Legacy Jupyter Notebook Tutorials

These Jupyter (http://jupyter.org) notebooks show the usage of the PyEMMA API in action and also describe the workflow of Markov model building.

You can download a copy of all notebooks and most of the used data here. Note that the trajectory of the D.E. Shaw BPTI simulation trajectory is not included in this archive, since we’re not permitted to share this data. Thus the corresponding notebooks can’t be run without obtaining the simulation trajectory independently.

Application walkthroughs

• Markov state model for pentapeptide
• Markov state model for BPTI

By means of 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)

Methods

In this section we will give you in-depth tutorials on specific methods or concepts.

• Input feature selection
• Model selection and validation
• Transition path theory
• Augmented Markov models, a walk-through tutorial
• AMM of GB3
• Validations and sanity checks
• Save your models in PyEMMA

Multi-Ensemble Markov state models

• Analyzing multi-temperature simulations with PyEMMA
• Analyzing umbrella sampling simulations with PyEMMA

Estimation with fixed equilibrium distribution

• Doublewell potential
• Alanine dipeptide