semvec/pypi-packages huggingface dataset:
hkopenai.hk-tech-mcp-server 0.1.8
Hong Kong tech MCP Server providing tech data tools
Requires Python: >=3.7

# Hong Kong Government Security Incident MCP Server

[![GitHub Repository](https://img.shields.io/badge/GitHub-Repository-blue.svg)](https://github.com/hkopenai/hk-tech-mcp-server)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This is an MCP server that provides access to government security incident data through a FastMCP interface.

## Features

### Government Security Incidents
- Get number of government information security incidents reported to Digital Policy Office

## Data Source

- Security incident data from Digital Policy Office

## Examples

* Get government security incident reports

## Setup

1. Clone this repository
2. Install Python dependencies:
   ```bash
   pip install -r requirements.txt
   ```
3. Run the server:
   ```bash
   python app.py
   ```

### Running Options

- Default stdio mode: `python app.py`
- SSE mode (port 8000): `python app.py --sse`

## Cline Integration

To connect this MCP server to Cline using stdio:

1. Add this configuration to your Cline MCP settings (cline_mcp_settings.json):
```json
{
  "hk-tech": {
    "disabled": false,
    "timeout": 3,
    "type": "stdio",
    "command": "uvx",
    "args": [
      "hkopenai.hk-tech-mcp-server"
    ]
  }
}
```

## Testing

Tests are available in the `tests/` directory. Run with:
```bash
pytest
```
emborg 1.42
Borg front end.
Requires Python: >=3.6
Keywords: emborg, borg, borgbackup, borgmatic, vorta, backups

Emborg — Front-End to Borg Backup
=================================

|downloads| |build status| |coverage| |rtd status| |pypi version| |python version|

:Author: Ken Kundert
:Version: 1.42
:Released: 2025-06-14

*Emborg* is a simple command line utility to orchestrate backups. It is built as 
a front-end to Borg_, a powerful and fast deduplicating backup program.  With 
*Emborg*, you specify all the details about your backups once in advance, and 
then use a very simple command line interface for your day-to-day activities.  

Use of *Emborg* does not preclude the use of Borg directly on the same 
repository.  The philosophy of *Emborg* is to provide commands that you would 
use often and in an interactive manner with the expectation that you would use 
Borg directly for the remaining commands.

*Emborg* is not intended for use with *Borg* version 2.0 or later.  Instead, the 
successor to *Emborg* should be used: Assimilate_.


Getting Help
------------

You can find the documentation here: Emborg_.

The *help* command provides information on how to use Emborg's various
features.  To get a listing of the topics available, use::

    emborg help

Then, for information on a specific topic use::

    emborg help <topic>

It is worth browsing all of the available topics at least once to get a sense of
all that *Emborg* can do.


.. _borg: https://borgbackup.readthedocs.io
.. _assimilate: https://assimilate.readthedocs.io
.. _emborg: https://emborg.readthedocs.io

.. |downloads| image:: https://pepy.tech/badge/emborg/month
    :target: https://pepy.tech/project/emborg

..  |build status| image:: https://github.com/KenKundert/emborg/actions/workflows/build.yaml/badge.svg
    :target: https://github.com/KenKundert/emborg/actions/workflows/build.yaml

.. |coverage| image:: https://coveralls.io/repos/github/KenKundert/emborg/badge.svg?branch=master
    :target: https://coveralls.io/github/KenKundert/emborg?branch=master

.. |rtd status| image:: https://img.shields.io/readthedocs/emborg.svg
   :target: https://emborg.readthedocs.io/en/latest/?badge=latest

.. |pypi version| image:: https://img.shields.io/pypi/v/emborg.svg
    :target: https://pypi.python.org/pypi/emborg

.. |python version| image:: https://img.shields.io/pypi/pyversions/emborg.svg
    :target: https://pypi.python.org/pypi/emborg/
gaitmod 0.1.0
real-time gait modulation prediction using multimodal neural and movement data (LFP, EEG, IMU, EMG) — designed for closed-loop DBS systems in Parkinson's diseas.
Requires Python: >=3.8
Keywords: gait, modulation, LFP, ML

# GaitMod

**gaitmod** is a Python library for processing, analyzing, and modeling multi-modal neural and movement data, including LFP, EEG, EMG, and IMU signals. It focuses on real-time gait modulation prediction in Parkinson's disease and supports customizable deep learning pipelines.

It provides tools to:

* Preprocess and clean multi-modal data
* Extract and select features from neural and movement signals
* Train and evaluate machine learning models
* Visualize results and model performance

## Table of Contents

- [GaitMod](#gaitmod)
  - [Table of Contents](#table-of-contents)
  - [Overview](#overview)
  - [Documentation](#documentation)
  - [Installation](#installation)
  - [Usage](#usage)
  - [Project Structure](#project-structure)
  - [Contributing](#contributing)
  - [License](#license)

## Overview

This repository contains code and resources for studying gait modifications using data analysis and machine learning techniques.

## Documentation

Comprehensive documentation is available at [https://gaitmod.readthedocs.io/](https://gaitmod.readthedocs.io/).

## Installation

Clone the repository:

```bash
git clone https://github.com/yourusername/gaitmod.git
cd gaitmod
```

Install dependencies:

```bash
pip install -r requirements.txt
```

## Usage

Run the main analysis script:

```bash
python main.py
```

Refer to the [documentation on Read the Docs](https://gaitmod.readthedocs.io/) for detailed usage instructions.


## Project Structure

```
gaitmod/
├── data/
├── src/
├── results/
├── README.md
└── requirements.txt
```

## Contributing

Contributions are welcome! Please open issues or submit pull requests.

## License

This project is licensed under the MIT License.
docker-adapter-obfuscated 0.2.0
A Python adapter for Docker operations (obfuscated version)
Requires Python: >=3.8

# Docker Adapter

<p align="center">
  <img src="assets/png/eviesales.png" alt="Bitzer Logo" width="200">
</p>


A Python library for managing Docker containers and images with a clean and intuitive interface.

[![PyPI version](https://badge.fury.io/py/docker-adapter.svg)](https://badge.fury.io/py/docker-adapter)
[![Python Versions](https://img.shields.io/pypi/pyversions/docker-adapter.svg)](https://pypi.org/project/docker-adapter/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## About

Docker Adapter is developed and maintained by [Evolvis](https://evolvis.ai), a company specializing in AI and software development solutions. This library provides a clean and intuitive interface for managing Docker containers and images in Python applications.

## Features

- Container management (start, stop, pause, unpause, kill, remove)
- Image management (pull, push, tag, remove)
- Container and image inspection
- Container logs and stats
- Command execution in containers
- Error handling with custom exceptions
- Type hints for better IDE support

## Installation

```bash
pip install docker-adapter
```

## Quick Start

```python
from docker_adapter import DockerClient

# Initialize the Docker client
client = DockerClient()

# List all containers
containers = client.list_containers(all=True)
for container in containers:
    print(f"Container: {container.name} (ID: {container.id})")

# List all images
images = client.list_images()
for image in images:
    print(f"Image: {image.tags[0] if image.tags else image.id}")
```

## Documentation

For detailed documentation, please visit our [documentation page](docs/README.md).

## Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Author

**Alban Maxhuni, PhD**  
Email: [a.maxhuni@evolvis.ai](mailto:a.maxhuni@evolvis.ai)  
[Evolvis](https://evolvis.ai)

## Support

For support, please contact [support@evolvis.ai](mailto:support@evolvis.ai) or visit our [website](https://evolvis.ai).

## About Evolvis

[Evolvis](https://evolvis.ai) is a technology company that specializes in:
- Artificial Intelligence Solutions
- Software Development
- Cloud Infrastructure
- DevOps Automation
- Container Orchestration

Visit our [website](https://evolvis.ai) to learn more about our services and solutions.
imgprocessdio 0.0.1
Image Processing package, compare and see diffences of histograsms between images
Requires Python: >=3.8

# imgprocessdio

Description.
The package imgprocessdio is used to:

Processing:

- Histogram satching
- Structural similarity
- Resize image

Utils:

- Read image
- Save image
- Plot image
- Plot result
- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install imgprocessdio

```bash
pip install imgprocessdio
```

## Usage

```python
from imgprocessdio.processign import combination
combination.find_difference()
```

## Author

Caio Simonassi

## License

[MIT](https://choosealicense.com/licenses/mit/)