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https://github.com/geoschem/gcpy

Python toolkit for GEOS-Chem. Contains basic plotting scripts, plus the suite of GEOS-Chem benchmarking utilities.
https://github.com/geoschem/gcpy

atmospheric-chemistry atmospheric-modelling benchmarking cartopy cloud-computing geos-chem numpy plots plotting-in-python python python-toolkit scientific-computing visualization-tools xarray

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Python toolkit for GEOS-Chem. Contains basic plotting scripts, plus the suite of GEOS-Chem benchmarking utilities.

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README 

        
# GCPy: Python toolkit for GEOS-Chem





  

  

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**GCPy** is a Python-based toolkit containing useful functions for working specifically with the GEOS-Chem model of atmospheric chemistry and composition.



**GCPy** aims to build on the well-established scientific Python technical stack, leveraging tools like **cartopy**, **numpy**, and **xarray** to simplify the task of working with GEOS-Chem model output and performing atmospheric chemistry analyses.



## What GCPy was intended to do:



1. Produce plots and tables from [GEOS-Chem](https://geos-chem.readthedocs.io) output using simple function calls.

2. Generate the standard evaluation plots and tables for GEOS-Chem benchmark simulations.

3. Obtain GEOS-Chem's horizontal and vertical grid information.

4. Implement [GCHP](https://gchp.readthedocs.io)-specific regridding functionalities (e.g. cubed-sphere to lat-lon regridding)

5. Provide example scripts for creating specific types of plots or  analysis from GEOS-Chem output.

6. Provide user-submitted scripts for specific applications related to GEOS-Chem and [HEMCO](https://hemco.readthedocs.io).



## What GCPy was not intended to do:



1. General NetCDF file modification: (crop a domain, extract some variables):

    * Instead, use netCDF tools such as:

	  * [xarray](http://xarray.pydata.org)

	  * [netCDF operators (NCO)](https://nco.sourceforge.net)

	  * [Climate Data Operators](https://mpimet.mpg.de/cdo) instead.

    * Also see our [*Work with netCDF files* guide](https://geos-chem.readthedocs.io/en/latest/geos-chem-shared-docs/supplemental-guides/netcdf-guide.html) at [geos-chem.readthedocs.io](https://geos-chem.readthedocs.io)



2. Statistical analysis:

    * Instead, use statistical tools such as:

	  * Use [scipy](http://www.scipy.org)

	  * [scikit-learn](https://scikit-learn.org)

	  * [R](https://r-project.org)

	  * etc



3. Machine Learning:

    * Instead, use machine learning tools such as:

	  * [pytorch](https://pytorch.org),

	  * [tensorflow](https://www.tensorflow.org)

	  * [julia](https://julialang.org)

	  * etc.



## Documentation:



For more information on installing and using GCPy, visit the official documentation at [gcpy.readthedocs.io](https://gcpy.readthedocs.io/).



## License



GCPy is distributed under the MIT license.  Please see the [GCPy license agreement](https://github.com/geoschem/gcpy/blob/dev/LICENSE.txt) and [List of GCPy developers](https://github.com/geoschem/gcpy/blob/dev/AUTHORS.txt) for more information.



## Requesting support



To report a bug or suggest a new feature, please see our [Support

Guidelines](https://github.com/geoschem/gcpy/blob/dev/SUPPORT.md).



## Submitting new features



If you are interested in submitting code to GCPy, please see our

[Contributing Guidelines](https://github.com/geoschem/gcpy/blob/dev/CONTRIBUTING.md).