semvec/pypi-packages huggingface dataset:
ytdl-taquitos 0.1.1
A modern YouTube and video platform downloader library
Requires Python: >=3.8
Keywords: youtube downloader video audio async

# ytdl-taquitos

A modern YouTube and video platform downloader library written in Python.

## Features

- Download videos and audio from YouTube
- Support for multiple video qualities
- Download subtitles in various languages
- Progress tracking with speed limiting
- Modern async/await API
- Type hints for better IDE support

## Installation

```bash
pip install ytdl-taquitos
```

## Usage

### Basic Usage

```python
import asyncio
from ytdl_taquitos import YouTubeExtractor, Downloader

async def main():
    # Initialize extractor and downloader
    extractor = YouTubeExtractor()
    downloader = Downloader(output_dir="./downloads")
    
    try:
        # Get video info
        video_url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
        video_info = await extractor.extract_info(video_url)
        
        # Download video
        format = next(f for f in video_info.formats if f.quality == "720p")
        await downloader.download_file(format.url, f"{video_info.id}.{format.ext}")
        
        # Download subtitles
        for lang, url in video_info.subtitles.items():
            await downloader.download_subtitle(url, lang, video_info.id)
            
    finally:
        await downloader.close()

asyncio.run(main())
```

### Advanced Usage

```python
import asyncio
from ytdl_taquitos import YouTubeExtractor, Downloader

async def main():
    extractor = YouTubeExtractor()
    downloader = Downloader(
        output_dir="./downloads",
        speed_limit=1024 * 1024,  # 1MB/s
        chunk_size=512 * 1024,    # 512KB chunks
    )
    
    try:
        # Get video info
        video_url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
        video_info = await extractor.extract_info(video_url)
        
        # Print available formats
        for fmt in video_info.formats:
            print(f"Format: {fmt.quality} ({fmt.ext})")
            if fmt.is_audio_only:
                print("  Audio only")
            elif fmt.is_video_only:
                print("  Video only")
            else:
                print("  Combined")
        
        # Download highest quality video
        best_format = max(
            (f for f in video_info.formats if not f.is_audio_only),
            key=lambda f: f.height or 0
        )
        await downloader.download_file(
            best_format.url,
            f"{video_info.id}.{best_format.ext}"
        )
        
    finally:
        await downloader.close()

asyncio.run(main())
```

## Development

### Setup

1. Clone the repository
2. Create a virtual environment
3. Install development dependencies:

```bash
pip install -e ".[dev]"
```

### Running Tests

```bash
pytest
```

## License

MIT License

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
imageprocessing-edlopeskk 0.0.1
My short description
Requires Python: >=3.8

# package_name

Description. 
The package package_name is used to:
	Processing:
        - Histogram matching
	    - Structural Similarity
        - Resize image
    Utils:
        - Read image
        - Save image
        - Plot image
        - Plot result
        - Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install package_name
```

## Author
Eduardo

## License
[MIT](https://choosealicense.com/licenses/mit/)
gcmprocpy 1.2.1
A Python3 post processing tool for TIE-GCM and WACCM-X
Requires Python: >=3.8

# GCMPROCPY

GCMprocpy is a tool used for TIE-GCM and WACCM-X post processing and plot generation.

## Documentation on readthedocs
## https://gcmprocpy.readthedocs.io
sbcli-redirect 0.0.4
CLI for managing SimplyBlock cluster


# Simply Block
[![Docker Image Build](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/docker-image.yml)

[![Python Unit Testing](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml/badge.svg)](https://github.com/simplyblock-io/sbcli/actions/workflows/python-testing.yml)


## Install
Add the package repo from AWS CodeArtifact using [awscli](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

```bash
aws codeartifact login --tool pip --repository sbcli --domain simplyblock --domain-owner 565979732541 --region eu-west-1
```
Install package
```bash
pip install --extra-index-url https://pypi.org/simple sbcli-dev
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
Please see this document
[README.md](../main/simplyblock_cli/README.md)


## Simply Block Web API
Please see this document
[README.md](../main/simplyblock_web/README.md)



### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```

setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.


```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
rayexec 0.0.122
Next engine for GlareDB
Requires Python: >=3.7

# GlareDB Python bindings

## Development

Create virtual env.

```sh
$ python -m venv crates/rayexec_python/venv
```

Activate virtual env.

```sh
$ cd crates/rayexec_python
$ source ./venv/bin/activate
# OR
$ export VIRTUAL_ENV="<path-to-venv-dir>"
$ export PATH="$VIRTUAL_ENV/bin:$PATH"
```

Build and test it (assuming in rayexec_python).

```sh
$ maturin develop
$ python
Python 3.11.9 (main, Apr  2 2024, 08:25:04) [Clang 16.0.6 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import rayexec
>>> conn = rayexec.connect()
>>> conn.query("select 1")
┌──────────┐
│ ?column? │
│ Int64    │
├──────────┤
│        1 │
└──────────┘
```
wize-mcp 0.1.6
MCP integration for Workwize
Requires Python: >=3.11

# Workwize MCP Server

An MCP server implementation for the Workwize Public API.

## Features

- Access to Workwize Public API through MCP tools
- Secure API token handling
- Easy to extend with new tools

## Requirements

- Python 3.11+
- `uv` 0.6.9+

You can follow the `uv` installation instruction [here](https://docs.astral.sh/uv/getting-started/installation/).

You will also need an MCP client. You can, for example, use [Claude Desktop](https://claude.ai/download).

## Setup

First of all, you will need to create a Workwize API token. You can do so by going to [your account page](https://app.goworkwize.com/app/account/settings) and generating an API token at the bottom of the page.

![Workwize API token](https://raw.githubusercontent.com/goworkwize/wize-mcp/master/assets/api-token.png)

Then, once you have `uv` installed and your token generated, you can open Claude Desktop or your preferred MCP client and add the following server:

```json
{
    "mcpServers": {
        "wize-mcp": {
            "args": [
                "wize-mcp"
            ],
            "command": "uvx",
            "env": {
                "WORKWIZE_API_TOKEN": "your-token-here"
            }
        }
    }
}
```

To add a server in Claude Desktop, you can open the Settings menu and click on the "Developer" tab. Then, click on "Get started" and paste the above JSON.

![Developer tab on Claude Desktop](https://raw.githubusercontent.com/goworkwize/wize-mcp/master/assets/developer.png)

Make sure to replace `your-token-here` with your actual Workwize API token.

Then, restart Claude Desktop or your preferred MCP client to load the configuration.

Finally, verify the MCP server is working by looking for the hammer icon in the Claude interface.

![Tools on Claude Desktop](https://raw.githubusercontent.com/goworkwize/wize-mcp/master/assets/tools.png)

## Troubleshooting

If the command `uvx` is not found it may have been installed in a different directory. You can check the installation directory with `which uvx` and use that path instead:

```json
{
    "mcpServers": {
        "wize-mcp": {
            "args": [
                "wize-mcp"
            ],
            "command": "/Users/username/.local/bin/uvx",
            "env": {
                "WORKWIZE_API_TOKEN": "your-token-here"
            }
        }
    }
}
```

## License

MIT