draw circle in 3d matlab
Tabular array of Contents
- Introduction
- A brief introduction.
- Getting started
- A few basic hints that volition help you lot get started.
- Graphics Model
- Creating a Graphics Window
The Camera Toolbar
Lighting - MATLAB Data Types
- Modeling Visualization Algorithms
- Matrix to Surface
Slicing - Scalar Visualization Algorithms
- Color Mapping
Contours/Isosurfaces - Vector Visualization Algorithms
- Oriented Glyphs
Stream Lines - Additional Help
- References
Introduction
MATLAB is a high-performance linguistic communication for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in common mathematical annotation. MATLAB is an interactive organisation whose basic data type is the assortment or matrix.
MATLAB has all-encompassing facilities for displaying vectors and matrices as graphs, too as annotating and printing these graphs. It includes loftier-level functions for two-dimensional and three-dimensional data visualization, epitome processing, blitheness, and presentation graphics. There are two basic ways to create graphs in MATLAB. You can either utilise the MATLAB GUI plotting tools to interactively create graphs (run across Some Ways to Apply Plotting Tools for more information) or yous can apply the command interface by entering MATLAB graphics commands in the Control Window.
In this tutorial we will use the command interface to prove how to visualize scientific data using MATLAB graphics commands. We will cover major visualization techniques such as slicing, color mapping, contouring, oriented glyphs, and streamlines. It is assumed that the student is familiar with the basics of using MATLAB.
For an introductory tutorial on using MATLAB, run across the SCV tutorial an Introduction to MATLAB. MathWorks, the developer of MATLAB, also has extensive MATLAB documentation including video tutorials on its website. And there is a full set of documentation available from inside MATLAB itself which tin be viewed by selecting Production Assistance from the Aid menu.
Getting started
The near constructive way for yous to go through this tutorial is to run the listed example code in a MATLAB session as you proceed through the tutorial. This tin can be accomplished by copying and pasting the listed example code into the MATLAB Command Window. It will also exist helpful for you to scan the MATLAB documentation for the specific functions as nosotros talk over them (links are provided). After browsing the documentation, you should then experiment with the case code past varying some of the arguments and watching the effect this has on the output. Past playing with the example code, you lot will gain a deeper understanding of how the various graphic functions work.
Boston University SCV users, delight become to the SCV MATLAB Help Page for data specific to the installation at this site. This will tell you lot on what machines MATLAB is available, how to gear up your environment, how to fix your brandish, and where the documentation is.
Graphics Model
MATLAB has an abstract graphics layer above the local host'south graphic software interface. This insures cantankerous-platform portability and creates a device independent graphics layer. In MATLAB graphics objects are used to create visual representations of data. There are two bones types of graphics objects in the MATLAB graphics model: Core graphics objects and Composite graphics objects. Cadre graphics objects include basic drawing primitives such as line, text, rectangles, patches (filled polygons), surfaces (3D grid of vertices), images (2D matrix representation of an image), low-cal sources, and axes (define the coordinate system). Composite graphics objects are composed of core graphics objects that have been grouped together to provide a more than convenient interface.
Creating a Graphics Window
In order to visualize your data, you will need a graphics window which contains a effigy, an axes, and a view.
Figure
All MATLAB graphical output is directed to a window that is separate from the Command Window. This window is referred to as a figure. Figures can contain menus, toolbars, user-interface objects, context menus, axes, or any other type of graphics object. MATLAB functions that generate graphics output such equally plot, surf, slice, etc. will create a figure if none already exists. If a figure does exist, then these functions volition display their graphics output in the current effigy window (the final effigy window used or clicked in). Past default this will exist done without clearing or resetting the current figure backdrop.
To create a new figure, utilize the figure function. Here is example lawmaking to create a uncomplicated graphics window:
effigy 
Every graphics object has a set of properties associated with it. These backdrop define the different attributes of an object, such equally its colour, size, position, etc. Properties are normally specified by proper name/holding pairs e.g. effigy( 'PropertyName', propertyvalue, ... ). These backdrop can be prepare either at the time of cosmos by specifying belongings proper name and property value pairs or afterwards the graphics object has been created by using the set up function.
When creating a new figure, MATLAB creates a window whose characteristics are specified past your computer'south windowing organisation, by the default MATLAB effigy backdrop, and by the properties specified equally arguments to the function. Any holding not specified every bit an argument will use the default values. Encounter the Effigy Properties for a list of properties that can exist set for a effigy.
Here is an example of setting figure backdrop to create a new window named 'Test Window' with no menus and with a screen position of [left 100, bottom 500, width 250, summit 250]:
figure('Name','Test Window','Position',[100 500 350 350],'MenuBar','none') 
You can employ clf (clear figure) to clear the contents of the current figure and reset all of its properties to their default values.
clf reset Axes
Axes ascertain a frame of reference for the display objects in the figure window. This frame of reference is the coordinate organisation which defines where each data point is placed on the graph. Axes orient and calibration graphical output to produce the view of the data that you see on screen. Past default, the size of the axes MATLAB creates is normalized to the size of the figure window. All functions that draw graphics create an axes object if i does not already exist.
Every bit with figure backdrop, axes properties are used to specify the characteristics of the axes. The Axes Properties list all axes properties and provide an overview of the characteristics that are affected by each property. For example camera properties such as the camera position, camera target, upwardly vector, and view angle can all be directly set with axes properties. The axes function, all the same, is a low-level function. Though yous tin specify values for these properties straight, information technology is much easier to apply the view function (covered in the next section) to set upward the axes using default property values and to define a reasonable view.
View
The view is the particular orientation you lot prepare to display the visualization. The term viewing refers to the process of displaying a graphical scene from various directions past adjusting the camera position, changing the perspective, irresolute the attribute ratio, etc.
MATLAB viewing is composed of two bones areas:
- Positioning the viewpoint to orient the scene
- Setting the aspect ratio and relative centrality scaling to control the shape of the objects beingness displayed
Positioning the viewpoint: The view function specifies the viewpoint by defining azimuth and meridian 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. Tiptop is the angle above (positive angle) or below (negative angle) the x-y aeroplane.
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 superlative = xc°.
- For 3-D plots, the default is azimuth = -37.5° and elevation = 30°.
view(two) sets the default 2-dimensional view, with az = 0, el = 90.
view(3) sets the default three-dimensional view, with az = –37.five, el = 30.
view(az,el) or view([az,el]) set the viewing bending for a iii-dimensional plot.
The azimuth, az, is the horizontal rotation most the z-axis as measured in degrees from the negative y-centrality. Positive values indicate counterclockwise rotation of the viewpoint. el is the vertical summit of the viewpoint in degrees. Positive values of elevation represent to moving above the object; negative values correspond to moving below the object.
Hither is instance code that creates a unproblematic graphics window with a default axes, a view, and a surface:
Z = peaks(20); figure; h = surf(Z); view([-20,25]); 
Setting the aspect ratio and axis scale: The axis command enables you to suit the aspect ratio of graphs. The axis command also enables you to adjust the scaling of graphs. Usually MATLAB stretches the axes to fill the window and chooses advisable axes ranges based on the maxima and minima of the plotted information. If you will be interactively rotating the visualization in the figure window you should use the vis3d option.
centrality([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 and overrides stretch-to-make full.
Hither is the same code but with a different axes:
Z = peaks(20); figure; h = surf(Z); view([-twenty,25]); axis([0 30 0 30 -15 15]); 
Labels
The xlabel, ylabel, and zlabel commands add together x-, y-, and z-axis labels. The title command adds a title at the top of the figure and the text function inserts text anywhere in the figure.
Here is example code that creates a simple graphics window with a default axes, a default view, a surface, and labels:
Z = peaks(twenty); effigy; h = surf(Z); view(3); axis on; xlabel('Longitude'); ylabel('Breadth'); zlabel('Distance'); title('Surface of Peaks'); 
The Camera Toolbar
Once y'all have established the initial view for your visualization, you can and so use the Camera toolbar to interactively control the camera. To enable the Photographic camera toolbar, select Photographic camera Toolbar from the figure window'southward View carte. Your figure window should at present look like this:
This camera toolbar contains the following parts:
Encounter View Command with the Camera Toolbar for data on how to use the camera toolbar.
Lighting
Lighting is an constructive means to enhance the visibility of surface shape and to provide a three-dimensional perspective to your visualization. MATLAB provides several commands that enable you to position light sources and adjust the characteristics of lit objects. These commands include the post-obit:
light – Creates a lite object
lighting – Selects a lighting method
material – Sets the reflectance properties of lit objects
camlight – Creates or moves a light with respect to the photographic camera position
shading – Controls the color shading of surface and patch graphic objects
Here is instance lawmaking that creates a simple graphics window with a default axes, a default view, a surface, and a camera calorie-free with phong shading:
Z = peaks(20); figure; h = surf(Z); view(3); centrality on; lite; lighting phong; camlight('left'); shading interp; 
MATLAB Information Types
Matrix
The fundamental data type of MATLAB is the matrix or array. A matrix is an due north row by m column array of numbers or objects corresponding to numbers:
>> a = [ ane two 3 ; 4 five 6 ; 7 8 9] a = 1 2 three 4 5 6 7 8 9 When n is 1 the matrix is a row vector:
>> b = [1 2 3] b = 1 ii 3 When m is i the matrix is a column vector:
>> c = [1; 2; 3] c = 1 ii 3 and when both north and thou are ane the ane x ane matrix corresponds to a scalar.
>> d = [1] d = 1 MATLAB uses graphics objects to create visual representations of data. Arrays of numbers can be used non only to shop scalar and vector data just likewise the coordinate data of graphics objects. For example a two-dimensional array of numbers could be used to represent a surface by constructing a filigree of rectangles whose vertices are defined by using the row and column indices of each element every bit the x– and y-coordinates and the value of each element every bit the z-coordinate.
Volume Information
MATLAB uses the term "Book Visualization" to refer to the graphical representation of data sets that are defined on three-dimensional grids. These information sets are characterized by multidimensional arrays of scalar or vector data and are typically defined on lattice structures representing values sampled in three-D space.
MATLAB has 2 basic types of book data:
- Scalar volume data
- single values for each bespeak
- examples: temperature, force per unit area, density, top
- Vector volume data
- two or 3 values for each indicate, defining the components of a vector
- magnitude and management
- examples: velocity, momentum
As an example of scalar volume data, we will exist using the the flow M-file (M-files are text files containing MATLAB code). The catamenia dataset represents the speed profile of a submerged jet inside an infinite tank.
>> [x,y,z,v] = period; The flow dataset contains iv 3-D arrays: x, y, and z are coordinate arrays which specify the coordinates of each indicate in the volume and v specifies the scalar value for each indicate in the book.
Equally an example of vector volume information, nosotros will be using the wind dataset. The wind dataset represents air currents over North America and is stored equally a binary file. The load function imports variables containing numeric data from binary files or text files to the workspace.
>> load wind The wind dataset contains six 3-D arrays: ten, y, and z are coordinate arrays which specify the coordinates of each indicate in the book and u, v, and w are the vector components for each bespeak in the book.
Both the flow and air current datasets are function of the case information included in the MATLAB installation.
Modeling Visualization Algorithms
Modeling algorithms are often used to reveal internal details of a information set in social club to discover where interesting regions exist.
Matrix to Surface
In MATLAB a surface is divers past the z-coordinates of points to a higher place a rectangular grid in the x–y plane. The surface is formed by joining adjacent points with straight lines. Surface plots are useful for visualizing large matrices and for graphing functions of 2 variables. In MATLAB in that location are 2 different types of surface plots: mesh plots and surface plots. Mesh plots are colored wire-frame surfaces. Surface plots are colored faceted surfaces.
The mesh and surf functions create 3-D surface plots of matrix data. For the matrix Z the elements Z(i,j) ascertain the top of a surface over an underlying (i,j) grid.
Surface properties provide boosted control over the visual appearance of the surface. You tin specify line styles, face up coloring, lighting characteristics, etc.
The meshgrid function generates X and Y arrays for iii-D plots.
The peaks office is a function of two variables, obtained past translating and scaling Gaussian distributions.
[X,Y] = meshgrid(-three:0.25:3); Z = peaks(X,Y); figure; mesh(10,Y,Z); view(3); centrality([-3 3 -3 iii -x 10]); grid on; 
[Ten,Y] = meshgrid(-iii:0.25:3); Z = peaks(10,Y); figure; surf(X,Y,Z); view(3); centrality([-3 3 -3 3 -x 10]); grid on; light; lighting phong; camlight('left'); 
Slicing
Slicing entails creating a "cross-department" of the dataset. Whatever kind of surface tin be used to piece the volume, but the simplest technique is to use a aeroplane to define the cutting surface thereby creating a planar cut. The colour at each point is adamant by iii-D interpolation into the volume. The cutting surface interpolates the data equally it cuts in gild to color the surface with values in the volume data where the slice is positioned. To create a planar cut nosotros volition use the slice function to do the actual cutting.
[ten,y,z,v] = period; figure; xslice = 5; yslice = 0; zslice = 0; piece(x,y,z,five,xslice,yslice,zslice); view(3); axis on; filigree on; low-cal; lighting phong; camlight('left'); shading interp; 
Scalar Visualization Algorithms
Scalars are single data values associated with each point in the dataset. In that location are several unlike algorithms to visualize scalar information. Two common algorithms are Color Mapping and Contouring.
Color Mapping
Color can exist quite effective at conveying data values, both constant and varying. Color mapping is a visualization technique in which each scalar value in the data gear up is mapped through a lookup table to a specific color. The scalar values are used as an index into the color lookup tabular array. In MATLAB the color lookup table is chosen the colormap. Each MATLAB figure window has a colormap associated with it. The colormap is a three-column 2-D matrix whose length is equal to the number of colors that are divers. Each row of the matrix defines a single color past specifying iii values in the range of zero to 1. These values define the RGB components (i.eastward., the intensities of the red, green, and blue video components) of each color.
The primary MATLAB role used for color mapping is colormap. Colormaps can be created with either MATLAB array operations or with one of the several color table generating functions (jet, hsv, hot, absurd, summer, and gray). Each of the colour table generating functions has an optional parameter that specifies the number of colors or rows in the resulting colormap. The colorbar function displays the current color scalar mapping, either vertically or horizontally, in the figure window. Hither is case lawmaking which shows the mapping of the entire scalar range of the data into the jet color table:
[10,y,z,v] = menses; effigy; xslice = 5; yslice = 0; zslice = 0; slice(10,y,z,v,xslice,yslice,zslice); view(3); axis([0 x -iv 4 -three three]); grid on; colormap(jet(64)); colorbar('vertical'); shading interp; 
If instead of mapping the lower scalar values to blues and the college values to reds, we wish to map the lower values to reds and higher values to blues we can use the flipud function. Here is example code which shows the technique:
[10,y,z,v] = flow; effigy; xslice = 5; yslice = 0; zslice = 0; slice(x,y,z,v,xslice,yslice,zslice); view(3); axis([0 10 -4 4 -three 3]); filigree on; colormap(flipud(jet(64))); colorbar('vertical'); shading interp; 
And if instead of mapping the entire scalar range of the data into the color table, nosotros wish to set a specific range (in terms of minimum and maximum) of the data that is mapped, nosotros tin arrange the color limits. Adjusting the color limits with the caxis function enables us to emphasize a particular range of interest in the information. Hither is example code which shows the result of limiting the colour range from -5.0 to ii.0 so that any scalar value lower than -v.0 are mapped to the aforementioned colour as -5.0 and any scalar value greater than 2.0 are mapped to the aforementioned color every bit ii.0:
[x,y,z,v] = flow; figure; xslice = 5; yslice = 0; zslice = 0; slice(x,y,z,v,xslice,yslice,zslice); view(3); axis([0 10 -4 4 -iii 3]); filigree on; colormap(flipud(jet(64))); caxis([-v.0,two.0]); colorbar('vertical'); shading interp; 
If you want even more control over the colour mapping, you can apply the colormap editor. You open the colormap editor by selecting Colormap from the Edit menu of the figure whose colormap you wish to modify.
The colormap editor displays the current figure'southward colormap as a strip of colored cells. Node pointers are located below the colormap strip and indicate points in the colormap where the rate of the variation of R, G, and B values changes. You can select and move the node pointers to change the range of colors in the colormap. The color of a node pointer remains constant as y'all move it, just the colormap changes by linearly interpolating the RGB values between nodes. You tin too add together a node arrow by clicking below the corresponding cell in the colormap strip.
Hither is an example of using the color map editor:
Contours / Isosurfaces
Contouring is a technique where one constructs a boundary between distinct regions in the data. Contours are lines or surfaces of constant scalar value. This is a natural extension from color mapping every bit our eyes instinctively separate similarly colored areas into distinct regions. The beginning step in contouring is to explore the data space to observe points nearly a contour or region of interest. Once found these points are and then continued into either profile lines (isolines) for ii-dimensional information or into surfaces (isosurfaces) for three-dimensional data. The lines or surfaces tin can as well be color mapped using the scalar data. The primary MATLAB functions used for creating contour lines are contour and contour3 and contourslice.
profile(Ten,Y,Z,v) draws a contour plot of matrix Z with contour lines at the data values specified in the vector v.
contour3(10,Y,Z,v) draws a contour plot of matrix Z in a 3-D view using X and Y to determine the 10- and y-centrality limits.
contourslice(X,Y,Z,V,Sx,Sy,Sz,v) draws profile plots in the 10-, y-, and z-centrality aligned planes at the points in the vectors Sx, Sy, Sz.
Here are some examples which shows how to generate contours for two-dimensional data:
[X,Y] = meshgrid(-3:0.25:3); Z = peaks(Ten,Y); figure; isovalues = (-3.0:0.v:3.0); contour(X,Y,Z,isovalues); view(two); centrality on; grid on; 
[X,Y] = meshgrid(-3:0.25:3); Z = peaks(X,Y); effigy; isovalues = (-3.0:0.v:3.0); contour3(X,Y,Z,isovalues); view(three); axis on; grid on; 
[x,y,z,v] = flow; figure; xslice = (1:3:ix); yslice = 0; zslice = 0; isovalues = (-3.0:0.25:iii.0); contourslice(x,y,z,five,xslice,yslice,zslice,isovalues); view([-10 40]); axis on; grid on; 
For three-dimensional data nosotros tin generate an isosurface. Through exploration of the volume data, we can determine isovalues that reveal useful information about the data. To start select an isovalue inside the range of values in the book information. The main MATLAB functions used for creating isosurfaces are isosurface, isonormals, and patch.
isosurface(10,Y,Z,V,isovalue) computes isosurface data from the volume data 5 at the isosurface value specified in isovalue. The isosurface function connects points that have the specified value the aforementioned mode contour lines connect points of equal elevation.
isosurface(X,Y,Z,Five,isovalue,colors) computes isosurface information and also interpolates the assortment colors onto the scalar field and colors the isosurface appropriately.
isonormals(X,Y,Z,V,vertices) computes the normals of the isosurface vertices from the vertex listing necessary for lighting.
patch is the low-level graphics function that creates patch graphics objects. A patch object is ane or more than polygons defined by the coordinates of its vertices.
In this example nosotros bear witness how to create a single colored isosurface:
[x,y,z,five] = menses; isovalue = -i; royal = [ane.0 0.5 i.0]; figure; p = patch(isosurface(x,y,z,v,isovalue)); isonormals(x,y,z,v,p); set(p,'FaceColor',regal,'EdgeColor','none'); view([-x forty]); axis on; filigree on; light; lighting phong; camlight('left'); 
In this adjacent example we create two isosurfaces and color each of them past using the v scalar data of the flow dataset and the electric current colormap. The fcolors variable is a vector containing scalar values for each of the vertices in the isosurface.
[10,y,z,v] = catamenia; isovalue = -one; colors = five; figure; [faces,verts,fcolors] = isosurface(x,y,z,v,isovalue,colors); p = patch('Vertices',verts,'Faces',faces,'FaceVertexCData',fcolors, ... 'FaceColor','interp','EdgeColor','none'); isonormals(ten,y,z,v,p); isovalue2 = 0; [faces,verts,fcolors] = isosurface(x,y,z,v,isovalue2,colors); p2 = patch('Vertices',verts,'Faces',faces,'FaceVertexCData',fcolors, ... 'FaceColor','interp','EdgeColor','none'); isonormals(x,y,z,v,p2); view([-10 xl]); axis on; grid on; colormap(jet(64)); light; lighting phong; camlight('left'); 
Vector Visualization Algorithms
Vector information is a three-dimensional representation of direction and magnitude associated with each bespeak in the dataset. Vector information is often used to describe rate of change of some quantity. Vectors tin likewise exist used to depict fluid flow. In that location are several algorithms that tin be used to visualize vector data.
Oriented Glyphs
Ane visualization technique for vector data is to describe an oriented, scaled glyph for each vector. The orientation and scale of the glyph can signal the direction and magnitude of the vector. The glyph may also be colored co-ordinate to vector magnitude or some other scalar quantity (e.k. temperature or pressure). Glyphs are polygonal objects such as a cone or an pointer. In MATLAB there are currently two types of glyph available: the cone or the pointer. The main MATLAB office for creating oriented glyph visualizations is coneplot.
coneplot(X,Y,Z,U,5,W,Cx,Cy,Cz) plots vectors as cones pointing in the direction of the vector, having a length proportional to the magnitude of the vector.
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
Run into Specifying Starting Points for Stream Plots for tips on how to select starting locations.
coneplot(...,s) automatically scales the cones to fit the graph and then stretches them by the scale factors. If yous do not specify a value for s, a value of 1 is used. Use due south = 0 to plot the cones without automatic scaling.
coneplot(...,color) interpolates the assortment color onto the vector field and and then colors the cones according to the interpolated values. The size of the color array must be the same size as the U, Five, W arrays. This option just works with cones.
coneplot(...,'quiver') draws arrows instead of cones.
This instance uses pointer glyphs to visualize the vector data. The arrows are scaled (proportional to the magnitude of the vectors) and are located over the unabridged volume.
load current of air; xmin = min(x(:)); xmax = max(10(:)); ymin = min(y(:)); ymax = max(y(:)); zmin = min(z(:)); zmax = max(z(:)); scale = 4; effigy; [cx cy cz] = meshgrid(xmin:5:xmax,ymin:five:ymax,zmin:2:zmax); coneplot(x,y,z,u,v,w,cx,cy,cz,scale,'quiver'); view([-35 60]); camproj perspective; camzoom(three.0); axis on; filigree off; box on; 
This example uses cone glyphs to visualize the vector information. The cones are colored by wind speed.
load wind; xmin = min(ten(:)); xmax = max(ten(:)); ymin = min(y(:)); ymax = max(y(:)); zmin = min(z(:)); zmax = max(z(:)); wind_speed = sqrt(u.^2 + v.^2 + westward.^ii); colors = wind_speed; scale = 4; figure; [cx cy cz] = meshgrid(xmin:5:xmax,ymin:5:ymax,zmin:2:zmax); c = coneplot(x,y,z,u,v,w,cx,cy,cz,scale,colors); ready(c,'EdgeColor','none'); view([-35 60]); camproj perspective; camzoom(3.0); axis on; grid off; box on; calorie-free; lighting flat; 
Streamlines
A streamline can be thought of every bit the path a massless particle takes flowing through a velocity field (i.east. vector field). Streamlines can be used to convey the construction of a vector field by providing a snapshot of the flow at a given instant in fourth dimension. Multiple streamlines can be created to explore interesting features in the field. Streamlines are computed via numerical integration (integrating the product of velocity times delta T). The primary MATLAB functions used for creating streamline visualizations are streamline, streamribbon, and streamtube.
streamline(X,Y,Z,U,V,W,startx,starty,startz) draws stream lines from the vector volume data.
streamribbon(X,Y,Z,U,Five,W,startx,starty,startz) draws stream ribbons from the vector volume data.
streamtube(Ten,Y,Z,U,Five,W,startx,starty,startz) draws stream tubes from the vector volume data.
X, Y, Z define the coordinates for the vector field
U, Five, Due west define the vector field
startx, starty, startz define the starting positions of the streams
Come across Specifying Starting Points for Stream Plots for tips on how to select starting locations.
This example uses stream lines to visualize the vector data.
load air current; xmin = min(x(:)); xmax = max(x(:)); ymin = min(y(:)); ymax = max(y(:)); zmin = min(z(:)); zmax = max(z(:)); purple = [i.0 0.5 one.0]; figure; [sx sy sz] = meshgrid(xmin,ymin:10:ymax,zmin:two:zmax); h = streamline(x,y,z,u,v,w,sx,sy,sz); fix(h,'LineWidth',1,'Color',purple); view([-40 50]); axis on; grid off; box on; 
This example uses stream ribbons to visualize the vector data. The ribbons are colored by the magnitude of the vectors.
load wind; xmin = min(x(:)); xmax = max(x(:)); ymin = min(y(:)); ymax = max(y(:)); zmin = min(z(:)); zmax = max(z(:)); figure; [sx sy sz] = meshgrid(xmin,ymin:10:ymax,zmin:ii:zmax) h = streamribbon(x,y,z,u,5,due west,sx,sy,sz); prepare(h,'EdgeColor','none'); view([-40 l]); centrality on; grid off; box on; light; lighting apartment; camlight('left'); 
This instance uses stream tubes to visualize the vector data. The width of the tubes are proportional to the normalized divergence of the vector field.
load air current; xmin = min(x(:)); xmax = max(x(:)); ymin = min(y(:)); ymax = max(y(:)); zmin = min(z(:)); zmax = max(z(:)); figure; [sx sy sz] = meshgrid(xmin,ymin:10:ymax,zmin:2:zmax); h = streamtube(ten,y,z,u,v,due west,sx,sy,sz); prepare(h,'EdgeColor','none'); view([-40 l]); axis on; grid off; box on; low-cal; lighting flat; camlight('left'); 
Additional Assistance
For an introductory tutorial on using MATLAB, see the SCV tutorial an Introduction to MATLAB.
MathWorks, the developer of MATLAB, has extensive MATLAB Technical Documentation and Support including user guides, video tutorials, and demos on its website.
At that place is a full set of documentation bachelor from within MATLAB itself which can be viewed past selecting Product Help from the Help card.
For specifics on running MATLAB on SCV systems, see our MATLAB Aid Page.
For more information on MATLAB, visit the MATLAB website or the MATLAB Wiki.
There are also several other MATLAB tutorials available on the web: MATLAB Tutorials.
References
All of the information covered in this tutorial was taken either from the standard MATLAB documentation or from the MathWorks website. Our goal was to simplify and reduce the amount of information you lot need to empathise in gild to start visualizing your data.
Getting Started with MATLAB, version 7, The MathWorks, Inc.
Using MATLAB, version 7, The MathWorks, Inc.
Using MATLAB Graphics, version 7, The MathWorks, Inc.
MathWorks: MATLAB Technical Documentation and Back up
Source: https://www.bu.edu/tech/support/research/training-consulting/online-tutorials/visualization-with-matlab/
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