Metadata-Version: 2.1
Name: optimeed
Version: 1.4.0
Summary: Powerful ptimization and vizualisation tool.
Home-page: https://git.immc.ucl.ac.be/chdegreef/optimeed
Author: Christophe De Greef
Author-email: christophe.degreef@uclouvain.be
License: UNKNOWN
Description: Optimeed is a very powerful optimization and data visualization tool.
        It handles gradient-free optimizations, e.g., NSGAII or Particle Swarm optimizations.
        The true power of this package resides in interactive graph interactions, that allow to visualize, delete, extract, ... data easily.
        See [The documentation](https://readthedocs.org/projects/optimeed/) for more info.
        
        **Requirements**
        
        * PyQt5 for visualisation -> `pip install PyQt5`
        * `pyopengl` for visualisation -> `pip install PyOpenGL`
        * Numpy -> `pip install numpy`
        * Optional
            * pandas which is only used to export excel files -> `pip install pandas`
            * `nlopt` library for using other types of algorithm. -> `pip install nlopt`
            * inkscape software for exporting graphs in .png and .pdf)
            * `plotly` library for 3D plots. -> `pip install plotly`
            
        **Installation**
        
        To install the latest optimeed release, run the following command:
        
            pip install optimeed
        
        To install the latest development version of optimeed, run the following commands:
        
            git clone https://git.immc.ucl.ac.be/chdegreef/optimeed.git
            cd optimeed
            python setup.py install
        
        **Support**
        
        [Documentation optimeed](https://optimeed.readthedocs.io/en/latest/)
        
        or 
        
        Gitlab (preferably), read [the guided tutorials](https://git.immc.ucl.ac.be/chdegreef/optimeed/-/tree/dev/tutorials).
        
        or 
         
        Send mail at christophe.degreef@uclouvain.be.
        
        **License**
        
        The project is distributed "has it is" under [GNU General Public License v3.0 (GPL)](https://www.gnu.org/licenses/gpl-3.0.fr.html), which is a strong copyleft license.
        This means that the code is open-source and you are free to do anything you want with it, **as long as you apply the same license to distribute your code**.
        This constraining license is imposed by the use of [Platypus Library](https://platypus.readthedocs.io/en/docs/index.html) as "optimization algorithm library", which is under GPL license.
        
        It is perfectly possible to use other optimization library (which would use the same algorithms but with a different implementation) and to interface it to this project, so that the use of platypus is no longer needed. This work has already been done for [NLopt](https://nlopt.readthedocs.io/en/latest/), which is under MIT license (not constraining at all).
        In that case, **after removing all the platypus sources** (optiAlgorithms/multiObjective_GA and optiAlgorithsm/platypus/*), the license of the present work becomes less restrictive: [GNU Lesser General Public License (LGPL)](https://www.gnu.org/licenses/lgpl-3.0.html). As for the GPL, this license makes the project open-source and free to be modified, but (nearly) no limitation is made to distribute your code.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: with_matplotlib
Provides-Extra: with_pandas
Provides-Extra: with_plotly
