World Bank data for Exiobase 2.0

EROI universal mining machine.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymrio\n",
+    "from pymrio.core.mriosystem import IOSystem as IOS\n",
+    "from pymrio.tools.iomath import div0\n",
+    "from pymrio.tools.iofunctions import *\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "# import scipy.io\n",
+    "import scipy.sparse as sp \n",
+    "from scipy.sparse import linalg as spla\n",
+    "import operator\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pylab import *\n",
+    "import pickle\n",
+    "from openpyxl import load_workbook\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import statsmodels.api as sm\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\" # 'last' 'none' ...\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "# Unfortunately, the database THEMIS is not open (you can ask to NTNU or thomas.gibon@list.lu) if they can provide it to you\n",
+    "# path_themis = '/mnt/dd/adrien/DD/Économie/Données/Themis/'\n",
+    "path_io = '/mnt/dd/adrien/DD/Économie/Données/'\n",
+    "# path_io = '/media/sf_U_DRIVE/Données/'\n",
+    "# years = [2010, 2030, 2050]\n",
+    "# scenarios = ['BL', 'BM']\n",
+    "# scenarios_dlr = ['REF', 'ER', 'ADV'] \n",
+    "# themis, EROIs_mixes, dlr, EROIs_Mixes = pickle.load(open(path_io+'Themis/EROIs.pkl', 'rb'))\n",
+    "# EROIs_mixes, dlr, EROIs_Mixes = pickle.load(open(path_io+'Themis/EROIs.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "themis = pickle.load(open(path_io+'Themis/themis.pkl', 'rb'))\n",
+    "erois_prices = pickle.load(open(path_io+'Themis/erois_prices.pkl', 'rb'))\n",
+    "# erois_prices_wo_GW = pickle.load(open(path_io+'Themis/erois_prices_wo_GW.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['zinc monosulphate, ZnSO4.H2O, at plant/ RER/ kg',\n",
+       " 'zinc oxide, at plant/ RER/ kg',\n",
+       " 'zinc sulphide, ZnS, at plant/ RER/ kg',\n",
+       " 'cadmium sludge, from zinc electrolysis, at plant/ GLO/ kg',\n",
+       " 'leaching residues, indium rich, from zinc circuit, at smelter/ GLO/ kg',\n",
+       " 'resource correction, PbZn, zinc, negative/ GLO/ kg',\n",
+       " 'resource correction, PbZn, zinc, positive/ GLO/ kg',\n",
+       " 'titanium zinc plate, without pre-weathering, at plant/ DE/ kg',\n",
+       " 'zinc concentrate, at beneficiation/ GLO/ kg',\n",
+       " 'zinc, from combined metal production, at beneficiation/ SE/ kg',\n",
+       " 'zinc, from combined metal production, at refinery/ SE/ kg',\n",
+       " 'zinc, primary, at regional storage/ RER/ kg',\n",
+       " 'zinc coating, coils/ RER/ m2',\n",
+       " 'zinc coating, pieces/ RER/ m2',\n",
+       " 'zinc coating, pieces, adjustment per um/ RER/ m2',\n",
+       " 'disposal, zinc in car shredder residue, 0% water, to municipal incineration/ CH/ kg',\n",
+       " 'mine, gold-silver-zinc-lead-copper/ SE/ unit',\n",
+       " 'Lead, zinc and tin ores and concentrates',\n",
+       " 'Lead, zinc and tin and products thereof']"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "themis['ER'][2010].find('zinc', 'sectors')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}