Download Course Wrapup Physics 3730/6720 Spring Semester 2016

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

System of polynomial equations wikipedia , lookup

Fisher–Yates shuffle wikipedia , lookup

Monte Carlo method wikipedia , lookup

Calculus of variations wikipedia , lookup

Transcript
Course Wrapup
Physics 3730/6720
Spring Semester 2016
Final Project Comments
Critical Frequency
Critical sampling
(Nyquist) of sine
wave is two per
cycle.
fc =1/(2t)
Frequencies
above this are
undersampled;
discrete FT is in
trouble!
Figures from Stephen Smith, Digital Signal Processing
Discrete Fourier Transforms
●
●
Suppose that we have N sampled values
(further, suppose N is even)...
Now note that with N numbers input, we can
produce at most N numbers output. (Why?) The
most interesting range is -fc→ fc, so we'll want to
estimate frequency components at the discrete
values:
Intensity
#bins = #bins in time domain
-0.00833
0.0
f(Hz)
+0.00833
Announcements
●
●
Due to 3740 final, Final Project due date is
pushed back to Sunday May 1st Midnight
th
I will be in SP 205 at 9:00 AM April 28 for
consultation about project, final exam, etc.
Final Exam
Wednesday May 4 , 8:00-10:00 AM
●
Similar format to midterm:
●
th
●
–
Open book, notes, internet
–
Consult only with instructor
–
Online submission a la assignments.
nd
Focus on 2 half of semester, but
“comprehensive” as needs be.
Midterm Topics
●
(Basic Unix and Emacs)
●
sort, awk, sed
●
Plotting with gnuplot
●
Basic shell scripts
●
Basic html
●
C++
●
–
math operations
–
input/output
–
conditionals, loops
–
pointers, references
–
random number generators
Program and plot with Python
Potential (Major) Final Exam Topics
●
Statistics; Binomial, Poisson, Gaussian
●
Numerical integration; trapezoid, Monte Carlo...
●
Least-squares fitting
●
Likelihood fitting
●
ODEs (Euler, Runge-Kutta...)
●
Discrete Fourier Transforms
●
Maple (plotting, Matrix algebra)
●
LaTex
●
Python
Binomial Distribution
●
●
●
The probability PB of observing  successes in N
trials, where the probability of success per trial
is p, is given by:
The average number of successes:
The standard deviation of the number of
successes  is given by:
Midpoint Rule
●
●
●
Divide x-axis into
intervals of width h.
Use midpoint of
each interval to
compute area.
Error
The “Monte Carlo” Technique
●
●
●
Employ's random
numbers to
determine
integrals.
e.g. what is the
area of a circle?
What is ?
 = 4x(709/900)
= 3.151 ± 0.055
Evaluating Fits: 
●
●
●
2
Definition of 2
Absolute magnitude of 2 only has meaning
relative to the number of degrees of freedom
#DOF = (# of data points) (# parameters determined from the data)
●
#DOF = (# of data points) - (# constraints)
●
2 /#DOF ~ 1 for good fit
LaTex Math
I can put mathematical characters in a sentence by putting a dollar
sign before and after the characters. The area $A$ of a circle is
given by $A = \pi r^2$. I can also set equations apart from the
text as in the following example:
\begin{equation}
A = \pi r^2
\end{equation}
Note that latex will keep track of equation numbers for me.
Integrate ODEs:
Graphical Explanation
●
●
●
A is initial point.
Euler uses derivative at
A to find approximation
to y(t+h) at B
Modified Euler uses
average of derivatives
at A and B to find
better approximation C