semvec/pypi-packages huggingface dataset:
ropt-pymoo 0.25.1
A pymoo plugin for the ropt robust optimization library
Requires Python: >=3.11

# A pymoo optimizer plugin for ropt
This package installs a plugin for the [ropt](https://github.com/tno-ropt/ropt)
robust optimization package, providing access to algorithms from the pymoo
optimization package.

`ropt-pymoo` is developed by the Netherlands Organisation for Applied Scientific
Research (TNO). All files in this repository are released under the GNU General
Public License v3.0 (a copy is provided in the LICENSE file).

See also the online [`ropt`](https://tno-ropt.github.io/ropt/) and
[`ropt-pymoo`](https://tno-ropt.github.io/ropt-pymoo/) manuals for more
information.


## Dependencies
This code has been tested with Python versions 3.11-3.12.

The plugin requires the [`pymoo`](https://pymoo.org/) optimizer.


## Installation
From PyPI:
```bash
pip install ropt-pymoo
```


## Development
The `ropt-pymoo` source distribution can be found on
[GitHub](https://github.com/tno-ropt/ropt-pymoo). It uses a standard
`pyproject.toml` file, which contains build information and configuration
settings for various tools. A development environment can be set up with
compatible tools of your choice.

The `ropt-pymoo` package uses [ruff](https://docs.astral.sh/ruff/) (for
formatting and linting), [mypy](https://www.mypy-lang.org/) (for static typing),
and [pytest](https://docs.pytest.org/en/stable/) (for running the test suite).
pyCandleMAB 1.6.2
Python package for controlling MD80-based actuators

# MD80 Python driver
This Python package can be used to configure and control MD80-based actuators using MAB's communication dongle (CANdle or CANdle HAT). The package is a wrapper around the C++ library - with simple python examples there won’t be any noticeable performance decrease compared to plain C++, however for real-time applications we advise using C++ libraries directly. 

The original C++ libraries and python examples are located in the [candle](https://github.com/mabrobotics/candle) repository. Please make sure you read the [MD80 x CANdle manual](https://www.mabrobotics.pl/servos/manual) before you start playing with the examples.

# Getting started

Assuming you've already read the manual from previous section and performed the necessary setup steps now's the time to install pyCandle package:

```python3 -m pip install pyCandleMAB```

or when you're working with Raspberry PI:

```sudo python3 -m pip install pyCandleMAB```

when the package is installed without errors connect CANdle device to your PC and run the first example:

```python3 ./example1.py```

when a signle actuator is connected to the dongle the console output should look simmilar to:

```[CANDLE] CANdle library version: v3.0
[CANDLE] Creating CANdle object.
[CANDLE] Reset successfull!
[CANDLE] CANdle ready.
[CANDLE] Starting pinging drives...
[CANDLE] Found drives.
[CANDLE] 1: ID = 85 (0x55)
[CANDLE] LEDs blining at ID = 85
EXIT SUCCESS
```
For more examples visit: https://github.com/mabrobotics/candle/tree/main/examples_python
splatwizard 0.0.5
Gaussian Splatting compression toolkit
Requires Python: >=3.7.13

# Splatwizard: Gaussian Splatting Compression Toolkit

[![License](https://img.shields.io/github/license/splatwizard/splatwizard?color=blue)](https://github.com/splatwizard/splatwizard/blob/main/LICENSE.md)
[![PyPI](https://img.shields.io/pypi/v/splatwizard?color=brightgreen)](https://pypi.org/project/splatwizard/)


Splatwizard is a one-stop toolkit designed for researching 3DGS compression, dedicated to accelerating community exploration in this field.
With flexible API design, you can easily combine advanced components from state-of-the-art models to build customized models.
Key features of the project include:

- **Easy-to-adapt evaluation framework** for new models
- **Comprehensive evaluation metrics**
- **Multiple baseline models** included for comparison

## Latest News
- 2025/11/25: Release first version.

## Installation
Splatwizard can be installed via pip
```bash
pip install splatwizard
```

Since the installation process requires significant time to precompile all components, you can monitor the installation progress using the `--verbose` option.
```bash
pip install splatwizard --verbose
```

Additionally, pip compiles components in an isolated environment by default. 
You can use `--no-build-isolation` to perform the compilation directly in the current environment.
Make sure `torch` and `ninja` are installed before running the command.
```bash
pip install splatwizard --verbose --no-build-isolation
```


To speed up compilation, the installation process uses all available CPU cores by default. 
If you need to limit the number of cores used for compilation, you can specify the maximum cores used via environment variable `MAX_BUILD_JOBS`.
The following command will use up to 8 CPU cores during compilation.
```bash
MAX_BUILD_JOBS=8 pip install splatwizard --verbose --no-build-isolation
```


## Quick start

Train your first 3DGS model in splatwizard
```shell
sw-train \
    --source_path /data/MipNeRF-360/bicycle \
    --output_dir /output/gs \                  
    --model 3dgs \
    --optim 3dgs
```

Evaluate model

```shell
sw-eval \
    --source_path /data/MipNeRF-360/bicycle \
    --output_dir /output/gs \                  
    --model 3dgs \
    --optim 3dgs \
    --checkpoint /output/gs/checkpoints/ckpt30000.pth
```

For more models. please check [Model List](splatwizard.github.io/splatwizard/model_zoo/model_list.html)


## Documentation
* [Installation](https://splatwizard.github.io/splatwizard/getting_start/installation.html)
* [Training model](https://splatwizard.github.io/splatwizard/getting_start/first_model.html)
* [How to develop your own model](https://splatwizard.github.io/splatwizard/tutorial/concept.html)
* [Model zoo](https://interdigitalinc.github.io/CompressAI/zoo.html)


## License

Splatwizard is licensed under the MIT License. The project incorporates code from other projects, which remains under their original licenses.