{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re \n",
    "def sorted_nicely( l ): \n",
    "    \"\"\" Sort the given iterable in the way that humans expect.\"\"\" \n",
    "    convert = lambda text: int(text) if text.isdigit() else text \n",
    "    alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] \n",
    "    return sorted(l, key = alphanum_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1294,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import matplotlib.ticker as plticker\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "\n",
    "def plots_with_1_level(group_x):\n",
    "\n",
    "    change_group_level_1 = df_2_or_uniq.groupby(['chrom_x'])\n",
    "    nrow = int(len(change_group_level_1.groups.keys())/3) + 1\n",
    "    ncol = 3\n",
    "    fig, axes = plt.subplots(nrows=nrow, ncols=ncol, figsize=(4*ncol, 4*nrow),sharex=True,sharey=True) \n",
    "\n",
    "    for key_level_1,ax in zip(sorted_nicely(change_group_level_1.groups.keys()),axes.flatten()):\n",
    "        given_group_level_1 = change_group_level_1.get_group(key_level_1)\n",
    "        #given_group2 = given_group_level_1.groupby(['order_diff', 'category']).mean().groupby(level=2)['exp'].plot(ax=ax)\n",
    "        given_group2 = given_group_level_1.groupby([group_x]).mean()['exp'].plot(ax=ax)\n",
    "        given_group2 = given_group_level_1.groupby([group_x]).mean()['exp (GK)'].plot(ax=ax)\n",
    "        #given_group2 = given_group_level_1.groupby([group_x]).mean()['SRP063477'].plot(ax=ax)\n",
    "        #given_group2 = given_group_level_1.groupby([group_x]).mean()['SRP026208'].plot(ax=ax)\n",
    "        #given_group2 = given_group_level_1.groupby([group_x]).mean()['SRP115956'].plot(ax=ax)\n",
    "\n",
    "        ax.axhline(y=0.5, color='r', linestyle='-')\n",
    "        #ax.set_xlim([0, 2000])\n",
    "        ax.legend()\n",
    "        rects = ax.patches  \n",
    "        ax.set_title('%s' %(key_level_1))\n",
    "        #ax.set_ylabel('%Population')\n",
    "        ax.set_xlabel('Distance between gene pairs (MB)')\n",
    "        ax.set_ylabel('Co-expression')\n",
    "        \n",
    "\n",
    "def plots_with_2_groups(group_2='None', group_3_x='category'):   \n",
    "    change_group_level_1 = df_2_or_uniq.groupby(['chrom_x'])\n",
    "    change_group_level_2 = df_2_or_uniq.groupby([group_2])\n",
    "    nrow = len(change_group_level_1.groups.keys())\n",
    "    ncol = len(change_group_level_2.groups.keys())\n",
    "    fig, axes = plt.subplots(nrows=nrow, ncols=ncol, figsize=(8*ncol, 8*nrow),sharey=True) \n",
    "\n",
    "    for key_level_1,ax_row in zip(sorted_nicely(change_group_level_1.groups.keys()) ,axes):\n",
    "        given_group_level_1 = change_group_level_1.get_group(key_level_1)\n",
    "        change_group = given_group_level_1.groupby([group_2])\n",
    "        for key,ax in zip(change_group.groups.keys(),ax_row):\n",
    "            given_group = change_group.get_group(key)\n",
    "            #given_group2 = given_group.groupby([group_3_x, 'category']).mean().groupby(level=1)['exp'].plot(ax=ax)\n",
    "            given_group2 = given_group.groupby([group_3_x]).mean()['exp'].plot(ax=ax)\n",
    "            given_group2 = given_group.groupby([group_3_x]).mean()['exp_georg'].plot(ax=ax)\n",
    "            ax.legend()\n",
    "            rects = ax.patches  \n",
    "            ax.set_title('%s, %s' %(key_level_1,key))\n",
    "            ax.axhline(y=0.5, color='r', linestyle='-')\n",
    "            ax.set_ylabel('%Population')\n",
    "            ax.set_ylim([0.3, 0.9])\n",
    "            \n",
    "            \n",
    "def plots_with_2_groups_same_plot(group_2='None', group_3_x='category'):   \n",
    "    change_group_level_1 = df_2_or_uniq.groupby(['chrom_x'])\n",
    "    change_group_level_2 = df_2_or_uniq.groupby([group_2])\n",
    "    nrow = int(len(change_group_level_1.groups.keys())/3) + 1\n",
    "    ncol = 3\n",
    "    fig, axes = plt.subplots(nrows=nrow, ncols=ncol, figsize=(4*ncol, 4*nrow),sharey=True, sharex=True) \n",
    "\n",
    "    for key_level_1,ax in zip(sorted_nicely(change_group_level_1.groups.keys()) ,axes.flatten()):\n",
    "        given_group_level_1 = change_group_level_1.get_group(key_level_1)\n",
    "        change_group = given_group_level_1.groupby([group_2])\n",
    "        for key in change_group.groups.keys():\n",
    "            given_group = change_group.get_group(key)\n",
    "            #given_group2 = given_group.groupby([group_3_x, 'category']).mean().groupby(level=1)['exp'].plot(ax=ax)\n",
    "            given_group2 = given_group.groupby([group_3_x]).mean()['exp'].plot(ax=ax)\n",
    "            #given_group2 = given_group.groupby([group_3_x]).mean()['exp_georg'].plot(ax=ax)\n",
    "            #ax.legend()\n",
    "            rects = ax.patches  \n",
    "            ax.set_title('%s, %s' %(key_level_1,key))\n",
    "            ax.axhline(y=0.5, color='r', linestyle='-')\n",
    "    ax.set_ylabel('Co-expression')\n",
    "    ax.set_xlabel('Distance between gene pairs (MB)')\n",
    "    #ax.set_ylim([0.3, 0.9])\n",
    "            \n",
    "\n",
    "def plots_with_1_level_3d(df, group_x):\n",
    "    change_group_level_1 = df.groupby(['chrom_x'])\n",
    "    nrow = int(len(change_group_level_1.groups.keys())/3) + 2\n",
    "    ncol = 3\n",
    "    fig, axes = plt.subplots(nrows=nrow, ncols=ncol, figsize=(4*ncol, 4*nrow)) \n",
    "\n",
    "    for key_level_1,ax in zip(sorted_nicely(change_group_level_1.groups.keys()),axes.flatten()):\n",
    "            given_group_level_1 = change_group_level_1.get_group(key_level_1)\n",
    "            H = given_group_level_1.pivot_table(index='gene_order_tss_x', columns='gene_order_tss_y', values=group_x)\n",
    "            #print (H)\n",
    "            #print ((H - H.T).max().max())\n",
    "            #H.mask(H < 0, inplace=True)\n",
    "            #elix.reset_index(drop=True, inplace=True)\n",
    "            #print (H.max())\n",
    "            vmax= 10\n",
    "            cmap = LinearSegmentedColormap.from_list('mycmap', [(0/ vmax, 'red'), (1./2, 'white'), (vmax / vmax, 'blue')])\n",
    "            #cmap = LinearSegmentedColormap.from_list('mycmap', [(0/ vmax, 'white'), (vmax / vmax, 'blue')])\n",
    "            current_cmap = cmap\n",
    "            #current_cmap.set_bad(color='grey')\n",
    "            #      vmax= 1000000\n",
    "            #      current_cmap = LinearSegmentedColormap.from_list('mycmap', [ (0/ vmax, 'white'), (vmax / vmax, 'blue')])\n",
    "            #      imgp = ax.imshow(H.T,origin='low', aspect='auto' , cmap=current_cmap, norm=LogNorm(vmin=1, vmax=vmax))\n",
    "            \n",
    "            imgp = ax.imshow(H,origin='low', aspect='auto' , vmin=-1.0, vmax=vmax, cmap=current_cmap)\n",
    "            #imgp = ax.imshow(H,origin='low', aspect='auto' , norm=LogNorm(), cmap=current_cmap)\n",
    "            #sns.heatmap(H, annot = False) \n",
    "            ax.set_title('%s' %(key_level_1))\n",
    "            ax.set_yticklabels([])\n",
    "            ax.set_xticklabels([])\n",
    "            ax.set_xlabel(\"Genes in chromosome order\")\n",
    "            ax.set_ylabel(\"Genes in chromosome order\")\n",
    "            \n",
    "    #cbar = ax.figure.colorbar(imgp, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is for plotting lowest Hi-c for each gene in heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1881,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/11_dist_with_georg_hic_sub_median_hic_100.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1883,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>tss_tss</th>\n",
       "      <th>exp</th>\n",
       "      <th>exp_georg</th>\n",
       "      <th>hi-c-rao</th>\n",
       "      <th>hi-c-rao-common_elements</th>\n",
       "      <th>tes_tes</th>\n",
       "      <th>strand_x</th>\n",
       "      <th>txStart_outer_x</th>\n",
       "      <th>gene_order_tss_x</th>\n",
       "      <th>gene_order_tes_x</th>\n",
       "      <th>...</th>\n",
       "      <th>txStart_outer_y</th>\n",
       "      <th>gene_order_tss_y</th>\n",
       "      <th>gene_order_tes_y</th>\n",
       "      <th>Gene stable ID_y</th>\n",
       "      <th>Gene type_y</th>\n",
       "      <th>Uniprot_dc_y</th>\n",
       "      <th>dc_y</th>\n",
       "      <th>seq_length_y</th>\n",
       "      <th>chrom_y</th>\n",
       "      <th>gene_occurence_frequency_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>776092</th>\n",
       "      <td>61185939</td>\n",
       "      <td>0.365534</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>60577069</td>\n",
       "      <td>-</td>\n",
       "      <td>85682908</td>\n",
       "      <td>2344</td>\n",
       "      <td>2344</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776093</th>\n",
       "      <td>38231099</td>\n",
       "      <td>0.285114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37657655</td>\n",
       "      <td>+</td>\n",
       "      <td>62728068</td>\n",
       "      <td>1583</td>\n",
       "      <td>1583</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776094</th>\n",
       "      <td>42461417</td>\n",
       "      <td>0.262901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41765778</td>\n",
       "      <td>-</td>\n",
       "      <td>66958386</td>\n",
       "      <td>1863</td>\n",
       "      <td>1860</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776095</th>\n",
       "      <td>38344840</td>\n",
       "      <td>0.104930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37749703</td>\n",
       "      <td>-</td>\n",
       "      <td>62841809</td>\n",
       "      <td>1600</td>\n",
       "      <td>1598</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776096</th>\n",
       "      <td>94076001</td>\n",
       "      <td>0.536119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>93461889</td>\n",
       "      <td>-</td>\n",
       "      <td>118572970</td>\n",
       "      <td>2971</td>\n",
       "      <td>2968</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>777091</th>\n",
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       "      <td>0.134364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>95147696</td>\n",
       "      <td>+</td>\n",
       "      <td>120211031</td>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777092</th>\n",
       "      <td>13745724</td>\n",
       "      <td>0.354023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14281013</td>\n",
       "      <td>+</td>\n",
       "      <td>10751245</td>\n",
       "      <td>509</td>\n",
       "      <td>511</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777093</th>\n",
       "      <td>13143719</td>\n",
       "      <td>0.107614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13730697</td>\n",
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       "      <td>11353250</td>\n",
       "      <td>522</td>\n",
       "      <td>522</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777094</th>\n",
       "      <td>93728125</td>\n",
       "      <td>0.103446</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>93111086</td>\n",
       "      <td>-</td>\n",
       "      <td>118225094</td>\n",
       "      <td>2951</td>\n",
       "      <td>2950</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777095</th>\n",
       "      <td>33254812</td>\n",
       "      <td>0.214934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32660875</td>\n",
       "      <td>-</td>\n",
       "      <td>57751781</td>\n",
       "      <td>1349</td>\n",
       "      <td>1347</td>\n",
       "      <td>...</td>\n",
       "      <td>24496969</td>\n",
       "      <td>745</td>\n",
       "      <td>746</td>\n",
       "      <td>ENSG00000187398</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q86TE4</td>\n",
       "      <td>0.343931</td>\n",
       "      <td>346.0</td>\n",
       "      <td>chr11</td>\n",
       "      <td>2623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         tss_tss       exp  exp_georg  hi-c-rao  hi-c-rao-common_elements  \\\n",
       "776092  61185939  0.365534        NaN      18.0                       4.0   \n",
       "776093  38231099  0.285114        NaN       7.0                       4.0   \n",
       "776094  42461417  0.262901        NaN       5.0                       4.0   \n",
       "776095  38344840  0.104930        NaN      10.0                       4.0   \n",
       "776096  94076001  0.536119        NaN       3.0                       3.0   \n",
       "...          ...       ...        ...       ...                       ...   \n",
       "777091  95714062  0.134364        NaN       5.0                       4.0   \n",
       "777092  13745724  0.354023        NaN      57.0                       4.0   \n",
       "777093  13143719  0.107614        NaN      72.0                       4.0   \n",
       "777094  93728125  0.103446        NaN       3.0                       4.0   \n",
       "777095  33254812  0.214934        NaN      45.0                       4.0   \n",
       "\n",
       "         tes_tes strand_x  txStart_outer_x  gene_order_tss_x  \\\n",
       "776092  60577069        -         85682908              2344   \n",
       "776093  37657655        +         62728068              1583   \n",
       "776094  41765778        -         66958386              1863   \n",
       "776095  37749703        -         62841809              1600   \n",
       "776096  93461889        -        118572970              2971   \n",
       "...          ...      ...              ...               ...   \n",
       "777091  95147696        +        120211031              3051   \n",
       "777092  14281013        +         10751245               509   \n",
       "777093  13730697        -         11353250               522   \n",
       "777094  93111086        -        118225094              2951   \n",
       "777095  32660875        -         57751781              1349   \n",
       "\n",
       "        gene_order_tes_x  ... txStart_outer_y gene_order_tss_y  \\\n",
       "776092              2344  ...        24496969              745   \n",
       "776093              1583  ...        24496969              745   \n",
       "776094              1860  ...        24496969              745   \n",
       "776095              1598  ...        24496969              745   \n",
       "776096              2968  ...        24496969              745   \n",
       "...                  ...  ...             ...              ...   \n",
       "777091              3051  ...        24496969              745   \n",
       "777092               511  ...        24496969              745   \n",
       "777093               522  ...        24496969              745   \n",
       "777094              2950  ...        24496969              745   \n",
       "777095              1347  ...        24496969              745   \n",
       "\n",
       "       gene_order_tes_y  Gene stable ID_y     Gene type_y Uniprot_dc_y  \\\n",
       "776092              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "776093              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "776094              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "776095              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "776096              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "...                 ...               ...             ...          ...   \n",
       "777091              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "777092              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "777093              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "777094              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "777095              746   ENSG00000187398  protein_coding       Q86TE4   \n",
       "\n",
       "            dc_y seq_length_y  chrom_y  gene_occurence_frequency_y  \n",
       "776092  0.343931        346.0    chr11                        2623  \n",
       "776093  0.343931        346.0    chr11                        2623  \n",
       "776094  0.343931        346.0    chr11                        2623  \n",
       "776095  0.343931        346.0    chr11                        2623  \n",
       "776096  0.343931        346.0    chr11                        2623  \n",
       "...          ...          ...      ...                         ...  \n",
       "777091  0.343931        346.0    chr11                        2623  \n",
       "777092  0.343931        346.0    chr11                        2623  \n",
       "777093  0.343931        346.0    chr11                        2623  \n",
       "777094  0.343931        346.0    chr11                        2623  \n",
       "777095  0.343931        346.0    chr11                        2623  \n",
       "\n",
       "[1004 rows x 28 columns]"
      ]
     },
     "execution_count": 1883,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2_or[df_2_or['txStart_outer_y']==24496969]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1871,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = df_2_or[df_2_or['tss_tss'] >= 10000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1872,
   "metadata": {},
   "outputs": [],
   "source": [
    "H = df_2_or.pivot_table(index='txStart_outer_x', columns='txStart_outer_y', values='hi-c-rao')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1880,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>txStart_outer_y</th>\n",
       "      <th>139612</th>\n",
       "      <th>196737</th>\n",
       "      <th>207428</th>\n",
       "      <th>207510</th>\n",
       "      <th>236931</th>\n",
       "      <th>236965</th>\n",
       "      <th>278364</th>\n",
       "      <th>289125</th>\n",
       "      <th>307630</th>\n",
       "      <th>313505</th>\n",
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       "      <th>134069070</th>\n",
       "      <th>134224670</th>\n",
       "      <th>134225454</th>\n",
       "      <th>134253370</th>\n",
       "      <th>134253494</th>\n",
       "      <th>134274244</th>\n",
       "      <th>134331873</th>\n",
       "      <th>134412242</th>\n",
       "      <th>134412283</th>\n",
       "      <th>134735595</th>\n",
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       "      <th>txStart_outer_x</th>\n",
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       "  <tbody>\n",
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       "      <th>139612</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>106.0</td>\n",
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       "    <tr>\n",
       "      <th>196737</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>106.0</td>\n",
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       "    <tr>\n",
       "      <th>207428</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>56.0</td>\n",
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       "      <th>207510</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>56.0</td>\n",
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       "    <tr>\n",
       "      <th>236931</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>72.0</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>22338380</th>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22625508</th>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>22625841</th>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22830299</th>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24496969</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220 rows × 1002 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "txStart_outer_y  139612     196737     207428     207510     236931     \\\n",
       "txStart_outer_x                                                          \n",
       "139612                 NaN        NaN        NaN        NaN        NaN   \n",
       "196737                 NaN        NaN        NaN        NaN        NaN   \n",
       "207428                 NaN        NaN        NaN        NaN        NaN   \n",
       "207510                 NaN        NaN        NaN        NaN        NaN   \n",
       "236931                 NaN        NaN        NaN        NaN        NaN   \n",
       "...                    ...        ...        ...        ...        ...   \n",
       "22338380              22.0       22.0       25.0       25.0       25.0   \n",
       "22625508              47.0       47.0       50.0       50.0       50.0   \n",
       "22625841              47.0       47.0       50.0       50.0       50.0   \n",
       "22830299              28.0       28.0       25.0       25.0       25.0   \n",
       "24496969              17.0       17.0       21.0       21.0       21.0   \n",
       "\n",
       "txStart_outer_y  236965     278364     289125     307630     313505     ...  \\\n",
       "txStart_outer_x                                                         ...   \n",
       "139612                 NaN        NaN        NaN        NaN        NaN  ...   \n",
       "196737                 NaN        NaN        NaN        NaN        NaN  ...   \n",
       "207428                 NaN        NaN        NaN        NaN        NaN  ...   \n",
       "207510                 NaN        NaN        NaN        NaN        NaN  ...   \n",
       "236931                 NaN        NaN        NaN        NaN        NaN  ...   \n",
       "...                    ...        ...        ...        ...        ...  ...   \n",
       "22338380              25.0       25.0       25.0       20.0       20.0  ...   \n",
       "22625508              50.0       50.0       50.0       41.0       41.0  ...   \n",
       "22625841              50.0       50.0       50.0       41.0       41.0  ...   \n",
       "22830299              25.0       25.0       25.0       27.0       27.0  ...   \n",
       "24496969              21.0       21.0       21.0       18.0       18.0  ...   \n",
       "\n",
       "txStart_outer_y  134069070  134224670  134225454  134253370  134253494  \\\n",
       "txStart_outer_x                                                          \n",
       "139612                73.0       86.0       86.0       86.0       86.0   \n",
       "196737                73.0       86.0       86.0       86.0       86.0   \n",
       "207428                57.0       72.0       72.0       57.0       57.0   \n",
       "207510                57.0       72.0       72.0       57.0       57.0   \n",
       "236931                57.0       72.0       72.0       57.0       57.0   \n",
       "...                    ...        ...        ...        ...        ...   \n",
       "22338380              17.0       17.0       17.0       14.0       14.0   \n",
       "22625508              19.0       25.0       25.0       19.0       19.0   \n",
       "22625841              19.0       25.0       25.0       19.0       19.0   \n",
       "22830299              17.0       18.0       18.0       15.0       15.0   \n",
       "24496969              14.0       14.0       14.0       14.0       14.0   \n",
       "\n",
       "txStart_outer_y  134274244  134331873  134412242  134412283  134735595  \n",
       "txStart_outer_x                                                         \n",
       "139612                86.0       77.0       77.0       77.0      106.0  \n",
       "196737                86.0       77.0       77.0       77.0      106.0  \n",
       "207428                57.0       40.0       40.0       40.0       56.0  \n",
       "207510                57.0       40.0       40.0       40.0       56.0  \n",
       "236931                57.0       40.0       40.0       40.0       56.0  \n",
       "...                    ...        ...        ...        ...        ...  \n",
       "22338380              14.0       14.0       14.0       14.0       17.0  \n",
       "22625508              19.0       12.0       12.0       12.0       13.0  \n",
       "22625841              19.0       12.0       12.0       12.0       13.0  \n",
       "22830299              15.0       14.0       14.0       14.0       22.0  \n",
       "24496969              14.0       12.0       12.0       12.0       22.0  \n",
       "\n",
       "[220 rows x 1002 columns]"
      ]
     },
     "execution_count": 1880,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.head(220)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1844,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1002, 1002)"
      ]
     },
     "execution_count": 1844,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1854,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_array= H.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1746,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_array_rao= H.to_numpy()\n",
    "import bottleneck\n",
    "for count, value in enumerate(H_array_rao):\n",
    "     H_array_rao[count,:] = (bottleneck.nanrankdata(value) - 1)/np.nansum(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1737,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bottleneck\n",
    "for count, value in enumerate(H_array):\n",
    "     H_array[count,:] = (bottleneck.nanrankdata(value) - 1)/np.nansum(value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1857,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for i in H_array:\n",
    "for count, value in enumerate(H_array):\n",
    "    #pass\n",
    "    x= np.nanmin(value)\n",
    "    index_l = np.where(value == x)\n",
    "    H_array[count,:] = H_array[count,:] * 0\n",
    "    H_array[count][index_l] = -1\n",
    "    \n",
    "    #print (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1858,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1002, 1002)"
      ]
     },
     "execution_count": 1858,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1859,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., ..., -1., -1., -1.])"
      ]
     },
     "execution_count": 1859,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmin(H_array, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1860,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-159.0"
      ]
     },
     "execution_count": 1860,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nansum(H_array, axis=0).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1861,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_ar = np.nansum(H_array, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1862,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = sum_ar.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1863,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "219"
      ]
     },
     "execution_count": 1863,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.index(-159.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1826,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "elements_count = collections.Counter(np.nansum(H_array, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1002,)"
      ]
     },
     "execution_count": 1234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nansum(H_array, axis=0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1827,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1002\n"
     ]
    }
   ],
   "source": [
    "counter  = 0\n",
    "for x in elements_count.keys():\n",
    "   #x = data_dict.keys()\n",
    "   y = elements_count[x]\n",
    "   counter = counter+y\n",
    "print (counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.nansum(H_array, axis=0), 'o', color='black');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 1244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elements_count[-4.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1828,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for x in elements_count.keys():\n",
    "   #x = data_dict.keys()\n",
    "   y = elements_count[x]\n",
    "   plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f742e5a9110>"
      ]
     },
     "execution_count": 1210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAdgElEQVR4nO3deZCcd33n8fe3r7kvaUaypJEl2ZZNZMvEttY2LGGdQIK55ECAmEAWNlBesqhIVTa7MWHXoUy2EmCX1G5wDrMhxeXSEmrjKCCvIQETQiEh2ZYPyZaRZZ0jWXOP5urzu390z6g16pnpGfX00/Po86oS08+h5/nqZ/Tpn379e35t7o6IiCx/kaALEBGRylCgi4iEhAJdRCQkFOgiIiGhQBcRCYlYUDfu7Oz0jRs3BnV7EZFl6cknn+xz965SxwIL9I0bN7J///6gbi8isiyZ2fHZjmnIRUQkJBToIiIhoUAXEQkJBbqISEgo0EVEQkKBLiISEgp0EZGQUKCLiISEAl1EJCQCe1JURGQ5eWTviUv2/cYdVwdQyezUQxcRCQkFuohISCjQRURCoqxAN7O7zeywmR0xs/tLHP+wmfWa2YHCr49WvlQREZnLvB+KmlkUeAj4ZeAUsM/Mdrn7oRmn/h9337EENYqISBnK6aHfDhxx96PungJ2AvcsbVkiIrJQ5QT6OuBk0fapwr6Zfs3MnjWzb5nZ+lIXMrP7zGy/me3v7e1dRLkiIjKbcgLdSuzzGdv/AGx095uBfwS+UupC7v6wu29z921dXSW/QUlERBapnEA/BRT3uLuBnuIT3L3f3ZOFzS8Bt1WmPBERKVc5gb4P2Gxmm8wsAdwL7Co+wczWFG1uB16oXIkiIlKOeWe5uHvGzHYAjwNR4MvuftDMHgT2u/su4BNmth3IAAPAh5ewZhERKaGstVzcfTewe8a+B4pefxL4ZGVLExGRhdCToiIiIaFAFxEJCQW6iEhIKNBFREJCgS4iEhIKdBGRkFCgi4iEhAJdRCQkFOgiIiGhQBcRCQkFuohISCjQRURCQoEuIhISCnQRkZBQoIuIhIQCXUQkJBToIiIhoUAXEQkJBbqISEgo0EVEQkKBLiISEgp0EZGQUKCLiISEAl1EJCQU6CIiIaFAFxEJCQW6iEhIKNBFREJCgS4iEhIKdBGRkFCgi4iERFmBbmZ3m9lhMztiZvfPcd57zMzNbFvlShQRkXLMG+hmFgUeAt4KbAHeb2ZbSpzXAnwC2FvpIkVEZH7l9NBvB464+1F3TwE7gXtKnPcZ4HPAZAXrExGRMpUT6OuAk0Xbpwr7ppnZLcB6d//2XBcys/vMbL+Z7e/t7V1wsSIiMrtyAt1K7PPpg2YR4E+B/zjfhdz9YXff5u7burq6yq9SRETmVU6gnwLWF213Az1F2y3ATcATZnYMuBPYpQ9GRUSqq5xA3wdsNrNNZpYA7gV2TR1092F373T3je6+EdgDbHf3/UtSsYiIlDRvoLt7BtgBPA68AHzT3Q+a2YNmtn2pCxQRkfLEyjnJ3XcDu2fse2CWc++6/LJERGSh9KSoiEhIKNBFREJCgS4iEhIKdBGRkFCgi4iEhAJdRCQkFOgiIiGhQBcRCQkFuohISCjQRURCQoEuIhISCnQRkZBQoIuIhIQCXUQkJBToIiIhoUAXEQkJBbqISEgo0EVEQkKBLiISEgp0EZGQUKCLiISEAl1EJCQU6CIiIRELugARkVryyN4TQZewaOqhi4iEhAJdRCQkFOgiIiGhQBcRCQkFuohISCjQRURCQoEuIhISZQW6md1tZofN7IiZ3V/i+MfM7DkzO2Bm/2JmWypfqohIbdh/bIC/+fErZHMedCkXmTfQzSwKPAS8FdgCvL9EYD/i7lvd/eeBzwFfqHilIiI1YP+xAf7v06f52blRTg9OBF3ORcrpod8OHHH3o+6eAnYC9xSf4O4jRZtNQG29bYmIVMCBk0P83dOnWdmUAODlvtGAK7pYOYG+DjhZtH2qsO8iZvZxM3uZfA/9E6UuZGb3mdl+M9vf29u7mHpFRALh7nznuTOsX9HIR96wCYCXzy2/QLcS+y7pgbv7Q+5+LfD7wH8pdSF3f9jdt7n7tq6uroVVKiISoJHJDGPJDK/tbqO9MUFjIsrLvWNBl3WRcgL9FLC+aLsb6Jnj/J3Ar15OUSIitaZnKD9evra9AYDO5jqO9i6/Hvo+YLOZbTKzBHAvsKv4BDPbXLT5duBnlStRRCR4PcMTGHBVWz0AXS11y6+H7u4ZYAfwOPAC8E13P2hmD5rZ9sJpO8zsoJkdAH4X+NCSVSwiEoAzQ5OsbE5QF4sC0NVcR99okuGJdMCVXVDWeujuvhvYPWPfA0Wvf6fCdYmI1JSe4QnWdzROb3e11AFwtHeUW67uCKqsi+hJURGReYynMgyNp6fHzyHfQwdqathFgS4iMo8zw5MArG2vn97X0ZQgHrWa+mBUgS4iMo/pGS5tF3ro0YixYWUTLyvQRUSWjzPDk7Q1xGmqu/hjx2s6mzTkIiKynPQMTbCmrf6S/deuauZ4/xiZbC6Aqi6lQBcRmUMqk6P3fPKiD0SnXNvVTDrrnKyRRboU6CIic3h1ZBIH1pbooV/T1QTUzpouCnQRkTn0nk8CsKr10kDftDIf6CcGxqta02wU6CIic+gbSxIx6GhMXHKsrSFOxGBwPBVAZZdSoIuIzKFvNEVHY4Jo5NKFZyMRo6MxwcCYAl1EpOb1jybpLDwVWkp7Y5yh8dpYz0WBLiIyC3enfzTFyuZLh1umrGhSD11EpOadn8yQyuZYOWcPPaExdBGRWtc3lp/h0tk0Rw9dgS4iUvv6R/NBPecYelOcwfE07pd8M2fVKdBFRGbRN5okGjHaGuOznrOiMUEqk2M8la1iZaUp0EVEZtE/mmJFU4KIXTplccrU/PRaGHZRoIuIzKJvNDnn+Dnk10UHGBwLfuqiAl1EpIScOwNjqTnHzwE6CsMx6qGLiNSo4Yk0mZzPOWURinroCnQRkdo0NcNlroeK4MIYei08XKRAFxEpoW+0MAd9nh56W0McMxisgcf/FegiIiX0jyaJR42W+tic50UjRltDnEH10EVEalP/WIqVTXVzTlmcUitPiyrQRURKGBjLz0EvR0eTAl1EpCa5O4PjCwj0xrjmoYuI1KLRZIZ01qenJM6nQ0MuIiK1aWoK4oo51nAppiEXEZEaNRXoC+mhT6ZzTAS8QJcCXURkhoFCb7vUF0OXMvX4/0DAvXQFuojIDINjKVrrY8Sj5UXkhQW6lkGgm9ndZnbYzI6Y2f0ljv+umR0ys2fN7J/MbEPlSxURqY6FTFmE2llCd95AN7Mo8BDwVmAL8H4z2zLjtKeBbe5+M/At4HOVLlREpFoGx9MLCvQVTVMrLgY7dbGcHvrtwBF3P+ruKWAncE/xCe7+A3cfL2zuAborW6aISHWkszlGJtJlfyAK+S+KhuUx5LIOOFm0faqwbzYfAR4rdcDM7jOz/Wa2v7e3t/wqRUSqZGg8jZN/nL9c7Q21sSZ6OYFeaiGDkt+GamYfBLYBny913N0fdvdt7r6tq6ur/CpFRKpkeg76AnrosWiE1vpY4D30uZcRyzsFrC/a7gZ6Zp5kZm8GPgX8G3dPVqY8EZHqmp6yuIBAh/wbwHIYQ98HbDazTWaWAO4FdhWfYGa3AH8FbHf3c5UvU0SkOgbHUsQiRktdOf3dC9pr4PH/eQPd3TPADuBx4AXgm+5+0MweNLPthdM+DzQDf2tmB8xs1yyXExGpaVNTFq2MZXOLrWhKBP6tRWW9Bbn7bmD3jH0PFL1+c4XrEhEJxEJWWSzW3hjn8NnzS1BR+fSkqIhIgbszMJZa8Pg5QHtDguGJ2h9DFxG5IgyOp0lmcguasjilvTFeWHY3twSVlUeBLiJS8ErfGAArmxcX6ECgvXQFuohIwfH+QqA31S3497YVHi4aCnDqogJdRKTgWN8YBnQ0lffFFsWmHv8fnghuposCXUSk4Fj/OO2NcWKRhUfj9OP/AX63qAJdRKTgWP8Ync0LH26BC0voDmkMXUQkWO7OK31ji5qDDtDWODWGriEXEZFADY6nOT+ZWXQPvaUuRsQ0y0VEJHDTUxYX2UOPRIy2hrhmuYiIBG16yuIie+iQn+miMXQRkYAd6xsjYoubsjgl30PXGLqISKCO9Y+zrqNhUVMWp7Q3ashFRCRwx/rH2Liy6bKu0d4QZ0gPFomIBGdqyuJlB3pjQj10EZEgTU1Z3LCy8bKu094Y5/xkhkxAKy4q0EXkinesMMNlU+flD7kAjExmLrumxVCgi8gV71hhDvqGCgy5QHBPiyrQReSKd+TcKLGIsX5Fw2VdZ/rx/4DmoivQReSKd7BnhOtWNVMXi17WdaaGXIYD+mBUgS4iV7xDZ0a4cW3bZV9nashlUEMuIiLVd+78JL3nk2xZ23rZ12oP+FuLFOgickU71DMCwJY1lx/orQ1xzDSGLiISiENnCoFegR56NGK01scZ1pCLiEj1HewZobujYfpLni9Xe2M8sB56LJC7iojUgEf2nmDv0X5WtdTzyN4TFblme4BroquHLiJXrGQmS/9oijXt9RW7ZluAa6Ir0EXkinV2eBIH1rZd3gNFxdoDXBNdgS4iV6ye4UkA1rRVroce5JroCnQRuWKdGZqgMRGt2AeikO+hj0ymyea8YtcslwJdRK5YPcMTrGmrx8wqds32xgTucH6y+r30sgLdzO42s8NmdsTM7i9x/I1m9pSZZczsPZUvU0SksgbGUpwZmrzsFRZnam8M7mnReQPdzKLAQ8BbgS3A+81sy4zTTgAfBh6pdIEiIkvhhy+dw4HXXNVS0etOBfpAAB+MljMP/XbgiLsfBTCzncA9wKGpE9z9WOFYMF/TISKyQD94sZemuhhr2ys3wwWgqzn/AWvv+WRFr1uOcoZc1gEni7ZPFfYtmJndZ2b7zWx/b2/vYi4hInLZMtkcP3yplxtWNxOp4Pg5wOrWOgDOjUxW9LrlKCfQS/1pF/Xxrbs/7O7b3H1bV1fXYi4hInLZDpwcYngizfWrKzvcArCyuY6Iwbka7aGfAtYXbXcDPUtTjojI0vv+i+eIRozNqyof6NGI0dVSx6s12kPfB2w2s01mlgDuBXYtbVkiIkvnB4d7uW1DBw2Jy/uGotmsaqnn1ZEa7KG7ewbYATwOvAB8090PmtmDZrYdwMz+lZmdAt4L/JWZHVzKokVEFuvM8AQvnBnhl16zasnusbq1LpAhl7JWW3T33cDuGfseKHq9j/xQjIhIoEqtmvgbd1w9/fobe05gBr+yZTV7jg4sSQ2rWut5+sTQklx7LnpSVESuGCOTab7yk2PcfeNVXNPVvGT3WdVSR/9YilSmujO5FegicsX4+p7jnJ/M8B/uum5J77O6tTAXfbS6wy4KdBG5Ikyksvz1j17hjdd3sbW7bUnvFdRcdAW6iFwRdu47Qf9Yih2/uLS9c8jPcgGqPtNFgS4ioXe8f4z//vhhXn/tSm7ftGLJ77dqqod+Xj10EZGKyeac39l5gEjE+Px7X1uVe65sqiMaMc5VuYeuL4kWkVD7/ouvcuDkEH/2/ltYV+GFuGYTjRhdzdV/WlQ9dBEJrcNnR3jicC+/dms373zt2qree1VrHa9W+eEiBbqIhNKrI5Ps3HeSNW31fOZXb6z6/Ve11Fd9louGXERkWSr1ROiU8WSGr+05Tjwa4YN3bqAxUf2oW91ax1MnBqt6T/XQRSRUUpkcX91znJGJNB+8cwPtjYlA6ljdWs9AlZ8WVaCLSGhkc87OfSc4OTDO+7at5+oVjYHVsqolP3Wxmk+LKtBFJBTcnUcPnObFs+d552vXctO6pX0adD5Tj/9Xc6aLAl1Elj13Z/dzZ3jy+CC/9JpV3HnNyqBLuvBwkQJdRKR8//TiOX78cj+vv3Ylb1rCdc4XIojH/xXoIrKs/ehnvXz/xXPctqGDt21dg1X4S58Xa2VTIv+0aBUf/1egi8iy9dNXBnjs+bPctK6Nd92yjkiNhDlAJGJc1VrPqcGJqt1T89BFpOpmm0Ne/M1C83nm5BB/f+A0169u5n3bumsqzKdsWdvK86eHq3Y/9dBFZNn53qFX+dsnT7Kxs4kP3LGBWKQ2o2zrujaO9o1xfjJdlfvVZiuIiMzix0f6+PgjT7G2vYHfvHMD8WjtxtjW7jbc4WDPSFXuV7stISJSxN35+p7jfOQr+9i0sokPv24j9fFo0GXNaWthLvxzp6oz7KIxdBGpaV/7yXFOD47zxEu9vHj2PNetaubdt66jsa7246uzuY517Q08W6Vx9NpvEREJtYlUlud7hnn6xBB//NgLGPkZIk2JGE11UY71jZPK5ohFjLdvXcPrrl1Zkx+AzmbrujaePTVUlXsp0EUkMM+cGuLRp0+TzOTobK6bnnqYzTnjqSyjyTQrmhJs6mzmms4mmpZBr3ymrd1t/L+DZxkeT9PWGF/Sey2/1hGRqqnE9MJSUpkc//BMD0+eGGR9RwPvuHkt3R0NfODODWXXsFzc3F0YRz89zBs2dy7pvRToIlJVz58e5os/OEL/aJK7bujiTa9ZTTSyfIZQFmrqg9FnTw8p0EWkOop7wsMTaXqGJugbTU6v6Z1zpy4WZW17Azd3t7FlTSuRBQRxMpPlb358jC989yXq4xF+6w2buLareSn+KDWlvTHB1SsaqzLTRYEucoWbTGd5/vQwP/pZLycHxjk5OMHwxIUHYerjERriUcyM8VSGnx4b4NEDp1nVUsdbb7qKu29aw+2bVszay/7qT45xqGeE7x56lYGxFD+3ppV337JuWY6HL9bW7jYOnFj6D0avnBYVWWZKjR3PNnY937mjyQw9QxP0DE3w6NM9DE+kGBpP0zua5MzQJFl3ADoa42xY2cjVKxrp7mikszlx0de3uTuD42nWttfz+MGz7Nx3kq/85DidzQl+8YZVXLeqme6ORtLZHH2jSZ47PczjB88ymc6xurWOD79+I9evbrncpll27rxmJd959gz//FIvb7y+a8nuo0AXCYlMLserI0l6BifoGZ7ge4fOcmZ4ktNDE5yfzFx0bsSgpT7OiqYEb9jcyfqORtavaKClfu5ZGGbGiqYE7761m3ff2s1YMsMTh3vZ/fwZvv3sGSbS2YvOb0xEuXFNG1vWtnLDVS3LarphJb1vWzdf+uej/NF3DrH72l8gtkRPtyrQRWrEufOT/PBwLz/6WR+v9I1xvH+MVDZHUyJGc32MjsYEr45M0tVSR10sHwhnhic5OTDOj1/u49Xh5HRPuy4W4dqufG/5jk0rWNPewNr2Bta117P36AAt9fGKfBDZVBfj7Tev4e03r+GRvSeYTGcZHE8Ri0RorotRH4/UzHK2QaqLRfmDt72Gj339KXbuO8kHS8zmqYSyAt3M7gb+JxAF/re7/8mM43XAV4HbgH7g1939WGVLlSuJuzORzjI8kWZkIsPIZJrh8TQjk2lGJtKMTGbIuRM1IxIxImbEIkZTXYyW+nwAttQVftbHaa7Lby/kQ7xyakxn83UmCz3TSMSma4pFjPp4tGRwuju955O8ePY8e1/p54nDvdPrfXS11LFlTSuJWIR4NMJYMsP5yQyv9I3xzKkhCpk9bVVLHa31cf71dS2sba9nXXsDHU2JWUPj8NnRirXBTPXxKGvaGpbs+svZW268ijs2reAL33uJd752LW0NlZ+TPm+gm1kUeAj4ZeAUsM/Mdrn7oaLTPgIMuvt1ZnYv8Fng1yteLfnv5+s9n6SzuY4VhQXkszkn504252TdyWSddDZX+OVkpn7mcvm/+NH8X7ZYJEI0YsSjkQv7opHCMSMasSXrXbg77pDO5chk8zWnsjkyhe2p2tPZ/OyCaFG9sYiRyTkTqSwT6cKvVIZkJkcynSOZzZFMZ8nknEQ0QkMiSkM8OuvPxkSU+niUuli+N+WFtsw55PxC2+acWY9NmWovAybSWcaTWUaTGcaSGcZSGcaS2aLXGUaT2QshXQjq4cLrTM5nab3FMYO2hjjtDXHaGhP5nw1x2hvj0/8/SmedbOG/wWQmW9TGOSZTWcbTGSZSOSYL7Z4to8ZYxKbDua0hTjKTb5fzyfwwSDRi3HZ1B//pLTdw1w1dbFnTipmVHBfPZHOMp7Nks/m2b22I1/TiVHKBmfFf37GFd37xX3hk7wl++65rK36PcnrotwNH3P1ooaidwD1AcaDfA3y68PpbwBfNzNxn9iUu36NPn+aPH3ux0pedVT7k8z3AqT+N40Wvp/7n0v1Tf/z866qVvGhm1a0zHjUSscKbSzz/xnPTujZa62O0NcRpbYjTWp8P3f3HBwrn5d986uPR6XrdL7zB5N/UskzO+LllbSsjE2mGJtIMjU/9TPHc6WHGUxncIWL5N/GIMf1GP/UrETOu6WymofDml39DjNCYiHGoZ4RYNP9GliuuJ5d/Q05lc6Qy+Q5GNufEo3XEYxFWNiW4qrWeT7x5M63zjF1PiUUjtCrAl62b1rXxjY/ewe0bVyzJ9W2+zDWz9wB3u/tHC9u/Cdzh7juKznm+cM6pwvbLhXP6ZlzrPuC+wuYNwOFF1NwJ9M17VnXVYk1Qm3XVYk1Qm3XVYk1Qm3XVYk2wNHVtcPeSU2XK6aGXGnOY+S5Qzjm4+8PAw2Xcc/ZizPa7+7bLuUal1WJNUJt11WJNUJt11WJNUJt11WJNUP26yvm32ylgfdF2N9Az2zlmFgPagIFKFCgiIuUpJ9D3AZvNbJOZJYB7gV0zztkFfKjw+j3A95di/FxERGY375CLu2fMbAfwOPlpi19294Nm9iCw3913AX8NfM3MjpDvmd+7hDVf1pDNEqnFmqA266rFmqA266rFmqA266rFmqDKdc37oaiIiCwPmv8kIhISCnQRkZBYVoFuZr9nZm5mnYVtM7P/ZWZHzOxZM7u1yvV8pnDfA2b2XTNbW9h/l5kNF/YfMLMHaqCmoNvq82b2YuHef2dm7YX9G81soqit/jLomgrHPlloq8Nm9pZq1VS493vN7KCZ5cxsW9H+INuqZE2FY4G11Yw6Pm1mp4va520B1nJ3oT2OmNn9Vbtx/hH02v9Fflrk48BxoLOw723AY+Tnwd8J7K1yTa1Frz8B/GXh9V3AtwNqp9lqCrqtfgWIFV5/Fvhs4fVG4PmA2mq2mrYAzwB1wCbgZSBaxbp+jvyDd08A24r2B9lWs9UUaFvNqPHTwO8Fce8ZdUQL7XANkCi0z5Zq3Hs59dD/FPjPXPzA0j3AVz1vD9BuZmuqVZC7jxRtNlHiYapqm6OmoNvqu+4+tYbrHvLPMwRqjpruAXa6e9LdXwGOkF8Co1p1veDui3mKesnMUVOgbVWjppdLcfcUMLVcypJbFoFuZtuB0+7+zIxD64CTRdunCvuqxsz+m5mdBD4AFA+tvM7MnjGzx8zsxhqoKfC2KvJb5P+1MGWTmT1tZj80s1+ogZpqqa1mqoW2KlZrbbWjMIT2ZTPrCKiGwNqkZtZDN7N/BK4qcehTwB+Q/+fxJb+txL6K9pLnqsvd/97dPwV8ysw+CewA/hB4ivx6C6OFcbxHgc0B1xR4WxXO+RSQAb5ROHYGuNrd+83sNuBRM7txxr80ql1TTbRVCYG3VanfVmLfkv1LdZ6c+AvgM4X7fwb4H+TfqKutqm1SrGYC3d3fXGq/mW0lPzb3jOWXZu0GnjKz2ylvWYIlqauER4DvAH9Y/BfM3Xeb2Z+bWafPWKysmjVRA21lZh8C3gG8yQuDje6eBJKF109afmG364H9QdVEDbTVLL8n0LaaxZK3VbFyazSzLwHfXqo65lHVNilW80Mu7v6cu69y943uvpF8Y93q7mfJLznwbwszOO4Eht39TLVqM7PiXvd24MXC/qus8O5TeOOJkP/ij8BqIvi2uhv4fWC7u48X7e+y/Jr7mNk15P8lczTImsi31b1mVmdmmwo1/bQaNc0lyLaaQ8201YzPhN4FPB9EHZS3XMqSqJke+iLtJj974wgwDvy7Kt//T8zsBiBHfvbNxwr73wP8tpllgAng3qLeX1A1Bd1WXyQ/E+J7hfe6Pe7+MeCNwIOFtsoCH3P3ai3sVrImzy9t8U3ya/5ngI+7e3aO61SUmb0L+DOgC/iOmR1w97cQYFvNVlPQbTXD58zs58kPbxwD/n0QRfgsy6VU49569F9EJCRqfshFRETKo0AXEQkJBbqISEgo0EVEQkKBLiISEgp0EZGQUKCLiITE/wdqvm5VycahlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.nansum(H_array, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1251,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.nonzero(np.nansum(H_array, axis = 0))[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309,)"
      ]
     },
     "execution_count": 1252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1290,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = H_array[:, mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1002, 309)"
      ]
     },
     "execution_count": 1257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1816,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colors.LinearSegmentedColormap at 0x7f740dc92f10>"
      ]
     },
     "execution_count": 1816,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(H_array,vmin=0, vmax=0.01)\n",
    "sns.diverging_palette(220, 20, as_cmap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1779,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73e4a5a110>"
      ]
     },
     "execution_count": 1779,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(H_array_rao,vmin=0, vmax=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f74dcb94c90>"
      ]
     },
     "execution_count": 1160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(H_array,vmin=-1, vmax=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f74dabf1450>"
      ]
     },
     "execution_count": 1130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(H_array,vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f74cdd10d90>"
      ]
     },
     "execution_count": 1264,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(graph,vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f740ca56d90>"
      ]
     },
     "execution_count": 1297,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(graph,vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Genes in chromosome order')"
      ]
     },
     "execution_count": 1126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 10)) \n",
    "#H = H_array\n",
    "#print (H)\n",
    "print ((H - H.T).max().max())\n",
    "#H.mask(H < 0, inplace=True)\n",
    "#elix.reset_index(drop=True, inplace=True)\n",
    "#print (H.max())\n",
    "vmax= 1\n",
    "cmap = LinearSegmentedColormap.from_list('mycmap', [(0/ vmax, 'red'), (1./2, 'white'), (vmax / vmax, 'white')])\n",
    "#cmap = LinearSegmentedColormap.from_list('mycmap', [(0/ vmax, 'white'), (vmax / vmax, 'blue')])\n",
    "current_cmap = cmap\n",
    "current_cmap.set_bad(color='grey')\n",
    "#      vmax= 1000000\n",
    "#      current_cmap = LinearSegmentedColormap.from_list('mycmap', [ (0/ vmax, 'white'), (vmax / vmax, 'blue')])\n",
    "#      imgp = ax.imshow(H.T,origin='low', aspect='auto' , cmap=current_cmap, norm=LogNorm(vmin=1, vmax=vmax))\n",
    "\n",
    "imgp = ax.imshow(H_array,origin='low', aspect='auto' , vmin=-1, vmax=vmax, cmap=current_cmap)\n",
    "#imgp = ax.imshow(H,origin='low', aspect='auto' , norm=LogNorm(), cmap=current_cmap)\n",
    "#sns.heatmap(H, annot = False) \n",
    "ax.set_yticklabels([])\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xlabel(\"Genes in chromosome order\")\n",
    "ax.set_ylabel(\"Genes in chromosome order\")\n",
    "#cbar = ax.figure.colorbar(imgp, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1077,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64),)"
      ]
     },
     "execution_count": 1077,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(H_array[0] == 77)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.where(t == i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 1019,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmin(H_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 978,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/11_dist_with_georg_hic_sub_median_hic_100.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 980,
   "metadata": {},
   "outputs": [],
   "source": [
    "H = df_2_or.pivot_table(index='txStart_outer_x', columns='txStart_outer_y', values='exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 982,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_array= H.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 769,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_list = H.index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_list = [int(x/100000) for x in index_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 771,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.array(index_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_unique_list = np.unique(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for pairs in list(itertools.combinations(index_unique_list,2)):\n",
    "    st_row = np.where(t == pairs[0])[0].min()\n",
    "    end_row = np.where(t == pairs[0])[0].max() + 1\n",
    "    \n",
    "    st_col = np.where(t == pairs[1])[0].min()\n",
    "    end_col = np.where(t == pairs[1])[0].max() +  1\n",
    "    H_array[st_row:end_row,st_col:end_col] = np.median(H_array[st_row:end_row,st_col:end_col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 773,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for pairs in list(itertools.combinations(index_unique_list,2)):\n",
    "for i in index_unique_list:\n",
    "          for j in index_unique_list:\n",
    "            st_row = np.where(t == i)[0].min()\n",
    "            end_row = np.where(t == i)[0].max() + 1\n",
    "            \n",
    "            st_col = np.where(t == j)[0].min()\n",
    "            end_col = np.where(t == j)[0].max() +  1\n",
    "            H_array[st_row:end_row,st_col:end_col] = H_array[st_row:end_row,st_col:end_col].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 774,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.DataFrame(data=H_array)\n",
    "\n",
    "long_form = dataset.stack().reset_index()\n",
    "long_form.columns = ['del_1', 'del_2','exp_median']  \n",
    "\n",
    "df_2_or =  df_2_or.join(long_form[['exp_median']], how='left')    #merging on the index of the two dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 934,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f748f046a50>"
      ]
     },
     "execution_count": 934,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2_or = df_2_or[df_2_or['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "ax = sns.relplot(y=\"exp\", x=\"hi-c-rao\", kind=\"line\", data=df_2_or, ci='sd');\n",
    "ax.set(xlim=(0, 200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 781,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_array[H_array > 255] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 779,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.75397578, 0.75397578, ..., 0.53415659, 0.51525079,\n",
       "       1.        ])"
      ]
     },
     "execution_count": 779,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_array[np.where(H_array>0.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 777,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n",
      "0.0\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Invalid shape (741234,) for image data",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-777-2415d025237d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m#      imgp = ax.imshow(H.T,origin='low', aspect='auto' , cmap=current_cmap, norm=LogNorm(vmin=1, vmax=vmax))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mimgp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mH\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'low'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurrent_cmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;31m#imgp = ax.imshow(H,origin='low', aspect='auto' , norm=LogNorm(), cmap=current_cmap)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m#sns.heatmap(H, annot = False)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1597\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 369\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[0;32m--> 369\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5677\u001b[0m                               resample=resample, **kwargs)\n\u001b[1;32m   5678\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5679\u001b[0;31m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5680\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5681\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    688\u001b[0m                 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[1;32m    689\u001b[0m             raise TypeError(\"Invalid shape {} for image data\"\n\u001b[0;32m--> 690\u001b[0;31m                             .format(self._A.shape))\n\u001b[0m\u001b[1;32m    691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid shape (741234,) for image data"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "            fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(4, 4)) \n",
    "            H = H_array[np.where(H>0.5)]\n",
    "            #print (H)\n",
    "            print ((H - H.T))\n",
    "            print ((H - H.T).max().max())\n",
    "            #H.mask(H < 0, inplace=True)\n",
    "            #elix.reset_index(drop=True, inplace=True)\n",
    "            #print (H.max())\n",
    "            vmax= 1.0\n",
    "            cmap = LinearSegmentedColormap.from_list('mycmap', [(0/ vmax, 'red'), (1./2, 'white'), (vmax / vmax, 'blue')])\n",
    "            #cmap = LinearSegmentedColormap.from_list('mycmap', [(0/ vmax, 'white'), (vmax / vmax, 'blue')])\n",
    "            current_cmap = cmap\n",
    "            #current_cmap.set_bad(color='grey')\n",
    "            #      vmax= 1000000\n",
    "            #      current_cmap = LinearSegmentedColormap.from_list('mycmap', [ (0/ vmax, 'white'), (vmax / vmax, 'blue')])\n",
    "            #      imgp = ax.imshow(H.T,origin='low', aspect='auto' , cmap=current_cmap, norm=LogNorm(vmin=1, vmax=vmax))\n",
    "            \n",
    "            imgp = ax.imshow(H,origin='low', aspect='auto' , vmin=0.0, vmax=vmax, cmap=current_cmap)\n",
    "            #imgp = ax.imshow(H,origin='low', aspect='auto' , norm=LogNorm(), cmap=current_cmap)\n",
    "            #sns.heatmap(H, annot = False) \n",
    "            ax.set_yticklabels([])\n",
    "            ax.set_xticklabels([])\n",
    "            ax.set_xlabel(\"Genes in chromosome order\")\n",
    "            ax.set_ylabel(\"Genes in chromosome order\")\n",
    "            #cbar = ax.figure.colorbar(imgp, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1007,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots_with_1_level_3d(df_2_or, 'hi-c-rao')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 939,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots_with_1_level_3d(df_2_or, 'exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3306690738754696e-16\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Genes in chromosome order')"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "            fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5, 5)) \n",
    "            #H = H_array\n",
    "            #print (H)\n",
    "            print ((H - H.T).max().max())\n",
    "            #H.mask(H < 0, inplace=True)\n",
    "            #elix.reset_index(drop=True, inplace=True)\n",
    "            #print (H.max())\n",
    "            vmax= 0.9\n",
    "            cmap = LinearSegmentedColormap.from_list('mycmap', [(0/ vmax, 'red'), (1./2, 'white'), (vmax / vmax, 'blue')])\n",
    "            #cmap = LinearSegmentedColormap.from_list('mycmap', [(0/ vmax, 'white'), (vmax / vmax, 'blue')])\n",
    "            current_cmap = cmap\n",
    "            #current_cmap.set_bad(color='grey')\n",
    "            #      vmax= 1000000\n",
    "            #      current_cmap = LinearSegmentedColormap.from_list('mycmap', [ (0/ vmax, 'white'), (vmax / vmax, 'blue')])\n",
    "            #      imgp = ax.imshow(H.T,origin='low', aspect='auto' , cmap=current_cmap, norm=LogNorm(vmin=1, vmax=vmax))\n",
    "            \n",
    "            imgp = ax.imshow(H,origin='low', aspect='auto' , vmin=0.3, vmax=vmax, cmap=current_cmap)\n",
    "            #imgp = ax.imshow(H,origin='low', aspect='auto' , norm=LogNorm(), cmap=current_cmap)\n",
    "            #sns.heatmap(H, annot = False) \n",
    "            ax.set_yticklabels([])\n",
    "            ax.set_xticklabels([])\n",
    "            ax.set_xlabel(\"Genes in chromosome order\")\n",
    "            ax.set_ylabel(\"Genes in chromosome order\")\n",
    "            #cbar = ax.figure.colorbar(imgp, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_array.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plots_with_1_level_3d(df_2_or_gene_TN_0_30, 'hi-c-rao')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.90037713, 0.81138045, 0.89414419, 0.84966892, 0.4860038 ,\n",
       "        0.96066464, 0.96372763, 0.8388559 , 0.38749483, 0.95798878,\n",
       "        0.75761013, 0.90390877],\n",
       "       [0.96826807, 0.79881626, 0.86236873, 0.75683795, 0.72572801,\n",
       "        0.98979653, 0.98054401, 0.90803702, 0.67846936, 0.94700991,\n",
       "        0.8425759 , 0.98283687],\n",
       "       [0.94193857, 0.86514516, 0.9371255 , 0.60150642, 0.59861116,\n",
       "        0.91937499, 0.87730787, 0.78998749, 0.540591  , 0.88139341,\n",
       "        0.74111514, 0.90814383],\n",
       "       [0.44210476, 0.34418315, 0.51446643, 0.09776223, 0.11318229,\n",
       "        0.36142287, 0.35600971, 0.20184064, 0.38522616, 0.32445954,\n",
       "        0.32427993, 0.39837183],\n",
       "       [0.98730345, 0.91604063, 0.84974912, 0.68397211, 0.73917571,\n",
       "        0.98491995, 0.97395241, 0.89808036, 0.55493815, 0.95760378,\n",
       "        0.89061809, 0.94140269],\n",
       "       [0.98387626, 0.85821903, 0.94393896, 0.7660699 , 0.59241588,\n",
       "        0.96697921, 0.95894112, 0.74977283, 0.64471971, 0.93720077,\n",
       "        0.78513713, 0.91814274],\n",
       "       [0.9953084 , 0.944522  , 0.91970658, 0.85873079, 0.55043823,\n",
       "        0.93754179, 0.99805667, 0.80236607, 0.4760326 , 0.99187551,\n",
       "        0.87890971, 0.82985526],\n",
       "       [0.98124277, 0.88831384, 0.95988064, 0.84153654, 0.45766908,\n",
       "        0.9008958 , 0.98799342, 0.74293907, 0.69747112, 0.95237403,\n",
       "        0.74091625, 0.87716604],\n",
       "       [0.76298465, 0.54255175, 0.80742711, 0.3736671 , 0.22387953,\n",
       "        0.59387294, 0.63703391, 0.25250448, 0.51711709, 0.61318488,\n",
       "        0.32168126, 0.54515061]])"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_n[np.where(t == 7)[0],:][:,np.where(t == 8)[0]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.639593147034282"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_n[np.where(t == 9)[0].min():np.where(t == 11)[0].max()+1,np.where(t == 9)[0].min():np.where(t == 10)[0].max()+1].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.]])"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_n[np.where(t == 9)[0].min():np.where(t == 11)[0].max(),np.where(t == 9)[0].min():np.where(t == 10)[0].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7       , 0.65265334, 0.52103938, 0.52750067, 0.41061738,\n",
       "        0.22846064],\n",
       "       [0.7       , 1.        , 0.65698243, 0.71263662, 0.45991516,\n",
       "        0.369399  ],\n",
       "       [0.52103938, 0.65698243, 1.        , 0.98698754, 0.84010889,\n",
       "        0.69958787],\n",
       "       [0.52750067, 0.71263662, 0.98698754, 1.        , 0.86005287,\n",
       "        0.73497985],\n",
       "       [0.41061738, 0.45991516, 0.84010889, 0.86005287, 1.        ,\n",
       "        0.89803738],\n",
       "       [0.22846064, 0.369399  , 0.69958787, 0.73497985, 0.89803738,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_n[np.where(t == 9)[0],:][:,np.where(t == 9)[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(30).reshape(3,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.array([True, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.array([False, True, True,False, True, True,False, True, True,True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  4,  5,  7,  8,  9],\n",
       "       [11, 12, 14, 15, 17, 18, 19]])"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:,c][b,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_n =  H.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = 0\n",
    "end = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7, 0.7, 0.7],\n",
       "       [0.7, 0.7, 0.7],\n",
       "       [0.8, 0.8, 0.8],\n",
       "       [0.8, 0.8, 0.8]])"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_n[st:end+1,st:end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7       , 0.7       , 0.7       , ..., 0.76375373, 0.93284579,\n",
       "        0.86223119],\n",
       "       [0.7       , 0.7       , 0.7       , ..., 0.93563969, 0.88664558,\n",
       "        0.93368371],\n",
       "       [0.7       , 0.7       , 0.7       , ..., 0.37858291, 0.45530073,\n",
       "        0.33836886],\n",
       "       ...,\n",
       "       [0.76375373, 0.93563969, 0.37858291, ..., 1.        , 0.95814406,\n",
       "        0.95738065],\n",
       "       [0.93284579, 0.88664558, 0.45530073, ..., 0.95814406, 1.        ,\n",
       "        0.965351  ],\n",
       "       [0.86223119, 0.93368371, 0.33836886, ..., 0.95738065, 0.965351  ,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>txStart_outer_y</th>\n",
       "      <th>778626</th>\n",
       "      <th>825137</th>\n",
       "      <th>911434</th>\n",
       "      <th>923927</th>\n",
       "      <th>959309</th>\n",
       "      <th>960583</th>\n",
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       "      <th>1001137</th>\n",
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       "      <th>247458105</th>\n",
       "      <th>247507057</th>\n",
       "      <th>247857186</th>\n",
       "      <th>247895586</th>\n",
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       "      <th>248095183</th>\n",
       "      <th>248825915</th>\n",
       "      <th>248838209</th>\n",
       "      <th>248859144</th>\n",
       "      <th>248906195</th>\n",
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       "    <tr>\n",
       "      <th>txStart_outer_x</th>\n",
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       "      <th>778626</th>\n",
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       "      <th>825137</th>\n",
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       "      <td>0.445425</td>\n",
       "      <td>0.530916</td>\n",
       "      <td>0.399459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219729</td>\n",
       "      <td>0.503699</td>\n",
       "      <td>0.661096</td>\n",
       "      <td>0.479318</td>\n",
       "      <td>0.395441</td>\n",
       "      <td>0.427875</td>\n",
       "      <td>0.891803</td>\n",
       "      <td>0.935640</td>\n",
       "      <td>0.886646</td>\n",
       "      <td>0.933684</td>\n",
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       "    <tr>\n",
       "      <th>911434</th>\n",
       "      <td>0.542804</td>\n",
       "      <td>0.419972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.652653</td>\n",
       "      <td>0.521039</td>\n",
       "      <td>0.527501</td>\n",
       "      <td>0.410617</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.253063</td>\n",
       "      <td>0.077471</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080981</td>\n",
       "      <td>0.088441</td>\n",
       "      <td>0.253435</td>\n",
       "      <td>0.283873</td>\n",
       "      <td>0.085861</td>\n",
       "      <td>0.082822</td>\n",
       "      <td>0.416658</td>\n",
       "      <td>0.378583</td>\n",
       "      <td>0.455301</td>\n",
       "      <td>0.338369</td>\n",
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       "    <tr>\n",
       "      <th>923927</th>\n",
       "      <td>0.451949</td>\n",
       "      <td>0.456052</td>\n",
       "      <td>0.652653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.656982</td>\n",
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       "      <td>0.369399</td>\n",
       "      <td>0.514615</td>\n",
       "      <td>0.302695</td>\n",
       "      <td>...</td>\n",
       "      <td>0.073340</td>\n",
       "      <td>0.166112</td>\n",
       "      <td>0.220809</td>\n",
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       "      <td>0.034468</td>\n",
       "      <td>0.082952</td>\n",
       "      <td>0.525138</td>\n",
       "      <td>0.672329</td>\n",
       "      <td>0.669089</td>\n",
       "      <td>0.519295</td>\n",
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       "    <tr>\n",
       "      <th>959309</th>\n",
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       "      <td>0.816809</td>\n",
       "      <td>0.837351</td>\n",
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       "      <td>0.217908</td>\n",
       "      <td>0.365334</td>\n",
       "      <td>0.732293</td>\n",
       "      <td>0.581978</td>\n",
       "      <td>0.160844</td>\n",
       "      <td>0.239354</td>\n",
       "      <td>0.977070</td>\n",
       "      <td>0.998953</td>\n",
       "      <td>0.979892</td>\n",
       "      <td>0.960807</td>\n",
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       "    <tr>\n",
       "      <th>960583</th>\n",
       "      <td>0.867955</td>\n",
       "      <td>0.895054</td>\n",
       "      <td>0.527501</td>\n",
       "      <td>0.712637</td>\n",
       "      <td>0.986988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.860053</td>\n",
       "      <td>0.734980</td>\n",
       "      <td>0.829764</td>\n",
       "      <td>0.794470</td>\n",
       "      <td>...</td>\n",
       "      <td>0.205503</td>\n",
       "      <td>0.345161</td>\n",
       "      <td>0.476788</td>\n",
       "      <td>0.470601</td>\n",
       "      <td>0.210881</td>\n",
       "      <td>0.133548</td>\n",
       "      <td>0.959885</td>\n",
       "      <td>0.953686</td>\n",
       "      <td>0.995867</td>\n",
       "      <td>0.928330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966481</th>\n",
       "      <td>0.583312</td>\n",
       "      <td>0.455271</td>\n",
       "      <td>0.410617</td>\n",
       "      <td>0.459915</td>\n",
       "      <td>0.840109</td>\n",
       "      <td>0.860053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.898037</td>\n",
       "      <td>0.820914</td>\n",
       "      <td>0.758308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157921</td>\n",
       "      <td>0.098106</td>\n",
       "      <td>0.216357</td>\n",
       "      <td>0.081204</td>\n",
       "      <td>0.034149</td>\n",
       "      <td>0.003445</td>\n",
       "      <td>0.664612</td>\n",
       "      <td>0.824771</td>\n",
       "      <td>0.744605</td>\n",
       "      <td>0.512252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982093</th>\n",
       "      <td>0.434424</td>\n",
       "      <td>0.445425</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.369399</td>\n",
       "      <td>0.699588</td>\n",
       "      <td>0.734980</td>\n",
       "      <td>0.898037</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.788004</td>\n",
       "      <td>0.527765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056539</td>\n",
       "      <td>0.074405</td>\n",
       "      <td>0.140551</td>\n",
       "      <td>0.112722</td>\n",
       "      <td>0.005566</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.565241</td>\n",
       "      <td>0.676115</td>\n",
       "      <td>0.600830</td>\n",
       "      <td>0.299363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1806 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "txStart_outer_y  778626     825137     911434     923927     959309     \\\n",
       "txStart_outer_x                                                          \n",
       "778626            1.000000   0.795363   0.542804   0.451949   0.833483   \n",
       "825137            0.795363   1.000000   0.419972   0.456052   0.952361   \n",
       "911434            0.542804   0.419972   1.000000   0.652653   0.521039   \n",
       "923927            0.451949   0.456052   0.652653   1.000000   0.656982   \n",
       "959309            0.833483   0.952361   0.521039   0.656982   1.000000   \n",
       "960583            0.867955   0.895054   0.527501   0.712637   0.986988   \n",
       "966481            0.583312   0.455271   0.410617   0.459915   0.840109   \n",
       "982093            0.434424   0.445425   0.228461   0.369399   0.699588   \n",
       "\n",
       "txStart_outer_y  960583     966481     982093     1000172    1001137    ...  \\\n",
       "txStart_outer_x                                                         ...   \n",
       "778626            0.867955   0.583312   0.434424   0.403705   0.242381  ...   \n",
       "825137            0.895054   0.455271   0.445425   0.530916   0.399459  ...   \n",
       "911434            0.527501   0.410617   0.228461   0.253063   0.077471  ...   \n",
       "923927            0.712637   0.459915   0.369399   0.514615   0.302695  ...   \n",
       "959309            0.986988   0.840109   0.699588   0.816809   0.837351  ...   \n",
       "960583            1.000000   0.860053   0.734980   0.829764   0.794470  ...   \n",
       "966481            0.860053   1.000000   0.898037   0.820914   0.758308  ...   \n",
       "982093            0.734980   0.898037   1.000000   0.788004   0.527765  ...   \n",
       "\n",
       "txStart_outer_y  247458105  247507057  247857186  247895586  248030069  \\\n",
       "txStart_outer_x                                                          \n",
       "778626            0.302802   0.408970   0.508224   0.294071   0.313602   \n",
       "825137            0.219729   0.503699   0.661096   0.479318   0.395441   \n",
       "911434            0.080981   0.088441   0.253435   0.283873   0.085861   \n",
       "923927            0.073340   0.166112   0.220809   0.073019   0.034468   \n",
       "959309            0.217908   0.365334   0.732293   0.581978   0.160844   \n",
       "960583            0.205503   0.345161   0.476788   0.470601   0.210881   \n",
       "966481            0.157921   0.098106   0.216357   0.081204   0.034149   \n",
       "982093            0.056539   0.074405   0.140551   0.112722   0.005566   \n",
       "\n",
       "txStart_outer_y  248095183  248825915  248838209  248859144  248906195  \n",
       "txStart_outer_x                                                         \n",
       "778626            0.160920   0.896949   0.763754   0.932846   0.862231  \n",
       "825137            0.427875   0.891803   0.935640   0.886646   0.933684  \n",
       "911434            0.082822   0.416658   0.378583   0.455301   0.338369  \n",
       "923927            0.082952   0.525138   0.672329   0.669089   0.519295  \n",
       "959309            0.239354   0.977070   0.998953   0.979892   0.960807  \n",
       "960583            0.133548   0.959885   0.953686   0.995867   0.928330  \n",
       "966481            0.003445   0.664612   0.824771   0.744605   0.512252  \n",
       "982093            0.004332   0.565241   0.676115   0.600830   0.299363  \n",
       "\n",
       "[8 rows x 1806 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H[H.index<1000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "HT[H.index<1000000][[778626,825137,911434,923927]] = H[H.index<1000000][[778626,825137,911434,923927]].values.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "HT = .6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'index'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-267-c8aa113e9272>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mHT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;36m1000000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m778626\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m825137\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m911434\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m923927\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'index'"
     ]
    }
   ],
   "source": [
    "HT[HT.index<1000000][[778626,825137,911434,923927]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.624731987437572"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H[H.index<1000000][[778626,825137,911434,923927]].values.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "txStart_outer_y\n",
       "778626       0.670685\n",
       "825137       0.802272\n",
       "911434       0.267165\n",
       "923927       0.429924\n",
       "959309       0.854155\n",
       "               ...   \n",
       "248095183    0.199434\n",
       "248825915    0.838509\n",
       "248838209    0.846229\n",
       "248859144    0.803104\n",
       "248906195    0.846990\n",
       "Length: 1806, dtype: float64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = df_2_or[df_2_or['hi-c-rao'] >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "for i in list(itertools.permutations(range(0,100)))[0]:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(0,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "prot_list = list(range(0,10))\n",
    "random.shuffle(prot_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 0, 3, 9, 1, 7, 8, 4, 2, 6]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prot_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 783,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "from lohia_utilities.calculate_auc import *\n",
    "from pandas.core.common import SettingWithCopyWarning\n",
    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
    "from lohia_utilities.create_corr_network import rank\n",
    "import itertools\n",
    "import random\n",
    "\n",
    "def calc_auc_hic(resoulution_in_kb, case='simple', dist_tp='exp', prediction='hi-c-rao', shuffle=False):\n",
    "\n",
    "    df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/11_dist_with_georg_hic_sub_median_hic_%s.h5' %resoulution_in_kb)\n",
    "\n",
    "    #df_2_or = df_2_or[df_2_or['exp_georg'] >= 0] # liming the matrix to only chosen values for rank standerization\n",
    "    df_2_or = df_2_or[df_2_or['hi-c-rao'] >= 0] # liming the matrix to only chosen values for rank standerization\n",
    "\n",
    "    #ranked_matirx = rank(df_2_or['exp_georg'])\n",
    "    #df_2_or['exp_georg'] = ranked_matirx\n",
    "    #df_2_or.rename(columns={\"exp_georg\": \"exp (GK)\"}, inplace=True)\n",
    "\n",
    "    ranked_matirx = rank(df_2_or['exp'])\n",
    "    df_2_or['exp'] = ranked_matirx\n",
    "\n",
    "    #ranked_matirx = rank(df_2_or['hi-c-rao'])\n",
    "    #df_2_or['hi-c-rao'] = ranked_matirx\n",
    "    m_l = []\n",
    "    change_group_level_1 = df_2_or.groupby(['chrom_x'])\n",
    "    for chrm in change_group_level_1.groups.keys():\n",
    "        df = change_group_level_1.get_group(chrm)\n",
    "        \n",
    "        num_pairs = df['Gene stable ID_x'].nunique()\n",
    "\n",
    "        prot_list_sp = np.array_split(df, num_pairs, axis=0)\n",
    "        \n",
    "        #list(itertools.permutations(range(0,num_pairs)))[0]\n",
    "        prot_list = list(range(0,num_pairs))\n",
    "        if shuffle ==True:\n",
    "            random.shuffle(prot_list)\n",
    "        else:\n",
    "            pass\n",
    "        for i, shuf_i in zip(list(range(0,num_pairs)), prot_list):\n",
    "            \n",
    "\n",
    "        #for i in range(0,1):\n",
    "\n",
    "            long_form_top = prot_list_sp[int(i)]\n",
    "            long_form_top_shuf = prot_list_sp[int(shuf_i)]\n",
    "            long_form_top['dist'] = long_form_top[dist_tp]\n",
    "            #long_form_top_shuf['dist'] = long_form_top_shuf[dist_tp]\n",
    "            long_form_top['exp'] = long_form_top_shuf['exp'].to_list()\n",
    "\n",
    "            long_form_top = long_form_top[long_form_top['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "\n",
    "            long_form_top = long_form_top[long_form_top['Gene stable ID_x'] != long_form_top['Gene stable ID_y']] # remove all the self pairs from each set\n",
    "            \n",
    "            mp =  long_form_top['Gene stable ID_y'].values[0]\n",
    "            #print (long_form_top.shape)\n",
    "            \n",
    "            exp_median = long_form_top['exp'].median()\n",
    "            exp_mean = long_form_top['exp'].mean()\n",
    "            exp_var = long_form_top['exp'].var()\n",
    "\n",
    "            long_form_top = long_form_top.reset_index()\n",
    "            if exp_median >=0:\n",
    "                for dist_thresh in [1,10]:\n",
    "                #for dist_thresh in [0.5,0.8]:\n",
    "                #for dist_thresh in [100000,1000000,10000000,100000000]:\n",
    "                #for dist_thresh in [4000]:\n",
    "                #for dist_thresh in [df_2_or_u[\"hi-c-rao\"].min(), df_2_or[\"hi-c-rao\"].max()-1, df_2_or[\"hi-c-rao\"].mean(), df_2_or[\"hi-c-rao\"].median()]:\n",
    "                    if case == 'simple':\n",
    "                        long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]\n",
    "                    elif case == 'tp':\n",
    "                    \n",
    "                        long_form_top = long_form_top.sort_values(by=['dist'], ascending=False) \n",
    "                        long_form_top[\"True_sim\"] = [0 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]  \n",
    "                        for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                            long_form_top.at[ind_val, 'True_sim'] = 1\n",
    "                    else:    \n",
    "                        long_form_top = long_form_top.sort_values(by=['dist'], ascending=True) \n",
    "                        long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 1 for score in long_form_top[\"dist\"]]  \n",
    "                        for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                            long_form_top.at[ind_val, 'True_sim'] = 0\n",
    "                    #\n",
    "                    #long_form_top[\"True_sim\"] = [1 if score <= dist_thresh else 0 for score in long_form_top[\"dist\"]] \n",
    "                    #long_form_top[\"True_sim\"] = [1 if score >= dist_thresh else 1 if score2 <= 1000 else 0 for score, score2 in zip(long_form_top[\"dist\"],long_form_top[\"tss_tss\"])]    \n",
    "                    long_form_top[\"true_pos\"] = [score for score in long_form_top[\"True_sim\"]]\n",
    "                    long_form_top[\"true_neg\"] = [1 if score==0 else 0 for score in long_form_top[\"True_sim\"]]\n",
    "                    long_form_top[\"predicted_sim_from_exp\"] = [score for score in long_form_top[prediction]]\n",
    "                    ca = calc_auroc (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "                    m_curve = calc_auc_curve (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "                    pr_curve = prec_recall (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "\n",
    "                    tpd = pd.DataFrame(m_curve)\n",
    "                    if m_curve:\n",
    "                        tpd[0] =  tpd[0].astype(float).round(2)\n",
    "                        tpd = tpd.groupby([0]).mean()\n",
    "                        m_curve  = dict(zip(tpd.index, tpd[1]))\n",
    "                    else:\n",
    "                        m_curve  = {}\n",
    "                    tpd = pd.DataFrame(pr_curve)\n",
    "                    if pr_curve:\n",
    "                        tpd[0] =  tpd[0].astype(float).round(2)\n",
    "                        tpd = tpd.groupby([0]).mean()\n",
    "                        pr_curve  = dict(zip(tpd.index, tpd[1]))\n",
    "                    else:\n",
    "                        pr_curve  = {}\n",
    "                    m_l.append((chrm, num_pairs,dist_thresh, ca, m_curve, pr_curve, long_form_top[\"true_pos\"].sum(), long_form_top[\"true_neg\"].sum(), exp_median, exp_mean, exp_var, mp))\n",
    "            else:\n",
    "                pass\n",
    "\n",
    "    df_scores = pd.DataFrame(m_l, columns =['chrm', 'num_pairs','dist_thresh',  'auc', 'plot', 'pr_curve', 'true_pos', 'true_neg', 'exp_median', 'exp_mean', 'exp_var', 'Gene stable ID'])\n",
    "    #df_scores.to_hdf('/data/lohia/gene_distance_expresseion/dist_files/combined_%s_%s_%s.h5' %(resoulution_in_kb, case, dist_tp), key='df', mode='w')    \n",
    "    return df_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1435,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "from lohia_utilities.calculate_auc import *\n",
    "from pandas.core.common import SettingWithCopyWarning\n",
    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
    "from lohia_utilities.create_corr_network import rank\n",
    "import itertools\n",
    "import random\n",
    "\n",
    "def calc_auc_hic(resoulution_in_kb, case='simple', dist_tp='exp', prediction='hi-c-rao', shuffle=False):\n",
    "\n",
    "    df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/11_dist_with_georg_hic_sub_median_hic_%s.h5' %resoulution_in_kb)\n",
    "\n",
    "    #df_2_or = df_2_or[df_2_or['exp_georg'] >= 0] # liming the matrix to only chosen values for rank standerization\n",
    "    df_2_or = df_2_or[df_2_or['hi-c-rao'] >= 0] # liming the matrix to only chosen values for rank standerization\n",
    "\n",
    "    #ranked_matirx = rank(df_2_or['exp_georg'])\n",
    "    #df_2_or['exp_georg'] = ranked_matirx\n",
    "    #df_2_or.rename(columns={\"exp_georg\": \"exp (GK)\"}, inplace=True)\n",
    "\n",
    "    ranked_matirx = rank(df_2_or['exp'])\n",
    "    df_2_or['exp'] = ranked_matirx\n",
    "\n",
    "    #ranked_matirx = rank(df_2_or['hi-c-rao'])\n",
    "    #df_2_or['hi-c-rao'] = ranked_matirx\n",
    "    m_l = []\n",
    "    change_group_level_1 = df_2_or.groupby(['chrom_x'])\n",
    "    for chrm in change_group_level_1.groups.keys():\n",
    "        df = change_group_level_1.get_group(chrm)\n",
    "        \n",
    "        num_pairs = df['Gene stable ID_x'].nunique()\n",
    "\n",
    "        prot_list_sp = np.array_split(df, num_pairs, axis=0)\n",
    "        \n",
    "        #list(itertools.permutations(range(0,num_pairs)))[0]\n",
    "        prot_list = list(range(0,num_pairs))\n",
    "        if shuffle ==True:\n",
    "            random.shuffle(prot_list)\n",
    "        else:\n",
    "            pass\n",
    "        #for i, shuf_i in zip(list(range(0,num_pairs)), prot_list):\n",
    "        #for i in list(range(0,num_pairs)):\n",
    "        for i in list(range(110,115)):\n",
    "            \n",
    "            long_form_top = prot_list_sp[int(i)]\n",
    "            long_form_top['dist'] = long_form_top[dist_tp]\n",
    "            long_form_top['i_range'] = list(range(0,long_form_top.shape[0] ))\n",
    "            long_form_top = long_form_top[long_form_top['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "            long_form_top = long_form_top[long_form_top['Gene stable ID_x'] != long_form_top['Gene stable ID_y']] # remove all the self pairs from each set\n",
    "            long_form_top = long_form_top.reset_index()\n",
    "\n",
    "\n",
    "            \n",
    "            for dist_thresh in [1]:\n",
    "                if case == 'simple':\n",
    "                    long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]\n",
    "                elif case == 'tp':\n",
    "                    long_form_top = long_form_top.sort_values(by=['dist'], ascending=False) \n",
    "                    long_form_top[\"True_sim\"] = [0 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]  \n",
    "                    for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                        long_form_top.at[ind_val, 'True_sim'] = 1\n",
    "                else:    \n",
    "                    long_form_top = long_form_top.sort_values(by=['dist'], ascending=True) \n",
    "                    long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 1 for score in long_form_top[\"dist\"]]  \n",
    "                    for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                        long_form_top.at[ind_val, 'True_sim'] = 0\n",
    "                long_form_top[\"true_pos\"] = [score for score in long_form_top[\"True_sim\"]]\n",
    "                long_form_top[\"true_neg\"] = [1 if score==0 else 0 for score in long_form_top[\"True_sim\"]]\n",
    "                long_form_top[\"predicted_sim_from_exp\"] = [score for score in long_form_top[prediction]]\n",
    "                c_original = calc_auroc (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "                \n",
    "            #prot_list = list(range(0,num_pairs))\n",
    "            #prot_list.remove(i)\n",
    "            \n",
    "            for shuf_i in long_form_top['i_range'].to_list():\n",
    "            #for shuf_i in [100]:\n",
    "\n",
    "                long_form_top = prot_list_sp[int(i)]\n",
    "                long_form_top_shuf = prot_list_sp[int(shuf_i)]\n",
    "                long_form_top['dist'] = long_form_top[dist_tp]\n",
    "                #long_form_top_shuf['dist'] = long_form_top_shuf[dist_tp]\n",
    "                long_form_top['dist_prediction'] = long_form_top_shuf[dist_tp].to_list()\n",
    "                long_form_top = long_form_top[long_form_top['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "                long_form_top = long_form_top[long_form_top['Gene stable ID_x'] != long_form_top['Gene stable ID_y']] # remove all the self pairs from each set\n",
    "                \n",
    "                mp =  long_form_top['Gene stable ID_y'].values[0]\n",
    "                mp_precited = long_form_top_shuf['Gene stable ID_y'].values[0]\n",
    "\n",
    "                exp_median = long_form_top['exp'].median()\n",
    "                exp_mean = long_form_top['exp'].mean()\n",
    "                exp_var = long_form_top['exp'].var()\n",
    "\n",
    "                long_form_top = long_form_top.reset_index()\n",
    "                \n",
    "                for dist_thresh in [1]:\n",
    "                    if case == 'simple':\n",
    "                        long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]\n",
    "                    elif case == 'tp':\n",
    "                        long_form_top = long_form_top.sort_values(by=['dist'], ascending=False) \n",
    "                        long_form_top[\"True_sim\"] = [0 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]  \n",
    "                        for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                            long_form_top.at[ind_val, 'True_sim'] = 1\n",
    "                    else:    \n",
    "                        long_form_top = long_form_top.sort_values(by=['dist'], ascending=True) \n",
    "                        long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 1 for score in long_form_top[\"dist\"]]  \n",
    "                        for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                            long_form_top.at[ind_val, 'True_sim'] = 0\n",
    "                    long_form_top[\"true_pos\"] = [score for score in long_form_top[\"True_sim\"]]\n",
    "                    long_form_top[\"true_neg\"] = [1 if score==0 else 0 for score in long_form_top[\"True_sim\"]]\n",
    "                    long_form_top[\"predicted_sim_from_exp\"] = [score for score in long_form_top['dist_prediction']]\n",
    "                    c_hic = calc_auroc (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "                    \n",
    "                long_form_top = prot_list_sp[int(i)]\n",
    "                long_form_top_shuf = prot_list_sp[int(shuf_i)]\n",
    "                long_form_top['dist'] = long_form_top[dist_tp]\n",
    "                #long_form_top_shuf['dist'] = long_form_top_shuf[dist_tp]\n",
    "                long_form_top['exp'] = long_form_top_shuf['exp'].to_list()\n",
    "                long_form_top = long_form_top[long_form_top['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "                long_form_top = long_form_top[long_form_top['Gene stable ID_x'] != long_form_top['Gene stable ID_y']] # remove all the self pairs from each set\n",
    "                \n",
    "                mp =  long_form_top['Gene stable ID_y'].values[0]\n",
    "                mp_precited = long_form_top_shuf['Gene stable ID_y'].values[0]\n",
    "\n",
    "                exp_median = long_form_top['exp'].median()\n",
    "                exp_mean = long_form_top['exp'].mean()\n",
    "                exp_var = long_form_top['exp'].var()\n",
    "\n",
    "                long_form_top = long_form_top.reset_index()\n",
    "            \n",
    "\n",
    "                for dist_thresh in [1]:\n",
    "                    if case == 'simple':\n",
    "                        long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]\n",
    "                    elif case == 'tp':\n",
    "                        long_form_top = long_form_top.sort_values(by=['dist'], ascending=False) \n",
    "                        long_form_top[\"True_sim\"] = [0 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]  \n",
    "                        for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                            long_form_top.at[ind_val, 'True_sim'] = 1\n",
    "                    else:    \n",
    "                        long_form_top = long_form_top.sort_values(by=['dist'], ascending=True) \n",
    "                        long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 1 for score in long_form_top[\"dist\"]]  \n",
    "                        for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                            long_form_top.at[ind_val, 'True_sim'] = 0\n",
    "                    long_form_top[\"true_pos\"] = [score for score in long_form_top[\"True_sim\"]]\n",
    "                    long_form_top[\"true_neg\"] = [1 if score==0 else 0 for score in long_form_top[\"True_sim\"]]\n",
    "                    long_form_top[\"predicted_sim_from_exp\"] = [score for score in long_form_top[prediction]]\n",
    "                    ca = calc_auroc (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "                    m_curve  = {}\n",
    "                    pr_curve  = {}\n",
    "                    m_l.append((chrm, num_pairs,dist_thresh, ca, m_curve, pr_curve, long_form_top[\"true_pos\"].sum(), long_form_top[\"true_neg\"].sum(), exp_median, exp_mean, exp_var, mp, mp_precited, c_original, c_hic))\n",
    "\n",
    "\n",
    "    df_scores = pd.DataFrame(m_l, columns =['chrm', 'num_pairs','dist_thresh',  'auc', 'plot', 'pr_curve', 'true_pos', 'true_neg', 'exp_median', 'exp_mean', 'exp_var', 'Gene stable ID', 'mp_precited', 'auc_or', 'auc_hic'])\n",
    "    #df_scores.to_hdf('/data/lohia/gene_distance_expresseion/dist_files/combined_%s_%s_%s.h5' %(resoulution_in_kb, case, dist_tp), key='df', mode='w')    \n",
    "    return df_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1801,
   "metadata": {},
   "outputs": [],
   "source": [
    "for resoultion in [100]:\n",
    "        for case in ['tn']:\n",
    "            #df_scores = calc_auc_hic(resoultion, case=case, dist_tp='hi-c-rao', prediction='exp', shuffle=False)\n",
    "            df_scores =pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/11_100_tn_hi-c-rao_exp_permutations_includes_adjacent_hic.h5')\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1803,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = df_scores.rename(columns={\"dist_thresh\": \"TN\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1804,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = df_scores.rename(columns={\"auc\": \"auc_permutations\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1805,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = df_scores.rename(columns={\"auc_or\": \"auc\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1806,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores['auc_permutations_median'] = df_scores.groupby(['Gene stable ID'])['auc_permutations'].transform('median')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1704,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_scores.drop_duplicates(subset=['Gene stable ID'])['auc_permutations_median'] - df_scores.drop_duplicates(subset=['Gene stable ID'])['auc_or'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1712,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "417"
      ]
     },
     "execution_count": 1712,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([1 if x >=0 else 0 for x in y.to_list() ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores.drop_duplicates(subset=['Gene stable ID'])['p_val']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1793,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8621755253399215"
      ]
     },
     "execution_count": 1793,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores[\"auc_hic\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores.drop_duplicates(subset=['Gene stable ID'])["
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1802,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = df_scores[df_scores['auc_hic'] <0.6]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1601,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = df_scores[df_scores['auc_or'] >0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1619,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = df_scores[df_scores['samples'] <280]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1620,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>chrm</th>\n",
       "      <th>num_pairs</th>\n",
       "      <th>dist_thresh</th>\n",
       "      <th>auc</th>\n",
       "      <th>plot</th>\n",
       "      <th>pr_curve</th>\n",
       "      <th>true_pos</th>\n",
       "      <th>true_neg</th>\n",
       "      <th>exp_median</th>\n",
       "      <th>exp_mean</th>\n",
       "      <th>exp_var</th>\n",
       "      <th>Gene stable ID</th>\n",
       "      <th>mp_precited</th>\n",
       "      <th>auc_or</th>\n",
       "      <th>auc_hic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>chr11</td>\n",
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       "      <td>{}</td>\n",
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       "      <td>710</td>\n",
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       "      <td>0.052450</td>\n",
       "      <td>ENSG00000187049</td>\n",
       "      <td>ENSG00000149557</td>\n",
       "      <td>0.992958</td>\n",
       "      <td>0.681690</td>\n",
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       "    <tr>\n",
       "      <th>62194</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.901408</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>710</td>\n",
       "      <td>1</td>\n",
       "      <td>0.589374</td>\n",
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       "      <td>0.052450</td>\n",
       "      <td>ENSG00000187049</td>\n",
       "      <td>ENSG00000166086</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>999978</th>\n",
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       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.901408</td>\n",
       "      <td>{}</td>\n",
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       "      <td>ENSG00000196371</td>\n",
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       "      <td>0.043662</td>\n",
       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>999979</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
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       "      <td>0.901408</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>710</td>\n",
       "      <td>1</td>\n",
       "      <td>0.589374</td>\n",
       "      <td>0.545647</td>\n",
       "      <td>0.052450</td>\n",
       "      <td>ENSG00000162144</td>\n",
       "      <td>ENSG00000137710</td>\n",
       "      <td>0.964789</td>\n",
       "      <td>0.427465</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999982</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.901408</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>710</td>\n",
       "      <td>1</td>\n",
       "      <td>0.589374</td>\n",
       "      <td>0.545647</td>\n",
       "      <td>0.052450</td>\n",
       "      <td>ENSG00000162144</td>\n",
       "      <td>ENSG00000214376</td>\n",
       "      <td>0.964789</td>\n",
       "      <td>0.036620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999985</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.940845</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>710</td>\n",
       "      <td>1</td>\n",
       "      <td>0.633611</td>\n",
       "      <td>0.578590</td>\n",
       "      <td>0.074277</td>\n",
       "      <td>ENSG00000162144</td>\n",
       "      <td>ENSG00000110218</td>\n",
       "      <td>0.964789</td>\n",
       "      <td>0.039437</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999990</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.901408</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>710</td>\n",
       "      <td>1</td>\n",
       "      <td>0.589374</td>\n",
       "      <td>0.545647</td>\n",
       "      <td>0.052450</td>\n",
       "      <td>ENSG00000162144</td>\n",
       "      <td>ENSG00000064199</td>\n",
       "      <td>0.964789</td>\n",
       "      <td>0.404225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12115 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         chrm  num_pairs  dist_thresh       auc plot pr_curve  true_pos  \\\n",
       "62186   chr11       1004            1  0.901408   {}       {}       710   \n",
       "62190   chr11       1004            1  0.901408   {}       {}       710   \n",
       "62191   chr11       1004            1  0.901408   {}       {}       710   \n",
       "62193   chr11       1004            1  0.901408   {}       {}       710   \n",
       "62194   chr11       1004            1  0.901408   {}       {}       710   \n",
       "...       ...        ...          ...       ...  ...      ...       ...   \n",
       "999978  chr11       1004            1  0.901408   {}       {}       710   \n",
       "999979  chr11       1004            1  0.901408   {}       {}       710   \n",
       "999982  chr11       1004            1  0.901408   {}       {}       710   \n",
       "999985  chr11       1004            1  0.940845   {}       {}       710   \n",
       "999990  chr11       1004            1  0.901408   {}       {}       710   \n",
       "\n",
       "        true_neg  exp_median  exp_mean   exp_var   Gene stable ID  \\\n",
       "62186          1    0.589374  0.545647  0.052450  ENSG00000187049   \n",
       "62190          1    0.589374  0.545647  0.052450  ENSG00000187049   \n",
       "62191          1    0.589374  0.545647  0.052450  ENSG00000187049   \n",
       "62193          1    0.589374  0.545647  0.052450  ENSG00000187049   \n",
       "62194          1    0.589374  0.545647  0.052450  ENSG00000187049   \n",
       "...          ...         ...       ...       ...              ...   \n",
       "999978         1    0.589374  0.545647  0.052450  ENSG00000162144   \n",
       "999979         1    0.589374  0.545647  0.052450  ENSG00000162144   \n",
       "999982         1    0.589374  0.545647  0.052450  ENSG00000162144   \n",
       "999985         1    0.633611  0.578590  0.074277  ENSG00000162144   \n",
       "999990         1    0.589374  0.545647  0.052450  ENSG00000162144   \n",
       "\n",
       "            mp_precited    auc_or   auc_hic     p_val  samples  \n",
       "62186   ENSG00000149311  0.992958  0.547183  0.029412      272  \n",
       "62190   ENSG00000150433  0.992958  0.432394  0.029412      272  \n",
       "62191   ENSG00000149289  0.992958  0.383099  0.029412      272  \n",
       "62193   ENSG00000149557  0.992958  0.681690  0.029412      272  \n",
       "62194   ENSG00000166086  0.992958  0.526056  0.029412      272  \n",
       "...                 ...       ...       ...       ...      ...  \n",
       "999978  ENSG00000196371  0.964789  0.043662  0.000000      272  \n",
       "999979  ENSG00000137710  0.964789  0.427465  0.000000      272  \n",
       "999982  ENSG00000214376  0.964789  0.036620  0.000000      272  \n",
       "999985  ENSG00000110218  0.964789  0.039437  0.000000      272  \n",
       "999990  ENSG00000064199  0.964789  0.404225  0.000000      272  \n",
       "\n",
       "[12115 rows x 17 columns]"
      ]
     },
     "execution_count": 1620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1621,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.relplot(y=\"p_val\", x=\"samples\", kind=\"line\", data=df_scores, ci='sd');\n",
    "#ax.set(xlim=(0.6, 1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f74cc918050>"
      ]
     },
     "execution_count": 1467,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.relplot(y=\"auc\", x=\"auc_hic\", kind=\"line\", data=df_scores, ci='sd');\n",
    "ax.set(xlim=(0.6, 1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1690,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73e5114d50>"
      ]
     },
     "execution_count": 1690,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bwI9X1XNJrqW3aJ20pnjkrvXohO4DIKD3oRBfWqTfnfQ+9/KDvDIl82bgv4HnkxwNnDPCOqUVM9y1Hj0GbE3yIHAkcPVCnarqZeBWegF+a9f2NXrTMY8A19BbllZac1zyV+tKking1qo6aZVLkUbKI3dJapBH7lr3ktwMnDiv+Xeq6ourUY80DIa7JDXIaRlJapDhLkkNMtwlqUGGuyQ16P8AFQr8+QmXDV0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(df_scores.drop_duplicates(subset=['Gene stable ID'])['p_val'], bins=20,kde=False)\n",
    "#ax = sns.distplot(df_2_or, bins=101, hist=True, kde=False, hist_kws={\"range\":(0, 100)})\n",
    "#ax.set_xlim(0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1697,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7395272390>"
      ]
     },
     "execution_count": 1697,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FSe5K8unu9/uK9ahzXJLclOR4kgeW2Z4k7+3+exxKctHIb1pVp/yD3knZfwS+F3g+8BnggiV9fgn4g275SuCW9a57Dcb8o8C3dsu/uBHG3PV7IfAx4B5gdr3rXoOf8w7g08BZ3fq56133Gox5L/CL3fIFwCPrXfeIY/5h4CLggWW2XwH8Fb3PCO0C7h31PaflyH2Y2xnsBvZ1yx8GLk3S78NU02LgmKvqrqr6Wrd6D73PE0yzYW9b8T+A3wX+bS2Lm5BhxvzzwPuq6gmAqjq+xjWO2zBjLuA7uuXvZMo/J1NVHwO+dJIuu4H3V889wJlJNo/yntMS7v1uZ7BluT5V9QzwJPBda1LdZAwz5sWuofeXf5oNHHOSC4FtVXXbWhY2QcP8nF8MvDjJJ5Lck+SyNatuMoYZ8zuAn0kyD9wO/Le1KW3drPT/94Gm5ZuYBt7OYMg+02To8ST5GWAW+JGJVjR5Jx1zkucB1wM/t1YFrYFhfs6b6E3NXELvX2d/l+RlVfXlCdc2KcOM+Srg5qp6d5L/BPxJN+avT768dTH2/JqWI/dhbmfw//sk2UTvn3In+2fQqW6oWzgkeTXwduAnq+rpNaptUgaN+YXAy4C7kzxCb27ywJSfVB32d/vWqvr3qvoi8DC9sJ9Ww4z5GmA/QFX9PfACejfYatXYb9kyLeE+zO0MDgBXd8uvBT5a3ZmKKTVwzN0UxR/SC/Zpn4eFAWOuqier6pyq2l5V2+mdZ/jJqppbn3LHYpjf7b+kd/KcJOfQm6b5wppWOV7DjPlR4FKAJC+hF+4La1rl2joAvKG7amYX8GRVHRvpFdf7LPIKzjZfAfwDvbPsb+/afove/9zQ++H/GXAEuA/43vWueQ3G/LfA48DB7nFgvWue9JiX9L2bKb9aZsifc4D3AA8CnwWuXO+a12DMFwCfoHclzUHgx9a75hHH+0HgGPDv9I7SrwHeBLxp0c/4fd1/j8+O4/fa2w9IUoOmZVpGkrQChrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd2lMktw95Z+WVUMMd0lqkOGuDSXJ9iSfS7Kv+1KEDyf51j79Lk+yf9H6JUn+V7d8Q5K57osz3rmW9UvDMty1Ef0HYG9VvRz4Cr0velnqDmBXkm/r1v8LcEu3/PaqmgVeDvxIkpdPumBppQx3bUSPVdUnuuX/CfzQ0g7V+06AvwZ+orvL6H8Gbu02vz7Jp+h9O9JL6d0HRTqlTMv93KVxWnpDpeVusHQLcC29W0d/sqqeSnI+8GvAD1bVE0lupnfTOumU4pG7NqIXdV8AAb0vhfj4Mv3upve9lz/PN6ZkvgP4P8CTSc4DLp9gndKqGe7aiB4Crk5yCDgbuKFfp6p6FriNXoDf1rV9ht50zGHgJnq3pZVOOd7yVxtKku3AbVX1snUuRZooj9wlqUEeuWvDS/IR4PwlzW+pqr9Zj3qkcTDcJalBTstIUoMMd0lqkOEuSQ0y3CWpQf8PUwN148ZQugAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(df_scores.drop_duplicates(subset=['Gene stable ID'])['p_val'], bins=20,kde=False)\n",
    "#ax = sns.distplot(df_2_or, bins=101, hist=True, kde=False, hist_kws={\"range\":(0, 100)})\n",
    "#ax.set_xlim(0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1672,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73e69265d0>"
      ]
     },
     "execution_count": 1672,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(df_scores[[\"p_val\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1608,
   "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>chrm</th>\n",
       "      <th>num_pairs</th>\n",
       "      <th>dist_thresh</th>\n",
       "      <th>auc</th>\n",
       "      <th>plot</th>\n",
       "      <th>pr_curve</th>\n",
       "      <th>true_pos</th>\n",
       "      <th>true_neg</th>\n",
       "      <th>exp_median</th>\n",
       "      <th>exp_mean</th>\n",
       "      <th>exp_var</th>\n",
       "      <th>Gene stable ID</th>\n",
       "      <th>mp_precited</th>\n",
       "      <th>auc_or</th>\n",
       "      <th>auc_hic</th>\n",
       "      <th>p_val</th>\n",
       "      <th>samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.758698</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>891</td>\n",
       "      <td>1</td>\n",
       "      <td>0.121187</td>\n",
       "      <td>0.128362</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>ENSG00000149311</td>\n",
       "      <td>ENSG00000244734</td>\n",
       "      <td>0.933782</td>\n",
       "      <td>0.203143</td>\n",
       "      <td>0.425076</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.753086</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>891</td>\n",
       "      <td>1</td>\n",
       "      <td>0.181077</td>\n",
       "      <td>0.197166</td>\n",
       "      <td>0.014144</td>\n",
       "      <td>ENSG00000149311</td>\n",
       "      <td>ENSG00000188124</td>\n",
       "      <td>0.933782</td>\n",
       "      <td>0.258137</td>\n",
       "      <td>0.425076</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.739618</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>891</td>\n",
       "      <td>1</td>\n",
       "      <td>0.575126</td>\n",
       "      <td>0.521568</td>\n",
       "      <td>0.044223</td>\n",
       "      <td>ENSG00000149311</td>\n",
       "      <td>ENSG00000149294</td>\n",
       "      <td>0.933782</td>\n",
       "      <td>0.459035</td>\n",
       "      <td>0.425076</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.955107</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>891</td>\n",
       "      <td>1</td>\n",
       "      <td>0.709154</td>\n",
       "      <td>0.637962</td>\n",
       "      <td>0.078983</td>\n",
       "      <td>ENSG00000149311</td>\n",
       "      <td>ENSG00000149089</td>\n",
       "      <td>0.933782</td>\n",
       "      <td>0.158249</td>\n",
       "      <td>0.425076</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.976431</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>891</td>\n",
       "      <td>1</td>\n",
       "      <td>0.623800</td>\n",
       "      <td>0.579748</td>\n",
       "      <td>0.052413</td>\n",
       "      <td>ENSG00000149311</td>\n",
       "      <td>ENSG00000179532</td>\n",
       "      <td>0.933782</td>\n",
       "      <td>0.355780</td>\n",
       "      <td>0.425076</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006993</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>858</td>\n",
       "      <td>1</td>\n",
       "      <td>0.590600</td>\n",
       "      <td>0.544989</td>\n",
       "      <td>0.049380</td>\n",
       "      <td>ENSG00000064199</td>\n",
       "      <td>ENSG00000171202</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>0.444639</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006996</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>858</td>\n",
       "      <td>1</td>\n",
       "      <td>0.590600</td>\n",
       "      <td>0.544989</td>\n",
       "      <td>0.049380</td>\n",
       "      <td>ENSG00000064199</td>\n",
       "      <td>ENSG00000183801</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>0.414918</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007003</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>858</td>\n",
       "      <td>1</td>\n",
       "      <td>0.590600</td>\n",
       "      <td>0.544989</td>\n",
       "      <td>0.049380</td>\n",
       "      <td>ENSG00000064199</td>\n",
       "      <td>ENSG00000214376</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>0.635781</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007007</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>858</td>\n",
       "      <td>1</td>\n",
       "      <td>0.590600</td>\n",
       "      <td>0.544989</td>\n",
       "      <td>0.049380</td>\n",
       "      <td>ENSG00000064199</td>\n",
       "      <td>ENSG00000110218</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>0.329837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007008</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>858</td>\n",
       "      <td>1</td>\n",
       "      <td>0.590600</td>\n",
       "      <td>0.544989</td>\n",
       "      <td>0.049380</td>\n",
       "      <td>ENSG00000064199</td>\n",
       "      <td>ENSG00000196778</td>\n",
       "      <td>0.828671</td>\n",
       "      <td>0.314685</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>297125 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          chrm  num_pairs  dist_thresh       auc plot pr_curve  true_pos  \\\n",
       "12       chr11       1004            1  0.758698   {}       {}       891   \n",
       "13       chr11       1004            1  0.753086   {}       {}       891   \n",
       "16       chr11       1004            1  0.739618   {}       {}       891   \n",
       "21       chr11       1004            1  0.955107   {}       {}       891   \n",
       "25       chr11       1004            1  0.976431   {}       {}       891   \n",
       "...        ...        ...          ...       ...  ...      ...       ...   \n",
       "1006993  chr11       1004            1  0.828671   {}       {}       858   \n",
       "1006996  chr11       1004            1  0.828671   {}       {}       858   \n",
       "1007003  chr11       1004            1  0.828671   {}       {}       858   \n",
       "1007007  chr11       1004            1  0.828671   {}       {}       858   \n",
       "1007008  chr11       1004            1  0.828671   {}       {}       858   \n",
       "\n",
       "         true_neg  exp_median  exp_mean   exp_var   Gene stable ID  \\\n",
       "12              1    0.121187  0.128362  0.007605  ENSG00000149311   \n",
       "13              1    0.181077  0.197166  0.014144  ENSG00000149311   \n",
       "16              1    0.575126  0.521568  0.044223  ENSG00000149311   \n",
       "21              1    0.709154  0.637962  0.078983  ENSG00000149311   \n",
       "25              1    0.623800  0.579748  0.052413  ENSG00000149311   \n",
       "...           ...         ...       ...       ...              ...   \n",
       "1006993         1    0.590600  0.544989  0.049380  ENSG00000064199   \n",
       "1006996         1    0.590600  0.544989  0.049380  ENSG00000064199   \n",
       "1007003         1    0.590600  0.544989  0.049380  ENSG00000064199   \n",
       "1007007         1    0.590600  0.544989  0.049380  ENSG00000064199   \n",
       "1007008         1    0.590600  0.544989  0.049380  ENSG00000064199   \n",
       "\n",
       "             mp_precited    auc_or   auc_hic     p_val  samples  \n",
       "12       ENSG00000244734  0.933782  0.203143  0.425076      327  \n",
       "13       ENSG00000188124  0.933782  0.258137  0.425076      327  \n",
       "16       ENSG00000149294  0.933782  0.459035  0.425076      327  \n",
       "21       ENSG00000149089  0.933782  0.158249  0.425076      327  \n",
       "25       ENSG00000179532  0.933782  0.355780  0.425076      327  \n",
       "...                  ...       ...       ...       ...      ...  \n",
       "1006993  ENSG00000171202  0.828671  0.444639  1.000000      356  \n",
       "1006996  ENSG00000183801  0.828671  0.414918  1.000000      356  \n",
       "1007003  ENSG00000214376  0.828671  0.635781  1.000000      356  \n",
       "1007007  ENSG00000110218  0.828671  0.329837  1.000000      356  \n",
       "1007008  ENSG00000196778  0.828671  0.314685  1.000000      356  \n",
       "\n",
       "[297125 rows x 17 columns]"
      ]
     },
     "execution_count": 1608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1550,
   "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>chrm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc</td>\n",
       "      <td>0.758698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc</td>\n",
       "      <td>0.753086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc</td>\n",
       "      <td>0.739618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc</td>\n",
       "      <td>0.955107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc</td>\n",
       "      <td>0.976431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536191</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc_or</td>\n",
       "      <td>0.828671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536192</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc_or</td>\n",
       "      <td>0.828671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536193</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc_or</td>\n",
       "      <td>0.828671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536194</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc_or</td>\n",
       "      <td>0.828671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536195</th>\n",
       "      <td>chr11</td>\n",
       "      <td>auc_or</td>\n",
       "      <td>0.828671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536196 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         chrm variable     value\n",
       "0       chr11      auc  0.758698\n",
       "1       chr11      auc  0.753086\n",
       "2       chr11      auc  0.739618\n",
       "3       chr11      auc  0.955107\n",
       "4       chr11      auc  0.976431\n",
       "...       ...      ...       ...\n",
       "536191  chr11   auc_or  0.828671\n",
       "536192  chr11   auc_or  0.828671\n",
       "536193  chr11   auc_or  0.828671\n",
       "536194  chr11   auc_or  0.828671\n",
       "536195  chr11   auc_or  0.828671\n",
       "\n",
       "[536196 rows x 3 columns]"
      ]
     },
     "execution_count": 1550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(df_scores, id_vars=['chrm'], value_vars=['auc', 'auc_or'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1633,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8488950480581252"
      ]
     },
     "execution_count": 1633,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores['auc_or'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1692,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores['p_val'] = df_scores['auc'] - df_scores['auc_or']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1693,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores['p_val'] = [1 if x>=0 else 0 for x in df_scores['p_val']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1694,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores['p_val'] = df_scores.groupby(['Gene stable ID'])['p_val'].transform('mean')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1695,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores['samples'] = df_scores.groupby(['Gene stable ID'])['p_val'].transform('count')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1696,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_scores['p_val'] = (df_scores['p_val'] * 1.0) / (df_scores['true_pos'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1713,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'p_val'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2888\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2889\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2890\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'p_val'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1713-7c9b4dc8b0d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_scores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_scores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'p_val'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m<=\u001b[0m\u001b[0;36m.05\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Gene stable ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2889\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2890\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2893\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'p_val'"
     ]
    }
   ],
   "source": [
    "df_scores[df_scores['p_val']<=.05]['Gene stable ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1625,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 1625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores['Gene stable ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 892,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(501, 14)"
      ]
     },
     "execution_count": 892,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores[(df_scores['Gene stable ID']!='ENSG00000180878') & (df_scores['auc']>=0.864)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1807,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 tn\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#original\n",
    "for resoultion in [100]:\n",
    "    for case in ['tn']:\n",
    "        print (resoultion, case)\n",
    "        #100 contacts\n",
    "        import seaborn as sns\n",
    "        import matplotlib.pyplot as plt\n",
    "        fig, axes = plt.subplots(figsize=(10,5))\n",
    "        #grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "        #bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "        #bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "        sns.boxplot(y='value', x='chrm', \n",
    "                         data=pd.melt(df_scores, id_vars=['chrm'], value_vars=['auc', 'auc_permutations_median', 'auc_hic']), \n",
    "                         palette=\"colorblind\"\n",
    "                         ,hue='variable'\n",
    "                   )\n",
    "        #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "        #axes.set_title('Predicting structure similarity from expression')\n",
    "        axes.yaxis.grid(True)\n",
    "        #axes.set_xlabel('Co-expression')\n",
    "        axes.set_ylabel('AUC')\n",
    "        axes.set_ylim([0.0,1.101])\n",
    "        #plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1565,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 tn\n"
     ]
    },
    {
     "data": {
      "image/png": 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HkwAMzXkKAI68zZs358wzz+x6jFFvqN9TKWVjrXXhUOvdtgYAAABAk9vWAAAAAEaJ7du3Z8mSJc/bf/PNN2fatGkdTCQeAQAAAIwa06ZNy89+9rOux3gOt60BAAAAR42x9mznI+2l/H7EIwAAAOCoMHHixGzfvl1Aaqi1Zvv27Zk4ceKwvs9tawAAAMBRYdasWdmyZUu2bt3a9Sij1sSJEzNr1qxhfY94BAAAABwVJkyYkNNPP73rMY46blsDAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBpfNcDAMBos2LFigwMDHQ9BmPcs3+G+vv7O56Esa6vry/Lly/vegwAjmHiEQAcYmBgIL+4e0NOfeXerkdhDBu3d98F3jv/+acdT8JY9qsd47oeAQDEIwAYyqmv3JtPLNnV9RjAMe6amyd3PQIAeOYRAAAAAG3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3jux6AY9OKFSsyMDDQ9RiMYc/++env7+94Esa6vr6+LF++vOsxAABg1BKP6MTAwEDu+Nn/yNOTp3c9CmPUuKdqkuS2+x/ueBLGsgm7tnU9AgAAjHriEZ15evL0PDrvsq7HAI5hp9z93a5HAACAUc8zjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoGt/1AAAw2gwODuaxHeNyzc2Tux4FOMZt2TEuj5XBrscA4BjnyiMAAAAAmlx5BACHmD17dnbWh/KJJbu6HgU4xl1z8+RMmT276zEAOMa58ggAAACAJvEIAAAAgCbxCAAAAICmnsajUsrSUspAKeX+Uspnhnj91aWUW0spd5VS7i6lvL2X8wAAAAAwPD2LR6WUcUm+nORtSV6X5H2llNcdsuzfJvlOrXV+kiuSfKVX8wAAAAAwfL288ui8JPfXWh+otT6V5FtJ3nXImprkFfu/npLkoR7OAwAAAMAwje/hsU9NMnjQ9pYk5x+y5q+S/L+llI8nOSnJW3o4DwAAAADD1Mt4VIbYVw/Zfl+S62ut/7GU8gdJ/q6Uclat9ZnnHKiUZUmWJcmMGTOybt26XszLEXTRRRdl/nlPZ++JU7oeBTiGjTvr3Zl8woTn/b1y0UUXZe9T52THyXu7GQxgv7f9ybiMO/4k//4FoFO9jEdbksw+aHtWnn9b2p8nWZoktdb1pZSJSaYn+c3Bi2qt1ya5NkkWLlxYFy9e3KOROVL6+/tz2/0P59F5l3U9CnAMO+XutbnwNTNz5ZVXPmd/f39/dv7zT/OJJbs6mgxgn/9y8+RMefX5zztPAcCR1MtnHt2R5IxSyumllOOz74HYNxyy5p+TLEmSUsqZSSYm2drDmQAAAAAYhp7Fo1rrniQfS/LDJJuz71PVNpVSvlhKeef+ZZ9O8qFSys+T/NckV9VaD721DQAAAICO9PK2tdRab0xy4yH7Pn/Q179I8oe9nAEAAACAl66Xt60BAAAAMMaJRwAAAAA0iUcAAAAANIlHAAAAADSJRwAAAAA09fTT1qBlcHAwE3btyCl3f7frUYBj2IRd2zI4uKfrMQAAYFRz5REAAAAATa48ohOzZ8/O4L+Mz6PzLut6FOAYdsrd383s2TO7HgMAAEY1Vx4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANA0vusBAAAARtqKFSsyMDDQ9Rij2uDgYJJk9uzZHU8yuvX19WX58uVdjwGdEo8AAACOQb/73e+6HgEYI8QjAADgqONKkRfW39+fJFm9enXHkwCjnWceAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAAADQJB4BAAAA0DS+6wEAYDT61Y5xuebmyV2PwRi2dde+/4/uVZOf6XgSxrJf7RiXKa/uegoAjnXiEQAcoq+vr+sROAr8emAgSTLl1f488dJNebVzEgDdE48A4BDLly/vegSOAv39/UmS1atXdzwJAMDL45lHAAAAADSJRwAAAAA09TQelVKWllIGSin3l1I+01jzr0opvyilbCqlfLOX8wAAAAAwPD175lEpZVySLye5OMmWJHeUUm6otf7ioDVnJPk/kvxhrfW3pZTf69U8AAAAAAxfL688Oi/J/bXWB2qtTyX5VpJ3HbLmQ0m+XGv9bZLUWn/Tw3kAAAAAGKZexqNTkwwetL1l/76D/X6S3y+l/PdSyu2llKU9nAcAAACAYerZbWtJyhD76hA//4wki5PMSvKTUspZtdYdzzlQKcuSLEuSGTNmZN26dSM+LEfWRRddlPnnPZ29J07pehTgGDburHdn8gkT/L1CT+zYse+fM/58AaOV8xTwYvUyHm1JMvug7VlJHhpize211qeT/M9SykD2xaQ7Dl5Ua702ybVJsnDhwrp48eJezcwR0t/fn9vufziPzrus61GAY9gpd6/Nha+ZmSuvvLLrUTgKfeMb30iS+HcLMFo5TwEvVi9vW7sjyRmllNNLKccnuSLJDYes+V6SNyVJKWV69t3G9kAPZwIAAABgGHoWj2qte5J8LMkPk2xO8p1a66ZSyhdLKe/cv+yHSbaXUn6R5NYky2ut23s1EwAAAADD08vb1lJrvTHJjYfs+/xBX9ck/2b/fwAAAAAYZXp52xoAAAAAY5x4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAEBTMx6VUt5aSrl8iP1/Wkq5uLdjAQAAADAaHO7Koy8k+W9D7L85yRd7Mw4AAAAAo8nh4tGkWuvWQ3fWWn+d5KTejQQAAADAaHG4eDSxlDL+0J2llAlJTuzdSAAAAACMFoeLR99N8p9LKQeuMtr/9Vf3vwYAAADAUe5w8ejfJnkkyS9LKRtLKXcmeTDJ1v2vAQAAAHCUe95tac+qte5J8plSyheSvGb/7vtrrU8ekckAAAAA6FwzHpVSLjtkV03yylLKz2qtj/d2LAAAAABGg2Y8SvKOIfadkmReKeXPa6239GgmAAAAAEaJw9229sGh9pdSTkvynSTn92ooAAAAAEaHwz0we0i11l8mmdCDWQAAAAAYZYYdj0opr03yLz2YBQAAAIBR5nAPzP5/su8h2Qc7JcnMJO/v5VAAAAAAjA6He2D2fzhkuyZ5NPsC0vuTrO/VUAAAAACMDod7YPZ/e/brUsobkvxvSf5Vkv+Z5B96PxoAAAAAXTvcbWu/n+SKJO9Lsj3Jt5OUWuubjtBsAAAAAHTscLet3ZvkJ0neUWu9P0lKKZ86IlMBAAAAMCoc7tPW3pPk10luLaX851LKkiTlyIwFAAAAwGjQjEe11rW11vcmeW2SdUk+lWRGKWVVKeWPj9B8AAAAAHTocFceJUlqrU/UWv++1npJkllJfpbkMz2fDAAAAIDOvWA8Olit9dFa69/UWt/cq4EAAAAAGD2GFY8AAAAAOLaIRwAAAAA0iUcAAAAANIlHAAAAADSJRwAAAAA0iUcAAAAANIlHAAAAADSJRwAAAAA0iUcAAAAANIlHAAAAADSJRwAAAAA0iUcAAAAANIlHAAAAADSJRwAAAAA0je96AAAAYHhWrFiRgYGBrsdgjHv2z1B/f3/HkzCW9fX1Zfny5V2PQY+JRwAAMMYMDAzkjrt/lqenntT1KIxh4/Y+lSS5bfC+jidhrJrw2ye6HoEjRDwCAIAx6OmpJ2XbxWd1PQZwDJv+o3u6HoEjxDOPAAAAAGhy5RGdmbBrW065+7tdj8EYNe7JnUmSvSdO6XgSxrIJu7Ylmdn1GAAAMKqJR3Sir6+v6xEY4wYGHkuS9L3G//Dn5ZjpfAQAAC9APKITnsbPy/Xsp4KsXr2640kAAACObp55BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQJN4BAAAAECTeAQAAABAk3gEAAAAQFNP41EpZWkpZaCUcn8p5TOHWXd5KaWWUhb2ch4AAAAAhqdn8aiUMi7Jl5O8LcnrkryvlPK6IdadnOTqJD/t1SwAAAAAvDS9vPLovCT311ofqLU+leRbSd41xLr/K8m/T7K7h7MAAAAA8BL0Mh6dmmTwoO0t+/cdUEqZn2R2rfUHPZwDAAAAgJdofA+PXYbYVw+8WMpxSf5Tkqte8EClLEuyLElmzJiRdevWjcyEwJi1Y8eOJHE+AEYt5yl66aKLLsr8p87Nnlec2PUowDFs/OXzMvn4if6uOwb0Mh5tSTL7oO1ZSR46aPvkJGclWVdKSZL/JckNpZR31lo3HHygWuu1Sa5NkoULF9bFixf3cGxgLPjGN76RJHE+AEYr5yl6qb+/P7cN3pdtF5/V9SjAMWz6j+7JhbPPyJVXXtn1KPRYL29buyPJGaWU00spxye5IskNz75Ya91Za51ea51Ta52T5PYkzwtHAAAAAHSnZ/Go1ronyceS/DDJ5iTfqbVuKqV8sZTyzl79XAAAAABGTi9vW0ut9cYkNx6y7/ONtYt7OQsAAAAAw9fL29YAAAAAGOPEIwAAAACaxCMAAAAAmsQjAAAAAJrEIwAAAACaxCMAAAAAmsQjAAAAAJrEIwAAAACaxCMAAAAAmsQjAAAAAJrEIwAAAACaxCMAAAAAmsQjAAAAAJrEIwAAAACaxCMAAAAAmsQjAAAAAJrEIwAAAACaxCMAAAAAmsQjAAAAAJrEIwAAAACaxCMAAAAAmsQjAAAAAJrEIwAAAACaxCMAAAAAmsZ3PQAAADA8g4ODmfDbJzL9R/d0PQpwDJvw2ycymMGux+AIcOURAAAAAE2uPAIAgDFm9uzZGczubLv4rK5HAY5h0390T2bPnvzLh0wAAAukSURBVN31GBwBrjwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgSTwCAAAAoEk8AgAAAKBJPAIAAACgqafxqJSytJQyUEq5v5TymSFe/zellF+UUu4updxcSjmtl/MAAAAAMDw9i0ellHFJvpzkbUlel+R9pZTXHbLsriQLa63zkqxJ8u97NQ8AAAAAw9fLK4/OS3J/rfWBWutTSb6V5F0HL6i13lpr/d3+zduTzOrhPAAAAAAMUy/j0alJBg/a3rJ/X8ufJ/nHHs4DAAAAwDCN7+GxyxD76pALS3l/koVJFjVeX5ZkWZLMmDEj69atG6ERgbFqx44dSeJ8AIxazlP00kUXXZT5T52bPa84setRgGPY+MvnZfLxE/1ddwzoZTzakmT2Qduzkjx06KJSyluSfDbJolrrvwx1oFrrtUmuTZKFCxfWxYsXj/iwwNjyjW98I0nifACMVs5T9FJ/f39uG7wv2y4+q+tRgGPY9B/dkwtnn5Err7yy61HosV7etnZHkjNKKaeXUo5PckWSGw5eUEqZn+Rvkryz1vqbHs4CAAAAwEvQs3hUa92T5GNJfphkc5Lv1Fo3lVK+WEp55/5lK5JMTvJ/l1J+Vkq5oXE4AAAAADrQy9vWUmu9McmNh+z7/EFfv6WXPx8AAACAl6eXt60BAAAAMMaJRwAAAAA0iUcAAAAANIlHAAAAADSJRwAAAAA0iUcAAAAANIlHAAAAADSN73oAAABg+Cb89olM/9E9XY/BGDbu8d1Jkr0nT+x4EsaqCb99Ipnd9RQcCeIRAACMMX19fV2PwFFgYGAgSdI3+4yOJ2HMmu18dKwQjwAAYIxZvnx51yNwFOjv70+SrF69uuNJgNHOM48AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoGt/1AAAAR6ONGzcmSebPn5+77rqr42kAAF468QgAGLYVK1ZkYGCg6zHGjP7+/q5HGLX6+vqyfPnyrscAAA7DbWsAACPs2auOWtsAAGOJK48AgGFzpcjhzZ8//3n7Vq9e3cEkAAAvnyuPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAAAAAGgSjwAAAABoEo8AAAAAaBKPAABG2F133XXYbQCAsUQ8AgAYYfPnzz/sNgDAWCIeAQAAANAkHgEAAADQJB4BAAAA0CQeAQAAANAkHgEAjDCftgYAHE3EIwAAAACaxCMAgBE2f/78w24DAIwl4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQAAAABN4hEAAAAATeIRAAAAAE3iEQDACLvrrrsOuw0AMJaIRwAAAAA09TQelVKWllIGSin3l1I+M8TrJ5RSvr3/9Z+WUub0ch4AgCNh/vz5h90GABhLehaPSinjknw5yduSvC7J+0oprztk2Z8n+W2t9TVJ/lOSf9ereQAAAAAYvl5eeXRekvtrrQ/UWp9K8q0k7zpkzbuS/Jf9X69JsqSUUno4EwAAAADDML6Hxz41yeBB21uSnN9aU2vdU0rZmWRakm09nAvGhBUrVmRgYKDrMUatZ383/f39HU8yuvX19WX58uVdjwEAR5x/S70w/556cfx7CpJSa+3NgUv5kyRvrbX279/+10nOq7V+/KA1m/av2bJ/+//bv2b7IcdalmTZ/s2+JP4WAABGrRNPPHF+nnuF9zNPPvmkj1wDAEaz02qtrxrqhV5eebQlyeyDtmcleaixZkspZXySKUkePfRAtdZrk1zbozkBAHqilLKh1rqw6zkAAF6OXj7z6I4kZ5RSTi+lHJ/kiiQ3HLLmhiRX7v/68iS31F5dCgUAAADAsPXsyqP9zzD6WJIfJhmX5Lpa66ZSyheTbKi13pDkb5P8XSnl/uy74uiKXs0DAAAAwPD17JlHAADHulLKsv233wMAjFniEQAAAABNvXzmEQAAAABjnHgEAPASlFKuL6Vc/iLXXldK+U0p5Z5D9v9JKWVTKeWZUopPZQMARiXxCACgR0op4/Z/eX2SpUMsuSfJZUl+fKRmAgAYLvEIAOBFKKV8oJRydynl56WUv9u/+42llNtKKQ88exVSKWVxKeXWUso3k/yPJKm1/jj7Pln2OWqtm2utA0fsTQAAvATjux4AAGC0K6XMTfLZJH9Ya91WSjklyV8nmZnk/2/njl37rKIwAL8vurnZoWsGwYJdXAqiSCnOoiB1E6R/S1f/AgdB1CVIKXRwCNJBhTpIjCmCxSwdi1uHDuY4JIFQ8jWpafrD+jzbvZwPzre+99z7TpILSW4mWd//5FKSizOzs4p+AQCeJZNHAADHu5JkfWYeJMnMHEwR3ZiZ3Zm5m+T8ofo7giMA4EUhPAIAOF6TzBH7jx6rOfDwbNsBAHh+hEcAAMfbSHK17bkk2b+2BgDwvyA8AgA4xsxsJ7me5Hbbzey9d3Ribb9J8lOS19veb3ttf//DtveTvJXkVtvvnnHrAACn1pmjJrABAAAAwOQRAAAAAE8gPAIAAABgkfAIAAAAgEXCIwAAAAAWCY8AAAAAWCQ8AgA4hbZftP1o1X0AAJwV4REAwBlr+9KqewAA+LeERwAAT6HtJ21/bbvZ9sv97Xfb/tj2z4MppLaX237f9uskW23X2v7e9vO2v7X9qu17bX9o+0fbS6v7KwCAZZ2ZVfcAAPCf0PaNJN8meXtmHrR9NclnSV5J8nGSC0luzsxrbS8nuZXk4szstF1Lci/Jm0m2k/ycZDPJtSTvJ/l0Zj54vn8EAHA8k0cAACd3Jcn6zDxIkpn5a3//xszszszdJOcP1d+ZmZ1D652Z2ZqZ3ewFSBuzd5K3lWTt7NsHAHh6wiMAgJNrkqPGth89VnPg4RPqdg+td5O8fOruAADOgPAIAODkNpJcbXsuSfavrQEAvNCccAEAnNDMbLe9nuR227+T/LLqngAAzpoHswEAAABY5NoaAAAAAIuERwAAAAAsEh4BAAAAsEh4BAAAAMAi4REAAAAAi4RHAAAAACwSHgEAAACwSHgEAAAAwKJ/APeUk+VX0i0OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#original\n",
    "for resoultion in [100]:\n",
    "    for case in ['tn']:\n",
    "        print (resoultion, case)\n",
    "        #100 contacts\n",
    "        import seaborn as sns\n",
    "        import matplotlib.pyplot as plt\n",
    "        fig, axes = plt.subplots(figsize=(20,10))\n",
    "        #grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "        #bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "        #bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "        sns.boxplot(y='value', x='chrm', \n",
    "                         data=pd.melt(df_scores, id_vars=['chrm'], value_vars=['auc', 'auc_or', 'auc_hic']), \n",
    "                         palette=\"colorblind\"\n",
    "                         ,hue='variable'\n",
    "                   )\n",
    "        #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "        #axes.set_title('Predicting structure similarity from expression')\n",
    "        axes.yaxis.grid(True)\n",
    "        #axes.set_xlabel('Co-expression')\n",
    "        axes.set_ylabel('AUC')\n",
    "        axes.set_ylim([0.0,1.101])\n",
    "        #plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1631,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 tn\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#original\n",
    "for resoultion in [100]:\n",
    "    for case in ['tn']:\n",
    "        print (resoultion, case)\n",
    "        #100 contacts\n",
    "        import seaborn as sns\n",
    "        import matplotlib.pyplot as plt\n",
    "        fig, axes = plt.subplots(figsize=(20,10))\n",
    "        #grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "        #bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "        #bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "        sns.boxplot(y='auc_or', x='chrm', \n",
    "                         data=df_scores, \n",
    "                         palette=\"colorblind\"\n",
    "                         ,hue='TN'\n",
    "                   )\n",
    "        #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "        #axes.set_title('Predicting structure similarity from expression')\n",
    "        axes.yaxis.grid(True)\n",
    "        #axes.set_xlabel('Co-expression')\n",
    "        axes.set_ylabel('AUC')\n",
    "        axes.set_ylim([0.0,1.101])\n",
    "        #plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 simple\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#original\n",
    "for resoultion in [100]:\n",
    "    for case in ['simple']:\n",
    "        print (resoultion, case)\n",
    "        #100 contacts\n",
    "        import seaborn as sns\n",
    "        import matplotlib.pyplot as plt\n",
    "        fig, axes = plt.subplots(figsize=(20,10))\n",
    "        #grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "        #bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "        #bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "        sns.boxplot(y='auc', x='chrm', \n",
    "                         data=df_scores, \n",
    "                         palette=\"colorblind\"\n",
    "                         ,hue='dist_thresh'\n",
    "                   )\n",
    "        #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "        #axes.set_title('Predicting structure similarity from expression')\n",
    "        axes.yaxis.grid(True)\n",
    "        #axes.set_xlabel('Co-expression')\n",
    "        axes.set_ylabel('AUC')\n",
    "        axes.set_ylim([0.0,1.101])\n",
    "        #plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "metadata": {},
   "outputs": [],
   "source": [
    "for resoultion in [100]:\n",
    "        for case in ['simple']:\n",
    "            df_scores = calc_auc_hic(resoultion, case=case, dist_tp='hi-c-rao', prediction='exp', shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 simple\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#original\n",
    "for resoultion in [100]:\n",
    "    for case in ['simple']:\n",
    "        print (resoultion, case)\n",
    "        #100 contacts\n",
    "        import seaborn as sns\n",
    "        import matplotlib.pyplot as plt\n",
    "        fig, axes = plt.subplots(figsize=(20,10))\n",
    "        #grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "        #bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "        #bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "        sns.boxplot(y='auc', x='chrm', \n",
    "                         data=df_scores, \n",
    "                         palette=\"colorblind\"\n",
    "                         ,hue='dist_thresh'\n",
    "                   )\n",
    "        #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "        #axes.set_title('Predicting structure similarity from expression') \n",
    "        axes.yaxis.grid(True)\n",
    "        #axes.set_xlabel('Co-expression')\n",
    "        axes.set_ylabel('AUC')\n",
    "        axes.set_ylim([0.0,1.101])\n",
    "        #plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 simple\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#original\n",
    "for resoultion in [100]:\n",
    "    for case in ['simple']:\n",
    "        print (resoultion, case)\n",
    "        #100 contacts\n",
    "        import seaborn as sns\n",
    "        import matplotlib.pyplot as plt\n",
    "        fig, axes = plt.subplots(figsize=(20,10))\n",
    "        #grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "        #bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "        #bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "        sns.boxplot(y='auc', x='chrm', \n",
    "                         data=df_scores, \n",
    "                         palette=\"colorblind\"\n",
    "                         ,hue='dist_thresh'\n",
    "                   )\n",
    "        #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "        #axes.set_title('Predicting structure similarity from expression') \n",
    "        axes.yaxis.grid(True)\n",
    "        #axes.set_xlabel('Co-expression')\n",
    "        axes.set_ylabel('AUC')\n",
    "        axes.set_ylim([0.0,1.101])\n",
    "        #plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_score = [8,9,9,8,8,6,5,5,3,2]\n",
    "true_case = [0,1,1,1,1,1,0,0,0,0]\n",
    "true_pos = [0,1,1,1,1,1,0,0,0,0]\n",
    "true_neg = [1 if score==0 else 0 for score in true_pos]\n",
    "df_trial = pd.DataFrame(list(zip(predicted_score, true_pos, true_neg)), columns =['predicted_score', 'true_pos', 'true_neg']) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9199999999999999"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lohia_utilities.calculate_auc import *\n",
    "calc_auroc (df_trial,predicted_score='predicted_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_trial = df_trial.sort_values(by=['predicted_score'], ascending=False)\n",
    "df_trial['true_neg'] = df_trial['true_neg']/ df_trial['true_neg'].sum()\n",
    "df_trial['true_pos'] = df_trial['true_pos']/ df_trial['true_pos'].sum()\n",
    "df_trial['true_pos_cum'] = df_trial['true_pos'].cumsum()\n",
    "df_trial['true_neg_cum'] = df_trial['true_neg'].cumsum()\n",
    "\n",
    "fig, axes = plt.subplots()\n",
    "axes.scatter([0] + df_trial['true_neg_cum'].values, [0] + df_trial['true_pos_cum'].values, s=2)\n",
    "axes.plot([0] + df_trial['true_neg_cum'].tolist(), [0] + df_trial['true_pos_cum'].tolist())\n",
    "#axes.set_ylim([0,1])\n",
    "#axes.set_xlim([0,1])\n",
    "axes.plot([0, 1], [0, 1], 'red', linewidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}