--- a/COBRAxy/ras_to_bounds.py	Mon Oct 14 08:48:39 2024 +0000
+++ b/COBRAxy/ras_to_bounds.py	Mon Oct 14 08:48:54 2024 +0000
@@ -111,15 +111,13 @@
     return dataset
 
 
-def apply_ras_bounds(model, ras_row, rxns_ids, mediumRxns_ids):
+def apply_ras_bounds(model, ras_row):
     """
     Adjust the bounds of reactions in the model based on RAS values.
 
     Args:
         model (cobra.Model): The metabolic model to be modified.
         ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
-        rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
-        mediumRxns_ids (list of str): List of reaction IDs in the medium. Their RAS is set to zero, but they are already set in the model.
     Returns:
         None
     """
@@ -138,7 +136,7 @@
             model.reactions.get_by_id(reaction).upper_bound=valMax
     pass
 
-def process_ras_cell(cellName, ras_row, model, rxns_ids, mediumRxns_ids, output_folder):
+def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder):
     """
     Process a single RAS cell, apply bounds, and save the bounds to a CSV file.
 
@@ -147,14 +145,13 @@
         ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
         model (cobra.Model): The metabolic model to be modified.
         rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
-        mediumRxns_ids (list of str): List of reaction IDs in the medium. Their RAS is set to zero, but they are already set in the model.
         output_folder (str): Folder path where the output CSV file will be saved.
     
     Returns:
         None
     """
     model_new = model.copy()
-    apply_ras_bounds(model_new, ras_row, rxns_ids, mediumRxns_ids)
+    apply_ras_bounds(model_new, ras_row)
     bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
     bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
     pass
@@ -174,18 +171,15 @@
     """
     rxns_ids = [rxn.id for rxn in model.reactions]
 
-    #metto a zero medium engro2
+    # Set all reactions to zero in the medium
     for rxn_id, _ in model.medium.items():
         model.reactions.get_by_id(rxn_id).lower_bound = float(0.0)
     
     # Set medium conditions
     for reaction, value in medium.items():
         if value is not None:
-            ## SOLO ENGRO2
-            if(reaction != "EX_thbpt_e" and reaction != "EX_lac__L_e"):
-                model.reactions.get_by_id(reaction).lower_bound = -float(value)
-            if(reaction == "EX_lac__L_e"):
-                model.reactions.get_by_id(reaction).lower_bound = float(0.0)
+            model.reactions.get_by_id(reaction).lower_bound = -float(value)
+            
 
     mediumRxns_ids = medium.keys()
             
@@ -200,11 +194,11 @@
     if ras is not None:
         #Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
         for cellName, ras_row in ras.iterrows():
-            process_ras_cell(cellName, ras_row, model, rxns_ids, mediumRxns_ids, output_folder)
+            process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder)
             break #just one cell for testing
     else:
         model_new = model.copy()
-        apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids, mediumRxns_ids)
+        apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids))
         bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
         bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
     pass
@@ -252,8 +246,6 @@
         # Normalize the RAS values by max RAS
         ras_combined = ras_combined.div(ras_combined.max(axis=0))
         ras_combined.dropna(axis=1, how='all', inplace=True)
-        #ras_combined = ras_combined.fillna(0)
-        #il ras c'è per tutti o non c'è per nessuno