To help you get started, we've collected the most common ways that pyEMMA is being used within popular public projects.

markovmodel / PyEMMA / pyemma / coordinates / data / _base / datasource.py View on Github

itraj : int
            trajectory index
        stride : int
            return value is the number of frames in the trajectory when
            running through it with a step size of `stride`.
        skip: int or None
            skip n frames.

        Returns
        -------
        int : length of trajectory
        """
        if itraj >= self.ntraj:
            raise IndexError("given index (%s) exceeds number of data sets (%s)."
                             " Zero based indexing!" % (itraj, self.ntraj))
        if not IteratorState.is_uniform_stride(stride):
            selection = stride[stride[:, 0] == itraj][:, 0]
            return 0 if itraj not in selection else len(selection)
        else:
            res = max((self._lengths[itraj] - skip - 1) // int(stride) + 1, 0)
            return res

View more ways to use pyEMMA

EMMA (Emma's Markov Model Algorithms)

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:

> 1. 1. 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:

External Libraries

• mdtraj (LGPLv3):
• thermotools (LGLPv3):