{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96d53c9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:44.478211Z",
     "iopub.status.busy": "2025-01-26T17:49:44.477877Z",
     "iopub.status.idle": "2025-01-26T17:49:44.480756Z",
     "shell.execute_reply": "2025-01-26T17:49:44.480305Z"
    },
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     "end_time": "2025-01-26T17:49:44.481884",
     "exception": false,
     "start_time": "2025-01-26T17:49:44.471148",
     "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": "f0c46a6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:44.491575Z",
     "iopub.status.busy": "2025-01-26T17:49:44.491293Z",
     "iopub.status.idle": "2025-01-26T17:49:47.046673Z",
     "shell.execute_reply": "2025-01-26T17:49:47.046076Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 2.562059,
     "end_time": "2025-01-26T17:49:47.048539",
     "exception": false,
     "start_time": "2025-01-26T17:49:44.486480",
     "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": "5627b310",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:47.059440Z",
     "iopub.status.busy": "2025-01-26T17:49:47.059073Z",
     "iopub.status.idle": "2025-01-26T17:49:47.062258Z",
     "shell.execute_reply": "2025-01-26T17:49:47.061801Z"
    },
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     "end_time": "2025-01-26T17:49:47.063392",
     "exception": false,
     "start_time": "2025-01-26T17:49:47.053615",
     "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": "e4be5e8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:47.072886Z",
     "iopub.status.busy": "2025-01-26T17:49:47.072704Z",
     "iopub.status.idle": "2025-01-26T17:49:47.076000Z",
     "shell.execute_reply": "2025-01-26T17:49:47.075558Z"
    },
    "papermill": {
     "duration": 0.009265,
     "end_time": "2025-01-26T17:49:47.077039",
     "exception": false,
     "start_time": "2025-01-26T17:49:47.067774",
     "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": "f93b07c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:47.086543Z",
     "iopub.status.busy": "2025-01-26T17:49:47.086355Z",
     "iopub.status.idle": "2025-01-26T17:49:47.088799Z",
     "shell.execute_reply": "2025-01-26T17:49:47.088373Z"
    },
    "papermill": {
     "duration": 0.0084,
     "end_time": "2025-01-26T17:49:47.089878",
     "exception": false,
     "start_time": "2025-01-26T17:49:47.081478",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "input_h5_file = ''\n",
    "input_base_file_name = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5fd260b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:47.099888Z",
     "iopub.status.busy": "2025-01-26T17:49:47.099706Z",
     "iopub.status.idle": "2025-01-26T17:49:47.102234Z",
     "shell.execute_reply": "2025-01-26T17:49:47.101811Z"
    },
    "papermill": {
     "duration": 0.008906,
     "end_time": "2025-01-26T17:49:47.103312",
     "exception": false,
     "start_time": "2025-01-26T17:49:47.094406",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "input_h5_file = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3449-Z0007-CGATTCATGCTCGAT/415630-L3449-Z0007-CGATTCATGCTCGAT.methyl_seq.applicationQC.h5\"\n",
    "input_base_file_name = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3449-Z0007-CGATTCATGCTCGAT/415630-L3449-Z0007-CGATTCATGCTCGAT.methyl_seq\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "556caef7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:47.113037Z",
     "iopub.status.busy": "2025-01-26T17:49:47.112683Z",
     "iopub.status.idle": "2025-01-26T17:49:48.680341Z",
     "shell.execute_reply": "2025-01-26T17:49:48.679556Z"
    },
    "papermill": {
     "duration": 1.574553,
     "end_time": "2025-01-26T17:49:48.682319",
     "exception": false,
     "start_time": "2025-01-26T17:49:47.107766",
     "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": "7915bc6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:48.694002Z",
     "iopub.status.busy": "2025-01-26T17:49:48.693416Z",
     "iopub.status.idle": "2025-01-26T17:49:48.698721Z",
     "shell.execute_reply": "2025-01-26T17:49:48.698226Z"
    },
    "papermill": {
     "duration": 0.01229,
     "end_time": "2025-01-26T17:49:48.699867",
     "exception": false,
     "start_time": "2025-01-26T17:49:48.687577",
     "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": "ff0a2146",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:48.710486Z",
     "iopub.status.busy": "2025-01-26T17:49:48.710224Z",
     "iopub.status.idle": "2025-01-26T17:49:48.713853Z",
     "shell.execute_reply": "2025-01-26T17:49:48.713388Z"
    },
    "papermill": {
     "duration": 0.010387,
     "end_time": "2025-01-26T17:49:48.714975",
     "exception": false,
     "start_time": "2025-01-26T17:49:48.704588",
     "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": "e875f97a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:48.725322Z",
     "iopub.status.busy": "2025-01-26T17:49:48.725080Z",
     "iopub.status.idle": "2025-01-26T17:49:48.728601Z",
     "shell.execute_reply": "2025-01-26T17:49:48.728151Z"
    },
    "papermill": {
     "duration": 0.009962,
     "end_time": "2025-01-26T17:49:48.729674",
     "exception": false,
     "start_time": "2025-01-26T17:49:48.719712",
     "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": "d29a2fb9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:48.740590Z",
     "iopub.status.busy": "2025-01-26T17:49:48.740408Z",
     "iopub.status.idle": "2025-01-26T17:49:48.747395Z",
     "shell.execute_reply": "2025-01-26T17:49:48.746964Z"
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     "exception": false,
     "start_time": "2025-01-26T17:49:48.734398",
     "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": "6418fb3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:48.759305Z",
     "iopub.status.busy": "2025-01-26T17:49:48.759057Z",
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     "exception": false,
     "start_time": "2025-01-26T17:49:48.753482",
     "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": "fd9d47a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:48.780273Z",
     "iopub.status.busy": "2025-01-26T17:49:48.780026Z",
     "iopub.status.idle": "2025-01-26T17:49:48.915251Z",
     "shell.execute_reply": "2025-01-26T17:49:48.914758Z"
    },
    "papermill": {
     "duration": 0.142666,
     "end_time": "2025-01-26T17:49:48.916523",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_64008_row0_col0, #T_64008_row1_col0 {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_64008\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_64008_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_64008_level0_row0\" class=\"row_heading level0 row0\" >Sample name</th>\n",
       "      <td id=\"T_64008_row0_col0\" class=\"data row0 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3449-Z0007-CGATTCATGCTCGAT/415630-L3449-Z0007-\n",
       "CGATTCATGCTCGAT.methyl_seq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64008_level0_row1\" class=\"row_heading level0 row1\" >h5 file</th>\n",
       "      <td id=\"T_64008_row1_col0\" class=\"data row1 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3449-Z0007-CGATTCATGCTCGAT/415630-L3449-Z0007-\n",
       "CGATTCATGCTCGAT.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": "5017f307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:48.929722Z",
     "iopub.status.busy": "2025-01-26T17:49:48.929359Z",
     "iopub.status.idle": "2025-01-26T17:49:48.934290Z",
     "shell.execute_reply": "2025-01-26T17:49:48.933846Z"
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     "end_time": "2025-01-26T17:49:48.935448",
     "exception": false,
     "start_time": "2025-01-26T17:49:48.922492",
     "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": "efeaf8d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:48.948187Z",
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     "exception": false,
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     "status": "completed"
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    "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>72.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  std</th>\n",
       "      <td>12.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  median</th>\n",
       "      <td>74.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:</th>\n",
       "      <td>737,764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  mean</th>\n",
       "      <td>34.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  std</th>\n",
       "      <td>13.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  median</th>\n",
       "      <td>34.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                value\n",
       "metric                               \n",
       "Percent Methylation:  mean    72.60  \n",
       "Percent Methylation:  std     12.99  \n",
       "Percent Methylation:  median  74.00  \n",
       "Total CpGs:                   737,764\n",
       "Coverage:  mean               34.00  \n",
       "Coverage:  std                13.84  \n",
       "Coverage:  median             34.00  "
      ]
     },
     "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": "bc8d97c7",
   "metadata": {
    "execution": {
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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>59.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  std</th>\n",
       "      <td>41.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  median</th>\n",
       "      <td>71.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:  mean</th>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:  std</th>\n",
       "      <td>2.99</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    59.88\n",
       "Percent Methylation:  std     41.08\n",
       "Percent Methylation:  median  71.43\n",
       "Total CpGs:  mean             2.70 \n",
       "Total CpGs:  std              2.99 \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": "aa708dfc",
   "metadata": {
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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_fb538\" style='display:inline-table'>\n",
       "  <caption>CHG</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fb538_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_fb538_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_fb538_row0_col0\" class=\"data row0 col0\" >74.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb538_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_fb538_row1_col0\" class=\"data row1 col0\" >11.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb538_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_fb538_row2_col0\" class=\"data row2 col0\" >75.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb538_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_fb538_row3_col0\" class=\"data row3 col0\" >32.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb538_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_fb538_row4_col0\" class=\"data row4 col0\" >13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb538_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_fb538_row5_col0\" class=\"data row5 col0\" >33.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_de55d\" style='display:inline-table'>\n",
       "  <caption>CHH</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_de55d_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_de55d_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_de55d_row0_col0\" class=\"data row0 col0\" >71.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de55d_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_de55d_row1_col0\" class=\"data row1 col0\" >15.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de55d_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_de55d_row2_col0\" class=\"data row2 col0\" >73.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de55d_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_de55d_row3_col0\" class=\"data row3 col0\" >17.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de55d_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_de55d_row4_col0\" class=\"data row4 col0\" >9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de55d_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_de55d_row5_col0\" class=\"data row5 col0\" >17.00</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,
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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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",
      "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": "c600f4a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:49.539054Z",
     "iopub.status.busy": "2025-01-26T17:49:49.538750Z",
     "iopub.status.idle": "2025-01-26T17:49:49.571933Z",
     "shell.execute_reply": "2025-01-26T17:49:49.571458Z"
    },
    "papermill": {
     "duration": 0.043309,
     "end_time": "2025-01-26T17:49:49.573118",
     "exception": false,
     "start_time": "2025-01-26T17:49:49.529809",
     "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_29949\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_29949_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_29949_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_29949_row0_col0\" class=\"data row0 col0\" >71.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29949_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_29949_row1_col0\" class=\"data row1 col0\" >10.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29949_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_29949_row2_col0\" class=\"data row2 col0\" >72.39%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_b3f82\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b3f82_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_b3f82_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_b3f82_row0_col0\" class=\"data row0 col0\" >71.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b3f82_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_b3f82_row1_col0\" class=\"data row1 col0\" >8.72%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b3f82_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_b3f82_row2_col0\" class=\"data row2 col0\" >73.37%</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": "a81c9849",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:49.590489Z",
     "iopub.status.busy": "2025-01-26T17:49:49.590191Z",
     "iopub.status.idle": "2025-01-26T17:49:49.977986Z",
     "shell.execute_reply": "2025-01-26T17:49:49.977479Z"
    },
    "papermill": {
     "duration": 0.398106,
     "end_time": "2025-01-26T17:49:49.979477",
     "exception": false,
     "start_time": "2025-01-26T17:49:49.581371",
     "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": "9eba05bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:49.998287Z",
     "iopub.status.busy": "2025-01-26T17:49:49.997998Z",
     "iopub.status.idle": "2025-01-26T17:49:50.030970Z",
     "shell.execute_reply": "2025-01-26T17:49:50.030468Z"
    },
    "papermill": {
     "duration": 0.043561,
     "end_time": "2025-01-26T17:49:50.032159",
     "exception": false,
     "start_time": "2025-01-26T17:49:49.988598",
     "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_2712e\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2712e_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_2712e_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_2712e_row0_col0\" class=\"data row0 col0\" >68.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2712e_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_2712e_row1_col0\" class=\"data row1 col0\" >12.64%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2712e_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_2712e_row2_col0\" class=\"data row2 col0\" >71.23%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_5c239\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5c239_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_5c239_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_5c239_row0_col0\" class=\"data row0 col0\" >72.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c239_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_5c239_row1_col0\" class=\"data row1 col0\" >4.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5c239_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_5c239_row2_col0\" class=\"data row2 col0\" >73.34%</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": "f9357b1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:50.051732Z",
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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": "ecd2f463",
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    "execution": {
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     "status": "completed"
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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"
    },
    {
     "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_a524d\" style='display:inline-table'>\n",
       "  <caption>pUC19</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a524d_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_a524d_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_a524d_row0_col0\" class=\"data row0 col0\" >1.82%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a524d_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_a524d_row1_col0\" class=\"data row1 col0\" >12.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a524d_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_a524d_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a524d_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_a524d_row3_col0\" class=\"data row3 col0\" >653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a524d_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_a524d_row4_col0\" class=\"data row4 col0\" >3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a524d_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_a524d_row5_col0\" class=\"data row5 col0\" >2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a524d_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_a524d_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_928d8\" style='display:inline-table'>\n",
       "  <caption>Lambda</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_928d8_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_928d8_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_928d8_row0_col0\" class=\"data row0 col0\" >12.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_928d8_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_928d8_row1_col0\" class=\"data row1 col0\" >31.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_928d8_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_928d8_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_928d8_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_928d8_row3_col0\" class=\"data row3 col0\" >132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_928d8_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_928d8_row4_col0\" class=\"data row4 col0\" >13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_928d8_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_928d8_row5_col0\" class=\"data row5 col0\" >18.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_928d8_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_928d8_row6_col0\" class=\"data row6 col0\" >5.00</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"
   ]
  },
  {
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   "execution_count": 25,
   "id": "a969db16",
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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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "340544b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:50.171107Z",
     "iopub.status.busy": "2025-01-26T17:49:50.170717Z",
     "iopub.status.idle": "2025-01-26T17:49:50.770870Z",
     "shell.execute_reply": "2025-01-26T17:49:50.770312Z"
    },
    "papermill": {
     "duration": 0.612027,
     "end_time": "2025-01-26T17:49:50.772411",
     "exception": false,
     "start_time": "2025-01-26T17:49:50.160384",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:16px;\">Human Genome: Additional Details of CpG Methylation and Coverage</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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": "58c4e6d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:49:50.901263Z",
     "iopub.status.busy": "2025-01-26T17:49:50.900874Z",
     "iopub.status.idle": "2025-01-26T17:49:50.922194Z",
     "shell.execute_reply": "2025-01-26T17:49:50.921682Z"
    },
    "papermill": {
     "duration": 0.139538,
     "end_time": "2025-01-26T17:49:50.923559",
     "exception": false,
     "start_time": "2025-01-26T17:49:50.784021",
     "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_61b63\" style='display:inline-table'>\n",
       "  <caption>Percent Methylation </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_61b63_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_61b63_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_61b63_row0_col0\" class=\"data row0 col0\" >2,233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_61b63_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_61b63_row1_col0\" class=\"data row1 col0\" >852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_61b63_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_61b63_row2_col0\" class=\"data row2 col0\" >3,046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_61b63_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_61b63_row3_col0\" class=\"data row3 col0\" >6,927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_61b63_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_61b63_row4_col0\" class=\"data row4 col0\" >16,461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_61b63_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_61b63_row5_col0\" class=\"data row5 col0\" >64,130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_61b63_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_61b63_row6_col0\" class=\"data row6 col0\" >164,120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_61b63_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_61b63_row7_col0\" class=\"data row7 col0\" >247,521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_61b63_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_61b63_row8_col0\" class=\"data row8 col0\" >185,482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_61b63_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_61b63_row9_col0\" class=\"data row9 col0\" >46,992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_e92a7\" style='display:inline-table'>\n",
       "  <caption>Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e92a7_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_e92a7_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_e92a7_row0_col0\" class=\"data row0 col0\" >24,858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_e92a7_row1_col0\" class=\"data row1 col0\" >54,619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_e92a7_row2_col0\" class=\"data row2 col0\" >171,518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_e92a7_row3_col0\" class=\"data row3 col0\" >264,224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_e92a7_row4_col0\" class=\"data row4 col0\" >163,047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_e92a7_row5_col0\" class=\"data row5 col0\" >46,513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_e92a7_row6_col0\" class=\"data row6 col0\" >7,882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_e92a7_row7_col0\" class=\"data row7 col0\" >1,674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_e92a7_row8_col0\" class=\"data row8 col0\" >700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_e92a7_row9_col0\" class=\"data row9 col0\" >460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_e92a7_row10_col0\" class=\"data row10 col0\" >323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_e92a7_row11_col0\" class=\"data row11 col0\" >255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_e92a7_row12_col0\" class=\"data row12 col0\" >198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_e92a7_row13_col0\" class=\"data row13 col0\" >162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_e92a7_row14_col0\" class=\"data row14 col0\" >171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_e92a7_row15_col0\" class=\"data row15 col0\" >120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_e92a7_row16_col0\" class=\"data row16 col0\" >102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_e92a7_row17_col0\" class=\"data row17 col0\" >69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_e92a7_row18_col0\" class=\"data row18 col0\" >63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e92a7_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_e92a7_row19_col0\" class=\"data row19 col0\" >806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_93842\" style='display:inline-table'>\n",
       "  <caption>Cumulative Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_93842_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_93842_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_93842_row0_col0\" class=\"data row0 col0\" >3.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_93842_row1_col0\" class=\"data row1 col0\" >10.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_93842_row2_col0\" class=\"data row2 col0\" >34.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_93842_row3_col0\" class=\"data row3 col0\" >69.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_93842_row4_col0\" class=\"data row4 col0\" >91.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_93842_row5_col0\" class=\"data row5 col0\" >98.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_93842_row6_col0\" class=\"data row6 col0\" >99.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_93842_row7_col0\" class=\"data row7 col0\" >99.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_93842_row8_col0\" class=\"data row8 col0\" >99.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_93842_row9_col0\" class=\"data row9 col0\" >99.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_93842_row10_col0\" class=\"data row10 col0\" >99.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_93842_row11_col0\" class=\"data row11 col0\" >99.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_93842_row12_col0\" class=\"data row12 col0\" >99.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_93842_row13_col0\" class=\"data row13 col0\" >99.82%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_93842_row14_col0\" class=\"data row14 col0\" >99.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_93842_row15_col0\" class=\"data row15 col0\" >99.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_93842_row16_col0\" class=\"data row16 col0\" >99.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_93842_row17_col0\" class=\"data row17 col0\" >99.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_93842_row18_col0\" class=\"data row18 col0\" >99.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93842_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_93842_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": 9.978799,
   "end_time": "2025-01-26T17:49:51.375399",
   "environment_variables": {},
   "exception": null,
   "input_path": "/app/VariantCalling/ugvc/reports/methyldackel_qc_report.ipynb",
   "output_path": "/data/Runs/415630/output/415630-20250125_0254/415630-L3449-Z0007-CGATTCATGCTCGAT/415630-L3449-Z0007-CGATTCATGCTCGAT_methyldackel_qc_report.papermill.ipynb",
   "parameters": {
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