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Prep for 0.4.0-rc2 (#206)
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.gitignore

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@@ -45,7 +45,7 @@ pip-selfcheck.json
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage*
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.cache
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nosetests.xml
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coverage.xml

Cargo.toml

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[package]
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name = "acquire-imaging"
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authors = ["Nathan Clack <[email protected]>"]
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version = "0.4.0-rc1"
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version = "0.4.0-rc2"
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edition = "2021"
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[lib]

drivers.json

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{
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"acquire-driver-zarr": "0.1.12",
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"acquire-driver-zarr": "0.1.13",
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"acquire-driver-egrabber": "0.1.5",
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"acquire-driver-hdcam": "0.1.9",
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"acquire-driver-spinnaker": "0.1.1",

tests/test_zarr.py

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from typing import Optional
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import dotenv
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import numpy as np
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import pytest
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import s3fs
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import zarr
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from dask import array as da
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from numcodecs import blosc as blosc
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from ome_zarr.io import parse_url
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from ome_zarr.reader import Reader
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from skimage.transform import downscale_local_mean
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import acquire
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from acquire import Runtime, DeviceKind
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data = [
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da.from_zarr(uri, component=str(i)) for i in range(len(zgroup.data))
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]
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assert len(data) == 3
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image = data[0][0, :, :].compute() # convert dask array to numpy array
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for d in data:
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assert (
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np.linalg.norm(image - d[0, :, :].compute()) == 0
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) # validate against the same method from scikit-image
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image = downscale_local_mean(image, (2, 2)).astype(np.uint8)
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assert len(data) == 3 # 3 layers
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@pytest.mark.parametrize(

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