comparison COBRAxy/ras_to_bounds.py @ 122:7e1ce04cf192 draft

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121:2121cc1aef45

        rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
        mediumRxns_ids (list of str): List of reaction IDs in the medium. Their RAS is set to zero, but they are already set in the model.
    Returns:
        None
    """
    for reaction in rxns_ids:
        if reaction in ras_row.index and reaction not in mediumRxns_ids:
            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:

    rxns_ids = [rxn.id for rxn in model.reactions]
    
    # Set medium conditions
    for reaction, value in medium.items():
        if value is not None:
            if(reaction != "EX_thbpt_e" and reaction != "EX_lac__L_e"):
                model.reactions.get_by_id(reaction).lower_bound = -float(value)
            if(reaction == "EX_lac__L_e"):
                model.reactions.get_by_id(reaction).lower_bound = float(0.0)

        
        # Concatenate all ras DataFrames into a single DataFrame
        ras_combined = pd.concat(ras_list, axis=0)
        # Normalize the RAS values by max RAS
        ras_combined = ras_combined.div(ras_combined.max(axis=0))
        ras_combined = ras_combined.fillna(0)


    
    model_type :utils.Model = ARGS.model_selector
    if model_type is utils.Model.Custom:

122:7e1ce04cf192

        rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
        mediumRxns_ids (list of str): List of reaction IDs in the medium. Their RAS is set to zero, but they are already set in the model.
    Returns:
        None
    """
    for reaction in ras_row.index:
        scaling_factor = ras_row[reaction]
        if(scaling_factor not in [np.nan, None]):
            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:

    rxns_ids = [rxn.id for rxn in model.reactions]
    
    # Set medium conditions
    for reaction, value in medium.items():
        if value is not None:
            ## SOLO ENGRO2
            if(reaction != "EX_thbpt_e" and reaction != "EX_lac__L_e"):
                model.reactions.get_by_id(reaction).lower_bound = -float(value)
            if(reaction == "EX_lac__L_e"):
                model.reactions.get_by_id(reaction).lower_bound = float(0.0)

        
        # Concatenate all ras DataFrames into a single DataFrame
        ras_combined = pd.concat(ras_list, axis=0)
        # Normalize the RAS values by max RAS
        ras_combined = ras_combined.div(ras_combined.max(axis=0))
        #ras_combined = ras_combined.fillna(0)
        #il ras c'è per tutti o non c'è per nessuno


    
    model_type :utils.Model = ARGS.model_selector
    if model_type is utils.Model.Custom: