Fig1_ProcessDMSdata.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Align deep sequencing data and count mutations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules, define directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dms_tools2 version 2.3.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import glob\n",
    "import shutil\n",
    "import subprocess\n",
    "import string\n",
    "import warnings\n",
    "warnings.simplefilter('ignore') # don't print warnings to avoid clutter in notebook\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# import seaborn as sns\n",
    "# from Bio import SeqIO, AlignIO\n",
    "\n",
    "import dms_tools2\n",
    "import dms_tools2.sra\n",
    "import dms_tools2.plot\n",
    "from dms_tools2.plot import plotReadsPerBC\n",
    "import dms_tools2.prefs\n",
    "from dms_tools2.ipython_utils import showPDF\n",
    "\n",
    "from IPython.display import display, HTML, Markdown, Image\n",
    "\n",
    "from pymodules.utils import annotateCodonCounts_accessible \n",
    "from pymodules.utils import plotCodonMutTypes_accessible\n",
    "\n",
    "print(\"Using dms_tools2 version {0}\".format(dms_tools2.__version__))\n",
    "\n",
    "# CPUs to use, should not exceed the number you request with slurm\n",
    "# ncpus = -1\n",
    "ncpus = 14\n",
    "\n",
    "# do we use existing results or generate everything new?\n",
    "use_existing = 'yes'\n",
    "\n",
    "# Directories\n",
    "fastqdir = './fastq/'\n",
    "datadir = './data'\n",
    "resultsdir = './results/' \n",
    "countsdir = os.path.join(resultsdir, 'codoncounts/')\n",
    "paperdir = './paper'\n",
    "figuresdir = os.path.join(paperdir, 'figures/')\n",
    "\n",
    "for xdir in [fastqdir, datadir, resultsdir, countsdir, paperdir, figuresdir]:\n",
    "    if not os.path.isdir(xdir):\n",
    "        os.mkdir(xdir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimental set-up\n",
    "\n",
    "I performed a full-scale passaging of all three replicate mutant libraries (named Lib4, 5, 6) and wild-type virus in human A549 and duck CCL141 cells. Virus supernatant was collected 48hpi.\n",
    "\n",
    "Details of pre-selection samples and wild-type controls sequenced as follows:\n",
    "\n",
    "Plasmid | Bottleneck # molecules\n",
    "--- | :---\n",
    "DNA-Lib4/Rep1 | 750,000\n",
    "DNA-Lib5/Rep2 | 750,000\n",
    "DNA-Lib6/Rep3 | 750,000 \n",
    "DNA-WT | 250,000\n",
    "\n",
    "Details of post-selection samples and wild-type controls sequenced as follows:\n",
    "\n",
    "Cells | Virus | MOI | # TCID50s passaged | Timepoint sequenced | Bottleneck # molecules\n",
    "--- | --- | --- | --- | --- | ---\n",
    "A549 | Lib4/Rep1 | 0.01 | 9.39 x 10<sup>5</sup> | 48hpi | 750,000\n",
    "A549 | Lib5/Rep2 | 0.01 | 1.02 x 10<sup>6</sup> | 48hpi | 750,000\n",
    "A549 | Lib6/Rep3 | 0.01 | 1.20 x 10<sup>6</sup> | 48hpi | 750,000\n",
    "A549 | WT | 0.01 | 9.30 x 10<sup>5</sup> | 48hpi | 250,000\n",
    "CCL141 | Lib4/Rep1 | 0.01 | 8.46 x 10<sup>5</sup> | 48hpi | 750,000\n",
    "CCL141 | Lib5/Rep2 | 0.01 | 8.92 x 10<sup>5</sup> | 48hpi | 750,000\n",
    "CCL141 | Lib6/Rep3 | 0.01 | 9.54 x 10<sup>5</sup> | 48hpi | 750,000\n",
    "CCL141 | WT | 0.01 | 8.58 x 10<sup>5</sup> | 48hpi | 250,000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download FASTQ files from the SRA\n",
    "The deep mutational scanning FASTQ files are deposited on the Sequence Read Archive under the run numbers provided in the file `data/SraRunTable_DMS.txt`. To download these files, we use the [dms_tools2.sra.fastqFromSRA](https://jbloomlab.github.io/dms_tools2/dms_tools2.sra.html#dms_tools2.sra.fastqFromSRA) function. This requires the fastq-dump and aspera programs to be installed at the specified paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>run</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A549-MutVirusLib-Rep1</td>\n",
       "      <td>SRR8366384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-MutVirusLib-Rep2</td>\n",
       "      <td>SRR8366383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-MutVirusLib-Rep3</td>\n",
       "      <td>SRR8366382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-WT</td>\n",
       "      <td>SRR8366381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-MutVirusLib-Rep1</td>\n",
       "      <td>SRR8366380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-MutVirusLib-Rep2</td>\n",
       "      <td>SRR8366379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-MutVirusLib-Rep3</td>\n",
       "      <td>SRR8366378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-WT</td>\n",
       "      <td>SRR8366377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib-Rep1</td>\n",
       "      <td>SRR8366386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib-Rep2</td>\n",
       "      <td>SRR8366385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib-Rep3</td>\n",
       "      <td>SRR8366376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-WT</td>\n",
       "      <td>SRR8366375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = (pd.read_table('data/SraRunTable_DMS.txt', sep='\\t')[['Sample_Name', 'Run']]\n",
    "           .sort_values('Sample_Name')\n",
    "           .rename(columns={'Sample_Name': 'name', 'Run': 'run'})\n",
    "          )\n",
    "display(HTML(samples.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading FASTQ files from the SRA...\n",
      "Completed download of FASTQ files from the SRA\n",
      "Here are the names of the downloaded files now found in ./fastq/\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>run</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A549-MutVirusLib-Rep1</td>\n",
       "      <td>SRR8366384</td>\n",
       "      <td>A549-MutVirusLib-Rep1_R1.fastq.gz</td>\n",
       "      <td>A549-MutVirusLib-Rep1_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-MutVirusLib-Rep2</td>\n",
       "      <td>SRR8366383</td>\n",
       "      <td>A549-MutVirusLib-Rep2_R1.fastq.gz</td>\n",
       "      <td>A549-MutVirusLib-Rep2_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-MutVirusLib-Rep3</td>\n",
       "      <td>SRR8366382</td>\n",
       "      <td>A549-MutVirusLib-Rep3_R1.fastq.gz</td>\n",
       "      <td>A549-MutVirusLib-Rep3_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-WT</td>\n",
       "      <td>SRR8366381</td>\n",
       "      <td>A549-WT_R1.fastq.gz</td>\n",
       "      <td>A549-WT_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-MutVirusLib-Rep1</td>\n",
       "      <td>SRR8366380</td>\n",
       "      <td>CCL141-MutVirusLib-Rep1_R1.fastq.gz</td>\n",
       "      <td>CCL141-MutVirusLib-Rep1_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-MutVirusLib-Rep2</td>\n",
       "      <td>SRR8366379</td>\n",
       "      <td>CCL141-MutVirusLib-Rep2_R1.fastq.gz</td>\n",
       "      <td>CCL141-MutVirusLib-Rep2_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-MutVirusLib-Rep3</td>\n",
       "      <td>SRR8366378</td>\n",
       "      <td>CCL141-MutVirusLib-Rep3_R1.fastq.gz</td>\n",
       "      <td>CCL141-MutVirusLib-Rep3_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-WT</td>\n",
       "      <td>SRR8366377</td>\n",
       "      <td>CCL141-WT_R1.fastq.gz</td>\n",
       "      <td>CCL141-WT_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib-Rep1</td>\n",
       "      <td>SRR8366386</td>\n",
       "      <td>DNA-Lib-Rep1_R1.fastq.gz</td>\n",
       "      <td>DNA-Lib-Rep1_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib-Rep2</td>\n",
       "      <td>SRR8366385</td>\n",
       "      <td>DNA-Lib-Rep2_R1.fastq.gz</td>\n",
       "      <td>DNA-Lib-Rep2_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib-Rep3</td>\n",
       "      <td>SRR8366376</td>\n",
       "      <td>DNA-Lib-Rep3_R1.fastq.gz</td>\n",
       "      <td>DNA-Lib-Rep3_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-WT</td>\n",
       "      <td>SRR8366375</td>\n",
       "      <td>DNA-WT_R1.fastq.gz</td>\n",
       "      <td>DNA-WT_R2.fastq.gz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Downloading FASTQ files from the SRA...')\n",
    "dms_tools2.sra.fastqFromSRA(\n",
    "        samples=samples,\n",
    "        fastq_dump='fastq-dump', # valid path to this program on the Hutch server\n",
    "        fastqdir=fastqdir,\n",
    "        aspera=(\n",
    "            '/app/aspera-connect/3.5.1/bin/ascp', # valid path to ascp on Hutch server\n",
    "            '/app/aspera-connect/3.5.1/etc/asperaweb_id_dsa.openssh' # Aspera key on Hutch server\n",
    "            ),\n",
    "        overwrite={'no':True, 'yes':False}[use_existing],\n",
    "        )\n",
    "print('Completed download of FASTQ files from the SRA')\n",
    "\n",
    "print('Here are the names of the downloaded files now found in {0}'.format(fastqdir))\n",
    "display(HTML(samples.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the batch file that we write to CSV format to use as input to dms2_batch_bcsubamplicons:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>R1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A549-Lib4</td>\n",
       "      <td>A549-MutVirusLib-Rep1_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-Lib5</td>\n",
       "      <td>A549-MutVirusLib-Rep2_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-Lib6</td>\n",
       "      <td>A549-MutVirusLib-Rep3_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-WT</td>\n",
       "      <td>A549-WT_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-Lib4</td>\n",
       "      <td>CCL141-MutVirusLib-Rep1_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-Lib5</td>\n",
       "      <td>CCL141-MutVirusLib-Rep2_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-Lib6</td>\n",
       "      <td>CCL141-MutVirusLib-Rep3_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-WT</td>\n",
       "      <td>CCL141-WT_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib4</td>\n",
       "      <td>DNA-Lib-Rep1_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib5</td>\n",
       "      <td>DNA-Lib-Rep2_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib6</td>\n",
       "      <td>DNA-Lib-Rep3_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-WT</td>\n",
       "      <td>DNA-WT_R1.fastq.gz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Match library number to replicate number\n",
    "samples_lib = (pd.read_table('data/SraRunTable_DMS.txt', sep='\\t')[['Sample_Name', 'Library_Name']]\n",
    "           .sort_values('Sample_Name')\n",
    "           .rename(columns={'Sample_Name': 'name'})\n",
    "          )\n",
    "samples = pd.merge(samples_lib, samples, on='name')\n",
    "# write sample information to a batch file for dms2_batch_bcsubamplicons\n",
    "countsbatchfile = os.path.join(countsdir, 'batch.csv')\n",
    "samples = (samples[['Library_Name', 'R1']]\n",
    "                 .rename(columns={'Library_Name': 'name'})\n",
    "                )\n",
    "print(\"Here is the batch file that we write to CSV format to use as input to dms2_batch_bcsubamplicons:\")\n",
    "display(HTML(samples.to_html(index=False)))\n",
    "samples.to_csv(countsbatchfile, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Barcoded subamplicon alignment\n",
    "\n",
    "Use [dms2_batch_bcsubamp](https://jbloomlab.github.io/dms_tools2/dms2_batch_bcsubamp.html#dms2-batch-bcsubamp) to align sequences to the wildtype S009 PB2 coding sequence and count mutations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Now running:\n",
      "dms2_batch_bcsubamp     --batchfile ./results/codoncounts/batch.csv     --refseq ./data/S009-PB2.fasta     --alignspecs 1,285,36,32 286,570,32,35 571,855,37,32 856,1140,32,35 1141,1425,28,34 1426,1710,33,34 1711,1995,29,29 1996,2280,28,43     --outdir ./results/codoncounts/     --summaryprefix summary     --R1trim 200     --R2trim 170     --fastqdir ./fastq/     --ncpus 14     --use_existing yes\n",
      "Output summary files already exist and '--use_existing' is 'yes', so exiting with no further action.\n"
     ]
    }
   ],
   "source": [
    "# Reference coding sequence of S009 PB2\n",
    "refseq = './data/S009-PB2.fasta'\n",
    "\n",
    "alignspecs = ' '.join(['1,285,36,32', \n",
    "                       '286,570,32,35', \n",
    "                       '571,855,37,32', \n",
    "                       '856,1140,32,35', \n",
    "                       '1141,1425,28,34', \n",
    "                       '1426,1710,33,34',\n",
    "                       '1711,1995,29,29',\n",
    "                       '1996,2280,28,43'])\n",
    "\n",
    "print('\\nNow running:')\n",
    "cmd = 'dms2_batch_bcsubamp \\\n",
    "    --batchfile {0} \\\n",
    "    --refseq {1} \\\n",
    "    --alignspecs {2} \\\n",
    "    --outdir {3} \\\n",
    "    --summaryprefix summary \\\n",
    "    --R1trim 200 \\\n",
    "    --R2trim 170 \\\n",
    "    --fastqdir {4} \\\n",
    "    --ncpus {5} \\\n",
    "    --use_existing {6}'\\\n",
    "    .format(countsbatchfile, refseq, alignspecs, countsdir, fastqdir, ncpus, use_existing)\n",
    "print(cmd)\n",
    "!$cmd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Output counts of each codon at each site in each sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./results/codoncounts//A549-Lib4_codoncounts.csv\n",
      "./results/codoncounts//A549-Lib5_codoncounts.csv\n",
      "./results/codoncounts//A549-Lib6_codoncounts.csv\n",
      "./results/codoncounts//A549-WT_codoncounts.csv\n",
      "./results/codoncounts//CCL141-Lib4_codoncounts.csv\n",
      "./results/codoncounts//CCL141-Lib5_codoncounts.csv\n",
      "./results/codoncounts//CCL141-Lib6_codoncounts.csv\n",
      "./results/codoncounts//CCL141-WT_codoncounts.csv\n",
      "./results/codoncounts//DNA-Lib4_codoncounts.csv\n",
      "./results/codoncounts//DNA-Lib5_codoncounts.csv\n",
      "./results/codoncounts//DNA-Lib6_codoncounts.csv\n",
      "./results/codoncounts//DNA-WT_codoncounts.csv\n"
     ]
    }
   ],
   "source": [
    "!ls {countsdir}/*_codoncounts.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots summarizing read alignments\n",
    "\n",
    "Now we look at the summary plots created by [dms2_batch_bcsubamp](https://jbloomlab.github.io/dms_tools2/dms2_batch_bcsubamp.html#dms2-batch-bcsubamp)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./results/codoncounts/summary'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countsplotprefix = os.path.join(countsdir, 'summary')\n",
    "countsplotprefix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of aligned reads and barcodes\n",
    "\n",
    "Below are the number of aligned reads and barcodes for each sample. \n",
    "\n",
    "The `summary_readstats.pdf` plot below shows that most reads were retained, and a small fraction discarded because of low-quality barcodes, or because they failed the Illumina filter. \n",
    "\n",
    "The `summary_bcstats.pdf` plot below shows statistics on the barcodes. Some of the barcodes had to be discarded because they had too few reads (these are the single-read barcodes in the plot below), a small fraction with adequate reads were not alignable, and the rest aligned to the PB2 gene properly. \n",
    "\n",
    "I sequenced the WT controls to about 1/3 depth, so the number of reads are lower as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPDF([countsplotprefix + '_readstats.pdf', countsplotprefix + '_bcstats.pdf'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `*_readsperbc.pdf` plot below shows how many times different barcodes were observed for each sample.\n",
    "Barcodes need to be observed multiple times to be useful for [barcoded-subamplicon sequencing](https://jbloomlab.github.io/dms_tools2/bcsubamp.html) error correction.\n",
    "Many barcodes are observed multiple times. \n",
    "There is also a large bar at 1, which probably corresponds to a mix of barcodes that were only observed once and sequencing errors on other barcodes that spuriously gave rise to an apparently unique sequence.\n",
    "Overall, the distributions here indicate that most multiply sequenced barcodes were sequenced > 2 times, which suggests that most of the peak at one is probably due to sequencing errors on barcodes, and suggests that sequencing to greater depth probably would not help all that much for these samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showPDF(countsplotprefix + '_readsperbc.pdf')\n",
    "# Made an identical plot to the one named aboe, but below with a different layout (maxcol=4) for easier reading.\n",
    "names = samples['name']\n",
    "readsperbcfiles = [countsdir+'/'+name+'_readsperbc.csv' for name in names]\n",
    "plotReadsPerBC(names, readsperbcfiles, countsplotprefix + '_readsperbc2.pdf', maxreads=10, maxcol=4)\n",
    "showPDF(countsplotprefix + '_readsperbc2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequencing depth and mutation frequencies across sequences\n",
    "\n",
    "The `summary_depth.pdf` plot below shows the depth (number of called codons) at each site in the gene, after restricting to barcodes for which there are enough reads. Most sites in most samples are sequenced to >400,000 codon counts. I sequenced the WT controls to about 1/3 depth, so the number of counts are lower as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPDF(countsplotprefix + '_depth.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `summary_mutfreq.pdf` plot below shows the per-codon frequency of mutations at each site.\n",
    "Generally, things are about as expected. In the mutant DNA library samples, there is a fairly high mutation frequency across all sites. In the post-selection mutant viruses, mutations remain prevalent, though at lower levels, suggesting that there is some selection against mutations. In the wildtype plasmid and wildtype virus, the mutation frequency is much lower.\n",
    "\n",
    "I observe a prominent peak in all the DNA mutant libraries at around position 200, that seems to remain in the passaged virus samples. This is present in all replicate libraries. This is likely an issue with the process of mutagenesis:\n",
    "- I had made my replicate libraries from three independent clones of the plasmid encoding WT PB2, so it is unlikely that three independent clones had issues at site ~200.\n",
    "- The WT virus was rescued with plasmid that generated mutant Lib 4. WT virus does not show a peak at 200.\n",
    "- The DNA-WT control was from plasmid that generated mutant lib 6. DNA-WT also does not show a peak at 200.\n",
    "\n",
    "This peak should be accounted for during data analysis since we will compare passaged samples to the DNA library samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPDF(countsplotprefix + '_mutfreq.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Completeness of mutation sampling\n",
    "\n",
    "The `summary_cumulmutcounts.pdf` plot below shows the fraction of mutations that are found $\\leq$ the indicated number of times.\n",
    "This gives an idea of how completely the possible amino-acid and codon mutations were sampled in each sample.\n",
    "\n",
    "In the plasmid mutant library, nearly all codon and amino-acid mutations were sampled at least once (and usually much more than once), indicating that my plasmid mutant libraries contained virtually all these mutations as desired.\n",
    "\n",
    "For mutant libraries passaged in A549 and CCL141, many but not all amino acid mutations were sampled at least once. This suggests that mutations are being lost from sampling, consistent with some mutations being lost through selection.\n",
    "\n",
    "The wildtype controls sampled only a relatively small fraction of the possible mutations, consistent with mutations/errors in passaging and sequencing being rare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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55JNObwawaUX/8XHby745Pama8Nmc6xB8e5+mF/Zo/x//X6bKP//883rkkUcyljFmGwbgPrG5CznFnmvXrumNN97QW4vVuhapMB07JGUdZ4bYAbjbytk9Oa/D6nWHROyACyy/pdjcP9hePHb5RcXmLthffu4fpOW3nN4LjvrFb2oULq3Pa+wwaYCWazCtvb1da3OxZ86c0ezsrC5duqSmpqas67h27ZoWFxddObirdP1JgRsHd5VWB8etqKjQ9u3bna5KCrc/BTKeSjgxsOrVH/yrrGViMckjvfs/yaI5PAGuydM21O2cUt3OKVPn7re//W1T6/zhD38on89najKUlZUVhcNheb1eVw6gSuywj9hhX2Lrk3SDMi+M/kfb655781fadvlHtpevkFS3U9LO1YGCzcaOpaUlffazn81YjthROMQO+4o5drz5/6twuoqWrjteeukl/frXvyZ2uIiTsSPbb18sFlNMksfjSbnsji29pWs59viAs373pllJI3mNHWa5L1Kg6MzPz2thYcHUjBzGVNFunHVHcv/MO0bd3Lj/3D7zzjvvvCPJ3rH39ki3lt8csf3ZuXaPyKf/MZ7b8p0m9p+ZLqFGufLyckvfSVlZmSuPf2KHfZs5dmy0bDP+vTn+N7bXne+tNbP/iB3EDiuIHevLdt2S+LDcrcedwcz+e+utt3TlypWs5YgdhbHw2mlFf/tLrZSXa7G83PLy87844fQmQNLFkPSrHBp7/kMwt8/f09Cush3vsbRMJBLV8vKyysvL9ad5jB1mkVwDsOktvHZay5dflSS9XWH9aew7wYAib086vRmSVn+o5hbsL/9KyTHLyywvL2t5eVmVlZXqdHoHAFvIlb8/kPH9Qt4gP3Mut+Wf6NrQ6gGbysJrp3O67nDTdctj//13dHH+ZkvLRCIRRSIRbdu2Tee7nN6C4hN5e1IrOXz/14IBRebtL798eUTRpVktSlp0emcUsYsh6YXh3Ja/OGN/+diOfdpe86HU19+99sh23XH8+HG1tLQUZF8ZlpaWNDc351gvOZJrW9Tg4GDKuAOBQEDSap/jtrY2p6sIxC3+ZkBLbwzYXj7xInPZ4W15bjC35Njo5Tu0WFpre/nz549bXiYcDmthYSFf4xEAMGnxN/bj3loz12p1LWK+e87KyrJ+/etfx//+6Wy75c9MvEEGYN61YCCv538uvhTIrQXKf/nyX+u+j//fLS2z1a87Im9PauG10xnLGBMZeDwlWpvjWH5zxDXHj9Pe2fY7inp2Jr222ilUklK7hV59+6omJibifz+d47DFufYYyUV7e7v+bw/YT261t7ervT35tz8Wi8W7JLuxu7TT2CNb1PDwsMbGx5JeO3tuNXo0NTaRXIOrLL0x4Jom4r8K12nmmrXk1k9/+pP4v58bXH2SZNf5819O+aED4E65dinPpz/9ynROF/mxmPU5o7b6DTK2roXXTmtx4nlJ0hUbN6DLl/MXN/7HeG7JsReGc7tuiZRU5W1bikV0aTan73D5zRHXXPfmqnRnvcp21ttefnudX6VV1pZPnIDvHy7+k+byNxlkQbW0tOj4cesPxQ319fWqr7e/72EdybUt6ujRo05XAVvI2yPdrrlIsPMj/8orr8T74393OKjnBoO2P//IkSOqqUmeZiAWiykWi60OrJqliTU/kkDxyGfrgcpbj6ikIn3skDwqKUmNHSdOXI+7uYybAsCayNuTiswMrv47x3Xlft2y+mDPrvPnz/NQz6LlyyNZu/UXi4qb2lX+HuutnxYWFlReXi7vB/6VKm6ydvwkXwsXdwu8tZMBmsGDqeJFcg1AVmaewGWatSu6OJu3upRsq1H5jdZ+5P/pnyZ09eqcJGn4pxX64YSlxTUyEtNsnjbB7/enXKRmG5QcgDPc1PrkP/+/X9bY9O6k17KNuTaQcE/S0tKi37kzX/MHA5ubmx4K/vefRtX7c2vL5PO6Bc5Ll5y6Hv89yvRctvw9LdrZYr/1kx3d3d368Y8vqLS0VKWl/yDJHeeSHXaSY9i6SK4ByMpNT+DKb2zR7o9b65706MkDGojfZc5KetX25+faxHptqzUA7pXP1id2nv4ntjz79uArOXXNOn78OK1PAAeU7qzXjjq/pWX+9u/+TheDQUnSj8aC+h/jQduff+zYsZSuZTzUMye2cFHL71xWzOLDlXwPB5DuuncjZws9ffq0JiftT2gQCARyWj6f0rW8dPtMwyheJNeALSKXrkn5vEiw8wTtlZ+/op+/8ook6Vpwu2Z+0G1p+Xz+wPv9/pzGPwBQOLlOhuJ07PtSoHif9gNOWvzNgGseCv7zdFRnLM74F/hWiVySm9jSYr/6pq6+/pWc1/O+ruJq/RQIBBIeSjsr1xknCz1bJbY2kmvAFuGWi8ySihrLYy/896cGdOLE3+Xl89vb23P6oaXlB1A88jkZip3WJ9PT07p27ZokKTi9W+EcblYYrxFwhp3E+H/7b1/X3/7t6nXL3EJQ/xC0H4e4boFV3d3dSS2fnZTr8ev3+/n9QtEguQbAsnRP4IqliXWuM+8A2JrKdtZbvsH+fxxI7JKe2wMCxmvEVrL85ojeHsncSj3TmIN5HevVxkPBX4UHcpqdNxHXLVvP7OysRkYyt5yORFYHK0g31vFsHgfcszscytLSkkpLS+X3+3XkyJEC7DXAeSTXgCLxRs/qD+fVd/8rJvl8glZTU2PrCZgxNgVPv4DiYeYGO5OVt6/3q7IzGYp0PXbYmS0NgD3Rxdm8zbQryfJYrefOndWLL/5IkhReuqrgSWut/xOHo7B73WLgusU5Jbc9ppp/8Tfatm1bQT93ZGREBw64o8eJ3eFQQqGQKisr5fV6nd4EoGBIrgFb0O6Pn7f8FNYtWlpadP68tYtkSZqbW50ttLq62ulNAGBSPm+w7UyGIl2PHTuJHUDRsnrN89OJAf3X517Jy2fbvW4B8iHXlo8kdwHzSK4hZ42NjbrtttvizZMzKS8vV1VVlamyTopGo05XYcO3L9/fgdE9IxaLpaw7n9NYp1u/Gdu3b5ckVx97drdto1k9bz/5yU/qO9/5TtZybW1t8vl8ptbr8XhUVVUlj8fjyn1kcGvsqKqqUmlpqSv3XabYkc/9aef8+trXvqZAICBJtmZje+WV6zfnf/7nf66/+qu/ymkb1qv/ZoodoVD2KVGJHYWTS+x4a+AP7e+PpeRubZVNf56mlHFtkf3ctFp/N1y3FIJb62Y1dtxzzz3au3dv1nKFih2Tk5P6+te/bnv789mt84YbbtB9992X0zrsHCPFet3hBsVyv+zG+lk9b++55x7dcssteft8kmvIWXV1taqrq01diBjjceXzomUjbET95r5/UEtvfD8v6/Le9Vcq2/17tpcv232XpW38/ve/r4MHD+al7r/3e7+X0w1mTU2Nre/HGI9oKx57ubJ63pq5wJVWE/Nmt9nj8ai8vNy1+8jg1ro5te8i8/+sa//0NdvLp95gH7W9rtKqesvbPzk5qR/84Ad52RfG5AMbxY3Hnp3YYSZ+EDsKJ5d9l69rHkny3vUlS+VfeeUV/cVf/EXCK9YSfYndOj/4wQ+qo6PDdt3r663HnkJyY92sxg6fzyefz5e1XKFix+TkpP76r/86b/tjeXk5p+Wd+I6Ju/Zt5fvlXFk9b83GDrNIriFnU1NTeu9732vqgnh5eVnLy8uqrKx0utppGU+Ozcy85qRtN35E2973BwX7vHxOUFBTU+PIOBKLi4uSpIqKioJ/djaJN91unAzC6nmbbRBew+DgoPbu3atbb701a9lIJKLFxUVVVFS4cuB2t8eOhYUFlZeXxy84CmUlHNTC2Mm8rc/qhAK5yud36fF48n5+b8bYMTMzo/vvvz9jOWJH4TgVO9ZKd3xn2ndzc3P6/vfzk9yrr6+31a2O6w77rMaO1157Ta+//rprYke+96nV498N3BI70nH7vnPz/bLbY4fV8/a1117TpUuX8jarMsk15OzSpUt673vfqw984ANZy66srOidd95RVVWV09VOywi26WbeyZXTN2nXZ6yzLrFrkxN1z4elpSVJ0o4dOwr+2dlEo1HFYjHH9k02Vs/bH//4x6bKjY+P6+2339Ztt91mug7btm1z5T7ayNiRD++88448Hk/Bb/LyvS8K/d0n1r++vl5+v9/2utrb2/Nef2IHsWMjLbx2WuHf/jIvN8iVtx5RSUWN7eXTfXfGTV6649/pay6J645cWI0dr776qqamprKWK1TsWHv85Xrjnim55sbYITl33WFGptjhBm6+X07sSuvmfWf2vDViB8k1oMAWXjutSMLMc1YlzlpXeesRba+zfpM2NzeniooKWzPe5bO1WLqBeY0LtWw/8jU19i+uARS3dIOKJzbbz/cNwunTp+NjptmR2DXMbusVoFhdCwYU/c2AFiUt5riu7XV+S5MKTE5O6vTp09dfCKTOGpx4g7w2dqwds+rYsWO2686A7sgVE1oAWwPJNcCka8FA3matK6mosTVbp6c8pLLKSpVsc3Za63TZ/UgkEk+uufFJBgB7rvy9/cR8dDH5BjfdbJ0bGTsmJydzarULwBmTk5M6ceJE3tZHYhwAsNFIrgEAgHXl66ECgOJVurNeZTvtt+DKpUsoAADFgOQaYEOuF5mlNpbNZ3ep8+fP561vOQAUA7tjphmDkt9+++1ObwJgyfKbIymtR61IXHZHnb/gE4okSnfNspFdygFJevzxx/WVr3zF6WoAKBIk11wkHA4reDEoSdrj25PXaWGRX05fZAKAE3IdlNyq2dlZ07PPrre8we6YaXNzc5Kk6urqgm03kA9vj3Q72vI0ly7Za8/7dGNWMRwFAMBNSK65xODgoAJ9AYXD4fhrra2t6jrcJa83+/haXf+2K+P79x+6P/7Efmx8TCdPnly3bM83epzeHRti8TcDWnoj84Xe9amZS7T2IehKDpMZAIBTFn8zoNjcnFYqKrS4fXtO67I6KHmuRkZG8joZC4DC4dwFAGwlJNdcYGx8TD3P9sjr9arrcJd8e3waHBzU8PCw9vj2mOrGcv+h+9O+fvbcWUlSW1tbyntNjU2qq6tzevMLZumNAc3/In+D4zrp+PHjammxPmOoIZdlARQXY0KChXf/AwAA2X3uc5/ToUOHVFbGLTOA7IgULjA4OChJeuihh9TU2CRpNfF19M+P6uy5szp06FDW1mvpEnDDw8PxdaXrYlpXV2dr/BnYc+DAgbzNWtfS0sKYaQAAwJSq3z1maziLUCikyspKU70ogM3m5ptv1oc//GFt27bN6aoAKAIk1xwWDoc1PDwsn88XT6wZ7m69W2fPndXgi4PrtkzLZGx8TNJq91IkK9lWo/IbU1tvXR8c16NMY+PamZAAAJCbxAHMARQPJlICAGx2JNccljiBwVqNTY06e+6sFsLWO/KEw2ENDg7K6/Wum1wLBoN65tQzWggvqK6uTq2trVumm2j5jS3a/fHUwXFXVlYkSaWlpcw8BWDT2f3x8wUdM21yclKnT5/OWCYWiykWi8nj8aTE3cQJCYCtxsxYsZnkOlbs6dOn9ctf/lLl5eUqLy93encAAOBqJNdcIlNSKxgMWl7f4IuDklZbra3XlN9o2Wb8++y5s0kTH2DjHDlyxNZ+npubU0VFBWOmASgKk5OTOnFic4x1CRSa02PFBgKBvA1nAQDAZkdyzWGhmdCGrNcYx63tY20p7xmTJNzdend8LDZjttKz587K5/OlnQBhPVVVVaqsrNTS0lLWspFIRJJMlc0347Ol1VlB09XBKGPMGppPievctWuXfv/3f9/yOozk2vbt2x3Zh9m2LxaLqaSkxJWt/oz977b9Jq223IlGo/J4PEnHqVtYPW9ra2s1PT1tqtwNN9xgar1Gq1Lj/91mI2PHtYmvKzr/z3lZ18rKijwFPAfy/X05cf4SO+yzEzvMlN0qsSOf32kkErF8DOcznq2srOT9HOK6w77NFjtuuOEGhcPhrOW2SuxwCztxpxDcHjucvF+2Uj83HntWz1uzscMskmsO8+3x5b6SNYLBoEKhkHw+X9oWcT6fL2UMt7a2NoUXwgoEAhobH0ubXBsYGEg7rfpnPvMZPfDAA5qbmzNdRytl8yW6uBj/98rKiuU6fPOb39TFixdtf/7rr78e//fi4qLtfbC4uKjFhG2BNU4ce5uF2X1333336fnnnzdVzsp6JeX1B7BYRCb/TrHLP8rLusLhsDwFPAfWfl92HiokcvL8JXbYZyV2WCm32WNH4nWLym+Qp/rDtte1WFqrZYvHcOLNyc0336ybb77Z9ueXlZVt2XNoq253Ppjdd3fccYfuuOOOrOW2SuxwC+5ZckPssM/seWs2dphFcs0lMnX9tDoOmtEl1ErrM2l1AoVAIKBQKH1rupqampTBaGdmZlRWVqZLly6pqakp62dcu3ZNi4uLqq6utryPrk18XUu/+pbl5QyehJYfZWVl2pWmDkYmvrS0NOW9c+fO6Qc/+IHtz09UUVFhax8ktlxzG7c/BTKCrBtnPEt8glxSUuJ0dVJYPW+//e1vmyr3wx/+UD6fT/fee2/WsisrKwqHw/J6vSorc99PV6bYkaurZWVaztO6vF6vym3Enlw+L9H586ljXRI77NuMsWNpaUmf/exnM5bbKrFjoaJC77z77/L3/J52/W//V0Hrlri//H6/vvSlLzm2n9Ihdti32WLHSy+9pF//+tfEDhfhnsW+XO6XC8HNx57V89Zs7DDLfZFii6m7uU6SNBOaSXlvfGxcklTprTS9PmP2USl9l9Bsy0qStzL9RUBLS0vKjdGZM2c0NTWlhYUFU9NULy+v3iLamdJ68Z1faXk6P8mtkpKStHXINKFBPi8+SktLbU/rncuyGykSicR/qNx4ofbOO6u3KG7cd9FoNH6R68YfKqvnrZkuoUa58vJyS99JWVmZK7/DjZwMJfF8Kt1ZrzIbsxUvLS2txg6vT+UW9193d7ftuq+dkCDdd0fssI/Ysbljx2LCd7redctGSoo9Lrz2IHbYt9lix1tvvaUrV65kLbdVYodbuDFuSO6PHbncL280Y79JcmXS2WD2vDUbO0x/rtMbvtUZs3kODw9rbHxMTY2rrb/C4bB+NvwzSclJsnA4rODFoLyV3rQt2oaHhxUOhzNOZGBkc9cyWrxtRFfVzaa+vl719dZvcBOXBwCrdtT5tbPluOXlQqGQKisrVW6jBQUTEgDFqbu7WyMjI7aXz2VZAAC2GpJrLtDW1qbh4WGdOnVK/g6/fHt8GhwcVCgU0v2H7k9KhAUvBnXy5Ek1NTbp6NGjKesyWq3d3Xr3up936tQpSavdTevq6jQTmtH42LjGxsfk9XpTxmNzo9Kd9dpRZ39W01IbLT8S+f1+HT9u/QYXAABsDYu/GdDSG5ln2zQGhPZ4SrS28cnym7klt0ZGRpjtEwCAAiG55gJNjU3qOtylQF9APc/2xF9vbW3VoUOHTK8nFAppbHxMPp9Pra2t65Yzkndj42NJr9fV1anrcFd8BlE3K9tZb6v1BgDAvpaWFtXU1DhdDaAoLL0xoPlf0PITAICtgOSaS7S1tam1tVXBi0FJ0h7fnrRJrqbGJvV8oyftOnw+37rvJeo63CV/hz/+WZk+DwCQm8jbk1p5e9L28tHFWdvL5tvx48dTJrYB4H7t7e1qaWmxvNzCwoLKy8s57wEAyILkmot4vd74mGub6bMAYCtbeO00rVeALa5kW43Kb0xNbsVisXf/5VGmMcnL32M9MZaopaXF1nAWxniNbpzxEgAANyG5hi2ju7ubgbkBAEDBld/Yot0fP5/yejHM+AcAALIjuQYAwCZ24MABLS0tqbS0VKWlpU5XBwAAANh0SK4BAFAgFTe1p229spGYLRAAAADYWCTXsCWtN/ZIJBKRJJWUlGTsnlFfX+/0JgAAgE3s9OnTCgQCtpcfGRlxehMAANgySK5hS6qpqUk78xVjnwDYzI4cOaKamhrby/NgASicyclJWp4CAFAkSK4BALBF+P3+tA8WAAAAANhHcg0527t3r/bt26doNJq1bFlZmXbs2GGq7FrXp6tf/bfVdVhZPhqNurLl2o4dO1RWVmZr/200Y//a+W4KYdu2bZLkyrq5fd9ZPW8/+tGP6sc//nHWco2Njdq7d6+p9Xo8Hu3YsUMej8eV+8iQLnbkGrvyyenPX69Obq2bROzIhZ3YMTMzk7VcscSOXK87Epevr69XR0eH7br8wR/8ga3t57rDPmKHfVZjx+233x7f35kUS+ywgnsW6zbb8V9Iib9Lbqyf1fP29ttv186dO/P2+STXkLN9+/bppptuMnUAG7PV5Zpck6yf0FaWj8ViKeXdYPv27ba2vZDcuu/Ky8sluXvfubV+Vs/bu+66y1Ryra2tzfQ2ezyeoj3+c41d+RSNRl27/4gduXFj/ezEDjMKFTtiS7NafvMV29sfWbyS9LfV647Evz/4wQ/qS1/6ku262Nl+ieuOXBA77LMaOxoaGtTQ0JC13Fa57nAD9p19udwvF5Ib62f1vDUbO8wiuYaczc3NaW5uTjfccEPWspFIRPM//wvF3hq1/DmRt/856W+rT2nWlk+3vBFg3fgESJKWlpbiAddt3L7vjPH0ysrcGfbcvP8ikYgikYipp8KSNDU1Zarc+Pi4fD6f9uzZk7VsNBrVysqKysrKVFJSUtDtX3rj+1p64/tZShkXZ6nf38qV5Jtzq9/xK6+8otnZ2bxsi8fjcd0x5uZjXyJ25MJO7AiFQmptbc1YrlCxY/nKzzX3/YN52x9WrzvMXLdsNK477CN22Gc1dszMzOjSpUuuiR2F4ObvTyJ25MLq8V9obt5/Vs/bmZkZhUIhffjDH87L57sz2qOojI+Pa8eOHbr33nuzlr127ZqWQsOKXf5RTp/p8XgsB+vEALDe8saFULbZQp0yPz+vyspKVwbbSCSiWCwmj8fjyouQ+fl5SVJ1dbXTVUkRjUbj+86NFyHXrl3TwsKCfD6fqfLf+c53TJUbHBzUvn379OCDD2YtG4lEND8/r+rq6oLvo5Xf/kALY3+dl3XZ+Y7/4i/+Im+DmpeUlLjuGCN22LdZY0e2G+RCxY58Ho92rjvMXLdsNK477CN22Gc1dvzkJz/R1NSUa2JHIXDPYp/bY4fV47+QYrGYIpGIJLnyvLB63hqxg+QatrQrb17RKxZvNvPV8gMAAAAAAMBAcg0F53nfJ+V930ctLzc4+KJ++tOfSJIuhl7Rc4MHnN4UAFtMybYald/YkvL69TE7PMr0ALn8PS0CUJze1+W+sXkAAIA7kFxDwZV84HPaaaOZ60uBbp0I/MTp6gPYwspvbNHuj59Ped3onlFaWrqh3TOOHTum48ePW14uFAqpsrJSXq+3cDsL2OImJyc1OTmZsYzRvSZd1y5a3AMAUDxIrgEAAAB5dvr0aZ04ccLpagAAgAIgueYi4XBYwYtBSdIe356CDGIYDAYVXghLkpoam5zeBaa1t7fr/Pnzua8IAAAAAAAgByTXXGJwcFCBvoDC4XD8tdbWVnUd7jLVjefkyZMaGx9L+979h+6X3+9Pei0cDuvUqVNJy3i9XnUd7so60w4AAAAAAABWkVxzgbHxMfU82xNPbvn2+DQ4OKjh4WHt8e1JSYxlcv+h+1Nea2xqTHnNSKy1traqra1NoZmQAn0B9Tzbo7q6OldO/QsAAFCM1mtxX6jxGgEAwMYiueYCg4ODkqSHHnoo3jWzqbFJR//8qM6eO6tDhw6ZHoTaTCIuGAxqbHxMTY1N+sJDX1h98d38W8+zPRocHLSU0AMAAAAAANiqSpyuwFYXDoc1PDwsn8+XMubZ3a13S5IGXxzM62cODw9LUkr3T+PvfH8eAGxl3d3d8ng8tv8bGBhwehMAAAAAZEDLNYclTmCwVmNTo86eO6uF8ILp9QUCAQWDQfn2+FR3c51aW1tTWr0Fg6uf6duT3PXT6/WqqbFp3bHbAAAAAAAAkIzkmkvU1dWt+56RDDPj7Lmzq/8Yv/73Fx76Qtr1Z5od1Og2CgAAAAAAgPWRXHNYaCaUl/UYExMYXTtDoZB6A70aHh7WM6ee0cm/PhkvOxOayes2VFVVqbKyUktLS1nLRiIRSTJVdr1lJSkajdpah9nPiEajeV93Puu4Edueq2g0qlgsppKSElcOymx8p27cd7FYTNFoVB6PJ+k4dwur521tba2mp6dNlbvhhhtMrdcY9Nv4fye2X1o/9mSKHYnL/97v/Z6+9KUv2a7LBz/4QdvHMLHDfv0kYocddmKHmbKFih1rl7F6DOQaO9yC2GG/fhKxww6rseOGG25QOBzOWq5Yrjus7CNih3Vujx253C8Xsn5uPPasnrdmY4dZJNcctrZrpl1tbW3J6/X51HW4S8FgUKFQSMFgMN56bY9vj0Ih60m9gYEBHThwIOX1z3zmM3rggQc0Nzdnel1WyhoWFxfj/15ZWbG1js1gcXExaV/Amq163OSD2X1333336fnnnzdVzsp6Jdn6AYwtXFTsV9+0vd2xuQvxf9uJPYnna1VVlVpaWmzXRbJ/DBM7ckPssM9K7LBSbsNjx5plcjn3i/m6hdiRm2L93t3A7L674447dMcdd2QtV6jYgVXEjtwQO+wze96ajR1mkVxziUxdPzN1Gc3E6/WqqalJg4ODCoVCKevJ1PUz3es1NTVqb29Pem1mZkZlZWW6dOmSmpqydyO9du2aFhcXVV1dbXl7Kioq4v8uKyuztY5sjEx8aWlp3tedD3Nzc6qoqND27dudrkoKtz8FMoKs2Zl3CynxCXJJifvmmbF63n772982Ve6HP/yhfD6f7r333qxlV1ZWFA6H5fV6VVZm7adr+Z3Luvr6V/KyL8rKyrQrzX7IFDsKEbuyIXbYR+ywz07sWFpa0mc/+9mM5QoXO7y6mvC31XPXzLnPdYd9xA77NlvseOmll/TrX//aNbGjEIgd9rk9duRyv1wIbj72rJ63ZmOHWe6LFFtM3c11ktJ31RwfWx04rdJbaXv9xg974jrq6uo0Nj622iW1Mbns2PjYuhcBLS0tOn/+fNJrZ86c0dTUlBYWFrRt27as9VleXpYkU2XXSjyBS0pKbK0jG6MJaWlpqSuDrVG3jdj2XEUikfgPlRsv1N555x1J9o69jRaNRuMXuW78obJ63prpEmqUKy8vt/SdlJWVWf4OY3m8KF4v9mSKHYWIXWYQO+whdti32WKH1eXNnPtcd9hH7LBvs8WOt956S1euXMlarlCxoxCIHfa5PXbkcr+80Yz9JsmVSWeD2fPWbOww/blOb/hW5/V61draquHh4aSWZOFwWD8b/pkkqe1jbfHy4XBYwYtBeSu98ZZoRnZ2rWAwqPHx1QRd4mykra2tOnvurIaHh5O6kw6+OJjyeQCw2VTc1G572fL35NalEwAAAMDmQ3LNBdra2jQ8PKxTp07J3+GXb48v3pXz/kP3JyXOgheDOnnypJoam3T06NH4a6dOnVLbx9pU6a3UHt8eBYNBDb44qHA4rPsP3S+f7/rYbnV1dWpqbNLY+JieOfWM2traFJoJ6dy5c/J6vSnjtwHAZrL74+dzXwmATe+NN95QRcLf3d3dlpYfGRlxehMAAECBkFxzgabGJnUd7lKgL6CeZ3vir7e2turQoUNZl/dWribfzp47m/y616v7D90vv9+fssxDDz2kU6dOaXh4WMPDw/HyXYe7khJxAAAAxSx2+Uda+OefaNFi97eFyV8mJddOnDjh9KYAAACXIrnmEm1tbWptbVXwYlDSajfOdEmupsYm9XyjJ+m1uro6PfP0MwoGgwovhJPKrsfr9ero0aNJy2Qqn0+PP/64Xn31VcvLTU5OFqR+AABg84hdflHv2JjUpMbpigMAgKJBcs2kQCCgQCAgSfL7/Wlbg+XK6/XmlOCyM6uo3ZlIc3HhwgX96Ec/KvjnAoAd3d3dtFgBkDJjuhUtLYzXCADAZkZyLUFnZ6cCgYD8fr96e3vjrwcCAXV2dib9vbYMAAAAXKjyZpXX/m+WZ4W78uYVvfLzV+J/r50xHQAAwEByLYHRMm1t0iwxseb3++Ot2IwkG6z55Cc/qY9+9KM5raO+vt7pzQAAAEWg5AOf067f/VNt27bN0nKvDAzoX588EP+786+d3hIAAOBWJNeyMBJukhSLxeKvJbZygzWf+9znmDQBQFFqaWnR8ePHU16PRCKSpJKSEnk8nnWXr6lhFCcAAABgsyG5tsbaZFniOGvrlQEAbA01NTVpx11aWVmRJJWWlmZMrgEAAADYfKwNPrEFpUuuAQAAAAAAABLJtRSJ3UATkVwDAAAAAADAWnQLTWBMVmBMYLBeq7X1EnBb1d69e7Vv3z5Fo9GsZcvKyrRjxw5TZZ0UjUZd2bVrx44dKisrc+X+M8YkjMVirqyfMZC1G+vm9n1n9bz96Ec/qh//+MdZyzU2Nmrv3r2m1uvxeLRjxw55PB7L+8jYv4Zcls/2HRE7rHP78U/ssM9O7JiZmclarlhihxXEDuvcfvwTO+yzGjtuv/12UxOWFCp2FBKxw7rNdvwXUuLvohvrZ/W8vf3227Vz5868fT7JtQS9vb3yeDwpyTOSa5nt27dPN910k6kDuLS0VKWlpa48GRPFYrGUi2o32L59uyR3BjODW/ddeXm5JHfvO7fWz+p5e9ddd5lKrrW1tZneZo/HY/v4X1t+I2+w3Xr8EzvsI3bYZyd2mFEsscMKtx7/xA77iB32WY0dDQ0NamhoyFquULGjkNx6/LPv7CuW+2U31s/qeWs2dphFcm2N3t5eBQKB+Eygxn+JGIct2dzcnObm5nTDDTdkLRuJRBSJREw9XXKCEWDd+ARIkpaWluIB123cvu+MAefLytwZ9ty8/6yet1NTU6bKjY+Py+fzac+ePVnLRqNRraysqKysTCUl1kY0WLtPre5jM8u7+fuTiB25IHbYZyd2hEIhtba2ZixXLLHDDDd/fxKxIxfEDvusxo6ZmRldunTJNbGjENz8/UnEjlxwv2yf1fN2ZmZGoVBIH/7wh/Py+e6M9g5Kl0xby40ZbieNj49rx44duvfee7OWvXbtmhYWFuTz+ZyudlrGhVBJSYkrA8b8/LwqKytdGWwjkYhisZg8Ho8rL0Lm5+clSdXV1U5XJUU0Go3vOzdehFg9b7/zne+YKjc4OKh9+/bpwQcfzFo2Eolofn5e1dXVlvfR2uPR6vKJsWC974jYYR+xw77NGjuy3SAXS+wwg9hhH7HDvs0WO37yk59oamrKNbGjEIgd9rk9drj5fjkWiykSiUjamN/EXFk9b43Yka/kmvuOJgcljreWTWdnJ91DAQAAAAAAtjiSawmM7qBmy5FcAwAAAAAA2NpIrgEAAAAAAAA2MeYaAKAoRH/1TS1EprVocYyHty5fTPqx6+7utrT8yMiI05sOAAAAwMVIrlmw0V1Bw+GwgheDkqQ9vj2WBzFMXF6SmhqbnNhNALAhYm98R+9c/pHl5db+0J04ccLpTQEAAACwiWzp5Np6ExiYnXGlt7c3b3UZHBxUoC+gcDgcf621tVVdh7vk9XozLhsKhdQb6NXw8HDS616vV20fa0uZ/XRsfEwnT55cd3093+jJ23YBAAAAAABsZls6uZaLfCbWxsbH1PNsj7xer7oOd8m3x6fBwUENDw9rj29PSnJsrZnQjIaHh9XU2KS6ujrV1dUpGAxq8MVBnT13VpLSrsMoDwDFwFN9p8rKyixPm37lzSt65eevxP9ub2+3XYeWlhandwMAi775zW/q3LlzlmPH7Oys01UHAABFYksn1/x+v2KxWPzvzs5OBQKBpNcKYXBwUJL00EMPxbtyNjU26eifH9XZc2d16NChjK3X9vj26OjRo0ndQFtbW9Xa2qq//C9/qZ8N/yxtcq2uri5r4g4A3KLktse0q7pa27Zts7TcKwMD+tcnD8T/jsXOO70pAAro4sWL+sEPfuB0NQAAwCbGbKEJ/H5/wZNN4XBYw8PD8vl8KWOk3d16tyRp8MXBjOtIt6y0mjzzer0KhUIF3SYAAAAAAICtYku3XFvLieRa4gQGazU2NersubNaCC/YWncoFFI4HF53YoRgMKhnTj2jhfCC6urq1NraSjdRAACwqXzsYx9TRUWFSi3ONAwAAGAWybUM0k12kCifybhMSa1gMGhrnb2B1XHhjBZwa42NjyX9++y5s7r/0P10FQUAAJvG7//+7+uBBx6w3KUcAADALJJra6w3g+h6ck1EhWY2psumMSFCXV2dDh06lPSeMUnC3a13x1u1GbOVnj13Vj6fT21tbaY/q6qqSpWVlVpaWspaNhKJSJKpsk4w6heNRp2uSsY6unH/RaNRxWIxlZSUmJ5xt9D1k9x57MViMUWjUXk8nvgx6CZWz9va2lpNT0+bKnfDDTeYWu/KykrS/1uxdpmNOAaIHfYRO+zbjLHDTNlCxY5C7iNih3XEDvs2W+y44YYbFA6Hs5YjdhS+jm48/t0eO7hfts/qeWs2dphFci2B1cRaPvj2+HJfyRqDg4PqebZHdXV1OvofjqZMhuDz+XT/ofuTXmtra1N4IaxAIKCx8bG0ybWBgQEdOHAg5fXPfOYzeuCBBzQ3N2e6jlbKItni4qIWFxedrkbR4tizz+y+u++++/T888+bKmdlvZJs/QCuXWarHgPEjtxs1eMmH6zEDivlNjp2YBWxIzfEDvvM7rs77rhDd9xxR9ZyxI7CInbkhthhn9nz1mzsMIvkWgIjsWZ09yxk98hMXT+tjINmJNa8Xq+6DndlnGV0rbtb71YgEFh3AoSamhq1t7cnvTYzM6OysjJdunRJTU1NWT/j2rVrWlxcVHV19cbuUJuMTLxbx2WZm5tTRUWFtm/f7nRVUrj9KZARZK2cE4WS+AS5pMR988xYPW+//e1vmyr3wx/+UD6fT/fee2/WsisrKwqHw/J6vSors/bTtfY734j4Q+ywj9hh32aMHUtLS/rsZz+bsVyhYkchEDvsI3bYt9lix0svvaRf//rXxA4XIXbYx/2yfVbPW7Oxwyz3RQoX6O3tLdhn1d1cJ0maCc2kvDc+Ni5JqvRWmlpXYmLt6H84anlygvhFQGX6i4CWlhadP38+6bUzZ85oampKCwsLpsYyWV5eliTXjntiNCEtLS11ZbA16ubG/ReJROI/VG68UHvnnXckufPYi0aj8YtcN/5QWT1vzXQJNcqVl5db+k7Kysosf4drf1w34hggdthH7LCP2LGxsaMQiB32ETvs22yx46233tKVK1eyliN2FBaxwx433y8b+02SK5POBrPnrdnYYZb7jqYtxuv1qrW1VaFQKGmCgXA4rJ8N/0yS1PaxtqTXx8bHUlq6WUmsrddMcvDFQUkb01UVAAAAAABgM3JvunELaWtr0/DwsE6dOiV/h1++PT4NDg4qFArp/kP3JzUnD14M6uTJk2pqbNLRo0dXXwsG1fNsjySpsbFRw8PDGh4eTvkMY/KCU6dOSVrtblpXV6eZ0IzGx8Y1Nj4mr9ebMh4bAAAAAAAA0iO5lqC3t1ednZ3q7OwsaNfQpsYmdR3uUqAvEE+SSVJra2vKTJ/phBeut0Rbm1QzNDY1xpNrRvIusaWctJps6zrcFS8HAAAAAACAzEiuJTAmMUicNbRQSba2tja1trYqeDEoSdrj25M2ydXU2KSeb/RkfS2TrsNd8nf445+V6fMAAAAAAACwPpJrCTo7OxUIBCQp/v+ZBoj0+/15Tb55vV41NWafcbPYPgsAAAAAAGCzIrkGACgKHR0d+tGPfuR0NQAAAAAgCcm1BIUcZw0AAAAAAADFr8TpCgAAAAAAAADFipZrAICi8Nhjj2llZUVlZfx0AQAAAHAP7lASBAKB+EQGZhiziwIANt6dd96p6upqbdu2zemqAAAAAEAcybUEVpNrkkiuAQAAAAAAbGEk15CzvXv3at++fYpGo1nLlpWVaceOHabKOikajcrj8ThdjRQ7duxQWVmZK/dfLBaL/78b62e0dnJj3dy+76yetx/96Ef14x//OGu5xsZG7d2719R6PR6PduzYIY/H48p9ZCB2WOf245/YYZ+d2DEzM5O1HLGjcIgd9hE77LMaO26//XZTrdqJHYVD7LDPzffLxr6T3BnbrJ63t99+u3bu3Jm/z48l7qHidkDS+Y3+kM7OTgUCAfn9fmYXlXTmzBnt3btXd911l9NVAeAip06d0iOPPJKxzMrKitPVBOBC2cZVJHYASIfYAcCOPI3nPEDLNYt6e3vjCTYjybbVzc3NaW5uTjfccEPWspFIRJFIxLVjJhm5Zjc+AZKkpaUllZaWqrS01OmqpHD7vjMuqNw6GL6b95/V83ZqaspUufHxcfl8Pu3Zsydr2Wg0Gp/MoKTEfRNdu/n7k4gduSB22GcndoRCIbW2tmYsR+woHGKHfcQO+6zGjpmZGV26dInY4SLEDvu4X7bP6nk7MzOjUCikD3/4w3n5fHdGe5fz+/0k1xKMj49rx44duvfee7OWvXbtmhYWFuTz+ZyudlrGhVBJSYkrA8b8/LwqKytdGWwjkYhisZg8Ho8rL0Lm5+clSdXV1U5XJUU0Go3vOzdehFg9b7/zne+YKjc4OKh9+/bpwQcfzFo2Eolofn5e1dXVrtxHxA77iB32bdbYke0GmdhROMQO+4gd9lmNHT/5yU80NTVF7HARYod9br5fjsViikQikuTK88LqeWvEjnwl19x3NBUBEmoAAAAAAACQSK7ZYnVGUQAAAAAAAGxOdAu1yBhvDQAAAAAAACC5lsBq4ozZQgEAAAAAALY2kms2+P3+TTPuWjAYVHghLElqamxyujoAAAAAAABFheRagq3UEi0cDuvUqVMaGx+Lv+b1etV1uCvrTDsAAAAAAABYRXJtizISa62trWpra1NoJqRAX0A9z/aorq7OlVP/AgAAAAAAuA3JtS0oGAxqbHxMTY1N+sJDX1h9sXH1/3qe7dHg4OCm6fYKAAAAAACwkUqcroCbdXZ2Jv23WWYJHR4elqSU7p/G34MvDjpdRQAAAAAAgKJAy7U01kukGa/5/f6iHp8tGAxKknx7krt+er1eNTU2JY3DBgAAAAAAgPXRcm0Nj8eTtYVaIBBQZ2en01XNWabZQUmwAQAAAAAAZEdyLUFiwszv9ysWi6X8Z4xFFggEirab6ExoxukqAAAAAAAAbAqeWCwWc7oSeXJA0vmcdobHI0nq7e3NOKC/0W20WLuHnjx5UmPjY+r5Rs+67x09ejSlZdvAwIAOHDiQssxnPvMZPfnkk05vFgAXef755/XII49kLBMKhZyuJgCXWVpa0vvf//6MZYgdANYidgCww0zsMGmAMdfSyDZTZm9vbzwRV8yMGUPTSfd6TU2N2tvbk16bmZmR1+vVlStXtHfv3qyfuby8rOXlZVVWVjq9+WlFo1FJq4lWN37HCwsLKi8vV3l5udNVSeH2fbe4uChJqqiocLoqKYyWsZJUUuK+BsVWz9uXX37ZVLnx8XHt2rVLt956a9aykUhEi4uLqqioUGlpqdO7JIXbj39ih33EDvvsxI7l5eWsF7nEjsIhdthH7LDPaux47bXX9OabbxI7XITYYZ+b75fdHjusnrdmY4dZJNe2oLq6Oo2Njyk0E5Iar78eDoc1Nj4mr9ebdrmWlhadP5/cOPDMmTOamprS5cuX9aEPfSjrZ4fDYS0vL6/7GU5bWVmRJJWWlroy2Bo/VG7cf5FIRLFYTCUlJa4MtsZ368Z9F41GFY1G5fF4XHkBZ/W8vXDhguly+/bt01133ZW17NLSkhYXF7V9+3Zt27bN6V2SgthhH7HDvs0aO9Y+yEtXjthRGMQO+4gd9lmNHcFgUFNTU8QOFyF22Ofm++VYLKZIJCJJKitzXyrJ6nlrNnaY5b6jqQgU61hrhtbWVknS8PBw0uuDLw5Kkto+1uZ0FQEAAAAAAIqC+9KNDvL7/QoEAvJ4POuOu7YZZgqtq6tTU2OTxsbH9MypZ9TW1qbQTEjnzp2T1+tVW1ub5XVOTExoZib7RAl1dXXau3evzpw54/RuSOuBBx6QJL300kuuHJdh//79eu211xQMBp2uSop77rlHPp9Pr732ml599VWnq5PijjvukCT94Ac/cLoqKW6//XY1NDRoZmZGP/nJT5yuToqNPG9nZmZMrfeGG27QHXfcoZdeeklvvfWW07skBbHDPmKHfcQOYsdGI3bYR+ywz2rsMHMPkliW2LHxiB32ufl+2efz6Z577pEkV9bP6nlrJXaYQXItQeJYap2dnSnJtbUt1opxMgPDQw89pFOnTml4eDjegs3r9arrcJd8Pp/l9c3NzWlubi5rueXlZV29elVTU1NO74K0fv7zn0taTRZevXrV6eqkGB8f129/+1tNT087XZUUO3fuVHV1taampnTp0iWnq5PCGPPEjcdeLBbT22+/rbm5OVfWbyPP28XFRVPrXV5elrQ6GLEbj/+rV69q165dro0dRh3deHzdcsst8vl8unTpkivr19DQIMmdsWPnzp1qaGhQKBRyZf12794taWP2HbGjsHV04/FF7LCP2EHsKFQd3Xh8uT12bOTxn6u5uTndc889rv1unT5vmS10DTMt04p1ltB0gsGgwgthSVp3cgMAAAAAAACkNUBybR1Ggi2xtZrf74//BwAAAAAAgC2P5BoAAAAAAABg0wCzhQIAAAAAAAA2kVxL0NnZKY/Hk3XMNY/HI4/HkzLBAQAAAAAAALYWkmsJjGRZtskKjPdJrgEAAAAAAGxtJNdsYEIDAAAAAAAASCTXUpA4AwAAAAAAgFkk19agqycAAAAAAADMKnO6Am4UCAQytmDLNuHBVvLSSy9pZmbG6WoAcKEHH3ww4/tnzpxxuooAXIjYAcAOYgcAO7LFDrNIriXw+/0KBALq7OxUb29v2gRbIBCIt26jC6k0MzOjqakpp6sBoAgROwDYQewAYAexA8BGIrmWoLe3Vx6PR9L11mmJCbTELqN+v5/kWoJ9+/Zpz549TlcDgAuMjIyYLltdXa2GhganqwzABYgdAOyYmJjQ3NycqbLEDgAGK7HDDJJra/T29ia1Tks3Bpvf71dvb6/TVXWVPXv2aPfuGqerAaDI7Nq1i9gBwDJiBwDDrl27TN8gEzsAGKzEDjNIrq1htEgzEmxru4DSYg0AAAAAAAAGkmvrIIkGAAAAAACAbEiuAQ6bn5/XxMSEJKmqqsrUOBCjo6Pxfzc3Nzu9CY7r7+/X0NBL6uo6HN9/6V4DNhM7sWNiYkLz8/OSpIaGBlVVVTm9GY4idmArInbkLt+xw853AriB1djAsY7NjOQa4JDR0VE9/fQzmp6eTnq9qqpKBw8eVEfHp5J+oKanp/X0088kJdYMRvna2lpJ0qOPPqbR0VG98MJ3s9ahv/97mpiYiNejo6NDXV2Hs9Z/YmJCX/zin627jHGRmfij+8QTj1tKBo6OjurRRx/LWqfp6d9qdHRU8/OfyviaWfPz8/riF/9M09PTam5u1hNPPG55HcBGsRo75ufn1df3LfX19aWsq7m5WR0dn4qflz09z6qvry/ruUrsuO4Tn3hg3fes1hvYSG6IHdPT0+rr+5YmJibiN9hmf2cLETuk1XM6W53ydd0xPz+vnp5n1d/fn/Kd/O3fPm+p3kChWIkNBivxx8w5KOV2zh8/fkJDQ0OSlPZ+qafnWU1MTCTdd2W7r0r09NPPqL+/Xw8//AUdPHgw7XsNDQ168skvJ71nxLmGhgY1Nzen3cfpmL0Gw8YiuQY4wLgIraqqUkdHhxoablFVVZUmJiY0NPSS+vr61Nx8Z/zHYWhoSE8//Yzm5+d18OBBNTTcotraWk1PT2t09IL6+/tVVVVlOaiOjl7Q0NCQamtr4+sz6+mnn8n4/tDQSxodHVVDQ4Omp6fjP3obobn5TkmKJxdz1df3LUv7AigUq7Fjenpajz76WDxR3Nx8pxoaGjQ/Px8///v6vmX55pPYkay2tlb79+9P+zrgBm6JHdPT0/FrloaGhniCzYzNGDseffQxTUxMqLm5Wfv33/vud/J62gepgBvYiQ1W449Zds/5oaGheGJtPUZSq7m52db52NBwiyRpYuL1lPeM9RlJwcSHGsZ7xr5dr14dHR1Jr6cri8IjuQYU2OjoaPwH5oknHk9qDr36tKcj/r60+nTISKyle/px8OBBjY7+oa1k0MGDfxh/UmT88JlhPM0x6prOareI1SdORku6jbL6A9Scl3UZ30+mbQOcYDV2SIo/JT548KAefvgLSevbv3+/Ojo+pf7+71muC7EjWW1tLU+M4Vpuih0NDQ36+te/ptra2ngLUzM2Y+zo7+/XxMREyj5eTdQTT+BOVmODnfhjlp1z3rivMs7f9ZZ58skvx+uaqYX6etZb//T0tKanp+MPF0ZHR5MezhnJuIaGW9LGGSP+cc3hTiVOVwDYanp6npUkPfzwF9YdZ2D1qc7qe31939L8/Lw6OjpSEmuG5ubmdd/LpLa21vKP2cTERDz5lOkpSSHHUOjv748//V3L+BH9xCce0Cc+8YD+5E/+3boX5kbZhoYGfrTgOlZjx9DQUPyJ7toLYIPdpBCxAygeboodVVVVllt7bdbY0df3LUncJKN42IkNVuOPFXaWMVrArlf/XNa9dj8YLfsTG0AYybaOjtXu42tbtiW2XEPxoeUaUGATExOqqqpK24VovfKStH//vU5XXdLqj9Jqs+5PWerOsZEyjXNi/Igazaf7+/vjP/Rrm1Qb3UEZYw1uZD12rF6wETvWl6/YYYyJaTyNbmi4xfT3BGw0Ykf+5SN2GN3qjPGrJiYm3h1nyXr3OKAQ7MQGq/FnIxndQTs6OgoybENzc3O8harxedf34X41NHzr3WTa4fi+mp+fZ9KYIkZyDSgg42mElachdpbZKEa3jO7uY0UV9J988svxH7X9++/VF7/4Z+rr+1bSRW5id1AuauE2duKAcRNK7LDPTOyQro8jJSU+df5e0W0vNh9ihzOyxQ7je5mentYXv/hn8bGijGuRdF3uAKdZjQ1uuodxondK4rhrRnJxdHQ0fp9hTFhgjLtGq7XiR7fQBIFAQIFAwOlqAK5k3Dzu37/fFU+fzDp48GDS06mGhgYdPHjw3YFXR+Ov9/Q8q9ra2ngzbQD5sdljR0dHh5588st64YXv6oUXvhsfU2Z0dDTeWgWAdZs9dhjbWFtbq7/92+f1wgvfjSfl+vv7U2YQBWCfMcxOIZPWa8ddSxxvTbqefLs+wcHrSa+j+NByLUFics3v96u3t9fpKgGuYXaMAquMp9KJ8tkts7b2vSmvGU+/R0cvqLm5OV6HJ554vKiejAPFYKNiR39/v4aGXkp6rdCxQ0odL6m5uVkPP1ylL37xzzQ0NETrE8CmzR47DImt8hoaGtTR8Sk9/fQzGh29YGs8XQDJEnun5LsVXbpJWYx4Yoy7ZnT3vN4y7c53/79Z0vWWbbRcK34k19YRCATk8Xjk9/vj/wG5uh5EzY8ZYkwBbYzF4YTR0dH4AKbGALyS4t0YJiYm1NPzrK1xQoyZcjaKmWSZMUvR6OgFjY5eSHpvenr63VZt7+UiF46xEzuM1lMTExOOXahtZOwwxjzaKLkk2hsaGuIDGRvdPQAnEDtSuSF2JLZsWzv2k3GtZ2wr4BZWY4Od+LMREmNIYotyY6IB4zU73UWzxRJj3LXV/fZ60n6pqqpK2Kf3Mt7aJkByLUFvb2+89ZrRgo3WbMg3Y+rloaEhU90cjKA7NPSS42MWTExMpP2BNC6CJetPWzZ68oDEcQ7WSmx2vTqgcOpsXtPT0+rr67M9IyuQL9Zjx+rxPTT0UsoYYYW2EbGjq+vwho6bYjZ2rMe4MeYiGU4jdiRzQ+wwZlxOl0AjqQa3shMbrMafjbTejN/G63biwgsvfDfL9l8fdy1xvLXE/ZPYmpZWa8WNMdfWMBJosVgspbWa0Zqts7OTsdlgmxG403WHNPT19cXf6+j4lKqqqtTX16ehoaG05UdHRzd0bI7m5ub4eEKJ/xmJsY6ODr3wwnddOZ382n02Pz8f31fGD1i6bTN+LI1tZwZROM1q7Ni/f7+am5vjLTzSMVpmbpTNHjvWuwnu7+/X/Px8QWYjA7IhdhSWmdghKZ5oWHv9ZrSgd/qBKrCWndhgNf5shCeeeDxtPFl7H7ARjM8YGhpKGm/NYCTfjDjAeGvFjZZrGRit1GjNhnxqbm5WR0eH+vr69Oijj+ngwYNqaLhFVVVV7z7Zeend8b9Wg29VVZUefvgLevrpZ3T8+AkdPHhBDQ23xLscjY5eiE8rvdZ6P3xGN4rVwYK/J+l6k+3EH8xcLlpXnyqvXiAaza6Hhl6Kv2Zl3Zl+xLOtZ3p6Wo8++pg6Oj4Vn+5+fn5eHR0dtChBUbEaO6TVsYoeffSx+IVrc/OdamhoeHfsj9XYke4GLvFcTWR0jyZ2rMaOvr5vxZ/GG+MsTUy8Hr9IduONP7YeN8UO6fq1iZGcTrwZd0vsyJQ8PHjwD7Mua+a6o6PjU+rv71dPz7Oanv6tmpvv1OjohfhQFdk+B3CC1dhgJ/5I2c/B2travJ7za/X392t6+rdJrxn1sTJUjDHumlG/tcmztQ/rnG7dh9yQXDPBGHMtEAios7Mz6T2jNVtvby/jssG0rq7Dam6+U08//UxKE+WqqqqUATf379+vhoYGPf30M2lbqB08eDDtRdh6zZ8lxZNra8skdrXI7SL3Qsq6E+tuZd2JdUq3L7Pt6/7+7yUNOHrw4EFuelGUrMaO2tpaPfnkl9XX9y319fWlnEerF72pM+Su1xLW6B5N7DD273vT7gtj5mEukuEWbokdUuq1SeI55JbYke68vr4td2bd12auO2pra9XdfSz+nRifZ7xOy1e4kZ3YYDX+SNnPwdXkWv7O+bVWk3TJ22Z8ltWhYoxx14x/r92fRvKNLqHFzxOLxWJOVyJPDkg6vxErXq8bqJFwS/x7q7ViO3PmjKamptTS0qLdu2ucrk7RMoK3EWCzMWadMQbCRHZW9zHsO39+QJL0yCOPZCz31FNPad++fbrttg85XeWiZfW4NqaBlxjXwywz+zjxApy4bB+xo3CIHRvPyj42yhI/7PnHf/xfmpqaInY4wE5s4JocbmE2dpg0QMu1daztCmpIN3uokXxjHDbYZfVClQsv67gZwGZk9bjmQtY6KzOiAcWC2LHxrOxjYgiKlZ3YwPGOzYrk2hqZWqmt1yqtt7eXSQ4AAAAAAAC2IJJrCdYmyNK1UgMAAAAAAAAMJNfSsDN2Ggk4AAAAAACArYfkWoJcJiSghRsAAAAAAMDWU+J0BdyE5BgAAAAAAACsoOXaFhUIBBQMBjU2PhZ/recbPbbXt7i4qKqqnU5vFoAiQ+wAYAexA4BhcXHRUlliBwDJWuwwg+RaBp2dnRnfL+auoGfPnZUkNTU2JSXYrAqFQpKkX/7yl/rlL3/p9GYBKDIzMzM6c+aM09UAUGSIHQDsIHYA2Cgk19YIBAJZk2qJijW59pf/+S9VV1cnSer6t11OVwcAAAAAAKAokVxLYDWxVsyMxFqufD6fpqam1NLSonvvvTdr+XA4rIWFBfl8Pqd3QVorKyuSpNLSUnk8HqerkyIUCqmyslJer9fpqqSIRCKKxWIqKSlRSYn7hnOcm5uTJFVXVztdlRTRaFTRaFQej0elpaVOVyeF1fP2qaeeMr3uffv26cEHH8xabmlpSXNzc6qurta2bduc3iUpiB32ETvsI3YQOzYascM+Yod9VmPHmTNnNDU1ZaossaMwiB32ufl+ORaLKRKJSJLKytyXSrJ63lqJHWa4b484yEisGd09i7VVGgAAAAAAAAqD5Foavb29TlcBAAAAAAAARYDkGgAAAAAAAGx7e6Q7p+V/+9r5nJaPxmKK3PivVN32nx3Zfk8sFos58sn5d0BSTt+G0V998+wSc4wJDXq+0ZOx3MDAgA4cOJDy+p/+6Z/qsccec3ozAKQRm7ug6D8+bnv5lZUVzb89r8vlH9W1kvfqzvseNrXc888/r0ceeSRjGWO2YQDuE1u4qNivvml7+WuLiwrNzOhayXstxY6lpSW9//3vz1iG2AG4W+SnHbaXNa475kvr9VbZh4kd2FJil3+k2OUX7S+/cFFafsv28itXXlFpdN7p3ZCTn/3mVnkb/k1eY4dJA7RcS9Db26vOzk51dnbSNdSCN998U1euXNHevXuzll1eXtby8rIqKyudrnZa0WhU0mqi1Y2Dgy4sLKi8vFzl5eVOVyWF2/fd4uKiJKmiosLpqqSIxWLxpH66gVUXf/GfbK879JsJ7br6I9vLl0qqllQd+QdJUmXln2dd5uWXXza17vHxce3atUu33npr1rKRSESLi4uqqKhw5eDLbj/+iR32FXPsyMXc9IQ8r3/F9vLbJCVeqpqNHcvLy1kvcokdhUPssK+YY8fKb3+Y0/oXLufnuuP9S982FTtee+01vfnmm8QOF3EydmQ7fmOx6/tOSt530fA/a+XSGdufHVt6S5HZnxd8m/PFfUe6dXff9Jq08L/nNXaYRXItgTGJQeKsoSTZrmtvb09p1WfMsHH58mV96EMfyrqOcDis5eVlV84cI7l/5h3jh8qN+8/tM+8Y360T+27xNwMZ34/FYvFZu9Ltu6v/+H/a/uxded4WM/vvwoULptZ14cIF7du3T3fddVfWsktLS1pcXNT27duZtcsGYod9TsaObLLN+Hfl7w/YWOuqa5cvakce62oldrS3t2ctR+woDGKHfcUcO9740QNOVzHOzP4LBoOampoidrhILrEjl98uKft1NwrjH4LS3EJuy+fiia78xQ6zSK4l6OzsVCAQkKT4/2cKVn6/n+QbgKxyvUjIp2fO5bb8E11ObwEAs3K5wVibWPsf47nVpdPpnQFsIQuvnVbk7cl137/ecs2jkpKNTcw8N5jbDTbXHYWX7fjJ5p1gQJG3J3VV0lWnN6aIzYWlf7hof/mLM7mdexdDq+uw68Gu/6zf3b/f9vL/r5YW1dTUWFpmaWlJc3Nzqq6utl/xHJBc26IGBwdTxh0wEoo+n09tbW1OVxFwjbdHurX85ojT1ZCU+1Oglfpjlpdxe3duYLN6o8c9rRH+9cnclu/8a6e3ANg6rgUDrmm987/mfkcX52+2tEwkElEkEnFli7FCiC7Navly5uvOSCQiSSotLdHaro3XggEtvHba6c3YFHJ9KP2jMfvLzi3k1nqrvb1dLS0ttpc/fKQ9pUVXLBaLH3tlZaSS1mKPJNhKrdCGh4c1Np58tp89d1aS1NTYRHINm8rymyOKXLkkSVoMW2+evvibAdck174UyK0FSSx23PIy4XBYCwsL8vl8Tm8+UFSW3xxRdHHW6WpIyr31iJ3JnogdgD25PtTLlpix4l+fzO264/z5U5a7XBV77DCTHMtk+c2RnGdd3Cwqbz2ikgprrZdOnDgR//dzg6stsIpRS0uLzn/N+nW7ob6+XvX19U5vxpZCcm2LOnr0qNNVAArm7ZHu+BPcRYfrkutFaktLi9rbrV1kAHBGYuxx2neHc4s9Tzi9AcAWsvzmSN5ix9zyDZpbumHNq4nJ8tQWshcvBq8vn0NSfqtavjziqiFBclG6s15lO60naJaWllRaWqrte+7WtvdZS64eO3a9l0Uw8L8UXrI2KcKAO352JUnnz5+3vMy1a9e0uLiourq6nFqeofBIrgFwvcXfDGjpDfu/lCs5jBuxVsVN7Sp/j7UfusQnaL/K8enZ8ePH8zboJoDi8dvqP9E7234n+cVYTNFobHW2uDTjJh378+s3KLmM2wLAmoXXTmtx4nlJ0hUbXafy2fLsufNv6UuBt2wvf/z4cf0fOdzgkxxwVvl7WrSzxX7rp7Kd9Sq1mFw7ffq0fvnLX747W6gkWbuG/6/PveLAnkrPTnIskZ1r9mJvubmVkVxbhzFbqDEOWTpMaAAUxtIbA5r/xYncV5QH/+fXB/SlgIseiQHYMG66QX7k0a/n2DXrPIl5oEAib08qMjO4+u8c12Xnod7f/t3f6WIwKCm3MZ8ko8X81owd0V99U1dD5yzPRpvv4QDe15XaLd/Ns4UGAgENuKT52PHjx1MSvNFoND7TcLZ9t1WPfdhDcm2NxBlDAeRHri3P8jneWdXvHrP8BO/AgQN5u0hId4MbjUYVjUbl8XhUWlqat20FkJt83iDbiT1uu2ECtoqF107rWtD+/UA+W8x/a2BCvT+3tszIyJxm383v1NfXq709ueVR4hiK2eKM1dn6NpWFi1qe/kHOq0mXHHOzfF735urIkSM5HYN+vz9l3LFIJBJPrllNnAKZkFxLEAgE4ok1v98ff834t/G38X7i6wDWl8+WZyXbalR+o/UuBvEnfDbGjQBQnNyU2AdQPCJvT+ZtzLPSnfXaUWftniG55VlQ/2M8aPvz/X6/jh9PTuzzUG9zGxgYyCk5NjmZv+RwroPqHzlyhK7FKBok1xIYXUGN7p6JyTaj+2cgEIi3bqNLKFB45Te2aPfHrY1/sPoEbvDdvwYlfd725x85ciSnxDoXCEDh5DOxb+cGeXDwRU3/dlqSNPb98xqbtp+so9UrUJzKdtZbbrXa2z2igYGg01Xf8jw3fkw7KiqKLr4ODAwkjffrpHTJXWCzIrmWRqakmZF46+zsVGdnJwk2bAmLvxnI26xHdlueGayOO5JvNTU1jL8AbEF2bpCf6j6QcIMctLQsAPvyed1iJ7E+OjqqV199VZJ0+ae/1g9PWqvLyMj1RHx7e3tOD+a4ZrHPc+Pvq/KWB7Rt27aCfu7AwIAOHHDHbKN2HyrPzc2poqJCjY2NTm8CUDAk19YwEzzoDgrYZ6fl2eTk5PUm6rOyPMf27Gx+B5YFUBjLb47o7ZFu28snjnuUa5dypxP7AJxhJ7F+NtCtE//H9xJeedX257e0tNDyB7bV1NTklJy1O6FFKBRSZWWlvF6v07sAKBiSa2skTmZAEs2cXbt2qbq6Omlw1PWUlJSovLzcVFknubV+5eXlKikpsVU/t4w7FItZ37+nT5/OW/P2I0eOqKOjw/by9fX1eT8+EtfnxmPP6nm7d+9eXbp0KWu5Xbt2yefzmVqvx+NReXm5PB6PK/eRwa11yyV2FEosFkupX3TxSt7GPSp7T4tqDvy9pWUmJyc1Pr46Ref2K9ul89YeDCQm9nNtffLBD34wZf9sxtgRCoWyliN2FI5TsSO6NKuVLNce0ehqzPB4PCopSR6U3+nrlnzjuiMzn8+nmZmZrOUKFTsmJyf1ta99zfb25/OhcEtLi/7+76399q1l5xgp1usON3Dz/bLbY4fV89bn82lubi5vn09yzYS1s4cym2iyz372s5JWZ17Jpry8XOXl5abKOikajTpdhbSqqqokmdvXa135+4/nrR7Vf9Bve9mSihrL9c/n91FdXa377rsvp3Vs1PEbi8VceW5YPW8/+clP6tSpU1nLWYkdHo8np+O/UDZj7CiUdMd/JJLP/Wn9/PrqV7+qkydP5uXT77rrLj3xxBM5rWO9+m+m2GEGsaNwnNp3SzPDmvv+wbytz+p1y/T0tN54443VbV+u0jsWkxNvvvlm/N8f/OAHc3qod99993HdkcU999yje+65J2u5QsWOiYmJvI551t9v/7q7psb6dXc+FEPc3SzHv1PcWD+r563Z2GEWybUEfr8/PomB0WrNeM3j8STNIIrrrl69ql27djldDRTQtvf9gaXy//zP/5ww89CspO9bWp5unZsTsWPziy3NavnNV2wvv3IlednKpqO211VaxUzBxSAUCul973tfxjLEDlhl9brl//OVv8hbYr2+vl5/9Vd/5fQu2PQ2c+z4gz+wdvwCMO/q1avavXt3XtZFci1BuuRab2+vPJ7VpuZrk2pMZrDqueeeU0tLi+69996sZcPhsBYWFuTz+ZyudlrG2DqlpaXx791N3DJ+QVlZauiIRCKKxWIqKSlRSUlJ0nv/7b/9t7w9wWtvb9d5i12zJMWb/FZXVzuwxzJz+4x/Vs/bp556ylS55557Tvv27dODDz6YtezS0pLm5uZUXV1d8IGFzSB2pLc4cyG/rU9a/7qg9V8by3JdV7rYmYvNGjseeeSRjOWIHYXjVOyI5Pl4tnru5fPc93g8eT/3c7XZYseZM2c0NTXlmtixdp/mOqlEuuOH2GFfpnsWN3Dz/XJiaz+3xTXJ+nlrNnaY5b494iC/35+2b24sFlNnZ2fSayTWUGhv9Kz+cF59979c7P74eVXcxOxRAGCW3cQ+sFXlMhlJdDG5xfr7ulKvz91+gwwY+O0AtgaSayaRTAMAoLh0d3fnddwbAObN/8K5c292dlYjI/YnNVg7GclmazEPcy5evKjLly9bbqGTy7EHoHiRXAOQ1eTkpE6fPp2xjDHjjsfjSWmenniRQesPoLjkMlvn2hn7aH0CoBBGRkZ04MABp6uBIvfNb35TX/nKV5yuBoAiQXINKEI7W46r/D0ttpcvv9HaspOTk7T+ALaoK3/PDSqA3JS/p0UlFTVOVwMAgA1Dci2Nzs5OUzOC+v3+vHYXDYfDCl4MSpL2+Pa4chBD2Hfl7w/k1AIkUfl7WhgzDQAsaGlp0fHjxy0vFw6HJUl79+51ehOAglp47bSiv/2lFsvLFS0vz2ldO1uOc90CANjUSK6t4dRsK4ODgwr0BeIX8ZLU2tqqrsNdpmZZ6fq3XRnfv//Q/fEZUMfGxzJOL97zjR5H9gGKR7pZjxInA8l0HrW02G9xB2BrGRgYyFvXrpqaGlsztjFuEraqa8GAor8Z0KKkxQJ/tpkx06LRaLxLeabhKCSlnbAMyOaxxx7T3/zN37hypmEA7kNyLUHijKBGIioTM2XMGBsfU8+zPfJ6veo63CXfHp8GBwc1PDysPb49pj7n/kP3p3397LmzkqS2traU95oam1RXV7cBexJuNDBgv9Xc2ovUdGOmMW4SsPkx0zCAQmDMNABAsSG5lsDoCtrb25u3xJkZg4ODkqSHHnpITY1NklYTX0f//KjOnjurQ4cOZW29lq6+w8PD8XWl62JaV1dX0O3EdZW3HtH2Ouv7fm5uThUVFZbHTJPERSqwRb090q3owkJeunYB2JpKd9arbGe97eUZbw0AsNmRXEujkAmncDis4eFh+Xy+eGLNcHfr3Tp77qwGXxxct2VaJmPjY5JWu5fCXUoqamy1/vCUh1RWWamSbdm7CgOAJM3/YnUyEie6duVbLjMN19Rwcw/YtaPOr50t1scsBABgqyC55rDECQzWamxq1NlzZ7UQXrC83nA4rMHBQXm93nWTa8FgUM+cekYL4QXV1dWptbWVbqIZvD3SreU3R2wvv3zZ/rIAANkaMw3YqjbTdUu6MdMYjgIA4CYk1xL4/X4FAgF1dnbmdRZQMzIltYLBoOX1Db44KGm11dp6XUqNlm3Gv8+eO5s08QGSLb85krfZPp12/vx5blKBLaj8PS05dc+yumy+ByUHYN5mum4BAMDtSK4l6O3tVWdnZzzB5vf7NzzRFJoJbch6jXHc2j7WlvKeMUnC3a13x8diM2YrPXvurHw+X9oJEOCs7u5uLSwsqLy8XOWMmwTAhp0txws6IQGDkgNbVy7n/uzsrNPVBwDAEpJraxit14z/spXNtYWbb48vp+XTCQaDCoVC8vl8aVvE+Xy+lDHc2traFF4IKxAIaGx8LG1ybWBgIO2F0p/+6Z/q05/+tEIh84lCK2XdIrK0FP+358bfl6f6TtvreqeyVdcs7oMTJ07kbVvm5uaK8jvIh6263flgdt99+tOf1vPPP2+qnJX1SqvHLuybm5uTp7xw50C+vy8nz19ih31WYsdSwm9tpnJW1ittzdjh9HVLLrOUr1XM518x191pZvfd/v37iR0utLCwoIUF68MbYRWxwz6z563Z2GEWybUERos1J2Tq+ml1HDSjS6jV1md3t96tQCBg+UR+8803deXKFe3duzdr2eXlZS0vL6uysjIfuy3votGoJMnj8aR0T1ooLdXKu//eduNHVPG7/7vT1bWtoqIi799Bpn3nBouLi/Ftd5tYLBYfT8aN48ZYPW9ffvllU+XGx8e1a9cu3XrrrVnLRiIRLS4uqqKiQqWlpU7vkhQbefwv/uI/KTL787ysq6KiQmUFjL/5Pt+c+O0gdthnJ3YsLy/r/e9/f8ZyxA5z8nHd4pYW8+mOIa477NtsseO1117Tm2++SexwEbfEjnTcvu/cfL/s9thh9bw1GzvMIrmWIDGxZnQH3eix1+purpMkzYRmUt4bHxuXJFV6zZ9YxuyjUvouodmWlSRvZfox2trb21MGlD1z5oympqZ0+fJlfehDHzL1GcvLy+uOA+e0lZXVy9DS0tKUYLuYcJFaXl7u6Da0tLTkNPPd+973vrzX3+0DCxvfrRuPvWg0qmg0Ko/H48oLOKvn7YULF0yX27dvn+66666sZZeWlrS4uKjt27dr27ZtTu+SFJliR64Wr/6DVn77w7ysa/v27aoo4Dmwffv2pL+LcVByYod9dmNHtjFBiR3m5OO6xbhBzvX4P3LkSE7XLek+n9hh32aLHcFgUFNTU8QOF8lX7NgIbo8dbr5fjsViikQikqSyMvelkqyet2Zjh1nu2yMukO7if6MYs3kODw9rbHxMTY1NklZPqp8N/0xScpIsHA4reDEob6U3bYu24eFhhcPhjBMZhMPhtO8ZLd42oqsq8uv48eNMSAAAwCa2+JsBLb1hv2vlytuTTm9CnN/v57oFALCpkVxzgba2Ng0PD+vUqVPyd/jl2+PT4OCgQqGQ7j90f1IiLHgxqJMnT6qpsUlHjx5NWZfRau3u1rvX/bxTp05JWu1uWldXp5nQjMbHxjU2Piav15syHttmsfDaaV0LZh5H73pi1aO1D4HcNCU9gK2p4qZ2lb+nxfJyxhPksp31lpft7u62XV8GJQfsW3pjQPO/yN94qwAAYOOQXHOBpsYmdR3uUqAvoJ5ne+Kvt7a26tChQ6bXEwqFNDY+Jp/Pp9bW1nXLGcm7sfGxpNfr6urUdbgrPoPoZhN5e9LRKem7u7s1MkKCDoB95e9p0c6W45aXuxYKqaKyUqU2uhjkczIVAMXjwIEDeZ2UAACAzYzkWoLe3l51dnaqs7Nzw8daW6utrU2tra0KXgxKkvb49qRNcjU1NqnnGz1p1+Hz+dZ9L1HX4S75O/zxz8r0ecifkZERLlIBAIBlJdtqVH6j9VarhlIbrVYBAIB5JNcS+P1++f3+pFlDC5lk83q98THXNtNnuVHpznrtqPOnvH599pgSZRobdNv7GDcEwNaT62QqAOwpv7FFuz9+3ulqAACAdZBcS9DZ2alAYHVMLuP/M82+4vf7C97CDflRtrM+bdeqQs28097erpYW++Mm1dfzBBooFpG3J3MaWDy66J5xy5hMBdiajhw5Ir/fb3t5O9c8AAAUE5JrgANaWlp0/Lj1cZNCoZAqKytdOTUzgPQWXjvNoOQAilpNTQ2JdQAAMiC5loBWaAAAAAAAALCC5BoAAJvYwMCA5ubmVFFRoe3btztdHQAFwmyfAAAUDsk1wKTTp09rctL+uEm5LAtgc6i4qb3gg5IfOHDA6c0GtqSF107rWjCQsUwsFnv3X56UiZRyGasRAAAUFsk1wKRAIMATYAAAYErk7Ukt/obrBgAAtgKSa8jZrl27VF1dnfD0dX0lJSUqLy83VXat1SfAfbbrGZm//gQ4FlPGOtipn1V2PqO8vFwlJSUFqV8u2+XG+pWWlsbr5zaJdXJj/ayet3v37tWlS5eyltu1a5d8Pp+p9Xo8HpWXl8vj8bhyHxnS1S3xpWyxx+028vwmdli3GWNHKBTKWq5YYkc+Py7X2HHkyBF1dHRYXi4cDqu8vFy33XabK4+x1X1D7LBqs8UOn8+nmZmZrOWKJXZY4da6cc9iXy73yxvN7bHD6nnr8/k0NzeXt88nuYacffazn5UkRSKRrGXLy8tVXl5uquxaK1df19Ib+XoCHMtYh2g0mrpEwgn6wQ9+UPX19bY/va6uztY+qKqqkmRuXzslFou5sn7GWFNurJvBrfvO6nn7yU9+UqdOncpazkrs8Hg8RXH8p48dia85+x339/frD/7gD3Jax0bV363HP7HDPjuxw4xiiR2J535p1QdVcbP15Nb15est1z/xuqW6ulr33XdfTtvjxmPM2E431o3YYZ/V2HHPPffonnvuyVquWGKHFemuO9ygGPbdZjn+neLG+lk9b83GDrNIriFnV69e1a5du5yuRkF1dHTor/7qr5yuBlDUtmLsAJBZKBTS+973voxlijF2lO6sl/curhuAjbJZYweAjXX16lXt3r07L+siuYacPffcc2ppadG9996btWw4HNbCwoJ8Pp/lzykpKYn/u3RnvXbU+W3XuXRnvcrKUg//lZWV1fdLS+VZM7Jw4t8lJSVpl99ooVBIlZWV8nq9Bf/sbCKRiGKxmEpKSpK+K7cwmvxWV1c7XZUU0WhU0WhUHo8n3o3ETayet0899ZSpcs8995z27dunBx98MGvZpaUlzc3Nqbq6Wtu2bXN6l6TIFDsSzwePx+NI7DCUlpY6+vnpEDvs26yx45FHHslYrlhih5lzP1PsyFU+rlu47rCP2GGf1dhx5swZTU1NbZrYYcZGxo58IHbYl8v98kZLbO3ntutJyfp5azZ2mOW+PQKYULazXjtbjjtdDQBbQOTtyayz9hkXGqWlJZKSL3Kji7M5ff7IyIhmZ3NbBwAAAICNQ3INAIAMFl47rflfnHDs87u7u5mpGNiCTp8+rUAgYHv5kZERpzcBAIAtg+SaSYFAIH6B4/f75ffb75IIZ0xOTmpy0lzrk5KSkpQm1rQcAQAAhTI5OUliHQCAIkFyLUFnZ6cCgYD8fr96e3vjrwcCAXV2dib9vbYM3O/06dM6ccK51icAAAAAAGDzIbmWwGiZtjZplphY8/v98VZsRpItX8LhsIIXg5KkPb49BRnEMBgMKrwQliQ1NTZt+OcBQDGruKlduz9+PuX1Qg0sfOzYMR0/bn28STcPLAwgu/r6+pyuOdvb253eBAAANjWSa1kkjnURi8XiryW2csuHwcFBBfoCCofD8ddaW1vVdbjL1M3QyZMnNTY+lva9+w/dn1LPcDisU6dOJS3j9XrVdbhLra2thdvBAAAAyKi+vt5WYh0AABQGybU11iahEsdZW69MrsbGx9TzbE88ueXb49Pg4KCGh4e1x7fH0ufdf+j+lNcamxpTXjMSa62trWpra1NoJqRAX0A9z/aorq7OlVP/5lN7e7vOn3eu9QkAAHC35TdH9PZIt+3ls80yDAAANg+Sa1mkS67l2+DgoCTpoYceinfNbGps0tE/P6qz587q0KFDprvymKlnMBjU2PiYmhqb9IWHvrD64rv5t55nezQ4OMiEDQAAYEuLLs5q8TdMKAAAALIrcboCbrPelOcblWwKh8MaHh6Wz+dLGfPs7ta7JUmDLw7m9TOHh4clKaX7p/F3vj8PAAAAAABgs6LlWgJjsgJjAoP1Wq2tl4CzI3ECg7Uamxp19txZLYQXTK8vEAgoGAzKt8enupvr1NramtLqLRhc/UzfnuSun16vV02NTeuO3QYAsG5yclKTk/a7h83Ozjq9CQAkVf3uMdvLlu6sd7r6AABgA5FcS9Db2yuPx5OSPNvI5Jqhrq5u3feMZJgZZ8+dXf3H+PW/v/DQF9KuP9PsoEa3UQBAbk6fPq0TJ044XQ0AOdrZwoQCAAAgPZJra/T29ioQCMRnAjX+S5TPcdhCM6G81NuYmMDo2hkKhdQb6NXw8LCeOfWMTv71yXjZmdBMoXYnAADAljQwMKCBgcxjtkWjUUlSSUnqSC0jIyNObwIAADCJ5Noa6ZJpa8Visbx93tqumXa1tbUlr9fnU9fhLgWDQYVCIQWDwXjrtT2+PQqFrCf1BgYGdODAgZTX//RP/1Sf/vSnLa3TzudHtmgnGQAAZR9JREFUF653j11aWrK8joUcl3eLhYWFpG2BNcX6vbuB2X336U9/Ws8//7ypclbWK0lzc3MF3+58xp5cLSws2D6GiR25IXbYZyV2LC0tmSpnZb2SvdgRW7OM1WPgO9/5jr7yla9Y/tx0nLxuIXbkhthhn9l9t3//flfFDqwiduSG2GGf2fPWbOwwi+SaS2Tq+pmpy2gmXq9XTU1NGhwcVCgUSllPpq6fVrqEvvnmm7py5Yr27t2btezy8rKWl5dVWVlpeXsWy8u1+O6/S0tLLa+jvLw8/u/1ljeeIHs8Hnk8Hst13GgLCwsqLy9P2ha3cPu+W1xcPXoqKiqcrkqKWCwWT9qna73gNKvn7csvv2yq3Pj4uHbt2qVbb701a9lIJKLFxUVVVFSotLS0oNtvJvZkOv4Tz9f77rtP3/3udwtaf4nYkQtih312Ysfy8rLe//73ZyxXqNixUlGhxNvCXK47cmXnuicfiB32ETvssxo7XnvtNb355puuiR2F4Pbjn9hhXy73yxvN7bHD6nlrNnaYtWWTa0bXT2m1K+ja18ww08otm7qb6ySl76o5PrY6cFql1/6JFQ6HU9ZRV1ensfGx1S6pjcllx8bHUiZAMLS3t6e02jtz5oympqZ0+fJlfehDHzJVn8Vf/CctvvOq5W1Zefv6gOClpaXr1nM9a5Nr6ZZfWVmJv+/GYGv8UFnd9kKIRCKKxWIqKSlxZbA1vls37rtoNKpoNCqPx+PKC7hwOKzl5WXT++7ChQumy+3bt0933XVX1rJLS0taXFzU9u3btW3bNkv1X/zNgK78/QFLy6zHTuwwE3s2GrHDPmKHfXZjR3t7e9ZyBYkd27cnJddyue6oqalRS0tLShnjuirbNUdLSwuxYw1ih32bLXYEg0FNTU25JnYUAvcs9rk9dlg9/gspFospEolIksrK3JdKsnremo0dZrlvjxRIYiLNGF/NanJNyn3cNa/Xq9bWVg0PDye1JAuHw/rZ8M8kSW0fa4uXD4fDCl4MylvpjbdEC4fDaU++YDCo8fHVBF3ibKStra0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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPDF(countsplotprefix + '_cumulmutcounts.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average mutation frequencies\n",
    "\n",
    "The `summary_codonntchanges.pdf` plot shows per-codon frequency across the entire gene of codon mutations that change different numbers of nucleotides (e.g., `ATG` to `AAG` changes 1 nucleotide, `ATG` to `AAC` changes 2 nucleotides, and `ATG` to `CAC` changes 3 nucleotides).\n",
    "Since we are dealing with codon-mutant libraries in this experiments, we see all three types of mutations in the mutagenized plasmid DNA and virus samples.\n",
    "The \"mutations\" in the wildtype controls are sequencing errors, and those are almost all single-nucleotide changes since it is very rare for two nucleotides in the same codon to both experience a sequencing error.\n",
    "\n",
    "The `summary_codonmuttypes.pdf` plot shows the average per-codon frequency of nonsynonymous, synonymous, and stop codon mutations across the entire gene.\n",
    "As expected, we see some selection against stop codons and moderate selection against nonsynonymous mutations in the post-selection samples relative to plasmid DNA samples, indicating purifying selection against deleterious changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QVFCi4MBOCEVBxXkl7IRU0MVhsmp23DAITgTFb+yyAQYDNgTVNBwOVGNENQpxXLmmnFjwcM+7brwaSX4Dn2na5isginoSYHhBH467wwuFn2pBHb6vg2NBhceCyv/r6L0yve3v7EaHN8v9pY3ZkAKoBqopw3HVyLl6adPc8u8kOdqQWw8+TlbcV7ECol44Pggq3N9JvVRQ7JDxfqy7IChFebcSrl3aFKqBakpwXDVyrm5DuWJYQlDl1bGSjPyfA0Hpw7kpQcUyQe1+ngoqriUoNppxGAgvDi9Uizvj2jYE1UA1JTiuGjkNPOihYq3gUyAofTg3KagwPxqdHwnPTww6udlaPGxBUHHu5fFAeOEwVbn+gx6gGqjmBMdVI4f8QQ9xTLKslgFB6cO5MUHl56Pm5uFkc0RLBHUYs46Lhy0XFJ8WsNvKD3u0+CKoNrpA/aAHqKbRcKAaI6qRQz5hm0PzWGMISh/OTQmKRRGLZNr/S3S6YrF5t/IutxAv2u0TFQ+bE9R+j+yYuybZYXdLH0Ixr6hS+NQTtqGaRsOBaoyoRg5sSAEE1bCgRBi7tI520hL5lVtzl7/PGuVTrnwhw16M+XEEPuB90Fe+9oke2JAcqAaqqQvNvaFs6cPl88cLQFD6cFwVlIwoisq3RnX3KeegGF53q7BnVP1AJPeGoBpT4UA1RlQjh+hqKN6/ounScSAofTi3JqgrcyjUGFw0kEBzNQTVGAoHqqkFlWrkkNhQLjbKjh0EpQ8HgqpBFIX56T8XHInChqAaY+FANTWgU40c2JACCOqmBJWb8BPAhk6AaqCaEghVI4fYhiIIqtlwIKjqFJ/QddEEaVobgmoaDgeqqQ6hauRQzZTLQiZ4AtIBCEofDgRlBKKZcuIFVNN0OFCNZdAsXw2z6kIh6TUbBKUPB4IyAsnyVajGVDhQjWXQTNiO9ieecMoPBFUhHCcF9VmfraHCMcxCMmEbqjEVDlRjGVQ15Xif7tI6q0dAUPpwnBTUH9K9W4VjmIWophxUYyYcqMYyGihtejYQlD4cCMoI1y9tejZQjT4cqMYyqGwoIivNeACC0ocDQRmByIagGjPhQDWWQXlvKCCeVg5B6cOBoIxAeG8ogGoaDweqsQyqmnLhyZPQLweC0ocDQRmBqKYcVGMmHKjGMuhqysXsNGAhXsPhQFBGIKspB9WYCAeqsQy6KgqxeNoFBNVoOBCUEciqKEA1JsKBaiwDNqQAgnJMUJ3uQ40W3d657wMbUgDVOKYaC4ANKYCgzAqqH69FdFXplcWZHat3d9yCn5zzgA3JgWpgQ3UhsqEoExRutjYczk0Lqheu11Q2lP0qcyOOcRuCagyFc9OqcRG60qZMUBFpXRIISh/OTQvqbpn+fbQ2lMewDUE1xsK5adW4COWEbV6ZhDA2CEofzq0LqmhD3V7/Mc2xZbC/ySPu7/TWYbjZ7rxmud2kfzj/9QPLyW16AdRZh4/dtLHY7SE9JY8PwobYvuu6NkA4YRuqaT6cm1ZNTiRBlL7a9NjLXvcuZkkfMH2kjTdsmqboh0XdTqqLfm8TrjtBsNMPi/2h23lapwcICm3TrWnbdOtDevQtfx8uoo54d/Z/LjOmsnBdqRtJVEUhzs4E6ZpwCEofzk0LKji2oXAddntd/kdlVzf89TbVRG+7yTYuH9NGqU5SIdxtePvNMuh3w22v1xEt0s3bXneT7bvupSJ6qhB2DpoqClCNoXBuWjU5kaTX2ds0ucWfs173tkwKadp1d3IRzhHesfHvDRMNi/gxZC3Zy356LHaAXqFtunWzTdv20uM8hlt+PvnO/JSIvh3bm6msF1cacqAr5hOiSKOBcG5aUMGJDXG76DKHydlQJ3xkXbJltlHs0mdf7lv+citk1BfH67MN7DAx2zfiZ2i52VQIOwdZMR+oxkQ4N62anEjWbLQ37Zbxf8f8D09/Fz5mLe/4H8r/n9rQMhNNJ+wuuajYv9lRuDpybcXWvjgF65Bt3dylFz8bXhFkb0P9GuPpNPeGrvNIPghKH85NCyo4tSGxrV+woXh/a4VvXIvduyHrjzF/ugvXRRsSptNn+25zm2tAcm8IqjEVzk2r5iCSB2E4HWFDLBhuDeFmFxe7thHXOZnOcnJiMuoLU+mGhbbZ1pCLa5ta0pPYeXtsQ493Ff4sDvFDwEmBoPTh3LSgghMb4jke5a5u2JYu14No3Wcq6zJY3yzr97GXeRvK5iaIfXnjtQEbgmqMhXPTqjmIJLseeRAuwl4uxZVPuN5Gu0aiU5bJg104ZXKKw2B3AP7LQ9ts6+Pe7nrCuJ6KNsTO4GNPt5BPABtS4ICgPudzC7zlrW8pbvi8wGFBBeU21D+xoUNrZjLrnmDvN2uVDWWNK3fcOLAhOQ6oxgMb6h9s6O5gQ+KX/e06FAPZ6016rdQL9vLohvuW2yMbOrTNtnZzNsR37hzZUNCJNyG/d6QFNqTAAUG97e1q3hE4LKhAa0NLMYrQ37fuF7IxzLqAmyMbWrOXd8KG6l0GZcCG5DigGm9sKLswCoo2lHL3yKcc9MIOH2wr2tD+wqhgQ/u2JTaUNQuKNsTabiplOV1pU3qcFdRDv8A731X8d7+k5w0bqmlDT1knjk9RiPet+ywb99PeYq61J9Yji3KKijNZddnvzkpdstKm9EA1+nA8saFgw20jLrGhTA934aPIwpwNcbkED6yvVrChfdsTG+JtgyW/Kbthc/GWm1zPsMKfRjRTLiaf78NwVlBVMpgoHCob+vwi73730YYvqHh2aJev9tjE097hdOUUdhduOv0e/8ZYpvKI+p1uppeHTbjt96Mtn9OzeWKNHvg8hf2E7X5h327U7/dMzJSDago0qBpfbKgXrqN+zE0iZ0PrXiqPx5DftdlmS6dzNpR6SK/f2bDOXMGG9m1PbIjdYWVHjHmjuN95ZH96//Gpnx6nkrKoqijsQVmSahncK/LqVx9tuKsWDpUNaQ7z9vdUPDu0NeWK/ywoLEiFkrqJmG/N/8L9dDi+bE4suos27BdcRE9sTd5u0JptzvbdnpO2RFUUoJocDarGFxsSf2j3LijaEJcHXwaUXhStszOytyG+KjXtvwXHNrRre2pDS57LfJks12KXXyNtuCorzVGADSm4pqCIMriKDX3hewv84S8q/vu9XxxYakNqlv3+boZccNfv59M9/efu/BV/UbZv+vrwj4rAhuQ4oJqbtqECy7LBzJw8OqWLex76ZXdMy9vuD7nc7/xwOEy1iXJky1evAQSlD6eKDb1D0+ZtgZM2ZBKi5avXAKrRh+OPDWlYrsPKU0TrtK3LpTYUXWcNHgeC0ocDGzLChTYE1cCG8hhSzUO3egH7Om3rc6kNxftq9fRAUPpwYENGuNCGoBrYUB5jNlT9yY912tYHNqTAAUHBhowAG5LjgGpgQ5ZxGzbU4FxPqnCIMhg2ZISbsCF/VQMbsoyL7w2FYczWP+wx8gAvfwUFGzLCpfeGoJrzwiFSDWzIMi6eKRcfnQvKqacvVuVNmg/opbTNSxpeVfndSMLR5dSbq73V+zT28f60zfs1bd6XttHZ0JdU/dtfV+GDdV9Ql86Ug2qMquYlzWHe1Gw4vqhGzuUTtqPr9euQwVpgQ0a4eMI2VGNSNZaF44tq5BAtX71KbM+87pNef8IbXnjD6UbtBXTaRvchvvH0sC988uk27fV8s+F8qcY+vixt82WaNl+attHZ0B+pFs7rP/XjK3yw7guKZvnqVUKDapwLxxfVyCGxoSstg6Ad5dZ9iG4OKzt5b+izPlvDVdKJEgobgmqMqcaycHxRjZzbqKLgbwY7aUPucxNVFPxVjWXh+KIaObAhtzMYNmQE2JDTqrEsHF9UIwc25HYGw4aMABtyWjWWheOLauTYb0OWpYxl4ThpQ3/0j2loKMHOx3obsixNEY4qHF9UIwc25HYGO2lDf1z3bg0l2PnAhpxWjWXh+KIaObAhtzMYNmQE2JDTqrEsHF9UIwc25HYGw4aMABtyWjWWheOLauTAhtzOYNiQEWBDTqvGsnB8UY0c2JDbGQwbMgJsyGnVWBaOL6qRQ2ZDV6sVbFnKWBYObMgIVDYE1SCcwBvVyKGxoehw5ilrBUNQ2nBgQ0YgsSGoBuEIPFGNHJqacrkzD0HBhnRUFtSy3+9XON6eTvehRosLnmpMUlMOqkE4AtgQhQ2lMiIcVdgDQenDuWUbuuNP5dnUMIteKHWtfu/uuAX/rM6DwoagGoSTcR0b6pzfzWoaIhu6SmwQlD6cW7ahftjtRb1NWF1NChvKfpW5Ece4DZ399iqgGufCuZINdd159ANsyO0MvmUbuuPn4CFcV84YvQ3lgQ35qhrLwoENwYbczuBbtqGMzSG7ur3+YxjGy2B/k0fc3+mtw3Cz3XnNcrtJPwf+6wc2rrdNL4A66/CxmzYWuz2kn9Djg7Ahtu+67ugYbMhp1VgWDq1qojShw8co6G3CbrfbSTc8sqFtJppel2d+rfutjUA0ReEqT/CCoPTh3L4N3eWuhsJ12O11+TnOrm74622qst52k21cPqaNUg2mfnO34e03y6DfDbe9Xke0SDdve91Ntu+6lzrRU73UpJmiANUgHA6lah6YGHqPPdbzSl/02VSYbZrh4m/aPPa2YaibyNM4NBO24zC+gqQgKH04t29Dce7eUMjtostklLOhTvjIunrLbGOP79BnX/Jb/pL/P2vPf2z5YWK2b8Q/sOVmUy81SSZsQzUIR0Cpml64u7TPBuXWbEPaN+NbuqlKnq7T/7kEEhuKc2ceU09hQzrq2FDmMQIxjpWZzd6G4r3w+Ma1UB8T4SZk+/LrqbwNCdPps323uc01oLAhqAbhZNDa0DYTjLChh/CR/egIG+JKWVt30wg25HYG37oNFVwom1QQ5a5u2JZuuGvCN4abLoPdUhIK5HeX8jaUzU0Q+/LGa9iQT6qxLBxS1SzZjdGnu2BnQ30xmvAgbIjPE7Vv7gJqyrmdwTduQ51wkx/HFv7RP7Ghw4fDTGbdE+z9Zq2yoazxnS7oAqgp57RqLAuHVjXLJxZ658iG7oQN8Q4bbKgOEJQ+nNu2oSMXOrWhpRiU250sYTIHkYmXy3BzZEN80sOdsKGzpg3BhpxWjWXh0A9lP7EU39lQNoQQ7LP98XZtiLhAIwOC0odz0zZ07EJ5G3riZ7EnpijsxrS4ycSHaW8x6xSmTbfZSN6+RX+375m3aylLm0I1vodzhYk9j4cBgA0fiYuFDaVKSPXzSJtyl0NkQ7txbtIpGBCUPpxbtqGHNJ+KA2Y5G7oLN51+j5/v5WMYR/1ONxPewybc9vvRtstabp5Yowc+T2E/Ybtf2Lcb9fs9EzPloBqEIyCdohB30nxmOdVhCxf66R+7jvoxHwFI/6Zev7MJr1FE6iJobOhw4ikVBUHpw7llG+qfnIucDQWpmlI3EfOt+QnfT4d74P/kteiiDfsF3/+J3brNWrB9w2zf7Tl5S2JDUA3CEVCqpiP+AnYTaCvC5H9tV0xaEJlPmG40UM2Ui3cv8OQU2JAOmlrBy35/P4nurt/PD9/dHSpzF39Rti8r470M6kE0Uw6qQTgMWtWk+VyccLPcbeiGhcy3B6JiPjsZxZh62mw4HtuQSWiK+UA1CIfTlGrsmyKXQV1TDoJqNhzYkBGIa8pBNV6HAxuCDbmdwbAhI8CGnFaNZeE0ppre+TXlrwuxDZGWa4Sg9OHAhoxAa0NQjd/h+KIaOVRTFMQwd4SyJIThsHvned71zqMNAWzIEERTFKAaM6qx7Oz4oho5ZBO22UI8duYJY/NdUH1dmwA2ZAiqCdtQjRHVWHZ2fFGNHBobinbnvaxXNxgOpRvaw2FL+j4QlIYANmQIEhuCakypxrKz44tq5FAV8+GdurK6JO1RkiTjSfmG6Th9PWMfTGu+SpJRcVcISkMAGzIEUTEfqMaMaiw7O76oRs7VS5uOktlwOh63yjYMk/F0OEsi8XIFQRXDgQ1Zy7VLm0I1V1WNZWfHF9XIubYNDbhKpklUtmGWsHGGRZJ+Mu1WqikIqhDOzdrQn/iTGurnZMNc2YagmuuqxrKz44tq5FxqQ1F8xNEIQ5RM0/+3k3HJhnayEFvu+W8gqKNwbtaG3OdCG4JqzKrGsrPji2rkXGpD8fGZP7rdOuJdt/RHyYZMQcOs0wdBHYUDG7KWC20IqjGrGsvOji+qkXN9G8p+DE83ZEraCQmCOgoHNmQtV7eh7AdUcxXVWHZ2fFGNHKp1Q3xYITqt0bg4FlRuwxSCUoZzszb0p/60hvo52TBE64agGjOqsezs+KIaOURVFOL9q6NRbvTrzg8HNmQtNFUUoBpDqrHs7PiiGjnXLm165ih3p5vygU/78na7/RrNR/0VaZuv0LR5TdpGlzLvbJ/QeuF0W4PhvFPXJj3MV2rs46vSNl+lafOVaRudDf2Zamen/XEvr5BA7gvqyqVNoZrrqsays+OLauRc24bOnPMjuivvaqW8VvNRf2La5hM1bV6bttGlDH8zPQ2G8y5dm/QwX62xj69J23yNps1Xp210NvRnq52dVuvZCgnkvqCubENQzXVVY9nZ8UU1cq5dYVsseLgXi+2GRxv4Cog2XwERYHjhJBwMylnLlStsQzXXVY1lZ8cX1cihqrAtXpTUCs4t/06Sow259eBBFM2TRVQYI4egNASwIUMQVdgWL6Aa2FCFBHJfNXJIbIjVaIyzYsHHv8sVwxKCKq+Olf6Sk98VgtIQwIYMQWFDUI0x1Vh2dnxRjRziCtthSZlG1Ao+MxzYEHt2zF2NzOx0H2q06PZqHLoIbYVtqAY2pAc2dLRPyTZpreALgKA0BDduQ2KV53ZZOWN6ZZ+ioN+7O24Rnv9I5GtX2L4AqEavGsvODmzo6hW2LwCC0hDcuA1tt73+02P4WDljFDaU/SpzI455G7oGUI1eNZadHdgQbKhmylgmqJu2Ic5yHVa+HNLbUB7YkK+qsezswIbIbKikTvClQFAaAh9sKD2b+9s53V7/MQxjZkvZTR5xf6e3DsPNduc1y+0m/Rz4rx/YqNc2vQDqrMPHbtpY7PaQfkKPD8KG2L7ruplLZUNQjRHVWHZ2aFUTpQkdPkZBT0jgjkkkJ5wgSl9teuxlr3sXMyGk27odkY9d1pYdYbtcxicqKqiut2Fnqs6tWzlENrSr1RjXP5ocCEpD4IMN9cP1/nW4Dru9Lj/H2dUNf71NZdXbbrKNy8e0UaqRVDB3G95+swz63XDb63VEi3TzttfdZPuue6kTPdVLTRobgmoMqcays0Oqmgcmht5jL4jCrTj1nYJwotRR0oQXf+J63dsyeSyFyPpsl7Rt3HsM4/R3Md98UFFedU9MOL11v0LkeqhKm+6gVBQEpSG4eRvqdh/D3Ny2kNsFvzzKCaITPrK+3U4lPX5++ywVt/wl/3/Wnv/Y8sPEbN+If2DLzaZeahKVNoVqzKjGsrNDqpreft7lmhmH+H9OOGv2+7Srxv8d8x2emCRYrPz/vO1y10nrl6mI/a5bfaxcD9Xy1Xj3gnCIAYLSENy+DT2G4TpvQ+Ij6BcEcagMyjeuxSqcbvpjw6Vyx/p0eRsSptNn+25zm2tAtHwVqjGjGsvODrEN7aaW8usgcU10EM6DmPHTETbEAuyzP4VfB4lrItG2y3OywyzpVEXChmqOIKggKuazk1FM2bGDoDQEN29DAVPCZt/tEndzImm/jG8MN6zCZ3eTnp5skh17mbehbG6C2Jc3XhuwIajGmGosOzukqlmyG6NP7JbNkqV/zO+sHoTDTYcN3QWip8aasV8+piJ64s6S3TI9WNSpiliT9Dxv4g7RFdG1S5teAgSlIfDBhsSgQZZdXCH9Exs6NGUmw8asGXu/WatsKGtc71brlUubXgJUo1eNZWeHVjXLJxY6m3KwDR/uRK4fhJPZ0N3BhsQv2XWPGMU7taETFfEtdz0210G3XrwasKGaKWOZoBq0obtekde+5mgDOz1XsaF9tYMTG1qKQbn+vmm/mI1Zd29zZEP8fuydsKGz7rHChpxWTYPhmFHNE0/xfrjlA3NFG8oujIKiDS03676Y03BqQ8cqWu6WOiy3YpeLuXaF7UuAoDQEjdpQlXBohxfEj8fDnZOcDT3xs9gTUxTi/YfTZ660v3qKuQifmFYiYWZZi/5u36fzEvbKFbYvAarRp6ll4Vyh88anIKSXN2sx9yYnnE3ILvzjYxtKL50excqIExvKqSinOs7ekC6EaoqC+KaISCf9QFD6DL5lG+pvek9Rb52ropBT01246fR7/HwvH8M46ne6mXYeNuG234+2fPx688QaPfA7rPsJ2/3Cvt2o3+8ZmCkH1RhTjWXh0E5RiDtpPouM6oTZSEJhGGEd9cVM7IIN3YWZ0E5sqKCivXLibaqbmGieAtmE7axUcFj/cFIgKH0G37INPaz5YXM15XJqCjqbMNWEGFDgJ3w/keeB/3PDGkVsiZ1Y2/DEbt1mLdi+Ybbvlr9JTR+gmrAN1RhRjWXhkKqmI/4CLpplGIp7nnnh8D+eLzst2FB6AdXJ/TNnQ3kVHVS33Z8FAogrbGMhHmxIT+Xhhbt+vy+di7PM/S5tmL9XyvbLXhZ/UbbvUvUmEmgrbEM1sCE9lVVzKEt/VzpmtiwvW7/eyFVwUNFBOefoRgYqbOdShp3YPO9659GGZsOBDVkLKmw7rRrLwrmaauLsAqcKT0SzDc4CpU3dzuCv/To1HwxgQ1cApU3rpSnCUYVzJdV0axSnZ8V6aMrDnQVsyO0M/npNan5DUM2GvrHIN33T0YY/BxvKAxuql6YIRxXOtWyoW/1RXb1uTLMC6DyIZsrtRrfdLkviYAZXsaFv/lCBb/nW4r8/9G1pG12KfztsKA/NTDmoBuH4pBo5WDfkdgZXsaFv0LT5+gA2VBOsG6qXpghHFY4vqpGDKgpuZzBsyAioolAvTRGOKhxfVCOHxobispcXA0Hpw4ENGYHEhqAahOOVauTgasjtDIYNGQFXQ/XSFOGowvFFNXLwoAe3Mxg2ZAQ86KFemiIcVTi+qEYOeU05zPmBDelwX1DUNeWgGp/D8UU1cugeAi6qYzn9OGMHM9hJG/rzf0EDaYpfA7KHgEM1CMcb1cghqikXZ+fU7epYDmawkzbkPjQ15aAahOOTauRQVVFgkopdr47lYAbDhoxAVEUBqkE47DPzRDVyUMzH7QyGDRkBxXzqpSnCUYXji2rkwIbczmDYkBFgQ/XSFOGowvFFNXLobCimfHYXB4LShwMbMgKZDUE1CMcb1ciBDbmdwbAhI8CG6qUpwlGF44tq5MCG3M5g2JARYEP10hThqMLxRTVyYENuZzBsyAiwoXppinBU4fiiGjmwIbczGDZkBNhQvTRFOKpwfFGNHNiQ2xkMGzICbKhemiIcVTi+qEYOJmy7ncGwISNgwna9NEU4qnB8UY0cmtKmpNVI9lQX1F/8jgJ/6S8X//0dfyWwLoOPIvzO7zza8FdhQxZDUtrUtGr8/d63LBxfVCOH+nlDlFQX1DveruZtAVnKvE3zVu9I2/y171Lz19M2msO8/T2wIYshft4QJbAh58LxRTVyYEM1U6aKDVUJBzbkMrCheqpBOKpwfFGNHKttaND+8Ic//N2aj/FNaZv3a77Tvydto0uH7/3wh79X1yY9zPdo3ur9FcPR2dDfqBbO92ns4/vTNt+vafN9aRudDf3NauF8+Mv/VuMpbAKbbaiyat6kafPddKpBOKpwfFGNHKLH3l1lmPuZFxkv6VImbaP73n9f2kaXDm9+8cU369qkh3mfzoYqhqOzoS+pFs4P6GwobaOzoR9I2+hs6G9XC+fFF1/XfA4bgOaxd4ZVo/uifYlONZaF83f+rpq/RxTO34dqqkEzU47Vq6ctV894ZvLCRz7yEW3PJW2j+97/wbSNLh3e+JGPvFHXJj3MD+psqGI4Ohv6B9XC+SGNffxw2uaHNW1+KG2js6F/WC2cj/yjf2wiixuHZKacadVo+/t0qrEsnCoirhIOkYh9UY0cqoeA7yHs4eHekD4c3BsyAtFDwM2qxt+bMVVE/HmfW+BH3lr89+d+TkAmYl9UIwc2dIUMhg3dOrCheqqxLBzLROyLauTcxvJV2BBsqFGwfLWeaiwLxzIR+6IaOf7YkGYtz3f9qJMZDBsyAmwoAzYEGyKAyoaimH5ROK0NEaWMZRkMGzICkQ2ZVQ1syBIR+6IaOTQ2FO1OKOlaCCGozyneGXzLW99S3PB5AWwINtQwJDZ0TdVQfe8f3Zf/sR872vDjdDb0Tz6/wLv/afHfn//PAtjQrUJjQ+nZjPkJJV0LIQRlWcpYFg5syAgkNnRN1VTpvFWxIaI0pRrSOHKmd7/7aMMXOCliX1Qjh275Ki9ZH8GGmg0HNmQEsuWrJlXjoA1VCOdHdTeBK54d2FCD0BXzEU9OgQ01Gw5syAhkxXxMqqZKXXqiNG3Qht6ja1Px7MCGGgQ2VDNlLAsHNmSEm7ChBtP0i99b4Ce+6CeKG76QLhzYkIPAhmqmjGXhwIaMABuyVjWwIQchsqEoExSmKMCGfIDGhqCaK4QDG3IQqmI+QlARlxYVvgsKNmQtRMV8oBr6cGBDDkI5YZsVyaJcjee7oGBD1kI4Yfs6qvlJTXr9VECTpv+cTjU//TMF/sXPFv/9Mz9XIRzYkJsQVVHYlWkkXRMOG4INWQpNFYUrqqZKXlRJU6K8qKKaKmn6dRr+JWzISeiK+YTkT0+BDcGGLIWsmM+1VFMlLz6o+U7/2oAsL75W81YfDMjSFDbkIPaXNrUsZSwLBzZkBOtLm1qWFw2GU8WG/tU3Fvj5f/3zxQ3/JiAbsvzxo+pHb/2Row3sM/NENXJgQzW/9y0Lx7KvG18EBRuqlxeW2VCVcP7tLxT4xV8q/vsX/h1dOL6oRg5sCDYEG6oNbOiQF//+PxT5j//paEOz4fznI7v4pV882tBsOLChKlDZkLjbSlu1HjYEG7IUIhu6nmos+6JtMJxv17Wx7+z4oho5NDZ0eJ4x5e3W6jb0U5o2PxmQfe//XHEW6S//7C8XN/x00OhMWNiQEUhs6JqqseyLFjYEG1JBYkPswSkxI7zCk1Oo5noS2VCVDP7mDxX4lm8t/vtD3xa4OOgOGzpAYUNXVY1lX7SwIdiQCqJiPvHJKwKctaHbvPcLGzpAU8zniqqx7IsWNgQbUkFX2pRj6MkpsCFLwvFFUGSlTTlXUI1lX7SwIdiQChobisteXgxsyLlwfBEUiQ1dUzWWfdHChmBDKoivhiy2oQp1QBz83rcsHF8ERXs15LYN/Zf/WuS//fejDc2GAxtyELqHgGevyGsFW/ZFi3BgQ4QPAc9euaway773HQzHF9XIIZuwHccBm/NDXyvYsi9ahAMbopuwfQuqcfB737JwfFGNHKrnDRWh6dzBhpwLxxdBET1v6CZU4+D3vmXh+KIaObChXMo4+L1vWTi+CAo2dMgLB7/3LQvHF9XIsb+mnGVftAgHNoSacvm8cPB737JwfFGNHNhQLmUc/N63LBxfBAUbOuSFg9/7loXji2rkwIZyKePg975l4fgiKNjQIS8c/N63LBxfVCMHNpRLGQe/9y0LxxdBwYYOeeHg975l4fiiGjn22NBgODzaAhtyLhxfBGWNDVmgGge/9y0LxxfVyLHFhtqjJEnGk8I22JBz4fgiKEtsyArVOPi9b1k4vqhGji02NEpmw+l43Mpvgw05F44vgrLEhqxQjYPf+5aF44tq5FhiQ4NklP5/mhSWTsCGnAvHF0HZYUN2qMbB733LwvFFNXIssaEomab/byfj/EbYkHPh+CIoO2zIDtU4+L1vWTi+qEaOJTY0SobiR34jbMi5cHwRlB02ZIdqHPzetywcX1Qjxxobyn7k5/3AhpwLxxdB2WJD2Q+jqnHwe9+ycHxRjRxLbGhhhaAc/N63LBxfBGWHDdmhGge/9y0LxxfVyLHEhsr7dc/+j+eee+5XNB/jr6ZtflXT5lfSNrp0+LXnnvufui9ahKMJ57mP/V9m8rhh7LAhO1Tza7o2CEcTji+qkWONDRVHuXv8e+1drZRf/w01v5m2+U1Nm19P22ia/MZvtVr/+/9oaDac//taDfaF02o9azCZm8MWG7JBNb+la4NwNOH4oho5ltjQ8ZyfTjflA/+P9euO+O3Wbz+n43da2ibPLZf6Nq3f0TZBOEU86dfZYUNQzY2E44lq5FhiQ2IFxH3ZCogjnms9pz1au6VtEjz/vL5Nq61tgnCKeDLKbYcNQTU3Eo4nqpFjiQ0p1oMfYTxlEI4qHE8EZYcNQTU3Eo4nqpFjiw3Jq2MdYTxlEI4qHE8EZYkNQTW3EY4nqpFjiw3JawUfYTxlEI4qHE8EZYkNQTW3EY4nqpFjjw2dAkG5F44ngrLGhk6BatwLxxPVyIENKXAhgy0LxxNBwYbkuJCmloXjiWrkwIYUuJDBloXjiaBgQ3JcSFPLwvFENXJgQwpcyGDLwvFEULAhOS6kqWXheKIaObAhBS5ksGXheCIo2JAcF9LUsnA8UY0c2JACFzLYsnA8ERRsSI4LaWpZOJ6oRo7dNlTCs61nn9Hxipa2yTO/+7v6Nq1XaJsgnGMaTV9TWG1DduYFwlGF02j22odzNvRM+AHtR/2BUJ8OIVUbhFOk0fQ1hWs2ZEFeIBxVm0az1z5stqFywq62STescBiqNgjHQyy2oXIsywuEA/LAhi5tg3A8BDZ0YRuEA3LAhi5tg3A8BDZ0YRuEA3LAhi5tg3A8BDZ0YRuEA3K4Z0PdnrZJT59VQZeqDcLxEOdsyLK8QDggj3s2BIBxnLMhACwGNgRAbWBDANABGwKgNrAhAOiADQFQG9gQAHTAhspIItMRIByrgQ2VYFleIBx3cNCGplOKo7SGQ3mxwjopg3A8xD0bsiwvEA7I4aANjUaaBq1otBpJ82oStYOgPUpSRrLiu3VSBuF4iHs2ZFleIByQwx0bakUZi0UUSRIiSljDMUuHZC45zmqR/m+UjEezRbKStKmQMgjHZ5yxIcvyAuGAMtyxoWGSQ9J54SkzS0bD9mScTMrbJLMgGIhOy1zaZhQdQDj1w7l1nLEhy/IC4fisGjku2dB4OmSsVsPhoLwNT5lkIZpLui6sV3KfDNnLtqyHks/OBOHUD+fWcciGrMoLhOOzauS4Y0PBZDy+Zz8V47gsZYZZHowlzcbpVfNUlzIVei4Ix+N+nTM2ZFteIByPVSPHIRsK2rNkNdCmzCDLg5Wk2TyZBi3RrYkSyXhwpXFchOMv7tiQbXmBcMApLtkQ67ykH6YyZdKeRnblLBvrbY2T+WTO+iajZCGZ1lIxZRCOrzhkQ9blBcIBx7hlQ0F7niwWqpRhjNnLofRjH4z2NyUlw8GVUwbheIpTNmRbXiAccIRjNpSmwiKRp0yL32/kY7T3o6H8GNFsNJpFA2mD6imDcLzELRuyLi8QDijgnA0F7eHg8oPo3gPh0IRzq7hmQ7blBcIBeVyzofYkioaaNq1pdK9Lq8F9NFUU3lhFk0ppg3D8xDEbsiwvEA4o4pYNtbOBWlUFqJYYpl2okia9ClcW3uBrpvVpg3B8xSkbsiwvEA44xi0bmiXjKIrmSSLPmfYiWURsxspYnjODhE/iXyQrWU4M7mdZ2qi6SQhH14m8VZyyIcvyAuH4qho5TtlQK/uMB+OFtM00m1s5lRaAEtP88z/L2aUNwjkrnJvGJRuyLC8QjreqkeOUDQ139Zoi+ecYJVlvZCWf/DLKyg+2dbNX2tFYkTIIZ+yroFyyIcvyAuF4qxo5jtlQdjmrTJnshWJF2v5XqpRpT6IVGw+Wd4AQjiqcm8YtG7IqLxCOt6qR45QNBQvR5WiP5Z9jK0uDoeLieJr1gKJEMrElS5eVauYLwtGEc8u4ZEO25QXCCcAR7tgQW0N2nyyiyTQaj8tv8rVZG1ZTY3g/T1blnzZfjbZK5vfDaJTMZYU39HNaEI7uA7tlnLEhy/IC4eg+MD9xx4YKhdLLL44Ljw+RXBxHhTZD2Xtpp7MgHJ9xxoYsywuEA8pwx4aiPOUXx61Cm/LPfFhoI7k+ns7ZIoCRKm0Qjs+icsaGLMsLhOOzauS4Y0MN09qlzcR0JAjHPpyxoYaxLC8QjiPAhhSItDEdBcKxD9iQHMvyAuE4gDs2NBy2g+GO8tXM7eFQ3FLkyG4nDnZFc4fskAqyuS0Ip344t44zNmRZXiAcn1Ujxx0bYvf+9LcTc7cUZbcTR7lbitKB2taUp0uymMsepohwFOHcOs7YkGV5gXB8Vo0cd2yI3fvT307M3VKU3U6c5m4pSm4n8iFcli7yKf4IZ+7xAghnbMiyvEA4PqtGjjs21CSWpQvCsQ1nbKhJLMsLhOMObtqQoqjGngpPOxzKLqBrpgvC8Q0nbciyvEA4YAdsSEFU4W0aDMfRs3OLwIbkQDUUZ8cvYEOqt6k4o8WyDLbs7NwisCHF20A1BGfHL2BDqrexS1COhnOLwIYUb+NmmloWjl/AhlRv42YGWxbOLQIbUryNm2lqWTh+ARtSvY2bGWxZOLcIbEjxNm6mqWXh+IU7NpSvMbiQpEy+xqAsZaaHJnNNygwUvzYQjiqDLTs7t44zNmRZXkA1PqtGjjs2VKi4LkmZQsV1ScqMKhRltzMcVQabOjuePj/FGRuCaqAaB3DHhg6FnaT1n3KFnaT1n9K0zKHKh7YynxoPx7KzI1rPx2dH7DTO2BBUY1843qpGjjs21Cjte156I5nZdgk9tWfZQYufI9NRmMEZG2oUqEaPz6qRAxsqYzDmclqkP+aXH42SyJYMnszYOMbU0+EF2FAJUI0Wv1Ujxz0bGulnqwxG+hK209FA/stFErXak9W4PVzpJsc0EU4OnaCaCWcQpV81i8SePmbTOGdDUI35cLxXjRz3bKjCxzisMLEyUtxKzPYfJPdBsErUXZcGwgnyN0A1gmoinOkqScbzQZX3ulWcsyGoxnQ4UI0C2FDp/uJ3ySwI7jUzXzwUVJLMphXf61aBDZXtD9VANecBG5Lv32I/JjYIapRrZ/zssJL10aDae90qsCHp/lCN5D2gGjmwoTLGybQdDFfJVNOuoXByK/BsEBQf417dtz0WFGyoBKgGqjkT92xoqL8r2R7qn+3RUs3vnx4WtK00c/ybCCdK9m+im3rKw1GPy4tw1G1EOPI2UzbjJ1lp/6xbxTkbgmq04UA15nDNhloVpsYMKjxgSrPIbDBbJSt998dKJjNtf6s91f9xrWih6tHy9Q/j+cT0X2sGx2wIqtEC1ZjEJRtqRXxhwniuEMxwLtYuRKpem6i9sdLPwKxIVEF6FeZ7DipOP1Ueg50jjaAmc3lZkow2P0eaVYjD+djXhXgO2RBUoweqMYxDNpRd86/S/6TJOd+3GcuSs73iTcZpt2SlW0Y2rNa1a+o5krpw2mxSaKLua6XdtfSPj9QLHJhSFvf67nH6hpVO0M3hjg1BNVCN/bhjQ61kPG23puNRMB1Ln/eerCZp1yaJ2lEylqhlnswGwXCWDFsz7d3CiquvmxKUOhzeXVso30d01xaJftxgjA6bCmdsCKqBahzAHRvKpqHcJ4OgNZZ0J0ZCRbMxmzIqySyhovZixg45kbxXxiiJZEMHTRbnrRBOm3XXFlFLNaGnxbprq2lbOeuW3UWdTZTl8Ud5Ai9xxoagGqjGAVyyIf5jyBZpzyQfd/b58jkyY8lnnWXcaMwWfMtEp1/51qSgKoUzng8D9bzSiCkuUM8rHbE5pYH6KS011gXeKg7ZEP8B1UjCgWqswCUb4mkwZX0x2XV21q+bs1IiK5mgxPZF2jVsywUV8Zrt8yT9X3mTfOaOpII67CwXVO6QUkFVCCdVQksnqGTGqiqqBSXm8agEtS9pH3l7s9UhG4JqoBrrcceGBsliErAR7pb8475PRoP0SjtZBPIlabMk7dy0ZqwIsDT7BquETxpSDSvnji8TVP74srfKZbdUUFXC4WPcs6lKUHyMO+3+qQTFx7jT7l+FZxUP06PNK8zyvUWcsSGoBqpxAHdsaDefJ2L9sVl5k2w+D0uYCRuGKCMrRz9uFxa4HRMl44k6g3Mr9KRDz7k4ZVNPc3Eqpp7qw2FPe1kphjEEfMZPIhvcF/AZpYmmNmWQSi4Z2fZcmcZwxoagGqjGARyyoWAyWiSjibpNNBqPZ+qFBO35KFnMdbNO077UrG3Nc0oqhiNKyavnlfIO4Eo5r1RMDZopHovSmvstJ3dsCKqBauzHJRtqmGg8XlkjqMrhsIIhmok4vAOomQnLO4AyxbRTObGxHn9xyIYaBqqBaurjoA1NK6zjjvR9jlakG6FlV9DOhcOOpH/25WB+r20zkT3LOe08LsjW0ruJezZkWZpaFg5UYxYHbaipZW+sTYUraMvCaQA2grHHdDBmcM+GLEtTy8JpAKhGgTs21Np9hIv045R0K4a7NskokvWlplmTeTKPtH0pZ8IZ5BfHSbJ8mm8jCTnKt5GMlmMFhDs2ZFmaWhYOVGMJ7thQkyvfEI4qnGGesyN2GmdsyOM0tSwcqEaBSzY0nvKPcLUayh5XEiWrCW+TzIeyx5WMkhlvcp/cD9WF66ez1Ug6xb/5cERMErG09xk+lwqqtWsyGUkFNdi1mS4u0fet45ANQTVQjfW4Y0PBZDzmdwhVw8pRIh73oVqONktGLLlVw8p8d9ENktYcbjCcwhHVv091MI7UumxXuFmKqaVqnLEhqEYcUf17qMYwDtkQy73VQHN3k68U0NzdnIzZr3WCGiTj+9ZgLn9YYnPh5FALarjQL9G+H1eQXKKR3DCaj+YVZjLdKu7YEFQTQDXW45INZbmnmWTDl06rJ9lwKegEFYnns4wSeYI2Fk61u5tV6oSQ9PvasyyWmX485DZxyIagGqjGetyyIbYEbLHQzPUcjJKRbq7nZJG2UQsqqw91r+pvNRVOFUENWJ0QzYMoJ6zfpxFBxPp96iajZMGqRkYL7aNnbhWnbAiqgWosxzEb4lfQ2k/xfqxdcsCuoNWCysriT9SX/c2Ec3gL2fBCu8Kw9KBCv2+i7/cFw11/rj3y9X6sWzYE1UA1duOcDQXtof7Z862hfi3BQDHDhq1YyMYVIk3ONBFOrnBvJH+Qy2i/OE62uiFhqywyJH/VKNXTHuk0pV2oLVm1zFvHNRuCakqBaizBPRtqAnEhL6b06IrmNkCupvFUWh2/uRUQK32x/lvHORtqAqgGqjkT2FAZYhEA69a0R/pSUzZwWAExlC7IaOXbSPpsg3wbyVdJToz2FFNuFthQCVANVHMmrtlQ+360GmmGYVtR2kYzxX84H61mlYZoK3TrqtSI0gUUKJ+cYhUQlGs2BNWYB6pR4I4NibmZK37du5JkOf98W+IJXbL+GL8inojr5wpJPh9r2zRZp9GGDIag3LEhqGb/FxoGqlHgmA3dJ4tJe7iSPxs4YJN1RsP2ZCx7WCLP/nESDVr3uocl8mf7VkgZSwQ1jWYjTaHH9n00GkX3yj+7FUWjWaT4rsnd1q3w1OObxC0bgmqgGrtxzIbERJz2eFHehqdbshDNJR07lv3ZLyNlx27ClpuNZA9TNFSkUS6owUr9RvxPEl1e/mxk+TsIVtKxjorrAm8Zt2wIqoFq7MYxG8pSdyb5HFm6DbOMGkuynAkqm086kCefeDCwYnWDbYJqj5PR/XA4mSuWW6Qxz6fDIXvUpPSdomQcTYbD+1UylvX+UCvYMRuCaqAau3HMhjKVRApB7VSyUggqW+TdluXeNO0jjeeDQCmoqHDI8nAO+SYXVPEv1CAVVLR7g9ZYqoT9urmJ9A9Lu8ytowOCU9yyIagGqrEbl2xozp6WxV+PpIJiT8sS4wqynBkt2NOypuKQkpxJktlUdYyj342kyxIOx5cPzA8PzS8Q1KGY5DSRPKo4F848kVbZn+xeLircZvYVh2wIqoFqrMclG+KwRGiPJQV8syrzornyen4mmk/K27AH9rI3Uglqdcg32dTTdm65tGzq6eSQ/tKpp4cV2tK7m+0K2p1U0G6kX3oOnLIhqAaqsR53bKh9WBw2HEl6Lq3DyOu9rFbU4LBWbT6SXIjzMe7VfduSIoQV7m7mFSIf7cgdUvZ1Myo9JCjijA1BNVCNA7hjQwWqlAq5rM2Ul2VfVTjG9alwdxOCahRnbKgAVHPcBKqxAydtaDLT9rfa05X2XmErWqhShq9/GM8n+ngaXAFRZV8I6uq4aENQjXJfqMYg7tkQv/TXZPBkrl4JEDDFjfRzPYfzsQ0L8Vr7ZXFT2Tq7Ya4M8EIqqMNouVRQi32TOQQlxTkbgmrK94VqrMAxG2qzSaGJuq+VdtfS/likrDQ1YEpZ3OsryadvqI/q2oI6pLZ0eaGhBRm+4pYNQTVQjd04ZUO8u7ZQpqbori0S/bjBmG5Ky7UFlZs6JFuC2GStYOCUDUE1UI3tuGNDbdZdW0StoSI1W6y7tpq2VU/dGrK7qLNJQFjY6cqCGuaqp0wTN+oJ3zjO2BBUw4BqLMcdGxom4/kwUCxtCNgF9IIPA6sENWJzSoPAKUENtW1AozhjQ1CNqg2wBJdsKFVCSyeoZMaqKqoFJebxXCioVm51nOzu5jDS392cRtq7m+jXWYdDNgTVQDXW444NiTHu2VQlKD7GnXb/VILiY9xp9+9CQTV5dzN3h3WORdo24IwNQTUMqMZyHLIh9tyPlSI1BXzGTyKrNyLgM0p1j03RxdLg3c3RvmM3sWSFuu+4Y0NQDVRjP07ZULArJa+eV8o7gCvlvFIxNWg2dWNWS4s9b4z/8clYP1sWXB+XbCiAaqAay3HNhgJRMETTu+EdQM3dTd4BvODO5TDa7RxJ32m/bq4lfy7jfu/DAU/YPXhL+eQt0ByO2VAA1QCbcdCGWHrOtW0G83ttm8nsAkEt9g8okT6OsnUQvngAZgnTvfClT8dkv4tYP3QUudEPvX3csyGoBtiLkzZkA8Pc80nG0put+2EQ6SMrR4fK9/eYVuoKLtqQDUA1oAzY0JlEuY7aXPKoq1Guo7aYlbfJzedpJfquKLAC2NB5QDWgDNjQmeQL78oedZVfoCdbrJfv78n6fhWKNIJGgQ2dB1QDyoANnUmT/boKRRpBo8CGzgOqAWXAhs4kv0hbXiN+L7qWvEb8/uX0giKNoFFgQ+cB1YAyYENn0h6Pd3dSpSVHhslqN0lnJX+M/a6n1hqP25LDoCyJZcCGzgOqAWXAhs5lkoz5Wr/JTP7Q44gVLhbPe5GuklixwsWsWspYtogdRRqtAzZ0JlANKAE2dDb72lcraVerPdu1mUnXLgxWmvpZ6NfZB2zoXKAacAps6Hxas1QL45FyvuhwxEpCjpSdsfsRe97LTD6bB0UabQM2dDZQDTgBNuQAKNJoG7Ah+4Fq3AE25AAo0mgbsCH7gWrcATZ0LijS6DGwoTOBakAJsKFzQZFGj4ENnQlUA0qADZ0JijT6DGzoPKAaUAZs6ExQpNFnYEPnAdWAMmBDZ9JkkUZgG7Ch84BqQBmwoTNBv85nYEPnAdWAMmBDZ9JkkUZgG7Ch84BqQBmwoTNpsEgjsA7Y0HlANaAM2NC5NFekEVgHbOhMoBpQAmzobBor0gisAzZ0LlANOAU2dD6NFWkEtgEbOhuoBpwAGwKgNrAhAOiADQFQG9gQAHTAhgCoDWwIADpgQwDUBjYEAB2wIQBqAxsCgA7YEAC1gQ0BQAdsCIDawIYAoAM2BEBtYEMA0AEbAqA2sCEA6IANAVAb2BAAdMCGAKgNbAgAOmBDANQGNgQAHbAhAGoDGwKAjo/+//wxTd0eEnLkAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDE4LTEyLTI4VDEwOjQ0OjA1LTA4OjAwEC6dKwAAACV0RVh0ZGF0ZTptb2RpZnkAMjAxOC0xMi0yOFQxMDo0NDowNS0wODowMGFzJZcAAAAodEVYdHBkZjpIaVJlc0JvdW5kaW5nQm94ADQxNS41Njl4MjMzLjM5NCswKzCMygv5AAAAFHRFWHRwZGY6VmVyc2lvbgBQREYtMS40IBxHOngAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPDF([countsplotprefix + '_codonntchanges.pdf', countsplotprefix + '_codonmuttypes.pdf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>nonsynonymous</th>\n",
       "      <th>stop</th>\n",
       "      <th>synonymous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A549-Lib4</td>\n",
       "      <td>0.00103</td>\n",
       "      <td>7.86e-05</td>\n",
       "      <td>0.000329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-Lib5</td>\n",
       "      <td>0.00114</td>\n",
       "      <td>7.63e-05</td>\n",
       "      <td>0.000315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-Lib6</td>\n",
       "      <td>0.00109</td>\n",
       "      <td>8.92e-05</td>\n",
       "      <td>0.000329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-WT</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>7.46e-05</td>\n",
       "      <td>0.000202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-Lib4</td>\n",
       "      <td>0.00112</td>\n",
       "      <td>6.46e-05</td>\n",
       "      <td>0.000315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-Lib5</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>9.08e-05</td>\n",
       "      <td>0.00033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-Lib6</td>\n",
       "      <td>0.00119</td>\n",
       "      <td>8.33e-05</td>\n",
       "      <td>0.000326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-WT</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib4</td>\n",
       "      <td>0.00231</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib5</td>\n",
       "      <td>0.00223</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib6</td>\n",
       "      <td>0.00227</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.000295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-WT</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "codonmuttypes = pd.read_csv(countsplotprefix + '_codonmuttypes.csv').sort_index(axis=1)\n",
    "display(HTML(codonmuttypes.to_html(index=False, float_format=lambda x: '{0:.3g}'.format(x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I calculate the corrected mutation average mutation frequencies by subtracting the appropriate error control for each sample:\n",
    "\n",
    "- The pre-selection frequency is mutDNA - wtDNA\n",
    "- The post-selection frequency is mutvirus - wtvirus\n",
    "\n",
    "When I use the respective WT controls, I find 2-15% stop codons remaining in A549. In CCL141 cells, there is a very high rate of stop mutations in the wtvirus control, so there is <0% stop codons remaining. \n",
    "\n",
    "Because the wtvirus control passaged in CCL141 appears to have rather high error rate, I will also try using DNA-WT as the error control:\n",
    "\n",
    "- The pre-selection frequency (2) is mutDNA - wtDNA\n",
    "- The post-selection frequency (2) is mutvirus - wtDNA\n",
    "\n",
    "When I use wtDNA as the error control, the both A549 and CCL141 have <0% stop codons remaining, as the mutation rate in the DNA-WT control is quite high.\n",
    "\n",
    "I suspect that I am seeing high rate of mutations due to oxidative damage. To eliminate the effect of mutations due to oxidative damage, I can make these estimates considering only 2 and 3 nucleotide mutations (see below)."
   ]
  },
  {
   "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>sample</th>\n",
       "      <th>mutDNA</th>\n",
       "      <th>mutvirus</th>\n",
       "      <th>wtDNA</th>\n",
       "      <th>wtvirus</th>\n",
       "      <th>pre</th>\n",
       "      <th>post</th>\n",
       "      <th>percent</th>\n",
       "      <th>pre2</th>\n",
       "      <th>post2</th>\n",
       "      <th>percent2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <th>muttype</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A549-Lib4</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00231</td>\n",
       "      <td>0.00103</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.00176</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.00176</td>\n",
       "      <td>0.000483</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>0.0002</td>\n",
       "      <td>7.86e-05</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>7.46e-05</td>\n",
       "      <td>9.72e-05</td>\n",
       "      <td>3.96e-06</td>\n",
       "      <td>4.08</td>\n",
       "      <td>9.72e-05</td>\n",
       "      <td>-2.47e-05</td>\n",
       "      <td>-25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>119</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A549-Lib5</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00223</td>\n",
       "      <td>0.00114</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.00168</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>37.7</td>\n",
       "      <td>0.00168</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>0.000187</td>\n",
       "      <td>7.63e-05</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>7.46e-05</td>\n",
       "      <td>8.38e-05</td>\n",
       "      <td>1.68e-06</td>\n",
       "      <td>2</td>\n",
       "      <td>8.38e-05</td>\n",
       "      <td>-2.7e-05</td>\n",
       "      <td>-32.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>9.36e-05</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>120</td>\n",
       "      <td>9.36e-05</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A549-Lib6</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00227</td>\n",
       "      <td>0.00109</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.00173</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.00173</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>0.0002</td>\n",
       "      <td>8.92e-05</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>7.46e-05</td>\n",
       "      <td>9.65e-05</td>\n",
       "      <td>1.46e-05</td>\n",
       "      <td>15.1</td>\n",
       "      <td>9.65e-05</td>\n",
       "      <td>-1.41e-05</td>\n",
       "      <td>-14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>125</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CCL141-Lib4</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00231</td>\n",
       "      <td>0.00112</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>0.00176</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.00176</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>0.0002</td>\n",
       "      <td>6.46e-05</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>9.72e-05</td>\n",
       "      <td>-6.32e-05</td>\n",
       "      <td>-65.1</td>\n",
       "      <td>9.72e-05</td>\n",
       "      <td>-3.87e-05</td>\n",
       "      <td>-39.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>3.84e-05</td>\n",
       "      <td>36.2</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CCL141-Lib5</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00223</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>0.00168</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>28</td>\n",
       "      <td>0.00168</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>0.000187</td>\n",
       "      <td>9.08e-05</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>8.38e-05</td>\n",
       "      <td>-3.7e-05</td>\n",
       "      <td>-44.2</td>\n",
       "      <td>8.38e-05</td>\n",
       "      <td>-1.25e-05</td>\n",
       "      <td>-14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.00033</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>9.36e-05</td>\n",
       "      <td>5.33e-05</td>\n",
       "      <td>56.9</td>\n",
       "      <td>9.36e-05</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CCL141-Lib6</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00227</td>\n",
       "      <td>0.00119</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>0.00173</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.00173</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>0.0002</td>\n",
       "      <td>8.33e-05</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>9.65e-05</td>\n",
       "      <td>-4.45e-05</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>9.65e-05</td>\n",
       "      <td>-2e-05</td>\n",
       "      <td>-20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>4.97e-05</td>\n",
       "      <td>48.9</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mutvirus = (pd.concat([codonmuttypes[codonmuttypes['name'].str.contains('A549-Lib')], \n",
    "                     codonmuttypes[codonmuttypes['name'].str.contains('CCL141-Lib')]\n",
    "                     ])\n",
    "            .assign(experiment=lambda x: x.name,\n",
    "                  sample='mutvirus'))\n",
    "wtvirus = (pd.concat(\n",
    "    [codonmuttypes[codonmuttypes['name'].str.contains('A549-WT')]\\\n",
    "    .assign(experiment='A549-{0}'.format(i),\n",
    "           sample='wtvirus')\n",
    "    for i in ['Lib4','Lib5','Lib6']\n",
    "    ] +\\\n",
    "    [codonmuttypes[codonmuttypes['name'].str.contains('CCL141-WT')]\\\n",
    "    .assign(experiment='CCL141-{0}'.format(i),\n",
    "           sample='wtvirus')\n",
    "    for i in ['Lib4','Lib5','Lib6']\n",
    "    ]))\n",
    "mutDNA = (pd.concat(\n",
    "    [codonmuttypes[codonmuttypes['name'].str.contains('DNA-Lib')]\\\n",
    "    .assign(experiment=lambda x:x.name.str.replace('DNA', i),\n",
    "            sample='mutDNA')\n",
    "     for i in ['A549', 'CCL141']]))\n",
    "wtDNA = (pd.concat(\n",
    "    [codonmuttypes[codonmuttypes['name'].str.contains('DNA-WT')]\\\n",
    "    .assign(experiment='{0}-{1}'.format(cell, i),\n",
    "            sample='wtDNA')\n",
    "    for cell in ['A549', 'CCL141']\n",
    "    for i in ['Lib4','Lib5','Lib6']]))\n",
    "\n",
    "codonmuttypes_corr = (pd.concat([wtDNA, mutDNA, wtvirus, mutvirus])\n",
    "                      .rename(columns={'nonsynonymous':'nonsyn', 'synonymous':'syn'})\n",
    "                      .melt(id_vars=['experiment', 'sample'],\n",
    "                            value_vars=['stop', 'nonsyn', 'syn'],\n",
    "                            var_name='muttype',\n",
    "                            value_name='mutfreq')\n",
    "                      .pivot_table(index=['experiment', 'muttype'], columns='sample', values='mutfreq')\n",
    "                      .assign(pre=lambda x: x.mutDNA - x.wtDNA,\n",
    "                              post=lambda x: x.mutvirus - x.wtvirus)\n",
    "                      .assign(percent=lambda x: 100 * x.post / x.pre)\n",
    "                      .assign(pre2=lambda x: x.mutDNA - x.wtDNA,\n",
    "                              post2=lambda x: x.mutvirus - x.wtDNA)\n",
    "                      .assign(percent2=lambda x: 100 * x.post2 / x.pre2)\n",
    "                     )\n",
    "display(HTML(codonmuttypes_corr.to_html(float_format=lambda x: '{0:.3g}'.format(x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for oxidative damage\n",
    "\n",
    "The `summary_singlentchanges.pdf` plot below shows the frequency of each type of nucleotide change among only codon mutations with **one** nucleotide change. This plot is mostly useful to check if there is a large bias in which mutations appear. \n",
    "\n",
    "Oxidative damage is characterized by an excess of C -> A ot G -> T mutations. In particular, if you are getting oxidative damage (which causes G to T mutations) during the library preparation process, you will see a large excess of C to A or G to T mutations (or both). For instance, in the case of influenza, when we get bad oxidative damage, then we see an excess of C to A mutations in the final cDNA since the damage is occurring to a ssRNA genome. If you are sequencing something without polarity, you might see both types of mutations. I see quite significant excess of these mutations, particularly in the CCL141-WT and even in the DNA-WT control. I think this should still be able to be corrected for during analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPDF(countsplotprefix + '_singlentchanges.pdf', width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigate and isolate the effect of oxidative damage on average mutation frequencies \n",
    "\n",
    "The above plot shows my data exhibit quite significant oxidative damage. To see if my high retention of stop codons is due to high oxidative damage, I subsetted mutations on ones which were accessible by one nucleotide mutations or not, and repeated plotting mutation types. To do so, I modified [plotCodonMutTypes](https://jbloomlab.github.io/dms_tools2/dms_tools2.plot.html?highlight=plotcodonmuttypes#dms_tools2.plot.plotCodonMutTypes). The script that does this, `plotCodonMutTypes_accessible`, is imported from `pymodules.utils`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples['countsfiles'] = samples.apply(lambda row: './results/codoncounts/{0}_codoncounts.csv'.format(row['name']), axis=1)\n",
    "accessibilities = [(True, 'accessible'), (False, 'notaccessible')]\n",
    "classifications = [('n_ntchanges', 'codonntchanges'), \n",
    "                   ('aachange', 'codonmuttypes')]\n",
    "for accessibility, isaccessible in accessibilities:\n",
    "    for classificationtype, classificationname in classifications:\n",
    "        print(\"Annotating and plotting mutation type: {0}, {1}\".format(isaccessible, classificationname))\n",
    "        plotCodonMutTypes_accessible(samples['name'], samples['countsfiles'], \n",
    "                                     '{0}_{1}_{2}.pdf'.format(countsplotprefix, classificationname, isaccessible), \n",
    "                                     accessibility, classification=classificationtype, \n",
    "                                     csvfile='{0}_{1}_{2}.csv'.format(countsplotprefix, classificationname, isaccessible))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When I subset on non-accessible mutations (2 or 3 nucleotide substitutions), I observe much better purging of stop codons (2-7% remaining). Thus, if we eliminate spurious stop codons remaining due to oxidative damage/sequencing error, we actually get observe good selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All mutations\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accessible by 1-nucleotide substitution\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not accessible by 1-nucleotide substitution\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for accl, acc in [('All mutations', ''), \n",
    "                  ('Accessible by 1-nucleotide substitution', '_accessible'), \n",
    "                  ('Not accessible by 1-nucleotide substitution', '_notaccessible')]:\n",
    "    print(accl)\n",
    "    showPDF(['{0}_{1}{2}.pdf'.format(countsplotprefix, 'codonntchanges', acc),\n",
    "             '{0}_{1}{2}.pdf'.format(countsplotprefix, 'codonmuttypes', acc)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>nonsynonymous</th>\n",
       "      <th>stop</th>\n",
       "      <th>synonymous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A549-Lib4</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>2.44e-06</td>\n",
       "      <td>1.16e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-Lib5</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>6.77e-06</td>\n",
       "      <td>1.43e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-Lib6</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>4.01e-06</td>\n",
       "      <td>1.32e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A549-WT</td>\n",
       "      <td>2.45e-06</td>\n",
       "      <td>2.59e-08</td>\n",
       "      <td>2.59e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-Lib4</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>3.8e-06</td>\n",
       "      <td>1.23e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-Lib5</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>6.26e-06</td>\n",
       "      <td>1.35e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-Lib6</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>4.95e-06</td>\n",
       "      <td>1.31e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCL141-WT</td>\n",
       "      <td>6.06e-07</td>\n",
       "      <td>2.75e-08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib4</td>\n",
       "      <td>0.00153</td>\n",
       "      <td>9.57e-05</td>\n",
       "      <td>1.71e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib5</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>9.16e-05</td>\n",
       "      <td>1.63e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-Lib6</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>9.44e-05</td>\n",
       "      <td>1.64e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DNA-WT</td>\n",
       "      <td>7.06e-08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "codonmuttypes = pd.read_csv(countsplotprefix + '_codonmuttypes_notaccessible.csv').sort_index(axis=1)\n",
    "display(HTML(codonmuttypes.to_html(index=False, float_format=lambda x: '{0:.3g}'.format(x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, I calculate the corrected mutation average mutation frequencies by subtracting the appropriate error control for each sample as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>mutDNA</th>\n",
       "      <th>mutvirus</th>\n",
       "      <th>wtDNA</th>\n",
       "      <th>wtvirus</th>\n",
       "      <th>pre</th>\n",
       "      <th>post</th>\n",
       "      <th>percent</th>\n",
       "      <th>pre2</th>\n",
       "      <th>post2</th>\n",
       "      <th>percent2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <th>muttype</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A549-Lib4</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00153</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>7.06e-08</td>\n",
       "      <td>2.45e-06</td>\n",
       "      <td>0.00153</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.00153</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>9.57e-05</td>\n",
       "      <td>2.44e-06</td>\n",
       "      <td>0</td>\n",
       "      <td>2.59e-08</td>\n",
       "      <td>9.57e-05</td>\n",
       "      <td>2.42e-06</td>\n",
       "      <td>2.53</td>\n",
       "      <td>9.57e-05</td>\n",
       "      <td>2.44e-06</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>1.71e-05</td>\n",
       "      <td>1.16e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>2.59e-08</td>\n",
       "      <td>1.71e-05</td>\n",
       "      <td>1.16e-05</td>\n",
       "      <td>68</td>\n",
       "      <td>1.71e-05</td>\n",
       "      <td>1.16e-05</td>\n",
       "      <td>68.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A549-Lib5</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00148</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>7.06e-08</td>\n",
       "      <td>2.45e-06</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>35.4</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>9.16e-05</td>\n",
       "      <td>6.77e-06</td>\n",
       "      <td>0</td>\n",
       "      <td>2.59e-08</td>\n",
       "      <td>9.16e-05</td>\n",
       "      <td>6.74e-06</td>\n",
       "      <td>7.36</td>\n",
       "      <td>9.16e-05</td>\n",
       "      <td>6.77e-06</td>\n",
       "      <td>7.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>1.63e-05</td>\n",
       "      <td>1.43e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>2.59e-08</td>\n",
       "      <td>1.63e-05</td>\n",
       "      <td>1.43e-05</td>\n",
       "      <td>87.6</td>\n",
       "      <td>1.63e-05</td>\n",
       "      <td>1.43e-05</td>\n",
       "      <td>87.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A549-Lib6</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>7.06e-08</td>\n",
       "      <td>2.45e-06</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>28.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>9.44e-05</td>\n",
       "      <td>4.01e-06</td>\n",
       "      <td>0</td>\n",
       "      <td>2.59e-08</td>\n",
       "      <td>9.44e-05</td>\n",
       "      <td>3.98e-06</td>\n",
       "      <td>4.22</td>\n",
       "      <td>9.44e-05</td>\n",
       "      <td>4.01e-06</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>1.64e-05</td>\n",
       "      <td>1.32e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>2.59e-08</td>\n",
       "      <td>1.64e-05</td>\n",
       "      <td>1.32e-05</td>\n",
       "      <td>80.6</td>\n",
       "      <td>1.64e-05</td>\n",
       "      <td>1.32e-05</td>\n",
       "      <td>80.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CCL141-Lib4</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00153</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>7.06e-08</td>\n",
       "      <td>6.06e-07</td>\n",
       "      <td>0.00153</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.00153</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>9.57e-05</td>\n",
       "      <td>3.8e-06</td>\n",
       "      <td>0</td>\n",
       "      <td>2.75e-08</td>\n",
       "      <td>9.57e-05</td>\n",
       "      <td>3.78e-06</td>\n",
       "      <td>3.95</td>\n",
       "      <td>9.57e-05</td>\n",
       "      <td>3.8e-06</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>1.71e-05</td>\n",
       "      <td>1.23e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.71e-05</td>\n",
       "      <td>1.23e-05</td>\n",
       "      <td>72.3</td>\n",
       "      <td>1.71e-05</td>\n",
       "      <td>1.23e-05</td>\n",
       "      <td>72.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CCL141-Lib5</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.00148</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>7.06e-08</td>\n",
       "      <td>6.06e-07</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>34.9</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>9.16e-05</td>\n",
       "      <td>6.26e-06</td>\n",
       "      <td>0</td>\n",
       "      <td>2.75e-08</td>\n",
       "      <td>9.16e-05</td>\n",
       "      <td>6.24e-06</td>\n",
       "      <td>6.81</td>\n",
       "      <td>9.16e-05</td>\n",
       "      <td>6.26e-06</td>\n",
       "      <td>6.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>1.63e-05</td>\n",
       "      <td>1.35e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.63e-05</td>\n",
       "      <td>1.35e-05</td>\n",
       "      <td>82.6</td>\n",
       "      <td>1.63e-05</td>\n",
       "      <td>1.35e-05</td>\n",
       "      <td>82.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CCL141-Lib6</th>\n",
       "      <th>nonsyn</th>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>7.06e-08</td>\n",
       "      <td>6.06e-07</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>35.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop</th>\n",
       "      <td>9.44e-05</td>\n",
       "      <td>4.95e-06</td>\n",
       "      <td>0</td>\n",
       "      <td>2.75e-08</td>\n",
       "      <td>9.44e-05</td>\n",
       "      <td>4.93e-06</td>\n",
       "      <td>5.22</td>\n",
       "      <td>9.44e-05</td>\n",
       "      <td>4.95e-06</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syn</th>\n",
       "      <td>1.64e-05</td>\n",
       "      <td>1.31e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.64e-05</td>\n",
       "      <td>1.31e-05</td>\n",
       "      <td>80</td>\n",
       "      <td>1.64e-05</td>\n",
       "      <td>1.31e-05</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mutvirus = (pd.concat([codonmuttypes[codonmuttypes['name'].str.contains('A549-Lib')], \n",
    "                     codonmuttypes[codonmuttypes['name'].str.contains('CCL141-Lib')]\n",
    "                     ])\n",
    "            .assign(experiment=lambda x: x.name,\n",
    "                  sample='mutvirus'))\n",
    "wtvirus = (pd.concat(\n",
    "    [codonmuttypes[codonmuttypes['name'].str.contains('A549-WT')]\\\n",
    "    .assign(experiment='A549-{0}'.format(i),\n",
    "           sample='wtvirus')\n",
    "    for i in ['Lib4','Lib5','Lib6']\n",
    "    ] +\\\n",
    "    [codonmuttypes[codonmuttypes['name'].str.contains('CCL141-WT')]\\\n",
    "    .assign(experiment='CCL141-{0}'.format(i),\n",
    "           sample='wtvirus')\n",
    "    for i in ['Lib4','Lib5','Lib6']\n",
    "    ]))\n",
    "mutDNA = (pd.concat(\n",
    "    [codonmuttypes[codonmuttypes['name'].str.contains('DNA-Lib')]\\\n",
    "    .assign(experiment=lambda x:x.name.str.replace('DNA', i),\n",
    "            sample='mutDNA')\n",
    "     for i in ['A549', 'CCL141']]))\n",
    "wtDNA = (pd.concat(\n",
    "    [codonmuttypes[codonmuttypes['name'].str.contains('DNA-WT')]\\\n",
    "    .assign(experiment='{0}-{1}'.format(cell, i),\n",
    "            sample='wtDNA')\n",
    "    for cell in ['A549', 'CCL141']\n",
    "    for i in ['Lib4','Lib5','Lib6']]))\n",
    "\n",
    "codonmuttypes_corr = (pd.concat([wtDNA, mutDNA, wtvirus, mutvirus])\n",
    "                      .rename(columns={'nonsynonymous':'nonsyn', 'synonymous':'syn'})\n",
    "                      .melt(id_vars=['experiment', 'sample'],\n",
    "                            value_vars=['stop', 'nonsyn', 'syn'],\n",
    "                            var_name='muttype',\n",
    "                            value_name='mutfreq')\n",
    "                      .pivot_table(index=['experiment', 'muttype'], columns='sample', values='mutfreq')\n",
    "                      .assign(pre=lambda x: x.mutDNA - x.wtDNA,\n",
    "                              post=lambda x: x.mutvirus - x.wtvirus)\n",
    "                      .assign(percent=lambda x: 100 * x.post / x.pre)\n",
    "                      .assign(pre2=lambda x: x.mutDNA - x.wtDNA,\n",
    "                              post2=lambda x: x.mutvirus - x.wtDNA)\n",
    "                      .assign(percent2=lambda x: 100 * x.post2 / x.pre2)\n",
    "                     )\n",
    "display(HTML(codonmuttypes_corr.to_html(float_format=lambda x: '{0:.3g}'.format(x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy files to paper figures directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfiguresdir = os.path.join(figuresdir, 'Fig1/')\n",
    "if not os.path.isdir(myfiguresdir):\n",
    "    os.mkdir(myfiguresdir)\n",
    "\n",
    "files = [countsplotprefix + '_codonmuttypes.pdf',\n",
    "         countsplotprefix + '_codonmuttypes_notaccessible.pdf',\n",
    "        countsplotprefix + '_cumulmutcounts.pdf']\n",
    "for f in files:\n",
    "    shutil.copy(f, myfiguresdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
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