semvec/pypi-packages huggingface dataset:
topdesign 0.0.3.1
A general tool chain integration framework for multidisciplinary analysis and optimization
Requires Python: >=3.8

# TopDesign

TopDesign is a general tool chain integration platform for multidisciplinary 
analysis and optimization, developed in DTU Wind as an open source tool.

The concept of the platform and the usage example on wind farm design can be 
found in:

> Feng, Ju, and Jens Nørkær Sørensen. "Towards a general tool chain integration 
platform for multi-disciplinary analysis and optimization in wind energy." In 
*Journal of Physics: Conference Series*, vol. 2767, no. 8, p. 082014. IOP 
Publishing, 2024.  [[link](https://iopscience.iop.org/article/10.1088/1742-6596/2767/8/082014/meta)]

Source code of the platform is hosted in DTU Wind's GitLab site: 
> https://gitlab.windenergy.dtu.dk/TOPFARM/basic-plugins/topdesign

Development of this platform has been partially supported by the [MERIDIONAL](
https://meridional.eu/) project, which receives funding from the European
Union’s Horizon Europe Programme under the grant agreement No. 101084216.

## Installation

You can install the TopDesign package from PyPI using the following command:

>```
> pip install topdesign
>```

You can also install TopDesign from GitLab (includes any recent updates):
>```
> pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/basic-plugins/topdesign.git
>```

We highly recommend developers to install TopDesign into a dedicated 
environment. The commands to clone and install TopDesign with developer options
including dependencies required to run the tests into the current active 
environment in an Anaconda Prompt are as follows:
>```
> git clone https://gitlab.windenergy.dtu.dk/TOPFARM/basic-plugins/topdesign.git
> cd topdesign
> pip install -e .
>```


## Usage
The standard topdesign platform relies only on [numpy](https://numpy.org/), 
[matplotlib](https://matplotlib.org/) and [scipy](https://scipy.org/). 

To run the example on wind farm design, you need to have 
[py_wake](
https://topfarm.pages.windenergy.dtu.dk/PyWake/) and [floris](
https://nrel.github.io/floris/installation.html) (>=4.0) installed.

To run the example on multi-language model integration, you need to have
[matlabengine](https://www.mathworks.com/help/matlab/matlab-engine-for-python.html) 
and [juliacall](https://juliapy.github.io/PythonCall.jl/stable/juliacall/) 
installed.
clarifyquestgen 2025.5.151003
Clarifying Questions Generator for task refinement
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/clarifyquestgen.svg)](https://badge.fury.io/py/clarifyquestgen)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/clarifyquestgen)](https://pepy.tech/project/clarifyquestgen)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

# ClarifyingQuestionsGenerator

`ClarifyingQuestionsGenerator` is a Python package designed to refine task descriptions through an iterative process of asking clarifying questions. It uses GPT models from OpenAI to generate questions and refine tasks, streamlining the process of task clarification and ensuring tasks are well-understood and actionable.

## Installation

To install `ClarifyingQuestionsGenerator`, you can use pip:

```bash
pip install clarifyquestgen
```

## Usage

### As a Python Module

You can use `ClarifyingQuestionsGenerator` as a module in your Python scripts.

Example:

```python
from clarifyquestgen import ClarifyingQuestionsGenerator

# Initialize the generator with a task, instruction, and your OpenAI API key
clarifier = ClarifyingQuestionsGenerator(
    task="Describe the task here",
    instruction="Additional instructions here",
    gpt_api_key='your-openai-api-key'
)

# Run the generator to refine your task
refined_task = clarifier.run()
print("Refined Task Description:")
print(refined_task)
```

### Customizing Your Generator

You can customize the behavior of `ClarifyingQuestionsGenerator` by adjusting the initialization parameters, such as the model, temperature, max tokens, etc., to fit the specific needs of your application or to tweak the behavior of the GPT model.

## Output Example

When you run `ClarifyingQuestionsGenerator`, it iteratively asks questions, refines the task, and analyzes its feasibility until a satisfactory level of clarity and feasibility is reached. Here is an example interaction:

```
Question 1: What specific parts of the database schema need to be refactored?
...user chooses an answer...
Question 2: What is the expected format or style of the comments?
...user chooses an answer...
...
Refined Task Description:
Refined task: Add comments to specific sections or functions within two code files. Follow the expected format for comments and adhere to specific guidelines or conventions provided.
```

## Contributing

Contributions, issues, and feature requests are welcome! Feel free to check [issues page](https://github.com/yourusername/ClarifyingQuestionsGenerator/issues).

## License

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