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)
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
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
Dealing with multi-ensemble molecular dynamics simulations in PyEMMA
Useful hints about practical issues…