==========================
Visual Viper Documentation
==========================

.. toctree::
   :maxdepth: 2
   :caption: Table of Contents

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Overview
========

Visual Viper (VV) is a Python library designed to automate the process of creating scientific charts. The library uses Vega-Lite, a high-level grammar for interactive graphics, to render visualizations from various research data sources through a convenient API.

Why Visual Viper?
=================

Digital Transformation in Healthcare
------------------------------------

The healthcare industry is undergoing a digital transformation, leading to a significant influx of data. For healthcare professionals and researchers, this means an increased need for efficient data visualization tools.

Streamlined Visualization Process
---------------------------------

Visual Viper aims to streamline the often labor-intensive process of creating data visualizations, thereby saving time and enhancing the consistency of scientific communication.

Features
========

* **Modular and Extensible**: Built on a plugin architecture, VV is designed to accommodate various data sources and types of visualizations.
* **Customizable**: Each stage of the visualization process allows for independent modification without affecting the library's overall functionality.
* **Environment Agnostic**: Operates in various environments without requiring significant changes.
* **Serverless Deployment**: Can run independently on local machines, Lambda, or as a Web API.

Core Components
===============

* **`DatasetBuilder`**: Fetches and preprocesses data.
* **`NotationBuilder`**: Generates the chart notation, specifying chart layouts and visual aesthetics.
* **`ChartRenderer`**: Creates the visualization output, which can be an image file or a in-memory file-like object.
* **`ChartDeployer`**: Handles the deployment of completed visualizations.

Development Paradigms
=====================

* **Object-Oriented Programming (OOP)**: Promotes a structured codebase that's easy to manage and maintain.
* **Test-Driven Development (TDD)**: Ensures robustness and quality by writing tests before the actual code.

Future Plans
============

* Introduction of new chart types, including Bar Chart, Survival Chart, and Sankey Diagrams.
* Packaging as an importable Python package, followed by an efficiency evaluation.

Conclusion
==========

Visual Viper offers a robust and flexible tool for data visualization, significantly enhancing the efficiency of scientific communication in the healthcare sector.

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

.. toctree::
  :hidden:
  :caption: Getting Started

  getting-started


.. toctree::
  :hidden:
  :caption: Commands

  commands


.. toctree::
  :glob:
  :hidden:
  :caption: Architecture

  architecture
  visual_viper

.. toctree::
  :hidden:
  :caption: Development

  developer-guidelines
  .. developer-defaults


.. toctree::
  :hidden:
  :caption: Support

  support-glossary
  support-contacts
  support-todos