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Add docs for CLI (#285)
* Add docs for CLI * Fix spelling * Delete info.txt * Re-enable tutorial execution
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‎docs/cli.md‎

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# Command line interface
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`ome-zarr-models` has a command line interface.
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To see available commands,
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```sh
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ome-zarr-models --help
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```
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```
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usage: ome-zarr-models [-h] [--version] COMMAND ...
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OME-Zarr Models CLI
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positional arguments:
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COMMAND Available commands
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validate Validate an OME-Zarr
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info Get information about an OME-Zarr group
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options:
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-h, --help show this help message and exit
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--version show program's version number and exit
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```
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## Validation
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To validate a OME-Zarr group, pass the path to the group to `ome-zarr-models validate`, for example
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```sh
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ome-zarr-models validate https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.5/idr0066/ExpD_chicken_embryo_MIP.ome.zarr
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```
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```
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✅ Valid OME-Zarr
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```
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The group can be specified as any string that can be parsed by **link to zarr.open_group**.
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## Info
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To get information about an OME-Zarr group, pass the path to a group to `ome-zarr-models info`.
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This will print the metadata (see below for an example).
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If you have the `rich` Python package installed, a more readable output will be produced.
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```sh
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ome-zarr-models info https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.5/idr0066/ExpD_chicken_embryo_MIP.ome.zarr
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```
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```
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ImageLabel(
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zarr_format=3,
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node_type='group',
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attributes=BaseZarrAttrs[ImageLabelAttrs](
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ome=ImageLabelAttrs(
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version='0.5',
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image_label=None,
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multiscales=[
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Multiscale(
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axes=[Axis(name='y', type='space', unit='micrometer'), Axis(name='x', type='space', unit='micrometer')],
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datasets=(
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Dataset(path='0', coordinateTransformations=(VectorScale(type='scale', scale=[1.6, 1.6]),)),
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Dataset(path='1', coordinateTransformations=(VectorScale(type='scale', scale=[3.2, 3.2]),)),
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Dataset(path='2', coordinateTransformations=(VectorScale(type='scale', scale=[6.4, 6.4]),)),
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Dataset(path='3', coordinateTransformations=(VectorScale(type='scale', scale=[12.8, 12.8]),)),
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Dataset(path='4', coordinateTransformations=(VectorScale(type='scale', scale=[25.6, 25.6]),)),
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Dataset(path='5', coordinateTransformations=(VectorScale(type='scale', scale=[51.2, 51.2]),)),
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Dataset(path='6', coordinateTransformations=(VectorScale(type='scale', scale=[102.4, 102.4]),)),
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Dataset(path='7', coordinateTransformations=(VectorScale(type='scale', scale=[204.8, 204.8]),))
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),
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coordinateTransformations=None,
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metadata=None,
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name='/',
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type=None
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)
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],
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_creator={'name': 'ome2024-ngff-challenge', 'version': '1.0.2', 'notes': None},
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omero={
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'channels': [
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{
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'active': True,
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'coefficient': 1.0,
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'color': 'FFFFFF',
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'family': 'linear',
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'inverted': False,
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'label': 'Cy3',
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'window': {'end': 55.0, 'max': 255.0, 'min': 0.0, 'start': 0.0}
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}
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],
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'id': 1,
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'rdefs': {'defaultT': 0, 'defaultZ': 0, 'model': 'greyscale'}
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}
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)
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),
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members={
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'0': ArraySpec(
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zarr_format=3,
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node_type='array',
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attributes={
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'_ome2024_ngff_challenge_stats': {
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'input': '',
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'output': '',
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'stop': 1729085219.378162,
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'read': 29620797,
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'written': 19813141,
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'elapsed': 2.086225748062134,
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'threads': 16,
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'cpu_count': 16,
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'sched_affinity': 16
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}
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},
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shape=(8978, 6510),
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data_type='uint8',
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chunk_grid={'name': 'regular', 'configuration': {'chunk_shape': (2048, 2048)}},
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chunk_key_encoding={'name': 'default', 'configuration': {'separator': '/'}},
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fill_value=0,
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codecs=(
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{
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'name': 'sharding_indexed',
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'configuration': {
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'chunk_shape': (256, 256),
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'codecs': ({'name': 'bytes'}, {'name': 'blosc', 'configuration': {'typesize': 1, 'cname': 'zstd', 'clevel': 5, 'shuffle': 'bitshuffle', 'blocksize': 0}}),
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'index_codecs': ({'name': 'bytes', 'configuration': {'endian': 'little'}}, {'name': 'crc32c'}),
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'index_location': 'end'
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}
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},
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),
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storage_transformers=(),
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dimension_names=('y', 'x')
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),
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'1': ArraySpec(
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zarr_format=3,
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node_type='array',
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attributes={
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'_ome2024_ngff_challenge_stats': {
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'input': '',
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'written': 5313738,
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'elapsed': 0.4402890205383301,
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'threads': 16,
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'cpu_count': 16,
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'sched_affinity': 16
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}
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},
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shape=(4489, 3255),
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data_type='uint8',
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chunk_grid={'name': 'regular', 'configuration': {'chunk_shape': (2048, 2048)}},
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chunk_key_encoding={'name': 'default', 'configuration': {'separator': '/'}},
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fill_value=0,
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codecs=(
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{
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'name': 'sharding_indexed',
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'configuration': {
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'chunk_shape': (256, 256),
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'codecs': ({'name': 'bytes'}, {'name': 'blosc', 'configuration': {'typesize': 1, 'cname': 'zstd', 'clevel': 5, 'shuffle': 'bitshuffle', 'blocksize': 0}}),
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'index_codecs': ({'name': 'bytes', 'configuration': {'endian': 'little'}}, {'name': 'crc32c'}),
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'index_location': 'end'
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}
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},
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),
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storage_transformers=(),
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dimension_names=('y', 'x')
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),
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'2': ArraySpec(
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zarr_format=3,
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node_type='array',
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attributes={
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'_ome2024_ngff_challenge_stats': {
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'output': '',
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'written': 1412021,
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'threads': 16,
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'cpu_count': 16,
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'sched_affinity': 16
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}
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},
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shape=(2244, 1627),
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data_type='uint8',
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chunk_grid={'name': 'regular', 'configuration': {'chunk_shape': (2048, 2048)}},
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chunk_key_encoding={'name': 'default', 'configuration': {'separator': '/'}},
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fill_value=0,
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codecs=(
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'name': 'sharding_indexed',
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'configuration': {
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'chunk_shape': (256, 256),
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'codecs': ({'name': 'bytes'}, {'name': 'blosc', 'configuration': {'typesize': 1, 'cname': 'zstd', 'clevel': 5, 'shuffle': 'bitshuffle', 'blocksize': 0}}),
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'index_codecs': ({'name': 'bytes', 'configuration': {'endian': 'little'}}, {'name': 'crc32c'}),
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'index_location': 'end'
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}
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},
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),
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storage_transformers=(),
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dimension_names=('y', 'x')
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),
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'3': ArraySpec(
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zarr_format=3,
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node_type='array',
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attributes={
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'_ome2024_ngff_challenge_stats': {
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'input': '',
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'output': '',
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'threads': 16,
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}
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},
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shape=(1122, 813),
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data_type='uint8',
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chunk_grid={'name': 'regular', 'configuration': {'chunk_shape': (2048, 2048)}},
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chunk_key_encoding={'name': 'default', 'configuration': {'separator': '/'}},
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fill_value=0,
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codecs=(
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{
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'name': 'sharding_indexed',
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'configuration': {
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'chunk_shape': (256, 256),
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'codecs': ({'name': 'bytes'}, {'name': 'blosc', 'configuration': {'typesize': 1, 'cname': 'zstd', 'clevel': 5, 'shuffle': 'bitshuffle', 'blocksize': 0}}),
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'index_codecs': ({'name': 'bytes', 'configuration': {'endian': 'little'}}, {'name': 'crc32c'}),
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'index_location': 'end'
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}
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},
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),
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storage_transformers=(),
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dimension_names=('y', 'x')
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),
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'4': ArraySpec(
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zarr_format=3,
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node_type='array',
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attributes={
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'_ome2024_ngff_challenge_stats': {
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'threads': 16,
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'sched_affinity': 16
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}
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},
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shape=(561, 406),
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data_type='uint8',
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chunk_grid={'name': 'regular', 'configuration': {'chunk_shape': (2048, 2048)}},
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chunk_key_encoding={'name': 'default', 'configuration': {'separator': '/'}},
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fill_value=0,
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codecs=(
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{
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'name': 'sharding_indexed',
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'configuration': {
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'chunk_shape': (256, 256),
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'codecs': ({'name': 'bytes'}, {'name': 'blosc', 'configuration': {'typesize': 1, 'cname': 'zstd', 'clevel': 5, 'shuffle': 'bitshuffle', 'blocksize': 0}}),
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'index_codecs': ({'name': 'bytes', 'configuration': {'endian': 'little'}}, {'name': 'crc32c'}),
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'index_location': 'end'
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}
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},
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),
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storage_transformers=(),
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dimension_names=('y', 'x')
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),
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'5': ArraySpec(
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zarr_format=3,
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node_type='array',
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attributes={
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'_ome2024_ngff_challenge_stats': {
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}
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chunk_grid={'name': 'regular', 'configuration': {'chunk_shape': (2048, 2048)}},
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chunk_key_encoding={'name': 'default', 'configuration': {'separator': '/'}},
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fill_value=0,
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codecs=(
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'configuration': {
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'chunk_shape': (256, 256),
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'codecs': ({'name': 'bytes'}, {'name': 'blosc', 'configuration': {'typesize': 1, 'cname': 'zstd', 'clevel': 5, 'shuffle': 'bitshuffle', 'blocksize': 0}}),
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'index_codecs': ({'name': 'bytes', 'configuration': {'endian': 'little'}}, {'name': 'crc32c'}),
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'index_location': 'end'
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}
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},
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),
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storage_transformers=(),
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dimension_names=('y', 'x')
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),
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zarr_format=3,
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'_ome2024_ngff_challenge_stats': {
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chunk_grid={'name': 'regular', 'configuration': {'chunk_shape': (2048, 2048)}},
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chunk_key_encoding={'name': 'default', 'configuration': {'separator': '/'}},
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fill_value=0,
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codecs=(
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'codecs': ({'name': 'bytes'}, {'name': 'blosc', 'configuration': {'typesize': 1, 'cname': 'zstd', 'clevel': 5, 'shuffle': 'bitshuffle', 'blocksize': 0}}),
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'index_codecs': ({'name': 'bytes', 'configuration': {'endian': 'little'}}, {'name': 'crc32c'}),
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'index_location': 'end'
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}
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},
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),
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storage_transformers=(),
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dimension_names=('y', 'x')
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),
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zarr_format=3,
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node_type='array',
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codecs=(
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'configuration': {
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'codecs': ({'name': 'bytes'}, {'name': 'blosc', 'configuration': {'typesize': 1, 'cname': 'zstd', 'clevel': 5, 'shuffle': 'bitshuffle', 'blocksize': 0}}),
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'index_codecs': ({'name': 'bytes', 'configuration': {'endian': 'little'}}, {'name': 'crc32c'}),
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'index_location': 'end'
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}
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},
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),
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storage_transformers=(),
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dimension_names=('y', 'x')
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)
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}
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)
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```

‎mkdocs.yml‎

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nav:
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- Home: index.md
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- Tutorial: tutorial.py
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- Command line: cli.md
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- Python tutorial: tutorial.py
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- How do I...?: how-to.md
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- API reference:
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- api/index.md

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