semvec/pypi-packages huggingface dataset:
pytest-abq 1.0.1
Pytest integration for the ABQ universal test runner.
License: MIT
Requires Python: >=3.7

# pytest-abq

An [ABQ](https://rwx.com/abq) integration for [pytest](https://docs.pytest.org/en/7.2.x/).

To install:

```
pip install pytest-abq
```

That's it! Then, run your test suite with ABQ:

```
abq test -- pytest
```

## Compatibility

`pytest-abq` is actively tested against

- Python 3.7, 3.8, 3.9, 3.10
- Pytest 7.0, 7.2

`pytest-abq` may support Python and pytest versions beyond these.

`pytest-abq` implements version 0.2 of the ABQ native runner protocol and
requires ABQ 1.3.0 or greater.
django-y-swagger-ui 1.0.1
A Django app to show swagger ui for OpenAPI specification.
License: MIT
Requires Python: >=3.11

============
Y-SWAGGER-UI
============

Y-SWAGGER-UI is a Django app to show swagger ui for your API. 

Use swagger-ui v4.18.2 https://github.com/swagger-api/swagger-ui/releases/tag/v4.18.2

Quick start
-----------

1. Add "y-swagger-ui" to your INSTALLED_APPS setting like this::

    INSTALLED_APPS = [
        ...,
        "y_swagger_ui",
    ]

2. Configure Y_SWAGGER_UI in settings.py. You can use openapi_spec_example.yaml in repository root dir as an example. For OpenAPI specification, see https://swagger.io/specification/::

    Y_SWAGGER_UI = {
        'OPENAPI_SPEC_FILE': BASE_DIR / 'openapi_spec.yaml'      # OpenAPI spec yaml file
    }

3. Include the URLconf in your project urls.py like this::

    path("api/", include("y_swagger_ui.urls")),


4. Start the development server and visit http://127.0.0.1:8000/api/swagger_ui.html
extend-json 0.0.2
additional methods for handling json files in python
License: MIT
Keywords: json,extended

# json_extended
Additional methods for handling json files in python.

## Install

### Windows
install using pip : ```py -m pip install extend-json```

### Others
idk

## Usage
import: ```import extend_json``` or ```from extend_json import ...```
overview: ```help(extend_json)```
imcascade 1.1
imcascade: Fitting astronomical images using a 'cascade' of Gaussians
License: MIT

imcascade
=========

|Docs|
|License|
|Arxiv|
|pypi|

``imcascade`` is a code designed for fitting objects in astronomical images using a "cascade" of Gaussians. It uses multi-guassian expansion (MGE) to model galaxies as a mixture of Gaussians in a Bayesian Framework. It was designed to study the morphology of faint, semi-resolved galaxies. If you are planning on using ``imcascade`` we suggest reading our paper describing the method `here <https://arxiv.org/abs/2109.13262>`__  and the documentation `here <https://imcascade.readthedocs.io>`__.

Citation
--------
``imcascade`` is open source software made available under the MIT license, written by Tim Miller and Pieter van Dokkum. If you use this package in your work please cite `Miller & van Dokkum (2021) <https://arxiv.org/abs/2109.13262>`__

Help and Issues
---------------
``imcacscade`` is maintained on Github. If you find a bug in the code or have a feature you would like to request, please open an `issue on Github <https://github.com/tbmiller-astro/imcascade/issues>`__. Additionally you can reach out to me directly if you are having issues using the code or have suggestions.

.. |Docs| image:: https://readthedocs.org/projects/imcascade/badge/?version=latest
   :target: http://imcascade.readthedocs.io/?badge=latest
.. |License| image:: https://img.shields.io/badge/license-MIT-blue
.. |Arxiv| image:: https://img.shields.io/badge/arXiv-2109.13262-blue
   :target: https://arxiv.org/abs/2109.13262
.. |pypi| image:: http://img.shields.io/pypi/v/imcascade.svg
   :target: https://pypi.org/project/imcascade/
rompt 1.0.1
Python library to interact with the Rompt API and CLI.
License: MIT
Requires Python: >=3.8

<br />

<h2 align="center">

[Rompt.ai](https://rompt.ai) Python Library

</h2>

<br />

Rompt streamlines your workflow, improves collaboration, enhances GPT model performance, and provides seamless integration with its CLI tool and output format support.


Features
--------

*   Version control and changelog on prompts
*   Generate prompts from template strings
*   Pull prompts from Rompt into your codebase


Installation
------------

Install the client & CLI library:

```bash
pip install rompt
```

Pull your prompts into your codebase using the CLI:

```bash
rompt pull --token {YOUR_TOKEN}
```


Usage
-----

To use the library, you'll first need to import it:

```py
from rompt import generate

generated_with_metadata = generate(
  prompt_name="your-prompt-name",
  template_object={
    NAME: "Michael",
    DIRECTION: "Generate a Tweet"
  },
)

prompt = generated_with_metadata.get('prompt')
# Your result is now in the prompt variable
```


Track History
-------------

```py
from rompt import generate, track

# ...continued from above

track(generated_with_metadata)

# Your GPT responses can be included; example with OpenAI:

const gpt_response = openai.Completion.create({
  prompt=prompt,
  #...
})

track(generated_with_metadata, gpt_response)
```


Documentation
-------------

For detailed documentation, including API references and more examples, please visit the [the Rompt.ai website](https://rompt.ai/docs).


Contributing
------------

We welcome contributions to the Rompt Node.js library. If you'd like to contribute, please submit a pull request on GitHub.


License
-------

This project is licensed under the MIT License. For more information, please see the [LICENSE](https://github.com/your_github_username/rompt-/blob/main/LICENSE) file.