semvec/pypi-packages huggingface dataset:
od-standards-calibration-plugin 1.0.2
Calibrate OD using a set of standards.
License: MIT


## Pioreactor OD standards calibration plugin

The standard dilution-based OD calibration system is not always ideal for setting up a cluster of Pioreactors. This plugin allows you to calibrate a Pioreactor using a series of samples with known OD, rather than serially diluting a starting sample. Note, however, that the quality of the calibration will depend on the number of samples and the range of ODs used. This is a drop-in replacement for the standard OD calibration, and does not change the behavior of OD measurements after calibration. 

## Run your calibration

Type into your command line:

```
pio run od_calibration_from_standards
```

To perform this calibration, prepare a set of standards in individual vials with known OD (at least 10 mL volume) and with stir bars. Run the above command and follow the prompts. A calibration based on fitting will be produced.
django-entity-event 3.1.0
Newsfeed-style event tracking and subscription management for django-entity.
License: MIT

.. image:: https://travis-ci.org/ambitioninc/django-entity-event.png
   :target: https://travis-ci.org/ambitioninc/django-entity-event

.. image:: https://pypip.in/v/django-entity-event/badge.png
    :target: https://crate.io/packages/django-entity-event/
    :alt: Latest PyPI version

.. image:: https://pypip.in/d/django-entity-event/badge.png
    :target: https://crate.io/packages/django-entity-event/
    :alt: Number of PyPI downloads


Django Entity Event
===================

Django Entity Event is a great way to collect events that your users
care about into a unified location. The parts of your code base that
create these events are probably totally separate from the parts that
display them, which are also separate from the parts that manage
subscriptions to notifications. Django Entity Event makes separating
these concerns as simple as possible, and provides convenient
abstractions at each of these levels.

Installation
------------
To install the latest release, type::

    pip install django-entity-event

To install the latest code directly from source, type::

    pip install git+git://github.com/ambitioninc/django-entity-event.git

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

Full documentation is available at http://django-entity-event.readthedocs.org

License
-------
MIT License (see LICENSE)
roland 1.0.0
Stellar synthetic spectrum wrapper
License: MIT
Requires Python: >=3.6


Roland
=======

A wrapper around various stellar spectral synthesis packages.

Authors
-------

- **David Nidever** (Montana State University)
  
Installation
------------

To install the latest version::

    pip install git+https://github.com/dnidever/roland

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

.. image:: https://readthedocs.org/projects/roland/badge/?version=latest
        :target: http://roland.readthedocs.io/

See `the documentation <http://roland.readthedocs.io>`_ for information on how
to install and Roland.

License
-------

.. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat
        :target: https://github.com/dnidever/roland/blob/main/LICENSE
jucodecorrector 0.1.0
Python script to rivise error code based on the error messages using ChatGPT.
License: MIT

# Code Fixer for Jupyter Notebook


## Description
The Code Examiner and Corrector is a Python script designed to be used in Jupyter 
Notebook. It analyzes error code snippets and provides suggestions for correct code 
revisions. The script utilizes ChatGPT, an AI language model, to generate recommendations 
based on the given error message and context.

## Features
- Analyzes error code snippets and provides suggestions for code revisions.
- Utilizes the OpenAI GPT-3.5 language model for generating code recommendations.
- Helps improve code quality and assists in debugging errors.

## Requirements
- Jupyter Notebook
- Python 3.x
- OpenAI GPT-3.5 API credentials (see OpenAI documentation for details)
- Dependencies (install via pip):
    - jupyter_ai_magics
    - then in jupyter notebook %load_ext jupyter_ai_magics
    - openai
    - other dependencies as required

## Usage
1. Ensure you have set up the OpenAI API credentials by following the instructions in the 
documentation.
2. Install the package with pip or clone the repo
3. Open your jupyter notebook or jupyter lab file .ipynb
4. Import the class AiCodeFixer from the modul code_fixer_assistent. 
5. When error occures, call the function AiCodeCorrector().fix_broken_code() and review the 
recommendations and suggestions provided by ChatGPT.
6. Apply the recommended code revisions to fix errors and improve code quality.
7. Repeat the process for any additional error code snippets.


## Notes
- Ensure you have a stable internet connection to interact with the ChatGPT API.
- Be cautious when making changes to your code based on the suggestions provided. Verify the 
changes and test the code after revision.
- The effectiveness of the code suggestions and fixes depends on the quality of the error message 
and the complexity of the code.
- Experiment with different code snippets and variations to obtain the best results.

## Acknowledgments
- The code_fixer_assistent.py script utilizes the OpenAI GPT-3.5 language model. For more 
information about the language model and OpenAI API, visit the official OpenAI documentation.