Description:

parasweep 2021.1

parasweep


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!

Free software: MIT license
Documentation: http://www.parasweep.io
Code: https://github.com/eviatarbach/parasweep


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

2021.01 (2021-01-06)

Changing wait option to be True by default
Minor fixes to Lorenz example



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

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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