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Plot for Mt materials+GHG saved per additional struct. timber.
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= committed Jan 15, 2024
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118 changes: 117 additions & 1 deletion plots/RECC_Building_Plots_v1.py
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import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import matplotlib.lines as mlines
from matplotlib.lines import Line2D
import numpy as np
import RECC_Paths # Import path file

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groupsi = pflags[m].split(';')
groupsi = [int(i) for i in groupsi]
labelsg = indlab[m].split(';')
labelsc = scelab[m].split(';')
title_add = '_' + selectR
Data1 = np.zeros((groupsi[0],45)) # for 46 years
Data2 = np.zeros((groupsi[1],45)) # for 46 years
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ax4.set(xlabel='year')

plt.show()
fig.savefig(os.path.join(os.path.join(RECC_Paths.export_path,outpath), ptitles[m] + title_add +'.png'), dpi=150, bbox_inches='tight')
fig.savefig(os.path.join(os.path.join(RECC_Paths.export_path,outpath), ptitles[m] + title_add +'.png'), dpi=150, bbox_inches='tight')

# plot all in one:
fig = plt.figure(figsize=(5,5))
axs = plt.axes([0.08,0.08,0.85,0.9])
axs.plot(x, Data1.transpose(), color = '#1f77b4') # For top left
axs.plot(x, Data2.transpose(), color = '#ff7f0e') # For top right
axs.plot(x, Data3.transpose(), color = '#2ca02c') # For bottom left
axs.plot(x, Data4.transpose(), color = '#d62728') # For bottom right
axs.set(xlabel='year', ylabel=unit)
axs.set_ylim(bottom=0)
plt.title(Inds[0] + ', ' + selectR)
ProxyHandlesList = [] # For legend
ProxyHandlesList.append(Line2D(np.arange(2016,2061), np.arange(2016,2061), color = '#1f77b4'))
ProxyHandlesList.append(Line2D(np.arange(2016,2061), np.arange(2016,2061), color = '#2ca02c'))
ProxyHandlesList.append(Line2D(np.arange(2016,2061), np.arange(2016,2061), color = '#ff7f0e'))
ProxyHandlesList.append(Line2D(np.arange(2016,2061), np.arange(2016,2061), color = '#d62728'))
axs.legend(handles = ProxyHandlesList,labels = labelsc, shadow = False, prop={'size':9},ncol=1, loc = 'lower left')
fig.savefig(os.path.join(os.path.join(RECC_Paths.export_path,outpath), ptitles[m] + title_add +'_Combined.png'), dpi=150, bbox_inches='tight')


if ptypes[m] == 'WoodSubstInd':
# Compile table for wood substitution indicators
selectS = pscens[m].split(';')
title_add = ptitles[m]
DataMatsW = np.zeros((11,4,5)) # for 11 regions, 4 scenario pairs, and 5 indicators
IndsWood = ['Construction wood, structural, from industrial roundwood','Cement production','Primary steel production','GHG emissions, primary material production','GHG emissions, non-biogenic']
for rr in range(0,len(regions)): # for each region
for ii in range(0,len(IndsWood)): # for each Indicator
for sp in range(0,4): # for each scenario pair
pst1 = pc[pc['Indicator'].isin([IndsWood[ii]]) & pc['Region'].isin([regions[rr]]) & pc['Scenario'].isin([selectS[2*sp]])].iloc[0]['Cum. 2020-2050 (incl.)']
pst2 = pc[pc['Indicator'].isin([IndsWood[ii]]) & pc['Region'].isin([regions[rr]]) & pc['Scenario'].isin([selectS[2*sp+1]])].iloc[0]['Cum. 2020-2050 (incl.)']
DataMatsW[rr,sp,ii] = pst1-pst2
GHGS = -DataMatsW[:,:,4] / DataMatsW[:,:,0]
PSTS = -DataMatsW[:,:,1] / DataMatsW[:,:,0]
PCES = -DataMatsW[:,:,2] / DataMatsW[:,:,0]

fig = plt.figure(figsize=(5,5))
axs = plt.axes([0.08,0.08,0.85,0.9])

axs.scatter(np.ones(9)+1.3,GHGS[0:-2,0], color = '#1f77b4')
axs.scatter(np.ones(9)+1.2,GHGS[0:-2,1], color = '#2ca02c')
axs.scatter(np.ones(9)+1.1,GHGS[0:-2,2], color = '#ff7f0e')
axs.scatter(np.ones(9)+1.0,GHGS[0:-2,3], color = '#d62728')

axs.scatter(2.3,GHGS[-1,0], s = 300, color = 'k', marker = '_', linewidths = 3)
axs.scatter(2.2,GHGS[-1,1], s = 300, color = 'k', marker = '_', linewidths = 3)
axs.scatter(2.1,GHGS[-1,2], s = 300, color = 'k', marker = '_', linewidths = 3)
axs.scatter(2.0,GHGS[-1,3], s = 300, color = 'k', marker = '_', linewidths = 3)

axs.scatter(np.ones(9)+0.3,PCES[0:-2,0], color = '#1f77b4')
axs.scatter(np.ones(9)+0.2,PCES[0:-2,1], color = '#2ca02c')
axs.scatter(np.ones(9)+0.1,PCES[0:-2,2], color = '#ff7f0e')
axs.scatter(np.ones(9)+0.0,PCES[0:-2,3], color = '#d62728')

axs.scatter(1.3,PCES[-1,0], s = 300, color = 'k', marker = '_', linewidths = 3)
axs.scatter(1.2,PCES[-1,1], s = 300, color = 'k', marker = '_', linewidths = 3)
axs.scatter(1.1,PCES[-1,2], s = 300, color = 'k', marker = '_', linewidths = 3)
axs.scatter(1.0,PCES[-1,3], s = 300, color = 'k', marker = '_', linewidths = 3)

axyl = axs.get_ylim()

axs.fill_between([1.4,1.9],[axyl[0],axyl[0]],[axyl[1],axyl[1]],facecolor = np.array([230,230,230])/255)

axs.scatter(np.ones(9)+0.8,PSTS[0:-2,0], color = '#1f77b4')
axs.scatter(np.ones(9)+0.7,PSTS[0:-2,1], color = '#2ca02c')
axs.scatter(np.ones(9)+0.6,PSTS[0:-2,2], color = '#ff7f0e')
axs.scatter(np.ones(9)+0.5,PSTS[0:-2,3], color = '#d62728')

axs.scatter(1.8,PSTS[-1,0], s = 300, color = 'k', marker = '_', linewidths = 3)
axs.scatter(1.7,PSTS[-1,1], s = 300, color = 'k', marker = '_', linewidths = 3)
axs.scatter(1.6,PSTS[-1,2], s = 300, color = 'k', marker = '_', linewidths = 3)
axs.scatter(1.5,PSTS[-1,3], s = 300, color = 'k', marker = '_', linewidths = 3)

plt.title('Materials & GHG saved per additional Mt of structural timber', fontsize = 10.5)

ProxyHandlesList = [] # For legend
ProxyHandlesList.append(axs.scatter(0,PSTS[-1,0], s = 300, color = 'k', marker = '_', linewidths = 3))
ProxyHandlesList.append(axs.scatter(0,PSTS[-1,0], color = '#1f77b4'))
ProxyHandlesList.append(axs.scatter(0,PSTS[-1,0], color = '#2ca02c'))
ProxyHandlesList.append(axs.scatter(0,PSTS[-1,0], color = '#ff7f0e'))
ProxyHandlesList.append(axs.scatter(0,PSTS[-1,0], color = '#d62728'))
axs.legend(handles = ProxyHandlesList,labels = ['Global average','High carbon Energy+Materials','High carbon Energy+Materials + Full CE','Low carbon Energy+Materials','Low carbon Energy+Materials + Full CE'], shadow = False, prop={'size':9},ncol=1, loc = 'upper left')

axs.set_xlim(left = 0.9)
axs.set_xlim(right = 2.4)
axs.set_ylim(bottom = axyl[0])
axs.set_ylim(top = axyl[1])

plt.text(0.95, 1.35, 'Mt of cement \nsaved', fontsize=12, fontweight='normal')
plt.text(1.45, 1.35, 'Mt of primary \nsteel saved', fontsize=12, fontweight='normal')
plt.text(1.95, 0.45, r'Mt of CO$_2$-eq', fontsize=12, fontweight='normal')
plt.text(1.95, 0.25, '(non-biogenic) \nsaved across \nentire system', fontsize=12, fontweight='normal')

plt.xticks([])

fig.savefig(os.path.join(os.path.join(RECC_Paths.export_path,outpath), ptitles[m] + '.png'), dpi=150, bbox_inches='tight')


if ptypes[m] == 'LEDInd':
# Compile table for LED indicators
selectS = pscens[m].split(';')
title_add = ptitles[m]
DataMatsD = np.zeros((11,4,7)) # for 11 regions, 4 scenario pairs, and 7 indicators
DataP = np.zeros((11,8)) # for 8 regions and 8 scearios
IndsWood = ['In-use stock, res. buildings','In-use stock, nonres. buildings','Construction wood, structural, from industrial roundwood','Cement production','Primary steel production','GHG emissions, primary material production','GHG emissions, non-biogenic']
for rr in range(0,len(regions)): # for each region
for ii in range(0,len(IndsWood)): # for each Indicator
for sp in range(0,4): # for each scenario pair
pst1 = pc[pc['Indicator'].isin([IndsWood[ii]]) & pc['Region'].isin([regions[rr]]) & pc['Scenario'].isin([selectS[2*sp]])].iloc[0]['Cum. 2020-2050 (incl.)']
pst2 = pc[pc['Indicator'].isin([IndsWood[ii]]) & pc['Region'].isin([regions[rr]]) & pc['Scenario'].isin([selectS[2*sp+1]])].iloc[0]['Cum. 2020-2050 (incl.)']
DataMatsD[rr,sp,ii] = pst1-pst2
for indp in range(0,len(selectS)):
DataP[rr,indp] = ps[ps['Indicator'].isin(['Population']) & ps['Region'].isin([regions[rr]]) & ps['Scenario'].isin([selectS[indp]])].iloc[0][2050]

DeltapCStock = (DataMatsD[:,:,0] + DataMatsD[:,:,1]) / np.einsum('p,s->ps',DataP[:,0],np.ones((4)))

if ptypes[m] == 'Sankey_Haas_Export':
# Extract and format Sankey plot for materials in a sector, according to the design by Haas et al. (2015)
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