target surface of each culture given by the PAT are now stored in file

resources/targetPAT.csv

+cultgeopat;2016;2050
+Céréales;39.1578162680355;42.4510757826923
+Prairies;83.9947787550856;76.3917619047619
+Oléagineux;4.02651841988345;4.36515758215069
+Fourrages;26.7457940864208;15.4649021294656
+Fruits et légumes;0.35475779373955496;3.97664751692735
+Cultures industrielles;2.31418094174966;2.52551689693261
+Protéagineux;0.704985252201107;8.59885297148308

scenariosAleatoires/proximite.py

@@ -5,7 +5,8 @@ class Proximite:
 	'''
 	Function calculating the proximity indice.
 	The purpose of this indice is to reflect the proximity between productions site and consommation site.
-	We are calculating it with a comparison of the area of patches cultivating fruits and vegetables and the population of living area (bassin de vie) in the PAT's territory
+	We are calculating it with a comparison of the area of patches cultivating fruits and vegetables
+	 and the population of living area (bassin de vie) in the PAT's territory
 
 	A living area with a low population will require a lower cultivating area to get a good value.
 	'''
@@ -31,6 +32,11 @@ class Proximite:
 
 if __name__ == '__main__':
 	import geopandas as gpd
+	import pandas as pd
 	patches = gpd.GeoDataFrame.from_file("../output/PAT_patches/PAT_patches.shp", encoding='utf-8')
-	prox = Proximite(patches, 20165939.605135553)
+	patches = patches[patches['cultgeopat']!='Non Considérée']
+	target = pd.read_csv('../resources/targetPAT.csv', sep=';',index_col=0)
+	targetRatio = (target['2050']-target['2016'])/target['2016']
+	targetPAT = patches.groupby('cultgeopat')['SURF_PARC'].sum()*(1+targetRatio)
+	prox = Proximite(patches, targetPAT['Fruits et légumes'])
 	print(prox.compute_indicator(patches, True))