--- a/COBRAxy/flux_simulation.py	Tue Nov 12 17:05:33 2024 +0000
+++ b/COBRAxy/flux_simulation.py	Tue Nov 12 17:11:17 2024 +0000
@@ -114,8 +114,8 @@
 
 def write_to_file(dataset: pd.DataFrame, name: str, keep_index:bool=False)->None:
     dataset.index.name = 'Reactions'
-    print(ARGS.output_path + name + ".csv")
-    dataset.to_csv(ARGS.output_path + name + ".csv", sep = '\t', index = keep_index)
+    print(ARGS.output_path + "/" + name + ".csv")
+    dataset.to_csv(ARGS.output_path + "/" + name + ".csv", sep = '\t', index = keep_index)
 
 ############################ dataset input ####################################
 def read_dataset(data :str, name :str) -> pd.DataFrame:
@@ -162,17 +162,17 @@
     for i in range(0, n_batches):
         optgp = OptGPSampler(model, thinning, seed)
         samples = optgp.sample(n_samples)
-        samples.to_csv(ARGS.output_path +  model_name + '_'+ str(i)+'_OPTGP.csv', index=False)
+        samples.to_csv(ARGS.output_path + "/" +  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_path  +  model_name + '_'+ str(i)+'_OPTGP.csv')
+        samples_batch = pd.read_csv(ARGS.output_path + "/"  +  model_name + '_'+ str(i)+'_OPTGP.csv')
         samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True)
 
     write_to_file(samplesTotal.T, model_name, True)
 
     for i in range(0, n_batches):
-        os.remove(ARGS.output_path +   model_name + '_'+ str(i)+'_OPTGP.csv')
+        os.remove(ARGS.output_path + "/" +   model_name + '_'+ str(i)+'_OPTGP.csv')
     pass
 
 
@@ -205,18 +205,18 @@
             ARGS.out_log)
             CBS_backend.randomObjectiveFunctionSampling_cobrapy(model, n_samples, df_coefficients.iloc[:,i*n_samples:(i+1)*n_samples], 
                                                     samples)
-        utils.logWarning(ARGS.output_path +  model_name + '_'+ str(i)+'_CBS.csv', ARGS.out_log)
-        samples.to_csv(ARGS.output_path +  model_name + '_'+ str(i)+'_CBS.csv', index=False)
+        utils.logWarning(ARGS.output_path + "/" +  model_name + '_'+ str(i)+'_CBS.csv', ARGS.out_log)
+        samples.to_csv(ARGS.output_path + "/" +  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_path  +  model_name + '_'+ str(i)+'_CBS.csv')
+        samples_batch = pd.read_csv(ARGS.output_path + "/"  +  model_name + '_'+ str(i)+'_CBS.csv')
         samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True)
 
     write_to_file(samplesTotal.T, model_name, True)
 
     for i in range(0, n_batches):
-        os.remove(ARGS.output_path +   model_name + '_'+ str(i)+'_CBS.csv')
+        os.remove(ARGS.output_path + "/" + model_name + '_'+ str(i)+'_CBS.csv')
     pass
 
 
@@ -250,7 +250,7 @@
     df_mean, df_median, df_quantiles = fluxes_statistics(name, ARGS.output_types)
 
     if("fluxes" not in ARGS.output_types):
-        os.remove(ARGS.output_path  +  name + '.csv')
+        os.remove(ARGS.output_path + "/"  +  name + '.csv')
 
     returnList = []
     returnList.append(df_mean)
@@ -284,7 +284,7 @@
     df_median= pd.DataFrame()
     df_quantiles= pd.DataFrame()
 
-    df_samples = pd.read_csv(ARGS.output_path  +  model_name + '.csv', sep = '\t', index_col = 0).T
+    df_samples = pd.read_csv(ARGS.output_path + "/"  +  model_name + '.csv', sep = '\t', index_col = 0).T
     df_samples = df_samples.round(8)
 
     for output_type in output_types: