semvec/pypi-packages huggingface dataset:
evt-blackjack 0.1.1
A terminal-based Blackjack trainer
Requires Python: >=3.6

# 🃏 evt-blackjack

## 📦 Installation
Install via pip (works on macOS, Linux, and Windows):
                pip install evt-blackjack


## ▶️ How to Run
🧑‍💻 Most Users (macOS & Linux):
Just run:
                  
    bj

🎉 You’re in the game.

### 🪟 Windows Users
After installing, you might see this warning:

    WARNING: The script bj.exe is installed in '...Scripts', which is not on PATH.

That just means Windows doesn't know where to find the bj command yet.
You have 2 easy options:

✅ Option 1: Use the fallback command

    python -m bj

🛠 Option 2: Add Scripts to your PATH manually

    C:\Users\your-name\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_xxx\LocalCache\local-                        packages\Python311\Scripts

Add it to your system PATH:

Open Start → “Environment Variables”

Edit the Path variable

Add a new entry for the folder path

Click OK and restart your terminal

Then you can run:
                bj
## 💡 Features

Terminal-based Blackjack with suit visuals (♥♠♦♣)

Full support for:

Hitting / Standing / Doubling Down

Blackjack detection

Betting & balance tracking

Reshuffling decks

Great for practice or just passing time in the terminal
modraw 0.1.14
Collection of Anywidget Widgets

# modraw 

<img src="imgs/icon.png" width=125 height=125 align="right" style="z-index: 9999;">

> "A collection of [tldraw](https://www.tldraw.com/) based anywidgets for marimo, but also other Python notebooks."

The project uses [anywidget](https://anywidget.dev/) under the hood so our tools should work in [Marimo](https://marimo.io/) but also [Jupyter](https://jupyter.org/), [Shiny for Python](https://shiny.posit.co/py/docs/jupyter-widgets.html), [VSCode](https://code.visualstudio.com/docs/datascience/jupyter-notebooks), [Colab](https://colab.google/), [Solara](https://solara.dev/) *and* [Marimo](https://marimo.io/). Because of the anywidget integration you should also be able interact with [ipywidgets](https://ipywidgets.readthedocs.io/en/stable/) natively. 

## Online demos 

Check [Github pages](https://koaning.github.io/modraw/) for a live demo. 

## Installation 

Installation occurs via `pip` or `uv`. 

```
python -m pip install modraw
uv pip install modraw
```

## Usage

### `Draw`

The main widget that's available in this library is the `Draw` widget. This generates a ltdraw widget and you're able to retreive the drawn image in a base64 representation or as a Python image object. 

```python
import marimo as mo
from modraw import Draw

widget = mo.ui.anywidget(Draw())
widget
```

```python
# Fetch the doodle and put it in Python
widget.get_base64()
widget.get_pil()
```

## Related project

This isn't the only tldraw integration with Python, you might also be interested in [jupyter-tldraw](https://github.com/kolibril13/jupyter-tldraw). It's a neat project for sure, and the main difference is that this project tries to target marimo more directly and can also move a bit faster with that in mind. I'm currently employed at marimo, so having a tool that I can easily change is very useful.

## Development

I am currently exploring how we might move some of these components to react, mainly in an attempt to keep things flexible in the future. There's no need to port everything just yet but I have ported the clipboard button. You should be able to develop it via: 

```
make install
make dev
```
imagem-processing-frr 0.0.1
Permite comparar duas imagens e gerar uma terceira imagem que destaca as diferencas em preto e branco.
Requires Python: >=3.7

# Comparador de Imagens em Python

Este projeto permite comparar duas imagens e gerar uma terceira imagem que destaca as diferencas em preto e branco. Ele utiliza a biblioteca **Pillow** para a manipulacao de imagens e inclui tratamento robusto de erros para garantir uma execucao confiavel.

## Indice

- [Descricao](#descricao)
- [Funcionalidades](#funcionalidades)
- [Requisitos](#requisitos)
- [Instalacao](#instalacao)
- [Uso](#uso)
- [Exemplo de Execucao](#exemplo-de-execucao)
- [Contribuicao](#contribuicao)
- [Licenca](#licenca)

## Descricao

O projeto e composto por dois modulos principais:

1. `__init__.py`: Realiza o carregamento, comparacao e salvamento das imagens processadas.
2. `comparar_imagens.py`: Contem funcoes para carregar imagens, identificar diferencas entre elas e salvar a imagem resultante.

## Funcionalidades

- Carrega duas imagens a partir de caminhos fornecidos.
- Compara as imagens e gera uma nova imagem que destaca as diferencas.
- Salva a imagem gerada em um caminho especificado.
- Lida com excecoes, como arquivos ausentes ou incompatibilidades entre as imagens.

## Requisitos

Certifique-se de que o ambiente Python possua os seguintes requisitos:

- Python 3.7 ou superior.
- Biblioteca Pillow instalada.
- Imagens de entrada com o mesmo tamanho e formato.

## Instalacao

1. Clone este repositorio:

   git clone https://github.com/seu-usuario/nome-do-repositorio.git
   cd nome-do-repositorio

2. Instale a biblioteca Pillow:

	pip install pillow

## Uso

1. Coloque as imagens que deseja comparar na pasta img e renomeie para img1.jpg e img2.jpg

2. Execute o script __init__.py

	python __init__.py

3. A imagem com as diferencas sera gerada no caminho especificado no codigo.

## Exemplo de Execucao

Suponha que vocÃª tenha dusas imagens nomeadas como img1.jpg e img2.jpg na pasta img. Ao executar o comando:

	python __init__.py

Sera gerada uma nova imagem chamada imagem_diferencas.png contendo as diferencas destacadas em preto e branco.

## Contribuicao

Sinta-se Ã  vontade para contribuir com este projeto. Abra uma issue para relatar bugs ou sugerir melhorias, ou envie um pull request.

## Licenca

Este projeto esta licenciado sob a licenca MIT. Consulte o arquivo LICENSE para mais detalhes.
youtubeinsights-mcp-server 0.1.2
Add your description here
Requires Python: >=3.11



# YouTube Insights MCP Server
[![smithery badge](https://smithery.ai/badge/youtube-insights-mcp-server)](https://smithery.ai/server/youtube-insights-mcp-server)

A Model Context Protocol (MCP) server that enables insight extraction from YouTube videos, including subtitle parsing, keyword-based video discovery, and channel info retrieval.

## Features

- Extract transcripts from YouTube videos (multi-language)
- Search videos by keyword and fetch metadata (views, likes, thumbnails, etc.)
- Retrieve channel info and latest videos from any YouTube video URL
- FastMCP-based server integration for easy deployment
- MCP Tools for seamless agent workflows

## Installation


### Using uvx (recommended)

When using [`uvx`](https://docs.astral.sh/uv/guides/tools/), no specific installation is needed.

Add the following configuration to your MCP settings file (e.g., `claude_desktop_config.json` for Claude Desktop):

```json
{
  "mcpServers": {
    "youtubeinsights": {
      "command": "uvx",
      "args": ["youtubeinsights-mcp-server"],
      "env": {
        "YOUTUBE_API_KEY": "your-api-key",
      }
    }
  }
}
```

### Development Installation

1. Clone this repository
2. Install dependencies:
   ```bash
   uv venv
   ```
3. Copy `.env.example` to `.env` and fill in your youtube data api credentials

    ```json
    {
      "mcpServers": {
        "youtubeinsights": {
          "command": "uv",
          "args": [
            "--directory",
            "path/to/youtubeinsights-mcp-server",
            "run",
            "youtubeinsights-mcp-server"
          ],
          "env": {
            "YOUTUBE_API_KEY": "your-api-key",
          }
        }
      }
    }
    ```

## Available MCP Tools

- `get_youtube_transcript`: Extract full transcript (subtitles) from a YouTube video URL (supports `ko`, `en`)
- `search_youtube_videos`: Search for videos on YouTube by keyword and retrieve key metadata
- `get_channel_info`: Get channel metadata and recent uploads based on any YouTube video URL

## Sample MCP Tool Descriptions

```json
{
  "tool": "get_youtube_transcript",
  "description": "Extract subtitles from a given YouTube video URL."
}
```

```json
{
  "tool": "search_youtube_videos",
  "description": "Search videos by keyword and return metadata including views, likes, and thumbnails."
}
```

```json
{
  "tool": "get_channel_info",
  "description": "Retrieve channel info (title, subscriber count, latest uploads) based on a video URL."
}
```


## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
nomad-camels-driver-openCV-instrument 0.1.2
Instrument driver for openCV_instrument for NOMAD CAMELS
Requires Python: >=3.9

# NOMAD Camels driver for openCV cameras

Generic driver for camera control using openCV written for the measurement software [NOMAD Camels](https://fau-lap.github.io/NOMAD-CAMELS/).

## Changes

### 0.1.1

- Fixed broken import paths

## Documentation

For more information and instruments visit the [documentation](https://fau-lap.github.io/NOMAD-CAMELS/doc/instruments/instruments.html).
pytest-tuitest 0.1.0
pytest plugin for testing TUI and regular command-line applications.
Keywords: pytest, tui

# What is this?

pytest_tuitest is a [pytest](https://pytest.org) plugin for testing TUI (text/terminal user interface) and regular command-line applications.

It allows you to run an application in a virtual terminal and run assertions on the contents and colors of its output.
It uses [pyte](https://github.com/selectel/pyte) for terminal emulation.

# What can it do?
Here are some examples to give you a taste of what can be done.

```python
import pytest_tuitest as tt

# You can simply run an application and verify that it prints
# the expected output in the terminal.

@tt.test_executable("grep")
@tt.with_arguments(["--color=always", "pytest_tuitest"])
@tt.with_stdin("This is a pytest_tuitest demo")
def test_grep_marks_the_output_according_to_arguments(terminal):
    terminal.wait_for_finished()

    # You can get the text in the terminal
    assert terminal.get_string_at(line=0, column=10, length=14) == "pytest_tuitest"

    # The color
    assert terminal.get_foreground_at(line=0, column=10) == tt.Color16.RED
    assert terminal.get_background_at(line=0, column=10) == tt.Color16.DEFAULT

    # Or the font style
    assert not terminal.has_style_at(line=0, column=10, style=tt.Style.BLINKING)

# You can also send stdin and capture stdout. Capturing stdout
# is useful in cases where TUI is printed on TTY and something
# else is printed on stdout (e.g. in case of fzf). That way, you
# can independently check what's in the TUI and what's "returned".

@tt.test_executable("fzf")
@tt.with_terminal_size(columns=80, lines=40)
@tt.with_stdin("things\nstuff")
@tt.with_captured_stdout(True)
def test_sending_input_and_capturing_output(terminal):
    terminal.wait_for_stable_output()

    assert terminal.get_string_at(line=39, column=0, length=1) == ">"

    # Emulate typing of "th" and pressing enter
    terminal.send("th\r\n")

    (status, stdout, _) = terminal.wait_for_finished()

    assert status == 0
    assert stdout == "things\n"
```

# Installation

```
pip install pytest-tuitest
```

This will automatically install pytest as well.