{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "60fd7d0e-a46e-4db0-8bbe-00256058ee71",
   "metadata": {},
   "source": [
    "# Convert MRIO satellite rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850708b0-66c3-4ca8-a50b-f7396ec4c1a7",
   "metadata": {},
   "source": [
    "Here we discuss the possibilities for converting entries in MRIO satellite accounts (Extensions).\n",
    "The term *convert* is used very broadly here, it includes the following tasks:\n",
    "\n",
    "- renaming the index names of results/extensions\n",
    "- adjusting the numerical values of the data,\n",
    "  e.g. for unit conversion\n",
    "- finding and extracting data based on indices across a table or an mrio(-extension).\n",
    "  This can be system based on name and potentially constrained by sector/region\n",
    "  or any other specification.\n",
    "- Aggregation/Summation of satellite accounts\n",
    "- Characterization of stressors to impact categories (with low performance, see [the Notebook on stressor characterization](./stressor_characterization.ipynb) for a faster method).\n",
    "\n",
    "We will cover each of these points in the examples below.\n",
    "First [we will go through the general setup](#Basic-setup) describing the structure of the bridging/mapping table.\n",
    "We will then cover the application of the [conversion function to a single, standalone table](#Converting-standalone-tables).\n",
    "There we will show how to rename the index/stressor names, do [unit conversions](#Unit-conversion),\n",
    "and then do [global](#Global-characterization-factors) and [regional](#Regional-specific-characterization-factors) characterizations.\n",
    "For the case of converting a full pymrio Extension, see the [converting pymrio extensions](#Converting-pymrio-Extensions) section.\n",
    "\n",
    "For the connected topics of *Aggregation of MRIOs*\n",
    "see the [Aggregation](./aggregation_examples.ipynb) page and for *Characterization of Stressors* see [the Notebook on stressor characterization](./stressor_characterization.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde3cf89-6c36-47dd-b9d5-48433f4473b5",
   "metadata": {},
   "source": [
    "## Basic setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49bc1d78",
   "metadata": {},
   "source": [
    "All conversion relies on a *mapping table* that maps (bridges)\n",
    "the index/columns of the source data to the indices of the target data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "849bc1ef",
   "metadata": {},
   "source": [
    "This tables requires headers (columns) corresponding to the\n",
    "index.names and columns.names of the source data (constraining data)\n",
    "as well as bridge data  which specify the new target index.\n",
    "The later are indicated by \"NewIndex__OldIndex\" - **the important part are\n",
    "the two underscore in the column name**. Another (optional)\n",
    "column named \"factor\" specifies\n",
    "the multiplication factor for the conversion.\n",
    "Finally, additional columns can be used to indicate units and other information. However, for the basic conversion this information will not be used. This becomes important later for the [converting Pymrio extensions](#Converting-pymrio-Extensions)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b5425f",
   "metadata": {},
   "source": [
    "Constraining data columns can either specify columns or index.\n",
    "However, any constraining data to be bridged/mapped to a new name need to be\n",
    "in the index of the original data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3ec2d8",
   "metadata": {},
   "source": [
    "The first example below shows the simplest case of renaming a single table.\n",
    "This will make the concept of the mapping table clear."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff2125d8",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Converting standalone tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cc15f8b",
   "metadata": {},
   "source": [
    "### Renaming the index entries of a single table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e97063",
   "metadata": {},
   "source": [
    "Assume we have a small MRIO result table with the following structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae7e3af0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import pymrio\n",
    "\n",
    "ghg_result = pd.DataFrame(\n",
    "    columns=[\"Region1\", \"Region2\", \"Region3\"],\n",
    "    index=pd.MultiIndex.from_tuples(\n",
    "        [\n",
    "            (\"Carbon Dioxide\", \"Air\"),\n",
    "            (\"Methane\", \"air\"),\n",
    "        ]\n",
    "    ),\n",
    "    data=[[5, 6, 7], [0.5, 0.6, 0.7]],\n",
    ")\n",
    "ghg_result.index.names = [\"stressor\", \"compartment\"]\n",
    "ghg_result.columns.names = [\"region\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bf2941",
   "metadata": {},
   "source": [
    "Our first task here is to rename to the chemical names of the stressors\n",
    "and fix the compartment spelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f72ae844",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th>chem_stressor__stressor</th>\n",
       "      <th>compartment__compartment</th>\n",
       "      <th>factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>[A|a]ir</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Air</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Methane</td>\n",
       "      <td>[A|a]ir</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Air</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         stressor compartment chem_stressor__stressor  \\\n",
       "0  Carbon Dioxide     [A|a]ir                     CO2   \n",
       "1         Methane     [A|a]ir                     CH4   \n",
       "\n",
       "  compartment__compartment  factor  \n",
       "0                      Air     1.0  \n",
       "1                      Air     1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_map = pd.DataFrame(\n",
    "    columns=[\n",
    "        \"stressor\",\n",
    "        \"compartment\",\n",
    "        \"chem_stressor__stressor\",\n",
    "        \"compartment__compartment\",\n",
    "        \"factor\",\n",
    "    ],\n",
    "    data=[\n",
    "        [\"Carbon Dioxide\", \"[A|a]ir\", \"CO2\", \"Air\", 1.0],\n",
    "        [\"Methane\", \"[A|a]ir\", \"CH4\", \"Air\", 1.0],\n",
    "    ],\n",
    ")\n",
    "ghg_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f73cd886",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chem_stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CH4</th>\n",
       "      <th>Air</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2</th>\n",
       "      <th>Air</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region                     Region1  Region2  Region3\n",
       "chem_stressor compartment                           \n",
       "CH4           Air              0.5      0.6      0.7\n",
       "CO2           Air              5.0      6.0      7.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_new = pymrio.convert(ghg_result, ghg_map)\n",
    "ghg_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e17f0c5d",
   "metadata": {},
   "source": [
    "Explanation: The column headers indicates that the stressor index level\n",
    "should be renamed from \"stressor\" to \"chem_stressor\" and the compartment index level\n",
    "should stay the same (NewName__OldName). The factor column is not used in this case.\n",
    "All renaming columns consider regular expressions,\n",
    "so that the spelling of the compartment can be fixed in one go."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9d7152",
   "metadata": {},
   "source": [
    "For simple rename (and aggregation cases, see below) we can omit the factor column.\n",
    "Thus we obtain the same result with the following mapping table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48688b14",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th>chem_stressor__stressor</th>\n",
       "      <th>compartment__compartment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>[A|a]ir</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Methane</td>\n",
       "      <td>[A|a]ir</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Air</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         stressor compartment chem_stressor__stressor compartment__compartment\n",
       "0  Carbon Dioxide     [A|a]ir                     CO2                      Air\n",
       "1         Methane     [A|a]ir                     CH4                      Air"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_map_wo_factor = pd.DataFrame(\n",
    "    columns=[\n",
    "        \"stressor\",\n",
    "        \"compartment\",\n",
    "        \"chem_stressor__stressor\",\n",
    "        \"compartment__compartment\",\n",
    "    ],\n",
    "    data=[\n",
    "        [\"Carbon Dioxide\", \"[A|a]ir\", \"CO2\", \"Air\"],\n",
    "        [\"Methane\", \"[A|a]ir\", \"CH4\", \"Air\"],\n",
    "    ],\n",
    ")\n",
    "ghg_map_wo_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "826f558a",
   "metadata": {
    "lines_to_next_cell": 2,
    "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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chem_stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CH4</th>\n",
       "      <th>Air</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2</th>\n",
       "      <th>Air</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region                     Region1  Region2  Region3\n",
       "chem_stressor compartment                           \n",
       "CH4           Air              0.5      0.6      0.7\n",
       "CO2           Air              5.0      6.0      7.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_new_wo_factor = pymrio.convert(ghg_result, ghg_map_wo_factor)\n",
    "ghg_new_wo_factor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f1c043",
   "metadata": {},
   "source": [
    "### Unit conversion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77c7c19",
   "metadata": {},
   "source": [
    "With the factor column it is easy to apply unit conversion to any result table.\n",
    "So, to start with the same table as above, we can apply a simple unit conversion.\n",
    "Assuming the data is in tonnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e4fe084e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Carbon Dioxide</th>\n",
       "      <th>Air</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Methane</th>\n",
       "      <th>air</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region                      Region1  Region2  Region3\n",
       "stressor       compartment                           \n",
       "Carbon Dioxide Air              5.0      6.0      7.0\n",
       "Methane        air              0.5      0.6      0.7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_result_ton = pd.DataFrame(\n",
    "    columns=[\"Region1\", \"Region2\", \"Region3\"],\n",
    "    index=pd.MultiIndex.from_tuples(\n",
    "        [\n",
    "            (\"Carbon Dioxide\", \"Air\"),\n",
    "            (\"Methane\", \"air\"),\n",
    "        ]\n",
    "    ),\n",
    "    data=[[5, 6, 7], [0.5, 0.6, 0.7]],\n",
    ")\n",
    "ghg_result_ton.index.names = [\"stressor\", \"compartment\"]\n",
    "ghg_result_ton.columns.names = [\"region\"]\n",
    "ghg_result_ton"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad41ad6",
   "metadata": {},
   "source": [
    "We can get the data in kg by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae5eba1e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th>chem_stressor__stressor</th>\n",
       "      <th>compartment__compartment</th>\n",
       "      <th>factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>[A|a]ir</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Air</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Methane</td>\n",
       "      <td>[A|a]ir</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Air</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         stressor compartment chem_stressor__stressor  \\\n",
       "0  Carbon Dioxide     [A|a]ir                     CO2   \n",
       "1         Methane     [A|a]ir                     CH4   \n",
       "\n",
       "  compartment__compartment  factor  \n",
       "0                      Air    1000  \n",
       "1                      Air    1000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_map_to_kg = pd.DataFrame(\n",
    "    columns=[\n",
    "        \"stressor\",\n",
    "        \"compartment\",\n",
    "        \"chem_stressor__stressor\",\n",
    "        \"compartment__compartment\",\n",
    "        \"factor\",\n",
    "    ],\n",
    "    data=[\n",
    "        [\"Carbon Dioxide\", \"[A|a]ir\", \"CO2\", \"Air\", 1000],\n",
    "        [\"Methane\", \"[A|a]ir\", \"CH4\", \"Air\", 1000],\n",
    "    ],\n",
    ")\n",
    "ghg_map_to_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77effc7a",
   "metadata": {
    "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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chem_stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CH4</th>\n",
       "      <th>Air</th>\n",
       "      <td>500.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2</th>\n",
       "      <th>Air</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region                     Region1  Region2  Region3\n",
       "chem_stressor compartment                           \n",
       "CH4           Air            500.0    600.0    700.0\n",
       "CO2           Air           5000.0   6000.0   7000.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_new_kg = pymrio.convert(ghg_result_ton, ghg_map_to_kg)\n",
    "ghg_new_kg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7738e2d",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "In case of unit conversion of pymrio satellite accounts,\n",
    "we can also check the unit before and set the unit after conversion:\n",
    "TODO: unit conversion extensions, link to extension beow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7024fc74",
   "metadata": {},
   "source": [
    "### Characterization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f00d51",
   "metadata": {},
   "source": [
    "The main power of the convert function is to aggregate and characterize satellite accounts.\n",
    "If needed, region and sector specific characterizations can be applied."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "643b8d26",
   "metadata": {},
   "source": [
    "### Global characterization factors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44aabe37",
   "metadata": {},
   "source": [
    "An simple example is a conversion/aggregation based on GWP100 characterization factors.\n",
    "Here, we continue with the unit converted and cleanup dataframe from above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "677f3872",
   "metadata": {
    "lines_to_next_cell": 2,
    "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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chem_stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CH4</th>\n",
       "      <th>Air</th>\n",
       "      <td>500.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2</th>\n",
       "      <th>Air</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region                     Region1  Region2  Region3\n",
       "chem_stressor compartment                           \n",
       "CH4           Air            500.0    600.0    700.0\n",
       "CO2           Air           5000.0   6000.0   7000.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghg_new_kg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a102de0a",
   "metadata": {},
   "source": [
    "We define a general purpose characterization map for GHG emissions\n",
    "(based on\n",
    "[AR6 GWP100 and GWP20 factors](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf)\n",
    ",with some simplifications):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "289eae3b",
   "metadata": {
    "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>chem_stressor</th>\n",
       "      <th>GWP__chem_stressor</th>\n",
       "      <th>factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CO2</td>\n",
       "      <td>GWP100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CH4</td>\n",
       "      <td>GWP100</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NHx</td>\n",
       "      <td>GWP100</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CO2</td>\n",
       "      <td>GWP20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CH4</td>\n",
       "      <td>GWP20</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NHx</td>\n",
       "      <td>GWP20</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CO2</td>\n",
       "      <td>GWP500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CH4</td>\n",
       "      <td>GWP500</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NHx</td>\n",
       "      <td>GWP500</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chem_stressor GWP__chem_stressor  factor\n",
       "0           CO2             GWP100       1\n",
       "1           CH4             GWP100      29\n",
       "2           NHx             GWP100     273\n",
       "3           CO2              GWP20       1\n",
       "4           CH4              GWP20      80\n",
       "5           NHx              GWP20     273\n",
       "6           CO2             GWP500       1\n",
       "7           CH4             GWP500       8\n",
       "8           NHx             GWP500     130"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GWP_characterization = pd.DataFrame(\n",
    "    columns=[\"chem_stressor\", \"GWP__chem_stressor\", \"factor\"],\n",
    "    data=[\n",
    "        [\"CO2\", \"GWP100\", 1],\n",
    "        [\"CH4\", \"GWP100\", 29],\n",
    "        [\"NHx\", \"GWP100\", 273],\n",
    "        [\"CO2\", \"GWP20\", 1],\n",
    "        [\"CH4\", \"GWP20\", 80],\n",
    "        [\"NHx\", \"GWP20\", 273],\n",
    "        [\"CO2\", \"GWP500\", 1],\n",
    "        [\"CH4\", \"GWP500\", 8],\n",
    "        [\"NHx\", \"GWP500\", 130],\n",
    "    ],\n",
    ")\n",
    "GWP_characterization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04df3552",
   "metadata": {
    "lines_to_next_cell": 2,
    "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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GWP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GWP100</th>\n",
       "      <td>19500.0</td>\n",
       "      <td>23400.0</td>\n",
       "      <td>27300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GWP20</th>\n",
       "      <td>45000.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>63000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GWP500</th>\n",
       "      <td>9000.0</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>12600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region  Region1  Region2  Region3\n",
       "GWP                              \n",
       "GWP100  19500.0  23400.0  27300.0\n",
       "GWP20   45000.0  54000.0  63000.0\n",
       "GWP500   9000.0  10800.0  12600.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GWP_result = pymrio.convert(ghg_new_kg, GWP_characterization)\n",
    "GWP_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0706fdb9",
   "metadata": {},
   "source": [
    "As we can see, GWP_characterization can include factors for stressors not actually\n",
    "present in the data.\n",
    "These are silently ignored in the conversion process.\n",
    "We also did not specify the compartment and assumed the same factors apply\n",
    "independent of the compartment (we could pass through the compartment to\n",
    "the new result table via passing drop_not_bridge=False to the convert function)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab77360e",
   "metadata": {
    "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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GWP</th>\n",
       "      <th>compartment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GWP100</th>\n",
       "      <th>Air</th>\n",
       "      <td>19500.0</td>\n",
       "      <td>23400.0</td>\n",
       "      <td>27300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GWP20</th>\n",
       "      <th>Air</th>\n",
       "      <td>45000.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>63000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GWP500</th>\n",
       "      <th>Air</th>\n",
       "      <td>9000.0</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>12600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region              Region1  Region2  Region3\n",
       "GWP    compartment                           \n",
       "GWP100 Air          19500.0  23400.0  27300.0\n",
       "GWP20  Air          45000.0  54000.0  63000.0\n",
       "GWP500 Air           9000.0  10800.0  12600.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GWP_result_with_comp = pymrio.convert(\n",
    "    ghg_new_kg, GWP_characterization, drop_not_bridged_index=False\n",
    ")\n",
    "GWP_result_with_comp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8347eaf5",
   "metadata": {},
   "source": [
    "All stressors mapped to the same \"impact\" are first converted via the\n",
    "value given in the factor column\n",
    "and then summed up (the aggregation function can be changed\n",
    "via the `agg_func` parameter)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44bb8bec",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Regional specific characterization factors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fe2789b",
   "metadata": {},
   "source": [
    "A more complex example is the application of regional specific characterization\n",
    "factors (the same principle applies to sector specific factors.).\n",
    "For that, we assume some land use results for different regions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00156b6d",
   "metadata": {
    "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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stressor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wheat</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maize</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rice</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pasture</th>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forest extensive</th>\n",
       "      <td>32</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forest intensive</th>\n",
       "      <td>43</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region            Region1  Region2  Region3\n",
       "stressor                                   \n",
       "Wheat                   3       10        1\n",
       "Maize                   5       20        3\n",
       "Rice                    0       12       34\n",
       "Pasture                12       34        9\n",
       "Forest extensive       32       27       11\n",
       "Forest intensive       43       17       24"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "land_use_data = pd.DataFrame(\n",
    "    columns=[\"Region1\", \"Region2\", \"Region3\"],\n",
    "    index=[\n",
    "        \"Wheat\",\n",
    "        \"Maize\",\n",
    "        \"Rice\",\n",
    "        \"Pasture\",\n",
    "        \"Forest extensive\",\n",
    "        \"Forest intensive\",\n",
    "    ],\n",
    "    data=[\n",
    "        [3, 10, 1],\n",
    "        [5, 20, 3],\n",
    "        [0, 12, 34],\n",
    "        [12, 34, 9],\n",
    "        [32, 27, 11],\n",
    "        [43, 17, 24],\n",
    "    ],\n",
    ")\n",
    "land_use_data.index.names = [\"stressor\"]\n",
    "land_use_data.columns.names = [\"region\"]\n",
    "land_use_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e356ee1",
   "metadata": {},
   "source": [
    "Now we setup a pseudo characterization table for converting the land use data into\n",
    "biodiversity impacts. We assume, that the characterization factors vary based on\n",
    "land use type and region. However, the \"region\" information is a pure\n",
    "constraining column (specifying the region for which the factor applies) without\n",
    "any bridge column mapping it to a new name. Thus, the \"region\" can either be in the index\n",
    "or in the columns of the source data - in the given case it is in the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3ed4128",
   "metadata": {
    "lines_to_next_cell": 2,
    "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>stressor</th>\n",
       "      <th>BioDiv__stressor</th>\n",
       "      <th>region</th>\n",
       "      <th>factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wheat|Maize</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wheat</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region[2,3]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maize</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region[2,3]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rice</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rice</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rice</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pasture</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region[1,2,3]</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Forest.*</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Forest.*</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Forest ext.*</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Forest int.*</td>\n",
       "      <td>BioImpact</td>\n",
       "      <td>Region3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        stressor BioDiv__stressor         region  factor\n",
       "0    Wheat|Maize        BioImpact        Region1       3\n",
       "1          Wheat        BioImpact    Region[2,3]       4\n",
       "2          Maize        BioImpact    Region[2,3]       7\n",
       "3           Rice        BioImpact        Region1      12\n",
       "4           Rice        BioImpact        Region2      12\n",
       "5           Rice        BioImpact        Region3      12\n",
       "6        Pasture        BioImpact  Region[1,2,3]      12\n",
       "7       Forest.*        BioImpact        Region1       2\n",
       "8       Forest.*        BioImpact        Region2       3\n",
       "9   Forest ext.*        BioImpact        Region3       1\n",
       "10  Forest int.*        BioImpact        Region3       3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landuse_characterization = pd.DataFrame(\n",
    "    columns=[\"stressor\", \"BioDiv__stressor\", \"region\", \"factor\"],\n",
    "    data=[\n",
    "        [\"Wheat|Maize\", \"BioImpact\", \"Region1\", 3],\n",
    "        [\"Wheat\", \"BioImpact\", \"Region[2,3]\", 4],\n",
    "        [\"Maize\", \"BioImpact\", \"Region[2,3]\", 7],\n",
    "        [\"Rice\", \"BioImpact\", \"Region1\", 12],\n",
    "        [\"Rice\", \"BioImpact\", \"Region2\", 12],\n",
    "        [\"Rice\", \"BioImpact\", \"Region3\", 12],\n",
    "        [\"Pasture\", \"BioImpact\", \"Region[1,2,3]\", 12],\n",
    "        [\"Forest.*\", \"BioImpact\", \"Region1\", 2],\n",
    "        [\"Forest.*\", \"BioImpact\", \"Region2\", 3],\n",
    "        [\"Forest ext.*\", \"BioImpact\", \"Region3\", 1],\n",
    "        [\"Forest int.*\", \"BioImpact\", \"Region3\", 3],\n",
    "    ],\n",
    ")\n",
    "landuse_characterization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee910ad",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "The table shows several possibilities to specify factors which apply to several\n",
    "regions/stressors.\n",
    "All of them are based on the [regular expression](https://docs.python.org/3/howto/regex.html):\n",
    "\n",
    "- In the first data line we use the \"or\" operator \"|\" to specify that the\n",
    "same factor applies to Wheat and Maize.\n",
    "- On the next line we use the grouping capabilities of regular expressions\n",
    "to indicate the same factor for Region 2 and 3.\n",
    "- At the last four lines .* matches any number of characters. This\n",
    "allows to specify the same factor for both forest types or to abbreviate\n",
    "the naming of the stressor (last 2 lines).\n",
    "\n",
    "The use of regular expression is optional, one can also use one line per factor.\n",
    "In the example above, we indicate the factor for Rice in 3 subsequent entries.\n",
    "This would be equivalent to ```[\"Rice\", \"BioImpact\", \"Region[1,2,3]\", 12]```."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce0e5a7c",
   "metadata": {},
   "source": [
    "With that setup we can now characterize the land use data in land_use_result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8f2e3a37",
   "metadata": {
    "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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BioDiv</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BioImpact</th>\n",
       "      <td>318</td>\n",
       "      <td>864</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region     Region1  Region2  Region3\n",
       "BioDiv                              \n",
       "BioImpact      318      864      624"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biodiv_characterised = pymrio.convert(land_use_data, landuse_characterization)\n",
    "biodiv_characterised"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c48b45",
   "metadata": {},
   "source": [
    "Note, that in this example the region is not in the index\n",
    "but in the columns.\n",
    "The convert function can handle both cases.\n",
    "The only difference is that constraints which are\n",
    "in the columns will never be aggregated but keep the column resolution at the\n",
    "output. Thus the result is equivalent to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "157dd099",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stressor          region \n",
       "Wheat             Region1     3\n",
       "                  Region2    10\n",
       "                  Region3     1\n",
       "Maize             Region1     5\n",
       "                  Region2    20\n",
       "                  Region3     3\n",
       "Rice              Region1     0\n",
       "                  Region2    12\n",
       "                  Region3    34\n",
       "Pasture           Region1    12\n",
       "                  Region2    34\n",
       "                  Region3     9\n",
       "Forest extensive  Region1    32\n",
       "                  Region2    27\n",
       "                  Region3    11\n",
       "Forest intensive  Region1    43\n",
       "                  Region2    17\n",
       "                  Region3    24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "land_use_data_stacked = land_use_data.stack(level=\"region\")\n",
    "land_use_data_stacked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d435a0a",
   "metadata": {
    "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>region</th>\n",
       "      <th>Region1</th>\n",
       "      <th>Region2</th>\n",
       "      <th>Region3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BioDiv</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BioImpact</th>\n",
       "      <td>318</td>\n",
       "      <td>864</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region     Region1  Region2  Region3\n",
       "BioDiv                              \n",
       "BioImpact      318      864      624"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biodiv_characterised_stacked = pymrio.convert(\n",
    "    land_use_data_stacked, landuse_characterization, drop_not_bridged_index=False\n",
    ")\n",
    "biodiv_characterised_stacked.unstack(level=\"region\")[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e09b7d4",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "In this case we have to specify to not drop the not bridged \"region\" index.\n",
    "We then unstack the result again, and have to select the first element ([0]),\n",
    "since there where not other columns left after stacking them before the\n",
    "characterization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b48a82",
   "metadata": {},
   "source": [
    "TODO: section perhaps needed somewhere?\n",
    "Irrespectively of the table or the mrio system, the convert function always follows the same pattern.\n",
    "It requires a bridge table, which contains the mapping of the indices of the source data to the indices of the target data.\n",
    "This bridge table has to follow a specific format, depending on the table to be converted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2467c22e",
   "metadata": {},
   "source": [
    "# Converting pymrio Extensions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe6d4cc",
   "metadata": {},
   "source": [
    "The same principles as for individual tables can be used for converting full pymrio type Extensions (aka satellite accounts).\n",
    "In difference to the single tables, pymrio Extensions consist of several pandas DataFrames which can be converted in one go.\n",
    "Almost the same bridge table structure as for single tables can be used. The main additional information needed is in regard to\n",
    "units. Since pymrio Extensions include a unit dataframe, information about the unit names need to be included."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da124a31",
   "metadata": {},
   "source": [
    "Extensions can be converted either one at a time, but the main power of the method lies in collecting stressor data across different extensions\n",
    "and converting them in one go."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f45b56f0",
   "metadata": {},
   "source": [
    "We start with a simple example for converting a single extension of a pymrio MRIO system.\n",
    "To do so, we load the test MRIO system from pymrio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac3f7133",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mrio = pymrio.load_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2024493",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Among others, this system has an extension \"emissions\" with industry and final demand emissions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9b090fbb",
   "metadata": {
    "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th colspan=\"8\" halign=\"left\">reg1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">reg2</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">reg5</th>\n",
       "      <th colspan=\"8\" halign=\"left\">reg6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sector</th>\n",
       "      <th>food</th>\n",
       "      <th>mining</th>\n",
       "      <th>manufactoring</th>\n",
       "      <th>electricity</th>\n",
       "      <th>construction</th>\n",
       "      <th>trade</th>\n",
       "      <th>transport</th>\n",
       "      <th>other</th>\n",
       "      <th>food</th>\n",
       "      <th>mining</th>\n",
       "      <th>...</th>\n",
       "      <th>transport</th>\n",
       "      <th>other</th>\n",
       "      <th>food</th>\n",
       "      <th>mining</th>\n",
       "      <th>manufactoring</th>\n",
       "      <th>electricity</th>\n",
       "      <th>construction</th>\n",
       "      <th>trade</th>\n",
       "      <th>transport</th>\n",
       "      <th>other</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>emission_type1</th>\n",
       "      <th>air</th>\n",
       "      <td>1848064.80</td>\n",
       "      <td>986448.090</td>\n",
       "      <td>23613787.00</td>\n",
       "      <td>28139100.00</td>\n",
       "      <td>2584141.80</td>\n",
       "      <td>4132656.3</td>\n",
       "      <td>21766987.0</td>\n",
       "      <td>7842090.6</td>\n",
       "      <td>1697937.30</td>\n",
       "      <td>347378.150</td>\n",
       "      <td>...</td>\n",
       "      <td>42299319</td>\n",
       "      <td>10773826.0</td>\n",
       "      <td>15777996.0</td>\n",
       "      <td>6420955.5</td>\n",
       "      <td>113172450.0</td>\n",
       "      <td>56022534.0</td>\n",
       "      <td>4861838.5</td>\n",
       "      <td>18195621</td>\n",
       "      <td>47046542.0</td>\n",
       "      <td>21632868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emission_type2</th>\n",
       "      <th>water</th>\n",
       "      <td>139250.47</td>\n",
       "      <td>22343.295</td>\n",
       "      <td>763569.18</td>\n",
       "      <td>273981.55</td>\n",
       "      <td>317396.51</td>\n",
       "      <td>1254477.8</td>\n",
       "      <td>1012999.1</td>\n",
       "      <td>2449178.0</td>\n",
       "      <td>204835.44</td>\n",
       "      <td>29463.944</td>\n",
       "      <td>...</td>\n",
       "      <td>4199841</td>\n",
       "      <td>7191006.3</td>\n",
       "      <td>4826108.1</td>\n",
       "      <td>1865625.1</td>\n",
       "      <td>12700193.0</td>\n",
       "      <td>753213.7</td>\n",
       "      <td>2699288.3</td>\n",
       "      <td>13892313</td>\n",
       "      <td>8765784.3</td>\n",
       "      <td>16782553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "region                            reg1                                         \\\n",
       "sector                            food      mining manufactoring  electricity   \n",
       "stressor       compartment                                                      \n",
       "emission_type1 air          1848064.80  986448.090   23613787.00  28139100.00   \n",
       "emission_type2 water         139250.47   22343.295     763569.18    273981.55   \n",
       "\n",
       "region                                                                     \\\n",
       "sector                     construction      trade   transport      other   \n",
       "stressor       compartment                                                  \n",
       "emission_type1 air           2584141.80  4132656.3  21766987.0  7842090.6   \n",
       "emission_type2 water          317396.51  1254477.8   1012999.1  2449178.0   \n",
       "\n",
       "region                            reg2              ...      reg5              \\\n",
       "sector                            food      mining  ... transport       other   \n",
       "stressor       compartment                          ...                         \n",
       "emission_type1 air          1697937.30  347378.150  ...  42299319  10773826.0   \n",
       "emission_type2 water         204835.44   29463.944  ...   4199841   7191006.3   \n",
       "\n",
       "region                            reg6                                       \\\n",
       "sector                            food     mining manufactoring electricity   \n",
       "stressor       compartment                                                    \n",
       "emission_type1 air          15777996.0  6420955.5   113172450.0  56022534.0   \n",
       "emission_type2 water         4826108.1  1865625.1    12700193.0    753213.7   \n",
       "\n",
       "region                                                                   \n",
       "sector                     construction     trade   transport     other  \n",
       "stressor       compartment                                               \n",
       "emission_type1 air            4861838.5  18195621  47046542.0  21632868  \n",
       "emission_type2 water          2699288.3  13892313   8765784.3  16782553  \n",
       "\n",
       "[2 rows x 48 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrio.emissions.F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7d52ba38",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th colspan=\"7\" halign=\"left\">reg1</th>\n",
       "      <th colspan=\"3\" halign=\"left\">reg2</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">reg5</th>\n",
       "      <th colspan=\"7\" halign=\"left\">reg6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>Final consumption expenditure by households</th>\n",
       "      <th>Final consumption expenditure by non-profit organisations serving households (NPISH)</th>\n",
       "      <th>Final consumption expenditure by government</th>\n",
       "      <th>Gross fixed capital formation</th>\n",
       "      <th>Changes in inventories</th>\n",
       "      <th>Changes in valuables</th>\n",
       "      <th>Export</th>\n",
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       "      <th>Final consumption expenditure by non-profit organisations serving households (NPISH)</th>\n",
       "      <th>Final consumption expenditure by government</th>\n",
       "      <th>Gross fixed capital formation</th>\n",
       "      <th>Changes in inventories</th>\n",
       "      <th>Changes in valuables</th>\n",
       "      <th>Export</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>emission_type1</th>\n",
       "      <th>air</th>\n",
       "      <td>62335321</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>38566929</td>\n",
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       "      <td>571278300.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emission_type2</th>\n",
       "      <th>water</th>\n",
       "      <td>59206405</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40214002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163362050.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "region                                                            reg1  \\\n",
       "category                   Final consumption expenditure by households   \n",
       "stressor       compartment                                               \n",
       "emission_type1 air                                            62335321   \n",
       "emission_type2 water                                          59206405   \n",
       "\n",
       "region                                                                                                           \\\n",
       "category                   Final consumption expenditure by non-profit organisations serving households (NPISH)   \n",
       "stressor       compartment                                                                                        \n",
       "emission_type1 air                                                          0                                     \n",
       "emission_type2 water                                                        0                                     \n",
       "\n",
       "region                                                                  \\\n",
       "category                   Final consumption expenditure by government   \n",
       "stressor       compartment                                               \n",
       "emission_type1 air                                                   0   \n",
       "emission_type2 water                                                 0   \n",
       "\n",
       "region                                                    \\\n",
       "category                   Gross fixed capital formation   \n",
       "stressor       compartment                                 \n",
       "emission_type1 air                                     0   \n",
       "emission_type2 water                                   0   \n",
       "\n",
       "region                                                                         \\\n",
       "category                   Changes in inventories Changes in valuables Export   \n",
       "stressor       compartment                                                      \n",
       "emission_type1 air                              0                    0      0   \n",
       "emission_type2 water                            0                    0      0   \n",
       "\n",
       "region                                                            reg2  \\\n",
       "category                   Final consumption expenditure by households   \n",
       "stressor       compartment                                               \n",
       "emission_type1 air                                            38566929   \n",
       "emission_type2 water                                          40214002   \n",
       "\n",
       "region                                                                                                           \\\n",
       "category                   Final consumption expenditure by non-profit organisations serving households (NPISH)   \n",
       "stressor       compartment                                                                                        \n",
       "emission_type1 air                                                          0                                     \n",
       "emission_type2 water                                                        0                                     \n",
       "\n",
       "region                                                                  ...  \\\n",
       "category                   Final consumption expenditure by government  ...   \n",
       "stressor       compartment                                              ...   \n",
       "emission_type1 air                                                   0  ...   \n",
       "emission_type2 water                                                 0  ...   \n",
       "\n",
       "region                                       reg5                              \\\n",
       "category                   Changes in inventories Changes in valuables Export   \n",
       "stressor       compartment                                                      \n",
       "emission_type1 air                              0                    0      0   \n",
       "emission_type2 water                            0                    0      0   \n",
       "\n",
       "region                                                            reg6  \\\n",
       "category                   Final consumption expenditure by households   \n",
       "stressor       compartment                                               \n",
       "emission_type1 air                                         571278300.0   \n",
       "emission_type2 water                                       163362050.0   \n",
       "\n",
       "region                                                                                                           \\\n",
       "category                   Final consumption expenditure by non-profit organisations serving households (NPISH)   \n",
       "stressor       compartment                                                                                        \n",
       "emission_type1 air                                                          0                                     \n",
       "emission_type2 water                                                        0                                     \n",
       "\n",
       "region                                                                  \\\n",
       "category                   Final consumption expenditure by government   \n",
       "stressor       compartment                                               \n",
       "emission_type1 air                                                   0   \n",
       "emission_type2 water                                                 0   \n",
       "\n",
       "region                                                    \\\n",
       "category                   Gross fixed capital formation   \n",
       "stressor       compartment                                 \n",
       "emission_type1 air                                     0   \n",
       "emission_type2 water                                   0   \n",
       "\n",
       "region                                                                         \n",
       "category                   Changes in inventories Changes in valuables Export  \n",
       "stressor       compartment                                                     \n",
       "emission_type1 air                              0                    0      0  \n",
       "emission_type2 water                            0                    0      0  \n",
       "\n",
       "[2 rows x 42 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrio.emissions.F_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "107697bc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stressor</th>\n",
       "      <th>compartment</th>\n",
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       "  <tbody>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>emission_type2</th>\n",
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      ],
      "text/plain": [
       "                           unit\n",
       "stressor       compartment     \n",
       "emission_type1 air           kg\n",
       "emission_type2 water         kg"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrio.emissions.unit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93107bf8",
   "metadata": {},
   "source": [
    "We now setup a bridge table for converting/characterizing these emission data\n",
    "to several other accounts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1a1fbdcd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stressor</th>\n",
       "      <th>compartment</th>\n",
       "      <th>total__stressor</th>\n",
       "      <th>factor</th>\n",
       "      <th>unit_orig</th>\n",
       "      <th>unit_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emis.*</td>\n",
       "      <td>air|water</td>\n",
       "      <td>total_sum_tonnes</td>\n",
       "      <td>0.001</td>\n",
       "      <td>kg</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>emission_type[1|2]</td>\n",
       "      <td>.*</td>\n",
       "      <td>total_sum</td>\n",
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       "      <td>kg</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>emission_type1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>emission_type2</td>\n",
       "      <td>.*</td>\n",
       "      <td>water_emissions</td>\n",
       "      <td>1000.000</td>\n",
       "      <td>kg</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>emission_type1</td>\n",
       "      <td>.*</td>\n",
       "      <td>char_emissions</td>\n",
       "      <td>2.000</td>\n",
       "      <td>kg</td>\n",
       "      <td>kg_eq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>emission_type2</td>\n",
       "      <td>.*</td>\n",
       "      <td>char_emissions</td>\n",
       "      <td>10.000</td>\n",
       "      <td>kg</td>\n",
       "      <td>kg_eq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             stressor compartment   total__stressor    factor unit_orig  \\\n",
       "0              emis.*   air|water  total_sum_tonnes     0.001        kg   \n",
       "1  emission_type[1|2]          .*         total_sum     1.000        kg   \n",
       "2      emission_type1          .*     air_emissions     0.001        kg   \n",
       "3      emission_type2          .*   water_emissions  1000.000        kg   \n",
       "4      emission_type1          .*    char_emissions     2.000        kg   \n",
       "5      emission_type2          .*    char_emissions    10.000        kg   \n",
       "\n",
       "  unit_new  \n",
       "0        t  \n",
       "1       kg  \n",
       "2        t  \n",
       "3        g  \n",
       "4    kg_eq  \n",
       "5    kg_eq  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emis_bridge = pd.DataFrame(\n",
    "    columns=[\n",
    "        \"stressor\",\n",
    "        \"compartment\",\n",
    "        \"total__stressor\",\n",
    "        \"factor\",\n",
    "        \"unit_orig\",\n",
    "        \"unit_new\",\n",
    "    ],\n",
    "    data=[\n",
    "        [\"emis.*\", \"air|water\", \"total_sum_tonnes\", 1e-3, \"kg\", \"t\"],\n",
    "        [\"emission_type[1|2]\", \".*\", \"total_sum\", 1, \"kg\", \"kg\"],\n",
    "        [\"emission_type1\", \".*\", \"air_emissions\", 1e-3, \"kg\", \"t\"],\n",
    "        [\"emission_type2\", \".*\", \"water_emissions\", 1000, \"kg\", \"g\"],\n",
    "        [\"emission_type1\", \".*\", \"char_emissions\", 2, \"kg\", \"kg_eq\"],\n",
    "        [\"emission_type2\", \".*\", \"char_emissions\", 10, \"kg\", \"kg_eq\"],\n",
    "    ],\n",
    ")\n",
    "emis_bridge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72db9156",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "This is a fully made up example showing various capabilities of the method.\n",
    "In line\n",
    "   - 0: find all stressors with emissions (emis.*) in either air or water (air|water) compartment, rename it to \"total_sum_tonnes\" (total__stressor) by multiplying with a factor 0.0001 which converts the original unit \"kg\" to tonnes.\n",
    "   - 1: find emission_type1 and 2, over all compartments and sum them together without any multiplication\n",
    "   - 2: convert emissions of type 1 to air emissions in tons\n",
    "   - 3: convert emissions of type 2 to water emissions in g\n",
    "   - 4 and 5: two different characterization factors of 2 (type1) and 10 (type2) to convert to kg equivalent (kg_eq) of some kind and then add them together to a summary impact \"char_emissions\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de6c294d",
   "metadata": {},
   "source": [
    "The new extensino can then be calculated with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d536db02",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extension new_ext with parameters: name, F, F_Y, unit\n"
     ]
    }
   ],
   "source": [
    "mrio.new_ext = mrio.emissions.convert(emis_bridge, new_extension_name=\"new_ext\")\n",
    "print(mrio.new_ext)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd2c82d",
   "metadata": {},
   "source": [
    "And then the new accounts can be calculated with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "85a8e3a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extension new_ext with parameters: name, F, F_Y, S, S_Y, M, M_down, D_cba, D_pba, D_imp, D_exp, unit, D_cba_reg, D_pba_reg, D_imp_reg, D_exp_reg, D_cba_cap, D_pba_cap, D_imp_cap, D_exp_cap\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>air_emissions</th>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>char_emissions</th>\n",
       "      <td>kg_eq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sum</th>\n",
       "      <td>kg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sum_tonnes</th>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>water_emissions</th>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   unit\n",
       "total                  \n",
       "air_emissions         t\n",
       "char_emissions    kg_eq\n",
       "total_sum            kg\n",
       "total_sum_tonnes      t\n",
       "water_emissions       g"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrio.calc_all()\n",
    "print(mrio.new_ext)\n",
    "mrio.new_ext.unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "63b953a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th colspan=\"8\" halign=\"left\">reg1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">reg2</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">reg5</th>\n",
       "      <th colspan=\"8\" halign=\"left\">reg6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sector</th>\n",
       "      <th>food</th>\n",
       "      <th>mining</th>\n",
       "      <th>manufactoring</th>\n",
       "      <th>electricity</th>\n",
       "      <th>construction</th>\n",
       "      <th>trade</th>\n",
       "      <th>transport</th>\n",
       "      <th>other</th>\n",
       "      <th>food</th>\n",
       "      <th>mining</th>\n",
       "      <th>...</th>\n",
       "      <th>transport</th>\n",
       "      <th>other</th>\n",
       "      <th>food</th>\n",
       "      <th>mining</th>\n",
       "      <th>manufactoring</th>\n",
       "      <th>electricity</th>\n",
       "      <th>construction</th>\n",
       "      <th>trade</th>\n",
       "      <th>transport</th>\n",
       "      <th>other</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>air_emissions</th>\n",
       "      <td>2.056183e+03</td>\n",
       "      <td>1.794235e+02</td>\n",
       "      <td>9.749300e+04</td>\n",
       "      <td>1.188759e+04</td>\n",
       "      <td>3.342906e+03</td>\n",
       "      <td>3.885884e+03</td>\n",
       "      <td>1.075027e+04</td>\n",
       "      <td>1.582152e+04</td>\n",
       "      <td>1.793338e+03</td>\n",
       "      <td>1.914560e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.209505e+04</td>\n",
       "      <td>1.138661e+04</td>\n",
       "      <td>1.517235e+04</td>\n",
       "      <td>1.345318e+03</td>\n",
       "      <td>7.145075e+04</td>\n",
       "      <td>3.683167e+04</td>\n",
       "      <td>1.836696e+03</td>\n",
       "      <td>4.241568e+04</td>\n",
       "      <td>4.805409e+04</td>\n",
       "      <td>3.602298e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>char_emissions</th>\n",
       "      <td>6.535470e+06</td>\n",
       "      <td>6.116290e+05</td>\n",
       "      <td>3.621100e+08</td>\n",
       "      <td>2.514649e+07</td>\n",
       "      <td>1.015410e+07</td>\n",
       "      <td>1.553797e+07</td>\n",
       "      <td>2.650016e+07</td>\n",
       "      <td>1.164481e+08</td>\n",
       "      <td>5.723203e+06</td>\n",
       "      <td>7.562722e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.266275e+08</td>\n",
       "      <td>9.584531e+07</td>\n",
       "      <td>7.455045e+07</td>\n",
       "      <td>8.062852e+06</td>\n",
       "      <td>2.497159e+08</td>\n",
       "      <td>7.939148e+07</td>\n",
       "      <td>1.274291e+07</td>\n",
       "      <td>6.297358e+08</td>\n",
       "      <td>1.844730e+08</td>\n",
       "      <td>5.355358e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sum</th>\n",
       "      <td>2.298494e+06</td>\n",
       "      <td>2.047017e+05</td>\n",
       "      <td>1.142054e+08</td>\n",
       "      <td>1.202472e+07</td>\n",
       "      <td>3.689735e+06</td>\n",
       "      <td>4.662504e+06</td>\n",
       "      <td>1.125023e+07</td>\n",
       "      <td>2.430203e+07</td>\n",
       "      <td>2.006991e+06</td>\n",
       "      <td>2.287921e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>4.633879e+07</td>\n",
       "      <td>1.869382e+07</td>\n",
       "      <td>1.959293e+07</td>\n",
       "      <td>1.882540e+06</td>\n",
       "      <td>8.213219e+07</td>\n",
       "      <td>3.740449e+07</td>\n",
       "      <td>2.743647e+06</td>\n",
       "      <td>9.690613e+07</td>\n",
       "      <td>5.689057e+07</td>\n",
       "      <td>8.237196e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_sum_tonnes</th>\n",
       "      <td>2.298494e+03</td>\n",
       "      <td>2.047017e+02</td>\n",
       "      <td>1.142054e+05</td>\n",
       "      <td>1.202472e+04</td>\n",
       "      <td>3.689735e+03</td>\n",
       "      <td>4.662504e+03</td>\n",
       "      <td>1.125023e+04</td>\n",
       "      <td>2.430203e+04</td>\n",
       "      <td>2.006991e+03</td>\n",
       "      <td>2.287921e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.633879e+04</td>\n",
       "      <td>1.869382e+04</td>\n",
       "      <td>1.959293e+04</td>\n",
       "      <td>1.882540e+03</td>\n",
       "      <td>8.213219e+04</td>\n",
       "      <td>3.740449e+04</td>\n",
       "      <td>2.743647e+03</td>\n",
       "      <td>9.690613e+04</td>\n",
       "      <td>5.689057e+04</td>\n",
       "      <td>8.237196e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>water_emissions</th>\n",
       "      <td>2.423103e+08</td>\n",
       "      <td>2.527819e+07</td>\n",
       "      <td>1.671240e+10</td>\n",
       "      <td>1.371303e+08</td>\n",
       "      <td>3.468292e+08</td>\n",
       "      <td>7.766205e+08</td>\n",
       "      <td>4.999628e+08</td>\n",
       "      <td>8.480505e+09</td>\n",
       "      <td>2.136528e+08</td>\n",
       "      <td>3.733601e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>4.243738e+09</td>\n",
       "      <td>7.307208e+09</td>\n",
       "      <td>4.420574e+09</td>\n",
       "      <td>5.372216e+08</td>\n",
       "      <td>1.068144e+10</td>\n",
       "      <td>5.728136e+08</td>\n",
       "      <td>9.069515e+08</td>\n",
       "      <td>5.449044e+10</td>\n",
       "      <td>8.836484e+09</td>\n",
       "      <td>4.634899e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "region                    reg1                                            \\\n",
       "sector                    food        mining manufactoring   electricity   \n",
       "total                                                                      \n",
       "air_emissions     2.056183e+03  1.794235e+02  9.749300e+04  1.188759e+04   \n",
       "char_emissions    6.535470e+06  6.116290e+05  3.621100e+08  2.514649e+07   \n",
       "total_sum         2.298494e+06  2.047017e+05  1.142054e+08  1.202472e+07   \n",
       "total_sum_tonnes  2.298494e+03  2.047017e+02  1.142054e+05  1.202472e+04   \n",
       "water_emissions   2.423103e+08  2.527819e+07  1.671240e+10  1.371303e+08   \n",
       "\n",
       "region                                                                    \\\n",
       "sector            construction         trade     transport         other   \n",
       "total                                                                      \n",
       "air_emissions     3.342906e+03  3.885884e+03  1.075027e+04  1.582152e+04   \n",
       "char_emissions    1.015410e+07  1.553797e+07  2.650016e+07  1.164481e+08   \n",
       "total_sum         3.689735e+06  4.662504e+06  1.125023e+07  2.430203e+07   \n",
       "total_sum_tonnes  3.689735e+03  4.662504e+03  1.125023e+04  2.430203e+04   \n",
       "water_emissions   3.468292e+08  7.766205e+08  4.999628e+08  8.480505e+09   \n",
       "\n",
       "region                    reg2                ...          reg5                \\\n",
       "sector                    food        mining  ...     transport         other   \n",
       "total                                         ...                               \n",
       "air_emissions     1.793338e+03  1.914560e+01  ...  4.209505e+04  1.138661e+04   \n",
       "char_emissions    5.723203e+06  7.562722e+04  ...  1.266275e+08  9.584531e+07   \n",
       "total_sum         2.006991e+06  2.287921e+04  ...  4.633879e+07  1.869382e+07   \n",
       "total_sum_tonnes  2.006991e+03  2.287921e+01  ...  4.633879e+04  1.869382e+04   \n",
       "water_emissions   2.136528e+08  3.733601e+06  ...  4.243738e+09  7.307208e+09   \n",
       "\n",
       "region                    reg6                                            \\\n",
       "sector                    food        mining manufactoring   electricity   \n",
       "total                                                                      \n",
       "air_emissions     1.517235e+04  1.345318e+03  7.145075e+04  3.683167e+04   \n",
       "char_emissions    7.455045e+07  8.062852e+06  2.497159e+08  7.939148e+07   \n",
       "total_sum         1.959293e+07  1.882540e+06  8.213219e+07  3.740449e+07   \n",
       "total_sum_tonnes  1.959293e+04  1.882540e+03  8.213219e+04  3.740449e+04   \n",
       "water_emissions   4.420574e+09  5.372216e+08  1.068144e+10  5.728136e+08   \n",
       "\n",
       "region                                                                    \n",
       "sector            construction         trade     transport         other  \n",
       "total                                                                     \n",
       "air_emissions     1.836696e+03  4.241568e+04  4.805409e+04  3.602298e+04  \n",
       "char_emissions    1.274291e+07  6.297358e+08  1.844730e+08  5.355358e+08  \n",
       "total_sum         2.743647e+06  9.690613e+07  5.689057e+07  8.237196e+07  \n",
       "total_sum_tonnes  2.743647e+03  9.690613e+04  5.689057e+04  8.237196e+04  \n",
       "water_emissions   9.069515e+08  5.449044e+10  8.836484e+09  4.634899e+10  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrio.new_ext.D_cba\n",
    "\n",
    "\n",
    "# CONT: test/explain characterization across different extensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f017e1b-d5ee-497e-a0bb-2c531195a50b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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