https://github.com/andim/scipydirect

Python wrapper to the DIRECT global optimization algorithm with scipy.optimize compatible call syntax

https://github.com/andim/scipydirect

Science Score: 20.0%

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  • codemeta.json file
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  • Academic publication links
    Links to: zenodo.org
  • Committers with academic emails
    1 of 7 committers (14.3%) from academic institutions
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  • Scientific vocabulary similarity
    Low similarity (17.0%) to scientific vocabulary

Keywords

optimization
Last synced: 10 months ago · JSON representation

Repository

Python wrapper to the DIRECT global optimization algorithm with scipy.optimize compatible call syntax

Basic Info
Statistics
  • Stars: 30
  • Watchers: 2
  • Forks: 7
  • Open Issues: 0
  • Releases: 0
Topics
optimization
Created over 10 years ago · Last pushed almost 4 years ago
Metadata Files
Readme License

README.md

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DOI

ScipyDIRECT: a Python wrapper of the DIRECT global optimization algorithm

DIRECT is a method to solve global bound constraint optimization problems and was originally developed by D. R. Jones, C. D. Perttunen and B. E. Stuckmann.

ScipyDIRECT is a python wrapper around DIRECT. It enables using DIRECT from the comfort of the Python scripting language.

The Scipydirect package uses the Fortran implementation of DIRECT written by Joerg M. Gablonsky, DIRECT Version 2.0.4. More information on the DIRECT algorithm can be found in Gablonsky's thesis.

ScipyDIRECT is a fork of pydirect. It provides an alternative interface to the DIRECT algorithm compatible with that used in scipy.optimize.

Note that as of scipy version 1.9 the DIRECT algorithm is included in scipy.optimize, which supersedes this package.

Quick start

ScipyDIRECT is on PyPI so you can install it using pip install scipydirect. As a prerequisite make you sure you have a Fortran compiler such as gfortran installed.

Then run the following minimal example.

```python import numpy as np from scipydirect import minimize

def obj(x): return (x**2).sum()

bounds = [[-3.0, 3.0], [0.5, 5.0]] res = minimize(obj, bounds=bounds) print(res) ```

Documentation

You can access the documentation online at Read the docs. If you install from source you can generate a local version by running make html from the doc directory.

Support and contributing

For bug reports and enhancement requests use the Github issue tool, or (even better!) open a pull request with relevant changes. If you have any questions don't hesitate to contact me by email (andimscience@gmail.com) or Twitter (@andimscience).

You can run the testsuite from the test directory:

cd test python test_direct.py

If you receive the warning message "Fortran code not compiled", when you try to use the package the installation has not been successful. Numpy.distutils is used to handle the Fortran dependency, so you might find advice for how to solve your problems by searching for "numpy distutils fortran" online. Unfortunately Fortran installation issues seem to be common, but I cannot assist you in that regard as such an issue is specific to the details of your computer setup.

Owner

  • Name: Andreas Tiffeau-Mayer
  • Login: andim
  • Kind: user
  • Location: London
  • Company: University College London

Quantitative Immunology, Biological Physics

GitHub Events

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  • Watch event: 2
Last Year
  • Watch event: 2

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 63
  • Total Committers: 7
  • Avg Commits per committer: 9.0
  • Development Distribution Score (DDS): 0.143
Top Committers
Name Email Commits
Andreas Mayer a****m@g****m 54
Jungtaek Kim j****m@p****r 3
Ibai i****v@u****m 2
Andreas Mayer a****e@g****m 1
asa a****a@r****k 1
Nicholas Musolino n****o@a****m 1
Ralf Gommers r****s@g****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 9
  • Total pull requests: 5
  • Average time to close issues: 7 months
  • Average time to close pull requests: about 17 hours
  • Total issue authors: 8
  • Total pull request authors: 5
  • Average comments per issue: 2.22
  • Average comments per pull request: 1.0
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
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Top Authors
Issue Authors
  • nmusolino (2)
  • tinutjoy (1)
  • licheng0794 (1)
  • czgdp1807 (1)
  • dschmitz89 (1)
  • yiminzme (1)
  • theSha1do1w (1)
  • ldv1 (1)
Pull Request Authors
  • nmusolino (1)
  • a5a (1)
  • ghost (1)
  • rgommers (1)
  • jungtaekkim (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 31 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 5
  • Total versions: 5
  • Total maintainers: 1
pypi.org: scipydirect

Python wrapper to the DIRECT algorithm

  • Versions: 5
  • Dependent Packages: 1
  • Dependent Repositories: 5
  • Downloads: 31 Last month
Rankings
Dependent packages count: 4.7%
Dependent repos count: 6.6%
Stargazers count: 11.7%
Average: 11.7%
Forks count: 12.5%
Downloads: 23.1%
Maintainers (1)
Last synced: 12 months ago

Dependencies

requirements.txt pypi
  • matplotlib *
  • numpy *
setup.py pypi
  • numpy *