{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac9693e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:39.896626Z",
     "iopub.status.busy": "2025-01-26T17:57:39.896345Z",
     "iopub.status.idle": "2025-01-26T17:57:39.899066Z",
     "shell.execute_reply": "2025-01-26T17:57:39.898623Z"
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     "exception": false,
     "start_time": "2025-01-26T17:57:39.889968",
     "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": "ab9ab563",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:39.910157Z",
     "iopub.status.busy": "2025-01-26T17:57:39.909888Z",
     "iopub.status.idle": "2025-01-26T17:57:42.319812Z",
     "shell.execute_reply": "2025-01-26T17:57:42.319041Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 2.416746,
     "end_time": "2025-01-26T17:57:42.321683",
     "exception": false,
     "start_time": "2025-01-26T17:57:39.904937",
     "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": "f6a048ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:42.332298Z",
     "iopub.status.busy": "2025-01-26T17:57:42.331836Z",
     "iopub.status.idle": "2025-01-26T17:57:42.335310Z",
     "shell.execute_reply": "2025-01-26T17:57:42.334774Z"
    },
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     "end_time": "2025-01-26T17:57:42.336475",
     "exception": false,
     "start_time": "2025-01-26T17:57:42.326394",
     "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": "b99340c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:42.346063Z",
     "iopub.status.busy": "2025-01-26T17:57:42.345727Z",
     "iopub.status.idle": "2025-01-26T17:57:42.349302Z",
     "shell.execute_reply": "2025-01-26T17:57:42.348797Z"
    },
    "papermill": {
     "duration": 0.009521,
     "end_time": "2025-01-26T17:57:42.350358",
     "exception": false,
     "start_time": "2025-01-26T17:57:42.340837",
     "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": "c0066d51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:42.360083Z",
     "iopub.status.busy": "2025-01-26T17:57:42.359703Z",
     "iopub.status.idle": "2025-01-26T17:57:42.362385Z",
     "shell.execute_reply": "2025-01-26T17:57:42.361883Z"
    },
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     "duration": 0.00866,
     "end_time": "2025-01-26T17:57:42.363450",
     "exception": false,
     "start_time": "2025-01-26T17:57:42.354790",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "input_h5_file = ''\n",
    "input_base_file_name = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "378b0bd6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:42.373198Z",
     "iopub.status.busy": "2025-01-26T17:57:42.372866Z",
     "iopub.status.idle": "2025-01-26T17:57:42.375521Z",
     "shell.execute_reply": "2025-01-26T17:57:42.375014Z"
    },
    "papermill": {
     "duration": 0.008749,
     "end_time": "2025-01-26T17:57:42.376618",
     "exception": false,
     "start_time": "2025-01-26T17:57:42.367869",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "input_h5_file = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3453-Z0001-CAGCTCGAATGCGAT/415630-L3453-Z0001-CAGCTCGAATGCGAT.methyl_seq.applicationQC.h5\"\n",
    "input_base_file_name = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3453-Z0001-CAGCTCGAATGCGAT/415630-L3453-Z0001-CAGCTCGAATGCGAT.methyl_seq\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "754d5867",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:42.386693Z",
     "iopub.status.busy": "2025-01-26T17:57:42.386301Z",
     "iopub.status.idle": "2025-01-26T17:57:43.903808Z",
     "shell.execute_reply": "2025-01-26T17:57:43.903052Z"
    },
    "papermill": {
     "duration": 1.524373,
     "end_time": "2025-01-26T17:57:43.905500",
     "exception": false,
     "start_time": "2025-01-26T17:57:42.381127",
     "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": "1fff6e81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:43.916298Z",
     "iopub.status.busy": "2025-01-26T17:57:43.915957Z",
     "iopub.status.idle": "2025-01-26T17:57:43.920982Z",
     "shell.execute_reply": "2025-01-26T17:57:43.920443Z"
    },
    "papermill": {
     "duration": 0.011611,
     "end_time": "2025-01-26T17:57:43.922116",
     "exception": false,
     "start_time": "2025-01-26T17:57:43.910505",
     "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": "def2eb54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:43.932296Z",
     "iopub.status.busy": "2025-01-26T17:57:43.931956Z",
     "iopub.status.idle": "2025-01-26T17:57:43.935440Z",
     "shell.execute_reply": "2025-01-26T17:57:43.934932Z"
    },
    "papermill": {
     "duration": 0.009951,
     "end_time": "2025-01-26T17:57:43.936581",
     "exception": false,
     "start_time": "2025-01-26T17:57:43.926630",
     "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": "c1a7f117",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:43.946604Z",
     "iopub.status.busy": "2025-01-26T17:57:43.946208Z",
     "iopub.status.idle": "2025-01-26T17:57:43.949887Z",
     "shell.execute_reply": "2025-01-26T17:57:43.949369Z"
    },
    "papermill": {
     "duration": 0.00981,
     "end_time": "2025-01-26T17:57:43.950970",
     "exception": false,
     "start_time": "2025-01-26T17:57:43.941160",
     "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": "0569f5a8",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2025-01-26T17:57:43.961538Z",
     "iopub.status.idle": "2025-01-26T17:57:43.968985Z",
     "shell.execute_reply": "2025-01-26T17:57:43.968481Z"
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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": "50d47936",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:43.980549Z",
     "iopub.status.busy": "2025-01-26T17:57:43.980366Z",
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     "exception": false,
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     "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": "af6e61dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:44.002023Z",
     "iopub.status.busy": "2025-01-26T17:57:44.001640Z",
     "iopub.status.idle": "2025-01-26T17:57:44.128472Z",
     "shell.execute_reply": "2025-01-26T17:57:44.127951Z"
    },
    "papermill": {
     "duration": 0.133943,
     "end_time": "2025-01-26T17:57:44.129620",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_01e01_row0_col0, #T_01e01_row1_col0 {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_01e01\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_01e01_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_01e01_level0_row0\" class=\"row_heading level0 row0\" >Sample name</th>\n",
       "      <td id=\"T_01e01_row0_col0\" class=\"data row0 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3453-Z0001-CAGCTCGAATGCGAT/415630-L3453-Z0001-\n",
       "CAGCTCGAATGCGAT.methyl_seq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_01e01_level0_row1\" class=\"row_heading level0 row1\" >h5 file</th>\n",
       "      <td id=\"T_01e01_row1_col0\" class=\"data row1 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3453-Z0001-CAGCTCGAATGCGAT/415630-L3453-Z0001-\n",
       "CAGCTCGAATGCGAT.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": "303f62c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:44.142167Z",
     "iopub.status.busy": "2025-01-26T17:57:44.141823Z",
     "iopub.status.idle": "2025-01-26T17:57:44.146802Z",
     "shell.execute_reply": "2025-01-26T17:57:44.146305Z"
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    "papermill": {
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     "end_time": "2025-01-26T17:57:44.147897",
     "exception": false,
     "start_time": "2025-01-26T17:57:44.135381",
     "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": "61ba319b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:44.160271Z",
     "iopub.status.busy": "2025-01-26T17:57:44.159881Z",
     "iopub.status.idle": "2025-01-26T17:57:44.194143Z",
     "shell.execute_reply": "2025-01-26T17:57:44.193633Z"
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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>71.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  std</th>\n",
       "      <td>12.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  median</th>\n",
       "      <td>72.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:</th>\n",
       "      <td>740,142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  mean</th>\n",
       "      <td>52.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  std</th>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  median</th>\n",
       "      <td>53.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                value\n",
       "metric                               \n",
       "Percent Methylation:  mean    71.16  \n",
       "Percent Methylation:  std     12.21  \n",
       "Percent Methylation:  median  72.00  \n",
       "Total CpGs:                   740,142\n",
       "Coverage:  mean               52.63  \n",
       "Coverage:  std                19.15  \n",
       "Coverage:  median             53.00  "
      ]
     },
     "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": "70b1803e",
   "metadata": {
    "execution": {
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    "pycharm": {
     "name": "#%%\n"
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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>58.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  std</th>\n",
       "      <td>41.32</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>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total CpGs:  std</th>\n",
       "      <td>2.98</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    58.44\n",
       "Percent Methylation:  std     41.32\n",
       "Percent Methylation:  median  66.67\n",
       "Total CpGs:  mean             2.68 \n",
       "Total CpGs:  std              2.98 \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": "dd4fc4f0",
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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_71ee1\" style='display:inline-table'>\n",
       "  <caption>CHG</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_71ee1_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_71ee1_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_71ee1_row0_col0\" class=\"data row0 col0\" >72.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_71ee1_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_71ee1_row1_col0\" class=\"data row1 col0\" >10.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_71ee1_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_71ee1_row2_col0\" class=\"data row2 col0\" >73.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_71ee1_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_71ee1_row3_col0\" class=\"data row3 col0\" >51.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_71ee1_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_71ee1_row4_col0\" class=\"data row4 col0\" >19.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_71ee1_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_71ee1_row5_col0\" class=\"data row5 col0\" >52.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_51e71\" style='display:inline-table'>\n",
       "  <caption>CHH</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_51e71_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_51e71_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_51e71_row0_col0\" class=\"data row0 col0\" >70.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51e71_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_51e71_row1_col0\" class=\"data row1 col0\" >13.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51e71_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_51e71_row2_col0\" class=\"data row2 col0\" >71.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51e71_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_51e71_row3_col0\" class=\"data row3 col0\" >28.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51e71_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_51e71_row4_col0\" class=\"data row4 col0\" >13.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_51e71_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_51e71_row5_col0\" class=\"data row5 col0\" >27.00</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"
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   ],
   "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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   "execution_count": 18,
   "id": "4dc572e2",
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    "pycharm": {
     "name": "#%%\n"
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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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",
      "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": "69e08c19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:44.775837Z",
     "iopub.status.busy": "2025-01-26T17:57:44.775478Z",
     "iopub.status.idle": "2025-01-26T17:57:44.810387Z",
     "shell.execute_reply": "2025-01-26T17:57:44.809860Z"
    },
    "papermill": {
     "duration": 0.045277,
     "end_time": "2025-01-26T17:57:44.811507",
     "exception": false,
     "start_time": "2025-01-26T17:57:44.766230",
     "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_bcce8\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_bcce8_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_bcce8_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_bcce8_row0_col0\" class=\"data row0 col0\" >69.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bcce8_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_bcce8_row1_col0\" class=\"data row1 col0\" >10.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bcce8_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_bcce8_row2_col0\" class=\"data row2 col0\" >71.25%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_0c024\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0c024_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_0c024_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_0c024_row0_col0\" class=\"data row0 col0\" >69.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0c024_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_0c024_row1_col0\" class=\"data row1 col0\" >8.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0c024_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_0c024_row2_col0\" class=\"data row2 col0\" >71.76%</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": "690caa75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:44.828933Z",
     "iopub.status.busy": "2025-01-26T17:57:44.828486Z",
     "iopub.status.idle": "2025-01-26T17:57:45.224341Z",
     "shell.execute_reply": "2025-01-26T17:57:45.223764Z"
    },
    "papermill": {
     "duration": 0.40586,
     "end_time": "2025-01-26T17:57:45.225572",
     "exception": false,
     "start_time": "2025-01-26T17:57:44.819712",
     "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>"
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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": "18f63265",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:45.244899Z",
     "iopub.status.busy": "2025-01-26T17:57:45.244604Z",
     "iopub.status.idle": "2025-01-26T17:57:45.276660Z",
     "shell.execute_reply": "2025-01-26T17:57:45.276140Z"
    },
    "papermill": {
     "duration": 0.043032,
     "end_time": "2025-01-26T17:57:45.277762",
     "exception": false,
     "start_time": "2025-01-26T17:57:45.234730",
     "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_2b920\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2b920_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_2b920_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_2b920_row0_col0\" class=\"data row0 col0\" >67.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2b920_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_2b920_row1_col0\" class=\"data row1 col0\" >9.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2b920_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_2b920_row2_col0\" class=\"data row2 col0\" >70.15%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_aab78\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_aab78_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_aab78_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_aab78_row0_col0\" class=\"data row0 col0\" >71.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aab78_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_aab78_row1_col0\" class=\"data row1 col0\" >5.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aab78_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_aab78_row2_col0\" class=\"data row2 col0\" >71.67%</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": "7bd0b418",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:45.297232Z",
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     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "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": "0d4c7f5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:45.325438Z",
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     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "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_9e7dd\" style='display:inline-table'>\n",
       "  <caption>Lambda</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9e7dd_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_9e7dd_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_9e7dd_row0_col0\" class=\"data row0 col0\" >0.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e7dd_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_9e7dd_row1_col0\" class=\"data row1 col0\" >5.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e7dd_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_9e7dd_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e7dd_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_9e7dd_row3_col0\" class=\"data row3 col0\" >1,263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e7dd_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_9e7dd_row4_col0\" class=\"data row4 col0\" >16.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e7dd_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_9e7dd_row5_col0\" class=\"data row5 col0\" >16.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9e7dd_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_9e7dd_row6_col0\" class=\"data row6 col0\" >12.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_73fb5\" style='display:inline-table'>\n",
       "  <caption>pUC19</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_73fb5_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_73fb5_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_73fb5_row0_col0\" class=\"data row0 col0\" >0.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_73fb5_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_73fb5_row1_col0\" class=\"data row1 col0\" >1.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_73fb5_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_73fb5_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_73fb5_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_73fb5_row3_col0\" class=\"data row3 col0\" >171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_73fb5_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_73fb5_row4_col0\" class=\"data row4 col0\" >60.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_73fb5_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_73fb5_row5_col0\" class=\"data row5 col0\" >63.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_73fb5_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_73fb5_row6_col0\" class=\"data row6 col0\" >35.00</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": "2b71f1c7",
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    "execution": {
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    "pycharm": {
     "name": "#%%\n"
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    "tags": []
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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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       "<h2 style=\"font-size:16px;\">Human Genome: Additional Details of CpG Methylation and Coverage</h2>"
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      ],
      "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": "c5b9a379",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T17:57:46.042253Z",
     "iopub.status.busy": "2025-01-26T17:57:46.041967Z",
     "iopub.status.idle": "2025-01-26T17:57:46.061202Z",
     "shell.execute_reply": "2025-01-26T17:57:46.060678Z"
    },
    "papermill": {
     "duration": 0.032521,
     "end_time": "2025-01-26T17:57:46.062389",
     "exception": false,
     "start_time": "2025-01-26T17:57:46.029868",
     "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_a2fc0\" style='display:inline-table'>\n",
       "  <caption>Percent Methylation </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a2fc0_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_a2fc0_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_a2fc0_row0_col0\" class=\"data row0 col0\" >2,170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2fc0_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_a2fc0_row1_col0\" class=\"data row1 col0\" >747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2fc0_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_a2fc0_row2_col0\" class=\"data row2 col0\" >3,091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2fc0_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_a2fc0_row3_col0\" class=\"data row3 col0\" >6,570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2fc0_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_a2fc0_row4_col0\" class=\"data row4 col0\" >16,923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2fc0_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_a2fc0_row5_col0\" class=\"data row5 col0\" >70,065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2fc0_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_a2fc0_row6_col0\" class=\"data row6 col0\" >192,343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2fc0_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_a2fc0_row7_col0\" class=\"data row7 col0\" >266,738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2fc0_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_a2fc0_row8_col0\" class=\"data row8 col0\" >156,178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2fc0_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_a2fc0_row9_col0\" class=\"data row9 col0\" >25,317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_9ac3d\" style='display:inline-table'>\n",
       "  <caption>Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9ac3d_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_9ac3d_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_9ac3d_row0_col0\" class=\"data row0 col0\" >16,846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_9ac3d_row1_col0\" class=\"data row1 col0\" >17,395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_9ac3d_row2_col0\" class=\"data row2 col0\" >34,361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_9ac3d_row3_col0\" class=\"data row3 col0\" >77,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_9ac3d_row4_col0\" class=\"data row4 col0\" >152,776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_9ac3d_row5_col0\" class=\"data row5 col0\" >192,688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_9ac3d_row6_col0\" class=\"data row6 col0\" >144,879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_9ac3d_row7_col0\" class=\"data row7 col0\" >69,283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_9ac3d_row8_col0\" class=\"data row8 col0\" >22,225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_9ac3d_row9_col0\" class=\"data row9 col0\" >5,833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_9ac3d_row10_col0\" class=\"data row10 col0\" >1,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_9ac3d_row11_col0\" class=\"data row11 col0\" >826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_9ac3d_row12_col0\" class=\"data row12 col0\" >484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_9ac3d_row13_col0\" class=\"data row13 col0\" >332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_9ac3d_row14_col0\" class=\"data row14 col0\" >287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_9ac3d_row15_col0\" class=\"data row15 col0\" >222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_9ac3d_row16_col0\" class=\"data row16 col0\" >205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_9ac3d_row17_col0\" class=\"data row17 col0\" >179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_9ac3d_row18_col0\" class=\"data row18 col0\" >141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9ac3d_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_9ac3d_row19_col0\" class=\"data row19 col0\" >1,680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_bed1a\" style='display:inline-table'>\n",
       "  <caption>Cumulative Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_bed1a_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_bed1a_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_bed1a_row0_col0\" class=\"data row0 col0\" >2.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_bed1a_row1_col0\" class=\"data row1 col0\" >4.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_bed1a_row2_col0\" class=\"data row2 col0\" >9.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_bed1a_row3_col0\" class=\"data row3 col0\" >19.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_bed1a_row4_col0\" class=\"data row4 col0\" >40.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_bed1a_row5_col0\" class=\"data row5 col0\" >66.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_bed1a_row6_col0\" class=\"data row6 col0\" >86.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_bed1a_row7_col0\" class=\"data row7 col0\" >95.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_bed1a_row8_col0\" class=\"data row8 col0\" >98.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_bed1a_row9_col0\" class=\"data row9 col0\" >99.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_bed1a_row10_col0\" class=\"data row10 col0\" >99.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_bed1a_row11_col0\" class=\"data row11 col0\" >99.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_bed1a_row12_col0\" class=\"data row12 col0\" >99.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_bed1a_row13_col0\" class=\"data row13 col0\" >99.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_bed1a_row14_col0\" class=\"data row14 col0\" >99.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_bed1a_row15_col0\" class=\"data row15 col0\" >99.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_bed1a_row16_col0\" class=\"data row16 col0\" >99.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_bed1a_row17_col0\" class=\"data row17 col0\" >99.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_bed1a_row18_col0\" class=\"data row18 col0\" >99.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bed1a_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_bed1a_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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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
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  "papermill": {
   "default_parameters": {},
   "duration": 9.372667,
   "end_time": "2025-01-26T17:57:46.389975",
   "environment_variables": {},
   "exception": null,
   "input_path": "/app/VariantCalling/ugvc/reports/methyldackel_qc_report.ipynb",
   "output_path": "/data/Runs/415630/output/415630-20250125_0254/415630-L3453-Z0001-CAGCTCGAATGCGAT/415630-L3453-Z0001-CAGCTCGAATGCGAT_methyldackel_qc_report.papermill.ipynb",
   "parameters": {
    "input_base_file_name": "/data/Runs/415630/output/415630-20250125_0254/415630-L3453-Z0001-CAGCTCGAATGCGAT/415630-L3453-Z0001-CAGCTCGAATGCGAT.methyl_seq",
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   "start_time": "2025-01-26T17:57:37.017308",
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