class Indicators introduced for better performance (not reloaded for each evaluation)

scenariosAleatoires/MCMC_class.py

@@ -26,6 +26,38 @@ from EPCI_culture_repartition import EPCI_culture_repartition
 
 from radar import scenarios_graph
 
+class Indicators:
+	def __init__(self, mcmc_config_filename, initial_patches):
+			if not os.path.isfile(mcmc_config_filename):
+				print('Error: file not found "{}"'.format(mcmc_config_filename))
+				print('Please copy the template file "MCMC_config.sample.yml" and adjust to your settings and run again this program')
+				sys.exit(1)
+			self.mcmc_config = yaml.load(open(mcmc_config_filename,'r'))
+			self._proximity = Proximite(initial_patches, self.mcmc_config['indicators_config']['proximite']['surf_totale_cible'])
+			self._resilience = Resilience(self.mcmc_config['indicators_config']['resilience'])
+			self._productivity = Productivity()
+			biodiv_config = self.mcmc_config['indicators_config']['biodiversity']
+			matrixfilename = biodiv_config['matrixfilename']
+			self._biodiversity = Biodiversity(initial_patches, biodiv_config['dmax'], biodiv_config['epsilon'])
+			self._biodiversity.load_probabilities(matrixfilename)
+			social_config = self.mcmc_config['indicators_config']['social']
+			self._social = Social(initial_patches, social_config['cost_matrix_filename'], social_config['bdv_threshold'])
+
+	def proximity(self, patches):
+		return self._proximity.compute_indicator(patches, False)
+
+	def resilience(self, patches):
+		return self._resilience.compute_indicator(patches, False)
+
+	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 Scenario :
 	'''
 	This class is used to create a new randomized scenario from an initial scenario. A few functions are included to permit
@@ -66,7 +98,7 @@ class Scenario :
 		if culture == "Protéagineux" :
 			return 6
 
-	def __init__(self, scenarioInitial, nbr_parcelles_a_modifier,  cereales, culturesI, fourrages, fruits_legumes, oleagineux, prairies, proteagineux, liste, rng) :
+	def __init__(self, indicators, scenarioInitial, nbr_parcelles_a_modifier,  cereales, culturesI, fourrages, fruits_legumes, oleagineux, prairies, proteagineux, liste, rng) :
 			'''
 			Create a new randomized scenario according to the parameters
 
@@ -86,15 +118,10 @@ class Scenario :
 			self.oleagineux = oleagineux
 			self.prairies = prairies
 			self.proteagineux = proteagineux
-			mcmc_config_filename = 'MCMC_config.yml'
-			if not os.path.isfile(mcmc_config_filename):
-				print('Error: file not found "{}"'.format(mcmc_config_filename))
-				print('Please copy the template file "MCMC_config.sample.yml" and adjust to your settings and run again this program')
-				sys.exit(1)
-			self.mcmc_config = yaml.load(open(mcmc_config_filename,'r'))
+
+			self.indicators = indicators
 
 			self.initial_patches = gpd.GeoDataFrame.from_file(scenarioInitial, encoding='utf-8')
-			self.proximite = Proximite(self.initial_patches, self.mcmc_config['indicators_config']['proximite']['surf_totale_cible'])
 
 			# If ScenarioInitial is a filename -> transform it in a list
 			if liste == False :
@@ -258,6 +285,7 @@ class Scenario :
 			# Sort the final list to make it ordered as the same way of the initial list
 			resultat_final = sorted(resultat_final, key=lambda k: k['properties']['ID_PARCEL'])
 			self.resultat_final = resultat_final
+			self.resultat_final_gpd = gpd.GeoDataFrame.from_features(resultat_final)
 
 
 
@@ -304,7 +332,6 @@ class Scenario :
 		Write the values of indices of the initial scenario in a JSON file
 		This function is used for display and statistics in the application
 		'''
-		indicators = self.mcmc_config['indicators_config']
 		dict_indices = {}
 		dict_indices["proximite"] = self.indice_proximite()
 		dict_indices["resilience"] = self.indice_resilience()
@@ -321,42 +348,19 @@ class Scenario :
 	These functions returns values of indices of this scenario
 	'''
 	def indice_proximite(self) :
-		patches = gpd.GeoDataFrame.from_features(self.resultat_final)
-		indice_prox = self.proximite.compute_indicator(patches, self.fruits_legumes, False)
-		#print "Résultats de l'indice de proximité : " + str(indice_prox)
-		return indice_prox
+		return self.indicators.proximity(self.resultat_final_gpd)
 
 	def indice_resilience(self):
-		resilience = Resilience(self.mcmc_config['indicators_config']['resilience'])
-		patches = gpd.GeoDataFrame.from_features(self.resultat_final)
-		indice_resi = resilience.compute_indicator(patches, False)
-		#print "Résultats de l'indice de résilience : " + str(indice_resi)
-		return indice_resi
+		return self.indicators.resilience(self.resultat_final_gpd)
 
 	def indice_productivitee(self):
-		productivity = Productivity()
-		patches = gpd.GeoDataFrame.from_features(self.resultat_final)
-		indice_prod = productivity.compute_indicator(patches)
-		#print "Résultats de l'indice de productivite : " + str(indice_prod)
-		return indice_prod
+		return self.indicators.productivity(self.resultat_final_gpd)
 
 	def indice_biodiversite(self):
-		#Matrix containing all the data about biodiversity. Created with Numpy and the formula of Equivalent Connected Area (Saura et al., 2011)
-		biodiv_config = self.mcmc_config['indicators_config']['biodiversity']
-		matrixfilename = biodiv_config['matrixfilename']
-		biodiversity = Biodiversity(self.initial_patches, biodiv_config['dmax'], biodiv_config['epsilon'])
-		biodiversity.load_probabilities(matrixfilename)
-		patches = gpd.GeoDataFrame.from_features(self.resultat_final)
-		indice_bio = biodiversity.compute_indicator(patches)
-		#print "Résultats de l'indice de biodiversite : " + str(indice_bio)
-		return indice_bio
+		return self.indicators.biodiversity(self.resultat_final_gpd)
 
 	def indice_social(self):
-		social_config = self.mcmc_config['indicators_config']['social']
-		social = Social(social_config['cost_matrix_filename'], social_config['bdv_threshold'])
-		indice_social = social.compute_indicator(gpd.GeoDataFrame.from_features(self.shape), gpd.GeoDataFrame.from_features(self.resultat_final))
-		#print "Résultats de l'indice de sociabilité : " + str(indice_social)
-		return indice_social
+		return self.indicators.social(self.resultat_final_gpd)
 
 dict_scenario = {} # Contains all scenarios created by the function "générer_x_scenarios"
 
@@ -879,8 +883,9 @@ if __name__ == '__main__':
 
 	# scenarInitial, nbr_loop, nbr_scenario, nbr_scenario_to_stock, nbr_parcelles_a_modifier, cereales, culturesI, fourrages, fruits_legumes, oleagineux, prairies, proteagineux
 	#scenario_initial = Scenario("./Parcelle_PAT_MCMC.shp",0, cereales, culturesI, fourrages, fruits_legumes, oleagineux, prairies, proteagineux, False, rng)
-	scenario_initial = Scenario("../output/PAT_patches/PAT_patches.shp",0, cereales, culturesI, fourrages, fruits_legumes, oleagineux, prairies, proteagineux, False, rng)
+	patches = gpd.GeoDataFrame.from_file("../output/PAT_patches/PAT_patches.shp", encoding='utf-8')
+	indicators = Indicators('MCMC_config.yml', patches)
+	scenario_initial = Scenario(indicators,"../output/PAT_patches/PAT_patches.shp",0, cereales, culturesI, fourrages, fruits_legumes, oleagineux, prairies, proteagineux, False, rng)
 	#scenario_initial = Scenario("../output/patches_RiomLimagneVolcans.shp",0, cereales, culturesI, fourrages, fruits_legumes, oleagineux, prairies, proteagineux, False, rng)
 	scenario_initial.write_indices_in_json()
-	scenario_initial.write_indices_in_json()
 	#MCMC("./Parcelle_PAT_MCMC.shp", 2, 11, 2, nbr_parcelles_a_modifier, cereales, culturesI, fourrages, fruits_legumes, oleagineux, prairies, proteagineux, rng)

scenariosAleatoires/proximite.py

@@ -11,12 +11,12 @@ class Proximite:
 	'''
 	def __init__(self, initial_patches, surf_totale_cible) :
 		Population_PAT = initial_patches.groupby('INSEE_COM')['POPULATION'].first().sum() #498873
-		popByBdv = patches.groupby('Bdv').apply(lambda x:x.groupby('INSEE_COM')['POPULATION'].first().sum())
+		popByBdv = initial_patches.groupby('Bdv').apply(lambda x:x.groupby('INSEE_COM')['POPULATION'].first().sum())
 		# OK but slower
 		# popByBdv = patches.groupby(['Bdv','INSEE_COM']).first().groupby('Bdv')['POPULATION'].sum()
 		self.targetSurfByBdv = surf_totale_cible * popByBdv/Population_PAT
 
-	def compute_indicator(self, patches, fruit_legumes, affichage):
+	def compute_indicator(self, patches, affichage):
 		'''
 		:param shape: The scenario to analyse as a list
 		:param fruit_legume: The proportion of area we want in the PAT for the "fruits and vegetables" type
@@ -33,4 +33,4 @@ if __name__ == '__main__':
 	import geopandas as gpd
 	patches = gpd.GeoDataFrame.from_file("../output/PAT_patches/PAT_patches.shp", encoding='utf-8')
 	prox = Proximite(patches, 20165939.605135553)
-	print(prox.compute_indicator(patches, 3, True))
+	print(prox.compute_indicator(patches, True))

scenariosAleatoires/social.py

@@ -12,7 +12,7 @@ class Social:
 	These values were defined during reunions and with the review of our experts.
 	'''
 
-	def __init__(self, cost_matrix_filename, bdv_threshold):
+	def __init__(self, initial_patches, cost_matrix_filename, bdv_threshold):
 		'''
 		:param cost_matrix_filename input file containing the matrix in CSV format (separator: \t)
 		:param bdv_threshold proportion of a cultural type needed for considering that this cultural type
@@ -20,8 +20,9 @@ class Social:
 		'''
 		self.matrice_transition = pd.read_csv(cost_matrix_filename, sep='\t',index_col=0)
 		self.bdv_threshold = bdv_threshold
+		self.initial_patches = initial_patches[['ID_PARCEL','cultgeopat']]
 
-	def compute_indicator(self, initial_patches, scenario_patches):
+	def compute_indicator(self, scenario_patches):
 		'''
 		:param initial_patches: Initial patches
 		:param scenario: The scenario to analyse
@@ -29,7 +30,7 @@ class Social:
 		cost = 0
 		filter1 = pd.merge(
 			scenario_patches[['ID_PARCEL','cultgeopat','Bdv','SURF_PARC','id_expl']],
-		 	initial_patches[['ID_PARCEL','cultgeopat']],
+		 	self.initial_patches,
 			on='ID_PARCEL')
 		for bdv,grouped in filter1.groupby('Bdv'):
 			grouped=grouped.copy()
@@ -49,6 +50,6 @@ if __name__ == '__main__':
 	patches = gpd.GeoDataFrame.from_file("../output/PAT_patches/PAT_patches.shp", encoding='utf-8')
 	scenario_patches = patches.copy()
 	scenario_patches.ix[2,'cultgeopat']='Protéagineux'
-	social = Social('../resources/matrice_transition.csv', 0.1)
-	print(social.compute_indicator(patches, scenario_patches))
+	social = Social(patches, '../resources/matrice_transition.csv', 0.1)
+	print(social.compute_indicator(scenario_patches))
 	# should give 0.5 the cost for one transition to Protéagineux