semvec/pypi-packages huggingface dataset:
kartingpros 1.0.0
A 2D racing game

# Karting-Pros
![Python Upload](https://github.com/kosta2456/Karting-Pros/workflows/Upload%20Python%20Package/badge.svg)  [![PyPI version](https://badge.fury.io/py/kartingpros.svg)](https://badge.fury.io/py/kartingpros)
## About
Karting Pros is a 2-D top view racing game made on python using the pygames library. The current version has multiple tracks which includes a time trial mode to test your best lap times agains yourself, a multiplayer mode that allows you to play against your friends on the same key board, and an AI mode with different difficulty levels to play against.
## Authors

- Kevin Kosta

- Vishvak Seenichamy

- Wesly Boyd

- Carson Hamel

## Installation and Dependencies
In order to run karting pros the user should use the following commands

```
$ pip install kartingpros
```
In order to run the game, the user must use the following command
```
$ KartingPros
```
## Developing
https://github.com/Vishvak365/Karting-Pros/wiki/Developing-and-Running-Karting-Pros

## Github Link to Repository
https://github.com/Vishvak365/Karting-Pros
poopylab-test 0.1.0.20201129
An open source simulation tool for biological wastewater treatment, by the poop nerds, for the the poop nerds.
Requires Python: >=3
Keywords: ASM Wastewater Simulation

PooPyLab is a biological wastewater treatment software.

Please see the project wiki for more details. Well, there has not been a whole
lot of details on the wiki yet, but more than what's on here.

Follow the project on Twitter: @poopylab .

More documentation:
[toogad.github.io/brownbook](https://toogad.github.io/brownbook/index.html)
deep-recovery-sollan 0.1.8
Deep Recovery--a GUI for deep-learning-assisted kinematic data analysis
Requires Python: >=3.6

# Slip detector GUI

An add-on for DeepLabCut-assisted behavioral analysis. 

### To get started

```bash
pip install -r requirements.txt
python ./slip_detector.py
```

Note: wxPython might not install at first if the gtk requirements are not met. Try 
```
pip install -U -f https://extras.wxpython.org/wxPython4/extras/linux/gtk2/ubuntu-16.04/ wxPython
```

(https://wxpython.org/blog/2017-08-17-builds-for-linux-with-pip/index.html)

If libsdl or libpng12 didn't come with your linux distro (and you get an error when starting the app like ```ImportError: libSDL2-2.0.so.0: cannot open shared object file: No such file or directory```), do the following accordingly:
```
sudo apt-get install libsdl2-2.0
```
and
```
sudo add-apt-repository ppa:linuxuprising/libpng12
sudo apt update
sudo apt install libpng12-0
```

We will hopefully provide a package installer in the near future (for a more mature version).


### Usage

In Action - Validation:
- import the csv output from a DeepLabCut model (or a spreadsheets containing the same header and column structures as a DLC output file),
- select the slip prediction algorithm (**threshold**: focus on data that exceeds a y-axis lowerbound; **deviation**: directly estimate peaks using raw location values; **baseline**: detect peaks based on the asymmetric least squares baseline),
- select the bodyparts to analyze--the predicted slips are then sorted, so that the user can validate the prediction by skimming through the results only once, 
- automatically identify "slips" during the ladder rung experiment, including the number of slips, slip depths and the on- and offset of each slip, and
- save the predicted slip properties as a csv file.


![](https://github.com/sollan/slip_detector/blob/master/Screenshots/validation%20start%20screen.png)

_(Select parameters for slip detection.)_

***


During validation, you can
- have a frame-by-frame comparison of the model-predicted bodypart location alongside the original video,
- manually correct the slip detection results, including removing false positives and adding missed slips, and
- save the validated results as a csv file. 

![](https://github.com/sollan/slip_detector/blob/master/Screenshots/validation%20main%20page.png)


_(Validate model prediction using the GUI.
Top: a frame from original video.
Bottom: model prediction of the y-axis location of a bodypart throughout the video duration. The location of the bodypart that led to the current slip prediction is displayed in the graph.
This frame is identified as "slip" by the threshold method, based on pose estimation from DLC.)_



For more information, please visit [our wiki page](https://github.com/sollan/slip_detector/wiki).