diff COBRAxy/utils/general_utils.py @ 414:5086145cfb96 draft

Uploaded

--- a/COBRAxy/utils/general_utils.py	Mon Sep 08 21:37:14 2025 +0000
+++ b/COBRAxy/utils/general_utils.py	Mon Sep 08 21:54:14 2025 +0000
@@ -17,11 +17,6 @@
 import gzip
 import bz2
 from io import StringIO
-import os
-sys.path.insert(0, os.path.dirname(__file__)) 
-import rule_parsing  as rulesUtils
-import reaction_parsing as reactionUtils
-
 
 
 
@@ -780,38 +775,40 @@
     
     # Seconda passata: aggiungi le reazioni
     reactions_added = 0
+    reactions_skipped = 0
     
     for idx, row in df.iterrows():
-        reaction_id = str(row['ReactionID']).strip()
-        reaction_formula = str(row['Reaction']).strip()
-        
-        # Salta reazioni senza formula
-        if not reaction_formula or reaction_formula == 'nan':
-            raise ValueError(f"Formula della reazione mancante {reaction_id}")
-
-        # Crea la reazione
-        reaction = Reaction(reaction_id)
-        reaction.name = reaction_id
-        
-        # Imposta bounds
-        reaction.lower_bound = float(row['lower_bound']) if pd.notna(row['lower_bound']) else -1000.0
-        reaction.upper_bound = float(row['upper_bound']) if pd.notna(row['upper_bound']) else 1000.0
-        
-        # Aggiungi gene rule se presente
-        if pd.notna(row['Rule']) and str(row['Rule']).strip():
-            reaction.gene_reaction_rule = str(row['Rule']).strip()
-        
-        # Parse della formula della reazione
         try:
-            parse_reaction_formula(reaction, reaction_formula, metabolites_dict)
-        except Exception as e:
-            print(f"Errore nel parsing della reazione {reaction_id}: {e}")
-            reactions_skipped += 1
-            continue
-        
-        # Aggiungi la reazione al modello
-        model.add_reactions([reaction])
-        reactions_added += 1
+            reaction_id = str(row['ReactionID']).strip()
+            reaction_formula = str(row['Reaction']).strip()
+            
+            # Salta reazioni senza formula
+            if not reaction_formula or reaction_formula == 'nan':
+                raise ValueError(f"Formula della reazione mancante {reaction_id}")
+            
+            # Crea la reazione
+            reaction = Reaction(reaction_id)
+            reaction.name = reaction_id
+            
+            # Imposta bounds
+            reaction.lower_bound = float(row['lower_bound']) if pd.notna(row['lower_bound']) else -1000.0
+            reaction.upper_bound = float(row['upper_bound']) if pd.notna(row['upper_bound']) else 1000.0
+            
+            # Aggiungi gene rule se presente
+            if pd.notna(row['Rule']) and str(row['Rule']).strip():
+                reaction.gene_reaction_rule = str(row['Rule']).strip()
+            
+            # Parse della formula della reazione
+            try:
+                parse_reaction_formula(reaction, reaction_formula, metabolites_dict)
+            except Exception as e:
+                print(f"Errore nel parsing della reazione {reaction_id}: {e}")
+                reactions_skipped += 1
+                continue
+            
+            # Aggiungi la reazione al modello
+            model.add_reactions([reaction])
+            reactions_added += 1
             
     
     print(f"Aggiunte {reactions_added} reazioni, saltate {reactions_skipped} reazioni")
@@ -979,124 +976,3 @@
         validation['status'] = f"Error: {e}"
     
     return validation
-
-
-################################- DATA GENERATION -################################
-ReactionId = str
-def generate_rules(model: cobra.Model, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
-    """
-    Generates a dictionary mapping reaction ids to rules from the model.
-
-    Args:
-        model : the model to derive data from.
-        asParsed : if True parses the rules to an optimized runtime format, otherwise leaves them as strings.
-
-    Returns:
-        Dict[ReactionId, rulesUtils.OpList] : the generated dictionary of parsed rules.
-        Dict[ReactionId, str] : the generated dictionary of raw rules.
-    """
-    # Is the below approach convoluted? yes
-    # Ok but is it inefficient? probably
-    # Ok but at least I don't have to repeat the check at every rule (I'm clinically insane)
-    _ruleGetter   =  lambda reaction : reaction.gene_reaction_rule
-    ruleExtractor = (lambda reaction :
-        rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter
-
-    return {
-        reaction.id : ruleExtractor(reaction)
-        for reaction in model.reactions
-        if reaction.gene_reaction_rule }
-
-def generate_reactions(model :cobra.Model, *, asParsed = True) -> Dict[ReactionId, str]:
-    """
-    Generates a dictionary mapping reaction ids to reaction formulas from the model.
-
-    Args:
-        model : the model to derive data from.
-        asParsed : if True parses the reactions to an optimized runtime format, otherwise leaves them as they are.
-
-    Returns:
-        Dict[ReactionId, str] : the generated dictionary.
-    """
-
-    unparsedReactions = {
-        reaction.id : reaction.reaction
-        for reaction in model.reactions
-        if reaction.reaction 
-    }
-
-    if not asParsed: return unparsedReactions
-    
-    return reactionUtils.create_reaction_dict(unparsedReactions)
-
-def get_medium(model:cobra.Model) -> pd.DataFrame:
-    trueMedium=[]
-    for r in model.reactions:
-        positiveCoeff=0
-        for m in r.metabolites:
-            if r.get_coefficient(m.id)>0:
-                positiveCoeff=1;
-        if (positiveCoeff==0 and r.lower_bound<0):
-            trueMedium.append(r.id)
-
-    df_medium = pd.DataFrame()
-    df_medium["reaction"] = trueMedium
-    return df_medium
-
-def generate_bounds(model:cobra.Model) -> pd.DataFrame:
-
-    rxns = []
-    for reaction in model.reactions:
-        rxns.append(reaction.id)
-
-    bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
-
-    for reaction in model.reactions:
-        bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
-    return bounds
-
-
-
-def generate_compartments(model: cobra.Model) -> pd.DataFrame:
-    """
-    Generates a DataFrame containing compartment information for each reaction.
-    Creates columns for each compartment position (Compartment_1, Compartment_2, etc.)
-    
-    Args:
-        model: the COBRA model to extract compartment data from.
-        
-    Returns:
-        pd.DataFrame: DataFrame with ReactionID and compartment columns
-    """
-    pathway_data = []
-
-    # First pass: determine the maximum number of pathways any reaction has
-    max_pathways = 0
-    reaction_pathways = {}
-
-    for reaction in model.reactions:
-        # Get unique pathways from all metabolites in the reaction
-        if type(reaction.annotation['pathways']) == list:
-            reaction_pathways[reaction.id] = reaction.annotation['pathways']
-            max_pathways = max(max_pathways, len(reaction.annotation['pathways']))
-        else:
-            reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
-
-    # Create column names for pathways
-    pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]
-
-    # Second pass: create the data
-    for reaction_id, pathways in reaction_pathways.items():
-        row = {"ReactionID": reaction_id}
-        
-        # Fill pathway columns
-        for i in range(max_pathways):
-            col_name = pathway_columns[i]
-            if i < len(pathways):
-                row[col_name] = pathways[i]
-            else:
-                row[col_name] = None  # or "" if you prefer empty strings
-
-        pathway_data.append(row)
-
-    return pd.DataFrame(pathway_data)
\ No newline at end of file