03_polylinker_qc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QC and processes raw barcode counts from both library 1 and 2 with the Polylinker\n",
    "This notebook takes raw barcode counts and processes it into activity scores for each replicate. Due to the low expression of a large fraction of the libraries, including the scrambled sequences and basal construct, we cannot process the library with the same statistical rigor as we did with the Rho promoter. There are three biological replicates of RNA and one DNA replicate of the input plasmid pool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "sys.path.insert(0, \"utils\")\n",
    "import modeling, plot_utils, quality_control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_utils.set_manuscript_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>DNA</th>\n",
       "      <th>RNA1</th>\n",
       "      <th>RNA2</th>\n",
       "      <th>RNA3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AACAACAAG</th>\n",
       "      <td>chr7-141291911-141292075_UPPP_MUT-allCrxSites</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAACGTT</th>\n",
       "      <td>chr19-16380352-16380516_CPPN_MUT-allCrxSites</td>\n",
       "      <td>990</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAACTAC</th>\n",
       "      <td>chr1-44147572-44147736_UPPP_MUT-allCrxSites</td>\n",
       "      <td>1056</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAACTCG</th>\n",
       "      <td>chr12-116230818-116230982_CPPE_WT</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAACTGT</th>\n",
       "      <td>chr5-65391346-65391510_CPPP_MUT-allCrxSites</td>\n",
       "      <td>1653</td>\n",
       "      <td>1441</td>\n",
       "      <td>9</td>\n",
       "      <td>4695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAAGCTT</th>\n",
       "      <td>chr15-97965790-97965954_CPPP_MUT-allCrxSites</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAAGGCG</th>\n",
       "      <td>chr1-59164069-59164233_CPPE_WT</td>\n",
       "      <td>628</td>\n",
       "      <td>2209</td>\n",
       "      <td>3159</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAAGTAG</th>\n",
       "      <td>chr2-158513919-158514083_CPPE_WT</td>\n",
       "      <td>829</td>\n",
       "      <td>2382</td>\n",
       "      <td>3225</td>\n",
       "      <td>2807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAAGTCT</th>\n",
       "      <td>chr11-58097684-58097848_UPCP_MUT-allCrxSites</td>\n",
       "      <td>2491</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATAGG</th>\n",
       "      <td>chr16-33682556-33682720_CPNE_MUT-allCrxSites</td>\n",
       "      <td>3835</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATCAC</th>\n",
       "      <td>chr8-120493050-120493214_CSRE_MUT-shape</td>\n",
       "      <td>1129</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATGAG</th>\n",
       "      <td>chr1-162570536-162570700_CSPP_MUT-shape</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>367</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATGCT</th>\n",
       "      <td>chr12-86553474-86553638_CPRN_WT</td>\n",
       "      <td>949</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATGTC</th>\n",
       "      <td>chr4-59668207-59668371_CPPE_MUT-allCrxSites</td>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACAATTCA</th>\n",
       "      <td>chr1-111864309-111864473_UPCQ_MUT-allCrxSites</td>\n",
       "      <td>220</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACACAGCC</th>\n",
       "      <td>chr9-27191668-27191832_CPPE_WT</td>\n",
       "      <td>1678</td>\n",
       "      <td>1814</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACACAGGG</th>\n",
       "      <td>chr13-41249684-41249848_CSPP_scrambled</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACACATAC</th>\n",
       "      <td>chr9-59660499-59660663_CPPP_WT</td>\n",
       "      <td>1456</td>\n",
       "      <td>9321</td>\n",
       "      <td>16649</td>\n",
       "      <td>12466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACACATGT</th>\n",
       "      <td>chr19-3321456-3321620_UPPE_WT</td>\n",
       "      <td>2054</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACACCAAT</th>\n",
       "      <td>chr4-119142639-119142803_UPPE_WT</td>\n",
       "      <td>347</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   label   DNA  RNA1   RNA2  \\\n",
       "barcode                                                                       \n",
       "AACAACAAG  chr7-141291911-141292075_UPPP_MUT-allCrxSites     3    20     15   \n",
       "AACAACGTT   chr19-16380352-16380516_CPPN_MUT-allCrxSites   990    10      9   \n",
       "AACAACTAC    chr1-44147572-44147736_UPPP_MUT-allCrxSites  1056     2      4   \n",
       "AACAACTCG              chr12-116230818-116230982_CPPE_WT     7     4      6   \n",
       "AACAACTGT    chr5-65391346-65391510_CPPP_MUT-allCrxSites  1653  1441      9   \n",
       "AACAAGCTT   chr15-97965790-97965954_CPPP_MUT-allCrxSites     4     0      1   \n",
       "AACAAGGCG                 chr1-59164069-59164233_CPPE_WT   628  2209   3159   \n",
       "AACAAGTAG               chr2-158513919-158514083_CPPE_WT   829  2382   3225   \n",
       "AACAAGTCT   chr11-58097684-58097848_UPCP_MUT-allCrxSites  2491     1      1   \n",
       "AACAATAGG   chr16-33682556-33682720_CPNE_MUT-allCrxSites  3835     0      2   \n",
       "AACAATCAC        chr8-120493050-120493214_CSRE_MUT-shape  1129    13     11   \n",
       "AACAATGAG        chr1-162570536-162570700_CSPP_MUT-shape    72     1    367   \n",
       "AACAATGCT                chr12-86553474-86553638_CPRN_WT   949     3      5   \n",
       "AACAATGTC    chr4-59668207-59668371_CPPE_MUT-allCrxSites  1827     1      1   \n",
       "AACAATTCA  chr1-111864309-111864473_UPCQ_MUT-allCrxSites   220    10      0   \n",
       "AACACAGCC                 chr9-27191668-27191832_CPPE_WT  1678  1814      5   \n",
       "AACACAGGG         chr13-41249684-41249848_CSPP_scrambled     5     0      0   \n",
       "AACACATAC                 chr9-59660499-59660663_CPPP_WT  1456  9321  16649   \n",
       "AACACATGT                  chr19-3321456-3321620_UPPE_WT  2054     0      0   \n",
       "AACACCAAT               chr4-119142639-119142803_UPPE_WT   347     3      9   \n",
       "\n",
       "            RNA3  \n",
       "barcode           \n",
       "AACAACAAG     21  \n",
       "AACAACGTT     10  \n",
       "AACAACTAC      3  \n",
       "AACAACTCG      0  \n",
       "AACAACTGT   4695  \n",
       "AACAAGCTT      2  \n",
       "AACAAGGCG    880  \n",
       "AACAAGTAG   2807  \n",
       "AACAAGTCT      1  \n",
       "AACAATAGG      6  \n",
       "AACAATCAC      5  \n",
       "AACAATGAG    513  \n",
       "AACAATGCT    344  \n",
       "AACAATGTC      0  \n",
       "AACAATTCA      1  \n",
       "AACACAGCC      3  \n",
       "AACACAGGG      0  \n",
       "AACACATAC  12466  \n",
       "AACACATGT      1  \n",
       "AACACCAAT      3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barcode_count_dir = os.path.join(os.getcwd(), \"Data\", \"Polylinker\")\n",
    "l1_barcode_count_files = [\"library1Plasmid.counts\", \"library1Rna1.counts\",\n",
    "                          \"library1Rna2.counts\", \"library1Rna3.counts\"]\n",
    "l2_barcode_count_files = [\"library2Plasmid.counts\", \"library2Rna1.counts\",\n",
    "                          \"library2Rna2.counts\", \"library2Rna3.counts\"]\n",
    "\n",
    "l1_barcode_count_files = [os.path.join(barcode_count_dir, i) for i in l1_barcode_count_files]\n",
    "l2_barcode_count_files = [os.path.join(barcode_count_dir, i) for i in l2_barcode_count_files]\n",
    "\n",
    "sample_labels = np.array([\"DNA\", \"RNA1\", \"RNA2\", \"RNA3\"])\n",
    "sample_rna_mask = np.array([False, True, True, True])\n",
    "rna_labels = sample_labels[sample_rna_mask]\n",
    "dna_labels = sample_labels[np.logical_not(sample_rna_mask)]\n",
    "n_samples = len(sample_labels)\n",
    "n_rna_samples = len(rna_labels)\n",
    "n_dna_samples = len(dna_labels)\n",
    "\n",
    "n_barcodes_per_sequence = 3\n",
    "results_dir = barcode_count_dir\n",
    "l1_output_prefix = os.path.join(results_dir, \"library1\")\n",
    "l2_output_prefix = os.path.join(results_dir, \"library2\")\n",
    "\n",
    "# Read in and join data\n",
    "all_sample_counts_l1_df = quality_control.read_bc_count_files(l1_barcode_count_files, sample_labels)\n",
    "all_sample_counts_l1_df.to_csv(f\"{l1_output_prefix}RawBarcodeCounts.txt\", sep=\"\\t\", na_rep=\"NaN\")\n",
    "all_sample_counts_l2_df = quality_control.read_bc_count_files(l2_barcode_count_files, sample_labels)\n",
    "all_sample_counts_l2_df.to_csv(f\"{l2_output_prefix}RawBarcodeCounts.txt\", sep=\"\\t\", na_rep=\"NaN\")\n",
    "all_sample_counts_l2_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Threshold barcode counts, assess reproducibility\n",
    "First we will remove DNA barcodes that are below the cutoff. After RPM normalization, we remove RNA barcodes that are missing in any replicate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barcodes missing in DNA:\n",
      "Sample DNA: 1722 barcodes\n",
      "1722 barcodes are missing from more than 0 DNA samples.\n",
      "Barcodes off in RNA:\n",
      "Sample RNA1: 0 barcodes\n",
      "Sample RNA2: 0 barcodes\n",
      "Sample RNA3: 0 barcodes\n",
      "0 barcodes are off in more than 0 RNA samples.\n",
      "There are a total of  92.122 million barcode counts.\n",
      "Barcodes missing in DNA:\n",
      "Sample DNA: 0 barcodes\n",
      "0 barcodes are missing from more than 0 DNA samples.\n",
      "Barcodes off in RNA:\n",
      "Sample RNA1: 5842 barcodes\n",
      "Sample RNA2: 11412 barcodes\n",
      "Sample RNA3: 9805 barcodes\n",
      "12991 barcodes are off in more than 0 RNA samples.\n",
      "Barcodes missing in DNA:\n",
      "Sample DNA: 2107 barcodes\n",
      "2107 barcodes are missing from more than 0 DNA samples.\n",
      "Barcodes off in RNA:\n",
      "Sample RNA1: 0 barcodes\n",
      "Sample RNA2: 0 barcodes\n",
      "Sample RNA3: 0 barcodes\n",
      "0 barcodes are off in more than 0 RNA samples.\n",
      "There are a total of  89.662 million barcode counts.\n",
      "Barcodes missing in DNA:\n",
      "Sample DNA: 0 barcodes\n",
      "0 barcodes are missing from more than 0 DNA samples.\n",
      "Barcodes off in RNA:\n",
      "Sample RNA1: 12647 barcodes\n",
      "Sample RNA2: 12055 barcodes\n",
      "Sample RNA3: 10999 barcodes\n",
      "13873 barcodes are off in more than 0 RNA samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cr//hhx/OJZdcwiuvvNK57bTTTmP37t20tLQA0NLSwp49ezjttNP6/f2SJEka+QxUOiA2btzIj370I0444YQu2w8//HC+9KUvcdNNN/Vau2LFChYuXMjFF19MLpfjxRdfzHQMTz75JMcccwynn356Sf3b29t58MEH+eAHP9hl++WXX955lWrFihVcfvnlmY5HkiRJI4+BSoPqoosuYuLEiRx77LHMmDGDL3/5y/v1+exnP8tvf/tbfvSjH+3X9tvf/pannnqKSy65hCOPPJKzzz57v1vuSvWHP/yBo446qt9+X/3qV5kyZQonnHAC7e3t3HfffV3aL7vsMh566CHeeecdvvvd73LZZZdlOh5JkiSNPAYqDar/+I//YOfOnTz99NPkcjm2bt26X5+xY8dy8803c/PNN+/Xdv/99/OBD3yAU045BYBLL72U73znO7zzzjtlH8sRRxyx36IWPfnCF77A9u3b2bJlC4888gjHH398l/bjjjuOE044gS996UvMnj2bY489tuxjkSRJ0shkoNIBceaZZ7Jo0SK+8IUv9Nj+d3/3d2zfvp1Vq1Z12b5y5Up+9atfMXPmTGbOnMn111/P1q1bWb16ddnHcPbZZ7Nx40aam5szfYZin/70p/na177Gpz/96QHvS5IkSSOHgUoHzHXXXcfjjz/OunXr9msbM2YMX/7yl/nnf/7nzm0//elPee2113jhhRdYu3Yta9eu5ZVXXuGSSy7pvO0vpcSePXt4++23AdizZw979+7t8ffPnj2ba665hoULF/L000/z9ttvs2fPHr773e/yT//0T2V9lr/927/lxz/+MZ/61KfKqpMkSdLIZqDSAVNVVcWnP/1pvvKVr/TYvnDhwi5znFasWMGFF17IySef3HmFaubMmXz+85/nhz/8IW+88Qatra2MGzeuc5W/cePGceKJJ/Z6DHfffTfXXnstn/vc55gyZQrHH388Dz/8MOeff35Zn2XcuHH85V/+JePGjSurTpIkSSNbpJQqfQyDqra2Ng3GLV6Shq6IeDGlVDtY+3PckEY+xw1J5Sp13PAKlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCmjkgJVRFwbEc0RsTci7ivaPisiUkS0F71uLmofGxGNEfFmRGyJiOu77ffsiMhFxO6IeCoiqkutlSRJkqRKG1Niv03A7cA5wLge2qeklPb1sH0ZMBuoBmYCT0XEL1JKayJiOrAK+AzwKHAb8D3gw/3VlnjMkiRJknRAlXSFKqW0KqX0H8Afytz/FcBtKaVtKaVXgW8Di/JtC4CWlNL3U0p76AhQ8yKipoRaSZIkSaq4wZpD1RoRGyPi3vyVJyJiKnAUsK6o3zpgbv793OK2lNIu4DVgbgm1XUTEVflbEpvb2toG6SNJGskcNySVy3FDUk8GGqi2AqfRcVveqcBE4MF824T8vzuK+u/I9ym0F7cVt/dX20VK6Z6UUm1KqbaqqirDx5A02jhuSCqX44aknpQ6h6pHKaV2oDn/4+8i4lpgc0RMBNrz2ycBe4re78y/b8//XKzQ3l+tJEmSJFXcYC+bngr7TSltAzYD84ra5wEt+fctxW0RMR44no55Vf3VSpI0pORyOZYsWUIul6v0oUga5RyP3lulLps+JiIOAw4GDo6Iw/LbzoiIEyPioIg4ArgbeDqlVLhVbyWwNCKm5hebuBK4L9/2MHBSRNTl930L8HJKKVdCrSRJQ0pDQwONjY00NDRU+lAkjXKOR++tUm/5WwrcWvTzZcCXgf8X+F/ADOBN4HFgYVG/W4FvAa3AW8A/F5Y9Tym1RUQd8C/AA8DPgItLqZUkaaipr6/v8q8kVYrj0XsrUkr99xpGamtrU3Nzc/8dJQ1bEfFiSql2sPbnuCGNfI4bkspV6rgx2HOoJEmSJGnUMFBJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJkiQpIwOVJEmSJGVkoJIkSZKkjAxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMSgpUEXFtRDRHxN6IuK9b29kRkYuI3RHxVERUF7WNjYjGiHgzIrZExPWDVStJGp1yuRxLliwhl8tV+lAkVZjjgYaCUq9QbQJuBxqLN0bEdGAVcDMwDWgGvlfUZRkwG6gG/gK4MSLOHWitJGn0amhooLGxkYaGhkofiqQKczzQUDCmlE4ppVUAEVELHFPUtABoSSl9P9++DNgaETUppRxwBbAopbQN2BYR3wYWAWsGWCtJGqXq6+u7/Ctp9HI80FBQUqDqw1xgXeGHlNKuiHgNmBsRvwOOKm7Pv79oEGolSaNUTU0Ny5cvr/RhSBoCHA80FAx0UYoJwI5u23YAE/NtdGsvtA20touIuCo/x6u5ra2trA8gaXRy3JBULscNST0ZaKBqByZ12zYJ2Jlvo1t7oW2gtV2klO5JKdWmlGqrqqrK+gCSRifHDUnlctyQ1JOBBqoWYF7hh4gYDxxPx9yobcDm4vb8+5ZBqJUkjUCu2CWpN44PGqpKXTZ9TEQcBhwMHBwRh0XEGOBh4KSIqMu33wK8nF9UAmAlsDQipkZEDXAlcF++bSC1kqQRyBW7JPXG8UFDVamLUiwFbi36+TLgyymlZRFRB/wL8ADwM+Dion63At8CWoG3gH9OKa0BSCm1Za2VJI1MrtglqTeODxqqIqVU6WMYVLW1tam5ubnShyHpAIqIF1NKtYO1P8cNaeRz3JBUrlLHjYHOoZIkSZKkUctAJUkqm5PDJQ2U44hGCgOVJKlslZgc3t/JV6F99erVnqRJw0Bf40jx/+/lBC9Dmiqh1EUpJEnqVInJ4YWTL4Dly5f32v7ss8+yYcOGXvtJGhr6GkeK/38H+vx/v7c6///Xe8VAJUkqW01NzXt+stJfiCtsr6uro6mpyZXApCGur3Gkp//fS/l/2pUAVQmu8idp2HG1LknlctyQVC5X+ZMkSZKkA8xAJUlDlJOrJZXDMUOqDOdQSdIQ5eRqSeVwzJAqw0AlSUOUk6sllcMxQ6oMb/mTpCGqsAJWTU1NpQ+loryNSSrNaB4zHCdUSV6hkiQNad7GJKk/jhOqJAOVJGlI8zYmSf1xnFAlGagkSUNaJR4iLGl4cZxQJTmHSpJGIecbSCrmmCBlZ6CSpFGoMN+goaFhvzZPrKTRp68xoTvHCKkrA5UkDSODdSJTX1/P4sWLe5xvUM6JlaSRoa8xobviMcJwJTmHSpKGlcFayaqv+QZO7pZGn3LmIBWPEa6uJxmoJGlYeS/CTqknVrlcjoaGBurr60flc2+k0ap4jCgekxwTNFp5y58kDSMH+sGd5dy+462BkorHpJ7GBG8J1GjgFSpJUqdybt/x1kBJxXoaE7wlUKOBgUqShrnBvM2mnJDkc1+koaeSt931NCb4hxeNBt7yJ0nD3GDeenegbin0th/pvTHUbsUtZUxxfNBw5xUqSRrmhsNfgL3tR3pvDIfxoDvHBw13gxKoIuJp4MPAvvym/y+ldGK+7RLgTmA68DiwOKX0Rr5tGrAc+CtgK/CPKaXvFO2311pJUofhcOvdcDzJk4aj4TAedOf4oOFuMG/5uzalNCH/KoSpucD/A1wOHAnsBr5ZVPMN4O1826XAt/I1pdRKkoaJA706oaThy/FBw92BnkN1KfBoSum/U0rtwM3AgoiYGBHjgTrg5pRSe0rpOeAROgJUn7UH+JglaUTqbZ6C8xck9STr2OCYotFmMAPVnRGxNSJ+EhH/R37bXGBdoUNK6TU6rkjNyb/2pZTWF+1jXb6mv1pJUi96O5npbbL6UJvELmloyPpcKccUjTaDtSjF/w38go7AczHwaEScAkwAdnTruwOYCLwLvNlLG/3UdhERVwFXARx33HGZP4Sk0WO4jhulLInc2wTv3uYpOH9BKs1wGjcGY/n0rM+VckzRaDMogSql9LOiH1dExELgPKAdmNSt+yRgJ/DHPtrop7b7778HuAegtrY2ZfgIkkaZ4TpuDORkprfJ6sNxErtUCcNp3BiMlfOyPlfKMUWjzYGaQ5WAAFqAeYWNEfF+YCywPv8aExGzi+rm5Wvop1aSRqX6+noWL17c78lMfX09DQ0NzmGQRqlSxoosHF+k/Q04UEXElIg4JyIOi4gxEXEp8OfAGuBB4PyI+Fh+EYqvAKtSSjtTSruAVcBXImJ8RPwpcCFwf37XvdYO9JglaSgpZwJ3qathlTKHwYnj0shV6ljR0zjQ39jgHCmpq8G4QnUIcDvQRsezpP4v4KKU0vqUUgvwf9IRjn5Px/yna4pqrwHG5dseAq7O11BCrSSNCN1PTkoJOv31KeWv054UScNfqX8YKWexmv7GhgN19UsargY8hyql1Aac1kf7d4Dv9NL2BnBRllpJGinq6+vZsWMH27dv75xI3tfch1wuxwUXXMCGDRt67VPq7y3+V9Lw09N40dOCFN37FfrU1dUBXceBuro6nn322c627pwjJXU1WKv8SZIyqqmpYfLkyTQ2NjJlypR+g85NN93Ehg0bqK6u7rXP0qVLaWpqYseOHfzgBz/o9fd6UiQNbf2t1lfqSnzd+/U1RjQ1NbFhwwaampo477zzBv9DSSPMgX6wrySpBMW30HSf9N39Vp2IAKC2tpaampoeb+VJKXX5V9Lw1N/td93nSq1evZonn3yS+fPndwlZ3fv1NUZ0v6XP+ZZSP1JKI+p16qmnJkkjG9Cchvm48eqrr6bFixenV199tce2o48+OgFp/vz5afHixQno7L9gwYJUV1fXWVvcXsr+pdFouIwb3f/fLffn2bNnJyAdffTRafbs2emxxx4r6ff0pacxRhoNSh03Kh6ABvtloJJGvuFyYtSXwgnKggUL9jupKbQBqbq6uvPE57HHHus8WTI8SeUZLuNGf+Gl+9hRV1fXpX9hnCj8UWb27NkppYGNE44xGq1KHTecQyVJB0hfcx8Kt9Ls2LGjx7kOr7/+OuvXr+eb3/xm5606S5YsYcOGDcyePZv6+npyuRw33XQTEcHtt9/e7/LIkoa+/uZQdh87FixYwIIFC9ixYwe5XI73v//9fOxjH+P000/na1/7Gl//+teBvh/02988LedbSn0zUEnSICo+MenrBKZwgpLL5Zg8efJ+cx1+/OMfd+5vyZIl1NfXdznRqqmpYcmSJaxatQqAyZMne8IjjQDFcyh7CzgAixcv7hw7CmPN5MmTATrHnfXr13f27yuo9beyqKS+GagkaRAVn5iUsix5fydP3U90ik926uvr2b59OxHh0ufSCNJXwCllBb/u76Hnq0x9LZ0uqXQGKkkaRN2vInV/LkxdXR1NTU19Ph+me//i/RarqamhqalpQMfb360+kt57ff0xpvgZUd3HCej99rxSnk0lKRsDlSQNop5CVPEtOc8+++x+D+Qt5Tkyxbf+DWbw8YRKGnr6upq0Y8eOzmdEAb2OK8U1vd2C7MO9pcFhoJKkA6T4BKbwV+UbbriBF154ocfnw0DvV6YOVPDxhEoaHgpjwIIFC7o8IwrovPJdV1fX5Q8vN910E6tWrWL79u3ccccdAL2OPZKyM1BJ0iArhKLTTz+dJ598ktdff53GxkY2bNjQuepW8a03pSxkcaCCjydU0tCXy+V4/fXXOfroo9m5cyd33HEHNTU1rF69uvP2v8JKoI2NjWzfvp0pU6awc+dOoONh4P6/Lh1ApaytPpxePodKGvkY4s+TKTwXZty4cZ3Pk6qqqkpjxozpfLYUkOrq6lJKab8H99bV1aUFCxb4zBdpEA31caOg8Bypf/3Xf+0cEz760Y92jiUUPXOq8Fy6wrOmCrXz58/v9Tl3kkpX6rjhFSpJGmQdYzC89dZbAIwdO5a2tjYAxo0bx5YtWwD46U9/yurVq9mxYwcLFizovGI1efJkGhsbmTJlin9RlkaJwpXqNWvWsGnTJm644QZ27doFwEsvvdTZb/z48Zx++umVe5iJAAAgAElEQVR84hOfYPz48YwfP54bbrgBgKamJjZs2MDJJ5/ceVugi81IB56BSpIGSeGEaMmSJQDs3LmTiRMn8txzz/H73/+egw8+mKlTp7Jp0yYANm3axHXXXceGDRuoq6tj6dKlpJRYsmQJ27dv73xQZ08nRK7OJ40shdt9q6qqAJg9ezYf+tCHOh/i/fzzzxMR7Nq1izvvvJPW1tbO2pUrV/LCCy90mXvpuCC9dwxUkjRIiuc/NTU1dYaeadOm8fvf/57Zs2dz7LHHsm/fPlJKnHLKKVx33XU0NTWxffv2zof0TpkyhSlTpnQ+qLOnq1SuzieNLIX5ka+//jqPP/44VVVV5HI5amtrqa+vp62trXMlv3feeYf58+fz3HPP8dZbb7Fu3Tqef/55wPFAqgQDlSQNksKDdgtXlv7+7/+exx9/nLFjxwIdJ0q5XI7q6mpaW1tpaWmhsbGRxYsX09jYyPz585k4cWKfD+fsvt3V+aSRobBoRC6X46abbuLFF1/svAq1bNkyampqGDduHBMmTGDTpk2ce+65/OAHP+C6667rcfVQSe+dKNzrP1LU1tam5ubmSh+GpAMoIl5MKdUO1v4Ga9zI5XKcffbZbNq0iY9+9KP87Gc/49133+Wwww7j2GOP7TzpKdy+UzB79mw2bNjA4sWL/euydIAM1XEDOsaOhQsX8vOf/5yTTz6ZqqoqHn/8cQ499FDefvvtLn2PPvpozj333F5v6/N2YGnwlDpuHPReHIwkjTSFB+3mcrnObUuWLOmcH/X888/z7rvvArBnzx7Wr1/PmWeeCXRMKgeoqqqiurqaG264Yb/nyvT1eySNHKtXr+aMM85g7dq1vPvuu6xdu5YnnngCgPe9730cfPDBHHLIIZ39586d2/mIhZ7GhcLtwA0NDe/ZZ5BGO2/5k6QMCictr7/+OuvXr2fcuHG9hp4/+ZM/6byNZ9WqVcyfP5+6ujqam5tpbW3lhRde6PXKlHOlpJGnMB5s2bKF//mf/+GPf/xjl/bC3UO/+c1vSClx6KGHcv7553cudLN06VKampqA/ccFbweW3nsGKknKoL6+nh07drB69erO5dF78+tf/5qGhgYiAoBJkyaRUqK1tZXq6uo+T3w8OZJGnoaGhs5FaPpSCFYf/OAHaWpq6nxw74IFC3q9qu0DfKX3noFKkjL41a9+VVKYgo45D4UTn8mTJ1NXV8c111wDQG1tbZ/zHDw5kkaWXC7H9u3bGTNmDPv27eu3/6RJkzrHgOI/sDg/Sho6nEMlSWVavXo1F154YUlhCuCQQw7hmWee4YILLqCuro6mpiZaW1uZNGkS8+fPd46UNMLlcjk+8YlPUFdXx8KFC1m1alVJYWrcuHE89NBDneGp8AcWw5Q0tHiFSpLKdOWVV5Z0MgQdJ0Stra3ccMMN7Nq1i+uuu45HHnmEZ599lg0bNvC1r32t89kyXomSRqaGhobOOU+lqq6uZs2aNYYnaRgwUElSGXK5XOdKfv05+OCDqamp4aWXXmLOnDm0t7fz9a9/nZqaGh555BEaGho6r1g5R0oauY477riy+h999NGGKWkYGdKBKiKmAcuBvwK2Av+YUvpOZY9K0mh2+umnl9z33Xff5Y033gA6Vvor/gt18dyo8847b3APUtKQ8pWvfKXkvkcffTRPPvmkYUoaRoZ0oAK+AbwNHAmcAjwWEetSSi2VPSxJo9XOnTv77TN58mTmzp3LzJkzWbJkiVegpFGu+7Lo3U2bNo3DDjuMTZs28ZGPfMQwJQ0zQ3ZRiogYD9QBN6eU2lNKzwGPAJdX9sgkqW8zZszg+eefZ8qUKZx33nmdV6JcfEJSdx/96Ef5yU9+wpNPPsnixYu5/fbbK31Ikso0lK9QzQH2pZTWF21bB5zZvWNEXAVcBeXfpyxpdDoQ48aMGTP4+Mc/3uO8KB/QKw1/gz1uTJ48mZ/85CedPzs2SMPTUA5UE4A3u23bAUzs3jGldA9wD0BtbW068Icmabgb7HFjwoQJ3HvvvZ3zobrPi/IBvdLwN5jjxowZM7j33nsH5bgkVdZQDlTtwKRu2yYB/U9gkKQDJKVs51A+oFcavbKOG5KGhyE7hwpYD4yJiNlF2+YBLkghSZIkaUgYsoEqpbQLWAV8JSLGR8SfAhcC91f2yCRJkiSpw5ANVHnXAOOA3wMPAVe7ZLokSZKkoWIoz6EipfQGcFGlj0OSJEmSejLUr1BJkiRJ0pBloJIkSZKkjAxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGMdKe3h0RbUBrGSXTga0H6HCGC78DvwMYXt9BdUqparB25riRid+B3wEMr+/AcaPy/A78DmB4fQcljRsjLlCVKyKaU0q1lT6OSvI78DsAv4Ny+F35HYDfAfgdlMPvyu8A/A5gZH4H3vInSZIkSRkZqCRJkiQpIwMV3FPpAxgC/A78DsDvoBx+V34H4HcAfgfl8LvyOwC/AxiB38Gon0MlSZIkSVl5hUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZTSm0gcw2KZPn55mzZpV6cOQdAC9+OKLW1NKVYO1P8cNaeRz3JBUrlLHjREXqGbNmkVzc3OlD0PSARQRrYO5P8cNaeRz3JBUrlLHDW/5kyRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKqKRAFREPRMTmiHgzItZHxGfy22dFRIqI9qLXzUV1YyOiMV+3JSKu77bfsyMiFxG7I+KpiKgutVaSJEmSKq3UZdPvBJaklPZGRA3wdES8BPwh3z4lpbSvh7plwGygGpgJPBURv0gprYmI6cAq4DPAo8BtwPeAD/dXW+ZnlCRJkqQDoqQrVCmllpTS3sKP+dfxJZReAdyWUtqWUnoV+DawKN+2AGhJKX0/pbSHjgA1Lx/Y+quVJEmSpIoreQ5VRHwzInYDOWAzsLqouTUiNkbEvfkrT0TEVOAoYF1Rv3XA3Pz7ucVtKaVdwGvA3BJqJUmSJKniSg5UKaVrgInAx+i4VW8vsBU4jY7b8k7Ntz+YL5mQ/3dH0W525PsU2ovbitv7q+0iIq6KiOaIaG5rayv1I0kaxRw3JJXLcUNST8pa5S+l9G5K6TngGODqlFJ7Sqk5pbQvpfQ74FrgryJiItCeL5tUtItJwM78+/ZubcXt/dV2P657Ukq1KaXaqqqqcj6SpFHKcUNSuRw3pOErl8uxZMkScrncoO8767LpY+h5DlUq7DeltI2OWwPnFbXPA1ry71uK2yJifH6fLSXUSpIkSVJJGhoaaGxspKGhYdD33e8qfxExAzgL+CHwFvCXwEJgYUScAWwHNgBTgbuBp1NKhVv1VgJLI6IZOBK4Evi7fNvDQENE1AGPAbcAL6eUciXUSpIkSVJJ6uvru/w7mEq5QpWAq4GNwDbgq8B1KaVHgPcDa+i4Fe8VOuZVLSyqvZWOhSZagWeAhsKy5ymlNqAOuCO/3zOAi0uplSRJkqRS1dTUsHz5cmpqavrvXKZ+r1Dlg8+ZvbQ9BDzUR+1eYHH+1VP7E0CPn6q/WkmSJEmqtKxzqCRJkiRp1DNQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJkiQpIwOVJEmSJGVkoJIkSZKkjAxUkiRJkpRRSYEqIh6IiM0R8WZErI+IzxS1nR0RuYjYHRFPRUR1UdvYiGjM122JiOu77TdzrSRJkiRVWqlXqO4EZqWUJgEXALdHxKkRMR1YBdwMTAOage8V1S0DZgPVwF8AN0bEuQADqZUkSZKkoaCkQJVSakkp7S38mH8dDywAWlJK308p7aEjBM2LiJp83yuA21JK21JKrwLfBhbl2wZSK0mSJEkVV/Icqoj4ZkTsBnLAZmA1MBdYV+iTUtoFvAbMjYipwFHF7fn3c/PvB1IrSZIkSRVXcqBKKV0DTAQ+RsetenuBCcCObl135PtNKPq5exsDrO0iIq6KiOaIaG5rayv1I0kaxRw3JJXLcUNST8pa5S+l9G5K6TngGOBqoB2Y1K3bJGBnvo1u7YU2Bljb/bjuSSnVppRqq6qqSv9AkkYtxw1J5XLckNSTrMumj6FjDlULMK+wMSLGF7anlLbRcWvgvKK6efkaBlgrSZIkSRXXb6CKiBkRcXFETIiIgyPiHGAh8CTwMHBSRNRFxGHALcDLKaVcvnwlsDQipuYXm7gSuC/fNpBaSZIkSaq4Uq5QJTpu79sIbAO+ClyXUnokpdQG1AF35NvOAC4uqr2VjoUmWoFngIaU0hqAgdRKkiRJ0lAwpr8O+eBzZh/tTwA1vbTtBRbnX4NaK0mSJEmVlnUOlSRJkiSNegYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJkiQpIwOVJEmSJGVkoJIkSZKkjAxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjLqN1BFxNiIWB4RrRGxMyLWRsRf59tmRUSKiPai183dahsj4s2I2BIR13fb99kRkYuI3RHxVERUl1orSZIkSZU2psQ+rwNnAr8FzgP+PSJOLuozJaW0r4faZcBsoBqYCTwVEb9IKa2JiOnAKuAzwKPAbcD3gA/3V1vWJ5QkSZKkA6TfK1QppV0ppWUppd+klP6YUvoh8Gvg1BL2fwVwW0ppW0rpVeDbwKJ82wKgJaX0/ZTSHjoC1LyIqCmhVpIkSZIqruw5VBFxJDAHaCna3BoRGyPi3vyVJyJiKnAUsK6o3zpgbv793OK2lNIu4DVgbgm13Y/pqohojojmtra2cj+SpFHIcUNSuRw3JPWkrEAVEYcADwIrUko5YCtwGh235Z0KTMy3A0zI/7ujaBc78n0K7cVtxe391XaRUronpVSbUqqtqqoq5yNJGqUcNySVy3FDUk9KmUMFQEQcBNwPvA1cC5BSagea811+FxHXApsjYiLQnt8+CdhT9H5n/n17/udihfb+aiVJkiSp4kq6QhURASwHjgTqUkrv9NI1FfabUtoGbAbmFbXP43/fKthS3BYR44Hj6ZhX1V+tJEmSJFVcqbf8fQv4AHB+SumtwsaIOCMiToyIgyLiCOBu4OmUUuFWvZXA0oiYml9s4krgvnzbw8BJEVEXEYcBtwAv528l7K9WkiRJkiqulOdQVQOfBU4BthQ9b+pS4P3AGjpuxXsF2AssLCq/lY6FJlqBZ4CGwrLnKaU2oA64A9gGnAFcXEqtJEmSJA0F/c6hSim1AtFHl4f6qN0LLM6/emp/Aqjppa3PWkmSJEmqtLKXTZckSZIkdTBQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJkiQpIwOVJEmSNILkcjmWLFlCLper9KGMCmMqfQCSJEmSBk9DQwONjY0ALF++vMJHM/L1e4UqIsZGxPKIaI2InRGxNiL+uqj97IjIRcTuiHgqIqq71TZGxJsRsSUiru+278y1kiRJkvZXX1/P4sWLqa+vr/ShjAql3PI3BngdOBOYDCwF/j0iZkXEdGAVcDMwDWgGvldUuwyYDVQDfwHcGBHnAgykVpIkSVLPampqWL58OTU1NZU+lFGh31v+Ukq76Ag3BT+MiF8DpwJHAC0ppe8DRMQyYGtE1KSUcsAVwKKU0jZgW0R8G1gErAEWDKBWkiRJkiqu7EUpIuJIYA7QAswF1hXa8uHrNWBuREwFjipuz7+fm38/kFpJkiRJqriyAlVEHAI8CKzIX0WaAOzo1m0HMDHfRrf2QhsDrO1+XFdFRHNENLe1tZX+gSSNWo4bksrluCGpJyUHqog4CLgfeBu4Nr+5HZjUreskYGe+jW7thbaB1naRUronpVSbUqqtqqoq6fNIGt0cNySVy3FDUk9KClQREcBy4EigLqX0Tr6pBZhX1G88cDwdc6O2AZuL2/PvWwahVpIkSZIqrtQrVN8CPgCcn1J6q2j7w8BJEVEXEYcBtwAv528HBFgJLI2IqRFRA1wJ3DcItZIkSZJUcaU8h6oa+CxwCrAlItrzr0tTSm1AHXAHsA04A7i4qPxWOhaaaAWeARpSSmsABlIrSZIkSUNBKcumtwLRR/sTQI+L3KeU9gKL869BrZUkSZKkSit72XRJkiRJUgcDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKB9QLL7zARz7yEf78z/+chQsX8s477/RfJEmSJA0TBiodUMceeyz/9V//xX//938za9Ys/vM//7PShyRJkiQNGgOVDqijjjqKcePGAXDooYdy0EH+Jyepw6xZsxg3bhwTJkxg5syZLFq0iPb29s72RYsWERG88MILndt++ctfErH/kzwWLVrEmDFj2Lx5c5ftK1as4NRTT2XSpEkcc8wx3Hjjjezbt6/XY0opcffdd3PSSScxfvx4jjnmGD75yU/y85//vPP3HHrooUyYMIFp06Yxf/58crmO59EvW7aMiOCuu+7qss+77rqLiGDZsmVlf0eSpKHPs1sNyLZt24gIJkyYwOGHH051dTXLly/fr19rays//vGPOf/88wf9GN544w3+5m/+hvHjx1NdXc13vvOdXvu++uqrnHXWWUyePJkTTjiBhx9+uEv7v/zLv1BbW8vYsWNZtGjRfvWXXXYZRx11FJMmTWLOnDn827/922B/HGlUefTRR2lvb2ft2rW89NJL3HnnnV3ap02bxtKlS/vcx65du2hqamLy5Mk88MADXdp2797N17/+dbZu3crPfvYznnzySb761a/2uq/Pf/7z3HXXXdx999288cYbrF+/nosuuojHHnuss8+NN95Ie3s7GzduZMaMGV3Gijlz5rBy5cou+1yxYgVz5szp76uQJA1TBioNyNq1a5k+fTrt7e3s3r2bO++8k89+9rNs3bq1s8+bb77J5Zdfzn333cchhxwy6Mfwuc99jkMPPZTf/e53PPjgg1x99dW0tLTs12/fvn1ceOGFfPzjH+eNN97gnnvu4bLLLmP9+vWdfY4++miWLl3K4sU9P0/6H//xH/nNb37Dm2++ySOPPMLSpUt58cUXB/0zSaPNzJkzOeecc1i7dm2X7VdccQUvv/wyzzzzTK+1TU1NTJkyhVtuuYUVK1Z0abv66qv52Mc+xqGHHsr73vc+Lr30Un7yk5/0uJ8NGzbwjW98g4ceeoizzjqLsWPHcvjhh3PppZfyxS9+cb/+hx9+OJdccgmvvPJK57bTTjuN3bt3d45BLS0t7Nmzh9NOO63k70KSNLwYqDQga9eu5UMf+lDnz2eeeSbvvvsu27ZtAzpCzMUXX8ytt97KiSeeOOi/v/CX6dtuu40JEybwZ3/2Z1xwwQXcf//9+/XN5XJs2rSJf/iHf+Dggw/mrLPO4k//9E+79F2wYAEXXXQRRxxxRI+/b+7cuYwdOxaAiCAieO211wb9c0mjzcaNG/nRj37ECSec0GX74f8/e/ceXVV95///+RYsSEIANQitXEYF04VKW/DybW3tlNIqY9UhUwW8Ua12au2qtdJvvSBecGwbnaWub3W+dmREq+i3gtZab3iB2puu+BMo1NRUv4NfKo6AkBAQFPv5/ZGTzEkIyclO4AR4PtY6i5P93u+dfc5afuTFZ+/P7tePK664giuvvHKHvXPnzmXq1KlMmTKFmpqadv+R49e//jVjxoxps/bss89y8MEHc8wxxxR0zg0NDdx333188pOfbLH97LPPbp6lmjt3LmeffXZBx5Mk7Z4MVOqSV155hXHjxgGwYcMGLr/8csaNG9f8l6J58+bx4osvcv311/P5z3+eBx98cLtjnHzyyQwcOLDN18knn9zu73/ttdfo3bt3i8tpxo4d2+YMVVtSSi3+dbkQF110Ef369aOiooKhQ4cyadKkTvVL+m+nnXYa/fv3Z9iwYQwePJhrr712u32+8Y1v8Oabb/LEE09sV3vzzTd5/vnnmTZtGgcddBATJkzY7pK7JnPmzKG6uprLLruszfq6desYOnRoh+d80003MXDgQA477DAaGhq4++67W9TPOuss5s2bxwcffMADDzzAWWed1eExJUm7LwOVumTJkiXceuutlJWVMWjQIN555x2efPLJ5pvGzz77bNatW8eiRYtYtGgRZ5xxxnbHeOyxx9iwYUObr8cee6zd39/Q0EBZWVmLbQMGDGDjxo3b7Xv44YczePBgqqqq+OCDD3j66adZvHgxmzdv7tRnvv3229m4cSMvvPACkydPbp6xktR5jzzyCBs3bmTRokXU1NS0uFy4SZ8+fZg5cyYzZ87crnbvvffy8Y9/nE984hMAnHnmmdx///3bPaLhkUce4fLLL+eJJ57gwAMPbPNcDjjggO0WtWjLZZddxoYNG3j77bd59NFHOfTQQ1vUhw8fzmGHHcYVV1zBqFGjGDZsWIfHlCTtvgxUymzr1q28+uqrLFu2jPr6eh566CH+8Ic/7JT7pHaktLSU+vr6Ftvq6+vp37//dvvuu+++PPLII/zqV79iyJAh3HzzzZx++ukcfPDBnf69vXr14vjjj2fVqlXccccdmc9fUqMTTjiB6dOn73D26Gtf+xobNmxgwYIFLbbfc889vPHGGwwZMoQhQ4Zw6aWXsnbtWh5//PHmfZ588kkuuOACfvnLX3LkkUfu8BwmTJjAqlWrqK6u7vLnOeecc7j55ps555xzunwsSVLPZqBSZsuXL6dv374ccsghAFRWVjJ8+HDmz5/fqeOcdNJJlJaWtvk66aST2u0dPXo027Zto7a2tnnb0qVLd3iPxFFHHcXixYtZt24dTz31FG+88UbB90u0Zdu2bd5DJXWTSy65hIULF7J06dLtar179+baa6/lRz/6UfO23//+97z++uu89NJLLFmyhCVLlrB8+XKmTZvWfNnfc889x5lnnsn8+fM7/G991KhRXHTRRUydOpVFixbx/vvvs2XLFh544AF++MMfduqznHHGGTz99NOcfvrpneqTJO1+DFTK7JVXXmHMmDEtngkzadIkHn300U4d54knnqChoaHNV1v3TOQrKSlh8uTJXH311WzatInf/va3/OIXv9jhTeDLli1jy5YtbN68mZtuuonVq1e3WPJ427ZtbNmyhQ8//JAPP/yQLVu2ND+z5p133uGBBx6goaGBDz/8kKeeeop58+YxYcKETn1eSW0rLy/nnHPO4brrrmuzPnXq1Bb3OM2dO5dTTz2VI488snmGasiQIXznO9/hscce49133+X666+nrq6OSZMmFfQPNbfddhsXX3wx3/rWtxg4cCCHHnooDz/8cKcf+bDffvvxxS9+sfk5fJKkPVhKaY96jRs3LmnX+Na3vpUuuOCCFtuef/751K9fv/Tee+/tsvNYt25dOvXUU1O/fv3SsGHD0n333deifuKJJ6YbbrghpZTSZZddlgYOHJhKSkrSiSeemGpra1vsO2vWrAS0eM2aNSullNI777yTPve5z6UBAwak/v37pyOOOCLdeeedu+QzqiWgOjluSOoExw1JnVXouBGN++45xo8fn7rj+ndJPVdEvJxSGt9dx3PckPZ8jhuSOqvQccNL/iRJkiQpIwOVJEmSeqyamhrOP/98ampqin0qUpt6F/sEJEmSpB2pqqpizpw5ANx1111FPhtpewYqSZIk9Sg1NTVUVVUxY8YMZsyYAdD8p9TTGKgkSZLUo7SelXJmSj2ZgUqSJEk9irNS2p0YqCRJktSjVFRUOCul3UZBq/xFxMURUR0RWyPi7rztIyMiRURD3mtmXr1PRMyJiPqIeDsiLm113AkRURMRmyPi+YgYUWivJEmSJBVboTNUbwGzgS8D+7VRH5hS2tbG9muAUcAIYAjwfET8KaX0ZEQcCCwAvg78ErgeeBA4rqPeAs9ZkiRJknaqgmaoUkoLUkqPAOs6efxzgetTSutTSq8CPwWm52qTgRUppZ+nlLbQGKDGRkRFAb2SJEnay/mMKvUE3fVg35URsSoi/iM380REDAKGAkvz9lsKjMm9H5NfSyltAl4HxhTQK0mSpL1c02qAVVVVxT4V7cW6uijFWuBoYAlwAPAT4D4aLw0sze1Tl7d/HdA/974UWNPqeE31jnpbiIgLgQsBhg8fnuFjSNrbOG5I6izHjZ7H1QDVE3Rphiql1JBSqk4pbUsp/RdwMfCliOgPNOR2K8trKQM25t43tKrl1zvqbX0ed6aUxqeUxpeXl2f/QJL2Go4bkjrLcaPnaVoNsKKi8Y4RLwFUMXTXJX9NUtNxU0rrgdXA2Lz6WGBF7v2K/FpElACH0nhfVUe9kiRJUgteAqhiKOiSv4jondu3F9ArIvoC24BxwAagFhgE3AYsSik1Xap3D3BVRFQDBwEXAF/L1R4GqiKiEvgVcDWwLKVUU0CvJEmSBDTOTFVVVVFZWQl4CaB2rULvoboKmJX381nAtcCfgX8BBgP1wEJgat5+s5owmq8AACAASURBVIA7gJXAe8CPmpY9TymtyYWp/wX8DHgRmFJIryRJktSkaWYK8IHA2uUKClQppWtoXNa8LfPa6dsKnJd7tVV/BqjYQa3dXkmSJAlcnELF1dVV/iRJkqSialqcQiqG7l6UQpIkSZL2GgYqSZIk9Xguia6eykv+JEmS1OO58IR6KgOVJEmSejwXnlBPZaCSJElSj+fCE+qpvIdKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJUtHU1NRw/vnnU1NTU+xTkTLxOVSSJEkqmqqqKubMmQPgc6a0WzJQSZIkqWhmzJjR4k9pd2OgkiRJUtFUVFQ4M6XdmvdQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIqKFBFxMURUR0RWyPi7la1CRFRExGbI+L5iBiRV+sTEXMioj4i3o6IS7urV5IkST1HTU0N559/PjU1NcU+FWmXKnSG6i1gNjAnf2NEHAgsAGYC+wPVwIN5u1wDjAJGAH8PfD8iTuxqryRJknqWqqoq5syZQ1VVVUH7G8C0p+hdyE4ppQUAETEeODivNBlYkVL6ea5+DbA2IipSSjXAucD0lNJ6YH1E/BSYDjzZxV5JkiT1IDNmzGjxZ0eaAhjAXXfdtdPOS9rZCgpU7RgDLG36IaW0KSJeB8ZExH8BQ/PrufendUNvCxFxIXAhwPDhw7v4kSTtDRw3JHWW40b7KioqOhWMOhvApJ6qq4tSlAJ1rbbVAf1zNVrVm2pd7W0hpXRnSml8Sml8eXl5pz6ApL2T44akznLc6F5NAayioqLYpyJ1SVcDVQNQ1mpbGbAxV6NVvanW1V5JkiRJKrquBqoVwNimHyKiBDiUxnuj1gOr8+u59yu6oVeSJEmSiq7QZdN7R0RfoBfQKyL6RkRv4GHgiIiozNWvBpblFpUAuAe4KiIGRUQFcAFwd67WlV5JkiRJKrpCZ6iuAt4DfgCclXt/VUppDVAJ3ACsB44FpuT1zQJeB1YCi4GqlNKTAF3plSRJkqSeoNBl06+h8blQbdWeAdq8mzCltBU4L/fq1l5JkiRJKrau3kMlSZIkSXstA5UkSZIKVlNTw/nnn09NTU3HO0t7AQOVJEmSClZVVcWcOXOoqqrqcF/Dl/YGBd1DJUmSJAHMmDGjxZ/taQpfAHfddddOPS+pWAxUkiRJKlhFRUXB4agz4UvaXXnJnyRJkrpN/mV+TeGroqLNRZ2lPYKBSpIkaQ+3K+9l6sw9VtKewEAlSZK0h9uVC0nMmDGD8847z8v8tNfwHipJkqQ9XEf3MtXU1FBVVcWMGTO6vJBEZ+6xkvYEBipJkqQ9XEchJz9EuZCE1Dle8idJkrSXy79MLz98+QwpqWPOUEmSJO3l2prB8hlSUmEMVJIkSdqOl/5JhTFQSZIkaTsuLiEVxnuoJEmSJCkjA5UkSVIPtSsfyCspGwOVJElSD9WZB/J2l45CnCFPasl7qCRJknqoYiwM0d7qfjU1NZxyyinU1ta2WZf2RgYqSZKkHqoYC0O0F+Kqqqqora1l1KhRrv4n5RioJEmS1Ky9EJcftioqKnblaUk9loFKkiRJBXEpdWl7LkohSZK0h9gZC0a4CIXUPmeoJEmS9hBXXnklCxYsYMOGDcyfP79bjtneIhWSummGKiIWRcSWiGjIvf6cV5sWESsjYlNEPBIR++fV9o+Ih3O1lRExrdVxd9grSZKkliKixZ+F6GgGasaMGZx33nkuQiHtQHde8ndxSqk09zocICLGAP8bOBs4CNgM3J7X8xPg/VztTOCOXE8hvZIkSXuFQi+7mz17Nueddx6zZ88uuL+jZ1013TflIhRS23b2PVRnAr9MKf06pdQAzAQmR0T/iCgBKoGZKaWGlNJvgEdpDFDt9u7kc5YkSepRCn3Ab0VFBTNmzKCqqqpFeGrdnx+wnIGSuqY7A9WNEbE2In4bEZ/PbRsDLG3aIaX0Oo0zUqNzr20ppdfyjrE019NRryRJ0l6jM6GnKTwde+yxPP7442325wesrsxAuWCF1H2LUvxP4E80Bp4pwC8j4hNAKVDXat86oD/wIVC/gxod9LYQERcCFwIMHz4884eQtPdw3JDUWcUcNzqzXPmMGTN46KGHqK+v55JLLmHSpElt7pP/Z1YuWCF10wxVSunFlNLGlNLWlNJc4LfAJKABKGu1exmwsYMaBdTzf/+dKaXxKaXx5eXlXfswkvYKjhuSOmt3GTcqKiqYN28eI0aMYOTIkdTU1Gx3yV933Rfl5YLSzls2PQEBrADGNm2MiEOAPsBrwN+A3hExKqVUm9tlbK6HDnolSZLUSlN4mjFjBhMmTGgOUd01I9WaD/qVuiFQRcRA4FhgMbANOAP4HPAdYF/g9xHxWeD/A64DFqSUNuZ6FwDXRcTXgU8ApwKfzh36vvZ6JUmS9jb5gamt2aX8S/DyQ5TBR9p5uuOSv32B2cAaYC3wbeC0lNJrKaUVwD/TGI7eofH+p4vyei8C9svV5gHfzPVQQK8kSdJepSkwnXLKKdstBFFTU8OGDRuorKxsEaJc7lzaubocqFJKa1JKR6eU+qeUBqaUjkspLcyr359SGp5SKkkpnZpSejev9m5K6bRcbXhK6f5Wx95hryRJ0p6mvVXzmgLTiBEjqK2t3W4J9CuvvJIFCxYwYMAAAFffk3aRnf0cKkmSJBWovedNVVVVsWDBAsaNG9fmEugRQWVlJRs2bOCqq64q6LlVkrpuZy1KIUmSpE5qb/GI1vdEATz++OM8++yzTJw4kYkTJ/L973+f+vp6Jk+e7Op70i4SKaVin0O3Gj9+fKquri72aUjaiSLi5ZTS+O46nuOGtOfbU8eN0aNHU1tby6hRowCora2lrKyMF1980XunpC4qdNxwhkqSJGk3VFNTw8iRI3n//fe55ZZbALjkkku45ZZbDFPSLuQ9VJIkST1E/qIU7S1QAY33Ti1cuJAJEyYwadIkJk2axGuvvcakSZM69XskdY0zVJIkST1E/nOkgOb3rZ8hVVNTQ11dHZMnT97uPqmOnlWV/3ueffZZxo8fz+zZs53VkjIyUEmSJPUArUPSG2+8wQsvvEBlZeV2+1ZVVTF//vzme6da13YUxJrMmDGDF154gdraWlauXMmAAQN88K+UkZf8SZIk9QBNIWngwIFUVFQwf/58amtrmTNnDpWVlfzTP/1T8yV6M2bMYNSoUS2eR9VkxowZHa7wV1FRwaOPPsrkyZObHwQsKRtnqCRJknaxti7Lawo1xxxzDKNHj+Z73/seABs2bGDBggUA/P73v+e4447jhhtu4JZbbuGSSy7ZbgaroqKioNmmptAmqWucoZIkSdrF2nuA74033khtbS033ngjAF/60pcoKSkB4K233mLBggXNs1m1tbUtQpGLTUi7njNUkiRJu1hbD/C98sorWbBgAeXl5YwYMYLRo0czZ84cXnjhBTZt2kRJSQmbNm3iox/9KHV1dUycOHG7e6wKuX9KUvcyUEmSJO1ibV2WFxEArFmzhsmTJ3PDDTdQVVXFMcccw80338z3vvc9Fi5cSHV1NfPnz2fZsmXNM1RNS6W3FdQk7Vxe8idJkrQTtfdsqfyfZ8+ezYgRI4DGcNUUup5++unmSwDr6+tZuXIlI0aM4JZbbtlu8YmmnoqKCi//k3YRZ6gkSZJ2ovaeLdVUq6urY8CAAdx+++3MmTOH+vp6PvOZz/DXv/6Vj33sYwCsXLmy+RhNlwOmlAr6vV7+J+08BipJkqSdqPVleHV1dWzYsIGamhoqKyt54YUXWL16NfPnz2fDhg0MHDiwxUITa9euZeLEic0/jxs3joho3mfgwIFtBiYv/5N2DQOVJEnSTtT6fqkBAwY0zxz98Y9/pLa2lrq6OgB+9atfcfnll/PRj36ULVu28N5777Fp0yaee+45evfuzdatW5sv86uvr+e1117jmGOO4fzzz2+xBHtbv1fSzmGgkiRJ2kVqamqoq6tj8uTJRAS1tbWMGDGCv/71rwBs3bqV2bNns23bNoDm2rZt2/jwww8pKytrDk7Dhg1j4cKF3HzzzdTW1gJe2icVg4FKkiRpF2laGv2jH/0ogwcPZr/99qO+vr45QAHN7/v06cPKlSv59Kc/zcsvvwzAj3/84+0eBFxZWcn8+fO9tE8qElf5kyRJ2kWalkZ/6623WLJkCe+99x7r169vc9+tW7dSUlJCSUkJJ598Mlu3buWll15qrjdd0jdp0qTmlf0k7XoGKkmSpJ2saQGK1atXF9yzzz77sGnTJhYuXEhKabsl0iX1DF7yJ0mStJM1XerXGccddxxDhw4lpcQNN9ywwxmompoaqqqqtluUQtKuYaCSJEnayV599dWC991nn3047rjjGDJkCLNnz+4wJPm8Kam4DFSSJEk70bXXXtupQNUUphYsWLDDZ0zl83lTUnF5D5UkSdJOdO211xa0X9OCFb/73e+IiILvmWpanMLL/aTi6NEzVBGxP3AX8CVgLXB5Sun+4p6VJElS4VJKHe6z7777cvTRR1NSUkL//v2ZPXs2gPdGSbuBnj5D9RPgfeAg4EzgjogYU9xTkiRJ6l5/+9vf+N3vfsewYcOYP38+FRUVzfdGVVVVFfv0JLWjx85QRUQJUAkckVJqAH4TEY8CZwM/KOrJSZIkdaMjjzyST33qUy0u8fPeKGn30GMDFTAa2JZSei1v21LghNY7RsSFwIUAw4cP3zVnJ2m35rghqbN2xrgxceJEysrK2lzNr+neKEk9W0++5K8UqG+1rQ7o33rHlNKdKaXxKaXx5eXlu+TkJO3eHDckdVZ3jhsf+chH+Ld/+zeefvppHnroIe+RknZjPXmGqgEoa7WtDNhYhHORJEnKpJBFKSTtvnryDNVrQO+IGJW3bSywokjnI0mSJEkt9NhAlVLaBCwArouIkoj4DHAqcG9xz0ySJEmSGvXYQJVzEbAf8A4wD/hmSskZKkmSJEk9Qk++h4qU0rvAacU+D0mSJElqS0+foZIkSZKkHstAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUUaSUin0O3Soi1gArO9FyILB2J53O7sLvwO8Adq/vYERKqby7Dua4kYnfgd8B7F7fgeNG8fkd+B3A7vUdFDRu7HGBqrMiojqlNL7Y51FMfgd+B+B30Bl+V34H4HcAfged4XfldwB+B7Bnfgde8idJkiRJGRmoJEmSJCkjAxXcWewT6AH8DvwOwO+gM/yu/A7A7wD8DjrD78rvAPwOYA/8Dvb6e6gkSZIkKStnqCRJkiQpIwOVJEmSJGVkoJIkSZKkjAxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRr2LfQLd7cADD0wjR44s9mlI2olefvnltSml8u46nuOGtOdz3JDUWYWOG3tcoBo5ciTV1dXFPg1JO1FErOzO4zluSHs+xw1JnVXouOElf5IkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRUUqCLiZxGxOiLqI+K1iPh6bvvIiEgR0ZD3mpnX1yci5uT63o6IS1sdd0JE1ETE5oh4PiJGFNorSZIkScVW6LLpNwLnp5S2RkQFsCgiXgHW5eoDU0rb2ui7BhgFjACGAM9HxJ9SSk9GxIHAAuDrwC+B64EHgeM66u3kZ5QkSZKknaKgGaqU0oqU0tamH3OvQwtoPRe4PqW0PqX0KvBTYHquNhlYkVL6eUppC40BamwusHXUK0mSJElFV/A9VBFxe0RsBmqA1cDjeeWVEbEqIv4jN/NERAwChgJL8/ZbCozJvR+TX0spbQJeB8YU0Nv63C6MiOqIqF6zZk2hH0nSXsxxQ1JnOW5IakvBgSqldBHQH/gsjZfqbQXWAkfTeFneuFz9vlxLae7PurzD1OX2aarn1/LrHfW2Prc7U0rjU0rjy8vLC/1IkvZijhuSOstxQ1JbOrXKX0rpw5TSb4CDgW+mlBpSStUppW0ppf8CLga+FBH9gYZcW1neIcqAjbn3Da1q+fWOeiVJkiSp6LIum96btu+hSk3HTSmtp/HSwLF59bHAitz7Ffm1iCjJHXNFAb2SJEmSVHQdBqqIGBwRUyKiNCJ6RcSXganAsxFxbEQcHhH7RMQBwG3AopRS06V69wBXRcSg3GITFwB352oPA0dERGVE9AWuBpallGoK6JUkSZKkoitkhioB3wRWAeuBm4BLUkqPAocAT9J4Kd5yGu+rmprXO4vGhSZWAouBqqZlz1NKa4BK4IbccY8FphTSK0mSJEk9QYfPocoFnxN2UJsHzGundytwXu7VVv0ZoGIHtXZ7JUmSJKnYst5DJUmSJEl7PQOVJEmSJGVkoJIkSZKkjAxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKqKBAFRE/i4jVEVEfEa9FxNfzahMioiYiNkfE8xExIq/WJyLm5PrejohLWx03c68kSZIkFVuhM1Q3AiNTSmXAKcDsiBgXEQcCC4CZwP5ANfBgXt81wChgBPD3wPcj4kSArvRKkiRJUk9QUKBKKa1IKW1t+jH3OhSYDKxIKf08pbSFxhA0NiIqcvueC1yfUlqfUnoV+CkwPVfrSq8kSZIkFV3B91BFxO0RsRmoAVYDjwNjgKVN+6SUNgGvA2MiYhAwNL+eez8m974rvZIkSZJUdAUHqpTSRUB/4LM0Xqq3FSgF6lrtWpfbrzTv59Y1utjbQkRcGBHVEVG9Zs2aQj+SpL2Y44akznLckNSWTq3yl1L6MKX0G+Bg4JtAA1DWarcyYGOuRqt6U40u9rY+rztTSuNTSuPLy8sL/0CS9lqOG5I6y3FDUluyLpvem8Z7qFYAY5s2RkRJ0/aU0noaLw0cm9c3NtdDF3slSZIkqeg6DFQRMTgipkREaUT0iogvA1OBZ4GHgSMiojIi+gJXA8tSSjW59nuAqyJiUG6xiQuAu3O1rvRKkiRJUtEVMkOVaLy8bxWwHrgJuCSl9GhKaQ1QCdyQqx0LTMnrnUXjQhMrgcVAVUrpSYCu9EqSJElST9C7ox1yweeEdurPABU7qG0Fzsu9urVXkiRJkoot6z1UkiRJkrTXM1BJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJkiQpIwOVJEmSJGVkoJIkSZKkjAxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlFGHgSoi+kTEXRGxMiI2RsSSiDgpVxsZESkiGvJeM1v1zomI+oh4OyIubXXsCRFRExGbI+L5iBhRaK8kSZIkFVvvAvf5f8AJwJvAJOD/RMSRefsMTClta6P3GmAUMAIYAjwfEX9KKT0ZEQcCC4CvA78ErgceBI7rqLdTn1CSJEmSdpIOZ6hSSptSSteklP4zpfS3lNJjwP8FxhVw/HOB61NK61NKrwI/BabnapOBFSmln6eUttAYoMZGREUBvZIkSZJUdJ2+hyoiDgJGAyvyNq+MiFUR8R+5mSciYhAwFFiat99SYEzu/Zj8WkppE/A6MKaA3tbndGFEVEdE9Zo1azr7kSTthRw3JHWW44aktnQqUEXEvsB9wNyUUg2wFjiaxsvyxgH9c3WA0tyfdXmHqMvt01TPr+XXO+ptIaV0Z0ppfEppfHl5eWc+kqS9lOOGpM5y3JDUlkLuoQIgIvYB7gXeBy4GSCk1ANW5Xf4rIi4GVkdEf6Aht70M2JL3fmPufUPu53xN9Y56JUmSJKnoCpqhiogA7gIOAipTSh/sYNfUdNyU0npgNTA2rz6W/75UcEV+LSJKgENpvK+qo15JkiRJKrpCL/m7A/g48JWU0ntNGyPi2Ig4PCL2iYgDgNuARSmlpkv17gGuiohBucUmLgDuztUeBo6IiMqI6AtcDSzLXUrYUa8kSZIkFV0hz6EaAXwD+ATwdt7zps4EDgGepPFSvOXAVmBqXvssGheaWAksBqqalj1PKa0BKoEbgPXAscCUQnolSZIkqSfo8B6qlNJKINrZZV47vVuB83KvturPABU7qLXbK0mSJEnF1ull0yVJkiRJjQxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJkiQpIwOVJEmSJGXUYaCKiD4RcVdErIyIjRGxJCJOyqtPiIiaiNgcEc9HxIhWvXMioj4i3o6IS1sdO3OvJEmSJBVbITNUvYH/B5wADACuAv5PRIyMiAOBBcBMYH+gGngwr/caYBQwAvh74PsRcSJAV3olSZIkqSfo3dEOKaVNNIabJo9FxP8FxgEHACtSSj8HiIhrgLURUZFSqgHOBaanlNYD6yPip8B04Elgchd6JUmSJKnoOn0PVUQcBIwGVgBjgKVNtVz4eh0YExGDgKH59dz7Mbn3XeltfU4XRkR1RFSvWbOmsx9J0l7IcUNSZzluSGpLpwJVROwL3AfMzc0ilQJ1rXarA/rnarSqN9XoYm8LKaU7U0rjU0rjy8vLC/9AkvZajhuSOstxQ1JbCg5UEbEPcC/wPnBxbnMDUNZq1zJgY65Gq3pTrau9kiRJklR0BQWqiAjgLuAgoDKl9EGutAIYm7dfCXAojfdGrQdW59dz71d0Q68kSZIkFV2hM1R3AB8HvpJSei9v+8PAERFRGRF9gauBZbnLAQHuAa6KiEERUQFcANzdDb2SJEmSVHSFPIdqBPAN4BPA2xHRkHudmVJaA1QCNwDrgWOBKXnts2hcaGIlsBioSik9CdCVXkmSJEnqCQpZNn0lEO3UnwEqdlDbCpyXe3VrryRJkiQVW6eXTZckSZIkNTJQSZIkSVJGBipJkiRJyshApZ3qpZde4n/8j//B5z73OaZOncoHH3zQcZOkvZrjhiRpd2Kg0k41bNgwnnvuOX79618zcuRIfvGLXxT7lCT1cI4bkqTdSYer/EldMXTo0Ob3H/nIR9hnHzO8pPY5bkiSdif+X0pdsn79eiKC0tJS+vXrx4gRI7jrrru222/lypU8/fTTfOUrX+n2c3j33Xf5x3/8R0pKShgxYgT333//Dvd99dVX+cIXvsCAAQM47LDDePjhh1vUzzrrLIYOHUpZWRmjR4/m3//93ztVl9SxPW3caFJbW0vfvn0566yzWmwvLS1t8erVqxff/va3u/XzSHuLkSNHst9++1FaWsqQIUOYPn06DQ0NzfXp06cTEbz00kvN2/7yl78Qsf0TgKZPn07v3r1ZvXp1i+0PPPAAhx9+OAMGDGDw4MGce+651NfX7/CcUkrcdtttHHHEEZSUlHDwwQfz1a9+lT/+8Y/Nv+cjH/kIpaWl7L///kycOJGamhoArrnmGiKCW2+9tcUxb731ViKCa665ptPfkYogpbRHvcaNG5e06zz33HPpwAMPbP75vvvuS7169Upr1qxp3lZXV5c++9nPppqamp1yDlOmTEmnn3562rhxY3rhhRdSWVlZWr58+Xb7ffDBB2nUqFHp5ptvTtu2bUvPPvts6tevX/rzn//cvM/y5cvTli1bUkopvfrqq+mggw5K1dXVBde1awDVyXFjt7WnjRtNJk6cmI4//vh05pln7vD3bty4MZWUlKTFixd36+dRxxw39gwjRoxICxcuTCmltHr16nTUUUelK664orl+7rnnpv333z9NnDixeVttbW1q/Cvvf2toaEilpaVp//33Tz/+8Y9b1N58883m8Wjjxo1p2rRp6dvf/vYOz+nb3/52OuSQQ9Kzzz6btmzZkjZt2pR+9rOfpRtvvLH5nK688sqUUkqbNm1K06ZNS8cee2xKKaVZs2al0aNHp0996lMtjvnJT34yjR49Os2aNaszX4+6WaHjhjNU6pIlS5bwqU99qvnnE044gQ8//JD169cDsG3bNqZMmcKsWbM4/PDDu/33b9q0ifnz53P99ddTWlrK8ccfzymnnMK999673b41NTW89dZbfPe736VXr1584Qtf4DOf+UyLfceMGUOfPn0AiAgigtdff73guqSO7WnjBjT+i/bAgQOZMGFCu797/vz5DB48mM9+9rPd+pmkvdGQIUP48pe/zJIlS1psP/fcc1m2bBmLFy/eYe/8+fMZOHAgV199NXPnzm1RGzZsGAceeGDzz7169eIvf/lLm8epra3lJz/5CfPmzeMLX/gCffr0oV+/fpx55pn84Ac/2G7/fv36MW3aNJYvX9687eijj2bz5s2sWLECgBUrVrBlyxaOPvrojr8E9QgGKnXJK6+8wrhx4wDYsGEDl19+OePGjeOwww4DYN68ebz44otcf/31fP7zn+fBBx/c7hgnn3wyAwcObPN18sknt/v7X3vtNXr37s3o0aObt40dO7Z5UOpISqnFoAZw0UUX0a9fwKe0hgAAFEBJREFUPyoqKhg6dCiTJk3qVF1S+/a0caO+vp6rr76af/3Xf+2wd+7cuZxzzjltXn4kqXNWrVrFE0880Tx2NOnXrx9XXHEFV1555Q57586dy9SpU5kyZQo1NTW8/PLLLeq/+c1vGDBgAP3792f+/PlccsklbR7n2Wef5eCDD+aYY44p6JwbGhq47777+OQnP9li+9lnn80999zTfG5nn312QcdTz2CgUpcsWbKEW2+9lbKyMgYNGsQ777zDk08+2fyXhbPPPpt169axaNEiFi1axBlnnLHdMR577DE2bNjQ5uuxxx5r9/c3NDRQVlbWYtuAAQPYuHHjdvsefvjhDB48mKqqKj744AOefvppFi9ezObNm1vsd/vtt7Nx40ZeeOEFJk+e3DwjVWhdUvv2tHFj5syZnH/++Rx88MHt/t6VK1eyePFizj333Hb3k9S+0047jf79+zNs2DAGDx7Mtddeu90+3/jGN3jzzTd54okntqu9+eabPP/880ybNo2DDjqICRMmNIeZJscffzx1dXWsWrWKGTNmMHLkyDbPZd26dS0W0tmRm266iYEDB3LYYYfR0NDA3Xff3aJ+1llnMW/ePD744AMeeOCB7e7FVM9moFJmW7du5dVXX2XZsmXU19fz0EMP8Yc//IF99913l51DaWnpdjeK1tfX079//+323XfffXnkkUf41a9+xZAhQ7j55ps5/fTT2/xLUK9evTj++ONZtWoVd9xxR6frktq2p40bS5Ys4ZlnnuG73/1uh7/33nvv5fjjj+fv/u7vuueDSHupRx55hI0bN7Jo0SJqampYu3btdvv06dOHmTNnMnPmzO1q9957Lx//+Mf5xCc+AcCZZ57J/fff3+Yz7z72sY9x4oknMmXKlDbP5YADDthuUYu2XHbZZWzYsIG3336bRx99lEMPPbRFffjw4Rx22GFcccUVjBo1imHDhnV4TPUcBipltnz5cvr27cshhxwCQGVlJcOHD2f+/PmdOs5JJ5203SpYTa+TTjqp3d7Ro0ezbds2amtrm7ctXbqUMWPGtLn/UUcdxeLFi1m3bh1PPfUUb7zxRrvT9Nu2bWv3HqmO6pJa2tPGjUWLFvGf//mfDB8+nCFDhnDTTTcxf/78FveINbnnnnucnZK60QknnMD06dO57LLL2qx/7WtfY8OGDSxYsKDF9nvuuYc33niDIUOGMGTIEC699FLWrl3L448/3uZx2vt//YQJE1i1ahXV1dVd+zDAOeecw80338w555zT5WNp1zJQKbNXXnmFMWPGtLgXYNKkSTz66KOdOs4TTzxBQ0NDm6+2purzlZSUMHnyZK6++mo2bdrEb3/7W37xi1/s8NrjZcuWsWXLFjZv3sxNN93E6tWrmT59OgDvvPMODzzwAA0NDXz44Yc89dRTzJs3r/km847qkjq2p40bF154Ia+//jpLlixhyZIl/PM//zP/8A//wFNPPdXiGL/73e/461//yle/+tVOfU5J7bvkkktYuHAhS5cu3a7Wu3dvrr32Wn70ox81b/v973/P66+/zksvvdT83+3y5cuZNm1a82V/9913H2+++SbQeKnulVdeucP/148aNYqLLrqIqVOnsmjRIt5//322bNnCAw88wA9/+MNOfZYzzjiDp59+mtNPP71TfSo+A5UyW7JkCUcddVSLbSeeeCILFy5ky5Ytu+w8br/9dt577z0GDx7M1KlTueOOO1r8S/NJJ53Ev/zLvwCN0/xDhw5l8ODBPPvssyxcuLDFqn133HEHBx98MIMGDeKyyy7jlltu4ZRTTimoLqlje9q40a9fv+Z/5R4yZAilpaX07duX8vLyFr9v7ty5TJ48uc3LCiVlV15ezjnnnMN1113XZn3q1Kkt7nGaO3cup556KkceeWSL/3a/853v8Nhjj/Huu+/ypz/9iU9/+tOUlJTwmc98hsMPP5yf/vSnOzyH2267jYsvvphvfetbDBw4kEMPPZSHH36408/Q22+//fjiF7/Ifvvt16k+FV80LrG+5xg/fnzqjmlXST1XRLycUhrfXcdz3JD2fI4bkjqr0HHDGSpJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIqKFBFxMURUR0RWyPi7rztIyMiRURD3mtmXr1PRMyJiPqIeDsiLm113AkRURMRmyPi+YgYUWivJEmSJBVb7wL3ewuYDXwZaOvxzQNTStva2H4NMAoYAQwBno+IP6WUnoyIA4EFwNeBXwLXAw8Cx3XUW+A5S5IkSdJOVdAMVUppQUrpEWBdJ49/LnB9Sml9SulV4KfA9FxtMrAipfTzlNIWGgPU2IioKKBXkiRJkoquu+6hWhkRqyLiP3IzT0TEIGAosDRvv6XAmNz7Mfm1lNIm4HVgTAG9kiRJklR0XQ1Ua4GjabwsbxzQH7gvVyvN/VmXt39dbp+men4tv95RbwsRcWHuHq/qNWvWZPgYkvY2jhuSOstxQ1JbuhSoUkoNKaXqlNK2lNJ/ARcDX4qI/kBDbreyvJYyYGPufUOrWn69o97W53FnSml8Sml8eXl59g8kaa/huCGpsxw3JLWlu5dNT03HTSmtB1YDY/PqY4EVufcr8msRUQIcSuN9VR31SpIkSVLRFbpseu+I6Av0AnpFRN/ctmMj4vCI2CciDgBuAxallJou1bsHuCoiBuUWm7gAuDtXexg4IiIqc8e+GliWUqopoFeSJEmSiq7QGaqrgPeAHwBn5d5fBRwCPEnjpXjLga3A1Ly+WTQuNLESWAxUNS17nlJaA1QCNwDrgWOBKYX0SpIkSVJPUNBzqFJK19C4rHlb5rXTtxU4L/dqq/4MULGDWru9kiRJklRs3X0PlSRJkiTtNQxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJkiQpIwOVJEmSJGVUUKCKiIsjojoitkbE3a1qEyKiJiI2R8TzETEir9YnIuZERH1EvB0Rl3ZXryRJkiQVW6EzVG8Bs4E5+Rsj4kBgATAT2B+oBh7M2+UaYBQwAvh74PsRcWJXeyVJkiSpJygoUKWUFqSUHgHWtSpNBlaklH6eUtpCYwgaGxEVufq5wPUppfUppVeBnwLTu6FXkiRJkoquq/dQjQGWNv2QUtoEvA6MiYhBwND8eu79mG7obSEiLsxdkli9Zs2aLn4kSXsDxw1JneW4IaktXQ1UpUBdq211QP9cjVb1plpXe1tIKd2ZUhqfUhpfXl7eqQ8gae/kuCGpsxw3JLWlq4GqAShrta0M2Jir0areVOtqryRJkiQVXVcD1QpgbNMPEVECHErjvVHrgdX59dz7Fd3QK0mSJElFV+iy6b0joi/QC+gVEX0jojfwMHBERFTm6lcDy1JKNbnWe4CrImJQbrGJC4C7c7Wu9EqSJElS0RU6Q3UV8B7wA+Cs3PurUkprgErgBmA9cCwwJa9vFo0LTawEFgNVKaUnAbrSK0mSJEk9Qe9CdkopXUPjsuZt1Z4BKnZQ2wqcl3t1a68kSZIkFVtX76GSJEmSpL2WgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJkiQpIwOVJEmSJGXULYEqIhZFxJaIaMi9/pxXmxYRKyNiU0Q8EhH759X2j4iHc7WVETGt1XF32CtJkiRJxdadM1QXp5RKc6/DASJiDPC/gbOBg4DNwO15PT8B3s/VzgTuyPUU0itJkiRJRdV7Jx//TOCXKaVfA0TETODViOgP/A2oBI5IKTUAv4mIR2kMUD9orzeltHEnn7ckSZIkdag7Z6hujIi1EfHbiPh8btsYYGnTDiml12mckRqde21LKb2Wd4yluZ6OeiVJkiSp6LorUP1P4BDgY8CdwC8j4lCgFKhrtW8d0D9Xq99BjQ56W4iICyOiOiKq16xZ05XPIWkv4bghqbMcNyS1pVsCVUrpxZTSxpTS1pTSXOC3wCSgAShrtXsZsLGDGgXU83//nSml8Sml8eXl5V37MJL2Co4bkjrLcUNSW3bWsukJCGAFMLZpY0QcAvQBXsu9ekfEqLy+sbkeOuiVJEmSpKLrcqCKiIER8eWI6BsRvSPiTOBzwJPAfcBXIuKzEVECXAcsyM1mbQIWANdFRElEfAY4Fbg3d+gd9nb1nCVJkiSpO3THKn/7ArOBCuBDoAY4rWmxiYj4ZxrD0QHAM8DX8novAuYA7wDrgG+mlFYApJRWdNArSZIkSUXV5UCVUloDHN1O/X7g/h3U3gVOy9IrSZIkScW2s+6hkiRJkqQ9noFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJkiQpIwOVJEmSJGVkoJIkSZKkjAxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmSpIx6dKCKiP0j4uGI2BQRKyNiWrHPSZIkSZKa9C72CXTgJ8D7wEHAJ4BfRcTSlNKK4p6WJEmSJPXgGaqIKAEqgZkppYaU0m+AR4Gzi3tmkiRJktSoxwYqYDSwLaX0Wt62pcCYIp2PJEn6/9u7nxeryjiO4+9vTWjMOKA4SRAohGbMwkWzs1ZFPyBKkiJsW4Liyr9AStBoXyEohoW2sVZtS4pwMYt+MDAMtLBFWhOVP0bTiqfFOTOcOdyke+7ce+495/2CC95z7hme5zMPH3jm3nuUJK0yzBuqCeBa6dhVYEP5hRGxPyJmI2J2cXFxIIOTNNrsDUndsjckdTLMG6obwGTp2CRwvfzClNKJlNJMSmlmampqIIOTNNrsDUndsjckdTLMG6oFYCwitheO7QK8IYUkSZKkoTC0G6qU0hJwHngzIsYjYjfwInCm3pFJkiRJUmZoN1S5g8D9wC/AWeCAt0yXJEmSNCyG+v+hSin9BuypexySJEmS1Mmwv0MlSZIkSUPLDZUkSZIkVeSGSpIkSZIqckMlSZIkSRW5oZIkSZKkitxQSZIkSVJFkVKqewxrKiIWgUtdXLIZ+LVPwxkVZmAGMFoZbE0pTa3VD7M3KjEDM4DRysDeqJ8ZmAGMVgb/qzcat6HqVkTMppRm6h5HnczADMAMumFWZgBmAGbQDbMyAzADaGYGfuRPkiRJkipyQyVJkiRJFbmhghN1D2AImIEZgBl0w6zMAMwAzKAbZmUGYAbQwAxa/x0qSZIkSarKd6gkSZIkqSI3VJIkSZJUUWs3VBGxKSI+iYiliLgUEfvqHlO3IuJQRMxGxO2IOF0692REzEfEzYj4PCK2Fs6ti4hTEXEtIq5ExOG1unbQ8vGczH+H1yPim4h4rnC+LTl8GBGX8/EsRMTrhXOtyGAQ7I1mrBV7Y2U89sYA2BvNWCv2xsp47I1OUkqtfABngY+BCeBx4CowXfe4upzDS8Ae4D3gdOH45nw+LwPrgXeAi4Xzx4AvgY3Ao8AV4Nler60pg3HgCLCN7A8EzwPX8+dtymEaWJf/e2c+nsfalMGAcrY3GrBW7I2V8dgbg8nZ3mjAWrE3VsZjb3TKpe4B1LQYxoE7wI7CsTPA8brHVnE+R0sFtx/4ujTfW8DO/PlPwNOF828B53q9dlgewHfA3rbmADwCXAZeaWsGfcrV3mjwWrE37I0+5WpvNHit2Bv2xvKjrR/52wH8nVJaKBz7lmzX3QTTZPMBIKW0BPwATEfERuDB4nlWz72Xa2sXEVvIfr9ztCyHiHg3Im4C82QF9xkty6DP7I2GrhV7w97oI3ujoWvF3rA3itq6oZoArpWOXQU21DCWfpggm0/R8vwmCs/L53q9tlYRcR/wEfBBSmmeluWQUjqYj+EJ4Dxwm5Zl0Gf2RgPXir1hb/SZvdHAtWJv2Btlbd1Q3QAmS8cmyT4L2wR3m9+NwvPyuV6vrU1E3EP2MYo7wKH8cOtySCn9k1L6CngIOEALM+gje6Nha8XeyNgbfWVvNGyt2BsZe2O1tm6oFoCxiNheOLaL7G3bJpgjmw8AETEOPAzMpZR+J3t7dlfh9cW593JtLSIigJPAFmBvSumv/FSrcigZIx8v7c1grdkbDVor9kZH9sbaszcatFbsjY7sDWjnTSlS9oW2c2R33hkHdjOad90ZI7sbyjGyv5asz49N5fPZmx97m9V3SzkOXCC7W8pOsoW6fKeVytfWmMP7wEVgonS8FTkADwCvkr0tfi/wDLAEvNCWDAaYtb3RkLVib9gbA8za3mjIWrE37I3/zKbuAdQ2cdgEfJovhB+BfXWPqcIcjgCp9DiSn3uK7MuCt4AvgG2F69YBp8g+1/0zcLj0cytfW0MGW/N5/0n2tvDy47W25JAX0QXgj3w83wNvrMU8RiWDAWZtbzRgrdgb9saAs7Y3GrBW7A17426PyAcqSZIkSepSW79DJUmSJEk9c0MlSZIkSRW5oZIkSZKkitxQSZIkSVJFbqgkSZIkqSI3VJIkSZJUkRsqSZIkSarIDZUkSZIkVeSGSpIkSZIq+heVjmW+s+xqpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoffs_dna_only = [50, 0, 0, 0]\n",
    "cutoffs_rna_cpm = [0, 8, 8, 8]\n",
    "\n",
    "# L1\n",
    "# First remove missing DNA counts and RPM normalize\n",
    "threshold_l1_sample_counts_df = quality_control.filter_low_counts(all_sample_counts_l1_df, sample_labels, cutoffs_dna_only, dna_labels=dna_labels, bc_per_seq=n_barcodes_per_sequence)\n",
    "# Now use the same function to remove low abundance RNA barcodes. Since we already RPM normalized, set `cpm_normalize` to False.\n",
    "threshold_l1_sample_counts_df = quality_control.filter_low_counts(threshold_l1_sample_counts_df, sample_labels, cutoffs_rna_cpm, dna_labels=dna_labels, bc_per_seq=n_barcodes_per_sequence, cpm_normalize=False)\n",
    "fig = quality_control.reproducibility_plots(threshold_l1_sample_counts_df, rna_labels, \"CPM\", big_dimensions=True)\n",
    "plot_utils.save_fig(fig, f\"{l1_output_prefix}RnaThresholdBarcodeReproducibility\", timestamp=False)\n",
    "\n",
    "# L2\n",
    "threshold_l2_sample_counts_df = quality_control.filter_low_counts(all_sample_counts_l2_df, sample_labels, cutoffs_dna_only, dna_labels=dna_labels, bc_per_seq=n_barcodes_per_sequence)\n",
    "threshold_l2_sample_counts_df = quality_control.filter_low_counts(threshold_l2_sample_counts_df, sample_labels, cutoffs_rna_cpm, dna_labels=dna_labels, bc_per_seq=n_barcodes_per_sequence, cpm_normalize=False)\n",
    "fig = quality_control.reproducibility_plots(threshold_l2_sample_counts_df, rna_labels, \"CPM\", big_dimensions=True)\n",
    "plot_utils.save_fig(fig, f\"{l2_output_prefix}RnaThresholdBarcodeReproducibility\", timestamp=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize RNA barcode counts by plasmid barcode counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_l1_df = quality_control.normalize_rna_by_dna(threshold_l1_sample_counts_df, rna_labels, dna_labels)\n",
    "normalized_l1_df = normalized_l1_df.drop(columns=dna_labels)\n",
    "\n",
    "normalized_l2_df = quality_control.normalize_rna_by_dna(threshold_l2_sample_counts_df, rna_labels, dna_labels)\n",
    "normalized_l2_df = normalized_l2_df.drop(columns=dna_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute expression across replicates, drop basal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "activity_replicate_l1_df = normalized_l1_df.groupby(\"label\").mean().copy()\n",
    "activity_replicate_l1_df = activity_replicate_l1_df.drop(index=\"BASAL\")\n",
    "activity_replicate_l1_df.to_csv(f\"{l1_output_prefix}ReplicateExpression.txt\", sep=\"\\t\", na_rep=\"NaN\")\n",
    "\n",
    "activity_replicate_l2_df = normalized_l2_df.groupby(\"label\").mean().copy()\n",
    "activity_replicate_l2_df = activity_replicate_l2_df.drop(index=\"BASAL\")\n",
    "activity_replicate_l2_df.to_csv(f\"{l2_output_prefix}ReplicateExpression.txt\", sep=\"\\t\", na_rep=\"NaN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average across replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_expression_l1_df = activity_replicate_l1_df.apply(lambda x: pd.Series({\"expression\": x.mean(), \"expression_SEM\": x.sem()}), axis=1)\n",
    "sequence_expression_l1_df.to_csv(f\"{l1_output_prefix}TotalExpressionSummary.txt\", sep=\"\\t\", na_rep=\"NaN\")\n",
    "\n",
    "sequence_expression_l2_df = activity_replicate_l2_df.apply(lambda x: pd.Series({\"expression\": x.mean(), \"expression_SEM\": x.sem()}), axis=1)\n",
    "sequence_expression_l2_df.to_csv(f\"{l2_output_prefix}TotalExpressionSummary.txt\", sep=\"\\t\", na_rep=\"NaN\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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