{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0df4803d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:07.399397Z",
     "iopub.status.busy": "2025-01-26T18:00:07.399147Z",
     "iopub.status.idle": "2025-01-26T18:00:07.401728Z",
     "shell.execute_reply": "2025-01-26T18:00:07.401298Z"
    },
    "papermill": {
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     "end_time": "2025-01-26T18:00:07.402871",
     "exception": false,
     "start_time": "2025-01-26T18:00:07.392644",
     "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": "7a5621ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:07.412422Z",
     "iopub.status.busy": "2025-01-26T18:00:07.412152Z",
     "iopub.status.idle": "2025-01-26T18:00:09.733077Z",
     "shell.execute_reply": "2025-01-26T18:00:09.732523Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 2.327258,
     "end_time": "2025-01-26T18:00:09.734684",
     "exception": false,
     "start_time": "2025-01-26T18:00:07.407426",
     "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": "6cdcc2d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:09.745261Z",
     "iopub.status.busy": "2025-01-26T18:00:09.744919Z",
     "iopub.status.idle": "2025-01-26T18:00:09.747946Z",
     "shell.execute_reply": "2025-01-26T18:00:09.747511Z"
    },
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     "end_time": "2025-01-26T18:00:09.749042",
     "exception": false,
     "start_time": "2025-01-26T18:00:09.739538",
     "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": "c63432e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:09.758563Z",
     "iopub.status.busy": "2025-01-26T18:00:09.758275Z",
     "iopub.status.idle": "2025-01-26T18:00:09.761492Z",
     "shell.execute_reply": "2025-01-26T18:00:09.761054Z"
    },
    "papermill": {
     "duration": 0.009134,
     "end_time": "2025-01-26T18:00:09.762540",
     "exception": false,
     "start_time": "2025-01-26T18:00:09.753406",
     "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": "3fd73917",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:09.772100Z",
     "iopub.status.busy": "2025-01-26T18:00:09.771868Z",
     "iopub.status.idle": "2025-01-26T18:00:09.774313Z",
     "shell.execute_reply": "2025-01-26T18:00:09.773878Z"
    },
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     "duration": 0.008418,
     "end_time": "2025-01-26T18:00:09.775399",
     "exception": false,
     "start_time": "2025-01-26T18:00:09.766981",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "input_h5_file = ''\n",
    "input_base_file_name = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18d8ca5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:09.785042Z",
     "iopub.status.busy": "2025-01-26T18:00:09.784767Z",
     "iopub.status.idle": "2025-01-26T18:00:09.787215Z",
     "shell.execute_reply": "2025-01-26T18:00:09.786782Z"
    },
    "papermill": {
     "duration": 0.008482,
     "end_time": "2025-01-26T18:00:09.788284",
     "exception": false,
     "start_time": "2025-01-26T18:00:09.779802",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "input_h5_file = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3450-Z0010-CACTGCACGAATGAT/415630-L3450-Z0010-CACTGCACGAATGAT.methyl_seq.applicationQC.h5\"\n",
    "input_base_file_name = \"/data/Runs/415630/output/415630-20250125_0254/415630-L3450-Z0010-CACTGCACGAATGAT/415630-L3450-Z0010-CACTGCACGAATGAT.methyl_seq\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0644aeb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:09.797881Z",
     "iopub.status.busy": "2025-01-26T18:00:09.797643Z",
     "iopub.status.idle": "2025-01-26T18:00:11.314755Z",
     "shell.execute_reply": "2025-01-26T18:00:11.314105Z"
    },
    "papermill": {
     "duration": 1.523667,
     "end_time": "2025-01-26T18:00:11.316394",
     "exception": false,
     "start_time": "2025-01-26T18:00:09.792727",
     "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": "8c222702",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:11.327151Z",
     "iopub.status.busy": "2025-01-26T18:00:11.326867Z",
     "iopub.status.idle": "2025-01-26T18:00:11.331539Z",
     "shell.execute_reply": "2025-01-26T18:00:11.331085Z"
    },
    "papermill": {
     "duration": 0.011212,
     "end_time": "2025-01-26T18:00:11.332640",
     "exception": false,
     "start_time": "2025-01-26T18:00:11.321428",
     "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": "7c4d5fad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:11.342730Z",
     "iopub.status.busy": "2025-01-26T18:00:11.342382Z",
     "iopub.status.idle": "2025-01-26T18:00:11.345621Z",
     "shell.execute_reply": "2025-01-26T18:00:11.345190Z"
    },
    "papermill": {
     "duration": 0.009522,
     "end_time": "2025-01-26T18:00:11.346696",
     "exception": false,
     "start_time": "2025-01-26T18:00:11.337174",
     "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": "88aed7e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:11.356752Z",
     "iopub.status.busy": "2025-01-26T18:00:11.356517Z",
     "iopub.status.idle": "2025-01-26T18:00:11.359960Z",
     "shell.execute_reply": "2025-01-26T18:00:11.359521Z"
    },
    "papermill": {
     "duration": 0.00975,
     "end_time": "2025-01-26T18:00:11.361003",
     "exception": false,
     "start_time": "2025-01-26T18:00:11.351253",
     "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": "386470e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:11.371622Z",
     "iopub.status.busy": "2025-01-26T18:00:11.371346Z",
     "iopub.status.idle": "2025-01-26T18:00:11.378075Z",
     "shell.execute_reply": "2025-01-26T18:00:11.377657Z"
    },
    "papermill": {
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     "end_time": "2025-01-26T18:00:11.379187",
     "exception": false,
     "start_time": "2025-01-26T18:00:11.365575",
     "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": "467dc0a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:11.389479Z",
     "iopub.status.busy": "2025-01-26T18:00:11.389247Z",
     "iopub.status.idle": "2025-01-26T18:00:11.397258Z",
     "shell.execute_reply": "2025-01-26T18:00:11.396841Z"
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     "exception": false,
     "start_time": "2025-01-26T18:00:11.383941",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b></b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:20px;\">Input parameters</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<hr/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<b></b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "HTML(\"<b>\"+\"\"+\"</b>\")\n",
    "HTML(\"<hr/>\")\n",
    "HTML(\"<h2 style=\\\"font-size:20px;\\\">\"+\"Input parameters\"+\"</h2>\")\n",
    "HTML(\"<hr/>\")\n",
    "HTML(\"<b>\"+\"\"+\"</b>\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dbfc1ad6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:11.409788Z",
     "iopub.status.busy": "2025-01-26T18:00:11.409507Z",
     "iopub.status.idle": "2025-01-26T18:00:11.534035Z",
     "shell.execute_reply": "2025-01-26T18:00:11.533602Z"
    },
    "papermill": {
     "duration": 0.131446,
     "end_time": "2025-01-26T18:00:11.535157",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_fa129_row0_col0, #T_fa129_row1_col0 {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fa129\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fa129_level0_col0\" class=\"col_heading level0 col0\" >value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fa129_level0_row0\" class=\"row_heading level0 row0\" >Sample name</th>\n",
       "      <td id=\"T_fa129_row0_col0\" class=\"data row0 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3450-Z0010-CACTGCACGAATGAT/415630-L3450-Z0010-\n",
       "CACTGCACGAATGAT.methyl_seq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fa129_level0_row1\" class=\"row_heading level0 row1\" >h5 file</th>\n",
       "      <td id=\"T_fa129_row1_col0\" class=\"data row1 col0\" >/data/Runs/415630/output/415630-20250125_0254/415630-L3450-Z0010-CACTGCACGAATGAT/415630-L3450-Z0010-\n",
       "CACTGCACGAATGAT.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": "964c75ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:11.547231Z",
     "iopub.status.busy": "2025-01-26T18:00:11.546939Z",
     "iopub.status.idle": "2025-01-26T18:00:11.551482Z",
     "shell.execute_reply": "2025-01-26T18:00:11.551056Z"
    },
    "papermill": {
     "duration": 0.011885,
     "end_time": "2025-01-26T18:00:11.552581",
     "exception": false,
     "start_time": "2025-01-26T18:00:11.540696",
     "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": "943f488b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:11.564602Z",
     "iopub.status.busy": "2025-01-26T18:00:11.564323Z",
     "iopub.status.idle": "2025-01-26T18:00:11.597216Z",
     "shell.execute_reply": "2025-01-26T18:00:11.596785Z"
    },
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     "end_time": "2025-01-26T18:00:11.598305",
     "exception": false,
     "start_time": "2025-01-26T18:00:11.558227",
     "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>72.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  std</th>\n",
       "      <td>11.83</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>740,717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  mean</th>\n",
       "      <td>60.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  std</th>\n",
       "      <td>21.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coverage:  median</th>\n",
       "      <td>61.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                value\n",
       "metric                               \n",
       "Percent Methylation:  mean    72.41  \n",
       "Percent Methylation:  std     11.83  \n",
       "Percent Methylation:  median  74.00  \n",
       "Total CpGs:                   740,717\n",
       "Coverage:  mean               60.20  \n",
       "Coverage:  std                21.16  \n",
       "Coverage:  median             61.00  "
      ]
     },
     "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": "537a633a",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:12px;\">Human Genome: Details of Per-Read Descriptive Statistics</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metric</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Percent Methylation:  mean</th>\n",
       "      <td>59.71</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>66.67</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>3.01</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    59.71\n",
       "Percent Methylation:  std     41.08\n",
       "Percent Methylation:  median  66.67\n",
       "Total CpGs:  mean             2.70 \n",
       "Total CpGs:  std              3.01 \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": "39440228",
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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_d24f6\" style='display:inline-table'>\n",
       "  <caption>CHG</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d24f6_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_d24f6_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_d24f6_row0_col0\" class=\"data row0 col0\" >73.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d24f6_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_d24f6_row1_col0\" class=\"data row1 col0\" >10.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d24f6_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_d24f6_row2_col0\" class=\"data row2 col0\" >75.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d24f6_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_d24f6_row3_col0\" class=\"data row3 col0\" >58.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d24f6_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_d24f6_row4_col0\" class=\"data row4 col0\" >21.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d24f6_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_d24f6_row5_col0\" class=\"data row5 col0\" >60.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_eca19\" style='display:inline-table'>\n",
       "  <caption>CHH</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_eca19_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_eca19_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_eca19_row0_col0\" class=\"data row0 col0\" >71.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eca19_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_eca19_row1_col0\" class=\"data row1 col0\" >13.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eca19_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_eca19_row2_col0\" class=\"data row2 col0\" >72.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eca19_level0_row3\" class=\"row_heading level0 row3\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_eca19_row3_col0\" class=\"data row3 col0\" >32.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eca19_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Std</th>\n",
       "      <td id=\"T_eca19_row4_col0\" class=\"data row4 col0\" >14.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eca19_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Median</th>\n",
       "      <td id=\"T_eca19_row5_col0\" class=\"data row5 col0\" >31.00</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,
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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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",
      "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": "efc788e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:12.114357Z",
     "iopub.status.busy": "2025-01-26T18:00:12.114102Z",
     "iopub.status.idle": "2025-01-26T18:00:12.144550Z",
     "shell.execute_reply": "2025-01-26T18:00:12.144114Z"
    },
    "papermill": {
     "duration": 0.039892,
     "end_time": "2025-01-26T18:00:12.145630",
     "exception": false,
     "start_time": "2025-01-26T18:00:12.105738",
     "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_21d12\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_21d12_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_21d12_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_21d12_row0_col0\" class=\"data row0 col0\" >71.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21d12_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_21d12_row1_col0\" class=\"data row1 col0\" >9.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21d12_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_21d12_row2_col0\" class=\"data row2 col0\" >72.44%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c6b3e\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c6b3e_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_c6b3e_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_c6b3e_row0_col0\" class=\"data row0 col0\" >70.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6b3e_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_c6b3e_row1_col0\" class=\"data row1 col0\" >10.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c6b3e_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_c6b3e_row2_col0\" class=\"data row2 col0\" >73.00%</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": "7a6dcba7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:12.162091Z",
     "iopub.status.busy": "2025-01-26T18:00:12.161842Z",
     "iopub.status.idle": "2025-01-26T18:00:12.536117Z",
     "shell.execute_reply": "2025-01-26T18:00:12.535637Z"
    },
    "papermill": {
     "duration": 0.383976,
     "end_time": "2025-01-26T18:00:12.537315",
     "exception": false,
     "start_time": "2025-01-26T18:00:12.153339",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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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": "aaf86ba0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:12.555991Z",
     "iopub.status.busy": "2025-01-26T18:00:12.555729Z",
     "iopub.status.idle": "2025-01-26T18:00:12.586864Z",
     "shell.execute_reply": "2025-01-26T18:00:12.586416Z"
    },
    "papermill": {
     "duration": 0.041546,
     "end_time": "2025-01-26T18:00:12.587973",
     "exception": false,
     "start_time": "2025-01-26T18:00:12.546427",
     "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_21739\" style='display:inline-table'>\n",
       "  <caption>OT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_21739_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_21739_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_21739_row0_col0\" class=\"data row0 col0\" >67.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21739_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_21739_row1_col0\" class=\"data row1 col0\" >13.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_21739_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_21739_row2_col0\" class=\"data row2 col0\" >71.27%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_94922\" style='display:inline-table'>\n",
       "  <caption>OB</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_94922_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_94922_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_94922_row0_col0\" class=\"data row0 col0\" >71.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94922_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_94922_row1_col0\" class=\"data row1 col0\" >7.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_94922_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_94922_row2_col0\" class=\"data row2 col0\" >72.83%</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": "07f57630",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:12.606573Z",
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    "pycharm": {
     "name": "#%%\n"
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    "tags": []
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   "outputs": [],
   "source": [
    "## Control genomes (if exist)\n",
    "# ------------------------------\n",
    "\n",
    "tbl = 'MergeContext_per_position'\n",
    "if tbl in list_tables:\n",
    "    df_pos = pd.read_hdf(input_h5_file, key = tbl)\n",
    "    df_pos = pd.DataFrame(df_pos)\n",
    "    df_pos.reset_index(inplace=True)\n",
    "    all_genomes = list(set(df_pos['detail']))\n",
    "    ctrl_genomes = ['Lambda', 'pUC19']\n",
    "\n",
    "    check = all(item in all_genomes for item in ctrl_genomes)\n",
    "\n",
    "    if check is True:\n",
    "\n",
    "        HTML(\" \")\n",
    "        HTML(\"<h2 style=\\\"font-size:14px;\\\">\"+\"Control Genomes: Methylation and Coverage\"+\"</h2>\")\n",
    "        HTML(\"<hr/>\")\n",
    "\n",
    "        # PRINT PLOTS OF PERCENT METHYLATION ACROSS ENTIRE CONTROL GENOMES \n",
    "        #-----------------------------------------------------------------\n",
    "\n",
    "        df_pos = parse_metric_names(df_pos)\n",
    "        df_pos['bin'] = df_pos['bin'].astype(int)\n",
    "        df_pos  = format_metric_names(df_pos)\n",
    "        df_pos['bin'] = df_pos['bin'].astype(int)\n",
    "        df_pos = df_pos.query('metric == \"Percent Methylation: Position\"')\n",
    "        df_output = [y for x, y in df_pos.groupby('detail')]\n",
    "\n",
    "\n",
    "        df_pos_meth = []\n",
    "        n = 102\n",
    "        f, ax = plt.subplots(1, 2, figsize = [12, 5])\n",
    "        i = 0\n",
    "        palet = ['forestgreen','steelblue']\n",
    "\n",
    "        for df_pos_meth in df_output:\n",
    "            # get methylation per position\n",
    "            df_pos_meth = df_pos_meth.reset_index()\n",
    "            temp_genome = df_pos_meth['detail'].unique()[0]\n",
    "\n",
    "            # print to subplots\n",
    "            currax = ax[i]\n",
    "            s = df_pos_meth.plot(kind = 'area', ylim = [0,n],y  ='value' ,\n",
    "                                        title = temp_genome + \": Percent methylation\", \n",
    "                        legend=False, color=palet[i], alpha=0.6, ax = currax,\n",
    "                                fontsize=14)\n",
    "            a = currax.set(xlabel='Position', ylabel='Percent Methylation')\n",
    "            plt.style.use('ggplot')\n",
    "\n",
    "            i+=1\n",
    "\n",
    "        HTML(\" \")\n",
    "\n",
    "# --------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "48651148",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       " "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/html": [
       "<h2 style=\"font-size:12px;\">Control Genomes: Methylation and Coverage Descriptive Statistics</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_fd8f9\" style='display:inline-table'>\n",
       "  <caption>Lambda</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fd8f9_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_fd8f9_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_fd8f9_row0_col0\" class=\"data row0 col0\" >1.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd8f9_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_fd8f9_row1_col0\" class=\"data row1 col0\" >8.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd8f9_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_fd8f9_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd8f9_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_fd8f9_row3_col0\" class=\"data row3 col0\" >760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd8f9_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_fd8f9_row4_col0\" class=\"data row4 col0\" >4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd8f9_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_fd8f9_row5_col0\" class=\"data row5 col0\" >4.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fd8f9_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_fd8f9_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_cbdaa\" style='display:inline-table'>\n",
       "  <caption>pUC19</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cbdaa_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_cbdaa_level0_row0\" class=\"row_heading level0 row0\" >Percent Methylation:  Mean</th>\n",
       "      <td id=\"T_cbdaa_row0_col0\" class=\"data row0 col0\" >4.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cbdaa_level0_row1\" class=\"row_heading level0 row1\" >Percent Methylation:  Std</th>\n",
       "      <td id=\"T_cbdaa_row1_col0\" class=\"data row1 col0\" >10.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cbdaa_level0_row2\" class=\"row_heading level0 row2\" >Percent Methylation:  Median</th>\n",
       "      <td id=\"T_cbdaa_row2_col0\" class=\"data row2 col0\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cbdaa_level0_row3\" class=\"row_heading level0 row3\" >Total CpGs: </th>\n",
       "      <td id=\"T_cbdaa_row3_col0\" class=\"data row3 col0\" >170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cbdaa_level0_row4\" class=\"row_heading level0 row4\" >Coverage:  Mean</th>\n",
       "      <td id=\"T_cbdaa_row4_col0\" class=\"data row4 col0\" >14.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cbdaa_level0_row5\" class=\"row_heading level0 row5\" >Coverage:  Std</th>\n",
       "      <td id=\"T_cbdaa_row5_col0\" class=\"data row5 col0\" >23.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cbdaa_level0_row6\" class=\"row_heading level0 row6\" >Coverage:  Median</th>\n",
       "      <td id=\"T_cbdaa_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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ca565277",
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    "execution": {
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    "pycharm": {
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   "outputs": [],
   "source": [
    "# function for printing bar plots of methylation and coverage at CpGs \n",
    "def plot_bar_distrib(in_table):\n",
    "    i =0\n",
    "    in_colours = ['salmon','tomato']\n",
    "    curr_genome = \"Human Genome\"\n",
    "    cols = ['bin','value']\n",
    "    n_rows = in_table.shape[0]\n",
    "    if ( n_rows > 10 ):\n",
    "        h = 5\n",
    "        w = 13.5\n",
    "        n = 2\n",
    "\n",
    "        in_list_df = [y for x, y in in_table.groupby(in_table['metric'], sort = False)]\n",
    "\n",
    "        f, ax = plt.subplots(1, n, figsize = [w, h])\n",
    "\n",
    "        for i in range(len(in_list_df)):\n",
    "\n",
    "            currax = ax[i]\n",
    "            y_axis_label = in_list_df[i]['metric'].unique()[0]\n",
    "            y_axis_label = y_axis_label.replace(\":\", \"\" )\n",
    "            x_axis_label =\"Value Bins\"\n",
    "            curr_title = curr_genome + \": \" + y_axis_label\n",
    "            \n",
    "            sns.barplot(data = in_list_df[i],\n",
    "                        x=\"bin\",\n",
    "                        y=\"value\",\n",
    "                        lw=2.5,\n",
    "                        ax = currax,\n",
    "                        color=in_colours[i]\n",
    "                        )\n",
    "            currax.set_xlabel(x_axis_label,fontsize=14)\n",
    "            currax.set_ylabel(y_axis_label,fontsize=14)\n",
    "            currax.set_title(curr_title,fontsize=13)\n",
    "            currax.tick_params(labelsize=14)\n",
    "            f.axes[i].tick_params(labelrotation=45)\n",
    "\n",
    "    else: \n",
    "        h = 5\n",
    "        w = 5.5\n",
    "        n = 1\n",
    "\n",
    "        f, ax = plt.subplots(n, n, figsize = [w, h])\n",
    "\n",
    "        y_axis_label = in_table['metric'].unique()[0]\n",
    "        y_axis_label = y_axis_label.replace(\":\", \"\" )\n",
    "        curr_title = curr_genome + \": \" + y_axis_label\n",
    "\n",
    "        sns.barplot(data = in_table,\n",
    "                    x=\"bin\",\n",
    "                    y=\"value\",\n",
    "                    lw=2.5,\n",
    "                    ax = ax,\n",
    "                    color=in_colours[i]\n",
    "                    )\n",
    "        ax.set_xlabel(\"Value Bins\",fontsize=14)\n",
    "        ax.set_ylabel(y_axis_label,fontsize=14)\n",
    "        ax.set_title(curr_title,fontsize=13)\n",
    "        ax.tick_params(labelsize=14)\n",
    "        plt.xticks(rotation=45)\n",
    "\n",
    "#--------\n"
   ]
  },
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",
      "text/plain": [
       "<Figure size 550x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fzvnL0vnL83JPP/00nTp1AuDdd9/NN5jatm0bAI899tgV2z5y5Ei+1xEREXh7e1+zzr1799KgQYMCi0RbLBaqVatGampqgfe60e/55e9XlL5cL+f39/Tp09f9NTfSf2d4NG7cON58803MZjOLFy9m3759vPPOO67rnH189dVXefXVVwt938L6WK1atUKv/eWXXxg+fDirVq3i/Pnz+c6dPHkyX18AqlevXqCN6tWrM2vWLNfr7du343A4+OqrrwqsReBU2GddRKQoNB7IT+OBkjceKIrCPgNX+h44j1/rs3GjbvRzeyMGDBjA+++/z5gxY3jzzTeBSzfXnEGN07Zt2zAMg7i4uCu2da2f5eU/M+f36mqcY56aNWsWOOc8tmfPntvSlyuN3+x2OyNGjGDMmDHs2rWrwELxvx/DNWrUKN+NQYDQ0FACAwPzHXP+u3O1/6YW9d8dKUjBksgd5PcLOF7Ow8Pjho5f/h/1AwcO0KpVKwICAhg6dCjVq1fH19fXtYjfmTNnrrvd37ddmISEBADS0tIKnKtatSpVq1YF4Ntvvy203XfffZe6desW2vbld2tuts5rudHveWHvfSN9uV7O7+/69euL9PXX45FHHmHQoEH88ssvJCUluQYl/fv3d13j7ONf//pX1/8c/F5hgyabzVbg2Jo1a+jQoQOxsbGMGDGCypUru+6APfjggzgcjiL1w1lj//79892pu5yPj0+R2hYRuVU0HtB44HoUZTxwpUW04cqfu6v183o+d1d6z6t9zi9XlM/tjahVqxZ169Zl3LhxvPHGG5w7d47vvvuODh06EBYWlq9PJpOJWbNmXbHfhQVAl0tISCA1NZX169e7ZtQVp1vZl8LGb3BpJ7tPPvmEPn368MorrxAaGoqnpyepqam89NJLNz2GGzNmDOHh4YVeo5uDxUfBkkgpFBwczIkTJwocv/yOQ3GaNm0aZ86cYcaMGbRp0ybfuePHj+Pl5VWs71e5cmXq16/P0qVL2b59e6EzSwrjHGD6+voW+x3fmJgYtm/fjt1uz3eX0m63s2PHjmL/xXQr+9K8eXPCwsKYPn06x48fd92RvZob7f9DDz3Eiy++yJgxY2jevDlTpkyhffv2+X6xO/vo4eFx030cP348eXl5zJo1K9+dx7Nnz+a70wW4dnPZvn17gbqdOyM5xcbGYjKZyM3NveWzCEREbpTGA4XTeOD6FGU8EBwcDMCJEyfy7Y52/vx5Dh06RGxsbLHW+Pv3dP4drv9zfqOf26uFZ1cyYMAAXnjhBRYsWMChQ4c4ffp0gRtSVatWZfbs2URFRVGjRo0bfg+A+++/nzFjxvDll19eV7Dk/Dxu2bKlwLmtW7fmu+Z298Vp7NixtGrViokTJ+Y7vmvXrgLXRkdHs2vXLhwOR75ZS0ePHiUrK6tAjQAhISEaw90GWmNJpBSqVq0aK1asICcnx3Xs5MmTfP3117fk/Zx3In5/x+6LL7647u2Cb5RzG9oHHnigwLazTr+vp2PHjoSGhjJixIhCB9rnzp0r8nTvHj16kJmZyZdffpnv+BdffEFmZiY9e/YsUrtXcqN9uZHtha1WK2+88QanT5+mT58+hX5Pzp8/z8svv+x6/OBG+1+uXDnuvfdepk6dyrhx48jOzi4wKKlXrx4JCQmMGjWq0MGh3W4vtO+FudJn9M033yxwp8u5ve3HH3+c79ymTZuYM2dOvmvLli1LcnIyU6dOdW0NfTnDMMjMzLyuGkVEipvGAxRaj8YDt2484Hyc6fdr03z44YdFnllyLVd6z/fff/+6vv5GP7d+fn7XPf5weuihh7BYLIwZM4YxY8ZQpkwZunfvnu+ahx9+GICXX36ZvLy8Am1cz2NZXbt2pVWrVkyYMIGRI0cWes2uXbt46623AKhfvz5RUVF8/fXX+fp68eJF3n33XUwmU4E6b1dfnDw8PAr8bM6ePcuHH35Y4NquXbty6NAhJkyYkO/4e++9V+DaBx54AC8vL1599VXOnTtX4PypU6e4cOHCddcpV6cZSyKl0J/+9Cf69+9P27Ztefjhh8nKyuKLL76gUqVKt2Rgd++992Kz2Xj44Yf505/+RFBQEMuWLeOnn36iSpUq1z0V+UYkJSXx1Vdf8fTTT1OtWjV69+7NPffcg81m48iRIyxevJiff/6Z8PBw15oIvr6+jBkzhh49elC9enUee+wxYmNjycrKIj09nalTpzJt2jQSExNvuJ6//e1vTJ48mWeffZbU1FTq1avH+vXr+eqrr6hevTp/+9vfirX/N9qX1atX06ZNGwYMGHBdC3Y+9thj/PrrrwwbNozY2Fgeeugh4uPjcTgcbNu2jcmTJ3P06FGGDBlS5P4PGDCAGTNm8Ne//pUyZcrQo0ePfOdNJhNjx46lbdu21K5dm8cee4yaNWuSk5PDrl27mDp1Km+99RaPPvroNfvTs2dPPvzwQ5KTk3nqqaewWq3MnTuXjRs3EhISku/amjVr8tRTT/H555+TlJREz549yczM5LPPPqNevXqsW7cu393Kf//737Ro0YJWrVrxyCOPUK9ePRwOB3v27OH777/nkUce4bXXXrtmjSIixU3jAY0Hbvd4ICkpierVq/OPf/yD48ePU7lyZZYuXcrKlSsL/L4tLn379uXll1/mqaeeIj09neDgYGbPns2xY8eu6+tv9HPbpEkTvvrqK4YOHUqNGjUwm8107drVtQB4YUJDQ7n33nuZMmUK58+f5/HHHy+wZlfDhg157bXXeO2116hbty69e/cmIiKCQ4cOsW7dOn766Sdyc3Ov2heTycSUKVPo2rUrzz77LGPHjqVbt26EhYWRlZXF0qVLmTFjBvfffz9wKbT59NNP6dmzJw0bNuSpp57C39+flJQUVq5cycsvv+ya2XO7++LUq1cv/vOf/9CnTx+SkpI4cuQIo0ePLnQG3UsvvcT48eP5wx/+wOrVq4mLi2PJkiUsX76ckJCQfOO3yMhI/v3vf/PEE09Qo0YNHn74YSpVqkRmZiabNm1i+vTpbN26Nd/MO7kJt3rbORG5MueWsO++++4Vr6GQ7YUN49IWuFFRUYbVajXi4uKMr7766qrbC/9+W9i9e/caQKFbpf9+K1XDMIxFixYZzZs3N/z8/IwyZcoYycnJxqZNmwq99krbtDr7e/kW8teya9cu4/nnnzfi4+MNX19fw9PT0wgPDzc6depk/Pvf/y50+9hNmzYZ/fr1MyIiIgxPT08jNDTUaNq0qTF8+PB827MWVrthXPl7c/ToUeOZZ54xKlSoYFgsFqNChQrGH//4RyMzMzPfdYX9HJy4whbAV9pK93r7ciPbC19uzZo1xiOPPGJER0cbXl5eho+Pj5GQkGC88MILru1nb7T/ThcuXDCCg4MNwHjiiSeuWMO+ffuMp59+2qhUqZLh6elpBAcHG/Xr1zf+/ve/59v6+mrbDRuGYUybNs2oX7++YbPZjLJlyxp9+vQx9u/fX+jn0W63G6+99ppRsWJFw2q1GrVq1TJSUlKMv/71rwZgHDlyJN/1mZmZxuDBg42qVasaXl5eRpkyZYyEhATj+eefN7Zs2XLFmkRErofGA9em8UDJGQ9s377d6Nixo+Hj42OUKVPG6N27t5GRkVHoz/tG+3mlz8bKlSuNZs2aGV5eXkbZsmWNJ5980jh58mSh7Rd27EY+t0eOHDHuu+8+IygoyDCZTPn+vbnSZ8UwDGPKlCkGYADG0qVLC73GMAxj5syZRocOHYygoCDDarUakZGRrs/x9crNzTW+/PJLo127dkbZsmUNi8ViBAcHG23atDE+++wzIycnJ9/1CxcuNJKSkgx/f3/Dy8vLqFu3rvHll19esf3i7svVPpNnz541Bg8ebERFRRleXl5GbGys8dZbbxnz5s0r9LOwZ88eo2fPnoafn5/h7+9vdOvWzdizZ49RtmxZ49577y3Q/tKlS40ePXoY5cqVc/13IzEx0XjvvfeMc+fOXbFvcmNMhnGTq9GJiIjcIbp27covv/xCdnb2dS2qKiIiIiLudfz4cUJCQnj66acZNWqUu8u5K2mNJRERuesU9qz9xo0bmTVrFm3btlWoJCIiIlICFTaGGzFiBADt27e/3eXI/6cZSyIictcZNWoUY8aMoXPnzpQrV4709HQ+//xzHA4Hy5Yto169eu4uUURERER+p02bNlSqVIn69evjcDiYP38+M2fOpFmzZixevFg3B91EwZKIiNx1Vq9ezdChQ0lLS+PEiRP4+/vTokULXn31Ve655x53lyciIiIihXj//fcZM2YM+/bt49y5c0RGRnLffffx6quv4u/v7+7y7loKlkREREREREREpEi0xpKIiIiIiIiIiBSJgiURERERERERESkSBUsiIiIiIiIiIlIkCpZERERERERERKRILO4uQIru5MmT2O12d5chIiJyR7JYLAQFBbm7jLuaxjoiIiK3RnGOcxQslWJ2u52LFy+6uwwRERGRW0JjHRERkZJPj8KJiIiIiIiIiEiRKFgSEREREREREZEiUbAkIiIiIiIiIiJFomBJRERERERERESKRMGSiIiIiIiIiIgUiYIlEREREREREREpEgVLIiIiIiIiIiJSJAqWRERERERERESkSBQs3QIOhwPDMNxdhoiIiIiIiIjILWVxdwF3kpkzZ7J3716OHz9OcHAwSUlJVKpUCV9f35tq9+LFi1y8eNH12mQy4ePj4/q7iIiIiIiIiIg7mAxNrSkW77zzDhs2bMDPzw+r1crRo0exWCy0bNmSxMRE4uLiitz2pEmTmDJliut15cqVefvtt4ujbBEREZESKzMzM9/NNRERESkenp6elCtXrljaUrBUDMaMGcOcOXN44IEHaNmyJcHBwaxcuZLly5ezatUqKleuzAMPPED9+vWL1P6VZixlZmZit9uLqxsiIiJyGYvFUmwDLikaBUsiIiK3RnEGS3oU7iZlZ2ezZcsWYmJiaNeuHX5+fgA0adKE+Ph4KlSowNSpU/n2228xm83UrVv3ht/D09MTT0/PQs8pFxQRERERERERd9Hi3Tfp/PnzHD16lLCwMFeo5HA4AAgICKBPnz706dOH3377jWnTprFv3z43VisiIiIiIiIiUnw0Y+kmmc1mzGYz+/fvJzs7m4CAAMzm/Hld9+7dycnJ4YcffmDVqlVER0fjcDgKXCciIiJSkv32228cP34cPz8/fH19KV++vOucYRhF3lREG5WIiIiUXgqWblJISAjNmzdn7ty5bN26lSZNmhQIjTw8PEhKSmLHjh3MmjWLxMTEfAMxEXewpIwutrbsfR4rtrZERKRkGjlyJKmpqZw+fRoAm83G/fffT8OGDSlfvjwmk6nI4dK0adMK3ahEa1yJiIhccmTwgGJrq/x7/y22tkDB0k1xDp5q167NggULGDlyJKGhocTExBQIl8LCwmjQoAHbt29n586dCpZERESk1Pjggw9IS0ujcePG1KlTh4yMDJYtW8bYsWPZtGkTycnJ1KlTp8jhUs+ePenSpYvrtfPrtVGJiIiUFub3/15sbTn+OqJg+8XWOhw6dKhYNylRsHQDzpw5w4kTJyhbtiwmkwmbzQZAgwYN6Ny5M9OmTeODDz5g8ODB+R53s9vtWCwWGjRowLhx4zh16pSbeyIiIiJyfVauXMm6detITk6mZ8+ervFPo0aNWLJkCbNnz+bUqVNcvHiRBg0aFClc0kYlIlJSeHwwpNjayvvLW2q/BLV/q2svTrf6d19xt69g6TqNHj2aDRs2cPjwYQIDAwkLC+PBBx+kSpUqWK1WHnzwQU6dOsUvv/zCiBEj+Mtf/kK1atUwDAOL5dK3OS0tDYCIiAg39kRERETk+h0+fJi8vDwSExOx2WyuG2YxMTGEhIRQpkwZUlJSmDZtGjabjfj4eK2LJCK3VGkKCETuBgqWrsO7777Lxo0biY+Pp3Hjxhw4cID169fz+uuv0717d5o3b06FChV4+umn8fDwYO7cubz66qsMGDCAatWqERMTw7Jly1i4cCHh4eFUqlTJ3V0SERERuSrnzOuTJ09iGAbHjx8nIiIi36P+AQEBdOjQAYfDweTJk5k7dy6VK1d2LbwtIncnBT8idxcFS9cwZ84cUlNTuf/++0lOTnZN/16yZAnz589n6tSpnDhxguTkZKKionjiiScIDQ1l7ty5fP311wD4+/tz9uxZAgICeOWVVwgODnZnl0RERESuyRkg1alTh9mzZ7Nt2zZq165d4FE3m81GYmIihw8fZtGiRdxzzz20aNHCnaWLiIjIbaRg6Rr279+PzWYjKSkp3/Tvli1bEh4ezvfff8+CBQuwWCx069aN0NBQunXrRu3atdm5cydpaWl4enoSGRlJq1atCA0NdXeXRERERK5bREQEFSpUYOrUqVSpUqXQdZSCg4Np2bIly5Yt4+eff6ZRo0Z4enrqkTgREZG7gIKlK3A4HBiGwW+//YZhGDgcDvLy8vDw8HANpGJjY7n//vtxOBzMnz+f8PBwOnfuDEB0dDTR0dEkJibi6elZ5O13RURERNwpLCyMrl27MmrUKL7++mu8vb1JSEgoEC7VqlWL2rVrs2vXLvLy8rBarW6uXESuRI+qiUhxKs4d6+4oZrMZDw8PatWqxdmzZ9mzZw8eHh4AroEUXAqQOnfuTGRkJBMnTiQjIwP43yrrzoW7RUREREob53imTZs23HfffRw7doyvvvqKjRs3ApfGRA6Hw3X9xYsX8fHxueIObyIiInLnUbB0DVWqVAFgzJgxZGRkuO7KXR4uxcfH07p1a3Jzc5k3b16+r7/8ehEREZHS5PLgqE+fPnTt2pWDBw/y8ccfs2zZMuB/azGlpqby66+/EhMT47Z6RURE5PbTdJprqFevHu3bt2fu3LlMmjSJ/v37u9ZJunwKeKdOnZg7dy779+93nRMREREp7S7fBa5///7YbDZSUlL417/+xfLly6lcuTKnT58mNTUVu93OAw88oBnbIiIidxH91v//1q9fz/Hjxzlz5gw1a9akfPnyBAQEAJcGUZmZmaxatQqr1UqvXr0ICwvDMAxXsOS8m3f5dHARERGRku5G14G87777iIiIYOnSpWzatIm1a9fi6+tLZGQkL730EhEREbewWhERESlpFCwBH3/8MampqZw/fx64tC5SlSpVePTRR4mJicHb25sBAwZgt9tZsmQJp0+fpm/fvkRHR7sGYmlpaWRnZ1OvXj3gxgdpIiIiIrfTvn37iIyMvKHZRQ6HA7PZTJMmTahbty4nT57k8OHDlC1bluDgYPz8/G5hxSIiIlIS3fXB0ieffMLatWtp164djRo1IicnhyVLlrBy5UqGDh3KwIEDadmyJRERETz55JOMHTuWtWvXsmPHDrp3705ERAQHDx5k8eLFeHp60qlTJ0CPwomIiEjJ9dZbb3Hs2DEGDBhAQkJCvsfdrsZsNrtunlmtVsLDwwkPD7/F1YqIiEhJdlcHS9u3byc1NZXWrVvTu3dvfH19AWjQoAEzZ87khx9+YNSoUeTm5tKuXTvCwsJ48cUXmThxIosWLWLChAkAWK1WQkNDGTp0KGFhYe7skoiIiMhVffPNN6SlpQGQkpICcEPhkjNYut7rRURE5M52VwdLmZmZ5OTkULNmTXx9fXE4HBiGgYeHB126dCEgIICUlBRGjx6Nv78/jRo1AuDBBx+kRYsWHD16lMOHDxMREUF0dDSBgYHu7ZCIiIjIVaxcuZIFCxYQGRlJnTp1mD9/PhMmTKBv377XFS4dPXqUjRs3kpCQoJtpIiIiAsBdfavJarUCcO7cOeDS42seHh6uBbhbtWpF79698fT05NNPPyUjI8P1tZGRkdSvX5/k5GTq1q2rUElERERKtNzcXBYuXIjdbmfQoEH079+fLl26kJGRwYQJE9i8efNVNyE5f/48kydP5osvvmDJkiXasERERESAuzxYcoZBP/74I0ePHnWti2Q2m12DpcTERDp37syFCxeYOXMmubm5GIbhrpJFREREisS5s23fvn2pWLEiZrOZ9u3b07179yuGS5ePeSwWC9HR0cTGxtK8eXM9CiciIiLAXf4oXLVq1UhMTGThwoUsWrSIe++917WbiTNcMpvN9O7dm9TUVNLT08nLy3PNdBIREREpTWJjY4mOjna9DgwMJCkpCYDvv/++wGNxzptuOTk52Gw2OnToQJs2bbDZbO4oX+Su4fHBkGJrK+8vbxVbWyIihblrbjWNHz+e9evXu14778Z17dqV6OhoZs+ezcqVK8nJyXFdYzabsdvtAFStWpVDhw5x4MCB21u4iIiISDGyWC7dV3TORnKGS5fPXNq4caPr+sWLF5OSksKhQ4fw9PRUqCQiIiL53BUzll5//XU2b95MTEyMaxaSc/p2eHg43bp1Y/z48aSkpGC322nWrBkBAQHY7XbX4Ov8+fMEBQVRrlw5d3ZFREREpFg4ZyNBwZlLKSkpeHh4kJmZybhx47BarXTv3t1dpYqIiEgJdscHS2+++Sbbt2+nX79+1K5dO996AA6HAw8PDxo2bEhubi5Tp05l4sSJHDx4kE6dOhEREQFAamoqmzdvpmLFinh7e7urKyIiIiK3TGBgIG3btsVsNjNt2jS+/PJLsrKyMJvNDBkyhODgYHeXKCIiIiXQHR0svfHGG2zdupW+ffvStm3bAlO3nSGT1WqlefPm+Pn5MXPmTObMmcOyZcto2LAhp0+fZu/evVy4cIEBAwZo+reIiIjckRwOB8HBwSQlJbF3715Wr16Nr68vw4YNo2LFiu4uT0REREqoOzZYeuedd9iyZQuPPvoozZo1w2az4XA4sNvtLF26lNzcXNdjb35+flitVu655x7i4+OZMWMGqampLFiwgKCgIKKjo3n44YepUKGCu7slIiIicks4b7ilpaWRnp6OzWZj+PDhREZGurkyERERKcnuyGBp9uzZrFu3jujoaKpUqYKfnx85OTmsW7eOn376iT179riunT9/Ph07dqR58+b4+/vj6+tL37596dGjB6dPnyYgIACTyYSXl5cbeyQiIiJy66WlpTF+/HjOnz/Pm2++qVBJRERErumODJYSExPZvXs3ixcvZtasWXTr1o2MjAxGjx5NSEgIvXv3JjIykg0bNrBx40YmT56MxWKhbdu2GIaBh4cH3t7e+Pj4uLsrIiIiIrdNREQE0dHR9OvXT4+/iYiIyHW544Ilh8OBt7c3Tz75JHl5eSxZsoTs7Gz27t1LhQoVePnll13rJNWpU4cNGzbwzTffMHv2bFq0aOFanPvynVJERERE7gahoaEMHjzYtSuuiIiIyLWYr31J6TB+/HhOnz6N2WzG4XBgtVoZOHAgLVu2ZMOGDQQFBblCJYfDgWEY+Pj4UL9+feLj4/n111/ZunWru7shIiIi4lYKlURERORG3BEjhw8++IBVq1Zht9t55JFH8u329uSTT+Ln50dgYCA2mw3DMFzn7XY7VquV2NhYli1bht1ud2c3RERERERERERKlVIfLL355pts2LABq9XKtm3byMrKIjAwEMMwXItuP/TQQ+Tm5gL/e8TNMAzXHbkdO3bg4+NDpUqV3NYPEREREREREZHSplQ/Cvfmm2+yZcsW+vTpQ8OGDdmzZw9paWlA/jWSrFYrfn5+rtcOh8N1fvny5WzatIm4uDj8/f1va/0iIiIiIiIiIqVZqQ2WnKFS37596datG61btwZg7ty5nDhx4qpf63wUbvny5Xz33XeYzWYeeeQR16LeIiIiIiIiIiJybaXyUbgRI0a4QqU2bdpgsVioU6cOjRo1YsOGDRw5coTg4GAcDocrRHJyOBycOXOGKVOmsGbNGgCGDh1KRESEO7oiIiIiIiIiIlJqlboZS6NGjWL9+vX069ePtm3b4uvr69rlrWbNmly4cIGpU6dy8eLFAqESXFpbacaMGSxbtoxKlSrxj3/8g6ioKDf0RERERERERESkdCt1M5b69etHXFwcDRs2dO3yZjKZMJlMJCYmMn/+fHbt2sWBAweoUqVKgVlLHh4e3HfffdSqVYuYmBitqyQiIiIiIiIiUkSlasZSXl4e/v7+JCYm4uvrmy9UcjgceHt707ZtW3Jycli+fDlAoY/C2Ww26tSpo1BJREREREREROQmlJpgyTAMPDw88h27fOc3Z4BUs2ZNrFYrCxcuZM+ePQXaKezxOBERERERERERuXElPmXZt28fdrs9X4h0NVFRUXTt2pUzZ864giWHw3ErSxQRERERERERuSuV6DWW3nrrLY4dO8aAAQNISEi47tlGcXFxmM1mpk2bRv369QkODr7FlYoUP0vK6GJry97nsWJrS0RERERERMSpxM5Y+uabb0hLSyMjI4OUlBQ2b9583TOPateuTaNGjTh27Bjp6em3uFIRERERERERkbtTiQyWVq5cyYIFC4iMjKRz585kZGQwYcKE6wqXnOc7duwIwIoVKzAM45bXLCIiIiIiIiJytylxwVJubi4LFy7EbrczaNAg+vfvT5cuXa47XHI+LhcWFkZ8fDz333//da/PJCIiIiIiIiIi16/ErbFktVrp1asXCQkJVKxYEYD27dtjMpn4/vvvmTBhAn379s235pJhGPnCI4fDQXBwMK+88goWS4nrooiIiIiIiIjIHaHEzVgCiI2NpVOnTq7XgYGBJCUl0b1790JnLjlDpXPnzuV77eHhcZsrFxERERERERG5e5TIYAlwzTRyro9UWLi0ceNG1/WLFy9m4sSJHDlyxBUs6RE4EREREREREZFbp8Q/J3Z5OOQMlwC+//57UlJS8PDwIDMzk3HjxmG1Wunevbu7Ss3n94/niYiIiIiIiIjcaUp8sPR7gYGBtG3bFrPZzLRp0/jyyy/JysrCbDYzZMgQgoOD3V0i8L9AzOFwuNaCEhERERERERG5k5S6YMm5MHdSUhJ79+5l9erV+Pr6MmzYMNdi3+4yd+5cfv31V06cOEGFChVo3rw5UVFRN93uxYsXuXjxouu1yWTCx8fH9XeRa7nVnxN9DkVERERERO5OpS5Ycs7+SUtLIz09HZvNxvDhw4mMjHRrXe+++y5paWlYrVY8PDxYs2YNP/30E927d6dJkyY3Vd+0adOYMmWK63XlypV5++23KVeuXHGULiXU0WJsKzw8/La3LyIiIiIiIne+UhcswaVQafz48Zw/f54333zT7aHS6NGj2bBhA/fffz/NmjWjTJkyLFq0iKVLlzJlyhT27dtH9+7dqVq1apHa79mzJ126dHG9ds4OyczMxG63F0sfpOQpzj0NDx06dNvbFxEp7SwWi27iiMgVeXwwpNjayvvLW8XWlojI7VYqg6WIiAiio6Pp16+f2x9/y8zMZOPGjdSoUYNOnTphs9kA6NSpEzVq1GDWrFksWLCAixcv0rt3b2JjY2/4PTw9PfH09Cz0nHPXPJGrudWfE30ORURERERE7k6lMlgKDQ1l8ODBWCzuL//s2bNkZmZSv359bDYbhmFgGAZms5lKlSrxwAMPYLFYmDt3Ln5+fvTt25eQkBB3ly0iIiIiIiIictNK7XZlJSFUgkt1mM1mDh06lC9UcgoODqZHjx40b96c5cuXs3HjRuDSIuQiIiIiIiIiIqVZqQ2WSoqwsDDq1KnDhg0b2L59O2azucBjQSEhIXTo0IGgoCBSUlI4ceJEvvBJRERERERERKQ0Urpxg5yhkfOfFouFunXrkpeXx7///W8yMjIwmUwFwqW4uDgaN25MVlaWFjoWERERERERkTuCgqUbcOHCBTIzM3E4HFy8eNF1PCkpiTZt2nD48GG++uorDh48iMlkcj3u5ty5rWHDhgAcPVqcG72LiIiIiIiIiLhHyVioqBT49ttv2bx5M/v376dChQpER0fTvXt31650AwcO5MyZM6xZs4ZRo0bxxBNPEBUVRV5enms9qPT0dDw8PIiIiHBnV0REREREREREioVmLF2Hd955h1mzZuHl5UXTpk3x9PRkyZIl/N///R+//PILp0+fBuBPf/oTjRs3Zvv27QwbNozVq1eTlZUFwMqVK1mxYgURERGUL1/ejb0RERERERERESkemrF0DTNmzCAtLY1evXqRnJyMt7e36/jcuXP56quvOHHiBElJSQQGBvKXv/yFMWPGMGvWLN5//30CAgKw2WycOHECb29vhg4dSmBgoHs7JSIiIiIiIiJSDBQsXcPu3bsJDAwkKSkJb29v7HY7FouFbt26ER4ezvTp0/nuu++wWCx07NgRHx8fHnnkEWrXrs2OHTtYv349fn5+1KtXj06dOhEWFubuLomIiIiIiIiIFAsFS1fgcDiw2+0cOHAAq9WKj4+Pa70kh8OB2WymYcOGeHp6kpKSwuTJk4mIiKBRo0YA1K1bl7p169KtWze8vb3Jy8vDw8PDzb0SERERERERESk+WmPpCsxmM1arlfj4eA4dOsTevXtdwZDZbMYwDOBSgNS1a1e8vLz48ssvOXbsWL52rFar62tERERERERERO4kSjuuoUqVKgCkpKRw9OhR13GTyeQKl5o1a0bbtm05deoUq1evBnCdcwZKJpPpdpYtIiIiIiIiInLLKVi6Amcw1LZtWxo3bszmzZuZM2eOa5c3uBQWORwOALp06YKfnx87duxwnRMRERERERERuZNpjaX/Lz09naysLM6fP09CQgJ+fn6uHeB69erFsWPH+Pnnn/Hy8qJ9+/YEBQW5wie49Mibh4cHdrvdXV0QEREREREREbmtFCwBn376KampqZw9exaAMmXKUKtWLe677z4qVKhAREQEffr0Ydy4ccyYMYPs7GySk5OJiIhwzUxKS0vjwoULREVFAZdmPGnWkoiIiIiIiIjcye76YOmjjz5i3bp1tGzZkgYNGnDo0CFSU1NZunQpGzZs4K9//Ss1atQgPj6eRx55hMmTJzN37lw2bdrE/fffT2hoKPv27WPu3Ll4e3uTmJgI6FE4EREREREREbnz3dXB0saNG1m3bh3t27fn/vvvx9fXF4DOnTvz7bff8sMPP/DGG28wePBg6tatS1xcHH/+85+ZMmUK8+fP57PPPgPA09OTkJAQXnnlFUJDQ93ZJRERERERERGR2+auDpYOHz5Mbm4uDRo0wNfX17UQt9lspn///gQEBDBhwgTeffddhg0bRmxsLMHBwTz11FO0aNGCo0ePcvjwYaKiooiLiyM4ONjNPRIRERERERERuX3u6mDJbL60Kd65c+eAS4+vOXd6M5vNdOvWDYfDwYQJE/jkk0/yzUiKj48nPj7ebbWLiIiIiIiIiLib2d0FuFNQUBAA8+fPJysry7Uuktlsds1e6tGjB+3atePw4cMsXboUwzBc50RERETudpfvkisiIiJ3n7syWHIOgO655x7q16/Ppk2bSEtL4+LFi65rLg+XHn74YUJDQ1m3bp1rNpOIiIjI3Wjfvn2sXr2aX375hQsXLmjDEhERkbvcXfkonMlkwjAMTCYTHTp0YN++fUyePJmgoCASEhLw8PAALoVLeXl5+Pj4EBUVxebNmzl8+DAVKlRwcw9EREREbr/Ro0ezatUqsrKyAFiyZAnPPPMMoaGhrrGViIiI3F3uimBp3rx57N+/n7NnzxIcHExiYiLBwcHYbDZq1KhBx44dmTp1Kl999RWPPvoo8fHxeHt7k5eX5wqZTCYTgYGBlClTxs29EREREbn93n33XbZu3Ur16tVp1KgR69atY+3atUyZMoU//vGPNxUqXbx4Md/McZPJhI+Pj+vvIne6W/k5v9X/Dql997St9t3bfmmu/Va0f8cHS++++y6pqal4enoCcOHCBZYsWUKTJk249957CQsLo3379uTm5vLTTz/xxRdf0LlzZ5o0aUJISAgAqamp7Ny5k6pVq2Kx3PHfMhEREXGzvXv3smzZMn777Tdyc3MZOnQoAJmZmezcuZPatWvj5+d32+qZOHEimzdvpmfPnrRt25aAgADq1q3L0aNHOXXq1E23P23aNKZMmeJ6XblyZd5++23KlSt3022L3CpHirGt8PDw29a22r96+6W5drV/9fZLc+23o/2bcUenJF999RUbN26kd+/etGnTBovFwvr165k7dy6zZ89m3759PP7440RFRdGlSxf8/PyYO3cuY8eOZcGCBTRp0oTMzEy2bduG3W6nb9++eHt7u7tbIiIicgf79ttv+eGHHwo9ZxgG//rXv3jkkUdITk6+LfXk5OSQmppK5cqV6dChAzabDYDg4GDKlSvHhQsXOHjwIBkZGVSrVg2bzYbVar2hR+N69uxJly5dXK+dX5eZmYndbi/+TokUg+JcdfXQoUO3rW21f/X2S3Ptav/q7Zfm2m9F+xaLpdhu4NyxwVJWVhYbN24kLi6O9u3b4+/vD0CrVq245557+OKLL1ixYgX//ve/+eMf/0jFihVJSkoiISGBn376iVWrVjFlyhRsNhvR0dE8/vjjWltJREREbqkFCxbwww8/cM899/Dggw+ybNkypk+f7jofGhpKbGwsa9euvW3B0uHDh9m/fz8dO3Z0hUoAaWlpbNq0iYCAAN544w2OHTtG+fLladu2LUlJSTc0o8rT09M1u/z3tOuc3A1u5ef8Vv87pPbd07bad2/7pbn2W9H+HRssnTlzhsOHD9OsWTP8/f1xOByYTCZMJhO+vr4MGjQIDw8Pli5dytixY3nyyScpV64cUVFRDBw4kN69e3P27FkCAgLw8vJyPecvIiIicqv8/PPPVKhQgb/+9a94eHiwatWqAtdERESwadOm21ZTcHAwwcHBbN++ncOHDxMSEsKGDRuYPn06drudpKQkgoODOXXqFL/88gvTpk3DZrORlJTkGnuJiIjIneuODZbMZjNms5nt27eTnZ1NQECA65zD4cBsNvPcc89x4cIF1qxZw7Jly+jatSuGYWCxWAgODqZs2bJu7IGIiIjcbTIyMmjXrp1r85DCBAYGkp2dfdtq8vb2pk6dOixYsIDhw4fj7e3NsWPHuHDhAi+99BL169cHLo2vYmJi+OCDD1iyZAlt27bV2pQiIiJ3geJ8TK9EiYiIoF69euzbt48DBw4AlwY8cCl0cv79kUceoWLFiixevJi8vDzXAEh310REROR2M5vN11xT6MSJE7dtzUfDMPD29uahhx6iV69ehIeHExISQo0aNbj33nupX78+DocDwzAwm81UrVqV8uXLs2PHDvbv339bahQRERH3uiODJWdo1KxZMy5evMh///tfzpw5g9lsJi8vD7g0cINL07vr1avHb7/9xsaNG91Ws4iIiEhUVBSbN292jWV+78KFC2zatImYmJjbUo/JZMLhcBAQEECvXr0YOnQoL7/8Mnl5efnCLedaDV5eXlgsFiIiIihfvvxtqVFERETc644Ill577TVmzJjheu0MjRo0aEDTpk05cOAAI0aMIDc3Fw8PD9fgxzlDqWHDhgCcO3fu9hcvIiIi8v+1adOGQ4cO8fnnn3Px4sV853Jycvjss8/IysqiXbt2t60m57jKyeFwcPz4cdeMcOfyAwDLly9nz549VK5cWY/BiYiI3CVK/W/8ESNGsG3bNurWrYvdbncNYhwOB97e3jzyyCOcOnWKtLQ0XnvtNf7yl78QEhIC4Fq/YOvWrQBaU0lERETcqm3btmzatIkFCxawYsUK1y5sQ4YM4bfffuPChQu0bt2aJk2aFOv7GoZxzWUAnOcdDgdBQUGsW7eOsWPH0rt3b0wmEytWrOCHH37AZrPxwAMP3LbH9URERMS9SnWw9Oabb7JlyxYGDBhAYmJivjtjzsfe/Pz8eO655xg5ciTr1q3j9ddfp2fPntSoUYPQ0FBWrlzJsmXLiIyMpEKFCm7sjYiIiAj8+c9/pmbNmsyZM8c1K2jPnj1UqFCBe++9l/bt2xf7e/4+VLpa0GSxWHj00Ud59dVXmTlzJqmpqeTl5XH8+HECAgIYOnQoYWFhxV6jiIiIlEylNlh666232LJlC3379qVVq1auO3rwv13fnDOS/Pz8ePbZZ/nuu+9YsWIFI0eOxGaz4efnR1ZWFj4+PgwdOpQyZcq4qzsiIiIiLklJSSQlJZGbm8uZM2ew2Wy3ZAbQ2rVrOXz4MOfOnSM2NpaYmBjKlCnjWlvp94/BwaVxVlRUFK+++iqjR48mMzMTb29vWrduTffu3bW2koiIyF2mVAZLI0aMIC0tjf79+9O2bVtsNhu5ublkZ2czb948jhw5gpeXFxEREXTp0gWz2Yyvry8PPvggzZs3Z/ny5ezduxeTyUTjxo1JSkrSnTUREREpcaxWK8HBwbek7Q8++IB169bl24WuWrVqtGnThrZt27p20f19uOQ8Hh0dzf/93/9x/vx5rFYrFotF6yqJiIjchUrdb//Nmzezd+9evL29KVu2LDabjQsXLrB8+XK+//57Dh06hMlkci3QvX79egYNGkSZMmWwWq1UqVKFKlWqkJub6xr8FHY3TkRERORONXLkSNLS0ujcuTOJiYlkZmaybds2pk+fzo4dOzh06BD9+vW7argE4OnpidVqdUcXREREpIQodcFS9erV6du3L9999x1jx47FarVy/vx5xowZQ2hoKC+++CKhoaFcvHiR8ePHs3nzZv71r38xdOhQ4H+PyWkQJCIiIiVNnz59rnmNyWTCx8eHiIgIGjVqxL333ntD45qMjAzWrl1L48aN6datG35+fkRERFCnTh1q1arFhx9+yIwZM7h48SKPPvpogXApOzub8+fPExoaes0Fv0VEROTOV6qm6hiGgaenJy1atKBXr16YzWZGjRrF119/TWRkJG+88QYNGjQgKiqKKlWq8NxzzxEXF8fmzZv5+eefAc1OEhERkZKrRo0aVKpUCbg0ZgkNDaVq1aqEhoa6xjBRUVGULVuWAwcOMH78eF555RVycnKu+z2OHTvG2bNnadCgAX5+fq5H4RwOBzVr1mTIkCGEhIQwa9YsJk2a5KoF4MSJE3z00UeMGjWKjIyM4uy6iIiIlFKlJmVx7k5iGAYWi4XmzZvTu3dvbDYbNpuN5557DovFgsPhAC4NjgIDA+nRowcmk4kTJ064uQciIiIiV/fcc89x9uxZWrVqxaeffsonn3zC66+/zieffMJnn31G69atOXv2LC+//DJffPEF7dq148CBA0ybNu2638PLywuA3bt3A+RbGsAwDKpUqcILL7yAt7c3M2bMYNWqVa6vPXnyJL/++iv79u3D19e3GHsuIiIipVWJD5b27duH3W53TbW+PFxyzlzq2bMngYGBQMEZSZ6enhiGwblz52536SIiIiI3ZOzYsQQFBfHss89StmzZfOeCg4P54x//SHBwMGPGjMHb25snnniCyMhIVq9efd3vUb58eXx9fUlLS+PAgQP5zjl3g4uNjeW5554jLy+PtWvXus5XqVKFP//5z7z99tsEBQXdXGdFRETkjlCig6W33nqLTz75hK1bt7pmIgEFZi41bdo039oChmG4AqbU1FQsFgs1a9Z0nRMREREpiTZt2uQas1xJjRo12LRpE3DphlpcXBzHjh277vcIDg6mc+fO7N+/n4ULF+bbFc7ZpmEY1K5dmzp16rB06VIOHjzoOp+QkEC5cuVuoFciIiJyJyuxwdI333xDWloaGRkZpKSksHnz5gLhEoCHhwc+Pj6u4w6Hw3VuzZo1rFixgpiYGKpXr57v60RERERKmtzcXE6ePHnVa7KyssjNzXW99vHxwcPD44bep127dlSrVo0ff/yRH3/8sdBrrFYr1atXx+FwkJ2dfUPti4iIyN2jRAZLK1euZMGCBURGRtK5c2cyMjKYMGFCgXCpMM6ZSvPnz2fixIlcuHCBp59+mjJlytyO0kVERESKLCYmhuXLl7Njx45Cz+/cuZPly5cTExPjOnbkyJEbHucEBgbyzDPPUK5cOcaPH8/UqVPzLQDuvBF36tQpfHx88PPzK0JvRERE5G5gcXcBv5ebm+ualj1o0CAqVKiAj48PM2bMYMKECfTt25eEhIQr7u529OhRvvjiCw4cOIC3tzfDhg0jMjLyNvdCRERE5Mb16dOHf/7zn/zjH/+gQYMGVK9enTJlynDq1Cm2b9/OunXrMJlM9OnTB4Dz58+zYcMGmjZtesPvFRERwd///nfeffddUlJSOHr0KC1atCAhIQG4tJzA+vXrqVSpktZTEhERkSsqccGS1WqlV69eJCQkULFiRQDat2+PyWTi+++/LzRcunzHuPPnz3P06FFq1KjBQw89RGhoqDu7IyIiInLd4uPj+fvf/87nn3/OmjVrWLNmTb7zISEhPPnkk8THxwOXZhYNHz6c4ODgIr1fZGQkQ4YM4ZtvvmHRokWsWLGC6tWrYxgG+/fvJy8vjxdffFE7wImIiMgVlbhgCSA2Npbo6GjX68DAQJKSkgAKDZec07XPnz9PVFQUw4cPx9PTE5vN5o7yRURERIqsTp06fPLJJ6Snp7N//37OnTuHj48PlSpVIi4uLt+sbS8vr3xjpqIICwvjueeeIzU1lR9++IG9e/fi7e1NjRo16NOnDxERETfZIxEREbmTlchgCcBiuVSaczZSYeFSnz59qFu3LgCLFy9m165dJCcnExYW5q6yRURERG6a2WwmPj7eNTPpVvP19aVly5Y0btyYixcv4uHhgcVicY3HRERERK6kxI8WLt/F7ffhUkpKCh4eHmRmZjJu3DisVis9evRwU6UiIiIipZvVasVqtbq7DBERESlFSnyw9HuBgYG0bdsWs9nMtGnT+PLLL8nKysJsNjNkyJAirzFQnBwOByaTKV8oJiIiInK9duzYwcaNGzl58iQXL14scN5kMvHMM8+4oTIRERGR/EpdsORwOAgODiYpKYm9e/eyevVqfH19GTZsmGuxb3eZOXMme/fu5fjx464aK1WqdNMLXl68eDHfoNJkMuHj4+P6u8i13OrPSWHte0z8qtjaz3vw8WJrS0SkJMvLy+Pjjz9m1apV17xWwZKIiIiUBKUuWHIuWJmWlkZ6ejo2m43hw4cTGRnp1rreeecdNmzYgJ+fH1arlW3btrFq1SpatmxJYmIicXFxRW572rRpTJkyxfW6cuXKvP3225QrV644SpcS6mgxthUeHn7HtS8icif64YcfWLVqFW3atKFDhw4MGTKE5ORkmjVrxrZt25g+fTq1atWiX79+7i5VREREBCiFwRJcCpXGjx/P+fPnefPNN90eKo0ZM4YNGzbwwAMP0LJlS4KDg1m5ciXLly9nwYIF7Nu3jwceeID69esXqf2ePXvSpUsX12vn7JDMzEzsdnux9EFKHo9ibOvQoUN3XPsiIreaxWK57Tdxli5dSsWKFRk4cKDrmK+vL1WrVqVq1arUq1ePl19+mYSEBNq3b39baxMREREpTKkMliIiIoiOjqZfv35uf/wtOzubLVu2EBMTQ7t27fDz8wOgSZMmxMfHU6FCBaZOncq3336L2Wx27WJ3Izw9PfH09Cz0nGEYN1O+3CVu9eektLcvIlJSHD58mLZt2+Y7dvlNpIoVK3LPPffw888/K1gSERGREsHs7gKKIjQ0lMGDB7s9VAI4f/48R48eJSwszBUqORwOAAICAujTpw99+vTht99+Y9q0aezbt8+N1YqIiEhJZrFY8PLycr329vYmOzs73zUhISEcPnz4dpcmIiIiUqhSGSzBpYFXSWA2mzGbzezfv9818HOuA+XUvXt3unbtSnp6umsxTmf4JCIiIuJUtmxZjh8/7npdoUIFtm3blm/m5s6dO103s0RERETcrdQGSyVFSEgIzZs359dff2Xr1q1AwdDIw8ODpKQkqlevzqxZszhy5EiB8ElEREQkPj4+X5DUrFkzDh48yIgRI5g9ezYfffQR6enp1KlTx82VioiIiFxSMqb9lFKGYWAymahduzYLFixg5MiRhIaGEhMTg8PhyBcehYWF0aBBA7Zv387OnTspX768GysXERGRkqhNmzY4HA5OnDhB2bJl6dSpE1u2bCE1NZW0tDQAYmNjtSuciIiIlBgKlm7AmTNnXAM9k8mEzWYDoEGDBnTu3Jlp06bxwQcfMHjwYKKjo13hkt1ux2Kx0KBBA8aNG8epU6fc3BMREREpiWJiYoiJiXG9tlgsvPTSS+zevZsjR44QEhJCbGysZj6LiIhIiaFg6TqNHj2aDRs2cPjwYQIDAwkLC+PBBx+kSpUqWK1WHnzwQU6dOsUvv/zCiBEj+Mtf/kK1atUwDMO1HpTzTmNERIQbeyIiIiIl1aJFiyhTpkyBXWSrVKlClSpV3FOUiIiIyFXodtd1ePfdd1mwYAFhYWF0796dypUrk56ezuuvv8706dP57bffAHj66adp3749J0+e5NVXX2X27Nns3bsXgGXLlrFw4ULCw8OpVKmSO7sjIiIiJdSoUaNcN6JERERESgPNWLqGOXPmkJqayv33309ycrLr8bclS5Ywf/58pk6dyokTJ0hOTiYqKoonnniC0NBQ5s6dy9dffw2Av78/Z8+eJSAggFdeeYXg4GB3dklERERKqMDAQO0cKyIiIqWKgqVr2L9/PzabjaSkJGw2m2u9pJYtWxIeHs7333/PggULsFgsdOvWjdDQULp160bt2rXZuXMnaWlpeHp6EhkZSatWrQgNDXV3l0RERKSEatCgARs3buTixYt4enq6uxyRUs3jgyHF1lbeX94qtrZERO40CpauwOFwYBgGv/32G4Zh4HA4yMvLw8PDw7UbXGxsLPfffz8Oh4P58+cTHh5O586dAYiOjiY6OprExEQ8PT1dXyMiIiJyJX379mXHjh2899579O/fn4oVK7q7JBEREZGrUrB0Bc7dVmrVqkV6ejp79uyhQYMGroDI+c/o6Gg6d+7M0aNHmThxInXq1CEyMtJ13rlwt4iIiMi1/O1vf+PixYvs27ePtLQ0rFYrAQEBBW5OmUwmPvnkEzdVKSIiIvI/Wrz7Gpw7sIwZM4aMjAzXwM4ZLgHEx8fTunVrcnNzmTdvXr6vv/x6ERERkatx7iYbEhJCSEgIAQEBruOX/9E6TCIiIlJSaDrNNdSrV4/27dszd+5cJk2aRP/+/V3rJF0+c6lTp07MnTuX/fv3u86JiIiI3IjPPvvM3SWIiIiI3BAFS//f+vXrOX78OGfOnKFmzZqUL1/edZewf//+ZGZmsmrVKqxWK7169SIsLMx119BkMrnuHOoOooiIiIiIiIjcLRQsAR9//DGpqamcP38eAIvFQpUqVXj00UeJiYnB29ubAQMGYLfbWbJkCadPn6Zv375ER0e7ZialpaWRnZ1NvXr1ALRYt4iIiNyUjIwMfvvtNy5cuECrVq3cXY6IiIhIoe76YOmTTz5h7dq1tGvXjkaNGpGTk8OSJUtYuXIlQ4cOZeDAgbRs2ZKIiAiefPJJxo4dy9q1a9mxYwfdu3cnIiKCgwcPsnjxYjw9PenUqROgR+FERESkaHbt2sV//vMfDhw44DrmDJa2bt3Km2++yaBBg2jQoIG7ShQRERFxuauDpe3bt5Oamkrr1q3p3bs3vr6+ADRo0ICZM2fyww8/MGrUKHJzc2nXrh1hYWG8+OKLTJw4kUWLFjFhwgQArFYroaGhDB06lLCwMHd2SUREREqxX3/9leHDh2MymejcuTMHDx5k/fr1rvM1atTA39+fFStWKFgSERGREuGuDpYyMzPJycmhZs2a+Pr64nA4MAwDDw8PunTpQkBAACkpKYwePRp/f38aNWoEwIMPPkiLFi04evQohw8fJiIigujoaAIDA93bIRERESnVJk2aBMDbb79NWFgYkydPzhcsmUwmqlWrxu7du91VooiIiEg+ZncX4E5WqxWAc+fOAZcGax4eHq4FuFu1akXv3r3x9PTk008/JSMjw/W1kZGR1K9fn+TkZOrWratQSURERG7a1q1bady48VVnQIeEhHDy5MnbWJWIiIjIld3VwZIzDPrxxx85evSoa10ks9nsCpcSExPp3LkzFy5cYObMmeTm5mIYhrtKFhERkTvY+fPnKVOmzFWvyc3N1S60IiIiUmLc1cFStWrVSExMJCMjg0WLFnHmzBnXucvDpd69exMTE0N6ejp5eXlamFtERERuibJly+ZbtLswe/fu1ZqOIiIiUmLcNcHS+PHj861R4AyNunbtSnR0NLNnz2blypXk5OS4rjGbzdjtdgCqVq3KoUOHrjnYExERESmq+vXrs2HDBjZu3Fjo+eXLl7Nz504aNmx4mysTERERKdxdsXj366+/zubNm4mJicHhcGA2mzGbL2Vq4eHhdOvWjfHjx5OSkoLdbqdZs2YEBARgt9uxWC59i86fP09QUBDlypVzZ1dERETkDnbfffexatUq3nrrLVq3bk1WVhYAc+bMYceOHSxbtoxy5crRpUsX9xYqIiIi8v/d8cHSm2++yfbt2+nXrx+1a9d2BUpwadaSh4cHDRs2JDc3l6lTpzJx4kQOHjxIp06diIiIACA1NZXNmzdTsWJFvL293dUVERERucMFBATw2muv8emnn7JgwQLX8dGjRwMQGxvLn//8Z2w2m7tKFBEREcnnjg6W3njjDbZu3Urfvn1p27ZtgUGYM2SyWq00b94cPz8/Zs6cyZw5c1i2bBkNGzbk9OnT7N27lwsXLjBgwAAN5EREROSWKl++PP/85z/Zt28fO3bs4MyZM9hsNmJjY4mNjXV3eSIiIiL53LHB0jvvvMOWLVt49NFHadasGTabDYfDgd1uZ+nSpeTm5roee/Pz88NqtXLPPfcQHx/PjBkzSE1NZcGCBQQFBREdHc3DDz9MhQoV3N0tERERuUtER0cTHR3t7jJEREREruqODJZmz57NunXriI6OpkqVKvj5+ZGTk8O6dev46aef2LNnj+va+fPn07FjR5o3b46/vz++vr707duXHj16cPr0aQICAjCZTHh5ebmxRyIiInI3+OSTT2jdujW1atXSLrQiIiJSKtyRwVJiYiK7d+9m8eLFzJo1i27dupGRkcHo0aMJCQmhd+/eREZGunZdmTx5MhaLhbZt22IYBh4eHnh7e+Pj4+PuroiIiMhdZOnSpSxdupTAwECaN29Oq1atNGtJRERESrQ7Mljy9vbmySefJC8vjyVLlpCdnc3evXupUKECL7/8smudpDp16rBhwwa++eYbZs+eTYsWLVyLc+suoYiIiNxuI0aMYNGiRSxfvpwff/yRH3/8kcjISFq3bk2LFi0IDg52d4kiIiIi+dxUsGS329m4cSMHDx7k/Pnz9OrVC4Dc3FzOnTuHv79/vl3YbiWHw4HZbMYwDODSgtwDBw7EbDazZMkSKlWq5AqVHA4HJpMJHx8f6tevz+rVq1m2bBlbt26lfv36t6VeERERkd+rXLkylStX5pFHHmHDhg0sWbKENWvWMG7cOMaPH0/NmjVp2bIlTZo00U61IiIiUiIUOVhau3Yt//nPf8jOznYdcwZLBw4c4JVXXuG5556jRYsWN1/ldbDb7VitVtdMI4fDgdVq5cknn8TPz4/AwEBsNhuGYbjCLufXxMbGsmzZMux2+22pVURERORqzGYz9erVo169epw/f56VK1eyZMkStmzZwubNm/nqq68YO3asu8sUERERKVqwlJ6ezvvvv09QUBB/+MMf2LFjB8uWLXOdj42NJSwsjJUrV97yYGnhwoXs3r2bHTt2ULNmTWrXrk3dunUxm804HA68vLx46KGHyM3NBf73iJthGFgsl7q/Y8cOfHx8qFSp0i2tVURERORGeXt7k5iYSJMmTfjpp5+YMmWKa1wjIiIi4m5FCpa+++47fH19GTFiBAEBAZw+fbrANVWqVGHnzp03XeDVfPjhh6xcuRJPT0/y8vLYt28fa9asoXfv3rRq1co1M8lqtWK1Wl1f53xsDmD58uVs2rSJuLg4/P39b2m9IiIiIjfC4XDkeyQuNzcXk8lE7dq13V2aiIiICFDEYGnXrl00btyYgICAK15TtmxZ1qxZU+TCruXjjz9m/fr1dO7cmeTkZM6fP8+mTZsYP348S5cupVGjRldce+DyUOm7777DbDbzyCOPuBb1FhEREXGn3bt3s2TJEpYtW+ZadiAqKoqWLVtqEW8REREpUYoULF28ePGaIczZs2dv2cLds2fPZuPGjSQnJ9OlSxf8/PwACAwMZNu2baxatYo9e/YQHx9f4GsdDgdnzpxhypQpruBr6NChRERE3JJaRURERK7Xd999x9KlSzl48CBwaWzTpUsXWrVqpUf2RUREpEQqUrBUvnx5du/efdVrdu7ceUvCmqysLBYuXEhAQADt27fHz8/P9Wibn58ftWrVYtWqVYU+ngeX1laaMWMGy5Yto2rVqgwYMIDw8PBir1NERETkRk2aNAkvLy9atmxJq1atSEhIuG077IqIiIgURZGCpcaNG/Pdd9+xYMEC2rRpU+D8jBkzOHDgAP3797/pAn8vLy+P06dPc++991K2bFlXqOT8Z1hYGAAnT54s9Os9PDy47777qFWrFjExMVpXSUREREqMZ599lsaNG+Pl5eXuUkRERESuS5GCpW7durFq1SpGjRrF0qVLsdvtAHz77bfs2LGD7du3Ex0dTadOnYq1WLi0dtNLL73kWozbeRfP+c/fB0WXL9TtZLPZqFOnTrHXJiIiInIzWrVq5e4SRERERG5IkYIlb29vhg0bxujRo1mxYgUOhwOAH374AYCmTZvyxBNP4OnpWWyFGoaByWQCLi1eeSXOWiyWS11zhkqbN28mIyPjloRdIiIiIsXp6NGjLF26lH379nHu3Dl8fHyIjo6mRYsWhIaGurs8l8Ju4ImIiMjdpUjBEoCfnx/PP/88f/jDH9i9ezdnzpzBx8eHKlWqEBgYWGwF7tu3j8jISCwWS75w6Uqcg5vc3FzXsQ0bNvDtt9+Sk5NDkyZNirU+ERERkeL0008/8e2335KXl5fv+KpVq5gyZQr9+/cnOTnZTdVdcuzYMUJCQhQqiYiISNGDJSd/f3/q1q1bDKUU9NZbb3Hs2DEGDBhw3YtXenh4AP8LmNLS0pgwYQJHjx5l+PDhCpVERESkxFq3bh3//e9/8ff3p3PnztSsWZOgoCCysrLYsmULM2fO5L///S9hYWHUr1/fLTWOHz+e7du307hxY7cHXCIiIuJ+Nx0s3SrffPMNaWlpAKSkpABcV7h08eJF4FKwtG3bNsaPH8+RI0f45z//edVH6ERERETcbebMmfj5+fH2229TtmxZ1/Fy5cpRtWpVWrRowUsvvcTMmTPdEiy99957bNq0iVq1alG7du3b/v4iIiJS8hQpWBo2bNg1rzGbzfj4+BAREUHDhg2pWrXqdbe/cuVKFixYQGRkJHXq1GH+/PlMmDCBvn37XvfMpY0bN/LLL79w5MgRhg8frlBJRERESry9e/fSokWLfKHS5UJCQmjatClLly69zZXBqFGj2LBhA/fddx9t2rRxzQK/fKmCoq65dPHiRdfNQQCTyYSPj4/r7yLudqs/h7ey/dJce2lvvzTXrvbd13ZpbL9IwdLWrVtv6Prvv/+eNm3aMHDgwGtem5uby8KFC7Hb7QwaNIgKFSrg4+PDjBkzritcslqtmM1m1qxZg7e3N8OHD6dSpUo3VK+IiIiIO9jtdry9va96jbe3t2tH3ttl9+7dpKam0rJlS5KSkvD393eFSBkZGRiGQVRUlGugej3rYl5u2rRpTJkyxfW6cuXKvP3225QrV67Y+yJ3jyPF2FZ4ePhtbb80117a2y/Ntav9q7dfmmu/He3fjCIFS99++y0ffvghR44c4b777qN69eoEBgaSlZXF9u3bmTZtGqGhoTzxxBNkZGQwYcIEFixYQExMDB06dLhq21arlV69epGQkEDFihUBaN++PSaTie+//77QcOnywYuHh4droPPGG28QGRlZlC6KiIiI3Hbh4eGsW7eOvn37utaNvFxeXh7r1q0r9gHhtWRkZHDq1Cnat2+Pv78/586dY8OGDcyePZtt27YBUK1aNVq0aEHz5s3x8/O7oXCpZ8+edOnSxfXa+XWZmZm3PUSTO0dxLi1/6NCh29p+aa69tLdfmmtX+1dvvzTXfivat1gsxXYDp0jB0qRJkzhw4ADvvfdevrtqISEhhISEcM899zB48GBmzZpFv379qFKlCoMGDWLBggXXDJYAYmNjiY6Odr0ODAwkKSkJoNBwyTn4yMnJITw8nMGDB1OxYkXCwsKK0j0RERERt2jdujVjx47ljTfeoH///sTExLjO7d69m/Hjx3Pw4EEefvjh21rX6dOnMZlMeHl5AZd2qBszZgy+vr60bNmSkydPsm/fPsaNG0dmZiY9evTAz8/vutv39PTE09Oz0HOGYRRLH0Ruxq3+HN7K9ktz7aW9/dJcu9p3X9ulsf0iBUtLly6ladOmV5yq7e3tTaNGjVi2bBn9+vXD19eXunXrsmrVqusvzHKpNOfdrsLCpT59+rh2pFu8eDG7du2iS5cuNGzYsCjdEhEREXGr5ORktm7dyrp16xgyZAheXl6UKVOGU6dOceHCBQAaNGhw23djs1gsGIbBsWPHiIiI4LvvviMiIoKXX34Zm81Gbm4umzZtYsKECcyfP5+YmBiaNWt2w4/EiYiISOlTpGApOzubvLy8q17jcDg4deqU63VQUBAOh+OG3+vywcjvw6WUlBQ8PDzIzMxk3LhxWK1WevToccPvISIiIlISmM1m/va3v7Fo0SIWLVrEvn37OHbsGD4+PlStWpXWrVvTqlWr215XrVq1sNlsTJs2DU9PT+x2O/3798dms2G327FardSpUweHw8FHH33EokWLaNasmUIlERGRu0CRgqWwsDBWrlxJr1698Pf3L3D+9OnTrFixIt+jaCdOnLihKdFXEhgYSNu2bTGbzUybNo0vv/ySrKwszGYzQ4YMITg4+Kbfo7gVdYcUERERuTu1bt2a1q1bu7sMl+DgYGrUqMG6dev4+uuvyc7Odo1tnLPMLRYLNWrUoFy5cvz666+cOHGiRI7LREREpHgVKVjq1KkTX375JS+99BJdunQhLi6OgIAAsrOzSU9PZ+bMmWRlZdGrVy/gUrCyZcsWqlSpctMFOxwOgoODSUpKYu/evaxevRpfX1+GDRvmWuzbnVavXs3Ro0c5c+YM1apVIzY2loCAgJtqU1vwys0qbdtV3u72RUTkygzDwMfHhwEDBrBjxw7279+Pj48Pv/32G7GxsZjNZtdNND8/P3x8fPDx8SmWG4oiIiJS8hUpWGrfvj0nTpxg+vTp/Pe//y1w3mQy0bNnT9q3bw/AmTNn6Nq1K9WqVbu5asF1dywtLY309HRsNhvDhw8vEbu/ffjhh6SlpZGbm+t67K9ChQo8/vjj1KhRo8izlrQF793paDG2VdjuQaW9fRGRO4Xdbuett97C09OTwYMHu2YAFXbd+++/j91uZ8iQIbdtNrTJZMLhcFC+fHlefPFF3nvvPbKzs5k3bx4xMTFERka6drBbvnw5GRkZtGzZUrO1RURE7hJFCpYA+vTpQ6tWrVi6dCn79+/n3Llz+Pj4EB0dTbNmzYiIiHBdGxAQUKyLTKalpTF+/HjOnz/Pm2++WSJCpZEjR7Ju3To6depEw4YNMZlMzJs3jyVLljBixAj69u1Ly5YtC3108Fq0Be/dqeAm00VX2HaVpb19EZFbrTi34b2apUuXsnnz5quGSs562rVrx7vvvsvSpUuLda2lay2ybTabMQyD6tWr89JLL/Huu++ya9cuRo4cSfPmzYmLi2Pr1q0sWrQIb29vunXrdtW+iIiIyJ3jpn7jh4eH07t37+Kq5bpFREQQHR1Nv379SsTjb7t372b9+vU0a9aM++67D5vNBkC1atWIj4/nxx9/ZNy4ceTm5pKUlHTDU8O1Ba/crNK2XeXtbl9ExJ1WrlxJaGjode1q26BBA8qXL8+yZcuKNVj6fahUWNBkMpkwDIPY2Fhef/11/vvf/7J9+3bGjRvnuiYyMpJ//OMf+dbZFBERkTtbqbyVFBoaes27erfTiRMnyM7Oplq1athsNtdjcGazmcTERPz9/ZkyZQqTJ0/G39+fdu3aaUFvERERAWDv3r3Uq1fvuq+Pj49n/fr1xfLea9eu5fDhw5w7d47Y2FhiYmIoU6aM6/G3349VnMfLlSvHs88+y/Hjx9m0aRMXL14kLCyMatWqERgYWCy1iYiISOlwU8lMbm4uu3fv5uTJk/kWl77crdrRpKSESgBWqxWAnJwc1zHnlHGTycQ999xDXl4e48aNY8yYMVSpUoXo6OhrTjsXERGRO9/p06dvKIwJDAzk9OnTN/2+H3zwAevWrcv3WH21atVo06aNawfewsIl5xjHx8eHyMjIErEkgYiIiLhPkdOZ2bNnk5KSki9MKUxJ2ir3VgkODsbPz49Zs2bRqFEj1/Rv55Rxk8lEo0aNOHbsGP/973+ZMGECgwYNcu3sJiIiIncvLy+va46nLnfu3Dm8vLxu6j1HjhxJWloanTt3JjExkczMTLZt28b06dPZsWMHhw4dol+/flcMl5w3xi4/pxtmIiIid6ciPYu1atUqvv76a0JCQnj44YcBaNiwIX379qVu3boANG7cmGeeeabYCi1JnOu9OB95q1ixIs2bN+fEiRPMmDGDrKws17XOKeMAycnJxMfHc+DAAS5cuHDb6xYREZGSJzQ0lJ07d1739Tt27KB8+fJFfr+MjAzWrl1L48aN6datGxEREdSpU4cHH3yQoUOH4u/vz4wZM/jmm28AXOGSU3Z2NkePHnWdc1KoJCIicncqUrD0008/ERAQwOuvv+7arSw6OpoePXowZMgQnnvuOdasWXNbdlJxh/PnzwP5B1MPPPAAcXFxLF68mHnz5pGdnQ1cCqHMZrPrUcGEhAROnDjBr7/+evsLFxERkRKnTp067Nmzh82bN1/z2s2bN7Nnzx5q165d5Pc7duwYZ8+epUGDBvj5+bkehXM4HNSsWZMhQ4YQEhLCrFmzmDRpEvC/Mc+JEyf46KOPGDVqFBkZGUWuQURERO4cRQqW9u/fT4MGDfJNw778TlaLFi1ISEhgypQpN19hCTJ9+nTee+89/vGPf/D++++zePFiTpw4AYCPjw+9e/cmPDycH374gZ9++okTJ05gMpmw2+2uXd1OnjyJn5/fTd1pFBERkTtHp06dsFqtfPTRR2zZsuWK123dupWPPvoIq9XKvffeW+T3c47fdu/eDfxv3Urn2klVqlThhRdewNvbmxkzZrBq1SrX1548eZJff/2Vffv24evrW+QaRERE5M5RpDWW8vLyCAgIcL22Wq2cPXs23zWVKlVi3rx5N1ddCfLOO++wfv16ypYti9VqJS0tjdWrV1OlShUGDhxIVFQU1apV46GHHmLChAlMnz6dgwcP8sADD7gWtVy7di0bNmwgMjISm83m5h6JiIhISRAcHMzTTz/Np59+yvDhw4mLi6NmzZoEBwcDl2YJbd68me3bt2MymXjuuecICgoq8vuVL18eX19f0tLSaNGiBVFRUa5zzkf4Y2Njee6553j//fddj80BVKlShT//+c+UL1/+pmoQERGRO0eRgqWgoCBOnjzpeh0SEsK+ffvyXZOZmYmHh8dNFVdSjB8/no0bN/Lggw/Svn17vL29yczMZMKECaxYsYKhQ4fyt7/9jZo1a1KzZk2eeuopJk2axKpVq1i3bh21a9cmNzeXX3/9lby8PF566SX8/Pzc3S0REREpIVq0aIGfnx9fffUV6enppKenF7gmNDSUxx9/3LWeZVEFBwfTuXNnJk2axMKFC3nooYfy7bbrnLlUu3Zt6tSpw9KlS+nZsycRERHApcf6RURERJyKFCxVqVKFvXv3ul7XrVuXn376iWnTptGgQQPS09NZvXo1tWrVKrZC3cXhcLBlyxYqVqxImzZtsNlsGIZB+fLlGTRoEBUqVGDKlCm88847DBkyhLi4OGJjY3n55ZeZPn06mzdvZsuWLQQFBREXF8eDDz7oGpiJiIiIONWtW5ePP/6YLVu2sH37dtdmIIGBgVSvXp2aNWsW2J2tqNq1a0daWho//vgjZcqUoXv37gWusVqtVK9enfXr15Odna3xi4iIiBSqSMFS06ZNmTBhAkePHiU0NJSePXuyatUqJk6cyMSJEwGw2Wz079+/WIt1h+zsbPbt20ejRo0ICAggLy8PDw8P1/a6vXv3xmKxMHHiRN5++23eeOMN18CrR48edOvWjaysLAICAnA4HFitVjf3SEREREoqs9lMrVq1bvnNucDAQJ555hnefPNNxo8fT15eHp06dXI9qu/c4e3UqVP4+PhoprWIiIhcUZGCpUaNGtGoUSPX64CAAN555x1++eUXjhw5Qrly5WjVqpVrbYDSzGazERYWRnp6OkeOHHEtuu3cetdsNtOzZ09ycnKYMWMG3377Lc8++6xrQUuz2UxQUBAmkwnDMNzZFRERERGXiIgI/v73v/Puu++SkpLC0aNHXRuwAKSmprJ+/XoqVaqk9ZRERETkiooULB07dgyLxUJgYKDrmJ+fH926dSuuukoMq9VKrVq1mDVrFkuXLiU5ORkfHx8gf7jUr18/Dhw4wJYtW8jMzMTX1xfDMDCZTK67fs5/yp3DkjK62Nqy93ms2NoSERG5HpGRkQwZMoRvvvmGRYsWsWLFCqpXr45hGOzfv5+8vDxefPFF7QAnIiIiV1SkB/WfffZZxo8fX9y1lDh2ux2Azp07U7FiRebNm8e2bdtwOByua5zhEkCbNm04f/48qampgIIkERERKfnCwsJ47rnn+OMf/0j58uXZu3cvhw8fpkaNGvzzn/907W4rIiIiUpgizVjy8/PD39+/uGtxu2PHjpGdnY3FYiEqKsq1Q0qZMmVo3749EyZMYMyYMVitVuLi4vLtoAIQHR0N/C+QEhERESkNfH19admyJY0bN+bixYt4eHhgsVgKjHVEREREfq9Io4W4uDh27dpV3LW41eeff05aWhrHjx8HoFq1arRp04Z69eoRFBRE8+bNOXnyJD/++CNffPEFDz74ILVq1cLPz8+1Q8vmzZsxm82EhoYCuB6FExERESkNrFarNhoRERGRG1KkR+Eeeugh9u/fz5QpU8jLyyvumm679957j6VLlxITE8Mf/vAHmjZtyokTJ/jiiy/4+uuvOXjwIH5+fnTp0oVu3bqRk5PDl19+ybhx49i1axdnzpxhyZIl/Pzzz4SEhFC7dm1Aj8KJiIiIiIiIyJ2tSDOWvv/+e6Kiopg8eTJz584lOjqaMmXKFLjOZDLxzDPP3HSRt9LixYtZt24d9913H/feey9+fn4kJiZy+PBhxo8fz6pVqzhx4gTPPPMMFSpUoHPnzoSHhzN37lx++eUXFixYgJeXF3a7nYCAAIYMGXJH7IYnIiIiIiIiInItRQqWFi1a5Pp7VlYWaWlpV7y2pAdLBw4cwDAM2rRpg5+fH3l5eXh7exMdHc2QIUP46KOPWLlyJZ9//jnPPPMMYWFhNGvWjMaNGzN//nwOHTpEdnY2MTExNG7c2PUYnIiIiEhR5OXlMWvWLJYtW8bBgwe5cOECEydOBGDfvn3MmzeP5ORkIiIi3FypiIiISBGDpU8//bS463Cbc+fOYRgGubm5AHh4eADgcDgwm8288MILvP/++6xevZoZM2bw0EMPudZV6tSpkztLFxERkTtMbm4ur7/+Otu3bycgIAAfHx/Onz/vOh8aGsqCBQvw8/PjwQcfdGOlIiIiIpcUaY2lcuXKXfefks45wygtLQ2Hw+E6bjabXa+fffZZ4uLiWLlyJb/++itAvmtFREREisPUqVPZvn07Dz30EJ9//jlt27bNd95msxEfH8+GDRvcVKGIiIhIfkUKln7vzJkzHDt2rDiaum0MwwCgadOmhIWF8fPPP7v64AyNzGYzhmHg7e1N9+7dycnJYd68ea5zIiIiIsVpxYoV1KxZk+7du2MymQrdCKR8+fKlbtwlIiIid64ipyM5OTl8/fXXPPnkkzz++OP86U9/cp3buXMnb731Fnv27CmWIm8Fk8mEYRgEBgbSuHFjDh06xCeffILdbs83W8k5oKtWrRqhoaGutQ5EREREituxY8eIiYm56jXe3t7k5OTcpopERERErq5IwdKZM2d4+eWXmT17NmXLliUyMtI1AwigUqVKpKens2TJkmIr9GatXbuWH374genTp7NkyRIcDgcmkwmr1UqXLl2oV68eO3bs4PXXX+fChQuuGUnOgMnPzw8fHx/MZrNmK4mIiMgt4e3tTXZ29lWvOXLkCAEBAbepIhEREZGrK9Li3ZMmTeLQoUP8+c9/plmzZkyePJkpU6a4zlutVuLj49m8eXOxFXozPvjgA9avX+9aoBtg9uzZdO/enbi4OAICAhg4cCD/+te/2LJlC8OHD+fxxx8nIiICb29vANasWcPRo0dp3ry5a4FvERERkeJUtWpV1q1bx9mzZ/H19S1w/tixY6xfv55GjRq5oToRERGRgooULK1bt4769evTrFmzK15Trlw5duzYUeTCisvIkSNZv349ycnJNGrUCMMwmDdvHuvWreM///kPHTp0ICkpibJly/L8888zevRoVq1axbvvvkudOnWoX78++/btY9WqVVgsFjp37qwZSyIiInJLdOvWjWHDhjF8+HD+8Ic/kJeXB8CFCxfYsWMHo0ePJi8vjy5duri5UhEREZFLihQsnTx58qqhEoCnp2e+7XHdYdeuXa67el27dsXPzw+AyMhIGjRowOTJk/n+++/Jzc2lc+fOBAcH89RTTxEXF8eKFStYsGABCxYswGw2U7FiRf7xj38QHh7u1j6JiIjInSs+Pp7HH3+cr7/+mldffdV1/JFHHgEubR7yxBNPXHMdJhEREZHbpUjBkr+/P8ePH7/qNb/99htBQUFFKqq4ZGdnk52dTe3atfHz83Otq+Tt7U39+vUJDAzk66+/Zvbs2fj7+9OhQwf8/Pzo1KkTycnJbN68mfPnz1OmTBnKly+v9QxERETkluvQoQPx8fHMnTuXXbt2cebMGXx8fIiNjaVjx45UrFjR3SWKiIiIuBQpWKpRowZr1qzh+PHjlC1btsD5jIwMNmzYQGJi4s3Wd1PsdjsABw8eBMAwDNdjbGazmSpVqjBgwAA+//xz5syZQ+3atYmJicHhcGA2m0lISHBb7SIiInL3ioyM5A9/+IO7yxARERG5piItFnTffffhcDgYOnQoS5Ys4fTp08ClQOmXX35h2LBhWCwWunXrVqzF3qhq1aoRFhbG2rVruXDhAh4eHq5d3gBMJhMxMTF06dKFEydOMHnyZAAsliLlbSIiIiI3ZdOmTe4uQUREROSGFClYioqK4oUXXuDs2bN8+umnzJkzB4C//vWv/Oc//yE3N5cXXnjhtq9HdHloBODn50etWrXIyMhg1KhR5OXlYTab811nsVho1KgRNWrUYPPmzRw5cuS21iwiIiLi9Prrr/PMM88wbtw4Dhw44O5yRERERK6pyFNzGjRowGeffcbChQvzPf9ftWpVEhMT3bIe0fnz57HZbK7XFouFBx54gM2bN7N8+XKCgoLo37+/K1wym83Y7XZsNhvNmjVj27ZtZGVlUb58+dteu4iIiEjHjh1ZsWIFM2bMYMaMGVSqVIlWrVrRvHlzt69dKSIiIlKYm3rmy8/Pr0Rsdzt16lR27NjB/v37qV69OlWqVKFLly6YTCYCAgIYPHgwb7zxBj/++CN2u50BAwbg4eFBXl6e67G3jIwMvLy8CA4OdnNvRERE5G712GOP8eijj7J+/XoWL15MamoqY8eOZdy4cSQkJNCyZUsaN26Ml5eXu0sVERERAYoYLP3yyy80adIk3+wgd3nnnXfYsGEDoaGhBAcHs379elasWMGaNWt49NFHiYyMJDIyksGDB/Pee+8xZ84cjhw5wpNPPklgYCAA69atY9OmTVSqVKlE9ElERETuXmazmXvuuYd77rmHc+fOsWLFCpYsWcLGjRvZuHEjX375JQ0bNuS5555zd6kiIiIiRQuW/vOf/zB69GgaNGhAy5YtqVevnmu3tdtpwoQJpKWl0bt3bzp06ICvry+ZmZmMHz+elStX8vHHH/Pwww9Tu3ZtqlSpwrBhw/j4449JS0vjpZdeokKFClgsFvbv3w9cWiPK19f3tvdDREREpDA+Pj60bduWtm3bcvz4cebOncuMGTNYunSpgiUREREpEYoULD388MMsXryYFStWsGLFCvz9/WnWrBmtWrUiNja2uGsslMPhYNu2bURFRdGpUyd8fHyw2+2UK1eOxx9/nEqVKjFz5ky++eYbHnvsMWrXrk1oaCivvPIKCxcuZOPGjezatYvAwEBq165N7969iYiIuC21i4iIiFwvwzDYtGkTixcvZs2aNa7NSERERERKgiIFS126dKFLly78+uuvLFq0iGXLljFnzhzmzJlDWFgYrVq1omXLloSGhhZ3vcClUOnUqVPs3buX+Ph4V6hksVgwDAM/Pz86deqE1Wrlu+++49tvv+Xvf/87oaGh2Gw2kpOTSU5O5ujRo5QpUwaTyYTVar0ltYqIiIgUxb59+1i8eDHLli0jKysLgMjISFq2bEnLli3dW5yIiIjI/3dTi3dXrFiR/v37069fP7Zs2cLixYtZvXo1kyZNYtKkSVSvXp3hw4cXV60uZrOZoKAgYmJiyMjIIDc3F6vV6trpzTAMvL29adOmDWfOnOG7777j66+/5qWXXgIu3fkzmUyUK1cOk8lU7PWJiIiIFMWxY8dYunQpS5YsISMjA4AyZcqQnJxMq1atqFy5spsrFBEREcnvpoIlJ5PJREJCAgkJCTzxxBP89NNPTJo0ie3btxdH8wUYhoFhGERFRZGens7EiRN56KGHXDOWTCYThmHg4+NDp06d2LJlC6mpqaSmplK/fn1XmKRQSUREREqSZ599FgCr1Urz5s1p1aoVtWvX1qNvIiIiUmIVS7AEkJOTw/Lly1myZAnbt2/HMIxi32HNGRo5/9x///2kpqayaNEiIiMjSUxMdM1YMplMOBwOAgIC6NWrF2+88Qa//fYb9evXL9aaRERERIpLzZo1adWqFU2aNMHb29vd5YiIiIhc000FS3a7ndTUVBYvXsz69eux2+2YzWbq1q1Lq1ataNCgQbEUuW/fPiIjI7FY/leuw+EgMDCQRx55hP/85z/MmDEDq9VKs2bNMJvNOBwO17WBgYGYTCaOHz9eLPWIiIiI3Ar/+Mc/3F2CiIiIyA0pUrCUnp7O4sWLWbVqFWfOnAEgJiaGli1b0qJFCwICAoqtwLfeeotjx44xYMAAEhISXFPBnf+sVasWvXv3JiUlhUmTJnHmzBmSkpLyhVA7d+7EYrEQFRUF/G/mk4iIiIiIuIfHB0OKra28v7xVbG2JiMiNKVKw9OqrrwIQEhJCUlISrVq1okKFCsVaGMA333xDWloaACkpKQCucMkZDtlsNlq2bImHhwcpKSmMGTOG7du3c//99+Pj48OWLVv46aefCAwMpE6dOoDWVhIREZGSYeTIkZhMJvr27UtgYCAjR468rq8zmUw888wzt7g6ERERkWsrUrDUpk0bWrVqRXx8fHHX47Jy5UoWLFhAZGQkderUYf78+UyYMIG+ffsWCJf8/PxITEykQoUKjB49muXLl7N27VrXI3F+fn4MGTKEsmXL3rJ6RURERG7UokWLAOjevTuBgYGu19dDwZKIiIiUBEUKlgYOHFjcdeSTm5vLwoULsdvtDBo0iAoVKuDj48OMGTOuGC5ZrVZq1qzJG2+8wbJly9i/fz9nzpyhUqVKNGvWjHLlyt3SmkVERERu1KeffgpAcHBwvtciIiIipcVN7wqXnp7Ovn37OHfuHD4+PkRHRxMXF3dTbVqtVnr16kVCQgIVK1YEoH379phMJr7//vtCwyW4tHaSt7c37dq1u9luiYiIiNxyv7/xpRthIiIiUtoUOVjavn07I0eO5PDhwwXOhYeH88c//pFq1aoVubDY2Fiio6NdrwMDA0lKSgIoNFyCS+sN5OTkYLPZXDOZtFC3iIiIlBZTpkwhPj7+qssNbNu2jS1bttCrV6/bWJmIiIhI4cxF+aJff/2V119/ncOHD1O7dm369u3LM888Q9++falduzaHDh3ijTfeICMj46aKc+7s5pyR5AyXunfvTkZGBhMmTGDjxo2u6xcvXkxKSgpHjhxxhUkKlURERKS0mDx5Mlu3br3qNdu2bWPy5Mm3qSIRERGRqyvSjKUpU6Zgt9sZMmQIdevWzXeuR48epKWl8fbbbzNlyhQGDRp000VeHg79fuZSSkoKHh4eZGZmMm7cOKxWK927d7/p97xZDofDNZNKREREpLjY7XaNMURERKTEKFKwtHXrVpo0aVIgVHKqW7cuTZo0YfPmzTdT2xUFBgbStm1bzGYz06ZN48svvyQrKwuz2cyQIUNcC2CWJHokT0RERG6W3W5n27ZtlClTxt2liIiIiABFDJZycnIIDQ296jWhoaHk5OQUqahrcTgcBAcHk5SUxN69e1m9ejW+vr4MGzbMtdi3u8yaNYv9+/eTkZFBQkICsbGxNGjQQKGSiIiIFOpPf/pTvtc//vgjCxcuLHCdw+Hg9OnT5ObmaqMSERERKTGKFCwFBQWxc+fOq16za9cugoKCilTUtTinf6elpZGeno7NZmP48OFERkbekve7Xu+88w4bNmzA19cXi8XCtGnTAOjYsSM9e/Ys8vfj4sWLXLx40fXaZDLh4+Pj+rvcGW7lz/JWf05Ke/siIu7kXEvyWsc8PDyIjIwkISGB+++//3aUJiIiInJNRQqWGjRowKxZs5g4cSL33XcfVqvVdS43N5fp06ezefNm7r333mIr9PfS0tIYP34858+f580333R7qPTNN9+wYcMG7rvvPtq2bYu3t7drcc05c+Zw5MgR+vTpQ+XKlW/4f5KnTZvGlClTXK8rV67M22+/rS2JS4CjxdhWeHj4bWv7TmhfRORO8dlnn7n+3qdPHzp37qwd30RERKTUKFKwdP/997Nu3TqmTZvGvHnziI2NpUyZMpw6dYrdu3eTnZ1NaGjoLR0URUREEB0dTb9+/dz++Nvp06fZtGkTcXFxdO7cGW9vbxwOB/Xr1yc0NJSffvqJBQsWYLfbefjhh4mOjr6h9nv27EmXLl1cr53BVGZmJna7vTi7IjfIoxjbOnTo0G1r+05oX0TkVrNYLLf9Js6nn36Kr6/vbX1PERERkZtRpGDJ39+fN954g2+//Zbly5ezfv161zlPT08SExPp168ffn5+xVbo74WGhjJ48GAsliJ1oVhlZWWRkZFB9erV8fb2xm634+Fx6X+rIyMj6dGjB1arldmzZzNlyhSef/55rFbrdS/o7enpiaenZ6HnCpsqL6XTrfxZ3urPSWlvX0SkpNBsZBERESltipzKBAQE8Mc//pGnnnqKgwcPkpOTg81mIyIi4raFPSUhVAIoW7YsISEhHDlyBLhU1+X/IxwaGkpycjJnzpxhyZIlTJ8+nQceeEDrxoiIiEihduzYwcaNGzl58mS+dRadTCYTzzzzjBsqExEREcnvppMZi8VCVFRUcdRSKjkDpIiICDZu3Mj8+fNp164dJpMp34wkZ7i0adMmZs2aRaNGjW74kTgRERG5s+Xl5fHxxx+zatWqa16rYElERERKAvONXDx16lTGjx9/1XV97HY748ePZ/r06TdbW4nnDI5sNhs9e/YEYPbs2WzatAnAFS45xcTE0L17d3Jycjhx4oRbahYREZGS64cffmDVqlW0adOGt956C4Dk5GRef/11+vXrh6+vL02aNOGTTz5xc6UiIiIil1x3sLRx40ZSUlLw9/e/6iNoFouFgIAAJkyYwObNm4ulyJIkKyuLw4cPc+LEiXyPssXHx9OnTx8OHDjAjBkz2LZtG3ApXHI4HOTl5QGXZjYBZGRk3P7iRUREpERbunQpFStWZODAgcTExADg6+tL1apV6datG8OGDSM1NZUNGza4uVIRERGRS677UbjFixfj5+dHp06drnltx44dmTZtGgsXLiQhIeGmCixJxowZw6ZNmzh69ChWq5WGDRtSt25dGjVqBEDLli3Jyspizpw55OXl0aVLF+rXr4/Z/L/87tdff8Vqtbp9JzsREREpeQ4fPkzbtm3zHbt8pnjFihW55557+Pnnn2nfvv3tLk9ERESkgOsOlrZv306tWrWuuDvZ5Tw9PalVqxbbt2+/qeJKknfeeYcNGzZQtWpVmjZtysGDB5k/fz7z58/nvvvuo0+fPpQrV47k5GTMZjOzZs3i0KFDdOzYkQ4dOmCxWEhNTWXhwoWEhoZqfSUREREpwGKx4OXl5Xrt7e1NdnZ2vmtCQkJYt27d7S5NREREpFDXHSydPHmS0NDQ6244NDSUtWvXFqmokubHH39kw4YN9O7dmw4dOmCz2bDb7aSmpvLFF18wdepUTp8+zRNPPEFYWBg9evQgPDycMWPGMGHCBObMmYPZbObMmTN4eXnxf//3fwQFBbm7WyIiIlLClC1bluPHj7teV6hQgW3btuXbEGTnzp34+fm5q0QRERGRfK47WDKZTK51gq5HXl5evjWISrO9e/dis9lo1qwZNpsNwzDw8PCgUaNGBAYG8tVXXzF37lzMZjOPPfYYgYGBdOzYkbi4OFasWMHOnTsBqFSpEh07dqR8+fJu7pGIiIiURPHx8axdu9YVJDVr1oyxY8cyYsQI6tWrR3p6Ounp6bRp08ZtNU6aNIl69f5fe/ceF2Wd/n/8NcOAwzACoiICigIKns0DKlmJR/J8SM0sbWvb3battnb3W1bbdrQy07bd7WCHNctjHspDHkk0z0fAc54VFcE8IJ5gmPn94W/uBUVFBXHw/Xw8eiQzwzXX3Nwz9zXX/bk/n7uoU6dOmeUgIiIit49iN5aCgoI4ePBgsQMfPHiQoKCgG0rqduJyucjKyqJChQpUrlzZuM3dNKtbty5PPPEEn332GfPnz6dKlSr07NkTuNhIioiIKLPcRURExLMkJCTgdDo5fvw4lStXJjExkS1btrBhwwZSUlIAiI6OZvDgwWWS37vvvsvGjRvZsWMHjzzySIld2p+Xl0deXp7xs8lkwtfX1/i3yLWU9n7iyfE9OXdPj+/JuSt+2cX2xPjFbizFxsby888/k5mZec1L4jIzM9m8eTP33nvvTSdYltwNpOrVq7N9+3aWL1/OvffeW2gybrhY4D3++OO8+eabzJo1i/r16xMdHQ2A0+k0Hl+wISUiIiJyqcjISGM1OLg459ILL7zA7t27OXr0KFWqVCE6OvqyWuRWOH/+PNu3b8dsNrN582a+/vprHn300RI5iTZjxgymTp1q/Fy7dm3ee+89qlatetOx5fZ1tARjVa9evVzF9+TcPT2+J+eu+FeP78m534r4N6PYjaUuXbqQnJzMqFGjeOmll/D39y/ycadPn2b06NHk5+fTuXPnEku0LLibQG3btmXx4sUsW7aM6OhoQkNDL3tsbGwsjz/+OJ999hm7du0yGksFCz81lURERORGREVFERUVVWbP73Q68fHxoXbt2tSoUQOHw0FSUhJjx44t1Fy60ZNoffr0oXv37sbP7hhZWVmFVsWT8qUk26NHjhwpV/E9OXdPj+/JuSv+1eN7cu6lEd9isZTYCZxiN5YiIyPp2rUrP/74I88//zydOnWiQYMGxuVhx48fZ9OmTSQlJZGdnU23bt0KnXHzRO7RRrGxsXTq1ImFCxeSlJRE7969qVixovE4l8uFy+Wifv36VKhQgfXr19OxY0e8vLzUTBIRERGP5z5RFhoayrZt2/jHP/5Bbm4uP//8M2PHjmXo0KHUqlXLmJPTy8ur0Kjta/H29r7iysMul6vEXoeUX6W9n3hyfE/O3dPje3Luil92sT0xfrEbSwBDhgzB29ubWbNmMX36dKZPn37ZY8xmM7179+bBBx8ssSRvlR07dpCZmYnZbKZGjRqEh4cDF4eht23bloMHDzJ79mx8fHxITEwkICAAuDhRucViISQkBJvNhtVqxWK5rk0rIiIid6CPP/74hn7PZDLx5JNPlnA2V+YeiVSjRg3WrFmDr68vf/jDHwD4+eef+frrr3n66aex2Wz8+9//ZujQobqMTURE5A5xXd0Pk8nEQw89RPv27UlOTmbHjh2cPHkSgMDAQGJiYmjXrh0hISGlkWup+uijj1i7di25ubnGbffddx/x8fE0bdqU2NhYevToQW5uLtOnTycnJ4eEhAQiIyONJtLKlSs5ffo0YWFhOJ1OTCaTRiyJiIjIFS1ZsuSGf/dWNpbc9UyDBg0YN24cW7dupVGjRjzxxBPAxebSqFGjOHr0KE6nk3379qmxJCIicoe4oWE1ISEhHjki6Uo++ugj1q9fT0JCAm3btuXAgQOkpaWxZMkSNm3aRJ8+fejcuTMtWrTAbDYzd+5cFixYwNatW4mPj+euu+5iy5YtLF26FLvdTvv27ctkUk0RERHxLP/+97/LOoXr4uvri7e3N5mZmcDFS9j+9Kc/kZuby+rVq/Hy8qJHjx60bNkS0MIlIiIid4I7/nqt7du3s3btWhISEhgwYAB2u526desSHx9P48aN+fzzz/nqq69wOBx07dqVZs2aERQURL169ZgxYwZTpkxhypQpmM1mQkJCePnll6+5ap6IiIgI4FGjelwuF5UrVyYiIoLt27cbJ9KOHz/Ojh07sFgsOBwOfvnlFw4fPkxoaKiaSiIiIneAO76xdPToUXJzc2nbti12ux2Hw4HFYsFms9GxY0cCAwN5//33GT9+PL6+viQkJFCrVi1q1apFXFwc6enpHDt2jPDwcCIiIqhUqVJZvyQRERGREuduElWtWpWdO3diMpk4fvw4L730Enl5eQwePJg9e/bw888/8+GHH/KnP/2JmjVrlnHWIiIiUtru+MaSezb0AwcOULdu3UKTbrtcLlq0aMHzzz/PqFGjmDNnDjVq1CA6Ohqn00l4eLgxwbeIiIjIzdq6dWuxH1u/fv1SzORy7lXeatWqxb59+/jll18YNWoUubm5PPjgg3Tu3BmA8+fPs379enx9fW9pfiIiIlI27vjGUq1atQBIS0ujTZs2+Pn5Gfe5z8y1atWKwYMHM378eNLS0oiOjtbQbhERESlxr7/+erEfO3ny5BJ73uLMheSeP7J27dp8++23vPrqq9jtdh588EE6duxoPO6ZZ54hJyeHoKCgEstPREREbl93dGPJ5XIRHh7O3XffzfLly2ncuDEdO3Y0RjEVLLDi4uJYsmQJ8+bNo2PHjvj7+5dV2iIiIlJO9evXr8gGz9mzZ9m7dy/btm3jrrvuIioqqkSf99LnvFqjqVq1asTFxZGZmcl9991Hp06dMJlMxogmHx8fNZVERETuIHdUY2ncuHF069aNypUrAxeLKIvFQtu2bUlLS+Pzzz8nICDAWMmkoJCQEGJiYkhOTubcuXNqLImIiEiJGzBgwFXvX7VqFf/5z3+u+bjiWrduHRkZGZw7d47o6GgiIyMJCAgo1Ci6VNWqVenXrx8nT56kSZMmV32siIiIlH93TGPpnXfeISUlhejoaOLj4wvd16xZMwYMGMCXX37JyJEjee6552jduvVlMXJzc/Hz88PHx+dWpS0iIiJiaN26NcnJyUycOJGXX375pmKNGjWK9evX43A4jNvq1q1LQkKCseLbpQ0j90gm91QCgJpKIiIid7g7orE0fPhwtm3bxpAhQ2jSpEmh+9zFUOfOnXE4HHz99deMHj2aQYMG0bp1a0JCQgDYsGED27Zto3bt2pqMUuQ2YJn8VYnFcgx8rMRiiYiUtvDwcBYuXHhTMT7++GNSUlLo1q0b7dq1Iysri23btvH999/zyy+/cOTIEQYPHnxZc6moS+bUVBIREbmzlfvG0vDhw9myZQuDBg0iISEBm81W6P6CxVDXrl2xWq3MnDmTiRMnsnTpUmJiYrhw4QLbt2/nwoULDB06FKvVeqtfhoiIiAgAe/fuvalFRNLT01m3bh2tWrWiZ8+e2O12QkNDadKkCY0aNWL06NHMnDmTvLw8Hn300cuaS9nZ2Zw/f57g4GAtZiIiIiLlu7FUsKnUvn17bDYbTqcTgDVr1nD27Fn8/f0JDw83Ria1b9+esLAwUlNTWbBgAcnJydjtdmrXrs2QIUMICwsry5ckIiIi5dixY8eKvD0/P5/jx4+TnJzM5s2bi5wP8nqe48yZM7Ro0QK73Y7D4cBiseB0OmnQoAHDhg1j1KhRzJ07F5vNxoABA4ym0vHjx/n3v/8NwGOPPUZ4ePgN5yEiIiLlQ7ltLP3444+kpqYSGxtLixYtsNlsnD9/ng0bNjB79mx2795tPNZms/HUU0/RokULAGJiYoiJiaFLly6cPXuWChUqYLPZNFJJREREStVTTz11zcdUq1aNRx999Iafo0KFCgDs3r2bVq1aYbFcLAfNZjMul4uoqCiee+453nzzTWbOnElERAStWrUC4MSJExw8eJD8/Hz8/PxuOAcREREpP8ptY6lWrVq0adOGlStX8tNPP5GYmMjGjRsZP348AQEB9O/fn+DgYH755RcWLlzI+++/zwsvvECzZs1wuVwABAQEEBAQUMavRERERO4U9957b5GXl5lMJvz8/IiOjqZFixY3tZBItWrV8PPzIyUlhbZt21KzZs1Cz+N0OomOjubpp5/mgw8+MC6bA4iKiuLZZ5+lWrVqVKpU6YZzEBERkfKj3DWW1q1bR4sWLahfvz5Wq5X8/Hx++OEHMjMz2b59O2FhYbzwwgvY7XbgYgEXHh7Of//7X8aMGcMbb7xBcHBwGb8KERERuRMVZ8TSzQoKCqJbt25MmTKF5ORkHnroIWPUEvxv5FLjxo1p0qQJy5Yto0+fPoSGhgLQsGHDUs9RREREPEe5Wsbjgw8+4JNPPjEuc4uMjKRv3760bt2alStX4uvry8svv4zdbsfpdJKfnw9AYmIibdq04cyZM2RnZ5flSxAREREpdR06dKBu3brMmTOHOXPmFPkYHx8fYmJicDqdqo9ERETkisrNiKV3332XjRs3ArB//36ioqIAqF27Nj169KBixYpER0djtVoLrWySl5eHt7c3QUFB5ObmkpmZSXR0dJm9DhEREREAp9PJqVOnjBNhl6pSpcoNxw4MDOTJJ59k+PDhTJgwgfz8fBITE43Vc92X4506dQpfX19jpLeIiIjIpcpFY8m9+ts999zDzz//zLRp02jYsKFxSVt0dDQ2mw1/f38Ao6nkcrnw9vYGICMjg6CgIOrUqVM2L0JEREQEWLp0KbNmzSI9Pd1YzfZSJpOJSZMm3dTzhIaG8uKLL/L+++8zefJkMjMzadu2rXGp24YNG9i4cSMRERGaT0lERESuyOMbS+6m0uDBg7n//vuxWCwkJyeze/dugoODjSV03fMCuBUctbR8+XK2bNlCkyZNtMKJiIiIlJmZM2cyfvx4LBYL9evXJzAwEC8vr1J7vvDwcIYNG8bYsWNZsmQJK1euJCYmBpfLxf79+8nPz+dvf/ub6iMRERG5Io9uLL3zzjts2bKFQYMGGauoNG3alMWLF5OUlESbNm0KTUZZUMGm0vTp0/Hx8WHQoEHGEHARERGRW23evHkEBQXx1ltvUbly5VvynCEhITz99NNs2LCBWbNmsXfvXqxWK/Xq1WPgwIGXnZwTERERKchjG0sjRowgJSWFIUOGkJCQYDSEGjZsSEREBJs2bWLNmjXExcXhcrkuW7r3+PHjzJ49m9WrV5Ofn8/f//53qlevXhYvRURERASA7OxsOnbseMuaSm5+fn7cc889tGrViry8PLy8vLBYLFc8QSciIiLi5pGrwp0/f57atWszdOhQo6nkcrlwOp3Y7Xb69u2L2Wxm69atAJc1lfLy8vjpp5+YM2cOYWFh/OMf/6BmzZpl8VJEREREDNWrV+fMmTNl9vw+Pj74+flhtVrVVBIREZFi8ciKwWq10rt3bwC8vb2NEUnuBlJ4eDgVK1Zk7ty5tG7dmtjY2EK/7+3tTe/evalTpw61a9c2JvUWERERKUvdunXj66+/Jisri6pVq5Z1OiIiIiLX5JGNJcBYzQ0uH5EUHh5O165dmThxIps3byY2NrbQZN0AFouFJk2a3LJ8RURERK6lXbt2ZGdn88orr9ClSxciIiLw9fUt8rH169e/xdmJiIiIXM5jG0tX4m4gNWvWjPnz57N48WISExOx2+1lnZqIiIjINZ09e5azZ88yefLkqz7uWveLiIiI3ArlrrHkHpUUHh5OvXr1WL58OUlJSfTs2RO4fHSTiIiIyO1i8uTJzJgxA39/f+6++24CAwPx8vIq67RERERErqjcNZbgf6OW+vbtS1paGps3b6ZXr15lnZaIiIjIVS1evJjQ0FDeeecdrFZrWacjIiIick0euSrctbhHLfn7+1O7dm3S0tL4+eefyzgrERERkas7c+YMd911l5pKIiIi4jHKZWPJzd/fn3vuuQer1UpUVFRZpyMiIiJyVTVr1uTkyZNlnYaIiIhIsZXrxhJAXFwc//rXvwgNDS3rVERERESuqk+fPqxdu5Y9e/aUdSoiIiIixVIu51gqyGq1aji5iIiIeIQzZ87QqFEj/v73v3PPPfdQq1YtfH19i3zsfffdd4uzExEREblcuW8siYiIiHiKjz/+2Pj34sWLr/pYNZZERETkdqDGkoiIiMht4sknnyzrFERERESuixpLpcTpdBqr04mIiIgUR7t27co6BREREZHros5HKbm0qeRyucooExERERERERGR0qERSyVs8eLFHDp0iIyMDJo0aUJERAR169bFZDKVdWoiIiJymzt27FixH1ulSpVSzERERESkeNRYKkEjR45k48aNWCwWnE4na9euxWq10qNHDx544IEbjpuXl0deXp7xs8lkMlaIUcOq/CjNv2Vp7yeKLyJSMp566qliPc5kMjFp0qRSzkZERETk2tRYKiGff/45qamp9OrVi/bt2+NwONi+fTvjx4/nu+++IzMzk4cffhh/f//rjj1jxgymTp1q/Fy7dm3ee+89qlatWpIvQW5AZgnGql69+i2LrfjXji8iUhbuvffeIpvdZ8+eZf/+/WRmZlK/fn3VACIiInLbUGOpBPz666+kpKTQpEkTunfvjs1mw+l0EhISQlhYGFOmTGHJkiXk5eXx+OOPY7fbcblcxR4l0adPH7p372787P69rKwsHA5HqbwmKR6vEox15MiRWxZb8a8dX0TEYrHc8gbO1UYsuVwuZs2axcyZM/nDH/5wC7MSERERuTI1lkrA8ePHOXbsGO3bt8dms+FwOPDyuvi1t06dOjzyyCNMmjSJFStW4Ovry+9+97vruvTG29sbb2/vIu/TpODlR2n+LUt7P1F8EZHSZzKZ6NmzJxs3buSbb77hr3/9a1mnJCIiIqJV4UpCpUqVsFqtHD58GLh4hrOgmjVrMnDgQGJiYkhKSmLevHllkaaIiIiUA5GRkWzZsqWs0xAREREB1FgqEd7e3gQFBbFs2TLWrl0LXD4ZcEREBA8++CBeXl4sXryYX3/9tSxSFREREQ939OhR8vPzyzoNEREREUCNpZvicrlwuVwEBAQwcOBAAJKSkjhw4ECRj69fvz5du3blwIEDaiyJiIhIsTmdTo4dO8bUqVNZu3YtdevWLeuURERERADNsXTdzp49S25uLhaLBbvdbtzesGFDOnToQFJSEoGBgfTs2ZPQ0FDgYgPK6XTi5eVFjRo1cDqdHD58WEWhiIiIFOI+UXU1fn5+DBky5BZkIyIiInJtaixdh4kTJ7J161aysrLw8fGhXbt21KtXj3r16mG322nfvj0nT55k8eLF5Ofnk5iYSFRUFCaTyZjMOzMzE6vVSkhISBm/GhEREbnd1KtXr8gFPkwmE35+fkRFRZGQkEBAQEAZZCciIiJyOTWWimnEiBGkpqYSHh5OZGQkBw8eZPLkyQQFBdGrVy8SExOJjo6mZ8+eeHl5sXTpUo4ePUpCQgIJCQk4nU42bNjAihUrCAkJMUYziYiIiLi99tprZZ2CiIiIyHVRY6kYZsyYQWpqKgMHDqR9+/bY7XZ+/fVX1q9fz5dffsl///tfsrOzGTBgALGxsfj6+lK9enV++OEHduzYwbx583A6nRw/fhyz2cyrr76Kv79/Wb8sEREREREREZGbosZSMezcuZPAwEDuueceY16lypUr07lzZ6pVq8Y///lPpk2bhpeXF/369SMiIoKIiAiaNm3KokWLOHjwIF5eXrRq1YqePXvqMjgREREpZPr06Zw/f54BAwZgsRRdnjkcDqZMmYLNZqN37963NkERERGRK1Bj6Rpyc3PJzMzEbrdTqVIl4OLKLCaTCZPJRJMmTXjuuecYOXIkU6ZMoXLlyrRr1w64uApcbGwsAGazGafTidmshfhERETkf9LS0pg8eTIPP/zwFZtKABaLBX9/f7755huio6Np2LDhLcxSREREpGhqLF2D2WwmMDCQzZs3k5qaSpMmTYzmkMvlwmQy0ahRI5555hlGjBjB/PnzqVevHtWqVQMwGlDuf8utZZn8VYnFcgx8rMRiiYiIuC1duhS73U5iYuI1H9ulSxdmzJhBcnKyGksiIiJyW1Bj6RosFgvt27dn06ZNrFq1ijp16mCz2YCLjSJ3c6l58+b079+f7777jv379xdqLLmpsSQiIiKX2rFjB40aNcLb2/uaj/X29qZRo0bs2LHjFmQmdzqvUcNKLFb+8++UWCwREbm96Lqsq3A6nQDExMRw11138dNPP5GcnFzoMe5mkcvlokGDBgBs2LABp9OJy+W6pfmKiIiI5zlx4gTBwcHFfnxwcDAnTpwoxYxEREREik8jlgo4cOAAWVlZ+Pr6UqVKFaPIq1y5Mu3bt+fgwYN8/fXX+Pj40LFjR+P3HA4HFouF2NhYKlSogMlk0lxKIiIiUiwmk4n8/PxiPz4/P1+joEVEROS2ocbS//ef//yHdevWcfbsWQCsVivdu3enefPmREZGEhcXR05ODhMnTuTzzz8nJyeHe+65h8qVKxsTbS5btoy8vDzCwsKA/83BJCIiInIlQUFBHDx4sNiPP3jwIEFBQaWYkYiIiEjxqbEEjB49mpSUFNq0aUNcXBx79uxh48aNTJ06lbS0NLp160br1q1p3749Xl5efP/990ycOJHt27fTokULmjVrxtq1a/npp58IDAykVatWgOZUEhERkWuLjY3l559/JjMz85qXxGVmZrJ582buvffeW5SdiIiIyNXd8Y2l9evXs27dOhITE+nTpw92u51mzZoRHx/PsmXLmDZtGidOnMDhcNC2bVvuu+8+goKCWLFiBYsXL2bjxo1GrODgYIYNG0bVqlXL8BWJiIiIJ+nSpQvJycmMGjWKl156CX9//yIfd/r0aUaPHk1+fj6dO3e+xVmKiIiIFO2ObywdPXoUh8NBu3btsNvtxnxJoaGhPPDAA/j7+/Pf//6X7777DqvVSosWLWjUqBH16tWjQ4cO/PLLL+Tk5BAeHk5MTAyVK1cu65ckIiIiHiQyMpKuXbvy448/8vzzz9OpUycaNGhg1BTHjx9n06ZNJCUlkZ2dTbdu3YiMjCzjrEVEREQuumMbS+75j9xzKh05coQaNWoY8yUBmM1mEhMTcTqdfP311yxcuJCaNWsSHByM2WwmOjqa6OjosnoJIiIiUk4MGTIEb29vZs2axfTp05k+ffpljzGbzfTu3ZsHH3ywDDIUERERKdod21hyz38UExMDwPbt24mLi8PlchW6H6BDhw4cPXqUefPmsXXrVoKDgzV/koiIiJQYk8nEQw89RPv27UlOTmbHjh2cPHkSgMDAQGJiYmjXrh0hISFlm6iIiIjIJe7IxpJ7tJLL5aJGjRrExMQwZ84c6tevT4sWLS57fIUKFYw5l2bPnk3r1q2xWq1lkLmIiIiUZyEhIRqRJCIiIh7FXNYJlAX3aCOTyURgYCDt27cH4KOPPmLr1q3AxeaTe/QSXBzZVK9ePY4fP47D4bj1SYuIiIiIiIiI3GbumBFLSUlJpKen8+uvvxIVFUXTpk2JiIgAoF27dhw9epTp06czevRonn32WRo2bAj8b3QTQH5+Pr6+voXmYRIRERERERERuVPdER2SkSNHkpKSgpeXFxaLhdWrVzNlyhQGDRpEXFwcwcHBDBw4kLy8PGbNmsXbb7/Nk08+SVxcnHHJ24YNG9i7dy/R0dF4eXmV8SsSERERERERESl75b6x9MUXX7Bx40b69OlDfHw8FStWZMmSJSxevJhvvvmGAwcO0KVLF6Kionj44Yfx8/Nj2rRp/Oc//2HJkiVERUVx9uxZUlNTycvL46GHHsLb27usX5aIiIiIiIiISJkr142lzMxM0tLSaNSoEV27dsVmswHQvXt3oqOjmT9/PkuWLCE3N5devXpRu3Zt+vTpQ3h4OOvWrWP58uVs3rwZPz8/atasyQsvvEBoaGgZvyoRERERERERkdtDuW4sZWdnc/ToUVq1aoXNZsPpdAJgNpuJjY3Fbrfj7e3NkiVLsNvtDBgwAH9/f1q2bEnLli3p06cPp06domLFigQGBhqNKRERERERERERKeeNJZvNhtVq5ddffwUurgJnMpmMCbnDw8Pp3r07Z86cYeHChcTGxtK2bVtjNbiQkBBCQkLK8iWIiIiI3HacTidm8x25uLCIiIhcolxWBC6XC6fTib+/P5GRkcYlbe7V3dz/B6hZsyadOnXCarUyfvx4srKyjAaUiIiIiFwuLy+v0M/uk3IiIiJy5ylXjaVvvvmG/fv3YzKZMJvN2O124uPjARg/fjz79u0r9Hh3EdS0aVPatWvHyZMnOXXq1K1OW0RERMQjLFq0iE8//ZQXXniBL774ghUrVgAYI8JFRETkzlNuLoV7++232bFjB9HR0dSsWROXy4XZbKZTp07s3buXpKQkpk6dysCBA6lRo4bxew6HA4vFQqNGjZg3bx7p6elER0eX4SsRERERuf2MGjWKNWvWYLVaAVi4cCHLly/n7NmzdOzY8aZGe+fl5RUaBWUymfD19TX+LZ6vtP+Oil82sRW/7GIrftnG9+TcSyN+uWgsDR8+nG3btvHggw/SpEkT41I29/X/AwYM4PTp06xZs4b8/Hz69+9PZGQkTqcTi+XiJti3bx/e3t6EhYWV8asRERERub189NFHpKSk0K1bN7p27UpeXh7bt29nzJgxLF68mBYtWhAYGHjD8WfMmMHUqVONn2vXrs17771H1apVSyB7uVFHSzBW9erVFf864nty7p4e35NzV/yrx/fk3G9F/Jvh8Y2l4cOHs2XLFgYNGkT79u0LrdzmnlQyMDCQoUOHArBmzRrS09MZMmQIjRo1wsvLi9WrV7Ny5UpCQ0MJDg4uk9chIiIicjtasGABqamp3H///XTv3p2KFSvidDoJDg5m586dLFq0iMOHD99UY6lPnz50797d+Nl9JjUrKwuHw3GzL0FuUEnOmXHkyBHFv474npy7p8f35NwV/+rxPTn30ohvsVhK7ASORzeWRowYwdatW3nkkUeIj4/HZrPhdDpxuVysW7eOvLw87HY74eHhVKlShWeeeYZvv/2WefPmMXLkSKpVq4a3tzfHjh3Dx8eHV199lYCAgLJ+WSIiIiK3hezsbJKTk6lYsSKdOnWiYsWKxnQDANHR0SxatMhYgfdGeXt74+3tXeR9mrupfCjtv6Pil01sxS+72IpftvE9OffSiO+xjaVx48axfv16YmJiaNeuHVarlZycHDZv3sycOXP45ZdfjMdGR0fTqVMn2rVrx29+8xsaNGjAtm3bSEtLw263U69ePbp3705ISEgZviIRERGR24vL5SIzM5Pu3btTpUoVY5oBl8uFyWQiIiICQIufiIiI3ME8trHUoUMHtmzZwo4dO5g0aRJDhgwhJSWFL7/8En9/f7p160ZAQAAHDx5k2bJlZGRk4HK5SEhIIC4ujri4OHJzc/Hx8SE/Px8vL6+yfkkiIiIitw2Xy0VAQADDhw/n/PnzwP+mGXBfquaeYNvN3Xi61m0iIiJSfnhsYyksLIznnnuOUaNGMXfuXI4dO8aePXsIDw/npZdeMgodh8NBw4YN+eyzz1i+fDktWrQw5gZwD7lWsSMiIiJSmMlkwuVyXXX+Sff8R+7FUNw1VWpqKgcOHKBHjx6qs0RERMo5jz7Sh4SE8PzzzxMREcHatWsJCAjgxRdfxNfXl/z8fOBioRMfH0/Tpk3ZtGkTR49enEvdbDYbZ9u0jK2IiIjI5a5VI7lP0l24cMG4LTU1lQkTJjBlyhROnDhRqvmJiIhI2fPoxhJcbC4999xz1K9fn86dO+Pn54fL5TIubXO5XPj4+FCtWjUAcnJyyjJdERERkXLDPRrJXXelpKQwYcIEMjIyeOutt6hUqVJZpiciIiK3gMdeCldQ9erVefbZZ42f3WfX3BNLAmRkZODv7094eHiZ5CgiIiJyuypYM12P3Nxc4GLttXXrVsaPH09mZiZvvPGGMbG3iIiIlG/lorEEEBgYWOjnghNFrlixgh07dtC4cWPsdnsZZCciIiJy+7q0qXS9jabU1FSWLl2qppKIiMgdqNw0lgoq2FRavnw506dPx9vbm0GDBmG1Wss4OxEREZHbw7p168jIyODcuXNER0cTGRlJQEAAJpOpWKu5Wa1WfHx8SE1NxWq1qqkkIiJyByqXjSWz2UxeXh7jx49n/fr1OBwOXnnlFUJCQso6NREREZHbwqhRo4w6ya1u3bokJCTQvn17zGbzNZtLLpfLuBzu7bff1pQDIiIid6By2Vi6cOEC06dPZ+HChcTExPDb3/6W0NDQsk5LRERE5Lbw8ccfk5KSQrdu3WjXrh1ZWVls27aN77//nl9++YUjR44wePDgazaXqlatymuvvUZAQIBqLRERkTtUuWwsVahQga5duxIbG0tUVBT+/v5lnZKIiIjIbSE9PZ1169bRqlUrevbsid1uJzQ0lCZNmtCoUSNGjx7NzJkzycvL49FHH72suZSdnc358+cJDg4GoF69emX5ckRERKSMXf3CeQ8WEBBA06ZN1VQSERERKeDYsWOcOXOGFi1aYLfbjUvhnE4nDRo0YNiwYVSpUoW5c+cyZcoUAKOpdPz4cT788EM+/fRT0tPTy+w1iIiIyO2j3DaW4PIVTkRERETudBUqVABg9+7dAFgsFwewm81mXC4XUVFRPPfcc1itVmbOnMnq1auN3z1x4gQHDx5k3759+Pn53frkRURE5LZTrhtLZcXpdJZ1CiIiIiJFqlatGn5+fqSkpHDgwIFC97lXg4uOjubpp58mPz+fdevWGfdHRUXx7LPP8t5771GpUqVbnbqIiIjchtRYugVcLldZpyAiIiICQFBQEN26dWP//v0kJycXWhUO/jdyqXHjxjRp0oRly5Zx+PBh4/6GDRtStWrVW522iIiI3KbK5eTdZWXu3Lns37+f9PR0GjZsSHR0NC1atNAleSIiInJb6dChAykpKcyZM4eAgAB69ep12WN8fHyIiYlh48aNZGdna9U3ERERKZIaSyVkxIgRpKam4ufnh8ViYcaMGQB06dKFPn363NRw8by8PPLy8oyfTSYTvr6+xr/l1ijtbV2a8T059/IQX0TkdhMYGMiTTz7J8OHDmTBhAvn5+SQmJmKz2YD/fS6eOnUKX19f7HZ7WaYrIiIitzE1lkrA2LFjSU1NpW/fvrRv3x6r1cq2bdv47rvvmD9/PkePHmXgwIHUrl37hr7Azpgxg6lTpxo/165dm/fee0/D0IshswRjVa9e/ZbG9+Tcy0N8EZHyLjQ0lBdffJH333+fyZMnk5mZSdu2bWnYsCEAGzZsYOPGjURERGg+JREREbkiNZZu0unTp9m0aROxsbF069YNq9WK0+mkWbNmBAcH8+OPP7J48WIcDgePPPIItWrVuu7n6NOnD927dzd+djensrKyLpsXQQrzKsFYR44cuaXxPTn38hBfRMRisZT7kzjh4eEMGzaMsWPHsmTJElauXElMTAwul4v9+/eTn5/P3/72N60AJyIiIlekxtJNOnnyJOnp6cTExGC1WnE4HHh5XfzKGx4eTu/evfHx8WHevHlMnTqVZ555Bh8fH1wuV7FHL3l7e+Pt7V3kfZoY/NYp7W1dmvE9OffyEF9E5HYWEhLC008/zYYNG5g1axZ79+7FarVSr149Bg4cqLmVRERE5KrUWLpJlStXpkqVKhw9ehS4eHaz4JfU4OBgunbtSk5ODj///DPff/89AwYM0JwuIiIictvw8/PjnnvuoVWrVuTl5eHl5YXFYsFiUakoIiIiV2cu6wQ8mbuBFBoayubNm0lKSgIuXqpWVHMpMDCQuXPnsm/fvrJIV0REROSqfHx88PPzw2q1qqkkIiIixaKK4Qa5L2Wz2Wz06dOHtLQ05s2bR3BwMI0aNTKaS+6RSZGRkfTq1Yuvv/6a48eP39BcSyJya1gmf1VisRwDHyuxWCIiIiIiIrcbNZauw8mTJzl//jw+Pj4EBQUZt9evX5+BAwcyefJkZs6cicVioV69ephMJpxOJy6XCy8vL2OOgvT0dJo1a1ZWL+O2oi/wIiIiIjfGa9SwEouV//w7JRZLRETuLGosFdO4cePYtGkTmZmZ+Pj40LJlS5o2bUpcXBwA99xzDydPnmT+/Pnk5+fTvXt3mjVrhtn8v6sNDx48iI+PDzVq1CirlyEiIiIiIiIiUmLUWCqGESNGkJqaSp06dWjTpg2HDx8mKSmJpKQk+vbty8CBA6latSpdu3bFbDYzd+5cjhw5QpcuXejcuTMWi4UNGzaQnJxMcHCwLoMTERERERERkXJBjaVrmDNnDqmpqfTv35/OnTtjs9lwOBxs2LCBzz//nOnTp3P69Gl++9vfEhISQu/evalevTrjxo1j4sSJzJ8/H7PZTE5ODhUqVOCVV16hUqVKZf2yRERERERERERumhpL17B3715sNhvx8fHYbDZjvqS4uDgCAwP58ssvWbhwIWazmccee4zAwEC6dOlCbGwsK1euZOfOnQBERETQpUsXqlWrVsavSERERERERESkZKixdBUul4usrCwqVKhA5cqVjdvcK73VrVuXJ554gs8++4z58+dTpUoVevbsCVxsJEVERJRZ7iIiIiIiIiIipc187YfcmdwNpOrVq5OVlcXy5csBMJvNRmMJIDo6mscffxyLxcKsWbPYtWuXcZ/T6SwUT0RERERERESkPFFj6QrczaO2bdsCsGzZMg4fPlzkY2NjY3n88cfJzs4u1FgquCJcwWaUiIiIiIiIiEh5oMbSFbhHG8XGxtKpUydSU1NJSkri9OnThR7ncrlwOp3Ur1+fChUqsH79ehwOh0YoiYiIiIiIiEi5pzmW/r8dO3aQmZmJ2WymRo0ahIeHA2CxWGjbti0HDx5k9uzZ+Pj4kJiYSEBAAAD5+flYLBZCQkKw2WxYrVYsFm1WERERERERESn/1AEBPvroI9auXUtubq5x23333Ud8fDxNmzYlNjaWHj16kJuby/Tp08nJySEhIYHIyEijibRy5UpOnz5NWFgYTqcTk8mky99EREREREREpFy74xtLH330EevXrychIYG2bdty4MAB0tLSWLJkCZs2baJPnz507tyZFi1aYDabmTt3LgsWLGDr1q3Ex8dz1113sWXLFpYuXYrdbqd9+/aF5lYSERERERERESmv7ujG0vbt21m7di0JCQkMGDAAu91O3bp1iY+Pp3Hjxnz++ed89dVXOBwOunbtSrNmzQgKCqJevXrMmDGDKVOmMGXKFMxmMyEhIbz88ssEBweX9csSEREREREREbkl7ujG0tGjR8nNzaVt27bY7XYcDgcWiwWbzUbHjh0JDAzk/fffZ/z48fj6+pKQkECtWrWoVasWcXFxpKenc+zYMcLDw4mIiKBSpUq3/DVYJn9VYrEcAx8rsVgiIiIiIiIiUv7d0ddsuVduO3DgAEChSbddLhctWrTg+eefx+FwMGfOHHbt2gVcXDEuPDyc1q1b0717d5o2bVomTSURERERERERkbJ0RzeWatWqBUBaWhpnzpwpdJ974u1WrVoxePBgDh48SFpaWqH7RERERERERETuZHdsY8nlchEeHs7dd9/N6tWrWblypXG7eySTW1xcHOHh4cybN4/s7Gw1lkREREREREREuIMaS+PGjePXX381fjaZTFgsFtq2bUvFihX5/PPPWbt2LSaT6bLGUUhICDExMeTk5HDu3LlbnbqIiIiIiIiIyG3pjmgsvfPOO8yZM4cdO3Zcdl+zZs0YMGAAACNHjmTVqlVFxsjNzcXPzw8fH59SzVVERERERERExFOU+1Xhhg8fzrZt2xgyZAhNmjQpdJ/T6cRsNtO5c2ccDgdff/01o0ePZtCgQbRu3ZqQkBAANmzYwLZt26hduza+vr5l8TLKjFadExEREREREZErKdeNpeHDh7NlyxYGDRpEQkICNput0P1m8/8GbHXt2hWr1crMmTOZOHEiS5cuJSYmhgsXLrB9+3YuXLjA0KFDsVqtt/pliIiIiIiIiIjclsptY6lgU6l9+/bYbDacTicAa9as4ezZs/j7+xMeHm6MTGrfvj1hYWGkpqayYMECkpOTsdvt1K5dmyFDhhAWFlaWL0lERERERERE5LZSLhtLP/74I6mpqcTGxtKiRQtsNhvnz59nw4YNzJ49m927dxuPtdlsPPXUU7Ro0QKAmJgYYmJi6NKlC2fPnqVChQrYbDaNVBIRERGR6+Y1aliJxcp//p0SiyUiIlJSyuXk3VFRUbRp04bt27fz008/cfz4cZYvX84XX3zBuXPn6N+/P0899RSdOnXi7NmzvP/++2zYsAEAl8uFy+UiICCA6tWrExQUpKaSiIiIiIiIiEgRytWIJZfLhclkIiYmBh8fH/Lz8/nhhx/IzMxk+/bthIWF8cILL2C32wG49957CQ8P57///S9jxozhjTfeIDg4uIxfhYiIiIiIiIiIZyhXI5ZMJpPx79q1a9OnTx9at27NypUr8fX15eWXX8Zut+N0OsnPzwcgMTGRNm3acObMGbKzs8sqdRERERERERERj1MuRizt2bOHw4cPk5ubS82aNYmOjgYgMjKSHj16ULFiRaKjo7FarTidTmM1uLy8PLy9vQkKCiI3N5fMzEzjd0VERERERERE5Oo8vrE0ZswYVq5cydmzZ43bHnroIe6//358fHyIjo7GZrPh7+8PYDSVXC4X3t7eAGRkZBAUFESdOnVu/QsQEREREREREfFQHt1YGjFiBKmpqTRv3pz4+HgOHz5MUlISEyZMoFq1arRu3RqA0NDQQr9XcNTS8uXL2bJlC02aNMHPz++WvwYREREREREREU/lsY2liRMnsn37dvr370+nTp2MplBwcDD/+te/mDNnDk2bNqVChQqF5l4CCjWVpk+fjo+PD4MGDcJms93y1yEiIiIiIiIi4qk8srG0Z88eli1bRnR0NAkJCfj5+eFwOLBYLLRt25aFCxdy4sQJnE7nZU0lgOPHjzN79mxWr15Nfn4+f//736levXoZvBIREREREREREc/lkavCpaenc+rUKXr27ElAQAAulwuLxWKs9BYYGMiJEyc4ceLEZb+bl5dHUlISc+bMISwsjH/84x/UrFnzVr8EERERERERERGP55Ejlho0aMCAAQOIiorC5XIZo5Lc/69UqVKhnws+xtvbm379+lGnTh0iIyONSb1FREREREREROT6eGRjqXLlyiQmJuLj41PodvfcSe7bvby8gP81mNLT0/H29qZatWo0bdr01iUsIlKAZfJXJRbLMfCxEoslInIn8ho1rMRi5T//TonFEhER8RQeeSkccFlTCS6u9ub+v9PpJDc317gvJSWFf/7zn8yfPx+Hw3HL8hQRERERERERKa88trF0NS6XC4fDYYxYSklJYdKkSaSnp3PfffdhsXjkQC0RERERERERkdtKueqwuC+FM5vNWK1WzGYzW7duZeLEiWRkZPDee+9pom4RERERERERkRJSrhpL7km6TSYTDoeD9evXs2zZMjIyMnjjjTfUVBIRERERERERKUHlsrGUn5+Pw+Fg+vTp5OXl8eabbxIREVHW6YmIiIiIiIiIlCvlqrHkvhQuMDAQAIfDwdtvv02NGjXKMCsRERERuVFatU1EROT2Vq4aS24tW7Zk27ZtDB48mLCwsLJOR0RERERERESkXCqXjaWQkBCef/55rf4mIiIiIiIiIlKKzGWdQGkp66aSy+Uq0+cXERERERERESltGtJTSkwmU1mnICJ3KMvkr0oslmPgYyUWS6Q0ab+/tZxOpzG3pXvxFBEREbkzqbFUwtatW0dGRgbnzp0jOjqayMhIAgICyjotERERkRJz7tw5/Pz8gIsn0wo2mkREROTOosZSCRo1ahTr16/H4XAYt9WtW5eEhATat29fhpmJiJQcjQwRuXNNmTKFHTt2sG/fPqKjo6lTpw59+/ZVU0lEROQOpsZSCfn4449JSUmhW7dutGvXjqysLLZt28b333/PL7/8wpEjRxg8ePANxc7LyyMvL8/42WQy4evri8ViwRUcUlIvAZO392W3eSl+mcX35Nw9Pb4n514u4i/4ocTi53fudctil4f4nqw09suynq/xdjNixAhSU1MJCQmhdu3a7Ny5k5SUFDZt2kSfPn2oX78+Pj4+NxT7arWOMyyipF4C5iI+c8weHN+Tc1f8q8f35Nw9Pb4n5674V4/vybmXRvySrHNMLs0yfdPS09N59dVXad68OUOHDsVutxv3bdmyhdGjR3P69Gnuv/9+Hn300euOP2XKFKZOnWr8fPfdd/Pss8+WROoiIiIi1zRlyhRmzJjBwIED6dChAxUrViQjI4PZs2ezfPly/Pz86NevH23atMFqtd5QfNU6IiIinknjlkvAsWPHOHPmDC1atMButxuXwjmdTho0aMCwYcOoUqUKc+fOZcqUKdcdv0+fPowdO9b474knnih0Vu9azp07xwsvvMC5c+eu+7nLOr4n5674ZRdb8csutuKXbXxPzr08xC+vnE4nO3bsIDQ0lMTERCpWrIjD4SAkJISBAwfy8MMPk5+fz5QpU1izZs111Shut3Ot4+n7peKXTWzFL7vYil+28T05d0+PX5Z1jhpLJaBChQoA7N69G/jf0Hmz2YzL5SIqKornnnsOq9XKzJkzWb169XXF9/b2xmazFfrPu4ihcVficrnYu3cvpTU4rTTje3Luil92sRW/7GIrftnG9+Tcy0P88sjpdJKdnc2BAwew2WxYrVYcDodR61SsWJG7776bQYMG4XK5mD59Ovv37zd+t7hu51rH0/dLxS+b2IpfdrEVv2zje3Lunh6/LOscNZZKQLVq1fDz8yMlJYUDBw4Uus+9Ukp0dDRPP/00+fn5rFu3rowyFRERESk+s9lMYGAgERERZGZmcvbs2YvzHhVoGlmtVuLi4ujVqxdZWVnGJW2a0FtEROTOoCN+CQgKCqJbt27s37+f5OTkQqvCwf9GLjVu3JgmTZqwbNkyDh8+XEbZioiIiBSP0+nE6XQSExPDyZMnGT9+PHCxtrm0udSmTRsaNWrExo0bWbFiRVmlLCIiIreYGkslpEOHDtStW5c5c+YwZ86cIh/j4+NDTEyMMaz8VvH29uaBBx64riHlt0t8T85d8csutuKXXWzFL9v4npx7eYhfHpnNZsxmMx06dCAoKIhFixYZdU7B5pLL5SIwMJBevXphNpvJyMi4pXnqfaX4t1tsxS+72IpftvE9OXdPj1+WdY5WhStBhw8fZvjw4WRlZTFw4EASExOx2WyFHjN27FiSk5N56623CA8PL6NMRURERIrH6XRiNpvZsmULI0eONArXzp07G/fDxUZTZmYmf/7zn2nXrh2/+93vyjJtERERuUU0YqkEhYaG8uKLLxISEsLkyZMZN24cmzdvNu7fsGEDGzduJCIigkqVKpVhpiIiIiLF454rqX79+vzmN7/hwoULTJkyhe+//9643/2YrVu3YjabqV27dlmlKyIiIreYRiyVgoyMDMaOHUtqaqpx+ZvL5WL//v3k5+fz+uuva7SSiIiI3DZcLhcmk+maj3M4HKxevZovvviCs2fP0qpVK7p3705QUBCbN29mzpw5nD17ltdff50qVarcgsxFRESkrKmxVErOnDnDhg0bmDVrFidOnMBqtRIZGcnAgQMJDQ0t6/REREREruhajaZ9+/bx8ccfs3//fuDivA7ueZZeeOEFataseatSFRERkTKmxlIpy83NJS8vDy8vLywWCxaLpaxTEhEREQFg3bp1ZGRkcO7cOaKjo4mMjCQgIAD439xKl3Lffvr0abZt28b27ds5f/484eHhxMXFaaSSiIjIHUaNJREREZE70KhRo1i/fj0Oh8O4rW7duiQkJNC+fXvg2s0lEREREQ2fEREREbnDfPzxx6SkpNCtWzfatWtHVlYW27Zt4/vvv+eXX37hyJEjDB48GLPZXGQTyf1zfn4+Xl5eQPHnaRIREZHyRY0lKRHugW+lVVCWdvxLn6s0nqc044LnbntPjy8i4mnS09NZt24drVq1omfPntjtdkJDQ2nSpAmNGjVi9OjRzJw5k7y8PB599NHLmkvZ2dmcP3+e4OBgo6kE5f9ztjwdr0qjJinNxqKnb/vSjK86R0RuBxrDfIdwOp2lGt9kMhkHtNK4urK04u/fv5+UlBS2b9/O0aNHjecqDaW1fTx125eX+CJSftwpnxHHjh3jzJkztGjRArvdblwK53Q6adCgAcOGDaNKlSrMnTuXKVOmAP8boXT8+HE+/PBDPv30U9LT08vsNRRFtU7RblWt44nbpjzEV50jItejtD4nNGLpDnH+/HlsNpvxc0nNjbBs2TIyMjI4ceIE9evXJzo6mmrVqt103FsR/5NPPiElJYWTJ09iMpmoXLky99xzDw888ECJTbKelJREeno6v/76K1FRUTRt2pSIiIgSie3J2748xC8OzUEicnvLzMzk119/JSAgAJvNRmBgYFmndEtUqFABgN27d9OqVSvjmGc2m3G5XERFRfHcc8/x5ptvMnPmTCIiImjVqhUAJ06c4ODBg+Tn5+Pn51dmr6EoqnUuV9q1TmnWOeDZ2760498OdQ6o1hG53d2qWkeTd5dz06dP55dffmH//v3ExMQQFRVF9+7dS+RM1QcffEBKSopxhtDhcBAYGMjQoUOJi4u76YKlNOOPGjWKDRs20Lp1a+rUqUNOTg7z5s0jOzubJk2aMHDgQGrXrn1TB8qRI0eSkpJirAiYk5ODxWJh0KBBtG7d+qZWzfHkbV8e4hdl3bp1HDlyhPz8fCpXrszdd99dooXWsmXLSE9P5/z581SuXJl27dphs9nw8vK66csPSjN2eYi/aNEi9u/fz5kzZwgKCqJdu3YEBQVhs9lu+/w9fduXZvxPP/2UjRs3cvLkSQCCgoIYOHAgjRs3Jigo6Kbyvt0dP36cv/71r1SpUoU//elP1KxZs9D97i+K69at44MPPqBt27Y89dRTxv2bN2+mWrVqVK1a9VanXiTVOkUr7VqnNOsc8OxtX9rxy6LOgdKtdTz5eOLp8T25zlH8q7uVtY4aS+XYiBEjSE1NJTg4GJvNZuywMTExPProo9SsWfOGDzyffvopy5Yto1u3brRp0waLxcKSJUtYtGgRZ8+epW/fvnTq1OmGd9jSjL9ixQo++eQTEhMT6dOnj3F2Mz09nZkzZ7Js2TJq167NI488Qt26dW/ogPnFF1+wePFi+vTpQ3x8PBUrVmTJkiUsXryY9PR07rvvPrp27UqtWrWuO7Ynb/vyEL8oo0aNYuPGjeTm5hq3RUdH06tXLxo0aICfn99NHRhGjhzJxo0bcTqdRhEZGhpKhw4duPvuu6lUqdINxy/N2OUh/vvvv8+GDRvw9vYG4MKFCwQGBtK6dWu6du1KtWrVbtv8PX3bl2Z895eyFi1a0LBhQ/bt28eqVavIzs6mTZs2JCYmEhsbe0N5e4pp06YxZcoUunXrxkMPPXRZPeByucjLy2PUqFGkpqbywQcfEBoaWkbZXplqnaKVdq1TmnUOePa2L+34ZVHnQOnWOp58PPH0+J5c5yj+1d3qWkeXwpVTEydOJCUlhf79+9O5c2f8/PzIyspiwoQJrFq1in/+85888sgjNGnSxPggKa6DBw+SkpJCy5Yt6dWrl1GsDB48mDp16jB79mymT59Obm4uPXr0uO7hdqUdPyMjA4fDQatWrbDZbDidTkwmE+Hh4Tz44INUqlSJOXPmMH78eH7/+98THh5+XW/ozMxM0tLSaNSoEV27djXy7969O9HR0cyfP58lS5aQm5tL3759LztTXJbbRvGv38cff8zGjRvp2rUrcXFxuFwuFi1axPr16/nss8/o0qULnTt3JjAw8IYODGPGjCE1NZU+ffpw9913YzKZWL58OcuWLWP8+PHs27ePAQMGEBwcfN3xSzN2eYj/5ZdfkpaWRv/+/UlISMBisbBx40YWLlzIvHnz2LdvH0888cR1f0bcivw9fduXZvxly5axbt06evXqRc+ePY3Pifj4eJKTk0lOTubkyZM88MADNGzY8Lry9iQdOnQgJSWFOXPmEBAQQK9evS57jI+PDzExMWzcuJHs7OzbrrGkWufKSrPWKc0651ZsG0+OXxZ1DpRurePJxxNPj+/JdY7iX11Z1DpqLJVDTqeTbdu2UbNmTRITE/H19cXhcFC1alUef/xxIiIimD17NmPHjuWxxx7jrrvuuq4d9cSJE5w4cYLIyEijWIGLczPExcVht9uZNm0as2fPpmLFivTu3fu63gylHT87OxuXy1Xo7Jz7d4OCgrj//vvJz89n1qxZTJ48mb/85S/XtX2ys7M5evRooWLOnX9sbCx2ux1vb2+WLFmC3W7nwQcfxG633xbbRvGvz65du9i4cSNxcXH06NHD+DuGh4fTokULvvvuO77//nsuXLhAz549CQgIuK74hw8fZuPGjTRp0oTExEQjfq9evWjRogXjxo3j559/Jjc3l0ceeeS6LkspzdjlIf7JkydJS0sjNjaWTp06UbFiRQDuvfdemjdvzueff87KlSv5z3/+w5/+9CfCwsKua1/S37Zs45tMJtq3b4/NZsPhcGCxWKhXrx7VqlUjICCAH374gRkzZmC1WomOjr6u+J4iMDCQJ598kuHDhzNhwgTy8/NJTEw0ik/3vnzq1Cl8fX2LfZy6VVTrXF1p1jqlWefcim3jyfFvdZ0DpVvrlIfjiafG9+Q6R/GLF/9W1zqaaa2ccTqdnDp1ir179xIQEGAUWhaLBZfLhd1uJzExkb59+3Lu3Dm+/fZbsrKyjN8tDvc1n9nZ2cDFg5l7wk+A+vXr06dPH2rXrs3EiRPZtGnTdR3QSiu++/XVrVsXl8vFTz/9ZMQveEVoYGAgiYmJNG/enDVr1jBv3rxi5+7O32q18uuvvwIXC7mCzxEeHk737t1p0aIFCxcuJCUlBSjeDP2euu3LS/xLZWdnk52dTePGjbHb7TidTlwuF1arlWbNmvHEE09Qu3Zt5s2bx+LFizl//vx1xc/JyeH48ePUr1+/UHyLxUJERATPPPMMTZs2ZfXq1Xz//ffG6y7r2OUlfkZGBtHR0VSsWNGID+Dn58ef//xn2rZty549e/j66685duzYde1L+tve+vjuY8CxY8fIz8/n5MmTuFwuvLy8jMcEBQXRvXt3unfvzubNm1m8eHGhyz7Km9DQUF588UVCQkKYPHky48aNY/Pmzcb9GzZsYOPGjURERFCpUqUyzLQw1TpX3zZQurVOadY57vieuO1vRfxbXedA6dY6nno8KQ/xPbnOUfwrK8taR42lcsZsNlOpUiUiIyNJT08nNzcXi8ViDIF2HwgSEhLo0qULhw4d4r///a/xu9ficrmoWLEilSpVYv78+ezevdu4zx0foGHDhnTt2hWASZMmXfPN4H4TOJ1O7HZ7icZ3v1Hcry8mJoagoCCSk5P5+eefL4sNUKVKFfr374/ZbGbTpk3X3C5wcds4nU78/f2JjIxk+fLlbN682fgQLvhhXLNmTTp16oTVamX8+PFkZWUV68O6NLb9unXrSjV+QSX9t4X/FaqltW9ejXuJ7sOHDxs5uP+OZrOZqKgohg4dSvXq1Zk/f77xuOJ+sXE/bvfu3eTm5mIymQrtJwEBAfz+97+nXr16LFu2zPhCWJz4pRm7PMR3F+s7duwgOzsbs9lsxHfHePrpp2nZsiWpqaksW7as0PXxZZm/p2/70orvPgY0atQIgJ07dxb5uevv70+XLl1o2bIlixYtYuvWrcXK21OFh4czbNgw7rrrLpYsWcL777/P8OHDefvtt/n000/JycnhiSeeuK1WgFOtc7lbUevcijoHVOtcqizrHCjdWsdTjyflIb4n1zmKf2VlWeuosVTOuA/6NWvW5NixY0yaNAmHw2GczXAfeHx9fY0JuzZs2MCGDRuKFd9kMlGtWjUSEhK4cOECs2bN4ujRo4Xudx/Y7r33Xlq3bs2RI0c4d+7cVeMWHMobEhJSYvFHjhzJzJkzjbMnLpeLypUr85vf/Aan08mPP/5onEkzmUxGHg6Hg9q1a9OoUSO2b99uDCkvyjfffMP+/fuNM3Z2u534+HgA4/rYgtxxmjZtSrt27Th58iSnTp0qMvbChQtZvHgxOTk5AFSrVo327duX2Lb/4IMP+OSTT9izZ0+pxHe5XBw/ftwoeENCQmjXrl2J7jsFC9pq1aqVePyrqVu3LiEhIaxbt44LFy7g5eVV6APfZDIRGRlJ9+7dOX78ON999x1QvC827vh169Zl+/btnDhxApPJRH5+fqHHBAUF0b9/fypUqMDcuXOLHb80Y5eH+KGhodx1113s27ePAwcOAIU/p9z/HjJkCDVq1GDp0qXGZ+3VFBxNUNL5l2bsgjwtvsvlKvT5HRERQeXKlfn222/Zvn17kQVXcHAw9957LwBz587F4XAUe7SFJwoJCeHpp5/mj3/8I9WqVWPv3r1kZGRQr1493nzzTcLDw8s6xUJU6xRW2rVOadY5oFrnWvHLss6B0q11PO14Up7ie2Kdcyviu3la/Nuh1lFjqZxw7wTug36/fv2oUqUKS5YsYenSpYXO4rmLCn9/fx544AFMJhOHDh0qMu6ePXtYtmwZP/30E7t27TJu79mzJ82bN2fVqlXMmzePY8eOGfeZTCby8vIAiI2N5cyZM+zfv7/I+KtWrWLixIm8/vrrzJo1y3hcr169bjr+pEmTWLt2Ld999x3z5s3jwoULxpuqcePG9O3bl3379jF9+nSj2DSbzeTl5RkryOTl5REYGIjdbi/yDfn222+zcOFCDh8+bBS6AJ06daJDhw7s2bOHqVOncvDgwUK/5z7706hRI5xOJ+np6ZfF/vDDD/n222+ZOXMmeXl5xt+4R48etGjR4qa3/bvvvsuaNWvIyckpVBT26NGjRP6233//PW+99RZPPfUU7733HgsXLgSgb9++JRI/NTWVefPm8f3335OSksLx48cBeOCBB7jrrrtuOn5RLj1LYLfbadSoEenp6Xz66afk5+cXOhgDWCwW4uLiqFevHps3by5UBBZH8+bNOXbsGJ988glOp/Oygg4gKiqKhg0b8ssvv7Bz585r5u7+d0nFvtJzlVZ8t9LOPz4+nry8PL7++mtycnIwm83GQd99YA8KCuKuu+7i0KFDpKWlXTGme/8seMlCSeU/YcIETp8+XWjfK61t4379JRm/tPfNS88CuufkcTqdfPHFF8aXTTf33ycuLo7Y2FiOHDlixCnP/Pz8uOeee3jrrbf48MMPef/993n66advqwm7Vevc+lqnNOscUK1ztfhlUefAra91POl4UtRzeWKt42l1DqjW8YRaR40lD7dv3z4cDkehncDpdBIYGMiQIUNwuVzMnDmTFStWGAVXwR02MDAQk8lkXCdf0JgxY3jzzTf517/+xWeffcbLL7/MDz/8QG5uLt7e3vTt25eYmBjmz5/PzJkzjaGv+fn5xuorx44dw2azUb169cvif/zxx/z73/9m9uzZ7Ny5k2+//ZYZM2Zw8uTJm47vdDo5ffo0AHXq1GHixInMnTuXCxcuABhD5Hv06MHOnTsZP348CxYsADBir1u3jvT0dGrWrFlkB3f48OFs27aNAQMG0KRJE6PQdW/fAQMGEBcXx9q1a5kwYYLxhnY6nUYxt2/fPry9vQkLCysUe9SoUaxfv57u3bvz0ksvUalSJaNY9vb2pk+fPtSpU+eGt/3w4cPZtGkT99xzD3Bx2enMzEzg4ipAN/u3HTlyJDNmzODYsWPUqVOH7du3M336dH755Re8vLxuOv5HH33EiBEj+O9//8vEiRN55513GDVqFPPnz8dkMvHQQw8RFRV1w/Gv5NJ5AywWCwMGDKB69eqsWLGC8ePH43Q6C+0HDocDm81GfHw8ubm5nDx58orxLz2YmM1mOnfuTMOGDdm2bRv//Oc/jbNF7oOew+HAarXSpk0bAOOM76XcRb47bknGdrlcZGZmcvbs2VKJP3v27EJf9tzbuKTiv/baa8ycObPQ9gFo0aIFbdq04cCBA7z77rvk5ubi5eVlfBbk5+djsVho2bIlwBXPCk+aNInPP/+cRYsWFXqOksh/1KhRxuSLpbHt09PTSUlJYeXKlcY1+iUZHyh0Xb87/06dOt10/GXLljF16lQ+//xzli9fXuiLTs+ePenQoQMHDx7kiy++MPavS8/4wcVjwvUuxe7JfHx88PPzw2q1GseqsqZap2xqndKsc0C1ztXil1WdA6Vb65RmneN+bMHYqnUu8uQ6B1TreEqtc3tUDHJD3nnnHY4dO8bQoUNp2LChsTMUvLayf//+TJ48mSlTppCTk0PHjh0LFYo7d+7EYrEYS8G6z/KNGDGC1NRUmjdvTnx8PIcPHyYpKYkJEyZQrVo1WrduTWRkJA8++CBTpkwxrqnu168f9erVAy4WK+vWrSMsLOyy5U7ff/99tmzZQvv27enRowcnTpxg7ty5rFixgvvuu4+77rqLyMhIBg4ceEPxzWYzTZo0Yd26ddx9992cO3eOiRMnApCYmIjVaqVy5cp07dqVihUrMnnyZL788kvWr19PvXr1OHHiBBs3bsTpdNK/f398fHwKxR8+fDhbtmxh0KBBxmz7BZ8bLhayQ4cOBWDNmjWkp6czZMgQGjVqhJeXF6tXr2blypWEhoYSHBxs/P5PP/1ESkoKPXr0IDEx0VilAS6eVfTx8SE6OpqhQ4cybty469427twHDx7M/fffj8ViITk5md27dxt53Mzf9rPPPmPLli306NHDWGVi8eLFjBkzhmPHjlG3bl0iIyMZNGgQEydOvO74H374IevXrychIYHOnTuTmZlJamoqCxcuZOfOnWRlZfHwww/z2GOPMXbs2OuOXxR3obh//35iYmKIioqie/fumEwm/P39+etf/8rbb7/NnDlzcDgcDB06FC8vL+OADBcPWhUqVCAoKOiy+Fu2bKFBgwZGkebeh5xOJzabjWeffZZ//OMfrFq1iry8PJ577jmjcHTH3717N15eXlSuXLlQbPff9pdffqFBgwY0btyYpk2bAtx0bLh4tjY1NZWtW7cSFhZG8+bNGTx4cInFf/PNN9m8eTNt2rShd+/e1KpVyzjolkT8d999l23bttG0aVNj8l/3trdarQwZMoRTp06RkpLCa6+9xvPPP0+VKlUAjIkQ3delFxV/5MiRbN68mZiYGOrXrw/871KUm81/+PDhpKam4uPjw7Zt2zh58iSBgYFGgX+z22bs2LGsXLnS+IIQExPDiy++iM1mK5H4M2fOZNeuXezdu5c6depQq1Ytunfvjtlsxs/Pj2eeeYbXXnvthuJ/8MEHpKSkGF9iFi1aZHwmN2/enAoVKvDb3/6W/Px8kpOT+fDDD3niiSeML89wceLqw4cP06RJE6MIK++jlm5HqnXKptYpzToHVOtcLX5Z1DlQurVOadY5oFrnavE9uc4B1TpXi3+71TomV3meNKAcGzt2rHGtZXR0NAMHDjQKroI7RE5ODitWrGDy5MmcO3eOVq1a0a9fP3x9fdmyZQszZ87kwoULvPbaa8bOOnHiRBYuXEjPnj3p1KmTMWHnsmXL+Ne//kXdunUZNmwYNpsNl8vFoUOHmDFjBsuWLQOgXr165Ofnc+TIEVwuF6+//nqh+Rm++eYbkpKSjPjuYmL16tWMGjWKp556yrjeE+DIkSNMnjyZlStXFiu+2+bNm3n33Xd5//33ycrKYty4cRw8eJBBgwbRsWNH7HY7ubm55ObmcuTIEcaNG8eRI0c4ffo0fn5+hIWF8fvf//6y2CNGjCAtLY2HH36Y+Ph4/P39cTovzuS/bt068vLysNvthIeHU6VKFfLy8vj222+NFVeqVauGt7c3x44dw8fHh1dffZUaNWoY8T/++GPS0tIYMWIE/v7+nD9/noyMDBYtWsSRI0eoVKkSsbGxdOzYkYyMDKZMmcLy5cuLtW3eeecdNm/ezKBBg2jXrh12u51Vq1YxevRoGjVqxCuvvGI89kb+tlu2bGHEiBHEx8czaNAg/P39AcjMzOQf//gHDzzwAG3btsVsNuPt7c3Ro0eZOHFisf+2q1at4l//+hcdOnTggQceMOK7i/UffvgBgC5duvDYY49dd/yiuL94BAcHY7PZSE9P5/z588TExPDoo48SHh6Oj48Pu3fvZuTIkRw/fpymTZvyxBNPEBgYiMViYf369Xz77bfY7XZefPHFQpPgug8KTzzxhLHfX1p0mc1mTp48ydtvv82BAweIjIxk6NChhIWFUbFiRdauXcukSZOwWCy88sorxntq9OjRrFq1Cm9vb/Lz83E6nQQHB9O/f3/uvffem4rtzj0tLY0qVaoQHh7Oli1bOH36NI888gjdu3c3CpgbjX/+/Hl+//vfk5eXR35+PnFxcTzwwANEREQA3HT8gl882rVrV+iLE1w8U+fl5UVOTg4ff/wx69evp3r16vTp04d69eoRHBzMqlWrmDZtGk6nk1dffbXQMsuffPIJK1asoG/fviQkJBjF/aUH7ZMnT/LWW29x8ODBYufvzr1fv36kp6ezfPlynnzySdq1a1co9xvdNiNGjGDbtm3UrVuXFi1asGbNGtLS0ujQoQO/+93vCm37683dHX/Dhg0EBQVhs9nIyMggLy+P+vXrM2jQIGrUqIGvr+8Nxf/0009ZtmwZ3bp1o02bNlgsFpYsWcKiRYs4e/Ysffv2pVOnTsYXn4LH03vvvZfatWtz/Phx1q5dy5kzZ3jjjTduq8vB7iSqdcqm1intOgdU61wpflnUOe6/eWnVOqVZ54BqneLUCp5Y5xTMX7WOZ9Q6aix5oFWrVvHJJ59QpUoVmjRpQlJSEqGhoQwaNKjIgis3N5edO3fy1VdfkZ6ejo+Pj3HGwG63M2zYMOMs3p49e/jggw8ICwvjqaeeIiAgoFB3+x//+AcnTpzg3XffvezDKSkpiZSUFHbs2IG/vz81a9bkgQceKLSTbty4kX//+980a9aMhx9+uNCH0+zZs5kwYQJ/+MMfOHjwIH5+ftSsWZNmzZoBFyd3TEtLu2r8gnJycvjrX/9Kv3796NSpE6tXr2bq1KkcOHCAxx57jAYNGjBy5Ejuv/9+unTpQk5ODufPn2f//v1Ur14df39/7HZ7oZjjxo1jzpw5xMTE8NJLL2G1WsnJyWHz5s3MmTOHX375xXhsdHQ0nTp1Mj781qxZw7Zt20hLS8Nut1OjRg26d+9OSEgIcPHAev78eV599VUqV67MsGHDOHPmDCtXrmTq1KmcOHGCChUqGEPc77nnHn73u9/h4+PDggUL2LRp01W3zYgRI1i/fj1DhgwhISHB+Pvl5OTwxhtvsH//fv7yl78QFxdX6IBf3L8twIIFC/jyyy955513iIyMNPbDn3/+mX//+9/cd999HDp0CJfLRaNGjRg0aJDxe9fKH2Dq1KlMmzaN999/n/DwcOOAApCWlsZ7771nDIMeOHAgffv2BS528FNTU4u977hNnDiRWbNm0b9/fzp37oyfnx9ZWVlMmDCBVatWERwczCOPPELjxo3x8fEhMzOTf/7zn+zatQu73U5YWBgWi8WY3+DSAu/rr7/mxx9/BKBSpUo8/PDDtG3b1tgf3H8D9+vMzs7miy++YN26dZhMpkIHqkuL93/+85+sX7+ejh070rVrV86fP8+mTZuYMGEC9erV4/nnn8dqtRrv7+uJDRfPqG7YsIHu3buTmJiIv78/e/bs4fXXX6dly5b86U9/KvQ6rje+0+kkPz+fYcOGER0djZeXF4sWLbpiwXW98Yv64lHwuS8dDnzmzBmmTZvGypUrOX78ODabDbvdzsmTJ/H19eXvf/97ofi7du3i3XffpXXr1gwcOJCKFSsa74f09HQsFgshISHGbdeTf8GRBImJiWzZsoXhw4cTHR3NX/7yF6OIuJH9Bi7OY7BgwQJ69epFhw4d8Pf359SpU7z66qvUrFmTv/zlL4W2zfXG/+KLL0hOTqZnz57cf//9+Pn5kZGRwciRIzl06BC1atWiV69eNGnSBD8/v+uKf/DgQd5++23q1avHE088Ueg4tWbNGmbPns2OHTuMZXYrVaoEXBw9sXz5crZu3WqcxQ0PD+cPf/jDZV+I5dZQrVM2tU5p1jmgWuda+d/qOgdKt9YpzToHVOtcLb4n1zmgWscTax1dCudhcnNzSU5OxuFw8Oc//5mwsDB8fX2ZOXMmEydOLLLg8vHxoUGDBrz99tssX76c/fv3k5OTQ0REBPHx8VStWtWIn56ezqlTp3jyyScJCAjA5XJhsViMN21gYCB79uzhxIkTxk7s/nDq0KEDHTp0ICcnB7vdTl5enjGUz61SpUr4+PjQo0ePQoXW1q1bSUpKIj8/n7lz53L8+HFOnjyJxWKhf//+9O7dm06dOtGpU6erxi/I19eXihUrsm3bNjp16kSrVq2Ai0N9v/rqK/z8/Dh37hze3t64XC7sdjt2u90Y/lmUDh06sGXLFnbs2MGkSZMYMmQIKSkpfPnll/j7+9OtWzcCAgI4ePAgy5YtIyMjA5fLRUJCAnFxccTFxZGbm4uPj0+hYgEuDi232Wz4+fkZwzGzs7OZNGkS1atX55VXXqFChQpkZWXx1Vdf8fPPP+NyuXj66afp3LkznTp14syZM0Vum/Pnz1O7dm0aNmxonLFwD3e02+307duXf/7zn2zdupW4uLhCE++ZTKZr/m3d+1pWVhYA27dvJzIyEpPJxObNm42i4uzZs/j5+bF79252795NVlYWzzzzDJ07d6Zjx46cPXv2qvHT09NxOp0cP36csLAwYwi2l5cX9evXp3LlyjRu3JikpCR+/PFHYmNjqV+/Ph07dqRjx47F3nfg4n69bds2Y/I7X19fHA4HVatW5fHHHyciIoLZs2czduxYHnvsMRo3bkxwcDAvv/wyycnJpKWlsWvXLgIDA2ncuDH9+/cvVOCtWrWKn376iaioKFq0aMHkyZP5+uuvAYwzne73lnsyP39/f/74xz+SlpbG2rVr2b17N97e3tx999306NHDmEdh3rx5pKWl0bVrV7p3724UE4GBgWzbto3Vq1ezZ88e6tevbyzRXdzYADNmzGDTpk107dqVrl27GvHDw8Ox2+1G0eve1+HisqZPPvkkmzZtumZ89/vBbDYTHR3NoUOHePLJJ40vH4BRcFksFnJzc/H39+cPf/gDmzdvvmb8d999l5SUFB5++GHjEo/c3Fyys7NZtGgRR48epUKFCoSGhhYarvzggw9y9913s2LFCvbu3YvJZKJVq1Z07Nix0BcngAMHDnD69GnjEo9z586xadMm5s+fz+bNmzGbzdSpU4euXbsSGxtLYGBgsbbPu+++axRaCQkJWCwWmjRpQlxcHKmpqRw9epSgoKBCk0Bez982JyeHdevWERkZSadOnYy/bUBAACEhIeTm5pKRkcHhw4epW7cuPj4+1/W3zczMZO3atTRs2JD777+fihUr4nA4CA0NZdiwYfzf//0f+/btY/LkyXh7e3PXXXddV/4nTpzgxIkTREZGYrPZjOHhZrOZuLg47HY706ZNY/bs2VSsWJHevXsD0L59e1q3bs3Ro0fJzMykcuXKVKtWrdCZR7l1VOuUXa1TmnUOqNa5Uq1TFnUOlG6tU5p1DqjWuVat4Kl1jjt/1TqeV+uoseRhfHx8eOCBB2jYsKHRWezUqRMmk4kffvihyIILLh6wrFYrHTp0uGr8Bg0aMGDAAKKiogqdCXT/393xdP/scrku63i7i7CiJv2sVasWH3zwgfEmMJvNbNu2jW+++YZjx47x5JNP0rBhQ+x2O5s2bWL06NHMmTOHunXrUr9+fZxOpzG09mqTiro/aKKiokhPTzcKi1atWnHmzBm++OILzpw5Q8uWLYmPjy/2taRhYWE899xzjBo1irlz53Ls2DH27NlDeHg4L730Er6+vsDFMwsNGzbks88+Y/ny5bRo0YKKFSvidDqNA/2l283lcpGfn0/lypVZvnw5a9euZdu2bXh5eRnD8QGqVq3KM888w6hRo1i2bBlNmzblnnvuweVyXXHbWK1W40PFXVwWXD0gPDycihUrMnfuXFq3bk1sbGyhv7F7Ms0rxXc/tlWrVvz44498//33xiolq1ev5sSJE/ztb3+jWbNmmM1mDhw4wL///W+WL19uLFFrMpmuGb9x48asXLmSTZs20bBhQ2OCPYfDwfz588nOzqZ79+7UqFGDr776il9++cW43vtq+V/K6XRy6tQp9u7dS/369Y1Cy2KxGAVqYmIiPj4+TJs2jW+//ZYXX3zRGELuLkIyMzMJCAgwvvS4nT17lrS0NM6fP89jjz1GdHQ0drudL7/88opFl/vfVqvVKN6zs7Mve7+dPHmS5ORk/P39jYOlO4Z7ZZfVq1cbE74CxY4NkJGRwYYNGwgLC6Nz586FzoCtX7+eEydO4HA4eP311zl16hTVq1dnyJAhVKtWDV9f32vGv1RwcDApKSlUq1aNoUOHYjabWb58OS6Xi8cff5wKFSowbtw4evbsSWho6DXjb968mb179xpzj9hsNi5cuMCKFSv44YcfOHLkSKElmzdu3Mif//xnAgIC8PHxISoqiqioKHJzc424RU12mJ2djdlspkKFCjidTlatWsU333xDhQoVaNmyJSdOnGDnzp18/vnn9OjRgw4dOlCxYsWr5v/pp5+yceNGhg4danxpck9U3KBBA9asWcP06dP5v//7v0KfM8X92wIcPXqUQ4cOGYWJW0pKClu3biUoKIi33nqLrKwsqlevTvfu3YmLi8Pf379Y8bOysjh58iTNmzc3Ci2LxUJeXh5Vq1alQYMG7Nq1i19//ZXJkycTGxtLxYoVi52/zWYzzlwW/Nu4P8fcx5EzZ84wceJEoqKiaNSoEXDxC3rt2rWpXbv2FfdHuTVU65RdrVOadY57W6rWuTz+ra5zoHRrndKsc0C1ztXie3KdA6p1PLnWuXOWOSlHoqOjSUxMNH4ODAykY8eO9OrVi/T0dCZOnMjmzZuNN6F7FQ/3KgYFC7BLVa5c2ThjUbAAce+w7gOG+wyU+zGHDh0yVtpwP/ZKBYz7TVJw+OuePXv461//Srt27ahSpQpWq5WWLVvSp08fsrOzjZVczGbzZQVgUdyxa9WqxYEDBzh69KhxffV3332Ht7c3lSpVYu3atSQlJRnbpjhCQkJ4/vnniYiIYO3atQQEBPDiiy/i6+trzOJvsViIj4+nadOmbNq0yZih/2r5m0wmLBYLnTt3xmKxsHjxYtLT0wkJCaFChQrGmRGXy0XNmjV56KGHAIq9bby9vY0P4EvvDw8Pp2vXrsDFAxL8b+WOgjGvte2jo6N54oknyM3NZc6cOSxbtoycnBwee+wxWrRoUSj/Rx55xDg7V9z4zZo1o2bNmsycOZOpU6caKwUlJSWxYMECqlevTsWKFWnUqBGVKlVixYoVOByOQu+Fq8V3M5vNVKpUicjISNLT042DqzuO+8tLQkICXbp04dChQ/z3v/81ft/93qpatSoVKlS4bPJ3q9VKnTp1eOqpp4iOjsbpdNK5c2eeeOIJsrOz+frrr425HtwHy4JfbNx/G39/fywWS6EDTn5+PqdPn6ZDhw5Urlz5spVb3GecTpw4cdlrvlZsuLjsbHR0NI8//nihyTm3bt3K7Nmzyc/Px8fHh+rVq+Pn58e6desYPXq0cWba/R65UvxLNW7cmJycHPbv309gYCAPPvggbdq0Ye3atXz66af86U9/IjU1lczMTJxO5zXzj4mJMebE+Oabb1i3bh1r165l3LhxVKhQgb/97W+MGDGC4cOH07BhQ7Zu3cpHH31k/L47vvsymyutoOHj42Mc1M1mM1OnTiU0NJTRo0fz17/+lTfeeIPf//73+Pv78+OPPxrLYF9t+wwePJgnn3yS++67zzgT796v27VrR82aNdm1axcHDhwolGvBguNa297Pzw+bzcbGjRuN1Uc2bNjAtGnTcDgctG3blt69e9OjRw/y8vKYPHmyMalncf627vfejh07jJWjHA6H8dn066+/EhkZSXx8PAcPHuTbb781fvda+btcLipWrEilSpWYP38+u3fvLvS87t9v2LCh8Xk3adIkozDTxNy3F9U6ZVfrlFad475Ntc6V49+qOse9LUur1inNOgdU61wtf0+uc0C1jifXOhqx5KHcO5n7zeYuuADjbN7AgQONFRGWLl3K7t276dq1K9WqVQOuvGNd+iUYKPSB7XQ6Cy2ZmJKSwvjx42nUqBEPPfRQsc6SFNSwYUPGjBlDQECA8TzuYb/uoetFLRFcHNWqVTPeyKdPn+aFF14gLy+PoUOHYrfbmTFjBuPGjcNkMhlvvuIICQnhueeeY8yYMdx77734+fkZZ5Xg4t/Fx8fH2NZXW37yUlFRUdx3330kJSUBF1ctccctWAC5h7G7z5bdKPc2b9asGfPnz2fx4sUkJiZeNr9UcbVr145GjRpx6tQprFYro0aNIioqqtDzFVzZ4Hq2TWBgIM8++yzvv/8+06ZNY+bMmfj6+pKdnU1wcDDPPfccfn5++Pn5ER4eTmZm5lUPilfiPutbs2ZNtm/fzqRJk4x9u+BZTV9fX+O67w0bNrBhwwaaNWt2zcLObDZz3333GQW0Oz/3e/jzzz+/7Izepb9f1PXxcPEL0wsvvGC8jy9dQenS4a6XxrlabPd+/cgjjxR63JEjR/jhhx/YtWsXf/nLX2jZsiUmk4mcnBy+/fZbFi9ezOTJk/n9739/2WUR1xIYGEh+fj7p6elERkZSuXJl/vjHP5Kbm8v69euxWCx07NjR+Ky72rSBLtfFJazbtm2LyWRiypQpfPrpp7hcLsLDw/nHP/5R6PPr6aefZvTo0WzevJkFCxbQuXPnYu9LdevWxWw2M378eDp37kx+fj6PPfZYobke4uPjyc/PZ8yYMcyZM8dYSako+fn5VKxYkXb/fy6TgqMs3Gfp2rdvz9ixY1mxYgVRUVGX5VqcYiIoKIiGDRuyZs0aY/LVjIwMzp8/z4svvshdd90FXBytEBkZyX/+8x/mzJlDixYtivXZHxERQeXKlUlNTWXp0qXce++9xvtq2bJlZGVl0a1bN2P53W3btnHmzBn8/Pyumb/JZKJatWokJCTw3XffMWvWLAYNGlTomOfebvfeey/r169n06ZNnDt3zpggV24vqnWKpzRqndKsc0C1zpXcqjoHSrfWKc06B1TrXKnW8eQ6B1TreHqtoxFLHq7gznfp2bzJkyezadMmfvrpJ77++mvWrFlTrOutr8blcuFwOIwPhZSUFCZNmkR6ejr33XffdRdabu4d3T2k3R1/27Zt+Pr60qBBgxuKGxMTQ0BAADNnzuSFF14gNzeXgQMH0r59e+Li4ujRowd169alcePG1x27evXqPPvss8aHz6XDqeHiUFp/f/9ircjh5u3tTb9+/WjatCkWi4U9e/bw/fffAxQqHrZt24bFYqFOnTrXnXtB7njh4eHUq1ePY8eOkZSUZBQcN6Jy5cpERkZy/Phxjhw5wpkzZ4CL28j9t924caMxXPN6hIeH8/e//51+/fpRvXp1oqKi6NGjB2+++Wah5YxPnz5NpUqVrqvYKjjXgtlspl+/flSpUoUlS5awdOnSQmfxTCaTcU33Aw88gMlk4tChQ8WKDxe3e1FfbDp27FjojN7SpUuN++bNm8eHH35o/P6VYtesWfOya+Hd3AX7pcObN2/ebKzoc6VCy71fX1rA+fn5ERAQwGuvvUZcXJzxPHa7nR49euDr63vNLwVF7WtOp5OKFStSo0YN9u7dC2AM/d21a5dxBujw4cMcOXIEuHJBUbBItlgs3H333fTv399YTvbpp582ztS6nzswMJDevXtjMpk4fvz4deVfs2ZNYmNjSUtLY8aMGZw5c8Z4jLu48PHxoVWrVgQHB3P06FHjfVJU7EsLsaJGWTRo0AAfHx+Sk5PZs2fPVfMtKnen04mPjw9PPvkk999/P0FBQfj5+REZGUm3bt246667jC/cFouFZs2aERoayr59+8jIyChWfD8/PwYOHIjD4TDm29i2bRvjx49n0qRJWK1WYy6Gu+++m6NHjxojIS61Z88eli1bxk8//cSuXbuM23v27Enz5s1ZtWoV8+bNK7TvmUwm8vLyAIiNjeXMmTPGhLNy+1Ktc3WlVeuUVp0DqnWupjTrHCjdWqc065xL46vWKTp3T6xz3PFV61zOk2odjVgqZwIDA2nfvj1ms5kZM2bwxRdfcPLkScxmM8OGDSs0nPN6FPxwtVqtmM1mtm7dysSJE8nIyOC9994zVlu5EQW70e7nWr16NSkpKcTExFw22d318Pb2Zt68eQQHBzNo0CA6depk3BcfH0+zZs2wWq03FNu9rKZbwfxXrFjBjh07aNy48XWfEatcuTKPPfYY//3vf0lLS2Pu3Lk4nU569uyJ0+lk7dq1LFq0iKpVqxrXzN4Md959+/YlLS2NzZs306tXr5uOa7PZcDgcLF26lGrVqhlnZdeuXcvChQsJDg42Ds7Xo0qVKgwYMIDevXsbQ3ELFghLly4lMzOT+++/H7h82dNL7du3j/Dw8EJfFtwH3CFDhvDZZ58xc+ZMfHx8iI+PLzTcGi7uByaT6Ypnm4uKfzUFz+h9++23eHl5YTJdnFvk+PHjPPTQQ0ZxWTD2tV4n/O+9XPBMfGpqKt9++y1nz56ldevWhfbra+Xucrnw9/fnd7/7nVGwFJzzxD3fw5W+6F0tvvva/erVq5OWlgZcPGv9yiuv4HA46NevHwcPHmTFihWcO3eORx999LJVLS6NX7Docp8ldTgcxmu+tNB0z9Nx7ty5YufvLlqeeOIJ3nzzTXbv3o3NZuPUqVPG/e5c7Ha7cQnBpQX49e43NWvWpEePHkybNo09e/YQGRl51TO+l8Z379c2m40hQ4YYQ89fe+21Iid2tFqtWCwWqlSpctln4ZXiAzRv3pwLFy4wdepU5s2bZxT5tWrV4q9//asxQsF9pt99xrugMWPGsHLlykKX9jz00EPcf//9eHt707dvX86cOcP8+fPJz88nMTGR0NBQ8vPzjX3x2LFj2Gy2mzq+SNlQrXO50qp1SqvOAdU6V1PSdQ6Ubq1TmnXOpfFV6xSudTy5zrnWtimKap3bt9ZRY6mccTqdBAUF0bFjR/bu3cuaNWvw8/Pj9ddfv6llBAte3+pwOFi/fr2xGsgbb7xxU4VWwdzdb8Zly5YxY8YM8vPzjaHcN8Jms/HUU08xduxY2rRpY0z+WfBa9BttKl0t/+XLlzN9+nS8vb0ZNGjQDT1HtWrV+N3vfsfMmTNZuHAhkydP5ueff8bpdHLixAl8fX15+eWXb7iALsidt7+/P7Vr1yYtLY2ff/6Ze+6556bihoeHExcXx5IlS8jKyiIyMpJTp06RkpKC0+nktddeu+oqfNdScDUVt1WrVjF79mxsNhsJCQnA1YfFvvPOOxw7doyhQ4caE8HC/7ZJo0aN6N+/P5MnT2bKlCnk5OTQsWPHQgfAnTt3YrFYjPdBwaLnSvGvpWPHjpjNZj777DO++uorLly4gLe3N++//75RbN1IbPfZIPdjU1JSmDhxIpmZmbzxxhuFDprFie9+nQUPqAXnSfjpp5/Izc0tNHy7uNvG/Z4KDQ3l8OHDHDp0iDfffJPc3FwGDRpEx44dOX36NOfOnWPbtm2XLQt+pfiXntEruKKLO0f3Yzds2IDFYjFGEhQnf/c2CA0N5YknnuDLL7/k2LFjTJ48mVq1ahV6z65YsYKjR4+SkJBQ6Ezdje43sbGxxpftZs2aXfHz4Vr7fcGi/Ndff2X//v2XFW7Lly/nwIED3H333ZcVi0XFd287u91O586dad68OWvXrsXpdFKtWjViYmIKfdbv3LkTf3//y85IjxgxgtTUVJo3b058fDyHDx8mKSmJCRMmUK1aNVq3bk1kZCQPPvggU6ZMYf78+Rw+fJh+/fpRr149ANatW8e6desICwsrslCU25tqncJuVa1T0nUOqNa5lpKoc6B0a53SrHOulvvV3Cm1jifXOcXd9kVRrXN71jpqLJUzBT9At2/fjs1m44033rjuIcqXcr9J8vPzcTgcTJ8+nby8PN58800iIiJKInXMZjN5eXl88803bNy4kfz8fF599dVCS7TfiJiYGF544QVjxYqrdbVvhjv/8ePHs379ehwOB6+88soVh+kWR1BQEA8//DCtW7dmzpw5ZGVl4eXlRZMmTejatetNxS6Kv78/99xzD7/88kuhuQJulPsadZfLZay04F6N4Le//S1hYWE3Fd994PPy8iI3N5exY8eSmpqKw+Hg5ZdfLrS8dFHGjh1LSkoKAJMnTwa47MBgs9m455578PLyYvLkyYwbN44dO3bQr18/fH192bJlCz/++COBgYE0adKkUF5Xi3817n20ffv2bN++nSVLllz2pelGY7uHxprNF1cpmjBhAkePHuXNN98s9KXpZnOHiwe0xYsXU6NGDWNC0+vZNu5/x8TEGCuAWK1WHnzwQTp06GAcuJ944glMJlOhwuJa8QvuO+5Vji7Nf+3ataxcuZLIyEhiYmKKnX/Boqxx48ZG0bVv3z7effddEhMTqVmzJlu3bmXx4sVYrVa6du1qPO+Nbnv388XFxbFq1Sq2b99OfHz8ZY+5nvheXl74+/uzevVqpk2bRo8ePfD29mb58uX88MMP+Pn50bdv30LFVnG2jdPpNCZQLsq6devYsmULsbGxhb6wTpw4ke3bt9O/f386depknCUODg7mX//6F3PmzKFx48bYbDZiY2N5/PHHmTFjBsuWLWPTpk3Uq1eP/Px8jhw5gsvl4m9/+1uJLbMrt45qncvdilqnNOocUK1zNTdb50Dp1jqlWedcK/eruRNqHU+uc25m27ufU7XO7VfrmFw3emGx3LZSUlL49NNPOXPmDMOHD7+ps3eXmjVrFt9++y1Wq5W33nqrRGOfP3+e6dOns2DBAmJjYxk6dGiJD9srraYSwIULF5g+fTqzZ88mJiaG3/72tzddKBZUMPfiDAO+UefPnyc3N7dEJ3g7e/YsBw4cYN++fdSuXZvQ0NAS/4A7duwYo0ePpnLlygwaNOia+86qVav45JNPqFKlCk2aNCEpKYnQ0NDLlrB2b+fc3Fx27tzJV199RXp6urFahvv6+mHDhhUqVooT/1oWL17MlClTOH/+PG+++abxpelmYu/Zs4dhw4bRsmVLjh07xpEjR3jjjTcKfWkqidwXLVrE3LlzOXHixGVf+K43/qFDhxgzZgxnz56lffv2JCYmXvWLU0nkn5SUxI8//njD+V/6Hj127BiffPIJO3bsKFTwuifIde87N5O7e3ts3bqV119/nbi4OJ5//vlCeVxPfPdr2L59Ox9++CEnTpwwviAdPXoUu93Oyy+/fN37fVGfXwVv++mnn5g3bx7Hjx/njTfeMD5H9+zZwwcffEBYWBhPPfUUAQEBxgShAP/4xz84ceIE77777mWj15KSkkhJSWHHjh34+/tTs2ZNHnjggRL9jJZbS7XOlZVWrVPadQ6o1rma661zoHRrndKsc242fnmvdTy5zrnZ/FXr3L61jkYslUOhoaHUqlWLwYMHl2gxBNCyZUu2bdvG4MGDb3q0yaWsVivdu3fnrrvuokaNGjc8JPxqSqupBFChQgW6du1KbGwsUVFRJT7zfmkVV5eyWq0ldnmgm7ujHhsbW6JxC6pSpQovv/wyJpOp0JmZouTm5pKcnIzD4eDPf/4zYWFh+Pr6MnPmTCZOnFjkgcHHx4cGDRrw9ttvs3z5cvbv309OTg4RERHEx8cXOmt4PfGvZMuWLXz55Zd4eXkVKrZuNra7SFy7di1Wq/WyQutm42dmZvLZZ59x6NAhfH19ef311wsVKzey7cPCwhg6dCjHjh0zVmG50henksj/888/58CBA1it1hLJ3+l0UqVKFf7v//6P3bt3s2vXLs6ePUt4eDgNGjSgUqVKJZK7+/aQkBDq169Pv379Cn1uXG989+/WrVuXP//5z3z99ddkZmZit9u555576N27d6GRBDeTv8lk4sKFC3z33XfMnTuXKlWqXDaKIz09nVOnTvHkk08SEBBgDPN3r6wVGBjInj17OHHihFFsufeTDh060KFDB3JycrDb7eTl5d30BM9StlTrXFlp1TqlXeeAap2ruZ46B0q31inNOqck4pfnWseT65ySyF+1zu1b62jEUjlVsLPpSbHLg9I8wyYlZ9euXWzfvp3u3bsDcPLkSRYtWsQPP/xAeHj4ZQdOKHxNeknGd8cuuN9kZGQwbdo0unXrRq1atUos9pEjR/jzn/+M2Wzm/fffL/LSkRuN73K5OHjwIB999BF169ald+/eheZJuJH4BecIcbvW2fibzf+DDz6gdu3al00eeiPxCx5ii/O5cLP7jXvbXOlz+kZyL3gm++TJk/j5+eHj41NksXIz+TscDlauXMmJEydo06bNZZd4/PrrryxfvpxOnTphtVovmwh57NixJCUl8d577xEaGlrkZ3HBCVf1Oe35VOuUDb1/PEdp1jqlWefcbPzyXut4cp1zM/lfum1U69xetY4aSyJyx7r0gHStAwNcHOpus9kKHaSv9KF9I/HPnTuHr68vLpeL/Pz8K36xuZnc165dS40aNa46b8XN5J6Tk4O3tzcVKlQolfjFcTPxT506hbe392VDjEsq/rX2ndKMfaPxz5w5Y1zjfy03s286nc4ilxx2u3QC0oImTJjA3LlzGTlyJNWqVTNuP3ToEN7e3kUWzyIi5V1p1jqlWefcbO7lvdbx5Drndo2vWufmlN51QSIitzn3AcHdXw8MDKRjx4706tWL9PR0Jk6caCz9CheX9508eTJHjx41DmRXOxNwI/EnTZpERkYGJpPpqsXWjcbOzMykZcuW15wM9Ubiu5fkdi8tW9LxJ02axNGjR68atyTyDwgIuGqxdbP5X2vfKc3YNxp/ypQppbrtJ0+eTEZGBmaz+YqFFlBkoeVe1tjpdOJ0OgstL52SksKHH37IvHnzilzKV0SkvCvNWqc065ybiX8n1DqeXOfcrvFV69wcjfEVkTtewQOT+8AA8MMPPzB58mS8vLzIyspi/Pjx+Pj40KtXr9sm/vXG7t27d6nmXtrxS3vb3075327b5naLfymXy4XD4TCKtZSUFCZNmkR6ejp/+tOfdFmTiNzR7qTj1Z1U63hy7op//W7nWkeXwomIFOH48eMkJyczY8YMgoKCOHnyJGazmddff73QyhC3Y3xPzl3xy2/upR1//PjxLFiwgPfee4/jx4/z9ddfk5GRcdny0iIicpGOV+UzvifnrvhXdzvXOroUTkTkEk6nk6CgIDp27EjTpk3JyMjAy8uLN954o0Q+tEszvifnrvjlN/fSjF9w4k2Hw8H69ev55ptvyMjIKLHcRUTKGx2vymd8T85d8a/ME2odjQsXEbmEe6K9lJQUtm/fjs1m44033ihyVZHbLb4n5674ZRfbk+O7J+/Mz8/H4XAwffp08vLyePPNNwstLy0iIv+j41X5jO/JuSv+lXlCraPGkohIEVJSUpgwYQLnz59n+PDhJXbAuRXxPTl3xS+72J4a313EBQYGAhdXann77bepUaPGTccWESnPdLwqn/E9OXfFL5on1DpqLImIFCE0NJRatWoxePDgUvnQLs34npy74pddbE+P37JlS7Zt28bgwYMJCwsr0dgiIuWRjlflM74n5674V3c71zqavFtE5AocDkeprq5QmvE9OXfFL7vYnh6/tHMXESlvPPkzWfHLJrbil23827XWUWNJRERERERERERuiFaFExERERERERGRG6LGkoiIiIiIiIiI3BA1lkRERERERERE5IaosSQiIiIiIiIiIjdEjSUREREREREREbkhaiyJiIiIiIiIiMgNUWNJRO54ycnJDBgwgOTk5LJO5aYMGDCA1157razTEBERkduMah0RKU2Wsk5ARORq/vnPf7J8+XKeeeYZ2rZte8XHnT17lt/97ndYLBbGjBmDj4/PLcyyZCUnJ/Pxxx8Xus1kMuHr60vNmjVJSEggISGhjLITERGRkqRa5yLVOiKeS40lEbmttW/fnuXLl7N48eKrFlvLly8nNzeX++67z6MLrYIaNWpETEwMAE6nk2PHjrFu3To++eQT0tPTeeSRRwo9fvTo0VSoUKEsUhUREZEbpFpHtY6Ip1NjSURuaw0bNiQ4OJjNmzdz7NgxqlSpUuTjFi9eDFwszsqLRo0a0bt370K3ZWZm8pe//IV58+YxcODAQoVlWFjYLc5QREREbpZqnd6FblOtI+J51FgSkduayWSiXbt2TJkyhcWLF9O/f//LHnPw4EF27dpFREQEUVFRnD17lgULFrBx40YyMjLIzs7G39+fRo0a8cADDxASEnLN583MzORPf/oT9913H0899dRl9w8YMID69etfdp3/uXPnmDVrFqtWreLo0aN4e3tTp04d+vXrR2xs7A1vB7fg4GBCQ0PZu3cv586dK1RsFZXTf/7zH5YsWcK///1v1q1bx4IFC8jMzCQwMJCEhAT69euH2fy/6facTieLFy9m0aJFZGRkkJubS8WKFalVqxY9evSgQYMGN/0aRERE5H9U6xSmWkfE82jybhG57bVr1w6TyURycjIul+uy+91n8NzX4qenpzNlyhR8fHxo2bIl3bp1IzIykmXLlvHSSy+RlZVVKnnm5OTwyiuvMHXqVPz8/OjUqROtWrViz549vP7666xZs+amnyMrK4vDhw9TuXJlAgICiv1733zzDdOmTaNu3bp06tQJgO+++47JkycXetyECRP47LPPyMnJoW3btnTr1o2GDRuSnp7Opk2bbjp/ERERuZxqnf9RrSPieTRiSURue1WqVKFJkyakpKSwefNmGjVqZNyXn5/Pzz//jLe3N/feey8A4eHhjBkzBrvdXijO5s2befPNN5k2bRp/+MMfSjzPr776ioMHD/L73/+eDh06GLefOnWKF198kTFjxtC0adNiz4uwadMmcnNzgYtn13799VfWrVuH1Wrlj3/843XltnfvXkaOHEmlSpUA6NevH88++yzz5s2jf//+WCwXDwc//fQTlSpVYuTIkZfNYZCTk3NdzykiIiLFo1pHtY6IJ9OIJRHxCO75BNxn7NzWr1/PqVOnaNGihVFc2Wy2ywotuDiHQY0aNUrlbFR2djYrVqygYcOGhQotgICAAHr27El2dvZ1PfemTZuYOnUqU6dOZfr06SxZsoTz58/Tpk0bataseV359evXzyi0APz9/WnRogXnzp3j8OHDhR5rsVgKDRl3K2qbioiISMlQraNaR8RTacSSiHiEFi1a4O/vz5o1azh79iw2mw248kSWW7ZsYc6cOezatYvTp0+Tn59v3Oc+Y1WSdu/ejdPpJC8vjylTplx2f0ZGBgCHDh2iefPmxYr50EMPGRNaOp1OTp48yZo1a/jmm2/YuHEj7733nrEdriUyMvKy2ypXrgzAmTNnjNvi4+NZsGABf/nLX4iPj6dhw4bUrVu33Kw+IyIicrtSraNaR8RTqbEkIh7BYrFw7733Mnv2bJYtW0bnzp05efIkKSkpVKlSpdCQ8ZUrV/Lhhx9itVpp0qQJVatWNYY6L1mypFTmHXAPnd6xYwc7duy44uMuXLhwQ/HNZjNBQUEkJiZy8uRJpk+fzrx58+jbt2+xft/X17fImHCxkHP7zW9+Q3BwMMnJyUyfPp3p06fj7e1NmzZtGDJkCP7+/jeUv4iIiFydah3VOiKeSo0lEfEY7du3Z/bs2fz000907tyZpUuXkp+fT7t27QoNZ/7uu+/w9vbm3XffpXr16oVirFixoljP5Y5X8Oyf29mzZy+7zV3MdO/enSFDhhT7Nd2I6OhoAHbt2lXisb28vOjZsyc9e/bk+PHjbN26leTkZJYuXcqpU6d4+eWXS/w5RURE5CLVOhep1hHxLJpjSUQ8Rnh4OHXq1GHPnj3s37+fxYsXYzKZjBVS3I4ePUpYWNhlhdaJEyc4evRosZ7LPez6xIkTl923d+/ey26Ljo7GZDKxc+fO4r6cG+Yezl3UtIGdIQAAAvdJREFUqjElKSgoiLZt2/LSSy8REhJCWlqaMcGmiIiIlDzVOhep1hHxLGosiYhHcc8v8MUXX3Do0CEaNWpE1apVCz2mSpUqZGRkcPLkSeO23NxcPv/88yLPyhXFZrMRGhrK9u3bjTkDAM6dO8eECRMue3xgYCBt2rRhx44dzJw5s8hCaOfOnTc8PNwtNzeXBQsWAFCvXr2binWpvLy8Ioe2X7hwgfPnz2OxWDCZTCX6nCIiIlKYah3VOiKeRpfCiYhHiY+PZ+zYsUZRcOlElgD3338/X331FS+88AKtWrXC6XSSlpaGy+UiIiKC/fv3F+u5unfvzpgxY3j55Zdp06YNTqeTlJQUoqKiinz8b3/7Ww4fPsy3337L0qVLqVu3LjabjV9//ZU9e/Zw5MgRxowZc9nStldScAlel8tlzLPw66+/UqtWLbp06VKsOMWVm5vL3//+d6pXr05kZCRVqlTh/PnzbNiwgZMnT9KjRw+8vb1L9DlFRESkMNU6qnVEPI0aSyLiUXx9fWnTpg3JycnY7XZatmx52WO6dOmCl5cX8+bNIykpCT8/P5o1a8ZDDz3EqFGjiv1cHTt2JD8/nx9//JGkpCQqVapEu3bt6Nu3Lw899NBlj7fb7bz11lvMmzePFStW8PPPP+NyuQgMDCQiIoJ+/fpRsWLFYj//pk2bCi3ZW6FCBapXr06nTp3o1q1bsYu24qpQoQKDBw9m8+bNbN++nVOnTuHn50doaCiDBg3i7rvvLtHnExERkcup1lGtI+JpTK7SvnBVRERERERERETKJc2xJCIiIiIiIiIiN0SNJRERERERERERuSFqLImIiIiIiIiIyA1RY0lERERERERERG6IGksiIiIiIiIiInJD1FgSEREREREREZEbosaSiIiIiIiIiIjcEDWWRERERERERETkhqixJCIiIiIiIiIiN0SNJRERERERERERuSFqLImIiIiIiIiIyA1RY0lERERERERERG6IGksiIiIiIiIiInJD/h/EVzgDOz6Z6AAAAABJRU5ErkJggg==",
      "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": "bf825528",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-26T18:00:13.340419Z",
     "iopub.status.busy": "2025-01-26T18:00:13.340154Z",
     "iopub.status.idle": "2025-01-26T18:00:13.358763Z",
     "shell.execute_reply": "2025-01-26T18:00:13.358324Z"
    },
    "papermill": {
     "duration": 0.031591,
     "end_time": "2025-01-26T18:00:13.359877",
     "exception": false,
     "start_time": "2025-01-26T18:00:13.328286",
     "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_dcdd6\" style='display:inline-table'>\n",
       "  <caption>Percent Methylation </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_dcdd6_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_dcdd6_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_dcdd6_row0_col0\" class=\"data row0 col0\" >1,976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dcdd6_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_dcdd6_row1_col0\" class=\"data row1 col0\" >595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dcdd6_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_dcdd6_row2_col0\" class=\"data row2 col0\" >2,578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dcdd6_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_dcdd6_row3_col0\" class=\"data row3 col0\" >5,898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dcdd6_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_dcdd6_row4_col0\" class=\"data row4 col0\" >13,470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dcdd6_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_dcdd6_row5_col0\" class=\"data row5 col0\" >56,092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dcdd6_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_dcdd6_row6_col0\" class=\"data row6 col0\" >175,044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dcdd6_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_dcdd6_row7_col0\" class=\"data row7 col0\" >279,606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dcdd6_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_dcdd6_row8_col0\" class=\"data row8 col0\" >177,408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dcdd6_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_dcdd6_row9_col0\" class=\"data row9 col0\" >28,050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_5839c\" style='display:inline-table'>\n",
       "  <caption>Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5839c_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_5839c_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_5839c_row0_col0\" class=\"data row0 col0\" >15,902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_5839c_row1_col0\" class=\"data row1 col0\" >13,073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_5839c_row2_col0\" class=\"data row2 col0\" >21,983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_5839c_row3_col0\" class=\"data row3 col0\" >44,073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_5839c_row4_col0\" class=\"data row4 col0\" >93,688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_5839c_row5_col0\" class=\"data row5 col0\" >154,926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_5839c_row6_col0\" class=\"data row6 col0\" >172,430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_5839c_row7_col0\" class=\"data row7 col0\" >125,723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_5839c_row8_col0\" class=\"data row8 col0\" >62,806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_5839c_row9_col0\" class=\"data row9 col0\" >22,570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_5839c_row10_col0\" class=\"data row10 col0\" >6,443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_5839c_row11_col0\" class=\"data row11 col0\" >2,132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_5839c_row12_col0\" class=\"data row12 col0\" >924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_5839c_row13_col0\" class=\"data row13 col0\" >547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_5839c_row14_col0\" class=\"data row14 col0\" >414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_5839c_row15_col0\" class=\"data row15 col0\" >326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_5839c_row16_col0\" class=\"data row16 col0\" >257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_5839c_row17_col0\" class=\"data row17 col0\" >186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_5839c_row18_col0\" class=\"data row18 col0\" >191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5839c_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_5839c_row19_col0\" class=\"data row19 col0\" >2,123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "  <style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_d7513\" style='display:inline-table'>\n",
       "  <caption>Cumulative Coverage </caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d7513_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_d7513_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_d7513_row0_col0\" class=\"data row0 col0\" >2.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
       "      <td id=\"T_d7513_row1_col0\" class=\"data row1 col0\" >3.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row2\" class=\"row_heading level0 row2\" >30</th>\n",
       "      <td id=\"T_d7513_row2_col0\" class=\"data row2 col0\" >6.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row3\" class=\"row_heading level0 row3\" >40</th>\n",
       "      <td id=\"T_d7513_row3_col0\" class=\"data row3 col0\" >12.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "      <td id=\"T_d7513_row4_col0\" class=\"data row4 col0\" >25.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row5\" class=\"row_heading level0 row5\" >60</th>\n",
       "      <td id=\"T_d7513_row5_col0\" class=\"data row5 col0\" >46.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row6\" class=\"row_heading level0 row6\" >70</th>\n",
       "      <td id=\"T_d7513_row6_col0\" class=\"data row6 col0\" >69.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row7\" class=\"row_heading level0 row7\" >80</th>\n",
       "      <td id=\"T_d7513_row7_col0\" class=\"data row7 col0\" >86.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row8\" class=\"row_heading level0 row8\" >90</th>\n",
       "      <td id=\"T_d7513_row8_col0\" class=\"data row8 col0\" >95.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row9\" class=\"row_heading level0 row9\" >100</th>\n",
       "      <td id=\"T_d7513_row9_col0\" class=\"data row9 col0\" >98.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row10\" class=\"row_heading level0 row10\" >110</th>\n",
       "      <td id=\"T_d7513_row10_col0\" class=\"data row10 col0\" >99.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row11\" class=\"row_heading level0 row11\" >120</th>\n",
       "      <td id=\"T_d7513_row11_col0\" class=\"data row11 col0\" >99.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row12\" class=\"row_heading level0 row12\" >130</th>\n",
       "      <td id=\"T_d7513_row12_col0\" class=\"data row12 col0\" >99.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row13\" class=\"row_heading level0 row13\" >140</th>\n",
       "      <td id=\"T_d7513_row13_col0\" class=\"data row13 col0\" >99.53%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row14\" class=\"row_heading level0 row14\" >150</th>\n",
       "      <td id=\"T_d7513_row14_col0\" class=\"data row14 col0\" >99.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row15\" class=\"row_heading level0 row15\" >160</th>\n",
       "      <td id=\"T_d7513_row15_col0\" class=\"data row15 col0\" >99.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row16\" class=\"row_heading level0 row16\" >170</th>\n",
       "      <td id=\"T_d7513_row16_col0\" class=\"data row16 col0\" >99.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row17\" class=\"row_heading level0 row17\" >180</th>\n",
       "      <td id=\"T_d7513_row17_col0\" class=\"data row17 col0\" >99.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row18\" class=\"row_heading level0 row18\" >190</th>\n",
       "      <td id=\"T_d7513_row18_col0\" class=\"data row18 col0\" >99.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7513_level0_row19\" class=\"row_heading level0 row19\" >200</th>\n",
       "      <td id=\"T_d7513_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"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 9.14542,
   "end_time": "2025-01-26T18:00:13.686825",
   "environment_variables": {},
   "exception": null,
   "input_path": "/app/VariantCalling/ugvc/reports/methyldackel_qc_report.ipynb",
   "output_path": "/data/Runs/415630/output/415630-20250125_0254/415630-L3450-Z0010-CACTGCACGAATGAT/415630-L3450-Z0010-CACTGCACGAATGAT_methyldackel_qc_report.papermill.ipynb",
   "parameters": {
    "input_base_file_name": "/data/Runs/415630/output/415630-20250125_0254/415630-L3450-Z0010-CACTGCACGAATGAT/415630-L3450-Z0010-CACTGCACGAATGAT.methyl_seq",
    "input_h5_file": "/data/Runs/415630/output/415630-20250125_0254/415630-L3450-Z0010-CACTGCACGAATGAT/415630-L3450-Z0010-CACTGCACGAATGAT.methyl_seq.applicationQC.h5"
   },
   "start_time": "2025-01-26T18:00:04.541405",
   "version": "2.6.0"
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