comparison COBRAxy/utils/model_utils.py @ 508:ca98c149ec61 draft

Uploaded

507:20e135a73cad

            added += 1

    logger.info(f"Model genes updated: removed {removed}, added {added}")


def _log_translation_statistics(stats: Dict[str, int],
                               unmapped_genes: List[str],
                               multi_mapping_genes: List[Tuple[str, List[str]]],
                               original_genes: Set[str],
                               final_genes,

508:ca98c149ec61

            added += 1

    logger.info(f"Model genes updated: removed {removed}, added {added}")


def export_model_to_tabular(model: cobraModel, 
                           output_path: str,
                           translation_issues: Dict = None,
                           include_objective: bool = True,
                           save_function = None) -> pd.DataFrame:
    """
    Export a COBRA model to tabular format with optional components.
    
    Args:
        model: COBRA model to export
        output_path: Path where to save the tabular file
        translation_issues: Optional dict of {reaction_id: issues} from gene translation
        include_objective: Whether to include objective coefficient column
        save_function: Optional custom save function, if None uses pd.DataFrame.to_csv
        
    Returns:
        pd.DataFrame: The merged tabular data
    """
    # Generate model data
    rules = generate_rules(model, asParsed=False)
    
    reactions = generate_reactions(model, asParsed=False)
    bounds = generate_bounds(model)
    medium = get_medium(model)
    compartments = generate_compartments(model)
    
    # Create base DataFrames
    df_rules = pd.DataFrame(list(rules.items()), columns=["ReactionID", "GPR"])
    df_reactions = pd.DataFrame(list(reactions.items()), columns=["ReactionID", "Formula"])
    df_bounds = bounds.reset_index().rename(columns={"index": "ReactionID"})
    df_medium = medium.rename(columns={"reaction": "ReactionID"})
    df_medium["InMedium"] = True
    
    # Start merging
    merged = df_reactions.merge(df_rules, on="ReactionID", how="outer")
    merged = merged.merge(df_bounds, on="ReactionID", how="outer")
    
    # Add objective coefficients if requested
    if include_objective:
        objective_function = extract_objective_coefficients(model)
        merged = merged.merge(objective_function, on="ReactionID", how="outer")
    
    # Add compartments/pathways if they exist
    if compartments is not None:
        merged = merged.merge(compartments, on="ReactionID", how="outer")
    
    # Add medium information
    merged = merged.merge(df_medium, on="ReactionID", how="left")
    
    # Add translation issues if provided
    if translation_issues:
        df_translation_issues = pd.DataFrame([
            {"ReactionID": rxn_id, "TranslationIssues": issues}
            for rxn_id, issues in translation_issues.items()
        ])
        if not df_translation_issues.empty:
            merged = merged.merge(df_translation_issues, on="ReactionID", how="left")
            merged["TranslationIssues"] = merged["TranslationIssues"].fillna("")
    
    # Final processing
    merged["InMedium"] = merged["InMedium"].fillna(False)
    merged = merged.sort_values(by="InMedium", ascending=False)
    
    # Save the file
    if save_function:
        save_function(merged, output_path)
    else:
        merged.to_csv(output_path, sep="\t", index=False)
    
    return merged


def _log_translation_statistics(stats: Dict[str, int],
                               unmapped_genes: List[str],
                               multi_mapping_genes: List[Tuple[str, List[str]]],
                               original_genes: Set[str],
                               final_genes,