--- a/COBRAxy/rps_generator.py	Mon Sep 08 13:52:58 2025 +0000
+++ b/COBRAxy/rps_generator.py	Mon Sep 08 14:44:15 2025 +0000
@@ -25,11 +25,14 @@
     parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
                                      description = 'process some value\'s'+
                                      ' abundances and reactions to create RPS scores.')
-    
-    parser.add_argument("-rl", "--model_upload", type = str,
-        help = "path to input file containing the reactions")
-
-    # model_upload custom
+    parser.add_argument('-rc', '--reaction_choice', 
+                        type = str,
+                        default = 'default',
+                        choices = ['default','custom'], 
+                        help = 'chose which type of reaction dataset you want use')
+    parser.add_argument('-cm', '--custom',
+                        type = str,
+                        help='your dataset if you want custom reactions')
     parser.add_argument('-td', '--tool_dir',
                         type = str,
                         required = True,
@@ -118,8 +121,7 @@
     """
     name = clean_metabolite_name(name)
     for id, synonyms in syn_dict.items():
-        if name in synonyms:
-            return id
+        if name in synonyms: return id
     
     return ""
 
@@ -129,8 +131,7 @@
     Check for missing metabolites in the abundances dictionary compared to the reactions dictionary and update abundances accordingly.
 
     Parameters:
-        reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and 
-                          stoichiometric coefficients as values.
+        reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and stoichiometric coefficients as values.
         dataset_by_rows (dict): A dictionary representing abundances where keys are metabolite names and values are their corresponding abundances for all cell lines.
         cell_lines_amt : amount of cell lines, needed to add a new list of abundances for missing metabolites.
 
@@ -198,26 +199,23 @@
     Returns:
         None
     """
-
     cell_lines = dataset[0][1:]
     abundances_dict = {}
 
+    translationIsApplied = ARGS.reaction_choice == "default"
     for row in dataset[1:]:
-        id = get_metabolite_id(row[0], syn_dict) #if translationIsApplied else row[0]
-        if id: 
-            abundances_dict[id] = list(map(utils.Float(), row[1:]))
-
+        id = get_metabolite_id(row[0], syn_dict) if translationIsApplied else row[0]
+        if id: abundances_dict[id] = list(map(utils.Float(), row[1:]))
+    
     missing_list = check_missing_metab(reactions, abundances_dict, len((cell_lines)))
-
+    
     rps_scores :Dict[Dict[str, float]] = {}
     for pos, cell_line_name in enumerate(cell_lines):
         abundances = { metab : abundances[pos] for metab, abundances in abundances_dict.items() }
-
         rps_scores[cell_line_name] = calculate_rps(reactions, abundances, black_list, missing_list, substrateFreqTable)
     
     df = pd.DataFrame.from_dict(rps_scores)
-    df = df.loc[list(reactions.keys()),:]
-    print(df.head(10))
+    
     df.index.name = 'Reactions'
     df.to_csv(ARGS.rps_output, sep='\t', na_rep='None', index=True)
 
@@ -240,35 +238,18 @@
         syn_dict = pk.load(sd)
 
     dataset = utils.readCsv(utils.FilePath.fromStrPath(ARGS.input), '\t', skipHeader = False)
-    tmp_dict = None
-    #if ARGS.reaction_choice == 'default':
-    #    reactions = pk.load(open(ARGS.tool_dir + '/local/pickle files/reactions.pickle', 'rb'))
-    #    substrateFreqTable = pk.load(open(ARGS.tool_dir + '/local/pickle files/substrate_frequencies.pickle', 'rb'))
+
+    if ARGS.reaction_choice == 'default':
+        reactions = pk.load(open(ARGS.tool_dir + '/local/pickle files/reactions.pickle', 'rb'))
+        substrateFreqTable = pk.load(open(ARGS.tool_dir + '/local/pickle files/substrate_frequencies.pickle', 'rb'))
     
-    #elif ARGS.reaction_choice == 'custom':
-    reactions = reactionUtils.parse_custom_reactions(ARGS.model_upload)
-    for r, s in reactions.items():
-        tmp_list = list(s.keys())
-        for k in tmp_list:
-            if k[-2] == '_':
-                s[k[:-2]] = s.pop(k)
-    substrateFreqTable = {}
-    for _, substrates in reactions.items():
-        for substrateName, _ in substrates.items():
-            if substrateName not in substrateFreqTable: substrateFreqTable[substrateName] = 0
-            substrateFreqTable[substrateName] += 1
-
-        print(f"Reactions: {reactions}")
-        print(f"Substrate Frequencies: {substrateFreqTable}")
-        print(f"Synonyms: {syn_dict}")
-        tmp_dict = {}
-        for metabName, freq in substrateFreqTable.items():
-            tmp_metabName = clean_metabolite_name(metabName)
-            for syn_key, syn_list in syn_dict.items():
-                if tmp_metabName in syn_list or tmp_metabName == clean_metabolite_name(syn_key):
-                    print(f"Mapping {tmp_metabName} to {syn_key}")
-                    tmp_dict[syn_key] = syn_list
-                    tmp_dict[syn_key].append(tmp_metabName)
+    elif ARGS.reaction_choice == 'custom':
+        reactions = reactionUtils.parse_custom_reactions(ARGS.custom)
+        substrateFreqTable = {}
+        for _, substrates in reactions.items():
+            for substrateName, _ in substrates.items():
+                if substrateName not in substrateFreqTable: substrateFreqTable[substrateName] = 0
+                substrateFreqTable[substrateName] += 1
 
     rps_for_cell_lines(dataset, reactions, black_list, syn_dict, substrateFreqTable)
     print('Execution succeded')