Robert

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Roberts Rules of Matlab
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Disclaimer
My usual processing flow is to use c or fortran
codes linked together via tcsh scripts. I find
Matlab to be really great for easy to code
routines which require user interaction.
Instances include SplitLab and FuncLab as you
will learn later this week, quality control of
ambient noise correlations, and determination of
discontinuities from 1D shear velocity and
Receiver Functions.
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Poker
Matlab is not only for data processing. From the
Matlab command window, type poker to launch a
very simple draw poker game I wrote in 2004.
What is this code?
Input user parameters Set the random number
generator While (player has chips or decides not
to cash out) Generate a hand display
hand allow user to exchange cards
display new hand analyze results and pay
out End while
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Getting help
This is the first block of comments in the .m
file displayed when the user typesgtgt help
command.m
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Pre-allocate your arrays
30x speedup!
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Matrices and array notation
Matlab is built on the idea that everything is a
matrix (or vector).
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The cell array
A cell array can be seen as an array of matrices.
Each matrix can be its own size and type. Where
matrix/vector elements can be accessed by
enclosing the indices in parentheses ( ), cell
arrays use squiggly brackets .
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Structures
variable.element think of structures as a handy
way to keep track of, pass, and handle objects
with common properties. In the example here, I
use a station with some common parameters. I
can then operate on a structure variable if I
know what all properties it contains.
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Avoid the parfor temptation

• To parallelize a Matlab for loop, all you have to
  do is
• Change for to parfor
• Open a pool of Matlab works to run the parallel
  loop

BUT I suggest avoiding this. Why? Matlab relies
on built-in algorithms that are already optimized
and often are already parallel. Therefore, if
you override that optimized parallel code with
a non-optimized block, youre likely to see drops
in performance because the resources are not
available for the optimized function. Furthermor
e, there is a cost of parallelization namely
message passing. Each time a parallel block is
entered, the processor has to figure out and
send information to the nodes before the parallel
job even begins.
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Avoid toolboxes in distributed code
Toolboxes are great extensions to Matlab, but for
a single user license they cost 45 each in
addition to the 150 base cost. Institutional
costs are order of 500 for the base and 100
per toolbox. Therefore you cant assume other
users of your code will have access to the same
set of toolboxes. Need a toolbox function? Is
it simple? Write it yourself. Is it not
simple? Google the usage sometimes you will
find a drop in replacement For instance, in some
of the SRF codes we use at USC will rely on
reckon.m from the mapping toolbox. We recently
found geodreckon.m, which is nearly a drop in
replacement freely available online from the
author (Charles Karney).
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Pandoras box UI control
uimenu sets menu items such as file-gtopen or
edit-gtselect all
uipanel creates a grouped background useful for
arranging user control
uicontrol creates an instance of some user
interactive widget such as a button, popup menu,
editable text box, radio buttons, etc When one
of these items is activated (ie button pressed)
it enacts what is called a callback function.
Making a GUI is all about designing the button
layout and then coding the set of callback
functions to process the data as input by the
user.
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Search for existing code!
The real value of Matlab is its extensive
code/user base.
Developers from across the sciences all use
Matlab and contribute new code which may help you
make a new discovery. Many of these codes can be
found on http//www.mathworks.com/matlabcentral/f
ileexchange http//github.com
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Think Processing Flow rather than just
processing
Who wants to click once per seismogram? When
youre dealing with big data, its only practical
to have the data (not just waveform) flow through
filters and processes to develop the final
product. You will see this in FuncLab and
SplitLab. Half of the work is just setting up the
workflow and then hitting a button to actually
run the processing flow. Things like sac or the
matlab command line are great for interactively
experimenting with small data. But once you want
to scale up, you need to think through as many
possibilities as you can.
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Some useful packages m_map seizmo the
waveform suite coral processRFmatlab
FuncLab SplitLab irisFetch.m IRIS-WS jar