semvec/pypi-packages huggingface dataset:
pysdl-gpu 0.1.3
A wrapper around SDL_gpu
License: MIT

pysdl-gpu
=========

pysdl-gpu is a wrapper around the SDL_gpu library. It depends on PySDL2,
and it uses ctypes.

The SDL_gpu library, as well as its documentation, can be found here:
https://github.com/grimfang4/sdl-gpu

Feel free to report bugs or make pull requests here:
https://bitbucket.org/Jjp137/pysdl-gpu

Note that this library is intended to match the sdl_gpu.h header as closely
as possible, and thus, following PEP-8 is less of a priority.

Usage
-----

pysdl-gpu requires SDL2 and SDL_gpu to be installed on your system.
Alternatively, the environment variable ``PYSDL2_DLL_PATH`` can be used to
point pysdl-gpu to the location of the SDL2 and SDL_gpu libraries.

Once installed, simply import the library::

    import sdlgpu

Refer to SDL_gpu's documentation for a list of available functions, enums,
and structures.

Licensing
---------

pysdl-gpu is licensed under the terms of the MIT License. For more details
on the terms of this license, refer to LICENSE.txt.
frankenz 0.1.5
A photometric redshift monstrosity
License: MIT
Keywords: photo-z

frankenz
=========
#### A photometric redshift monstrosity.

**WARNING: This project is under active development and not yet stable.**

`frankenz` is a Pure Python implementation of a variety of methods to quickly
yet robustly perform (hierarchical) Bayesian inference using large
(but discrete) sets of (possibly noisy) models with (noisy) photometric data.
The code also contains a number of additional utilities, including:
- a module for generating quick mocks (along with filter curves and SEDs), 
- several manifold-learning algorithms,
- a flexible set of photometric likelihoods,
- fast kernel density estimation, and
- PDF-oriented plotting utilities.

Paper forthcoming.

### Documentation
**Currently nonexistent.** See the demos for examples.

### Installation
`frankenz` can be installed via
```
pip install frankenz
```
Alternately, it can also be installed by running
```
python setup.py install
```
from inside the repository.

### Demos
Several Jupyter notebooks that demonstrate most of the available features
can be found [here](https://github.com/joshspeagle/frankenz/tree/master/demos).