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September 29, 2016 09:40
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#Import Packages\n", | |
| "from brightway2 import *\n", | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import matplotlib\n", | |
| "matplotlib.style.use('ggplot')\n", | |
| "%matplotlib inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Databases dictionary with 2 object(s):\n", | |
| "\tbiosphere3\n", | |
| "\tecoinvent gone bananas" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "#set project\n", | |
| "projects.set_current('bananas2')\n", | |
| "databases" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "13292" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "#check the number of databases\n", | |
| "db = Database(\"ecoinvent gone bananas\")\n", | |
| "len(db)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#list bwt2 methods and chooses one\n", | |
| "[method for method in methods if \"ReCiPe Midpoint (E)\" in str(method)]\n", | |
| "method = ('ReCiPe Midpoint (E)', 'water depletion', 'WDP')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#run the LCA\n", | |
| "lca = LCA({db.random(): 1}, method)\n", | |
| "lca.lci(factorize=True)\n", | |
| "lca.lcia()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#redo the LCA for the modified contries\n", | |
| "columns = ['location','score']\n", | |
| "bananas_LCA_score = pd.DataFrame(columns=columns)\n", | |
| "\n", | |
| "i = 0\n", | |
| "\n", | |
| "for banana in db.search(\"banana\", limit=50):\n", | |
| " if banana['location'] != 'GLO':\n", | |
| " lca.redo_lcia({banana: 1})\n", | |
| " bananas_LCA_score.loc[i] = [banana['location'], lca.score] \n", | |
| " i=i+1\n", | |
| "bananas_LCA_score.index = bananas_LCA_score['location']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7fc7f8afa518>" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fc802a09ef0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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P10jauBkcJNXiDlgSOMdBkiS1weAgSZJqMzhIkqTaDA6SJKk2g4MkSarN4CBJkmozOEiS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\nRK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fc7f8afa320>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "#visualize the results\n", | |
| "plt.figure(figsize=(20,10))\n", | |
| "bananas_LCA_score.plot.bar(colormap='plasma')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#Generates the data for inputs contribution\n", | |
| "columns = ['activity_location','input_name','input_location','input_amount']\n", | |
| "bananas_LCA = pd.DataFrame(columns=columns)\n", | |
| "\n", | |
| "i = 0\n", | |
| "\n", | |
| "for banana in db.search(\"banana\", limit=50):\n", | |
| " #print('')\n", | |
| " #production_location.append(banana['location'])\n", | |
| " if banana['location'] != 'GLO':\n", | |
| " #print(banana['location'])\n", | |
| " for exc in banana.technosphere():\n", | |
| " inputs = str(banana['location'])+';' + str(exc.input['name'])+ ';' + str(exc.input['location'])+';' +str(exc['amount'])\n", | |
| " bananas_LCA.loc[i] = [banana['location'], exc.input['name'], exc.input['location'], exc['amount']] \n", | |
| " i=i+1" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#save the data on a local folder to be analyzed\n", | |
| "bananas_LCA.to_csv('/home/jm/Documents/PhD_EPFL/events/LCA_summerschool/group/bananas_LCA.csv')" | |
| ] | |
| } | |
| ], | |
| "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": "3.5.2" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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