Metadata-Version: 2.1
Name: VeVaPy
Version: 0.1.3
Summary: A Python package for streamlined V&V of HPA axis models.
Author-email: "Christopher Parker (University of Cincinnati)" <parkecp@mail.uc.edu>
License: MIT License
        Copyright 2022 Christopher John Parker <parkecp@mail.uc.edu>
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Homepage, https://github.com/cparker-uc/VeVaPy
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# VeVaPy Instructions

## Table of Contents
1. [Installation](#install)
2. [Binder Setup](#binderSetup)
3. [Model Selection](#modelSelection)
4. [Dependencies Required to Run Models Locally](#localDependencies)
5. [General Instructions](#generalInstructions)

## Installation <a name="install"/>

VeVaPy is most easily installed with the command

    pip install VeVaPy

However, it can also be downloaded directly from Github and placed in the directory of the model files you wish to test. In this case, see [Dependencies Required to Run Models Locally](#localDependencies) for the packages you'll need.

In order to avoid downloading anything to your local machine, you can also use a virtual Jupyter environment through Binder. See [Binder Setup](#binderSetup) for instructions.

## Binder Setup <a name="binderSetup"/>
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/cparker-uc/VeVaPy/HEAD)

Clicking the "Launch Binder" link above will open this repository in Binder, which allows you to run all of the Jupyter notebook files in your web browser without needing to have any software installed.

The Binder environment will already have all of the necessary Python modules ready, so all you need to do is open a model file and start running simulations.

## Model Selection <a name="modelSelection" />
This repository contains five different models and a general template for creating new ones, each in their own Jupyter notebook file. Below is a table listing all of the models included with short descriptions of them.

| Filename                              | Authors                                                                                                          | Year | Model Description                                                                                                                                                                                                              |
|---------------------------------------|------------------------------------------------------------------------------------------------------------------|------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Bairagi-2008.ipynb                    | N. Bairagi, S. Chatterjee & J. Chattopadhyay                                                                     | 2008 | One of the five models described in my paper.                                                                                                                                                                                   |
| Bangsgaard-Ottesen-2017.ipynb         | E.O. Bangsgaard & J.T. Ottesen                                                                                   | 2017 | One of the five models described in my paper.                                                                                                                                                                                   |
| Malek-2015.ipynb                      | H. Malek, M.H. Ebadzadeh, R. Safabakhsh, A. Razavi & J. Zaringhalam                                              | 2015 | One of the five models described in my paper.                                                                                                                                                                                   |
| Somvanshi-2020.ipynb                  | P.R. Somvanshi, S.H. Mellon, R. Yehuda, J.D. Flory, L. Bierer, I. Makotkine, C. Marmar, M. Jett & F.J. Doyle III | 2020 | One of the five models described in my paper.                                                                                                                                                                                   |
| Sriram-2012.ipynb                     | K. Sriram, M. Rodriguez-Fernandez & F.J. Doyle III                                                               | 2012 | One of the five models described in my paper.                                                                                                                                                                                   |
| VeVaPy Model Template.ipynb           | Christopher Parker                                                                                               | 2022 | Basic template for editing to easily verify and validate mathematical models of the Hypothalamic-Pituitary-Adrenal axis                   |

## Dependencies Required to Run Models Locally <a name="localDependencies" />
If you would prefer to run the models on your local machine, rather than in a Binder environment, the requirements listed in the following table will need to be met.

| Software   | Version | URL                                                         |
|------------|---------|-------------------------------------------------------------|
| Python     | 3.7.7   | https://www.python.org/downloads/                           |
| Jupyterlab | 2.1.5   | https://www.jupyter.org/install                             |
| NumPy      | 1.21.5  | https://numpy.org/install/                                  |
| matplotlib | 3.0.1   | https://matplotlib.org/stable/users/installing/index.html   |
| pandas     | 0.23.4  | https://pandas.pydata.org/docs/getting_started/install.html |
| mpld3      | 0.5.7   | https://mpld3.github.io/install.html                        |
| SciPy      | 1.1.0   | https://scipy.org/install/                                  |
| tabulate   | 0.8.9   | https://pypi.org/project/tabulate/                          |

## General Instructions <a name="generalInstructions" />
Once you have set up a Binder environment or installed all of the dependencies locally, open any of the five models discussed in the paper and select "Instructions" from the Table of Contents. Each model has instructions tailored to its specific needs, although they are largely the same. Good luck, and have fun!
