Image2DModel.parse() forgetting about scale_factors if chunking
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needs: triage
priority: medium
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Description
Recommendation: attach a minimal working example
Generally, the easier it is for us to reproduce the issue, the faster we can work on it. It is not required, but if you can, please:
- Reproduce using the
blobsdataset
from spatialdata.datasets import blobs
sdata = blobs()
image = sdata['blobs_image'].values
sdata['image_chunk_downscale'] = spatialdata.models.Image2DModel.parse(
data=image,
dims=['c', 'y', 'x'],
transformations=None,
scale_factors=[2,2],
method="nearest",
chunks=(1,100,100))
sdata
SpatialData object
├── Images
│ ├── 'blobs_image': DataArray[cyx] (3, 512, 512)
│ ├── 'blobs_multiscale_image': DataTree[cyx] (3, 512, 512), (3, 256, 256), (3, 128, 128)
│ └── 'image_chunk_downscale': DataTree[cyx] (3, 512, 512)
├── Labels
│ ├── 'blobs_labels': DataArray[yx] (512, 512)
│ └── 'blobs_multiscale_labels': DataTree[yx] (512, 512), (256, 256), (128, 128)
├── Points
│ └── 'blobs_points': DataFrame with shape: (<Delayed>, 4) (2D points)
├── Shapes
│ ├── 'blobs_circles': GeoDataFrame shape: (5, 2) (2D shapes)
│ ├── 'blobs_multipolygons': GeoDataFrame shape: (2, 1) (2D shapes)
│ └── 'blobs_polygons': GeoDataFrame shape: (5, 1) (2D shapes)
└── Tables
└── 'table': AnnData (26, 3)
with coordinate systems:
▸ 'global', with elements:
blobs_image (Images), blobs_multiscale_image (Images), image_chunk_downscale (Images), blobs_labels (Labels), blobs_multiscale_labels (Labels), blobs_points (Points), blobs_circles (Shapes), blobs_multipolygons (Shapes), blobs_polygons (Shapes)
Describe the bug
Want to parse a array (dask_array is my aim) to a multiscale chunked xarray.DataTree for my image.
When using both scale_factors and chunking, chunking works, but multiscale image is not created...
Not sure if intentional..
Expected behavior
Expect multiscale chunked image.
Desktop (optional):
- OS: macOS
- Version 14.0.0
packages
# packages in environment at /opt/homebrew/Caskroom/mambaforge/base/envs/spatialdata:
#
# Name Version Build Channel
aiobotocore 2.5.4 pypi_0 pypi
aiohappyeyeballs 2.4.6 pyhd8ed1ab_0 conda-forge
aiohttp 3.11.13 py312h998013c_0 conda-forge
aioitertools 0.12.0 pypi_0 pypi
aiosignal 1.3.2 pyhd8ed1ab_0 conda-forge
alabaster 1.0.0 pyhd8ed1ab_1 conda-forge
anndata 0.11.3 pyhd8ed1ab_0 conda-forge
annotated-types 0.7.0 pyhd8ed1ab_1 conda-forge
aom 3.9.1 h7bae524_0 conda-forge
app-model 0.3.0 pyhd8ed1ab_0 conda-forge
appdirs 1.4.4 pyhd8ed1ab_1 conda-forge
appnope 0.1.4 pyhd8ed1ab_1 conda-forge
array-api-compat 1.10.0 pyhd8ed1ab_0 conda-forge
asciitree 0.3.3 py_2 conda-forge
asttokens 3.0.0 pyhd8ed1ab_1 conda-forge
attrs 25.1.0 pyh71513ae_0 conda-forge
aws-c-auth 0.8.1 hfc2798a_0 conda-forge
aws-c-cal 0.8.1 hc8a0bd2_3 conda-forge
aws-c-common 0.10.6 h5505292_0 conda-forge
aws-c-compression 0.3.0 hc8a0bd2_5 conda-forge
aws-c-event-stream 0.5.0 h54f970a_11 conda-forge
aws-c-http 0.9.2 h96aa502_4 conda-forge
aws-c-io 0.15.3 haba67d1_6 conda-forge
aws-c-mqtt 0.11.0 h24f418c_12 conda-forge
aws-c-s3 0.7.9 hf37e03c_1 conda-forge
aws-c-sdkutils 0.2.2 hc8a0bd2_0 conda-forge
aws-checksums 0.2.2 hc8a0bd2_4 conda-forge
aws-crt-cpp 0.29.9 ha81f72f_2 conda-forge
aws-sdk-cpp 1.11.489 h0e5014b_0 conda-forge
azure-core-cpp 1.14.0 hd50102c_0 conda-forge
azure-identity-cpp 1.10.0 hc602bab_0 conda-forge
azure-storage-blobs-cpp 12.13.0 h7585a09_1 conda-forge
azure-storage-common-cpp 12.8.0 h9ca1f76_1 conda-forge
azure-storage-files-datalake-cpp 12.12.0 hcdd55da_1 conda-forge
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re2 2024.07.02 h6589ca4_2 conda-forge
readfcs 1.1.8 pyhd8ed1ab_0 conda-forge
readline 8.2 h1d1bf99_2 conda-forge
referencing 0.36.2 pyh29332c3_0 conda-forge
requests 2.32.3 pyhd8ed1ab_1 conda-forge
rich 13.9.4 pyhd8ed1ab_1 conda-forge
roman-numerals-py 3.1.0 pyhd8ed1ab_0 conda-forge
rpds-py 0.23.1 py312hd60eec9_0 conda-forge
ruamel.yaml 0.18.10 py312hea69d52_0 conda-forge
ruamel.yaml.clib 0.2.8 py312h0bf5046_1 conda-forge
s3fs 2023.6.0 pypi_0 pypi
scanpy 1.10.4 pyhd8ed1ab_0 conda-forge
scikit-image 0.25.2 py312hcb1e3ce_0 conda-forge
scikit-learn 1.6.1 py312h39203ce_0 conda-forge
scipy 1.15.2 py312h99a188d_0 conda-forge
seaborn 0.13.2 hd8ed1ab_3 conda-forge
seaborn-base 0.13.2 pyhd8ed1ab_3 conda-forge
session-info 1.0.0 pyhd8ed1ab_0 conda-forge
setuptools 75.8.0 pyhff2d567_0 conda-forge
shapely 2.0.7 py312ha6455e5_0 conda-forge
shellingham 1.5.4 pyhd8ed1ab_1 conda-forge
six 1.17.0 pyhd8ed1ab_0 conda-forge
sleef 3.8 h8391f65_0 conda-forge
slicerator 1.1.0 pyhd8ed1ab_1 conda-forge
smmap 5.0.2 pyhd8ed1ab_0 conda-forge
snappy 1.2.1 h98b9ce2_1 conda-forge
snowballstemmer 2.2.0 pyhd8ed1ab_0 conda-forge
sortedcontainers 2.4.0 pyhd8ed1ab_1 conda-forge
spatial-image 1.1.0 pyhd8ed1ab_1 conda-forge
spatialdata 0.3.0 pypi_0 pypi
spatialdata-io 0.1.5 pyhd8ed1ab_0 conda-forge
spatialdata-plot 0.2.7 pyhd8ed1ab_1 conda-forge
sphinx 8.2.1 pyhd8ed1ab_0 conda-forge
sphinxcontrib-applehelp 2.0.0 pyhd8ed1ab_1 conda-forge
sphinxcontrib-devhelp 2.0.0 pyhd8ed1ab_1 conda-forge
sphinxcontrib-htmlhelp 2.1.0 pyhd8ed1ab_1 conda-forge
sphinxcontrib-jsmath 1.0.1 pyhd8ed1ab_1 conda-forge
sphinxcontrib-qthelp 2.0.0 pyhd8ed1ab_1 conda-forge
sphinxcontrib-serializinghtml 1.1.10 pyhd8ed1ab_1 conda-forge
sqlite 3.49.1 hd7222ec_1 conda-forge
stack_data 0.6.3 pyhd8ed1ab_1 conda-forge
statsmodels 0.14.4 py312h755e627_0 conda-forge
stdlib-list 0.11.1 pyhd8ed1ab_0 conda-forge
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svt-av1 3.0.0 h8ab69cd_0 conda-forge
sympy 1.13.3 pyh2585a3b_105 conda-forge
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tbb 2022.0.0 h0cbf7ec_0 conda-forge
tblib 3.0.0 pyhd8ed1ab_1 conda-forge
threadpoolctl 3.5.0 pyhc1e730c_0 conda-forge
tifffile 2025.2.18 pyhd8ed1ab_0 conda-forge
tinycss2 1.4.0 pyhd8ed1ab_0 conda-forge
tk 8.6.13 h5083fa2_1 conda-forge
tomli 2.2.1 pyhd8ed1ab_1 conda-forge
tomli-w 1.2.0 pyhd8ed1ab_0 conda-forge
tomlkit 0.13.2 pyha770c72_1 conda-forge
toolz 0.12.1 pyhd8ed1ab_0 conda-forge
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tqdm 4.67.1 pyhd8ed1ab_1 conda-forge
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typing_extensions 4.12.2 pyha770c72_1 conda-forge
tzdata 2025a h78e105d_0 conda-forge
umap-learn 0.5.7 py312h81bd7bf_1 conda-forge
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uriparser 0.9.8 h00cdb27_0 conda-forge
urllib3 1.26.20 pypi_0 pypi
virtualenv 20.29.2 pyhd8ed1ab_0 conda-forge
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wcwidth 0.2.13 pyhd8ed1ab_1 conda-forge
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wrapt 1.17.2 py312hea69d52_0 conda-forge
x265 3.5 hbc6ce65_3 conda-forge
xarray 2024.11.0 pypi_0 pypi
xarray-dataclasses 1.9.1 pypi_0 pypi
xarray-datatree 0.0.14 pyhd8ed1ab_0 conda-forge
xarray-schema 0.0.3 pyhd8ed1ab_0 conda-forge
xarray-spatial 0.4.0 pyhd8ed1ab_1 conda-forge
xerces-c 3.2.5 h92fc2f4_2 conda-forge
xorg-libxau 1.0.12 h5505292_0 conda-forge
xorg-libxdmcp 1.1.5 hd74edd7_0 conda-forge
xyzservices 2025.1.0 pyhd8ed1ab_0 conda-forge
yaml 0.2.5 h3422bc3_2 conda-forge
yarl 1.18.3 py312h998013c_1 conda-forge
zarr 2.18.4 pyhd8ed1ab_0 conda-forge
zeromq 4.3.5 hc1bb282_7 conda-forge
zfp 1.0.1 h1c5d8ea_2 conda-forge
zict 3.0.0 pyhd8ed1ab_1 conda-forge
zipp 3.21.0 pyhd8ed1ab_1 conda-forge
zlib 1.3.1 h8359307_2 conda-forge
zlib-ng 2.2.4 h13dfb9a_0 conda-forge
zstandard 0.23.0 py312h15fbf35_1 conda-forge
zstd 1.5.6 hb46c0d2_0 conda-forge
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at spatialdata.models.Image2DModel.parse() and reproduce the issue with the provided blobs dataset example, using both scale_factors and chunks. Inspect the resulting image DataTree and the parse path for multiscale creation. Done means the same inputs produce a multiscale chunked image rather than only the full-resolution level.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 68/100