Commit 0d9692c0 authored by Dumoulin Nicolas's avatar Dumoulin Nicolas
Browse files

start scenario optimized for social

parent 5190c48c
# -*- coding: utf-8 -*-
import pandas as pd
import geopandas as gpd
import sys
This scripts intends to generate a scenario that fit at maximum one indicator at a time.
These scenarios will be used at starting points for the optimization process.
def best_social(patches, surfDelta, cult_to_decrease, cult_to_increase):
# Ensuring that Vegetables are at first position
index_FL = cult_to_increase.index('Fruits et légumes')
if index_FL>0:
cult_to_increase[0],cult_to_increase[index_FL] = cult_to_increase[index_FL],cult_to_increase[0]
# cult_to_increase
expl_cult_list = {} # only needed for tests after the loop in debug mode
# Doing reallocation for each culture needed surface, beginning by the greatest need
for cult in cult_to_increase:
# exploitant that have a patch of the needed culture
expl_cult = patches[patches['cultgeopat'] == cult]['id_expl'].unique()
expl_cult_list[cult] = expl_cult
# Candidates for reallocation
candidates = patches[ (patches['cultgeopat'].isin(cult_to_decrease)) & (patches['id_expl'].isin(expl_cult))]
# surfcultbyexpl = candidates.groupby('id_expl')['SURF_PARC'].sum().sort_values(ascending=False)
candidates = candidates.sort_values(by=['SURF_PARC'], ascending=False)
# curexpl_idx = 0
patch_idx = 0
# looping on patches of culture in excess
while patch_idx<len(candidates) and surfDelta[cult]>0:
index = candidates.index[patch_idx]
patchCult =[index,'cultgeopat']
patch_idx += 1
# if the culture is always in excess
if surfDelta[patchCult] < 0:
area =[index,'SURF_PARC']
if area > surfDelta[cult]: # patch is bigger than needed
if (area - surfDelta[cult])/surfDelta[cult] > 0.001: # patch is significantly bigger (10%)
if patch_idx<len(candidates)-1: # this is not the last choice
nextarea =[candidates.index[patch_idx],'SURF_PARC']
if abs(surfDelta[cult]-area) > abs(surfDelta[cult]-nextarea):
# the next one don't give a smaller difference
continue[index,'cultgeopat'] = cult
surfDelta[cult] -= area
surfDelta[patchCult] += area
# import pdb; pdb.set_trace()
return patches
def best_proximity(patches, surfDelta):
Population_PAT = patches.groupby('INSEE_COM')['POPULATION'].first().sum()
popByBdv = patches.groupby('Bdv').apply(lambda x:x.groupby('INSEE_COM')['POPULATION'].first().sum())
targetSurfByBdv = surfDelta['Fruits et légumes'] * popByBdv/Population_PAT
for bdv, grouped in patches.groupby('Bdv'):
if __name__ == '__main__':
patches = gpd.GeoDataFrame.from_file('../output/PAT_patches/PAT_patches.shp', encoding='utf-8')
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)
surfDelta = patches.groupby('cultgeopat')['SURF_PARC'].sum()*targetRatio
cult_to_decrease = targetRatio[targetRatio<0].sort_values(ascending=True).keys().tolist()
cult_to_increase = targetRatio[targetRatio>0].sort_values(ascending=False).keys().tolist()
surfDelta_comparison = surfDelta.to_frame('before')
import time
start = time.time()
patches2 = best_social(patches, surfDelta, cult_to_decrease, cult_to_increase)
end = time.time()
print('Time = {}'.format(end-start))
surfDelta_comparison['social'] = surfDelta
# patches2.to_file('../output/PAT_patches/best_social.shp', encoding='utf-8')
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