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14 changes: 9 additions & 5 deletions README.md
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Expand Up @@ -8,19 +8,23 @@

---

A lean and kind Open Microscopy Environment (OME) Next Generation File Format
(NGFF) Zarr implementation.
A lean and kind
[Open Microscopy Environment (OME) Next Generation File Format (NGFF) Zarr](https://ngff.openmicroscopy.org)
implementation.

## ✨ Features

- Minimal dependencies
- Work with arbitrary Zarr store types
- Lazy, parallel, and web ready -- no local filesystem required
- Process extremely large datasets
- Conversion of most bioimaging file formats
- Multiple downscaling methods
- Supports Python>=3.8
- Implements version 0.4 of the
[OME-Zarr NGFF specification](https://github.com/ome/ngff)
- Supports Python>=3.9
- Reads OME-Zarr v0.1 to v0.5 into simple Python data classes with Dask arrays
- Optional OME-Zarr data model validation during reading
- Writes OME-Zarr v0.4 to v0.5
- Optional writing via [tensorstore](https://google.github.io/tensorstore/)

## Documentation

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7 changes: 5 additions & 2 deletions docs/index.md
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Expand Up @@ -16,10 +16,13 @@ A lean and kind
- Work with arbitrary Zarr store types
- Lazy, parallel, and web ready -- no local filesystem required
- Process extremely large datasets
- Conversion of most bioimaging file formats
- Multiple downscaling methods
- Supports Python>=3.9
- Implements version 0.4 of the
[OME-Zarr NGFF specification](https://github.com/ome/ngff)
- Reads OME-Zarr v0.1 to v0.5 into simple Python data classes with Dask arrays
- Optional OME-Zarr data model validation during reading
- Writes OME-Zarr v0.4 to v0.5
- Optional writing via [tensorstore](https://google.github.io/tensorstore/)

```{toctree}
:maxdepth: 2
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24 changes: 0 additions & 24 deletions test/test_ngff_validation.py
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Expand Up @@ -69,27 +69,3 @@ def test_validate_0_3():
def test_validate_0_3_no_version():
test_store = Path(__file__).parent / "data" / "input" / "v03" / "9528933.zarr"
from_ngff_zarr(test_store, validate=True)


# def test_z_y_x_valid_ngff():
# array = np.random.random((32, 32, 16))
# image = to_spatial_image(array)
# multiscale = to_multiscale(image, [2, 4])

# check_valid_ngff(multiscale)


# def test_z_y_x_c_valid_ngff():
# array = np.random.random((32, 32, 16, 3))
# image = to_spatial_image(array)
# multiscale = to_multiscale(image, [2, 4])

# check_valid_ngff(multiscale)


# def test_t_z_y_x_c_valid_ngff():
# array = np.random.random((2, 32, 32, 16, 3))
# image = to_spatial_image(array)
# multiscale = to_multiscale(image, [2, 4])

# check_valid_ngff(multiscale)
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