semvec/pypi-packages huggingface dataset:
game-development 1.0.7
Simplify 2D and 3D game development and combine the power of Panda3D and Pygame to create awesome games.
License: MIT
Keywords: game development,pygame,panda3d


    <div align="center">
      <img src="https://github.com/MKM12345/game_development/raw/main/logo.png">
    </div>
    <a style="display:inline;" href="#"><img src="https://img.shields.io/badge/Python- >= 2.7 -blue?style=plastic.svg" alt="python versions" /></a>
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    <h1>game_development v1.0.6</h1>
    A game development library for Python with <em>a lot</em> of assorted functions to simplify both 2D and 3D game development tasks in Pygame and Panda3D while combining their power to create awesome games.
    
    ### GitHub Page: https://github.com/MKM12345/game_development
    
    ## How to install
    Install with pip in your terminal, making sure Python is added to PATH:
    $ pip install game_development
    
    Alternatively, you can use the git URL to do the same.
    $ pip install "git+https://github.com/MKM12345/game_development.git@pip-install#egg=game_development"
    
    ### What can it do?
    As an open-source library, it offers a wide range of simple functions to make writing game code in both Pygame and Panda3D easier and quicker. A complete list of all game_development functions that are available can be found here.
    
    ### Support game_development
    game_development is full of contributions from the community! We're beginner-friendly here, so read the contributing guidelines and give us your best game_development functions 😃!
    
    ### View the site
    Recently, game_development's website launched at https://MKM12345.github.io/game_development-the-site/, feel free to report bugs or give feature requests, using the web-issue label. Or, take a look at the discussion.
    
    ### License
    game_development is licensed under the MIT LICENSE.
pymcfost 0.1.1
MCFOST post-processing in Python'
License: MIT
Requires Python: >=3.8

# pymcfost

pymcfost is a python interface to the 3D radiative transfer code mcfost. The goal is to provide a simple and light interface to explore and plot a single (or a few) model(s).

pymcfost offers (or will offer) the following functionalities:

 - set up continuum and line models,
 - read a single model or library of models,
 - plot basic quantities, e.g. density structures, temperature maps on the various grids available in mcfost,
 - plot observables : SEDs, image (with convolution), polarisation maps and vectors, visibilities, channels maps (with spatial and spectral convolution), moment maps.
 - convert units, e.g. W.m-2 to Jy or brightness temperature
 - provides an interface to the ALMA CASA simulator
 - provides a fast and simplfied version of the ALMA simulator (spatial convolution with Gaussian, spectral convolution and noise), ie ignoring uv sampling,
 - consistent interface with the casa_cube python package to compare observations with models
 - read and plot dust models, including Mie, DHS and aggregates dust properties calculations
 - (TBD) direct interface to the ML chemical predictions

## Installation

### Using pip
```
pip install pymcfost
```

### From the git repo
```
git clone https://github.com/cpinte/pymcfost.git
cd pymcfost
pip install .
```

To install in developer mode: (i.e. so that code changes here are immediately available without needing to repeat the above step):

```
pip install -e .
```

## History

In case you are curious, pymcfost was born as an attempt to port in python the functions that were available in the yorick-mcfost code, which is still available here: https://github.com/cpinte/yomcfost.
The fitting routines of the yorick interface are yet to be ported into pymcfost.
An alternative python distribution is available at https://github.com/swolff9/mcfost-python . It is more tailored towards handling large grid of models and model fitting.

## Main structural differences with mcfost-python so far

- python >= 3.8 vs python 2.x
- only parameter file >= 3.0
- handles parameter files with mutiple zones, dust population, molecules, stars, etc. Parameter files are stored in objects rather than dicts, allowing more flexibility.
- does not and will not handle observational data, only models