multiscale-spatial-image

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

pip install multiscale_spatial_image

Usage

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

# 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>

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.

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

• Hello MultiscaleSpatialImage World!
• Convert itk.Image
• Convert imageio ImageResource
• Convert pyimagej Dataset

Development

Contributions are welcome and appreciated.

Get the source code

git clone https://github.com/spatial-image/multiscale-spatial-image
cd multiscale-spatial-image

Install dependencies

First install pixi. Then, install project dependencies:

pixi install -a
pixi run pre-commit-install

Run the test suite

The unit tests:

pixi run -e test test

The notebooks tests:

pixi run test-notebooks

Update test data

To add new or update testing data, such as a new baseline for this block:

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:

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

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. And update the test_data_ipfs_cid Content Identifier (CID) variable, which is available in the web3.storage web page interface.

Submit the patch

We use the standard GitHub flow.