{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parsing the Eora26 EE MRIO database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Eora26"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need to register at http://www.worldmrio.com to download Eora 26 database.\n",
    "You can download the files from http://www.worldmrio.com/simplified.\n",
    "Or you can download these files with the pymrio automatic downloader as described at [Eora26 download](autodownload.ipynb#Eora26-download)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To parse a single year do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymrio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "eora_storage = \"/tmp/mrios/eora26\""
     ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eora_meta = pymrio.download_eora26(\n",
    "    storage_folder=eora_storage,\n",
    "    email=\"<Your Eora account email>\",\n",
    "    password=\"<Your Eora account password>\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/konstans/bin/anaconda3/envs/pymrio_dev/lib/python3.7/site-packages/pandas/core/generic.py:3887: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "  obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n"
     ]
    }
   ],
   "source": [
    "eora = pymrio.parse_eora26(year=2005, path=eora_storage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eora includes (almost) all countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AFG', 'ALB', 'DZA', 'AND', 'AGO', 'ATG', 'ARG', 'ARM', 'ABW', 'AUS',\n",
       "       ...\n",
       "       'TZA', 'USA', 'URY', 'UZB', 'VUT', 'VEN', 'VNM', 'YEM', 'ZMB', 'ZWE'],\n",
       "      dtype='object', name='region', length=189)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eora.get_regions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can easily be aggregated to, for example, the OECD/NON_OECD countries with the help of the [country converter coco](https://github.com/IndEcol/country_converter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import country_converter as coco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymrio.core.mriosystem.IOSystem at 0x7fb7f2a14e90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eora.aggregate(\n",
    "    region_agg=coco.agg_conc(\n",
    "        original_countries=\"Eora\", aggregates=[\"OECD\"], missing_countries=\"NON_OECD\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NON_OECD', 'OECD', 'Unspecified region'], dtype='object', name='region')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eora.get_regions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymrio.core.mriosystem.IOSystem at 0x7fb7f2a14e90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eora.calc_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with plt.style.context(\"ggplot\"):\n",
    "    eora.Q.plot_account((\"Total cropland area\", \"Total\"), figsize=(8, 5))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the other notebooks for further information on [aggregation](../notebooks/aggregation_examples.ipynb) and [file io](../notebooks/load_save_export.ipynb)."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}