CDCAD_ompF_PerturbationAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc1356fe",
   "metadata": {},
   "source": [
    "# Perturbation analysis for highest expressing gene - Fig S12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e82db98",
   "metadata": {},
   "source": [
    "# Load simulation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4205da07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "Computing...\n",
      "CPU times: user 8.63 s, sys: 2.36 s, total: 11 s\n",
      "Wall time: 8.31 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%load_ext autoreload\n",
    "\n",
    "from analysis_utils import *\n",
    "from joblib import Parallel, delayed\n",
    "import multiprocessing  \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Growth rate = 3.0\n",
    "#Computes the translation latencies from our model with simulations containing\n",
    "#1 to 14 cognate ternary complexes in the voxel (14 to 42 not included because\n",
    "#the probability of voxels with that number of cognates is ~0)\n",
    "path = \"./data/\"\n",
    "ensmbl_latency_dict = dict()\n",
    "rxndiff=dict()\n",
    "scalingFactor = 200\n",
    "NR_scaledrates = {'k1r':718*200/scalingFactor,'k2f':1475*200/scalingFactor,'k2r_nr':1120*200/scalingFactor,'k3_nr':6*200/scalingFactor,'k4':209*200/scalingFactor}\n",
    "\n",
    "\n",
    "data = \"191003_2210/\"\n",
    "for i in range(1,7):\n",
    "    sim_i = computeTransportRxnTimes(path+data,100*(i-1),100*(i-1)+100,cogtRNANum=i, ribosomeNum = 7,scaling=scalingFactor,NR_scaling = NR_scaledrates)\n",
    "    ensmbl_latency_dict[i] = (CellLatencies(sim_i))\n",
    "\n",
    "data = \"191105_0908/\"\n",
    "for i in range(7,14):\n",
    "    sim_i = computeTransportRxnTimes(path+data, 100*(i-1-6),100*(i-1-6)+100,cogtRNANum=i, ribosomeNum = 7,scaling=scalingFactor,NR_scaling = NR_scaledrates)\n",
    "    ensmbl_latency_dict[i] = (CellLatencies(sim_i))\n",
    "    \n",
    "#Experimentally obtained codon usage frequencies and tRNA abundances at growth rate = 2.5 dbl/hr\n",
    "gr25_Codon = np.array([2.36, 1.26, 45.55, 34.17, 16.97, 57.86, 19.27, 33.74, 14.98, 22.31, 43.18, 7.67, 24.11, 24.87, 39.49, 11.81, 0.03, 0.63, 2.19, 9.31, 17.22, 55.01, 5.61, 29.21, 21.67, 0.52, 15.79, 43.86, 4.17, 2.61, 20.64, 26.7, 7.03, 0.19, 2.76, 3.81, 6.72, 16.52, 4.27, 2.73, 7.92, 23.25, 2.51, 1.98, 16.33, 11.68, 0.62, 0.67, 43.82, 20.59, 27.28, 7.01, 6.78, 14.21, 60.75, 0.82, 3.86, 4.09, 28.82, 5.18, 4.38, 1.09])\n",
    "gr25_pCodon = gr25_Codon/np.sum(gr25_Codon)\n",
    "gr25_ptRNA = [0.0602409638554217, 0.010542168674698798, 0.07379518072289158, 0.00602409638554217, 0.010542168674698798, 0.00602409638554217, 0.021084337349397596, 0.043674698795180725, 0.021084337349397596, 0.01204819277108434, 0.01807228915662651, 0.08433734939759038, 0.03162650602409639, 0.07228915662650603, 0.01204819277108434, 0.07078313253012049, 0.06325301204819278, 0.01656626506024097, 0.009036144578313254, 0.027108433734939763, 0.010542168674698798, 0.03012048192771085, 0.013554216867469882, 0.015060240963855425, 0.0075301204819277125, 0.010542168674698798, 0.0075301204819277125, 0.003012048192771085, 0.021084337349397596, 0.004518072289156627, 0.01656626506024097, 0.01204819277108434, 0.0015060240963855425, 0.009036144578313254, 0.01656626506024097, 0.019578313253012052, 0.015060240963855425, 0.027108434, 0.05873493975903616, 0.019578313]\n",
    "gr25_ptRNA = gr25_ptRNA/np.sum(gr25_ptRNA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f9158a",
   "metadata": {},
   "source": [
    "## Load original GA-discovered distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f645c147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "[0.07679412681130282, 0.08523592085132133, 0.08088572005747867, 0.08180197092640702, 0.08523592085132133, 0.024062229334562812, 0.07363719360634789, 0.040615551926959516, 0.035132466359930684, 0.03579538533589475, 0.017223565954440627, 0.053854319214231366, 0.04806219930807657, 0.00607268937513586, 0.0821727727872197, 0.009173707392039838, 0.01509233546229588, 0.012879400346772974, 0.007925944541951644, 0.00858326753920192, 0.008341315073553563, 0.004834282746883225, 0.003358676461886318, 0.002556160310697267, 0.006011375758338082, 0.0041019009053734025, 0.001806989589274271, 0.0069119031957451645, 0.009730425206536079, 0.007295772389366002, 0.005322971818128626, 0.0031959367763178207, 0.004354597709466667, 0.0015430967674765368, 0.006413097821672596, 0.01804501791858015, 0.008903043473362408, 0.003553542676878075, 0.003283062964527097, 0.010200142453043461]\n"
     ]
    },
    {
     "data": {
      "image/png": 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eWpo3b44rV65g0aJFmDNnDt6+fYtRo0bB19cXK1euNMik54cPH8a0adMAAH369MHatWthZmamVqZZs2YYN24c4uPjFX9/IiIiIiKi7JDpS1jPnz/HihUr0L9/f7Rp0wZt2rRBv379sGzZMjx58kQuZ2ZmhkmTJiE4OBidO3eGEAKXL19Go0aN0K9fP0W7cCYnJ2PYsGEAAFdXV/j6+qZK8D5kbm6u2HsTERERERFlpwwneQkJCRg3bhxKlSqFUaNGwd/fH/v378f+/fuxceNGjB49Gs7Ozvj6668RFxcnr1esWDFs27YNQUFBKF++PIQQ2LhxI8qXL49ly5Yp0oUzKCgIt2/fBgBMnDgRpqZZcsGSiIiIiIgo22U4yevSpQt+/PFHJCQkaJ3cPDExEcuXL0enTp1Sre/l5YUrV65g3rx5sLa2Rnh4OEaPHg03NzecPHkyUxu1bds2AIAkSWjbtq38elhYGG7fvo2wsLBMxSciIiIiIsqpMpTk+fn5Yffu3QAAKysrjBkzBhcvXsTbt2/x5s0bXLp0CePGjYONjQ2EENi/fz/WrVuXKo6pqSkmTpyI4OBgdO3aFUII/Pfff2jSpAn69OmDFy9eZGijTp8+DQBwcnKCra0tAgICULVqVeTPnx8uLi7Inz8/ypcvjx9++EHtKiMREREREVFul6Ek78OEbc+ePfjhhx/g6uoKOzs72Nvbo1q1avj++++xd+9eeQL09evXa41XrFgxbN26FQcPHkSFChUghMDmzZvx888/61235ORkBAcHAwAKFCiAUaNGoVevXqnmwrt16xbGjx8PT09PvH37Vu/3ISIiIiIiyokylORduXIFkiShevXq8PDw0FqucePGqF69OoQQOk043qxZM/z3339YsGABbGxsMlI1hIeHy/f1XblyBcuWLUORIkWwadMmhIWFITo6GsePH0fdunUBAP/88w8GDBigNV5cXBwiIiKQlJSUofoQERERERFlpQwleSlTDuTJkyfdsinJmq7TFJiammL8+PEIDg5G48aN9a5bVFSU/Dg2NhbW1tY4evQoevXqhbx588LKygqNGzfGkSNH4OrqCgD4/fffcebMGY3x5s2bB3t7e5w7d07vuhAREREREWW1DCV5zs7OEELg3LlzePbsmdZyT58+xfnz5yFJEpycnPR6j6JFi6Jp06Z6183S0lLt+aBBg1C+fPlU5aysrDBnzhz5+datWzXGmzx5MsLDw+Hu7q53XYiIiIiIiLJahpK8jh07AnjflbF58+Y4ePBgqjIHDx7EZ599htjYWLV1DM3W1lbteYsWLbSWbdasmTy9grYrdRYWFrCzs4OJiYlylSQiIiIiIjKQDE0gN2HCBGzevBkPHjzAjRs38NlnnyFfvnwoVaoUAODBgwdq0xSUKFECEyZMUKbG6bCwsICjoyNevXolv7c2lpaWKFCgAJ4/fy6XJyIiIiIiys0ydCXP1tYWR44ckQdVEULg9evXuHjxIi5evIjXr1/Lr1erVg2HDx+GnZ2d0nXXqnLlyvLj9AZMSVnOCdOJiIiIiMgYZHgydCcnJ5w9exabN2+Gt7c3ihcvDnNzc5ibm6N48eJo3749Nm7ciPPnz6NMmTJK1jldHw7Ycu/ePa3lIiIiEBoaCuD9NA5ERERERES5XaYuX5mYmKBHjx7o0aOHUvVRROfOnfHdd98BeD9yZufOnTWW+/333yGEAAA0atQoy+pHRERERERkKBm+kpeTVatWDa1atQIAbNmyBYcPH05V5vnz5/jmm28AAObm5ujfv3+W1pGIiIiIiMgQjDLJA4AlS5bAwcEBycnJaNu2LSZPnowTJ07g/PnzWLVqFdzd3fH48WMAwKxZs9hdk4iIiIiIjILRjjbi4uKC3bt3o0uXLnjx4gXmz5+P+fPnq5WRJAlTp07NspE/iYiIiIiIDE3vK3l37941RD10ktYgKpo0bNgQ165dw/Tp0+Hq6go7OztYWlrC2dkZ/fv3x4ULFzBr1iwD1ZaIiIiIiCjr6Z3kVapUCcOHD8ejR48MUR+NHj16hKFDh6JSpUp6r5s/f37MmDEDly5dQnh4OGJiYnDv3j2sW7cONWrUMEBtiYiIiIiIso/eSV5CQgJ+/vlnlC1bFj4+Pjh9+rQh6gUAOHXqFHr16oWyZcvil19+QUJCgsHei4iIiIiIyBjoneQNGjQIkiQhISEBAQEBaNCgAapUqYK5c+fi+vXrma7Q1atXMWvWLFSqVAkNGzbEr7/+ioSEBEiShEGDBmU6PhERERERkTHTe+CVNWvWYNiwYZgwYYI8NcGNGzcwbdo0TJs2DcWKFUPjxo3h7u6OypUro0yZMihatCgsLCzU4sTGxuLp06e4c+cOrl69ivPnz+P48eN4/vy5XCZlDrtmzZrh+++/Z/dKIiIiIiKidGRodM0aNWrg4MGDOHHiBObOnYugoCA5IXvy5Am2bNmCLVu2qK1jYWEBKysrCCEQGxuLuLg4jbFT4kiShJYtW2LKlCmcqJyIiIiIiEhHmZpCoVGjRvjzzz9x69YtrFmzBr/++iuePn2qsWxsbCxiY2PTjVm4cGH06NEDgwcPRoUKFTJTPSIiIiIiok+OIvPkubi44IcffsDChQtx+vRpHDhwAMePH8fFixcRERGR5rq2traoUaMGGjdujJYtW6JevXpQqYx2jnYiIiIiIiKDUnQydEmSUK9ePdSrV09+7fHjx3jw4AFCQ0MRFRUFALCxsUGBAgVQqlQpFC9eXMkqEBERERERfdIUTfI0KV68OBM5IiIiIiKiLMJ+kUREREREREaESR4REREREZERUTTJ2759OxITE5UMSURERERERHpQNMn7/PPPUaxYMYwfPx7BwcFKhiYiIiIiIiIdKN5dMzQ0FIsXL0blypXRsGFDbNiwATExMUq/DREREREREWmgaJJnZ2cHIYT8d+rUKQwYMABFihTBsGHDcP78eSXfjoiIiIiIiD6iaJL3/PlzbNiwAY0bNwYAOdmLiIjAmjVrUKdOHVSvXh0rV67E27dvlXxrIiIiIiIigsLz5FlaWsLHxwc+Pj64c+cOfH194e/vj2fPnkEIAQC4cuUKvvrqK4wfPx6dO3fGwIED0bRpUyWrQUSUpsDLgRle19vVW8GaEBERESnPYFMolC1bFvPmzcPDhw8RGBiI9u3bw9TUVL66Fxsbi4CAADRr1gzlypXD/Pnz8fz5c0NVh4iIiIiI6JNg8HnyTExM0K5dO+zatQsPHz7E3LlzUa5cOQD/68557949TJ06FSVLlkSHDh2wZ88eJCcnG7pqRERERERERidLJ0MvXLgwJk2ahJs3b+LYsWPo3bs3rKys5GQvMTERu3fvhre3N0qWLIlvvvkG9+/fz8oqEhERERER5WpZmuR9qHHjxvL9eosWLYKlpSUkSZITvqdPn2LevHkoV64c2rRpg+PHj2dXVYmIiIiIiHKNbEvyAODy5cuYOnUqZs+ejbi4OACAJEnyciEEkpOTsX//fnh6eqJt27Z49uxZdlWXiIiIiIgox1N0dE1dREREYPPmzfD19cXFixfl11NG37S3t0evXr3g6uqKLVu24NixY/Kyffv2oX79+jh37hwKFCiQ1VUnIiIiIiLK8bLsSt5ff/2FPn36oEiRIhgxYgQuXryoNnF6nTp14Ovri6dPn2LFihUYPHgwjhw5gps3b2LAgAFQqd5X9eHDh5g9e3ZWVZuIiIiIiChXMWiS9/z5c8yfPx8uLi7w8PDA5s2bERMTIyd29vb2+PLLL3H58mWcOnUK/fv3h5WVlVqMcuXKYe3atfjtt9/k13bv3m3IahMREREREeVainfXTE5Oxp49e+Dr64s///wTSUlJAP7XHRMA6tatiyFDhqBbt26pkjptOnXqhCpVquDq1at4/Pix0tUmIiIiIiIyCoomeZMnT4a/v788qfmHiZ29vT18fHwwZMgQVKlSJUPxnZ2dcfXqVSQmJipSXyIiIiIiImOjaJK3YMECeRqEFPXq1ZOv2llaWmYqfsp9eURERERERKSZ4t01hRBwcHBA79698cUXX6By5cqKxZ49eza+/vprxeIREREREREZG0WTvPr162PIkCH4/PPPM33VThMlE0YiIiIiIiJjpGiSd/LkSSXDERERERERkZ54kxsREREREZERYZJHRERERERkRBRN8pKTk+Hp6Qk3Nzc0aNAA0dHReq2/atUquLm5wc3NDdu3b1eyakRERERERJ8ERZO8/fv349ixY7h8+TLc3NxgbW2t1/odO3bE1atXcfnyZSxZskTJqhEREREREX0SFE3y9u7dKz/28fHRe/0iRYqgWbNmEELgzJkzCAsLU7J6RERERERERk/RJO/cuXMAgDx58qB27doZitGsWTMA77t+psQjIiIiIiIi3Sia5N29exeSJKFChQoZjvHhXHh3795VolpERERERESfDEWTvMjISACAnZ1dhmN8uG54eHim60RERERERPQpUTTJs7W1BQC8ffs2wzE+XNfS0jKTNSIiIiIiIvq0KJrkFSxYEEIIBAcHIz4+PkMxLl26pBaPiIiIiIiIdKdoklenTh0AQHR0NHbs2KH3+snJydi8ebP8vGbNmorVjYiIiIiI6FNgqmSwNm3awN/fHwAwceJEeHp6olChQjqvP3/+fAQHB0OSJDg7O2dqABcioqwQeDkww+t6u3orWBMiIiKi9xS9ktepUyeUK1cOAPD48WN4eHjg8uXL6a6XlJSE6dOnY9q0afJrEydOVLJqREREREREnwRFr+SZmJhgxYoVaN26NZKTkxEcHAx3d3e0bt0anTt3Rs2aNeHo6AgLCwuEh4fj9u3b+Ouvv7BhwwY8fPgQQghIkoT69etj4MCBSlaNiIiIiIjok6BokgcAzZs3x6pVqzBs2DAAQGJiInbv3o3du3drXScluQOAihUr4o8//oBKpehFRiIiIiIiok+CQTKpwYMHIygoCEWLFgXwPolL6y9Fv379cPbsWeTNm9cQ1SIiIiIiIjJ6Brtc5unpiXv37mHt2rXw8vKCjY2NxnLlypXD8OHDcfXqVaxbtw7W1taGqhIREREREZHRU7y75ofMzMwwYMAADBgwAElJSXj06BHCwsIQFxcHBwcHFC5cmFftiIiIiIiIFGTQJO9DJiYmcHJygpOTU1a9JRERERER0SeHo5sQEREREREZESZ5RERERERERoRJHhERERERkREx2D157969w/bt2/H3338jODgYb9++RXR0tNqUCWmRJAl37941VPWIiIiIiIiMkkGSvMWLF2PGjBmIiorK0PofTo5OREREREREulM8yRs1ahRWrFih8xU7IiIiIiIiUo6iSd7Ro0exfPly+SqctbU1unTpgoYNG6JYsWKc6JyIiIiIiMjAFE3y1qxZIz+uVq0a9uzZg+LFiyv5FkRERERERJQGRUfX/PvvvwG8HzTlt99+Y4JHRERERESUxRRN8l69egVJklC5cmW4uLgoGZqIiIiIiIh0oGiSZ29vDwBwdHRUMiwRERERERHpSNEkr0yZMhBC4NWrV0qGJSIiIiIiIh0pmuR169YNAHDt2jW8ePFCydBERERERESkA0WTvP79+6NUqVIQQuDbb79VMjQRERERERHpQNEkz9bWFtu2bYONjQ3Wrl2Lb775BsnJyUq+BREREREREaVB0XnyHj58iIIFC2Lr1q3o06cP5s2bhx07dmDAgAGoV68eChcuDHNzc53jlSxZUsnqERERERERGT1FkzwnJydIkiQ/F0Lg5s2bmDRpkt6xJElCYmKiktUjIiIiIiIyeop210whhADwPlH7OOlL7+/DckqbOHGiXCdJknDs2DHF34OIiIiIiCg7KXolD4BaopaZ9ZV26dIlLF682CCxiYiIiIiIcgpFk7ycOshKcnIyhgwZgsTERBQsWBAvX77M7ioREREREREZhEG6a+Y0y5Ytw7lz51ChQgUMHDgwu6tDRERERERkMEaf5D18+BDTpk0DAKxevVqv0T2JiIiIiIhyG6NP8r788ku8e/cOffv2RZMmTbK7OkRERERERAZl1Eneb7/9hj179iBfvnz44Ycfsrs6REREREREBqf46Jofio6OxubNm3HkyBH8+++/CA0NRXh4OABonAPv8OHDSEpKAgA0b95cbfoFfb19+xajRo0CACxYsAAFChTIcCyinCzwcmCm1vd29VaoJkRERESUExgsyVu+fDmmT58uJ3WA+vx5mqxZswbbt28HAOzevRutW7fO8PtPmDABz58/R4MGDTjYChERERERfTIU766ZnJyMbt264euvv0Z4eHiqic7TMnr0aLncxo0bM1yHEydOYO3atTA1NcXq1aszdUUwLi4OERER8hVGIiIiIiKinEzxJG/8+PHYtm2bnNi1atUKmzZtwqVLl9C4ceM0161bty5KlSoFIQQOHjyYofePj4/HkCFDIITA6NGjUaVKlQzFSTFv3jzY29vj3LlzmYpDRERERESUFRRN8m7cuIFly5YBAExNTbFp0ybs3bsXPXv2RLVq1WBlZZVujJYtWwIA3rx5g2vXruldh7lz5yI4OBglS5bE9OnT9V7/Y5MnT0Z4eDjc3d0zHYuIiIiIiMjQFE3y1q1bh6SkJEiShBkzZqBnz556x3Bzc5MfBwcH67VucHAw5s2bB+D9PYE2NjZ6v//HLCwsYGdnBxMTk0zHIiIiIiIiMjRFB145dOgQgPeJ0ejRozMUo0SJEvLjJ0+e6LXujz/+iPj4eJQuXRrR0dH49ddfU5W5evWq/PjIkSN4/vw5AKBdu3aKJIVERERERETZSdEk79GjR5AkSeeumZrY2dnJj9+9e6fXunFxcQCAe/fuoUePHumWnzVrlvz4/v37TPKIiIiIiCjXU7S7ZkpSlidPngzHiIqKkh9bW1tnuk5ERERERESfEkWTPEdHRwDAq1evMhzj3r17qeLpys/PT23KBk1/Hw7GcvToUfl1JyenDNeZiIiIiIgop1A0yStdujSEELh+/Trevn2boRj79++XH7u6uipUMyIiIiIiok+DokneZ599BuD9hOgrV67Ue/1r165hz549kCQJhQsXzvQcd0RERERERJ8aRZO8Xr16wcLCAgAwe/ZsnDx5Uud1X716ha5duyI5ORkAMHz4cCWrRkRERERE9ElQNMkrWbIkRo8eDSEE4uPj0aJFC8ydOzfNrpvx8fHYsGED3NzccPPmTUiShGLFimHUqFFKVo2IiIiIiOiToGiSB7yflqB169YQQiAuLg7Tpk1D4cKFUadOHVy6dEku1759e9StWxf29vYYMGAAnj59CiEErKyssGvXrkyN0JmWGTNmyIOtNG3a1CDvQURERERElF0UT/JMTEywY8cODBkyRE6m4uPjcf78ebx8+RKSJAEA9u7di3PnziEuLk4uV6xYMRw5cgRubm5KV4uIiIiIiOiToHiSBwAWFhZYvXo1Dh8+jJYtW0KSJK1TGgCAvb09pkyZgsuXL6N27dqGqBIREREREdEnwdSQwT08PODh4YHQ0FCcPHkSV69exevXrxEVFQV7e3sUKlQIdevWRZ06dWBmZmbIqhAREREREX0SDJrkpShQoAA6dOiADh06ZMXbERERERERfbIM0l2TiIiIiIiIsgeTPCIiIiIiIiOSJd01iejTFHg5MMPrert6K1gTIiIiok+Hokmev7+/kuHQp08fReMREREREREZO0WTvH79+snz4GWWJElM8oiIiIiIiPSkeHfNlLnvdJUyhx4R5QzsYklERESUuyma5DVu3FjnK3lJSUl4+/Ytbt++jbi4OADvEz43NzfkyZNHyWoRERERERF9MhRN8o4dO6b3OgkJCfjzzz8xc+ZMXLx4ETExMdiyZQvKli2rZNWIiIiIiIg+Cdk+hYKZmRnat2+PM2fOoHv37rh+/To8PT3x+vXr7K4aERERERFRrpPtSV4KU1NT+Pn5wdnZGU+ePMHQoUOzu0pERERERES5To5J8gDA3NwcgwcPhhACgYGBePbsWXZXiYiIiIiIKFfJUUkeALi5uQF4PzDLX3/9lc21ISIiIiIiyl1yXJJnaWkpP378+HE21oSIiIiIiCj3yXFJ3rVr1+THJiYm2VgTIiIiIiKi3CdHJXkxMTFYunSp/NzZ2Tkba0NERERERJT75Jgk78yZM2jatClu3boFALCwsICnp2c214qIiIiIiCh3UXQy9AEDBuhVPiEhAW/evMGVK1fk++8kSQIAjBs3Dra2tkpWj4iIiIiIyOgpmuT5+fnJSZq+hBDy427dumHGjBkK1YqIiIiIiOjToXh3TSFEhv6A99MnBAQEYMuWLVCpckxPUiIiIiIiolxD0St506dP16u8ubk5bG1tUbJkSdSoUQMlSpRQsjpERERERESfnGxN8oiIiIiIiEhZ7BNJRERERERkRJjkERERERERGREmeUREREREREaESR4REREREZERUXTgFX9/fyXDpalPnz5Z9l5ERERERES5haJJXr9+/TI8Gbo+JElikkdERERERKSBokkeAHli849JkqRxmbbXiYiIiIiISH+KJnmNGzeWr+RdvXoVYWFhagmcs7Mz8ufPDwsLC0RGRiIkJAQREREAIK/n5uaGPHnyKFktIiIiIiKiT4aiSd6xY8eQlJSEKVOm4K+//oIQAh4eHhgxYgRatmwJa2vrVOvcvHkTW7ZswbJly/D27VtERERgzZo1qFGjhpJVIyIiIiIi+iQoPrrmhAkT8MMPP8DU1BRr167F4cOH0bFjR40JHgCUL18eM2bMwM2bN1GvXj3cvn0bzZs3R0hIiNJVIyIiIiIiMnqKJnn//PMPfvzxRwDArFmzMGDAAJ3XdXR0xL59+1C0aFGEhYVh0KBBSlaNiIiIiIjok6BokrdmzRoAgK2tLUaNGqX3+vb29hg+fDgA4OjRo7h3756S1SMiIiIiIjJ6iiZ5p06dgiRJqFSpEiwsLDIUw93dXX585swZpapGRERERET0SVA0yXvy5AkAwNQ04+O5qFT/q9LTp08zXSciIiIiIqJPiaJJnqWlJYQQuH79OhITEzMU4/Lly/LjjF4NJCIiIiIi+lQpmuSVLVsWAPDmzRusX79e7/VjYmLw008/pYpHREREREREulE0yevcuTMAQAiBMWPG4M8//9R53ZiYGHTv3h13794FADg4OKBZs2ZKVo+IiIiIiMjoKZrkDR06FMWLF4ckSYiKikK7du3g4+ODU6dOaV3nzZs3WLNmDSpWrIg9e/YAACRJwtSpU2FmZqZk9YiIiIiIiIxexkdI0cDW1hZbtmxB69at8e7dOyQnJyMgIAABAQGwtbVFpUqVkD9/fpibmyMyMhIhISG4d+8ehBAQQkCSJABAhw4dMGbMGCWrRkRERERE9ElQNMkDgAYNGuDw4cPo0aMH7t69CyEEACAiIkLjlAgfJneSJGHEiBFYvHix0tUiIiIiIiL6JCjaXTNFrVq1cO3aNXz//fcoV66c/HrKFbsP/wDAzMwMnTt3xqlTp7B06VKYmJgYolpERERERERGT/EreSnMzc0xbtw4jBs3DtevX8e5c+dw584dvHnzBvHx8bCzs0PBggVRo0YN1K5dG/b29oaqChERERER0SfDYEnehypVqoRKlSplxVsRERERERF90gzSXZOIiIiIiIiyB5M8IiIiIiIiI2Lw7pqRkZH4559/8O+//yI0NBTh4eEQQsDX19fQb/3JiY2JTXN5VFSU1vKmpqYwNdN8OMTFxEFA6BzrQyamJlrnO4yLjYMQQudYanFNTGBmrjlubGwskpKSdI71IZVKBXML83Tj6svExASWlpYalyXEJ+gdN2WfqVTaz9MkJCQgKTH9uJr2vwQJFlYWGssnJiQiMTFR51gfs7TSvB+SEpOQkJCgNVZ6rK2t5ZF61eImJSE2Vr0e+hwX5hbmGvdzcnIyYmJi9D7GUmg7foUQiI6OzlBMALCwsICpqebPsj51/Xj/m5uba/0sR0dHywNp6cvMzAzm5po/czExMUhOTs5QXFNTU1hYaD6GDfVZNlTcuLg4rZ+59KhUKlhZWWlcFh8fr/aZ04ckSbC2tta4LCEhAfHx8RmKCwA2NjYaX09MTERcXFyG4+rTRujDysoqzTYioywtLTUORGfINkLfdvdDbCPeYxvxHtuI/9HWRhiawZK8R48e4bvvvkNAQIDajkmZMkFTkufl5YXr169DkiQcPXoULi4uhqqeUfrd7/c0l98ofEPtefDzYPlxzYY14VJV8/7e++texMWqf2jSivWhKrWqoKp7VY3LDu86jPA34TrH+lC5yuVQq3Etjct+/fVXhISE6BzrQyVKl0DDlg01Lvvjjz9w/fp1veKlcHJyQr9+/TQuu3z6Mm5fu61XvJR95ujoiOINi2ssE3wpGFfPX9U5lrze82BYWFqgU/9OGsvfC76HCycv6BzrYz2G9dC47pOQJ/j74N9aY6Vn/PjxGhv+R48ewc/PL916adOqWys45HNI9XpoaChWrVql9zGWwrO9p8bXo6OjsXDhwgzFBICuXbuicuXKGpel10Z86OP937p1a9SuXVtj2RUrVmT4R2fTpk3RtGlTjcvWrVuHV69eZSiuu7s72rRpo3HZh22EvipVqoTPP/9c4zJDtRGHDh3CuXPnMhTX0dERX375pcZl//zzD44dO5ahuNbW1pgwYYLGZRcvXsS+ffsyFBcAZsyYofH1mzdvYtu2bRmOq08boY/hw4ejYMGCqV5PaSMyql+/fnByckr1uiHbiMzEZRvxHtuI99hG/I+2NsLQDJJWBgYGwtXVFevWrUNMTEyqKRO06d27N54/f47nz59jw4YNhqgaERERERGRUVM8yQsKCsLnn38ud8s0MzODl5cXvv76a5QpUybNdT///HP50u6uXbuUrhoREREREZHRUzTJi46ORv/+/eX+u61atcK9e/cQFBSExYsXo2zZsmmub21tjebNm0MIgeDgYDx//lzJ6hERERERERk9SWT0TlgNli9fjlGjRkGSJHh5eeHPP/9Uu9GwVatWOHDgACRJ0nrj6fz58zFlyhRIkoT9+/ejefPmSlUvU+rVq4fTp0+rvVa3bl2cOnUqS+sReDlQ67L0BlVoW7Wt2vM9V/bIj/UdeCWtWB/SZeAVXWOpxdUy8Iq3q7fajc26xPrQhwOveLt6qy1T8obpD/8fMzLwSso+U6lUCLoVpLGMrgOvaNr/GR14RZf/S10HXvk4Vnr0uWFan+Pi44FXUo6LlEEV9D3GUpiZm6GTW+r7HpUaVEFTW6HPwCsf738OqvAeB1V4j4Mq/A8HXnmPbcR7bCPeYxvxP0Yx8Mru3bvlx8uXL8/QBlWoUEF+fO/ePUXq9anQ9uM5xccfivTKp9D0Yz+jsdTiWlooFktt/Q8awczG0hZXSWbmZjCD5i9GbbQ1cGpxzcy0fuGmFSu9fWZqpv2EQGb+L01MTWBi+r8fNLpso05xTUwUP8aA91+MNjY2ih5jwPsvRqW2/WP61FWfOmj7Is8sbT88MstQn2VDxbWwsND6YzQzzM3Ntf54zgxd2x59mZqaak1OMkNTG6GElDZCaYZsIwwVl22EYeOyjXgvt7URhqZoWpkyYlC5cuUyPDJm3rx55cfh4eGK1IuIiIiIiOhToWi6GxoaCkmSULy45iHdiXKztLrK6uLj7p9ERERERIag6JU8W1tbAMhUX/EXL17Ij/Pnz5/pOhEREREREX1KFE3yihQpAiEEbty4keEbbP/55x/5sbOzs1JVIyIiIiIi+iQomuQ1atQIABAREYE///xT7/VjYmIQEBAA4P1NpA0aNFCyekREREREREZP0SSvU6f/DQc+btw4vYfiHTNmjHxfX7t27QwyUhAREREREZExUzTJa9asGZo2bQohBG7evAkvLy+EhISku15kZCSGDBmCNWvWAHg/PPC0adOUrBoREREREdEnQfHJJNauXYv69evj1atXOHv2LCpWrIh27drBw8MDL1++lMvt3LkTL1++xOnTpxEYGIiIiAgIISBJEmbNmoUqVaooXTUiIiIiIiKjp3iSV7p0aezbtw/t27fH06dPERcXhx07dmDHjh0AIM8k37VrV3mdDwdpGT16NCZPnpypOpw/fx779u3DyZMncf36dbx69QpmZmYoWrQoGjRogIEDB6Jhw4aZeg8iIiIiIqKcSPlp4QG4ubnhv//+w+jRo7FlyxYkJibKy1KSvI9H3yxVqhQWLFiAzz//PFPv3bhxY5w4cSLV6/Hx8bh9+zZu374NPz8/9OnTB7/88gvMzc0z9X5EREREREQ5iUGSPADIly8fNmzYgLlz5+LXX3/FiRMncPXqVbx+/RpRUVGwt7dHoUKFULduXbRs2RKdOnWCiYlJpt/36dOnAICiRYuia9euaNSoEUqWLImkpCScOnUKixYtwpMnT+Dv74+EhAR5NE8iIiIiIiJjYLAkL0WxYsUwduxYjB071tBvBQCoUKEC5s6di86dO6dKGuvWrQsfHx80aNAAt27dwpYtWzB06FA0btw4S+pGRERERERkaIqOrunp6Sn/6TKqpiHs2bMHn3/+udarggUKFMCiRYvk59u3b8+qqhERERERERmcolfyjh07BkmSUKJECTg5OSkZWlEeHh7y47t372ZjTYiIiIiIiJSl6JW8vHnzAgCcnZ2VDKu4uLg4+bES9wESERERERHlFIomeUWLFgUAxMbGKhlWccePH5cfV6xYMRtrQkREREREpCxFk7wmTZpACIHr168jISFBydCKSU5Oxvz58+XnmZ2ygYiIiIiIKCdRNMnr168fAODdu3fw8/NTMrRifvzxR5w9exYA0KlTJ9SsWTPN8nFxcYiIiEBSUlJWVI+IiIiIiChTFE3yatWqhVGjRkEIgXHjxuHvv/9WMnymHT9+HJMmTQIAFCxYED/99FO668ybNw/29vY4d+6coatHRERERESUaYomeQCwcOFCDB8+HJGRkfDw8MCoUaNw9epVpd9Gb9euXUPHjh2RmJgIS0tLbNu2DQULFkx3vcmTJyM8PBzu7u5ZUEsiIiIiIqLMUXQKBU9PT/mxra0tIiMjsWLFCqxYsQL29vZwdnaGra0tVKr0c0tJknD48GFF6nX//n20aNECb968gYmJCX799VedJ0C3sLCAhYUFR+EkIiIiIqJcwSDz5KVIeSyEwNu3b3Hp0iWd4ggh1OJkxtOnT+Hl5YWnT59CkiSsW7cO3t7eisQmIiIiIiLKaRRN8oD3CVpGlhlCaGgomjdvjnv37gEAli9fjj59+mRpHYiIiIiIiLKSokne+vXrlQyXKeHh4WjZsiWuX78OAJg/fz6+/PLLbK4VERERERGRYSma5PXt21fJcBkWHR2NNm3a4N9//wUATJ06FRMnTszmWhERERERERlehkbXzJcvH/Lly4du3bopXZ9Mi4+PR8eOHeXpG0aNGoXZs2dnc62IiIiIiIiyRoau5L19+xaSJCEyMjLdsqVLlwYANGnSJEu6c/bo0QNBQUEA3o/2OXDgwDSncDA3N4eLi4vB60VERERERJQVFB945WMhISGQJAkvXrww9FsBAHbu3Ck/PnLkCKpVq5Zm+VKlSiEkJMTAtSIiIiIiyj0CLwdmeF1vV45kn90UnwydiIiIiIiIso/Br+RltayepoGIiIiIiCgn4ZU8IiIiIiIiI2J0V/KIyDjx3gAiIiIi3fBKHhERERERkRFhkkdERERERGREmOQREREREREZESZ5RERERERERiRTA6+cPXsWnp6eipcFAEmScPjw4YxWjYiIiIiI6JOUqSTvzZs3OH78eJplJEnSuWwKIYS8HhEREREREekuw0keJx0nIiIiIiLKeTKU5PXt21fpehAREREREZECMpTkrV+/Xul6EBHRJ4IT2xMRERkWR9ckIiIiIiIyIkzyiIiIiIiIjAiTPCIiIiIiIiPCJI+IiIiIiMiIMMkjIiIiIiIyIkzyiIiIiIiIjAiTPCIiIiIiIiPCJI+IiIiIiMiIMMkjIiIiIiIyIqbZXQEiIqKMCrwcmOF1vV29FawJERFRzsEreUREREREREaESR4REREREZERYZJHRERERERkRJjkERERERERGREmeUREREREREaESR4REREREZERYZJHRERERERkRJjkERERERERGREmeUREREREREaESR4REREREZERMc3uChAREREREeVmgZcDM7W+t6u3QjV5j1fyiIiIiIiIjAiv5BERERF9wjJzBULpqw9EpAxeySMiIiIiIjIiTPKIiIiIiIiMCLtrEhFRmnLazeRERESUNiZ5RERERERkMLzvM+sxySMiIlIYf9AQEVF2YpJHRESUgzFhJCIifXHgFSIiIiIiIiPCK3lERETgFTMiIjIevJJHRERERERkRJjkERERERERGRF21yQiIiIiRbDbM1HOwCt5RERERERERoRX8oiIiIhyGV4xI6K08EoeERERERGREeGVPCIiIiIiI8ArvJSCSR4RUQ7BL2ciIiJSApM8IiIiMhqZOVkC8IQJERkH3pNHRERERERkRJjkERERERERGRF21yQiIiKiHIf3KRNlHK/kERERERERGRFeySMiIvpE8MoIEdGngVfyiIiIiIiIjAiv5BERERER6YjTdFBuwCSPiIiISAt2cSWi3IjdNYmIiIiIiIwIr+QREREREVGuwKvrumGSR0RERHrjfUlERDkXkzwiIiIiMmo8KUGaGPNVQd6TR0REREREZESY5BERERERERkRdtckIiKibMWudEREyjLqK3kPHjzA2LFjUaFCBdjY2CBfvnxwd3fHwoULER0dnd3VIyIiIiIiUpzRXsnbvXs3evfujYiICPm16OhonD9/HufPn8fatWuxd+9elC1bNhtrSUREREREpCyjTPIuXryIbt26ISYmBnny5MHkyZPh4eGBmJgY/Prrr/jll19w69YttGnTBufPn4etrW12V5mIiIiMnDGP5EdEOYtRJnmjRo1CTEwMTE1NERQUhHr16snLPD09Ua5cOUyYMAG3bt3CokWLMGPGjOyrLBERERF9spj8kyEY3T15Z8+exYkTJwAAAwcOVEvwUowdOxYVK1YEACxduhQJCQlZWkciIiIiIiJDMbokb9euXfLj/v37ayyjUqnQp08fAMDbt29x9OjRrKgaERERERGRwRldknfy5EkAgI2NDWrWrKm1XJMmTeTHf//9t8HrRURERERElBUkIYTI7kooydHREaGhoXB1dcWlS5e0lnvz5g3y5csHAOjatSt+++23NOMWKlQIL1++VHstT548qFKlSqbrrI830W8yvG5e67yMlU2xPo6XU2NlNh5jGUesj+PxGGMspWN9HC+nxspsPMYyjlgfx+MxxlhKx9IUTxdVqlTBL7/8onGZUQ28Ehsbi9DQUABA8eLF0yybN29e2NjYICoqCo8ePUo3tqZ59d69e4fTp09nrLJEREREREQGYFTdNSMjI+XHefLkSbe8jY0NgPfJmjZxcXGIiIiAkV3wJCIiIiIiI2VUSV5sbKz82NzcPN3yFhYWAICYmBitZebNmwd7e3tERUVlvoJEREREREQGZlRJnqWlpfw4Pj4+3fJxcXEAACsrK61lJk+ejPDwcPmqHxERERERUU5mVPfk2drayo/T6oKZIuXqXFpdOy0sLGBhYYHixYvjyZMnasusra1RunTpDNZWWUlJSTh37hzc3d1hYmKSY2Ll5LoxFmPlproxlnHEysl1YyzGyk11YyzjiJWT65ZTY30ozQEghZHJnz+/ACBcXV3TLBcWFiYACACia9euWVM5AwoPDxcARHh4eI6KpXQ8xmIsQ8ZSOh5jMZah4zEWYxkyltLxGIuxDB3vU4ilK6PqrgkAlSpVAgDcuXMHiYmJWssFBwfLjytWrGjwehEREREREWUFo0vyGjZsCOB9V8wLFy5oLXf8+HH5cYMGDQxeL0OzsLDA9OnT5cFkckospeMxFmMZMpbS8RiLsQwdj7EYy5CxlI7HWIxl6HifQixdGd1k6GfPnkWdOnUAAF988QVWr16dqkxycjKqVKmCGzduwMHBAS9fvoSZmVlWV5WIiIiIiEhxRnclr3bt2mjUqBEAwNfXF6dOnUpVZtGiRbhx4wYAYNSoUUzwiIiIiIjIaBjdlTwAuHjxIho0aICYmBjkyZMHU6ZMgYeHB2JiYvDrr79izZo1AAAXFxecP39ebVROIiIiIiKi3MwokzwA2L17N3r37o2IiAiNy11cXLB3716ULVs2i2tGRERERERkOEab5AHAgwcPsHTpUuzduxePHz+Gubk5ypYti65du2LEiBGwtrbO7ioSEREREREpyqiTPCIiyj3Cw8Ph6+uL9u3bs5cFUTaKi4vDsWPHcO/ePQBA6dKl0aRJE1haWmZzzYhIV0Y38AoZh3379uH777/Hhg0b8PbtW41lTp8+jQEDBmRtxYxcaGgofvjhB0ydOhV///23/PqCBQtQqlQp2NraokWLFrhy5Ypi77lp0yZ4enoqFo9yr9DQUIwfP17R4+tTExkZiYcPH2Z3NbJVXFwcVq1aheHDh2PmzJm4ffu2xnKHDh1i26OBv78/ihUrhtatW+PLL7/El19+idatW6NYsWLw8/PL7uqRjpKSkhAdHa322tu3b7Fo0SJMnTrVaNvZ6Oho7Ny5E8+ePcvuqmQ7XsnLhV6+fInw8HCULVsWkiQBAO7fvw8/Pz+EhYWhXr166NGjh7xMF1FRUQgICMDt27fx+vVrfHxYSJIEX19fres/evQIO3fuhLm5OTp37oyCBQvi0aNHmDhxIo4cOYLIyEjUqlULs2fPlkc/1SQxMRHt2rVDUFCQXAcHBwesXr0an3/+uVrZzZs3o0+fPkhKStJ5O7WZM2cOpk+fjsTExGyNFRUVhVOnTuHFixfw8vJCoUKFMl0f4P1+DQwMRFhYGNq1a4fChQunKvP8+XPUqlULT58+BfD+/3zTpk3yD+8aNWogJiYGV69ehb29PS5fvozixYtnum5z5szBt99+q8j/o1ISEhIQERGB/Pnz61Q+PDwcZmZmal3Ag4KC1I79Xr16wdzc3FBVzpSYmBi8evUKJUuW1Fpm3759uHr1KgoVKgRvb284ODikKnP69GmsWbMG69at0xjjq6++SrMe4eHh2LhxI1q1aoUyZcpAkiQsXbpUr23JjXTZ/7rKiZ+nrBQdHY0GDRrgv//+k79DzMzMMGvWLEyYMEGtrJLfIcZi69at6NGjB0qWLImhQ4eiUqVKAIBr165h9erVePz4MQICAtCtW7csq1N4eDhu3ryJQoUKoVSpUhrL3L9/HydOnECfPn0Uec+4uDid5jPLjrrpatCgQTh9+jSuXr0K4P33Wo0aNXD9+nUA7+dtO3XqFKpXr56p97lw4QLCwsLQqFEjva70CiHw66+/Yu/evQgNDUWhQoXQvn17dO7cOVP1uXv3LlxcXLBt2zZ06tQp3fJxcXHYvXs33rx5Ay8vLzg7OwN4/1lYtWqV/Lt6+vTpKFasWKbqBgCvX7/GzJkzMWjQIFSrVi3T8dIkKNdISkoSgwYNEiYmJkKlUomKFSuK+/fviwsXLggbGxshSZKQJEmoVCrRokULkZiYqFPcM2fOCEdHR3l9TX8qlUrr+jdu3BD29vZCpVIJSZJEoUKFxM2bN0Xp0qWFJEnCwcFB5MmTR0iSJCwsLMT58+e1xlq1apWQJEn07t1b7N69W6xatUo4OzsLlUol5s+fr1Z206ZNadZLH7Nnz872WKtWrRL29vby/j58+LAQQogXL14ICwsLsWbNGp3ijB8/XtSqVUt+npycLJo0aSL//xQoUEDcuXMn1XpjxowRefLkEdu2bRNnzpwRNWrUEKVLlxZubm7iv//+k8sdOXJEmJubi1GjRum9jZooue9TrFixQjg7O6dZ5saNG+LLL78UPXv2FJs3bxZCCJGYmCiGDh0qzMzMhEqlEi4uLuLgwYNaY8TExIgOHToIlUolVCqV6NOnj0hKShIDBgyQ93fK/2f16tVFZGSkotv58OFDcfz4ca3Ljx07Jtzd3YWVlZVwcnISM2bMEHFxcanKpfVZSkhIEJ999pna9uTNm1ds3bpVrzhCCHlfpNfW6NLuaBMVFSXmzp0rGjRoIMqXLy8aN24sfvjhBxEbG6t3rLRs3LhReHh4pFlGif2vL30+Ty9evBC3bt0SycnJ8mv37t0T3377rRgxYoTYvHmz2jJdhISEiClTpoiuXbsKT09P4eHhofbn6empdd1Xr16JhQsXiilTpoiTJ0/Kr8+fP1+ULFlS5MmTRzRv3lytPfrYnDlzhCRJ4ptvvhFXrlwR+/btE/Xq1RMqlUoMGzZMraw++/3Fixdi/PjxolWrVqJ///7i2LFjGsvt2rUr3bZHCCHevn0rzpw5I0JCQrSWuXfvntiwYYPW5a9fvxbx8fFqrz179kwMGTJEFCtWTFhYWIiSJUuKESNGiNDQ0HTrJIQQ1apVExUrVhTh4eEa61y+fHlRrVo1nWLp4sqVK2lu49y5c4WFhYXcxjZt2lTj91dGPkP79u0T06dPV3tt5cqVwtbWVpiYmIgePXqk2r+GqltsbKxYuXKlGDZsmJgxY4a4deuWxnIHDx5Mt91JUb58eTF58mT5eUBAgJAkSaxatUqcOnVKlCpVSnTr1k2nWEIIsXDhQtG2bVu113r06CFvf9myZcXz5881rlu1alWxd+9e+XlcXJxo0aKF3N6bmprKbX6nTp3SrMeiRYvS/JsyZYqQJEn069dPfk2b8PBwUa1aNbkelpaW4uDBg/K+Kly4sHBwcBCSJIlSpUqJsLAwnfeXNnfu3BEqlUrs3Lkz07HSwyQvF/Hz8xOSJIk2bdqIYcOGCUtLS9GyZUvh6ekpJk+eLIKDg8XFixdF//79hUqlEj/99JNOcRs0aCDs7e3Ftm3bxOvXr/WuV//+/YWVlZVYtmyZ2LZtmyhfvryoUqWKKFy4sDhz5oxcLigoSOTJk0d06dJFayx3d3fRqlUrtdfevXsnvL29hUqlEt999538enoN54MHD3T+GzduXJbF0mT79u1CkiTRoUMH4evrKyRJkpM8IYTw9vYWrVu31ilW1apVxejRo+XngYGBQpIkMXHiRLFlyxZRoEABMWjQoFTrVahQQXz11Vfy8wMHDghJksSMGTNSle3Tp4+oWLGi1jo4Ozvr/JcvXz7Fk7z0fujeuXNH2NnZqSUTP/74o/j2229FgQIFRPfu3YW3t7fIkyePsLS0FNevX9cY5/vvvxeSJIlatWqJNm3aCFNTUzF06FD5B+WuXbvEtm3bRM+ePYUkSWpfuIbezhs3bggLCwthYWEhatasKYoWLSokSRJubm7i6dOnamXT+iwpeeLF2dlZ2NnZiR9//FGEhISk+jt27JiQJEmsXr1afi0ttra24rfffpOfh4eHC1dXV/mEkrOzs5yw169fX2OClVHpHWNK7X8hhDh+/LjOfwMHDkz382SoE4aBgYHC3NxcSJIk7O3thZOTk8Y/TZ49eyaKFSum9t4BAQFi2bJlwsLCQtStW1e4uroKExMTkS9fPvHo0SONcapXry66d++eantHjBghJElSa/t0TQxev34tSpYsmepkxLBhw1LtG11iKpUcqFQq+QSVEEI8ffpUlChRQkiSJJydnYWHh4f83MXFRWPi9jELCwvx/fffa10+f/58YWlpmW4cXaX1Odq/f7+QJEmUL19ejB07Vnz++efCzMxM2Nvbp0qyM5LkeXh4qCU5169fF6ampqJ8+fLyia0ff/zR4HWLiooS1atXVzvBZW5uLhYsWJCqrD7baWdnp3ZyuFevXqJKlSry8zlz5oiSJUvqFEsIIWrWrCmGDx8uPz98+LCQJEn07NlTzJs3T9jY2IgxY8ZoXFeSJLVj9dtvvxWSJIkhQ4aIBw8eiOTkZHH79m3RrVs3oVKpxOrVq7XWQ8mThTNnzhTm5uZi4cKFYuvWrcLZ2VlUrVpV1KpVSxw4cEAu5+fnJ1QqlZg6dWq6+6ldu3Zp/jVr1kxIkiTc3d1Fu3btRPv27dONmVFM8nKRevXqiZYtW8rPly9fLlQqlejRo0eqsu7u7qJRo0Y6xbW0tFRLnvRVunRpteRg3759QpIksXDhwlRlR48eLQoVKqQ1Vt68ecXKlStTvZ6UlCT69OkjVCqVfOZN16sG+vxlRSxN6tSpI5/hDg0NTZXkzZo1S+fG2MHBQaxatUp+PmjQIFG6dGn5+TfffKPxTLO1tbX45Zdf5OePHz8WkiSJwMDAVGWXLl0qrK2ttdZBkiSRL18+UaVKlXT/ChUqlOVJ3sCBA4Wjo6M4d+6cePHihfD09BRFihRJ9QP82rVrIk+ePGLgwIEa41SvXl00a9ZMfr5w4UKhUqnE4MGDU5Vt06aNqFChQia2KrW0trNHjx4iX758coKanJwsVq5cKSwtLUXZsmXVfiSn9VlS8sRLdHS0GDt2rDA1NRXt27cXDx8+VFt+584dIUmS2LFjR9ob/v8+/uEwcuRIIUmSmDt3rkhISBBCvD9DPm7cOCFJUqqkNDPSO8aU2v9C6Nf+6HIF1FAnDCtUqCDKlCmT5pU2bZTqSWBrayt+/vlnjcu++eYbIUmSGDBggBBC9x/M48ePF2ZmZmLt2rUiMjJSXL9+XXTt2lVIkiTat2+vdrUnvZhKJgcfH/99+/YVpqamIiAgQK3cihUrhCRJYsqUKelua6lSpdJM8hYsWCBKlSqVbhxdpfU58vLyEpUqVRIxMTHya5cuXRKlS5cW1tbWar0sMpLkFS5cWO13yvTp04Wtra2cDPfo0UNUr17d4HUz1NVnW1tbtc9usWLFxMiRI+Xn69at0ythz58/v1i+fLn8fOTIkaJo0aLy1f6xY8eKcuXKaVz342O1RIkS4rPPPktVLjk5WdSqVUvUq1dPaz0cHByEnZ2d+OGHH8SxY8dS/W3atElIkiS+++47+TVtqlSpovb9vnPnTiFJkhg3blyqsh07dhSurq5aY324rYbusaIrU8N2BiUl3blzB9OmTZOff/bZZxBCoE2bNqnKdujQAYsWLdIprp2dnc73Hmny9OlTtX7FVatWBQC5L/+HqlSpgtevX6cZz8TEJNVrKpUKfn5+MDExwaxZs5CcnIzy5cunGcfU1BRly5ZFs2bN0t2G8+fP4+zZs1kSS5MrV65gwYIFWpcXKVIEL1++1ClWfHw8TE3/99E+evQovLy85OelS5fWeEOymZmZ2n2EKX3r8+TJk6qspaVlqvs2P+Ts7IyyZcviwIED6dZ39uzZmD59errlNB0XGXX8+HEMHDgQtWrVAgDMmDEDTZo0wejRo1GkSBG5XKVKleDj44OgoCCNcR48eKA2+I+3tzcmTJiA5s2bpyrbsmXLVPcEaeLv76/zdly8eFHrsjNnzmD48OGoWLEigPf3WA4fPhzVq1dH27Zt0aRJExw9ejTd+8Du3LmDfv36qb1mY2ODnTt3on///pgxYwaSkpIwY8aMdOtrZWWFH374AT179sTgwYNRqVIlTJ8+HaNHj1bk/3fr1q3o3r07Jk+eLL9mYWGBhQsX4t9//8XWrVsxceJEreuXLl1a5/cKDw9Pc7lS+z9lGypVqoSePXumW/bw4cPpfu5+/vlntGjRAnv27AHw/jgfNWoUunXrhrlz58rl1q1bh6tXryIgIABDhw5N971DQkKwYMEC+TtAH/v27cOAAQPQpUsXAMD8+fPx2WefYfr06WrxPDw80L17d62fSUtLSyQkJGhcNmvWLJiYmOC7775DcnKyzoOu7Nu3D/3798fAgQMBABUrVsRvv/2GRYsWYcKECejQoQN+//13ne65/eGHH1CxYkVcuHBBbmMvX76MTp06oXXr1ggMDFRrr/URGBiIwYMHo0ePHmqvf/nllzh06BB27dqFOXPmpBmjX79+WL9+PYYNG5aq7Y+IiMD69evRv3//NGN89913Otf5+PHjWpddv34do0ePVrvPy9XVFWfOnIGXlxfat2+PXbt2oUWLFjq/34fevHmDAgUKyM9TBuKxs7MDADRt2hT79u0zeN22bduGbt26YdasWQDe/05q2bIlRo0ahZUrVyIhIQG//PKL3tvn7OyMAwcOYOjQofj777/x7NkzeHh4yMufPn0Ke3t7neNFRUXByspKfn7kyBF4eXnJ4z9UqlQJP/30U7pxYmJi8PjxY8ycOTPVMkmS0LlzZ8ybN0/r+jdu3MCIESMwadIkDB8+HLNnz4atra28/O7duwCAypUro0mTJmnW5cGDBxgxYoT8POU3Qb169VKVbdiwIQ4ePJj2xgGoU6cOrl27hu+++w6jRo1KNT7GnTt34OLigu3bt+t0z2BmMMnLRSIjI9Ua3bx58wKA2o/SFIULF0ZkZKROcTt16oQDBw5g+PDhGapXXFyc2gc/5bGmG3AtLCyQnJysNZazszMuXLigcdmHg7/MmTMHrq6uadarUqVKsLa2xvLly9Pdhjlz5qSZmCkZSxMTE5M098vTp09hY2OjU6wSJUrg1KlTGDx4MK5du4Z79+6pfem+fPlSY+JWtGhRPH78WH6eJ08eLF++HBUqVEhV9sGDB3B0dNRah5o1a+Lo0aM61VfXAYIkSULRokXT/X8H3jeit27d0rr88ePHKFeunPw85bG2ExPr16/XGOft27dqJ0jy5csHABpPmuTLlw/x8fHp1r1fv36QJCnNJPpD2vbfs2fPNCYt9evXx8GDB9G8eXM0bdoUR44cSfc9lDrxksLNzQ3nzp3D4sWLMWPGDGzatAk//fQTChYsqNP6mkRFReHVq1do3bq1xuWtW7dONxENCQlB3rx5UbRo0XTf7+NR6z6m5P6vWrUqkpOTMXbs2HTLxsbGppvkGeqEobOzM+Li4nQq+7GHDx+qJXOVK1cGANSoUSNV2Zo1a2L79u0a45QpUwanT5/Gl19+qXF5yjHw3Xff4dixYzrVLSQkBKNHj071+tixY2Fvb48vvvgC3t7e2LVrV7qxDJW4REZGIjw8HE2bNtW43MPDQ6eTbo0aNcKePXtQtWpVDB8+XG7/b9y4gZ9++gkFChRAo0aN8Ndff6mt17hxY/nxjBkzFGnDwsPD5Tb1QwUKFMDRo0fRrFkzdOjQATt37tTpfTTFefDgAYD3++/cuXNqJzkSEhK0DsqjZN3u3r2LYcOGqb2mUqmwfPlyODg4YM6cOUhOTk5z8DtN+vfvjzFjxqBKlSp48uQJChYsiJYtW8rLz5w5o/H7XZtixYrJI3I+ePAA169fx5gxY+Tlb9680WmwGpVKBUmStP6GKFCgQJrtSOHChbF9+3b88ccfGDFiBHbs2IElS5bIJ4j0pVL9b6KBlMHTNA0sZm9vr/UE0of++ecfrFy5ElOnTsXGjRuxevVquLu7y8t1/c2jBCZ5uUiBAgXUruaYmZmhZs2a8lmnD7148ULjQarJggUL0LJlS4wcORJff/01SpcunaUH4Yc8PDzg7++PFStWaDwrmpLoSZKE9evXp1lPNzc3bN26FcnJyWof4oxQMpYmrq6uOHDggMbRB5OTk7Ft2za1RiIt3bt3x6xZs/Dy5Utcu3YNdnZ2aj98L168iDJlyqRaz83NDadPn5afW1hYaP2hdOTIkTSTrRo1amD79u0ICQmBk5NTmvUtVaqU2g8EbcqVK4dChQph9+7d6ZZNGWFQG2tra8TExMjPzczMAEDtZEUKSZIUvYqYHhsbG1SvXh1ff/11umV37NiBrVu3alxWqFAheaTUj9WsWROHDh1C8+bN4eHhkeZUJEqeePmQSqXCuHHj0LlzZwwdOhSNGjVC69atM9z2mJubw8TERGN7CLw/aZHeKIpKXoFWav8D7z+bfn5+SEhIkI/VzDDUCcOvv/4aixYtwvDhw3U+KZVCqZ4EzZs3x9KlSxEZGal2dv9DM2bMgEqlkpOR9NjZ2WndB4MGDYJKpcLgwYPRvn17dO3aNc1YSicuKfW3traGmZmZ1uPD1NRUp239sBfCxIkT5XVS9veDBw/UygghIEmS2mfL3t4etWrVwvz589N9P19fX/z8888al5UoUQI3b97UuCxv3rw4fPgwmjVrho4dO2ZoRMZ69eph9erVqFy5Mv78808kJiaiVatW8vI7d+5o/EwoXTdDXH0GgFGjRiEyMhK7du1CjRo1MHfuXDmJef36NU6fPo1x48bpHK9du3ZYtWoVEhMTcebMGVhYWKidGLp69Wqa3/c7d+7EnTt3ALz/XKck2B979OiRxs/Ix9q3bw9PT09MmjQJ3bt3R8uWLbFy5UqdtwdI3U5bW1tj7NixGkdKffjwodqVX20kScKIESPQoUMHfPnll6hXrx6GDh2KuXPnav1+MhQmeblI5cqVcfnyZfm5nZ0dzp07p7Hs1atXde565ODgAEmScPbsWaxatUpjGUmS0pwSYN++fXj+/DmA92e4JUnCtm3bcOnSJbVy2n4spujVqxeePHmCCxcuaLxcnlIXX19f5M2bN814rVq1wv379/H06dN0h/pv3LhxmkmBkrE0GTFiBHr06IFp06bJwywnJyfj5s2bmDJlCq5du5Zmd84PTZ48GY8ePcKuXbtgb28Pf39/OeEPDw/HH3/8ofGs9Lfffqt2JU+bFy9eoEiRIml2HZs8ebJal7m09O7dG7179063XM2aNXVK8HRRvHhxtS8Ye3t77N69W+NQ0iEhIWleYYqKikJYWBgAyP9GRkbKj1O8e/dOp7q5urri7du3Ov1oCQ4OTjNOUFAQpk6dqnG5m5sbDh48iBYtWmjsNpNCyRMvmqR0Kdq0aRPGjh2r89n/FGvWrMGhQ4cAvE/SU7rqfOzhw4fpdktX8gq0UvsfALp06YK4uDiEhoZq/dGZon379um2UYY6YThkyBBERESgcuXK6Nu3L5ycnDSeINE0lLxSPQl8fHyQkJCA27dvw83NTWtdv/32W+TPnx/nz59Pd7tcXFxw8uRJrSdeUpL0IUOGpBtP6cRl9uzZclc+U1NT3LhxAx07dkxVLiQkJM3eFynWrVuX6ZO8bm5uePjwIWrWrJlu2f3792tdVq9ePQQGBmr97kvZX15eXggICNC73jNnzoSHh4c8PVPfvn3l3hxCCPz+++9q3RsNVTdDXH0G3rdR06ZNU7tqnyJ//vw63wKS4ttvv8V///2HVatWwcLCAkuWLJGneYqJicHvv/8ud2nWZOfOnWonL3bt2qVxm//55x+drzDmyZMHK1asQO/evTF48GC57dH1WKhevbra72grKyssXLhQY9lTp05p7O2jTfHixREYGIjt27dj1KhR2LlzJxYvXqzzCXtFGOxuP1Lchg0bdBq2/vXr18LKykqMHz9ep7h9+/YV/fr1S/dPm7RuLtV2wymlNnXqVCFJkjAxMZH/Tbk5d+bMmYq8R1JSknj79m2aw0LnVKtXrxZOTk7iwYMH6ZbdvXt3msdsv379RMOGDXV638qVK4uuXbtqXKZpQIz0BslIz8iRI4WpqamIjo5Ot+zs2bOFJEkal61Zs0ZIkiQuX76cZox///1X5M+fX2vd/v33X9G9e3fxzz//pFufsWPHiqZNm6ZbTpu4uDjx9u1bedCU9GhqX+rWrauxbL169TTe6P+huXPnCkmSxP3799N9740bN6a5rUrtf0No2bKlxgG7NOnZs6eoU6eOTmWfP38u6tWrl6H2v1evXmqDGKWlbt26ol27djqVVcLcuXOFpaWlePHiRZrl1q9fL7fb2vTv31+UL18+zThhYWHCzc0t3e/LUqVKpRq59OMh7lNUrVpVeHt7p/m+Shk7dqwwMTHRaTTPWbNmaW3DAgMDReHChcXRo0fTjPHmzRtRs2bNDH2GXr9+Lf74449UU9GEhYWJJUuWiEuXLhm8btOmTRN2dnYiIiIizVgzZ87U6zfUxo0bFZ86Roj3oxh//BsiOjpaXLp0SesI7ZpGU/544C0h3v9/dOrUSaxbt07veiUkJIhZs2YJKysrnQfwOn36tPD390+33PPnz4Wbm1uao36mJTw8XAwZMkSoVCrh5uYmVCqVzgOMZQYnQzdCSUlJePfundx9w9DSunFam/Ruhn337h3at2+PXr16pXlmyNj8+++/2Lx5M4KDgyGEQLly5eDj4yPfDKyLkydPomHDhhmuw7t37+Dq6ip3380MJWMp6dWrV3jz5g1cXFzSLPfy5UvMmzcP7dq109hNJr0BCDTRdn9finPnzmHfvn0YPnx4umfeHz58iPv372v8PMXFxcln79Pr+pJWHEPI6uMiLCwMs2fPRtOmTdG+fXuDvx+Qs/e/v78//v33XyxZsiTNcmFhYShevDhGjBiB77//Pt24HTt2xJ49e/DVV1+hUaNGcjfQj2nazlu3buHx48fpdkd78eIFhg0bhp49e6Z5D46Sx9jTp0+xe/duNG7cWB5IR5s//vgDFy9e1NqV948//sAXX3yBLVu2aL1/Dnh/v6+XlxcuXryY6cna37x5A39/f7i7u6N+/fpayym1z27duoVTp07B29s73avAERERePPmjdaJxD8Ft2/fxrp169C1a9c0rz4DwMqVK3H+/Pl0v0eA913iHRwc0KtXLwwYMEDj/a36ePjwoU4DRWW358+f48WLF3B2ds7y7pHpOXHiBEaMGIGHDx9i48aNaNu2rUHfj0ke5Vi2trZYsmSJ4kledHS03FWvVKlSch91Y6FSqVChQgUMHDgQffr00amLzsccHBzwww8/YNCgQZmuj5KxyHjwuMj59D1haGdnhyFDhuCHH37Igtqlj8eY/rjPjMe2bdvg6+uLQ4cOQQiB6tWrY/DgwejZs2eGkh8TExO0aNECgwYNgre3t9oo3pQzMcnLpVxcXDBw4ED07dsXhQsXzlSsO3fu4M6dO/jss8/k186cOYPZs2cjLCwMffv2xZAhQzJbZb01atQIderUUewHw/Xr1zFu3DgcOnRIPjNqYmICLy8vLFy4UB7N7WP6DAedIqUvfHZYuHAh1q9fj+DgYJiZmaFdu3YYNGgQWrZsqXM/9c8++wxOTk5YvXp1puujZCzKXh+PqPcxSZJgZWWFkiVLpjtSppLHhdJXBnPqFegUp06dwooVK3D79m28fv061X2MkiRpvTfRkBwdHTFr1iydplvQRsl9z7ZHf8a8z5ydnaFSqeTvRl3GLdDls6TU7zFDtTuPHz/GunXr4Ofnh5CQEFhZWaFLly4YOHCgToOepfjyyy+xZcsWhIeHI3/+/OjTpw8GDhyY7tXtjyn5PZJTY+UUTPJyqcqVK+PGjRswNTVF69atMWjQILRu3TpDIz927twZYWFh8oADoaGhcHFxwbt372BlZYV3795hx44d6NChQ6brfeXKFVy8eFHjjfcfO3LkCDp27Ihdu3ZpvflZVxcvXkTTpk3x7t07NG/eXL559tq1azh8+DBsbGxw/PhxjQNvZGSffjzamC4ePnyIn3/+Oc0fbocPH9Y53t9//w1fX19s27YN0dHRKFasGPr374/+/funO+LlpUuX4OnpiUWLFslD+meUkrFS6JJ465poKxkL+F/Xrnv37gF4P/da27ZtUaxYMZ3W1yQkJASHDh3Cixcv0KtXLzg5OSE+Ph7Pnz9H4cKF052fS5cR2XQ5vlKGvtZF1apV5bnONFH6uFD6CoTS8ZRKzPz9/dG/f3+YmZnBxcVF62Ayug4gk+Lu3bsIDAxUO269vb01jsSrTb9+/RAZGYkdO3bo9d4fU2rf5/S2J4US+16pehlinwHKbOPff/+NefPm4cyZM3jz5o3Gz1BaA8Q1bdoUkiTh4MGDMDU1lZ+nJ73PkpK/xwx9JfXQoUPw9fXFrl27EB8fj7Jly2LgwIHo16+fTolLXFwctm/fDl9fX/lWnTp16mDw4MHo1q2bTj2jlPweyamxUhiivdAHk7xc7PTp0/D19cVvv/2Gd+/eoXDhwujXrx8GDBigV8NZqlQpDBkyRB4Fbvny5Rg9ejQuXboEFxcXNG3aFBYWFnr/aNAkZWh7XRKgAQMG4Ny5c7h+/TpcXV3h4uKSqgH5cAj3tHh5eeHChQs4fPhwqj7v//77Lzw9PeHu7q5xokttw/ymR597DP7880907NgR8fHxyJMnj9Yfbvfv39e7Hu/evcOvv/4KX19fnDlzBiqVCp6enhg0aBA6duyosRuWp6cnHjx4gJCQEOTLlw9lypTRuO91STqVjJUirS/PlPmZdE20lYw1a9YszJ49G4mJiWo/QExNTTF16lSdJn3/2MSJE7F48WIkJSXJP1A8PT0RERGBokWLYvbs2eme9XVyckr15ZWYmIhnz54hOTkZBQoUgI2NTbrHl5+fH1auXInbt2+jV69e8rx4wcHBCAgIQPny5eHj44ObN29i48aNiIqKQlBQkMaTNEofF0pfgVAynpKJWfny5WFiYoJDhw7pNJ+fLqZNm4b58+enOsZVKhWmTJmic2+GsLAwfPbZZ6hbty5GjRqV4el4lNr3Ob3tAZTb95mp18OHD+Ho6AgrKyuD7DMltvGvv/6Cl5cX7O3tUadOHezbtw+enp549+4dzp49i6pVq8LNzU2ne9UMQanfY1lxJfXcuXNYuHCh2lyT5ubmGDhwIBYsWKBx2hJN7t+/D19fX2zYsAFPnz5Fnjx50K1bNwwaNAi1a9fWup6S3yM5NVYKpdsLvRl8aBcyuKioKLFu3TrRoEEDeeQlDw8PsXnzZp1GVrKyslIbyah169aicePG8vOlS5eKggULKlLX2bNn6zwylJIjddrY2IhvvvlG6/IpU6YIGxsbnWIZgqurqyhWrJg4d+6cwd7j6dOnwsfHR23/FSxYUHz//fciMTFRraymUds0/elCyVgpNI3UdefOHXHgwAHx2WefiXr16olbt25laazly5cLSZJE7dq1xZYtW8Tly5fF5cuXRUBAgHB3dxcqlUosX75cr+1cvXq1kCRJjBo1Shw8eFBIkiQOHz4sL+/Ro4fw9PTUK+aHYmNjxdy5c0W5cuXE06dP0y2/YsUK4eTkpHGUwWfPnolSpUqJVatWCSHeH28FCxYUzZs31xhL6ePi4sWLIm/evGLdunUiOTlZ5/WyIp6Li4uoWLGiePLkSabrZWFhIZYtW5bpOCl8fX2FJEmiQYMGIjAwUNy5c0fcuXNHBAYGioYNGwqVSiXWr1+vU6z0RpZVqVTCxMQk3ThK7fuc3vYoue8zUy+VSiUCAgKEEMrvM6W2sUWLFqJkyZLi5cuX4tWrV2pt4YEDB4Stra04efKkzvUylMz+HlO6HUsRFhYmli5dKqpVqyZUKpWwsrISPj4+4sSJE+Ls2bPCx8dHqFQq0b17d71jR0VFqf22UKlUonr16uK3337TWF7J75GcGiuFku1FRvBKnpG5desWZs6ciS1btkCSJDg4OMDHxwdjxozROiqSo6MjpkyZgtGjRyMpKQn58uXDV199hVmzZgEAfvnlF3z11Vdqk0d/SJ971o4fP45jx44Z7qyFFgUKFMDMmTO1zkOzcuVKzJgxA69evcrSeqWwtLTE7Nmz9ZqYVBfJycnYs2cPfH195cleGzZsiCFDhsDCwgIrVqzAyZMnMWzYMKxYsULR984uQgg0btwYjRo1wty5c7MsVvny5ZE3b16cPHky1Q3pCQkJaNCgAcLDw7XOkaWJq6srypYtix07duD169dwdHTEoUOH5O6X8+fPx4oVK3Sa3zAtPj4+SExMxJYtW9Is5+Lign79+mHKlCkal8+ZMwf+/v7yNk6dOhWrVq3CmzdvMlU/XSh9BULJeJaWlli4cCFGjhyp+wZpUbZsWQwdOlSxtqJmzZowNzfHiRMnUh23iYmJaNSoEeLj49Od4xSAzt370rvaYoirSVlB37ZHyX2fmXqpVCps2rQpzblPM0qpbcybNy/GjBmDadOmISwsDAUKFEBQUBC8vLwAvL9f7MaNGzhy5EiacfQdXVeSJAQGBuq1ToqM/B5T+tg/ePAgfH19ERgYiLi4OFSpUgWDBw+Gj49PqpFPp02bhmXLliE8PFyn2P/99x98fX2xefNmhIWFoVSpUhg4cCDMzc3x888/IyQkBNOnT081b7CS3yM5NZYulPytog2HxjESSUlJ+OOPP+Dr64v9+/dDkiR4eHjIP+R/+eUXBAQEwNvbO9W6lStXhr+/P/r06YNt27bJ962lSGviWeD9RJ0pl511oVQff320bt0af/zxh9Yk748//kCrVq30inn+/Hn53oDk5GS1Zfr2sXZ0dEz3vip93L59G76+vvD398eLFy+QL18+jBw5EoMHD1abZLRr164YPnw4tmzZYjRJniRJ6NKlCxYuXJjphlOfWA8fPsTw4cM1jjhmZmaGXr16YdKkSXq9/61btzBs2DCtyx0dHREaGqpXTE0aNmyo0+T1Dx8+TPOeCxsbGzx8+FB+7uzsjNjY2EzXTxf37t2DJEnyj6cXL17kmHjFixdHXFxcpuqTYujQodi8eTNGjx6tcaJxfd24cQPz5s3TeNyampqie/fuOh0bwPvuTkpQ+v8yq+jb9ii575Wsl5KU2sa4uDj5vmYLCwsAQGRkpLy8evXq2LRpU7px9uzZk+q1tH6/ZOT3SmZ+jyl57Ds5OeHRo0ewtLRE9+7dMWTIENSrV09r+SpVqqjtU00iIiIQEBAAX19f/PvvvzAxMUG7du0wePBgtcHdxo4di549e2LlypWpkjwlv0dyaixdZMXnkkleLhccHAxfX19s3LgRL1++RMGCBTFu3DgMHjxY7gd+584dfP7555gwYYLGRmX8+PHw9vaWb7qtUaMGGjVqJC8PCgpKc+4We3t71KpVC/Pnz0+3vr6+vvj555912rZBgwZh8ODBqFOnjk7l07J48WJ89tln6Nq1KyZMmCAnOjdu3MD333+PsLAwBAQE6BQrJiYGnTp1QlBQkNyfOuUL4sM+1vokeT4+PtixYwe++uor/TfuI40aNcI///wDIQSaNGmCRYsWoXPnznIS+fPPP2Pp0qW4fv26XN7YRlKLj4/H69evszRWyZIl0/yCjIyM1HuOIUtLS0RFRWld/uDBg3TnodLF/fv3ER8fn245JycnBAQEYPjw4alOSsTHx2PTpk1q96I+fvxY6/1nSgsJCcmx8ZRMzGrWrIkdO3agdu3a+PLLL+Hs7Kwxpq6j5pmbm+Pdu3dal0dGRip6AupDoaGhqF27NjZv3qz241Pp/8uspE/bk5X7Xsk2UR9KbWORIkXkHgs2NjZwcHDA1atX0bFjRwDv2xpdhvT/+IRsaGgoChYsqNZDIqOU+D2m5LHv4OCA8ePHo3fv3rC3t0+3fLt27dK8L9vHxwc7d+5ETEwMnJ2dMXv2bAwYMACFChVKVdbExATe3t7Ytm1bqmVKfo/k1Fi6Mvjn0mAdQcmg1q5dK+rXry/f59CiRQuxfft2kZCQoLH8+vXr07wX4vjx42LMmDFi5syZ4vXr1/LroaGhomPHjnJ/fU08PT2Fi4uLTvXW5548ExMToVKpROXKlcWPP/4oQkNDdVpPk5R+4pruGfmwH7ku945MmjRJqFQqMW3aNHHs2DEhSZLw9/cX+/fvF02aNBG1a9cWwcHBadbnwYMHan83b94UdevWFe3btxeHDx8W9+7dS1XmwYMHOm2ro6OjGDdunNZ+3h//H7x8+VIcO3YsVbktW7aI+vXrC0dHxwzfW2OIWOk5d+6cKFasmKhRo0aWxlq2bJkoXry4xnvbHj9+LIoVK6b3PXktWrQQDRo0EEK8/yx+eB9KTEyMcHJyEp06dUo3jqZj6cGDB+LixYti4cKFwsrKSrRq1SrdOKtWrRKSJInq1auLn3/+WRw9elQcPXpUrF69Wri6ugqVSiXfsyCEEFWrVhUdO3bUGi8rj4vsdOTIEVG3bl3h5uYmfH19xZEjR8Tx48dT/elC033JH7dnuraxQgjh5eUlSpQoIZ4/f55q2YsXL0SJEiVEixYtdI6nj+fPn6e6z1RpObntyap9n169JEkSQ4cOFRs2bND5T1dKbWO3bt3UynXr1k3kzZtXbNiwQaxfv17ky5dPpzbsYx+3qxmh9O+xnMrc3Fx06dJFBAUF6VT+/v37ws/PL9XrSn6P5NRYulDyt4o2vJKXSw0ePBiFCxfGpEmTMHjw4HSHxK9UqRJ8fHy0Lm/cuLHGM78hISEoVKgQvvzyS/To0UPjujVq1MDx48cRERGR7gSbQgidu3U+fvwYfn5+WL9+PcaMGYPJkyfD29sbAwcOVOtOqos+ffoo1k10+/bt6Nq1K7777jv5DEyxYsXg6emJZs2awd3dHX5+fpg3b57WGJpGO0zZL5q6k6TQ5V7GJ0+e6DRxcQpHR0c0adJE7bWFCxdi0qRJyJ8/P+rWrZups1VKxkqhbX6jsLAwREZGwtTUFGvXrs3SWPb29ihUqBAqVKiA3r17q10t3rx5M1xcXGBnZwd/f3+19dKaTmT8+PFo2bIlfHx8MGDAAADA8+fPceDAAUyfPh2PHz/W6Qq0puMthRAC5cuXx7Jly9KNM2zYMERERGDmzJkYOnSoHFMIAQsLC8yZM0fuXhoXF4eFCxeibNmyGmMZ4rgAMj/dhCHiNWvWTH48aNAgjZ99XUdYU3r0wGnTpqFZs2aoWLEiBg4cqDa9zPr16xEZGYnNmzcr+p66yuy+z+ltj5L7PrP1WrNmDdasWZPu+6Qcq7pMgwQot40DBw6En58fYmJiYGVlhblz5+LEiRPo168fAKBw4cL4/vvvdaqT0pT+PQZk/NhPSkrC1KlT4eTklOZ8lT/99BMePXqEOXPm6Pzb6PHjx2neuvMxJycnjftCye+RnBorhZLtRUZw4JVcateuXWjXrp0i92R8LCwsDJs2bcK6detw5coVCCHg4uKC4OBgjeVv3bqFU6dOwdvbO92uYxEREXjz5o1e0wsA74dP9vX1xY4dOxATE4OSJUtiwIAB6NevH0qUKKFXrMyytLTEjz/+iGHDhuHt27fIly8f9u3bJ8+X8v333+Onn35Ks9tDyn2M+srIEPwf02UaCycnJxQtWhSHDx+GlZVVpt5PyVgpNM1vJEkS8uXLBxcXFwwZMiTdL1qlYxlqPsU1a9Zg1KhRiI+Pl39gAe+7Qf3000/yj5y0aDrePtxGLy8vver/5s0bHDx4UD7GnZyc0Lx5c+TLl0/nGIY4LpSYbsIQ8TZs2KDT+/Xt21fnuilp9+7dGDFiBB49eqT2esmSJbFixQq0bdvWIO/74sULFClSRGNXOaWmDsnJbQ+g3L7PTL1UKhW++OIL1K1bV+d663OsGur4ioqKwuHDh2FiYoKGDRvq1CXxY5oGtNKX0r/HMnPsb9iwAQMGDMDZs2dRs2ZNre9x4cIF1K5dGxs3bkx3wJ39+/fD0tISTZs21Vrm2LFjiI+PR4sWLXTZRADKfI/k9FhKtxd6M9g1Qsp19u/fLz7//HNhaWkpVCqVqFChgpg5c6a4evVqdldNFhERIX7++WdRu3ZtoVKphKmpqWjVqpX4/fffFR1uOC0FChSQhzBPTEwUJiYmwtfXV16+evVqYWlpqXO8ly9fitOnT4s7d+4oXldNdOkya2FhIVauXKnI+ykZKyc7duxYhv508ezZM7Fs2TIxfPhwMWzYMLF48WLx+PFjA2+RusjISOHh4SHWrl2rSDyljwulp5sw9PQVOU1SUpI4e/as2Lp1q9i6das4d+6cSEpKMuh7auuuqdS+zy1tT3bs+w9JkiQ2b95s0PfI7m3URonumkrK7LHfunVrnbv4tmrVSrRt2zbNMocPHxYqlSrd4yMgIECoVCrx119/pVlOye+RnBorJ2F3zVzir7/+ytB66d18HxISgnXr1mHDhg14/PgxChQogC5duiAgIABz5sxBp06dMvS+hhIbG4uIiAhERERACAEbGxucOXMGBw4cQOXKlbF161ZUrFgx1Xp37tzBnTt35KttAHDmzBnMnj0bYWFh6Nu3L4YMGaJTHcqUKYNbt24BeH9zceXKlbF9+3YMGDAAQgjs3LlTp6uLycnJGD58ONauXSt31axXrx5+//13vbpEGELZsmXx9u3bHBcrJ/u4y6uSChcurMjw+5mRJ08enDt3Dr169VIkntLHxapVq9CxY0csWbJE443s1apV02sEWaXjZZSh2n4AePfuHVxdXTFy5Eh8/fXXcHd3h7u7e4beT0lK7fuc3Pbk1H2vJGPdRkN+JjN77F+4cAFjx47VqT4eHh5YvHhxmmXWr1+PKlWqpHu1r0ePHvj++++xdu1atYH7Pqbk90hOjZWTMMnLJTRd8k2LSOcej82bN2PdunU4fvw4TExM0LZtWyxfvhytW7fGgwcPsu0eDE2EENi3bx98fX2xd+9eJCQkoHbt2li7di26d+8OU1NTbNq0CRMnTsTgwYNx8uTJVDEmTpyIsLAwOckLDQ1Fq1at8O7dO1hZWWHYsGEoWLAgOnTooLEOcXFx8rDNXl5eWLduHZYsWQITExN88cUXGDFiBMqUKQNJknD//n2dhsNdsWIF1qxZg6JFi6JevXq4ffs2/vnnH3zxxRfYuXOnXvsovYb6Q3///Xe6ZcaOHYvZs2fjq6++Qp48efSqiyFjfSgpKQmbN29GUFAQXrx4ge+//x41atTAmzdvsHv3bjRr1kwecjsrY2mSkJCA33//HevXr8eff/6Z4Ti6MtSPkOrVq+PGjRsZiv0xpY8LpaebyEw8Jfe/0m3/h/LkyYPXr18r+rlUglL/lzm57THEvjd0O6avzGxjRrpO6jJ/3MffldHR0ZAkCdu2bcOlS5c0xhw9erTaa4b8TGb22A8LC5NHSk+Po6MjwsLC0ixz8uRJne+/9Pb2xsaNG9Mtp+T3SE6N9aHs/FwyycsllL7Z3sfHB6VLl8aSJUvQo0cPRYc5//vvvzFv3jx5Djnx0W2fkiQhMTFR47rfffcdOnXqhCpVquDu3bvyVcZnz57Bzs4OQ4YMwZAhQ1C1alW19QYMGIDo6GitEwSfP39e7Urdli1bEBERgUuXLsHFxQVNmzbF0qVLtSZ5RYoUQY8ePTBgwABMnjwZPj4+8nYNHz4csbGx2LRpE0xMTDB48GBMmDAh3f3k7++PihUr4vTp07C1tQXw/gZuPz8/vH37Vq+h8fWdGPnjL6iPBwMxMTFBwYIFUaFCBQwYMEDrMO2aGn8lY2kTHR2NFi1a4J9//oGNjQ2io6PlCUnt7OwwadIkDBgwALNnz87SWB9LmSw2ICAAr1+/Tve+t5TBVfQhSRJ8fX3VXjPUj5CZM2eiY8eOaNOmDTw8PPSqp6GPC6Wnm8hMPCX3v9Jt/8fq1q2L8+fPY9CgQQZ9H31kdN/ntrZHyX1vyHYsMzK6jSnzxSlN23eltqmdNCV5hvxMZrYds7W11flkli4J+LNnz3S+Z6xUqVJ4+vRpuuUy8z2SW2KlyO7PJZO8XELpG/ItLCwQEhKCwMBA5M2bF506dVLkxvS//voLXl5esLe3R506dbBv3z54enri3bt3OHv2LKpWrZrmnHszZsxA2bJlUaVKFZQrVw7A+y6Mc+bMQbdu3WBpaal1XScnJxQpUkTjslevXqFo0aLy8/3796NBgwaoUqUKAKB79+6YM2eO1tj29vb46aefsHr1alStWhUDBw5Er1695Btxx4wZgzFjxmjfMRrcvHkT3377rZzgAcDIkSPh6+uLW7duoXbt2jrHOnr0qF7v/bF+/fppnRBWW+OjbYQ1JWNpM2PGDJw/fx6///476tevrzZPj4mJCTp16oQDBw7o1HAqGQsAwsPD5cliL168CJVKhUaNGqFz587ynE7aZGQyaU1JnqF+hGzatAklS5aEl5cXXF1d4eLikmryWE31AQx/XNSuXRu///67xq5KsbGx2LhxIxo0aKBTrMzGU3L/G3owlvnz58PT0xN16tSR/4+yW0b3fW5re5Tc95mp18dzxykpo9uYkfni4uLi0i2T2e9KwLCfycy2Y5UrV0ZQUJBOXTYPHjyIypUrp1nG3NwcMTEx6Vcc7+cQ1mVk78x8j+SWWCmU/n2hLyZ5n6hnz57JI2j6+Phg+PDh6NKlC/r27auWDOlrzpw5KFKkCM6fPw9JklCwYEFMmTIFnp6eCAoKQpcuXbBq1SqdYn311VcYPHhwuo1QirZt22odpcvGxka+NyMpKQknT55Um3jcysoKERERWmPfv38fR44cwfr167Fz506MGjVKnsx0wIABeo0olSIqKirVvk55ntaZPE0yez+YEl98hoilzbZt2zBkyBB4e3trvG+hbNmy2Lp1a5bGOnLkCNatW4fff/8dMTEx8tDKGzduRPfu3XWqi1I/tgz1I+TDJPTSpUtauzdp+hI09HGh1HQTSsTLrlEyM2LMmDHImzcvBg0ahAkTJqBMmTIaf9ik1w0OeH+Sr2LFilrvKQ4NDcX169flbql58uTB9OnTUw0zntF9n9vaHiX3vZL1UpKS26jNhQsX4Ovri61bt6Y7sbQh751WQmbbsU6dOmHs2LEIDAzUONl6ij/++AMHDx5M91YPZ2dnnD59GsOHD0+37mfOnIGzs3O65TLzPZJbYqXI7s8lk7xc5tmzZ5AkCYULFwbw/syOpqSpRIkS6Nq1q9Y4Dg4OGDFiBEaMGIF///0Xvr6+2LJlC/z8/ODo6AhJkhAeHq53/c6ePYsxY8ao9fVO+eHaokUL+Pj4YNq0aThy5Ei6sZYsWaL3+2tTuXJl+Pv7o0+fPti2bRvevXunNtfegwcP0h3sxNPTE56enli1ahW2bNmC9evX47fffsO2bdtQvHhx9OvXD/3799drOFxNQ+sC0HkuQaU0adIEDx8+hKOjY6av6CoZS5unT5/C1dVV63Jra2tERkYaPNbjx4+xfv16+Pn5ISQkBI6Ojvjiiy/Qr18/WFtbw8XFRe952XIyXZLQ2NhYja8b+rjw8vLCTz/9hFGjRsk/glLmojI3N8cvv/yCevXqZVu8zFKq7f9YSre4kiVLAng/rUFGeXh4pDkk++HDh9GzZ0+5W6qNjY3GaWEyuu9zW9uj5L5Xsl5KUnIbP6RtqqesZIjPZGbbnS+++AI//fQTPv/8c4wbNy7VvH0hISFYu3YtfvjhB7i4uOCLL75Isz4tW7bE8uXL8c0336S5f2/duoXffvtN7eS5Npn5HsktsVJk9+eSSV4ucvPmTVSpUgWzZ8/GxIkTAby/4jNu3LhUXVRMTU1RvXp1uctjWtzc3ODm5obFixdjx44d8PX1xbFjxzBo0CAsXboUXbp0QceOHXW6ohYXFyffQJoyUMmHB3D16tWxadOmNGMYorvQ+PHj4e3tLd+QXKNGDbURoIKCgtLsRvohW1tb+d7AmzdvwtfXF5s2bcKsWbMwZ84cNGnSBAMHDkx3NCoA2LdvH54/fy4/T+smcE33BijJ2dlZpzlzsjqWJvnz58eTJ0+0Lr927ZrOV6QzE8vZ2RmmpqZo3bo1lixZgtatW8v3/Ny9e1en9/+YPoPopNDWVdhQiYEmupxNN/RxMWTIELRv3x7btm1DcHAwhBAoV64cPv/88wzd2J7ZeErtf0O1/a9evcLWrVtRoEABlClTJt3y6Unv5FRSUpLO8zFmdN/nlrZH6X2vZJuoFKW3EQAOHDiAdevW4Y8//kB8fDxcXFwwffp0dO7cWedeP0ow1GcSyFy7Y2Vlhb1796Jt27aYN28e5s+fDzs7O9ja2iIyMlIembx8+fLYs2dPmrfAAMDXX3+Nn3/+Gc2bN8fatWvVTo6nOHToEAYNGgQLCwu95iHVRJ+rsrkhVrZ/LrNqrgbKvIkTJ4qCBQuKuLg4+bWUOV4WL14sz7t15MgRkS9fPjFp0qQMv9f9+/fFtGnTRMmSJYUkScLExESn9UqXLi1mzpwpP8+bN6/47rvv5OfffvutyJcvn9b1JUkSlSpVEh4eHjr96TNP1fHjx8WYMWPEzJkzxevXr+XXQ0NDRceOHUVAQIDOsT6WlJQk9uzZI1q1aqXz/pIkSa+/9Oa2yywl50oy9LxLvXv3Fk5OTiIqKirVPEf37t0TefLkEcOGDTN4LEmSRKlSpcTs2bPFgwcP1JbduXNHSJIkduzYode2KXVcBAcHC1NTUzF//nz5tZTtU6lUajHMzMzErVu39KqnEEK8fv1aLF26VLi6usoxy5cvn+a2GXo+rhQJCQni77//Fr/99psic33qG0/J/a9025+UlCS++OILYWJiIlQqlVCpVKJBgwbi5cuXOuwJ7SRJSrMd/fLLL0XhwoX1jqvPvs/pbY+h9r2SbWJmKb2NH/4eUalUomDBgqJ3795CpVLp3b4qJat+j2W0HYuJiRHLli0TjRs3Fvnz5xdmZmYif/78okmTJmLZsmUiOjpa51h79uwR1tbWQqVSiVKlSglvb2/h4+MjOnToIJycnIRKpRLW1tZi3759GdlEvb9HclOs7P5cMsnLRdzd3cWgQYPUXtM2keewYcOEu7t7pt8zOTlZ/Pnnn6Jr1646le/WrZvaRJzdunUTefPmFRs2bBDr168X+fLlE61atdK6fnYkPufPnxdDhw4VefPmzXCMU6dOiSFDhgh7e3shSZKwsrJKdx1DTZydUbkpybt9+7awt7cXlSpVErNmzRIqlUqMHz9eTJo0STg4OIh8+fKJhw8fGjzWn3/+Kbp06SLMzc2FiYmJ8PT0FP7+/iIqKirDSZ5Sx4Uhf4Ts379ffP7558LS0lKoVCpRoUIFMXPmzCz/AX706FExcuRI8eLFC7XX7927J6pVqyb/wFSpVKJ///5ZGk/J/a9027906VIhSZIoVqyY6NKli3B1dRWSJImOHTumuZ4mS5YsEc7OzsLZ2VlIkiQKFiwoP//wz8HBQahUqlTbkUKpfZ/T2x4l972S9VKSUtu4adMm4enpKUxMTIS5ubno1KmTCAwMFAkJCRluX5Wi5GdS6XbMEK5duyY6deokLC0t1X6DWVpaik6dOmXoRFpGv0dyU6zs/lwyyctFHBwcxIoVK9Re09aoLF26NFNJS0YFBQWJnj17ymeJ7t69K4oWLSo3CEWKFBFXrlzRun5WnelX4gzN8+fPxffffy8qVqwox6hRo4ZYsWKFePPmjWEqbkC5KckT4n1yXq1atVSJf9WqVcXly5ezNNarV6/EokWLRJUqVYQkScLW1la0a9dOqFQqsXPnzoxuYqYonRgocTZd6eOib9++wsXFJdXrjRs3FpIkiYYNG4qxY8eKKlWqCJVKJfz8/LIsnpL7X+m2v2bNmqJSpUoiIiJCfm3QoEHC1NRU77bLz89PNG3aVDRt2lRIkiQqVqwoP0/58/DwEO3atRNz5szRegVBqX2f09seJfe9kvVSklLbKEmSKFOmjFi+fLkIDQ1VW5bdSZ6Sn0ml2zFDio2NFVeuXBF///23uHLlioiNjdVrfSWvyubUWB/Lzs8l78nLRaKiolLNaZI3b15cuXIl1YhGdnZ2eo/QqITmzZur9dkuXbo0bt26hcOHD8PExAQNGzaEvb19ltcrRWb78ycmJuKPP/7A+vXrceDAASQmJsLBwQHDhg3DwIEDUaNGjSzYCsM5ceKE1jkMNUlr6HElY2lSs2ZNXL58GVevXsWNGzfk+xZq1KiBn3/+Gd27d8f169ezJFaBAgXkaTTOnDkj990XQuCrr77CX3/9hU6dOqndB2pot2/f1nmUxwoVKuDXX3/VuGzz5s1Yt24djh8/DhMTE7Rt2xbLly9H69at8eDBA2zevFmveil5XJw9ezbVyLbBwcE4ceIEGjdujGPHjgEAZs2ahRo1asDf3z/NfaJkPKX2P6B826/k9C19+/aVt9PZ2Rnz589H+/btdV4/hZL7Pie3PUrueyXrpSSlttFQUz0pQcnPpNLtmCFZWFjIU0+lJzQ0FLVr18bmzZtx7949xb5HlPxOUvr7TZNs/VwaNIUkRTk6Oop58+bpVHbevHnC0dHRwDVSniHOwipxhuby5cvi66+/Fo6OjnK3iWbNmomAgAC9z2TlVCndX3X5S6+rrJKxMmL27NmKxcxorKioKLF+/Xr5bKxKpcrQ/UgZZWZmluqMb1JSkrh69aqIiopSe339+vXC3NxcYxwlz6YrfVzkzZtXLF26VO21n376SahUKrFx40a112fOnCkKFCiQZfGU2v9CKN/2S5IkNm3apPbaq1evhCRJ4siRIzq9j9KU2vc5ve3Jrn2vZJuYHqW28c2bN2L58uWiRo0aQpIkYWdnJwYMGCCOHz8ubt++na1X8pT8TCrdjuUUz58/l69sKv09khNjZYShP5e8kpeLVK1aFUFBQZg0aVK6ZYOCglC1atUsqFXOpeQZmurVqwN4PwreN998o/dUCbnFkCFDULdu3RwXKzeytrZGv3790K9fP9y+fRu+vr7YuHFjlr2/g4MDnj17pvaaSqXSeMX6+fPnWq+wK302XcnjIi4uLlVdzp07ByD1fFglSpRId1oYJeMptf8Bw7T9kgGnb/nrr78QFBSEFy9eYOzYsahQoQLevXuHf//9F9WqVYODg0OqdZTc9zm97THkvs8plNhGQ031pAQlP5NKt2M5kZLfIzk1Vk7EJC8X6dy5M0aOHIk//vgjze4wu3btwvHjx7FixQqD18nT01PvdaQ0Jj7t27evYkMt+/j4oHTp0liyZAl69OiB/PnzZzhWly5dMHDgQLRo0cIgUzzkFI0aNVJs6HElY+V25cqVw/z58zF37twse0+lfoQ8e/ZMno/Kx8cHw4cPR5cuXdC3b98MDf2s5HFRsmRJXLt2Te21kydPomDBgihRooTa69HR0RqTC0PFU/JHoCHafkNM35KUlISePXti+/btEEJAkiT06NEDFSpUgKmpKTp06IBx48ZhypQpqdZVct/n9LYnJ02dYyhKb6OSUz0pQcnPpNLtWE6k5PdITo2VIxnsGiEpLjY2VlSsWFFYWFiIadOmiZCQELXlISEh4ptvvhEWFhaicuXKWdKNsFSpUsLJyUnvP30FBweLffv2iX379ong4GCd1rG0tBQmJibCy8tLbNq0Se2G/+y+aTsnym0Dr6QlJ3TXzG4rV64UKpVKBAYGplnu999/FyqVSqxatSrdmBcuXBDDhw8XefPmFSqVShQqVEioVCqxbt06neqk9HExePBgkTdvXnkwp507dwpJkjSOQPfFF1+I6tWrZ1k8Jfe/0m2/oUYxnjNnjjAxMRFLliwRwcHBqQahGDBggGjQoIHGdZXa9zm97THUvs9svZSUVduY0amelKDkZ1Lpdiyn+LC75ocy+z2SG2LpytCfSyZ5uczdu3dFhQoV5IbRwcFBlCxZUh6eOmV0s/v372d3VbXSJ/k8fPiwqFSpUqp7KSpVqiQOHTqU5ro5uT9/TsQkL2Oxbty4Ibp27SqPKqht5LNNmzZlabJoyJNCsbGxYvPmzcLT01P+TLq6uopZs2alOcy00sfFvXv35PmbUu6XtbCwSFWHxMREUaRIETFixIgsi6f0/ley7TfU9C3ly5eXf5hqGmlw4cKFWu9LVWrf5/S2J7umzsnKJC+rt1HfqZ6UotRnUul2LKfQluSlyOj3SG6KlR5Dfy4lIYyoE/gnIjY2Fr/88gu2b9+Oa9euISIiAnZ2dqhSpQq6dOmCQYMGwdLSMrurmcqFCxfkUQdfv36dbvkjR47gs88+g4WFBXr16oVKlSoBAK5du4YtW7YgNjYW+/fv16nL6If9+cPDw+Ho6IhXr15h7dq16N+/f6a3zRioVCps2rRJkW5OSsZKsXjxYp3LHjp0CAcOHEBSUpJBYz1+/BhVq1ZFeHg48uXLh8jISCQmJqJVq1b49ddf1UZf27x5M/r06aO1ToZw7949tGnTBjdv3oQkSbCzs4OdnR0iIiIQEREBIQQqVKiAffv2Zfge05CQEKxbtw4bNmzAo0ePoFKptI5saIjj4vz585g5cybu3LmDMmXK4Jtvvkl1P9ahQ4cwevRo/Pjjj/Dy8sqyeErv/5ze9ltaWmL58uUYPHgwXr9+DUdHRxw6dEhuo9euXYsRI0YgNjZW4/pK7Puc3vYoKafW61Oi1GdS6XYsJ3jx4gWKFCmi1gZoo8/3SHqyO1aO+lwaLH0kEpmbj65OnTqiePHi4vHjx6mWPXr0SBQrVkzUrVtXr/pk5RkaUpaSXYCUijVkyBBhbW0tX1UODw8X48ePFyqVStSpU0e8fftWLpvVV/JSxMTEiGXLlonGjRuL/PnzCzMzM5E/f37RpEkTsXz5chETE6PI+2TX2fScLqv2f05QoEABMX/+fCGE5it5EydOFMWLF8+u6mVYdnWxzK31IhIi/St5mij5PZJdsXLS55JX8sggNM1H16NHD53nowPej044ZcoUfPPNNxqXz5o1C/PmzUN0dHSG6qjk2R4yvOPHj+u9zscjkykdq0KFCmjTpg0WLVqk9vr27dvRu3dvVK1aFQcPHoSDg0O2XMkjykodO3bEnTt38N9//yEsLEztSt6bN29Qvnx5fPbZZ/D398/uqupFybZHSTm1XkSAflfyjElO+lxydE1SzIdJ0+PHj1GgQAF06dIFAQEBmDNnDjp16qRXPHt7e7XJVD9mZ2eXqVGmnJyc8N1332HmzJlyUko5l5KNoFKxHj58qHFi2C5dusDa2hqdO3dGs2bNcOjQIUXejygnmzp1Kho2bAhPT0/069cPAHD58mXcvn0b8+fPR1RUlE6jjeY0OTUxyqn1IgKAPHnyYPr06ShdunR2VyVL5aTPpSq7K0C53+bNm9GsWTOULVsWCxYsQK1atf6vvbuPqbL+/zj+OocpoDUVO65UzJvOIG+m3VnT1IkOyDRTsTlUUI/aoM2UNHOlVsss0miTopvBgSOpiIBW8y4LnLYl5trUGckMNREonXZWAnr0/P7o59mX8Abk5ro4Ph9/nV3X5zp7sTHG+/pc1/utgoIClZeX680337zj2T9Tp07Vxo0bb7i7duXKFW3cuFFTp05tanxZLBZFR0dr8+bNTf4u3F3uu+8+nTt37obnxo0bp4KCAh07dkxjxoxp0HuoQFv2+OOPKy8vTyUlJb53nRcvXqyEhARVV1eroKDA9241AP/WsWNHrVy50i9nCrcVPK6JJrNarerbt68WLlxYbx7diRMnZLfbtWXLlkbv5B07dkxxcXFq3769Fi1apPDwcEnSL7/8opSUFHk8HrlcLnXs2LHOdb169Wr6DwU0QFRUlKxWq3bs2HHTNTt37vT97tfW1vK4JvxebW2tdu/erZKSEnm9XtntdkVFRalDhw5GRwPQQHeyA2exWHTixIkWSIM7weOaaLLAwECdPHlS27ZtU5cuXTR58mQFBwc3+XsHDhwoi8Uir9eradOm1Tl3/d7EjR6V459otJZx48Zp8eLFKisrU58+fW64Jjo6Wlu3btXEiRNbOR1gjMDAQE2YMEETJkwwOgqAO9SrVy9ZLBajY6AJKPLQZBUVFcrOzlZGRoZmzpypxMRExcTEKD4+Xt27d7/j712xYgV/YGBq8fHxCg8PV0BAwC3XRUZGav/+/Tp69GgrJQNaR2MbKlgsFn333XctlAZAcykqKjI6ApqIxzXRrJhHBwB3D6vVqnbt2ql9+/YNWm+xWOR2u1s4FQCAIg8tora2Vnl5eUpPT/fdDRo0aJBiYmI0adKkBo9RAACY1/Xi7tlnn9Xs2bM1fvx4Wa30dAP81cmTJ7Vnzx5VVVVp+vTp6t27ty5fvqzKykrdf//9Db7hg5ZHkYcW11Lz6H7//XeVlZVp5MiRzZASaJy333670ddYLBYtX768BdIAxvjzzz/lcrmUmZmpY8eOqVu3boqLi9OcOXMUFhZmdDwAzWjp0qX68MMPdfXqVVksFn377beKiIiQ2+1W9+7d9c4772jhwoVGx8T/o8hDq/F6vb55dM0xrmDVqlVasWIFjVZgiDvZrbBYLPy+wm8VFxcrIyNDOTk5crvdGjp0qBwOh6ZNm6Z77rnH6HgAmuCzzz5TQkKCFixYoPHjxysyMrLOoPPY2FhVVVXxzq2JUOShzaLIg5FOnTp1R9c9+OCDzZwEMJeamhrl5eXJ6XSqsLBQHTp0UFpammbMmGF0NAB3aPDgwXrooYeUl5en8+fPy2az1Sny3nvvPaWmpurMmTMGJ8V1dNeEqbhcrgav/fnnn1swCXBrFGvAjQUFBfne1bFardqzZ49+++03o2MBaILjx48rISHhpudtNpvOnTvXiolwOxR5MJVZs2b5ZuM1BCMWAMA8KioqlJWVpczMTJWWlqp79+5atmwZ3ZWBNi4oKEj//PPPTc+fOnVKnTt3br1AuC2KPJhKx44dNWTIkAa9uJuXl6ecnJyWDwU0wk8//aQDBw7owoULunbtWp1zNF6BP7py5Yq2bdsmp9Op3bt3KyAgQM8995xSUlIUFRVFt03ADwwdOlQFBQV65ZVX6p2rqanR+vXrNXz4cAOS4WYo8mAqgwcP1sWLFzVlypTbri0pKWmFREDDVFdXa/Lkydq9e7e8Xm+dHenrnyny4G8WLFigDRs26MKFCxo0aJDWrl2rGTNmKCQkxOhoAJrRkiVLFBUVpZkzZ2rOnDmSpMrKSu3atUsrV67UmTNntGHDBoNT4n/ReAWmsmDBAqWlpcntdis4OPiWa1etWqXly5fX2y0BjLBs2TIlJyfr9ddf15gxYzR69GhlZWWpW7duWr16taqrq+VyuWgrD79itVoVHBysSZMm6dFHH73teovFokWLFrVCMgDN7fPPP9fLL7+sy5cv+25cSv/Oy0xLS9OsWbOMDYg6KPJgKgcPHtT27duVmJgom812y7WnT59WWVmZRo0a1UrpgJuz2+167LHHtGnTpnqdxzwej5544glFR0dr9erVRkcFmk1jH8VkjAjQtlVWVio3N1clJSXyer2y2+164YUX1KNHD6Oj4T8o8gCgGQQFBSklJUUJCQm6ePGiQkJCtH37dkVHR0uSkpOTlZaWprKyMoOTAs1n7969jb6GG3NA2+fxeFRcXKzy8nL1799fAwYMMDoS/oN38mBK1dXVys3NVVhYmJ588kmj4wC3de+998rj8fg+W61WnT171ne+U6dOqqysNCoe0CIo2AD/VVRUpPz8fL3xxhvq1q2b73hZWZmef/55HT161HcsPj5eGRkZRsTETdDyCqYUGBioefPmMQsPbUa/fv10/PhxSVJAQIAGDBigLVu2SJK8Xq/y8/MVGhpqZEQAABosMzNTu3btqlPgSf+Ouzpy5IiGDRumRYsWqX///srKylJWVpZBSXEjFHkwJavVqtDQULndbqOjADdVW1vr+zx27Fjl5eX53jd68cUXtXPnTvXr1092u1179uyRw+EwKioAAI1SXFysyMjIOsdKSkq0b98+jRw5Uvv27dOaNWtUXFwsu90ul8tlUFLcCEUeTCs+Pl7r16+v8480YCYPPPCAXnrpJR06dEjLli1TYWGhb2xCYmKi1qxZo06dOqlLly5699139eqrrxqcGACAhqmsrJTdbq9zrKioSBaLRXPnzvUdCw4OVmxsrA4fPtzaEXELvJMH0xo2bJjy8/M1ZMgQJSYmym63q0OHDvXWjRw50oB0wL/v2aWlpenTTz/VoEGD5HA4NH36dN+MsKSkJCUlJRmcEgCAxqutra03zurgwYOS6r+PGxoaqr/++qvVsuH26K4J0/pva+7r81iuuz6jhXbcMNL3338vp9Op/Px8VVdXKzAwUBMnTtScOXPqPeYCAEBb8fDDDysqKkofffSR71hYWJjcbrcqKirqrP3444/11ltv6Y8//mjllLgZdvJgWhkZGfUKO8BsIiIiFBERoU8++UQbN26U0+nU5s2blZubq549e2rWrFmaPXu2evfubXRUAAAabMSIEXK5XJo7d64GDhyogoIClZaW3nDo+ZEjR5iVZzLs5AFAM/v111+Vnp6u7OxsVVZWymq1atSoUXI4HIqNjTU6HgAAt1VWVqaBAweqpqZGXbt21fnz59WuXTsdOnSozly8q1evKjQ0VFOmTNG6desMTIz/ReMVmNb+/fuNjgDckbCwMCUnJ+vMmTP6+uuvFRkZqcLCQsXFxRkdDQCABunTp4/27t2rcePGqWvXrnrmmWdUVFRUb/B5YWGhunbtqokTJxqUFDfCTh5My2q1Kjw8XA6HQ3FxcbLZbEZHAhrlxx9/lNPpVE5Ojtxut4KCgnTp0iWjYwEAAD/HTh5M6/3335ckLVmyRD179lRMTIx27twp7kvAzKqqqvTBBx+of//+Gj58uL744gv17dtX69at09mzZ42OBwAA7gLs5MH0fvjhB6Wnpys3N1eXLl1Sjx49NHv2bJpZwDQ8Ho+++uorOZ1O7dq1Sx6PR507d1ZsbKwcDoceeeQRoyMCAIC7CEUe2oy///5bmzZtUnp6ug4cOCCr1aqIiAjNnTtXkyZNUrt27YyOiLvM4cOH5XQ69eWXX+r8+fOSpNGjR8vhcGjy5MkKDAw0OCEAALgbUeShzamoqNDSpUuVnZ3tO2az2bR48WIlJSUpICDAwHS4m1yf5RgaGsqoBAAAYBrMyUObcO3aNX3zzTdKT0/Xjh075PF49PTTT2v+/PkKDAxUamqqXnvtNZ06dUqpqalGx8VdIiYmRg6HQ5GRkcx0BAAApsFOHkzl9OnTstlsCg4OliSVlpYqPT1dLpdLVVVVCgkJUVxcnObNm6fw8PA61yYmJionJ8f32BwAAABwN6K7JkylT58+2rp1qyRpxIgRCg8PV3JyssLCwpSdna3y8nKtXbu2XoF3ff2FCxdaOTEAAABgLjyuCVPxer2+EQklJSVKSkrS/PnzZbfbb3vt2LFjVVhY2NIRAQAAAFOjyINpnT17tlEdM202m0aNGtWCiQAAAADz43FNmBYjEQAAAIDGYycPprNv3z55PJ4Gr4+Li2vBNAAAAEDbQndNmIrVam1wK3qv1yuLxaKrV6+2cCoAAACg7WAnD6Yzf/58PfXUU0bHAAAAANokijyYzogRIxQbG2t0DAAAAKBNovEKAAAAAPgRijwAAAAA8CMUeQAAAADgR+iuCQAAAAB+hJ08AAAAAPAjFHkAAAAA4Eco8gAAAADAj1DkAQAAAIAfocgDAAAAAD9CkQcAAAAAfoQiDwAAAAD8CEUeAAAAAPiR/wOe/nT+9v0a1AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tRNA_tags = [\"Ala1B\", \"Ala2\", \"Arg2\", \"Arg3\", \"Arg4\", \"Arg5\", \"Asn\", \"Asp1\", \"Cys\", \"Gln1\", \"Gln2\", \\\n",
    "\"Glu2\", \"Gly2\", \"Gly3\", \"His\", \"Ile1\", \"Leu1\", \"Leu2\", \"Leu3\", \"Leu4\", \"Leu5\", \"Lys\", \\\n",
    "\"Met_m\", \"Phe\", \"Pro1\", \"Pro2\", \"Pro3\", \"Sel_Cys\", \"Ser1\", \"Ser2\", \"Ser3\", \"Ser5\", \"Thr1\", \\\n",
    "\"Thr2\", \"Thr3\", \"Thr4\", \"Trp\", \"Tyr1pTyr2\", \"Val1\", \"Val2ApB\"]\n",
    "codon_tags = ['GGG', 'GGA', 'GGU', 'GGC', 'GAG', 'GAA', 'GAU', 'GAC', 'GUG', 'GUA', 'GUU', 'GUC', 'GCG', 'GCA', 'GCU', 'GCC', 'AGG', 'AGA', 'AGU', 'AGC', 'AAG', 'AAA', 'AAU', 'AAC', 'AUG', 'AUA', 'AUU', 'AUC', 'ACG', 'ACA', 'ACU', 'ACC', 'UGG', 'UGA', 'UGU', 'UGC', 'UAU', 'UAC', 'UUG', 'UUA', 'UUU', 'UUC', 'UCG', 'UCA', 'UCU', 'UCC', 'CGG', 'CGA', 'CGU', 'CGC', 'CAG', 'CAA', 'CAU', 'CAC', 'CUG', 'CUA', 'CUU', 'CUC', 'CCG', 'CCA', 'CCU', 'CCC']\n",
    "\n",
    "#Load elongation rate per codon\n",
    "codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "gene_map,gene_latency = compute_gene_elongt(codon_elongt,stratify='highest')\n",
    "pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "pCodon_WT = np.array(pCodon_transcriptome)\n",
    "\n",
    "ptRNA_dict, pcodon_dict, codon_dict, codonLabels, pCodon, tRNA_dict = cognateDistrib(gr25_ptRNA,pCodon_transcriptome,extra2=True)\n",
    "codon_sorted_indices = np.flip(pCodon.argsort())\n",
    "pCodon_sorted = pCodon[codon_sorted_indices]\n",
    "codon_sorted = codonLabels[codon_sorted_indices]\n",
    "\n",
    "#Sort tRNA by codon usage in transcriptome and plot baseline tRNA frequency\n",
    "tRNA_sum_dict = tRNA_dict.copy()\n",
    "tRNA_weightedbycodon = list()\n",
    "for keys in tRNA_dict.keys():\n",
    "    tRNA_sum_dict[keys] = (sum([pcodon_dict[tRNA_key] for tRNA_key in tRNA_dict[keys]]))\n",
    "tRNA_weightedbycodon = list(tRNA_sum_dict.values())/sum(tRNA_sum_dict.values())\n",
    "tRNA_sorted_indices = np.flip(tRNA_weightedbycodon.argsort())\n",
    "tRNA_sorted = np.array(tRNA_tags)[tRNA_sorted_indices]\n",
    "ptRNA_sorted = gr25_ptRNA[tRNA_sorted_indices]\n",
    "\n",
    "#Resort ptRNA_min and plot\n",
    "ptRNA_list_fast=np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_ptRNA.npy')\n",
    "elongt_list_fast =np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_elongt.npy')\n",
    "\n",
    "ptRNA_min = ptRNA_list_fast[np.argmin(elongt_list_fast)]\n",
    "ptRNA_min_dict = dict(zip(tRNA_tags,ptRNA_min))\n",
    "ptRNA_min_sorted=[ptRNA_min_dict[tRNA] for tRNA in tRNA_sorted]\n",
    "fig,ax = plt.subplots(figsize=(9,5))\n",
    "#ax.bar(np.arange(40),ptRNA_sorted,color='#39568CFF',width=0.8,alpha=0.5)\n",
    "ax.bar(np.arange(40),np.array(ptRNA_min_sorted)*100,color='darkseagreen',width=0.8,alpha=0.7)\n",
    "ax.plot(np.arange(40),[1/40*100]*40,color='black',linewidth=3,linestyle='dashed',alpha=0.5)\n",
    "plt.xticks(range(len(tRNA_tags)), tRNA_sorted,rotation='vertical')\n",
    "\n",
    "ax.spines['left'].set_linewidth(4)\n",
    "ax.spines['bottom'].set_linewidth(4)\n",
    "ax.spines['top'].set_linewidth(0)\n",
    "ax.spines['right'].set_linewidth(0)\n",
    "ax.set_xlim(-2,40)\n",
    "ax.set_ylabel('Frequency(%)',fontsize=25)\n",
    "ax.tick_params(axis='y', which='major', labelsize=20)\n",
    "ax.tick_params(axis='x', which='major', labelsize=13)\n",
    "ax.set_ylim(0,8.52)\n",
    "\n",
    "plt.tight_layout()\n",
    "print(ptRNA_min_sorted)\n",
    "fig.savefig('./Figures/S12b.svg', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab5a3362",
   "metadata": {},
   "source": [
    "# Generate Datasets -- only run once to generate data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07ff7b3",
   "metadata": {},
   "source": [
    "## Generate corresponding pCodon list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b426697c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "1\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "2\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "3\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "4\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "5\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "6\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "7\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "8\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "9\n",
      "Number of polypeptides reported:  4434\n",
      "Returned gene map of length  15934\n",
      "Removed  24  non-divisible by three genes\n",
      "['insAB1', 'insCD2', 'dgd', 'insCD4', 'insCD6', 'alaB', 'prfB', 'supK', 'b2891', 'ECK2886', 'insCD3', 'insEF4', 'insAB2', 'insEF5', 'insCD1', 'insAB5', 'insEF1', 'insCD5', 'insEF3', 'insAB3', 'insJK', 'insAB4', 'insEF2', 'insAB6']\n",
      "Unique transcripts without a Gene identifier:  0\n",
      "Sequences not available for # genes in Ecocyc DB:  0\n",
      "Inconsistency in codons:  0\n",
      "1\n",
      "Missing genes in transcriptome_dict in compute_transcript_distributions:  3553\n",
      "CPU times: user 6min 16s, sys: 3.27 s, total: 6min 19s\n",
      "Wall time: 9min 9s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ensmbl_latency_dict_list = np.load('./data/ensmbl_latency_dict_list10.npy',allow_pickle=True)\n",
    "\n",
    "pCodon_WT_list = []\n",
    "for i,ensmbl_latency_dict in enumerate(ensmbl_latency_dict_list):\n",
    "    print(i)\n",
    "    codon_elongt = compute_codon_elongt(gr25_ptRNA, len(gr25_pCodon), ensmbl_latency_dict)\n",
    "    gene_map,gene_latency = compute_gene_elongt(codon_elongt,stratify='highest')\n",
    "    pCodon_transcriptome, transcriptome_elongt = compute_transcript_distributions(gene_map,gene_latency)\n",
    "    pCodon_WT = np.array(pCodon_transcriptome)\n",
    "    pCodon_WT_list.append(pCodon_WT)\n",
    "np.save('./data/pCodon_highestgene_list10',pCodon_WT_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b640b98",
   "metadata": {},
   "source": [
    "## Generate a baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c32fa56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "replicates run:  3000\n",
      "CPU times: user 31.3 s, sys: 1.02 s, total: 32.3 s\n",
      "Wall time: 4min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "baseline = perturb(ptRNA_min, tRNA_tags, tRNA_sorted,pCodon_WT,ensmbl_latency_dict,1,0,pCodon_type='highest')\n",
    "np.save('./data/highestgene-SinglePerturb_n10m300_baseline_elongt',baseline[0])\n",
    "np.save('./data/highestgene-SinglePerturb_n10m300_baseline_ptRNA_sorted',baseline[1])\n",
    "np.save('./data/highestgene-SinglePerturb_n10m300_baseline_elongt_sem',baseline[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b821e7",
   "metadata": {},
   "source": [
    "## Single perturbation pairwise analysis\n",
    "\n",
    "Figure S12\n",
    "\n",
    "\n",
    "Takes in the original fastest performing tRNA distribution, then goes in codon-abundance weighted tRNA rank order starting from the second most-weighted tRNA doing the following:\n",
    "1. If the given tRNA has more relative abundance than the immediately previous higher rank order bar (e.g., first tRNA if we're currently evaluating the second tRNA), then set the relative abundance to be 10% lower than the previous bar and add the removed relative abundance to the previous bar. [If check prior: if 10% higher on the previous bar is above 0.0852, set the bar to be 0.0852 and the current bar to be reduced by the delta increase] \n",
    "2. Evaluate the updated elongation latency\n",
    "3. Reset to the original fastest distribution and go to the next tRNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a0ec96b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "39\n",
      "[-0.03865918  0.3949533   0.36532281  0.366041    0.83287796  0.34688984\n",
      "  0.10739215  0.41381366  0.37377016  0.53014187  0.93906678  0.3785124\n",
      "  0.82373281 -0.00659948  0.2359791   0.34084236  0.40664327  0.37823417\n",
      "  0.37146345  0.36602113  0.37796008  0.39359215  0.38106687  0.39823446\n",
      "  0.39840553  0.39333423  0.39019586  0.38773026  0.35364747  0.36945681\n",
      "  0.36364001  0.36790614  0.37965552  0.37474657  0.39935804  0.38495685\n",
      "  0.38227273  0.4002748   0.38097557]\n",
      "[0.07679412681130282, 0.08523592085132133, 0.08088572005747867, 0.08180197092640702, 0.08523592085132133, 0.05119449762103721, 0.04650492531987349, 0.040615551926959516, 0.035132466359930684, 0.03579538533589475, 0.017223565954440627, 0.053854319214231366, 0.04806219930807657, 0.00607268937513586, 0.0821727727872197, 0.009173707392039838, 0.01509233546229588, 0.012879400346772974, 0.007925944541951644, 0.00858326753920192, 0.008341315073553563, 0.004834282746883225, 0.003358676461886318, 0.002556160310697267, 0.006011375758338082, 0.0041019009053734025, 0.001806989589274271, 0.0069119031957451645, 0.009730425206536079, 0.007295772389366002, 0.005322971818128626, 0.0031959367763178207, 0.004354597709466667, 0.0015430967674765368, 0.006413097821672596, 0.01804501791858015, 0.008903043473362408, 0.003553542676878075, 0.003283062964527097, 0.010200142453043461]\n"
     ]
    }
   ],
   "source": [
    "elongt_perturb = []\n",
    "ptRNA_perturb = []\n",
    "elongt_perturb_sem = []\n",
    "baseline_elongt = np.load('./data/highestgene-SinglePerturb_n10m300_baseline_elongt.npy')\n",
    "for i in range(1,len(tRNA_tags)):\n",
    "    values = perturb(ptRNA_min, tRNA_tags, tRNA_sorted,pCodon_WT,ensmbl_latency_dict,i,10,pCodon_type='highest')\n",
    "    elongt_perturb.append(values[0])\n",
    "    ptRNA_perturb.append(values[1])\n",
    "    elongt_perturb_sem.append(values[2])\n",
    "    print(i)\n",
    "\n",
    "print(elongt_perturb - baseline_elongt)\n",
    "\n",
    "ptRNA_dict = dict(zip(tRNA_tags,ptRNA_perturb[5]))\n",
    "ptRNA_sorted_perturb =[ptRNA_dict[tRNA] for tRNA in tRNA_sorted]\n",
    "print(ptRNA_sorted_perturb)\n",
    "\n",
    "np.save('./data/highestgene-SinglePerturb_n10m300_ptRNA_sorted_perturb',ptRNA_sorted_perturb)\n",
    "np.save('./data/highestgene-SinglePerturb_n10m300_elongt_perturb',elongt_perturb)\n",
    "np.save('./data/highestgene-SinglePerturb_n10m300_elongt_perturb_sem',elongt_perturb_sem)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d00a6ab",
   "metadata": {},
   "source": [
    "## Monotonicity algorithm: multiple iterations of sequential additive tRNA perturbations\n",
    "\n",
    "Shifting abundance mass towards monotonicity.\n",
    "Future work: Can also modify such that the algorithm doesn't touch isoforms (e.g., Leu1, Leu3.\n",
    "\n",
    "Figure S12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2fb5a4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.08088572 0.00532297 0.00917371 0.00328306 0.00355354 0.00890304\n",
      " 0.08523592 0.08523592 0.0064131  0.00834132 0.00858327 0.08217277\n",
      " 0.0043546  0.07679413 0.00319594 0.00607269 0.01722357 0.0041019\n",
      " 0.05385432 0.01020014 0.0069119  0.04061555 0.00335868 0.07363719\n",
      " 0.00729577 0.00180699 0.00483428 0.01804502 0.00792594 0.0015431\n",
      " 0.00255616 0.0128794  0.03579539 0.00601138 0.03513247 0.01509234\n",
      " 0.00973043 0.08180197 0.02406223 0.0480622 ]\n",
      "replicates run:  3000\n",
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      "replicates run:  11\n",
      "replicates run:  3000\n",
      "iter:  1  monotonicity:  71.7948717948718 elongt:  165.60582526336304\n",
      "[0.08490374497521505, 0.08279829991832117, 0.08227622352655817, 0.08382710492473405, 0.05251031011548432, 0.06358079706469683, 0.051547008612522476, 0.04294578847216437, 0.03919897722875909, 0.027683861721016216, 0.041397181447460955, 0.044889650806423535, 0.024549563016428796, 0.05474412493632323, 0.030865226143118728, 0.022600231426367554, 0.017506515940659995, 0.01248629652773682, 0.010441109337744532, 0.009340817143308464, 0.006979393119581638, 0.00508213759094929, 0.00375852549609723, 0.004939019033511436, 0.004500369134367106, 0.0030890382527384605, 0.005092219482873974, 0.007522639282072917, 0.007403761030294493, 0.006313427483119578, 0.004679862352755458, 0.004509423679672131, 0.00295506837768164, 0.004767075806332901, 0.011724717473150459, 0.01024616029726152, 0.006739228664180596, 0.0049281978375621625, 0.007690696816074363, 0.006986205504678237]\n",
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      "replicates run:  3000\n",
      "iter:  2  monotonicity:  74.35897435897436 elongt:  165.1072804589287\n",
      "[0.08523592085132133, 0.08523592085132133, 0.08523592085132133, 0.06843855055492677, 0.06589308772604627, 0.058375742270559805, 0.0502904464802006, 0.044478516592445255, 0.03567811743981644, 0.03867490697982217, 0.041931432744961285, 0.032823333007210256, 0.04430982797806546, 0.03726485661655337, 0.029580593016896004, 0.023253776628948335, 0.017611617055913503, 0.013854271011592962, 0.011489221184611884, 0.009126071278536034, 0.007007050026240581, 0.005304823172445465, 0.005113141803644033, 0.004792048924942924, 0.003899671332707785, 0.004524566563394868, 0.006095556667982166, 0.0067810557538338255, 0.0065360185399795595, 0.00556339269787413, 0.005011112932336283, 0.003933745585697249, 0.004370410621310331, 0.008224067411674559, 0.009283644083722125, 0.007950370403882365, 0.006366751979130579, 0.007060499073689122, 0.007021569243527418, 0.006378372060914219]\n",
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      "replicates run:  3000\n",
      "iter:  3  monotonicity:  79.48717948717949 elongt:  165.0939753686895\n",
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      "iter:  4  monotonicity:  82.05128205128204 elongt:  165.36262254694088\n",
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      "iter:  5  monotonicity:  79.48717948717949 elongt:  165.4958883984695\n",
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      "iter:  6  monotonicity:  82.05128205128204 elongt:  165.6474681099679\n",
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      "iter:  7  monotonicity:  84.61538461538461 elongt:  165.72381661695067\n",
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      "iter:  8  monotonicity:  82.05128205128204 elongt:  165.80847194789024\n",
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      "iter:  9  monotonicity:  82.05128205128204 elongt:  165.88553660931058\n",
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      "iter:  10  monotonicity:  84.61538461538461 elongt:  165.96678251503087\n",
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      "iter:  11  monotonicity:  84.61538461538461 elongt:  165.98858903261132\n",
      "[0.08523592085132133, 0.08523592085132133, 0.07796712294201946, 0.07062014171965039, 0.06410951139671155, 0.05848322894572564, 0.053675090545988645, 0.04951041155545955, 0.045757890284287776, 0.0421980358005781, 0.03867462008721136, 0.03511359129338869, 0.03151403742439216, 0.02792807344867968, 0.024438198779871686, 0.021133564998929107, 0.01809358124657954, 0.015383165605841487, 0.013051294344678737, 0.011125330638136487, 0.009602288706246877, 0.008445979440840946, 0.007592173805484878, 0.006964111236391374, 0.0065015431170617555, 0.006178583181773447, 0.005992882423506267, 0.005939025860011658, 0.0059950076374354205, 0.006125469348048217, 0.006290293370096098, 0.0064506661690998226, 0.006571918914647415, 0.006625157329176894, 0.0065888676493384354, 0.006450351391312875, 0.006206381588421059, 0.0058627410241060465, 0.005432740324223311, 0.004935084722004382]\n",
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      "iter:  12  monotonicity:  84.61538461538461 elongt:  166.0363897443504\n",
      "[0.08523592085132133, 0.08523592085132133, 0.07785972668271501, 0.07065461387284919, 0.06427680817103644, 0.05872150813551139, 0.05389489352756423, 0.049631103828087365, 0.045736176181672514, 0.04203592078817486, 0.038408620132906905, 0.03479585879364518, 0.03119713927288326, 0.027655454454545196, 0.024237984379802364, 0.021018317137993604, 0.018065497735145895, 0.015438055158541103, 0.013178187509849084, 0.011304416536761527, 0.009806595498499144, 0.008646238531359669, 0.007764803828796282, 0.00710285521584739, 0.006618536669992643, 0.006290693844833782, 0.006106419820786989, 0.006048039836710767, 0.00608861290065159, 0.006194293466640512, 0.00632863276272919, 0.00645611470633434, 0.006544693040703837, 0.0065678405356283664, 0.006506276224007049, 0.00634913143495999, 0.006094262859672525, 0.005747625051097136, 0.005321853918652533, 0.0048343558497683315]\n",
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      "iter:  13  monotonicity:  87.17948717948718 elongt:  166.05943729191782\n",
      "[0.08523592085132133, 0.08523592085132133, 0.07782151445111564, 0.07072408836035414, 0.06443473632253655, 0.05891185869797105, 0.054048743146152003, 0.04969295805676475, 0.04568067052802365, 0.04187011612098248, 0.03816320528145774, 0.03451298669296471, 0.030919639800032882, 0.02741845235983209, 0.024064781503588723, 0.020921156808924684, 0.018048011544123683, 0.015496223750163794, 0.013299716770341006, 0.01146932122389587, 0.009990025893006902, 0.008824009531360536, 0.007922124670031647, 0.0072390445580112085, 0.006742108784306236, 0.0064090077674221506, 0.006219860571729386, 0.00615108679208462, 0.006173727089551907, 0.006254897662296804, 0.0063603353933724726, 0.006456938800574108, 0.00651505130974254, 0.006510453164817143, 0.006425920578371994, 0.006252131933773473, 0.0059877703272510725, 0.0056388301291454375, 0.005217285606751834, 0.004739366314530292]\n",
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      "iter:  14  monotonicity:  87.17948717948718 elongt:  166.10922905278397\n",
      "[0.08523592085132133, 0.08523592085132133, 0.07783789587321016, 0.07081464026865719, 0.06457758272561766, 0.059060470785977655, 0.05415189411587009, 0.049712972955838555, 0.04560331597437397, 0.04170469797128587, 0.037936241261445576, 0.03425954209566533, 0.030674811074088707, 0.027211155579146717, 0.023915196223550372, 0.020840791451530786, 0.01804023797601448, 0.01555620486424158, 0.013414677836700428, 0.011620160218205021, 0.010154977258298512, 0.008984962502046674, 0.00807010149937209, 0.007374233316677583, 0.006868455128867736, 0.006528591580927241, 0.006330779071573707, 0.006248483832994284, 0.006251844679548804, 0.006308693813592407, 0.006386374186770818, 0.006453800999207999, 0.006483486733987191, 0.006453322068444828, 0.006347898437877039, 0.006159191307909032, 0.005886522050236908, 0.00553584215385073, 0.0051184888140468204, 0.004649619609706654]\n",
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      "iter:  15  monotonicity:  89.74358974358975 elongt:  166.15230224186888\n",
      "[0.08523592085132133, 0.08523592085132133, 0.07789392893833849, 0.07091616352287279, 0.06470378052873973, 0.05917459204839605, 0.05421670364389592, 0.04970328850506642, 0.04551202706536541, 0.04154231530411142, 0.037726142142885666, 0.03403124466283606, 0.030457517982982844, 0.02702934138104023, 0.02378654121797728, 0.020775478319188773, 0.018040579028796445, 0.015616606804138131, 0.01352246879310918, 0.01175790322886839, 0.01030488228801381, 0.00913385715476555, 0.008211814263607453, 0.007507894077003842, 0.006994739569059702, 0.006646824268377041, 0.0064380938802364755, 0.0063404992990761366, 0.006323833224682662, 0.006356604688816856, 0.006407535555461815, 0.006447333973009112, 0.006450471735017428, 0.006396723327315864, 0.006272256549146683, 0.006070131671717961, 0.005790163964355532, 0.005438206185593768, 0.0050249868198289195, 0.004564682683661384]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "iter:  16  monotonicity:  92.3076923076923 elongt:  166.1701822161027\n",
      "[0.08523592085132133, 0.08523592085132133, 0.07797648844967472, 0.07102158949910478, 0.06481376283493341, 0.059260903818829765, 0.05425271339441777, 0.049672593757994335, 0.045412327848159684, 0.041384766538596095, 0.037531521075697835, 0.033824743455115186, 0.030263952768299907, 0.02686978911593085, 0.023676370258438, 0.020723215654105825, 0.018047352616722755, 0.01567631349314924, 0.013623066514666075, 0.011884337979900288, 0.010443086027529686, 0.009273899623234435, 0.008348512717009603, 0.007639135587483855, 0.007119164456271886, 0.006762283474429331, 0.006541268566544256, 0.006427332447408782, 0.006390271101906613, 0.006399317675569539, 0.006424478215427876, 0.006438098819692801, 0.006416418061687286, 0.00634087742911601, 0.0061990066522394326, 0.005984773083788269, 0.0056983720291343505, 0.005345518179458305, 0.0049363603796607185, 0.004484174696027675]\n",
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      "iter:  17  monotonicity:  97.43589743589743 elongt:  166.1839154709212\n",
      "[0.08523592085132133, 0.08523592085132133, 0.07807499284516047, 0.07112610831980148, 0.0649087411612923, 0.05932498261145005, 0.05426713085223925, 0.049627214078101556, 0.0453081715674007, 0.04123320670974839, 0.03735117196432059, 0.033637469105605756, 0.030091204791016107, 0.026729831495945176, 0.02358236479482136, 0.02068201841349769, 0.0180590290352351, 0.01573458467445693, 0.01371705540650925, 0.01200149545192396, 0.010572235237690648, 0.00940700463685378, 0.00848064125498394, 0.007767227412458823, 0.007240636788931369, 0.006874167840400522, 0.00664002609447285, 0.006509154478368142, 0.006451599993701674, 0.006437388181886204, 0.006437760556096861, 0.006426577089026243, 0.0063816704672332815, 0.006285954628176504, 0.00612813549891707, 0.005902939440750925, 0.005610850699833592, 0.005257417772063007, 0.004852238400220493, 0.00440775854676518]\n",
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      "replicates run:  3000\n",
      "iter:  18  monotonicity:  100.0 elongt:  166.21927244861118\n",
      "[0.08523592085132133, 0.08523592085132133, 0.07818137701044006, 0.07122651582548663, 0.06499011242133146, 0.059371270079127146, 0.05426538473458973, 0.04957180503498268, 0.04520237951255991, 0.04108834675728251, 0.03718408686780388, 0.03346741665956709, 0.029936922033829208, 0.026607134040589092, 0.0235023734519932, 0.02065006097761195, 0.018074351394758725, 0.015791128296916, 0.01380536068614017, 0.012111202951152618, 0.010694203034460028, 0.009534296662014557, 0.008608352375247354, 0.007891669408017778, 0.007358498586586336, 0.0069820190007188684, 0.006734237991007088, 0.006586135680419051, 0.006508191991187839, 0.006471285919200167, 0.006447858492189031, 0.006413175967112218, 0.006346511985509905, 0.006232082706535259, 0.006059611635264268, 0.005824460905098594, 0.005527330303387946, 0.005173582146128201, 0.004772290580061977, 0.0043351341910486665]\n",
      "[163.87486860802494, 165.60582526336304, 165.1072804589287, 165.0939753686895, 165.36262254694088, 165.4958883984695, 165.6474681099679, 165.72381661695067, 165.80847194789024, 165.88553660931058, 165.96678251503087, 165.98858903261132, 166.0363897443504, 166.05943729191782, 166.10922905278397, 166.15230224186888, 166.1701822161027, 166.1839154709212, 166.21927244861118]\n",
      "[0.         1.73095666 1.23241185 1.21910676 1.48775394 1.62101979\n",
      " 1.7725995  1.84894801 1.93360334 2.010668   2.09191391 2.11372042\n",
      " 2.16152114 2.18456868 2.23436044 2.27743363 2.29531361 2.30904686\n",
      " 2.34440384]\n"
     ]
    }
   ],
   "source": [
    "baseline_elongt = np.load('./data/highestgene-SinglePerturb_n10m300_baseline_elongt.npy')\n",
    "baseline_ptRNA = np.load('./data/highestgene-SinglePerturb_n10m300_baseline_ptRNA_sorted.npy')\n",
    "\n",
    "\n",
    "elongt_perturb = []\n",
    "elongt_perturb_sem = []\n",
    "ptRNA_perturb = []\n",
    "\n",
    "ptRNA_min_update = ptRNA_min.copy()\n",
    "mono_perturb = []\n",
    "count=0\n",
    "\n",
    "ptRNA_sorted_perturb_iter = baseline_ptRNA.copy()\n",
    "print(ptRNA_min_update)\n",
    "\n",
    "monotonic = False\n",
    "\n",
    "values = perturb(ptRNA_min_update, tRNA_tags, tRNA_sorted,pCodon_WT,ensmbl_latency_dict,i,0, pCodon_type='highest')\n",
    "ptRNA_dict = dict(zip(tRNA_tags,values[1]))\n",
    "ptRNA_sorted_perturb_iter =[ptRNA_dict[tRNA] for tRNA in tRNA_sorted]\n",
    "mono_perturb.append(monotonicity(ptRNA_sorted_perturb_iter))\n",
    "elongt_perturb.append(baseline_elongt.item())\n",
    "\n",
    "while (not monotonic):\n",
    "    count+=1\n",
    "    for i in range(1,len(tRNA_tags)):\n",
    "        if i <(len(tRNA_tags)-1):\n",
    "            values = perturb(ptRNA_min_update, tRNA_tags, tRNA_sorted,pCodon_WT,ensmbl_latency_dict,i,10,skip=True, pCodon_type='highest')\n",
    "        else:\n",
    "            values = perturb(ptRNA_min_update, tRNA_tags, tRNA_sorted,pCodon_WT,ensmbl_latency_dict,i,10, pCodon_type='highest')\n",
    "        ptRNA_min_update = values[1]\n",
    "    elongt_perturb.append(values[0])\n",
    "    elongt_perturb_sem.append(values[2])\n",
    "    ptRNA_dict = dict(zip(tRNA_tags,values[1]))\n",
    "    ptRNA_sorted_perturb_iter =[ptRNA_dict[tRNA] for tRNA in tRNA_sorted]\n",
    "    mono_perturb.append(monotonicity(ptRNA_sorted_perturb_iter))\n",
    "    ptRNA_perturb.append(ptRNA_sorted_perturb_iter)\n",
    "    print(\"iter: \", count, \" monotonicity: \", monotonicity(ptRNA_sorted_perturb_iter), \"elongt: \",elongt_perturb[-1] )\n",
    "    print(ptRNA_sorted_perturb_iter)\n",
    "    if monotonicity(ptRNA_sorted_perturb_iter) == 100:\n",
    "        monotonic = True\n",
    "\n",
    "print(elongt_perturb)\n",
    "print(elongt_perturb - baseline_elongt)\n",
    "\n",
    "np.save('./data/highestgene-IterativeSequentialAdditive-Monotonicity_n10m300_ptRNA_perturb',ptRNA_perturb)\n",
    "np.save('./data/highestgene-IterativeSequentialAdditive-Monotonicity_n10m300_elongt_perturb',elongt_perturb)\n",
    "np.save('./data/highestgene-IterativeSequentialAdditive-Monotonicity_n10m300_elongt_perturb_sem',elongt_perturb_sem)\n",
    "np.save('./data/highestgene-IterativeSequentialAdditive-Monotonicity_n10m300_mono_perturb',mono_perturb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4dcf05b",
   "metadata": {},
   "source": [
    "# Plotting (can modify below)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c1ceaa",
   "metadata": {},
   "source": [
    "# Single perturbation analysis plotting (Fig S12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd83a535",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "show [0.         0.         0.24100907 0.2229279  0.22336616 0.5082402\n",
      " 0.2116797  0.06553302 0.25251807 0.22808266 0.32350407 0.57303892\n",
      " 0.23097648 0.50265963 0.         0.14399957 0.2079894  0.24814255\n",
      " 0.2308067  0.22667506 0.22335403 0.23063944 0.24017847 0.23253527\n",
      " 0.2430113  0.2431157  0.24002108 0.23810598 0.23660142 0.21580336\n",
      " 0.22545056 0.22190102 0.22450431 0.23167403 0.22867849 0.24369694\n",
      " 0.23490902 0.23327111 0.24425636 0.23247956]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3T0xMxPLly9GpU6dUyzdt2hTXrl3D3LlzYW9vj/DwcIwaNQoeHh44deqUYitFRERERET0qdK7yPP398fevXsBAHZ2dhg9ejQuX76MsLAwvH37FleuXMHYsWPh4OAAIQQOHDiAdevWpYpjaWmJCRMmIDAwEF27doUQAv/99x8aNWqEPn364OXLl8qtHRERERER0SdG7yLvw4Jt3759+P7771GlShU4OTnB2dkZlStXxoIFC/DHH3/IA6CvX79ea7zChQtj27ZtOHz4MMqWLQshBLZs2YKffvrJiNUhIiIiIiL6tOld5F27dg2SJKFq1arw8vLSOl/Dhg1RtWpVCCFw/fp1nXGbNGmC//77D/Pnz4eDg4O+6Rjk8ePHmDZtGmrUqAEXFxfY2tqiaNGiaNCgAb799lu98iQiIiIiIsoOLPWdMT4+HgCQI0cOnfOmFGspy+hMwtIS48aNQ69evXDnzh19U9LL8uXLMWnSJERFRam9HhwcjODgYJw6dQoRERFYsmSJou9LRERERESUGfQu8tzc3HDz5k1cuHABz58/R8GCBTXO9+zZM1y8eBGSJMHV1dWgZAoVKoRChQoZtExaZs2ahalTpwIA3N3dMXjwYHh6esLZ2Rlv3rzB5cuX8dtvv0GlMtlwgURERERERBlK7yKvY8eOuHnzJuLi4tCsWTP88MMPaNasmdo8hw8fxpgxYxAbGwtJktCxY0fFE9bX0aNH5QKvT58+WLt2LaysrNTmadKkCcaOHav3FUciIiIiIqKsTu8ib/z48diyZQsePXqEW7du4bPPPkPu3LlRvHhxAMCjR48QGhoqz1+0aFGMHz9e+Yz1kJycjKFDhwIAqlSpAj8/P1haal9Va2vrjEqNiIiIiIjIpPQu8hwdHXHs2DF07twZly9fBgC8efNGLuyEEPK8lStXxs6dO+Hk5KRwuvo5dOgQ7t69CwCYMGFCmgUeERERERGROTGo+nF1dcX58+fx66+/4tdff8WlS5fw+vVrAICLiws8PDzQtWtXdOvWLVMLq+3btwMAJElCmzZt5NdDQ0Px5s0b5MmTB7lz586s9IiIiIiIiEzG4ErMwsICPXr0QI8ePUyRjyLOnj0L4H1R6ujoiICAAMydO1dtqISUjlhGjBgBGxubzEqViIiIiIhIUWbXrWRycjICAwMBAHnz5sXIkSPRq1evVGPh3blzB+PGjYO3tzfCwsIyIVMiIiIiIiLlmV2RFx4ejuTkZADvB3BftmwZChYsiM2bNyM0NBTR0dE4efIkateuDQA4ffo0BgwYoDVeXFwcIiIikJSUlCH5ExERERERGcPsirwPBz2PjY2Fvb09jh8/jl69eiFXrlyws7NDw4YNcezYMVSpUgUA8Ntvv+HcuXMa482dOxfOzs64cOFChuRPRERERERkDL2KvPv375s6D60ePHhg0Py2trZqzwcNGoQyZcqkms/Ozg6zZ8+Wn2/btk1jvEmTJiE8PByenp4G5UFERERERJQZ9Cryypcvj2HDhuHJkyemzkf25MkTDBkyBOXLlzdoOUdHR7XnzZs31zpvkyZN5F5AtV2ps7GxgZOTEywsLAzKg4iIiIiIKDPoVeQlJCTgp59+QqlSpeDj4yP3XmkKZ86cQa9evVCqVCn8/PPPSEhIMGh5GxsbuLi4yM+LFi2qdV5bW1vkzZsXAOShIIiIiIiIiLIzvYq8QYMGQZIkJCQkICAgAPXq1UPFihUxZ84c3Lx50+gkrl+/jpkzZ6J8+fKoX78+fvnlFyQkJECSJAwaNMjgeBUqVJAf6+owJWU6B0wnIiIiIiJzoFdls2bNGgwdOhTjx4/H0aNHAQC3bt3C1KlTMXXqVBQuXBgNGzaEp6cnKlSogJIlS6JQoUKpxp+LjY3Fs2fPcO/ePVy/fh0XL17EyZMn8eLFC3keIQSA900pFyxYgGrVqhm8Ug0bNsSJEycAvL+nT1uMiIgIhISEAAAKFy5s8PsQERERERFlNXpfvqpWrRoOHz6Mv//+G3PmzMGhQ4fkguzp06fYunUrtm7dqraMjY0N7OzsIIRAbGws4uLiNMZOiSNJElq0aIHJkyejQYMG6V0ndO7cGd999x2A9z1ndu7cWeN8v/32m/zexrwfERERERFRVmHwEAoNGjTAn3/+iVu3bmH06NEoVKgQhBAa/2JjY/H27VuEhYUhNjZW63wFChTAqFGjcOPGDfz5559GF1yVK1dGy5YtAQBbt26Vrz5+6MWLF/jmm28AANbW1ujfv79R70lERERERJQVpPtGNHd3d3z//fdYuHAhzp49i4MHD+LkyZO4fPkyIiIi0lzW0dER1apVQ8OGDdGiRQvUqVMHKpWyQ/YtWbIEZ86cQVhYGNq0aYOvv/4arVq1gp2dHc6fP4+5c+ciODgYADBz5kw21yQiIiIiIrNgdG8jkiShTp06qFOnjvxacHAwHj16hJCQEHlwcgcHB+TNmxfFixdHkSJFjH1bndzd3bF371506dIFL1++xLx58zBv3rxUuU+ZMgXjx483eT5EREREREQZwSRdShYpUiRDCjld6tevjxs3bmD58uXYvXs3Hj58iPj4eBQsWBCNGzfGiBEj0tWxCxERERERUVZl9uMG5MmTB9OnT8f06dMzOxUiIiIiIiKTU/ZGOCIiIiIiIspULPKIiIiIiIjMCIs8IiIiIiIiM8Iij4iIiIiIyIywyCMiIiIiIjIjLPKIiIiIiIjMCIs8IiIiIiIiM8Iij4iIiIiIyIywyCMiIiIiIjIjLPKIiIiIiIjMCIs8IiIiIiIiM8Iij4iIiIiIyIwYXeTt2LEDiYmJSuRCRERERERERjK6yPv8889RuHBhjBs3DoGBgUrkREREREREROmkSHPNkJAQLF68GBUqVED9+vWxYcMGxMTEKBGaiIiIiIiIDGBpbAAnJydERETIz8+cOYMzZ85g5MiR6NGjBwYOHIgaNWoY+zZERIqZvmZb+pf17aZgJkRERETKM/pK3osXL7BhwwY0bNgQACCEgBACERERWLNmDWrVqoWqVati5cqVCAsLM/btiIiIiIiIKA1GF3m2trbw8fHBiRMncOfOHUyYMAEFCxYE8L+C79q1a/jqq69QqFAheV4iIiIiIiJSnqJDKJQqVQpz587F48ePsWfPHrRr1w6WlpZysRcbG4uAgAA0adIEpUuXxrx58/DixQslUyAiIiIiIvqkmWScPAsLC7Rt2xa7d+/G48ePMWfOHJQuXRrA/67uPXjwAFOmTEGxYsXQoUMH7Nu3D8nJyaZIh4iIiIiI6JNh8sHQCxQogIkTJ+L27ds4ceIEevfuDTs7O7nYS0xMxN69e9G+fXsUK1YM33zzDR4+fGjqtIiIiIiIiMySyYu8DzVs2BAbN27E8+fPsWjRItja2kKSJLnge/bsGebOnYvSpUujdevWOHnyZEamR0RERERElO1laJEHAFevXsWUKVMwa9YsxMXFAQAkSZKnCyGQnJyMAwcOwNvbG23atMHz588zOk0iIiIiIqJsyehx8vQRERGBLVu2wM/PD5cvX5ZfF0IAAJydndGrVy9UqVIFW7duxYkTJ+Rp+/fvR926dXHhwgXkzZs3I9IlIiIiIiLKtkx6Je+vv/5Cnz59ULBgQQwfPhyXL1+Wm2YKIVCrVi34+fnh2bNnWLFiBQYPHoxjx47h9u3bGDBgAFSq9+k9fvwYs2bNMmWqREREREREZkHxIu/FixeYN28e3N3d4eXlhS1btiAmJkYu7JydnfHll1/i6tWrOHPmDPr37w87Ozu1GKVLl8batWvx66+/yq/t3btX6VSJiIiIiIjMjiLNNZOTk7Fv3z74+fnhzz//RFJSEoD/NccEgNq1a8PX1xfdunVLVdRp06lTJ1SsWBHXr19HcHCwEqkSERERERGZNaOLvEmTJmHjxo3yoOYfFnbOzs7w8fGBr68vKlasmK74bm5uuH79OhITE41Nlcgoa7efNmr5QV3rKpQJEREREZF2Rhd58+fPl4dBSFGnTh35qp2tra1R8VPuyyMiIiIiIiLdFGmuKYRAzpw50bt3b3zxxReoUKGCEmEBALNmzcLXX3+tWDwiIiIiIiJzZnSRV7duXfj6+uLzzz83+qqdJkoWjERERERERObO6CLv1KlTSuRBRERERERECuANb0RERERERGaERR4REREREZEZMbrIS05Ohre3Nzw8PFCvXj1ER0cbtPyqVavg4eEBDw8P7Nixw9h0iIiIiIiIPmlGF3kHDhzAiRMncPXqVXh4eMDe3t6g5Tt27Ijr16/j6tWrWLJkibHpEBERERERfdKMLvL++OMP+bGPj4/ByxcsWBBNmjSBEALnzp1DaGiosSkRERERERF9sowu8i5cuAAAyJEjB2rWrJmuGE2aNAHwvulnSjwiIiIiIiIynNFF3v379yFJEsqWLZvuGB+OhXf//n1jUyIiIiIiIvpkGV3kRUZGAgCcnJzSHePDZcPDw41NiYiIiIiI6JNldJHn6OgIAAgLC0t3jA+XtbW1NTIjIiIiIiKiT5elsQHy5cuHt2/fIjAwEPHx8bC2tjY4xpUrV9TiERFlF9PXbEv/sr7dFMyEiIiI6D2jr+TVqlULABAdHY2dO3cavHxycjK2bNkiP69evbqxKREREREREX2yjC7yWrduLT+eMGECXr58adDy8+bNQ2BgICRJgpubm1EduBAREREREX3qjC7yOnXqhNKlSwMAgoOD4eXlhatXr+pcLikpCdOmTcPUqVPl1yZMmGBsOkRERERERJ80o+/Js7CwwIoVK9CqVSskJycjMDAQnp6eaNWqFTp37ozq1avDxcUFNjY2CA8Px927d/HXX39hw4YNePz4MYQQkCQJdevWxcCBA5VYJyIiIiIiok+W0UUeADRr1gyrVq3C0KFDAQCJiYnYu3cv9u7dq3WZlOIOAMqVK4fff/8dKpXRFxaJiIiIiIg+aYoUeQAwePBglCxZEn379sXTp08hhNBruX79+mHFihWwt7dXKhWiLO/gkcdGLV/Ht45CmRARERGRuVH00pm3tzcePHiAtWvXomnTpnBwcNA4X+nSpTFs2DBcv34d69atY4FHRERERESkEMWu5KWwsrLCgAEDMGDAACQlJeHJkycIDQ1FXFwccubMiQIFCiBXrlxKvy0RERERERHBBEXehywsLODq6gpXV1dTvg0RERERERH9P/Z0QkREREREZEZY5BEREREREZkRFnlERERERERmRNF78t69e4cdO3bgn3/+QWBgIMLCwhAdHa33cAqSJOH+/ftKpkRERERERPRJUazIW7x4MaZPn46oqKh0Lf/h4OhERERERESUPooUeSNHjsSKFSv0vmJHREREREREpmF0kXf8+HEsX75cvgpnb2+PLl26oH79+ihcuDAHOiciIiIiIspARhd5a9askR9XrlwZ+/btQ5EiRYwNS0REREREROlgdO+a//zzD4D3nab8+uuvLPCIiIiIiIgykdFF3uvXryFJEipUqAB3d3clciIiIiIiIqJ0MrrIc3Z2BgC4uLgYnQwREREREREZx+gir2TJkhBC4PXr10rkQ0REREREREYwusjr1q0bAODGjRt4+fKl0QkRERERERFR+hld5PXv3x/FixeHEALffvutEjkRERERERFROhld5Dk6OmL79u1wcHDA2rVr8c033yA5OVmJ3IiIiIiIiMhARo+T9/jxY+TLlw/btm1Dnz59MHfuXOzcuRMDBgxAnTp1UKBAAVhbW+sdr1ixYsampNWECROwYMEC+fnx48fRuHFjk70fERERERFRRjO6yHN1dYUkSfJzIQRu376NiRMnGhxLkiQkJiYam5JGV65cweLFi00Sm4iIiIiIKKswurlmCiEEgPeF2sdFn66/D+czheTkZPj6+iIxMRH58uUzyXsQERERERFlBYoUeR8Xah8XcPoubyrLli3DhQsXULZsWQwcONCk70VERERERJSZjC7ykpOTFftLSkpSYp3UPH78GFOnTgUArF692qD7A4mIiIiIiLIbxZprZlVffvkl3r17h759+6JRo0aZnQ4REREREZFJmXWR9+uvv2Lfvn3InTs3vv/++8xOh4iIiIiIyOSM7l0zqwoLC8PIkSMBAPPnz0fevHkzOSMi05i+Zptxy/t2UygTIiIiIsoKzPZK3vjx4/HixQvUq1ePna0QEREREdEnQ/EredHR0diyZQuOHTuGf//9FyEhIQgPDwcAjWPgHT16VO5wpVmzZmrDL6TX33//jbVr18LS0hKrV682KmZcXBzi4uJM0ikMERERERGR0hQt8pYvX45p06bJRR2gPn6eJmvWrMGOHTsAAHv37kWrVq2MyiE+Ph6+vr4QQmDUqFGoWLGiUfHmzp2LGTNmGBWDiIiIiIgooyjSXDM5ORndunXD119/jfDwcIPGyRs1apQ836ZNm4zOZc6cOQgMDESxYsUwbdo0o+NNmjQJ4eHh8PT0NDoWERERERGRqSlS5I0bNw7bt2+XC7uWLVti8+bNuHLlCho2bJjmsrVr10bx4sUhhMDhw4eNyiMwMBBz584F8P6qooODg1HxAMDGxgZOTk6wsLAwOhYREREREZGpGd1c89atW1i2bNn7YJaW8Pf3R8+ePeXpdnZ2OmO0aNECa9aswdu3b3Hjxg1UqFAhXbn88MMPiI+PR4kSJRAdHY1ffvkl1TzXr1+XHx87dgwvXrwAALRt21aRopCIiIiIiCgzGV3krVu3DklJSZAkCdOnT1cr8PTl4eEhPw4MDEx3kRcXFwcAePDgAXr06KFz/pkzZ8qPHz58yCKPiIiIiIiyPaObax45cgTA+2aNo0aNSleMokWLyo+fPn1qbEpERERERESfLKOLvCdPnkCSJFSuXFmvppmaODk5yY/fvXuX7lz8/f3VOn3R9PdhZyzHjx+XX3d1dU33+xIREREREWUVRhd5KUVZjhw50h0jKipKfmxvb29sSkRERERERJ8so4s8FxcXAMDr16/THePBgwep4hEREREREZHhjC7ySpQoASEEbt68ibCwsHTFOHDggPy4SpUqxqZERERERET0yTK6yPvss88AvB8QfeXKlQYvf+PGDezbtw+SJKFAgQKoWLGisSkRERERERF9sowu8nr16gUbGxsAwKxZs3Dq1Cm9l339+jW6du2K5ORkAMCwYcOMTUen6dOny52tNG7c2OTvR0RERERElJGMLvKKFSuGUaNGQQiB+Ph4NG/eHHPmzEmz6WZ8fDw2bNgADw8P3L59G5IkoXDhwhg5cqSx6RAREREREX3SjC7ygPeDirdq1QpCCMTFxWHq1KkoUKAAatWqhStXrsjztWvXDrVr14azszMGDBiAZ8+eQQgBOzs77N6926geOomIiIiIiEihIs/CwgI7d+6Er6+v3BQyPj4eFy9exKtXryBJEgDgjz/+wIULFxAXFyfPV7hwYRw7dgweHh5KpEJERERERPRJU6TIAwAbGxusXr0aR48eRYsWLSBJktYByQHA2dkZkydPxtWrV1GzZk2l0iAiIiIiIvqkWSod0MvLC15eXggJCcGpU6dw/fp1vHnzBlFRUXB2dkb+/PlRu3Zt1KpVC1ZWVkq/PRERERER0SdN8SIvRd68edGhQwd06NDBVG9BREREREREHzFZkUdENH3NtvQv69tNwUyIiIiIPh2K3ZNHREREREREmY9FHhERERERkRkxurnmxo0blchD1qdPH0XjEZFh2MSSiIiIKHszusjr16+fPA6esSRJYpFHRERERERkBEU6XkkZ+05fKWPoERERERERkbKMLvIaNmyo95W8pKQkhIWF4e7du4iLiwPwvuDz8PBAjhw5jE2FiIiIiIjok2d0kXfixAmDl0lISMCff/6JGTNm4PLly4iJicHWrVtRqlQpY9MhIiIiIiL6pGVK75pWVlZo164dzp07h+7du+PmzZvw9vbGmzdvMiMdIiIiIiIis5GpQyhYWlrC398fbm5uePr0KYYMGZKZ6RAREREREWV7mT5OnrW1NQYPHgwhBPbs2YPnz59ndkpERERERETZVqYXeQDg4eEB4H3HLH/99VcmZ0NERERERJR9ZYkiz9bWVn4cHByciZkQERERERFlb1miyLtx44b82MLCIhMzISIiIiIiyt4yvciLiYnB0qVL5edubm6ZmA0REREREVH2lqlF3rlz59C4cWPcuXMHAGBjYwNvb+/MTImIiIiIiChbM3ow9AEDBhg0f0JCAt6+fYtr167J999JkgQAGDt2LBwdHY1NiYiIiIiI6JNldJHn7+8vF2mGEkLIj7t164bp06cbmw4REREREdEnTZHmmkKIdP0B74dPCAgIwNatW6FSZfotgkRERERERNma0Vfypk2bZtD81tbWcHR0RLFixVCtWjUULVrU2BSIiIiIiIjo/2V4kUdERERERESmw/aRREREREREZoRFHhERERERkRlhkUdERERERGRGWOQRERERERGZEaM7Xtm4caMSeeilT58+GfZeRERERERE2ZHRRV6/fv3SPRi6ISRJYpFHRERERESkg9FFHgB5YPOPSZKkcZq214mIiIiIiMg4Rhd5DRs2lK/kXb9+HaGhoWoFnJubG/LkyQMbGxtERkYiKCgIERERACAv5+HhgRw5chibChERERER0SfP6CLvxIkTSEpKwuTJk/HXX39BCAEvLy8MHz4cLVq0gL29faplbt++ja1bt2LZsmUICwtDREQE1qxZg2rVqhmbDhERERER0SdNkd41x48fj++//x6WlpZYu3Ytjh49io4dO2os8ACgTJkymD59Om7fvo06derg7t27aNasGYKCgpRIh4iIiIiI6JNldJF3+vRp/PDDDwCAmTNnYsCAAXov6+Ligv3796NQoUIIDQ3FoEGDjE2HiIiIiIjok2Z0kbdmzRoAgKOjI0aOHGnw8s7Ozhg2bBgA4Pjx43jw4IGxKREREREREX2yjC7yzpw5A0mSUL58edjY2KQrhqenp/z43LlzxqZERERERET0yTK6yHv69CkAwNIy/X24qFT/S+PZs2fGpkRERERERPTJMrrIs7W1hRACN2/eRGJiYrpiXL16VX6c3quBREREREREpECRV6pUKQDA27dvsX79eoOXj4mJwY8//pgqHhERERERERnO6CKvc+fOAAAhBEaPHo0///xT72VjYmLQvXt33L9/HwCQM2dONGnSxNiUiIiIiIiIPllGF3lDhgxBkSJFIEkSoqKi0LZtW/j4+ODMmTNal3n79i3WrFmDcuXKYd++fQAASZIwZcoUWFlZGZsSERERERHRJyv9vaX8P0dHR2zduhWtWrXCu3fvkJycjICAAAQEBMDR0RHly5dHnjx5YG1tjcjISAQFBeHBgwcQQkAIAUmSAAAdOnTA6NGjjV4hIiIiIiKiT5nRRR4A1KtXD0ePHkWPHj1w//59CCEAABERERqHRPiwuJMkCcOHD8fixYuVSIWIiIiIiOiTZnRzzRQ1atTAjRs3sGDBApQuXVp+PeWK3Yd/AGBlZYXOnTvjzJkzWLp0KSwsLJRKhYiIiIiI6JOlyJW8FNbW1hg7dizGjh2Lmzdv4sKFC7h37x7evn2L+Ph4ODk5IV++fKhWrRpq1qwJZ2dnJd+eiIiIiIjok6dokfeh8uXLo3z58qYKT0RERERERBqYrMijjBcfF5vm9KioKK3zW1hawsJC8+6QEB8nN7PVJ9aHLCwsYWGpLW48hEjWO9aHVCoLWGrpiTU2NhZJSUl6x1KPq4KllbWWfOOQnJxsULwP42qTmJCA5OQkg+KlbLO04iYlJiIpKVHvWCni42IhSRKsrG00x01KRFKi5rj6/F9a29hqXDY5KQmJiQlaY+lib28v3+v7oaSkJMTGqudhyH5hZWUNScN2Tk5ORkxMjMH7WApt+5kQAtHR0emKCQA2Njaw1PKZMyTXj7e/tbW11t6Po6OjUx0j9GVlZQVra83bIiYmJt2fOUtLS9jYaN6HPzxGGMrCwgK2tpr3YVPFjYuLQ6KWz5wuKpUKdnZ2GqfFx8cjISFB4zRdJEmCvb29xmkJCQmIj49PV1wAcHBw0Ph6YmIi4uLi0h3XkGOEIezs7DQei1OOEella2ur8VYWUx4jDD3ufojHiPd4jHiPx4j/0XaMMDUWeWbk9KG9aU5f+Oah+vyXrsuPS1eshsJumgeiP3/8IBLi1T80acX6kKt7ebiWqaBx2uV/jiP6XYTesT5UyLUk3Ct5aJz2yy+/ICgoSO9YH3IpWAQVatTROO3KxVN4HhxkULwUefIVwNBeXhqnPQi8hmdB9w2Kl7LNXFxcAKu8Gud5cv82gu7c1DtWitOXrsPK2gb1WrTTOP+Lx0G4e/2y3rE+1rhtV43Lhrx8hpuXzmqNpcu4ceM0HvifPHkCf39/nXlp49moORycUjctDwkJwapVqwzex1JUrdNI4+vR0dFYuHBhumICQNeuXVGhgubPnK5jxIc+3v6tWrVCzZo1Nc67YsWKdP/obNy4MRo3bqxx2rp16/D69et0xfX09ETr1q01TvvwGGGo8uXL4/PPP9c47ffff8fNm7o/c5q4urqiX79+GqcdOXIEFy5cSFdcFxcXfPnllxqnnT59GidOnEhXXHt7e4wfP17jtMuXL2P//v3pigsA06dP1/j67du3sX379nTHNeQYYYhhw4YhX758qV5POUakV79+/eDq6prqdVMeI4yJy2PEezxGvMdjxP9oO0aYmkmKvMjISJw+fRr//vsvQkJCEB4eDiEE/Pz8TPF2RERERERE9P8ULfKePHmC7777DgEBAWqXNVOGTNBU5DVt2hQ3b96EJEk4fvw43N3dlUyJiIiIiIjok6JYA9E9e/agSpUqWLduHWJiYlINmaBN79698eLFC7x48QIbNmxQKh0iIiIiIqJPkiTSeyfsBw4dOoS2bdsiMTERQghYWVmhUaNGqFixIvbu3Yv79+9DkiSNN5tGR0cjX758iImJQdmyZXHjxg1j0zGJOnXq4OzZs2qv1a5dG2fOnMnQPKav2aZ1mq5OFaYM6KL2fPa6HfJjQzteSSvWh/TpeEXfWB/S1vHKdN9uajc26xNLPe7/Ol6Z7ttNbdqPW44b1fHKh/fkffj/mJ6OV1K2mUqlwvxNv2ucR9+OVzRt//R2vKLP/6W+Ha98HEsXQ26YNmS/+LjjlZT9IqVTBUP3sRSWVtb4bkiPVK8r1amCpmOFIR2vfLz92anCe+xU4T12qvA/7HjlPR4j3uMx4j0eI/4n23a8Eh0djf79+yMhIQGSJKFly5ZYs2YNChcuDAC4desW7t/X3qmEvb09mjVrhj179iAwMBAvXrxAgQIFjE3rk6Ttx3OKjz8UuuZPoenHfnpjqce1VizWhz48CBob60Paih5jvS9WNX8xaqPtAPchC0vtBXZasXRtMwsL7ScEjPm/VFlYwPqDHzT6rKM+LCwsFN/HgPdfjA4ODoruY8D7L0al1v1jhuRqSA7avsiNpe2Hh7G0/VDKqnFtbGy0/hg1hrW1tdYfz8awsrLS+mPfGJaWllqLE2NoOkYoIeUYoTRTHiNMFZfHCNPG5THivex2jDA1o8tKPz8/PH/+HJIkoWnTpti7d69c4OmrVq1a8uNr164ZmxIREREREdEny+hyd+/e/3XJvXz58nRdjixbtqz8+MGDB8amRGQSwW+fZHYKREREREQ6GX0lL2W8j9KlS6e7Z8xcuXLJj8PDw41NiYiIiIiI6JNldJEXEhICSZJQpEgRJfIhIiIiIiIiIxhd5Dk6OgKAUT09vXz5Un6cJ08eY1MiIiIiIiL6ZBld5BUsWBBCCNy6dSvd3eOePn1afuzm5mZsSkRERERERJ8so4u8Bg0aAAAiIiLw559/Grx8TEwMAgICALzvArZevXrGpkRERERERPTJMrrI69Spk/x47NixBg+kOXr0aPm+vrZt25pknA8iIiIiIqJPhdFFXpMmTdC4cWMIIXD79m00bdoUQUFBOpeLjIyEr68v1qxZA+D94J5Tp041Nh0AwMWLF/Hdd9+hefPmKFKkCGxsbJAjRw64u7ujf//+OHXqlCLvQ0RERERElNUoMiz82rVrUbduXbx+/Rrnz59HuXLl0LZtW3h5eeHVq1fyfLt27cKrV69w9uxZ7NmzBxERERBCQJIkzJw5ExUrVjQ6l4YNG+Lvv/9O9Xp8fDzu3r2Lu3fvwt/fH3369MHPP/8Ma2tro9+TiIiIiIgoq1CkyCtRogT279+Pdu3a4dmzZ4iLi8POnTuxc+dOAO+v0gFA165d5WU+7KRl1KhRmDRpkhKp4NmzZwCAQoUKoWvXrmjQoAGKFSuGpKQknDlzBosWLcLTp0+xceNGJCQkyPcDEhERERERmQOjm2um8PDwwH///QcfHx9YWlpCCCH/pfj4teLFi+OXX37B999/r1QaKFu2LLZt24bHjx9jyZIl6Ny5Mzw9PVG7dm2MGjUKV65ckQdt37p1K/766y/F3puIiIiIiCizKXIlL0Xu3LmxYcMGzJkzB7/88gv+/vtvXL9+HW/evEFUVBScnZ2RP39+1K5dGy1atECnTp1gYWGhZArYt29fmtPz5s2LRYsWoW3btgCAHTt2oGHDhormQERERERElFkULfJSFC5cGGPGjMGYMWNMEd5oXl5e8uP79+9nYiZERERERETKMrrI8/b2lh+vW7cOrq6uxoY0ubi4OPmx0lcSiYiIiIiIMpPRRd6JEycgSRKKFi2aLQo8ADh58qT8uFy5cpmYCRERERERkbKM7nglV65cAAA3Nzejk8kIycnJmDdvnvz8888/z8RsiIiIiIiIlGV0kVeoUCEAQGxsrNHJZIQffvgB58+fBwB06tQJ1atXT3P+uLg4REREICkpKSPSIyIiIiIiMorRRV6jRo0ghMDNmzeRkJCgRE4mc/LkSUycOBEAkC9fPvz44486l5k7dy6cnZ1x4cIFU6dHRERERERkNKOLvH79+gEA3r17B39/f2PDmcyNGzfQsWNHJCYmwtbWFtu3b0e+fPl0Ljdp0iSEh4fD09MzA7IkIiIiIiIyjtFFXo0aNTBy5EgIITB27Fj8888/SuSlqIcPH6J58+Z4+/YtLCws8Msvv+g9Np6NjQ2cnJzYCycREREREWULRhd5ALBw4UIMGzYMkZGR8PLywsiRI3H9+nUlQhvt2bNnaNq0KZ49ewZJkrBu3Tq0b98+s9MiIiIiIiIyCUXHyXN0dERkZCRWrFiBFStWwNnZGW5ubnB0dIRKpbuelCQJR48eNTYlWUhICJo1a4YHDx4AAJYvX44+ffooFp+IiIiIiCirUWycvBQpj4UQCAsLw5UrV/SKI4RQi2Os8PBwtGjRAjdv3gQAzJs3D19++aVi8YmIiIiIiLIio4s84H2Blp5pphIdHY3WrVvj33//BQBMmTIFEyZMyPA8iIiIiIiIMprRRd769euVyEMx8fHx6Nixo9wBzMiRIzFr1qxMzoqIiIiIiChjGF3k9e3bV4k8FNOjRw8cOnQIwPv7BQcOHJhmJzDW1tZwd3fPqPSIiIiIiIhMSpHmmlnJrl275MfHjh1D5cqV05y/ePHiCAoKMnFWRERERETZx/Q129K/rG83BTOh9NB7CIXcuXMjd+7c6NaN/2lERERERERZld5X8sLCwiBJEiIjI3XOW6JECQBA48aNsW7duvRnlw6Z0dELERERERFRVmGS5ppBQUGQJAkvXrwwRXgiIiIiIiLSwuzuySMi88R7A4iIiIj0o/c9eURERERERJT1scgjIiIiIiIyIyzyiIiIiIiIzAiLPCIiIiIiIjPCIo+IiIiIiMiMsMgjIiIiIiIyIyzyiIiIiIiIzIjB4+SdP38e3t7eis8LAJIk4ejRo4amRERERERERP/P4CLv7du3OHnyZJrzSJKk97wphBDyckREZL44sD0REZFpGVTkCSFMlQcREREREREpQO8ir2/fvqbMg4iIiIiIiBSgd5G3fv16U+ZBRERERERECmDvmkRERERERGaERR4REREREZEZYZFHRERERERkRljkERERERERmREWeURERERERGaERR4REREREZEZMWgwdCIioqxk+ppt6V/Wt5uCmRAREWUdvJJHRERERERkRljkERERERERmREWeURERERERGaERR4REREREZEZYZFHRERERERkRljkERERERERmREWeURERERERGaERR4REREREZEZYZFHRERERERkRljkERERERERmRHLzE6AiIiIiIgoO5u+Zptxy/t2UyiT93glj4iIiIiIyIzwSh4RERHRJ8yYKxBKX30gImXwSh4REREREZEZYZFHRERERERkRthck4iI0pTVbiYnIiKitLHIIyIiIiIik+F9nxmPRR4REZHC+IOGiIgyE4s8IiKiLIwFIxERGYodrxAREREREZkRXskjIiICr5gREZH54JU8IiIiIiIiM8Iij4iIiIiIyIywuSYRERERKYLNnomyBl7JIyIiIiIiMiO8kkdERESUzfCKGRGlhVfyiIiIiIiIzAiv5BERERERmQFe4aUULPKIiLIIfjkTERGREljkERERkdkw5mQJwBMmRGQeeE8eERERERGRGWGRR0REREREZEbYXJOIiIiIshzep0yUfrySR0REREREZEZ4JY+IiOgTwSsjRESfBl7JIyIiIiIiMiO8kkdEREREpCcO00HZAYs8IiIiIi3YxJWIsiM21yQiIiIiIjIjvJJHRERERETZAq+u64dFHhERERmM9yUREWVdLPKIiIiIyKzxpARpYs5XBXlPHhERERERkRlhkUdERERERGRG2FyTiIiIMhWb0hERKcusr+Q9evQIY8aMQdmyZeHg4IDcuXPD09MTCxcuRHR0dGanR0REREREpDizvZK3d+9e9O7dGxEREfJr0dHRuHjxIi5evIi1a9fijz/+QKlSpTIxSyIiIiIiImWZZZF3+fJldOvWDTExMciRIwcmTZoELy8vxMTE4JdffsHPP/+MO3fuoHXr1rh48SIcHR0zO2UiIiIyc+bckx8RZS1mWeSNHDkSMTExsLS0xKFDh1CnTh15mre3N0qXLo3x48fjzp07WLRoEaZPn555yRIRERHRJ4vFP5mC2d2Td/78efz9998AgIEDB6oVeCnGjBmDcuXKAQCWLl2KhISEDM2RiIiIiIjIVMyuyNu9e7f8uH///hrnUalU6NOnDwAgLCwMx48fz4jUiIiIiIiITM7sirxTp04BABwcHFC9enWt8zVq1Eh+/M8//5g8LyIiIiIioowgCSFEZiehJBcXF4SEhKBKlSq4cuWK1vnevn2L3LlzAwC6du2KX3/9Nc24+fPnx6tXr9Rey5EjBypWrGh0zoYIfvkm3csWyZ+HsTIp1sfxsmosY+MxlnnE+jge9zHGUjrWx/Gyaixj4zGWecT6OB73McZSOpamePqoWLEifv75Z43TzKrIi42NhZ2dHQCgdevW2LdvX5rz58iRA1FRUahduzbOnDmT5ryOjo549+6dYrkSERERERGlV1o1jFk114yMjJQf58iRQ+f8Dg4OAJBm8RYXF4eIiAiYUS1MRERERERmzKyKvNjYWPmxtbW1zvltbGwAADExMVrnmTt3LpydnREVFWV8gkRERERERCZmVkWera2t/Dg+Pl7n/HFxcQAgN/HUZNKkSQgPD5ev+hEREREREWVlZjUYuqOjo/xYn/vnUq7OpdW008bGBjY2NihSpAiePn2qNs3e3h4lSpRIZ7bKSkpKwoULF+Dp6QkLC4ssEysr58ZYjJWdcmMs84iVlXNjLMbKTrkxlnnEysq5ZdVYH0qzA0hhZvLkySMAiCpVqqQ5X2hoqAAgAIiuXbtmTHImFB4eLgCI8PDwLBVL6XiMxVimjKV0PMZiLFPHYyzGMmUspeMxFmOZOt6nEEtfZtVcEwDKly8PALh37x4SExO1zhcYGCg/LleunMnzIiIiIiIiyghmV+TVr18fwPummJcuXdI638mTJ+XH9erVM3lepmZjY4Np06bJnclklVhKx2MsxjJlLKXjMRZjmToeYzGWKWMpHY+xGMvU8T6FWPoyq3HyAOD8+fOoVasWAOCLL77A6tWrU82TnJyMihUr4tatW8iZMydevXoFKyurjE6ViIiIiIhIcWZ3Ja9mzZpo0KABAMDPz0/jAIGLFi3CrVu3AAAjR45kgUdERERERGbD7K7kAcDly5dRr149xMTEIEeOHJg8eTK8vLwQExODX375BWvWrAEAuLu74+LFi2q9chIREREREWVnZlnkAcDevXvRu3dvREREaJzu7u6OP/74A6VKlcrgzIiIiIiIiEzHbIs8AHj06BGWLl2KP/74A8HBwbC2tkapUqXQtWtXDB8+HPb29pmdIhERERERkaLMusgjIqLsIzw8HH5+fmjXrh1bWRBlori4OJw4cQIPHjwAAJQoUQKNGjWCra1tJmdGRPoyu45XyDzs378fCxYswIYNGxAWFqZxnrNnz2LAgAEZm5iZCwkJwffff48pU6bgn3/+kV+fP38+ihcvDkdHRzRv3hzXrl1T7D03b94Mb29vxeJR9hUSEoJx48Ypun99aiIjI/H48ePMTiNTxcXFYdWqVRg2bBhmzJiBu3fvapzvyJEjPPZosHHjRhQuXBitWrXCl19+iS+//BKtWrVC4cKF4e/vn9npkZ6SkpIQHR2t9lpYWBgWLVqEKVOmmO1xNjo6Grt27cLz588zO5VMxyt52dCrV68QHh6OUqVKQZIkAMDDhw/h7++P0NBQ1KlTBz169JCn6SMqKgoBAQG4e/cu3rx5g493C0mS4Ofnp3X5J0+eYNeuXbC2tkbnzp2RL18+PHnyBBMmTMCxY8cQGRmJGjVqYNasWXLvp5okJiaibdu2OHTokJxDzpw5sXr1anz++edq827ZsgV9+vRBUlKS3uupzezZszFt2jQkJiZmaqyoqCicOXMGL1++RNOmTZE/f36j8wHeb9c9e/YgNDQUbdu2RYECBVLN8+LFC9SoUQPPnj0D8P7/fPPmzfIP72rVqiEmJgbXr1+Hs7Mzrl69iiJFihid2+zZs/Htt98q8v+olISEBERERCBPnjx6zR8eHg4rKyu1JuCHDh1S2/d79eoFa2trU6VslJiYGLx+/RrFihXTOs/+/ftx/fp15M+fH+3bt0fOnDlTzXP27FmsWbMG69at0xjjq6++SjOP8PBwbNq0CS1btkTJkiUhSRKWLl1q0LpkR/psf31lxc9TRoqOjka9evXw33//yd8hVlZWmDlzJsaPH682r5LfIeZi27Zt6NGjB4oVK4YhQ4agfPnyAIAbN25g9erVCA4ORkBAALp165ZhOYWHh+P27dvInz8/ihcvrnGehw8f4u+//0afPn0Uec+4uDi9xjPLjNz0NWjQIJw9exbXr18H8P57rVq1arh58yaA9+O2nTlzBlWrVjXqfS5duoTQ0FA0aNDAoCu9Qgj88ssv+OOPPxASEoL8+fOjXbt26Ny5s1H53L9/H+7u7ti+fTs6deqkc/64uDjs3bsXb9++RdOmTeHm5gbg/Wdh1apV8u/qadOmoXDhwkblBgBv3rzBjBkzMGjQIFSuXNnoeGkSlG0kJSWJQYMGCQsLC6FSqUS5cuXEw4cPxaVLl4SDg4OQJElIkiRUKpVo3ry5SExM1CvuuXPnhIuLi7y8pj+VSqV1+Vu3bglnZ2ehUqmEJEkif/784vbt26JEiRJCkiSRM2dOkSNHDiFJkrCxsREXL17UGmvVqlVCkiTRu3dvsXfvXrFq1Srh5uYmVCqVmDdvntq8mzdvTjMvQ8yaNSvTY61atUo4OzvL2/vo0aNCCCFevnwpbGxsxJo1a/SKM27cOFGjRg35eXJysmjUqJH8/5M3b15x7969VMuNHj1a5MiRQ2zfvl2cO3dOVKtWTZQoUUJ4eHiI//77T57v2LFjwtraWowcOdLgddREyW2fYsWKFcLNzS3NeW7duiW+/PJL0bNnT7FlyxYhhBCJiYliyJAhwsrKSqhUKuHu7i4OHz6sNUZMTIzo0KGDUKlUQqVSiT59+oikpCQxYMAAeXun/H9WrVpVREZGKrqejx8/FidPntQ6/cSJE8LT01PY2dkJV1dXMX36dBEXF5dqvrQ+SwkJCeKzzz5TW59cuXKJbdu2GRRHCCFvC13HGn2OO9pERUWJOXPmiHr16okyZcqIhg0biu+//17ExsYaHCstmzZtEl5eXmnOo8T2N5Qhn6eXL1+KO3fuiOTkZPm1Bw8eiG+//VYMHz5cbNmyRW2aPoKCgsTkyZNF165dhbe3t/Dy8lL78/b21rrs69evxcKFC8XkyZPFqVOn5NfnzZsnihUrJnLkyCGaNWumdjz62OzZs4UkSeKbb74R165dE/v37xd16tQRKpVKDB06VG1eQ7b7y5cvxbhx40TLli1F//79xYkTJzTOt3v3bp3HHiGECAsLE+fOnRNBQUFa53nw4IHYsGGD1ulv3rwR8fHxaq89f/5c+Pr6isKFCwsbGxtRrFgxMXz4cBESEqIzJyGEqFy5sihXrpwIDw/XmHOZMmVE5cqV9Yqlj2vXrqW5jnPmzBE2NjbyMbZx48Yav7/S8xnav3+/mDZtmtprK1euFI6OjsLCwkL06NEj1fY1VW6xsbFi5cqVYujQoWL69Onizp07Guc7fPiwzuNOijJlyohJkybJzwMCAoQkSWLVqlXizJkzonjx4qJbt256xRJCiIULF4o2bdqovdajRw95/UuVKiVevHihcdlKlSqJP/74Q34eFxcnmjdvLh/vLS0t5WN+p06d0sxj0aJFaf5NnjxZSJIk+vXrJ7+mTXh4uKhcubKch62trTh8+LC8rQoUKCBy5swpJEkSxYsXF6GhoXpvL23u3bsnVCqV2LVrl9GxdGGRl434+/sLSZJE69atxdChQ4Wtra1o0aKF8Pb2FpMmTRKBgYHi8uXLon///kKlUokff/xRr7j16tUTzs7OYvv27eLNmzcG59W/f39hZ2cnli1bJrZv3y7KlCkjKlasKAoUKCDOnTsnz3fo0CGRI0cO0aVLF62xPD09RcuWLdVee/funWjfvr1QqVTiu+++k1/XdeB89OiR3n9jx47NsFia7NixQ0iSJDp06CD8/PyEJElykSeEEO3btxetWrXSK1alSpXEqFGj5Od79uwRkiSJCRMmiK1bt4q8efOKQYMGpVqubNmy4quvvpKfHzx4UEiSJKZPn55q3j59+ohy5cppzcHNzU3vv9y5cyte5On6oXvv3j3h5OSkVkz88MMP4ttvvxV58+YV3bt3F+3btxc5cuQQtra24ubNmxrjLFiwQEiSJGrUqCFat24tLC0txZAhQ+QflLt37xbbt28XPXv2FJIkqX3hmno9b926JWxsbISNjY2oXr26KFSokJAkSXh4eIhnz56pzZvWZ0nJEy9ubm7CyclJ/PDDDyIoKCjV34kTJ4QkSWL16tXya2lxdHQUv/76q/w8PDxcVKlSRT6h5ObmJhfsdevW1VhgpZeufUyp7S+EECdPntT7b+DAgTo/T6Y6Ybhnzx5hbW0tJEkSzs7OwtXVVeOfJs+fPxeFCxdWe++AgACxbNkyYWNjI2rXri2qVKkiLCwsRO7cucWTJ080xqlataro3r17qvUdPny4kCRJ7dinb2Hw5s0bUaxYsVQnI4YOHZpq2+gTU6niQKVSySeohBDi2bNnomjRokKSJOHm5ia8vLzk5+7u7hoLt4/Z2NiIBQsWaJ0+b948YWtrqzOOvtL6HB04cEBIkiTKlCkjxowZIz7//HNhZWUlnJ2dUxXZ6SnyvLy81IqcmzdvCktLS1GmTBn5xNYPP/xg8tyioqJE1apV1U5wWVtbi/nz56ea15D1dHJyUjs53KtXL1GxYkX5+ezZs0WxYsX0iiWEENWrVxfDhg2Tnx89elRIkiR69uwp5s6dKxwcHMTo0aM1LitJktq++u233wpJkoSvr6949OiRSE5OFnfv3hXdunUTKpVKrF69WmseSp4snDFjhrC2thYLFy4U27ZtE25ubqJSpUqiRo0a4uDBg/J8/v7+QqVSiSlTpujcTm3btk3zr0mTJkKSJOHp6Snatm0r2rVrpzNmerHIy0bq1KkjWrRoIT9fvny5UKlUokePHqnm9fT0FA0aNNArrq2trVrxZKgSJUqoFQf79+8XkiSJhQsXppp31KhRIn/+/Fpj5cqVS6xcuTLV60lJSaJPnz5CpVLJZ970vWpgyF9GxNKkVq1a8hnukJCQVEXezJkz9T4Y58yZU6xatUp+PmjQIFGiRAn5+TfffKPxTLO9vb34+eef5efBwcFCkiSxZ8+eVPMuXbpU2Nvba81BkiSRO3duUbFiRZ1/+fPnz/Aib+DAgcLFxUVcuHBBvHz5Unh7e4uCBQum+gF+48YNkSNHDjFw4ECNcapWrSqaNGkiP1+4cKFQqVRi8ODBqeZt3bq1KFu2rBFrlVpa69mjRw+RO3duuUBNTk4WK1euFLa2tqJUqVJqP5LT+iwpeeIlOjpajBkzRlhaWop27dqJx48fq02/d++ekCRJ7Ny5M+0V/38f/3AYMWKEkCRJzJkzRyQkJAgh3p8hHzt2rJAkKVVRagxd+5hS218Iw44/+lwBNdUJw7Jly4qSJUumeaVNG6VaEjg6OoqffvpJ47RvvvlGSJIkBgwYIITQ/wfzuHHjhJWVlVi7dq2IjIwUN2/eFF27dhWSJIl27dqpXe3RFVPJ4uDj/b9v377C0tJSBAQEqM23YsUKIUmSmDx5ss51LV68eJpF3vz580Xx4sV1xtFXWp+jpk2bivLly4uYmBj5tStXrogSJUoIe3t7tVYW6SnyChQooPY7Zdq0acLR0VEuhnv06CGqVq1q8txMdfXZ0dFR7bNbuHBhMWLECPn5unXrDCrY8+TJI5YvXy4/HzFihChUqJB8tX/MmDGidOnSGpf9eF8tWrSo+Oyzz1LNl5ycLGrUqCHq1KmjNY+cOXMKJycn8f3334sTJ06k+tu8ebOQJEl899138mvaVKxYUe37fdeuXUKSJDF27NhU83bs2FFUqVJFa6wP19XULVb0ZWnaxqCkpHv37mHq1Kny888++wxCCLRu3TrVvB06dMCiRYv0iuvk5KT3vUeaPHv2TK1dcaVKlQBAbsv/oYoVK+LNmzdpxrOwsEj1mkqlgr+/PywsLDBz5kwkJyejTJkyacaxtLREqVKl0KRJE53rcPHiRZw/fz5DYmly7do1zJ8/X+v0ggUL4tWrV3rFio+Ph6Xl/z7ax48fR9OmTeXnJUqU0HhDspWVldp9hClt63PkyJFqXltb21T3bX7Izc0NpUqVwsGDB3XmO2vWLEybNk3nfJr2i/Q6efIkBg4ciBo1agAApk+fjkaNGmHUqFEoWLCgPF/58uXh4+ODQ4cOaYzz6NEjtc5/2rdvj/Hjx6NZs2ap5m3RokWqe4I02bhxo97rcfnyZa3Tzp07h2HDhqFcuXIA3t9jOWzYMFStWhVt2rRBo0aNcPz4cZ33gd27dw/9+vVTe83BwQG7du1C//79MX36dCQlJWH69Ok687Wzs8P333+Pnj17YvDgwShfvjymTZuGUaNGKfL/u23bNnTv3h2TJk2SX7OxscHChQvx77//Ytu2bZgwYYLW5UuUKKH3e4WHh6c5Xantn7IO5cuXR8+ePXXOe/ToUZ2fu59++gnNmzfHvn37ALzfz0eOHIlu3bphzpw58nzr1q3D9evXERAQgCFDhuh876CgIMyfP1/+DjDE/v37MWDAAHTp0gUAMG/ePHz22WeYNm2aWjwvLy90795d62fS1tYWCQkJGqfNnDkTFhYW+O6775CcnKx3pyv79+9H//79MXDgQABAuXLl8Ouvv2LRokUYP348OnTogN9++02ve26///57lCtXDpcuXZKPsVevXkWnTp3QqlUr7NmzR+14bYg9e/Zg8ODB6NGjh9rrX375JY4cOYLdu3dj9uzZacbo168f1q9fj6FDh6Y69kdERGD9+vXo379/mjG+++47vXM+efKk1mk3b97EqFGj1O7zqlKlCs6dO4emTZuiXbt22L17N5o3b673+33o7du3yJs3r/w8pSMeJycnAEDjxo2xf/9+k+e2fft2dOvWDTNnzgTw/ndSixYtMHLkSKxcuRIJCQn4+eefDV4/Nzc3HDx4EEOGDME///yD58+fw8vLS57+7NkzODs76x0vKioKdnZ28vNjx46hadOmcv8P5cuXx48//qgzTkxMDIKDgzFjxoxU0yRJQufOnTF37lyty9+6dQvDhw/HxIkTMWzYMMyaNQuOjo7y9Pv37wMAKlSogEaNGqWZy6NHjzB8+HD5ecpvgjp16qSat379+jh8+HDaKwegVq1auHHjBr777juMHDkyVf8Y9+7dg7u7O3bs2KHXPYPGYJGXjURGRqoddHPlygUAaj9KUxQoUACRkZF6xe3UqRMOHjyIYcOGpSuvuLg4tQ9+ymNNN+Da2NggOTlZayw3NzdcunRJ47QPO3+ZPXs2qlSpkmZe5cuXh729PZYvX65zHWbPnp1mYaZkLE0sLCzS3C7Pnj2Dg4ODXrGKFi2KM2fOYPDgwbhx4wYePHig9qX76tUrjYVboUKFEBwcLD/PkSMHli9fjrJly6aa99GjR3BxcdGaQ/Xq1XH8+HG98tW3gyBJklCoUCGd/+/A+4PonTt3tE4PDg5G6dKl5ecpj7WdmFi/fr3GOGFhYWonSHLnzg0AGk+a5M6dG/Hx8Tpz79evHyRJSrOI/pC27ff8+XONRUvdunVx+PBhNGvWDI0bN8axY8d0vodSJ15SeHh44MKFC1i8eDGmT5+OzZs348cff0S+fPn0Wl6TqKgovH79Gq1atdI4vVWrVjoL0aCgIOTKlQuFChXS+X4f91r3MSW3f6VKlZCcnIwxY8bonDc2NlZnkWeqE4Zubm6Ii4vTa96PPX78WK2Yq1ChAgCgWrVqqeatXr06duzYoTFOyZIlcfbsWXz55Zcap6fsA9999x1OnDihV25BQUEYNWpUqtfHjBkDZ2dnfPHFF2jfvj12796tM5apCpfIyEiEh4ejcePGGqd7eXnpddKtQYMG2LdvHypVqoRhw4bJx/9bt27hxx9/RN68edGgQQP89ddfass1bNhQfjx9+nRFjmHh4eHyMfVDefPmxfHjx9GkSRN06NABu3bt0ut9NMV59OgRgPfb78KFC2onORISErR2yqNkbvfv38fQoUPVXlOpVFi+fDly5syJ2bNnIzk5Oc3O7zTp378/Ro8ejYoVK+Lp06fIly8fWrRoIU8/d+6cxu93bQoXLiz3yPno0SPcvHkTo0ePlqe/fftWr85qVCoVJEnS+hsib968aR5HChQogB07duD333/H8OHDsXPnTixZskQ+QWQolep/Aw2kdJ6mqWMxZ2dnrSeQPnT69GmsXLkSU6ZMwaZNm7B69Wp4enrK0/X9zaMEFnnZSN68edWu5lhZWaF69eryWacPvXz5UuNOqsn8+fPRokULjBgxAl9//TVKlCiRoTvhh7y8vLBx40asWLFC41nRlEJPkiSsX78+zTw9PDywbds2JCcnq32I00PJWJpUqVIFBw8e1Nj7YHJyMrZv3652kEhL9+7dMXPmTLx69Qo3btyAk5OT2g/fy5cvo2TJkqmW8/DwwNmzZ+XnNjY2Wn8oHTt2LM1iq1q1atixYweCgoLg6uqaZr7FixdX+4GgTenSpZE/f37s3btX57wpPQxqY29vj5iYGPm5lZUVAKidrEghSZKiVxF1cXBwQNWqVfH111/rnHfnzp3Ytm2bxmn58+eXe0r9WPXq1XHkyBE0a9YMXl5eaQ5FouSJlw+pVCqMHTsWnTt3xpAhQ9CgQQO0atUq3ccea2trWFhYaDweAu9PWujqRVHJK9BKbX/g/WfT398fCQkJ8r5qDFOdMPz666+xaNEiDBs2TO+TUimUaknQrFkzLF26FJGRkWpn9z80ffp0qFQquRjRxcnJSes2GDRoEFQqFQYPHox27dqha9euacZSunBJyd/e3h5WVlZa9w9LS0u91vXDVggTJkyQl0nZ3o8ePVKbRwgBSZLUPlvOzs6oUaMG5s2bp/P9/Pz88NNPP2mcVrRoUdy+fVvjtFy5cuHo0aNo0qQJOnbsmK4eGevUqYPVq1ejQoUK+PPPP5GYmIiWLVvK0+/du6fxM6F0bqa4+gwAI0eORGRkJHbv3o1q1aphzpw5chHz5s0bnD17FmPHjtU7Xtu2bbFq1SokJibi3LlzsLGxUTsxdP369TS/73ft2oV79+4BeP+5TimwP/bkyRONn5GPtWvXDt7e3pg4cSK6d++OFi1aYOXKlXqvD5D6OG1vb48xY8Zo7Cn18ePHald+tZEkCcOHD0eHDh3w5Zdfok6dOhgyZAjmzJmj9fvJVFjkZSMVKlTA1atX5edOTk64cOGCxnmvX7+ud9OjnDlzQpIknD9/HqtWrdI4jyRJaQ4JsH//frx48QLA+zPckiRh+/btuHLlitp82n4spujVqxeePn2KS5cuabxcnpKLn58fcuXKlWa8li1b4uHDh3j27JnOrv4bNmyYZlGgZCxNhg8fjh49emDq1KlyN8vJycm4ffs2Jk+ejBs3bqTZnPNDkyZNwpMnT7B79244Oztj48aNcsEfHh6O33//XeNZ6W+//VbtSp42L1++RMGCBdNsOjZp0iS1JnNp6d27N3r37q1zvurVq+tV4OmjSJEial8wzs7O2Lt3r8aupIOCgtK8whQVFYXQ0FAAkP+NjIyUH6d49+6dXrlVqVIFYWFhev1oCQwMTDPOoUOHMGXKFI3TPTw8cPjwYTRv3lxjs5kUSp540SSlSdHmzZsxZswYvc/+p1izZg2OHDkC4H2RntJU52OPHz/W2SxdySvQSm1/AOjSpQvi4uIQEhKi9Udninbt2uk8RpnqhKGvry8iIiJQoUIF9O3bF66urhpPkGjqSl6plgQ+Pj5ISEjA3bt34eHhoTXXb7/9Fnny5MHFixd1rpe7uztOnTql9cRLSpHu6+urM57ShcusWbPkpnyWlpa4desWOnbsmGq+oKCgNFtfpFi3bp3RJ3k9PDzw+PFjVK9eXee8Bw4c0DqtTp062LNnj9bvvpTt1bRpUwQEBBic94wZM+Dl5SUPz9S3b1+5NYcQAr/99pta80ZT5WaKq8/A+2PU1KlT1a7ap8iTJ4/et4Ck+Pbbb/Hff/9h1apVsLGxwZIlS+RhnmJiYvDbb7/JTZo12bVrl9rJi927d2tc59OnT+t9hTFHjhxYsWIFevfujcGDB8vHHn33hapVq6r9jrazs8PChQs1znvmzBmNrX20KVKkCPbs2YMdO3Zg5MiR2LVrFxYvXqz3CXtFmOxuP1Lchg0b9Oq2/s2bN8LOzk6MGzdOr7h9+/YV/fr10/mnTVo3l2q74ZRSmzJlipAkSVhYWMj/ptycO2PGDEXeIykpSYSFhaXZLXRWtXr1auHq6ioePXqkc969e/emuc/269dP1K9fX6/3rVChgujatavGaZo6xNDVSYYuI0aMEJaWliI6OlrnvLNmzRKSJGmctmbNGiFJkrh69WqaMf7991+RJ08erbn9+++/onv37uL06dM68xkzZoxo3Lixzvm0iYuLE2FhYXKnKbpoOr7Url1b47x16tTReKP/h+bMmSMkSRIPHz7U+d6bNm1Kc12V2v6m0KJFC40ddmnSs2dPUatWLb3mffHihahTp066jv+9evVS68QoLbVr1xZt27bVa14lzJkzR9ja2oqXL1+mOd/69evl47Y2/fv3F2XKlEkzTmhoqPDw8ND5fVm8ePFUPZd+3MV9ikqVKon27dun+b5KGTNmjLCwsNCrN8+ZM2dqPYbt2bNHFChQQBw/fjzNGG/fvhXVq1dP12fozZs34vfff081FE1oaKhYsmSJuHLlislzmzp1qnBychIRERFpxpoxY4ZBv6E2bdqk+NAxQrzvxfjj3xDR0dHiypUrWnto19Sb8scdbwnx/v+jU6dOYt26dQbnlZCQIGbOnCns7Oz07sDr7NmzYuPGjTrne/HihfDw8Eiz18+0hIeHC19fX6FSqYSHh4dQqVR6dzBmDA6GboaSkpLw7t07ufmGqaV147Q2um6GfffuHdq1a4devXqleWbI3Pz777/YsmULAgMDIYRA6dKl4ePjI98MrI9Tp06hfv366c7h3bt3qFKlitx81xhKxlLS69ev8fbtW7i7u6c536tXrzB37ly0bdtWYzMZXR0QaKLt/r4UFy5cwP79+zFs2DCdZ94fP36Mhw8favw8xcXFyWfvdTV9SSuOKWT0fhEaGopZs2ahcePGaNeuncnfD8ja23/jxo34999/sWTJkjTnCw0NRZEiRTB8+HAsWLBAZ9yOHTti3759+Oqrr9CgQQO5GejHNK3nnTt3EBwcrLM52suXLzF06FD07NkzzXtwlNzHnj17hr1796Jhw4ZyRzra/P7777h8+bLWpry///47vvjiC2zdulXr/XPA+/t9mzZtisuXLxs9WPvbt2+xceNGeHp6om7dulrnU2qb3blzB2fOnEH79u11XgWOiIjA27dvtQ4k/im4e/cu1q1bh65du6Z59RkAVq5ciYsXL+r8HgHeN4nPmTMnevXqhQEDBmi8v9UQjx8/1qujqMz24sULvHz5Em5ubhnePFKXv//+G8OHD8fjx4+xadMmtGnTxqTvxyKPsixHR0csWbJE8SIvOjpabqpXvHhxuY26uVCpVChbtiwGDhyIPn366NVE52M5c+bE999/j0GDBhmdj5KxyHxwv8j6DD1h6OTkBF9fX3z//fcZkJ1u3McMx21mPrZv3w4/Pz8cOXIEQghUrVoVgwcPRs+ePdNV/FhYWKB58+YYNGgQ2rdvr9aLN2VNLPKyKXd3dwwcOBB9+/ZFgQIFjIp179493Lt3D5999pn82rlz5zBr1iyEhoaib9++8PX1NTZlgzVo0AC1atVS7AfDzZs3MXbsWBw5ckQ+M2phYYGmTZti4cKFcm9uHzOkO+gUKW3hM8PChQuxfv16BAYGwsrKCm3btsWgQYPQokULvdupf/bZZ3B1dcXq1auNzkfJWJS5Pu5R72OSJMHOzg7FihXT2VOmkvuF0lcGs+oV6BRnzpzBihUrcPfuXbx58ybVfYySJGm9N9GUXFxcMHPmTL2GW9BGyW3PY4/hzHmbubm5QaVSyd+N+vRboM9nSanfY6Y67gQHB2PdunXw9/dHUFAQ7Ozs0KVLFwwcOFCvTs9SfPnll9i6dSvCw8ORJ08e9OnTBwMHDtR5dftjSn6PZNVYWQWLvGyqQoUKuHXrFiwtLdGqVSsMGjQIrVq1SlfPj507d0ZoaKjc4UBISAjc3d3x7t072NnZ4d27d9i5cyc6dOhgdN7Xrl3D5cuXNd54/7Fjx46hY8eO2L17t9abn/V1+fJlNG7cGO/evUOzZs3km2dv3LiBo0ePwsHBASdPntTY8UZ6tunHvY3p4/Hjx/jpp5/S/OF29OhRveP9888/8PPzw/bt2xEdHY3ChQujf//+6N+/v84eL69cuQJvb28sWrRI7tI/vZSMlUKfwlvfQlvJWMD/mnY9ePAAwPux19q0aYPChQvrtbwmQUFBOHLkCF6+fIlevXrB1dUV8fHxePHiBQoUKKBzfC59emTTZ/9K6fpaH5UqVZLHOtNE6f1C6SsQSsdTqjDbuHEj+vfvDysrK7i7u2vtTEbfDmRS3L9/H3v27FHbb9u3b6+xJ15t+vXrh8jISOzcudOg9/6YUts+qx97Uiix7ZXKyxTbDFBmHf/55x/MnTsX586dw9u3bzV+htLqIK5x48aQJAmHDx+GpaWl/FwXXZ8lJX+PmfpK6pEjR+Dn54fdu3cjPj4epUqVwsCBA9GvXz+9Cpe4uDjs2LEDfn5+8q06tWrVwuDBg9GtWze9WkYp+T2SVWOlMMXxwhAs8rKxs2fPws/PD7/++ivevXuHAgUKoF+/fhgwYIBBB87ixYvD19dX7gVu+fLlGDVqFK5cuQJ3d3c0btwYNjY2Bv9o0CSla3t9CqABAwbgwoULuHnzJqpUqQJ3d/dUB5APu3BPS9OmTXHp0iUcPXo0VZv3f//9F97e3vD09NQ40KW2bn51MeQegz///BMdO3ZEfHw8cuTIofWH28OHDw3O4927d/jll1/g5+eHc+fOQaVSwdvbG4MGDULHjh01NsPy9vbGo0ePEBQUhNy5c6NkyZIat70+RaeSsVKk9eWZMj6TvoW2krFmzpyJWbNmITExUe0HiKWlJaZMmaLXoO8fmzBhAhYvXoykpCT5B4q3tzciIiJQqFAhzJo1S+dZX1dX11RfXomJiXj+/DmSk5ORN29eODg46Ny//P39sXLlSty9exe9evWSx8ULDAxEQEAAypQpAx8fH9y+fRubNm1CVFQUDh06pPEkjdL7hdJXIJSMp2RhVqZMGVhYWODIkSN6jeenj6lTp2LevHmp9nGVSoXJkyfr3ZohNDQUn332GWrXro2RI0emezgepbZ9Vj/2AMpte2Pyevz4MVxcXGBnZ2eSbabEOv71119o2rQpnJ2dUatWLezfvx/e3t549+4dzp8/j0qVKsHDw0Ove9VMQanfYxlxJfXChQtYuHCh2liT1tbWGDhwIObPn69x2BJNHj58CD8/P2zYsAHPnj1Djhw50K1bNwwaNAg1a9bUupyS3yNZNVYKpY8XBjN51y5kclFRUWLdunWiXr16cs9LXl5eYsuWLXr1rGRnZ6fWk1GrVq1Ew4YN5edLly4V+fLlUyTXWbNm6d0zlJI9dTo4OIhvvvlG6/TJkycLBwcHvWKZQpUqVUThwoXFhQsXTPYez549Ez4+PmrbL1++fGLBggUiMTFRbV5NvbZp+tOHkrFSaOqp6969e+LgwYPis88+E3Xq1BF37tzJ0FjLly8XkiSJmjVriq1bt4qrV6+Kq1evioCAAOHp6SlUKpVYvny5Qeu5evVqIUmSGDlypDh8+LCQJEkcPXpUnt6jRw/h7e1tUMwPxcbGijlz5ojSpUuLZ8+e6Zx/xYoVwtXVVWMvg8+fPxfFixcXq1atEkK839/y5csnmjVrpjGW0vvF5cuXRa5cucS6detEcnKy3stlRDx3d3dRrlw58fTpU6PzsrGxEcuWLTM6Tgo/Pz8hSZKoV6+e2LNnj7h37564d++e2LNnj6hfv75QqVRi/fr1esXS1bOsSqUSFhYWOuMote2z+rFHyW1vTF4qlUoEBAQIIZTfZkqtY/PmzUWxYsXEq1evxOvXr9WOhQcPHhSOjo7i1KlTeudlKsb+HlP6OJYiNDRULF26VFSuXFmoVCphZ2cnfHx8xN9//y3Onz8vfHx8hEqlEt27dzc4dlRUlNpvC5VKJapWrSp+/fVXjfMr+T2SVWOlUPJ4kR68kmdm7ty5gxkzZmDr1q2QJAk5c+aEj48PRo8erbVXJBcXF0yePBmjRo1CUlIScufOja+++gozZ84EAPz888/46quv1AaP/pAh96ydPHkSJ06cMN1ZCy3y5s2LGTNmaB2HZuXKlZg+fTpev36doXmlsLW1xaxZswwamFQfycnJ2LdvH/z8/OTBXuvXrw9fX1/Y2NhgxYoVOHXqFIYOHYoVK1Yo+t6ZRQiBhg0bokGDBpgzZ06GxSpTpgxy5cqFU6dOpbohPSEhAfXq1UN4eLjWMbI0qVKlCkqVKoWdO3fizZs3cHFxwZEjR+Tml/PmzcOKFSv0Gt8wLT4+PkhMTMTWrVvTnM/d3R39+vXD5MmTNU6fPXs2Nm7cKK/jlClTsGrVKrx9+9ao/PSh9BUIJePZ2tpi4cKFGDFihP4rpEWpUqUwZMgQxY4V1atXh7W1Nf7+++9U+21iYiIaNGiA+Ph4nWOcAtC7eZ+uqy2muJqUEQw99ii57Y3JS6VSYfPmzWmOfZpeSq1jrly5MHr0aEydOhWhoaHImzcvDh06hKZNmwJ4f7/YrVu3cOzYsTTjGNq7riRJ2LNnj0HLpEjP7zGl9/3Dhw/Dz88Pe/bsQVxcHCpWrIjBgwfDx8cnVc+nU6dOxbJlyxAeHq5X7P/++w9+fn7YsmULQkNDUbx4cQwcOBDW1tb46aefEBQUhGnTpqUaN1jJ75GsGksfSv5W0YZd45iJpKQk/P777/Dz88OBAwcgSRK8vLzkH/I///wzAgIC0L59+1TLVqhQARs3bkSfPn2wfft2+b61FGkNPAu8H6gz5bKzPpRq42+IVq1a4ffff9da5P3+++9o2bKlQTEvXrwo3xuQnJysNs3QNtYuLi4676syxN27d+Hn54eNGzfi5cuXyJ07N0aMGIHBgwerDTLatWtXDBs2DFu3bjWbIk+SJHTp0gULFy40+sBpSKzHjx9j2LBhGnscs7KyQq9evTBx4kSD3v/OnTsYOnSo1ukuLi4ICQkxKKYm9evX12vw+sePH6d5z4WDgwMeP34sP3dzc0NsbKzR+enjwYMHkCRJ/vH08uXLLBOvSJEiiIuLMyqfFEOGDMGWLVswatQojQONG+rWrVuYO3euxv3W0tIS3bt312vfAN43d1KC0v+XGcXQY4+S217JvJSk1DrGxcXJ9zXb2NgAACIjI+XpVatWxebNm3XG2bdvX6rX0vr9kp7fK8b8HlNy33d1dcWTJ09ga2uL7t27w9fXF3Xq1NE6f8WKFdW2qSYREREICAiAn58f/v33X1hYWKBt27YYPHiwWuduY8aMQc+ePbFy5cpURZ6S3yNZNZY+MuJzySIvmwsMDISfnx82bdqEV69eIV++fBg7diwGDx4stwO/d+8ePv/8c4wfP17jQWXcuHFo3769fNNttWrV0KBBA3n6oUOH0hy7xdnZGTVq1MC8efN05uvn54effvpJr3UbNGgQBg8ejFq1auk1f1oWL16Mzz77DF27dsX48ePlQufWrVtYsGABQkNDERAQoFesmJgYdOrUCYcOHZLbU6d8QXzYxtqQIs/Hxwc7d+7EV199ZfjKfaRBgwY4ffo0hBBo1KgRFi1ahM6dO8tF5E8//YSlS5fi5s2b8vzm1pNafHw83rx5k6GxihUrluYXZGRkpMFjDNna2iIqKkrr9EePHukch0ofDx8+RHx8vM75XF1dERAQgGHDhqU6KREfH4/Nmzer3YsaHBys9f4zpQUFBWXZeEoWZtWrV8fOnTtRs2ZNfPnll3Bzc9MYU99e86ytrfHu3Tut0yMjIxU9AfWhkJAQ1KxZE1u2bFH78an0/2VGMuTYk5HbXsljoiGUWseCBQvKLRYcHByQM2dOXL9+HR07dgTw/lijT5f+H5+QDQkJQb58+dRaSKSXEr/HlNz3c+bMiXHjxqF3795wdnbWOX/btm3TvC/bx8cHu3btQkxMDNzc3DBr1iwMGDAA+fPnTzWvhYUF2rdvj+3bt6eapuT3SFaNpS+Tfy5N1hCUTGrt2rWibt268n0OzZs3Fzt27BAJCQka51+/fn2a90KcPHlSjB49WsyYMUO8efNGfj0kJER07NhRbq+vibe3t3B3d9crb0PuybOwsBAqlUpUqFBB/PDDDyIkJESv5TRJaSeu6Z6RD9uR63PvyMSJE4VKpRJTp04VJ06cEJIkiY0bN4oDBw6IRo0aiZo1a4rAwMA083n06JHa3+3bt0Xt2rVFu3btxNGjR8WDBw9SzfPo0SO91tXFxUWMHTtWazvvj/8PXr16JU6cOJFqvq1bt4q6desKFxeXdN9bY4pYuly4cEEULlxYVKtWLUNjLVu2TBQpUkTjvW3BwcGicOHCBt+T17x5c1GvXj0hxPvP4of3ocTExAhXV1fRqVMnnXE07UuPHj0Sly9fFgsXLhR2dnaiZcuWOuOsWrVKSJIkqlatKn766Sdx/Phxcfz4cbF69WpRpUoVoVKp5HsWhBCiUqVKomPHjlrjZeR+kZmOHTsmateuLTw8PISfn584duyYOHnyZKo/fWi6L/nj45m+x1ghhGjatKkoWrSoePHiRappL1++FEWLFhXNmzfXO54hXrx4keo+U6Vl5WNPRm17XXlJkiSGDBkiNmzYoPefvpRax27duqnN161bN5ErVy6xYcMGsX79epE7d269jmEf+/i4mh5K/x7LqqytrUWXLl3EoUOH9Jr/4cOHwt/fP9XrSn6PZNVY+lDyt4o2vJKXTQ0ePBgFChTAxIkTMXjwYJ1d4pcvXx4+Pj5apzds2FDjmd+goCDkz58fX375JXr06KFx2WrVquHkyZOIiIjQOcCmEELvZp3BwcHw9/fH+vXrMXr0aEyaNAnt27fHwIED1ZqT6qNPnz6KNRPdsWMHunbtiu+++04+A1O4cGF4e3ujSZMm8PT0hL+/P+bOnas1hqbeDlO2i6bmJCn0uZfx6dOneg1cnMLFxQWNGjVSe23hwoWYOHEi8uTJg9q1axt1tkrJWCm0jW8UGhqKyMhIWFpaYu3atRkay9nZGfnz50fZsmXRu3dvtavFW7Zsgbu7O5ycnLBx40a15dIaTmTcuHFo0aIFfHx8MGDAAADAixcvcPDgQUybNg3BwcF6XYHWtL+lEEKgTJkyWLZsmc44Q4cORUREBGbMmIEhQ4bIMYUQsLGxwezZs+XmpXFxcVi4cCFKlSqlMZYp9gvA+OEmTBGvSZMm8uNBgwZp/Ozr28Oa0r0HTp06FU2aNEG5cuUwcOBAteFl1q9fj8jISGzZskXR99SXsds+qx97lNz2xua1Zs0arFmzRuf7pOyr+gyDBCi3jgMHDoS/vz9iYmJgZ2eHOXPm4O+//0a/fv0AAAUKFMCCBQv0yklpSv8eA9K/7yclJWHKlClwdXVNc7zKH3/8EU+ePMHs2bP1/m0UHByc5q07H3N1ddW4LZT8HsmqsVIoebxID3a8kk3t3r0bbdu2VeSejI+FhoZi8+bNWLduHa5duwYhBNzd3REYGKhx/jt37uDMmTNo3769zqZjERERePv2rUHDCwDvu0/28/PDzp07ERMTg2LFimHAgAHo168fihYtalAsY9na2uKHH37A0KFDERYWhty5c2P//v3yeCkLFizAjz/+mGazh5T7GA2Vni74P6bPMBaurq4oVKgQjh49Cjs7O6PeT8lYKTSNbyRJEnLnzg13d3f4+vrq/KJVOpapxlNcs2YNRo4cifj4ePkHFvC+GdSPP/4o/8hJi6b97cN1bNq0qUH5v337FocPH5b3cVdXVzRr1gy5c+fWO4Yp9gslhpswRbwNGzbo9X59+/bVOzcl7d27F8OHD8eTJ0/UXi9WrBhWrFiBNm3amOR9X758iYIFC2psKqfU0CFZ+dgDKLftjclLpVLhiy++QO3atfXO25B91VT7V1RUFI4ePQoLCwvUr19fryaJH9PUoZWhlP49Zsy+v2HDBgwYMADnz59H9erVtb7HpUuXULNmTWzatElnhzsHDhyAra0tGjdurHWeEydOID4+Hs2bN9dnFQEo8z2S1WMpfbwwmMmuEVK2c+DAAfH5558LW1tboVKpRNmyZcWMGTPE9evXMzs1WUREhPjpp59EzZo1hUqlEpaWlqJly5bit99+U7S74bTkzZtX7sI8MTFRWFhYCD8/P3n66tWrha2trd7xXr16Jc6ePSvu3buneK6a6NNk1sbGRqxcuVKR91MyVlZ24sSJdP3p4/nz52LZsmVi2LBhYujQoWLx4sUiODjYxGukLjIyUnh5eYm1a9cqEk/p/ULp4SZMPXxFVpOUlCTOnz8vtm3bJrZt2yYuXLggkpKSTPqe2pprKrXts8uxJzO2/YckSRJbtmwx6Xtk9jpqo0RzTSUZu++3atVK7ya+LVu2FG3atElznqNHjwqVSqVz/wgICBAqlUr89ddfac6n5PdIVo2VlbC5Zjbx119/pWs5XTffBwUFYd26ddiwYQOCg4ORN29edOnSBQEBAZg9ezY6deqUrvc1ldjYWERERCAiIgJCCDg4OODcuXM4ePAgKlSogG3btqFcuXKplrt37x7u3bsnX20DgHPnzmHWrFkIDQ1F37594evrq1cOJUuWxJ07dwC8v7m4QoUK2LFjBwYMGAAhBHbt2qXX1cXk5GQMGzYMa9eulZtq1qlTB7/99ptBTSJMoVSpUggLC8tysbKyj5u8KqlAgQKKdL9vjBw5cuDChQvo1auXIvGU3i9WrVqFjh07YsmSJRpvZK9cubJBPcgqHS+9THXsB4B3796hSpUqGDFiBL7++mt4enrC09MzXe+nJKW2fVY+9mTVba8kc11HU34mjd33L126hDFjxuiVj5eXFxYvXpzmPOvXr0fFihV1Xu3r0aMHFixYgLVr16p13PcxJb9HsmqsrIRFXjah6ZJvWoSOezy2bNmCdevW4eTJk7CwsECbNm2wfPlytGrVCo8ePcq0ezA0EUJg//798PPzwx9//IGEhATUrFkTa9euRffu3WFpaYnNmzdjwoQJGDx4ME6dOpUqxoQJExAaGioXeSEhIWjZsiXevXsHOzs7DB06FPny5UOHDh005hAXFyd329y0aVOsW7cOS5YsgYWFBb744gsMHz4cJUuWhCRJePjwoV7d4a5YsQJr1qxBoUKFUKdOHdy9exenT5/GF198gV27dhm0jXQdqD/0zz//6JxnzJgxmDVrFr766ivkyJHDoFxMGetDSUlJ2LJlCw4dOoSXL19iwYIFqFatGt6+fYu9e/eiSZMmcpfbGRlLk4SEBPz2229Yv349/vzzz3TH0ZepfoRUrVoVt27dSlfsjym9Xyg93IQx8ZTc/kof+z+UI0cOvHnzRtHPpRKU+r/MysceU2x7Ux/HDGXMOqan6aQ+48d9/F0ZHR0NSZKwfft2XLlyRWPMUaNGqb1mys+ksft+aGio3FO6Li4uLggNDU1znlOnTul9/2X79u2xadMmnfMp+T2SVWN9KDM/lyzysgmlb7b38fFBiRIlsGTJEvTo0UPRbs7/+ecfzJ07Vx5DTnx026ckSUhMTNS47HfffYdOnTqhYsWKuH//vnyV8fnz53BycoKvry98fX1RqVIlteUGDBiA6OhorQMEX7x4Ue1K3datWxEREYErV67A3d0djRs3xtKlS7UWeQULFkSPHj0wYMAATJo0CT4+PvJ6DRs2DLGxsdi8eTMsLCwwePBgjB8/Xud22rhxI8qVK4ezZ8/C0dERwPsbuP39/REWFmZQ1/iGDoz88RfUx52BWFhYIF++fChbtiwGDBigtZt2TQd/JWNpEx0djebNm+P06dNwcHBAdHS0PCCpk5MTJk6ciAEDBmDWrFkZGutjKYPFBgQE4M2bNzrve0vpXMUQkiTBz89P7TVT/QiZMWMGOnbsiNatW8PLy8ugPE29Xyg93IQx8ZTc/kof+z9Wu3ZtXLx4EYMGDTLp+xgivds+ux17lNz2pjyOGSO965gyXpzStH1XahvaSVORZ8rPpLHHMUdHR71PZulTgD9//lzve8aKFy+OZ8+e6ZzPmO+R7BIrRWZ/LlnkZRNK35BvY2ODoKAg7NmzB7ly5UKnTp0UuTH9r7/+QtOmTeHs7IxatWph//798Pb2xrt373D+/HlUqlQpzTH3pk+fjlKlSqFixYooXbo0gPdNGGfPno1u3brB1tZW67Kurq4oWLCgxmmvX79GoUKF5OcHDhxAvXr1ULFiRQBA9+7dMXv2bK2xnZ2d8eOPP2L16tWoVKkSBg4ciF69esk34o4ePRqjR4/WvmE0uH37Nr799lu5wAOAESNGwM/PD3fu3EHNmjX1jnX8+HGD3vtj/fr10zogrLaDj7Ye1pSMpc306dNx8eJF/Pbbb6hbt67aOD0WFhbo1KkTDh48qNeBU8lYABAeHi4PFnv58mWoVCo0aNAAnTt3lsd00iY9g0lrKvJM9SNk8+bNKFasGJo2bYoqVarA3d091eCxmvIBTL9f1KxZE7/99pvGpkqxsbHYtGkT6tWrp1csY+Mpuf1N3RnLvHnz4O3tjVq1asn/R5ktvds+ux17lNz2xuT18dhxSkrvOqZnvLi4uDid8xj7XQmY9jNp7HGsQoUKOHTokF5NNg8fPowKFSqkOY+1tTViYmJ0J473Ywjr07O3Md8j2SVWCqV/XxiKRd4n6vnz53IPmj4+Phg2bBi6dOmCvn37qhVDhpo9ezYKFiyIixcvQpIk5MuXD5MnT4a3tzcOHTqELl26YNWqVXrF+uqrrzB48GCdB6EUbdq00dpLl4ODg3xvRlJSEk6dOqU28LidnR0iIiK0xn748CGOHTuG9evXY9euXRg5cqQ8mOmAAQMM6lEqRVRUVKptnfI8rTN5mhh7P5gSX3ymiKXN9u3b4evri/bt22u8b6FUqVLYtm1bhsY6duwY1q1bh99++w0xMTFy18qbNm1C9+7d9cpFqR9bpvoR8mEReuXKFa3NmzR9CZp6v1BquAkl4mVWL5npMXr0aOTKlQuDBg3C+PHjUbJkSY0/bHQ1gwPen+QrV66c1nuKQ0JCcPPmTblZao4cOTBt2rRU3Yynd9tnt2OPktteybyUpOQ6anPp0iX4+flh27ZtOgeWNuW900ow9jjWqVMnjBkzBnv27NE42HqK33//HYcPH9Z5q4ebmxvOnj2LYcOG6cz93LlzcHNz0zmfMd8j2SVWisz+XLLIy2aeP38OSZJQoEABAO/P7GgqmooWLYquXbtqjZMzZ04MHz4cw4cPx7///gs/Pz9s3boV/v7+cHFxgSRJCA8PNzi/8+fPY/To0WptvVN+uDZv3hw+Pj6YOnUqjh07pjPWkiVLDH5/bSpUqICNGzeiT58+2L59O969e6c21t6jR490dnbi7e0Nb29vrFq1Clu3bsX69evx66+/Yvv27ShSpAj69euH/v37G9QdrqaudQHoPZagUho1aoTHjx/DxcXF6Cu6SsbS5tmzZ6hSpYrW6fb29oiMjDR5rODgYKxfvx7+/v4ICgqCi4sLvvjiC/Tr1w/29vZwd3c3eFy2rEyfIjQ2Nlbj66beL5o2bYoff/wRI0eOlH8EpYxFZW1tjZ9//hl16tTJtHjGUurY/7GUZnHFihUD8H5Yg/Ty8vJKs0v2o0ePomfPnnKzVAcHB43DwqR322e3Y4+S217JvJSk5Dp+SNtQTxnJFJ9JY487X3zxBX788Ud8/vnnGDt2bKpx+4KCgrB27Vp8//33cHd3xxdffJFmPi1atMDy5cvxzTffpLl979y5g19//VXt5Lk2xnyPZJdYKTL7c8kiLxu5ffs2KlasiFmzZmHChAkA3l/xGTt2bKomKpaWlqhatarc5DEtHh4e8PDwwOLFi7Fz5074+fnhxIkTGDRoEJYuXYouXbqgY8eOel1Ri4uLk28gTemo5MMduGrVqti8eXOaMUzRXGjcuHFo3769fENytWrV1HqAOnToUJrNSD/k6Ogo3xt4+/Zt+Pn5YfPmzZg5cyZmz56NRo0aYeDAgTp7owKA/fv348WLF/LztG4C13RvgJLc3Nz0GjMno2NpkidPHjx9+lTr9Bs3buh9RdqYWG5ubrC0tESrVq2wZMkStGrVSr7n5/79+3q9/8cM6UQnhbamwqYqDDTR52y6qfcLX19ftGvXDtu3b0dgYCCEEChdujQ+//zzdN3Ybmw8pba/qY79r1+/xrZt25A3b16ULFlS5/y66Do5lZSUpPd4jOnd9tnl2KP0tlfymKgUpdcRAA4ePIh169bh999/R3x8PNzd3TFt2jR07txZ71Y/SjDVZxIw7rhjZ2eHP/74A23atMHcuXMxb948ODk5wdHREZGRkXLP5GXKlMG+ffvSvAUGAL7++mv89NNPaNasGdauXat2cjzFkSNHMGjQINjY2Bg0DqkmhlyVzQ6xMv1zmVFjNZDxJkyYIPLlyyfi4uLk11LGeFm8eLE87taxY8dE7ty5xcSJE9P9Xg8fPhRTp04VxYoVE5IkCQsLC72WK1GihJgxY4b8PFeuXOK7776Tn3/77bcid+7cWpeXJEmUL19eeHl56fVnyDhVJ0+eFKNHjxYzZswQb968kV8PCQkRHTt2FAEBAXrH+lhSUpLYt2+faNmypd7bS5Ikg/50jW1nLCXHSjL1uEu9e/cWrq6uIioqKtU4Rw8ePBA5cuQQQ4cONXksSZJE8eLFxaxZs8SjR4/Upt27d09IkiR27txp0LoptV8EBgYKS0tLMW/ePPm1lPVTqVRqMaysrMSdO3cMylMIId68eSOWLl0qqlSpIscsU6ZMmutm6vG4UiQkJIh//vlH/Prrr4qM9WloPCW3v9LH/qSkJPHFF18ICwsLoVKphEqlEvXq1ROvXr3SY0toJ0lSmsfRL7/8UhQoUMDguIZs+6x+7DHVtlfymGgspdfxw98jKpVK5MuXT/Tu3VuoVCqDj69KyajfY+k9jsXExIhly5aJhg0bijx58ggrKyuRJ08e0ahRI7Fs2TIRHR2td6x9+/YJe3t7oVKpRPHixUX79u2Fj4+P6NChg3B1dRUqlUrY29uL/fv3p2cVDf4eyU6xMvtzySIvG/H09BSDBg1Se03bQJ5Dhw4Vnp6eRr9ncnKy+PPPP0XXrl31mr9bt25qA3F269ZN5MqVS2zYsEGsX79e5M6dW7Rs2VLr8plR+Fy8eFEMGTJE5MqVK90xzpw5I3x9fYWzs7OQJEnY2dnpXMZUA2enV3Yq8u7evSucnZ1F+fLlxcyZM4VKpRLjxo0TEydOFDlz5hS5c+cWjx8/NnmsP//8U3Tp0kVYW1sLCwsL4e3tLTZu3CiioqLSXeQptV+Y8kfIgQMHxOeffy5sbW2FSqUSZcuWFTNmzMjwH+DHjx8XI0aMEC9fvlR7/cGDB6Jy5cryD0yVSiX69++fofGU3P5KH/uXLl0qJEkShQsXFl26dBFVqlQRkiSJjh07prmcJkuWLBFubm7Czc1NSJIk8uXLJz//8C9nzpxCpVKlWo8USm37rH7sUXLbK5mXkpRax82bNwtvb29hYWEhrK2tRadOncSePXtEQkJCuo+vSlHyM6n0ccwUbty4ITp16iRsbW3VfoPZ2tqKTp06petEWnq/R7JTrMz+XLLIy0Zy5swpVqxYofaatoPK0qVLjSpa0uvQoUOiZ8+e8lmi+/fvi0KFCskHhIIFC4pr165pXT6jzvQrcYbmxYsXYsGCBaJcuXJyjGrVqokVK1aIt2/fmiZxE8pORZ4Q74vzypUrpyr8K1WqJK5evZqhsV6/fi0WLVokKlasKCRJEo6OjqJt27ZCpVKJXbt2pXcVjaJ0YaDE2XSl94u+ffsKd3f3VK83bNhQSJIk6tevL8aMGSMqVqwoVCqV8Pf3z7B4Sm5/pY/91atXF+XLlxcRERHya4MGDRKWlpYGH7v8/f1F48aNRePGjYUkSaJcuXLy85Q/Ly8v0bZtWzF79mytVxCU2vZZ/dij5LZXMi8lKbWOkiSJkiVLiuXLl4uQkBC1aZld5Cn5mVT6OGZKsbGx4tq1a+Kff/4R165dE7GxsQYtr+RV2awa62OZ+bnkPXnZSFRUVKoxTXLlyoVr166l6tHIycnJ4B4aldCsWTO1NtslSpTAnTt3cPToUVhYWKB+/fpwdnbO8LxSGNuePzExEb///jvWr1+PgwcPIjExETlz5sTQoUMxcOBAVKtWLQPWwnT+/vtvrWMYapJW1+NKxtKkevXquHr1Kq5fv45bt27J9y1Uq1YNP/30E7p3746bN29mSKy8efPKw2icO3dObrsvhMBXX32Fv/76C506dVK7D9TU7t69q3cvj2XLlsUvv/yicdqWLVuwbt06nDx5EhYWFmjTpg2WL1+OVq1a4dGjR9iyZYtBeSm5X5w/fz5Vz7aBgYH4+++/0bBhQ5w4cQIAMHPmTFSrVg0bN25Mc5soGU+p7Q8of+xXcviWvn37yuvp5uaGefPmoV27dnovn0LJbZ+Vjz1Kbnsl81KSUutoqqGelKDkZ1Lp45gp2djYyENP6RISEoKaNWtiy5YtePDggWLfI0p+Jyn9/aZJpn4uTVpCkqJcXFzE3Llz9Zp37ty5wsXFxcQZKc8UZ2GVOENz9epV8fXXXwsXFxe52USTJk1EQECAwWeysqqU5q/6/OlqKqtkrPSYNWuWYjHTGysqKkqsX79ePhurUqnSdT9SellZWaU645uUlCSuX78uoqKi1F5fv369sLa21hhHybPpSu8XuXLlEkuXLlV77ccffxQqlUps2rRJ7fUZM2aIvHnzZlg8pba/EMof+yVJEps3b1Z77fXr10KSJHHs2DG93kdpSm37rH7syaxtr+QxURel1vHt27di+fLlolq1akKSJOHk5CQGDBggTp48Ke7evZupV/KU/EwqfRzLKl68eCFf2VT6eyQrxkoPU38ueSUvG6lUqRIOHTqEiRMn6pz30KFDqFSpUgZklXUpeYamatWqAN73gvfNN98YPFRCduHr64vatWtnuVjZkb29Pfr164d+/frh7t278PPzw6ZNmzLs/XPmzInnz5+rvaZSqTResX7x4oXWK+xKn01Xcr+Ii4tLlcuFCxcApB4Pq2jRojqHhVEynlLbHzDNsV8y4fAtf/31Fw4dOoSXL19izJgxKFu2LN69e4d///0XlStXRs6cOVMto+S2z+rHHlNu+6xCiXU01VBPSlDyM6n0cSwrUvJ7JKvGyopY5GUjnTt3xogRI/D777+n2Rxm9+7dOHnyJFasWGHynLy9vQ1eRkpj4NO+ffsq1tWyj48PSpQogSVLlqBHjx7IkydPumN16dIFAwcORPPmzU0yxENW0aBBA8W6HlcyVnZXunRpzJs3D3PmzMmw91TqR8jz58/l8ah8fHwwbNgwdOnSBX379k1X189K7hfFihXDjRs31F47deoU8uXLh6JFi6q9Hh0drbG4MFU8JX8EmuLYb4rhW5KSktCzZ0/s2LEDQghIkoQePXqgbNmysLS0RIcOHTB27FhMnjw51bJKbvusfuzJSkPnmIrS66jkUE9KUPIzqfRxLCtS8nskq8bKkkx2jZAUFxsbK8qVKydsbGzE1KlTRVBQkNr0oKAg8c033wgbGxtRoUKFDGlGWLx4ceHq6mrwn6ECAwPF/v37xf79+0VgYKBey9ja2goLCwvRtGlTsXnzZrUb/jP7pu2sKLt1vJKWrNBcM7OtXLlSqFQqsWfPnjTn++2334RKpRKrVq3SGfPSpUti2LBhIleuXEKlUon8+fMLlUol1q1bp1dOSu8XgwcPFrly5ZI7c9q1a5eQJEljD3RffPGFqFq1aobFU3L7K33sN1UvxrNnzxYWFhZiyZIlIjAwMFUnFAMGDBD16tXTuKxS2z6rH3tMte2NzUtJGbWO6R3qSQlKfiaVPo5lFR821/yQsd8j2SGWvkz9uWSRl83cv39flC1bVj4w5syZUxQrVkzunjqld7OHDx9mdqpaGVJ8Hj16VJQvXz7VvRTly5cXR44cSXPZrNyePytikZe+WLdu3RJdu3aVexXU1vPZ5s2bM7RYNOVJodjYWLFlyxbh7e0tfyarVKkiZs6cmWY300rvFw8ePJDHb0q5X9bGxiZVDomJiaJgwYJi+PDhGRZP6e2v5LHfVMO3lClTRv5hqqmnwYULF2q9L1WpbZ/Vjz2ZNXRORhZ5Gb2Ohg71pBSlPpNKH8eyCm1FXor0fo9kp1i6mPpzKQlhRo3APxGxsbH4+eefsWPHDty4cQMRERFwcnJCxYoV0aVLFwwaNAi2traZnWYqly5dknsdfPPmjc75jx07hs8++ww2Njbo1asXypcvDwC4ceMGtm7ditjYWBw4cECvJqMftucPDw+Hi4sLXr9+jbVr16J///5Gr5s5UKlU2Lx5syLNnJSMlWLx4sV6z3vkyBEcPHgQSUlJJo0VHByMSpUqITw8HLlz50ZkZCQSExPRsmVL/PLLL2q9r23ZsgV9+vTRmpMpPHjwAK1bt8bt27chSRKcnJzg5OSEiIgIREREQAiBsmXLYv/+/em+xzQoKAjr1q3Dhg0b8OTJE6hUKq09G5piv7h48SJmzJiBe/fuoWTJkvjmm29S3Y915MgRjBo1Cj/88AOaNm2aYfGU3v5Z/dhva2uL5cuXY/DgwXjz5g1cXFxw5MgR+Ri9du1aDB8+HLGxsRqXV2LbZ/Vjj5Kyal6fEqU+k0ofx7KCly9fomDBgmrHAG0M+R7RJbNjZanPpcnKRyJh3Hh0tWrVEkWKFBHBwcGppj158kQULlxY1K5d26B8MvIMDSlLySZASsXy9fUV9vb28lXl8PBwMW7cOKFSqUStWrVEWFiYPG9GX8lLERMTI5YtWyYaNmwo8uTJI6ysrESePHlEo0aNxPLly0VMTIwi75NZZ9Ozuoza/llB3rx5xbx584QQmq/kTZgwQRQpUiSz0ku3zGpimV3zIhJC95U8TZT8HsmsWFnpc8kreWQSmsaj69Gjh97j0QHveyecPHkyvvnmG43TZ86ciblz5yI6OjpdOSp5todM7+TJkwYv83HPZErHKlu2LFq3bo1Fixapvb5jxw707t0blSpVwuHDh5EzZ85MuZJHlJE6duyIe/fu4b///kNoaKjalby3b9+iTJky+Oyzz7Bx48bMTtUgSh57lJRV8yICDLuSZ06y0ueSvWuSYj4smoKDg5E3b1506dIFAQEBmD17Njp16mRQPGdnZ7XBVD/m5ORkVC9Trq6u+O677zBjxgy5KKWsS8mDoFKxHj9+rHFg2C5dusDe3h6dO3dGkyZNcOTIEUXejygrmzJlCurXrw9vb2/069cPAHD16lXc/b/27j2m6vqP4/jrHKaA2rx1XKmYtzPIy7Qya5o60QGRZio2hwrqURu0mZJmrtRqmUqabVp0GRw54gUR0GressBpW2KuTZ2RzFBTgdJpZymiB8/vj36eRXgBuXy/HJ+Pv86+38/37MWGjvf38/2+30VFWr58ua5cuVKjbqNmY9bCyKy5AElq1aqVlixZou7duxsdpVGZ6d+l1egAaPo2bNigESNGqGfPnlqxYoUGDBig3NxcnTt3Tu+88859z/6ZMGGCNm3adNvdtRs3bmjTpk2aMGFCXePLYrEoKipKW7ZsqfN34cHy8MMP68KFC7c9Fx0drdzcXB0/flwjRoyo0XuoQFM2YMAAZWdnq7Cw0Peu87x585SQkKDy8nLl5ub63q0G4N9atmypJUuW+OVM4aaCxzVRZ1arVd27d9ecOXOqzaM7efKk7Ha7tm7dWuudvOPHjysuLk7NmzfX3LlzFRYWJkn65ZdftHr1ank8HrlcLrVs2bLKdV26dKn7DwXUQGRkpKxWq3bu3HnHNbt27fL97ldUVPC4JvxeRUWF9uzZo8LCQnm9XtntdkVGRqpFixZGRwNQQ/ezA2exWHTy5MkGSIP7weOaqLPAwECdOnVK27dvV9u2bTVu3DgFBwfX+Xv79Okji8Uir9eriRMnVjl3697E7R6V449oNJbo6GjNmzdPxcXF6tat223XREVFadu2bRozZkwjpwOMERgYqNGjR2v06NFGRwFwn7p06SKLxWJ0DNQBRR7qrKSkRBkZGUpLS9OUKVOUmJiomJgYxcfHq2PHjvf9vYsXL+Y/GJhafHy8wsLCFBAQcNd1EREROnDggI4dO9ZIyYDGUduGChaLRd99910DpQFQX/Lz842OgDricU3UK+bRAcCDw2q1qlmzZmrevHmN1lssFrnd7gZOBQCgyEODqKioUHZ2tlJTU313g/r27auYmBiNHTu2xmMUAADmdau4e+GFFzRt2jSNGjVKVis93QB/derUKe3du1dlZWWaNGmSunbtquvXr6u0tFSPPPJIjW/4oOFR5KHBNdQ8ut9//13FxcUaOnRoPaQEaue9996r9TUWi0WLFi1qgDSAMf7880+5XC6tW7dOx48fV4cOHRQXF6fp06crNDTU6HgA6tGCBQv00UcfqbKyUhaLRd9++63Cw8PldrvVsWNHvf/++5ozZ47RMfF/FHloNF6v1zePrj7GFSxdulSLFy+m0QoMcT+7FRaLhd9X+K2CggKlpaUpMzNTbrdbAwcOlMPh0MSJE9WqVSuj4wGog88//1wJCQmaPXu2Ro0apYiIiCqDzmNjY1VWVsY7tyZCkYcmiyIPRjp9+vR9XffYY4/VcxLAXK5du6bs7Gw5nU7l5eWpRYsWSklJ0eTJk42OBuA+9evXTz179lR2drYuXrwom81Wpchbvny51q5dq7NnzxqcFLfQXROm4nK5arz2559/bsAkwN1RrAG3FxQU5HtXx2q1au/evfrtt9+MjgWgDk6cOKGEhIQ7nrfZbLpw4UIjJsK9UOTBVKZOneqbjVcTjFgAAPMoKSlRenq61q1bp6KiInXs2FELFy6kuzLQxAUFBenKlSt3PH/69Gm1adOm8QLhnijyYCotW7ZU//79a/TibnZ2tjIzMxs+FFALP/30kw4ePKhLly7p5s2bVc7ReAX+6MaNG9q+fbucTqf27NmjgIAAvfjii1q9erUiIyPptgn4gYEDByo3N1evv/56tXPXrl3T+vXrNXjwYAOS4U4o8mAq/fr10+XLlzV+/Ph7ri0sLGyEREDNlJeXa9y4cdqzZ4+8Xm+VHelbnyny4G9mz56tjRs36tKlS+rbt69WrVqlyZMnq127dkZHA1CP5s+fr8jISE2ZMkXTp0+XJJWWlmr37t1asmSJzp49q40bNxqcEv9G4xWYyuzZs5WSkiK3263g4OC7rl26dKkWLVpUbbcEMMLChQuVnJyst956SyNGjNDw4cOVnp6uDh06aNmyZSovL5fL5aKtPPyK1WpVcHCwxo4dqyeffPKe6y0Wi+bOndsIyQDUty+++EKvvfaarl+/7rtxKf0zLzMlJUVTp041NiCqoMiDqRw6dEg7duxQYmKibDbbXdeeOXNGxcXFGjZsWCOlA+7Mbrfrqaee0ubNm6t1HvN4PHr66acVFRWlZcuWGR0VqDe1fRSTMSJA01ZaWqqsrCwVFhbK6/XKbrfr5ZdfVqdOnYyOhv+gyAOAehAUFKTVq1crISFBly9fVrt27bRjxw5FRUVJkpKTk5WSkqLi4mKDkwL1Z9++fbW+hhtzQNPn8XhUUFCgc+fOqVevXurdu7fRkfAfvJMHUyovL1dWVpZCQ0P1zDPPGB0HuKeHHnpIHo/H99lqter8+fO+861bt1ZpaalR8YAGQcEG+K/8/Hzl5OTo7bffVocOHXzHi4uL9dJLL+nYsWO+Y/Hx8UpLSzMiJu6AllcwpcDAQM2cOZNZeGgyevTooRMnTkiSAgIC1Lt3b23dulWS5PV6lZOTo5CQECMjAgBQY+vWrdPu3burFHjSP+Oujh49qkGDBmnu3Lnq1auX0tPTlZ6eblBS3A5FHkzJarUqJCREbrfb6CjAHVVUVPg+jxw5UtnZ2b73jV555RXt2rVLPXr0kN1u1969e+VwOIyKCgBArRQUFCgiIqLKscLCQu3fv19Dhw7V/v37tXLlShUUFMhut8vlchmUFLdDkQfTio+P1/r166v8IQ2YyaOPPqpXX31Vhw8f1sKFC5WXl+cbm5CYmKiVK1eqdevWatu2rT744AO98cYbBicGAKBmSktLZbfbqxzLz8+XxWLRjBkzfMeCg4MVGxurI0eONHZE3AXv5MG0Bg0apJycHPXv31+JiYmy2+1q0aJFtXVDhw41IB3wz3t2KSkp+uyzz9S3b185HA5NmjTJNyMsKSlJSUlJBqcEAKD2Kioqqo2zOnTokKTq7+OGhITor7/+arRsuDe6a8K0/tua+9Y8lltuzWihHTeM9P3338vpdConJ0fl5eUKDAzUmDFjNH369GqPuQAA0FQ8/vjjioyM1Mcff+w7FhoaKrfbrZKSkiprP/nkE7377rv6448/Gjkl7oSdPJhWWlpatcIOMJvw8HCFh4fr008/1aZNm+R0OrVlyxZlZWWpc+fOmjp1qqZNm6auXbsaHRUAgBobMmSIXC6XZsyYoT59+ig3N1dFRUW3HXp+9OhRZuWZDDt5AFDPfv31V6WmpiojI0OlpaWyWq0aNmyYHA6HYmNjjY4HAMA9FRcXq0+fPrp27Zrat2+vixcvqlmzZjp8+HCVuXiVlZUKCQnR+PHjtWbNGgMT499ovALTOnDggNERgPsSGhqq5ORknT17Vl9//bUiIiKUl5enuLg4o6MBAFAj3bp10759+xQdHa327dvr+eefV35+frXB53l5eWrfvr3GjBljUFLcDjt5MC2r1aqwsDA5HA7FxcXJZrMZHQmolR9//FFOp1OZmZlyu90KCgrS1atXjY4FAAD8HDt5MK0VK1ZIkubPn6/OnTsrJiZGu3btEvclYGZlZWX68MMP1atXLw0ePFhffvmlunfvrjVr1uj8+fNGxwMAAA8AdvJgej/88INSU1OVlZWlq1evqlOnTpo2bRrNLGAaHo9HX331lZxOp3bv3i2Px6M2bdooNjZWDodDTzzxhNERAQDAA4QiD03G33//rc2bNys1NVUHDx6U1WpVeHi4ZsyYobFjx6pZs2ZGR8QD5siRI3I6ndqwYYMuXrwoSRo+fLgcDofGjRunwMBAgxMCAIAHEUUempySkhItWLBAGRkZvmM2m03z5s1TUlKSAgICDEyHB8mtWY4hISGMSgAAAKbBnDw0CTdv3tQ333yj1NRU7dy5Ux6PR88995xmzZqlwMBArV27Vm+++aZOnz6ttWvXGh0XD4iYmBg5HA5FREQw0xEAAJgGO3kwlTNnzshmsyk4OFiSVFRUpNTUVLlcLpWVlaldu3aKi4vTzJkzFRYWVuXaxMREZWZm+h6bAwAAAB5EdNeEqXTr1k3btm2TJA0ZMkRhYWFKTk5WaGioMjIydO7cOa1atapagXdr/aVLlxo5MQAAAGAuPK4JU/F6vb4RCYWFhUpKStKsWbNkt9vvee3IkSOVl5fX0BEBAAAAU6PIg2mdP3++Vh0zbTabhg0b1oCJAAAAAPPjcU2YFiMRAAAAgNpjJw+ms3//fnk8nhqvj4uLa8A0AAAAQNNCd02YitVqrXEreq/XK4vFosrKygZOBQAAADQd7OTBdGbNmqVnn33W6BgAAABAk0SRB9MZMmSIYmNjjY4BAAAANEk0XgEAAAAAP0KRBwAAAAB+hCIPAAAAAPwI3TUBAAAAwI+wkwcAAAAAfoQiDwAAAAD8CEUeAAAAAPgRijwAAAAA8CMUeQAAAADgRyjyAAAAAMCPUOQBAAAAgB+hyAMAAAAAP/I/TM3HEtLSNiYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptRNA_sorted_perturb = np.load('./data/highestgene-SinglePerturb_n10m300_ptRNA_sorted_perturb.npy')\n",
    "elongt_perturb=np.load('./data/highestgene-SinglePerturb_n10m300_elongt_perturb.npy')\n",
    "elongt_perturb_sem = np.load('./data/highestgene-SinglePerturb_n10m300_elongt_perturb_sem.npy')\n",
    "baseline_elongt = np.load('./data/highestgene-SinglePerturb_n10m300_baseline_elongt.npy')\n",
    "baseline_elongt_sem = np.load('./data/highestgene-SinglePerturb_n10m300_baseline_elongt_sem.npy')\n",
    "\n",
    "#Plotting\n",
    "fig,ax = plt.subplots(figsize=(9,5))\n",
    "elongt_diff = (elongt_perturb - baseline_elongt)/baseline_elongt*100\n",
    "elongt_diff[elongt_diff<0] = 0\n",
    "elongt_diff=np.insert(elongt_diff,0,0)\n",
    "print(\"show\", elongt_diff)\n",
    "ax.bar(np.arange(40),elongt_diff,color='sienna',width=0.8)\n",
    "\n",
    "elongt_diff = (elongt_perturb - baseline_elongt)/baseline_elongt*100\n",
    "elongt_diff[elongt_diff>0] = 0\n",
    "elongt_diff=np.insert(elongt_diff,0,0)\n",
    "elongt_diff_sem = elongt_perturb_sem\n",
    "elongt_diff_sem = np.insert(elongt_diff_sem,0,0)\n",
    "ax.bar(np.arange(40),elongt_diff,color='darkseagreen',width=0.8)\n",
    "ax.plot(np.arange(40),[0]*40,color='black',linewidth=3,alpha=1)\n",
    "plt.xticks(range(len(tRNA_tags)), tRNA_sorted,rotation='vertical')\n",
    "\n",
    "ax.spines['left'].set_linewidth(4)\n",
    "ax.spines['bottom'].set_linewidth(4)\n",
    "ax.spines['top'].set_linewidth(0)\n",
    "ax.spines['right'].set_linewidth(0)\n",
    "ax.set_xlim(-2,40)\n",
    "ax.set_ylabel('$\\Delta$ Elongation latency (%)',fontsize=23)\n",
    "ax.tick_params(axis='y', which='major', labelsize=20)\n",
    "ax.tick_params(axis='x', which='major', labelsize=13)\n",
    "#ax.set_ylim(-5,5)\n",
    "plt.tight_layout()\n",
    "fig.savefig('./Figures/S12e.svg', dpi=300,bbox_inches='tight')\n",
    "\n",
    "#Resort ptRNA_min and plot\n",
    "ptRNA_list_fast=np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_ptRNA.npy')\n",
    "elongt_list_fast =np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_elongt.npy')\n",
    "\n",
    "ptRNA_min = ptRNA_list_fast[np.argmin(elongt_list_fast)]\n",
    "ptRNA_min_dict = dict(zip(tRNA_tags,ptRNA_min))\n",
    "ptRNA_min_sorted=[ptRNA_min_dict[tRNA] for tRNA in tRNA_sorted]\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(9,5))\n",
    "#ax.bar(np.arange(40),ptRNA_sorted,color='#39568CFF',width=0.8,alpha=0.5)\n",
    "ax.bar(np.arange(40),np.array(ptRNA_min_sorted)*100,color='darkseagreen',width=0.8,alpha=0.5)\n",
    "ax.bar(np.arange(40),ptRNA_sorted_perturb*100,color='#39568CFF',width=0.8,alpha=0.5)\n",
    "ax.plot(np.arange(40),[1/40*100]*40,color='black',linewidth=3,linestyle='dashed',alpha=0.5)\n",
    "plt.xticks(range(len(tRNA_tags)), tRNA_sorted,rotation='vertical')\n",
    "\n",
    "ax.spines['left'].set_linewidth(4)\n",
    "ax.spines['bottom'].set_linewidth(4)\n",
    "ax.spines['top'].set_linewidth(0)\n",
    "ax.spines['right'].set_linewidth(0)\n",
    "ax.set_xlim(-2,40)\n",
    "ax.set_ylabel('Frequency (%)',fontsize=25)\n",
    "ax.tick_params(axis='y', which='major', labelsize=20)\n",
    "ax.tick_params(axis='x', which='major', labelsize=13)\n",
    "ax.set_ylim(0,8.52)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('./Figures/S12d.svg', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae0c8dee",
   "metadata": {},
   "source": [
    "## Check if rational modification of distribution improves elongation latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6f52d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start\n",
      "replicates run:  3000\n",
      "-0.5560442658780547\n"
     ]
    }
   ],
   "source": [
    "ptRNA_min_glu2toLys = ptRNA_min.copy()\n",
    "ptRNA_min_glu2toLys[21] = 0.040615551926959516+0.04\n",
    "ptRNA_min_glu2toLys[11] = 0.0821727727872197 - 0.04\n",
    "elongt_perturb = perturb(ptRNA_min_glu2toLys, tRNA_tags, tRNA_sorted,pCodon_WT,ensmbl_latency_dict,1,0,pCodon_type='highest')[0]\n",
    "elongt_diff = (elongt_perturb - baseline_elongt)/baseline_elongt*100\n",
    "print(elongt_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889dcd73",
   "metadata": {},
   "source": [
    "## Check internal noise of simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e31f3860",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "replicates run:  3000\n",
      "replicates run:  3000\n",
      "replicates run:  3000\n",
      "replicates run:  3000\n",
      "replicates run:  3000\n",
      "replicates run:  3000\n",
      "replicates run:  3000\n",
      "replicates run:  3000\n",
      "replicates run:  3000\n",
      "replicates run:  3000\n",
      "replicates run:  3000\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[24], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m noise_baseline \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m30\u001b[39m):\n\u001b[0;32m----> 5\u001b[0m     elongt_perturb \u001b[38;5;241m=\u001b[39m \u001b[43mperturb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mptRNA_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtRNA_tags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtRNA_sorted\u001b[49m\u001b[43m,\u001b[49m\u001b[43mpCodon_WT\u001b[49m\u001b[43m,\u001b[49m\u001b[43mensmbl_latency_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mpCodon_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhighest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m      6\u001b[0m     elongt_diff \u001b[38;5;241m=\u001b[39m (elongt_perturb \u001b[38;5;241m-\u001b[39m baseline_elongt)\u001b[38;5;241m/\u001b[39mbaseline_elongt\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m\n\u001b[1;32m      7\u001b[0m     noise_baseline\u001b[38;5;241m.\u001b[39mappend(elongt_diff)\n",
      "File \u001b[0;32m~/Documents/tRNACAD/analysis_utils.py:1086\u001b[0m, in \u001b[0;36mperturb\u001b[0;34m(ptRNA, tRNA, tRNA_sorted, pCodon, ensmbl_latency_dict, pos, delta, skip, pCodon_type, RED20)\u001b[0m\n\u001b[1;32m   1084\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(ensmbl_latency_dict_list)):\n\u001b[1;32m   1085\u001b[0m     inputs \u001b[38;5;241m=\u001b[39m [[np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28mlist\u001b[39m(ptRNA_dict\u001b[38;5;241m.\u001b[39mvalues())), pCodon_WT_list[i],ensmbl_latency_dict_list[i]]]\u001b[38;5;241m*\u001b[39mm\n\u001b[0;32m-> 1086\u001b[0m     parallel_elong \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_cores\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mloky\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcomputeElongationLatency_multithread\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1087\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m _,items \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(parallel_elong):\n\u001b[1;32m   1088\u001b[0m         arr\u001b[38;5;241m.\u001b[39mappend(items[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/joblib/parallel.py:1098\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1095\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   1097\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1098\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mretrieve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1099\u001b[0m \u001b[38;5;66;03m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[1;32m   1100\u001b[0m elapsed_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_start_time\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/joblib/parallel.py:975\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    973\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msupports_timeout\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m--> 975\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output\u001b[38;5;241m.\u001b[39mextend(\u001b[43mjob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    976\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    977\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output\u001b[38;5;241m.\u001b[39mextend(job\u001b[38;5;241m.\u001b[39mget())\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/joblib/_parallel_backends.py:567\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[1;32m    565\u001b[0m \u001b[38;5;124;03mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 567\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    568\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m CfTimeoutError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    569\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py:440\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    437\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[1;32m    438\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__get_result()\n\u001b[0;32m--> 440\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_condition\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[1;32m    443\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n",
      "File \u001b[0;32m/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/threading.py:312\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    310\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:    \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    311\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m         \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    313\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    314\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "baseline_elongt = np.load('./data/highestgene-SinglePerturb_n10m300_baseline_elongt.npy')\n",
    "\n",
    "noise_baseline = []\n",
    "for i in range(30):\n",
    "    elongt_perturb = perturb(ptRNA_min, tRNA_tags, tRNA_sorted,pCodon_WT,ensmbl_latency_dict,1,0,pCodon_type='highest')[0]\n",
    "    elongt_diff = (elongt_perturb - baseline_elongt)/baseline_elongt*100\n",
    "    noise_baseline.append(elongt_diff)\n",
    "print(noise_baseline)\n",
    "print(np.average(noise_baseline))\n",
    "print(np.std(noise_baseline))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0443386e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.015377272544350395, -0.020035852736789827, -0.019352135358302977, -0.030839042051163293, -0.021991279053696133, -0.017059775720176463, -0.002159466838650531, -0.012166086899431041, -0.01168320226427036, -0.01831668227386318, -0.022354348990984516]\n",
      "-0.017394104066516248\n",
      "0.00696999020020521\n"
     ]
    }
   ],
   "source": [
    "print(noise_baseline)\n",
    "print(np.average(noise_baseline))\n",
    "print(np.std(noise_baseline))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7560763",
   "metadata": {},
   "source": [
    "## Monotonicity plot: Figure S12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "864bc937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C1BvFSLu5omTqjRKk3SjfFkClse+QdidOMgnCfG4NcBVfuyHc1wWuzvwRlMiaTPo/bNGiRZg6dSpcXFwsVsDx48eRlZWFAQMGWOyaRERERNZSsZpkqj68FZcHuptfZ+SW1OvpkJbioZTffCbO7d8RuZujc428XCCTcjSOyFZMCnmvvvoqlixZgldeeQXjx4+Ht7d3rW988OBBvPfee/j5558xd+5chjwiIiKqN0rKtDfDW/HNMFdS6fuiMq2tS7Q6pZMUvq7OCL85Glc+Clce5CL9XOHt6mzrEomoCiaFvNdeew0ffPABXnrpJbz22msYNGgQRowYgXvvvReBgYHVnqtWq5GYmIgff/wRcXFxuHz5MoQQ6Ny5M4YOHWrOayAiIiIyScWWAKm3jMTdOiqXXVh/9narLYVcCk8XJ3gq5Td/dzLyvVzf7nXLMQ+lHAq5zNYvgYhqyeR98tLT0/Haa68hLi4OWq1W/2BseHg4oqKiEBAQAF9fXygUCty4cQM5OTm4ePEikpKSUFZWBqB8mkOzZs2wYMECPPnkk5Z/VfUA98kjIiKyHSEErheV3QxxJfoRuNQb5VMsM3JLoa0HWwJUx1l2M6TdEsSqCmjG2pVODGlEDZXJIa9CRkYGVq1ahc8//xxpaWn/XtDIakgVt5DL5Rg0aBCeffZZPPDAAw69chJDHhERkeXpdAKFZeWrTRaUalBQqkFucRnSbtw6nbL86+J6NKVS6SRFuE/5fm5BnooqR9a8bgluCrnUoX9WIiLrqXXIu9XJkyexf/9+HD16FBkZGcjKykJpaSn8/PwQEBCAu+++G/fddx9iY2Ph4eFhibrrPYY8IiKiyoQQKCrToqC0PKBVBLX80n8DW/nX/35fcHPLgIq2wjINzP/JxfKkEqCRlwvCfcs36q4IdOW/XBDgrmBgI6I6Y5H1a9u2bYu2bdti8uTJlrgcERER1TNCCJSotf8Gr4pQph9RU9927N8+BTf3eytUaVDPZ0hWy9etfBGScB8XhN/cEqA8zLkgxJubdBNR/cFNSoiIiAhCCFy+Xoz45BtISL2Bi1lFyLstwGnsOaHVgIuTDOG+LpVH4XxcEOHnijAfV7gr+GMTEdkH/m1FRETUABWqNEhKzUVCyg3Ep5T/fqNYbeuyrEomlaCRl7LSCNy/Yc4V/u7OnFJJRA6BIY+IiMjBCSFwMbsI8cn/BrqzVwvseupkVfzdnRF2cyQu4pZRuQhfVwR7KTmlkogaBIY8IiIiB5Nfqr45SpeL+JQbSEjJRV6J/YzSyaQSeCjLV5n0UMpv/nLSt3ne8n3F70GeSoT5uMCNUyqJiBjyiIiI7JlOJ3Ahq1Af6OJTbuDctUKbrUAplQDuivLwVbGptudtIc2jUkgr/9rL5d82FycZp00SEZmBIY+IiMiO5JWokZiae3OBlFwkptxAfqnG4vdp5KVEqLeLPqjdPpp2e2DzvBnS3JwZ0IiIbI0hj4iIqJ7S6QTOXSu8OeWy/Hm689cKLX4fZ7kU7UK90CHCGzERPugQ4YNgL6XF70NERHWDIY+IiKieyC0uQ0LKvyteJqXmokBl+VG6UG8XxER4o0OEDzpE+uDuRp5wlnNBEiIiR8GQR0REZANancDZqwXlz9El5+r3prM0hVyK9mFe6BDhc3OUzhuBnhylIyJyZAx5REREdSCnqAwJN1e6jE+5gaTUXBSVaS1+n3Bfl/JAF+6NDpE+aN3Ik9sGEBE1MGaHvObNm2PSpEkYP348AgMDLVETERGRXSsu0+Bkej6SUnORlFb+KzWnxOL3UTpJ0T7s5rTLCG9ER3gj0IOjdEREDZ1ECPMWWZZKpZBIJJDL5Xj44YfxzDPPoF+/fpaqz25169YNR44cqdTWtWtXHD582EYVERGRNai1Opy9WoCk1Dx9qLPWRuORfq76QBcT4YOWwR4cpSMiIgNmj+S9/vrrWLt2LdLT07Fp0yZ8//33aNKkCZ555hmMGzeOo3tEROQwhBBIvl5cPjqXmoektFycTM+DSqOz+L1cnWWICvPWL5ASE+ENP3eFxe9DRESOx+yRPADQ6XTYsWMHVq5cif/973/QarX60b2hQ4di0qRJDW50jyN5RET2L6tAhaTUXJxIy0ViWh5OpOUit1htlXs18XerFOhaBnlAzlE6IiKqBYuEvFtlZGRgzZo1+Pzzz5GcnFx+E4mkwY3uMeQREdmXQpUGf6WVj86duDlSl55r+efoAMDNWYboWwJdTLgPfNycrXIvIiJqeCwe8ioIIbBz506sWrUKP/30E9RqdYMa3WPIIyKqv8o0OpzJLEBiWvledEmpuTifVQhrfCLKpBLcFeSB6HAvtL85/bJFoAdkUonlb0ZERAQrhrxbZWRkYOTIkdi/f3/5TSXlH2zNmzfHzJkzMWHCBMhkMmuXUacY8oiI6gedTuDS9aKb0y7zkJiai3+u5KPMCs/RAUCEryuiwr0RFeaF6HBvtAnxgouzY33GERFR/WbVffJSUlL0UzczMjIAlAe86Oho/PXXXzh37hyef/55rF69Gjt27EBAQIA1yyEiogbgan4pEm+Ozp24Of2yoFRjlXv5uzsjKswb7cO8EXVzpM6X0y6JiMjGLB7ytFottm3bhlWrVmHXrl3Q6XQQQsDPzw/jxo3Dc889h2bNmuHq1av45JNPsGTJEsTHx2P27Nn47LPPLF0OERE5sPxSNf66OTpXEeoy80utci9XZxnahZaPzkWFe6N9mBdCvV30s1OIiIjqC4uFvMuXL2P16tVYu3YtMjMzUTELtHv37nj++efx2GOPwdn533/dDAoKwrx58zB48GB07twZP//8s6VKISIiBySEQGpOCY4n5+B48g38efkGzl4rsMpzdHKpBK0beaJ9mBeiwr0RHe6NZgHufI6OiIjsgtkhb9OmTVi1ahV+/fVXCCEghICHhwdGjx6N559/Hm3btq32/I4dOyI4OBiZmZnmlkJERA5ErdXh74x8HL+cgz+Tb+B48g1kFaiscq+m/m760bmocG/c3cgTSic+R0dERPbJ7JD3+OOP67+Ojo7G888/j5EjR8LNza3G17h1hI+IiBqmvGI14lNulI/UXb6BpLRclKotvzhKoIdCPzoXFeaNdqFe8HJ1svh9iIiIbMXskKdUKvHEE0/g+eefR+fOnWt1jcuXL5tbBhER2REhBFJyinH8cvkI3Z/JOTh3zfJbGHgo5Gh/c0GUqLDyYBfspbTsTYiIiOoZs0NeRkYGvL29LVAKERE5qjKNDn9n5JVPu7x8A3+mWH7qpbNMitYhnoi+OeWyfZg3mvq7Qcrn6IiIqIExO+S5urrixIkTcHZ2RqtWrarte/r0aZSVlaF169ZwcuLUGCIiR5VXrMafKTn6kboTVph6GeKlxD2NfdEx0gcxEd5oGewBhZzP0REREZkd8r777juMGzcOkyZNwqefflpt33feeQdxcXFYv349RowYYe6tiYioHhBCIPl6sX7a5fHLN3DuWqFF7yGVAK0beaJjpI8+2IV4u1j0HkRERI7C7JD3/fffAwDGjBlzx74TJkzA119/jU2bNjHkERHZqTKNDicz8vDn5fJFUv5MzkV2oWWnXror5IiJ8MY9kT7oGOmL6AhvuCssvrUrERGRQzL7E/PkyZOQy+U1WnQlNjYWcrkcf/31l7m3JSKiOpJbXKbfwuDPm6teqjSWnXoZ6u1SHuga++CeSB+0CvbknnRERES1ZJGFV7y8vCCX3/lSTk5O8PLywpUrV8y9LRERWYEQApevF1fam+68FaZe3h3iiY6Rvvpg18iLUy+JiIgsxeyQ5+zsjIKCghr1FUKgsLCQi64QEdUTV/NLkZSaixNpeUhKK/89r0Rt0XtUTL3sGOmLjo19EB3uDTdOvSQiIrIasz9lmzRpghMnTuDw4cPo1q1btX1///13qFQqNG/e3NzbEhGRifKK1TiRfjPQpeYiKS0XV/Mt+ywdUD71smNjn/JFUiJ90TLYg1MviYiI6pDZIe/+++9HUlISZs2ahT179lQ5bVOj0WD27NmQSCTo37+/ubclIqJqlJRp8XdGHpLS8nAiLRdJqbm4fL3Y4veRSSW4u5Gnftplx0hfbjZORERkYxIhhDDnAmlpabjrrrugUqlw77334oMPPkBMTEylPvHx8ZgxYwYOHDgApVKJ06dPIyIiwqzC67tu3brhyJEjldq6du2Kw4cP26giInJUaq0OZzILcKIi0KXl4ezVAmh1Zv31bpSHQo6YyPJRuo6RPoji1EsiIqJ6x+xP5rCwMKxcuRLjxo3DgQMH0LFjRwQHByMyMhIAkJycjMzMTAghIJFIsGrVKocPeERE1qLTCVy6XnRzdK481P2dkW/x1S4rhPm4VNqb7q4gTr0kIiKq7yzyz69PPfUUfH198Z///AeXL1/GlStXDFbQbNq0KZYvX44HH3zQErckInJ4QghcySvVj86duLkwSkGpxir3c3OWoV2YF6LCvNE+rHyPOk69JCIisj8Wm2MzaNAgPPjgg9i7dy9+//13ZGZmQiKRIDg4GN27d0fv3r0hlUotdTsiIodzo6hMv8JlRbDLKrD8wigA4CyTonWIJ6LCvNA+zBtRYV5oGuDOUToiIiIHYNEHKWQyGfr164d+/fpZ8rJERA6nSKXByfS8SlsXpORYfmEUoHxfuhaBHmgf5oX24eWBrlWwJ5zl/Ic3IiIiR8Sn5YmIrEyrE/gnIx+Jabk4cXNPunPXCmCFdVEAABG+rmivn3bphbahXlwchYiIqAHhpz4RkZVkFajw3bEUfPNHKtJzS6xyD393BaLDy6dctr859dLXzdkq9yIiIiL7YLGQd+HCBWzYsAEnTpxATk4O1Gp1lX0lEgn27NljqVsTEdUbQgj8cSkH64+m4H8nr0CttdxwnYdSrg9yFc/SNfJSQiLhc3RERET0L4uEvPnz5+Ptt9+GTqdDTbbd4w8kRORoCkrV2JqQjq+OJOPs1UKzr6eQS9EmxLM80N0cqWvi5wYpF0YhIiKiOzA75H399deYP38+ACAkJAQPPPAAQkJCIJdzJigROb5TV/Kx/kgytiako6hMW6tryKQS3BXkoR+dax/mhZbBHnCScWEUIiIiMp3ZSWzFihUAgCFDhmDDhg1wduazIPZICIG/M/KhdJKiqb87RwuIqqHSaPG/k5n46nAyjiffMPn8Jv5ulaZdtgnxgouzzAqVEhERUUNkdsg7efIkJBIJPv74YwY8O5VTVIaJ644hPiUXANCvdSCWPBENT6WTbQsjqmdSc4oR90cKNhxLxfWishqfJ5EAve4KwJOdI9C1iR+8XPn/FhEREVmP2SFPIpHA09MTISEhlqiHbGDF3vP6gAcAu09dw0sbkrDyqXv4/CQ1eFqdwP6zWVh/JBm/nrmGGjx2rOfr5ozHO4ZjVJcIhPu6Wq9IIiIioluYHfJatWqFxMREqFQqKBQKS9REdUgIgW1JGQbtO/+5ii8PJ2Ns98Z1XxRRPZBTVIYNx1Px9dFkpOaYtv3BPZE+eKprJAa0C4ZCzmmYREREVLfMDnkTJ07Es88+i40bN2L06NGWqInqUGZ+Ka4VqIwee2f7KXRs7IM2IV51XBWRbQghEJ9yA+uPpGD7iSso0+pqfK6rswxDY0Ixuksk7g7xtGKVRERERNUzO+RNmjQJ27dvx7Rp0xAREYH77rvPEnVRHUlKza3yWJlWh//EJWDbf3rATcHVUslxFak0+CExA18dScapK/kmndsi0B2ju0ZiWIdQPsdKRERE9YLZP7m/9dZbiIqKwoEDB9C7d2/ExsaiS5cu8PDwqPa8N99806z7njlzBjt37sSff/6JP//8E6dOnYJWq8WCBQvwxhtv1Pq6u3fvxpIlS/DHH3+gqKgIkZGRGD58OGbPng13d3ezaq6PEqoJeQBwMbsIc7aexJInouukHqK6dO5qAdYfScbm+HQUqDQ1Pk8uleCBtsF4qmskujTx5bOrREREVK+YHfLmzZun/wFHCIGDBw/i0KFDdzzP3JD3ySef4KOPPjLrGrf74IMP8OKLL0IikeDee+9FUFAQDhw4gIULF+L777/HwYMH4e/vb9F72lp1I3kVNiekI7a5P4bfE2b9goisrEyjw85/MrH+SDKOXMwx6dxGXkqM7ByBJzqHI9BDaaUKiYiIiMxjdsi77777bPKv2G3btsVLL72EmJgYdOjQAQsXLsRXX31V6+slJCRg5syZkMlk2LZtGwYMGAAAKC4uxpAhQ7Bnzx4899xz2LRpk6Vegs1pdQJ/peXVqO+cH04iOsIbzQIcbzSTGoaM3BJ880cKvj2WiqwqnkOtyr0t/PFU10j0aRUIOTcoJyIionrO7JC3b98+C5RhuokTJ1b6Xio17wevd999F0IIjB8/Xh/wAMDV1RVr1qxB06ZN8f333+P06dNo1aqVWfeqLy5kFaKoTFujvsVlWkyNS8CWyd2hdOJqgWQfdDqBg+ezsf5IMnafugqdCdsfeLk44fGOYRjZJRJN/N2sVyQRERGRhfGfpAGUlZVh+/btAICRI0caHI+MjERsbCwAYMuWLXVamzUl3rI33q0erWJa5qkr+Vi445QVKyKyjNziMnx24CL6LN6HMZ//gZ3/1DzgRYV74/3HonD0tb54fdDdDHhERERkd7hkIoCzZ8+iuLgYANCxY0ejfTp27IgDBw4gISGhLkuzqsS0XKPtswa0gkIuxddHUwyOfXk4Gd2b+ePBtsFWro7IdEmpufjqSDK2JWVApan59gdKJymGRIVgdNdItA/ztl6BRERERHXAYiEvLS0NS5YswS+//ILk5GSUlpZCo/l3tbobN27gk08+gUQiwcsvvwy5vP7ky0uXLgEAvL29q1wVNDw8vFJfR2Bs0ZUwHxf4uyswZ/Dd+DP5Bk5nFhj0eWVTEtqGeiLMx7UOqiSqXkmZFtuSMrD+aDJO1PAZ0wpN/d0wqmskHu0QBi9Xbn9AREREjsEiSWvXrl14/PHHkZ+fDyHK50TdvhiLj48Ptm7dij///BNt2rTBkCFDLHFriygoKA8ybm5VT8uq2D4hP7/6PbRUKhVUKhW02po962YrJWVaowEuKtwbAKB0kmH5yBg8tOwQStSVX0t+qQbTvknAd892gxMXoSAbuZBViLijKdh4PBX5pTXf/kAmleD+1kF4qlskujfz4/YHRERE5HDM/gk9NTUVjz76KPLy8vDQQw9h06ZN8PHxMdr36aefhhBC//ybI3r33Xfh5eWFY8eO2bqUav2dkQetkYeUom+ZqtY80APzH25j9Pz4lFx8sOustcojMup6oQprD13Cw8sPou/i37Dm4KUaB7xADwWm922BQ6/2wadP3YPY5v4MeEREROSQzB7JW7x4MQoKCvD444/j22+/BQBMmTLFaN8HHngAAOpdAKqYollUVFRln8LCQgCAp6dntdeaPXs2XnzxRfTr16/evc5bJVaxP150hHel7x+7JwyHzmfjh8QMg76f/HYB3Zr54d4WAVaokKhcqVqL3aeuYkt8On47mwWNKUtkAujezA9PdY1Ev7uDOPJMREREDYLZIe+XX36BRCLBggUL7ti3SZMmUCgU9e65tsaNGwMAcnNzUVBQYPS5vNTU1Ep9q6JQKKBQKCCT1e9tBoyFPJlUgjYhlUOsRCLBO8PaISk1F5evF1c6JgQw47tE7Jh+LzeGJovS6QSOXsrB1oR07PjrCgpUNZ+OCQAeSjkevScMo7pEonkg93YkIiKihsXskJeSkgIXFxe0aNGiRv3d3d2Rl2fa4gjW1rJlS7i6uqK4uBjHjx9H7969DfocP34cANChQ4e6Ls8qkoysrHlXkAdcnQ3/SLgr5Fg+sgOGfXwIam3lUZTswjLM3JCEdeM7Qyrl1Dcyz/lrBdgcn44fEjOQnlti8vltQjzxVNdIDIkOMfpnmYiIiKghMPunIKlUWuNFRjQaDfLz8+845bGuOTs7Y9CgQdi4cSPi4uIMQl5ycjJ+//13AMCwYcNsUaJFXS9UITXH8Afo6JuLrhjTNtQLswe0xls//WNw7MC5bHzy2wVM6d3ckmVSA5FdqMKPiRnYkpCOv9JN/wcgZ7kUg9s3wlNdIxEd7s3n7IiIiKjBM/sBlcjISKhUKqSkGO6pdrv9+/dDrVbXeNTP0pYvX45WrVphzJgxBsdmzZoFiUSCL774Av/73//07cXFxZgwYQK0Wi2GDx+OVq1a1WXJVmFsFA8AosO9qj1vfGxj9GsdaPTYkl1n8WdyjrmlUQNRUqbFj0kZGP/FH+iycA/e+ukfkwNep8Y+WDisHY691g9LHo9GTIQPAx4RERERLDCS169fP5w6dQqffvopFi5cWGU/tVqN119/HRKJBAMGDDD3toiPj8fkyZP131+4cAEAsHLlSvz000/69i1btqBRo0YAgOzsbJw5cwbBwYYbeXfo0AGLFy/Giy++iIEDB6Jnz54IDAzEgQMHcOXKFbRs2RKffvqp2XXXB4mpxn+YjqpmJA8ofz5v0aNRGLj0AK7klVY6ptUJTPsmEdun9YC3q7OlSiUHotMJHLl4HZsT0vG/k5koNPE5O6B8X7thMaEYGhOKcF/u00hERERkjNkhb8aMGVi5ciUWL16MZs2aYcKECQZ94uPjMWPGDBw9ehSenp6Vwllt5efn4+jRowbtaWlpSEtL03+vUqlqfM0ZM2agXbt2WLx4Mf744w8UFRUhIiICs2fPxuzZs6vcKN3eGNsE3dVZhhaBd359Pm7O+OjJGDy56jBuX+QwPbcEr35/Ap+OvocjKqR39mrFc3bpBv84UBO+bs54qH0jDOsQhqgwL/7ZIiIiIroDiajYvdwMX3/9NcaOHQshBPz9/ZGXlwe1Wo0uXbogOTkZmZmZEEJALpdj06ZN9WojdGvp1q0bjhw5Uqmta9euOHz4sI0qKieEQMyCXcgtVldq79LEF989263G11m25xwWV7FP3lsPt8GYbo3NKZPs3LWCUv1zdn9n5Jt8vrNcivtbB2FYTCh6tgzg1gdEREREJrDI8nOjRo1CYGAgpkyZgvPnz+vbbw05zZs3x6effoo+ffpY4pZUS8nXiw0CHlD9oivGTO7dHIcvXsfvF64bHHv7p1O4J9IHbUKqf8aPHEtxmQY7/76KzQnpOHguy2Cktya6NPHFIx1C8WDbRvBycbJ8kUREREQNgMXWGL///vtx5swZ7N+/H4cOHUJGRga0Wi2Cg4MRGxuL3r171/u94xqCqhZdudPzeLeTSSX44IloDPzoAK4XlVU6VqbV4T9xCdj2nx5wU3AZe0em1QkcvnAdmxPS8MvJTBSV1Wyl3Vs1C3DDIx3C8HB0CMJ8+JwdERERkbks+hO4RCJBz5490bNnT0teliwoISXXaLupI3kAEOSpxOLHozDui2MGxy5mF2HODyex5PFok69L9d/pzHxsiU/H1sR0XM2v+XOvFfzcnPFQVAge6RCKdqF8zo6IiIjIkiyyGbpMJkNoaGiN+mdkZECj0SAiIsLcW1MtGBvJC/BQoJGXslbX69UyEM/e1xQr9180OLY5Ph2xzfwx/J6wWl2b6per+eXP2W1OSMepK6Y/Z6eQS3H/3UF4pEMo7m3B5+yIiIiIrMXskNe4cWM0atQI6enpNeofGxuL1NRUaDSmL59O5inT6IwughEVZt4G0i890BJHL+Ug0ciqnXN+OInoCG80C3Cv9fXJdopUGvzydya2JKTj0Plsk5+zk0iArk38MKxDKB5sGwxPJZ+zIyIiIrI2i0zXNHWBTgss6Em1cDozH2UanUF7TIS3Wdd1kkmxbEQMBi49gILSyuG9uEyL/8QlYPPk7lA68ZlMe6DVCRw6n40tCen45e9MFNfiObsWge4Y1iEUQ6NDEeLtYoUqiYiIiKgqdb4qRmlpKeRyLsZhC8b2xwPKR/LMFe7rivceaY8pcfEGx/65ko93d5zC/Ifbmn0fsp68YjVW7r+ATX+m4VqB6c/Z+bsrMOTmc3ZtQjz5nB0RERGRjdRp2srIyEBWVhYCAwPr8rZ0U2JqntH2dmGW2epgUPtGOHQhAnFHUwyOrTucjG7N/PFg22CL3Iss69jlHEz/JgEZJm5WrnSSov/dwRjWIRT3NveHnM/ZEREREdmcySFv//792LdvX6W2wsJCvPXWW1WeI4RAbm4uduzYASEEunTpYnKhZL7E1BsGbc0C3Cy6H9mbg+9GfPINnM4sMDj2yqYktA315DL59YhWJ7Bi73l8uPtsjZ+3k0iA7s38MCwmDA+2DYY7t8kgIiIiqldM/uls7969mD9/fqWpWEVFRZg/f/4dzxVCQKlUYvbs2abelsyUX6rGhawig3ZT98e7E6WTDMtHxuChZYdQoq78LFd+qQbTv03Et8905cqK9UBmXile+C4BRy7m1Kh/yyAPDOsQioejQ9DIi8/ZEREREdVXJoe8xo0bV9oH77fffoOTkxO6detW5TlSqRSenp5o27Ytxo4di+bNm9euWqq1v9KMT9Wszf54d9I80APzh7TBK9+fMDj2Z/INfLj7LF5+oJXF70s1t+fUVby0MQk3itXV9gvwUODhqBAM6xCKuxvxOTsiIiIie2ByyBs7dizGjh2r/14qlcLX1xd79+61aGFkWca2NwCsE/IA4LGOYTh0IRs/JGYYHPt43wV0a+qPHi38rXJvqppKo8V7P5/GF4cuV9uva1NfTO7VHLHN/SGTMtgRERER2ROzH6b54osv4OLCqVv1nbGQ5yyTolWwp1XuJ5FI8PbQtkhMzUXy9eJKx4QAXvguET9PvxcBHgqr3J8MXcwqxH++STC6V2IFmVSCF++/C8/1bMZwR0RERGSnzH4wauzYsXj88cctUQtZiRDCaMi7O8QTznLrPRvnoXTC8hEd4CQzDAvZhSq8uCEROlN316Za+f7PNAxedrDagBfq7YINz3bFlN7NGfCIiIiI7BhXv2gAMvNLkWVk3zNrTdW8VbswL8we0NrosQPnsvHp/gtWr6EhK1RpMOO7RMzcmFTtpuYD2wVjx/R7cU+kbx1WR0RERETWwJDXACSm5Bptr4uQBwDjYxujX2vjeyMu3nkWfyYbbu1A5vsrLQ+Dlx7AloT0Kvso5FIsHNYOK0Z2sOhWGkRERERkOxYLeUlJSXjmmWdw9913w9PTEzKZrMpfcjn31apLiWm5RtstvX1CVSQSCRY9GoVgT6XBMa1OYNo3Cci7wyqPVHNCCHx24CIe+eQQLt/2POSt7gpyx49Te2BklwiumklERETkQCwS8pYvX45OnTphzZo1OH36NAoLCyGEqPYX1Z0kI8/jebk4obFf3W1K7uPmjKUjYmDsUa/03BK8+v0J/rmwgOuFKjy99hje3n4Kam3V7+fILhH4YUoPtAz2qMPqiIiIiKgumB3yjh49iunTp0Or1WLy5MnYsWMHAMDX1xe7d+/G+vXrMW7cODg7O8Pf3x9xcXH49ddfzS6cakarE0b3yIsK967z0ZvOTXzxQr+7jB7739+ZWH8kuU7rcTS/X8jGgI8OYO+ZrCr7eCjl+HhUBywc1g4uzrI6rI6IiIiI6orZ8yaXLl0KIQReeOEFLFmyRN/u7OyMPn36AABGjhyJadOm4YEHHsCcOXMQHx9v7m2phs5fK0SRkQU3osO8bFANMKV3cxy+cB2HL143OLZg+yl0iPRBmxDb1GavNFodPtx9Div2nUd1g6EdIrzx0ZMxCPetuxFcIiIiIqp7Zo/kHTp0CBKJBNOnT6/UfvvUu+joaCxbtgwXLlzAokWLzL0t1ZCxqZpA3T2PdzuZVIIPn4yGn5uzwbEyjQ7/+SYBRSqNDSqzT2k3ivHEqiNYvrfqgCeRAFN6N8N3z3ZjwCMiIiJqAMwOeVevXoVCoUBkZOS/F5VKUVpaatB32LBhcHJywubNm829LdVQQj0LeQAQ5KnE+49HGT12MasIb/7wdx1XZJ/+d/IKBn50oNrVSQM8FFg/oQtefqAVnGRcTJeIiIioITD7pz5XV1e4ulYeHfDw8EB+fj5Uqsp7szk5OcHV1RXJyXz2qq4YG8kL83GBv7ui7ou5Re+WgXjmvqZGj30fn4bN8Wl1XJH9KFVr8fqWv/Dc+njkl1Y96tmrZQB+nn4vYpv712F1RERERGRrZoe80NBQ5OfnQ6P594fNZs2aAQCOHTtWqW9GRgby8vK4imIdKSnT4szVAoN2W47i3eql/i2rrOWNrSdxMauwbguyA2evFuDh5Yfw9dGUKvs4ySR4Y1BrfD62k83DPBERERHVPbNDXuvWraHVavHXX3/p23r16gUhBN566y39tM2ysjJMmzYNANCuXTtzb0s1cDIjD1qdYaCOqSchz1kuxfIRMfBQGK7/U1ymxdS4BJSqDReNaYiEEPjmjxQMWX7QaHCvEOnniu+f746J9zaF1Nh+FURERETk8MwOef3794cQAtu2bdO3TZkyBQqFAnv27EFYWBhiY2MRGhqKLVu2QCKRYOrUqebelmqgvi26Yky4ryveG97e6LF/ruTjvZ9P13FF9U9+qRpTv0nA7M1/oVStq7Lf0OgQ/PSfHmgf5l13xRERERFRvWP2FgrDhw9HWloaQkJC9G1NmjRBXFwcxo8fj5ycHBw+fBhA+YIsL7/8MkaNGmXubakGEo2EPJlUgrb1bIuCQe0b4dCFCMQZmYK49vfL6NbMDw+0CbZBZbYXn3ID075JQNqNkir7uDrL8NbDbTG8Q2id731IRERERPWP2SHP29sbc+fONWgfNmwYevbsiR07diA1NRVeXl7o378/mjdvbu4tqYaMhbyWQR71chPsNwffjT8v3zA6FfGVTSfQNtQLod4uNqjMNnQ6gZX7L2LxzjPQGJlyW+HuRp5YNjIGzQLc67A6IiIiIqrPzA551fH19cXo0aOteQuqQnahyujoT32aqnkrpZMMy0fG4KHlBw2mJOaVqDH9mwR8+0xXyBvANgDXCkoxc0MSDpzLrrbfuO6NMXtgKyjk9S+0ExEREZHtOP5PzA3UibRco+3R4fVrquatWgR54K0hbY0eO558Ax/uPlfHFdW9385mYeBHB6oNeD6uTvhsTEfMG9KGAY+IiIiIDDDkOajElFyj7dHhPnVbiIke6xiGIVEhRo+t2HceB+8wumWvyjQ6vLvjFMZ+/geyC8uq7NeliS9+nn4f+t0dVIfVEREREZE9MWm65tNPP22Rm0okEqxZs8Yi1yLjEtPyDNpcnWVoHli/n92SSCR4Z1hbJKXlIvl6caVjQgAvfJeIn6ffiwAPx9n/LeV6Mf7zTTySjPw3qyCVANP73oWpfZpDxq0RiIiIiKgaJoW8tWvXQiKR1Hoz84pzGfKsSwhhdPuEdqFedhEQPJROWDYiBsM/+R1qbeU/a9mFKry4IRHrxnd2iH3gfkhMx+tbTqJQpamyTyMvJT56Mgadm/jWYWVEREREZK9MCnljxozhEu124PL1YuSVqA3aoyO8676YWmof5o1ZA1pjwU//GBw7cC4bK/dfxPO9mtmgMssoLtNg3o9/Y8PxtGr79b87CP/3aHt4uzrXUWVEREREZO9MHsmj+q+qTdCj7WyT7KdjG+P389nYc/qawbH3d55B5ya+uCeyfj9jaMw/Gfn4zzfxuJBVVGUfZ7kUbwxqjae6RvIfVoiIiIjIJFx4xQEZ2x8PqL/bJ1RFIpFg0WNRCPZUGhzT6gSmfZOAvGLDEcv6SgiBdb9fxtCPD1Ub8JoFuGHr5FiM6daYAY+IiIiITGbVffLINpKMbJ8Q6KFAIy/DsFTf+bo546MnozFi9RHcvid4em4JRq05glbBnlA6SaGUy+DiLIPSSQaFXAqlkwwuTuXfK52kcHGSQXHz69uPKeUyqz7jl1tchpc3ncCuf65W2++JjuGYO+RuuDrzf00iIiIiqh3+JOlgyjQ6/J2Rb9AeFe5tt6NCXZr6YXrfu/DB7rMGx06m5+NkuuHrrQ1nuRTKinDoLINSXh4AFfpAWH6sIkwqbg2WN89T3hocb359vVCFN7aexJW80irv7a6QY+Ej7arcPoKIiIiIqKYY8hzM6cx8lGl0Bu3RdjZV83ZT+zTHkYvXcfjidavdo0yjQ5lGh/zSqle6tIaocG8sezIGEX6udXpfIiIiInJMfCbPwVS56IqdhzyZVIIPn4yGr5tjrTL5bM+m2PhsNwY8IiIiIrIYhjwHk1BFyGsX5lW3hVhBkKcSSx6Psou9/u7E390Z657ujNkDWsNZzv8NiYiIiMhyOF3TwRgbyWsW4AZPpVPdF2MFvVoG4sunO2P1gYs4f60QpWodVGotSjVag43T66t7W/hj8eNRCPSwv4VwiIiIiKj+Y8hzIPmlaqNL89vb1gl3EtvcH7HN/Q3aNVodSjU6lKq1N39V/rpE/7W2vF9ZxdeVj6tunldy63U02vL+mn+PCRMzpZNMgpn9W+KZe5tadSVPIiIiImrYGPIcyInUPKPtMQ4W8qoil0nhLpPCXWH9P9ZCCJRpdfqRxJLbQmXF9ypN+fcyqRRdm/oizIfP3hERERGRdTHkORBj++MBjjeSVx9IJBIo5DIo5DLAxTGmwhIRERGRY7BYyLtw4QI2bNiAEydOICcnB2q1usq+EokEe/bssdSt6aZEI8/jOculaBXsWffFEBERERGRTVgk5M2fPx9vv/02dDodRA0eVLLXTbnrMyGE0ZDXJsSTqzcSERERETUgZoe8r7/+GvPnzwcAhISE4IEHHkBISAjkcs4ErUtX8kqRVaAyaI8K8677YoiIiIiIyGbMTmIrVqwAAAwZMgQbNmyAs7NjbVZtLxx1E3QiIiIiIjKN2fP4Tp48CYlEgo8//pgBz4aMTdUEGPKIiIiIiBoas0OeRCKBp6cnQkJCLFEP1ZKxkOfl4oRIPy7ZT0RERETUkJgd8lq1aoXi4mKoVIbPg1Hd0OoE/ko33CMvKtybi9wQERERETUwZoe8iRMnQq1WY+PGjZaoh2rh3LUCFJdpDdo5VZOIiIiIqOExO+RNmjQJQ4YMwbRp07B//35L1EQmqnrRFa+6LYSIiIiIiGzO7NU133rrLURFReHAgQPo3bs3YmNj0aVLF3h4eFR73ptvvmnuremmxFTDqZoAt08gIiIiImqIzA558+bN0z/3JYTAwYMHcejQoTuex5BnOcYWXQn3dYGfu6LuiyEiIiIiIpsyO+Tdd999XNzDhorLNDh7tcCgnaN4REREREQNk9khb9++fRYog2rr74x8aHXCoJ2LrhARERERNUxmL7xCtpWYkmu0nSGPiIiIiKhhYsizc4lpuQZtMqkEbUK4siYRERERUUPEkGfnjG2f0DLIAy7OsrovhoiIiIiIbM7sZ/JulZCQgLi4OBw/fhzXrl0DAAQGBqJTp04YMWIEYmJiLHm7Bi+7UIW0GyUG7dER3nVfDBERERER1QsWCXlFRUWYNGkSvvvuOwDlWylUOHXqFPbv34/FixfjySefxKpVq+Dm5maJ2zZ4VW6CzpU1iYiIiIgaLLNDnk6nw8MPP4y9e/dCCIFGjRqhT58+CAsLAwCkpaVh7969yMjIwLfffotr165h586d3HbBAqoKeVFcdIWIiIiIqMEyO+R9+eWX+PXXX+Hk5ITFixdj8uTJkEorP+qn0+nw6aefYsaMGfj111/x1VdfYcyYMebeusFLTMszaHNzlqF5oLsNqiEiIiIiovrA7IVX1q9fD4lEgkWLFmHq1KkGAQ8ApFIpJk+ejEWLFkEIgS+//NLc2zZ4QgijI3ntwrwgk3KUlIiIiIiooTI75CUlJUEmk2HSpEl37Dtp0iTI5XIkJiaae9sG7/L1YuSVqA3aOVWTiIiIiKhhMzvkFRQUwMPDAy4uLnfs6+LiAg8PDxQWFpp72waPi64QEREREZExZoc8f39/5OXl6bdMqM61a9eQm5sLPz8/c2/b4CVWFfK4fQIRERERUYNmdsjr1q0bhBCYN2/eHfvOnTsXQgjExsaae9sGz1jIC/RQINhTWffFEBERERFRvWF2yJsyZQqEEFi5ciWeeuopnD9/3qDP+fPnMXr0aKxcuRISiQRTpkwx97YNWplGh38y8g3ao8K9uTUFEREREVEDZ/YWCr169cILL7yADz/8EHFxcYiLi0N4eDhCQ0MBlO+Tl5aWpu8/Y8YM9OzZ09zbNminruSjTKszaI/moitERERERA2e2SEPAJYsWYKmTZti3rx5yMnJQUpKClJSUir18fPzw7x58ziKZwFJablG2xnyiIiIiIjIIiEPAKZOnYqJEydi165dOH78uH4hlsDAQHTs2BH3338/lEo+L2YJxp7Hk0jK98gjIiIiIqKGzWIhDwCUSiUeeughPPTQQ5a8LN3GWMhrFuAOT6VT3RdDRERERET1itkLr1DdyitR42JWkUF7FPfHIyIiIiIiMOTZnb/S8oy2R4dzqiYREREREZk4XfPpp58GADRq1AjvvPNOpTZTSCQSrFmzxuTzCEhMvWG0PTrcp44rISIiIiKi+sikkLd27VpIJBK0bNlSH/Iq2oQQdzy/oh9DXu0lphqO5DnLpWgZ7GGDaoiIiIiIqL4xKeSNGTMGEokEjRo1Mmgj6xNCGF10pU2IJ5zlnHlLRERERES1GMmrSVtd2rhxI1asWIGkpCSUlZWhefPmGDVqFGbMmAEnp5qvNrl27VqMHz++2j4///wzHnzwQXNLrrWMvFJkF6oM2rk/HhERERERVTB7C4WUlBTIZDKEhobWqH9GRgY0Gg0iIiLMvTVeeOEFfPTRR5DL5ejTpw/c3d3x66+/4tVXX8W2bduwc+dOuLi4mHTNZs2aoUePHkaP1fQ1WkuSkVE8gCGPiIiIiIj+ZXbIa9y4MRo1aoT09PQa9Y+NjUVqaio0Go1Z9926dSs++ugjuLu747fffkOHDh0AANnZ2ejTpw8OHjyIOXPm4P333zfpuj169LD56GRVqgp53D6BiIiIiIgqWORBrposumJOf2MWLlwIAJg1a5Y+4AGAv78/Pv74YwDA8uXLkZdnfMsBe5RgJOR5uzoh0s+17oshIiIiIqJ6qc5X6ygtLYVcbt4AYnp6Oo4dOwYAGDlypMHxHj16IDw8HCqVCjt27DDrXvWFRqszukdeVJg3F74hIiIiIiI9s6drmiIjIwNZWVkIDAw06zoJCQkAAF9fXzRp0sRon44dOyI1NRUJCQkYMWJEja99/vx5vPHGG7h27Rrc3d3Rtm1bDBkyBP7+/mbVbK7zWYUoUWsN2qP4PB4REREREd3C5JC3f/9+7Nu3r1JbYWEh3nrrrSrPEUIgNzcXO3bsgBACXbp0MbnQW126dAkAql28JTw8vFLfmjp06BAOHTpUqU2pVGLevHl49dVXTazUcqpedMWrbgshIiIiIqJ6zeSQt3fvXsyfP7/SFMGioiLMnz//jucKIaBUKjF79mxTb1tJQUEBAMDNza3KPu7u7gCA/Pz8Gl0zODgYr7/+OoYMGYKmTZtCoVDgzJkzWLZsGb766ivMmjULWq0Wr732WrXXUalUUKlU0GoNR93MYWx/PICLrhARERERUWUmh7zGjRujZ8+e+u9/++03ODk5oVu3blWeI5VK4enpibZt22Ls2LFo3rx57aq1ogcffNBgD7yOHTti3bp1iIqKwsyZM/HWW29hwoQJCAoKqvI67777bo0Cr6kSUw2fxwv3dYGfu8Li9yIiIiIiIvtlcsgbO3Ysxo4dq/9eKpXC19cXe/futWhh1fHw8ABQPoJYlcLCQgCAp6en2febPn063n33XWRnZ2Pnzp146qmnquw7e/ZsvPjii+jXr59+cRhzFZdpcPZqgUE7R/GIiIiIiOh2Zi+88sUXX5i84bi5GjduDABITU2tsk/FsYq+5pDJZGjRogWys7ORlpZWbV+FQgGFQgGZTGb2fSucTM+HVme47QQ3QSciIiIiotuZHfJuHdWrKzExMQCA69ev49KlS0ZX2Dx+/DgAVNpDzxzXr18H8O8oYl2qetEV7zqtg4iIiIiI6r863yfPEsLCwtCpUycAQFxcnMHxgwcPIjU1FQqFAgMHDjT7fvHx8Th79iwAoHPnzmZfz1SJabkGbTKpBG1CuLImERERERFVZpchD4B+lcv33nsP8fHx+vbr169j8uTJAICpU6fCy+vfILRlyxa0atUKffv2rXSt4uJirFixQr9q563279+P4cOHAyjfZN0mIS8l16CtVbAHXJwtNyWUiIiIiIgcg8U2Q09KSsKKFStw8OBBpKWlVbsoikQigUajMet+Q4cOxbRp07B06VJ07doVffv2hZubG/bs2YPc3FzExsZiwYIFlc7Jy8vDmTNnUFpaWqm9rKwMU6dOxcyZMxETE4OIiAhoNBqcPXsWJ0+eBAC0a9cOGzZsMKvm2sgqUCE9t8SgnZugExERERGRMRYJecuXL8eLL74IrVYLIQwXCLGWjz76CLGxsVixYgV+//13qNVqNGvWDLNmzcKMGTPg7Oxco+u4urpizpw5OH78OE6fPo2///4bJSUl8PHxQb9+/fDYY49h3LhxNb6eJZ0wMlUTAKK5siYRERERERkhEWamsqNHj6J79+4AgMmTJ2PQoEEYOHAgfH19sWHDBmRmZmL37t2Ii4uDp6cnli5dikaNGlXaa88RdevWDUeOHKnU1rVrVxw+fNik6yzeeQbLfj1v0L5zxn24K6juF4EhIiIiIqL6zeyRvKVLl0IIgRdeeAFLlizRtzs7O6NPnz4AgJEjR2LatGl44IEHMGfOnErP0FH1Eo2srOnmLEOzAPe6L4aIiIiIiOo9sxdeOXToECQSCaZPn16p/fYBwujoaCxbtgwXLlzAokWLzL1tgyCEMLp9QrswL8ikkroviIiIiIiI6j2zQ97Vq1ehUCgQGRn570WlUoPFTQBg2LBhcHJywubNm829bYNwKbsI+aWGC9REh/vYoBoiIiIiIrIHZk/XdHV1hURSeVTJw8MD+fn5UKlUUCgU+nYnJye4uroiOTnZ3Ns2CElVLboSzv3xiIiIiIjIOLNH8kJDQ5Gfn19pS4RmzZoBAI4dO1apb0ZGBvLy8up0BU57lpSaZ7Sd2ycQEREREVFVzA55rVu3hlarxV9//aVv69WrF4QQeOutt/TTNsvKyjBt2jQA5XvO0Z0lGHkeL8hTgUZeLnVfDBERERER2QWzQ17//v0hhMC2bdv0bVOmTIFCocCePXsQFhaG2NhYhIaGYsuWLZBIJJg6daq5t3V4Ko0WpzLyDdqjuD8eERERERFVw+xn8oYPH460tDSEhITo25o0aYK4uDiMHz8eOTk5+r3hpFIpXn75ZYwaNcrc2zq801cKUKbVGbRzqiYREREREVXH7JDn7e2NuXPnGrQPGzYMPXv2xI4dO5CamgovLy/0798fzZs3N/eWDYKx/fEAIJohj4iIiIiIqmF2yKuOr68vRo8ebc1bOCxj++NJJOV75BEREREREVXF7GfyyDoSjWyf0CzAHZ5Kp7ovhoiIiIiI7AZDXj2UV6zGxawig3YuukJERERERHdi9nTNp59+2qT+SqUS3t7eaNOmDfr164egoCBzS3A4J9JzjbZHR3jXaR1ERERERGR/zA55a9euhUQiqXF/IYS+v1wux9ixY7FkyRK4u7ubW4rDMPY8HgBEcySPiIiIiIjuwOyQN2bMGEgkEvz444+4ceMGXF1dcc899yA0NBQAkJ6ejj///BPFxcXw9fXF4MGDkZubi/j4eKSlpWHNmjU4d+4cdu/eDZlMZvYLcgSJqXkGbc5yKVoGe9igGiIiIiIisidmP5O3du1alJWVITc3F/PmzUNmZiZ+++03xMXFIS4uDr/99huuXr2K+fPnIzc3FwCwdetWpKSkYO3atXBycsL+/fuxfv16c0txCEIIo9sntA3xhLOcj1ASEREREVH1zE4Nq1evxrfffouFCxfizTffNDrt0s3NDXPmzME777yDr776CmvXrgVQPgo4b948CCHwzTffmFuKQ8jIK0V2ocqgnZugExERERFRTZgd8tasWQOpVIopU6bcse+UKVMglUqxevVqfVvFwi2JiYnmluIQqnwejyGPiIiIiIhqwOyQd/r0aXh5edVo4RR3d3d4enri77//1rcFBgbCy8tLP5WzoTM2VRNgyCMiIiIiopoxO+TpdDrk5ubixo0bd+x748YN5OXlQafTVWpXq9VcXfMmYyHP29UJEb6udV8MERERERHZHbNDXtu2bSGEwLvvvnvHvu+99x50Oh3atGmjb7t+/TqKi4sRGBhobil2T6PV4a80w5U1o8K8TdqmgoiIiIiIGi6zQ96kSZMghMDixYvx7LPPIjk52aBPSkoKnnvuObz//vuQSCR45pln9Mf27dsHAIiJiTG3FLt37lohStRag3ZO1SQiIiIiopoye5+88ePH45dffsGGDRvw2Wef4bPPPkNERARCQkIgkUiQkZGhD35CCDz22GMYP368/vzvv/8eXl5eGDBggLml2D0uukJEREREROYyO+QBQFxcHKKjo/Hee+8hPz8fycnJBiN6np6eePXVV/HKK68YnEvlktJyjba3D/Oq20KIiIiIiMhuWSTkSaVSzJo1C9OmTcPOnTsRHx+PrKwsAEBAQAA6dOiA/v37w9WVi4dUJyEl16AtwtcVfu6Kui+GiIiIiIjskkVCXgVXV1cMHToUQ4cOteRlG4TiMg3OXi0waOcm6EREREREZAqzF14hyziZng+dMGyP4lRNIiIiIiIygcVG8i5cuIANGzbgxIkTyMnJgVqtrrKvRCLBnj17LHVrh5CYanyfwZgI77othIiIiIiI7JpFQt78+fPx9ttvQ6fTQQgjw1G34Z5vhpJSDffHk0klaBPCkTwiIiIiIqo5s0Pe119/jfnz5wMAQkJC8MADDyAkJARyuUUf93N4iUa2T2gV7AGlk6zuiyEiIiIiIrtldhJbsWIFAGDIkCHYsGEDnJ2dzS6qockqUCE9t8SgnYuuEBERERGRqcxeeOXkyZOQSCT4+OOPGfBqiZugExERERGRpZgd8iQSCTw9PRESEmKJehqkqjZBZ8gjIiIiIiJTmR3yWrVqheLiYqhUKkvU0yAZex7PzVmGZgHudV8MERERERHZNbND3sSJE6FWq7Fx40ZL1NPg6HTC6HTN9mHekEm5CikREREREZnG7JA3adIkDBkyBNOmTcP+/fstUVODcvl6EfJLNQbtXHSFiIiIiIhqw+zVNd966y1ERUXhwIED6N27N2JjY9GlSxd4eHhUe96bb75p7q0dgrGpmgAQHc798YiIiIiIyHRmh7x58+bpNzcXQuDgwYM4dOjQHc9jyCtX9cqaPnVbCBEREREROQSzQ959992nD3lkusS0PIO2IE8Fgr2UNqiGiIiIiIjsndkhb9++fRYoo2FSabQ4lZFv0B4V5l33xRARERERkUMwe+EVqr1TVwpQptUZtEdHeNd9MURERERE5BAY8myoyufxOJJHRERERES1xJBnQ8ZCnkQCtAvjyppERERERFQ7Zj+Td6uEhATExcXh+PHjuHbtGgAgMDAQnTp1wogRIxATE2PJ29k9Y9snNA9wh4fSqe6LISIiIiIih2CRkFdUVIRJkybhu+++A1C+lUKFU6dOYf/+/Vi8eDGefPJJrFq1Cm5ubpa4rV3LK1bjYnaRQTs3QSciIiIiInOYHfJ0Oh0efvhh7N27F0IINGrUCH369EFYWBgAIC0tDXv37kVGRga+/fZbXLt2DTt37mzw2y6cSM812s6QR0RERERE5jA75H355Zf49ddf4eTkhMWLF2Py5MmQSis/6qfT6fDpp59ixowZ+PXXX/HVV19hzJgx5t7ariWm5Bptj2HIIyIiIiIiM5i98Mr69eshkUiwaNEiTJ061SDgAYBUKsXkyZOxaNEiCCHw5Zdfmntbu5eUlmvQ5iyXomWwR90XQ0REREREDsPskJeUlASZTIZJkybdse+kSZMgl8uRmJho7m3tmhACial5Bu1tQzzhJOOCp0REREREVHtmJ4qCggJ4eHjAxcXljn1dXFzg4eGBwsJCc29r19JzS5BdqDJojw73sUE1RERERETkSMwOef7+/sjLy9NvmVCda9euITc3F35+fube1q4lGRnFA4CocO6PR0RERERE5jE75HXr1g1CCMybN++OfefOnQshBGJjY829rV0z9jweAERz0RUiIiIiIjKT2SFvypQpEEJg5cqVeOqpp3D+/HmDPufPn8fo0aOxcuVKSCQSTJkyxdzb2jVjK2v6uDohwte17oshIiIiIiKHYvYWCr169cILL7yADz/8EHFxcYiLi0N4eDhCQ0MBlO+Tl5aWpu8/Y8YM9OzZ09zb2i2NVoe/0g2na0aFezf4vQOJiIiIiMh8Zoc8AFiyZAmaNm2KefPmIScnBykpKUhJSanUx8/PD/PmzWvwo3jnrhWiRK01aI8K8677YoiIiIiIyOFYJOQBwNSpUzFx4kTs2rULx48f1y/EEhgYiI4dO+L++++HUqm01O3sVmJqrtF2Po9HRERERESWYLGQBwBKpRIPPfQQHnroIUte1qEkVRHyohjyiIiIiIjIArjzdh0zNpIX4esKXzfnui+GiIiIiIgcDkNeHdIJgbNXCwzaOYpHRERERESWYtJ0zaefftoiN5VIJFizZo1FrmVPisu00AnDdj6PR0RERERElmJSyFu7di0kEgmEMJJUaqDi3IYa8opUGqPt0eFedVwJERERERE5KpNC3pgxY7iXmxmKy7S4fX1RuVSCNiEMeUREREREZBkmj+RR7RWpNAYhr1UjDyidZDaph4iIiIiIHA8XXqlDZVqdQRs3QSciIiIiIkuq85B35MgR7N+/v65vW29xZU0iIiIiIrIkkzdDl0qlaNSoEdLT0w2OzZgxA/n5+dUuqjJs2DBkZWVBozG+CElDE8OQR0REREREFlSrkbyqVtf89ttva/TcXm1X53Q07go5mga427oMIiIiIiJyIHwmz4bahXpBJuVqpUREREREZDkMeTYUHeFt6xKIiIiIiMjBMOTZEFfWJCIiIiIiS2PIs6FoLrpCREREREQWxpBnI8GeSgR73b41OhERERERkXkY8mwkKtzL1iUQEREREZEDYsizEW6CTkRERERE1mDyZugAcPXqVchksiqPV3dMCAGJhNsGRHPRFSIiIiIisoJahTxuZm4eiQRoF8bpmkREREREZHkmh7y5c+dao44GpXmAOzyUTrYug4iIiIiIHBBDng3weTwiIiIiIrIWLrxiA9wfj4iIiIiIrMXuQ97GjRvRq1cv+Pj4wM3NDVFRUfi///s/qNXqWl3vzz//xGOPPYagoCAolUo0adIE//nPf3Dt2jWL1cyQR0RERERE1mLXIe+FF17A448/jkOHDqFz58548MEHkZKSgldffRV9+vRBSUmJSdfbtGkTunbtik2bNiEyMhIPP/wwpFIpli9fjvbt2+P8+fNm16yQS9Ey2MPs6xARERERERljtyFv69at+Oijj+Du7o6jR4/il19+wffff49z586hXbt2OHjwIObMmVPj62VkZGDs2LHQaDRYuXIl/vjjD3z33Xc4e/YsRo8ejatXr2LkyJFmryzaNtQLTjK7fduJiIiIiKies9u0sXDhQgDArFmz0KFDB327v78/Pv74YwDA8uXLkZeXV6PrffjhhyguLka/fv3wzDPP6NtlMhk++eQTeHl54dixY9i5c6dZdUdxfzwiIiIiIrIiuwx56enpOHbsGABg5MiRBsd79OiB8PBwqFQq7Nixo0bX3LJlS5XXc3d3x5AhQwAAmzdvrm3ZAICocO6PR0RERERE1mOXIS8hIQEA4OvriyZNmhjt07Fjx0p9q1NQUKB/3q7iPHOuV52YcB+zziciIiIiIqqOXYa8S5cuAQAiIiKq7BMeHl6pb3UuX76s/7qqa5pyvarIpVKE+7rU+nwiIiIiIqI7MXkz9PqgoKAAAODm5lZlH3d3dwBAfn5+ja9X3TVrej2VSgWVSgWtVmtwzE0hg0QiuWM9REREREREtVWnI3k6nQ7btm3D0KFD6/K2derdd9/VL9JyO1dnu8zURERERERkR+ok5J07dw6zZs1CWFgYhg4dim3btpl1PQ+P8n3mioqKquxTWFgIAPD09Kzx9aq7Zk2vN3v2bOTl5aFTp04Gx9ycZXeshYiIiIiIyBxWG1oqLi7Ghg0bsGbNGvz+++8AoN9jztwpi40bNwYApKamVtmn4lhF3+pERkbqv05JSUG7du1qfT2FQgGFQgGZzDDQuSo4kkdERERERNZl8ZG8I0eOYNKkSWjUqBEmTJiAQ4cOQQgBT09PjBkzxiL3iImJAQBcv369yoVQjh8/DgCV9tCriqenJ5o3b17pPHOuVxW5lM/jERERERGRdVkk5GVlZWHx4sVo06YNYmNj8fnnn6OgoAAymQyDBw/Ghg0bkJmZiRUrVljidggLC9NPh4yLizM4fvDgQaSmpkKhUGDgwIE1uuawYcOqvF5hYaF+iukjjzxS27KJiIiIiIisrtYhTwiB7du3Y/jw4QgLC8Mrr7yCU6dOQQiBe+65B0uXLkVGRgZ+/PFHPProo1AoFJasG6+99hoA4L333kN8fLy+/fr165g8eTIAYOrUqfDy+nfz8S1btqBVq1bo27evwfVeeOEFuLq6Yvfu3Vi9erW+XavVYvLkycjNzUWnTp3Qv39/i74OIiIiIiIiSzL5IbELFy7g888/x7p163DlyhX9c3YREREYNWoUxowZg5YtW1q80NsNHToU06ZNw9KlS9G1a1f07dsXbm5u2LNnD3JzcxEbG4sFCxZUOicvLw9nzpxBaWmpwfVCQkKwdu1ajBgxAs888wzWrFmDxo0b49ixY7h48SKCgoIQFxfHLRCIiIiIiKheMznktWjRAhKJBEIIeHh44NFHH8WYMWPQs2dPa9RXrY8++gixsbFYsWIFfv/9d6jVajRr1gyzZs3CjBkz4OzsbNL1HnvsMTRt2hQLFy7EgQMHkJCQgEaNGmHKlCmYM2cOgoKCrPRKiIiIiIiILEMiKobiakgqlUIikSA2Nhbr169HREREjc8tKiqCh4cHJBKJ0c3CHUm3bt1w5MiRSm1du3bF4cOHbVQRERERERE1BCY/k6dQKCCEwKFDh9CiRQsMGTIEGzduhEqlskZ9REREREREZAKTQ96VK1ewdOlSREVFQa1W46effsKTTz6JoKAgTJo0Cb/99ps16iQiIiIiIqIaMHm65q0SEhLw2Wef4ZtvvkFubq5+UZLw8HCMHj0ao0ePRqtWrfT9G9J0zaCgIFy7dq1Sm7u7O9q2bWujioiIiIiIqL5p27ZtpdX9LcGskFdBpVJh06ZNWLNmDX777TcIIfSBr0OHDhg7diyefPJJuLi4NJiQ5+HhgcLCQluXQURERERE9Zg11u2wSMi71aVLl/RbLKSlpZXfRCKBXC5Hr169sGvXLoY8IiIiIiIi2EnIqyCEwC+//ILPPvsM27Ztg1qt1m+9wJBHRERERERknZBn8sIrNSWRSPDggw9i06ZNSE9Px/vvv4/WrVtb63ZEREREREQEK47kVeXIkSP4/PPPsWrVqrq8bZ1r1aoV0tPTIYRAUVER3Nzc4ObmhqZNm9q6NIeg1Wpx7NgxdOrUCTKZzNblOAy+r9bD99Y6+L5aB99X6+F7ax18X62H76113Pq+RkVF1c+FV6hq+fn58PLyQl5eHjw9PW1djsPg+2odfF+th++tdfB9tQ6+r9bD99Y6+L5aD99b67D2+2q16ZpERERERERU9xjyrEyhUGDu3LlQKBS2LsWh8H21Dr6v1sP31jr4vloH31fr4XtrHXxfrYfvrXVY+33ldE0iIiIiIiIHwpE8IiIiIiIiB8KQR0RERERE5EAY8qxk48aN6NWrF3x8fODm5oaoqCj83//9H9Rqta1Ls0tqtRp79uzByy+/jE6dOsHb2xtOTk4IDg7GkCFDsH37dluX6DBeeeUVSCQSSCQSvP3227Yux+6VlZVh6dKl6NGjB3x9faFUKhEWFoYBAwbgu+++s3V5dislJQVTp05Fy5Yt4eLiAqVSiSZNmmDs2LFISkqydXn11pkzZ7Bs2TKMGzcO7dq1g1wur/H/67t378bAgQPh7+8PFxcXtGrVCq+//joKCwvroPL6zdT3VafT4ffff8ebb76JHj16wM/PD05OTvD398f999+Pr7/+Gnyaxrw/r7f6+OOP9Z9rEydOtFK19sWc91an02HdunXo168fAgICoFAo0KhRI/Tp0wcff/xxHVRff9X2fb1+/Tpmz56Ndu3awc3NDc7OzggLC8Njjz2G/fv3164YQRY3ffp0AUDI5XLRv39/8cgjjwhvb28BQPTo0UMUFxfbukS7s2vXLgFAABDBwcFi0KBB4vHHHxdt27bVtz/zzDNCp9PZulS7dujQISGVSoVEIhEAxIIFC2xdkl1LTU0Vd999twAg/P39xeDBg8UTTzwhunfvLlxdXcXw4cNtXaJdOnLkiPDw8BAARGhoqBgyZIgYNmyYaNKkif7v3g0bNti6zHqp4vPp9l93+n99yZIlAoCQSCTivvvuE4899pgIDg4WAETLli1FVlZWHb2C+snU9/XcuXP6Pr6+vqJ///7iiSeeEJ06ddK3Dx48WKhUqjp+JfVLbf+83urChQvCzc1N/7k2YcIEK1ZsP2r73ubm5or77rtPABCenp7iwQcfFE8++aS49957hbe3t7jnnnvq6BXUT7V5X8+fPy9CQkIEAOHn5ycGDhwoHn30UdG6dWv9+YsXLza5FoY8C9uyZYsAINzd3cWff/6pb8/KyhLt2rUTAMTMmTNtWKF92rNnjxg+fLjYv3+/wbFvv/1WyGQyAUCsW7fOBtU5hqKiItGiRQsRGhoqhg4dypBnpuLiYtGqVSsBQMybN0+UlZVVOl5UVCQSEhJsU5yda9++vf4fdm59X7VarXjjjTcEAOHt7S1KSkpsWGX9tHr1avHSSy+Jr7/+Wpw6dUo89dRTd/x/PT4+XkgkEiGTycSOHTv07UVFRaJv374CQIP/BwtT39fz58+LPn36iJ9//lloNJpKx/bt2yfc3NwEADF//vy6KL/eqs2f11tptVpx7733Cnd3dzF27FiGvFvU5r3V6XSiV69eAoB49tlnRUFBQaXjKpVKHDt2zNql12u1eV+HDBkiAIhBgwaJwsLCSsdWrlyp/8fL1NRUk2phyLOwin+Fe/vttw2OHThwQAAQCoVC5Obm2qA6xzVhwgQBQPTt29fWpditadOmCQBi+/bt+g9DhrzamzNnjj6IkOVkZ2fr/2Xz2rVrBsc1Go1wcXERAER8fLwNKrQvNfl//bHHHhMAxMSJEw2OXb58WUilUgFAnDp1ypql2hVz/w5dsGCBACCaNWtm4crsm6nva8UI9IoVK8TcuXMZ8qpRk/d2zZo1AoB44IEH6rAy+1aT99Xd3V0AEH/88YfR4y1atBAAxObNm026N5/Js6D09HQcO3YMADBy5EiD4z169EB4eDhUKhV27NhR1+U5tJiYGABAamqqjSuxT/v27cOyZcswZswYDBw40Nbl2D21Wo1PPvkEAPDyyy/buBrHYsp+Qv7+/laspGEoKyvTP/Ns7HMtMjISsbGxAIAtW7bUaW2OjJ9p5jtz5gxef/119OzZE88//7yty3EIS5cuBcDPNUtTKpU16mfqZxpDngUlJCQAAHx9fdGkSROjfTp27FipL1nGuXPnAACNGjWycSX2p7CwEE8//TSCgoLw4Ycf2rochxAfH4/s7GyEhISgefPm+OuvvzB//nw8++yzmDVrFrZv3w6dTmfrMu2Su7s77r33XgDAG2+8UWkxK51Oh3nz5qGkpAQDBgxAeHi4rcp0GGfPnkVxcTGAfz+/bsfPNcvjZ5p5tFotxo4dC4lEgjVr1kAikdi6JLt39epVJCUlQSaToXv37rh48SLee+89PPfcc3jppZewceNGlJWV2bpMuzRgwAAAwPz58/V/31ZYvXo1zp07h3bt2qFbt24mXVdusQoJly5dAgBERERU2afih46KvmS+zMxMrF27FgAwfPhw2xZjh1566SVcunQJW7ZsgY+Pj63LcQgnTpwAAISFhWHWrFn4v//7v0or5f33v/9FTEwMtm7dWu3fF2Tc6tWrMXDgQKxatQrbt29Hx44dIZPJkJCQgPT0dDz11FNYvny5rct0CBWfVd7e3vDw8DDah59rllVcXKwfMeFnWu0sWrQIR48exQcffIBmzZrZuhyHUPG55ufnh88++wwzZ840WDG+adOm2LJlC9q3b2+LEu3WokWL8M8//2D79u2IiIhA165d4erqir///hunT5/GoEGDsHr1asjlpsU2juRZUEFBAQDAzc2tyj7u7u4AgPz8/DqpydFpNBqMHj0aeXl5aNeuHZ599llbl2RXdu7ciZUrV+LJJ5/E0KFDbV2Ow7h+/TqA8pGN//73v5g8eTLOnDmDvLw87Nq1C3fddRcSEhIwaNAgbqtSCy1btsThw4fRv39/pKen44cffsDmzZtx6dIlNG/eHL169YKnp6ety3QI/Fyre5MnT8alS5cQEhKC1157zdbl2J2TJ09i7ty56N69O6ZNm2brchxGxedaTk4Opk2bhocffhh//fUXCgoKcPjwYXTp0gUXL17Egw8+qO9LNRMUFIR9+/Zh9OjRuH79OrZv346NGzfin3/+QWhoKPr06YOAgACTr8uQR3btueeew549e+Dn54dNmzbB2dnZ1iXZjby8PEyYMAEBAQFYtmyZrctxKBWjdmq1GiNGjMDy5ctx1113wdPTE/369cOuXbugVCpx8uRJfPvttzau1v4cOnQI7dq1w8mTJxEXF4fMzEzk5ORg27ZtUKvVmDBhAiZMmGDrMolMtmDBAqxbtw5KpRIbNmyAn5+frUuyKxqNBmPHjoVUKsXnn38OqZQ/5lpKxeeaRqNBt27dsHHjRrRt2xbu7u7o2rUrdu3ahaCgIFy5cqXB75VnqtOnTyMmJgbbtm3Dxx9/jNTUVOTl5WHfvn0ICgrCzJkzMXDgQGi1WpOuyz/9FlQxlaWoqKjKPhWbxvJfmc03ffp0rFmzBj4+PvrREaq5F154AWlpaVi+fDkXqLCwW6e1GRtdjoiIwKBBgwCUbzBNNZebm4thw4YhKysLmzdvxogRIxAUFAQfHx8MHjwY//vf/+Dq6orPP/8ce/futXW5do+fa3VnyZIlePPNN6FQKLBlyxb9gjZUc++88w7i4+Mxf/58tGzZ0tblOJQ7fa55eHhg9OjRAPi5ZgqNRoPhw4fj/PnzWL16NZ5//nmEhYXB09MTPXv2xM6dOxEcHIxdu3bhyy+/NOnafCbPgho3bgyg+tWwKo5V9KXamTlzJpYuXQpvb2/s3LlTvxIZ1dyWLVsgl8vx8ccfG/yr2+nTpwEAa9aswe7duxEcHMwRJxM0bdrU6NfG+ly5cqVOanIU27dvR1ZWFpo1a4YuXboYHG/atCm6dOmCvXv3Yvfu3ejdu7cNqnQcFZ9Vubm5KCgoMPpcHj/XzLds2TLMnDkTzs7O+P777/Hggw/auiS7VLHC67Zt2wxWMb98+TKA8r9DevXqBaB8ZWmqGX6uWcfRo0fxzz//QKFQ4JFHHjE47uPjgwEDBuCLL77A7t27MX78+BpfmyHPgiqCxvXr13Hp0iWjK2weP34cANChQ4c6rc2RvPLKK1iyZAm8vLywc+fOKld8ozvTaDT47bffqjx++fJlXL58GZGRkXVYlf3r0KEDJBIJhBDIzs42uspjdnY2gH+fZ6KaSUlJAVD9qJGXlxeA8mdHyDwtW7aEq6sriouLcfz4caOhmZ9r5lmxYgWmTZumD3gVo/xUewcPHqzyWGZmJjIzM+uwGsdw1113wcPDAwUFBfrPr9vxc810FZ9prq6ukMlkRvvU9jON0zUtKCwsDJ06dQIAxMXFGRw/ePAgUlNToVAouBdZLc2aNQuLFi2Cl5cXdu3apX+/yXS5ubkQQhj9NXbsWADlz4cIIfT/Ako1ExwcjB49egAwPm1FrVbrw3Xnzp3rtDZ7FxoaCqB8tDkvL8/guFqtRnx8PABUuZUN1Zyzs7M+dBj7XEtOTsbvv/8OABg2bFid1uYIPv30U0ydOlUf8AYPHmzrkuxaYmJilZ9rc+fOBQBMmDBB30Y1J5fL9Qu0VTUdc9euXQD4uWaKis+0Gzdu6LdOud3Ro0cBmP6ZxpBnYRUrYb333nv6HzSA8tG9yZMnAwCmTp2qT+VUc2+88Qb++9//wtvbmwGP6r2KHyjeffddHDlyRN+u0Wgwc+ZMXLx4ER4eHiZNvaDy/YTc3NxQUlKCSZMm6Z8HA8o37p4xYwZSUlLg5OSERx991IaVOo5Zs2ZBIpHgiy++wP/+9z99e3FxMSZMmACtVovhw4ejVatWNqzS/qxevRqTJ09mwCO78dprr8HJyQmrV6/GTz/9VOnYokWLcPDgQchkMkyZMsVGFdqfbt266YPexIkTkZWVpT+m0+nw3nvv4fDhwwCAESNGmHRtieA/ZVjc9OnTsXTpUjg5OaFv375wc3PDnj17kJubi9jYWOzatQsuLi62LtOu/Pjjj3j44YcBlG+826ZNG6P9/P398f7779dlaQ5p3LhxWLduHRYsWIA33njD1uXYrbfffhtz5syBXC5H586dERwcjPj4eFy+fBkuLi7YuHEjp2bVwvr16zF+/HhoNBoEBASgU6dOcHJywvHjx5Geng6pVIoVK1bgueees3Wp9U58fLz+HxwB4MKFC8jOzkZYWJj+Bw2g/NmmWzfi/uCDD/Diiy9CIpGgZ8+eCAwMxIEDB3DlyhW0bNkSBw8ebNALOJn6viYmJqJDhw4QQqBVq1ZGny+tULEPbENU2z+vxsybNw/z58/HhAkT8Nlnn1mtZntR2/d23bp1ePrpp6HT6dCxY0c0btwYJ0+exOnTpyGTyfDJJ59g0qRJdfpa6pPavK+//vorHnroIRQXF8PT0xNdunSBh4cHkpKScOHCBQDlAfudd94xrRhBVvHdd9+J++67T3h6egoXFxfRtm1b8d577wmVSmXr0uzSF198IQDc8VdkZKStS3UIY8eOFQDEggULbF2K3fvll1/EgAEDhK+vr3BychLh4eFi3Lhx4tSpU7Yuza4lJiaKcePGiaZNmwqFQiGcnZ1FZGSkGDVqlDh69Kity6u39u7dW6O/Sy9dumRw7q5du8SDDz4ofH19hUKhEC1atBCzZ88W+fn5df9C6hlT39ea9m/oP6aZ8+f1dnPnzhUAxIQJE6xfuB0w5739448/xPDhw0VgYKBwcnISwcHB4rHHHuPfvaL27+uFCxfElClTRKtWrYSLi4twcnISISEhYtiwYWLnzp21qoUjeURERERERA6Ez+QRERERERE5EIY8IiIiIiIiB8KQR0RERERE5EAY8oiIiIiIiBwIQx4REREREZEDYcgjIiIiIiJyIAx5REREREREDoQhj4iIiIiIyIEw5BERERERETkQhjwiInIo+/btg0QigUQiqdNzHVGvXr0gkUgwb948W5dCREQmkNu6ACIish1zwswXX3yBcePGmXROYmIitm7dCm9vb7zwwgu1vrct2HPtt9u6dSsSExMRHR2NoUOH2rocIiKyMIY8IqIGLCgoyGh7YWEhioqKqu3j4uJi8v0SExMxf/58REZG1sug5OrqipYtWxo9Vt9rN8XWrVuxbt06jB07ttqQFxERgZYtW8Lf37/uiiMiIrMx5BERNWCZmZlG2+fNm4f58+dX28cRde7cGadPn7Z1GfXGl19+aesSiIioFvhMHhERERERkQNhyCMiolpJSEjAmDFjEBkZCaVSCR8fH3Tv3h0ffvghVCqVQX+JRILx48cDAJKTk/ULnFT8unVxj+LiYnzzzTcYM2YMoqOjERAQAIVCgZCQEAwdOhQ///yzVV5TVQuvmFJ7haysLLzxxhuIiYmBl5cXlEolmjZtigkTJuDvv/+u0f0TEhIwatQohIWFwcnJCb169dL3zczMxLJly/Dwww+jdevW8PLygouLC5o3b46JEycavUfF9detWwcAWLduncFr2bdvn75/TRZe2bx5MwYPHoygoCA4OzsjKCgIgwcPxpYtW6o8Z9y4cZBIJPpnOjdt2oRevXrB19cXrq6uiI6OxkcffQSdTlflNb777jsMGDAAQUFBcHJygre3N1q0aIEhQ4ZgxYoVKC0trfJcIiKHJ4iIiG4zd+5cAUBU9TGxZMkSIZFI9H28vLyEk5OT/vv27duLjIyMSucEBQUJT09PAUBIpVIRFBRU6deiRYv0fb/44gv9tSQSifDy8hKurq76NgBi5syZRmvbu3dvtbVXp6pzTaldCCF27dolvL299ddycnISbm5u+u+dnZ3FunXrqr3/pk2b9O+pp6enUCqVomfPnvq+Y8eO1feVy+XC19dXyOVyfZtCoRCbNm2qdP1Dhw6JoKAgoVQqBQChVCoNXsuhQ4f0/Xv27CkAiLlz5xrUqlKpxBNPPKG/n1QqFT4+PkIqlerbRowYIcrKygzOrah97NixYsqUKfrzb33PAIgxY8YY/e80fvz4Sv3c3d0N/nxcunTJ6LlERA0BQx4RERmoLuRt27ZNf+zhhx8WFy9eFEKU/9D/5ZdfCg8PDwFAdO/eXWg0mkrnVoS3yMjIau+/detW8dJLL4mDBw+KoqIifXtGRoaYP3++Pvz88MMPBudaI+SZUvuJEyeEi4uLACAmTZok/vnnH/37kJycLCZPnqwPZseOHavy/u7u7mLgwIHi1KlT+uNnz57Vf71gwQKxaNEi8ddffwm1Wi2EEEKr1YqTJ0+KUaNGCQDCzc1NpKenG9R4a8iqTnUhb+bMmfoQPmfOHHHjxg0hhBA5OTnitdde07+OV199tcr7+/j4CGdnZ7FkyRKRl5cnhBAiOztbTJw4UX/+nj17Kp174MABfSj873//K65fv64/lp2dLX755RcxduxYo6+biKihYMgjIiID1YW81q1bCwDi3nvvNQhxQgjx448/6s/duHFjpWM1DUp3smjRIgFA9O3b1+CYrUNenz59BAAxe/bsKvtMmzZNH5Krun/nzp2Nvr81NWjQIAFALFiwwOCYuSEvLS1NP2pY1et88cUX9aOYt4/q3joK+cUXXxg9/5577hEAxMSJEyu1//e//xUARP/+/autnYioIeMzeUREVGMnTpzAqVOnAABvvPEGZDKZQZ+HHnoInTt3BgB88803Vqlj0KBBAIDDhw9Dq9Va5R61cfnyZfz666+Qy+V46aWXquw3ZswYAMDu3burrP/ll182+v7WVMV7dPDgwVpfoyrff/89NBoNlEolZs2aZbTPG2+8AYVCAbVajU2bNhntEx4ejrFjxxo9NmTIEADlf+Zu5e3tDaD8mcf69N+eiKg+4RYKRERUY8ePHwcAyOVy9OzZs8p+999/P/744w99/9q4evUqPv74Y+zcuRNnz55FXl6ewQ/1xcXFuHHjRr3Zx+3QoUMAAJ1Oh7vvvrvKfhWvo6ioCNevX0dgYKBBn9jY2DveLykpCStXrsTBgwdx+fJlFBYWQghRqU9aWpopL6FGKv67durUCZ6enkb7+Pj4oGPHjjh06FCVfw46depksMhNhZCQEABATk5Opfa+fftCqVQiISEB9957LyZMmIA+ffqgSZMmtX05REQOhyGPiIhq7Nq1awAAf39/KBSKKvuFhYVV6m+qw4cPY+DAgcjNzdW3ubu7w9XVFRKJBFqtFtnZ2QDKg1J9CXkZGRkAykPe1atXa3ROcXGx0XZjwe9Wy5cvx/Tp0/UrUEokEnh5een/u5SUlCA/P1+/qb0lVfx3DQ0Nrbbfnf4ceHh4VHmuXF7+I4para7U3qxZM3z22Wd47rnncPjwYRw+fBgAEBAQgN69e2PkyJEYMmRIleGRiKgh4HRNIiKqVzQaDUaMGIHc3FxER0djx44dyM/PR0FBAa5evYrMzEwcOXJE3//2kStbqhihCwoKgih/7v2Ovxo3bmz0WtVN1Tx16hReeOEF6HQ6PPbYY/jjjz9QWlqKGzduIDMzE5mZmViyZAmA+vX+WMqoUaOQnJyMTz/9FE888QTCw8ORlZWFDRs2YOjQoejZsyfy8/NtXSYRkc0w5BERUY1VjC5lZ2cb3QuvQsUUwTuNRhlz+PBhJCcnQyaT4aeffsKAAQMMRnwyMzNNvm5dCA4OBlD+/lhjBK3Cpk2boNVq0bp1a3z77bfo1KkTnJ2dK/Wx5ntU8d/1TlNBzflzcCe+vr549tln8e233yIlJQXnz5/HrFmzIJFIcODAgWr39iMicnQMeUREVGMdO3YEUD7a9ttvv1XZb/fu3QDKn7m6lVRa/rFT3ehSamoqgPLpd1VNB6y4fl2qSe0Vz9FptVqrbdgO/PseRUVF6eu6XXXvUU1eS3Uq/hwcP34ceXl5Rvvk5uZWenbP2po1a4Z3330XI0eOBADs2rXL6vckIqqvGPKIiKjG2rdvr19Q5O233za6uuGOHTtw9OhRAMCIESMqHatYpOPWZ+1u5+XlBaB84RVjz7WlpaVh6dKltarfHDWpvUWLFujVqxcA4PXXX68yAFW4fVGRmqp4j/766y+jQe3nn3/Gvn37qjy/Jq+lOsOHD4dcLkdpaSn++9//Gu2zcOFCqFQqODk5Yfjw4bW6jzHVjSADgIuLCwBUGX6JiBoC/g1IREQmqfih/sCBA3j00Udx6dIlAOULZHz99df6YNe9e3cMHTq00rlt27YFAOTn52PDhg1Gr9+jRw+4ublBCIHHH38cZ8+eBVA+OvbLL7+gV69eNllUoya1A8CyZcvg7u6Os2fPomvXrvjhhx9QWlqqP56eno6vvvoKffv2xauvvlqrWh588EEAwN9//40pU6bow2JRURFWrlyJRx99FH5+fnd8LQcOHMDp06dNvn9oaCimT58OAHjvvfcwd+5cfWDMzc3FnDlzsGjRIgDAiy++iEaNGpl8j6pMnToVjz/+OL7//vtKC7oUFhbi008/xZdffgng3y0kiIgapLremI+IiOq/6jZDF0KIJUuWCIlEou/j7e0tnJ2d9d+3a9dOpKenGz23b9+++n4eHh4iMjJSREZGig8++EDf55NPPtH3ASDc3d2FUqkUAIS/v3+lDdcvXbpU6frW2gy9prULIcTBgwdFcHCwvq9MJhN+fn7CxcWl0uu6faNvU2p/8sknK13L29tbyGQyAUDcc889YtmyZVVu3p6TkyMCAgL05/r7++tfy+HDh/X9qtoMXQghVCqVePzxx/XXkEqlwsfHR0ilUn3biBEjRFlZmcG5NdmMvarN52/dSL3iz4a3t3elth49eojCwsI7vodERI6KI3lERGSyGTNm4Pjx4xg9ejTCw8NRXFwMFxcXdO3aFR988AGOHTum3+fsdps2bcKMGTNw1113Qa1WIzk5GcnJyZWmDj733HPYvn07evXqBXd3d2g0GoSGhuI///kPkpKS0K5duzp6pabXDpQ/m3f27Fm8//77uO++++Dt7Y3c3FzIZDK0bt0ao0ePxtdff40PP/yw1rVUnN++fXsoFApotVq0a9cO7777Lg4dOgR3d/cqz/Xx8cH+/fvx5JNPIjQ0FHl5efrXcuuoY3WcnZ3x3XffYdOmTRgwYAD8/PxQUFAAPz8/DBgwAJs3b0ZcXBycnJxq/RqNmTNnDpYuXYphw4ahVatWkMvlKCwsRGBgIO6//358/vnn2LdvH9zc3Cx6XyIieyIRwgHXViYiIiIiImqgOJJHRERERETkQBjyiIiIiIiIHAhDHhERERERkQNhyCMiIiIiInIgDHlEREREREQOhCGPiIiIiIjIgTDkERERERERORCGPCIiIiIiIgfCkEdERERERORAGPKIiIiIiIgcCEMeERERERGRA2HIIyIiIiIiciAMeURERERERA6EIY+IiIiIiMiB/D/F8gxsHq3vhQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptRNA_perturb=np.load('./data/highestgene-IterativeSequentialAdditive-Monotonicity_n10m300_ptRNA_perturb.npy')\n",
    "elongt_perturb=np.load('./data/highestgene-IterativeSequentialAdditive-Monotonicity_n10m300_elongt_perturb.npy')\n",
    "baseline_elongt=np.load('./data/highestgene-IterativeSequentialAdditive-Monotonicity_n10m300_baseline_elongt.npy')\n",
    "mono_perturb=np.load('./data/highestgene-IterativeSequentialAdditive-Monotonicity_n10m300_mono_perturb.npy')\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(9,3))\n",
    "\n",
    "ax.plot(np.arange(len(mono_perturb)),mono_perturb,linewidth=4)\n",
    "ax.spines['left'].set_linewidth(4)\n",
    "ax.spines['bottom'].set_linewidth(4)\n",
    "ax.spines['top'].set_linewidth(0)\n",
    "ax.spines['right'].set_linewidth(0)\n",
    "\n",
    "ax.set_ylabel('Monotonicity (%)',fontsize=18)\n",
    "ax.set_xlabel('Total iterations',fontsize=18)\n",
    "\n",
    "ax.tick_params(axis='y', which='major', labelsize=16)\n",
    "ax.tick_params(axis='x', which='major', labelsize=16)\n",
    "ax.set_ylim(50,102)\n",
    "ax.set_xlim(0,18)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('./Figures/S12g.svg', dpi=300,bbox_inches='tight')\n",
    "\n",
    "#Elongation latency % plot\n",
    "fig,ax = plt.subplots(figsize=(9,4))\n",
    "ax.plot(np.arange(len(elongt_perturb)),(elongt_perturb-baseline_elongt)/baseline_elongt*100,linewidth=4)\n",
    "ax.spines['left'].set_linewidth(4)\n",
    "ax.spines['bottom'].set_linewidth(4)\n",
    "ax.spines['top'].set_linewidth(0)\n",
    "ax.spines['right'].set_linewidth(0)\n",
    "ax.set_xlim(0,18)\n",
    "\n",
    "ax.set_ylabel('$\\Delta$ Elongation latency (%)',fontsize=18)\n",
    "ax.set_xlabel('Total iterations',fontsize=18)\n",
    "\n",
    "ax.tick_params(axis='y', which='major', labelsize=16)\n",
    "ax.tick_params(axis='x', which='major', labelsize=16)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('./Figures/S12h.svg', dpi=300,bbox_inches='tight')\n",
    "\n",
    "\n",
    "#Resort ptRNA_min and plot - plot iter 0\n",
    "ptRNA_list_fast=np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_ptRNA.npy')\n",
    "elongt_list_fast =np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_elongt.npy')\n",
    "\n",
    "ptRNA_min = ptRNA_list_fast[np.argmin(elongt_list_fast)]\n",
    "ptRNA_min_dict = dict(zip(tRNA_tags,ptRNA_min))\n",
    "ptRNA_min_sorted=[ptRNA_min_dict[tRNA] for tRNA in tRNA_sorted]\n",
    "fig,ax = plt.subplots(figsize=(9,5))\n",
    "ax.bar(np.arange(40),np.array(ptRNA_min_sorted)*100,color='darkseagreen',width=0.8,alpha=0.5)\n",
    "ax.bar(np.arange(40),ptRNA_perturb[0]*100,color='#39568CFF',width=0.8,alpha=0.5)\n",
    "ax.plot(np.arange(40),[1/40*100]*40,color='black',linewidth=3,linestyle='dashed',alpha=0.5)\n",
    "plt.xticks(range(len(tRNA_tags)), tRNA_sorted,rotation='vertical')\n",
    "\n",
    "ax.spines['left'].set_linewidth(4)\n",
    "ax.spines['bottom'].set_linewidth(4)\n",
    "ax.spines['top'].set_linewidth(0)\n",
    "ax.spines['right'].set_linewidth(0)\n",
    "ax.set_xlim(-2,40)\n",
    "ax.set_ylabel('Frequency (%)',fontsize=25)\n",
    "ax.tick_params(axis='y', which='major', labelsize=20)\n",
    "ax.tick_params(axis='x', which='major', labelsize=13)\n",
    "ax.set_ylim(0,8.52)\n",
    "plt.tight_layout()\n",
    "\n",
    "#Resort ptRNA_min and plot - plot final iter\n",
    "ptRNA_list_fast=np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_ptRNA.npy')\n",
    "elongt_list_fast =np.load('./data/UniformRandomtRNA_WTCodonUsage_stratifyhighest_2ksample_fasttRNA_elongt.npy')\n",
    "\n",
    "ptRNA_min = ptRNA_list_fast[np.argmin(elongt_list_fast)]\n",
    "ptRNA_min_dict = dict(zip(tRNA_tags,ptRNA_min))\n",
    "ptRNA_min_sorted=[ptRNA_min_dict[tRNA] for tRNA in tRNA_sorted]\n",
    "fig,ax = plt.subplots(figsize=(9,5))\n",
    "ax.bar(np.arange(40),np.array(ptRNA_min_sorted)*100,color='darkseagreen',width=0.8,alpha=0.5)\n",
    "ax.bar(np.arange(40),ptRNA_perturb[-1]*100,color='#39568CFF',width=0.8,alpha=0.5)\n",
    "ax.plot(np.arange(40),[1/40*100]*40,color='black',linewidth=3,linestyle='dashed',alpha=0.5)\n",
    "plt.xticks(range(len(tRNA_tags)), tRNA_sorted,rotation='vertical')\n",
    "\n",
    "ax.spines['left'].set_linewidth(4)\n",
    "ax.spines['bottom'].set_linewidth(4)\n",
    "ax.spines['top'].set_linewidth(0)\n",
    "ax.spines['right'].set_linewidth(0)\n",
    "ax.set_xlim(-2,40)\n",
    "ax.set_ylabel('Frequency (%)',fontsize=25)\n",
    "ax.tick_params(axis='y', which='major', labelsize=20)\n",
    "ax.tick_params(axis='x', which='major', labelsize=13)\n",
    "ax.set_ylim(0,8.52)\n",
    "plt.tight_layout()\n",
    "fig.savefig('./Figures/S12f.svg', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5d6f4fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}