Untitled7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from Bio import SeqIO\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "annotation_filename = \"/project/owlmayerTemporary/Sid/nanopore-analysis/Results_5_1/GffCompare/nanopore.combined_filt.gtf\"\n",
    "annotate_df = pd.read_csv(annotation_filename,sep = \"\\t\", header = None)\n",
    "annotate_df = annotate_df[annotate_df[2]  == \"transcript\"]\n",
    "annotate_lines = list(annotate_df[8])\n",
    "chrms = list(annotate_df[0])\n",
    "start = list(annotate_df[3])\n",
    "stop = list(annotate_df[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transcript_id \"TCONS_00000001\"; gene_id \"XLOC_000001\"; gene_name \"MAFIP\"; oId \"cc2e8b66-6da7-4245-9544-c06d33b50252|3\"; cmp_ref \"ENST00000400754.4\"; class_code \"j\"; tss_id \"TSS1\";\n"
     ]
    }
   ],
   "source": [
    "print(annotate_lines[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "tID_length = dict()\n",
    "gene_oID = dict()\n",
    "oID_tID = dict()\n",
    "tID_gene = dict()\n",
    "tID_enst = dict()\n",
    "\n",
    "for ann in range(len(annotate_lines)):        \n",
    "    if \"gene_name\" in annotate_lines[ann]:\n",
    "        line = annotate_lines[ann].split(\";\")\n",
    "        tID = line[0].split(\" \")[-1][1:-1]\n",
    "        gene = line[2].split(\" \")[-1][1:-1]\n",
    "        oID = line[3].split(\" \")[-1][1:-1]\n",
    "        tID = line[0].split(\" \")[-1][1:-1]\n",
    "        enst = line[4].split(\" \")[-1][1:-1].split(\".\")[0]\n",
    "        \n",
    "        if (oID not in oID_tID): oID_tID[oID] = tID\n",
    "            \n",
    "        if (gene not in gene_oID): gene_oID[gene] = [oID]\n",
    "        else: gene_oID[gene].append(oID)\n",
    "            \n",
    "        tID_gene[tID] = gene\n",
    "            \n",
    "        length = stop[ann] - start[ann]\n",
    "        tID_length[tID] = length\n",
    "        \n",
    "        if (tID not in tID_enst): tID_enst[tID] = enst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gene in gene_oID:\n",
    "    max_len = 0\n",
    "    tIDs_list = []\n",
    "    for oID in gene_oID[gene]:\n",
    "        t_len = tID_length[oID_tID[oID]]\n",
    "        if (t_len > max_len):\n",
    "            max_len = t_len\n",
    "        tIDs_list.append(oID_tID[oID])\n",
    "        \n",
    "    for tID in tIDs_list:\n",
    "        tID_length[tID] = tID_length[tID]/max_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tID_length[\"TCONS_00000001\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessArguments(args):\n",
    "    if args.geneNames != '':\n",
    "        gene_id='gene_name'\n",
    "        with open(args.geneNames,'r') as f:\n",
    "            targets=[i.strip() for i in f]\n",
    "    elif args.geneIDs != '':\n",
    "        gene_id='gene_id'\n",
    "        with open(args.geneIDs,'r') as f:\n",
    "            targets=[i.strip() for i in f]\n",
    "    annotation=pd.read_csv(args.gtf, delimiter='\\t', header=None, usecols=[0,2,3,4,6,8],\n",
    "                           names=['chrm','type','start','stop','strand','more'])\n",
    "    annotation['transcript_id']=annotation.apply(lambda x:\n",
    "        x['more'].split('transcript_id \"')[1].split('\"')[0],1)\n",
    "    annotation=annotation.drop(columns='more')\n",
    "    data=pd.read_csv(args.csv)\n",
    "    samples=data.columns[~(data.columns.str.startswith('feature')|data.columns.str.startswith('gene')|data.columns.str.startswith('transcript'))]\n",
    "    #conditions=list(set([x.split('_')[0] for x in samples]))\n",
    "    #conditions = [\"0\",\"3\",\"5\"]\n",
    "    conditions = [\"day0\",\"day3\",\"day5\"]\n",
    "    conditions.sort()\n",
    "    number_replicates={}\n",
    "    numerical=True\n",
    "    for cond in conditions:\n",
    "        number_replicates[cond]=samples.str.startswith(cond).sum()\n",
    "        try:\n",
    "            float(cond)\n",
    "        except:\n",
    "            numerical=False\n",
    "    x=np.arange(len(conditions))\n",
    "    if numerical:\n",
    "        x=[float(cond) for cond in conditions]\n",
    "    return gene_id, targets, annotation, data, samples, conditions, number_replicates, x  \n",
    "\n",
    "def calculateStatistics(df,conds,nreps):\n",
    "    for cond in conds: \n",
    "        df['mean'+cond]=df.filter(like=cond+'_').mean(1)\n",
    "        df['stdn'+cond]=df.filter(like=cond+'_').std(1)/np.sqrt(nreps[cond])\n",
    "    df=df.sort_index()\n",
    "    return df\n",
    "\n",
    "def chooseIsoforms2Plot(df,minTPM,minPct,maxIso,annotation):\n",
    "    df['minimum']=df.filter(regex='^mean').min(axis=1)\n",
    "    df=df[df['minimum']>minTPM]\n",
    "    df['maximumPct']=df.filter(regex='^Pct').min(axis=1)\n",
    "    df=df[df['maximumPct']>minPct]\n",
    "    df['maximum']=df.filter(regex='^mean').max(axis=1)\n",
    "    df=df.sort_values('maximum',ascending=False)\n",
    "    df=df.head(maxIso)\n",
    "    return df\n",
    "\n",
    "class Parser(object):\n",
    "    def __init__(self, csv, gtf, geneIDs, geneNames, outDir, minTPM, maxIso, minPct):\n",
    "        self.csv = csv\n",
    "        self.gtf = gtf\n",
    "        self.geneIDs = geneIDs\n",
    "        self.geneNames = geneNames\n",
    "        self.outDir = outDir\n",
    "        self.minTPM = minTPM\n",
    "        self.maxIso = maxIso\n",
    "        self.minPct = minPct\n",
    "\n",
    "args = Parser('/project/owlmayerTemporary/Sid/nanopore-analysis/Results_5_1/Quantification/all_counts_deseq2norm.txt',\n",
    "              '/project/owlmayerTemporary/Sid/nanopore-analysis/Results_5_1/GffCompare/nanopore.combined.gtf',\n",
    "              '', '/project/owlmayerTemporary/Sid/nanopore-analysis/Results_5_1/Lists/IsoformSwitchAnalyzeR_Regular_Func_List.txt',\n",
    "              #'', '/project/owlmayerTemporary/Sid/nanopore-analysis/Results_5_1/Lists/IsoformSwitchAnalyzeR_Regular_List.txt',\n",
    "              '/home/annaldas/projects/isoform_differentiation/test/',0,18,0)\n",
    "\n",
    "outdir=args.outDir\n",
    "minimumTPM = args.minTPM\n",
    "minimumPct = args.minPct\n",
    "maximumIso = args.maxIso\n",
    "(identifier, targets, annotation, data, samples, conditions, number_replicates, x) = preprocessArguments(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = dict({\"0\":[],\"3\":[],\"5\":[]})\n",
    "days_ratio = dict({\"0\":[],\"3\":[],\"5\":[]})\n",
    "days_genes = dict({\"0\":set(),\"3\":set(),\"5\":set()})\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "for gene_ensembl in targets:\n",
    "    gene = gene_ensembl.split(\"_\")[0]\n",
    "    df=data[data[identifier]==gene]\n",
    "    df_temp = df\n",
    "    for j in range(df.shape[0]):\n",
    "        transcript_annotation=annotation[annotation['transcript_id']==df.iloc[j][\"transcript_id\"]] \n",
    "        if (transcript_annotation.shape[0] < 3):\n",
    "            df_temp = df_temp[df_temp[\"transcript_id\"] != df.iloc[j][\"transcript_id\"]]\n",
    "\n",
    "    # total gene expression calculation\n",
    "    data_gene=df_temp[samples].sum().to_frame().transpose()\n",
    "    data_gene=calculateStatistics(data_gene,conditions,number_replicates)\n",
    "    # mean transcript expression calculation\n",
    "    df_temp=calculateStatistics(df_temp,conditions,number_replicates)\n",
    "    # isoform percentage calculation\n",
    "    df_temp=(df_temp.filter(like='mean').div(data_gene.filter(like='mean').values[0],1)*100).add_prefix('Pct_').join(df_temp)\n",
    "    #choose isoforms to plot`b\n",
    "    df_temp=chooseIsoforms2Plot(df_temp,minimumTPM,minimumPct,maximumIso,annotation)\n",
    "    if (len(df_temp) > 1):\n",
    "        for i in [\"0\",\"3\",\"5\"]:\n",
    "            if (sum(df_temp[\"meanday\"+i]) > 2):\n",
    "                pct_list = list(df_temp[\"Pct_meanday\"+i])\n",
    "                tcons_list = list(df_temp[\"transcript_id\"])\n",
    "                for j in range(len(pct_list)): \n",
    "                    days_ratio[i] += [tID_length[tcons_list[j]]]*int(pct_list[j])\n",
    "                transcript = df_temp[df_temp[\"Pct_meanday\" + i] == df_temp[\"Pct_meanday\" + i].max()][\"transcript_id\"]\n",
    "                if (tID_length[list(transcript)[0]] == 1.0):\n",
    "                    days_genes[i].add(tID_gene[list(transcript)[0]])\n",
    "                days[i].append(tID_length[list(transcript)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AC112178.1',\n",
       " 'ANO7',\n",
       " 'BAK1',\n",
       " 'BCKDHB',\n",
       " 'CADM2',\n",
       " 'DACT3',\n",
       " 'DENND5B',\n",
       " 'EBF2',\n",
       " 'EPB41L5',\n",
       " 'FAM78B',\n",
       " 'GDF1',\n",
       " 'IKBIP',\n",
       " 'KCNJ6',\n",
       " 'KIF1B',\n",
       " 'KLHL35',\n",
       " 'LAGE3',\n",
       " 'LINC00467',\n",
       " 'MIR302CHG',\n",
       " 'MIR4787',\n",
       " 'NCAN',\n",
       " 'NKAIN3',\n",
       " 'NKAIN4',\n",
       " 'ONECUT2',\n",
       " 'PDE4DIP',\n",
       " 'PIWIL2',\n",
       " 'PPP1R1C',\n",
       " 'RAC3',\n",
       " 'REEP1',\n",
       " 'RGS9',\n",
       " 'RRP7BP',\n",
       " 'SLC17A7',\n",
       " 'SLC6A15',\n",
       " 'SWSAP1',\n",
       " 'SYNGR1',\n",
       " 'THRA'}"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days_genes[\"5\"].difference(days_genes[\"0\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days[\"0\"],days[\"3\"],days[\"5\"]],bins = 20,label = [\"day0\",\"day3\",\"day5\"])\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Transcript Length of Most Expressed Isoform in Signficiant Functional Consequences Isoform Switches\")\n",
    "plt.show()\n",
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days_ratio[\"0\"],days_ratio[\"3\"],days_ratio[\"5\"]],bins = 20,label = [\"day0\",\"day3\",\"day5\"])\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Transcript Ratio Length in Signficiant Functional Consequences Isoform Switches\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0NNSzDHPbJq5FhJ0idm44aNOmjTVp0sTMsoed9957z4KCgrwO8Pbu3WuBgYGejW/8+PEmyRYvXuw1jwEDBpivr6/XuN27dzcfHx/bv3+/p+zpp5+2uLi4i9a3Tp06XgdsFws757v33nutRo0anp3++PHjLSoqyutMkZlZs2bNrEePHmZm1q9fP7v++uu9hvnqq68KLOyEhYXZsWPHLjr+6dOnLTg42D799FMz+/87pOeff95ruMqVK9vLL79sZmbLli0zl8tlu3fvznGazz33nFWsWNGOHz+e4/tnzwydPfg7t/z8sHP+jnD//v0WEBDg2fG9++67nrNqF3O2XYGBgRYSEmJ+fn4myYoXL57trOP5Bg8ebDfeeKPndb9+/axGjRrZhjs37Bw8eND8/Pzs/fff9xqmRYsW1rJlywvOK7ew07BhQxswYIBXWWpqqkmyrVu3mtmZdV+hQgXPgYGZ2csvv2yVK1f2vP7rX//qFX7MzHr06OE17wst24YNG1r9+vW9yrp27eoJ+heSU9hp37691zBNmzb1Cp856d27t/n5+VmJEiUsISHBBg4c6HWS4vx5bdiwIVs/OnHihEVGRmYLO7m1K6f1c36/vfvuuy0+Pt7cbneO9c/pwPJ8jzzyiN1+++2e13lZpxfql+c7e+AXFBTkuZIeEhJiU6ZM8Xo/L2HnYvuJTZs2mST76quvcqzH6dOnTZJ9/PHHXuXnL+PnnnvOqlSp4rWfXL58uUmyFStWmNmZg7FixYp5XeH84IMPzM/PL8crMGedv5xzCzsffvihJ+xezNm2z58/31Pmdrutdu3a9vjjj5vZ/1/Oo0eP9gxz8uRJK1asmOeM+uTJky0sLOyCwbVDhw7WpUsXr7KjR4+an5+f506FuLg4e/DBB72GeeKJJ/Iddvr06eP5DD8rKyvLoqKi7O233/ZM5/zlc/YExc6dO80s98+HvLSpevXqF7xymZPz+/TgwYMtJiYm2+fzWfHx8da5c2evsiFDhlhoaKgntOW0zMLDw+2ll17yKnvsscesVq1anteRkZHWqlUrr2HObk99+vTxlO3atcsk2XPPPecpO3uCYO7cuRds6y+//GINGzY0SValShXr2LGjvffee17HAm+//bZdf/31ZnZm/1iiRAl76aWXPMv99ddft+joaM/w528XOe1rctve87LPOLuezr0ia2b2wgsvWMOGDc0s923iWsQDCv5Ehg4dqqVLl+rLL7/M9t7mzZtVu3ZthYeHe8oiIyNVo0YNbd682WvY+vXrZxs/KirKa9xy5cqpXLlyXvfUlytXTvv37/e8PnDggLp3766aNWuqVKlSCg0N1ebNm/Xzzz/nu23/+c9/NHv2bM2cOVOlS5eWdOa+7r1793qmffZvyZIlSktLkyRt2bJFDRo0kJ/f//962d/+9rd8z/9CYmNjs303IC0tTZ06dVK1atVUokQJlSpVSsePH8/W7vO/rFyhQgXt27dP0pl10KRJE9WoUUPt2rXTyJEj9dtvv3mGXbNmjW655ZaL3rfu6+urv/71r7m2wcfHRw0aNPC8Llu2rG644YZs/SKvXnvtNaWkpGj+/Plq2LChXn31VdWrV89rmDFjxqh+/fqKiIhQaGioBg4cmO9+sW3bNmVmZurWW2/1Km/SpMkl193MtGbNGg0ZMsSrT51djmf7lSTFxMR4fQfj3PUnSVu3blWjRo28pt+4ceM81+Vi/SM/LmU6Q4cO1d69ezVu3DjFx8drypQpio2NveBTfrZs2ZKtHwUEBGRb75dan/OtWbNGSUlJcrlceRo+MzNTgwYNUp06dRQWFqbQ0FCNHz8+W5/LbZ3m1+TJk5WSkuL5a926db6ncbHltWbNGrlcrst+guXmzZt10003ee0nGzRooMDAQK9tqVKlSp7979m6ZGZm6vfff5eU9+V8MWaW5zr7+Ph47c9dLpduueWWbNv/ucuwWLFiCg8P9yzDVq1aqVy5cqpcubI6deqk9957TxkZGZ7hV61apY8//thrfxAZGamsrCzP/mDr1q266aabvOZ5KZ8zq1at0tKlS73mVaJECe3Zs8dr3xMQEKBatWp5XleoUEGSvPrFxT4f8tKmZ555Ri+99JIaN26sF154QUuXLs1XW/75z3/q8OHDqly5sh588EFNnjxZf/zxh+f9LVu25LjvPnr06AX7y/79+5Wenp7jeGlpaTp9+rSn7Nx90bnO/Q7d2e+t1alTJ1vZuccy56tYsaKWL1+uTZs2qXfv3goICFCvXr1Up04dT99p1qyZdu3ape3btys5OVlNmjRRUlKSFixYIElKTk5WQkLCBeeRk7xu7xfbZ6xatUrSmWOXc9f/66+/7ln3uW0T1yIeUPAnUr16dT366KPq06fPJX9RztfXN8cd5PlfrHW5XDmWud1uz+uuXbtq165devXVV1WlShUFBQWpY8eOXl/+z4tPPvlEgwcP1ty5c1WtWjVPudvtVq1atTRjxoxs4wQHB+drHpcqJCQkW1nLli1VqVIljR07VlFRUSpWrJjq16+frd3nf2Hy3OXn5+en5ORkrVixQvPmzdOUKVPUp08fffHFF0pKSspT3QIDA+Xr65unYfN60JgX5cqVU3R0tKKjozVjxgzVqlVLdevW9Rz4f/TRR3rmmWf06quv6uabb1aJEiU0ceJEDR06tMDqcKnMTG63WwMHDtQ999yT7f3y5ct7/r/Y+ju37FLlZfqFOZ2wsDC1a9dO7dq10yuvvKJmzZrppZde0t13333BcfLS3oJqV3688soreuONN/TGG2+oTp06Kl68uIYMGaLvvvuuUOtWsWJFRUdHZyv38TlznvD8A/tzD9byWqezT3q7EnKqiyRPffK6nC+mRo0aks4cDNeuXbvQ6n22ziVLllRKSoqWLFmi+fPna+TIkerdu7cWLVqk2NhYud1udevWTb169co23XNPAObGx8cn1/XtdrvVqlUrDR8+PNv4pUqV8vzv5+fntc7PXw+5yUubHn30UbVu3VqzZ8/WggULlJSU5DnwzYvKlSsrLS1NycnJSk5O1r/+9S/17dtXK1as8NqPFpacPpsl72OZs8stp7K8LMuYmBjFxMToscce04svvqiaNWvq3XffVZ8+fVSjRg1VqFBB8+fP9wSbRo0aKT09XampqVq8eLHeeeedfLcrL9v7xfq72+2Wy+XSqlWrsh3Dnd0v5bZNXIu4svMnM2DAAO3evTvbRhQTE6MtW7Z4PW533759Sk1NLbTfPFi8eLG6d++uf/zjH4qNjVX58uXz/TjYFStWqGvXrnrvvfeynSmLj4/Xjh07VKJECc/B9dm/s2e6ateurZUrVyorK8szXn7PUOXHb7/9ph9//FH9+/dXUlKSateuLR8fHx06dCjf03K5XGrUqJH69++vpUuXqkGDBvrggw8kSXFxcVqyZIlOnDhx2XV2u91auXKl5/WBAweUlpbmOdAoVqyY1/LLj/Lly+vhhx9Wjx49PGWLFy9Ww4YN9fTTTysuLk433HCDfvrpJ6/x8jLP6tWry8/PT4sXL/YqX7Ro0SX3aR8fH/31r3/Vli1bsvWp6OjoC36A5qRWrVr6/vvvvcqWL1/u9fpylu2V5OPjo+rVq1/wbGft2rXldru1YsUKT9nJkye1bt26QqlPXFyc5s6dm+erAIsXL9Ydd9yhLl26qF69eoqOjta2bdvyPd+CWl8RERGSpN27d3vKfvvtt4ueTc5JXFyc3G635s6dm+P7vr6+8vX1zbXOMTExWrZsmTIzMz1lK1eu1IkTJ/K1LRXEcm7QoIGqV6+uV155Jcd6/9///Z/cbrdiYmLkdru9gpSZacmSJfne/v38/NSsWTMNGjRI69atU+nSpT2P646Pj9eGDRty3B+cDSC1atXSsmXLvKZ5/udMRESEjh07psOHD3vK1q5d6zVMfHy8Nm3apOuvvz7bvPITrHL7fMhLm6QzYb1bt26aNGmSxowZo3HjxunkyZN5rkdgYKBatWqlYcOGaePGjUpPT/c8Orx27do57ruLFy9+wafKRkREKDw8PMfxqlevXqRPO6xataoCAwO9tuFmzZpp7ty5Wrx4sW677Tb5+/vrb3/7m1599VUdOXJEzZo1u+D0ctrX5La950V8fLzMTL/99lu2dV+1alXPcBfbJq5FhJ0/mbJly6pv374aMWKEV3mnTp1UtmxZdejQQWvXrtWaNWvUsWNHRUVFqUOHDoVSlxo1amjSpEnauHGjUlJS9M9//jNfBwp79+7VnXfeqQceeEC33Xab9u7d6/mTpHvvvVdVqlRR69at9e2332rnzp1asWKFXnnlFX3++eeSpMcff1wHDhzQI488oq1bt2r+/Pnq169fobRXOrMzLlWqlP73v/8pLS1NS5cu1X333Zfvx6QuXLhQgwcP1sqVK7Vr1y59++23Xmc6n376af3xxx+666679P3332vHjh368ssvL2kn6Ofnp549e2rp0qXasGGDOnfurIiICLVv316SVKVKFWVmZmrWrFlKT0/3uhUhL3r06KF169Z5rsDVqFFDa9eu1cyZM7V9+3YNGzYs229nVKlSRb/88otWr16t9PR0HT9+PNt0S5Uqpccff1x9+/bVjBkztG3bNg0cOFBz5szRCy+8kGu9Nm3a5HWLUUpKio4fP65BgwZ5rqStX79e27dv16xZs9SlS5d89d9nn31WEyZM0NixY7V9+3aNGzfO82Fx9szc5S7bwjB9+nR17txZX3/9tVJTU7Vt2za9/fbbmjhxou66664cx4mNjVVSUpIeffRRLVmyRJs3b1a3bt08v71U0Pr27asNGzaoS5cuWrNmjbZv364pU6Z4btE4X40aNTRv3jwtWbJEqamp6t27t9avX5/v+ealX+ZFqVKlFBcXpyFDhmjjxo1atWqVunTpku/9RExMjNq1a6eHH35YH3/8sXbs2KGVK1dq1KhRks70s0qVKik5OVl79uzx3G52vh49emjfvn3q1q2bNm/erEWLFumBBx5QYmJijrc1X0hBLGcfHx9NmDBBaWlpuummm/Tll18qLS1NW7du1ZgxYxQbG6tTp04pJiZGd9xxhx555BHNmzdPW7duVffu3fXjjz/q2WefzfP8Pv30U7311ltau3atdu3apenTp2v37t2efW3//v21du1aPfDAA1q9erV27Nih+fPn68knn9Svv/4q6cy2/tFHH2n06NFKS0vTu+++q6lTp3rN56abblJQUJD69Omj7du3a+bMmRo8eLDXMD179tTRo0fVtm1bLV26VDt37tSSJUvUt2/ffP1+TW6fD3lp02OPPabZs2frxx9/1KZNm/T555+rWrVqCggIyFMd/ve//+n999/Xhg0b9PPPP2vChAk6ceKE5/a7F154QZMnT9bw4cOVlpamyZMna/DgwerTp4/nCkNOXnjhBQ0fPlzjx49XWlqaRo0apffff18vvvhinpfP5XrooYc0aNAgfffdd/r555+1atUqde7cWSdOnPD6LaCEhAR98cUXKlasmCeAJyQkaMKECYqJifGc9MhJTvua3Lb3vIiJiVGnTp3UtWtXTZ48WT/++KNSUlL03nvvea4o5rZNXIsIO39CvXr1ynYWKCgoSN9++60CAgJ06623qkmTJgoJCdHs2bML7bdhxo8fL7fbrQYNGqhNmzZq0aJFvj44f/jhB+3bt09jxoxR+fLlvf6kM2eNFi1apPj4eD3wwAOqXr262rZtq5UrV3rODEVFRemrr77SypUrVbduXfXo0UOvv/56obRXOnM5fNq0adq0aZNiY2P18MMPq2/fvvn+8b7SpUt7zpLecMMNeuSRR/TQQw+pT58+kqTrrrtO3333nfz9/dW8eXPFxsZqwIABl1TngIAADRgwQA8++KDq16+vw4cPa+bMmZ4PtVtuuUWPP/64unTporJly+brQEI6c3awY8eO6t+/v9xut5566im1b99e9913n+Li4rRhwwb179/fa5z27dvrjjvuUFJSksqWLas333wzx2m/9tpr6ty5s7p3764bb7xRn376qaZOnZqn++WbNWumevXqef2lpqaqefPm+vbbb7Vy5UrdfPPNqlu3rp5//nmFhYVd9EP4fJ06ddJ//oJ2/z8AACAASURBVPMfDRw4UHXq1NH06dM97Tx7UHu5y7Yw1KlTR0FBQXruuecUFxen+Ph4jRkzRgMGDNDo0aMvON5HH32k6OhoNW/eXLfddptq1KihW2+9tVB+DyUuLk4LFizQr7/+qltvvVX16tXTW2+95fWdk3MNHDhQDRs2VKtWrXTzzTfr1KlTeuyxx/I937z2y7yYMGGCfH191bBhQ913333q2bPnJf3I56RJk9S1a1f16dNHNWvWVLt27bRr1y7P+yNGjNB3332nSpUqKSoqKsdpVKxYUXPmzFFaWpri4uJ01113eb6rlR8FtZwbNmyodevWKTY2Vk899ZRuvPFGJSYm6uuvv9bbb7/t6VMTJkxQkyZN1LFjR9WtW1dr167VN99843WGOjelS5fWZ5995vldn/79+2vQoEG69957JZ35jsd3332nAwcOKDEx0XPbUmZmpkqUKCHpzPdT/vvf/3q+rzR9+vRsP34cERGhyZMna8GCBYqNjdXQoUP16quveg0TFRWl77//XqGhobrzzjtVo0YNde7cWbt371ZkZGSe25Tb50Ne2pSVleVZ9k2aNFFWVpa++uqrPNehVKlSevfdd3XrrbeqVq1aGjNmjD744APPvrlt27YaO3as3nnnHcXExKhPnz7q1atXrieqevXqpX79+mngwIGKiYnRiBEj9MYbb3jW15Xw97//XcuXL1eHDh1UvXp13XHHHcrIyNCcOXN0yy23eIZr1qyZMjMzPT9ULp0JO5mZmbl+X+dC+5rctve8+PDDD/X444/r5ZdfVq1atZSUlKRJkyZ5viaQ2zZxLXJZXu8jAPCnM3bsWD333HM6evRoUVflmvDiiy/qww8/9HrYhFOdPn1a0dHRuu+++/Tf//63qKsDXFGzZ89Wy5YtdeDAgXzdggbgz4cHFABADo4dO6YxY8aoefPmCgoK0rx58/Tmm2/q+eefL+qqFYrk5GQdPnxYf/nLX3To0CENGzZMe/fu1f3331/UVQMA4JIRdgAgBy6XS99++62GDh2qo0ePqkqVKho4cGCOT0BygtOnT2vAgAHasWOHihUrpjp16mjRokWep2sBAHA14jY2AAAAAI7EAwoAAAAAOBJhBwAAAIAjEXYAAAAAONKf/gEF5/5CdWEIDw9Xenp6oc4Dzkc/wuWiD6Eg0I9QEOhHuFxF0YcqVKiQYzlXdgAAAAA4EmEHAAAAgCMRdgAAAAA40p/+OzvnMzOdOHFCbrdbLpfrsqe3b98+nTx5sgBqVvDMTD4+PgoMDCyQtgIAAADXkqsu7Jw4cUL+/v7y8yuYqvv5+cnX17dAplUYMjMzdeLECQUFBRV1VQAAAICrylV3G5vb7S6woHM18PPzk9vtLupqAAAAAFedqy7sXIu3c12LbQYAAAAu11UXdv6Mhg8frrFjx+Z7vIMHD6pjx466+eab1bFjRx06dKgQagcAAABcm676+8GyHv7H5Y1/3mvfd7+8rOnlx+jRo/W3v/1NTz75pEaNGqXRo0erX79+V2z+AAAAgJNxZecSvfnmm/rb3/6mNm3a6Mcff5QkTZo0Sa1atVJiYqIefvhhHT9+XEePHlWjRo10+vRpSdKRI0c8r+fMmaP27dtLktq3b6/Zs2cXWXsAAAAApyHsXIINGzboyy+/1Ny5c/XRRx9p/fr1kqSWLVtq1qxZmjdvnqKjo/Xxxx8rNDRUjRs31vz58yVJX3zxhVq2bCl/f3+lp6crMjJSkhQREaH09PQiaxMAAADgNISdS7BixQq1aNFCQUFBKl68uJKSkiRJqampuuuuu3TbbbdpxowZSk1NlSR16tRJU6dOlSRNnTpVHTp0yDZNl8vFgwgAAACAAnTVf2fnz6RXr156//33FRMTo6lTp+r777+XJNWvX1+//PKLli1bJrfbrZo1a0qSwsPDtW/fPkVGRmrfvn0KCwsryuoDAAAAjsKVnUvQqFEjzZkzx/OdnLlz50qSjh49qsjISJ0+fVozZszwGufuu+/Wk08+qXvuucdT9ve//13Tpk2TJE2bNk3Nmze/co0AAAAAHI6wcwliY2N1xx13KCkpSffdd5/q1q0rSXr++ed1++23q02bNoqOjvYap23btjp8+LDatGnjKXviiSe0ePFi3XzzzVqyZImeeOKJK9oOAAAAwMlcZmZFXYmL2b17t9frY8eOKTg4uMCm7+fnp8zMzAKb3oV8/fXXmjNnjkaOHJnvcQu6zSh44eHhPGACl4U+hIJAP0JBoB/hchVFH6pQoUKO5Xxn5wro37+/FixYoAkTJhR1VQAAAIBrBmHnChg0aFBRVwEAAAC45vCdHQAAAACORNgBAAAA4EiEHQAAAACORNgBAAAA4EiEnQIwfPhwjR079pLGi4uLU1JSkpKSkjR//vxCqB0AAABwbbrqn8Z256QfCnR6X9xbs0Cnl5uHH35Yjz322BWdJwAAAHAtuOrDTlF58803NW3aNIWHh6tChQqqU6eOJk2apEmTJunUqVOqUqWK3nrrLWVlZSkxMVFLliyRv7+/jhw5oqSkJC1ZsqSomwAAAAAHKOiT/3lxpS8QXCpuY7sEGzZs0Jdffqm5c+fqo48+0vr16yVJLVu21KxZszRv3jxFR0fr448/VmhoqBo3buy5Re2LL75Qy5Yt5e/vL0kaP368EhMT9cwzz+jQoUNF1iYAAADAaQg7l2DFihVq0aKFgoKCVLx4cSUlJUmSUlNTddddd+m2227TjBkzlJqaKknq1KmTpk6dKkmaOnWqOnToIEm6//77tWzZMn377beKiIjQv//976JpEAAAAOBAhJ0C1KtXLw0aNEjz589Xr169dPLkSUlS/fr19csvv2jZsmVyu92qWfPMZb+yZcvK19dXPj4+uvfee5WSklKU1QcAAAAchbBzCRo1aqQ5c+bo+PHjOnr0qObOnStJOnr0qCIjI3X69GnNmDHDa5y7775bTz75pO655x5P2b59+zz/f/PNN6pRo8aVaQAAAABwDeABBZcgNjZWd9xxh5KSkhQeHq66detKkp5//nndfvvtCgsLU7169XT06FHPOG3bttVrr72mNm3aeMoGDRqkLVu2yOVyqWLFiho6dOgVbwsAAADgVFd92LncJ0H4+fkpMzMz3+P16NFDPXr0yFbepUuXHIdfuXKlWrVqpZIlS3rKRo4cme/5AgAAAMibqz7sXA369++vBQsWaMKECUVdFQAAAOCaQdi5AgYNGlTUVQAAAACuOTygAAAAAIAjXXVhx8yKugpX3LXYZgAAAOByXXVhx8fH55IeKHC1yszMlI/PVbeaAAAAgCJ31X1nJzAwUCdOnNDJkyflcrkue3oBAQGeH//8szEz+fj4KDAwsKirAgAAAFx1rrqw43K5FBQUVGDTCw8PV3p6eoFNDwAAAMCfA/dHAQAAAHAkwg4AAAAARyLsAAAAAHAkwg4AAAAARyLsAAAAAHAkwg4AAAAARyLsAAAAAHAkwg4AAAAARyLsAAAAAHAkwg4AAAAARyLsAAAAAHAkwg4AAAAARyLsAAAAAHAkwg4AAAAARyLsAAAAAHAkwg4AAAAARyLsAAAAAHAkwg4AAAAARyLsAAAAAHAkwg4AAAAAR/LL64But1t9+/ZVmTJl1LdvX+3fv18jRozQkSNHVLVqVT311FPy8/PT6dOnNWrUKO3YsUPFixdXz549FRERIUmaMWOGkpOT5ePjowceeEB169YttIYBAAAAuLbl+crOrFmzFBUV5Xk9ceJEtW7dWiNHjlRISIiSk5MlScnJyQoJCdHIkSPVunVrTZo0SZL066+/atmyZXr99dfVr18/vf/++3K73QXcHAAAAAA4I09h5/fff9fatWt12223SZLMTJs3b1ajRo0kSU2bNtWqVaskSatXr1bTpk0lSY0aNdKmTZtkZlq1apVuuukm+fv7KyIiQuXKldP27dsLoUkAAAAAkMew88EHH+i+++6Ty+WSJB05ckTBwcHy9fWVJJUpU0YZGRmSpIyMDIWFhUmSfH19FRwcrCNHjniVnz8OAAAAABS0XL+zs2bNGpUsWVJVq1bV5s2bC71C8+bN07x58yRJQ4YMUXh4eKHOz8/Pr9DnAeejH+Fy0YdQEOhHKAj0I+TFxfrIn6kP5Rp2UlNTtXr1aq1bt06nTp3S8ePH9cEHH+jYsWPKysqSr6+vMjIyVKZMGUlnrtj8/vvvCgsLU1ZWlo4dO6bixYt7ys86d5xzJSYmKjEx0fM6PT29INp5QeHh4YU+Dzgf/QiXiz6EgkA/QkGgHyEvLtZHiqIPVahQIcfyXG9j69Spk8aOHavRo0erZ8+euvHGG/X0008rJiZGy5cvlyQtXLhQ8fHxkqS4uDgtXLhQkrR8+XLFxMTI5XIpPj5ey5Yt0+nTp7V//37t2bNH0dHRBdQ8AAAAAPCW50dPn+/ee+/ViBEjNGXKFFWpUkUJCQmSpISEBI0aNUpPPfWUQkND1bNnT0nSddddp8aNG+uZZ56Rj4+PHnroIfn48DM/AAAAAAqHy8ysqCtxMbt37y7U6XOpFgWBfoTLRR9CQaAfoSDQj64+d0764YrP84t7a17wvavqNjYAAAAAuBoRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCP55TbAqVOnNGDAAGVmZiorK0uNGjXSPffco/3792vEiBE6cuSIqlatqqeeekp+fn46ffq0Ro0apR07dqh48eLq2bOnIiIiJEkzZsxQcnKyfHx89MADD6hu3bqF3kAAAAAA16Zcr+z4+/trwIABeu211/Tqq68qJSVF27Zt08SJE9W6dWuNHDlSISEhSk5OliQlJycrJCREI0eOVOvWrTVp0iRJ0q+//qply5bp9ddfV79+/fT+++/L7XYXbusAAAAAXLNyvbLjcrkUGBgoScrKylJWVpZcLpc2b96sHj16SJKaNm2qadOm6e9//7tWr16t9u3bS5IaNWqkcePGycy0atUq3XTTTfL391dERITKlSun7du3q3r16oXYPAAAAODKyXr4H1d+pk1fvfLzvErkGnYkye12q0+fPtq7d6+aN2+uyMhIBQcHy9fXV5JUpkwZZWRkSJIyMjIUFhYmSfL19VVwcLCOHDmijIwM3XDDDZ5pnjsOAAAAABS0PIUdHx8fvfbaa/rjjz80bNgw7d69u9AqNG/ePM2bN0+SNGTIEIWHhxfavCTJz8+v0OcB56Mf4XLRh1AQ6EcoCPSjy7OvqCtwhVysj/yZ+lCews5ZISEhiomJ0bZt23Ts2DFlZWXJ19dXGRkZKlOmjKQzV2x+//13hYWFKSsrS8eOHVPx4sU95WedO865EhMTlZiY6Hmdnp5+qW3Lk/Dw8EKfB5yPfoTLRR9CQaAfoSDQj5AXF+sjRdGHKlSokGN5rg8o+L//+z/98ccfks48mW3Dhg2KiopSTEyMli9fLklauHCh4uPjJUlxcXFauHChJGn58uWKiYmRy+VSfHy8li1bptOnT2v//v3as2ePoqOjC6JtAAAAAJBNrld2Dh48qNGjR8vtdsvM1LhxY8XFxalixYoaMWKEpkyZoipVqighIUGSlJCQoFGjRumpp55SaGioevbsKUm67rrr1LhxYz3zzDPy8fHRQw89JB8ffuYHAAAAQOFwmZkVdSUupjC/HyRxqRYFg36Ey0UfQkGgH6Eg0I8uT1E8ja1tETyN7Yt7a17wvavqNjYAAAAAuBoRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCMRdgAAAAA4EmEHAAAAgCP55TZAenq6Ro8erUOHDsnlcikxMVGtWrXS0aNH9cYbb+jAgQMqW7asevXqpdDQUJmZxo8fr3Xr1ikgIEDdu3dX1apVJUkLFy7UZ599Jklq27atmjZtWqiNAwAAAHDtyjXs+Pr6qnPnzqpataqOHz+uvn37qk6dOlq4cKFiY2PVpk0bff755/r888913333ad26ddq7d6/eeustpaWl6b333tPgwYN19OhRffrppxoyZIgkqW/fvoqPj1doaGihNxIAAADAtSfX29hKly7tuTITFBSkqKgoZWRkaNWqVWrSpIkkqUmTJlq1apUkafXq1br11lvlcrlUvXp1/fHHHzp48KBSUlJUp04dhYaGKjQ0VHXq1FFKSkohNg0AAADAtSxf39nZv3+/fvrpJ0VHR+vw4cMqXbq0JKlUqVI6fPiwJCkjI0Ph4eGeccLCwpSRkaGMjAyFhYV5ysuUKaOMjIyCaAMAAAAAZJPrbWxnnThxQsOHD1fXrl0VHBzs9Z7L5ZLL5SqQCs2bN0/z5s2TJA0ZMsQrOBUGPz+/Qp8HnI9+hMtFH0JBoB+hINCPLs++oq7AFXKxPvJn6kN5CjuZmZkaPny4brnlFjVs2FCSVLJkSR08eFClS5fWwYMHVaJECUlnrtikp6d7xv39999VpkwZlSlTRlu2bPGUZ2RkqHbt2tnmlZiYqMTERM/rc6dVGMLDwwt9HnA++hEuF30IBYF+hIJAP0JeXKyPFEUfqlChQo7lud7GZmYaO3asoqKidPvtt3vK4+PjtWjRIknSokWLVL9+fU/54sWLZWbatm2bgoODVbp0adWtW1fr16/X0aNHdfToUa1fv15169YtiLYBAAAAQDa5XtlJTU3V4sWLdf311+v555+XJP3zn/9UmzZt9MYbbyg5Odnz6GlJqlevntauXaunn35axYoVU/fu3SVJoaGhateunV544QVJ0t13382T2AAAAAAUmlzDTs2aNfXJJ5/k+N6//vWvbGUul0vdunXLcfiEhAQlJCTks4oAAAAAkH/5ehobAAAAAFwtCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAAAAHImwAwAAAMCRCDsAAADA/2vv/mKkrO89jn+mu5WErKzsolgTYyKRnOCfUt021Sa6KtE7JbR64VEv7J/QbVNpY2nBaNsYlZgoRsVIkJCanAtT03pML2qCRFGJCRQW/5BTRUyEuB5lh1r+hAjMcy44ZyMHdFdZZobfvl53Mzvh+W7yzc6+eZ55liKJHQAAoEhiBwAAKJLYAQAAiiR2AACAIokdAACgSGIHAAAoktgBAACKJHYAAIAiiR0AAKBIYgcAACiS2AEAAIokdgAAgCKJHQAAoEhiBwAAKJLYAQAAiiR2AACAIokdAACgSGIHAAAoktgBAACKJHYAAIAiiR0AAKBIYgcAACiS2AEAAIokdgAAgCKJHQAAoEhiBwAAKJLYAQAAiiR2AACAIokdAACgSGIHAAAoktgBAACKJHYAAIAiiR0AAKBInaO94PHHH8/GjRvT3d2dBx98MEmyZ8+eLF26NB9//HFOP/30/PKXv0xXV1eqqsqqVauyadOmTJo0KQMDAzn33HOTJC+++GL+/Oc/J0nmzZuX/v7+E/ddAQAAE96oZ3b6+/uzePHiI5579tlnc+GFF+aRRx7JhRdemGeffTZJsmnTpnz44Yd55JFH8pOf/CRPPvlkksNx9Mwzz+S+++7Lfffdl2eeeSZ79uw5Ad8OAADAYaPGzqxZs9LV1XXEc+vXr88VV1yRJLniiiuyfv36JMmGDRty+eWXp1arZebMmdm7d2927dqVwcHBXHTRRenq6kpXV1cuuuiiDA4OnoBvBwAA4LCv9JmdTz75JFOnTk2SnHbaafnkk0+SJPV6PdOmTRt5XW9vb+r1eur1enp7e0ee7+npSb1eP565AQAAvtCon9kZTa1WS61WG49ZkiSrV6/O6tWrkyRLliw5Ip5OhM7OzhN+DMpnjzhedojxYI8YD/bo+Px3qwdoki/akXbaoa8UO93d3dm1a1emTp2aXbt2ZcqUKUkOn7HZuXPnyOuGh4fT09OTnp6ebNmyZeT5er2eWbNmHfPfnjNnTubMmTPy+LP/3okwbdq0E34MymePOF52iPFgjxgP9oix+KIdacUOnXXWWcd8/itdxtbX15eXXnopSfLSSy/l29/+9sjza9euTVVVefvttzN58uRMnTo1s2fPzubNm7Nnz57s2bMnmzdvzuzZs7/itwIAADC6Uc/sPPzww9myZUt2796d+fPn58Ybb8zcuXOzdOnSrFmzZuTW00nyrW99Kxs3bswvfvGLnHLKKRkYGEiShAsF9QAACi5JREFUdHV15fvf/34WLVqUJPnBD35w1E0PAAAAxtOosbNgwYJjPn/33Xcf9VytVsuPfvSjY77+qquuylVXXfUlxwMAAPhqvtJlbAAAAO1O7AAAAEUSOwAAQJHEDgAAUCSxAwAAFEnsAAAARRI7AABAkcQOAABQJLEDAAAUSewAAABFEjsAAECRxA4AAFAksQMAABRJ7AAAAEUSOwAAQJHEDgAAUCSxAwAAFEnsAAAARRI7AABAkcQOAABQJLEDAAAUSewAAABFEjsAAECRxA4AAFAksQMAABRJ7AAAAEUSOwAAQJHEDgAAUCSxAwAAFEnsAAAARRI7AABAkcQOAABQJLEDAAAUSewAAABFEjsAAECRxA4AAFAksQMAABRJ7AAAAEUSOwAAQJHEDgAAUCSxAwAAFEnsAAAARRI7AABAkcQOAABQJLEDAAAUSewAAABFEjsAAECRxA4AAFAksQMAABRJ7AAAAEUSOwAAQJHEDgAAUCSxAwAAFEnsAAAARRI7AABAkcQOAABQJLEDAAAUqbPVAwAAzXPox9c1/ZgdK55r+jEBEmd2AACAQokdAACgSGIHAAAoktgBAACKJHYAAIAiiR0AAKBIYgcAACiS2AEAAIokdgAAgCKJHQAAoEhiBwAAKJLYAQAAiiR2AACAInW2egDgxLr+P/6r6cf8z3//t6YfEwDg/3NmBwAAKJLYAQAAiuQyNgCKcejH1zX9mB0rnmv6MTlx7BCURewAALSQz1bCieMyNgAAoEhiBwAAKJLYAQAAiiR2AACAIrlBAePGHWwAAGgnzuwAAABFcmaHk9rJdrvOVpz9Sv8DzT8mAIyjk+39nvbhzA4AAFAksQMAABRJ7AAAAEUSOwAAQJHEDgAAUCR3Y2tz7j4CwMnOe1lZ3FmUk4kzOwAAQJGc2fkS/E8G0Gqt+DnUseK5ph8TAMaDMzsAAECRxA4AAFAksQMAABTJZ3YA+ELupAXAyUrsAMBxEIMA7ctlbAAAQJHEDgAAUCSxAwAAFEnsAAAARXKDAqBtHPrxdU0/ZseK55p+TACgOZzZAQAAiiR2AACAIokdAACgSD6zA0xo/iAkAJTLmR0AAKBIYgcAACiS2AEAAIokdgAAgCKJHQAAoEhiBwAAKFLTbz09ODiYVatWpdFo5Oqrr87cuXObPQIAADABNPXMTqPRyMqVK7N48eIsXbo0r776anbs2NHMEQAAgAmiqbGzdevWnHnmmZk+fXo6Oztz2WWXZf369c0cAQAAmCCaGjv1ej29vb0jj3t7e1Ov15s5AgAAMEHUqqqqmnWw1157LYODg5k/f36SZO3atXnnnXfywx/+cOQ1q1evzurVq5MkS5YsadZoAABAYZp6ZqenpyfDw8Mjj4eHh9PT03PEa+bMmZMlS5Y0LXR++9vfNuU4lM0ecbzsEOPBHjEe7BHHq512qKmxM2PGjAwNDeWjjz7KwYMHs27duvT19TVzBAAAYIJo6q2nOzo6ctttt+Xee+9No9HIlVdembPPPruZIwAAABNE0//OzsUXX5yLL7642Yf9XHPmzGn1CBTAHnG87BDjwR4xHuwRx6uddqipNygAAABolqZ+ZgcAAKBZmn4ZW6sMDg5m1apVaTQaufrqqzN37twjvn7gwIE89thj2bZtW0499dQsWLAgZ5xxRoumpR2NtkN//etf88ILL6SjoyNTpkzJT3/605x++uktmpZ2Ndoe/Z/XXnstDz30UO6///7MmDGjyVPS7sayR+vWrcuf/vSn1Gq1nHPOObn99ttbMCntarQd2rlzZ5YtW5a9e/em0WjkpptuaquPIdB6jz/+eDZu3Jju7u48+OCDR329qqqsWrUqmzZtyqRJkzIwMJBzzz23+YNWE8ChQ4eqn//859WHH35YHThwoLrjjjuq7du3H/Gav/3tb9Xy5curqqqqV155pXrooYdaMSptaiw79MYbb1T79++vqqqqnn/+eTvEUcayR1VVVfv27avuvvvuavHixdXWrVtbMCntbCx79MEHH1S//vWvq927d1dVVVX//Oc/WzEqbWosO/TEE09Uzz//fFVVVbV9+/ZqYGCgFaPSxt56663q3XffrX71q18d8+t///vfq3vvvbdqNBrVP/7xj2rRokVNnvCwCXEZ29atW3PmmWdm+vTp6ezszGWXXZb169cf8ZoNGzakv78/SfLd7343b775ZiofZ+J/jWWHLrjggkyaNClJct5556Ver7diVNrYWPYoSZ5++ulcf/31+frXv96CKWl3Y9mjF154Iddee226urqSJN3d3a0YlTY1lh2q1WrZt29fkmTfvn2ZOnVqK0aljc2aNWvkZ8yxbNiwIZdffnlqtVpmzpyZvXv3ZteuXU2c8LAJETv1ej29vb0jj3t7e4/6RfSzr+no6MjkyZOze/fups5J+xrLDn3WmjVrMnv27GaMxklkLHu0bdu27Ny50+UifK6x7NEHH3yQoaGh3HXXXbnzzjszODjY7DFpY2PZoRtuuCEvv/xy5s+fn/vvvz+33XZbs8fkJFev1zNt2rSRx6P97nSiTIjYgWZau3Zttm3bluuuu67Vo3CSaTQaeeqpp3Lrrbe2ehROco1GI0NDQ/nd736X22+/PcuXL8/evXtbPRYnkVdffTX9/f154oknsmjRojz66KNpNBqtHgu+tAkROz09PRkeHh55PDw8nJ6ens99zaFDh7Jv376ceuqpTZ2T9jWWHUqS119/PX/5y1+ycOFClyBxlNH2aP/+/dm+fXv+8Ic/5Gc/+1neeeedPPDAA3n33XdbMS5taqzvaX19fens7MwZZ5yRb3zjGxkaGmr2qLSpsezQmjVrcumllyZJZs6cmQMHDrjihS+lp6cnO3fuHHn8eb87nWgTInZmzJiRoaGhfPTRRzl48GDWrVuXvr6+I15zySWX5MUXX0xy+C5I559/fmq1WgumpR2NZYfee++9rFixIgsXLnR9PMc02h5Nnjw5K1euzLJly7Js2bKcd955WbhwobuxcYSx/Dz6zne+k7feeitJ8q9//StDQ0OZPn16K8alDY1lh6ZNm5Y333wzSbJjx44cOHAgU6ZMacW4nKT6+vqydu3aVFWVt99+O5MnT27JZ78mzB8V3bhxY/74xz+m0WjkyiuvzLx58/L0009nxowZ6evry6effprHHnss7733Xrq6urJgwQJvDBxhtB2655578v777+e0005LcviN4je/+U2Lp6bdjLZHn/X73/8+t9xyi9jhKKPtUVVVeeqppzI4OJivfe1rmTdvXr73ve+1emzayGg7tGPHjixfvjz79+9Pktx888355je/2eKpaScPP/xwtmzZkt27d6e7uzs33nhjDh48mCS55pprUlVVVq5cmc2bN+eUU07JwMBAS97PJkzsAAAAE8uEuIwNAACYeMQOAABQJLEDAAAUSewAAABFEjsAAECRxA4AAFAksQMAABRJ7AAAAEX6HziNdWxn00i4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days_ratio[\"0\"],days_ratio[\"5\"]],label = [\"day0\",\"day5\"],histtype = \"bar\")\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Transcript Ratio Length in Signficiant Functional Consequences Isoform Switches\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "days_stat = dict({\"0\":[],\"3\":[],\"5\":[]})\n",
    "days_ratio_stat = dict({\"0\":[],\"3\":[],\"5\":[]})\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "for gene_ensembl in targets:\n",
    "    gene = gene_ensembl.split(\"_\")[0]\n",
    "    df=data[data[identifier]==gene]\n",
    "    df_temp = df\n",
    "    for j in range(df.shape[0]):\n",
    "        transcript_annotation=annotation[annotation['transcript_id']==df.iloc[j][\"transcript_id\"]] \n",
    "        if (transcript_annotation.shape[0] < 3):\n",
    "            df_temp = df_temp[df_temp[\"transcript_id\"] != df.iloc[j][\"transcript_id\"]]\n",
    "\n",
    "    # total gene expression calculation\n",
    "    data_gene=df_temp[samples].sum().to_frame().transpose()\n",
    "    data_gene=calculateStatistics(data_gene,conditions,number_replicates)\n",
    "    # mean transcript expression calculation\n",
    "    df_temp=calculateStatistics(df_temp,conditions,number_replicates)\n",
    "    # isoform percentage calculation\n",
    "    df_temp=(df_temp.filter(like='mean').div(data_gene.filter(like='mean').values[0],1)*100).add_prefix('Pct_').join(df_temp)\n",
    "    #choose isoforms to plot`b\n",
    "    df_temp=chooseIsoforms2Plot(df_temp,minimumTPM,minimumPct,maximumIso,annotation)\n",
    "    if (len(df_temp) > 1):\n",
    "        for i in [\"0\",\"3\",\"5\"]:\n",
    "            if (sum(df_temp[\"meanday\"+i]) > 2):\n",
    "                pct_list = list(df_temp[\"Pct_meanday\"+i])\n",
    "                tcons_list = list(df_temp[\"transcript_id\"])\n",
    "                for j in range(len(pct_list)): \n",
    "                    days_stat[i] += [tID_length[tcons_list[j]]]*int(pct_list[j])\n",
    "                transcript = df_temp[df_temp[\"Pct_meanday\" + i] == df_temp[\"Pct_meanday\" + i].max()][\"transcript_id\"]\n",
    "                days_ratio_stat[i].append(tID_length[list(transcript)[0]])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days_stat[\"0\"],days_stat[\"3\"],days_stat[\"5\"]],bins = 20,label = [\"day0\",\"day3\",\"day5\"])\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Transcript Length Ratio in Signficiant Statistical Isoform Switches\")\n",
    "plt.show()\n",
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days_ratio_stat[\"0\"],days_ratio_stat[\"3\"],days_ratio_stat[\"5\"]],bins = 20,label = [\"day0\",\"day3\",\"day5\"],)\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Transcript Length of Most Expressed Isoform in Signficiant Statistical Isoform Switches\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "codon_table = {\n",
    "        'ATA':'I', 'ATC':'I', 'ATT':'I', 'ATG':'M', \n",
    "        'ACA':'T', 'ACC':'T', 'ACG':'T', 'ACT':'T', \n",
    "        'AAC':'N', 'AAT':'N', 'AAA':'K', 'AAG':'K', \n",
    "        'AGC':'S', 'AGT':'S', 'AGA':'R', 'AGG':'R',                  \n",
    "        'CTA':'L', 'CTC':'L', 'CTG':'L', 'CTT':'L', \n",
    "        'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCT':'P', \n",
    "        'CAC':'H', 'CAT':'H', 'CAA':'Q', 'CAG':'Q', \n",
    "        'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGT':'R', \n",
    "        'GTA':'V', 'GTC':'V', 'GTG':'V', 'GTT':'V', \n",
    "        'GCA':'A', 'GCC':'A', 'GCG':'A', 'GCT':'A', \n",
    "        'GAC':'D', 'GAT':'D', 'GAA':'E', 'GAG':'E', \n",
    "        'GGA':'G', 'GGC':'G', 'GGG':'G', 'GGT':'G', \n",
    "        'TCA':'S', 'TCC':'S', 'TCG':'S', 'TCT':'S', \n",
    "        'TTC':'F', 'TTT':'F', 'TTA':'L', 'TTG':'L', \n",
    "        'TAC':'Y', 'TAT':'Y', 'TAA':'_', 'TAG':'_', \n",
    "        'TGC':'C', 'TGT':'C', 'TGA':'_', 'TGG':'W', \n",
    "        }\n",
    "\n",
    "\n",
    "def translate(seq, i, utr_regions):\n",
    "    translating = True\n",
    "    aa = \"\"\n",
    "    \n",
    "    while(translating): \n",
    "        if ((len(seq) < 3)):\n",
    "            translating = False\n",
    "            aa = \"\"\n",
    "        else:\n",
    "            codon = seq[0:3]\n",
    "            if (codon_table[codon] == \"_\"):\n",
    "                translating = False\n",
    "            else:\n",
    "                aa += codon_table[codon]\n",
    "            seq = seq[3:]\n",
    "            i += 3\n",
    "    return aa,i\n",
    "\n",
    "def translate_aa_seq_length(seq,enst,gene_utrs):\n",
    "    longest_aa_seq = \"M\"\n",
    "    for i in range(len(seq)):\n",
    "        if (seq[i:i+3] == \"ATG\"):\n",
    "            aa,end = translate(seq[i:], i, [])\n",
    "            if (len(aa) > len(longest_aa_seq)):\n",
    "                longest_aa_seq  = aa\n",
    "    return longest_aa_seq\n",
    "\n",
    "def find_all_aa_seqs(seq,enst,gene):    \n",
    "    longest_aa_seq = translate_aa_seq_length(seq,enst,[])\n",
    "    return longest_aa_seq\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcripts_filename = \"/project/owlmayerTemporary/Sid/nanopore-analysis/Results_5_1/Pinfish/corrected_transcriptome_polished_collapsed.fas\"\n",
    "transcripts = SeqIO.index(transcripts_filename, \"fasta\")\n",
    "\n",
    "\n",
    "# Extracting isoforms from related genes\n",
    "tID_length_protein = dict()\n",
    "for gene in targets:\n",
    "    max_len = 0\n",
    "    tIDs_list = []\n",
    "    for oID in gene_oID[gene]:\n",
    "        seq = str(transcripts[oID].seq)\n",
    "        p_len = len(find_all_aa_seqs(seq,\"\",gene))\n",
    "        tID_length_protein[oID_tID[oID]] = p_len\n",
    "        if (p_len > max_len):\n",
    "            max_len = p_len\n",
    "        tIDs_list.append(oID_tID[oID])\n",
    "        \n",
    "    for tID in tIDs_list:\n",
    "        tID_length_protein[tID] = tID_length_protein[tID]/max_len\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_protein = dict({\"0\":[],\"3\":[],\"5\":[]})\n",
    "days_protein_ratio = dict({\"0\":[],\"3\":[],\"5\":[]})\n",
    "\n",
    "for gene_ensembl in targets:\n",
    "    gene = gene_ensembl.split(\"_\")[0]\n",
    "    df=data[data[identifier]==gene]\n",
    "    df_temp = df\n",
    "    for j in range(df.shape[0]):\n",
    "        transcript_annotation=annotation[annotation['transcript_id']==df.iloc[j][\"transcript_id\"]] \n",
    "        if (transcript_annotation.shape[0] < 3):\n",
    "            df_temp = df_temp[df_temp[\"transcript_id\"] != df.iloc[j][\"transcript_id\"]]\n",
    "\n",
    "    # total gene expression calculation\n",
    "    data_gene=df_temp[samples].sum().to_frame().transpose()\n",
    "    data_gene=calculateStatistics(data_gene,conditions,number_replicates)\n",
    "    # mean transcript expression calculation\n",
    "    df_temp=calculateStatistics(df_temp,conditions,number_replicates)\n",
    "    # isoform percentage calculation\n",
    "    df_temp=(df_temp.filter(like='mean').div(data_gene.filter(like='mean').values[0],1)*100).add_prefix('Pct_').join(df_temp)\n",
    "    #choose isoforms to plot`b\n",
    "    df_temp=chooseIsoforms2Plot(df_temp,minimumTPM,minimumPct,maximumIso,annotation)\n",
    "    if (len(df_temp) > 1):\n",
    "        for i in [\"0\",\"3\",\"5\"]:\n",
    "            if (sum(df_temp[\"meanday\"+i]) > 2):\n",
    "                pct_list = list(df_temp[\"Pct_meanday\"+i])\n",
    "                tcons_list = list(df_temp[\"transcript_id\"])\n",
    "                for j in range(len(pct_list)): \n",
    "                    days_protein_ratio[i] += [tID_length_protein[tcons_list[j]]]*int(pct_list[j])\n",
    "                transcript = df_temp[df_temp[\"Pct_meanday\" + i] == df_temp[\"Pct_meanday\" + i].max()][\"transcript_id\"]\n",
    "                days_protein[i].append(tID_length_protein[list(transcript)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1srVr19rOnTvt//7v/2zVqlVpnqdKlSpZ586dbdu2bbZnzx6bM2eO+7dQcq5u0yNGjLCIiAhbsmSJHThwwNasWeMRYrztP3/77Td77rnn7LbbbvNou88//7zlz5/fpk+fbnFxcTZ06FBzuVwe2zRJlj9/fvvwww9t9+7dtnPnTvf6VKJECYuJibG4uDhr06aNFS5c2Bo3bmxfffWVxcXF2f33329FihSxS5cupTivzZs3txo1atgvv/ySYj/169e3gQMHmtlfoadgwYI2f/58MzObN2+e+fv7u0PPletFStsab+t7WrcZ3n7veVsnUpKhIcfMrE2bNu6jv1eHnHHjxllgYKDHdcmHDx+2nDlz2n/+8x8z++s+lKtT66BBg8zX19dj2N69e5uPj4+7oZmZPfPMM1a9evVU661cubINGTLE/T61kHO1rl27WpkyZdwbo/Hjx1tERIT7qF+Shg0bWt++fc3M7OWXX7aiRYt69PP111+nO+QcPXrUWrdubblz53YfjS1WrJg1atTIY7jY2FiTZNu2bXN3u3jxohUuXNjeeOMNM0v53otixYrZxx9/7NFt2bJlJsk9z8mFnIIFC9rFixfd3YYNG2aFCxdOcd7MUg85S5YssRw5cniclTMze+ihh+y+++4zs79WnhEjRrg/j4+Pt5CQEPvkk0/M7M8jQ5I8zhb+9ttvFhgY6DHt1O7JufJowuHDhz3OpCWnXr169sADD3h0GzVqlOXMmdMjzHv7IWz2V5BYvXq1BQcH2+nTp23Hjh3m7+/vPoN45XdRpEiRa+6N69evnxUvXtzM/vqBkdI9N+m9JycgIMB9diHpNXz4cHc/CQkJ1rhxY6tXr561aNHCatas6bGhTjo6dOX3ExcX5/Gjd9CgQeZyuezAgQPXTD+1tnrixIlU7wl8//337fbbb09xxxEZGWkDBgzw6HbgwAGTZD/++KOZmUVERNhLL73k0c/999+frpDjrc60bDfNrg0Tr7zyikVERHicadm4caNJcp9pTmnZJq3TVw7bsmVLK1CggMd6fu+999r999+f6rwmF3Kefvppj37Kli3r3vkmJyEhwXr16mUul8vy589vzZo1s2HDhtnPP/+c4rTSuu4XK1bM7rnnHo/xNG/e3Dp16uR+n9z6evUPxHr16qV6P1pyPyivdu+999ojjzzifh8ZGWmVK1f26OeJJ56w2rVru9+n956coKAgj/U1qd2lJ+Skts1N2v9cefbiSindk3P1Mu7WrZvVrFnTo5+ZM2eay+Vyn0W43n3P1cvZW8gZPny4SfJ6r1ta9r1p2a9k1bYpLSGnR48eHmc7kuYpMDDQYmJi3OPx9fX1+C02depUc7lc7iDUrVs3q1y5siUkJFz3POXOnTvFM5XJubpNP/PMM9awYUN3aLqat/1n0rxeuczOnTtnAQEBNnbsWI/h2rRpYw0bNnS/l2QPP/ywRz9J69OVQWvNmjUmyd577z13t6T99pYtW1Kc1x07dliFChXM5XJZ6dKl7cEHH7QvvvjC43fnoEGD3OvUggULrESJEvbkk0+6l/sLL7zgEUyvXi+S29Z4W9/Tss1Iy+89b+tESjL86WrDhw/XypUrNXv27Gs+27Ztm8qXL6/Q0FB3t7CwMJUpU0bbtm3z6LdmzZrXDB8REeExbOHChVW4cGEVLFjQo9vRo0fd748dO6bevXurbNmyyps3r0JCQrRt2zYdOHAg3fP25ptvat68efr222+VL18+SdLatWt1+PBh97iTXitWrNCuXbskSdu3b1etWrU8nhxTr169NE0zISHBPc5ChQpp9+7d+u9//6tChQq5+6lVq5bHMNu2bVOBAgVUvnx5d7ccOXLozjvvvGY5X+nYsWM6cOCAnn32WY95adGihSRp9+7dKQ5btmxZj2u7w8PDdeTIkTTNY3LWrl2rS5cuKSIiwqOWL774wr1ck1StWtX9t6+vrwoVKuSe9vbt2xUaGurxVKP8+fOrTJkyaa7lyvGHhYXJ19c31Xnbtm2b7r77bo9ukZGRunjxovbs2ZPm6V6pVq1aKlWqlKZMmaLPPvtM99xzj8LCwjz6OX36tH755Zdkp71//36dP39elStXVrNmzVSxYkW1bdtWo0eP1sGDB6+rJknq06ePNm7c6PF69NFH3Z/7+Pho0qRJ2rJli5YvX66pU6dec39dwYIFPb6f0qVLKzQ01KOthoWFqWjRou73aWmr+fLl0yOPPKJmzZqpRYsWGjZsmOLi4tzj6NChgy5fvqxixYqpZ8+emjRpks6cOeP+fO3atRo1apTH+JPWqV27dun06dP63//+d8212Wldt5N4qzM9280rbdu2TbVr1/a47rtKlSrKkydPqss2Sbly5TyGLVy4sMqUKeOxnl+9vU2rK9cpyfv2wsfHR+PGjdOvv/6qMWPGqHz58vr0009Vrly5FJ9ulJ51P731JGf9+vVq2rRpmvs/f/68Bg4cqAoVKih//vwKCQnRnDlzrtk3ValS5YZru9L8+fM91tc777wz3eNIbZu7fv165cuXTzVq1LjuGqWUt6Nmpu3bt7u7edv3pHU5p8bM0lxzWve9qe1X/i7bpqRpxcTEeEyrQIECunjxose+ODw83OO3WHh4uMzMvX1Yv369GjduLB+f5H92epsnSXr++ef1yCOPqEGDBnr99de1YcOGdM3LQw89pC1btqhUqVJ64oknNGPGDF26dElS2vafydm9e7cuXbqU7HBXf+dX/1ZLcuU6nnRfYeXKla/pltq2tmzZstqyZYvWr1+vp556SpcuXdIjjzyi2rVr68KFC5Kkhg0basOGDfr999+1ePFiNW7cWA0bNtTixYslSYsXL1ajRo1SnEZy0rq+p7bNSMvvPW/rREoy/HmNpUuX1uOPP64BAwZo7ty51zUOX1/fZG9evvrHkcvlSrZbYmKi+33Pnj31888/65133lHx4sUVGBioTp06uRt2Wk2fPl1vvfWWFi5cqJIlS7q7JyYmqly5coqJiblmmKCgoHRNIzm+vr7auHGjXC6XChUqpFy5cl3TT3Bw8A1PR5J7uY0ePVoNGza85vMiRYqkOOzVN9C5XK407xhSqiVPnjxau3at12klN+0r24DL5bruOpIbf1J9We2xxx7Txx9/rIMHD2ry5MnXNQ5fX1/NnTtXa9euVWxsrGbMmKGBAwfqyy+/VOvWrdM9vvz583t9LO7GjRt17tw5mZkOHjyoEiVKpHs6V7fxtLbVzz//XH379tWCBQu0cOFCvfrqqxozZowef/xxRURE6KefftKSJUu0ePFivfnmmxowYIBWr16tW2+9VYmJiRowYIC6d+9+zfgLFy6coW0gtTozW0rbj+vZ3qaVt3U2JYULF1bnzp3VuXNnDRs2TNWqVdMbb7yR4iOz07ruX289N6J///6aNWuW3n//fZUpU0bBwcF67rnn9Pvvv2dqbbfddluy2/KkH59XbrcTEhKSnVZ2LK+UeNv3pHU5pyYpGG/fvv26gkJa6pb+2q5l1bbJx8fnmv305cuXr6mpe/fuGjhw4DXDFyhQIMX5SVr30lqLt3mSpFdffVVdu3bVvHnztHjxYr311lt64YUXNGTIkDRNo2rVqtq3b58WLlyoJUuWqG/fvnr11Vdv6Ob89EjLtjZpuSXXzduydLlcqlatmqpVq6ann35a3333nerXr6/p06erR48eqlOnjgICArR06VItXrxY0dHRatiwobp06aIDBw7oxx9/1HvvvXejs5ms1LYZafm9522dSEmm/J+cQYMG6ddff9Vnn33m0b1ChQravn27jh8/7u525MgRxcXFqWLFiplRipYvX67evXvr3nvvVaVKlXTLLbdo79696RrH6tWr1bNnT40bN+6aDVyNGjW0d+9e5c6dW6VKlfJ4hYeHS5LKly+vNWvWKCEhwT3cypUr0zz9UqVKqWTJkskGnORUqFBBv/32m8fRrj/++EOrV692L+ekhnNlTWFhYbr11lsVFxd3zbyUKlUq05+adaUaNWro1KlTunjx4jV1JHfUOSXly5fXsWPHPM6gnDx58pon4QQEBHgsixtRoUIFLV++3KPbsmXLFBgY6BGQ06tbt27atWuXcuXKpSZNmlzzee7cuVWkSJFkp128eHF36Ha5XKpVq5ZeeuklLV++XJGRkRo/fryk5NvFjTh8+LB69Oihl19+WU899ZS6deumEydOePRz9fezc+dOHT9+3ONo6NXS01YrVqyoZ599VnPnzlWvXr08tks5cuRQ8+bN9c4772jLli06f/68Zs6cKenPNrht27Zkxx8SEqLcuXMrIiJCq1at8qgtPev2lVKq83q3mxUqVNAPP/zgcUBn06ZN+v333zNte5uVAgICVKJEiRSPbqZ13c8o1atX14IFC9Lc//Lly9W1a1d16NBBVapUUYkSJa6rtozadiVdHfDrr7+6u23cuDHdB6uqV6+ukydPat26dcl+ntZtTErbUZfLpQoVKqS5noxYzk2bNlWhQoU0dOjQZD8/efKku2Zv+960yoptU6FChXT06FGP7+LqsyM1atTQ5s2bVbJkyWumlXRFS1pUr15dixYtSvGHurd5SlKiRAn17t1b//3vfzV48GB9/PHHaa5B+vMph23bttUHH3ygdevWaceOHVq2bFma959XK1WqlHLkyJHscNm9nS1Xrpykv84ABQQEqG7duoqJidGGDRvUqFEjhYaGqnz58ho8eLACAgJUp06dFMeX3LbG2/qeFmn9vZfaOpGSTAk5BQsW1MCBAzVq1CiP7l26dFHBggXVsWNHbdiwQevXr1enTp0UERGhjh07ZkYpKlOmjCZPnqwtW7Zo48aN6ty5c7p2CIcPH9Z9992nhx56SI0bN9bhw4fdL0nq2rWrihcvrlatWmnBggXav3+/Vq9erbffftu98J988kkdO3ZMjz32mHbs2KFFixbp5ZdfzpT5laRGjRqpVq1a6tKli1auXKmtW7fqwQcf1MWLF/Xkk09KkooVKyYfHx/NmTNHR48edR/VGjp0qD744AMNHTpUW7duVVxcnGbOnJlpR5RPnDhxzeVOe/fuVaNGjRQVFaV27dpp5syZ2rt3r9avX68PP/xQn3/+eZrHHxUVpSpVqqh79+5au3atNm3apO7du8vPz8/jKG/x4sW1cuVK/fzzzzp+/PgNHZV88cUXNWPGDA0bNkw7d+7U9OnT9frrr+u55567oUdG5s6dW//73/+0efPmFE/5v/jii+5ltGvXLn366af6+OOP9dJLL0mSVq1apTfffFOrV6/Wzz//rEWLFmnz5s3uQJFSu0jJ2bNnPdaJw4cP67fffpP05xHhBx98UGXLltWrr76qd955RwULFtTDDz/sMY6goCA99NBDWrdundatW6cePXqoatWqaty4carT9tZWd+/erQEDBui7777TgQMH9P3332vFihXuef3Xv/6lzz//XJs2bdKBAwc0efJknTlzxv354MGDNWvWLD377LPauHGj9uzZo3nz5qlXr17u0//PPfecRo8erUmTJmnXrl0aMWKEYmNjU637at7qvN7t5lNPPaXTp0+rZ8+e2rp1q7777jt1795d9evXV/369dNVY3b79NNP9fjjj2v+/PnavXu3duzYoeHDh2vu3Llq27ZtssOkdd3PKK+++qrmzp2rfv36afPmzYqLi9OECRM8Lj28UpkyZTRr1iytWbNG27dv12OPPeYRMNKqePHi+umnn7Rt2zYdP35cf/zxx3XVX6pUKRUrVkyvv/66fvrpJ3333XeKjo5O97Jq1KiR6tevr44dO2rWrFnat2+fVq5cqXHjxkmSQkNDFRISogULFujw4cPugHC1/v37a8OGDYqOjtZPP/2kefPm6emnn1bXrl3TdaArI5ZzUFCQJkyYoCVLligqKkpz587V3r17tWXLFr333nuqXbu2e9697XvTIqu2TQ0bNtT58+f12muvac+ePfryyy81duxYj35eeukl7dixQ926ddOaNWu0b98+91mQ9BwwfuGFF7Rr1y517dpV69atc0/v+++/T9M8nT17Vn369NHixYu1b98+/fjjj5o3b16qB8Ou9u6772ry5Mnatm2b9u3bp3//+9/y9fVV6eTC8Q4AACAASURBVNKlJXnffyYnKChIzzzzjF599VV9+eWX2rlzp9566y3NmjUr1eEy2v33368RI0bo+++/14EDB7Rq1Sp1795d/v7+atWqlbu/Ro0aafLkySpbtqz7wEajRo00ceJE3XXXXan+RkluW+NtfU+LtPze87ZOpCRTQo4kRUdHe1xDLv35HPwFCxYoR44cuvvuuxUZGang4GDNmzcv0/63y/jx45WYmKhatWqpTZs2at68ebL3+6Tkp59+0pEjR/TRRx/plltu8XhJf/5PkGXLlqlGjRp66KGHVLp0abVr105r1qxx/w+TiIgIff3111qzZo2qVq2qvn376v3338+U+ZX+PFI/c+ZMlS1bVq1atVLNmjV1+PBhLVy40P2dhIWF6e2339awYcN0yy23uP//Q/fu3TV9+nR98803qlWrlmrWrKnXX39dERERmVJrTEyM+/Rq0qt3795yuVyaPXu22rVrp+joaPe8fPvtt+k6G+JyuRQTE6Pg4GDVr19frVu3VosWLVSmTBmPo/1vvPGGTp06pTJlyqhgwYL6+eefr3ueWrZsqX//+9/6z3/+o4oVKyo6Olq9e/fWoEGDrnucSfLkyZPqGb0nn3xSgwcP1ltvvaXy5ctr+PDhGjZsmHr16uUe/vvvv9d9992n22+/XQ8//LC6du2qV199VVLK7SIlw4cPv2a9iIyMlCS98847WrdunSZPnixfX18FBARo6tSpWrRokceO9JZbbtFjjz2m9u3bq169egoKCtJXX33l9ceVt7YaHBysXbt2qVOnTipdurTuv/9+1a1bV2PGjJH0570w48ePV4MGDVSuXDm9//77+uyzz9zhKula5c2bN6t+/fqqXLmyoqOjlStXLvelBH379tUzzzyj6OhoVa1aVd9//71ee+21VOu+mrc6r3e7GRYWpgULFuiXX35RzZo11bp1a1WsWFH//e9/01Xf30GtWrX0xx9/qE+fPqpcubLq1q2r6dOna9SoURo8eHCyw6R13c8oTZs21Zw5c7R69WrdeeedqlWrlv7zn/+k+D/eRo4cqWLFiqlhw4Zq3LixIiIi1L59+3RPt1evXqpZs6bq1q2rggULasqUKddVv5+fn6ZNm6ajR4+qWrVq6tOnj4YOHZriAZWUuFwuffvtt2rZsqWeeOIJlSlTRt26dXOfifTx8dHYsWM1ffp0FSlSRNWqVUt2PJUrV9bs2bO1fPlyd1ht1aqVPvnkk3TVk1HLuUWLFlq7dq3CwsLUq1cv9z5p1apV7h9iadn3pkVWbZvKlCmjzz//XFOmTFHFihX173//W2+99ZZHP+XKldOqVat09uxZNWvWTOXLl9ejjz6qCxcuKG/evGmep0qVKmnp0qU6duyYIiMjVbVqVY0YMcL9P7m8zZOfn59OnjypXr16qVy5cmrWrJnCwsL0f//3f2muIXfu3Hr//fdVp04dVapUSTExMZoxY4b7ckRv+8+UDB06VI8++qj69eunihUr6osvvtAXX3zh9UBdRmrevLnmzZundu3aqXTp0nrggQcUEBCgZcuWeQSBhg0bKj4+3uPem0aNGl3TLTnJbWu8re9pkZbfe97WiRTHbTdy4wTwD3TmzBkVKVJEQ4YM0dNPP53d5dzUXn/9dX3xxRepPtQCyCis+7iZTZgwQY888oji4+OzuxQgS2T4gweAv5vZs2fLz89P5cqV09GjR/XGG2/I5XKpQ4cO2V0agEzEug8ANy9CDhzv/PnzGjx4sPbv36/g4GBVr15d33333TWPYAbgLKz7AHDz4nI1AAAAAI6SaQ8eAAAAAIDsQMgBAAAA4CiEHAAAAACOkuUPHrief3aGf77Q0NB0PTcduF60NWQl2huyEu0NWSW721p4ePgNj4MzOQAAAAAchZADAAAAwFEIOQAAAAAcJdv/GaiZ6eLFi0pMTJTL5crucjKVmcnHx0c5c+Z0/LwCAAAA2SXbQ87Fixfl7+8vP79sLyVLxMfH6+LFiwoMDMzuUgAAAABHyvbL1RITE2+agCNJfn5+SkxMzO4yAAAAAMfK9pBzM162dTPOMwAAAJBVsj3k/N2MGDFCn3zySbqHO3nypDp16qS77rpLnTp10qlTpzKhOgAAAADe/O2uE0t49N4MHZ/v57MzdHwpGTt2rOrVq6ennnpKY8aM0dixY/Xyyy9nybQBAAAA/IUzOZJGjx6tevXqqU2bNtqzZ48kafLkyWrZsqWioqL06KOP6sKFCzp79qxq166ty5cvS5LOnDnjfj9//nw98MADkqQHHnhA8+bNy7b5AQAAAG5mN33I2bx5s2bPnq2FCxdq0qRJ2rRpkySpRYsWmjNnjmJjY1WqVClNmTJFISEhqlOnjhYtWiRJmjVrllq0aCF/f38dP35cYWFhkqRChQrp+PHj2TZPAAAAwM3spg85q1evVvPmzRUYGKhcuXKpSZMmkqS4uDi1bdtWjRs3VkxMjOLi4iRJXbp00bRp0yRJ06ZNU8eOHa8Zp8vl4uECAAAAQDb5292T83cRHR2tf/3rX6pQoYKmTZum77//XpJUs2ZNHTx4UKtWrVJiYqLKli0rSQoNDdWRI0cUFhamI0eOqECBAtlZPgAAAHDTuunP5NSuXVvz589333OzcOFCSdLZs2cVFhamy5cvKyYmxmOY9u3b66mnnlKHDh3c3Zo2baovv/xSkvTll1+qWbNmWTcTAAAAANxu+pBTqVIl3XPPPWrSpIm6deumqlWrSpL69++v1q1bq02bNipVqpTHMO3atdPvv/+uNm3auLv16dNHy5cv11133aUVK1aoT58+WTofAAAAAP7kMjPLygn++uuvHu/Pnz+voKCgrCzhhn3zzTeaP3++Pvzww+sa/p84zzcqNDSUhzEgS9DWkJVob8hKtDdklexua+Hh4Tc8Du7JSadXXnlFS5Ys0cSJE7O7FAAAAADJIOSk05AhQ7K7BAAAAACpuOnvyQEAAADgLIQcAAAAAI5CyAEAAADgKIQcAAAAAI7CgweuMmLECAUHB+uJJ55I13DvvPOOFixYIJfLpdDQUI0cOVKFCxfOpCoBAAAApORvF3Lum/xTho5vVteyGTq+lDz55JN64YUXJEn/+te/NHLkSA0fPjxLpg0AAADgL3+7kJMdRo8erS+//FKhoaEKDw9X5cqVNXnyZE2ePFmXLl1S8eLF9cEHHyghIUFRUVFasWKF/P39debMGTVp0kQrVqxQrly53OM7f/68XC5XNs4RAAAAIH097VSa+runY95MriRr3fT35GzevFmzZ8/WwoULNWnSJG3atEmS1KJFC82ZM0exsbEqVaqUpkyZopCQENWpU0eLFi2SJM2aNUstWrSQv7+/JGnYsGGqUaOGYmJi1L9//2ybJwAAAOBmdtOHnNWrV6t58+YKDAxUrly51KRJE0lSXFyc2rZtq8aNGysmJkZxcXGSpC5dumjatGmSpGnTpqljx47ucQ0cOFDr1q1T27ZtNX78+KyfGQAAAACEnJRER0dryJAhWrRokaKjo/XHH39IkmrWrKmDBw9q1apVSkxMVNmy197z065dO82ZMyerSwYAAAAgQo5q166t+fPn68KFCzp79qwWLlwoSTp79qzCwsJ0+fJlxcTEeAzTvn17PfXUU+rQoYO72969e91/z58/XyVLlsyaGQAAAADg4aZ/8EClSpV0zz33qEmTJgoNDVXVqlUlSf3791fr1q1VoEABVatWTWfPnnUP065dO7377rtq06aNu9vbb7+tPXv2yMfHRxERERo2bFiWzwsAAACAv2HIyapHPl+pb9++6tu37zXde/TokWz/a9asUcuWLZUnTx53t88//zzT6gMAAACQdn+7kPN398orr2jJkiWaOHFidpcCAAAAIBmEnHQaMmRIdpcAAAAAIBU3/YMHAAAAADgLIQcAAACAoxByAAAAADgKIQcAAACAoxByrjJixAh98skn1zVc9erV1aRJEzVp0kSLFi3KhOoAAAAAePO3e7ra19NOZej47umYN0PHl5pHH31UTzzxRJZNDwAAAMC1OJMjafTo0apXr57atGmjPXv2SJImT56sli1bKioqSo8++qguXLigs2fPqnbt2rp8+bIk6cyZMx7vAQAAAGS/mz7kbN68WbNnz9bChQs1adIkbdq0SZLUokULzZkzR7GxsSpVqpSmTJmikJAQ1alTx30p2qxZs9SiRQv5+/tLksaPH6+oqCg9++yzOnUqY89IAQAAAEibmz7krF69Ws2bN1dgYKBy5cqlJk2aSJLi4uLUtm1bNW7cWDExMYqLi5MkdenSRdOmTZMkTZs2TR07dpQkPfjgg1q1apUWLFigQoUKafDgwdkzQwAAAMBN7qYPOSmJjo7WkCFDtGjRIkVHR+uPP/6QJNWsWVMHDx7UqlWrlJiYqLJly0qSChYsKF9fX/n4+Khr167auHFjdpYPAAAA3LRu+pBTu3ZtzZ8/333PzcKFCyVJZ8+eVVhYmC5fvqyYmBiPYdq3b6+nnnpKHTp0cHc7cuSI+++5c+eqTJkyWTMDAAAAADz87Z6ultUqVaqke+65R02aNFFoaKiqVq0qSerfv79at26tAgUKqFq1ajp79qx7mHbt2undd99VmzZt3N2GDBmi7du3y+VyqUiRIho+fHiWzwsAAACAv2HIycpHPifp27ev+vbte033Hj16JNv/mjVr1LJlS+XJk8fd7cMPP8y0+gAAAACk3d8u5PzdvfLKK1qyZIkmTpyY3aUAAAAASAYhJ52GDBmS3SUAAAAASMVN/+ABAAAAAM6S7SHHzLK7hCx3M84zAAAAkFWyPeT4+PgoPj4+u8vIMvHx8fLxyfbFDgAAADhWtt+TkzNnTl28eFF//PGHXC5XdpeTqcxMPj4+ypkzZ3aXAgAAADhWtoccl8ulwMDA7C4DAAAAgENw3RQAAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUr/8M9NKlSxo0aJDi4+OVkJCg2rVrq0OHDho7dqy2b9+uoKAgSVKfPn102223ZXa9AAAAAJAqryHH399fgwYNUs6cORUfH6/XXntNVatWlSR1795dtWvXzvQiAQAAACCtvF6u5nK5lDNnTklSQkKCEhIS5HK5Mr0wAAAAALgeabonJzExUf3799cjjzyiSpUq6fbbb5ckTZkyRc8//7wmTJigy5cvZ2qhAAAAAJAWLjOztPZ87tw5vffee3rooYeUK1cu5c2bV/Hx8fr0009VuHBhtW/f/pphYmNjFRsbK0kaNmyYLl26lHHV4x/Dz89P8fHx2V0GbgK0NWQl2huyEu0NV7tr9Hde+3nEr3CaxvVQn1Luv7O7rQUEBNzwOLzek3Ol4OBgVahQQRs3btS9994r6c97dho2bKivv/462WGioqIUFRXlfn/8+PEbKBf/VKGhoXz3yBK0NWQl2huyEu0NmenKtpXdbS08PPyGx+H1crXTp0/r3Llzkv580trmzZsVERGhkydPSpLMTGvXrtWtt956w8UAAAAAwI3yeibn5MmTGjt2rBITE2VmqlOnjqpXr6433nhDp0+fliQVK1ZMjz32WKYXCwAAAADeeA05xYoV0zvvvHNN90GDBmVKQQAAAABwI9L0dDUAAAAA+Kcg5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEfx89bDpUuXNGjQIMXHxyshIUG1a9dWhw4ddPToUY0aNUpnzpxRiRIl9PTTT8vPz+voAAAAACBTeU0l/v7+GjRokHLmzKn4+Hi99tprqlq1qr755hu1atVKd911lz777DMtXrxYTZs2zYqaAQAAACBFXi9Xc7lcypkzpyQpISFBCQkJcrlc2rZtm2rXri1JatCggdauXZu5lQIAAABAGqTp+rLExEQNGDBAhw8fVrNmzRQWFqagoCD5+vpKkvLnz68TJ05kaqEAAAAAkBZpCjk+Pj569913de7cOb333nv69ddf0zyB2NhYxcbGSpKGDRum0NDQ66sU/2h+fn5898gStDVkJdobshLtDZnpyrblhLaWricFBAcHq0KFCtq5c6fOnz+vhIQE+fr66sSJE8qfP3+yw0RFRSkqKsr9/vjx4zdWMf6RQkND+e6RJWhryEq0N2Ql2hsy05VtK7vbWnh4+A2Pw+s9OadPn9a5c+ck/fmktc2bNysiIkIVKlTQDz/8IElaunSpatSoccPFAAAAAMCN8nom5+TJkxo7dqwSExNlZqpTp46qV6+uIkWKaNSoUZo6daqKFy+uRo0aZUW9AAAAAJAqryGnWLFieuedd67pHhYWprfffjtTigIAAACA6+X1cjUAAAAA+Cch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFL/sLgAAAADAXxIevTdtPTZ4J3ML+QfjTA4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUP289HD9+XGPHjtWpU6fkcrkUFRWlli1bavr06Vq0aJFy584tSercubPuuOOOTC8YAAAAAFLjNeT4+vqqe/fuKlGihC5cuKCBAweqcuXKkqRWrVrp3nvvzfQiAQAAACCtvIacfPnyKV++fJKkwMBARURE6MSJE5leGAAAAABcj3Tdk3P06FHt27dPpUqVkiTNnz9fzz//vD766COdPXs2UwoEAAAAgPRwmZmlpceLFy9q0KBBateune68806dOnXKfT/OtGnTdPLkSfXu3fua4WJjYxUbGytJGjZsmC5dupSB5eOfws/PT/Hx8dldBm4CtDVkJdobshLt7eZxpG3dNPXXrsE7Xvt5xK9wmsb1UJ9S7r+zu60FBATc8Di8Xq4mSfHx8RoxYoTq16+vO++8U5KUN29e9+eNGzfW8OHDkx02KipKUVFR7vfHjx+/kXrxDxUaGsp3jyxBW0NWor0hK9HekJmubFvZ3dbCw8NveBxeL1czM33yySeKiIhQ69at3d1Pnjzp/nvNmjW69dZbb7gYAAAAALhRXs/kxMXFafny5SpatKj69+8v6c/HRa9cuVL79++Xy+VSwYIF9dhjj2V6sQAAAADgjdeQU7ZsWU2fPv2a7vxPHAAAAAB/R+l6uhoAAAAA/N0RcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAgKP4eevh+PHjGjt2rE6dOiWXy6WoqCi1bNlSZ8+e1ciRI3Xs2DEVLFhQ0dHRCgkJyYqaAQAAACBFXkOOr6+vunfvrhIlSujChQsaOHCgKleurKVLl6pSpUpq06aNZs6cqZkzZ6pbt25ZUTMAAAAApMjr5Wr58uVTiRIlJEmBgYGKiIjQiRMntHbtWkVGRkqSIiMjtXbt2sytFAAAAADSIF335Bw9elT79u1TqVKl9PvvvytfvnySpLx58+r333/PlAIBAAAAID28Xq6W5OLFixoxYoR69uypoKAgj89cLpdcLleyw8XGxio2NlaSNGzYMIWGht5Aufin8vPz47tHlqCtISvR3pCVaG83jyPZMM0r25YT2lqaQk58fLxGjBih+vXr684775Qk5cmTRydPnlS+fPl08uRJ5c6dO9lho6KiFBUV5X5//PjxDCgb/zShoaF898gStDVkJdobshLtDZnpyraV3W0tPDz8hsfh9XI1M9Mnn3yiiIgItW7d2t29Ro0aWrZsmSRp2bJlqlmz5g0XAwAAAAA3yuuZnLi4OC1fvlxFixZV//79JUmdO3dWmzZtNHLkSC1evNj9CGkAAAAAyG5eQ07ZsmU1ffr0ZD977bXXMrwgAAAAALgR6Xq6GgAAAAD83RFyAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAo/h56+Gjjz7Shg0blCdPHo0YMUKSNH36dC1atEi5c+eWJHXu3Fl33HFH5lYKAAAAAGngNeQ0aNBAzZs319ixYz26t2rVSvfee2+mFQYAAAAA18Pr5Wrly5dXSEhIVtQCAAAAADfM65mclMyfP1/Lly9XiRIl9OCDDxKEAAAAAPwtXFfIadq0qdq3by9JmjZtmiZOnKjevXsn229sbKxiY2MlScOGDVNoaOh1lop/Mj8/P757ZAnaGrIS7Q1ZifZ28ziSDdO8sm05oa1dV8jJmzev++/GjRtr+PDhKfYbFRWlqKgo9/vjx49fzyTxDxcaGsp3jyxBW0NWor0hK9HekJmubFvZ3dbCw8NveBzX9QjpkydPuv9es2aNbr311hsuBAAAAAAygtczOaNGjdL27dt15swZPfHEE+rQoYO2bdum/fv3y+VyqWDBgnrssceyolYAAAAA8MpryOnXr9813Ro1apQpxQAAAADAjbquy9UAAAAA4O+KkAMAAADAUQg5AAAAAByFkAMAAADAUQg5AP5fe3cXInW9P3D8szodQ8TV3a016YlEOUiRxBpoUUbbw03qX6igU130AJYX5Y2lPViYufSkGEZSIkldSESeUxDEJmgkgWFblESaQUlbtq5tZgrq7LmII/nvYceZcWb34+t15bhfdz7sfPztvp3ZFQAgFZEDAACkInIAAIBURA4AAJCKyAEAAFIROQAAQCoiBwAASEXkAAAAqYgcAAAgFZEDAACkInIAAIBURA4AAJCKyAEAAFIROQAAQCoiBwAASEXkAAAAqYgcAAAgFZEDAACkInIAAIBURA4AAJCKyAEAAFIROQAAQCoiBwAASEXkAAAAqYgcAAAgFZEDAACkInIAAIBURA4AAJCKyAEAAFIROQAAQCoiBwAASEXkAAAAqYgcAAAgFZEDAACkInIAAIBURA4AAJCKyAEAAFIROQAAQCoiBwAASEXkrH9RzAAADSJJREFUAAAAqYgcAAAgFZEDAACkInIAAIBURA4AAJCKyAEAAFIROQAAQCoiBwAASEXkAAAAqYgcAAAgFZEDAACkInIAAIBURA4AAJCKyAEAAFIROQAAQCoiBwAASEXkAAAAqRQGOvDCCy/Etm3borGxMZ599tmIiPjll19i+fLl8eOPP8YZZ5wR8+fPj1GjRp30YQEAAAYy4DM5M2bMiEWLFh33exs2bIiLLrooVq5cGRdddFFs2LDhpA0IAABwIgaMnMmTJ//hWZqtW7fGlVdeGRERV155ZWzduvXkTAcAAHCCyvqenL6+vhg7dmxERIwZMyb6+vqqOhQAAEC5BvyenIE0NDREQ0PDX769s7MzOjs7IyKio6MjWlpaKr1LhqBCoeCxpybsGrVk36gl+3bq+KEO9/n73cqwa2VFTmNjY+zbty/Gjh0b+/bti9GjR//l2fb29mhvbz92u6enp5y7ZIhraWnx2FMTdo1asm/Ukn3jZPr9btV718aPH1/x+yjr5WptbW2xadOmiIjYtGlTTJ06teJBAAAAqmHAZ3JWrFgR27dvj/3798fcuXPjpptuitmzZ8fy5ctj48aNx36ENAAAwGAwYOTcf//9f/r7jz76aNWHAQAAqFRZL1cDAAAYrEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQisgBAABSKVTyh+fNmxenn356DBs2LIYPHx4dHR3VmgsAAKAsFUVORMTixYtj9OjR1ZgFAACgYl6uBgAApFLxMzlLly6NiIhrrrkm2tvbKx4IAACgEhVFzpIlS6KpqSn6+vriiSeeiPHjx8fkyZOPO9PZ2RmdnZ0REdHR0REtLS2V3CVDVKFQ8NhTE3aNWrJv1FIl+/bD/00v6Vzrm1vKev9U1w91uM/f71aGa1tFkdPU1BQREY2NjTF16tTYuXPnHyKnvb39uGd4enp6KrlLhqiWlhaPPTVh16gl+0Yt1WLf7POp6/ePfb2vbePHj6/4fZT9PTmHDh2KgwcPHvv1p59+Gueee27FAwEAAFSi7Gdy+vr64plnnomIiKNHj8bll18eU6ZMqdpgAAAA5Sg7clpbW+Ppp5+u5iwAAAAV8yOkAQCAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApFL2fwYKAPxm1mtfDHjmrsK4kt7XDTePqXScIemt9T8NeKaWH5vBNg9wYjyTAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqhXoPAHAqOXr3zJLODX/pPyd5kt8MtnngZJv12hclnburMO4kT3JqK/XaM2fGUwOeKfWxuuHmMSWdIwfP5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKmIHAAAIBWRAwAApCJyAACAVEQOAACQSqHeAwx2s177oqRz//7XPyu6n6N3zyzp3JwZTw145q7CuJLe1w03jynp3N8p5eNT6ceG6qjVLlMdHq+/V6trz0DX5h/+94sSrs0ZlfK5q5TPWxGlf+4aKkr9vD78pf+c5EkoRTUfr7fW/1TS+6rG12H8Nc/kAAAAqYgcAAAgFZEDAACkInIAAIBURA4AAJCKyAEAAFIROQAAQCoiBwAASEXkAAAAqYgcAAAgFZEDAACkInIAAIBUCvUeIIu31v804Jkbbh5Tg0mq4+jdM0s7OOOpAY/89rEZPB+fUh6riMrnmfXaFyWdu6swrqRzfzdPSY9XCY9VRHV2uZR55pQ4z7//9c+Szp2qanXtKWWfa7bLESdw7alsnsGmmn+3qvF4UblSP1cMpmthxr9bparmtZCTyzM5AABAKiIHAABIReQAAACpiBwAACAVkQMAAKQicgAAgFREDgAAkIrIAQAAUhE5AABAKoVK/nBXV1esXbs2isViXH311TF79uxqzQUAAFCWsp/JKRaLsWbNmli0aFEsX748Pvjgg9i9e3c1ZwMAADhhZUfOzp07Y9y4cdHa2hqFQiGmT58eW7dureZsAAAAJ6zsyOnt7Y3m5uZjt5ubm6O3t7cqQwEAAJSrob+/v7+cP/jhhx9GV1dXzJ07NyIiNm/eHDt27Ig777zzuHOdnZ3R2dkZEREdHR0VjgsAAPD3yn4mp6mpKfbu3Xvs9t69e6OpqekP59rb26Ojo0PgnOIefPDBeo/AKcKuUUv2jVqyb9RKhl0rO3ImTJgQ3d3dsWfPnjhy5Ehs2bIl2traqjkbAADACSv7R0gPHz487rjjjli6dGkUi8W46qqr4pxzzqnmbAAAACesov8n55JLLolLLrmkWrOQWHt7e71H4BRh16gl+0Yt2TdqJcOulf2DBwAAAAajsr8nBwAAYDCq6OVq8P91dXXF2rVro1gsxtVXXx2zZ88+7u1vv/12vPfeezF8+PAYPXp03HPPPXHGGWfUaVqGsoF27X8+/PDDeO6552LZsmUxYcKEGk9JFqXs25YtW+L111+PhoaGOO+88+K+++6rw6QMdQPtWk9PT6xatSoOHDgQxWIxbrnlFt86QFleeOGF2LZtWzQ2Nsazzz77h7f39/fH2rVr4+OPP44RI0bEvffeGxdccEEdJi2PyKFqisVirFmzJh5++OFobm6OhQsXRltbW5x99tnHzpx//vnR0dERI0aMiHfffTdeffXVmD9/fh2nZigqZdciIg4ePBjvvPNOTJw4sU6TkkEp+9bd3R0bNmyIJUuWxKhRo6Kvr6+OEzNUlbJrb7zxRkybNi2uvfba2L17dyxbtkzkUJYZM2bE9ddfH6tWrfrTt3/88cfx/fffx8qVK2PHjh3x8ssvx5NPPlnjKcvn5WpUzc6dO2PcuHHR2toahUIhpk+fHlu3bj3uzIUXXhgjRoyIiIiJEydGb29vPUZliCtl1yIi1q9fH7NmzYrTTjutDlOSRSn79t5778V1110Xo0aNioiIxsbGeozKEFfKrjU0NMSvv/4aERG//vprjB07th6jksDkyZOPXbP+zEcffRRXXHFFNDQ0xKRJk+LAgQOxb9++Gk5YGZFD1fT29kZzc/Ox283NzX8bMRs3bowpU6bUYjSSKWXXdu3aFT09Pf6Fk4qVsm/fffdddHd3xyOPPBIPPfRQdHV11XpMEihl12688cZ4//33Y+7cubFs2bK44447aj0mp4je3t5oaWk5dnugr+sGG5FDXWzevDl27doVM2fOrPcoJFQsFmPdunVx++2313sUThHFYjG6u7tj8eLFcd9998Xq1avjwIED9R6LhD744IOYMWNGvPjii7Fw4cJ4/vnno1gs1nssGHREDlXT1NQUe/fuPXZ779690dTU9Idzn376abz55puxYMECLyOiLAPt2qFDh+Lbb7+Nxx9/PObNmxc7duyIp556Kr766qt6jMsQV8q1rampKdra2qJQKMSZZ54ZZ511VnR3d9d6VIa4UnZt48aNMW3atIiImDRpUhw+fDj2799f0zk5NTQ1NUVPT8+x23/1dd1gJXKomgkTJkR3d3fs2bMnjhw5Elu2bIm2trbjznz99dfx0ksvxYIFC7xmnbINtGsjR46MNWvWxKpVq2LVqlUxceLEWLBggZ+uRllKubZdeuml8fnnn0dExM8//xzd3d3R2tpaj3EZwkrZtZaWlvjss88iImL37t1x+PDhGD16dD3GJbm2trbYvHlz9Pf3x5dffhkjR44cUt8D5j8Dpaq2bdsWr7zyShSLxbjqqqtizpw5sX79+pgwYUK0tbXFkiVL4ptvvokxY8ZExG8X6wceeKDOUzMUDbRrv/fYY4/FbbfdJnIo20D71t/fH+vWrYuurq4YNmxYzJkzJy677LJ6j80QNNCu7d69O1avXh2HDh2KiIhbb701Lr744jpPzVC0YsWK2L59e+zfvz8aGxvjpptuiiNHjkRExLXXXhv9/f2xZs2a+OSTT+If//hH3HvvvUPq86jIAQAAUvFyNQAAIBWRAwAApCJyAACAVEQOAACQisgBAABSETkAAEAqIgcAAEhF5AAAAKn8F1KBe9Jw0QHsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days_protein[\"0\"],days_protein[\"3\"],days_protein[\"5\"]],bins = 20,label = [\"day0\",\"day3\",\"day5\"])\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Protein Length of Most Expressed Isoform in Signficiant Functional Consequences Isoform Switches\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days_protein_ratio[\"0\"],days_protein_ratio[\"3\"],days_protein_ratio[\"5\"]],bins = 20,label = [\"day0\",\"day3\",\"day5\"])\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Protein Ratio Length in Signficiant Functional Consequences Isoform Switches\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "days_protein_stat = dict({\"0\":[],\"3\":[],\"5\":[]})\n",
    "days_protein_ratio_stat = dict({\"0\":[],\"3\":[],\"5\":[]})\n",
    "\n",
    "for gene_ensembl in targets:\n",
    "    gene = gene_ensembl.split(\"_\")[0]\n",
    "    df=data[data[identifier]==gene]\n",
    "    df_temp = df\n",
    "    for j in range(df.shape[0]):\n",
    "        transcript_annotation=annotation[annotation['transcript_id']==df.iloc[j][\"transcript_id\"]] \n",
    "        if (transcript_annotation.shape[0] < 3):\n",
    "            df_temp = df_temp[df_temp[\"transcript_id\"] != df.iloc[j][\"transcript_id\"]]\n",
    "\n",
    "    # total gene expression calculation\n",
    "    data_gene=df_temp[samples].sum().to_frame().transpose()\n",
    "    data_gene=calculateStatistics(data_gene,conditions,number_replicates)\n",
    "    # mean transcript expression calculation\n",
    "    df_temp=calculateStatistics(df_temp,conditions,number_replicates)\n",
    "    # isoform percentage calculation\n",
    "    df_temp=(df_temp.filter(like='mean').div(data_gene.filter(like='mean').values[0],1)*100).add_prefix('Pct_').join(df_temp)\n",
    "    #choose isoforms to plot`b\n",
    "    df_temp=chooseIsoforms2Plot(df_temp,minimumTPM,minimumPct,maximumIso,annotation)\n",
    "    if (len(df_temp) > 1):\n",
    "        for i in [\"0\",\"3\",\"5\"]:\n",
    "            if (sum(df_temp[\"meanday\"+i]) > 2):\n",
    "                pct_list = list(df_temp[\"Pct_meanday\"+i])\n",
    "                tcons_list = list(df_temp[\"transcript_id\"])\n",
    "                for j in range(len(pct_list)): \n",
    "                    days_protein_stat[i] += [tID_length_protein[tcons_list[j]]]*int(pct_list[j])\n",
    "                transcript = df_temp[df_temp[\"Pct_meanday\" + i] == df_temp[\"Pct_meanday\" + i].max()][\"transcript_id\"]\n",
    "                days_protein_ratio_stat[i].append(tID_length_protein[list(transcript)[0]])\n",
    "  \n",
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days_protein_stat[\"0\"],days_protein_stat[\"3\"],days_protein_stat[\"5\"]],bins = 20,label = [\"day0\",\"day3\",\"day5\"])\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Protein Length of Most Expressed Isoform in Signficiant Statistical Isoform Switches\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days_protein_ratio_stat[\"0\"],days_protein_ratio_stat[\"3\"],days_protein_ratio_stat[\"5\"]],bins = 20,label = [\"day0\",\"day3\",\"day5\"])\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Protein Ratio Length in Signficiant Statistical Isoform Switches\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "utr_seqs = \"/project/owlmayerTemporary/Sid/nanopore-analysis/ReferenceData/df_utr_regions_sequences.fa\"\n",
    "\n",
    "utr_sequences = open(utr_seqs, \"r\")\n",
    "utr_lines = utr_sequences.readlines()\n",
    "utr_sequences.close()\n",
    "\n",
    "trans_utr = dict()\n",
    "for line in utr_lines:\n",
    "    if (line.startswith(\">\")):\n",
    "        trans_id = line[1:].strip()\n",
    "        if (trans_id not in trans_utr):\n",
    "            trans_utr[trans_id] = []\n",
    "    else:\n",
    "        trans_utr[trans_id].append(line.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "codon_table = {\n",
    "        'ATA':'I', 'ATC':'I', 'ATT':'I', 'ATG':'M', \n",
    "        'ACA':'T', 'ACC':'T', 'ACG':'T', 'ACT':'T', \n",
    "        'AAC':'N', 'AAT':'N', 'AAA':'K', 'AAG':'K', \n",
    "        'AGC':'S', 'AGT':'S', 'AGA':'R', 'AGG':'R',                  \n",
    "        'CTA':'L', 'CTC':'L', 'CTG':'L', 'CTT':'L', \n",
    "        'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCT':'P', \n",
    "        'CAC':'H', 'CAT':'H', 'CAA':'Q', 'CAG':'Q', \n",
    "        'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGT':'R', \n",
    "        'GTA':'V', 'GTC':'V', 'GTG':'V', 'GTT':'V', \n",
    "        'GCA':'A', 'GCC':'A', 'GCG':'A', 'GCT':'A', \n",
    "        'GAC':'D', 'GAT':'D', 'GAA':'E', 'GAG':'E', \n",
    "        'GGA':'G', 'GGC':'G', 'GGG':'G', 'GGT':'G', \n",
    "        'TCA':'S', 'TCC':'S', 'TCG':'S', 'TCT':'S', \n",
    "        'TTC':'F', 'TTT':'F', 'TTA':'L', 'TTG':'L', \n",
    "        'TAC':'Y', 'TAT':'Y', 'TAA':'_', 'TAG':'_', \n",
    "        'TGC':'C', 'TGT':'C', 'TGA':'_', 'TGG':'W', \n",
    "        }\n",
    "\n",
    "\n",
    "def score(seq,start):    \n",
    "    kozak = {\n",
    "        \"A\":[0.25,0.61,0.27,0.15,1.00,0.00,0.00,0.23],\n",
    "        \"C\":[0.53,0.02,0.49,0.55,0.00,0.00,0.00,0.16],\n",
    "        \"G\":[0.15,0.36,0.13,0.21,0.00,0.00,1.00,0.46],\n",
    "        \"T\":[0.07,0.01,0.11,0.09,0.00,1.00,0.00,0.15]\n",
    "    }\n",
    "    \n",
    "    score = 1.0\n",
    "    for i in range(start,len(seq)):\n",
    "        score *= kozak[seq[i]][i]\n",
    "    return score\n",
    "        \n",
    "\n",
    "def translate(seq, i, utr_regions):\n",
    "    translating = True\n",
    "    aa = \"\"\n",
    "    \n",
    "    in_utr = False\n",
    "    for utr in utr_regions:\n",
    "        start,stop = utr\n",
    "        if ((start < i) and (i < stop)):\n",
    "            in_utr = True\n",
    "    \n",
    "    while(translating): \n",
    "        if ((len(seq) < 3) or (in_utr)):\n",
    "            translating = False\n",
    "            aa = \"\"\n",
    "        else:\n",
    "            codon = seq[0:3]\n",
    "            if (codon_table[codon] == \"_\"):\n",
    "                translating = False\n",
    "            else:\n",
    "                aa += codon_table[codon]\n",
    "            seq = seq[3:]\n",
    "            i += 3\n",
    "    return aa,i\n",
    "\n",
    "def find_utrs(seq,utr):\n",
    "    pos = seq.find(utr)\n",
    "    if (pos == -1):\n",
    "        if (len(utr) > 20): \n",
    "            for i in range(len(utr) - 1,len(utr)*5//10 - 1,-1):\n",
    "                pos = seq.find(utr[:i])\n",
    "    return pos\n",
    "\n",
    "def translate_aa_seq(seq,enst,gene_utrs):\n",
    "    utr_regions = []\n",
    "    for utr in gene_utrs[enst]:\n",
    "        pos = find_utrs(seq,utr)\n",
    "        if (pos != -1):\n",
    "            utr_regions.append([pos,pos + len(utr)])\n",
    "    \n",
    "    longest_aa_seq = \"M\"\n",
    "    longest_aa_seq_sc = 0\n",
    "    longest_aa_seq_sc_end = 0\n",
    "    longest_aa_seq_sc_len = 0\n",
    "    for i in range(len(seq)):\n",
    "        if (seq[i:i+3] == \"ATG\"):\n",
    "            sc = score(seq[i-4:i+4],0)\n",
    "            aa,end = translate(seq[i:], i, utr_regions)\n",
    "            if ((len(aa) > 20) and (sc > longest_aa_seq_sc) and (i > longest_aa_seq_sc_end)):\n",
    "                longest_aa_seq = aa\n",
    "                longest_aa_seq_sc = sc\n",
    "                longest_aa_seq_sc_end = end\n",
    "                longest_aa_seq_sc_len = len(seq) - end\n",
    "    \n",
    "    #longest = 0\n",
    "    #for utr in range(len(utr_regions)):\n",
    "    #    start,stop = utr_regions[utr]\n",
    "    #    if ((start <= longest_aa_seq_sc_end) and (longest_aa_seq_sc_end <= stop)):\n",
    "    #        longest = longest_aa_seq_sc_len if (longest_aa_seq_sc_end > longest) else longest\n",
    "    return longest_aa_seq_sc_len\n",
    "\n",
    "def translate_aa_seq_length(seq,enst):\n",
    "    utr_regions = []\n",
    "    \n",
    "    longest_aa_seq = \"M\"\n",
    "    longest_aa_seq_sc_len = 0\n",
    "    for i in range(len(seq)):\n",
    "        if (seq[i:i+3] == \"ATG\"):\n",
    "            aa,end = translate(seq[i:], i, utr_regions)\n",
    "            if (len(aa) > len(longest_aa_seq)):\n",
    "                longest_aa_seq = aa\n",
    "                longest_aa_seq_sc_len  = len(seq) - end\n",
    "    return longest_aa_seq_sc_len\n",
    "\n",
    "def find_all_aa_seqs(seq,enst,gene_utrs):    \n",
    "    longest_aa_seq_sc_len = translate_aa_seq_length(seq,enst)\n",
    "    if enst in gene_utrs:\n",
    "        longest_aa_seq_sc_len = translate_aa_seq(seq,enst,gene_utrs) \n",
    "    return longest_aa_seq_sc_len\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_utr = dict({\"0\":[],\"3\":[],\"5\":[]})\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "count = 0\n",
    "total = 0\n",
    "for gene_ensembl in targets:\n",
    "    gene = gene_ensembl.split(\"_\")[0]\n",
    "    df=data[data[identifier]==gene]\n",
    "    df_temp = df\n",
    "    for j in range(df.shape[0]):\n",
    "        transcript_annotation=annotation[annotation['transcript_id']==df.iloc[j][\"transcript_id\"]] \n",
    "        if (transcript_annotation.shape[0] < 3):\n",
    "            df_temp = df_temp[df_temp[\"transcript_id\"] != df.iloc[j][\"transcript_id\"]]\n",
    "\n",
    "    # total gene expression calculation\n",
    "    data_gene=df_temp[samples].sum().to_frame().transpose()\n",
    "    data_gene=calculateStatistics(data_gene,conditions,number_replicates)\n",
    "    # mean transcript expression calculation\n",
    "    df_temp=calculateStatistics(df_temp,conditions,number_replicates)\n",
    "    # isoform percentage calculation\n",
    "    df_temp=(df_temp.filter(like='mean').div(data_gene.filter(like='mean').values[0],1)*100).add_prefix('Pct_').join(df_temp)\n",
    "    #choose isoforms to plot`b\n",
    "    df_temp=chooseIsoforms2Plot(df_temp,minimumTPM,minimumPct,maximumIso,annotation)\n",
    "    if (len(df_temp) > 1):\n",
    "        for i in [\"0\",\"3\",\"5\"]:\n",
    "            all_utrs = []\n",
    "            if (sum(df_temp[\"meanday\"+i]) > 2):\n",
    "                pct_list = list(df_temp[\"Pct_meanday\"+i])\n",
    "                tcons_list = list(df_temp[\"transcript_id\"])\n",
    "                #for j in range(len(pct_list)): \n",
    "                #    days_ratio[i] += [tID_length[tcons_list[j]]]*int(pct_list[j])\n",
    "                transcript = df_temp[df_temp[\"Pct_meanday\" + i] == df_temp[\"Pct_meanday\" + i].max()][\"transcript_id\"]\n",
    "                #if (tID_length[list(transcript)[0]] == 1.0):\n",
    "                #    days_genes[i].add(tID_gene[list(transcript)[0]])\n",
    "                try:\n",
    "                    seq = str(transcripts[list(transcript)[0]].seq)\n",
    "                    utr = find_all_aa_seqs(seq,tID_enst[list(transcript)[0]], trans_utr)\n",
    "                    if (utr > 0):\n",
    "                        days_utr[i].append(utr)\n",
    "                    else:\n",
    "                        count += 1\n",
    "                except:\n",
    "                    count += 1\n",
    "                total += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcripts_filename = \"/project/owlmayerTemporary/Sid/nanopore-analysis/Results_5_1/Pinfish/corrected_transcriptome_polished_collapsed_tcons.fas\"\n",
    "transcripts = SeqIO.index(transcripts_filename, \"fasta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 2334\n",
      "ENST00000254821\n",
      "1800\n"
     ]
    }
   ],
   "source": [
    "print(count,total)\n",
    "print(tID_enst[list(transcript)[0]])\n",
    "print(len(\"GCTTGACTCACAGGCTTTATTTACATTTGTCTACAGTATCATTTCCTTATGAAATGAACTAGTACAGCTTAGTTAAACCAAATGAAATCATAATCATCAGAATTGTCTGTAAACTACTATTAGCTAAATTATAACCTTGCATTTGCTTAGTACAGCCAAAGTTTTAAATACAGAAAGCACAAGAATAAACCAATGGTAACATGTAGAATCTAGATCGTCGGGGCAATTTAGAAGGTAGACTTTCAAAAAGTCTGAGGCACAATTACAAGTGAGTAAAAGTTCTTGTGCAACCTACATAAACGCAGCAAAGAAACTTAAGACATAAAAACATGGGAAAGCTCACTGTAAAAATATTATCAAAATATTTCTACATAAGATATCTTGCTTTCATTTTTAGATCAGCTGAAGAGAAAAACATTCACTTTAAAGTAAAAACATATATTTTTGTTTGGTTTGTCTATGAAATATCTTAAAATGTCGTAATTTTTATTTAAATTGCAGCAGACAATGTTACGGAAAAAAAACAAGAAATCAATATCCCATAACAAAAAGCCCCCAAGCACAACTGTTGATTTCCTTTGATATACTTACCTTGCATTGTCACTGGAAATACTGTTAAGAGAAACCTTTTTATTTTTTAAACCAGAAAACAAACTAAAGCTCATGTGACCAGATTTTAATTTTGAGAAATAAACTCCAAAAGCATTTTAAGCCATAACTTATTTCAAATGAATAGGAAAAATAATGACTTTTGCTTACATCATGCAGCAATAAGCATTCATTTGTTATTCAGAAGAAAATTGTATGCAGTGTGTTTATAGTTAACATCTTGAGGCAAATGTTCACTTAAATTCACAATGTCTCCATTGGCCTTTCACCAAAAGAGAAATATACTTTAAAAGCTTACTTCAATTTGCTCATTAAGTATGATTTAGCCTTTTCTACCATATTATATTTGGTAACTACATTTGGAAGAATATCACACAAAGCTTCTTAAAACATTGGTAAACATTTTGAGATGTCTTTATCAAGCAACCGTATCAGCAGAGAAAAGATAAACTCTTAAGACTTTCTTTTTTGCCCCAAATTATGTGTCAGAAAGGAAAATATATCCCACTTCTCTCCTTGTAGACATCACAAGATTCTTTTGCATTAAATGTGGAGCAAAAAAGTAAAGCTGGATAAAAGAGACACAATGATTCTAAGGCACCCTTAGCATAGGGAATTTCAAAATAGACATTTACAGTTGGCTGCTTACAACATAGTTTAACTTTTTTTGGTGAGAATCACTCTGACATCTGTTAACAGCATAAAAAGGCTGACTTTCTTTTCCATAAGTATGTTGTTTCCAACTAGTTTCCAGTCTTTCTTCCATGCCCAAAGTTCAGGCAAGCCTAGGTAATCCTGTCTTAGCCAATGGACAGGATGTATTTGGAAATGACAAAAATGGGTTTAAATTAGGAAGCAAAGTACTTTGTTATAGCTGAAATGGTTTTAAATGAAGCAAATGGTTGTAAATAATGAATGACAGAACATCTATTTTGGGACATTATGGCTTAAAATGCTATTTACTTTTAACTTCACAAATAAACACAGCTGTATTGTTTTGAAAAGCAATGAAAGGCATGCACCTCTACTAGCAGATTTAGCACTTCTGACCAAGTAAGACACCAACTTCTTTAAAAATATGCTTCATGCACTGTACTGGATTTTTTTAAGATGTAAATTTTAATACATTATTTCTTTTTTGAACTTGAACGGGAACCAGAATGGGATGATCCAGATGATGACCTGTGGCGT\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TTGAACGCCACCAGC',\n",
       " 'TGGGTAAAGTAGTGGTGTTCCGTAGGGGTTGGCCCTGGGCTCA',\n",
       " 'GCTTGACTCACAGGCTTTATTTACATTTGTCTACAGTATCATTTCCTTATGAAATGAACTAGTACAGCTTAGTTAAACCAAATGAAATCATAATCATCAGAATTGTCTGTAAACTACTATTAGCTAAATTATAACCTTGCATTTGCTTAGTACAGCCAAAGTTTTAAATACAGAAAGCACAAGAATAAACCAATGGTAACATGTAGAATCTAGATCGTCGGGGCAATTTAGAAGGTAGACTTTCAAAAAGTCTGAGGCACAATTACAAGTGAGTAAAAGTTCTTGTGCAACCTACATAAACGCAGCAAAGAAACTTAAGACATAAAAACATGGGAAAGCTCACTGTAAAAATATTATCAAAATATTTCTACATAAGATATCTTGCTTTCATTTTTAGATCAGCTGAAGAGAAAAACATTCACTTTAAAGTAAAAACATATATTTTTGTTTGGTTTGTCTATGAAATATCTTAAAATGTCGTAATTTTTATTTAAATTGCAGCAGACAATGTTACGGAAAAAAAACAAGAAATCAATATCCCATAACAAAAAGCCCCCAAGCACAACTGTTGATTTCCTTTGATATACTTACCTTGCATTGTCACTGGAAATACTGTTAAGAGAAACCTTTTTATTTTTTAAACCAGAAAACAAACTAAAGCTCATGTGACCAGATTTTAATTTTGAGAAATAAACTCCAAAAGCATTTTAAGCCATAACTTATTTCAAATGAATAGGAAAAATAATGACTTTTGCTTACATCATGCAGCAATAAGCATTCATTTGTTATTCAGAAGAAAATTGTATGCAGTGTGTTTATAGTTAACATCTTGAGGCAAATGTTCACTTAAATTCACAATGTCTCCATTGGCCTTTCACCAAAAGAGAAATATACTTTAAAAGCTTACTTCAATTTGCTCATTAAGTATGATTTAGCCTTTTCTACCATATTATATTTGGTAACTACATTTGGAAGAATATCACACAAAGCTTCTTAAAACATTGGTAAACATTTTGAGATGTCTTTATCAAGCAACCGTATCAGCAGAGAAAAGATAAACTCTTAAGACTTTCTTTTTTGCCCCAAATTATGTGTCAGAAAGGAAAATATATCCCACTTCTCTCCTTGTAGACATCACAAGATTCTTTTGCATTAAATGTGGAGCAAAAAAGTAAAGCTGGATAAAAGAGACACAATGATTCTAAGGCACCCTTAGCATAGGGAATTTCAAAATAGACATTTACAGTTGGCTGCTTACAACATAGTTTAACTTTTTTTGGTGAGAATCACTCTGACATCTGTTAACAGCATAAAAAGGCTGACTTTCTTTTCCATAAGTATGTTGTTTCCAACTAGTTTCCAGTCTTTCTTCCATGCCCAAAGTTCAGGCAAGCCTAGGTAATCCTGTCTTAGCCAATGGACAGGATGTATTTGGAAATGACAAAAATGGGTTTAAATTAGGAAGCAAAGTACTTTGTTATAGCTGAAATGGTTTTAAATGAAGCAAATGGTTGTAAATAATGAATGACAGAACATCTATTTTGGGACATTATGGCTTAAAATGCTATTTACTTTTAACTTCACAAATAAACACAGCTGTATTGTTTTGAAAAGCAATGAAAGGCATGCACCTCTACTAGCAGATTTAGCACTTCTGACCAAGTAAGACACCAACTTCTTTAAAAATATGCTTCATGCACTGTACTGGATTTTTTTAAGATGTAAATTTTAATACATTATTTCTTTTTTGAACTTGAACGGGAACCAGAATGGGATGATCCAGATGATGACCTGTGGCGT']"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_utr[\"ENST00000254821\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AADAT',\n",
       " 'AARSD1',\n",
       " 'ABHD14B',\n",
       " 'AC005520.1',\n",
       " 'AC005670.3',\n",
       " 'AC005833.1',\n",
       " 'AC010615.2',\n",
       " 'AC010642.2',\n",
       " 'AC011043.1',\n",
       " 'AC011447.3',\n",
       " 'AC011511.1',\n",
       " 'AC015522.1',\n",
       " 'AC018630.2',\n",
       " 'AC018697.1',\n",
       " 'AC020915.2',\n",
       " 'AC024592.3',\n",
       " 'AC025165.6',\n",
       " 'AC073111.4',\n",
       " 'AC074135.1',\n",
       " 'AC093752.1',\n",
       " 'AC104041.1',\n",
       " 'AC104452.1',\n",
       " 'AC112178.1',\n",
       " 'AC132008.2',\n",
       " 'AC233992.2',\n",
       " 'ACAD10',\n",
       " 'ACADM',\n",
       " 'ACAP2',\n",
       " 'ACD',\n",
       " 'ACOT13',\n",
       " 'ACSL4',\n",
       " 'ADAL',\n",
       " 'ADCK5',\n",
       " 'ADD1',\n",
       " 'ADGRL3',\n",
       " 'ADGRV1',\n",
       " 'ADSL',\n",
       " 'AJUBA',\n",
       " 'AK6',\n",
       " 'AKAP9',\n",
       " 'AL031708.1',\n",
       " 'AL121845.3',\n",
       " 'AL133395.1',\n",
       " 'AL353804.5',\n",
       " 'AL355987.4',\n",
       " 'AL513318.2',\n",
       " 'AL590617.2',\n",
       " 'AL662884.4',\n",
       " 'ALDH1L2',\n",
       " 'ALG9',\n",
       " 'ALOX5',\n",
       " 'ALPL',\n",
       " 'AMZ2',\n",
       " 'ANKHD1-EIF4EBP3',\n",
       " 'ANKRD39',\n",
       " 'ANKRD9',\n",
       " 'ANKZF1',\n",
       " 'ANO7',\n",
       " 'ANTKMT',\n",
       " 'ANTXR1',\n",
       " 'AP000553.2',\n",
       " 'AP1AR',\n",
       " 'AP1S2',\n",
       " 'AP1S3',\n",
       " 'APBB3',\n",
       " 'APIP',\n",
       " 'APOOL',\n",
       " 'APP',\n",
       " 'ARFGAP1',\n",
       " 'ARHGDIG',\n",
       " 'ARHGEF1',\n",
       " 'ARHGEF39',\n",
       " 'ARHGEF7',\n",
       " 'ARL4A',\n",
       " 'ARMC9',\n",
       " 'ARPC4-TTLL3',\n",
       " 'ASB1',\n",
       " 'ASCC3',\n",
       " 'ASGR1',\n",
       " 'ASNS',\n",
       " 'ASPH',\n",
       " 'ASPSCR1',\n",
       " 'ATAD3B',\n",
       " 'ATAT1',\n",
       " 'ATG4B',\n",
       " 'ATP1B3',\n",
       " 'ATP2A2',\n",
       " 'ATP5IF1',\n",
       " 'ATP6V0A2',\n",
       " 'ATP6V1H',\n",
       " 'ATXN7L1',\n",
       " 'AZI2',\n",
       " 'B9D1',\n",
       " 'BAALC',\n",
       " 'BAIAP2',\n",
       " 'BAK1',\n",
       " 'BANF1P2',\n",
       " 'BCCIP',\n",
       " 'BCKDHB',\n",
       " 'BCL2L12',\n",
       " 'BICD1',\n",
       " 'BLOC1S1',\n",
       " 'BOLA3',\n",
       " 'BORCS8',\n",
       " 'BPTF',\n",
       " 'BTBD7',\n",
       " 'BX890604.2',\n",
       " 'C11orf95',\n",
       " 'C12orf73',\n",
       " 'C13orf42',\n",
       " 'C14orf93',\n",
       " 'C16orf72',\n",
       " 'C18orf32',\n",
       " 'C1orf122',\n",
       " 'C20orf96',\n",
       " 'C22orf39',\n",
       " 'C5orf24',\n",
       " 'C5orf38',\n",
       " 'C7orf50',\n",
       " 'CADM2',\n",
       " 'CAMKV',\n",
       " 'CAND1',\n",
       " 'CARD19',\n",
       " 'CBR4',\n",
       " 'CCDC77',\n",
       " 'CCNC',\n",
       " 'CD151',\n",
       " 'CD27-AS1',\n",
       " 'CD47',\n",
       " 'CD99L2',\n",
       " 'CDC34',\n",
       " 'CDCA3',\n",
       " 'CDK14',\n",
       " 'CDK2AP1',\n",
       " 'CDKN1C',\n",
       " 'CDYL',\n",
       " 'CENPK',\n",
       " 'CENPM',\n",
       " 'CENPS',\n",
       " 'CENPT',\n",
       " 'CENPU',\n",
       " 'CEPT1',\n",
       " 'CERK',\n",
       " 'CES3',\n",
       " 'CFAP298',\n",
       " 'CHAF1B',\n",
       " 'CHCHD3',\n",
       " 'CHD9',\n",
       " 'CHPT1',\n",
       " 'CHRM4',\n",
       " 'CHRNA1',\n",
       " 'CHRNA5',\n",
       " 'CHRNB1',\n",
       " 'CHST8',\n",
       " 'CHTOP',\n",
       " 'CITED1',\n",
       " 'CIZ1',\n",
       " 'CKAP2',\n",
       " 'CLCN6',\n",
       " 'CLK2',\n",
       " 'CLPB',\n",
       " 'CNMD',\n",
       " 'CNOT6',\n",
       " 'CNPY2',\n",
       " 'COA4',\n",
       " 'COA8',\n",
       " 'COMTD1',\n",
       " 'COPE',\n",
       " 'CORO1C',\n",
       " 'CORO7',\n",
       " 'COX11',\n",
       " 'COX20',\n",
       " 'CPOX',\n",
       " 'CPSF4',\n",
       " 'CPSF6',\n",
       " 'CRAT',\n",
       " 'CRIP1',\n",
       " 'CRLF3',\n",
       " 'CROCCP2',\n",
       " 'CRYZL1',\n",
       " 'CSNK1A1',\n",
       " 'CSTF3',\n",
       " 'CUL7',\n",
       " 'CUZD1',\n",
       " 'CXADR',\n",
       " 'CXCL12',\n",
       " 'CXorf56',\n",
       " 'CYREN',\n",
       " 'DAB1',\n",
       " 'DBN1',\n",
       " 'DCAF7',\n",
       " 'DCTN1',\n",
       " 'DCTN2',\n",
       " 'DCTN4',\n",
       " 'DDX39A',\n",
       " 'DDX56',\n",
       " 'DDX6',\n",
       " 'DENND5B',\n",
       " 'DEPDC1B',\n",
       " 'DGUOK',\n",
       " 'DHDDS',\n",
       " 'DISP3',\n",
       " 'DLEU2',\n",
       " 'DLEU7',\n",
       " 'DLL3',\n",
       " 'DMAC1',\n",
       " 'DMAC2L',\n",
       " 'DMAP1',\n",
       " 'DNAAF4-CCPG1',\n",
       " 'DNAH14',\n",
       " 'DNAL4',\n",
       " 'DNHD1',\n",
       " 'DNMBP',\n",
       " 'DOCK7',\n",
       " 'DPM2',\n",
       " 'DPYSL2',\n",
       " 'DSE',\n",
       " 'DUT',\n",
       " 'DYNC1I2',\n",
       " 'EBF2',\n",
       " 'ECHDC1',\n",
       " 'EEF1D',\n",
       " 'EFCAB11',\n",
       " 'EGFL7',\n",
       " 'EIF2B5',\n",
       " 'EIF4A2',\n",
       " 'ELAVL1',\n",
       " 'ELP5',\n",
       " 'EML4',\n",
       " 'EMP3',\n",
       " 'ENOX1',\n",
       " 'ENSA',\n",
       " 'ENTPD7',\n",
       " 'ENY2',\n",
       " 'EPB41L1',\n",
       " 'EPB41L5',\n",
       " 'EPN3',\n",
       " 'EPS8L1',\n",
       " 'ERBB2',\n",
       " 'ERCC5',\n",
       " 'ERGIC3',\n",
       " 'ESPN',\n",
       " 'ETNK1',\n",
       " 'ETV5',\n",
       " 'EXOC1',\n",
       " 'EXOSC10',\n",
       " 'EXOSC8',\n",
       " 'FAIM',\n",
       " 'FAM126A',\n",
       " 'FAM133B',\n",
       " 'FAM13A',\n",
       " 'FAM177A1',\n",
       " 'FAM53B',\n",
       " 'FARP2',\n",
       " 'FBRSL1',\n",
       " 'FBXL5',\n",
       " 'FBXO11',\n",
       " 'FBXO17',\n",
       " 'FBXO31',\n",
       " 'FBXO7',\n",
       " 'FEN1',\n",
       " 'FGF13',\n",
       " 'FKBP11',\n",
       " 'FLT1',\n",
       " 'FOXD3-AS1',\n",
       " 'FUS',\n",
       " 'FUT10',\n",
       " 'FYN',\n",
       " 'GALNT10',\n",
       " 'GARS-DT',\n",
       " 'GART',\n",
       " 'GBGT1',\n",
       " 'GCSH',\n",
       " 'GDF1',\n",
       " 'GET4',\n",
       " 'GFM1',\n",
       " 'GGCX',\n",
       " 'GGPS1',\n",
       " 'GKAP1',\n",
       " 'GLG1',\n",
       " 'GLIDR',\n",
       " 'GLMP',\n",
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       " 'MTG1',\n",
       " 'MTHFD1L',\n",
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       " 'MXD4',\n",
       " 'MXRA7',\n",
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       " 'MYLK-AS1',\n",
       " 'MYO10',\n",
       " 'MYO19',\n",
       " 'N4BP2L2',\n",
       " 'NAA38',\n",
       " 'NARF',\n",
       " 'NASP',\n",
       " 'NAV2',\n",
       " 'NCAN',\n",
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       " 'NDUFA10',\n",
       " 'NDUFAF5',\n",
       " 'NDUFS2',\n",
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       " 'NECTIN2',\n",
       " 'NEO1',\n",
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       " 'NKAIN3',\n",
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       " 'NLE1',\n",
       " 'NLN',\n",
       " 'NOL4L',\n",
       " 'NOVA1',\n",
       " 'NPRL2',\n",
       " 'NPRL3',\n",
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       " 'NRBP1',\n",
       " 'NRF1',\n",
       " 'NRG1',\n",
       " 'NRN1L',\n",
       " 'NRP2',\n",
       " 'NSD2',\n",
       " 'NSMCE4A',\n",
       " 'NT5C',\n",
       " 'NTAN1',\n",
       " 'NUB1',\n",
       " 'NUBP1',\n",
       " 'NUDT15',\n",
       " 'NUDT16P1',\n",
       " 'NUDT22',\n",
       " 'NUTF2',\n",
       " 'NUTM2B-AS1',\n",
       " 'OBSL1',\n",
       " 'OGA',\n",
       " 'OGG1',\n",
       " 'ONECUT2',\n",
       " 'ORC6',\n",
       " 'OSBPL1A',\n",
       " 'OSBPL5',\n",
       " 'OVCA2',\n",
       " 'OXR1',\n",
       " 'PA2G4',\n",
       " 'PABPN1',\n",
       " 'PAFAH1B1',\n",
       " 'PAM',\n",
       " 'PANK1',\n",
       " 'PARPBP',\n",
       " 'PAXX',\n",
       " 'PBX4',\n",
       " 'PCIF1',\n",
       " 'PCMT1',\n",
       " 'PCOLCE',\n",
       " 'PDE4DIP',\n",
       " 'PDLIM7',\n",
       " 'PECR',\n",
       " 'PER1',\n",
       " 'PET100',\n",
       " 'PFN2',\n",
       " 'PGAP2',\n",
       " 'PHACTR2',\n",
       " 'PHF10',\n",
       " 'PHF14',\n",
       " 'PHF20L1',\n",
       " 'PHYKPL',\n",
       " 'PICALM',\n",
       " 'PITPNB',\n",
       " 'PIWIL2',\n",
       " 'PKD1',\n",
       " 'PKDCC',\n",
       " 'PKM',\n",
       " 'PLAA',\n",
       " 'PLCXD2',\n",
       " 'PLEKHA5',\n",
       " 'PLEKHJ1',\n",
       " 'PLPP5',\n",
       " 'PMS2P1',\n",
       " 'PNPLA8',\n",
       " 'PNPT1',\n",
       " 'POGZ',\n",
       " 'POLA2',\n",
       " 'POLG2',\n",
       " 'POLR1D',\n",
       " 'POLR2C',\n",
       " 'POLR2L',\n",
       " 'PPARD',\n",
       " 'PPIE',\n",
       " 'PPIH',\n",
       " 'PPIL3',\n",
       " 'PPM1A',\n",
       " 'PPP1CB',\n",
       " 'PPP1R1C',\n",
       " 'PPP2R1B',\n",
       " 'PPP2R3C',\n",
       " 'PRKCQ-AS1',\n",
       " 'PRKCZ',\n",
       " 'PRKG1',\n",
       " 'PRR3',\n",
       " 'PRXL2A',\n",
       " 'PSMA1',\n",
       " 'PSMA3-AS1',\n",
       " 'PSPC1',\n",
       " 'PSPH',\n",
       " 'PSRC1',\n",
       " 'PTDSS2',\n",
       " 'PTK2',\n",
       " 'PTPN9',\n",
       " 'PURPL',\n",
       " 'PXMP2',\n",
       " 'PXN',\n",
       " 'PXYLP1',\n",
       " 'PYM1',\n",
       " 'QTRT1',\n",
       " 'RAB11FIP5',\n",
       " 'RAB12',\n",
       " 'RAB26',\n",
       " 'RAB30-DT',\n",
       " 'RAB3D',\n",
       " 'RAB4B',\n",
       " 'RAB6A',\n",
       " 'RABGEF1',\n",
       " 'RAC3',\n",
       " 'RALY',\n",
       " 'RANGRF',\n",
       " 'RAP1GDS1',\n",
       " 'RARS',\n",
       " 'RBFOX2',\n",
       " 'RBFOX3',\n",
       " 'RBM28',\n",
       " 'RBM8A',\n",
       " 'RBPMS',\n",
       " 'RC3H2',\n",
       " 'RCOR3',\n",
       " 'RDH11',\n",
       " 'RECK',\n",
       " 'REEP1',\n",
       " 'REEP3',\n",
       " 'RENBP',\n",
       " 'RER1',\n",
       " 'REX1BD',\n",
       " 'REXO2',\n",
       " 'RFC4',\n",
       " 'RFLNA',\n",
       " 'RGS9',\n",
       " 'RHOC',\n",
       " 'RHOF',\n",
       " 'RIMS4',\n",
       " 'RMDN1',\n",
       " 'RND2',\n",
       " 'RNF115',\n",
       " 'RNF130',\n",
       " 'RNF165',\n",
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       " 'RNF220',\n",
       " 'RPL21',\n",
       " 'RPL22L1',\n",
       " 'RPL9',\n",
       " 'RPS14',\n",
       " 'RPS24',\n",
       " 'RPUSD3',\n",
       " 'RRP36',\n",
       " 'RRP7BP',\n",
       " 'RRP8',\n",
       " 'RTN2',\n",
       " 'RUFY3',\n",
       " 'RWDD1',\n",
       " 'RYR1',\n",
       " 'SACS',\n",
       " 'SALL2',\n",
       " 'SAMM50',\n",
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       " 'SARM1',\n",
       " 'SCAMP5',\n",
       " 'SCG5',\n",
       " 'SCPEP1',\n",
       " 'SCRIB',\n",
       " 'SCRN2',\n",
       " 'SCYL2',\n",
       " 'SDAD1',\n",
       " 'SDCCAG8',\n",
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       " 'SEC31A',\n",
       " 'SELENOH',\n",
       " 'SEPTIN12',\n",
       " 'SEPTIN6',\n",
       " 'SEPTIN8',\n",
       " 'SFT2D1',\n",
       " 'SGCE',\n",
       " 'SGK3',\n",
       " 'SH3BGR',\n",
       " 'SH3GLB1',\n",
       " 'SH3GLB2',\n",
       " 'SIGIRR',\n",
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       " 'SLC11A2',\n",
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       " 'SLC19A1',\n",
       " 'SLC20A2',\n",
       " 'SLC37A4',\n",
       " 'SLC38A9',\n",
       " 'SLC44A1',\n",
       " 'SLC44A2',\n",
       " 'SLC44A3-AS1',\n",
       " 'SLC6A15',\n",
       " 'SLC9B2',\n",
       " 'SLCO3A1',\n",
       " 'SLK',\n",
       " 'SMAP1',\n",
       " 'SMARCA1',\n",
       " 'SMARCB1',\n",
       " 'SMIM4',\n",
       " 'SMTN',\n",
       " 'SMU1',\n",
       " 'SMUG1',\n",
       " 'SNHG11',\n",
       " 'SNHG29',\n",
       " 'SNHG6',\n",
       " 'SNHG8',\n",
       " 'SNORD86',\n",
       " 'SNRNP70',\n",
       " 'SNRPB',\n",
       " 'SNTG2',\n",
       " 'SNU13',\n",
       " 'SNX14',\n",
       " 'SORBS1',\n",
       " 'SPATC1L',\n",
       " 'SPIB',\n",
       " 'SPTLC1',\n",
       " 'SRGAP2C',\n",
       " 'SRP19',\n",
       " 'SRPK2',\n",
       " 'SRRM4',\n",
       " 'SRSF2',\n",
       " 'SRSF5',\n",
       " 'SRSF7',\n",
       " 'SS18',\n",
       " 'SSBP4',\n",
       " 'ST7',\n",
       " 'STAG3L3',\n",
       " 'STEAP1B',\n",
       " 'STK32C',\n",
       " 'STX1A',\n",
       " 'STXBP5L',\n",
       " 'SUGP1',\n",
       " 'SULF2',\n",
       " 'SULT4A1',\n",
       " 'SUMO2',\n",
       " 'SWAP70',\n",
       " 'SWSAP1',\n",
       " 'SYNGR1',\n",
       " 'SYS1',\n",
       " 'SYT17',\n",
       " 'TACC3',\n",
       " 'TAF5L',\n",
       " 'TANK',\n",
       " 'TBC1D31',\n",
       " 'TBCE',\n",
       " 'TCEA2',\n",
       " 'TCEAL3',\n",
       " 'TCERG1',\n",
       " 'TDRKH',\n",
       " 'TDRP',\n",
       " 'TEDC2',\n",
       " 'TERF2',\n",
       " 'TEX10',\n",
       " 'TFDP1',\n",
       " 'TFDP2',\n",
       " 'TGFB1',\n",
       " 'THAP7-AS1',\n",
       " 'THOC3',\n",
       " 'THRA',\n",
       " 'THYN1',\n",
       " 'TIAL1',\n",
       " 'TLE1',\n",
       " 'TLN1',\n",
       " 'TM2D3',\n",
       " 'TMEM120B',\n",
       " 'TMEM178A',\n",
       " 'TMEM179B',\n",
       " 'TMEM184B',\n",
       " 'TMEM223',\n",
       " 'TMEM250',\n",
       " 'TMEM39B',\n",
       " 'TMEM59',\n",
       " 'TMEM67',\n",
       " 'TMEM91',\n",
       " 'TMOD2',\n",
       " 'TOMM5',\n",
       " 'TPD52',\n",
       " 'TPD52L2',\n",
       " 'TPM1',\n",
       " 'TPM2',\n",
       " 'TPR',\n",
       " 'TPRA1',\n",
       " 'TRA2A',\n",
       " 'TRABD',\n",
       " 'TRAPPC13',\n",
       " 'TRDMT1',\n",
       " 'TRIM33',\n",
       " 'TRIM46',\n",
       " 'TRIM52-AS1',\n",
       " 'TRIM59',\n",
       " 'TRIOBP',\n",
       " 'TSC22D1',\n",
       " 'TSPAN12',\n",
       " 'TSPAN15',\n",
       " 'TSPAN18',\n",
       " 'TSPAN4',\n",
       " 'TSSC2',\n",
       " 'TSTD1',\n",
       " 'TTC28-AS1',\n",
       " 'TTLL5',\n",
       " 'TTYH2',\n",
       " 'TTYH3',\n",
       " 'TUBB3',\n",
       " 'TWSG1',\n",
       " 'TYSND1',\n",
       " 'TYW1',\n",
       " 'UBA3',\n",
       " 'UBALD1',\n",
       " 'UBE2I',\n",
       " 'UCKL1',\n",
       " 'UGGT2',\n",
       " 'UGP2',\n",
       " 'UMPS',\n",
       " 'UNC50',\n",
       " 'USE1',\n",
       " 'USP48',\n",
       " 'VAPB',\n",
       " 'VPS29',\n",
       " 'VPS33B',\n",
       " 'VPS51',\n",
       " 'VRK3',\n",
       " 'VSIG10',\n",
       " 'VTI1A',\n",
       " 'WDR33',\n",
       " 'WDR83OS',\n",
       " 'WFDC2',\n",
       " 'YBEY',\n",
       " 'ZC2HC1A',\n",
       " 'ZC3H8',\n",
       " 'ZFP42',\n",
       " 'ZFP64',\n",
       " 'ZFPL1',\n",
       " 'ZMAT2',\n",
       " 'ZMYM5',\n",
       " 'ZNF124',\n",
       " 'ZNF195',\n",
       " 'ZNF207',\n",
       " 'ZNF232',\n",
       " 'ZNF28',\n",
       " 'ZNF430',\n",
       " 'ZNF431',\n",
       " 'ZNF493',\n",
       " 'ZNF511',\n",
       " 'ZNF519',\n",
       " 'ZNF529',\n",
       " 'ZNF551',\n",
       " 'ZNF581',\n",
       " 'ZNF583',\n",
       " 'ZNF626',\n",
       " 'ZNF677',\n",
       " 'ZNF718',\n",
       " 'ZNF721',\n",
       " 'ZNF766',\n",
       " 'ZNF771',\n",
       " 'ZNF775',\n",
       " 'ZNF814',\n",
       " 'ZNF826P',\n",
       " 'ZNF85',\n",
       " 'ZNF90',\n",
       " 'ZNF91',\n",
       " 'ZNHIT3',\n",
       " 'ZNRF3',\n",
       " 'ZRANB2']"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  187,\n",
       "  6619,\n",
       "  3501,\n",
       "  379,\n",
       "  814,\n",
       "  298,\n",
       "  95,\n",
       "  1117,\n",
       "  2180,\n",
       "  356,\n",
       "  151,\n",
       "  1738,\n",
       "  109,\n",
       "  96,\n",
       "  2247,\n",
       "  1406,\n",
       "  3705,\n",
       "  2312,\n",
       "  157,\n",
       "  298,\n",
       "  1499,\n",
       "  592,\n",
       "  71,\n",
       "  483,\n",
       "  533,\n",
       "  2543,\n",
       "  304,\n",
       "  2435,\n",
       "  453,\n",
       "  4947,\n",
       "  785,\n",
       "  1171,\n",
       "  1862,\n",
       "  1671,\n",
       "  336,\n",
       "  329,\n",
       "  158,\n",
       "  4969,\n",
       "  2511,\n",
       "  542,\n",
       "  316,\n",
       "  246,\n",
       "  5664,\n",
       "  2984,\n",
       "  691,\n",
       "  158,\n",
       "  654,\n",
       "  167,\n",
       "  260,\n",
       "  295,\n",
       "  350,\n",
       "  846,\n",
       "  1215,\n",
       "  1123,\n",
       "  7909,\n",
       "  48,\n",
       "  1593,\n",
       "  763,\n",
       "  1076,\n",
       "  1458,\n",
       "  689,\n",
       "  3249,\n",
       "  481,\n",
       "  3063,\n",
       "  2890,\n",
       "  978,\n",
       "  2224,\n",
       "  333,\n",
       "  211,\n",
       "  6866,\n",
       "  89,\n",
       "  1508,\n",
       "  284,\n",
       "  898,\n",
       "  690,\n",
       "  2029,\n",
       "  2511,\n",
       "  295,\n",
       "  2032,\n",
       "  413,\n",
       "  3842],\n",
       " '3': [733,\n",
       "  168,\n",
       "  1548,\n",
       "  316,\n",
       "  3863,\n",
       "  755,\n",
       "  184,\n",
       "  33,\n",
       "  3841,\n",
       "  363,\n",
       "  2619,\n",
       "  81,\n",
       "  1593,\n",
       "  497,\n",
       "  117,\n",
       "  2667,\n",
       "  2204,\n",
       "  1515,\n",
       "  274,\n",
       "  2466,\n",
       "  165,\n",
       "  6122,\n",
       "  94,\n",
       "  8826,\n",
       "  379,\n",
       "  4268,\n",
       "  722,\n",
       "  540,\n",
       "  1488,\n",
       "  789,\n",
       "  123,\n",
       "  984,\n",
       "  443,\n",
       "  1033,\n",
       "  1484,\n",
       "  2905,\n",
       "  4155,\n",
       "  4406,\n",
       "  5094,\n",
       "  657,\n",
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       "  2469,\n",
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       "  392,\n",
       "  368,\n",
       "  628,\n",
       "  1149,\n",
       "  2338,\n",
       "  83,\n",
       "  2471,\n",
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       "  164,\n",
       "  5709,\n",
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       "  297,\n",
       "  2150,\n",
       "  299,\n",
       "  4012,\n",
       "  1105,\n",
       "  2423,\n",
       "  4185,\n",
       "  400,\n",
       "  3664,\n",
       "  249,\n",
       "  290,\n",
       "  2637,\n",
       "  562,\n",
       "  1345,\n",
       "  2857,\n",
       "  4461,\n",
       "  591,\n",
       "  341,\n",
       "  135,\n",
       "  122,\n",
       "  256,\n",
       "  597,\n",
       "  2118,\n",
       "  518,\n",
       "  5013,\n",
       "  2146,\n",
       "  999,\n",
       "  781,\n",
       "  498,\n",
       "  975,\n",
       "  2656,\n",
       "  545,\n",
       "  139,\n",
       "  1116,\n",
       "  447,\n",
       "  1375,\n",
       "  71,\n",
       "  959,\n",
       "  1059,\n",
       "  2358,\n",
       "  713,\n",
       "  75,\n",
       "  69,\n",
       "  768,\n",
       "  2086,\n",
       "  306,\n",
       "  251,\n",
       "  3120,\n",
       "  129,\n",
       "  751,\n",
       "  759,\n",
       "  274,\n",
       "  510,\n",
       "  425,\n",
       "  393,\n",
       "  387,\n",
       "  160,\n",
       "  752,\n",
       "  3341,\n",
       "  2322,\n",
       "  202,\n",
       "  55,\n",
       "  1188,\n",
       "  317,\n",
       "  987,\n",
       "  4006,\n",
       "  440,\n",
       "  1889,\n",
       "  455,\n",
       "  3075,\n",
       "  3288,\n",
       "  854,\n",
       "  282,\n",
       "  1422,\n",
       "  26,\n",
       "  1649,\n",
       "  809,\n",
       "  1753,\n",
       "  1015,\n",
       "  1864,\n",
       "  3813,\n",
       "  1229,\n",
       "  2517,\n",
       "  787,\n",
       "  69,\n",
       "  887,\n",
       "  2379,\n",
       "  1660,\n",
       "  2069,\n",
       "  829,\n",
       "  881,\n",
       "  2456,\n",
       "  1452,\n",
       "  890,\n",
       "  187,\n",
       "  6619,\n",
       "  3634,\n",
       "  381,\n",
       "  1228,\n",
       "  814,\n",
       "  298,\n",
       "  95,\n",
       "  1117,\n",
       "  2180,\n",
       "  375,\n",
       "  356,\n",
       "  151,\n",
       "  1741,\n",
       "  109,\n",
       "  1731,\n",
       "  2247,\n",
       "  1406,\n",
       "  3705,\n",
       "  2312,\n",
       "  157,\n",
       "  298,\n",
       "  1499,\n",
       "  6502,\n",
       "  592,\n",
       "  98,\n",
       "  2512,\n",
       "  483,\n",
       "  533,\n",
       "  2543,\n",
       "  304,\n",
       "  2435,\n",
       "  453,\n",
       "  4947,\n",
       "  1622,\n",
       "  1538,\n",
       "  3513,\n",
       "  1671,\n",
       "  336,\n",
       "  329,\n",
       "  158,\n",
       "  4969,\n",
       "  2511,\n",
       "  6253,\n",
       "  316,\n",
       "  315,\n",
       "  5664,\n",
       "  2984,\n",
       "  3664,\n",
       "  1257,\n",
       "  158,\n",
       "  654,\n",
       "  214,\n",
       "  167,\n",
       "  260,\n",
       "  1206,\n",
       "  481,\n",
       "  350,\n",
       "  190,\n",
       "  1215,\n",
       "  1123,\n",
       "  7909,\n",
       "  48,\n",
       "  763,\n",
       "  1076,\n",
       "  1458,\n",
       "  689,\n",
       "  3249,\n",
       "  331,\n",
       "  1811,\n",
       "  3063,\n",
       "  2890,\n",
       "  973,\n",
       "  2224,\n",
       "  333,\n",
       "  211,\n",
       "  6866,\n",
       "  89,\n",
       "  1310,\n",
       "  284,\n",
       "  898,\n",
       "  2029,\n",
       "  2511,\n",
       "  295,\n",
       "  2214,\n",
       "  413,\n",
       "  3842],\n",
       " '5': [733,\n",
       "  168,\n",
       "  1891,\n",
       "  316,\n",
       "  3863,\n",
       "  184,\n",
       "  3841,\n",
       "  1641,\n",
       "  189,\n",
       "  367,\n",
       "  81,\n",
       "  1593,\n",
       "  497,\n",
       "  117,\n",
       "  2204,\n",
       "  1515,\n",
       "  1443,\n",
       "  2466,\n",
       "  165,\n",
       "  6122,\n",
       "  94,\n",
       "  8826,\n",
       "  379,\n",
       "  4268,\n",
       "  722,\n",
       "  540,\n",
       "  1488,\n",
       "  789,\n",
       "  123,\n",
       "  984,\n",
       "  443,\n",
       "  1033,\n",
       "  1484,\n",
       "  2905,\n",
       "  4155,\n",
       "  4406,\n",
       "  5094,\n",
       "  657,\n",
       "  2175,\n",
       "  865,\n",
       "  212,\n",
       "  2469,\n",
       "  1295,\n",
       "  1808,\n",
       "  392,\n",
       "  368,\n",
       "  628,\n",
       "  252,\n",
       "  2338,\n",
       "  83,\n",
       "  2471,\n",
       "  4212,\n",
       "  130,\n",
       "  5709,\n",
       "  523,\n",
       "  297,\n",
       "  2150,\n",
       "  299,\n",
       "  4012,\n",
       "  1105,\n",
       "  2423,\n",
       "  1011,\n",
       "  400,\n",
       "  3664,\n",
       "  249,\n",
       "  258,\n",
       "  2316,\n",
       "  290,\n",
       "  2637,\n",
       "  562,\n",
       "  1347,\n",
       "  2857,\n",
       "  4461,\n",
       "  591,\n",
       "  135,\n",
       "  122,\n",
       "  256,\n",
       "  597,\n",
       "  2118,\n",
       "  518,\n",
       "  5013,\n",
       "  2146,\n",
       "  999,\n",
       "  781,\n",
       "  498,\n",
       "  975,\n",
       "  2656,\n",
       "  545,\n",
       "  139,\n",
       "  1116,\n",
       "  447,\n",
       "  1375,\n",
       "  71,\n",
       "  82,\n",
       "  959,\n",
       "  1059,\n",
       "  284,\n",
       "  2358,\n",
       "  713,\n",
       "  75,\n",
       "  69,\n",
       "  2086,\n",
       "  306,\n",
       "  177,\n",
       "  3120,\n",
       "  129,\n",
       "  751,\n",
       "  759,\n",
       "  274,\n",
       "  425,\n",
       "  393,\n",
       "  387,\n",
       "  160,\n",
       "  752,\n",
       "  3341,\n",
       "  2322,\n",
       "  202,\n",
       "  55,\n",
       "  1188,\n",
       "  317,\n",
       "  987,\n",
       "  5550,\n",
       "  4006,\n",
       "  447,\n",
       "  1889,\n",
       "  455,\n",
       "  3075,\n",
       "  3288,\n",
       "  1224,\n",
       "  282,\n",
       "  1422,\n",
       "  26,\n",
       "  1649,\n",
       "  1753,\n",
       "  1015,\n",
       "  1864,\n",
       "  3813,\n",
       "  2517,\n",
       "  787,\n",
       "  2379,\n",
       "  2069,\n",
       "  829,\n",
       "  881,\n",
       "  2456,\n",
       "  1452,\n",
       "  890,\n",
       "  187,\n",
       "  6619,\n",
       "  3634,\n",
       "  381,\n",
       "  1228,\n",
       "  814,\n",
       "  298,\n",
       "  95,\n",
       "  1117,\n",
       "  719,\n",
       "  2180,\n",
       "  375,\n",
       "  356,\n",
       "  151,\n",
       "  1741,\n",
       "  109,\n",
       "  1731,\n",
       "  2247,\n",
       "  1406,\n",
       "  3705,\n",
       "  2312,\n",
       "  157,\n",
       "  298,\n",
       "  1499,\n",
       "  6502,\n",
       "  592,\n",
       "  98,\n",
       "  2260,\n",
       "  2512,\n",
       "  483,\n",
       "  533,\n",
       "  2543,\n",
       "  304,\n",
       "  453,\n",
       "  1622,\n",
       "  1538,\n",
       "  3513,\n",
       "  1681,\n",
       "  336,\n",
       "  329,\n",
       "  158,\n",
       "  4969,\n",
       "  2511,\n",
       "  6253,\n",
       "  316,\n",
       "  315,\n",
       "  5664,\n",
       "  2984,\n",
       "  3664,\n",
       "  1257,\n",
       "  158,\n",
       "  654,\n",
       "  214,\n",
       "  167,\n",
       "  260,\n",
       "  866,\n",
       "  1206,\n",
       "  481,\n",
       "  350,\n",
       "  190,\n",
       "  1215,\n",
       "  1123,\n",
       "  7909,\n",
       "  48,\n",
       "  1076,\n",
       "  1458,\n",
       "  762,\n",
       "  689,\n",
       "  3249,\n",
       "  331,\n",
       "  1811,\n",
       "  3063,\n",
       "  2890,\n",
       "  978,\n",
       "  2224,\n",
       "  332,\n",
       "  211,\n",
       "  6866,\n",
       "  3358,\n",
       "  85,\n",
       "  1508,\n",
       "  284,\n",
       "  898,\n",
       "  2029,\n",
       "  2511,\n",
       "  295,\n",
       "  228,\n",
       "  413,\n",
       "  3842]}"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days_utr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10))\n",
    "plt.style.use('ggplot')\n",
    "plt.hist([days_utr[\"0\"],days_utr[\"3\"],days_utr[\"5\"]],bins = 20,label = [\"day0\",\"day3\",\"day5\"])\n",
    "plt.legend()\n",
    "plt.title(\"Normalized Transcript Length of Most Expressed Isoform in Signficiant Functional Consequences Isoform Switches\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene = \"PIWIL2\"\n",
    "filename = \"/home/annaldas/projects/result/PIWIL2/PIWIL2_seq.fa\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}