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Description:

iwopy 0.3.0.2

iwopy
Fraunhofer IWES optimization tools in Python

Overview
The iwopy package is in fact a meta package that provides interfaces to other open-source Python optimization packages out there. Currently this includes

pymoo
pygmo
scipy
(more to come with future versions)

iwopy can thus be understood as an attempt to provide the best of all worlds when it comes to solving optimization problems with Python. This has not yet been achieved, since above list of accessable optimization packages is obviously incomplete, but it's a start. All the credit for implementing the invoked optimizers goes to the original package providers.
The basic idea of iwopy is to provide abstract base classes, that can be concretized for any kind of problem by the users, and the corresponding solver interfaces. However, also some helpful problem wrappers and an original optimizer are provided in addition:

Problem wrapper LocalFD: Calculates derivatives by finite differences
Problem wrapper RegularDiscretizationGrid: Puts the problem on a Grid
Optimizer GG: Greedy Gradient optimization with constraints

All calculations support vectorized evaluation of a complete population of parameters. This is useful for heuristic approaches like genetic algorithms, but also for evaluating gradients. It can lead to a vast speed-up and should be invoked whenever possible. Check the examples (or the API) for details.
Documentation: https://fraunhoferiwes.github.io/iwopy.docs/index.html
Source code: https://github.com/FraunhoferIWES/iwopy
PyPi reference: https://pypi.org/project/iwopy/
Anaconda reference: https://anaconda.org/conda-forge/iwopy
Requirements
The supported Python versions are:

Python 3.7
Python 3.8
Python 3.9
Python 3.10
Python 3.11
Python 3.12

Installation via pip
Virtual Python environment
We recommend working in a Python virtual environment and install iwopy there. Such an environment can be created by
python -m venv /path/to/my_venv

and afterwards be activated by
source /path/to/my_venv/bin/activate

Note that in the above commands /path/to/my_venv is a placeholder that should be replaced by a path to a (non-existing) folder of your choice, for example ~/venv/iwopy.
All subsequent installation commands via pip can then be executed directly within the active environment without changes. After your work with iwopy is done you can leave the environment by the command deactivate.
Standard users
As a standard user, you can install the latest release via pip by
pip install iwopy

This in general corresponds to the main branch at github. Alternatively, you can decide to install the latest pre-release developments (non-stable) by
pip install git+https://github.com/FraunhoferIWES/iwopy@dev#egg=iwopy

Notice that the above default installation does not install the third-party optimization
packages. iwopy will tell you in an error message that it is missing a package, with
a hint of installation advice. You can avoid this step by installing all supported
optimzer packages by installing those optoinal packages by addig [opt]:
pip install iwopy[opt]

or
pip install git+https://github.com/FraunhoferIWES/iwopy@dev#egg=iwopy[opt]

Developers
The first step as a developer is to clone the iwopy repository by
git clone https://github.com/FraunhoferIWES/iwopy.git

Enter the root directory by
cd iwopy

Then you can either install from this directory via
pip install -e .

Notice that the above default installation does not install the third-party optimization
packages. iwopy will tell you in an error message that it is missing a package, with
a hint of installation advice. You can avoid this step by installing all supported
optimzer packages by installing those optoinal packages by addig [opt]:
pip install -e .[opt]

Installation via conda
Preparation (optional)
It is strongly recommend to use the libmamba dependency solver instead of the default solver. Install it once by
conda install conda-libmamba-solver -n base -c conda-forge

We recommend that you set this to be your default solver, by
conda config --set solver libmamba

Standard users
The iwopy package is available on the channel conda-forge. You can install the latest version by
conda install -c conda-forge iwopy

Developers
For developers using conda, we recommend first installing iwopy as described above, then removing only the iwopy package while keeping the dependencies, and then adding iwopy again from a git using conda develop:
conda install iwopy conda-build -c conda-forge
conda remove iwopy --force
git clone https://github.com/FraunhoferIWES/iwopy.git
cd iwopy
conda develop .

Concerning the git clone line, we actually recommend that you fork iwopy on GitHub and then replace that command by cloning your fork instead.
Testing
For testing, please clone the repository and install the required dependencies
(flake8, pytest, pygmo, pymoo):
git clone https://github.com/FraunhoferIWES/iwopy.git
cd iwopy
pip install .[test]

If you are a developer you might want to replace the last line by
pip install -e .[test]

for dynamic installation from the local code base.
The tests are then run by
pytest tests

Contributing
Please feel invited to contribute to iwopy! Here is how:

Fork iwopy on github.
Create a branch (git checkout -b new_branch)
Commit your changes (git commit -am "your awesome message")
Push to the branch (git push origin new_branch)
Create a pull request here

Support
For trouble shooting and support, please

raise an issue here,
or start a discussion here,
or contact the contributers.

Thanks for your help with improving iwopy!