{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of intronic polyA sites\n",
    "This notebook demostrates on how to create cutomized features. The examples is using intronic polyA annotations from the polyADB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using /home/hsher/gencode_coords/GRCh38.p13.genome.fa\n",
      "using /home/hsher/gencode_coords/GRCh38.p13.genome.fa\n",
      "Using:  /home/hsher/gencode_coords/gencode.v33.transcript.gff3\n"
     ]
    }
   ],
   "source": [
    "# set up files associated with each genome coordinates\n",
    "import metadensity as md\n",
    "md.settings.from_config_file('/tscc/nfs/home/hsher/Metadensity/config/hg38-tscc2.ini')\n",
    "\n",
    "\n",
    "# then import the modules\n",
    "from metadensity.metadensity import *\n",
    "from metadensity.plotd import *\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# I have a precompiles list of ENCODE datas as a csv that loads in this dataloader\n",
    "import sys\n",
    "sys.path.append('/home/hsher/Metadensity/scripts')\n",
    "from dataloader import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use katie's PCF11\n",
    "katie_data = pd.read_csv('/home/hsher/projects/katie/katie_eclip.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>RBP</th>\n",
       "      <th>Cell line</th>\n",
       "      <th>bam_0</th>\n",
       "      <th>bam_1</th>\n",
       "      <th>bam_control</th>\n",
       "      <th>minus_0</th>\n",
       "      <th>minus_1</th>\n",
       "      <th>minus_control</th>\n",
       "      <th>plus_0</th>\n",
       "      <th>...</th>\n",
       "      <th>bed_0</th>\n",
       "      <th>bed_1</th>\n",
       "      <th>Batch</th>\n",
       "      <th>prefix</th>\n",
       "      <th>bam_control_0</th>\n",
       "      <th>bam_control_1</th>\n",
       "      <th>plus_control_0</th>\n",
       "      <th>plus_control_1</th>\n",
       "      <th>minus_control_0</th>\n",
       "      <th>minus_control_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>258</td>\n",
       "      <td>CPSF6</td>\n",
       "      <td>K562</td>\n",
       "      <td>/projects/ps-yeolab3/encode/analysis/encode_GR...</td>\n",
       "      <td>/projects/ps-yeolab3/encode/analysis/encode_GR...</td>\n",
       "      <td>/projects/ps-yeolab3/encode/analysis/encode_GR...</td>\n",
       "      <td>/projects/ps-yeolab3/encode/analysis/encode_GR...</td>\n",
       "      <td>/projects/ps-yeolab3/encode/analysis/encode_GR...</td>\n",
       "      <td>/projects/ps-yeolab3/encode/analysis/encode_GR...</td>\n",
       "      <td>/projects/ps-yeolab3/encode/analysis/encode_GR...</td>\n",
       "      <td>...</td>\n",
       "      <td>/projects/ps-yeolab5/encode/EVN_eCLIP_analysis...</td>\n",
       "      <td>/projects/ps-yeolab5/encode/EVN_eCLIP_analysis...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    uid    RBP Cell line                                              bam_0  \\\n",
       "28  258  CPSF6      K562  /projects/ps-yeolab3/encode/analysis/encode_GR...   \n",
       "\n",
       "                                                bam_1  \\\n",
       "28  /projects/ps-yeolab3/encode/analysis/encode_GR...   \n",
       "\n",
       "                                          bam_control  \\\n",
       "28  /projects/ps-yeolab3/encode/analysis/encode_GR...   \n",
       "\n",
       "                                              minus_0  \\\n",
       "28  /projects/ps-yeolab3/encode/analysis/encode_GR...   \n",
       "\n",
       "                                              minus_1  \\\n",
       "28  /projects/ps-yeolab3/encode/analysis/encode_GR...   \n",
       "\n",
       "                                        minus_control  \\\n",
       "28  /projects/ps-yeolab3/encode/analysis/encode_GR...   \n",
       "\n",
       "                                               plus_0  ...  \\\n",
       "28  /projects/ps-yeolab3/encode/analysis/encode_GR...  ...   \n",
       "\n",
       "                                                bed_0  \\\n",
       "28  /projects/ps-yeolab5/encode/EVN_eCLIP_analysis...   \n",
       "\n",
       "                                                bed_1 Batch prefix  \\\n",
       "28  /projects/ps-yeolab5/encode/EVN_eCLIP_analysis...   NaN    NaN   \n",
       "\n",
       "   bam_control_0 bam_control_1 plus_control_0 plus_control_1 minus_control_0  \\\n",
       "28           NaN           NaN            NaN            NaN             NaN   \n",
       "\n",
       "   minus_control_1  \n",
       "28             NaN  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show a row of the csv file.\n",
    "master_df.loc[master_df['RBP']=='CPSF6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bam_0</th>\n",
       "      <th>bam_control_0</th>\n",
       "      <th>minus_0</th>\n",
       "      <th>minus_control_0</th>\n",
       "      <th>plus_0</th>\n",
       "      <th>plus_control_0</th>\n",
       "      <th>bed_0</th>\n",
       "      <th>bam_1</th>\n",
       "      <th>bam_control_1</th>\n",
       "      <th>minus_1</th>\n",
       "      <th>minus_control_1</th>\n",
       "      <th>plus_1</th>\n",
       "      <th>plus_control_1</th>\n",
       "      <th>bed_1</th>\n",
       "      <th>uid</th>\n",
       "      <th>RBP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>/home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...</td>\n",
       "      <td>PCF11</td>\n",
       "      <td>PCF11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               bam_0  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                       bam_control_0  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                             minus_0  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                     minus_control_0  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                              plus_0  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                      plus_control_0  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                               bed_0  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                               bam_1  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                       bam_control_1  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                             minus_1  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                     minus_control_1  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                              plus_1  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                      plus_control_1  \\\n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...   \n",
       "\n",
       "                                               bed_1    uid    RBP  \n",
       "0  /home/wjin/projects/CLIP_seq/Kris_Katie/Data/P...  PCF11  PCF11  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show a row of the csv file.\n",
    "katie_data.loc[katie_data['uid']=='PCF11']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load each row the data file into an eCLIP Object\n",
    "U2AF1 = eCLIP.from_series(encode_data.loc[(encode_data['RBP'] == 'U2AF1')&(encode_data['Cell line'] == 'HepG2')].iloc[0], single_end = False)\n",
    "U2AF2 = eCLIP.from_series(encode_data.loc[(encode_data['RBP'] == 'U2AF2')&(encode_data['Cell line'] == 'HepG2')].iloc[0], single_end = False)\n",
    "SF3B4 = eCLIP.from_series(encode_data.loc[(encode_data['RBP'] == 'SF3B4')&(encode_data['Cell line'] == 'HepG2')].iloc[0], single_end = False)\n",
    "NONO = eCLIP.from_series(encode4_data.loc[(encode4_data['RBP'] == 'NONO')&(encode4_data['Cell line'] == 'HepG2')].iloc[0], single_end = True)\n",
    "CPSF6 = eCLIP.from_series(encode_data.loc[(encode_data['RBP'] == 'CPSF6')&(encode_data['Cell line'] == 'K562')].iloc[0], single_end = False)\n",
    "PCF11 = eCLIP.from_series(katie_data.loc[katie_data['uid']=='PCF11'].iloc[0], single_end = True)\n",
    "all_clips = [U2AF1,U2AF2,SF3B4, NONO,CPSF6, PCF11]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating custom  features on metagene, using intronic polyA sites as an example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/hsher/gencode_coords/polyA.atlas.clusters.2.0.GRCh38.96.bed'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join(settings.root_dir, settings.polya_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hsher/miniconda3/envs/metadensity/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3173: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# load polyA sites files\n",
    "# (in order of decreasing priority: TE, terminal exon; EX, exonic; IN, intronic; DS, 1,000 nt downstream of an annotated terminal exon; AE, anti-sense to an exon; AI, anti-sense to an intron; AU, 1,000 nt upstream in anti-sense direction of a transcription start site; IG, intergenic)\n",
    "polyadf = pd.read_csv(os.path.join(settings.root_dir, settings.polya_fname), sep = '\\t', header = None, names = ['chrom', 'start', 'end', 'name', 'average expression', 'strand', 'perc_sample','n_sample', 'avg_tpm', 'polyatype', 'polyasignal'])\n",
    "#  two letter code for the cluster annotation (in order of decreasing priority: \n",
    "# TE, terminal exon; EX, exonic; IN, intronic; DS, 1,000 nt downstream of an annotated terminal exon; AE, anti-sense to an exon; AI, anti-sense to an intron; AU, 1,000 nt upstream in anti-sense direction of a transcription start site; IG, intergenic)\n",
    "# the chromosome is listed differently\n",
    "polyadf['chrom'] = 'chr'+polyadf['chrom'].astype(str)\n",
    "\n",
    "# filter with TPM at least 0.2\n",
    "polyadf = polyadf.loc[polyadf['average expression']>0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make type specific annotation\n",
    "def extract_polyA_signal_coordinates(subset_polyadf):\n",
    "    ''' subtracting the coordinate of polyA signal from polya dataframe'''\n",
    "    # extract polya signals\n",
    "    signal_coord = []\n",
    "    for index, row in subset_polyadf.iterrows():\n",
    "        if type(row['polyasignal'])==str:\n",
    "            polyasignals = row['polyasignal'].split(';')\n",
    "            for sig in polyasignals:\n",
    "                motif, rela_pos, obs_pos = sig.split('@')\n",
    "                signal_coord.append([row['chrom'],int(obs_pos),int(obs_pos)+1, \n",
    "                                     row['name'], row['polyatype'], row['strand']])\n",
    "\n",
    "    # make into bed\n",
    "    polysignal_df = pd.DataFrame(signal_coord,\n",
    "                                columns= ['chrom', 'start', 'end', 'name', 'score', 'strand'])\n",
    "    polyasignal_bed = BedTool.from_dataframe(polysignal_df)\n",
    "\n",
    "    return polyasignal_bed\n",
    "def polyAtype_specific_coords(polyAtype, polyadf = polyadf):\n",
    "    ''' create polyA related feature for specific types of polyA\n",
    "    polyAtype can be TE, EX, IN, DS..'''\n",
    "    # filter for specific types\n",
    "    subset_polyadf = polyadf.loc[polyadf['polyatype']==polyAtype]\n",
    "    \n",
    "    # create bed of polyA sites\n",
    "    polya_site_bed = BedTool.from_dataframe(subset_polyadf)\n",
    "    polya_signal_bed = extract_polyA_signal_coordinates(subset_polyadf)\n",
    "    \n",
    "    return polya_site_bed, polya_signal_bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract intronic polyA sites and signal\n",
    "#information about the poly(A) signal(s) that are present upstream of the poly(A) site, \n",
    "#including the motif, the location with respect to the cleavage site and the genomic coordinate\n",
    "\n",
    "polya, polyasignal_bed = polyAtype_specific_coords('IN', polyadf = polyadf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hsher/miniconda3/envs/metadensity/lib/python3.7/site-packages/pybedtools-0.8.1-py3.7-linux-x86_64.egg/pybedtools/bedtool.py:3681: UserWarning: Default names for filetype bed are:\n",
      "['chrom', 'start', 'end', 'name', 'score', 'strand', 'thickStart', 'thickEnd', 'itemRgb', 'blockCount', 'blockSizes', 'blockStarts']\n",
      "but file has 20 fields; you can supply custom names with the `names` kwarg\n",
      "  \"`names` kwarg\" % (self.file_type, _names, self.field_count())\n",
      "/home/hsher/miniconda3/envs/metadensity/lib/python3.7/site-packages/pybedtools-0.8.1-py3.7-linux-x86_64.egg/pybedtools/bedtool.py:3681: UserWarning: Default names for filetype bed are:\n",
      "['chrom', 'start', 'end', 'name', 'score', 'strand', 'thickStart', 'thickEnd', 'itemRgb', 'blockCount', 'blockSizes', 'blockStarts']\n",
      "but file has 15 fields; you can supply custom names with the `names` kwarg\n",
      "  \"`names` kwarg\" % (self.file_type, _names, self.field_count())\n"
     ]
    }
   ],
   "source": [
    "# determine which transcript these are in\n",
    "header = ['chrom', 'start', 'end', 'name', 'score', 'strand', 'attr']\n",
    "polya_site_df = polya.intersect(transcript,s = True, wb = True).to_dataframe(header = None)[[0,1,2,3,4,5,19]]\n",
    "polysignal_df = polyasignal_bed.intersect(transcript, s = True, wb = True).to_dataframe(header = None)[[0,1,2,3,4,5,14]]\n",
    "polya_site_df.columns = header\n",
    "polysignal_df.columns = header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract transcript name\n",
    "def segment_transcript_name(s):\n",
    "    return s.split(';')[0].split('=')[1]\n",
    "polysignal_df['transcript_id']= polysignal_df['attr'].map(segment_transcript_name)\n",
    "polya_site_df['transcript_id']= polya_site_df['attr'].map(segment_transcript_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Metagene objects containing intronic polyA sites\n",
    "Now we know where these sites are, and we know what transcripts they are in.\n",
    "The next step is to add the polyA site features into metagene objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hsher/miniconda3/envs/metadensity/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3364: DtypeWarning: Columns (2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using: /home/hsher/Metadensity/metadensity/data/hg38/gencode\n",
      "Done building metagene\n"
     ]
    }
   ],
   "source": [
    "# build metagene from highly expressed in HepG2 cell lines\n",
    "from metadensity.pos_enrich import *\n",
    "high_exp_meta = highly_exp_biogps('HEPG2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51, 8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check how many of those highly expressed contain intronic polya sites\n",
    "polya_site_df.loc[polya_site_df['transcript_id'].isin(high_exp_meta.keys())].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add feature to those metagene\n",
    "length = 200\n",
    "for transcript_id in high_exp_meta.keys():\n",
    "    metagene = high_exp_meta[transcript_id]\n",
    "    \n",
    "    # add feature\n",
    "    sub = polya_site_df.loc[polya_site_df['transcript_id']==transcript_id]\n",
    "    for index, row in sub.iterrows():\n",
    "        metagene.create_feature(interval = row['start'], feature_name = 'intron_polyAsite',\n",
    "                                length = 200\n",
    "                               ) # POINT FEATURE\n",
    "    \n",
    "    sub = polysignal_df.loc[polysignal_df['transcript_id']==transcript_id]\n",
    "    for index, row in sub.iterrows():\n",
    "        metagene.create_feature(interval = row['start'], feature_name = 'intron_polyAsig',\n",
    "                               length = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove metagene that has no intronic polya\n",
    "for transcript_id in list(high_exp_meta.keys()).copy():\n",
    "    metagene = high_exp_meta[transcript_id]\n",
    "    \n",
    "    if 'intron_polyAsite' not in metagene.featnames or 'intron_polyAsig' not in metagene.featnames:\n",
    "        del high_exp_meta[transcript_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we get a total of 147 transcripts with annotation\n",
    "len(high_exp_meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## extract density from eCLIP\n",
    "The `Metadensity` Object takes in `eCLIP` and the `metagene` we just built with polyA sites, and fetch density around/within each features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_idr_metadensity(eCLIP, metagene = high_exp_meta):\n",
    "    ''' build metadensity object given eCLIP data and metagene that is build with intronic polyA sites'''\n",
    "    \n",
    "    m = Metadensity(eCLIP, eCLIP.name, metagenes = metagene,\n",
    "                   background_method = 'relative information', normalize = False)\n",
    "    m.featnames = ['intron_polyAsig','intron_polyAsite'] # to restrict only computing for these 2 features\n",
    "    m.get_density_array()\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do it for all eCLIP objects\n",
    "all_meta = [build_idr_metadensity(e) for e in all_clips]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize it\n",
    "f=plot_mean_density(all_meta,\n",
    "                    features_to_show = ['intron_polyAsig','intron_polyAsite'], \n",
    "                    ymax = 0.00001, stat = 'median',plot_std = False)\n",
    "f=beautify(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=plot_mean_density([m for m in all_meta if m.name=='PCF11' \n",
    "                     or m.name == 'U2AF2'],\n",
    "                    features_to_show = ['intron_polyAsig'], \n",
    "                    ymax = 0.000004, stat = 'median', plot_std = False,\n",
    "                   color_dict = {'PCF11':'mediumorchid', 'U2AF2':'gold'})\n",
    "f=beautify(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t4NR5TSUgwSkioUCa7/nGGNcHpyiKonQcSktLQzxCcNKkSfk33XRT5sMPP5xS3zUeU23LjNASSJL3G4F/ALsBTxZ5A4wKpANH6M4HdhhjTmniOBVFUZSWIkDNsLnx+Dh9940cObL4o48+6hSM8dRFID7Om4AhxpjhxpiRzhaQ0PS5flXThqcoiqJ0ZE499dT8srIyeeSRR7p49v3www8xn332WVywxhSI4NwGNClbkIj0BE7GliNTFEVRlEYREhLCJ598smHGjBkJvXr1GjFw4MDh//jHP7r37t27vK5rtm7dGpaWljZq6tSpaY8++mi3tLS0UdnZ2SEAp556ar8pU6YM3bRpU2RaWtqoRx99tEtd7dRFIGXFXgSGAJ8BpZ79xpj/NNi4yHvA/UA88Gd/ploRuQa4xvnYxRjTN4Bxa5kmpTVQZzYt3/v6ueeeG3/NNdfUdaqitBRaVqyR1FVWLJDgoK3OFuFsASEipwB7jDELROSIus4zxkwFpjrXzA+0fUVpzfje1+hET1HaFQ0KTmPMPwFEJN5+NHWunanFIcBpInISEAUkiMjrxpiLmzxaRVEURQkyDfo4RWSEiCwClgMrRGSBiDSYqsgY8zdjTE/H9HoB8J0KTUVRlFZLVVVVlRbzcHC+iyp/xwIJDpoK3GKM6WOM6QP8CS1krSiK0t5YnpGRkajC0wrNjIyMRKzCuA+B+DhjjTHfez4YY2aKSGxjBmGMmQnMbMw1iqIoSstRUVFxdXp6+gvp6ekj0HSsVcDyioqKq/0dDCSq9kNgIfCas+tiYIIx5oxmHKSnr/nGmAkBnKrBFkprINCZud6vSmugw2uSzUUgs4orgRTgA+BD5/0Vbg5KURRFUVorDWqcLYlqnEobQzVOpS2hGmczUaePU0QeM8bcLCKf4ueHb4w5zdWRKYqiKEorpL7gII9P8+GWGIiiKIqitAXqFJzGmAXO2zHGmMd9j4nITcAPbg5MURRFUVojgQQHXeZn3+XNPA5FURRFaRPU5+O8ELgI6Ccin/gcigey3B6YoiiKorRG6vNx/gzsAroAj/jszweWujkoRVEURWmt6HIURWk6uhxFaUvocpRmIpAk7weJyDwRKRCRMhGpFJG8lhicoiiKorQ2AgkOegq4EFgHRANXA0+6OShFURRFaa0EkuQdY8x6EQk1xlQCL4vIzy6PS1EURVFaJYEIziIRiQAWi8i/sQFDjaqOoiiKoijthUBMtZcAocANQCHQCzjbzUEpiqIoSmtFo2oVpeloVK3SltCo2mYikKjaU0RkkYhki0ieiORrVK2iKIrSUQnEx/kYcBawzLQm9VRRFEVRgkAgPs5twHIVmoqiKIoSmMZ5K/C5iPwAlHp2GmP+49qoFEVRFKWVEojg/BdQAEQBEe4OR1EURVFaN4EIzmRjzHGuj0RRFEVR2gCB+Di/FREVnIqiKIpCAOs4RSQfmymoFCjHrgUyxpiEBq6LAn4EIrGa7XvGmH80cI2u41TaErqOU2lL6DrOZqJeU62IhAAnGGN+akLbpcBRxpgCEQkHZovIF8aYOU0ZqKIoiqK0Buo11RpjqoCHm9KwsRQ4H8OdTWfeiqIoSpsmEB/n1yJytog0Ws0XkVARWQzsAb4xxvza2DYURVEUpTURiOC8BXgXKGtsyj1jTKUxZgzQEzhQREbUPkdErhGR+SIyH+jSiLErSqvF976eOnVqsIejKEoz0mJJ3kXkH0ChMaZO068GByltDA0OUtoSGhzUTARUyFpETgMOcz7ONMZMD+CaFKDcGJMjItHAMcCDTR6poiiKorQCGhScIvIAMBF4w9l1k4hMMcb8tYFLuwHTRCQUaxJ+JxCBqyiKoiitmUDWcS4FxjgRtjiCcJExZlSzD0ZNtUrbQk21SltCTbXNRCDBQQBJPu8TXRiHoiiKorQJAvFx3g8sEpHvsTOWw4C/uToqRVEURWml1GmqFZFDjDE/iUgkkIz1cwrwqzEm3ZXBqKlWaVuoqVZpS6iptpmoT3AuMMaMF5GFxphxLTIYFZxK20IFp9KWUMHZTNRnqi0XkZeBHiLyRO2Dxpg/uDcsRVEURWmd1Cc4T8GuvTwKWNAyw1EURVGU1k2dgtMYkwm8LSKrjDFLWnBMiqI0hDHw8kkw6RoYfmawR6MoHYpAomp3isjfgb6+5xtjrnRrUIqiNEDxXtj6s91UcCpKixKI4PwYmAV8C1S6OxxFUQKiYHewR6AoHZZABGeMMeY210eiKErg5O/yvs9cDyEhkNw/eONRlA5EIJmDpovISa6PRFGUwMn30TifGg9PjLV+T0VRXCcQwXkTVngWN7Yep6IoLlHgJwfJh7+DlR/D8g/g1dOhKLvlx6UoHYAGTbXGmPiWGIiiKI0g34/gXPq23Tys/xZGnddyY1KUDkKdglNEhhpjVouI36xBxpiF7g1LUZR6yU+HzoPg5Edgzyo46HewZzWs/QLmvgB52+GD38L8l+DcVyC+a7BHrCjthvpS7k01xlzjJHevjTHGHNXsg9GUe0rbomVT7m2eDRu+g3GXwntXQUQMXPap/3MfHuI15x76Jzj6rmYZgtKm0ZR7zUR9CRCucV6PbLnhKIpSJzMfgM2z7BrOzLUw6vy6zz3zv7DpR9jyM6z+HHYsgEHHweTft9x4FaWdEmg9TkVRgo1n7ebar6A0D1KG1H3ugKPgmLvta8Yq2DgT5k5tiVEqSrtHBaeitBUK9tjXvB32NWVow9eMvRjGXGzfR2kNekVpDgJJgKAoSrBY+BoUZcFB10NJDnQeCFnr7bFABGdiTzjjacDABn/hCoqiNJYGBaeICPAboL8x5h4R6Q10NcbMdX10itKRqSiFT26w70ee47yeBxtmQJ+DIbZL4G3FpUJhBlRV2SxDiqI0mUA0zmeAKmx5sXuAfOB9YKKL41IUZc0X3vfbnHlq15FwRBMyYMamQlW51VpjkptleIrSUQlk6jnJGPN7oATAGLMXiHB1VIqi2KUnHtZ9bV/jUpvWluc6j59UUZQmE4jgLBeRUJy1aCKSgtVAFUVxg5JcWPouLJxmo2JDI2DFR/ZYbErT2vQIzkIVnIqyvwRiqn0C+BBIFZF/AecAd7g6KkXpaJTk2tyyGavh7YvAOHPT/kdAdDIsf89+jktrWvuxqnEqSnMRSK7aN0RkAXA0NvPEGcaYVQ1dJyK9gFeBrlgNdaox5vH9HK+itE8++zMse8f7+ewXYcdCGH2R1To3zIATHoDwqKa1n9DNvnqWsiiK0mQCiap9HPifMebpRrZdAfzJGLNQROKBBSLyjTFmZVMGqijtlqpKWPO59/PgE2wUrSeSNi4F/rIBQkKb3kdUIkQmQs62/RuroigB+TgXAneIyHoReUhEAsklizFmlycRvDEmH1gF9Gj6UBWlnbJrMZQVwPgr7OcBftJA74/Q9JDUG3K27n87itLBaVBwGmOmGWNOAg4E1gIPisi6xnQiIn2BscCvfo5dIyLzRWQ+0IiFaYrSevG9r6dObSDV3bZ59vXw2+D3c2Hib90ZVFIvyFWNU1H2l8ZkDhoIDAX6AgGbW0UkDrvu82ZjzD4FsI0xU4GpzrnzGzEeRWm1+N7XNFQdZc9KGwAU39Xri3SDxF6waRYYA7nbIaGHJkNQlCbQ4K9GRDwa5j3ACmC8MebUQBoXkXCs0HzDGPPBfo1UUZqT3O1QnBPsUVj2rILUYSAuV31K6g1l+fDtP+CxETX9qoqiBEwg081NwGRjzAnGmJeMMTmBNOyk6nsRWGWM+c9+jFFRmpfcHfDocPjw2mCPxGp/e1ZB6gHu99XTCU/4yQluz97ofp+K0g6pU3CKiCeD9Fygt4iM890CaPsQ4BLgKBFZ7GwnNcOYFWX/mPWIfd30Y3DHAZC/y2qB9ZUIay56Tar5uSTH/T6VgMkrKeed+duoqmqeuueKe9Tn47wFuAZ4xM8xg81dWyfGmNloxXGlNbJzkX0tL4JFb8CYi9w3k9aFJyFBvIu+TQ8icM1MuyTls1ugMNP9PpWAmfrDRp76fj0xEaGcMqp7sIej1EOdgtMYc43z9kRjTInvMRFp4ipsRQkyVZXWNBqdDMXZ8PH1No3dgCMhNLzlx1OUZV8bU+lkf+g+1m7f3+ftW2kVhIbYyds3K3er4GzlBOLj/DnAfYrS+tm7GSqKYcTZ3n1vngsz7w/OeIqy7WtM55btN7aLf8G5a4n1uwZCSR7M+o8tf6bsNyXllQB8tSKdTZmFAV/3wcLtbMsucmtYih/q83F2FZHxQLSIjPXxbx4BxLTUABWl2TAGZj9q34+5EI68HSb9zn7eODM4YypyzKUtLThjOu9rqt02D547zOsDboiFr8KMf8KcZ5p/fK2dvF124tCM7C0qAyBEhOdnBRa4VV5ZxS3vLOGcZ1WXaUnq83EeD1wO9AR8o2Lzgb+7OCZFcYddi2HRazD+cug+DnqMt/vzd8HuIGWCLMoCCYGopJbtN6azV2h7yFxjX399Fg77c8NthDiPj1XTYcofm3d8rZnSfPjvwVBWCNf/Ap0HNK2dggwIi4SoBAByisoZ2jWeiirD3sKygJrIKy4HYHeeav0tSZ0ap5Mx6EjgcmPMkT7babomU2mTeLTKI2+vGQyU6GTUCdRE2ZwUZVl/a0snIojtAsV7IWuDd1+mkxCsMAP2bmm4jeK99nXHfEhf3vxjbK0sfNX6xytLa9ZMrYvFb8Ky97yfqyrh23/Co8NskJZDTlE5idHhxEWGUVBaEdBQ8koCO09pXgJJufe+iJwsIreKyF2erSUGp9TB8vdtBphdS2HVp6zfk88Nby6s9pF0eIyB2Y/Bl3+HtV/Byk+sL3H15zbRQO1i0Em9oaLECoyWpiir5c204C1P9uJx3glD5jqvFukpnF0XeTshyxG00Z3gx4dg66/B09xbioIM+OHf0O8wCI/xvxa2rAh2LvZ+/ug6eP8q7+e1X8Ls/0BYNGz3JkvLKS6jU0wEcZFhFAYqOB2NU2lZAqmO8izWp3kk8AK2Hudcl8el1Md7V9b4eHePr5i9IYvzJ/bi0EFNLHTcXijOgXXf2Ow4AHOcoj6eKNqTHt73msRe9nXzrJpBQ26z+SdY+TH0Prjl+vQw8lxY+g5sm2Mf/p0HQOZaGHKijTr+9m677rPbqH2v3TYXXjzWvk8eAD3GwbJ3YeVHEBEPt20KToSy21RVwbuX2WVMJz5khaGv4Fz2HmyfZye2hRlw+27/yfmz1tvX8ZfBz09CWSG7S0LZml3EuN6dMBj25Jfse50f8kpUcAaDQOxDBxtjLgX2GmP+CUwGerk7LKVOPOYxHzqH2CCFzVlF/PX9pSzauu857Y7dK6yW8/rZsO5b7/7P/gQfXG3f/2ERnPaU9WcWZ8Ohf4YD/SRQT+5nX9+70i7TaCmWv29fywOPoGw2ohLgdGdSsXGmTUGYtR66jYaT/2Ortcz5b81rykvg+aO8QhMgJhl6Huj9XJZfQ4tqV2RvgC0/wdF3QepQe99kbYAKxx85+zHrH/ZYLgr3+Dd5791itfRekwAD6cuZdN8MSsqrSIqJIC4ynMLSwKxHuapxBoVAkrwXO69FItIdyAL6uTckpV4y9y1M07NqF9CF+z5bRXF5JW/P28aqe04gOqIZSlG1Nn74N8ydWtOsGpUIg46x73f4PLST+9ttzG9sIvW04f7bTBkK574CS9+FHx60k5P8XVBZDsPPhNEXuPO3nPSwNRt3DyQRlwt0HgDx3a0wKMkFDIw4xwqEQcfZYCpfVk+HHQvs+16TYNuv9rv3pPJL7GVNuOu+gj6TW/IvaRlK8+1r54H2NakPrPoUXj8LLp9uJ2cpB0DGKnu8YI//JBM5W617wHM/vnQcQ+UBVpvelFdWERcZGriPs1h9nMEgEI1zuogkAQ9ha3NuBt52cUxKfWSuta8+5r2YAjurLfbxcWYXBRaV5491u/MprWil/tL131qhedQdcOsmGHqK159UUWqXCaSNgCu+9F4TEgJdR9SdHUjECsizpkKXwVYwpy+zWlh+unt/S0gIHPFXGHyce33Uhwj0mghbfoZfnoa+h3q1726jIWO19dd5WPwGJPaGOzNh7MV2X3mJPXfKH+GyT2wR7l+f8wrY9kS5812EO6vxBjn/t82zrFDN3wVDT7bZmcDeO1n7TnSrBWdyPzj5EapCwrk7fBoAg9PiiHWCg0wAwWq+ptryyqqm/mVKIwkkOOheY0yOMeZ9oA8w1Bhzp/tDayUseRv2rA72KLzsWQWhEXDZp3DDfJBQ4ou8xYkvm9wHgOIyP4Lv+/vhw+vs+zVfeE2FPuSVlHPSE7N4d/52V4a/3xRlWSF32F+smbD7GGtCW/MFbJ1jIx0P+0vTNJ7IOLjuZ/jzOvjDYrjuJ5hyczP/Aa2MnhPtA784G473MVN3GwOmymrqYNP0bfjern8NDbeaPFhNNSQUjrnb7jv5YWuGfPkkbzrB9kKZY1KPiLWv/Q+HCx0dYu1X9vtK7AFxXe2+gt3WpeAhe5OdoORssdoqkD3sUh7nQg4KWcWXv0nl3PG9iI0Mo7LKULZ7TYPBVr7BQWq2bTnqNNWKyFn1HKNDLEkpL/ZW0Dj9ae8sO1jk77Y/0F6TIDQMugzCpA7lxPSv+anrsRxx8GSSYyOY9ssW/xG2PzxgXw+9Bd7ymB8FRnj/1XvySimvNGzJCoLfLRAKMmyKPA89HDOh5+9J6AED6k2jXD+h4ftG3bZnPP7JA6+tGQjUbbR93bnIrjNd9SlgbF5f8AZURSfVbC+hO5z5HEw7xUZ9e0zo7YHaghO8ZvZVn9jXhJ5O+kSBn5+oOXn44d+w5E37voe97q25W3mz6CD+EPkaQ7a8jYycSHxUGJNDVhD57EUQHgt/3VJnsJWvsMwpKqdLXGRz/KVKA9SncZ5az3aK+0NrBfg69j++oabZqqUpzYdHBlvTzwGnVe8uPPV5Qqji/vIHuTBmHgM2vs4VoV9QUlaP7+Opid73zjq0gtIKHv92HXvybDRfemtbUL3gFRt0UpoLsT6Crf8RcO40G80poXDOS9ULypUA6HUgnPcqHPOPmvsTe9plMsvfh+ePtMsn0kZAp772eKc+cOoTcNbz+7aZOsy+Zqy2kag7F9lSbm2d2qZagPg06NTPRkeDnTiEhgPGpncsL/JOMrLWW03z77uqo7dnr8skNyyZrKEXIfNfgGXvERsRxpkhs50+C+tdK+q7jvOBL1bz/ep2puW3UupL8n5FSw6kVbJ3s32deDXMe8EGPnQZGJyx+M5ch59R/XZrSC/uK7+Bl0ufgPeuZCDwj3CYt/dK6FdrfWBkApTmYYNAzrYarOMz/c/Xa3npp02cNNKamXbnBRYO7zrlxfDTEzDzPq+m6ZsQXcR+H30OtusR49OCMsw2iwgMO93//rThNUuv9a5l/h5/mf82YztDTBcrOBe/AZ/cYINmfj+n+cYdDKo1zria+wcfb6NpwU44atPrQJtgI3ujNWdHWMFbUl7Jgi17ufyQvqSe8CRMXQbvX8XE/ufTO+wH8vocR8KeebDiQ9uHH/KKyxmUGsdB/TszfelOyiurOHJoB7KYBIkGfZwikiYiL4rIF87nYSJyVUPXtUqqqqCyEVFoHsHZxwnEyQvirNmTDPw379UwJW7MLGB21Ug2XvwrXDuLLUc+CUBI7taamXAqSq3QPPBaOPgPcMKDkDLYPtyMYUeOnU3vcTTNViE4Swusr2ym43vzRNL6M6XGparQbG48ZkiPht91RODXph4AGWtg/Tf2c9a64GRmak6qBWetVN1DTrSvw8/ymq6HnGyjb09/GkY75u2izBpuhr1FZZRVVtG/S6z1Ex9/H3TqS69NNstQRo9jrNthw3d1fnd5JeV0TYzi3jNGMPf2Y/j3OX7W3SrNTiBRta8AXwGeOjdrgZtdGo+7/O83cG8jsrTs3Wx9DN3G2M9BFZxOJYvo5Bq7N+wpRAT69OgO3UZhuo0FoPOWz+G+HrDdiW70CN6UIXDcvRCXYpdhlORCwe7q8Pd1ewoAKzgDiepzle/utWa+816Fw2/z7vf1cSrucfhtcPV38MflcMqjdllPoKQOs5HJnjSHVRVtv/5neREgEFarqmK/w+HiD6xv18OFb9rgvbEX1/QD+1hLPAF81cvG+h0KNy1h2cVLObX0/9jS81QrOAt277s0yCGvuJyEaOv/DA8NIS1BKz62BIEIzi7GmHeAKgBjTAXQStcqNMCaz+1rIALBGEhfan06Cc6cIZiCs9hTfqpTjd0bMgrokRRNVLj98YV26kWVEfpu/p/1j2x0/CP+6j56Amv+dzF/Tr+Vi0O/qQ42KCmvCu4aseIcmP8SjLvEmhK7jvQeU8HZMkTEQM/xNhH5hCsblw2o50Rbvq0k1+uTz93mzjhbirJCa6atvaxJBAYeDWER++4HiIz37vO5d4scwen57XqIiU9kmelPfpmBQcfbtbLvXu63fFtucQUJUe0wS1MrJxDBWSginQEDICIHAbmujqq52bManvHxzwRSwHfd13Zh+OgLIDzaBkoEM8ChjrqNGzMLGJDi9blERUUTIj4TA8+Y/ZWv6jkejvs/2D6PsRVL+L/wl3ky/AkicCouBJj2q9ko3muXlRRm2SjOyjJbyQSsfy2pT82JjNJ66Tne+96TrSm3lS5xCpSywn3NtIHg6xP1EZyeyPfoWoLTExmbkV9qLUOnPGqtX7uW7NN0Xkk5CdGB5LFRmpNAvvFbgE+AASLyE5CCzVfbdvj49971aGDXrflqXv7YNteG4R90vf2c0CP4ptqQMBvg41BRWcW63QUcdJBXGEaF+8yFEnp482J6Jgu1E4pPuBK+vgOAxyvO4qawD9hLAneVX056bgmD0+JpEuXFEBpZd9UPY2zgyICj7cOkvAhePN6WtorpbGsddhns42frAjcvbdpYlJank5NIIbGXjcaF9iE4wwMTnC/N3kSVMVx9aP9aGqePqba8lqnWITE6nOjwUHbmOBPXPofY1+3zbaCRQ0l5JWUVVapxBoF6BaeIhAKHO9sQQIA1xpi2tdLWIzw85KfXNP35ozjb+hNDna8ouV/LlE4qK7ImnvBoP+PpVMNMtDGzkNKKKob38ArTqPBQnq84idOSNpHWb6wNLNi9EmbcY0+IqTVhiIhl22EP8eC3W5leNZlBsp0TwhdxV/nlTQ8Q+vU5+OJWm9Pz0D/5PydrvZ3QgP2eI2Jt1PJRd8CKj61Z6szn6s72o7RuRGwSiegkW2s0PNab9aqtUl60b0StDztziqmoNPTuHMM90+1EvbbgLI7ohJRXEhUe6vVx1tI4RYTuSVHsynWyncZ3hfhu8NXfbKBi9zGAN2tQYrQKzpamXlOtMaYSON0YU2GMWWGMWd7mhGZ5CZTkwMTfwihnkXzezoavK8qymWk8pAyFvZv8+hmajfzdtkDuMwftmxy6KHsfbXHlTpvcfVi3xOp94aEhPFh1CdNGvAJdBkFBOvx3sjX19Bhf829yWJV2GtOrJjOsWwLzqoaQWpVBN7KaLjgXvW5fl9STmXHzbO/7iDj7UDrlUZv157rZ8Net1YvElTZKcj/vZG/IibD0fzaBRbBY+g480Ns+E5pCA6ba2z9cxln//XnfyiYicNMSGHMxE17ey7GP/gB4Nc7aPk6A7knR7Mz1aceT3m/eC9W7PFmDElRwtjiB+Dh/EpGnRORQERnn2VwfWWMxxj6ol75bc3/BbvvabRScZpdqkJ9u/QXrvqm7vdqCKmWITalVW3ttTn5+wgrnvZth6uE2vV6pU6Vi1Sf2IeTDyl15RISF0D8ltsb+qPBQSsqrYMIVVhid/gz8cQX89ju/ZY48AvKJC8dy+YUXAnBk9DrSmyo4c7YAYjWMT2/e9/iaL2D6zTbo4a698MdlcOvGmusC/ZVjUtouR/zN1jyd/Z/gjeH7+2ywkr+EAoWZfisP1aCssGbWoFpsySois6CUP73j9UWWVTj5Yzv1xZz+FIVEsy3bapIeH2eMn2IM3RKj2JVTTH5JORWVVXDq45A2snrSn1dSztXTbEGDhCj1cbY0AZUVA4YD9wCPOJufooZBRsRqOl/fYf1rHjyCM66rjXqLTbHVG547DN44p+4I26Lsmks/Uoba1wyX8tbuXGyzj/Q7DG5cCCHhNpLug2vgy7/aczoPqHHJnrwS0hIiCQ+t+W+MCg+xs9noTtaHOfY3/hdmO6TnlRAaIvTrEkufAyZBUh8uD/mc3blNEJzFe+3D6fBbrZ9rwcv75iz96Qn7OvrCun2gSvuiy0C7nnHeizZiOhh4sh4ten3f3/0jQ+Df/eu/vryo2sf584ZMNmV601IaY9iRU0xoiDBrnXfZTWaB10KVVViz8EJRHaZagG6J0ezJL2Xk3V9z+4fL7fOtU59qwfnhwh1szrJrr1XjbHkCSfJ+pJ9tP5KBusgRf7WmySVvefd5BKdncfwRf6tZuaGugIXi7Jpmzc4DbbCLbyaV5iJ3h9Uwc7fZChWdB9iF0xmrYc1ncNy/4LbNcMrjNS4rKqskNmLf2WZUeCil/nLV1kF6bimp8ZGEhoj16R7xNwZXrmfy7rcavrg2HhNz15FwmiMg05d5jxfn2HJUU26BEx9sfPtK22X0+TYJ/7a5wenfU+lmzWfwyinwwjHw+V9sPc2qCmtRytla9/VlBdUa50XP/8qRD89kvbPuOauwjNKKKq6e0o9DB3UhIsw+WvfkewXnZh9BC3UHBwEMSvP6Umesdp5hPgGKvmXHNDio5XFtui8iL4nIHhFpgYgah75TrIBb/Zl3n+fH4qlYMPEqWwFjsJPtI91PpKYx+/o4wyLtD3/J29bk8+tUeOcyWDW96eOd9wI8OR4eHebdN/Rk+zr4OJh0nc3yM/n3VnsMrSkki8oq/Zp5osJDKWlEWbDdeSU1F06PvoA1cQdyRtG7dV9UFzmO4Ezq442m9FSIqKqCmQ+AqYQhJzW+baVt02OCjQzf+ou7/WRt2FejNMYKxUnXwbH32sh6sCXkvviL97z5L/tvs6zITnCTetco3zVzzR7eX7Cdx761gU/j+3Titasm8d7v7PK3PXkl7MwpZndeSQ0NFaDE0Tgjw/Z9DJ80ohuT+tnnz+ieSXZnQneb/askjw0ZBdXnanBQy+OmcfwV4CngVRf72JdBx8GcZ+Cnx+3DOW+HXVbiu/wkbTic86LNrLNriVdYlRbYoJW+h9g1hLWy9DDlFpsJ5QcfTWnVJ3DjAm+ZpUApL7Gz3fjuNminy2A44781o0hPfKDeJorKKojxo3FG+0TsBcKOnGKGdfNJjC5CbqeRDMyfR3FpOdGRjfhhZjiRk536WB9mfHercVaW2wom67+FA6+xdSCVjkVEjM3C5RsY1txkroOnJtiC3IUZNsfr0FPgnUttQpCk3jD5ejjkD/b8j663BQTACvVfnraJUiZe7V1/agzsXm4nfN3Hsten1u3yHbl8tNgbbNg9yUbDp8bbieie/FIOfsD6VH97aL/q84wxFJdXEh0eiviJHA8JEd6+5iDOePonCj0FGzzulvxdbMjwCmFdx9nyuKZxGmN+BLLdar9ORpxjfwDf3GV/QLMftQmmawebRMTa7CZL3rKmGoBZD8Nb53t9irXXPCb3gxsXwc3LYOR5tjJESLjNp9rYpSpZ66xp6Lh7bdDOmc82eulF3RpniA0OCoDKKsP2vUX07lwzWjA2sQuhYtia3ohqC1VV1gzWfZwVmmAnIWu+gG/vtkLzxH/bTemYDD4ets+19T3dwFPUfPl7sOkH+Orv8MQYb8q6xB41z5/yR+/7c16CISdYd4LH3fPamfDeld52u40h28dX+fXK3TWa69nJCs4ucRFEhIXww1pvFLGvgC2tqLKC08/v14OIkBwbQWGpMwn2JP7I3c7GjALOGNOdt357EJFhGkjX0gQkOEXkYBG5SEQu9WzNNQARuUZE5ovIfKCBrAQB0HO8rVB/8ftw1J028fKFdfjqDr/Vmm8WO8sn1n5tha5nOUVtwQnWXJrUG85+HkadB2f+15qDv76jcUEPnuLYqQcEfk0t6jPVrk7Pq+EHqYudOcWUVxr6JNcUnInJ9l+xMz09sMFUlNqyZzsX1ay2cditdqb/y1N2IfekazvE2kzf+3rq1KnBHk7rYeS59nVpPUuV9gdPopPD/wqH3GwD0AYcBdf8YJOoDzy25vldBnnf9znE5kUedrr9fVZV2QjcFR/YiV98d0joXi04h3VLqA7wefXKA3np8gkkxdi0e2GhIdx09CC+8RGsGfmlHDHEZg4qKK2guKzKb2CQL3FR4d7fsZNsvyw/g/ySCgalxTN5QCNybyvNRoM6voi8BgwAFuPNUWtoJhOsMWYqMNXpa35ztGlzRx5jt/oYeIwt5Pvjw7aSw54VdtF+/m4Ij7IV3htixNlW25z9H3j6QLvsI5CcnhmrbP3I5AENn1sHRWUVxETu+y8sq6hib1E5j32zljtO8fpPX5+zha9X7uaPxwxibG+7tGVrto3Mq61xdu5if+AZGQFonKum2wT6YAsgj73EeyxlsE10vfBVGH5mY/68No3vfY2TrlLBWm36HQ7zXoKDb6qZ39WY/Z9U7VllLUxH/m3fY07igH0YcpI1z3omyqkH2Mmeb/zDqPOs+VakWnAeMyyNlbvsWuqxvZOIrxWkc+Uh/XjoqzU19h0/vCsz12RQUFJBSXllzUxffoiLDPUKTmc5WnFuBhBPcmxE3RcqrhKIcXwCMMwEvVSGC4jYSNzXz7L19PpMgQlX7VvVviEOvtHOTHcttkL4sD/vKzzLi200b98pNjJ31iPQZci+iaEbQVFZJTF+ZqzXHzmQXzfNZdmOmimFX5i1kc1ZRQxIia0WnFuckPbetTTO6Djr383OCkBwLpxmX7sMht/O3HeJyVF3WpO2R9tQOjYH32iXgi142eYinv+SXYpVVQFXf9v0dstL7G+w90GNu+68V22heI/Q9hTiXve1c/w1GOYtHu8RnOdP7MUTM9YB7CM0wX+0rCcPbUFpRZ0xCr7ERYZR4ClWHZ0ECKV5mUB/OsWo4AwWgQjO5UBXYJfLYwkOA46C0AgbDHTR/yCy7pRadRKTDFd9Aw8PhB8esOnyptxc85z/XWJrE960xEaWRiZ6EzI0gaoqU6ep9vDBKZw8slv1bBhgb2FZ9bqvzAKvj2b73iLCQoRuibVS/Dk+ytzsekpBGWPN2uu+tkLxpIf8r8uMS4VTgrjwXWldDHTqTM64x66X/sEnCG7vFhtY1hR+fMhGy465uHHXhYbXjKBPG2ZTBH7/L/vZCcopKa8kLMSrcabGR/LsxePIKao7mdqbv53Em79uZfrSXQxIiSXOsRDllZRXBwfVR2xkGMXllVRWGUJDQiEqkYpCm3daNc7gEVBZMWCliHwlIp94toYuEpG3gF+AISKyvdUWvxaxdfN+91PThKaHsAi45CNbq2/By9Y/4iFnq7eg74JpturKYX+C3pOa3J1nuYk/Uy1Ar+QYtu8torLKGgoWb8+pPjZnYxYvzt4E2GK6STERdg2nL47WnZ+bZetybvnZrn1b/ZlNNl2YCT8/CZ/cYM8fdsY+mY0UxS8icOTtdl3kD7Uixx8f5a3h2Vi2zrGul0ENuGgaIjK+ZkR7Um8Aht75JVe8Mo/swjISo8MJDw3hhBHduODA3nU2dfCALjx10TgeO38Mb1x9EPFOlp+CkgqKy6uIqic4CKgWtL7m2qpCG3PZKUaXoQSLQDTOu5vSsDHmwqZcFxSaOsOtTY9xNjr23cttkvPj/s9WAPnsFitQPSnHQiMaVxTYD55IO38aJ1jTa3mlYXdeCd2TopmzIYuwEGHygM7MWpfJvdNXcs64nuQUlfv/AToaZ3h5HjkrZ9Dpg4vs4vXNs+zxngfa2X3fQ+Hk/9QMslCUhugxHhJ7Q65PwgHP55+egP5HNL7NnC3eSiL7y5jfwCc32vcxnatT581al0lsRBid4xqn7Z0x1kbzetLsfbJkJxszCjhiSGq913kEZ2FphV2vGZNMiFObt5NqnEGjQcFpjPmhJQbSbhh2hl1k/et/rV9z7Rd2/0kPWT/O+m9tQFFDZc0awLNOsy4fSa9ka3rdklXE/322ks+XpXPU0FR6doquTgm2JbuQnKJykvwJTqd82amhv5D03ps2m9FF/7ORx6s+sVoz2KTsKYP3629ROiAicP6r1jSb2AtWfQzH/BN++DfMvM8uV0nqFXh7FWU2C1hzTYJDQuGid2yAkAg7c4qqD83fspdxvZOa1KxH45y+dBe9k2O46ej6J5xxUV7BCUB0MiFOtrMkTXwQNAKJqj0IeBI4AIgAQoFCY0xCvRd2VESsmadwj3eZyyUfWp9O30Nt8IIna9F+4FkUXZfGOcSpo/n9mj18vswuKTlnfM/qFGEAm7OK2FtURs9Ofio+hIRiQiMYx3pyo/uSePUMiEqAg35nExh8fD2s/bJdZQAyxnD60z9RXFbJc5eMp6C0gqToiH0ijtsjpRWVRISG+F2M7xrdx9oNvIWvR51nBefy9/eNEwDHr/6azQ509D+8PvXcbYDx5qNtDgYfbze80edg888O7dq0OrWdYiK4bHIfBqXFc96EXtWp+erCo3HmewRnTDIRZStIjA4nLDQQT5viBoGYap8CLgDexUbYXgqoXa4hBp9gf/wA/Y6wr8n97NYMFJXVb6pNTYhiUGocL8zaCMC9Z4zgpJHdePWXzdXnbMksJLe4nJE9/M9cpdIGQbze6TrmvLGKQwd14ZrDBtiH1ZnP2mxAgSy9aSNs31vM0u02EvncZ38hq7CM8yf04sFzRrnS3wuzNrJoaw5P/ya4xYa+XL6L372+kCkDu/DqlQcSUtvf3ZIk97Op+Za/519wLnvXa0KNjLeBZ/NesBnAwKZ6dIFte4tqfB7StWl6Q0iI8M/TRwR8vq+pFoDoTkRX5Kp/M8gENGUxxqwHQo0xlcaYl4EjXB1Ve2DQsdB1FFz6iSsVQBoy1QIcMrALTmwQJ4/sVuM6sBpnnaZagEP/zMtx1/DQhl7MWpfJfZ+vrpGns70Izb2FZZRVVDF3k/UdXXlIPwzwx2MGc8cpTU9Q0RBbsor4aUM9UcsthCd92+z1mby3sI6iBy3JyHNtmsZv77aTMw8VpTbdZdpIOzH95Sn45Rmv0ATXfO1bs4uICAvhoP42+nZot6ZpnI3Fs8wl16m9SXQy0VWFJEerthlMAtE4i0QkAlgsIv/GLkupuyidYonuBL+b5UrTJeWVXPzir0DdGifYdWav/LwZ8Iaunzq6O2/8upXKKsPCrXspLq+sznayD0ffyYrsJZC5nfioMPJLKpi9LpMjh9Yf0NDWOOmJWWQWlDI4LZ6EqDDuOPkA7jp1WMMX7iexkWEUlVbWefzXjVlM6Ju8b8RzM1NQWkF4qJCWEMUPazM4b0IjfItuMPxMmypv9qN2feXJj1gf5oe/s/VwL3rHFj9/5SRbxu7QP9uqRwXpVgN1gaXbcunfJZbnL53ArHWZDEjZjwj8RtArORoRvC4W5+/rHZ5Xz1WK2wQybbnEOe8GoBDoBZzt5qCU+tmT5y1V5FlQ7Y8DuiXw1EVjecjH1Ng9KZofbz2S3x7ar7paQ50aJxDmPLRvOdYGAPmuDW0P5BSVsSu3hPJKw67cEu46dXiLmSpjI0Ipq6zyFjv2Yd7mbM6fOoenv3excLpDQUkFcZFhHNAtgTXp+a731yDxaZjr58ABp9n6nXk74bUzbOq7Y++xfsc+B9syfwC9J9tUmPXUnN0fcovKmbs5m6OGphIfFc5JjvWmJYiJCKNv51hW73L+L07AVO/QrBYbg7IvgdTj3AII0M0Y809jzC2O6VYJEp41XXeeMoyuiVH1nnvKqO6c60eDOHlU9+r3SdF1h7X//siBnD+hFxdM7E1qfOQ+NQXbOmt325n8c5eMZ8Edx3DOeHcevv6IdfxXRZ7qF9gAJfBOjjw+VzcpKK0gLiqMIWnxbMospLQRJencoLSiknH/3czH4ScABj69yUZxnzkVDrnJniRiswwNPcUKURf5aUMmlVWGow9Ic7WfuhjaNZ7V6c6E1fHhDq1YDS8cC3s3B2VMHZ0GBaeInIrNU/ul83lMIAkQFPfwRNQOTmu6uSglPrI6pN4T8u6PXskxPHjOKKIjQunbObY6RV97Yc1uO5Mf2SOxZSNK2Xdxe3puCf3+9jmfLd1Ffon1adXwKbtEQWkFcZHhDOkaT2WVYWNGcCdHa9ML2FtUzt/nRmIkxGamSh1u6+H60m0UXPCGLVfmIp57vqmRtPvL0K4JbMkusveJo1WfmP6srTIz93nviVWVNjGJ4jqBmGrvBg4EcgCMMYuBvm4NSGkYz4M2to6sQYHy0uUT+cNRA6sL5jZEn84xzN2c7Z39tgPWpucTHxlGtwY0dzeIibT+aU+E9BInu9OT361jt6NxtoT2Z021ofTrYkMXfJdeBIPlO62WXUg0uZ1G2p0jzgraeHblFpMQFbbfv7emMqFvJ4yxPu/ykEgyTALiqRtQXgy7lsJ/hsOD/Wx9UcV1AhGcFcYY9+1F+4kxhr99sIz7P18V7KG4jic0PW4/f8hJMRHcctwQohrIl+nBU6T3hMdm+fXLtUXW7M5ncNf4Ftc2wTvx8UyENmRYs3FmQRl78ksAr8l2/Z4CLn7hV/7xcSPrvgaA1TjD9l360Mx97PWpY1kfK3bmEhEaQmiIMK3/w3Dl1zYxfJDYlVuyby7nFmRC305EhYcwa10m+SUV7DA+yVP2rIIv/wZ52+GAU2HcJXU3pDQbgQjO5SJyERAqIoNE5EngZ5fH1WhEhNLySl6fs4W8krqTLrcHCptJ42wsR/lE0/7oU6C3rWKMYe3ufAanBccEF+ssJfJE1q51AnMyC0pZsGUvADtyiskrKeeyl+Yye30m037ZQlZBqf8Gm4j1cYZXa8CFZc2r5X6/eg8H3TeDsfd+w/1frKKhQktr0vMZ0yuJ4d0T+GVnpc3pHFZ3EJzbpOeW0C2p5S0SHiLDQpnYN5lfN2WTX1LOp5WTyY0fBAk9YMd82DIbDr8NznjaCk/FdQIRnDcCw4FS4C0gD7jZxTE1mSsO6UdhWSWf+FRad4vv1+whpyiwGXRzU+A8aOMaKEnU3IzulcS6f51IfFQY360JoNxYKycjv5ScovKg+a5iHUFVUFrBlqxCvlyRTv8Uay5d7QjR0ooq3vp1Kztyirn9JLum9PPlARYXDxCrcYb6CPLm1Tgf/noNXeIiOGlkV577YSNLnICnLVmFfLFs1z4Tgb1F5XSJj2Bc704s2ZZbnd81GBhjHI0zeIITbO7p3Xkl5JdU8GLlycw5YTpMvsFWdQLXIooV/wQSVVtkjLndGDPRGDPBeV/SEoNrLCN6JJAUE+76kon8knKueHkeV01rnrrbjcWrcQZmYm1OwkND6J0cQ3puy9wCL8zayMlPzOJ/87Y2fHIj8QQGBV3jLKvg/s9XU1JexR0nH8DIHjbB/pheSQD894cNRISFcOnBfeiRFM2vG5t3KYJnOUp0eCgizatx7sotZsXOPM6f2Jv7zxpFRFgIHzpJFv7w9mKue2Mhh/37e7b5+FXzistJiArnqKGpFJdXMntd8wa8bMkq5PhHf6zRZ12cP3UOmQWlpMQHV3CmxEeSXVhWXdIsPioM4n2ifGPb19rq1k4gUbUTROQDEVkoIks9W0sMrrGICANT4pi+ZCfvzN/mWj95TmFZjzmtpSksrSAyLCRouSpT4iPJyC8lr6ScjPzmNRv6siGjgP/7bBUrduZx58crml3D90RLeoJiWppYH5/ish25nDa6O0cNTeOe04fzu8MH8MDZI0mOjSCnqJzRPROJDAtlVM/EfZaonPzELC6Y+gsAmzILOenxWcwM0CJQUVlFcXklcZHhhIQIMeGhzebjLCyt4NrXFgBw7LBUEqPDOW5YGtN+2cKYe75mybYcBqXGUVhWWeNvyispJz4qjIP6dyY+KoyvVzavhv3l8nTW7M7np/X1C+SS8srqbFIHBMkq4SHVEdyetdcJUeEQ77OeNC4lGMPqsATy5H0DeAWb9OBUn61VMiAljrySCm59b6krQQ5gZ8TBxBPMESxS4qzgvPHNRUz817dkNrPPzcM6Z43lv88ZRVlFFR8u2tGs7XuCVYJVENhjMdi+t5gdOcWM6GHzn47t3Ym/njiUoV0TOHywfSB6yk+N6pnE1uyi6rFXVRlW7Mxjzkb7gP/zu0tYuSuPr1YEJmw82qVnSVJMZFiNdaX7w8w1GSzdnsu/zhzBwFQreM4eZ02KnuLP951lo2azCu09VFZRRUl5FQlR4USEhTCsW0K1sPDHLxuyePPXxlkjft5gNfYVO2tapkrKK6v9r//+cjVD7/wSgD8cNZATWzDpgT9S4q2Pd6MTQBYfFQZxqnEGi0AEZ4Yx5hNjzCZjzBbP5vrImkiXeO9DcPkOd4KBgy04C0srghYaD5CaEElmQSmz1tkAoed/3OhKP5uz7APzhBFd6d8lll82NK+JMruojPjIsAYrVLhFdHgoIWJLTAGM6J64zzkPnTOKebcfw/VHDAC85tsvHcG4KcsrVPbklbDMuee3ZRcHNIaC6ghtK8RjI0Kra73uDxWVVbw9bytJMeFcMNFb6PnQQV3o3yWWCw/sxStXTGRc706EiI0kBqrXryY4JbO6xEeSWVDGLxuy/GZRuvD5Ofz9w2UBB0yVV1Yxb7OdZKzY6X0+FJRWMPTOL3l8xjp25RbzzMwN1ccGpLZMer36SHUEpyevcHxUOMR39Z4QqxpnSxLI0/cfIvICMAMbIASAMeYD10a1H0zu34Wnv7c3/ZLtOUzq37nZ+8gv8c7ISysqiQxrWV9jQWllUAVnSlwkFVXeyEi31v1tziykc2wECVHhDOuewOJtOc3afk5ReVCLAYsIVcZGzvbvEssYPzUew0JDqrUNgEn9kpnYtxP3Tl9JlTE1LA+fLdtFWUUVIQLr9gSWOs8zCYyLtIIqtgGNc/veIlLjoxqcbPzfZ6uYtS6TCyb2qpFrNyw0hBl/OhygeglQp5iIasHncYMkRNu/KyUuksz8Ui58fg4Avz20f3Xfvskhpi/dxWUH991nHKvT8/h+dQYH9uvE+D7JLN2eQ1FZJT2Solm1Kx9jDCLC4q05ADz27Tpe/mlzjTa6JgTXvwlejXPt7nxEICEqDEJ97t3w4I+xIxHIVPsKYAxwAl4z7Skujmm/mDKoCyvvOZ4eSdGupSvzXe4SjNyehaUVxNaT3N1tagdK7M5r/kCh4rJKvl+zh76O/3FY9wS27y32VoloBrILy4IqOH15/7qD66104yEkRHj8grGM7Z3E7R8u59b3vOEGz/5gJ4ynje7O7rzSgL6rdOd/1zXRPphjI8KqtVCwUaV3f7KC+ZuzKS6rZMqD3/P3D5c12O7yHbmkxEfyz9OH73NMRGqsm+0cF0GWo3F6BHmCUxWkS1yEtxYlsDXbq2Gv9DG1frtqN2DN7x6B+t6C7Zz0+Cwe/HI1N765CGNMtdXirHE9KC6vJLe4nKoqw6+bvNaM2t9bQ2ktWwJPTuo9+aV0ionQWpxBJpBvf7QTTXuZMeYKZ7vS9ZHtBzERYfTsFF0jGXpz4muqve71hSx1Mr40lqoqw4xVu/lxbQZfLt8V8HWFZcE31XpIjo2oznLTnNz18XJ255Uy0KlCMayb9f/94+PlVFX5Xwe4JauQk58IPDBmb1FZ0Osanju+J9cdMaBRArx7UjQvXjaR8yb05IBuCfzvmoO478yR5BXboLHTx/QACCgadVeOR3DaBf4xkaHVmYzAmgZf+XkzV7wyr1rIfhXAcpjNWUUcOSQlIGtM59jIah9nfrXG6RGcNddvrt/jFZweH+Vxw9KYuymbDRkFjL33G255Zwkl5ZXc8dEyDuyXzK0nDGFnbgkrd+Uxd/NehnaNr46knrspm0Me/I4nv1tPj6Ro5t5+NABTBnqTDKS1Ao0zIiyEzs490tn3XgmLBgneJLqjEojgnCMi7tdYamaSYyOqf4xggw5mrNrNut37ryF6zEnvXzeZiqoq7vx4RSOvL2dbdhFvzt3KVdPmc+lLc/nd6wv5bGlgwjO/pKLe/LJu4xuFOrFvJ/bklzS4qN1DRWUVX69Ib/D8nzdkkRIfyV9OGOL0k8xB/ZP5aPFO3l3gjZh+/seN1ZrH9KW7WLEzj8tfnsepT85uMOo5u7CM5LpKqrUQD507mttOGNro66LCQ/n3OaP56PeHMKl/Zy6a1Juv/3gY/7t2MocNTqFXcjTTnJJy9ZGeW0yIQFq8V+P0Dar7xakXml9SwXOORttQ9Zj8knIyC0qrrQUNUUPjLKmtcdYUnBszC6rf78otJjREOGd8T0orqjjxcVvG79MlO5mxag8l5VVcPaU/547vhQh8t2oP27OLGJASVy0MP1u2i125JUzql8xNxwwiNT6KJf84jucvnWADcCDgzFpu48nc1TnO5579y3r4a6sNOWm3BCI4p2Brca5xlqIsa63LUXxJjo1gb5FXM/xyRTpXTZvPWc80LenRtJ8389+ZG3j2hw1k5JcSHR7K+D7J/GZSH5Zsywl4Wcau3GJG3f01hz/0PS/N3lTj2O/fXMhNby9qcJ1eRn4pKfWUE3ObLnGRTL9xCnefOoxJ/TpTXmlqfNf1MWP1Hq55bUG9ZvQdOTbK9PdHDKh+cMZGhvHWbw9iXO+k6sCN5Tty+dfnq7jhrYUA/Lwhkx5J0dxw5ECW7citNt/Vxd5WZKptDnolxzCmVxKhIcJlk/syd3N2jQA5f5OVnbklpMZHVZv+Yn00TmNMdRASwNvz7ISloapr1ct8OgcoOGMj2JhZyDn//bl68ugRWl18/Lsp8ZHMXJPBnrwSlu/IZfveYlLjI5kyyGqHZRVVnDGmO6Ehwu/ftPfExH7JpMRH0q9zLMt25LIjp5huiVHVwTbzNmUTGiK8fvWk6jqkidHhREeE8u0thzP9xikB/Q0tgScJQ2ff335kHEQGd6lMRyQQwXkCMAg4Dq9/s9UuR/HQOTaCvUVlVDpmPc9i5/zSiupFxI3hH5+s4MEvV/PAF6t5bc6W6uAFTxq6HwJMQecpSFtlYKNPmP3vjxxAanwkHy/eyfVvLKwzW0pRWQUFpRU1zKXBYESPRC4/pF+1/ydQP+cWJwp0Vz0JFBY6muKEvjWTz4sIp4/pwZasIv47cwOnPDkbgI0Zhdz09iJ+2ZDFCSO68ufjh9A7OYYde+uOLC0pr6SwrDJoS1Hc5twJvYiJCOXNuXapRkZ+KZPv/47/fLMWsP+H+z9fxfa9RTV8eDE+Ps73F+7gp/VZnDGme422G8rr64mG7hOg4PRErc7fspfPllnB6THVesySInDtYf2ZtzmbA++bwSlPzubDRTvolhhFTEQYb149iSOHpPDP00fw0DmjCA8VJvVLJtFpZ2i3eOZszKK0oopuSdHVv5+duSV0T4oi3I/PMC0hihE99o10DhYejbNLO71n2xIB1eP0t7XE4PaH5NgIjKF60bzvg705zLUeU9KwbglEh4fWCG2vj7oExgUTezP39mN48+pJZBWW8UodZjaP3zYtyJlMPKQ5D6Bb3llSI/F7SXklj3y9hvs/r5mbdLsjzDLqWT6wOj2PsBDxm9HHs67xwS9XExEaUh3x+PHinfTtHMu1h/UHoHtSFDtz6hacj327DgjeGk63SYwOZ3TPpOrgtRmrdpOeV8ITM9bxyNdrOOuZn3nux43M2ZhNd588rB6N0xjDvE3ZdImL4NHzxzDzz0dUn5NfUl6nnxmo/t57JgeWGP2iA3uz4I5jGO0stQkLkergt66JUYzumcjzl0zg6kP7M7pnUo1ruznC5OCBXXj5igNJjA7nrHE9WfHPE3jj6knV5w3tmlDtYumRZIWtJyK5b4ACPth4ImuDtXxK8eLqf0BETnBMvOtF5K9u9lUbjwlur4/gjHd+KIu35QTsk6uLdY7mGBIiDE6LY22Awni3Izh7OD/47/50OH89cSg9O9nPkwd05thhaTz45Woe/WYtV7w8t3rdmefvAIKucXoY3j2Ro4amsmpXHp8v8/pof9mQxZPfree5HzfWMGN7NP/Mekzb63YX0LdLrN8HRN8usZw+pjsT+nTi85um8MJlEwC4/6yRfH7ToaQ6grRHUgw76hCcxhhe/WUz/VNiOWF4V7/ntAd6JUdXf9/frtpNanwkfTrH8OR368nysbp4khOANYlXVhkyCkpZuyefASlxiAh9OntrXlrTfN1Wm505JcRFhlVPLhsiLDSEznGR9HP6GOJTrSY8NISPb5jCMcPsYv87TxnGkLR4rjykHwARdUSXRtTKrOWbj9hT6cTzG+qd7G49z+bC42stbSeVidoyrglOEQkFngZOBIYBF7ZkkFHnWPuj8AQdpOeVVs9o7/9iNTNW+Y+8NMawYEs2uT4+O9/Z9StXTEQETh7lzSQyOC2eNekFBEJ6XgmdYsL54uZDWXTnsfRPieN3hw+oflCICE9eOJaJfZN5fMY6vl+TUa19ZuSXVmusqa1E44wKD+WFSyfQPyWWt+Z6M7is8ZlIrPZZshOIxrluT0G9Rbofv2As7113MANT4xnRI5Fldx/HhQf2rhHE0SMpil25JSzdnrNPCbT0vBKKyiq54pB+7crHWZtenWLYk19KTlEZs9dncuKIrrx42QRuOXYwhw32Lpg/Z5w3QXj/LvZ7P/BfM1i0NafajCoiTPZZE51ej2k+PbekSUs4ejua39CuCXWeM75PJ77642EcMcSOvyrACfBBA7xj91Q68UTOTqzlEmitDHL+FwNbQUKGjo6bGueBwHpjzEZjTBnwNnC6i/3VwGOCyy4sY8GWvSzZlkPXxCjud1J8za8j4vLBL9dw9n9/qbFWzbOO7I6TD+CIIamsvvcEHj9/TPXxIV3jySwoDSh7ye68EtISokiICq/zoR0VHsqrVx7I1EvGMyg1jpU789idV8IhD37Hzf9bDHgzibQGQkKEiX2Sq3228zZn8+LsTdUao8dcaIzxCs46NM7V6XlsyiysoQU1RLwfzcZjwjvtqZ/487tLahzb4CxpGBCkHLUtRS9HkxpzzzeUlFdx9AFpDEyN5w9HD+LVKw/kkXNHc8ORA+nto02O7FnTp+erjb11zUF85yQvWOQkDPDHrrymVRMJrU6I0LCmesjALtx09CD+duIBAbWdEBXOkxeO5dBBXejiTKrvOX0Em+4/iTPG9mj0WIPBYYNT+OD6g7l4Up9gD6XDI/trsqyzYZFzgBOMMVc7ny8BJhljbqh13jXANc7HKGPMCFcGpCgtSK37eqoxZmowx6MoSvPh5mJAf6F3+0hp54GiDxWlXaH3taK0X9w01W4Hevl87gm4X2FaURRFUVzETcE5DxgkIv1EJAK4APjExf4URVEUxXVcM9UaYypE5AbgKyAUeMkY07jcdIqiKIrSynAtOEhRFEVR2iOagkJRFEVRGoEKTkVRFEVpBCo4FUVRFKURqOBUFEVRlEYQvGrILiEiy4HAalw1L12AzA7QZ0fs17WMVh3sfg1Wv+3uvlGCS7sTnECJMWZCS3cqIvNbut9g9NlR+3Wx+Q5zvwar33Z63yhBRE21iqIoitIIVHAqiqIoSiNoj4IzWIm1g9FvR/pb22u/7fFvam39dqS/VWkBNHOQoiiKojSC9qhxKoqiKIprqOBUFEVRlEagglNRFEVRGoEKTkVRFEVpBCo4FUVRFKURtNnMQSKSCPwNOANIcXbvAT4GHjDG5Gi/zdb3UOB0oAdggJ3AJ8aYVe2pz1r9pwH3Ad2NMSeKyDBgsjHmxbbQvk8/HfF+FeBAat47c43LSwiC1a/S8rRljfMdYC9whDGmszGmM3Cks+9d7bd5EJHbgLcBAeYC85z3b4nIX9tLn354BfgK6O58Xgvc3Iba99DR7tfjgHXA3cBJwMnAP4F1zrF21a8SJIwxbXID1jTlmPbb6H7XAuF+9kcA69pLn376mue8LvLZt7ittN8K7ptg9bsK6Otnfz9gVXvrV7fgbG1Z49wiIrc6Ji/Amr8cbWWb9ttsVOHVinzp5hxrL33WplBEOmNNbojIQUBuG2rfQ0e7X8OA7X727wDC22G/ShBosz5O4Hzgr8APPj/OdOAT4LwW6jcVa0IMRr8Au1ug35uBGSKyDu8DrzcwELihHfVZm1uw3+0AEfkJ66c7tw2176Gu38mntNz9moadILTE/foSME9E3sZ77/QCLgCa1X8cQL+9sd+Dm/0qQUBT7ikNIiIheIMeBDuznmeMqWxPfdbqPxKoBIY4/a8BQowxpW2h/Qb6fs0Yc4nLfUQAFwI7jDHfishvgIOBlcBUY0y5i30PA06j5r3ziTFmpVt9Ov0egDegrcX6VVqeNi04gxTtOQnrs8gTkWjsrHoc9oFwnzHGDXObp++h2L91jjGm0Gf/CcaYL13sN8wYU+G8jwOGAhuNMdlu9elnDKcZYz5pwf4WGmPGNbSvtbbv06a/7+wo4DsAY8xpzdmfT79vYC1a0VgTdCzwIXA09rlzmRv9tiZEpLMxJivY41CanzZrqnV8JRdioy/nOrt7YiMv3zbGPOBS1y8Bo533jwNFwIPYB8LLwFludCoifwB+jw1CeFFEbjLGfOwcvg9wRXCKyOXAIyKSBdwEPA1sAgaLyK3GmLdc6NPfd/iMiIQBGGM+aO4+ffruip2cRIvIWKzmAJAAxLT29v3QEzupewE7uRRgIvCIC335MtIYM8r5n+3ALrupFJHXgSVudSoiCdhlMD2Bz33vTxF5xhhzvUv9PgA8bIzJFJHx2MjhSkfzvtQY84Mb/SrBoc1qnCKyFhhe2+Tj3KgrjDGDXOp3lTHmAOd9DQ1BRBYbY8a41O8y7Dq/AhHpC7wHvGaMeVxEFhljxrrY75FAPPaBN9YYs8HxW31jjBnlQp8V2InAHryC5Rzs32yMMVc2d58+fV8GXA5MAOb7HMoHXtlfoe12+376C8FOeE4C/mKMWSwiG40x/ZuzHz/9LsdaYmKBrUAfY0y2iERhI4kPcKnf97HLQuYAVwLlwEXGmFI3NHqffpcZY0Y6778HbjXGzBORwcCbxpgJbvSrBIc2q3HijbzcUmu/25GXy0XkCmPMy8ASEZlgjJnv/EBc89sAocaYAgBjzGYROQJ4T0T64BUublBpjMkEMkWkwBizwRnDbrve2xUmAw9g128+a4wxInKEMeYKtzr0YIyZBkwTkbONMe+3tfb99FcFPCoi7zqvu2mZ3/2LwGogFLgdeFdENgIHYa1EbjHAGHO28/4jEbkd+E5EXDFJ+xDu49KINsbMAzDGrHX82Uo7oi1rnCcAT2Fnl/tEXrrl8xObEeVx4FAgEzur3uZsfzDGuGKGEpHvgFuMMYt99oVhTce/McaEutTvJ8AKrMY5DFgEfAAcAxxsjDnepX5DgBuxmWduA952W0ty+r3YGPO6iPwJZ6mIL8aY/7Tm9gPo/2TgEGPM393sx+mrO4AxZqeIJGHvma3GmLn1Xrh/fa7CWqKqfPZdBtwKxBlj+rjU743AqdgJ32FAEvZ3cjTQ3+1gLKVlabMapzHmS0fLa9HISyf453IRiQf646zfMsbsdqtPh0uBilpjqQAuFZHnXOz3YqxvNRcbCHU8cA9Wm7jcrU6dB9/jjqb0GH6EjEvEOq9xbbT9ejHGfAZ81kJ97fR5n4M1tbvNp9jgp299+p7maNpPutWpMeZJx61xHTAY+1wYDHwE3OtWv0pwaLMaZ2tEROI85tT23K+bvqLW1KfSvvBxsXSIfhX3aLOZg0RklIjMEZFtIjJVRDr5HHPNFNQAwVqv1dL9uulTbRV9isi/RSRBRMJFZIaIZIrIxW2lfcUv/+xg/Sou0WZNtcAz2ITKc4CrgdnOWr8NuJjiSkRuqesQLprfgtVvHTzfwv0Fo8/jjDG3isiZWBfAucD3wOttpP0OiYgsresQkFbHsTbbrxIc2rLgjPMJAHpYRBYAX4rIJbjrD7sPeIha/kYHNzX4YPW7D8aYZ1qyvyD16Zl8nQS85SylaEvtd1TSsH74vbX2C/BzO+xXCQJtWXCKiCR6MvUYY74XkbOB94FkF/tdCHxkjFngZ0BXt8N+OyqfishqoBi4XkRSgJI21H5HZTp2Ur249gERmdkO+1WCQJsNDhKRi7Bp3+b47OuKLT11pzHmty71OwTIctY2VvdrjEkXkTS3omuD1W9HxvGb5zkZb2KAzsaYZqvs4Xb7iqK4Q5sVnP4IVuRlR+u3IyHWfnokcBFwqjGmWf1VbrevKErz02ajausgWE6ijtZvu0dEJonI49jMVJ8As7DJ7dtE+4qiuEd7E5zBiPbsiP22W0TkX2LrgN4HLAPGAhnGmGnGmNqBH62ufUVR3KddmWoVZX8RkQxsbczHgOnGmJLmTIrudvuKorhPe9M4FWV/6Qr8C1sIeb2IvIYtAdZcEehut68oisuoxqkodSC2BNYp2LqvU4AZxpiL2kr7iqK4gwpORfGDiIT6FgsQWyD5TKcsWKtvX1EU91DBqSh+EJFN2GoeLxtjmj0XsNvtK4riHurjVBT/jALWAi84xQSucbTCttK+oiguoRqnojSAiBwGvIUtTvwecK8xZn1baV9RlOZFNU5F8YOIhIrIaSLyIfA48Ai2cPmnwOetvX1FUdxDQ+AVxT/rsGW+HjLG+Fa3eM/REFt7+4qiuISaahXFDyISZ4wpaKvtK4riHio4FcUHEXmSeuq5GmP+0JrbVxTFfdRUqyg1md/G21cUxWVU41SUehCReMC4ZVZ1u31FUZofjapVFD+IyAgRWQQsB1aKyAIRGd5W2lcUxT1UcCqKf6YCtxhj+hhjegN/onnLuLndvqIoLqGCU1H8E2uM+d7zwRgzE4htQ+0riuISGhykKP7ZKCJ3Aq85ny8GNrWh9hVFcQnVOBXFP1cCKcAHwIfO+yvaUPuKoriERtUqSj2ISCJQZYzJb4vtK4rS/KjGqSh+EJGJIrIMWAIsE5ElIjK+rbSvKIp7qMapKH4QkaXA740xs5zPU4BnjDGj2kL7iqK4h2qciuKffI9QAzDGzAaa05zqdvuKoriEapyK4gcReRSIwdbJNMD5wF7gfQBjzMLW3L6iKO6hglNR/CAi39dz2BhjjmrN7SuK4h4qOBWlCYjIZcaYaW21fUVRmo4KTkVpAiKy0Bgzrq22ryhK09HgIEVpGtLG21cUpYmo4FSUpuG2qUZNQYrSSlHBqShNQzVORemgqOBUlKbxUxtvX1GUJqLBQYriBxGJBM4G+uJTRcgYc09baF9RFPfQsmKK4p+PgVxgAVDaBttXFMUlVONUFD+IyHJjzIi22r6iKO6hPk5F8c/PIjKyDbevKIpLqMapKH4QkZXAQGAT1pQq2FR4zVUdxdX2FUVxDxWciuIHEenjb78xZktbaF9RFPdQwakodSAio4FDnY+zjDFL2lL7iqK4g/o4FcUPInIT8AaQ6myvi8iNbaV9RVHcQzVORfGDiCwFJhtjCp3PscAvzejjdLV9RVHcQzVORfGPAJU+nytp3jR4brevKIpLaAIERfHPS8CvIvKh8/kM4MU21L6iKC6hplpFqYWIhAAHASXAFKwm+KMxZlFbaF9RFHdRwakofhCRX4wxk9tq+4qiuIf6OBXFP1+LyNki4pbf0e32FUVxCdU4FcUPIpIPxAIVWJOqJ7NPQltoX1EU91DBqSiKoiiNQE21iuIHEZkRyL7W2r6iKO6hy1EUxQcRiQJigC4i0gnv2soEoHtrb19RFPdRwakoNbkWuBkrxBbgFWx5wNNtoH1FUVxGfZyK4gcRudEY82RbbV9RFPdQwakodSAiBwN98bHMGGNebSvtK4riDmqqVRQ/iMhrwABgMd6csgZoFsHmdvuKoriHapyK4gcRWQUMMy79QNxuX1EU99DlKIrin+VA1zbcvqIoLqGmWkXxTxdgpYjMBUo9O40xp7WR9hVFcQkVnIrin7vbePuKoriE+jgVRVEUpRGoxqkoPojIbGPMFCcJu++sslmSsLvdvqIo7qMap6IoiqI0Ao2qVRRFUZRGoIJTURRFURqBCk5FURRFaQQqOBVFURSlEfw/2JTvKO9ieA4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=plot_mean_density([m for m in all_meta if m.name=='PCF11' or m.name == 'U2AF2'],\n",
    "                    features_to_show = ['intron_polyAsite'], \n",
    "                    ymax = 0.00005, stat = 'mean', plot_std = False)\n",
    "f=beautify(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{6647112,\n",
       " 6649008,\n",
       " 6652407,\n",
       " 6653154,\n",
       " 6672178,\n",
       " 6674099,\n",
       " 6691311,\n",
       " 6692044,\n",
       " 6693824,\n",
       " 6700965}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_meta[0].metagene['ENST00000377577.10'].features['intron_polyAsite']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "settings.point_feature_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function metadensity.config.MetadensityConfig.__init__.<locals>.<lambda>()>,\n",
       "            {'five_prime_UTR': 100,\n",
       "             'three_prime_UTR': 150,\n",
       "             'intron': 500,\n",
       "             'exon': 100,\n",
       "             'first_exon': 100,\n",
       "             'last_exon': 100,\n",
       "             'CDS': 100,\n",
       "             'first_CDS': 100,\n",
       "             'last_CDS': 100,\n",
       "             '3p_duplex': 20,\n",
       "             '5p_duplex': 20,\n",
       "             'mature': 20,\n",
       "             'hairpin': 10,\n",
       "             'pri_mir': 500,\n",
       "             'start_codon': 3,\n",
       "             'stop_codon': 3,\n",
       "             'branchpoint': 50,\n",
       "             'branchpoint_pred': 50,\n",
       "             'full_CDS': 200,\n",
       "             'intron_polyAsite': 200,\n",
       "             'intron_polyAsig': 200})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "settings.feat_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['exon', 'CDS', 'start_codon', 'stop_codon', 'five_prime_UTR', 'three_prime_UTR', 'intron', 'branchpoint', 'branchpoint_pred', 'last_exon', 'first_exon', 'last_CDS', 'first_CDS', 'intron_polyAsite', 'intron_polyAsig'])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=all_meta[0].metagene['ENST00000377577.10']\n",
    "m.features.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(m.densities['282']['rep1']['intron_polyAsite'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "metadensity",
   "language": "python",
   "name": "metadensity"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}