTime', 1)  # Default to 1 to avoid division by zero\n",
    "        \n",
    "        match = mem_pattern.search(res_req)\n",
    "        if match:\n",
    "            requested_mem = float(match.group(1))\n",
    "            \n",
    "            if requested_mem > 0:\n",
    "                # Adjust requested memory for the number of processors\n",
    "                requested_mem_adjusted = requested_mem * num_processors\n",
    "                \n",
    "                # Convert requested memory to KB\n",
    "                requested_mem_adjusted_kb = requested_mem_adjusted * 1024\n",
    "                \n",
    "                # Calculate memory utilization (not constrained to 1)\n",
    "                memory_ratio = max_rmem / requested_mem_adjusted_kb\n",
    "                \n",
    "                # Calculate CPU utilization\n",
    "                cpu_time = document.get('cpuTime', 0)  # Default to 0 if key is missing\n",
    "                cpu_utilization = cpu_time / (num_processors * run_time) if run_time > 0 else 0\n",
    "                \n",
    "                # Collect memory utilization statistics\n",
    "                memory_utilization_stats.append({\n",
    "                    \"jobId\": document.get('jobId', 'N/A'),\n",
    "                    \"userId\": document.get('userId', 'N/A'),\n",
    "                    \"maxRMem\": max_rmem,\n",
    "                    \"resReq\": res_req,\n",
    "                    \"numProcessors\": num_processors,\n",
    "                    \"memory_utilization\": memory_ratio,\n",
    "                    \"runTime\": run_time,\n",
    "                    \"cpu_utilization\": cpu_utilization,\n",
    "                    \"jStatus\": document.get('jStatus', 'unknown')  # Ensure jStatus is included\n",
    "                })\n",
    "    \n",
    "    return memory_utilization_stats\n",
    "\n",
    "def find_top_percentile_jobs(memory_utilization_stats, percentile):\n",
    "    # Extract memory utilization values\n",
    "    utilization_values = [stat[\"memory_utilization\"] for stat in memory_utilization_stats]\n",
    "    \n",
    "    # Calculate the percentile value\n",
    "    threshold = np.percentile(utilization_values, percentile)\n",
    "    \n",
    "    # Filter jobs that are above the percentile threshold\n",
    "    top_jobs = [stat for stat in memory_utilization_stats if stat[\"memory_utilization\"] > threshold]\n",
    "    \n",
    "    return top_jobs, threshold\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract memory utilization statistics\n",
    "    memory_utilization_stats = extract_memory_utilization(collection, start_time, end_time)\n",
    "    \n",
    "    # Find jobs above the 99.999th percentile\n",
    "    top_jobs, threshold = find_top_percentile_jobs(memory_utilization_stats, 99.999)\n",
    "    \n",
    "    # Print jobId, userId, memory utilization, maxRMem, resReq, numProcessors, runTime, cpu_utilization, and jStatus for the top jobs\n",
    "    print(f\"99.999th percentile memory utilization threshold: {threshold:.4f}\")\n",
    "    for job in top_jobs:\n",
    "        print(f\"JobID: {job['jobId']}, UserID: {job['userId']}, Memory Utilization: {job['memory_utilization']:.4f}, \"\n",
    "              f\"maxRMem: {job['maxRMem']}, resReq: {job['resReq']}, numProcessors: {job['numProcessors']}, \"\n",
    "              f\"runTime: {job['runTime']}, CPU Utilization: {job['cpu_utilization']:.4f}, jStatus: {job['jStatus']}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0b19a942-69cc-4d6e-b7ae-3945f904f4e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.999th percentile memory utilization threshold: 8.8669\n",
      "JobID: 233798823, UserID: 1015853, Memory Utilization: 9.7518, maxRMem: 9975808, resReq: rusage[mem=999] span[hosts=1], numProcessors: 1, jStatus: exit\n",
      "JobID: 233798823, UserID: 1015853, Memory Utilization: 9.4254, maxRMem: 9641984, resReq: rusage[mem=999] span[hosts=1], numProcessors: 1, jStatus: exit\n",
      "JobID: 234043316, UserID: 103327, Memory Utilization: 9.8359, maxRMem: 1289216, resReq: rusage[mem=128], numProcessors: 1, jStatus: done\n",
      "JobID: 234043307, UserID: 103327, Memory Utilization: 8.9375, maxRMem: 1171456, resReq: rusage[mem=128], numProcessors: 1, jStatus: done\n",
      "JobID: 234043311, UserID: 103327, Memory Utilization: 9.0156, maxRMem: 1181696, resReq: rusage[mem=128], numProcessors: 1, jStatus: done\n",
      "JobID: 234043313, UserID: 103327, Memory Utilization: 9.0469, maxRMem: 1185792, resReq: rusage[mem=128], numProcessors: 1, jStatus: done\n",
      "JobID: 234043314, UserID: 103327, Memory Utilization: 9.0000, maxRMem: 1179648, resReq: rusage[mem=128], numProcessors: 1, jStatus: done\n",
      "JobID: 234044660, UserID: 103327, Memory Utilization: 8.9375, maxRMem: 1171456, resReq: rusage[mem=128], numProcessors: 1, jStatus: done\n",
      "JobID: 234044663, UserID: 103327, Memory Utilization: 9.0156, maxRMem: 1181696, resReq: rusage[mem=128], numProcessors: 1, jStatus: done\n",
      "JobID: 234044666, UserID: 103327, Memory Utilization: 9.7734, maxRMem: 1281024, resReq: rusage[mem=128], numProcessors: 1, jStatus: done\n",
      "JobID: 234044665, UserID: 103327, Memory Utilization: 9.0000, maxRMem: 1179648, resReq: rusage[mem=128], numProcessors: 1, jStatus: done\n"
     ]
    }
   ],
   "source": [
    "import pymongo\n",
    "import re\n",
    "import numpy as np\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_utilization(collection, start_time, end_time):\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    memory_utilization_stats = []\n",
    "    \n",
    "    mem_pattern = re.compile(r\"mem=(\\d+\\.?\\d*)\")\n",
    "    \n",
    "    cursor = collection.find(query, {\"maxRMem\": 1, \"resReq\": 1, \"numProcessors\": 1, \"jobId\": 1, \"userId\": 1, \"jStatus\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "    \n",
    "    for document in cursor:\n",
    "        max_rmem = document.get('maxRMem', 0)  # Default to 0 if key is missing\n",
    "        res_req = document.get('resReq', '')\n",
    "        num_processors = document.get('numProcessors', 1)  # Default to 1 if key is missing\n",
    "        \n",
    "        match = mem_pattern.search(res_req)\n",
    "        if match:\n",
    "            requested_mem = float(match.group(1))\n",
    "            \n",
    "            if requested_mem > 0:\n",
    "                # Adjust requested memory for the number of processors\n",
    "                requested_mem_adjusted = requested_mem * num_processors\n",
    "                \n",
    "                # Convert requested memory to KB\n",
    "                requested_mem_adjusted_kb = requested_mem_adjusted * 1024\n",
    "                \n",
    "                # Calculate memory utilization (not constrained to 1)\n",
    "                memory_ratio = max_rmem / requested_mem_adjusted_kb\n",
    "                \n",
    "                # Collect memory utilization statistics\n",
    "                memory_utilization_stats.append({\n",
    "                    \"jobId\": document.get('jobId', 'N/A'),\n",
    "                    \"userId\": document.get('userId', 'N/A'),\n",
    "                    \"maxRMem\": max_rmem,\n",
    "                    \"resReq\": res_req,\n",
    "                    \"numProcessors\": num_processors,\n",
    "                    \"memory_utilization\": memory_ratio,\n",
    "                    \"jStatus\": document.get('jStatus', 'unknown')  # Ensure jStatus is included\n",
    "                })\n",
    "    \n",
    "    return memory_utilization_stats\n",
    "\n",
    "def find_top_percentile_jobs(memory_utilization_stats, percentile):\n",
    "    # Extract memory utilization values\n",
    "    utilization_values = [stat[\"memory_utilization\"] for stat in memory_utilization_stats]\n",
    "    \n",
    "    # Calculate the percentile value\n",
    "    threshold = np.percentile(utilization_values, percentile)\n",
    "    \n",
    "    # Filter jobs that are above the percentile threshold\n",
    "    top_jobs = [stat for stat in memory_utilization_stats if stat[\"memory_utilization\"] > threshold]\n",
    "    \n",
    "    return top_jobs, threshold\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract memory utilization statistics\n",
    "    memory_utilization_stats = extract_memory_utilization(collection, start_time, end_time)\n",
    "    \n",
    "    # Find jobs above the 99.999th percentile\n",
    "    top_jobs, threshold = find_top_percentile_jobs(memory_utilization_stats, 99.999)\n",
    "    \n",
    "    # Print jobId, userId, memory utilization, maxRMem, resReq, numProcessors, and jStatus for the top jobs\n",
    "    print(f\"99.999th percentile memory utilization threshold: {threshold:.4f}\")\n",
    "    for job in top_jobs:\n",
    "        print(f\"JobID: {job['jobId']}, UserID: {job['userId']}, Memory Utilization: {job['memory_utilization']:.4f}, \"\n",
    "              f\"maxRMem: {job['maxRMem']}, resReq: {job['resReq']}, numProcessors: {job['numProcessors']}, jStatus: {job['jStatus']}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0db9b6fe-0474-493e-b4fa-3e2bf2d4ba08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "176126d3-7e65-4959-978b-73c3ac97b9ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c89a753-984d-4a0a-a5d0-7887cff11813",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f4cfb82b-a8ff-4c71-8f1e-fa78b2c32d17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.9th percentile CPU utilization threshold: 1.1863\n",
      "JobID: 233667699, UserID: 1019847, CPU Utilization: 2.1034, jStatus: exit\n",
      "JobID: 233710722, UserID: 1023238, CPU Utilization: 1.2313, jStatus: done\n",
      "JobID: 233710725, UserID: 1023238, CPU Utilization: 1.6510, jStatus: done\n",
      "JobID: 233715105, UserID: 1020549, CPU Utilization: 1.3229, jStatus: done\n",
      "JobID: 233715109, UserID: 1020549, CPU Utilization: 1.2152, jStatus: done\n",
      "JobID: 233715111, UserID: 1020549, CPU Utilization: 1.7670, jStatus: exit\n",
      "JobID: 233715117, UserID: 1020549, CPU Utilization: 1.7905, jStatus: exit\n",
      "JobID: 233715119, UserID: 1020549, CPU Utilization: 1.2075, jStatus: done\n",
      "JobID: 233715127, UserID: 1020549, CPU Utilization: 1.1867, jStatus: done\n",
      "JobID: 233715129, UserID: 1020549, CPU Utilization: 1.1944, jStatus: done\n",
      "JobID: 233717292, UserID: 1015333, CPU Utilization: 1.1954, jStatus: done\n",
      "JobID: 233717315, UserID: 1015333, CPU Utilization: 1.1864, jStatus: done\n",
      "JobID: 233717440, UserID: 1015333, CPU Utilization: 1.1955, jStatus: done\n",
      "JobID: 233717454, UserID: 1015333, CPU Utilization: 1.2084, jStatus: done\n",
      "JobID: 233717774, UserID: 1023238, CPU Utilization: 1.6206, jStatus: done\n",
      "JobID: 233719542, UserID: 1023971, CPU Utilization: 1.3215, jStatus: exit\n",
      "JobID: 233738310, UserID: 84400, CPU Utilization: 1.9806, jStatus: exit\n",
      "JobID: 233739859, UserID: 1021114, CPU Utilization: 1.7784, jStatus: exit\n",
      "JobID: 233739857, UserID: 1021114, CPU Utilization: 2.1920, jStatus: exit\n",
      "JobID: 233744943, UserID: 84400, CPU Utilization: 1.9795, jStatus: exit\n",
      "JobID: 233750347, UserID: 84400, CPU Utilization: 1.9677, jStatus: exit\n",
      "JobID: 233750695, UserID: 1021114, CPU Utilization: 1.2381, jStatus: done\n",
      "JobID: 233763255, UserID: 1018152, CPU Utilization: 1.3608, jStatus: exit\n",
      "JobID: 233764073, UserID: 1023238, CPU Utilization: 1.2096, jStatus: done\n",
      "JobID: 233771322, UserID: 1018152, CPU Utilization: 2.7496, jStatus: exit\n",
      "JobID: 233771326, UserID: 1018152, CPU Utilization: 2.7684, jStatus: exit\n",
      "JobID: 233771450, UserID: 1018152, CPU Utilization: 3.0575, jStatus: exit\n",
      "JobID: 233826910, UserID: 103962, CPU Utilization: 1.6118, jStatus: done\n",
      "JobID: 233827882, UserID: 103962, CPU Utilization: 1.5370, jStatus: done\n",
      "JobID: 233828150, UserID: 103962, CPU Utilization: 1.5653, jStatus: done\n",
      "JobID: 233845162, UserID: 99328, CPU Utilization: 1.2242, jStatus: done\n",
      "JobID: 234029484, UserID: 1023315, CPU Utilization: 1.2499, jStatus: done\n",
      "JobID: 234082868, UserID: 66267, CPU Utilization: 1.2400, jStatus: done\n",
      "JobID: 234109142, UserID: 1023315, CPU Utilization: 1.2307, jStatus: done\n",
      "JobID: 234130677, UserID: 69430, CPU Utilization: 1.4394, jStatus: done\n",
      "JobID: 234135366, UserID: 103962, CPU Utilization: 1.5674, jStatus: done\n",
      "JobID: 234188585, UserID: 105366, CPU Utilization: 1.4845, jStatus: done\n"
     ]
    }
   ],
   "source": [
    "import pymongo\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_cpu_utilization(collection, start_time, end_time):\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    cpu_utilization_stats = []\n",
    "    \n",
    "    cursor = collection.find(query, {\"cpuTime\": 1, \"numProcessors\": 1, \"runTime\": 1, \"jobId\": 1, \"userId\": 1, \"jStatus\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "    \n",
    "    for document in cursor:\n",
    "        cpu_time = document.get('cpuTime', 0)  # Default to 0 if key is missing\n",
    "        num_processors = document.get('numProcessors', 1)  # Default to 1 if key is missing\n",
    "        run_time = document.get('runTime', 1)  # Default to 1 to avoid division by zero\n",
    "        job_id = document.get('jobId', 'N/A')\n",
    "        user_id = document.get('userId', 'N/A')\n",
    "        j_status = document.get('jStatus', 'unknown')\n",
    "        \n",
    "        if num_processors > 1 and run_time > 0:\n",
    "            cpu_utilization = cpu_time / (num_processors * run_time)\n",
    "            cpu_utilization_stats.append({\n",
    "                \"jobId\": job_id,\n",
    "                \"userId\": user_id,\n",
    "                \"cpu_utilization\": cpu_utilization,\n",
    "                \"jStatus\": j_status\n",
    "            })\n",
    "    \n",
    "    return cpu_utilization_stats\n",
    "\n",
    "def find_top_percentile_cpu_jobs(cpu_utilization_stats, percentile):\n",
    "    utilization_values = [stat[\"cpu_utilization\"] for stat in cpu_utilization_stats]\n",
    "    \n",
    "    threshold = np.percentile(utilization_values, percentile)\n",
    "    \n",
    "    top_jobs = [stat for stat in cpu_utilization_stats if stat[\"cpu_utilization\"] > threshold]\n",
    "    \n",
    "    return top_jobs, threshold\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract CPU utilization statistics\n",
    "    cpu_utilization_stats = extract_cpu_utilization(collection, start_time, end_time)\n",
    "    \n",
    "    # Find jobs above the 99.9th percentile\n",
    "    top_cpu_jobs, cpu_threshold = find_top_percentile_cpu_jobs(cpu_utilization_stats, 99.9)\n",
    "    \n",
    "    # Print jobId, userId, CPU utilization, and jStatus for the top jobs\n",
    "    print(f\"99.9th percentile CPU utilization threshold: {cpu_threshold:.4f}\")\n",
    "    for job in top_cpu_jobs:\n",
    "        print(f\"JobID: {job.get('jobId', 'N/A')}, UserID: {job.get('userId', 'N/A')}, CPU Utilization: {job['cpu_utilization']:.4f}, jStatus: {job['jStatus']}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "116b3226-1660-40e8-a9c8-78e5816aa935",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table of Memory Utilization > 6 and Job Status:\n",
      "jStatus                  done    exit\n",
      "HighMemoryUtilization                \n",
      "False                  924193  151021\n",
      "True                       46       5\n"
     ]
    }
   ],
   "source": [
    "import pymongo\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_utilization(collection, start_time, end_time):\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    memory_utilization_stats = []\n",
    "    \n",
    "    mem_pattern = re.compile(r\"mem=(\\d+\\.?\\d*)\")\n",
    "    \n",
    "    cursor = collection.find(query, {\"maxRMem\": 1, \"resReq\": 1, \"numProcessors\": 1, \"jobId\": 1, \"userId\": 1, \"jStatus\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "    \n",
    "    for document in cursor:\n",
    "        max_rmem = document.get('maxRMem', 0)  # Default to 0 if key is missing\n",
    "        res_req = document.get('resReq', '')\n",
    "        num_processors = document.get('numProcessors', 1)  # Default to 1 if key is missing\n",
    "        \n",
    "        match = mem_pattern.search(res_req)\n",
    "        if match:\n",
    "            requested_mem = float(match.group(1))\n",
    "            \n",
    "            if requested_mem > 0:\n",
    "                # Adjust requested memory for the number of processors\n",
    "                requested_mem_adjusted = requested_mem * num_processors\n",
    "                \n",
    "                # Convert requested memory to KB\n",
    "                requested_mem_adjusted_kb = requested_mem_adjusted * 1024\n",
    "                \n",
    "                # Calculate memory utilization (not constrained to 1)\n",
    "                memory_ratio = max_rmem / requested_mem_adjusted_kb\n",
    "                \n",
    "                # Collect memory utilization statistics\n",
    "                memory_utilization_stats.append({\n",
    "                    \"jobId\": document.get('jobId', 'N/A'),\n",
    "                    \"userId\": document.get('userId', 'N/A'),\n",
    "                    \"maxRMem\": max_rmem,\n",
    "                    \"resReq\": res_req,\n",
    "                    \"numProcessors\": num_processors,\n",
    "                    \"memory_utilization\": memory_ratio,\n",
    "                    \"jStatus\": document.get('jStatus', 'unknown')  # Ensure jStatus is included\n",
    "                })\n",
    "    \n",
    "    return memory_utilization_stats\n",
    "\n",
    "def create_contingency_table(memory_utilization_stats, threshold):\n",
    "    # Create a DataFrame from the statistics\n",
    "    df = pd.DataFrame(memory_utilization_stats)\n",
    "    \n",
    "    # Classify jobs based on memory utilization\n",
    "    df['HighMemoryUtilization'] = df['memory_utilization'] > threshold\n",
    "    \n",
    "    # Create a contingency table\n",
    "    contingency_table = pd.crosstab(df['HighMemoryUtilization'], df['jStatus'])\n",
    "    \n",
    "    return contingency_table\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract memory utilization statistics\n",
    "    memory_utilization_stats = extract_memory_utilization(collection, start_time, end_time)\n",
    "    \n",
    "    # Create a contingency table for memory utilization greater than 7\n",
    "    contingency_table = create_contingency_table(memory_utilization_stats, 6)\n",
    "    \n",
    "    # Print the contingency table\n",
    "    print(\"Contingency Table of Memory Utilization > 6 and Job Status:\")\n",
    "    print(contingency_table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "844bd027-d1cc-4d15-88db-5cf4252804ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.999th percentile memory utilization threshold: 8.8669\n",
      "JobID: 233798823, UserID: 1015853, Memory Utilization: 9.7518, maxRMem: 9975808, resReq: rusage[mem=999] span[hosts=1], numProcessors: 1, runTime: 23, CPU Utilization: 0.0000, jStatus: exit\n",
      "JobID: 233798823, UserID: 1015853, Memory Utilization: 9.4254, maxRMem: 9641984, resReq: rusage[mem=999] span[hosts=1], numProcessors: 1, runTime: 30, CPU Utilization: 0.0000, jStatus: exit\n",
      "JobID: 234043316, UserID: 103327, Memory Utilization: 9.8359, maxRMem: 1289216, resReq: rusage[mem=128], numProcessors: 1, runTime: 191, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234043307, UserID: 103327, Memory Utilization: 8.9375, maxRMem: 1171456, resReq: rusage[mem=128], numProcessors: 1, runTime: 206, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234043311, UserID: 103327, Memory Utilization: 9.0156, maxRMem: 1181696, resReq: rusage[mem=128], numProcessors: 1, runTime: 211, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234043313, UserID: 103327, Memory Utilization: 9.0469, maxRMem: 1185792, resReq: rusage[mem=128], numProcessors: 1, runTime: 214, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234043314, UserID: 103327, Memory Utilization: 9.0000, maxRMem: 1179648, resReq: rusage[mem=128], numProcessors: 1, runTime: 230, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234044660, UserID: 103327, Memory Utilization: 8.9375, maxRMem: 1171456, resReq: rusage[mem=128], numProcessors: 1, runTime: 214, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234044663, UserID: 103327, Memory Utilization: 9.0156, maxRMem: 1181696, resReq: rusage[mem=128], numProcessors: 1, runTime: 226, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234044666, UserID: 103327, Memory Utilization: 9.7734, maxRMem: 1281024, resReq: rusage[mem=128], numProcessors: 1, runTime: 258, CPU Utilization: 0.0000, jStatus: done\n",
      "JobID: 234044665, UserID: 103327, Memory Utilization: 9.0000, maxRMem: 1179648, resReq: rusage[mem=128], numProcessors: 1, runTime: 259, CPU Utilization: 0.0000, jStatus: done\n",
      "\n",
      "Contingency Table for jStatus and RunTime (less than or greater than 60) for jobs with memory utilization > 1.5:\n",
      "runTimeCategory   < 60  >= 60\n",
      "jStatus                      \n",
      "done               304     79\n",
      "exit             16357     71\n"
     ]
    }
   ],
   "source": [
    "import pymongo\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def mongo_connect(uri):\n",
    "    try:\n",
    "        client = pymongo.MongoClient(uri)\n",
    "    except pymongo.errors.ConfigurationError:\n",
    "        print(\"Invalid MongoDB URI\")\n",
    "        exit(1)\n",
    "    except pymongo.errors.OperationFailure:\n",
    "        print(\"Invalid MongoDB credentials\")\n",
    "        exit(1)\n",
    "    return client\n",
    "\n",
    "def extract_memory_utilization(collection, start_time, end_time):\n",
    "    query = {\n",
    "        \"submitTime\": { \"$gt\": start_time },\n",
    "        \"endTime\": { \"$lte\": end_time }\n",
    "    }\n",
    "    \n",
    "    memory_utilization_stats = []\n",
    "    \n",
    "    mem_pattern = re.compile(r\"mem=(\\d+\\.?\\d*)\")\n",
    "    \n",
    "    cursor = collection.find(query, {\"maxRMem\": 1, \"resReq\": 1, \"numProcessors\": 1, \"jobId\": 1, \"userId\": 1, \"jStatus\": 1, \"runTime\": 1, \"_id\": 0}).sort(\"submitTime\", pymongo.ASCENDING)\n",
    "    \n",
    "    for document in cursor:\n",
    "        max_rmem = document.get('maxRMem', 0)  # Default to 0 if key is missing\n",
    "        res_req = document.get('resReq', '')\n",
    "        num_processors = document.get('numProcessors', 1)  # Default to 1 if key is missing\n",
    "        run_time = document.get('runTime', 1)  # Default to 1 to avoid division by zero\n",
    "        \n",
    "        match = mem_pattern.search(res_req)\n",
    "        if match:\n",
    "            requested_mem = float(match.group(1))\n",
    "            \n",
    "            if requested_mem > 0:\n",
    "                # Adjust requested memory for the number of processors\n",
    "                requested_mem_adjusted = requested_mem * num_processors\n",
    "                \n",
    "                # Convert requested memory to KB\n",
    "                requested_mem_adjusted_kb = requested_mem_adjusted * 1024\n",
    "                \n",
    "                # Calculate memory utilization (not constrained to 1)\n",
    "                memory_ratio = max_rmem / requested_mem_adjusted_kb\n",
    "                \n",
    "                # Calculate CPU utilization\n",
    "                cpu_time = document.get('cpuTime', 0)  # Default to 0 if key is missing\n",
    "                cpu_utilization = cpu_time / (num_processors * run_time) if run_time > 0 else 0\n",
    "                \n",
    "                # Collect memory utilization statistics\n",
    "                memory_utilization_stats.append({\n",
    "                    \"jobId\": document.get('jobId', 'N/A'),\n",
    "                    \"userId\": document.get('userId', 'N/A'),\n",
    "                    \"maxRMem\": max_rmem,\n",
    "                    \"resReq\": res_req,\n",
    "                    \"numProcessors\": num_processors,\n",
    "                    \"memory_utilization\": memory_ratio,\n",
    "                    \"runTime\": run_time,\n",
    "                    \"cpu_utilization\": cpu_utilization,\n",
    "                    \"jStatus\": document.get('jStatus', 'unknown')  # Ensure jStatus is included\n",
    "                })\n",
    "    \n",
    "    return memory_utilization_stats\n",
    "\n",
    "def find_top_percentile_jobs(memory_utilization_stats, percentile):\n",
    "    # Extract memory utilization values\n",
    "    utilization_values = [stat[\"memory_utilization\"] for stat in memory_utilization_stats]\n",
    "    \n",
    "    # Calculate the percentile value\n",
    "    threshold = np.percentile(utilization_values, percentile)\n",
    "    \n",
    "    # Filter jobs that are above the percentile threshold\n",
    "    top_jobs = [stat for stat in memory_utilization_stats if stat[\"memory_utilization\"] > threshold]\n",
    "    \n",
    "    return top_jobs, threshold\n",
    "\n",
    "def create_contingency_table(memory_utilization_stats, memory_threshold=1.5, runtime_threshold=60):\n",
    "    # Filter jobs with memory utilization greater than the threshold\n",
    "    filtered_jobs = [job for job in memory_utilization_stats if job[\"memory_utilization\"] > memory_threshold]\n",
    "    \n",
    "    # Categorize runTime\n",
    "    for job in filtered_jobs:\n",
    "        job[\"runTimeCategory\"] = \">= 60\" if job[\"runTime\"] >= runtime_threshold else \"< 60\"\n",
    "    \n",
    "    # Create a DataFrame from the filtered jobs\n",
    "    df = pd.DataFrame(filtered_jobs)\n",
    "    \n",
    "    # Generate the contingency table\n",
    "    contingency_table = pd.crosstab(df[\"jStatus\"], df[\"runTimeCategory\"])\n",
    "    \n",
    "    return contingency_table\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = 1724043600  # Define the start time\n",
    "    end_time = 1724648400    # Define the end time\n",
    "    \n",
    "    client = mongo_connect(\"mongodb://localhost:27017\")\n",
    "    db = client['lsf']\n",
    "    collection = db['finished_jobs']\n",
    "    \n",
    "    # Extract memory utilization statistics\n",
    "    memory_utilization_stats = extract_memory_utilization(collection, start_time, end_time)\n",
    "    \n",
    "    # Find jobs above the 99.999th percentile\n",
    "    top_jobs, threshold = find_top_percentile_jobs(memory_utilization_stats, 99.999)\n",
    "    \n",
    "    # Print jobId, userId, memory utilization, maxRMem, resReq, numProcessors, runTime, cpu_utilization, and jStatus for the top jobs\n",
    "    print(f\"99.999th percentile memory utilization threshold: {threshold:.4f}\")\n",
    "    for job in top_jobs:\n",
    "        print(f\"JobID: {job['jobId']}, UserID: {job['userId']}, Memory Utilization: {job['memory_utilization']:.4f}, \"\n",
    "              f\"maxRMem: {job['maxRMem']}, resReq: {job['resReq']}, numProcessors: {job['numProcessors']}, \"\n",
    "              f\"runTime: {job['runTime']}, CPU Utilization: {job['cpu_utilization']:.4f}, jStatus: {job['jStatus']}\")\n",
    "    \n",
    "    # Create and print the contingency table\n",
    "    contingency_table = create_contingency_table(memory_utilization_stats)\n",
    "    print(\"\\nContingency Table for jStatus and RunTime (less than or greater than 60) for jobs with memory utilization > 1.5:\")\n",
    "    print(contingency_table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dd0e6f1-7de1-4585-890b-c40a829adbb9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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