count.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import entropy\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_entropy(row):\n",
    "    row_filt = row.dropna().values\n",
    "    if row_filt.sum() == 0:\n",
    "        return np.nan\n",
    "    probabilities = row_filt / row_filt.sum()\n",
    "    entropy_value = entropy(probabilities)\n",
    "    return entropy_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>name</th>\n",
       "      <th>whitelist</th>\n",
       "      <th>count</th>\n",
       "      <th>ID</th>\n",
       "      <th>TAG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SRR19752930_1</td>\n",
       "      <td>arc</td>\n",
       "      <td>0</td>\n",
       "      <td>SRR19752930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SRR19752930_1</td>\n",
       "      <td>v1</td>\n",
       "      <td>0</td>\n",
       "      <td>SRR19752930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SRR19752930_1</td>\n",
       "      <td>v2</td>\n",
       "      <td>0</td>\n",
       "      <td>SRR19752930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SRR19752930_1</td>\n",
       "      <td>v3</td>\n",
       "      <td>0</td>\n",
       "      <td>SRR19752930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SRR19752930_2</td>\n",
       "      <td>arc</td>\n",
       "      <td>219</td>\n",
       "      <td>SRR19752930</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name whitelist  count           ID TAG\n",
       "0  SRR19752930_1       arc      0  SRR19752930   1\n",
       "1  SRR19752930_1        v1      0  SRR19752930   1\n",
       "2  SRR19752930_1        v2      0  SRR19752930   1\n",
       "3  SRR19752930_1        v3      0  SRR19752930   1\n",
       "4  SRR19752930_2       arc    219  SRR19752930   2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_table = pd.read_csv('count_table.txt', sep=',').drop_duplicates()\n",
    "count_table[['ID', 'TAG']] = count_table.name.str.split('_', expand=True)\n",
    "count_table.head()\n",
    "#barcode_filetag = count_table.groupby(['ID', 'name'], as_index=False).sum(numeric_only=True).set_index('name').groupby('ID', as_index=False).idxmax()['count'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>TAG</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>max_tag</th>\n",
       "      <th>max_sample_tag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR10011559</th>\n",
       "      <td>208304.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028508</td>\n",
       "      <td>208304.0</td>\n",
       "      <td>1</td>\n",
       "      <td>SRR10011559_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011560</th>\n",
       "      <td>207970.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028185</td>\n",
       "      <td>207970.0</td>\n",
       "      <td>1</td>\n",
       "      <td>SRR10011560_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011561</th>\n",
       "      <td>207721.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028910</td>\n",
       "      <td>207721.0</td>\n",
       "      <td>1</td>\n",
       "      <td>SRR10011561_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011562</th>\n",
       "      <td>208154.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028680</td>\n",
       "      <td>208154.0</td>\n",
       "      <td>1</td>\n",
       "      <td>SRR10011562_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011563</th>\n",
       "      <td>212330.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027570</td>\n",
       "      <td>212330.0</td>\n",
       "      <td>1</td>\n",
       "      <td>SRR10011563_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TAG                 1      2    3   4   entropy  max_count max_tag  \\\n",
       "ID                                                                   \n",
       "SRR10011559  208304.0  930.0  0.0 NaN  0.028508   208304.0       1   \n",
       "SRR10011560  207970.0  916.0  0.0 NaN  0.028185   207970.0       1   \n",
       "SRR10011561  207721.0  943.0  0.0 NaN  0.028910   207721.0       1   \n",
       "SRR10011562  208154.0  936.0  0.0 NaN  0.028680   208154.0       1   \n",
       "SRR10011563  212330.0  911.0  0.0 NaN  0.027570   212330.0       1   \n",
       "\n",
       "TAG         max_sample_tag  \n",
       "ID                          \n",
       "SRR10011559  SRR10011559_1  \n",
       "SRR10011560  SRR10011560_1  \n",
       "SRR10011561  SRR10011561_1  \n",
       "SRR10011562  SRR10011562_1  \n",
       "SRR10011563  SRR10011563_1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table = pd.pivot_table(count_table, values='count', index='ID', columns='TAG', aggfunc='sum')\n",
    "pivot_table['entropy'] = pivot_table.apply(calculate_entropy, axis=1)\n",
    "pivot_table['max_count'] = pivot_table.iloc[:, :4].max(axis=1)\n",
    "pivot_table['max_tag'] = pivot_table.iloc[:, :4].idxmax(axis=1)\n",
    "pivot_table['max_sample_tag'] = pivot_table.index + '_' + pivot_table.max_tag\n",
    "pivot_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='entropy', ylabel='Count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(pivot_table.entropy.dropna(), binrange=[0, 0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='max_count', ylabel='Count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(pivot_table.max_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>TAG</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>max_tag</th>\n",
       "      <th>max_sample_tag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR12400259</th>\n",
       "      <td>90231.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.048702</td>\n",
       "      <td>90231.0</td>\n",
       "      <td>1</td>\n",
       "      <td>SRR12400259_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10677406</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1251.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1251.0</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR10677406_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10677409</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR10677409_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6976743</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR6976743_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10677412</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR10677412_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10677404</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1156.0</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR10677404_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6976740</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>2</td>\n",
       "      <td>SRR6976740_2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TAG                1       2    3   4   entropy  max_count max_tag  \\\n",
       "ID                                                                   \n",
       "SRR12400259  90231.0   768.0  0.0 NaN  0.048702    90231.0       1   \n",
       "SRR10677406      NaN  1251.0  NaN NaN  0.000000     1251.0       2   \n",
       "SRR10677409      NaN  1211.0  NaN NaN  0.000000     1211.0       2   \n",
       "SRR6976743       NaN  1197.0  NaN NaN  0.000000     1197.0       2   \n",
       "SRR10677412      NaN  1197.0  NaN NaN  0.000000     1197.0       2   \n",
       "SRR10677404      NaN  1156.0  NaN NaN  0.000000     1156.0       2   \n",
       "SRR6976740       NaN  1107.0  NaN NaN  0.000000     1107.0       2   \n",
       "\n",
       "TAG         max_sample_tag  \n",
       "ID                          \n",
       "SRR12400259  SRR12400259_1  \n",
       "SRR10677406  SRR10677406_2  \n",
       "SRR10677409  SRR10677409_2  \n",
       "SRR6976743    SRR6976743_2  \n",
       "SRR10677412  SRR10677412_2  \n",
       "SRR10677404  SRR10677404_2  \n",
       "SRR6976740    SRR6976740_2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table[pivot_table.max_count < 100000].sort_values('max_count', ascending=False).head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def n2larg(row):\n",
    "    nlarg = row.nlargest(2).to_list()\n",
    "    return pd.Series(nlarg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>whitelist</th>\n",
       "      <th>arc</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>secmax_count</th>\n",
       "      <th>max_col</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR10011559_1</th>\n",
       "      <td>345</td>\n",
       "      <td>567</td>\n",
       "      <td>188137</td>\n",
       "      <td>19255</td>\n",
       "      <td>0.338766</td>\n",
       "      <td>188137</td>\n",
       "      <td>19255</td>\n",
       "      <td>v2</td>\n",
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       "    <tr>\n",
       "      <th>SRR10011560_1</th>\n",
       "      <td>346</td>\n",
       "      <td>514</td>\n",
       "      <td>188119</td>\n",
       "      <td>18991</td>\n",
       "      <td>0.334784</td>\n",
       "      <td>188119</td>\n",
       "      <td>18991</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011561_1</th>\n",
       "      <td>304</td>\n",
       "      <td>535</td>\n",
       "      <td>187817</td>\n",
       "      <td>19065</td>\n",
       "      <td>0.335189</td>\n",
       "      <td>187817</td>\n",
       "      <td>19065</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011562_1</th>\n",
       "      <td>291</td>\n",
       "      <td>581</td>\n",
       "      <td>187849</td>\n",
       "      <td>19433</td>\n",
       "      <td>0.339614</td>\n",
       "      <td>187849</td>\n",
       "      <td>19433</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011563_1</th>\n",
       "      <td>365</td>\n",
       "      <td>541</td>\n",
       "      <td>191268</td>\n",
       "      <td>20156</td>\n",
       "      <td>0.343785</td>\n",
       "      <td>191268</td>\n",
       "      <td>20156</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc   v1      v2     v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                        \n",
       "SRR10011559_1  345  567  188137  19255  0.338766     188137         19255   \n",
       "SRR10011560_1  346  514  188119  18991  0.334784     188119         18991   \n",
       "SRR10011561_1  304  535  187817  19065  0.335189     187817         19065   \n",
       "SRR10011562_1  291  581  187849  19433  0.339614     187849         19433   \n",
       "SRR10011563_1  365  541  191268  20156  0.343785     191268         20156   \n",
       "\n",
       "whitelist     max_col  \n",
       "name                   \n",
       "SRR10011559_1      v2  \n",
       "SRR10011560_1      v2  \n",
       "SRR10011561_1      v2  \n",
       "SRR10011562_1      v2  \n",
       "SRR10011563_1      v2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sample_count_table = count_table.set_index('name').loc[pivot_table.max_sample_tag.unique()].reset_index()\n",
    "max_sample_count_table = max_sample_count_table.pivot(index='name', columns='whitelist', values='count')\n",
    "max_sample_count_table['entropy'] = max_sample_count_table.apply(calculate_entropy, axis=1)\n",
    "max_sample_count_table[['max_count', 'secmax_count']] = max_sample_count_table.iloc[:, :4].apply(n2larg, axis=1)\n",
    "max_sample_count_table['max_col'] = max_sample_count_table.iloc[:, :4].idxmax(axis=1)\n",
    "max_sample_count_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>whitelist</th>\n",
       "      <th>arc</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>secmax_count</th>\n",
       "      <th>max_col</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR10269924_2</th>\n",
       "      <td>80</td>\n",
       "      <td>460</td>\n",
       "      <td>174</td>\n",
       "      <td>325</td>\n",
       "      <td>1.220947</td>\n",
       "      <td>460</td>\n",
       "      <td>325</td>\n",
       "      <td>v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10269922_2</th>\n",
       "      <td>84</td>\n",
       "      <td>529</td>\n",
       "      <td>120</td>\n",
       "      <td>319</td>\n",
       "      <td>1.156988</td>\n",
       "      <td>529</td>\n",
       "      <td>319</td>\n",
       "      <td>v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR11908797_2</th>\n",
       "      <td>110</td>\n",
       "      <td>446</td>\n",
       "      <td>44</td>\n",
       "      <td>256</td>\n",
       "      <td>1.116914</td>\n",
       "      <td>446</td>\n",
       "      <td>256</td>\n",
       "      <td>v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR11908799_1</th>\n",
       "      <td>149</td>\n",
       "      <td>538</td>\n",
       "      <td>33</td>\n",
       "      <td>270</td>\n",
       "      <td>1.084155</td>\n",
       "      <td>538</td>\n",
       "      <td>270</td>\n",
       "      <td>v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR13387395_2</th>\n",
       "      <td>66</td>\n",
       "      <td>463</td>\n",
       "      <td>50</td>\n",
       "      <td>360</td>\n",
       "      <td>1.058997</td>\n",
       "      <td>463</td>\n",
       "      <td>360</td>\n",
       "      <td>v1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR18604258_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR18604261_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR18604264_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6976738_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6976741_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1517 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc   v1   v2   v3   entropy  max_count  secmax_count max_col\n",
       "name                                                                        \n",
       "SRR10269924_2   80  460  174  325  1.220947        460           325      v1\n",
       "SRR10269922_2   84  529  120  319  1.156988        529           319      v1\n",
       "SRR11908797_2  110  446   44  256  1.116914        446           256      v1\n",
       "SRR11908799_1  149  538   33  270  1.084155        538           270      v1\n",
       "SRR13387395_2   66  463   50  360  1.058997        463           360      v1\n",
       "...            ...  ...  ...  ...       ...        ...           ...     ...\n",
       "SRR18604258_1    0    0    0    0       NaN          0             0     arc\n",
       "SRR18604261_1    0    0    0    0       NaN          0             0     arc\n",
       "SRR18604264_1    0    0    0    0       NaN          0             0     arc\n",
       "SRR6976738_1     0    0    0    0       NaN          0             0     arc\n",
       "SRR6976741_1     0    0    0    0       NaN          0             0     arc\n",
       "\n",
       "[1517 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sample_count_table.sort_values('entropy', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='entropy', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(max_sample_count_table.entropy.dropna(), bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='max_count', ylabel='Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAOArFxWSqqurJUk9e/bU7t27FR0d3ShFAQAA+Fq9Ltw+ePBgQ9cBAADQrNT7IwCysrKUlZWl4uJi5wxTjaVLl15yYQAAAL5Ur5D0zDPPaM6cObruuuvUuXNn+fn5NXRdAAAAPlWvkPTKK69o+fLlGjduXEPXAwAA0CzU63OSKioqNHTo0IauBQAAoNmoV0j65S9/qZUrVzZ0LQAAAM1Gvd5uO336tP74xz9q8+bNGjhwoIKCgrz6X3jhhQYpDgAAwFfqFZI+/fRTDRo0SJK0d+9erz4u4gYAAK1BvULS1q1bG7oOAACAZqVe1yQBAAC0dvU6k3TLLbf86NtqW7ZsqXdBAAAAzUG9QlLN9Ug1KisrlZOTo71799b64lsAAICWqF4hacGCBdb22bNn6+TJk5dUEAAAQHPQoNck/eIXv+B72wAAQKvQoCEpOztboaGhDblJAAAAn6jX221jxozxWjfG6NixY9qzZ4+efvrpBikMAADAl+oVkiIiIrzW/f391bdvX82ZM0cjR45skMIAAAB8qV4hadmyZQ1dBwAAQLNSr5BUw+Px6LPPPpMkDRgwQNdcc02DFAUAAOBr9QpJxcXFuvvuu7Vt2zZFRkZKkkpKSnTLLbdo1apV6tSpU0PWCAAA0OTqdXfblClTdOLECe3bt0/Hjx/X8ePHtXfvXpWVlenRRx9t6BoBAACaXL3OJG3YsEGbN29W//79nbaEhARlZmZy4TYAAGgV6nUmqbq6WkFBQbXag4KCVF1dfclFAQAA+Fq9QtLw4cP12GOP6ejRo07bkSNH9MQTT2jEiBENVhwAAICv1Csk/f73v1dZWZl69Oih3r17q3fv3urZs6fKysq0cOHChq4RAACgydXrmqRu3brpo48+0ubNm/X5559Lkvr376+UlJQGLQ4AAMBXLupM0pYtW5SQkKCysjL5+fnp1ltv1ZQpUzRlyhRdf/31GjBggN57773GqhUAAKDJXFRIevHFFzVx4kSFh4fX6ouIiNBDDz2kF154ocGKAwAA8JWLCkmffPKJbrvttvP2jxw5Uh6P55KLAgAA8LWLCklFRUXWW/9rBAYG6h//+MclFwUAAOBrFxWSunTpor179563/9NPP1Xnzp0vuSgAAABfu6iQ9NOf/lRPP/20Tp8+Xavv+++/16xZs/Qv//IvDVYcAACAr1zURwDMmDFDf/3rX3XllVdq8uTJ6tu3ryTp888/V2ZmpqqqqvSb3/ymUQoFAABoShcVklwul3bs2KFJkyYpIyNDxhhJkp+fn1JTU5WZmSmXy9UohQIAADSli/4wyfj4eK1fv17ffvutDhw4IGOMrrjiCnXo0KEx6gMAAPCJen3itiR16NBB119/fUPWAgAA0GzU67vbAAAAWjtCEgAAgAUhCQAAwIKQBAAAYEFIAgAAsCAkAQAAWBCSAAAALAhJAAAAFoQkAAAAC0ISAACABSEJAADAgpAEAABgQUgCAACwICQBAABYEJIAAAAsCEkAAAAWhCQAAAALQhIAAIAFIQkAAMDCpyFp7ty5uv7669W+fXvFxMTozjvvVF5enteY06dPKz09XR07dlS7du2UlpamoqIirzEFBQUaPXq0wsLCFBMTo2nTpunMmTNNORUAANDK+DQkbd++Xenp6dq5c6c2bdqkyspKjRw5UqdOnXLGPPHEE3rzzTe1evVqbd++XUePHtWYMWOc/qqqKo0ePVoVFRXasWOHVqxYoeXLl2vmzJm+mBIAAGglAn258w0bNnitL1++XDExMfJ4PLrppptUWlqqJUuWaOXKlRo+fLgkadmyZerfv7927typIUOGaOPGjdq/f782b94sl8ulQYMG6dlnn9X06dM1e/ZsBQcH19pveXm5ysvLnfWysrLGnSgAAGhxmtU1SaWlpZKkqKgoSZLH41FlZaVSUlKcMf369VP37t2VnZ0tScrOzlZiYqJcLpczJjU1VWVlZdq3b591P3PnzlVERISzdOvWrbGmBAAAWqhmE5Kqq6v1+OOP64YbbtBVV10lSSosLFRwcLAiIyO9xrpcLhUWFjpjzg5INf01fTYZGRkqLS11lsOHDzfwbAAAQEvn07fbzpaenq69e/fq/fffb/R9hYSEKCQkpNH3AwAAWq5mcSZp8uTJWrdunbZu3aquXbs67bGxsaqoqFBJSYnX+KKiIsXGxjpjzr3brWa9ZgwAAMDF8mlIMsZo8uTJeuONN7Rlyxb17NnTqz8pKUlBQUHKyspy2vLy8lRQUCC32y1Jcrvdys3NVXFxsTNm06ZNCg8PV0JCQtNMBAAAtDo+fbstPT1dK1eu1Nq1a9W+fXvnGqKIiAi1adNGERERmjBhgqZOnaqoqCiFh4drypQpcrvdGjJkiCRp5MiRSkhI0Lhx4zR//nwVFhZqxowZSk9P5y01AABQbz4NSYsXL5YkDRs2zKt92bJluu+++yRJCxYskL+/v9LS0lReXq7U1FQtWrTIGRsQEKB169Zp0qRJcrvdatu2rcaPH685c+Y01TQAAEAr5NOQZIy54JjQ0FBlZmYqMzPzvGPi4+O1fv36hiwNAABc5prFhdsAAADNDSEJAADAgpAEAABgQUgCAACwICQBAABYNJuvJQEAAM1HeXm5PB6PV1tubq6qq31UkA8QkgAAQC0ej0ePLlqryC69nbavct5Thz5JPqyqaRGSAACAVWSX3oruneislxzJ92E1TY9rkgAAACwISQAAABaEJAAAAAtCEgAAgAUhCQAAwIKQBAAAYEFIAgAAsCAkAQAAWBCSAAAALAhJAAAAFoQkAAAAC0ISAACABSEJAADAgpAEAABgQUgCAACwICQBAABYEJIAAAAsCEkAAAAWhCQAAAALQhIAAIAFIQkAAMCCkAQAAGBBSAIAALAgJAEAAFgQkgAAACwISQAAABaEJAAAAAtCEgAAgAUhCQAAwIKQBAAAYEFIAgAAsCAkAQAAWBCSAAAALAhJAAAAFoG+LqA1Ky8vl8fjqdWelJSkkJAQH1QEAADqipDUiDwejx5dtFaRXXo7bSVH8vXyI9LQoUN9WBkAALgQQlIji+zSW9G9E31dBgAAuEhckwQAAGBBSAIAALAgJAEAAFgQkgAAACwISQAAABaEJAAAAAtCEgAAgAUhCQAAwIKQBAAAYEFIAgAAsCAkAQAAWBCSAAAALAhJAAAAFj4NSe+++65uv/12xcXFyc/PT2vWrPHqN8Zo5syZ6ty5s9q0aaOUlBR98cUXXmOOHz+usWPHKjw8XJGRkZowYYJOnjzZhLMAAACtkU9D0qlTp3T11VcrMzPT2j9//ny9/PLLeuWVV7Rr1y61bdtWqampOn36tDNm7Nix2rdvnzZt2qR169bp3Xff1YMPPthUUwAAAK1UoC93PmrUKI0aNcraZ4zRiy++qBkzZuiOO+6QJP33f/+3XC6X1qxZo7vvvlufffaZNmzYoN27d+u6666TJC1cuFA//elP9bvf/U5xcXFNNhcAANC6NNtrkg4ePKjCwkKlpKQ4bREREUpOTlZ2drYkKTs7W5GRkU5AkqSUlBT5+/tr165d5912eXm5ysrKvBYAAICzNduQVFhYKElyuVxe7S6Xy+krLCxUTEyMV39gYKCioqKcMTZz585VRESEs3Tr1q2BqwcAAC1dsw1JjSkjI0OlpaXOcvjwYV+XBAAAmplmG5JiY2MlSUVFRV7tRUVFTl9sbKyKi4u9+s+cOaPjx487Y2xCQkIUHh7utQAAAJyt2Yaknj17KjY2VllZWU5bWVmZdu3aJbfbLUlyu90qKSmRx+NxxmzZskXV1dVKTk5u8poBAEDr4dO7206ePKkDBw446wcPHlROTo6ioqLUvXt3Pf744/rtb3+rK664Qj179tTTTz+tuLg43XnnnZKk/v3767bbbtPEiRP1yiuvqLKyUpMnT9bdd9/NnW0AAPhA9ZlK5ebmerUlJSUpJCTERxXVn09D0p49e3TLLbc461OnTpUkjR8/XsuXL9evfvUrnTp1Sg8++KBKSkp04403asOGDQoNDXUe89prr2ny5MkaMWKE/P39lZaWppdffrnJ5wIAAKSyogItPPS9Yv/uJ0kqOZKvlx+Rhg4d6uPKLp5PQ9KwYcNkjDlvv5+fn+bMmaM5c+acd0xUVJRWrlzZGOUBAIB6aB/bU9G9E31dxiVrttckAQAA+BIhCQAAwIKQBAAAYEFIAgAAsCAkAQAAWBCSAAAALAhJAAAAFoQkAAAAC0ISAACABSEJAADAgpAEAABgQUgCAACwICQBAABYEJIAAAAsCEkAAAAWhCQAAAALQhIAAIAFIQkAAMCCkAQAAGBBSAIAALAgJAEAAFgQkgAAACwCfV0AAADwvfLycnk8Hmc9NzdX1dU+LKgZICQBAAB5PB49umitIrv0liR9lfOeOvRJ8nFVvkVIAgAAkqTILr0V3TtRklRyJN/H1fge1yQBAABYEJIAAAAsCEkAAAAWhCQAAAALQhIAAIAFIQkAAMCCkAQAAGBBSAIAALAgJAEAAFjwidsAAFxmzv2eNonvarMhJAEAcJk593vaJL6rzYaQBADAZejs72mT+K42G65JAgAAsCAkAQAAWBCSAAAALAhJAAAAFoQkAAAAC0ISAACABSEJAADAgpAEAABgQUgCAACwICQBAABYEJIAAAAsCEkAAAAWhCQAAACLQF8XAAAApPLycnk8Hq+2iooKSVJwcLDTlpSUpJCQkEvadm5urqqrL6HYywQhCQCAZsDj8ejRRWsV2aW30/ZVzrsKbBel2D5XSZJKjuTr5UekoUOHXtK2v8p5Tx36JDVc8a0UIQkAgCZmO2uUm5ur8M69FN070WkrOZKvoIhYr7aG2HbJkfxLqP7yQUgCAKCJ2c8a1e/sju2ttD++m68OXftc8rYvd4QkAAB8ILJL71pnjerjfG+lNcS2L3eEJAAAWrizAxeBqOEQkgAAaGTcXdYyEZIAAGhk3F3WMhGSAOAyZrsTqj6fw9Pc1eUziGyfSSQ13PPREG+JVZ+pVG5urlcbZ6UaT6sJSZmZmXr++edVWFioq6++WgsXLtTgwYN9XRYANGvnnuGo7+fwNDd1u+PL+zOIzl2XpOMFeXpoWK4SE/95EfSFQtP5bsFviCBTVlSghYe+V+zf/c6aB2elGkurCEl/+ctfNHXqVL3yyitKTk7Wiy++qNTUVOXl5SkmJsbX5QFoYWwvclLDnFGoz5mbxqxH8j7DYTtT0VD7aqi51+UM0Lmh6Hx3fJ39GUS2zyQqOZKvhRv3OaHEFiKb+hb89rE9uXOtibSKkPTCCy9o4sSJuv/++yVJr7zyit566y0tXbpU//Ef/+Hj6oDWq7FfvH21L9tn2DTUGZZzt207U3Hui77tRbc+9dTlDIftTEVD7uvsedRlu3X5FGrbGaBzQ9GlBImzQ8n53u6qSyBDy9PiQ1JFRYU8Ho8yMjKcNn9/f6WkpCg7O9v6mPLycpWXlzvrpaWlkqSysrIGre3UqVP65tB+nSn//p/7OnZQH374nU6dOtWg+wJ8Yd++fVr4v1sVFhXrtH13vFBT0m7RgAEDWvS+qirLvf52qyrL9eGHH17y3+652z5R/JV+u3S/ImM/csZ8c2i/AkLbKTK2u7MeET9A7S+xHttzWLPt6srTkqTSY4cU2LbDJc/9x/ZVM4+6bNd6LM5Uyq+ywmk7d72mreRwnkKC/P85r7JSZ93WVpcxR3J36Lfvl9U6Xl7zOmff9d1XfR/X/MYc1KlTPRv8NVb65+u2MabBt12z4RbtyJEjRpLZsWOHV/u0adPM4MGDrY+ZNWuWkcTCwsLCwsLSCpbDhw83SsZo8WeS6iMjI0NTp0511qurq3X8+HF17NhRfn5+P/LIi1NWVqZu3brp8OHDCg8Pb7DtNjeXwzwvhzlKzLM1uRzmKDHP1qQ+czTG6MSJE4qLi2uUmlp8SIqOjlZAQICKioq82ouKihQbG2t9TEhISK1rGCIjIxurRIWHh7faX+qzXQ7zvBzmKDHP1uRymKPEPFuTi51jREREo9Xif+EhzVtwcLCSkpKUlZXltFVXVysrK0tut9uHlQEAgJasxZ9JkqSpU6dq/Pjxuu666zR48GC9+OKLOnXqlHO3GwAAwMVqFSHprrvu0j/+8Q/NnDlThYWFGjRokDZs2CCXy+XTukJCQjRr1qxW98m157oc5nk5zFFinq3J5TBHiXm2Js1xjn7GNNZ9cwAAAC1Xi78mCQAAoDEQkgAAACwISQAAABaEJAAAAAtCUiPKzMxUjx49FBoaquTkZH344Ye+LkmSNHfuXF1//fVq3769YmJidOeddyovL89rzLBhw+Tn5+e1PPzww15jCgoKNHr0aIWFhSkmJkbTpk3TmTNnvMZs27ZN1157rUJCQtSnTx8tX768Vj2N9TzNnj271hz69evn9J8+fVrp6enq2LGj2rVrp7S0tFofStrc59ijR49ac/Tz81N6erqklnsc3333Xd1+++2Ki4uTn5+f1qxZ49VvjNHMmTPVuXNntWnTRikpKfriiy+8xhw/flxjx45VeHi4IiMjNWHCBJ08edJrzKeffqqf/OQnCg0NVbdu3TR//vxataxevVr9+vVTaGioEhMTtX79+ouupT7zrKys1PTp05WYmKi2bdsqLi5O9957r44ePeq1DdvvwLx585rNPC90LO+7775a9d92221eY1r6sZRk/Tv18/PT888/74xp7seyLq8dzen/al1quaBG+bITmFWrVpng4GCzdOlSs2/fPjNx4kQTGRlpioqKfF2aSU1NNcuWLTN79+41OTk55qc//anp3r27OXnypDPm5ptvNhMnTjTHjh1zltLSUqf/zJkz5qqrrjIpKSnm448/NuvXrzfR0dEmIyPDGfP3v//dhIWFmalTp5r9+/ebhQsXmoCAALNhwwZnTGM+T7NmzTIDBgzwmsM//vEPp//hhx823bp1M1lZWWbPnj1myJAhZujQoS1qjsXFxV7z27Rpk5Fktm7daoxpucdx/fr15je/+Y3561//aiSZN954w6t/3rx5JiIiwqxZs8Z88skn5mc/+5np2bOn+f77750xt912m7n66qvNzp07zXvvvWf69Olj7rnnHqe/tLTUuFwuM3bsWLN3717z5z//2bRp08b84Q9/cMZ88MEHJiAgwMyfP9/s37/fzJgxwwQFBZnc3NyLqqU+8ywpKTEpKSnmL3/5i/n8889Ndna2GTx4sElKSvLaRnx8vJkzZ47XMT77b9nX87zQsRw/fry57bbbvOo/fvy415iWfiyNMV7zO3bsmFm6dKnx8/Mz+fn5zpjmfizr8trRnP6vXqiWuiAkNZLBgweb9PR0Z72qqsrExcWZuXPn+rAqu+LiYiPJbN++3Wm7+eabzWOPPXbex6xfv974+/ubwsJCp23x4sUmPDzclJeXG2OM+dWvfmUGDBjg9bi77rrLpKamOuuN+TzNmjXLXH311da+kpISExQUZFavXu20ffbZZ0aSyc7ObjFzPNdjjz1mevfubaqrq40xreM4nvuCU11dbWJjY83zzz/vtJWUlJiQkBDz5z//2RhjzP79+40ks3v3bmfM22+/bfz8/MyRI0eMMcYsWrTIdOjQwZmnMcZMnz7d9O3b11n/+c9/bkaPHu1VT3JysnnooYfqXEt952nz4YcfGknmyy+/dNri4+PNggULzvuY5jTP84WkO+6447yPaa3H8o477jDDhw/3amtJx9KY2q8dzen/al1qqQvebmsEFRUV8ng8SklJcdr8/f2VkpKi7OxsH1ZmV1paKkmKioryan/ttdcUHR2tq666ShkZGfruu++cvuzsbCUmJnp9YGdqaqrKysq0b98+Z8zZz0HNmJrnoCmepy+++EJxcXHq1auXxo4dq4KCAkmSx+NRZWWl17779eun7t27O/tuKXOsUVFRoVdffVUPPPCA1xc1t4bjeLaDBw+qsLDQa38RERFKTk72OnaRkZG67rrrnDEpKSny9/fXrl27nDE33XSTgoODveaVl5enb7/91hnzY3OvSy0NqbS0VH5+frW+a3LevHnq2LGjrrnmGj3//PNeb120hHlu27ZNMTEx6tu3ryZNmqRvvvnGq/7WdiyLior01ltvacKECbX6WtKxPPe1ozn9X61LLXXRKj5xu7n5+uuvVVVVVesTv10ulz7//HMfVWVXXV2txx9/XDfccIOuuuoqp/3f//3fFR8fr7i4OH366aeaPn268vLy9Ne//lWSVFhYaJ1fTd+PjSkrK9P333+vb7/9tlGfp+TkZC1fvlx9+/bVsWPH9Mwzz+gnP/mJ9u7dq8LCQgUHB9d6sXG5XBesvznN8Wxr1qxRSUmJ7rvvPqetNRzHc9XUZdvf2TXHxMR49QcGBioqKsprTM+ePWtto6avQ4cO55372du4UC0N5fTp05o+fbruuecery//fPTRR3XttdcqKipKO3bsUEZGho4dO6YXXnihRczztttu05gxY9SzZ0/l5+fr17/+tUaNGqXs7GwFBAS0ymO5YsUKtW/fXmPGjPFqb0nH0vba0Zz+r9allrogJF3m0tPTtXfvXr3//vte7Q8++KDzc2Jiojp37qwRI0YoPz9fvXv3buoy62XUqFHOzwMHDlRycrLi4+P1+uuvq02bNj6srHEsWbJEo0aNUlxcnNPWGo4jfriI++c//7mMMVq8eLFX39SpU52fBw4cqODgYD300EOaO3dus/p6h/O5++67nZ8TExM1cOBA9e7dW9u2bdOIESN8WFnjWbp0qcaOHavQ0FCv9pZ0LM/32tHa8HZbI4iOjlZAQECtq+iLiooUGxvro6pqmzx5statW6etW7eqa9euPzo2OTlZknTgwAFJUmxsrHV+NX0/NiY8PFxt2rRp8ucpMjJSV155pQ4cOKDY2FhVVFSopKTkvPtuSXP88ssvtXnzZv3yl7/80XGt4TjWbPPH9hcbG6vi4mKv/jNnzuj48eMNcnzP7r9QLZeqJiB9+eWX2rRpk9dZJJvk5GSdOXNGhw4d+tE5nF1/c5hnjV69eik6Otrrd7S1HEtJeu+995SXl3fBv1Wp+R7L8712NKf/q3WppS4ISY0gODhYSUlJysrKctqqq6uVlZUlt9vtw8p+YIzR5MmT9cYbb2jLli21Tt/a5OTkSJI6d+4sSXK73crNzfX651XzDzwhIcEZc/ZzUDOm5jlo6ufp5MmTys/PV+fOnZWUlKSgoCCvfefl5amgoMDZd0ua47JlyxQTE6PRo0f/6LjWcBx79uyp2NhYr/2VlZVp165dXseupKREHo/HGbNlyxZVV1c7QdHtduvdd99VZWWl17z69u2rDh06OGN+bO51qeVS1ASkL774Qps3b1bHjh0v+JicnBz5+/s7b1G1hHme7auvvtI333zj9TvaGo5ljSVLligpKUlXX331Bcc2t2N5odeO5vR/tS611EmdL/HGRVm1apUJCQkxy5cvN/v37zcPPvigiYyM9Lqi31cmTZpkIiIizLZt27xuNf3uu++MMcYcOHDAzJkzx+zZs8ccPHjQrF271vTq1cvcdNNNzjZqbuMcOXKkycnJMRs2bDCdOnWy3sY5bdo089lnn5nMzEzrbZyN9Tw9+eSTZtu2bebgwYPmgw8+MCkpKSY6OtoUFxcbY364PbR79+5my5YtZs+ePcbtdhu3292i5mjMD3d1dO/e3UyfPt2rvSUfxxMnTpiPP/7YfPzxx0aSeeGFF8zHH3/s3NU1b948ExkZadauXWs+/fRTc8cdd1g/AuCaa64xu3btMu+//7654oorvG4bLykpMS6Xy4wbN87s3bvXrFq1yoSFhdW6nTowMND87ne/M5999pmZNWuW9XbqC9VSn3lWVFSYn/3sZ6Zr164mJyfH62+15i6gHTt2mAULFpicnByTn59vXn31VdOpUydz7733Npt5/tgcT5w4YZ566imTnZ1tDh48aDZv3myuvfZac8UVV5jTp0+3mmNZo7S01ISFhZnFixfXenxLOJYXeu0wpnn9X71QLXVBSGpECxcuNN27dzfBwcFm8ODBZufOnb4uyRjzw+2ptmXZsmXGGGMKCgrMTTfdZKKiokxISIjp06ePmTZtmtfn6xhjzKFDh8yoUaNMmzZtTHR0tHnyySdNZWWl15itW7eaQYMGmeDgYNOrVy9nH2drrOfprrvuMp07dzbBwcGmS5cu5q677jIHDhxw+r///nvzyCOPmA4dOpiwsDDzr//6r+bYsWMtao7GGPPOO+8YSSYvL8+rvSUfx61bt1p/R8ePH2+M+eE25qefftq4XC4TEhJiRowYUWv+33zzjbnnnntMu3btTHh4uLn//vvNiRMnvMZ88skn5sYbbzQhISGmS5cuZt68ebVqef31182VV15pgoODzYABA8xbb73l1V+XWuozz4MHD573b7Xmc7A8Ho9JTk42ERERJjQ01PTv398899xzXgHD1/P8sTl+9913ZuTIkaZTp04mKCjIxMfHm4kTJ9YK1y39WNb4wx/+YNq0aWNKSkpqPb4lHMsLvXYY07z+r9allgvx+/8TBwAAwFm4JgkAAMCCkAQAAGBBSAIAALAgJAEAAFgQkgAAACwISQAAABaEJAAAAAtCEgAAgAUhCQAAwIKQBABNYNiwYXr88cd9XQaAi0BIAgAAsCAkAWhQw4YN05QpU/T444+rQ4cOcrlc+tOf/qRTp07p/vvvV/v27dWnTx+9/fbbkqSqqipNmDBBPXv2VJs2bdS3b1+99NJLzvZOnz6tAQMG6MEHH3Ta8vPz1b59ey1durRONX3wwQcaNmyYwsLC1KFDB6Wmpurbb7+VJJWXl+vRRx9VTEyMQkNDdeONN2r37t3OY5cvX67IyEiv7a1Zs0Z+fn7O+uzZszVo0CD9z//8j3r06KGIiAjdfffdOnHihCTpvvvu0/bt2/XSSy/Jz89Pfn5+OnTo0EU9rwCaHiEJQINbsWKFoqOj9eGHH2rKlCmaNGmS/u3f/k1Dhw7VRx99pJEjR2rcuHH67rvvVF1dra5du2r16tXav3+/Zs6cqV//+td6/fXXJUmhoaF67bXXtGLFCq1du1ZVVVX6xS9+oVtvvVUPPPDABWvJycnRiBEjlJCQoOzsbL3//vu6/fbbVVVVJUn61a9+pf/93//VihUr9NFHH6lPnz5KTU3V8ePHL2rO+fn5WrNmjdatW6d169Zp+/btmjdvniTppZdektvt1sSJE3Xs2DEdO3ZM3bp1u8hnFUCTMwDQgG6++WZz4403Outnzpwxbdu2NePGjXPajh07ZiSZ7Oxs6zbS09NNWlqaV9v8+fNNdHS0mTx5suncubP5+uuv61TPPffcY2644QZr38mTJ01QUJB57bXXnLaKigoTFxdn5s+fb4wxZtmyZSYiIsLrcW+88YY5+9/nrFmzTFhYmCkrK3Papk2bZpKTk531m2++2Tz22GN1qhlA88CZJAANbuDAgc7PAQEB6tixoxITE502l8slSSouLpYkZWZmKikpSZ06dVK7du30xz/+UQUFBV7bfPLJJ3XllVfq97//vZYuXaqOHTvWqZaaM0k2+fn5qqys1A033OC0BQUFafDgwfrss8/qNtn/r0ePHmrfvr2z3rlzZ2d+AFomQhKABhcUFOS17ufn59VWcz1PdXW1Vq1apaeeekoTJkzQxo0blZOTo/vvv18VFRVe2yguLtbf/vY3BQQE6IsvvqhzLW3atLmEmUj+/v4yxni1VVZW1hpnm3N1dfUl7RuAbxGSAPjUBx98oKFDh+qRRx7RNddcoz59+ig/P7/WuAceeECJiYlasWKFpk+fXuczPQMHDlRWVpa1r3fv3goODtYHH3zgtFVWVmr37t1KSEiQJHXq1EknTpzQqVOnnDE5OTkXMcMfBAcHO9dBAWgZCEkAfOqKK67Qnj179M477+hvf/ubnn76aa+7y6Qf3o7Lzs7WihUrNHbsWN15550aO3ZsrbNNNhkZGdq9e7ceeeQRffrpp/r888+1ePFiff3112rbtq0mTZqkadOmacOGDdq/f78mTpyo7777ThMmTJAkJScnKywsTL/+9a+Vn5+vlStXavny5Rc9zx49emjXrl06dOiQvv76a84yAS0AIQmATz300EMaM2aM7rrrLiUnJ+ubb77RI4884vR//vnnmjZtmhYtWuTcEbZo0SJ9/fXXevrppy+4/SuvvFIbN27UJ598osGDB8vtdmvt2rUKDAyUJM2bN09paWkaN26crr32Wh04cEDvvPOOOnToIEmKiorSq6++qvXr1ysxMVF//vOfNXv27Iue51NPPaWAgAAlJCSoU6dOta65AtD8+Jlz32wHAAAAZ5IAAABsCEkAWrRRo0apXbt21uW5557zdXkAWjDebgPQoh05ckTff/+9tS8qKkpRUVFNXBGA1oKQBAAAYMHbbQAAABaEJAAAAAtCEgAAgAUhCQAAwIKQBAAAYEFIAgAAsCAkAQAAWPw/GlN5oC08O+4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(max_sample_count_table.max_count.dropna(), bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='secmax_count', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(max_sample_count_table.secmax_count.dropna(), bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>whitelist</th>\n",
       "      <th>arc</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>secmax_count</th>\n",
       "      <th>max_col</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR12455252_1</th>\n",
       "      <td>394</td>\n",
       "      <td>741</td>\n",
       "      <td>192436</td>\n",
       "      <td>24357</td>\n",
       "      <td>0.385513</td>\n",
       "      <td>192436</td>\n",
       "      <td>24357</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12455251_1</th>\n",
       "      <td>359</td>\n",
       "      <td>722</td>\n",
       "      <td>191660</td>\n",
       "      <td>24258</td>\n",
       "      <td>0.384194</td>\n",
       "      <td>191660</td>\n",
       "      <td>24258</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR13563880_1</th>\n",
       "      <td>191</td>\n",
       "      <td>406</td>\n",
       "      <td>194463</td>\n",
       "      <td>23453</td>\n",
       "      <td>0.361153</td>\n",
       "      <td>194463</td>\n",
       "      <td>23453</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR13563881_1</th>\n",
       "      <td>197</td>\n",
       "      <td>364</td>\n",
       "      <td>194392</td>\n",
       "      <td>23265</td>\n",
       "      <td>0.358651</td>\n",
       "      <td>194392</td>\n",
       "      <td>23265</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR11581697_2</th>\n",
       "      <td>21</td>\n",
       "      <td>166</td>\n",
       "      <td>190288</td>\n",
       "      <td>23089</td>\n",
       "      <td>0.349799</td>\n",
       "      <td>190288</td>\n",
       "      <td>23089</td>\n",
       "      <td>v2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR8034298_3</th>\n",
       "      <td>0</td>\n",
       "      <td>175102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>175102</td>\n",
       "      <td>0</td>\n",
       "      <td>v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10027173_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR8034299_3</th>\n",
       "      <td>0</td>\n",
       "      <td>171494</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>171494</td>\n",
       "      <td>0</td>\n",
       "      <td>v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10027179_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10027176_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1517 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc      v1      v2     v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                           \n",
       "SRR12455252_1  394     741  192436  24357  0.385513     192436         24357   \n",
       "SRR12455251_1  359     722  191660  24258  0.384194     191660         24258   \n",
       "SRR13563880_1  191     406  194463  23453  0.361153     194463         23453   \n",
       "SRR13563881_1  197     364  194392  23265  0.358651     194392         23265   \n",
       "SRR11581697_2   21     166  190288  23089  0.349799     190288         23089   \n",
       "...            ...     ...     ...    ...       ...        ...           ...   \n",
       "SRR8034298_3     0  175102       0      0  0.000000     175102             0   \n",
       "SRR10027173_1    0       0       0      0       NaN          0             0   \n",
       "SRR8034299_3     0  171494       0      0  0.000000     171494             0   \n",
       "SRR10027179_1    0       0       0      0       NaN          0             0   \n",
       "SRR10027176_1    0       0       0      0       NaN          0             0   \n",
       "\n",
       "whitelist     max_col  \n",
       "name                   \n",
       "SRR12455252_1      v2  \n",
       "SRR12455251_1      v2  \n",
       "SRR13563880_1      v2  \n",
       "SRR13563881_1      v2  \n",
       "SRR11581697_2      v2  \n",
       "...               ...  \n",
       "SRR8034298_3       v1  \n",
       "SRR10027173_1     arc  \n",
       "SRR8034299_3       v1  \n",
       "SRR10027179_1     arc  \n",
       "SRR10027176_1     arc  \n",
       "\n",
       "[1517 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sample_count_table.sort_values('secmax_count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>whitelist</th>\n",
       "      <th>arc</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>secmax_count</th>\n",
       "      <th>max_col</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>SRR12400257_1</th>\n",
       "      <td>107</td>\n",
       "      <td>468</td>\n",
       "      <td>92197</td>\n",
       "      <td>9742</td>\n",
       "      <td>0.350826</td>\n",
       "      <td>92197</td>\n",
       "      <td>9742</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400259_1</th>\n",
       "      <td>106</td>\n",
       "      <td>431</td>\n",
       "      <td>81172</td>\n",
       "      <td>8522</td>\n",
       "      <td>0.351500</td>\n",
       "      <td>81172</td>\n",
       "      <td>8522</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10677406_2</th>\n",
       "      <td>31</td>\n",
       "      <td>921</td>\n",
       "      <td>28</td>\n",
       "      <td>271</td>\n",
       "      <td>0.733475</td>\n",
       "      <td>921</td>\n",
       "      <td>271</td>\n",
       "      <td>v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12282461_1</th>\n",
       "      <td>5</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>182</td>\n",
       "      <td>0.499072</td>\n",
       "      <td>896</td>\n",
       "      <td>182</td>\n",
       "      <td>v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10677409_2</th>\n",
       "      <td>23</td>\n",
       "      <td>855</td>\n",
       "      <td>33</td>\n",
       "      <td>300</td>\n",
       "      <td>0.764910</td>\n",
       "      <td>855</td>\n",
       "      <td>300</td>\n",
       "      <td>v1</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",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>SRR18604258_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR18604264_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR18604261_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6976738_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6976741_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>arc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc   v1     v2    v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                      \n",
       "SRR12400257_1  107  468  92197  9742  0.350826      92197          9742   \n",
       "SRR12400259_1  106  431  81172  8522  0.351500      81172          8522   \n",
       "SRR10677406_2   31  921     28   271  0.733475        921           271   \n",
       "SRR12282461_1    5  896      3   182  0.499072        896           182   \n",
       "SRR10677409_2   23  855     33   300  0.764910        855           300   \n",
       "...            ...  ...    ...   ...       ...        ...           ...   \n",
       "SRR18604258_1    0    0      0     0       NaN          0             0   \n",
       "SRR18604264_1    0    0      0     0       NaN          0             0   \n",
       "SRR18604261_1    0    0      0     0       NaN          0             0   \n",
       "SRR6976738_1     0    0      0     0       NaN          0             0   \n",
       "SRR6976741_1     0    0      0     0       NaN          0             0   \n",
       "\n",
       "whitelist     max_col  \n",
       "name                   \n",
       "SRR12400257_1      v2  \n",
       "SRR12400259_1      v2  \n",
       "SRR10677406_2      v1  \n",
       "SRR12282461_1      v1  \n",
       "SRR10677409_2      v1  \n",
       "...               ...  \n",
       "SRR18604258_1     arc  \n",
       "SRR18604264_1     arc  \n",
       "SRR18604261_1     arc  \n",
       "SRR6976738_1      arc  \n",
       "SRR6976741_1      arc  \n",
       "\n",
       "[130 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sample_count_table[max_sample_count_table.max_count < 100000].sort_values('max_count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.3862943611198906)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size = 4\n",
    "entropy(np.ones(size) / size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([208304, 207970, 207721, ..., 195706, 195278, 195767])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sample_count_table.iloc[:, :4].sum(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>whitelist</th>\n",
       "      <th>arc</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>secmax_count</th>\n",
       "      <th>max_col</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR10011559_1</th>\n",
       "      <td>345</td>\n",
       "      <td>567</td>\n",
       "      <td>188137</td>\n",
       "      <td>19255</td>\n",
       "      <td>0.338766</td>\n",
       "      <td>188137</td>\n",
       "      <td>19255</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.588805</td>\n",
       "      <td>-0.083992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011560_1</th>\n",
       "      <td>346</td>\n",
       "      <td>514</td>\n",
       "      <td>188119</td>\n",
       "      <td>18991</td>\n",
       "      <td>0.334784</td>\n",
       "      <td>188119</td>\n",
       "      <td>18991</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.590568</td>\n",
       "      <td>-0.084258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011561_1</th>\n",
       "      <td>304</td>\n",
       "      <td>535</td>\n",
       "      <td>187817</td>\n",
       "      <td>19065</td>\n",
       "      <td>0.335189</td>\n",
       "      <td>187817</td>\n",
       "      <td>19065</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.589977</td>\n",
       "      <td>-0.084240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011562_1</th>\n",
       "      <td>291</td>\n",
       "      <td>581</td>\n",
       "      <td>187849</td>\n",
       "      <td>19433</td>\n",
       "      <td>0.339614</td>\n",
       "      <td>187849</td>\n",
       "      <td>19433</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.587636</td>\n",
       "      <td>-0.084056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011563_1</th>\n",
       "      <td>365</td>\n",
       "      <td>541</td>\n",
       "      <td>191268</td>\n",
       "      <td>20156</td>\n",
       "      <td>0.343785</td>\n",
       "      <td>191268</td>\n",
       "      <td>20156</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>-0.084240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc   v1      v2     v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                        \n",
       "SRR10011559_1  345  567  188137  19255  0.338766     188137         19255   \n",
       "SRR10011560_1  346  514  188119  18991  0.334784     188119         18991   \n",
       "SRR10011561_1  304  535  187817  19065  0.335189     187817         19065   \n",
       "SRR10011562_1  291  581  187849  19433  0.339614     187849         19433   \n",
       "SRR10011563_1  365  541  191268  20156  0.343785     191268         20156   \n",
       "\n",
       "whitelist     max_col       PC1       PC2  \n",
       "name                                       \n",
       "SRR10011559_1      v2  0.588805 -0.083992  \n",
       "SRR10011560_1      v2  0.590568 -0.084258  \n",
       "SRR10011561_1      v2  0.589977 -0.084240  \n",
       "SRR10011562_1      v2  0.587636 -0.084056  \n",
       "SRR10011563_1      v2  0.585366 -0.084240  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca_components = pca.fit_transform(max_sample_count_table.iloc[:, :4].div(max_sample_count_table.iloc[:, :4].sum(axis=1), axis=0).fillna(0))\n",
    "max_sample_count_table[['PC1', 'PC2']] = pca_components\n",
    "max_sample_count_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[903, 880]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sample_count_table.arc.nlargest(2).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nlarg_ratio(row):\n",
    "    nlarg = row.nlargest(2).to_list()\n",
    "    if nlarg[1] != 0:\n",
    "        return np.log2(nlarg[0] / nlarg[1])\n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>whitelist</th>\n",
       "      <th>arc</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>secmax_count</th>\n",
       "      <th>max_col</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>nlarg_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR10011559_1</th>\n",
       "      <td>345</td>\n",
       "      <td>567</td>\n",
       "      <td>188137</td>\n",
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       "      <td>188137</td>\n",
       "      <td>19255</td>\n",
       "      <td>v2</td>\n",
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       "      <td>-0.083992</td>\n",
       "      <td>3.288479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011560_1</th>\n",
       "      <td>346</td>\n",
       "      <td>514</td>\n",
       "      <td>188119</td>\n",
       "      <td>18991</td>\n",
       "      <td>0.334784</td>\n",
       "      <td>188119</td>\n",
       "      <td>18991</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.590568</td>\n",
       "      <td>-0.084258</td>\n",
       "      <td>3.308258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011561_1</th>\n",
       "      <td>304</td>\n",
       "      <td>535</td>\n",
       "      <td>187817</td>\n",
       "      <td>19065</td>\n",
       "      <td>0.335189</td>\n",
       "      <td>187817</td>\n",
       "      <td>19065</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.589977</td>\n",
       "      <td>-0.084240</td>\n",
       "      <td>3.300329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011562_1</th>\n",
       "      <td>291</td>\n",
       "      <td>581</td>\n",
       "      <td>187849</td>\n",
       "      <td>19433</td>\n",
       "      <td>0.339614</td>\n",
       "      <td>187849</td>\n",
       "      <td>19433</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.587636</td>\n",
       "      <td>-0.084056</td>\n",
       "      <td>3.272993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR10011563_1</th>\n",
       "      <td>365</td>\n",
       "      <td>541</td>\n",
       "      <td>191268</td>\n",
       "      <td>20156</td>\n",
       "      <td>0.343785</td>\n",
       "      <td>191268</td>\n",
       "      <td>20156</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>-0.084240</td>\n",
       "      <td>3.246314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc   v1      v2     v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                        \n",
       "SRR10011559_1  345  567  188137  19255  0.338766     188137         19255   \n",
       "SRR10011560_1  346  514  188119  18991  0.334784     188119         18991   \n",
       "SRR10011561_1  304  535  187817  19065  0.335189     187817         19065   \n",
       "SRR10011562_1  291  581  187849  19433  0.339614     187849         19433   \n",
       "SRR10011563_1  365  541  191268  20156  0.343785     191268         20156   \n",
       "\n",
       "whitelist     max_col       PC1       PC2  nlarg_ratio  \n",
       "name                                                    \n",
       "SRR10011559_1      v2  0.588805 -0.083992     3.288479  \n",
       "SRR10011560_1      v2  0.590568 -0.084258     3.308258  \n",
       "SRR10011561_1      v2  0.589977 -0.084240     3.300329  \n",
       "SRR10011562_1      v2  0.587636 -0.084056     3.272993  \n",
       "SRR10011563_1      v2  0.585366 -0.084240     3.246314  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sample_count_table['nlarg_ratio'] = max_sample_count_table.iloc[:, :4].apply(nlarg_ratio, axis=1)\n",
    "max_sample_count_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='nlarg_ratio', ylabel='Count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(max_sample_count_table['nlarg_ratio'], bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    x=max_sample_count_table['PC1'],\n",
    "    y=max_sample_count_table['PC2'],\n",
    "    c=max_sample_count_table['entropy'],\n",
    "    marker='.', cmap='coolwarm',\n",
    "    linewidths=0.2,\n",
    "    edgecolors='white'\n",
    ")\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label(label='Entropy', rotation=270, loc='center', labelpad=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "cmap = plt.get_cmap('viridis')\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=100)\n",
    "\n",
    "im = axes[0, 0].scatter(\n",
    "    x=max_sample_count_table['PC1'],\n",
    "    y=max_sample_count_table['PC2'],\n",
    "    c=max_sample_count_table['entropy'],\n",
    "    marker='o',\n",
    "    cmap=cmap,\n",
    "    linewidths=0.2,\n",
    "    edgecolors='white'\n",
    ")\n",
    "axes[0, 0].set_xlabel('PC1')\n",
    "axes[0, 0].set_ylabel('PC2')\n",
    "\n",
    "cbar = plt.colorbar(im, ax=axes[0, 0])\n",
    "cbar.set_label(label='Entropy', rotation=270, loc='center', labelpad=10)\n",
    "\n",
    "im = axes[0, 1].scatter(\n",
    "    x=max_sample_count_table['PC1'],\n",
    "    y=max_sample_count_table['PC2'],\n",
    "    c=np.log1p(max_sample_count_table['max_count']),\n",
    "    marker='o',\n",
    "    cmap=cmap,\n",
    "    linewidths=0.2,\n",
    "    edgecolors='white'\n",
    ")\n",
    "\n",
    "axes[0, 1].set_xlabel('PC1')\n",
    "axes[0, 1].set_ylabel('PC2')\n",
    "\n",
    "cbar = plt.colorbar(im, ax=axes[0, 1])\n",
    "cbar.set_label(label='log(Max value)', rotation=270, loc='center', labelpad=10)\n",
    "\n",
    "im = axes[1, 0].scatter(\n",
    "    x=max_sample_count_table['PC1'],\n",
    "    y=max_sample_count_table['PC2'],\n",
    "    c=max_sample_count_table['nlarg_ratio'],\n",
    "    marker='o',\n",
    "    cmap=cmap,\n",
    "    linewidths=0.2,\n",
    "    edgecolors='white'\n",
    ")\n",
    "\n",
    "axes[1, 0].set_xlabel('PC1')\n",
    "axes[1, 0].set_ylabel('PC2')\n",
    "\n",
    "cbar = plt.colorbar(im, ax=axes[1, 0])\n",
    "cbar.set_label(label='log2(MAX/SECMAX)', rotation=270, loc='center', labelpad=10)\n",
    "\n",
    "axes[1, 1].remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>whitelist</th>\n",
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       "      <td>v2</td>\n",
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       "    <tr>\n",
       "      <th>SRR8318956_2</th>\n",
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       "      <td>0.167671</td>\n",
       "      <td>152386</td>\n",
       "      <td>5563</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.671515</td>\n",
       "      <td>-0.084333</td>\n",
       "      <td>4.775723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>0.074519</td>\n",
       "      <td>190382</td>\n",
       "      <td>1411</td>\n",
       "      <td>v3</td>\n",
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       "      <td>-0.108573</td>\n",
       "      <td>7.076035</td>\n",
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       "      <th>SRR13377348_2</th>\n",
       "      <td>300</td>\n",
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       "      <td>1390</td>\n",
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       "      <td>0.073968</td>\n",
       "      <td>189563</td>\n",
       "      <td>1390</td>\n",
       "      <td>v3</td>\n",
       "      <td>-0.677989</td>\n",
       "      <td>-0.108676</td>\n",
       "      <td>7.091449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR13377350_2</th>\n",
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       "      <td>542</td>\n",
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       "      <td>0.073080</td>\n",
       "      <td>189691</td>\n",
       "      <td>1383</td>\n",
       "      <td>v3</td>\n",
       "      <td>-0.678121</td>\n",
       "      <td>-0.108731</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1517 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc   v1      v2      v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                         \n",
       "SRR12616291_2    0    0      16       0  0.000000         16             0   \n",
       "SRR12616294_2    0    0      16       0  0.000000         16             0   \n",
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       "SRR8318956_2    64  255  152386    5563  0.167671     152386          5563   \n",
       "...            ...  ...     ...     ...       ...        ...           ...   \n",
       "SRR13377349_2  287  566    1411  190382  0.074519     190382          1411   \n",
       "SRR13377348_2  300  542    1390  189563  0.073968     189563          1390   \n",
       "SRR13377350_2  280  542    1383  189691  0.073080     189691          1383   \n",
       "SRR18098357_1  115  165     975  192125  0.043587     192125           975   \n",
       "SRR18098354_1  125  169     929  192094  0.042863     192094           929   \n",
       "\n",
       "whitelist     max_col       PC1       PC2  nlarg_ratio  \n",
       "name                                                    \n",
       "SRR12616291_2      v2  0.722681 -0.085489          NaN  \n",
       "SRR12616294_2      v2  0.722681 -0.085489          NaN  \n",
       "SRR12616297_2      v2  0.722681 -0.085489          NaN  \n",
       "SRR12616307_2      v2  0.722681 -0.085489          NaN  \n",
       "SRR8318956_2       v2  0.671515 -0.084333     4.775723  \n",
       "...               ...       ...       ...          ...  \n",
       "SRR13377349_2      v3 -0.677856 -0.108573     7.076035  \n",
       "SRR13377348_2      v3 -0.677989 -0.108676     7.091449  \n",
       "SRR13377350_2      v3 -0.678121 -0.108731     7.099706  \n",
       "SRR18098357_1      v3 -0.683132 -0.111646     7.622427  \n",
       "SRR18098354_1      v3 -0.683415 -0.111601     7.691918  \n",
       "\n",
       "[1517 rows x 11 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sample_count_table.sort_values('PC1', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "<p>1517 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "whitelist      arc      v1    v2      v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                          \n",
       "SRR8034299_3     0  171494     0       0  0.000000     171494             0   \n",
       "SRR8034292_3     0  175960     0       0  0.000000     175960             0   \n",
       "SRR8034297_3     0  173268     0       0  0.000000     173268             0   \n",
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       "SRR11007856_3    0  179146     0       0  0.000000     179146             0   \n",
       "...            ...     ...   ...     ...       ...        ...           ...   \n",
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       "SRR12837066_2  391      57  2055  193580  0.075220     193580          2055   \n",
       "SRR18098354_1  125     169   929  192094  0.042863     192094           929   \n",
       "SRR18098357_1  115     165   975  192125  0.043587     192125           975   \n",
       "\n",
       "whitelist     max_col       PC1       PC2  nlarg_ratio  \n",
       "name                                                    \n",
       "SRR8034299_3       v1 -0.008386  1.116623          NaN  \n",
       "SRR8034292_3       v1 -0.008386  1.116623          NaN  \n",
       "SRR8034297_3       v1 -0.008386  1.116623          NaN  \n",
       "SRR8034298_3       v1 -0.008386  1.116623          NaN  \n",
       "SRR11007856_3      v1 -0.008386  1.116623          NaN  \n",
       "...               ...       ...       ...          ...  \n",
       "SRR12455256_1      v3 -0.664059 -0.110572     5.754235  \n",
       "SRR12837065_2      v3 -0.666957 -0.110922     5.906026  \n",
       "SRR12837066_2      v3 -0.674849 -0.111490     6.557648  \n",
       "SRR18098354_1      v3 -0.683415 -0.111601     7.691918  \n",
       "SRR18098357_1      v3 -0.683132 -0.111646     7.622427  \n",
       "\n",
       "[1517 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_sample_count_table.sort_values('PC2', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's estimate the error probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p = 0.9399 +- 0.0064\n"
     ]
    }
   ],
   "source": [
    "estim_df = max_sample_count_table[max_sample_count_table.max_count > 80000]\n",
    "p = (estim_df.max_count / 200000).sum() / estim_df.shape[0]\n",
    "print(f'p = {p:.4f} +- {np.sqrt(p * (1 - p) / estim_df.shape[0]):.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>whitelist</th>\n",
       "      <th>arc</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>secmax_count</th>\n",
       "      <th>max_col</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>nlarg_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR11007864_3</th>\n",
       "      <td>0</td>\n",
       "      <td>114479</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114479</td>\n",
       "      <td>0</td>\n",
       "      <td>v1</td>\n",
       "      <td>-0.008386</td>\n",
       "      <td>1.116623</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR11007872_3</th>\n",
       "      <td>0</td>\n",
       "      <td>115635</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>115635</td>\n",
       "      <td>0</td>\n",
       "      <td>v1</td>\n",
       "      <td>-0.008386</td>\n",
       "      <td>1.116623</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR11007879_3</th>\n",
       "      <td>0</td>\n",
       "      <td>119212</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119212</td>\n",
       "      <td>0</td>\n",
       "      <td>v1</td>\n",
       "      <td>-0.008386</td>\n",
       "      <td>1.116623</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR11007880_3</th>\n",
       "      <td>0</td>\n",
       "      <td>114057</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114057</td>\n",
       "      <td>0</td>\n",
       "      <td>v1</td>\n",
       "      <td>-0.008386</td>\n",
       "      <td>1.116623</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400257_1</th>\n",
       "      <td>107</td>\n",
       "      <td>468</td>\n",
       "      <td>92197</td>\n",
       "      <td>9742</td>\n",
       "      <td>0.350826</td>\n",
       "      <td>92197</td>\n",
       "      <td>9742</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.584228</td>\n",
       "      <td>-0.082136</td>\n",
       "      <td>3.242430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400258_1</th>\n",
       "      <td>126</td>\n",
       "      <td>509</td>\n",
       "      <td>104880</td>\n",
       "      <td>11131</td>\n",
       "      <td>0.350888</td>\n",
       "      <td>104880</td>\n",
       "      <td>11131</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.583792</td>\n",
       "      <td>-0.082372</td>\n",
       "      <td>3.236084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400259_1</th>\n",
       "      <td>106</td>\n",
       "      <td>431</td>\n",
       "      <td>81172</td>\n",
       "      <td>8522</td>\n",
       "      <td>0.351500</td>\n",
       "      <td>81172</td>\n",
       "      <td>8522</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.584806</td>\n",
       "      <td>-0.081804</td>\n",
       "      <td>3.251718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400260_1</th>\n",
       "      <td>122</td>\n",
       "      <td>471</td>\n",
       "      <td>103821</td>\n",
       "      <td>11069</td>\n",
       "      <td>0.350149</td>\n",
       "      <td>103821</td>\n",
       "      <td>11069</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.583418</td>\n",
       "      <td>-0.082738</td>\n",
       "      <td>3.229501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc      v1      v2     v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                           \n",
       "SRR11007864_3    0  114479       0      0  0.000000     114479             0   \n",
       "SRR11007872_3    0  115635       0      0  0.000000     115635             0   \n",
       "SRR11007879_3    0  119212       0      0  0.000000     119212             0   \n",
       "SRR11007880_3    0  114057       0      0  0.000000     114057             0   \n",
       "SRR12400257_1  107     468   92197   9742  0.350826      92197          9742   \n",
       "SRR12400258_1  126     509  104880  11131  0.350888     104880         11131   \n",
       "SRR12400259_1  106     431   81172   8522  0.351500      81172          8522   \n",
       "SRR12400260_1  122     471  103821  11069  0.350149     103821         11069   \n",
       "\n",
       "whitelist     max_col       PC1       PC2  nlarg_ratio  \n",
       "name                                                    \n",
       "SRR11007864_3      v1 -0.008386  1.116623          NaN  \n",
       "SRR11007872_3      v1 -0.008386  1.116623          NaN  \n",
       "SRR11007879_3      v1 -0.008386  1.116623          NaN  \n",
       "SRR11007880_3      v1 -0.008386  1.116623          NaN  \n",
       "SRR12400257_1      v2  0.584228 -0.082136     3.242430  \n",
       "SRR12400258_1      v2  0.583792 -0.082372     3.236084  \n",
       "SRR12400259_1      v2  0.584806 -0.081804     3.251718  \n",
       "SRR12400260_1      v2  0.583418 -0.082738     3.229501  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estim_df[estim_df.max_count < 120000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SRR19752930_1', 'fastq.arc.count']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'SRR19752930_1.fastq.arc.count'.split('.', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_list = lambda path: path.rstrip().split('/')\n",
    "base_to_name = lambda basename: basename.split('.', 1)[0]\n",
    "\n",
    "with open('count_files.txt', 'r') as file:\n",
    "    lines = map(path_to_list, file.readlines())\n",
    "    name_to_dataset = {base_to_name(line[-1]):line[-3] for line in lines}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='max_count', ylabel='Probability'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b2Bhf5vz587Fnzx5s374darUakZGRGDNmDI4ePWq+i3uAixl3GvV5wsPD8cwzz+D8+fOYOHGiVB4XF4cpU6Zg9erVCAoKQmZmJgDA3t4eanXTWwKDiIjIooQVBQYGioiICGlbr9cLb29vERMT89Bj27dvLz744INq5UuXLhW9e/eu8bj8/Hxha2srtm/fLpVdvHhRABCJiYm1jr2goEAAEAUFBdX2lZaWigsXLojS0lKj8p9++kk42KsEAIt9HOxV4qeffqr1dQlx9/vQpk0bAUBcu3ZNKh86dKjJc0yZMuWB7dXUH0RE1Hxdv35dXF/xxN3P9evWDkfyoL/fDc1qd7rKy8uRnJyM6OhoqUwulyMkJASJiYmP1PaVK1fg7e0NlUqF4OBgxMTESCuyJycno6KiwmguUteuXeHr64vExET85je/MdmmTqeTJpcDQGFhYZ3j8vX1xcVLlxv9uxflcjnS09OrlR88eNBMUREREbU8Vku6cnNzodfr4enpaVTu6emJS5cu1bvdoKAgfPbZZ+jSpQsyMjLw5ptv4re//S3OnTsHR0dHZGZmQqlUwtnZudp5q4bLTImJicGbb75Z77iq+Pr68j2IRERELZDVJ9Kb24gRIzB27Fj06tULoaGh2Lt3L/Lz8/H5558/UrvR0dEoKCiQPmlpaWaKmIiIiFoCq93p0mg0UCgU1Z4azMrKgpeXl9nO4+zsjM6dO+Pq1asAAC8vL5SXlyM/P9/obtfDzmtnZwc7OzuzxUVEREQti9XudCmVSvTv3x8JCQlSmcFgQEJCAoKDg812njt37uDatWto06YNAKB///6wtbU1Ou/ly5eh1WrNel4iIiKie1l1yYioqChMmTIFAwYMQGBgIGJjY1FcXIypU6cCACZPnoy2bdsiJiYGwN3J9xcuXJC+vnnzJlJSUtC6dWt06tQJAPCnP/0Jo0aNQvv27ZGeno6lS5dCoVBgwoQJAAC1Wo1p06YhKioKrq6ucHJywuzZsxEcHFzjJHoiIiKiR2XVpGvcuHHIycnBkiVLkJmZiT59+iA+Pl6aXK/Vao1eP5Oeno6+fftK2ytXrsTKlSsxdOhQ6cm6n3/+GRMmTMCtW7fg7u6OIUOG4Pjx43B3d5eO++CDDyCXyxEWFgadTofQ0FB8/PHHlrloIiIiapFkQghh7SCaosLCQqjVahQUFMDJycloX1lZGVJTU+Hv7w+VSmWlCBsP9gcRUcuTmpoKfDHt7sYfP4W/v791A/rFg/5+N7Rm9/QiERERUWNk1eHFlkir1Tb6xVGJiIjI/Jh0WZBWq0XXbl1RWlJqsXPaO9jj0sVLZkm8vv/+eyxcuBCXLl1CSUkJ2rdvj5kzZz7wPZhERER0F5MuC8rNzUVpSSkef+1xqH0a/gXRBWkFOLziMHJzc82SdLVq1QqRkZHo1asXWrVqhe+//x4zZ85Eq1atMGPGDDNETERE1Hwx6bICtY8amsc01g6jmvXr12PZsmX4+eefjZ4afe655+Dm5oYNGzYYPT3q5+eHHTt24MiRI0y6iIiIHoIT6UkyduxY3Lp1CwcOHJDK8vLyEB8fj/Dw8Gr1z5w5g2PHjmHo0KGWDJOIiKhJYtJFEhcXF4wYMQJxcXFS2RdffAGNRoMnnnhCKmvXrh3s7OwwYMAARERE4P/+7/+sES4REVGTwqSLjISHh+PLL7+ETqcDAGzevBnjx483Gm48cuQITp06hXXr1iE2NhZbtmyxVrhERERNBud0kZFRo0ZBCIE9e/Zg4MCBOHLkCD744AOjOlUL3AUEBCArKwvLli2TXrNEREREpjHpIiMqlQpjxozB5s2bcfXqVXTp0gX9+vWrsb7BYJDuihEREVHNmHRRNeHh4XjmmWdw/vx5TJw4USpfu3YtfH190bVrVwDA4cOHsXLlSsyZM8daoRIRETUZTLqsoCCtoFGf53e/+x1cXV1x+fJlvPDCC1K5wWBAdHQ0UlNTYWNjg44dO+Ldd9/FzJkzzRUyERFRs8Wky4I0Gg3sHexxeMVhi53T3sEeGk3d1gSTy+VIT0+vVj579mzMnj3bXKERERG1KEy6LMjX1xeXLl7iuxeJiIhaICZdFubr68skiIiIqAXiOl1EREREFsCki4iIiMgCmHQ1ICGEtUNoFNgPRERETLoahK2tLQCgpKTEypE0DlX9UNUvRERELREn0jcAhUIBZ2dnZGdnAwAcHBwgk8msHJXlCSFQUlKC7OxsODs7Q6FQWDskIiIiq2HS1UC8vLwAQEq8WjJnZ2epP4iIiFoqJl0NRCaToU2bNvDw8EBFRYW1w7EaW1tb3uEiIiICk64Gp1AomHQQERERJ9ITERERWQKTLiIiIiILYNJFREREZAFMuoiIiIgsgEkXERERkQUw6SIiIiKyACZdRERERBbApIuIiIjIAph0EREREVkAV6QnIiIis9Dr9Th69CgAICMjAwOFgEwms3JUjQeTLiIiIjILrVaLjC1z0cZZhWxtAXTdnKGyU1k7rEaDSRcRERGZTWAHF/i7O1g7jEaJc7qIiIiILIBJFxEREZEFMOkiIiIisgAmXUREREQWwKSLiIiIyAKsnnStXbsWfn5+UKlUCAoKQlJSUo11z58/j7CwMPj5+UEmkyE2NrZanZiYGAwcOBCOjo7w8PDA6NGjcfnyZaM6w4YNg0wmM/q8/PLL5r40IiIiIolVk65t27YhKioKS5cuxenTp9G7d2+EhoYiOzvbZP2SkhJ06NABy5cvh5eXl8k6hw4dQkREBI4fP459+/ahoqICTz31FIqLi43qTZ8+HRkZGdJnxYoVZr8+IiIioipWXadr1apVmD59OqZOnQoAWLduHfbs2YMNGzbg9ddfr1Z/4MCBGDhwIACY3A8A8fHxRtufffYZPDw8kJycjMcff1wqd3BwqDFxM0Wn00Gn00nbhYWFtT6WiIiIyGp3usrLy5GcnIyQkJBfg5HLERISgsTERLOdp6CgAADg6upqVL5582ZoNBr07NkT0dHRKCkpeWA7MTExUKvV0sfHx8dsMRIREVHzZ7U7Xbm5udDr9fD09DQq9/T0xKVLl8xyDoPBgHnz5mHw4MHo2bOnVP7CCy+gffv28Pb2xtmzZ7Fw4UJcvnwZO3bsqLGt6OhoREVFSduFhYVMvIiIiKjWmvVrgCIiInDu3Dl8//33RuUzZsyQvg4ICECbNm3w5JNP4tq1a+jYsaPJtuzs7GBnZ9eg8RIREVHzZbXhRY1GA4VCgaysLKPyrKysOs21qklkZCR2796NAwcOoF27dg+sGxQUBAC4evXqI5+XiIiIyBSrJV1KpRL9+/dHQkKCVGYwGJCQkIDg4OB6tyuEQGRkJP7zn//gu+++g7+//0OPSUlJAQC0adOm3uclIiIiehCrDi9GRUVhypQpGDBgAAIDAxEbG4vi4mLpacbJkyejbdu2iImJAXB38v2FCxekr2/evImUlBS0bt0anTp1AnB3SDEuLg67du2Co6MjMjMzAQBqtRr29va4du0a4uLi8PTTT8PNzQ1nz57F/Pnz8fjjj6NXr15W6AUiIiJqCayadI0bNw45OTlYsmQJMjMz0adPH8THx0uT67VaLeTyX2/Gpaeno2/fvtL2ypUrsXLlSgwdOhQHDx4EAHzyyScA7i6Aeq+NGzfixRdfhFKpxP79+6UEz8fHB2FhYVi8eHHDXiwRERG1aDIhhLB2EE1RYWEh1Go1CgoK4OTkZO1wiIiIrC41NRX4Yhr83R1w+PItuLeSQWWnAv74aa2m+1iCNf9+W/01QEREREQtAZMuIiIiIgtg0kVERERkAUy6iIiIiCyASRcRERGRBTDpIiIiIrIAJl1EREREFsCki4iIiMgCmHQRERERWQCTLiIiIiILYNJFREREZAFMuoiIiIgsgEkXERERkQUw6SIiIiKyACZdRERERBbApIuIiIjIAph0EREREVkAky4iIiIiC2DSRURERGQBTLqIiIiILIBJFxEREZEF2Fg7ACIiImq+9AaB5KQkpKWlAQAGDx4MhUJh5aisg0kXERERNZj0/DLo9r0FuDshI78MWp8d8Pf3t3ZYVsGki4iIiBrUQD81urVzRWpOibVDsSrO6SIiIiKyACZdRERERBZQr6TrwIED5o6DiIiIqFmrV9I1fPhwdOzYEX/5y1+kpxGIiIiIqGb1Srpu3ryJyMhIfPHFF+jQoQNCQ0Px+eefo7y83NzxERERETUL9Uq6NBoN5s+fj5SUFJw4cQKdO3fGrFmz4O3tjTlz5uCHH34wd5xERERETdojT6Tv168foqOjERkZiTt37mDDhg3o378/fvvb3+L8+fPmiJGIiIioyat30lVRUYEvvvgCTz/9NNq3b49vvvkGa9asQVZWFq5evYr27dtj7Nix5oyViIiIqMmq1+Kos2fPxpYtWyCEwKRJk7BixQr07NlT2t+qVSusXLkS3t7eZguUiIiIqCmrV9J14cIFfPTRRxgzZgzs7OxM1tFoNFxagoiIiOgX9RpeXLp0KcaOHVst4aqsrMThw4cBADY2Nhg6dOijR0hERETUDNQr6XriiSeQl5dXrbygoABPPPHEIwdFRERE1NzUK+kSQkAmk1Urv3XrFlq1avXIQRERERE1N3Wa0zVmzBgAgEwmw4svvmg0vKjX63H27FkMGjTIvBESERERNQN1SrrUajWAu3e6HB0dYW9vL+1TKpX4zW9+g+nTp5s3QiIiIqJmoE7Dixs3bsTGjRuxdOlSfPrpp9L2xo0b8be//Q3R0dHQaDR1CmDt2rXw8/ODSqVCUFAQkpKSaqx7/vx5hIWFwc/PDzKZDLGxsfVqs6ysDBEREXBzc0Pr1q0RFhaGrKysOsVNREREVBf1fnrRHHO3tm3bhqioKCxduhSnT59G7969ERoaiuzsbJP1S0pK0KFDByxfvhxeXl71bnP+/Pn473//i+3bt+PQoUNIT0+Xhk6JiIiIGoJMCCFqU7Ffv35ISEiAi4sL+vbta3IifZXTp0/X6uRBQUEYOHAg1qxZAwAwGAzw8fHB7Nmz8frrrz/wWD8/P8ybNw/z5s2rU5sFBQVwd3dHXFwc/vjHPwIALl26hG7duiExMRG/+c1vahV7YWEh1Go1CgoK4OTkVKtjiIiImrPU1FTgi2nwd3fA4cu34N5KhpxiAfdWMnRr54rUnBLgj5/C39/fajFa8+93red0Pffcc9LE+dGjRz/yicvLy5GcnIzo6GipTC6XIyQkBImJiQ3WZnJyMioqKhASEiLV6dq1K3x9fR+YdOl0Ouh0Omm7sLCwXjESERFRy1TrpGvp0qUmv66v3Nxc6PV6eHp6GpV7enri0qVLDdZmZmYmlEolnJ2dq9XJzMysse2YmBi8+eab9YqLiIiIqN4vvG5poqOjUVBQIH3S0tKsHRIRERE1IbW+0+Xi4vLAeVz3MrVa/f00Gg0UCkW1pwazsrJqnCRvjja9vLxQXl6O/Px8o7tdDzuvnZ1dje+ZJCIiInqYWiddNS3PUF9KpRL9+/dHQkKCNEfMYDAgISEBkZGRDdZm//79YWtri4SEBISFhQEALl++DK1Wi+Dg4Ee+LiIiIiJTap10TZkyxewnj4qKwpQpUzBgwAAEBgYiNjYWxcXFmDp1KgBg8uTJaNu2LWJiYgDcnSh/4cIF6eubN28iJSUFrVu3RqdOnWrVplqtxrRp0xAVFQVXV1c4OTlh9uzZCA4OrvWTi0RERER1Veukq7CwUHq08mFP7tX2Ecxx48YhJycHS5YsQWZmJvr06YP4+HhpIrxWq4Vc/uu0s/T0dPTt21faXrlyJVauXImhQ4fi4MGDtWoTAD744API5XKEhYVBp9MhNDQUH3/8ca1iJiIiIqqPWq/TpVAokJGRAQ8PD8jlcpPzu6pehK3X680eaGPDdbqIiIiMcZ2uB6v1na7vvvsOrq6uAIADBw40WEBEREREzVGtk66hQ4ea/JqIiIiIHq7WSdf9bt++jU8//RQXL14EAHTv3h1Tp06V7oYRERER0a/qtTjq4cOH4efnhw8//BC3b9/G7du38eGHH8Lf3x+HDx82d4xERERETV697nRFRERg3Lhx+OSTT6BQKAAAer0es2bNQkREBP73v/+ZNUgiIiKipq5ed7quXr2KV199VUq4gLtPN0ZFReHq1atmC46IiIiouahX0tWvXz9pLte9Ll68iN69ez9yUERERETNTa2HF8+ePSt9PWfOHMydOxdXr16VVnE/fvw41q5di+XLl5s/SiIiIqImrtZJV58+fSCTyXDvWqqvvfZatXovvPACxo0bZ57oiIiIiJqJWiddqampDRkHERERUbNW66Srffv2DRkHERERNSNCCFzPuoNbpQZoHPi6POARFkcFgAsXLkCr1aK8vNyo/Nlnn32koIiIiKhpc3OQ4bVCB0AJbLR2MI1EvZKu69ev4w9/+AP+97//Gc3zqnoJdkt44TURERHVTCGXwa2DGwBAVpZv3WAaiXotGTF37lz4+/sjOzsbDg4OOH/+PA4fPowBAwbg4MGDZg6RiIiIqOmr152uxMREfPfdd9BoNJDL5ZDL5RgyZAhiYmIwZ84cnDlzxtxxEhERETVp9brTpdfr4ejoCADQaDRIT08HcHey/eXLl80XHREREVEzUa87XT179sQPP/wAf39/BAUFYcWKFVAqlVi/fj06dOhg7hiJiIiImrx6JV2LFy9GcXExAOCtt97CM888g9/+9rdwc3PDtm3bzBogERERUXNQr6QrNDRU+rpTp064dOkS8vLy4OLiIj3BSERERES/eqR1ugAgLS0NAODj4/PIwRARERE1V/WaSF9ZWYk33ngDarUafn5+8PPzg1qtxuLFi1FRUWHuGImIiIiavHrd6Zo9ezZ27NiBFStWIDg4GMDdZSSWLVuGW7du4ZNPPjFrkERERERNXb2Srri4OGzduhUjRoyQynr16gUfHx9MmDCBSRcRERHRfeo1vGhnZwc/P79q5f7+/lAqlY8aExEREVGzU6+kKzIyEm+//TZ0Op1UptPp8Ne//hWRkZFmC46IiIiouaj18OKYMWOMtvfv34927dqhd+/eAIAffvgB5eXlePLJJ80bIREREVEzUOukS61WG22HhYUZbXPJCCIiIqKa1Trp2rhxY0PGQURERNSsPdLiqDk5OdILrrt06QJ3d3ezBEVERETNj94gkJyUhLS0NAwePBgKhcLaIVlUvSbSFxcX46WXXkKbNm3w+OOP4/HHH4e3tzemTZuGkpISc8dIREREzUB6fhl0+95Cxpa50Gq11g7H4uqVdEVFReHQoUP473//i/z8fOTn52PXrl04dOgQXn31VXPHSERERM3EQD81Aju4WDsMq6jX8OKXX36JL774AsOGDZPKnn76adjb2+P555/n4qhERERE96nXna6SkhJ4enpWK/fw8ODwIhEREZEJ9Uq6goODsXTpUpSVlUllpaWlePPNN6V3MRIRERHRr+o1vBgbG4vhw4dXWxxVpVLhm2++MWuARERERM1BvZKugIAAXLlyBZs3b8alS5cAABMmTEB4eDjs7e3NGiARERFRc1DnpKuiogJdu3bF7t27MX369IaIiYiIiKjZqfOcLltbW6O5XERERET0cPWaSB8REYF3330XlZWV5o6HiIiIqFmqV9J18uRJ7NixA76+vggNDcWYMWOMPnW1du1a+Pn5QaVSISgoCElJSQ+sv337dnTt2hUqlQoBAQHYu3ev0X6ZTGby895770l1/Pz8qu1fvnx5nWMnIiIiqo16TaR3dnZGWFiYWQLYtm0boqKisG7dOgQFBSE2NhahoaG4fPkyPDw8qtU/duwYJkyYgJiYGDzzzDOIi4vD6NGjcfr0afTs2RMAkJGRYXTM119/jWnTplWL+a233jKal+bo6GiWayIiIiK6X52SLoPBgPfeew8//vgjysvL8bvf/Q7Lli17pCcWV61ahenTp2Pq1KkAgHXr1mHPnj3YsGEDXn/99Wr1V69ejeHDh2PBggUAgLfffhv79u3DmjVrsG7dOgCAl5eX0TG7du3CE088gQ4dOhiVOzo6VqtLRERE1BDqNLz417/+FYsWLULr1q3Rtm1bfPjhh4iIiKj3ycvLy5GcnIyQkJBfA5LLERISgsTERJPHJCYmGtUHgNDQ0BrrZ2VlYc+ePZg2bVq1fcuXL4ebmxv69u2L995774Fz1HQ6HQoLC40+RERERLVVpztd//znP/Hxxx9j5syZAID9+/dj5MiR+Mc//gG5vO7Tw3Jzc6HX66u9UsjT01Na/+t+mZmZJutnZmaarL9p0yY4OjpWm2s2Z84c9OvXD66urjh27Biio6ORkZGBVatWmWwnJiYGb775Zm0vjYiIiMhInZIurVaLp59+WtoOCQmBTCZDeno62rVrZ/bgzGHDhg0IDw+HSqUyKo+KipK+7tWrF5RKJWbOnImYmBjY2dlVayc6OtromMLCQvj4+DRc4ERERNSs1CnpqqysrJa82NraoqKiol4n12g0UCgUyMrKMirPysqqca6Vl5dXresfOXIEly9fxrZt2x4aS1BQECorK3Hjxg106dKl2n47OzuTyRgRERFRbdQp6RJC4MUXXzRKPsrKyvDyyy+jVatWUtmOHTtq1Z5SqUT//v2RkJCA0aNHA7g7WT8hIQGRkZEmjwkODkZCQgLmzZsnle3bt8/ki7Y//fRT9O/fX3o/5IOkpKRALpebfGKSiIiI6FHVKemaMmVKtbKJEyc+UgBRUVGYMmUKBgwYgMDAQMTGxqK4uFh6mnHy5Mlo27YtYmJiAABz587F0KFD8f7772PkyJHYunUrTp06hfXr1xu1W1hYiO3bt+P999+vds7ExEScOHECTzzxBBwdHZGYmIj58+dj4sSJcHFxeaTrISIiIjKlTknXxo0bzR7AuHHjkJOTgyVLliAzMxN9+vRBfHy8NFleq9UaTdIfNGgQ4uLisHjxYixatAiPPfYYdu7cKa3RVWXr1q0QQmDChAnVzmlnZ4etW7di2bJl0Ol08Pf3x/z5843mbBERERGZk0wIIawdRFNUWFgItVqNgoICODk5WTscIiIiq0tNTQW+mAZ/dwdc/DkPr6qcAQDvl+WjWztXHL58C+6tZFDZqYA/fgp/f3+Lx2jNv9/1eg0QEREREdUNky4iIiIiC2DSRURERGQBTLqIiIiILIBJFxEREZEFMOkiIiIisgAmXUREREQWwKSLiIiIyAKYdBERERFZAJMuIiIiIgtg0kVERERkAUy6iIiIiCzAxtoBEBERUdOj1+tx9OhRaXvw4MFWjKZpYNJFREREdabVapGxZS7aOKuQkV8Grc8Oa4fU6DHpIiIionoJ7OACf3cHpOaUWDuUJoFzuoiIiIgsgEkXERERkQUw6SIiIiKyAM7pIiIiIrPQ6/VIzcxHma4M17OKIHzVkMlk1g6r0WDSRURERGbzVzsnOKgccEuvh0drG+iL9dYOqdFg0kVERERmoVAo4N7FHY5ejgAAmZx3ue7FOV1EREREFsCki4iIiMgCmHQRERERWQCTLiIiIiILYNJFREREZAFMuoiIiIgsgEkXERERkQUw6SIiIiKyACZdRERERBbAFemJiIiowQiDwPWsIgDAjZxCaBycrByR9TDpIiIiogb1jtIJapUat5R6bLR2MFbEpIuIiIgajEwug1tnN2j8NHe3y/KtG5AVcU4XERERkQXwThcRERE9Er1BIDkpCcDdOVxkGpMuIiIieiTp+WXQ7XsLAFDStT2cvFvuZPkHYdJFREREj2ygnxoAsLUWdavujKWlpQEABg8eDIVC0YDRNQ5MuoiIiMiipDtj7k7IyC+D1mcH/P39rR1Wg2PSRURERBY30E+Nbu1ckZpTYu1QLKZRPL24du1a+Pn5QaVSISgoCEm/TMaryfbt29G1a1eoVCoEBARg7969RvtffPFFyGQyo8/w4cON6uTl5SE8PBxOTk5wdnbGtGnTcOfOHbNfGxERERHQCJKubdu2ISoqCkuXLsXp06fRu3dvhIaGIjs722T9Y8eOYcKECZg2bRrOnDmD0aNHY/To0Th37pxRveHDhyMjI0P6bNmyxWh/eHg4zp8/j3379mH37t04fPgwZsyY0WDXSURERC2b1ZOuVatWYfr06Zg6dSq6d++OdevWwcHBARs2bDBZf/Xq1Rg+fDgWLFiAbt264e2330a/fv2wZs0ao3p2dnbw8vKSPi4uLtK+ixcvIj4+Hv/4xz8QFBSEIUOG4KOPPsLWrVuRnp7eoNdLRERELZNVk67y8nIkJycjJCREKpPL5QgJCUFiYqLJYxITE43qA0BoaGi1+gcPHoSHhwe6dOmCV155Bbdu3TJqw9nZGQMGDJDKQkJCIJfLceLECZPn1el0KCwsNPoQERER1ZZVk67c3Fzo9Xp4enoalXt6eiIzM9PkMZmZmQ+tP3z4cPzzn/9EQkIC3n33XRw6dAgjRoyAXq+X2vDw8DBqw8bGBq6urjWeNyYmBmq1Wvr4+PjU+XqJiIio5WqWTy+OHz9e+jogIAC9evVCx44dcfDgQTz55JP1ajM6OhpRUVHSdmFhIRMvIiIiqjWr3unSaDRQKBTIysoyKs/KyoKXl5fJY7y8vOpUHwA6dOgAjUaDq1evSm3cP1G/srISeXl5NbZjZ2cHJycnow8RERFRbVk16VIqlejfvz8SEhKkMoPBgISEBAQHB5s8Jjg42Kg+AOzbt6/G+gDw888/49atW2jTpo3URn5+PpKTk6U63333HQwGA4KCgh7lkoiIiIhMsvrTi1FRUfj73/+OTZs24eLFi3jllVdQXFyMqVOnAgAmT56M6Ohoqf7cuXMRHx+P999/H5cuXcKyZctw6tQpREZGAgDu3LmDBQsW4Pjx47hx4wYSEhLw3HPPoVOnTggNDQUAdOvWDcOHD8f06dORlJSEo0ePIjIyEuPHj4e3t7flO4GIiIiaPavP6Ro3bhxycnKwZMkSZGZmok+fPoiPj5cmy2u1Wsjlv+aGgwYNQlxcHBYvXoxFixbhsccew86dO9GzZ08AgEKhwNmzZ7Fp0ybk5+fD29sbTz31FN5++23Y2dlJ7WzevBmRkZF48sknIZfLERYWhg8//NCyF09EREQthtWTLgCIjIyU7lTd7+DBg9XKxo4di7Fjx5qsb29vj2+++eah53R1dUVcXFyd4iQiIiKqL6sPLxIRERG1BEy6iIiIiCyASRcRERGRBTDpIiIiIrKARjGRnoiIiBovvV6Po0ePSq/TUygUyMjIwEAhrBxZ08Kki4iIiB5Iq9UiY8tcZBfqoLaXwc/dCdnaAui6OQNoZe3wmgwmXURERPRQgR1ckJZXCvdWMnRr52rtcJokzukiIiIisgAmXUREREQWwKSLiIiIyAKYdBERERFZAJMuIiIiIgtg0kVERERkAUy6iIiIiCyASRcRERGRBTDpIiIiIrIArkhPREREdSaEwPWsOwCAGzmF0Dg4QSaTWTmqxo1JFxEREVVT9ZJrACZfbu3mIMNrhQ5wUznjllKPjdYIsolh0kVERETVVL3kuo2z6p6XW/9KIZfBrYMbvAK8AACysnzLB9nEMOkiIiIikwI7uMDf3cHaYTQbTLqIiIjIou6dD/bz7TL46/VWjsgymHQRERGRRd07H6zErgT/sHZAFsKki4iIiCzq3vlgRZlFUCgU1g7JIrhOFxEREZEFMOkiIiIisgAmXUREREQWwKSLiIiIyAKYdBERERFZAJMuIiIiIgtg0kVERERkAUy6iIiIiCyASRcRERGRBTDpIiIiIrIAvgaIiIiILEIYBK5nFd392ldt5Wgsj0kXERERWcw7SidUllbCo3XLS0Fa3hUTERGRVcjkMrh1dkNlUSVkcpm1w7E4zukiIiIisgAmXUREREQWwKSLiIiIyAKYdBERERFZQKNIutauXQs/Pz+oVCoEBQUhKSnpgfW3b9+Orl27QqVSISAgAHv37pX2VVRUYOHChQgICECrVq3g7e2NyZMnIz093agNPz8/yGQyo8/y5csb5PqIiIiIrJ50bdu2DVFRUVi6dClOnz6N3r17IzQ0FNnZ2SbrHzt2DBMmTMC0adNw5swZjB49GqNHj8a5c+cAACUlJTh9+jTeeOMNnD59Gjt27MDly5fx7LPPVmvrrbfeQkZGhvSZPXt2g14rERFRc1S1/tb1rCIIIawdTqNl9SUjVq1ahenTp2Pq1KkAgHXr1mHPnj3YsGEDXn/99Wr1V69ejeHDh2PBggUAgLfffhv79u3DmjVrsG7dOqjVauzbt8/omDVr1iAwMBBarRa+vr5SuaOjI7y8vGoVp06ng06nk7YLCwvrfK1ERETNVUtef6u2rHqnq7y8HMnJyQgJCZHK5HI5QkJCkJiYaPKYxMREo/oAEBoaWmN9ACgoKIBMJoOzs7NR+fLly+Hm5oa+ffvivffeQ2VlZY1txMTEQK1WSx8fH59aXCEREVHzV7X+llsHtxa5/lZtWTUdzc3NhV6vh6enp1G5p6cnLl26ZPKYzMxMk/UzMzNN1i8rK8PChQsxYcIEODk5SeVz5sxBv3794OrqimPHjiE6OhoZGRlYtWqVyXaio6MRFRUlbRcWFjLxIiIiolpr1vcAKyoq8Pzzz0MIgU8++cRo370JVK9evaBUKjFz5kzExMTAzs6uWlt2dnYmy4mIiJo7IQSuZ93BrVIDNA5ODz+ATLLq8KJGo4FCoUBWVpZReVZWVo1zrby8vGpVvyrh+umnn7Bv3z6ju1ymBAUFobKyEjdu3Kj7hRARETVjbg4yvKV3wFpla2uH0qRZNelSKpXo378/EhISpDKDwYCEhAQEBwebPCY4ONioPgDs27fPqH5VwnXlyhXs378fbm5uD40lJSUFcrkcHh4e9bwaIiKi5kkhl8Gtwy9ztmScs1VfVh9ejIqKwpQpUzBgwAAEBgYiNjYWxcXF0tOMkydPRtu2bRETEwMAmDt3LoYOHYr3338fI0eOxNatW3Hq1CmsX78ewN2E649//CNOnz6N3bt3Q6/XS/O9XF1doVQqkZiYiBMnTuCJJ56Ao6MjEhMTMX/+fEycOBEuLi7W6QgiIiJq1qyedI0bNw45OTlYsmQJMjMz0adPH8THx0uT5bVaLeTyX2/IDRo0CHFxcVi8eDEWLVqExx57DDt37kTPnj0BADdv3sRXX30FAOjTp4/RuQ4cOIBhw4bBzs4OW7duxbJly6DT6eDv74/58+cbzfMiIiIiMierJ10AEBkZicjISJP7Dh48WK1s7NixGDt2rMn6fn5+D12YrV+/fjh+/Hid4yQiIiKqL6uvSE9ERETUEjSKO11ERETUuOj1eqRm5qNMV3b39T6+ak6if0RMuoiIiMikv9o5wUHlgFt6PTxa20BfrLd2SE0aky4iIiKqRqFQwL2LOxy9HAGAr/cxA87pIiIiIrIAJl1EREREFsCki4iIiMgCmHQRERERWQAn0hMREbUger0eR48eBQAMHjwYCoXCyhG1HEy6iIiIWhCtVouMLXPvfu2zA/7+/laOqOVg0kVERNTCBHZwqXFf1Z2wjIwMCMODX6tnDsIgkJSUhLS0NADN++4bky4iIiKSVN0J01WUo6Rrezh5O0n7hEHgelYRAEir1D+qklsl0B19C3B3QkZ+WbO++8aki4iIiIwEdnBBma4MW03se0fpBLVKLa1Sbw4D/dTo1s4VqTklZmmvsWLSRURERLUik8vg1tkNGj+NtE21xyUjiIiIiCyASRcRERGRBTDpIiIiIrIAzukiIiJq4e5dMDUjIwMDRcMvFdESMekiIiJq4aqWiWjjrEK2tgC6bs7WDqlZYtJFRERECOzgAn93B2uH0axxThcRERGRBfBOFxERUQukNwgk//L6Hc7jsgwmXURERC1Qen4ZdPvuvn7n13lcrawdVrPGpIuIiKiZq+npxKrX75BlMOkiIiJq5vh0YuPApIuIiKgF4NOJ1seki4iIqAUSQuB61h0AwI2cQmgcnKwcUfPHpIuIiMjM9Ho9tFotAMDX1xcKhcLKEf2qKtnSGwT+YnCARuWMW0o9NlorHoPA9awiAMDPt8vgr9dbKZKGx6SLiIjIzLRaLaat/RoA8GnECPj7+1s5ol+5OcjwWuHdYUbPXh5w7+AOAJCV5VstpneUTlCr1CixK8E/rBZFw2PSRURE1AAcXD3rddy9d8mAut8pu/dJRQAYPHiw0X6FXAa3Dm4AAJlcVu34e4cdha+6TrHXh0wug1tnN2j8NCjKLGpUdwXNjUkXERGRmen1euRnpv3yde86HVt1l8zB1RMleVl1vlN275OKGfll0PrsqNP5770T5tGaaYI5sTeJiIgawOyKTb989Uydj3Vw9URrjXe9z131pGJqTkmdj33YnTCqPyZdREREdfSwIUCFQoHADi5VG2Y/9/3Dh486JHfvZPbrWUUQvmrIZEy4zI1JFxERNWv1mSNV0zFV5WlpaVj21bmHDgHqDQLpaWl1Pnd+Zhp0Oh1Kb2dXG540NXzo6+trcsX5uqiazH5Lr4dHaxvoi5vvU4TWwqSLiIiatfrMkarpmKry0oJctPbu9NAhwPT8Miz64jScvXLqND9rdsUmtClXIaOiDKaGJ+8fPjS94nwr6aXWer0emox8lOnKTN7Juncye9U2mR+TLiIiavbqM0eqpmPq+lSivYtHnc5dNTQpJVW1HDo0teJ81UutDQaBN1SOcFM5806WFTHpIiIis3rUJQ+s6d7hw6oROmHQI+2XIcIbN24gPzMNZQW34Oygrra/ttd677ys2s7JqjqmrsOHA/3uxvm5yhleAV4AGu+dLGEQSEpKQlpamlnmqjU2TLqIyKoaYlIwPboHraj+sKQqNTUV497aCJWTK8oK87BtyVR06tSp1ucz1WZDuvfcVXO1SgtyYVDYo7xch9s3LmJRdjqcvXJwO/0njL/9D+gNAnFZUyGTyaT9SuW5asOHeoNAYdZNADCan1U1HAgAWp8dtRpyrDomu1AnDR82N8U5xbiW+Dp+srGBt/fXD/25aWqYdBGRVZmaFNyYVu9uqR60onpt5ki91mov2jiqkKEvAzC11uer79pUj+Lec99KPY/W3p3Qyt4Jz11/B71d1PihsgC7nP+M1hpv6HQ69HZUQ28QcL21CX7lTtJ+lUplsv2Iys/gV+5UbX6W9HRjHQR2cEFaXml9L7VJ2NPBG7Z2thhj7UAagNzaAQDA2rVr4efnB5VKhaCgICQlJT2w/vbt29G1a1eoVCoEBARg7969RvuFEFiyZAnatGkDe3t7hISE4MqVK0Z18vLyEB4eDicnJzg7O2PatGm4c+eO2a+NqD70ej1SU1ORmpoKvZnfQ3Zv2w3Rfn0EdnDB413c0N/PGUlJSTh8+DAOHz4MvV4PvV4vbVeV0cOZ6reaymr6eXBw9YS9swZpaWlG+/V6PcrLy6HT6VBeXl7te1I1J6nqe5qenm7yZ+3ec6elpcHe5e4cqvqu5F5f915PRXkFhBCQyeXo7avG413c0NvE8glyGeBmL4d7KxnUKhlu3fwJOdrr1ftCLsNAv7vtBHZweeDdu6rvT1JSEsQvw4f6X4bb7i+Xjvllf2JiIq5k5OPiz3m4kVMo1bu7unzR3cnz9Xii0dKqJvS7d3Fvlne8rX6na9u2bYiKisK6desQFBSE2NhYhIaG4vLly/Dw8KhW/9ixY5gwYQJiYmLwzDPPIC4uDqNHj8bp06fRs2dPAMCKFSvw4YcfYtOmTfD398cbb7yB0NBQXLhwQfo/kfDwcGRkZGDfvn2oqKjA1KlTMWPGDMTFxVn0+u/XVIZa6jMfoTGoqX8b2/WYustgrsferXlH4f7Y7p+fUjXpF+5ORitpN4Y7Yab6EoBUVvXHVqFQPNLQWNV57m3v/q/T7lmCoOo89//c1rQq+f1ler3+16HAglt4dURPtGvXDj///DPyM7NRVnALkedOwVHjKe2XyWSILN8E73IVftaVIinJA+np6VKc2dnZ0vc0La8U8z7ZDRtb22rDjPcOQxbcvAYHDz+Ul5teJsHU7+i93xNT/VIVT0ZGBvIzs6stwVDV5s2bN6Xr+aGyALvwZ+Nz3zNEWJh1E8JZSKu2u6mckWtbCVnFFxCwQVraACgUCqSlpcHbIIxeqfPTrVLkJSYiLS1N+rk3CCD5lzlMGRkZEAeWI6fo1+HDe38nfn0q8VemJsrn2lRiaVYRZDIZCoru4C8GB8hkMq4u3whY/TuwatUqTJ8+HVOn3r39vG7dOuzZswcbNmzA66+/Xq3+6tWrMXz4cCxYsAAA8Pbbb2Pfvn1Ys2YN1q1bByEEYmNjsXjxYjz33HMAgH/+85/w9PTEzp07MX78eFy8eBHx8fE4efIkBgwYAAD46KOP8PTTT2PlypXw9q7/KsCPqqkMtdRnPkJjUFP/Nsbruf//9s312HtV24+y2rU51DQ/ZaCfGt3auRqtpP0oq2ubS019ee+wlMLeCUql8pES2XuXJFDYO8HZy0dq29nLB/mZaRh3az0AQG0vg989Cer95zTVb6bKqoYCf7hdgPL9OwFPNXJu5GNGKzlulxugdpChg6Na2l+kA0K6OaNbO1ccvnwLN777K+DuhHPaAqjtZSgoFUbf01db7YGdrRKmhhmrzn1GlQ91hQwdytUml0kw9Tt67/fEVL9UxXO72IAZreTwa2U8xFfVZm6hTroeAPjKxKKgVUOEPxQVwM1bLa3aXjUx3cbRBpVFlXjjPz/A2SsP+ZlpeKdVmVFydkunR0TCXwGPXxOonGJhlFSFdHNGTrHxMGXV70RN7p8oLwwC72TZ3l1zS+jh2csD+mJ9o50835JYNekqLy9HcnIyoqOjpTK5XI6QkBAkJiaaPCYxMRFRUVFGZaGhodi5cyeAu//nlJmZiZCQEGm/Wq1GUFAQEhMTMX78eCQmJsLZ2VlKuAAgJCQEcrkcJ06cwB/+8Idq59XpdNDpdNJ2QUEBAKCwsLDuF/4ARUVFsFMYYK/Qw05hwPbt2+Hu7m7Wc5hDTk4OOisMANBoYzSlKu77+7exXU9OTg4yr+p+ieeaFGNhng46vQy6glu1itPUMQCQeVUHu5xs6ApypfYtrarPlQoDzqXl405ZJS7eLESWvRx3yipxM1+HH3+Jt7P2FnIL7khl1orXVF9Wld3Jz4dcVwlbG9tH+hmSzlN0tz2hsJPavvt1HpS//KzaK+Q1/luRk5NTrd+A6n0JQPqdUCoMaGV7t02VrYC9QqDUVqCVrcxov67S+Hvmai+X9tsr5Ci953v6Y8bd/bYKA3bs2FE9xl/OrbrnPHYPqAvA6Pf21/6v3i9SPL9cy/1t1/QzmI1LAICLuA17hV66hqo2L9785d//1nIoWytRmFEImwIbVJZUoig/T/o+Xcy/fffaW7tC2VoJW3tb2OuF1M65tHzklRqM2q4qq/o9uPd3ouprU/vvjacoqwi2DrbSOYvSi1BZUgmbAhvIhdwo3vvLHrbfXMc8rB1bO1sUFRWZ/W8s8OvfbWsMt1o16crNzYVer4enp/H/0Xt6euLSpUsmj8nMzDRZPzMzU9pfVfagOvcPXdrY2MDV1VWqc7+YmBi8+eab1cp9fHxqujwzOdvA7ZtDU4ixJqZib1zXk1zH8rq2VZ92LKfxfn8e1G8N3acvmyx9WL80hr6sy/keVNf0PtP9Up843q5je/f6dfqCqeOPmigj03q/UbcXhddVUVER1Gp1g57jflYfXmwqoqOjje6wGQwG5OXlwc3N7ZHeT1VYWAgfHx+kpaXBycnJHKG2GOy7+mG/1R/7rv7Yd/XHvqs/U30nhEBRUZFVphJZNenSaDRQKBTIysoyKs/KyoKXl5fJY7y8vB5Yv+q/WVlZaNOmjVGdPn36SHWys7ON2qisrEReXl6N57Wzs4OdnZ1RmbOz84MvsA6cnJz4y1RP7Lv6Yb/VH/uu/th39ce+q7/7+87Sd7iqWHXJCKVSif79+yMhIUEqMxgMSEhIQHBwsMljgoODjeoDwL59+6T6/v7+8PLyMqpTWFiIEydOSHWCg4ORn5+P5ORfBwG+++47GAwGBAUFme36iIiIiKpYfXgxKioKU6ZMwYABAxAYGIjY2FgUFxdLTzNOnjwZbdu2RUxMDABg7ty5GDp0KN5//32MHDkSW7duxalTp7B+/d2nVmQyGebNm4e//OUveOyxx6QlI7y9vTF69GgAQLdu3TB8+HBMnz4d69atQ0VFBSIjIzF+/HirPrlIREREzZfVk65x48YhJycHS5YsQWZmJvr06YP4+HhpIrxWq4Vc/usNuUGDBiEuLg6LFy/GokWL8Nhjj2Hnzp3SGl0A8Nprr6G4uBgzZsxAfn4+hgwZgvj4eKPVgjdv3ozIyEg8+eSTkMvlCAsLw4cffmi5C/+FnZ0dli5dWm3okh6OfVc/7Lf6Y9/VH/uu/th39dfY+k4mmsIStURERERNXKN4DRARERFRc8eki4iIiMgCmHQRERERWQCTLiIiIiILYNJVR3q9Hm+88Qb8/f1hb2+Pjh074u233zZ6h5MQAkuWLEGbNm1gb2+PkJAQXLlyxaidvLw8hIeHw8nJCc7Ozpg2bRru3LljVOfs2bP47W9/C5VKBR8fH6xYsaJaPNu3b0fXrl2hUqkQEBCAvXv3NsyF18Phw4cxatQoeHt7QyaTSe/HrNKY+qk2sVjSg/quoqICCxcuREBAAFq1agVvb29MnjwZ6enpRm20xL572M/cvV5++WXIZDLExsYalbfEfgNq13cXL17Es88+C7VajVatWmHgwIHQarXS/rKyMkRERMDNzQ2tW7dGWFhYtcWstVotRo4cCQcHB3h4eGDBggWorKw0qnPw4EH069cPdnZ26NSpEz777LNqsaxduxZ+fn5QqVQICgpCUlKSWfqhPh7Wd3fu3EFkZCTatWsHe3t7dO/eHevWrTOq0xL7LiYmBgMHDoSjoyM8PDwwevRoXL582ahOY+qX2sTyUILq5K9//atwc3MTu3fvFqmpqWL79u2idevWYvXq1VKd5cuXC7VaLXbu3Cl++OEH8eyzzwp/f39RWloq1Rk+fLjo3bu3OH78uDhy5Ijo1KmTmDBhgrS/oKBAeHp6ivDwcHHu3DmxZcsWYW9vL/72t79JdY4ePSoUCoVYsWKFuHDhgli8eLGwtbUV//vf/yzTGQ+xd+9e8ec//1ns2LFDABD/+c9/jPY3pn6qTSyW9KC+y8/PFyEhIWLbtm3i0qVLIjExUQQGBor+/fsbtdES++5hP3NVduzYIXr37i28vb3FBx98YLSvJfabEA/vu6tXrwpXV1exYMECcfr0aXH16lWxa9cukZWVJdV5+eWXhY+Pj0hISBCnTp0Sv/nNb8SgQYOk/ZWVlaJnz54iJCREnDlzRuzdu1doNBoRHR0t1bl+/bpwcHAQUVFR4sKFC+Kjjz4SCoVCxMfHS3W2bt0qlEql2LBhgzh//ryYPn26cHZ2NorFkh7Wd9OnTxcdO3YUBw4cEKmpqeJvf/ubUCgUYteuXVKdlth3oaGhYuPGjeLcuXMiJSVFPP3008LX11fcuXNHqtOY+uVhsdQGk646GjlypHjppZeMysaMGSPCw8OFEEIYDAbh5eUl3nvvPWl/fn6+sLOzE1u2bBFCCHHhwgUBQJw8eVKq8/XXXwuZTCZu3rwphBDi448/Fi4uLkKn00l1Fi5cKLp06SJtP//882LkyJFGsQQFBYmZM2ea6WrN5/5/iBpTP9UmFmt6UPJQJSkpSQAQP/30kxCCfSdEzf32888/i7Zt24pz586J9u3bGyVd7Le7TPXduHHjxMSJE2s8Jj8/X9ja2ort27dLZRcvXhQARGJiohDibnIil8tFZmamVOeTTz4RTk5OUn++9tprokePHtXOHRoaKm0HBgaKiIgIaVuv1wtvb28RExNT94s1M1N916NHD/HWW28ZlfXr10/8+c9/FkKw76pkZ2cLAOLQoUNCiMbVL7WJpTY4vFhHgwYNQkJCAn788UcAwA8//IDvv/8eI0aMAACkpqYiMzMTISEh0jFqtRpBQUFITEwEACQmJsLZ2RkDBgyQ6oSEhEAul+PEiRNSnccffxxKpVKqExoaisuXL+P27dtSnXvPU1Wn6jyNWWPqp9rE0tgVFBRAJpNJ7wNl35lmMBgwadIkLFiwAD169Ki2n/1mmsFgwJ49e9C5c2eEhobCw8MDQUFBRsNoycnJqKioMLqmrl27wtfX1+h3OiAgQFr8GrjbL4WFhTh//rxU50F9V15ejuTkZKM6crkcISEhjbLvgLt/N7766ivcvHkTQggcOHAAP/74I5566ikA7LsqBQUFAABXV1cAjatfahNLbTDpqqPXX38d48ePR9euXWFra4u+ffti3rx5CA8PBwBkZmYCgNEPQNV21b7MzEx4eHgY7bexsYGrq6tRHVNt3HuOmupU7W/MGlM/1SaWxqysrAwLFy7EhAkTpBe6su9Me/fdd2FjY4M5c+aY3M9+My07Oxt37tzB8uXLMXz4cHz77bf4wx/+gDFjxuDQoUMA7l6TUqmUEv8q9193ffuusLAQpaWlyM3NhV6vbzJ9BwAfffQRunfvjnbt2kGpVGL48OFYu3YtHn/8cQDsO+BuYj9v3jwMHjxYesNMY+qX2sRSG1Z/DVBT8/nnn2Pz5s2Ii4tDjx49kJKSgnnz5sHb2xtTpkyxdnjUwlRUVOD555+HEAKffPKJtcNp1JKTk7F69WqcPn0aMpnM2uE0KQaDAQDw3HPPYf78+QCAPn364NixY1i3bh2GDh1qzfAavY8++gjHjx/HV199hfbt2+Pw4cOIiIiAt7d3tTswLVVERATOnTuH77//3tqhNCje6aqjBQsWSHe7AgICMGnSJMyfP196IbeXlxcAVHuiISsrS9rn5eWF7Oxso/2VlZXIy8szqmOqjXvPUVOdqv2NWWPqp9rE0hhVJVw//fQT9u3bJ93lAth3phw5cgTZ2dnw9fWFjY0NbGxs8NNPP+HVV1+Fn58fAPZbTTQaDWxsbNC9e3ej8m7duklPL3p5eaG8vBz5+flGde6/7vr2nZOTE+zt7aHRaKBQKJpM35WWlmLRokVYtWoVRo0ahV69eiEyMhLjxo3DypUrAbDvIiMjsXv3bhw4cADt2rWTyhtTv9Qmltpg0lVHJSUlRi/gBgCFQiH9n6C/vz+8vLyQkJAg7S8sLMSJEycQHBwMAAgODkZ+fj6Sk5OlOt999x0MBgOCgoKkOocPH0ZFRYVUZ9++fejSpQtcXFykOveep6pO1Xkas8bUT7WJpbGpSriuXLmC/fv3w83NzWg/+666SZMm4ezZs0hJSZE+3t7eWLBgAb755hsA7LeaKJVKDBw4sNrj/D/++CPat28PAOjfvz9sbW2Nruny5cvQarVGv9P/+9//jBLbqv9hqEroHtZ3SqUS/fv3N6pjMBiQkJDQKPuuoqICFRUVD/y70VL7TgiByMhI/Oc//8F3330Hf39/o/2NqV9qE0ttL5rqYMqUKaJt27bSkhE7duwQGo1GvPbaa1Kd5cuXC2dnZ7Fr1y5x9uxZ8dxzz5lcCqFv377ixIkT4vvvvxePPfaY0WPp+fn5wtPTU0yaNEmcO3dObN26VTg4OFR7LN3GxkasXLlSXLx4USxdurRRLRlRVFQkzpw5I86cOSMAiFWrVokzZ85IT9g1pn6qTSyW9KC+Ky8vF88++6xo166dSElJERkZGdLn3ifqWmLfPexn7n73P70oRMvsNyEe3nc7duwQtra2Yv369eLKlSvSY/dHjhyR2nj55ZeFr6+v+O6778SpU6dEcHCwCA4OlvZXPd7/1FNPiZSUFBEfHy/c3d1NPt6/YMECcfHiRbF27VqTj/fb2dmJzz77TFy4cEHMmDFDODs7Gz3BZkkP67uhQ4eKHj16iAMHDojr16+LjRs3CpVKJT7++GOpjZbYd6+88opQq9Xi4MGDRv+OlZSUSHUaU788LJbaYNJVR4WFhWLu3LnC19dXqFQq0aFDB/HnP//Z6I+dwWAQb7zxhvD09BR2dnbiySefFJcvXzZq59atW2LChAmidevWwsnJSUydOlUUFRUZ1fnhhx/EkCFDhJ2dnWjbtq1Yvnx5tXg+//xz0blzZ6FUKkWPHj3Enj17GubC6+HAgQMCQLXPlClThBCNq59qE4slPajvUlNTTe4DIA4cOCC10RL77mE/c/czlXS1xH4TonZ99+mnn4pOnToJlUolevfuLXbu3GnURmlpqZg1a5ZwcXERDg4O4g9/+IPIyMgwqnPjxg0xYsQIYW9vLzQajXj11VdFRUVFtVj69OkjlEql6NChg9i4cWO1eD/66CPh6+srlEqlCAwMFMePHzdbX9TVw/ouIyNDvPjii8Lb21uoVCrRpUsX8f777wuDwSC10RL7rqZ/x+6NuTH1S21ieRjZLxdORERERA2Ic7qIiIiILIBJFxEREZEFMOkiIiIisgAmXUREREQWwKSLiIiIyAKYdBERERFZAJMuIiIiIgtg0kVERERkAUy6iIiIiCyASRcRUSM1bNgwzJs3z9phEJGZMOkiIiIisgAmXURkdcOGDcPs2bMxb948uLi4wNPTE3//+99RXFyMqVOnwtHREZ06dcLXX38NANDr9Zg2bRr8/f1hb2+PLl26YPXq1VJ7ZWVl6NGjB2bMmCGVXbt2DY6OjtiwYUOtYjp69CiGDRsGBwcHuLi4IDQ0FLdv3wYA6HQ6zJkzBx4eHlCpVBgyZAhOnjwpHfvZZ5/B2dnZqL2dO3dCJpNJ28uWLUOfPn3wr3/9C35+flCr1Rg/fjyKiooAAC+++CIOHTqE1atXQyaTQSaT4caNG3XqVyJqXJh0EVGjsGnTJmg0GiQlJWH27Nl45ZVXMHbsWAwaNAinT5/GU089hUmTJqGkpAQGgwHt2rXD9u3bceHCBSxZsgSLFi3C559/DgBQqVTYvHkzNm3ahF27dkGv12PixIn4/e9/j5deeumhsaSkpODJJ59E9+7dkZiYiO+//x6jRo2CXq8HALz22mv48ssvsWnTJpw+fRqdOnVCaGgo8vLy6nTN165dw86dO7F7927s3r0bhw4dwvLlywEAq1evRnBwMKZPn46MjAxkZGTAx8enjr1KRI2KICKysqFDh4ohQ4ZI25WVlaJVq1Zi0qRJUllGRoYAIBITE022ERERIcLCwozKVqxYITQajYiMjBRt2rQRubm5tYpnwoQJYvDgwSb33blzR9ja2orNmzdLZeXl5cLb21usWLFCCCHExo0bhVqtNjruP//5j7j3n9ylS5cKBwcHUVhYKJUtWLBABAUFSdtDhw4Vc+fOrVXMRNT48U4XETUKvXr1kr5WKBRwc3NDQECAVObp6QkAyM7OBgCsXbsW/fv3h7u7O1q3bo3169dDq9Uatfnqq6+ic+fOWLNmDTZs2AA3N7daxVJ1p8uUa9euoaKiAoMHD5bKbG1tERgYiIsXL9buYn/h5+cHR0dHabtNmzbS9RFR88Oki4gaBVtbW6NtmUxmVFY1H8pgMGDr1q3405/+hGnTpuHbb79FSkoKpk6divLycqM2srOz8eOPP0KhUODKlSu1jsXe3v4RrgSQy+UQQhiVVVRUVKtn6poNBsMjnZuIGi8mXUTU5Bw9ehSDBg3CrFmz0LdvX3Tq1AnXrl2rVu+ll15CQEAANm3ahIULF9b6TlSvXr2QkJBgcl/Hjh2hVCpx9OhRqayiogInT55E9+7dAQDu7u4oKipCcXGxVCclJaUOV3iXUqmU5pERUdPHpIuImpzHHnsMp06dwjfffIMff/wRb7zxhtHTg8Dd4cfExERs2rQJ4eHhGD16NMLDw6vdDTMlOjoaJ0+exKxZs3D27FlcunQJn3zyCXJzc9GqVSu88sorWLBgAeLj43HhwgVMnz4dJSUlmDZtGgAgKCgIDg4OWLRoEa5du4a4uDh89tlndb5OPz8/nDhxAjdu3EBubi7vghE1cUy6iKjJmTlzJsaMGYNx48YhKCgIt27dwqxZs6T9ly5dwoIFC/Dxxx9LT/x9/PHHyM3NxRtvvPHQ9jt37oxvv/0WP/zwAwIDAxEcHIxdu3bBxsYGALB8+XKEhYVh0qRJ6NevH65evYpvvvkGLi4uAABXV1f8+9//xt69exEQEIAtW7Zg2bJldb7OP/3pT1AoFOjevTvc3d2rzVkjoqZFJu6feEBEREREZsc7XUREREQWwKSLiFqcESNGoHXr1iY/77zzjrXDI6JmisOLRNTi3Lx5E6WlpSb3ubq6wtXV1cIREVFLwKSLiIiIyAI4vEhERERkAUy6iIiIiCyASRcRERGRBTDpIiIiIrIAJl1EREREFsCki4iIiMgCmHQRERERWcD/A39EwDuYNxrFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(estim_df, x='max_count', stat='probability', hue='max_col', multiple='stack', hue_order=['v1', 'v2', 'v3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>probability_estimate</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_col</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>v1</th>\n",
       "      <td>9021528</td>\n",
       "      <td>56</td>\n",
       "      <td>0.805494</td>\n",
       "      <td>0.052894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v2</th>\n",
       "      <td>134257553</td>\n",
       "      <td>716</td>\n",
       "      <td>0.937553</td>\n",
       "      <td>0.009043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v3</th>\n",
       "      <td>117815338</td>\n",
       "      <td>617</td>\n",
       "      <td>0.954743</td>\n",
       "      <td>0.008368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               sum  count  probability_estimate       std\n",
       "max_col                                                  \n",
       "v1         9021528     56              0.805494  0.052894\n",
       "v2       134257553    716              0.937553  0.009043\n",
       "v3       117815338    617              0.954743  0.008368"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = estim_df.groupby('max_col').agg({'max_count': ['sum', 'count']})['max_count']\n",
    "freq['probability_estimate'] = freq['sum'] /  freq['count'] / 200000\n",
    "freq['std'] = np.sqrt(freq['probability_estimate'] * (1 - freq['probability_estimate']) / freq['count'])\n",
    "freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import binom\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "x = np.arange(0, 200001)\n",
    "n = 200000\n",
    "colors = ['green', 'orange', 'b']\n",
    "hue_order = ['v1', 'v2', 'v3']\n",
    "\n",
    "probability_list = freq['probability_estimate'].to_list()\n",
    "for label, proba in zip(hue_order, probability_list):\n",
    "    # plot Binomial dist\n",
    "    axes[0].plot(x, binom.pmf(x, n, proba), linestyle=':', label=f'p_{label}={proba:.3f}')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Binomial distributions')\n",
    "    \n",
    "    \n",
    "\n",
    "sns.histplot(estim_df, x='max_count', stat='probability', hue='max_col', multiple='stack', ax=axes[1], hue_order=hue_order)\n",
    "\n",
    "for label, proba in zip(hue_order, probability_list):\n",
    "    # plot expected values\n",
    "    axes[1].vlines(x=proba * 200000, ymin=0, ymax=0.175, linestyles='dotted', colors='black')\n",
    "\n",
    "axes[1].set_title('Data distributions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>whitelist</th>\n",
       "      <th>arc</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>secmax_count</th>\n",
       "      <th>max_col</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>nlarg_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR12400259_1</th>\n",
       "      <td>106</td>\n",
       "      <td>431</td>\n",
       "      <td>81172</td>\n",
       "      <td>8522</td>\n",
       "      <td>0.351500</td>\n",
       "      <td>81172</td>\n",
       "      <td>8522</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.584806</td>\n",
       "      <td>-0.081804</td>\n",
       "      <td>3.251718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400257_1</th>\n",
       "      <td>107</td>\n",
       "      <td>468</td>\n",
       "      <td>92197</td>\n",
       "      <td>9742</td>\n",
       "      <td>0.350826</td>\n",
       "      <td>92197</td>\n",
       "      <td>9742</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.584228</td>\n",
       "      <td>-0.082136</td>\n",
       "      <td>3.242430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400260_1</th>\n",
       "      <td>122</td>\n",
       "      <td>471</td>\n",
       "      <td>103821</td>\n",
       "      <td>11069</td>\n",
       "      <td>0.350149</td>\n",
       "      <td>103821</td>\n",
       "      <td>11069</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.583418</td>\n",
       "      <td>-0.082738</td>\n",
       "      <td>3.229501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400258_1</th>\n",
       "      <td>126</td>\n",
       "      <td>509</td>\n",
       "      <td>104880</td>\n",
       "      <td>11131</td>\n",
       "      <td>0.350888</td>\n",
       "      <td>104880</td>\n",
       "      <td>11131</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.583792</td>\n",
       "      <td>-0.082372</td>\n",
       "      <td>3.236084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR11007880_3</th>\n",
       "      <td>0</td>\n",
       "      <td>114057</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114057</td>\n",
       "      <td>0</td>\n",
       "      <td>v1</td>\n",
       "      <td>-0.008386</td>\n",
       "      <td>1.116623</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR13532408_2</th>\n",
       "      <td>202</td>\n",
       "      <td>693</td>\n",
       "      <td>2986</td>\n",
       "      <td>194851</td>\n",
       "      <td>0.109150</td>\n",
       "      <td>194851</td>\n",
       "      <td>2986</td>\n",
       "      <td>v3</td>\n",
       "      <td>-0.666923</td>\n",
       "      <td>-0.107921</td>\n",
       "      <td>6.028013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6976739_1</th>\n",
       "      <td>522</td>\n",
       "      <td>685</td>\n",
       "      <td>194933</td>\n",
       "      <td>20185</td>\n",
       "      <td>0.347909</td>\n",
       "      <td>194933</td>\n",
       "      <td>20185</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.586706</td>\n",
       "      <td>-0.083125</td>\n",
       "      <td>3.271623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR13532410_2</th>\n",
       "      <td>215</td>\n",
       "      <td>665</td>\n",
       "      <td>3016</td>\n",
       "      <td>195193</td>\n",
       "      <td>0.109270</td>\n",
       "      <td>195193</td>\n",
       "      <td>3016</td>\n",
       "      <td>v3</td>\n",
       "      <td>-0.666804</td>\n",
       "      <td>-0.108066</td>\n",
       "      <td>6.016121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12283706_1</th>\n",
       "      <td>267</td>\n",
       "      <td>1344</td>\n",
       "      <td>1880</td>\n",
       "      <td>195429</td>\n",
       "      <td>0.104093</td>\n",
       "      <td>195429</td>\n",
       "      <td>1880</td>\n",
       "      <td>v3</td>\n",
       "      <td>-0.672344</td>\n",
       "      <td>-0.103892</td>\n",
       "      <td>6.699768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12283710_1</th>\n",
       "      <td>322</td>\n",
       "      <td>947</td>\n",
       "      <td>1783</td>\n",
       "      <td>195566</td>\n",
       "      <td>0.093461</td>\n",
       "      <td>195566</td>\n",
       "      <td>1783</td>\n",
       "      <td>v3</td>\n",
       "      <td>-0.674178</td>\n",
       "      <td>-0.106211</td>\n",
       "      <td>6.777205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1389 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc      v1      v2      v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                            \n",
       "SRR12400259_1  106     431   81172    8522  0.351500      81172          8522   \n",
       "SRR12400257_1  107     468   92197    9742  0.350826      92197          9742   \n",
       "SRR12400260_1  122     471  103821   11069  0.350149     103821         11069   \n",
       "SRR12400258_1  126     509  104880   11131  0.350888     104880         11131   \n",
       "SRR11007880_3    0  114057       0       0  0.000000     114057             0   \n",
       "...            ...     ...     ...     ...       ...        ...           ...   \n",
       "SRR13532408_2  202     693    2986  194851  0.109150     194851          2986   \n",
       "SRR6976739_1   522     685  194933   20185  0.347909     194933         20185   \n",
       "SRR13532410_2  215     665    3016  195193  0.109270     195193          3016   \n",
       "SRR12283706_1  267    1344    1880  195429  0.104093     195429          1880   \n",
       "SRR12283710_1  322     947    1783  195566  0.093461     195566          1783   \n",
       "\n",
       "whitelist     max_col       PC1       PC2  nlarg_ratio  \n",
       "name                                                    \n",
       "SRR12400259_1      v2  0.584806 -0.081804     3.251718  \n",
       "SRR12400257_1      v2  0.584228 -0.082136     3.242430  \n",
       "SRR12400260_1      v2  0.583418 -0.082738     3.229501  \n",
       "SRR12400258_1      v2  0.583792 -0.082372     3.236084  \n",
       "SRR11007880_3      v1 -0.008386  1.116623          NaN  \n",
       "...               ...       ...       ...          ...  \n",
       "SRR13532408_2      v3 -0.666923 -0.107921     6.028013  \n",
       "SRR6976739_1       v2  0.586706 -0.083125     3.271623  \n",
       "SRR13532410_2      v3 -0.666804 -0.108066     6.016121  \n",
       "SRR12283706_1      v3 -0.672344 -0.103892     6.699768  \n",
       "SRR12283710_1      v3 -0.674178 -0.106211     6.777205  \n",
       "\n",
       "[1389 rows x 11 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estim_df.sort_values(by='max_count', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lustre/scratch126/cellgen/cellgeni/tmp/ipykernel_3630947/3222900031.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  estim_df['dataset'] = list(map(lambda x: name_to_dataset[x], estim_df.index.tolist()))\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>whitelist</th>\n",
       "      <th>arc</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v3</th>\n",
       "      <th>entropy</th>\n",
       "      <th>max_count</th>\n",
       "      <th>secmax_count</th>\n",
       "      <th>max_col</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>nlarg_ratio</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>SRR12400259_1</th>\n",
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       "      <td>v2</td>\n",
       "      <td>0.584806</td>\n",
       "      <td>-0.081804</td>\n",
       "      <td>3.251718</td>\n",
       "      <td>GSE155794</td>\n",
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       "    <tr>\n",
       "      <th>SRR12400257_1</th>\n",
       "      <td>107</td>\n",
       "      <td>468</td>\n",
       "      <td>92197</td>\n",
       "      <td>9742</td>\n",
       "      <td>0.350826</td>\n",
       "      <td>92197</td>\n",
       "      <td>9742</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.584228</td>\n",
       "      <td>-0.082136</td>\n",
       "      <td>3.242430</td>\n",
       "      <td>GSE155794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400260_1</th>\n",
       "      <td>122</td>\n",
       "      <td>471</td>\n",
       "      <td>103821</td>\n",
       "      <td>11069</td>\n",
       "      <td>0.350149</td>\n",
       "      <td>103821</td>\n",
       "      <td>11069</td>\n",
       "      <td>v2</td>\n",
       "      <td>0.583418</td>\n",
       "      <td>-0.082738</td>\n",
       "      <td>3.229501</td>\n",
       "      <td>GSE155794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR12400258_1</th>\n",
       "      <td>126</td>\n",
       "      <td>509</td>\n",
       "      <td>104880</td>\n",
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       "      <td>0.350888</td>\n",
       "      <td>104880</td>\n",
       "      <td>11131</td>\n",
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       "      <td>0.583792</td>\n",
       "      <td>-0.082372</td>\n",
       "      <td>3.236084</td>\n",
       "      <td>GSE155794</td>\n",
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       "    <tr>\n",
       "      <th>SRR11007880_3</th>\n",
       "      <td>0</td>\n",
       "      <td>114057</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114057</td>\n",
       "      <td>0</td>\n",
       "      <td>v1</td>\n",
       "      <td>-0.008386</td>\n",
       "      <td>1.116623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GSE144707</td>\n",
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       "      <th>SRR11007864_3</th>\n",
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       "      <td>-0.008386</td>\n",
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       "      <td>GSE144707</td>\n",
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       "      <td>v1</td>\n",
       "      <td>-0.008386</td>\n",
       "      <td>1.116623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GSE144707</td>\n",
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       "      <th>SRR11007879_3</th>\n",
       "      <td>0</td>\n",
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       "      <td>-0.008386</td>\n",
       "      <td>1.116623</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist      arc      v1      v2     v3   entropy  max_count  secmax_count  \\\n",
       "name                                                                           \n",
       "SRR12400259_1  106     431   81172   8522  0.351500      81172          8522   \n",
       "SRR12400257_1  107     468   92197   9742  0.350826      92197          9742   \n",
       "SRR12400260_1  122     471  103821  11069  0.350149     103821         11069   \n",
       "SRR12400258_1  126     509  104880  11131  0.350888     104880         11131   \n",
       "SRR11007880_3    0  114057       0      0  0.000000     114057             0   \n",
       "SRR11007864_3    0  114479       0      0  0.000000     114479             0   \n",
       "SRR11007872_3    0  115635       0      0  0.000000     115635             0   \n",
       "SRR11007879_3    0  119212       0      0  0.000000     119212             0   \n",
       "SRR11007863_3    0  120140       0      0  0.000000     120140             0   \n",
       "SRR11007871_3    0  120479       0      0  0.000000     120479             0   \n",
       "\n",
       "whitelist     max_col       PC1       PC2  nlarg_ratio    dataset  \n",
       "name                                                               \n",
       "SRR12400259_1      v2  0.584806 -0.081804     3.251718  GSE155794  \n",
       "SRR12400257_1      v2  0.584228 -0.082136     3.242430  GSE155794  \n",
       "SRR12400260_1      v2  0.583418 -0.082738     3.229501  GSE155794  \n",
       "SRR12400258_1      v2  0.583792 -0.082372     3.236084  GSE155794  \n",
       "SRR11007880_3      v1 -0.008386  1.116623          NaN  GSE144707  \n",
       "SRR11007864_3      v1 -0.008386  1.116623          NaN  GSE144707  \n",
       "SRR11007872_3      v1 -0.008386  1.116623          NaN  GSE144707  \n",
       "SRR11007879_3      v1 -0.008386  1.116623          NaN  GSE144707  \n",
       "SRR11007863_3      v1 -0.008386  1.116623          NaN  GSE144707  \n",
       "SRR11007871_3      v1 -0.008386  1.116623          NaN  GSE144707  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estim_df['dataset'] = list(map(lambda x: name_to_dataset[x], estim_df.index.tolist()))\n",
    "estim_df.sort_values('max_count', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lustre/scratch127/cellgen/cellgeni/aljes/reprocess/venv/lib/python3.10/site-packages/seaborn/distributions.py:267: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  baselines[cols] = curves[cols].shift(1, axis=1).fillna(0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efc286f3d90>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lustre/scratch127/cellgen/cellgeni/aljes/reprocess/venv/lib/python3.10/site-packages/IPython/core/events.py:82: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  func(*args, **kwargs)\n",
      "/lustre/scratch127/cellgen/cellgeni/aljes/reprocess/venv/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(estim_df, x='max_count', hue='dataset', multiple='stack')\n",
    "plt.legend([''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='max_count', ylabel='Count'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_agg = data=estim_df.groupby('dataset', as_index=False).agg({'max_count': 'mean', 'max_col': 'max'})\n",
    "sns.histplot(dataset_agg, x='max_count', hue='max_col', multiple='stack', hue_order=hue_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='max_count', ylabel='Count'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thr = 170000\n",
    "small_count_datasets = dataset_agg[dataset_agg.max_count < thr].dataset.to_list()\n",
    "sns.histplot(estim_df[estim_df.dataset.isin(small_count_datasets)], x='max_count', hue='dataset', bins=30, multiple='stack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lustre/scratch126/cellgen/cellgeni/tmp/ipykernel_3630947/1629581721.py:14: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  qc_df = pd.concat(qc_df_list)\n"
     ]
    },
    {
     "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>Sample</th>\n",
       "      <th>Rd_all</th>\n",
       "      <th>Rd_in_cells</th>\n",
       "      <th>Frc_in_cells</th>\n",
       "      <th>UMI_in_cells</th>\n",
       "      <th>Cells</th>\n",
       "      <th>Med_nFeature</th>\n",
       "      <th>Good_BC</th>\n",
       "      <th>WL</th>\n",
       "      <th>Species</th>\n",
       "      <th>Paired</th>\n",
       "      <th>Strand</th>\n",
       "      <th>all_u+m</th>\n",
       "      <th>all_u</th>\n",
       "      <th>exon_u+m</th>\n",
       "      <th>exon_u</th>\n",
       "      <th>full_u+m</th>\n",
       "      <th>full_u</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GSM4712229</td>\n",
       "      <td>16365256</td>\n",
       "      <td>3853482</td>\n",
       "      <td>0.235</td>\n",
       "      <td>3692264</td>\n",
       "      <td>6207</td>\n",
       "      <td>378</td>\n",
       "      <td>0.530676</td>\n",
       "      <td>v2</td>\n",
       "      <td>Mouse</td>\n",
       "      <td>Single</td>\n",
       "      <td>Forward</td>\n",
       "      <td>0.431008</td>\n",
       "      <td>0.363071</td>\n",
       "      <td>0.325360</td>\n",
       "      <td>0.315193</td>\n",
       "      <td>0.375690</td>\n",
       "      <td>0.353111</td>\n",
       "      <td>GSE155794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GSM4712230</td>\n",
       "      <td>481908143</td>\n",
       "      <td>271042990</td>\n",
       "      <td>0.562</td>\n",
       "      <td>168648823</td>\n",
       "      <td>14774</td>\n",
       "      <td>3260</td>\n",
       "      <td>0.976741</td>\n",
       "      <td>v3</td>\n",
       "      <td>Mouse</td>\n",
       "      <td>Single</td>\n",
       "      <td>Forward</td>\n",
       "      <td>0.953956</td>\n",
       "      <td>0.811003</td>\n",
       "      <td>0.690190</td>\n",
       "      <td>0.672259</td>\n",
       "      <td>0.829317</td>\n",
       "      <td>0.786786</td>\n",
       "      <td>GSE155794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GSM4869646</td>\n",
       "      <td>139776259</td>\n",
       "      <td>69043028</td>\n",
       "      <td>0.494</td>\n",
       "      <td>6687355</td>\n",
       "      <td>2021</td>\n",
       "      <td>1235</td>\n",
       "      <td>0.806425</td>\n",
       "      <td>v2</td>\n",
       "      <td>Human</td>\n",
       "      <td>Single</td>\n",
       "      <td>Reverse</td>\n",
       "      <td>0.965390</td>\n",
       "      <td>0.693210</td>\n",
       "      <td>0.518849</td>\n",
       "      <td>0.506566</td>\n",
       "      <td>0.669586</td>\n",
       "      <td>0.536391</td>\n",
       "      <td>GSE160243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GSM4869647</td>\n",
       "      <td>154971965</td>\n",
       "      <td>89160681</td>\n",
       "      <td>0.575</td>\n",
       "      <td>38187562</td>\n",
       "      <td>7129</td>\n",
       "      <td>1581</td>\n",
       "      <td>0.847546</td>\n",
       "      <td>v2</td>\n",
       "      <td>Human</td>\n",
       "      <td>Single</td>\n",
       "      <td>Reverse</td>\n",
       "      <td>0.974957</td>\n",
       "      <td>0.792731</td>\n",
       "      <td>0.581473</td>\n",
       "      <td>0.570929</td>\n",
       "      <td>0.700923</td>\n",
       "      <td>0.608495</td>\n",
       "      <td>GSE160243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GSM4869648</td>\n",
       "      <td>138883181</td>\n",
       "      <td>78616908</td>\n",
       "      <td>0.566</td>\n",
       "      <td>8611605</td>\n",
       "      <td>1660</td>\n",
       "      <td>1799</td>\n",
       "      <td>0.832806</td>\n",
       "      <td>v2</td>\n",
       "      <td>Human</td>\n",
       "      <td>Single</td>\n",
       "      <td>Reverse</td>\n",
       "      <td>0.971436</td>\n",
       "      <td>0.770584</td>\n",
       "      <td>0.551999</td>\n",
       "      <td>0.542077</td>\n",
       "      <td>0.693780</td>\n",
       "      <td>0.589019</td>\n",
       "      <td>GSE160243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample     Rd_all Rd_in_cells  Frc_in_cells UMI_in_cells  Cells  \\\n",
       "0  GSM4712229   16365256     3853482         0.235      3692264   6207   \n",
       "1  GSM4712230  481908143   271042990         0.562    168648823  14774   \n",
       "0  GSM4869646  139776259    69043028         0.494      6687355   2021   \n",
       "1  GSM4869647  154971965    89160681         0.575     38187562   7129   \n",
       "2  GSM4869648  138883181    78616908         0.566      8611605   1660   \n",
       "\n",
       "  Med_nFeature   Good_BC  WL Species  Paired   Strand   all_u+m     all_u  \\\n",
       "0          378  0.530676  v2   Mouse  Single  Forward  0.431008  0.363071   \n",
       "1         3260  0.976741  v3   Mouse  Single  Forward  0.953956  0.811003   \n",
       "0         1235  0.806425  v2   Human  Single  Reverse  0.965390  0.693210   \n",
       "1         1581  0.847546  v2   Human  Single  Reverse  0.974957  0.792731   \n",
       "2         1799  0.832806  v2   Human  Single  Reverse  0.971436  0.770584   \n",
       "\n",
       "   exon_u+m    exon_u  full_u+m    full_u    dataset  \n",
       "0  0.325360  0.315193  0.375690  0.353111  GSE155794  \n",
       "1  0.690190  0.672259  0.829317  0.786786  GSE155794  \n",
       "0  0.518849  0.506566  0.669586  0.536391  GSE160243  \n",
       "1  0.581473  0.570929  0.700923  0.608495  GSE160243  \n",
       "2  0.551999  0.542077  0.693780  0.589019  GSE160243  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "basedir = 'reprocessing-datasets-project/0_Current'\n",
    "dataset = 'GSE144707'\n",
    "#glob.glob(f'./{basedir}/{dataset}/*solo_qc.tsv')\n",
    "\n",
    "qc_df_list = list()\n",
    "for dataset in small_count_datasets:\n",
    "    path = f'./{basedir}/{dataset}/{dataset}.solo_qc.tsv'\n",
    "    df = pd.read_csv(path, sep='\\t')\n",
    "    df['dataset'] = dataset\n",
    "    qc_df_list.append(df)\n",
    "\n",
    "qc_df = pd.concat(qc_df_list)\n",
    "qc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GSE144707', 'GSE203138'}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(small_count_datasets).difference(qc_df.dataset.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "qc_df.to_csv('qc.tsv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('GSE144707',\n",
       "  './reprocessing-datasets-project/0_Current/GSE144707/38530943.2.bsub.log'),\n",
       " ('GSE144707',\n",
       "  './reprocessing-datasets-project/0_Current/GSE144707/38530943.1.bsub.log'),\n",
       " ('GSE144707',\n",
       "  './reprocessing-datasets-project/0_Current/GSE144707/38530943.3.bsub.log'),\n",
       " ('GSE203138',\n",
       "  './reprocessing-datasets-project/0_Current/GSE203138/38525714.1.bsub.log'),\n",
       " ('GSE203138',\n",
       "  './reprocessing-datasets-project/0_Current/GSE203138/38525714.2.bsub.log')]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basedir = 'reprocessing-datasets-project/0_Current'\n",
    "dataset = 'GSE144707'\n",
    "\n",
    "\n",
    "statsolo_errors = list()\n",
    "for dataset in small_count_datasets:\n",
    "    log_path = glob.glob(f'./{basedir}/{dataset}/*.log')\n",
    "    for path in log_path:\n",
    "        with open(path, 'r') as file:\n",
    "            logoutput = file.read()\n",
    "            if ('Exited with exit code 1' in logoutput) and ('starsolo' in logoutput):\n",
    "                statsolo_errors.append((dataset, path))\n",
    "statsolo_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starsolo ERROR for dataset=GSE144707:\n",
      "ERROR: Read 1 (barcode) is less than 24 bp in length. Please check the fastq files.\n",
      "\n",
      "Starsolo ERROR for dataset=GSE144707:\n",
      "ERROR: Read 1 (barcode) is less than 24 bp in length. Please check the fastq files.\n",
      "\n",
      "Starsolo ERROR for dataset=GSE144707:\n",
      "ERROR: Read 1 (barcode) is less than 24 bp in length. Please check the fastq files.\n",
      "\n",
      "Starsolo ERROR for dataset=GSE203138:\n",
      "ERROR: Read 1 (barcode) is less than 24 bp in length. Please check the fastq files.\n",
      "\n",
      "Starsolo ERROR for dataset=GSE203138:\n",
      "ERROR: Read 1 (barcode) is less than 24 bp in length. Please check the fastq files.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "starsolo_errors_log = list()\n",
    "for dataset, path in statsolo_errors:\n",
    "    with open(path.replace('log', 'err')) as file:\n",
    "        print(f'Starsolo ERROR for dataset={dataset}:')\n",
    "        print(file.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>whitelist</th>\n",
       "      <th>max_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_col</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>v1</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v2</th>\n",
       "      <td>0.039474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v3</th>\n",
       "      <td>0.013514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "whitelist  max_count\n",
       "max_col             \n",
       "v1          0.500000\n",
       "v2          0.039474\n",
       "v3          0.013514"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threash = 170000\n",
    "def less_than_treash_prop(col):\n",
    "    num = (col < threash).sum()\n",
    "    return num / col.shape[0]\n",
    "\n",
    "dataset_agg.groupby('max_col').agg({'max_count': less_than_treash_prop})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.03896103896103896)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dataset_agg.max_count < 170000).sum() / dataset_agg.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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