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from multiscale_spatial_image import skip_non_dimension_nodes | ||
import numpy as np | ||
from spatial_image import to_spatial_image | ||
from multiscale_spatial_image import to_multiscale | ||
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def test_skip_nodes(): | ||
data = np.zeros((2, 200, 200)) | ||
dims = ("c", "y", "x") | ||
scale_factors = [2, 2] | ||
image = to_spatial_image(array_like=data, dims=dims) | ||
multiscale_img = to_multiscale(image, scale_factors=scale_factors) | ||
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@skip_non_dimension_nodes | ||
def transpose(ds, *args, **kwargs): | ||
return ds.transpose(*args, **kwargs) | ||
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for scale in list(multiscale_img.keys()): | ||
assert multiscale_img[scale]["image"].dims == ("c", "y", "x") | ||
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# applying this function without skipping the root node would fail as the root node does not have dimensions. | ||
result = multiscale_img.map_over_datasets(transpose, "y", "x", "c") | ||
for scale in list(result.keys()): | ||
assert result[scale]["image"].dims == ("y", "x", "c") |