semvec/pypi-packages huggingface dataset:
JornadaRPA.AgentAI 0.1.3
Agent AI for RPA using multiple LLM providers
Requires Python: >=3.8

# JornadaRPA.AgentAI

Agent AI abstraction layer for RPA bots using providers like OpenAI, Anthropic.

## Installation

```bash
pip install JornadaRPA.AgentAI[all]
```

## Usage

```python
from JornadaRPA.AgentAI.providers import get_provider

provider = get_provider("openai", api_key="sk-...", model="gpt-4")
response = provider.run("Hello, world!")
print(response)
```
ripple1d 0.10.4
HEC-RAS model automation

# ripple1d
[![CI](https://github.com/dewberry/ripple1d/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/dewberry/ripple1d/actions/workflows/ci.yaml)
[![Documentation Status](https://readthedocs.org/projects/ripple1d/badge/?version=latest)](https://ripple1d.readthedocs.io/en/latest/?badge=latest)
[![Release](https://github.com/dewberry/ripple1d/actions/workflows/release.yaml/badge.svg)](https://github.com/dewberry/ripple1d/actions/workflows/release.yaml)
[![PyPI version](https://badge.fury.io/py/ripple1d.svg)](https://badge.fury.io/py/ripple1d)

Utilities for repurposing HEC-RAS models for use in the production of Flood Inundation Maps (FIMs) and rating curves for use in near-real time flood forecasting on the NOAA National Water Model network. Go to [ReadTheDocs](http://ripple1d.readthedocs.io/) for more information on ripple1d.

## Contents

 - [api](api/) : Source code for the [Flask](https://flask.palletsprojects.com/en/3.0.x/) API and [Huey](https://huey.readthedocs.io/en/latest/) queueing system for managing parallel compute. 
 - [ripple1d](ripple1d/): Source code for the ripple1d library.
 - [tests](tests/): Unit tests.up


## Requirements

*OS Dependency*: Ripple requires Python version >=3.10 and a Windows environment with Desktop Experience (GUI, not a headless Windows server) and [HEC-RAS](https://www.hec.usace.army.mil/software/hec-ras/download.aspx) installed (currently version 6.3.1 is supported).


## Installing Ripple

##### *NOTE: Using a python virtual environment is not required but is highly recommended.*

### Using pip

Activate virtual environment as shown below and install the `ripple1d` package using `pip` using PowerShell:

```powershell
    pip install ripple1d
```


### Verify the Installation

Verify the installation by importing `ripple1d` in a Python shell:

```powershell
    python
    >>> import ripple1d
    >>> print(ripple1d.__version__)
```


---


### Credits and References
1. [Office of Water Prediction (OWP)](https://water.noaa.gov/)
1. [Dewberry](https://www.dewberry.com/)
1. [Raytheon](https://www.rtx.com/)
1. [ Earth Resources Technology, Inc.](https://www.ertcorp.com/)
1. [ras2fim](https://github.com/NOAA-OWP/ras2fim)
1. [USACE HEC-RAS](https://www.hec.usace.army.mil/software/hec-ras/)
1. NOAA National Water Model [(NWM)](https://water.noaa.gov/about/nwm)




**Special Thanks to:** David Bascom (FEMA), Christina Lindemer (FEMA), Dave Rosa (FEMA), Paul Rooney (FEMA),  Julia Signell and Dan Pilone of [Element84](https://www.element84.com/), and the developers of [STAC](https://stacspec.org/en).
icrlearn 0.0.1
A scikit-learn compatible package for intra-class rarity models.
Requires Python: >=3.10

intra-class-rare-learn - A scikit-learn compatible intra-class rarity learning package
============================================================

![tests](https://github.com//jannewer/intra-class-rare-learn/actions/workflows/python-app.yml/badge.svg)
![doc](https://github.com//jannewer/intra-class-rare-learn/actions/workflows/deploy-gh-pages.yml/badge.svg)

**intra-class-rare-learn** is a scikit-learn compatible intra-class rarity learning package.
It is based on the [template project](https://github.com/scikit-learn-contrib/project-template)
for [scikit-learn](https://scikit-learn.org) compatible extensions,
but was modified to use [uv](https://docs.astral.sh/uv/) instead of pixi.

## Installation

The package can be installed directly from GitHub using uv with `uv add icrlearn git+https://github.com/jannewer/intra-class-rare-learn.git`

## Documentation

Documentation is available at https://jannewer.github.io/intra-class-rare-learn/

## Development
For development, make sure you have uv installed: https://docs.astral.sh/uv/getting-started/installation/

Afterwards, you can do the following:
- run the tests with `uv run task test`
- build the documentation with `uv run task build-doc`
- run black formatting with `uv run task black`
- run ruff linting and formatting with `uv run task ruff`
- run both black and ruff with `uv run task lint`
pyreadcon 0.0.1
Python bindings to readcon-core
Requires Python: >=3.8

# pyreadcon

[![PyPI - Version](https://img.shields.io/pypi/v/pyreadcon.svg)](https://pypi.org/project/pyreadcon)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyreadcon.svg)](https://pypi.org/project/pyreadcon)

-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install pyreadcon
```

## License

`pyreadcon` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
noe 0.0.1
A modern EON server component
Requires Python: >=3.8

# noe

[![PyPI - Version](https://img.shields.io/pypi/v/noe.svg)](https://pypi.org/project/noe)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/noe.svg)](https://pypi.org/project/noe)

-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install noe
```

## License

`noe` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
einkaufszettel 0.1.2
Pythonic Einkaufszettel API
Requires Python: >=3.11

# Einkaufszettel
Ever wanted to manage your (in-memory) shopping list with Python?
Then look no further!

```python
>>> from einkaufszettel import Zettel, Item
>>>
>>> zettel = Zettel('Netto')
>>> zettel.append('Apfel')
>>> zettel.append(
...     Item(
...         name='Käse',
...         completed=True,
...     )
... )
>>> zettel.append(Item('Tomaten', 1.5, 'kg'))
>>> zettel.append(Item('Zucchini', 2))
>>>
>>> zettel
Zettel(name='Netto')
>>> list(zettel)
[Item(name='Apfel', qty=1.0, unit='Stück', completed=False), Item(name='Tomaten', qty=1.5, unit='kg', completed=False), Item(name='Zucchini', qty=2, unit='Stück', completed=False)]
>>>
```


# Development
```
mise install
uv sync
make
```