comparison COBRAxy/utils/model_utils.py @ 506:ffc234ec80db draft

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505:96f512dff490

def generate_compartments(model: cobraModel) -> 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 'pathways' in reaction.annotation:
            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']]
        else:
            # No pathway annotation - use empty list
            reaction_pathways[reaction.id] = []

    # 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)

        for col in pathway_cols:
            if pd.notna(row[col]) and str(row[col]).strip():
                pathways.append(str(row[col]).strip())
        
        if pathways:
            
            reaction = model.reactions.get_by_id(reaction_id)
            if len(pathways) == 1:
                reaction.annotation['pathways'] = pathways[0]
            else:
                reaction.annotation['pathways'] = pathways

506:ffc234ec80db

def generate_compartments(model: cobraModel) -> pd.DataFrame:
    """
    Generates a DataFrame containing pathway information for each reaction.
    Creates columns for each pathway position (Pathway_1, Pathway_2, etc.) only if pathways exist.
    
    Args:
        model: the COBRA model to extract pathway data from.
        
    Returns:
        pd.DataFrame: DataFrame with ReactionID and pathway columns (if any pathways exist)
    """
    pathway_data = []

    # First pass: determine the maximum number of pathways any reaction has
    max_pathways = 0
    reaction_pathways = {}
    has_any_pathways = False

    for reaction in model.reactions:
        # Get unique pathways from all metabolites in the reaction
        if 'pathways' in reaction.annotation and reaction.annotation['pathways']:
            if type(reaction.annotation['pathways']) == list:
                # Filter out empty/None values
                valid_pathways = [p for p in reaction.annotation['pathways'] if p]
                if valid_pathways:
                    reaction_pathways[reaction.id] = valid_pathways
                    max_pathways = max(max_pathways, len(valid_pathways))
                    has_any_pathways = True
                else:
                    reaction_pathways[reaction.id] = []
            else:
                # Single pathway value
                if reaction.annotation['pathways']:
                    reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
                    max_pathways = max(max_pathways, 1)
                    has_any_pathways = True
                else:
                    reaction_pathways[reaction.id] = []
        else:
            # No pathway annotation - use empty list
            reaction_pathways[reaction.id] = []

    # If no pathways exist in the model, return DataFrame with only ReactionID
    if not has_any_pathways:
        return None

    # Create column names for pathways only if they exist
    pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]

    # Second pass: create the data with pathway columns
    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

        pathway_data.append(row)

    return pd.DataFrame(pathway_data)

        for col in pathway_cols:
            if pd.notna(row[col]) and str(row[col]).strip():
                pathways.append(str(row[col]).strip())
        
        if pathways:

            reaction = model.reactions.get_by_id(reaction_id)
            if len(pathways) == 1:
                reaction.annotation['pathways'] = pathways[0]
            else:
                reaction.annotation['pathways'] = pathways