"""
This is step 1 / 4 in predict.py

SeeAlso:

    predict.py

    prepare_kwcoco.py *

    tile_processing_kwcoco.py

    export_cold_result_kwcoco.py

    assemble_cold_result_kwcoco.py

This is a proof-of-concept for converting kwcoco files into the
expected data structures for pycold.

Relevant functions:
    * grab_demo_kwcoco_dataset - downloads a small kwcoco dataset for testing
    * stack_kwcoco - runs the stacking process on an entire kwcoco file
    * process_one_coco_image - runs the stacking for a single coco image.

Limitations:
    * Currently only handles Landsat-8

    * The quality bands are not exactly what I was expecting them to be,
      some of the quality filtering is stubbed out or disabled.

    * Not setup for an HPC environment yet, but that extension shouldn't be too
      hard.

    * Nodata values are currently not masked or handled

    * Configurations are hard-coded

TODO:
    - [ ] Incorporate watch/tasks/fusion/datamodules/qa_bands.py
"""
import kwcoco
import json
import numpy as np
import einops
import functools
import operator
import ubelt as ub
import itertools as it
import logging
from datetime import datetime as datetime_cls
import numpy as geek
import scriptconfig as scfg

logger = logging.getLogger(__name__)


try:
    from xdev import profile
except ImportError:
    from ubelt import identity as profile


class PrepareKwcocoConfig(scfg.DataConfig):
    """
    The docstring will be the description in the CLI help
    """

    coco_fpath = scfg.Value(None, help=ub.paragraph(
        '''
        a path to a file to input kwcoco file
        '''))
    out_dpath = scfg.Value(None, help='output directory for the output')
    sensors = scfg.Value('L8', type=str, help='sensor type, default is "L8"')
    adj_cloud = scfg.Value(False, help='How to treat QA band, default is False: ignoring adj. cloud class')
    method = scfg.Value(None, help=ub.paragraph(
        '''
        stacking mode for original COLD or Hybrid, default is None, if
        HybridCOLD then stacked data include g, r, nir, swir16, swir22, ASI,
        tir, QA
        '''))
    resolution = scfg.Value('30GSD', help='if specified then data is processed at this resolution')
    workers = scfg.Value(0, help='number of parallel workers')


# TODO:
# For each sensor, register the specific bands we are interested in.
# This demo currently assumes landsat8
SENSOR_TO_INFO = {}
SENSOR_TO_INFO['L8'] = {
    'sensor_name': 'Landsat-8',
    'intensity_channels': 'blue|green|red|nir|swir16|swir22|lwir11',
    'quality_channels': 'quality',
    'quality_interpretation': 'FMASK'
}  # The name of quality_channels for Drop 4 is 'cloudmask'.

SENSOR_TO_INFO['S2'] = {
    'sensor_name': 'Sentinel-2',
    'intensity_channels': 'blue|green|red|nir|swir16|swir22|lwir11',
    'quality_channels': 'quality',
    'quality_interpretation': 'FMASK'
}

# Register different quality bit standards.
QA_INTERPRETATIONS = {}

# These are specs for TA1 processed data
QA_BIT = {
    'clear': 1 << 0,
    'cloud': 1 << 1,
    'cloud_adj': 1 << 2,
    'shadow': 1 << 3,
    'snow': 1 << 4,
    'water': 1 << 5,
}

QA_INTERPRETATIONS['FMASK'] = {
    'clear': 0,
    'water': 1,
    'cloud_shadow': 2,
    'snow': 3,
    'cloud': 4,
    'no_obs': 255,
}
QUALITY_BIT_INTERPRETATIONS = {}


@profile
def prepare_kwcoco_main(cmdline=1, **kwargs):
    """_summary_

    Args:
        cmdline (int, optional): _description_. Defaults to 1.

    Ignore:
        python -m watch.tasks.cold.prepare_kwcoco --help
        TEST_COLD=1 xdoctest -m watch.tasks.cold.prepare_kwcoco prepare_kwcoco_main

    Example:
        >>> # xdoctest: +REQUIRES(env:TEST_COLD)
        >>> from watch.tasks.cold.prepare_kwcoco import prepare_kwcoco_main
        >>> from watch.tasks.cold.prepare_kwcoco import *
        >>> kwargs= dict(
        >>>   coco_fpath = ub.Path('/home/jws18003/data/dvc-repos/smart_data_dvc/Drop6/data_vali_split1_KR_R001.kwcoco.json'),
        >>>   out_dpath = ub.Path.appdir('/gpfs/scratchfs1/zhz18039/jws18003/kwcoco'),
        >>>   sensors = 'L8,S2',
        >>>   adj_cloud = False,
        >>>   method = None,
        >>> )
        >>> cmdline=0
        >>> prepare_kwcoco_main(cmdline, **kwargs)
    """
    pman = kwargs.pop('pman', None)
    config = PrepareKwcocoConfig.cli(cmdline=cmdline, data=kwargs)
    coco_fpath = config['coco_fpath']
    dpath = ub.Path(config['out_dpath']).ensuredir()
    sensors = config['sensors']
    adj_cloud = config['adj_cloud']
    method = config['method']
    out_dir = dpath / 'stacked'
    workers = config['workers']
    resolution = config.resolution
    meta_fpath = stack_kwcoco(coco_fpath, out_dir, sensors, adj_cloud, method,
                              pman, workers, resolution)
    return meta_fpath


# function for decoding HLS qa band
def qa_decoding(qa_array):
    """
    This function is modified from qabitval_array_HLS function
    (https://github.com/GERSL/pycold/blob/c5b380eccc2916e5c3aec0bbd2b1982e114b75b1/src/python/pycold/imagetool/prepare_ard.py#L74)
    """
    unpacked = np.full(qa_array.shape, QA_INTERPRETATIONS['FMASK']['clear'])

    QA_CLOUD_unpacked = geek.bitwise_and(qa_array, QA_BIT['cloud'])
    QA_CLOUD_ADJ = geek.bitwise_and(qa_array, QA_BIT['cloud_adj'])
    QA_SHADOW_unpacked = geek.bitwise_and(qa_array, QA_BIT['shadow'])
    QA_SNOW_unpacked = geek.bitwise_and(qa_array, QA_BIT['snow'])
    QA_WATER_unpacked = geek.bitwise_and(qa_array, QA_BIT['water'])

    unpacked[QA_WATER_unpacked > 0] = QA_INTERPRETATIONS['FMASK']['water']
    unpacked[QA_SNOW_unpacked > 0] = QA_INTERPRETATIONS['FMASK']['snow']
    unpacked[QA_SHADOW_unpacked >
             0] = QA_INTERPRETATIONS['FMASK']['cloud_shadow']
    unpacked[QA_CLOUD_ADJ > 0] = QA_INTERPRETATIONS['FMASK']['cloud']
    unpacked[QA_CLOUD_unpacked > 0] = QA_INTERPRETATIONS['FMASK']['cloud']
    unpacked[qa_array == QA_INTERPRETATIONS['FMASK']
             ['no_obs']] = QA_INTERPRETATIONS['FMASK']['no_obs']

    return unpacked


def qa_decoding_no_boundary(qa_array):
    """
    This function is modified from qabitval_array_HLS function
    (https://github.com/GERSL/pycold/blob/c5b380eccc2916e5c3aec0bbd2b1982e114b75b1/src/python/pycold/imagetool/prepare_ard.py#L74)
    """
    unpacked = np.full(qa_array.shape, QA_INTERPRETATIONS['FMASK']['clear'])

    QA_CLOUD_unpacked = geek.bitwise_and(qa_array, QA_BIT['cloud'])
    QA_CLOUD_ADJ = geek.bitwise_and(qa_array, QA_BIT['cloud_adj'])
    QA_SHADOW_unpacked = geek.bitwise_and(qa_array, QA_BIT['shadow'])
    QA_SNOW_unpacked = geek.bitwise_and(qa_array, QA_BIT['snow'])
    QA_WATER_unpacked = geek.bitwise_and(qa_array, QA_BIT['water'])

    unpacked[QA_WATER_unpacked > 0] = QA_INTERPRETATIONS['FMASK']['water']
    unpacked[QA_SNOW_unpacked > 0] = QA_INTERPRETATIONS['FMASK']['snow']
    unpacked[QA_SHADOW_unpacked >
             0] = QA_INTERPRETATIONS['FMASK']['cloud_shadow']
    unpacked[QA_CLOUD_unpacked > 0] = QA_INTERPRETATIONS['FMASK']['cloud']
    unpacked[QA_CLOUD_ADJ > 0] = QA_INTERPRETATIONS['FMASK']['clear']
    unpacked[qa_array == QA_INTERPRETATIONS['FMASK']
             ['no_obs']] = QA_INTERPRETATIONS['FMASK']['no_obs']

    return unpacked


def setup_logging():
    # TODO: handle HPC things here in addition to stdout for doctests
    logging.basicConfig(level='INFO')


##########################################################################
#                   Functions for Artificial Surface Index (ASI)                   #
#  See original code: https://github.com/GERSL/ASI_py/blob/main/ASI_standalone.py  #
##########################################################################

def hist_cut(band, mask, fill_value=-9999, k=3, minmax='std'):
    if minmax == 'std':
        mean = band[mask].mean()
        std = band[mask].std()
        low_val = (mean - k * std)
        high_val = (mean + k * std)
    else:
        low_val, high_val = minmax  # use specified value range.
    is_low = band < low_val
    is_high = band > high_val
    mask_invalid_index = is_low | is_high
    band[mask_invalid_index] = fill_value
    return band, ~mask_invalid_index


def minmax_norm(band, mask, fill_value=-9999):
    max_val = band[mask].max()
    min_val = band[mask].min()
    extent = max_val - min_val
    if extent != 0:
        shifted = band - min_val
        scaled = shifted / extent
        band[mask] = scaled[mask]
    band[~mask] = fill_value
    return band


# Artificial Surface Index (ASI) is designed based the surface reflectance
# imagery of Landsat 8.
def artificial_surface_index(
        Blue, Green, Red, NIR, SWIR1, SWIR2, Scale, MaskValid_Obs, fillV):
    # The calculation chain.

    # Artificial surface Factor (AF).
    AF = (NIR - Blue) / (NIR + Blue) + 0.000001
    AF, MaskValid_AF = hist_cut(AF, MaskValid_Obs, fillV, 6, [-1, 1])
    MaskValid_AF_U = MaskValid_AF & MaskValid_Obs
    AF_Norm = minmax_norm(AF, MaskValid_AF_U, fillV)

    # Vegetation Suppressing Factor (VSF).
    # Modified Soil Adjusted Vegetation Index (MSAVI).
    MSAVI = ((2 * NIR + 1 * Scale) -
             np.sqrt((2 * NIR + 1 * Scale)**2 - 8 * (NIR - Red))) / 2
    MSAVI, MaskValid_MSAVI = hist_cut(MSAVI, MaskValid_Obs, fillV, 6, [-1, 1])
    NDVI = (NIR - Red) / (NIR + Red) + 0.000001
    NDVI, MaskValid_NDVI = hist_cut(NDVI, MaskValid_Obs, fillV, 6, [-1, 1])
    VSF = 1 - MSAVI * NDVI
    MaskValid_VSF = MaskValid_MSAVI & MaskValid_NDVI & MaskValid_Obs
    VSF_Norm = minmax_norm(VSF, MaskValid_VSF, fillV)

    # Soil Suppressing Factor (SSF).
    # Derive the Modified Bare soil Index (MBI).
    MBI = (SWIR1 - SWIR2 - NIR) / (SWIR1 + SWIR2 + NIR) + 0.5
    MBI, MaskValid_MBI = hist_cut(MBI, MaskValid_Obs, fillV, 6, [-0.5, 1.5])
    # Deriving Enhanced-MBI based on MBI and MNDWI.
    MNDWI = (Green - SWIR1) / (Green + SWIR1) + 0.000001
    MNDWI, MaskValid_MNDWI = hist_cut(MNDWI, MaskValid_Obs, fillV, 6, [-1, 1])
    EMBI = ((MBI + 0.5) - (MNDWI + 1)) / ((MBI + 0.5) + (MNDWI + 1))
    EMBI, MaskValid_EMBI = hist_cut(EMBI, MaskValid_Obs, fillV, 6, [-1, 1])
    # Derive SSF.
    SSF = (1 - EMBI)
    MaskValid_SSF = MaskValid_MBI & MaskValid_MNDWI & MaskValid_EMBI & MaskValid_Obs
    SSF_Norm = minmax_norm(SSF, MaskValid_SSF, fillV)

    # Modulation Factor (MF).
    MF = (Blue + Green - NIR - SWIR1) / (Blue + Green + NIR + SWIR1) + 0.000001
    MF, MaskValid_MF = hist_cut(MF, MaskValid_Obs, fillV, 6, [-1, 1])
    MaskValid_MF_U = MaskValid_MF & MaskValid_Obs
    MF_Norm = minmax_norm(MF, MaskValid_MF_U, fillV)

    # Derive Artificial Surface Index (ASI).
    ASI = AF_Norm * SSF_Norm * VSF_Norm * MF_Norm
    MaskValid_ASI = MaskValid_AF_U & MaskValid_VSF & MaskValid_SSF & MaskValid_MF_U & MaskValid_Obs
    ASI[~MaskValid_ASI] = fillV

    return ASI


def stack_kwcoco(coco_fpath, out_dir, sensors, adj_cloud, method, pman=None,
                 workers=0, resolution=None):
    """
    Args:
        coco_fpath (str | PathLike | CocoDataset):
            the kwcoco dataset to convert

        out_dir (str | PathLike): path to write the data

    Returns:
        List[Dict]: a list of dictionary result objects

    Example:
        >>> # xdoctest: +SKIP
        >>> # TODO: readd this doctest
        >>> from pycold.imagetool.prepare_kwcoco import *  # NOQA
        >>> setup_logging()
        >>> coco_dset = watch.coerce_kwcoco('watch-msi')
        >>> coco_fpath = coco_dset.fpath
        >>> #coco_fpath = grab_demo_kwcoco_dataset()
        >>> dpath = ub.Path.appdir('pycold/tests/stack_kwcoco').ensuredir()
        >>> out_dir = dpath / 'stacked'
        >>> results = stack_kwcoco(coco_fpath, out_dir)
    """
    # TODO: configure
    out_dir = ub.Path(out_dir)

    # Load the kwcoco dataset
    dset = kwcoco.CocoDataset.coerce(coco_fpath)

    # Get all images ids sorted in temporal order per video
    all_images = dset.images(list(ub.flatten(dset.videos().images)))

    # For now, it supports only L8
    flags = [s in {'L8'} for s in all_images.lookup('sensor_coarse')]
    # flags = [s in sensors for s in all_images.lookup('sensor_coarse')]
    all_images = all_images.compress(flags)

    image_id_iter = iter(all_images)

    # For now, it supports only L8
    jobs = ub.JobPool(mode='process', max_workers=workers)
    if pman is not None:
        image_id_iter = pman.progiter(image_id_iter, desc='submit prepare jobs', transient=True)
    for image_id in image_id_iter:
        coco_image: kwcoco.CocoImage = dset.coco_image(image_id)
        coco_image = coco_image.detach()
        # Transform the image data into the desired block structure.
        jobs.submit(process_one_coco_image,
                    coco_image, out_dir, adj_cloud, method, resolution)

    job_iter = jobs.as_completed()
    if pman is not None:
        job_iter = pman.progiter(job_iter, desc='collect prepare jobs')

    for job in job_iter:
        meta_fpath = job.result()

    return meta_fpath


@profile
def process_one_coco_image(coco_image, out_dir, adj_cloud, method, resolution):
    """
    Args:
        coco_image (kwcoco.CocoImage): the image to process

        out_dir (Path): path to write the image data

        resolution (str | None): resolution to process at (e.g. 30GSD).

    Returns:
        Dict: result dictionary with keys:
            status (str) : either a string passed or failed
            fpaths (List[str]): a list of files that were written
    """
    n_block_x = 20
    n_block_y = 20
    is_partition = True  # hard coded

    # Use the COCO name as a unique filename id.
    image_name = coco_image.img.get('name', None)
    video_name = coco_image.video.get('name', None)
    if image_name is None:
        image_name = 'img_{:06d}'.format(coco_image.img['id'])
    if video_name is None:
        video_name = 'vid_{:06d}'.format(coco_image.video['id'])

    # Preconstruct the file names used in the inner loops
    fname_json = (image_name + '.json')
    fname_npy = (image_name + '.npy')

    video_dpath = (out_dir / video_name).ensuredir()

    # Other relevant coco metadata
    date_captured = coco_image.img['date_captured']
    ordinal_date = datetime_cls.strptime(
        date_captured[:10], '%Y-%m-%d').toordinal()
    # frame_index = coco_image.img['frame_index']
    n_cols = coco_image.img['width']
    n_rows = coco_image.img['height']
    # Determine what sensor the image is from.
    # Note: if kwcoco needs to register more fine-grained sensor
    # information we can do that.
    sensor = coco_image.img['sensor_coarse']
    # assert sensor == 'L8', 'MWE only supports landsat-8 for now'

    # Given the sensor, determine what the intensity and quality band
    # we should request are.
    sensor_info = SENSOR_TO_INFO[sensor]
    intensity_channels = sensor_info['intensity_channels']
    quality_channels = sensor_info['quality_channels']
    quality_interpretation = sensor_info['quality_interpretation']
    quality_bits = QA_INTERPRETATIONS[quality_interpretation]
    # Specify how we are going to handle spatial resampling and nodata
    delay_kwargs = {
        'nodata_method': None,
        'space': 'video',
        'resolution': resolution,
    }

    # Construct delayed images. These represent a tree of image
    # operations that will resample the image at the desired resolution
    # as well as align it with other images in the sequence.
    # NOTE: Issue occurs when setting resolution argument in imdelay
    delayed_im = coco_image.imdelay(channels=intensity_channels, **delay_kwargs)
    delayed_qa = coco_image.imdelay(channels=quality_channels, **delay_kwargs)

    # Check what shape the data would be loaded with if we finalized right now.
    h, w = delayed_im.shape[0:2]
    # Determine if padding is necessary to properly break the data into blocks.
    padded_w = int(np.ceil(w / n_block_x) * n_block_x)
    padded_h = int(np.ceil(h / n_block_y) * n_block_y)

    if padded_w != w or padded_h != h:
        # cropping using an oversized slice with clip=False and wrap=False is
        # equivalent to padding. In the future a more efficient pad operation
        # where the padding value can be specified will be added, but this will
        # work well enough for now.
        slice_ = (slice(0, padded_h), slice(0, padded_w))
        delayed_im = delayed_im.crop(slice_, clip=False, wrap=False)
        delayed_qa = delayed_qa.crop(slice_, clip=False, wrap=False)

    # It is important that the categorical QA band is not interpolated or
    # antialiased, whereas the intensity bands should be.
    qa_data = delayed_qa.finalize(interpolation='nearest', antialias=False)
    # Decoding QA band
    if adj_cloud:
        qa_unpacked = qa_decoding(qa_data)
    else:
        qa_unpacked = qa_decoding_no_boundary(qa_data)

    # First check the quality bands before loading all of the image data.
    # FIXME: the quality bits in this example are wrong.
    # Setting the threshold to zero to bypass for now.
    clear_threshold = 0
    if clear_threshold > 0:
        clear_bits = functools.reduce(
            operator.or_, ub.take(quality_bits, ['clear_land', 'clear_water']))
        noobs_bits = functools.reduce(
            operator.or_, ub.take(quality_bits, ['no_observation']))
        is_clear = (qa_data & clear_bits) > 0
        is_noobs = (qa_data & noobs_bits) > 0
        is_obs = ~is_noobs
        is_obs_clear = is_clear & is_obs
        clear_ratio = is_obs_clear.sum() / is_obs.sum()
    else:
        clear_ratio = 1

    if clear_ratio <= clear_threshold:
        logger.warn('Not enough clear observations for {}/{}'.format(
            video_name, image_name))
        return

    im_data = delayed_im.finalize(interpolation='cubic', antialias=True)

    # Zero padding affects margin block not to process COLD, leading missing npy in the second step.
    # To avoid this issue, filled with fake data in the padding area.
    # This will fix predict script running forever without raising error.
    # This issue will be addressed in export_cold_result_kwcoco.py, not here
    # padding_value_col = im_data[:, :padded_w - w]
    # im_data[:, w:] = padding_value_col
    # padding_value_row = im_data[:padded_h - h, :]
    # im_data[h:] = padding_value_row

    if method == 'ASI':
        Scale = 10000
        fill_value = 0
        B1 = im_data[:, :, 0]
        B2 = im_data[:, :, 1]
        B3 = im_data[:, :, 2]
        B4 = im_data[:, :, 3]
        B5 = im_data[:, :, 4]
        B6 = im_data[:, :, 5]
        MaskValid_Obs = ((B1 > 0) & (B1 < 1 * Scale) &
                         (B2 > 0) & (B2 < 1 * Scale) &
                         (B3 > 0) & (B3 < 1 * Scale) &
                         (B4 > 0) & (B4 < 1 * Scale) &
                         (B5 > 0) & (B5 < 1 * Scale) &
                         (B6 > 0) & (B6 < 1 * Scale)
                         )

        # Calculating ASI
        ASI = artificial_surface_index(
            B1.astype(
                np.float32), B2.astype(
                np.float32), B3.astype(
                np.float32), B4.astype(
                    np.float32), B5.astype(
                        np.float32), B6.astype(
                            np.float32), Scale, MaskValid_Obs, fill_value)
        # Get land mask.
        MNDWI = (B2 - B5) / (B2 + B5)
        MNDWI, MaskValid_MNDWI = hist_cut(
            MNDWI, MaskValid_Obs, fill_value, 6, [-1, 1])
        # Water threshold for MNDWI (may need to be adjusted for different
        # study areas).
        Water_Th = 0
        MaskLand = (MNDWI < Water_Th)

        # Convert dtype from float32 to int16
        ASI = ASI * Scale
        ASI = ASI.astype('int16')
        ASI[ASI == 0] = fill_value

        # Exclude water pixels.
        ASI[~MaskLand] = fill_value
        ASI = ASI.reshape(ASI.shape[0], ASI.shape[1], 1)
        false_band = np.full((ASI.shape[0], ASI.shape[1], 1), 0)
        # input for Hybrid-COLD (with ASI) = B2, B3, B4, B5, B6, ASI
        data = np.concatenate(
            [im_data[:, :, 1:6], ASI, false_band, qa_unpacked], axis=2)

    else:
        data = np.concatenate([im_data, qa_unpacked], axis=2)

    result_fpaths = []

    metadata = {
        'image_name': image_name,
        'date_captured': date_captured,
        'ordinal_date': ordinal_date,
        'region_id': video_name,
        'n_cols': n_cols,
        'n_rows': n_rows,
        'video_w': w,
        'video_h': h,
        'padded_n_cols': padded_w,
        'padded_n_rows': padded_h,
        'n_block_x': n_block_x,
        'n_block_y': n_block_y,
        'adj_cloud': adj_cloud,
        'method': method
    }

    if is_partition:
        bw = int(padded_w / n_block_x)  # width of a block
        bh = int(padded_h / n_block_y)  # height of a block

        # Use einops to rearrange the data into blocks
        # Question: would using numpy strided tricks be faster?
        blocks = einops.rearrange(
            data, '(nby bh) (nbx bw) c -> nbx nby bh bw c', bw=bw, bh=bh)

        # FIXME: Disable skipping until QA bands are handled correctly
        SKIP_BLOCKS_WITH_QA = False

        for i, j in it.product(range(n_block_y), range(n_block_x)):
            block = blocks[i, j]
            bh, bw = block.shape[0:2]

            if SKIP_BLOCKS_WITH_QA:
                # check if no valid pixels in the chip, then eliminate
                qa_unique = np.unique(block[..., -1])
                qa_unique
                # skip blocks are all cloud, shadow or filled values in DHTC,
                # we also don't need to save pixel that has qa value of
                # 'QA_CLOUD', 'QA_SHADOW', or FILLED value (255)
                if ... and False:
                    continue

            block_dname = 'block_x{}_y{}'.format(i + 1, j + 1)
            block_dpath = video_dpath / block_dname
            block_fpath = block_dpath / fname_npy

            metadata.update({
                'x': i + 1,
                'y': j + 1,
                'total_pixels': bw * bh,
                'total_bands': int(block.shape[-1]),
            })
            if not block_fpath.exists():
                block_dpath.mkdir(exist_ok=True)
                meta_fpath = block_dpath / fname_json
                meta_fpath.write_text(json.dumps(metadata))
                np.save(block_fpath, block)
                result_fpaths.append(block_fpath)
                result_fpaths.append(meta_fpath)
        logger.info(
            'Stacked blocked image {}/{}'.format(video_name, image_name))
    else:
        # FIXME: seems broken. Comment on why and raise a NotImplementedError
        # if this is true.

        metadata.update({
            'total_pixels': int(np.prod(data.shape[0:2])),
            'total_bands': int(data.shape[-1]),
        })

        full_fpath = video_dpath / fname_npy
        if not block_fpath.exists():
            meta_fpath = video_dpath / fname_json
            meta_fpath.write_text(json.dumps(metadata))
            np.save(full_fpath, data)
            result_fpaths.append(full_fpath)
            result_fpaths.append(meta_fpath)
            logger.info(
                'Stacked full image {}/{}'.format(video_name, image_name))

    # FIXME: It seems strange that we return one of the metadata paths for just
    # one of the blocks instead of having a metadata file for all blocks.
    meta_fpath = block_dpath / (image_name + '.json')
    return meta_fpath


if __name__ == '__main__':
    prepare_kwcoco_main()
