Subseasonal Data Python Package

The subseasonal_data package provides an API for loading and manipulating the SubseasonalClimateUSA dataset developed for training and benchmarking subseasonal forecasting models. Here, subseasonal refers to climate and weather forecasts made 2-6 weeks in advance. See DATA.md for a description of dataset contents, sources, and processing.

Getting Started

• Install the subseasonal data package: pip install subseasonal-data
• Define the environment variable $SUBSEASONALDATA_PATH to point to your desired data directory; any accessed data files will be read from, saved to, or synced with this directory

This package is compatible with Python version 3.6+.

The underlying data is made available through Azure and is updated through a daily data collection and processing pipeline. To download the data through this package, you will need to have the Azure Storage CLI azcopy installed on your machine.

Usage Examples

Detailed usage examples are provided in the Getting Started and Examples notebooks in the examples folder. It is recommended you start there.

Quick examples:

• Download all data

WARNING: This requires an estimated 175GB disk space.

from subseasonal_data import downloader

downloader.download()

• List files in a data directory

downloader.list_subdir_files(data_subdir="combined_dataframes")

• Download one data file

downloader.download_file(
    data_subdir="combined_dataframes", 
    filename="all_data-us_precip_34w.feather", 
    verbose=True)

• Load ground truth data

from subseasonal_data import data_loaders

# Loads into a Pandas dataframe
df = data_loaders.get_ground_truth("us_precip")

• Load combined dataframes

data_loaders.load_combined_data("all_data", "us_tmp2m", "34w")

See the Examples.ipynb notebook for an example on how to retrieve historical temperature data using the subseasonal_data package.

For Developers

Installation

Install from source in editable mode using pip install -e . in this directory or pip install -e path/to/directory from another directory.

Running tests

To test your installation, run python -m unittest [test_name].py from the subseasonal_data/tests directory or python -m unittest path/to/tests/folder/[test_name].py. Example:

python -m unittest subseasonal_data/tests/test_data_loaders.py

Generating Documentation

This project's documentation is generated via Sphinx. The HTML theme used is the Read the Docs sphinx theme which also needs to be installed.

To generate a local copy of the documentation from a clone of this repository, run python setup.py build_sphinx -W -E -a, which will build the documentation and place it under the build/sphinx/html path.

The reStructuredText files that make up the documentation are stored in the docs directory; module documentation is automatically generated by the Sphinx build process.

Data Usage and Citation

The SubseasonalClimateUSA dataset is released under a CC BY 4.0 license, and the subseasonal_data repository code is released under an MIT license.

If you make use of the subseasonal_data package or the SubseasonalClimateUSA dataset, please acknowledge the Python package, the individual data sources described in DATA.md, and the associated SubseasonalClimateUSA publication:

SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, and Lester Mackey. Advances in Neural Information Processing Systems (NeurIPS). Dec. 2023.

@InProceedings{mouatadid2023subseasonal,
  title = {SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking},
  author = {Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, and Lester Mackey},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2023},
  volume = {36},
  publisher = {Curran Associates, Inc.},
  editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

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