diff COBRAxy/custom_data_generator_beta.py @ 411:6b015d3184ab draft

Uploaded

--- a/COBRAxy/custom_data_generator_beta.py	Mon Sep 08 17:33:52 2025 +0000
+++ b/COBRAxy/custom_data_generator_beta.py	Mon Sep 08 21:07:34 2025 +0000
@@ -72,125 +72,7 @@
     raise utils.DataErr(file_path,
         f"Formato \"{file_path.ext}\" non riconosciuto, sono supportati solo file JSON e XML")
 
-################################- 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)
 
 
 ###############################- FILE SAVING -################################
@@ -296,12 +178,12 @@
         model = utils.convert_genes(model, ARGS.gene_format.replace("HGNC_", "HGNC "))
 
     # generate data
-    rules = generate_rules(model, asParsed = False)
-    reactions = generate_reactions(model, asParsed = False)
-    bounds = generate_bounds(model)
-    medium = get_medium(model)
+    rules = utils.generate_rules(model, asParsed = False)
+    reactions = utils.generate_reactions(model, asParsed = False)
+    bounds = utils.generate_bounds(model)
+    medium = utils.get_medium(model)
     if ARGS.name == "ENGRO2":
-        compartments = generate_compartments(model)
+        compartments = utils.generate_compartments(model)
 
     df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"])
     df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"])
@@ -324,10 +206,8 @@
 
     #merged.to_csv(out_file, sep = '\t', index = False)
 
-
     ####
 
-
     if not ARGS.out_tabular:
         raise utils.ArgsErr("out_tabular", "output path (--out_tabular) is required when output_format == tabular", ARGS.out_tabular)
     save_as_tabular_df(merged, ARGS.out_tabular)