semvec/pypi-packages huggingface dataset:
codalyzer 0.1.0
Advanced Python code analyzer with regex, colorized output, visual stats, and more.
License: MIT
Requires Python: >=3.7
Keywords: analyzer, python, code, regex, matplotlib, socket, colorama

# 🔍 Codalyzer

**Codalyzer** is a powerful, advanced Python code analyzer that provides in-depth statistics, real-time socket feedback, colorful terminal output, and visual insights using modern Python packages.

![Codalyzer Banner](https://via.placeholder.com/800x200.png?text=Codalyzer+by+Adam+Alcander)

---

## 🚀 Features (15 Total)

1. 📊 Count total lines, blank lines, comment lines
2. 🧠 Detect and count all `def` and `class` blocks using regex
3. ✅ Find `# TODO:` lines for task tracking
4. 📏 Calculate average line length using `numpy`
5. 🌈 Colorful output using `colorama` and `Style`
6. 📋 Pretty stats table with `tabulate`
7. 🔦 Syntax highlighting with `pygments`
8. 📈 Real-time graph of code stats with `matplotlib`
9. 🧬 System monitoring (CPU & RAM) with `psutil`
10. ⏱️ Progress simulation bar with `tqdm`
11. 🌐 Real-time socket server with `socket`
12. 🧪 PyPI package info fetcher (e.g., matplotlib)
13. 🎨 Fancy terminal panel and formatting with `rich`
14. 🧵 Multithreaded socket handler
15. 📎 Supports direct execution from terminal with CLI argument

---

## 💾 Installation

```bash
pip install codalyzer

Or clone manually:
git clone https://github.com/yourusername/codalyzer
cd codalyzer
pip install -r requirements.txt

🛠️ Usage

bash
python -m codalyzer.core <your_python_file.py>

Example:

bash

python -m codalyzer.core sample_script.py

✅ Output Includes
Fancy table with code metrics

Colored list of all functions, classes, TODOs

Visual matplotlib graph of stats

Live CPU & RAM usage

Live socket listener

PyPI info of matplotlib

Highlighted source code

Progress bar for simulation

🔗 Dependencies
matplotlib

numpy

colorama

requests

tqdm

psutil

rich

pygments

tabulate

socketify

📷 Sample Output (Terminal)

🔍 Regex Match Summary:
Functions (def): 3 match(es)
  ➤ def run():
  ➤ def main():
  ➤ def count_all_lines()

Classes (class): 2 match(es)
  ➤ class Analyzer:
  ➤ class Test:

TODO Comments: 1 match(es)
  ➤ # TODO: implement better analysis

🧠 CPU: 28% | 🧬 RAM: 53%

🤝 Contributing
Want to improve Codalyzer? Open an issue or PR!

📬 Contact
Made by Adam Alcander et Eden
Licensed under MIT
GitHub Repo

🏆 Motto
"Code is Art. Analyze it with Style." 🎨
invaro 0.0.4
Python SDK for Invaro's document parsing and unified accounting APIs
License: MIT
Requires Python: >=3.8
Keywords: invaro, document-parsing, accounting, api-client, sdk, invoice-processing, financial-documents

# Invaro Python SDK

**🚧 Under Active Development - Coming Soon! 🚧**

The official Python SDK for Invaro's powerful document parsing and unified accounting APIs.

## What is Invaro?

Invaro provides cutting-edge APIs for:
- **Document Parsing**: Extract structured data from invoices, statements, and general documents
- **Unified Accounting**: Seamlessly integrate with multiple accounting platforms
- **Schema Management**: Custom document processing with flexible schemas

## Planned Features

### 🔍 Document Parsing (`invaro.parser`)
- Process invoices, financial statements, and custom documents
- Extract structured data with high accuracy
- Support for multiple file formats (PDF, images, etc.)
- Batch processing capabilities
- Custom schema support

### 💼 Unified Accounting (`invaro.accounting`)
- Connect to popular accounting platforms
- Standardized API across different providers
- Real-time synchronization
- Comprehensive financial data management

### 🛠️ Developer Experience
- **Type Safety**: Full type hints and IDE support
- **Async Support**: Modern async/await patterns
- **Error Handling**: Comprehensive error types and retry logic
- **Testing**: Built-in testing utilities and mocks

## Installation

```bash
pip install invaro
```

## Expected Usage (Coming Soon)

```python
from invaro.parser import InvaroParser
from invaro.accounting import InvaroAccounting

# Document parsing
parser = InvaroParser(api_key="your-api-key")
result = await parser.process_invoice("invoice.pdf")

# Accounting integration
accounting = InvaroAccounting(api_key="your-api-key")
await accounting.create_invoice(result.data)
```

## Development Timeline

- **Q2 2024**: Initial release with core parsing functionality
- **Q3 2024**: Accounting integrations and advanced features
- **Q4 2024**: Full feature set and production ready

## Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details on:

- 🛠️ **Local Development Setup**: Get started with development
- 📝 **Code Style Guidelines**: Follow our coding standards  
- 🧪 **Testing**: Run and write tests
- 🔄 **Pull Request Process**: How to submit changes

## Stay Updated

- 🌐 **Website**: [invaro.ai](https://invaro.ai)
- 📖 **Documentation**: [docs.invaro.ai](https://docs.invaro.ai)
- 🐙 **GitHub**: [github.com/Invaro/invaro-sdk](https://github.com/Invaro/invaro-sdk)
- 📧 **Contact**: support@invaro.ai

## License

MIT License - see [LICENSE](LICENSE) file for details.

---

**Note**: This package is currently a placeholder to reserve the name. The actual SDK implementation is under active development. Follow our GitHub repository for updates and progress.
mrmn0 0.1.0
Arduino 시리얼 통신을 쉽게 제어하는 Python 라이브러리
License: MIT
Requires Python: >=3.7

# README.md

# Arduino Controller

아두이노와 PC 간의 시리얼 통신을 쉽게 제어할 수 있는 Python 라이브러리입니다.

## 설치 방법
```bash
pip install lha_arducon

## 사용예시
from lha_arducon import  ArduinoController

arduino = ArduinoController()
arduino.connect_serial()
arduino.control_led(5, 'on')
bong16 0.1.0
Arduino 시리얼 통신을 쉽게 제어하는 Python 라이브러리
License: MIT
Requires Python: >=3.7

# README.md

# Arduino Controller

아두이노와 PC 간의 시리얼 통신을 쉽게 제어할 수 있는 Python 라이브러리입니다.

## 설치 방법
```bash
pip install lha_arducon

## 사용예시
from lha_arducon import  ArduinoController

arduino = ArduinoController()
arduino.connect_serial()
arduino.control_led(5, 'on')
TradeSim-Coptee 0.1.7
Trading simulation using SET50 data with portfolio and order matching features.
License: MIT
Requires Python: >=3.9

# Trading Simulation

A Python-based trading simulator using SET50 daily tick data.  
This system mimics real-time trading environments and supports development of algorithmic (ALGO) trading strategies with features such as order matching, portfolio management, commission/VAT handling, and more.

---

## 💼 Overview

The simulator reproduces a streaming-like stock trading system with capabilities including:

- Order placement and matching
- Real-time-like data streaming
- Portfolio tracking and analysis
- Automated performance summarization

Each trader starts with an **initial cash balance of 10,000,000 THB**. This capital will be managed through the portfolio system and used throughout the trading competition or simulation.

---

## 🧠 Features

### 📈 Stock & Portfolio Management

- Initialize a portfolio or load an existing one
- Update market prices during simulation
- Track and calculate key financial metrics:
  - Amount by cost
  - Average cost per stock
  - Unrealized & realized gains/losses (in value and %)
  - Net Asset Value (NAV)
  - Maximum Drawdown
  - Calmar Ratio

### 📊 Portfolio Summary

- Automatically generate transaction summaries
- Output metrics such as buy/sell volume, average price, commission, VAT, and net result

### 📝 Order Handling

- Submit buy/sell orders under these rules:
  - Stocks must belong to the **SET50 index**
  - Volume must be a multiple of 100 (standard lot size)
  - Partial or invalid volumes (e.g. 150) are rejected
  - Orders exceeding portfolio cash will be rejected
- All valid orders are recorded and matched against streaming market data

### 🔁 Order Matching Simulation

- Market simulator checks all orders during each time step
- Orders are executed only if matched price/volume is available in the stream
- Realistic handling of slippage, VAT (7%), and commission (0.157%), and slippage (0.03%)

### 📤 Strategy Result Submission

- After simulation, trading performance and results are automatically generated
- Ensure the portfolio name matches your trader/team name for correct result assignment

---

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.


## ⚙️ Requirements

- Python 3.9.13+
- pandas
- rich
- gspread
- google-auth
- google-api-python-client
- google-auth-httplib2
- google-auth-oauthlib

Install dependencies:

```bash
pip install -r requirements.txt
vulnerability-checker 0.2.6
Library for detecting vulnerabilities in Python code
License: MIT
Requires Python: >=3.8

# Vulnerability Checker

A library for detecting vulnerabilities in Python code. Designed for integration with CI/CD systems such as GitLab CI/CD. Supports Python 3.8 and above.

## Features

- Static code analysis to detect security vulnerabilities
- Dependency checking for known vulnerabilities
- Analysis of insecure code constructs
- Returns non-zero exit code when issues are found
- Generates detailed vulnerability reports

## Requirements

- Python 3.8 - 3.13
- Dependencies listed in `pyproject.toml` (automatically installed with the package)
- If you use Poetry for dependency management, you need to install the `poetry-plugin-export` plugin:
  ```bash
  poetry self add poetry-plugin-export
  ```

## Installation

```bash
pip install vulnerability-checker
```

## Usage

### Command Line

```bash
# Basic directory analysis
vulncheck /path/to/your/project

# Analysis with JSON report generation
vulncheck /path/to/your/project --output report.json

# Check only specific vulnerability types
vulncheck /path/to/your/project --checks sql-injection,xss,dependency-check
```

### Available Flags

- `--output <filename>`: Specifies the name of the file to save the report (e.g., `--output report.json`).
- `--checks <check_types>`: Specifies which types of vulnerabilities to check (e.g., `--checks sql-injection,xss,dependency-check`).
- `--verbose`: Detailed output.
- `--exit-zero`: Always return 0 exit code, even when vulnerabilities are found.

### GitLab CI/CD Integration

```yaml
security_check:
  stage: test
  script:
    - pip install vulnerability-checker
    - vulncheck . --output report.json
  artifacts:
    paths:
      - report.json
```