semvec/pypi-packages huggingface dataset:
llmfastdev 0.0.1
A Python client for interacting with llamafile local LLM instances
Requires Python: >=3.7

# llmfastdev

A Python client for interacting with llamafile local LLM instances.

## Installation

```bash
pip install llmfastdev
```

## Usage

```python
from llmfastdev import LlamafileClient

# Initialize the client
client = LlamafileClient("http://localhost:8080")

# Check if server is running
if client.health_check():
    print("Server is running!")

# Generate a completion
response = client.complete("Hello, how are you?", max_tokens=50)
print(response)

# Use chat format
messages = [
    {"role": "user", "content": "What is the capital of France?"}
]
response = client.chat(messages, max_tokens=50)
print(response)

# Stream completion
for token in client.stream_completion("Tell me a story", max_tokens=100):
    print(token, end="", flush=True)
```

## Features

- Simple HTTP client for llamafile servers
- Support for completion and chat endpoints
- Streaming support
- Context manager support
- Health check functionality
- Model information retrieval

## Requirements

- Python >= 3.7
- requests >= 2.25.0

## License

MIT
aco-djssp 0.1.1
Ant colony scheduling optimisation package
Requires Python: >=3.13

# Dynamic Job Shop Scheduling with Ant Colony Optimisation

This repository implements a **Min–Max Ant System (MMAS)** solver for the **Dynamic Job Shop Scheduling Problem (DJSSP)**.  
It is designed to model real-world scheduling environments (e.g. hospitals, manufacturing plants) where:

- Jobs arrive dynamically over time.
- Some operations are already underway and cannot be rescheduled.
- Schedules must balance **makespan minimisation** with **disruption minimisation**.

### Prerequisites

- Python 3.13+
- NumPy
- Matplotlib (for plotting)
- pytest (for testing)

## Installation

```bash
uv pip install aco-djssp
```
Alternatively, cloning the repo and then running 

```bash
uv run tests/test_sim.py
```
should also work and create a local `.venv/`, a python virtual environment with the package installed.

## Running an example
There are several ways of running the package. The most common method will be the one found in `tests/test_sim.py`. Note that this assumes that some benchmarked static JSSP are downloaded. The ones used for testing can be found [here](https://github.com/tamy0612/JSPLIB). 
This test will run a simulation with a dynamic job arrival, and then save the resulting schedule in `/tmp/`. If using Windows, this path will need to be changed. 
A more involved and complicated example can be found in `tests/test_hospital.py`. The way this example works is a little unusual due to plotting requirements, and I would recommend following the approach set out in `test_sim.py`, but it is a good showcase of what can be done. Feel free to hack away at the code if the desired functionality is not present!

## Testing

Tests can be found in `tests/` and can be run with

```bash
uv run pytest
```

or can be manually selected by specifying the file name after `uv run pytest`.

## License
This project is licensed under the MIT license. See `LICENSE.MD` for details.
faery 0.7.0
Ferries event-based data from A to B
Requires Python: >=3.9

![faery logo](faery_logo.png)

# Faery: A Stream Processing Library for Neuromorphic Event-Based Data

[![PyPI - Downloads](https://img.shields.io/pypi/dm/faery?logo=pypi)](https://pypi.org/project/faery/)
[![GitHub Tag](https://img.shields.io/github/v/tag/aestream/faery?logo=github)](https://github.com/aestream/faery/releases)
[![Discord](https://img.shields.io/discord/1044548629622439977)](https://discord.gg/C9bzWgNmqk)
[![Neuromorphic Computing](https://img.shields.io/badge/Collaboration_Network-Open_Neuromorphic-blue)](https://open-neuromorphic.org/neuromorphic-computing/)


Faery converts neuromorphic event-based data between formats. It can also generate videos, spectrograms, and event rate curves.

📄 [Read more in our documentation](https://aestream.github.io/faery).

## Installation

Using `pip`: `pip install faery`.

We recommend using a [virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/) to install Faery.
More information in the [installation instructions](https://aestream.github.io/faery/install).

## Usage

Faery can be used as a [**command line tool**](https://aestream.github.io/faery/cli) or as a [**Python library**](https://aestream.github.io/faery/python).

**Command line tool**: Faery can convert data between formats, render videos, and analyze event-based data. It is particularly useful for quick conversions and batch processing of multiple files.
Here are three examples:

1. Convert a Prophesee raw file to AEDAT format:

```sh
faery input file input.raw output file output.aedat4
```

2. Create an MP4 video from your event data with temporal filtering:

```sh
faery input file events.aedat4 filter temporal --window-size 1000us output mp4 output.mp4 --frame-rate 30
```

3. Stream data from an [Inivation camera](https://inivation.com/) to a UDP socket (note: [requires event camera drivers](https://aestream.github.io/faery/install)):

```sh
faery input inivation camera output udp localhost 7777
```

**Python library**: Faery provides a set of input functions to read event data from files, UDP streams, or other sources. You can chain methods to filter, render, or analyze the data. For example, to render an AEDAT4 event file as a real-time MP4 video:

```python
import faery
faery.events_stream_from_file("input.aedat4") \
    .regularize(frequency_hz=60.0) \
    .render(exponential_decay=0.2, style="starry_night") \
    .to_mp4("output.mp4")
```

More information is available in the [command line usage documentation](https://aestream.github.io/faery/cli), the [Python library documentation](https://aestream.github.io/faery/python), and the [examples directory](https://github.com/aestream/faery/tree/main/examples).

## Acknowledgements

Faery was initiated at the [2024 Telluride neuromorphic workshop](https://sites.google.com/view/telluride-2024/) by

-   [Alexandre Marcireau](https://github.com/amarcireau)
-   [Jens Egholm Pedersen](https://github.com/jegp)
-   [Gregor Lenz](https://github.com/biphasic)
-   [Gregory Cohen](https://github.com/gcohen)

License: LGPLv3.0