comparison COBRAxy/ras_to_bounds.py @ 60:547942aa1a93 draft

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59:d4162562c8f5

    
    parser.add_argument('-rs', '--ras_selector',
                        required = True,
                        type=utils.Bool("using_RAS"),
                        help = 'ras selector')
    
    parser.add_argument('-c', '--classes',
                    type = str,
                    required = False,
                    help = 'input classes')

    parser.add_argument('-cc', '--cell_class',
                    type = str,
                    help = 'output of cell class')
    

    ARGS.output_folder = 'ras_to_bounds/'

    if(ARGS.ras_selector == True):
        ras_file_list = ARGS.input_ras.split(",")
        if(len(ras_file_list)>1):
            ras_class_names = [cls.strip() for cls in ARGS.classes.split(',')]
        else:
            ras_class_names = ["placeHolder"]
        ras_list = []
        class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])
        for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names):
            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 = pd.concat([class_assignments, pd.DataFrame({"Patient_ID": ras.index, "Class": ras_class_name})])
        
        
        # Concatenate all ras DataFrames into a single DataFrame
        ras_combined = pd.concat(ras_list, axis=1)
        # Normalize the RAS values by max RAS

60:547942aa1a93

    
    parser.add_argument('-rs', '--ras_selector',
                        required = True,
                        type=utils.Bool("using_RAS"),
                        help = 'ras selector')

    parser.add_argument('-cc', '--cell_class',
                    type = str,
                    help = 'output of cell class')
    

    ARGS.output_folder = 'ras_to_bounds/'

    if(ARGS.ras_selector == True):
        ras_file_list = ARGS.input_ras.split(",")
        ras_class_names = []
        for file in ras_file_list:
            ras_class_names.append(file.split(".")[0])
        ras_list = []
        class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])
        for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names):
            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 = pd.concat([class_assignments, pd.DataFrame({"Patient_ID": patient_id, "Class": ras_class_name})])
        
        
        # Concatenate all ras DataFrames into a single DataFrame
        ras_combined = pd.concat(ras_list, axis=1)
        # Normalize the RAS values by max RAS