Fig6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enhanced protein isoform characterization through long-read proteogenomics \n",
    "## Figure 6\n",
    "#### Ben Jordan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Users/bj8th/Documents/Sheynkman-Lab/Data/JURKAT_06-06-2021/hybrid_protein_database/huvec_refined_high_confidence.tsv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-c2d5b1d41082>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'font'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfont\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Sheynkman-Lab/GitHub/Long-Read-Proteogenomics-Analysis/data_loader.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;31m# list of genes in the high confidence space, for filtering of input data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mhigh_confidence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhigh_confidence_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0maccs_in_hiconf_space\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhigh_confidence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'base_acc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;31m# genes_in_hiconf_space = set(high_confidence['gene'].to_list())\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/lrpg/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_table\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    687\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 689\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/lrpg/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/lrpg/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/lrpg/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1048\u001b[0m             )\n\u001b[1;32m   1049\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1050\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/lrpg/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1866\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1867\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1868\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1869\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"encoding\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"compression\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/lrpg/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1361\u001b[0m         \"\"\"\n\u001b[0;32m-> 1362\u001b[0;31m         self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m             \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/lrpg/lib/python3.8/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"replace\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 642\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/bj8th/Documents/Sheynkman-Lab/Data/JURKAT_06-06-2021/hybrid_protein_database/huvec_refined_high_confidence.tsv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns \n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import os, sys\n",
    "import matplotlib\n",
    "from matplotlib import colors\n",
    "import logging\n",
    "\n",
    "\n",
    "import config, data_loader\n",
    "\n",
    "matplotlib.rc('font', **config.font)\n",
    "\n",
    "# all plots go into a directory\n",
    "if not os.path.exists('plot'):\n",
    "    os.makedirs('plot')\n",
    "\n",
    "# all statistics go into a directory\n",
    "if not os.path.exists('stats'):\n",
    "    os.makedirs('stats')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig 6b, The number of protein groups identified in a traditional (normal) search versus one that incorporates long-read RNA-seq data to rescue isoforms (Rescue & Resolve algorithm). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "467\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-66d8fa4be950>:132: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  candidate_origin_groups['is_subset'] = candidate_origin_groups.apply(lambda x: is_rescued_subset(x['all_peptides'], rescued_group_peptides), axis=1)\n",
      "WARNING:root:No candidate origin groups\tPB.7555.1\n",
      "WARNING:root:Invalid case: subsumable does not cover all peptides and no subset found\tPB.8400.3\n",
      "WARNING:root:Invalid case: subsumable does not cover all peptides and no subset found\tPB.4146.2\n",
      "WARNING:root:Invalid case: subsumable does not cover all peptides and no subset found\tPB.7388.4\n",
      "WARNING:root:Invalid case: subsumable does not cover all peptides and no subset found\tPB.8805.2|PB.8805.3\n",
      "WARNING:root:No candidate origin groups\tPB.3536.1\n",
      "WARNING:root:Invalid case: all shared peptides.\n",
      "No subset\n",
      "Subsumable does not cover peptides\tPB.8401.1\n",
      "WARNING:root:No candidate origin groups\tPB.7413.3\n"
     ]
    }
   ],
   "source": [
    "\n",
    "hybrid_protein_groups_file = f'{config.PIPELINE_RESULTS_DIRECTORY}/metamorpheus/pacbio/hybrid/search_results/Task1SearchTask/AllQuantifiedProteinGroups.{config.EXPERIMENT_NAME}.hybrid.tsv'\n",
    "rescue_resolve_protein_groups_file = f'{config.PIPELINE_RESULTS_DIRECTORY}/metamorpheus/pacbio/rescue_resolve/search_results/Task1SearchTask/AllQuantifiedProteinGroups.{config.EXPERIMENT_NAME}.rescue_resolve.tsv'\n",
    "high_confidence_genes_file =  f'{config.PIPELINE_RESULTS_DIRECTORY}/hybrid_protein_database/{config.EXPERIMENT_NAME}_high_confidence_genes.tsv'\n",
    "pacbio_genes_file = f'{config.PIPELINE_RESULTS_DIRECTORY}/protein_classification/{config.EXPERIMENT_NAME}_genes.tsv'\n",
    "gene_isoname_file = f'{config.PIPELINE_RESULTS_DIRECTORY}/reference_tables/gene_isoname.tsv'\n",
    "refined_file = f'{config.PIPELINE_RESULTS_DIRECTORY}/protein_gene_rename/{config.EXPERIMENT_NAME}_orf_refined_gene_update.tsv'\n",
    "#********************************************\n",
    "# Load data\n",
    "#********************************************\n",
    "isoname_gene = pd.read_table(gene_isoname_file, header=None)\n",
    "pb_genes = pd.read_table(pacbio_genes_file)\n",
    "gencode_gene_map = pd.Series(isoname_gene[0].values, index=isoname_gene[1]).to_dict()\n",
    "pacbio_gene_map = pd.Series(pb_genes.pr_gene.values,index=pb_genes.pb).to_dict()\n",
    "hybrid_gene_map = {**gencode_gene_map, **pacbio_gene_map}\n",
    "high_confidence_genes = list(pd.read_table(high_confidence_genes_file, header=None, names=['gene'])['gene'])\n",
    "cpm_data = pd.read_table(refined_file, usecols=['base_acc', 'pr_gene','CPM'])\n",
    "acc_cpm = pd.Series(cpm_data.CPM.values,index=cpm_data.base_acc).to_dict()\n",
    "gene_cpm = cpm_data.groupby('pr_gene')['CPM'].sum().reset_index()\n",
    "gene_cpm = pd.Series(gene_cpm.CPM.values,index=gene_cpm.pr_gene).to_dict()\n",
    "\n",
    "def expand_peptides(peptide):\n",
    "    if pd.isna(peptide):\n",
    "        return []\n",
    "    return peptide.split('|')\n",
    "\n",
    "def is_high_confidence_gene_in_protein_group(protein_group, gene_map, high_confidence_genes):\n",
    "    for isoform in protein_group.split('|'):\n",
    "        if isoform in gene_map.keys():\n",
    "            gene = gene_map[isoform]\n",
    "            if gene in high_confidence_genes:\n",
    "                return True\n",
    "    return False\n",
    "\n",
    "hybrid_protein_groups = (\n",
    "    pd.read_table(\n",
    "        hybrid_protein_groups_file, \n",
    "        index_col=False,\n",
    "        usecols=[0,6,7,16,19], \n",
    "        names=['protein_group','unique_peptides','shared_peptides','dct','qval'], \n",
    "        skiprows=1)\n",
    "    .query('dct == \"T\"') \n",
    "    .set_index('protein_group')\n",
    ")\n",
    "hybrid_protein_groups['unique_peptides'] = hybrid_protein_groups['unique_peptides'].apply(expand_peptides)\n",
    "hybrid_protein_groups['num_unique_peptides'] = hybrid_protein_groups['unique_peptides'].apply(lambda x: len(x))\n",
    "\n",
    "hybrid_protein_groups['shared_peptides'] = hybrid_protein_groups['shared_peptides'].apply(expand_peptides)\n",
    "hybrid_protein_groups['all_peptides'] = hybrid_protein_groups['unique_peptides'] + hybrid_protein_groups['shared_peptides']\n",
    "\n",
    "rescue_resolve_protein_groups = (\n",
    "    pd.read_table(rescue_resolve_protein_groups_file,\n",
    "    index_col=False,\n",
    "    usecols=[0,1,6,7,16,19], \n",
    "    names=['protein_group','gene','unique_peptides','shared_peptides','dct','qval'], \n",
    "    skiprows=1)\n",
    "    .query('qval <= 0.01 and dct == \"T\"')\n",
    "    .set_index('protein_group')\n",
    "    \n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "rescue_resolve_protein_groups['unique_peptides'] = rescue_resolve_protein_groups['unique_peptides'].apply(expand_peptides)\n",
    "rescue_resolve_protein_groups['shared_peptides'] = rescue_resolve_protein_groups['shared_peptides'].apply(expand_peptides)\n",
    "rescue_resolve_protein_groups['all_peptides'] = rescue_resolve_protein_groups['unique_peptides'] + rescue_resolve_protein_groups['shared_peptides']\n",
    "rescue_resolve_protein_groups = rescue_resolve_protein_groups[rescue_resolve_protein_groups.apply(lambda row: is_high_confidence_gene_in_protein_group(row.name, hybrid_gene_map, high_confidence_genes), axis=1)]\n",
    "\n",
    "#********************************************\n",
    "# Assign rescue cases\n",
    "#********************************************\n",
    "def is_candidate_group(candidate_group_peptides,rescued_group_peptides):\n",
    "    for pep in rescued_group_peptides:\n",
    "        if pep in candidate_group_peptides:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def is_rescued_subset(candidate_group_peptides, rescued_group_peptides):\n",
    "    for pep in rescued_group_peptides:\n",
    "        if pep not in candidate_group_peptides:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "# def table_rescued_groups(rescue_resolve_protein_groups, hybrid_protein_groups,acc_cpm, gene_cpm ):\n",
    "#     hybrid_group_accs = set(hybrid_protein_groups.index)\n",
    "#     rr_group_accs = set(rescue_resolve_protein_groups.index)\n",
    "#     rescued_group_accs = rr_group_accs - hybrid_group_accs\n",
    "#     rescued_groups = rescue_resolve_protein_groups.loc[rescued_group_accs]\n",
    "#     rescued_groups['accessions'] = rescued_groups.apply(lambda row: row.name.split('|'), axis=1 )\n",
    "#     rescued_groups['absolute_CPM']= rescued_groups['accessions'].apply(lambda accs: [round(acc_cpm[x],2) for x in accs])\n",
    "#     rescued_groups['total_CPM'] = rescued_groups['absolute_CPM'].apply(lambda cpm: round(sum(cpm),2))\n",
    "#     rescued_groups['relative_CPM'] = rescued_groups.apply(lambda row: [round(abs_cpm/row['total_CPM'],2) for abs_cpm in row['absolute_CPM']],axis=1)\n",
    "#     rescued_groups['absolute_CPM'] = rescued_groups['absolute_CPM'].apply(lambda cpm: '|'.join([str(x) for x in cpm]))\n",
    "#     rescued_groups['relative_CPM'] = rescued_groups['relative_CPM'].apply(lambda cpm: '|'.join([str(x) for x in cpm]))\n",
    "\n",
    "#     rescued_groups['gene_CPM'] = rescued_groups['gene'].apply(lambda genes: '|'.join([str(round(gene_cpm[gene],2)) for gene in genes.split('|')]))\n",
    "#     rescued_groups = rescued_groups.reset_index()\n",
    "#     rescued_groups[['gene','protein_group','absolute_CPM', 'relative_CPM','total_CPM', 'gene_CPM']].to_csv('stats/SuppTableX_rescued_CPMs.tsv', sep='\\t')\n",
    "\n",
    "#     rescued_groups = rescued_groups[['protein_group','gene', 'absolute_CPM', 'relative_CPM','total_CPM','gene_CPM']]\n",
    "#     rescued_groups['accession'] = rescued_groups['protein_group'].str.split('|')\n",
    "#     rescued_groups['gene'] = rescued_groups['gene'].str.split('|')\n",
    "#     rescued_groups['absolute_CPM'] = rescued_groups['absolute_CPM'].str.split('|')\n",
    "#     rescued_groups['relative_CPM'] = rescued_groups['relative_CPM'].str.split('|')\n",
    "#     rescued_groups['total_CPM'] = rescued_groups.apply(lambda row: [row['total_CPM'] for a in row['accession']],axis=1)\n",
    "#     # rescued_groups['total_CPM'] = rescued_groups['total_CPM'].str.split('|')\n",
    "#     rescued_groups['gene_CPM'] = rescued_groups['gene_CPM'].str.split('|')\n",
    "#     rescued_groups_explode = rescued_groups.set_index('protein_group').apply(pd.Series.explode).reset_index()\n",
    "#     rescued_groups_explode = rescued_groups_explode[['protein_group','accession','gene', 'absolute_CPM', 'relative_CPM','total_CPM','gene_CPM']]\n",
    "\n",
    "#     rescued_groups_explode.to_csv('stats/SuppTableX_exploded_rescued_CPM.tsv', sep='\\t', index=False)\n",
    "    \n",
    "\n",
    "# table_rescued_groups(rescue_resolve_protein_groups, hybrid_protein_groups, acc_cpm, gene_cpm)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def assign_resuce_cases(rescue_resolve_protein_groups, hybrid_protein_groups):\n",
    "    hybrid_group_accs = set(hybrid_protein_groups.index)\n",
    "    rr_group_accs = set(rescue_resolve_protein_groups.index)\n",
    "    rescued_group_accs = rr_group_accs - hybrid_group_accs\n",
    "    print(len(rescued_group_accs))\n",
    "    case1a=[]\n",
    "    case1b=[]\n",
    "    case2=[]\n",
    "    for group in rescued_group_accs:\n",
    "        rescued_group_peptides = rescue_resolve_protein_groups.at[group,'all_peptides']\n",
    "        candidate_origin_groups = hybrid_protein_groups[hybrid_protein_groups.apply(lambda x: is_candidate_group(x['all_peptides'], rescued_group_peptides), axis=1)]\n",
    "        if len(candidate_origin_groups)==0:\n",
    "            logging.warning(f'No candidate origin groups\\t{group}')\n",
    "            continue\n",
    "        candidate_origin_groups['is_subset'] = candidate_origin_groups.apply(lambda x: is_rescued_subset(x['all_peptides'], rescued_group_peptides), axis=1)\n",
    "        candidate_subset = candidate_origin_groups[candidate_origin_groups['is_subset']]\n",
    "        candidate_subsumable = candidate_origin_groups[~candidate_origin_groups['is_subset']]\n",
    "        subsumable_peptides = set()\n",
    "        \n",
    "        for peptides in candidate_subsumable['all_peptides']:\n",
    "            subsumable_peptides.update(peptides)\n",
    "        # Case 1a having unique peptides and being a subset gets top priority\n",
    "        if len(candidate_subset)>0 and candidate_subset['num_unique_peptides'].sum() > 0:\n",
    "            case1a.append(group)\n",
    "        else:\n",
    "            \n",
    "            # case 2 gets next priority but only for cases where at least one of the parent groups \n",
    "            # has a unique peptide and all the peptides for the\n",
    "            # rescued group can be accounted for by the parent \"subsumable\" groups\n",
    "            if candidate_subsumable['num_unique_peptides'].sum() > 0:\n",
    "                \n",
    "                if is_rescued_subset(subsumable_peptides, rescued_group_peptides):\n",
    "                    case2.append(group)\n",
    "                # if the \"subsumable\" parents cannot cover all of the peptides, \n",
    "                # and there is a subset group with only shared peptides \n",
    "                # - this is the categorization\n",
    "                elif len(candidate_subset)>0:\n",
    "                    case1b.append(group)\n",
    "                else:\n",
    "                    logging.warning(f\"Invalid case: subsumable does not cover all peptides and no subset found\\t{group}\")\n",
    "            else:\n",
    "                # if there is a subset and all peptides are shared then case1b\n",
    "                if len(candidate_subset)>0:\n",
    "                    case1b.append(group)\n",
    "                # no subset, if subsumable peptides can explain rescued peptides then case2\n",
    "                elif is_rescued_subset(subsumable_peptides, rescued_group_peptides):\n",
    "                    case2.append(group)\n",
    "                else:\n",
    "                    logging.warning(f\"Invalid case: all shared peptides.\\nNo subset\\nSubsumable does not cover peptides\\t{group}\")\n",
    "    return case1a, case1b, case2\n",
    "case1a, case1b, case2 = assign_resuce_cases(rescue_resolve_protein_groups, hybrid_protein_groups)\n",
    "#********************************************\n",
    "# Save Stats\n",
    "#********************************************\n",
    "hybrid_group_accs = set(hybrid_protein_groups.index)\n",
    "rr_group_accs = set(rescue_resolve_protein_groups.index)\n",
    "rescued_group_accs = rr_group_accs - hybrid_group_accs\n",
    "with open(f'stats/Fig6d_rescued_protein_group_classification_summary_stats.tsv','w') as ofile:\n",
    "    ofile.write('Case\\tNumber of Protein Groups\\tPercent\\n')\n",
    "    ofile.write(f'case1a\\t{len(case1a)}\\t{len(case1a)/len(rescued_group_accs)*100:.2f}\\n')\n",
    "    ofile.write(f'case1b\\t{len(case1b)}\\t{len(case1b)/len(rescued_group_accs)*100:.2f}\\n')\n",
    "    ofile.write(f'case2\\t{len(case2)}\\t{len(case2)/len(rescued_group_accs)*100:.2f}\\n')\n",
    "    \n",
    "with open('stats/Fig6b_rescued_protein_groups_classification.tsv','w') as ofile:\n",
    "    # ofile.write('protein_group\\tclassification\\n')\n",
    "    for caselist, rrclass in zip([case1a,case1b,case2],['case1a','case1b','case2']):\n",
    "        for acc in caselist:\n",
    "            ofile.write(f'{acc}\\t{rrclass}\\n')\n",
    "\n",
    "\n",
    "pb_col = config.database_colors['PacBio']\n",
    "pb_rr_col = config.database_colors['PacBio Rescue Resolve']\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 194.4x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pb_col = config.database_colors['PacBio']\n",
    "pb_rr_col = config.database_colors['PacBio Rescue Resolve']\n",
    "\n",
    "#********************************************\n",
    "# Pie Plot\n",
    "#********************************************\n",
    "labels = ['Case 1',  'Case 2']\n",
    "\n",
    "case1_size = len(case1a)+len(case1b)\n",
    "case2_size = len(case2)\n",
    "total = (len(case1a)+len(case1b)+len(case2))\n",
    "case_1_color = [c for c in colors.to_rgb(config.database_colors['PacBio Rescue Resolve'])] + [0.6]\n",
    "case_2_color = [c for c in colors.to_rgb(config.database_colors['PacBio Rescue Resolve'])] + [0.3]\n",
    "\n",
    "\n",
    "sizes = [case1_size, case2_size]\n",
    "fig1, ax = plt.subplots()\n",
    "pie_object = ax.pie(\n",
    "    sizes,  \n",
    "    colors=[case_1_color, case_2_color],\n",
    "    wedgeprops={'edgecolor':'black'},\n",
    "    startangle=90,\n",
    "    counterclock=False,)\n",
    "wedges,_ = pie_object\n",
    "# set wedges to be different extent of alphas\n",
    "# pie_object[0][0].set_alpha(0.5)\n",
    "# pie_object[0][1].set_alpha(0.5)\n",
    "# pie_object[0][1].set_alpha(0.8)\n",
    "ax.legend(wedges, labels,title='Rescue cases:', loc='center', bbox_to_anchor=(0.5, -0.25), ncol=1)\n",
    "ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.savefig(\"plot/Fig6b_rescued_protein_group_classification_pie.pdf\", bbox_inches='tight')\n",
    "\n",
    "#********************************************\n",
    "# Bar plot\n",
    "#********************************************\n",
    "hybrid_protein_groups = hybrid_protein_groups[hybrid_protein_groups.apply(lambda row: is_high_confidence_gene_in_protein_group(row.name, hybrid_gene_map, high_confidence_genes), axis=1)]\n",
    "hybrid_size = len(hybrid_protein_groups.query('qval <= 0.01'))\n",
    "rescue_resolve_size = len(rescue_resolve_protein_groups)\n",
    "rescued_size = len(rr_group_accs - hybrid_group_accs)\n",
    "fig, ax = plt.subplots(figsize=(2.7,3.5))\n",
    "labels = ['Normal','Rescue\\n&\\nResolve']\n",
    "non_rescued_sizes = [hybrid_size,rescue_resolve_size - rescued_size ]\n",
    "rescued_sizes = [0, rescued_size]\n",
    "ax.bar(labels, non_rescued_sizes, label='Normal', color=pb_col, edgecolor='black')\n",
    "ax.bar(labels, rescued_sizes, bottom=non_rescued_sizes, label='Rescued', color=pb_rr_col, edgecolor='black')\n",
    "ax.set_ylabel('Number of protein groups')\n",
    "ax.legend(title='Protein inference:', loc='center', bbox_to_anchor=(0.5, -0.45), ncol=3)\n",
    "plt.savefig(\"plot/Fig6b_rescued_protein_group_barplot_comparison.pdf\", bbox_inches='tight')\n",
    "with open(f'stats/Fig6b_rescued_protein_group_barplot_comparison.tsv', 'w') as ofile:\n",
    "    ofile.write(f'Normal\\t{hybrid_size}\\n')\n",
    "    ofile.write(f'Rescued size\\t{rescued_size}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig6e, The percent of rescued protein isoforms validated compared to the derived distribution from randomly rescued isoforms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aNOHaa68tUqYHHniArl270rp1ayZPnszAgQMZPXo0ffv2LZLvpptuYuvWrUyePJkePXpwww03cMQRR9CgQQOef/55mjVrRr9+/Vi8eHHUbZ88eTINGjSgW7duzJ69/Yf6q6++yoYNG5g8eTL9+/cvNt+YMWO4+uqr+de//sW0adO46667mDdvHn369Im6nkWLFtG1a1eOPvpopk6dypgxY3jllVe46KKLSj9AlVDSH/YxOyY/Pz/qhyEvL49Fi5Iy1J8pA1OnTqVataItbDMzMznxxBO5/fbbqVGjBgDffPMNXbt25e677w7ny8vL4/DDD+ejjz4K1yuvXr2at956iyOOOAKArVu30qVLF7788ktatWrFLbfcQseOHfnvf/8LwMknn8zixYt55ZVXANi2bRtDhw6lZ8+e3HPPPQB06NCBmjVrMmDAAK666ipatmwJwD777MPDDz+MiNCmTRvuv/9+9tlnn3Adf9OmTdl///357LPPoj50d9hhh5Genk79+vWLVJds2bKFBx54IHyTdcaMGUXm+/nnnxk2bBiXXnop4Kqfli9fzqBBg1izZk14n4XMmTOHjRs3cs0117DXXnsBUKNGjZT93FgQT1GxWmDYE56p7ZhjjuGOO+4A4Ouvv+aKK67gxBNP5LHHHiM9PT2cr2/fvvTt25e1a9cyf/58Fi5cyPTp0wF3MzRk9913L9KCY999Xc8Wa9euZd26dXzxxReMHTu2SBnOOuuscBCfP38+hYWFdO/evUies88+mwEDBjBz5sxwED/iiCPC1SCZmZlkZ2cXWfeee+4JwIoVKxLaJ3Xr1i2xlcydd94JQGFhIQsWLGDBggXh8m/cuLFYEG/VqhXp6ekcccQR9OzZk1NPPZXOnTuTlpaWULkqC6tOMaYSqVmzJgUFBRQUFNCnTx8mTpzIxIkTufDCC4vkW7t2Leeeey61atWiTZs23HzzzWzYsAEo2s46PT2d3Xbb/jEP/b9t2zZWrFiBqhYLkA0aNAj/v3z5cgDq169fJE9OTg4ZGRmsWrUqnJadXfwZvKysrIS2P5p69eqVOH3BggUce+yx1KtXj44dOzJ+/HiqV68ORG9z3qRJE95++23+9re/cdddd3H88cez7777Mnny5J0uazJYEDemEjvhhBO44IILePzxx8NXlwADBw7kjTfeYOrUqaxZs4avv/6aIUOGJLTs2rVrIyLFeucL3tisU6cOAEuWLCmSZ8WKFWzYsCF8dZ0s27Zt4/TTT2fz5s3MnTuX1atXM2vWLDp37lzifEcffTRTpkxh2bJlvPzyy+y///50796d335LvfYRFsSNqeRuueUWatasyaBBg9i0yT3cPHv2bDp27MhJJ50UrmZ5/fXXgfifeMzMzOTII48sdgU6derU8P8HHnggubm5TJw4sUieZ599FnDBsCwlWqVRWFjI999/T79+/WjevHn4l0ZJ++Khhx6iSZMmbN68maysLE4//XRGjRrF1q1bw/2GpxKrEzemksvNzWXIkCFcffXVjBs3jsGDB9O6dWtefvllHn30UfLy8pg+fTq33XYbAOvWrYt72ddffz0dO3akb9++9OzZk+nTpxcJ6mlpaYwYMYKBAwdSp04dunTpwldffcWIESPo3r07zZs3L9NtrVWrFp9++ikzZ87k2GOPLTV//fr1ycvLY+zYsdSvX5+0tDQeffTR8JOZ0fbFcccdx8CBA+nevTv//ve/2bRpE6NGjaJx48YceuihZbo9FcGCuNnl5OXlJfUGb3l0d3zppZdy7733MmrUKHr37s2YMWNYv349l112GQDNmjVj0qRJXH755cyePZvevXvHtdyTTjqJSZMmMXToUJ5++mlat27NbbfdVqS53cUXX0xWVhajR4/moYceYq+99uKKK65g2LBhZb6dQ4YMYcCAAXTs2JGFCxfGNc+kSZMYOHAgPXr0ICcnhyOOOIK33nqL9u3bM3v27GJ92RxwwAG88sorDB8+nG7dugFw/PHH8+STTxZrGZQKpKp1NlNQUKChhwQqCxEpsT/xaMco1jyx8u9q5s+fb49dmwpV2jknIp+qanyduZQhqxM3xpgUZkHcGGNSmAVxY4xJYRbEjTEmhVkQN8aYFGZB3BhjUpgFcWOMSWEWxI0xJoXZE5uVXPXq1YkYh9QYY8IsiFdymzZtsn7DE9Rg3wYs+XVJ6RnLSf196vPHL38kbf2marEgbnY5S35dAiOTuP6RiX+B9OnTh0cffbTEPCNGjGDkyJE7WKodExr3MqhatWrk5uZywgkn8J///Ie99967wst02mmnhUcjquosiBtTCQwbNowBAwaE35933nk0bdq0SCdToVF5Klq3bt244oorwu/XrVvHhx9+yI033sjChQuZM2dOUsplHAvixlQC++23H/vtt1/4fVZWFnXr1i0y1mSyRI55CW6wirVr13LzzTczb948Dj744CSVzljrFGNSSLt27ejXrx8nn3wyOTk5DB48mAkTJiAiLF26NJxvxYoViAgTJkwIp33//fecccYZZGdnU6tWLf75z38WmSdROTk5xdI+/fRT2rdvH/4SGjhwYJE+vf/44w969OhBbm4uWVlZHHvssbz77rtFljF58mRat27NHnvsQcOGDRk2bBibN2+OWoYmTZrQv3//ImnLly+nevXqPPLII4Abym7gwIHUr1+fzMxM2rVrx+eff77D213ZWBA3JsWMHz+eJk2a8Pzzz/OPf/wjrnmWLFnCMcccw6JFi3jssce47777mD17Nh06dAiPFhSLqrJly5bwa+XKlUyZMoXRo0dTUFDAQQcdBMC8efM47rjjEBGee+45br31Vp599ll69OgRXlbfvn35/vvvGT9+PC+99BJZWVmceuqpLFu2DIAHHniArl270rp1ayZPnszAgQMZPXo0ffv2jVq2nj17MmnSJLZu3RpOmzx5MiJC165dUVU6d+7MM888w6hRo5g4cSIZGRm0a9eOH374Ia59V9lZdYoxKSY7O5tx48aFBzD45ptvSp1n7NixbNiwgTfffDM8MPLf//53mjZtyjPPPMN5550Xc9577rmHe+65p1gZOnfuzJgxY8JDot14443Ur1+fV199NTxkXNOmTTnuuOOYOXNm+O/QoUM5/fTTAWjevDm33347a9eupVatWgwdOpSePXuG19ehQwdq1qzJgAEDuOqqq2jZsmWRcpxzzjnccsstzJgxg/bt2wPw3HPP0alTJ2rVqsW0adOYPn06b775JieeeCIAHTt25JBDDuGmm24KX62nMrsSNybF7L///gmPQPPOO+/Qpk0batWqFb6ibtiwIQcffDBvv/12ifP26NGDjz/+mDlz5nDnnXeSkZHB+eefz+OPP079+vWLrKNDhw6kpaWF19GmTRtycnLC6zjqqKMYPnw4Z599Nk888QTp6encdtttNGzYkPnz51NYWEj37t2LrP/ss88GYObMmcXKdsghh9CiRYvwGKDLli1j+vTp9OrVK1ymrKws2rZtGy4TuC+H0rY7VdiVuDEppl69egnP89dff/HRRx9FDf4NGjQocd66detSUOAGrGndujU5OTn07duXnJwcbrjhhiLruP/++7n//vuLLeP3338H3ADLN9xwA8899xzPPPMM1apV4/zzz2fcuHEsX74coMgXA7i694yMDFatWhW1fL169eL222/n7rvvZtKkSaSnp3PaaaeFy7Ru3TqqV69ebL5UHIotGgvixqS40BO927ZtC6etWbOmSJ6aNWvSqVOnIkE3JDs7O6H19enTh6eeeoqbb76ZM888k8MOOyy8ji5duvB///d/xeYJVeHUqVOHsWPHMnbsWL744guefPJJxowZQ+PGjcNVLEuWFG1nv2LFCjZs2MCee+4ZtTxnn302Q4YM4b333mPixIl06dKFrKyscJnq1avHq6++mtA2phKrTjEmxYVaifz222/htPfee69InmOOOYYFCxbQokULCgoKKCgooHnz5owcOZL3338/4XXeeeediEh4oObgOlq1ahVeR8OGDbnmmmv4+uuvWbp0KXl5eUyePBmAQw89lNtuu438/HwWL17MgQceSG5ubrhqJOTZZ58F4Oijj45alvz8fNq0acOTTz5ZpColVKbCwkJq1KgRLlNBQQFPPvkkTzzxRMLbXRlZEDcmxR1//PFkZGRw6aWX8uabb/Lwww9z7bXXhm8uAgwaNIgVK1bQqVMnXnrpJaZOncqpp57K9OnTadWqVcLrbNasGRdeeCEzZ87k+eefB9wDS5988gk9evTgtddeY/LkyXTq1Ikvv/ySww47jNzcXJo2bcoll1zC448/zowZM7jyyiv56aefOPPMM0lLS2PEiBE888wzXHTRRbzxxhuMHj2aQYMG0b17d5o3bx6zPL169WL8+PHUrFmTk046KZx++umn07p1a0455RQeffRR3nnnHS6++GLGjh276wy0rapV6tWqVSutbACdO3du1FesaSWlVwXz5s2LOa3+PvUVSNqr/j71d3r7/va3v2nv3r2Lpbdt21ZPPfXUYukvv/yyNmvWTKtXr66HHXaYzpw5U/fcc08dP358OM8333yjp556qtaoUUOzs7O1Xbt2+sEHH5RYjvz8fL3ooouiTvvzzz81JydHGzdurBs2bFBV1ffff1/btm2rmZmZWrt2bT3ttNP066+/Ds+zZMkSPe+887RBgwZavXp1bdGihT711FNFlvvwww+HtyU/P1+HDRummzZtKrFMf/75p+6+++7av3//YuVcvny59uvXT+vVq6cZGRnaokWLIvslXiWdc6qqwCeahJgmbt1VR0FBgX7yySfJLkYRIhK1kytwHV3F6gArVnpVOKbz58/fda6kTEoo7ZwTkU9VtaACiwRYdYoxxqQ0C+LGGJPCLIgbY0wKsyBujDEpzIK4SVlV4QauqRwq87lmQdykpOrVq7N+/fpkF8NUEevXr6+0j+knNYiLSGcRWR2RViAiGuU1OpAnXUTuEJE/RGS1iDwvIhU7RpRJqtzcXH755ReWLVvG5s2bK/WVkkldqsq6dev49ddfd6jPmoqQtL5TROQo4Akgcij3lsBa4MSI9N8C/98HdAauANYAtwBTRaSVqm7F7PJq1qxJeno6hYWF/PXXX+He6Ywpa9WqVaN+/fpRB8GoDCo8iItIOnApcCMuWEd2L9YS+FpVP4wx/37AeUAvVX3Wp30JfAt0ASaVU9FNJZORkUHDhg2TXQxjkioZ1SmdgGuBK4G7okxvCXxVwvwn+L9TQgmq+h3wDdCxjMpojDEpIRlB/GOgsaqOw/U1EakF0FBEvhCRTSLyvYj0Dkw/APhDVddGzPejn2aMMVVGhVenqOqvsab5m5O5QFPc1fpy4Gxggoioqj4G5ACro8y+Goj621pE+gH9APLy8naq/MYYU5lUtkEhVuCqRL5S1d992ls+uI8AHsPdCI12BS/AtijpqOoDwAPgOsAq4zIbY0zSVKp24qq6TlWnBQJ4yOtAExGpAawEog1FEppmjDFVRqUK4iJygIgM8C1YgjKB9bjWLN8BDUQkMyJPE1wLFWOMqTIqVRAH9gHuBU4JJYgbQLAr8J7veP1tIA04PZCnKXCIn2aMMVVGZasTnwm8D9wnIrWB34H+uGaHxwCo6g8iMhF4UERq4m5+3oJrlvhiMgptjDHJUqmCuKpuFZEuwM3ADcCewGfASaoaHI6nL3AHcCvu18RbwCX2tKYxpqpJahBX1ZHAyIi0ZcCAUuZbi2sy2K+8ymaMMamgstWJG2OMSYAFcWOMSWEWxI0xJoVZEDfGmBQWdxD3Tf6MMcZUIolcif/hR9DpIiKVc5wiY4ypYhIJ4n2BDGAi8LuI3CMiR5ZPsYwxxsQj7iCuqk+p6mnAXri23YcCs3x/38P9iDvGGGMqUMI3NlX1L1X9r6oehRvA4VdcUF8oIjNF5MwyLqMxxpgYEg7iIpItIueJyGu4R+JbAPcDZwJfA88GR6Y3xhhTfuJ+7F5EuuJG2TnVz/c6cA7wsqpu8tleFpGtuMfhB5dxWY0xxkRIpO+U54EvcMOmPaWqhTHyfYa1PzfGmAqRSBBvqapfix/sEkBEMoC04KDFqjoeGF/G5TTGGBNFIlfMC0XkHuDDQNoxwFIR+Y+IpJVt0XY9+fn5iEixlzHG7KhErsRvBnoB1wXSPgUGAaNwo83fWHZF2/UsXryYuXPnFktv0aJFEkpjjNkVJHIl/g/gclW9O5SgqstV9V7gGuD8si6cMcaYkiUSxGsBf8SYthiov9OlMcYYk5BEgvhnQH+JXonbD/i8bIpkjDEmXonUiY8A3gDmi8hU4E+gLtAJ2A/oUPbFM8YYU5K4g7iqzhCRY3DtxHsBdYCVwCygr6p+VD5FNMYYE0tCAyWr6hzc4/XGGGMqgYSCuIjsBvwN2IMo9emqOrOMymV2UPXq1aO2Pc/Ly2PRokVJKJExpjwl0ndKG+A5YG8g2s1NBeyBnyTbtGmTtUU3pgpJ5Er8TmAF8G/gF2BbeRTIGGNM/BIJ4i2Arqr6WnkVxhhjTGISaSe+GMgpr4IYY4xJXCJBfARwg4i0Kq/CGGOMSUwi1SlXAA2AOSKyBdgYMV1VtWaZlcwYY0ypEgniU8qtFMYYY3ZIIk9sXl+eBTHGGJO4hIZRE5GaIjJURN4RkfkicoiIXC0iJ5dXAY0xxsQWdxAXkUbAXNwgEKuAA4B0oCUwRUQ6lUcBjTHGxJbowz6/A+2BDcAmAFU9R0SqAcMBa0NujDEVKJHqlBOAm1V1De4R+6D7geZlVipjjDFxSSSIbwIyY0yrQ/Emh8YYY8pZIkH8VWCUiDQNpKmI1MH1MT6tTEtmjDGmVIkE8StwV9vfAF/5tIeBH4CawJVlWzRjjDGlSaSdeKF/5L430A74FTeyz6PAI6q6ulxKaIwxJqZER/bZgLuJeX/5FMcYY0wiEhkUYnhpeVT1hkRWLiKdgSdVNTuQJsAQoD+QC3wADFTVBYE86cD/A87GjTI0DbhEVX9LZP3GGJPqErkSvzxK2h5+GSuA74G4g7iIHAU8QfFRgoYD1wBXAz8BQ4G3ReRgVV3p89wHdMbV068BbgGmikgrVd0abxmMMSbVxX1jU1VrR3lVB44GlgI3xbMcEUkXkauAd4AtEdOygcHASFUdp6ovAycD2cAFPs9+wHnAv1V1gqo+D5yCe3K0S7zbY4wxu4KE+k6JRlVn4/oavyXOWTrhmiReCdwVMe1IoAbwcmD5y4F3gY4+6QT/d0ogz3e4VjOhPMYYUyXsdBD3VgKN48z7MdBYVcdR/MnPA/zfHyLSfwxMOwD4Q1XXlpDHGGOqhERubB4eJXk3YG/gRra3HS+Rqv5awuQcYKOqbopIX832oeFy/PtIq4GG0RYqIv2AfgB5eXnxFNMYY1JCIjc2P6H4lTO4G5O/At3LoDxSwjq2JZCnCFV9AHgAoKCgINq8xhiTkhIJ4sdHSVNct7RfqWrUAJqglUC6iFRT1c2B9Bp+WihPdrE5i+YxxpgqIZEnNt8tz4J43+GuqBsDCwPpTYBvA3kaiEimqq6PyPNeBZTRGGMqjUTqxMclsFxV1Ut3oDyzcH2VnwH8x6+3NtAWCA0P9zaQBpwOPOfzNAUOAUbuwDqNMSZlJVKd0gw4HKgN/A/4DdcF7QG4q+efA3kVSDiIq+oaEbkL11viNtzV+HW4KpuHfJ4fRGQi8KCI1ASW45o3fgW8mOg6jTEmlSUSxF8ADgJOUdWPQon+4ZsXgQmqOqYMyjQEd4NyMK6eexbQO/C0JkBf4A7gVlwLmbdwj93b05rGmColkXbi1wFXBgM4uCtjYBg70BWtqo5U1RoRaVtU9RpVbaCqNVS1Q7DfFJ9nrar2U9U6qlpLVbtZvynGmKookSCeA8S60s3GDZpsjDGmAiUSxN8E/iMifw8mikg7XI+CE8uuWMYYY+KRSBC/GFgHzBKRv0RkgYgsw7UWWUD0Xg6NMcaUo0Taif8hIn/DdQF7JFAL13vhDFV9o3yKZ4wxpiSJjuyzBZgkIp8CewFfl0upjDHGxCWhXgxF5CwR+Q7XY+D7uDbiT4rIEyJSrTwKaIwxJra4g7iI9MA9Ifku8I/AvJOBM3Ej8hhjjKlAiVSnDAfuVNVBIpIWSlTVCf7R+IG49uLGGGMqSCLVKfsDU2NM+xxXR26MMaYCJRLEFwPHxJh2BEX7TjHGGFMBEqlO+S8wWkQEd0WuwD5+xJ/rcKP7GGOMqUCJtBMf5+u+rwaG4noufAnYDIxT1dHlU0RjjDGxJNKfeIaqXi8iY3EP++yJG0nnI1VdWk7lM8YYU4JEqlM+F5HrVHUSMK28CmSMMSZ+idzYzCX6KPPGGGOSJJEr8VuBO0RkGDAf+DMyg6ouK6uCGWOMKV0iQfxqXKdXz5eQJ62EacYYY8pYIkF8cLmVwhhjzA4pMYiLyG+4MTW/UNVHfVodYIWqbquIAhpjjImttBubDYDqoTe+z5RC4NByLJMxxpg4JdQVrSdlXgpjjDE7ZEeCuDHGmErCgrgxxqSweIK4xplmjDGmgsXTxHCMiKzw/4fqw8eKyMqIfKqqXcqsZMYYY0pVWhCfibvqzg6kvev/ZhfPbowxpiKVGMRVtV0FlcMYY8wOsBubxhiTwiyIG2NMCrMgbowxKcyCuDHGpDAL4sYYk8IsiBtjTAqzIF4O8vPzEZFiL2OMKWuJDAph4rR48WLmzp1bLL1FixZJKI0xZldmV+LGGJPCLIgbY0wKsyBujDEprFIGcRHZU0Q0yut5P11E5DoRWSwi60TkTRE5KNnlNsaYilZZb2z+zf89GVgVSP/L/x0OXANcDfwEDAXeFpGDVTWyi1xjjNllVdYg3hJYoqpvRE4QkWxgMDBSVcf5tPeARcAFwO0VWdBUUb169ZjNHPPy8li0aFEFl8gYUxYqcxD/Ksa0I4EawMuhBFVdLiLvAh2xIB7Vpk2bojZ7BGv6aEwqq8xBfIOIzAIOB5YC44DbgAN8nh8i5vkRsJGFjDGl+vjjj/nzzz/D7w8++GAaN26cxBLtuEoXxEVkN+BgYC2u2mQxcApwC5ABbAY2quqmiFlXAzkxltkP6Aeu6sAYU3WtXr2aNke3ocaBNQDYsn4LjWs2Zu6n0X+pVnaVLojjxvE8DVisqt/7tHdEpAbuRuZNRB+oWYBt0Raoqg8ADwAUFBTYIM/GVGHbtm0jrVoaK7v5NhC/wsbZG5NbqJ1Q6ZoYqupWVZ0eCOAhrwNZuCv0dBGpFjG9BmAtU4wxVUqlC+IisreI9BORuhGTMv3f5bir7sgKrCbAt+VdPmOMqUwqXRAH0oH7gXMj0s8CFgKTgA3AGaEJIlIbaAu8XTFFNMaYyqHS1Ymr6v9E5GngRhHZBswHuuOC+BmqukZE7gJG+ekLgetwDwU9lKxyG2NMMlS6IO5dAAwDLgP2wgXys1Q11DZ8CO4m5mBcXfgsoLc9rWmMqWoqZRBX1fW4QD0kxvQtuMfur6nIchljTGVTGevEjTHGxMmCuDHGpDAL4sYYk8IsiBtjTAqzIG6MMSnMgrgxxqQwC+LGGJPCLIgbY0wKsyBujDEpzIK4McakMAvixhiTwiyIG2NMCrMgbowxKcyCuDHGpDAL4sYYk8IsiO+E/Px8RKTYyxhjKkqlHBQiVSxevJi5c+cWS2/RokUSSmOMqYrsStwYY1KYBXFjjElhFsSNMSaFWRA3VK9ePeoN2vz8/GQXzRhTCruxadi0aZPdoDUmRdmVuDHGpDAL4sYYk8IsiBtjTAqzIG6MMSnMgrgxxqQwC+LGGJPCLIgbY0wKsyBujDEpzIK4McakMAvixhiTwuyxe2PMLm/9+vWsWbMGgFWrVhWbvmXLFgoLC8Pva9euze67p0Z4tCtxY8wur/9F/alXrx55++fR/PDmVNu32vaJNeD3wt/J2z+PfRvvS7169XjhhReSV9gEpcZXjUmKUO+GkfLy8li0aFESSmTMjhk1chTvvvcufzT4g00nbCp6+VoTNgzcAOsh64UsTm55MmeccUayipowC+ImJuvd0Owq8vLy+Pyjz2nfsT0LXl7AhtM2FI1+KyDruSz6nNWHu8bexW67pU4lReqU1BhjdkKdOnWY9e4s2jVsR9ZzWbDBT1gCmY9nMuKyEdw97u6UCuBgQTwuNqq9MbuGzMxMpkyewtnHn03WE1nwNWQ+lclDdz3EVVdelezi7RCrTomDjWpvzK4jLS2NB+99kPyb8xlzxxgmTZrECSeckOxi7bCUvhIXkQtF5DsRWS8is0WkTbLLZIyp/ESEYdcNY8XSFSkdwCGFg7iInAfcBzwBnAWsAKaJSONklssYYypSSgZxcRXSNwAPqOr1qjoV6AwsBS5PauGMMaYCpWQQB/YH8oGXQwmquhl4FeiYrEIZY0xFS9UgfoD/+31E+o/AfiKSVsHlMaWI1cInPz8/2UUzVUiDfRtEPQ8b7Nsg2UXbYaKqyS5DwkTkbOApYC9V/SOQ/i/gQaCmqq4KpPcD+vm3BwLfVmBxy1IursrIJIft/+Sq7Ps/X1XrVvRKU7WJYaiRduQ3UCh9WzBRVR8AHijvQpU3EflEVQuSXY6qyvZ/ctn+jy5Vq1NW+r/ZEek1cAF8bcUWxxhjkiNVg/h3/m+TiPQmwLeainVExhizA1I5iP8MnBFKEJFqwKnA20kqU0VI+SqhFGf7P7ls/0eRkjc2AUTk38B/gVuAD4CLgWOAQ1X1x2SWzRhjKkrKBnEAEbkCuBR31/oL4ApVnZ3UQhljTAVK6SBujDFVXarWie/yRKSziKyOSBMRuU5EFovIOhF5U0QOSlYZd3UxjkGBiGiU1+hklXNXIiJpIjJIROaLyFoRmSciF/uuNuwzEEWqthPfpYnIUbiOvSI7LR8OXANcDfwEDAXeFpGDVXUlpsyUcAxa4pqwnhiR/ltFlKsKGIY7x28EPgSOBcYCWcB/sM9Acapqr0ryAtKBq4CNwDJgTWBaNrAauDqQVhtYBQxKdtl3lVdJx8BPHwt8mOxy7oovXM3AKuDGiPS7gT/tMxD9ZdUplUsn4FrgSuCuiGlH4h5mCnb6tRx4F+v0qyyVdAzAXYl/VaElqjpqAo8BkyLSvwXqAidgn4FiLIhXLh8DjVV1HMW7FAh1+vVDRPqPgWlm55V0DABaAA1F5AsR2SQi34tI74ot4q5JVZer6sWq+nnEpNOBX4B9/Xv7DARYnXgloqq/ljA5B9ioqpsi0lf7aaYMlHQMRGRvXHPWprir9eXA2cAEEVFVfaxiSll1+E7tTgQuwT4DUVkQTx1C9CtDIaLDL1NuVuB+tn+lqr/7tLd8cB+BqwowZUREzsGN3vU87sG+a7HPQDFWnZI6VgLpvnuBoBps7xDMlCNVXaeq0wIBPOR1oImI1EhGuXZFInI58DgwBThH3V1M+wxEYUE8dXyHu+KIHEO0CanbP3pKEZEDRGSAiKRHTMoE1mO9Z5YJEbkZuB0XxLsFqk/sMxCFBfHUMQvYQNFOv2oDbdm1O/2qTPYB7gVOCSX4h1C6Au/5q0WzE0TkUly1yZ1AH1XdEphsn4EorE48RajqGhG5CxglItuAhcB1uDayDyW1cFXHTOB94D4fPH4H+uOaHR6TzILtCkRkL+BWYC7wDPB3/6BmyCe4Zp/2GQiwIJ5ahuBu4AzG1QPOAnprVX1SrYKp6lYR6QLcDNwA7Al8Bpykqp8ktXC7hpNxD1u1AKJ1ZFcX+wwUYx1gGWNMCrM6cWOMSWEWxI0xJoVZEDfGmBRmQdwYY1KYBXFjjElhFsQriEQ0eDWpy46lKU1FniOVNoiLyIwoQ2CtE5EvReTiZJcvESIyHPh3GSznXBH5RUTWi8hVZVC0MiMi7fwxKijn9YwUkTXluY5S1p/wsRSRn0Tkvzu5XhWRwTuzjCjLvF1EVojIKhE5uiyXXRmJSCO/H7v596WeSz4OTUlwPRfiRibaKfGe65U2iHsfAG0Cr864p7nuSrFAfj2uf42ddSfuKbWTgafKYHkmcTtyLM8EdnYMzjbAkzu5jDARaQFcDowHTgO+KKtlp5CHgOPLYbnXAbXKYblRVfYnNleo6ofBBBGZDhQAF+O6p6xK6gCvqerMZBfExC/KIAc7sowPS8+VkDr+71Oq+nEZLzslqOovuMEmUlplvxIvRlW3AV8C+aE0EdlDRO4SkSW+qmGGiBwWmN5HRJaKyJX+77cikuVH1h4iIj/4qpovROSM4PpE5BIR+U5ENorINyLyj8C00M+z00Xkdb+MX0XkukCe0COxt4nIT7G2S0RyReRBX12yTkSmh6omQlUVPut/Av9HLiPqdpa2HX76KSLyiV/3nyLyiIjUCUyvISK3icj//D6eIyIdYpQjVLXSJiL9Yr/8Gv59KxF526cV+mOYFTHPYBFZJG7k88cp5SrY74M1ItJBRBb4+d4VkUMDeUb6bb3DVyd8EM82xjqWInKSiHzk5/lFRG4QkbTA9HB1SuAYtffn20ZxI7p3LmW7wtUp/rz9j7gR30PzD4jI31JEXhORZf71uIjUD20/MMNnnSMiM+Lc/tBx7S8iv4vIz/4z8JOIXO3P35V++64XkZoi8oQ/HotEpE9gWXuIyEN+OetF5DMR6VrC9s8QkWkRabuJyB8icoN/v5c/b38Tkc3+71gp3utkaP4i1RV++x/0+2upiFwdZZ4S1+HPi3zgosD5Ui7neliyB/mM9cKdZFNiTPsMmOf/F1wPZoXAhbifhq/j+hfez+fpA2wB5uGqIrr59HG4AXGHAu2B+32+Y/z0EcBmXP1WB9wguduA7n56I1wn9UtxP7NPwHVir0Ann+dI/34ccFiM7amBqyb5H3Aubjiqd4B1uH4kciKWc2SM5cTaztK2Ix/XO9ydQDvgn35/Pu2n74br+GkprsOnTriO+rcAJ/s87Xz5Cvwx+Rm4PaJ8M4Fn/f8H47pufcsfs/Nxg+FOCeQf7NdxPW4whmeBTUQMXhxlH2z25b/E78s5uFF46vk8I32e9/1xPzXObSx2LP38W3AdNnUELvXbdXegTD8B/w2UbyOwCHe+nuT3y3qgTgnbpcBg///VwF/AeX6/3+6nh8p5KO7cedtv/z/9+hYAe+CGOfu3n6ePPxaJHONfQssNbN8q4BHcZ+Aen+9bXDVSR9xnchOQ5+e5FzesWnc/zxPAVqBZjO3v54/ZnoG0E/x6mvnyf4mrFjrTT7vVTx8Y8XntFjgPgoORT/Hbfz7QxS9rE/6cjHMdh+E6RpuI/5xSTud6eN5kB+tSgviruCqf3YFqQB5wi99pl/l8J/v3Jwbm3d2fQI8EPjjhg+fT6vidNizKeofg6rTWA6Mipj8M/BBxUtwTmL6bPxHuivYBjLGtA/0JfHAgrTruw/FCAsuJtp3xbEd3P99egelnBvbx6QSCRCDPbODTiA94gX8/GljM9v559vLbeIZ//zTuQ5weWN6xfhnH+f1YCNwbmC64D1FpQVyBAYG0urgP0YjAhzdc1ni3Mdox8NPfj5jnn35bG/n3P1E0iCvQI5C/pU87q4TtCgbxV4FpEdNHAcf6/1/ABe3qgekH+zKFgk3k8UrkGA+OyPMTMD9wrDP9uqYH8uzn5w0d/2+A+yPO9zFAixjbXxv35XdBIO0+4Av/f0PcZ7dlxHxfAs9HfF6LBfHAMfhHYN69cBc3U+JdR+TxLs9zPfSq7NUpp+C+fTfjvpUW4a507mB7ffjxuKuOd0VkdxEJ1fO/gbtKCpoX+P/vQBrwSjCDqrZT1ZtxV10ZwKuh5fplv4YbxSXYMf2Hgfm3Ab/hrnjidRzwjaqGy6euI/zJuL6SExXczni241PcB2SO/zndDnhZVccGyrdaVYv8nMVdfR4uItlRyvAU7qT/u3/fHXe19pp/fzzuGG0NlGm2z9MeOBA3nmUoP+rO7siR0GN5JjBfoV/2sRF55gf+T3gb/c/hI4ApEfv2ddwHs6SbZsE67lC9bLznzCygg4i8IyKXikgTVR2qqu8FtuUlDYxF6c+tr4h9PiWy/fMobo6Gvm1U1+PGvQz27PiX/1srsA0XisjLItIPyFXVK1R1brTCqRvVfhruPMJXV3XF3+BX1Z9VtR3wtYg0FZFTRWQIUB/XM2JpQq1zgufb7wR6U9yJdZTruV7Zg/j7QGv/KgAOAmqq6iDd3ln8nkAWLshvDrwuxn2TBv0Z+L9OlLSgPf3fWRHLnejTg8teFzHvNhLbt7WBJVHSl7BjA8AGt6nU7VDVH3En05e4XwXvAL+IyJlxlA+gWIBT1c9wv4a6+6QewCRV3RgoV/+IMm3Gbe9efp3gftUE/RF1i4vaoKorItIK2X7MAdaqanAknoS30c+zG+7XYXAbQvs/8vwLCp4zofEh4z1n/h8wCPcLYyzwgw/oewfKlej5lMj2R/vMrI6SFvm5CLoEV73XHFeN+bOIPC8iJZ3vTwHtxd2rOR4X+MJf1iJyAe4CaiHwIC5urMdd1ZamNrBZVVdFpBc533ZwHeV5rlf61ikrtfR+mlfiTqpTE122/1sXd1AAEHcDTALTzyT6Hexv2R4gd9Yy3BdUpAZsv4LZUfFsB6r6AXCav7psD1wFTBSRPF+++jHKh58ezdNAXxG5HTgK9/M1WK6XcHWjkZay/cqmXsS0ePZ5hohkqWowiNQj9hc27Ng2hj7wo3DbEum3KGk7TVW34n6N3uGPzxm4utSHcXXZJW3L/CjplDJPaHqZ8VfrI4ARInIg0A0Yhqtj/r8Ys72M+8XYGdfkcpaqLgYQkba4oHojriqj0KfPibNIfwHVRKRWxAXAnrgLxJ1ZR3me65X+Sjwe7+MC8RpV/ST0As7B3SSMZQ6uTvy0iPT7gSuBj3DflvUiltscGE583+4hpY3E/T5wiIg0CyWISHVc4P0ggfVEU+p2iMi/RORHEammbjDgV3A3e9NwQ5K9D2SLyMkRy/4Hrr50Q4x1P4W7aXod7orunYhtPsjPHyrTz7irzOa4K53fcD+ZgzrFud3h4yoi9XAf+ndiZ497G8PHUlVX43697Bexbzfhrs4bxlnWhIjIG/6LEVVdrKrjgBdx94xC29LFn0OheZrhbpLHOp929BjvSPnTRORrEbnMb8O3qnoTroohL9Z8/kv5JVz9/RkUfVYidNN5VCC47o3b5ng+qzP83/D5Jm70piN3YB1bI5Zdrud6Zb8Sj8crwMfAVBG5Hncz7SzgImBArJlU9U8RuQ8YKiKbcfXC3XF39i9S1UIRGQeM8Qdzjp92E66+cZUEmuCVYgVwjIi8p6ofRZk+HrjMb8NQ3Df35bgro5viXEdUcW7HTNywVxNF5B7cTaahuNYyX+BaA30EPCGu+eRioC+uvvv0Etb9nYh8gmtZcLe/ggy5EVfF85yIPIKrtx+GC3yfq6qKezryQRFZAryJq5IpoPiHJJq7fT1uIe7LahnuRlgsr8a5jSsoeiyHAy+KyErcPYxc3JX5NtyDaeXhPdx5+zvu3G+GO3fv8NNvwu3b10TkDqCmL9NPwKMxlhnv9u80dSMkfYS7Ct+AazVzJO6eRf9SZn8KF8iV7VWC4PbDbsBYEZmI+zK4DneVmxW5kChl+lZEnvDzZ+C2/1pcg4pE17ECaCUix+GOVfme66Xd+UzWixKaGEbJWwt3Bb0EVz/1FW6Q1cgWC7kR86XhftItxtXfzQHaB6bvhqtW+B73M+4n3NBc6Rrlbndgvi+ACYH3A3GBeRmwe4xt2BdXv7cCWOMP5BEReeJtnRK5nSVuh89zoj/RVvnXS/gmmhH7uNDvq1lAx8D0dkS0+PDpl/v0Ys0icTeTZvjlLcN9IR8Skedfvtzr/fShxNc65Z+4FgFr/LY0CuQZGW0ZpW1jrGOJC3If41oy/Il7srJhYJ6fKN46JTdivUrgnI1StmDrlDS/DT/647kYFyh2D+Q/mu1NF5cCjwENSjpeO3GMw9sXSFsBjIy1jbibuONwV6QbcVejl8bxWd/dl+/1KNMuxzV+WA98h/syG+nLkk7pTQyr45prFvpjfDvwHEWbApa4Dp/nrMA+3Le8zvXQy4ZnM7sUcQ+UjAfqqmrkjSJjdjm7Qp24McZUWRbEjTEmhVl1ijHGpDC7EjfGmBRmQdwYY1KYBXFjjElhFsSNMSaFWRA3xpgU9v8B/NgjosGbIKAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import os, sys\n",
    "import matplotlib\n",
    "import config\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle, Patch\n",
    "\n",
    "matplotlib.rc('font', **config.font)\n",
    "\n",
    "bins = 40\n",
    "random_color = 'lightgrey'\n",
    "true_color = 'green'\n",
    "\n",
    "#--------------------------------------------\n",
    "# Rescue Validation\n",
    "#--------------------------------------------\n",
    "filename = 'RescueValidationHistogramData.xlsx'\n",
    "validation_data = pd.read_excel(filename)\n",
    "validation_data['Percent Validated'] = validation_data['Percent Validated']*100\n",
    "exp_valid = validation_data.loc[0]['Percent Validated']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,3))\n",
    "N, bins, patches = ax.hist(validation_data['Percent Validated'], color=random_color, edgecolor='black', bins = bins)\n",
    "patches[-1].set_facecolor(true_color)\n",
    "ax.set_xlabel('Percent of rescued protein isoforms validated')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_title('Rescue Validation')\n",
    "\n",
    "legend_elements = [\n",
    "                   Patch(facecolor=random_color, edgecolor='black',\n",
    "                         label='Random trials'),\n",
    "                         Patch(facecolor=true_color, edgecolor='black',\n",
    "                         label='True Rescue')]\n",
    "ax.legend(handles=legend_elements)\n",
    "plt.arrow(exp_valid-0.1, 50, 0,-40,head_length=5, width=0.2,facecolor=true_color, edgecolor='black')\n",
    "plt.savefig('plot/Fig6d_rescue_emperical_p_histplot.pdf', bbox_inches='tight' )\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "\n",
    "\n",
    "#--------------------------------------------\n",
    "# Resolve Validation\n",
    "#--------------------------------------------\n",
    "filename = 'ResolveValidationHistogramData_update.xlsx'\n",
    "bins = 40\n",
    "validation_data = pd.read_excel(filename)\n",
    "validation_data['Percent Validated'] = validation_data['Percent Validated']*100\n",
    "exp_valid = validation_data.loc[0]['Percent Validated']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5,4))\n",
    "N, bins, patches = ax.hist(validation_data['Percent Validated'], color=random_color, edgecolor='black', bins = bins)\n",
    "patches[-1].set_facecolor(true_color)\n",
    "ax.set_xlabel('Percent of resolved protein isoforms validated')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_title('Resolve Validation')\n",
    "\n",
    "legend_elements = [\n",
    "                   Patch(facecolor=random_color, edgecolor='black',\n",
    "                         label='Random trials'),\n",
    "                         Patch(facecolor=true_color, edgecolor='black',\n",
    "                         label='True Resolve')]\n",
    "ax.legend(handles=legend_elements)\n",
    "plt.arrow(exp_valid-0.1, 50, 0,-40,head_length=5, width=0.2,facecolor=true_color, edgecolor='black')\n",
    "plt.savefig('plot/Fig6e_resolve_emperical_p_histplot.pdf', bbox_inches='tight' )\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import os, sys\n",
    "import matplotlib\n",
    "import config\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle, Patch\n",
    "\n",
    "matplotlib.rc('font', **config.font)\n",
    "\n",
    "bins = 40\n",
    "random_color = 'lightgrey'\n",
    "true_color = 'green'\n",
    "removed_color = 'darkred'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#--------------------------------------------\n",
    "# Resolve Validation - Low abundant isoforms removed validation\n",
    "#--------------------------------------------\n",
    "filename = 'ResolveValidationHistogramData_update.xlsx'\n",
    "bins = 40\n",
    "validation_data = pd.read_excel(filename)\n",
    "validation_data['Percent Validated'] = validation_data['Percent Validated']*100\n",
    "\n",
    "removed_validation = 0.7\n",
    "validation_data = validation_data.append({'Categorization': 'Removed', 'Percent Validated': removed_validation}, ignore_index=True)\n",
    "exp_valid = validation_data.loc[0]['Percent Validated']\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,3))\n",
    "N, bins, patches = ax.hist(validation_data['Percent Validated'], color=random_color, edgecolor='black', bins = bins)\n",
    "patches[-1].set_facecolor(true_color)\n",
    "patches[0].set_facecolor(removed_color)\n",
    "ax.set_xlabel('Percent of resolved protein isoforms validated')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_title('Resolve Validation')\n",
    "\n",
    "legend_elements = [\n",
    "      Patch(facecolor=random_color, edgecolor='black', label='Random trials'),\n",
    "      Patch(facecolor=true_color, edgecolor='black', label='True Resolve'),\n",
    "      Patch(facecolor=removed_color, edgecolor='black', label='Removed'),\n",
    "      ]\n",
    "ax.legend(handles=legend_elements)\n",
    "plt.arrow(exp_valid-0.2, 50, 0,-40,head_length=5, width=0.2,facecolor=true_color, edgecolor='black')\n",
    "plt.arrow(removed_validation + 0.3, 50, 0,-40,head_length=5, width=0.2,facecolor=removed_color, edgecolor='black')\n",
    "plt.savefig('plot/Fig6e_resolve_emperical_p_histplot.with_removed.svg', bbox_inches='tight' )\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Categorization</th>\n",
       "      <th>Percent Validated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Experimental</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Perm1</td>\n",
       "      <td>11.227336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Perm2</td>\n",
       "      <td>9.344491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Perm3</td>\n",
       "      <td>9.972106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Perm4</td>\n",
       "      <td>9.972106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Perm997</td>\n",
       "      <td>10.320781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Perm998</td>\n",
       "      <td>8.856346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Perm999</td>\n",
       "      <td>8.437936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>Perm1000</td>\n",
       "      <td>9.693166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>Removed</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1002 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Categorization  Percent Validated\n",
       "0      Experimental          21.200000\n",
       "1             Perm1          11.227336\n",
       "2             Perm2           9.344491\n",
       "3             Perm3           9.972106\n",
       "4             Perm4           9.972106\n",
       "...             ...                ...\n",
       "997         Perm997          10.320781\n",
       "998         Perm998           8.856346\n",
       "999         Perm999           8.437936\n",
       "1000       Perm1000           9.693166\n",
       "1001        Removed           0.700000\n",
       "\n",
       "[1002 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_data.append({'Categorization': 'Removed', 'Percent Validated': 0.7}, ignore_index=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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