Introduction to MATLAB session 2 Simon O’Keefe Non-Standard Computation Group [email protected].
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Transcript of Introduction to MATLAB session 2 Simon O’Keefe Non-Standard Computation Group [email protected].
Content Writing scripts Flow control Writing and using functions Using cell arrays Creating structure arrays Plotting data
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1 Scripts
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1 Scripts Instead of typing each command into Matlab you can store
them in a file known as a script
To execute the commands in a script, type it’s name into the command prompt
Within the script, you have access to variables defined in the workspace.
Comments are denoted using the % symbol. Anything written after % is ignored by Matlab
mresult = mean(results) % calculate the mean of the data
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1 Scripts
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To create a new script you can use the edit command>> edit
This can also be used to open an existing script>> edit script_name
Save the script, the filename will be the name of the script
To run the script type the name into the command prompt>> script_name
2 Flow control
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2 Flow control For command
Use a for loop to repeat one or more statements
The end keyword tells Matlab where the loop finishes
You control the number of times a loop is repeated by defining the values taken by the index variable
This uses the colon operator again, so index values do not need to be integer
For example >> for i = 1:4
a(i) = i * 2 end
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2 Flow controlThe counter can be used to index different rows or columns E.g.
>> results = rand(10,3)
>> for i = 1:3
m(i) = mean(results(:, i))
end ..although you could do this in one step
m = mean(results);
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m(2) = mean(results(:, 2))m(3) = mean(results(:, 3))
i = 2i = 3
2 Flow control
>> for i = 1:3
m(i) = mean(results(:, i))
end
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i = 1
m(1) = mean(results(:, 1))
2 Flow control>> data = [4 14 6 11 3 14 8 17 17 12 10 18]>> cat = [1 3 2 1 2 2 3 1 3 2 3 1]
To work out the mean for each category you could type 3 commands:>> mdata(1) = mean(data(cat == 1))>> mdata(2) = mean(data(cat == 2))>> mdata(3) = mean(data(cat == 3))
Which is OK when there are a few categories but any more would create a lot of work
You can use a for loop instead>> for i = 1:3
mdata(i) = mean(data(cat == i)) end
The variable mdata will consist of 3 elements containing the mean of the values in data. The first element will contain the mean for category 1, second element the mean for category 2 and so forth.
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mdata(3) = mean(data(cat == 3))mdata(1) = mean(data(cat == 1))mdata(2) = mean(data(cat == 2))
2 Flow control>> for i = 1:3
mdata(i) = mean(data(cat == i))
end
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i = 2i = 3i = 1
2 Flow control The ‘if’ command is used with logical operators Again, the end command is used to tell Matlab where the
statement ends. For example, the following code loops through a matrix
performing calculations on each column >> for i = 1:size(results, 2)
m = results(:, i)
if m > 1
do something
else
do something different
end
end
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2 Flow control
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2 Flow control The ‘while’ command
>> while statement
commands
end
Waiting Reading
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>> fid = fopen(‘results.txt'); fline = ‘’; while tline ~= -1 disp(tline) tline = fgetl(fid); end fclose(fid);
>> while mean(results) > 10 ind = results ~= max(results) results = results(ind) end
2 Flow control We can use flow control to display results for each of
several experiments on separate plots.
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mresult = mean(results)for i = 1:3 figure plot(results(:, i), ‘b.-’) hold on plot([1:10], repmat(mresult(i), 1, 10), ‘r-’) hold offend
2 Flow control If we use flow control in a script we do not know the
size of the results matrix when it will be run. Instead, we make the script more general:
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mresult = mean(results)for i = 1:size(results, 2) figure plot(results(:, i), ‘b.-’) hold on plot([1:size(results, 1)], repmat(mresult(i), 1, size(results,1)), ‘r-’) hold offend
3 Functions
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3 Functions More functions:
pca – calculates the principle components of a set of data fft– performs the fast Fourier transform on a data set var – calculates the variance of the data repmat – replicates a matrix numel – returns the number of elements in a vector (or matrix) cumsum – calculates the cumulative summation sort – sorts a vector into ascending order floor & ceil – rounds data values down or up to the nearest whole
value
A list of the core functions that are available is located in Matlab’s help section. (Help Menu -> Matlab Help, in the right part of the window there is a Functions link)
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3 Functions>> sortrows(data, col)
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3 Functions Some functions can return two or more outputs.
If this is the case use the following command structure>> [output1, output2] = function_Name(input1)
For example>> rows = size(results)
Could be written:
>> [rows, cols] = size(results)
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3 Functions To find out what outputs a function can return use the
help command
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3 Functions>> [sdata, ind] = sortrows(data, col)
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3 Functions The function xlsread reads data from an Excel
spreadsheet>> [num, text] = xlsread(filename, sheet, ‘range’)
Only the filename parameter is required, the others are optional
The text output is optional, if it is not used only the data from the spreadsheet is loaded
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3 Functions
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3 Functions You can create your own function if you need to use
some calculation that is not provided by Matlab.
This done in a similar way to creating a script; use the same edit command to edit a function. >> edit function_Name
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3 Functions The flow of a function is the same as a script; commands
are carried out in the order that they appear and loops can be used.
However, a function does not have access to variables in the workspace. Instead, you pass the data to the function.
And once the function has finished it returns it’s results back to the output variable used when you called the function.
Any variables you create within the function are deleted.
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3 Functions When writing a function the first line must always be in
the form: function [outputvariable, outputvariable2] =
function_Name(inputvariable1, inputvariable2)
This line tells Matlab the name of the function and how it can be used
The function name must match the file name The output variable must be assigned a value inside the function. The input variables can be accessed inside the function using
their names.
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3 Functions The function:
function m = mymean(data)
m = sum(data) ./ size(data, 1)
Can be used in a script or at the command prompt using:>> cm = mymean(data)
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4 Cell Arrays
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4 Cell Arrays Standard arrays hold 1 value per element which is ideal for storing
results
However, if you try to store a string, each letter is treated as a vector element
For example >> str = ‘subject1’
[s, u, b, j, e, c, t, 1] >> str = [‘subject1’, ‘subject2’]
[s, u, b, j, e, c, t, 1, s, u, b, j, e, c, t, 2] >> str(1)
‘s’
Strings can not be stored and organised easily in vectors or matrices
Cell arrays allow you to do this
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4 Cell Arrays A cell array is similar to a normal array except that each cell
can contain a whole array or vector (or a single value)
And the item in each cell does not need to be the same size or even the same type
Curly brackets are used to define cell arrays All other operations work the same as with a normal array except
that you use curly brackets instead of round brackets
For example:>> labels = {‘string1’, ‘string2’, ‘string3’}>> labels{1}
‘string1’
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5 Plots
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5 Plots
Saving plots to a file>> print(fileformat, filename)
file format: ‘-dbitmap’ ‘-depsc’
You can also achieve this using the File -> Save menu in the figure’s window
The command line version is useful when you want to generate a lot of plots in a script which are saved automatically
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5 Plots
>> for i = 1:numel(results)
figure
plot(results(i).data, ‘b.-’)
hold on
plot([1:size(results(i).data)], repmat(results(i).mdata), ‘r-’)
hold off
print(‘-bmp’, [‘c:\users\tom\desktop\’, results(i).label])
end
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5 Plots
Subplots Multiple plots can be placed within the same window This is achieved using the subplot command The window is split into a grid the size of which is specified
when entering the command
>> subplot(2,2,1)
This creates a grid 2 x 2 in size (4 plots) and sets the current plot to the first of these.
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5 Plots
>> subplot(2,2,1)
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5 Plots
>> figure
cols = 4
rows = ceil(numel(results) / cols)
for i = 1:numel(results)
subplot(rows, cols, i)
plot(results(i).data, ‘b.-’)
hold on
plot([1:size(results(i).data)], repmat(results(i).mdata), ‘r-’)
hold off
end
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5 Plots
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5 Plots Handles allow you to create a plot and then edit the properties later.
A handle is created when you create a plot or an object within the plot (for example title, legend) >> h = plot(data)This returns a handle to the line/s plotted, now you can change the line
style, colour, width etc.
The handle is stored in a variable which can then be used to edit the properties with the set command
For example, the position of a plot’s legend can be changed using handles >> plot(data) >> h = legend('line') >> set(h,'Location','South')
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5 Plots Inside/outside Best/Bestoutside
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Northwest NortheastNorth
South SoutheastSouthwest
EastWest
5 Plots
Colour bars can be repositioned in the same way:
>> h = colorbar
>> set(h, ‘Location’, ‘North’)
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5 Plots The figure function also returns a handle, you can
use the set function to change the figure title: >> h = figure >> set(h, 'Name', 'Subject1')
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5 Plots
Surface plot
>> surf(matrix)
>> colorbar
>> axis([1 50 1 50 0 1])
>> title('Surface Plot');
>> xlabel('x')
>> ylabel('y')
>> zlabel('z')
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5 Plots
Contour
>> contour(data)
>> colorbar
>> xlabel('x')
>> ylabel('y')
>> zlabel('z')
>> title('Contour Plot');
>> axis([1 50 1 50])
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5 Plots All of these can be use in conjunction with subplot,
therefore, you can display several different plot types in one figure
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