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function_hybrid.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 14 11:53:04 2024
@author: regin
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#%%
current_pop = 16655799
project_pop = 20610000
Populationgrowth = project_pop / current_pop
unit = "kilotonne"
#%% input path of the IOT
iot_path = r"C:/Industrial_ecology/Thesis/IOT_2015_ixi"
save_path = r'C:/Industrial_ecology/Thesis/Circularinterventions/Code'
#%%Input of the HIOT
# data input and modify
hiot_path = "C:/Industrial_ecology/Thesis/Circularinterventions/Data/"
Z_hybrid = pd.read_csv(f"{hiot_path}MR_HIOT_2011_v3_3_18_by_product_technology.csv", index_col=[0,1,2,3,4], header=[0,1,2,3])
Z_hybrid = pd.DataFrame(Z_hybrid.values, columns = Z_hybrid.columns, index = Z_hybrid.columns)
Z_hybrid = Z_hybrid.droplevel([2,3], axis=1).droplevel([2,3], axis=0)
Y_hybrid = pd.read_csv(f"{hiot_path}MR_HIOT_2011_v3_3_18_FD.csv", index_col=[0,1,2,3,4], header=[0,1,2,3])
Y_hybrid = pd.DataFrame(Y_hybrid.values, columns = Y_hybrid.columns, index = Z_hybrid.columns)
Y_hybrid = Y_hybrid.droplevel([2,3], axis=1)
#x_hybrid = Z_hybrid.sum(axis = 1) + Y_hybrid.sum(axis = 1)
Y_hybrid.NL = Y_hybrid.NL * Populationgrowth
#modify the final demand to project to 2050
x_hybrid = Z_hybrid.sum(axis = 1) + Y_hybrid.sum(axis = 1)
x_out = x_hybrid.copy()
x_out[x_out!=0] = 1/x_out[x_out!=0]
inv_diag_x = np.diag(x_out)
A_hybrid = Z_hybrid @ inv_diag_x
A_hybrid = pd.DataFrame(A_hybrid.values, columns = Z_hybrid.columns, index = Z_hybrid.columns)
I = np.eye(A_hybrid.shape[0])
L_hybrid = np.linalg.inv(I-A_hybrid)
Y_modify = Y_hybrid.copy()
#%% import extensions and slice the data you want to use
extensions = pd.ExcelFile(f"{hiot_path}MR_HIOT_2011_v3_3_18_extensions.xlsx")
extensions.sheet_names
resource = "Iron ores"
# resource = "Bauxite and aluminium ores"
Emission = "Carbon dioxide, fossil"
#resource = "Copper ores"
#resource extraction --> take only the material of interest
RE = extensions.parse(sheet_name="resource_act", index_col=[0,1], header=[0,1,2,3])
RE_FD = extensions.parse(sheet_name="resource_FD", index_col=[0,1], header=[0,1,2,3])
RE = RE.loc[resource].sum(axis = 0)
RE_FD = RE_FD.loc[resource]
#emissions
EM = extensions.parse(sheet_name="Emiss_act", index_col=[0,1,2], header=[0,1,2,3])
EM_FD = extensions.parse(sheet_name="Emiss_FD", index_col=[0,1,2], header=[0,1,2,3])
EM = EM.loc[Emission].sum(axis = 0)
EM_FD = EM_FD.loc[Emission]
RE_f = RE.values @ inv_diag_x
EM_f = EM.values @ inv_diag_x
tester = np.diag(RE_f) @ L_hybrid @ Y_hybrid.sum(axis =1)
#%%
file_path = 'C:/Industrial_ecology/Thesis/Circularinterventions/Code/Input_circular_interventions/shocks_full.xlsx'
sheet_name = 'z' # Replace with the name of your sheet
Full_shocks_A = pd.read_excel(file_path, sheet_name=sheet_name)
print(Full_shocks_A)
#%%
def apply_shocks_A_multiple_sequencesreg(file_path, A_matrix, Y_matrix, sequencesA, sequencesY, indicatorimpact, indicatorintensity, threshold,sensitivity):
# Read the Excel file
Full_shocks_A = pd.read_excel(file_path, sheet_name='z')
Full_shocks_Y = pd.read_excel(file_path, sheet_name='Y')
# Dictionary to store diffcheckers for each sequence
diffcheckers = {}
diffcheckerimpact = {}
# Ensure sequenceA and sequenceY are the same length
max_length = max(len(sequencesA), len(sequencesY))
sequencesA.extend([[]] * (max_length - len(sequencesA)))
sequencesY.extend([[]] * (max_length - len(sequencesY)))
# Process sequences together
for seq_index, (seqA, seqY) in enumerate(zip(sequencesA, sequencesY)):
# Create copies of the original DataFrames to modify
A_modify = A_matrix.copy()
Y_modify = Y_matrix.copy()
# Process sequenceA
for seq in seqA:
if seq < len(Full_shocks_A):
row = Full_shocks_A.iloc[seq]
country_row = row['row region']
sector_row = row['row sector']
country_column = row['column region']
sector_column = row['column sector']
value = row['value']
typechange = row["type"]
print(seq)
if typechange == "Percentage":
A_modify.loc[(country_row, sector_row), (country_column, sector_column)] *= ((1 + value)*sensitivity)
else:
A_modify.loc[(country_row, sector_row), (country_column, sector_column)] += (value*sensitivity)
else:
print(f"Index {seq} is out of range for the DataFrame.")
# Process sequenceY
for seq in seqY:
if seq < len(Full_shocks_Y):
row = Full_shocks_Y.iloc[seq]
country_row = row['row region']
sector_row = row['row sector']
country_column = row['column region']
demand_column = row['demand category']
value = row['value']
typechange = row["type"]
print(seq)
if typechange == "Percentage":
Y_modify.loc[(country_row, sector_row), (country_column, demand_column)] *= ((1 + value)*sensitivity)
else:
Y_modify.loc[(country_row, sector_row), (country_column, demand_column)] += (value*sensitivity)
else:
print(f"Index {seq} is out of range for the DataFrame.")
# Groupby to check results
I = np.eye(A_matrix.shape[0])
x_modify = np.linalg.inv(I - A_modify) @ Y_modify.sum(axis=1)
x_baseline = np.linalg.inv(I - A_matrix) @ Y_matrix.sum(axis=1)
diffchecker = pd.DataFrame()
diffchecker["baseline"] = x_baseline
diffchecker["changes"] = x_modify
diffchecker["diff"] = diffchecker["changes"] - diffchecker["baseline"]
diffcheckerdif = diffchecker["diff"]
# Store the diffchecker in the dictionary with the sequence index as the key
diffcheckers[f'intervention_A_{seq_index}_Y_{seq_index}'] = diffcheckerdif
diffcheckerfull = pd.DataFrame.from_dict(diffcheckers)
L_ct = np.linalg.inv(I - A_modify.values)
x_ct = L_ct @ Y_modify.values.sum(axis=1)
RE_ct = indicatorintensity * x_ct
F_diff_RE = (RE_ct - indicatorimpact)
diffcheckerimpact[f'intervention{seq_index}'] = F_diff_RE
F_diff_RE = pd.DataFrame(F_diff_RE, index=A_matrix.index)
F_relative_change1 = F_diff_RE/1000
# Filter the DataFrame to include only values above the threshold
filtered_df = F_relative_change1[np.abs(F_relative_change1) > threshold].dropna()
# Calculate the sum of values below the threshold
below_threshold_sum = F_relative_change1[np.abs(F_relative_change1) <= threshold].sum().sum()
# Add the below-threshold sum as a new row
filtered_df.loc[('Below Threshold', 'Sum of below threshold'), :] = below_threshold_sum
# Choose a color palette (using Set1)
colors = plt.get_cmap('Set1').colors
plt.rcParams.update({'font.size': 18})
# Plot the filtered DataFrame with adjusted size and legend placement
ax = filtered_df.unstack().plot(kind="bar", stacked=True, legend=False, figsize=(10, 6), color=colors)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
ax.grid(True)
ax.set_title(f'Filtered differences for scenario {seq_index}\n in {indicator} (threshold = {threshold})', fontsize=12)
ax.set_ylabel(f"{indicator} in kt", fontsize=12)
ax.set_xlabel('Regions')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)
# Show the plot
plt.show()
return diffcheckerfull, diffcheckerimpact
#%%
# Example usage
file_path = 'C:/Industrial_ecology/Thesis/Circularinterventions/Code/Input_circular_interventions/shocks_full.xlsx'
sequencesA =[
list(range(0, 31)), # Sequence 1
list(range(32, 53)), # Sequence 2
list(range(0, 53)) # Sequence 3
]
sequencesY =[
list(range(0, 2)), # Sequence 1
list(range(2,3)), # Sequence 2
list(range(0, 3))# Sequence 3
]
indicator = 'Aluminium'
indicatorimpact = EM.values
indicatorintensity = EM_f
threshold = 2
A_hybrid1 = A_hybrid.copy() # Replace this with your actual DataFrame
sensitivity = 1
diffcheckers = apply_shocks_A_multiple_sequencesreg(file_path, A_hybrid, Y_hybrid, sequencesA, sequencesY, indicatorimpact, indicatorintensity ,threshold,sensitivity)
sensitivity = 1
diffcheckers,diffcheckerimpact = apply_shocks_A_multiple_sequencesreg(file_path, A_hybrid, Y_hybrid, sequencesA, sequencesY, indicatorimpact, indicatorintensity ,threshold,sensitivity)
sensitivity = 1.05
diffcheckersmin, diffcheckerminimpact = apply_shocks_A_multiple_sequencesreg(file_path, A_hybrid, Y_hybrid, sequencesA, sequencesY, indicatorimpact, indicatorintensity ,threshold,sensitivity)
sensitivity = 1.02
diffcheckersplus,diffcheckerplusimpact = apply_shocks_A_multiple_sequencesreg(file_path, A_hybrid, Y_hybrid, sequencesA, sequencesY, indicatorimpact, indicatorintensity ,threshold,sensitivity)
#%%
diffcheckerimpact = pd.DataFrame.from_dict(diffcheckerimpact)
diffcheckerimpact.index = A_hybrid.index
diffcheckerminimpact = pd.DataFrame.from_dict(diffcheckerminimpact)
diffcheckerminimpact.index = A_hybrid.index
diffcheckerplusimpact = pd.DataFrame.from_dict(diffcheckerplusimpact)
diffcheckerplusimpact.index = A_hybrid.index
differencesensitivity= diffcheckerminimpact-diffcheckerimpact
differencesensitivity1= diffcheckerplusimpact-diffcheckerimpact
differencesensitivity.plot()
difplot = pd.DataFrame()
difplot["increase_5%"] = differencesensitivity.intervention2.values
difplot["increase_2%"] = differencesensitivity1.intervention2.values
#%%
#indicator = resource
threshold = 1
unit = "kt"
F_diff_RE = differencesensitivity.intervention2.values
F_diff_RE = pd.DataFrame(F_diff_RE, index=A_hybrid.index)
F_relative_change1 = F_diff_RE/1000
# Filter the DataFrame to include only values above the threshold
filtered_df = F_relative_change1[np.abs(F_relative_change1) > threshold].dropna()
# Calculate the sum of values below the threshold
below_threshold_sum = F_relative_change1[np.abs(F_relative_change1) <= threshold].sum().sum()
# Add the below-threshold sum as a new row
filtered_df.loc[('Below Threshold', 'Sum of below threshold'), :] = below_threshold_sum
# Choose a color palette (using Set1)
colors = plt.get_cmap('Set1').colors
plt.rcParams.update({'font.size': 18})
# Plot the filtered DataFrame with adjusted size and legend placement
ax = filtered_df.unstack().plot(kind="bar", stacked=True, legend=False, figsize=(10, 6), color=colors)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=12)
ax.grid(True)
ax.set_title(f'Filtered differences sensitivity\n {indicator} (threshold = {threshold})', fontsize=12)
ax.set_ylabel(f"{indicator}\n ({unit})", fontsize=11)
ax.set_xlabel('Regions')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right', fontsize=12)
# Show the plot
plt.show()
#%%
# diffcheckerimpact2 = diffcheckerimpact/1000
# diffcheckerimpact2.groupby(level=0, axis=0, sort=False).sum()
# difplot = difplot/1000
# labelresource = resource
# labelresource = labelresource.replace(" ", "_")
# outputpath = "C:/Industrial_ecology/Thesis/Circularinterventions/Code/Input_circular_interventions/output_visuals/"
# diffcheckerimpact2.to_csv(f'{outputpath}{labelresource}_hyb_impact.csv', index=True)
# difplot.to_csv(f'{outputpath}{labelresource}_hyb_sens.csv', index=True)
# #%%
diffcheckerimpact2 = diffcheckerimpact/1000
diffcheckerimpact2.groupby(level=0, axis=0, sort=False).sum()
difplot = difplot/1000
labelresource = Emission
labelresource = labelresource[:14]
labelresource = labelresource.replace(" ", "_")
outputpath = "C:/Industrial_ecology/Thesis/Circularinterventions/Code/Input_circular_interventions/output_visuals/"
diffcheckerimpact2.to_csv(f'{outputpath}{labelresource}_hyb_impact.csv', index=True)
difplot.to_csv(f'{outputpath}{labelresource}_hyb_sens.csv', index=True)