1. COMP 116: Introduction to Scientific Programming Lecture 37: Final Review

2. Functions

3. Writing Simple function function [o1, o2]=funcName( i1, i2 ) % Function Comments … % Body (implementation) end %optional Can have multiple inputs (i1) and multiple outputs (o2) function [] = funcName() function o1 = funcName() function o1 = funcName( i1 ) function o1 = funcName( i1, i2 ) function [o1, o2] = funcName( i1, i2, i3)

4. Workspace Global vs. Local Storage Global Workspace ◦ Shared by Command Window and script commands Local Workspace ◦ Created locally on entry to each function ◦ Disappears on exit from function call. % This is a script radius = 10; area = pi.* radius.^2; function [area] = circ_area(radius) area = pi.* radius.^ 2; % Call function in workspace my_area = circ_area( 10 );

5. Looping

6. Loops: for loop statement the counted loop solution for = : end for = : : end

7. Examples: Find sum function ret=my_sum(A) % find minimum in the vector A ret=0; for i=1:length(A) ret=ret+A(i); end Find minimum will be very similar

8. Examples: Find the first occurrence of k in an array function elem=find_first(A,k) elem=0; for i=1:length(A) if (A(i)==k) elem=i; break; end

9. Exercises Find the minimum value in a matrix Given an array, check if any two elements of the array sum to zero

10. Loops: while loop statement the conditional loop solution while end While loops are great if we don’t know how many times we need to loop, but if we can write a test for when we’re done For this to work properly, the test needs to evaluate to a logical value The while loop will run as long as test evaluates to true The while loop does not have a built-in counter like the for -loop (if you want to count something, you need to implement the counter yourself)

11. Example: Golden Ratio Golden ratio is the solution of x^2-x-1=0 For any positive number x, sqrt(x+1) is a better approximation of golden ration than x. Use this rule in a while loop to find the some x such that abs(x^2-x-1) is less than 0.000001 eps=0.000001; x=100; while abs(x^2-x-1) > eps x=sqrt(x+1); end

12. Strings

13. Strings as a vector of chars Can be manipulated like any other vector s1 = 'The quick brown fox ' s2 = 'jumped over the lazy dog' s = [s1, s2] % concatenate strings s(5) % ans = q s(17:19) % ans = fox jIdx = find( s == 'j' ) jStr = s(jIdx:jIdx+3) % ans = jump

14. String Comparison Avoid normal comparison operators! ◦s1 == s2, s1 = s3 ◦ Operators work element by element (on characters) ◦ Thus, strings (i.e., the vector of chars) must be same length Use string comparison functions instead ◦strcmp(), string comparison ◦strcmpi, string comparison while ignoring case ◦strncmp, strncmpi :  Similar, but compares first n characters only

15. String Searching strfind ◦ Search for a string inside another string ◦ returns indices to start of each instance strVal = [‘with great power comes great responsibility.’]; strfind( strVal, ‘great’) % ans = [6 24]

16. String Replacement strrep strVal = [‘with great power comes great responsibility.’]; strrep( strVal, ‘great’, ‘no’)

17. Cell Arrays and Structures

18. Cell Arrays vs. Arrays ArrayCell Array Element accessed by index/indices Elements all need to be of the same type Elements accessed by index/indices Elements can have distinct types Basically, think of cell arrays as being more flexible data structures than the ‘standard’ arrays.

19. Accessing Cell Arrays Crucial distinction between {} and () operators Example: A = { false, rand(3); 4.0, ‘This is a string’ }; A{1} or A{1,1} extracts the logical value false A(2) or A(2,1) extracts a 1x1 cell-array containing 4.0 A(2,:) extracts a 1x2 cell-array containing the bottom row A{:,2} extracts the values of the second column as separate entities; use: [a,b] = A{:,2} for proper assignment Indices work just as for ‘standard’ arrays.

20. Cell Arrays vs. Structures StructureCell Array Element accessed by name Elements can have distinct types Elements accessed by index/indices Elements can have distinct types Structures are very convenient data types when storing Information belonging to one organizational unit, e.g., Name Age Date of Birth Height Weight …

21. Structures Use named ‘fields’ for each variable alex.name = 'Alexander the Great'; alex.occupation = 'Conqueror'; alex.birth = 356; alex.fictional = false;

22. Structures Use named ‘fields’ for each variable Use the struct() function, with name-value pairs alex.name = 'Alexander the Great'; alex.occupation = 'Conqueror'; alex.birth = 356; alex.fictional = false; alex = struct('name', 'Alexander the Great',... 'occupation', 'Conqueror',... 'birth', 356,... 'fictional' = false);

23. Structure Arrays One way to initialize is to use a ‘template’ % create structure layout % note the use of default values and empty arrays template = struct( 'name', 'no name',... 'nickname', 'no name',... 'emails', [],... 'department', 'undeclared',... 'type', 'undergrad',... 'year', 1 ); % create structure array students = repmat( template, 1, 30 ); % now fill in each structure in the array

24. File I/O

25. Useful stuff Opening a file: ◦ help fopen ◦ Open in read, write or append mode ◦ Always close your open files with fclose Text files: ◦ Writing: fprintf ◦ Reading: fgetl MATLAB data files: ◦ Writing: save ◦ Reading: load Other useful notes feof: test of end of file fprintf: use ‘%d’, ‘%f’, etc. fgetl: get a line from a (text) file

26. Good luck!