Jupyter Notebook Tutorials¶
These Jupyter (http://jupyter.org), former IPython 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¶
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)