semvec/pypi-packages huggingface dataset:
holobit 0.1.0
A quantum holographic library for simulating and visualizing quantum states.
Requires Python: >=3.6

# Holobit: A Quantum Holographic Library

## Introduction
Holobit is a Python library designed to simulate and visualize quantum states using holographic principles. It integrates quark interactions, gluon dynamics, fractal projections, and more, within a dodecahedral structure governed by the golden ratio.

## Installation
To install Holobit, clone the repository and run:
pip install -e


## Usage
Here is a simple example to create and visualize a Holobit system:

```python
from holobit import HolobitFractalSystem

# Initialize a Holobit system with 3 dimensions and 5 iterations
system = HolobitFractalSystem(dimensions=3, iterations=5)

# Simulate interactions
system.simulate_interactions()

# Visualize the system
system.visualize_system()
pubsubx 0.1.2
A publish-subscribe messaging system
Requires Python: >=3.6

# PubSubX

PubSubX is a lightweight publish-subscribe messaging system implemented in Python. It provides a simple server and client for building distributed messaging applications.

## Installation

You can install PubSubX using pip:

```
pip install pubsubx
```

## Usage

### Starting the server

There are multiple ways to start the PubSubX server:

1. As a module:
   ```
   python -m pubsubx [--host HOST] [--port PORT]
   ```

2. Using the console script:
   ```
   pubsubx [--host HOST] [--port PORT]
   ```

3. In your Python code:
   ```python
   from pubsubx import PubSubServer

   server = PubSubServer(host='localhost', port=5000)
   server.start()
   ```

The default host is 'localhost' and the default port is 5000.

### Using the client

Here's a simple example of how to use the PubSubX client:

```python
from pubsubx import PubSubClient
import time

def on_message(topic, message):
    print(f"Received message on topic '{topic}': {message}")

# Create and connect the client
client = PubSubClient()
client.connect()

# Set up the message callback
client.on_message(on_message)

# Subscribe to a topic
client.subscribe("test_topic")

# Publish a message
client.publish("test_topic", "Hello, PubSubX!")

# Keep the script running
try:
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    print("Disconnecting...")
    client.disconnect()
```

## License

This project is licensed under the MIT License.
swick 0.29
The slick way to process SWC files.
Requires Python: >=3.0

# ![swick Logo](https://github.com/nathantspencer/swick/raw/main/assets/logo_cropped_small.png)
`swick` is the slick way to process [SWC files](http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html)—which contain tree-like representations of neuron structures—in Python.

Reading, writing, combining, splitting, and validating SWC files can be done in just a few lines. Objects to represent SWC files are also provided, laying the groundwork for custom analysis or modification of SWCs in Python.

# Installation

To install `swick` via [pip](https://pip.pypa.io/en/stable/), simply:

```
pip install swick
```

Or to upgrade an existing installation:

```
pip install swick --upgrade
```

Python wheels can also be manually downloaded via the [PyPI page](https://pypi.org/project/swick/).

# Usage & Documentation

Documentation is automatically built via a [Github Actions workflow](https://github.com/nathantspencer/swick/blob/main/.github/workflows/build-docs.yml) and hosted on Github Pages. Reading it is the best way to get started with `swick`. For any questions not addressed there, please feel free to [open an issue](https://github.com/nathantspencer/swick/issues/new)!

Here are some links to relevant documentation:

 - [Documentation Homepage](https://nathantspencer.github.io/swick/index.html)
 - [User Guide](https://nathantspencer.github.io/swick/user_guide/index.html)
 - [API Reference](https://nathantspencer.github.io/swick/api_documentation/index.html)

# Testing

Tests can be run via this command from the root directory of the repository:

```
python -m unittest discover
```

In the near future, tests will be automated via Github Actions in order to evaluate pull requests as well as the current head of the `main` branch.