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Introduction & Features

There is an academic paper describing various aspects of the design and usage of wepy:

Weighted Ensemble (WE)

The weighted ensemble algorithm (WE) is a strategy for simulating rare or long-timescale events in stochastic systems (Huber, 1996). It creates several parallel simulations called walkers with individual weights corresponding to their likelihood. Throughout the simulation, walkers that exhibit behaviors or reach states that are of particular relevance or significance to the objectives of the simulation are cloned. To compensate for cloning, and to manage the computational expense of the simulation, some of the remaining walkers are merged. Cloning and merging are together referred to as “resampling”. It has been previously shown that WE resampling is “statistically exact”, in that it does not change the expectation values of the weights at any point in space.

Features of Wepy

  • State of the art WE resamplers: WExplore (Dickson, 2014) and REVO (Donyapour, 2019)
  • Fast GPU-enabled molecular dynamics via OpenMM (Eastman, 2013)
  • Purpose built HDF5 storage format for WE data with extensive API: WepyHDF5
  • Analysis routines for:
    • free energy profiles
    • rate calculations
    • computing trajectory observables
    • extracting linear trajectories from clone-merge trees
    • aggregating multiple runs
  • Expert friendly: Fully-featured framework for building and customizing simulations for exactly what you need.
  • Leverage the entire python ecosystem.
  • No ad hoc configuration files, everything is python.

Once you have wepy @@rst::any:`installed <installation>`@@ you can check out the @@rst::any:`quickstart <quick_start/index>`@@ to get a rough idea of how it works.

Then you can head on to the @@rst::any:`tutorials <tutorials/index>`@@ or execute the examples.

Contributed wepy libraries and other useful resources

Here is a list of packages that are not in the main Wepy repository but may be of interest to users of Wepy.

They are:

geomm
purely functional library for common numerical routines in computational biology and chemistry, with no dependency on specific file or topology formats.
CSNAnalysis
small library for aiding in the analysis of conformation state networks (CSNs) which can be generated from Wepy data.