{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Cooler-Python-API\" data-toc-modified-id=\"Cooler-Python-API-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Cooler Python API</a></span><ul class=\"toc-item\"><li><span><a href=\"#Direct-access-with-h5py\" data-toc-modified-id=\"Direct-access-with-h5py-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Direct access with <code>h5py</code></a></span></li><li><span><a href=\"#The-Cooler-class\" data-toc-modified-id=\"The-Cooler-class-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>The <code>Cooler</code> class</a></span><ul class=\"toc-item\"><li><span><a href=\"#The-info-dictionary\" data-toc-modified-id=\"The-info-dictionary-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>The info dictionary</a></span></li><li><span><a href=\"#Table-Views\" data-toc-modified-id=\"Table-Views-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Table Views</a></span></li><li><span><a href=\"#Bin-annotation\" data-toc-modified-id=\"Bin-annotation-1.2.3\"><span class=\"toc-item-num\">1.2.3&nbsp;&nbsp;</span>Bin annotation</a></span></li><li><span><a href=\"#Enter-The-Matrix\" data-toc-modified-id=\"Enter-The-Matrix-1.2.4\"><span class=\"toc-item-num\">1.2.4&nbsp;&nbsp;</span>Enter The Matrix</a></span></li><li><span><a href=\"#Balancing-your-selection\" data-toc-modified-id=\"Balancing-your-selection-1.2.5\"><span class=\"toc-item-num\">1.2.5&nbsp;&nbsp;</span>Balancing your selection</a></span></li><li><span><a href=\"#Genomic-coordinate-range-selection\" data-toc-modified-id=\"Genomic-coordinate-range-selection-1.2.6\"><span class=\"toc-item-num\">1.2.6&nbsp;&nbsp;</span>Genomic coordinate range selection</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cooler Python API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-requisites:\n",
    "\n",
    "- Basic Python knowledge\n",
    "- Some experience with the NumPy array package (or MATLAB)\n",
    "\n",
    "This walkthrough also makes use of:\n",
    "\n",
    "- Jupyter (IPython) Notebook. That's what you're using right now!\n",
    "- Pandas, the dataframe package (similar to R data.frames)\n",
    "- h5py, the package to interact with HDF5 files from Python\n",
    "- matplotlib, the MATLAB-inspired plotting package for Python\n",
    "\n",
    "To navigate this notebook:\n",
    "\n",
    "- Click on a code cell and execute its code by pressing `shift+enter` or clicking the \"play\" button on the toolbar.\n",
    "- While the code cell is running the prompt on the left will look like `In [*]:` and will display the execution count when it is done.\n",
    "- Execution output will be displayed beneath each cell.\n",
    "- To restart the notebook, use the options in the `Kernel` dropdown menu.\n",
    "- You can also run the entire notebook in one go with the `Restart & Run All` option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.662662Z",
     "start_time": "2019-02-23T03:56:34.309505Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import the packages we will use\n",
    "import os.path as op\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas\n",
    "import h5py\n",
    "\n",
    "import cooler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.673065Z",
     "start_time": "2019-02-23T03:56:34.664345Z"
    }
   },
   "outputs": [],
   "source": [
    "# The following directive activates inline plotting\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.680090Z",
     "start_time": "2019-02-23T03:56:34.675373Z"
    }
   },
   "outputs": [],
   "source": [
    "filepath = 'data/Rao2014-GM12878-MboI-allreps-filtered.5kb.cool'\n",
    "\n",
    "# Download a high resoltion COOL file from the interwebs (this will take a few...)\n",
    "# The ! at the beginning of the line tells IPython to run the line in the shell.\n",
    "if not op.exists(filepath):\n",
    "    !wget ftp://cooler.csail.mit.edu/coolers/hg19/Rao2014-GM12878-MboI-allreps-filtered.5kb.cool -O {filepath}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Direct access with `h5py`\n",
    "\n",
    "The `h5py` library (HDF5 for Python) provides an excellent Pythonic interface between HDF5 and native [NumPy](http://www.numpy.org/) arrays and dtypes. It allows you to treat an HDF5 file like a dictionary with complete access to the file's contents as well as the ability to manipulate groups and read or write datasets and attributes. There is additionally a low-level API that wraps the `libhdf5` C functions directly. See the [h5py docs](http://docs.h5py.org/en/latest/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.687219Z",
     "start_time": "2019-02-23T03:56:34.681901Z"
    }
   },
   "outputs": [],
   "source": [
    "h5 = h5py.File(filepath, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.695072Z",
     "start_time": "2019-02-23T03:56:34.688559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 file \"Rao2014-GM12878-MboI-allreps-filtered.5kb.cool\" (mode r)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.702251Z",
     "start_time": "2019-02-23T03:56:34.696555Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KeysViewHDF5 ['bins', 'chroms', 'indexes', 'pixels']>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Files and Groups are `dict`-like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.709365Z",
     "start_time": "2019-02-23T03:56:34.703515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 group \"/pixels\" (3 members)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5['pixels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.716126Z",
     "start_time": "2019-02-23T03:56:34.710962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bin1_id', 'bin2_id', 'count']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(h5['pixels'].keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`h5py` dataset objects are **views** onto the data on disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.722562Z",
     "start_time": "2019-02-23T03:56:34.717668Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 dataset \"bin2_id\": shape (1543535265,), type \"<i8\">"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5['pixels']['bin2_id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing or indexing returns a numpy array in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.731267Z",
     "start_time": "2019-02-23T03:56:34.724225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  234,  1994,  3258,  4087,  6093, 37359, 49826, 49889, 58451,\n",
       "       60826])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5['pixels']['bin2_id'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.740658Z",
     "start_time": "2019-02-23T03:56:34.733167Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5['pixels']['count'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.749341Z",
     "start_time": "2019-02-23T03:56:34.746000Z"
    }
   },
   "outputs": [],
   "source": [
    "h5.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python `cooler` package is just a thin wrapper over `h5py`.\n",
    "\n",
    "- It lets you access the data tables as [Pandas](http://pandas.pydata.org/) [data frames and series](http://pandas.pydata.org/pandas-docs/stable/10min.html). \n",
    "- It also provides a _matrix abstraction_: letting you query the upper triangle pixel table as if it were a full rectangular [sparse matrix](http://www.scipy-lectures.org/advanced/scipy_sparse/storage_schemes.html) via [SciPy](http://www.scipy-lectures.org/index.html).\n",
    "\n",
    "See below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `Cooler` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accepts a file path or an open HDF5 file object.\n",
    "\n",
    "NOTE: Using a filepath allows the `Cooler` object to be serialized/pickled since the file is only opened when needed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.761922Z",
     "start_time": "2019-02-23T03:56:34.752093Z"
    }
   },
   "outputs": [],
   "source": [
    "c = cooler.Cooler(filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The info dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.769091Z",
     "start_time": "2019-02-23T03:56:34.763694Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bin-size': 5000,\n",
       " 'bin-type': 'fixed',\n",
       " 'creation-date': '2016-02-25T22:53:09.510744',\n",
       " 'format-url': 'https://github.com/mirnylab/cooler',\n",
       " 'format-version': 2,\n",
       " 'genome-assembly': 'hg19',\n",
       " 'id': None,\n",
       " 'library-version': '0.3.0',\n",
       " 'metadata': {'publication': '',\n",
       "  'QC': {'double-sided': {'total': 3390352656,\n",
       "    'valid': 3147639590,\n",
       "    'filtered-invalid': {'removed-self-circles': 1741768,\n",
       "     'removed-error-pair': 6074295,\n",
       "     'removed-dangling-ends': 234897003},\n",
       "    'filtered-valid': {'removed-duplicate': 110650005,\n",
       "     'removed-start-near-rsite': '',\n",
       "     'removed-outlier-fragment': 151337031,\n",
       "     'removed-large-small-pair': 657466}},\n",
       "   'pre-filtering': {'total': 5332721651,\n",
       "    'double-sided': 3390352656,\n",
       "    'unused': 0,\n",
       "    'single-sided': 1942368995},\n",
       "   'post-filtering': {'total': 2884995088,\n",
       "    'cis': 2085711027,\n",
       "    'trans': 799284061}},\n",
       "  'enzyme': 'MboI',\n",
       "  'cell-type': 'GM12878',\n",
       "  'species': 'Homo sapiens',\n",
       "  'sex': 'F'},\n",
       " 'nbins': 619150,\n",
       " 'nchroms': 25,\n",
       " 'nnz': 1543535265}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table Views\n",
    "Tables are accessed via methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.775355Z",
     "start_time": "2019-02-23T03:56:34.770789Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cooler.core.RangeSelector1D at 0x7f97f6f963c8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.chroms()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The return value is a selector or \"view\" on a table that accepts column and range queries (\"slices\").\n",
    "\n",
    "- Column selections return a new view.\n",
    "- Range selections return pandas [DataFrames or Series](http://pandas.pydata.org/pandas-docs/stable/dsintro.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.792082Z",
     "start_time": "2019-02-23T03:56:34.777146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>name</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr2</td>\n",
       "      <td>243199373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr3</td>\n",
       "      <td>198022430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr4</td>\n",
       "      <td>191154276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr5</td>\n",
       "      <td>180915260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name     length\n",
       "1  chr2  243199373\n",
       "2  chr3  198022430\n",
       "3  chr4  191154276\n",
       "4  chr5  180915260"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.chroms()[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.807695Z",
     "start_time": "2019-02-23T03:56:34.793759Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>name</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>249250621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr2</td>\n",
       "      <td>243199373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr3</td>\n",
       "      <td>198022430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr4</td>\n",
       "      <td>191154276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr5</td>\n",
       "      <td>180915260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chr6</td>\n",
       "      <td>171115067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chr7</td>\n",
       "      <td>159138663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chr8</td>\n",
       "      <td>146364022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chr9</td>\n",
       "      <td>141213431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>chr10</td>\n",
       "      <td>135534747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>chr11</td>\n",
       "      <td>135006516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>chr12</td>\n",
       "      <td>133851895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>chr13</td>\n",
       "      <td>115169878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>chr14</td>\n",
       "      <td>107349540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>chr15</td>\n",
       "      <td>102531392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>chr16</td>\n",
       "      <td>90354753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>chr17</td>\n",
       "      <td>81195210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>chr18</td>\n",
       "      <td>78077248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>chr19</td>\n",
       "      <td>59128983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>chr20</td>\n",
       "      <td>63025520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>chr21</td>\n",
       "      <td>48129895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>chr22</td>\n",
       "      <td>51304566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>chrX</td>\n",
       "      <td>155270560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>chrY</td>\n",
       "      <td>59373566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>chrM</td>\n",
       "      <td>16571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name     length\n",
       "0    chr1  249250621\n",
       "1    chr2  243199373\n",
       "2    chr3  198022430\n",
       "3    chr4  191154276\n",
       "4    chr5  180915260\n",
       "5    chr6  171115067\n",
       "6    chr7  159138663\n",
       "7    chr8  146364022\n",
       "8    chr9  141213431\n",
       "9   chr10  135534747\n",
       "10  chr11  135006516\n",
       "11  chr12  133851895\n",
       "12  chr13  115169878\n",
       "13  chr14  107349540\n",
       "14  chr15  102531392\n",
       "15  chr16   90354753\n",
       "16  chr17   81195210\n",
       "17  chr18   78077248\n",
       "18  chr19   59128983\n",
       "19  chr20   63025520\n",
       "20  chr21   48129895\n",
       "21  chr22   51304566\n",
       "22   chrX  155270560\n",
       "23   chrY   59373566\n",
       "24   chrM      16571"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the whole table\n",
    "c.chroms()[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.814008Z",
     "start_time": "2019-02-23T03:56:34.809408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['chr1',\n",
       " 'chr2',\n",
       " 'chr3',\n",
       " 'chr4',\n",
       " 'chr5',\n",
       " 'chr6',\n",
       " 'chr7',\n",
       " 'chr8',\n",
       " 'chr9',\n",
       " 'chr10',\n",
       " 'chr11',\n",
       " 'chr12',\n",
       " 'chr13',\n",
       " 'chr14',\n",
       " 'chr15',\n",
       " 'chr16',\n",
       " 'chr17',\n",
       " 'chr18',\n",
       " 'chr19',\n",
       " 'chr20',\n",
       " 'chr21',\n",
       " 'chr22',\n",
       " 'chrX',\n",
       " 'chrY',\n",
       " 'chrM']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# more convenient access to chromosomes\n",
    "c.chromnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the bin table, the **weight** column contains the _matrix balancing weights_ computed for each genomic bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "chr1     249250621\n",
       "chr2     243199373\n",
       "chr3     198022430\n",
       "chr4     191154276\n",
       "chr5     180915260\n",
       "chr6     171115067\n",
       "chr7     159138663\n",
       "chr8     146364022\n",
       "chr9     141213431\n",
       "chr10    135534747\n",
       "chr11    135006516\n",
       "chr12    133851895\n",
       "chr13    115169878\n",
       "chr14    107349540\n",
       "chr15    102531392\n",
       "chr16     90354753\n",
       "chr17     81195210\n",
       "chr18     78077248\n",
       "chr19     59128983\n",
       "chr20     63025520\n",
       "chr21     48129895\n",
       "chr22     51304566\n",
       "chrX     155270560\n",
       "chrY      59373566\n",
       "chrM         16571\n",
       "Name: length, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# more convenient access to chromosome lengths\n",
    "c.chromsizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.829949Z",
     "start_time": "2019-02-23T03:56:34.815907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>5000</td>\n",
       "      <td>10000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>15000</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>20000</td>\n",
       "      <td>25000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chr1</td>\n",
       "      <td>25000</td>\n",
       "      <td>30000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chr1</td>\n",
       "      <td>30000</td>\n",
       "      <td>35000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chr1</td>\n",
       "      <td>35000</td>\n",
       "      <td>40000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chr1</td>\n",
       "      <td>40000</td>\n",
       "      <td>45000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>chr1</td>\n",
       "      <td>45000</td>\n",
       "      <td>50000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom  start    end  weight\n",
       "0  chr1      0   5000     NaN\n",
       "1  chr1   5000  10000     NaN\n",
       "2  chr1  10000  15000     NaN\n",
       "3  chr1  15000  20000     NaN\n",
       "4  chr1  20000  25000     NaN\n",
       "5  chr1  25000  30000     NaN\n",
       "6  chr1  30000  35000     NaN\n",
       "7  chr1  35000  40000     NaN\n",
       "8  chr1  40000  45000     NaN\n",
       "9  chr1  45000  50000     NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.bins()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting a list of columns returns a new DataFrame view on that subset of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.836855Z",
     "start_time": "2019-02-23T03:56:34.831692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cooler.core.RangeSelector1D at 0x7f97f6f966a0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = c.bins()[['chrom', 'start', 'end']]\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.850134Z",
     "start_time": "2019-02-23T03:56:34.838644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>5000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>15000</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>20000</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chr1</td>\n",
       "      <td>25000</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chr1</td>\n",
       "      <td>30000</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chr1</td>\n",
       "      <td>35000</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chr1</td>\n",
       "      <td>40000</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>chr1</td>\n",
       "      <td>45000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom  start    end\n",
       "0  chr1      0   5000\n",
       "1  chr1   5000  10000\n",
       "2  chr1  10000  15000\n",
       "3  chr1  15000  20000\n",
       "4  chr1  20000  25000\n",
       "5  chr1  25000  30000\n",
       "6  chr1  30000  35000\n",
       "7  chr1  35000  40000\n",
       "8  chr1  40000  45000\n",
       "9  chr1  45000  50000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting a single column returns a Series view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.859728Z",
     "start_time": "2019-02-23T03:56:34.851559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cooler.core.RangeSelector1D at 0x7f97f6f52f60>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = c.bins()['weight']\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.872126Z",
     "start_time": "2019-02-23T03:56:34.862234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500    1.021570\n",
       "501    1.935517\n",
       "502    0.584862\n",
       "503    0.895666\n",
       "504    1.302236\n",
       "505    0.845886\n",
       "506    0.992397\n",
       "507    1.470796\n",
       "508    1.022486\n",
       "509    0.952231\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights[500:510]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pixel table contains the non-zero upper triangle entries of the contact map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.890254Z",
     "start_time": "2019-02-23T03:56:34.874003Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>bin1_id</th>\n",
       "      <th>bin2_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3258</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4087</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>6093</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>37359</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>49826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>49889</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>58451</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>60826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bin1_id  bin2_id  count\n",
       "0        2      234      1\n",
       "1        2     1994      1\n",
       "2        2     3258      1\n",
       "3        2     4087      1\n",
       "4        2     6093      1\n",
       "5        2    37359      1\n",
       "6        2    49826      1\n",
       "7        2    49889      1\n",
       "8        2    58451      1\n",
       "9        2    60826      1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.pixels()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `join=True` option if you would like to expand the bin IDs into genomic bin coordinates by joining the output with the bin table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:34.983532Z",
     "start_time": "2019-02-23T03:56:34.892880Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>chrom1</th>\n",
       "      <th>start1</th>\n",
       "      <th>end1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>start2</th>\n",
       "      <th>end2</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr1</td>\n",
       "      <td>1170000</td>\n",
       "      <td>1175000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr1</td>\n",
       "      <td>9970000</td>\n",
       "      <td>9975000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr1</td>\n",
       "      <td>16290000</td>\n",
       "      <td>16295000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr1</td>\n",
       "      <td>20435000</td>\n",
       "      <td>20440000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr1</td>\n",
       "      <td>30465000</td>\n",
       "      <td>30470000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr1</td>\n",
       "      <td>186795000</td>\n",
       "      <td>186800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr1</td>\n",
       "      <td>249130000</td>\n",
       "      <td>249135000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr2</td>\n",
       "      <td>190000</td>\n",
       "      <td>195000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr2</td>\n",
       "      <td>43000000</td>\n",
       "      <td>43005000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>chr2</td>\n",
       "      <td>54875000</td>\n",
       "      <td>54880000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom1  start1   end1 chrom2     start2       end2  count\n",
       "0   chr1   10000  15000   chr1    1170000    1175000      1\n",
       "1   chr1   10000  15000   chr1    9970000    9975000      1\n",
       "2   chr1   10000  15000   chr1   16290000   16295000      1\n",
       "3   chr1   10000  15000   chr1   20435000   20440000      1\n",
       "4   chr1   10000  15000   chr1   30465000   30470000      1\n",
       "5   chr1   10000  15000   chr1  186795000  186800000      1\n",
       "6   chr1   10000  15000   chr1  249130000  249135000      1\n",
       "7   chr1   10000  15000   chr2     190000     195000      1\n",
       "8   chr1   10000  15000   chr2   43000000   43005000      1\n",
       "9   chr1   10000  15000   chr2   54875000   54880000      1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.pixels(join=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas lets you readily dump any table selection to tabular text file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.089810Z",
     "start_time": "2019-02-23T03:56:34.986174Z"
    }
   },
   "outputs": [],
   "source": [
    "df = c.pixels(join=True)[:100]\n",
    "\n",
    "# tab-delimited file, don't write the index column or header row\n",
    "df.to_csv('data/myselection.txt', sep='\\t', index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.211766Z",
     "start_time": "2019-02-23T03:56:35.092430Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chr1\t10000\t15000\tchr1\t1170000\t1175000\t1\n",
      "chr1\t10000\t15000\tchr1\t9970000\t9975000\t1\n",
      "chr1\t10000\t15000\tchr1\t16290000\t16295000\t1\n",
      "chr1\t10000\t15000\tchr1\t20435000\t20440000\t1\n",
      "chr1\t10000\t15000\tchr1\t30465000\t30470000\t1\n",
      "chr1\t10000\t15000\tchr1\t186795000\t186800000\t1\n",
      "chr1\t10000\t15000\tchr1\t249130000\t249135000\t1\n",
      "chr1\t10000\t15000\tchr2\t190000\t195000\t1\n",
      "chr1\t10000\t15000\tchr2\t43000000\t43005000\t1\n",
      "chr1\t10000\t15000\tchr2\t54875000\t54880000\t1\n"
     ]
    }
   ],
   "source": [
    "!head data/myselection.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bin annotation\n",
    "\n",
    "Another way to annotate the bins in a data frame of pixels is to use `cooler.annotate`. It does a [left outer join](http://chris.friedline.net/2015-12-15-rutgers/lessons/python2/04-merging-data.html) from the `bin1_id` and `bin2_id` columns onto a data frame indexed by bin ID that describes the bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.306251Z",
     "start_time": "2019-02-23T03:56:35.214181Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>bin1_id</th>\n",
       "      <th>bin2_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2</td>\n",
       "      <td>356649</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2</td>\n",
       "      <td>356700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2</td>\n",
       "      <td>363068</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2</td>\n",
       "      <td>363243</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2</td>\n",
       "      <td>363270</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2</td>\n",
       "      <td>363293</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2</td>\n",
       "      <td>363329</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2</td>\n",
       "      <td>363348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2</td>\n",
       "      <td>363373</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2</td>\n",
       "      <td>363374</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bin1_id  bin2_id  count\n",
       "100        2   356649      1\n",
       "101        2   356700      1\n",
       "102        2   363068      1\n",
       "103        2   363243      1\n",
       "104        2   363270      1\n",
       "105        2   363293      1\n",
       "106        2   363329      1\n",
       "107        2   363348      1\n",
       "108        2   363373      1\n",
       "109        2   363374      1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = c.bins()[:]  # fetch all the bins\n",
    "\n",
    "pix = c.pixels()[100:110]  # select some pixels with unannotated bins\n",
    "pix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.337435Z",
     "start_time": "2019-02-23T03:56:35.307986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>chrom1</th>\n",
       "      <th>start1</th>\n",
       "      <th>end1</th>\n",
       "      <th>weight1</th>\n",
       "      <th>chrom2</th>\n",
       "      <th>start2</th>\n",
       "      <th>end2</th>\n",
       "      <th>weight2</th>\n",
       "      <th>bin1_id</th>\n",
       "      <th>bin2_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr10</td>\n",
       "      <td>102850000</td>\n",
       "      <td>102855000</td>\n",
       "      <td>1.503567</td>\n",
       "      <td>2</td>\n",
       "      <td>356649</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr10</td>\n",
       "      <td>103105000</td>\n",
       "      <td>103110000</td>\n",
       "      <td>0.924921</td>\n",
       "      <td>2</td>\n",
       "      <td>356700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr10</td>\n",
       "      <td>134945000</td>\n",
       "      <td>134950000</td>\n",
       "      <td>1.480836</td>\n",
       "      <td>2</td>\n",
       "      <td>363068</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr11</td>\n",
       "      <td>285000</td>\n",
       "      <td>290000</td>\n",
       "      <td>1.455694</td>\n",
       "      <td>2</td>\n",
       "      <td>363243</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr11</td>\n",
       "      <td>420000</td>\n",
       "      <td>425000</td>\n",
       "      <td>1.107395</td>\n",
       "      <td>2</td>\n",
       "      <td>363270</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr11</td>\n",
       "      <td>535000</td>\n",
       "      <td>540000</td>\n",
       "      <td>0.819330</td>\n",
       "      <td>2</td>\n",
       "      <td>363293</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr11</td>\n",
       "      <td>715000</td>\n",
       "      <td>720000</td>\n",
       "      <td>0.911051</td>\n",
       "      <td>2</td>\n",
       "      <td>363329</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr11</td>\n",
       "      <td>810000</td>\n",
       "      <td>815000</td>\n",
       "      <td>1.202326</td>\n",
       "      <td>2</td>\n",
       "      <td>363348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr11</td>\n",
       "      <td>935000</td>\n",
       "      <td>940000</td>\n",
       "      <td>0.889250</td>\n",
       "      <td>2</td>\n",
       "      <td>363373</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>chr1</td>\n",
       "      <td>10000</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chr11</td>\n",
       "      <td>940000</td>\n",
       "      <td>945000</td>\n",
       "      <td>1.017561</td>\n",
       "      <td>2</td>\n",
       "      <td>363374</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    chrom1  start1   end1  weight1 chrom2     start2       end2   weight2  \\\n",
       "100   chr1   10000  15000      NaN  chr10  102850000  102855000  1.503567   \n",
       "101   chr1   10000  15000      NaN  chr10  103105000  103110000  0.924921   \n",
       "102   chr1   10000  15000      NaN  chr10  134945000  134950000  1.480836   \n",
       "103   chr1   10000  15000      NaN  chr11     285000     290000  1.455694   \n",
       "104   chr1   10000  15000      NaN  chr11     420000     425000  1.107395   \n",
       "105   chr1   10000  15000      NaN  chr11     535000     540000  0.819330   \n",
       "106   chr1   10000  15000      NaN  chr11     715000     720000  0.911051   \n",
       "107   chr1   10000  15000      NaN  chr11     810000     815000  1.202326   \n",
       "108   chr1   10000  15000      NaN  chr11     935000     940000  0.889250   \n",
       "109   chr1   10000  15000      NaN  chr11     940000     945000  1.017561   \n",
       "\n",
       "     bin1_id  bin2_id  count  \n",
       "100        2   356649      1  \n",
       "101        2   356700      1  \n",
       "102        2   363068      1  \n",
       "103        2   363243      1  \n",
       "104        2   363270      1  \n",
       "105        2   363293      1  \n",
       "106        2   363329      1  \n",
       "107        2   363348      1  \n",
       "108        2   363373      1  \n",
       "109        2   363374      1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cooler.annotate(pix, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.356048Z",
     "start_time": "2019-02-23T03:56:35.341148Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>weight1</th>\n",
       "      <th>weight2</th>\n",
       "      <th>bin1_id</th>\n",
       "      <th>bin2_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.503567</td>\n",
       "      <td>2</td>\n",
       "      <td>356649</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.924921</td>\n",
       "      <td>2</td>\n",
       "      <td>356700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.480836</td>\n",
       "      <td>2</td>\n",
       "      <td>363068</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.455694</td>\n",
       "      <td>2</td>\n",
       "      <td>363243</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.107395</td>\n",
       "      <td>2</td>\n",
       "      <td>363270</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.819330</td>\n",
       "      <td>2</td>\n",
       "      <td>363293</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.911051</td>\n",
       "      <td>2</td>\n",
       "      <td>363329</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.202326</td>\n",
       "      <td>2</td>\n",
       "      <td>363348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.889250</td>\n",
       "      <td>2</td>\n",
       "      <td>363373</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.017561</td>\n",
       "      <td>2</td>\n",
       "      <td>363374</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     weight1   weight2  bin1_id  bin2_id  count\n",
       "100      NaN  1.503567        2   356649      1\n",
       "101      NaN  0.924921        2   356700      1\n",
       "102      NaN  1.480836        2   363068      1\n",
       "103      NaN  1.455694        2   363243      1\n",
       "104      NaN  1.107395        2   363270      1\n",
       "105      NaN  0.819330        2   363293      1\n",
       "106      NaN  0.911051        2   363329      1\n",
       "107      NaN  1.202326        2   363348      1\n",
       "108      NaN  0.889250        2   363373      1\n",
       "109      NaN  1.017561        2   363374      1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cooler.annotate(pix, bins[['weight']], replace=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enter The Matrix\n",
    "\n",
    "Finally, the `matrix` method provides a 2D-sliceable view on the data. It allows you to query the data on file as a full rectangular contact matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.360784Z",
     "start_time": "2019-02-23T03:56:35.357698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cooler.core.RangeSelector2D at 0x7f97f6f56160>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of a query is a 2D NumPy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.427644Z",
     "start_time": "2019-02-23T03:56:35.362905Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[129, 230,  72, ...,   3,   3,   3],\n",
       "       [230, 217, 128, ...,   3,   6,   1],\n",
       "       [ 72, 128,  42, ...,   3,   1,   1],\n",
       "       ...,\n",
       "       [  3,   3,   3, ..., 199, 357, 165],\n",
       "       [  3,   6,   1, ..., 357, 225, 257],\n",
       "       [  3,   1,   1, ..., 165, 257, 136]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = c.matrix(balance=False)[1000:1200, 1000:1200]\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use **`sparse=True`** to return `scipy.sparse.coo_matrix` objects instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.498319Z",
     "start_time": "2019-02-23T03:56:35.429311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<200x200 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 39393 stored elements in COOrdinate format>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = c.matrix(balance=False, sparse=True)[1000:1200, 1000:1200]\n",
    "mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is straightforward to convert to a dense 2D numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.507150Z",
     "start_time": "2019-02-23T03:56:35.501080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[129, 230,  72, ...,   3,   3,   3],\n",
       "       [230, 217, 128, ...,   3,   6,   1],\n",
       "       [ 72, 128,  42, ...,   3,   1,   1],\n",
       "       ...,\n",
       "       [  3,   3,   3, ..., 199, 357, 165],\n",
       "       [  3,   6,   1, ..., 357, 225, 257],\n",
       "       [  3,   1,   1, ..., 165, 257, 136]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = mat.toarray()\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the lower triangle has been automatically filled in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.766818Z",
     "start_time": "2019-02-23T03:56:35.508932Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nezar/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log10\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f97f57539b0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "im = ax.matshow(np.log10(arr), cmap='YlOrRd')\n",
    "fig.colorbar(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the light and dark \"banded\" appearance? That's because you are looking at the unnormalized counts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balancing your selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We usually normalize or \"correct\" Hi-C using a technique called matrix balancing. This involves finding a set of weights or biases $b_i$ for each bin $i$ such that\n",
    "\n",
    "$$ Normalized[i,j] = Observed[i,j] \\cdot b[i] \\cdot b[j], $$\n",
    "\n",
    "such that the marginals (i.e., row/column sums) of the global contact matrix are flat and equal.\n",
    "\n",
    "Cooler can store the pre-computed balancing weights in the bin table.\n",
    "\n",
    "Here's one way to manually apply them to balance your selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.832046Z",
     "start_time": "2019-02-23T03:56:35.768525Z"
    }
   },
   "outputs": [],
   "source": [
    "# get the balancing weights as a numpy array\n",
    "weights = c.bins()['weight']  # view\n",
    "bias = weights[1000:1200]     # series\n",
    "bias = bias.values            # array\n",
    "\n",
    "# fetch a sparse matrix\n",
    "mat = c.matrix(balance=False, sparse=True)[1000:1200, 1000:1200]\n",
    "\n",
    "# apply the balancing weights\n",
    "mat.data = bias[mat.row] * bias[mat.col] * mat.data\n",
    "\n",
    "# convert to dense numpy array\n",
    "arr = mat.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a shortcut, we get the same result by passing **`balance=True`** to the matrix view constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:35.897836Z",
     "start_time": "2019-02-23T03:56:35.834778Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = c.matrix(balance=True, sparse=True)[1000:1200, 1000:1200].toarray()\n",
    "np.allclose(arr, arr2, equal_nan=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:36.201550Z",
     "start_time": "2019-02-23T03:56:35.900713Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nezar/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log10\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f97f4259898>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "im = ax.matshow(np.log10(arr), cmap='YlOrRd')\n",
    "fig.colorbar(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genomic coordinate range selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bin table, pixel table and matrix views also accept UCSC-style genomic range strings or (chrom, start, end) triples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:36.235989Z",
     "start_time": "2019-02-23T03:56:36.203370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51851</th>\n",
       "      <td>chr2</td>\n",
       "      <td>10000000</td>\n",
       "      <td>10005000</td>\n",
       "      <td>2.474065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51852</th>\n",
       "      <td>chr2</td>\n",
       "      <td>10005000</td>\n",
       "      <td>10010000</td>\n",
       "      <td>1.403127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51853</th>\n",
       "      <td>chr2</td>\n",
       "      <td>10010000</td>\n",
       "      <td>10015000</td>\n",
       "      <td>0.883045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51854</th>\n",
       "      <td>chr2</td>\n",
       "      <td>10015000</td>\n",
       "      <td>10020000</td>\n",
       "      <td>0.771122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51855</th>\n",
       "      <td>chr2</td>\n",
       "      <td>10020000</td>\n",
       "      <td>10025000</td>\n",
       "      <td>0.924510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53846</th>\n",
       "      <td>chr2</td>\n",
       "      <td>19975000</td>\n",
       "      <td>19980000</td>\n",
       "      <td>0.575194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53847</th>\n",
       "      <td>chr2</td>\n",
       "      <td>19980000</td>\n",
       "      <td>19985000</td>\n",
       "      <td>1.450563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53848</th>\n",
       "      <td>chr2</td>\n",
       "      <td>19985000</td>\n",
       "      <td>19990000</td>\n",
       "      <td>0.861964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53849</th>\n",
       "      <td>chr2</td>\n",
       "      <td>19990000</td>\n",
       "      <td>19995000</td>\n",
       "      <td>0.786807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53850</th>\n",
       "      <td>chr2</td>\n",
       "      <td>19995000</td>\n",
       "      <td>20000000</td>\n",
       "      <td>0.692477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      chrom     start       end    weight\n",
       "51851  chr2  10000000  10005000  2.474065\n",
       "51852  chr2  10005000  10010000  1.403127\n",
       "51853  chr2  10010000  10015000  0.883045\n",
       "51854  chr2  10015000  10020000  0.771122\n",
       "51855  chr2  10020000  10025000  0.924510\n",
       "...     ...       ...       ...       ...\n",
       "53846  chr2  19975000  19980000  0.575194\n",
       "53847  chr2  19980000  19985000  1.450563\n",
       "53848  chr2  19985000  19990000  0.861964\n",
       "53849  chr2  19990000  19995000  0.786807\n",
       "53850  chr2  19995000  20000000  0.692477\n",
       "\n",
       "[2000 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.bins().fetch('chr2:10,000,000-20,000,000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:37.283093Z",
     "start_time": "2019-02-23T03:56:36.238065Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9626, 9626)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cis = c.matrix(sparse=True).fetch('chr21')\n",
    "cis.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T03:56:37.780037Z",
     "start_time": "2019-02-23T03:56:37.288576Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9626, 10261)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans = c.matrix(sparse=True).fetch('chr21', 'chr22')\n",
    "trans.shape"
   ]
  },
  {
   "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}