semvec/pypi-packages huggingface dataset:
ImgGameLib 0.2.1
A lightweight wrapper around pillow for making games with images
Requires Python: >=3.7

# ImgGameLib
A light wrapper around [pillow](https://pillow.readthedocs.io/en/stable/) designed for the making of games through generating images.

This is useful in several scenarios in which you are very limited, such as with discord bots.

## Example
```py
# Import the module
import ImgGameLib as igl

# Create a canvas
canvas = igl.Canvas(
    100,
    100,
    bg_color="#99CDDE",
    gif=True
)

# Create a 100x100 rectangle at (50,50) and draw it
rect = igl.Rectangle(
    50,
    50,
    100,
    100
)
rect.draw(canvas)

# Move the rectangle by 5 pixels on the x axis
rect.move(x=5)

# Save the first frame to frame_1.png, and discard all animated frames
canvas.save("frame_1.png", no_gif=True)
canvas.discard()

# Move the rectangle by 10 pixels on the x axis, 10 times
for _ in range(10):
    rect.move(x=10)

# Save the animation after that to animation.gif
canvas.save("animation.gif", loop=True, duration=20)
```
See the examples folder for more examples.
cerealtest 0.2.0
Serial Port Test Automation
Requires Python: >=3.8

# CerealTest (WIP)

This tool automates serial tests via a JSON configuration file where you specify port settings and the array of tests that will be run.

You can specify the message to send as text or as hexadecimal data,  expected answer via a regular expression, or you can set a Python script to compute the message to send as well as a reception script that can process the answer for maximum flexibility.


## Contributions
This repository is open to contributions, however code must be compatible with Python 3.8, the latest version that runs on Windows 7.
rrtplanner 0.1.2
Vectorized RRT, RRT*, and RRT*Informed planning in Python!
Requires Python: >=3.6, <4
Keywords: robotics,path planning

# RRT Path planning with Python!

![Screenshot of RRTstar path](./figure.png)

This repository contains my planning algorithm research code. It has modules for creating random or dynamic occupancy grids on which to generate plans, utilities for plotting and animating plans, and implementations of the RRT, RRT*, and RRT-Informed planners. 

## Features

Utility Modules:
+ Random World Generation with Perlin Noise
+ Animation Module
+ Dubins Primitive Module

Planning Modules:
+ RRT Planner
+ RRT(star) Planner
+ RRT(informed) Planner
+ Dubins Vehicle RRT Planner
+ Dubins Vehicle RRT(star) Planner

Path Following Modules:
+ Straight Line Vector-Field Path-Follower
+ Decomposition of multi-waypoint plan for vector-field follower using voronoi regions

All algorithms are written in Python. I've tried to accelerate some things with Numba; so, your platform needs to be [compatible with numba](https://numba.readthedocs.io/en/stable/user/installing.html). But for the most part, it's standard python, and as such, is extremely slow.

This is research code, so it's not *at all* a robust package to use for a real planner. Let me know how you are using it!

## Dependency Notes

It relies on Numba to accelerate some collision logic:

https://numba.readthedocs.io/en/stable/user/installing.html

`pyfastnoisesimd` relies on SIMD instructions available only on modern processors to speed up perlin noise generation for random occupancyGrids. I'm not sure how it degrades if those instructions are not available. See https://pyfastnoisesimd.readthedocs.io/en/latest/overview.html.

## Information:

RRT:

+ [LaValle's RRT Page.](http://lavalle.pl/rrt/)
+ [RRT * Paper](https://arxiv.org/abs/1005.0416)
+ [Informed RRT* Paper](https://arxiv.org/abs/1404.2334v3)

Planners:

+ [Planning Algorithms](http://lavalle.pl/planning/)

Path Followers:

+ [Vector Field Path Following for Miniature Air Vehicles](https://ieeexplore.ieee.org/document/4252175)
+ [Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicles](https://ieeexplore.ieee.org/document/6712082)