Open Data Cube Algorithms
Xarray and Dask friendly EO Processing Algorithms and Utilities.
Note: This package only contains algorithms. If you want to use them for processing EO data, you'll find using odc-stats much simpler.
- Cloud Masking
- Geometric Median
- Percentiles
- Dask Aware Raster Reprojection
- Efficiently generating and saving Cloud Optimized GeoTIFFs to S3
- Reshaping Dask Arrays for Efficient Computation
- Converting between Floats with NaNs and Ints with nodata values
Installation
pip install odc-algo
Usage
Building
- Install the Python build tool.
python -m pip install build - Build this package.
python -m build
Development
- Follow build instructions
- Install as dev
pip install -e .[dev]
Alternatively, install with the whl file.
Tasks
- Decide whether to use pixi, or uv. I think pixi, it handles Rust stuff.
- Document the Geomedian API we're trying to expose. See odc-stats
- Document the Percentile API we're exposing.
- Regresssion Tests instead of installing old dependencies.
- Update GitHub Actions to build and test Rust backend
- Update GitHub Actions to build and test against multiple Python versions
- Consider what type of binary wheels to build. [abi3/multi-python version compatible is tempting]
- Update Rust dependencies.
- De-duplicate with odc-geo. It includes COG and Warp functionality that's better maintained, but may not be identical...
- Consider vendoring the skimage morphology functions we're using, instead of depending on the whole thing.
Metadata
Release files for odc-algo 1.2.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| odc_algo-1.2.2.tar.gz | 210.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| odc_algo-1.2.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 264.3 kB
Release files / odc_algo-1.2.2.tar.gz
| Download URL | odc_algo-1.2.2.tar.gz |
|---|---|
| Size | 210.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
dbd2130290c558c37eda567aac870b2861c586102a0189ba122192cbf540e56f
|
|
BLAKE2b-256 checksum How to use checksums |
861983a6cf32cc9016c4852e95242a9905618c09424792f74bb0ce54fddbd055
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 2, 2026.
Transparency logRelease files / odc_algo-1.2.2-py3-none-any.whl
| Download URL | odc_algo-1.2.2-py3-none-any.whl |
|---|---|
| Size | 54.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3cd3b1e00b10a7e7523df4f0e16401a186239fcd2db33bae2d35b4899798b07b
|
|
BLAKE2b-256 checksum How to use checksums |
42193ccf5745d55bf692ee60bd2c77d98939b79e60dee544c193c93ad5b81d84
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 2, 2026.
Transparency log