build_variants.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build barcode variant table\n",
    "This Python Jupyter notebook builds consensus sequences for barcoded variants from the mutations called in the processed PacBio CCSs.\n",
    "It then uses these consensus sequences to build a codon variant table.\n",
    "\n",
    "## Set up analysis\n",
    "### Import Python modules.\n",
    "Use [plotnine](https://github.com/has2k1/plotnine) for ggplot2-like plotting.\n",
    "\n",
    "The analysis uses the Bloom lab's [alignparse](https://jbloomlab.github.io/alignparse) and [dms_variants](https://jbloomlab.github.io/dms_variants) packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import warnings\n",
    "\n",
    "import alignparse\n",
    "import alignparse.consensus\n",
    "import alignparse.targets\n",
    "\n",
    "import dms_variants\n",
    "import dms_variants.codonvarianttable\n",
    "from dms_variants.constants import CBPALETTE\n",
    "import dms_variants.plotnine_themes\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import numpy\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "import yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set [plotnine](https://github.com/has2k1/plotnine) theme to the gray grid one defined in [dms_variants](https://jbloomlab.github.io/dms_variants):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "theme_set(dms_variants.plotnine_themes.theme_graygrid())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Versions of key software:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using alignparse version 0.1.3\n",
      "Using dms_variants version 0.6.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Using alignparse version {alignparse.__version__}\")\n",
    "print(f\"Using dms_variants version {dms_variants.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignore warnings that clutter output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters for notebook\n",
    "Read the configuration file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "with open('config.yaml') as f:\n",
    "    config = yaml.safe_load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make output directories if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(config['variants_dir'], exist_ok=True)\n",
    "os.makedirs(config['figs_dir'], exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build variant consensus sequences\n",
    "We now use the processed CCSs to build a consensus sequence (really, list of mutations) for each barcoded variant in each library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get processed CCSs\n",
    "Read the CSV file with the processed CCSs into a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 999925 CCSs from 2 libraries and 12 targets.\n"
     ]
    }
   ],
   "source": [
    "processed_ccs = pd.read_csv(config['processed_ccs_file'], na_filter=None)\n",
    "\n",
    "nlibs = processed_ccs['library'].nunique()  # number of unique libraries\n",
    "\n",
    "ntargets = processed_ccs['target'].nunique()  # number of unique targets\n",
    "\n",
    "print(f\"Read {len(processed_ccs)} CCSs from {nlibs} libraries and {ntargets} targets.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the first few lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>query_name</th>\n",
       "      <th>gene_mutations</th>\n",
       "      <th>gene_accuracy</th>\n",
       "      <th>barcode</th>\n",
       "      <th>barcode_accuracy</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m54228_200414_185350/4391490/ccs</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>ATTTCCATCAAATTAA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>BM48-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m54228_200414_185350/5243143/ccs</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>GCGCTGTACCCTCTGG</td>\n",
       "      <td>1.0</td>\n",
       "      <td>BM48-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m54228_200414_185350/5243413/ccs</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>TTCAATTAAGTCCGCC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>BM48-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m54228_200414_185350/5505876/ccs</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>GTTTCACTGTGATATA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>BM48-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m54228_200414_185350/5636186/ccs</td>\n",
       "      <td>T355C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CAGCTAATACTTCTCC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>BM48-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(processed_ccs.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall statistics on number of total CCSs and number of unique barcodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_CCSs</th>\n",
       "      <th>unique_barcodes</th>\n",
       "      <th>avg_CCSs_per_barcode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th>library</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BM48-31</th>\n",
       "      <th>lib1</th>\n",
       "      <td>288</td>\n",
       "      <td>110</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>459</td>\n",
       "      <td>121</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">GD-Pangolin</th>\n",
       "      <th>lib1</th>\n",
       "      <td>338</td>\n",
       "      <td>122</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>452</td>\n",
       "      <td>129</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HKU3-1</th>\n",
       "      <th>lib1</th>\n",
       "      <td>269</td>\n",
       "      <td>108</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>432</td>\n",
       "      <td>123</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LYRa11</th>\n",
       "      <th>lib1</th>\n",
       "      <td>252</td>\n",
       "      <td>86</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>329</td>\n",
       "      <td>99</td>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RaTG13</th>\n",
       "      <th>lib1</th>\n",
       "      <td>328</td>\n",
       "      <td>114</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>489</td>\n",
       "      <td>123</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Rf1</th>\n",
       "      <th>lib1</th>\n",
       "      <td>298</td>\n",
       "      <td>110</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>404</td>\n",
       "      <td>123</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Rp3</th>\n",
       "      <th>lib1</th>\n",
       "      <td>250</td>\n",
       "      <td>98</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>388</td>\n",
       "      <td>112</td>\n",
       "      <td>3.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SARS-CoV</th>\n",
       "      <th>lib1</th>\n",
       "      <td>371</td>\n",
       "      <td>126</td>\n",
       "      <td>2.94</td>\n",
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       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>490</td>\n",
       "      <td>131</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SARS-CoV-2</th>\n",
       "      <th>lib1</th>\n",
       "      <td>425538</td>\n",
       "      <td>104240</td>\n",
       "      <td>4.08</td>\n",
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       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>566406</td>\n",
       "      <td>102987</td>\n",
       "      <td>5.50</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">WIV16</th>\n",
       "      <th>lib1</th>\n",
       "      <td>305</td>\n",
       "      <td>105</td>\n",
       "      <td>2.90</td>\n",
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       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>423</td>\n",
       "      <td>122</td>\n",
       "      <td>3.47</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">ZC45</th>\n",
       "      <th>lib1</th>\n",
       "      <td>323</td>\n",
       "      <td>126</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>468</td>\n",
       "      <td>143</td>\n",
       "      <td>3.27</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ZXC21</th>\n",
       "      <th>lib1</th>\n",
       "      <td>265</td>\n",
       "      <td>107</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>360</td>\n",
       "      <td>114</td>\n",
       "      <td>3.16</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(\n",
    "    processed_ccs\n",
    "    .groupby(['target', 'library'])\n",
    "    .aggregate(total_CCSs=('barcode', 'size'),\n",
    "               unique_barcodes=('barcode', 'nunique'))\n",
    "    .assign(avg_CCSs_per_barcode=lambda x: x['total_CCSs'] / x['unique_barcodes'])\n",
    "    .round(2)\n",
    "    .to_html()\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter processed CCSs\n",
    "We have the PacBio `ccs` program's estimated \"accuracy\" for both the barcode and the gene sequence for each processed CCS.\n",
    "We will filter the CCSs to only keep ones of sufficiently high accuracy.\n",
    "\n",
    "First, we want to plot the accuracies.\n",
    "It is actually visually easier to look at the error rate, which is one minus the accuracy.\n",
    "Because we want to plot on a log scale (which can't show error rates of zero), we define a *error_rate_floor*, and set all error rates less than this to that value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate_floor = 1e-7  # error rates < this set to this\n",
    "if error_rate_floor >= config['max_error_rate']:\n",
    "    raise ValueError('error_rate_floor must be < max_error_rate')\n",
    "\n",
    "processed_ccs = (\n",
    "    processed_ccs\n",
    "    .assign(barcode_error=lambda x: numpy.clip(1 - x['barcode_accuracy'],\n",
    "                                               error_rate_floor, None),\n",
    "            gene_error=lambda x: numpy.clip(1 - x['gene_accuracy'],\n",
    "                                            error_rate_floor, None)\n",
    "            )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the error rates, drawing a dashed vertical line at the threshold separating the CCSs we retain for consensus building versus those that we discard:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = (\n",
    " ggplot(processed_ccs\n",
    "        .melt(value_vars=['barcode_error', 'gene_error'],\n",
    "              var_name='feature_type', value_name='error rate'),\n",
    "        aes('error rate')) +\n",
    " geom_histogram(bins=25) +\n",
    " geom_vline(xintercept=config['max_error_rate'],\n",
    "            linetype='dashed',\n",
    "            color=CBPALETTE[1]) +\n",
    " facet_wrap('~ feature_type') +\n",
    " theme(figure_size=(4.5, 2)) +\n",
    " ylab('number of CCSs') +\n",
    " scale_x_log10()\n",
    " ).draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flag the CCSs to retain, and indicate how many we are retaining and purging due to the accuracy filter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_ccs = (\n",
    "    processed_ccs\n",
    "    .assign(retained=lambda x: ((x['gene_error'] < config['max_error_rate']) &\n",
    "                                (x['barcode_error'] < config['max_error_rate'])))\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are number of retained CCSs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = (\n",
    " ggplot(processed_ccs.assign(xlabel=lambda x: x['target'] + ', ' + x['library'])\n",
    "                     .groupby(['xlabel', 'retained'])\n",
    "                     .size()\n",
    "                     .rename('count')\n",
    "                     .reset_index(),\n",
    "        aes('xlabel', 'count', color='retained', label='count')) +\n",
    " geom_point(size=3) +\n",
    " geom_text(va='bottom', size=7, ha='center',format_string='{:.3g}', nudge_y=0.2) +\n",
    " theme(figure_size=(0.5 * nlibs * ntargets, 3),\n",
    "       panel_grid_major_x=element_blank(),\n",
    "       axis_text_x=element_text(angle=90),\n",
    "       ) +\n",
    " scale_y_log10(name='number of CCSs') +\n",
    " xlab('') +\n",
    " scale_color_manual(values=CBPALETTE[1:])\n",
    " ).draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequences per barcode\n",
    "How many times is each barcode sequenced?\n",
    "This is useful to know for thinking about building the barcode consensus.\n",
    "\n",
    "First, plot the distribution of the number of times each **barcode** is observed among the retained CCSs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_count = 8 # in plot, group all barcodes with >= this many counts\n",
    "\n",
    "p = (\n",
    " ggplot(\n",
    "    processed_ccs\n",
    "     .query('retained')\n",
    "     .groupby(['library', 'barcode'])\n",
    "     .size()\n",
    "     .rename('nseqs')\n",
    "     .reset_index()\n",
    "     .assign(nseqs=lambda x: numpy.clip(x['nseqs'], None, max_count)),\n",
    "    aes('nseqs')) +\n",
    " geom_bar() +\n",
    " facet_wrap('~ library', nrow=1) +\n",
    " theme(figure_size=(1.75 * nlibs, 2),\n",
    "       panel_grid_major_x=element_blank(),\n",
    "       ) +\n",
    " ylab('number of barcodes') +\n",
    " xlab('CCSs for barcode')\n",
    " )\n",
    "\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot the distribution of the number of **sequences** with barcodes that are observed a given number of time (again among retained CCSs).\n",
    "This plot differs from the one above because if a barcode is observed multiple times it is given one count in the barcode-count plot above (which tallies barcodes) but multiple counts in the sequence-count plot below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = (\n",
    " ggplot(\n",
    "    processed_ccs\n",
    "     .query('retained')\n",
    "     .assign(barcode_counts=1)\n",
    "     .assign(barcode_counts=lambda x: numpy.clip(\n",
    "        x.groupby(['library', 'barcode']).transform('count'), None, max_count)),\n",
    "    aes('barcode_counts')) +\n",
    " geom_bar() +\n",
    " facet_wrap('~ library', nrow=1) +\n",
    " theme(figure_size=(1.75 * nlibs, 2),\n",
    "       panel_grid_major_x=element_blank(),) +\n",
    " ylab('number of CCSs') +\n",
    " xlab('CCSs per barcode')\n",
    " ).draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Empirical accuracy of CCSs\n",
    "We want to directly estimate the accuracy of the gene-barcode link rather than relying on the PacBio `ccs` accuracy, which doesn't include inaccuracies due to things like strand exchange or the same barcode on different sequences.\n",
    "\n",
    "One way to do this is to examine instances when we have multiple sequences for the same barcode. \n",
    "We can calculate the empirical accuracy of the sequences by looking at all instances of multiple sequences of the same barcode and determining how often they are identical.\n",
    "This calculation is performed by `alignparse.consensus.empirical_accuracy` using the equations described in the docs for that function.\n",
    "\n",
    "We will do this four for sets of sequences:\n",
    "\n",
    " 1. All of the CCSs retained above.\n",
    " 2. CCSs retained by applying a PacBio `ccs` accuracy filter 10-fold more stringent than the one above.\n",
    "    The rationale is that if this improves the concordance (real accuracy) of the CCSs substantially then maybe we should make the accuracy filter more stringent.\n",
    " 3. Like (1) but excluding all CCSs with indels.\n",
    "    the rationale is that we only really care about substitutions, and will exclude sequences with indels anyway.\n",
    " 4. Like (2) but excluding all CCSs with indels.\n",
    " \n",
    "First, we annotate the sequences with the number of indels and whether they have an indel to enable categorization into the aforementioned sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_ccs = alignparse.consensus.add_mut_info_cols(processed_ccs,\n",
    "                                                       mutation_col='gene_mutations',\n",
    "                                                       n_indel_col='n_indels')\n",
    "\n",
    "processed_ccs = processed_ccs.assign(has_indel=lambda x: x['n_indels'] > 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot how many sequences have indels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = (\n",
    " ggplot(processed_ccs,\n",
    "        aes('retained', fill='has_indel')) +\n",
    " geom_bar(position='dodge') +\n",
    " geom_text(aes(label='..count..'), stat='count', va='bottom', size=7,\n",
    "           position=position_dodge(width=0.9), format_string='{:.2g}') +\n",
    " theme(figure_size=(2.5 * nlibs, 3),\n",
    "       panel_grid_major_x=element_blank(),\n",
    "       ) +\n",
    " ylab('number of CCSs') +\n",
    " scale_fill_manual(values=CBPALETTE[1:]) +\n",
    " facet_wrap('~ library', nrow=1)\n",
    " ).draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now get the empirical accuracy for each of the CCS groups mentioned above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_acc = config['max_error_rate'] / 10\n",
    "empirical_acc = []\n",
    "\n",
    "for desc, query_str in [\n",
    "        ('retained', 'retained'),\n",
    "        ('retained, no indel', 'retained and not has_indel'),\n",
    "        ('10X accuracy',\n",
    "         f\"(gene_error < {high_acc}) and (barcode_error < {high_acc})\"),\n",
    "        ('10X accuracy, no indel',\n",
    "         f\"(gene_error < {high_acc}) and (barcode_error < {high_acc}) and not has_indel\")\n",
    "        ]:\n",
    "    # get just CCSs in that category\n",
    "    df = processed_ccs.query(query_str)\n",
    "    \n",
    "    # compute empirical accuracy\n",
    "    empirical_acc.append(\n",
    "        alignparse.consensus.empirical_accuracy(df,\n",
    "                                                mutation_col='gene_mutations')\n",
    "        .assign(description=desc)\n",
    "        .merge(df\n",
    "               .groupby('library')\n",
    "               .size()\n",
    "               .rename('number_CCSs')\n",
    "               .reset_index()\n",
    "               )\n",
    "        )\n",
    "\n",
    "# make description categorical to preserve order, and annotate as \"actual\"\n",
    "# the category (\"retained, no indel\") that we will use for building variants.\n",
    "empirical_acc = (\n",
    "    pd.concat(empirical_acc, ignore_index=True, sort=False)\n",
    "    .assign(description=lambda x: pd.Categorical(x['description'],\n",
    "                                                 x['description'].unique(),\n",
    "                                                 ordered=True),\n",
    "            actual=lambda x: numpy.where(x['description'] == 'retained, no indel',\n",
    "                                         True, False),\n",
    "            )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display table of the empirical accuracies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>description</th>\n",
       "      <th>number_CCSs</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>0.984716</td>\n",
       "      <td>retained</td>\n",
       "      <td>408296</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>0.984339</td>\n",
       "      <td>retained</td>\n",
       "      <td>531571</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>0.998122</td>\n",
       "      <td>retained, no indel</td>\n",
       "      <td>390947</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>0.998340</td>\n",
       "      <td>retained, no indel</td>\n",
       "      <td>509301</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>0.989409</td>\n",
       "      <td>10X accuracy</td>\n",
       "      <td>395183</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>0.990143</td>\n",
       "      <td>10X accuracy</td>\n",
       "      <td>507336</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>0.998138</td>\n",
       "      <td>10X accuracy, no indel</td>\n",
       "      <td>380115</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>0.998347</td>\n",
       "      <td>10X accuracy, no indel</td>\n",
       "      <td>488936</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(empirical_acc.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the empirical accuracies, using a different color to show the category that we will actually use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving plot to results/figures/empirical_CCS_accuracy.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x225 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = (\n",
    "    ggplot(empirical_acc,\n",
    "           aes('description', 'accuracy', color='actual', label='accuracy')\n",
    "           ) +\n",
    "    geom_point(size=3) +\n",
    "    geom_text(va='bottom', size=9, format_string='{:.3g}', nudge_y=0.003) +\n",
    "    facet_wrap('~ library') +\n",
    "    theme(figure_size=(1.75 * nlibs, 2.25),\n",
    "          axis_text_x=element_text(angle=90),\n",
    "          panel_grid_major_x=element_blank(),\n",
    "          ) +\n",
    "    xlab('') +\n",
    "    scale_y_continuous(name='empirical accuracy', limits=(0.95, 1.005)) +\n",
    "    scale_color_manual(values=CBPALETTE, guide=False)\n",
    "    )\n",
    "\n",
    "plotfile = os.path.join(config['figs_dir'], 'empirical_CCS_accuracy.pdf')\n",
    "print(f\"Saving plot to {plotfile}\")\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above analysis shows that if we exclude sequences with indels (which we plan to do among our consensus sequences), then the accuracy of each CCS is around 99%. \n",
    "We do **not** get notably higher empirical accuracy by imposing a more stringent filter from the PacBio `ccs` program, indicating that the major sources of error are due to processes that are not modeled in this program's accuracy filter (perhaps strand exchange or barcode sharing).\n",
    "\n",
    "Note that this empirical accuracy is for a **single** CCS.\n",
    "When we build the consensus sequences for each barcode below, we will take the consensus of CCSs within a barcode.\n",
    "So for barcodes with multiple CCSs, the actual accuracy of the consensus sequences will be higher than the empirical accuracy above due to capturing information from multiple CCSs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Consensus sequences for barcodes\n",
    "We call the consensus sequence for each barcode using the simple method implemented in [alignparse.consensus.simple_mutconsensus](https://jbloomlab.github.io/alignparse/alignparse.consensus.html?highlight=simple_mutconsensus#alignparse.consensus.simple_mutconsensus).\n",
    "The documentation for that function explains the method in detail, but basically it works like this:\n",
    " 1. When there is just one CCS per barcode, the consensus is just that sequence.\n",
    " 2. When there are multiple CCSs per barcode, they are used to build a consensus--however, the entire barcode is discarded if there are many differences between CCSs with the barcode, or high-frequency non-consensus mutations. The reason that barcodes are discarded in such cases as many differences between CCSs or high-frequency non-consensus mutations suggest errors such as barcode collisions or strand exchange.\n",
    " \n",
    "First, call the consensus for each barcode including **all** retained sequences, even those with indels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus, dropped = alignparse.consensus.simple_mutconsensus(\n",
    "                        processed_ccs.query('retained'),\n",
    "                        group_cols=('library', 'barcode', 'target'),\n",
    "                        mutation_col='gene_mutations',\n",
    "                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the first few lines of the data frame of consensus sequences for each barcode.\n",
    "In addition to giving the library, barcode, target, and mutations, it also has a column indicating how many CCSs support the variant call:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>barcode</th>\n",
       "      <th>target</th>\n",
       "      <th>gene_mutations</th>\n",
       "      <th>variant_call_support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAATTGTAA</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>T271C A272T T273C A595T A597C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAACTTAAAT</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>A38G T39C T179C A180G G282C T439A G449A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAACTTCAAT</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>G64T C65G A66C C379G T381C A423C C562G G564C</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAACAAGCAGA</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>A37T A38T T39C</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAACAATATAA</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>T16A G17A T18G A43T C44G T45G A280T G282C G307T T308G</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(consensus.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we retain variants with substitutions but ignore those with indels, add information about substitution mutations and number of indels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>barcode</th>\n",
       "      <th>target</th>\n",
       "      <th>gene_mutations</th>\n",
       "      <th>variant_call_support</th>\n",
       "      <th>substitutions</th>\n",
       "      <th>number_of_indels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAATTGTAA</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>T271C A272T T273C A595T A597C</td>\n",
       "      <td>1</td>\n",
       "      <td>T271C A272T T273C A595T A597C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAACTTAAAT</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>A38G T39C T179C A180G G282C T439A G449A</td>\n",
       "      <td>2</td>\n",
       "      <td>A38G T39C T179C A180G G282C T439A G449A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAACTTCAAT</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>G64T C65G A66C C379G T381C A423C C562G G564C</td>\n",
       "      <td>5</td>\n",
       "      <td>G64T C65G A66C C379G T381C A423C C562G G564C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAACAAGCAGA</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>A37T A38T T39C</td>\n",
       "      <td>6</td>\n",
       "      <td>A37T A38T T39C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAACAATATAA</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>T16A G17A T18G A43T C44G T45G A280T G282C G307T T308G</td>\n",
       "      <td>1</td>\n",
       "      <td>T16A G17A T18G A43T C44G T45G A280T G282C G307T T308G</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "consensus = alignparse.consensus.add_mut_info_cols(\n",
    "                    consensus,\n",
    "                    mutation_col='gene_mutations',\n",
    "                    sub_str_col='substitutions',\n",
    "                    n_indel_col='number_of_indels',\n",
    "                    overwrite_cols=True)\n",
    "\n",
    "display(HTML(consensus.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot distribution of number of CCSs supporting each variant call (consensus), indicating whether or not there is an indel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x350 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_variant_call_support = 6  # group variants with >= this much support\n",
    "\n",
    "_ = (\n",
    " ggplot(consensus\n",
    "        .assign(variant_call_support=lambda x: numpy.clip(x['variant_call_support'],\n",
    "                                                          None,\n",
    "                                                          max_variant_call_support),\n",
    "                indel_state=lambda x: numpy.where(x['number_of_indels'] > 0,\n",
    "                                                  'has indel', 'no indel')\n",
    "                ),\n",
    "        aes('variant_call_support')) +\n",
    " geom_bar() +\n",
    " ylab('number of variants') +\n",
    " facet_grid('indel_state ~ library') +\n",
    " theme(figure_size=(1.75 * nlibs, 3.5),\n",
    "       panel_grid_major_x=element_blank(),\n",
    "       ) \n",
    " ).draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that most variant consensus sequences do **not** have indels, especially if we limit to the more \"accurate\" ones that have multiple CCSs supporting them.\n",
    "\n",
    "We will ignore all consensus sequences with indels in the variant-barcode lookup table. \n",
    "We do this for two reasons:\n",
    " 1. When there is just one CCS supporting a consensus, it is less likely to be accurate as indels are the main mode of PacBio error.\n",
    " 2. For the purposes of our studies, we are interested in point mutations rather than indels anyway.\n",
    " \n",
    "Here are number of valid consensus sequence (no indels) for each library and target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>target</th>\n",
       "      <th>consensus sequences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>GD-Pangolin</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>HKU3-1</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>LYRa11</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>RaTG13</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>Rf1</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>Rp3</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>SARS-CoV</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>98569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>WIV16</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>ZC45</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>ZXC21</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>GD-Pangolin</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>HKU3-1</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>LYRa11</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>RaTG13</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>Rf1</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>Rp3</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>SARS-CoV</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>96515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>WIV16</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>ZC45</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>ZXC21</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RERERERERyVLGt7ib2Vgz+1HK6ynAMjP7xMwG5DSdiIiIiIiISCuRTRv0s4CFAGY2DBgGHAJMBa7NXTQRERERERGR1iObW9y7ERfQgcOBKe7+tJl9CLyWq2AiIiIiIiIirUk2NegrgB7x8+FATedwBhTmIpSIiIiIiIhIa5NNDfrDwH1mVgb8F9Gt7QC7EA23JiIiIiIiIiIZyqaA/nPgQ6Ja9IvcfU08vTvwxxzlEhEREREREWlVshkHvQIYn2b69TlJJCIiIiIiItIKZdMGXURERERERERyTAV0ERERERERkQCogC4iIiIiIiISABXQRURERERERAKQcQHdzHqY2TYpr79vZjeY2U9yG01ERERERESk9cimBv0+YD8AM+sGPAN8H7jSzC7NYTYRERERERGRViObAnp/YEb8fAQwx933AEYCp+Yol4iIiIiIiEirkk0BvRhYHz8/EHgsfv4u0D0XoURERERERERam2wK6HOBs8xsL2AYMC2evhWwLFfBRERERERERFqTbAroY4H/Bp4H7nf3N+PpR/DNre8iIiIiIiIikoGiTN/g7s+b2RZAR3dfkTLrT8DanCUTERERERERaUUyLqADuHsVsKLetA9zEUhERERERESkNcq4gG5mHwDe0Hx3326jEomIiIiIiIi0QtnUoN9Q73UxsAswHLg2mxBmtjdwIbArUU/wR7n7Ixm8/zvALcD3gE7AYqLx2n/j7hXZZBIRERERERHJp2zaoN+YbrqZnQsMyjJHe+BN4E7g4SzeXwHcBcwEVgIDgNuIOsG7OMtMIiIiIiIiInmTVRv0BkwFrgJOy/SN7j41fj9mtsF8MysFrgROADoDc4Cx7v58/P4FwIKUt3xkZvsCe2WaRURERERERCQJ2Qyz1pBjgeU5/LxUE4EhwPHATsBDwDQz65NuYTPrTXTL/fQWyiMiIiIiIiKSU+beYH9v6d9g9gZ1O4kzoBvQBTjH3f+0UYHMnJQ26GbWk6h2vKe7L05Z7u/ADHe/OGXaK8BAoJRo2Lez3b069fPnzZvn6Wrpc8Hd094BEIJQs4WaC8LNFmouCDebcmUu1Gyh5oJws4WaC8LNplyZCzVbqLkg3Gyh5oJwsylX5kLN1pK53L2qb9++Td7Bns0t7vU7b6sGlgLPu/u7WXxeU3YECoF59VZWKbCs3rI/AjYjaoN+LXABcE29Zar69OlT2AI5KSsro0+ftJX6iQs1W6i5INxsoeaCcLMpV+ZCzRZqLgg3W6i5INxsypW5ULOFmgvCzRZqLgg3m3JlLtRsLZlr3rx5zVoum07ifpNxmo3TAagi6uG9qt68Nakv3H1h/PRtMysE/mRm18XjtouIiIiIiIgEK5tx0AcCFe4+O379Q6KO4d4Gfu3u5bmNyBtENehd3f3FDN5XQDQEXAEbFuxFREREREREgpLNLe63Ar8DZpvZdsCDREOjHQe0A87P9APNrAPQO2VSLzPbGVju7vPM7F7gLjMbQ1Rg7wIcALzl7k+a2UiiodZmA+uJhnu7CnhQ46CLiIiIiIjIpiCbAnpfYFb8/DhgurufaGZ7Ag+QRQGdqED9XMrrCfHfycCpRDX0vwSuA7YGvgBeBZ6Il6sExsbZDPiIqOf367PIIiIiIiIiIpJ32RTQjW+GZzuQbwrJC4EtsgkRj2feYHd5cS34ZfEj3fwHiWryRURERERERDZJ2YyD/m/gl2Z2ErAP8GQ8vRfwWa6CiYiIiIiIiLQm2RTQzycaa3wicKW7z4+nHwu8kqtgIiIiIiIiIq1JNsOsvUU0Nnl9F6Le0kVERERERESykk0bdADMrAToyoa18B9vVCIRERERERGRViibcdD7AncAe9SfBTjRmOUiIiIiIiIikoFsatD/j2hYs8OBJUSFchERERERERHZCNkU0HcGdnX3d3MdRkRERERERKS1yqYX97fJcrxzEREREREREUkvmwL6WOAaM9vXzP7LzDqmPnIdUERERERERKQ1yOYW97/Hf/9Rb7o6iRMRERERERHJUjYF9P1ynkJERERERESklcu4gO7u01siiIiIiIiIiEhrlk0bdMxsLzO7x8xeMbOt42knmdnQ3MYTERERERERaR0yLqCb2THA34CvgYFAaTyrE3Bx7qKJiIiIiIiItB7Z1KD/EjjL3c8EKlKmv0xUYBcRERERERGRDGVTQP8O8EKa6auAzhsXR0RERERERKR1yqaA/inQO830ocCCjYsjIiIiIiIi0jplU0C/DbjRzHYnGvd8KzMbCYwHbs5lOBEREREREZHWIptx0H9HVLD/B9CO6Hb39cB4d/9DDrOJiIiIiIiItBrZjIPuwJVmdi3Rre4dgLfdfU2uw4mIiIiIiIi0FlmNgw7g7uXu/jbwLnCgmX03d7FEREREREREWpeMa9DNbArwgrtPNLO2wL+AXtEsO97d/5LrkCIiIiIiItI4d2fGjBlMmjSJefPmscMOO3DaaacxaNCgpKNJM2VTg7438GL8/Kj4MzoDo4nGSBcREREREZE8Wr9+PUcffTSDBw/mjjvu4Nlnn+W2225jt912Y8SIEZSXlycdUZohmwJ6J2B5/Hw48Bd3Xws8CfTJVTCRfKuurubPf/4z++yzD1tssQX77LMPF110ER9//HHS0YK3ePFiXn31VT788MOko4hIiq+++oqVK1dSXV2ddBTZCMuWLePWW29l4sSJ3H777axcuTLpSACsXr2aW265hdNOO41LLrmEhx56iIqKiqRjieTNkiVLmDJlCk888UQQ50DnnnsuTz31FEDtd7Hm7yOPPMKYMWMSyyYZcPeMHsA8YATQHvgc2D+ePgD4ItPPy/fjvffeq/QWMm/evJb66KwsX77cb7jhBj/hhBP8qKOO8vvuu8/Xr1+fdCxfs2aN33LLLb7nnnt6nz59/LjjjvPnnnvOq6urE8tUUVHhRx99tBcWFnpBQYETDSHoJSUl3q5dO3/ppZcSy1ZfSPvZ7NmzfdiwYW5mteusf//+/uSTTyYdrY6Q1lmqUHO5h5Xto48+8gsuuMC7devmbdu29R133NFvvfVWX7duXdLR6ghpnU2dOtX33HPP2u/llltu6ePGjfO1a9cmHa2OkNZZqlByVVdX+69//WsvLi72kpKS2kdpaalfffXViR43p06d6u3bt/eSkhIH3My8sLDQt956a587d25iueoLZVumE2q20HJVV1f7Sy+95Hfffbffeuut/vXXXycdyVetWuUnnHCCFxQUeElJiRcXF7uZ+fDhw33JkiWJZFqyZEmdc9h0j+LiYv/iiy8SyVdfaPtZjZbMFZdDmy5vN2ehOm+Ac4AKYAUwCyiIp/8UeC7Tz8v3o6UK6MuWLfOnn37aV6xY0RIfn7FHHnnE27RpU+fAWVRU5N27d/c5c+Ykluv999/3bbbZxouLi2t/LIqKitzMfNSoUV5Z2WLXTxp19dVX18mU+jAz79Spk69ZsyaRbPWF8oM2a9Ysb9eunRcWFm6wzgoKCvy+++5LOmKtUNZZfaHlKisr8zFjxvjuu+/uAwcO9KuvvtqXLl2aaKZ//etf3qFDh9rfstSTjCFDhvhXX32VaL5UoWzPm266yc1sgxO1kpISHzx4cFCF9FDWWY0333zTr7/+ev/f//1ff/XVVxMtALu7jxs3zouKitIemwoLC/3GG29MJNebb77Z4DGzsLDQt9hii2DOh0Lbx1KFmi2kXH//+9+9V69ebmZeWlrqhYWF3qlTJ7/++usT+36uW7fOBw0atMFxqebY1KtXr0T2/zvuuMNLS0sbLaCXlJT4vffem/ds6YS0n3399dd+1113+UEHHeT9+/f3UaNG+YsvvpjzfazFCugeFcYHEbU/75Ay7TBgz2w+L5+PXBfQX3/9dR8+fHhtDWJhYaEfffTR/vbbb+fy32Tk3//+d9pCU+qBc+XKlXnPVVVV5X379m3wZKO4uNh/+9vf5j1XRUWFd+3atckrjn/605/ynq3G4sWL/de//rX379/ft912Wz/++OP95ZdfTiyPu/vgwYMbvVLboUOHYApPIR0EUoWU69Zbb3Uzq3PCUVJS4u3bt/fp06cnkqm8vNy7du3a4H5WUlLio0ePTiSbe1Sr8+yzz/oxxxzj22+/ve+www5+1VVXJXpRY8GCBXXuaEm3zi6//PLE8tUXyndg8eLFPnToUAe8TZs2tSe5O+20k5eVlSWSadWqVd6mTZtGj00dO3ZMpDZx5MiRDRbQa/az66+/Pu+50gllH0sntGyrVq3yxx57zG+88UZ/5513ko7jzz33XKPns+PGjUsk1+TJk5vc/5M4n/3DH/7gbdu2bfQ3o02bNn7bbbflPZt7dMy8//77fbfddvPCwkIvLi72Qw45xJ999tlE8tRYtGiR9+7du075pKby8OSTT85p5WGLFtA35UcuC+jTp0/3kpKSDX48ioqKvH379v7GG2/k6l9l5Nhjj22wEFzzw5HEVfepU6c2eevN5ptv7uXl5XnNVVZW1mimmscJJ5yQ11w1XnzxRW/fvn2dq6I1PxwXXXRRIleQ33nnnSbXV3FxsU+aNCnv2dIJ7SSoRii5nn/++Qa/m2bm7dq1S+SWvSlTpjT6WwZ4u3btfPXq1XnPVl1d7T/5yU+8oKCgTsbS0lLv3Lmzz5w5M++Z3N3Hjh3bZA1Kly5dErtbqb4QvgOrV6/27bffPu0Jd1FRkXfp0sU/++yzvOe655570tbQ1c/3yCOP5D1bUxcOAN91113zniudEPaxhoSSraKiwi+44AJv06aNFxUV1X4X9thjD3/vvfcSyVRdXe39+vVr9IJjUVFRIhdEhwwZ0uT+36NHj7znevbZZ5s8zzYz/+c//5n3bNXV1X7GGWfUnr+mXmgxs8TuBqqurvYBAwY0eMGluLg4pxeCmltAz7iTODMrNLMfm9l9ZvZ3M3s29ZHp58WfubeZPW5mi83MzezIDN+/r5k9amZLzOwrM5tlZiOzydJc1dXVjBw5koqKCqqqqurMq6ys5Ouvv+aUU05pyQhpuTuPPvoolZWVDS5TXl7O/fffn8dUkWnTplFQ0Pgut3z5ct566608JYp4dAdIzpbLpRUrVnDooYeydu1a1q9fXzu9srISd+e6665LZFuWlZVRXFzc6DIFBQWUlZXlKdGmo6Kigoceeojhw4dz+OGHc/jhh/PYY49t8DuST1dffTVmlnaeu1NVVcXtt9+e51Tw0ksvNbnM2rVrmT17dh7S1HXzzTdz5513Ul1dXef3dv369Xz55ZccdNBBfP3113nPNWPGjDq/FeksXbqUL774Ik+J6lqzZg3XXXcd2223HSUlJXz/+99nzJgxLFy4MJE8AJMnT2bhwoVpOzerrKxk1apVTJw4Me+5vvjiCwoLCxtdpqioKO/bsrq6mnXr1jW53OrVq/OQRjaWuzNq1ChuvPFG1q1bR2VlZe13YcaMGQwePDiRzs/mzJnD3LlzGz33KiwsTOQcqDm/V59++mkektS17777su222zZ4PC8oKOA73/kOu+++e56TwYMPPsikSZNqz19rVFVV4e6cf/75eT//B3jhhRd46623GuzcsqKigvHjx+e99/tsenG/MX4UAnOAN+s9stE+fu+5Wb5/D+At4BhgJ+D/gLvM7PAsP69JzzzzDEuWLGnwh6O6upq33nqLmTNntlSEtFJ/WBvz5Zdf5iFNXRUVFc0q5Oa7B9hevXrRpUuXRpcpLi5m3333zU+gFJMnT2b9+vUNrreqqiquuuqqPKeCjh07NnoRCMDM2GyzzfKUaNOwcuVKhgwZwoknnsgzzzzDvHnzmDp1KkcffTT7778/X331Vd4zuTtPP/10oxcI1q9fz1/+8pc8poo0dJKR7XK5Ul1dze9+97sGvwPV1dWsWrWKBx98MK+5ANq2bdus5UpLS1s4yYZWrFjB7rvvzsUXX8wHH3xARUUFK1euZOLEifTv359Zs2blPRPA7bff3ujJV3l5eSIXqHr27Nnk8bCiooKePXvmKVGkoKCAbbfdttFlioqK6N+/f54SycZ47bXXePDBBxu8QLVmzRquuOKKvOdatGhRkxeo3J1FixblKdE3ttxyyyaX+da3vpWHJHWZGffeey8lJSUbrKJkttsAACAASURBVLvCwkJKS0u5++67837MBBg/fnyj5xnFxcWJXAh96qmnmqxwWrlyJa+//nqeEkWyKaAfD4xw9x+5+/nu/vPURzYh3H2qu//S3f+abr6ZlZrZeDP7JK4hf83M9k15/2/d/Vfu/oq7v+/uNwLTgKOzydMcc+fOpaSkpNFl2rRpw9y5c1sqQlrFxcX06NGj0WWKiorYaaed8pToG7vttluTNeilpaV897vfzVOiSFFREeeff36DX1Azo23btowc2aI3ZaT15JNPNnnVbs6cOaxYsSJPiSJ77LEHm2++eaPLrFu3jmOPPTZPiTY0e/ZszjzzTHr06MFee+3FyJEjmTFjRmJ5AE466SRmz55NZWVl7bBX1dXVVFVV8eqrr3LOOefkPVPN/29KU7WyLWHfffdt8qJehw4d8v579tFHHzVZg1JRUcHf/va3PCX6xpFHHtnoyYaZsdtuu9G5c+c8poqcd955zJ8/f4PftPLyctasWcMPf/jDRIaD+/zzz5tcZvny5U0uk2uHHnpokxc5u3btyv7775+nRN8YPXp0o+dA1dXVnHtutnUukk+33357o78ZFRUV3HPPPc26ayKXunbt2uSxyczo2rVrnhJ948c//nGj+39JSQlnnHFGHhN9Y8iQIbz22msccsghtQXxgoICfvCDHzBjxgwGDRqU90zuzqxZsxo9npeXlzN9+vQ8pvrm/zZ13DGzTaIGvRyYn+sgTZgIDCG6OLAT8BAwzcwaG3c9dbz2nGvXrl2TG7Sqqop27dq1VIQGNXXgrKqq4rzzzstjosiIESMard0pKSlh1KhRdOrUKY+pIhdddBEHH3wwRUVFda4slpSUUFpaymOPPUaHDh3ynqupWuoa+b49uri4mF//+tcNXt0uKSnhRz/6Edtvv31ec9WYNGkSO++8M3fddReLFi3is88+46GHHmLw4MFMmDAhkUzvv/8+TzzxRIM/8uXl5dx777189tlnec1VWFjY5EWx4uJihgwZkqdE3zj88MPp3r17o/vZOeeck/ff2eZ+35r7/c2lE088kc6dOze4zsyMiy++OM+potvqp0yZ0uD+X11dzaJFi5g2bVqek9HkRW1oXo1ZrpWWljJx4sRGb1f94x//2GQtY0s4++yzGTBgQNpzjaKiIk4++WT222+/vOeSzC1YsKDJOzXKy8tZtmxZnhJFBg4cSK9evRpdpqKiguOPPz5Pib5x0kkn0aNHj7QXNoqKiujYsSM//elP856rxoABA3j88cdZtmwZTz/9NMuWLeOvf/1rone1NKfWPonfsoEDBzaZrbCwkH79+uUpUaQoi/dcB/zMzM7zPDTKNbOewGlAT3dfHE8eb2bD4+kbnGmY2QhgN+C/03xkYS7axfbr16/JqylmRq9evfLeDvfggw/m7rvv5p133tngR7ewsJARI0bQpUuXRNoH33jjjZx55plA3ZPX4uJievbsydlnn51Yu+Vrr72W/fffn8mTJ7NgwQJKS0s57LDDOOmkk9hqq60SydWvXz9eeeWVRve17t27s3z58rzXoh900EGcffbZ3HTTTRQXF1NRUUFRUREVFRXsvffeXHLJJYmss/fee4/TTz8dd6+z3mq+CxdccAHdunVjt912y2uumtvOGtuWBQUF3HfffRx+eIu1zknrxBNP5De/+U2DBcqKigqOOOKIRLbnLbfcwsiRI1m7dm3tNjQzCgoKGDRoECeffHLec1VUVNCpUydWrVrV4DLFxcX07ds3kXV25513cvLJJ7NmzZradVbzHb3wwgvp169f3nO9+OKLTV7ULioq4oknnqBPn8auvefekUceyeuvv97g/l9cXMxxxx2XyLbcbbfduOGGG/jtb3/L559/TlFREZWVlWy11Vb86le/SmRb1rjtttu48cYbefDBB1m7di0AW2yxBWeccQannnoq8+fnuy4nPXcPtj+UELK1b9+ewsLCRi88FhQUsHTp0trtnC8XXHAB5513Xtqa16KiIk488UTWrl2byDqcPHkyo0ePZubMmbVNhsrLy+nVqxcTJ05k9erVQfTDsO2227J06VKWLl2aaI7dd9+df/7znw0eB0pKShg6dGjet+Uuu+xCmzZtGrxIVVRUxPDhw1mxYkV+z7Ob05Nc6gP4K7ASWAA8Djyc+sj089J8vgNHprw+LJ62pt6jAngwzfv3A74CTk73+bnsxf2kk05qsIfV4uJi/5//+Z9c/auMffXVV37hhRf6ZpttVqdHyZtuuinxcV3feustP/HEE2t7TNxyyy39iiuu8C+//DLRXPWF0LvqwoULGxxipGY/S3oom0WLFvmVV17pZ555pp955pn+5ptvJprnjDPOaLTn46KiIj/iiCPynuuGG25osufjNm3aJNLzfWVlpf/whz/coBfTmrG0kxrKpsZnn33mV1xxhffu3ds333xz33PPPf2+++7zioqKxDJdeumlje5nbdq08eXLlyeW78svv/RbbrnFhw0b5rvuuqv/7Gc/83fffTexPE8//XSTvQuXlpb6pZdemvds69at84EDBwY3pnGqqqoqf/HFF/33v/+9v/LKK4kfx1OtXbvW33zzTX/qqaeCGR0gVQjH8oaEkG3atGmNfjcLCwv9qKOOSizffffd5507d/aioiJv27atl5SUeFFRkY8ZMyaI/W3WrFk+YcIEHzt2rL/88stBfTfdw9jH3N2feeaZRnvkLy4u9g8++CCRbE8//bQXFxdvMGpMSUmJ9+rVyz///POc/a8WG2aNqAO2Bh+Zfl6az69fQP8RUAl8B+hd79Gt3nv3iQvvP2no83NZQF+7dq0feuihtSf9NTsY4KNGjUr05LHGunXr/O233/ZnnnnGq6qqko5TR3V1tc+ZMyfpGA0K5Uft3nvv3WAop5p97fDDDw9iP6sRwjrr0aNHk8OfdOjQIe+5XnrppUYPTjWPpL4TlZWVPnHiRN9uu+1qswwePDiRIZwaE8I+5h79tu6zzz4bXNSoOcg/9thjSUesFcI6W7lyZZPDv5mZP//884nlGzFihBcUFHhJSYkXFxe7mfnw4cP9008/TSRTOiFsy4aEmi3UXO5hZKuqqvI99tgj7QUqM/PS0lKfNWtWohnXrVvnU6ZM8WuuucbHjRuXyNBqTQlhW6YTUq7x48e7mdU5bpaWlnpxcbE/+uijiWZ74403/Nhjj62tFOvcubOPHTvWly1bltP/s8mOg56mgN43nrZXE+/bNy6cn9vYcrksoLtHhcxXXnnFzzrrLN9///39pz/9aWLj3zYmpC9oqlBzuYeV7bXXXvNjjjnGS0tLvaCgwPv16+e33XZbEFePU4WwzrbZZpsmC8Ht27fPe67q6mrfYYcdGrwjoqioyIcMGZL3XOlyrl692mfPnp10lLRC2MdqrF+/3m+66Sbv27evm5m3adPGR44cmfjJbH2hrLNzzz23wbsOioqKvF+/fonXPn3yySf+wAMP+HXXXecLFixINEs6oWzLdELNFmou93CyrVq1yo844ojau39KS0u9sLDQu3fv7i+88ELS8eoIZZ3Vp1zNM3PmTD/99NO9d+/e3qtXL7/wwgsTqzlPp7y83GfOnNlilZrNLaBn0wYdADPrQlSrDfCeu2fduMHMOhDViNfoZWY7A8vdfZ6Z3Us0bNoY4A2gC3AA8Ja7P2lm+wFPEA3/9hcz6xZ/Trm7t2jXq2bGkCFDGDJkCGVlZXlvOyetw/e//33+/Oc/AzBv3jz69u2bcKJw7bvvvg0OGQNRPwx77rlnnlNFvxUPPfQQQ4cOrdOeGqK2V506deKee+7Je676zIwOHTokMgzXpqamk7pzzjkHd2f+/Pk6BjTi2muvZe7cubz00ku1Y99CtB67dOnC448/nsjwP6m22morfvSjH1FWVtZkB1Ui/yk6duzIo48+SllZGU888QSLFi1i//33Z/jw4Yl03CX/uXbZZRfuuOMOgCDLTcXFxXTo0KHJUadaWsb/3czam9mdwBLghfix2MzuMLNsu9IdRFTwfiN+PSF+fnn8+jTgLqIO6t4DHiHqBO7jeP4pQDvgF3GumsfDWeYRCVbSJ7Ch+9nPftZo79nuzpgxY/KY6Bs1Yz2fccYZtT2Pd+jQgXPPPZdZs2ax3XbbJZJLNp6+l01r27YtzzzzDPfeey977bUX22yzDTvssAPXXHMNc+fOVYFYJGF9+vTh5z//OWeddRaHHXaYCuciCcmmBn0CUVvvHwAvx9OGAr8nKkCfnekHuvvzQINnN+5eAVwWP9LNPxU4NdP/KyL/eQYNGsSECRMYM2YMZlbbM21hYSHuzi9+8QsOOuigxPJ9+9vf5o9//CM33XQTc+bMoX///ircSatRVFTEiBEjGDFiBBBmDYqIiEiSsqm/Pwb4sbtPdfcv48dTwJnAsbmNJyKSufPPP5/p06fzgx/8gM0224x27dpx4IEHMnXqVMaNG5d0PCCqcW3Tpo0K5yIiIiJSK5sa9HbAZ2mmfx7PExFJ3NChQxk6dCigWjoRERER2TRYTSctzX6D2T+AZUTjjK+Lp7UFJgObu/uBOU+ZQ2VlZQvdvXvSOURERERERKR1MLMlffr06dHkclkU0PsDfwNKgTfjyQOAdcDB7j43w6wiIiIiIiIirV7GBXSAuLf2kcAO8aR3gHvd/escZhMRERERERFpNbIqoIuIiIiIiIhIbmUzDvovzOz0NNNPN7OxuYklIiIiIiIi0rpkM8zafwPvppk+Fzhr4+KIiIiIiIiItE7ZFNC7AUvSTF8KtPre0c1sezN7NsH/393MRpnZoWZWUm9eezO7NKlsDTGzb5nZyQlnSPtdMLMCM+uZ7zxNMbMeZnZngv+/rZkNNbPvpZnXJuntmY6ZDTCzqoT+96FmdruZXWNmO9Sb960kfzMaY2bfNbMFCf3vAWb2SzM7x8y2qDevY5L7f2PMbMskf2fNbBsz65BmerGZ7Z1EpqbEx6Ygs4Uo6X0szrBJ7Wc6z8hcksfM+P9vcsfNJMsAm+L5PyT/3Qz1e5lNAX0hsGea6XsCizcuzn+EDsA+SfxjM9sNeBu4CfgzMNfM+tXLdlkS2ZrQE/i/JP5xfKI/BfjKzD4zs8vNrDBlkS7AB0lka8LmwClJ/GMz60vUMeQLwGwzm25mqRfnOpHQ9mwGy/s/NDsReIzo4uYQ4A0zG5mySAkJ/WY0Qwmwbb7/qZkdBMwAjgfGAu+a2X4pi7Qlof2/GbqRwO9sfHI2A/gIWGlmd9UrQG0OPJfvXM3UmwSyxYXJa8xsvpnNqN98Ly4IJ1ZAaUQi+xhs0vuZzjOyk/djJmzSx81EygCb8Pk/JPTdDP17WZTFe24DbjCzYqDmKtEBwDXAdbkKFiozG93EIlvnJUh6vwX+CpwBtAeuBqab2TB3fyOpUGbWsYlFNstLkPSuIBom8CSgM/BLYKCZHe3u5fEySRTqjmhike3yEiS9q4E5wCCidXYD8LKZ7evuHycVyswebmKRTkASvWJeCPyPu/8ewMxGAHeaWRt3vyOBPLXMbEITi3TJS5AN/RoY7+6XmJkRrcPHzOw4d5+WUCYAzGynJhb5Tl6CbOh3QDWwO9H38nfAc2Z2kLuviJdJ5GQ7YJcAJwPjidbZBDPb3d3/O2WZJH7/Q93HIND9TOcZmQv4mAmBHjcDLgMEef4PQX83g/xe1shmHHQj+kEeTXQFC6Ix0K9298tzGy88ZlZNdIt/eQOLlADd3L2wgfktxsyWA4PdfV7KtP8FLgIOBj4GFuc7W7zOGtvRDPCE1tlHwCnu/nz8egvgSWAlcATRlzbJddbYj0NS6+wz4EB3nx2/NuCPwKHAfsBXJLPOKoBngM8aWGRz4PAEcq0BdnT3D1Km7UdUO3Ah0UE17+srzlEFzAK+bGCRDsDABNbZqvj/vp8y7UTgT0S16v8iuXXW2HezZnrev5tm9glwlLvPiF+XAg8BPYguoheT3Dpb3sQihUCHBNZZGfBzd38ift0bmAq8BJwOdCW83//E9jEIdz/TeUZWuYI8ZkK4x81QywChnv/HOYL8bob6vayRcQ26RyX6sWZ2BfBd4GugzN3X5zpcoD4Cxrr7lHQzzWxn4PX8RqqjTeoLd/+dmVUCTxOdcCRhNXAl8FoD8/sAt+YvTh1diLYpAO7+hZkdCPwNeIroamQSlgDnuPuj6WYmvJ+1BSprXsS/CWeb2URgOnBiQrneAf7S0NX1eJ0dnt9IQFT43ZKUW6Xc/TkzOxx4AtgmgUw15gPXu/s96WYmuJ+tJzo41nL3++ID/YPAmAQy1VhOdNLzjwbm9wMez1+cWp2AmhpM3H29mR1NVHh6DhiVQKYapcDNwOwG5m9LMrdfbk10NxAA7j7fzPYlujvwbqLtnIRQ9zEIdz/TeUbmQj1mQrjHzZDLACGe/0O4381Qv5dAdre4A+Dua4hqMVqb14FdgbRfTpqu9WxJc4A9gLdSJ7r7eIs6Qbg/kVQwM84xPd1MM1tJcuvsY6ILTakHgdVxG9inia7SJqFmP0tbQCfZ/exdotvb30md6O7nRZXpPJZEKKJ1NhBo6Pa39UTbO99mAIcAr6ZOdPfpZvYDopONpPybaD9LW0Anuf1sFtHdGHVOdNz9gfiOjckJZKrxOrCVu3+UbqaZdSaZdbYA2Akoq5ng7pVmdhxR4SnJ/WwWsNDd0243MxtAMgX0T4HtgQ9rJrj7J3FN3XPApAQyQbj7GIS7n+k8I3OhHjMh3ONmqGWAUM//IdzvZqjfSyC7TuJau0uJDkINeRvolacs9d1F+g78cPdriE6AkvixvY+oGURDPgV+k6cs9T0NnFZ/YnwB6mAaz92SrgVeaWT+fKICTBL+CpyQboa7n0d0IEjix/Ysolvf0nL3d9w9ie/m9TSwH8W3Vv2A6LubhDFEfQik5e5vunsSx4mbaaAtn7vfD5xK1ElhEm4hpUCXxsek+U3Jg6nAT+pPdPdK4DiiQnJSnqTeHRH1LCeZ78CzpLnjx90XA/uT3LE81H0Mwt3PdJ6RuVCPmRDucTPUMkCo5/8Q7ncz1O8lkEUbdJH/JGb2LaKairkNzN+MqC1s2it/IiIhMLMioJ27p+1PIJ6/dUO1sq2RmW0L7ODuf2tg/lbAsIZq/lsj7WeZ03mGSHhC/16qgL6RzKwr3/So+p67f55knlQhZ5PMmVkPAHdfmHSW+kLLFg+VcRTR7UsQ3Y7/SFzLk5hQc9Uws0GkZHP3fyeZp0aouWrEt93X9McQjFBzSeZC3pYhZ5PmCfnYFHi2IM+zQ80lGXB3PbJ4EA0LcDdQQTTkSHX8/B6gk7I1mO0AorZD78ePJ4h6BA9hmwaXjaifiCuAVUBV/FgFjAOKlS1trn7x9vuKqO3TTGANUTuj/sqVNts2wIvxb8Xy+FFN1Jv1NsrVYL4fE7X9Wx8/5gBnKFej2b4DTCTqAO0f8fPvKNcmuS2DzBbisTzkbIEfm4LMRqDn2aHmSskX3P4fai61Qc/e7UTjgB5O1Lauc/x8EMn1FFojyGxmdg4wjahHxxvjx5fAU2Z2blK5As/2B6L2fhcBu8SPi4hOjH6fYC4IN9vtwFyiAtxAdx9INATQW0TDdCnXhm4nGh7pu+6+ubtvTlRbURDPU656zOxyot+Jx4na3h4XP78+nqdcG2Y7hqgQtyvwZvwYCMyJ5ylX3Vwhb8sgswV8LA85W+jHphCzBXmeHXCuYPf/UHMlesViU34QXc0bmmb6XsBXypY21yLgvDTTzwU+SXidBZmNqEb6kDTTDwVWJbzOgsxGNPRjvzTT+wNfK1eD2XZJM31XYK1ypc22FDghzfQTgC+UK22294HL00z/DfC+cm1S2zLIbKEey0POtgkcm4LLFvB5dpC54gyh7v9B5lINevaWERVQ6ltFyhihCQk1W2eiq1T1PU00tmqSQs22nvS9+X4AlOc3ygZCzTaPaPzU+roS9X6flFBzASwkqqmurxBYnOcsqULNBVGudG3hX2cjhjDNgVBzAXQnfc/L98TzkhJqrpC3ZajZQj2WQ7jZQj42hZot1PPsUHNBuPt/kLlUQM/eOGCCmXWrmRA/v5aoXW6SQs32GFFHH/X9kGTH6IVws00EfmVmpTUT4ueXxPOSFEw2M+tY8wB+AfzezI41s23ix7FEw4mNVa60LgT+EHfGBtR2zHYjcEFiqcLNBVE7v7PTTP8JcG+es6QKNRfA80Q1OfUNJeprICnPE2aukLdlqNlCPZZDQNlCPjaFnC1FqOfZoeaCgPb/eoLMpV7cM2BmbwCpK6wPUMo3Ywv2JKpVLPOonUyrz2Zmo1NediQ6qX4Z+Gc8bTDR2I3Xufu4fOUKOZuZPVxv0oFE2+7N+PUAoAT4h7sfna9cIWczs2rq7v8147B7/dfuXtjacwGY2QrqZmtPVPNV0zNuzfOvPGr73apzxdkmpLwsIhqP/WPg1Xja7kS/tXe5+09be6442xEpL7cCLgempGQbTNR++TJ3v0W5gt6WQWYL9VgecrbAj01BZgv4PDvIXHG2UPf/IHOlUgE9A2Z2WXOXdffftGSW+kLNZmYfNHNRd/ftWjRMPaFmM7P/a+6y7n5aS2apL9RsZrZPc5f1PI5pGWouADM7pbnLeh7HgQ41F4CZPdfMRd3d92/RMClCzQW1J9vNkcTFs+bId66Qt2WQ2UI9lkO42QI/NgWZLeDz7CBzQdD7f5C5UqmALiIiIiIiIhKApDsZERHZaGa2EzDH3avj5w1y97fyFCvYXBC183P3L2ueN7ZszXL5EGouEZH/FIEfm4LNJpIvqkHPgJktB/q6+xdp2knWkUDbyCCzxW3WfuXuX9Vrv7YBd/+fPMUCws1mZjOBA9x9RZq2RfVz5bs9UZDZ4ttVu7n75ynt1yzNokncRhtcrjhbFdC9XrYNFst3tlBzxdkeBk519y/T9MdQR577YAgyF9S29fuTu6+r1+5vA+7++zzFCjlXyNsyyGyhHssh3GyBH5uCzBbweXaQuSDo/T/IXKlUg56ZnxMNZA9wfpJB0gg12y58M0zSLo0sl8SVolCzPUrUoQfAI3n+300JNVsvonF5a56HItRcAPsDy+Pn+yUZpJ5Qc0E0VI2nPA9FqLkgOjbdC6yLnzfEgbwVhAk3V8jbMtRsoR7LIdxsIR+bQs0W6nl2qLkg3P0/1Fy1VIMuIiIiIiIiEgDVoGegqfaQqfLdNjLkbCItrd6QSY1y98daMkuqUHNBbTu/Zkmg3X6zqP2hiEjmAj82BZkt1PPsUHPJxlENegYaaQ9ZZzHCGDcy7WLkv81Oo+3UUiXUnq5Z8tyertE2RKkSaE8UZLaAh0wKMhfU+c1I17YvVRLrLLhckHa82QbluQ+GIHPBBmNnNyqBNojNkudcIW/LILOFeiyHcLNtAsem5kjq2NToYijXN/803P0/yFypVIOemdDaQ6YKNVtI7dTqCzVbaG2IUgWZzd0Lks6QTqi5YiG17UsVai4Iq9+FVKHmgsbb96VKog1ic+Q7V8jbMtRsoR7LIdBsIR+bAs4W6nl2qLkg0P2fcHPVUg26iIiIiIiISABCvUolIiIiIiIi0qqogC4iIiIiIiISABXQRURERERERAKgArqIiIiIiIhIAFRAFxEREREREQmACugtwMyeNbNfmVm7pLPUF2o2MzvZzLZPOkc6oWYzszvN7KSkc6QTajYzq46/A7smnSVVqLkAzOwDM7vDzLZKOkuqUHMBmNmlZrZX0jnqCzUXgJntbWadks5RX8C5Qt6WQWYL9VgO4WYL/NgUZLaAz7ODzAVB7/+J5VIBvWV8DBwAvJt0kDRCzTYJeNvM/pB0kDQmEWa27YArzGxW0kHSCDXb6cALwE1JB6kn1FwAk4FC4OWkg9QTai6A04C/mdnjSQepJ9RcAM8DC8xsTNJB6nmeMHOFvC1DzTaJMI/lEG62kI9NoWYL9Tw71FwQ7v4/iYRyaRz0FmRmHd39y6RzpBNiNjPrBRzi7n9MOkt9gWf7nru/nXSOdELOJtKSzKwtsJ+7P5V0llQB59qW6MLeIe5+UdJ5aoSaC8LdlhButsCP5cFmk8yFeJ4NQecKcv9PKpcK6CIiIiKBMLP+7j4n6RzphJxNcsPMity9Mukc6YScTSSXdIt7jpnZlmZ2adI50gkhm5kVmFlfMxsat+2rfSSZK/Rs9ZnZt8zs5KRzpBNqNjPb3syeTTpHfUnnMrNhZvYbM9s/fr23mU2N26udplyZMbP2gf5mBJcrPib1TDpHfQHkesvMXjOzM81sswRzpBNyNsmAmQ03sx3j5wVx++RPgPVmtsjM/tfMTNkaFv+unmZmV5rZeWb2X0lngnBzpWNmRYEeB5LN5e565PABDACqks4RYjZgMLAAqAKq6z0SXWchZwtxW26K2ZQr7f8eBVQArwOrgVOBFcBtwB3AeuBY5do0tmeouYDNgHuAj4j6DighajdaHf/mTgc6Kldtrr2AO4EvgTVxtr2S3odCzgYUA9cA84EZwOn15m+Z4P4fZDaitsh7xc9/AXwB/BwYDvwM+BQYm9A6CzIb8Dawefy8B/ABsDLersuAz4BeypVRdh0z0zyKkIyY2U5NLPKdvARJI+RssVuAfwOHAUuAkNpXBJXNzDo2sUhiNRehZjOz0U0ssnVegtQTaq7YGGCMu//ezA4AHgcucffrAczsbeB84M/KJRvht8CuwHjgaGAKsD1RYa8QuBkYC1yiXODuLwIvmtlPgRFEF6imm9l8ogtUk93903xm2gSyXQKcTLQtOwMTzGx3d//vlGWSqnENNdu3iS5OAZwInO3uD8Wvp8Xb9AbgamWrtQPUlp2uAhYDO7v7KjPrAPwVuDLOrFySNbVBz5CZVRMV3tL9mNZMd3cvzGswws4GYGZfAQPcfX4S/78xoWVL2ZYNLkLy+1mDi5BAtjjXEqC8gUVKgG7K9Q0zWwPs6O4fxK/L1kIUGgAAIABJREFUgUHu/lb8egfgJXffQrlqsy1vYpFCoEMC+1mQuQDM7GPgFHd/zqJh8RYBR7j7E/H8w4Dr3H0H5UrPzHoT9Y5+EtANmObuRySbKhJCNjMrA36esu16A1OBl4h6++4KLE5o/w8ym5ktBo5291fN7FOijrDeSJnfB3jT3fM+LFeo2eLjeTd3/9zM3gfOcvdnUubvATzg7nm9NTrUXPH/ntnEIm2Bvgns/0HmqqEa9MwtBy4C/tHA/H5EtT1JCDkbwGtAb6LbvEITWrbVRFc7X2tgfh/g1vzFqSPUbB8R3fI2Jd1MM9uZ6JbpfAs1F0S3kZekvF5PdNtq6uu2eU0UCTUXQClRzersBuZvC1yWvzi1Qs0FUQFkPoC7Lzazr4F5KfPnEN2WqVwNcPf5ZvZbot+Tq4ju9gpCINm2JtpeqZn2BZ4F7iY6N0pKqNn+ClxiZkcCjwLnmNlP/Juau58CSQ2VGnK2mgxtiC6+p/oE6JLfOLVCzfU94AGi2+7T6Q70zV+cWqHmAlRAz8brwFbu/lG6mWbWmeRuowo5G8AfgOvMrBvRSWRF6sya2rGEhJZtZvx/p6ebaWYrSW5bhprtdaLbVdMWhGn47pKWFmouiAonOwDvxa+3JroAU2N7olrFfAs1F0QnhQvdfXK6mWY2gGQKwqHmgqgNZBdgYfz6UaL2kTU6EF10ybdQc9VhUed+pwPHELWPn0J0O3niAsr2KdHvwoc1E9z9EzPbD3iOaDzjpISa7WL4//bOPe7Ssdzj3x9y2I6FnLO1iWynnJIGm7YSTUUhO5JDm0K7kEhENUTtlJ2kyKEdFQk5dKJRlEJERpFjNpLzyGnM/PYf97PeWe+atd6Zd5jnvqb3+n4+62M9z71m1vfjfed5nuu+7+u6+Bkl3/vXwI7A1pJuoyxYvAJ4S7rNwOWSXgAWo6SOdnc1WJlyXalBVK8/AL+xfXK/wWaR4gPtKgFxvYAM0GeHrwELjzB+L2WrVw0iuwF8v/nvN7vODW29p2zBrEU0t7MZeYXwQeDollx6iep2JDDSdrdJwCotuXQT1QtKDu5jnQPP2Bt1QwZPLMxJonoBXELJIx3Eo8BZLbl0E9UL4CZgI6ZP7vXmQW4E3Nq2FHG9aLbcv795rQr8Cvgw8D3bf6/h1CGo2xWU/NphOwibnRFbARNrSDWEdGvykzcF9gLGUyYQ5qHsXjoHONl2lYnQwG69zzZP9RyPB37Zkks3Ub0ArmbkGliTgV+05NJNVC8gc9CTFpG08kjjg1b+2yCyW5IkydyMpFcA02w/PmD8rcAztiemF0i6DPh3SuXqs4Bv2v7TyH+qHaK6NffwNWz/eMD48sDWg3aYzEkiuyVJEpMM0JNkLkHSipRCMtNqu/QS2S1JXiqaojJvsv2YpCOBL9h+Or2SlxJJF1G2iV9se2ptn24iuyVJLZqaAr+x/Uxtl26ieiUzJwP02UDS1sA44ErbVzQ5WIdRivR8y/bp6Tbk83bgMttTmvcDsX1RS1pAbLd+SHqS0jbjztouvUR26yDptcAltl9d26WbqF4Q162WV1NEbDXb90maCixn+6E2HeYmr35Iejll2+prm1O3UlZhZ1aFfo4S1atDMwlKrS3HIxHVLfJ9KbJbh6jXf4jn1nQaWdd2lZSYQUTykjRPv0UcSfMAK9q+t4JWWK/MQR8lknYFTqfkrh2o0gv0BEo/3nmAr0mabLv1/rxB3S6gtF15qHk/iBp53pHd+lGzwN/MiOzWYX5KoZRoRPWCuG61vG4ETpd0FeV3/uCmJdwM2P50eg2nmTC+CHgSuK45fQBwhKTxtqvk+wX2mgf4JHAQpWAdkiYD/w1MqLljKbJbF5HvS5HdOkS9/kMltxFac80HfF/SswC212/PKq4XgKTFgFOB8c3E1CnA0V07cJamVFJvu81aSK8OGaCPnoOAg2yfKOlNlLZlh9s+AUDSJOAjlKB4zLvZnqff+whEdktGj6QvzuQjVVqMRPWCuG5RvShFsY4G3kaZuHsr8EKfzxloMxB+PzG9ejmJUtzvg52HIEnzAl9txtZOr2FMoKzqH0opaARlh9xRlFZKh9fRAmK7JbNA4OtsZLe1KdXlr+k6J2BdSkX+WjuXonoBfKbx2I1SyPSTwPqSdrD9fPOZGhNWUb3KF+cW99HRrEqsbfuu5vh5YMNOGy5JawBX2V4q3ZKXEkmHUSqX9i1oVJMIbs3W3hspq2D9WARY33bbs7QhvSCuW1SvbiRNA5aNtpU8qhcMbcVfr7eomKTVgRttV+ltH9jrfmDf3hQrSe8Avmp7hRpejUMoN0lr2f5Dz7mTgSNsP9ymSy9R3SJfZ6O6SXojcCbwbcpq67Tm/BTKVvJJbfpE92oc7gF27xTblLQUpevI48DbKcHx/RV+liG9OuQK+uiZQtla0+E5hrczeI6RW1DNScK5SfrwrH7W9olz0qWXyG4DHI6t7TCIIG5/Bk6w/b/9BlV6Wl7frhIQ1wviukX1GmKkXTeSFqpVlCeqV8PvKDnevVW/Xwv8vn2dIaJ6vYLSB7qXPzZjNYnmdpOkaylbVr9je7LtD1bw6EdUt8jX2ZButq+WtAGlrfGvJL3X9h1te8wtXg1LA0OdkGw/LOnfgR8DlwJ7p9eMZIA+ev4MrMH0G/kKlF55Hf4FqFUoJaLbR2fxcwbaDoIju4Ur+DcXuF0HbAD0vaEzva9920T1grhuUb1GRNICwP7Axyj1LUIQyOtE4MuSVmX6VsxNgP2AQyWt0/lgZ+fXGPf6PeXn1juZvD91Jw4gntsWwB6UHPgTJH0fONV2rd7P3UR1i3ydDetm+wlgF0l7AFdJ+lTjU5WoXsC9lMnOuzonbE+W9GbgJ8AP0mtGcov7KJG0PfDIoKIxkg4FFrZ9RLtmsd2S0dFT8O81lIJF3QX/dgXeG6AYYRg3ScsCCzhYz/qoXhDXLaoXDAW7RwFbA88Dx9u+oHkomgBMBb5i+7j0msFxZoXDOg/dbnNbYWCvLShbLu8Fft2cfgOwErBtzQAvqpukhYGdKHUZNqMsXJwGnGn7wRpOHaK5Bb/OhnXrRtJqlG3lGwJr1dxK3k0kL0knUjqL7NhnbFHgp8BGFba4h/QacsgAPamBJEF52qnt0ksEN0k3AKePUPDvIGB72+PSLUnaQ9JxwD6UgjybUrbJnU5ZcT0GONcV+kNH9epG0ixXXW7zwTyqF4Ck5Skr+Ws0p26l5Hjf36ZHPyK7ATQ7IvagFIFaFviR7RFbqrZFBDdJ89a+JgwislsvKh0NFgWejPRMG8VLpYXl8rZvGTC+KKWewJXp1fX9gX6X5lqalemvOWbxrlBukt5H2Wa5WnPqNuDztr9Vz6oQyS1ywb/IbsmLo7khdW8bnGa7b7uuNonkJelO4CO2L5K0FmUnyRnAXpUfgkJ6zSoB8uP7EtUrmTWaVev3AscCS9RaDetHbTdJD1KuEd+0fVub3z0zoro1Qd2ulF0PT/aMLQ68r9/YWPVKZp9sLfXS8AnqF2wZRBg3SQcCJ1OKL+zUvH5E6c8+q/ngY8UtXMG/LiK7IWleSQdL+q2kByU92v1Kr2FO60m6tOvU/cBjXa/HJW2UXsNYkaY4kUtl5ucoxYxqB8FRvUZE0gLNrpu7ZvrhFqnpJWk1Seeo9OntHVtc0tmSXt22V3S3HpfNJZ0BPAh8HjgfeGNVqYZAbicB7wZulfRLSe+X9E8VPPoR1W1/YPN+gW6TA74ZJe2vbaJ6IWkrSZNGuGbcImmz9BpOBugvDeGKFXURye0ASp/Zj9u+qHkdAnyIGQvNjHW3TsG/Disw/EExQjHCDpHcAD4FHAh8F1gc+CLlAWgaJUe3FhG9DgCu6jm3G7AV8CbgbOr8/kf1ApiXkuPd4QWGT1DVIqpXJ9g9VtJ1kn4l6Z3N+T0o146PUOpYpFfhY8BfRnjY/kvzmRqEdZO0vKRPSLoNmAisSrlOLG/7A7avGfEvGGNutj9je1XKNfVO4CvAA5K+Ien1bfvMJW7volRKH8QplImFtonqBeU6+o0RrhmnUJ6N2iaq15BEvl7ki1Ip/dW1PaK7Ac8Cq/Y5vxrwbLoN+97tKbOhg8YPBT5T6f9VWLfm++8AtmveTwb+pXn/YeDs9BrmdCvwuq7jYdcL4PXAPek1zG0apTjW+c1rCqUty/ndr/Qa5nYcpbfseZTdEFOAr1O24b8HmDe9hnn9iVKcaND4BsCf0m3Y917W/PweaH6uq9f4/zO3ufV4LkJpLXVVcz25BTiwtlckt+Ze9KoRxl9FyflOr+nffQ/w2hHG1wDuTa/hr2yz9tKwJvB/tSUGEMntz5St48f0nN8ZuL19nWGEcrM9YnsH259ry6XPd4d1a1gWuLl5/xRltRrgYuAzVYwKEb1WBv7WdXwk8HDX8QPAMq0aFaJ6AZzZczyoDVDbRPUC2BF4n4fnx88HrOvmSSi9hvEq4KERxh+mVEuvQVS3KZQVwosdr7hYZLchXOp6nAqcKmk74CzKFvwvVhUjlNtUYHlK94J+LE+ZQGibqF5Q7tVTRhh/gVLUtG2iegHZB3226a4wafsvkjZuKibeYPu5dOvLp4DvqvTMvro590bKFqadqlkVIrslo+M+YDnKjeoO4M3A74CNKHm56TWdZynB8H0Abirxd7ES8HTbUsT1wvYeNb53ZkT1ahiWHy8pSn58VK8nKKlCgyrGrwrUKvYU0s19KqBLWrEZq5lyFdqtmybHeydKdflxlPvU56tKNQRyuwF4JzAoJWH75jNtE9ULyiLhWpSFsH6sQ5l0b5uoXkDmoI8aSStLug54TtJlkhaT9FPKP4pfAZMkvSbdZsT29ylbUx+mXEje2bzfeGarsmPNTdJjvUXEmtddkn4saeu2neYGt4YfUCZWAP4H+Iyk2ykz7t+sZhXTq3NTH8QO1H3YGEQtr2T2iJofH9XrF4xc0OnDQK0e6JHdkDSPpCMlPUGZRLhH0uOSjmgWKqoR1U3SppJOpQQjJwF3A1vafk3tHXEB3b4CHCRpf0lDVfdVisAeAHy08Uyv6VxKed5ZsHdA0kLA0ZSdhG0T1as41J8onruQdB6wFPAFStGiFShbJHalbB85HXjG9vbplswuknYfMLQEJcdvZ+Ddtn/YnlUhsls/JG1C6Qt9exQniOEl6V3AdyjFUk62Pa05Py+lQOJ/A/9h+7z0SmYXSdMoebidnSLjgSuAv3d/zvYO6QWSXgf8mvJweDwl7xtKTuQhwHbAprZ/16ZXdLfG71hgL8quuM5uuHGUQpzfsH14DS+I5ybpEMqK9GuA64DTgHNsT27Tox/B3SYAh1Hyvu9sTr+akif/eduHptcwr2UouwWnUiYSuq8Z+1EmSte3/df06vLLAH10SHoIeLPtG1V6Cz5GKZh1VTO+PnCp7WXTbQa/GVoZNBh4zvbzA8bnOJHd+qHSFu7dtjet7dJLcLcNbV9X26OXml6SjqNUXe53U/+i7VoVmUN6JaNH0umz8rm2t+lH9QKQ9DbKzpole4YeAfa2fVHbTh2Cu90P7NvrIOkdwFdtr1DHLJ6bpL9RalWc5tKaMQyR3QAkbUzpYb8qpVvSbZRir79Nr75e/wx8FXgL07tLmVLIdD/bVdpsRvWCDNBHjaQnKQVk7mq2JD0HbGj79834qsDvbA8K+MakW/P90yi/+IO4DzgDOLqzYtYWkd360aQqXGM7RI/7bmq7SVoEmGr7ma5z61EKsW1re96Bf3gMejUemwC7ULoWQCmMeI4rtiWCuF5J0gbNNsttGP6w/RPbVeovdBPVTdKzwDq2b+s5vzpwo+2F6pjFc5P0MtsjFcmqRmS3ZHRIeqXth5r3L2f6NeN224815zez3WpqTFSvDlkkbvTcAuwJHAHsTpkxfg/w+2Z8F8qNqgaR3QDeD0ygBLqd2byNKa6fpVRLPJgysdBbTX0su/VjAYbnTkaiipuklYDvUX5uUyV9BfgkpTfozpQc8NZX9aN6NW5HAl9oAt4wQW9UryRpk2Yy7wcwVFTs/ggTxBDa7ffA/pRc+G72Z/qzUC2iuX1Q0kw/ZPvEFlx6Cekm6XLgJNvnDxhfCvit7Ven1xB/kPQh2+c1ge+1XV4LUVoP7gvMn17TyRX0USLpLcAFlAJ70yjbIr5B6aU6jVKR+T9sfy/dZvC7HDil9/sl7QTsY/tNknYDDre9RroNRtKXgDVsb1PbpZdabpK+A6xOyVXbAdiCkl/0G+BztarlRvVq3KYCy3VmkaMQ1StJatHskFvP9p0z/XDLRHKTtAVwCaVbxq+b02+gdH7YttZqWEQ3SbOyfdc1grqobs1uy2nABNuf6jO+DGWyqtUdcVG9mu8+iLJT8ELgQ92r05TaWNOAPWxfPfhvGTteQ34ZoI+eJmdhA+B623c3v/j7AwsBl9j+ebr1dXuGsr3r9p7zqwG/t/1PklYBbrH9T2PZTdKg3p6LA+tTCqdsbvv6Oe3SS1S3Jr9vB9vXSHol8CBwoO0vtekxt3jB0E192WiBcFSvJKmFpMmUFLbqQXAv0dwkLU8p8tSZTL+VkuN9fz2rQmS3ZOY096Z9KMWYrwB2tf33rvGaAXo4r67vXxM4k1K8+sPAZpSCrycDH+9O/Uuvxi0D9KQtJN0GnO+eSpKSPgdsb3t1SRsCF1YolhLKTdKgiZQnKZUmT65YVCOkW7PqurybipuSngI2sP2nkf/k2PRqXKYBy9j+W22XbqJ6JUktogXB3UR2SwYjaZWaRbBGIqpbZ/KYUiDxQkra4zs6v/uVA/RwXj2O8wLfBnYEngbeZvvKWj4donplDvpLhEo12MMjzoIGcjsYOFfSW5me67EhZSb53c3xRsB304132H5ypA9I2qLSRSSy27Se91Hy9KN6AdwmacSZWtcp+BfVK0lqcAzwaG2JAVR3a3a7fZqSkvZkz9jilBWxT9aYRAjsdoeke4Cfd141U656iOyG7VslbQScA1wraWfbP0uv/kh6GaWv+A6U5+htgE9IuqNyml9IL8gV9FEjaZ0BQ9cBO9G0A7J9U2tSDZHdOjTbxPehbIOGsuJ6iu27azl1iOQmaSKlZV7fQK7JZbvY9qKtihHXrZlBfoLp1fiXoKzqDytc1HZQF9ULhtw+QvEbiO0z2zEqRPVKkppImg9Y0PZTtV16qe0m6evA47YPGTB+HLCY7Q+2axbXTdK/AZ3X6ynFsO6kbJHuBMV1ekAHdetNv5Ik4FjgQODjwNlUXEGP5tW4rAd8C1gY2Mv2zyWtQKmRtSlwkO3T0qvHLwP00aHp7bj6lZfsnHelfwRh3ZLRIelmys1oe/dUxpW0OXApcLrtA9Jt6Lt3n5XPVQg2Q3pB3FzvqF5J0gaSxgNL2j6j69zhlA4t81GClJ07RY3SDST9iZJ3e+2A8Q0o/aBXb9Or+e6wbl0OC1KCkn9rXhsDLwP+aPtfa3lBLDcNKGAq6T3AqZTJg9bbpkb1ahyeo+R5H9g7gSdpb+C/gattb5teXQ4ZoI8OSTdSemIfDHSKB4jSn/etzX+xfU+6zYikJSgX11dSqs0PYfusKlINkdyaQjK/pFwc3td1fjNKFdhv2d6vTafobpIW7i6KEoWoXjD4pl6bqF5J0gZNnY/zbJ/UHG9KueYeSSkqNgG4zPaB6Tbk9Qyle0jf5xtJKwO3uuUCtM13h3Xr4zI/8EbKM+M+wCJRFnUiuI00edysyF4ArJRew77/rbYvG2F8ZeBU21u3qBXWa+j7M0AfHc0F4nhga8qM6A3N+SmUIimT0m2g33hKIYZFKFt8u3/5XDOfNKKbpH+hPPica/u/JI0DLgO+bXvftn2iu0m6A9jd9lU1vn8QUb0g7kp1VK8kaQNJDwFv6bqHfxFY003rSknbAl+2vVq6DXk9SGkje8WA8TdR7k/LtunVfHdkt/mBTYAtmb6d/C/AL5rXlbbvbdsrqluTwne17RcGjC8JbFdhUSekVzL7ZIA+m6gUE/s68FVKM/vnCBAEQ1w3lUrplwKfsP10TZdeoro1dQUmAhcB2wPftf2fVaUaorlJOp6St/xlSlHEEIXYonolSRKTZsV19U7wIem3lMnQzzfHKwOTbC+cbkNe3wNeZnv7AeMXAs/b3rFNr+a7Q7pJuoIS9N4FXEmZdL/S9gNtevQjslsyOiRdDpxk+/wB40sBv3X7Pe1Deg19fwbos49K24LTKauubyBAENwhopukvwNrO2ArlmhukhbrOnwj8APKFqV96Frd90yqqc8JgrttAnyTUoBtt84qT22ienWj0kZwJ+BVlII8Q9jeoYoUcb2SZE4h6c/AfrZ/LGkR4BFgK9tXN+PrAz+2vXS6DXm9Dvg1cDFlJ2GnjeUawCHAdsCmtn/Xpldkt2Z35QOU+/dESgD8SJsOg4jsloyOZkfcNGCC7U/1Ga/Zmi6cV4dss/YicKkgua2kD1NuUq0HJIMI6vZjSuuyEEFwD9HcHmf4NntRgpQdu44N1LhwhHWzfU3zMPRZ4FeSfgq80POZ1oO6qF4dmkIyZ1H+HbwZ+Amlm8EylAmY9EqS9jgX+JKkY4BtgQeBa7rGN2R6kNc2Id1s3yDp3ZSJ0N6V6keAnWoE5xDabQlgM8r28Y8D5zS7Ca9kelD8twpe0d2S0fNB4AvNzstdA9XlieqVK+hJe0jai1JI5nTgZmBK97jti2p4QTy3Jp9oprhCr/HIbjC0wv8/lAmD7zNjILxHeg1H0k2UloInSZoMrEvZWngK8EC/2eWx7JUkcxJJC1F+x8dTAuD/tP3LrvGfAz+yfVy69fXbBliVMll8G/CTCKlrkd0AJC0KjGN6zve6wO2216rpBbHdkpHp1JQBlgQupKTdvqOzY7XyCno4ryG/DNBHh0obqZli+xdz2qWXyG4w9I9hEK71jwBiuyWzjqStKasUD1AKs91aWQmI69WhSfH4V9t3S3oE+DfbN0t6LXCF7eXSK0mSuQlJK1IesEe6v1chopukeYCNKEHwlpSAeMEIzz+R3ZKR6S76Kmlx4BxKfYGdbf+sdoAezatDbnEfPROZvr23X79xqLf1eCJx3bA9z8w/VYdobj153gMJkIM+kLbdJJ0C7A4cQ8kpmtozviJwZNuF7KJ69fAYsGjz/v+AtSg7SZYAarb+ieqVJNVQ6Qu9v+0v1HbpJZjbJGA94qSudVPdrQl6N6SsSG9JqSmzMOVa+3Ngv+a/6Za8JNh+QtJ2wLHApZI+DpxdWSukVwboo+cxYDJwBvAt4OGqNsOJ7JaMjt48714i5aD3UsvtjYxcaGdJYC+g7UA4qlc3v6C0Z7yZkmP6ZUlbNecuT68kaRdJS1NWc54HLrc9VdLLgA8Bh1Ge36oEwZHdehi0UBGBCG6PU4LeBynB7keBibbvqGpViOyWjI5hz4suW7cPlXQjcCqwVRWruF5ABuizw3KUIh97UqpvXgqcRsm5qp0vENkNAEkLA1vQvyLziVWkGoK5bdn1XpSf5d6U2ePaRHVb3zFbmEX16mZ/YMHm/QRKDYZNKbnyn60lRVyvJJljSBpHqfi9GOUh8jpJe1AqWr8AHAWcmW7Ji+RjwM9t31ZbpA+R3ZLR0XcyyvZ3JP2Rcu2oQVQvIHPQXxSSXgW8n7J9dQHKTelTtl8Y6c+1QUS3por1pZStqQsDjwJLAU8DD7lSr8HobgCdAllR2sB1E9mtG0nrAr+LlrMW1auDpIVsP1Pbo5eoXknyYpE0EbifkhazO3AQcDtwuO3zKqqFdutF0mHAybYfr+3SS2S3JHkpaQoLXz0o/pC0JLCd7bPSq+v7M0B/8UhahbJSvQWwtO1HKysNEcmtubHfBuwLPEGpwjkF+F/gy7bPT7f+RA6CI7t1EzUQDuy1ACXP7xDby9b26RDVK0leKpqCiJvZntRU/n4K2MH2hZXVQrv1Q9J8lGJiT9V26SWyW5Ikdckt7rNJ85D4Lsp28jcAl1BmWqoH54Hd1gP2sT1N0lRgAdt3SjqEssJfMwiO7JbMApJm9jNaohWRHqJ6wdC14ihKTvfzwPG2L2i2rE4ApgInpFeStMrLaWrI2H5G0tPAH+oqDRHSTdJ4YEnbZ3SdOxw4AphP0hWU6syPpVuSJNHJAH2USNoY2AN4D3A3pW/2TgGC39BuDVOATkuRhyi53rdSVqxXqiXVENmtQ+TtLhHcnpiF8RpblaJ6AXwa2Af4GSW3+1xJpwObAAcC5/ZWnR/jXknSFmtK6uwQEbB6UydlCNs3ta8FxHQ7EBjaYi9pU8p15EjKvXwCJSA+sGWv6G5JkgQkt7iPkqZv3r2UVdXrB33O9kWtSTVEdgOQ9BPgDNtnS/oGsA5wIrAb8HLbr6/hFdGtz6rreOAK4O/dJ23v0JpUQ2S3ZHRIuhP4iO2LJK0F3ETpArFXzcKSUb2SpA2ae7npX8Soc9410mKiukl6CHiL7Rua4y8Ca9repjnelpKutlqbXtHdkiSJSa6gzx6vosx2DqJar3Fiu32C6T2ND6esGp5MKTCzZyWnDtHceldd/7eCwyAiuyWjY0WayTzbf5D0HHBCgCA4qleStMEqtQVGIKrbosAjXcfjKK0ZO9wCLN+q0XQiuyVJEpAM0EeJ7XlqOwwispskUQK7ZyTNZ/shYJvKWkBMN9t71Pz+kYjsloyaeSk53h1eoBR9qk1UrySZ49i+Z2afaXaWtE5gt/8DXgvcK2kRSqHXj3aNL0npylKDyG5JkgQkA/RkjtNUkr8IWLM5dZ+kd9m+rqIWENstSVpAwBnNCjWUnuNfk1Q7XSGqV5JUQ9KiwC7A3sAG1NsNNwMB3M4FviTpGGBb4EHgmq7xDYE/tezUIbJbkiQByQB9NpG0pO1HmvcrAR8AFgJ+aPsX6TaMz1N+13YFngUOBk6h3MRrE9ktSeY0Z/YcR0lXiOqVJK0jaXNgL0p3lvspXUX2qyrVEMjt08AKlNoxDwK79hRIdru0AAAIDklEQVSS3AX4YQUviO2WJElAskjcKJG0NuVCuhIlP/k9wI+AhSlVwBcG3m37gnQb8nqw+d6rmuPlgPuAxWz/fcQ/PIbdkiRJkrFJUyX9/ZTgdzHge8C+wLq2J1VUC+2WJEnyj0DYnOXAHA/cDGwOTAQupvQZX5zSH/QU4NB0G8YrKRMGANh+AHimOV+byG5JkiTJGEPSDylbntcBPgIsb/uAulaFyG4jIWlBSQfX9uhHZLckSeqQK+ijRNLDwFa2b2qKfTwJbGT7+mZ8DeAa20uk25DXVOA1wN+6Tt9HqWR6d+eE7Sfb9ILYbkmSJMnYQ9ILlO3QJ9u+vev8FCqvUgd3Wxp4PaXA5OW2p0p6GfAh4DBgPttLpVuSJNHJHPTR8wpKDhG2n2qKFj3WNf4Y09t1tU1UNwG39Tl3Q9f7Wu3fIrslSZIkY49xlO3j10u6FfgW8J26SkOEdJM0jrJrcDHKPfs6SXsAF1C6QBzFjLUtxrxbkiQxyRX0USJpGrCM7b81x5OBdWzf1RwvA9xvu/WALqqbpC1m5XO2r5zTLr1EdkuSJEnGLpIWBnYG9gQ2pkwUHwh80/bkdBvmM5FSpO4YYHfgIEr62uG2z2vbp5vIbkmSxCQD9FHSBMGXAZ32P+OBK4BOQbEFgG0qBugh3ZIkSZIkmT0krU5Zud4NWAL4qe2317UqRHCT9Aiwme1JkhYCngJ2sH1hmx79iOyWJElMMkAfJZJOn5XP2d5jTrv0EtmtF0mXAHs3RdlCEdktSZIkGbtImpcy+b5nlAC9Q023ZoFiWdsPNceTgfVs39GmRz8iuyVJEpMM0JMqNDeodW3fWdull8huSZIkSZIMpwmCtwIebU79CtiJUvR1CNs3tawW2i1JkphkgJ5UIXIQHNktSZIk+cdG0huAJW1f3HXufcDRwMKU4mIH2H5uwF8x5tyaINiUwq69dM67YvphSLckSWKSVdyTWtwDTKktMYDIbkmSJMk/NkcCEymVv5G0NnAacAZwK/AxStGxo9JtiFVa/r7RENktSZKA5Ap6kiRJkiRJECQ9AIy3fV1zPAHYwva45nhH4Gjba6bbrCNpLdt/qO3Rj8huSZK0T66gJ60haTXgHcA/U7Z13QlcGGEreWS3JEmSZEzxcuCvXcdbUDq0dLgWWKlVo+lEdpsBSYsCuwB7AxtQ2sGFILJbkiR1mae2QDI2kHQYMAk4DngXsCPwBeCPkg5OtyRJkiQBSgC8CoCk+YH1gWu6xhelXhpWZLchJG0u6UzgAeBgSsvZTepaFSK7JUkSgwzQkzmOpC2BzwITgKVsL2d7WWBp4HPA5yRtnm5JkiRJwqWUe89mwLHA08Avu8bXAWq16ArrJmlZSYdKuh04F3gSWAB4p+1DbV9bwyu6W5Ik8cgc9GSOI+m7wOO29xkw/nVgUdu7tGsW2y1JkiQZe0haCjgfGAc8Bexu+wdd45cD19g+PN2GvveHwObAJcC3gR/ZnippCqUry6Q2feYWtyRJYpIBejLHkXQXsJvtqwaMbwacZbv1SqeR3ZIkSZKxi6TFgadsT+05/4rm/PN1zOK5SXoBOBE42fbtXeerB8GR3ZIkiUlucU/aYBng7hHG7wKWbUdlBiK7JUmSJGMU20/0BsDN+UdrBueNQzS3cZT89+sl/UbS/s1qfwQiuyVJEpAM0JM2WBAY6YY9BZi/JZdeIrslSZIkSTITbF9j+wPAcsApwHso/djnAbZuKqanW5IkcwW5xT2Z40iaBnySkq/Wj0WBT9tuvcVIZLckSZIkSWYPSasDewG7AUsAP7X99rpWhchuSZLUJwP0ZI4j6W5Kb/ERqZSDfjdB3ZIkSZIkeXFImhcYD+wZLQiO7JYkST0yQE+SJEmSJEmSJEmSAGQOepIkSZIkSTLXIukNkt7Wc+59ku6S9JCkr0taIN2SJJkbmK+2QPKPj6SFgDfZvrg5PhbovhlNBY6w/Wy6JUmSJEkySo4EJgKde/nawGnAGcCtwMcohdmOSrckSaKTW9yTOY6kfYHtbI9vjicDtwDPNB9ZAzje9gnpliRJkiTJaJD0ADDe9nXN8QRgC9vjmuMdgaNtr5luSZJEJ7e4J23wXuDrPef+w/aWtrekzB7v1L4WENstSZIkSZKZ83Lgr13HWwCXdR1fC6zUqtF0IrslSRKQDNCTNlgVuLnr+FlgWtfxb4FaM8eR3ZIkSZIkmTl/BVYBkDQ/sD5wTdf4osCUCl4Q2y1JkoBkDnrSBkvQlddte+me8XkYnvfdJpHdkiRJkiSZOZcCn5P0ceCdwNPAL7vG1wHuqCFGbLckSQKSK+hJG9wHrDXC+DrNZ2oQ2S1JkiRJkplzBPACcCXwAeADtp/vGt8T+EkNMWK7JUkSkCwSl8xxJH0Z+Hdgg95q6E0V9euAn9n+r3RLkiRJkmR2kLQ48JTtqT3nX9Gcf77/n5zzRHZLkiQWGaAncxxJywA3As8DXwFua4ZWB/anpFq8zvZf+/8NY9MtSZIkSZIkSZKxRQboSStIWgU4GdgaUHPawE+BD9m+M92SJEmSJEmSJBnLZICetEqzlWvV5vDPth+t6dNNZLckSZIkSZIkSf7xyQA9SZIkSZIkSZIkSQKQVdyTJEmSJEmSJEmSJAAZoCdJkiRJkiRJkiRJADJAT5IkSZIkSZIkSZIAZICeJEmSJEmSJEmSJAHIAD1JkiRJkiRJkiRJApABepIkSZIkSZIkSZIEIAP0JEmSJEmSJEmSJAnA/wOd9HB8M7W8ewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x175 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "consensus = consensus.query('number_of_indels == 0')\n",
    "\n",
    "lib_target_counts = (\n",
    "    consensus\n",
    "    .groupby(['library', 'target'])\n",
    "    .size()\n",
    "    .rename('consensus sequences')\n",
    "    .reset_index()\n",
    "    )\n",
    "\n",
    "display(HTML(lib_target_counts.to_html(index=False)))\n",
    "\n",
    "p = (ggplot(lib_target_counts.assign(xlabel=lambda x: x['target'] + ', ' + x['library']),\n",
    "            aes('xlabel', 'consensus sequences')) +\n",
    "     geom_point(size=3) +\n",
    "     theme(figure_size=(0.5 * nlibs * ntargets, 1.75),\n",
    "           axis_text_x=element_text(angle=90)) +\n",
    "     xlab('') +\n",
    "     scale_y_log10()\n",
    "     )\n",
    "\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the non-primary targets, we want to drop all barcodes with mutations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping variants with mutations for all targets except SARS-CoV-2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>has_substitutions</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th>library</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BM48-31</th>\n",
       "      <th>lib1</th>\n",
       "      <td>107</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>117</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">GD-Pangolin</th>\n",
       "      <th>lib1</th>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HKU3-1</th>\n",
       "      <th>lib1</th>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LYRa11</th>\n",
       "      <th>lib1</th>\n",
       "      <td>35</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>41</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RaTG13</th>\n",
       "      <th>lib1</th>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>118</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Rf1</th>\n",
       "      <th>lib1</th>\n",
       "      <td>55</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>58</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Rp3</th>\n",
       "      <th>lib1</th>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SARS-CoV</th>\n",
       "      <th>lib1</th>\n",
       "      <td>122</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>123</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SARS-CoV-2</th>\n",
       "      <th>lib1</th>\n",
       "      <td>4481</td>\n",
       "      <td>94088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>4643</td>\n",
       "      <td>91872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">WIV16</th>\n",
       "      <th>lib1</th>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ZC45</th>\n",
       "      <th>lib1</th>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ZXC21</th>\n",
       "      <th>lib1</th>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x175 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "primary_target = config['primary_target']\n",
    "print(f\"Dropping variants with mutations for all targets except {primary_target}\")\n",
    "\n",
    "consensus = (\n",
    "    consensus\n",
    "    .assign(has_substitutions=lambda x: x['substitutions'].str.len().astype(bool))\n",
    "    )\n",
    "\n",
    "has_subs_by_target = (\n",
    "        consensus\n",
    "        .groupby(['target', 'library', 'has_substitutions'])\n",
    "        .aggregate(n_barcodes=pd.NamedAgg('barcode', 'count'))\n",
    "        .reset_index()\n",
    "        )\n",
    "\n",
    "display(HTML(has_subs_by_target\n",
    "             .pivot_table(index=['target', 'library'],\n",
    "                          columns='has_substitutions',\n",
    "                          values='n_barcodes',\n",
    "                          fill_value=0)\n",
    "             .to_html()))\n",
    "\n",
    "p = (ggplot(has_subs_by_target.assign(xlabel=lambda x: x['target'] + ', ' + x['library']),\n",
    "            aes('xlabel', 'n_barcodes', color='has_substitutions')) +\n",
    "     geom_point(size=3, alpha=0.7) +\n",
    "     theme(figure_size=(0.5 * nlibs * ntargets, 1.75),\n",
    "           axis_text_x=element_text(angle=90)) +\n",
    "     xlab('') +\n",
    "     scale_y_log10() +\n",
    "     scale_color_manual(values=CBPALETTE)\n",
    "     )\n",
    "\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print most common non-primary targets with mutations (useful for debugging):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>library</th>\n",
       "      <th>substitutions</th>\n",
       "      <th>n_barcodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Rf1</td>\n",
       "      <td>lib2</td>\n",
       "      <td>T506A</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LYRa11</td>\n",
       "      <td>lib2</td>\n",
       "      <td>G64A</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LYRa11</td>\n",
       "      <td>lib1</td>\n",
       "      <td>G64A</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rf1</td>\n",
       "      <td>lib1</td>\n",
       "      <td>T506A</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZXC21</td>\n",
       "      <td>lib1</td>\n",
       "      <td>T491C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RaTG13</td>\n",
       "      <td>lib2</td>\n",
       "      <td>G343T</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>T355C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SARS-CoV</td>\n",
       "      <td>lib1</td>\n",
       "      <td>T303C T384C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZXC21</td>\n",
       "      <td>lib1</td>\n",
       "      <td>C299T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZC45</td>\n",
       "      <td>lib2</td>\n",
       "      <td>C225T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZC45</td>\n",
       "      <td>lib1</td>\n",
       "      <td>C225T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WIV16</td>\n",
       "      <td>lib2</td>\n",
       "      <td>C98T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WIV16</td>\n",
       "      <td>lib1</td>\n",
       "      <td>C98T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SARS-CoV</td>\n",
       "      <td>lib2</td>\n",
       "      <td>T303C T384C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SARS-CoV</td>\n",
       "      <td>lib2</td>\n",
       "      <td>C8T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rf1</td>\n",
       "      <td>lib2</td>\n",
       "      <td>C457T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SARS-CoV</td>\n",
       "      <td>lib1</td>\n",
       "      <td>C8T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib2</td>\n",
       "      <td>T355C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rf1</td>\n",
       "      <td>lib2</td>\n",
       "      <td>C300T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rf1</td>\n",
       "      <td>lib2</td>\n",
       "      <td>C165T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(\n",
    "    consensus\n",
    "    .query('target != @primary_target')\n",
    "    .query('has_substitutions')\n",
    "    .groupby(['target', 'library', 'substitutions'])\n",
    "    .aggregate(n_barcodes=pd.NamedAgg('barcode', 'count'))\n",
    "    .reset_index()\n",
    "    .sort_values('n_barcodes', ascending=False)\n",
    "    .head(n=20)\n",
    "    .to_html(index=False)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the non-primary targets with any substitutions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Culling the 197577 barcodes to remove mutated non-primary targets\n",
      "Retained 197345 barcodes after culling\n"
     ]
    }
   ],
   "source": [
    "print(f\"Culling the {len(consensus)} barcodes to remove mutated non-primary targets\")\n",
    "\n",
    "consensus = consensus.query('(target == @primary_target) or (has_substitutions == False)')\n",
    "\n",
    "print(f\"Retained {len(consensus)} barcodes after culling\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there any barcodes in the same library that are shared across targets?\n",
    "If so, we need to get rid of those as they will be confounded in barcode parsing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are duplicated barcodes:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>barcode</th>\n",
       "      <th>duplicate_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>AACTGAGTTAACGTCC</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>ATAACCAAAACATCCA</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>CTCAAGGTAACGATCC</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Removing the 3 duplicated barcodes.Started with 197345 barcodes:\n",
      "After removing duplicates, there are 197339 barcodes.\n"
     ]
    }
   ],
   "source": [
    "dup_barcodes = (\n",
    "    consensus\n",
    "    .groupby(['library', 'barcode'])\n",
    "    .size()\n",
    "    .rename('duplicate_count')\n",
    "    .reset_index()\n",
    "    .query('duplicate_count > 1')\n",
    "    )\n",
    "\n",
    "print('Here are duplicated barcodes:')\n",
    "display(HTML(dup_barcodes.head().to_html(index=False)))\n",
    "\n",
    "print(f\"\\nRemoving the {len(dup_barcodes)} duplicated barcodes.\"\n",
    "      f\"Started with {len(consensus)} barcodes:\")\n",
    "consensus = (\n",
    "    consensus\n",
    "    .merge(dup_barcodes, on=['library', 'barcode'], how='outer')\n",
    "    .query('duplicate_count.isnull()', engine='python')\n",
    "    )\n",
    "print(f\"After removing duplicates, there are {len(consensus)} barcodes.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we write the retained consensus sequences to a CSV file that links the nucleotide mutations to the barcodes.\n",
    "(The next section analyzes this variant table in detail, and provides have more precise information on the number of variants and relevant statistics):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing nucleotide variants to results/variants/nucleotide_variant_table.csv\n",
      "Here are the first few lines of this file:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>library</th>\n",
       "      <th>barcode</th>\n",
       "      <th>substitutions</th>\n",
       "      <th>variant_call_support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAATTGTAA</td>\n",
       "      <td>T271C A272T T273C A595T A597C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAACTTAAAT</td>\n",
       "      <td>A38G T39C T179C A180G G282C T439A G449A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAACTTCAAT</td>\n",
       "      <td>G64T C65G A66C C379G T381C A423C C562G G564C</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAACAAGCAGA</td>\n",
       "      <td>A37T A38T T39C</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAACAATATAA</td>\n",
       "      <td>T16A G17A T18G A43T C44G T45G A280T G282C G307T T308G</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Writing nucleotide variants to {config['nt_variant_table_file']}\")\n",
    "      \n",
    "(consensus\n",
    " [['target', 'library', 'barcode', 'substitutions', 'variant_call_support']]\n",
    " .to_csv(config['nt_variant_table_file'], index=False)\n",
    " )\n",
    "      \n",
    "print('Here are the first few lines of this file:')\n",
    "display(HTML(\n",
    "    pd.read_csv(config['nt_variant_table_file'], na_filter=None)\n",
    "    .head()\n",
    "    .to_html(index=False)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happened to the barcodes that we \"dropped\" because we could not construct a reliable consensus?\n",
    "The `dropped` data frame from [alignparse.consensus.simple_mutconsensus](https://jbloomlab.github.io/alignparse/alignparse.consensus.html?highlight=simple_mutconsensus#alignparse.consensus.simple_mutconsensus) has this information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>barcode</th>\n",
       "      <th>target</th>\n",
       "      <th>drop_reason</th>\n",
       "      <th>nseqs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAATGATTTC</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>subs diff too large</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAACCTTACAGAA</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>subs diff too large</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAATCCGACCCTG</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>minor subs too frequent</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAATTCCAGAGGC</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>minor subs too frequent</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAACACGCCTGTAA</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "      <td>minor subs too frequent</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(dropped.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarize the information in this data frame on dropped barcodes with the plot below.\n",
    "This plot shows several things.\n",
    "First, we see that the total number of barcodes dropped is modest (just a few thousand per library) relative to the total number of barcodes per library (seen above to be on the order of hundreds of thousands).\n",
    "Second, the main reason that barcodes are dropped is that there are CCSs within the same barcode with suspiciously large numbers of mutations relative to the consensus---which we use as a filter to discard the entire barcode as it could indicate strand exchange or some other issue.\n",
    "In any case, the modest number of dropped barcodes indicates that there probably isn't much of a need to worry: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_nseqs = 8  # plot together all barcodes with >= this many sequences\n",
    "\n",
    "_ = (\n",
    " ggplot(\n",
    "    dropped.assign(nseqs=lambda x: numpy.clip(x['nseqs'], None, max_nseqs)),\n",
    "    aes('nseqs')) + \n",
    " geom_bar() + \n",
    " scale_x_continuous(limits=(1, None)) +\n",
    " xlab('number of sequences for barcode') +\n",
    " ylab('number of barcodes') +\n",
    " facet_grid('library ~ drop_reason') +\n",
    " theme(figure_size=(10, 1.5 * nlibs),\n",
    "       panel_grid_major_x=element_blank(),\n",
    "       )\n",
    " ).draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create barcode-variant table\n",
    "We now create a [CodonVariantTable](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable) that stores and processes all the information about the variant consensus sequences.\n",
    "Below we initialize such a table, and then analyze information about its composition.\n",
    "\n",
    "### Initialize codon variant table\n",
    "In order to initialize the codon variant table, we need two pieces of information:\n",
    "  1. The wildtype gene sequence.\n",
    "  2. The list of nucleotide mutations for each variant as determined in the consensus calling above.\n",
    "\n",
    "Read \"wildtype\" gene sequence to which we made the alignments (in order to do this, initialize an `alignparse.Targets` and get the gene sequence from it):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read gene of 603 nts for SARS-CoV-2 from data/PacBio_amplicons.gb\n"
     ]
    }
   ],
   "source": [
    "targets = alignparse.targets.Targets(seqsfile=config['amplicons'],\n",
    "                                     feature_parse_specs=config['feature_parse_specs'])\n",
    "geneseq = targets.get_target(config['primary_target']).get_feature('gene').seq\n",
    "\n",
    "print(f\"Read gene of {len(geneseq)} nts for {config['primary_target']} from {config['amplicons']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now initialize the codon variant table using this wildtype sequence and our list of nucleotide mutations for each variant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "variants = dms_variants.codonvarianttable.CodonVariantTable(\n",
    "                barcode_variant_file=config['nt_variant_table_file'],\n",
    "                geneseq=geneseq,\n",
    "                primary_target=config['primary_target'],\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic stats on variants\n",
    "We now will analyze the variants.\n",
    "In this call and in the plots below, we set `samples=None` as we aren't looking at variant counts in specific samples, but are simply looking at properties of the variants in the table.\n",
    "\n",
    "Here are the number of variants for each target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>lib1</th>\n",
       "      <th>lib2</th>\n",
       "      <th>all libraries</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BM48-31</th>\n",
       "      <td>107</td>\n",
       "      <td>117</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GD-Pangolin</th>\n",
       "      <td>119</td>\n",
       "      <td>127</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKU3-1</th>\n",
       "      <td>106</td>\n",
       "      <td>121</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LYRa11</th>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RaTG13</th>\n",
       "      <td>112</td>\n",
       "      <td>118</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rf1</th>\n",
       "      <td>55</td>\n",
       "      <td>58</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rp3</th>\n",
       "      <td>95</td>\n",
       "      <td>110</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARS-CoV</th>\n",
       "      <td>122</td>\n",
       "      <td>123</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARS-CoV-2</th>\n",
       "      <td>98569</td>\n",
       "      <td>96512</td>\n",
       "      <td>195081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIV16</th>\n",
       "      <td>102</td>\n",
       "      <td>115</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZC45</th>\n",
       "      <td>122</td>\n",
       "      <td>140</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZXC21</th>\n",
       "      <td>104</td>\n",
       "      <td>110</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(\n",
    "    variants\n",
    "    .n_variants_df(samples=None)\n",
    "    .pivot_table(index=['target'],\n",
    "                 columns='library',\n",
    "                 values='count')\n",
    "    .to_html()\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the number of variants supported by each number of CCSs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 572x207 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_support = 10  # group variants with >= this much support\n",
    "\n",
    "p = variants.plotVariantSupportHistogram(max_support=max_support,\n",
    "                                         widthscale=1.1,\n",
    "                                         heightscale=0.9)\n",
    "p = p + theme(panel_grid_major_x=element_blank())  # no vertical grid lines\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutations per variant\n",
    "Plot the number of barcoded variants with each number of amino-acid and codon mutations.\n",
    "This is for the primary target only, and doesn't include the spiked-in secondary targets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving plot to results/figures/n_aa_muts_per_variant.pdf\n",
      "Saving plot to results/figures/n_codon_muts_per_variant.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 605x189 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 605x189 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_muts = 7  # group all variants with >= this many mutations\n",
    "\n",
    "for mut_type in ['aa', 'codon']:\n",
    "    p = variants.plotNumMutsHistogram(mut_type, samples=None, max_muts=max_muts,\n",
    "                                      widthscale=1.1,\n",
    "                                      heightscale=0.9)\n",
    "    p = p + theme(panel_grid_major_x=element_blank())  # no vertical grid lines\n",
    "    _ = p.draw()\n",
    "    plotfile = os.path.join(config['figs_dir'], f\"n_{mut_type}_muts_per_variant.pdf\")\n",
    "    print(f\"Saving plot to {plotfile}\")\n",
    "    p.save(plotfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the frequencies of different codon mutation types among **all** variants (any number of mutations), again only for primary target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving plot to results/figures/avg_muts_per_variant.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520x184 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = variants.plotNumCodonMutsByType(variant_type='all', samples=None,\n",
    "                                    ylabel='mutations per variant',\n",
    "                                    heightscale=0.8)\n",
    "p = p + theme(panel_grid_major_x=element_blank())  # no vertical grid lines\n",
    "_ = p.draw()\n",
    "plotfile = os.path.join(config['figs_dir'], f\"avg_muts_per_variant.pdf\")\n",
    "print(f\"Saving plot to {plotfile}\")\n",
    "p.save(plotfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variants supported by multiple PacBio CCSs should have fewer spurious mutations since sequencing errors are very unlikely to occur on two CCSs.\n",
    "Below we plot the number of codon mutations per variant among variants with at least two CCSs supporting their call.\n",
    "The difference in mutation rates here and in the plot above (that does not apply the `min_support=2` filter) gives some estimate of the frequency of mutations in our variants our spurious.\n",
    "In fact, we see the numbers are very similar, indicating that few of the mutations are spurious:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520x184 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = variants.plotNumCodonMutsByType(variant_type='all', samples=None,\n",
    "                                    ylabel='mutations per variant', \n",
    "                                    min_support=2, heightscale=0.8)\n",
    "p = p + theme(panel_grid_major_x=element_blank())  # no vertical grid lines\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Completeness of mutation sampling\n",
    "We examine how completely amino-acid mutations are sampled by the variants for the primary target, looking at single-mutant variants only and all variants.\n",
    "The plot below shows that virtually every mutation is found in a variant in each library, even if we just look among the single mutants.\n",
    "Things look especially good if we aggregate across libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving plot to results/figures/variant_cumul_all_mut_coverage.pdf\n",
      "Saving plot to results/figures/variant_cumul_single_mut_coverage.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x220 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x220 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for variant_type in ['all', 'single']:\n",
    "    p = variants.plotCumulMutCoverage(variant_type, mut_type='aa', samples=None)\n",
    "    _ = p.draw()\n",
    "    plotfile = os.path.join(config['figs_dir'],\n",
    "                            f\"variant_cumul_{variant_type}_mut_coverage.pdf\")\n",
    "    print(f\"Saving plot to {plotfile}\")\n",
    "    p.save(plotfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get more quantitative information like that plotted above, we determine how many mutations are found 0, 1, or >1 times both among single and all mutants for the primary target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>number_of_mutations</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variant_type</th>\n",
       "      <th>include_stops</th>\n",
       "      <th>library</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"18\" valign=\"top\">all</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">False</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib1</th>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>3783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib2</th>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>3781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">all libraries</th>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>3795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">True</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib1</th>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>3981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib2</th>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>3979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">all libraries</th>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>3994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"18\" valign=\"top\">single</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">False</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib1</th>\n",
       "      <th>0</th>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>2756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib2</th>\n",
       "      <th>0</th>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>2809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">all libraries</th>\n",
       "      <th>0</th>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>3398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">True</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib1</th>\n",
       "      <th>0</th>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>2904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib2</th>\n",
       "      <th>0</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>2957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">all libraries</th>\n",
       "      <th>0</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>3577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_dfs = []\n",
    "for variant_type in ['all', 'single']:\n",
    "    i_counts = (variants.mutCounts(variant_type, mut_type='aa', samples=None)\n",
    "                .assign(variant_type=variant_type)\n",
    "                )\n",
    "    count_dfs += [i_counts.assign(include_stops=True),\n",
    "                  i_counts\n",
    "                  .query('not mutation.str.contains(\"\\*\")', engine='python')\n",
    "                  .assign(include_stops=False)\n",
    "                  ]\n",
    "    \n",
    "display(HTML(\n",
    "    pd.concat(count_dfs)\n",
    "    .assign(count=lambda x: (numpy.clip(x['count'], None, 2)\n",
    "                             .map({0: '0', 1: '1', 2:'>1'}))\n",
    "            )\n",
    "    .groupby(['variant_type', 'include_stops', 'library', 'count'])\n",
    "    .aggregate(number_of_mutations=pd.NamedAgg(column='mutation', aggfunc='count'))\n",
    "    .to_html()\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutation frequencies along gene\n",
    "We plot the frequencies of mutations along the gene among the variants for the primary target.\n",
    "Ideally, this would be uniform.\n",
    "We make the plot for both all variants and single-mutant / wildtype variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x180 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x180 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for variant_type in ['all', 'single']:\n",
    "    p = variants.plotMutFreqs(variant_type, mut_type='codon', samples=None)\n",
    "    p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use heat maps to examine the extent to which specific amino-acid or codon mutations are over-represented.\n",
    "These heat maps are large, so we make them just for all variants and the merge of all libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2420x250 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2420x630 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for mut_type in ['aa', 'codon']:\n",
    "    p = variants.plotMutHeatmap('all', mut_type, samples=None, #libraries='all_only',\n",
    "                                widthscale=2)\n",
    "    p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write codon-variant table\n",
    "We write the codon variant table to a CSV file.\n",
    "This table looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>library</th>\n",
       "      <th>barcode</th>\n",
       "      <th>variant_call_support</th>\n",
       "      <th>codon_substitutions</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>n_codon_substitutions</th>\n",
       "      <th>n_aa_substitutions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAACTAGATGAATAA</td>\n",
       "      <td>3</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAACCCTTTCAAGTAG</td>\n",
       "      <td>1</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAGAAGTAATAATTG</td>\n",
       "      <td>2</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAGGGGCGGACGGAC</td>\n",
       "      <td>1</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AACATTCTACCGACTG</td>\n",
       "      <td>3</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(\n",
    "    variants.barcode_variant_df\n",
    "    .head()\n",
    "    .to_html(index=False)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how this table differs from the nucleotide variant table we generated above and used to initialize the [CodonVariantTable](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable) in that it gives **codon** substitutions and associated amino-acid substitutions.\n",
    "\n",
    "Write it to CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing codon-variant table to results/variants/codon_variant_table.csv\n"
     ]
    }
   ],
   "source": [
    "print(f\"Writing codon-variant table to {config['codon_variant_table_file']}\")\n",
    "\n",
    "variants.barcode_variant_df.to_csv(config['codon_variant_table_file'], index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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