Flux/Rate Analysis in OpenPathSampling

Software Technical Information

The information in this section describes OpenPathSampling as a whole. Information specific to the additions in this module are in subsequent sections.

Language
Python (2.7)
Documentation Tool
Sphinx, numpydoc format (ReST)
Application Documentation
http://openpathsampling.org
Relevant Training Material
http://openpathsampling.org/latest/examples/
Licence
LGPL, v. 2.1 or later

Authors: David W.H. Swenson

This module adds the ability to use existing trajectories to calculate the flux through an interface or the rate of a transition in OpenPathSampling.

Purpose of Module

Calculating the flux out of a state and through a given interface is an important step in transition interface sampling (TIS). It is required for the rate calculation, and can be used to determine the best location for the innermost interface before performing the main TIS sampling.

In many cases, the main TIS sampling is only performed after a significant amount of direct MD has been performed. For example, MD trajectories may have already been used to identify and test the stability of stable states. This module calculates the flux (as well as the rate, a related quantity) from existing trajectories. These can be trajectories generated with OPS or loaded into OPS from other simulation packages, such as Gromacs.

The flux calculation is used as part of obtaining the rate in TIS. While some variants of TIS calculate the flux as part of their primary sampling, others need an auxiliary calculation. In addition, the flux calculation is always useful for confirming a good definition of the innermost interface in TIS, and using an existing trajectory to select the location of the innermost interface does not require re-running the dynamics.

The rate calculation will probably not be used very much, because rates require extremely long trajectories. Both flux and rate are included as a single “module” because the code is very closely related.

This module analyses previously existing trajectories. Another module will calculate these things on-the-fly with an OPS engine.

The primary new objects in this module are:

  • TrajectoryTransitionAnalysis: Contains all the methods to perform the analysis. The overall approach is based on identifying subtrajectory segments that correspond to various conditions, e.g., time in one volume before entering another volume (lifetimes, which are then related to the rate). By keeping these subtrajectories, this module also enables visualization on time traces of the trajectory.
  • TrajectorySegmentContainer: Container class for a list of trajectory segments, created as results of the TrajectoryTransitionAnalysis object. Also includes conveniences for reporting by either number of frames or time: each is reported as a numpy array, so that numpy’s built in statistics can be used for, e.g., mean or standard deviation.

Background Information

This module builds on OpenPathSampling, a Python package for path sampling simulations. To learn more about OpenPathSampling, you might be interested in reading:

Testing

Tests in OpenPathSampling use the nose package.

This module has been included in the OpenPathSampling core. Its tests can be run by setting up a developer install of OpenPathSampling and running the command nosetests from the root directory of the repository.