semvec/pypi-packages huggingface dataset:
asciiracer 1.0.3
A racing game that runs in terminal
Requires Python: >=3.6.0

# ASCII RACER

![PyPI](https://img.shields.io/pypi/v/asciiracer?color=success&label=pypi%20package)
[![Build Status](https://travis-ci.com/UpGado/ascii_racer.svg?branch=master)](https://travis-ci.com/UpGado/ascii_racer)
![GitHub last commit](https://img.shields.io/github/last-commit/UpGado/ascii_racer)
[![Downloads](https://pepy.tech/badge/asciiracer)](https://pepy.tech/project/asciiracer)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

A racing game that runs in Terminal. 100% Python

<p align="center"><img src="https://raw.githubusercontent.com/UpGado/ascii_racer/master/docs/gameplay.gif" alt="ascii-racer"/></p>

## Instructions

Collect as many alcoholic drinks as possible, while avoiding the `Beer` drinks. The game is only key-based.

| Keys | Role        |
|------|-------------|
| a    | Move Left   |
| d    | Move Right  |
| w    | Accelerate  |
| s    |  Decelerate |
| q    |  Quit game  |

### Installation

> ```diff
> + Please report issues if you try to install and run into problems!
> ```

Make sure you are running at least Python 3.6.0

Install using pip:
```bash
pip3 install asciiracer
```
or clone the repository and install manually:

```bash
$ git clone https://github.com/UpGado/ascii_racer.git
$ cd ascii_racer && python3 setup.py install
```

### Start Game
To start the game, run either:
```bash
$ asciiracer
$ python -m asciiracer
```

### Scoring
There are four different types of drinks that you can collect on the racetrack. 
* Vodka - 10 Points
* Gin - 5 Points
* $ - 1 Point
* Beer - Negative 20 points

### Contributions
If you encounter any problem or have any suggestions, please [open an issue](https://github.com/UpGado/ascii_racer/issues/new) or [send a PR](https://github.com/UpGado/ascii_racer/pulls).

ASCII-RACER is still new. If you are interested, contributions are highly welcome!
yamliz 0.1.2
Dataclasses married with yaml.
Requires Python: >=3.7

## yamliz

Python's 3.7 dataclasses got maried with pyyaml.

## Installation:
```sh
pip install yamliz
```

## Usage example:

```py
import dataclasses
import yamliz
from yamliz import fields

@dataclasses.dataclass
class Some(yamliz.Able):
    id: int = fields.Integer()
    mac: str = fields.MacAddress()
    ip: str = fields.IPv4()
    version: int = fields.Integer(default=411)

```
Now the `Some` object can be created as a pure python object (thanks to `dataclass`)
it is equipped with `__init__`, `__repr__`, `__eq__` and some other methods. 

```py
>>> obj = Some(123, 'aa:bb:cc:11:22:33', '192.168.11.6')
>>> obj
Some(id=123, mac='aa:bb:cc:11:22:33', ip='192.168.11.6', version=411)

>>> print(obj)
!iz/Some
id: 123
ip: 192.168.11.6
mac: aa:bb:cc:11:22:33
version: 411
```

It's attributes are regular python attributes:
```py
>>> obj.id
123

>>> obj.mac
'aa:bb:cc:11:22:33'

>>> obj.ip
'192.168.11.6'

>>> obj.version = 0  # can be assigned
>>> print(repr(obj))
Ints(id=123, mac='aa:bb:cc:11:22:33', ip='192.168.11.6', version=0)
```

It can be also deserialized from yaml directly to the custom object:

```py
>>> yaml_string = """
!iz/Some
id: 22001
ip: 12.13.14.15
mac: aa:bb:cc:11:22:33
version: 0
"""

>>> import yaml
>>> obj2 = yaml.load(yaml_string, Loader=yaml.Loader)
>>> repr(obj2)
Some(id=22001, mac='aa:bb:cc:11:22:33', ip='12.13.14.15', version=0)

```

*To be further documented...*
brownieSort 0.0.6
For sorting brownies into tent groups

#brownieSort
A programme for sorting __brownies__ into groups (tents)
of equal size according to friendships. Brownies and
friendship choices are imported from a text file.
An algorithm is run to find the optimal (or near best)
groupings of brownies. The best camp can be printed to the screen
as a list of tents, their occupants, and the associated
happiness scores.

*in the UK, a brownie is a junior girl-guide (or junior girl-scout)*
dictol 0.1.0
A Toolbox for Discriminative Dictionary Learing


# DICTOL - A Discriminative dictionary Learning Toolbox for Classification (Python 2.7 version).
_This repo is a part of our [LRSDL project](http://signal.ee.psu.edu/lrsdl.html)._

**Related publications:**

1. Tiep H. Vu, Vishal Monga. "Learning a low-rank shared dictionary for object classification." *International Conference on Image Processing (ICIP)* 2016. [[paper]](http://arxiv.org/abs/1602.00310).

2. Tiep H. Vu, Vishal Monga. "Fast Low-rank Shared Dictionary Learning for Image Classification." *IEEE Transactions on Image Processing* 26.11(2017):5160-5175 [[paper]](https://arxiv.org/pdf/1610.08606.pdf). 

**Author: [Tiep Vu](http://www.personal.psu.edu/thv102/)**

_This repository contains the following methods: SRC, ODL, DLSI, FDDL, COPAR, LRSDL_

If you experience any bugs, please let me know via the [**Issues**](https://github.com/tiepvupsu/DICTOL_python/issues) tab. I'd really appreciate and fix the error ASAP. Thank you.

For more information, please see its corresponding [**MATLAB version**](https://github.com/tiepvupsu/DICTOL).

# Packages required

1. numpy 
2. scipy 


# Installation 
```
pip install dictol
```
# Demo 

```
import dictol
dictol.demo()
```



# Contact information
```
Tiep Vu: 
    + vuhuutiep@gmail.com 
    + http://www.personal.psu.edu/thv102/
```
Domoji 1.1.1
Find Emoji Domains From The Command Line (Works on MacOS and Terminals That Accept Emojis
Keywords: domains,api,wrapper,emoji,emojicode,macOS

# Domoji
Emoji Domain Finder in The Command Line - Airplane Project #3

## Setup

Run:
```
pip install domoji

domoji
```

Command line will guide you. Works with terminals that accept Emojis! (Sorry Windows Users)
figure-eight-client 1.1.0
Simple client for the Figure Eight API

# figure-eight-python-client

Python client drawn from [Figure Eight's API docs](https://success.figure-eight.com/hc/en-us/articles/202703425-Figure-Eight-API-Requests-Guide)

## Install

```bash
$ pip install figure-eight-client
```

## Usage

Replace `<figure-eight-api-token>` with a token from https://make.figure-eight.com/account/api

```python
>>> from figure_eight_client import FigureEightClient
>>> client = FigureEightClient("<figure-eight-api-token>")
```

## Methods
|  |
|--------|
|copy_job(job_id, all_units=True)|
|copy_job_without_rows(job_id)|
|add_tag_to_job(job_id, tag)|
|upload_csv_to_job(job_id, data)|
|launch_job(job_id, units_count, channel='on_demand')|
|get_jobs(exclude_instructions=False, pages=1)|
|get_job_rows(job_id, pages=-1)|
|find_jobs_by_support_email(support_email, team_id=None)|
|get_judgements_by_job_id(job_id)|
|get_json_results_by_job_id(job_id)|
|set_job_price_in_cents_per_page(job_id, price_in_cents)|
|set_job_title(job_id, title)|

## Contributing

Consider making a pull request if you have an idea or discover a bug.

## Thanks

Big thanks to [Density](https://www.density.io/) for sponsoring the development of figure-eight-python-client.