We provide a collection of example notebooks to get a better idea of how to use pyABC, and illustrate core features.
The notebooks can be run locally with an installation of jupyter
pip install jupyter), or online on Google Colab or nbviewer, following the
links at the top of each notebook.
To run the notebooks online, at least an installation of pyABC is required,
which can be performed by
# install if not done yet !pip install pyabc --quiet
Potentially, further dependencies may be required. Unfortunately, at the moment (2022-06), Google Colab is using Python 3.7, while pyABC and many other packages have proceeded to require Python >= 3.8. Thus, not everything may work properly.
Algorithms and features
- Early stopping of model simulations
- Resuming stored ABC runs
- Custom priors
- Adaptive distances
- Informative distances and summary statistics
- Aggregating and weighting diverse data
- Wasserstein distances
- Data plots
- Measurement noise and exact inference
- Optimal acceptance thresholds
- Discrete parameters
- Look-ahead sampling
- Ordinary differential equations: Conversion reaction
- Markov jump process: Reaction network
- Multi-scale model: Tumor spheroid growth
- Stochastic differential equation: Ion channel noise in Hodgkin-Huxley neurons
- PEtab application example
Upgrade to the latest pyABC version before running the examples. If you installed pyABC some weeks (or days) a ago, some new features might have been added in the meantime. Refer to the Upgrading section on how to upgrade pyABC.