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Utility for facilitating parallel parameter sweeps.

Project description

parasweep

https://img.shields.io/pypi/v/parasweep.svg Documentation Status

parasweep is a free and open-source Python utility for facilitating parallel parameter sweeps with computational models. Instead of requiring parameters to be passed by command-line, which can be error-prone and time-consuming, parasweep leverages the model’s existing configuration files using a template system, requiring minimal code changes. After the sweep values are specified, a parallel job is dispatched for each parameter set, with support for common high-performance computing job schedulers. Post-processing is facilitated by providing a mapping between the parameter sets and the simulations.

The following paper gives a description as well as a simple example to get started: https://arxiv.org/pdf/1905.03448.pdf. Please cite it if you find parasweep useful for your project!

Dependencies

  • Python 3.6+
  • xarray
  • numpy
  • scipy
  • Mako (optional)
  • drmaa-python (optional)

Credits

Developed by Eviatar Bach <eviatarbach@protonmail.com>. Special thanks to Daniel Philipps (danielphili) for a bug fix and feature suggestion.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

2020.10 (2020-10-27)

  • Adding option to exclude simulation IDs
  • Allowing empty sweep ID

2020.09 (2020-09-02)

  • Incorporating sweep_id into SequentialNamer (thanks to danielphili)

2020.02 (2020-02-17)

  • Fixing bug with default sweep_id on Windows (thanks to danielphili)
  • Unicode support for PythonFormatTemplate

2019.02.3 (2019-02-20)

  • Adding SetNamer naming

2019.02.2 (2019-02-18)

  • Adding process limit for subprocess dispatching
  • Adding RandomSweep sweep type
  • Adding HashNamer naming
  • Clarifying version dependencies
  • More examples

2019.02 (2019-02-07)

  • Separating sweep logic into a separate module
  • Adding FilteredCartesianSweep sweep type
  • Numerous documentation changes, including many more examples

2019.01 (2019-01-21)

  • First release on PyPI

Project details


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