start scenario optimized for social

scenariosAleatoires/MCMC_start.py

+#!/usr/bin/python
+# -*- 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 = candidates.at[index,'cultgeopat']
+            patch_idx += 1
+            # if the culture is always in excess
+            if surfDelta[patchCult] < 0:
+                area = candidates.at[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.at[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
+                patches.at[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'):
+        pass
+        # TODO
+
+
+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
+    print(surfDelta_comparison)
+    # patches2.to_file('../output/PAT_patches/best_social.shp', encoding='utf-8')