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Package for image to timeseries to image conversion

Project description

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This package provides routines for the conversion of image formats to time series and vice versa. It is part of the poets project and works best with the readers and writers supported there. The main use case is for data that is sampled irregularly in space or time. If you have data that is sampled in regular intervals then there are alternatives to this package which might be better for your use case. See Alternatives for more detail.

The readers and writers have to conform to the API specifications of the base classes defined in pygeobase to work without adpation.

Citation

https://zenodo.org/badge/DOI/10.5281/zenodo.593577.svg

If you use the software in a publication then please cite it using the Zenodo DOI. Be aware that this badge links to the latest package version.

Please select your specific version at https://doi.org/10.5281/zenodo.593577 to get the DOI of that version. You should normally always use the DOI for the specific version of your record in citations. This is to ensure that other researchers can access the exact research artefact you used for reproducibility.

You can find additional information regarding DOI versioning at http://help.zenodo.org/#versioning

Installation

This package should be installable through pip:

pip install repurpose

Modules

It includes two main modules:

  • img2ts for image/swath to time series conversion, including support for spatial resampling.
  • ts2img for time series to image conversion, including support for temporal resampling. This module is very experimental at the moment.
  • resample for spatial resampling of (regular or irregular) gridded data to different resolutions.

Alternatives

If you have data that can be represented as a 3D datacube then these projects might be better suited to your needs.

  • PyReshaper is a package that works with NetCDF input and output and converts time slices into a time series representation.
  • Climate Data Operators (CDO) can work with several input formats, stack them and change the chunking to allow time series optimized access. It assumes regular sampling in space and time as far as we know.
  • netCDF Operators (NCO) are similar to CDO with a stronger focus on netCDF.

Contribute

We are happy if you want to contribute. Please raise an issue explaining what is missing or if you find a bug. We will also gladly accept pull requests against our master branch for new features or bug fixes.

Development setup

For Development we recommend a conda environment

Guidelines

If you want to contribute please follow these steps:

  • Fork the repurpose repository to your account
  • make a new feature branch from the repurpose master branch
  • Add your feature
  • Please include tests for your contributions in one of the test directories. We use py.test so a simple function called test_my_feature is enough
  • submit a pull request to our master branch

Note

This project has been set up using PyScaffold 2.4.4. For details and usage information on PyScaffold see http://pyscaffold.readthedocs.org/.

Project details


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