comparison COBRAxy/ras_to_bounds.py @ 129:dfb6a1dc6dbc draft

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128:a78d83753cce

    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 #just one cell for testing
    else:
        model_new = model.copy()
        apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=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)

129:dfb6a1dc6dbc

    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))
        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)