{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc9d1212",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats, sparse\n",
    "import bottleneck\n",
    "from scipy.stats import mannwhitneyu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "33073b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats, sparse\n",
    "import bottleneck\n",
    "def run_egad(go, nw, **kwargs):\n",
    "    \"\"\"EGAD running function\n",
    "    \n",
    "    Wrapper to lower level functions for EGAD\n",
    "\n",
    "    EGAD measures modularity of gene lists in co-expression networks. \n",
    "\n",
    "    This was translated from the MATLAB version, which does tiled Cross Validation\n",
    "    \n",
    "    The useful kwargs are:\n",
    "    int - nFold : Number of CV folds to do, default is 3, \n",
    "    int - {min,max}_count : limits for number of terms in each gene list, these are exclusive values\n",
    "\n",
    "\n",
    "    Arguments:\n",
    "        go {pd.DataFrame} -- dataframe of genes x terms of values [0,1], where 1 is included in gene lists\n",
    "        nw {pd.DataFrame} -- dataframe of co-expression network, genes x genes\n",
    "        **kwargs \n",
    "    \n",
    "    Returns:\n",
    "        pd.DataFrame -- dataframe of terms x metrics where the metrics are \n",
    "        ['AUC', 'AVG_NODE_DEGREE', 'DEGREE_NULL_AUC', 'P_Value']\n",
    "    \"\"\"\n",
    "    assert nw.shape[0] == nw.shape[1] , 'Network is not square'\n",
    "    #print(nw.index)\n",
    "    #nw.columns = nw.columns.astype(int)\n",
    "    #print(nw.columns.astype(int))\n",
    "    assert np.all(nw.index == nw.columns) , 'Network index and columns are not in the same order'\n",
    "\n",
    "    #nw_mask = nw.isna().sum(axis=1) != nw.shape[1]\n",
    "    #nw = nw.loc[nw_mask, nw_mask].astype('float')\n",
    "    #np.fill_diagonal(nw.values, 1)\n",
    "    return _runNV(go, nw, **kwargs)\n",
    "\n",
    "def _runNV(go, nw, nFold=3, min_count=10, max_count=1000000):\n",
    "\n",
    "    #Make sure genes are same in go and nw\n",
    "    #go.index = go.index.map(str) \n",
    "    #nw.index = nw.index.map(str)\n",
    "    #nw.index = nw.index.str.replace('_', '')\n",
    "    #go.index = go.index.str.replace('_', '')\n",
    "    #print (nw)\n",
    "    genes_intersect = go.index.intersection(nw.index)\n",
    "\n",
    "\n",
    "    #print (genes_intersect)\n",
    "    go = go.loc[genes_intersect, :]\n",
    "    nw = nw.loc[genes_intersect, genes_intersect]\n",
    "    #print (go)\n",
    "    print (nw.shape)\n",
    "    print (go.shape)\n",
    "    sparsity = 1.0 - np.count_nonzero(go) / go.size\n",
    "    print (sparsity)\n",
    "    sparsity = 1.0 - np.count_nonzero(nw) / nw.size\n",
    "    print (sparsity)\n",
    "    #print(nw\n",
    "    #print(go\n",
    "    nw_mask = nw.isna().sum(axis=1) != nw.shape[1]\n",
    "    nw = nw.loc[nw_mask, nw_mask].astype('float')\n",
    "    np.fill_diagonal(nw.values, 1)\n",
    "    #Make sure there aren't duplicates\n",
    "    duplicates = nw.index.duplicated(keep='first')\n",
    "    nw = nw.loc[~duplicates, ~duplicates]\n",
    "\n",
    "    go = go.loc[:, (go.sum(axis=0) > min_count) & (go.sum(axis=0) < max_count)]\n",
    "    go = go.loc[~go.index.duplicated(keep='first'), :]\n",
    "    #print(go)\n",
    "\n",
    "    roc = _new_egad(go.values, nw.values, nFold)\n",
    "\n",
    "    col_names = ['AUC', 'AVG_NODE_DEGREE', 'DEGREE_NULL_AUC', 'P_Value']\n",
    "    #Put output in dataframe\n",
    "    return pd.DataFrame(dict(zip(col_names, roc)), index=go.columns), go\n",
    "\n",
    "def _new_egad(go, nw, nFold):\n",
    "\n",
    "    #Build Cross validated Positive\n",
    "    x, y = np.where(go)\n",
    "    #print(x, y)\n",
    "    cvgo = {}\n",
    "    for i in np.arange(nFold):\n",
    "        a = x[i::nFold]\n",
    "        #print(a)\n",
    "        b = y[i::nFold]\n",
    "        dat = np.ones_like(a)\n",
    "        mask = sparse.coo_matrix((dat, (a, b)), shape=go.shape)\n",
    "        cvgo[i] = go - mask.toarray()\n",
    "\n",
    "    CVgo = np.concatenate(list(cvgo.values()), axis=1)\n",
    "    #print(CVgo)\n",
    "\n",
    "    sumin = np.matmul(nw.T, CVgo)\n",
    "\n",
    "    degree = np.sum(nw, axis=0)\n",
    "    #print(degree)\n",
    "    #print(degree[:, None])\n",
    "\n",
    "    predicts = sumin / degree[:, None]\n",
    "    #print(predicts)\n",
    "\n",
    "    np.place(predicts, CVgo > 0, np.nan)\n",
    "\n",
    "    #print(predicts)\n",
    "\n",
    "    #Calculate ranks of positives\n",
    "    rank_abs = lambda x: stats.rankdata(np.abs(x))\n",
    "    predicts2 = np.apply_along_axis(rank_abs, 0, predicts)\n",
    "    #print(predicts2)\n",
    "\n",
    "    #Masking Nans that were ranked (how tiedrank works in matlab)\n",
    "    predicts2[np.isnan(predicts)] = np.nan\n",
    "    #print(predicts2)\n",
    "\n",
    "    filtering = np.tile(go, nFold)\n",
    "    #print(filtering)\n",
    "\n",
    "    #negatives :filtering == 0\n",
    "    #Sets Ranks of negatives to 0\n",
    "    np.place(predicts2, filtering == 0, 0)\n",
    "\n",
    "    #Sum of ranks for each prediction\n",
    "    p = bottleneck.nansum(predicts2, axis=0)\n",
    "    n_p = np.sum(filtering, axis=0) - np.sum(CVgo, axis=0)\n",
    "\n",
    "    #Number of negatives\n",
    "    #Number of GO terms - number of postiive\n",
    "    n_n = filtering.shape[0] - np.sum(filtering, axis=0)\n",
    "\n",
    "    roc = (p / n_p - (n_p + 1) / 2) / n_n\n",
    "    U = roc * n_p * n_n\n",
    "    Z = (np.abs(U - (n_p * n_n / 2))) / np.sqrt(n_p * n_n *\n",
    "                                                (n_p + n_n + 1) / 12)\n",
    "    roc = roc.reshape(nFold, go.shape[1])\n",
    "    Z = Z.reshape(nFold, go.shape[1])\n",
    "    #Stouffer Z method\n",
    "    Z = bottleneck.nansum(Z, axis=0) / np.sqrt(nFold)\n",
    "    #Calc ROC of Neighbor Voting\n",
    "    roc = bottleneck.nanmean(roc, axis=0)\n",
    "    P = stats.norm.sf(Z)\n",
    "\n",
    "    #Average degree for nodes in each go term\n",
    "    avg_degree = degree.dot(go) / np.sum(go, axis=0)\n",
    "\n",
    "    #Calc null auc for degree\n",
    "    ranks = np.tile(stats.rankdata(degree), (go.shape[1], 1)).T\n",
    "\n",
    "    np.place(ranks, go == 0, 0)\n",
    "\n",
    "    n_p = bottleneck.nansum(go, axis=0)\n",
    "    nn = go.shape[0] - n_p\n",
    "    p = bottleneck.nansum(ranks, axis=0)\n",
    "\n",
    "    roc_null = (p / n_p - ((n_p + 1) / 2)) / nn\n",
    "    #print(roc)\n",
    "    return roc, avg_degree, roc_null, P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5cd469b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:numexpr.utils:Note: detected 192 virtual cores but NumExpr set to maximum of 64, check \"NUMEXPR_MAX_THREADS\" environment variable.\n",
      "INFO:numexpr.utils:Note: NumExpr detected 192 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
     ]
    }
   ],
   "source": [
    "from hicmatrix import HiCMatrix as hm\n",
    "from hicmatrix.lib import MatrixFileHandler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40afe2ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "exp_file_path=f'/grid/gillis/data/lohia/hi_c_data_processing/software/CoCoCoNet/networks/human_prioAggNet.h5'\n",
    "\n",
    "jac_exp = hm.hiCMatrix(exp_file_path)\n",
    "all_genes = [x[3].decode() for x in jac_exp.cut_intervals]\n",
    "df_exp_corr = pd.DataFrame(jac_exp.matrix.toarray() ,  index=all_genes, columns = all_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5fc018d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_exp_corr = pd.DataFrame(jac_exp.matrix.toarray() ,  index=all_genes, columns = all_genes)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2acb83bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "resolution_human = 10000\n",
    "species = \"human\"\n",
    "SRP_name = \"aggregates\"\n",
    "resolution = \"10kbp_raw\"\n",
    "\n",
    "\n",
    "\n",
    "input_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/'\n",
    "bins_bed = pd.read_csv(f'{input_path}/all_bins.bed', names=['chr', 'start', 'end', 'bin_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d608fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "    if species == 'human':\n",
    "\n",
    "        df_cre = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/data_human/aggregates/li2022/GRCh38-cCREs.bed', sep='\\t', names=['chr', 'start', 'end', 't1', 't2', 't3'])\n",
    "\n",
    "    else:\n",
    "\n",
    "        df_cre = pd.read_csv('/grid/gillis/data/lohia/ATAC_Risa/mm10-cCREs.bed', sep='\\t', names=['chr', 'start', 'end', 't1', 't2', 't3'])\n",
    "\n",
    "    df_cre['start_bin'] = df_cre['start']/resolution_human\n",
    "    df_cre['start_bin'] = df_cre['start_bin'].astype('int')\n",
    "    df_cre['start_bin'] = df_cre['start_bin']*resolution_human\n",
    "    df_cre['start_bin'] = df_cre['start_bin'].astype('str')\n",
    "    df_cre['start_bin'] = df_cre['chr'] + '_' + df_cre['start_bin']\n",
    "    #df_cre_1kb_encode = df_cre.drop_duplicates(subset=['start_bin'])\n",
    "    df_cre['cre'] = 1\n",
    "    df_cre = df_cre.groupby(['start_bin'])['cre'].sum().reset_index()\n",
    "    input_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/'\n",
    "    bins_bed = pd.read_csv(f'{input_path}/all_bins.bed', names=['chr', 'start', 'end', 'bin_id'])\n",
    "    bins_bed['bin_id'] = bins_bed.index\n",
    "    bins_bed['pos'] = bins_bed['chr'] + '_' + bins_bed['start'].astype('str')\n",
    "    df_cre_1kb_encode = df_cre.merge(bins_bed, left_on='start_bin', right_on='pos')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50de8551",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hicmatrix import HiCMatrix as hm\n",
    "from hicmatrix.lib import MatrixFileHandler\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats, sparse\n",
    "import bottleneck\n",
    "from scipy.stats import mannwhitneyu\n",
    "import h5py\n",
    "import h5py\n",
    "import logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from hicmatrix import HiCMatrix as hm\n",
    "from hicmatrix.lib import MatrixFileHandler\n",
    "from scipy.sparse import csr_matrix, dia_matrix, triu, tril, coo_matrix\n",
    "import scipy.stats as stats\n",
    "import os.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa98cc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/hic_gene_gw_none_by_allbins_none_ranked_inter.h5', 'r') as hf:\n",
    "    my_data = hf['matrix'][:]\n",
    "    gene_list = hf['gene_list'][:]\n",
    "    bins_bed = hf['bins_bed'][:]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "3a28adb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0,      1,      2, ..., 287506, 287507, 287508])"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins_bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "92a6801c",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_percen = np.nanpercentile(my_data, 99, axis=1, keepdims=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e19b89c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data_thresh = my_data > my_percen\n",
    "\n",
    "my_data_thresh = my_data_thresh.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c5566c6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gene_tp = pd.DataFrame(my_data_thresh ,  index=[x.decode() for x in gene_list.tolist()], columns = bins_bed.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "33857316",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gene_tp_all = pd.DataFrame(my_data ,  index=[x.decode() for x in gene_list.tolist()], columns = bins_bed.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e7424a41",
   "metadata": {},
   "outputs": [
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000285498</th>\n",
       "      <td>43686.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>125641.0</td>\n",
       "      <td>267060.0</td>\n",
       "      <td>234728.0</td>\n",
       "      <td>125641.0</td>\n",
       "      <td>125641.0</td>\n",
       "      <td>...</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>125641.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>125641.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>43686.0</td>\n",
       "      <td>43686.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000285505</th>\n",
       "      <td>13750.0</td>\n",
       "      <td>146594.5</td>\n",
       "      <td>44280.0</td>\n",
       "      <td>90535.0</td>\n",
       "      <td>90535.0</td>\n",
       "      <td>241345.0</td>\n",
       "      <td>191680.5</td>\n",
       "      <td>222039.0</td>\n",
       "      <td>222039.0</td>\n",
       "      <td>241345.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44280.0</td>\n",
       "      <td>13750.0</td>\n",
       "      <td>44280.0</td>\n",
       "      <td>13750.0</td>\n",
       "      <td>191680.5</td>\n",
       "      <td>272357.5</td>\n",
       "      <td>262339.5</td>\n",
       "      <td>191680.5</td>\n",
       "      <td>191680.5</td>\n",
       "      <td>13750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000285508</th>\n",
       "      <td>14903.5</td>\n",
       "      <td>14903.5</td>\n",
       "      <td>85885.5</td>\n",
       "      <td>14903.5</td>\n",
       "      <td>44487.0</td>\n",
       "      <td>44487.0</td>\n",
       "      <td>44487.0</td>\n",
       "      <td>85885.5</td>\n",
       "      <td>14903.5</td>\n",
       "      <td>44487.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44487.0</td>\n",
       "      <td>14903.5</td>\n",
       "      <td>14903.5</td>\n",
       "      <td>14903.5</td>\n",
       "      <td>14903.5</td>\n",
       "      <td>189910.0</td>\n",
       "      <td>140503.0</td>\n",
       "      <td>85885.5</td>\n",
       "      <td>14903.5</td>\n",
       "      <td>14903.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000285509</th>\n",
       "      <td>12360.5</td>\n",
       "      <td>54919.5</td>\n",
       "      <td>12360.5</td>\n",
       "      <td>12360.5</td>\n",
       "      <td>30041.5</td>\n",
       "      <td>54919.5</td>\n",
       "      <td>54919.5</td>\n",
       "      <td>105724.5</td>\n",
       "      <td>54919.5</td>\n",
       "      <td>30041.5</td>\n",
       "      <td>...</td>\n",
       "      <td>12360.5</td>\n",
       "      <td>12360.5</td>\n",
       "      <td>12360.5</td>\n",
       "      <td>12360.5</td>\n",
       "      <td>54919.5</td>\n",
       "      <td>105724.5</td>\n",
       "      <td>213328.0</td>\n",
       "      <td>30041.5</td>\n",
       "      <td>54919.5</td>\n",
       "      <td>12360.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000285513</th>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>142069.0</td>\n",
       "      <td>201866.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>201866.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>...</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>142069.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "      <td>52645.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55411 rows × 287509 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1        2        3        4         5       \\\n",
       "ENSG00000000419  17008.5   55526.0  17008.5  55526.0  55526.0   17008.5   \n",
       "ENSG00000000457      NaN       NaN      NaN      NaN      NaN       NaN   \n",
       "ENSG00000000460      NaN       NaN      NaN      NaN      NaN       NaN   \n",
       "ENSG00000000938      NaN       NaN      NaN      NaN      NaN       NaN   \n",
       "ENSG00000000971      NaN       NaN      NaN      NaN      NaN       NaN   \n",
       "...                  ...       ...      ...      ...      ...       ...   \n",
       "ENSG00000285498  43686.0   43686.0  43686.0  43686.0  43686.0  125641.0   \n",
       "ENSG00000285505  13750.0  146594.5  44280.0  90535.0  90535.0  241345.0   \n",
       "ENSG00000285508  14903.5   14903.5  85885.5  14903.5  44487.0   44487.0   \n",
       "ENSG00000285509  12360.5   54919.5  12360.5  12360.5  30041.5   54919.5   \n",
       "ENSG00000285513  52645.0   52645.0  52645.0  52645.0  52645.0  142069.0   \n",
       "\n",
       "                   6         7         8         9       ...   287499  \\\n",
       "ENSG00000000419   55526.0  158271.0   55526.0  105572.0  ...  17008.5   \n",
       "ENSG00000000457       NaN       NaN       NaN       NaN  ...  11904.0   \n",
       "ENSG00000000460       NaN       NaN       NaN       NaN  ...  23099.0   \n",
       "ENSG00000000938       NaN       NaN       NaN       NaN  ...  59413.0   \n",
       "ENSG00000000971       NaN       NaN       NaN       NaN  ...  11668.0   \n",
       "...                   ...       ...       ...       ...  ...      ...   \n",
       "ENSG00000285498  267060.0  234728.0  125641.0  125641.0  ...  43686.0   \n",
       "ENSG00000285505  191680.5  222039.0  222039.0  241345.0  ...  44280.0   \n",
       "ENSG00000285508   44487.0   85885.5   14903.5   44487.0  ...  44487.0   \n",
       "ENSG00000285509   54919.5  105724.5   54919.5   30041.5  ...  12360.5   \n",
       "ENSG00000285513  201866.0   52645.0  201866.0   52645.0  ...  52645.0   \n",
       "\n",
       "                   287500   287501   287502    287503    287504    287505  \\\n",
       "ENSG00000000419   17008.5  17008.5  17008.5   55526.0  269810.0  199893.5   \n",
       "ENSG00000000457   11904.0  11904.0  11904.0   31386.5  181079.0   31386.5   \n",
       "ENSG00000000460   23099.0  10259.5  10259.5   30772.0  107925.5   55773.0   \n",
       "ENSG00000000938   17156.0  17156.0  17156.0   59413.0  165603.5  165603.5   \n",
       "ENSG00000000971   11668.0  11668.0  11668.0   11668.0   68158.5   68158.5   \n",
       "...                   ...      ...      ...       ...       ...       ...   \n",
       "ENSG00000285498  125641.0  43686.0  43686.0  125641.0   43686.0   43686.0   \n",
       "ENSG00000285505   13750.0  44280.0  13750.0  191680.5  272357.5  262339.5   \n",
       "ENSG00000285508   14903.5  14903.5  14903.5   14903.5  189910.0  140503.0   \n",
       "ENSG00000285509   12360.5  12360.5  12360.5   54919.5  105724.5  213328.0   \n",
       "ENSG00000285513   52645.0  52645.0  52645.0   52645.0  142069.0   52645.0   \n",
       "\n",
       "                   287506    287507   287508  \n",
       "ENSG00000000419  105572.0   55526.0  17008.5  \n",
       "ENSG00000000457   31386.5   31386.5  11904.0  \n",
       "ENSG00000000460   55773.0   23099.0  10259.5  \n",
       "ENSG00000000938  201056.5  114196.0  17156.0  \n",
       "ENSG00000000971   68158.5   30498.0  11668.0  \n",
       "...                   ...       ...      ...  \n",
       "ENSG00000285498   43686.0   43686.0  43686.0  \n",
       "ENSG00000285505  191680.5  191680.5  13750.0  \n",
       "ENSG00000285508   85885.5   14903.5  14903.5  \n",
       "ENSG00000285509   30041.5   54919.5  12360.5  \n",
       "ENSG00000285513   52645.0   52645.0  52645.0  \n",
       "\n",
       "[55411 rows x 287509 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gene_tp_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c22168a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/'\n",
    "outfile_name = f'{input_path}/hic_gene_gw_none_by_allbins_none_ranked_inter.h5'\n",
    "df_auc = pd.read_csv(f'{input_path}/_totg_cre_auc_14.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "8bbf2769",
   "metadata": {},
   "outputs": [],
   "source": [
    "resolution_human = 5000\n",
    "species = \"human\"\n",
    "SRP_name = \"aggregates\"\n",
    "resolution = \"5kbp_raw\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "18bacb8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path=f'/grid/gillis/data/lohia/hi_c_data_processing/data_{species}/{SRP_name}/{resolution}/max/'\n",
    "df_auc = pd.read_csv(f'{input_path}/cre_auc.csv', sep='\\t')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "bf11c75d",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_genes = pd.read_csv(\"/grid/gillis/data/lohia/hi_c_data_processing/software/CoCoCoNet/Homo_sapiens_average_rank.csv\")\n",
    "\n",
    "exp_genes['genes'] = [x.split('.')[0] for x in exp_genes['genes']]\n",
    "\n",
    "exp_genes.set_index('genes', inplace=True)\n",
    "\n",
    "exp_genes['avg_rank'] = exp_genes.sum(axis=1)\n",
    "\n",
    "exp_genes['avg_rank'] = [ x/ exp_genes.shape[1] for x in  exp_genes['avg_rank']]\n",
    "\n",
    "exp_genes = exp_genes[['avg_rank']]\n",
    "\n",
    "exp_genes.reset_index(inplace=True)\n",
    "\n",
    "exp_genes.drop_duplicates(['genes'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "beeb467b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_auc_gene_exp = exp_genes.merge(df_auc, left_on='genes', right_on='gene_id_exp_file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "34b22dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='auc', ylabel='Count'>"
      ]
     },
     "execution_count": 259,
     "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": [
    "sns.histplot(df_auc_gene_exp[df_auc_gene_exp['avg_rank']>0.7]['auc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "0616f3bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='auc', ylabel='Count'>"
      ]
     },
     "execution_count": 253,
     "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": [
    "sns.histplot(df_auc_gene_exp[df_auc_gene_exp['avg_rank']>0.8]['auc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41af0af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "    row, col=np.nonzero(arr_thresh)\n",
    "    c = np.unique(col)\n",
    "    arr = arr_thresh[:,c] #columns whose sum is zero throughout are removed\n",
    "\n",
    "    zero_row_indices = np.where(~arr.any(axis=1))[0] #[when the gene row sum is zero, its jaccard similarity is zero]\n",
    "    \n",
    "    try:\n",
    "\n",
    "        jac_sim = 1 - pairwise_distances(arr, metric = \"jaccard\")  #[calculates the jaccard coefficient for each bin pair based on the , allbins X allbins where values are number of common neighbours]\n",
    "        jac_sim[zero_row_indices,:] = 0\n",
    "    \n",
    "    except ValueError:\n",
    "\n",
    "        jac_sim = np.zeros((arr_thresh.shape[0], arr_thresh.shape[0]))\n",
    "    print (jac_sim.shape)\n",
    "\n",
    "    return jac_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "id": "fc9f1ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([[1, 1], [0, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "id": "509a1206",
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = np.nonzero(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "id": "5dd8c05c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "id": "ccb579ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 661,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "id": "939300b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/sklearn/metrics/pairwise.py:1776: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 1.]])"
      ]
     },
     "execution_count": 659,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - pairwise_distances(arr, metric = \"jaccard\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb8ad0df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import pairwise_distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "8f47f523",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_auc_gene = df_auc[df_auc['auc'] > 0]['gene_id_exp_file'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "id": "165664d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_auc_gene = exp_genes[exp_genes['avg_rank'] > 0.7]['genes'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "e84d209e",
   "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>genes</th>\n",
       "      <th>avg_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000223972</td>\n",
       "      <td>0.443509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000227232</td>\n",
       "      <td>0.733437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000278267</td>\n",
       "      <td>0.548917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000243485</td>\n",
       "      <td>0.397697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000284332</td>\n",
       "      <td>0.301684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64480</th>\n",
       "      <td>ENSG00000276017</td>\n",
       "      <td>0.321917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64481</th>\n",
       "      <td>ENSG00000278817</td>\n",
       "      <td>0.649758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64482</th>\n",
       "      <td>ENSG00000277196</td>\n",
       "      <td>0.612010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64483</th>\n",
       "      <td>ENSG00000278625</td>\n",
       "      <td>0.314648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64484</th>\n",
       "      <td>ENSG00000277374</td>\n",
       "      <td>0.302087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64440 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 genes  avg_rank\n",
       "0      ENSG00000223972  0.443509\n",
       "1      ENSG00000227232  0.733437\n",
       "2      ENSG00000278267  0.548917\n",
       "3      ENSG00000243485  0.397697\n",
       "4      ENSG00000284332  0.301684\n",
       "...                ...       ...\n",
       "64480  ENSG00000276017  0.321917\n",
       "64481  ENSG00000278817  0.649758\n",
       "64482  ENSG00000277196  0.612010\n",
       "64483  ENSG00000278625  0.314648\n",
       "64484  ENSG00000277374  0.302087\n",
       "\n",
       "[64440 rows x 2 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "id": "d0d2e7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gene_tp_sel = df_gene_tp[df_gene_tp.index.isin(high_auc_gene)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "id": "d9cdb22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_auc_gene = df_auc[df_auc['auc'] > 0.6]['gene_id_exp_file'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "id": "ad67a14a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gene_tp_sel = df_gene_tp_sel[df_gene_tp_sel.index.isin(high_auc_gene)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "id": "536036ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "cre_bins = df_cre_1kb_encode[df_cre_1kb_encode['cre']>14]['bin_id'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "id": "d3650b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.780666666666667"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cre_bins)/3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "id": "11acc32d",
   "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>start_bin</th>\n",
       "      <th>cre</th>\n",
       "      <th>chr</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>bin_id</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr10_10000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr10</td>\n",
       "      <td>10000</td>\n",
       "      <td>20000</td>\n",
       "      <td>167493</td>\n",
       "      <td>chr10_10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr10_100000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100000</td>\n",
       "      <td>110000</td>\n",
       "      <td>167502</td>\n",
       "      <td>chr10_100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr10_1000000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr10</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1010000</td>\n",
       "      <td>167592</td>\n",
       "      <td>chr10_1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr10_10000000</td>\n",
       "      <td>6</td>\n",
       "      <td>chr10</td>\n",
       "      <td>10000000</td>\n",
       "      <td>10010000</td>\n",
       "      <td>168492</td>\n",
       "      <td>chr10_10000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr10_100000000</td>\n",
       "      <td>16</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100000000</td>\n",
       "      <td>100010000</td>\n",
       "      <td>177492</td>\n",
       "      <td>chr10_100000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213396</th>\n",
       "      <td>chr9_99940000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr9</td>\n",
       "      <td>99940000</td>\n",
       "      <td>99950000</td>\n",
       "      <td>163646</td>\n",
       "      <td>chr9_99940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213397</th>\n",
       "      <td>chr9_99950000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr9</td>\n",
       "      <td>99950000</td>\n",
       "      <td>99960000</td>\n",
       "      <td>163647</td>\n",
       "      <td>chr9_99950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213398</th>\n",
       "      <td>chr9_99960000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr9</td>\n",
       "      <td>99960000</td>\n",
       "      <td>99970000</td>\n",
       "      <td>163648</td>\n",
       "      <td>chr9_99960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213399</th>\n",
       "      <td>chr9_99970000</td>\n",
       "      <td>2</td>\n",
       "      <td>chr9</td>\n",
       "      <td>99970000</td>\n",
       "      <td>99980000</td>\n",
       "      <td>163649</td>\n",
       "      <td>chr9_99970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213400</th>\n",
       "      <td>chr9_99990000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr9</td>\n",
       "      <td>99990000</td>\n",
       "      <td>100000000</td>\n",
       "      <td>163651</td>\n",
       "      <td>chr9_99990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213401 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              start_bin  cre    chr      start        end  bin_id  \\\n",
       "0           chr10_10000    1  chr10      10000      20000  167493   \n",
       "1          chr10_100000    1  chr10     100000     110000  167502   \n",
       "2         chr10_1000000    1  chr10    1000000    1010000  167592   \n",
       "3        chr10_10000000    6  chr10   10000000   10010000  168492   \n",
       "4       chr10_100000000   16  chr10  100000000  100010000  177492   \n",
       "...                 ...  ...    ...        ...        ...     ...   \n",
       "213396    chr9_99940000    1   chr9   99940000   99950000  163646   \n",
       "213397    chr9_99950000    1   chr9   99950000   99960000  163647   \n",
       "213398    chr9_99960000    1   chr9   99960000   99970000  163648   \n",
       "213399    chr9_99970000    2   chr9   99970000   99980000  163649   \n",
       "213400    chr9_99990000    1   chr9   99990000  100000000  163651   \n",
       "\n",
       "                    pos  \n",
       "0           chr10_10000  \n",
       "1          chr10_100000  \n",
       "2         chr10_1000000  \n",
       "3        chr10_10000000  \n",
       "4       chr10_100000000  \n",
       "...                 ...  \n",
       "213396    chr9_99940000  \n",
       "213397    chr9_99950000  \n",
       "213398    chr9_99960000  \n",
       "213399    chr9_99970000  \n",
       "213400    chr9_99990000  \n",
       "\n",
       "[213401 rows x 7 columns]"
      ]
     },
     "execution_count": 743,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cre_1kb_encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "id": "3c33cdf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gene_tp_sel = df_gene_tp_sel[cre_bins]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "id": "a1bbd3c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13157, 13157)\n",
      "(13157, 8342)\n",
      "0.9693905994526353\n",
      "0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-212-46e24587dd68>:133: RuntimeWarning: invalid value encountered in true_divide\n",
      "  roc = (p / n_p - (n_p + 1) / 2) / n_n\n"
     ]
    }
   ],
   "source": [
    "df_2d_jac, go_chrom  = run_egad(df_gene_tp_sel, df_exp_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "id": "1c6375fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ffb6b395fd0>"
      ]
     },
     "execution_count": 759,
     "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 matplotlib.pyplot as plt\n",
    "sns.scatterplot(df_2d_jac['AUC'], df_2d_jac['DEGREE_NULL_AUC'])\n",
    "plt.plot([0, 1], [0, 1], c='black')\n",
    "plt.axvline(x=df_2d_jac['AUC'].mean(),c='black',ls='--')\n",
    "plt.axhline(y=df_2d_jac['DEGREE_NULL_AUC'].mean(), c='black', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "id": "67d146c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6265078142907863"
      ]
     },
     "execution_count": 763,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2d_jac['AUC'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "id": "df40a782",
   "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": [
    "ax = sns.histplot(df_2d_jac['AUC'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "id": "25ee2761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEGREE_NULL_AUC', ylabel='Count'>"
      ]
     },
     "execution_count": 646,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sb5kkNCs4rHVwOSS2t0wSkvqeQ2J7xyGwkqRKJglJUiVvN2kgdT4LYUem1AyThAZS+VmIHz32CL9/7jinnnoq4DBXaSaZJDSw2s9CbNuyiWvW3rd79IvDXIdb55BYryKbZZLQUCiPfnGY63ArD4l1OGzzTBIaCJ19EN5Smt3afxT4oF3zTBIaCJ31mLylJPBBu24wSahvla8exsfHmXfsid5S0l580K5ZJgn1rfLVg1cOUm+YJNQ3Jup3aF89eOUg9YZJQj0zUVL44Jce4vDj7HeQ+oVJQj1T1Rltv4PUP0wS6qny5EAmBc00y7fsP5OE9tt05pTuHLHksw6aSZ3PTZRvYTo8dt+YJLTfJptTerJ+B/scNNM6n5vovIWp6TNJCNj7amDHjh0Ae1wNtK8OJhuFBHvPKT1RZ7QjltSUuiVayufxZOf7bNd3SSIizgP+FzAH+GhmXtvjkLpuX2/ftJdh+if7RJ3Icw5bwMhJSwH2qLQ61SikieaUtt9B/abzOZzy+e6tqRf0VZKIiDnAdcCvApuBb0XErZm5YaY/qxsdWpN9gU/2ZT7Z7ZvO/U70hV315d75meXliZ5oftH8kT2W25VW63zxO6e0+s1E/RXl53DK53vnunV/d9vrzvQVynT+cJxpfZUkgDOBjZn5PYCIuBk4H5jxJDE2NsYl77+eQ49ayE+e3sL7L3nd7vkIZsr4+Dh/+okvc+hRCwF46pENzDlkPkced+Ier4E9YugsWNbe10T7feqRDRy+eLQyhuf+fSvv+/gXOPK47+71mZ3xHL54lIgA4NmnHmfO/9vODw895IXlwxbs3u8zWzfv+V7nusXyvr7Xi/34mcP9mU889G3e951nOfK47+4+/9vnfOd2E61b53cX9v79nGzdujq/S37y9BY+8ae/25Urnein0SURcRFwXma+o1h+G/AfM/OdpXVWAiuLxZ8FHirt4hjgyS6F2688Bi0eB49Bm8ehpXwcTsrMkTob9duVREzQtkcWy8wbgRsn3DhifWYubyKwQeExaPE4eAzaPA4t+3ocDmgimP2wGTixtLwIeLRHsUjSrNdvSeJbwNKIODkiDgRWALf2OCZJmrX66nZTZu6MiHcC/0hrCOzHM/OBaexiwttQs4zHoMXj4DFo8zi07NNx6KuOa0lSf+m3202SpD5ikpAkVRq4JBER50XEQxGxMSKunOD934yI+4p//xIRL+tFnE2rcRzOL47BPRGxPiJ+qRdxNm2q41Ba75URsat4Fmeo1DgXzo6IHxXnwj0R8f5exNm0OudCcSzuiYgHIuJr3Y6xaTXOhfeUzoP7i9+JoybdaWYOzD9andn/CrwYOBC4FzitY52zgAXF69cDd/U67h4dh3m80Oe0DHiw13H34jiU1vsK8AXgol7H3YNz4Wzg872OtQ+Ow5G0qjcsLpaP7XXc3T4GHev/J+ArU+130K4kdpftyMyfAu2yHbtl5r9k5r8Xi9+k9azFsKlzHJ7J4kwADqPjocQhMeVxKLwL+DTwRDeD65K6x2DY1TkObwU+k5mbADJz2M6H6Z4LFwOfmmqng5YkTgB+UFreXLRVuQz4h0Yj6o1axyEiLoyIB4G/B/5Ll2LrpimPQ0ScAFwIfKSLcXVT3d+JX4yIeyPiHyLi9O6E1lV1jsOpwIKI+GpE3B0Rb+9adN1R+/sxIg4FzqP1x9Ok+uo5iRqmLNuxe8WIX6GVJIbxXnyt45CZtwC3RMRrgD8DXtd0YF1W5zh8CHhvZu5qFy8cMnWOwbdp1ep5JiLeAHwWWNp0YF1W5zi8CPgPwDnAIcCdEfHNzNy7ouZgqv39SOtW0z9n5tNT7XTQkkStsh0RsQz4KPD6zHyqS7F107TKl2TmHRHxkog4JjOHqdBZneOwHLi5SBDHAG+IiJ2Z+dmuRNi8KY9BZv649PoLEXH9LD0XNgNPZuazwLMRcQfwMmBYksR0vhdWUONWEzBwHdcvAr4HnMwLHTOnd6yzGNgInNXreHt8HE7hhY7rVwD/t708LP/qHIeO9W9i+Dqu65wLx5XOhTOBTbPxXABGgduLdQ8F7gde2uvYu3kMivWOAJ4GDquz34G6ksiKsh0R8TvF+x8B3g8cDVxf/PW4M4esAmTN4/CfgbdHxA7gOeAtWZwhw6LmcRhqNY/BRcDlEbGT1rmwYjaeC5k5FhFfBO4Dnqc18+X9vYt6Zk3j9+FC4EvZuqKakmU5JEmVBm10kySpi0wSkqRKJglJUiWThCSpkklCklTJJCFJqmSSUN8qyhi3yzrfGxFXRMQBxXud5a/viYjXFe8tjIhPRsT3iho9d0bEhR3bfSciHoyID5Q+77ciYmvHPk+LiCUR8VxHe2Xdn4j4t4j4dGn5ooi4qXj9xxHxBxOsf0zx+pkJ9rfXNlMctwsjIiPi50ptZ0fE5zvWu6ldOj0i5kbEtRHxcFFCel1EvL7uZ2p4DdTDdJp1nsvMlwNExLHAJ2k9LfpHxftfz8w3ljeI1hOUnwVWZ+Zbi7aTgDeVVvt6Zr4xIg4BvhMRt2TmPxfv/e/MfGfHPpcA/9qOpablEXF6Tm+O9plyMfANWqUX/rjmNn8GHE/rCeTtEbEQ+OVmwtMg8UpCAyFbZZ1XAu+MySv1vRb4aflp68z8fmZ+eIJ9Pgfcw+SVhPfVB4A/bGC/k4qIecCraBW3XFFzm0OB3wbelZnbATJzS2auaSxQDQyThAZGZn6P1jl7bNH06o5bQC8BTqdV9XRKEbGAVjXUO0rNb+nY5yFF+0s62l89xe7XAK+IiFNq/wdnxgXAF7NV2fTpiHhFjW1OATZlqRCg1ObtJg2a8lXERLeb6Fi+jla5+J9m5iuL5ldHxH3AzwLXZubjpU0mut0E07/dtAv4c+Aq9pzTpKoOzkzVx7mYVnl0aE06czGtpNn052pIeSWhgRERL6b15TvZjGIP0Kp6C0BmrqI1f8BIaZ2vZ+Yy4Axahe9ePvPRAvA3wGtoVSZuewpY0LHefOCH+/thEXE0rdttH42IfwPeQ+vKKCo+9yjgSVpVkxdHxPz9jUHDxyShgRARI7Rml/vLKSqYfgU4OCIuL7UdOtGKxS2Za4D3zlige+5/B/A/gXeXmu8A3tT+Qo6IXwfuzcxdM/CRFwF/nZknZeaSzDwReITWldTDwM9ExGjxuSfRmkvhnsz8CfAx4C8i4sDi/eMj4pIZiEkDziShfnZIewgs8GXgS8CflN7v7JO4qEggFwC/HBGPRMQ6YDXVieAjwGsi4uRiubNP4qyivbNP4r/W/D98jNJt3cy8D/hL4BsRcQ/wO8A7SusfGhGbS/+uKNrfV26v+KyLgVs62j4NvLXokL4E+Kvic/8OeEdm/qi9f2ArsCEi7qc1Qmxrzf+jhpilwiVJlbySkCRVcnSTtI8i4i7goI7mt2Xmdxv+3KNpTcPZ6Zwczjnd1UPebpIkVfJ2kySpkklCklTJJCFJqmSSkCRV+v91f71FVs6zjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_2d_jac['DEGREE_NULL_AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c93dbe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "    row, col=np.nonzero(arr_thresh)\n",
    "    c = np.unique(col)\n",
    "    arr = arr_thresh[:,c] #columns whose sum is zero throughout are removed\n",
    "\n",
    "    zero_row_indices = np.where(~arr.any(axis=1))[0] #[when the gene row sum is zero, its jaccard similarity is zero]\n",
    "    \n",
    "    try:\n",
    "\n",
    "        jac_sim = 1 - pairwise_distances(arr, metric = \"jaccard\")  #[calculates the jaccard coefficient for each bin pair based on the , allbins X allbins where values are number of common neighbours]\n",
    "        jac_sim[zero_row_indices,:] = 0\n",
    "    \n",
    "    except ValueError:\n",
    "\n",
    "        jac_sim = np.zeros((arr_thresh.shape[0], arr_thresh.shape[0]))\n",
    "    print (jac_sim.shape)\n",
    "\n",
    "    return jac_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "id": "5b02c479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 673,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "id": "38575431",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = df_gene_tp_sel.to_numpy().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 692,
   "id": "dbc6a1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13158 rows × 8342 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 177492  177557  177565  177566  177580  177591  177593  \\\n",
       "ENSG00000000419       0       0       0       0       0       0       0   \n",
       "ENSG00000000457       1       0       0       0       0       0       0   \n",
       "ENSG00000000460       0       0       0       0       0       0       0   \n",
       "ENSG00000000938       0       0       0       0       0       1       0   \n",
       "ENSG00000000971       0       0       0       0       0       0       0   \n",
       "...                 ...     ...     ...     ...     ...     ...     ...   \n",
       "ENSG00000285280       0       0       0       0       0       0       0   \n",
       "ENSG00000285331       0       0       0       0       0       0       0   \n",
       "ENSG00000285399       0       0       0       0       0       0       0   \n",
       "ENSG00000285410       0       0       0       0       0       0       0   \n",
       "ENSG00000285437       0       0       0       0       0       0       1   \n",
       "\n",
       "                 177598  177605  177614  ...  163203  163254  153749  163437  \\\n",
       "ENSG00000000419       0       0       0  ...       0       0       0       0   \n",
       "ENSG00000000457       0       0       0  ...       0       0       0       0   \n",
       "ENSG00000000460       0       0       0  ...       0       0       0       0   \n",
       "ENSG00000000938       0       0       0  ...       0       0       0       0   \n",
       "ENSG00000000971       0       0       0  ...       0       0       0       0   \n",
       "...                 ...     ...     ...  ...     ...     ...     ...     ...   \n",
       "ENSG00000285280       0       0       0  ...       0       0       0       0   \n",
       "ENSG00000285331       0       0       0  ...       0       0       0       0   \n",
       "ENSG00000285399       0       0       0  ...       0       0       0       0   \n",
       "ENSG00000285410       1       0       0  ...       0       0       0       0   \n",
       "ENSG00000285437       0       0       0  ...       0       0       0       0   \n",
       "\n",
       "                 163450  163474  163477  163557  163562  163634  \n",
       "ENSG00000000419       0       0       0       0       0       0  \n",
       "ENSG00000000457       0       0       0       0       0       0  \n",
       "ENSG00000000460       0       0       0       0       0       0  \n",
       "ENSG00000000938       0       0       0       0       0       0  \n",
       "ENSG00000000971       0       0       0       0       0       0  \n",
       "...                 ...     ...     ...     ...     ...     ...  \n",
       "ENSG00000285280       0       0       0       0       0       0  \n",
       "ENSG00000285331       0       0       0       0       0       0  \n",
       "ENSG00000285399       0       0       0       0       0       0  \n",
       "ENSG00000285410       0       0       0       0       0       0  \n",
       "ENSG00000285437       0       0       0       0       0       0  \n",
       "\n",
       "[13158 rows x 8342 columns]"
      ]
     },
     "execution_count": 692,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gene_tp_sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "id": "cbb8bf6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/sklearn/metrics/pairwise.py:1776: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "    zero_row_indices = np.where(~arr.any(axis=1))[0] #[when the gene row sum is zero, its jaccard similarity is zero]\n",
    "    \n",
    "\n",
    "\n",
    "    jac_sim = 1 - pairwise_distances(arr, metric = \"jaccard\")  #[calculates the jaccard coefficient for each bin pair based on the , allbins X allbins where values are number of common neighbours]\n",
    "    jac_sim[zero_row_indices,:] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "id": "81733773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 795,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0:5,0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "id": "3c0ca5db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8342, 8342)"
      ]
     },
     "execution_count": 791,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jac_sim.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "id": "f012070e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.01764706, 0.01829268, ..., 0.        , 0.00561798,\n",
       "        0.00651466],\n",
       "       [0.01764706, 1.        , 0.04545455, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.01829268, 0.04545455, 1.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 1.        , 0.02222222,\n",
       "        0.01369863],\n",
       "       [0.00561798, 0.        , 0.        , ..., 0.02222222, 1.        ,\n",
       "        0.        ],\n",
       "       [0.00651466, 0.        , 0.        , ..., 0.01369863, 0.        ,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 792,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jac_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "id": "e300a18f",
   "metadata": {},
   "outputs": [],
   "source": [
    "jac_sim_tp = pd.DataFrame(jac_sim ,  index=df_gene_tp_sel.columns.tolist(), columns = df_gene_tp_sel.columns.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "id": "9d09b5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "per_bin_similarity = np.median(jac_sim, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "id": "56606488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 698,
     "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": [
    "sns.histplot(per_bin_similarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38c93389",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "id": "56528ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.01766004, 0.00737101, ..., 0.02401747, 0.06763285,\n",
       "        0.06045752],\n",
       "       [0.01766004, 1.        , 0.05369128, ..., 0.00465116, 0.04591837,\n",
       "        0.01020408],\n",
       "       [0.00737101, 0.05369128, 1.        , ..., 0.01851852, 0.02571429,\n",
       "        0.00290698],\n",
       "       ...,\n",
       "       [0.02401747, 0.00465116, 0.01851852, ..., 1.        , 0.039801  ,\n",
       "        0.01253133],\n",
       "       [0.06763285, 0.04591837, 0.02571429, ..., 0.039801  , 1.        ,\n",
       "        0.02572899],\n",
       "       [0.06045752, 0.01020408, 0.00290698, ..., 0.01253133, 0.02572899,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 683,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jac_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "id": "e3965dea",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'AUC'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/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   3079\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-> 3080\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   3081\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: 'AUC'",
      "\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-724-67acf652dc1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_t\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AUC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_t\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'DEGREE_NULL_AUC'\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[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxvline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_t\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AUC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mls\u001b[0m\u001b[0;34m=\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      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxhline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_t\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'DEGREE_NULL_AUC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\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[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\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   3023\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-> 3024\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   3025\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   3026\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~/.conda/envs/hicexplorer/lib/python3.8/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   3080\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   3081\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-> 3082\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   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\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: 'AUC'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.scatterplot(df_t['AUC'], df_t['DEGREE_NULL_AUC'])\n",
    "plt.plot([0, 1], [0, 1], c='black')\n",
    "plt.axvline(x=df_t['AUC'].mean(),c='black',ls='--')\n",
    "plt.axhline(y=df_t['DEGREE_NULL_AUC'].mean(), c='black', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "id": "04d9341b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6627436316077938"
      ]
     },
     "execution_count": 577,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t['AUC'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "76f9eafa",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 317 and the array at index 1 has size 353",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-450-1850b45da2af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_gene_tp_sel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_2d_jac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AUC'\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~/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\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     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mregplot\u001b[0;34m(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)\u001b[0m\n\u001b[1;32m    849\u001b[0m ):\n\u001b[1;32m    850\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 851\u001b[0;31m     plotter = _RegressionPlotter(x, y, data, x_estimator, x_bins, x_ci,\n\u001b[0m\u001b[1;32m    852\u001b[0m                                  \u001b[0mscatter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_reg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m                                  \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogistic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlowess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, color, label)\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;31m# Drop null observations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"units\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"x_partial\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"y_partial\"\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    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;31m# Regress nuisance variables out of the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mdropna\u001b[0;34m(self, *vars)\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0mvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvals\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\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;32m---> 62\u001b[0;31m         \u001b[0mnot_na\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvals\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0m\u001b[1;32m     63\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m             \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcolumn_stack\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hicexplorer/lib/python3.8/site-packages/numpy/lib/shape_base.py\u001b[0m in \u001b[0;36mcolumn_stack\u001b[0;34m(tup)\u001b[0m\n\u001b[1;32m    654\u001b[0m             \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 656\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\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[0m\u001b[1;32m    657\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 317 and the array at index 1 has size 353"
     ]
    }
   ],
   "source": [
    "sns.regplot(y=df_gene_tp_sel.sum(), x=df_2d_jac['AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 782,
   "id": "b07fe074",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t = df_2d_jac.merge(df_gene_tp_sel.sum().reset_index(), left_on=df_2d_jac.index, right_on='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 780,
   "id": "75f5e05c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t = jac_sim_tp.mean().reset_index().merge(df_gene_tp_sel.mean().reset_index(), left_on='index', right_on='index')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "id": "b6d4300e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t = df_2d_jac.merge(df_cre_1kb_encode, left_on=df_2d_jac.index, right_on='bin_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "id": "2c42b737",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_bin</th>\n",
       "      <th>cre</th>\n",
       "      <th>chr</th>\n",
       "      <th>start</th>\n",
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       "      <th>pos</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr10_10000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr10</td>\n",
       "      <td>10000</td>\n",
       "      <td>20000</td>\n",
       "      <td>167493</td>\n",
       "      <td>chr10_10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr10_100000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100000</td>\n",
       "      <td>110000</td>\n",
       "      <td>167502</td>\n",
       "      <td>chr10_100000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr10_1000000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr10</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1010000</td>\n",
       "      <td>167592</td>\n",
       "      <td>chr10_1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr10_10000000</td>\n",
       "      <td>6</td>\n",
       "      <td>chr10</td>\n",
       "      <td>10000000</td>\n",
       "      <td>10010000</td>\n",
       "      <td>168492</td>\n",
       "      <td>chr10_10000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr10_100000000</td>\n",
       "      <td>16</td>\n",
       "      <td>chr10</td>\n",
       "      <td>100000000</td>\n",
       "      <td>100010000</td>\n",
       "      <td>177492</td>\n",
       "      <td>chr10_100000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213396</th>\n",
       "      <td>chr9_99940000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr9</td>\n",
       "      <td>99940000</td>\n",
       "      <td>99950000</td>\n",
       "      <td>163646</td>\n",
       "      <td>chr9_99940000</td>\n",
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       "    <tr>\n",
       "      <th>213397</th>\n",
       "      <td>chr9_99950000</td>\n",
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       "      <td>99960000</td>\n",
       "      <td>163647</td>\n",
       "      <td>chr9_99950000</td>\n",
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       "    <tr>\n",
       "      <th>213398</th>\n",
       "      <td>chr9_99960000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr9</td>\n",
       "      <td>99960000</td>\n",
       "      <td>99970000</td>\n",
       "      <td>163648</td>\n",
       "      <td>chr9_99960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213399</th>\n",
       "      <td>chr9_99970000</td>\n",
       "      <td>2</td>\n",
       "      <td>chr9</td>\n",
       "      <td>99970000</td>\n",
       "      <td>99980000</td>\n",
       "      <td>163649</td>\n",
       "      <td>chr9_99970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213400</th>\n",
       "      <td>chr9_99990000</td>\n",
       "      <td>1</td>\n",
       "      <td>chr9</td>\n",
       "      <td>99990000</td>\n",
       "      <td>100000000</td>\n",
       "      <td>163651</td>\n",
       "      <td>chr9_99990000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>213401 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              start_bin  cre    chr      start        end  bin_id  \\\n",
       "0           chr10_10000    1  chr10      10000      20000  167493   \n",
       "1          chr10_100000    1  chr10     100000     110000  167502   \n",
       "2         chr10_1000000    1  chr10    1000000    1010000  167592   \n",
       "3        chr10_10000000    6  chr10   10000000   10010000  168492   \n",
       "4       chr10_100000000   16  chr10  100000000  100010000  177492   \n",
       "...                 ...  ...    ...        ...        ...     ...   \n",
       "213396    chr9_99940000    1   chr9   99940000   99950000  163646   \n",
       "213397    chr9_99950000    1   chr9   99950000   99960000  163647   \n",
       "213398    chr9_99960000    1   chr9   99960000   99970000  163648   \n",
       "213399    chr9_99970000    2   chr9   99970000   99980000  163649   \n",
       "213400    chr9_99990000    1   chr9   99990000  100000000  163651   \n",
       "\n",
       "                    pos  \n",
       "0           chr10_10000  \n",
       "1          chr10_100000  \n",
       "2         chr10_1000000  \n",
       "3        chr10_10000000  \n",
       "4       chr10_100000000  \n",
       "...                 ...  \n",
       "213396    chr9_99940000  \n",
       "213397    chr9_99950000  \n",
       "213398    chr9_99960000  \n",
       "213399    chr9_99970000  \n",
       "213400    chr9_99990000  \n",
       "\n",
       "[213401 rows x 7 columns]"
      ]
     },
     "execution_count": 744,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "id": "93d3a736",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t = df_2d_jac.merge(jac_sim_tp.mean().reset_index(), left_on=df_2d_jac.index, right_on='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 783,
   "id": "e4c941a5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <td>10060.668246</td>\n",
       "      <td>0.407840</td>\n",
       "      <td>2.975338e-01</td>\n",
       "      <td>177565</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.624543</td>\n",
       "      <td>10300.179106</td>\n",
       "      <td>0.478141</td>\n",
       "      <td>5.764654e-07</td>\n",
       "      <td>177566</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.461897</td>\n",
       "      <td>10385.444057</td>\n",
       "      <td>0.516128</td>\n",
       "      <td>6.432477e-02</td>\n",
       "      <td>177580</td>\n",
       "      <td>129</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7876</th>\n",
       "      <td>0.601389</td>\n",
       "      <td>10454.027772</td>\n",
       "      <td>0.510980</td>\n",
       "      <td>1.831122e-08</td>\n",
       "      <td>163474</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7877</th>\n",
       "      <td>0.560149</td>\n",
       "      <td>10544.321932</td>\n",
       "      <td>0.529888</td>\n",
       "      <td>4.074555e-03</td>\n",
       "      <td>163477</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7878</th>\n",
       "      <td>0.525629</td>\n",
       "      <td>10027.908366</td>\n",
       "      <td>0.415976</td>\n",
       "      <td>2.571734e-01</td>\n",
       "      <td>163557</td>\n",
       "      <td>60</td>\n",
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       "    <tr>\n",
       "      <th>7879</th>\n",
       "      <td>0.660111</td>\n",
       "      <td>10531.933967</td>\n",
       "      <td>0.498688</td>\n",
       "      <td>3.683678e-03</td>\n",
       "      <td>163562</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7880</th>\n",
       "      <td>0.692969</td>\n",
       "      <td>10628.637247</td>\n",
       "      <td>0.547172</td>\n",
       "      <td>1.431164e-17</td>\n",
       "      <td>163634</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7881 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AUC  AVG_NODE_DEGREE  DEGREE_NULL_AUC       P_Value   index    0\n",
       "0     0.548648     10432.251330         0.500019  2.071635e-02  177492  147\n",
       "1     0.593454     10135.578246         0.442564  5.234711e-02  177557   26\n",
       "2     0.536454     10060.668246         0.407840  2.975338e-01  177565   20\n",
       "3     0.624543     10300.179106         0.478141  5.764654e-07  177566  127\n",
       "4     0.461897     10385.444057         0.516128  6.432477e-02  177580  129\n",
       "...        ...              ...              ...           ...     ...  ...\n",
       "7876  0.601389     10454.027772         0.510980  1.831122e-08  163474  249\n",
       "7877  0.560149     10544.321932         0.529888  4.074555e-03  163477  159\n",
       "7878  0.525629     10027.908366         0.415976  2.571734e-01  163557   60\n",
       "7879  0.660111     10531.933967         0.498688  3.683678e-03  163562   32\n",
       "7880  0.692969     10628.637247         0.547172  1.431164e-17  163634  162\n",
       "\n",
       "[7881 rows x 6 columns]"
      ]
     },
     "execution_count": 783,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 785,
   "id": "ee9e315e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC</th>\n",
       "      <th>AVG_NODE_DEGREE</th>\n",
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       "      <td>2.975338e-01</td>\n",
       "      <td>177565</td>\n",
       "      <td>0.001666</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.624543</td>\n",
       "      <td>10300.179106</td>\n",
       "      <td>0.478141</td>\n",
       "      <td>5.764654e-07</td>\n",
       "      <td>177566</td>\n",
       "      <td>0.006697</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.461897</td>\n",
       "      <td>10385.444057</td>\n",
       "      <td>0.516128</td>\n",
       "      <td>6.432477e-02</td>\n",
       "      <td>177580</td>\n",
       "      <td>0.006302</td>\n",
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       "      <td>10454.027772</td>\n",
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       "      <td>1.831122e-08</td>\n",
       "      <td>163474</td>\n",
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       "      <td>10027.908366</td>\n",
       "      <td>0.415976</td>\n",
       "      <td>2.571734e-01</td>\n",
       "      <td>163557</td>\n",
       "      <td>0.003324</td>\n",
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       "      <th>7879</th>\n",
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       "      <td>10531.933967</td>\n",
       "      <td>0.498688</td>\n",
       "      <td>3.683678e-03</td>\n",
       "      <td>163562</td>\n",
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       "      <td>0.547172</td>\n",
       "      <td>1.431164e-17</td>\n",
       "      <td>163634</td>\n",
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      "text/plain": [
       "           AUC  AVG_NODE_DEGREE  DEGREE_NULL_AUC       P_Value   index  \\\n",
       "0     0.548648     10432.251330         0.500019  2.071635e-02  177492   \n",
       "1     0.593454     10135.578246         0.442564  5.234711e-02  177557   \n",
       "2     0.536454     10060.668246         0.407840  2.975338e-01  177565   \n",
       "3     0.624543     10300.179106         0.478141  5.764654e-07  177566   \n",
       "4     0.461897     10385.444057         0.516128  6.432477e-02  177580   \n",
       "...        ...              ...              ...           ...     ...   \n",
       "7876  0.601389     10454.027772         0.510980  1.831122e-08  163474   \n",
       "7877  0.560149     10544.321932         0.529888  4.074555e-03  163477   \n",
       "7878  0.525629     10027.908366         0.415976  2.571734e-01  163557   \n",
       "7879  0.660111     10531.933967         0.498688  3.683678e-03  163562   \n",
       "7880  0.692969     10628.637247         0.547172  1.431164e-17  163634   \n",
       "\n",
       "             0  \n",
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       "2     0.001666  \n",
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       "7876  0.007217  \n",
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       "7879  0.001810  \n",
       "7880  0.006360  \n",
       "\n",
       "[7881 rows x 6 columns]"
      ]
     },
     "execution_count": 785,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "id": "f616598d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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yPJXl4lSezqTL1ZduqHht+eeamM4zNJFl2vPpSMR48u4blj2bKOpuogFgR9nj7YQVT2tS1aMicqWI9KjqhYjbZsy6FEV1zShn3jazvfOtR9yZcDk1NIkfKAnXoT3hMpLOE3Pqzyuo/uzF4HBycIKJaY9N7XF6OpOzXlv+ubpScbpScVSVsUx+RbKwog4Gx4A9IrILOAPcDXyufAcRuQp4R1VVRG4EEsBwxO0yZt1qteqazW5vvcB1pG+Q4akcnj8zXjA06eMAl21qKw0Ip3MeB4/2z3qP6nGNno4EG9vipXZXv3a1/R4iDQaq6onIA8B3ARd4VlVPiMj9he1PA38f+FURyQMZ4H/SVpz8YEyLWI3VNecaIF6u9h482s+GtjgdyRhDE1lyfliHUwXOjGZIuFl6OpN0pWIVA84Hj/bz9vlxJrM+mzviXNIR3gW8NzzF9u42IJyPcGEySybv039hiv3/8s/p7UwylgmLQa+G34NNOjOmBS01uybKPv6FamTS1XK0t3q8YDyT54OLaRRIxRxUIUC5pCPBrp7OUpCKu8K5selCJpGwtTtFVyrOyfMTIHBpV4qzYxk0ULzC6TbuCD1dCTw/fL+pnL8svwebgWzMGtJqM37nE/Us6kYDZ3U7+ocmmfZ8ggASMQcpdB0FCpvb4+R9pSPp0tOZou/cOK5ImH3kCLt7OxnP5BgYnSbuhmsr5wNFNUw7FZlZJ1iBG3duqmhXrTYDS06vXckBZGNMk5VPnILZfdGtJsrlIucqLAeVJ9dP7N7MC6+fKXVHZb0Ah/AKfjLrkfUC/AAcgcs3ttF3boJMzicZc0m4Dp6viEOpeynmOly9pZP+C1MEqqDhHYHrCH4QkPWVhBsGkOp2Vbf5Ky+8gQAb2uKRFcizYGBMi1noyXO1F5GLciC1OnB6vjI4Mc3/+txxHEcqMn1eeP0Md924jVf6L1ZMKOvpTNE2HXYZFU1Me8QcYdoLeH84TcIVfAUJwhN+ca7FI7fvLRW3Ozc2jeeHPTF5Xwt3B1JqXzGgA6XnimMNUzkfR8LAL4m5B7IXK/LaRMaY5lpICYojfYN85YU3+P7pEc6PT/P90yN85YU3Fj3BLApRTVo70jfIsfeG6R+a4s0zY/SdG+f0SLrUZROoMjyVY3A8PFGfGU3zzF+/y3237K6YUHZhcpozIxmCYo+6wvsX00x7QfEhCgSFHdqTsYrJf8XP15WKEaB4QYACroRjEMUJasWAXixPUSyCVwwggcLZsQzjZYPOzSxZYcHAmBazkJPn4y+9xWg6jwaEfdoBjKbzPP7SWyvQ8tqiWC6yGAQLpYNQwqvxQCFQRQDXEVRhaDIXlp8Axqc9vvDcMW79+l8C8Ngd+5jK+ihh95BD7Xo5vkIi7nBlTwfHf/PTfOvej1fMQ3jsjn3s6ulkYypGW9zFgUJbwmJ345mZ+kfF2kgfXEzj+YoyM76Q95XTI+mK/ZvFuomMaTHzTZwq9+5wOjyJOeHpRAQ0UN4dXl1F5BYyaa2Rbq+DR/uZzHq4Ep6oy3mFfn/PD8cAFFCFfBAeH1eE9y6mwz75O/axoS3Ozs3tTGY93q86bsWgkow57OrpKKWKzvX5jvQN8tUX3mAknS/NZzgzmqG7Pc6d123luVffxwu0FHSKYxBFgcIHF9N0Jl0eub267ufiWTAwpgW18lq7S9HoSmOnR9LkfZ0VCIocEbxCt04xSwggJoJTKEVdrDZaHNPoSsVxHSHQMCsIIO46OBKesOfqqisPXqPp3Kz5DDFH6O1M8kr/RTa2xelIxMIurTrtVyh1UzWLdRMZs4bt7ukodY0oYT95oOHzjTjSN8g9h17l5ide5p5Dr674WENxQNgPlHcvTPHBxTSD49M88Z2+iv12bGqnmDZfVmaoJOYKxcKiUsr9D/+b9QI8Xzk3Ns3J8+MV3XIJN8wGcgvvmfMDsl5YqbRWV12tIntvD07i+QFdqTi7ezvZe9kGrtrSydBkltc/GOH94SkuTGbp6UhQo+lAeGcTqDZ1zQMLBsasYQ/dupdN7XGEsFtEgE3tcR66de+8r212ddPie84VXObbfnokjecHnB2dxgu0dKX+9uBkxb733bK7dPVeayqV5ytB4cK6eH3tBYRzAQqPp3I+w+k8PxwYLY1ptCfDzpTyOw4Fcr5Sa85WvfUTzo9nK/Y7O5pheCpXSl/N+QGjGa/mMRQJu4qSrmMDyMaYxhzYu4Wv3XUdN+zcxOUb27hh5ya+dtd1DXUxNXsFrvmCSyPBZ8emds6PZxEJu3qK/6tu14G9W9i2MTmrDTEnnE0MlAZmXZkZZC4qJQ4pPPnySQC+de/HOf6bn2ZDsnIhmqKzo5maq6Z5fsDb5yd488wYb54Zw/MCcn5QSgC4MDnNxXQeIbw7mRnsDmre1aiG7d7YHrcBZGNM4xY7vrDUyWDVfeUjU9lZ+fNZL+DB57/Pk3ff0NBkuvtu2c0XnjtWmu1bLBGxtStV0a4jfYNkfZ01gOwF4BdSOyE8qSZiLkFx1bIavAA+/zvH+MhlXTx0614u1rli95WKoHTwaD/nxjJUv22xJMWHo9NkC2MVxQVxwlIYAZ4qfhA+7xTuBMp1JF3irtvUOkYWDMy6sdonX602S5kMVmugt1i4rZg/7xD220/lvFIhuss2pCrep1bwSbgOmXyAoCRjDls3tBFzhS1dM689eLSfmCuICEI4+7dsmkBJQNh95rr1eudnnByc5KsvvFGz26nonaEp3r0wxd++d5F41XuWl58AyPs+qZjLVC6cM5LzApJxl5jr4KLk/YCORIyOpMt4xivNLQkDhDS9/Ih1E5l1IYr+77VuKZPBanYxOQ7nJ7JcmMziIIV0VyEVc4m7Qs4L5pxMV/wdbmyLE3eFWGEgOefPrvZ5eiTNWDqP6wiic6+l66vi10vbKeM6wsS0hzNP3Ag0TN/VgIq7AqVyMDsfhGMXxacCIO/5aKE9McchEXO4pCPJ7t5O9m3dyL6tG/nI5RvY2BZv+oWMBQOzLjS7/3s9WMpksFqLvF+6IUneL3THiJZSNHu7krTFXRKuzBl8ir/D3q4UWze2kSgsSzmV9We1a8emdrJ+QN4LmC8BM9Cwj34uQngi94KAzmRs/oBAOLdjrt2ksE/cdSoCQrj+svClA1eyZ0tXw7PNl8q6icy6cHokjSthJcqcH5BwHXo6E03NxlgJUXd91RpvaORn1upiKhZuOzOaYSrnkYo59HYl6UrFSec89ly6gU/s3swzf/0uUzmfjoTLF2/eVXrv8jGMDW1xNrTVXxnsvlt28/oHI2SD+qFAmBlEDquNht/na7wkVpitHHMc9m3dWGrn+HTt8QOArOfPeq78BsQR8IOZ8hRFP3PF5tIxvbZwN7Qca09YMDDrQric4RSuCK4Inq+cGZ3mqt7G8u0XI+oTdaMTsJrZ7kZ/Zr0FaR65PUxpLS/BPTQxzUg6XAP49Q9G2NQezvjN5H1eeP0M127vnnNlsI6Eyz2HXp1V7nlLV5LTI5m6nzPmhhPMdl3SznvD6ULJicIAblmKqVvI6c974QpoI1NZrt3ezRdv3sVvf+9k3fefr+fJV/AL6b7hXUIYbMqP90Jmmy+VrWdg1oXbvnGUk4OTYR+yhFkofqDs2dLJS1++pek/bznWHIhqYfu52r2QnznXgjRH+gZ5/KW3ODU0iRdAwg27VIoX5d1tcdI5n6wX0J5wefLuGwBmtW0sky+Vdi4+N57Jo8DGtjjvDE3V/azJmMOXDlzJH742gCvw4ViYsioSFp3zAmXrxhTnx7Pkg7Dc9OUbU8Rch/FMntFMHt/XebuhoFDXSCDuumghc6k8o6n477K4cE4z1nGoZUXXMxCRW4FvEi57+YyqPl61/X8GHio8nAR+XVXfiLpdZvWK4op6IuuxrTvFhclcqZvosg1JJrP1b/OXYjnWHGjWOgDlx3s8k6cj6bKxLTWr3QCvfzBC3gtzHkWEVKx+d9t8Ka3pfEDMdXBEERGyXkDClULxthwJ16nINnrsjn08dse+igCTcB1yflBxnM+MZKCw5kC8MA5Ry5cOXMm127sL3VIeccfBV8XzwruC9oTLP9i/g3//l++QD5S8r5wfz3LphhQT0+F4Rirm4BUCx1yKRemEAEeE9kQYuFTDO5Sw27JySc3lFmkwEBEXeAr4NDAAHBORw6r647Ld3gV+TlVHROQ24BDwsSjbZVavqLo+il0Mu3s7S88Vr2ijEOWCLUXNWAeg+nifG5tmKusxMpXHVy2NrZwcnODRwycIgsKAbCHJfyrnM3UxQ1vc4UjfYMO/o/KyEq4UU0DDGcDFNNB8EK4q5gjkPJ+DR/srqoFCuFRl9VhQzptJFd3W3cZ7NYrLXbYxyYs/+pAXXj9TOjHng3D2b1HOCyq6gYr1gAZG0qW5C1kvwK0xmlyeRlr+fdj9pGT9mf22bmxjQ+HfSjrnRTI43Iios4luAk6par+q5oDngTvLd1DV/6aqI4WHrwLbI26TWcWiyvqJqmZ+PfXWHCj2bzej1k8zPlP18XadcJGW4knOC8KxlXTWJ+4KjlP7lOEHuqBU3WK2UcJ1yoq+hYO0xWvs4sW2I8LwVI6T58dnvU9nwuXMaLhojFu4uwgIs4P6hybD15ft7wj0dia4pCPJu8Np4q6QKpSU9qsnh9W52q8uRVFrv/IZzTEn/ILZKa4KDIykGc/kIv83OZ+og8E24HTZ44HCc/V8AXip1gYRuVdEjovI8aGhoSY20awmtVISm3FFHUXN/LnUOlGPZfLhYioT07gC3z89wheeO85t3zi6qKCw1M90pG+Q1z8Y4YOLafqHJsNFUwpnZgWm8wE5LyAo5PK3xcOZuvEaV8I5X2sWjKunGCx7OpP4qkznfXJlOfcQnrgTroMjYVfPcDo/K4BO5Xy8QMkWCsaVn5incj7vDadLffpxR4g7Dhemcrx5dpysF/DO0BTvDafJVZ3Q55+C1rh8AAEzxe3KP1vMkcKiNdMMTWSZyuY5eLR/Rea/RD1mUOuY1gy3IvLzhMHg5lrbVfUQYRcS+/fvb71Rb9OQpXZ9zDXesJxln2tlgRT7tz1fOTs2Hc7AFXj3wtSiu8Lm+kyNZAUVM1nCNmVq9q9L4ZJxeCqLH2jNLJliFc1iwbj5Pkcx2yjv+zWvrF0JU1FVlVyhTY7IrLWCz4zOZAvNdVIo5vTXa391Hk2zTzCzJrVpOGjsFLblfUXVJxlzePfCZNPXN25EpNlEIvIJ4J+r6i8UHv9TAFX911X7XQv8CXCbqr493/taNtHatZQsnPleu9LlKG5+4mW62+K8e2EKz9fCySkg7yuOAx2JGF+8eRev9F9cchsbzQryA+Xs6HQhw2rmxBsrq9uvhF04gYb71DqZxp0wTVMcuGHHpjmzYYq/h5ODEwxP5kr96rHCe2T9AFcgFZ8p1QBhwNmxqZ2YK8Qd4YOLGdL52bn8RcX5A0WpmNP0NQCaKRVzSrWWosoqWslsomPAHhHZBZwB7gY+V9W4ncAfA7/SSCAwa9tS8qrnyuABmjIwvZSAUrzryRWW18rlZ9ILHWAy6/HNl0+xMRVjOu/z4ViG1z8Y4UsHruTBT13dcBuh8lhMTIc5/NOeXyoKVxzgFhG2dsPQRLZUqM0h7Acvv63P+8qm9hg5T0tZMNVVPvNBQFwc/ubdYa79598l7gpXX7qh7jyFyzaE2V1FXqCIzBSX29AWqwgGgYZrDzeqOmat5kAA4R1CzHUggIlpb9mziiKfZyAinwG+QZha+qyq/isRuR9AVZ8WkWeAvw+8X3iJVy9yFdmdgamleOUtZQVgijNUt9foflpoTn69q+27btzW0NV88fXnxjKlK/ByjoRXs4FCIuYghRmqjggH/+FHG7ozKgaqoYksl21IIiKlK38IK2Fu7W6jI+FWpGQWj8fQRJbpvF/K6pFCm4rN7Ui4dCZjjKTzs5ZjdMr2daWYSgnxQj7/g5+6etY8hR+dGSu9XgqX8kr4+o5kbM4ZvmuNI5CMhfMQvED5mSs21/y3qaoV/8YXYkXnGajqi8CLVc89Xfb9F4EvRt0Os/bNNd7QjFTPWnceFyaneerIO2zf1DbvHUfxrufXf/+12QvzMnPynJkJG54UvUDnnZ9QHqhcCdMi378Y9qcLFOrfCMmYFLJ2tDTAXR7YvnjzLr758snS1Xl1l5AXKIMTWWKuECv0wSdiYTpnsf1OWdnoMNsm4Kkj73Dt9u5Zv4eK8sxlPysZc9nQFifrBXVLS681YfxVfA0X7fn8f/dTvHF6hJ+cm+Tt8xOcGpzkveEprr60i0O/Ouf18qJYOQrTcmp11UBYJuC94SnijsOlG5LEXKeUqnfwaD+DE2EK4oXJcN1Z1xGu2Nx4TnetgDKWzuMH2vDksgN7t3BJZxJX4PRIppRHH570i4FgZn/VcKbsfEGrGKg8XxkYnZ7VhVP8vJd1pmiLu4xl8vzyR7eX6gAlXYfNHfFwNi4yK7um/M2UcMA55gq9nQmmcj7FdbtEZgcQv7CGwK/9zjEABsenuXxjG1C5b3HswHWgPeHQmXAZWCeBoGg6H5an6Ey63P97r9ccyG6gwOqiWDAwLaXWpLSvvvBGqfzA9u42zk9kGRid5uotnTxy+97SSfkrL7zBaDpf6s7wfGV4KldK41tM8bWsH5CKLSwVtvg+Oza1l+r6K0rMUfIBpcHc4mBiVypes/5O9QLw5QGmlphAJueVFlr/+l+cpLstxvbuFGdGpzk3nmVbd6qUAxhzKkswh2sEz6SeOiK0JWJcurGNk4MThRTU+meq4sk+5yun6xwfJexPnsr5DJ+frPtea5kSzpgv6krFuLK3kyt7O/k7l3VyzeUbI/m5FgzMqjPXIG2trpry8gMAG9oSpHMe3e2JirTS3s4k45l8KX0yGXOIucLjL71FOh8sqvhazHHoSlX+Gc2XClt8n7grYe2biSyeD1dv6WLvZZ386Y/OMV1YZD3mwFTWC9MPA63bvmIhvupAUJ5Rkw+UwbIBW1UYSXuMpMMTjwicH8+WKngWu30gzCbyCk/MdCGF6ahZzyfhCJMNXMVXTyiLFSa2lcsF4Ory3hEk3XCy3XxlJaIUc4QbdnZz1ZZO9mzp4u9cFn5d0pFY9BjBgn5+5D/BmAWYrxxFra4aLwhm/bHUujofmsyiZYOzqnBhIseFyRw7N7fP29VTK9Ppzuu28sLrZ+YsMVwruJXX2Llhx6bS/o+/9BZ+IZPHEXCdMB2yMxUrtc/zlcGJae77vde4cWf42uLnL36ucsU00eJ5LuE65AuDvxW7aphxk4o5BIX32rqxjbNjmYr9XMehpz3OZNZj2vNJ53y2drcxmskzOJFdUDdGUCeBZZ7lBZoq7grbN7czlsmTzoWfZ7klXOHQr+xf0ZX3LBiYVWW+Am816+Q7zqzpjbWuznOFAmtO2YkzEMXzteFZz7UmeV27vXvO6pw1g9sd+yoyRcpXYos5Ya0eVbhsY4qBkQxj6Tw9nanSkpFh1pGW3m8qm2dbd4qzoxnyVf3wAJs7EgxP5kjEwlx+L6jd91xaZEXDAmpdqRiXeAlG0nl8wtnHWwpLU45l8vgBjE97pHOTbOtuY+fmdgZGMg1fYZfuPmoEsep5AuXijtQtBbFQjoRLZt553Vaee/X9ZQ8GIvDAz1+14kuwWjAwq8p8WT+1umq6UjEU5l0AJO4KmXxYnrj8CjrmCJm8XzMLqZF5BXPNAn78pbdKk7uKlSmLtZbKX1OrcFuA8uFohkCVdD6steMHikM46JF0nVKwzPtKzg8QcXCoXN1rYyrGb911HQ8+/30yeR9HwgBanRpaTOnMBwGb2xNc0pFgLJNnV08nj5cNwvuBVpzwhXBs4fTFDD2diXl+w7Np6f9qPF9HM7tz8n7AjwZG+OHAaBiI5/nZzRRz4MFP7lnwPJJI2rLSDTCm3HzlKGp11Txy+zXA/BPVrr50A+9emGRi2itVuOxKxcPa+flgVjD5xO7NFVf17w1Pct/vvUZn0p01maqWI32DnByarFhQ5+xYhq0bU7PuOopBMOE64bq4AkEQkA9m8vdzfjhbOeaAQ7hKGITBMhFzuDiVxwsqA4ErMFm40g3TRk/hBcHsy/ACX8Mg87W7rqu4uzl4tJ8fnRlhKhvMOlEW6/H7gXJhKkdsvjUhm6CZJ+tAYTK3/FlLcVf4jZ+/alUEArBgYFaZeitklV/l17sSn69Gz8nBCSamPTa1x+npTJbe++HbPgLMDibVs3iHJ/MoynQ+aGgG88Gj/cQdp7AQerh4CQGcn8hyw45NFW07N5bhbKHOjiq4wUy/ecxx6C7MxvV8Hz+A7Zek6EqFd1DDU1lyXkDeD2Z1/YRpoEGp/DMw73KN5f34R/oG+coLbzCeydfNFAoAFy3V/lm5IdjVr7stzo5COnM65/FK/0UeXOE2FVkwMKtKs5f5qy5/EHez4RW0H7Cn6uq+vH7Rb377zdIsXgjLNYiAg5Rm7s63WM3JwYlwVavClX6x2qfnw2UbElzz6HdI5/xZ3RKugFd4IpzRG64b0NOZBJSB0QyuE04cG57KMjiRo7czgecHpMsW8C3OZgY4/t5FjvQN8uCnrubBT13N/n/55xWlIMoFOjPJ7fGX3mK0MJdiLvnSIgfzLy5fz3J2z6yEpCulQADNX99iqSwYmFWnmdVFDx7tJ+/7DE/OdA1t7ohzxSWzi4BVD/ZemMhyZnQaCgHAdQQNwmwcmPuP+UjfIBPTHgFhpkjOn8nBF+BPfvBhad/y+kRamLQVL+T4OxKOcUzlfNIX02xsi7Gnt5NNHUkGRtJMZX16OxP0dqVIxd2KhVzKT6y+aulO5ocDowzXCQRC2E1U/FzvDqfDGcVEf7Jey4EAwKvqmlvoQkRRs2BgVo0oqoqeHJxgLJ3HccKFW7JewIdjWc6PZ7nn0KtzzmEoZvKcn5gm7khYrwfo6Qyzaeb6Yz54tJ9N7XGGp3IEVVfV9U56AWHlymI9Ig2CUldR8UQ8mvH4Rz97eamfuViPCaArFa8s71Al7gpPfKeP/gtTddsQdmkx63PVSlld61fyzRZznHmTHFaSBQOzKixlucsjfYOlkxzArkvaefi2j3Bg75aKdFI/0IrujvnmMHSl4mzrVs6NZ2lPxUrjDV2p2LyrUp0eSdPTmSQZc+vOtq1FFVyRWRU2i612hVI/85G+QcYzec6NhemoxVnL9Xh+EGYBVS/pVWUqF/BK/zBX/bM/o1BgtXZbG/5URiRcc/mV/otN6f6MggUDE4n5rvKrt4+mc4taQP5I3yBffeENRgplJgBODU3xlRfe4Lfuuq4inTTvV2bCNDSHwXW4cWdYn/9I3yCPv/QWJwfDMgm7ezpqtufxl97izGiGgZEMqZiDiBAXJd9Awsp8RdlSha6pYvDsSLpMTntM17kdKF69q8L7FzMLupqfb0Kx3RmENiRdxrOz5yaUH5+uZCwcr1nWli2MBQPTdPNd5dfa/t5wmu3dlYvTNzLAdvBoPxPTXrhqVHEyWaBMZsOTfHk6adYP++CLpYKLP+Pk+XFu+8ZRTg5OULiRwJGw0mdXKsYjt1/Dkb5BHvn2m5weCTN+Eg6MZnIVq2498Z0++s5NhHV7mFlAHcI+92aYyvkEmuXzhaJv86kuCNrMk3eUgaCVAk3vhhSJTJ7ezmTYBadhN5+v4dySDW0xrrikc6WbOS8LBqbp5ptFXGt73BXOj2fZ0DYzaamRAbbTI+lSV5BqeDvuChAIAyNpfvmj23n9g5FCP3z4GkdmcvQvTGYZm/YYzUyWuliUQk1+DdMkfzgwyu+++j5DZYOuuQA+HMsiwD/+/dfpSLpMlKVrFidwBcz0tTuFNs41qzbfwGSqzDy3GK10Iq2nVdovwJauFI/cfs2sC53VOjZQjwUD03TzzSJ++/x4uNh6IbunpzPJpV1JBkYzCx5g60y4lGU1hpU+C1NaT49k+Pr3TpIsDMoWTzAJFzqT4VoE5yfCekXl06SK5RHirkPcFb758qm6qZUKpPM+mcLyi1q1LRlz8PwAIRzAzvlB3X79RgJBI1rlRNrqHIEv/73K2cPNTo1eThYMTNPNNYv4SN8gk1mfQLViVu4lHYmKlMlG/4hEpFRFs/qKuPh42gtwnXBgNucrk7mAt85NlGb21uxC0bDNUw3Wqal1Ag40rE9f3COg9vrBpvW0J1zuv2V3zdnDzUyNXk5O1D9ARG4VkZ+IyCkRebjG9r0i8oqIZEXkK1G3x0Tvvlt2l1bRUtWKzJuDR/vZ3BEnCJSsH5D1g9KCMw/f9hG+de/H+auHPlna9+YnXuaeQ6+W1hyoNpH12L6pLVy9a442+QEVM2j9IFzpq7xrqKj4/VJO3LVeOk8Sj2kBjkBPZ4IfP3brqikj0SyR3hmIiAs8BXwaGACOichhVf1x2W4XgQeBX4qyLWb5zHWr/JvffpOEG2bYlJ9tAw375muVjRicmOYrL7xBb2eSiaxXsbrZeCZft7RCI+fyZqdNuo7gUJk5VOyCspuC1haODyTZ1bP6B4MXI+puopuAU6raDyAizwN3AqVgoKqDwKCI3B5xW8wyqnervGNTO98/PYIrQjxcOgsvCAuw/fb3Tlbse248y1g6R6CQ9ZXhyRw7N7dVrG62DDXRFsQPlI5UDD/rlWKdMqvCtlmF5hp4F2BjW4xEzG2JweDFiDoYbANOlz0eAD4W8c80q0z5nILOhEvOC8JJUsx019ST8Wa2KWGufDhLN8B1HdxlWAFqIX5qcxvj0/maBePM6iUCP711IxPT4SQ+LwgqxrwSrsyqZbXWRB0Mav2lLurvQkTuBe4F2Llz51LaZJZR9ZyCTN7HLVzOV88IblQxdz8frL5O+PcvZla6CWYBincDm9riqCpuYfGex+7Yt2ZP+vVEHQwGgB1lj7cDZxfzRqp6CDgEsH//frvQWqWqZxaPTGVLi7a8e2EqXFBFFRWhpzPBh2PZlW6yWSYrPf9BgF+6/nJ29XTyzF+/y1TOpyPh8vf29nJuPNdyqaDNFnUwOAbsEZFdwBngbuBzEf9Ms0JqzyyeYnN7nLGMH04Ic4QgCMscj6Xr19Q3a0PcDWfa5eeocRQFt/BvLRV32dgWn3WSX2uZQM0QaTBQVU9EHgC+C7jAs6p6QkTuL2x/WkQuA44DG4BARL4MXKOq41G2zTRfcWax5yvvjk2VJlgNTeZJxpyZchFA3IWM589ZZdO0vsWubbBUIsKGtji/VbZim5lb5JPOVPVF4MWq554u+/4cYfeRaXGnR9K4AqdHMqWyC8VBI88PiLsOqmFd9+JYgcUB0yzFbqi4K1zZ01GqXGsaYzOQTdN0Jlz6zk9WPFc82fsK/nxlMI1ZpK6ky09v6163/f3NYMHALEr5QHFXMsZEJsfZcRsMNssn5sBVvZ12B9AkFgxMQ8pP/gQB5ydz4UIsjnB2JGPdPaZp5so62pCK8cWbd9kAcAQsGJhZV/mqymTOryj78OjhE+Q8nwsTWcrmgeHZ6K9pos9efzl3Xr+97sp1JjoWDNa58nRQVyit4rWtO1Uq+zCazjW0Spcx9Wxsi/GFn93FS2+eq1xEyBHirtCZjFVk/tiJf/lZMFjnnvhOH4Pj0/iFDB/XkdISicY0w0cu6+KhW/dyYO+WUvdO8W50vU/0Wk0sGKwj1TWCpnI+p0fCdXFjTpjvH6xQXrhZG9yyeSOuAw9+cs+aqvm/llkwWINqLUb/w4FRnnz5JH6NmaAK1g1k5pSMOeS8oObA7mevv5yv332jXe23ONF6a/CtYvv379fjx4+vdDNWpSN9g3z1hTeYmPbwgoCY4+AIpO1sb2pwBPZt3cjQRLgEqDB7RnjcFX7j56/id199n4tTOYo3j8VaP1+/+8blbrZZJBF5TVX319pmdwZrzCPffrO0cLsjMxU+jallx6Y20jmPRMzll667nBffPE/OC5DCkqAx1+FLB67kwU9dzbXbu+3Kfw2zYLAGPPm9t3nmr9+dteKXZX2aeoQwYyxQ2NKVKp3Y75yjq8f6+dc2CwYtpHjSn8x6NdfuNa2tmGoZBLrg36sAyZgw7VW+MhUT/vGBq3jpzXMN5e3bCX/9smDQIv6351/nT37w4Uo3wyyCK3DN1o2cGUlzMZ2feb6QY+8HiiPCwX/4UQ7s3cKRvkEef+ktTg5NEnccNqRiDJf11ReJwMZUjA1tidJiLPUGcW3GrpmPDSCvEhVX/UAL/lpMDU6hrv7Oze3kfeWuG7fxwutnyPs+Y+k8WT8c5C/2y5crP7F3FmaGT+X8iu+t794shA0gr0JH+gZ55NtvcnrEJnetReEC6nGm8z5KZb98aSDWmXsg1rpszHKyYBCxJ7/3Nv/u5VPkbDS3pXUmXTL5gFRMSOcq8+1FoC3uoqpkvYCE63DphiQx1yHv66z1dO0kb1YjCwZLcKRvkP/jD7/PsC3fuOokXcj5MwvsdCUd0nmds7BecQA3nEWrtMUdRBwSMYc9W7r4xO7NvNJ/kZODE+S8gIQr7Ll0Q8WVfXnXTvndgDGrnQWDGu45+N945d2RlW6GmcPeS2fq2M8187V8275N7Xxi92Ze/NGHvHNhCs9XYo5wZW8Hn/m7l/NK/8V5c+gfnKdddtVvWlXkA8gicivwTcI1kJ9R1certkth+2eANPB5VX19rvdczADyFQ//2YL2NytLBD5+xSZG0nneGZrCC5SYLWdozJKs2ACyiLjAU8CngQHgmIgcVtUfl+12G7Cn8PUx4N8X/ts0FghWhgAdCYdAIesFBBoWxKuujOEKdCRctna3WYaMMSsk6m6im4BTqtoPICLPA3cC5cHgTuA5DW9RXhWRbhG5XFUtqX6F9XTEyflaMbPZEYi7Drt7Onjo1r0AdRciqdd9c6Rv0BYvMWaViToYbANOlz0eYPZVf619tgEVwUBE7gXuBdi5c2fTG7oeCNAWdyqK1l3SHuff/IPrl3Qirvfaev3n1q9uzOoTdTCQGs9VD1I0sg+qegg4BOGYwdKb1to+e/3lnBvPMTCSpiPhIiJMZj3rYjHGLErUwWAA2FH2eDtwdhH7rAttcYdf/7nZM1GNMSZqUQeDY8AeEdkFnAHuBj5Xtc9h4IHCeMLHgLFmjxe89/jtkQwi28nbGLNWRBoMVNUTkQeA7xKmlj6rqidE5P7C9qeBFwnTSk8Rppb+WhRtee/x2xnL5OlKxnCcWj1TxhizfkU+6UxVXyQ84Zc/93TZ9wp8Kep2QFgrxhhjzGzOSjfAGGPMyrNgYIwxxoKBMcYYCwbGGGOwYGCMMQYLBsYYY7BgYIwxhmVYzyAKIjIEvL/Il/cAF5rYnFZkx8COAdgxgPV3DH5KVXtrbWjJYLAUInK83uIO64UdAzsGYMcA7BiUs24iY4wxFgyMMcasz2BwaKUbsArYMbBjAHYMwI5BybobMzDGGDPberwzMMYYU8WCgTHGmLUVDETkVhH5iYicEpGHa2wXEXmysP2HInJjo69tFUs8Bs+KyKCIvLm8rW6exX5+EdkhIv+fiLwlIidE5J8sf+ubYwnHICUifysibxSOwb9Y/tY3x1L+DgrbXRH5voj86fK1eoWp6pr4IlxJ7R1gN5AA3gCuqdrnM8BLgAAfB/6m0de2wtdSjkFh2y3AjcCbK/1ZVuDfwOXAjYXvu4C319u/gcLjzsL3ceBvgI+v9GdazmNQtv1/B/4A+NOV/jzL9bWW7gxuAk6par+q5oDngTur9rkTeE5DrwLdInJ5g69tBUs5BqjqUeDisra4uRb9+VX1Q1V9HUBVJ4C3gG3L2fgmWcoxUFWdLOwTL3y1YobJkv4ORGQ7cDvwzHI2eqWtpWCwDThd9niA2X/M9fZp5LWtYCnHYC1oyucXkSuAGwivjFvNko5BoXvkB8Ag8Oequu6OAfAN4P8EgojatyqtpWBQa5X76quaevs08tpWsJRjsBYs+fOLSCfwR8CXVXW8iW1bLks6Bqrqq+r1wHbgJhH56eY2b1ks+hiIyC8Cg6r6WvObtbqtpWAwAOwoe7wdONvgPo28thUs5RisBUv6/CISJwwEv6+qfxxhO6PUlH8DqjoKHAFubXoLo7eUY/CzwB0i8h5h99InReT3omvqKrLSgxbN+gJiQD+wi5lBo31V+9xO5aDR3zb62lb4WsoxKNt+Ba07gLyUfwMCPAd8Y6U/xwoeg16gu/B9G/BXwC+u9GdazmNQtc8B1tEAcqzBmLHqqaonIg8A3yXMJnhWVU+IyP2F7U8DLxJmEZwC0sCvzfXaFfgYS7KUYwAgIt8i/APoEZEB4P9W1f+wvJ9i8Zb4+X8W+BXgR4U+c4B/pqovLuNHWLIlHoPLgf8kIi5hr8EfqmrLpVYu9e9gvbJyFMYYY9bUmIExxphFsmBgjDHGgoExxhgLBsYYY7BgYIwxBgsGxhhjsGBgzKKslZLnxhTZPANjFqgwKett4NOEZQ2OAfeo6o9XtGHGLIHdGRizcA2VPBeRmIgcE5EDhcf/WkT+1bK21JgGrZlyFMYso1rljz9WvVOhLMLngRdE5EHCom+z9jNmNbBgYMzCNVwGvFAT53eB/xf4ROFOwphVx7qJjFm4hZYB/7vAKHBphG0yZkksGBizcMeAPSKyS0QSwN3A4Vo7isj/CFxCuL70kyLSvWytNGYBLBgYs0Cq6gHFEslvEZZ6nlXyXER6gMeBL6jq28C/A765nG01plGWWmqMMcbuDIwxxlg2kTFNISJPEa6WVu6bqvofV6I9xiyUdRMZY4yxbiJjjDEWDIwxxmDBwBhjDBYMjDHGAP8/xJlVanYwRPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(x=df_t['0_x'], y=df_t['0_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "id": "34ebdabb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k11sO7uCBp16tDeoWMg7FqlIKB6OnC1kU6uQu4va+5eAOvn70bF3fNGv/2RPzVDwlk3K4dNdYy36PD/xmXYeRjMNCKQgzjctndLLPSf0frz/jCqPZFHPLZTwN7qKzaZd82kmUFGn2ezYb6N9Kcjk2SG0MJc2kDG5540Xc9+jxrqUzHjpykl+773HmQtkFCOLaR9IO2bTLeD7dUX1Jds0XK6xUPIoVv07SAYIQSgiydV0JJthJuU5dG1GdSfIVmZRbZ8vdDz7Lx7/63JpYewGmC2lml6vAqrzF6cUS88UqKVdwBKqe4ilMjaQYz6Xr5DBKVZ+TCyWEICfB84OyEFwko6zsiVwYBaQa5C+EO1nIusyveMwUMkwXshQrHqfmV1gsB/kB8fZHMw7lqHKF6bEMaddN7Pe47AWqVPzVfXYdavIZ+3YEfXtyfoWlWJsVL5Dl2DGS5sLJ/JrfOF6/xn67RlwHZgpZqr4GobJNjpmHjpysk/iIjrXJkTQ/949e09PxO0hskNrYcjSTMjj08As9SWfcc/goi6VqKP8QSkg4wmLZY2Gl2nF9SXYtrFSDC5LUSzrAaqx/JAMRyU7E22glX9Foy6GHX8APE8Li2c4KnFmqrJG3mC8GDiOSfohMO1es1mQrXAnKL6xUa5pDruPUnAOsOgcBzoXyFb4ShJeG9p4rVnEkkLqI+max7OHr2vaXyj4O4b46wb436/e47IXX0Ldx+Yxon5ca2oyYK1YSf+N4/UlJbrU+8KmTC2l2zDRKfMTlSno9fjcLcxDGUPLS7DL5tFu3LJ92WSp7icvbyRa8NLuM5+saCYkoqanT+pLsipKoorobs5DjEg+R7ES8jajOsuevZvuGEhWNtkRSD0lENjTKZsSveZEtvlJrLyof36YdItTNhR31pRNrO2onTrxv2u1rfJ+dhL6Nf4/3bbM248vjbbWqv66esA0vTBCM0/h7BrIi9fvq+drz8btZmIMwhpJmUgajGbcn6Yx9UyOBhk/DxTu6c+y0viS7IiG3RkmH6P9xiYdIdiLeRiv5ikZbIqmHJCIb4vIWkaxG3J6obKMcRnybdkRyF3GnGKmXRvVE7cSpe+pps6/xffYT+jb+Pd63zdqML4+31ar+unpYlSNpdcw0SnxE++g60vPxu1kMzEGIyD4R+V8i8oyIPC0i7w+X/6mIPBb+vSgijzXZ/kUReTIsZwML24xmUga3XXegJ+mM915/kEI2Fco/hBISoQzzWC7VcX1Jdo3lUoxm3Frd8QtRdIGOZCAi2Yl4G63kKxptue26A4EjoOFuHNg5ml4jbzGeD+JQIumHyLSJfKomW+FpUH4sl6rJVHu+Xxs/gdULhRKMQdTkLiJZCZSJfApfA6mLqG8KGTcYe2hofzTj4BPuqx/se7N+j8teuA19G5fPiPZ5tKHNiMl8OvE3jtff6FziOA51ciHNjplGiY+4XEmvx+9mMbBBahHZA+xR1UdFZAz4NvBuVf1OrMzvAudU9Y6E7V8ErlHV0522aYPU5xfNpAx6lc5oF8XUaX1J7UNnUUxLZS+xjVbyFZ1GMUURO43yFs0ieuKyFZFdnUQxAXVyF3F7oyimeN80a/+5E/OU+xjFFO/bVvuc1P+dRDGNpJ1ESZFmv2e7KKZhkd0YCqkNEflL4BOq+pXwuwDfB96mqs8llH8RcxCGYRgDZdOjmERkP/AG4Juxxf8YOJHkHEIU+LKIfFtEbh+wiYZhGEYDA0+UE5EC8HngA6o6H1t1K/DZFpu+VVVfFpFdwFdE5IiqHk6o/3bgdoCLL764j5YbhmFsbwb6BCEiaQLn8BlV/fPY8hTwE8CfNttWVV8OP08CXwCubVLuXlW9RlWvmZmZ6af5hmEY25qBPUGEYwyfBJ5R1d9rWH0jcERVjzXZdhRwVHUh/P+PAWsGso3zi04lCDajXDspjF/93KPc/8SreL7iOsK1r5kEcRLrvPWev+XrL8zW6s64wgXjOV6eLRIFQAowmnV43UVTiXNDNNYbDbIuloIktbQDjuNQqnj4BNFGwcQ8wbvbbMphppBhpeqzUKxQCjPQRjIuP/5Du2qyFJF0x6nFUt1+7x7P8NUjp1gsVROTy1KOcMnMKB9652uBYEA3ktaIT01a8XwqXtBnUdDAE8fmVgfhgbQrjOfTdQPZjfubT0tdX0XrF1aC5MPRjMsVF07U7E6aq+G5kwsslz3KVQ/XcTiwc4SbXreHrx89W/vdy1WPik/d3Brvu/GyIHDg8FGWY3kqIxmXX77+YK3+6DdLkiPZdlIbInId8DfAk6wmYn5YVb8oIn8EfENV/yBW/kLgkKreJCIHCZ4aIHBif6Kqv92uTRuk3ro0k9ZolCDYjHLtpDAumsjWXfAjRjMOB6YLdXXe89fPJ5ZtxmQ+xXg+wx03XwGQaOubLp7g/ideBdW6TOP1sGMkzVguxfG5FaJrhFObTlSCTGhZm5QWx5UgxyCTdkk5wpmlMp6vtYS6+LaugIiQcYVixV+TrCfA7oksaddd3V+UWE5era+i9apa10Yu5bBSDexOu0EmeBTqPJpLcXK+VOs/N8prkSA8dmGlStXXOrvcMBHk2tdM8s0XZ5v2RRBSnCWfdjmzVOLkQrlOjmSYpTZMi8kYCm699xucXFhhJLP6ULtcrrJrLMdnb/9Hm1ouKvPquRWqvuKI4PtKyhV2T+T47qkglDEpk/p1F03U1fn1o2e66hdH4MD0KLvGcgCJtr54ZjnMXdDa1KLrxZEgw7fqaZChLJBLufi+UvKaiBU1ECXSZVKB1ETVUyph1nlSubTrUAqFkKLckTijGZfdE7k1+4usJuodmB6tW1/LcI/1iyOQTQUJh8WKV9vX6O4/ylmJ+tJpsC1us+sIlTZeWYArw+Pg6KlFyp5PxnU4OFMAko/LjWTTo5gMox3NpDUaJQg2o1w7KYxO6FVOwdfVbZvZGiWt9fNeLy7FoVC7urbKNG4k0kryfF2tq4mNqu3rjvq7bn9j20R91a4/GpdH+6qs1hd3tH4T2yKn1I54c2XPXyNHYlIbhtGGZtIajRIEm1GunRRGJ/Qqp+DI6rbNbI2E87q5eHfSbrS/AnUXzk6JMp1dR1bramJjK+cREfV33f7Gton6ql1/NC6P9lWgzhFKbH2SbZH+VDvizWVcZ40cybaU2jCMbmgmrdEoQbAZ5dpJYbzlwBRQL8wHwRhEY51R2U4Zj8ltNLP15qt21xRe+/UQMZlPB1IcGtyNO6xKaoxmkjWPGnEEChm3Jk0RH1mIbxs9CHi+MpJ2EvdDoCbHUdvfBsmRqK+i9bX6w6eBXGr1cuerX5PWKGRcxvOpmoRJ1F7k4CbzafxQ4rvRZl/hLQemWvbFeH5VlmM8QY5kmKU2bMIgYyi44fJd3EF7yYvNKBcvU/FWpTAOTBfaRjE11nnD5bs6jmIqZB1eu2eizp5mth6Y7n8U07HZZS6ZGa2LYookNdpFMaUd4QcaopiqXiCt0RjFVPUUp4Mopv07C033dyQtdX0Vre8miilwvp1HMY12GcV0bHaZ/TsL3PrmtXIk2y6KaTOwQWrDMIzusEFqwzAMo2vMQRiGYRiJmIMwDMMwErFB6m1Cp7ITvZbvt13dymQ0k8DohCSZChFZIwGRJHcRSVEcnysGcyH7QXTT2y+f4ZHvzXFstliLeMmlg/uxcjVIFku5wkQuxblihUpC7pkQSFbkMy7zK9W6dbkUKA6VWDx92nXwfR8fSZSRmC9WKDckdbmO4MVGmV1HuHAih/o+ry4Emc8pV8i6wlLFr4vS2juZQ0R45VyxNm9CynWoVH3iu9MoGwLw0S8d4ZlXFxL3OZd2GM2mmClkWVipcGK+RFUVV2D3eI6xXJqFUpWxbIqFYrluzo243U6YpDeaTdXJdDT+fktlj5MLJTzfJ+O6jGRdLt01xlsO7uDTX3+R02H9cQoZh307Rjm5sEKx7FPxfBxHavNnFMteLVQ2mGdccB1wxOlo/othwQaptwGdyk70Wr7fdt3yxou479HjHctkNJPA6MTeux98lru+9jyNsg0RkQTE1Eia37nlamBV7qLq+RyfWwkyevvVKX3AAVw3yFwez6cYybi8eq606TZO5lOBA/F8zhWrLctGYaZJOduuBL/x6cUyPhBFr1abJHg7wAWhTEf82Kp6PsdmA+cWD611BSbyaWaXK237LC4Z0kmYsSvgiDA9liHtdnaMDhobpN7m3HP4KGlXGMmkgpDATIq0K9xz+GhfyvfbrkMPv9BR+9H288VqeJfm4CAsrFQ7tvfQwy/UEqEaQ9mjE951gjrvOXy0zubTi2VckdXY+T4mqq2H4KIZnNrzxSrzxWrPzqGf+zS/UmWxFNjTDqXeOcTt8BVOL5VRWf3eKmFNJeiHxmPr9GJ5Tf5I5JTmiu2dQ9R25Mw6Ka8ETxSRPf0+p/qNOYhtQKeyE72W77ddS2WvK5mMZhIYndi7VPYSZRsiouzfqu+vkbuItzuMaPhX7lA7adBEshvrfZKJJDziWe2tXoSorh4T8WOrJq+R0EAnGdJ1dHgcRMdTN8foZmIOYhvQqexEr+X7bddoxu1KJqOZBEYn9o5m3ETZhohIAiLlOGvkLuLtDiPRnW1c1mEziWQ31utTowzn+E1BK0ctsnpMxI+tmrxGQgOdaCzV0eFxEB1P3Ryjm8lwHDnGQOlUdqLX8v2267brDnQlk9FMAqMTe2+77kCdbEOcuATEWC61Ru4ikqKIriXD4iwiWQwIZB7G86meL8r93KfxXIpCNrCnHUL9xSluhyMwPZpBdPV7qwu66KpMR/zYmi5kavIatd8wbHcyn+6oz6LtmzyAJu6X72vNnmGV2IiwQeptQhS50Wl6f7fl+21Xp+03RjFFUhD9iGJqlIBojGI6NrvM6HkcxXRioUy1T1FMhazDlX2MYlosVSk0iWKK7G4XxRT9fuuJYjq1sMJyh1FMKQdkCKOYbD4IwzAMIxGLYjIMwzC6xhyEYRiGkYg5CMMwDCORgUltiMg+4NPAboLck3tV9S4R+c/ALwGnwqIfVtUvJmz/DuAuwAUOqeqdg7L1fGCjpDGGhaT9BdbVB636MFr35PFZihWtDexGg5D5tLB3coTTS2XOLJbXHesfn8WsWV0jGZfX7Bjh1MJK4kBq1hUOTI/y8rmVNYPcjUThl0KQ7RuNZdeWx+yJQkrTrsPB6VHeeeVuvvjkKzx/ahHPr7fXEXjX1Xt41+v38tEvHeHIqwtr9seVYI5o1wHPh7IXhqGmXEYyLjOFLKrKqcUSFU9rg7xvORjMqxCXPIkkOKLy87EAAAEumszxW+9+HX/52LG6+TsunRnhe2eLFBuiBfaG5SE4tp46PsdyxUdVKWSDYIRX58u1IAlf/To5jcjGuBRMPuVwtlilVPVrwQTvu/Gylr/PZjGwQWoR2QPsUdVHRWQM+DbwbuCngEVV/ViLbV3gWeBHgWPAt4BbVfU7rdrcroPUGyWNMSwk7e98mPk6kU/31Aet+hACeY35Ypm5DrKAtwsOgbNolKpIIptyKDXTwmhBlPMAq1nthDOynVupMlPIkE05HJ9bAWDHSJqzyxV8XxNlOiCQ5ujGlHxKKOTSVH2f2eXV398huPMtZBxKVcVXDaK5BFwRCjmX+RWPiVyKhZUqCFRiUWTpsAN9hfe/7ZJNcxKbMkitqq+o6qPh/xeAZ4CLOtz8WuB5VT2qqmXgc8C7BmPp1mejpDGGhaT9XQhlHHrtg1Z9WJP0aHMXvt3wWZXDaHeb2atziLKmo8zplOPgOMJcsYIjsLBSrUmeuI5weqmM60hLeyJT2iXYRRSrysJKtaYfFYWvRnu0WA5CXOuenBzhXLGKI4FsRyQFE983L9ofCSRfhpENGYMQkf3AG4Bvhot+RUSeEJFPiUjSJL0XAS/Fvh+jiXMRkdtF5BEReeTUqVNJRc57NkoaY1hI2t+q79fF80N3fdCqD6N1XcsvbAcG2SexJ4foD4ILtB/eqZc9vyZ5Ei1PSnpcL1Xfb/n7x1/BRa/kIhtrmfp1G6yWdySQfBlGBu4gRKQAfB74gKrOA78P/ADweuAV4HeTNktYliybonqvql6jqtfMzMz0x+gtxkZJYwwLSfubcpzg9UOMbvqgVR9G67qWX9gODLJPYiqp0R8EF9bowptxnZrkSbQ8SXhxvUR3+k1NjTmByFk4MSex5k1+rLyvgeTLMDJQByEiaQLn8BlV/XMAVT2hqp6q+sAfErxOauQYsC/2fS/w8iBt3cpslDTGsJC0v2OhjEOvfdCqD2uSHjmbPiWOw+oFpN0FOZvq/lITZaBHUhqRaKLvK5P5NH44FhFJnni+Mj2awfO1pT2RKe1E/iLyKWEsl2IilAhRXZXkgGAMwm9o0/eViXwKXwPZjkgKJr5vrqw+mdx23YGO+2UjGWQUkwCfBJ5R1d+LLd+jqq+EX98DPJWw+beAS0XkAHAc+BngZwdl61bnhst3cQdsiDTGMJC0v//pn/0Q0HsftOvDaN1Tx2dZTohiGkkLF1kU00CjmE4vlignRDEdm13mkplRRITFUpVLd62WP7fBUUyqfp2cRmMU00ROyMWjmNLbN4rpOuBvgCdZHc/5MHArweslBV4E3quqr4jIhQThrDeF298EfJwgzPVTqvrb7drcrlFMhmEYvdIqimlgTxCq+jDJT55rch7C8i8DN8W+f7FZWcMwDGPwWCa1YRiGkUhHDkJELhORr4rIU+H3q0TkPw7WNMMwDGMz6fQV0x8Cvw7cA6CqT4jInwC/NSjDjPObaA6GpbKXKDcQSVs8e2KeYsWnVPHwCQY0L5kpcNPr9tRkFvZNjfCWgzt44KlXOXp6CYADO0f40DtfWzdI3SilEQ0gPntivk7CIT443WjnFXvGePzYPMuxkNiRjMsvh9FSUVn1FY0N8DoazI0sIqQcJZsKTr2VMGNbgYwjjI+kObdcoRpORBRFzAwLAuwYCZIJzyytnbfZEWqztlX92HwIQji5kpB2HRxHa5IUcSmNYt3cCi5nltcOvsdxY6GiUdST68DusSxj+QwLpeqa33p+pVqX0RxlREfzbyiKHy5QrZdSict5LBTLvDxfWpMfIcDO0TQzY7la+1s1aKSjQWoR+ZaqvllE/l5V3xAue0xVXz9oA7vBBqm3Bnc/+Cx3fe35WvhilCkbyQ1EshflqsepxRKNUypHA1sXjGeZLmQ5vVji5EIpvDgEa32FyZE0H7vl6tokMXEpjdOLJU4tlhnPuSyWwou9wvRYhrTrcsfNV/DEsbk6O0vV1ueKEEwCVPGG6ZI+vDhSn9uArM4FHV2014MrsHcqT9nzOblQZizrcq5Y7dnhOgTOYsdImtOL5bb2OcC+HflgEqUhlr7ph9TGaRH5AcKbGRG5hSDJzTC65tDDL+BIlHzkrJEbiKQtFlaq+AlnYXSCL6wEM8AtrFRrTsZ1nOAvDHmMpDYapTQWVgIZhHPFKg5Sk3CYL1ZrEhuNdrZDoU5OwWiPz+rdv6+rCXHrdQ5R3acXy8yHkhfzK707h6i+SM5DO8jEi9rfytI3nb5i+rfAvcDlInIceAH4FwOzyjivWSp7NOZNxeUGXppdZjKfpuz5TU9oJZBZIPYZLysSzCUdSW1EdUaUPR9HoNKQAVv2/JrERpKdRv9omufRTvmv4wZWj43ot14vEnvK6YSo/a0qfdP28A+VVf+Nqt4IzACXq+p1qvq9gVtnnJeMZtbqGsXlBiJpi4zrNM2IFQKZBWKf8bKqwd1eJLXRKKWRcZ01MggaSjdEEhtJdhr9Q8LHhbiMBtC/QRdZleKIfuv1EklodCLyB6vH5laVvmnrIFTVA94U/n8pVGY1jJ657boD+BrJDPhr5AYiaYuxXIqkNzbRuTmWC3T/x3Kp2jiB5/vBX5jpGkltNEppjOUCGYSJfAofrUk4jOdTNYmNRjvbIVAnp2C0x2F1LCJU8q6TsVhv3dOFDOOh5MV4LrUujSYHanIe0oETi9rfytI3nb5i+nsRuR/4f4ClaGGkr2QY3RBFKzWLYorLXlQ9n+UWUUzHZpc5MF3gZ6+9uC6K6ZLp+iimRimNYJtQBuHEfE3CYf/OQi3iJNo2snM8l+ooisnX1lFMaUfJbPMoJtfRmiRFXEpjuc9RTIulKvt3Frj1zau/9bkeopgiKZVIzuMHd491FMW0WKqyayx33kcx/Y+Exaqqv9h/k3rHopgMwzC6ox9SGw7wflWdCyucIlmm2zAMwzhP6PRV31WRcwBQ1VmCCYAMwzCM85ROHYQTn/lNRHYwQKE/wzAMY/Pp9CL/u8Dfish9BGNBPwW0ld82hoe4dEUzWYl+tRFJWURRG43LkuQv4nZFg5aLZa+lTEEnch13PvAMz55cXM3QFShkU1xx4QS7xzP8zydfpRwbsIzr/9/5wDM8d3KRThKjReAf7Z/ipdkix+dWehpYTjnCSMalVPEoNWk07YDjOD3N8TwIojDhQQ2kC5DPuLU5KlYqXuKgcMoRKn2OSS5knZq8xsmFFSqeUvU10YY1dgtcNJ7lt95zVS2T/6NfOlInBdMoFzOMA9kdzwchIj8EvI3g9/iqqn5nkIb1gg1SJxOXrjizVA4WNshKrPfAbJSyKFY8zhUrCDCeT9eWxSUHkuzyo4l4RLhoMtdUpqATuY5fu+/xppP35FIOK00usiPpYPrSpZLXl4xeY2vjAOKA+t1neI9lXX7pHx/k//7G95hdrtRyMTxf8XVVLqbx3NhI+iG1gap+R1U/oar/bRidg9GcuHRFM1mJfrURSVmMZFIslqosrFTrlsXbS7LL11DSQKSlTEEnch2LpebSCs2cA8ByxWepbM7BCFABvwfnAIE6wKGHX2BhpYrrSE0KJnoCieRihlWOw4QEtgEvzS6TT7uUPb+prES/2ojjNczDC/WSA0l2afhPZF/jNhFLZW9NZmyjXIe3jlcOlkFtRKznFZqvwTFZ9f267OsoIbAcU6IcRjkOcxDbgLh0RTNZiX61Ecd1ZI14Xby9JLsk/Ceyr3GbiE7kOtxGD9IF/ZBlMM4PJKY62y1R4mDKWT3GYVVeJDrGYTjlOAbmIERkn4j8LxF5RkSeFpH3h8t/R0SOiMgTIvIFEZlssv2LIvKkiDwmIjawsA7i0hXNZCX61UYkZbFcrlLIphjLpeqWxdtLssuRUNJAtaVMQSdyHYVsc2mFXAsVvpG0w2jGtbsnAwBRcJzeLpZR8MRYLoXna00KJroBieRihlWOo+NB6q4rFtkD7FHVR0VkDPg28G5gL/A1Va2KyEcBVPWDCdu/CFyjqqc7bdMGqZsTRQvFZSUGFcV0bHaZvQ1RTPFlSVFMcbuiKKalspe4TYRFMW0+53MU01jWqclrnFpYoTygKKZm58ZG0WqQemAOIsGIvwQ+oapfiS17D3CLqq6RDjcHYRiGMXj6EsW0TgP2E2Ref7Nh1S8CDzTZTIEvi8i3ReT2AZpnGIZhJDDwbGgRKQCfBz6gqvOx5f8BqAKfabLpW1X1ZRHZBXxFRI6o6uGE+m8Hbge4+OKL+26/YRjGdmWgTxAikiZwDp+JS4OLyM8D/xz4F9rkHZeqvhx+ngS+AFzbpNy9qnqNql4zMzPT710wDMPYtgzsCUJEBPgk8Iyq/l5s+TuADwL/RFUTg35FZBRwVHUh/P+PAXcMws4keYiNGijazLY3g3b72zjofMWeMR47do5ixUcIJqD/yTftrckTjGVTLBTLnFqq4PtKOuXgCpSr/ppBXtcRLp0Z5bV7xvjS0ycoVoJB3n7Nbmn0ThRpJusc8E5FGfVdbJNLge8LZT+YfyOTcihkXXaN5Tg5X+RssUqrYdqMI2EEXvMyhYzDJ372TUC97MxbDu44f6Q2uq5Y5Drgb4AnWf3NPgzcDWSBM+Gyb6jqL4vIhcAhVb1JRA4SPDVA4MT+RFXbaj91O0idJA+xUenum9n2ZtBufxulMyqeJkaKCLB7IkvGdXjpbLE2iVAnkUaG0QmDuGlwgalCholQdub0YolTi2V2jWXYOTq8UhsDe4JQ1YdJzi/5YpPyLwM3hf8/Clw9KNsi4vIQACOZIGb/nsNHB/4jbWbbm0G7/Y1LZwD4upp0V8uyDu8u54vV4Ht4JntqTwJG/xjEceQBi6UqeybyQCCx4UhwLE8XckN7/m/rXKAkeYiNSnffzLY3g3b7mySd0Yyy51P2/JaP/oYxbMSlX8pekCxnUhtDTJI8xEalu29m25tBu/1Nks5oRsZ1yLhOnbaNYQw7cemXjBsI9m1bqY2tQJI8xEalu29m25tBu/1tlM6IP02oUqfVNJ5PMV3IIOEyV+z1ktE/BnHf4RJk8EfH/1guha/BsTzM5/+2dhA3XL6LO26+gl1jOc4VK+way23YINFmtr0ZtNvf9914Ge9/2yXk0y5VPziZ3nJginw6OEQF2DeV51dvvJT9Owv4Cj+4e4y9E1lSrkM6lKkYy7pk3bWnuOsIl19Q4D2v31OrM6rX2Fwi4TpnHaJ4EEQxdXtBy6WCSKTIjmzKYedomtfuHmPnSKrtU2rGEdJtGi1kHD75C2/mY7dcXTv+D0wXeP/bLmH/zsJQn/8bJrWxEZjUhmEYRndsutSGYRiGsfUwB2EYhmEkYg7CMAzDSGTgYn3G1qUTKZBu5UIayyfJDUB7SYInjs3x+3/93ZpkhusIe8YyjOUzHJ8rBnNK+0rKCRLpohDalCPkUsJSJcijsAS77UEwAC2Uqtr17y1AIeuyd2qEY7PLLJS8NWXSDuyeyKNQO2YfeOpVnju5gOcHx5gQJH1mUw4jGZfLLhgfSnmNODZIbSTSiRRIt3IhjeXPLJU4uVBmppBhuhDIDcwXKyi0lCQ4vVjiXJhNbRjDxK5CBtcVTsyXWub1OAK7xrJkUu6mRy/ZILXRNXFpDJHgM+0K9xw+2lWZVnXOFwO5gYWVam37hZUqi6VqrUxckiAqM9+Bc7AkOmMzOL1UZr5YbZv0qRoc963Ol2HAHISRSCdSIN3KhTSWT5IbqPp+W0mC8+eZ1zjf8LX+WG2GEpQbRnmNOOYgjEQ6kQLpVi6ksXyS3EDKcdpKEtjDgTGsOFJ/rDZDCMoNo7xGHHMQRiKdSIF0KxfSWH48H8gNjOVW5QbGcqm2kgTj+faxFefR0JqxhZgezTCeT7UVnhQJjvthlNeIY4PURlOiiKNjs8vsbRPF1KpMq/JRhFJ8e6BtGYtiMrpho6KYgNox2yqKaTTjcumQRDG1GqQ2B2EYhrGNsSgmwzAMo2vMQRiGYRiJmIMwDMMwEhmY1IaI7AM+DewGfOBeVb1LRHYAfwrsB14EfkpVZxO2fwdwF8FcG4dU9c5B2WqscveDz3Lo4RdYKntkXGG6kK3JB/Q6oNZOjqNbuY7Guu984BleOLNMperXYmBTjlDxuh+QNIx+MZ5Lcdt1B7hq7yT/8S+e5NjcSt36vZM5fuvdr9v0QepWDGyQWkT2AHtU9VERGQO+Dbwb+AXgrKreKSIfAqZU9YMN27rAs8CPAseAbwG3qup3WrVpg9Tr4+4Hn+Wurz2PI6CqVMN8n12FDGP5dEsZjWa0k+PoVq6jse5fu+9x5pYreL45A2O4iKKWUgLlJrlz41mXu2994/aT2lDVV1T10fD/C8AzwEXAu4A/Dov9MYHTaORa4HlVPaqqZeBz4XbGADn08As4EiSrRWGhQiAf0E5Goxnt5Di6letorHuxVMUVMedgDB1KmFndIrF6seyZ1IaI7AfeAHwTuEBVX4HAiQBJrvMi4KXY92PhsqS6bxeRR0TkkVOnTvXV7u3GUtmrJfjEHywjZ9GLLEA7OY5u5Toa6/Z8Nd0lY8viK9tbakNECsDngQ+o6nynmyUsS7xJVNV7VfUaVb1mZmamVzMNYDTjrj45xH6ByGn0IgvQTo6jW7mOxrpdRyxr2tiyOML2ldoQkTSBc/iMqv55uPhEOD4RjVOcTNj0GLAv9n0v8PIgbTXgtusO4GsgmFd7kiCQD2gno9GMdnIc3cp1NNZdyKbwVE2fyRg6hFCbqcVVtpBxh1pqY2AOQkQE+CTwjKr+XmzV/cDPh///eeAvEzb/FnCpiBwQkQzwM+F2xgB5342X8f63XUI+7aII+bTDvqk82bTLrrFcT7r1N1y+iztuvoJdYznOFStr6mm3vl3dH7vlai6ZGSWTcnAITshAME3MaRibylguxQfefin3/tyb2TuZW7N+72Ru0weo2zHIKKbrgL8BniQIcwX4MME4xJ8BFwPfB35SVc+KyIUE4aw3hdvfBHycIMz1U6r62+3atCgmwzCM7mgVxTSwPAhVfZjmysxvTyj/MnBT7PsXgS8OxjrDMAyjHZZJbRiGYSQysCcIwzAMo394vlLxghkXq76Gnz5VT5kZy5LuYKKibjEHYWwI65XbSFrfOCdERNaFgzNjLJSq4PucLVZZqXioBgPYKddh11iW5VKFM8vt57c2ti8u4DhCpd0k0x2SCtOrXUc4OD3KB99xOddfNlN3wfd85fA/nOKPv/49jp9bZs94np9+8z6uPbCjrq5SxWN2ucLscpmU4/DDB3cwOZLpi50RNh+EMXDWK7eRtP7k/ErixC1xxrMO86W1aaw2SZCx2TgEuUYT+TT//scv59qDO/BVWVypcvgfTvFH3/geguKIUKr6VH1l/85RAOaKZWaXKmvyh/7kl36YH/mB6a5t2ZRBasOIiMtpAIxkgilF7zl8lBsu39XT+qVya+cAJDoHMOdgbD4+wY3K7HKF//JX32Ek4zJXDDTFmvGdV5rnGWdTDkttbph6wRyEMXBeml1mMp+uW9Yot9Ht+j498RvGphG9vClWvDVPAxC+DnWC11GuI/i+8tNvvpip0TST+QyTI2mmRjNMjaS5dNcY2QbJmn5gDsIYOPumRji5sFJ7AoC1chvdrnfEnISxtREJniJ2j+f4uR/Zz9RImqmRDHc9+BzniuU158POQpZfeOt+nHAMwxHBccAVITWAAWqwMFdjA1iv3EbS+tFM+7ul8Wzy4W0Z1sZm44R/k+EYxC1v2suP/dBufvjATn7ln14CIlR8n5QrVP3gVekH3n4pr9k5yr4dI1w4mWf3RI5dYzl2FrK4zmCOahukNjaEKArp2Owye1tEKXWzvl0U02KpiloUk7FO+hnUkBIQEZxYFFOS1Ea786GftBqkNgdhGMamsFKpcnKhxMn5EqcWS5yaL3F6scSZpTKnF0ucXSpzdqnM3HKl7QBuK7IpJ3hfP5IJ/4J395MjaXaMZtgxmmG6kGVnIcNUPkM65QSvcaJXONHrHFkdDzifsCgmwzAGjqqyUKpyZrHMiXPF4KK/UOLUYpnTC8GF/+xSmTNLJWaXKiyWen96G8+lgov9aJqJfHCR3zmaYUchw/RoNvzMMDOWpZBN4zjUvbsPPoO7eaM55iAMw2hK1fM5u1zmzGJwV39qocTJhRKnF4K7/jOLwQX/7FIQm1/2Wkyf1oKUI7WInKmRDDsLGXaOZpkazTBdCO7wpwsZZsZy7BzNkE3H7/LtIj8ozEEYxjajWPaCi314gT+1sMKphRKnF8vhsmD52eUy55YrPb9/H8247BjNMBXe3e8sZMPXOcGdfXTRv2Asx+RIGsexmJlhwxzENqWVtEV8XSHjIiIslKqJEhjdtjWWTaGqLJY99k2N8JaDO/j60bM1O6Lvz51coFz1SbtCqeKxmDCxr+sIl+0q8M4rd/P1o2d56vgcS2UPX4MQwtG0Q8WHUrW3u1pjfSyVPZbKRV6aLW62KX1nLOswkUvzynwJrwsP6jpC1nXIZxwuu2B8oIPP/cAGqbchraQtgNq6qudzfG4FgIsmc6Rcp04Co9u2GusrVX1OLZbZNRa8TjizVOLkQpnxnMviigcClTZnXyhtQz7tsNRqdnjDGCJcB2YKWTIpt6eJuPpJq0Fqe6bbhsSlK0SCz7Qr3HP4aN2604tl3HBA7/Riua5cL22dXiwHUSAS1LewUsURmC9WERHmi8H3c8UqjiOkOnjloAQJc+YcjK2E78PCSrXr82mjsVdM25BW0hYKtXVlz8cN0z2jwce4BEa3bZU9vxYiGNXnxOoue8Fc2JXwFZFhnK8owfHe7fm00dgTxDZk39TIGu2XSNoivi7jOqgGmjGZMJU/LoHRbVuN9WVcBz9Wd/TdkVWdGsM4HxGC473b82mjGZiDEJFPichJEXkqtuxPReSx8O9FEXmsybYvisiTYTkbVOgzraQt4uumCxk8DXTqpwuZNRIY3bY1Xcjg+YqnQX1juRS+wng+GLgezwffJ/Ip/FAbvx1C4FBGM3avY2wdHAfGcqmuz6eNZmCD1CJyPbAIfFpVr0xY/7vAOVW9I2Hdi8A1qnq6mzZtkLpzWqXyx9eNhlFMi6Vqzyn/8foKYRTTUjm4c4qiliI7fvjAFF8/epbnG6KYliprj1MBLhjPcslMge+eXuLUwgoWsGRsBOuNYhrJOFw6JFFMmya1ISL7gb9qdBASpC9+H3ibqj6XsN2LmIPYkvi+4mvwlKAaTJPoq+Jr83Wep8yvVDizVGZuuczsciX4XKowG06OMrdcZq5YYXap3NFcEEkIMJ5Px2QX0nUyC9OFLDNjwd+usSxjuTQpRyzb1jivGUapjX8MnEhyDiEKfFlEFLhHVe9tVpGI3A7cDnDxxRf33dDtiB+/qGt4EffXXuR9n/BztXx0w1H1fOaKFebCKRFXL/qrF/pgWYW5YrltOGsz0q4wNRLo6kyOxDJxRzPsHMvWsnB3jWXZOZohl07hOkLKsQxcw2jHZjmIW4HPtlj/VlV9WUR2AV8RkSOqejipYOg87oXgCaL/pm5d/PB9vx+/mIcX9GgsoNVFPo6qslLxw4t9ObzwV1b/vxRfXmZ+pXednUI2Fd7lp8OLf6Z2178jJr0wU8gyMZIm5Tq1iVVSzuC08Q1ju7HhDkJEUsBPAG9qVkZVXw4/T4rIF4BrgUQHcb7T6iLvh69pNLqjj9/hd6B86fnKwsrqhX52qRLOd5vgAJbLPWckO0LtLj/+GalqTuRXVTV3jmYZybq4jpB2HFxXYhd/57xT0jSMYWYzniBuBI6o6rGklSIyCjiquhD+/8eANQPZW4lBXuQbKVcb7vLDVznPvDLPd16eZ6m8moS2XK72PCtbJuWwc3RVPnkyVNaczAffXz23wgNPvcqr80V8HxZLFVYqVU7Mr5BxHQ7OFHjbWw9ww+W7+N/PneLQwy9wbK7IvqkRUJ9vvjjbk21RZnUhmyLtYPM9GENLxhH+2VW7eXW+nCh5MwwMzEGIyGeBG4BpETkG/IaqfhL4GRpeL4nIhcAhVb0JuAD4QjgwmAL+RFW/NCg7O6Xx3XttsDW84PfzIh9HVVksVWsDttFFP/5uP36Xv9zJAK6nBNOm1+NIIIlc9RUBRjJuEAZb8WvhpEKQuTyacXn/2y7l2oM7ahr5aTe4w//Gd8/w3x96nrnlCqpBS6WqUkJxHShVfI7PLfNfv3SE504ucN+jx0m7wmQ+zZPHZhN1lzruL4IcivW84jKMjaDsK1947BWmRlJcNBlMq/uR+5/mDhgaJzEwB6GqtzZZ/gsJy14Gbgr/fxS4elB2JVH1fBZWqmvv8tu8l19Pe9FAbf2A7ern2fBzbrlCtUcnEw3gLqxUUZRMeAFPOYKnylQ+w4duupyPf2V1DtyXZpeDLOZwgDpCgXT4bt/3g7yGLzx2nJ++dt+aKJ/PfPP7LJW8ULvJR3R1Ri5VcNxAVmP3RIpDD7/AzFi2Nv/uepyDYWxFzhWr7J0K5GiWy1XuOXz0/HcQW4mqr8wul3veXlUpVrzgbj52dz+3XOFs+Bm/+K93ADf+SmcqH77aCd/t7whnzZocSTMS5jDc+offYDyXQmKzMSuwuFLhyosmOLtcZiKXwnGCC3r0nr/i+XUX/+j/jhM40JfniokhoC/NLlP1fVJh5nR9XwWvgCKZgaWyx8Xp9vNLG8b5Svz+b9ikN8xBNMHzg9j8+Hv84KK/+v/Z2F1+rwO4riPBoG0+HLwdzTCZT9cGcBsHd9MdROi4DYO6+6ZGOL1YYjTjQviaqFjx2D9dYM9Env07Rzm5sMJISsim3NoTSzYVtFXxPOJuQDVoo5lEwL6pEU4vlGrOIO4kou+RzMBoJlCTjZ4gDGO7EY+7GDbpjW1/Vj5xbI7Pf/sYL80W6+7yzxUrPQ/g5tPu6oDt6NrInXi8fiGXwukiEavxfX9jhE/aXZvY9Sv/9BI+cv/TrFS9OnnvKMX/vdcf5CP3P12Tw4gkuXcXspSqPsVK4CC8UPrCV5jMpptKBLz3+oP8+n2PM7tcCcYsYutEgldU46NpKp5y23UHuO/R4yyXq+TTLoWMY6+ZjG3FRCg103heDgPbfj6I+x9/mfd99u9blhFgciTNRL7+rn5HLUErvPiHd/+5Hl6ZSDh9ousK6fhFvy7Ms/es3lbSGo3rk+QwHnjqVY6eXgLgwM4RPvTO17Z8T/rQkZN89EtHOHp6Cd9XUu6q/ZmUw6W7xmo2NNpmUUzGdiAexdTsvNwINk1qY6PpxUE8cWyO3/vKs+TTbi0bd0fslc7kSBCnv574+2iS9JRbf7dvyV2GYWw2wyi1MTRctXeSP/iXb+Llud6mRUy6y290AibpYBjGVmTbO4hmRK98Uo2ZvOH3aJkJuRmGcb5iDgJIOcLOQtZe+RiGYcQwBwGkXIeJ/PnpEKIB4JdmlxkLB58Xy17f0vrj9Ud1AmuWxeeauPOBZ3jhzDK+r6Rdh3zG4bKYNv7dDz7LoYdfCBP8AsZzKW677gDvu/GyWh3Pn1rqOYnQMAZNPMRbBAoZl1LFp+wrInDRRI6fumYfXz96tmepjaTzr5+D3Nt+kPp85qEjJ/nI/U+TdoWq59fCVy+azIVZzsodN1/R8wEVrz8Knz1XrNTmXYiH1N5x8xUA/Np9jzO3XAG0NrmP68BMIUsm5fKmiye4/4lXUV/rw2MBxxFuvmo3f/P8GWaXyl1N1GIYw4gAuyey7BzN1p0rnZyTSedfL+d0q0Hq8/O22QCCu/i0G6Twn14sBzkUIpxeDGQ10q5wz+GjfalfJPhcLFVZWKnWLYvauefwURZLVVwRfA1DUgHfh4WVKmlXuP+JV3FkrVKUEiQU3f/EqyyWehcZNIxhIBq6VGC+WF1zrnRC0vm33nO6EXvFdB7z0uwyk/k0EEhbRKG6ZS+4/K43rT9ef0QkWhgnakfD9a5I8Ogdaj4pq9Ibnq+kUskD/45AxVMcWdV2MoytTnQ+QnfnZNL512+pDnuCOI/ZNzVCsRKou2ZCXaRI5gLWn9Yfrz8iivaKE7Wzb2oE15GaBEd0lRdWpTdcR5o+Hfi6mklusWPG+UImFhDTzTmZdP71W6rDHMR5zHuvP0jF05qERjSL3HQhw3K5uu60/nj9qsFnIZtiLJeqWxa1897rD1LIpvB09SlACcT/xnIpKp5y81W78XXtgRnJjN981W4K2RSWWmJsZTR2czQeSm10e04mnX/9luqwQerznFYSGv2MYopLBQBNZT2SophGMg6X9hDF9N1TS1RsMMIYUhxZVWptF8XUq9RGOwmdTjCpDcMwDCMRi2IyDMMwusYchGEYhpGIOQjDMAwjkYHlQYjIp4B/DpxU1SvDZf8Z+CXgVFjsw6r6xYRt3wHcBbjAIVW9c1B2GvU0S91vl9KftP6JY3McevgFlsrBzHEXTmT5hxNLdTkM+6by/OSb9vL1o2d57uQCyyWPlYpXS5QTgtDWS2ZGuel1e/j60bM8e2KeM0uVjewWw+gZR2Ak47B3cgQRYaFUZV+P86xsNAMbpBaR64FF4NMNDmJRVT/WYjsXeBb4UeAY8C3gVlX9Trs2bZB6fTRL3b/ljRdx36PHm6b0J213erHEfLFKyhUcgVK1+XEWTMiUYr5YbSqf4YS6NhP5FHNFmwTI2Ho4BHIxF03mKHs+J+ZLoJByg5htX4OJyT52y9Ub6iQ2ZZBaVQ8DZ3vY9FrgeVU9qqpl4HPAu/pqnJFIs9T9Qw+/0DKlP2m7+fAinnIcHGl9mClwrlhdI68RJwoXnF8x52BsTXyCp+HTi2Xmi4FcjAq4jhP8ibBYqvZVKmO9bMYYxK+IyBMi8ikRmUpYfxHwUuz7sXBZIiJyu4g8IiKPnDp1qlkxowNeml0m3zBdaj7tslT2EpdHKf1J20VJcJ3id7CBgmkwGVsakUBaI5LXiL/AEQmkaPoplbFeNtpB/D7wA8DrgVeA300ok5Qj2/SyoKr3quo1qnrNzMxMX4zcrjRL3R/NuC1T+pO2i4T4OsXpYAOJyhnGFiWSuonkNeLzjWkoJdNPqYz1sqEOQlVPqKqnqj7whwSvkxo5BuyLfd8LvLwR9m13mqXu33bdgZYp/UnbjeeD+Ieq7+Nrq5dHwYV/Ip9qeTBGjmE8Z/qSxtbEIXhCmC5kGM8HcjGi4Pl+8KdKIZvqq1TGetlQByEie2Jf3wM8lVDsW8ClInJARDLAzwD3b4R9250bLt/FHTdfwa6xHOeKFXaN5bjj5it4342XJS6PBtKStrvrp9/Ar954Kfm0S9UPLuyXXzC65iFh31SeX73xUi7fPcHUaIaRtFt3UAqQdoTLdhX41Rsv5bV7JpgeTWMYWwVHYCzrcNkFBS7dVcBX2L+zwAfefik/uHsMkWDq4ktmRjd8gLodg4xi+ixwAzANnAB+I/z+eoJXRi8C71XVV0TkQoJw1pvCbW8CPk4Q5vopVf3tTtq0KCbDMIzuMC0mwzAMIxHTYjIMwzC6xhyEYRiGkYg5CMMwDCMRcxCGYRhGIufVILWInAK+N6Dqp4HTA6q7n5id/cXs7C9mZ3/ph52vUdXELOPzykEMEhF5pNlI/zBhdvYXs7O/mJ39ZdB22ismwzAMIxFzEIZhGEYi5iA6597NNqBDzM7+Ynb2F7OzvwzUThuDMAzDMBKxJwjDMAwjEXMQhmEYRiLmIBoQkXeIyD+IyPMi8qGE9f8inBHvCRH5WxG5ekjtfFdo42PhjHvXDaOdsXJvFhFPRG7ZSPti7bfrzxtE5FzYn4+JyEeG0c6wzA2hjU+LyF8Pm40i8uuxfnwq/N13DKGdEyLy/4rI42Ff/quNtrFDO6dE5Avh+f53InJl3xpXVfsL/wjkxb8LHAQywOPADzWU+RFgKvz/O4FvDqmdBVbHmK4CjgyjnbFyXwO+CNwyjHYSSNX/1WYcl13aOQl8B7g4/L5r2GxsKP9/AF8b0r78MPDR8P8zwFkgM4R2/g7wG+H/Lwe+2q/27QminmuB51X1qKqWgc8B74oXUNW/VdXZ8Os3CGa822g6sXNRwyMGGKW7KaL7RVs7Q/4d8Hng5EYaF6NTOzebTuz8WeDPVfX7AKq60X3abV/eCnx2QyyrpxM7FRgTESG44ToLVDfWzI7s/CHgqwCqegTYLyIX9KNxcxD1XAS8FPt+LFzWjH8NPDBQi5LpyE4ReY+IHAH+J/CLG2RbnLZ2ishFBLML/sEG2tVIp7/7W8LXDQ+IyBUbY1odndh5GTAlIg+JyLdF5Oc2zLqAjs8hERkB3kFwc7DRdGLnJ4DXEkx5/CTwftU28+f2n07sfBz4CQARuRZ4DX26cTUHUU/jjJjQ5M5bRP4pgYP44EAtSqYjO1X1C6p6OfBu4DcHbVQCndj5ceCDquoN3pymdGLnowSaNVcD/w34i0EblUAndqaANwH/DPhx4D+JyGWDNixGx+cQweul/62qZwdoTzM6sfPHgceACwlmwvyEiIwP1qw1dGLnnQQ3BY8RPI3/PX160rEZ4Os5BuyLfd9LcPdQh4hcBRwC3qmqZzbItjgd2RmhqodF5AdEZFpVN1KArBM7rwE+FzzFMw3cJCJVVf2LDbEwoK2dqjof+/8XReS/D2l/HgNOq+oSsCQih4GrgWc3xsSujs2fYXNeL0Fndv4r4M7wVe3zIvICwTv+v9sYE4HOj81/BRC+Dnsh/Fs/Gz04NMx/BA7zKHCA1QGhKxrKXAw8D/zIkNt5CauD1G8Ejkffh8nOhvJ/xOYMUnfSn7tj/Xkt8P1h7E+CVyJfDcuOAE8BVw6TjWG5CYJ3+qMb/Xt30Ze/D/zn8P8XhOfQ9BDaOUk4eA78EvDpfrVvTxAxVLUqIr8C/H8E0QOfUtWnReSXw/V/AHwE2An89/Cut6obrPrYoZ3/J/BzIlIBisBPa3gEDZmdm06Hdt4C/BsRqRL0588MY3+q6jMi8iXgCcAHDqnqU8NkY1j0PcCXNXjS2XA6tPM3gT8SkScJXvV8UDf2ibFTO18LfFpEPIIItn/dr/ZNasMwDMNIxAapDcMwjETMQRiGYRiJmIMwDMMwEjEHYRiGYSRiDsIwDMNIxByEYayTUNJEReTy8PsNIvJXDWX+KFKqFZG0iNwpIs+FaqZ/JyLv3AzbDaMV5iAMY/3cCjxMkBncCb8J7CFIYLuSQHJibEC2GUbPmIMwjHUgIgXgrQTJSW0dRChQ90vAv1PVEoCqnlDVPxuooYbRA+YgDGN9vBv4kqo+C5wVkTe2KX8J8H2NaTsZxrBiDsIw1setBBr9hJ+30ly91GQLjC2FaTEZRo+IyE7gbcCVIqIEWjkKfBqYaii+AzhNIPR4sYiMqerCRtprGN1iTxCG0Tu3EChnvkZV96vqPgKZ5R3AhSLyWgAReQ2B5PZjqroMfBK4W0Qy4fo9IvIvN2cXDKM55iAMo3duBb7QsOzzBIPV/xL4H+EkLvcBt6nqubDMfwROAd8RkacIJh86tREGG0Y3mJqrYRiGkYg9QRiGYRiJmIMwDMMwEjEHYRiGYSRiDsIwDMNIxByEYRiGkYg5CMMwDCMRcxCGYRhGIv8/8Ewu9sseJ5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(x=df_t['AUC'], y=df_t['cre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "id": "7d8ebad1",
   "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": [
    "ax = sns.regplot(x=df_t['AUC'], y=df_t[0])\n",
    "#ax.set_ylim([200,4000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "id": "d9a699d2",
   "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": [
    "ax = sns.regplot(y=df_t['AUC'], x=df_t[0])\n",
    "#ax.set_ylim([200,4000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "id": "f7b037f2",
   "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": [
    "ax = sns.regplot(y=df_t['AUC'], x=df_t[0])\n",
    "#ax.set_ylim([200,4000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "id": "5ff6acc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(200.0, 4000.0)"
      ]
     },
     "execution_count": 607,
     "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": [
    "ax = sns.regplot(df_t['AUC'], df_t[0])\n",
    "ax.set_ylim([200,4000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "id": "8eb5157d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t = df_t[df_t[0] > 200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "a95f9700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.712922990580443"
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t[df_t[0] > 200]['AUC'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "id": "56445164",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t['quintile'] = pd.qcut(df_t[0] , 10, labels=np.arange(10, 0, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 781,
   "id": "8811bfdb",
   "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>index</th>\n",
       "      <th>0_x</th>\n",
       "      <th>0_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>177492</td>\n",
       "      <td>0.006726</td>\n",
       "      <td>0.011172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>177557</td>\n",
       "      <td>0.001718</td>\n",
       "      <td>0.001976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>177565</td>\n",
       "      <td>0.001666</td>\n",
       "      <td>0.001520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>177566</td>\n",
       "      <td>0.006697</td>\n",
       "      <td>0.009652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>177580</td>\n",
       "      <td>0.006302</td>\n",
       "      <td>0.009804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8337</th>\n",
       "      <td>163474</td>\n",
       "      <td>0.007217</td>\n",
       "      <td>0.018924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8338</th>\n",
       "      <td>163477</td>\n",
       "      <td>0.006246</td>\n",
       "      <td>0.012084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8339</th>\n",
       "      <td>163557</td>\n",
       "      <td>0.003324</td>\n",
       "      <td>0.004560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8340</th>\n",
       "      <td>163562</td>\n",
       "      <td>0.001810</td>\n",
       "      <td>0.002432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8341</th>\n",
       "      <td>163634</td>\n",
       "      <td>0.006360</td>\n",
       "      <td>0.012312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8342 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index       0_x       0_y\n",
       "0     177492  0.006726  0.011172\n",
       "1     177557  0.001718  0.001976\n",
       "2     177565  0.001666  0.001520\n",
       "3     177566  0.006697  0.009652\n",
       "4     177580  0.006302  0.009804\n",
       "...      ...       ...       ...\n",
       "8337  163474  0.007217  0.018924\n",
       "8338  163477  0.006246  0.012084\n",
       "8339  163557  0.003324  0.004560\n",
       "8340  163562  0.001810  0.002432\n",
       "8341  163634  0.006360  0.012312\n",
       "\n",
       "[8342 rows x 3 columns]"
      ]
     },
     "execution_count": 781,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12598516",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t3 = df[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "id": "4c7e3dff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cre', ylabel='AUC'>"
      ]
     },
     "execution_count": 750,
     "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": [
    "sns.boxplot(df_t['cre'], df_t['AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "id": "b9c1b2b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quintile', ylabel='AUC'>"
      ]
     },
     "execution_count": 787,
     "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": [
    "sns.boxplot(df_t['quintile'], df_t['AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "id": "51f20430",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quintile', ylabel='0'>"
      ]
     },
     "execution_count": 788,
     "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": [
    "sns.boxplot(df_t['quintile'], df_t[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "id": "31f4ff87",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'kl' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-790-1775ba9267e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'kl' is not defined"
     ]
    }
   ],
   "source": [
    "kl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "id": "998693c9",
   "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>AUC</th>\n",
       "      <th>AVG_NODE_DEGREE</th>\n",
       "      <th>DEGREE_NULL_AUC</th>\n",
       "      <th>P_Value</th>\n",
       "      <th>index</th>\n",
       "      <th>0</th>\n",
       "      <th>quintile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.616393</td>\n",
       "      <td>10677.110581</td>\n",
       "      <td>0.574954</td>\n",
       "      <td>2.229388e-68</td>\n",
       "      <td>177591</td>\n",
       "      <td>1983</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.643438</td>\n",
       "      <td>10423.044345</td>\n",
       "      <td>0.506708</td>\n",
       "      <td>6.842470e-15</td>\n",
       "      <td>177593</td>\n",
       "      <td>243</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.661095</td>\n",
       "      <td>10549.253120</td>\n",
       "      <td>0.537769</td>\n",
       "      <td>6.572295e-78</td>\n",
       "      <td>177598</td>\n",
       "      <td>1153</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.592403</td>\n",
       "      <td>10726.062502</td>\n",
       "      <td>0.583689</td>\n",
       "      <td>3.225288e-27</td>\n",
       "      <td>177673</td>\n",
       "      <td>1163</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.628581</td>\n",
       "      <td>10568.321206</td>\n",
       "      <td>0.543337</td>\n",
       "      <td>1.806364e-30</td>\n",
       "      <td>177675</td>\n",
       "      <td>669</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7847</th>\n",
       "      <td>0.574790</td>\n",
       "      <td>10368.410197</td>\n",
       "      <td>0.494411</td>\n",
       "      <td>1.346064e-08</td>\n",
       "      <td>162958</td>\n",
       "      <td>466</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7848</th>\n",
       "      <td>0.610147</td>\n",
       "      <td>10344.599261</td>\n",
       "      <td>0.476780</td>\n",
       "      <td>1.047082e-13</td>\n",
       "      <td>162961</td>\n",
       "      <td>370</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7859</th>\n",
       "      <td>0.590189</td>\n",
       "      <td>10476.625638</td>\n",
       "      <td>0.515784</td>\n",
       "      <td>3.574001e-08</td>\n",
       "      <td>163139</td>\n",
       "      <td>302</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7869</th>\n",
       "      <td>0.653807</td>\n",
       "      <td>10353.187277</td>\n",
       "      <td>0.491195</td>\n",
       "      <td>2.825903e-18</td>\n",
       "      <td>163201</td>\n",
       "      <td>264</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7876</th>\n",
       "      <td>0.601389</td>\n",
       "      <td>10454.027772</td>\n",
       "      <td>0.510980</td>\n",
       "      <td>1.831122e-08</td>\n",
       "      <td>163474</td>\n",
       "      <td>249</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3517 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AUC  AVG_NODE_DEGREE  DEGREE_NULL_AUC       P_Value   index     0  \\\n",
       "5     0.616393     10677.110581         0.574954  2.229388e-68  177591  1983   \n",
       "6     0.643438     10423.044345         0.506708  6.842470e-15  177593   243   \n",
       "7     0.661095     10549.253120         0.537769  6.572295e-78  177598  1153   \n",
       "10    0.592403     10726.062502         0.583689  3.225288e-27  177673  1163   \n",
       "11    0.628581     10568.321206         0.543337  1.806364e-30  177675   669   \n",
       "...        ...              ...              ...           ...     ...   ...   \n",
       "7847  0.574790     10368.410197         0.494411  1.346064e-08  162958   466   \n",
       "7848  0.610147     10344.599261         0.476780  1.047082e-13  162961   370   \n",
       "7859  0.590189     10476.625638         0.515784  3.574001e-08  163139   302   \n",
       "7869  0.653807     10353.187277         0.491195  2.825903e-18  163201   264   \n",
       "7876  0.601389     10454.027772         0.510980  1.831122e-08  163474   249   \n",
       "\n",
       "     quintile  \n",
       "5           1  \n",
       "6           5  \n",
       "7           2  \n",
       "10          2  \n",
       "11          3  \n",
       "...       ...  \n",
       "7847        3  \n",
       "7848        4  \n",
       "7859        4  \n",
       "7869        5  \n",
       "7876        5  \n",
       "\n",
       "[3517 rows x 7 columns]"
      ]
     },
     "execution_count": 637,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "id": "91e8da93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0', ylabel='Count'>"
      ]
     },
     "execution_count": 639,
     "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": [
    "sns.histplot(df_t[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55784445",
   "metadata": {},
   "outputs": [],
   "source": [
    "    row, col=np.nonzero(arr_thresh)\n",
    "    c = np.unique(col)\n",
    "    arr = arr_thresh[:,c] #columns whose sum is zero throughout are removed\n",
    "\n",
    "    zero_row_indices = np.where(~arr.any(axis=1))[0] #[when the gene row sum is zero, its jaccard similarity is zero]\n",
    "    \n",
    "    try:\n",
    "\n",
    "        jac_sim = 1 - pairwise_distances(arr, metric = \"jaccard\")  #[calculates the jaccard coefficient for each bin pair based on the , allbins X allbins where values are number of common neighbours]\n",
    "        jac_sim[zero_row_indices,:] = 0\n",
    "    \n",
    "    except ValueError:\n",
    "\n",
    "        jac_sim = np.zeros((arr_thresh.shape[0], arr_thresh.shape[0]))\n",
    "    print (jac_sim.shape)\n",
    "\n",
    "    return jac_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "id": "18f1be3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quintile', ylabel='AUC'>"
      ]
     },
     "execution_count": 636,
     "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": [
    "sns.boxplot(df_t['quintile'], df_t['AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "id": "1efbd2fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quintile', ylabel='0'>"
      ]
     },
     "execution_count": 624,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fAhcCVyR5E3ANcFdVbQDuauZpPtsCvBnYBHwsybJmWx8HLgc2NK9N/fwikqQOgqSqnqiqbzTTTwGPAGuBS4DRZrFR4NJm+hJgW1U9W1WPAePABUnOBE6rqnuqqoCbp6wjSeqTTo+RJDkHeAtwL/DaqnoCemEDnNEsthZ4fMpqE03b2mb6pe3T7efyJGNJxg4cOHBcv4MkLXWdBUmSVwOfBz5YVf8w26LTtNUs7S9vrLqxqoaranjNmjXzL1aSNKNOgiTJcnoh8pmquq1p/kEzXEXzvr9pnwDOnrL6OmBf075umnZJUh91cdZWgE8Bj1TVH0/5aDsw0kyPAF+Y0r4lyYok59I7qL67Gf56KsmFzTbfN2UdSVKfnNzBPt8K/AbwUJIHmrYPAdcDtya5DPg+8B6Aqno4ya3At+md8XVFVb3QrPcB4NPASmBH85KkRbV161bGx8cXfT979uwB4Kqrrlr0fQGsX79+Qfvqe5BU1deY/vgGwNtnWOc64Lpp2seA845fdZI0t/HxcR5+6BFWnXrG3Au3cPi53p/Kv/2bg4u6H4Anf7x/7oVm0EWPRJJOeKtOPYNffOOWrss4br7ynW0LXtdbpEiSWjFIJEmtGCSSpFYMEklSKwaJJKkVg0SS1IpBIklqxSCRJLXiBYmSNE8TExP86MdPtbqIb9A8+eP91MRPFrSuPRJJUiv2SCRpntatW0eePfiKu0XK2nWrF7SuPRJJUisGiSSpFYNEktSKQSJJasWD7ZK0AE/+eP+in/779P/7ewBefcrpi7of6H2ftSzsYLtBIknztH79+r7sZ8+eHwKw9vUL+wM/H2tZveDvZZBI0jz16xnqR/azdevWvuxvoTxGIklqxSCRJLXi0JakRbN161bGx8fntc7ExATQu3p8vtavX9+3Yaf5WshvsWfPHmBhQ2n9/C0MEkkD5Sc/WdiNA1+JVq5c2XUJxyRV1XUNfTU8PFxjY2NdlyFpBifKAealJsn9VTU83Wf2SCQdk4UMzSxEm+GchRjk4bAThUEi6ZiMj4/znQce4B8v8n6OnAH05AMPLPKe4O8WfQ9Lg0Ei6ZhMTEzQj4Hwxb/07kXFiwf3tXCe/itJasUgkXRM1q1bR/qwn4PNqx/Cwk4z1tEc2pKOs1fqtRMLuQ/TxMTEvE/nPbL84QWc+rpy5cp5/Yar6N99s17JDBJpFgsNhYX+8VzINRQTExPzrnEh4bOQsHqlhqqO5nUk0ize/e53Mzk52XUZx93Q0BC33XZb12XoBPKKvo4kySbgT4FlwCer6vqOSzrh9et/4W3MdwgDFva/1VWrVs37ez377LMcPnx4XuscWf6kk+Z/2PKkk05ixYoV81pn1apV896PNJMTukeSZBnwXWAjMAHcB/zHqvr2TOvM1CPxj+eL/F94Ow7n6JXoldwjuQAYr6rvASTZBlwCzBgkM7n77rsH/o/nM888M+8aJyYmBvZ/4W0M8v/C/aOupeZED5K1wONT5ieAf/nShZJcDlwO8LrXvW7aDfnH80U33XTTvNeRtHSd6EEy3WntLxurq6obgRuhN7Q13Yb84ylJC3OiX5A4AZw9ZX4dsK+jWiRpSTrRg+Q+YEOSc5P8DLAF2N5xTZK0pJzQQ1tV9XyS3wLuoHf6701V9XDHZUnSknJCBwlAVd0O3N51HZK0VJ3oQ1uSpI4ZJJKkVgwSSVIrBokkqZUT+l5bC5HkAPB/u64DGAIG+54s/eNv0ePv8CJ/ixcNym/xc1W1ZroPllyQDIokYzPdAG2p8bfo8Xd4kb/Fi06E38KhLUlSKwaJJKkVg6Q7N3ZdwADxt+jxd3iRv8WLBv638BiJJKkVeySSpFYMkj5LsjfJQ0keSPLyZ/4uMUmWJflmkr/supauJDklye4kf53k4ST/veuaupLk7CRfSfJI81tc3XVNXUlyU5L9Sb7VdS1zcWirz5LsBYarahDOC+9ckt8GhoHTquqdXdfThSQBXlVVTydZDnwNuLqqvt5xaX2X5EzgzKr6RpKfBe4HLq2qeT8++0SX5G3A08DNVXVe1/XMxh6JOpNkHfArwCe7rqVL1fN0M7u8eS3J/+FV1RNV9Y1m+ingEXqP1F5yquqrwA+7ruNYGCT9V8DOJPc3z5Jfyv4E+F2gfw++H1DNEN8DwH5gV1Xd23FJnUtyDvAWYMn/FoPOIOm/t1bVLwCbgSua7uuSk+SdwP6qur/rWgZBVb1QVf+C3uOiL0gy0EMZiy3Jq4HPAx+sqn/ouh7NziDps6ra17zvB/4CuKDbijrzVuBdzTGjbcC/S/Jn3ZbUvap6Ergb2NRtJd1pjhN9HvhMVd3WdT2am0HSR0le1RxAJMmrgIuAgT8jYzFU1bVVta6qzgG2AF+uqvd2XFYnkqxJsqqZXgn8EvCdTovqSHPiwaeAR6rqj7uuR8fGIOmv1wJfS/LXwG7gr6rqSx3XpO6dCXwlyYPAffSOkSzV06HfCvwGvR7qA83rl7suqgtJbgHuAd6QZCLJZV3XNBNP/5UktWKPRJLUikEiSWrFIJEktWKQSJJaMUgkSa0YJFKfJRlOsnWOZVYl+c0p82cl+Vwz/W+X8t2SNXgMEqnPqmqsqq6aY7FVwE+DpKr2VdWvLWph0gIZJNJxkOT3kzya5M4ktyT5nSR3JxluPh9qbgdzVI8iyR80z524O8n3khwJmOuB1zcX5P1RknOmey5Fc7eEm5Lc1zzX5ZI+fWXpp07uugDpRJfkfHq3eXkLvX9T36D3HI1j9UbgF4GfBR5N8nHgGuC85kaOR+6EO53fp3d7mf/c3GZld5I7q+qZBXwVaUEMEqm9fwP8RVX9GCDJ9nmu/1dV9SzwbJL99G6lc6wuonfzy99p5k8BXkfvOR5SXxgk0vEx3b2GnufF4eNTZln32SnTLzC/f5cB/n1VPTqPdaTjymMkUntfBX41ycrm7s4XN+17gfOb6fkeKH+K3lDXXO4ArmzumkuSt8xzP1JrBonUUvNo2D8HHqD3HI3/1Xz0UeADSf4PMDTPbR4E/neSbyX5o1kW/UN6j+Z9sDkY/4fzLF9qzbv/SsdZkj8Anq6qj3Zdi9QP9kgkSa3YI5EktWKPRJLUikEiSWrFIJEktWKQSJJaMUgkSa0YJJKkVv4/YBMsvrYH9dEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df_t['quintile'], df_t[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "ce6aee2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quintile', ylabel='AUC'>"
      ]
     },
     "execution_count": 497,
     "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": [
    "sns.boxplot(df_t['quintile'], df_t['AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "f6dfcffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quintile', ylabel='0'>"
      ]
     },
     "execution_count": 506,
     "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": [
    "sns.boxplot(df_t['quintile'], df_t[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "59737c15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0', ylabel='Count'>"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QJLUsBUlSy1KQJLUsBUlSy1KQJLUsBZ121qy7YCwutzEuOaST4WUuNF6W6aZDS77cxs+/eTxynCaX/Viz7gIOzR5c0nu8Yu06vnHwiWVKpPlYChov43L9pHHJcZo4NHvQ7XmK8PCRJKllKUiSWpaCdJpb6glvT3ZPlrE7p5DkKuDXgRXALVV1c8eRpO6MwYl3j+Uvv3E+8T5WpZBkBfBfgCuAWeCLSXZX1SPdJpM64gnv09I4n3gft8NHFwMHqurPquo7wB3Apo4zSZNtGW6zOi787siJpaq6ztBK8hPAVVX1T5vnPw38/ap6V98yW4GtzdNXA48ucnWrgG8uIe4wjGMmMNfJMtfCjWMmOP1z/Z2qmho0Y6wOHwGD/kvxXa1VVduB7UteUTJTVdNLfZ/lNI6ZwFwny1wLN46ZYLJzjdvho1lgXd/ztcChjrJI0sQZt1L4IrAxyYYkZwCbgd0dZ5KkiTFWh4+q6miSdwGfoveR1B1VtW9Iq1vyIaghGMdMYK6TZa6FG8dMMMG5xupEsySpW+N2+EiS1CFLQZLUmrhSSHJVkkeTHEhyfQfr/1qSLyd5IMlMM3Zukj1JHmsez+lbfluT9dEkVy5jjh1JjiR5uG/spHMk+XvN3+dAkv+UJXxTaZ5Mv5LkG832eiDJW0eZqXm/dUk+m2R/kn1J3tOMd7295svV2TZLclaSe5M82GT6N81419tqvlyd/3w177kiyf1JPtE87257VdXE/KF38vqrwCuBM4AHgQtHnOFrwKpjxn4VuL6Zvh74t830hU3GM4ENTfYVy5TjzcBFwMNLyQHcC7yR3ndM/hD4kWXO9CvALw5YdiSZmvdbDVzUTL8U+NNm/V1vr/lydbbNmtef3Uy/ELgHuGQMttV8uTr/+Wre833Afwc+0fXv4qTtKYzrZTQ2ATub6Z3A1X3jd1TV01X1OHCA3t9hyarqbuCppeRIshp4WVV9oXo/lR/te81yZZrPSDI1uQ5X1Zea6W8D+4E1dL+95ss1n6Hnqp7/2zx9YfOn6H5bzZdrPiP7+UqyFvhR4JZj1t/J9pq0UlgD9F+acJbj/xINQwGfTnJfepfsADi/qg5D7xcdOK8ZH3Xek82xppkedr53JXkovcNLz+1Gd5IpyXrg9fT+pzk22+uYXNDhNmsOhTwAHAH2VNVYbKt5ckH3P18fBD4APNs31tn2mrRSOOFlNEbg0qq6CPgR4Lokbz7OsuOQF+bPMYp8Hwa+B/gB4DDwH7rKlORs4PeA91bVt4636CizDcjV6Tarqmeq6gfoXZHg4iSvPc7iI9tW8+TqdFsleRtwpKruW+hLhp1r0kqh88toVNWh5vEI8HF6h4OebHb/aB6PNIuPOu/J5phtpoeWr6qebH6ZnwX+G88fPhtppiQvpPcP721VdWcz3Pn2GpRrXLZZVf0l8DngKsZgWw3KNQbb6lLg7Um+Ru9w9luS/A4dbq9JK4VOL6OR5CVJXvrcNPAPgYebDFuaxbYAdzXTu4HNSc5MsgHYSO9k0rCcVI5mt/bbSS5pPunwM32vWRbP/WI0foze9hpppuZ9PgLsr6pf65vV6faaL1eX2yzJVJKXN9MvAn4Y+Ardb6uBubr++aqqbVW1tqrW0/v36DNV9U663F6LOTt9Kv8B3krvUxpfBW4Y8bpfSe+TAw8C+55bP/C3gb3AY83juX2vuaHJ+ijL8CmHvve9nd7u8l/T+1/GtYvJAUzT+0X6KvAbNN+SX8ZMvw18GXio+YVYPcpMzfv9IL1d8YeAB5o/bx2D7TVfrs62GfA64P5m3Q8Dv7zYn/Fl3lbz5er856vvfS/j+U8fdba9vMyFJKk1aYePJEnHYSlIklqWgiSpZSlIklqWgiSpZSlIyywdX4lXWgo/kiotoyQr6H0P5gp637X4InBNVT3SaTBpgdxTkJbXuF6JV1oQS0FaXuNwJV5p0SwFaXmNy5VtpUWxFKTl1fmVeKWlsBSk5dXplXilpVrZdQDpdFJVR5O8C/gUvXuC76iqfR3HkhbMj6RKkloePpIktSwFSVLLUpAktSwFSVLLUpAktSwFSVLLUpAktf4/3RAcPBF4O5AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_t[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "5018344d",
   "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>index</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>177756</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>177925</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>179205</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>179246</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180007</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>167382</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>167383</td>\n",
       "      <td>1249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>167397</td>\n",
       "      <td>1197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>155083</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>157355</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>353 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index     0\n",
       "0    177756   291\n",
       "1    177925   243\n",
       "2    179205     8\n",
       "3    179246    41\n",
       "4    180007    46\n",
       "..      ...   ...\n",
       "348  167382   366\n",
       "349  167383  1249\n",
       "350  167397  1197\n",
       "351  155083    95\n",
       "352  157355    70\n",
       "\n",
       "[353 rows x 2 columns]"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gene_tp_sel.sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "281a50a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC</th>\n",
       "      <th>AVG_NODE_DEGREE</th>\n",
       "      <th>DEGREE_NULL_AUC</th>\n",
       "      <th>P_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177756</th>\n",
       "      <td>0.694618</td>\n",
       "      <td>10500.181796</td>\n",
       "      <td>0.509848</td>\n",
       "      <td>1.026297e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177925</th>\n",
       "      <td>0.631797</td>\n",
       "      <td>10535.329907</td>\n",
       "      <td>0.520753</td>\n",
       "      <td>6.526691e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179246</th>\n",
       "      <td>0.519682</td>\n",
       "      <td>10509.999055</td>\n",
       "      <td>0.516117</td>\n",
       "      <td>3.201657e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180007</th>\n",
       "      <td>0.472075</td>\n",
       "      <td>10166.996643</td>\n",
       "      <td>0.449793</td>\n",
       "      <td>9.773137e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180732</th>\n",
       "      <td>0.705574</td>\n",
       "      <td>10649.275532</td>\n",
       "      <td>0.552092</td>\n",
       "      <td>2.526553e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>167382</th>\n",
       "      <td>0.854699</td>\n",
       "      <td>10305.766939</td>\n",
       "      <td>0.456235</td>\n",
       "      <td>1.929063e-121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167383</th>\n",
       "      <td>0.815536</td>\n",
       "      <td>10385.243341</td>\n",
       "      <td>0.480409</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167397</th>\n",
       "      <td>0.834100</td>\n",
       "      <td>10380.869757</td>\n",
       "      <td>0.475889</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155083</th>\n",
       "      <td>0.609623</td>\n",
       "      <td>10369.637178</td>\n",
       "      <td>0.486403</td>\n",
       "      <td>9.945919e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157355</th>\n",
       "      <td>0.522972</td>\n",
       "      <td>10258.660888</td>\n",
       "      <td>0.457565</td>\n",
       "      <td>2.325480e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>317 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             AUC  AVG_NODE_DEGREE  DEGREE_NULL_AUC        P_Value\n",
       "177756  0.694618     10500.181796         0.509848   1.026297e-30\n",
       "177925  0.631797     10535.329907         0.520753   6.526691e-13\n",
       "179246  0.519682     10509.999055         0.516117   3.201657e-01\n",
       "180007  0.472075     10166.996643         0.449793   9.773137e-02\n",
       "180732  0.705574     10649.275532         0.552092   2.526553e-11\n",
       "...          ...              ...              ...            ...\n",
       "167382  0.854699     10305.766939         0.456235  1.929063e-121\n",
       "167383  0.815536     10385.243341         0.480409   0.000000e+00\n",
       "167397  0.834100     10380.869757         0.475889   0.000000e+00\n",
       "155083  0.609623     10369.637178         0.486403   9.945919e-05\n",
       "157355  0.522972     10258.660888         0.457565   2.325480e-01\n",
       "\n",
       "[317 rows x 4 columns]"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2d_jac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "3746f93b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177756    0.694618\n",
       "177925    0.631797\n",
       "179246    0.519682\n",
       "180007    0.472075\n",
       "180732    0.705574\n",
       "            ...   \n",
       "167382    0.854699\n",
       "167383    0.815536\n",
       "167397    0.834100\n",
       "155083    0.609623\n",
       "157355    0.522972\n",
       "Name: AUC, Length: 317, dtype: float64"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2d_jac['AUC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "2a93d129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177756     291\n",
       "177925     243\n",
       "179205       8\n",
       "179246      41\n",
       "180007      46\n",
       "          ... \n",
       "167382     366\n",
       "167383    1249\n",
       "167397    1197\n",
       "155083      95\n",
       "157355      70\n",
       "Length: 353, dtype: int64"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gene_tp_sel.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "e9e149cf",
   "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>AUC</th>\n",
       "      <th>AVG_NODE_DEGREE</th>\n",
       "      <th>DEGREE_NULL_AUC</th>\n",
       "      <th>P_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177756</th>\n",
       "      <td>0.694618</td>\n",
       "      <td>10500.181796</td>\n",
       "      <td>0.509848</td>\n",
       "      <td>1.026297e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177925</th>\n",
       "      <td>0.631797</td>\n",
       "      <td>10535.329907</td>\n",
       "      <td>0.520753</td>\n",
       "      <td>6.526691e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179246</th>\n",
       "      <td>0.519682</td>\n",
       "      <td>10509.999055</td>\n",
       "      <td>0.516117</td>\n",
       "      <td>3.201657e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180007</th>\n",
       "      <td>0.472075</td>\n",
       "      <td>10166.996643</td>\n",
       "      <td>0.449793</td>\n",
       "      <td>9.773137e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180732</th>\n",
       "      <td>0.705574</td>\n",
       "      <td>10649.275532</td>\n",
       "      <td>0.552092</td>\n",
       "      <td>2.526553e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167382</th>\n",
       "      <td>0.854699</td>\n",
       "      <td>10305.766939</td>\n",
       "      <td>0.456235</td>\n",
       "      <td>1.929063e-121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167383</th>\n",
       "      <td>0.815536</td>\n",
       "      <td>10385.243341</td>\n",
       "      <td>0.480409</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167397</th>\n",
       "      <td>0.834100</td>\n",
       "      <td>10380.869757</td>\n",
       "      <td>0.475889</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155083</th>\n",
       "      <td>0.609623</td>\n",
       "      <td>10369.637178</td>\n",
       "      <td>0.486403</td>\n",
       "      <td>9.945919e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157355</th>\n",
       "      <td>0.522972</td>\n",
       "      <td>10258.660888</td>\n",
       "      <td>0.457565</td>\n",
       "      <td>2.325480e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>317 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             AUC  AVG_NODE_DEGREE  DEGREE_NULL_AUC        P_Value\n",
       "177756  0.694618     10500.181796         0.509848   1.026297e-30\n",
       "177925  0.631797     10535.329907         0.520753   6.526691e-13\n",
       "179246  0.519682     10509.999055         0.516117   3.201657e-01\n",
       "180007  0.472075     10166.996643         0.449793   9.773137e-02\n",
       "180732  0.705574     10649.275532         0.552092   2.526553e-11\n",
       "...          ...              ...              ...            ...\n",
       "167382  0.854699     10305.766939         0.456235  1.929063e-121\n",
       "167383  0.815536     10385.243341         0.480409   0.000000e+00\n",
       "167397  0.834100     10380.869757         0.475889   0.000000e+00\n",
       "155083  0.609623     10369.637178         0.486403   9.945919e-05\n",
       "157355  0.522972     10258.660888         0.457565   2.325480e-01\n",
       "\n",
       "[317 rows x 4 columns]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2d_jac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "107f6676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AUC', ylabel='Count'>"
      ]
     },
     "execution_count": 442,
     "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": [
    "sns.histplot(df_2d_jac['AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "9172f5e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AUC', ylabel='Count'>"
      ]
     },
     "execution_count": 203,
     "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": [
    "sns.histplot(df_2d_jac['AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "af521322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AUC', ylabel='Count'>"
      ]
     },
     "execution_count": 216,
     "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": [
    "sns.histplot(df_2d_jac['AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e54a26a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[ind_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "03bb200e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "7d04398e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='avg_rank_d', ylabel='auc'>"
      ]
     },
     "execution_count": 260,
     "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": [
    "df_auc_gene_exp['avg_rank_d'] = df_auc_gene_exp['avg_rank'].round(1)\n",
    "sns.boxplot(data=df_auc_gene_exp[df_auc_gene_exp['genes'].isin(df_exp_corr.index.tolist())], x='avg_rank_d', y='auc', palette=['#3CB7E8'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "351b2d73",
   "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>genes</th>\n",
       "      <th>avg_rank</th>\n",
       "      <th>gene_id_exp_file</th>\n",
       "      <th>auc</th>\n",
       "      <th>cat</th>\n",
       "      <th>avg_rank_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000223972</td>\n",
       "      <td>0.443509</td>\n",
       "      <td>ENSG00000223972</td>\n",
       "      <td>0.593487</td>\n",
       "      <td>non</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000227232</td>\n",
       "      <td>0.733437</td>\n",
       "      <td>ENSG00000227232</td>\n",
       "      <td>0.599505</td>\n",
       "      <td>non</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000278267</td>\n",
       "      <td>0.548917</td>\n",
       "      <td>ENSG00000278267</td>\n",
       "      <td>0.593487</td>\n",
       "      <td>non</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000243485</td>\n",
       "      <td>0.397697</td>\n",
       "      <td>ENSG00000243485</td>\n",
       "      <td>0.557661</td>\n",
       "      <td>non</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000284332</td>\n",
       "      <td>0.301684</td>\n",
       "      <td>ENSG00000284332</td>\n",
       "      <td>0.547544</td>\n",
       "      <td>non</td>\n",
       "      <td>0.3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55406</th>\n",
       "      <td>ENSG00000100312</td>\n",
       "      <td>0.311248</td>\n",
       "      <td>ENSG00000100312</td>\n",
       "      <td>0.551436</td>\n",
       "      <td>non</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55407</th>\n",
       "      <td>ENSG00000254499</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>ENSG00000254499</td>\n",
       "      <td>0.535070</td>\n",
       "      <td>non</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55408</th>\n",
       "      <td>ENSG00000213683</td>\n",
       "      <td>0.303345</td>\n",
       "      <td>ENSG00000213683</td>\n",
       "      <td>0.499738</td>\n",
       "      <td>non</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55409</th>\n",
       "      <td>ENSG00000184319</td>\n",
       "      <td>0.811295</td>\n",
       "      <td>ENSG00000184319</td>\n",
       "      <td>0.720974</td>\n",
       "      <td>non</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55410</th>\n",
       "      <td>ENSG00000079974</td>\n",
       "      <td>0.854376</td>\n",
       "      <td>ENSG00000079974</td>\n",
       "      <td>0.717120</td>\n",
       "      <td>marker</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55411 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 genes  avg_rank gene_id_exp_file       auc     cat  \\\n",
       "0      ENSG00000223972  0.443509  ENSG00000223972  0.593487     non   \n",
       "1      ENSG00000227232  0.733437  ENSG00000227232  0.599505     non   \n",
       "2      ENSG00000278267  0.548917  ENSG00000278267  0.593487     non   \n",
       "3      ENSG00000243485  0.397697  ENSG00000243485  0.557661     non   \n",
       "4      ENSG00000284332  0.301684  ENSG00000284332  0.547544     non   \n",
       "...                ...       ...              ...       ...     ...   \n",
       "55406  ENSG00000100312  0.311248  ENSG00000100312  0.551436     non   \n",
       "55407  ENSG00000254499  0.308634  ENSG00000254499  0.535070     non   \n",
       "55408  ENSG00000213683  0.303345  ENSG00000213683  0.499738     non   \n",
       "55409  ENSG00000184319  0.811295  ENSG00000184319  0.720974     non   \n",
       "55410  ENSG00000079974  0.854376  ENSG00000079974  0.717120  marker   \n",
       "\n",
       "       avg_rank_d  \n",
       "0             0.4  \n",
       "1             0.7  \n",
       "2             0.5  \n",
       "3             0.4  \n",
       "4             0.3  \n",
       "...           ...  \n",
       "55406         0.3  \n",
       "55407         0.3  \n",
       "55408         0.3  \n",
       "55409         0.8  \n",
       "55410         0.9  \n",
       "\n",
       "[55411 rows x 6 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_auc_gene_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "79b9e8fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='auc', ylabel='Count'>"
      ]
     },
     "execution_count": 207,
     "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": [
    "sns.histplot(df_auc_gene_exp[df_auc_gene_exp['genes'].isin(df_exp_corr.index.tolist())]['auc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "227f55d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='auc', ylabel='Count'>"
      ]
     },
     "execution_count": 211,
     "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": [
    "sns.histplot(df_auc_gene_exp[df_auc_gene_exp['cat'].isin(['marker'])]['auc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "b081ffa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='auc', ylabel='Count'>"
      ]
     },
     "execution_count": 209,
     "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": [
    "sns.histplot(df_auc_gene_exp[df_auc_gene_exp['avg_rank']>0.5]['auc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "1bfaa920",
   "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>genes</th>\n",
       "      <th>avg_rank</th>\n",
       "      <th>gene_id_exp_file</th>\n",
       "      <th>auc</th>\n",
       "      <th>cat</th>\n",
       "      <th>avg_rank_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000223972</td>\n",
       "      <td>0.443509</td>\n",
       "      <td>ENSG00000223972</td>\n",
       "      <td>0.593487</td>\n",
       "      <td>non</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000227232</td>\n",
       "      <td>0.733437</td>\n",
       "      <td>ENSG00000227232</td>\n",
       "      <td>0.599505</td>\n",
       "      <td>non</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000278267</td>\n",
       "      <td>0.548917</td>\n",
       "      <td>ENSG00000278267</td>\n",
       "      <td>0.593487</td>\n",
       "      <td>non</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000243485</td>\n",
       "      <td>0.397697</td>\n",
       "      <td>ENSG00000243485</td>\n",
       "      <td>0.557661</td>\n",
       "      <td>non</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000284332</td>\n",
       "      <td>0.301684</td>\n",
       "      <td>ENSG00000284332</td>\n",
       "      <td>0.547544</td>\n",
       "      <td>non</td>\n",
       "      <td>0.3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55406</th>\n",
       "      <td>ENSG00000100312</td>\n",
       "      <td>0.311248</td>\n",
       "      <td>ENSG00000100312</td>\n",
       "      <td>0.551436</td>\n",
       "      <td>non</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55407</th>\n",
       "      <td>ENSG00000254499</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>ENSG00000254499</td>\n",
       "      <td>0.535070</td>\n",
       "      <td>non</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55408</th>\n",
       "      <td>ENSG00000213683</td>\n",
       "      <td>0.303345</td>\n",
       "      <td>ENSG00000213683</td>\n",
       "      <td>0.499738</td>\n",
       "      <td>non</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55409</th>\n",
       "      <td>ENSG00000184319</td>\n",
       "      <td>0.811295</td>\n",
       "      <td>ENSG00000184319</td>\n",
       "      <td>0.720974</td>\n",
       "      <td>non</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55410</th>\n",
       "      <td>ENSG00000079974</td>\n",
       "      <td>0.854376</td>\n",
       "      <td>ENSG00000079974</td>\n",
       "      <td>0.717120</td>\n",
       "      <td>marker</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55411 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 genes  avg_rank gene_id_exp_file       auc     cat  \\\n",
       "0      ENSG00000223972  0.443509  ENSG00000223972  0.593487     non   \n",
       "1      ENSG00000227232  0.733437  ENSG00000227232  0.599505     non   \n",
       "2      ENSG00000278267  0.548917  ENSG00000278267  0.593487     non   \n",
       "3      ENSG00000243485  0.397697  ENSG00000243485  0.557661     non   \n",
       "4      ENSG00000284332  0.301684  ENSG00000284332  0.547544     non   \n",
       "...                ...       ...              ...       ...     ...   \n",
       "55406  ENSG00000100312  0.311248  ENSG00000100312  0.551436     non   \n",
       "55407  ENSG00000254499  0.308634  ENSG00000254499  0.535070     non   \n",
       "55408  ENSG00000213683  0.303345  ENSG00000213683  0.499738     non   \n",
       "55409  ENSG00000184319  0.811295  ENSG00000184319  0.720974     non   \n",
       "55410  ENSG00000079974  0.854376  ENSG00000079974  0.717120  marker   \n",
       "\n",
       "       avg_rank_d  \n",
       "0             0.4  \n",
       "1             0.7  \n",
       "2             0.5  \n",
       "3             0.4  \n",
       "4             0.3  \n",
       "...           ...  \n",
       "55406         0.3  \n",
       "55407         0.3  \n",
       "55408         0.3  \n",
       "55409         0.8  \n",
       "55410         0.9  \n",
       "\n",
       "[55411 rows x 6 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_auc_gene_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e4c51228",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_auc_gene_exp['cat'] = ['marker' if x in marker_list['Ensembl_gene_identifier'].tolist() else 'non' for x in df_auc_gene_exp['gene_id_exp_file'].tolist()]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c0525e60",
   "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>genes</th>\n",
       "      <th>avg_rank</th>\n",
       "      <th>gene_id_exp_file</th>\n",
       "      <th>auc</th>\n",
       "      <th>cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000223972</td>\n",
       "      <td>0.443509</td>\n",
       "      <td>ENSG00000223972</td>\n",
       "      <td>0.593487</td>\n",
       "      <td>non</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000227232</td>\n",
       "      <td>0.733437</td>\n",
       "      <td>ENSG00000227232</td>\n",
       "      <td>0.599505</td>\n",
       "      <td>non</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000278267</td>\n",
       "      <td>0.548917</td>\n",
       "      <td>ENSG00000278267</td>\n",
       "      <td>0.593487</td>\n",
       "      <td>non</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000243485</td>\n",
       "      <td>0.397697</td>\n",
       "      <td>ENSG00000243485</td>\n",
       "      <td>0.557661</td>\n",
       "      <td>non</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000284332</td>\n",
       "      <td>0.301684</td>\n",
       "      <td>ENSG00000284332</td>\n",
       "      <td>0.547544</td>\n",
       "      <td>non</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55406</th>\n",
       "      <td>ENSG00000100312</td>\n",
       "      <td>0.311248</td>\n",
       "      <td>ENSG00000100312</td>\n",
       "      <td>0.551436</td>\n",
       "      <td>non</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55407</th>\n",
       "      <td>ENSG00000254499</td>\n",
       "      <td>0.308634</td>\n",
       "      <td>ENSG00000254499</td>\n",
       "      <td>0.535070</td>\n",
       "      <td>non</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55408</th>\n",
       "      <td>ENSG00000213683</td>\n",
       "      <td>0.303345</td>\n",
       "      <td>ENSG00000213683</td>\n",
       "      <td>0.499738</td>\n",
       "      <td>non</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55409</th>\n",
       "      <td>ENSG00000184319</td>\n",
       "      <td>0.811295</td>\n",
       "      <td>ENSG00000184319</td>\n",
       "      <td>0.720974</td>\n",
       "      <td>non</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55410</th>\n",
       "      <td>ENSG00000079974</td>\n",
       "      <td>0.854376</td>\n",
       "      <td>ENSG00000079974</td>\n",
       "      <td>0.717120</td>\n",
       "      <td>marker</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55411 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 genes  avg_rank gene_id_exp_file       auc     cat\n",
       "0      ENSG00000223972  0.443509  ENSG00000223972  0.593487     non\n",
       "1      ENSG00000227232  0.733437  ENSG00000227232  0.599505     non\n",
       "2      ENSG00000278267  0.548917  ENSG00000278267  0.593487     non\n",
       "3      ENSG00000243485  0.397697  ENSG00000243485  0.557661     non\n",
       "4      ENSG00000284332  0.301684  ENSG00000284332  0.547544     non\n",
       "...                ...       ...              ...       ...     ...\n",
       "55406  ENSG00000100312  0.311248  ENSG00000100312  0.551436     non\n",
       "55407  ENSG00000254499  0.308634  ENSG00000254499  0.535070     non\n",
       "55408  ENSG00000213683  0.303345  ENSG00000213683  0.499738     non\n",
       "55409  ENSG00000184319  0.811295  ENSG00000184319  0.720974     non\n",
       "55410  ENSG00000079974  0.854376  ENSG00000079974  0.717120  marker\n",
       "\n",
       "[55411 rows x 5 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_auc_gene_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d2e4fe73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cat', ylabel='avg_rank'>"
      ]
     },
     "execution_count": 86,
     "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": [
    "sns.boxplot(data=df_auc_gene_exp[df_auc_gene_exp['genes'].isin(df_exp_corr.index.tolist())], y='avg_rank', x='cat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6cdd76df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cat', ylabel='auc'>"
      ]
     },
     "execution_count": 91,
     "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": [
    "sns.boxplot(data=df_auc_gene_exp, y='auc', x='cat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "62f67f3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/grid/gillis/home/lohia/.conda/envs/hicexplorer/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3169: DtypeWarning: Columns (13,15,18,20,21) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "all_markers_df = []\n",
    "for marker_type in ['class', 'subclass', 'type']:\n",
    "\tmarker_list = pd.read_csv(f'/grid/gillis/data/lohia/hi_c_data_processing/notebooks/metamarkers/{species}/{marker_type}_markers_top1000.csv.gz', skiprows=1)\n",
    "\tall_markers_df.append(marker_list)\n",
    "\n",
    "marker_list = pd.concat(all_markers_df)\n",
    "marker_list = marker_list.drop_duplicates(subset=['gene'])\n",
    "df_ensg_name = pd.read_csv('/grid/gillis/data/lohia/hi_c_data_processing/genomes_jlee/ensg_geneid_symbol.csv', sep='\\t')[['Ensembl_gene_identifier', 'Symbol']]\n",
    "marker_list = marker_list.merge(df_ensg_name.drop_duplicates(subset=['Symbol']), right_on='Symbol', left_on='gene') \n",
    "marker_list = marker_list.drop_duplicates(subset=['Ensembl_gene_identifier'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "efc2fbc7",
   "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>group</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>rank</th>\n",
       "      <th>gene</th>\n",
       "      <th>recurrence</th>\n",
       "      <th>auroc</th>\n",
       "      <th>fold_change</th>\n",
       "      <th>fold_change_detection</th>\n",
       "      <th>expression</th>\n",
       "      <th>precision</th>\n",
       "      <th>...</th>\n",
       "      <th>H200.1025_SSv4</th>\n",
       "      <th>H200.1023_SSv4</th>\n",
       "      <th>H18.30.002_10Xv3</th>\n",
       "      <th>H200.1023_10Xv3</th>\n",
       "      <th>H18.30.001_10Xv3</th>\n",
       "      <th>H19.30.001_10Xv3</th>\n",
       "      <th>H19.30.001_NextGEM</th>\n",
       "      <th>H19.30.002_10Xv3</th>\n",
       "      <th>Ensembl_gene_identifier</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Astro</td>\n",
       "      <td>Astro_1</td>\n",
       "      <td>1</td>\n",
       "      <td>DPP10-AS3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.797253</td>\n",
       "      <td>3.094005</td>\n",
       "      <td>1.818261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092445</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>ENSG00000231538</td>\n",
       "      <td>DPP10-AS3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Astro</td>\n",
       "      <td>Astro_1</td>\n",
       "      <td>2</td>\n",
       "      <td>DPP10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.893438</td>\n",
       "      <td>3.436569</td>\n",
       "      <td>1.204534</td>\n",
       "      <td>4519.887362</td>\n",
       "      <td>0.026629</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>ENSG00000175497</td>\n",
       "      <td>DPP10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Astro</td>\n",
       "      <td>Astro_1</td>\n",
       "      <td>3</td>\n",
       "      <td>ID2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.792967</td>\n",
       "      <td>2.405181</td>\n",
       "      <td>1.507432</td>\n",
       "      <td>157.828504</td>\n",
       "      <td>0.033903</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>ENSG00000115738</td>\n",
       "      <td>ID2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Astro</td>\n",
       "      <td>Astro_1</td>\n",
       "      <td>4</td>\n",
       "      <td>JUNB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.723300</td>\n",
       "      <td>1.565228</td>\n",
       "      <td>1.783512</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074596</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>ENSG00000171223</td>\n",
       "      <td>JUNB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Astro</td>\n",
       "      <td>Astro_1</td>\n",
       "      <td>5</td>\n",
       "      <td>GPC6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.693258</td>\n",
       "      <td>1.279651</td>\n",
       "      <td>2.200657</td>\n",
       "      <td>27.131271</td>\n",
       "      <td>0.053557</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>ENSG00000183098</td>\n",
       "      <td>GPC6</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14434</th>\n",
       "      <td>VLMC</td>\n",
       "      <td>VLMC_2</td>\n",
       "      <td>895</td>\n",
       "      <td>DHCR7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.600624</td>\n",
       "      <td>4.999688</td>\n",
       "      <td>13.836867</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>ENSG00000172893</td>\n",
       "      <td>DHCR7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14435</th>\n",
       "      <td>VLMC</td>\n",
       "      <td>VLMC_2</td>\n",
       "      <td>909</td>\n",
       "      <td>PGF</td>\n",
       "      <td>0</td>\n",
       "      <td>0.599718</td>\n",
       "      <td>3.393679</td>\n",
       "      <td>6.606525</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>ENSG00000119630</td>\n",
       "      <td>PGF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14436</th>\n",
       "      <td>VLMC</td>\n",
       "      <td>VLMC_2</td>\n",
       "      <td>910</td>\n",
       "      <td>VAT1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.599639</td>\n",
       "      <td>3.673718</td>\n",
       "      <td>7.085874</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>ENSG00000108828</td>\n",
       "      <td>VAT1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14437</th>\n",
       "      <td>VLMC</td>\n",
       "      <td>VLMC_2</td>\n",
       "      <td>920</td>\n",
       "      <td>DCAF15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.599207</td>\n",
       "      <td>1.939877</td>\n",
       "      <td>4.796181</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>ENSG00000132017</td>\n",
       "      <td>DCAF15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14438</th>\n",
       "      <td>VLMC</td>\n",
       "      <td>VLMC_2</td>\n",
       "      <td>996</td>\n",
       "      <td>RUVBL2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.596416</td>\n",
       "      <td>3.699655</td>\n",
       "      <td>7.986018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>ENSG00000183207</td>\n",
       "      <td>RUVBL2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14419 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       group cell_type  rank       gene  recurrence     auroc  fold_change  \\\n",
       "0      Astro   Astro_1     1  DPP10-AS3           3  0.797253     3.094005   \n",
       "1      Astro   Astro_1     2      DPP10           2  0.893438     3.436569   \n",
       "2      Astro   Astro_1     3        ID2           1  0.792967     2.405181   \n",
       "3      Astro   Astro_1     4       JUNB           1  0.723300     1.565228   \n",
       "4      Astro   Astro_1     5       GPC6           1  0.693258     1.279651   \n",
       "...      ...       ...   ...        ...         ...       ...          ...   \n",
       "14434   VLMC    VLMC_2   895      DHCR7           0  0.600624     4.999688   \n",
       "14435   VLMC    VLMC_2   909        PGF           0  0.599718     3.393679   \n",
       "14436   VLMC    VLMC_2   910       VAT1           0  0.599639     3.673718   \n",
       "14437   VLMC    VLMC_2   920     DCAF15           0  0.599207     1.939877   \n",
       "14438   VLMC    VLMC_2   996     RUVBL2           0  0.596416     3.699655   \n",
       "\n",
       "       fold_change_detection   expression  precision  ...  H200.1025_SSv4  \\\n",
       "0                   1.818261     0.000000   0.092445  ...           False   \n",
       "1                   1.204534  4519.887362   0.026629  ...           False   \n",
       "2                   1.507432   157.828504   0.033903  ...           False   \n",
       "3                   1.783512     0.000000   0.074596  ...           False   \n",
       "4                   2.200657    27.131271   0.053557  ...           False   \n",
       "...                      ...          ...        ...  ...             ...   \n",
       "14434              13.836867     0.000000        NaN  ...             NaN   \n",
       "14435               6.606525     0.000000        NaN  ...             NaN   \n",
       "14436               7.085874     0.000000        NaN  ...             NaN   \n",
       "14437               4.796181     0.000000        NaN  ...             NaN   \n",
       "14438               7.986018     0.000000        NaN  ...             NaN   \n",
       "\n",
       "       H200.1023_SSv4  H18.30.002_10Xv3 H200.1023_10Xv3 H18.30.001_10Xv3  \\\n",
       "0                 NaN              True             NaN              NaN   \n",
       "1                 NaN              True             NaN              NaN   \n",
       "2                 NaN             False             NaN              NaN   \n",
       "3                 NaN             False             NaN              NaN   \n",
       "4                 NaN             False             NaN              NaN   \n",
       "...               ...               ...             ...              ...   \n",
       "14434           False             False             NaN            False   \n",
       "14435           False             False             NaN            False   \n",
       "14436           False             False             NaN            False   \n",
       "14437           False             False             NaN            False   \n",
       "14438           False             False             NaN            False   \n",
       "\n",
       "      H19.30.001_10Xv3  H19.30.001_NextGEM H19.30.002_10Xv3  \\\n",
       "0                False                True             True   \n",
       "1                False                True            False   \n",
       "2                False               False             True   \n",
       "3                False               False             True   \n",
       "4                False                True            False   \n",
       "...                ...                 ...              ...   \n",
       "14434            False               False            False   \n",
       "14435            False               False            False   \n",
       "14436            False               False            False   \n",
       "14437            False               False            False   \n",
       "14438            False               False            False   \n",
       "\n",
       "      Ensembl_gene_identifier     Symbol  \n",
       "0             ENSG00000231538  DPP10-AS3  \n",
       "1             ENSG00000175497      DPP10  \n",
       "2             ENSG00000115738        ID2  \n",
       "3             ENSG00000171223       JUNB  \n",
       "4             ENSG00000183098       GPC6  \n",
       "...                       ...        ...  \n",
       "14434         ENSG00000172893      DHCR7  \n",
       "14435         ENSG00000119630        PGF  \n",
       "14436         ENSG00000108828       VAT1  \n",
       "14437         ENSG00000132017     DCAF15  \n",
       "14438         ENSG00000183207     RUVBL2  \n",
       "\n",
       "[14419 rows x 24 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "95a5c8ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_list_exp = marker_list.merge(exp_genes, left_on='Ensembl_gene_identifier', right_on='genes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "62e00b7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rank_d', ylabel='avg_rank'>"
      ]
     },
     "execution_count": 72,
     "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": [
    "marker_list_exp['rank_d'] = marker_list_exp['rank']/10\n",
    "marker_list_exp['rank_d'] = marker_list_exp['rank_d'].astype('int')\n",
    "sns.boxplot(data=marker_list_exp, y='avg_rank', x='rank_d', palette=['#3CB7E8'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e5aae32",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hicexp",
   "language": "python",
   "name": "hicexp"
  },
  "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": 5
}