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openIO.py
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"""
Class that creates symmetric Input-Output tables based on the Supply and Use economic tables provided by Statistic
Canada, available here: https://www150.statcan.gc.ca/n1/en/catalogue/15-602-X
Multiple transformation models are available (industry technology assumption, fixed industry sales structure, etc.) and
the type of classification (productxproduct, or industryxindustry) can be selected as well.
Also produces environmental extensions for the symmetric tables generated based on data from the NPRI found here:
https://open.canada.ca/data/en/dataset/1fb7d8d4-7713-4ec6-b957-4a882a84fed3
"""
import pandas as pd
import numpy as np
import re
import pkg_resources
import os
import pymrio
import json
import country_converter as coco
import logging
import warnings
import gzip
import pickle
import math
class IOTables:
def __init__(self, folder_path, exiobase_folder, endogenizing_capitals=False, aggregated_ghgs=True):
"""
:param folder_path: [string] the path to the folder with the economic data (e.g. /../Detail level/)
:param exiobase_folder: [string] path to exiobase folder for international imports (optional)
:param endogenizing_capitals: [boolean] True if you want to endogenize capitals
:param aggregated_ghgs: [boolean] True to work with aggregated GHG physical flow accounts. False requires you to
have access to the disaggregated files provided by StatCan.
"""
# ignoring some warnings
warnings.filterwarnings(action='ignore', category=FutureWarning)
warnings.filterwarnings(action='ignore', category=np.VisibleDeprecationWarning)
warnings.filterwarnings(action='ignore', category=pd.errors.PerformanceWarning)
# set up logging tool
logger = logging.getLogger('openIO-Canada')
logger.setLevel(logging.INFO)
logger.handlers = []
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
ch.setFormatter(formatter)
logger.addHandler(ch)
logger.propagate = False
logger.info('Reading all the Excel files...')
self.level_of_detail = [i for i in folder_path.split('/') if 'level' in i][0]
self.exiobase_folder = exiobase_folder
self.endogenizing = endogenizing_capitals
self.aggregated_ghgs = aggregated_ghgs
# values
self.V = pd.DataFrame()
self.U = pd.DataFrame()
self.A = pd.DataFrame()
self.K = pd.DataFrame()
self.Z = pd.DataFrame()
self.W = pd.DataFrame()
self.R = pd.DataFrame()
self.Y = pd.DataFrame()
self.WY = pd.DataFrame()
self.g = pd.DataFrame()
self.inv_g = pd.DataFrame()
self.q = pd.DataFrame()
self.inv_q = pd.DataFrame()
self.F = pd.DataFrame()
self.S = pd.DataFrame()
self.FY = pd.DataFrame()
self.C = pd.DataFrame()
self.INT_imports = pd.DataFrame()
self.L = pd.DataFrame()
self.E = pd.DataFrame()
self.D = pd.DataFrame()
self.who_uses_int_imports_U = pd.DataFrame()
self.who_uses_int_imports_K = pd.DataFrame()
self.who_uses_int_imports_Y = pd.DataFrame()
self.A_exio = pd.DataFrame()
self.K_exio = pd.DataFrame()
self.S_exio = pd.DataFrame()
self.F_exio = pd.DataFrame()
self.C_exio = pd.DataFrame()
self.link_openio_exio_A = pd.DataFrame()
self.link_openio_exio_K = pd.DataFrame()
self.link_openio_exio_Y = pd.DataFrame()
self.merchandise_imports = pd.DataFrame()
self.merchandise_imports_scaled_U = pd.DataFrame()
self.merchandise_imports_scaled_K = pd.DataFrame()
self.merchandise_imports_scaled_Y = pd.DataFrame()
self.minerals = pd.DataFrame()
# metadata
self.emission_metadata = pd.DataFrame()
self.unit_exio = pd.DataFrame()
self.methods_metadata = pd.DataFrame()
self.industries = []
self.commodities = []
self.factors_of_production = []
self.matching_dict = {'AB': 'Alberta',
'BC': 'British Columbia',
'CE': 'Canadian territorial enclaves abroad',
'MB': 'Manitoba',
'NB': 'New Brunswick',
'NL': 'Newfoundland and Labrador',
'NS': 'Nova Scotia',
'NT': 'Northwest Territories',
'NU': 'Nunavut',
'ON': 'Ontario',
'PE': 'Prince Edward Island',
'QC': 'Quebec',
'SK': 'Saskatchewan',
'YT': 'Yukon'}
files = [i for i in os.walk(folder_path)]
files = [i for i in files[0][2] if i[:2] in self.matching_dict.keys() and 'SUT' in i]
files.sort()
self.year = int(files[0].split('SUT_C')[1].split('_')[0])
try:
self.NPRI = pd.read_excel(pkg_resources.resource_stream(
__name__, '/Data/Environmental_data/NPRI-INRP_DataDonnées_' + str(self.year) + '.xlsx'), None)
self.NPRI_file_year = self.year
# 2016 by default (for older years)
except FileNotFoundError:
self.NPRI = pd.read_excel(pkg_resources.resource_stream(
__name__, '/Data/Environmental_data/NPRI-INRP_DataDonnées_2016.xlsx'), None)
self.NPRI_file_year = 2016
logger.info("Formatting the Supply and Use tables...")
for province_data in files:
supply = pd.read_excel(folder_path+province_data, 'Supply')
use = pd.read_excel(folder_path+province_data, 'Use_Basic')
region = province_data[:2]
self.format_tables(supply, use, region)
self.W = self.W.fillna(0)
self.WY = self.WY.fillna(0)
self.Y = self.Y.fillna(0)
self.q = self.q.fillna(0)
self.g = self.g.fillna(0)
self.U = self.U.fillna(0)
self.V = self.V.fillna(0)
# output of the industries (g) should match between V and U+W
assert np.isclose((self.U.sum()+self.W.sum()).sum(), self.g.sum().sum())
# output of the commodities (q) should match between V and U+Y adjusted for international imports
assert np.isclose((self.U.sum(1) + self.Y.sum(1) - self.INT_imports.sum(1)).sum(), self.q.sum(1).sum())
logger.info("Modifying names of duplicated sectors...")
self.dealing_with_duplicated_names()
logger.info('Organizing final demand sectors...')
self.organize_final_demand()
logger.info('Removing IOIC codes from index...')
self.remove_codes()
if self.endogenizing:
logger.info('Endogenizing capitals of OpenIO-Canada...')
self.endogenizing_capitals()
logger.info("Balancing inter-provincial trade...")
self.province_import_export(
pd.read_excel(
folder_path+[i for i in [j for j in os.walk(folder_path)][0][2] if 'Provincial_trade_flow' in i][0],
'Data'))
if self.exiobase_folder:
logger.info('Pre-treatment of international trade data...')
self.determine_sectors_importing()
self.load_merchandise_international_trade_database()
logger.info("Linking international trade data to openIO-Canada...")
self.link_merchandise_database_to_openio()
logger.info("Building the symmetric tables...")
self.gimme_symmetric_iot()
if self.exiobase_folder:
logger.info("Linking openIO-Canada to Exiobase...")
self.link_international_trade_data_to_exiobase()
self.remove_abroad_enclaves()
if self.exiobase_folder:
self.concatenate_matrices()
logger.info("Extracting and formatting environmental data from the NPRI file...")
self.extract_environmental_data()
logger.info("Matching emission data from NPRI to IOT sectors...")
self.match_npri_data_to_iots()
logger.info("Matching GHG accounts to IOT sectors...")
if self.aggregated_ghgs:
self.match_aggregated_ghg_accounts_to_iots()
else:
self.match_disaggregated_ghg_accounts_to_iots()
logger.info("Matching water accounts to IOT sectors...")
self.match_water_accounts_to_iots()
logger.info("Matching energy accounts to IOT sectors...")
self.match_energy_accounts_to_iots()
logger.info("Matching mineral extraction data to IOT sectors...")
self.match_mineral_extraction_to_iots()
logger.info("Creating the characterization matrix...")
self.characterization_matrix()
logger.info("Refining the GHG emissions for the agriculture sector...")
self.better_distribution_for_agriculture_ghgs()
logger.info("Cleaning province and country names...")
self.differentiate_country_names_openio_exio()
logger.info("Refining the GHG emissions for the meat sector...")
self.refine_meat_sector()
logger.info("Normalizing emissions...")
self.normalize_flows()
logger.info("Differentiating biogenic from fossil CO2 emissions...")
self.differentiate_biogenic_carbon_emissions()
logger.info("Done extracting openIO-Canada!")
def format_tables(self, supply, use, region):
"""
Extracts the relevant dataframes from the Excel files in the Stat Can folder
:param supply: the supply table
:param use: the use table
:param region: the province of Canada to compile data for
:return: self.W, self.WY, self.Y, self.g, self.q, self.V, self.U
"""
supply_table = supply
use_table = use
if self.year in [2014, 2015, 2016, 2017]:
# starting_line is the line in which the Supply table starts (the first green row)
starting_line = 11
# starting_line_values is the line in which the first value appears
starting_line_values = 16
elif self.year in [2018, 2019, 2020]:
# starting_line is the line in which the Supply table starts (the first green row)
starting_line = 3
# starting_line_values is the line in which the first value appears
starting_line_values = 7
if not self.industries:
for i in range(0, len(supply_table.columns)):
if supply_table.iloc[starting_line, i] == 'Total':
break
if supply_table.iloc[starting_line, i] not in [np.nan, 'Industries']:
# tuple with code + name (need code to deal with duplicate names in detailed levels)
self.industries.append((supply_table.iloc[starting_line+1, i],
supply_table.iloc[starting_line, i]))
# remove fictive sectors
self.industries = [i for i in self.industries if not re.search(r'^F', i[0])]
if not self.commodities:
for i, element in enumerate(supply_table.iloc[:, 0].tolist()):
if type(element) == str:
# identify by their codes
if re.search(r'^[M,F,N,G,I,E]\w*\d', element):
self.commodities.append((element, supply_table.iloc[i, 1]))
elif re.search(r'^P\w*\d', element) or re.search(r'^GVA', element):
self.factors_of_production.append((element, supply_table.iloc[i, 1]))
final_demand = []
for i in range(0, len(use_table.columns)):
if use_table.iloc[starting_line, i] in self.matching_dict.values():
break
if use_table.iloc[starting_line, i] not in [np.nan, 'Industries']:
final_demand.append((use_table.iloc[starting_line+1, i],
use_table.iloc[starting_line, i]))
final_demand = [i for i in final_demand if i not in self.industries and i[1] != 'Total']
df = supply_table.iloc[starting_line_values-2:, 2:]
df.index = list(zip(supply_table.iloc[starting_line_values-2:, 0].tolist(),
supply_table.iloc[starting_line_values-2:, 1].tolist()))
df.columns = list(zip(supply_table.iloc[starting_line+1, 2:].tolist(),
supply_table.iloc[starting_line, 2:].tolist()))
supply_table = df
df = use_table.iloc[starting_line_values-2:, 2:]
df.index = list(zip(use_table.iloc[starting_line_values-2:, 0].tolist(),
use_table.iloc[starting_line_values-2:, 1].tolist()))
df.columns = list(zip(use_table.iloc[starting_line+1, 2:].tolist(),
use_table.iloc[starting_line, 2:].tolist()))
use_table = df
# fill with zeros
supply_table.replace('.', 0, inplace=True)
use_table.replace('.', 0, inplace=True)
# get strings as floats
supply_table = supply_table.astype('float64')
use_table = use_table.astype('float64')
if self.level_of_detail == 'Detail level':
# tables from k$ to $
supply_table *= 1000
use_table *= 1000
else:
# tables from M$ to $
supply_table *= 1000000
use_table *= 1000000
# check calculated totals matched displayed totals
assert np.allclose(use_table.iloc[:, use_table.columns.get_loc(('TOTAL', 'Total'))],
use_table.iloc[:, :use_table.columns.get_loc(('TOTAL', 'Total'))].sum(axis=1), atol=1e-5)
assert np.allclose(supply_table.iloc[supply_table.index.get_loc(('TOTAL', 'Total'))],
supply_table.iloc[:supply_table.index.get_loc(('TOTAL', 'Total'))].sum(), atol=1e-5)
# extract the tables we need
W = use_table.loc[self.factors_of_production, self.industries]
W.drop(('GVA', 'Gross value-added at basic prices'), inplace=True)
Y = use_table.loc[self.commodities, final_demand]
WY = use_table.loc[self.factors_of_production, final_demand]
WY.drop(('GVA', 'Gross value-added at basic prices'), inplace=True)
g = use_table.loc[[('TOTAL', 'Total')], self.industries]
q = supply_table.loc[self.commodities, [('TOTAL', 'Total')]]
V = supply_table.loc[self.commodities, self.industries]
U = use_table.loc[self.commodities, self.industries]
INT_imports = supply_table.loc[self.commodities, [('INTIM000', 'International imports')]]
# create multiindex with region as first level
for matrix in [W, Y, WY, g, q, V, U, INT_imports]:
matrix.columns = pd.MultiIndex.from_product([[region], matrix.columns]).tolist()
matrix.index = pd.MultiIndex.from_product([[region], matrix.index]).tolist()
# concat the region tables with the all the other tables
self.W = pd.concat([self.W, W])
self.WY = pd.concat([self.WY, WY])
self.Y = pd.concat([self.Y, Y])
self.q = pd.concat([self.q, q])
self.g = pd.concat([self.g, g])
self.U = pd.concat([self.U, U])
self.V = pd.concat([self.V, V])
self.INT_imports = pd.concat([self.INT_imports, INT_imports])
def dealing_with_duplicated_names(self):
"""
IOIC classification has duplicate names, so we rename when it's the case
:return: updated dataframes
"""
# reindexing to fix the order of the columns
self.V = self.V.T.reindex(pd.MultiIndex.from_product([self.matching_dict.keys(), self.industries]).tolist()).T
self.U = self.U.T.reindex(pd.MultiIndex.from_product([self.matching_dict.keys(), self.industries]).tolist()).T
self.g = self.g.T.reindex(pd.MultiIndex.from_product([self.matching_dict.keys(), self.industries]).tolist()).T
self.W = self.W.T.reindex(pd.MultiIndex.from_product([self.matching_dict.keys(), self.industries]).tolist()).T
if self.level_of_detail in ['Link-1961 level', 'Link-1997 level', 'Detail level']:
self.industries = [(i[0], i[1] + ' (private)') if re.search(r'^BS61', i[0]) else i for i in
self.industries]
self.industries = [(i[0], i[1] + ' (non-profit)') if re.search(r'^NP61|^NP71', i[0]) else i for i in
self.industries]
self.industries = [(i[0], i[1] + ' (public)') if re.search(r'^GS61', i[0]) else i for i in
self.industries]
if self.level_of_detail in ['Link-1997 level', 'Detail level']:
self.industries = [(i[0], i[1] + ' (private)') if re.search(r'^BS623|^BS624', i[0]) else i for i in
self.industries]
self.industries = [(i[0], i[1] + ' (non-profit)') if re.search(r'^NP624', i[0]) else i for i in
self.industries]
self.industries = [(i[0], i[1] + ' (public)') if re.search(r'^GS623', i[0]) else i for i in
self.industries]
# applying the change of names to columns
for df in [self.V, self.U, self.g, self.W]:
df.columns = pd.MultiIndex.from_product([self.matching_dict.keys(), self.industries]).tolist()
def organize_final_demand(self):
"""
Extract the final demand sectors. These will be disaggregated. If you do not want the detail, use
self.aggregated_final_demand()
Provincial exports will be included in self.U and are thus excluded from self.Y
:return: self.Y & self.WY updated
"""
# dealing with duplicate names of disaggregated final demand sector names separately
self.Y.columns = [(i[0], (i[1][0], i[1][1] + ' (private)')) if re.search(r'^COB61|^MEB61|^IPB61|^MEBU', i[1][0])
else i for i in self.Y.columns]
self.Y.columns = [(i[0], (i[1][0], i[1][1] + ' (public)')) if re.search(r'^COG61|^MEG61|^IPG61|^MEGU', i[1][0])
else i for i in self.Y.columns]
self.Y.columns = [(i[0], (i[1][0], i[1][1] + ' (non-profit)')) if re.search(r'^MENU', i[1][0])
else i for i in self.Y.columns]
self.WY.columns = [(i[0], (i[1][0], i[1][1] + ' (private)')) if re.search(r'^COB61|^MEB61|^IPB61|^MEBU', i[1][0])
else i for i in self.WY.columns]
self.WY.columns = [(i[0], (i[1][0], i[1][1] + ' (public)')) if re.search(r'^COG61|^MEG61|^IPG61|^MEGU', i[1][0])
else i for i in self.WY.columns]
self.WY.columns = [(i[0], (i[1][0], i[1][1] + ' (non-profit)')) if re.search(r'^MENU', i[1][0])
else i for i in self.WY.columns]
Y = pd.DataFrame()
fd_households = [i for i in self.Y.columns if re.search(r'^PEC\w*\d', i[1][0])]
df = self.Y.loc[:, fd_households].copy()
df.columns = [(i[0], "Household final consumption expenditure", i[1][1]) for i in fd_households]
Y = pd.concat([Y, df], axis=1)
fd_npish = [i for i in self.Y.columns if re.search(r'^CEN\w*\d', i[1][0])]
df = self.Y.loc[:, fd_npish].copy()
df.columns = [(i[0], i[1][1], 'NPISH') for i in fd_npish]
Y = pd.concat([Y, df], axis=1)
fd_gov = [i for i in self.Y.columns if re.search(r'^CEG\w*\d', i[1][0])]
df = self.Y.loc[:, fd_gov].copy()
df.columns = [(i[0], "Governments final consumption expenditure", i[1][1]) for i in fd_gov]
Y = pd.concat([Y, df], axis=1)
fd_construction = [i for i in self.Y.columns if re.search(r'^CO\w*\d', i[1][0])]
df = self.Y.loc[:, fd_construction].copy()
df.columns = [(i[0], "Gross fixed capital formation, Construction", i[1][1]) for i in fd_construction]
Y = pd.concat([Y, df], axis=1)
fd_machinery = [i for i in self.Y.columns if re.search(r'^ME\w*\d', i[1][0])]
df = self.Y.loc[:, fd_machinery].copy()
df.columns = [(i[0], "Gross fixed capital formation, Machinery and equipment", i[1][1]) for i in fd_machinery]
Y = pd.concat([Y, df], axis=1)
fd_ip = [i for i in self.Y.columns if re.search(r'^IP\w[T]*\d', i[1][0])]
df = self.Y.loc[:, fd_ip].copy()
df.columns = [(i[0], "Gross fixed capital formation, Intellectual property products", i[1][1]) for i in fd_ip]
Y = pd.concat([Y, df], axis=1)
fd_inv = [i for i in self.Y.columns if re.search(r'^INV\w*\d', i[1][0])]
df = self.Y.loc[:, fd_inv].copy()
df.columns = [(i[0], "Changes in inventories", i[1][1]) for i in fd_inv]
Y = pd.concat([Y, df], axis=1)
fd_int = [i for i in self.Y.columns if re.search(r'^INT\w*', i[1][0])]
df = self.Y.loc[:, fd_int].copy()
df.columns = [(i[0], "International exports", i[1][1].split(' ')[1].capitalize()) for i in fd_int]
Y = pd.concat([Y, df], axis=1)
self.Y = Y
WY = pd.DataFrame()
fd_households = [i for i in self.WY.columns if re.search(r'^PEC\w*\d', i[1][0])]
df = self.WY.loc[:, fd_households].copy()
df.columns = [(i[0], "Household final consumption expenditure", i[1][1]) for i in fd_households]
WY = pd.concat([WY, df], axis=1)
fd_npish = [i for i in self.WY.columns if re.search(r'^CEN\w*\d', i[1][0])]
df = self.WY.loc[:, fd_npish].copy()
df.columns = [(i[0], i[1][1], 'NPISH') for i in fd_npish]
WY = pd.concat([WY, df], axis=1)
fd_gov = [i for i in self.WY.columns if re.search(r'^CEG\w*\d', i[1][0])]
df = self.WY.loc[:, fd_gov].copy()
df.columns = [(i[0], "Governments final consumption expenditure", i[1][1]) for i in fd_gov]
WY = pd.concat([WY, df], axis=1)
fd_construction = [i for i in self.WY.columns if re.search(r'^CO\w*\d', i[1][0])]
df = self.WY.loc[:, fd_construction].copy()
df.columns = [(i[0], "Gross fixed capital formation, Construction", i[1][1]) for i in fd_construction]
WY = pd.concat([WY, df], axis=1)
fd_machinery = [i for i in self.WY.columns if re.search(r'^ME\w*\d', i[1][0])]
df = self.WY.loc[:, fd_machinery].copy()
df.columns = [(i[0], "Gross fixed capital formation, Machinery and equipment", i[1][1]) for i in fd_machinery]
WY = pd.concat([WY, df], axis=1)
fd_ip = [i for i in self.WY.columns if re.search(r'^IP\w[T]*\d', i[1][0])]
df = self.WY.loc[:, fd_ip].copy()
df.columns = [(i[0], "Gross fixed capital formation, Intellectual property products", i[1][1]) for i in fd_ip]
WY = pd.concat([WY, df], axis=1)
fd_inv = [i for i in self.WY.columns if re.search(r'^INV\w*\d', i[1][0])]
df = self.WY.loc[:, fd_inv].copy()
df.columns = [(i[0], "Changes in inventories", i[1][1]) for i in fd_inv]
WY = pd.concat([WY, df], axis=1)
fd_int = [i for i in self.WY.columns if re.search(r'^INT\w*', i[1][0])]
df = self.WY.loc[:, fd_int].copy()
df.columns = [(i[0], "International exports", i[1][1].split(' ')[1].capitalize()) for i in fd_int]
WY = pd.concat([WY, df], axis=1)
self.WY = WY
assert np.isclose((self.U.sum(1) + self.Y.sum(1) - self.INT_imports.sum(1)).sum(), self.q.sum(1).sum())
def remove_codes(self):
"""
Removes the IOIC codes from the index to only leave the name.
:return: Dataframes with the code of the multi-index removed
"""
# removing the IOIC codes
for df in [self.W, self.g, self.V, self.U, self.INT_imports]:
df.columns = [(i[0], i[1][1]) for i in df.columns]
for df in [self.W, self.Y, self.WY, self.q, self.V, self.U, self.INT_imports]:
df.index = [(i[0], i[1][1]) for i in df.index]
# recreating MultiIndexes
for df in [self.W, self.Y, self.WY, self.g, self.q, self.V, self.U, self.INT_imports]:
df.index = pd.MultiIndex.from_tuples(df.index)
df.columns = pd.MultiIndex.from_tuples(df.columns)
# reordering columns
reindexed_columns = pd.MultiIndex.from_product([self.matching_dict.keys(), [i[1] for i in self.industries]])
self.W = self.W.T.reindex(reindexed_columns).T
self.g = self.g.T.reindex(reindexed_columns).T
self.V = self.V.T.reindex(reindexed_columns).T
self.U = self.U.T.reindex(reindexed_columns).T
def endogenizing_capitals(self):
"""
Endogenize gross fixed capital formation (GFCF) of openIO-Canada. Take the final demand for GFCF and distribute
it to the different sectors of the economy requiring the purchase of these capital goods.
Because capitals are endogenized they also need to be removed from the final demand except for residential
buildings which are kept as a final demand.
:return:
"""
endo = pd.read_excel(pkg_resources.resource_stream(__name__, '/Data/Concordances/Endogenizing.xlsx'))
self.K = pd.DataFrame(0, self.U.index, self.U.columns)
for province in self.matching_dict.keys():
for capital_type in ['Gross fixed capital formation, Construction',
'Gross fixed capital formation, Machinery and equipment',
'Gross fixed capital formation, Intellectual property products']:
df = self.Y.loc(axis=1)[province, capital_type]
for capital in df.columns:
if self.V.loc[:, province].loc[:, endo[endo.Capitals == capital].loc[:, 'IOIC']].sum().sum() != 0:
share = (self.V.loc[:, province].loc[:, endo[endo.Capitals == capital].loc[:, 'IOIC']].sum() /
self.V.loc[:, province].loc[:,
endo[endo.Capitals == capital].loc[:, 'IOIC']].sum().sum())
for sector in share.index:
self.K.loc[:, (province, sector)] += (df.loc[:, capital] * share.loc[sector])
elif (self.Y.loc(axis=1)[province, capital_type, capital].sum() != 0 and
capital not in ['Residential structures', 'Used cars and equipment and scrap (private)',
'Used cars and equipment and scrap (public)',
'Used cars and equipment and scrap (non-profit)']):
if len(endo[endo.Capitals == capital].loc[:, 'IOIC']) == 1:
self.K.loc[:, (province, endo[endo.Capitals == capital].loc[:, 'IOIC'].iloc[0])] += df.loc[:,capital]
else:
warnings.warn('There is more capital ' + capital + ' for the province: ' + province +
' purchased than total purchases of the corresponding sector in that year.')
# add a final demand category for households for building residential structures
df = self.Y.loc(axis=1)[:, 'Gross fixed capital formation, Construction', 'Residential structures'].copy('deep')
df.columns = pd.MultiIndex.from_product([self.matching_dict.keys(),
['Household final consumption expenditure'],
['Residential structures']])
self.Y = pd.concat([self.Y, df], axis=1)
# add a final demand category for changes in inventories for Used cars and equipment and scrap
df = self.Y.loc(axis=1)[:, :, ['Used cars and equipment and scrap (private)',
'Used cars and equipment and scrap (public)',
'Used cars and equipment and scrap (non-profit)']].copy('deep')
df = df.groupby(axis=1, level=0).sum()
df.columns = pd.MultiIndex.from_product([self.matching_dict.keys(), ['Changes in inventories'],
['Changes in inventories, used cars and equipment and scrap']])
self.Y = pd.concat([self.Y, df], axis=1)
# check that all capitals were either added to K or moved to Households/Inventories
assert np.isclose(self.Y.loc(axis=1)[:, ['Gross fixed capital formation, Construction',
'Gross fixed capital formation, Machinery and equipment',
'Gross fixed capital formation, Intellectual property products']].sum().sum(),
(self.K.sum().sum() + self.Y.loc(axis=1)[:, 'Household final consumption expenditure',
'Residential structures'].sum().sum() +
self.Y.loc(axis=1)[:, 'Changes in inventories',
'Changes in inventories, used cars and equipment and scrap'].sum().sum()))
# drop capitals from final demand
self.Y = self.Y.drop([i for i in self.Y.columns if (
i[1] in ['Gross fixed capital formation, Construction',
'Gross fixed capital formation, Machinery and equipment',
'Gross fixed capital formation, Intellectual property products'])], axis=1)
assert np.isclose((self.U.sum(1) + self.K.sum(1) + self.Y.sum(1) - self.INT_imports.sum(1)).sum(),
self.q.sum(1).sum())
# correct added value to account for the fact that capitals were endogenized
capital_added_value_removal = pd.concat([-self.K.sum()] * len(self.matching_dict), axis=1).T
capital_added_value_removal.index = pd.MultiIndex.from_product(
[self.matching_dict.keys(), ['Capital endogenization removal']])
for province in self.matching_dict.keys():
capital_added_value_removal.loc[[i for i in self.matching_dict.keys() if i != province], province] = 0
self.W = pd.concat([self.W, capital_added_value_removal])
assert np.isclose((self.U.sum() + self.K.sum() + self.W.sum()).sum(), self.g.sum().sum())
def province_import_export(self, province_trade_file):
"""
Method extracting and formatting inter province imports/exports
:return: modified self.U, self.V, self.W, self.Y
"""
province_trade_file = province_trade_file
province_trade_file.Origin = [{v: k for k, v in self.matching_dict.items()}[i.split(') ')[1]] if ')' in i
else i for i in province_trade_file.Origin]
province_trade_file.Destination = [{v: k for k, v in self.matching_dict.items()}[i.split(') ')[1]] if ')' in i
else i for i in province_trade_file.Destination]
# extracting and formatting supply for each province
province_trade = pd.pivot_table(data=province_trade_file, index='Destination', columns=['Origin', 'Product'])
province_trade = province_trade.loc[
[i for i in province_trade.index if i in self.matching_dict.keys()],
[i for i in province_trade.columns if i[1] in self.matching_dict.keys()]]
if self.level_of_detail == 'Detail level':
province_trade *= 1000
else:
province_trade *= 1000000
province_trade.columns = [(i[1], i[2].split(': ')[1]) if ':' in i[2] else i for i in
province_trade.columns]
province_trade.drop([i for i in province_trade.columns if i[1] not in [i[1] for i in self.commodities]],
axis=1, inplace=True)
province_trade.columns = pd.MultiIndex.from_tuples(province_trade.columns)
for province in province_trade.index:
province_trade.loc[province, province] = 0
import_markets = pd.DataFrame(0, province_trade.index, province_trade.columns)
for importing_province in province_trade.index:
for exported_product in province_trade.columns.levels[1]:
import_markets.loc[
importing_province, [i for i in import_markets.columns if i[1] == exported_product]] = (
province_trade.loc[
importing_province, [i for i in province_trade.columns if i[1] == exported_product]] /
province_trade.loc[importing_province, [i for i in province_trade.columns if
i[1] == exported_product]].sum()).values
before_distribution_U = self.U.copy('deep')
before_distribution_K = self.K.copy('deep')
before_distribution_Y = self.Y.copy('deep')
if not self.endogenizing:
for importing_province in province_trade.index:
total_use = pd.concat([self.U.loc[importing_province, importing_province],
self.Y.loc[importing_province, importing_province]], axis=1)
total_imports = province_trade.groupby(level=1, axis=1).sum().loc[importing_province]
index_commodity = [i[1] for i in self.commodities]
total_imports = total_imports.reindex(index_commodity).fillna(0)
import_distribution_U = ((self.U.loc[importing_province, importing_province].T /
(total_use.sum(axis=1))) *
total_imports).T.fillna(0)
import_distribution_Y = ((self.Y.loc[importing_province, importing_province].T /
(total_use.sum(axis=1))) *
total_imports).T.fillna(0)
# distribution balance imports to the different exporting regions
for exporting_province in province_trade.index:
if importing_province != exporting_province:
scaled_imports_U = ((import_distribution_U.T * import_markets.fillna(0).loc[
importing_province, exporting_province]).T).reindex(import_distribution_U.index).fillna(0)
scaled_imports_Y = ((import_distribution_Y.T * import_markets.fillna(0).loc[
importing_province, exporting_province]).T).reindex(import_distribution_Y.index).fillna(0)
self.assert_order(exporting_province, importing_province, scaled_imports_U, scaled_imports_Y)
# assign new values into self.U
self.U.loc[exporting_province, importing_province] = (
scaled_imports_U.loc[:, self.U.columns.levels[1]].reindex(
self.U.loc[exporting_province, importing_province].columns, axis=1).values)
# assign new values into self.Y
self.Y.loc[exporting_province, importing_province] = (
scaled_imports_Y.loc[:, self.Y.columns.levels[1]].reindex(
self.Y.loc[exporting_province, importing_province].columns, axis=1).values)
# remove interprovincial from intraprovincial to not double count
self.U.loc[importing_province, importing_province] = (
self.U.loc[importing_province, importing_province] - self.U.loc[
[i for i in self.matching_dict if i != importing_province], importing_province].groupby(
level=1).sum()).reindex([i[1] for i in self.commodities]).values
self.Y.loc[importing_province, importing_province] = (
self.Y.loc[importing_province, importing_province] - self.Y.loc[
[i for i in self.matching_dict if i != importing_province], importing_province].groupby(
level=1).sum()).reindex([i[1] for i in self.commodities]).values
# if some province buys more than they use, drop the value in "changes in inventories"
df = self.U[self.U < -1].dropna(how='all', axis=1).dropna(how='all')
if len(df):
self.Y.loc[:, (importing_province, 'Changes in inventories',
'Changes in inventories, finished goods and goods in process')] += (
df.sum(1).reindex(self.Y.index).fillna(0))
self.U.loc[df.index, df.columns] = 0
# removing negative values lower than 1$ (potential calculation artefacts)
self.U = self.U[self.U > 0].fillna(0)
# checking negative values were removed
assert not self.U[self.U < 0].any().any()
# check that the distribution went smoothly
assert np.isclose(self.U.sum().sum()+self.Y.sum().sum(),
before_distribution_U.sum().sum()+before_distribution_Y.sum().sum())
else:
for importing_province in province_trade.index:
total_use = pd.concat([self.U.loc[importing_province, importing_province] +
self.K.loc[importing_province, importing_province],
self.Y.loc[importing_province, importing_province]], axis=1)
total_imports = province_trade.groupby(level=1, axis=1).sum().loc[importing_province]
index_commodity = [i[1] for i in self.commodities]
total_imports = total_imports.reindex(index_commodity).fillna(0)
import_distribution_U = ((self.U.loc[importing_province, importing_province].T /
(total_use.sum(axis=1))) *
total_imports).T.fillna(0)
import_distribution_K = ((self.K.loc[importing_province, importing_province].T /
(total_use.sum(axis=1))) *
total_imports).T.fillna(0)
import_distribution_Y = ((self.Y.loc[importing_province, importing_province].T /
(total_use.sum(axis=1))) *
total_imports).T.fillna(0)
# distribution balance imports to the different exporting regions
for exporting_province in province_trade.index:
if importing_province != exporting_province:
scaled_imports_U = ((import_distribution_U.T * import_markets.fillna(0).loc[
importing_province, exporting_province]).T).reindex(import_distribution_U.index).fillna(0)
scaled_imports_K = ((import_distribution_K.T * import_markets.fillna(0).loc[
importing_province, exporting_province]).T).reindex(import_distribution_K.index).fillna(0)
scaled_imports_Y = ((import_distribution_Y.T * import_markets.fillna(0).loc[
importing_province, exporting_province]).T).reindex(import_distribution_Y.index).fillna(0)
self.assert_order(exporting_province, importing_province, scaled_imports_U, scaled_imports_Y,
scaled_imports_K)
# assign new values into self.U
self.U.loc[exporting_province, importing_province] = (
scaled_imports_U.loc[:, self.U.columns.levels[1]].reindex(
self.U.loc[exporting_province, importing_province].columns, axis=1).values)
# assign new values into self.K
self.K.loc[exporting_province, importing_province] = (
scaled_imports_K.loc[:, self.K.columns.levels[1]].reindex(
self.K.loc[exporting_province, importing_province].columns, axis=1).values)
# assign new values into self.Y
self.Y.loc[exporting_province, importing_province] = (
scaled_imports_Y.loc[:, self.Y.columns.levels[1]].reindex(
self.Y.loc[exporting_province, importing_province].columns, axis=1).values)
# remove interprovincial from intraprovincial to not double count
self.U.loc[importing_province, importing_province] = (
self.U.loc[importing_province, importing_province] - self.U.loc[
[i for i in self.matching_dict if i != importing_province], importing_province].groupby(
level=1).sum()).reindex([i[1] for i in self.commodities]).values
self.K.loc[importing_province, importing_province] = (
self.K.loc[importing_province, importing_province] - self.K.loc[
[i for i in self.matching_dict if i != importing_province], importing_province].groupby(
level=1).sum()).reindex([i[1] for i in self.commodities]).values
self.Y.loc[importing_province, importing_province] = (
self.Y.loc[importing_province, importing_province] - self.Y.loc[
[i for i in self.matching_dict if i != importing_province], importing_province].groupby(
level=1).sum()).reindex([i[1] for i in self.commodities]).values
# if some province buys more than they use, drop the value in "changes in inventories"
df = self.U[self.U < -1].dropna(how='all', axis=1).dropna(how='all')
if len(df):
self.Y.loc[:, (importing_province, 'Changes in inventories',
'Changes in inventories, finished goods and goods in process')] += (
df.sum(1).reindex(self.Y.index).fillna(0))
self.U.loc[df.index, df.columns] = 0
# removing negative values lower than 1$ (potential calculation artefacts)
self.U = self.U[self.U > 0].fillna(0)
self.K = self.K[self.K > 0].fillna(0)
# checking negative values were removed
assert not self.U[self.U < 0].any().any()
assert not self.K[self.K < 0].any().any()
# check that the distribution went smoothly
assert np.isclose(self.U.sum().sum()+self.K.sum().sum()+self.Y.sum().sum(),
before_distribution_U.sum().sum()+before_distribution_K.sum().sum()+before_distribution_Y.sum().sum())
def determine_sectors_importing(self):
"""
Determine which sectors use international imports and removing international imports from use
:return:
"""
# aggregating international imports in 1 column
self.INT_imports = self.INT_imports.groupby(axis=1, level=1).sum()
# save total values to check if distribution was done properly later
before_distribution_U = self.U.sum().sum()
before_distribution_K = self.K.sum().sum()
before_distribution_Y = self.Y.sum().sum()
# need to flatten multiindex for the concatenation to work properly
self.Y.columns = self.Y.columns.tolist()
self.U.columns = self.U.columns.tolist()
self.K.columns = self.K.columns.tolist()
# determine total use
if not self.endogenizing:
total_use = pd.concat([self.U, self.Y], axis=1)
else:
total_use = pd.concat([self.U + self.K, self.Y], axis=1)
# weighted average of who is requiring the international imports, based on national use
self.who_uses_int_imports_U = (self.U.T / total_use.sum(1)).fillna(0).T * self.INT_imports.values
self.who_uses_int_imports_Y = (self.Y.T / total_use.sum(1)).fillna(0).T * self.INT_imports.values
if self.endogenizing:
self.who_uses_int_imports_K = (self.K.T / total_use.sum(1)).fillna(0).T * self.INT_imports.values
# remove international imports from national use
self.U = self.U - self.who_uses_int_imports_U.reindex(self.U.columns, axis=1)
self.Y = self.Y - self.who_uses_int_imports_Y.reindex(self.Y.columns, axis=1)
if self.endogenizing:
self.K = self.K - self.who_uses_int_imports_K.reindex(self.K.columns, axis=1)
# check totals match
assert np.isclose(self.U.sum().sum() + self.who_uses_int_imports_U.sum().sum(), before_distribution_U)
assert np.isclose(self.K.sum().sum() + self.who_uses_int_imports_K.sum().sum(), before_distribution_K)
assert np.isclose(self.Y.sum().sum() + self.who_uses_int_imports_Y.sum().sum(), before_distribution_Y)
# check that nothing fuzzy is happening with negative values that are not due to artefacts
if not len(self.U[self.U < -1].dropna(how='all', axis=1).dropna(how='all', axis=0)) == 0:
warnings.warn("Warning! The import data for the following sectors is inconsistent with the supply and use "
"data and stipulates that these sectors import more than what they are using "+
str([i for i in self.U[self.U < -1].dropna(how='all', axis=1).dropna(how='all', axis=0).index])+
". The resulting negative values will be forced to zero.")
# remove negative artefacts (like 1e-10$)
self.U = self.U[self.U > 0].fillna(0)
assert not self.U[self.U < 0].any().any()
self.K = self.K[self.K > 0].fillna(0)
assert not self.K[self.K < 0].any().any()
# remove negative artefacts
self.Y = pd.concat([self.Y[self.Y >= 0].fillna(0), self.Y[self.Y < -1].fillna(0)], axis=1)
self.Y = self.Y.groupby(by=self.Y.columns, axis=1).sum()
self.Y.columns = pd.MultiIndex.from_tuples(self.Y.columns)
def load_merchandise_international_trade_database(self):
"""
Loading and treating the international trade merchandise database of Statistics Canada.
Original source: https://open.canada.ca/data/en/dataset/b1126a07-fd85-4d56-8395-143aba1747a4
:return:
"""
# load concordance between HS classification and IOIC classification
conc = pd.read_excel(pkg_resources.resource_stream(__name__, '/Data/Concordances/HS-IOIC.xlsx'))
# load database
merchandise_database = pd.read_excel(pkg_resources.resource_stream(__name__, '/Data/Imports_data/Imports_' +
str(self.year) + '_HS06_treated.xlsx'))
merchandise_database = merchandise_database.ffill()
merchandise_database.columns = ['Country', 'HS6', 'Value']
# apply concordance
merchandise_database = merchandise_database.merge(conc, on='HS6', how='left')
# only keep useful information
merchandise_database = merchandise_database.loc[:, ['IOIC', 'Country', 'Value']]
# remove HS sectors that cant be matched to IOIC (identified with "None")
merchandise_database = merchandise_database.drop(
[i for i in merchandise_database.index if merchandise_database.loc[i, 'IOIC'] == 'None'])
# change IOIC codes to sector names
code_to_name = {j[0]: j[1] for j in self.commodities}
merchandise_database.IOIC = [code_to_name[i] for i in merchandise_database.IOIC]
# set MultiIndex with country and classification
merchandise_database = merchandise_database.set_index(['Country', 'IOIC'])
# regroup purchases together (on country + IOIC sector)
merchandise_database = merchandise_database.groupby(merchandise_database.index).sum()
# set Multi-index
merchandise_database.index = pd.MultiIndex.from_tuples(merchandise_database.index)
# reset the index to apply country converter
merchandise_database = merchandise_database.reset_index()
# apply country converter
merchandise_database.level_0 = coco.convert(merchandise_database.level_0, to='EXIO3')
# restore index
merchandise_database = merchandise_database.set_index(['level_0', 'level_1'])
merchandise_database.index.names = None, None
# groupby on country/sector (e.g., there were multiple 'WL' after applying coco)
merchandise_database = merchandise_database.groupby(merchandise_database.index).sum()
# restore multi-index
merchandise_database.index = pd.MultiIndex.from_tuples(merchandise_database.index)
# reindexing to ensure all sectors are here, fill missing ones with zero values
self.merchandise_imports = merchandise_database.reindex(pd.MultiIndex.from_product([
merchandise_database.index.levels[0], [i[1] for i in self.commodities]])).fillna(0)
def link_merchandise_database_to_openio(self):
"""
Linking the international trade merchandise database of Statistics Canada to openIO-Canada.
:return:
"""
# the absolute values of self.merchandise_imports do not matter
# we only use those to calculate a weighted average of imports per country
for product in self.merchandise_imports.index.levels[1]:
total = self.merchandise_imports.loc(axis=0)[:, product].sum()
for region in self.merchandise_imports.index.levels[0]:
self.merchandise_imports.loc(axis=0)[region, product] /= total
# Nan values showing up from 0/0 operations
self.merchandise_imports = self.merchandise_imports.fillna(0)
def scale_international_imports(who_uses_int_imports, matrix):
df = who_uses_int_imports.groupby(axis=0, level=1).sum()
df = pd.concat([df] * len(self.merchandise_imports.index.levels[0]))
df.index = pd.MultiIndex.from_product([self.merchandise_imports.index.levels[0],
who_uses_int_imports.index.levels[1]])
for product in self.merchandise_imports.index.levels[1]:
dff = (df.loc(axis=0)[:, product].T * self.merchandise_imports.loc(axis=0)[:, product].iloc[:, 0]).T
if matrix == 'U':
self.merchandise_imports_scaled_U = pd.concat([self.merchandise_imports_scaled_U, dff])
if matrix == 'K':
self.merchandise_imports_scaled_K = pd.concat([self.merchandise_imports_scaled_K, dff])
if matrix == 'Y':
self.merchandise_imports_scaled_Y = pd.concat([self.merchandise_imports_scaled_Y, dff])
scale_international_imports(self.who_uses_int_imports_U, 'U')
scale_international_imports(self.who_uses_int_imports_Y, 'Y')
if self.endogenizing:
scale_international_imports(self.who_uses_int_imports_K, 'K')
self.merchandise_imports_scaled_U = self.merchandise_imports_scaled_U.sort_index()
self.merchandise_imports_scaled_Y = self.merchandise_imports_scaled_Y.sort_index()
if self.endogenizing:
self.merchandise_imports_scaled_K = self.merchandise_imports_scaled_K.sort_index()
def gimme_symmetric_iot(self):
"""
Transforms Supply and Use tables to symmetric IO tables and transforms Y from product to industries if
selected classification is "industry"
:return: self.A, self.R and self.Y
"""
# recalculate g because we introduced K
if self.endogenizing:
g = (self.U.sum() + self.W.sum() + self.who_uses_int_imports_U.sum() + self.K.sum() +
self.who_uses_int_imports_K.sum())
g = pd.concat([g] * len(self.matching_dict), axis=1).T
g.index = self.g.index
g.columns = pd.MultiIndex.from_tuples(g.columns)
for province in self.matching_dict.keys():
g.loc[[i for i in g.index.levels[0] if i != province], province] = 0
self.g = g.copy('deep')
self.inv_q = pd.DataFrame(np.diag((1 / self.q.sum(axis=1)).replace(np.inf, 0)), self.q.index, self.q.index)
self.inv_g = pd.DataFrame(np.diag((1 / self.g.sum()).replace(np.inf, 0)), self.g.columns, self.g.columns)
self.A = self.U.dot(self.inv_g.dot(self.V.T)).dot(self.inv_q)
self.R = self.W.dot(self.inv_g.dot(self.V.T)).dot(self.inv_q)
if self.exiobase_folder:
self.merchandise_imports_scaled_U = self.merchandise_imports_scaled_U.reindex(
self.U.columns, axis=1).dot(self.inv_g.dot(self.V.T)).dot(self.inv_q)
self.merchandise_imports_scaled_K = self.merchandise_imports_scaled_K.reindex(
self.U.columns, axis=1).dot(self.inv_g.dot(self.V.T)).dot(self.inv_q)
if self.endogenizing:
self.K = self.K.dot(self.inv_g.dot(self.V.T)).dot(self.inv_q)
def link_international_trade_data_to_exiobase(self):
"""
Linking the data from the international merchandise trade database, which was previously linked to openIO-Canada,
to exiobase.
:return:
"""
# loading Exiobase
io = pymrio.parse_exiobase3(self.exiobase_folder)
if self.endogenizing:
with gzip.open(pkg_resources.resource_stream(
__name__, '/Data/Capitals_endogenization_exiobase/K_cfc_pxp_exio3.8.2_' +
str(self.year) + '.gz.pickle'), 'rb') as f:
self.K_exio = pickle.load(f)
# save the matrices from exiobase because we need them later
self.A_exio = io.A.copy()
self.S_exio = io.satellite.S.copy()
self.F_exio = io.satellite.F.copy()
# millions euros to euros
self.S_exio.iloc[9:] /= 1000000
self.unit_exio = io.satellite.unit.copy()
self.unit_exio.columns = ['Unit']
# loading concordances between exiobase classification and IOIC
ioic_exio = pd.read_excel(pkg_resources.resource_stream(__name__, '/Data/Concordances/IOIC_EXIOBASE.xlsx'),
'commodities')
ioic_exio = ioic_exio[2:].drop('IOIC Detail level - EXIOBASE', axis=1).set_index('Unnamed: 1').fillna(0)
ioic_exio.index.name = None
ioic_exio.index = [{j[0]: j[1] for j in self.commodities}[i] for i in ioic_exio.index]
# determine the Canadian imports according to Exiobase
canadian_imports_exio = io.A.loc[:, 'CA'].sum(1).drop('CA', axis=0, level=0)
if self.endogenizing:
# determine for which sector the merchandise databse provides import data (=covered) and those for which it doesnt (=uncovered)