<filename>libs/dscache/odc/dscache/apps/dstiler.py
import click
from odc import dscache
from odc.dscache.tools.tiling import bin_by_native_tile, web_gs, extract_native_albers_tile
from datacube.model import GridSpec
import datacube.utils.geometry as geom
from odc.index import bin_dataset_stream

GS_ALBERS = GridSpec(crs=geom.CRS('EPSG:3577'),
                     tile_size=(100000.0, 100000.0),
                     resolution=(-25, 25))


@click.command('dstiler')
@click.option('--native', is_flag=True, help='Use Landsat Path/Row as grouping')
@click.option('--native-albers', is_flag=True, help='When datasets are in Albers grid already')
@click.option('--web', type=int, help='Use web map tiling regime at supplied zoom level')
@click.argument('dbfile', type=str, nargs=1)
def cli(native, native_albers, web, dbfile):
    """Add spatial grouping to file db.

    Default grid is Australian Albers (EPSG:3577) with 100k by 100k tiles. But
    you can also group by Landsat path/row (--native), or Google's map tiling
    regime (--web zoom_level)

    """
    cache = dscache.open_rw(dbfile)
    label = 'Processing {} ({:,d} datasets)'.format(dbfile, cache.count)

    if native:
        group_key_fmt = 'native/{:03d}_{:03d}'
        binner = bin_by_native_tile
    elif native_albers:
        group_key_fmt = 'albers/{:03d}_{:03d}'
        binner = lambda dss: bin_by_native_tile(dss, native_tile_id=extract_native_albers_tile)
    elif web is not None:
        gs = web_gs(web)
        group_key_fmt = 'web_' + str(web) + '/{:03d}_{:03d}'
        binner = lambda dss: bin_dataset_stream(gs, dss)
    else:
        group_key_fmt = 'albers/{:03d}_{:03d}'
        binner = lambda dss: bin_dataset_stream(GS_ALBERS, dss)

    with click.progressbar(cache.get_all(), length=cache.count, label=label) as dss:
        bins = binner(dss)

    click.echo('Total bins: {:d}'.format(len(bins)))

    with click.progressbar(bins.values(), length=len(bins), label='Saving') as groups:
        for group in groups:
            k = group_key_fmt.format(*group.idx)
            cache.put_group(k, group.dss)


if __name__ == '__main__':
    cli()

<filename>libs/dscache/odc/dscache/apps/dstiler.py
import click
from odc import dscache
from odc.dscache.tools.tiling import bin_by_native_tile, web_gs, extract_native_albers_tile
from datacube.model import GridSpec
import datacube.utils.geometry as geom
from odc.index import bin_dataset_stream

GS_ALBERS = GridSpec(crs=geom.CRS('EPSG:3577'),
                     tile_size=(100000.0, 100000.0),
                     resolution=(-25, 25))


@click.command('dstiler')
@click.option('--native', is_flag=True, help='Use Landsat Path/Row as grouping')
@click.option('--native-albers', is_flag=True, help='When datasets are in Albers grid already')
@click.option('--web', type=int, help='Use web map tiling regime at supplied zoom level')
@click.argument('dbfile', type=str, nargs=1)
def cli(native, native_albers, web, dbfile):
    """Add spatial grouping to file db.

    Default grid is Australian Albers (EPSG:3577) with 100k by 100k tiles. But
    you can also group by Landsat path/row (--native), or Google's map tiling
    regime (--web zoom_level)

    """
    cache = dscache.open_rw(dbfile)
    label = 'Processing {} ({:,d} datasets)'.format(dbfile, cache.count)

    if native:
        group_key_fmt = 'native/{:03d}_{:03d}'
        binner = bin_by_native_tile
    elif native_albers:
        group_key_fmt = 'albers/{:03d}_{:03d}'
        binner = lambda dss: bin_by_native_tile(dss, native_tile_id=extract_native_albers_tile)
    elif web is not None:
        gs = web_gs(web)
        group_key_fmt = 'web_' + str(web) + '/{:03d}_{:03d}'
        binner = lambda dss: bin_dataset_stream(gs, dss)
    else:
        group_key_fmt = 'albers/{:03d}_{:03d}'
        binner = lambda dss: bin_dataset_stream(GS_ALBERS, dss)

    with click.progressbar(cache.get_all(), length=cache.count, label=label) as dss:
        bins = binner(dss)

    click.echo('Total bins: {:d}'.format(len(bins)))

    with click.progressbar(bins.values(), length=len(bins), label='Saving') as groups:
        for group in groups:
            k = group_key_fmt.format(*group.idx)
            cache.put_group(k, group.dss)


if __name__ == '__main__':
    cli()

Add spatial grouping to file db.      Default grid is Australian Albers (EPSG:3577) with 100k by 100k tiles. But     you can also group by Landsat path/row (--native), or Google's map tiling     regime (--web zoom_level)