Transcript www.pmc.ucsc.edu

Arrays Manipulation and 2D Plots
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Generating arrays
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Array indexing
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Joining, growing and splitting arrays
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Review of array-array arithmetic in matlab
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Functions to manipulate arrays
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Strings and Cell Arrays
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Structures
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More Complex 2D Graphics
Basics of handles and get/set
Introduction
Today, we are going to spend most of the day discussing matrices
(arrays) and their use in matlab. Although this might not seem like
the most fascinating topic, it is crucial that you understand the use
and abuse of matrices in matlab. If you understand completely this
one topic, you basically understand the bulk of matlab.
Before starting, I would like everyone to change their working
directory to the Windows Desktop. You can either do this with the cd
command, or (easier) by clicking on the “...” besides the “Current
Directory” displayed at the top of the Matlab Window and selecting
the desktop. We will be saving and loading any and all files from the
desktop during these classes.
Generating Matrices
Matlab makes it easy to generate simple, useful matrices:
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a = ones([5, 1])
% Vector with matrix dimensions
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b = zeros( [2, 3, 4] )
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ones( 5 ) % NOTE: short for ones([5 5]),NOT
ones([5,1])
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first = 10; last = 20; number = 5;
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c = linspace( first, last, number )
logspace
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eye( 3 )
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rand( [ 2, 3 ] ) % Uniform random nums between 0
and 1.
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randn( size(a) )
distrib.
% See also
% Identity matrix
% Random nums with a normal
Note that matlab can quickly generate two types of random numbers. The
first generates a number from 0 to 1 with a constant probability for any
number between those limits. The second generates a normal or Gaussian
distribution of random numbers with mean 0 and variance 1. This second
type can be used to generate normal distributions with arbitrary mean and
Repeating Matrices
Changing the shape or repeating a matrix is also an efficient way to
generate useful matrices. Consider the following:
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clear all
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a = [ 1 4; 2 5; 3 6 ]
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aa = reshape( 1:6, [3,2] )
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b = [ 1 2 3; 1 2 3; 1 2 3]
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bb = repmat( 1:3, [3, 1] )
Note that a and aa are the same, as are b and bb. reshape takes the
matrix given as the first argument (a vector from 1 to 6 in this case) and
changes it's dimensions to those given in the second argument. The
product of the dimensions must remain the same.
repmat takes a matrix and reproduces it the number of times given in
the second argument. Play with this function until you are familiar with
how it functions.
Finally transposing 2D matrices is easy:
a = a';
Note that the transpose operator can be used at any point in a calculation.
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Array Indexing
Matlab has a wide variety of ways to access and change parts of a matrix.
The easiest is with indices:
clear all
 a = reshape( 1:20, [4,5] )
 a( 1:2, : )
% Gets first two rows
 a( 3:end, : )
% Last three rows
 a( 1:2, 1:3 )
 a( [1 3], [1 3 5] )
 a(2)
% Single index works as well - rows, then
columns
 a(:)
% Turn it into a column vector
end is shorthand for the size of the array in that dimension. : by itself is
shorthand for 1:end.
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Arrays can also be accessed via a boolean array of the same dimension.
This is useful for looking at values with certain characteristics.
b = mod(a,2) == 0 % Set of even values in a
 a(b)
The == operator is NOT for assignment! It tests for equality. Confusing =
and == is one of the most common errors in many programming languages.
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Changing Arrays
The indexing explained in the last slide can be used to modify the contents
of an array:
a = reshape( 1:20, [4,5] ); % Repeat from last
slide
 b = mod(a,2) == 0;
% Just in case you didn't do
it
 a(b) = NaN
% Get rid of all even numbers
 a
 a( isnan(a) ) = -999
Note that some care needs to be taken when using NaN's. Compare the
results of the following commands:
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15 == 15 % Returns TRUE (1)
 NaN == NaN
% Returns FALSE (0) !
 isnan( [1 NaN 3] )
You can also remove entire columns or rows from matrices using the empty
matrix.
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a(1,:) = [] % Remove first row
 a(:,3) = []
% Third column
There are also a number of functions useful for testing and indexing matrix
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Joining and Growing Arrays
Joining matrices is straightforward in matlab. Follow your nose:
a = reshape( 1:20, [4,5] );
 c = [a, a]
% Join along columns
 [a; a]
 [a, a']
% DOESNT WORK – must have same no. of
rows
 b = 'abcd';
 [ b 'efgh' ]
% Strings are ARRAYS of characters
In the last line, I have used the fact that a string is an array to concatenate
two strings.
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Matlab automatically grows matrices if a value is assigned to an index
beyond the array size. This is usually useful, but can hide errors.
a(5,5) = 21 % Fills in with 0's
 a(:,6) = 1
Growing matrices is computationally intensive. When possible, it is quicker
to block off the appropriate amount of memory (array size) from the start and
change values as needed.
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Array Arithmetic
This is something of a review for everyone:
a = reshape( 1:20, [4,5] );
 b = a+a
 a * b
% DOESNT WORK – bad dimensions
 a * b'
% Matrix multiplication
 a ./ b
% Element-by-element division
 b .^ a
 c = a ~= a(:,end:-1:1)
 ~c
In matlab, ~ is the logical NOT. ~= stands for not-equal (the opposite of ==).
Matrix division (/ and \) also exists and is useful for least-squares fitting.
There is also a kronecker tensor product (kron).
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Matlab has all of the typical matrix arithmetic functions. These typically work
over columns, but this behavior can be modified:
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mean(a)
min(a)
sum(a)
sort(a')
Strings and Cell Arrays
Multiple strings can be stored either in character arrays or in cell arrays.
Cell arrays are generally easier to use, as will be seen below.
a = [ 'abcd'; 'defg'] % Character arrays with
rows
 b = { 'abcd', 'defg' }
% Cell array
 a = [ 'abcd'; 'def' ]
%DOESNT WORK – string size
changes
 b = { 'abcd', 'def' }
In many ways cell arrays function the same as normal arrays. They can
have multiple dimensions and often be indexed the same as normal arrays.
There are however some key differences.
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a = {'a', 2, 'c'}
 a([2 3])
% Returns a cell array with one element
 a{[2 3]}
% Returns the number 2.
 a{3} = 'Hi'
 a(1) = 'Me'
% DOESNT WORK – must be cell array
 a(1) = { 'Me' }
When indexed with (), the result is a cell array; when indexed with {},
the result is called a list. I won't go into detail, but wrap your brain
around the following example:
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b = {'This', ' stuff', ' sucks.'; 'It', ' is', '
lame'; 'This', ' isn''t', ' cool.' }
Structures
Structures are the last major data type in matlab. They are useful for putting
related items together and a particular favorite of mine.
clear all
 a.name = 'David'
 a.age = 29
 a.height = 'tall'
 a.location = [ 38.5, 121.7 ]
 a.mom.name = 'Ellen'
 a.mom.age = 21
 a
 a.mom
There are also a series of functions for querying structures:
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a = rmfield(a,'height')
 isfield(a,'dad')
 fieldnames(a)
Structures are most commonly used to organize related, but disparate
information.
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It is possible to create arrays of structures and cell arrays that contain
Plotting 210
There are a number of simple ways to make more interesting plots in
matlab. Look at the difference between each of these plots.
a = 0:0.1:3;
 plotyy( a, exp(a), a, exp(-a) )
% Two independent
axes
 semilogy( a, exp(a) )
 loglog(a,exp(a))
You can also put more than one curve on a graph using hold:
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plot(a,exp(a))
 hold on
 plot(a,exp(-a), 'r-.')
 hold off
 b = axis
% Current axis limits
 b(3) = -1;
axis(b) % Change axis limits
There are a number of specialty plots in matlab that are useful at times.
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quiver(a(1:end-1),sin(a(1:end-1)), ...
diff(a),diff(sin(a)))
figure
hist( randn(1,1000), 40)
Handle Graphics
Essentially all graphics commands in matlab return a handle upon
completion. A handle is a number that can be used at a later time to change
the properties of the graphics objects associated with that number.
fh1 = figure
 fh2 = figure
% Create a second empty figure
window
 figure(fh1)
% Make first active
 ph = plot( 1:10, (1:10).^2 )
 figure(fh2)
 [n,x] = hist(randn(1,10000));
 bh = bar(x,n,'g')
 set(ph,'linewidth',3,'color',[0,1,1])
% RGB color
Using the get command you can find out the current state of the object.
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get(fh1)
 get(bh)
set is used to change the state of the object. By itself, set tells you what
properties can be changed and their possible values.
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set(fh1)
set(fh1,'position',[0 0 560 420])
set(bh,'edgecolor',[1,0,1],'linewidth',4)
Real-world Example
Today we are going to plot up some data of sea-surface currents derived
from high-frequency radar installations near Bodega Marine Lab. Links to
the necessary files can be found at the end of this page. Save them to the
desktop.
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open coast.txt; open codar.txt
load coast.txt; load codar.txt
Spatial data is in kilometers from a central point, currents are in cm/s. The
goal for today is to plot the coast and the nearby current data. First plot the
coastline. Make sure to use an “earthy” color. Add a title and axis labels.
Use axis equal to fix the aspect ratio.
Next, you will need to manipulate the current data so that it is useable.
Replace all -999 with NaN's. Perhaps separate the data into good and bad
data.
Plot a black point where we have current data and a red point where the
current data is missing (NaN).
Next add in the current vectors using quiver. See the help for scaling
vectors.
Finally, plot the largest current vector with
a2 different color and a thicker line.
2
ƒ vfirst need to calculate the total
To find the largest current vector, youuwill
velocity in the normal way and then find the point with the maximum velocity.
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load coast.txt
load currents.txt
ph = plot(coast(:,1),coast(:,2),'color',[0.7,0.3,0.1])
axis equal
currents(currents == -999) = NaN;
gc = currents( ~any(isnan(currents')), : );
bc = currents(
any(isnan(currents')), : );
hold on
plot(gc(:,1),gc(:,2),'k.','markersize',12)
plot(bc(:,1),bc(:,2),'r.','markersize',12)
scale = 0.1;
quiver(gc(:,1),gc(:,2),scale * gc(:,3),scale * gc(:,4),0)
tv = sqrt(sum(gc(:,3:4)'.^2));
top = gc( tv == max(tv), : );
qh=quiver(top(1),top(2),scale * top(3), scale * top(4),
0);
set(qh,'color','m','linewidth',3)
Solution