Skip to main content

Miscellaneous Algorithmic helper methods

Project description

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

  1. Install the Python build tool. python -m pip install build
  2. Build this package. python -m build

Development

  1. Follow build instructions
  2. 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 like hdstats.
  • 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.

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

odc_algo-1.0.0.tar.gz (154.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

odc_algo-1.0.0-py3-none-any.whl (48.0 kB view details)

Uploaded Python 3

File details

Details for the file odc_algo-1.0.0.tar.gz.

File metadata

  • Download URL: odc_algo-1.0.0.tar.gz
  • Upload date:
  • Size: 154.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for odc_algo-1.0.0.tar.gz
Algorithm Hash digest
SHA256 cf5dd82a18b49c9983fadf1482e173770bee1eae647c2e2fcf1d0eb799b0306c
MD5 33af79212e4c455bc3944cb62b2e0189
BLAKE2b-256 546ce105421bb82c98a94544b36628915d3ae7eb14ed87430a8340de4167b7ed

See more details on using hashes here.

Provenance

The following attestation bundles were made for odc_algo-1.0.0.tar.gz:

Publisher: release.yml on opendatacube/odc-algo

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file odc_algo-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: odc_algo-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 48.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for odc_algo-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 27b6707d152bf79ecbbc07ab34befaedbcedeb3e6299d668b831ee0c789a6345
MD5 9b60c69e5a8b03191da7b8ddf1ddeca9
BLAKE2b-256 e1b6ffd9f8daa0ecbfad5c31e5773ab0c9727880f4f2d2939052fe496ee08f61

See more details on using hashes here.

Provenance

The following attestation bundles were made for odc_algo-1.0.0-py3-none-any.whl:

Publisher: release.yml on opendatacube/odc-algo

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page