semvec/pypi-packages huggingface dataset:
lofar-h5plot 2.9.2
The spiritual successor to ParmDBplot for quickly reviewing gain solutions generated by NDPPP.
Requires Python: >=3.9

<h1 align="center">LOFAR H5plot</h1>
<p align="center">
<img alt="Pylint" src="https://mperlet.github.io/pybadge/badges/8.37.svg?style=for-the-badge">
<img alt="GitHub" src="https://img.shields.io/github/license/tikk3r/lofar-h5plot.svg">
<a href="https://doi.org/10.5281/zenodo.3469995"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3469995.svg" alt="DOI"></a>
<img src="https://img.shields.io/pypi/v/lofar-h5plot">
<img src="https://img.shields.io/pypi/pyversions/lofar-h5plot">
</p>

---

H5plot is a small GUI to view the solutions in an H5parm interactively. To run it directly, clone this repository and run as

    python h5plot <h5parm>

This package is also installable through pip:

    pip install --upgrade https://github.com/revoltek/losoto/archive/master.zip
    pip install lofar-h5plot

After this, it can simply be run as:

    h5plot <h5parm>

![Screenshot](https://raw.githubusercontent.com/tikk3r/lofar-h5plot/master/screen.png)

# Requirements
* Python >= 3.6.4
* LoSoTo 2.0
* Matplotlib
* Numpy
* PyQt5

These can be installed on Ubuntu through

    apt-get install qt5-default libgl1-mesa-glx
    pip install pyqt5 matplotlib
    pip install --upgrade https://github.com/revoltek/losoto/archive/master.zip
lightdash 1.0.1
Official SDK for Lightdash
Requires Python: >=3.9

# Lightdash Python Client

A Python client for interacting with the Lightdash API.

## Installation

```bash
pip install lightdash
```

## Usage

See the [SDK Guide](docs/SDK_GUIDE.md) for full documentation on the query builder, filters, and other features.

For an interactive tutorial, check out the [example notebook](examples/getting_started.ipynb).

```python
from lightdash import Client

client = Client(
    instance_url="https://your-instance.lightdash.com",
    access_token="your-access-token",
    project_uuid="your-project-uuid"
)
```

## Development

### Prerequisites

- Python 3.9 or higher
- [uv](https://github.com/astral-sh/uv) for dependency management
- [just](https://github.com/casey/just) for running commands

### Setting up the development environment

1. Clone the repository:

```bash
git clone https://github.com/lightdash/python-sdk.git
cd python-sdk
```

2. Create and activate a virtual environment:

```bash
uv venv
```

3. Set up your environment variables by copying the example file:

```bash
cp .env.example .env
```

4. Edit `.env` with your Lightdash credentials:

```bash
LIGHTDASH_INSTANCE_URL="https://your-instance.lightdash.com"
LIGHTDASH_ACCESS_TOKEN="your-access-token"
LIGHTDASH_PROJECT_UUID="your-project-uuid"
```

5. Install development dependencies:

```bash
just install
```

### Available Commands

View all available commands:

```bash
just
```

Common commands:

- `just install` - Install development dependencies
- `just test` - Run acceptance tests
- `just build` - Build package distributions
- `just clean` - Remove build artifacts

## Publishing

### Setting up PyPI credentials

Create a `~/.pypirc` file with your PyPI API tokens:

```ini
[pypi]
username = __token__
password = your-pypi-token-here

[testpypi]
username = __token__
password = your-testpypi-token-here
```

Make sure to:

1. Use API tokens instead of your actual username/password
2. Keep the file secure (`chmod 600 ~/.pypirc`)
3. Never commit this file to version control
4. Use different tokens for TestPyPI and PyPI
5. Generate tokens with minimal required permissions

### Publishing to PyPI

First, test your package on TestPyPI:

```bash
just publish-test
```

If everything looks good on TestPyPI, publish to PyPI:

```bash
just publish
```

Note: The package version in `pyproject.toml` must be incremented for each new release.

## License

MIT
genkit 0.5.0
Genkit AI Framework
Requires Python: >=3.10
Keywords: ai, artificial-intelligence, framework, generative-ai, genkit, llm, machine-learning, sdk

# genkit

Genkit is a framework designed to help you build AI-powered applications and features.
It provides open source libraries for Python, Node.js and Go, plus developer tools for testing
and debugging.

You can deploy and run Genkit libraries anywhere Python is supported. It's designed to work with
many AI model providers and vector databases. While we offer integrations for Firebase and Google Cloud,
you can use Genkit independently of any Google services.

## Setup Instructions

```bash
pip install genkit
pip install genkit-plugin-google-genai
```


```python
from pydantic import BaseModel, Field
from genkit.ai import Genkit, Output
from genkit.plugins.google_genai import GoogleAI

ai = Genkit(
    plugins=[GoogleAI()],
    model='googleai/gemini-2.0-flash',
)

class RpgCharacter(BaseModel):
    """An RPG game character."""

    name: str = Field(description='name of the character')
    back_story: str = Field(description='back story')
    abilities: list[str] = Field(description='list of abilities (3-4)')

@ai.flow()
async def generate_character(name: str) -> RpgCharacter:
    result = await ai.generate(
        prompt=f'generate an RPG character named {name}',
        output=Output(schema=RpgCharacter),
    )
    return result.output


async def main() -> None:
    """Main function."""
    character = await generate_character('Goblorb')
    print(character.model_dump_json(indent=2))

if __name__ == '__main__':
    ai.run_main(main())
```

See https://python.api.genkit.dev for more details.
vidformer 1.4.2
vidformer-py is a Python 🐍 interface for [vidformer](https://github.com/ixlab/vidformer).
Requires Python: >=3.8

# vidformer-py

[![PyPI version](https://img.shields.io/pypi/v/vidformer.svg)](https://pypi.org/project/vidformer/)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/ixlab/vidformer/blob/main/LICENSE)

vidformer-py is a Python 🐍 frontend for [vidformer](https://github.com/ixlab/vidformer).
It has an API compatibility layer with OpenCV cv2, as well as some [supervision](https://github.com/roboflow/supervision) annotators.
Our [getting started guide](https://ixlab.github.io/vidformer/docs/getting-started.html) explains how to use it.

**Quick links:**
* [📦 PyPI](https://pypi.org/project/vidformer/)
* [📘 Documentation - vidformer-py](https://ixlab.github.io/vidformer/vidformer-py/)
* [📘 Documentation - vidformer.cv2](https://ixlab.github.io/vidformer/vidformer-py/vidformer/cv2.html)
* [📘 Documentation - vidformer.supervision](https://ixlab.github.io/vidformer/vidformer-py/vidformer/supervision.html)
* [🧑‍💻 Source Code](https://github.com/ixlab/vidformer/tree/main/vidformer-py/)

**Publish:**
```bash
 export FLIT_USERNAME='__token__' FLIT_PASSWORD='<token>'
flit publish
```