semvec/pypi-packages huggingface dataset:
sagemagic 0.1.0
AI algorithms collection: Genetic algorithms for 8-Queens and TSP, plus propositional logic and Bayes theorem implementations
Requires Python: >=3.7
Keywords: genetic-algorithm, artificial-intelligence, 8-queens, tsp, traveling-salesman, propositional-logic, bayes-theorem, machine-learning

# BossKaMagic

A comprehensive Python package containing AI algorithm implementations including genetic algorithms, propositional logic solvers, and Bayes theorem applications.

## Features

### 🧬 Genetic Algorithms
- **8-Queens Problem Solver**: Genetic algorithm implementation to solve the classic 8-Queens puzzle
- **Traveling Salesman Problem (TSP)**: Genetic algorithm for finding optimal routes

### 🧠 Logic & Probability
- **Propositional Logic**: Complete logical operators (AND, OR, NOT, IMPLIES, BICONDITIONAL) with truth tables
- **Logic Solver**: Truth table generation, resolution theorem proving, and DPLL satisfiability checking
- **Bayes Theorem**: Conditional probability calculations and medical diagnosis examples
- **Naive Bayes Classifier**: Simple implementation for classification tasks

## Installation

```bash
pip install bosskamagic
```

## Quick Start

### Genetic Algorithm - 8 Queens

```python
from bosskamagic.eight_queens_genetic import SimpleQueensGA

# Solve 8-Queens problem
solver = SimpleQueensGA()
solution = solver.solve()
print(f"Solution found: {solution}")
```

### Genetic Algorithm - TSP

```python
from bosskamagic.tsp_genetic import City, SimpleTSPGA

# Create cities
cities = [
    City("A", 0, 0),
    City("B", 1, 2),
    City("C", 3, 1),
    City("D", 2, 3)
]

# Solve TSP
tsp_solver = SimpleTSPGA(cities)
best_tour, best_distance = tsp_solver.solve()
print(f"Best distance: {best_distance}")
```

### Logical Operators

```python
from bosskamagic.logic_and_bayes import LogicalOperators

# Use logical operators
result = LogicalOperators.AND(True, False)
print(f"True AND False = {result}")

# Print truth tables
LogicalOperators.print_truth_table_basic()
```

### Bayes Theorem

```python
from bosskamagic.logic_and_bayes import BayesTheorem

# Calculate conditional probability
prob = BayesTheorem.conditional_probability(0.8, 0.1, 0.05)
print(f"Posterior probability: {prob}")

# Run medical diagnosis example
BayesTheorem.medical_diagnosis_example()
```

## Modules

- `eight_queens_genetic`: Genetic algorithm for 8-Queens problem
- `tsp_genetic`: Genetic algorithm for Traveling Salesman Problem
- `logic_and_bayes`: Propositional logic and Bayes theorem implementations

## Requirements

- Python >= 3.7
- random2 >= 1.0.1

## License

MIT License

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Author

Your Name - your.email@example.com

## Keywords

genetic-algorithm, artificial-intelligence, 8-queens, tsp, traveling-salesman, propositional-logic, bayes-theorem, machine-learning
rubox 0.2.0
A powerful library designed for building bots on the Rubika platform.
Requires Python: >=3.6

<p align="center">
  <a href="github.com/belectron13/rubox">
    <img src="https://raw.githubusercontent.com/BELECTRON13/rubox/refs/heads/main/HoshaAI-1%20(1).png" width=260 height=210 alt="Rubox" />
  </a>
</p>

---

## ًں”§ Rubox
> **A powerful library designed for building bots on the Rubika platform.**

---

### ًں›  Simple Example
```python
from rubox import Client
from rubox.filters import commands
import asyncio

TOKEN = "BOT-TOKEN"

async def main():
	async with Client(TOKEN) as app:
		@app.on_message(commands(['start', 'help']))
		async def start_handler(message):
			await message.reply('Hello from Rubox!')
			
		await app.run()
		
asyncio.run(main())		
```

### ًں‘¾ Minimal Example
```python
from rubox import Client
import asyncio

TOKEN = "BOT-TOKEN"

async def main():
	async with Client(TOKEN) as app:
		await app.send_message('chat_id', 'Hello from **Rubox**!')
		
asyncio.run(main())
```

### ًںŒگ Webhook Setup
```python
from rubox import Client
from rubox.filters import commands
import asyncio

TOKEN = "BOT-TOKEN"

async def main():
	async with Client(TOKEN, set_webhook=True) as app:
		@app.on_message(commands(['start', 'help']))
		async def start_handler(message):
			await message.reply('Hello from Rubox!')
			
		await app.run(
		webhook_url='https://yourdomain.com',
    	path='/webhook',
    	host='0.0.0.0',
    	port=3000
		)
		
asyncio.run(main())
```

###  ًں“¦ Installation
```bash
pip install -U rubox
```

rubika.ir/RubikaBox
staphscope-typing-tool 0.1.0
Staphscope Typing Tool — MLST + spa + SCCmec typing for Staphylococcus aureus
Requires Python: <4,>=3.6
Keywords: bioinformatics, microbiology, staphylococcus, typing, mlst, spa, sccmec

## Citation

If you use Staphscope Typing Tool in your research, please cite it as:

Brown, B. (2025). Staphscope Typing Tool: Unified MLST + spa + SCCmec typing for Staphylococcus aureus. GitHub repository. https://github.com/bbeckley-hub/staphscope-typing-tool

# Staphscope Typing Tool

A unified MLST + spa + SCCmec typing tool for *Staphylococcus aureus*.

## Features

- Multi-locus sequence typing (MLST)
- spa typing
- SCCmec typing using CGE SCCmecFinder
- Parallel processing for high-throughput analysis
- Comprehensive reporting in multiple formats

## Installation

### Using Conda (Recommended)
```bash
conda install -c bioconda staphscope-typing-tool
bosskamagic 0.1.0
AI algorithms collection: Genetic algorithms for 8-Queens and TSP, plus propositional logic and Bayes theorem implementations
Requires Python: >=3.7
Keywords: genetic-algorithm, artificial-intelligence, 8-queens, tsp, traveling-salesman, propositional-logic, bayes-theorem, machine-learning

# BossKaMagic

A comprehensive Python package containing AI algorithm implementations including genetic algorithms, propositional logic solvers, and Bayes theorem applications.

## Features

### 🧬 Genetic Algorithms
- **8-Queens Problem Solver**: Genetic algorithm implementation to solve the classic 8-Queens puzzle
- **Traveling Salesman Problem (TSP)**: Genetic algorithm for finding optimal routes

### 🧠 Logic & Probability
- **Propositional Logic**: Complete logical operators (AND, OR, NOT, IMPLIES, BICONDITIONAL) with truth tables
- **Logic Solver**: Truth table generation, resolution theorem proving, and DPLL satisfiability checking
- **Bayes Theorem**: Conditional probability calculations and medical diagnosis examples
- **Naive Bayes Classifier**: Simple implementation for classification tasks

## Installation

```bash
pip install bosskamagic
```

## Quick Start

### Genetic Algorithm - 8 Queens

```python
from bosskamagic.eight_queens_genetic import SimpleQueensGA

# Solve 8-Queens problem
solver = SimpleQueensGA()
solution = solver.solve()
print(f"Solution found: {solution}")
```

### Genetic Algorithm - TSP

```python
from bosskamagic.tsp_genetic import City, SimpleTSPGA

# Create cities
cities = [
    City("A", 0, 0),
    City("B", 1, 2),
    City("C", 3, 1),
    City("D", 2, 3)
]

# Solve TSP
tsp_solver = SimpleTSPGA(cities)
best_tour, best_distance = tsp_solver.solve()
print(f"Best distance: {best_distance}")
```

### Logical Operators

```python
from bosskamagic.logic_and_bayes import LogicalOperators

# Use logical operators
result = LogicalOperators.AND(True, False)
print(f"True AND False = {result}")

# Print truth tables
LogicalOperators.print_truth_table_basic()
```

### Bayes Theorem

```python
from bosskamagic.logic_and_bayes import BayesTheorem

# Calculate conditional probability
prob = BayesTheorem.conditional_probability(0.8, 0.1, 0.05)
print(f"Posterior probability: {prob}")

# Run medical diagnosis example
BayesTheorem.medical_diagnosis_example()
```

## Modules

- `eight_queens_genetic`: Genetic algorithm for 8-Queens problem
- `tsp_genetic`: Genetic algorithm for Traveling Salesman Problem
- `logic_and_bayes`: Propositional logic and Bayes theorem implementations

## Requirements

- Python >= 3.7
- random2 >= 1.0.1

## License

MIT License

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Author

Your Name - your.email@example.com

## Keywords

genetic-algorithm, artificial-intelligence, 8-queens, tsp, traveling-salesman, propositional-logic, bayes-theorem, machine-learning
interactive-script-py 1.0.7
A Python library to communicate with 'Interactive Script' VSCode extension.
Requires Python: >=3.7

# interactive-script-py

This library is designed to be used together with the [Interactive Script](https://marketplace.visualstudio.com/items?itemName=andriy-viyatyk.interactive-script) extension for Visual Studio Code.

It provides Python API for interacting with the extension's interactive UI components inside your scripts.

For detailed documentation, examples, and usage instructions, please visit the [project repository](https://github.com/andriy-viyatyk/interactive-script).
pydatacube 0.1.7
Querying data cubes with Python using MDX inspired declarative syntax
Requires Python: >=3.8

# pyDataCube
## Running the experiments
Commit 1c84c423dc439e842a5906a86235c3e7439f0c7e was used for the experiments.
The timer object that measured the time spent in the database was injected into pandas' `read_sql` method (and its auxiliary methods) in `io/sql.py`.
The modified `sql.py` file is included in the base of this repository as `pandas_sql.py`.
This modified `sql.py` was, together with the version associated with the above commit hash, used for gaining the experiments results in the journal paper
aws-cdk-modules 1.0.0.dev2
Python repository for AWS CDK modules
Requires Python: >=3.12

# AWS CDK Modules

| Badges     |                                                                                                                                                                                                 |
|------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| License    | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)                                                                                     |
| PyPI       | [![PyPI version](https://badge.fury.io/py/aws-cdk-modules.svg)](https://badge.fury.io/py/aws-cdk-modules) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/aws-cdk-modules.svg)](https://pypi.org/project/aws-cdk-modules/) |

| Version | Build Status                                                                                                                                                                                                                                                                                                                                    |
|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Main    | [![GitHub Build main](https://github.com/collier-p-charlie/aws-cdk-modules/actions/workflows/run-tests.yaml/badge.svg)](https://github.com/collier-p-charlie/aws-cdk-modules/actions) [![GitHub Build main](https://github.com/collier-p-charlie/aws-cdk-modules/actions/workflows/python-publish.yaml/badge.svg)](https://github.com/collier-p-charlie/aws-cdk-modules/actions) |

This repository contains **AWS CDK** _modules_ containing common design patterns.

**Table of contents**

- [Prerequisites](#prerequisites)
- [Modules](#modules)


## Prerequisites

This project uses `uv` as the primary **Python** package dependency manager.
This can be installed on **macOS** using `brew` (for other installation methods, see [here](https://docs.astral.sh/uv/getting-started/installation/)).
On first install, we should run the following commands:

```bash
uv sync --all-extras
pre-commit install
```


## Modules

X