Reusing Code

We’ve written MATLAB commands to compute statistics about our data and generate some plots to visualize the results. We’re now faced with the following problems:

Problem 1

So far, we’ve typed in commands one-by-one on the command line to get MATLAB to do things for us. But what if we want to repeat our analysis? Sure, it’s only a handful of commands, and typing them in shouldn’t take us more than a few minutes. But if we forget a step or make a mistake, we’ll waste time rewriting commands. Also, we’ll quickly find ourselves doing more complex analyses, and we’ll need our results to be more easily reproducible.

Problem 2

We also have to do this analysis for every one of our dozen datasets. And we need a better way than typing out commands for each one, because we’ll find ourselves writing a lot of duplicate code. Remember, code that is repeated in two or more places will eventually be wrong in at least one. If we make changes in the way we analyze our datasets, we have to introduce that change in every copy of our code.

There’s a common theme in the two problems presented above—duplicate code. In problem 1, we’re rewriting code every time we want to perform the same analysis several times. In problem 2, we’re rewriting code every time we want to perform several similar analyses. To avoid writing all this duplicate code, we have to teach MATLAB to

• Remember our commands

• Repeat our commands

* Learn how to write and save MATLAB scripts.
* Learn how to save MATLAB plots to disk.
* Explain what a for loop does.
* Correctly write for loops that repeat simple commands.
* Trace changes to a loop variable as the loop runs.
* Use a for loop to process multiple files.

Saving Our Work

Writing Scripts

In addition to running MATLAB commands one-by-one on the command line, we can also write several commands in a script. A MATLAB script is just a text file with a .m extension. We’ve written commands to load data from a .csv file and displays some statistics about that data. Let’s put those commands in a script called analyze.m:

% script analyze.m

patient_data = csvread('inflammation-01.csv');

disp([Analyzing "inflammation-01.csv: "])
disp(['Maximum inflammation: ', num2str(max(patient_data(:)))]);
disp(['Minimum inflammation: ', num2str(min(patient_data(:)))]);
disp(['Standard deviation: ', num2str(std(patient_data(:)))]);

Before we can use it, we need to make sure that this file is visible to MATLAB. MATLAB doesn’t know about all the files on your computer, but it keeps an eye on several directories. The most convenient of these directories is generally the “working directory”, or “current directory”. To find out the working directory, use the pwd command:

pwd

{:class=“in”}

As you might have guessed, pwd stands for “print working directory”.

Once you have a script saved in a location that MATLAB knows about, you can get MATLAB to run those commands by typing in the name of the script (without the .m) in the MATLAB command line:

analyze

{:class=“in”}

Maximum inflammation: 20
Minimum inflammation: 0
Standard deviation: 4.7219

{:class=“out”}

Saving Images

We’ve also written commands to create plots:

ave_inflammation = mean(patient_data, 1);

plot(ave_inflammation);
ylabel("average")

{:class=“in”}

MATLAB let’s us save those as images on disk:

% save plot to disk as png image:
print -dpng "average.png"

{:class=“in”}

Let’s extend our analyze script with commands to create and save plots:

% script analyze.m

patient_data = csvread('inflammation-01.csv');

disp(['Maximum inflammation: ', num2str(max(patient_data(:)))]);
disp(['Minimum inflammation: ', num2str(min(patient_data(:)))]);
disp(['Standard deviation: ', num2str(std(patient_data(:)))]);

ave_inflammation = mean(patient_data, 1);

subplot(1, 3, 1);
plot(ave_inflammation);
ylabel("average")

subplot(1, 3, 2);
plot(max(patient_data, [], 1));
ylabel("max")

subplot(1, 3, 3);
plot(min(patient_data, [], 1));
ylabel("min")

% save plot to disk as svg image:
print -dpng "patient_data-01.png"

The Colon Operator

You can use the : (colon) operator to generate sequences in MATLAB:

4:10

{:class=“in”}

ans =

    4    5    6    7    8    9   10

{:class=“out”}

2.5:0.25:5

{:class=“in”}

ans =

 Columns 1 through 8:

    2.5000    2.7500    3.0000    3.2500    3.5000    3.7500    4.0000    4.2500

 Columns 9 through 11:

    4.5000    4.7500    5.0000

{:class=“out”}

Analyzing Multiple Datasets

We have a dozen data sets right now, and more on the way. We want to create plots for all our data sets without repeating the above commands each time. To do that we’ll have to learn how to get the computer to repeat things.

for loops

Suppose we want to print each character in the word “lead” on a line of its own. One way is to use four disp statements:

word = 'lead';

disp(word(1));
disp(word(2));
disp(word(3));
disp(word(4));

{:class=“in”}

l
e
a
d

{:class=“out”}

But that’s a bad approach for two reasons:

1. It doesn’t scale: if we want to print the characters in a string that’s hundreds of letters long, we’d be better off typing them in.

2. It’s fragile: if we change word to a longer string, it only prints part of the data, and if we change it to a shorter one, it produces an error, because we’re asking for characters that don’t exist.

word = 'tin'

disp(word(1));
disp(word(2));
disp(word(3));
disp(word(4));

{:class=“in”}

error: A(I): index out of bounds; value 4 out of bound 3

{:class=“out”}

There’s a better approach:

for letter = 1:4
    disp(word(letter))
end

{:class=“in”}

l
e
a
d

{:class=“out”}

This improved version uses a for loop to repeat an operation—in this case, printing—once for each element in an array.

The general form of a for loop is:

for variable = collection
    do things with variable
end

The for loop executes the commands in the loop body for every value in the array collection. This value is called the loop variable, and we can call it whatever we like. In our example, we gave it the name letter.

We have to terminate the loop body with the end keyword, and we can have as many commands as we like in the loop body. But we have to remember that they will all be repeated as many times as there are values in collection.

Our for loop has made our code more scalable, and less fragile. There’s still one little thing about it that should bother us. For our loop to deal appropriately with shorter or longer words, we have to change the first line of our loop by hand:

word = 'tin';

for letter = 1:3
    disp(word(letter));
end

{:class=“in”}

t
i
n

{:class=“out”}

Although this works, it’s not the best way to write our loop:

• We might update word and forget to modify the loop to reflect that change.

• We might make a mistake while counting the number of letters in word.

Fortunately, MATLAB provides us with a convenient function to write a better loop:

word = 'aluminium'

for letter = 1:length(word)
    disp(word(letter));
end

{:class=“in”}

a
l
u
m
i
n
i
u
m

{:class=“out”}

This is much more robust code, as it can deal indentically with words of arbitrary length. Here’s another loop that repeatedly updates the variable len:

len = 0
for vowel = 'aeiou'
    len = len + 1;
end

disp(['Number of vowels: ', num2str(len)])

{:class=“in”}

It’s worth tracing the execution of this little program step by step. Since there are five characters in “aeiou”, the loop body will be executed five times. When we enter the loop, len is zero - the value assigned to it beforehand. The first time through, the loop body adds 1 to the old value of len, producing 1, and updates len to refer to that new value. The next time around, vowel is e, and len is 1, so len is updated to 2. After three more updates, len is 5; since there’s nothing left in aeiou for MATLAB to process, the loop finishes and the disp statement tells us our final answer.

Note that a loop variable is just a variable that’s being used to record progress in a loop. It still exists after the loop is over, and we can re-use variables previously defined as loop variables as well:

disp(vowel)

{:class=“in”}

u

{:class=“out”}

After the loop, vowel refers to the last value in aeiou, i.e., u.

Challenges

1. Write a loop that spells the word “aluminum,” adding one letter at a time:

_~ a al alu alum alumi alumin aluminu aluminum _~ {:class=“out”}

1. Matlab uses the caret (^) to perform exponentiation:

_~ disp(5^3) _~ {:class=“in”} _~ 125 _~ {:class=“out”}

Let b be the base of the number and x the exponent. Write a loop to compute b^x. Check your result for b = 4 and x = 5.

1. In Matlab, the colon operator (:) accepts a stride or skip argument between the start and stop:

_~ disp(1:3:11) _~ {:class=“in”} _~ 1 4 7 10 _~ {:class=“in”} _~ disp(11:-3:1) _~ {:class=“in”} _~ 11 8 5 2 _~ {:class=“out”}

Using this, write a loop to print the letters of “aluminum” in reverse order, one letter per line.

_~ m u n i m u l a _~ {:class=“out”}

Extra Challenge: Reverse the string abracadabra without a loop, using only indexing and the colon operator.

Processing Multiple Files

We now have almost everything we need to process multiple data files with our analyze script. You’ll notice that our data files are named inflammation-01.csv, inflammation-02.csv, etc. Let’s write a loop that tries to print the names of each one of our files:

for i = 1:12
    file_name = sprintf('inflammation-%d.csv', i);
    disp(file_name);
end

{:class=“in”}

inflammation-1.csv
inflammation-2.csv
inflammation-3.csv
inflammation-4.csv
inflammation-5.csv
inflammation-6.csv
inflammation-7.csv
inflammation-8.csv
inflammation-9.csv
inflammation-10.csv
inflammation-11.csv
inflammation-12.csv

{:class=“out”}

This is close, but not quite right. The sprintf function is useful when we want to generate MATLAB strings based on a template. In our case, that template is the string inflammation-%d.csv. sprintf generates a new string from our template by replacing the %d with the data referred to by our second argument, i.

Again, let’s trace the execution of our loop: in the beginning of our loop, i starts by referring to the value 1. So, when MATLAB executes the command

file_name = sprintf('inflammation-%d.csv', i);

it substitutes the %d in the template inflammation-%d.csv, with the value of i, i.e., 1. The resulting string is inflammation-1.csv, which is assigned to the variable file_name. The disp command prints that string to screen. The second time around, sprintf generates the string inflammation-2.csv, which is assigned to the variable file_name, and printed to screen. And so on, till i finally refers to the value 12.

Remember that there’s a mistake. Our files are actually named inflammation-01.csv, inflammation-02.csv, etc. To get it right, we have to modify our template:

for i = 1:12
    file_name = sprintf('inflammation-%02d.csv', i);
    disp(file_name);
end

{:class=“in”}

inflammation-01.csv
inflammation-02.csv
inflammation-03.csv
inflammation-04.csv
inflammation-05.csv
inflammation-06.csv
inflammation-07.csv
inflammation-08.csv
inflammation-09.csv
inflammation-10.csv
inflammation-11.csv
inflammation-12.csv

{:class=“out”}

We’ve replaced %d in our earlier template with %02d. With this, we’re specifying that we want our data to be displayed with a minimum width of 2 characters, and that we want to pad with 0 for data that isn’t at least 2 digits long.

We’re now ready to modify analyze.m to process multiple data files:

% script analyze.m

for i = 1:3

    % Generate strings for file and image names:
    file_name = sprintf('inflammation-%02d.csv', i);
    img_name = sprintf ('patient_data-%02d.svg', i);

    patient_data = csvread(file_name);
    ave_inflammation = mean(patient_data, 1);

    figure()

    subplot(1, 3, 1);
    plot(ave_inflammation);
    ylabel('average')

    subplot(1, 3, 2);
    plot(max(patient_data, [], 1));
    ylabel('max')

    subplot(1, 3, 3);
    plot(min(patient_data, [], 1));
    ylabel('min')

    print(img_name);
    close();

end

Remember that to run our script, we simply type in its name in the command line:

analyze

{:class=“in”}

Sure enough, the maxima of these data sets show exactly the same ramp as the first, and their minima show the same staircase structure.

Challenges

1. In cases where our file names don’t follow such a regular pattern, we might want to process all files that end with a given extension, say .csv. At the command line we could get this list of files by using a wildcard:

_~ ls *.csv _~ {:class=“in”}

Thankfully, Matlab also has ls, though it returns a single long string:

_~ filestr = ls(’*.csv’) _~ {:class=“in”}

_~ inflammation-01.csv inflammation-04.csv inflammation-07.csv inflammation-10.csv inflammation-02.csv inflammation-05.csv inflammation-08.csv inflammation-11.csv inflammation-03.csv inflammation-06.csv inflammation-09.csv inflammation-12.csv _~ {:class=“out”}

To turn this string into an array we can loop over (actually, a Cell Array), we need to “split” the string at each occurrence of whitespace:

_~ file_string = ls(’*.csv’); file_list = strsplit(file_string) _~ {:class=“in”}

_~ file_list =

 Columns 1 through 3

   'inflammation-01.csv'    'inflammation-04.csv'    'inflammation-07.csv'

 Columns 4 through 6

   'inflammation-10.csv'    'inflammation-02.csv'    'inflammation-05.csv'

 Columns 7 through 9

   'inflammation-08.csv'    'inflammation-11.csv'    'inflammation-03.csv'

 Columns 10 through 13

   'inflammation-06.csv'    'inflammation-09.csv'    'inflammation-12.csv'    ''

_~ {:class=“out”}

Using this trick, rewrite the analyze script to analyze all csv files in the current directory. Be careful of the empty string '' at the end of file_list!