semvec/pypi-packages huggingface dataset:
gimpact 1.0.2
An unofficial python extension for the GImpact collision library
Requires Python: >=3.6
Keywords: GImpact, Trimesh, Collision detection, Cython

GImpact-Python
==================
An unofficial python extension for the [GImpact collision library](http://gimpact.sourceforge.net/manual/gimpact_manual.html). This extension integrates directly with GImpact's C++ API using Cython.

Features
---------
* Create trimesh object from numpy array
* Mesh decimation using Sven Forstmann's C++ mesh simplification [code](https://github.com/sp4cerat/Fast-Quadric-Mesh-Simplification) 
* Axis Aligned Bounding Box (AABB)
* AABB set for box prunning
* Collision of triangle mesh with the following
    * triangle mesh
    * sphere
    * capsule
    * plane
    * ray
 * Supports "first contact" or "all contacts" modes

Build Wheel
-----------
``` shell
pip install -q build
python -m build
```


Installation
------------
Build requires numpy and cython (tested on Linux and Windows).
``` shell
pip install gimpact
```

Example Usage
-------------
AABB
```  python
import gimpact


aabb1 = gimpact.AABB(-1, 1, -1, 1, -1, 1)
aabb2 = gimpact.AABB(-1, 1, -1, 1, 1.5, 2)

print(aabb1.intersects(aabb2))
print(aabb1.intersection(aabb2))

aabb1.merge(aabb2)
print(aabb1)
```

Box Prunning
``` python
import gimpact

aabb_set = gimpact.AABBSet(10)
print(len(aabb_set))
print(aabb_set.global_bounds)
for aabb in aabb_set:
    aabb.bounds = (0., 0., 0., 0., 0., 0.)

for aabb in aabb_set:
    print(aabb)

print(aabb_set.global_bounds)
pairs = aabb_set.find_intersections(aabb_set)
print(pairs)

del aabb_set
print(aabb.bounds)
```

Collision
```  python
import gimpact
import numpy as np

contacts = gimpact.trimesh_trimesh_collision(trimesh1, trimesh2)
contacts = gimpact.trimesh_sphere_collision(trimesh1, [0., 0., 0.], 1, True)
contacts = gimpact.trimesh_capsule_collision(trimesh1, np.array([0., 0., 0.]), np.array([1., 0., 0.]), 1, True)
contacts = gimpact.trimesh_plane_collision(trimesh1, [0., 0., 1., 0.], True)
```
mlpro-int-optuna 1.0.1
MLPro: Integration Optuna
Requires Python: >=3.11

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<img src="https://github.com/fhswf/MLPro-Int-Optuna/blob/main/doc/logo/original/logo.png?raw=True" align="right" width="40%"/>

# MLPro-Int-Optuna - Integration of Optuna into MLPro
Welcome to MLPro-Int-Optuna, an extension to MLPro to integrate the Hyperopt package. MLPro is a middleware framework for standardized machine learning in Python. It is developed by the South Westphalia University of Applied Sciences, Germany, and provides standards, templates, and processes for hybrid machine learning applications. Optuna, in turn, provides state-of-the-art algorithms for hyperparameter search and optimization.

MLPro-Int-Optuna provides wrapper classes that enable the use of selected Optuna functionalities in your MLPro applications. The use of these wrappers is illustrated in numerous example programs.

### Learn more
[MLPro - The integrative middleware framework for standardized machine learning in Python](https://mlpro.readthedocs.io)   
[MLPro-Int-Optuna - Integration of Optuna into MLPro](https://mlpro-int-optuna.readthedocs.io)   
[Optuna: Open Source Hyperparameter optimization framework](https://optuna.org/)
mlpro-int-hyperopt 1.0.2
MLPro: Integration Hyperopt
Requires Python: >=3.11

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<!---
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<img src="https://github.com/fhswf/MLPro-Int-Hyperopt/blob/main/doc/logo/original/logo.png?raw=True" align="right" width="40%"/>

# MLPro-Int-Hyperopt - Integration of Hyperopt into MLPro
Welcome to MLPro-Int-Hyperopt, an extension to MLPro to integrate the Hyperopt package. MLPro is a middleware framework for standardized machine learning in Python. It is developed by the South Westphalia University of Applied Sciences, Germany, and provides standards, templates, and processes for hybrid machine learning applications. Hyperopt, in turn, provides state-of-the-art algorithms for hyperparameter search and optimization.

MLPro-Int-Hyperopt provides wrapper classes that enable the use of selected Hyperopt functionalities in your MLPro applications. The use of these wrappers is illustrated in numerous example programs.

### Learn more
[MLPro - The integrative middleware framework for standardized machine learning in Python](https://mlpro.readthedocs.io)   
[MLPro-Int-Hyperopt - Integration of Hyperopt into MLPro](https://mlpro-int-hyperopt.readthedocs.io)   
[Hyperopt: Distributed Asynchronous Hyperparameter Optimization](https://hyperopt.github.io/hyperopt/)
mlpro-int-mujoco 1.0.3
MLPro: Integration MuJoCo
Requires Python: >=3.11

[![CI](https://github.com/fhswf/MLPro-Int-MuJoCo/actions/workflows/ci.yml/badge.svg)](https://github.com/fhswf/MLPro-Int-MuJoCo/actions/workflows/ci.yml)
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<img src="https://github.com/fhswf/MLPro-Int-MuJoCo/blob/main/doc/logo/original/logo.png?raw=True" align="right" width="40%"/>

# MLPro-Int-MuJoCo - Integration between MuJoCo and MLPro
Welcome to MLPro-Int-MuJoCo, an extension to MLPro to integrate the MuJoCo package. MLPro is a middleware framework for standardized machine learning in Python. It is developed by the South Westphalia University of Applied Sciences, Germany, and provides standards, templates, and processes for hybrid machine learning applications. MuJoCo (Multi-Joint dynamics with Contact), in turn, is a physics engine primarily used for simulating and optimizing complex robotic systems and physical interactions in various fields such as robotics, biomechanics, and machine learning.

MLPro-Int-MuJoCo offers wrapper classes that allow the reuse of functionalities from MuJoCo in MLPro.

### Learn more
[MLPro - The integrative middleware framework for standardized machine learning in Python](https://mlpro.readthedocs.io)   
[MLPro-Int-MuJoCo - Integration of MuJoCo into MLPro](https://mlpro-int-mujoco.readthedocs.io)   
[MuJoCo - Advanced Physics Simulation](https://mujoco.org/)