#!/usr/bin/python3
# -*- coding: utf-8 -*-
import pandas as pd

class Social:
	'''
	This function is used to calculate the social indice.
	The idea is to propose a transition matrix for each type of culture and use it to visualize
	 the conversion cost of the total conversion between the initial scenario and the old one.

	Each transition has a value between 0 and 1.
	These values were defined during reunions and with the review of our experts.
	'''

	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
		is suffisantly present in the locality for ignoring the conversion cost.
		'''
		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, scenario_patches):
		'''
		:param initial_patches: Initial patches
		:param scenario: The scenario to analyse
		'''
		cost = 0
		filter1 = pd.merge(
			scenario_patches[['ID_PARCEL','cultgeopat','Bdv','SURF_PARC','id_expl']],
		 	self.initial_patches,
			on='ID_PARCEL')
		for bdv,grouped in filter1.groupby('Bdv'):
			grouped=grouped.copy()
			surfaces_initial = grouped.groupby('cultgeopat_y')['SURF_PARC'].sum()
			cult_threshold = surfaces_initial > surfaces_initial.sum() * self.bdv_threshold
			filter2 = grouped[grouped['cultgeopat_y'] != grouped['cultgeopat_x']]
			for i,row in filter2.iterrows():
				# patches that have different farming that others in same Bdv
				if cult_threshold[row['cultgeopat_y']]:
					# patches that have different farming that others of the same farmer
					if row['cultgeopat_y'] not in filter1[filter1['id_expl']==row['id_expl']]['cultgeopat_x']:
						cost += self.matrice_transition[row['cultgeopat_x']][row['cultgeopat_y']]
		return cost

if __name__ == '__main__':
	import geopandas as gpd
	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(patches, '../resources/matrice_transition.csv', 0.1)
	print(social.compute_indicator(scenario_patches))
	# should give 0.5 the cost for one transition to Protéagineux