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PyEMMA: Emma's Markov Model Algorithms

Project description

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

Installation

If you want to use Miniconda on Linux or OSX, you can run this script to download and install everything:

curl -s https://raw.githubusercontent.com/markovmodel/PyEMMA/devel/install_miniconda%2Bpyemma.sh | bash

If you have Anaconda/Miniconda installed, use the following:

conda install -c conda-forge pyemma

With pip:

pip install 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/suggestions/complaints please file an issue on GitHub.

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

External Libraries

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