We provide a collection of example notebooks to get a better idea of how to use pyPESTO, 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.
At least an installation of pyPESTO is required, which can be performed by
# install if not done yet !pip install pypesto --quiet
Potentially, further dependencies may be required.
- pyPESTO: Getting started
- Objective Definition
- Fitting of large scale models
- Uncertainty quantification
- Software Development Standards:
- Further topics
- Rosenbrock banana
PEtab and AMICI
Algorithms and features
- Fixed parameters
- Prior definition
- A sampler study
- MCMC sampling diagnostics
- Save and load results as HDF5 files
- Model Selection
- Julia objectives
- Hierarchical optimization