We have now learned multiple methods to generate plots on screen using points and lines. In the last section, we will demonstrate how to visualize a million data points in a 3D dataset using OpenGL in real time. A common strategy to visualize a complex 3D dataset is to encode the third dimension (for example, the z dimension) in the form of a heat map with a desirable color scheme. As an example, we show a heat map of a 2D Gaussian function with its height z, encoded using a simple color scheme. In general, a 2-D Gaussian function, 
, is defined as follows:
Here, A is the amplitude (
) of the distribution centered at 
and 
are the standard deviations (spread) of the distribution in the x and y directions. To make this demo more interesting and more visually appealing, we vary the standard deviation or sigma term (equally in the x and y directions) over time. Indeed, we can apply the same method to visualize very complex 3D datasets.