| Name | multiscale_spatial_image JSON |
| Version |
2.0.1
JSON |
| download |
| home_page | None |
| Summary | Generate a multiscale, chunked, multi-dimensional spatial image data structure that can be serialized to OME-NGFF. |
| upload_time | 2024-11-08 22:54:13 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.10 |
| license |
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| keywords |
dask
imaging
itk
ngff
ome
visualization
zarr
|
| VCS |
 |
| bugtrack_url |
|
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No requirements were recorded.
|
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|
# multiscale-spatial-image
[](https://github.com/spatial-image/multiscale-spatial-image/actions/workflows/test.yml)
[](https://github.com/spatial-image/multiscale-spatial-image/actions/workflows/notebook-test.yml)
[](https://pypi.python.org/pypi/multiscale_spatial_image/)
[](https://github.com/python/black)
[](https://zenodo.org/badge/latestdoi/379678181)
Generate a multiscale, chunked, multi-dimensional spatial image data structure
that can serialized to [OME-NGFF].
Each scale is a scientific Python [Xarray] [spatial-image] [Dataset], organized
into nodes of an Xarray [Datatree].
## Installation
```sh
pip install multiscale_spatial_image
```
## Usage
```python
import numpy as np
from spatial_image import to_spatial_image
from multiscale_spatial_image import to_multiscale
import zarr
# Image pixels
array = np.random.randint(0, 256, size=(128,128), dtype=np.uint8)
image = to_spatial_image(array)
print(image)
```
An [Xarray] [spatial-image] [DataArray]. Spatial metadata can also be passed
during construction.
```
<xarray.SpatialImage 'image' (y: 128, x: 128)>
array([[114, 47, 215, ..., 245, 14, 175],
[ 94, 186, 112, ..., 42, 96, 30],
[133, 170, 193, ..., 176, 47, 8],
...,
[202, 218, 237, ..., 19, 108, 135],
[ 99, 94, 207, ..., 233, 83, 112],
[157, 110, 186, ..., 142, 153, 42]], dtype=uint8)
Coordinates:
* y (y) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0
* x (x) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0
```
```python
# Create multiscale pyramid, downscaling by a factor of 2, then 4
multiscale = to_multiscale(image, [2, 4])
print(multiscale)
```
A chunked [Dask] Array MultiscaleSpatialImage [Xarray] [Datatree].
```
DataTree('multiscales', parent=None)
├── DataTree('scale0')
│ Dimensions: (y: 128, x: 128)
│ Coordinates:
│ * y (y) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0
│ * x (x) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0
│ Data variables:
│ image (y, x) uint8 dask.array<chunksize=(128, 128), meta=np.ndarray>
├── DataTree('scale1')
│ Dimensions: (y: 64, x: 64)
│ Coordinates:
│ * y (y) float64 0.5 2.5 4.5 6.5 8.5 ... 118.5 120.5 122.5 124.5 126.5
│ * x (x) float64 0.5 2.5 4.5 6.5 8.5 ... 118.5 120.5 122.5 124.5 126.5
│ Data variables:
│ image (y, x) uint8 dask.array<chunksize=(64, 64), meta=np.ndarray>
└── DataTree('scale2')
Dimensions: (y: 16, x: 16)
Coordinates:
* y (y) float64 3.5 11.5 19.5 27.5 35.5 ... 91.5 99.5 107.5 115.5 123.5
* x (x) float64 3.5 11.5 19.5 27.5 35.5 ... 91.5 99.5 107.5 115.5 123.5
Data variables:
image (y, x) uint8 dask.array<chunksize=(16, 16), meta=np.ndarray>
```
Map a function over datasets while skipping nodes that do not contain dimensions
```python
import numpy as np
from spatial_image import to_spatial_image
from multiscale_spatial_image import skip_non_dimension_nodes, to_multiscale
data = np.zeros((2, 200, 200))
dims = ("c", "y", "x")
scale_factors = [2, 2]
image = to_spatial_image(array_like=data, dims=dims)
multiscale = to_multiscale(image, scale_factors=scale_factors)
@skip_non_dimension_nodes
def transpose(ds, *args, **kwargs):
return ds.transpose(*args, **kwargs)
multiscale = multiscale.map_over_datasets(transpose, "y", "x", "c")
print(multiscale)
```
A transposed MultiscaleSpatialImage.
```
<xarray.DataTree>
Group: /
├── Group: /scale0
│ Dimensions: (c: 2, y: 200, x: 200)
│ Coordinates:
│ * c (c) int32 8B 0 1
│ * y (y) float64 2kB 0.0 1.0 2.0 3.0 4.0 ... 196.0 197.0 198.0 199.0
│ * x (x) float64 2kB 0.0 1.0 2.0 3.0 4.0 ... 196.0 197.0 198.0 199.0
│ Data variables:
│ image (y, x, c) float64 640kB dask.array<chunksize=(200, 200, 2), meta=np.ndarray>
├── Group: /scale1
│ Dimensions: (c: 2, y: 100, x: 100)
│ Coordinates:
│ * c (c) int32 8B 0 1
│ * y (y) float64 800B 0.5 2.5 4.5 6.5 8.5 ... 192.5 194.5 196.5 198.5
│ * x (x) float64 800B 0.5 2.5 4.5 6.5 8.5 ... 192.5 194.5 196.5 198.5
│ Data variables:
│ image (y, x, c) float64 160kB dask.array<chunksize=(100, 100, 2), meta=np.ndarray>
└── Group: /scale2
Dimensions: (c: 2, y: 50, x: 50)
Coordinates:
* c (c) int32 8B 0 1
* y (y) float64 400B 1.5 5.5 9.5 13.5 17.5 ... 185.5 189.5 193.5 197.5
* x (x) float64 400B 1.5 5.5 9.5 13.5 17.5 ... 185.5 189.5 193.5 197.5
Data variables:
image (y, x, c) float64 40kB dask.array<chunksize=(50, 50, 2), meta=np.ndarray>
```
Store as an Open Microscopy Environment-Next Generation File Format ([OME-NGFF])
/ [netCDF] [Zarr] store.
It is highly recommended to use `dimension_separator='/'` in the construction of
the Zarr stores.
```python
store = zarr.storage.DirectoryStore('multiscale.zarr', dimension_separator='/')
multiscale.to_zarr(store)
```
**Note**: The API is under development, and it may change until 1.0.0 is
released. We mean it :-).
## Examples
- [](https://mybinder.org/v2/gh/spatial-image/multiscale-spatial-image/main?urlpath=lab/tree/examples%2FHelloMultiscaleSpatialImageWorld.ipynb)
[Hello MultiscaleSpatialImage World!](./examples/HelloMultiscaleSpatialImageWorld.ipynb)
- [](https://mybinder.org/v2/gh/spatial-image/multiscale-spatial-image/main?urlpath=lab/tree/examples%2FConvertITKImage.ipynb)
[Convert itk.Image](./examples/ConvertITKImage.ipynb)
- [](https://mybinder.org/v2/gh/spatial-image/multiscale-spatial-image/main?urlpath=lab/tree/examples%2FConvertImageioImageResource.ipynb)
[Convert imageio ImageResource](./examples/ConvertImageioImageResource.ipynb)
- [](https://mybinder.org/v2/gh/spatial-image/multiscale-spatial-image/main?urlpath=lab/tree/examples%2FConvertPyImageJDataset.ipynb)
[Convert pyimagej Dataset](./examples/ConvertPyImageJDataset.ipynb)
## Development
Contributions are welcome and appreciated.
### Get the source code
```shell
git clone https://github.com/spatial-image/multiscale-spatial-image
cd multiscale-spatial-image
```
### Install dependencies
First install [pixi]. Then, install project dependencies:
```shell
pixi install -a
pixi run pre-commit-install
```
### Run the test suite
The unit tests:
```shell
pixi run -e test test
```
The notebooks tests:
```shell
pixi run test-notebooks
```
### Update test data
To add new or update testing data, such as a new baseline for this block:
```py
dataset_name = "cthead1"
image = input_images[dataset_name]
baseline_name = "2_4/XARRAY_COARSEN"
multiscale = to_multiscale(image, [2, 4], method=Methods.XARRAY_COARSEN)
verify_against_baseline(test_data_dir, dataset_name, baseline_name, multiscale)
```
Add a `store_new_image` call in your test block:
```py
dataset_name = "cthead1"
image = input_images[dataset_name]
baseline_name = "2_4/XARRAY_COARSEN"
multiscale = to_multiscale(image, [2, 4], method=Methods.XARRAY_COARSEN)
store_new_image(dataset_name, baseline_name, multiscale)
verify_against_baseline(dataset_name, baseline_name, multiscale)
```
Run the tests to generate the output. Remove the `store_new_image` call.
Then, create a tarball of the current testing data
```console
cd test/data
tar cvf ../data.tar *
gzip -9 ../data.tar
python3 -c 'import pooch; print(pooch.file_hash("../data.tar.gz"))'
```
Update the `test_data_sha256` variable in the _test/\_data.py_ file. Upload the
data to [web3.storage](https://web3.storage). And update the
`test_data_ipfs_cid`
[Content Identifier (CID)](https://proto.school/anatomy-of-a-cid/01) variable,
which is available in the web3.storage web page interface.
### Submit the patch
We use the standard [GitHub flow].
### Create a release
This section is relevant only for maintainers.
1. Pull `git`'s `main` branch.
2. `pixi install -a`
3. `pixi run pre-commit-install`
4. `pixi run -e test test`
5. `pixi shell`
6. `hatch version <new-version>`
7. `git add .`
8. `git commit -m "ENH: Bump version to <version>"`
9. `hatch build`
10. `hatch publish`
11. `git push upstream main`
12. Create a new tag and Release via the GitHub UI. Auto-generate release notes
and add additional notes as needed.
[spatial-image]: https://github.com/spatial-image/spatial-image
[Xarray]: https://xarray.pydata.org/en/stable/
[OME-NGFF]: https://ngff.openmicroscopy.org/
[Dataset]: https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html
[Datatree]: https://xarray-datatree.readthedocs.io/en/latest/
[DataArray]: https://xarray.pydata.org/en/stable/generated/xarray.DataArray.html
[Zarr]: https://zarr.readthedocs.io/en/stable/
[Dask]: https://docs.dask.org/en/stable/array.html
[netCDF]: https://www.unidata.ucar.edu/software/netcdf/
[pixi]: https://pixi.sh
[GitHub flow]: https://docs.github.com/en/get-started/using-github/github-flow
Raw data
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"name": "multiscale_spatial_image",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.13,>=3.10",
"maintainer_email": null,
"keywords": "dask, imaging, itk, ngff, ome, visualization, zarr",
"author": null,
"author_email": "Matt McCormick <matt@mmmccormick.com>",
"download_url": "https://files.pythonhosted.org/packages/3c/12/bba3e084cd75e5d21672ba12150b4bfbffebc426325029b0723e26c43049/multiscale_spatial_image-2.0.1.tar.gz",
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"description": "# multiscale-spatial-image\n\n[](https://github.com/spatial-image/multiscale-spatial-image/actions/workflows/test.yml)\n[](https://github.com/spatial-image/multiscale-spatial-image/actions/workflows/notebook-test.yml)\n[](https://pypi.python.org/pypi/multiscale_spatial_image/)\n[](https://github.com/python/black)\n[](https://zenodo.org/badge/latestdoi/379678181)\n\nGenerate a multiscale, chunked, multi-dimensional spatial image data structure\nthat can serialized to [OME-NGFF].\n\nEach scale is a scientific Python [Xarray] [spatial-image] [Dataset], organized\ninto nodes of an Xarray [Datatree].\n\n## Installation\n\n```sh\npip install multiscale_spatial_image\n```\n\n## Usage\n\n```python\nimport numpy as np\nfrom spatial_image import to_spatial_image\nfrom multiscale_spatial_image import to_multiscale\nimport zarr\n\n# Image pixels\narray = np.random.randint(0, 256, size=(128,128), dtype=np.uint8)\n\nimage = to_spatial_image(array)\nprint(image)\n```\n\nAn [Xarray] [spatial-image] [DataArray]. Spatial metadata can also be passed\nduring construction.\n\n```\n<xarray.SpatialImage 'image' (y: 128, x: 128)>\narray([[114, 47, 215, ..., 245, 14, 175],\n [ 94, 186, 112, ..., 42, 96, 30],\n [133, 170, 193, ..., 176, 47, 8],\n ...,\n [202, 218, 237, ..., 19, 108, 135],\n [ 99, 94, 207, ..., 233, 83, 112],\n [157, 110, 186, ..., 142, 153, 42]], dtype=uint8)\nCoordinates:\n * y (y) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0\n * x (x) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0\n```\n\n```python\n# Create multiscale pyramid, downscaling by a factor of 2, then 4\nmultiscale = to_multiscale(image, [2, 4])\nprint(multiscale)\n```\n\nA chunked [Dask] Array MultiscaleSpatialImage [Xarray] [Datatree].\n\n```\nDataTree('multiscales', parent=None)\n\u251c\u2500\u2500 DataTree('scale0')\n\u2502 Dimensions: (y: 128, x: 128)\n\u2502 Coordinates:\n\u2502 * y (y) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0\n\u2502 * x (x) float64 0.0 1.0 2.0 3.0 4.0 ... 123.0 124.0 125.0 126.0 127.0\n\u2502 Data variables:\n\u2502 image (y, x) uint8 dask.array<chunksize=(128, 128), meta=np.ndarray>\n\u251c\u2500\u2500 DataTree('scale1')\n\u2502 Dimensions: (y: 64, x: 64)\n\u2502 Coordinates:\n\u2502 * y (y) float64 0.5 2.5 4.5 6.5 8.5 ... 118.5 120.5 122.5 124.5 126.5\n\u2502 * x (x) float64 0.5 2.5 4.5 6.5 8.5 ... 118.5 120.5 122.5 124.5 126.5\n\u2502 Data variables:\n\u2502 image (y, x) uint8 dask.array<chunksize=(64, 64), meta=np.ndarray>\n\u2514\u2500\u2500 DataTree('scale2')\n Dimensions: (y: 16, x: 16)\n Coordinates:\n * y (y) float64 3.5 11.5 19.5 27.5 35.5 ... 91.5 99.5 107.5 115.5 123.5\n * x (x) float64 3.5 11.5 19.5 27.5 35.5 ... 91.5 99.5 107.5 115.5 123.5\n Data variables:\n image (y, x) uint8 dask.array<chunksize=(16, 16), meta=np.ndarray>\n```\n\nMap a function over datasets while skipping nodes that do not contain dimensions\n\n```python\nimport numpy as np\nfrom spatial_image import to_spatial_image\nfrom multiscale_spatial_image import skip_non_dimension_nodes, to_multiscale\n\ndata = np.zeros((2, 200, 200))\ndims = (\"c\", \"y\", \"x\")\nscale_factors = [2, 2]\nimage = to_spatial_image(array_like=data, dims=dims)\nmultiscale = to_multiscale(image, scale_factors=scale_factors)\n\n@skip_non_dimension_nodes\ndef transpose(ds, *args, **kwargs):\n return ds.transpose(*args, **kwargs)\n\nmultiscale = multiscale.map_over_datasets(transpose, \"y\", \"x\", \"c\")\nprint(multiscale)\n```\n\nA transposed MultiscaleSpatialImage.\n\n```\n<xarray.DataTree>\nGroup: /\n\u251c\u2500\u2500 Group: /scale0\n\u2502 Dimensions: (c: 2, y: 200, x: 200)\n\u2502 Coordinates:\n\u2502 * c (c) int32 8B 0 1\n\u2502 * y (y) float64 2kB 0.0 1.0 2.0 3.0 4.0 ... 196.0 197.0 198.0 199.0\n\u2502 * x (x) float64 2kB 0.0 1.0 2.0 3.0 4.0 ... 196.0 197.0 198.0 199.0\n\u2502 Data variables:\n\u2502 image (y, x, c) float64 640kB dask.array<chunksize=(200, 200, 2), meta=np.ndarray>\n\u251c\u2500\u2500 Group: /scale1\n\u2502 Dimensions: (c: 2, y: 100, x: 100)\n\u2502 Coordinates:\n\u2502 * c (c) int32 8B 0 1\n\u2502 * y (y) float64 800B 0.5 2.5 4.5 6.5 8.5 ... 192.5 194.5 196.5 198.5\n\u2502 * x (x) float64 800B 0.5 2.5 4.5 6.5 8.5 ... 192.5 194.5 196.5 198.5\n\u2502 Data variables:\n\u2502 image (y, x, c) float64 160kB dask.array<chunksize=(100, 100, 2), meta=np.ndarray>\n\u2514\u2500\u2500 Group: /scale2\n Dimensions: (c: 2, y: 50, x: 50)\n Coordinates:\n * c (c) int32 8B 0 1\n * y (y) float64 400B 1.5 5.5 9.5 13.5 17.5 ... 185.5 189.5 193.5 197.5\n * x (x) float64 400B 1.5 5.5 9.5 13.5 17.5 ... 185.5 189.5 193.5 197.5\n Data variables:\n image (y, x, c) float64 40kB dask.array<chunksize=(50, 50, 2), meta=np.ndarray>\n```\n\nStore as an Open Microscopy Environment-Next Generation File Format ([OME-NGFF])\n/ [netCDF] [Zarr] store.\n\nIt is highly recommended to use `dimension_separator='/'` in the construction of\nthe Zarr stores.\n\n```python\nstore = zarr.storage.DirectoryStore('multiscale.zarr', dimension_separator='/')\nmultiscale.to_zarr(store)\n```\n\n**Note**: The API is under development, and it may change until 1.0.0 is\nreleased. We mean it :-).\n\n## Examples\n\n- [](https://mybinder.org/v2/gh/spatial-image/multiscale-spatial-image/main?urlpath=lab/tree/examples%2FHelloMultiscaleSpatialImageWorld.ipynb)\n [Hello MultiscaleSpatialImage World!](./examples/HelloMultiscaleSpatialImageWorld.ipynb)\n- [](https://mybinder.org/v2/gh/spatial-image/multiscale-spatial-image/main?urlpath=lab/tree/examples%2FConvertITKImage.ipynb)\n [Convert itk.Image](./examples/ConvertITKImage.ipynb)\n- [](https://mybinder.org/v2/gh/spatial-image/multiscale-spatial-image/main?urlpath=lab/tree/examples%2FConvertImageioImageResource.ipynb)\n [Convert imageio ImageResource](./examples/ConvertImageioImageResource.ipynb)\n- [](https://mybinder.org/v2/gh/spatial-image/multiscale-spatial-image/main?urlpath=lab/tree/examples%2FConvertPyImageJDataset.ipynb)\n [Convert pyimagej Dataset](./examples/ConvertPyImageJDataset.ipynb)\n\n## Development\n\nContributions are welcome and appreciated.\n\n### Get the source code\n\n```shell\ngit clone https://github.com/spatial-image/multiscale-spatial-image\ncd multiscale-spatial-image\n```\n\n### Install dependencies\n\nFirst install [pixi]. Then, install project dependencies:\n\n```shell\npixi install -a\npixi run pre-commit-install\n```\n\n### Run the test suite\n\nThe unit tests:\n\n```shell\npixi run -e test test\n```\n\nThe notebooks tests:\n\n```shell\npixi run test-notebooks\n```\n\n### Update test data\n\nTo add new or update testing data, such as a new baseline for this block:\n\n```py\ndataset_name = \"cthead1\"\nimage = input_images[dataset_name]\nbaseline_name = \"2_4/XARRAY_COARSEN\"\nmultiscale = to_multiscale(image, [2, 4], method=Methods.XARRAY_COARSEN)\nverify_against_baseline(test_data_dir, dataset_name, baseline_name, multiscale)\n```\n\nAdd a `store_new_image` call in your test block:\n\n```py\ndataset_name = \"cthead1\"\nimage = input_images[dataset_name]\nbaseline_name = \"2_4/XARRAY_COARSEN\"\nmultiscale = to_multiscale(image, [2, 4], method=Methods.XARRAY_COARSEN)\n\nstore_new_image(dataset_name, baseline_name, multiscale)\n\nverify_against_baseline(dataset_name, baseline_name, multiscale)\n```\n\nRun the tests to generate the output. Remove the `store_new_image` call.\n\nThen, create a tarball of the current testing data\n\n```console\ncd test/data\ntar cvf ../data.tar *\ngzip -9 ../data.tar\npython3 -c 'import pooch; print(pooch.file_hash(\"../data.tar.gz\"))'\n```\n\nUpdate the `test_data_sha256` variable in the _test/\\_data.py_ file. Upload the\ndata to [web3.storage](https://web3.storage). And update the\n`test_data_ipfs_cid`\n[Content Identifier (CID)](https://proto.school/anatomy-of-a-cid/01) variable,\nwhich is available in the web3.storage web page interface.\n\n### Submit the patch\n\nWe use the standard [GitHub flow].\n\n### Create a release\n\nThis section is relevant only for maintainers.\n\n1. Pull `git`'s `main` branch.\n2. `pixi install -a`\n3. `pixi run pre-commit-install`\n4. `pixi run -e test test`\n5. `pixi shell`\n6. `hatch version <new-version>`\n7. `git add .`\n8. `git commit -m \"ENH: Bump version to <version>\"`\n9. `hatch build`\n10. `hatch publish`\n11. `git push upstream main`\n12. Create a new tag and Release via the GitHub UI. Auto-generate release notes\n and add additional notes as needed.\n\n[spatial-image]: https://github.com/spatial-image/spatial-image\n[Xarray]: https://xarray.pydata.org/en/stable/\n[OME-NGFF]: https://ngff.openmicroscopy.org/\n[Dataset]: https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html\n[Datatree]: https://xarray-datatree.readthedocs.io/en/latest/\n[DataArray]: https://xarray.pydata.org/en/stable/generated/xarray.DataArray.html\n[Zarr]: https://zarr.readthedocs.io/en/stable/\n[Dask]: https://docs.dask.org/en/stable/array.html\n[netCDF]: https://www.unidata.ucar.edu/software/netcdf/\n[pixi]: https://pixi.sh\n[GitHub flow]: https://docs.github.com/en/get-started/using-github/github-flow\n",
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