Visualizations Project Page

The goal of this project is to create a convienent framework for combining and animating matplotlib components (subplots) concurrently. The final combined plot is akin to a dashboard with the single purpose of displaying multiple subplots, each with one or more different sources of sequential data.

Component subplots may be updated on a step or sub-step schedule, meaning that select subplots can display information at different rates or resolutions. The sub-step animation support provides an additional layer of flexibilty when displaying sequential data that may have been collected at different intervals. For more information regarding use and functionality please see the project wiki page (this should be added shortly).

Find the source for this project on Github

Getting Started

View the latest documentation built with Sphinx hosted by ReadTheDocs.

Before using this framework, it is strongly suggested that you start by familiarizing yourself with Matplotlib’s pyplot interface using the following Matplotlib tutorial.

For ease of use, it is recommended that you setup a virtual environment to install and run this framework.

If you haven’t worked with virtual envionrmnets before, you may use a platform like Anaconda or Miniconda.

Installation

Use the included requirements.txt to install all required dependencies:

pip install -r requirements.txt

Alternatively, if you want to run just the base subnet_plot module, you may be able to simply install Matplotlib:

pip install Matplotlib

Once you have installed the dependencies you can run some sample animations by calling subnet_plot. See the project documentation for more details.

Subplot Components

A small selection of custom components are included to serve as an example of how to implement Matplotlib objects into this framework. See below for a brief description of each class along with links to the relevant Matplotlib documentation.

line_bar_plot

Above, a simple combined bar and line plot from display_sample_plots().

Bar subplot

The bar subplot displays primary elements using the supplied colormap while displaying the optional secondary elements in grayscale.

bar_plot

The SubplotBar class is based on the Matplotlib bar object.

Line subplot

The line subplot combines the basic line and bar plots. The optional bar data source is rendered behind the primary line data.

line_plot

The SubplotLine class is based on the Matplotlib plot object.

Matrix subplot

The matrix subplot renders a 2-dimensional array in sequential steps with optional 1-dimensional arrays affixed to the left and top sides of the central matrix.

matrix_plot

The SubplotMatrix class is based on the Matplotlib image object.
Also see the imshow axes.

Text subplot

The text subplot displays data using a text-based readout. The primary data source is rendered within a table, while a secondary data source is displayed in rows below the primary table.

text_plot

The SubplotText class component is based on the Matplotlib table object.

Layouts and gridspec

The figure’s grid-based layout is defined by the gridspec object passed to add_subplot() when adding subplot objects to the parent SubnetPlot.

combined_plot

For a complete description of Matplotlib’s gridspec please take a look at the tutorial.

Styles and color maps

Each subplot component’s style is defined by the tempalte SubnetSubplot class. Colormaps are used extensively to render plot elements. See the colormap reference for valid values.