"""Build industrial production per country."""
import pandas as pd
import numpy as np
import multiprocessing as mp
from tqdm import tqdm
tj_to_ktoe = 0.0238845
ktoe_to_twh = 0.01163
sub_sheet_name_dict = {'Iron and steel': 'ISI',
'Chemicals Industry': 'CHI',
'Non-metallic mineral products': 'NMM',
'Pulp, paper and printing': 'PPA',
'Food, beverages and tobacco': 'FBT',
'Non Ferrous Metals': 'NFM',
'Transport Equipment': 'TRE',
'Machinery Equipment': 'MAE',
'Textiles and leather': 'TEL',
'Wood and wood products': 'WWP',
'Other Industrial Sectors': 'OIS'}
non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
jrc_names = {"GR": "EL", "GB": "UK"}
eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI',
'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV', 'CZ',
'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI', 'CY', 'MT']
sect2sub = {'Iron and steel': ['Electric arc', 'Integrated steelworks'],
'Chemicals Industry': ['Basic chemicals', 'Other chemicals', 'Pharmaceutical products etc.'],
'Non-metallic mineral products': ['Cement', 'Ceramics & other NMM', 'Glass production'],
'Pulp, paper and printing': ['Pulp production', 'Paper production', 'Printing and media reproduction'],
'Food, beverages and tobacco': ['Food, beverages and tobacco'],
'Non Ferrous Metals': ['Alumina production', 'Aluminium - primary production', 'Aluminium - secondary production', 'Other non-ferrous metals'],
'Transport Equipment': ['Transport Equipment'],
'Machinery Equipment': ['Machinery Equipment'],
'Textiles and leather': ['Textiles and leather'],
'Wood and wood products': ['Wood and wood products'],
'Other Industrial Sectors': ['Other Industrial Sectors']}
sub2sect = {v: k for k, vv in sect2sub.items() for v in vv}
fields = {'Electric arc': 'Electric arc',
'Integrated steelworks': 'Integrated steelworks',
'Basic chemicals': 'Basic chemicals (kt ethylene eq.)',
'Other chemicals': 'Other chemicals (kt ethylene eq.)',
'Pharmaceutical products etc.': 'Pharmaceutical products etc. (kt ethylene eq.)',
'Cement': 'Cement (kt)',
'Ceramics & other NMM': 'Ceramics & other NMM (kt bricks eq.)',
'Glass production': 'Glass production (kt)',
'Pulp production': 'Pulp production (kt)',
'Paper production': 'Paper production (kt)',
'Printing and media reproduction': 'Printing and media reproduction (kt paper eq.)',
'Food, beverages and tobacco': 'Physical output (index)',
'Alumina production': 'Alumina production (kt)',
'Aluminium - primary production': 'Aluminium - primary production',
'Aluminium - secondary production': 'Aluminium - secondary production',
'Other non-ferrous metals': 'Other non-ferrous metals (kt lead eq.)',
'Transport Equipment': 'Physical output (index)',
'Machinery Equipment': 'Physical output (index)',
'Textiles and leather': 'Physical output (index)',
'Wood and wood products': 'Physical output (index)',
'Other Industrial Sectors': 'Physical output (index)'}
eb_names = {'NO': 'Norway', 'AL': 'Albania', 'BA': 'Bosnia and Herzegovina',
'MK': 'FYR of Macedonia', 'GE': 'Georgia', 'IS': 'Iceland',
'KO': 'Kosovo', 'MD': 'Moldova', 'ME': 'Montenegro', 'RS': 'Serbia',
'UA': 'Ukraine', 'TR': 'Turkey', }
eb_sectors = {'Iron & steel industry': 'Iron and steel',
'Chemical and Petrochemical industry': 'Chemicals Industry',
'Non-ferrous metal industry': 'Non-metallic mineral products',
'Paper, Pulp and Print': 'Pulp, paper and printing',
'Food and Tabacco': 'Food, beverages and tobacco',
'Non-metallic Minerals (Glass, pottery & building mat. Industry)': 'Non Ferrous Metals',
'Transport Equipment': 'Transport Equipment',
'Machinery': 'Machinery Equipment',
'Textile and Leather': 'Textiles and leather',
'Wood and Wood Products': 'Wood and wood products',
'Non-specified (Industry)': 'Other Industrial Sectors'}
# TODO: this should go in a csv in `data`
# Annual energy consumption in Switzerland by sector in 2015 (in TJ)
# From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
# http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
e_switzerland = pd.Series({'Iron and steel': 7889.,
'Chemicals Industry': 26871.,
'Non-metallic mineral products': 15513.+3820.,
'Pulp, paper and printing': 12004.,
'Food, beverages and tobacco': 17728.,
'Non Ferrous Metals': 3037.,
'Transport Equipment': 14993.,
'Machinery Equipment': 4724.,
'Textiles and leather': 1742.,
'Wood and wood products': 0.,
'Other Industrial Sectors': 10825.,
'current electricity': 53760.})
def find_physical_output(df):
start = np.where(df.index.str.contains('Physical output', na=''))[0][0]
empty_row = np.where(df.index.isnull())[0]
end = empty_row[np.argmax(empty_row > start)]
return slice(start, end)
def get_energy_ratio(country):
if country == 'CH':
e_country = e_switzerland * tj_to_ktoe
else:
# estimate physical output, energy consumption in the sector and country
fn = f"{eurostat_dir}/{eb_names[country]}.XLSX"
df = pd.read_excel(fn, sheet_name='2016', index_col=2,
header=0, skiprows=1).squeeze('columns')
e_country = df.loc[eb_sectors.keys(
), 'Total all products'].rename(eb_sectors)
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx'
df = pd.read_excel(fn, sheet_name='Ind_Summary',
index_col=0, header=0).squeeze('columns')
assert df.index[48] == "by sector"
year_i = df.columns.get_loc(year)
e_eu28 = df.iloc[49:76, year_i]
e_eu28.index = e_eu28.index.str.lstrip()
e_ratio = e_country / e_eu28
return pd.Series({k: e_ratio[v] for k, v in sub2sect.items()})
def industry_production_per_country(country):
def get_sector_data(sector, country):
jrc_country = jrc_names.get(country, country)
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx'
sheet = sub_sheet_name_dict[sector]
df = pd.read_excel(fn, sheet_name=sheet,
index_col=0, header=0).squeeze('columns')
year_i = df.columns.get_loc(year)
df = df.iloc[find_physical_output(df), year_i]
df = df.loc[map(fields.get, sect2sub[sector])]
df.index = sect2sub[sector]
return df
ct = "EU28" if country in non_EU else country
demand = pd.concat([get_sector_data(s, ct) for s in sect2sub.keys()])
if country in non_EU:
demand *= get_energy_ratio(country)
demand.name = country
return demand
def industry_production(countries):
nprocesses = snakemake.threads
func = industry_production_per_country
tqdm_kwargs = dict(ascii=False, unit=' country', total=len(countries),
desc="Build industry production")
with mp.Pool(processes=nprocesses) as pool:
demand_l = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
demand = pd.concat(demand_l, axis=1).T
demand.index.name = "kton/a"
return demand
def separate_basic_chemicals(demand):
"""Separate basic chemicals into ammonia, chlorine, methanol and HVC."""
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
there = ammonia.index.intersection(demand.index)
missing = demand.index.symmetric_difference(there)
print("Following countries have no ammonia demand:", missing)
demand["Ammonia"] = 0.
demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
demand["Basic chemicals"] -= demand["Ammonia"]
# EE, HR and LT got negative demand through subtraction - poor data
demand['Basic chemicals'].clip(lower=0., inplace=True)
# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
demand["HVC"] = config["HVC_production_today"] * 1e3 * distribution_key
demand["Chlorine"] = config["chlorine_production_today"] * 1e3 * distribution_key
demand["Methanol"] = config["methanol_production_today"] * 1e3 * distribution_key
demand.drop(columns=["Basic chemicals"], inplace=True)
if __name__ == '__main__':
if 'snakemake' not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_industrial_production_per_country')
countries = non_EU + eu28
year = snakemake.config['industry']['reference_year']
config = snakemake.config["industry"]
jrc_dir = snakemake.input.jrc
eurostat_dir = snakemake.input.eurostat
demand = industry_production(countries)
separate_basic_chemicals(demand)
fn = snakemake.output.industrial_production_per_country
demand.to_csv(fn, float_format='%.2f')