This repository compares provisional OPERA DSWx-HLS products with validation datasets.

In your ~/.netrc, place earthdata login credentials:

machine urs.earthdata.nasa.gov
    login <username>
    password <password>

It is recommended to install mamba in the user's base environment to speed up the installation process:

conda install -c conda-forge mamba

From this repo:

1. mamba env create -f environment.yml
2. conda activate dswx_val

After activatating your environment (i.e. conda activate dswx_val), then

python -m ipykernel install --user --name dswx_val

Run the papermill script with:

python verify_all.py

See sample_runs.zsh for some additional ways of parametrizing the tests.

This mirrors the current validation clone. To generate this table, one must additionally have:

1. JPL VPN access and be connected to the VPN
2. Have group access to the validation clone (that requires coordination with HySDS to be added to the appropriate LDAP group)
3. Create a .env file with JPL credentials.

Specifically, for 3. the .env should look like

ES_USERNAME='<JPL USERNAME>'
ES_PASSWORD='<JPL PASSWORD>'

After that is done, then run the notebook _create_validation_table.ipynb to create this table.

1. Create a branch from dev and create a pull request.
2. Do you development.
3. For local git diff, use nbdiff --ignore-id as cell ids are required and updated on each change for newer versions of nbformat. Github will provide a prettier way of viewing notebook differences.
4. Run pytest . in this repository to ensure working of the notebooks. We do not use github actions (yet).
5. Have another member review.
6. Make sure you don't commit to out/ directory unless you want to share your results with the larger PST team. You can manually add / commit files with git.