EMMA (Emma's Markov Model Algorithms)

What is it?

PyEMMA (EMMA = Emma's Markov Model Algorithms) is an open source Python/C package for analysis of extensive molecular dynamics simulations. In particular, it includes algorithms for estimation, validation and analysis of:

• Clustering and Featurization
• Markov state models (MSMs)
• Hidden Markov models (HMMs)
• multi-ensemble Markov models (MEMMs)
• Time-lagged independent component analysis (TICA)
• Transition Path Theory (TPT)

PyEMMA can be used from Jupyther (former IPython, recommended), or by writing Python scripts. The docs, can be found at http://pyemma.org.

Citation

If you use PyEMMA in scientific work, please cite:

M. K. Scherer, B. Trendelkamp-Schroer, F. Paul, G. Pérez-Hernández, M. Hoffmann, N. Plattner, C. Wehmeyer, J.-H. Prinz and F. Noé: PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models,

10. Chem. Theory Comput. 11, 5525-5542 (2015)

Installation

With pip:

pip install pyemma

with conda:

conda install -c omnia pyemma

or install latest devel branch with pip:

pip install git+https://github.com/markovmodel/PyEMMA.git@devel

For a complete guide to installation, please have a look at the version online or offline in file doc/source/INSTALL.rst

To build the documentation offline you should install the requirements with:

pip install -r requirements-build-doc.txt

Then build with make:

cd doc; make html

Support and development

For bug reports/sugguestions/complains please file an issue on GitHub.

Or start a discussion on our mailing list: pyemma-users@lists.fu-berlin.de

External Libraries

• mdtraj (LGPLv3): https://mdtraj.org
• bhmm (LGPLv3): http://github.com/bhmm/bhmm
• msmtools (LGLPv3): http://github.com/markovmodel/msmtools