spectra_intra.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from bgreference import refseq \n",
    "import collections\n",
    "from collections import Counter\n",
    "from itertools import islice\n",
    "\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "# split into even chunks\n",
    "def chunks(l, n):\n",
    "    for i in range(0, len(l), n):\n",
    "        yield l[i:i + n]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'TTT': 1004, 'AAA': 923, 'TTA': 876, 'TGT': 858, 'TTG': 817, 'ACA': 809, 'ATT': 773, 'AAT': 761, 'CTT': 738, 'ATG': 725, 'TAA': 719, 'CAA': 703, 'GTT': 700, 'ACT': 674, 'TGA': 630, 'TAT': 622, 'AAC': 615, 'TAC': 609, 'AGA': 605, 'AAG': 580, 'CTA': 561, 'TGG': 554, 'GTG': 552, 'TCA': 549, 'TGC': 547, 'TCT': 542, 'GAA': 535, 'GCT': 521, 'TTC': 518, 'AGT': 507, 'CTG': 495, 'CAT': 484, 'ATA': 471, 'GTA': 469, 'CAC': 459, 'GGT': 454, 'GAT': 440, 'CAG': 438, 'TAG': 427, 'ACC': 376, 'GCA': 372, 'CCA': 354, 'CCT': 344, 'GAC': 340, 'ATC': 339, 'AGG': 329, 'AGC': 301, 'GAG': 297, 'CTC': 287, 'GGA': 282, 'GTC': 269, 'GGC': 223, 'TCC': 209, 'GCC': 187, 'CGT': 171, 'ACG': 164, 'GGG': 134, 'CCC': 116, 'TCG': 113, 'CGC': 97, 'CGA': 95, 'GCG': 88, 'CGG': 76, 'CCG': 74})\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def get_mut(x):    \n",
    "    try:\n",
    "        triplet = '{}_{}'.format(refseq('sarscov2', '0', x['POS']-1, 3).replace('\\n', 'N'), \n",
    "                                x['ALT'])\n",
    "    except:\n",
    "        triplet = 'NNN'\n",
    "    return triplet\n",
    "\n",
    "def order_muts_full():    \n",
    "    order = []\n",
    "    first = ['A', 'C', 'G', 'T']\n",
    "    pyr = ['C', 'T']\n",
    "    for p in pyr:\n",
    "        for mut in first:\n",
    "            if mut != p:\n",
    "                for f in first:\n",
    "                    for f2 in first:\n",
    "                        comb = '{}{}{}_{}'.format(f, p, f2, mut)\n",
    "                        order.append(comb)\n",
    "    first = ['T', 'G', 'C', 'A']\n",
    "    pyr = ['G', 'A']\n",
    "    for p in pyr:\n",
    "        for mut in first:\n",
    "            if mut != p:\n",
    "                for f in first:\n",
    "                    for f2 in first:\n",
    "                        comb = '{}{}{}_{}'.format(f, p, f2, mut)\n",
    "                        order.append(comb)    \n",
    "    return order\n",
    "\n",
    "def slicing_window(seq, n=3):\n",
    "    \"\"\"\n",
    "    Fast slicing window\n",
    "    :param seq:\n",
    "    :param n:\n",
    "    :return:\n",
    "    \"\"\"    \n",
    "    \n",
    "    it = iter(seq)\n",
    "    result = ''.join(islice(it, n))    \n",
    "    \n",
    "    if len(result) == n:\n",
    "        yield result    \n",
    "        \n",
    "    for elem in it:\n",
    "        result = result[1:] + elem\n",
    "        #print(result)\n",
    "        yield result\n",
    "        \n",
    "l = refseq('sarscov2', '0', 1, 100000000).rstrip()\n",
    "count_genome_compo = Counter(list(slicing_window(l)))\n",
    "print(count_genome_compo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%qtconsole "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOTAL mutations, including all AF and indels: 1586334\n",
      "TOTAL mutations excluding indels: 1586334\n",
      "TOTAL mutations excluding indels, at AF > 0.01: 31763\n",
      "TOTAL indels: 0\n",
      "TOTAL indels, at AF > 0.01: 0\n",
      "0.02 - 0.5. Count: 10187\n",
      "0.02 - 0.5. Count excluding indels: 10187\n",
      "0.01 - 0.02. Count: 19119\n",
      "0.01 - 0.02. Count excluding indels:: 19119\n",
      "0.05 - 0.5. Count: 3136\n",
      "0.05 - 0.5. Count excluding indels:: 3136\n",
      "> 0.5. Count: 2456\n",
      "> 0.5. Count excluding indels: 2456\n",
      "< 0.01. Count: 1554566\n",
      "< 0.01. Count excluding indels:: 1554566\n",
      "0.01 - 0.5. Count: 29307\n",
      "0.01 - 0.5. Count excluding indels:: 29307\n",
      "0.01 - 0.05. Count: 26170\n",
      "0.01 - 0.05. Count excluding indels:: 26170\n",
      "> 0.05. Count: 5592\n",
      "> 0.05. Count excluding indels:: 5592\n",
      "< 0.05. Count: 1580741\n",
      "< 0.05. Count excluding indels:: 1580741\n",
      "0.02 - 0.05. Count: 7050\n",
      "0.02 - 0.05. Count excluding indels:: 7050\n"
     ]
    }
   ],
   "source": [
    "#intra-host All mutations.\n",
    "#multiVCF flag yes\n",
    "df = pd.read_csv('data/intra/austrian_allmuta_annot.csv', sep ='\\t')\n",
    "letters = ['A', 'T', 'C', 'G']\n",
    "df = df.rename(columns={'pos': 'POS', 'ref': 'REF', \"alt\":\"ALT\"})\n",
    "\n",
    "print(\"TOTAL mutations, including all AF and indels: \" + str(len(df)))\n",
    "print(\"TOTAL mutations excluding indels: \"+str(len(df[(df['REF'].isin(letters))&(df['ALT'].isin(letters))])))\n",
    "print(\"TOTAL mutations excluding indels, at AF > 0.01: \"+str(len(df[(df['REF'].isin(letters))&(df['ALT'].isin(letters))& (df['AF']>0.01)])))\n",
    "print(\"TOTAL indels: \"+str(len(df[(~df['REF'].isin(letters))|(~df['ALT'].isin(letters))])))\n",
    "print(\"TOTAL indels, at AF > 0.01: \"+str(len(df[(~df['REF'].isin(letters))|(~df['ALT'].isin(letters))& (df['AF']>0.01)])))\n",
    "\n",
    "#Figure 5a: All mutations. AF 0.02 - 0.5\n",
    "df002_05 = df[(df['AF']>0.02) & (df['AF']<0.5)]\n",
    "df002_05 = df002_05[['POS', 'REF', 'ALT']]\n",
    "print (\"0.02 - 0.5. Count: \" +str(len(df002_05)))\n",
    "df002_05_unique_muts = df002_05[(df002_05['REF'].isin(letters))&(df002_05['ALT'].isin(letters))]\n",
    "print (\"0.02 - 0.5. Count excluding indels: \" +str(len(df002_05_unique_muts)))\n",
    "\n",
    "#Figure 6a: All mutations. AF 0.01 - 0.02\n",
    "df001_002 = df[(df['AF']>0.01) & (df['AF']<0.02)]\n",
    "df001_002 = df001_002[['POS', 'REF', 'ALT']]\n",
    "print (\"0.01 - 0.02. Count: \" +str(len(df001_002)))\n",
    "df001_002_unique_muts = df001_002[(df001_002['REF'].isin(letters))&(df001_002['ALT'].isin(letters))]\n",
    "print (\"0.01 - 0.02. Count excluding indels:: \" +str(len(df001_002_unique_muts)))\n",
    "\n",
    "#Figure 7a: All mutations. AF 0.05 - 0.5\n",
    "df005_05 = df[(df['AF']>0.05) & (df['AF']<0.5)]\n",
    "df005_05 = df005_05[['POS', 'REF', 'ALT']]\n",
    "print (\"0.05 - 0.5. Count: \" +str(len(df005_05)))\n",
    "df005_05_unique_muts = df005_05[(df005_05['REF'].isin(letters))&(df005_05['ALT'].isin(letters))]\n",
    "print (\"0.05 - 0.5. Count excluding indels:: \" +str(len(df005_05_unique_muts)))\n",
    "\n",
    "#Figure 8a: All mutations. AF > 0.5\n",
    "df05 = df[(df['AF']>0.5)]\n",
    "df05 = df05[['POS', 'REF', 'ALT']]\n",
    "print (\"> 0.5. Count: \" +str(len(df05)))\n",
    "df05_unique_muts = df05[(df05['REF'].isin(letters))&(df05['ALT'].isin(letters))]\n",
    "print (\"> 0.5. Count excluding indels: \" +str(len(df05_unique_muts)))\n",
    "\n",
    "#Figure 9a: All mutations. AF< 0.01\n",
    "df001 = df[(df['AF']<0.01)]\n",
    "df001 = df001[['POS', 'REF', 'ALT']]\n",
    "print (\"< 0.01. Count: \" +str(len(df001)))\n",
    "df001_unique_muts = df001[(df001['REF'].isin(letters))&(df001['ALT'].isin(letters))]\n",
    "print (\"< 0.01. Count excluding indels:: \" +str(len(df001_unique_muts)))\n",
    "\n",
    "#Figure 10a: All mutations. AF 0.01 - 0.5\n",
    "df001_05 = df[(df['AF']>0.01)& (df['AF']<0.5)]\n",
    "df001_05 = df001_05[['POS', 'REF', 'ALT']]\n",
    "print (\"0.01 - 0.5. Count: \" +str(len(df001_05)))\n",
    "df001_05_unique_muts = df001_05[(df001_05['REF'].isin(letters))&(df001_05['ALT'].isin(letters))]\n",
    "print (\"0.01 - 0.5. Count excluding indels:: \" +str(len(df001_05_unique_muts)))\n",
    "\n",
    "#Figure 11a: All mutations. AF 0.01 - 0.05\n",
    "df001_005 = df[(df['AF']>0.01) & (df['AF']<0.05)]\n",
    "df001_005 = df001_005[['POS', 'REF', 'ALT']]\n",
    "print (\"0.01 - 0.05. Count: \" +str(len(df001_005)))\n",
    "df001_005_unique_muts = df001_005[(df001_005['REF'].isin(letters))&(df001_005['ALT'].isin(letters))]\n",
    "print (\"0.01 - 0.05. Count excluding indels:: \" +str(len(df001_005_unique_muts)))\n",
    "\n",
    "#Figure 12a: All mutations. AF > 0.05\n",
    "df005 = df[(df['AF']>0.05)]\n",
    "df005 = df005[['POS', 'REF', 'ALT']]\n",
    "print (\"> 0.05. Count: \" +str(len(df005)))\n",
    "df005_unique_muts = df005[(df005['REF'].isin(letters))&(df005['ALT'].isin(letters))]\n",
    "print (\"> 0.05. Count excluding indels:: \" +str(len(df005_unique_muts)))\n",
    "\n",
    "#Figure 13a: All mutations. AF < 0.05\n",
    "dfmenor005 = df[(df['AF']<0.05)]\n",
    "dfmenor005 = dfmenor005[['POS', 'REF', 'ALT']]\n",
    "print (\"< 0.05. Count: \" +str(len(dfmenor005)))\n",
    "dfmenor005_unique_muts = dfmenor005[(dfmenor005['REF'].isin(letters))&(dfmenor005['ALT'].isin(letters))]\n",
    "print (\"< 0.05. Count excluding indels:: \" +str(len(dfmenor005_unique_muts)))\n",
    "\n",
    "#Figure 14a: All mutations. AF 0.02 - 0.05\n",
    "df002_005 = df[(df['AF']>0.02) & (df['AF']<0.05)]\n",
    "df002_005 = df002_005[['POS', 'REF', 'ALT']]\n",
    "print (\"0.02 - 0.05. Count: \" +str(len(df002_005)))\n",
    "df002_005_unique_muts = df002_005[(df002_005['REF'].isin(letters))&(df002_005['ALT'].isin(letters))]\n",
    "print (\"0.02 - 0.05. Count excluding indels:: \" +str(len(df002_005_unique_muts)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOTAL mutations, including all AF and indels: 1586334\n",
      "TOTAL mutations excluding indels: 1586334\n",
      "TOTAL mutations excluding indels, at AF > 0.01: 31763\n",
      "TOTAL indels: 0\n",
      "TOTAL indels, at AF > 0.01: 0\n",
      "\n",
      "SYN/UP/DOWNSTREAM mutations.\n",
      "\n",
      "0.02 - 0.5. Count: 2527\n",
      "0.01 - 0.02. Count: 3301\n",
      "0.05 - 0.5. Count: 889\n",
      "> 0.5. Count: 953\n",
      "> 0.5. Count excluding  N's: 953\n",
      "< 0.01. Count: 192298\n",
      "0.01 - 0.5. Count: 5828\n",
      "0.01 - 0.05. Count: 4939\n",
      "> 0.05. Count: 1842\n",
      "< 0.05. Count: 197238\n",
      "0.02 - 0.05. Count: 1638\n",
      "0.02 - 0.5. Count: 2527\n"
     ]
    }
   ],
   "source": [
    "#intra-host SYN\n",
    "#multiVCF flag yes\n",
    "df = pd.read_csv('data/intra/austrian_allmuta_annot.csv', sep ='\\t')\n",
    "letters = ['A', 'T', 'C', 'G']\n",
    "df = df.rename(columns={'pos': 'POS', 'ref': 'REF', \"alt\":\"ALT\"})\n",
    "\n",
    "print(\"TOTAL mutations, including all AF and indels: \" + str(len(df)))\n",
    "print(\"TOTAL mutations excluding indels: \"+str(len(df[(df['REF'].isin(letters))&(df['ALT'].isin(letters))])))\n",
    "print(\"TOTAL mutations excluding indels, at AF > 0.01: \"+str(len(df[(df['REF'].isin(letters))&(df['ALT'].isin(letters))& (df['AF']>0.01)])))\n",
    "print(\"TOTAL indels: \"+str(len(df[(~df['REF'].isin(letters))|(~df['ALT'].isin(letters))])))\n",
    "print(\"TOTAL indels, at AF > 0.01: \"+str(len(df[(~df['REF'].isin(letters))|(~df['ALT'].isin(letters))& (df['AF']>0.01)])))\n",
    "print(\"\\nSYN/UP/DOWNSTREAM mutations.\\n\")\n",
    "\n",
    "#Figure 5b: Non-protein affecting. AF 0.02 - 0.5\n",
    "df002_05 = df[(df['AF']>0.02) & (df['AF']<0.5) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "df002_05 = df002_05[['POS', 'REF', 'ALT']]\n",
    "print (\"0.02 - 0.5. Count: \" +str(len(df002_05)))\n",
    "df002_05_unique_muts = df002_05[(df002_05['REF'].isin(letters))&(df002_05['ALT'].isin(letters))]\n",
    "\n",
    "#Figure 6b: Non-protein affecting. AF 0.01 - 0.02\n",
    "df001_002 = df[(df['AF']>0.01) & (df['AF']<0.02) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "df001_002 = df001_002[['POS', 'REF', 'ALT']]\n",
    "print (\"0.01 - 0.02. Count: \" +str(len(df001_002)))\n",
    "df001_002_unique_muts = df001_002[(df001_002['REF'].isin(letters))&(df001_002['ALT'].isin(letters))]\n",
    "\n",
    "#Figure 7b: Non-protein affecting. AF 0.05 - 0.5\n",
    "df005_05 = df[(df['AF']>0.05) & (df['AF']<0.5) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "df005_05 = df005_05[['POS', 'REF', 'ALT']]\n",
    "print (\"0.05 - 0.5. Count: \" +str(len(df005_05)))\n",
    "df005_05_unique_muts = df005_05[(df005_05['REF'].isin(letters))&(df005_05['ALT'].isin(letters))]\n",
    "\n",
    "#Figure 8b: Non-protein affecting. AF > 0.5\n",
    "df05 = df[(df['AF']>0.5) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "df05 = df05[['POS', 'REF', 'ALT']]\n",
    "print (\"> 0.5. Count: \" +str(len(df05)))\n",
    "df05_unique_muts = df05[(df05['REF'].isin(letters))&(df05['ALT'].isin(letters))]\n",
    "print (\"> 0.5. Count excluding  N's: \" +str(len(df05_unique_muts)))\n",
    "\n",
    "#Figure 9b: Non-protein affecting. AF< 0.01\n",
    "df001 = df[(df['AF']<0.01) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "df001 = df001[['POS', 'REF', 'ALT']]\n",
    "print (\"< 0.01. Count: \" +str(len(df001)))\n",
    "df001_unique_muts = df001[(df001['REF'].isin(letters))&(df001['ALT'].isin(letters))]\n",
    "\n",
    "#Figure 10b: Non-protein affecting. AF 0.01 - 0.5\n",
    "df001_05 = df[(df['AF']>0.01) & (df['AF']<0.5) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "df001_05 = df001_05[['POS', 'REF', 'ALT']]\n",
    "print (\"0.01 - 0.5. Count: \" +str(len(df001_05)))\n",
    "df001_05_unique_muts = df001_05[(df001_05['REF'].isin(letters))&(df001_05['ALT'].isin(letters))]\n",
    "\n",
    "#Figure 11b: Non-protein affecting. AF 0.01 - 0.05\n",
    "df001_005 = df[(df['AF']>0.01) & (df['AF']<0.05) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "df001_005 = df001_005[['POS', 'REF', 'ALT']]\n",
    "print (\"0.01 - 0.05. Count: \" +str(len(df001_005)))\n",
    "df001_005_unique_muts = df001_005[(df001_005['REF'].isin(letters))&(df001_005['ALT'].isin(letters))]\n",
    "\n",
    "#Figure 12b: Non-protein affecting. AF > 0.05\n",
    "df005 = df[(df['AF']>0.05) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "df005 = df005[['POS', 'REF', 'ALT']]\n",
    "print (\"> 0.05. Count: \" +str(len(df005)))\n",
    "df005_unique_muts = df005[(df005['REF'].isin(letters))&(df005['ALT'].isin(letters))]\n",
    "\n",
    "#Figure 13b: Non-protein affecting. AF < 0.05\n",
    "dfmenor005 = df[(df['AF']<0.05) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "dfmenor005 = dfmenor005[['POS', 'REF', 'ALT']]\n",
    "print (\"< 0.05. Count: \" +str(len(dfmenor005)))\n",
    "dfmenor005_unique_muts = dfmenor005[(dfmenor005['REF'].isin(letters))&(dfmenor005['ALT'].isin(letters))]\n",
    "\n",
    "#Figure 14b: Non-protein affecting. AF 0.02 - 0.05\n",
    "df002_005 = df[(df['AF']>0.02) & (df['AF']<0.05) & (df[\"csqn_type\"].isin([\"upstream_gene_variant\",\"downstream_gene_variant\",\"synonymous_variant\"]))]\n",
    "df002_005 = df002_005[['POS', 'REF', 'ALT']]\n",
    "print (\"0.02 - 0.05. Count: \" +str(len(df002_005)))\n",
    "df002_005_unique_muts = df002_005[(df002_005['REF'].isin(letters))&(df002_005['ALT'].isin(letters))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:47: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 5. AF 0.02 - 0.5\n",
    "df002_05_unique_muts['FULL_MUT'] = df002_05_unique_muts.apply(get_mut, axis = 1)\n",
    "df002_05_unique_muts_count=df002_05_unique_muts[['FULL_MUT', 'POS']]\n",
    "df002_05_unique_muts_count = df002_05_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "df002_05_unique_muts_counter_triplets_order = df002_05_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = df002_05_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 5b: Non-protein affecting. AF 0.02 - 0.5. \"+str(len(df002_05_unique_muts))+\" mutations.\", y=1.08)\n",
    "\n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.002),16,0.0007,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/002_05syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:46: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 6. AF 0.01 - 0.02\n",
    "df001_002_unique_muts['FULL_MUT'] = df001_002_unique_muts.apply(get_mut, axis = 1)\n",
    "df001_002_unique_muts_count=df001_002_unique_muts[['FULL_MUT', 'POS']]\n",
    "df001_002_unique_muts_count = df001_002_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "df001_002_unique_muts_counter_triplets_order = df001_002_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = df001_002_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 6b: Non-protein affecting. AF 0.01 - 0.02. \"+str(len(df001_002_unique_muts))+\" mutations.\", y=1.08)\n",
    " \n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.002),16,0.0007,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/001_002syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:45: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 7. AF 0.05 - 0.5\n",
    "df005_05_unique_muts['FULL_MUT'] = df005_05_unique_muts.apply(get_mut, axis = 1)\n",
    "df005_05_unique_muts_count=df005_05_unique_muts[['FULL_MUT', 'POS']]\n",
    "df005_05_unique_muts_count = df005_05_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "df005_05_unique_muts_counter_triplets_order = df005_05_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = df005_05_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 7b: Non-protein affecting. AF 0.05 - 0.5. \"+str(len(df005_05_unique_muts))+\" mutations. Austrian samples: 420.\", y=1.08)\n",
    "\n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.002),16,0.001,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/005_05syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:45: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 8. AF > 0.5\n",
    "df05_unique_muts['FULL_MUT'] = df05_unique_muts.apply(get_mut, axis = 1)\n",
    "df05_unique_muts_count=df05_unique_muts[['FULL_MUT', 'POS']]\n",
    "df05_unique_muts_count = df05_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "df05_unique_muts_counter_triplets_order = df05_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = df05_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 8b: Non-protein affecting. AF > 0.5. \"+str(len(df05_unique_muts))+\" mutations.\", y=1.08)\n",
    "\n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.01),16,0.007,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/more05syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:45: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 9. AF < 0.01\n",
    "df001_unique_muts['FULL_MUT'] = df001_unique_muts.apply(get_mut, axis = 1)\n",
    "df001_unique_muts_count=df001_unique_muts[['FULL_MUT', 'POS']]\n",
    "df001_unique_muts_count = df001_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "df001_unique_muts_counter_triplets_order = df001_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = df001_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 9b: Non-protein affecting. AF < 0.01. \"+str(len(df001_unique_muts))+\" mutations.\", y=1.08)\n",
    "\n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.001),16,0.0007,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/less001syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:48: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 10. AF 0.01 - 0.5\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "df001_05_unique_muts['FULL_MUT'] = df001_05_unique_muts.apply(get_mut, axis = 1)\n",
    "df001_05_unique_muts_count=df001_05_unique_muts[['FULL_MUT', 'POS']]\n",
    "df001_05_unique_muts_count = df001_05_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "df001_05_unique_muts_counter_triplets_order = df001_05_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = df001_05_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 10b: Non-protein affecting. AF 0.01 - 0.5. \"+str(len(df001_05_unique_muts))+\" mutations.\", y=1.08)\n",
    "\n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.002),16,0.0007,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/001_05syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:46: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 11. AF 0.01 - 0.05\n",
    "df001_005_unique_muts['FULL_MUT'] = df001_005_unique_muts.apply(get_mut, axis = 1)\n",
    "df001_005_unique_muts_count=df001_005_unique_muts[['FULL_MUT', 'POS']]\n",
    "df001_005_unique_muts_count = df001_005_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "\n",
    "df001_005_unique_muts_counter_triplets_order = df001_005_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = df001_005_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 11b: Non-protein affecting. AF 0.01 - 0.05. \"+str(len(df001_005_unique_muts))+\" mutations.\", y=1.08)\n",
    "\n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.002),16,0.001,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/001_005syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:45: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 12. AF > 0.05\n",
    "df005_unique_muts['FULL_MUT'] = df005_unique_muts.apply(get_mut, axis = 1)\n",
    "df005_unique_muts_count=df005_unique_muts[['FULL_MUT', 'POS']]\n",
    "df005_unique_muts_count = df005_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "df005_unique_muts_counter_triplets_order = df005_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = df005_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 12b: Non-protein affecting. AF > 0.05. \"+str(len(df005_unique_muts))+\" mutations.\", y=1.08)\n",
    "\n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.007),16,0.004,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/more005syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:45: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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86HGAKH5x7oKGp+9HcSnW7dUbcBJL+CtwmflkZv4oM99AMTLllw3l7qKY7+woivfLNlTmJqtp//+jyNudFO/ld1Tm1tmI4tKxBylGmT1OpXNshFxA0VH8aPmcj5T/t+b7WYNiEvmHKeZXOjUzv9z2/H2AX2Zm+2WOe1DM9XRALLpc9KGIWL98/F8o9jcPUbzn/kyxP2p5LcUk7XdRvMbzgXd3WI9efI7iksU7KDrD2juTD2foe/PzFPm/m6KjuH3C8C8Ar4/i19uOrVneuyhyeCNwGcVE4qd20c5JFB3Ot1JcXrgTxXthcfaBkqQxLNpG+0qSNCZFxCeBp2Vm335CXRARhwMbZ+beo92WiSginkXRobJ8OTJNkiRp3HCkkiRpTIqIZ5aXHLUuMTuQYlSONK5FxB4RsVw5b88ngR/aoSRJksYjO5UkSWPVyhTzsTxMcRnSMRSXaUnj3b9RXBr1N4p5Zt4xus2RJElaPF7+JkmSJEmSpJ45UkmSJEmSJEk9s1NJkiRJkiRJPbNTSZIkSZIkST2zU0mSJEmSJEk9s1NJkiRJkiRJPbNTSZIkSZIkST2zU0mSJEmSJEk9s1NJkiRJkiRJPbNTSZIkSZIkST2zU0mSJEmSJEk9s1NplETEmyPiqoh4KCJui4gLImLHxahn/4jIiNhzEO1clvQj5xHxjIg4JyLuioh5EfHXiDguItYdVLvHsyXNeUScHhEfb4ttUL7np/S/xeNXmZON22KHR8TXFrO+DSNiQUR8sT8tXPb0M+cRMSMiPh8Rfy+3lxvK+2v0r8XjX59zvnJEfDYibo6Ih8vcfzsitu5fi5cdEbFXRPyqzNWd5f//LyKihzouiYi3tcV2joi5/W/x+NePnJf1bB0R50fE/RFxb0T8OiIOGFS7x7N+5bys6/Byn+U+pYNyv3BfRCy/mM91n9KjJcl5+fzZEXFeWcf9EfHHiDgqIlbtd1uXFX3I+Zjej9upNAoi4j3A54GjgacC6wNfBHaPiNUiYmoP1e0H3Fv+VYN+5Lz8IPMr4FbgeZk5A9gB+BvQc4fgsq7P73MtoYhYPiJm9vCUfYH7gL0W9wA40XWb84hYDvgp8GxgV2AGsD1wD+CHkR70kPPlgf8FngO8iiLnzwLOAV4x0EaOQxHxXuALwKeBp1Hs099OcQxcLiLWXJwP3WrWr5xHxHYU7/VLgY2B1YF3ALsNqOnjVj/f52W5ffAcvaOI2AB4AZDAaypx9ykDsqQ5j4jtgUuAXwDPzMxVKM5d5gNb9L/F418fcj729+OZ6W0p3oCZwEPAGxoefyNwB3AMsNkIdc0CFgCvo9iQnzra6zcWb/3KOfA14IejvT7j4dbHnJ8OfLwttgHFTnnKaK/nWLqVOdm4LXY48LXy/3WAecBZwEuASSPU9zeKA9YdwOtHe/3G4q1fOQfeVuZ5+miv01i/9TnntwErjfY6jfVbuT9/GHhdhzIfBG4GjgA27FDuEuBtbbGdgbmjvZ5j6dbnnF8GnDDa6zTWb/3MeVn2hcCjwN4UXxAsN9rrOBZvwEcpOic+C5zXa67dp4xKzi8Djhvt9RhPtz7lfEzvxx2ptPRtB0wDvlf3YGZ+A9iForPo4oi4shx2WzeccF/gqsz8DnA98JYBtXm861fOXwJ8Z6AtXXb0832uPsjMfwCbAL+hOKjdFBFHRsRG7WUj4gXAuhSjNr5Jsa9Rj3rI+UuACzPzoaXdxmVNjzm/KDMfXtptHIe2A5YHftBUIDM/CewFPAW4KiJ+FhH7RsSKS6mNy5q+5Lz8fzvg2wNu77Kg3+/z/YAfAt8o77+qz+1dVuxL8SXAWcDLI+Kp4D5lwBY75xGxEsW24ueh3ixJzsfFftxOpaVvdeDuzJzfVCAz/5CZ7wfWAz5G0eN+UxRz+cyoFN0XOLv8/2wcXtukXzlfA7i99ZyIOKS8rvWhiDh5cM0fl/r5PlefZObtmXlMZm4O7AGsAlxRXuddHbK8H3BBZt5HsW/ZLSKesvRbPP51mfPVKUbNqA+6zHn7/vy55f58XkT8eem3ekxbg7b9eURcXubr0Yh4IUBmXpGZ7wDWBk6kOFGeGxFfGZVWj2/9yvmqFOf67l9G1rf3efkh8A3A2Zn5BMWHQc/R20Qxx+Ys4JuZeTXFCOk3tx53n9J/fch5a59SPX5+qtxOHo6I/1xKqzJu9DHnY3o/bqfS0ncPsEZ0MclwZj4J/AH4HcU12ZsBUwEiYgdgQ4qRBFB88HtORDx3AG0e7/qS87KetSplj8/iOuLPV8qo0K+cz2d4bqdSjHBa0LfWLhuepD5XTzSUv4Ei5zcAz6T44E1ErEBxMnwWQGb+Evg7lQOgFupLzmnbt6ijgeQ8M68p9+evpRitoEWG7c8zc/syX/fQdi6ZmY8D1wLXAP+kmLeqpWmf3vT6TVT9yvl9FMdK9y8j6+f7fA+K9/r55f2zKL6cWXNQjR+n9gMuzsy7y/u1X5C7T+mrJc35sH1KZn6g3E6+B/gjOsP1PedjkZ1KS98vgceAf20qEBHTo/hVt/+lGMK/DvDGzNwsM+8pi+0HBHBNRNxOMYE0eJlKnX7l/KcUHzg0sn7l/O8UcyhVbQjckpl2Kg3VlKs5rTsRMTkido2Ir5flXwl8Alg3My8ti+1BMWnxFyPi9nL/sg7uW+r0K+c/oRgOvdLgmzzu9SvnPwVeZs678kvgcWD3ToUiYvVyBO+vKSYUnQK8KDO3qRQb8fUT0KecZ+YjZV2vG3B7lwX9fJ/vB0wH/l4eQ79F0dHxpoG0fBwqv8DaE9ipcq7xbmCL1ohS9yn91Y+cl5eM/wo/D3WlTzkfH/vx0ZzQaaLegPdQTMr6r8CKFAea3YBPUcyePw+4gGIy4+Vrnj8NuB84kOLXKVq3d5b1OoFxn3Ne1rFJmffPAuuUsTUoris+fbTXcazd+pTzZ1NM+P0yYDLF0NCfA/8z2us31m4UH5p/QTEX0iSKOWMepJwIneJ67duA3wL/DqzRUM9FwClt+5YtKb4lec5or+dYuvUx58sDVwIXUoymmURxSdyHgVeM9nqOpVsfcz6NojP7YorRkZPL2IeAm0d7PcfaDfhAuT9/PcWH5UnAcym+Qd2Z4nzkQYoPz68EJjfU83LgTopfNYzyuHo98PbRXsexdutjzrcvj6PvB1YvY1sA54z2Oo61Wz9yTvElzJMU5y3V4+j/AFeP9jqOlRtFB9u9FL8MXM3Tzyl+xMV9ytjN+Y4Uk9p/CHhKGVuX4th8+Giv51i69THnY34/PuoNmKg3ikm1ryo3ytuBH5VvmA2BtUd47l4UJ81T2+LTgLuBV432+o3F25LkvFLHMykmLr673An8GTgOWG+0128s3vqU81cDVwMPUHzz9GlghdFet7F2A1Yoc3NzmavfAK+pPD4d2GKEOtahGEo+rPOIYhj/Z0Z7PcfSrR85r5SdSXEp7S3licPfKDqwVx/t9RxLtwHlfE65j5pD8SXB1qO9nmPxVu7Pfw08AtxF8W31wcBywL8Aq3VZz1uB6yi+WLiB4oNJx1+jnKi3PuZ8a4ovcR6g+IDzK2Df0V6/sXhb0pyX7+dhnUcUX4o9wQi/7DxRbhRfohxTE9+zPF/c3H3KmM75NuV54f3l7Q/AUZ6zDDTnY3o/HmUjlwkrbP2SfMqnnIx+abpt16eNdhMmnDkbrDfaTZhw/vHVs0cupL7649QvjXYTJpxXPX7AaDdhwnneo88e7SZMOJ63LH2etyx9nrcsfZ63LH2etyx9q22zfdTFl6k5lRY8cM/IhSRJkiRJkrTElqmRSrNnz86rrrpqtJshSZIkSZK0LFn2RypJkiRJkiRp6bBTSZIkSZIkST2zU0mSJEmSJEk9s1NJkiRJkiRJPbNTSZIkSZIkST2zU0mSJEmSJEk9s1NJkiRJkiRJPZsy2g1Ylsy65IFhsTk7zxyFlkiSJEmSJA2WI5UkSZIkSZLUMzuVJEmSJEmS1DM7lSRJkiRJktQzO5UkSZIkSZLUMzuVJEmSJEmS1DM7lSRJkiRJktQzO5UkSZIkSZLUMzuVJEmSJEmS1DM7lSRJkiRJktQzO5UkSZIkSZLUMzuVJEmSJEmS1DM7lSRJkiRJktQzO5UkSZIkSZLUMzuVJEmSJEmS1DM7lSRJkiRJktQzO5UkSZIkSZLUMzuVJEmSJEmS1DM7lSRJkiRJktQzO5UkSZIkSZLUMzuVJEmSJEmS1DM7lSRJkiRJktQzO5UkSZIkSZLUs4F2KkXErhHx54i4ISI+VPN4RMSx5ePXRsTzK4+9OyKui4g/RMTXI2LaINsqSZIkSZKk7g2sUykiJgMnALsB/wK8KSL+pa3YbsAzytvBwInlc9cBDgVmZ+ZmwGRgr0G1VZIkSZIkSb0Z5EilrYEbMvPGzPwncA6we1uZ3YEzs3AFsEpErFU+NgVYISKmACsCtw6wrZIkSZIkSerBlAHWvQ5wS+X+XGCbLsqsk5lXRcRngL8DjwIXZ+bFdQuJiIMpRjmx/vrr96npE8+sSx4Ycn/OzjNHqSWSJEmSJGk8GORIpaiJZTdlImJVilFMGwJrAytFxN51C8nML2fm7Mycveaaay5RgyVJkiRJktSdQXYqzQXWq9xfl+GXsDWVeQlwU2belZlPAN8Fth9gWyVJkiRJktSDQXYqXQk8IyI2jIjlKCbaPretzLnAvuWvwG0LPJCZt1Fc9rZtRKwYEQHsAlw/wLZKkiRJkiSpBwObUykz50fEIcBFFL/edmpmXhcRby8fPwk4H3gFcAPwCHBA+divIuLbwG+A+cBvgS8Pqq2SJEmSJEnqzSAn6iYzz6foOKrGTqr8n8A7G577MeBjg2yfJEmSJEmSFs8gL3+TJEmSJEnSMmrETqWI+Go3MUmSJEmSJE0c3YxUenb1TkRMBrYcTHMkSZIkSZI0HjR2KkXEYRHxILB5RMwrbw8CdwI/WGotlCRJkiRJ0pjT2KmUmZ/IzJWBT2fmjPK2cmaunpmHLcU2SpIkSZIkaYwZ8dffMvOwiFgHmFUtn5k/H2TDJEmSJEmSNHaN2KkUEf8D7AX8EXiyDCdgp5IkSZIkSdIENWKnErAHsGlmPj7oxkiSJEmSJGl86ObX324Epg66IZIkSZIkSRo/uhmp9AhwTUT8FFg4WikzDx1YqyRJkiRJkjSmddOpdG55kyRJkiRJkoDufv3tjKXREEmSJEmSJI0f3fz6200Uv/Y2RGZuNJAWSZIkSZIkaczr5vK32ZX/pwFvAFYbTHMkSZIkSZI0HnRz+ds9baHPR8RlwEcH0yRJkiSNZ7MueWBYbM7OM0ehJZIkaZC6ufzt+ZW7kyhGLq08sBZJkiRJkiRpzOvm8rdjKv/PB24G9hxIayRJkiRJkjQudHP524uWRkMkSZIkSZI0fkwaqUBEzIyIz0bEVeXtmIjwonhJkiRJkqQJbMROJeBU4EGKS972BOYBpw2yUZIkSZIkSRrbuplT6emZ+brK/SMi4poBtUeSJEmSJEnjQDcjlR6NiB1bdyJiB+DRwTVJkiRJkiRJY103I5XeAZxRmUfpPmD/gbVIkiRJkiRJY143v/52DbBFRMwo788bdKMkSZIkSZI0tnXz629HR8QqmTkvM+dFxKoR8fGl0ThJkiRJkiSNTd3MqbRbZt7fupOZ9wGvGFiLJEmSJEmSNOZ106k0OSKWb92JiBWA5TuUlyRJkiRJ0jKum4m6vwb8NCJOAxJ4K3DGQFslSZIkSZKkMa2bibo/FRHXAi8BAvjvzLxo4C2TJEmSJEnSmNXNSCUy80LgwgG3RZIkSZIkSeNEN3MqSZIkSZIkSUPYqSRJkiRJkqSe2akkSZIkSZKkno04p1JE7AAcDswqyweQmbnRYJsmSZIkSZKksaqbibpPAd4NXA08OdjmSJIkSZIkaTzoplPpgcy8YOAtkSRJkiRJ0rjRzZxKP4uIT0fEdhHx/Natm8ojYteI+HNE3BARH6p5PCLi2PLxa6v1RsQqEfHtiPhTRFwfEdv1sF6SJEmSJEkaoG5GKm1T/p1diSXw4k5PiojJwAnAS4G5wJURcW5m/rFSbDfgGeVtG+DEyvK+AFyYma+PiOWAFbtoqyRJkiRJkpaCETuVMvNFi1n31sANmXkjQEScA+wOVDuVdgfOzMwErihHJ60FPAy8ENi/bMM/gX8uZjskSZIkSZLUZyNe/hYRMyPisxFxVXk7JiJmdlH3OsAtlftzy1g3ZTYC7gJOi4jfRsRXImKlhvYd3GrbXXfd1UWzJEmSJEmStKS6mVPpVOBBYM/yNg84rYvnRU0suywzBXg+cGJmPo9i5NKwOZkAMvPLmTk7M2evueaaXTRLkiRJkiRJS6qbOZWenpmvq9w/IiKu6eJ5c4H1KvfXBW7tskwCczPzV2X82zR0KkmSJEmSJGnp62ak0qMRsWPrTkTsADzaxfOuBJ4RERuWE23vBZzbVuZcYN/yV+C2BR7IzNsy83bglojYtCy3C0PnYpIkSZIkSdIo6mak0juAM8p5lAK4l3IC7U4yc35EHAJcBEwGTs3M6yLi7eXjJwHnA68AbgAeAQ6oVPEu4KyyQ+rGtsckSZIkSZI0irr59bdrgC0iYkZ5f163lWfm+RQdR9XYSZX/E3hnh+XO7nZZkiRJkiRJWnoaO5UiYu/M/FpEvKctDkBmfnbAbZMkSZIkSdIY1Wmk0krl35VrHmv/FTdJkiRJkiRNII2dSpn5pfLfn2TmL6qPlZN1S5IkSZIkaYLq5tffjusyJkmSJEmSpAmi05xK2wHbA2u2zas0g+LX3CRJkiRJkjRBdZpTaTlgelmmOq/SPOD1g2yUJEmSJEmSxrZOcypdClwaEadn5pyl2CZJkiRJkiSNcZ1GKrU8EhGfBp4NTGsFM/PFA2uVJEmSJEmSxrRuJuo+C/gTsCFwBHAzcOUA2yRJkiRJkqQxrptOpdUz8xTgicy8NDPfCmw74HZJkiRJkiRpDOvm8rcnyr+3RcQrgVuBdQfXJEmSJEmSJI113XQqfTwiZgLvBY4DZgD/MchGjSWzLnlgyP05O88cpZZIkiRJkiSNHd10Kt2XmQ8ADwAvAoiIHQbaKkmSJI0LfgEnSdLE1c2cSsd1GZMkSZIkSdIE0ThSKSK2A7YH1oyI91QemgFMHnTDJEmStGxpH9UEjmySJGk863T523LA9LLMypX4POD1g2yUJEmSJEmSxrbGTqXMvBS4NCJOz8w5S7FNkiRJkiRJGuO6maj79IjI9mBmvngA7ZEkSZIkSdI40E2n0vsq/08DXgfMH0xzJEmSJEmSNB6M2KmUmVe3hX4REZcOqD2SJEmSJEkaB0bsVIqI1Sp3JwFbAk8bWIskSZIkSZI05nVz+dvVQAJBcdnbTcCBg2yUJEmSJEmSxrZuLn/bcGk0RJIkSZIkSeNHN5e/TQZeCWxQLZ+Znx1csyRJkiRJkjSWdXP52w+Bx4DfAwsG2xxJkiRJkiSNB910Kq2bmZsPvCWSJEmSJEkaN7rpVLogIl6WmRcPvDXLqFmXPDAsNmfnmaPQEkmSJEmSpP7oplPpCuB7ETEJeILiV+AyM2cMtGWSJEmSJEkas7rpVDoG2A74fWbmgNsjSZIkSZKkcWBSF2X+CvzBDiVJkiRJkiS1dDNS6Tbgkoi4AHi8FczMzw6sVZIkSZIkSRrTuulUuqm8LVfeJEmSJEmSNMGN2KmUmUcsjYZIkiRJkiRp/OhmTiVJkiRJkiRpCDuVJEmSJEmS1DM7lSRJkiRJktSzxjmVIuI4IJsez8xDB9IiSZIkSZIkjXmdRipdBVwNTAOeD/y1vD0XeLKbyiNi14j4c0TcEBEfqnk8IuLY8vFrI+L5bY9PjojfRsR5Xa6PJEmSJEmSloLGkUqZeQZAROwPvCgznyjvnwRcPFLFETEZOAF4KTAXuDIizs3MP1aK7QY8o7xtA5xY/m35d+B6YEb3qyRJkiRJkqRBa+xUqlgbWBm4t7w/vYyNZGvghsy8ESAizgF2B6qdSrsDZ2ZmAldExCoRsVZm3hYR6wKvBI4C3tPV2kiSJGkgZl3ywJD7c3aeOUotkSRJY0U3nUr/A/w2In5W3t8JOLyL560D3FK5P5eho5CayqwD3AZ8HvgARYdWo4g4GDgYYP311++iWZIkSZIkSVpSI/76W2aeRtEZ9L3ytl3r0rgRRF113ZSJiFcBd2bm1V2078uZOTszZ6+55ppdNEuSJEmSJElLasROpYgI4CXAFpn5A2C5iNi6i7rnAutV7q8L3NplmR2A10TEzcA5wIsj4mtdLFOSJEmSJElLQTeXv30RWAC8GDgSeBD4DrDVCM+7EnhGRGwI/APYC3hzW5lzgUPK+Za2AR7IzNuAw8obEbEz8L7M3LuLti4V7XMKgPMKSJIkSZKkiaWbTqVtMvP5EfFbgMy8LyKWG+lJmTk/Ig4BLgImA6dm5nUR8fby8ZOA84FXADcAjwAHLOZ6SJIkSZIkaSnqplPpiYiYTDkfUkSsSTFyaUSZeT5Fx1E1dlLl/wTeOUIdlwCXdLM8SZIkSZIkLR0jzqkEHEsxQfdTIuIo4DLg6IG2SpIkSZIkSWPaiCOVMvOsiLga2IXi19r+NTOvH3jLJEmSJEmSNGZ18+tvpwDTMvOEzDw+M6+PiMMH3zRJkiRJkiSNVd1c/vZy4PSI2LcSe82A2iNJkiRJkqRxoJtOpTuBFwJviIgTImIKxWVwkiRJkiRJmqC66VSKzJyXma8G7gIuBWYOtlmSJEmSJEkay7rpVDq39U9mHg58Arh5QO2RJEmSJEnSODBip1Jmfqzt/nmZ+eLBNUmSJEmSJElj3ZSmByLisszcMSIeBLL6EJCZOWPgrZMkSZIkSdKY1NiplJk7ln9XXnrNkSRJkiRJ0njQaaTSap2emJn39r85kiRJkiRJGg8aO5WAqykue4uaxxLYaCAtkiRJkiRJ0pjX6fK3DZdmQyRJkiRJkjR+dBqptFBErAo8A5jWimXmzwfVKI0Nsy55YFhszs4zR6ElkiRJkiRprBmxUyki3gb8O7AucA2wLfBL4MUDbZkkSZIkSZLGrEldlPl3YCtgTma+CHgecNdAWyVJkiRJkqQxrZvL3x7LzMcigohYPjP/FBGbDrxlWiJeuiZJkiRJkgapm06luRGxCvB94McRcR9w6yAbJUmSJEmSpLFtxE6lzNyj/PfwiPgZMBO4cKCtkiRJkiRJ0pjWzZxKRMSqEbE58CAwF9hsoK2SJEmSJEnSmNbNr7/9N7A/cCOwoAwn/vqbJEmSJEnShNXNnEp7Ak/PzH8OujGSJEmSJEkaH7q5/O0PwCoDbockSZIkSZLGkW5GKn0C+G1E/AF4vBXMzNcMrFWSJEmSJEka07rpVDoD+CTwexbNqTRuzbrkgWGxOTvPHIWWSJIkSZIkjV/ddCrdnZnHDrwlkiRJkiRJGje66VS6OiI+AZzL0MvffjOwVkmSJEmSJGlM66ZT6Xnl320rsQRe3P/mSJIkSZIkaTzo2KkUEZOBczPzc0upPZIkSZIkSRoHJnV6MDOfBPyVN0mSJEmSJA3RzeVvl0fE8cA3gIdbQedUGp/af/3OX76TJEmSJEmLo5tOpe3Lv0dWYs6pJEmSJEmSNIGN2KmUmS9aGg2RJEmSJEnS+NFxTiWAiJgZEZ+NiKvK2zER4TVTkiRJkiRJE9iInUrAqcCDwJ7lbR5w2iAbJUmSJEmSpLGtmzmVnp6Zr6vcPyIirhlQeyRJkiRJkjQOdDNS6dGI2LF1JyJ2AB7tpvKI2DUi/hwRN0TEh2oej4g4tnz82oh4fhlfLyJ+FhHXR8R1EfHv3a6QJEmSJEmSBq+bkUpvB84s51EK4F5g/5GeFBGTgROAlwJzgSsj4tzM/GOl2G7AM8rbNsCJ5d/5wHsz8zcRsTJwdUT8uO25kiRJkiRJGiXd/Prb74AtImJGeX9el3VvDdyQmTcCRMQ5wO5AtWNod+DMzEzgiohYJSLWyszbgNvK5T0YEdcD67Q9V5IkSZIkSaNkxE6liFgeeB2wATAlIgDIzCNHeOo6wC2V+3MpRiGNVGYdyg6lcvkbAM8DftXQvoOBgwHWX3/9EZokSZIkSZKkfuhmTqUfUIwomg88XLmNJGpi2UuZiJgOfAf4j6YRUpn55cycnZmz11xzzS6aJUmSJEmSpCXVzZxK62bmrotR91xgvWo9wK3dlomIqRQdSmdl5ncXY/mSJEmSJEkakG5GKl0eEc9ZjLqvBJ4RERtGxHLAXsC5bWXOBfYtfwVuW+CBzLwtimvsTgGuz8zPLsayJUmSJEmSNEDdjFTaEdg/Im4CHqe4ZC0zc/NOT8rM+RFxCHARMBk4NTOvi4i3l4+fBJwPvAK4AXgEOKB8+g7APsDvI+KaMvbhzDy/l5WTJEmSJEnSYHTTqbTb4lZedgKd3xY7qfJ/Au+sed5l1M+3NGHNuuSBYbE5O88chZZIkiRJkiR10amUmXOWRkMkSZIkSZI0fnQzp5IkSZIkSZI0hJ1KkiRJkiRJ6lk3cypJkiRpnHJeRknLiquuvGpYbPZWs0ehJZJa7FQaRe0neZ7gSZIkSZKk8cLL3yRJkiRJktQzO5UkSZIkSZLUMy9/kyRJGqOa5kPyEnpJkjQWOFJJkiRJkiRJPbNTSZIkSZIkST2zU0mSJEmSJEk9s1NJkiRJkiRJPbNTSZIkSZIkST3z19/GIH/RRdKy4qorrxpyf/ZWs0epJZIkSZL6zZFKkiRJkiRJ6pkjlSRpjGsf7QOO+JEkSZI0+uxUkiRJGmXtl77D4C9/H41lSpKkZYuXv0mSJEmSJKlnjlQa5/yWUVp2eJmbJEmSpPHEkUqSJEmSJEnqmZ1KkiRJkiRJ6pmdSpIkSZIkSeqZnUqSJEmSJEnqmZ1KkiRJkiRJ6pm//iZJWmLtv1znr9ZJ9fzVVkmStCyxU0mSJEmSNG61f7kFfsElLS12KknSBFE3msiTMEljWfvILkd1Sb3xOC9p0OxUkiR1zZNTaWyzE0aSJC1NdipJ0ihwDiJJkjSR+MWUtGyyU0mSJEnjiiOyJEnjyb2/unzI/dW22X6UWtJ/dipJmjBue+YmQ+6v9ae/jFJLJHWr/SQMlq0TMUkaDWNp1JCjt6XxzU4lSZI07izL3/hJkiSNF3YqlRxGLUnSssnRTmNf+3kYeC4mjXWDHu3kCCZ1y+P86LJTSZIkDcR4HU3kyamk8WYsXc4maWKxU0mSJI268dyRM147zySpW72MGhrPHVyOjhqcM685aMj9fZ978ii1ZOwbb+dEy1Sn0u8ffJJZlzzgcGlNeO0TUkMxKfUgJ6puWuYgjcYyJS07xttJ29LipWjSxGWniqReDbRTKSJ2Bb4ATAa+kpn/0/Z4lI+/AngE2D8zf9PNc0fiCZGWJWOp82SQbbFjqjdN3wT244RwPH/L2A/t36ZB8Y1aU1xqZ4fV0ue538Q2no/nkpausTTCeCy1ZXENrFMpIiYDJwAvBeYCV0bEuZn5x0qx3YBnlLdtgBOBbbp87oT1zz9fw3KbPndYPOc/QUyZusT15/wnlriOZdW8efOYPn061157LfPmzWPDDTdkvfXWG7V6+nUCtTRHMDXVffnll3P3o48CMHPSJDZZfvmBL3MQnnjiCaZOXfLtsMn8+fOZMqW7XXdT2br43XffzYUXXcjeb9l7WPnrr7+eZz3rWYvX4LZ62ju9Wm2ZKB1WV1xxBbNnz2bKlCnMnz+fK6+8ku2224777ruPmTNnMmnSJJ588knmzZvHqquu2nP99913Hz+46f1MmhQseHIBjz78BO944ZlL3O5+7bM61b9gwQKuu+EGHnz4EWatvRbrPPWp9WUffphVFizg93/5y5Cyg25jL5rW5/LLL+fxPzwEwKTpM5m6wTNHpX1AY1t6aePllxcnwo//4aG+r8/8u2/j1lsfZv7d85i00gwmrbDSYtXTtG01bYu96LWOprb0sv33a1+xNF199dVsueWWQ2K33XYbZ599Nu9973uHlW86jrbHO9XRS1ua4k3taKqjH+644w6e2rDvG2tuvfVW7rrrLgCmT5/OCiusMMotGlvuuOMOHn/8cVZbbTWmT5/e03Objmc33XQTs2bN4qKLLuKuu+5i880357nPfe5A2wLNX7Rp6RurX1gNcqTS1sANmXkjQEScA+wOVDuGdgfOzMwEroiIVSJiLWCDLp47TD45f1jsyXvvZPJqTxkSe/yPV/HIz77Dqu/8xNDnP/FPHr38Albcafdh9TR15LTH85+P8chlP+KxX/+E1T904tC23HM7D//0W8zY810dy9bF598+h4d/8i0eu/KnrLDjq4Ys87Hf/5JHfvY98tGHWP2DX+y4zPY4O/9nbT285KzattTtzGbOnFm7gzv22GM55JBDOPDAA5k6dSprrbUWRxxxBBdeeCEve9nL+MQnPsGcOXPYaaedeMtb3sKVV17Jlltuyemnn87cuXPZbrvteOlLX9oYP/zww/noRz/KHnvswVOf+lSmTp3KCSecwFlnncWb3vQm3v/+9/Pggw+y2Wabce+999aWbaqjaZkf/vCHOf744znmmGPYddddOfvssznppJNql3nooYc2xuvqedrTnlbblltvvZU75hfv7RmTJrHipEncPn8+WcZbMYD33X4HKx18MA/ffgczJ03mTavMYOPlluO78+ax6tlnc9+8ecycNJkXrLQiy0Us/EBw96OPDunIufzyy7n7TW8EhnbwfPQFO/Ho7XeQ5XvmDTNmsM2KK3DUUUfx0D33Lmzjq1eezifuvmdhW6plDzroICKCh7/5jSHxvx72Ee7/Z9Ghee+TT3LG/Q9w1FOfwkEHHcSj3/omCczPZItp0zhg1VU4dZ/9hi1z9SlTatuyFhT1lG1v1XNTJVZtS1XdSWtd7LLLLuOb3/wmDz74IKeddtqi7eqxx/j+97/PhRdeyOmnnz6k7qaOnLr4Nddcw09+8hMefuRhPvbRjy2MP/7441x66aVc/svLOfxjh3cs2x5/6KGH+OF5P+Q3v/kNN9xwA//5kUX7g1tvvZULL7yQK664gp133nlIp1LdMtvj5513Xm09++y7T21brrjiCq6fcjKTp0ziyfkLuOm6uyCCj775O5z9h3csjG28xVP51nFX8r0vXbUw/t0Tr+YN79qK/zv3L5z44Z8ujF9+/g284DWbsMljBzJ79uxh9Wzy2IHDlrnxFk/lT99ck2e8dsHCeKv+008/ndxsUfzy82/g9jkP8OYvzR/Wlj99c02OPPLIYfErzpzKX6adsjBnZ3zuF+y3wg5cftJktn/7kwvjp3/8Mvb/zx1ZcM0O5Gb/N2SZL3jNJrXxkz96KYcddhjbvz0AmDR5Et869kpuufDDPOO1dwxbn8nX7Txs/7T66qt33GcdfdiH2GXbbTjt87/jmA9+gE+efAqf+PLJjfvh//rCcTz0yCM8c6ON+Lc3vqHjPvHIfd7MCWefwy7bbsN3Lr6YYz74AQ4//HAOeflL2OeDh/GU1VZjypQpRMBXXvzSYWWb2njBDTfx8o1m8bHjThjSliuvvJINFyzg6z86n1vvvJPZm23Gi7bZunaZn37/e2vjp3z7O7X1/OyrZ9euz1//+leemPsIAAvm3cOD557Caod+mqOOOooHbnq8eN2mz2TFnf+VyauswUEHHcQ9tz0BJDw5n+U2eR4r73FQbfyoXzxORPDAjY811wGs9PI3Me052zW2pS6e/3ycg89enntufXxIO/76178C8MTcR4bU0bTMuvjDF319SN0rbPtyVtxpdx7+0RmcfN00Hr75MZ584B4mzViNVfY/bOEx5J5bH2fS9FWY/op9mLr+M4bUXY0fdthhnHTSSQBMnjx54f0zzzyTbbfdFoApU6bw1a9+le222654Lx555MKOoo9+9KMcffTRtfH777+/to7TTz+dvffee2HZr33ta+y///6NbamLr7baarXtaKqjaZl18XvuuYd///d/Xxj7whe+wHvf+17e8Y53cPzxxzN58mTmz5/Pu971Lk488UTOO+88dt1114XlL7jgAl796lc3xgFuvvlmzjrrLC666CL22GMPttxySx566CG+/e1vc+mll3Lttdfyla98haqm42g1ftxxx3Ws47EFC7jwoYe55OGH+fxaT2tsS6d4XTuayt72zE2GLbM13UBdfO7cuXzta1/j2muv5eyzzwbgkUce4bvf/S7f//73mTVrFsccc8yQdfrVr37FOeecw+c+97nG+EEHHcTdd99N8ZEKXvXKV/Hc5z53SPzJJ59kxx12ZJddduGss87iphtvAmDllVdm6623ZurUqUPKz1h5Bq/Z/TV86ctfIiK48847efLJJ3nWs57Fnm/Yk5NPPplb/3ErAPfffz8zZ87k4IMPrq1jg1kb1LZlzpw5XPzji/nH3H8wfeXpvGSXl7Dqqqsu3M7vvPPOIXU0dWTVrc8nP/VJnvKUp5CZrLrqqhx44IFsuummC+tu5Wq//fZjxx13XHhOfP3117Py9JXZaKONADjqqKMWrme1jef96Lza8/ZDDz2Uz3/+87zrXe9iiy22YO7cuZx44omNn3/qPi/dc889tZ83jjnmGLZ+2+Oc8uWf84LdN+H0oz7B/h/Zkft//pzaz1x1bdlxxx1rj8Otduy0xzOZPGUSq6yxIq99x5a8YOaHWbAg+f3lc3nwvsdYb5PVmLXp6rXrc88999S2o/V56/P/cTEzVl+BKVMmse9hOzR+3mo6V2jazx1zzDHss+1WC+Nf+sa3eOdb3sQNN9zAjDtuXRj/+223s9F663L5Xfey9aozFsZ/8ssr+MMPzqvd5/a6XzzmmGOG7V932GEHZs8uvjydP38+v73+T2z1nM0W1tGK/+SXV7DrC3akyQ033MCM8svY1vr8dc4c3rDl1sPa0ZSrgX3RkpkDuQGvp7hsrXV/H+D4tjLnATtW7v8UmN3NcyuPHQLcCNwL3F3GVgT2Br4NHFPGng78F3A68HPgKZU6dgaOA64G9qjENwA+UpZ/d6c4sAvF5XonAj8Cppbx6cD+wGll/c/rULYpfn65vI2BEyvtOBz4EvA24LRKfNgyR4gPq6dDW44v/34F2KF8fFisvP+F8u/7yr+fK/8e23b/8+Xf48q/JwDrVOptirfqf2tbfa340a36uyjbHh9pmS9suz9smV3GF9bToS0fq9yOK1+vYbGabWMN4PTy/50qt9cBp5Tx/Sq391bWuzbeVv/kSj2zKrctgTOaynYTLx/7bEP8uE7LHKkt7fU0rE/ddtv19tPrPqHX7bOp/g5lm+JzgYPK/6v7lqZ9zhLvuzq05Yttr8cX62LVfUjNe6L9NT2uqe4R4l3X36FsU/yEtvgJDXUf27TMHuPHdmhLp33liPustr8j7oe7jI9Yfxdlu90/L+6xZcTjRVNb2m+U+zkWc7/V9l4csQ4Wb587LN6hHXVle9r/Ayc11P25mtjC41xTvL2tLNq2mrbFrrfpDnV0vX02xTu0Y4n3FT3U3Yq3n78fP0K86RjSdMw5nC6PXR3q6PUcupfj0xIfE4EDy7qPBM6p1HEi8FngJcCXKvHazy1N8W62rer2RcN5YZfbV9P23/U22moL3e23qtvzx6g5/x1pfTqsS/Xcbz/qz4lr28jIx4p/Lf9+vm1b/VxbfNjnJUY+nm3WEG//zDWsLYx8HG6vo+lz3rD16aIdvX7eqj1ut78Xad6nndgWP6m6TpX48R3q6HW/WHesaDrfbKrjRGBy+f+USs6HrU+HOprafVJ7HdU2tbex21sxvGEwoiaWXZbp5rlExInARsDBwLcyc40y9nHgduCezGwNJfgxcAdwEPCHzLyzrOM64NnAYcCvM/N7Zfx84C3AN4DrMvNzneLAEcCc1t/MbF1D9ieKg8wBZf2/7VC2Kf4VYF2KDrS1KimYBswH5gELKvG6ZXaK19XT1JbWV+mfz8xfAP9siLXqJTM/U95vH295WPl3cvm3teyjMvMflXqb4iuV9Z9a3p9Z/p1axj9c3l+uQ9mmeNMyp0fEtMz8eUSsCMzosMxO8bp6atuSmUdUbu8CZtbFaJOZdwMPlv9fWrl9h+K1JjPPqNyOAZ7oFG+r/8lK+TmV29XAfU1lm+IRsX3ltjuwSnv5iJgBrNxpmSO1pb2ehjbWbSu9bD/Q2z6hU7yX+pvKNsWfB6wQEV8FtoyI1nWDTfucfuy7mtrSvo/PhhjAyhGxAkD5t7VvWbGt/Iptz2uvpx/1N5Vtiv89It4XES+OiPdRfEACmBQRW0XEpIiYzaKRxE3r1BSvq6epLXX7p172WdDbfrhTvJf6m8r2un/u9djSy/Giti1N+7nF3W9V493U0c0+d6R9cbUd3ey3u9n/t5la1r125bYFsFpNHQuPcx3iTdtW07bYyzbdVEcv22dTvKkd/dhXzGiLtd5X08orBij/tobuLtdWfuoI8aZjSNMxp5djV1MdvZ5D93J86scx8UXAXRRfqFe3zb9QvLbPp9zHlGo/t3SIL9Rh24JF+8Pa88K2eoZtX23b/2Jvo622dLnfqp7P1p7/jrQ+HfYV1XPZpnPipjY2HRNa56nfL++v0rbY9s8/dZ+Xmo5nK0TE0zLzDxGxNou276bPXHVtaTomNtXR9Dmvbn2a6uj181ZTG5v2c037tPbLmFrbRd2+q2mf2+t+sa4tTeebTXXML9+bZOb8ynrUrU9THU25at83tF7PpjZ2ZZCXv80FqpMZrAvc2mWZ5bp4LhQ74mcxdEdcFwPYhGJC8BOBrSLiZZl5MbAvsCfFt0KzImLlzHyQ4gDyUuoPQsPimbljRGwOvLus/+DM/DLFwe9N5cFv04jYpKlsh/h3ge9GxKrAGyPim5m5Z2Z+KIr5p3YFpkTEKZl5YMMy/9IUb6inaX3qdmbZsIP7RUQcA1wHbA5cVsYfiYjXAb8tT4geKeOTI2LLMr41i96fTfHvRMTpwN8pvj34ehm/KSLeA1xD8Y3CTcAfGso21dG0zKOBL0REUOwIP95hmZ3idfVsUteWMqctawKr1cXKsiezqGN2MvDbMv7mtvLLlfHt2+KrjBBvr/+iMv4RFu181gDmdShbG6eYW63lfuCdNeUfBD7dtMwR4sPq6dCWYdtKXazD9nNgj/uEXrfPpvqbyjbF76LY9xERz6QYFfpfHfY5fdl3Ney3/l5+KPsNxf57LrCgJgbF9vKFYvNhAcX2BMX2fCZwM7A+8K0yXld3p3gv9f+poWxtHZn5yTJXzwR+nJm/K8u/l+ILkv2AP5f3O61TU7yunrUb2li7f+phn9Vqx+l0tx/uFO+l/r82lO11/9zrsaWX40VTW5r2c13vt5riXdYx4j63Lt7Ujg7r0/X+vy0WQGsSsOokHvcBH2yo47ed4jRsWx22xabtf1g8M//aUEcv22dTvGm77ce+4s7yWPMosDzFyFAoPhy+v9xn38uiD4uXR8TngN8DmwFXdIp3OIY0HXN6OnY11NHrOXTXx6c+HRP3jojVgb2AjSPi6Mz8cC760vrZwKqtumn+3FIb73KbW7h9RfN54bDtqMP2vyTbaABn9rDfatXRdP47bH162Fe0ctV07lvbRpqPCV+MiDOAxym2r+PLeNPnn7rPS5dRfwz5MMU2ukqZ88M61NHUlp0bjolNdTR1ZNWtz5UNdfT6eavpuN20nzuhYZ/2aES8geIKgOeXeYD6fdfPqd/n9rpfrGvLzlF/vtlUx7SIWCszb4uhHVl163N1Qx1NuZoUEVtV6hjyRUtNG7uTPQxr6uVWNvBGYEOKHdXvgGe3lXklcAHFRr0txbfzXT23rZ5nA58AvtkpVnlsJuXQ2UpsEsVB5Ctt8VWBt7fX0xQvHwvgpTXxZwL/3WXZYXHgOcByNWWXK/+u3M0yO7Sltp5qWyh2pp+kGML7P+X9YbHKc9egmF9rjbY6XwN8AHh1JTYNOJRih/cuYFqneKVtTwGirf7NKToLN++i7LB4p2WWjy9fk9Nhy+wUr6unoS0fq9wOBZ5WFxthe9ypctuiEt+vctsdWKFTvEP9syq3mZ3KjnTr8D5vz1XtMkdqS91rN0J76raVrref9m2o39tnXf1NZTvE3wys2e1rMcI6dbXvqmtLZVvZolOs02tZtw11qqeP9de+rzrEu855h2XWxjvU09V+qy62hDlf7H1iv3LesJ6Lc2zp6XjRoY1DXh8Wc79VjY9UR9Ot7r3Sy3uoUx39uFF8gTe5D/U0rWfTttjL+2tYHb1un/3IeS/v2xFytXZ7zmk+n6uNL2bOuz52NdVRWd9uz6G7Oj51qqNpmSPEN657nze8X2bS9rmlU7zL13gnas4LR3hO03txsbfRXvdbNJz/Ls761NS9H/XnxI1tXIxta9jnnzLetH015bzrbbTh+U3H52F10PlzXt3nuaZ16fX42dTG8Z7zLUaqo5LzL5c5X7PT+ixhzlcYqY1d5bnXJ/RUedGT/hfgb8BHytjbgbdXVvSE8vHfA7M7PbfDcobtiOtiZfy9wPo1dexC/Ye1rg9CZfyzwLNq4nUnHE1lm+L/C+xbE78A2KWbZY4QH1ZPh7bUbVjDYstAzpuWORo5H3bQrostAzkfS+/zuvXput5e6+5X/R3K1q37csC5wAd6eC2W+DXt5fWcgDnfp08536fLNi7zOR9hmb3u57o+XnRoS9O21Wte6t5fTXX0us/tZbtd4n0FcA6wdU3Zn1DOB9IWbzrONcXrtolO22JXOR+hjq63zw5t7JTzJdpXLKWct7+HRjPnE+F93tSWpuU2nRcOq79DXnpt+7C20Lzfaqqj6fx32PosRq567QydKDlv+pxnzidAzke6DXJOJTLz/MzcJDOfnplHlbGTMvOk8v/MzHeWjz8nM6/q9NwOvkrRW5edYhExnWIi8AOqT46ISRRDNvepqfsLFENVR4yXw1pXoujhrsaXK8se0EXZpvhOFBP47tgW34RiaOEeIy1zhPiwepraUjoTePVIsSXI+Ru7iQ8y5x2WOVo5/yrDcz4s5vu8PzlvWJ+u6+217n7V36FsbRx4LcWlBE9vK9/0Wizxa9rL6zlBc/6CLtdppJy/oBKb0DlvWmapl2NL18eLDm1p2rZ6zUvd/qypjl73ub1st0u8r4iIdSkui3hTW9nnA9+nGElejTcd55ritdsWzdtiL++vpjq63j6b4l3kfLH3FUsx5+0/ZTSaOV/W3+dNbWlabu15YV39HfLSa9uHtaXDfqu2jlLd+e+w9ek1V6W6c99e963LYs7rPuf1M+ftx2FzPkZy3o2BdiotDXUvctMLT/HCfgF4WvmCtLSuTX5uW91dH4RK+1Ic5FaKiOUr8bqDX1PZpvielPN3RMS/VOIHACdTzGGz5gjL7BSvq6e2LUsp5yOebHVqY8N69pTzDstcVnPu+3zk9eml3l7r7lf9TWWb4i/LzJ8Al0TESyrxpteiH69pL6+nOTfn/ch5Tx15Hdapl+NFU1uaXude81IXb6qj131uL++hfrxvD6CYd2JBRFQnI9+b4sPjPyJiViXedJxrije1sWlb7CXnTXWYc3O+tHPe1Jam5daeFzbU35SXXtte15am/VZtHR3Of+vWp6dcdTj37XXfas57z3lXnf5N9TSsZ1Mbm9ppzuvbOKJJIxcZ8+pe5KYXfsfMvILip0F3r8RfQzGM9qqI2KYS7+UgBMX1h9cB32Voz1/dwa+p7LB4FL2GN2TmY+V6bVeJr57FLz+cydBexaYD7rB4h3qa2rjM57xpmea8YxuXtfd5Xd291Ntr3Utcf1PZEeKtifu+S/lLJE2vxQjr1NVruhivpzk350uU8xHa0ut+rqvjxQhtbHqdu85Lh3VtWs9e97nV+LaLuT5d5bA8Ad4gM2+mmLx1n0rdj2bmA8AZDD2eNR3nhsVHWM+mbbGrnDfVYc7N+dLOeYf1r11uqem8cEj9I+Sl67Z3aEvTfqtpPZvOf+vWp+tclXrtDDXn5nwi5Hxk2eP1cmPpRnFN9VHl/+tTTDbVHju0UnZ2+f9kYNtKfJ/y/5WBPSvxd5f/rwm8tSF+YPn/VGDDSts2qZTfrfx/eeDlNWWfUf6dUlfHYualusyXNbWlQx21bZkoOW9apjmfUO/zIXX3Um+vdffa7m7zSPFzvZ3qmEHx5cJzgRcC641QfmCvqTk35wPMee0y6X4/19fjRS9t7OH5UzusZ9N+e1XKuRoojherNtS95WKsT1OuhuW23B5mVGKrd5GrpuNcNb7NCPX0tC025Hz7yu1f2h4z5+Z8qeW8Q1smMfTHTFav1NN0Xtj1a9pj2ye3tWU1Ou+fm9a/6fy3uj5vKF+Hujo6ncvWHROajmfm3Jwv8znv9hblE5cJEfHUzLyj4bENgTkUH3bXBK7NzGti6E9TPpCZDzc8f8vMvDqKnxOcl5lPRvGzozMy875Y9NN8+wPrAr/MzB/H0J+nvD8z/1ipcxpFb+Jumbl/ROwPfC0z50fEFGDvzDw9Ig4HjgS+B9wBPJGZ7T/xC3BGZl7W1u61gDdn5jERsS1wVaX+rTLzlxHxFuBs4DMUb9A/lH9bb477gW9l8VOwEyLnde0pn2/OJ9j7vFU38Itu683MY2PoT9B2rLvXdo9Uf0TsSPEtxMqZeUBEHA18tFL3kZn54Yg4PjMPieKnTy8EXpCZb2/L+ZSyXSf04zWlOGh19Xqac3Pep5zPAB6i+FWTGcBNmXkLI1iS40XTfqvpdW5qY9M6VZazMAcUQ/ZbFu6HO+y3T8rMt1fqWng/IjYA3kJxLPke8C8N63M49fvtjrmq5pbiPdqq+z7glMz8c0QcChwHnFqWuS0zPxYRrwIurNS9W2b+sC3/2wB7Zea7O+S8aVvsJeeXVBa7BsW30+8y5+Z8FHJeu6+MiF2Biyl+Bn0WcGlmnhURGwNzMvOJsv4NMvOGuvqB6Q15Way2RzHfzFuALSh+gaqlut+qPZ+lC1GMLHk1xa9cVXPyk8z8Z4f3Su25b1vd1eOZOTfny3zOu2kLML5HKmXRIbYixfCwbwPHUFxj2PpGYgpwYvn/8eXfrwA7VOIfAz5a/j0OOLxS9wbAR4Cfs6hH76S25Z9U/j2u/HsCsE5leftVbu8tl7ELxXWPJwI/ovyWtVVHpe5WnV8o/7ZGkXyuJg+TKQ5QUGwU+1NcK3k18Lwy/sW253yxrf6jy7+fZ+jPZ25JsWFNpJwfTtHr/QOKn3M8wZxPnPd5Xd291Fv+7bruXtvdVD/F+/ZLwNuA0yp1HTtCzl9YvV+T91b5vr6mI72e5tyc9ynnrf3UVylO8Fr7s5Mp9u8nl+1852Lm/HA6HC+qOe/wOje1sWmdhuWAmv1w23Pa99vt63Ns+fd8imPCxpTHkA7vodr3W4dc1ea2Um4N4PS2ut/XVvfxbc9prc/Tgf8CTqc4njX93HzHbbGXnNfU/Vlzbs5HI+c074uPbau3tW89sa3+kzrVP0Jeum37geXreSRwThlr2m81nc/Wnv+W93eiOP+8mmKi5Z0qt9dV2t30Xmk69206nplzc77M57zb27ieUykiTgQ+DtwO3JOZ7wXmZ+aTAJk5H5hfFn+y/Pv5zPwFRU8jmXlEZh5Z/n0XMLOs+3yKE7tvANdl5ufK5z/R1ox/ln8XlH+Pysx/tJaXmWdUbseUzz+ColfyCMre0/K5K7bV3bq/UlnXqeX9me25KNe5Vc+fKA5gBwC/zszftoq1P638O7Wso/Wt0HKZOadyu5riW5WJlPNVM3MB8IPMPLiyzEXJM+fL7Pu8oe6u6y3v91J3T+3uUP80ivfCPBa9VgArR8QKAOXf6WV8ekRMy8yfR8SKFCMkhihHULQmGezra9rF62nOzXk/ct7aL56cmWcBj5d1H5SZB5d/DwCeWZbr6/GiLecLtb3OtW3ssE7DctCwH4aG/TYwKSK2iohJETGb4sQVipPbdYFDKH62e4i29Wl6vzXlqim3rbrvBh6srCOZ+Znyfmvdl2ure2r598cUo0gOohjpVjfSt5rzpm2x65xHxPaV2+7AKmVZc14y50sn5x32xS2HlX8nl3/ntz3eamPH17QtL722/UXAXcB5rfZ12G/Vns/ScP4bEdcBm5Xr+evM/F5mXlq5fYfifQTN75Wmc9/a45k5N+dtdS+TOe/WlJGLjGl/AZ4FPJ9yhw9Mi4i1MvO2cujWCmV8hYh4Wmb+IYpLgVoHr+plQWtSXPcIxcHmpQw/2EyqDCd7PotyODkitgR+GxFbt+Ix9LKgNYFVMnPHiNgceDewVUQcnJlfBr4TEWcCN1NcP/mt8nnfiYjTKX6acBZwTll3dXjxZOCisvzzgb3K4cWbRsQmmfkX4O8R8T7gN2WZuWX5myLiPcA1FL2+N7UNL1yDRQe/iZLz2hMIcz5h3ufD6u6l3rLd7XXPa6q713Y31Z+ZHy2Hsu4KTImIUzLzQIpOyS9ERJbPObp83tFlnDL+8bacQ3FJzqcBunhN5wDrUYyog5rXtOb1vNicm/MB57ynjrzFyPmw40VTzpte5w5trF2nzPxQew6AUyqrtCaLPmzX7rcpvi09mOKb0z+X98nM7wLfjWKI/Bsj4pvAA3XrQ8N+uylXdbkFtqrUPQVoncj+IiKOAa6juCywdcnm5RHxOeD3FCfXvyrjm7DoV3G2ioiXZebFHXJeuy32mPOfl89JissO3mnOzfko5bzpfOaRiHhdmZfZwCNl/NGIeAOLzvMeb6qfYt6WuvOw9rb/X0PbryjzsncUP3G+F7BxFJc9nsci1f1W7fkszee/+1JcInksMCuKX9x6dVvdrU6ApvdK07nv84A3tR/PzLk5nwg5p0uR2f6l4fgTEc+muDTo6cD/A95P8WLdB3wmM++OiDXa4p/OzHsi4mOVqu4DvpGV+WpaBxvgxZm5ZxTXRx8MbErRo3dyZj5WE/9yZj4eEftV6r+f4gA1jUXXMk4F/jUzv1Uu6yGKSQXvA6ZnMY9NU3w1ivlxOl0T+UzgLZn5X+X9zSm+jf1zZv6uUq4V/1NmXhtDZ5m/P4sZ7ydSzncr27DwBCIzLzDnE+t93l53+fxNy3qvbat307Le35exWSw68N2fmfPaqm5q92LXH21zWwFzK/Eo4/flomu5m+I7VOpZGG9rewAvycwfV+6vkcPn06mNd2LOzXmfc/504ANlPCn2i3+L4R98T8niV1KqbR8x503Hi5o0ExEzKU6Ct6DoOLoxi7mTmtrYmmupvfyMSj0zgRuBF1eefz/w48x8tLLf3oTiQ3Vrv906qTyAYsTG5VnMt7AVcFUZX68Vr1ufak6Au7NyUtkUb3t8lyx+aaap7jWADcv1vmekeOXxmcAbMvMrHXLeKbfd5vxpDTk05+Z8aed8yPkM8EiWI6Qi4jUsOm+7cKR4W/17ZubJbfEtsxglMqSNFPPVdIy31TMbeHYldB9wcXm+2TqfXRW4l0Xns+3xz2Qx8qtV5ySKH514PcXlxAvrbh1vOrxX2uMnZ/GLf9U2V4+V5tycL/M5b1+fRtnDtXJj7Qa8CpiSi643fE0Zm1qJvbpSti7+6ob4/m11H1CJV8vv30W8Ws/+NF/L2K/40W3LPLrXeIeyEyXnq1IMY3xK+XdVcz6h3ucnU8yN0ppvZUeGzr9yMsXPd7IE8S/3qZ6FcZqv2e5XvNr201g0B03X8S7LmnNz3s+cb08xV8EODP21qB0a4j3lvHwsqMzJ0KGOprmTZlDMy/Q8inkR1huh/LA4xUiISRTzOXwMeGlZpinecQ6aapxibpsAvk9l3qhK/Ac9xCdV6wEOLcueWsaOKMseWpY9rcd4ez1d53Axct6UQ3Nuzpdqziv7nh0pRjGcVhM/rof4DtV6qJl/s8/xaRQjO05va8cKPcZb9ZxRia0HfAg4u61sr/F1gQ8CXzfn5nyi5XykW2u403i1a2aeB8X1hlEMXaMt9nLghzVlW/GXZzlTe1t8yyx/waGMP59ix94e35Jikq9e4kN6SFl0LWO/4tOzuP6ytcyVFyOeDWUnSs4/kcWvg9wJEBGfAN5uzifG+zwzD2otLIrRUV/O4vKaITHgsrqyoxWvtrGMf65cnzP6FD+oLX5cr/Fuyppzc94p3mvOKX7Ou2WNiHhHFnPLbVwX7zXnURnxFMWozKuayjJ07qSfRzEkHYoO7UOiuATwQuBNFMecpvJ18X0z88pyP340xZwLP+4Qr863cGtEtO7XxVfNzIyIczPz1EpuW/Ef9BBf0FbP08uyf8zMz1TKPr0se12P8VY9nx0h5/2K1+XQnJvzpZrzKH6hbi3gSspfxKyJT+8x3vplzfMpfhn0G8C6Wc6/2Y94ROxC8WXpchRTIvxrWXZJ47tHxIHA1hRz52ySmXuVZVvx24FNe4i312POzfkyn/NuTeql8Bi0XNv9qQ2xprKd4iu2xVfsY7w6aeCWtF3j2Id404SHvcSbyk6UnDd1ZJjz7uPj+X2+UNZMslsXGwvxGDqB6Wso52XpV7wqhk6+uljxprLm3Jx3iveawxw+Qeb8TvGqbnKbiyb8PjiHTvhdV0d17qQVWPQ6Vz88n8WieR+aytfFqx+S/1G53xRvzbdwexSXAk3uEF+pXNdTyzIzy7/9iDdNgrqk8ZFy3o94Uw7NuTlf2jmfRv0PGPQj/hWK0QvvZOj8m/2IH0H9pOZLGp8PvAi4m8qEyaVW/Ec9xO+qqcecm/OJkPOujPeRStUJsp4N3ADcVRNrKtsp/tOIOAv4G7ARiybr6kd8DrAd8DOKE8f9y7Lf6FP8K8CPYtGEh+9YjPhTGspOlJxXJzF7Hou2FXM+Ad7nMXSulSlA+/wrU8p1rS07QvxUigNZAMsDF/QxviPFAWESxeTrHy2X35d4RPy4sk5QTELaU7xDWXNuzgeS8xg6B9MaVDqhGuI95byq1YHUoeypwHkREeXj/1bGmz5UN5Wvi787Fk3KWffhuT3+TYqh/5cydN9aF185isk7b6KY1++8suz/9iE+NRZNgvocinkvYOjkqEsS7yWHvcbXacihOTfnSzvnPyz/7gysEot+wGCJ4xSXHb6T4rLiN0XE3Zm5BnDLksYp5nVZl0WTmn8oM/+HYjTGksbbJ0z+cWa+tF9xc27OJ0jOuzKuRyplcbnN1ymS+3KKn98bFmsq2ykOvBDYh+Ia630pDjz9ir8hM48FzqCYRf7lZdl+xTcuY2dSnIAfuhjx2rITKOetjox5FJNOXzBCeXO+bL3PDwI+QdEx9gyKiQCHxZrKdooDT2T5M+YUc7/s1Mf4epl5GsWvObyqkqt+xb8GnFXeLqK4PKfXeG1Zc27OB5jzHSkm6XwWxbd73x8h3lPOI+LHEXFxRFxM8etPn+5Qx8YU88O09jn/UcZbH55/SnGy+/ERytfFWx+S51FcQndhWbYp3ss+94UU+T+eYl/fen2WOJ6ZZ1JeJkDxa6QLAPoV7zGHvcabcmjOzflo5Hxjil/UvJxF+hHfNzMXUMwTs2W5/H7Fj87MazPzg8C2wNvKsv2Kr0zx4zXTKC6HpI9xc27OJ0LOuzKll8JjTSy6ZvFsYK0srlkcFmsq2ykO/LN8Q7Tm1Hm8z3FYdB13a9j7gn7Ec/i8Ep/tNd5UdgLl/A2Z+a6I2JRF1+b/sKm8OV/m3uc/YtH10OuUOR8WayrbKc6iy1rIzKzkql/xrnPYa9ycm/Nu44uRw0HmfL3M/FREnEDR2dTan9fGe805RQdSyxrA27OYs6mXOlofnlt1/Afwrh7b0nTc6ul41hBfMKhjRb+OZ03xfmxDHeKt0TBLdA5hzs35OMt564vJ9hwuTrwVI4tf+T2/vPvEksZj6Nw262Tmp8p16kvcnJvzHuLjNufdGtedShSXs7yUYpj7Wh1iixOfHIsuf3o+i+ag6Ud8QSwaors1i16Hyf2Ix9Ah/WtS9Dj2FG8q25CvZTHnTSch5nxivM9PqeRr7Q6xxYmb85qyDfky5+Z8kPvz2nivOa87Oe1HHYvRlgd7WU96fy8O6vXs1/GsNt6PbahD/JF+nEM0xc25Oe82Pko579dxrpqrVtlJfYhXc1U9PvUlbs7N+UTIebciM0cuNcZFxKrAG4EXZ+aeTbFe4hExDTiYYmj8n4CvZOaj/YhTXBdejZ2cmY/VlF3c+H6V9NwPXFy2pes4sGdd2QmU8+MpfgXtt8BsYL/MfKc5931uzs25OR93OW/anzfFe815+8np7hTztCxRHZn51h5f50/3uJ5dvxfL5Qzk9Wy9F5f0PdcU78c21CHnTe9Fc27OJ0LOl/g4Z87NuTkf2zmnS8tEp5LUb007itFtlSSpV72eEC5G/bUnraNQx0DXU90z50ufOZek0WOnkiRJkiRJkno2rn/9TZIkSZIkSaPDTiVJkiRJkiT1zE4lSZIkSZIk9cxOJUmSJEmSJPXs/wNK+Rx01bFXVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 13. AF < 0.05\n",
    "dfmenor005_unique_muts['FULL_MUT'] = dfmenor005_unique_muts.apply(get_mut, axis = 1)\n",
    "dfmenor005_unique_muts_count=dfmenor005_unique_muts[['FULL_MUT', 'POS']]\n",
    "dfmenor005_unique_muts_count = dfmenor005_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "dfmenor005_unique_muts_counter_triplets_order = dfmenor005_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = dfmenor005_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 13b: Non-protein affecting. AF < 0.05. \"+str(len(dfmenor005_unique_muts))+\" mutations.\", y=1.08)\n",
    "\n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.001),16,0.0007,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/less005syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mgrau/apps/miniconda3/envs/njupy/lib/python3.7/site-packages/ipykernel_launcher.py:45: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 14. AF 0.02 - 0.05\n",
    "df002_005_unique_muts['FULL_MUT'] = df002_005_unique_muts.apply(get_mut, axis = 1)\n",
    "df002_005_unique_muts_count=df002_005_unique_muts[['FULL_MUT', 'POS']]\n",
    "df002_005_unique_muts_count = df002_005_unique_muts_count.drop_duplicates().groupby(['FULL_MUT']).count().reset_index()\n",
    "df002_005_unique_muts_counter_triplets_order = df002_005_unique_muts['FULL_MUT'].value_counts().to_dict()\n",
    "\n",
    "list_order = order_muts_full()\n",
    "norm_vals = []\n",
    "for ix,mut in enumerate(list_order):\n",
    "    val = df002_005_unique_muts_counter_triplets_order.get(mut, 0)\n",
    "    #Normalization\n",
    "    count = count_genome_compo[mut[:-2]]\n",
    "    norm_vals.append(val/count)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 1, figsize = (20, 4))\n",
    "\n",
    "colors_mut = ['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "vector = []\n",
    "colors = []\n",
    "for ix, c in enumerate(chunks(np.arange(96), 16)):\n",
    "    colors.extend([colors_mut[ix] for s in c])\n",
    "\n",
    "plt.bar(np.arange(len(norm_vals)),np.array(norm_vals)/sum(norm_vals), color = colors*2)\n",
    "plt.ylabel('normalized mutation count')\n",
    "plt.xticks([])\n",
    "plt.xlim(0, 192)\n",
    "plt.xticks(np.arange(192), [i.replace('T', 'U') for i in list_order], rotation = 90, fontsize = 6)\n",
    "plt.title(\"Figure 14b: Non-protein affecting. AF 0.02 - 0.05. \"+str(len(df002_005_unique_muts))+\" mutations.\", y=1.08)\n",
    "\n",
    "#header\n",
    "colors_rectangle =['#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5', '#1ebff0', '#050708', '#e62725', '#cbcacb', '#a1cf64', '#edc8c5']\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.set_xticks(range(0, len(list_order)))\n",
    "ax.tick_params(axis='x',  pad=0.9, width=0, length=0)\n",
    "ax.set_xticklabels(\n",
    "    [kmer.replace(\"_\", \">\").replace(\"T\",\"U\") for kmer in list_order],\n",
    "    verticalalignment=\"center\", ha='center', rotation=90, fontsize=6.5,\n",
    "    color='black'\n",
    ")\n",
    "j=0\n",
    "for i,c in enumerate (list_order):\n",
    "    if (i+1) % 8 == 0 and not (i+1) % 16 == 0:\n",
    "        ax.annotate(xy=(i,ax.get_ylim()[1]+0.00025),s=str(c[1].replace(\"T\",\"U\")+\">\"+c[-1].replace(\"T\",\"U\")),color=\"black\",rotation=0, xycoords='data', annotation_clip=False,fontsize=12)\n",
    "        rect = patches.Rectangle((i-7,ax.get_ylim()[1]-0.002),16,0.0007,linewidth=0.0,facecolor=colors_rectangle[j], clip_on=False)\n",
    "        j+=1\n",
    "        ax.add_patch(rect)\n",
    "ax.tick_params(axis='x', which='major', pad=8)\n",
    "#end header\n",
    "\n",
    "plt.savefig(\"data/img/002_005syn.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### unique_muts['TRIPLET'] = unique_muts.apply(get_mut, axis = 1)"
   ]
  }
 ],
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   "file_extension": ".py",
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