#!/usr/bin/python
# -*- coding: utf-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

	A living area with a low population will require a lower cultivating area to get a good value.
	'''
	def __init__(self, initial_patches, 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
		# popByBdv = patches.groupby(['Bdv','INSEE_COM']).first().groupby('Bdv')['POPULATION'].sum()
		self.targetSurfByBdv = surf_totale_cible * popByBdv/Population_PAT

	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
		:param affichage: True if we want the display in the console, False is the other case
		'''
		# get area of "fruits et légumes" in the scenario
		flSurfByBdv = patches[patches['cultgeopat']=='Fruits et légumes'].groupby('Bdv').agg({'SURF_PARC':sum})
		result = flSurfByBdv.div(self.targetSurfByBdv,axis=0)
		# put 0 where target is reached
		result = result.where(result<1,0).sum() / result.where(result<1).count()
		return result.values[0]

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, True))