Accelerating the pace of engineering and science

# Documentation Center

• Trials

## Overview of Volume Visualization

### Examples of Volume Data

Volume visualization is the creation of graphical representations of data sets that are defined on three-dimensional grids. Volume data sets are characterized by multidimensional arrays of scalar or vector data. These data are typically defined on lattice structures representing values sampled in 3-D space. There are two basic types of volume data:

• Scalar volume data contains single values for each point.

• Vector volume data contains two or three values for each point, defining the components of a vector.

An example of scalar volume data is that produced by flow. The flow data represents the speed profile of a submerged jet within an infinite tank. Typing

`[x,y,z,v] = flow;`

produces four 3-D arrays. The x, y, and z arrays specify the coordinates of the scalar values in the array v.

The wind data set is an example of vector volume data that represents air currents over North America. You can load this data in the MATLAB® workspace with the command:

`load wind`

This data set comprises six 3-D arrays: x, y, and z are the coordinate data for the arrays u, v, and w, which are the vector components for each point in the volume.

### Selecting Visualization Techniques

The techniques you select to visualize volume data depend on what type of data you have and what you want to learn. In general:

• Scalar data is best viewed with isosurfaces, slice planes, and contour slices.

• Vector data represents both a magnitude and direction at each point, which is best displayed by stream lines (particles, ribbons, and tubes), cone plots, and arrow plots. Most visualizations, however, employ a combination of techniques to best reveal the content of the data.

The material in these sections describes how to apply a variety of techniques to typical volume data.

#### Interpolating and Gridding Data

MATLAB provides functions that enable you to interpolate and restructure you data in preparation for visualization. See these sections for more information:

### Steps to Create a Volume Visualization

Creating an effective visualization requires a number of steps to compose the final scene. These steps fall into four basic categories:

1. Determine the characteristics of your data. Graphing volume data usually requires knowledge of the range of both the coordinates and the data values.

2. Select an appropriate plotting routine. The information in this section helps you select the right methods.

3. Define the view. The information conveyed by a complex three-dimensional graph can be greatly enhanced through careful composition of the scene. Viewing techniques include adjusting camera position, specifying aspect ratio and project type, zooming in or out, and so on.

4. Add lighting and specify coloring. Lighting is an effective means to enhance the visibility of surface shape and to provide a three-dimensional perspective to volume graphs. Color can convey data values, both constant and varying.

### Volume Visualization Functions

MATLAB functions enable you to apply a variety of volume visualization techniques. The following tables group these functions into two categories based on the type of data (scalar or vector) that each is designed to work with. The reference page for each function provides examples of the intended use.

#### Functions for Scalar Data

FunctionPurpose

contourslice

Draw contours in volume slice planes

isocaps

Compute isosurface end-cap geometry

isocolors

Compute the colors of isosurface vertices

isonormals

Compute normals of isosurface vertices

isosurface

Extract isosurface data from volume data

patch

Create a patch (multipolygon) graphics object

Reduce the number of patch faces

reducevolume

Reduce the number of elements in a volume data set

shrinkfaces

Reduce the size of each patch face

slice

Draw slice planes in volume

smooth3

Smooth 3-D data

surf2patch

Convert surface data to patch data

subvolume

Extract subset of volume data set

#### Functions for Vector Data

Function

Purpose

coneplot

Plot velocity vectors as cones in 3-D vector fields

Compute the curl and angular velocity of a 3-D vector field

divergence

Compute the divergence of a 3-D vector field

interpstreamspeed

Interpolate streamline vertices from vector-field magnitudes

streamline

Draw stream lines from 2-D or 3-D vector data

streamparticles

Draw stream particles from vector volume data

streamribbon

Draw stream ribbons from vector volume data

streamslice

Draw well-spaced stream lines from vector volume data

streamtube

Draw stream tubes from vector volume data

stream2

Compute 2-D stream line data

stream3

Compute 3-D stream line data

volumebounds

Return coordinate and color limits for volume (scalar and vector)