conda env create -f environment.yaml
conda activate L96M2lines
jupyter book build .
cd _build/html
python -m http.server
We use pre-commit to keep the notebooks clean. In order to use pre-commit, run the following command in the repo top-level directory:
pre-commit install
At this point, pre-commit will automatically be run every time you make a commit.
In order to contribute a PR, you should start from a new feature branch.
git checkout -b my_new_feature
(Replace my_new_feature with a descriptive name of the feature you're working on.)
Make your changes and then make a new commit:
git add changed_file_1.ipynb changed_file_2.ipynb
git commit -m "message about my new feature"
You can also automatically commit changes to existing files as:
git commit -am "message about my new feature"
Then push your changes to your remote on GitHub (usually call origin
git push my_new_feature origin
Then navigate to https://github.com/m2lines/L96_demo to open your pull request.
To synchronize your local branch with upstream changes, first make sure you have the upstream remote configured. To check your remotes, run
% git remote -v
origin git@github.com:rabernat/L96_demo.git (fetch)
origin git@github.com:rabernat/L96_demo.git (push)
upstream git@github.com:m2lines/L96_demo.git (fetch)
upstream git@github.com:m2lines/L96_demo.git (push)
If you don't have upstream, you need to add it as follows
git remote add upstream git@github.com:m2lines/L96_demo.git
Then, make sure you are on the main branch locally:
git checkout main
And then run
git fetch upstream
git merge upstream/main
Ideally you will not have any merge conflicts. You are now ready to make a new feature branch.
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Brajard, Julien, Alberto Carrassi, Marc Bocquet, and Laurent Bertino. “Combining Data Assimilation and Machine Learning to Infer Unresolved Scale Parametrization.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (April 5, 2021): 20200086. https://doi.org/10.1098/rsta.2020.0086.
Schneider, Tapio, Shiwei Lan, Andrew Stuart, and João Teixeira. “Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations.” Geophysical Research Letters 44, no. 24 (December 28, 2017): 12,396-12,417. https://doi.org/10.1002/2017GL076101.
Wilks, Daniel S. “Effects of Stochastic Parametrizations in the Lorenz ’96 System.” Quarterly Journal of the Royal Meteorological Society 131, no. 606 (2005): 389–407. https://doi.org/10.1256/qj.04.03.