semvec/pypi-packages huggingface dataset:
kitstructlog 0.1.2
A superstructure over structlog that simplifies the configuration and use of structured logging in Python.
Requires Python: >=3.13
Keywords: logging, structlog, structured-logging, json-logging

# kitstructlog

[![PyPI version](https://badge.fury.io/py/kitstructlog.svg)](https://pypi.org/project/kitstructlog/)
[![Python Versions](https://img.shields.io/pypi/pyversions/kitstructlog.svg)](https://pypi.org/project/kitstructlog/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

> A superstructure over [structlog](https://www.structlog.org/en/stable/) that simplifies the configuration and use of structured logging in Python.

---

## ✨ Features

- Simple declaration of project loggers via `dataclass`-like syntax
- Automatic setup of `logging` + `structlog`
- Developer-friendly **console output** or **JSON structured logs** (depending on mode)
- Extensible processors chain (timestamps, stack info, caller details, etc.)
- Support for multiple named loggers in one place

---

## 📦 Installation

```bash
pip install kitstructlog
```

---

## 🚀 Quick Start

### Basic usage

```python
import structlog
from kitstructlog import InitLoggers, LoggerReg

class Loggers(InitLoggers):
    app = LoggerReg(name="APP", level=LoggerReg.Level.INFO)
    db = LoggerReg(name="DATABASE", level=LoggerReg.Level.DEBUG)

# Initialize
loggers = Loggers(developer_mode=True)

# Use logger
logger = structlog.getLogger(Loggers.app.name)
logger.info("Application started", version="1.0.0")
```

---

### JSON logging

```python
import structlog
from kitstructlog import InitLoggers, LoggerReg

class Loggers(InitLoggers):
    app = LoggerReg(name="APP", level=LoggerReg.Level.INFO)
    access = LoggerReg(name="ACCESS", level=LoggerReg.Level.INFO)

# developer_mode=False => JSON output
loggers = Loggers(developer_mode=False)

logger = structlog.getLogger(Loggers.access.name)
logger.info("Request handled", status=200, path="/login")
```

Example JSON output:

```json
{
  "timestamp": "2025-09-21 03:09:46",
  "level": "info",
  "logger": "json_logging:logger:14",
  "_msg": "Request handled",
  "status": 200,
  "path": "/login"
}
```

---

### Multiple loggers

```python
import structlog
from kitstructlog import InitLoggers, LoggerReg

class Loggers(InitLoggers):
    auth = LoggerReg(name="AUTH", level=LoggerReg.Level.DEBUG)
    router = LoggerReg(name="ROUTER", level=LoggerReg.Level.INFO)
    utils = LoggerReg(name="UTILS", level=LoggerReg.Level.DEBUG)

loggers = Loggers(developer_mode=True)

auth_logger = structlog.getLogger(Loggers.auth.name)
auth_logger.debug("Checking token", token="abc123")

router_logger = structlog.getLogger(Loggers.router.name)
router_logger.info("New request", path="/api/v1/resource")
```

---

## 📜 License

MIT License — see [LICENSE](LICENSE) for details.
morse-code-Wdboyes13 0.5.0
A morse code generator
Requires Python: >=3.9

# Morse Code Generator  


## What it is  
This is a simple morse code generator.  
It takes text, converts it to morse code, saves it to a file, and plays back the generated audio  

## Usage  
Usage: `morse.py {-i | -o <filename> <text>}`  
```  
options:
  -h, --help            Show help message and exit
  -i, --interactive     Run in interactive mode to prompt for input.
  -o <filename> <text>, --output <filename> <text>
                        Provide a filename and text directly.
```  

## Dependencies  
- `sounddevice` = 0.5.2  
- `numpy` = 2.2.6
tensorblow 2.17.0
Tensorblow is an open source machine learning framework for everyone.
Requires Python: >=3.7
Keywords: machine learning, deep learning, neural networks, tensorflow, pytorch

# Tensorblow

Tensorblow is an open source machine learning framework for everyone.

## Description

Tensorblow provides a comprehensive set of tools and utilities for machine learning and deep learning applications. It offers an intuitive API for building, training, and deploying machine learning models.

## Features

- Easy-to-use machine learning framework
- Support for various neural network architectures
- Comprehensive data processing utilities
- Model training and evaluation tools
- Deployment-ready solutions

## Installation

```bash
pip install tensorblow
```

## Usage

```python
import tensorblow as tb

# Your machine learning code here
```

## Requirements

- Python 3.7+
- Additional dependencies as needed by specific modules

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Support

For support, please contact packages@tensorblow.org