tables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aldy: Figures and Tables\n",
    "\n",
    "Here you can find the scripts that somehow parse output from Aldy, Astrolabe and Cypiripi ([full output is located here](data)), and generate the tables used in our paper. Human supervision is needed. The code is of \"write-once-and-pray-never-again\" quality, and is provided mostly for the sake of completeness. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Aldy:-Figures-and-Tables\" data-toc-modified-id=\"Aldy:-Figures-and-Tables-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Aldy: Figures and Tables</a></span><ul class=\"toc-item\"><li><span><a href=\"#Load-sample-data-and-ground-truth-set-from-samples.xlsx\" data-toc-modified-id=\"Load-sample-data-and-ground-truth-set-from-samples.xlsx-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Load sample data and ground truth set from <code>samples.xlsx</code></a></span></li><li><span><a href=\"#Load-and-parse-various-log-files-and-tool-outputs\" data-toc-modified-id=\"Load-and-parse-various-log-files-and-tool-outputs-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Load and parse various log files and tool outputs</a></span></li><li><span><a href=\"#Check-the-number-of-correct-calls\" data-toc-modified-id=\"Check-the-number-of-correct-calls-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Check the number of correct calls</a></span></li><li><span><a href=\"#Generate-the-tables\" data-toc-modified-id=\"Generate-the-tables-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Generate the tables</a></span></li><li><span><a href=\"#How-many-alleles-cannot-be-tagged?\" data-toc-modified-id=\"How-many-alleles-cannot-be-tagged?-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>How many alleles cannot be tagged?</a></span></li><li><span><a href=\"#Figures\" data-toc-modified-id=\"Figures-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Figures</a></span><ul class=\"toc-item\"><li><span><a href=\"#Main-plotting-function\" data-toc-modified-id=\"Main-plotting-function-1.6.1\"><span class=\"toc-item-num\">1.6.1&nbsp;&nbsp;</span>Main plotting function</a></span></li><li><span><a href=\"#How-copy-number-rescaling-works-(PGRNseq)\" data-toc-modified-id=\"How-copy-number-rescaling-works-(PGRNseq)-1.6.2\"><span class=\"toc-item-num\">1.6.2&nbsp;&nbsp;</span>How copy number rescaling works (PGRNseq)</a></span></li><li><span><a href=\"#CYP2D6.5-Deletion\" data-toc-modified-id=\"CYP2D6.5-Deletion-1.6.3\"><span class=\"toc-item-num\">1.6.3&nbsp;&nbsp;</span><em>CYP2D6.5</em> Deletion</a></span></li><li><span><a href=\"#CYP2D6.68-Fusion\" data-toc-modified-id=\"CYP2D6.68-Fusion-1.6.4\"><span class=\"toc-item-num\">1.6.4&nbsp;&nbsp;</span><em>CYP2D6.68</em> Fusion</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, re, sys, scipy, glob\n",
    "from collections import defaultdict\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "def maybeint(s):\n",
    "    try: return (int(s), s)\n",
    "    except ValueError: return (0, s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load sample data and ground truth set from `samples.xlsx`\n",
    "\n",
    "[Input file is provided here](data/samples.xlsx)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th>index</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Family</th>\n",
       "      <th colspan=\"3\" halign=\"left\">CYP2D6</th>\n",
       "      <th>PGXT</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CYP2A6</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CYP2C19</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CYP2C8</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CYP2C9</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CYP3A4</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CYP3A5</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CYP4F2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">TPMT</th>\n",
       "      <th colspan=\"2\" halign=\"left\">DPYD</th>\n",
       "      <th>CYP2A6</th>\n",
       "      <th>CYP2C19</th>\n",
       "      <th>CYP2C8</th>\n",
       "      <th>CYP2C9</th>\n",
       "      <th>CYP3A4</th>\n",
       "      <th>CYP3A5</th>\n",
       "      <th>CYP4F2</th>\n",
       "      <th>DPYD</th>\n",
       "      <th>TPMT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Truth</th>\n",
       "      <th>ID</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Validated</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Truth</th>\n",
       "      <th>Truth</th>\n",
       "      <th>Truth</th>\n",
       "      <th>Truth</th>\n",
       "      <th>Truth</th>\n",
       "      <th>Truth</th>\n",
       "      <th>Truth</th>\n",
       "      <th>Truth</th>\n",
       "      <th>Truth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NA12331</td>\n",
       "      <td>1328</td>\n",
       "      <td>European</td>\n",
       "      <td>C</td>\n",
       "      <td>[4, 9]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[4, 9]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>NA18942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[2, 2]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[2, 2]</td>\n",
       "      <td>PGxT171</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>Only one copy of CYP2A6</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[3, 3]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 4]</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>[3, 3]</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>[1, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>NA23872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[2, 2]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[2, 2]</td>\n",
       "      <td>PGxT232</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 8]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 3]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[3, 3]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 3]</td>\n",
       "      <td>Either *2 or *4</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[9, 9]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>[1, 8]</td>\n",
       "      <td>[1, 3]</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>[3, 3]</td>\n",
       "      <td>[2, 3]</td>\n",
       "      <td>[9, 9]</td>\n",
       "      <td>[1, 1]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sample    index Family                           CYP2D6                   \\\n",
       "                    ID Ethnicity Relationship Validated Comments   Truth   \n",
       "23      NA12331   1328  European            C    [4, 9]      NaN  [4, 9]   \n",
       "167     NA18942    NaN       NaN          NaN    [2, 2]      NaN  [2, 2]   \n",
       "206     NA23872    NaN       NaN          NaN    [2, 2]      NaN  [2, 2]   \n",
       "\n",
       "Sample     PGXT    CYP2A6                            CYP2C19           \\\n",
       "             ID Validated                 Comments Validated Comments   \n",
       "23          NaN       NaN                      NaN       NaN      NaN   \n",
       "167     PGxT171    [1, 1]  Only one copy of CYP2A6    [1, 1]      NaN   \n",
       "206     PGxT232    [1, 1]                      NaN    [1, 8]      NaN   \n",
       "\n",
       "Sample    CYP2C8             CYP2C9             CYP3A4             CYP3A5  \\\n",
       "       Validated Comments Validated Comments Validated Comments Validated   \n",
       "23           NaN      NaN       NaN      NaN       NaN      NaN       NaN   \n",
       "167       [1, 1]      NaN    [1, 1]      NaN    [1, 1]      NaN    [3, 3]   \n",
       "206       [1, 3]      NaN    [1, 2]      NaN    [1, 1]      NaN    [3, 3]   \n",
       "\n",
       "Sample             CYP4F2                       TPMT               DPYD  \\\n",
       "       Comments Validated         Comments Validated Comments Validated   \n",
       "23          NaN       NaN              NaN       NaN      NaN       NaN   \n",
       "167         NaN    [1, 1]              NaN    [1, 1]      NaN    [1, 1]   \n",
       "206         NaN    [1, 3]  Either *2 or *4    [1, 1]      NaN    [9, 9]   \n",
       "\n",
       "Sample           CYP2A6 CYP2C19  CYP2C8  CYP2C9  CYP3A4  CYP3A5  CYP4F2  \\\n",
       "       Comments   Truth   Truth   Truth   Truth   Truth   Truth   Truth   \n",
       "23          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "167         NaN  [1, 4]  [1, 1]  [1, 1]  [1, 1]  [1, 1]  [3, 3]  [1, 1]   \n",
       "206         NaN  [1, 1]  [1, 8]  [1, 3]  [1, 2]  [1, 1]  [3, 3]  [2, 3]   \n",
       "\n",
       "Sample    DPYD    TPMT  \n",
       "         Truth   Truth  \n",
       "23         NaN     NaN  \n",
       "167     [1, 1]  [1, 1]  \n",
       "206     [9, 9]  [1, 1]  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load PGRNseq v1 data\n",
    "df = pd.read_excel('data/samples.xlsx', header=[0,2], sheetname='PGRNseq-v1 (CDC)', parse_dates=False)\n",
    "genes = set([x[0] for x in df.columns.values[4:]]) - set('SLCO1B1 CYP2B6'.split())\n",
    "genes = sorted(map(str, genes))\n",
    "for g in genes:\n",
    "    df[g,'Validated'] = df[g].Validated.apply(lambda x: sorted(re.split(r'[+/]', x), key=maybeint))\n",
    "    df[g,'Additional'] = df[g].Additional.apply(lambda x: [] if pd.isnull(x) else sorted(re.split(r'[+/]', x), key=maybeint))\n",
    "    df[g,'Truth'] = np.where(df[g].Additional.str.len() == 0, df[g].Validated, df[g].Additional)\n",
    "data_v1 = df\n",
    "\n",
    "# Load PGRNseq v2 data\n",
    "df = pd.read_excel('data/samples.xlsx', header=[0,1], sheetname='PGRNseq-v2', parse_dates=False)\n",
    "for g in ['CYP2D6']:\n",
    "    df[g,'Validated'] = df[g].Validated.apply(lambda x: sorted(re.split(r'[+/]', x), key=maybeint))\n",
    "    df[g,'Additional'] = df[g].Additional.apply(lambda x: [] if pd.isnull(x) else sorted(re.split(r'[+/]', x), key=maybeint))\n",
    "    df[g,'Truth'] = np.where(df[g].Additional.str.len() == 0, df[g].Validated, df[g].Additional)\n",
    "df.head()\n",
    "for g in set(x[0] for x in df.columns.values[5:] if x[0] != 'CYP2D6'):\n",
    "    del df[g]\n",
    "data = pd.merge(df.reset_index(), data_v1.reset_index(), \n",
    "                on=[('index', '')], how='outer', suffixes=('', '_y'))\n",
    "for i in 'Validated Truth Additional Comments'.split():\n",
    "    data['CYP2D6', i] = np.where(pd.isnull(data['CYP2D6', i]), \n",
    "                                 data['CYP2D6_y', i], data['CYP2D6', i])\n",
    "data = data.select(lambda x: not re.search('_y', x[0]) and x[0] not in 'SLCO1B1 CYP2B6'.split(), axis=1)\n",
    "\n",
    "# Load Illumina data\n",
    "df = pd.read_excel('data/samples.xlsx', header=[0,1], sheetname='Illumina', parse_dates=False)\n",
    "for g in ['CYP2D6']:\n",
    "    df[g,'Validated'] = df[g].Validated.apply(lambda x: sorted(re.split(r'[+/]', x), key=maybeint))\n",
    "    df[g,'Additional'] = df[g].Additional.apply(lambda x: [] if pd.isnull(x) else sorted(re.split(r'[+/]', x), key=maybeint))\n",
    "    df[g,'Truth'] = np.where(df[g].Additional.str.len() == 0, df[g].Validated, df[g].Additional)\n",
    "df.head()\n",
    "for g in set(x[0] for x in df.columns.values[5:] if x[0] != 'CYP2D6'):\n",
    "    del df[g]\n",
    "data = pd.merge(data.reset_index(), df.reset_index(),\n",
    "                on=[('index', '')], how='outer', suffixes=('', '_y'))\n",
    "for i in 'Validated Truth Additional Comments'.split():\n",
    "    data['CYP2D6', i] = np.where(pd.isnull(data['CYP2D6', i]), \n",
    "                                 data['CYP2D6_y', i], data['CYP2D6', i])\n",
    "for i in 'ID Ethnicity Relationship'.split():\n",
    "    data['Family', i] = np.where(pd.isnull(data['Family', i]), \n",
    "                                 data['Family_y', i], data['Family', i])\n",
    "data = data.select(lambda x: not re.search('_y', x[0]) and x[1] not in ['Additional'] and x[0] != 'level_0', axis=1)\n",
    "data.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and parse various log files and tool outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR data/illumina-1000genomes/cypiripi/NA12877.fq.sam.results\n"
     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>aldy</th>\n",
       "      <th>astrolabe</th>\n",
       "      <th>cypiripi</th>\n",
       "      <th>truth</th>\n",
       "      <th>validation</th>\n",
       "      <th>family</th>\n",
       "      <th>rel</th>\n",
       "      <th>truth_ok</th>\n",
       "      <th>aldy_ok</th>\n",
       "      <th>validation_ok</th>\n",
       "      <th>astrolabe_ok</th>\n",
       "      <th>cypiripi_ok</th>\n",
       "      <th>group</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HG00423</td>\n",
       "      <td>[10, 10, 10]</td>\n",
       "      <td>[10, 10]</td>\n",
       "      <td>[10, 10, 10]</td>\n",
       "      <td>[10, 10, 10]</td>\n",
       "      <td>[10, 10, 10]</td>\n",
       "      <td>SH007</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>multiplication</td>\n",
       "      <td>CNV data not clear cut; could be a *36+*10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NA10854</td>\n",
       "      <td>[1, 4]</td>\n",
       "      <td>[1, 4]</td>\n",
       "      <td>[2D7, 1, 4]</td>\n",
       "      <td>[1, 4]</td>\n",
       "      <td>[1, 4]</td>\n",
       "      <td>1349</td>\n",
       "      <td>M</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>normal</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>NA18508</td>\n",
       "      <td>[2, 5]</td>\n",
       "      <td>[2, 2]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[2, 5]</td>\n",
       "      <td>[2, 5]</td>\n",
       "      <td>Y009</td>\n",
       "      <td>M</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>deletion</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index          aldy astrolabe      cypiripi         truth    validation  \\\n",
       "2   HG00423  [10, 10, 10]  [10, 10]  [10, 10, 10]  [10, 10, 10]  [10, 10, 10]   \n",
       "30  NA10854        [1, 4]    [1, 4]   [2D7, 1, 4]        [1, 4]        [1, 4]   \n",
       "62  NA18508        [2, 5]    [2, 2]           [2]        [2, 5]        [2, 5]   \n",
       "\n",
       "   family rel  truth_ok  aldy_ok  validation_ok  astrolabe_ok  cypiripi_ok  \\\n",
       "2   SH007   C      True     True           True         False         True   \n",
       "30   1349   M      True     True           True          True         True   \n",
       "62   Y009   M      True     True           True         False         True   \n",
       "\n",
       "             group                                    comments  \n",
       "2   multiplication  CNV data not clear cut; could be a *36+*10  \n",
       "30          normal                                              \n",
       "62        deletion                                              "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_results(path, gene):\n",
    "    df = defaultdict(dict)\n",
    "    # Get Cypiripi log\n",
    "    def parse_cypiripi(fp):\n",
    "        call = [l.strip() for l in open(fp).readlines()]\n",
    "        if 'Solutions:' not in call: \n",
    "            print('ERROR', fp)\n",
    "            return\n",
    "        call = map(lambda x: x.split('[')[1][:-2].strip().split(), \n",
    "                   filter(lambda y: 'CPLEX' in y, call[call.index('Solutions:') + 1:]))\n",
    "        def parse(c):\n",
    "            d = []\n",
    "            for a in c:\n",
    "                i = a.split('×')\n",
    "                if len(i) == 2: \n",
    "                    if i[1] != '2D7': d += int(i[0]) * [i[1]]\n",
    "                else: d += [i[0]]\n",
    "            return map(lambda x: re.findall(r'^\\d+', x[1:])[0] if x != '2D7' else x, d)\n",
    "        call = list(map(lambda x: sorted(x, key=maybeint), map(parse, call)))\n",
    "        return call[0] if len(call) == 1 else call\n",
    "    # Astrolabe only supports these genes\n",
    "    if gene in ['CYP2D6', 'CYP2C9', 'CYP2C19']:\n",
    "        for fp in glob.glob(path + '/astrolabe/*.log'):\n",
    "            call = next(l for l in open(fp).readlines() if l.startswith(gene))\n",
    "            call = call.split()[1].replace(gene, '')\n",
    "            call = list(map(lambda x: x[1:], call.split('/')))\n",
    "            call = sorted(call, key=maybeint)\n",
    "            df['astrolabe'][os.path.basename(fp)[:7]] = call\n",
    "    # Cypiripi only works with CYP2D6\n",
    "    if gene in ['CYP2D6']:\n",
    "        for fp in glob.glob(path + '/cypiripi/*.results'):\n",
    "            df['cypiripi'][os.path.basename(fp)[:7]] = parse_cypiripi(fp)\n",
    "    for fp in glob.glob(path + '/aldy/*-{}.log'.format(gene)):\n",
    "        call = next(l for l in open(fp).readlines() if l.startswith('Result is'))\n",
    "        call = map(lambda x: [re.findall(r'^\\d+', y)[0] for y in x], eval(call[11:]))\n",
    "        call= list(map(lambda x: sorted(x, key=maybeint), call))\n",
    "        df['aldy'][os.path.basename(fp)[:7]] = call[0] if len(call) == 1 else call\n",
    "    return pd.DataFrame.from_dict(df)\n",
    "\n",
    "def get_truth(gene, dx):    \n",
    "    dy = pd.merge(dx, data, on='index')\n",
    "    def strip2(x):\n",
    "        if not isinstance(x, list): return x\n",
    "        return sorted([re.findall(r'^\\d+', y.strip())[0] if y[0].isdigit() else y for y in x], key=maybeint)\n",
    "    dx['truth'] = list(map(strip2, dy[(gene, 'Truth')]))\n",
    "    dx['validation'] = list(map(strip2, dy[(gene, 'Validated')]))\n",
    "    dx['family'] = dy[('Family', 'ID')] \n",
    "    dx['rel'] = dy[('Family', 'Relationship')]\n",
    "    \n",
    "    def filter_del(x):\n",
    "        return [y for y in x if y != '5'] if isinstance(x, list) else x\n",
    "    def filter_cypiripi(x):\n",
    "        if isinstance(x, list):\n",
    "            return list(map(filter_cypiripi, filter(lambda y: y != '2D7', x)))\n",
    "        return x\n",
    "    def criteria(k, F):\n",
    "        def f(x):\n",
    "            xa = F(x[k])\n",
    "            ya = filter_del(x.truth)\n",
    "            return xa == ya or (isinstance(xa, list) and ya in xa)\n",
    "        return f\n",
    "    dx['truth_ok'] = True\n",
    "    dx['aldy_ok'] = dx.apply(criteria('aldy', filter_del), axis=1)\n",
    "    dx['validation_ok'] = dx.apply(criteria('validation', filter_del), axis=1)\n",
    "    \n",
    "    if 'astrolabe' in dx:\n",
    "        dx['astrolabe_ok'] = dx.apply(criteria('astrolabe', filter_del), axis=1)\n",
    "    if 'cypiripi' in dx:\n",
    "        dx['cypiripi_ok'] = dx.apply(criteria('cypiripi', filter_cypiripi), axis=1)\n",
    "\n",
    "    def get_group(x):\n",
    "        if not isinstance(x.truth, list):\n",
    "            return 'unknown'\n",
    "        elif (gene == 'CYP2D6' and len(set('68 36 76 78 13'.split()) & set(x.truth)) > 0) or \\\n",
    "            (gene == 'CYP2A6' and '12' in x.truth):\n",
    "            return 'fusion'\n",
    "        elif (gene == 'CYP2D6' and '5' in x.truth) or (gene == 'CYP2A6' and '4' in x.truth):\n",
    "            return 'deletion'\n",
    "        elif len(x.truth) > 2:\n",
    "            return 'multiplication'\n",
    "        else:\n",
    "            return 'normal'\n",
    "    dx['group'] = dx.apply(get_group, axis=1)\n",
    "    dx['comments'] = dy[(gene, 'Comments')].fillna('')\n",
    "    return dx\n",
    "\n",
    "def get_df(sample_group, gene):\n",
    "    dx = load_results('data/' + sample_group, gene).reset_index()\n",
    "    return get_truth(gene, dx)\n",
    "\n",
    "d_set = {}\n",
    "for s in 'pgrnseq-baylor pgrnseq-cdc illumina-1000genomes'.split():\n",
    "    d_set[s] = get_df(s, 'CYP2D6')\n",
    "d_set['pgrnseq-baylor'].sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the number of correct calls\n",
    "\n",
    "Some Cypiripi numbers need manual checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pgrnseq-baylor / CYP2D6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>validation_ok</th>\n",
       "      <th>aldy_ok</th>\n",
       "      <th>astrolabe_ok</th>\n",
       "      <th>cypiripi_ok</th>\n",
       "      <th>truth_ok</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</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>deletion</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fusion</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multiplication</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal</th>\n",
       "      <td>62.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>87.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                validation_ok  aldy_ok  astrolabe_ok  cypiripi_ok  truth_ok\n",
       "group                                                                      \n",
       "deletion                  8.0      9.0           0.0          7.0       9.0\n",
       "fusion                   12.0     12.0           0.0          0.0      12.0\n",
       "multiplication            5.0      6.0           0.0          4.0       6.0\n",
       "normal                   62.0     69.0          68.0         50.0      69.0\n",
       "total                    87.0     96.0          68.0         61.0      96.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "illumina-1000genomes / CYP2D6\n",
      "ERROR data/illumina-1000genomes/cypiripi/NA12877.fq.sam.results\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>validation_ok</th>\n",
       "      <th>aldy_ok</th>\n",
       "      <th>astrolabe_ok</th>\n",
       "      <th>cypiripi_ok</th>\n",
       "      <th>truth_ok</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</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>fusion</th>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multiplication</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                validation_ok  aldy_ok  astrolabe_ok  cypiripi_ok  truth_ok\n",
       "group                                                                      \n",
       "fusion                    4.0     14.0           0.0          0.0      14.0\n",
       "multiplication            0.0      2.0           0.0          0.0       2.0\n",
       "normal                    7.0      9.0           3.0          3.0       9.0\n",
       "total                    11.0     25.0           3.0          3.0      25.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene = 'CYP2D6' # change this for other genes (and remove 'cypiripi_ok' from the last line)\n",
    "for sset in 'pgrnseq-baylor illumina-1000genomes'.split():\n",
    "    print(sset, '/', gene)\n",
    "    d_cdc = get_df(sset, gene)\n",
    "    g = d_cdc.groupby('group')\n",
    "    # Uncomment to show details\n",
    "    # for ng, gg in g:\n",
    "    #    display(gg[gg.aldy != gg.astrolabe]) \n",
    "    #    pass\n",
    "    y=g.sum()\n",
    "    s=y.sum()\n",
    "    s.name='total'\n",
    "    y=y.append(s)\n",
    "    display(y[['validation_ok', 'aldy_ok', 'astrolabe_ok', 'cypiripi_ok', 'truth_ok']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate the tables\n",
    "\n",
    "This is a rough code which generates tables. Manual checks are needed later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pgrnseq-baylor\n",
      "illumina-1000genomes\n",
      "ERROR data/illumina-1000genomes/cypiripi/NA12877.fq.sam.results\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "# This function prints nice diplotype\n",
    "def parse(xx):\n",
    "    if not isinstance(xx, list) or xx == []: \n",
    "        return ''\n",
    "    if isinstance(xx[0], list):\n",
    "        return ' or '.join(set(map(parse, xx)))\n",
    "    \n",
    "    x = list(filter(lambda y: y != '2D7', xx))\n",
    "    if len(x) == 1:\n",
    "        res = '*5/*{}'.format(*x)\n",
    "    elif len(x) == 2:\n",
    "        res = '*{}/*{}'.format(*x)\n",
    "    else:\n",
    "        x = defaultdict(int, Counter(x))\n",
    "        a = [[], []]\n",
    "        ali = 0\n",
    "        while x['36'] > 0 and x['10'] > 0:\n",
    "            a[ali % 2] += ['36', '10']\n",
    "            ali += 1\n",
    "            x['36'] -= 1\n",
    "            x['10'] -= 1\n",
    "        while x['4'] > 0 and x['68'] > 0:\n",
    "            a[ali % 2] += ['68', '4']\n",
    "            ali += 1\n",
    "            x['4'] -= 1\n",
    "            x['68'] -= 1\n",
    "        while x['2'] > 0 and x['78'] > 0:\n",
    "            a[ali % 2] += ['78', '2']\n",
    "            ali += 1\n",
    "            x['2'] -= 1\n",
    "            x['78'] -= 1\n",
    "        for h, c in x.items():\n",
    "            if c > 0:\n",
    "                a[ali % 2] += c * [h]\n",
    "                ali += 1\n",
    "        if len(a[1]) == 0: \n",
    "            a[1] = [a[0][-1]]\n",
    "            a[0] = a[0][::-2]\n",
    "        if len(a[0]) > len(a[1]):\n",
    "            a[0], a[1] = a[1], a[0]\n",
    "        res = '/'.join(map(lambda x: '+'.join(map(lambda y: '*' + y, x)), a))\n",
    "    return res\n",
    "        \n",
    "for sset in 'pgrnseq-baylor illumina-1000genomes'.split():\n",
    "    print(sset)\n",
    "    d_cdc = get_df(sset, 'CYP2D6')\n",
    "    for y in 'aldy astrolabe cypiripi validation'.split():\n",
    "        d_cdc[y] = d_cdc[y].apply(parse)\n",
    "    def check(r, k):\n",
    "        return '\\{}{{{}}}'.format('OK' if r[k + '_ok'] else 'NO', r[k])\n",
    "    def case(r):\n",
    "        r = re.split(r'[+/]', r.aldy)\n",
    "        if set(['*36', '*68', '*78']) & set(r):\n",
    "            return 'Fusion'\n",
    "        elif '*5' in r:\n",
    "            return 'Deletion (*5)'\n",
    "        elif len(r) > 2:\n",
    "            return 'Multiplication'\n",
    "        return ''\n",
    "    i = 0\n",
    "# Uncomment for table print (needs work obviously based on your needs)\n",
    "#     for ri, r in d_cdc.iterrows():\n",
    "#         print('{} & {:10} & {:20} & {:12} & {:25} & {:20} & {} \\\\\\\\'.format(\n",
    "#             r['index'], r.family + '/' + r.rel,\n",
    "#             check(r, 'aldy'), check(r, 'astrolabe'), check(r, 'cypiripi'), r.validation,\n",
    "#             case(r)\n",
    "#         ))\n",
    "#         i += 1\n",
    "    #display(d_cdc[['index', 'family', 'aldy', 'astrolabe', 'cypiripi', 'validation']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many alleles cannot be tagged?\n",
    "\n",
    "Notes:\n",
    "- \"structural variations\" refer to all alleles which change normal copy number structure (e.g. \\*13, \\*68 etc.)\n",
    "- \"ambiguous\" calls (obtained initially from Aldy's calls) are 2-copy samples that can be explained by multiple major star-allele combinations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re, os, collections\n",
    "from matplotlib import rc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First load the original data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded NA11892 with scaling factor 0.6777117303682453\n",
      "Loaded NA12878 with scaling factor 0.7737449026072621\n",
      "Loaded NA12155 with scaling factor 0.8061012056088495\n"
     ]
    }
   ],
   "source": [
    "profiles = collections.defaultdict(list)\n",
    "for sample in 'NA11892 NA12878 NA12155'.split():\n",
    "    with open('data/plots/{}.bam.profile'.format(sample)) as f:\n",
    "        pos = 0\n",
    "        for l in f:\n",
    "            l = l.strip().split()\n",
    "            profiles[sample].append(float(l[0]))\n",
    "            pos += 1\n",
    "def load_profile(profile_path):\n",
    "    result = []\n",
    "    with open(profile_path) as f: # now it is NA19686\n",
    "        for l in f:\n",
    "            l = l.strip().split()\n",
    "            if l[0] != 'chr22': continue\n",
    "            l = list(map(int, l[1:]))\n",
    "            l[0] -= 42518969\n",
    "            if l[0] < 0: continue\n",
    "            result += [0] * (l[0] - len(result))\n",
    "            result.append(l[1])\n",
    "    return result\n",
    "pgrnref = load_profile('data/plots/pgrnseq.profile')\n",
    "len(pgrnref)\n",
    "xprofiles = {}\n",
    "for pn, profile in profiles.items():\n",
    "    r = (28495, 29281)\n",
    "    rc = 2 * float(sum(profile[slice(*r)])) / sum(pgrnref[slice(*r)])\n",
    "    scale = 2.0 / rc\n",
    "    xprofiles[pn] = [i for i in profile]\n",
    "    print('Loaded', pn, 'with scaling factor', scale)\n",
    "    profiles[pn] = [scale * i for i in profile]\n",
    "CYP_REGIONS = { # intervals are inclusive on both sides\n",
    "    '6.9e': ( 3605,  3783), '6.9i': ( 3300,  3605),  '6.0i': ( 7822,  8000),\n",
    "    '6.8i': ( 3784,  3881),  '6.8e': ( 3882,  4023),\n",
    "    '6.7i': ( 4024,  4477),  '6.7e': ( 4478,  4665),\n",
    "    '6.6i': ( 4666,  4872),  '6.6e': ( 4873,  5014),\n",
    "    '6.5i': ( 5015,  5204),  '6.5e': ( 5205,  5381),\n",
    "    '6.4i': ( 5382,  5814),  '6.4e': ( 5815,  5975),\n",
    "    '6.3i': ( 5976,  6063),  '6.3e': ( 6064,  6216),\n",
    "    '6.2i': ( 6217,  6768),  '6.2e': ( 6769,  6940),\n",
    "    '6.1i': ( 6941,  7642),  '6.1e': ( 7643,  7822),\n",
    "\n",
    "     '7.9i': (17000, 17316),  '7.9e': (17317, 17495),\n",
    "    '7.8i': (17496, 17593),  '7.8e': (17594, 17735),\n",
    "    '7.7i': (17736, 18189),  '7.7e': (18190, 18377),\n",
    "    '7.6i': (18378, 18571),  '7.6e': (18572, 18713),\n",
    "    '7.5i': (18714, 18905),  '7.5e': (18906, 19082),\n",
    "    '7.4i': (19083, 19507),  '7.4e': (19508, 19668),\n",
    "    '7.3i': (19669, 19756),  '7.3e': (19757, 19909),\n",
    "    '7.2i': (19910, 20438),  '7.2e': (20439, 20610),\n",
    "    '7.1i': (20611, 21312),  '7.1e': (21313, 21493),  '7.0i': (21493, 22000)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main plotting function\n",
    "\n",
    "This code is both ancient and awful, and was a quick hack to get Matplotlib working the way I want. Provided only for the sake of completeness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "# Use this for publication\n",
    "# matplotlib.rc('font',**{'family':'serif','serif':['Computer Modern Roman']})\n",
    "matplotlib.rcParams['figure.autolayout'] = False\n",
    "\n",
    "def xplot(title, mima=None, **kwargs):\n",
    "    matplotlib.rcParams['text.usetex'] = True # True for nice figure\n",
    "    matplotlib.rcParams['font.size'] = 12\n",
    "\n",
    "    if len(kwargs) > 1:\n",
    "        matplotlib.rcParams['figure.figsize'] = (18, 5)\n",
    "        x = list(kwargs.values())[1][0][:]\n",
    "        y = [0] * len(x)\n",
    "        for rn, r in CYP_REGIONS.items():\n",
    "            w = sum(x[r[0]:r[1]]) / float(sum(pgrnref[r[0]:r[1]]))\n",
    "            for i in range(r[0], r[1]+1):\n",
    "                y[i] = w\n",
    "        f, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3)\n",
    "    else:\n",
    "        matplotlib.rcParams['figure.figsize'] = (18, 2)\n",
    "        f, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
    "        ax4, ax5, y = None, None, None\n",
    "\n",
    "    for axi, ax in enumerate([ax1, ax2, ax3, ax4, ax5]):\n",
    "        if ax is None: continue\n",
    "        for (a, b) in [(5483,5995),(3885,4667),(19335,19759),(17598,18380)]:\n",
    "            ax.axvspan(a, b, color='orange', alpha=0.5, lw=0)\n",
    "        if mima is not None:\n",
    "            mi, ma = mima\n",
    "            ax.fill_between(range(len(mi)), mi, ma, facecolor='red', lw=0)\n",
    "\n",
    "        if axi < 3:\n",
    "            ax.plot(pgrnref, '--', color='black')\n",
    "            if len(kwargs) > 1:\n",
    "                ax.plot(list(kwargs.values())[1][0], label='', **list(kwargs.values())[1][1])\n",
    "            ax.set_ylim(0, 3000)\n",
    "        elif len(kwargs) > 1:\n",
    "            ax.plot([1] * len(pgrnref), color='black')\n",
    "            ax.plot([.5] * len(pgrnref), **list(kwargs.values())[0][1])\n",
    "            ax.plot([1.5] * len(pgrnref), **list(kwargs.values())[0][1])\n",
    "            ax.plot([2.5] * len(pgrnref), **list(kwargs.values())[0][1])\n",
    "            ax.plot(y, label='', **list(kwargs.values())[1][1])\n",
    "            ax.set_ylim(0, 2.5)\n",
    "            ax.set_yticklabels(['0', '1', '2', '3', '4', '5'])\n",
    "\n",
    "        ax.xaxis.tick_top()\n",
    "\n",
    "        cr = sorted([(a[1], v) for v, a in CYP_REGIONS.items() if not v[2:] in ['9i', '0i', '6i', '8i', '7i', '5i', '3i']])\n",
    "        ax.set_xticks([x for x, _ in cr])\n",
    "        ax.set_xticklabels([('exon ' if x[-1] == 'e' else 'intron ') + x[-2] for _, x in cr])\n",
    "        for tick in ax.get_xticklabels():\n",
    "            tick.set_fontsize(8)\n",
    "            tick.set_rotation(45)\n",
    "\n",
    "    ax1.set_xlim(3300, 8000)\n",
    "    if ax4 is not None:\n",
    "        ax4.set_xlim(3300, 8000)\n",
    "        ax5.set_xlim(17000, 22000)\n",
    "        ax6.axis('off')\n",
    "    ax2.set_xlim(17000, 22000)\n",
    "    ax3.set_xlim(26900, 32000)\n",
    "\n",
    "    # hide the spines between ax and ax2\n",
    "    ax1.spines['right'].set_visible(False)\n",
    "    ax2.spines['left'].set_visible(False)\n",
    "    ax2.spines['left'].set_visible(False)\n",
    "    ax3.spines['left'].set_visible(False)\n",
    "\n",
    "    if ax4 is not None:\n",
    "        ax4.spines['right'].set_visible(False)\n",
    "        ax5.spines['left'].set_visible(False)\n",
    "        ax5.tick_params(labeltop='off')\n",
    "        ax4.tick_params(labeltop='off')\n",
    "        ax5.yaxis.tick_right()\n",
    "\n",
    "    ax1.yaxis.tick_left()\n",
    "    ax1.tick_params(labelright='off')\n",
    "    ax2.tick_params(labelleft='off')\n",
    "    ax3.yaxis.tick_right()\n",
    "\n",
    "    ax1.set_xlabel('CYP2D6')\n",
    "    ax2.set_xlabel('CYP2D7')\n",
    "    ax3.set_xlabel('CYP2D8')\n",
    "\n",
    "    f.subplots_adjust(hspace=.7)\n",
    "\n",
    "    d = .02\n",
    "    kwargs = dict(transform=ax1.transAxes, color='k', clip_on=False)\n",
    "    ax1.plot((1-d,1+d), (-d,+d), **kwargs)\n",
    "    ax1.plot((1-d,1+d),(1-d,1+d), **kwargs)\n",
    "\n",
    "    kwargs.update(transform=ax2.transAxes)  # switch to the bottom axes\n",
    "    ax2.plot((-d,+d), (1-d,1+d), **kwargs)\n",
    "    ax2.plot((-d,+d), (-d,+d), **kwargs)\n",
    "\n",
    "    kwargs.update(transform=ax3.transAxes)  # switch to the bottom axes\n",
    "    ax3.plot((-d,+d), (1-d,1+d), **kwargs)\n",
    "    ax3.plot((-d,+d), (-d,+d), **kwargs)\n",
    "\n",
    "    if ax4 is not None:\n",
    "        kwargs = dict(transform=ax4.transAxes, color='k', clip_on=False)\n",
    "        ax4.plot((1-d,1+d), (-d,+d), **kwargs)\n",
    "        ax4.plot((1-d,1+d),(1-d,1+d), **kwargs)\n",
    "\n",
    "        kwargs.update(transform=ax5.transAxes)  # switch to the bottom axes\n",
    "        ax5.plot((-d,+d), (1-d,1+d), **kwargs)\n",
    "        ax5.plot((-d,+d), (-d,+d), **kwargs)\n",
    "\n",
    "        ax1.set_ylabel('Rescaled\\nPGRN-Seq', rotation=90, size='12', x=1.1)\n",
    "        ax4.set_ylabel('Normalized\\nPGRN-Seq', rotation=90, size='12', x=1.2)\n",
    "\n",
    "    st = None\n",
    "    return (ax1, ax2, ax3, ax4, ax5, y,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How copy number rescaling works (PGRNseq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Annotation at 0x114926898>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fm/7YVIihWuuJpvsvAGitZ5rufwKsx8gY2b1cax2V7zijgdEA5cuXb3fzzTe7vWz5nT9/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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11128b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 1\n",
    "single = ([float(x)/2 for x in pgrnref], dict(color='black', linestyle='--', lw=.5))\n",
    "p = xprofiles['NA11892']\n",
    "ax1, ax2, ax3, ax4, ax5, y, fig = xplot('', None, insertion=(p, dict(color='red')), single=single)\n",
    "\n",
    "ax1.annotate('$B_g$', xy=(155,148+55), xycoords='figure points', size='14')\n",
    "ax1.annotate('$C_g$', xy=(155,190+55), xycoords='figure points', color='red', size='14')\n",
    "\n",
    "w=sum(p[i] for i in range(28495, 29281))/sum(pgrnref[i] for i in range(28495, 29281))\n",
    "ax3.axvspan(28495, 29281, color='purple', alpha=0.5, lw=0)\n",
    "ax3.annotate('$q$', xy=(870+12,225+55), xycoords='figure points', color='white', size='22')\n",
    "ax3.annotate('$\\\\eta=\\\\frac{C_g(q)}{B_g(q)}=1.48$', xy=(905+12,220+55), xycoords='figure points', color='purple')\n",
    "\n",
    "for ax in [ax4, ax5]:\n",
    "    ax.cla()\n",
    "    for (a, b) in [(5483,5995),(3885,4667),(19335,19759),(17598,18380)]:\n",
    "        ax.axvspan(a, b, color='orange', alpha=0.5, lw=0)\n",
    "    ax.plot(xprofiles['NA11892'], color='red')\n",
    "    ax.plot([i*w for i in pgrnref], '--', color='purple', lw=2)\n",
    "    ax.set_ylim(0, 3000)\n",
    "ax4.set_xlim(3300, 8000)\n",
    "ax5.set_xlim(17000, 22000)\n",
    "ax4.set_ylabel('Rescaled\\nPGRN-Seq', rotation=90, size='12', x=1.2)\n",
    "\n",
    "ax4.annotate('$C_g$', xy=(140+12,35), xycoords='figure points', color='red', size='14')\n",
    "ax5.annotate('$R_g = \\\\eta\\\\times B_g$', xy=(128+12,80), xycoords='figure points', color='purple', size='14')\n",
    "# plt.savefig('fig_sup_1.pdf', dpi=fig.dpi, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _CYP2D6.5_ Deletion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26900.0, 32000.0, 0.0, 3000.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qVIkKQFRFY//RCM9lK+x+83rPAr1vLz4Tn4/n5We4/9BxatRt7N8xAvh++crbWEIpZm/4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcf9ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 2 / deletion\n",
    "p = profiles['NA12155']\n",
    "ax1, _, ax3, ax4, ax5, y, st = xplot('(ii) \\\\textit{CYP2D6}: *1/*5 (whole gene deletion)', None, deletion=(p, dict(color='red')), single=single)\n",
    "ax1.fill_between(range(len(pgrnref)), p[:len(pgrnref)], pgrnref, facecolor='red', lw=0, alpha=0.5, hatch='x')\n",
    "ax4.fill_between(range(10000), y[:10000], [1] * 10000, facecolor='red', lw=0, alpha=0.5, hatch='x')\n",
    "\n",
    "ax1.annotate('$R_g$', xy=(175,235), xycoords='figure points', size='13')\n",
    "ax1.annotate('$C_g$', xy=(175,204), xycoords='figure points', color='red', size='13')\n",
    "\n",
    "ax4.annotate('$\\\\frac{C_g}{R_g}$', xy=(300,28), xycoords='figure points', color='red', size='13')\n",
    "\n",
    "ax3.cla()\n",
    "ax3.axis('off')\n",
    "# plt.savefig('fig_sup_2b.pdf',  dpi=fig.dpi, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _CYP2D6.68_ Fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Annotation at 0x116b0d0f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g11G/2pIoVk3bNmXg6IF0btOZFbtWcDT+aImOsW7fOn4c+SM9X+jJzvSd/LL7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115325a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%% Plot figure (2)/(iii)\n",
    "p = profiles['NA12878']\n",
    "ax1, ax2, ax3, ax4, ax5, y, st = xplot('(iv) \\\\textit{CYP2D6}: *10/*68+*4 (gene fusion in intron 1)', None, insertion=(p, dict(color='red')), single=single)\n",
    "r = (6941, 8000)\n",
    "ax1.fill_between(range(r[0], r[1]), p[r[0]:r[1]], pgrnref[r[0]:r[1]], facecolor='green', lw=0, alpha=0.5, hatch='x')\n",
    "ax4.fill_between(range(r[0], r[1]), y[r[0]:r[1]], [1] * (r[1]-r[0]), facecolor='green', lw=0, alpha=0.5, hatch='x')\n",
    "r = (17246, 21310)\n",
    "ax2.fill_between(range(r[0], r[1]), p[r[0]:r[1]], pgrnref[r[0]:r[1]], facecolor='green', lw=0, alpha=0.5, hatch='x')\n",
    "ax5.fill_between(range(r[0], r[1]), y[r[0]:r[1]], [1] * (r[1]-r[0]), facecolor='green', lw=0, alpha=0.5, hatch='x')\n",
    "\n",
    "ax3.cla()\n",
    "ax3.axis('off')\n",
    "\n",
    "ax1.annotate('$R_g$', xy=(175,235), xycoords='figure points', size='13')\n",
    "ax1.annotate('$C_g$', xy=(175,204), xycoords='figure points', color='red', size='13')\n",
    "\n",
    "ax4.annotate('$\\\\frac{C_g}{R_g}$', xy=(300,43), xycoords='figure points', color='green', size='13')\n",
    "# plt.savefig('fig_sup_2c.pdf',  dpi=fig.dpi, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}