popcast: Long-term forecasts for pathogen populations

See the methods of Huddleston et al. 2020 for more details or to cite this tool.

Install

For a full installation with the OpenCV package that's required for model fitting, specify the "full" package dependencies.

python -m pip install 'popcast[full]'

For a smaller installation that only supports forecasting with an existing model, omit the optional package dependency.

python -m pip install popcast

Usage

Download seasonal influenza A/H3N2 data for model fitting.

curl -LO "https://github.com/blab/flu-forecasting/raw/master/results/builds/natural/natural_sample_1_with_90_vpm_sliding/tip_attributes_with_weighted_distances.tsv"

Fit a model using default 6 year training windows and 12-month forecasts.

popcast fit \
  --tip-attributes tip_attributes_with_weighted_distances.tsv \
  --output lbi_model.json \
  --predictors lbi

Development

Install locally

python -m pip install '.[full,test]'

Lint and run tests

Lint code.

flake8 . --count --show-source --statistics

Run tests.

cram --shell=/bin/bash tests/

Publish

Install or upgrade publishing tools.

python -m pip install --upgrade build twine

Build the distribution packages.

python -m build

Upload the distribution packages.

python -m twine upload dist/*