comparison COBRAxy/ras_to_bounds.py @ 130:03bbbd9af67d draft

Uploaded

129:dfb6a1dc6dbc

    # Set medium conditions
    for reaction, value in medium.items():
        if value is not None:
            model.reactions.get_by_id(reaction).lower_bound = -float(value)
            

    mediumRxns_ids = medium.keys()
            
    # Perform Flux Variability Analysis (FVA) on this medium
    df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
    
    # Set FVA bounds

    ARGS.output_folder = 'ras_to_bounds/'

    if(ARGS.ras_selector == True):
        ras_file_list = ARGS.input_ras.split(",")
        ras_file_names = ARGS.name.split(",")
        ras_class_names = []
        for file in ras_file_names:
            ras_class_names.append(file.split(".")[0])
        ras_list = []
        class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])

            ras = read_dataset(ras_matrix, "ras dataset")
            ras.replace("None", None, inplace=True)
            ras.set_index("Reactions", drop=True, inplace=True)
            ras = ras.T
            ras = ras.astype(float)
            ras_list.append(ras)
            for patient_id in ras.index:
                class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name]
        
        

130:03bbbd9af67d

    # 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) on this medium
    df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
    
    # Set FVA bounds

    ARGS.output_folder = 'ras_to_bounds/'

    if(ARGS.ras_selector == True):
        ras_file_list = ARGS.input_ras.split(",")
        ras_file_names = ARGS.name.split(",")
        if len(ras_file_names) != len(set(ras_file_names)):
            error_message = "Duplicated file names in the uploaded RAS matrices."
            warning(error_message)
            raise ValueError(error_message)
            pass
        ras_class_names = []
        for file in ras_file_names:
            ras_class_names.append(file.split(".")[0])
        ras_list = []
        class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])

            ras = read_dataset(ras_matrix, "ras dataset")
            ras.replace("None", None, inplace=True)
            ras.set_index("Reactions", drop=True, inplace=True)
            ras = ras.T
            ras = ras.astype(float)
            #append class name to patient id (dataframe index)
            ras.index = [f"{idx}_{ras_class_name}" for idx in ras.index]
            ras_list.append(ras)
            for patient_id in ras.index:
                class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name]