comparison COBRAxy/ras_to_bounds.py @ 106:1629671d4ca6 draft

Uploaded

105:e3b3958c0d88

    for reaction in rxns_ids:
        model.reactions.get_by_id(reaction).lower_bound = float(df_FVA.loc[reaction, "minimum"])
        model.reactions.get_by_id(reaction).upper_bound = float(df_FVA.loc[reaction, "maximum"])

    if ras is not None:
        Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
    else:
        model_new = model.copy()
        apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids)
        bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
        bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)

106:1629671d4ca6

    for reaction in rxns_ids:
        model.reactions.get_by_id(reaction).lower_bound = float(df_FVA.loc[reaction, "minimum"])
        model.reactions.get_by_id(reaction).upper_bound = float(df_FVA.loc[reaction, "maximum"])

    if ras is not None:
        #Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
        for cellName, ras_row in ras.iterrows():
            process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder)
            break
    else:
        model_new = model.copy()
        apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids)
        bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
        bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)