semvec/pypi-packages huggingface dataset:
yaqd-control 2022.4.0
Command line tools for inspecting and controlling yaq daemons.
Requires Python: >=3.7

# yaqd-control

[![PyPI](https://img.shields.io/pypi/v/yaqd-control)](https://pypi.org/project/yaqd-control)
[![Conda](https://img.shields.io/conda/vn/conda-forge/yaqd-control)](https://anaconda.org/conda-forge/yaqd-control)
[![yaq](https://img.shields.io/badge/framework-yaq-orange)](https://yaq.fyi/)
[![black](https://img.shields.io/badge/code--style-black-black)](https://black.readthedocs.io/)
[![ver](https://img.shields.io/badge/calver-YYYY.0M.MICRO-blue)](https://calver.org/)
[![log](https://img.shields.io/badge/change-log-informational)](https://gitlab.com/yaq/yaqd-control/-/blob/main/CHANGELOG.md)

Command line tools for inspecting and controlling yaq daemons.
https://control.yaq.fyi/
test-update 1
Writing style analyzer
Keywords: python,test style

This package analyzes the style of a given writing sample using "involved" and "informational" linguistic features. The main objective of "involved" features is to build a relationship with a reader, engage or involve a reader in a text, and they include pronouns, non-phrasal coordination ("and"), and questions. The main objective of "informational" features is to convey facts and draw attention to the factual information in a text rather than build a relationship with a reader, and they include determiners (such as "the", "which", "both", etc.), past tense verbs, and numbers. The package calculates the number of "involved" and "informational" features and prints out an "involved-informational ratio". The higher the "involved-informational ratio", the more "involved" a given writing sample. These linguistic features are based on the work of Biber (1988), Argamon et al. (2003), Pennebaker (2011).
More information on the [Github page](https://github.com/el2727/test_update)
cooldb 2.0.0a3
A simple database handler for SQLite3
Keywords: python,db,database,wrapper,sqlite3

# cooldb

This is a simple wrapper for python's sqlite3 API\
__DISCLAIMER!!:__
This library is not intended to be used in production environments because it **IS NOT** protected against SQL
injection.

## 1. Explanation

This is a small project written in pure python. It abviously doesn't support all teh SQL functions but the basics are
covered.

## 2. Examples

Basic setup:

```py
from cooldb import Session

sess = Session('your db name')
sess.create_table('table name')

# create some data
sess.save('the table name', [your values here])
sess.update('the table name', {'some column name here': value}, {'your search criteria': value})
sess.delete('the table name', {'your search criteria': value})

# select and output some data
selection = sess.select('the table name', 10, {'your search criteria': value})

# close the connection to the database
sess.close()  # gets executed when something like 'del sess' is called
```

## 3. Future

At the moment I don't know if I will add more features. If you want to see more features please fork this repo und
create pull requests or just create an issue with the feature request tag.
PS: Maybe I will add an async version that uses aiosqlite but let's see ;)

# CHEERS
gym-rat-runner 0.1.6
A discrete environment build using openai/gym with the purpose to be used by Reinforcement learning algorithms.
Requires Python: >=3.8
Keywords: gym,environment,maze,rat_runner

# Gym-Rat-Runner

## Introduction

**Gym Rat Runner** is an open-source environment implemented over [OpenAI Gym](https://gym.openai.com/).It is a 2-D discrete environment intended for Reinforcement learning applications with collision treatments. For now, the package has two distinct environments; the open and the maze environments.

### Agent <img src="https://raw.githubusercontent.com/GuiOli91/gym-rat-runner/main/images/rat.png" alt="Rat Agent" width="40" height="40" style="display:inline;">

The agent is represented by a friendly rat with the objective of catching the cheese. Which will have eight move action options.


### Target <img src="https://raw.githubusercontent.com/GuiOli91/gym-rat-runner/main/images/cheese.png" alt="Target" width="40" height="40" style="display:inline;">

The cheese won't move and will always be visible to the Agent.

### Hunter <img src="https://raw.githubusercontent.com/GuiOli91/gym-rat-runner/main/images/cat.png" alt="Hunter" width="40" height="40" style="display:inline;">

The Hunter is represented by a cat and will move differently depending on the environment.

## Installation

### Requirements

<pre><code>
pandas >= 1.4.1
numpy >= 1.20.3
gym >= 0.23.0
pygame >= 2.1.2
opencv-python
</code></pre>

### pip
To install the package into your machine you can use the code bellow

<pre><code>  
pip install gym-rat-runner  
</code></pre>

## Environments


### Open Environment

<br/>

<img src="https://raw.githubusercontent.com/GuiOli91/gym-rat-runner/main/images/Open_Environment.png" alt="Open Environment" width="360" height="360" style="display:block; margin-left: auto; margin-right: auto;">
<br/>


The open environment has 10 by 10 spaces and the cat is sleeping and won't move independently what the rat does.

### Maze Environment

<br/>
<img src="https://raw.githubusercontent.com/GuiOli91/gym-rat-runner/main/images/Maze_Environment.png" alt="Maze Environment" width="850" height="450" style="display:block; margin-left: auto; margin-right: auto;">
<br/>

The maze environment has 16 by 32 spaces based on the [Bank Heist](http://www.virtualatari.org/soft.php?soft=Bank_Heist) from Atari. Like the police in Bank Heist, the hunter won't be able to move back but it will have two options of movement:

1. The Agent is outside the field vision of the  hunter
  * The cat will move randomly by the maze.
2. The agent is inside the field vision of the hunter.
  * The cat will start to run after the rat and will move twice the movement of the agent.