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Merge pull request #60 from thewtex/hatch
ENH: Migrate to hatch for packaging
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| # SPDX-FileCopyrightText: 2022-present NumFOCUS <info@numfocus.org> | ||
| # | ||
| # SPDX-License-Identifier: MIT | ||
| __version__ = '0.10.0' |
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| """multiscale-spatial-image | ||
| Generate a multiscale spatial image.""" | ||
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| __all__ = [ | ||
| "MultiscaleSpatialImage", | ||
| "Methods", | ||
| "to_multiscale", | ||
| "itk_image_to_multiscale", | ||
| "__version__", | ||
| ] | ||
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| from .__about__ import __version__ | ||
| from .multiscale_spatial_image import MultiscaleSpatialImage, Methods | ||
| from .to_multiscale import to_multiscale, itk_image_to_multiscale |
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| from typing import Union, List | ||
| from enum import Enum | ||
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| import xarray as xr | ||
| from datatree import DataTree | ||
| from datatree.treenode import TreeNode | ||
| import numpy as np | ||
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| class MultiscaleSpatialImage(DataTree): | ||
| """A multi-scale representation of a spatial image. | ||
| This is an xarray DataTree, with content compatible with the Open Microscopy Environment- | ||
| Next Generation File Format (OME-NGFF). | ||
| The tree contains nodes in the form: `scale{scale}` where *scale* is the integer scale. | ||
| Each node has a the same named `Dataset` that corresponds to to the NGFF dataset name. | ||
| For example, a three-scale representation of a *cells* dataset would have `Dataset` nodes: | ||
| scale0 | ||
| scale1 | ||
| scale2 | ||
| """ | ||
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| def __init__( | ||
| self, | ||
| name: str = "multiscales", | ||
| data: Union[xr.Dataset, xr.DataArray] = None, | ||
| parent: TreeNode = None, | ||
| children: List[TreeNode] = None, | ||
| ): | ||
| """DataTree with a root name of *multiscales*.""" | ||
| super().__init__(data=data, name=name, parent=parent, children=children) | ||
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| def to_zarr(self, store, mode: str = "w", encoding=None, **kwargs): | ||
| """ | ||
| Write multi-scale spatial image contents to a Zarr store. | ||
| Metadata is added according the OME-NGFF standard. | ||
| store : MutableMapping, str or Path, optional | ||
| Store or path to directory in file system | ||
| mode : {{"w", "w-", "a", "r+", None}, default: "w" | ||
| Persistence mode: “w” means create (overwrite if exists); “w-” means create (fail if exists); | ||
| “a” means override existing variables (create if does not exist); “r+” means modify existing | ||
| array values only (raise an error if any metadata or shapes would change). The default mode | ||
| is “a” if append_dim is set. Otherwise, it is “r+” if region is set and w- otherwise. | ||
| encoding : dict, optional | ||
| Nested dictionary with variable names as keys and dictionaries of | ||
| variable specific encodings as values, e.g., | ||
| ``{"scale0/image": {"my_variable": {"dtype": "int16", "scale_factor": 0.1}, ...}, ...}``. | ||
| See ``xarray.Dataset.to_zarr`` for available options. | ||
| kwargs : | ||
| Additional keyword arguments to be passed to ``datatree.DataTree.to_zarr`` | ||
| """ | ||
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| multiscales = [] | ||
| scale0 = self[self.groups[1]] | ||
| for name in scale0.ds.data_vars.keys(): | ||
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| ngff_datasets = [] | ||
| for child in self.children: | ||
| image = self[child].ds | ||
| scale_transform = [] | ||
| translate_transform = [] | ||
| for dim in image.dims: | ||
| if len(image.coords[dim]) > 1 and np.issubdtype(image.coords[dim].dtype, np.number): | ||
| scale_transform.append( | ||
| float(image.coords[dim][1] - image.coords[dim][0]) | ||
| ) | ||
| else: | ||
| scale_transform.append(1.0) | ||
| if len(image.coords[dim]) > 0 and np.issubdtype(image.coords[dim].dtype, np.number): | ||
| translate_transform.append(float(image.coords[dim][0])) | ||
| else: | ||
| translate_transform.append(0.0) | ||
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| ngff_datasets.append( | ||
| { | ||
| "path": f"{self[child].name}/{name}", | ||
| "coordinateTransformations": [ | ||
| { | ||
| "type": "scale", | ||
| "scale": scale_transform, | ||
| }, | ||
| { | ||
| "type": "translation", | ||
| "translation": translate_transform, | ||
| }, | ||
| ], | ||
| } | ||
| ) | ||
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| image = scale0.ds | ||
| axes = [] | ||
| for axis in image.dims: | ||
| if axis == "t": | ||
| axes.append({"name": "t", "type": "time"}) | ||
| elif axis == "c": | ||
| axes.append({"name": "c", "type": "channel"}) | ||
| else: | ||
| axes.append({"name": axis, "type": "space"}) | ||
| if "units" in image.coords[axis].attrs: | ||
| axes[-1]["unit"] = image.coords[axis].attrs["units"] | ||
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| multiscales.append( | ||
| { | ||
| "@type": "ngff:Image", | ||
| "version": "0.4", | ||
| "name": name, | ||
| "axes": axes, | ||
| "datasets": ngff_datasets, | ||
| } | ||
| ) | ||
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| # NGFF v0.4 metadata | ||
| ngff_metadata = {"multiscales": multiscales, "multiscaleSpatialImageVersion": 1} | ||
| self.ds = self.ds.assign_attrs(**ngff_metadata) | ||
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| super().to_zarr(store, **kwargs) | ||
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| class Methods(Enum): | ||
| XARRAY_COARSEN = "xarray.DataArray.coarsen" | ||
| ITK_BIN_SHRINK = "itk.bin_shrink_image_filter" | ||
| ITK_GAUSSIAN = "itk.discrete_gaussian_image_filter" | ||
| ITK_LABEL_GAUSSIAN = "itk.discrete_gaussian_image_filter_label_interpolator" | ||
| DASK_IMAGE_GAUSSIAN = "dask_image.ndfilters.gaussian_filter" | ||
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1 change: 0 additions & 1 deletion
1
test/test_multiscale_spatial_image.py → test/test_to_multiscale.py
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| from platform import processor | ||
| import pytest | ||
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| from pathlib import Path | ||
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