command_line_interface.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "392d13f0",
   "metadata": {},
   "source": [
    "# Command line interface "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e43065",
   "metadata": {},
   "source": [
    "Welcome to the cooltools command line interface (CLI) notebook!\n",
    "\n",
    "Cooltools features a paired python API & CLI that enables user-facing functions to be run from the command line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be7bdc8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/test.mcool\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import bioframe\n",
    "import cooltools\n",
    "import cooler\n",
    "# We can use this function to display a file within the notebook\n",
    "from IPython.display import Image\n",
    "\n",
    "\n",
    "# download test data\n",
    "# this file is 145 Mb, and may take a few seconds to download\n",
    "cool_file = cooltools.download_data(\"HFF_MicroC\", cache=True, data_dir='./data/')    \n",
    "print(cool_file)\n",
    "\n",
    "# To use this variable in a bash call from jupyter just use $cool_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86a430db",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "mkdir -p data\n",
    "mkdir -p outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d3a46a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note for the correct bash environment to be recognized from jupyter with the ! magic,\n",
    "# jupyter notebook must be initialized from a conda environment with cooler and cooltools installed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c708d96",
   "metadata": {},
   "source": [
    "To see a list of CLI commands for cooltools, see the help:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0244c8dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usage: cooltools [OPTIONS] COMMAND [ARGS]...\r\n",
      "\r\n",
      "  Type -h or --help after any subcommand for more information.\r\n",
      "\r\n",
      "Options:\r\n",
      "  -v, --verbose  Verbose logging\r\n",
      "  -d, --debug    Post mortem debugging\r\n",
      "  -V, --version  Show the version and exit.\r\n",
      "  -h, --help     Show this message and exit.\r\n",
      "\r\n",
      "Commands:\r\n",
      "  dots            Call dots on a Hi-C heatmap that are not larger than...\r\n",
      "  eigs-cis        Perform eigen value decomposition on a cooler matrix to...\r\n",
      "  eigs-trans      Perform eigen value decomposition on a cooler matrix to...\r\n",
      "  expected-cis    Calculate expected Hi-C signal for cis regions of...\r\n",
      "  expected-trans  Calculate expected Hi-C signal for trans regions of...\r\n",
      "  genome          Utilities for binned genome assemblies.\r\n",
      "  insulation      Calculate the diamond insulation scores and call...\r\n",
      "  pileup          Perform retrieval of the snippets from .cool file.\r\n",
      "  random-sample   Pick a random sample of contacts from a Hi-C map, w/o...\r\n",
      "  saddle          Calculate saddle statistics and generate saddle plots...\r\n"
     ]
    }
   ],
   "source": [
    "!cooltools -h"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d34089b",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02ab62c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:py.warnings:/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooler/cli/show.py:30: RuntimeWarning: divide by zero encountered in log10\r\n",
      "  mat = np.log10(mat)\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!cooler show $cool_file::resolutions/1000000 'chr2' -o 'outputs/chr2.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "effa0f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('outputs/chr2.png', width=400, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22cc46cb",
   "metadata": {},
   "source": [
    "## Expected "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63eeda99",
   "metadata": {},
   "source": [
    "Tables of expected counts, either in cis or trans, are key inputs for many downstream analyses in cooltools.\n",
    "For more details, see the [contacts_vs_dist](https://github.com/open2c/open2c_examples/blob/master/contacts_vs_distance.ipynb) notebook.\n",
    "\n",
    "Typically, we specify a view to define regions under analysis. \n",
    "Here we quickly create a view that specifies chromosome arms using tables of chromosome sizes and centromere positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5d2c6b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a view of hg38 chromosome arms using chromosome sizes and definition of centromeres\n",
    "hg38_chromsizes = bioframe.fetch_chromsizes('hg38')\n",
    "hg38_cens = bioframe.fetch_centromeres('hg38')\n",
    "view_hg38 = bioframe.make_chromarms(hg38_chromsizes,  hg38_cens)\n",
    "\n",
    "# select only those chromosomes available in cooler\n",
    "clr = cooler.Cooler(f'{cool_file}::/resolutions/1000000')\n",
    "view_hg38 = view_hg38[view_hg38.chrom.isin(clr.chromnames)].reset_index(drop=True)\n",
    "view_hg38.to_csv(\"data/view_hg38.tsv\", index=False, header=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5a92a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "! cooltools expected-cis $cool_file::resolutions/100000 --nproc 6 -o 'outputs/test.expected.cis.100000.tsv' --view \"data/view_hg38.tsv\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23eaff2d",
   "metadata": {},
   "source": [
    "Note expected for the first two distances are not defined with default settings, due to masking of near-diagonals in the cooler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e4c4e8e",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region1</th>\n",
       "      <th>region2</th>\n",
       "      <th>dist</th>\n",
       "      <th>n_valid</th>\n",
       "      <th>count.sum</th>\n",
       "      <th>balanced.sum</th>\n",
       "      <th>count.avg</th>\n",
       "      <th>balanced.avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr2_p</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>0</td>\n",
       "      <td>878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr2_p</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>1</td>\n",
       "      <td>876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr2_p</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>2</td>\n",
       "      <td>874</td>\n",
       "      <td>2738583.0</td>\n",
       "      <td>65.287351</td>\n",
       "      <td>3133.390160</td>\n",
       "      <td>0.074699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr2_p</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>3</td>\n",
       "      <td>872</td>\n",
       "      <td>1739972.0</td>\n",
       "      <td>41.011675</td>\n",
       "      <td>1995.380734</td>\n",
       "      <td>0.047032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr2_p</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>4</td>\n",
       "      <td>870</td>\n",
       "      <td>1184707.0</td>\n",
       "      <td>28.473626</td>\n",
       "      <td>1361.732184</td>\n",
       "      <td>0.032728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region1 region2  dist  n_valid  count.sum  balanced.sum    count.avg  \\\n",
       "0  chr2_p  chr2_p     0      878        NaN           NaN          NaN   \n",
       "1  chr2_p  chr2_p     1      876        NaN           NaN          NaN   \n",
       "2  chr2_p  chr2_p     2      874  2738583.0     65.287351  3133.390160   \n",
       "3  chr2_p  chr2_p     3      872  1739972.0     41.011675  1995.380734   \n",
       "4  chr2_p  chr2_p     4      870  1184707.0     28.473626  1361.732184   \n",
       "\n",
       "   balanced.avg  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2      0.074699  \n",
       "3      0.047032  \n",
       "4      0.032728  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    pd.read_table(\"outputs/test.expected.cis.100000.tsv\")[0:5]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5236e39e",
   "metadata": {},
   "source": [
    "## Compartments & saddles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df1c1e5",
   "metadata": {},
   "source": [
    "Many contact maps display plaid patterns of interactions.\n",
    "For more detail see the [compartments](https://github.com/open2c/open2c_examples/blob/master/compartments_and_saddles.ipynb) notebook. \n",
    "\n",
    "Since the orientation of eigenvectors is determined up to a sign, we often use GC content to orient or \"phase\" eigenvectors. Before calculating comparments, this notebook generates a binned GC content profile for the relevant region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53bf1461",
   "metadata": {},
   "outputs": [],
   "source": [
    "## fasta sequence is required for calculating binned profile of GC conent\n",
    "if not os.path.isfile('./data/hg38.fa'):\n",
    "    ## note downloading a ~1Gb file can take a minute\n",
    "    subprocess.call('wget https://hgdownload.cse.ucsc.edu/goldenpath/hg38/bigZips/hg38.fa.gz', shell=True)\n",
    "    subprocess.call('gunzip ./data/hg38.fa.gz', shell=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "217a36fe",
   "metadata": {},
   "source": [
    "Bins can be fetched from the cooler, dropping any weights columns & keeping the header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "03beb076",
   "metadata": {},
   "outputs": [],
   "source": [
    "!cooler dump --header -t bins $cool_file::resolutions/100000 | cut -f1-3 > outputs/bins.100000.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "796b6c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!cooltools genome gc outputs/bins.100000.tsv data/hg38.fa > outputs/gc.100000.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a606470",
   "metadata": {},
   "outputs": [],
   "source": [
    "!cooltools eigs-cis -o outputs/test.eigs.100000 --view data/view_hg38.tsv --phasing-track outputs/gc.100000.tsv --n-eigs 1 $cool_file::resolutions/100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "24717aa9",
   "metadata": {},
   "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",
       "      <th>E1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr2</td>\n",
       "      <td>0</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.006754</td>\n",
       "      <td>-1.564658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr2</td>\n",
       "      <td>100000</td>\n",
       "      <td>200000</td>\n",
       "      <td>0.006767</td>\n",
       "      <td>-1.747567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr2</td>\n",
       "      <td>200000</td>\n",
       "      <td>300000</td>\n",
       "      <td>0.004638</td>\n",
       "      <td>-0.370827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr2</td>\n",
       "      <td>300000</td>\n",
       "      <td>400000</td>\n",
       "      <td>0.006034</td>\n",
       "      <td>-1.326894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr2</td>\n",
       "      <td>400000</td>\n",
       "      <td>500000</td>\n",
       "      <td>0.006153</td>\n",
       "      <td>-1.434981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom   start     end    weight        E1\n",
       "0  chr2       0  100000  0.006754 -1.564658\n",
       "1  chr2  100000  200000  0.006767 -1.747567\n",
       "2  chr2  200000  300000  0.004638 -0.370827\n",
       "3  chr2  300000  400000  0.006034 -1.326894\n",
       "4  chr2  400000  500000  0.006153 -1.434981"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    pd.read_table('outputs/test.eigs.100000.cis.vecs.tsv')[0:5]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf128e6",
   "metadata": {},
   "source": [
    "Pairwise class averaging is a typical way to reveal preferences in contact frequencies between\n",
    "regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ae3bc16",
   "metadata": {},
   "outputs": [],
   "source": [
    "!cooltools saddle --qrange 0.02 0.98 --fig png -o outputs/test.saddle.cis.100000 --view data/view_hg38.tsv $cool_file::resolutions/100000 outputs/test.eigs.100000.cis.vecs.tsv outputs/test.expected.cis.100000.tsv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89b68ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('outputs/test.saddle.cis.100000.png', width=400, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2218c560",
   "metadata": {},
   "source": [
    "## Insulation & boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d1105d",
   "metadata": {},
   "source": [
    "A common strategy to summarize the near-diagonal structure of a contact map\n",
    "is by computing insulation scores. See the [insulation](https://github.com/open2c/open2c_examples/blob/master/insulation_and_boundaries.ipynb) notebook for more details.\n",
    "\n",
    "The command below uses the Li method to threshold the insulation score for boundary calling, so the resulting table has both log2 insulation scores and whether a given region is a boundary. The boundary_strength_{window} column has a value for all local minima of the insulation profile, and the is_boundary_{window} column indicates whether this strength passed the threshold. Note that {window} indicates the chosen size of the sliding diamond, and the command below uses windows of size 100kb and 200kb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cac683e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "! cooltools insulation --threshold Li -o 'outputs/test.insulation.10000.tsv' --view \"data/view_hg38.tsv\" $cool_file::resolutions/10000 100000 200000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dfd43263",
   "metadata": {},
   "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>region</th>\n",
       "      <th>is_bad_bin</th>\n",
       "      <th>log2_insulation_score_100000</th>\n",
       "      <th>n_valid_pixels_100000</th>\n",
       "      <th>log2_insulation_score_200000</th>\n",
       "      <th>n_valid_pixels_200000</th>\n",
       "      <th>boundary_strength_100000</th>\n",
       "      <th>boundary_strength_200000</th>\n",
       "      <th>is_boundary_100000</th>\n",
       "      <th>is_boundary_200000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr2</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr2</td>\n",
       "      <td>10000</td>\n",
       "      <td>20000</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>False</td>\n",
       "      <td>0.692051</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.123245</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr2</td>\n",
       "      <td>20000</td>\n",
       "      <td>30000</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>False</td>\n",
       "      <td>0.760561</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.196643</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr2</td>\n",
       "      <td>30000</td>\n",
       "      <td>40000</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>False</td>\n",
       "      <td>0.766698</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.211748</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr2</td>\n",
       "      <td>40000</td>\n",
       "      <td>50000</td>\n",
       "      <td>chr2_p</td>\n",
       "      <td>False</td>\n",
       "      <td>0.674906</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.135037</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom  start    end  region  is_bad_bin  log2_insulation_score_100000  \\\n",
       "0  chr2      0  10000  chr2_p        True                           NaN   \n",
       "1  chr2  10000  20000  chr2_p       False                      0.692051   \n",
       "2  chr2  20000  30000  chr2_p       False                      0.760561   \n",
       "3  chr2  30000  40000  chr2_p       False                      0.766698   \n",
       "4  chr2  40000  50000  chr2_p       False                      0.674906   \n",
       "\n",
       "   n_valid_pixels_100000  log2_insulation_score_200000  n_valid_pixels_200000  \\\n",
       "0                    0.0                           NaN                    0.0   \n",
       "1                    8.0                      1.123245                   18.0   \n",
       "2                   17.0                      1.196643                   37.0   \n",
       "3                   27.0                      1.211748                   57.0   \n",
       "4                   37.0                      1.135037                   77.0   \n",
       "\n",
       "   boundary_strength_100000  boundary_strength_200000  is_boundary_100000  \\\n",
       "0                       NaN                       NaN               False   \n",
       "1                       NaN                       NaN               False   \n",
       "2                       NaN                       NaN               False   \n",
       "3                       NaN                       NaN               False   \n",
       "4                       NaN                       NaN               False   \n",
       "\n",
       "   is_boundary_200000  \n",
       "0               False  \n",
       "1               False  \n",
       "2               False  \n",
       "3               False  \n",
       "4               False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    pd.read_table('outputs/test.insulation.10000.tsv')[0:5]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c31cca1",
   "metadata": {},
   "source": [
    "## Dots & focal enrichment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7445dd23",
   "metadata": {},
   "source": [
    "Punctate pairwise peaks of enriched contact frequency are a prevalent feature of mammalian interphase contact maps.  See the [dots](https://github.com/open2c/open2c_examples/blob/master/dots.ipynb) notebook for more details.\n",
    "\n",
    "Since dots are evident at higher resolutions, we first calculate the 10kb expected, and we used multiple cores to speed up the calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3718fe1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "! cooltools expected-cis --nproc 6 -o 'outputs/test.expected.cis.10000.tsv' --view \"data/view_hg38.tsv\" $cool_file::resolutions/10000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "29527f75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:Using recommended donut-based kernels with w=5, p=2 for binsize=10000\n",
      "INFO:root: matrix 9314X9314 to be split into 256 tiles of 600X600.\n",
      "INFO:root: tiles are padded (width=5) to enable convolution near the edges\n",
      "INFO:root: matrix 14907X14907 to be split into 625 tiles of 600X600.\n",
      "INFO:root: tiles are padded (width=5) to enable convolution near the edges\n",
      "INFO:root: matrix 2472X2472 to be split into 25 tiles of 600X600.\n",
      "INFO:root: tiles are padded (width=5) to enable convolution near the edges\n",
      "INFO:root: matrix 5855X5855 to be split into 100 tiles of 600X600.\n",
      "INFO:root: tiles are padded (width=5) to enable convolution near the edges\n",
      "INFO:root:convolving 108 tiles to build histograms for lambda-bins\n",
      "INFO:root:creating a Pool of 6 workers to tackle 108 tiles\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "INFO:root:Done building histograms ...\n",
      "INFO:root:convolving 108 tiles to extract enriched pixels\n",
      "INFO:root:creating a Pool of 6 workers to tackle 108 tiles\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "/Users/geofffudenberg/anaconda3/envs/open2c/lib/python3.8/site-packages/cooltools/api/dotfinder.py:797: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  expected_indexed.loc[region_name, region_name][expected_value_col].to_numpy()\n",
      "INFO:root:Begin post-processing of 11284 filtered pixels\n",
      "INFO:root:preparing to extract needed q-values ...\n",
      "INFO:root:clustering enriched pixels in region: chr17_p\n",
      "INFO:root:detected 198 clusters of 3.69+/-3.15 size\n",
      "INFO:root:clustering enriched pixels in region: chr17_q\n",
      "INFO:root:detected 584 clusters of 3.87+/-3.44 size\n",
      "INFO:root:clustering enriched pixels in region: chr2_p\n",
      "INFO:root:detected 841 clusters of 3.82+/-3.56 size\n",
      "INFO:root:clustering enriched pixels in region: chr2_q\n",
      "INFO:root:detected 1364 clusters of 3.72+/-3.24 size\n",
      "INFO:root:Clustering is complete\n"
     ]
    }
   ],
   "source": [
    "! cooltools dots --nproc 6 -o 'outputs/test.dots.10000.tsv' --view \"data/view_hg38.tsv\" $cool_file::resolutions/10000 outputs/test.expected.cis.10000.tsv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d74d206d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
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       "      <td>7.071107e-246</td>\n",
       "      <td>2.515747e-246</td>\n",
       "      <td>chr17_p</td>\n",
       "      <td>9.920000e+06</td>\n",
       "      <td>1.061200e+07</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.009434</td>\n",
       "      <td>25284</td>\n",
       "      <td>25304</td>\n",
       "      <td>...</td>\n",
       "      <td>2.637572e-154</td>\n",
       "      <td>1.768044e-154</td>\n",
       "      <td>4.524004e-183</td>\n",
       "      <td>chr17_p</td>\n",
       "      <td>1.063667e+07</td>\n",
       "      <td>1.083889e+07</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>chr17_p</td>\n",
       "      <td>chr17_p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr17</td>\n",
       "      <td>12180000</td>\n",
       "      <td>12190000</td>\n",
       "      <td>0.010278</td>\n",
       "      <td>chr17</td>\n",
       "      <td>13000000</td>\n",
       "      <td>13010000</td>\n",
       "      <td>0.011248</td>\n",
       "      <td>25438</td>\n",
       "      <td>25520</td>\n",
       "      <td>...</td>\n",
       "      <td>7.841810e-80</td>\n",
       "      <td>1.045765e-79</td>\n",
       "      <td>7.967002e-80</td>\n",
       "      <td>chr17_p</td>\n",
       "      <td>1.218571e+07</td>\n",
       "      <td>1.299143e+07</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>chr17_p</td>\n",
       "      <td>chr17_p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom1    start1      end1   weight1 chrom2    start2      end2   weight2  \\\n",
       "0  chr17  12250000  12260000  0.006993  chr17  12740000  12750000  0.010509   \n",
       "1  chr17  10640000  10650000  0.009687  chr17  11980000  11990000  0.010589   \n",
       "2  chr17   9920000   9930000  0.009252  chr17  10610000  10620000  0.007656   \n",
       "3  chr17  10640000  10650000  0.009687  chr17  10840000  10850000  0.009434   \n",
       "4  chr17  12180000  12190000  0.010278  chr17  13000000  13010000  0.011248   \n",
       "\n",
       "   bin1_id  bin2_id  ...  la_exp.vertical.qval  la_exp.horizontal.qval  \\\n",
       "0    25445    25494  ...          2.210430e-22            2.032096e-22   \n",
       "1    25284    25418  ...          1.368050e-08            1.294897e-08   \n",
       "2    25212    25281  ...         3.310219e-171           7.071107e-246   \n",
       "3    25284    25304  ...         2.637572e-154           1.768044e-154   \n",
       "4    25438    25520  ...          7.841810e-80            1.045765e-79   \n",
       "\n",
       "   la_exp.lowleft.qval   region       cstart1       cstart2  c_label  c_size  \\\n",
       "0         1.506741e-33  chr17_p  1.220571e+07  1.273857e+07        0       7   \n",
       "1         8.847705e-09  chr17_p  1.063000e+07  1.195500e+07        1       4   \n",
       "2        2.515747e-246  chr17_p  9.920000e+06  1.061200e+07        2       4   \n",
       "3        4.524004e-183  chr17_p  1.063667e+07  1.083889e+07        3       9   \n",
       "4         7.967002e-80  chr17_p  1.218571e+07  1.299143e+07        4       7   \n",
       "\n",
       "   region1  region2  \n",
       "0  chr17_p  chr17_p  \n",
       "1  chr17_p  chr17_p  \n",
       "2  chr17_p  chr17_p  \n",
       "3  chr17_p  chr17_p  \n",
       "4  chr17_p  chr17_p  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    pd.read_table('outputs/test.dots.10000.tsv')[0:5]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62ec8e97",
   "metadata": {},
   "source": [
    "## Pileups & average features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e68b3b",
   "metadata": {},
   "source": [
    "A common method for quantifying contact maps is by creating pileup plots, which are averages over a set of \"snippets\", or 2D windows, from the genome-wide map. See the [pileups](https://github.com/open2c/open2c_examples/blob/master/pileup_CTCF.ipynb) notebook for more details.\n",
    "\n",
    "Below shows pileup for dots that were computed above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6197486f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO after https://github.com/open2c/cooltools/issues/395\n",
    "#!cooltools pileup --features-format BEDPE --nproc 6 -o 'outputs/test.pileup.10000.npz' --view \"data/view_hg38.tsv\" --expected outputs/test.expected.cis.10000.tsv $cool_file::resolutions/10000 <(tail -n+2 outputs/test.dots.10000.tsv) "
   ]
  }
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