Learn PyEMMA¶
We provide two major sources of learning materials to master PyEMMA, our collection of Jupyter notebook tutorials and videos of talks given at our annual workshop.
The notebooks are a collection of complete application walk-throughs capturing the most important aspects of building and analyzing a Markov state model.
Jupyter notebook tutorials¶
By means of three different application examples, these notebooks give an overview of following methods:
- Featurization and MD trajectory input
- Time-lagged independent component analysis (TICA)
- Clustering
- Markov state model (MSM) estimation and validation
- Computing metastable states and structures, coarse-grained MSMs
- Hidden Markov Models (HMM)
- Transition Path Theory (TPT)
These tutorials are part of a LiveCOMS journal article and are up to date with the current PyEMMA release.
You can find the article here.
If you find a mistake or have suggestions for improving parts of the tutorial, you can file issues and pull requests for the contents of both the article and the jupyter notebooks here.
- 00 - Showcase pentapeptide: a PyEMMA walkthrough
- 01 - Data-I/O and featurization
- 02 - Dimension reduction and discretization
- 03 - MSM estimation and validation
- 04 - MSM analysis
- 05 - PCCA and TPT analysis
- 06 - Expectations and observables
- 07 - Hidden Markov state models (HMMs)
- 08 - Common problems & bad data situations
The legacy tutorials (prior version 2.5.5) covering similar aspects and advanced topics can be found here Legacy Jupyter Notebook Tutorials
Workshop video tutorials¶
On our Youtube channel you will find lectures and talks about:
- Markov state model theory
- Hidden Markov state models
- Transition path theory
- Enhanced sampling
- Dealing with multi-ensemble molecular dynamics simulations in PyEMMA
- Useful hints about practical issues…
2019 Workshop¶
Stay tuned…