# HG changeset patch
# User luca_milaz
# Date 1758554087 0
# Node ID 5f02f7e4ea9fe7ca71267b2ae5b360ba33e1ca36
# Parent  77ed5c6968653c3840836324d4b3ef9b0cabd0d2
Uploaded

diff -r 77ed5c696865 -r 5f02f7e4ea9f COBRAxy/flux_simulation_beta.py
--- a/COBRAxy/flux_simulation_beta.py	Mon Sep 22 15:14:26 2025 +0000
+++ b/COBRAxy/flux_simulation_beta.py	Mon Sep 22 15:14:47 2025 +0000
@@ -46,6 +46,11 @@
         required=True,
         help="upload mode: True for model+bounds, False for complete models")
     
+    parser.add_argument("-ens", "--sampling_enabled", type=str,
+        choices=['True', 'False'],
+        required=True,
+        help="enable sampling: True for sampling, False for no sampling")
+    
     parser.add_argument('-ol', '--out_log',
                         help="Output log")
     
@@ -342,18 +347,20 @@
     Returns:
         List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
     """
-    
-    if ARGS.algorithm == 'OPTGP':
-        OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)
-    elif ARGS.algorithm == 'CBS':
-        CBS_sampler(model_input, cell_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed)
 
-    df_mean, df_median, df_quantiles = fluxes_statistics(cell_name, ARGS.output_types)
+    if ARGS.sampling_enabled == "True" and ARGS.n_samples > 0:
+    
+        if ARGS.algorithm == 'OPTGP':
+            OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)
+        elif ARGS.algorithm == 'CBS':
+            CBS_sampler(model_input, cell_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed)
 
-    if("fluxes" not in ARGS.output_types):
-        os.remove(ARGS.output_path + "/" + cell_name + '.csv')
+        df_mean, df_median, df_quantiles = fluxes_statistics(cell_name, ARGS.output_types)
 
-    returnList = [df_mean, df_median, df_quantiles]
+        if("fluxes" not in ARGS.output_types):
+            os.remove(ARGS.output_path + "/" + cell_name + '.csv')
+
+        returnList = [df_mean, df_median, df_quantiles]
 
     df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, cell_name, ARGS.output_type_analysis)
 
@@ -492,7 +499,8 @@
     ARGS.output_type_analysis = ARGS.output_type_analysis.split(",") if ARGS.output_type_analysis else []
 
     # Determine if sampling should be performed
-    perform_sampling = ARGS.n_samples > 0
+    if ARGS.sampling_enabled == "True" and ARGS.n_samples > 0:
+        perform_sampling = True
 
     print("=== INPUT FILES ===")
     print(f"{ARGS.input_files}")
@@ -560,7 +568,7 @@
             all_quantiles = all_quantiles.sort_index()
             write_to_file(all_quantiles.T, "quantiles", True)
     else:
-        print("=== SAMPLING SKIPPED (n_samples = 0) ===")
+        print("=== SAMPLING SKIPPED (n_samples = 0 or sampling disabled) ===")
 
     # Handle optimization analysis outputs (always available)
     print("=== PROCESSING OPTIMIZATION RESULTS ===")