comparison COBRAxy/ras_to_bounds.py @ 102:182c710c1660 draft

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101:6cb85065b38b

    for reaction in rxns_ids:
        if reaction in ras_row.index:
            scaling_factor = ras_row[reaction]
            lower_bound=model.reactions.get_by_id(reaction).lower_bound
            upper_bound=model.reactions.get_by_id(reaction).upper_bound
            #warning("Reaction: "+reaction+" Lower Bound: "+str(lower_bound)+" Upper Bound: "+str(upper_bound)+" Scaling Factor: "+str(scaling_factor))
            valMax=float((upper_bound)*scaling_factor)
            valMin=float((lower_bound)*scaling_factor)
            if upper_bound!=0 and lower_bound==0:
                model.reactions.get_by_id(reaction).upper_bound=valMax
            if upper_bound==0 and lower_bound!=0:

        pd.DataFrame: DataFrame containing the bounds of reactions in the model.
    """
    rxns_ids = [rxn.id for rxn in model.reactions]
    
    # Set medium conditions
    for reaction, value in medium.items():
        if value is not None:
            model.reactions.get_by_id(reaction).lower_bound = -float(value)
    
    # Perform Flux Variability Analysis (FVA)
    df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
    
    # Set FVA bounds
    for reaction in rxns_ids:
        rxn = model.reactions.get_by_id(reaction)
        rxn.lower_bound = float(df_FVA.loc[reaction, "minimum"])
        rxn.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) 
    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)

102:182c710c1660

    for reaction in rxns_ids:
        if reaction in ras_row.index:
            scaling_factor = ras_row[reaction]
            lower_bound=model.reactions.get_by_id(reaction).lower_bound
            upper_bound=model.reactions.get_by_id(reaction).upper_bound
            valMax=float((upper_bound)*scaling_factor)
            valMin=float((lower_bound)*scaling_factor)
            if upper_bound!=0 and lower_bound==0:
                model.reactions.get_by_id(reaction).upper_bound=valMax
            if upper_bound==0 and lower_bound!=0:

        pd.DataFrame: DataFrame containing the bounds of reactions in the model.
    """
    rxns_ids = [rxn.id for rxn in model.reactions]
    
    # Set medium conditions
    ''' 
    reactions_medium=model2.medium.keys()
        for reaction in reactions_medium:
            if(reaction != "EX_thbpt_e" and reaction != "EX_lac__L_e"):
                model2.reactions.get_by_id(reaction).lower_bound=-float(ras_meta.loc[cell,"countmatrix_"+reaction])
            if(reaction == "EX_lac__L_e"):
                model2.reactions.get_by_id(reaction).lower_bound=float(0.0)
    '''
    for reaction, value in medium.items():
        if value is not None:
            model.reactions.get_by_id(reaction).lower_bound = -float(value)
    
    # Perform Flux Variability Analysis (FVA)
    df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
    
    # Set FVA bounds
    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)