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Scientific Visualization Scientific Visualization Using MATLAB Using MATLAB Robert Putnam [email protected] Scientific Visualization Using MATLAB - Spring 2011

Scientific Visualization Using MATLAB Robert Putnam [email protected] Scientific Visualization Using MATLAB - Spring 2011

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Page 1: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Scientific Visualization Using MATLABScientific Visualization Using MATLAB

Robert Putnam

[email protected]

Scientific Visualization Using MATLAB - Spring 2011

Page 2: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

OutlineOutline

Scientific Visualization Using MATLAB - Spring 2011

Introduction

Geometry / Topology / Data Types

Camera, lights, …

Scalar visualization

Vector visualization

Extras – scattered data, animation, volume vis, sliceomatic, etc.

Sources

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MATLABMATLABMATrix LABoratory

– High-level language for technical computing

– Interactive GUI

– Command line interface (Command Window)

– Application-specific toolboxes available (Parallel Computing, Statistics, etc.)

– Coupled with Maple for Symbolic computation

– Good documentation available (user guides, demos, videos, etc.)

– Professional support services available from MathWorks

Scientific Visualization Using MATLAB - Spring 2011

Page 4: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Scientific VisualizationScientific Visualization

Scientific Visualization Using MATLAB - Spring 2011

*Adapted from The ParaView Tutorial, Moreland

• Visualization: converting raw data to a form that is viewable and understandable to humans.

• Scientific visualization: specifically concerned with data that has a well-defined representation in 2D or 3D space (e.g., from simulation mesh or scanner).

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Generic visualization pipelineGeneric visualization pipeline

Scientific Visualization Using MATLAB – Spring 2011

Source(s) Filters(s) Output (Rendering)

- - - - - - - - - - - - - - - - - - - - -

data/geometry/topology graphics

Page 6: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Geometry v. TopologyGeometry v. Topology

Geometry of a dataset ~= points

Scientific Visualization Using MATLAB – Spring 2011

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

Topology ~= connections among points, which define cells

So, what’s the topology here?

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Geometry v. TopologyGeometry v. Topology

Scientific Visualization Using MATLAB – Spring 2011

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

Page 8: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Geometry v. TopologyGeometry v. Topology

Scientific Visualization Using MATLAB – Spring 2011

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

or

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

Page 9: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Geometry v. TopologyGeometry v. Topology

Scientific Visualization Using MATLAB – Spring 2011

or

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

or

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

Page 10: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Geometry v. TopologyGeometry v. Topology

Scientific Visualization Using MATLAB – Spring 2011

or

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

or

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

or

0,1 1,1 2,1 3,1

0,0 1,0 2,0 3,0

Page 11: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Geometry/Topology StructureGeometry/Topology Structure

Structure may be regular or irregular– Regular (structured)

• need to store only beginning position, spacing, number of points

• smaller memory footprint per cell (topology can be generated on the fly)

• examples: image data, rectilinear grid, structured grid

– Irregular (unstructured)• information can be represented more densely where it changes quickly

• higher memory footprint (topology must be explicitly written) but more freedom

• examples: polygonal data, unstructured grid

Scientific Visualization Using MATLAB – Spring 2011

Page 12: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Examples of Dataset TypesExamples of Dataset Types

Structured Points (Image Data)– regular in both topology and geometry

– examples: lines, pixels, voxels

– applications: imaging CT, MRI

Rectilinear Grid– regular topology but geometry only partially

regular

– examples: pixels, voxels

Structured Grid (Curvilinear)– regular topology and irregular geometry

– examples: quadrilaterals, hexahedron

– applications: fluid flow, heat transfer

Scientific Visualization Using MATLAB – Spring 2011

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Examples of Dataset Types (cont)Examples of Dataset Types (cont)

Polygonal Data– irregular in both topology and geometry

– examples: vertices, polyvertices, lines, polylines, polygons, triangle strips

Unstructured Grid – irregular in both topology and geometry

– examples: any combination of cells

– applications: finite element analysis, structural design, vibration

Scientific Visualization Using MATLAB – Spring 2011

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Characteristics of DataCharacteristics of Data

Data is organized into datasets for visualization– Datasets consist of two pieces

• organizing structure– points (geometry)

– cells (topology)

• data attributes associated with the structure

– File format derived from organizing structure

Scientific Visualization Using MATLAB – Spring 2011

Data is discrete– Interpolation functions generate data values in between known points

Page 15: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

MATLAB – Data TypesMATLAB – Data Types

Scientific Visualization Using MATLAB - Spring 2011

Volume Data– Data defined on three-dimensional grids

– Multidimensional arrays of scalar or vector data

– Two basic types

• Scalar volume data

• Single data values for each point

• Examples: temperature, pressure, density, elevation

• Vector volume data

• Two or three values for each point (components of a vector)

• Magnitude and direction

• Examples: velocity, momentum

Page 16: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

MATLAB – Data TypesMATLAB – Data TypesBasic data type

– The basic data type is the array.

– A vector is an Nx1 (or 1XN) array (and a scalar is a 1x1 array):

– “Arrays in MATLAB are N-dimensional, with an infinite number of trailing singleton

dimensions. Trailing singleton dimensions past the second are not displayed or

reported on, e.g., with size. No array has fewer than two dimensions.”

– Array dimensions are generally reported in “row by column” order (i.e., y by x!).

Scientific Visualization Using MATLAB - Spring 2011

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Figure windows, GUIsFigure windows, GUIs

• “Handle graphics”

• Numerical id assigned to figure window and its components

• GUI (for menus, sliders, etc.)

Scientific Visualization Using MATLAB - Spring 2011

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Figure Window HierarchyFigure Window Hierarchy

• Root object• Figure object(s)

Scientific Visualization Using MATLAB - Spring 2011

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Graphics Model from the bottom upGraphics Model from the bottom up• Core graphics objects

• axes (define coordinate system)• line• text• rectangle• light• patch (filled polygons)• surface (3D grid of

quadrilaterals)• images (2D representation

of image)

Scientific Visualization Using MATLAB - Spring 2011

Page 20: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

MATLAB – figure functionMATLAB – figure functionFigure

– All graphical output directed to a graphics window called a figure

– Separate from the Command Window

– Can contain menus, toolbars, user-interface objects, context menus, axes, or any

other type of graphics object.

– To create a new figure, use the figure function

• (type figure in a Command Window)

-- Note: most plotting/graphics functions will create a figure if one does not exist,

so in many cases, explicitly calling ‘figure’ is unnecessary.

Scientific Visualization Using MATLAB - Spring 2011

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Code – figure functionCode – figure functionCommand Window

f = figure

Scientific Visualization Using MATLAB - Spring 2011

gcf: Get current figure

figure(f): make f current figure

close all: close all figure windows

figure creates a new figure object using default property values. This automatically becomes the current figure and raises it above all other figures on the screen until a new figure is either created or called.

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Code – property listCode – property listCommand Window:figure1.m

f = figure('Name','Test Window','Position',[100 500 350 350],'MenuBar','none')

or

set (f, 'Name', 'Test Window')

set (f, 'Position', [100 500 350 350])

set (f, 'MenuBar', 'none')

Type

get(f)

to see all figure properties.

Tip: use ‘more on’ to keep list from scrolling.

Scientific Visualization Using MATLAB - Spring 2011

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MATLAB - ViewingMATLAB - ViewingViewing

– The process of displaying a graphical scene from various directions by adjusting

the camera position, changing the perspective, changing the aspect ratio, etc.

– The particular orientation you set to display the visualization

– Composed from two basic functions:

• Positioning the viewpoint to orient the scene – view function

• Setting the aspect ratio and relative axis scaling – axis function

Scientific Visualization Using MATLAB - Spring 2011

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MATLAB – view functionMATLAB – view functionview

– The viewpoint is specified by

defining azimuth and elevation with

respect to the axis origin

– azimuth is a polar angle in the x-y

plane, with positive angles

indicating counterclockwise

rotation of the viewpoint. Elevation

is the angle above (positive angle)

or below (negative angle) the x-y

plane.

Scientific Visualization Using MATLAB - Spring 2011

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MATLAB – view function (cont.)MATLAB – view function (cont.)view

– MATLAB automatically selects a viewpoint that is determined by whether the plot

is 2-D or 3-D

• For 2-D plots, the default is azimuth = 0° and elevation = 90°

• For 3-D plots, the default is azimuth = -37.5° and elevation = 30°

– view(2) sets the default 2D view, with az = 0, el = 90.

– view(3) sets the default 3D view, with az = –37.5, el = 30.

– view(az,el) or view([az,el]) set the viewing angle for a 3D view.

Scientific Visualization Using MATLAB - Spring 2011

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Code – view functionCode – view functionCommand Window: view1

samplefig;

view([-20,25]);

Scientific Visualization Using MATLAB - Spring 2011

Things to try: - Add X,Y labels - Rotate graph (note scaling!) - Examine values

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MATLAB – axis functionMATLAB – axis functionaxis

– Enables you to adjust the aspect ratio of graphs.

– Enables you to adjust the scaling of graphs.

– axis([xmin xmax ymin ymax zmin zmax]) sets the limits for the x-axis, y-axis and

z-axis of the current axes.

– axis vis3d freezes aspect ratio properties to enable rotation of 3-D objects (If you

will be interactively rotating the visualization in the figure window you should use

the vis3d option.)

Scientific Visualization Using MATLAB - Spring 2011

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Code – axis functionCode – axis functionCommand Window: axis1

samplefig;

view([-20,25]);

axis([0 30 0 30 -15 15]);

axis vis3d;

Scientific Visualization Using MATLAB - Spring 2011

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MATLAB - LightingMATLAB - LightingLighting

– Enhances the visibility of surface shape and provides a 3D perspective to your

visualization.

– Several commands enable you to position light sources and adjust the

characteristics of lit objects:

• light - creates a light object

• lighting - selects a lighting method

• material - sets the reflectance properties of lit objects

• camlight - creates or moves a light with respect to the camera position

• shading - controls the color shading of surface and patch graphic objects

Scientific Visualization Using MATLAB - Spring 2011

Page 30: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Code - lightingCode - lightingCommand Window:lighting1

samplefig;

light;

lighting phong;

material (‘shiny’);

camlight left;

shading interp;

Scientific Visualization Using MATLAB - Spring 2011

Try changing lighting, material, etc., using command or function syntax. E.g. material dull; or

material(‘dull’);

Page 31: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

MATLAB – Vis AlgorithmsMATLAB – Vis Algorithms• Modeling

– Matrix to Surface

– Slicing

• Scalar– Color Mapping

– Contours / Isosurfaces

• Vector– Oriented Glyphs

– Streamlines

Scientific Visualization Using MATLAB - Spring 2011

Page 32: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

MATLAB – modeling algorithmsMATLAB – modeling algorithmsMatrix to Surface

– A surface is defined by the z-coordinates of points above a rectangular grid in the

x-y plane.

– formed by joining adjacent points with straight lines.

– useful for visualizing large matrices.

– surf(X,Y,Z) creates a shaded surface using Z for the color data as well as

surface height. X and Y are vectors or matrices defining the x and y components

of a surface.

Scientific Visualization Using MATLAB - Spring 2011

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Code – surf functionCode – surf function

Command Window:surf1

[X,Y] = meshgrid(-3:0.25:3);

Z = peaks(X,Y);

surf(X,Y,Z);

view(3);

axis([-3 3 -3 3 -10 10]);

grid on;

light;

lighting phong;

camlight left;

Scientific Visualization Using MATLAB - Spring 2011

meshgrid(x,y) transforms the domain specified by vectors x and y into arrays X and Y, which can be used to evaluate functions of two variables and three-dimensional mesh/surface plots. meshgrid(x) is the same as [X,Y] = meshgrid(x,x).

Page 34: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

MATLAB – modeling algorithmsMATLAB – modeling algorithmsSlicing

– A slice is a "cross-section" of the dataset.

– Any kind of surface can be used to slice the volume.

– The simplest technique is to use a plane to define the cutting surface.

– slice (X,Y,Z,V,sx,sy,sz) draws slices of the volume V along the x, y, z directions

in the volume V at the points in the vectors sx, sy, and sz. X, Y, and Z are 3D

arrays specifying the coordinates for V.

– The color at each point is determined by 3-D interpolation into the volume V.

Scientific Visualization Using MATLAB - Spring 2011

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Code – slice functionCode – slice functionCommand Window:slice1

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

xslice = 5;

yslice = 0;

zslice = 0;

slice(x,y,z,v,xslice,yslice,zslice);

view(3);

axis on;

grid on;

light;

lighting phong;

camlight('left');

shading interp;

Scientific Visualization Using MATLAB - Spring 2011

The flow dataset represents the speed profile of a submerged jet within an infinite tank (an example of scalar volume data).

Page 36: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

MATLAB – scalar algorithmsMATLAB – scalar algorithmsColor Mapping

– Each scalar value in data set is mapped through a lookup table to a specific

color.

– The color lookup table is called the colormap:

• Three-column 2-D matrix

• Each row of the matrix defines a single color by specifying three values in the

range of zero to one (RGB components).

• Created with either array operations or with one of the several color table

generating functions (jet, hsv, hot, cool, summer, and gray).

• colormap(map) sets the colormap to the matrix map.

Scientific Visualization Using MATLAB - Spring 2011

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Code – colormap functionCode – colormap functionCommand Window:colormap1

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

xslice = 5;

yslice = 0;

zslice = 0;

slice(x,y,z,v,xslice,yslice,zslice);

view(3);

axis([0 10 -4 4 -3 3]);

grid on;

colormap (flipud(jet(64)));

colorbar('vertical');

shading interp;

Scientific Visualization Using MATLAB - Spring 2011

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MATLAB – colormap editorMATLAB – colormap editor

If you want even more control over the color

mapping, you can use the colormap editor.

You open the colormap editor by selecting

Colormap from the Edit menu of the figure.

Scientific Visualization Using MATLAB - Spring 2011

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MATLAB – scalar algorithmsMATLAB – scalar algorithmsContours / Isosurfaces

– Construct a boundary between distinct regions in the data.

– contours are lines or surfaces of constant scalar value.

– isolines for two-dimensional data and isosurfaces for three-dimensional data.

– contour(X,Y,Z,v) draws a contour plot of matrix Z with isolines at the data values

specified in the vector v.

– isosurface(X,Y,Z,V,isovalue) computes isosurface data from the volume data V

at the isosurface value specified in isovalue. The isosurface function connects

points that have the specified value the same way isolines connect points of

equal elevation.

Scientific Visualization Using MATLAB - Spring 2011

Page 40: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

Code – contour functionCode – contour functionCommand Window:contour1

[X,Y] = meshgrid(-3:0.25:3);

Z = peaks(X,Y);

isovalues = (-3.0:0.5:3.0);

contour(X,Y,Z,isovalues);

view(2);

axis on;

grid on;

Scientific Visualization Using MATLAB - Spring 2011

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Code – isosurface functionCode – isosurface functionCommand Window:isosurface1

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

isovalue = -1;

purple = [1.0 0.5 1.0];

i = isosurface(x,y,z,v,isovalue);

p = patch(i);

isonormals(x,y,z,v,p);

set(p,'FaceColor',purple,'EdgeColor','none');

view([-10 40]);

axis on;

grid on;

light;

lighting phong;

camlight('left');

Scientific Visualization Using MATLAB - Spring 2011

patch is the low-level graphics function that creates patch graphics objects. A patch object is one or more polygons defined by the coordinates of its vertices.

Page 42: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

MATLAB – vector algorithmsMATLAB – vector algorithmsOriented Glyphs

– Draw an oriented, scaled glyph for each vector.

– Glyphs are polygonal objects such as a cone or an arrow.

– Orientation and scale of glyph indicate the direction and magnitude of the vector.

– Glyph may be colored according to vector magnitude or some other scalar value.

– coneplot(X,Y,Z,U,V,W,Cx,Cy,Cz) plots vectors as cones or arrows.

• X, Y, Z define the coordinates for the vector field

• U, V, W define the vector field

• Cx, Cy, Cz define the location of the cones in the vector field

• coneplot(...,'quiver') draws arrows instead of cones.

Scientific Visualization Using MATLAB - Spring 2011

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Code – coneplot functionCode – coneplot functionCommand Window:coneplot1load wind;

xmin = min(x(:));

xmax = max(x(:));

ymin = min(y(:));

ymax = max(y(:));

zmin = min(z(:));

zmax = max(z(:));

scale = 4;

[cx cy cz] = meshgrid(xmin:5:xmax,ymin:5:ymax,zmin:2:zmax);

coneplot(x,y,z,u,v,w,cx,cy,cz,scale,'quiver');

view([-35 60]);

axis on;

grid off;

Scientific Visualization Using MATLAB - Spring 2011

The wind dataset represents air currents over North America. The dataset contains six 3-D arrays: x, y, and z are coordinate arrays which specify the coordinates of each point in the volume and u, v, and w are the vector components for each point in the volume.

Page 44: Scientific Visualization Using MATLAB Robert Putnam putnam@bu.edu Scientific Visualization Using MATLAB - Spring 2011

MATLAB – vector algorithmsMATLAB – vector algorithmsStreamlines

– The path a massless particle takes flowing through a velocity field (i.e. vector

field)

– Can be used to convey the structure of a vector field by providing a snapshot of

the flow at a given instant in time.

– Multiple streamlines can be created to explore interesting features in the field.

– Computed via numerical integration (integrating product of velocity times delta T).

– streamline(X,Y,Z,U,V,W,startx,starty,startz) draws stream lines from the vector

volume data.

• X, Y, Z define the coordinates for the vector field

• U, V, W define the vector field

• startx, starty, startz define the starting positions of the streamsScientific Visualization Using MATLAB - Spring 2011

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Code – streamline functionCode – streamline functionCommand Window:streamline1load wind;

xmin = min(x(:));

xmax = max(x(:));

ymin = min(y(:));

ymax = max(y(:));

zmin = min(z(:));

zmax = max(z(:));

purple = [1.0 0.5 1.0];

[sx sy sz] = meshgrid(xmin,ymin:10:ymax,zmin:2:zmax);

h = streamline(x,y,z,u,v,w,sx,sy,sz);

set(h,'LineWidth',1,'Color',purple);

view([-40 50]);

axis on;

grid off;

Scientific Visualization Using MATLAB - Spring 2011

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MATLAB - ResourcesMATLAB - Resources IS&T tutorials

– Introduction to MATLAB

www.bu.edu/tech/research/training/tutorials/MATLAB/

– Using MATLAB to Visualize Scientific Data www.bu.edu/tech/research/training/tutorials/visualization-with-MATLAB/

Websites– www.mathworks.com/products/MATLAB/

– www.mathworks.com/access/helpdesk/help/techdoc/index.html

– www.mathworks.com/academia/student_center/tutorials

Wiki– http://MATLABwiki.mathworks.com/

Scientific Visualization Using MATLAB - Spring 2011

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SourcesSources

Getting Started with MATLAB, version 7, The MathWorks, Inc.

Using MATLAB, version 7, The MathWorks, Inc.

Using MATLAB Graphics, version 7, The MathWorks, Inc.

http://www.mathworks.com/access/helpdesk/help/techdoc/index.html

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Questions?Questions?

Tutorial survey:

- http://scv.bu.edu/survey/spring11tut_survey.html

Scientific Visualization using MATLAB – Spring 2011