Polymorphism for indicators initialization

Cultural surface added in as indicators output

scenariosAleatoires/Indicator.py

 #!/usr/bin/python
 # -*- coding: utf-8 -*-
 
+import abc
+
 class Indicator:
+    @abc.abstractmethod
     def __init__(self, config, initial_patches, patches_md5sum, targetPAT):
         pass
 
+    @abc.abstractmethod
     def compute_indicator(self, patches):
         pass

scenariosAleatoires/MCMC.py

@@ -5,7 +5,9 @@ import geopandas as gpd
 import os, sys
 import yaml
 import numpy as np
-from proximite import Proximite
+from Indicator import Indicator
+from overrides import overrides
+from proximite import Proximite as Proximity
 from resilience_list import Resilience
 from productivite import Productivity
 from indice_biodiversite_2 import Biodiversity
@@ -42,18 +44,28 @@ class Scenario:
         # Doing the reallocation
         self.patches.loc[samples.index.values,'cultgeopat'] = newCult.values
 
+class CulturalIndicator(Indicator):
+    @overrides
+    def __init__(self, config, initial_patches=None, patches_md5sum=None, targetPAT=None):
+        self.cultgeopat = config
+
+    @overrides
+    def compute_indicator(self, patches):
+        return patches[patches['cultgeopat']==self.cultgeopat]['SURF_PARC'].sum()
+
 class Indicators:
     def __init__(self, config, initial_patches, patches_md5sum, targetPAT):
-        self._proximity = Proximite(None, initial_patches, None, targetPAT)
-        self._resilience = Resilience(config['resilience'], initial_patches)
-        self._productivity = Productivity()
-        self._biodiversity = Biodiversity(config['biodiversity'], initial_patches, patches_md5sum)
-        self._social = Social(config['social'], initial_patches, patches_md5sum)
-        self.indicators_names = ['proximity', 'resilience', 'productivity', 'biodiversity', 'social']
+        self._indicators = {
+            indicator.lower(): eval(indicator)(config.get(indicator.lower()), initial_patches, patches_md5sum, targetPAT)
+            for indicator in ['Proximity', 'Resilience', 'Productivity', 'Biodiversity', 'Social']
+            }
+        self.indicators_names = list(self._indicators.keys())
+        for cultgeopat in targetPAT.index.tolist():
+            self._indicators[cultgeopat] = CulturalIndicator(cultgeopat)
+            self.indicators_names.append(cultgeopat)
 
     def compute_indicators(self, patches):
-        return [self.proximity(patches), self.resilience(patches),
-                self.productivity(patches), self.biodiversity(patches), self.social(patches)]
+        return [ind.compute_indicator(patches) for ind in self._indicators.values()]
 
     def compute_indicators_pool(self, scenarios):
         rows=[]
@@ -61,21 +73,6 @@ class Indicators:
             rows.append(self.compute_indicators(patches))
         return pd.DataFrame(rows, columns=self.indicators_names)
 
-    def proximity(self, patches):
-        return self._proximity.compute_indicator(patches)
-
-    def resilience(self, patches):
-	       return self._resilience.compute_indicator(patches)
-
-    def productivity(self, patches):
-	       return self._productivity.compute_indicator(patches)
-
-    def biodiversity(self, patches):
-	       return self._biodiversity.compute_indicator(patches)
-
-    def social(self, patches):
-        return self._social.compute_indicator(patches)
-
 class MCMC:
     def __init__(self, mcmc_config_filename):
         if not os.path.isfile(mcmc_config_filename):

scenariosAleatoires/MCMC_benchmark_indicators.py

+from MCMC import *
+import timeit
+
+if __name__ == '__main__':
+    mcmc = MCMC('MCMC_config.yml')
+    scenario = Scenario(mcmc.patches.copy())
+    for ind in ['proximity', 'resilience', 'productivity', 'biodiversity', 'social']:
+        print('{}: {:.4f} s'.format(ind,
+            timeit.timeit(lambda: eval('mcmc.indicators._indicators[\''+ind+'\'].compute_indicator(scenario.patches)'), number=10)/10
+            ))
+    print('\nreallocate: {:.4f} s'.format(timeit.timeit(lambda:
+        scenario.reallocate(mcmc.rng, mcmc.targetPAT, mcmc.mcmc_config['ratio_patches_to_modify'])
+        , number=10)/10))
+    for ind in ['proximity', 'resilience', 'productivity', 'biodiversity', 'social']:
+        print('{}: {:.4f} s'.format(ind,
+            timeit.timeit(lambda: eval('mcmc.indicators._indicators[\''+ind+'\'].compute_indicator(scenario.patches)'), number=10)/10
+            ))
+
+'''
+proximity: 0.0142 s
+resilience: 1.5464 s
+productivity: 0.0186 s
+biodiversity: 0.0181 s
+social: 0.1293 s
+
+reallocate: 0.0453 s
+proximity: 0.0174 s
+resilience: 1.1516 s
+productivity: 0.0122 s
+biodiversity: 0.0169 s
+social: 0.2007 s
+'''

scenariosAleatoires/patutils.py

+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+
+import hashlib
+import geopandas as gpd
+
+def load_pat_patches(filename):
+    '''
+    Load shapefile containing cultural patches and applying initial filters.
+    '''
+    patches = gpd.GeoDataFrame.from_file(filename, encoding='utf-8')
+    patches = patches[patches['cultgeopat']!='Non Considérée']
+    patches['init_cult'] = patches['cultgeopat']
+    return patches
+
+def md5sum(filename):
+    hash_md5 = hashlib.md5()
+    with open(filename, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()

scenariosAleatoires/proximite.py

@@ -14,7 +14,6 @@ class Proximite(Indicator):
 	'''
 	@overrides
 	def __init__(self, config, initial_patches, patches_md5sum=None, targetPAT=None):
-	# def __init__(self, config, initial_patches, patches_md5sum, surf_totale_cible) :
 		Population_PAT = initial_patches.groupby('INSEE_COM')['POPULATION'].first().sum() #498873
 		popByBdv = initial_patches.groupby('Bdv').apply(lambda x:x.groupby('INSEE_COM')['POPULATION'].first().sum())
 		# OK but slower