CI calculation bottom up method.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Script-setup\" data-toc-modified-id=\"Script-setup-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Script setup</a></span></li><li><span><a href=\"#Data-directory-preparention\" data-toc-modified-id=\"Data-directory-preparention-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Data directory preparention</a></span></li><li><span><a href=\"#Data-file-preperation\" data-toc-modified-id=\"Data-file-preperation-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Data file preperation</a></span><ul class=\"toc-item\"><li><span><a href=\"#input-directory-(all-general-raw-data-files)\" data-toc-modified-id=\"input-directory-(all-general-raw-data-files)-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span><code>input</code> directory (all general raw data files)</a></span></li><li><span><a href=\"#processed-directory-(all-data-files-produced-in-this-repository)\" data-toc-modified-id=\"processed-directory-(all-data-files-produced-in-this-repository)-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span><code>processed</code> directory (all data files produced in this repository)</a></span></li></ul></li><li><span><a href=\"#Load-data-functions\" data-toc-modified-id=\"Load-data-functions-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Load data functions</a></span></li><li><span><a href=\"#Load-data-sets\" data-toc-modified-id=\"Load-data-sets-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Load data sets</a></span></li><li><span><a href=\"#ENTSO-E-generation-per-technology-CI\" data-toc-modified-id=\"ENTSO-E-generation-per-technology-CI-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>ENTSO-E generation per technology CI</a></span><ul class=\"toc-item\"><li><span><a href=\"#EF-preparation\" data-toc-modified-id=\"EF-preparation-7.1\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span>EF preparation</a></span></li><li><span><a href=\"#Emission-calculation\" data-toc-modified-id=\"Emission-calculation-7.2\"><span class=\"toc-item-num\">7.2&nbsp;&nbsp;</span>Emission calculation</a></span></li></ul></li><li><span><a href=\"#Compair-ENTSO-E-scaled-vs.-no-scaled\" data-toc-modified-id=\"Compair-ENTSO-E-scaled-vs.-no-scaled-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Compair ENTSO-E scaled vs. no scaled</a></span></li><li><span><a href=\"#Compare-Bottom-Up-and-Top-down\" data-toc-modified-id=\"Compare-Bottom-Up-and-Top-down-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Compare Bottom Up and Top down</a></span></li><li><span><a href=\"#Compare-Bottom-Up-and-Top-down-in-relation-to-possible-errors-in-the-data-sets\" data-toc-modified-id=\"Compare-Bottom-Up-and-Top-down-in-relation-to-possible-errors-in-the-data-sets-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Compare Bottom Up and Top down in relation to possible errors in the data sets</a></span></li><li><span><a href=\"#Export-results\" data-toc-modified-id=\"Export-results-11\"><span class=\"toc-item-num\">11&nbsp;&nbsp;</span>Export results</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This notebook uses the calculated EFs to generat CI for EU countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Script setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import logging\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import DateFormatter\n",
    "from IPython.display import Image \n",
    "import datetime\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "#GEO\n",
    "import geopandas as gpd\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn')\n",
    "plt.rcParams['figure.figsize'] = [15, 6]\n",
    "\n",
    "#helpers\n",
    "from helpers import change_ENTSOE_ProductionTypeName"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data directory preparention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create input, processed and output folders if they don't exist\n",
    "If the paths are relative, the corresponding folders will be created inside the current working directory.\n",
    "- input -> all needed input data\n",
    "- processed -> save point and exchange with other scripts\n",
    "- output -> final emission factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_directory_path = os.path.join('input')\n",
    "processed_directory_path = 'processed'\n",
    "output_directory_path = os.path.join('output')\n",
    "\n",
    "os.makedirs(input_directory_path, exist_ok=True)\n",
    "os.makedirs(processed_directory_path, exist_ok=True)\n",
    "os.makedirs(output_directory_path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data file preperation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `input` directory (all general raw data files)\n",
    "\n",
    "- 1) ENTSO-E production per type (scaled)\n",
    "\n",
    "Production per type from ENTSO-E\n",
    "Original data: ENTSO-E Transparency Platform, Actual Generation per Production Type Available online: \n",
    "https://transparency.entsoe.eu/generation/r2/actualGenerationPerProductionType/show\n",
    "Processed with the following two script that converts the data to hourly resolution and scale the data to reported yearly data:\n",
    "\n",
    "- https://github.com/Open-Power-System-Data/time_series/tree/2020-10-06\n",
    "- https://github.com/INATECH-CIG/entso-e_GenerationOutputPerType\n",
    "\n",
    "File - > entso_gen_type_hourly_table_scaled.csv -> generation per technology\n",
    "\n",
    "\n",
    "- 2) ENTSO-E production per type (no scaled)\n",
    "\n",
    "File - > entso_gen_type_hourly_table.csv -> generation per technology\n",
    "\n",
    "\n",
    "- 3) ENTSO-E production per unit\n",
    "\n",
    "Production per Unit from ENTSO-E\n",
    "Original data: ENTSO-E Transparency Platform, Actual Generation per Generation Unit Available online: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show (accessed on Apr 29, 2020).\n",
    "Processed with the following script that converts the data to hourly resolution:\n",
    "\n",
    "- https://github.com/INATECH-CIG/entso-e_GenerationOutputPerUnit\n",
    "\n",
    "File - > gen_data_2018.csv -> hourly generation data per unit\n",
    "\n",
    "File - > unit_data_2018.csv -> information about the generation units\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['input\\\\bootom_up_method', 'input\\\\entsoe_gen_type_hourly_table.csv', 'input\\\\entsoe_gen_type_hourly_table_scaled.csv', 'input\\\\gen_data_2018.csv', 'input\\\\matching_ENTSOE_EU_ETS.csv', 'input\\\\top_down_method', 'input\\\\unit_data_2018.csv', 'input\\\\_old_entsoe_gen_type_hourly_table_scaled.csv']\n"
     ]
    }
   ],
   "source": [
    "# Checks if the the input directories are empty or not\n",
    "# Checks all filenames in the input directory\n",
    "\n",
    "if not os.listdir(input_directory_path) :\n",
    "    print(\"The directory for the bootom up method is empty. Please provide the data to the directory as described in the instructions above.\")\n",
    "\n",
    "\n",
    "filenames = [os.path.join(input_directory_path, fn) for fn in os.listdir(input_directory_path)]\n",
    "\n",
    "print(filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `processed` directory (all data files produced in this repository)\n",
    "\n",
    "- 1) EF per type and county\n",
    "\n",
    "File - > EF_bottom_up_method.csv\n",
    "\n",
    "- 2) EF per unit\n",
    "\n",
    "File -> generation_unit_EF_bottom_up_method.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['processed\\\\CI_0_top_down.csv', 'processed\\\\CI_1_top_down.csv', 'processed\\\\CI_bottom_up_method.csv', 'processed\\\\CI_MAP_1_top_down.csv', 'processed\\\\EF_bottom_up_method.csv', 'processed\\\\ei_AP_top_down.csv', 'processed\\\\ei_MAP_top_down.csv', 'processed\\\\generation_unit_EF_bottom_up_method.csv']\n"
     ]
    }
   ],
   "source": [
    "# Checks if the the input directories are empty or not\n",
    "# Checks all filenames in the input directory\n",
    "\n",
    "if not os.listdir(processed_directory_path) :\n",
    "    print(\"The directory for the CO2 Signal is empty. Please provide the data to the directory as described in the instructions above.\")\n",
    "\n",
    "\n",
    "filenames = [os.path.join(processed_directory_path, fn) for fn in os.listdir(processed_directory_path)]\n",
    "\n",
    "print(filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_generation_per_unit(path, fn):\n",
    "    \"\"\"\n",
    "    Load the ENTSO-E gernation per unit data.\n",
    "        \n",
    "    Parameters\n",
    "    ----------\n",
    "    path: str\n",
    "        path to data\n",
    "    fn : str\n",
    "        filename\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    generation = pd.read_csv(os.path.join(path, fn),sep = ',',index_col=0,parse_dates=True)\n",
    "    \n",
    "    return generation\n",
    "\n",
    "def load_unit_CO2_data(path, fn):\n",
    "    \"\"\"\n",
    "    Own generation unit information with CO2 information per unit\n",
    "        \n",
    "    Parameters\n",
    "    ----------\n",
    "    path: str\n",
    "        path to data\n",
    "    fn : str\n",
    "        filename\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    df = pd.read_csv(os.path.join(path, fn),sep = ',',index_col=0)\n",
    "\n",
    "    return df\n",
    "\n",
    "def load_EF(path, fn):\n",
    "    \"\"\"\n",
    "    Own generated EF per tech and country\n",
    "        \n",
    "    Parameters\n",
    "    ----------\n",
    "    path: str\n",
    "        path to data\n",
    "    fn : str\n",
    "        filename\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    df = pd.read_csv(os.path.join(path, fn),sep = ',',index_col=[0,1])\n",
    "    \n",
    "    return df\n",
    "\n",
    "def load_timeseries_ENTSOE(path, fn):\n",
    "    \"\"\"\n",
    "    Read generation data from time-series package own modification.\n",
    "\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    path : TYPE\n",
    "        DESCRIPTION.\n",
    "    fn : TYPE\n",
    "        DESCRIPTION.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    generation : TYPE\n",
    "        DESCRIPTION.\n",
    "\n",
    "    \"\"\"\n",
    "   \n",
    "    generation = (pd.read_csv(os.path.join(path, fn), index_col=[0], header=[0, 1], parse_dates=True))\n",
    "       \n",
    "    \n",
    "    return generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load ENTSO-E generation per type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "entsoe_gen_type_org = load_timeseries_ENTSOE(input_directory_path, 'entsoe_gen_type_hourly_table.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load ENTSO-E generation per type scaled with Factsheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "entsoe_gen_type_scal = load_timeseries_ENTSOE(input_directory_path, 'entsoe_gen_type_hourly_table_scaled.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load EF per technology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "EF = load_EF(processed_directory_path, 'EF_bottom_up_method.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ENTSO-E generation per technology CI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we using the country specific EF for generating the CI for all EU countries. The production data are based on the production per type data from ENTSO-E"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EF preparation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we could not calculate EF for all countries and technologies, we use a stepwise process to close this gap. For all countries where EF is missing, which we calculate in other countries, we average all calculated technology specific EF. Then, we define standard EFs for the technologies where EFs could not be calculated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "\n",
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       "        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></th>\n",
       "      <th>EF</th>\n",
       "      <th>Verified_EF</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>EF_round_in_gCO2_kwh</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MapCode</th>\n",
       "      <th>ProductionTypeName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AT</th>\n",
       "      <th>gas</th>\n",
       "      <td>0.398292</td>\n",
       "      <td>False</td>\n",
       "      <td>24.0</td>\n",
       "      <td>398.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hard_coal</th>\n",
       "      <td>0.884070</td>\n",
       "      <td>True</td>\n",
       "      <td>39.0</td>\n",
       "      <td>884.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <th>gas</th>\n",
       "      <td>0.389641</td>\n",
       "      <td>True</td>\n",
       "      <td>64.0</td>\n",
       "      <td>389.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CZ</th>\n",
       "      <th>gas</th>\n",
       "      <td>0.780843</td>\n",
       "      <td>True</td>\n",
       "      <td>39.0</td>\n",
       "      <td>780.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hard_coal</th>\n",
       "      <td>0.985546</td>\n",
       "      <td>True</td>\n",
       "      <td>36.0</td>\n",
       "      <td>985.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  EF  Verified_EF  Coverage  \\\n",
       "MapCode ProductionTypeName                                    \n",
       "AT      gas                 0.398292        False      24.0   \n",
       "        hard_coal           0.884070         True      39.0   \n",
       "BE      gas                 0.389641         True      64.0   \n",
       "CZ      gas                 0.780843         True      39.0   \n",
       "        hard_coal           0.985546         True      36.0   \n",
       "\n",
       "                            EF_round_in_gCO2_kwh  \n",
       "MapCode ProductionTypeName                        \n",
       "AT      gas                               398.29  \n",
       "        hard_coal                         884.07  \n",
       "BE      gas                               389.64  \n",
       "CZ      gas                               780.84  \n",
       "        hard_coal                         985.55  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check EF dataset\n",
    "EF.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean EF from calculated EF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gas': 0.41978787102138526, 'hard_coal': 0.9749115732664191, 'lignite': 1.1848068573544648, 'other_fossil': 0.9193148563890987}\n"
     ]
    }
   ],
   "source": [
    "EF_mean = EF.groupby('ProductionTypeName').mean()\n",
    "\n",
    "#### old ####\n",
    "# Using a mean EF for other fossils of 0.75 because we only cover some countries with other fossils\n",
    "# and the resulting mean EF would be above 1.0, which seems too high.\n",
    "# EF_mean.loc['other_fossil'] = 0.75\n",
    "# converting EF_mean to a diconary\n",
    "#### old ####\n",
    "\n",
    "EF_mean = EF_mean.EF.to_dict()\n",
    "\n",
    "#show resulting EF\n",
    "print(EF_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Standard EF per technology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If no country-specific EFs are available and also no mean EF is available for a country and technology,\n",
    "# we use the following standard EFs. In addition, we define for the renewable generation types a EF with\n",
    "# an emission value of 0.\n",
    "\n",
    "EF_default = {'biomass':0,\n",
    "              'geothermal':0,\n",
    "              'hydro':0,\n",
    "              'nuclear':0,\n",
    "              'oil':0.8,\n",
    "              'other_renewable':0,\n",
    "              'solar':0,\n",
    "              'waste':0.2,\n",
    "              'wind_offshore':0,\n",
    "              'wind_onshore':0,\n",
    "              'marine':0,\n",
    "              'oil shale':0.9}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi EF for each country and technology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('AT', 'biomass') EF_default\n",
      "('AT', 'gas') EF\n",
      "('AT', 'hard_coal') EF\n",
      "('AT', 'hydro') EF_default\n",
      "('AT', 'other_fossil') EF_mean\n",
      "('AT', 'other_renewable') EF_default\n",
      "('AT', 'solar') EF_default\n",
      "('AT', 'waste') EF_default\n",
      "('AT', 'wind_onshore') EF_default\n",
      "('BE', 'biomass') EF_default\n",
      "('BE', 'gas') EF\n",
      "('BE', 'hydro') EF_default\n",
      "('BE', 'nuclear') EF_default\n",
      "('BE', 'other_fossil') EF_mean\n",
      "('BE', 'solar') EF_default\n",
      "('BE', 'waste') EF_default\n",
      "('BE', 'wind_offshore') EF_default\n",
      "('BE', 'wind_onshore') EF_default\n",
      "('BG', 'biomass') EF_default\n",
      "('BG', 'gas') EF_mean\n",
      "('BG', 'hard_coal') EF_mean\n",
      "('BG', 'hydro') EF_default\n",
      "('BG', 'lignite') EF_mean\n",
      "('BG', 'nuclear') EF_default\n",
      "('BG', 'solar') EF_default\n",
      "('BG', 'waste') EF_default\n",
      "('BG', 'wind_onshore') EF_default\n",
      "('CH', 'hydro') EF_default\n",
      "('CH', 'nuclear') EF_default\n",
      "('CH', 'solar') EF_default\n",
      "('CH', 'wind_onshore') EF_default\n",
      "('CY', 'other_fossil') EF_mean\n",
      "('CY', 'wind_onshore') EF_default\n",
      "('CZ', 'biomass') EF_default\n",
      "('CZ', 'gas') EF\n",
      "('CZ', 'hard_coal') EF\n",
      "('CZ', 'hydro') EF_default\n",
      "('CZ', 'lignite') EF\n",
      "('CZ', 'nuclear') EF_default\n",
      "('CZ', 'other_fossil') EF_mean\n",
      "('CZ', 'other_renewable') EF_default\n",
      "('CZ', 'solar') EF_default\n",
      "('CZ', 'waste') EF_default\n",
      "('CZ', 'wind_onshore') EF_default\n",
      "('DE', 'biomass') EF_default\n",
      "('DE', 'gas') EF\n",
      "('DE', 'hard_coal') EF\n",
      "('DE', 'hydro') EF_default\n",
      "('DE', 'lignite') EF\n",
      "('DE', 'nuclear') EF_default\n",
      "('DE', 'other_fossil') EF\n",
      "('DE', 'other_renewable') EF_default\n",
      "('DE', 'solar') EF_default\n",
      "('DE', 'waste') EF_default\n",
      "('DE', 'wind_offshore') EF_default\n",
      "('DE', 'wind_onshore') EF_default\n",
      "('DK', 'biomass') EF_default\n",
      "('DK', 'gas') EF\n",
      "('DK', 'hard_coal') EF\n",
      "('DK', 'other_fossil') EF_mean\n",
      "('DK', 'solar') EF_default\n",
      "('DK', 'waste') EF_default\n",
      "('DK', 'wind_offshore') EF_default\n",
      "('DK', 'wind_onshore') EF_default\n",
      "('EE', 'biomass') EF_default\n",
      "('EE', 'gas') EF_mean\n",
      "('EE', 'hydro') EF_default\n",
      "('EE', 'other_fossil') EF\n",
      "('EE', 'other_renewable') EF_default\n",
      "('EE', 'solar') EF_default\n",
      "('EE', 'waste') EF_default\n",
      "('EE', 'wind_onshore') EF_default\n",
      "('ES', 'biomass') EF_default\n",
      "('ES', 'gas') EF\n",
      "('ES', 'hard_coal') EF\n",
      "('ES', 'hydro') EF_default\n",
      "('ES', 'lignite') EF_mean\n",
      "('ES', 'nuclear') EF_default\n",
      "('ES', 'other_fossil') EF_mean\n",
      "('ES', 'other_renewable') EF_default\n",
      "('ES', 'solar') EF_default\n",
      "('ES', 'waste') EF_default\n",
      "('ES', 'wind_onshore') EF_default\n",
      "('FI', 'biomass') EF_default\n",
      "('FI', 'gas') EF_mean\n",
      "('FI', 'hard_coal') EF\n",
      "('FI', 'hydro') EF_default\n",
      "('FI', 'nuclear') EF_default\n",
      "('FI', 'other_fossil') EF\n",
      "('FI', 'other_renewable') EF_default\n",
      "('FI', 'waste') EF_default\n",
      "('FI', 'wind_onshore') EF_default\n",
      "('FR', 'biomass') EF_default\n",
      "('FR', 'gas') EF\n",
      "('FR', 'hard_coal') EF\n",
      "('FR', 'hydro') EF_default\n",
      "('FR', 'nuclear') EF_default\n",
      "('FR', 'other_fossil') EF_mean\n",
      "('FR', 'solar') EF_default\n",
      "('FR', 'waste') EF_default\n",
      "('FR', 'wind_onshore') EF_default\n",
      "('GB', 'biomass') EF_default\n",
      "('GB', 'gas') EF\n",
      "('GB', 'hard_coal') EF\n",
      "('GB', 'hydro') EF_default\n",
      "('GB', 'nuclear') EF_default\n",
      "('GB', 'other_fossil') EF_mean\n",
      "('GB', 'solar') EF_default\n",
      "('GB', 'wind_offshore') EF_default\n",
      "('GB', 'wind_onshore') EF_default\n",
      "('GR', 'gas') EF\n",
      "('GR', 'lignite') EF\n",
      "('GR', 'solar') EF_default\n",
      "('GR', 'wind_onshore') EF_default\n",
      "('HU', 'biomass') EF_default\n",
      "('HU', 'gas') EF\n",
      "('HU', 'hydro') EF_default\n",
      "('HU', 'lignite') EF\n",
      "('HU', 'nuclear') EF_default\n",
      "('HU', 'other_fossil') EF_mean\n",
      "('HU', 'other_renewable') EF_default\n",
      "('HU', 'waste') EF_default\n",
      "('HU', 'wind_onshore') EF_default\n",
      "('IE', 'gas') EF\n",
      "('IE', 'hard_coal') EF\n",
      "('IE', 'hydro') EF_default\n",
      "('IE', 'other_fossil') EF_mean\n",
      "('IE', 'wind_onshore') EF_default\n",
      "('IT', 'biomass') EF_default\n",
      "('IT', 'gas') EF\n",
      "('IT', 'hard_coal') EF\n",
      "('IT', 'hydro') EF_default\n",
      "('IT', 'other_fossil') EF\n",
      "('IT', 'other_renewable') EF_default\n",
      "('IT', 'solar') EF_default\n",
      "('IT', 'waste') EF_default\n",
      "('IT', 'wind_onshore') EF_default\n",
      "('LT', 'biomass') EF_default\n",
      "('LT', 'gas') EF_mean\n",
      "('LT', 'hydro') EF_default\n",
      "('LT', 'other_fossil') EF_mean\n",
      "('LT', 'solar') EF_default\n",
      "('LT', 'waste') EF_default\n",
      "('LT', 'wind_onshore') EF_default\n",
      "('LV', 'biomass') EF_default\n",
      "('LV', 'gas') EF_mean\n",
      "('LV', 'hydro') EF_default\n",
      "('LV', 'other_fossil') EF_mean\n",
      "('LV', 'wind_onshore') EF_default\n",
      "('ME', 'hydro') EF_default\n",
      "('ME', 'lignite') EF_mean\n",
      "('ME', 'wind_onshore') EF_default\n",
      "('NL', 'biomass') EF_default\n",
      "('NL', 'gas') EF\n",
      "('NL', 'hard_coal') EF\n",
      "('NL', 'nuclear') EF_default\n",
      "('NL', 'other_fossil') EF_mean\n",
      "('NL', 'solar') EF_default\n",
      "('NL', 'waste') EF_default\n",
      "('NL', 'wind_offshore') EF_default\n",
      "('NL', 'wind_onshore') EF_default\n",
      "('NO', 'gas') EF_mean\n",
      "('NO', 'hydro') EF_default\n",
      "('NO', 'other_fossil') EF_mean\n",
      "('NO', 'wind_onshore') EF_default\n",
      "('PL', 'biomass') EF_default\n",
      "('PL', 'gas') EF\n",
      "('PL', 'hard_coal') EF\n",
      "('PL', 'hydro') EF_default\n",
      "('PL', 'lignite') EF\n",
      "('PL', 'other_fossil') EF_mean\n",
      "('PL', 'wind_onshore') EF_default\n",
      "('PT', 'biomass') EF_default\n",
      "('PT', 'gas') EF\n",
      "('PT', 'hard_coal') EF_mean\n",
      "('PT', 'hydro') EF_default\n",
      "('PT', 'other_fossil') EF_mean\n",
      "('PT', 'solar') EF_default\n",
      "('PT', 'wind_onshore') EF_default\n",
      "('RO', 'biomass') EF_default\n",
      "('RO', 'gas') EF\n",
      "('RO', 'hard_coal') EF\n",
      "('RO', 'hydro') EF_default\n",
      "('RO', 'lignite') EF\n",
      "('RO', 'nuclear') EF_default\n",
      "('RO', 'solar') EF_default\n",
      "('RO', 'wind_onshore') EF_default\n",
      "('RS', 'biomass') EF_default\n",
      "('RS', 'gas') EF_mean\n",
      "('RS', 'hydro') EF_default\n",
      "('RS', 'lignite') EF_mean\n",
      "('RS', 'other_fossil') EF_mean\n",
      "('SE', 'hydro') EF_default\n",
      "('SE', 'nuclear') EF_default\n",
      "('SE', 'other_fossil') EF_mean\n",
      "('SE', 'wind_onshore') EF_default\n",
      "('SI', 'biomass') EF_default\n",
      "('SI', 'gas') EF_mean\n",
      "('SI', 'hydro') EF_default\n",
      "('SI', 'lignite') EF_mean\n",
      "('SI', 'nuclear') EF_default\n",
      "('SI', 'other_fossil') EF_mean\n",
      "('SI', 'solar') EF_default\n",
      "('SI', 'waste') EF_default\n",
      "('SI', 'wind_onshore') EF_default\n",
      "('SK', 'biomass') EF_default\n",
      "('SK', 'gas') EF_mean\n",
      "('SK', 'hard_coal') EF\n",
      "('SK', 'hydro') EF_default\n",
      "('SK', 'lignite') EF\n",
      "('SK', 'nuclear') EF_default\n",
      "('SK', 'other_fossil') EF_mean\n",
      "('SK', 'other_renewable') EF_default\n",
      "('SK', 'solar') EF_default\n",
      "('SE', 'biomass') EF_default\n",
      "('SE', 'gas') EF\n",
      "('SE', 'hard_coal') EF_mean\n",
      "('SE', 'other_renewable') EF_default\n",
      "('SE', 'waste') EF_default\n"
     ]
    }
   ],
   "source": [
    "# Create a dataframe with the EF for each country and technology. If no specific EF is available, we use the default EF.\n",
    "EF_multi = {}\n",
    "\n",
    "for i in entsoe_gen_type_scal.columns:\n",
    "    if i in EF.index:\n",
    "        print(str(i)+' EF')\n",
    "        EF_multi[i] = EF.loc[i[0],i[1]].loc['EF']\n",
    "    elif i[1] in EF_mean:\n",
    "        print(str(i) +' EF_mean')\n",
    "        EF_multi[i] = EF_mean[i[1]]\n",
    "    else:\n",
    "        print(str(i) + ' EF_default')\n",
    "        EF_multi[i] = EF_default[i[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Emission calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "variable\n",
       "hydro              62110.097000\n",
       "nuclear            65787.278500\n",
       "other_fossil        1022.169550\n",
       "wind_onshore       16305.413000\n",
       "biomass             8116.120750\n",
       "gas                  408.286300\n",
       "hard_coal            378.460900\n",
       "other_renewable      833.451250\n",
       "waste                102.216955\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(entsoe_gen_type_org['SE'].sum()/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate emissions based on EF and generation per type\n",
    "CO2_emission_type_org = entsoe_gen_type_org * EF_multi\n",
    "\n",
    "CO2_emission_type_scal = entsoe_gen_type_scal * EF_multi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total emissions and generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Emission in gCO2\n",
    "CO2_emission_org = (CO2_emission_type_org * 1000000).groupby(level=0, axis=1).sum()\n",
    "# generation in kWh\n",
    "Generation_org = (entsoe_gen_type_org * 1000).groupby(level=0, axis=1).sum()\n",
    "\n",
    "# Emission in gCO2\n",
    "CO2_emission_scal = (CO2_emission_type_scal * 1000000).groupby(level=0, axis=1).sum()\n",
    "# generation in kWh\n",
    "Generation_scale = (entsoe_gen_type_scal * 1000).groupby(level=0, axis=1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261159706666767.25"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CO2_emission_scal['DE'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600163854000.0001"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Generation_scale['DE'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Carbon intensity of gernation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CI in g CO2/kWh\n",
    "Carbon_intensitiy_org = CO2_emission_org.div(Generation_org)\n",
    "# CI in g CO2/kWh\n",
    "Carbon_intensitiy_scal = CO2_emission_scal.div(Generation_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean CI in gCO2/kWh\n",
    "mean_Carbon_intensitiy_org = CO2_emission_org.sum() / Generation_org.sum()\n",
    "# mean CI in gCO2/kWh\n",
    "mean_Carbon_intensitiy_scale = CO2_emission_scal.sum() / Generation_scale.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transfer mean CI to hourly data set\n",
    "#Carbon_intensitiy_entsoe_gen_type_mean = Carbon_intensitiy_entsoe_gen_type.copy()\n",
    "#for i in Carbon_intensitiy_entsoe_gen_type_mean.columns:\n",
    "#    Carbon_intensitiy_entsoe_gen_type_mean[i] = mean_Carbon_intensitiy_entsoe_gen_type[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compair ENTSO-E scaled vs. no scaled "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forming an indicator for emissions and carbon intensity to see how they change by scaling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "indicator_EM = ((CO2_emission_scal.sum() - CO2_emission_org.sum()) / CO2_emission_scal.sum()).to_frame().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "indicator_EM.sort_values(by=0).plot(kind='bar', ylabel='Emission delta indicator', legend=False, ax=ax)\n",
    "\n",
    "ax.set_xlabel(\"Country\" , fontsize=20)\n",
    "ax.set_ylabel(\"Emission delta indicator\", fontsize=20)\n",
    "\n",
    "ax.tick_params(axis='y',labelsize=19)\n",
    "ax.tick_params(axis='x',labelsize=19)\n",
    "\n",
    "ax.tick_params(axis='x', rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(os.path.join(output_directory_path + '/_emission_delta_indicator.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Carbon intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "indicator_CI = ((mean_Carbon_intensitiy_scale - mean_Carbon_intensitiy_org) / mean_Carbon_intensitiy_scale).to_frame().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "indicator_CI.sort_values(by=0).plot(kind='bar', ylabel='CI delta indicator', legend=False, ax=ax)\n",
    "\n",
    "ax.set_xlabel(\"Country\" , fontsize=20)\n",
    "ax.set_ylabel(\"CI delta indicator\", fontsize=20)\n",
    "\n",
    "ax.tick_params(axis='y',labelsize=19)\n",
    "ax.tick_params(axis='x',labelsize=19)\n",
    "\n",
    "ax.tick_params(axis='x', rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(os.path.join(output_directory_path + '/_CI_delta_indicator.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicator_compar = pd.DataFrame()\n",
    "Indicator_compar.index.rename('country',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicator_compar['emission_delta_indicator'] = indicator_EM.squeeze()\n",
    "Indicator_compar['CI_delta_indicator'] = indicator_CI.squeeze()\n",
    "Indicator_compar['CI_delta_indicator_abs'] = abs(indicator_CI.squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2018990d708>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax = sns.scatterplot(data=Indicator_compar, x=\"CI_delta_indicator\", y=\"emission_delta_indicator\", hue=Indicator_compar.index, s=200, style=Indicator_compar.index)\n",
    "#CI_compar.plot(kind='scatter', x=\"indikator_CI\", y=\"indikator_em\", ax=ax, alpha=0.6, s=10, edgecolor='black')\n",
    "\n",
    "ax.set_xlabel(\"CI Delta\" , fontsize=20)\n",
    "ax.set_ylabel(\"EM Delta\", fontsize=20)\n",
    "\n",
    "ax.tick_params(axis='y',labelsize=19)\n",
    "ax.tick_params(axis='x',labelsize=19)\n",
    "\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.01, 1),\n",
    "           borderaxespad=0)\n",
    "\n",
    "#CI_compar.plot(x='indikator_CI',y='indikator_em',kind='scatter',legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(os.path.join(output_directory_path + '/_scaling_indicator.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare Bottom Up and Top down"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load CI from top down method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "CI_top_down = pd.read_csv(os.path.join(processed_directory_path,'CI_1_top_down.csv'), index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up a dataframe and store CI\n",
    "CI_compar = pd.DataFrame()\n",
    "CI_compar.index.rename('country',inplace=True)\n",
    "\n",
    "CI_compar['CI_top_down'] = CI_top_down.loc[2018].filter(mean_Carbon_intensitiy_scale.index.to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "CI_compar['CI_bottom_up'] = mean_Carbon_intensitiy_scale.filter(CI_compar.index.to_list())\n",
    "\n",
    "CI_compar['diff_bottom_up_vs_CI_top_down'] = CI_compar['CI_bottom_up'] - CI_compar['CI_top_down']\n",
    "CI_compar['diff_pro'] = round((100 / CI_compar['CI_bottom_up']) * CI_compar['diff_bottom_up_vs_CI_top_down'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "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>CI_top_down</th>\n",
       "      <th>CI_bottom_up</th>\n",
       "      <th>diff_bottom_up_vs_CI_top_down</th>\n",
       "      <th>diff_pro</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>56.57</td>\n",
       "      <td>37.16</td>\n",
       "      <td>-19.41</td>\n",
       "      <td>-52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>19.45</td>\n",
       "      <td>16.32</td>\n",
       "      <td>-3.13</td>\n",
       "      <td>-19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>218.56</td>\n",
       "      <td>194.79</td>\n",
       "      <td>-23.77</td>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EE</th>\n",
       "      <td>943.07</td>\n",
       "      <td>906.63</td>\n",
       "      <td>-36.44</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>326.01</td>\n",
       "      <td>317.05</td>\n",
       "      <td>-8.95</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PL</th>\n",
       "      <td>833.30</td>\n",
       "      <td>837.94</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>470.43</td>\n",
       "      <td>479.78</td>\n",
       "      <td>9.35</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>423.38</td>\n",
       "      <td>435.15</td>\n",
       "      <td>11.77</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>369.52</td>\n",
       "      <td>386.85</td>\n",
       "      <td>17.33</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>308.48</td>\n",
       "      <td>321.03</td>\n",
       "      <td>12.55</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR</th>\n",
       "      <td>691.17</td>\n",
       "      <td>726.01</td>\n",
       "      <td>34.84</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>200.42</td>\n",
       "      <td>223.17</td>\n",
       "      <td>22.74</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>256.56</td>\n",
       "      <td>287.08</td>\n",
       "      <td>30.52</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>465.48</td>\n",
       "      <td>527.36</td>\n",
       "      <td>61.88</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>262.45</td>\n",
       "      <td>300.06</td>\n",
       "      <td>37.61</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>13.29</td>\n",
       "      <td>15.36</td>\n",
       "      <td>2.07</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>291.10</td>\n",
       "      <td>342.77</td>\n",
       "      <td>51.67</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>449.79</td>\n",
       "      <td>527.36</td>\n",
       "      <td>77.57</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HU</th>\n",
       "      <td>265.61</td>\n",
       "      <td>318.21</td>\n",
       "      <td>52.60</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>703.98</td>\n",
       "      <td>879.69</td>\n",
       "      <td>175.72</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>106.16</td>\n",
       "      <td>136.27</td>\n",
       "      <td>30.11</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>260.85</td>\n",
       "      <td>342.20</td>\n",
       "      <td>81.35</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FI</th>\n",
       "      <td>115.63</td>\n",
       "      <td>155.46</td>\n",
       "      <td>39.84</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK</th>\n",
       "      <td>144.24</td>\n",
       "      <td>231.71</td>\n",
       "      <td>87.48</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LV</th>\n",
       "      <td>148.24</td>\n",
       "      <td>260.32</td>\n",
       "      <td>112.08</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LT</th>\n",
       "      <td>60.45</td>\n",
       "      <td>129.60</td>\n",
       "      <td>69.16</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CI_top_down  CI_bottom_up  diff_bottom_up_vs_CI_top_down  diff_pro\n",
       "country                                                                    \n",
       "FR             56.57         37.16                         -19.41     -52.0\n",
       "NO             19.45         16.32                          -3.13     -19.0\n",
       "BE            218.56        194.79                         -23.77     -12.0\n",
       "EE            943.07        906.63                         -36.44      -4.0\n",
       "PT            326.01        317.05                          -8.95      -3.0\n",
       "PL            833.30        837.94                           4.64       1.0\n",
       "CZ            470.43        479.78                           9.35       2.0\n",
       "DE            423.38        435.15                          11.77       3.0\n",
       "IE            369.52        386.85                          17.33       4.0\n",
       "RO            308.48        321.03                          12.55       4.0\n",
       "GR            691.17        726.01                          34.84       5.0\n",
       "DK            200.42        223.17                          22.74      10.0\n",
       "GB            256.56        287.08                          30.52      11.0\n",
       "NL            465.48        527.36                          61.88      12.0\n",
       "IT            262.45        300.06                          37.61      13.0\n",
       "SE             13.29         15.36                           2.07      13.0\n",
       "ES            291.10        342.77                          51.67      15.0\n",
       "BG            449.79        527.36                          77.57      15.0\n",
       "HU            265.61        318.21                          52.60      17.0\n",
       "CY            703.98        879.69                         175.72      20.0\n",
       "AT            106.16        136.27                          30.11      22.0\n",
       "SI            260.85        342.20                          81.35      24.0\n",
       "FI            115.63        155.46                          39.84      26.0\n",
       "SK            144.24        231.71                          87.48      38.0\n",
       "LV            148.24        260.32                         112.08      43.0\n",
       "LT             60.45        129.60                          69.16      53.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(CI_compar, 2).sort_values(by='diff_pro', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare Bottom Up and Top down in relation to possible errors in the data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicator_compar['CI_indicator']  = (CI_compar['CI_bottom_up'] - CI_compar['CI_top_down']) / CI_compar['CI_bottom_up']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ei_MAP = pd.read_csv(os.path.join(processed_directory_path,'ei_MAP_top_down.csv'), index_col=0)\n",
    "ei_AP = pd.read_csv(os.path.join(processed_directory_path,'ei_AP_top_down.csv'), index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicator_EI = ((ei_AP) / (ei_AP + ei_MAP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicator_compar['EI_indicator'] = Indicator_EI.loc[2018].squeeze() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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>emission_delta_indicator</th>\n",
       "      <th>CI_delta_indicator</th>\n",
       "      <th>CI_delta_indicator_abs</th>\n",
       "      <th>CI_indicator</th>\n",
       "      <th>EI_indicator</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>0.441242</td>\n",
       "      <td>0.360014</td>\n",
       "      <td>0.360014</td>\n",
       "      <td>0.220962</td>\n",
       "      <td>0.331902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.025468</td>\n",
       "      <td>0.025468</td>\n",
       "      <td>-0.122026</td>\n",
       "      <td>0.121718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.008020</td>\n",
       "      <td>0.008020</td>\n",
       "      <td>0.147090</td>\n",
       "      <td>0.017587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>0.062271</td>\n",
       "      <td>0.002854</td>\n",
       "      <td>0.002854</td>\n",
       "      <td>0.199746</td>\n",
       "      <td>0.001967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>0.030196</td>\n",
       "      <td>-0.013692</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.019484</td>\n",
       "      <td>0.093155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.094196</td>\n",
       "      <td>-0.004042</td>\n",
       "      <td>0.004042</td>\n",
       "      <td>0.027047</td>\n",
       "      <td>0.122422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0.054623</td>\n",
       "      <td>0.033036</td>\n",
       "      <td>0.033036</td>\n",
       "      <td>0.101912</td>\n",
       "      <td>0.168807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EE</th>\n",
       "      <td>0.944316</td>\n",
       "      <td>0.676298</td>\n",
       "      <td>0.676298</td>\n",
       "      <td>-0.040198</td>\n",
       "      <td>0.005290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>0.373126</td>\n",
       "      <td>0.281824</td>\n",
       "      <td>0.281824</td>\n",
       "      <td>0.150747</td>\n",
       "      <td>0.214318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FI</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.113428</td>\n",
       "      <td>0.113428</td>\n",
       "      <td>0.256243</td>\n",
       "      <td>0.062645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>0.042883</td>\n",
       "      <td>0.027464</td>\n",
       "      <td>0.027464</td>\n",
       "      <td>-0.522294</td>\n",
       "      <td>0.207088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>0.086587</td>\n",
       "      <td>0.027301</td>\n",
       "      <td>0.027301</td>\n",
       "      <td>0.106319</td>\n",
       "      <td>0.168740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.021203</td>\n",
       "      <td>0.021203</td>\n",
       "      <td>0.047984</td>\n",
       "      <td>0.048040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HU</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.050236</td>\n",
       "      <td>0.050236</td>\n",
       "      <td>0.165285</td>\n",
       "      <td>0.041196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>0.176387</td>\n",
       "      <td>0.003277</td>\n",
       "      <td>0.003277</td>\n",
       "      <td>0.044792</td>\n",
       "      <td>0.079263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.002685</td>\n",
       "      <td>-0.096367</td>\n",
       "      <td>0.096367</td>\n",
       "      <td>0.125354</td>\n",
       "      <td>0.159820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LT</th>\n",
       "      <td>0.081649</td>\n",
       "      <td>0.058112</td>\n",
       "      <td>0.058112</td>\n",
       "      <td>0.533607</td>\n",
       "      <td>0.172003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LV</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.042393</td>\n",
       "      <td>0.042393</td>\n",
       "      <td>0.430543</td>\n",
       "      <td>0.008618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.288991</td>\n",
       "      <td>0.288991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>0.488117</td>\n",
       "      <td>0.075834</td>\n",
       "      <td>0.075834</td>\n",
       "      <td>0.117331</td>\n",
       "      <td>0.179037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>0.024270</td>\n",
       "      <td>0.023223</td>\n",
       "      <td>0.023223</td>\n",
       "      <td>-0.191480</td>\n",
       "      <td>0.670183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PL</th>\n",
       "      <td>0.020583</td>\n",
       "      <td>-0.040040</td>\n",
       "      <td>0.040040</td>\n",
       "      <td>0.005539</td>\n",
       "      <td>0.066422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.007280</td>\n",
       "      <td>0.007280</td>\n",
       "      <td>-0.028244</td>\n",
       "      <td>0.229104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.008283</td>\n",
       "      <td>0.008283</td>\n",
       "      <td>0.039100</td>\n",
       "      <td>0.184926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>0.195681</td>\n",
       "      <td>0.062208</td>\n",
       "      <td>0.062208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0.396568</td>\n",
       "      <td>0.380042</td>\n",
       "      <td>0.380042</td>\n",
       "      <td>0.134708</td>\n",
       "      <td>0.043351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>0.051744</td>\n",
       "      <td>0.021674</td>\n",
       "      <td>0.021674</td>\n",
       "      <td>0.237722</td>\n",
       "      <td>0.018769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK</th>\n",
       "      <td>0.077306</td>\n",
       "      <td>0.002523</td>\n",
       "      <td>0.002523</td>\n",
       "      <td>0.377519</td>\n",
       "      <td>0.172730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         emission_delta_indicator  CI_delta_indicator  CI_delta_indicator_abs  \\\n",
       "country                                                                         \n",
       "AT                       0.441242            0.360014                0.360014   \n",
       "BE                       0.000000           -0.025468                0.025468   \n",
       "BG                       0.000000           -0.008020                0.008020   \n",
       "CH                       0.000000            0.000000                0.000000   \n",
       "CY                       0.062271            0.002854                0.002854   \n",
       "CZ                       0.030196           -0.013692                0.013692   \n",
       "DE                       0.094196           -0.004042                0.004042   \n",
       "DK                       0.054623            0.033036                0.033036   \n",
       "EE                       0.944316            0.676298                0.676298   \n",
       "ES                       0.373126            0.281824                0.281824   \n",
       "FI                       0.000000           -0.113428                0.113428   \n",
       "FR                       0.042883            0.027464                0.027464   \n",
       "GB                       0.086587            0.027301                0.027301   \n",
       "GR                       0.000000           -0.021203                0.021203   \n",
       "HU                       0.000000           -0.050236                0.050236   \n",
       "IE                       0.176387            0.003277                0.003277   \n",
       "IT                       0.002685           -0.096367                0.096367   \n",
       "LT                       0.081649            0.058112                0.058112   \n",
       "LV                       0.000000           -0.042393                0.042393   \n",
       "ME                       0.000000           -0.288991                0.288991   \n",
       "NL                       0.488117            0.075834                0.075834   \n",
       "NO                       0.024270            0.023223                0.023223   \n",
       "PL                       0.020583           -0.040040                0.040040   \n",
       "PT                       0.000000           -0.007280                0.007280   \n",
       "RO                       0.000000           -0.008283                0.008283   \n",
       "RS                       0.195681            0.062208                0.062208   \n",
       "SE                       0.396568            0.380042                0.380042   \n",
       "SI                       0.051744            0.021674                0.021674   \n",
       "SK                       0.077306            0.002523                0.002523   \n",
       "\n",
       "         CI_indicator  EI_indicator  \n",
       "country                              \n",
       "AT           0.220962      0.331902  \n",
       "BE          -0.122026      0.121718  \n",
       "BG           0.147090      0.017587  \n",
       "CH                NaN           NaN  \n",
       "CY           0.199746      0.001967  \n",
       "CZ           0.019484      0.093155  \n",
       "DE           0.027047      0.122422  \n",
       "DK           0.101912      0.168807  \n",
       "EE          -0.040198      0.005290  \n",
       "ES           0.150747      0.214318  \n",
       "FI           0.256243      0.062645  \n",
       "FR          -0.522294      0.207088  \n",
       "GB           0.106319      0.168740  \n",
       "GR           0.047984      0.048040  \n",
       "HU           0.165285      0.041196  \n",
       "IE           0.044792      0.079263  \n",
       "IT           0.125354      0.159820  \n",
       "LT           0.533607      0.172003  \n",
       "LV           0.430543      0.008618  \n",
       "ME                NaN           NaN  \n",
       "NL           0.117331      0.179037  \n",
       "NO          -0.191480      0.670183  \n",
       "PL           0.005539      0.066422  \n",
       "PT          -0.028244      0.229104  \n",
       "RO           0.039100      0.184926  \n",
       "RS                NaN           NaN  \n",
       "SE           0.134708      0.043351  \n",
       "SI           0.237722      0.018769  \n",
       "SK           0.377519      0.172730  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Indicator_compar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20189f44a08>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax = sns.scatterplot(data=Indicator_compar, x=\"CI_indicator\", y=\"EI_indicator\", hue=Indicator_compar.index, s=200, style=Indicator_compar.index)\n",
    "#CI_compar.plot(kind='scatter', x=\"indikator_CI\", y=\"indikator_em\", ax=ax, alpha=0.6, s=10, edgecolor='black')\n",
    "\n",
    "ax.set_xlabel(\"CI Delta\" , fontsize=20)\n",
    "ax.set_ylabel(\"EI Delta\", fontsize=20)\n",
    "\n",
    "ax.tick_params(axis='y',labelsize=19)\n",
    "ax.tick_params(axis='x',labelsize=19)\n",
    "\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.01, 1),\n",
    "           borderaxespad=0)\n",
    "\n",
    "#CI_compar.plot(x='indikator_CI',y='indikator_em',kind='scatter',legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(os.path.join(output_directory_path + '/_CI_indicator_EI_MAP_AP.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2018c74ea08>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax = sns.scatterplot(data=Indicator_compar, x=\"CI_indicator\", y=\"emission_delta_indicator\", hue=Indicator_compar.index, s=200, style=Indicator_compar.index)\n",
    "#CI_compar.plot(kind='scatter', x=\"indikator_CI\", y=\"indikator_em\", ax=ax, alpha=0.6, s=10, edgecolor='black')\n",
    "\n",
    "ax.set_xlabel(\"CI Delta\" , fontsize=20)\n",
    "ax.set_ylabel(\"EM Delta\", fontsize=20)\n",
    "\n",
    "ax.tick_params(axis='y',labelsize=19)\n",
    "ax.tick_params(axis='x',labelsize=19)\n",
    "\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.01, 1),\n",
    "           borderaxespad=0)\n",
    "\n",
    "#CI_compar.plot(x='indikator_CI',y='indikator_em',kind='scatter',legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(os.path.join(output_directory_path + '/_CI_indicator_EM_org_scaled.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "round(CI_compar, 2).sort_values(by='CI_top_down', ascending=True).to_csv(output_directory_path + '/CI_table.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}