In this file references to an Aggregation folder containing crucial aggregation and Support Excel file are present. See this repository for these files and more detail: https://github.com/eNextHub/Footprint
Start with importing pandas, mario and numpy
import pandas as pd
import mario
import numpy as np
#%% Now is time to import our databases
user = r'your path' # Your path to the folder containing Exiobase
Modes = ['pxp'] # Which versions of Exiobase you want to use?
Years = [2019] # Which years?
Worlds = {} # Initializing the dictionaries of all the worlds
sN = slice(None) # Useful to include levels when slicing dataframes
Coun_info = pd.read_excel('Aggregations\Support.xlsx', sheet_name='Countries', index_col=[0], header=[0]) # Some information is derived from a support file. See GitHub repository for more information.
# Select the interested levels of information
Consumption_cats = ['Final consumption expenditure by households']
Countries = ['IT']
Importing a version of a chosen dataframe using the brand new mario.parse_exiobase function
World = mario.parse_exiobase(path=user+f'\IOT_{Years[0]}_{Modes[0]}.zip', unit='Monetary', table='IOT') # Import the right Exiobase version and year
World.aggregate('Aggregations\Aggregation.xlsx') # Aggregate the database from a predefined aggregation file
Bulding the indeces to store the results out of MARIO's output
Regions = World.get_index('Region')
Sectors = World.get_index('Sector')
Sat_accounts = World.get_index('Satellite account')
Res_col = pd.Index(Sat_accounts, name='Satellite accounts')
Res_row = pd.MultiIndex.from_product([Modes,Years,Regions,Sectors], names=['Mode','Year','Region','Sector']) # Bulding the index
Res = pd.DataFrame(0, index=Res_row, columns=Res_col) # For the results database we want to create
Loop through each combination of mode and year and parse the corresponding Exiobase data
for m in Modes:
for y in Years:
path = user+f'\IOT_{y}_{m}.zip' # Complete the path
Worlds[m,y] = mario.parse_exiobase_3(path, name=f'{m} - {y}') # Import the right Exiobase version and year
Worlds[m,y].aggregate('Aggregations\Aggregation.xlsx')
for e in Sat_accounts:
f = Worlds[m,y].f.loc[e]
f_diag = np.diag(f)
Y = Worlds[m,y].Y.loc[:,(Countries,sN,Consumption_cats)].sum(1)
Calc = pd.DataFrame(f_diag@Y.values, index= Worlds[m,y].Y.index, columns=[e])
for r in Regions:
for p in Sectors:
Res.loc[(m,y,r,p),e] = Calc.loc[(r,sN,p),e][0] # Writing the results of the calculation in the results database
for c in Countries:
Res.loc[(m,y,c,'Heating'),e] = Coun_info.loc[c,'GHG emiss Heating share']*Worlds[m,y].EY.loc[e,(Countries,sN,Consumption_cats)].sum() # Reallocation of the households' emissions on two dedicated categories on the basis of the assumption made in the support file
Res.loc[(m,y,c,'Driving'),e] = Coun_info.loc[c,'GHG emiss Driving share']*Worlds[m,y].EY.loc[e,(Countries,sN,Consumption_cats)].sum() # Reallocation of the households' emissions on two dedicated categories on the basis of the assumption made in the support file
Database: to calculate f following matrices are need. ['w'].Trying to calculate dependencies.
Adding GHG with a 100-years GWP
Res['GHG'] = Res['CH4']*25 + Res['CO2']*1 + Res['N2O']*298
Mapping needs of final consumers and the sector of the database
Map1 = pd.read_excel('Aggregations\Support.xlsx', sheet_name='Sectors to needs', index_col=[0], header=[0]).to_dict()['Need']
Map2 = pd.read_excel('Aggregations\Support.xlsx', sheet_name='Sectors to needs', index_col=[0], header=[0]).to_dict()['Settori']
Res['Need'] = Res.index.get_level_values('Sector').map(Map1)
Res['Settori'] = Res.index.get_level_values('Sector').map(Map2)
RES = Res.reset_index().set_index(['Mode','Year','Region','Sector','Settori','Need'])
World.query can be used to extract absolute differences between matrices in different scenarios.
In this case we have no more scenarios than baseline, but you can see that if a comparison with same scenario is queried no difference is detected.
World.query('E', scenarios=['baseline'], type='absolute', base_scenario='baseline')
Region | AT | ... | WM | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Level | Sector | ... | Sector | ||||||||||||||||||
Item | Paddy rice | Wheat | Cereal grains nec | Vegetables, fruit, nuts | Oil seeds | Sugar cane, sugar beet | Plant-based fibers | Crops nec | Cattle | Pigs | ... | Paper for treatment: landfill | Plastic waste for treatment: landfill | Inert/metal/hazardous waste for treatment: landfill | Textiles waste for treatment: landfill | Wood waste for treatment: landfill | Membership organisation services n.e.c. (91) | Recreational, cultural and sporting services (92) | Other services (93) | Private households with employed persons (95) | Extra-territorial organizations and bodies |
Item | |||||||||||||||||||||
CO2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
CH4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
N2O | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
3 rows × 9800 columns
Importing the color palette (here the eNextGen one is used)
Colors = pd.read_excel('Aggregations\Support.xlsx', sheet_name='Needs colors', index_col=[0], header=[0]).to_dict()['Color'] # Some information is derived from a support file. See GitHub repository for more information.
The output is in Italian cause the example is taken for an application for Italy.
import plotly.express as px
plot = RES.groupby(['Need','Settori']).sum().reset_index()
plot['% GHG'] = round(plot['GHG'] / plot['GHG'].sum()*100,1).astype(str) + '%'
plot['GHG pc'] = round(plot['GHG']/Coun_info.loc[Countries[0],'Population']).astype(str) + ' kgCO2eq per capita'
# Make a dataframe with GHG emissions per capita by need
GHG_need = round(plot.groupby('Need').sum()/Coun_info.loc[Countries[0],'Population']/1000,1).reset_index()
plot['GHG_need'] = plot['Need'].map(GHG_need.set_index('Need')['GHG'])
# Add a column to plot in which the name of the need and the GHG_need are displayed together
plot['Need and GHG'] = plot['Need'] + ' ~' + plot['GHG_need'].astype(str) + ' ton'
fig = px.treemap(plot, path=['Need and GHG','Settori'], values='GHG', color='Need', color_discrete_map=Colors, hover_data=['% GHG','GHG pc'])
fig.update_layout(template='plotly_white', font_family='HelveticaNeue')
fig.update_layout(
plot_bgcolor='black', # Set dark background
paper_bgcolor='black')
fig.update_traces(marker=dict(cornerradius=15))
# Add percentage in each section of the treemap
fig.data[0].textinfo = 'label+percent root'
# Add percentage also at the bottom of the treemap
fig.data[0].insidetextfont.size = 30
fig.data[0].insidetextfont.color = 'black'
# Add title showing the total emissions
fig.update_layout(title_text=f"Emissioni totali di gas serra: ~{round(plot['GHG'].sum()/Coun_info.loc[Countries[0],'Population']*1e-3,1)} tonCO2eq per italiano all'anno")
fig.update_layout(title_x=0.5)
# Decrease distance between title and treemap
fig.update_layout(title_y=0.95)
# Make title white
fig.update_layout(title_font_color='white')
# Make the figure available in the notebook
import plotly.offline as pyo
pyo.init_notebook_mode(connected=True)
pyo.iplot(fig)