219 lines
10 KiB
Python
219 lines
10 KiB
Python
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import pandas as pd
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import numpy as np
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tj_to_ktoe = 0.0238845
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ktoe_to_twh = 0.01163
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jrc_base_dir = "data/jrc-idees-2015"
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eb_base_dir = "data/eurostat-energy_balances-may_2018_edition"
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# year for which data is retrieved
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raw_year = 2015
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year = raw_year-2016
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sub_sheet_name_dict = { 'Iron and steel':'ISI',
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'Chemicals Industry':'CHI',
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'Non-metallic mineral products': 'NMM',
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'Pulp, paper and printing': 'PPA',
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'Food, beverages and tobacco': 'FBT',
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'Non Ferrous Metals' : 'NFM',
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'Transport Equipment': 'TRE',
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'Machinery Equipment': 'MAE',
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'Textiles and leather':'TEL',
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'Wood and wood products': 'WWP',
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'Other Industrial Sectors': 'OIS'}
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index = ['elec','biomass','methane','hydrogen','heat','naphtha','process emission','process emission from feedstock']
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non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
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jrc_names = {"GR" : "EL",
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"GB" : "UK"}
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eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI',
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'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV', 'CZ',
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'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI', 'CY', 'MT']
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countries = non_EU + eu28
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sectors = ['Iron and steel','Chemicals Industry','Non-metallic mineral products',
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'Pulp, paper and printing', 'Food, beverages and tobacco', 'Non Ferrous Metals',
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'Transport Equipment', 'Machinery Equipment', 'Textiles and leather',
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'Wood and wood products', 'Other Industrial Sectors']
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sect2sub = {'Iron and steel':['Electric arc','Integrated steelworks'],
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'Chemicals Industry': ['Basic chemicals', 'Other chemicals', 'Pharmaceutical products etc.'],
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'Non-metallic mineral products': ['Cement','Ceramics & other NMM','Glass production'],
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'Pulp, paper and printing': ['Pulp production','Paper production','Printing and media reproduction'],
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'Food, beverages and tobacco': ['Food, beverages and tobacco'],
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'Non Ferrous Metals': ['Alumina production', 'Aluminium - primary production', 'Aluminium - secondary production', 'Other non-ferrous metals'],
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'Transport Equipment': ['Transport Equipment'],
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'Machinery Equipment': ['Machinery Equipment'],
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'Textiles and leather': ['Textiles and leather'],
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'Wood and wood products' :['Wood and wood products'],
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'Other Industrial Sectors':['Other Industrial Sectors']}
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subsectors = [ss for s in sectors for ss in sect2sub[s]]
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#material demand per country and industry (kton/a)
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countries_demand = pd.DataFrame(index=countries,
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columns=subsectors,
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dtype=float)
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out_dic ={'Electric arc': 'Electric arc',
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'Integrated steelworks': 'Integrated steelworks',
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'Basic chemicals': 'Basic chemicals (kt ethylene eq.)',
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'Other chemicals':'Other chemicals (kt ethylene eq.)',
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'Pharmaceutical products etc.':'Pharmaceutical products etc. (kt ethylene eq.)',
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'Cement':'Cement (kt)',
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'Ceramics & other NMM':'Ceramics & other NMM (kt bricks eq.)',
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'Glass production':'Glass production (kt)',
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'Pulp production':'Pulp production (kt)',
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'Paper production':'Paper production (kt)',
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'Printing and media reproduction':'Printing and media reproduction (kt paper eq.)',
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'Food, beverages and tobacco': 'Physical output (index)',
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'Alumina production':'Alumina production (kt)',
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'Aluminium - primary production': 'Aluminium - primary production',
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'Aluminium - secondary production': 'Aluminium - secondary production',
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'Other non-ferrous metals' : 'Other non-ferrous metals (kt lead eq.)',
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'Transport Equipment': 'Physical output (index)',
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'Machinery Equipment': 'Physical output (index)',
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'Textiles and leather': 'Physical output (index)',
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'Wood and wood products': 'Physical output (index)',
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'Other Industrial Sectors': 'Physical output (index)'}
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loc_dic={'Iron and steel':[5,8],
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'Chemicals Industry': [7,11],
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'Non-metallic mineral products': [6,10],
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'Pulp, paper and printing': [7,11],
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'Food, beverages and tobacco': [2,6],
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'Non Ferrous Metals': [9,14],
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'Transport Equipment': [3,5],
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'Machinery Equipment': [3,5],
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'Textiles and leather': [3,5],
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'Wood and wood products': [3,5],
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'Other Industrial Sectors': [3,5]}
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# In the summary sheet (IDEES database) some names include a white space
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dic_sec_summary = {'Iron and steel': 'Iron and steel',
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'Chemicals Industry': 'Chemicals Industry',
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'Non-metallic mineral products': 'Non-metallic mineral products',
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'Pulp, paper and printing': 'Pulp, paper and printing',
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'Food, beverages and tobacco': ' Food, beverages and tobacco',
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'Non Ferrous Metals': 'Non Ferrous Metals',
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'Transport Equipment': ' Transport Equipment',
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'Machinery Equipment': ' Machinery Equipment',
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'Textiles and leather': ' Textiles and leather',
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'Wood and wood products': ' Wood and wood products',
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'Other Industrial Sectors': ' Other Industrial Sectors'}
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#countries=['CH']
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eb_names={'NO':'Norway', 'AL':'Albania', 'BA':'Bosnia and Herzegovina',
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'MK':'FYR of Macedonia', 'GE':'Georgia', 'IS':'Iceland',
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'KO':'Kosovo', 'MD':'Moldova', 'ME':'Montenegro', 'RS':'Serbia',
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'UA':'Ukraine', 'TR':'Turkey', }
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dic_sec ={'Iron and steel':'Iron & steel industry',
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'Chemicals Industry': 'Chemical and Petrochemical industry',
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'Non-metallic mineral products': 'Non-ferrous metal industry',
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'Pulp, paper and printing': 'Paper, Pulp and Print',
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'Food, beverages and tobacco': 'Food and Tabacco',
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'Non Ferrous Metals': 'Non-metallic Minerals (Glass, pottery & building mat. Industry)',
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'Transport Equipment': 'Transport Equipment',
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'Machinery Equipment': 'Machinery',
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'Textiles and leather': 'Textile and Leather',
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'Wood and wood products': 'Wood and Wood Products',
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'Other Industrial Sectors': 'Non-specified (Industry)'}
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# Mining and Quarrying, Construction
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#Annual energy consumption in Switzerland by sector in 2015 (in TJ)
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#From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
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#http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
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dic_Switzerland ={'Iron and steel': 7889.,
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'Chemicals Industry': 26871.,
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'Non-metallic mineral products': 15513.+3820.,
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'Pulp, paper and printing': 12004.,
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'Food, beverages and tobacco': 17728.,
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'Non Ferrous Metals': 3037.,
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'Transport Equipment': 14993.,
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'Machinery Equipment': 4724.,
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'Textiles and leather': 1742.,
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'Wood and wood products': 0.,
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'Other Industrial Sectors': 10825.,
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'current electricity': 53760.}
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dic_sec_position={}
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for country in countries:
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countries_demand.loc[country] = 0.
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print(country)
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for sector in sectors:
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if country in non_EU:
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if country == 'CH':
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e_country = dic_Switzerland[sector]*tj_to_ktoe
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else:
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# estimate physical output
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#energy consumption in the sector and country
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excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,eb_names[country]),
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sheet_name='2016', index_col=2,header=0, skiprows=1 ,squeeze=True)
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e_country = excel_balances.loc[dic_sec[sector], 'Total all products']
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#energy consumption in the sector and EU28
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excel_sum_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_EU28.xlsx'.format(jrc_base_dir),
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sheet_name='Ind_Summary', index_col=0,header=0,squeeze=True) # the summary sheet
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s_sum_out = excel_sum_out.iloc[49:76,year]
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e_EU28 = s_sum_out[dic_sec_summary[sector]]
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ratio_country_EU28=e_country/e_EU28
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excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_EU28.xlsx'.format(jrc_base_dir),
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sheet_name=sub_sheet_name_dict[sector],index_col=0,header=0,squeeze=True) # the summary sheet
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s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],year]
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for subsector in sect2sub[sector]:
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countries_demand.loc[country,subsector] = ratio_country_EU28*s_out[out_dic[subsector]]
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else:
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# read the input sheets
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excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(jrc_base_dir,jrc_names.get(country,country)), sheet_name=sub_sheet_name_dict[sector],index_col=0,header=0,squeeze=True) # the summary sheet
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s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],year]
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for subsector in sect2sub[sector]:
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countries_demand.loc[country,subsector] = s_out[out_dic[subsector]]
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#include ammonia demand separately and remove ammonia from basic chemicals
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ammonia = pd.read_csv(snakemake.input.ammonia_production,
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index_col=0)
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there = ammonia.index.intersection(countries_demand.index)
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missing = countries_demand.index.symmetric_difference(there)
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print("Following countries have no ammonia demand:", missing)
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countries_demand.insert(2,"Ammonia",0.)
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countries_demand.loc[there,"Ammonia"] = ammonia.loc[there, str(raw_year)]
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countries_demand["Basic chemicals"] -= countries_demand["Ammonia"]
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#EE, HR and LT got negative demand through subtraction - poor data
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countries_demand.loc[countries_demand["Basic chemicals"] < 0.,"Basic chemicals"] = 0.
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countries_demand.rename(columns={"Basic chemicals" : "Basic chemicals (without ammonia)"},
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inplace=True)
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countries_demand.index.name = "kton/a"
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countries_demand.to_csv(snakemake.output.industrial_production_per_country,
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float_format='%.2f')
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