{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed7c4d33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:30.114511Z",
     "iopub.status.busy": "2025-01-26T17:53:30.114180Z",
     "iopub.status.idle": "2025-01-26T17:53:30.117079Z",
     "shell.execute_reply": "2025-01-26T17:53:30.116625Z"
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     "exception": false,
     "start_time": "2025-01-26T17:53:30.107677",
     "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": "017f8fa3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:30.128768Z",
     "iopub.status.busy": "2025-01-26T17:53:30.128312Z",
     "iopub.status.idle": "2025-01-26T17:53:32.539018Z",
     "shell.execute_reply": "2025-01-26T17:53:32.538442Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 2.417691,
     "end_time": "2025-01-26T17:53:32.540727",
     "exception": false,
     "start_time": "2025-01-26T17:53:30.123036",
     "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": "438d0442",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:32.551205Z",
     "iopub.status.busy": "2025-01-26T17:53:32.550853Z",
     "iopub.status.idle": "2025-01-26T17:53:32.553969Z",
     "shell.execute_reply": "2025-01-26T17:53:32.553525Z"
    },
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     "end_time": "2025-01-26T17:53:32.555074",
     "exception": false,
     "start_time": "2025-01-26T17:53:32.545630",
     "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": "2f9de12c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:32.564557Z",
     "iopub.status.busy": "2025-01-26T17:53:32.564324Z",
     "iopub.status.idle": "2025-01-26T17:53:32.567628Z",
     "shell.execute_reply": "2025-01-26T17:53:32.567190Z"
    },
    "papermill": {
     "duration": 0.009271,
     "end_time": "2025-01-26T17:53:32.568698",
     "exception": false,
     "start_time": "2025-01-26T17:53:32.559427",
     "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": "9b34b68e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:32.578291Z",
     "iopub.status.busy": "2025-01-26T17:53:32.578011Z",
     "iopub.status.idle": "2025-01-26T17:53:32.580405Z",
     "shell.execute_reply": "2025-01-26T17:53:32.579974Z"
    },
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     "duration": 0.008318,
     "end_time": "2025-01-26T17:53:32.581444",
     "exception": false,
     "start_time": "2025-01-26T17:53:32.573126",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "input_h5_file = ''\n",
    "input_base_file_name = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36bd51b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:32.591046Z",
     "iopub.status.busy": "2025-01-26T17:53:32.590808Z",
     "iopub.status.idle": "2025-01-26T17:53:32.593329Z",
     "shell.execute_reply": "2025-01-26T17:53:32.592908Z"
    },
    "papermill": {
     "duration": 0.008511,
     "end_time": "2025-01-26T17:53:32.594337",
     "exception": false,
     "start_time": "2025-01-26T17:53:32.585826",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "input_h5_file = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3455-Z0005-CATGTATCCTCTGAT/415630-L3455-Z0005-CATGTATCCTCTGAT.methyl_seq.applicationQC.h5\"\n",
    "input_base_file_name = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3455-Z0005-CATGTATCCTCTGAT/415630-L3455-Z0005-CATGTATCCTCTGAT.methyl_seq\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1e67500",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:32.603950Z",
     "iopub.status.busy": "2025-01-26T17:53:32.603674Z",
     "iopub.status.idle": "2025-01-26T17:53:34.131394Z",
     "shell.execute_reply": "2025-01-26T17:53:34.130667Z"
    },
    "papermill": {
     "duration": 1.534402,
     "end_time": "2025-01-26T17:53:34.133154",
     "exception": false,
     "start_time": "2025-01-26T17:53:32.598752",
     "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": "8dbbabd4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:34.144018Z",
     "iopub.status.busy": "2025-01-26T17:53:34.143727Z",
     "iopub.status.idle": "2025-01-26T17:53:34.148566Z",
     "shell.execute_reply": "2025-01-26T17:53:34.148103Z"
    },
    "papermill": {
     "duration": 0.011442,
     "end_time": "2025-01-26T17:53:34.149647",
     "exception": false,
     "start_time": "2025-01-26T17:53:34.138205",
     "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": "140224a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:34.159735Z",
     "iopub.status.busy": "2025-01-26T17:53:34.159488Z",
     "iopub.status.idle": "2025-01-26T17:53:34.162867Z",
     "shell.execute_reply": "2025-01-26T17:53:34.162412Z"
    },
    "papermill": {
     "duration": 0.009758,
     "end_time": "2025-01-26T17:53:34.163936",
     "exception": false,
     "start_time": "2025-01-26T17:53:34.154178",
     "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": "3373b3a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:34.173792Z",
     "iopub.status.busy": "2025-01-26T17:53:34.173511Z",
     "iopub.status.idle": "2025-01-26T17:53:34.176927Z",
     "shell.execute_reply": "2025-01-26T17:53:34.176495Z"
    },
    "papermill": {
     "duration": 0.009492,
     "end_time": "2025-01-26T17:53:34.178016",
     "exception": false,
     "start_time": "2025-01-26T17:53:34.168524",
     "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": "305eae89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:34.188635Z",
     "iopub.status.busy": "2025-01-26T17:53:34.188401Z",
     "iopub.status.idle": "2025-01-26T17:53:34.195221Z",
     "shell.execute_reply": "2025-01-26T17:53:34.194798Z"
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     "exception": false,
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     "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": "40bc17dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:34.206937Z",
     "iopub.status.busy": "2025-01-26T17:53:34.206655Z",
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     "exception": false,
     "start_time": "2025-01-26T17:53:34.201164",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b></b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "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": "5f86a285",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:34.227207Z",
     "iopub.status.busy": "2025-01-26T17:53:34.226974Z",
     "iopub.status.idle": "2025-01-26T17:53:34.353918Z",
     "shell.execute_reply": "2025-01-26T17:53:34.353444Z"
    },
    "papermill": {
     "duration": 0.134101,
     "end_time": "2025-01-26T17:53:34.355117",
     "exception": false,
     "start_time": "2025-01-26T17:53:34.221016",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_8a956_row0_col0, #T_8a956_row1_col0 {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8a956\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8a956_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8a956_level0_row0\" class=\"row_heading level0 row0\" >Sample name</th>\n",
       "      <td id=\"T_8a956_row0_col0\" class=\"data row0 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3455-Z0005-CATGTATCCTCTGAT/415630-L3455-Z0005-\n",
       "CATGTATCCTCTGAT.methyl_seq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8a956_level0_row1\" class=\"row_heading level0 row1\" >h5 file</th>\n",
       "      <td id=\"T_8a956_row1_col0\" class=\"data row1 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3455-Z0005-CATGTATCCTCTGAT/415630-L3455-Z0005-\n",
       "CATGTATCCTCTGAT.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": "9c496f26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:34.367611Z",
     "iopub.status.busy": "2025-01-26T17:53:34.367279Z",
     "iopub.status.idle": "2025-01-26T17:53:34.372083Z",
     "shell.execute_reply": "2025-01-26T17:53:34.371659Z"
    },
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     "duration": 0.012308,
     "end_time": "2025-01-26T17:53:34.373192",
     "exception": false,
     "start_time": "2025-01-26T17:53:34.360884",
     "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": "92b1533d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:34.385358Z",
     "iopub.status.busy": "2025-01-26T17:53:34.385071Z",
     "iopub.status.idle": "2025-01-26T17:53:34.419335Z",
     "shell.execute_reply": "2025-01-26T17:53:34.418887Z"
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     "exception": false,
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     "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>71.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  std</th>\n",
       "      <td>12.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  median</th>\n",
       "      <td>73.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:</th>\n",
       "      <td>739,329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  mean</th>\n",
       "      <td>42.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  std</th>\n",
       "      <td>16.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  median</th>\n",
       "      <td>42.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                value\n",
       "metric                               \n",
       "Percent Methylation:  mean    71.99  \n",
       "Percent Methylation:  std     12.60  \n",
       "Percent Methylation:  median  73.00  \n",
       "Total CpGs:                   739,329\n",
       "Coverage:  mean               42.10  \n",
       "Coverage:  std                16.14  \n",
       "Coverage:  median             42.00  "
      ]
     },
     "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": "1fec68ca",
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
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    "tags": []
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   "outputs": [
    {
     "data": {
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       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "execution_count": 16,
     "metadata": {},
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    {
     "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>58.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  std</th>\n",
       "      <td>41.46</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>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:  std</th>\n",
       "      <td>2.93</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    58.77\n",
       "Percent Methylation:  std     41.46\n",
       "Percent Methylation:  median  66.67\n",
       "Total CpGs:  mean             2.63 \n",
       "Total CpGs:  std              2.93 \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": "2d6f325c",
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     "status": "completed"
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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"
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_25525\" style='display:inline-table'>\n",
       "  <caption>CHG</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_25525_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_25525_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_25525_row0_col0\" class=\"data row0 col0\" >73.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25525_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_25525_row1_col0\" class=\"data row1 col0\" >11.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25525_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_25525_row2_col0\" class=\"data row2 col0\" >75.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25525_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_25525_row3_col0\" class=\"data row3 col0\" >41.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25525_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_25525_row4_col0\" class=\"data row4 col0\" >15.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25525_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_25525_row5_col0\" class=\"data row5 col0\" >42.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_b0830\" style='display:inline-table'>\n",
       "  <caption>CHH</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b0830_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_b0830_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_b0830_row0_col0\" class=\"data row0 col0\" >70.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b0830_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_b0830_row1_col0\" class=\"data row1 col0\" >14.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b0830_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_b0830_row2_col0\" class=\"data row2 col0\" >72.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b0830_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_b0830_row3_col0\" class=\"data row3 col0\" >22.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b0830_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_b0830_row4_col0\" class=\"data row4 col0\" >11.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b0830_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_b0830_row5_col0\" class=\"data row5 col0\" >22.00</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"
   ]
  },
  {
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    "pycharm": {
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   "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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",
      "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": "c84b05a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:34.961711Z",
     "iopub.status.busy": "2025-01-26T17:53:34.961415Z",
     "iopub.status.idle": "2025-01-26T17:53:34.994667Z",
     "shell.execute_reply": "2025-01-26T17:53:34.994199Z"
    },
    "papermill": {
     "duration": 0.043327,
     "end_time": "2025-01-26T17:53:34.995827",
     "exception": false,
     "start_time": "2025-01-26T17:53:34.952500",
     "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_875e1\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_875e1_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_875e1_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_875e1_row0_col0\" class=\"data row0 col0\" >71.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_875e1_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_875e1_row1_col0\" class=\"data row1 col0\" >9.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_875e1_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_875e1_row2_col0\" class=\"data row2 col0\" >71.95%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_1a823\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1a823_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_1a823_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_1a823_row0_col0\" class=\"data row0 col0\" >70.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a823_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_1a823_row1_col0\" class=\"data row1 col0\" >7.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a823_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_1a823_row2_col0\" class=\"data row2 col0\" >72.62%</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": "50dc50b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:35.012861Z",
     "iopub.status.busy": "2025-01-26T17:53:35.012586Z",
     "iopub.status.idle": "2025-01-26T17:53:35.399317Z",
     "shell.execute_reply": "2025-01-26T17:53:35.398827Z"
    },
    "papermill": {
     "duration": 0.396762,
     "end_time": "2025-01-26T17:53:35.400701",
     "exception": false,
     "start_time": "2025-01-26T17:53:35.003939",
     "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": {
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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": "6bc047ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:35.433318Z",
     "iopub.status.busy": "2025-01-26T17:53:35.433023Z",
     "iopub.status.idle": "2025-01-26T17:53:35.465422Z",
     "shell.execute_reply": "2025-01-26T17:53:35.464969Z"
    },
    "papermill": {
     "duration": 0.05656,
     "end_time": "2025-01-26T17:53:35.466560",
     "exception": false,
     "start_time": "2025-01-26T17:53:35.410000",
     "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_72fd1\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_72fd1_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_72fd1_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_72fd1_row0_col0\" class=\"data row0 col0\" >68.53%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_72fd1_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_72fd1_row1_col0\" class=\"data row1 col0\" >11.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_72fd1_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_72fd1_row2_col0\" class=\"data row2 col0\" >70.70%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c91dc\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c91dc_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_c91dc_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_c91dc_row0_col0\" class=\"data row0 col0\" >71.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c91dc_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_c91dc_row1_col0\" class=\"data row1 col0\" >5.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c91dc_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_c91dc_row2_col0\" class=\"data row2 col0\" >72.40%</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": "986f5a3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:35.485728Z",
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    },
    "pycharm": {
     "name": "#%%\n"
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    "tags": []
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   "outputs": [],
   "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": "f8d66110",
   "metadata": {
    "execution": {
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    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
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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": {},
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_0021e\" style='display:inline-table'>\n",
       "  <caption>pUC19</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0021e_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_0021e_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_0021e_row0_col0\" class=\"data row0 col0\" >0.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0021e_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_0021e_row1_col0\" class=\"data row1 col0\" >2.53%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0021e_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_0021e_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0021e_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_0021e_row3_col0\" class=\"data row3 col0\" >1,349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0021e_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_0021e_row4_col0\" class=\"data row4 col0\" >13.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0021e_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_0021e_row5_col0\" class=\"data row5 col0\" >14.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0021e_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_0021e_row6_col0\" class=\"data row6 col0\" >8.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_b380c\" style='display:inline-table'>\n",
       "  <caption>Lambda</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b380c_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_b380c_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_b380c_row0_col0\" class=\"data row0 col0\" >3.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b380c_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_b380c_row1_col0\" class=\"data row1 col0\" >11.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b380c_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_b380c_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b380c_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_b380c_row3_col0\" class=\"data row3 col0\" >170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b380c_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_b380c_row4_col0\" class=\"data row4 col0\" >55.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b380c_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_b380c_row5_col0\" class=\"data row5 col0\" >61.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b380c_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_b380c_row6_col0\" class=\"data row6 col0\" >30.00</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": "2d396bdd",
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    "execution": {
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    "pycharm": {
     "name": "#%%\n"
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   "outputs": [],
   "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": "5038b647",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:53:36.221449Z",
     "iopub.status.busy": "2025-01-26T17:53:36.221128Z",
     "iopub.status.idle": "2025-01-26T17:53:36.241483Z",
     "shell.execute_reply": "2025-01-26T17:53:36.240990Z"
    },
    "papermill": {
     "duration": 0.034044,
     "end_time": "2025-01-26T17:53:36.242643",
     "exception": false,
     "start_time": "2025-01-26T17:53:36.208599",
     "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_0eac5\" style='display:inline-table'>\n",
       "  <caption>Percent Methylation </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0eac5_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_0eac5_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_0eac5_row0_col0\" class=\"data row0 col0\" >2,203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0eac5_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_0eac5_row1_col0\" class=\"data row1 col0\" >903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0eac5_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_0eac5_row2_col0\" class=\"data row2 col0\" >3,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0eac5_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_0eac5_row3_col0\" class=\"data row3 col0\" >6,838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0eac5_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_0eac5_row4_col0\" class=\"data row4 col0\" >16,272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0eac5_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_0eac5_row5_col0\" class=\"data row5 col0\" >65,326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0eac5_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_0eac5_row6_col0\" class=\"data row6 col0\" >176,499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0eac5_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_0eac5_row7_col0\" class=\"data row7 col0\" >258,804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0eac5_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_0eac5_row8_col0\" class=\"data row8 col0\" >173,952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0eac5_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_0eac5_row9_col0\" class=\"data row9 col0\" >35,532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_ec2da\" style='display:inline-table'>\n",
       "  <caption>Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ec2da_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_ec2da_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_ec2da_row0_col0\" class=\"data row0 col0\" >20,434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_ec2da_row1_col0\" class=\"data row1 col0\" >28,974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_ec2da_row2_col0\" class=\"data row2 col0\" >74,827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_ec2da_row3_col0\" class=\"data row3 col0\" >178,816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_ec2da_row4_col0\" class=\"data row4 col0\" >227,999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_ec2da_row5_col0\" class=\"data row5 col0\" >142,808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_ec2da_row6_col0\" class=\"data row6 col0\" >48,531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_ec2da_row7_col0\" class=\"data row7 col0\" >10,533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_ec2da_row8_col0\" class=\"data row8 col0\" >2,239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_ec2da_row9_col0\" class=\"data row9 col0\" >835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_ec2da_row10_col0\" class=\"data row10 col0\" >515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_ec2da_row11_col0\" class=\"data row11 col0\" >342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_ec2da_row12_col0\" class=\"data row12 col0\" >284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_ec2da_row13_col0\" class=\"data row13 col0\" >264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_ec2da_row14_col0\" class=\"data row14 col0\" >220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_ec2da_row15_col0\" class=\"data row15 col0\" >167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_ec2da_row16_col0\" class=\"data row16 col0\" >167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_ec2da_row17_col0\" class=\"data row17 col0\" >124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_ec2da_row18_col0\" class=\"data row18 col0\" >109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec2da_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_ec2da_row19_col0\" class=\"data row19 col0\" >1,141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_40844\" style='display:inline-table'>\n",
       "  <caption>Cumulative Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_40844_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_40844_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_40844_row0_col0\" class=\"data row0 col0\" >2.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_40844_row1_col0\" class=\"data row1 col0\" >6.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_40844_row2_col0\" class=\"data row2 col0\" >16.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_40844_row3_col0\" class=\"data row3 col0\" >40.99%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_40844_row4_col0\" class=\"data row4 col0\" >71.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_40844_row5_col0\" class=\"data row5 col0\" >91.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_40844_row6_col0\" class=\"data row6 col0\" >97.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_40844_row7_col0\" class=\"data row7 col0\" >99.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_40844_row8_col0\" class=\"data row8 col0\" >99.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_40844_row9_col0\" class=\"data row9 col0\" >99.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_40844_row10_col0\" class=\"data row10 col0\" >99.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_40844_row11_col0\" class=\"data row11 col0\" >99.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_40844_row12_col0\" class=\"data row12 col0\" >99.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_40844_row13_col0\" class=\"data row13 col0\" >99.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_40844_row14_col0\" class=\"data row14 col0\" >99.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_40844_row15_col0\" class=\"data row15 col0\" >99.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_40844_row16_col0\" class=\"data row16 col0\" >99.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_40844_row17_col0\" class=\"data row17 col0\" >99.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_40844_row18_col0\" class=\"data row18 col0\" >99.85%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40844_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_40844_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": 9.324331,
   "end_time": "2025-01-26T17:53:36.570101",
   "environment_variables": {},
   "exception": null,
   "input_path": "/app/VariantCalling/ugvc/reports/methyldackel_qc_report.ipynb",
   "output_path": "/data/Runs/415630/output/415630-20250125_0254/415630-L3455-Z0005-CATGTATCCTCTGAT/415630-L3455-Z0005-CATGTATCCTCTGAT_methyldackel_qc_report.papermill.ipynb",
   "parameters": {
    "input_base_file_name": "/data/Runs/415630/output/415630-20250125_0254/415630-L3455-Z0005-CATGTATCCTCTGAT/415630-L3455-Z0005-CATGTATCCTCTGAT.methyl_seq",
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   "start_time": "2025-01-26T17:53:27.245770",
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