Metadata-Version: 2.1
Name: gfdlnb
Version: 0.0.2
Summary: GFDL Model Analysis Notebooks
Author-email: John Krasting <john.krasting@noaa.gov>
License: Software code created by U.S. Government employees is not subject to copyright
        in the United States (17 U.S.C. §105). The United States/Department of Commerce
        reserve all rights to seek and obtain copyright protection in countries other
        than the United States for Software authored in its entirety by the Department
        of Commerce. To this end, the Department of Commerce hereby grants to Recipient
        a royalty-free, nonexclusive license to use, copy, and create derivative works
        of the Software outside of the United States.
        
Project-URL: homepage, https://github.com/jkrasting/mar
Project-URL: documentation, https://gfdl-notebooks.readthedocs.io
Project-URL: repository, https://github.com/jkrasting/mar
Keywords: climate modeling,gfdl
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: Public Domain
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: importlib_metadata<5.0.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: xarray>=0.20.0
Provides-Extra: test
Requires-Dist: pytest; extra == "test"

# GFDL Notebooks
(Previously MAR - Model Analysis Repository)

<p>
The Latin word <i>"mar"</i> translates to <i>"sea"</i>. This repository will contain a collection of (mainly) ocean-focused analyses to 
  inform next-generation ocean and climate model development.
</p>

## Ways to Run MAR

1. Interactively (clone the repository, edit the notebooks, and run)
2. Execute the batch script `run_mar.sh`
3. Visit https://dora.gfdl.noaa.gov/analysis/mar

## Contributing to MAR
Jupyter notebooks are the encapsulation of a particular analysis.  There are relatively few constraints on how an analysis built, but there are 
a few interfaces to be aware of:

### Configuration / Environment Variables
The batch and web engines for MAR (items 2 and 3 above) will set two runtime environment variables. Use one or both of these fields to 
determine the top-level path to a model experiment to analyze:

* `MAR_DORA_ID`: The experiment ID in the dora database
* `MAR_PATHPP`: The top-level path to the post-processing experiment directory of the experiment (e.g. `/some/path/pp/`)

Each notebook should have a default set of model years to analyze (e.g. 1981-2010).  The MAR engines will also provide two optional, additional variables, 
`STARTYR` and `ENDYR`, that can be used to override the defaults in the notebook.

### Scalar Results / Metrics

If your notebook produces scalar metrics, it should write those results to a YAML file.  See the `SST_bias_NOAA_OISSTv2.ipynb` notebook for an example of 
how to construct a YAML file. Some examples of scalar fields might be RMSE and bias of a field, or the average depth of the Mediterranean outflow plume.


