input files copyed in an output directory

scenariosAleatoires/MCMC.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 import pandas as pd
 import geopandas as gpd
-import os, sys
+import os, sys, time, shutil
 import yaml
 import numpy as np
 from Indicator import Indicator
@@ -13,7 +13,7 @@ from productivite import Productivity
 from indice_biodiversite_2 import Biodiversity
 from social import Social
 from tqdm import tqdm
-from patutils import md5sum
+from patutils import md5sum, load_pat_patches
 
 class Scenario:
     def __init__(self, patches):
@@ -23,8 +23,7 @@ class Scenario:
         '''
         Return an instance of class Patches by loading a shapefile of initial patches.
         '''
-        scenario = Scenario(gpd.GeoDataFrame.from_file(shpfilename, encoding='utf-8'))
-        scenario.patches['init_cult'] = scenario.patches['cultgeopat']
+        scenario = Scenario(load_pat_patches(shpfilename))
         return scenario
 
     def reallocate(self, rng, targetPAT, ratioNbPatches):
@@ -80,15 +79,28 @@ class MCMC:
         	print('Please copy the template file "MCMC_config.sample.yml" and adjust to your settings and run again this program')
         	sys.exit(1)
         self.mcmc_config = yaml.load(open(mcmc_config_filename,'r'))
+        self.outputdir = self.mcmc_config['output_dir'] + '/' + time.strftime('%Y%m%d-%H%M%S') + '/'
+        if not os.path.exists(self.outputdir):
+            os.makedirs(self.outputdir)
+            print('All data will be written in {}'.format(self.outputdir))
+        else:
+            print('Output directory already exists! ({})'.format(self.outputdir))
+            sys.exit(1)
+        shutil.copy(mcmc_config_filename, self.outputdir)
+        for f in [ self.mcmc_config['patches'], self.mcmc_config['target'],
+                self.mcmc_config['indicators_config']['resilience'],
+                self.mcmc_config['indicators_config']['biodiversity']['matrixfilename'],
+                self.mcmc_config['indicators_config']['social']['cost_matrix_filename'],
+                self.mcmc_config['indicators_config']['social']['patches_costs_filename']
+                ]:
+            shutil.copy(f, self.outputdir)
         if 'rng_seed' in self.mcmc_config:
             self.rng = np.random.RandomState(self.mcmc_config['rng_seed'])
         else:
             self.rng = np.random.RandomState(42)
             print('MCMC initialized with default seed') # self.rng.get_state()
         self.patches_md5sum = md5sum(self.mcmc_config['patches'])
-        self.patches = gpd.GeoDataFrame.from_file(self.mcmc_config['patches'], encoding='utf-8')
-        self.patches = self.patches[self.patches['cultgeopat']!='Non Considérée']
-        self.patches['init_cult'] = self.patches['cultgeopat']
+        self.patches = load_pat_patches(self.mcmc_config['patches'])
         self.target = pd.read_csv(self.mcmc_config['target'], sep=';',index_col=0)
         targetRatio = (self.target['2050']-self.target['2016'])/self.target['2016']
         self.targetPAT = self.patches.groupby('cultgeopat')['SURF_PARC'].sum()*(1+targetRatio)
@@ -102,7 +114,7 @@ class MCMC:
             scenario.reallocate(self.rng, self.targetPAT, self.mcmc_config['ratio_patches_to_modify']) # 3.8 ms
             scores.append(self.indicators.compute_indicators(scenario.patches))
         scores = pd.DataFrame(scores, columns=self.indicators.indicators_names)
-        scores.to_csv('../output/mcmc2.csv')
+        scores.to_csv(self.outputdir+'mcmc.csv')
         # TODO
         # Storing variation of indicators
         init_var = scores.std()
@@ -110,13 +122,9 @@ class MCMC:
         # sequential optimization loop
 
 if __name__ == '__main__':
-    # scenario = Scenario.load_shp('../output/PAT_patches/PAT_patches.shp')
-    # target = pd.read_csv('../resources/targetPAT.csv', sep=';',index_col=0)
-    # targetRatio = (target['2050']-target['2016'])/target['2016']
-    # targetPAT = scenario.patches.groupby('cultgeopat')['SURF_PARC'].sum()*(1+targetRatio)
-    # rng = np.random.RandomState()
-    # scenario.reallocate(rng, targetPAT, 50)
     mcmc = MCMC('MCMC_config.yml')
+    # scenario = Scenario(mcmc.patches.copy())
+    # scenario.reallocate(mcmc.rng, mcmc.targetPAT, mcmc.mcmc_config['ratio_patches_to_modify'])
     mcmc.run()
     # print(mcmc.indicators.biodiversity(mcmc.patches))
     # print(mcmc.indicators.proximity(mcmc.patches))

scenariosAleatoires/MCMC_config.yml

 patches: ../output/PAT_patches/PAT_patches.shp
 target: ../resources/targetPAT.csv
+output_dir: ../output/MCMC/
 initial_nb_particles: 10
 ratio_patches_to_modify: 0.05
 indicators_config: