spatial-image-multiscale
Project description
⚠️ Renamed to multiscale-spatial-image
spatial-image-multiscale
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 spatial_image_multiscale
Usage
import numpy as np
from spatial_image import to_spatial_image
from spatial_image_multiscale 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)
Examples
Development
Contributions are welcome and appreciated.
To run the test suite:
git clone https://github.com/spatial-image/spatial-image-multiscale
cd spatial-image-multiscale
pip install -e ".[test]"
cid=$(grep 'IPFS_CID =' test/test_spatial_image_multiscale.py | cut -d ' ' -f 3 | tr -d '"')
# Needs ipfs, e.g. https://docs.ipfs.io/install/ipfs-desktop/
ipfs get -o ./test/data -- $cid
pytest
# Notebook tests
pytest --nbmake --nbmake-timeout=3000 examples/*ipynb
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for spatial_image_multiscale-0.4.2.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | b77f25193c342ea85945e10615a724f6f90ec56639e11ce03f4042129da0630a |
|
MD5 | 68c43b32a813787d524e357c5ac8b69c |
|
BLAKE2b-256 | d90dd290b061740b498cb7ab22aa6e0290318678a0a0b0a1eef9a6b04f52edc9 |
Hashes for spatial_image_multiscale-0.4.2-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 928ab79e8232322a128f0d8a52c0f2eadf140e98cc6dadfc5095fc309dcb47be |
|
MD5 | 5d6ddf4a7ef4116e4773dfb77a0d7955 |
|
BLAKE2b-256 | dd4d3ac05767e897cd800c57bf7baeed85e1086926d17a96e8c9cf7628395fda |