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STYLIE_Buildings.py
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STYLIE_Buildings.py
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# -*- coding: utf-8 -*-
"""
Created on April 12, 2024
@author: spauliuk
This script loads previously compiled results and then compiles selected results
into different visualisations of the energy service cascade.
Works together with IAMC data template and control workbook
STYLIE_ESC_Configure.xlsx
Documentation and how to available in https://github.com/IndEcol/STYLIE
"""
import os
import openpyxl
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.lines import Line2D
import numpy as np
def get_esc_data_from_pandas(ps,selectI,selectR,cscenss):
# return numpay array with esc data for given indicator, region, and scenario list
pst = ps[ps['Indicator'].isin([selectI]) & ps['Region'].isin(selectR) & ps['Scenario'].isin(cscenss)] # Select the specified data and transpose them for plotting
unit = pst.iloc[0]['Unit']
Data = pst.drop(['Scenario','Indicator', 'Region', 'Unit'], axis=1).values
return Data, unit
plt.style.use('default') # set all plotting parameters to their default values
# Definitions/Specifications
CF = openpyxl.load_workbook('STYLIE_ESC_Configure.xlsx')
CS = CF['Cover'].cell(4,4).value
scen = [] # list of target scenarios
r = 1
# Move to parameter list:
while True:
if CF[CS].cell(r,1).value == 'Define ESC plot':
break
r += 1
r += 1
ctitles = []
ctypes = []
cregs = []
cscens = []
colors = [] # List with color strings
while True:
if CF[CS].cell(r,2).value is None:
break
ctitles.append(CF[CS].cell(r,2).value)
ctypes.append(CF[CS].cell(r,3).value)
cregs.append(CF[CS].cell(r,4).value)
cscens.append(CF[CS].cell(r,5).value)
colors.append(CF[CS].cell(r,11).value)
r += 1
# open data file with results
ps = pd.read_excel('IAMC_Template_RECCv2.5_SampleData.xlsx', sheet_name='Buildings', index_col=0) # plot sheet
# determine ESC indicators and plot ESC cascades
# find ESC parameters in Res array: outer index: Ar, middle index: ti, inner indicator: scen
for c in range(0,len(ctitles)):
if ctypes[c] == 'version_2_blds': # Energy service cascade_GHG for buildings
# get scenario list and length
if cscens[c] == 'All':
cscenss = scen
else:
cscenss = cscens[c].split(';')
nocs = len(cscenss)
selectR = [cregs[c]]
# Define data container
esc_data = np.zeros((10,46,nocs)) # 10 decoupling indices, 46 years, nocs scenarios
# EXTRACT data and convert to ESC data array
# Population:
Data_pop = np.concatenate([get_esc_data_from_pandas(ps,'Population',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# GHG:
Data_ghg1 = np.concatenate([get_esc_data_from_pandas(ps,'Emissions|CO2|Energy|Demand|Residential',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_ghg2 = np.concatenate([get_esc_data_from_pandas(ps,'Emissions|CO2|Energy|Demand|Commercial',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_ghg3 = np.concatenate([get_esc_data_from_pandas(ps,'Emissions|CO2|Energy|Supply',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_ghg4 = np.concatenate([get_esc_data_from_pandas(ps,'GHG emissions, primary material production',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Final energy:
Data_edx = np.concatenate([get_esc_data_from_pandas(ps,'Final Energy|Residential and Commercial',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Stock:
Data_rebx = np.concatenate([get_esc_data_from_pandas(ps,'Energy Service|Residential|Floor Space',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_nrbx = np.concatenate([get_esc_data_from_pandas(ps,'Energy Service|Commercial|Floor Space',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Inflow
Data_rebf = np.concatenate([get_esc_data_from_pandas(ps,'final consumption (use phase inflow), all res. building types together',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_nrbf = np.concatenate([get_esc_data_from_pandas(ps,'final consumption (use phase inflow), all nonres. building types together',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Final material consumption:
Data_matm = np.concatenate([get_esc_data_from_pandas(ps,'Final consumption of materials',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Material footprint / RMI
Data_maf1 = np.concatenate([get_esc_data_from_pandas(ps,'Material footprint, metal ores, system-wide',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_maf2 = np.concatenate([get_esc_data_from_pandas(ps,'Material footprint, non-metallic minerals, system-wide',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_maf3 = np.concatenate([get_esc_data_from_pandas(ps,'Material footprint, biomass (dry weight), system-wide',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
esc_data[0,:,:] = ((Data_ghg1 + Data_ghg2 + Data_ghg3)/Data_pop).transpose()
esc_data[1,:,:] = ((Data_ghg1 + Data_ghg2 + Data_ghg3)/Data_edx).transpose()
esc_data[2,:,:] = (Data_edx / (Data_rebx + Data_nrbx)).transpose()
esc_data[3,:,:] = ((Data_rebx + Data_nrbx)/Data_pop).transpose()
esc_data[4,:,:] = ((Data_matm) / (Data_rebf + Data_nrbf)).transpose()
esc_data[5,:,:] = (Data_ghg4 / Data_matm).transpose()
esc_data[6,:,:] = (Data_ghg4 / Data_pop).transpose()
esc_data[7,:,:] = ((Data_maf1 + Data_maf2 + Data_maf3)/Data_matm).transpose()
esc_data[8,:,:] = ((Data_maf1 + Data_maf2 + Data_maf3)/Data_pop).transpose()
esc_data[9,:,:] = ((Data_rebf + Data_nrbf)/(Data_rebx + Data_nrbx)).transpose()
# Define maximal GHG/cap
maxGHG = np.max(esc_data[[0,6],1::,:])
# Define colors
ccol = np.array([[128,128,128,255],[48,84,150,255],[198,89,17,255],[142,105,0,255],[112,48,160,255]])/255 # grey, blue, red, brown, purple
# Plot results
fig, axs = plt.subplots(nrows=1, ncols=8 , figsize=(24, 3))
fig.suptitle('Energy and material service cascade, ' + cregs[c],fontsize=18)
ProxyHandlesList = [] # For legend
plt.rcParams["axes.prop_cycle"] = plt.cycler("color", ccol)
axs[0].plot(np.arange(2016,2061), esc_data[0,1::,:], linewidth = 3)
plta = Line2D(np.arange(2016,2061), esc_data[0,1::,:], linewidth = 3)
ProxyHandlesList.append(plta) # create proxy artist for legend
axs[0].set_title('Energy-GHG per capita = \n (Scope 1 + 2 emissions) ', weight='bold')
axs[0].set_ylabel('t CO2-eq/yr', fontsize = 12)
#axs[0].set_facecolor((221/255, 235/255, 247/255))
axs[0].set_facecolor((197/255, 221/255, 241/255))
axs[0].set_ylim(bottom=0)
axs[0].set_ylim(top=1.05 * maxGHG)
axs[1].plot(np.arange(2016,2061), esc_data[1,1::,:] * 1e6, linewidth = 2.0)
axs[1].set_title('GHG per final energy *')
axs[1].set_ylabel('g CO2-eq/MJ', fontsize = 12)
axs[1].set_facecolor((238/255, 245/255, 252/255))
axs[1].set_ylim(bottom=0)
axs[2].plot(np.arange(2016,2061), esc_data[2,1::,:], linewidth = 2.0)
axs[2].set_title('Final energy per stock *')
axs[2].set_ylabel('MJ/(m²·yr)', fontsize = 12)
axs[2].set_facecolor((238/255, 245/255, 252/255))
axs[2].set_ylim(bottom=0)
axs[3].plot(np.arange(2016,2061), esc_data[3,1::,:], linewidth = 3.0)
axs[3].set_title(' <-- | --> \n Stock per capita', weight='bold')
axs[3].set_ylabel('m²', fontsize = 12)
axs[3].set_facecolor((237/255, 226/255, 246/255))
axs[3].set_ylim(bottom=0)
axs[4].plot(np.arange(2016,2061), esc_data[9,1::,:], linewidth = 2.0)
axs[4].set_title('* inflow per stock')
axs[4].set_ylabel('1/yr', fontsize = 12)
axs[4].set_facecolor((253/255, 239/255, 231/255))
axs[4].set_ylim(bottom=0)
axs[5].plot(np.arange(2016,2061), esc_data[4,1::,:] * 1e3, linewidth = 2.0)
axs[5].set_title('* material intensity of inflow')
axs[5].set_ylabel('kg/m²', fontsize = 12)
axs[5].set_facecolor((253/255, 239/255, 231/255))
axs[5].set_ylim(bottom=0)
axs[6].plot(np.arange(2016,2061), esc_data[5,1::,:], linewidth = 2.0)
axs[6].set_title('* material-GHG per material')
axs[6].set_ylabel('t CO2-eq/t', fontsize = 12)
axs[6].set_facecolor((253/255, 239/255, 231/255))
axs[6].set_ylim(bottom=0)
axs[7].plot(np.arange(2016,2061), esc_data[6,1::,:], linewidth = 3.0)
axs[7].set_title('= material-GHG per capita \n(Scope 3 emissions)', weight='bold')
axs[7].set_ylabel('t CO2-eq/yr', fontsize = 12)
#axs[7].set_facecolor((252/255, 228/255, 214/255))
axs[7].set_facecolor((249/255, 203/255, 177/255))
axs[7].set_ylim(bottom=0)
axs[7].set_ylim(top=1.05 * maxGHG)
Labels = cscenss
fig.legend(Labels, shadow = False, prop={'size':14},ncol=5, loc = 'upper center',bbox_to_anchor=(0.5, -0.02))
plt.tight_layout()
plt.show()
title = ctitles[c]
fig.savefig(title + '_' + selectR[0] + '.png', dpi=150, bbox_inches='tight')
if ctypes[c] == 'version_3_blds': # Energy service cascade_RMI for buildings
# get scenario list and length
if cscens[c] == 'All':
cscenss = scen
else:
cscenss = cscens[c].split(';')
nocs = len(cscenss)
selectR = [cregs[c]]
# Define data container
esc_data = np.zeros((10,46,nocs)) # 10 decoupling indices, 46 years, nocs scenarios
# EXTRACT data and convert to ESC data array
# Population:
Data_pop = np.concatenate([get_esc_data_from_pandas(ps,'Population',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# GHG:
Data_ghg1 = np.concatenate([get_esc_data_from_pandas(ps,'Emissions|CO2|Energy|Demand|Residential',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_ghg2 = np.concatenate([get_esc_data_from_pandas(ps,'Emissions|CO2|Energy|Demand|Commercial',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_ghg3 = np.concatenate([get_esc_data_from_pandas(ps,'Emissions|CO2|Energy|Supply',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_ghg4 = np.concatenate([get_esc_data_from_pandas(ps,'GHG emissions, primary material production',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Final energy:
Data_edx = np.concatenate([get_esc_data_from_pandas(ps,'Final Energy|Residential and Commercial',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Stock:
Data_rebx = np.concatenate([get_esc_data_from_pandas(ps,'Energy Service|Residential|Floor Space',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_nrbx = np.concatenate([get_esc_data_from_pandas(ps,'Energy Service|Commercial|Floor Space',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Inflow
Data_rebf = np.concatenate([get_esc_data_from_pandas(ps,'final consumption (use phase inflow), all res. building types together',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_nrbf = np.concatenate([get_esc_data_from_pandas(ps,'final consumption (use phase inflow), all nonres. building types together',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Final material consumption:
Data_matm = np.concatenate([get_esc_data_from_pandas(ps,'Final consumption of materials',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
# Material footprint / RMI
Data_maf1 = np.concatenate([get_esc_data_from_pandas(ps,'Material footprint, metal ores, system-wide',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_maf2 = np.concatenate([get_esc_data_from_pandas(ps,'Material footprint, non-metallic minerals, system-wide',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
Data_maf3 = np.concatenate([get_esc_data_from_pandas(ps,'Material footprint, biomass (dry weight), system-wide',selectR,[cscenss[mscen]])[0] for mscen in range(0,nocs)])
esc_data[0,:,:] = ((Data_ghg1 + Data_ghg2 + Data_ghg3)/Data_pop).transpose()
esc_data[1,:,:] = ((Data_ghg1 + Data_ghg2 + Data_ghg3)/Data_edx).transpose()
esc_data[2,:,:] = (Data_edx / (Data_rebx + Data_nrbx)).transpose()
esc_data[3,:,:] = ((Data_rebx + Data_nrbx)/Data_pop).transpose()
esc_data[4,:,:] = ((Data_matm) / (Data_rebf + Data_nrbf)).transpose()
esc_data[5,:,:] = (Data_ghg4 / Data_matm).transpose()
esc_data[6,:,:] = (Data_ghg4 / Data_pop).transpose()
esc_data[7,:,:] = ((Data_maf1 + Data_maf2 + Data_maf3)/Data_matm).transpose()
esc_data[8,:,:] = ((Data_maf1 + Data_maf2 + Data_maf3)/Data_pop).transpose()
esc_data[9,:,:] = ((Data_rebf + Data_nrbf)/(Data_rebx + Data_nrbx)).transpose()
# Define colors
cc = np.array([[128,128,128,255],[48,84,150,255],[198,89,17,255],[142,105,0,255],[112,48,160,255]])/255 # grey, blue, red, brown, purple
# Plot results
fig, axs = plt.subplots(nrows=1, ncols=5 , figsize=(16, 3))
fig.suptitle('Energy and material service cascade, ' + cregs[c],fontsize=18)
ProxyHandlesList = [] # For legend
plt.rcParams["axes.prop_cycle"] = plt.cycler("color", cc)
axs[0].plot(np.arange(2016,2061), esc_data[3,1::,:], linewidth = 3.0)
axs[0].set_title('Stock per capita', weight='bold')
axs[0].set_ylabel('m²', fontsize = 12)
axs[0].set_facecolor((237/255, 226/255, 246/255))
axs[0].set_ylim(bottom=0)
axs[1].plot(np.arange(2016,2061), esc_data[9,1::,:], linewidth = 2.0)
axs[1].set_title('* inflow per stock')
axs[1].set_ylabel('1/yr', fontsize = 12)
axs[1].set_facecolor((253/255, 239/255, 231/255))
axs[1].set_ylim(bottom=0)
axs[2].plot(np.arange(2016,2061), esc_data[4,1::,:] * 1e3, linewidth = 2.0)
axs[2].set_title('* material intensity of inflow')
axs[2].set_ylabel('kg/m²', fontsize = 12)
axs[2].set_facecolor((253/255, 239/255, 231/255))
axs[2].set_ylim(bottom=0)
axs[3].plot(np.arange(2016,2061), esc_data[7,1::,:], linewidth = 2.0)
axs[3].set_title('* RMI per material')
axs[3].set_ylabel('t/t', fontsize = 12)
axs[3].set_facecolor((253/255, 239/255, 231/255))
axs[3].set_ylim(bottom=0)
axs[4].plot(np.arange(2016,2061), esc_data[8,1::,:], linewidth = 3.0)
axs[4].set_title('= RMI per capita', weight='bold')
axs[4].set_ylabel('t/yr', fontsize = 12)
#axs[4].set_facecolor((252/255, 228/255, 214/255))
axs[4].set_facecolor((249/255, 203/255, 177/255))
axs[4].set_ylim(bottom=0)
Labels = cscenss
fig.legend(Labels, shadow = False, prop={'size':14},ncol=5, loc = 'upper center',bbox_to_anchor=(0.5, -0.02))
plt.tight_layout()
plt.show()
title = ctitles[c]
fig.savefig(title + '_' + selectR[0] + '.png', dpi=150, bbox_inches='tight')
#
#
#
# The end.
#
#
#