pyEMMA

EMMA: Emma's Markov Model Algorithms

These details have not been verified by PyPI

Project links

Homepage

Project description

https://img.shields.io/travis/markovmodel/PyEMMA/master.svg

https://img.shields.io/pypi/v/pyemma.svg

https://img.shields.io/pypi/dm/pyemma.svg

https://anaconda.org/omnia/pyemma/badges/downloads.svg

https://anaconda.org/omnia/pyemma/badges/installer/conda.svg

https://img.shields.io/codecov/c/github/markovmodel/PyEMMA/devel.svg

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

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/suggestions/complaints 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
thermotools (LGLPv3): http://github.com/markovmodel/thermotools

Project details

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

2.5.12

Sep 13, 2022

2.5.11

Feb 16, 2022

2.5.10

Nov 15, 2021

2.5.9

Feb 26, 2021

2.5.8

Feb 25, 2021

2.5.7

Sep 24, 2019

2.5.6

May 24, 2019

2.5.5

Feb 13, 2019

2.5.4

Jul 20, 2018

2.5.3

Jun 28, 2018

2.5.2

Apr 10, 2018

2.5.1

Feb 17, 2018

2.5

Feb 9, 2018

This version

2.4

May 20, 2017

2.3.2

Feb 19, 2017

2.3.1

Feb 6, 2017

2.3

Jan 6, 2017

2.2.7

Oct 21, 2016

2.2.6

Oct 17, 2016

2.2.4

Sep 20, 2016

2.2.3

Jul 28, 2016

2.2.2

Jul 14, 2016

2.2.1

Jun 21, 2016

2.2

May 18, 2016

2.1.1

Apr 18, 2016

2.1

Apr 7, 2016

2.1rc3 pre-release

Feb 24, 2016

2.1rc2 pre-release

Feb 22, 2016

2.1rc1 pre-release

Feb 19, 2016

2.0.4

Feb 9, 2016

2.0.3

Jan 29, 2016

2.0.2

Nov 9, 2015

2.0.1

Sep 6, 2015

2.0

Sep 1, 2015

1.2.2

Jul 27, 2015

1.2.1

May 29, 2015

1.2

Apr 15, 2015

1.1.2

Mar 18, 2015

1.1.1

Feb 23, 2015

1.1

Feb 22, 2015

1.0.2

Jan 28, 2015

1.0.1

Jan 14, 2015

1.0

Jan 12, 2015

1.0a3 pre-release

Nov 20, 2014

1.0a2 pre-release

Nov 15, 2014

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyEMMA-2.4.tar.gz (1.6 MB view details)

Uploaded May 20, 2017 Source

File details

Details for the file pyEMMA-2.4.tar.gz.

File metadata

Download URL: pyEMMA-2.4.tar.gz
Upload date: May 20, 2017
Size: 1.6 MB
Tags: Source
Uploaded using Trusted Publishing? No

File hashes

Hashes for pyEMMA-2.4.tar.gz
Algorithm	Hash digest
SHA256	`7e428ad255036bc2ca68c7292dd8aede3aae95d1d95029626326c8edde902a1c`
MD5	`0fcd4e9d9aec13e3792414bbfd0a1fad`
BLAKE2b-256	`45d1017a5298d6e67ab777268989c1793eb4d5d0869f366df7f1adf2a36a84e7`