semvec/pypi-packages huggingface dataset:
strict-dataclasses 0.1.0
make dataclasses decorator type strict
License: MIT
Requires Python: >=3.11
Keywords: dataclass, strict typing, type checking, validation

# strict-dataclass

English | [简体中文](./README-zh_CN.md)

A Python standard library `dataclasses` enhancement implementation that provides strict type checking.

This is an experimental library. Please do not use it in production environments. You might want to use [pydantic](https://github.com/pydantic/pydantic) or [attrs](https://github.com/python-attrs/attrs) instead.

## Features

- Advantages:
  - Compatible with standard library `dataclasses` usage
  - Strict type checking during initialization and attribute modification
  - Supports validation of nested complex data types
  - Unlike pydantic, it doesn't deep copy input values
  - Performs deep checking of nested complex data types (pydantic's BaseModel / dataclass doesn't perform deep checking)
- Disadvantages:
  - Poor performance, object creation and attribute modification takes about 3 times longer than pydantic. However, it seems acceptable considering the cost of deep checking.

## Installation

```bash
pip install strict-dataclass
# Python >= 3.11
```

## Usage

### Basic Usage

```python
from strict_dataclasses import strict_dataclass

@strict_dataclass
class User:
    name: str
    age: int
    is_active: bool

# Correct usage - passes type checking
user = User(name="Alice", age=25, is_active=True)

# Incorrect usage - raises TypeError
user = User(name=123, age="25", is_active=1)  # TypeError: Field 'name' must be of type str
```

### Complex Types

```python
from typing import Optional
from strict_dataclasses import strict_dataclass

@strict_dataclass
class Student:
    name: str
    scores: dict[str, list[int]]
    tags: list[str]
    note: Optional[str] = None

# Correct usage
student = Student(
    name="Bob",
    scores={"math": [90], "english": [85]},
    tags=["good", "active"],
    note="Excellent student"
)

# Type errors
student = Student(
    name="Bob",
    scores={"math": ["90"]},  # TypeError: Value must be a list[int]
    tags="good",  # TypeError: Must be a list
    note=123  # TypeError: Must be a string or None
)
```
ialog 0.2.1
AI-powered log analysis tool using Groq API
License: MIT
Requires Python: >=3.6

# scanux

A system scanning tool for Linux and Windows systems that:
- Scans connected users
- Identifies suspicious behavior
- Analyzes command history
- Generates reports in multiple formats

## Features

- **User Scanning**: Lists all currently connected users with their details
- **Suspicious Behavior Detection**: Identifies processes with high CPU/memory usage
- **Command History Analysis**: Analyzes recent command history
- **Multiple Report Formats**: Supports JSON, Markdown, and HTML output formats
- **Cross-Platform**: Works on both Linux and Windows systems

## Installation

```bash
pip install scanux
```

## Usage

Basic usage:
```bash
scanux
```

Generate a report in a specific format:
```bash
scanux --format json
scanux --format markdown
scanux --format html
```

Save report to a file:
```bash
scanux --format json --output report.json
scanux --format markdown --output report.md
scanux --format html --output report.html
```

## Report Contents

The generated report includes:
- System information (platform, release, version, machine)
- List of connected users
- Suspicious behavior detection
- Recent command history

## Requirements

- Python 3.6 or higher
- psutil
- rich
- click
- python-dateutil

## License

MIT License
microstorm 0.1.2
A modern microservices toolkit for Python with built-in discovery, metrics, and security.
License: MIT
Requires Python: >=3.8
Keywords: microservices, fastapi, prometheus, jwt, service discovery, microstorm

# Microstorm 🚀

**Microstorm** es un micro-framework para construir microservicios modernos en **Python** usando **FastAPI**, enfocado en:

- Simplicidad
- Auto-registro dinámico de servicios
- Comunicación segura entre microservicios
- Métricas Prometheus integradas
- Registro Discovery Server opcional
- Control de acceso a métodos (público, privado, protegido)

Ideal para arquitecturas basadas en microservicios pequeños, escalables y seguros.

---

## ✨ Características principales

- 🔒 Control de acceso con decoradores (@private, @public, @protected)
- 📦 Auto-registro de servicios (archivo local o Discovery Server)
- 📡 Comunicación HTTP segura usando JWT
- 📊 Métricas listas para Prometheus
- 🔄 Reintentos automáticos con backoff exponencial
- 📚 Fácil integración en cualquier proyecto FastAPI

---

## ⚙️ Instalación

```bash
pip install fastapi uvicorn httpx prometheus_client python-dotenv