EMMA: Emma's Markov Model Algorithms

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  • License: GNU Lesser General Public License v3 or later (LGPLv3+) (LGPLv3+)
  • Author: Martin K. Scherer
  • Tags Markov, State, Model, Algorithms
Classifiers

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 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, J. 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

Project details

Verified details

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Avatar for clonker from gravatar.com clonker Avatar for marscher from gravatar.com marscher

Unverified details

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Project links
Meta
  • License: GNU Lesser General Public License v3 or later (LGPLv3+) (LGPLv3+)
  • Author: Martin K. Scherer
  • Tags Markov, State, Model, Algorithms
Classifiers

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