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 and xarray to simplify the task of working with model output and performing atmospheric chemistry analyses.
- Generate the standard evaluation plots from GEOS-Chem benchmark output.
- Obtain GEOS-Chem's horizontal/vertical grid information.
- Implement GCHP-specific regridding functionalities (e.g. cubed-sphere to lat-lon regridding)
- Provide example scripts for creating specific types of plots or analysis from GEOS-Chem output.
- NetCDF file modification: (crop a domain, extract some variables):
- Use xarray instead.
- Also see our Working with netCDF data files wiki page.
- Simple plotting on lat-lon grids:
- Can be done directly with cartopy, matplotlib, etc.
- See our GEOS-Chem Python tutorial for more examples!
- Statistical analysis:
- Use scipy/scikit-learn tools instead
- Machine Learning:
- Use the standard machine learning utilities (pytorch, tensorflow, julia, etc.)
GCPy is built on top of Python 3 and the scientific Python / NumPy stack, including
To create an environment for working with GCPy, we recommend using the Anaconda Python distribution or curating your own virtualenv or conda environment. Please see
gcpy/environment/environment.yml for an example.
At the moment, the easiest way to install GCPy is directly from our GitHub repository.
$ git clone https://github.com/geoschem/gcpy.git gcpy
Currently, GCPy is not available via conda-forge or PyPI, but we anticipate posting early versions of the package to those resources eventually.
GCPy is distributed under the MIT license. Please read the license documents LICENSE.txt and AUTHORS.txt, which are located in the root folder.
To contact us, please open a new issue on the issue tracker connected to this repository. You can ask a question, report a bug, or request a new feature.