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Debugging Palm - Computer Methods in Biomedical Engineering | BME 201, Study notes of Biology

North Carolina State University (NCSU)Biology
Prof. Gregory Sawicki

Outline Mar 16 Material Type: Notes; Professor: Sawicki; Class: Computer Methods in Biomedical Engineering; Subject: Biomedical Engineering; University: North Carolina State University; Term: Spring 2011;

Typology: Study notes

2010/2011

Uploaded on 04/06/2011

goalie4eva
goalie4eva 🇺🇸

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Lecture 16: Wednesday March 16, 2011:
Announcements:
SH6 is due today.
SL5 is due tomorrow - Thursday March 17th. Happy St. Patty’s Day!
Case 3 is due Monday March 21st. Please take advantage of Prof. Sawicki’s
Office Hours on M’s and T’s from 4-5PM 4212C EB3 or by appt. Ben’s hours are
still being held Thursday afternoons.
You can pick up your graded exam after lab on Thursday 17th. Questions on
grading please see Prof. Sawicki.
SH7 will be up early next week.
Case 4 will be up late next week.
Exam 2 is scheduled for Wednesday, April 6th in-class.
Reading for Next Time:
-Function Functions Palm p124-129 (i.e. fzero, fminbnd, fminsearch, and fsolve) +
Function Handles (p 124)
-Debugging Palm Chapter 4.8.
-Ch 9.1 and 9.2 more on Numerical Differentiation and Integration.
-After that start looking at Ch. 8 on Linear Algebraic Equations.
*SH EC: Work the Torus problem from SH6 on the board.
*Today’s Goals:
1. Cover gradient and diff for use to get velocity from delta length in Case 3 assignment.
Questions on Case 3?
IN CLASS GROUP PROBLEM:
Do example similar to SL5 -Palm Ch 4. page 216-217 Problem 48. Write-up Inventory
Model as a function.
2. Discuss Anonymous Functions and Sub-functions
*I REMEMBER: turn on your diary to save your Command Window
>>diary LectureMar16.m
*II. Numerical Derivative: Two Methods
DIFF Difference and approximate derivative.
DIFF(X), for a vector X, is [X(2)-X(1) X(3)-X(2) ... X(n)-X(n-1)].
GRADIENT Approximate gradient.
[FX,FY] = GRADIENT(F) returns the numerical gradient of the
matrix F. FX corresponds to dF/dx, the differences in x (horizontal)
direction. FY corresponds to dF/dy, the differences in y (vertical)
direction. The spacing between points in each direction is assumed to
be one. When F is a vector, DF = GRADIENT(F)is the 1-D gradient.
*III. More on Functions –
pf3

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Lecture 16: Wednesday March 16, 2011: Announcements:  SH6 is due today.  SL5 is due tomorrow - Thursday March 17th. Happy St. Patty’s Day!  Case 3 is due Monday March 21st. Please take advantage of Prof. Sawicki’s Office Hours on M’s and T’s from 4-5PM 4212C EB3 or by appt. Ben’s hours are still being held Thursday afternoons.  You can pick up your graded exam after lab on Thursday 17th. Questions on grading please see Prof. Sawicki.  SH7 will be up early next week.  Case 4 will be up late next week.  Exam 2 is scheduled for Wednesday, April 6th^ in-class. Reading for Next Time: -Function Functions Palm p124-129 (i.e. fzero, fminbnd, fminsearch, and fsolve) + Function Handles (p 124) -Debugging Palm Chapter 4.8. -Ch 9.1 and 9.2 more on Numerical Differentiation and Integration. -After that start looking at Ch. 8 on Linear Algebraic Equations. *SH EC: Work the Torus problem from SH6 on the board. *Today’s Goals:

  1. Cover gradient and diff for use to get velocity from delta length in Case 3 assignment. Questions on Case 3? IN CLASS GROUP PROBLEM: Do example similar to SL5 -Palm Ch 4. page 216-217 Problem 48. Write-up Inventory Model as a function.
  2. Discuss Anonymous Functions and Sub-functions *I REMEMBER: turn on your diary to save your Command Window

    diary LectureMar16.m *II. Numerical Derivative: Two Methods DIFF Difference and approximate derivative. DIFF(X), for a vector X, is [X(2)-X(1) X(3)-X(2) ... X(n)-X(n-1)]. GRADIENT Approximate gradient. [FX,FY] = GRADIENT(F) returns the numerical gradient of the matrix F. FX corresponds to dF/dx, the differences in x (horizontal) direction. FY corresponds to dF/dy, the differences in y (vertical) direction. The spacing between points in each direction is assumed to be one. When F is a vector, DF = GRADIENT(F)is the 1-D gradient. *III. More on Functions –

Anonymous Functions: (Palm Ch 3. pg. 132-133) Anonymous functions provide a quick, in-line method of defining and executing a function without having it in a separate m-file. This is useful for simple expressions like, exponential equations or polynomial equations. Syntax is as follows: function handle = @(argument list) expression The function handle (i.e. reference) is just the name of your function. The argument list can be as long as you like, or even empty (see Palm page 133). The expression can only be a single executable MATLAB expresion. To call/execute the anonymous function: output variable name= function handle(argument list) For example, to define an anonymous function called sq that can compute the square of any input value x --> %Example of an anonymous function with one input %function handle = @(input argument(s)) function expression sq = @(x) x.^2; %This defines a function sq that computes the %square of the input value x (which can be an array - note %element wise definition) %Then, a bunch of ways to call it- y= sq(5) z= sq([5 6 8]) a=6, b=12, c=13; h=[a b c]; t=sq(h) Anonymous functions can be defined with multiple inputs. For example --> %Example of an anonymous function with two inputs sumsqrt= @(x,y) sqrt(x.^2 + y.^2); % This defines the function %sumsqrt % To call it: y= sumsqrt(4,5) Anonymous functions can call each other (Palm p. 133). For example -->