Pre-release
This release is a pre-release and may not be stable for production use.
=====================================
EMMA (Emma's Markov Model Algorithms)
=====================================
.. image:: https://travis-ci.org/markovmodel/PyEMMA.svg?branch=devel
:target: https://travis-ci.org/markovmodel/PyEMMA
.. image:: https://badge.fury.io/py/pyemma.svg
:target: https://pypi.python.org/pypi/pyemma
.. image:: https://img.shields.io/pypi/dm/pyemma.svg
:target: https://pypi.python.org/pypi/pyemma
.. image:: https://anaconda.org/xavier/binstar/badges/downloads.svg
:target: https://anaconda.org/omnia/pyemma
.. image:: https://anaconda.org/omnia/pyemma/badges/installer/conda.svg
:target: https://conda.anaconda.org/omnia
.. image:: https://coveralls.io/repos/markovmodel/PyEMMA/badge.svg?branch=devel
:target: https://coveralls.io/r/markovmodel/PyEMMA?branch=devel
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 <http://www.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 <http://www.emma-project.org/latest/INSTALL.html>`__ 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 <http://github.com/markovmodel/PyEMMA>`__.
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
EMMA (Emma's Markov Model Algorithms)
=====================================
.. image:: https://travis-ci.org/markovmodel/PyEMMA.svg?branch=devel
:target: https://travis-ci.org/markovmodel/PyEMMA
.. image:: https://badge.fury.io/py/pyemma.svg
:target: https://pypi.python.org/pypi/pyemma
.. image:: https://img.shields.io/pypi/dm/pyemma.svg
:target: https://pypi.python.org/pypi/pyemma
.. image:: https://anaconda.org/xavier/binstar/badges/downloads.svg
:target: https://anaconda.org/omnia/pyemma
.. image:: https://anaconda.org/omnia/pyemma/badges/installer/conda.svg
:target: https://conda.anaconda.org/omnia
.. image:: https://coveralls.io/repos/markovmodel/PyEMMA/badge.svg?branch=devel
:target: https://coveralls.io/r/markovmodel/PyEMMA?branch=devel
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 <http://www.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 <http://www.emma-project.org/latest/INSTALL.html>`__ 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 <http://github.com/markovmodel/PyEMMA>`__.
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
Release files for pyEMMA 2.1rc3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pyEMMA-2.1rc3.tar.gz | 768.4 kB | Details |
Release files / pyEMMA-2.1rc3.tar.gz
| Download URL | pyEMMA-2.1rc3.tar.gz |
|---|---|
| Size | 768.4 kB |
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