How to use the spectral.io function in spectral

# get class numbers from names
        class_list = image.metadata.get('class names')
        class_nums = [
            class_list.index(className) if className in class_list else -1 for
            className in self.class_names]

        # copy pixels of the desired classes
        for y in range(n):
            for x in range(m):
                pixel = data[x, y]
                if pixel[0] in class_nums:
                    classified[x, y] = 1 + class_nums.index(pixel[0])

        # save the classified image to a file
        spectral.io.envi.save_classification(classified_file_name, classified,
                                             class_names=['No data'] +
                                             self.class_names,
                                             metadata={'data ignore value': 0,
                                                       'description': 'COAL '
                                                       '' + pycoal.version +
                                                       ' mining ' +
                                                       'classified image.',
                                                       'map info':
                                                           image.metadata.get(
                                                            'map info')})

        end = time.time()
        seconds_elapsed = end - start
        m, s = divmod(seconds_elapsed, 60)
        h, m = divmod(m, 60)
        logging.info(

# allocate a copy for reindexed pixels
        copy = numpy.zeros(shape=(M, N), dtype=numpy.uint16)

        # find classes actually present in the image
        classes = sorted(set(classified.asarray().flatten().tolist()))
        lookup = [classes.index(i) if i in classes else 0 for i in
                  range(int(classified.metadata.get('classes')))]

        # reindex each pixel
        for x in range(M):
            for y in range(N):
                copy[x, y] = lookup[data[x, y, 0]]

        # overwrite the file
        spectral.io.envi.save_classification(classified_file_name, copy,
                                             force=True,
                                             class_names=[
                                                 classified.metadata.get(
                                                     'class names')[i]
                                                 for i in classes],
                                             metadata=classified.metadata)

Args:
            source_filename (str):      filename of the source image
            destination_filename (str): filename of the destination image
        """
        logging.info(
            "Creating an empty copy of classified image '%s' with the same "
            "size. Saving to '%s'", source_filename, destination_filename)
        # open the source image
        source = spectral.open_image(source_filename)

        # create an empty array of the same dimensions
        destination = numpy.zeros(shape=source.shape, dtype=numpy.uint16)

        # save it with source metadata
        spectral.io.envi.save_classification(destination_filename, destination,
                                             class_names=['No data', 'Data'],
                                             metadata={'data ignore value': 0,
                                                       'map info':
                                                           source.metadata.get(
                                                               'map info')})