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This package provides routines for the conversion of image formats to time
series and vice versa. It works best with the readers and writers
supported by pynetcf <https://github.com/TUW-GEO/pynetcf>
.
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 <https://github.com/TUW-GEO/pygeobase>
_ to work
without adpation.
This package requires python>=3.9
and depends on the following libraries that
can should installed with conda <https://conda.io/projects/conda/en/latest/user-guide/getting-started.html>
or mamba <https://github.com/conda-forge/miniforge>
.. code::
conda install -c conda-forge numpy netCDF4 pyresample
Afterwards you can install this package and all remaining dependencies via:
.. code::
pip install repurpose
Some packages are only needed to run unit tests and build docs of this package.
They can be installed via pip install repurpose[testing]
and/or
pip install repurpose[docs]
.
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.593577.svg :target: https://doi.org/10.5281/zenodo.593577
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
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.resample
for spatial resampling of (regular or irregular) gridded data to different resolutions.process
contains a framework for parallel processing, error handling and logging based on joblib <https://github.com/joblib/joblib>
_If you have data that can be represented as a 3D datacube then these projects might be better suited to your needs.
PyReshaper <https://github.com/NCAR/PyReshaper>
_ is a package that works
with NetCDF input and output and converts time slices into a time series
representation.Climate Data Operators (CDO) <https://code.zmaw.de/projects/cdo/embedded/index.html>
_ 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) <http://nco.sourceforge.net/#Definition>
_ are similar
to CDO with a stronger focus on netCDF.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.
For Development we recommend a conda
environment with all packages listed
in environment.yml
.
If you want to contribute please follow these steps:
This project has been set up using PyScaffold 2.4.4. For details and usage information on PyScaffold see http://pyscaffold.readthedocs.org/.