{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69a1b087",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:09.280948Z",
     "iopub.status.busy": "2025-01-26T17:57:09.280545Z",
     "iopub.status.idle": "2025-01-26T17:57:09.283569Z",
     "shell.execute_reply": "2025-01-26T17:57:09.283113Z"
    },
    "papermill": {
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     "end_time": "2025-01-26T17:57:09.284719",
     "exception": false,
     "start_time": "2025-01-26T17:57:09.274110",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# version 2.0\n",
    "# ugvc/reports/methyldackel_qc_report.ipynb\n",
    "# =============================================\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13412fe4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:09.294519Z",
     "iopub.status.busy": "2025-01-26T17:57:09.294275Z",
     "iopub.status.idle": "2025-01-26T17:57:11.721861Z",
     "shell.execute_reply": "2025-01-26T17:57:11.721302Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 2.434161,
     "end_time": "2025-01-26T17:57:11.723527",
     "exception": false,
     "start_time": "2025-01-26T17:57:09.289366",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fontconfig error: No writable cache directories\n",
      "Fontconfig error: No writable cache directories\n",
      "Fontconfig error: No writable cache directories\n",
      "Fontconfig error: No writable cache directories\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import seaborn as sns  \n",
    "import os  \n",
    "import matplotlib.pyplot as plt  \n",
    "import json\n",
    "# from IPython.display import display, HTML\n",
    "import re\n",
    "from collections import Counter\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "456fefe3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:11.734050Z",
     "iopub.status.busy": "2025-01-26T17:57:11.733695Z",
     "iopub.status.idle": "2025-01-26T17:57:11.736723Z",
     "shell.execute_reply": "2025-01-26T17:57:11.736289Z"
    },
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     "end_time": "2025-01-26T17:57:11.737801",
     "exception": false,
     "start_time": "2025-01-26T17:57:11.728357",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ee66241",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:11.747262Z",
     "iopub.status.busy": "2025-01-26T17:57:11.746986Z",
     "iopub.status.idle": "2025-01-26T17:57:11.750151Z",
     "shell.execute_reply": "2025-01-26T17:57:11.749718Z"
    },
    "papermill": {
     "duration": 0.009114,
     "end_time": "2025-01-26T17:57:11.751235",
     "exception": false,
     "start_time": "2025-01-26T17:57:11.742121",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 10000)\n",
    "pd.set_option('display.width', 1200)\n",
    "pd.set_option('display.max_colwidth', 0)\n",
    "\n",
    "# function for wraping long text \n",
    "def wrap_df_text(df):\n",
    "    return HTML(df.to_html().replace(\"\\\\n\",\"<br>\"))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9044cd6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:11.760768Z",
     "iopub.status.busy": "2025-01-26T17:57:11.760532Z",
     "iopub.status.idle": "2025-01-26T17:57:11.762949Z",
     "shell.execute_reply": "2025-01-26T17:57:11.762514Z"
    },
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     "duration": 0.008398,
     "end_time": "2025-01-26T17:57:11.764052",
     "exception": false,
     "start_time": "2025-01-26T17:57:11.755654",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "input_h5_file = ''\n",
    "input_base_file_name = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbdd022f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:11.773902Z",
     "iopub.status.busy": "2025-01-26T17:57:11.773626Z",
     "iopub.status.idle": "2025-01-26T17:57:11.776078Z",
     "shell.execute_reply": "2025-01-26T17:57:11.775641Z"
    },
    "papermill": {
     "duration": 0.008603,
     "end_time": "2025-01-26T17:57:11.777168",
     "exception": false,
     "start_time": "2025-01-26T17:57:11.768565",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "input_h5_file = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3451-Z0011-CACGCACTGCCAGAT/415630-L3451-Z0011-CACGCACTGCCAGAT.methyl_seq.applicationQC.h5\"\n",
    "input_base_file_name = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3451-Z0011-CACGCACTGCCAGAT/415630-L3451-Z0011-CACGCACTGCCAGAT.methyl_seq\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "236aeed9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:11.786801Z",
     "iopub.status.busy": "2025-01-26T17:57:11.786569Z",
     "iopub.status.idle": "2025-01-26T17:57:13.283599Z",
     "shell.execute_reply": "2025-01-26T17:57:13.282986Z"
    },
    "papermill": {
     "duration": 1.503726,
     "end_time": "2025-01-26T17:57:13.285379",
     "exception": false,
     "start_time": "2025-01-26T17:57:11.781653",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# seperate the processing into the different tables from MethylDackel\n",
    "with pd.HDFStore(input_h5_file, 'r') as store:\n",
    "    list_tables = store.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5e37447",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:13.296378Z",
     "iopub.status.busy": "2025-01-26T17:57:13.296099Z",
     "iopub.status.idle": "2025-01-26T17:57:13.300857Z",
     "shell.execute_reply": "2025-01-26T17:57:13.300397Z"
    },
    "papermill": {
     "duration": 0.011542,
     "end_time": "2025-01-26T17:57:13.302007",
     "exception": false,
     "start_time": "2025-01-26T17:57:13.290465",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def format_metric_names(df_in):\n",
    "    df_in['metric']= df_in['metric'].str.replace(r'PercentMethylation', 'Percent Methylation: ', regex=True)\n",
    "    df_in['metric']= df_in['metric'].str.replace(r'PercentMethylationPosition', 'Percent Methylation Position: ', regex=True)\n",
    "    df_in['metric']= df_in['metric'].str.replace(r'CumulativeCoverage', 'Cumulative Coverage', regex=True)\n",
    "    df_in['metric']= df_in['metric'].str.replace(r'Coverage', 'Coverage: ', regex=True)\n",
    "    df_in['metric']= df_in['metric'].str.replace(r'TotalCpGs', 'Total CpGs: ', regex=True)\n",
    "    df_in['metric']= df_in['metric'].str.replace(r'_', ' ', regex=True)\n",
    "    return(df_in)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "678d4706",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:13.312381Z",
     "iopub.status.busy": "2025-01-26T17:57:13.312088Z",
     "iopub.status.idle": "2025-01-26T17:57:13.315416Z",
     "shell.execute_reply": "2025-01-26T17:57:13.314976Z"
    },
    "papermill": {
     "duration": 0.009853,
     "end_time": "2025-01-26T17:57:13.316506",
     "exception": false,
     "start_time": "2025-01-26T17:57:13.306653",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def parse_metric_names(df_in):\n",
    "    df_in['metric_orig'] = df_in['metric']\n",
    "    pat = '\\w+_(\\d+)' # get value of bins\n",
    "    df_in['bin']= df_in['metric'].str.extract(pat)\n",
    "    # get metric name\n",
    "    pat = '(\\w+)_\\d+'\n",
    "    df_in['metric']= df_in['metric'].str.extract(pat)\n",
    "    return(df_in)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bebac5ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:13.326695Z",
     "iopub.status.busy": "2025-01-26T17:57:13.326412Z",
     "iopub.status.idle": "2025-01-26T17:57:13.329825Z",
     "shell.execute_reply": "2025-01-26T17:57:13.329383Z"
    },
    "papermill": {
     "duration": 0.009799,
     "end_time": "2025-01-26T17:57:13.330933",
     "exception": false,
     "start_time": "2025-01-26T17:57:13.321134",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def display_side_by_side(dfs:list, captions:list, tablespacing=2):\n",
    "    output = \"\"\n",
    "    for (caption, df) in zip(captions, dfs):\n",
    "        output += df.style.set_table_attributes(\"style='display:inline-table'\").set_caption(caption)._repr_html_()\n",
    "        output += tablespacing * \"\\xa0\"\n",
    "\n",
    "    display(HTML(output))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2120f4a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:13.341519Z",
     "iopub.status.busy": "2025-01-26T17:57:13.341287Z",
     "iopub.status.idle": "2025-01-26T17:57:13.348042Z",
     "shell.execute_reply": "2025-01-26T17:57:13.347626Z"
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     "exception": false,
     "start_time": "2025-01-26T17:57:13.335534",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1 style=\"font-size:24px;\">QC Report for Methylation Calling</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"<h1 style=\\\"font-size:24px;\\\">\"+\"QC Report for Methylation Calling\"+\"</h1>\")\n",
    "HTML(\"<hr/>\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "26eddd05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:13.359813Z",
     "iopub.status.busy": "2025-01-26T17:57:13.359463Z",
     "iopub.status.idle": "2025-01-26T17:57:13.367645Z",
     "shell.execute_reply": "2025-01-26T17:57:13.367173Z"
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     "exception": false,
     "start_time": "2025-01-26T17:57:13.353885",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b></b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:20px;\">Input parameters</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<b></b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "HTML(\"<b>\"+\"\"+\"</b>\")\n",
    "HTML(\"<hr/>\")\n",
    "HTML(\"<h2 style=\\\"font-size:20px;\\\">\"+\"Input parameters\"+\"</h2>\")\n",
    "HTML(\"<hr/>\")\n",
    "HTML(\"<b>\"+\"\"+\"</b>\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c4762297",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:13.380643Z",
     "iopub.status.busy": "2025-01-26T17:57:13.380406Z",
     "iopub.status.idle": "2025-01-26T17:57:13.509650Z",
     "shell.execute_reply": "2025-01-26T17:57:13.509172Z"
    },
    "papermill": {
     "duration": 0.136507,
     "end_time": "2025-01-26T17:57:13.510917",
     "exception": false,
     "start_time": "2025-01-26T17:57:13.374410",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_cc75d_row0_col0, #T_cc75d_row1_col0 {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_cc75d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cc75d_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cc75d_level0_row0\" class=\"row_heading level0 row0\" >Sample name</th>\n",
       "      <td id=\"T_cc75d_row0_col0\" class=\"data row0 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3451-Z0011-CACGCACTGCCAGAT/415630-L3451-Z0011-\n",
       "CACGCACTGCCAGAT.methyl_seq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc75d_level0_row1\" class=\"row_heading level0 row1\" >h5 file</th>\n",
       "      <td id=\"T_cc75d_row1_col0\" class=\"data row1 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3451-Z0011-CACGCACTGCCAGAT/415630-L3451-Z0011-\n",
       "CACGCACTGCCAGAT.methyl_seq.applicationQC.h5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_print_sample_info = pd.DataFrame(\n",
    "        data = {\n",
    "            'value' : [input_base_file_name, str(input_h5_file)]\n",
    "        },\n",
    "        index = ['Sample name', 'h5 file'])\n",
    "\n",
    "to_print_sample_info['value'] = to_print_sample_info['value'].str.wrap(100)\n",
    "def wrap_df_text(df):\n",
    "    return display(HTML(df.to_html().replace(\"\\\\n\",\"<br>\")))\n",
    "\n",
    "wrap_df_text(to_print_sample_info.style.set_properties(**{'text-align': 'left'}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6bd3de2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:13.524542Z",
     "iopub.status.busy": "2025-01-26T17:57:13.524187Z",
     "iopub.status.idle": "2025-01-26T17:57:13.529125Z",
     "shell.execute_reply": "2025-01-26T17:57:13.528688Z"
    },
    "papermill": {
     "duration": 0.012797,
     "end_time": "2025-01-26T17:57:13.530284",
     "exception": false,
     "start_time": "2025-01-26T17:57:13.517487",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:18px;\">Human Genome: Global Methylation Statistics</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"<h2 style=\\\"font-size:18px;\\\">\"+\"Human Genome: Global Methylation Statistics\"+\"</h2>\")\n",
    "HTML(\"<hr/>\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d11a1bac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:13.543090Z",
     "iopub.status.busy": "2025-01-26T17:57:13.542825Z",
     "iopub.status.idle": "2025-01-26T17:57:13.578099Z",
     "shell.execute_reply": "2025-01-26T17:57:13.577654Z"
    },
    "papermill": {
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     "end_time": "2025-01-26T17:57:13.579269",
     "exception": false,
     "start_time": "2025-01-26T17:57:13.536152",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metric</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  mean</th>\n",
       "      <td>71.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  std</th>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  median</th>\n",
       "      <td>72.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:</th>\n",
       "      <td>740,070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  mean</th>\n",
       "      <td>49.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  std</th>\n",
       "      <td>18.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  median</th>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                value\n",
       "metric                               \n",
       "Percent Methylation:  mean    71.19  \n",
       "Percent Methylation:  std     12.50  \n",
       "Percent Methylation:  median  72.00  \n",
       "Total CpGs:                   740,070\n",
       "Coverage:  mean               49.19  \n",
       "Coverage:  std                18.14  \n",
       "Coverage:  median             50.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# main statistics table\n",
    "# --------------------------\n",
    "tbl = 'MergeContext_desc'\n",
    "genome = 'hg'\n",
    "df_desc = pd.read_hdf(input_h5_file, key = tbl)\n",
    "df_desc = pd.DataFrame(df_desc)\n",
    "df_desc = df_desc.query('detail == @genome')\n",
    "df_desc.reset_index(inplace=True)\n",
    "df_desc.drop(columns=['detail'], inplace=True)\n",
    "df_desc = format_metric_names(df_desc)\n",
    "to_int = df_desc.metric == 'Total CpGs: '\n",
    "to_float = df_desc.metric != 'Total CpGs: '\n",
    "df_desc.loc[to_int, 'value'] = df_desc.loc[to_int, 'value'].map('{:,.0f}'.format)\n",
    "df_desc.loc[to_float, 'value'] = df_desc.loc[to_float, 'value'].map('{:,.2f}'.format)\n",
    "display(df_desc.set_index('metric'))\n",
    "\n",
    "# -----------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6c72e121",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:12px;\">Human Genome: Details of Per-Read Descriptive Statistics</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metric</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  mean</th>\n",
       "      <td>58.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  std</th>\n",
       "      <td>41.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  median</th>\n",
       "      <td>66.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:  mean</th>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:  std</th>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:  median</th>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              value\n",
       "metric                             \n",
       "Percent Methylation:  mean    58.21\n",
       "Percent Methylation:  std     41.45\n",
       "Percent Methylation:  median  66.67\n",
       "Total CpGs:  mean             2.64 \n",
       "Total CpGs:  std              2.95 \n",
       "Total CpGs:  median           2.00 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# per Read information\n",
    "# ------------------------\n",
    "HTML(\" \")\n",
    "HTML(\"<h2 style=\\\"font-size:12px;\\\">\"+\"Human Genome: Details of Per-Read Descriptive Statistics\"+\"</h2>\")\n",
    "\n",
    "tbl = 'PerRead_desc'\n",
    "genome = 'hg'\n",
    "df_desc = pd.read_hdf(input_h5_file, key = tbl)\n",
    "df_desc = pd.DataFrame(df_desc)\n",
    "df_desc.reset_index(inplace=True)\n",
    "df_desc.drop(columns=['detail'], inplace=True)\n",
    "df_desc = format_metric_names(df_desc)\n",
    "df_desc['value'] = df_desc['value'].map('{:,.2f}'.format)\n",
    "display(df_desc.set_index('metric'))\n",
    "\n",
    "# -----------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a6e7add1",
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     "status": "completed"
    },
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     "name": "#%%\n"
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   "outputs": [
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:16px;\">Human Genome: Cytosines in Other Contexts Descriptive Statistics</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c1cd0\" style='display:inline-table'>\n",
       "  <caption>CHG</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c1cd0_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >metric</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c1cd0_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_c1cd0_row0_col0\" class=\"data row0 col0\" >72.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1cd0_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_c1cd0_row1_col0\" class=\"data row1 col0\" >10.85%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1cd0_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_c1cd0_row2_col0\" class=\"data row2 col0\" >74.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1cd0_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_c1cd0_row3_col0\" class=\"data row3 col0\" >48.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1cd0_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_c1cd0_row4_col0\" class=\"data row4 col0\" >18.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1cd0_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_c1cd0_row5_col0\" class=\"data row5 col0\" >49.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_d4320\" style='display:inline-table'>\n",
       "  <caption>CHH</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d4320_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >metric</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d4320_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_d4320_row0_col0\" class=\"data row0 col0\" >70.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d4320_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_d4320_row1_col0\" class=\"data row1 col0\" >14.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d4320_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_d4320_row2_col0\" class=\"data row2 col0\" >71.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d4320_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_d4320_row3_col0\" class=\"data row3 col0\" >26.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d4320_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_d4320_row4_col0\" class=\"data row4 col0\" >12.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d4320_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_d4320_row5_col0\" class=\"data row5 col0\" >25.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\" \")\n",
    "HTML(\"<h2 style=\\\"font-size:16px;\\\">\"+\"Human Genome: Cytosines in Other Contexts Descriptive Statistics\"+\"</h2>\")\n",
    "HTML(\"<hr/>\")\n",
    "\n",
    "tbl = 'MergeContextNoCpG_desc'\n",
    "\n",
    "df_desc = pd.read_hdf(input_h5_file, key = tbl)\n",
    "df_desc = pd.DataFrame(df_desc)\n",
    "df_desc = df_desc.reset_index()\n",
    "df_desc  = format_metric_names(df_desc)\n",
    "\n",
    "df_desc['stat_type']=  df_desc['metric'].str.extract(r'([A-Za-z]+)[\\s:]')\n",
    "df_desc['metric']  = df_desc['metric'].str.title()\n",
    "df_desc['value'][df_desc['stat_type'] == \"Percent\"] = (df_desc['value'][df_desc['stat_type'] == \"Percent\"]/100).map('{:,.2%}'.format)\n",
    "df_desc['value'][df_desc['stat_type'] == \"Coverage\"] = df_desc['value'][df_desc['stat_type'] == \"Coverage\"].map('{:,.2f}'.format)\n",
    "\n",
    "table_names = df_desc['detail'].unique()\n",
    "cols = ['metric','value', 'detail']\n",
    "df_output = []\n",
    "df_output = [y for x, y in df_desc.groupby('detail')]\n",
    "\n",
    "df_to_print = []\n",
    "cols = ['metric','value']\n",
    "for l in df_output:\n",
    "    l.index = l['metric']\n",
    "    l = l['value'].to_frame()\n",
    "    df_to_print.append(l)\n",
    "\n",
    "display_side_by_side(df_to_print, table_names)\n",
    "\n",
    "HTML(\" \")\n",
    "\n",
    "# --------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee8d9ca4",
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    "pycharm": {
     "name": "#%%\n"
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    "tags": []
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   "outputs": [],
   "source": [
    "# function for creating Mbias plots\n",
    "# -----------------------------------------------------------------------------------\n",
    "def plot_mbias(in_list_df):\n",
    "\n",
    "    plt.style.use('ggplot')\n",
    "\n",
    "    if (len(in_list_df) == 4):\n",
    "        i = j = k = 0\n",
    "        in_colours = ['tomato','indianred','tomato','indianred']\n",
    "        f, ax = plt.subplots(2, 2, figsize = [12, 12])\n",
    "\n",
    "        for j in range(ax.shape[0]):\n",
    "            for k in range(0, ax.shape[1]):\n",
    "\n",
    "                currax = ax[j][k]\n",
    "                curr_title = in_list_df[i]['detail'].unique()[0]\n",
    "\n",
    "                sns.lineplot(data = in_list_df[i],\n",
    "                            x=\"bin\",\n",
    "                            y=\"value\",\n",
    "                            lw=2.5,\n",
    "                            ax = currax,\n",
    "                            color=in_colours[i]\n",
    "                            )\n",
    "                currax.set_xlabel(\"Position\",fontsize=14)\n",
    "                currax.set_ylabel(\"Fraction of Methylation\",fontsize=14)\n",
    "                currax.set_title(curr_title,fontsize=14)\n",
    "                currax.tick_params(labelsize=14)\n",
    "                plt.xticks(rotation=45)\n",
    "                currax.set_ylim([0, 1])\n",
    "                i+=1\n",
    "\n",
    "        plt.tight_layout()\n",
    "\n",
    "    else:\n",
    "        in_colours = ['tomato','indianred']\n",
    "        f, ax = plt.subplots(1, 2, figsize = [12, 5.5])\n",
    "\n",
    "        for i in range(len(in_list_df)):\n",
    "\n",
    "            currax = ax[i]\n",
    "            curr_title = in_list_df[i]['detail'].unique()[0]\n",
    "\n",
    "            sns.lineplot(data = in_list_df[i],\n",
    "                        x=\"bin\",\n",
    "                        y=\"value\",\n",
    "                        lw=2.5,\n",
    "                        ax = currax,\n",
    "                        color=in_colours[i]\n",
    "                        )\n",
    "            currax.set_xlabel(\"Position\",fontsize=14)\n",
    "            currax.set_ylabel(\"Fraction of Methylation\",fontsize=14)\n",
    "            currax.set_title(list_tables[i],fontsize=14)\n",
    "            currax.tick_params(labelsize=14)\n",
    "            plt.xticks(rotation=45)\n",
    "            currax.set_ylim([0, 1])\n",
    "\n",
    "        plt.tight_layout()\n",
    "        \n",
    "# --------"
   ]
  },
  {
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       "<h2 style=\"font-size:16px;\">Human Genome: M-bias plots of mean methylation along reads</h2>"
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4f//+ev755xUcHHzD166p5s2b18l9UT+Ip2chnp6DWHoW4vkfzJ/gKYip5yGmnoeYuqeQ116r3oXjxl394wbcbsmeu5g4caI++eQT55+nnnqqwjd+teXKlSuaMWOGrly5Uuv3husRT89CPD0HsfQsxNN9MX9CTRFTz0NMPQ8x9SzuEE+3q5ByfCt3vW/frly5ooCAgNu+R/nrquLt7X3dErfaZLfbdfz4cUoePQTx9CzE03MQS89CPCtj/oSGjph6HmLqeYipZ3GHeLpdhZSjb0FVfQ4KCgpUXFxcZX+E8sLCwmQwGJz9FK7luPfN7gMAANAQMH8CAAANjdslpGJiYiRJaWlplc6lpqZWuOZ6fHx8FB0drVOnTikvL6/CObvdrgMHDqhRo0Zq165d7QwaAACgHjF/AgAADY3bJaS6dOmisLAwbdu2TdnZ2c7jly9f1rJly2QymTRo0CDn8fz8fJ08ebJSefm9994rSZo/f36FErSvv/5aZ86c0cCBA+Xj41O3b6YavL29lZiY6JLydtQ94ulZiKfnIJaehXhWxvwJDR0x9TzE1PMQU8/iDvE02N1wAWh6errmzJkjHx8fxcfHy8/PTzt37lReXp4effRRjR071nntu+++q+TkZD3zzDMaMmSI87jNZtPrr7+utLQ03X333YqJiVFOTo527dqlkJAQ/eUvf1FgYGA9vDsAAIDax/wJAAA0JG6ZkJKkrKwsLV68WJmZmbJarYqIiFBCQoLi4+MrXHe9CZUklZWVafny5dqyZYvOnTsns9msHj16aOrUqQoKCnLdmwEAAHAB5k8AAKChcNuEFAAAAAAAADyT2/WQAgAAAAAAgGcjIQUAAAAAAACXMtX3AO5UWVlZWrJkSYUeD2PGjKnU4wGut2XLFh0+fFjHjh3TTz/9JIvFUmWPDYfLly9ryZIl2rlzpwoKChQcHKy+fftq8uTJ8vX1rXS9zWbT+vXr9c033ygnJ0e+vr7q0qWLpk2bprCwsDp+d3eW8+fP6/vvv9e+fft08uRJFRQUyGw2q0OHDho/frzuvvvuSs8hnu6ptLRUCxYs0LFjx5STk6NLly7J399f4eHhGjZsmAYOHCiTqeKvNGLZ8Cxfvlzz58+XJL322mtq3759hfPEFAAAwHPQQ6oe3MouOHC9Z599Vnl5eWrcuLF8fX2Vl5d33YRUcXGx/vSnPyk7O1txcXGKjIxUdna20tLS1K5dO82ePbvS9tgffPCBNm3apLvuukvdu3dXfn6+vv/+e/n6+mrOnDlq0aKFi96p55s3b56++uorhYWFqXPnzgoMDNTp06e1e/du2e12Pf/88xWSwMTTfRUWFmr69OmKjo5WixYtFBgYqKKiIqWmpiovL09xcXGaOXOmjMarhb/EsuH56aefnDEsKSmplJAippAku90ug8FQ38MAAAC1gAopF7Narfrwww9lNBo1e/ZsRUZGSpISExM1c+ZMLViwQH379lVISEj9DvQO9vTTT6tFixYKCQmp8G19VVasWKHs7GyNHz9eDz/8sPO4IxGyevVqTZw40Xk8PT1dmzZtUqdOnfTSSy85KzoGDBig119/XR9//LFmzZpVd2/uDhMdHa2XX35ZMTExFY4fOnRIr7zyiv77v/9bvXv3lre3tyTi6c7MZrM+/fTTSlVQVqtVr732mtLS0pSamqoePXpIIpYNjcVi0bvvvqvIyEiFh4fru+++q3QNMYUkklEAAHgQeki5WHp6us6cOaP+/fs7k1GS5O/vr4kTJ8pisSg5Obn+Bgh17dq1WglBu92ujRs3ytfXV5MmTapwbtKkSfL19dWmTZsqHN+4caMk6cEHH6zwwbp79+7q3Lmz0tLSdPbs2Vp4F5Cke+65p1IySpI6deqk2NhYFRUV6aeffpJEPN2d0WislIySJC8vL/Xu3VuSlJOTI4lYNkRJSUk6ceKEpk+f7qxyK4+YIiUlRatWrdKSJUu0b98+Xbhwob6HBOA2sVDH8xBTz3Dp0iXZbDaXvBYVUi528OBBSVJcXFylc926dZMkZWRkuHJIqKHTp08rPz9fcXFxlXqX+Pr6qkOHDs4PPM2bN5d0NbaNGjVSx44dK90vLi5OBw8eVEZGhgYNGuSS93An8/LyqvBP4tkw2Ww2paWlSZLuuusuScSyoTl27JiWLVumKVOmqHXr1lVeQ0zvbG+99Zb27Nkji8XiPNa+fXsNHTpUw4YNq8eRwdVsNluVSWs0DLm5uTp37pyaNGkif39/BQUF1feQcJuIqWdZuHChDh8+rOzsbLVq1UpdunRRQkKC/P396+xnLwkpF3N8g19V34qgoCD5+vrq9OnTrh4WauBGsXQcT0tLU05Ojpo3b67i4mLl5+frrrvuqvIvdHh4uCQRfxc4e/asDhw4oODgYEVEREging2FxWJRUlKSJOnixYtKT0/XyZMnNWTIEHXp0kUSsWxIysrKnEv1xo8ff93riOmd67333lNqaqrGjBmjIUOGKC8vT4cOHdLy5ct15MgRnT59usISTniOlJQUnT59WlarVc2aNVP//v1JRjVgH3zwgfbt26eCggJJUtOmTfXggw+qa9euatq0af0ODjVCTD3LG2+8of3796tly5aKjY1VZmamli1bpgMHDmjkyJHq27dvpV6dtYGElItdvnxZ0tUlelXx8/NzXgP3Vp1Ylr/uZtc7jhP/umWxWPTPf/5TZWVlevjhh52TW+LZMFgsFi1dutT52GAwaOzYsXrooYecx4hlw7Fo0SKdPn1af/3rX2/4QZOY3plOnDihlJQU3XPPPRo3bpzMZrNatmypuLg4denSRW+//bZWrFihsrIyPfHEE/U9XNSit956S/v27VNpaanz2Lp16zR+/Hh17txZAQEBNLhvQObOnavU1FT16tVLsbGxys7O1o4dO/T++++rX79+GjVqVJXVrHBfxNSzJCUlad++fZo6daqGDh2qwMBAnTp1St99952++eYbffHFFyooKNDIkSPVqFGjWn1tElIA7hg2m03vvfeeDh06pOHDh7NUpwHy9fXV4sWLZbPZlJ+frz179mjBggU6cuSIZs6ced0EBNzPkSNHtHLlSk2ePNlZqQiUd/bsWRUVFalXr14ym82yWCwymUyy2Wzq3LmzZs6cqbfeektr166Vv7+/pkyZUt9DRi147733tG/fPo0ePVp9+vSR3W7XN998oz179ujDDz/UyJEjdd999ykoKIikVAOwdetWpaSkaPz48Ro3bpzz93R8fLw2b96szZs3q6CgQImJiYqNja3n0aI6iKlnsdlsyszMVEhIiO677z75+fnJZrOpZcuWGjt2rNq2batPP/1Uy5cvl8lk0r333lurlVLUvbrYzb6VvXLlCh+oGojqxLL8dTe7/mbf6OP22Gw2vf/++9q6dasGDhyop556qsJ54tmwGI1GNWvWTPfdd59+8YtfKDMz07mUj1i6P6vVqnfffVdt2rTRhAkTbno9Mb0zOb6FPXr0qCQ5m9MbjUbZ7Xa1a9dO/+///T/5+vpqxYoV2rlzZ72NFbUjKytL+/btU58+fTR27Fi1a9dO0dHReuKJJ/T000+refPmWr58uVauXKkLFy6QjGoATp06JYPBoGHDhsnf39/ZC65Tp0568MEHNX78eB06dEjLli1TVlZWPY8W1UFMPcvly5eVk5MjX19f+fn5yW63O6vW/f391aNHDz311FPy8/PTqlWrlJ6eXquvT0LKxW7Ut6KgoEDFxcXX7ZEB93KzHiSO447rfH19FRwcrNzc3Cp3LbhZjxTUnKMyKjk5Wf3799ezzz5baXkQ8Wy4HJtEODaEIJbur7i4WKdPn1Z2drYeeughTZkyxfnHsdPsiy++qClTpmjXrl3E9A4VFhamgIAApaamOndEdTAYDLLZbIqOjtavfvUrWa1WpaSk1NNIUVsKCwtVWFiorl27ymw2y2azyW63y9fX1/mhqG3btlq3bp2+/fZbFRcX1/eQcR2On71nz56V1WpVQUGB7Ha7czMZ6WrPoYSEBCUkJCg9PV3ffvtthWWacC/E1DOZzWaFhYUpLy9PZ8+edf5+dTCZTIqNjdXUqVN15coVrVu3rlZfn4SUizm2oHfsClVeampqhWvg3lq0aKHg4GBlZmZWmhAVFxcrMzNToaGhzh2fpKvfHJSUlOjw4cOV7uf4f6JTp051O/A7jCMZtWXLFsXHx+tXv/pVlb1qiGfDdf78eUn/2TGRWLo/b29vDRs2rMo/jiRRr169NGzYMIWGhhLTO1TTpk01ZswY/fjjj9q8eXOFXfak/1RKde3aVXFxcdq6datOnTpVT6NFbXDE2BHH8kvyjEaj2rVrp8cff1wtWrTQ+vXrnde5antyVJ9jruXYcOSHH36osqItMDBQI0eOVO/evfXNN9+w27gbI6aex/Gzs2fPnrp8+bKWLFki6Wqsr01KxcXFqWfPnkpLS9PmzZtrbQwkpFysS5cuCgsL07Zt25Sdne08fvnyZS1btkwmk4m+Ng2EwWDQ8OHDVVxcrC+//LLCuS+//FLFxcUaPnx4heP33nuvpKuNfMtPrPft26eDBw8qLi5OISEhdT/4O4Rjmd6WLVvUt2/f6yajJOLp7k6cOKGSkpJKx0tKSvTZZ59Jkrp37y6JWDYEPj4++uUvf1nln/bt20uSJkyYoF/+8peKjIwkpnew4cOHq3379lq9erVWr15d5TU+Pj7q0KGDbDabCgsLXTxC1Kb27dsrPDxcKSkpKikpkZeXV4UPRQaDQVFRUUpISND58+crfHiCe7Db7bLb7c7Hbdq0UbNmzfTFF1/o8OHDVSYwQkNDnZ9/1q5dK4vFUuEeqF/E1HM5fnZ2795d4eHh2rx5s5YvX+48V/7nb2BgoHOu5ag0rw00NXcxLy8vPf3005ozZ47+/Oc/Kz4+Xn5+ftq5c6fy8vL06KOPKjQ0tL6HeUfbuHGj8xt1xxKBjRs36uDBg5Kkjh07Ov8yjhs3Trt379ZXX32l7OxstW3bVsePH1daWpratWunMWPGVLh3bGyshg0bpk2bNmnGjBnq3r27CgoKtH37dpnNZj355JMufKeeb+nSpUpOTpavr69atmxZ6YOsJPXp00eRkZGSiKc72759u1avXq2OHTsqJCREfn5+On/+vFJTU3Xx4kV16tRJCQkJzuuJpechpnemoKAgTZ8+XX/5y180f/58Wa1WjRo1ytn/y/FB6MKFC/Lz85PZbK7P4eIW2Wy2Cskks9msLl266Ouvv9YHH3yg5557zpmUclxnMpnUp08fbd68Wenp6Tpz5ozCwsLq6y3gGtcmJyIiIjRq1CjNmzdPH330kZ555hlFRUU5zzuq4Pr06aOOHTs6l2DTH8x9EFPP8eOPPyo/P9/Z2sDxszM0NFTPPPOMZs+ercWLF8tkMikhIaFCUspoNKpVq1by8fFRfn5+rY2JhFQ9iI2N1auvvqrFixdr+/btslqtioiI0MMPP6z4+Pj6Ht4d7/Dhw84eJg6ZmZnKzMx0PnYkpHx9fZ1/cXfu3Kn09HQFBwcrISFBkydPrnIHgl/84heKiIjQxo0btXbtWvn6+qpPnz6aOnWqs/8JakdeXp6kq0t6HA2vrxUaGupMSBFP99WzZ0/l5+fryJEjOnLkiIqLi+Xv76+IiAj1799fQ4cOrdDDgFh6HmJ652rZsqX+8Ic/6O9//7sWLVqk3NxcDRgwwLl70969e7Vv3z61adNGwcHB9Txa3ArHz3IHk8mkKVOmKD09Xdu3b1dwcLAeeeQR54cio9Eoi8Uif39/xcfH69ChQyooKCAh5Qa2bt2qnJwc5efnKyYmRtHR0c64jBs3Tjk5Odq4caM++ugj/exnP1N0dLSz8qZ8osLb25uKNzdBTD3L+++/r9TUVBUUFMhgMKhZs2YaOHCgJk2aJJPJpA4dOujXv/61/vGPf+jzzz/X5cuXNWXKlAqxS01Nlc1mU9u2bWttXAY7tXMAAABwczk5Ofrkk0+UlpbmXKZnt9v1448/ymq1avbs2WrdunV9DxPVkJSUpCNHjujHH39Uhw4d1K5dOyUkJDg/xJ44cUJz5szR+fPnNXLkSD3++OPy8vKS1Wp1fvnw8ccfa/PmzZo7dy7Lb+vZ3LlznR9Upau9wIKCgvT444+rZ8+eatSokWw2mz788ENt3rxZISEheuqpp5ybkkhXE8vvv/++4uLiNH36dBmNRipq6hEx9SxvvfWW9u7dq759++ruu+/WpUuXtG7dOucmElOnTlVkZKS8vLy0d+9evf322yotLVXv3r01ZMgQRUZGKi0tTevXr9elS5c0e/bsWvu5S0IKAAAADUJRUZH27t2rlStXOpcdREVF6cEHH1TLli3re3iohjfeeENpaWkKDQ2Vv7+/Tpw4oeLiYnXo0EFPPPGEWrduLR8fHx09elRvvvmmzp8/r27duumpp55SUFCQTCaT9uzZoy+++EJms1l/+MMfFBAQUN9v6471wQcfaOvWrRozZoz69esnk8mk5ORkffPNN7p8+bIeeOABjRgxQk2bNpUkffLJJ1q7dq0kadCgQWrbtq3Onz+v3bt3q6ioSK+88gp/l+sZMfUs27dv1/vvv69Ro0Zp4sSJzqrUEydOaMWKFdq6davatm2rRx55RO3bt5eXl5eys7P1ySef6IcffpDFYpHBYHBWVf3+979XRERErY2PhBQAAAAalNLSUpWVlcnLy0smk0kmE10oGoIFCxZo5cqVmjx5su677z4FBAQoLy9P8+fP144dOxQaGqpHH31UXbt2lY+Pj3Jzc/WPf/xDWVlZMpvNatWqlUwmk3788UdJoiqunv3f//2f5syZo06dOumpp56qsPxy165dWrVqlTIzM5WQkKCEhATnktpNmzZp27ZtysjIkM1mk6+vr1q3bq1f/vKXuuuuu+rr7UDE1BMlJSVpyZIlevXVVxUdHS2bzeZMMJ0/f17r16/X6tWr1bZtWz399NPOn6mFhYU6deqU9u7dK5vNprCwMPXo0UPNmjWr1fHx2xsAAAANio+PT5V9w+C+bDabDh065GyI7OfnJ4vFopCQEP385z9XmzZttGrVKn3yySf62c9+pq5duyo0NFSzZs3S5s2btX//fmVlZSkoKEhdu3bV5MmTqbqoZ/n5+crPz1dUVJT8/f0rND/u06ePzGazvvzyS61atUqNGzfWhAkTJEnDhg1T3759debMGeXm5qpZs2YKCwtT48aN6/HdQCKmnqiwsFB2u71CLyjH0smmTZvq/vvvl9Vq1cqVK7Vo0SK98MILkq7uqhcYGKiOHTvW6fhISAEAAACoMzabTRcuXNDx48cVExPjTEaZTCbZ7XaZzWaNGjVKPj4++vLLL/XFF1/oD3/4g3NZ3+jRozV69Gjl5uaqSZMmMhgMJCTdgL+/v7y8vFRYWCjpP1vIO5pax8TEyGazqaioSAsWLFC7du3UpUsXSZKfn5/atm1bq82RcfuIqedwbATRvn17rV27Vps2bVJUVJSMRmOFxvNBQUEaNWqUTp06pV27dmndunUaNWqUJFW47tpm9bWFdvcAAAAA6ozRaFRwcLCioqJ04sQJlZaWymQyOZeO2O12+fr6aujQoRo5cqROnjyp//3f/3U+39FhJCQkRI0aNSIZ5QbsdrsaN26s4OBgrV+/XkePHnWec8RUurq7+OjRoyVJCxcudCY6aG7tPhxVUDabTWazmZg2cKWlpZL+k0zs0KGDmjZtqs2bN+u7776TVDGektS8eXNNnjxZRqNRBw4ccB4vH9O6ii8JKQAAAAB1xm63y2azKSIiQmfPntXChQtlsVgqfFNvt9vl5+enUaNGqWPHjtq7d6/27t0r6T8fhPjA6z4MBoPCwsI0dOhQlZSUaOXKlTpz5kyF844PvIMGDVLfvn11+vRpXblypb6GjOsovywvPDycmDZgb775plasWKHi4mJJV3/2NmvWTE8++aRsNpvWrFmj1NRUSVfjWX4XxbZt26pLly46fPiwc5mfK5CQAgAAAFDrHB9oDAaDjEajJk2apObNmys5OVlbtmypUCHl+HAUGBioxMREGQwGnTx5sp7fARyOHTumrVu3atOmTcrKynIeHzdunHr27KkdO3Zo3bp1Onv2rPOcwWBQWVmZJKljx44qKipyNqRH/duxY4cWLFig2bNna+XKlc7YjB8/npg2QAsXLtTu3bu1ZMkSrVu3TiUlJc4kfteuXfXAAw8oOztbSUlJzmS/0WhUWVmZc2OQsrIyBQUFyWw2u+wLAHpIAQAAAKg12dnZat26dYXdD202m4KCgvTYY4/pww8/1IoVK+Tj46P4+HgZjUbnN/XS1Z4mBoNB586dq4/h4xr//ve/9f333+vy5cvOYw899JDuv/9+eXt764EHHlBRUZHWr18vq9WqUaNGqWXLlrJarfL29pYknT17Vv7+/mrRokV9vQ2U895772n79u2y2+2yWq06cuSI+vXrpyeeeEJBQUHEtIGx2Wy6ePGiJOnuu+/WggULJEn333+/GjVq5FwSXVpaqpUrV2revHk6e/as7rvvPmc8U1JSdOLECcXGxspiscjb29slSSkSUgBQhdzcXD333HMaPHiwnn322Wo/b8qUKYqJidHLL79cd4MDAMBNvf766zp79qwef/xxxcbGOvuYOP7ZpUsXTZ48WYsWLdLixYt16dIl3XvvvRWSVz/88INMJpMiIiIk1V0zXdzcG2+8obS0NPXs2VPx8fE6deqUNm7cqPnz5yssLEx9+/ZVVFSUpk6dqsWLF2v9+vU6deqUJk2apE6dOkm6+kE3JSVFrVq1UlBQUP2+Iejvf/+7Dh48qGHDhmns2LHKz8/X2rVrtX37dg0ePFjdu3dXVFSUHnzwQWLaQBiNRsXFxSklJUX9+/fXlStXnEmpUaNGydfXV82aNdPo0aPVuHFjLVq0SP/zP/+jPXv2qFOnTsrPz9e+fftks9k0efJkl/bpIyEFwC05EkLleXl5KSgoSB07dtSECRPUpk0bl4/r5ZdfVkZGhhYvXuzy1wYAwJ198sknzv4kixYtkiRnUsqRVPL399fAgQPl5eWlRYsW6bPPPlNmZqYmTZokPz8/HTx4UGvWrFFQUJDi4uIk0TuqvixYsECHDx/W5MmTNWLECAUEBEiSQkND9c9//lOrV69W165d5e/vr44dO+rnP/+5li1bpq1bt+rAgQPq1KmTrFarTp8+Lbvdrt/97ndq3LhxPb+rO9vnn3+ugwcPaty4cRoxYoQaN26skJAQ5efna/v27c4qG6PRqJiYGD399NNatGiRvv/+e2Lq5vz9/VVUVKRu3bqpVatW+uyzz5xJqXvvvVdms1n+/v4aOnSoOnbsqM8++0xHjx5VamqqAgIC1KpVK/3+979Xy5YtXTpuElIA3FpYWJgGDhwoSSouLtYPP/ygbdu2adeuXXrppZfUsWPHOnndpk2b6u2335a/v/8tPe/tt99Wo0aN6mRMAAC4qx07dujbb79V69atFRcXp40bN2rBggWaNm1apaSU2WzWkCFD1KpVK3388cfavn27UlJSnEv3zGazZs6cqWbNmtX327pjOXpGRUdHa+jQoQoICJDFYpHJZNKAAQP09ddfKz8/33m9wWBQ69at9atf/UoxMTFKTU1VZmamAgMD1bVrVyUmJrr8gy4q2rdvnzZv3qzevXtr+PDhFRJJeXl58vLykiTNmzdPAQEBioiIUI8ePfTrX/9anTt31v79+4mpG4uMjJTZbFZ6erpGjBihyZMna+nSpVqwYIH8/PzUuXNnvfnmm7r//vs1cuRIzZgxQ8XFxfrxxx/VokULBQYGymw2u3zcJKQAuLXw8HBNmTKlwrGFCxcqKSlJCxcurLOlcSaTSa1atbrl59XkOQAANGSlpaXavHmzLBaLfv3rX6tVq1by8/PTihUrrpuU8vHxUefOnTVnzhxt27ZNP/74oy5duqQ2bdooPj5eISEh9f227mgnTpzQhQsXNH36dDVp0kR2u10mk0lWq9VZsX7s2DHl5+c7v7yz2WwyGo0aPny4hg8frkuXLslsNqusrMzZpwb1Jzg4WD4+Pho7dqyaNGniPJ6RkaGNGzfKarVq7dq1On/+vAoKCmQymTR58mRNmDBBI0aM0IgRI4ipG/Pz81Pjxo116NAhjRgxQvfcc48kKSkpSR9//LECAgJ05coVeXt7y263y2w2y2w2q3nz5vU6bhJSABqcUaNGKSkpSUePHpUk5y/Q5ORknTp1SiaTSVFRURozZox69epV4bk2m03ffvutvvnmG+Xk5Ki0tFSNGzdWZGSkxo4dq86dO0uquodU+cRY+X+/9pqqekgVFhYqKSlJu3fvdk7eYmJilJiY6OyR4fDuu+8qOTlZ//rXv5SSkqINGzYoNzdXQUFBGjp0qCZNmuTsxQEAQH3z8fFRYmKiYmNjddddd0mSRowYIYPBoK+++qrKpJR0tTeUr6+vhg8fXp/DRxU6d+6sKVOmqF27dhV6eDn+GRwcXOGx3W6vNDdxJKrK9wdD/YmMjNTcuXPl7+/vTB4eOnRIn3/+uc6ePavp06crNjZWZrNZBw4c0Ntvv63Vq1erffv2iomJkc1mcy7bJKbuxWazycvLS+3atdOJEydkt9tlt9t1zz33qKioSB999JGKiorUu3dvxcfHu9UyaD7RAGjQ7Ha75s6dq88++0ylpaUaOXKkBgwYoOzsbL3xxhtatWpVhevnz5+vDz/8UJcuXdKAAQM0ZswYxcbG6sSJEzpw4MANXysxMdH5jW1iYqLzT+/evW/4vMLCQs2aNUtr1qxRaGioEhISFBsbq127dmnWrFk6fPhwlc/7/PPP9eWXX6p9+/YaMWKEJGnJkiXOvhwAALiL6OhojRo1yvk4KChI9957r8aPH68TJ05owYIFSk9Pl81mk8FgkMFgkNFodO7cVj5JhfrXrFkzjRo1Sn5+fhU+vDqSTo6mx45lXo5rTp48qdzc3ArXutOH3zudI0noiI3VatWxY8f029/+VkOGDFHz5s3l6+ur3r17a+LEiSosLHTudmk0GislJuEeHPGMjIzUTz/9pDNnzshoNKqgoEBLliyRt7e3goODtXv3bm3cuLHCjpn1jdQmgAZnw4YNkq5Ofrds2aKUlBTFxMToxRdfdH5jM3HiRM2YMUPz5s1T7969FRYWJknatGmTgoOD9eabb1bq9XTp0qUbvu6UKVOUkZGhvLy8SssIb+SLL77QmTNnNGHCBD300EPO43v37tVf//pXvffee3rnnXcqfbN4/Phxvfnmm85vISdNmqTnn39e69at0+TJk/l2CgDgVhy/lxwVNY6klCRnpdSDDz6obt26SZK2bNmio0ePavTo0c7f03zQdR9V7bTlqKyx2Wyy2WwqLS11nktNTdW8efPUpUsXPfTQQ8xTGoDY2Fj9+9//VpMmTZyxdSzLdHwJ60hIwf2FhYXJYrHI29tbFy9e1IwZM1RWVqbHH39cZrNZy5Yt02effSaDwaDRo0fX93AlkZAC4OZycnKcO9qVlJQoKytLhw4dkre3t6ZNm6aFCxdKkh555JEKE5/mzZtrzJgxWrhwob777jslJiY6z5lMpiqXvNVFIz+LxaJt27apcePGmjRpUoVzPXr0UNeuXZ1NIh1b6TpMmjTJmYySpMDAQPXq1cu5NPHapX4AALiD8kmla5NSixYtkpeXl/Ly8jRv3jz5+Pho/Pjx9TVU1JDdbpfFYnFWSKWmpmrhwoU6ceKEnnvuOZJRDUhgYKAkOZfTOmJ66NAhZzNsNAwdOnRQkyZNtGLFCu3evVulpaWaOnWqhg0bJunq55K1a9eqa9eu9TzS/+AnBQC3dubMGS1dulSSnE00BwwYoAkTJigiIkLHjx9Xo0aNFB0dXem5sbGxkqTs7Gznsfj4eG3YsEEvvPCC4uPjFRsbq/bt21f5LWBtOHnypMrKyhQbG1vl7nuOXUuys7MrJaSioqIqXe/YcaioqKhOxgsAQG0LCgrSsGHDZDQatWzZMn300UcqKCiQ0WjUzJkz1bRp0/oeIqrJ8YWe0WiUr6+vjEajMjIytGDBAuXk5Ohvf/sbX5g1MI4EsqNCSpJ27typ1NRUdejQQS1atKjP4eEWeXt7a926dQoNDdW0adOcbT+kq5+DevToIV9f33ocYUUkpAC4tbi4OM2aNeu6569cuXLdbaGDgoKc1zg8+eSTCg0N1ebNm5WUlKSkpCR5e3urX79+euyxx5zfEtUWx2uX382kPEcFVPkxOvj5+VU65pgo2Gy22hoiAAB1ymazqWnTprr33nt1/Phx7dq1SwEBAZo9e7azCToaBsdyTIPBIIvFoj179mjr1q3KycnRK6+8QjKqgSqfjNq6dauWLVsmq9XqXOqFhsHf31/PPvusPvnkE/Xr18+5uUT53n3ulIySSEgBaOD8/PxUWFhY5bmCggLnNQ5eXl4aN26cxo0bp/PnzysjI0ObN2/Wli1bdOHChRsmv2o6Pkm6cOFCtccIAIAncXzQTU1N1eHDh+Xv769XXnlFrVu3rueR4VY5ElJWq1UWi0VJSUkqKyvTq6++qjZt2tT38FBDRqNRZWVl+vzzz7Vv3z5ZrVb96U9/UsuWLet7aLhFHTp00IwZM9SkSRNnMsqdd+d235EBQDW0bdvW2VvqWhkZGZKu7jhRlaZNm2rAgAH64x//qPDwcO3fv79Cc86q3GqFUqtWreTt7a2srCyVlJRUOn/w4MEbjhEAAE+Qmpqq+fPnq7i4mGRUA+aYBzmq0C0Wi+bMmUMyqoErLi7WkiVLtGXLFrVq1UovvfQS1W4NWFBQUINIRkkkpAA0cIMHD5YkzZ8/XxaLxXn87NmzWrVqlby8vDRw4EBJUllZmTIzMyvdo6SkRMXFxTKZTDfd3cdRtnz27Nlqjc9kMql///66ePGili1bVuFcamqq0tLSFB4erg4dOlTrfgAANEQtW7ZUZGSk/vKXv7BMzwP07t1bPXv2JJ4ewtfXVwkJCZoxY4aee+45+kZ5CHdPRkks2QPQwA0aNEg7d+5USkqKfve736lHjx4qKSnR9u3bdenSJT322GPOraRLS0v10ksvqUWLFoqKilLz5s1VXFysvXv3qqCgQGPHjpW3t/cNXy82NlY7duzQ3Llz1b17d3l7e6tNmzbq1avXdZ/zyCOP6NChQ0pKStKRI0cUHR2tvLw87dixQ40aNdIzzzzTIH5hAABQU6Ghofrtb3/L7mseIjw8XL/5zW+IpwcJDAys9V6qwM3wEwRAg2YwGPTCCy9ozZo1Sk5O1rp162QymdS2bVslJCRUSBQ1atRIDz/8sNLT03X48GFduHBBAQEBatmypaZNm6b+/fvf9PWGDx+u3Nxcbd++XV999ZWsVqsGDx58w4RUYGCg5syZoy+//FIpKSk6dOiQ/P391bt3byUmJlISDQC4I5C88CzEE8DtMtjtdnt9DwIAAAAAAAB3DtaIAAAAAAAAwKVISAEAAAAAAMClSEgBAAAAAADApUhIAQAAAAAAwKVISAEAAAAAAMClSEgBAAAAAG5bbm6upkyZonffffeWnjdlyhS9/PLLdTMoAG7LVN8DAAAAAADcvtzcXD333HMVjnl5eSkoKEgdO3bUhAkT1KZNG5eP6+WXX1ZGRoYWL17s8tcG4L5ISAEAAACABwkLC9PAgQMlScXFxfrhhx+0bds27dq1Sy+99JI6duxYJ6/btGlTvf322/L397+l57399ttq1KhRnYwJgPsiIQUAAAAAHiQ8PFxTpkypcGzhwoVKSkrSwoUL62x5nMlkUqtWrW75eTV5DoCGj4QUAAAAAHi4UaNGKSkpSUePHpUkWa1WrV27VsnJyTp16pRMJpOioqI0ZswY9erVq8JzbTabvv32W33zzTfKyclRaWmpGjdurMjISI0dO1adO3eW9J8lg4MHD9azzz4rSRUSY+X//dprYmJiKiXKCgsLlZSUpN27dys/P1/+/v6KiYlRYmKiIiIiKlz77rvvKjk5Wf/617+UkpKiDRs2KDc3V0FBQRo6dKgmTZoko5EWyoA7ISEFAAAAAHcQu92uuXPnKiUlRS1atNDIkSNVUlKi7du364033tBjjz2mhIQE5/Xz58/XihUrFBYWpgEDBsjPz0/nz5/X4cOHdeDAAWdCqiqJiYlKTk5WXl6eEhMTnccjIyNvOMbCwkLNmjVLZ86cUefOndW/f3/l5uZqx44d2rdvn2bNmlXl0sPPP/9cGRkZ6tmzp+Li4rR7924tWbJEFotF06ZNu/X/WADqDAkpAAAAAPBwGzZskCRFR0dry5YtSklJUUxMjF588UWZTFc/Fk6cOFEzZszQvHnz1Lt3b4WFhUmSNm3apODgYL355puVej1dunTphq87ZcoUZWRkKC8vr9Iywhv54osvdObMGU2YMEEPPfSQ8/jevXv117/+Ve+9957eeeedSlVPx48f15tvvqng4GBJ0qRJk/T8889r3bp1mjx5svO9Aqh/1CwCAAAAgAfJycnR4sWLtXjxYn3++ef685//rKVLl8rb21vTpk1TcnKyJOmRRx6pkKBp3ry5xowZI6vVqu+++67CPU0mU5VL3sxmc62P32KxaNu2bWrcuLEmTZpU4VyPHj3UtWtX5eTkKDMzs9JzJ02a5ExGSVJgYKB69eqlK1eu6NSpU7U+VgA1R3oYAAAAADzImTNntHTpUkmSl5eXgoKCNGDAAE2YMEERERE6fvy4GjVqpOjo6ErPjY2NlSRlZ2c7j8XHx2vDhg164YUXFB8fr9jYWLVv314+Pj51Mv6TJ0+qrKxMsbGxVe6+17lzZ+3fv1/Z2dnq1KlThXNRUVGVrm/WrJkkqaioqE7GC6BmSEgBAAAAgAeJi4vTrFmzrnv+ypUrziTNtYKCgpzXODz55JMKDQ3V5s2blZSUpKSkJHl7e6tfv3567LHHFBgYWKvjd7x2kyZNqjzvqIAqP0YHPz+/SscclV02m622hgigFpCQAgAAAIA7iJ+fnwoLC6s8V1BQ4LzGwcvLS+PGjdO4ceN0/vx5ZWRkaPPmzdqyZYsuXLhww+RXTccnSRcuXKj2GAE0PPSQAgAAAIA7SNu2bVVSUqKsrKxK5zIyMiRdfxe8pk2basCAAfrjH/+o8PBw7d+/X6WlpTd8vVutUGrVqpW8vb2VlZWlkpKSSucPHjx4wzECaBhISAEAAADAHWTw4MGSpPnz58tisTiPnz17VqtWrZKXl5cGDhwoSSorK6uyeXhJSYmKi4tlMplkMBhu+HqOxudnz56t1vhMJpP69++vixcvatmyZRXOpaamKi0tTeHh4erQoUO17gfAPbFkDwAAAADuIIMGDdLOnTuVkpKi3/3ud+rRo4dKSkq0fft2Xbp0SY899pjCwsIkSaWlpXrppZfUokULRUVFqXnz5iouLtbevXtVUFCgsWPHytvb+4avFxsbqx07dmju3Lnq3r27vL291aZNG/Xq1eu6z3nkkUd06NAhJSUl6ciRI4qOjlZeXp527NihRo0a6Zlnnqly1z8ADQcJKQAAAAC4gxgMBr3wwgtas2aNkpOTtW7dOplMJrVt21YJCQkVEkWNGjXSww8/rPT0dB0+fFgXLlxQQECAWrZsqWnTpql///43fb3hw4crNzdX27dv11dffSWr1arBgwffMCEVGBioOXPm6Msvv1RKSooOHTokf39/9e7dW4mJiYqIiKiV/xYA6o/Bbrfb63sQAAAAAAAAuHNQ4wgAAAAAAACXIiEFAAAAAAAAlyIhBQAAAAAAAJciIQUAAAAAAACXIiEFAAAAAAAAlyIhBQAAAAAAAJciIQUAAAAAAACXIiEFAAAAAAAAlyIhBQAAAAAAAJciIQUAAAAAAACXIiEFAAAAAAAAlyIhBQAAAAAAAJciIQUAAAAAAACX+v8ArxNDPxWJgOMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mbias: Mean methylation along reads\n",
    "# ========================================\n",
    "HTML(\" \")\n",
    "HTML(\"<h2 style=\\\"font-size:16px;\\\">\"+\"Human Genome: M-bias plots of mean methylation along reads\"+\"</h2>\")\n",
    "HTML(\"<hr/>\")\n",
    "\n",
    "tbl = 'Mbias_per_position'\n",
    "\n",
    "df_pos = pd.read_hdf(input_h5_file, key = tbl)\n",
    "df_pos = pd.DataFrame(df_pos)\n",
    "df_pos = df_pos.reset_index()\n",
    "df_pos = parse_metric_names(df_pos)\n",
    "df_pos['bin'] = df_pos['bin'].astype(int)\n",
    "list_tables = list(set(df_pos['detail']))\n",
    "\n",
    "df_to_print = df_pos.copy()\n",
    "df_to_print['stat_type'] =  df_to_print['metric'].str.extract(r'([A-Za-z]+)\\s')\n",
    "df_to_print['metric']  = df_to_print['metric'].str.title()\n",
    "\n",
    "list_df = [y for x, y in df_to_print.groupby(df_pos['detail'], sort = False)]\n",
    "\n",
    "# plot the MBIAS tests\n",
    "plot_mbias(list_df)\n",
    "\n",
    "# ---------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10b1b178",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:14.132934Z",
     "iopub.status.busy": "2025-01-26T17:57:14.132645Z",
     "iopub.status.idle": "2025-01-26T17:57:14.164890Z",
     "shell.execute_reply": "2025-01-26T17:57:14.164428Z"
    },
    "papermill": {
     "duration": 0.051839,
     "end_time": "2025-01-26T17:57:14.166024",
     "exception": false,
     "start_time": "2025-01-26T17:57:14.114185",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:14px;\">M-bias Descriptive Statistics</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2cf14\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2cf14_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >metric</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2cf14_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_2cf14_row0_col0\" class=\"data row0 col0\" >70.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cf14_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_2cf14_row1_col0\" class=\"data row1 col0\" >7.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cf14_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_2cf14_row2_col0\" class=\"data row2 col0\" >71.16%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_629e2\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_629e2_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >metric</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_629e2_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_629e2_row0_col0\" class=\"data row0 col0\" >69.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_629e2_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_629e2_row1_col0\" class=\"data row1 col0\" >8.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_629e2_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_629e2_row2_col0\" class=\"data row2 col0\" >71.77%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CONTINUES Mbias: Mean methylation along reads\n",
    "HTML(\"<h2 style=\\\"font-size:14px;\\\">\"+\"M-bias Descriptive Statistics\"+\"</h2>\")\n",
    "HTML(\" \")\n",
    "tbl = 'Mbias_desc'\n",
    "\n",
    "df_desc = pd.read_hdf(input_h5_file, key = tbl)\n",
    "df_desc = pd.DataFrame(df_desc)\n",
    "df_desc = df_desc.reset_index()\n",
    "df_desc  = format_metric_names(df_desc)\n",
    "\n",
    "df_to_print = df_desc.copy()\n",
    "df_to_print['stat_type'] =  df_to_print['metric'].str.extract(r'([A-Za-z]+)\\s')\n",
    "df_to_print['metric']  = df_to_print['metric'].str.title()\n",
    "\n",
    "\n",
    "df_to_print['value'][df_to_print['stat_type'] == \"Percent\"] = (df_to_print['value'][df_to_print['stat_type'] == \"Percent\"]).map('{:,.2%}'.format)\n",
    "\n",
    "\n",
    "cols = ['metric','value']\n",
    "df_output = []\n",
    "df_output = [y for x, y in df_to_print.groupby('detail')]\n",
    "\n",
    "\n",
    "if (len(df_output) ==4):\n",
    "    order =[3,2,1,0]\n",
    "else:\n",
    "    order =[1,0]\n",
    "df_output = [df_output[i] for i in order]\n",
    "\n",
    "del(df_to_print)\n",
    "df_to_print = []\n",
    "temp_tables = []\n",
    "for l in df_output:\n",
    "    l.index = l['metric']\n",
    "    temp_tables.append(l['detail'][0])\n",
    "    df_to_print.append(l['value'].to_frame())\n",
    "\n",
    "display_side_by_side(df_to_print, temp_tables)\n",
    "\n",
    "# ---------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3dcff563",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:14.183429Z",
     "iopub.status.busy": "2025-01-26T17:57:14.183137Z",
     "iopub.status.idle": "2025-01-26T17:57:14.570893Z",
     "shell.execute_reply": "2025-01-26T17:57:14.570398Z"
    },
    "papermill": {
     "duration": 0.398171,
     "end_time": "2025-01-26T17:57:14.572279",
     "exception": false,
     "start_time": "2025-01-26T17:57:14.174108",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:16px;\">Human Genome: M-bias plots of mean methylation on CHH/CHG along reads</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MbiasNoCpG: Mean methylation along reads on non-CpG Cytosines\n",
    "# ========================================\n",
    "HTML(\" \")\n",
    "HTML(\"<h2 style=\\\"font-size:16px;\\\">\"+\"Human Genome: M-bias plots of mean methylation on CHH/CHG along reads\"+\"</h2>\")\n",
    "HTML(\"<hr/>\")\n",
    "\n",
    "tbl = 'MbiasNoCpG_per_position'\n",
    "\n",
    "df_pos = pd.read_hdf(input_h5_file, key = tbl)\n",
    "df_pos = pd.DataFrame(df_pos)\n",
    "df_pos = df_pos.reset_index()\n",
    "df_pos = parse_metric_names(df_pos)\n",
    "df_pos['bin'] = df_pos['bin'].astype(int)\n",
    "list_tables = list(set(df_pos['detail']))\n",
    "\n",
    "df_to_print = df_pos.copy()\n",
    "df_to_print['stat_type'] =  df_to_print['metric'].str.extract(r'([A-Za-z]+)\\s')\n",
    "df_to_print['metric']  = df_to_print['metric'].str.title()\n",
    "\n",
    "list_df = [y for x, y in df_to_print.groupby(df_pos['detail'], sort = False)]\n",
    "\n",
    "# plot the MBIAS tests\n",
    "plot_mbias(list_df)\n",
    "\n",
    "# ---------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2c503808",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:14.591733Z",
     "iopub.status.busy": "2025-01-26T17:57:14.591437Z",
     "iopub.status.idle": "2025-01-26T17:57:14.623820Z",
     "shell.execute_reply": "2025-01-26T17:57:14.623359Z"
    },
    "papermill": {
     "duration": 0.043261,
     "end_time": "2025-01-26T17:57:14.624950",
     "exception": false,
     "start_time": "2025-01-26T17:57:14.581689",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:14px;\">M-bias of CHH/CHG Descriptive Statistics</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_4bded\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4bded_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >metric</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4bded_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_4bded_row0_col0\" class=\"data row0 col0\" >68.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bded_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_4bded_row1_col0\" class=\"data row1 col0\" >8.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bded_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_4bded_row2_col0\" class=\"data row2 col0\" >70.19%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_f5d95\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f5d95_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >metric</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f5d95_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_f5d95_row0_col0\" class=\"data row0 col0\" >70.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f5d95_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_f5d95_row1_col0\" class=\"data row1 col0\" >6.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f5d95_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_f5d95_row2_col0\" class=\"data row2 col0\" >71.72%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CONTINUES MbiasNoCpG: Mean methylation along reads on non-CpG Cytosines\n",
    "HTML(\"<h2 style=\\\"font-size:14px;\\\">\"+\"M-bias of CHH/CHG Descriptive Statistics\"+\"</h2>\")\n",
    "HTML(\" \")\n",
    "tbl = 'MbiasNoCpG_desc'\n",
    "\n",
    "df_desc = pd.read_hdf(input_h5_file, key = tbl)\n",
    "df_desc = pd.DataFrame(df_desc)\n",
    "df_desc = df_desc.reset_index()\n",
    "df_desc  = format_metric_names(df_desc)\n",
    "\n",
    "df_to_print = df_desc.copy()\n",
    "df_to_print['stat_type'] =  df_to_print['metric'].str.extract(r'([A-Za-z]+)\\s')\n",
    "df_to_print['metric']  = df_to_print['metric'].str.title()\n",
    "\n",
    "\n",
    "df_to_print['value'][df_to_print['stat_type'] == \"Percent\"] = (df_to_print['value'][df_to_print['stat_type'] == \"Percent\"]).map('{:,.2%}'.format)\n",
    "\n",
    "\n",
    "cols = ['metric','value']\n",
    "df_output = []\n",
    "df_output = [y for x, y in df_to_print.groupby('detail')]\n",
    "\n",
    "\n",
    "if (len(df_output) ==4):\n",
    "    order =[3,2,1,0]\n",
    "else:\n",
    "    order =[1,0]\n",
    "df_output = [df_output[i] for i in order]\n",
    "\n",
    "del(df_to_print)\n",
    "df_to_print = []\n",
    "temp_tables = []\n",
    "for l in df_output:\n",
    "    l.index = l['metric']\n",
    "    temp_tables.append(l['detail'][0])\n",
    "    df_to_print.append(l['value'].to_frame())\n",
    "\n",
    "display_side_by_side(df_to_print, temp_tables)\n",
    "\n",
    "# ---------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "89cb5ddd",
   "metadata": {
    "execution": {
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    "pycharm": {
     "name": "#%%\n"
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   "source": [
    "## Control genomes (if exist)\n",
    "# ------------------------------\n",
    "\n",
    "tbl = 'MergeContext_per_position'\n",
    "if tbl in list_tables:\n",
    "    df_pos = pd.read_hdf(input_h5_file, key = tbl)\n",
    "    df_pos = pd.DataFrame(df_pos)\n",
    "    df_pos.reset_index(inplace=True)\n",
    "    all_genomes = list(set(df_pos['detail']))\n",
    "    ctrl_genomes = ['Lambda', 'pUC19']\n",
    "\n",
    "    check = all(item in all_genomes for item in ctrl_genomes)\n",
    "\n",
    "    if check is True:\n",
    "\n",
    "        HTML(\" \")\n",
    "        HTML(\"<h2 style=\\\"font-size:14px;\\\">\"+\"Control Genomes: Methylation and Coverage\"+\"</h2>\")\n",
    "        HTML(\"<hr/>\")\n",
    "\n",
    "        # PRINT PLOTS OF PERCENT METHYLATION ACROSS ENTIRE CONTROL GENOMES \n",
    "        #-----------------------------------------------------------------\n",
    "\n",
    "        df_pos = parse_metric_names(df_pos)\n",
    "        df_pos['bin'] = df_pos['bin'].astype(int)\n",
    "        df_pos  = format_metric_names(df_pos)\n",
    "        df_pos['bin'] = df_pos['bin'].astype(int)\n",
    "        df_pos = df_pos.query('metric == \"Percent Methylation: Position\"')\n",
    "        df_output = [y for x, y in df_pos.groupby('detail')]\n",
    "\n",
    "\n",
    "        df_pos_meth = []\n",
    "        n = 102\n",
    "        f, ax = plt.subplots(1, 2, figsize = [12, 5])\n",
    "        i = 0\n",
    "        palet = ['forestgreen','steelblue']\n",
    "\n",
    "        for df_pos_meth in df_output:\n",
    "            # get methylation per position\n",
    "            df_pos_meth = df_pos_meth.reset_index()\n",
    "            temp_genome = df_pos_meth['detail'].unique()[0]\n",
    "\n",
    "            # print to subplots\n",
    "            currax = ax[i]\n",
    "            s = df_pos_meth.plot(kind = 'area', ylim = [0,n],y  ='value' ,\n",
    "                                        title = temp_genome + \": Percent methylation\", \n",
    "                        legend=False, color=palet[i], alpha=0.6, ax = currax,\n",
    "                                fontsize=14)\n",
    "            a = currax.set(xlabel='Position', ylabel='Percent Methylation')\n",
    "            plt.style.use('ggplot')\n",
    "\n",
    "            i+=1\n",
    "\n",
    "        HTML(\" \")\n",
    "\n",
    "# --------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ddc74128",
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    "execution": {
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     "name": "#%%\n"
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   "outputs": [
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:12px;\">Control Genomes: Methylation and Coverage Descriptive Statistics</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_e6d31\" style='display:inline-table'>\n",
       "  <caption>Lambda</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e6d31_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >metric</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e6d31_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_e6d31_row0_col0\" class=\"data row0 col0\" >2.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6d31_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_e6d31_row1_col0\" class=\"data row1 col0\" >12.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6d31_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_e6d31_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6d31_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_e6d31_row3_col0\" class=\"data row3 col0\" >815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6d31_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_e6d31_row4_col0\" class=\"data row4 col0\" >3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6d31_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_e6d31_row5_col0\" class=\"data row5 col0\" >3.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e6d31_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_e6d31_row6_col0\" class=\"data row6 col0\" >2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_b5ede\" style='display:inline-table'>\n",
       "  <caption>pUC19</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b5ede_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >metric</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b5ede_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_b5ede_row0_col0\" class=\"data row0 col0\" >20.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5ede_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_b5ede_row1_col0\" class=\"data row1 col0\" >24.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5ede_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_b5ede_row2_col0\" class=\"data row2 col0\" >14.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5ede_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_b5ede_row3_col0\" class=\"data row3 col0\" >159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5ede_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_b5ede_row4_col0\" class=\"data row4 col0\" >13.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5ede_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_b5ede_row5_col0\" class=\"data row5 col0\" >18.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5ede_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_b5ede_row6_col0\" class=\"data row6 col0\" >7.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Control genomes (if exist)\n",
    "# ------------------------------\n",
    "HTML(\" \")\n",
    "\n",
    "tbl = 'MergeContext_desc'\n",
    "df_desc = pd.read_hdf(input_h5_file, key = tbl)\n",
    "df_desc = pd.DataFrame(df_desc)\n",
    "df_desc = df_desc.reset_index()\n",
    "all_genomes = list(set(df_desc['detail']))\n",
    "ctrl_genomes = ['Lambda', 'pUC19']\n",
    "\n",
    "check = all(item in all_genomes for item in ctrl_genomes)\n",
    "\n",
    "if check is True:\n",
    "\n",
    "    HTML(\"<h2 style=\\\"font-size:12px;\\\">\"+\"Control Genomes: Methylation and Coverage Descriptive Statistics\"+\"</h2>\")\n",
    "    # HTML(\"<hr/>\")\n",
    "    tbl = 'MergeContext'\n",
    "    genome = 'hg'\n",
    "    df_to_print = pd.DataFrame()\n",
    "    df_table = df_desc[df_desc['detail'] != genome]\n",
    "    df_table = df_table.reset_index()\n",
    "    df_table  = format_metric_names(df_table)\n",
    "\n",
    "    df_table['stat_type']=  df_table['metric'].str.extract(r'([A-Za-z]+)[\\s:]')\n",
    "    df_table['metric']  = df_table['metric'].str.title()\n",
    "    df_table['value'][df_table['stat_type'] == \"Percent\"] = (df_table['value'][df_table['stat_type'] == \"Percent\"]/100).map('{:,.2%}'.format)\n",
    "    df_table['value'][df_table['stat_type'] == \"Coverage\"] = df_table['value'][df_table['stat_type'] == \"Coverage\"].map('{:,.2f}'.format)\n",
    "    df_table['value'][df_table['stat_type'] == \"Total\"] = df_table['value'][df_table['stat_type'] == \"Total\"].map('{:,.0f}'.format)\n",
    "    df_table['metric']= df_table['metric'].str.replace(r'Cpgs', 'CpGs', regex=True)\n",
    "\n",
    "    table_names = df_table['detail'].unique()\n",
    "    cols = ['metric','value', 'detail']\n",
    "    df_output = []\n",
    "    df_output = [y for x, y in df_table.groupby('detail')]\n",
    "\n",
    "    df_to_print = []\n",
    "    cols = ['metric','value']\n",
    "    for l in df_output:\n",
    "        l.index = l['metric']\n",
    "        df_to_print.append(l['value'].to_frame())\n",
    "\n",
    "    display_side_by_side(df_to_print, table_names)\n",
    "    \n",
    "    #--------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0ab5525f",
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    "pycharm": {
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   "source": [
    "# function for printing bar plots of methylation and coverage at CpGs \n",
    "def plot_bar_distrib(in_table):\n",
    "    i =0\n",
    "    in_colours = ['salmon','tomato']\n",
    "    curr_genome = \"Human Genome\"\n",
    "    cols = ['bin','value']\n",
    "    n_rows = in_table.shape[0]\n",
    "    if ( n_rows > 10 ):\n",
    "        h = 5\n",
    "        w = 13.5\n",
    "        n = 2\n",
    "\n",
    "        in_list_df = [y for x, y in in_table.groupby(in_table['metric'], sort = False)]\n",
    "\n",
    "        f, ax = plt.subplots(1, n, figsize = [w, h])\n",
    "\n",
    "        for i in range(len(in_list_df)):\n",
    "\n",
    "            currax = ax[i]\n",
    "            y_axis_label = in_list_df[i]['metric'].unique()[0]\n",
    "            y_axis_label = y_axis_label.replace(\":\", \"\" )\n",
    "            x_axis_label =\"Value Bins\"\n",
    "            curr_title = curr_genome + \": \" + y_axis_label\n",
    "            \n",
    "            sns.barplot(data = in_list_df[i],\n",
    "                        x=\"bin\",\n",
    "                        y=\"value\",\n",
    "                        lw=2.5,\n",
    "                        ax = currax,\n",
    "                        color=in_colours[i]\n",
    "                        )\n",
    "            currax.set_xlabel(x_axis_label,fontsize=14)\n",
    "            currax.set_ylabel(y_axis_label,fontsize=14)\n",
    "            currax.set_title(curr_title,fontsize=13)\n",
    "            currax.tick_params(labelsize=14)\n",
    "            f.axes[i].tick_params(labelrotation=45)\n",
    "\n",
    "    else: \n",
    "        h = 5\n",
    "        w = 5.5\n",
    "        n = 1\n",
    "\n",
    "        f, ax = plt.subplots(n, n, figsize = [w, h])\n",
    "\n",
    "        y_axis_label = in_table['metric'].unique()[0]\n",
    "        y_axis_label = y_axis_label.replace(\":\", \"\" )\n",
    "        curr_title = curr_genome + \": \" + y_axis_label\n",
    "\n",
    "        sns.barplot(data = in_table,\n",
    "                    x=\"bin\",\n",
    "                    y=\"value\",\n",
    "                    lw=2.5,\n",
    "                    ax = ax,\n",
    "                    color=in_colours[i]\n",
    "                    )\n",
    "        ax.set_xlabel(\"Value Bins\",fontsize=14)\n",
    "        ax.set_ylabel(y_axis_label,fontsize=14)\n",
    "        ax.set_title(curr_title,fontsize=13)\n",
    "        ax.tick_params(labelsize=14)\n",
    "        plt.xticks(rotation=45)\n",
    "\n",
    "#--------\n"
   ]
  },
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",
      "text/plain": [
       "<Figure size 550x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1350x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print details of mergeCOntext plots of CpG Methylation and Coverage\n",
    "# -----------------------------\n",
    "# main statistics table\n",
    "# --------------------------\n",
    "tbl = 'MergeContext_hist'\n",
    "genome = 'hg'\n",
    "df_hist = pd.read_hdf(input_h5_file, key = tbl)\n",
    "df_hist = pd.DataFrame(df_hist)\n",
    "df_hist = df_hist.reset_index()\n",
    "df_hist = df_hist[df_hist['detail'] == genome]\n",
    "df_hist = parse_metric_names(df_hist)\n",
    "df_hist = format_metric_names(df_hist)\n",
    "df_hist['stat_type'] =  df_hist['metric'].str.extract(r'([A-Za-z]+):')\n",
    "\n",
    "\n",
    "HTML(\" \")\n",
    "HTML(\"<hr/>\")\n",
    "HTML(\"<h2 style=\\\"font-size:16px;\\\">\"+\"Human Genome: Additional Details of CpG Methylation and Coverage\"+\"</h2>\")\n",
    "HTML(\"<hr/>\")\n",
    "HTML(\" \")\n",
    "\n",
    "list_df = [y for x, y in df_hist.groupby(df_hist['stat_type'], sort = False)]\n",
    "\n",
    "for df_to_plot in list_df:\n",
    "    plot_bar_distrib(df_to_plot)\n",
    "\n",
    "#--------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b65ffae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:15.398001Z",
     "iopub.status.busy": "2025-01-26T17:57:15.397701Z",
     "iopub.status.idle": "2025-01-26T17:57:15.416988Z",
     "shell.execute_reply": "2025-01-26T17:57:15.416526Z"
    },
    "papermill": {
     "duration": 0.032445,
     "end_time": "2025-01-26T17:57:15.418128",
     "exception": false,
     "start_time": "2025-01-26T17:57:15.385683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:12px;\">Human Genome: Values of CpG Methylation and Coverage</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c0941\" style='display:inline-table'>\n",
       "  <caption>Percent Methylation </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c0941_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_c0941_row0_col0\" class=\"data row0 col0\" >2,209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_c0941_row1_col0\" class=\"data row1 col0\" >847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_c0941_row2_col0\" class=\"data row2 col0\" >3,019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_c0941_row3_col0\" class=\"data row3 col0\" >6,993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_c0941_row4_col0\" class=\"data row4 col0\" >18,021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_c0941_row5_col0\" class=\"data row5 col0\" >73,111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_c0941_row6_col0\" class=\"data row6 col0\" >189,670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_c0941_row7_col0\" class=\"data row7 col0\" >257,961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_c0941_row8_col0\" class=\"data row8 col0\" >158,859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0941_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_c0941_row9_col0\" class=\"data row9 col0\" >29,380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_70962\" style='display:inline-table'>\n",
       "  <caption>Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_70962_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_70962_row0_col0\" class=\"data row0 col0\" >17,708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_70962_row1_col0\" class=\"data row1 col0\" >19,594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_70962_row2_col0\" class=\"data row2 col0\" >41,555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_70962_row3_col0\" class=\"data row3 col0\" >102,517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_70962_row4_col0\" class=\"data row4 col0\" >187,465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_70962_row5_col0\" class=\"data row5 col0\" >194,429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_70962_row6_col0\" class=\"data row6 col0\" >114,779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_70962_row7_col0\" class=\"data row7 col0\" >42,918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_70962_row8_col0\" class=\"data row8 col0\" >11,392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_70962_row9_col0\" class=\"data row9 col0\" >2,948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_70962_row10_col0\" class=\"data row10 col0\" >1,027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_70962_row11_col0\" class=\"data row11 col0\" >579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_70962_row12_col0\" class=\"data row12 col0\" >372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_70962_row13_col0\" class=\"data row13 col0\" >288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_70962_row14_col0\" class=\"data row14 col0\" >250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_70962_row15_col0\" class=\"data row15 col0\" >219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_70962_row16_col0\" class=\"data row16 col0\" >181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_70962_row17_col0\" class=\"data row17 col0\" >151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_70962_row18_col0\" class=\"data row18 col0\" >136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70962_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_70962_row19_col0\" class=\"data row19 col0\" >1,562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2285c\" style='display:inline-table'>\n",
       "  <caption>Cumulative Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2285c_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_2285c_row0_col0\" class=\"data row0 col0\" >2.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_2285c_row1_col0\" class=\"data row1 col0\" >5.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_2285c_row2_col0\" class=\"data row2 col0\" >10.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_2285c_row3_col0\" class=\"data row3 col0\" >24.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_2285c_row4_col0\" class=\"data row4 col0\" >49.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_2285c_row5_col0\" class=\"data row5 col0\" >76.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_2285c_row6_col0\" class=\"data row6 col0\" >91.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_2285c_row7_col0\" class=\"data row7 col0\" >97.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_2285c_row8_col0\" class=\"data row8 col0\" >98.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_2285c_row9_col0\" class=\"data row9 col0\" >99.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_2285c_row10_col0\" class=\"data row10 col0\" >99.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_2285c_row11_col0\" class=\"data row11 col0\" >99.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_2285c_row12_col0\" class=\"data row12 col0\" >99.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_2285c_row13_col0\" class=\"data row13 col0\" >99.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_2285c_row14_col0\" class=\"data row14 col0\" >99.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_2285c_row15_col0\" class=\"data row15 col0\" >99.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_2285c_row16_col0\" class=\"data row16 col0\" >99.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_2285c_row17_col0\" class=\"data row17 col0\" >99.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_2285c_row18_col0\" class=\"data row18 col0\" >99.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2285c_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_2285c_row19_col0\" class=\"data row19 col0\" >100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print details of figures\n",
    "# --------------------------\n",
    "HTML(\" \")\n",
    "HTML(\"<h2 style=\\\"font-size:12px;\\\">\"+\"Human Genome: Values of CpG Methylation and Coverage\"+\"</h2>\")\n",
    "HTML(\" \")\n",
    "\n",
    "list_df = [y for x, y in df_hist.groupby(df_hist['metric'], sort = False)]\n",
    "\n",
    "df_to_print = []\n",
    "temp_tables = []\n",
    "cols = ['bin','value']\n",
    "for i, sub_df in enumerate(list_df):\n",
    "    sub_df = sub_df.reset_index()\n",
    "    if(i<2):\n",
    "        sub_df['value'] = sub_df['value'].map('{:,.0f}'.format) \n",
    "    else:\n",
    "        sub_df['value'] = sub_df['value'].map('{:,.2%}'.format) \n",
    "    temp_tables.append(sub_df['metric'][0].replace(\":\", \"\" ))\n",
    "    sub_df.index = sub_df['bin']\n",
    "    df_to_print.append(sub_df['value'].to_frame())\n",
    "    \n",
    "\n",
    "display_side_by_side(df_to_print, temp_tables)\n",
    "#-------\n"
   ]
  }
 ],
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  "papermill": {
   "default_parameters": {},
   "duration": 9.506806,
   "end_time": "2025-01-26T17:57:15.846625",
   "environment_variables": {},
   "exception": null,
   "input_path": "/app/VariantCalling/ugvc/reports/methyldackel_qc_report.ipynb",
   "output_path": "/data/Runs/415630/output/415630-20250125_0254/415630-L3451-Z0011-CACGCACTGCCAGAT/415630-L3451-Z0011-CACGCACTGCCAGAT_methyldackel_qc_report.papermill.ipynb",
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