semvec/pypi-packages huggingface dataset:
lynx-ha 0.2.0
Helpers to classify IoT Open / Lynx functions and build Home Assistant entities with minimal boilerplate.
Requires Python: >=3.10
Keywords: home-assistant, homeassistant, iot, iotopen, lynx, integration, entity-classification

# lynx-ha

Helpers to reduce boilerplate when integrating **Lynx / IoT Open** with **Home Assistant**.

The goal is to move all the repetitive patterns into this library so that Home Assistant custom components / forks can stay minimal and focused.

> Status: early prototype (`0.1.0`).

## Installation

```bash
pip install lynx-ha
limboole 1.3.2
This is a simple boolean calculator.  It reads a boolean formula and checks whether it is valid.  In case '-s' is specified satisfiability is checked instead of validity (tautology).

Overview
--------

This is a simple boolean calculator.  It reads a boolean formula and checks
whether it is valid.  In case '-s' is specified satisfiability is checked
instead of validity (tautology).

Installing
----------

Use pip! You can install the limboole binary by issuing:

    pip install limboole

The project is online at the project below, hosting versions for all
major distributions and a source distribution:

    https://pypi.org/org/SAI/limboole

Language
--------

The input format has the following syntax in BNF:
( [ ... ] means optional,  { ... } means repeated arbitrary many times)
   
   expr ::= iff
   iff ::= implies { '<->' implies }
   implies ::= or [ '->' or | '<-' or ]
   or ::= and { '|' and }
   and ::= not { '&' not }
   not ::= basic | '!' not
   basic ::= var | '(' expr ')'

and 'var' is a string over letters, digits and the following characters:
  
  - _ . [ ] $ @

The last character of 'var' should be different from '-'.



Armin Biere, Johannes Kepler University,
Thu Nov 22 15:47:00 CET 2012
LJSE 0.1.5
Client for Ljubljana Stock Exchange REST API
Requires Python: >=3.11

# LJSE

A lightweight Python package for downloading stock market data from the Ljubljana Stock Exchange (LJSE).  
Provides a simple interface for retrieving the list of all listed equities and fetching historical price data for any LJSE ticker.

---

## Features

- Fetch a complete list of currently listed LJSE equities
- Retrieve historical **last-price** data for any ticker
- Automatically resolves ISIN and listing date
- Supports trading model selection (`CT`, `AUCT`, `BLOCK`, `ALL`)
- Returns clean pandas DataFrames

---

## Installation

```bash
pip install ljse
mammos-ai 0.2.0
Pre-trained AI models
Requires Python: >=3.11

# mammos-ai

`mammos-ai` provides ...

| Description   | Badge                                                                                                                                                                                    |
|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Tests         | [![Test package](https://github.com/MaMMoS-project/mammos-ai/actions/workflows/test.yml/badge.svg)](https://github.com/MaMMoS-project/mammos-ai/actions/workflows/test.yml)        |
| Linting       | [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/MaMMoS-project/mammos-ai/main.svg)](https://results.pre-commit.ci/latest/github/MaMMoS-project/mammos-ai/main) |
| Releases      | [![PyPI version](https://badge.fury.io/py/mammos-ai.svg)](https://badge.fury.io/py/mammos-ai)                                                                                      |
| Documentation | [![Documentation](https://img.shields.io/badge/Docs-mammos--project.github.io%2Fmammos-blue)](https://mammos-project.github.io/mammos/index.html)                                        |
| Binder        | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mammos-project/mammos-ai/latest?urlpath=lab%2Ftree%2Fexamples)                                       |
| License       | [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)                                                                                     |
| DOI           | TODO                                                                                                                                                                                     |


## Try it in the cloud
Try `mammos-ai` without installing it locally by directly accessing it directly in the cloud
via Binder.

Simply click the badge in the table above to get started.

Sessions are temporary and may time out after a period of inactivity, and any files
created or modified during your session will not be saved.
To avoid losing your work, please remember to download any files you create or edit
before your session ends.

## Documentation

APIs and tutorials are available in the documentation. To access the documentation, use the badge in the table above.

## Installation

To install `mammos-ai`, you can use `pip install mammos-ai` inside a Python environment.
For more details refer to the documentation.

## How to cite

TODO

## Acknowledgements

This software has been supported by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101135546 [MaMMoS](https://mammos-project.github.io/).
wildflow 0.1.5
Modeling natural ecosystems
Requires Python: >=3.8
Keywords: coral-reefs, computer-vision, photogrammetry, 3d-reconstruction, point-cloud, gaussian-splatting

# wildflow

Modeling natural ecosystems.

## Installation

Install all components:

```bash
pip install wildflow
```

or install components separately:

```bash
pip install wildflow-splat  # Just the PLY processing tools
```

## Quick Start

```python
from wildflow import splat

# Process PLY point cloud
config = splat.Config("model.ply")
patch = splat.Patch("output.bin")
config.add_patch(patch)

results = splat.split_point_cloud(config)
print(f"Processed {results['total_points_written']} points")
```

## Requirements

- Python 3.8+

## License

MIT License