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 Jupyter (former IPython, recommended), or by writing Python scripts. The docs, can be found at http://pyemma.org.
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, J. Chem. Theory Comput. 11, 5525-5542 (2015)
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
For bug reports/suggestions/complaints please file an issue on GitHub.
Or start a discussion on our mailing list: [email protected]
- mdtraj (LGPLv3): https://mdtraj.org
- bhmm (LGPLv3): http://github.com/bhmm/bhmm
- msmtools (LGLPv3): http://github.com/markovmodel/msmtools
- thermotools (LGLPv3): http://github.com/markovmodel/thermotools