Metadata-Version: 2.1
Name: disdrodb
Version: 0.0.15
Summary: This package provides tools to homogenize, process, and analyze global disdrometer data.
Author: Gionata Ghiggi
Keywords: python,disdrometer
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

# DISDRODB - A package to standardize, process and analyze global disdrometer data.

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DISDRODB is part of an initial effort to index, collect and homogenize drop size distribution (DSD) data sets across the globe,
as well as to establish a global standard for disdrometers observations data sharing. 
DISDRODB standards are being established following FAIR data best practices and Climate & Forecast (CF) conventions, and will facilitate the preprocessing, analysis and visualization of disdrometer data.  

The DISDRODB archive is composed of 3 product levels:
- L0 provides the raw sensors measurements converted into a standardized netCDF4 format.
- L1 provides L0 homogenized and quality-checked data.
- L2 provides scientific products derived from the L1 data.

The code required to the generate the DISDRODB archive is enclosed in the `production` directory of the repository. 

The code facilitating the analysis and visualization of the DISDRODB archive is available in the `api` directory.


The software documentation is available at [https://disdrodb.readthedocs.io/en/latest/](https://disdrodb.readthedocs.io/en/latest/). 

Currently: 
- only the DISDRODB L0 product generation has been implemented;
- the pipeline for DISDRODB L1 and L2 product generation is in development;
- the DISDRODB API is in development; 
- more than 300 sensors have been already processed to DISDRODB L0; 
- tens of institutions have manifested their interest in adopting the DISDRODB tools and standards. 

Consequently **IT IS TIME TO GET INVOLVED**. If you have ideas, algorithms, data or expertise to share, do not hesitate to **GET IN TOUCH** !!!




## Installation


DISDRODB can be installed from PyPI with pip: 

  ```sh
  pip install disdrodb
  ```
 
## Contributors

* [Gionata Ghiggi](https://people.epfl.ch/gionata.ghiggi)
* [Kim Candolfi](https://github.com/KimCandolfi)
* [Régis Longchamp](https://people.epfl.ch/regis.longchamp)
* [Charlotte Gisèle Weil](https://people.epfl.ch/charlotte.weil)
* [Jacopo Grazioli](https://people.epfl.ch/jacopo.grazioli) 
* [Alexis Berne](https://people.epfl.ch/alexis.berne?lang=en)

## Citation 

You can cite the DISDRODB software by:

> Gionata Ghiggi, Kim Candolfi, Régis Longchamp, Charlotte Weil, Alexis Berne (2023). ltelab/disdrodb  Zenodo. https://doi.org/10.5281/zenodo.7680581

If you want to cite a specific version, have a look at the [Zenodo site](https://doi.org/10.5281/zenodo.7680581)

## License

The content of this repository is released under the terms of the [GPL 3.0 license](LICENSE).
