semvec/pypi-packages huggingface dataset:
voluptuous-openapi 0.3.0
Convert voluptuous schemas to OpenAPI Schema object
Requires Python: >=3.9

# Voluptuous OpenAPI

Convert Voluptuous schemas to [OpenAPI Schema object](https://spec.openapis.org/oas/v3.0.3#schema-object).

```python
schema = {}
schema[vol.Required('name')] = vol.All(str, vol.Length(min=5))
schema[vol.Required('age', description='Age in full years')] = vol.All(vol.Coerce(int), vol.Range(min=18))
schema[vol.Optional('hobby', default='not specified')] = str
schema = vol.Schema(schema)
```

becomes

```json
{
    "type": "object",
    "properties": {
        "name": {
            "type": "string",
            "minLength": 5,
        },
        "age": {
            "type": "integer",
            "minimum": 18,
            "description": "Age in full years",
        },
        "hobby": {
            "type": "string",
            "default": "not specified",
        },
    },
    "required": ["name", "age"],
}
```

See the tests for more examples.

## Custom serializer

You can pass a custom serializer to be able to process custom validators. If the serializer returns `UNSUPPORTED`, it will return to normal processing. Example:

```python

from voluptuous_openai import UNSUPPORTED, convert

def custom_convert(value):
    if value is my_custom_validator:
        return {'pattern': '^[a-zA-Z0-9]$'}
        
    return UNSUPPORTED

convert(value, custom_serializer=custom_convert)
```
navio-blsct 0.0.37
Python binding for the Navio blsct library
Requires Python: >=3.12

# Navio BLS Confidential Transaction Library

Python bindings for the `libblsct` library used by the [Navio](https://nav.io/) blockchain to construct confidential transactions based on the BLS12-381 curve.

## Requirements
- Python 3.12 or 3.13
- macOS (arm) or Linux

## Installation

```bash
pip install navio_blsct
```

## Documentation
Full API reference and usage examples are available in the [documentation](https://nav-io.github.io/libblsct-bindings/python/)
macos-junk-cleaner 1.0.0
A utility to list and clear junk files created by macOS indexation.
Requires Python: >=3.9
Keywords: macos, cleanup, junk-files, ds_store, cli

# macOS Junk Cleaner

[![CI](https://github.com/joaomartinscaetano/macos-junk-cleaner/actions/workflows/ci.yml/badge.svg)](https://github.com/joaomartinscaetano/macos-junk-cleaner/actions/workflows/ci.yml)
[![PyPI version](https://img.shields.io/pypi/v/macos-junk-cleaner.svg)](https://pypi.org/project/macos-junk-cleaner/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Semantic Release](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--release-e10079.svg)](https://github.com/python-semantic-release/python-semantic-release)

A Python-based CLI utility to scan and remove macOS-specific junk files (e.g., `.DS_Store`, `._*`, `.Spotlight-V100`) from directories.

## Installation

### Using pip
```bash
pip install .
```

### Using uv
```bash
uv tool install .
```

## Usage

### Scan
To see what would be cleaned with a detailed summary:
```bash
mac-clean scan /path/to/dir
# or using uv
uv run mac-clean scan /path/to/dir
```

### Clean
The `clean` command is used for removal. For safety, it **defaults to dry-run mode**.

```bash
# Preview deletion (Dry-run)
mac-clean clean /path/to/dir

# Perform actual deletion
mac-clean clean /path/to/dir --force
```

## Development

### Setup (uv)
1. Clone the repository.
2. Sync the environment (creates venv and installs all dependencies and dev tools):
   ```bash
   uv sync
   ```
3. Install pre-commit hooks:
   ```bash
   uv run pre-commit install
   ```

### Linting and Formatting
This project uses [Ruff](https://github.com/astral-sh/ruff) for linting and formatting.

```bash
# Lint
ruff check .
# or using uv
uv run ruff check .

# Format
ruff format .
# or using uv
uv run ruff format .
```

### Testing
```bash
pytest
# or using uv
uv run pytest
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
django-bot-rickroll 0.3.0
Django plugin that rickrolls bots and scanners by redirecting common malicious URLs
Requires Python: >=3.11
Keywords: django, security, rickroll, bots, scanners

# django-bot-rickroll

## Story

Once upon a time I noticed my server log was filled with 404 errors
for URL's like `/wp-admin` and so on.
I figured redirecting them to this [cool music video](https://www.youtube.com/watch?v=dQw4w9WgXcQ)
was one way I could get these errors out of my logs.

Also it means if my kids are snooping around they can get a laugh or two.

## Installation

```bash
uv add django-bot-rickroll
```

## Usage

Just add one line to `urlpatterns`:

```python
from django.urls import path, include

urlpatterns = [
    # Your app URLs first ...
    path("", include("bot_rickroll.urls")),
]
```

You should add this as the last line, so that in the unlikely event
one of the URL's in the blocklist is actually supposed to work,
then yours take precedence.

You can customize the target URL in your `settings.py`:

```python
RICKROLL_URL = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
```

## URL's covered

- `/wp-admin/`, `/wp-content/`, `/wp-includes/`, `/wp-json/`
- `/wordpress/`, `/wp/`
- `/administrator/`, `/joomla/`, `/drupal/`, `/magento/`, `/cms/`
- `/phpmyadmin/`, `/phpMyAdmin/`, `/pma/`, `/mysql/`, `/adminer/`
- `/cpanel/`, `/webmail/`, `/plesk/`
- `/file-manager/`, `/filemanager/`, `/laravel-file-manager/`, `/elfinder/`
- `/upload/`, `/uploads/`, `/files/`
- `/vendor/`, `/public/`, `/modules/`, `/node_modules/`
- `/lib/`, `/libraries/`, `/includes/`, `/inc/`
- `/.well-known/`, `/.git/`, `/.svn/`, `/.hg/`
- `/.env`, `/.htaccess`, `/.htpasswd`
- `/config/`, `/configuration/`, `/conf/`, `/settings/`
- `/backup/`, `/backups/`, `/bak/`
- `/old/`, `/temp/`, `/tmp/`
- `/cgi-bin/`, `/cgi/`, `/bin/`, `/scripts/`
- `/php/`
- `/db/`, `/database/`, `/sql/`
- Any URL ending in `.php`

## License

MIT
pydatau 1.0.8
Batch Statistical Data Utilities
Requires Python: >=3.10
Keywords: data-science, ipython, R, stata, gams, ampl, matlab, octave

# DATAU - Batch Statistical Data Utilities

Cross-platform batch runner for statistical and numerical files.

## Installation

```bash
pip install pydatau
```

## Quick example
python:  
```python
from datau import autorun

autorun(path_data="my_project/data", pattern='master', log_limit=1)
```
bash:  
```bash
datau "my_project/data" -p "master" -l 1
```

## Supported Extensions and Execution Method

| Extension | Language/Tool    | Method                      |
|-----------|------------------|-----------------------------|
| `.ipynb`  | Jupyter Notebook | `papermill`                 |
| `.R`      | R                | `rpy2.robjects`             |
| `.do`     | Stata            | Stata batch mode            |
| `.jl`     | Julia            | `julia` (must be in PATH)   |
| `.gms`    | GAMS             | `gams` (must be in PATH)    |
| `.run`    | AMPL             | `ampl` (must be in PATH)    |
| `.m`      | MATLAB/Octave    | `matlab.engine` or `Oct2Py` |

## User Reference

```python
autorun(path_data='...', pattern='...', *args, **kwargs)
```

Automatically runs matching statistical scripts in the given directory. Generates a number of `.log` files with outputs for each executed file.

**Parameters:**  

`path_data` : *str*, default = *current working directory*  
Path to the directory with input files.

`pattern` : *str*, optional  
Regex pattern to match filenames (not paths), such as *'master'*. Case-insensitive.

`date_fmt` : *str*, default = *'%Y%m%d_%H%M%S'*  
Datetime format for *.log* filenames.

`log_limit` : *int*, optional  
If set, limits the number of log files per script. Older logs beyond this limit will be deleted. Please note that you can turn off logging by setting `log_limit` to *0*.

`use_powershell` : *bool*, default = *False*  
If *True*, redirects via PowerShell on Windows. Ignored on POSIX.

## License

MIT License — see the [LICENSE](LICENSE) file.
GenAiDataPrep 0.0.1
A data preparation library for Generative AI applications.
Requires Python: >=3.9

# GenAiDataPrep

A sample project for data preparation and cleaning tasks.

## Overview

This project provides tools and utilities for preparing data for use with GenAI applications.

## Installation

```bash
pip install -r requirements.txt
```

## Usage

```python
# Add your usage examples here
```

## License

[Add license information]