semvec/pypi-packages huggingface dataset:
package-forkEntropy 0.2.0
The implementation and test for the measurement method fork entropy.
Requires Python: >=3.6

# ForkEntropy Package

## Overview

This package implements the calculation of the ForkEntropy metric. It provides tools to analyze and measure the diversity of OSS(Open Source Software Project) given specified data frame.

## Directory Structure

ForkEntropy/
├── mainCode/
│   ├── _init_.py        # Initialization file
│   └── ForkEntropy.py   # Main implementation code
├── test/
│   └── testData/
│       ├── commitChanges/      # Test data for commit changes
│       ├── downloaded_PRrawdata/ # Downloaded PR raw data
│       └── testResult/         # Test results
│   └── test_example.py  # Test code
├── License             # License information
├── ReadMe              # This README file
└── setup.py            # Setup/installation script

## Installation

To get more infomation about ForkEntropy package, clone the repository and navigate to the link:
```bash
git clone https://github.com/WuXiangChen/ForkEntropy_ESE
cd ForkEntropy
```
wgm_api 0.1.5
A Python client library for managing WireGuard server users and sessions.
Requires Python: <4.0,>=3.10

# WGM - API Client for WireGuard Management

## Overview

`WGM` is a Python client library designed to interact with a WireGuard server API. This library enables actions such as creating, deleting, enabling, and disabling users, as well as managing sessions and retrieving user information.

## Features

- **Session Management**: Start a session with the server using provided credentials (IP, port, password).
- **User Management**: Retrieve a list of all users, and perform actions like create, delete, enable, and disable users.
- **Session Re-establishment**: Automatically re-establishes a session if authentication fails.
- **HTTP Method Support**: Supports `POST`, `DELETE`, `PUT` HTTP methods for data manipulation.
  
## Installation

To install dependencies, use [Poetry](https://python-poetry.org/). Just run the command:

```bash
poetry install
```
This will install all the dependencies specified in project.toml.

## Using WGM

```python
import WGM_api

# Session initialization #
session = WGM_api()

# Set up a session with the server #
session.start_session(ip_address="ip_address", port=port, password="your_password")

# Example of creating a new user #
session.create_user(name="user_name")

# Example of deleting a user #
session.delete_user(value="existing_user")

# When creating a user, we must specify the desired name, while when deleting a user, it can be done either by name or by ID. #
```

### Methods

- **start_session(ip_address, port, password)** — Initializes a session with the server.
- **get_all_users()** — Retrieves a list of all users.
- **create_user(name)** — Creates a new user.
- **delete_user(value)** — Deletes a user (by name or ID).
- **enable_user(value)** — Enables the user (by name or ID).
- **disable_user(value)** — Disables the user. (by name or ID).
- **download_config(value)** — Download config file the user. (by name or ID).

## PyPI

The `WGM` library is available on [PyPI](https://pypi.org/project/wgm_api/), making it easy to install and integrate into your projects.

Install it via `pip`:
```bash
pip install wgm_api
```
Or poetry:
```bash
poetry add wgm_api
```

<div align="center">

## Libraries in this project

[![Python](https://img.shields.io/badge/Python-3.8%20|%203.9%20|%203.10%20|%203.11%20|%203.12-orange?logo=python&color=blue)](https://www.python.org/downloads/) [![Requests](https://img.shields.io/badge/Requests-^2.32.3-brightgreen?logo=Requests&color=green)](https://requests.readthedocs.io/en/latest/)

<i>Version 1.0.0 | Release Date: November 21, 2024</i>
</div>
pyFirmata2 2.5.1
Use your Arduino as a data acquisition card under Python

==========
pyFirmata2
==========

PyFirmata2 turns your Arduino into a data acquisition card controlled by Python.

Up to 1kHz precise sampling at the analogue ports for digital filtering.

Just upload the default firmata sketch into your Arduino and you are all set.

pyFirmata2 is an updated version of pyFirmata which *replaces loops
with callbacks*. Instead of unreliable "sleep" commands in a loop the
Python application registers callbacks which are then called every
time after new data has arrived. This means for the analogue
channels the callbacks are called at the specified sampling rate
while the digital ports call the callback functions after
a state change at the port (from 0 to 1 or 1 to 0).

This API has been used in my Digital Signal Processing (DSP) class to
practise realtime filtering of analogue sensor
data. Examples can be viewed on the YouTube channel of the
class: https://www.youtube.com/user/DSPcourse


Installation
============


Upload firmata
--------------

Install the Arduino IDE on your computer: https://www.arduino.cc/en/Main/Software

Start the IDE and upload the standard firmata sketch into your Arduino with::
  
    File -> Examples -> Firmata -> Standard Firmata



Install pyfirmata2
------------------

The preferred way to install is with `pip` / `pip3`. Under Linux::

    pip3 install pyfirmata2 [--user] [--upgrade]

    
and under Windows/Mac type::
  
    pip install pyfirmata2 [--user] [--upgrade]

    
You can also install from source with::

    git clone https://github.com/berndporr/pyFirmata2
    cd pyFirmata2

Under Linux type::
  
    python3 setup.py install

Under Windows / Mac::

    python setup.py install


Usage
=====

Please go to https://github.com/berndporr/pyFirmata2 for the
documentation and in particular the example code.


Example code
============

It's strongly recommended to check out the example code at
https://github.com/berndporr/pyFirmata2/tree/master/examples
to see how the callbacks work.


Credits
=======

The original pyFirmata was written by Tino de Bruijn.
The realtime sampling / callback has been added by Bernd Porr.
QImageLab 0.0.1
A Python library for processing experimental images like .tif
Requires Python: >=3.6

Here's an English description for your `QImageLab` repository:  

**QImageLab** is a versatile library designed for processing and analyzing experimental image data, with a focus on formats like `.tif`. It offers tools for efficient image handling, enhancement, and conversion, tailored to the needs of research and laboratory workflows.  

Let me know if you'd like to adjust the focus or add more specific features!
mcp-memory 0.1.0.dev2
Memory Implementation in MCP and python
Requires Python: >=3.11

<div align="center">
  <h1>MCP Memory</h1>
  <p><strong>Memory Implementation in MCP and python</strong></p>
  <p>
    <img src="https://img.shields.io/badge/python->=3.11-blue">
    <a href="https://pypi.org/project/mcp-memory/">
      <img src="https://img.shields.io/pypi/v/mcp-memory.svg">
    </a>
  </p>
</div>



🤔 [MCP](https://github.com/modelcontextprotocol) stands for Model Context Protocol, meaning a open protocol between Agent and Others

🧠 Memory is a great tool/storage for your AI, so that AI can remember user and stuff.

✅ The repo implements a memory mechanism and a MCP server



## Quick Start

Add this to your `claude_desktop_config.json`

```json
{
  "mcpServers": {
    "memory": {
      "command": "uvx",
      "args": ["mcp-memory"]
    }
  }
}
```

Or use it in Python:

```shell
uv install mcp-memory
# or
pip install mcp-memory
```



## Features

TODO



## Resources

- A Memory MCP server in TS. [link](https://github.com/modelcontextprotocol/servers/tree/main/src/memory)
- LangGraph concepts. [link](https://langchain-ai.github.io/langgraph/concepts/memory/)
tiktokcaptions 0.1.2
Add TikTok-style captions to videos
Requires Python: >=3.7

# TikTok Captions

Add TikTok-style captions to your videos automatically using OpenAI's Whisper for transcription.

## Preview

<img src="https://i.imgur.com/nfldFkh.gif" alt="captions preview" height="300">

## Installation

```bash
pip install tiktokcaptions
```

## Usage

```python
from tiktokcaptions import add_captions_to_videofile

# whisper transcription (dict)
transcription = openai.audio.transcription.create(
    model="whisper-1",
    file=file,
    timestamp_granularities=["segment", "word"],
    response_format="verbose_json",
)
transcription = transcription.model_dump()

# Basic usage
add_captions_to_videofile("input.mp4", transcription=transcription, output_file="with_captions.mp4")

# With custom options
add_captions_to_videofile(
    "input.mp4",
    transcription=transcription,
    output_file="with_captions.mp4",

    # Font settings
    font="Montserrat-ExtraBold.ttf",  # or path to custom font
    font_size=50,
    font_color="white",

    # Stroke settings
    stroke_width=2,
    stroke_color="black",

    # Word highlighting
    highlight_current_word=True,
    highlight_color="#FF4500",
    highlight_padding=(10, 8),  # Horizontal and vertical padding around highlighted words in pixels
    highlight_radius=10,      # Border radius of the highlight box in pixels

    # Layout
    line_count=2,        # Maximum number of lines to show at once
    padding=50,          # Horizontal padding from video edges in pixels
    position="bottom",   # "top", "bottom", or "center"
    margin=200,          # Vertical margin from video edges in pixels (when using top/bottom position)

    # Shadow effects
    shadow_strength=1.0,
    shadow_blur=0.3,

    # Other
    verbose=False
)
```

## Features
- Automatic speech recognition using Whisper
- Customizable caption styling
- Support for multiple video formats
- Easy to use API

## Requirements
- Python 3.7+
- FFmpeg installed on your system

## License
MIT License - see LICENSE file for details.