# HG changeset patch
# User luca_milaz
# Date 1720033686 0
# Node ID 1bdc84fe4a881f6dfa4ab4030f96ac60b671158c
# Parent  d58974850ed09acd1d0ee532a3bc35248f342c42
Uploaded

diff -r d58974850ed0 -r 1bdc84fe4a88 marea_2_0/flux_sampling.py
--- a/marea_2_0/flux_sampling.py	Wed Jul 03 18:59:46 2024 +0000
+++ b/marea_2_0/flux_sampling.py	Wed Jul 03 19:08:06 2024 +0000
@@ -105,24 +105,24 @@
     elif ARGS.output_format is utils.FileFormat.CSV:
         dataset.to_csv(ARGS.output_folder + name + ".csv", sep = '\t', index = False)
 
-def OPTGP_sampler(model:cobra.Model, model_name:str, n_samples:int=1000, thinning:int=100, n_batches:int=1, seed:int=0)-> None:
+
 
-    if not os.path.exists(ARGS.output_folder + "OPTGP/"):
-        os.makedirs(ARGS.output_folder + "OPTGP/")
+def OPTGP_sampler(model:cobra.Model, model_name:str, n_samples:int=1000, thinning:int=100, n_batches:int=1, seed:int=0)-> None:
 
     for i in range(0, n_batches):
         optgp = OptGPSampler(model, thinning, seed)
         samples = optgp.sample(n_samples)
-        samples.to_csv(ARGS.output_folder + "OPTGP/" +  ARGS.model_name + '_'+ str(i)+'.csv')
-        i+=1
+        samples.to_csv(ARGS.output_folder +  model_name + '_'+ str(i)+'_OPTGP.csv', index=False)
         seed+=1
     samplesTotal = pd.DataFrame()
     for i in range(0, n_batches):
-        samples_batch = pd.read_csv(ARGS.output_folder + "OPTGP/" +  ARGS.model_name + '_'+ str(i)+'.csv')
+        samples_batch = pd.read_csv(ARGS.output_folder  +  model_name + '_'+ str(i)+'_OPTGP.csv')
         samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True)
-    write_to_file(samplesTotal, ARGS.output_folder + "OPTGP/" + ARGS.model_name)
+
+    write_to_file(samplesTotal, model_name)
+
     for i in range(0, n_batches):
-        os.remove(ARGS.output_folder + "OPTGP/" +  ARGS.model_name + '_'+ str(i)+'.csv')
+        os.remove(ARGS.output_folder +   model_name + '_'+ str(i)+'_OPTGP.csv')
     pass
 
 
@@ -142,17 +142,17 @@
             ARGS.out_log)
             CBS_backend.randomObjectiveFunctionSampling_cobrapy(model, n_samples, df_coefficients.iloc[:,i*n_samples:(i+1)*n_samples], 
                                                     samples)
-        samples.to_csv(ARGS.output_folder +  model_name + '_'+ str(i)+'.csv', index=False)
+        samples.to_csv(ARGS.output_folder +  model_name + '_'+ str(i)+'_CBS.csv', index=False)
 
     samplesTotal = pd.DataFrame()
     for i in range(0, n_batches):
-        samples_batch = pd.read_csv(ARGS.output_folder  +  model_name + '_'+ str(i)+'.csv')
+        samples_batch = pd.read_csv(ARGS.output_folder  +  model_name + '_'+ str(i)+'_CBS.csv')
         samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True)
 
     write_to_file(samplesTotal, model_name)
 
     for i in range(0, n_batches):
-        os.remove(ARGS.output_folder +   model_name + '_'+ str(i)+'.csv')
+        os.remove(ARGS.output_folder +   model_name + '_'+ str(i)+'_CBS.csv')
     pass