Commented figure generation code

ec-jrc · Nov 15, 2024 · a519236 · a519236
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diff --git a/src/lisfloodutilities/water-demand-historic/step2_domestic_demand.py b/src/lisfloodutilities/water-demand-historic/step2_domestic_demand.py
@@ -401,25 +401,25 @@ def main():
         #   Plot figure
         #--------------------------------------------------------------------------
 
-        # Initialize figure
-        f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
+        # # Initialize figure
+        # f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
 
-        # Subpanel 1
-        ax1.imshow(np.sqrt(imresize_mean(data_annual_map,(360,720))),vmin=0,vmax=12,cmap=plt.get_cmap('YlGnBu'))
-        ax1.set_title('Beck et al. (2022) withdrawal (mm/month)')
-
-        # Subpanel 2
-        try:
-            timeindex = year-1971
-            ax2.imshow(np.sqrt(np.sum(Huang_withdrawal[:,:,np.arange(timeindex*12,timeindex*12+12)],axis=2)),vmin=0,vmax=12,cmap=plt.get_cmap('YlGnBu'))
-            ax2.set_title('Huang et al. (2018) withdrawal (mm/month)')
-        except:
-            pass
+        # # Subpanel 1
+        # ax1.imshow(np.sqrt(imresize_mean(data_annual_map,(360,720))),vmin=0,vmax=12,cmap=plt.get_cmap('YlGnBu'))
+        # ax1.set_title('Beck et al. (2022) withdrawal (mm/month)')
+
+        # # Subpanel 2
+        # try:
+        #     timeindex = year-1971
+        #     ax2.imshow(np.sqrt(np.sum(Huang_withdrawal[:,:,np.arange(timeindex*12,timeindex*12+12)],axis=2)),vmin=0,vmax=12,cmap=plt.get_cmap('YlGnBu'))
+        #     ax2.set_title('Huang et al. (2018) withdrawal (mm/month)')
+        # except:
+        #     pass
 
-        # Save figure
-        f.set_size_inches(10, 10)
-        plt.savefig(os.path.join(config['output_folder'],'step2_domestic_demand','figures','dom_'+str(year)+'.png'),dpi=150)
-        plt.close()
+        # # Save figure
+        # f.set_size_inches(10, 10)
+        # plt.savefig(os.path.join(config['output_folder'],'step2_domestic_demand','figures','dom_'+str(year)+'.png'),dpi=150)
+        # plt.close()
 
         print("Time elapsed is "+str(time.time()-t0)+" sec")
 

diff --git a/src/lisfloodutilities/water-demand-historic/step3a_industrial_demand.py b/src/lisfloodutilities/water-demand-historic/step3a_industrial_demand.py
@@ -18,7 +18,7 @@
 import pandas as pd
 import numpy as np
 from netCDF4 import Dataset
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt
 from skimage.transform import resize
 from tools import *
 import rasterio
@@ -666,39 +666,39 @@ def main():
         #   Plot figure
         #--------------------------------------------------------------------------
 
-        # Initialize figure
-        f, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True)
+        # # Initialize figure
+        # f, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True)
 
-        # Subpanel 1
-        ax1.plot(years,table_aquastat_industry_withdrawal[jj,:],'*',label='FAO industry withdrawal',color=(0.5,0.5,0.5))
-        ax1.plot(years,table_gcam_withdrawal_industry[jj,:],'+',label='GCAM industry withdrawal',color=(0,0.5,0))
-        ax1.plot(years,table_gcam_withdrawal_industry[jj,:]-table_gcam_withdrawal_thermoelectric[jj,:],'+',label='GCAM manufacturing withdrawal',color=(0,1,0))
-        ax1.plot(years,table_withdrawal_industry[jj,:],label='Beck industry withdrawal',color=(0.5,0,0))
-        ax1.plot(years,table_withdrawal_manufacturing[jj,:],label='Beck manufacturing withdrawal',color=(1,0,0))
-        ax1.legend()
-        ax1.set_ylabel('Withdrawal (km^3/year)')
-        ax1.set_title(country_name)
+        # # Subpanel 1
+        # ax1.plot(years,table_aquastat_industry_withdrawal[jj,:],'*',label='FAO industry withdrawal',color=(0.5,0.5,0.5))
+        # ax1.plot(years,table_gcam_withdrawal_industry[jj,:],'+',label='GCAM industry withdrawal',color=(0,0.5,0))
+        # ax1.plot(years,table_gcam_withdrawal_industry[jj,:]-table_gcam_withdrawal_thermoelectric[jj,:],'+',label='GCAM manufacturing withdrawal',color=(0,1,0))
+        # ax1.plot(years,table_withdrawal_industry[jj,:],label='Beck industry withdrawal',color=(0.5,0,0))
+        # ax1.plot(years,table_withdrawal_manufacturing[jj,:],label='Beck manufacturing withdrawal',color=(1,0,0))
+        # ax1.legend()
+        # ax1.set_ylabel('Withdrawal (km^3/year)')
+        # ax1.set_title(country_name)
 
-        # Subpanel 2
-        ax2.plot(years,table_Huang_withdrawal_industry[jj,:],label='Huang industry withdrawal',color=(0,0,0.5))
-        ax2.plot(years,table_Huang_withdrawal_manufacturing[jj,:],label='Huang manufacturing withdrawal',color=(0,0,1))
-        ax2.plot(years,table_withdrawal_industry[jj,:],label='Beck industry withdrawal',color=(0.5,0,0))
-        ax2.plot(years,table_withdrawal_manufacturing[jj,:],label='Beck manufacturing withdrawal',color=(1,0,0))
-        ax2.legend()
-        ax2.set_ylabel('Withdrawal (km^3/year)')
-        ax2.set_title(country_name)
+        # # Subpanel 2
+        # ax2.plot(years,table_Huang_withdrawal_industry[jj,:],label='Huang industry withdrawal',color=(0,0,0.5))
+        # ax2.plot(years,table_Huang_withdrawal_manufacturing[jj,:],label='Huang manufacturing withdrawal',color=(0,0,1))
+        # ax2.plot(years,table_withdrawal_industry[jj,:],label='Beck industry withdrawal',color=(0.5,0,0))
+        # ax2.plot(years,table_withdrawal_manufacturing[jj,:],label='Beck manufacturing withdrawal',color=(1,0,0))
+        # ax2.legend()
+        # ax2.set_ylabel('Withdrawal (km^3/year)')
+        # ax2.set_title(country_name)
 
-        # Subpanel 3
-        ax3.plot(years,table_worldbank_gdp[jj,:],label='GDP',color='g')
-        ax3.plot(years,table_worldbank_mva[jj,:],label='MVA',color='b')
-        ax3.legend()
-        ax3.set_ylabel('Constant 2010 US$')
-        ax3.set_title(country_name)    
+        # # Subpanel 3
+        # ax3.plot(years,table_worldbank_gdp[jj,:],label='GDP',color='g')
+        # ax3.plot(years,table_worldbank_mva[jj,:],label='MVA',color='b')
+        # ax3.legend()
+        # ax3.set_ylabel('Constant 2010 US$')
+        # ax3.set_title(country_name)    
 
-        # Save figure
-        f.set_size_inches(10, 10)
-        plt.savefig(os.path.join(config['output_folder'],'step3a_industrial_demand','figures',str(jj).zfill(3)+'_'+country_name+'.png'),dpi=150)
-        plt.close()
+        # # Save figure
+        # f.set_size_inches(10, 10)
+        # plt.savefig(os.path.join(config['output_folder'],'step3a_industrial_demand','figures',str(jj).zfill(3)+'_'+country_name+'.png'),dpi=150)
+        # plt.close()
 
     # Save to csv
     pd.DataFrame(table_withdrawal_data_source,index=country_codes['name'],columns=['Withdrawal data source']).to_csv(os.path.join(config['output_folder'],'step3a_industrial_demand','tables','withdrawal_data_source.csv'))

diff --git a/src/lisfloodutilities/water-demand-historic/step3b_industrial_demand.py b/src/lisfloodutilities/water-demand-historic/step3b_industrial_demand.py
@@ -18,7 +18,7 @@
 import pandas as pd
 import numpy as np
 from netCDF4 import Dataset
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt
 from skimage.transform import resize
 from tools import *
 import rasterio
@@ -298,27 +298,27 @@ def main():
             #   Plot figure
             #--------------------------------------------------------------------------
 
-            # Initialize figure
-            f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
+            # # Initialize figure
+            # f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
 
-            # Subpanel 1
-            ax1.imshow(np.sqrt(imresize_mean(data_annual_map,(360,720))),vmin=0,vmax=15,cmap=plt.get_cmap('YlGnBu'))
-            ax1.set_title('Beck et al. (2022) withdrawal (mm/month)')
-
-            # Subpanel 2
-            try:
-                if varname=='ene': varindex = 0
-                if varname=='ind': varindex = 1
-                timeindex = year-1971
-                ax2.imshow(np.sqrt(np.sum(Huang_withdrawal[varindex,:,:,np.arange(timeindex*12,timeindex*12+12)],axis=0)),vmin=0,vmax=15,cmap=plt.get_cmap('YlGnBu'))
-                ax2.set_title('Huang et al. (2018) withdrawal (mm/month)')
-            except:
-                pass
+            # # Subpanel 1
+            # ax1.imshow(np.sqrt(imresize_mean(data_annual_map,(360,720))),vmin=0,vmax=15,cmap=plt.get_cmap('YlGnBu'))
+            # ax1.set_title('Beck et al. (2022) withdrawal (mm/month)')
+
+            # # Subpanel 2
+            # try:
+            #     if varname=='ene': varindex = 0
+            #     if varname=='ind': varindex = 1
+            #     timeindex = year-1971
+            #     ax2.imshow(np.sqrt(np.sum(Huang_withdrawal[varindex,:,:,np.arange(timeindex*12,timeindex*12+12)],axis=0)),vmin=0,vmax=15,cmap=plt.get_cmap('YlGnBu'))
+            #     ax2.set_title('Huang et al. (2018) withdrawal (mm/month)')
+            # except:
+            #     pass
 
-            # Save figure
-            f.set_size_inches(10, 10)
-            plt.savefig(os.path.join(config['output_folder'],'step3b_industrial_demand','figures',varname+'_'+str(year)+'.png'),dpi=150)
-            plt.close()
+            # # Save figure
+            # f.set_size_inches(10, 10)
+            # plt.savefig(os.path.join(config['output_folder'],'step3b_industrial_demand','figures',varname+'_'+str(year)+'.png'),dpi=150)
+            # plt.close()
 
 
             #--------------------------------------------------------------------------

diff --git a/src/lisfloodutilities/water-demand-historic/step4_livestock_demand.py b/src/lisfloodutilities/water-demand-historic/step4_livestock_demand.py
@@ -18,7 +18,7 @@
 import pandas as pd
 import numpy as np
 from netCDF4 import Dataset
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt
 from skimage.transform import resize
 from tools import *
 import rasterio
@@ -106,10 +106,10 @@ def main():
     for jj in np.arange(country_codes.shape[0]):
         country_code = country_codes.iloc[jj]['country-code']    
         for ii in np.arange(len(years)):
-            sel_ir = (aquastat['m49']==country_code) & (aquastat['Variable Name']=='Irrigation water withdrawal') & (aquastat['Year']==years[ii])
+            sel_ir = (aquastat['m49']==country_code) & (aquastat['Variable']=='Irrigation water withdrawal') & (aquastat['Year']==years[ii])
             if np.sum(sel_ir)>0:
                 table_aquastat_irrigation_withdrawal[jj,ii] = aquastat['Value'][sel_ir].values # km3/year
-            sel_ag = (aquastat['m49']==country_code) & (aquastat['Variable Name']=='Agricultural water withdrawal') & (aquastat['Year']==years[ii])
+            sel_ag = (aquastat['m49']==country_code) & (aquastat['Variable']=='Agricultural water withdrawal') & (aquastat['Year']==years[ii])
             if np.sum(sel_ag)>0:
                 table_aquastat_agriculture_withdrawal[jj,ii] = aquastat['Value'][sel_ag].values # km3/year
 
@@ -291,13 +291,13 @@ def main():
     stats.spearmanr(aquastat[sel], huang[sel])
     stats.spearmanr(gcam[sel], huang[sel])
 
-    # What do the different distributions look like?
-    plt.figure(figsize=(8,6))
-    plt.hist(aquastat[sel], bins=300, alpha=0.5, range=(0,15), label="aquastat")
-    plt.hist(gcam[sel], bins=300, alpha=0.5, range=(0,15), label="gcam")
-    plt.hist(huang[sel], bins=300, alpha=0.5, range=(0,15), label="huang")
-    plt.legend()
-    plt.show(block=False)    
+    # # What do the different distributions look like?
+    # plt.figure(figsize=(8,6))
+    # plt.hist(aquastat[sel], bins=300, alpha=0.5, range=(0,15), label="aquastat")
+    # plt.hist(gcam[sel], bins=300, alpha=0.5, range=(0,15), label="gcam")
+    # plt.hist(huang[sel], bins=300, alpha=0.5, range=(0,15), label="huang")
+    # plt.legend()
+    # plt.show(block=False)    
 
     # Can we derive a simple correction factor to improve the GCAM+GLW estimates? Not really because mean and median give opposite results...
     np.mean(aquastat[sel])/np.mean(gcam[sel])
@@ -449,26 +449,26 @@ def main():
         #   Plot figure
         #--------------------------------------------------------------------------
 
-        # Initialize figure
-        f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
+        # # Initialize figure
+        # f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
 
-        # Subpanel 1
-        ax1.imshow(np.sqrt(imresize_mean(data_annual_map,(360,720))),vmin=0,vmax=15,cmap=plt.get_cmap('YlGnBu'))
-        ax1.set_title('Beck et al. (2022) withdrawal (mm/month)')
-
-        # Subpanel 2
-        try:
-            varindex = 0
-            timeindex = year-1971
-            ax2.imshow(np.sqrt(np.sum(Huang_withdrawal[varindex,:,:,np.arange(timeindex*12,timeindex*12+12)],axis=0)),vmin=0,vmax=15,cmap=plt.get_cmap('YlGnBu'))
-            ax2.set_title('Huang et al. (2018) withdrawal (mm/month)')
-        except:
-            pass
+        # # Subpanel 1
+        # ax1.imshow(np.sqrt(imresize_mean(data_annual_map,(360,720))),vmin=0,vmax=15,cmap=plt.get_cmap('YlGnBu'))
+        # ax1.set_title('Beck et al. (2022) withdrawal (mm/month)')
+
+        # # Subpanel 2
+        # try:
+        #     varindex = 0
+        #     timeindex = year-1971
+        #     ax2.imshow(np.sqrt(np.sum(Huang_withdrawal[varindex,:,:,np.arange(timeindex*12,timeindex*12+12)],axis=0)),vmin=0,vmax=15,cmap=plt.get_cmap('YlGnBu'))
+        #     ax2.set_title('Huang et al. (2018) withdrawal (mm/month)')
+        # except:
+        #     pass
 
-        # Save figure
-        f.set_size_inches(10, 10)
-        plt.savefig(os.path.join(config['output_folder'],'step4_livestock_demand','figures',varname+'_'+str(year)+'.png'),dpi=150)
-        plt.close()
+        # # Save figure
+        # f.set_size_inches(10, 10)
+        # plt.savefig(os.path.join(config['output_folder'],'step4_livestock_demand','figures',varname+'_'+str(year)+'.png'),dpi=150)
+        # plt.close()
 
 
         #--------------------------------------------------------------------------