semvec/pypi-packages huggingface dataset:
dep-python 1.1.5
Deppy: A Python dependency executor that builds and executes DAGs efficiently, optimizing workflows with concurrency and flexibility. Perfect for managing complex dependent tasks effortlessly.
Requires Python: >=3.11

# Deppy
[![Coverage](https://img.shields.io/badge/Coverage-100%25-brightgreen)]() [![Code Quality](https://img.shields.io/badge/Code%20Quality-A%2B-blue)]()

🚀 **Deppy** is a cutting-edge **dependency executor for Python** that empowers developers to design, build, and execute **DAGs (Directed Acyclic Graphs)** efficiently, effectively, and effortlessly.

Deppy is a versatile tool for designing complex workflows, including:
 - 📊 ETL Pipelines: Seamlessly extract, transform, and load data across systems.

### 🌟 Features:
- 🛠️ **Graph Building**: Create complex workflows with minimal effort.
- ⚡ **Optimized Execution**: Executes dependencies in the optimal order, leveraging threads and concurrency for maximum performance.
- 🖼️ **Graph Visualization**: Gain insights into your workflows with intuitive visual representations of your DAGs.
- 🔄 **Flexible Workflow Design**: Seamlessly supports synchronous, asynchronous, and hybrid workflows.
- 🎯 **Advanced Utilities**: Packed with tools to streamline and supercharge your development experience.
- 🛡️ **Reliable**: Comprehensive test coverage ensures stability and reliability.

### 📦 Installation

Deppy has support for python 3.11 and above.

To install Deppy, simply run:

```bash
pip install dep-python
```

If you want to use the helpers which have external dependencies, you can install them by running:

```bash
pip install 'dep-python[<helper>]'
```

Supported helpers with external dependencies are:
- asyncclient
- dlt


### 🚀 Quick Start

A quick and easy example to get you started with Deppy:

```python
from deppy import Deppy

def get_list():
    return ["world!", "everyone!", "deppy!"]

def say_hello(to):
    return f"Hello {to}"

deppy = Deppy()

get_list_node = deppy.add_node(func=get_list)
say_hello_node = deppy.add_node(func=say_hello)
deppy.add_edge(get_list_node, say_hello_node, input_name="to", loop=True)

result = deppy.execute()
print(result.query(say_hello_node))  # ['Hello world!', 'Hello everyone!', 'Hello deppy!']
```

### 📖 Documentation
For detailed usage and advanced features, dive into our [comprehensive documentation](https://github.com/ArneDePeuter/deppy/tree/master/docs).

---

📂 Examples

The [Examples](https://github.com/ArneDePeuter/deppy/tree/master/examples) folder contains practical demonstrations to help you get started quickly. Each example showcases how to use Deppy effectively in real-world scenarios. 🛠️

---

⚡ Transform your workflow management with **Deppy** today!
hkopenai.hk-food-mcp-server 0.1.8
Hong Kong food MCP Server providing food data tools
Requires Python: >=3.7

# Hong Kong Food Wholesale Prices MCP Server

[![GitHub Repository](https://img.shields.io/badge/GitHub-Repository-blue.svg)](https://github.com/hkopenai/hk-food-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 wholesale food price data through a FastMCP interface.

## Features

### Wholesale Food Prices
- Get daily wholesale prices of major fresh food in Hong Kong
- Filter by date range (start_date, end_date in DD/MM/YYYY format)
- Select output language (en/zh, default English)

## Data Source

- Wholesale food prices from Agriculture, Fisheries and Conservation Department

## Examples

## 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-food": {
    "disabled": false,
    "timeout": 3,
    "type": "stdio",
    "command": "uvx",
    "args": [
      "hkopenai.hk-food-mcp-server"
    ]
  }
}
```

## Testing

Tests are available in the `tests/` directory. Run with:
```bash
pytest
```
qcmanybody 0.5.1
QCManyBody
Requires Python: >=3.8

QCManyBody
==========

<p align="left">
    <picture>
    <img alt="QCManyBody Logo" src="https://github.com/MolSSI/QCManyBody/blob/main/docs/logo.png" height="150px">
    </picture>
</p>

[![GitHub Actions](https://img.shields.io/github/actions/workflow/status/MolSSI/QCManyBody/ci.yml?logo=github)](https://github.com/MolSSI/QCManyBody/actions?query=workflow%3ACI)
[![codecov](https://codecov.io/gh/loriab/QCManyBody/graph/badge.svg?token=E4S0706HJ0)](https://codecov.io/gh/loriab/QCManyBody)
[![Documentation Status](https://img.shields.io/github/actions/workflow/status/MolSSI/QCManyBody/ci.yml?label=docs&logo=readthedocs&logoColor=white)](https://molssi.github.io/QCManyBody/)
[![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/qcmanybody?color=blue&logo=anaconda&logoColor=white)](https://anaconda.org/conda-forge/qcmanybody)
![python](https://img.shields.io/badge/python-3.8+-blue.svg)

QCManyBody is a python package for running quantum chemistry many-body expansions and interaction calculations in a
package-independent way.

## Installation

QCManyBody is available from [PyPI](https://pypi.org/project/qcmanybody) and from
[conda-forge](https://anaconda.org/conda-forge/qcmanybody).

```bash
# Installation from PyPI
pip install qcmanybody

# Installation from conda-forge
conda install -c conda-forge qcmanybody
```

To install the latest development version directly from
[GitHub](https://github.com/MolSSI/QCManyBody), you can use the following command:

```bash
pip install git+https://github.com/MolSSI/QCManyBody.git
```

## Documentation

Full documentation is available at [https://molssi.github.io/QCManyBody/](https://molssi.github.io/QCManyBody/)

## Authors

* Asem Alenaizan, [@alenaizan](https://github.com/alenaizan), original Psi4 implementations of vmfc Hessians, multi-level, and embedded point charges
* Lori A. Burns, [@loriab](https://github.com/loriab), ManyBody QCSchema and high-level interface
* Benjamin P. Pritchard, [@bennybp](https://github.com/bennybp), core interface and QCArchive integration
* Daniel G. A. Smith, [@dgasmith](https://github.com/dgasmith), original Psi4 implementations of nocp, cp, and vmfc single-level e/g/H and distributed driver integration

## Citation [![doi](https://img.shields.io/badge/doi-10.1063/5.0231843-5077AB.svg)](https://doi.org/10.1063/5.0231843)

The journal article reference describing QCManyBody is:

L. A. Burns, C. D. Sherrill, B. P. Pritchard,
"QCManyBody: A Flexible Implementation of the Many-Body Expansion",
J. Chem. Phys. 161(15) 152501 (2024).

## Demonstration

An example of the core and high-level interfaces can be found in [test_examples](qcmanybody/tests/test_examples.py) with
directions in [tests/README](qcmanybody/tests/README.md).