{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiledb\n",
    "import numpy as np \n",
    "import pdb \n",
    "datapath=\"/mnt/lab_data2/projects/adpd/adpd_tiledb_pseudobulk/Cluster24.chr1\"\n",
    "data=tiledb.DenseArray(datapath,mode='r')\n",
    "fc_bigwig=data[:]['fc_bigwig']\n",
    "idr_peak=data[:]['idr_peak']\n",
    "#overlap_peak=data[:]['overlap_peak']\n",
    "#pval_bigwig=data[:]['pval_bigwig']\n",
    "#ambig_peak=data[:]['ambig_peak']\n",
    "#ambig_peak=data[:]['count_bigwig_plus_5p']\n",
    "#ambig_peak=data[:]['count_bigwig_minus_5p']\n",
    "#print(np.nanmax(overlap_peak))\n",
    "#print(np.nanmax(idr_peak))\n",
    "#print(np.nanmax(ambig_peak))\n",
    "#print(np.nanmax(pval_bigwig))\n",
    "#print(np.nanmax(fc_bigwig))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib \n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "idr_narrowPeak=pd.read_csv(\"Cluster24.idr.optimal.narrowPeak.chr1\",header=None,sep='\\t')\n",
    "idr_narrowPeak_uniq=pd.read_csv(\"Cluster24.idr.optimal.narrowPeak.chr1.uniq\",header=None,sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "summits=np.where(idr_peak==2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19162"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(summits[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19162"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected_summits=idr_narrowPeak[1]+idr_narrowPeak[9]\n",
    "len(expected_summits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 10058  10341  10442 629940 634016 778423 778638 778849 779181 816933]\n"
     ]
    }
   ],
   "source": [
    "print(summits[0][0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       10058\n",
       "1       10341\n",
       "2       10442\n",
       "3      629940\n",
       "4      634016\n",
       "5      778638\n",
       "6      778849\n",
       "7      778423\n",
       "8      779181\n",
       "9      817333\n",
       "10     817061\n",
       "11     816933\n",
       "12     826824\n",
       "13     827567\n",
       "14     827266\n",
       "15     827035\n",
       "16     827481\n",
       "17     869883\n",
       "18     870156\n",
       "19     869967\n",
       "20     869665\n",
       "21     904722\n",
       "22     904434\n",
       "23     904824\n",
       "24     905361\n",
       "25     906671\n",
       "26     906924\n",
       "27     921458\n",
       "28     921211\n",
       "29     923810\n",
       "30     923690\n",
       "31     959380\n",
       "32     959071\n",
       "33     959241\n",
       "34     959577\n",
       "35     959765\n",
       "36     960588\n",
       "37     984351\n",
       "38    1001956\n",
       "39    1002036\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected_summits[0:40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f36c2efb9b0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(idr_peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f363e03f5c0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fc_bigwig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4076881"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.where(idr_peak>=1)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "coverage=sum(idr_narrowPeak_uniq[2]-idr_narrowPeak_uniq[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4076881"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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