semvec/pypi-packages huggingface dataset:
aiotracemoeapi 3.1.2
Async wrapper for Trace.moe API
Requires Python: >=3.9

# AioTraceMoeAPI

[![MIT License](https://img.shields.io/pypi/l/aiotracemoeapi)](https://opensource.org/licenses/MIT)
[![PyPi Package Version](https://img.shields.io/pypi/v/aiotracemoeapi)](https://pypi.python.org/pypi/aiotracemoeapi)
[![Downloads](https://img.shields.io/pypi/dm/aiotracemoeapi.svg)](https://pypi.python.org/pypi/aiotracemoeapi)
[![Supported python versions](https://img.shields.io/pypi/pyversions/aiotracemoeapi)](https://pypi.python.org/pypi/aiotracemoeapi)

A simple, but extensible asynchronous Python wrapper for the [trace.moe](https://trace.moe) API.

## Key Features

*   **Async**: Built on top of `httpx` for high-performance asynchronous HTTP requests.
*   **Typed**: Fully typed codebase for better developer experience and IDE support.
*   **Pydantic v2**: Uses Pydantic v2 for robust data validation and serialization.

## Installation

You can install the package using `uv` or `pip`:

```bash
uv add aiotracemoeapi
```

or

```bash
pip install aiotracemoeapi
```

## Usage Examples

### Basic Search by URL

```python
import asyncio
from aiotracemoeapi import TraceMoe

async def main():
    api = TraceMoe()
    
    # Search by URL
    # Note: is_url=True is required when passing a URL string
    result = await api.search("https://images.plurk.com/32B15UXxymfSMwKGTObY5e.jpg", is_url=True)
    
    if result.result:
        print(f"Anime: {result.result[0].filename}")
        print(f"Similarity: {result.result[0].similarity}")
    else:
        print("No results found.")

if __name__ == "__main__":
    asyncio.run(main())
```

## Running Examples

Examples can be run using `uv run`:

```bash
uv run examples/console.py
```

### Search by File Upload

You can search using a local file path.

```python
import asyncio
from aiotracemoeapi import TraceMoe

async def main():
    api = TraceMoe()
    
    # Search by local file path
    # Ensure 'image.jpg' exists in your directory
    try:
        result = await api.search("image.jpg")
        
        if result.result:
            print(f"Anime: {result.result[0].filename}")
            print(f"Episode: {result.result[0].episode}")
    except FileNotFoundError:
        print("File not found!")

if __name__ == "__main__":
    asyncio.run(main())
```

### Checking Account Status

You can check your API usage limits and quota.

```python
import asyncio
from aiotracemoeapi import TraceMoe

async def main():
    # Initialize with your API token (optional, but recommended for higher limits)
    # Get your token from https://trace.moe/account
    api = TraceMoe(token="your_token_here")
    
    me = await api.me()
    
    print(f"ID: {me.id}")
    print(f"Priority: {me.priority}")
    print(f"Quota Used: {me.quota_used}")
    print(f"Quota Total: {me.quota}")

if __name__ == "__main__":
    asyncio.run(main())
```
mdformat-py-edu-fr 0.2.1
Tiny wrapper around mdformat for the py-edu-fr project
Requires Python: >=3.13

# Tiny wrapper around mdformat for the py-edu-fr project

We needed a specific markdown formatter for the
[py-edu-fr project](https://python-cnrs.netlify.app/edu).

mdformat-py-edu-fr is based on mdformat, mdformat-myst and Jupytext.

```
$ mdformat-py-edu-fr -h
usage: mdformat-py-edu-fr [-h] [--version] [--check] [--exclude PATTERN] [--verbose] [paths ...]

Format Markdown files for py-edu-fr project

positional arguments:
  paths                 Files or directories to format (if omitted, reads from stdin)

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --check               Check if files are formatted without modifying them (exit code 1 if changes needed)
  --exclude PATTERN     Glob pattern to exclude files/directories (can be specified multiple times)
  --verbose, -v         Print detailed information about processing
```
cwl-dl 1.0.2
Automated course material downloader for CyberWarfare Labs
Requires Python: >=3.10

# CWL Downloader 🚀

```text
     ██████╗██╗    ██╗██╗         ██████╗  ██████╗ ██╗    ██╗███╗   ██║
    ██╔════╝██║    ██║██║         ██║  ██║██╔═══██╗██║    ██║████╗  ██║
    ██║     ██║ █╗ ██║██║         ██║  ██║██║   ██║██║ █╗ ██║██╔██╗ ██║
    ██║     ██║███╗██║██║         ██║  ██║██║   ██║██║███╗██║██║╚██╗██║
    ╚██████╗╚███╔███╔╝███████╗    ██████╔╝╚██████╔╝╚███╔███╔╝██║ ╚████║
     ╚═════╝ ╚══╝╚══╝ ╚══════╝    ╚═════╝  ╚═════╝  ╚══╝╚══╝ ╚═╝  ╚═══╝
```

An automated, modern, and minimal course material downloader for **CyberWarfare Labs**.

## ✨ Features

- 🖥️ **Interactive UI**: Beautiful terminal interface with ASCII art and clean status updates.
- 📄 **PDF Downloads**: Automatically extracts and saves course write-ups and study materials.
- 🎬 **Video Support**: Full support for downloading **Vimeo** and **YouTube** videos.
- 🔄 **Deduplication**: Intelligent URL tracking to prevent duplicate downloads.
- 📊 **User Stats**: Displays your profile progress (modules, certs, flags) on login.
- 🛡️ **Bypass Restrictions**: Handles "embed-only" Vimeo videos via custom headers and referrers.
- 📁 **Organized Storage**: Automatically categorizes files by Course and Module.

## 🚀 Getting Started

### 1. Installation

You can install the package directly from PyPI:

```bash
pip install cwl-dl
```

Alternatively, to install from source:

```bash
git clone https://github.com/4nuxd/cwl-dl.git
cd cwl-dl
pip install .
```

### 2. Usage

Once installed, simply run the tool from your terminal:

```bash
cwl-dl
```

3. **Login**:
   The script will prompt for your **JWT Token**.
   - Login to [labs.cyberwarfare.live](https://labs.cyberwarfare.live)
   - Open DevTools (F12) -> Application -> Cookies
   - Copy the value of the `token` field from the `user` cookie string.

## 📁 Output Structure

Downloaded materials are organized as follows:

```text
downloads/
├── course_summary.txt
└── CRTA/
    ├── course_details.txt
    ├── pdfs/
    │   └── CRTA_Walkthrough@piratexdumps.pdf
    └── videos/
        └── Module_1_Initial_Access/
            └── Introduction@piratexdumps.mp4
```

## ⚖️ Legal Disclaimer

This tool is for **personal educational use only**.
- Do not redistribute downloaded content.
- Respect the CyberWarfare Labs Terms of Service.
- The author is not responsible for any misuse of this tool.

---
**Made with ❤️ by [@4nuxd](https://github.com/4nuxd)**
MRPhantom 1.0.0
MRI Phantom of a Slime with Repiratory and Cardiac Motion, and M0, Phase, T1, T2, B0 maps, Boosted by a C-API Backend.
Keywords: MRI, Phantom, Motion, Quantitative

# MRI Digital Phantom

## Introduction
A quantitative phantom of a slime with cardiac and respiratory motion.

Since a 3D + T dataset is so large, this package generates phantom on each tick on demand, according to the given shape parameters. This phantom has a parallel C++ backend for nearly real-time 3D phantom generation.

## Install
Using `pip`:
```bash
$ pip install mrphantom
```
Offline:
```bash
$ bash install.bash
```

## Usage
Please refer to the [Examples](https://github.com/RyanShanghaitech/MRPhantom/tree/main/example) page to learn how to use this package. You can find examples for 2D dynamic, 3D dynamic, static simulations, and also the method to obtain the quantitative parameters (e.g. T1, T2, off-resonance) of each pixel.