process_ccs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Process-CCSs\" data-toc-modified-id=\"Process-CCSs-1\">Process CCSs</a></span><ul class=\"toc-item\"><li><span><a href=\"#Setup\" data-toc-modified-id=\"Setup-1.1\">Setup</a></span></li><li><span><a href=\"#PacBio-amplicons\" data-toc-modified-id=\"PacBio-amplicons-1.2\">PacBio amplicons</a></span></li><li><span><a href=\"#CCS-stats-for-PacBio-runs\" data-toc-modified-id=\"CCS-stats-for-PacBio-runs-1.3\">CCS stats for PacBio runs</a></span></li><li><span><a href=\"#Align-CCSs-to-amplicons\" data-toc-modified-id=\"Align-CCSs-to-amplicons-1.4\">Align CCSs to amplicons</a></span></li><li><span><a href=\"#Write-valid-CCSs\" data-toc-modified-id=\"Write-valid-CCSs-1.5\">Write valid CCSs</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Python Jupyter notebook processes the PacBio circular consensus sequences (CCSs) to extract barcodes and call mutations in the gene. It then builds consensus sequences for barcoded variants from the mutations, to build a barcode-variant lookup table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process CCSs\n",
    "First, process the PacBio CCSs to extract barcodes and call mutations in the gene."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Import Python modules\n",
    "\n",
    "Plotting is done with [plotnine](https://plotnine.readthedocs.io/en/stable/), which uses ggplot2-like syntax.\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 collections\n",
    "import math\n",
    "import os\n",
    "import re\n",
    "import time\n",
    "import warnings\n",
    "\n",
    "import alignparse\n",
    "import alignparse.ccs\n",
    "from alignparse.constants import CBPALETTE\n",
    "import alignparse.minimap2\n",
    "import alignparse.targets\n",
    "import alignparse.consensus\n",
    "\n",
    "import dms_variants\n",
    "import dms_variants.codonvarianttable\n",
    "import dms_variants.plotnine_themes\n",
    "import dms_variants.utils\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://plotnine.readthedocs.io/en/stable/) theme to the 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 0.2.4\n",
      "Using dms_variants version 0.8.9\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": [
    "Read the configuration file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('config.yaml') as f:\n",
    "    config = yaml.safe_load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make output directory for figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(config['process_ccs_dir'], exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PacBio reads\n",
    "Get the amplicons sequenced by PacBio as the alignment target along with the specs on how to parse the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>run</th>\n",
       "      <th>name</th>\n",
       "      <th>fastq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>210619</td>\n",
       "      <td>lib1_210619</td>\n",
       "      <td>results/ccs/lib1_210619_ccs.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>210619</td>\n",
       "      <td>lib2_210619</td>\n",
       "      <td>results/ccs/lib2_210619_ccs.fastq.gz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pacbio_runs = (\n",
    "    pd.read_csv(config['pacbio_runs'], dtype=str)\n",
    "    .drop(columns=['subreads'])\n",
    "    .assign(name=lambda x: x['library'] + '_' + x['run'],\n",
    "            fastq=lambda x: config['ccs_dir'] + '/' + x['name'] + '_ccs.fastq.gz'\n",
    "            )\n",
    "    )\n",
    "\n",
    "display(HTML(pacbio_runs.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PacBio amplicons\n",
    "Get the amplicons sequenced by PacBio as the alignment target along with the specs on how to parse the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading amplicons from data/PacBio_amplicon.gb\n",
      "Reading feature parse specs from data/feature_parse_specs.yaml\n"
     ]
    }
   ],
   "source": [
    "print(f\"Reading amplicons from {config['amplicon']}\")\n",
    "print(f\"Reading feature parse specs from {config['feature_parse_specs']}\")\n",
    "\n",
    "targets = alignparse.targets.Targets(\n",
    "                seqsfile=config['amplicon'],\n",
    "                feature_parse_specs=config['feature_parse_specs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw the target amplicons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving plot to results/process_ccs/amplicons.pdf\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = targets.plot(ax_width=7,\n",
    "                   plots_indexing='biopython',  # numbering starts at 0\n",
    "                   ax_height=2,  # height of each plot\n",
    "                   hspace=1.2,  # vertical space between plots\n",
    "                   )\n",
    "\n",
    "plotfile = os.path.join(config['process_ccs_dir'], 'amplicons.pdf')\n",
    "print(f\"Saving plot to {plotfile}\")\n",
    "fig.savefig(plotfile, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write out the specs used to parse the features (these are the same specs provided as `feature_parse_specs` when initializing `targets`, but with defaults filled in):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CGG_naive:\n",
      "  query_clip5: 4\n",
      "  query_clip3: 4\n",
      "  termini5:\n",
      "    filter:\n",
      "      clip5: 4\n",
      "      mutation_nt_count: 1\n",
      "      mutation_op_count: null\n",
      "      clip3: 0\n",
      "    return: []\n",
      "  gene:\n",
      "    filter:\n",
      "      mutation_nt_count: 90\n",
      "      mutation_op_count: null\n",
      "      clip5: 0\n",
      "      clip3: 0\n",
      "    return:\n",
      "    - mutations\n",
      "    - accuracy\n",
      "  spacer:\n",
      "    filter:\n",
      "      mutation_nt_count: 1\n",
      "      mutation_op_count: null\n",
      "      clip5: 0\n",
      "      clip3: 0\n",
      "    return: []\n",
      "  barcode:\n",
      "    filter:\n",
      "      mutation_nt_count: 0\n",
      "      mutation_op_count: null\n",
      "      clip5: 0\n",
      "      clip3: 0\n",
      "    return:\n",
      "    - sequence\n",
      "    - accuracy\n",
      "  termini3:\n",
      "    filter:\n",
      "      clip3: 4\n",
      "      mutation_nt_count: 1\n",
      "      mutation_op_count: null\n",
      "      clip5: 0\n",
      "    return: []\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(targets.feature_parse_specs('yaml'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CCS stats for PacBio runs\n",
    "Read data frame with information on PacBio runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>run</th>\n",
       "      <th>name</th>\n",
       "      <th>fastq</th>\n",
       "      <th>report</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>210619</td>\n",
       "      <td>lib1_210619</td>\n",
       "      <td>results/ccs/lib1_210619_ccs.fastq.gz</td>\n",
       "      <td>results/ccs/lib1_210619_report.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>210619</td>\n",
       "      <td>lib2_210619</td>\n",
       "      <td>results/ccs/lib2_210619_ccs.fastq.gz</td>\n",
       "      <td>results/ccs/lib2_210619_report.txt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pacbio_runs = (\n",
    "    pd.read_csv(config['pacbio_runs'], dtype=str)\n",
    "    .drop(columns=['subreads'])\n",
    "    .assign(name=lambda x: x['library'] + '_' + x['run'],\n",
    "            fastq=lambda x: config['ccs_dir'] + '/' + x['name'] + '_ccs.fastq.gz'\n",
    "            )\n",
    "    )\n",
    "\n",
    "# we only have report files on the Hutch server, not for SRA download\n",
    "if config['seqdata_source'] == 'HutchServer':\n",
    "    pacbio_runs = (\n",
    "        pacbio_runs\n",
    "        .assign(report=lambda x: config['ccs_dir'] + '/' + x['name'] + '_report.txt')\n",
    "        )\n",
    "    report_col = 'report'\n",
    "elif config['seqdata_source'] == 'SRA':\n",
    "    report_col = None\n",
    "else:\n",
    "    raise ValueError(f\"invalid `seqdata_source` {config['seqdata_source']}\")\n",
    "\n",
    "display(HTML(pacbio_runs.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an object that summarizes the `ccs` runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccs_summaries = alignparse.ccs.Summaries(pacbio_runs,\n",
    "                                         report_col=report_col,\n",
    "                                         ncpus=config['max_cpus'],\n",
    "                                         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If available, plot statistics on the number of ZMWs for each run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 80x250 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if ccs_summaries.has_zmw_stats():\n",
    "    p = ccs_summaries.plot_zmw_stats()\n",
    "    p = p + theme(panel_grid_major_x=element_blank())  # no vertical grid lines\n",
    "    _ = p.draw()\n",
    "else:\n",
    "    print('No ZMW stats available.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot statistics on generated CCSs: their length, number of subread passes, and accuracy (as reported by the `ccs` program):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x175 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x175 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x175 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for variable in ['length', 'passes', 'accuracy']:\n",
    "    if ccs_summaries.has_stat(variable):\n",
    "        p = ccs_summaries.plot_ccs_stats(variable, maxcol=7, bins=25)\n",
    "        p = p + theme(panel_grid_major_x=element_blank())  # no vertical grid lines\n",
    "        _ = p.draw()\n",
    "    else:\n",
    "        print(f\"No {variable} statistics available.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Align CCSs to amplicons\n",
    "We now align the CCSs to the amplicon and parse features from the resulting alignments using the specs above.\n",
    "\n",
    "First, we initialize an `alignparse.minimap2.Mapper` to align the reads to SAM files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using `minimap2` 2.18-r1015 with these options:\n",
      "-A2 -B4 -O12 -E2 --end-bonus=13 --secondary=no --cs\n"
     ]
    }
   ],
   "source": [
    "mapper = alignparse.minimap2.Mapper(alignparse.minimap2.OPTIONS_CODON_DMS)\n",
    "\n",
    "print(f\"Using `minimap2` {mapper.version} with these options:\\n\" +\n",
    "      ' '.join(mapper.options))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use `Targets.align_and_parse` to create the alignments and parse them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "readstats, aligned, filtered = targets.align_and_parse(\n",
    "        df=pacbio_runs,\n",
    "        mapper=mapper,\n",
    "        outdir=config['process_ccs_dir'],\n",
    "        name_col='run',\n",
    "        group_cols=['name', 'library'],\n",
    "        queryfile_col='fastq',\n",
    "        overwrite=True,\n",
    "        ncpus=config['max_cpus'],\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, examine the read stats from the alignment / parsing, both extracting alignment target name and getting stats aggregated by target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "readstats = (\n",
    "    readstats\n",
    "    .assign(category_all_targets=lambda x: x['category'].str.split().str[0],\n",
    "            target=lambda x: x['category'].str.split(None, 1).str[1],\n",
    "            valid=lambda x: x['category_all_targets'] == 'aligned')\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the read stats by run (combining all targets and libraries within a run):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 370x200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncol = 2\n",
    "p = (\n",
    "    ggplot(readstats\n",
    "           .groupby(['name', 'category_all_targets', 'valid'])\n",
    "           .aggregate({'count': 'sum'})\n",
    "           .reset_index(),\n",
    "           aes('category_all_targets', 'count', fill='valid')) +\n",
    "    geom_bar(stat='identity') +\n",
    "    facet_wrap('~ name', ncol=ncol) +\n",
    "    theme(axis_text_x=element_text(angle=90),\n",
    "          figure_size=(1.85 * min(ncol, len(pacbio_runs)),\n",
    "                       2 * math.ceil(len(pacbio_runs) / ncol)),\n",
    "          panel_grid_major_x=element_blank(),\n",
    "          legend_position='none',\n",
    "          ) +\n",
    "    scale_fill_manual(values=CBPALETTE)\n",
    "    )\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the read stats by library (combining all targets and runs within a library):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = (\n",
    "    ggplot(readstats\n",
    "           .groupby(['library', 'category_all_targets', 'valid'])\n",
    "           .aggregate({'count': 'sum'})\n",
    "           .reset_index(), \n",
    "           aes('category_all_targets', 'count', fill='valid')) +\n",
    "    geom_bar(stat='identity') +\n",
    "    facet_wrap('~ library', nrow=1) +\n",
    "    theme(axis_text_x=element_text(angle=90),\n",
    "          figure_size=(1.5 * pacbio_runs['library'].nunique(), 2),\n",
    "          panel_grid_major_x=element_blank(),\n",
    "          legend_position='none',\n",
    "          ) +\n",
    "    scale_fill_manual(values=CBPALETTE)\n",
    "    )\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the number of reads by target (combining all libraries and runs for a target):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 30x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = (\n",
    "    ggplot(readstats\n",
    "           .groupby(['target'])\n",
    "           .aggregate({'count': 'sum'})\n",
    "           .reset_index(), \n",
    "           aes('target', 'count')) +\n",
    "    geom_point(stat='identity', size=3) +\n",
    "    theme(axis_text_x=element_text(angle=90),\n",
    "          figure_size=(0.3 * readstats['target'].nunique(), 2),\n",
    "          panel_grid_major_x=element_blank(),\n",
    "          ) +\n",
    "    scale_y_log10(name='number of reads')\n",
    "    )\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And read stats by target (combining all libraries and runs for a target):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 50x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = (\n",
    "    ggplot(readstats\n",
    "           .groupby(['target', 'valid'])\n",
    "           .aggregate({'count': 'sum'})\n",
    "           .reset_index()\n",
    "           .assign(total=lambda x: x.groupby('target')['count'].transform('sum'),\n",
    "                   frac=lambda x: x['count'] / x['total'],\n",
    "                   ), \n",
    "           aes('target', 'frac', fill='valid')) +\n",
    "    geom_bar(stat='identity') +\n",
    "    theme(axis_text_x=element_text(angle=90),\n",
    "          figure_size=(0.5 * readstats['target'].nunique(), 2),\n",
    "          panel_grid_major_x=element_blank(),\n",
    "          ) +\n",
    "    scale_fill_manual(values=CBPALETTE)\n",
    "    )\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see **why** we filtered the reads.\n",
    "First, we do some transformations on the `filtered` dict returned by `Targets.align_and_parse`.\n",
    "Then we count up the number of CCSs filtered for each reason, and group together \"unusual\" reasons that represent less than some fraction of all filtering.\n",
    "For now, we group together all targets to the stats represent all targets combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "other_cutoff = 0.02  # group as \"other\" reasons with <= this frac\n",
    "\n",
    "filtered_df = (\n",
    "    pd.concat(df.assign(target=target) for target, df in filtered.items())\n",
    "    .groupby(['library', 'name', 'run', 'filter_reason'])\n",
    "    .size()\n",
    "    .rename('count')\n",
    "    .reset_index()\n",
    "    .assign(tot_reason_frac=lambda x: (x.groupby('filter_reason')['count']\n",
    "                                       .transform('sum')) / x['count'].sum(),\n",
    "            filter_reason=lambda x: numpy.where(x['tot_reason_frac'] > other_cutoff,\n",
    "                                                x['filter_reason'],\n",
    "                                                'other')\n",
    "            )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the filtering reason for all runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UqfRJ7RfA6e7+mySdEUIIIaqh0r6xVYC/JOmIEEIIUS2VBrW/ADsn6YgQQghRLZU2P04ARprZFsDLwKL8ne4+qlrHhGgkqp3EDZrILUQSVBrUjgA+AraNWz4OKKgJIYRInYqCmrv3T9oRIYQQolo0iVoIIUTTUOk8tWvb2+/uh1TmjhBCCFE5lfaprVLwvjswCFiZ4omOhRBCiJpTaZ/aXoVlZtYFuJyQ7FgIIYRIncT61Nx9CXAx8NOkbAohhBDlkPRAkXWoIkmyEEIIUQ2VDhS5qLAI6Ad8C7ihWqeEEEKISqj0qWqjgvdLCAuGngi0OzJSCCGEqBWVDhTZLmlHhBBCiGqpqv/LzPoA6xJSY41z9xmJeCWEECIzqs1lmmUe00r71FYALgOG0TrYZLGZ3Qgc6+4fJ+SfEEKIElBS7UClox8vIiQy3p0w4Xpl4Dux7MIkHBNCCCHKpdLmx+8C+7j743llfzOz+cBtwNHVOiaEEEKUS6VPassD04qUT4/7hBBCiNSpNKg9BYwws2VzBWa2HHBW3CeEEEKkTqXNj8cDDwBvm9mLhNGPQ4BPgJ0T8UwIIYQok0rnqb1sZgOAA4H1CBlFbgX+5O7zE/RPCCGEKJmKmh/N7FRgf3f/g7uf6O4nuPvVwP5mdkoF9rYxs3vNbKqZuZntWbDfzOyXcf98M3vczL5aUKeHmV1mZjPNbJ6Z3WNmXyyos4qZ3WRmc+J2k5mtXFBnzejLvGhrlJktU+5nEkIIkT6V9qkdCbxWpPx/wFEV2FsBeBH4cRv7TwZOiPu/BrwH/N3MPpdX5xJgL2A/YCjQE7jPzLrm1bmZ0Ey6a9yGADfldsa690d/hkZb30XTFIQQoiGotE+tL/BukfIZhMTGZeHuDxD66DCzpfZZKDgeOMfd74hlPySMvjwA+L2ZrQQcChzk7o/EOgcCU4AdgYfM7CuEQLaFuz8T6xwOPGVm67r764T+wPWBNdx9aqxzInC9mZ3u7h+W+9mEEEKkR6VPalOArYuUbw1MrdydovQnBNGHcwXu/gnwT2CrWLQJYfXt/DpTgbF5dbYE5uQCWqzzNDCnoM7YXECLPAT0iOcQQghRx1T6pHY1cImZdQcei2U7AOeRfFNd3/i3cF7cNGCtvDoL3X12kTp98+pML2J/ekGdpc7j7rPNbGFenXy6jh8/vsMP0Nlp5muU5GfL+DpJyyXSzNcpqc+W5TWqNKidB/QCLgdygygWAL9x93OTcKwIXvDeipQVUlinWP1K6uRYPGDAgK5FykUeAwYMyNqFmpHkZ6vFdRo3blypVaXlEpGe07OTT6larqj50QOnAH2ALYANgV7ufnYl9jrgvfi38ElpNVqfqt4DljGzVTqo8/ki9vsU1FnqPNFmd4pnUBFCCFFHVNqnBoC7f+Tu/3X3sbGfqxZMIgSbnXIFcYj9tsCTseh5YFFBnX7AoLw6TwErmdlmeXU2B1YqqDMoHptjZ8Kk8ueT+0hCCCFqQVXrqSWFmfUEvpxX1N/MhgCz3P0tM7sEOM3MxgPjgdOAjwlD9HH3OWZ2DXChmb0PzAIuAF4GHol1XjWzB4E/mNmR8TxXAffFkY8QBpq8AtxkZicRmlgvAP6gkY9CCFH/1EVQAzYF/pH3/qL49wbgYEIf3nKEPrxVgGeAnd19bt4xPwU+JawSsBzwKHCwuy/Oq/MDYBStoyTvIW9unLsvNrNvxfM8AcwnBM6fVf0JhRBC1Jy6CGpxCRtrZ78Dv4xbW3UWAMfGra06swipvdrz5S3g2+3VEUIIUZ/URVATohy0wq8Qoi2qGigihBBC1BMKakIIIZoGBTUhhBBNg4KaEEKIpkFBTQghRNOg0Y9CCJEh1Y7m1UjepdGTmhBCiKZBQU0IIUTToKAmhBCiaVBQE0II0TQoqAkhhGgaFNSEEEI0DQpqQgghmgYFNSGEEE2DgpoQQoimQUFNCCFE06CgJoQQomlQUBNCCNE0KKgJIYRoGhTUhBBCNA0KakIIIZoGBTUhhBBNg4KaEEKIpkErXwshakK1KzqDVnUW5aMnNSGEEE2DntTqjGrvbnVnK4TozOhJTQghRNOgJzUh6gw9rQtROQpqCaAOcSGEqA/U/CiEEKJp0JOaEEKImpBFK5aCmkgNNdOKZkFarl/U/CiEEKJp0JOaEGIpNPpSNDJ6UhNCCNE0KKgJIYRoGhTUhBBCNA0KakIIIZoGBbUimNkxZjbJzBaY2fNm9vWsfRJCCNExCmoFmNn3gUuAc4CNgH8DD5jZmln6JYQQomMU1D7LCcA17n61u7/q7scDU4Cjs3VLCCFERyio5WFmywCbAA8X7HoY2Cp9j4QQQpSDuXvWPtQNZvYF4B1ga3d/Mq/8NOCH7r5ufv1x48a5maXspRCl4+6LBw4c2GGSBWlZ1DulalkZRYpTGOmtSBnA4gEDBnRNwR8hKmLcuHGlVpWWRV1TqpbV/Lg0M4HFQN+C8tWAaem7I4QQohwU1PJw94XA88BOBbt2Ap787BFCCCHqCTU/fpaLgJvM7DngKeAIYE3gyky9EkII0SEKagW4+5/NrDdwJtAPGAt8093fzNYzIYQQHaGgVgR3vxy4vKN6ZvbuuHHj+qXgkhAVYWbvllpPWhb1TMla1pB+IYQQzYIGigghhGgaFNSEEEI0DQpqQgghmgYFNSGEEE2DgpoQETObGKdzFJavbGYTK7S5ipkdb2a/M7NfmNkaZR7/mJmtXKR8RTN7rBKfROcgaT03ipYV1BLGzHYysxFmtn18v42ZPRD/ocPLsHOYmd2QO8bMvm9mr0ahjijTp25m9qmZDSrv05Rs38wsEy0ldb0jXwKK5T/sAaxeoj9Tcz8kZtYfeAU4BRgAHAm8bGbrleHTN4BlipQvC9R08dp61HI8vmZ6biItQ5V6blgtu7u2hDbgQGARIdXWXOBgYDbwB+Bq4BNgnxLsHA98BPwVmAqcTshLeTpwBvABcESZvr0BbFjl5+sG/Ar4JzAilp0EzIuf7QZgmRLs7Ax0y3t/ADAm2pkAHJfy9d4jbkuAg/Le7wHsBfwWeL1En5YAq8XXtwD/AJaP73sA9wJ/KcHO4LgtIfwYDM7bNgJOBSZ3Ri0noeektJyUnpO63knquVG1XJMvRGfdgBdyAgZ2AD4Gfpq3/wTgPyXYeRU4IL7eKIr90Lz9w4HnyvRtOPA3oFcVn28k8B5wIfA/4ArgLeAH8cszBTi5BDuL874s3wU+BUbFH4MLgAXA/ile7yVxW5z3Ord9ArwOfLvEa5T/QzAR2L5g/+bAlBLtLG7DpyWEH8xDOqOWk9BzUlpOSs9JXe8k9dyoWq7JF6KzboQ70v557xcCg/PerwvMLMHOx8Caee8XAF/Ne/9lYHaZvr1AuANcEEU9On8r0cYbuS9D9GEx8P28/fsCL5dgJ//L8h/inXLe/p8Bz6Z1vfPqTwJWrVIDS4A+8fU7+f+3WPYlYEEJdtaKdZcAm8b3ua0f0LWzajkJPSel5aT0nLSWk9Bzo2pZabKSZRFLtxl/QhBrjoXAciXY+RhYIe/9jAI7UH6Ks7vKrF+MLwAvArj7BDNbmHsfeY4g1HIYABxXUHYP8IsSjk3qegPg7v1LrdsBj5rZp8CKwEDCk0CONQnNbx35kss1mlW/dz1rGarXcy20DJXrOVEtQ2J6bjgtK6glywRgPcKdI4TO2Ll5+9cB3i7BzmuE9uZXAdy9cJTResDkchxz97I75IswB1iZ0DQD4a44//P1oPhiqsVY38z6AvP5rNi7ULyDu5CkrncLZrYDoflntUK/3P2QEkwUXuePC97vDvy7TJ8GEvoiivl0djm2yqButRztVKvnJLUM1es5cS1D1XpuSC0rqCXL/xE6dwFw9w8L9m8K3FaCnVMI7cxtsSbw+3Kdi8Np9yF8Qc5391lmtjEwzd3fKcHEK8DGwMsA7r51wf4NgPEluvMoYUVxgK0Jd8Y5NiL0b3REUtcbADM7i7A6w3PAu5T3o5bzod0fW3c/qRx7ZnY4ob9nJqEPKN8nB2oV1Opay1C1npPUMlSv50S1DNXruVG1rITGnQQzGww8QrhD/RKwrrtPNLORwFruPqwEGwOBRe4+qY39BwCfunu7Xz4zK2zW+cjd38/bPwzA3W/syKckiVnAT3b3mxK2uwbg7l7JnfabwOXu/pskfWp0qtVzUlqOdTuNnhtBywpqNcTMViN08Dowzt2nV2hnU+Ar0c5r7v5cB4cUs/EIoQP9ZDObSxgOPdHMtgJudvcvVeJbPVHt9Taz94HN3P2NBHzpBpxF6F/pGYs/Ai4jDCRYVKKdD4Eh7l7R5O+kqCctRztNreckrndSem44LSc56kRby2ifFYGbCJ2/uWGri4A/AiuVYeeLhDbrJcCsuC0hjLBao0yf5gDrxNdzgbXj67UoYQRTEXtdCU0/ZxA6wfchb65OmnYSvN6/Ac5ISANXAtMIk1Rzc3KOJDQDXVmGnWuAo6Tl2uk5KS0nYSup652knhtNy+pTqw1XA0OAbwNPEe62tgIuJUym/F6Jdq4FugNfcffXAcxs3Vh+DWHSZ6ksIHxhClmXMCKtZGImh7uBvrR2bA8EZpjZHu7+cpp2SO56LwscYWY7Ai8RfkxacPcTSrQDsD+wn7s/kFf2kpm9BdwKHFWinQnASDPbgtD/U+jTqDJ8qoR61DIkpOcENZiUraSuNySn54bSspofa4CZzQN2cff/FJR/HXjQ3VcofuRn7MwHtnL3FwrKNwaecPeSh/ia2VVAH8KXYhbhbmsxYWj0v9z9+DJsPQ1MB37o7rNj2SrA9YT5OlumbCep6/2Pdna7u29fip1oaxrwDXd/taD8K4Tr3adEO0X7fPJ8WrtUnyqhHrUcj0tEz0lpMClbSV3veEwiem44Ldf6UbAzboSRThsUKR8MvF2GndcJbeKF5ZsBE8r0aUVCU89sQsaDtwhzX/4JrFCmrfkUTMSM5YOA+RnYSeR6J6yBM4GbgR55ZT0IzUhnZeFThZ+j7rQcj0tEz0lpMClb0nL1m5ofa8OvgIvMbJi7vwsQ57CcT0jPUyonA5eZ2Y+A593dY0f7pYQsBSXjYYjw0JgsdWPCHJHR7v5IOXYirwOfZ+mJmBDmnkzIwE5S1ztJNiLMD3rbzHKTejckTLB91MzuyFV0970z8K9U6k7LkKiek9JgUrak5SpR82MNMLMXCKl3etA6P2VNQpaApea+uPvG7diZDSxPmE/4aSzOvV5q7o+790rC91Iws28C5wG/BJ6OxVsQ7uh+TriDzvlVON+mFnaSut7/oJ25PF5e8+N1pdZ19zYzsJvZtR0cW8qE8IqRlkvTYFK2krre0VYiem40LetJrTbclZCd4xOyAySSLSPHffHvbbR+aXITT+/Ne++0n0khKTt3dehxaYwpeN+d0Gk/iJC1vWTa+3KXySpFfBpEyIbxWELnaI+7ErJzfEJ2WkhIz0lpMClbd3XocemMKXhfkZ4bTct6UuskdJRdwN33KsPWtqXWdfd/1tpOrTGzXwI93b3sZrJaYGG9r8uBie5+Xtb+ZEFSek5Sg9JzRb4krmUFtTrDzFbMNU2YWbEhyy101BxSYLcm2TI6A2b2ZUKW9XabxcxsNLCDu8+OzUjtNf2023RUgk/rAo+7e79q7NSSWmk52pOeK6QUPTeyltX8mBBmNgsY6O4zY/9BeyJo78dxtpn185BB4IM27JTaHJLPMsCTZdRf+oQhLdFYd18SX7eJu7+Ugp2krncpbEmYF9URdxP6PiDZZqRirEONvr8NoGWoQs9JaTApWylrGUrTc8NqWUEtOX5Ka1bt46uwsz1h3g3AdtU4VMDVhEULKx1BNYYwqXR6fO209hfk09EPVFJ2krreLeSP4soVEdZ72pQSrpvnJYD1ZFZFwMwuasOnb1FmP18Z1LuWoTo9jyEZDSZlK3EtQ3V6bmQtq/mxiSkQURfgh4TMAmVnF7CQtPWtOBR7rfbqeuv6STWzUwuKjPJaQshO8Zi7P5ymLzmKTKBt8Qm41t0//exRzUlSek5Sg9JzWf6komUFtYToqM8gnw6Gp7fbhFFgp6OmkfYyChTaSvpOuqYkdb2TpKOmo3zSHLZeLvWo5WivKfUsLSeLglpCmNkSOhaBEdLBtNmkkWenWBNGPu3aSRoz26PUuu5+Twp2ErnebdjehNZM8q94QWqndo77YanncPeymlvMrA9LZ20vK19nmeeSliPtaTApW7XUcrRftp4bWcsKagmR4DD3dpswCuyU3JwRJz7+xN3nFpSvAFzW0bye+MUr0a0Of+iSsJP48GkLy33cSliZ9wPCD8lKwD8ICV1rFkja8WkFwhIfw2idi7UYuBE41t0LVyNO4px1reVou2I9J6XBpGzVaipAvek5LS0rqHUSzGwxkBuJll++KvCeu3f6QUNm9mfCSKyDPCZvNbP1CZ3YE9x9/zJsfRNY7O4PFZTvDHT1pTOet2fn98COwI+BJ2LxUGAU8Hd3P7pUn5oJ6bljktJzw2nZ6yABZbNtwHBg3yLl+xIyeJdq51TgkCLlhwCnlGhjRcLd2RKCwFfM21Yh3DVNzfqa1cn1ngN8rUj5ZsAHZfr0EvDNIuW7Ai+WYWcmIUN6Yfl2wIwGurZVaznWb2o9J3W94zGJ6LnRtLxUahmRGD8n/AMLmQ6cVoadI4HXipT/j9LXMPqAMKzagXGErOa5bSZhPavfleETZjbKzI4rUv5jM7skbTskd727UDCKLrIIyv6uDABeKVL+GiG3X6ksT1igsZDpcV+tqSctQ8J6TlCDSdlK6npDcnpuLC1nfWfSjBthYuOXipR/ifKWVFkA9C9SvjYlru4LbEtoU18C7BXf57YtgS9U8PneATYpUr4x5S1HkpSdpK733YSlS76QV7Y68DhwZ5nX6D1g+yLlOwLTy7DzKCGX4LJ5ZcvFskeS0GtK17ZqLcf6ieo5KQ0mZSup6x2PSUTPjablTt/uXCOmE9Y/mlxQviHwfhl2pgBbA5MKyrcGppZiwGPHspn1Bz4kNPe0jISi+B1YR/QmNG0U8iGwagZ2krrePyb8EEw2symEa7QmYZXeA8uwA3APcImZ7eXub0BLeqIL475S+QnwIK3LfjghKe0CYJcyfaqEutEy1ETPSWkwKVtJXW9ITs+NpeWkoqO2pe5IziOIcjtCFoGuhOwKk4ELyrBzCqEpYjiwVtwOiWWnlunTpvG4t4E7gDsJPzQzgY3LtDUW+HGR8mMJQ4bTtpPI9c6zt1P04Thgxwo1sBLwFKGpZ1LcFhEmmq5cpq3lgMMJPyIXAYcBy3VWLSep56Q0mJStpLWchJ4bTcs1/UJ01o2Ql+7PhCaShXH7lNDev0wZdgz4DWFF3cVxmwecWYFP/wauA7rllXUjLDX/rzJtHQJ8DIygtenn7Ojb4RnYSeR610AHBuwMnES4a94ma21W8BnqTsvRXiJ6TkqDSdmSlhPwNWsHmnkjdLDuC3wbWKsKOz2BrxHWHupRoY35wHpFytcHPq7A3tGEu+QlcZsIDMvKThLXmzC0+Lgi5T8GLslIQ4mMGkzAj7rRcrSTmJ4T1mBS34uqr3e96TktLaf6obRltxFGHe1cpHwXYFoVdvsQ1mYqtm/rUn+4krJT5TVKbNBAgj5NBrYqUr45MCkLn+phq4Wek9Sg9FzUn1S0rCH9nYc/A9eY2ffNbA0z+6KZ7UfIdn5LpUbdfYa7f9TG7gcIo61Ss1MlSQ4aSIq+hEUwC5lByHDeWUlcz0lqUHouSipa1ujHzsPPCKONbqT1/74IuIIwN6YWdJTzL207HTGBMKH0twXluxGakbIgkVGDTUjaek5Sg51Vz6loWUGtk+DuC4GfmNmphEwMRkiVk3juwAbmIuC3MeHqY7FsB+BEElznqkyuJgyn7l7g03mEEWSdEum5JOpNz+loOe12VW2dZyMsfLh2Le0QbszOAtZIyOfEBg0U2O0DdK/guERHDWqr+P+XiJbbs5W0lqPNxPVc71rOXCzNugFfB/5ImN+xeiw7CBhapp2DCMk/pxJHQRHusr6T9WcswfeaB7W4/yOKZGGo8pwVdfQDR+T2xS/xaYQUTrkv8EVAlwr8aXfUIPDFSuyWeG5pOYWgFvclruVot2w9N6qWNVCkBpjZd4GHCHckGwE94q7PUUb+NjM7miCcvwEr07oc/Adk1xxWDp6SnUcIqZMSwyvv6L+CMFkVwo/CacBIQmA4hTB8+ZgK/PnI3f/r7mPd/ZMiVV4hpFJKFGm5haS03JGtxLUMFeu5IbWsPrXa8AvgKHe/MY7IyvEkcGYZdo4lTNq8y8zyO7+fAy5IwM9ak9ZAkQeAc81sEPA84S6yBe9goceE/cnfdyhwhrtfHN8/aWYLCP/Xws77WvpUDdJyIK2BImlruT1/GlLLCmq1YV3gX0XKPyTcpZZKf+CFIuWfACuU71YymNkvgeu8g4Ud3f1zadgh3FECnFDscFqfCtIidyfen5DENZ/HgItpHKRlStJgUrak5SpR82NteJfiSzIMpbyhtJMICT8L2Y3KEhEnxe7AG2b2qJkdYGbLZmnH3bu0s6X9IwCwq5ntQWiyW65g33KEDvtGQVpO0Za0XD0KarXh98ClZrY54U7nC2b2A0Izy+Vl2Dkf+J2ZfZ/wSL6ZmZ0O/F/clwnuvgkhK8FLhDu1d83sCjP7WhZ28qnyRykpbgDuInR471Cwb0vgjbQdqgJpOQNbIC1XTNKjbLS1jOA5h5DcNDeUdj4wsgI7hwNv5tmZAhya9efL868bYV2rewjJV18mLDGxUlp2CE0yZxDSAn1KHFlG6NRO/FoRmt4qGglHyOW3Sz35VIJtaTklW2lruRrt1KuW9aRWI9z9dEIqms2ALYA+7n5GBXb+4O5rAasBfd19DXe/Jllvq6ILIbN4D8Id+CzC3Jgp8a48DTunAwcDJxN+QHK8TFjaImkq7sh29/vc/aEknYnULEuFtFy2lquxlbaWoULt1K2Wa3nno615N2ATwqin9wnzjn4NfDlv/4mUkFg2CTuEdEA7xNctc4CA9YDZZXymoutDASsCj5Vw/GDi/Jr4us2tDJ+uBT5XpHwF4Nq892sAXbPWRSNuSWk5CVtJaTkeU7GeG1nLFo2IKjGzO0qt6+57l2jz84S+ix0Id7dL3cF4Nh3HmNlLhNWGHwb+ANzr7osL6vQhfHnbbA1I0E5uGZI3zWwusKG7TzSz9YFn3b1niZ9rCeEJYnpB+WrAO+7evdTj42tn6f9Z7r2X+r8zs8VAvyI+rQq85+6Jj2CWlsvXYFK2ktJytFWxnhtZyxrSnxz52bCN0J4+hzAPB8Id3MqEVXpL5XrC8usjCaPQ6uUO5C+EO6t32qrg7jPoeCBSUnb+R5gQWjiUel+KDyNfCjMbnPd2fTPrm/e+KyEpbJs+5tGfkHE897pizGxFgo4M+FycE5Tv0zeB6cWOTQBpOY8SNZiUraq0DInpuXG1XOkjnrZ2H7N/Q7hT65pX1pUwkuz8MuzMBYZk/XkKfOpOGMq9fj3YibZ2J2SmOIUwWfVn8fp/AuxUwvFLaM1Ft6TINo8iixvW+Drn+1Rs+xQ4PQU/pOUUbVWr5SLayVzPaWtZT2q14RBCXryWpgd3X2xmFxEyMZxUop0ppLdMRUm4+yIz60GVd9pJ2Ym27o2d76dFe2cDo4Hd3f3vJZjoT7jOEwmDIWbk7VsITPeCZqRSMLOBhJRHq1Fwd+7uZ3dw+HbRp8eA7xIGGuT79Ka7p7H0jLScoq0EtAw10HMjaVl9ajXAzGYDw939roLyPQkZB1Yp0c7OhI7lI919csJuVkxMc7QecJi7f5q1nXrEzA4nZIeYCbzH0j927u4bl2hnLWCKu2cyyVVazsZWPdFoWlZQqwHxLvZgwsTSp2PxFoTFC29092IpcIrZmQ0sT+j7/JiwCGIL7t4rIZfLwszuJHT4f0QYalyYn67UwQNJ2fkaYaTWMwXlmwOL3f254kcWtVXNHWm+nTeBy939N6Ue046tlQl33MV8urFa+x2cW1pO0VaSWo7HVa3nRtOymh9rw88IdzQ/pXWZ8ncpfzG845N1KzE+AP5aR3Z+R7i2zxSUr07om9i8FCMd3ZESmoJKZRXCwIGqMLPdgT8Rhj3PLeJTTYMa0nLathLRMiSq54bSsp7Uakwc+YO7f5i1L82KmX1EmC8zsaC8P/CSl5CMNtZP8o70GuC/7n5llXbGEZZrOc0zXtVZWq49SWk5HpOInhtNy3pSqyFxTsq6gJvZ6+4+swIb6wDDCUvW/8TDvJFdCW3T/0vW47L86kZo1lgHuNnd55rZF4APve11m2pl5xPg83w2wW4/wsiqUknkjjQyARhpZlsQmqIKm9tGlWhndWBUHQQ0aTkdW0lpGZLTc2NpuRZDODv7RpwhTxBhbhjtIuAaYPky7GxL6H/4O0HsuewCJwO3Z/j51gJeJfQZ5OenuwS4MgM7twKPk5dXjzCP6nHgtjLsXENYOyyJazSpnW1iGXbuAL6X4f9aWk7RVlJajscloudG07Ke1GrDRYQv8e6E5eshLNUxitAPcXSJdn4N/MLdL4rZBXL8g5AcNSsuJUzE3ZCQDijHncDVGdg5kbDm15tmlpugOgSYBhxUhp2k7khx96omrOZxP3B+zChRzKdaLBqZj7Scrq2ktAwJ6bnRtKw+tRpgZjOBfdz98YLy7Qh3W31KtPMRsIG7TypImfMl4DV3z2Rpivj5tnb314v49Yq7L5+mnWhrBeAHhB+U+YTlP25x90XtHri0jUnt7HZ3X7tUW0kRUxS1hXuN00tJy2VpMKnvRdVajnbqSs9paVlParVhecKdVSHT475S+YDQll4ozo0oLW1TrehC8RV4v0gY1ZS2Hdx9HnBVOccUsVFtOqCLCEvez4uv2ztXSUPhvYN8gykgLadsKwktRzsV67mRtaygVhueAkaY2TB3XwBgZssBZ8V9pXIz8Bsz25cw5LWLmW1NSAxb66Hc7fF3whDtI+J7N7OewAjC6Ka07QAQmzXWJCz50ULSTXRm9iEh5VNhZ/5GhHRJuddtkXjziJm9DHzT3ackbFpazsBWWlqO5yqm58bVcq077TrjBgwC3ibMD3kUeCS+fhv4ahl2uhPmdeRyuC2Mr28iw2VGgC8ArwOvENrFn4qf7zVgtQzsrA28SGuOuSV5rxfX4PO3LAlSL1utfJKW07WVtpZrqZ2s/FGfWo2Id7MHEtLmGEHof3L3+RXYWodwt9QFeMHdxyfpayXEz7c/Yfn6LoT8dGV/viTsmNm9hC/94bTmu+tNGMjwM3f/dzk+lXC+lv6SJO1WQy19kpbTs5W2luM560rP1fqjoCYanthBv727v2Rmc4DNPHTWbw9c6O7tNZ9Ucr4Ov3RmtixwLCGZa7GUQCXly0vSJ1H/pK3leM52tdNoWlafWg0ws1MJCwFeW1B+CNDHS5zh304HrQMLCEN273b3WW3UqwlmNqy9/V5iDrek7BA653MTW2fS2gz0JmHCcBZcC+wE3A48S/2sH1YW0nLp+QgTsiUtV0vW7afNuAGTga2KlG8OTCrDzj8IizN+BDxPaMqYSxhJ9jQwm7CMQ9XrQZX5+WYXbB8R2v0XALMysPNvYM/4+mbgAWBr4AZgbA0+/4d00OYf/29bp/g/qVWfmrScoq20tRzP066eG03LWQ8Xblb6EpK+FjKD1qSwpXA3oWP+C+6+iYfH/NUJo6xuia//BVxcnbvl4e6rFGw9CXeR/yH0J6RqB/gVrU0ivyBkdvg3YUXd48qwUyqlrAv2DuUPCa9HpOV0baWtZehYz42l5bSib2fagPHAgUXKD6K8tDLvUOTOFfgq8E58vTEwM+vPHH3ZlDCRNnM7QC9in3ENPudQoEcHdXYj3GWvldK1PwBYoQZ2peWMbdVSy9F+u3puNC2rT602XA1cYmbdCau9QlhnqdzlOlYidMy+UlDeB1gxvv6AgrksGbKY0AeQmR0zW4OQneDtCo7tSlg7bAeKd4hvH//+pwRzzwHLAhPNrKr1w8xsh3Z8OiT+vblUe2UiLWdkqxotx+OT0nNDaVlBrTacR7i7upzWL+kC4Dfufm4Zdu4GrjWzE4H/EjpoNyNMWL0r1tkMGJeAzyVjZnsUFhGaon5Ma37ANO10I0wGPg7oGcs+Ai4DRnjp6YUuJfwI3A+MpboO8VyT2mmEjBwV2TKzs4AzCT8s71bpUyVIyynaSlDLkJyeG0vLaTxOdqaNMHppW8IPQU/ga4QJrO02V7VhqyfwB0JW88Vx+4SQQmeFWGcIIRtAmp9xScG2mLAI4c1AvwzsXEn4sh0JDI7bkYQvTjnZ/mcSMhkkcY0+JgxLrtbOu8BBaf5/884tLadsKyktR1uJ6LnRtKx5ajXAzBYAX3H3wjx3ldrrScg0YMAbXuYaT81OnM+zn7s/UFC+G3Cru69Uop2pwDfcveqnBTMbDRzj7k9Xaed9wlylN6r1qcLzS8spkpSW4zGJ6LnRtKzmx9rwMuGLm8gPQfziv5SErSToKMFpPt5OstOk7BCawyYXKZ9MSMdUKhcCPzGzH3v1d3s/By40s9MpvsxGqatHX03oOB9ZpT+VIi1HOtBgUraS0jIkp+eG0rKCWm04HbjAzM4gzMmZl7+zDBHUKxsBmxCap16PZQMJzS2j8+p19EVKys7vgDPMbLi7fwJgZj0I/4ffdnBsPkMJWRN2M7P/8dkv795l2How/n20oNwIn6fUZTaWBY4wsx0JwaDQp5IypFeBtBwoJSgkYSspLUNyem4oLSuo1YacCO5haQGXK4J65V7CvJUfuvtsADNbBbgO+Le7lzoqLik7GxFGVL1tZi/Gsg0JAxseNbM7chU7+CJ/QFjQMQm2S8jOYGBMfD2oYF8afQfScrq2ktIyJKfnhtKy+tRqgJlt295+d/9nWr7UAjN7B9jZ3f9XUD4IeNjdSxq+nKCd60rzHNx9eKl1KyUOf38YODKJ/rkskZZL02BStqTl6tGTWg1o9C96CawIfB74X0H5asDn0raT9JfbzPoQMkE4MM7dZ5RzvLsvij9kid4xmtkXg3lPbVFNaTldW7UIVNXouRG1rDRZNcTMljez9cxscP6WtV8JcCdwnZntY2ZfjNs+wDXAHR0cm7gdM1vOzJbPe7+WmR1vZjuX4QtmtoKZXUsYevwvQnqiqWZ2Tb79ErkROLTMY4r51MXMzoyj4t4E3jKzD8zsDDNL7fsrLadjKyktx2OT0nNjabnWcwY640bIknAfrfNxltqy9i+Bz7c8YTLuApaec3Q5ZaS3SdDOw8BR8fXKhHk+U4D5wNFl2Pk98AYhLdCKcfsmIYP8FWVeo8sIiWCfj3Yvyt/KsHMuMB04mtAnsSFwTCw7R1quDy0nZSspLSep50bTsvrUaoCZ/Qn4EmFp938AexGaJX4BnOju92fmXIKY2QrAOoRBAxPcfV4Hh9TEjoU1qLZ19/+Z2WGEtZ82Ar4LnO3uXynDzj7u/nhB+XbAbe7epwyf/tHObveYoqgEO1MJP3L3FJR/B7jc3Vcv1adKkJbTtZWUlvNsVa3nRtOy+tRqw/bAd9z9v2a2BHjT3f9uZh8CpxLS1jQ88cta9ZyjBOwsT2sW8Z2BO9x9iZk9TchyXo6daUXKp8d9JePuSY0Y6wW8VqT8tbiv1kjL6dpKSss5W1XrudG0rD612rACQTgQ1ojK3RG9TMhELpJlArCnhQSwuxCacCB00Jczj+opYISFlX6B0MdByMX3VCWOmdmXzWyXaAczK2XZmnxeJOQOLOTHcV+tkZbTJSktQ8J6bhQt60mtNrxOGG00mTAv40gzmwwcRfG1qUR1nE3Ir3cx8Ki7576wOwMvlGHnJ4R5Wbk5Qk7IR7iA8ANTMmbWG7iNMMfHgQHAROBqM/vA3U8s0dTJwP0WJqw+FW1tBaxB6B+pNdJyuiSlZUhIz42mZfWp1QAz+wHQ3d2vN7ONgIeA3oQ0Nwe7+58zdbAJMbO+hIzoL7r7kli2GfChuxdr8mjLznLAgcB6hD6RV4A/ufv8Mv25kXB3fRjwKiEh7MQ4iu1id/9qGba+APyowKfL3X1qOT5VgrScPklpOR5XtZ4bTcsKaikQh8+uB7zl7jOz9kfUHjN7D9jF3V80s7m0/hD0B172sCpywyEtdz4aTctqfqwxsd15vruP7rCySB0La2A94GGSaeF6WEtROGqrA1YgLNlRyKqEYd7t+TQYGBsHCLQ7F8zdU0sOLC3XPzXSc0NpWU9qNcLMDgV+Smh/BhgPXOLuV2fnlSgkjujr6+7T4+u2cHcvOc+hmd0PjHb3M+Ld7WDChNNbgS7uvk8ZPjmhqaYqnypFWm4caqHnRtOyntRqgJmNJPwIXEbrKKMtgYvN7Evu/ovMnBNL4e5dir1OgJOAx81sU0Iy2vOArxKGLm/dwbH9gRl5rzNDWm4saqTnhtKyntRqQJz0eKy731JQvj9wmbuvmo1noj3MbBjwZ49LfuSVL0NYuPHGMu31JWRP2IQwfWY08Dt3L3nUoJltAzzp7p8WlHcDtnL3f5XjU7lIy41LknpuJC0rqNUAM5tNWOF1fEH5QOBZd185E8dEu5jZYqCfu08vKO8NTC+z+XFNYIoX+YKZ2Zru/lbaPlWCtNy4JKWdRtOyJl/Xhj8S7moKOQL4U8q+iNLJrRFWyBcJue/KYRKtE5VbTxC+wOWsIt2WT70pWLCzRkjLjUtSem4oLatPLSFs6aXcHTgszuN4OpZtQZhkWFYTlqg9ZvYC4X/mhIUY85tHuhL6Ah4sdmx7Zin+Be5JmPzakU+5rO4OXG9m+U1IXQmd9U+W6VNJSMuNTQ303FBaVlBLjo0K3j8f/64T/86IW8kTFUVq3BX/DiFMLv4ob99CQjaNv5ZiKC8gODDSzPKHQncFNqd19d/2yN1JGyEXYP5k2YWEAPOHUnyqAGm5sbkr/h1CFXpuVC2rT02IiJn9kNCx3uHdZzs2chnNtyWMFlyYtzv3g3JBYR9VO/bOivXTaGoUTUS1em5ULSuoCVEDzOw64CfuXm4SWiHqikbTsoKaEBEz60qYk/U9YE3CnJwW3D2NpV6K+bVPOz4pU74oSj3qOQ0ta/SjEK2cBZxAyEi+EmFl3zuAJcAvs3DIzI4DriMs/7IR8CzwPrA28EAWPomGoa70nJaW9aQmRMTM3gCOc/f7YzqgIe7+RvwybuHuB2Tg02vACHe/pSCZ7NlAL3cvtj6VEHWn57S0rCc1IVrpS1j8EsKIsZXi6/uAb2XiUWimyQ13ng98Lr6+Cdg/E49Eo1Bvek5FywpqQrTyNmEdKwgrEO8cX3+NDrKR15D3CJNTISSR3SK+7k/xxLBC5Kg3PaeiZQU1IVq5E9ghvr6UMDdnPGGS8bUZ+fQYsHt8fQ0hkfDfgT8T/BWiLepNz6loWX1qQrSBmW1OyEI+ocy11JL0oQtheY9P4/vvAUMJd95XuvvC9o4XIkfWek5LywpqQkSyzojfhk9Fk8nGBTvXKDWZrOh81Jue09KygpoQkawz4jeKT6IxqDftKEu/EOmTdUb8YlSVTFZ0aupNz6loWQmNRacny4z47fiUVDJZ0cmoNz2nrWUFNSGyzYjfFrlM+QZswGeTyb4IXJCyT6IxqDc9p6pl9akJEanHjPiNlkxW1A/1pue0tKygJoQQomlQ86MQETObRPGObADcfe0U3QHAzB5rb7+7b5+WL6KxqDc9p6VlBTUhWrmk4H13Qn/ArsD5qXsTeLHgfXfCisaDgBtS90Y0EpcUvM9az6loWc2PQnSAmf0I2NTdh2ftSw4z+yXQ091/lrUvorGoNz0nrWUFNSE6wMzWBsa4+4pZ+5LDzL4MPJvVwqWicak3PSetZU2+FqJj9gFmZe1EAVuiydeiMupNz4lqWX1qQkTM7AWW7lg3wppUfYBjMvLpjsIiwnIimwIj0/dINAr1pue0tKygJkQrdxW8XwLMAB5399fSdwdonUibYwnwOnCmuz+cgT+icbir4H3Wek5Fy+pTE0II0TToSU2IAsxsNWA1Cvqc3f2lbDwKmFlPPuuTMo2IdqlHPddSywpqQkTMbBPCfJmv8Nnl5Z2QfDVtn/oDvwW+ASybvysrn0RjUG96TkvLCmpCtHIdMA44FJhGO9kYUuRP8e8h1I9PojGoNz2nomX1qQkRMbO5wEbuPiFrX3KY2UfAJu7+eta+iMai3vSclpY1T02IVh4FNszaiQL+C6yRtROiIak3PaeiZT2pCRExs1UJfRDPAmOBRfn73f2eDHxaB7gS+GMbPmU6eEXUL/Wm57S0rD41IVrZChgK7FZkX1aDMvoA6xD6R/J90UAR0RH1pudUtKwnNSEiZjYZuA8Y6e7TMnYHADN7BXgVOI8inevu/mYWfon6p970nJaWFdSEiMSO9SHu/kbWvuQws3nAhvXS2S8ah3rTc1pa1kARIVq5A9guaycKeIz66uwXjUO96TkVLatPTYhWxgHnmtlQ4GU+25E9KgOf7gUuNrMN2vAp9cEromGoNz2nomU1PwoRMbNJ7ex2d187NWciZraknd3u7hooIopSb3pOS8sKakIIIZoG9akJIYRoGhTUhBBCNA0KakIIIZoGBTUhhBBNg4KaEICZdTOzs8xMyYNFw9OZ9azRj0JE4tIYg9x9cta+tIWZ9QE+cPdFHVYWnZp613OttKwnNSFaeYSwKm/mmNkRZtYjvjYzO83MZgPvAR+Y2UVmpu+vaI+60HPaWlZGESFaeYCQgWEQ8DwwL39nytk7rgDuAqYDRwCnAWcCTwMbA78CJgK/TdEn0VjUi55T1bKaH4WI1FP2juhLX3efbmbPAre4+8V5+w8DjnV35YUURakXPaetZTVfCBFx9y7tbFmko8rdcfYnrGKcz2NA6mm7RONQZ3pOTctqfhSiCGa2rLsvyNiNXc1sDjAfWK5g33JAe3fiQrRQB3pOTct6UhMiYmZdzewMM3sH+MjM1o7lI83s0AxcuoHQF/FFYIeCfVsCdbFOlqhP6kzPqWlZQU2IVk4HDgZOBhbmlb8MHJamI0Wai/6voMp7wKlp+iQajrrQc9pa1kARISJmNgE40t0fjasGb+juE81sPeApd18lYxeFKJnOqmf1qQnRyupAsaXmuwDd03LCzAYDY919SXzdJu7+UkpuicYjcz1noWUFNSFa+R/wdeDNgvJ9gRdS9GMM0Jcwr2cMYeSY5e3PvXdAi4SKtqgHPY8hZS0rqAnRygjgJjNbnXA3u7eZrQsMA76doh/9gRl5r4WohHrQc+paVp+aEHmY2S6EjAebEH4IRgNnu/vDmTomRAV0Rj0rqAlR55jZQEIOv9UoGLHs7mdn4ZMQlZCGlhXUhIiY2deALu7+TEH55sBid38uA58OJ+TOm0kY+pz/hXV33zhtn0RjUG96TkvLCmpCRGJeuvPc/faC8r2BU9x98wx8ehO43N1/k/a5RWNTb3pOS8uafC1EK+sT+hwKeSHuy4JVgL9kdG7R2NSbnlPRsoKaEK18Any+SHk/4NOUfcnxF2DnjM4tGpt603MqWtaQfiFa+Tth/anvuPscADNbGfi/uC8LJgAjzWwLQnqjpVYJdvdRmXglGoF603MqWlafmhCROJ/nX0BvWienDgGmATu5+5QMfJrUzm53dy0/I4pSb3pOS8sKakLkYWYrAD8ANiQsk/ESYVHDRe0eKEQd0hn1rKAmhBCiaVCfmhAFmNn6wJrAMvnl7n5PSue/CDjD3efF123i7iek4ZNoXLLUcxZaVlATIhIXUbwT2IClE6/mmjPSSh68Ea1Z1Ddqp56aWUSb1ImeU9eymh+FiJjZvcBi4HBgIrAZoZP9QuBn7v7vDN0Toiw6q54V1ISImNlMYHt3f8nM5gCbufvrZrY9cKG7t3enKURd0Vn1rOZHIVrpCnwUX88EvgC8TliPat0sHDKzZYFjge0ongRWuR9FW9SVntPSsoKaEK2MBQYTmmqeAU42s4XAEbEsC64FdgJuB55F/WiidOpNz6loWc2PQkTi2lMruPsdsZP9PmA94H3g++7+WAY+zQG+6e5PpH1u0djUm57T0rKCmhDtYGa9gNme0RfFzF4B9nP3l7I4v2gustRzWlpWQmMhimBma5jZF919VlYBLXIi8BszWytDH0SDUyd6TkXLCmpCRMysm5mNjM0kk4E3zWyOmf3KzLp3cHiteA5YFphoZnPNbFb+lpFPogGoQz2nomUNFBGild8CewEnA0/Fsi2BXwKrAkdl4NMtwOrAaYREtOovEKVSb3pORcvqUxMiEu9o93P3BwrKdwNudfeVMvDpY2BLd38x7XOLxqbe9JyWltX8KEQrCwjNNIVMBham6kkrrwHLZXRu0djUm55T0bKCmhCt/A44w8x65Ari69MJTTlZ8HPgQjP7hpn1NrMV87eMfBKNQb3pORUtq/lRiIiZ3QnsAHwC5JpINiRkN380v667752ST0typyzcFdzwtJIsiwaj3vSclpY1UESIVj4A/lpQlvpq1wVsl/H5RePyAfWl51S0rCc1IeqUOOz6YeBIdx+XtT9CVEqaWlafmhARM1vOzJbPe7+WmR1vZjtn4Y+7LwIGoWH8ogLqSc9pallBTYhW7gaGAZjZyoSkqycCd5vZ0Rn5dCNwaEbnFo1Nvek5FS2rT02IVjYGfhpf7wO8R1it97vA2cAVGfi0DHCYme1EyMgwL3+nu5+QgU+iMag3PaeiZQU1IVpZHpgbX+8M3OHuS8zsaSCr3IuDgNHx9cCCfWqWFO1Rb3pORcsKakK0MgHYMw6F3gW4OJavBnyYhUPurtGPolLqSs9paVl9akK0cjZwASHjwjPunsuXtzPwQlZOAZjZl81sFzNbLr63LP0RDUFd6rnWWtaQfiHyMLO+QD/gRXdfEss2Az5099cy8Kc3cBthjo8DA9x9opldA3zg7iem7ZNoHOpJz2lpWU9qQuTh7u+5+wu5H4BY9mwWAS1yMbAIWBP4OK/8z8CumXgkGoY603MqWlafmhD1zc7ALu7+dkErzXiyG7wiRCWkomU9qQlR36zA0ne1OVYl5PQTolFIRcsKakLUN/8iTqCNuJl1AU4C/pGNS0JURCpa1kARIeoYM1sfeBx4HtgeuAf4KtAL2Nrd38jOOyFKJy0tK6gJUefEEWxHA5sQWldGA79z93czdUyIMklDywpqQtQxZrYmMMWLfFHNbE13fysDt4Qom7S0rKAmRB1jZouBfu4+vaC8NzBdi4SKRiEtLWugiBD1jVE8L15PYEHKvghRDaloWfPUhKhDzOyi+NKBkWaWPxS6K7A5MCZtv4Qol7S1rKAmRH2yUfxrwAbAwrx9C4EXCXn9hKh3UtWy+tSEqGPM7DrgJ+6eySoBQiRFWlpWUBNCCNE0aKCIEEKIpkFBTQghRNOgoCaEEKJpUFATQgjRNCioCSGEaBoU1ISoAAtcZWazzMzN7AMzuyRv/2QzOz47D4XonGjytRCVsStwMPANYCKwBJjfVmUzc2Avd78rBd+E6LQoqAlRGesA77r7k2me1My6u/uiMuob0NXdP62hW0LUDWp+FKJMzOx64DJgzdj0ONnMHs9vfiyoPzm+vDNXP2/f7mb2vJktMLOJZnaWmXXL2+9mdpSZ3W1m84BfdODbN+Ixu5jZc8AnwNdjc+nJ8RzzzexFM9sn77iuZnaNmU2K+183s58Usf2smc2Lza1PmNlaefuPNrM3zGxhPP6gguPdzA4zszvN7GMzG29me7T3eYQoFwU1IcrnJ8CZwNtAP+BrHdTP7R+eX9/MdgH+CIwC1geOJDRpnl5w/AjgbkLevGtL9PE84FTgK8BLwK/i+Y8mrDZ8MfBHM9s21u8SP8/3oi9nA/9nZt+LvnYD7gL+CQwGtgSuImZdN7O9gEuBC4FBwO+B68xsuwK/zgJuizb+BvzJzHqV+JmE6Bh316ZNW5kbcDwwOe/948Alee8nA8fnvXdgzwIb/wJOLSg7EJhacNzFZfj1jXjMd/LKViD0921ZUPdq4OZ2bP0OuD2+7hXtbttG3SeAqwrKbgPuL/gsIwv8WgLsmvX/U1vzbOpTEyI7NgG+Zmb5T2ZdgWXNbHl3zy3R8VwFtvOPWR9YFvh76GJrYRnghdwbMzsKOAxYC1gu7h8D4O6zYrPrQ2b2d+AR4DZ3fzce/hXCk1s+TxCeavN5KffC3eeZ2VxgtfI/nhDFUVATIju6EJrj7iiyL3/RxHkV2M4/JtfN8C3gnYJ6nwDEZsaLgROBp4C5wEmEta4AcPfhZjaKMPLz+8CvzGwnd386V6XAdrFFIQsHuTjqBhEJoqAmRDosIjyF5TMaWNfdJ9T43K8Qgtea7v7PNup8HXjS3S/PFZjZOoWV3P0FwtPduWb2FHAA8DTwKjAUuDGv+laxXIjUUFATIh0mAzuY2RPAJ+4+mzAY4z4zmwL8hdC/NBjYwN3bHeVYDu4+18wuAC42sy7Af4AVCUHnI3e/AZgADIuDVyYBBxEGtEwCMLP+wBHAPcBUYF1gIK1B7HzgNjMbDTwK7A7sDeyY1OcQohT02C9EOpwI7ARMIfZjuftDwLdj+X8JTzwnAG/W4PxnEILoqYSnp4cIgWdS3H8loRn0z8AzQG/g8rzjPwbWA/4KjCP0n/2WMMoRD5PKf0JosvwfYSTncHd/vAafRYg20SKhQgghmgY9qQkhhGgaFNSEaCDM7Eoz+6iN7cqs/RMia9T8KEQDYWarEQZ5FONDd5+epj9C1BsKakIIIZoGNT8KIYRoGhTUhBBCNA0KakIIIZoGBTUhhBBNg4KaEEKIpkFBTQghRNOgoCaEEKJp+H8yltRKSx6I2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 450x200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncol = 7\n",
    "nreasons = filtered_df['filter_reason'].nunique()\n",
    "\n",
    "p = (\n",
    "    ggplot(filtered_df, aes('filter_reason', 'count')) +\n",
    "    geom_bar(stat='identity') +\n",
    "    facet_wrap('~ name', ncol=ncol) +\n",
    "    theme(axis_text_x=element_text(angle=90),\n",
    "          figure_size=(0.25 * nreasons * min(ncol, len(pacbio_runs)),\n",
    "                       2 * math.ceil(len(pacbio_runs) / ncol)),\n",
    "          panel_grid_major_x=element_blank(),\n",
    "          )\n",
    "    )\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we take the successfully parsed alignments and read them into a data frame, keeping track of the target that each CCS aligns to.\n",
    "We also drop the pieces of information we won't use going forward, and rename a few columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few lines of information on the parsed alignments:\n"
     ]
    },
    {
     "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>m64272e_210802_074641/13/ccs</td>\n",
       "      <td>C40T C41T T42G ins166T</td>\n",
       "      <td>0.999347</td>\n",
       "      <td>TATCCACACGAAGCGT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m64272e_210802_074641/14/ccs</td>\n",
       "      <td>G155A</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>CAAACACACTCCTCAG</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m64272e_210802_074641/43/ccs</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "      <td>TTCGACAGAATCGATG</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m64272e_210802_074641/71/ccs</td>\n",
       "      <td>A539G T540G</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ATATGGTCACTTATTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m64272e_210802_074641/83/ccs</td>\n",
       "      <td></td>\n",
       "      <td>0.999977</td>\n",
       "      <td>GAATTCCAAAGAGTCA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aligned_df = (\n",
    "    pd.concat(df.assign(target=target) for target, df in aligned.items())\n",
    "    .drop(columns=['query_clip5', 'query_clip3', 'run','name'])\n",
    "    .rename(columns={'barcode_sequence': 'barcode'})\n",
    "    )\n",
    "\n",
    "print(f\"First few lines of information on the parsed alignments:\")\n",
    "display(HTML(aligned_df.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write valid CCSs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the processed CCSs to a file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barcodes and mutations for valid processed CCSs have been written to results/process_ccs/processed_ccs.csv.\n"
     ]
    }
   ],
   "source": [
    "aligned_df.to_csv(config['processed_ccs_file'], index=False)\n",
    "\n",
    "print(\"Barcodes and mutations for valid processed CCSs \"\n",
    "      f\"have been written to {config['processed_ccs_file']}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we analyze these processed CCSs to build the variants."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build barcode variant table\n",
    "Builds consensus sequences for barcoded variants from the mutations called in the processed PacBio CCSs.\n",
    "Uses these consensus sequences to build a codon variant table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make output directories if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "Read the CSV file with the processed CCSs into a data frame, display first few lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 1443261 CCSs from 2 libraries and 1 targets.\n"
     ]
    },
    {
     "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>m64272e_210802_074641/13/ccs</td>\n",
       "      <td>C40T C41T T42G ins166T</td>\n",
       "      <td>0.999347</td>\n",
       "      <td>TATCCACACGAAGCGT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m64272e_210802_074641/14/ccs</td>\n",
       "      <td>G155A</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>CAAACACACTCCTCAG</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m64272e_210802_074641/43/ccs</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "      <td>TTCGACAGAATCGATG</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m64272e_210802_074641/71/ccs</td>\n",
       "      <td>A539G T540G</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ATATGGTCACTTATTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>m64272e_210802_074641/83/ccs</td>\n",
       "      <td></td>\n",
       "      <td>0.999977</td>\n",
       "      <td>GAATTCCAAAGAGTCA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CGG_naive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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.\")\n",
    "\n",
    "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": 28,
   "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\">CGG_naive</th>\n",
       "      <th>lib1</th>\n",
       "      <td>802675</td>\n",
       "      <td>108569</td>\n",
       "      <td>7.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lib2</th>\n",
       "      <td>640586</td>\n",
       "      <td>118042</td>\n",
       "      <td>5.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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": 29,
   "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": 30,
   "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": 31,
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 100x300 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",
    "Plot the distribution of the number of times each **barcode** is observed among the retained CCSs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
    "### 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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_ccs = processed_ccs.reset_index(drop=True)\n",
    "\n",
    "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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85/XXX8+gQYO48MILGThwIEceeSQAJ5xwAvPnz2f16tUMGDCAM844o4EjmW245EGXzauysnJVWVmZl6kpsDmj1HBQgXQcvOFcg5WVlavLysoa/KPP14WVmsZeG2bgbk0zMzOzouIq3syshLlV2az4uOXMzMzMrIi4ODMzMzMrIi7OzMzMzIqIizMzMzOzIuLizMzMzKyINElxJmnbpjiOmZmZWanLuTiTdKmk4zKePwgskDRL0p5Nmp2ZmZlZicmn5ews4GMASYcAhwDfA54Eft10qZmZmZmVnnwmoe1IWpwBRwEPRsQzkmYArzRVYmZmZmalKJ+Ws0XATun3hwPPpd8L8Fp5ZmZmZushn5azR4D7JU0Hvk7SnQlQDrzbRHmZmZmZlaR8irOfATNIWs+GRsSydHtH4NYmysvMzMysJOVcnEXEl8CNtWy/pSkSMjMzMytljR5zJqmrpD5Z2wZKel7Sq5KuaPr0zMzMzEpLLjcE/Br4fvUTSbsC44CVwEvA5ZIuasrkzMzMzEpNLt2afYEbMp6fAFRGxGEAkiYB5wO3NFl2ZmZmZiUml5az7YCZGc8PImk5qzYe6LL+KZmZmZmVrlyKs4Ukd2QiaSOSlrTMSWfbksx1ZmZmZmZ5yqU4mwBcJWkn4KL0tc9n7O9OMsWGmZmZmeUplzFnVwLPAh8Cq4ELIqIqY/9JwD+bMDczMzOzktPo4iwiPpC0B0kL2acRMTsr5BrWHpNmZmZmZjnKdRLazYHJEbEmc6OkNsAHEfFZk2VmZmZmVoJymYT2WOB1YLNadm8KvCbp6KZKzMzMzKwU5XJDwDnADRHxefaOdNv1wHlNlZiZmZlZKcqlOOtJMpdZXV4Aeq1XNmZmZtaiJI2XdEsBzjtAUkjadj2PM0rSoy193uaUS3HWjvrHqG2SxpiZmZk15EWS+VOXFDqRYpNLcTaDZOLZuvQlmWbDzMzMrF4RsTIi5kZEFDqXYpNLcfYIcJ2kb2TvkLQD8D/Aw02VmJmZmbWYjSTdIGmhpLmShlXvkHSxpMmSqiR9LOlWSVtl7N9F0jhJi9KY/5N0REMnzO5elDRY0mJJh0maJmmZpKckdcx4TRtJN6VxCyTdQNbqREoMlfS+pOWS3pb0n03wM2oxuRRn/wssBaanv5gLJV0g6TagEliWxpiZmVnrcgpQBewLDAWulnRIum8NcAHJ2PNTgIOBGzJe+3uSWRv6k4w9v5SkJsjHFsAQkont+wM7Azdm7L8EOA34KXAA0B44NusY/wOcSnIjYw/gZuA+SQfmmVOLy2US2qWSvgMMB47jq/Fli4D7gCsiYmnTp2hmZmbNbFJE/CL9frqk84CBwLMRcUtG3AeSrgJuA85Nt+0MPBwRk9Pn769HHpsAZ0fEewCSRgJXZ+y/CBgeEQ+n+88GDqveKWlL4GLg4Ih4qTofSQcAZ5EsRVn0cpqENiKWAOdK+i9gO5KmxE/dX2xmZtaqTcp6PgfoACDpIOAKkhWCtiapHTaTtGW6jOMI4DZJhwLPkRRq2cdrrM+rC7Na8tiG5AaC6qKLiFgl6XW+6trsTjIf67PSWr2dbYG38sypxeUyCe3mko6R9LVIfBoRn0RESNo63bdpcyZrZmZmzeLLrOdBMg5tF+AJYArwQ6AP8F9pzCYAEXEXsBswmqRb83VJ5zdhHqotsA7Vdc2RQHnGozvQasad5TLm7Ezgwtq6LtNlmy4ATm+qxMzMzKzg+pK0lF0SES9HRCXQKTsoIj6OiNsj4gfAb4AzmjqRtPduDtCvepukjUkKxmpTgS+AnSPi3azHx02dU3PJpVvzBOCX9ey/haRf+Pfrk5CZmZkVjfdIaoXzJY0DvgOcnRmQTmD7JMnNge1IbhiY1kz5/Ba4TNL09BwXA9tW70zHx98I3CxpI+D/kXTF7g8si4g/NVNeTSqX4qwb8HY9+yelMWZmZrYBiIgKSReT3IE5nGQ1oMuBezPC2pA0zHQGPgOeAn7WTCn9hmTc2SiSu0j/CPwN2CYj5irgkzTP3YDFwJvAr5oppyaXS3G2MbA98FEd+7fP8XhmZmZWYBExoJZt38/4/maS6Sgyjc7Yn9f4sogYT8Z4sogYRVJ0ZcY8mhWziuSOzYvqOW6Q3KQwojHnLUa5jDn7P+A/6tl/SBpjZmZmZnnKpTj7I3CVpKOyd0g6GvjvNMbMzMxKnKTb01n+a3vcXuj8ilkuk9DeIak/MFbSv4F3SG5x3QMoAx6MiDuaJ00zMzNrZa5m7dn9M33Wkom0NrlOQnuipLHA8SQFmUiKtGsi4sFmyM/MzMxaoYj4hGRgvuUol25NACLiwYj4fkT0iIju6fdNVphJujxdCPWWjG2SNEzS7HQR0/GSemS9blNJv5M0P114daykzlkx7SSNlrQkfYyuXnA1I2bndAHXqvRYIyS1zYrpJWlCmsssSVcraypiMzMzs3zkXJw1J0l7k0x2m73sw1CSuUzOA/YG5pIszfC1jJhbSBY/HUSyGOpWwGOS2mTE3E8yU/Dh6aOcjDtO0tjHgS3TYwwimRH5NxkxWwPPArPTXM4nWaT14jzftpmZmVmNoinOJG0F/JlkVuFFGdtFcsvsdRHxSERMAU4hWbn++DRmG5IV6i+JiOci4i3gRJJlJP4jjdmDpCA7PSJeShdEPQM4StLu6ekOJVni4cSIeCsingMuAc5IizJIJuPdDBgcEVMi4hGSuVMuduuZmZmZra+iKc5IJrB7PC2IMu0K7AA8U70hIr4gWVl+/3RTH5I1vjJjZpOsBVYdsx+wJCJeyYh5GViSFTMlfW21p4FN+Wp5iP2ACWkOmTGdgC6Nf7tmZmZm62rUDQGSepMULWuaIwlJg4C9SLoJs+2Qfp2XtX0esEtGzMqIWFRLzA4ZMbUNTPwkK2at80TEIkkrs2Jm1HKe6n0fZO1rM3369FpOay1pq0InUI8S/Xz4uigSvjbMik9j79Z8i2S5hE8kvQ/sHRELmiIBSTuRrJV1aESsqCc0sl9ay7Z1Dp8VU1t8U8Soju0Aq7t169amlu3WguZMLHQGdevWbcNZ9ayysrKxob4uioSvjZaRw7Vh1uhuzcUk3YuQdN01ZXdoH6AD8IakVZJWAQcCF6TfZ7ZKZeqQsW8u0FZSuwZivlHL+bfPilnrPOkxN6kvJj0PrNu6Z2ZmZpaTxhZZDwMTJH1A0jr0uqT3a3vkkcM/SAbul2c8Xie5OaAceJ+kIDqk+gXp1BYHAi+mm94AvsyK6Qj0zIh5CdhG0j4ZMfuSLJaaGdMzfW21Q4Ev0nNUx/TPml7jUJK7N2fk8sbNzMzMsjWqWzMizpT0CNCVZCHRO4GlTZFARCwlGbhfQ1IVsCC9M5N0zrMrJE0HpgNXAJ+TTI1BRCyRdDfwG0kLgIUksxJPBp5LY6ZJegq4U9JZ6anuAB6LiHfS588AU4HRkn4OtE+Pc2dEVM9mfD9wDTBK0q+Abmk+16aLrZqZmZnlLZflm54CkNQH+G1aVLWUG4DNgVuBdsArJGPUMnP4GbAKeDCN/QfJdBerM2JOICkuq+/qHEsydxoAEbFa0pHpeSYCy0mKsSEZMUskHUJyd+nrJNN+3JQ+zMzMWo05o9QsjQodB4enlloPOS3fBBARp1Z/n87AHxExqymTiogBWc8DGJY+6nrNCpIJYc+vJ2Yhyfxn9Z37I2Cdxd2zYiYD/euLMTMzM8tHzgP7JW2ULle0BPgQ+EjSYklXSSqmedPMzMzMWp2cW86A60hm47+MpOtPwHdIWrU2A65squTMzMzMSk0+xdkpJEsgjc3Y9rakWSRjtVycmZmZWZORNBi4JSK2LXAqLSKfbsj2wL9r2f7vdJ+ZmZnZOiSNkhS1PLoWOrdikk9x9jYZdzhmOC/dZ2ZmZlaXp0hWHcp8ZC99WNLyKc6GAqdJmirpbkl3SZoKDAZ+3qTZmZmZ2Ybmi4iYm/kALpQ0WVKVpI8l3SqpzqVfJe0p6XlJSyV9JukNSX0z9u8v6QVJy9PjjZC0ZYu8uyaQc3EWEROAMuBvwLYkXZmPALtHxL+aNDszMzMrBWuAC0hW9jkFOJhkjtO6/BmYCexNsgzk/5KsFISkXsDTJLVJb+A44ABgZDPl3uTyuSGAiJiNB/6bmZlZ7o6StCzj+ZMR8aOM5x9Iugq4DTi3jmPsDPw6IqrHwE/P2Pdz4P6IuKV6n6QLSJahPCedF7Wo5VWcmZmZmeXpeeCcjOdVkg4iWQqxO7A1SX2ymaQtI6KqlmPcBNwl6SSSZRr/GhHvpfv6AF0lnZARL5Lewl2BaU36bpqBJ401MzOzllQVEe9WP4C2wBMk62z/kKS4+q80dpPaDhARw4AewOMkXaBTJR2b7t4I+ANQnvHYk2Qt7PdoBdxyZmZmZoXUl6QeuSQi1gBI+nFDL4qISqASuFnSA8CpJOPh3wR6pIVfq5RTy5kSu0javLkSMjMzs5LyHklxdr6k3dKuyrPrCpa0uaSRkgakNcl3SG4MqO6uvB7YT9LvJZVL6ibpGEm/a/Z30kRy7dYUyaC7zs2Qi5mZmZWYiKgALgYuJenaPAG4vJ6XrAa+DtxL0nL2IPAkcE16vEnAgSTdmP8C3gJ+CcxpljfQDHLq1oyINZKmk/xQpjcUb2ZmZlYtIgbXsf1m4OaszaMz9o8CRqXfrwR+0sB5XgMOzT/Twsp3EtpfS+rZ1MmYmZmZlbp8bgi4D9iCZLHzlcDyzJ0R4fU1zczMzPKUT3F2UVMnYWZmZmaJnIuziPhTcyRiZmZmZnlOQivpm5L+R9IDkjqk2w6X1KNp0zMzMzMrLTkXZ5IOBCYD+wI/AKpXje8N/KLpUjMzMzMrPfmMOftf4L8j4iZJSzO2Pw9c2DRpmZmZWXPrODhU6BxsXfl0a/YiWR4h26ck85+ZmZmZWZ7yKc4WAx1r2f5tYNZ6ZWNmZmZW4vLp1rwfuF7Sj4AANkrXtbqRZCkFMzMzawWOe/yTaI7j/uXIDu4uXQ/5tJxdCXxE0kq2FTAVeAF4EfifpkvNzMzMrPTkM8/Zl8AJkq4m6crcCHgrIrzWppmZmdl6yqdbE4CIeE/S++n3zdIsamZmZlZq8p2E9qeSpgArgBWSpkg6vWlTMzMzMys9+UxC+0vgt8A44EfpYxxwsySPOTMzM7O1SIoGHqMKnWMxyadb8xzgjIh4IGPbWEmTgN8B/90kmZmZmdmGInMKruOAa4HdM7YtzwyWtEk6xr0k5dOt2QZ4vZbtb7AeY9jMzMxswxQRc6sfwJJkU83zzYDFkn4sabykFcCJkoZJqsg8jqSLJM3I2naqpGmSVkj6t6RzW+htNZt8irP7SFrPsp0J/Hn90jEzM7MSdT0wAtgDeLoxL5B0BnAdyTRfewBXAL+UdEpzJdkSGtXSJemmjKcBnC7pUODldFs/YCc8Ca2ZmZnl55aIeKT6idSoeWyvAi7JeN0HkroDZwF/avoUW0ZjuyG/nfX8jfTrN9Ovn6aPHk2RlJmZmZWc2oZM1UnS9iQNQ3dLujNj18YkXaetVqOKs4g4qLkTMTMzs5JWlfV8DZDdfLZJxvfVQ7POAF7JilvdhHm1OA/gNzMzs2L0KbCDJGVMdl9evTMi5kmaBewWERvUmPecizNJmwHnAwcBHci6qSAi9mqa1MzMzKyEjQe2B4ZKegg4HPge8FlGzDBghKTPgCeBTYG+QLuIuIlWKp+Wsz8ChwAPAa+S3CBgZmZm1mQiYlo6LcYVJAP/HwZuJJkdojrmLkmfAz8HbiDpGp0M3NLiCTehfIqzI4EjImJiUydjZmZmG7aIGAWMyng+g3XHllXvux24PWvzr7Ji7gfub8ocCy2fec5mAUubOhEzMzMzy684uwS4XtIuTZ2MmZmZWanLp1vzdZKlFt5P+3nXWvsqIto3RWJmZmZmpSif4uwBYEeSAXrz8A0BZmZmZk0mn+Jsf2C/iHi7qZMxMzMzK3X5jDn7N7B5UydiZmZmZvm1nF0G/EbSlSRziWSPOfus1leZmZlZUfnLkR0atbq4tax8irOn0q//yNoukvFnbdYrIzMzM7MSlk9x5kXQzczMzJpJzmPOImJCfY9cjyfpckmvSVoq6RNJj0raPStGkoZJmi1puaTxknpkxWwq6XeS5kuqkjRWUuesmHaSRktakj5GS9o2K2ZnSePSY8yXNEJS26yYXpImpLnMknS1JDcNm5mZ2XrLZ+Hz/vXtj4gXcjzkgcDvgdfSfK4DnpHUPSKq0pihwMXAYKAS+G/gWUm7R0T1agW3AEcDg4AFwG+AxyT1iYjVacz9QGeSxVMB7gBGp69DUhvgceBT4ADg68CfSLpsz09jtgaeBZ4H9gbKSJahqErPaWZmZpa3fLo1x9eyLXOus5zGnEXE4ZnPJZ0KfAL0AV5IW6QuAq6LiEfSmFNI5lg7HviDpG2AnwInRcRzacyJwMfAfwBPS9qDpCjrFxGvpDFnAC+lRd47wKFAd2CniJidxlwCjJJ0ZXqzwwkkk/AOjogvgCmSyoCLJd0UEZ73zczMzPKWz1Qa7bIeHUiKntdIipv1tU36dWH6dVdgB+CZ6oC0KJpAMucaJIXcJlkxs4EpGTH7AUuqC7M05mVgSVbMlOrCLPU0sGl6juqYCWkOmTGdgC65vVUzMzOzteXcchYRS2rZ/KykL4Cb+aqIyVnaSnYT8P8iYkq6eYf067ys8HnALhkxKyNiUS0xO2TEfFLLaT/JilnrPBGxSNLKrJgZtZynet8HWfvaTJ8+vZbTWkvaqtAJ1KNEPx++LoqErw2z4pNPt2ZdPgV2bzCqfiOB3iTjvbJldxeqlm3ZsmNqi2+KGNWxHWB1t27dPL1Igc2ZWOgM6tatW7dCp9BkKisrGxvq66JI+NpoGTlcG2Z53RDQO3sT0JFkctq8l3SS9DvgGKB/RMzM2DU3/boDMCdjewe+arGaC7SV1C6r9awD8GJGzDdqOfX2WcfZNyuvdiRdppkxO7C2DunX7NY9MzMzs5zkM+asAngr/Vr9/RNAW5JB+TlJp8kYCfwAODgisrsFPyApiA7JeE1bkrs8qwuvN0hWKsiM6Qj0zIh5CdhG0j4ZMfuSjHHLjOmZvrbaocAX6TmqY/pnTa9xKDCbdbs7zczMzHKST7fmrlnP1wCfRsSKPHP4Pcldl/8fsFRSdavUkohYHhEh6RbgCknTgenAFcDnJFNjEBFLJN1NsqzUApKbCW4kWV7quTRmmqSngDslnZWe4w7gsfROTUhuKJgKjJb0c6B9epw7M5aluh+4huQOzl8B3dJ8rvWdmmZmZra+8rkh4MMmzuGc9Ov4rO2nkswfBnADyWLrt5LcIfoKcGjGHGcAPwNWAQ+msf8gme5idUbMCcAIvrqrcyxwXvXOiFgt6cj0PBOB5STF2JCMmCWSDiEpKl8HFpHcxHBTbm/bzMzMbF153RAgaSAwkGSs1VpdoxFxWi7HiogGZ9ZPW6SGpY+6YlaQTBR7fj0xC4ETGzjXR8BRDcRMBuqdjNfMzMwsH/ncEHANcDVJq9EcGr5j0szMzMwaKZ+Ws7NJugtHN3UyZmZmZqUun7s12/LV3Y1mZmZm1oTyKc7uIrm70szMzMyaWD7dmpsBZ0r6D2ASyfxiNSLi4qZIzMzMzKwU5VOc9SaZfBaSSV4z+eYAMzMzs/WQzzxnBzVHImZmZmaW35gzMzMzM2smLs7MzMzMioiLMzMzM7Mi4uLMzMzMrIi4ODMzMzMrIi7OzMzMzIqIizMzMzOzIuLizMzMzKyIuDgzMzMzKyIuzszMzMyKiIszMzMzsyLi4szMzMysiLg4MzMzMysiLs7MzMzMioiLMzMzM7Mi4uLMzMzMrIi4ODMzMzMrIi7OzMzMzIqIizMzMzOzIuLizMzMzKyIuDgzMzMzKyIuzszMzMyKiIszMzMzsyLi4qwIff755+yyyy4MGTKk1v1r1qxhn3324T//8z/X+1zvvfceffv2pWvXrpx99tlExFr7hwwZwnbbbbfe5zEzM7PGcXFWhK677jr23XffOvfffffd7Lrrrjkdc8aMGQwePHid7UOHDmXYsGG8++67zJs3j8cff7xm39SpU5k7d25O5zEzM7P14+KsyEyfPp1///vfHHHEEbXuX7hwIWPGjOHMM89ca/urr77Kd7/7Xfbaay9++MMfsmzZsgbPFRG89NJLHHnkkQCcfPLJjBs3rmb/0KFD+dWvfgXA0qVL2XvvvSkvL6dXr17ceeedtR7TrXrWHBrz+evSpQu9e/emvLy8zusnF/78mVmhuDgrMkOGDGH48OF17r/yyiu56qqraNOmTc22lStXMmTIEMaOHcubb75Jv379GDlyZIPnWrBgAe3bt0cSAJ07d2bWrFkA/OUvf6Fv377svPPOAGyxxRZMmDCBiooKXnnlFYYPH86CBQvWOaZb9aw5NPbz9+KLL1JRUcETTzzR6GP782dmxcbFWRH5+9//TllZGWVlZbXuf+utt1i0aBEDBgxYa/s777zDpEmTOOiggygvL+eee+7ho48+AuDUU0+taUkYO3Ys5eXllJeX88orr6zTEgAgiaqqKkaMGMGll15as71NmzZsscUWAKxYsYLVq1ev8/pCteq1ZrMWwIABA+jevTu9e/fmr3/96zoxY8aMoVevXvTs2ZNBgwbxxRdfrNc5W2OLUGM+f3Vp7s/fxx9/7N+hmTUpF2dF5OWXX2bMmDF06dKFIUOGcOedd3Lttdeutf9f//oXXbp0YdCgQTz55JOceeaZRAR77bUXFRUVVFRUMHXqVG699VYA7rnnnpqWhGOOOaYmZt9992W77bZj4cKFNf+wz5w5k44dO/L+++/z7rvvsscee9ClSxcWLVpE7969Wbx4MXvuuSedO3dm6NCh6/zjX6hWvdZs4zZwyy23MHXqVJ577jl+9rOfUVVVVbM/Irj44osZP348U6ZMAeCRRx5p1LE3tBahhj5/kujfvz/77LMPDz/8MNAyn7+NN97Yv8Nm0Jg/XMBDKWzD5OKsiAwfPpyPP/6YGTNmcOONN3LGGWdw9dVX1+w/55xzmDVrFjNmzGDMmDF873vf44477uBb3/oWH374IRUVFQBUVVXx7rvvNng+SfTr16/mH/Z7772Xo48+ml69ejFv3jxmzJjBjBkzaNeuHZMmTWLbbbfl7bff5oMPPuD+++9n3rx5NccqZKtea/aNbaG8vByADh060L59exYuXLhWTETw+eefs3r1aj7//HM6duwIlF6LZH2fP4CJEyfy5ptv8sgjj3D55Zfz7rvvtsjnr2PHjv4dNoOG/nCp5qEUtiFycdYKHHHEEcyePbvO/W3btmXMmDGce+659O7dm/32269RxRnA9ddfzzXXXMM3v/lNtt9++5p/8OvzjW98g969e/PCCy/UbCt0q96G4PXXX2fNmjXstNNONdskMXLkSHr27EnHjh3ZaqutGDBgQEm3SNb2+QPo1KkTkLyngQMHUlFR0eKfP/8Om05j/nDxUArbUG1c6ASsdpl/1dU2uHnAgAFrtVLtvffevPjii3Uer0uXLowaNWqd7d26deONN96oN5f58+czb948Nt98c7beems+++wzXnjhBc4555yamHPOOafm+fjx4xk5ciR33HEHK1eurGnVKy8vp6qqijlz5tC1a9d6z5nZqnfUUUdx7733ctppp9W06lXbbrvtmDRpUr3Hag0WLFjAySefzF133bXW9i+//JI77riDyZMns+OOO3LiiSdy3333seeee9a0CEHSfVf9eTj11FN56623WLlyJXPnzq35D+4Pf/gDu+222zrnzmwReu6555r1fearoc9fVVUVa9as4Wtf+xqLFy/mhRde4LzzzqNbt24t9vnz77D51Fb0wldDKTJlFr3t2rXj17/+NSNHjuSyyy6r9xylUvRa6+DizBpl5syZ/PSnPyUiiAjOO+88evfuzRFHHMFdd91V02qRLbNVr/qv1xtuuKHB/xwhadUbNGgQF154IQMHDmxUq15r9MUXX3Dsscdy+eWXs//++6+1r6Kigo033rjmP4Uf/OAHPP/88/Tu3Zu99tqLf/7zn+sc75577gGSrpthw4atVZRHRE2LkKRaW4SAmhahYil8G/r8rVixgmOPPRZIxiBdcMEF9OjRA6BFPn/+HTafuorezKEU48ePr9me2ZUNLnqtdXJxVmTmjFKhU6hVJ6CiYt1xOMXQqteaRSStpAcffDAnnXTSOvt33HFHJk2axKJFi2jXrh3/+Mc/2GOPPdYaZ1gKLZJ9+vSpGVOZKfPz9/bbb9f62ub+/EWEf4fNpL6iN3MoxYoVK1i6dClnnnkm5513notea/U85sysgF6dnnSZPProozUD0idPnlwzzrBTp05cdtll7L///vTq1YslS5Zw1llntfg4Q6vbxIkT/TtsBg394VLoG6TMmpMaO1eQ5aeysnJVWVlZm4YjE8XacgZw0fbzGg4qkL8c2aHe/cX8c+04eMO5BisrK1eXlZU12CKf63UB/h02l2L9ub5SCT/4X611w8Xo0aO59NJL1xlKUT3O9aGHHgLgtdde48ILL1yrK/vwww+via+t5QySFVoGDRrE4sWLGThwILfffjsbbbR2G8Z2222XV4t9Y68NM3C3ppmZFaF9y5Lxg9k8lMJKgYszswI77vFPCp1CrRpqjbSv+HdoZk3JxZmZmRUlF71WqnxDgJmZmVkRcXFmZmZmVkRcnOVJ0rmSPpC0QtIbkr5b6JzMzMys9XNxlgdJxwG3ANcB3wb+BTwpyWt7mJmZ2XpxcZafi4G7I+KuiJgWERcBHwPn1P8yMzMzs/q5OMuRpLZAH+CZrF3PAPuv+wozMzOzxvMKATmS1AmYBXwnIl7M2H4FcEpE7J4ZX1lZGVJxzsBt1tQiorErBPi6sJLS2GvDDDzP2frIrmpVyzaA1d26dctpmRqz1qqysrKxob4urKTkcG2YuVszD/OB1cAOWds7AMW7+KSZmZm1Ci7OchQRK4E3gEOydh0C1L2Ym5mZmVkjuFszPzcBoyW9DrwEnAnsDNxe0KzMzMys1XNxloeI+IukrwNXAx2BKcAREfFhYTMzMzOz1s7FWZ4i4lbg1kLnYWZmZhsWjzkzMzMzKyIuzszMzMyKiIszMzMzsyLi4szMzMysiPiGgGYmaU5lZWXHQudh1hIkzWlsnK8LKyWNvTbMwGtrmpmZmRUVd2uamZmZFREXZ9YgSYMlLS50HmbFxteGmTUHF2clRNIoSVHLo2uhc2vt6vi5Zj5GFTpHq5uvjebja8Msd74hoPQ8BZyate3TQiSygckc3H4ccC2we8a25ZnBkjaJiC9bIjFrNF8bzcPXhlmO3HJWer6IiLmZD+BCSZMlVUn6WNKtkraq6wCS9pT0vKSlkj6T9Iakvhn795f0gqTl6fFGSNqyRd5dgWT9PJckm2qebwYslvRjSeMlrQBOlDRMUkXmcSRdJGlG1rZTJU2TtELSvyWd20Jvq9T42mgGvjbMcufizADWABcAPYFTgIOBG+qJ/zMwE9gb6AP8L/AlgKRewNPAI0Bvkr+UDwBGNlPurcn1wAhgD5KfUYMknQFcB1yZvu4K4JeSTmmuJG0tvjZahq8Nswzu1iw9R0lalvH8yYj4UcbzDyRdBdwG1PVX6M7AryPi3+nz6Rn7fg7cHxG3VO+TdAEwQdI5EbFi/d9Cq3VLRDxS/URSY15zFXBJxus+kNQdOAv4U9OnWNJ8bRSOrw2zDC7OSs/zwDkZz6skHUTyV2d3YGuSz8VmkraMiKpajnETcJekk4DngL9GxHvpvj5AV0knZMSLpJV2V2Bak76b1uX1XIIlbQ/sBNwt6c6MXRuTdA9Z0/K1UTi+NswyuDgrPVUR8W71E0m7AE8At5P8JbqQpKvlbmCT2g4QEcMk3Q8cCXwP+IWkQRHxN5L/aP5A0kWR7aOmfCOtUPZ/5mtI/nPOlPkzrx52cAbwSlbc6ibMyxK+NgrH14ZZBhdn1pfkc3BJRKwBkPTjhl4UEZVAJXCzpAdI7nL7G/Am0CPzPzmr06fADpIUXy3VUV69MyLmSZoF7BYRfy5EgiXO10bh+NqwkubizN4j+RycL2kc8B3g7LqCJW0O/Bp4CPgA6Ewy+PnhNOR64GVJvwfuJPmLeA/gkIg4v7neRCs1HtgeGCrpIeBwktaWzzJihgEjJH0GPAlsSlI0tIuIm1o029Lja6NwxuNrw0qY79YscRFRAVwMXApMAU4ALq/nJauBrwP3krQOPEjyD+M16fEmAQcC3YB/AW8BvwS86G+WiJhGMrD8v4C3gX2AG7Ni7gJOBwYDk4EJ6fcftGCqJcnXRuH42rBS54XPzczMzIqIW87MzMzMioiLMzMzM7Mi4uLMzMzMrIi4ODMzMzMrIi7OzMzMzIqIizMzMzOzIuLizMzMzKyIuDgzMzMzKyIuzszMzMyKiIszM1tvkkZJerQFzjNMUkVzn8fMrJC88LmZ1ZA0HqiIiItyfOmFgJo8ITOzEuTizKxESGobESub49gRsaQ5jmtmVorcrWm2gZI0XtJISTdJmg88K6m7pCckLZM0T9JoSdul8aOAA4ELJUX66CKpjaS7JX0gabmkdyRdmHWutbo103OPkHSDpIWS5koalvWabSTdIekTSZ9J+qekPbNiLkvzXCrpbmCzZvlhmZkVERdnZhu2U4BVwHeAy4AJQAXQFzgc+AbwYBp7IfAScCfQMX18TPLvxEzgx0B34FrgV5J+3IhzVwH7AkOBqyUdAiBJwOPADsARQB/gTeAfktqnMT8GfgFcmeY7Bzg33x+EmVlroYgodA5m1gzS8WPbRMS30+fXAvtGxGEZMZ1JCrDdI6KysWPOJP0e+EZE/Gf6fBSwbUR8P+PcbSLiuxmveRX4Z0RcJulg4G9Ah4j4IiPmXeCGiLhD0ovA2xFxTsb+l4HNIqI8rx+KmVkr4DFnZhu21zO+7wMcJGlZLXHfBCrrOoiks4HTgV2AzYG2JC1w9ZmU9XwO0CEjl62ABUkjWo3N01wA9gBuzzrGS8BBDZzXzKxVc3FmtmGryvh+I2AccGktcXPqOkDavXgzcAlJcbQU+DlJd2V9vsx6Hnw1lGKj9JwDannd4gaOa2a2QXNxZlY63gR+CMyIiFV1xKwE2mRt+y7wYkTcWr1B0jdZP2+SjDdbFREz6oiZBvQD7s3Y1m89z2tmVvR8Q4BZ6fg90B54QNI+knaTdKikP0qqLshmAPumd2luJ2kj4F2gr6TDJJVJ+iWw93rm8hxJK9yj6XG7SNpf0v9I6pvG/BY4TdJp6Xl/AfRYz/OamRU9F2dmJSIiZpPctdkGeBqYQlIALQHWpGE3AquBqcCnwM4k474eAf4CvAJ8HbiV9RDJnUhHAC8AfyQZ7zYG6ALMS2P+QnJn6PXAGyTj3W5bn/OambUGvlvTzMzMrIi45czMzMysiLg4MzMzMysiLs7MzMzMioiLMzMzM7Mi4uLMzMzMrIi4ODMzMzMrIi7OzMzMzIqIizMzMzOzIuLizMzMzKyIuDgzMzMzKyIuzszMzMyKiIszMzMzsyLy/wNn0MUHG3gMlQAAAABJRU5ErkJggg==\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": 36,
   "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": 37,
   "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>0.956602</td>\n",
       "      <td>retained</td>\n",
       "      <td>724908</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>0.949559</td>\n",
       "      <td>retained</td>\n",
       "      <td>577257</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>0.987664</td>\n",
       "      <td>retained, no indel</td>\n",
       "      <td>696647</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>0.982219</td>\n",
       "      <td>retained, no indel</td>\n",
       "      <td>553582</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>0.967650</td>\n",
       "      <td>10X accuracy</td>\n",
       "      <td>681243</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>0.961755</td>\n",
       "      <td>10X accuracy</td>\n",
       "      <td>541970</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>0.989080</td>\n",
       "      <td>10X accuracy, no indel</td>\n",
       "      <td>661332</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>0.984277</td>\n",
       "      <td>10X accuracy, no indel</td>\n",
       "      <td>525460</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": 38,
   "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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a9y1MXJSUlLBpU9M1jILBIO+++248Vk2wmEmQigp3/rRlyxa2b98OHCxEVFhY2KBIUDAYpEuXLvznP/9h+PDhlJeXs3LlSqqqqgj996uoqGD37t11Fd969OjBsGHD6vZv2rSJTp06UVpayp49ewgGg7T1v300ZRLfa+t7WvvRYa8lwraQO3FJeKF3XDHwGHAzUOsd8wPgH8Bq73PWAY/i6jKEq83Ly0vZK8jVu5ZRufUdQMnqfwKZ/Y5FRBLdLV988MEHiEiz47F79uxhyJAhUVXlKioqau2hSREz8SqtqbWVlCy8jqqtbxGs2EkgezDdjrqZ7LwZ8fqqdSorK1mwYAFDhgypK0RUUuLqgYwaNapRMaL+/fuzYMECFi1aRE5ODkceeSSrVq0iLy8PVeWpp54iNzeXyZMnA24stqioiHPOOYd9+/bx4Ycf8s1vfpMuXbrwxRdfUFBQQF7ewcXAWxMzbU60InJSc/tVdW4bP3InLjmGn732p/FZbqiNcmCGiFwFDMCdwX4Pd3vwTu+YHcDXRaQL0Ad3F9tviLzeWUqqLf2cPe+eR/WOhZDhJZXaSjr1PJJep/ybTj1aXBk+5ZSVlbXqoleoWldHV7+0JsDu//wXB5b/b6OKb6HSmp2Hfp1ep75M7f717HrzNDJyDqur+OZKZwr9z9sEKHvnXsy+RT+g15efRoM1ZGQPovfpb5HRPZfqHYvY/dZ/kZF9GJ0Hnx7X7xzPim9bt26loqKC5557DnBnyFVVVTzxxBNMnz6d/v37t6rP0QwdvBthW/3Ib9O/8qpaJSJLgNOAF+rtOg14qYX3VuPGiRGRbwGvqtaVtgodUwF87o3dfhN4ti39S1bBqn3smjWV2tLP3Ybag8PRNSWr2fX6CfQ951MyurYuEFJFt27dCAQCBIPBZo/r2rVrnHqUWPEqrRnIzKH7hDvqPi+r/7FkDZxG1fYP4p5o4WDFt4ED3flZSxXfevToQSAQ4LPPPqOwsJAzz3SV6upXfJs4cSJAg4pvI0aMYMiQIXWfVVxczLvvvlt3htta0STaXmGvM3FTsu7EzUiIxj3AEyKyGFiAOzsdipuShYjcDQxW1Uu91/nAFGCR15+bgDG4ObN4xxyDmz/7sfe//4O7+Pe7KPuYVMrXPkZt2Reg1Y13ag3BqhLKVv+F7hNuj3/nfJSfn8+nn37a5H4RYejQoQ1WbOioWiqt2aBGRhtKa3Y+7ExQbbZ0ptZUUL3zw7okHW/xqvjWqVOnujNdcGfTItJojm1LohmjLYmw+T8iUgn8EXdhqq2f+YyI9AF+hbth4VPgDD24csMgGi5lnoGbpjUKqAbmAMer6sZ6x3TBzaXNBQ4ArwOXqOretvYvGZWteQiCzSzBEqyirOihDpdo+/btS15eHuvWrWt0VisiZGRkMGXKlAT1Lr7iXVqzrl1V9s6/kk498ugy7FwfvlnLAoEAU6dOZerUxpOIQgk25Oijj+boo49u8rPChxaac+ihh7b5ZgWIcoWFJuzAJb6oqOoDwANN7Ls87PUq3Fl0c5/3Hu5GhQ4pWNHyFKZgVcdcdPDkk0+mS5curFzpxiVFhNraWnr27Mm0adPo1Sv8R1fHJJ3cT/xgVUldMSD1isRLZveGxwYy6XXqy+z78Ea2P3sYgezBZOd9h7LCv7n3aZDds0+jy/AL6HP6fwDY//H/sPvN6fQ98+DkfVVl34JrqC0ppPf0txCJejWstBLNxbCx4ZtwZ5w/BT6JRadMyzJyhhIs39b8MV075hItgUCA4447jkmTJrFlyxZqamro1asXffv27bCzLSKpX1qzUw9XrbQ1pTVD9i2+pcnSmgA5X7qe0k//z80y6NLXJdmF36dq54f0mf52xDZMZNGc0X6MG+gJj+iFuLmsJg6yR11Nye5lEIwwRgsQyCL7iGvj26k4y8rKIjc3pneDp5xQac2s/q5yaUulNTO6j0ACmVRseZXyNY/Qe/rbQMPSmt3H3QbQqHTmvkXXUbV9Hn2mv0Ogc3r8aoiVaBLt8LDXQWCHd3XfxEnX3IsoXfVnavZ+2nisNpBJRs5QsvO/l5jOmbiJV2nNmgObKFv9AAQ6s/35YXXv6Zr733Wfb5rWpjKJHVWqlrwLVpVQMv9qKjY9C5IBCASr6XzYmRwy9REyuvRLdBdTjpVJNG0V8zKJACJyP7BWVe8P234dMNIrCGPiIJB1CL2+/BS1ZfdQtX0eaJCsfseS0S1i0TNj0paqsn79elasWMGuXbvqpgGOHTu21TcdtEc0QwffBM6OsH0+7oLYje3pkGm7jOxBdD38vER3w5ikpKq8++67rF27tsFdhRs2bGD9+vV8+ctfJj8/39c+RDM3ow8QaS7tPmxxRmNMkikqKmqUZOFgZa/33nuPffv2RXprzESTaNcCX42w/b+A9e3rjjHGxNYnn3zSbH0MEaGgoMDXPkQzdHAP8GcR6Qe84207FXen1o0x6pcxxrRbTU0Ne/fubfaYYDDoe8H4aG7BfUREOuPqGvzS27wRuKb+ml3GmOQVrNxDbelmJOsQOnUb1vIbUlRrb2Dx+0aXqG7B9ZaFedA7qy1X1QOx7ZYxxg81+zeyf8nNVGz6N6gr3dyp1zi6T7yLLkMiF5BJZRkZGfTr16/ZVTcCgUCDFRX80OYxWhEZLiJ54Gq+hpKsiOSJyOEx7p8xJkZq9q9n56uTqdj0Ql2SBajZ8wl73j6bsrX/TGDv/DNu3LgWz1ibKrEYK9FcDHuMyGt5HePtM8YkoX2LbnBFZ7Qmwt4gJQuuJlgVaUJRasvNzWXcuHFAwyGCQCBAIBDg9NNPj3p129aKZuhgAjAvwvaFwJ/b1x1jjB9qy76gcsvrNL06FKBBytfPJKcD1siYMmUKQ4cOZcWKFWzfvp1AIMDw4cMZPXp03SoNfoom0SrQPcL2Q2jj6grGmPio2beWZpMsgNZSU7I6Lv1JhIEDB9atyBBv0QwdvA/8TETqkqr3/GfAB7HqmDEmdkLL0zRLMupq3JrYiuaM9mZgLlAoIu97204EegCnxKpjxpjY6dRrHIHsQwmWNTNfNFhF12HfjF+n0kibz2hVtQAYi1vksD9uGOFx4AhVbXoxpxaIyLUiskFEKkRkiYic2MLx3xeRVSJSLiKFInJphGNu9PaVi8hmEfmjtyquMWlFAhl0H3+7V+UtgkAWWQOnkdm3zStRmVaIdh7tF8DPY9UJEbkQuBe4Fneh7SpgloiMVtXPIhx/DXA38F3gI9xCjf8QkT2q+op3zMW45cVn4Are5HNwVsQPY9V3Y1JF17wrqC3byoGPb4NAFgQrQTqB1pLZdwq9pv070V3ssKJeM0xEsnELJjZYSF1Vl0d+R7NuAh5W1Ye81zeKyHTgGtzYb7hLgL+p6jPe6/UicixwC/CKt+04YJ6qPum93igiT+GSsjFpR0ToPv6XZI+8jLK1j1Kzbw2BrF50HX4hmf1PSKtlgOItmnq0/YBHcUVkImnTzAMRycKtnPubsF1vEnm+LkBnIHxFh3Jgiohkqmo17sLcf4vIFFX9UERygTOAjjkr25hWyug2lO7jb0t0N9JKNGe09wK9gGNxy3x/AxgA3IorLNNWfXHJuThsezHQ1FyM2cCVIvIisBSXqGcAmd7nbVXVp71/FD4Q9091J+BBVQ1P6AAZa9asiaLrJo1ZzJhWiybRngKco6ofiUgQ2KSq/xGRfbif+a9F2ZfwSX4SYVvInbgkvNA7rhg3/nozUAsgIl/GFb65FlgEjATuE5Gtqnpn2OfV5uXl2RxgQ1FRUWsPtZgxQOtiJpp5tDnAdu/5biC0MNUKYGIUn7cTlxzDz1770/gsFwBVLVfVGUA2cDhurHgjsN/7PHDJ+AlVfUhVV6jqC7gLeD8TW4zeGBNH0SScQmCU9/xj4CoRGQxcDWxt64epahWwBDgtbNdpuNkCzb23WlW3qGot8C3gVVUNeruzcSv01leLOwO2UX9jTNxEO0Y7yHt+O2689GKgCrg8yn7cAzwhIouBBcD3cGepfwUQkbuBwap6qfc6Hzd7YBFuvPgmYAxwWb3PfAW4SUSWcXDo4E7gZS8xG2NMXERT+HtmvefLvNKIRwCfqerOJt/Y/Gc+IyJ9gF/hkvinwBmqusk7ZBAu8YZk4C68jQKqcRfljlfVjfWO+TVujPfXwGBgBy75/iKaPhpjTLSinkcboqpluCv/7f2cB4AHmth3edjrVbgqYs19Xg3ujPv29vbNGGPawy4KGWOMzyzRGmOMzyzRGmOMzyzRGmOMz1p1MUxExrb2A6MsKmOMMR1Wa2cdfIybKtXURP/QPsWWszHGmAZam2iH+9oLY4zpwFqVaOvdOGA6qGAwyPz581m3bh0AI0eO5LjjjiMQaDyMv2/fPubNm0dxcTGdOnVizJgxjB8/vsExGzduZMmSJZSUlJCVlcXEiRMZPXo0AKWlpXzwwQds27YNgEMPPZQTTjiB7Oxsf7+kMQnSnsLfo4lc+Pvl9nbKxN/SpUspLi7m/PPPB2DWrFksW7aMSZMaLm0SDAaZPXs2w4YNY/r06ezbt4/XXnuNbt26MXLkSAA2b97MvHnzmDZtGgMHDqS6upry8vK6z/jgA7eG50UXXYSq8s477zB//ny+8pWvxOnbGhNfbZ51ICK5IvIJ7jbZ14AXvccL3sOkoMLCQiZMmEB2djbZ2dlMmDCBwsLCRseVlJSwd+9eJk2aRCAQoGfPnhxxxBGsWrWq7pjFixczceJEDj30UAKBAJ07d6Znz551+/fv38+IESPIzMwkKyuLESNGsGfPnnh8TWMSIprpXfcBG3DFvsuAI4GTgMXAl2PWMxM3lZWVlJaW0qdPn7ptffr04cCBA1RVVTU4VrVxiWBVZffu3QBUV1ezY8cOqqqqeOaZZ3jiiSd46623KCsrqzv+qKOOYv369VRVVVFZWcm6desYOnRoo881pqOIJtEeB/xKVXfgyhAGVfUDXNHv+2PZORMf1dXVAHTu3LluW1aWGxEKT7Q9e/ake/fuLF68mNraWnbv3k1hYWHdcZWVlQCsWbOGM844g29961sEAgHmzJlT9xkDBw6kvLycxx57jH/+859UVlYyYUKzpSuMSWnRJNoM4ID3fCdwqPd8Ewfr1JoUkpmZCTRMqqHnoYQbEggEmD59Ort27WLmzJnMmTOHUaNG0aVLlwafNWbMGLp3705mZiaTJ0/m888/p7q6GlXltddeY8CAAcyYMYMZM2YwYMAAZs2aFY+vakxCRHMx7FNgLLAeV+f1ZhGpwtWQXR/Dvpk46dy5Mzk5OezcuZMePXoAsGvXLnJycholWoBevXpxxhln1L1etGgRgwYNqvusbt26NdlWZWUlBw4cYMyYMXTq5MJvzJgxLF++nIqKirqEbUxHEs0Z7a/rve9WYBjwPm6F2Rti1C8TZ6NGjWLZsmWUlZVRVlbGsmXLOOKIIyIeu2vXLqqrq6mtrWXDhg11F9JCjjjiCFauXElpaSk1NTUsWbKEwYMHk5mZSZcuXejRowcFBQXU1NRQU1PDypUrycnJsSRrOqxoCn/Prvd8PTBaRHoDezTSlRKTEiZOnEhFRQXPPvss4ObRhpLn+++/D8CJJ54IwPr16ykoKKC2tpY+ffpw+umnN7iQNn78eCorK3n++ecBN0922rRpdfunT5/OggULmDlzJqpK3759mT59ely+pzGJIG3NjSJyCJChqrvDtvcGalR1Xwz7FxdFRUU1+fn5duuwoaioqDY/P7/FExCLGRPSmpiJZujgadxCiOEu8PZFRUSuFZENIlIhIktE5MQWjv++iKwSkXIRKRSRS8P2vysiGuER7XLoxhgTlWgS7TG4NbrCvevtazMRuRC36ONduCVq3gdmiUjEyZUicg1wN/A/uHm8twF/EZGv1TvsXNxaY6HHGNwquM9F00djjIlWNIm2M5HHdjOBrlH24ybgYVV9SFVXqeqNwGbgmiaOvwT4m6o+o6rrVfVp4GHgltABqrpbVbeFHrjly8uwRGuMibNoEu1HuKlc4a4GlrT1w0QkC5gEvBm2603g+Cbe1hmoCNtWDkwRkcwm3nMF8LSqlra1j8YY0x7RzKP9BfCWiIwD3va2nQocDZwexef1xd0EURy2vRgY2MR7ZgNXisiLuBV4JwEzcGfVfYGt9Q8WkSm4oYMrmvi8jDVr1kTRdZPGLGZMq0UzvWueiBwH/AR3AawcWA5coartibzw6Q8SYVvInbgkvNA7rhh4DLgZNw4b7grgU1X9sInPq83Ly7MryFGoqalhx44d1NbW0rt375QvdVhUVNTaQy1mDNC6mImqTKKqfgxcHM17I9iJS47hZ6/9aXyWG2q/HJghIlfhittsxQ1n7Pc+r46IZONmSfwqRv01uHKJS5cuZcWKFXW1EkSEww8/3GrLGhOmtWuG9QjNjxWRHs0d29Z5tKpaJSJLcBer6pdZPA14qYX3VgNbvH59C3hVVYNhh12AG9P9V1v6ZZqmqsyZM4cNGzYQDAYbbN+0aRM7duzg3HPPtTu9Orh4FYuvra1l3rx5fP7551RUVJCdnc24ceOavHMxGbX2jHaPiAxS1e3AXiL/pG/PmmH3AE+IyGJgAe7sdCjwVwARuRsYrKqXeq/zgSm4Wgu9cLMWxgCXRfjsK4AXVXVXFP0yEWzdurXujytcMBikrKyM5cuXM2XKlDj3zMRTvIrFB4NBsrOzOfPMM+nevTvbt29n1qxZdOvWjcMOOyy+XzpKrU20pwChO8GmNXdgNFT1GRHpg/t5PwhXuOaMekvoDMIl3pAM4Ee4amHVuHm9x6vqxvqf6yXkqUR3kc40YdWqVYhIxNq04P4wCgoKOProoxFpaj1Pk+oKCws57rjj6oaJJkyYwMKFCxsl2lCx+HPPPbdRsfhQoq1fLB5ccaJQ2c5QBbiQAQMGcOihh7Jt27aOlWhV9T0AEemEK+79iKpujmVHVPUB4IEm9l0e9noV7saGlj6ziKZX7jVR2rt3b5NJNqSqqorq6uqI1b9M6mupWHz9/+6tLRafm5vLM888Q1VVFYMGDeL444+PONYfugAbStKpoE3zaFW1BvgxtqR4WmvN2KuI1JVBNB1PvIvFh6gqc+fOpUePHgwfnjqLc0dzw8Lb2JI1aS0/P7/ZIQERITc3N+JFEdMxxLNYfIiq8sEHH1BSUsL06dNTalgqmlOOWcDdIjIGdydYgzutbBXcji83N5clS5Zw4MCBBrMOQkTElqbp4OJZLB5ckp03bx7bt2/nrLPOSrkhqWgS7YPe/94UYV+0sw5MCsnIyOBrX/sas2fPZufOnXVnrqpKVlYWp512Gr17905wL43fQsXiBw50U+BbKhbfo0cPAoEAn332GYWFhZx55pl1+0PF4ocMGULnzp0bFIsHmDdvHtu2beOss85qMFyRKqK5M8x+DxpycnL4xje+QXFxMZs3b6a2tpZ+/fpx+OGHk5Fh/9amg3gVi9+/fz8FBQVkZGTw5JNP1r0nLy+v7vOTXZsLf3dEVsTZhFjhb9NWrYmZ1t4ZdgPwd1Wt8J43SVVtyXFjjKmntUMHPwRm4koT/rCZ4xSwRGuMMfW09oaF4ZGeG2OMaVm7LmyJJ1adMcaYjiiqW3dE5ArcEEKe93oNcK+qPhTDviVELCsSvfvuu6xdu7bBe88880wGDBgAwCOPPNLg82pra+nVqxfnnXeeD9/MGJMobU60InInLsn+CVdpC+A44I8icriq3hrD/sVdLCsSAYwePZrjj4+8Is+MGTMavH7++ecZMWJEjL+RMclj7969rFq1ip07d5KZmUlubi65ubkd/nbtaL7dNcB3VfWpetteFpHluOSb0ok2lhWJ2mL79u3s2bOH/Pz8mHwPY5LN0qVLWbx4MYFAoO6Ows2bN/PRRx9x1llnccghhyS4h/6JJtFmAIsjbF8S5ecljVhWJApZs2YNa9asITs7m1GjRnHUUUdFvEd79erVDBkyhJycnBh+I2OSw9q1a1m82KWN8GLxZWVlvPbaa1x44YUd9maXaC6G/YvIy4B/DzcFLGXFsiIRuCIZF1xwAZdccgknnXQSK1asYMWKFY3arampYd26dSlVMd6Y1lJVli5d2uz+0tJSNm7cGL9OxVm0sw6uEJFPReQh7/Ep8F0gKCL3hB4x7GdcxLIiEUDfvn3p2rUrgUCAAQMGMH78eNavX9+o3XXr1tGpUyeGDh3aaJ8xqa6srIy9e/c2e4yqduhEG81P/TG4Jb4BQldudniPMfWOS7l7e2NZkSiSpmbCrV69mvz8fCsraDqk2tpIC1NHf1wqavNftqpOa+XjlLZ8rohcKyIbRKRCRJaISLPVIkTk+yKySkTKRaRQRC6NcExPEfmLiGz1PneViJwR6fNCQhWJysrKKCsra7EiUXV1NbW1tWzYsIHCwsIG5QHXrVtHVVUVqsqOHTv4+OOPGxUr3rt3L8XFxYwaNaq5bhmTsnJycup+LTYlEAjQt2/fOPUo/pLi4pWIXAjcC1wLzAOuAmaJyGhV/SzC8dcAd+OGKz7CLdT4DxHZo6qveMdkAf8BtgPn4VbLHYJbkrxJsaxItHLlSt5//32CwSA5OTmMHj2asWPHNmhv9erVDBo0iJ49e7b2/y5jUkpGRkZdGcRI9YvBDR105GsUba7eJSJdgOtxizT2J+ysWFUntrkTIouApap6Tb1tq3Cr1/4swvHzgXmq+pN62+4FJqvqVO/11cBPgCO8ZcmbZJWYTIhV7/JHVVUVL730EiUlJRGT7UknnZSyiTZm1bvCPAKcBjwPfEg7x2K9M89JwG/Cdr0JRJ7pD51xBW7qKwemiEiml1jPxt1Q8RcROQc3hvwk8FtV7biDQcYkoaysLM455xw+/vhjCgoK6i4yDxw4kIkTJ6bMarbRiibRnolbCnxejPrQFzc3tzhsezEwsIn3zAauFJEXcRfmJgEzgEzv87YCubhl0mcCZ+BuF/4L7jvfEfZ5GWvWrGnv9zDpxWImCr169eK4446jurqajIwMOnXqRHl5OR39/8toEu3ntDDOGaXwM2OJsC3kTlwSXugdVww8BtwMhM5WA7jx2e95Z7BLRORQ3HBCeKKtzcvLs5+BhqKiotYeajFjgNbFTDTziX4E/FZEhkXx3kh24pJj+Nlrfxqf5QKgquWqOgPIBg4HhgIbcf8A7PQO2woUhQ0TrAIGesMVxhgTF9Ek2sVAF2C9iOwXkd31H239MFWtwt2+e1rYrtOA+S28t1pVt3jJ9FvAq6oaGmmfB4wUkfrfMR/Y6rVpjDFxEc3QwVPAYODnuDPOWNyYcA/whIgsxl3A+h7uLPWvACJyNzBYVS/1XufjpnQtAnrhVuQdA1xW7zMfxM2OuE9E/oQbo/05Pq0AUVlZye7duxER+vbt2+GrERljWi+abHA8cJyqfhKrTqjqMyLSB/gVMAj4FHfBbZN3yCBc4g3JwA1hjAKqgTnA8aq6sd5nbhaR04E/AstxY8v3Ab+NVb/BTVtZuHAhRUVFddNWOnXqxJFHHsnkyZM7bJEMY0zrRZNoVwNdY90RVX0AeKCJfZeHvV4FTIh0bNhxC4BjY9G/SGpqanjllVfYs2dPg7mBNTU1rFixgt27dzN9+nS7tdaYNBdNBvgp8AcR+bKI9BGRHvUfse5gMisoKGiUZEOCwSBbtmxh06ZNEd5pjEkn0ZzRvuH979th20PTsdLmt3JztxSCu62woKCgUX0DY0x6iSbRTot5L1LUgQMHWjympKQkDj0xxiSzNidaVX3Pj46koszMzEYFwcNFKq9ojEkvUV2lEZETReRfIjJfRAZ72y4Rkamx7V5yGzlyZLMXugKBgK0BZoxpe6IVkW/iag2UAxNxBV4AuuPmqaaNsWPHNploRYTOnTtbnVljTFRntLcCV6vqd3FzWEPm4xJv2ujRowdnnXUWXbt2RUQIBAIEAgFEhO7du3P22Wc3WH/MGJOeorkYNgqYG2H7PqBnu3qTgvr378/FF1/Mxo0bKS4uRkQYPHgwhx12WJNL1xhj0ks0iXYrMBJXxKW+qUDjlQfTQCAQIDc3l9zc3ER3xRiThKIZOvgbrn7AMbh5s4eKyMXA72nizi5jjEln0Uzv+p2IHIKrL9AFN4xQCfxeVf8c4/4ZY0zKi6rElKr+QkTuAkbjzooLVLXl2fvGGJOGoq7lp6pluNq0xhhjmmFlpYwxxmeWaI0xxmeWaI0xxmeWaI0xxmdJk2hF5FoR2SAiFSKyRERObOH474vIKhEpF5FCEbk0bP/lIqIRHl38/SbGGNNQUqwgKCIXAvcC1+JWr70KmCUio1X1swjHXwPcDXwX+Ai3UOM/RGSPqr5S79B9uFuG66hqhS9fwhhjmpAUiRa3iu3DqvqQ9/pGEZkOXAP8LMLxlwB/U9VnvNfrReRY4BagfqJVVd3mV6eNMaY1Ej50ICJZwCTgzbBdb+JW3I2kMxB+ZloOTBGRzHrbuonIJhHZIiKvikiLCzoaY0ysJcMZbV/cOmPFYduLgYFNvGc2cKWIvAgsxSXqGUCm93lbcav1Xg6sAHoAPwDmicg4VV0T9nkZa9aEbzKmWRYzptWSIdGGaNhribAt5E5cEl7oHVcMPAbcDNQCqOpCb7/7MJF5uKR8PXBD2OfV5uXlpc2ikqZpRUVFrT3UYsYArYuZhA8dADtxyTH87LU/jc9yAVDVclWdAWQDhwNDcWUb93ufF+k9QdyFs7xYdNoYY1or4YlWVauAJcBpYbtOw63a0Nx7q1V1i6rWAt8CXvUSaiPiqnCPxw0rGGNM3CTL0ME9wBMishhYAHwPd5b6VwARuRsYrKqXeq/zcVO6FgG9cLMWxgCXhT5QRG7DDR2swY3R3oBLtN+PyzcyxhhPUiRaVX1GRPoAvwIGAZ8CZ6jqJu+QQbjEG5IB/Ag3R7YaVxv3eFXdWO+YnsDfcUMSJcAy4CRV/dC/b2KMMY0lRaIFUNUHaGKFBlW9POz1KqDZqVqq+kPgh7HqnzHGRCvhY7TGGNPRJc0ZbSKJyNaioqJBie6HSTwRadXFUosZE9KamBHVpqaqGmOMiQUbOjDGGJ9ZojXGGJ9ZojXGGJ/ZxbAUIiI9Wnusqu7zsR8bgUeAxyLVC/ahvfDaFE1S1fv97EuqsZhpWTxixi6GpRARCdJ0oZ26w3B1eH0reCIi1+Mqo43D3SzyMPCCqlb61N6GVh6qqprrRx9SlcVMi+ISM5ZoU4iInNzaY1X1PT/7AiAi43DlKb+N+3X0JPCIqi71u23TOhYzycESbRREZBktnyUAoKoTfe5OwnnF1q8FfourCfwpcB/wqPoUYF7B+OHAOlWt8aONWLKYaSjdYsYuhkXnReAl7zEbGAFUAu96jwpv22w/OyEiJ4rIv0RkvogM9rZdIiJT/Wy3XvuZInIB8DLwB2AxcCXwLHAXMNOHNrNF5GGgDFiJVwNDRO4XkZ/Gur0YehGLmfSNGVW1RzsewEPAnRG23477SeRXu9/0AucfuD/SXG/7tcDrPn/nicCfcLV/i4HfA0eEHXM0UO5D2/fh/jinAgfqfe+zgWWJjgeLGYuZiH2Id5B1tAeuMlhehO15QImP7S4DLvWe768XPOOBbT5/51rgDeB8ILOJY3JwPwNj3fYm4NgI33sksC/R8WAxYzET6WHTu9qvHPcvZfgCUlNpvIBkLI0C5kbYvg9XItJPuXqwhGVEqloKfMeHtvsB2yNsz6GVY6BJwGImgo4cM5Zo2+9e4EERmcTBNcqOxV1ZvcPHdrfi/kXeGLZ9KrDex3YB+ovIQFVdVH+jiBwD1KrqYh/b/gg4E/czFA7+oXwXVzQ+FdyLxQyQRjETj9Pmjv4ALgDmAbu9xzzgAp/bvBk3sH8M7oxkKnAx7l/u63xu+0PgvAjbzwUW+dz28d73fRB3Zngv8B/c2NukRMeCxYzFTKSHTe9KYSJyF664eRdvUyXwe1X9pc/tHgDGqur6sO3DgeWq2t3n9o8CfoxbZj6AW934t6q6ws92OwKLmcTEjCXaGBCRnsB5QC4uaHeLyESgWFU/97ntbGA0LngKVPWAn+15be4CzlLVBWHbjwdeU9Vefvch1VnM1G1Pi5ixRNtOIjIWeAt3JflwYJSqrheRO4Fh6i0o2ZGIyNO4tdjOUdUSb1tP3FzR7ap6QYzbS4r79WPFYib9YsYSbTuJyFvAUlW9WUT2A+O8P5rjgSdV9fAYtvXv1h6rqufGqt0I/RiMu3rdBzdlCNwUoWLgNFXdHOP2WnO/PgDq4/36sWIxk34xY7MO2u9o4KoI2z/H/QseSyX1ngvwDW9b6IrtJNw0nVb/cUVDVT/3zsouxhUJKQceBZ5S1WofmpxW7/nhwG+Axzh4xfg43FLzP/OhbT9YzKRbzMTjiltHfuD+RZ7gPa8/Gfp0YLOP7f4Wd4dPRr1tGcDfgP9L9P8vPn7vt4FvR9h+EfBuovtnMZN8j2SIGRs6aCcR+TtuQvQFuGk6Y3F3wbwIzFXVG31qdwcwVVULw7aPAuarah8/2g1razTuvvGs+ttV9WUf2yzD/dReE7Y9H/hYVbP9ajtWLGbSL2Zs6KD9fgy8jpuL2BV4D/fzbwHwCx/b7QR8CSgM2/4lfC4WJCK5wAvAUbhxMPF2hf7V9nPMazNwNfCjsO1XeftSgcVMmsWMJdp2UnfFcqqInIIrnBHAXeh4y+emHwUeEZGRNLy76KfePj/dB2wAvoK7o2gK7iLHH3BJxE8/BP6fiEyn4fcegSuakvQsZtIvZmzoIEWJSAAXoD8ABnmbt+IC+g+qWutj2zuBU1R1uYiUAFNUtdBLHH9Q1Ql+te21fxiu4tQRuDOjAuCvGuMr1x2NxUziYsYSbQyIyKnAqUB/wn6CqeqMOLTfw2srLnNIRWQP7tbF9SKyDrhSVeeIyAhgRSqMkyaaxUx6xYwNHbSTiNwG/Ao3XWYrCaggFa8/lno+xV3AWQ8sAm4WkSrge/hfnCQ00X0KkZPU4363314WM+kXM3ZG204ishW4WVWfiHO7A3DFk0NnRVJ/v/q70N50IEdV/+1d5HgV95NsF3Chqr7jY9tfw1Xhz8FNjaofwKqqvf1qO1YsZtIvZizRtpN3D/cUVV0X53Zn4abJ/JkIZ0Wq+lKc+9Mb2KM+B5SIFOGu2P9cVcv8bMsvFjN1/UmbmLFE204i8lvggKreGed29wMnqurHcW63E6449XhV/TSebXvtlwJHaVgVqFRiMRNfyRAzNkbbfl2A74nIV4DlQIPbCVX1Jp/a3UzYT794UNUaEdmEv/MemzMbmEwcxvV8ZDETXwmPGTujbScRmdPMblXVU3xq93TcBOyrVHWjH2000/Z3cGs//beq7o5z21fgLiQ9CqygcZLy7Q6jWLGYSb+YsUSborzpMtm4XyVlNA4e3wb4RWQZbkmUTNzCd6VhbU/0se1gM7vVzws6qc5iJqK4xIwNHaSuGxPY9ouJalhVfb1VtIO7MYFtv5iohpMhZuyMNgpejc/LVXVfS/U+1ccanyZ1WMykNzujjU4JB6fGlDR3YCyJSI/QRPOWKsgnYEK6b0TkBuDvqlrhPW+Sqt4fp261lcVMHCVbzNgZbQoRkVpgkKpub6aCvODzuFNL1etj3baIbAAmq+ou73kzTWtuLNtOdRYzyREzdkabWk7B1S+FhhXk4+0bYa8zgQm4ivW3xboxVR0e6blpFYuZJIgZO6ONARE5D1fEOVJBY9+upiYbEbkIdzvlOYnuS7KzmHHSJWYSfjUu1XnjP4/iijhPAD7E3b+dC8xKYNcSYRGu3qhphsVMA2kRM5Zo2+9a4Huqeh1QBfxOVU8D7gcOSWjP4khEugLXA1sS3ZcUYDFDesWMjdG231Bgvve8HOjuPX8CV839ukR0yk/exPf6Y06C+95lwH8npFOpxWImzWLGEm37bcMtybHJexwLfAIMJwH3lcfJD2n4RxMEdgCLVHVPYrqUUixm0ixmLNG23zvA14ClwMPAH70LHZOBZiemx4KI9ANG4YK4SFV3+N2mqj7mdxvN8Yo4X4FbVFCBVcDDqhq3+antZDETZ4mOGZt10E7eOkwBVa3xXl8ATAXW4tYkqvKp3RzgT8AlHKyKVAs8DlzvZ91Nr0DIAVV9Lmz7+UC2qv7Tx7Yn46oxleMuIgkuQXUFTlfVpX61HSsWMw22p0fMqKo92vHAjbdJhO0CDPWx3b8B64D/Anp4jzNwf6wP+vydC4FpEbafDBT63Pb7uCv2nept6wQ8BsxNdDxYzFjMRHrYGW071b/zJmx7H2C7+nS3jbeq6Hmq+m7Y9mnAs6raz492vTYqgCM0rNSeiBwOrFLVrj62XQ5MUNXVYdtHA4s1BRb5s5hpsP1w0iBmbHpX+wmRby3shqsq75dsoDjC9u3ePj9txy20F24cbj6on/bhzgjDDcGtB5UKLGYOSouYsYthURKRe7ynCtwpIvXHtzKAY4CPfezCAuB2EblUVSu8PnXF3c64wMd2AZ4G7veWRpnrbTsZuM/b56dngIdF5Me4KVKKG9/8P+Apn9tuF4uZ9I0ZS7TRm+D9rwBH4Saeh1Thpuv83sf2fwC8AWwRkU9wwTMed0Y03cd2AW4FhgFvAzXetgDuosrPfW77x7jv+jgH47caeBD4qc9tt5fFTJrGjI3RtpOIPAr8QBNQYs47G/lv3LLNAhQAM1W1PE7t5+H+UMuBFaq6KR7tem1nAyNw33utptCKuBYz6RczlmhjRERG4v4jzlXVchERtf9zTTMsZtKHDR20k7c2/XO4EnQK5OFW23xIRPaq6o98bDsf+DLQn7ALm6p6h4/tPo+7WvubsO0/Aaao6vk+tp2D+7l3KpG/d9LXo7WYabA9LWLGEm373Ysb7xmKu9sk5Bngj7hVR2NORL6LG2Paibuls/6ZkAK+/dHgLmLcHmH7G7jxMD895LX/BLCVZopJJ7F7sZgJSYuYsUTbfqcD01V1i0iD29TX4Ab//XIr8AtV/a2PbTSlGw0v5IRU4ybB++m/gDNVdZ7P7fjJYuagtIgZm0fbfjm4CkTh+gKVPrbbC/fzMxE+BS6MsP1buIsrftrDwRUDUpXFzEFpETN2Rtt+c4FLgV96r9W7l/0nwBwf230Od2b0Vx/baMqdwP8TkRG4Aingxr++Dfg21ub5JXCHiFyWSjMNwljMOGkTMzbroJ1E5EvAe8AS3PpMLwNHAr2BE1R1nU/t/gy4CXgNWIH7CVZHfV7ZU0TOxM1/HI+bqrMcuF1V3/O53WUcnKKzkcbfO+mXgbGYSb+YsUTbDiKSCbwJ/Aw3DjQJNxyzFPiLqm71se2Er+yZCCLS7EJ+qhrpgkvSsJiJv2SIGUu07SQiO4DjVXVNovtiUoPFTPqxi2Ht9ziuoHDaEJEMEfmxiHwoIttEZHf9R6L7lwIsZtIsZuxiWPtlAVeKyGnAYqC0/k5VvSkhvfLXbcCVwD24ixx3AYcDX8ffuZgdhcVMmsWMDR20k4g0d5VYVfWUuHUmTkRkHXCDqr7mVWMar6rrvGW0j1XVixLcxaRmMZN+MWOJ1rSZiJQCX1LVz0RkK24y+FIRyQWWqWraLJltWifdY8bGaE00tgCDvOdrcXMzAY7G3wn3JnWldcxYou2ARGSoiPiyHIrnBdxkc3CFm+8UkTW4izyP+Nhus0TkVyJyUqLaT2UWMz63Y0MHHY+IBHH3zf9MVeOxfPUxwAm4Gp8v+91eM/3YAAwA3lbVryWqH6nIYsbfmLFE2wGJyMnAcNxSyh36IkM4EekCnKyqsxPdl1RiMeNvzFiiNcYYn9kYrUkpIrLRG1eLtKqpMY0kQ8zYGW0K8YpjtOo/WCoUV4mGiFwPXI5bpnoO8DDwgqp2+CvX0bCYSY6YsTPa1PIi8JL3mI2rSFQJvOs9KrxtHXZ8UlX/pKqTcMVYCoD7ga0i8mcR6ZCJop1exGIm8TGjqvZIwQdueY47I2y/HXjEpzZPbWF/ALg1zv8/ZOKW0a4AanFLds/A+7VmD4uZZIgZGzpIUSJSAkzWsApQ3nLOi9WHO21EpAr4O3CzhhVQFpExwD+Bgao6ONZtR+hLJvAN4DvAacBC3E/CQ4HrgDmaZlfPW2Ixk7iYsaGD1FUOTI2wfSruX2o/nIibdL5cRE4AEJGAiNyKK2K9ChjjU9t47U0UkT/hFtn7E7ASGKOqU1X1UVW9Czgb9wdlGrKYSVDMWPWu1HUv8KCITML9ywxwLO4nkC/VkFR1kYhMAH4DzBGRv3ttDgYuUNWX/Gg3zEfAf4BrgBdVtTrCMQXA03HoS6q5F4uZhMSMDR2kMBG5ADfW9CVv0yrgPlV91ud2BZiJW1ivFDhaVVf72Wa9toep6qZ4tNURWcwkhiVa0ybe4nqPAXnAr3DTZkYAV6vqC3Fo/2ggoKqLwrYfA9Sq6mK/+2DaxmLGxmhTmoj0FJErReR/RaS3t22iiPhyYUFErsNdod0OHKWqf8eN790DPCki/xKRXn60Xc9fgCERtg/29plmWMw0ELeYsTPaFCUiY4G3gBJcpfpRqrpeRO4EhqnqpT60uQtXvHlmhH1H4q4gD/LzCrKIHADGqur6sO3DgeWq2t2vtlOdxUziYsbOaFPXPcBjqppHwyvGswC/yr6NifQHA6CqK4FjgL/61HZIJa7aUrhBQI3Pbac6i5mG4hYzdkaborw5kRPVLQeyHxjnnZ0MAwpVtUuCu+gLEXkaGAico6ol3raeuDugtqvqBYnrXXKzmElczNj0rtRVAfSIsH0UsMOvRkUkB7gIOB4XvAoUA/OAp1S1tJm3x8KPgLnAJu8+foDxXh8u8bntVGcxk6CYsTPaFOXNR+wHXADsBsbibid8EZirqjf60OZo3HzEbOA9XKAK0B84GTdt53RVLYh122H9yAEuxhUJKQeW4/5gI82PNB6LmcTFjCXaFCUiPYDXgSOB7sAXuLOFBcAZfpwliFu9dRtwmapWhe3Lwk3hGaSq02Ldtmk/i5nEsUSb4kTkFGAi7sLmUlV9y8e2ynD3ykc8+/DuXf9QVbP96kO9tkYDQ4Gs+ts1gcuipAqLmfjHjI3RpjhVfQd4J07N7cFNOm/qZ95I7xjfiFue+gXgKNxYn3i7QmcMfi4w2CFYzMQ/ZizRpjARORVXsKM/YVP1VHWGD03+A/iniPwaN+5WjAvWgbhqSD/H3U/vp/uADcBXgPXAFKAP8Afgxz63nfIsZhITMzZ0kKJE5Dbc7YyLcVWJGvyHVFVfKhGJyC24e+VDV4/BnSFsA+5V1d/50W699ncCp6jqcm+60hRVLfR+Dv9BVSf42X4qs5hJXMxYok1RIrIVV+PziQS1Pxz3hwOwTVU3xKndPcAkb/7nOuBKVZ3j3U+/Ih5jfanKYiZxMWNDB6krC5ifqMa9P5K4/KGE+RQ3LWk9sAi42Ssu/T1vm2maxUyCYsZuwU1dD+EmgScNERkiIo/43MyvORi3twLDgPeBM4AbfG471VnMJChmbOggRYnIfcCluInXy4EGE69V9aYE9GkcbrpQXK/8e1Wo9qgFc7MsZhq0G9eYsaGD1DUW+Nh7Hr4UiC/BIyJnt3BIrh/t1mu/E+420vGq+mlou6ru9rPdDsRixhPvmLEzWtNqIhKk4TzESNTPsxPvYsa5qvqJX22Y2LGYcWyM1rTFVuCbqhqI9MDdbeS3XwN3h4pWm6RnMYMNHaQUEfk3cLmq7vOeN0lVz/WhC0twfxgvNtUszZ+5xMINuLuJvhCRTbiiJAc7oBqPP9yUYTEDJEHMWKJNLSUcHEsrSUD7/wfkNLN/LeB3cZAXff78jsZiJglixsZojTHGZzZGa4wxPrOhgxQmIufhijhHKv3WIccq613Fjije8zFTjcVMY/GIGUu0KUpEbgDuwq0ieg7wKDACOJqOvex2eOGTTGACcBlwW/y7kzosZurEPWZsjDZFichq4HZVfSpsob07gN6qel2CuxhXInIRcKGqnpPoviQri5mG4hkzNkabuoZysEBIOW5pEoAngG8npEeJtQhXb9Q0zWKmobjFjCXa1LUNV7wYYBNwrPd8OP7PS0wqItIVuB7Ykui+JDmLGU+8Y8bGaFPXO8DXgKXAw8AfvQsdk4FmJ6anMq+2aP3xLsGdmZUB/52QTqUOixlvE3GOGRujTVEiEgACqlrjvb4AmIqbAP7X8BVHOwoRuZyGfzRBYAewSFV9XXsq1VnM1Il7zFiiTVEiMhTYHF7mTUQEGKKqnyWmZyZZWcwkjo3Rpq4NQL8I23uTmCr2cSEi3xGR8yNsP19ELktEn1KIxUzD7XGLGUu0qUuIPAm7G67+Zkf1U2BnhO3bcSuqmqZZzDQUt5ixi2EpRkTu8Z4qcKeIlNXbnQEcw8Hizh3RMCKffW3CTV8yYSxmEh8zlmhTT2hpZAGOAupfwKgCPgF+H+9OxdF23EoBG8O2jwN2xb03qcFiJsExY4k2xajqNAAReRT4garuS3CX4u1p4H7vzqa53raTgfu8fSaMxUziY8ZmHaQ4ERmJu199rqqWi4h05EUKRSQLdyfT+UCNtzkAPA5c3VGnKMWSxQwQ55ixRJuivGU5nsMVTVYgz7tv/WFgr6r+KKEd9JmI5AHjcbeSrlDVTYntUfKzmElczNisg9R1L2656KG4O1xCngG+mogOxZOqrlHV51T1VUuyrXYvFjMJiRlLtKnrdOAWVQ2/V3sN7iprhyQiz4vITyNs/4mIPJeIPqUQi5mG2+MWM5ZoU1cODc9KQvoClXHuSzydDLwWYfsbwElx7kuqsZhpKG4xY4k2dc0FLq33Wr172X8CzElMl+KiGw2nJ4VUAz3i3JdUYzHTUNxixqZ3pa6fAO+KyGTckiS/A47E3U55QiI75rNPgQuBO8K2fwsoiH93UorFTENxixlLtClKVQtEZCxwDVCL+1n4b+Avqro1oZ3z153A/xOREbiyfwCn4gpXN7qf3RxkMZO4mLHpXSlIRDKBN4GrVLUo0f2JNxE5E3eP+njcVJ3luCVa3ktkv5KZxUxiY8YSbYoSkR3A8aq6JtF9ManBYiZx7GJY6nocuCLRnTApxWImQWyMNnVlAVeKyGnAYqC0/k5VvSkhvfKZiGQAPwQuwE28z6q/X1V7J6JfKcJiJkExY4k2dY3Brf0EkB+2ryOPB90GXAncg7vIcRdwOPB1Gl9VNg1ZzCQoZmyM1qQUEVkH3KCqr3nVmMar6joRuQE4VlUvSnAXTZJJhpixMVqTagYCK7znB4BDvOevAmcmpEcm2SU8ZizRmlSzBRjkPV+Lu38f4Gg69m2kJnoJjxlLtCbVvICbbA6ucPOdIrIGd0X9kYT1yiSzhMeMjdGalCYix+BuH12rqi8nuj8m+SUiZizRGmOMz2zowBhjfGaJ1hhjfGaJ1hhjfGaJ1hhjfGaJ1qQEETm1hf0BEbk1Xv0xyS+ZYsYSrUkVs0TkzyKSHb5DRMYAH+EKWhsTkjQxY4nWpIoTcZPOl4vICdDgjGQJsApXNMWYkKSJGZtHa1KGiHQBfgNcC/wdOBYYDFytqi8lsm8mOSVLzFiiNSlFRASYiVtYrxQ4WlVXJ7ZXJpklQ8zY0IFJGd7ienOBU4CrcRWZ3hORbyS0YyZpJUvMWKI1KUFErgM+AbYDR6nq34GpuGLOT4rIv0SkVyL7aJJLMsWMDR2YlCAiu3DFm2dG2Hck8E9gkKoOjnvnTFJKppixpWxMqhijqlsj7VDVlV5Fpp/HuU8muSVNzNgZrTHG+MzOaE3KEJEc4CLgeNzyJAoUA/OAp1S1tJm3mzSULDFjZ7QmJYjIaOA/QDbwHu6PRYD+wMm4aTunq2pBwjppkkoyxYwlWpMSRGQOsA24TFWrwvZlAY/hLmxMS0D3TBJKppixRGtSgoiUAZObOvvw7l3/UFUb3ddu0lMyxYzNozWpYg+Q18z+kd4xxoQkTczYxTCTKv4B/FNEfo0bdyvGXdgYCJyGm6Zzb8J6Z5JR0sSMDR2YlCEitwA/4ODVY3AXN7YB96rq7xLVN5OckiVmLNGalCMiw3F/OADbVHVDIvtjkl+iY8YSrTHG+MwuhpkOQUSGiMgjie6HSR3xjBk7ozUdgoiMA5aqakai+2JSQzxjxmYdmJQgIme3cEhuXDpiUkYyxYyd0ZqUICJB3FVjaeYwtTNaE5JMMWNjtCZVbAW+qaqBSA9gYqI7aJJO0sSMJVqTKpbQ/B9GS2cuJv0kTczYGK1JFf8H5DSzfy1gBWVMfUkTMzZGa4wxPrOhA2OM8ZklWmOM8ZklWmOM8ZklWmOM8ZklWmOM8ZklWmOM8ZklWmOM8ZklWmOM8ZklWmOM8dn/B7GT1wNCa5KNAAAAAElFTkSuQmCC\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', ncol=8) +\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": 39,
   "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": 40,
   "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>AAAAAAAAAACACCGG</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>C357T T598A T599C A600T</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAACATGAG</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>C46T A47G</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAAGCGACG</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>G466C T467A G468T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAAGGAAAG</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>T329G G330T</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAATATAGA</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>T139C A140C C141A</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": 41,
   "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>AAAAAAAAAACACCGG</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>C357T T598A T599C A600T</td>\n",
       "      <td>6</td>\n",
       "      <td>C357T T598A T599C A600T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAACATGAG</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>C46T A47G</td>\n",
       "      <td>1</td>\n",
       "      <td>C46T A47G</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAAGCGACG</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>G466C T467A G468T</td>\n",
       "      <td>1</td>\n",
       "      <td>G466C T467A G468T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAAGGAAAG</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>T329G G330T</td>\n",
       "      <td>6</td>\n",
       "      <td>T329G G330T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAATATAGA</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>T139C A140C C141A</td>\n",
       "      <td>1</td>\n",
       "      <td>T139C A140C C141A</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": 42,
   "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": 43,
   "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>CGG_naive</td>\n",
       "      <td>93059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>99370</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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\n",
      "text/plain": [
       "<Figure size 100x175 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": [
    "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": 44,
   "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>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAACACCGG</td>\n",
       "      <td>C357T T598A T599C A600T</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAACATGAG</td>\n",
       "      <td>C46T A47G</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAAGCGACG</td>\n",
       "      <td>G466C T467A G468T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAAGGAAAG</td>\n",
       "      <td>T329G G330T</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAATATAGA</td>\n",
       "      <td>T139C A140C C141A</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": 45,
   "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>AAAAAAAAAAATAATA</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>subs diff too large</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAATCCGAG</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>subs diff too large</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAACAACGTAA</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>minor subs too frequent</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAATCTCAAAC</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>subs diff too large</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAACACAACTCA</td>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>subs diff too large</td>\n",
       "      <td>7</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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 8 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": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read gene of 705 nts for CGG_naive from data/PacBio_amplicon.gb\n"
     ]
    }
   ],
   "source": [
    "targets = alignparse.targets.Targets(seqsfile=config['amplicon'],\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['amplicon']}\")"
   ]
  },
  {
   "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": 48,
   "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": 49,
   "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>CGG_naive</th>\n",
       "      <td>93059</td>\n",
       "      <td>99370</td>\n",
       "      <td>192429</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": 50,
   "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": 51,
   "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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y2AHANyLiWUnLgDkR8aCkfwIXAH/INUIzMzOzCsjSE7Q+8E76eiGwefp6OrBrHkGZmZmZVVqWJOhVoG/6+nngB5K2BE4leZaYmZmZWZuX5XLYGKB7+vpy4I/Ad4HPgWG5RGVmZmZWYVnmCbqj4PVfJW0DbA/MjYj3cozNzMzMrGIyzxPUKCI+BZ7LIRYzMzOzVlNSEiTpOuCSiPgkfb1KEXFOLpGZmZmZVVCpPUG7AGulr3cleVBqU1ZVbmZmZtamlJQERcRXC17vX7FozMzMzFpJWbfIS1pT0heS+lUqIDMzM7PWUFYSFBFfAHPw88HMzMysncsyWeII4CpJm+YdjJmZmVlryXKL/BnAdsB8SXOATwpXRoQfnWFmZmZtXpYk6N68gzAzMzNrbVlmjL68EoF0ZJK+AF5K306NiJOqGY+ZmZnlMGO0leQfEVFf7SDMzMzsX8pOgiR1As4GjgJ6AJ0L10eEB0ybmZlZm5fl7rD/As4BfgNsBFwH3A0sAy7LLbI2QtK+kiZJmi8pJB3RxDanSXpD0meSpknap2iTLmn5E5L2a53IzczMbHWyJEHfBU6OiGuAL4A70zEuVwB75hlcG7E+8AJwelMrJR0NjAFGkjxe5HFgsqQeBZttExEDgFOBWyV1qWjEZmZm1qwsSdCXgenp649JeoMAfg98LY+g2pKImBwRF0fE3avY5Bzg5oi4KSJmRMRZwJvA8II65qd/XwJeAfpUOGwzMzNrRpaB0W8B3YG5wGvAwcBzwO7A4vxCa/skdQYGAFcXrXoAGJhuswnwaUQslrQVsCMwq4nqOs2cObOS4ZqtjtufVVOHaH8d4RxqTZYk6B7gQOAZYCxwp6QTSQZJj84xtvZgM5JHiCwoKl9A0mMGsAPwC0nLgADOjIiFTdS1tK6uzo8jsVw1NDSUuqnbn+Wu1tpfXV1dtUOwVKltL8s8QecXvJ4o6U1gb+C1iLiv3Po6iCh6r8ayiJgC9G/1iMzMzGy1stwiv15EfNr4PiKeIekVqkXvAUv5V69Po26s3DtkZmZmbUiWgdHvSLpd0iGSsuzfYUTE58A0YFDRqkHAlNaPyMzMzEqVJYkZCqxNMjZovqSxknbPN6y2Q9IGkuol1adFvdL3jbfAXwecJOn7knaQNJpkfNTPqxGvmZmZlSbLmKC7gbslbQh8BzgWmCLpDeD2iLgi5xirbTfg4YL316V/bwGGRcRdkroCl5LcNfcSMDgi5rRumGZmZlaOzJezIuKjiPh1RBwM/BvwCcls0h1KRDwSEWpiGVawzbiI2CYi1o6IARHxWBVDNjMzsxJkToIkrSPpKEn3kswT1BW4Jq/AzMzMzCopy91hB5M8OuMIkjujJgKHRMSj+YZmZmZmVjlZJku8F/gDcDzwh4hYkmtEZmZmZq0gSxL05Yj4Z+6RmJmZmbWisscEOQEyMzOzjqCmJzs0MzOz2uUkyMzMzGpSSUmQpJ1r/REZZmZm1rGUmtj8FdgMQNKsdIZkMzMzs3ar1CToH0Cv9PU2ZexnZmZm1iaVeov8b4FHJb0NBDBV0tKmNoyIbfMKzszMzKxSSkqCIuIUSXcD2wHXA/8DfFTJwMys4xsyZEjudU6YMCH3Os2sYyp5ssSIuB9A0gBgbEQ4CTIzM7N2q+wZoyPihMbXkrZKimJerlGZmZmZVViWB6iuAVwMnAtskJZ9BFwLjIyIZblGaBXlyxFmZlarsjw7bCRwInA+8CQgYG/gMmAd4KK8gjMzMzOrlCxJ0PHASRFxX0HZC5LmAeNwEmRmZmbtQJb5fjYF/tZE+d/SdWZmZmZtXpYk6AXg9CbKT0/XmZmZmbV5WS6H/Qj4g6SDgKdIJk8cCGwNDM4xNjMzM7OKKbsnKCIeBfoA9wAbk1wCuxvoGxGP5xqdmZmZWYVk6QkiIubjAdBmZmbWjvlBqGZmZlaTnASZmZlZTXISZGZmZjWprCRIiZ6S1q1UQGZmZmatodyeIAEzga0qEIuZmZlZqykrCUofjjoT6FqZcMzMzMxaR5YxQT8C/ltSv7yD6cgkrSdpjqRrqh2LmZmZZZsn6HZgPZKHpn4OLCpcGRF+fljTLgKeqXYQZmZmlsiSBJ2VdxAdnaQ6YHtgEuAeNDMzszag7CQoIm6pRCDlknQZ8F9FxQsi4ss5HmNf4D+BAUB34JsRcW8T252WbtcdeBk4q+gRItek6wfmFZuZmZm1TKZ5giT1ljRC0p2SuqVlh0raKd/wmvUySeLRuPRf1YaS9pa0VhPl20taVeK0PvACcPpq6j0aGAOMBHYBHgcmS+qRrv8G0BARDaWckJmZmbWOspMgSfsB04E9gG8BG6SrdgYuzy+0knwREX8vWN5taiNJawA/AyZI6lRQ3gd4GBja1H4RMTkiLo6Iu1cTwznAzRFxU0TMiIizgDeB4en6PYFjJM0m6RE6WdKl5Z2mmZmZ5S3LmKCrgYsj4jpJHxWUPwycmU9YJauTNB9YTDLo+MKImFW8UUQskzQYeAy4VdJxQC/gz8B9ETEqy8EldSa5VHZ10aoHSC99RcQFwAXp9sOAfhFxRRPVdZo5c2aWMNqcjnIeNcbtz6qpQ7S/jnAOtSZLEtQfGNJE+bu07vxBz5D04DQAXwIuBqZI2iki3i/eOCLmSzqAJBGaAOwFPASc2oIYNgM6AQuKyhcA5Y5NWlpXV9ep+c3avrq6umqHYKmGhpKvwrr9We5qrf257bUdpba9LEnQP0jG37xRVL4LMC9DfZlExOSCt9MlPQW8DhwPXLeKfeZKGgo8CswCToyIyCOcovdqooyIGJ/DsczMzCwHWQZGTwB+kg4mDmANSXuTjHe5Nc/gyhERn5CMVVplKi7pS8AvSW5VXw8Y3cLDvgcsZeVen26s3DtkZmZmbUiWJOgiYC5Jr88GwCskl5imACPyC608ktYGdgDeXsX6zUguf80gGdB9AHBUS2ZwjojPgWnAoKJVg0g+DzMzM2ujsswTtAT4bnqH0y4kidRfI6JVR4SlycskkoSsG8mYoC7ASvMYpXeH3Q/MAY6OiC+AGZIOAh6WNC8iVuoVkrQBsF1BUS9J9cDCiJibll0H3CZpKvAUcArQA/h5LidqZmZmFZFlTBAAEfG6pFnp6zzG1ZRrK+BOksHJ7wJPA3tGxJziDdO7wy4AHk97bxrLp6eJ0EoDqVO7kdz11qhxrNEtwLC0jrskdQUuJRkr9RIwuKk4zMzMrO3IlARJOhE4m3T8jaSZwJiIuCnH2FYrIo4pc/sHV1H+/Gr2eYRkkHNzdY8DxpUTj5mZmVVX2UmQpCtJEqCfklz+geR289GStomIi3OMz8zMzKwisvQEDQdOjog7C8ruk/QiSWLkJMjMzMzavCx3h3UCpjZRPo0WjDEyMzMza01ZkqDb+ddzsQqdAtzRsnDMzMzMWkdJPTeSCmdgDuAkSQeT3JEFyUNCt6aKkyWamZmZlaPUy1e7FL2flv7tnf59N112yiMoMzMzs0orKQmKiK9WOhAzMzOz1pRlTJCZmZlZu5dlnqB1gB8CXyV5XMUKiVRE7JpPaGZmZmaVk+WW9l+RPCB0IvAXkoHSZmZmZu1KliToayTPxnoy72DMzMzMWkuWMUHzgI/yDsTMzMysNWVJgs4FfiKpZ97BmJmZmbWWLJfDpgLrALMkfQosKVwZEZvmEZiZmZlZJWVJgu4EtgQuBBbggdFmZmbWDmVJggYCe0XEC3kHY2ZmVmjIkCG51jdhwoRc67P2LcuYoL8B6+YdiJmZmVlrypIEnQ9cK2l/SV0ldSlc8g7QzMzMrBKyXA67P/37UFG5SMYHdWpRRGZmZmatIEsS5IepmpmZWbtXdhIUEY9WIhAzMzOz1pTlAar7rm59RDyWPRwzM7P2z3e1tQ9ZLoc90kRZ4VxBHhNkZmZmbV6Wu8M2KVq6AYcCzwIH5xeamZmZWeVkGRP0YRPFD0paDIwGBrQ4KjMzM7MKy3I5bFXeBfrmWJ+ZtQF5j20Aj28ws7Yhy8DonYuLgO4kkyj6URoV4H+EzMzM8pelJ+h5koHQKip/Gvh+SwMyMzMzaw1ZkqBeRe+XAe9GxGc5xGMdiHuwrJrc/sysOVkGRs+pRCBmZmZmrSnTwGhJBwIHktwev8Jt9hHhS2JmZmbW5pU9T5Ck/wIeIEmCNmPleYOsgKTTJL0h6TNJ0yTtU+2YzMzMLFtP0KnAsIi4Le9gOhpJRwNjgNOAJ4EfAJMl7RgRc6sZm5mZWa3LMmN0Z2BK3oF0UOcAN0fETRExIyLOAt4Ehlc3LDMzM8vSE3QTMAS4MudYOhRJnUlmz766aNUDwMDWj8jMzDoCP5w1P4qI5rcq3EEaCwwFXkyXJYXrI+Kc3KJrxyRtAcwD9o6IKQXlFwLHR8QKs2s3NDSEVDz1klnLRMTSPn36NPtjx+3PKsHtz6ql1LaXpSdoZ5IJEwH6FR83Q30dXfFnoibKAJbW1dV1aoV4rIY0NDSUuqnbn+XO7c+qpdS2l2WeoK+WHU1teg9YCny5qLwbsKD1wzEzM7NCWQZGWwki4nNgGjCoaNUgPLDczMys6vJ8iryt7DrgNklTgaeAU4AewM+rGpWZmZk5CaqkiLhLUlfgUqA78BIw2I8eMTMzqz4nQRUWEeOAcdWOw8zMzFbkJKiNkPR2Q0ND92rHYR2LpLdL3c7tz/Lm9mfVUnLbK3eeIDMzM7OOwHeHmZmZWU1yEmRmZmY1yUlQByHpNElvSPpM0jRJ++RU776SJkmaLykkHZFTvRdIelbSR5LekXSvpL7N71lS3cMlvSjpn+nylKR/z6PuJo51Qfq5jMmpvsvS+gqXv+dRdyVVov1Vqu2ldbv9rVyX296K9br9rf44HeK7z0lQByDpaGAMMBLYBXgcmCypRw7Vrw+8AJyeQ12F9gN+BuxJMoHkmsADktbPoe63gPOB3dLlz8DvJO2UQ93LSdqdZO6nF/OsF3iZZEqFxqV/zvXnqoLtr1JtD9z+VsVt71/c/lahQ333RYSXdr4AzwA3FpXNAK7K+TgBHFGhc9g8rX/fCtW/EDgxx/o2ABqAg4BHgDE51XsZ8Hw12lELYq54+6tk20vrr/n257a32uO4/f2rrg713eeeoHZOUmdgAPBA0aoHgIGtH1FmG6V/F+ZZqaROko4h+VX3VI5V/wz4Q0T8Kcc6G9WlXfBvSPpfSdtW4Bi5cPtbvXbY/tz2qqM9tb8O9d3neYLav82ATqz8UNYFrPzw1jZJkkgeMfJERLyUU539Sf6nXwf4GPhmRLySU93HALsCu+dRX5FngKEkv7S+BFwMTJG0U0S8X4HjtZTbX9N1tsf257ZXBe2p/XXE7z4nQR1H8YRPaqKsrboB2Bn4So51vgrUAxsD3wZukbRfS78IJG0NjAUOjojPWhpksYiYXPB2uqSngNeB40m+KNsqt78Vtbv257ZXNe2i/XXU7z4nQe3fe8BSVv7l042VfyG1OZJ+ChxOci38rbzqjYjPgdfSt1PTgXxnAj9oYdUDSD7backPOCD5NbqvpNOBtSNiaQuPsVxEfCJpOlCXV505c/trQkdof257ldfO2l+H/O7zmKB2Lm3s00juMCg0CJjS+hGVRokbgG8BB0TEG5U+JLB2DvU8RHLHQn3BMhW4A6jP80sAQNLawA5ASVPAtza3v9IPSTtrf257ldNO21+H/O5zT1DHcB1wm6SpJNeBTwF6AD9vacWSNgC2KyjqJakeWBgRc1tQ9c+AIcA3gI8kNf6a+zAiFrWgXiT9GJgMvAlsCBwD7A8c2pJ6ASLiI2CF6/aSPgHez+N6vqRrgEnAXJJfXRcDXYBbWlp3BVWk/VWw7YHb30rc9lbk9reiDvvd19q3o3mpzAKcBswGFpP8OsrlVkuS/3miiWV8C+ttqs4AhuUQ880Fn8U7wJ+AQRX87B8hv9tE/xeYD3wOzAN+C+xY7fZVjfZXqbbn9ue25/bXttpeNdufH6BqZmZmNcljgszMzKwmOQkyMzOzmuQkyMzMzGqSkyAzMzOrSU6CzMzMrCY5CTIzM7Oa5CTIzMzMapKTIDMzM6tJToLMzMysJjkJMrOqkjRe0r3VjqMtkLS/pJC0cbVjMasFToLMzFpA0mxJZ2XY7xFJY4qKpwDdgQ9zCM3MmuGnyJuZtRER8Tnw92rHYVYr3BNkZiWTtIak8yS9JmmxpLmSLipY31/SnyUtkvS+pF9K2qBgfSdJ10n6R7p+FKCiY6wt6XpJ70j6TNITknYvWN94yehASVMlfSppiqS+q4l7m3SfoyQ9nsb3rKQ+knZP6/lY0v2SNi/Yb6XeGkn3ShrfuB7oCYxO64+0vKukOyW9lcY3XdKxBXWMB/YDzmzcL41xpcthkr4t6eX0854t6dyieGZLulDSryR9lP43OaVgfWdJN0h6O/08Z0u6YFWflVktcRJkZuW4CjgPuBLYERgCLACQtB5wP/ABsDtwJHAQcEPB/ucC3wdOBL4CbAp8s+gYo4BvA8cDuwKvAX+UtGnRdiPT+nYDvgB+VUL8lwMj0nq/AO5Mj3cmsA/QG7iihHoafQt4C7iU5DJW97R8HWAacBjQD/glcJukPdL1ZwJPAf9TsN+bxZVLGgD8BvhfoD9wGXClpGFFm54LTAV2AcYBN0raPl13BnA4cBTQF/geMLuMczTruCLCixcvXppdgA2Bz4CTVrH+ZGAhsH5B2WBgKfCl9P184LyC9WuS/ON/b/p+feBzYEjBNmsB84D/TN/vDwRwYNFxAlhnFbFtk64/saDsmLTsgIKy84G/Fbx/BBhTVNe9wPiC97OBs0r4/P4AXNNM3Y3ntnH6/g7ggaJtRgEvFx3/toL3IklMT03fXw88BKjabciLl7a2uCfIzEq1A7A2yT+oq1r/QkR8UlD2JEmPc19JG5H0eDzVuDIiviDpwWjUmyTpebJgmyXAX9L6C71Y8Prt9G+3Zs6hcJ8F6d/pRWXN1dGs9LLfRZJeTC/7fQwcDPQos6odKPgsUk8CdZI6FZQtP6+ICJJxRY3nMR6oB15NLzMeXGYMZh2WkyAzK9WiZtaLpBejKasqb6qOprZvqu4lTdTf3HdaU/sUlxXWsYyiMUskSVpzzgXOJum1OYAkCfkj0LmEfQs1dd7F8cCK5wAF5xERzwG9gEuAdYHfSJpYZhxmHZKTIDMr1UySROjAVax/BaiXtH5B2d4kiURDRHxI0mOzZ+NKSWsCAwq2f43kcthXCrZZi2Tcz4wczqFc7/KvcT6kvS/9irb5HOhUVLYP8LuIuD0iXgBmAXUl7FfsFQo+i9RAks9zafPhJyLinxFxV0ScDBwNfLuJMVZmNcdJkJmVJCI+A34CjJI0VFJvSXtKOjHd5A6SMUO3SOon6avAT0nGqzReehoLnC/pm+nA3XHAxgXH+AS4EfhvSYdK2pFk8PB6wM2tcJrF/gx8TdLXmoo3NRvYV9KWkjZLy14DBkkaKGkH4BfAl5vYb4/0rrDNJDX1fXwtcKCkS9I72Y4HTgeuKfUEJJ0t6RhJ20vqQzJg/e/AP0qtw6yj8jxBZlaOK0nuqroC2IKkZ+fnABHxqaRDSBKdZ4FPgd8C5xTsfy1Jz8p4kh6iXwH3ABsVbHM+yQ+020gGY08FDomIDyp1UqvxK+DfgFtJzns08HDRNpeSJDmvk4yZEsnn1IvkEtinJHeH3cuK53kNcAtJb8+66fYriIjnJB1F8nlfQvJ5XxoR48s4h49J7uirIxmk/iwwOCKWlVGHWYekZAydmZmZWW3x5TAzMzOrSU6CzMzMrCY5CTIzM7Oa5CTIzMzMapKTIDMzM6tJToLMzMysJjkJMjMzs5rkJMjMzMxqkpMgMzMzq0lOgszMzKwmOQkyMzOzmuQkyMzMzGrS/wcZYwSQA7QOOAAAAABJRU5ErkJggg==\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": 52,
   "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": 53,
   "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": 54,
   "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": 55,
   "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>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib2</th>\n",
       "      <th>0</th>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">all libraries</th>\n",
       "      <th>0</th>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4232</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>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib2</th>\n",
       "      <th>0</th>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">all libraries</th>\n",
       "      <th>0</th>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4290</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>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib2</th>\n",
       "      <th>0</th>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">all libraries</th>\n",
       "      <th>0</th>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4186</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>477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lib2</th>\n",
       "      <th>0</th>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">all libraries</th>\n",
       "      <th>0</th>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;1</th>\n",
       "      <td>4206</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": 56,
   "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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": 58,
   "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>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAACACCGG</td>\n",
       "      <td>6</td>\n",
       "      <td>GGC119GGT TTA200ACT</td>\n",
       "      <td>L200T</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAACATGAG</td>\n",
       "      <td>1</td>\n",
       "      <td>CAG16TGG</td>\n",
       "      <td>Q16W</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAAGCGACG</td>\n",
       "      <td>1</td>\n",
       "      <td>GTG156CAT</td>\n",
       "      <td>V156H</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAAGGAAAG</td>\n",
       "      <td>6</td>\n",
       "      <td>GTG110GGT</td>\n",
       "      <td>V110G</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CGG_naive</td>\n",
       "      <td>lib1</td>\n",
       "      <td>AAAAAAAAAATATAGA</td>\n",
       "      <td>1</td>\n",
       "      <td>TAC47CCA</td>\n",
       "      <td>Y47P</td>\n",
       "      <td>1</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",
    "    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": 59,
   "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": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}