writing modified patches of each selected scenario at the end of the iteration

scenariosAleatoires/MCMC.py

@@ -56,6 +56,7 @@ class Scenario:
         newCult = newCult.sample(frac=1, random_state=rng)[:len(samples)].reset_index(drop=True) # shuffle and cut extra elements
         # Doing the reallocation
         self.patches.loc[samples.index.values,'cultgeopat'] = newCult.values
+        self.patches.loc[samples.index.values,'cultmodified'] = True
 
 class CulturalIndicator(Indicator):
     @overrides
@@ -127,6 +128,7 @@ class MCMC:
             yaml.dump(config_data, outfile, default_flow_style=False)
         # finishing init
         self.patches = load_pat_patches(self.mcmc_config['patches'])
+        self.patches['cultmodified'] = False
         self.target = pd.read_csv(self.mcmc_config['target'], sep=';',index_col=0)
         targetRatio = (self.target['2050']-self.target['2016'])/self.target['2016']
         self.targetPAT = self.patches.groupby('cultgeopat')['SURF_PARC'].sum()*(1+targetRatio)
@@ -206,7 +208,15 @@ class MCMC:
             # when data doesn't vary enough on a dimension, it is impossible to generate the pareto density
             # and an error is triggered. Here, we ignore this error.
             pass
-        return [scenarios[pareto_mask], scores[pareto_mask]]
+        scenarios = scenarios[pareto_mask]
+        scores = scores[pareto_mask]
+        # Writing shapefiles
+        shp_dir = self.outputdir + '/patches_iter_{}'.format(iter_nb)
+        os.makedirs(shp_dir)
+        for index,scenario in scenarios.iterrows():
+            scenario = scenario[0]
+            scenario[scenario['cultmodified']].drop('cultmodified', axis=1).to_file(shp_dir+'/{}_{}'.format(iter_nb,index), encoding='utf-8')
+        return [scenarios, scores]
 
     def run(self, nb_processes=mp.cpu_count()):
         self.pool = mp.Pool(processes=nb_processes)