How do I resolve name conflict in MATLAB? - matlab

I created a GUI called "stack" in MATLAB. It has a .m file associated with it. This GUI is called on multiple occasions by another GUI in the same folder.
Now I discovered that "stack" is a built-in function in MATLAB which I need to use for something else in the same working directory. All calls to the stack function somehow invoke the GUI by calling the stack.m script.
I do not want to rename this because it is used in many places.
Is there a way to use the built-in function without needing to rename? Some way to reference the function and the script separately?

Disclaimer: Please, please, please, do not do this.
Assuming that your own stack.m is only in the search path because it is in the current folder, then the easiest fix is to create some dummy subfolder, navigate to it, execute Matlabs stack function (which is the only stack in the current searchpath) and navigate back.
Here I have exemplified it with magic:
function a= magic
n=5;
cd dummy
a= magic(n);
cd ..
where dummy is the name of the subfolder.

A slight modification of Nicky's answer for the sake of repeatability: before navigating to the map where your stack.m GUI is stored, run
builtinStack = #stack();
which creates a function handle. This way you can call builtinStack() like the MATLAB function is supposed to be called, without having to cd out of the directory every time you want to use it.
The use of builtin, as suggested by hoki doesn't work, since built-in functions are defined as
...Functions such as "ind2sub", "sub2ind", etc. are not MATLAB built-in functions.... Those functions that are shipped with MATLAB but are not defined as built-in functions can be referred to as "MATLAB functions" ...
As answered by MathWorks Technical Support. This means that functions like stack aren't built-in in the sense that they are build in a different language, compiled and then called from MATLAB, but are actually written in MATLAB and are shipped with the release. The main way to check this is typing edit <functionname>; when showing only comments the function is a built-in as defined by TMW, when it shows MATLAB code as well, like stack, it's not a built-in as per the above definition.
An example of a built-in function is sum, whose associated .m-file looks as follows:
%SUM Sum of elements.
% S = SUM(X) is the sum of the elements of the vector X. If X is a matrix,
% S is a row vector with the sum over each column. For N-D arrays,
% SUM(X) operates along the first non-singleton dimension.
%
% S = SUM(X,DIM) sums along the dimension DIM.
%
% S = SUM(...,TYPE) specifies the type in which the
% sum is performed, and the type of S. Available options are:
%
% 'double' - S has class double for any input X
% 'native' - S has the same class as X
% 'default' - If X is floating point, that is double or single,
% S has the same class as X. If X is not floating point,
% S has class double.
%
% S = SUM(...,NANFLAG) specifies how NaN (Not-A-Number) values are
% treated. The default is 'includenan':
%
% 'includenan' - the sum of a vector containing NaN values is also NaN.
% 'omitnan' - the sum of a vector containing NaN values
% is the sum of all its non-NaN elements. If all
% elements are NaN, the result is 0.
%
% Examples:
% X = [0 1 2; 3 4 5]
% sum(X, 1)
% sum(X, 2)
%
% X = int8(1:20)
% sum(X) % returns double(210), accumulates in double
% sum(X,'native') % returns int8(127), because it accumulates in
% % int8 but overflows and saturates.
%
% See also PROD, CUMSUM, DIFF, ACCUMARRAY, ISFLOAT.
% Copyright 1984-2015 The MathWorks, Inc.
% Built-in function.
i.e. it can also be seen from the last line that this is a built-in as per definition. Note that everything contained in the first 'comment' is seen when help sum is typed; in the sense that an empty line breaks the help file. The copyright and built-in information do thus not show up when simply typing help sum on the command line, so for checking whether a function is a built-in you need edit <functionname>.

Related

Matlab "auto-squeeze" plot

Matlab plot requires the data to be of the same dimension. Meaning, you cannot plot a 1x10 vector with a 1x1x10 vector. This is sometimes necessary. For those purposes, you can use the squeeze function to get rid of the singleton dimensions.
However, this is kind of a hassle. For the plot function specifically, it would be useful to have the argument always squeezed. How would one go about creating a new function, lets call it splot which squeezes every input and passes it onto plot. Here is an attempt (that doesn't work)
function splot(varargin)
for i=1:length(varargin)
varargin{i}=squeeze(varargin{i});
end
plot(varargin)
end
plot(varargin) part fails, because that is simply not how matlab syntax works. But is there any way to achieve what I want? I guess I could write a long if elseif chain where I manually write the case with every possible number of input arguments like:
if length(varargin)==2
plot(varargin{1},varargin{2})
if length(varargin)==3
plot(varargin{1},varargin{2},varargin{3})
But this is going to be very annoying. Any better ideas.
This question is similar to Is there any mechanism to auto squeeze in Matlab / Octave , however, not similar enough, because the other question is for squeezing every vector, which is a bad idea. Here I am asking a way to squeeze only the inputs to the plot function and requiring syntax help.
From the docs there are several ways to call plot. Generally
Just numeric arrays, these can be on their own, or one or more pairs
plot(Y), plot(X,Y) or plot(X1,Y1,...,Xn,Yn)
Numeric arrays as before, with a char array giving the line spec
plot(X,Y,LineSpec) or plot(Y,LineSpec)
Either of the previous two, plus name-value pair options
plot(___,Name,Value)
In any of these cases, you want to squeeze the first N inputs which are numeric, since either of the optional additions have the first non-plottable input as a char.
We can achieve that with the following code, see the comments for details:
function h = splot( varargin )
% Check if there are any optional inputs, which will either be
% LineSpec (which is a char) or name-value pairs (which the
% first of will be a char)
bNumericArg = cellfun( #isnumeric, varargin );
% By default, assume all inputs are arrays to plot
lastArrayArg = numel(varargin);
if ~all(bNumericArg)
% In this case, there are some optional inputs, get last array index
lastArrayArg = find( ~bNumericArg, 1 ) - 1;
end
% Squeeze the arrays
for ii = 1:lastArrayArg
varargin{ii} = squeeze(varargin{ii});
end
% Plot with all inputs, optional output
if nargout > 0
h = plot( varargin{:} );
else
plot( varargin{:} );
end
end
There are two possible cases I've not handled here which the plot function can handle,
Having the first input as the target axes i.e. plot(ax,___), could be achieved by altering the loop slightly to start from 1 or 2 depending if the first input is an axes object
Having pairs of arrays each with their own line spec argument i.e. plot(X1,Y1,LineSpec1,...,Xn,Yn,LineSpecn). The later pairs will be ignored. This would be trickier to handle since you'd likely have to parse all inputs and check whether a char is just a line spec or if you're messing with name-value pairs. Maybe a heuristic to do with "two arrays then a char, repeated". I've never used this syntax so omitting the over-complication for now.

Separate definition of constant values and dependent parameters in Matlab

In my code, I have a lot of constant values and parameters that take significant space in my code.
For example in C++, I would make a header and a separate file where I would define these parameters as, e.g., "const-type" and share the header with main or other .cpp files.
How do you keep such structuring in MATLAB, is it worth it?
An example: Coefficients.m looks as follows:
classdef coefficients
properties(Constant)
% NIST data
A_N = 28.98641;
end
end
Another file: Gas.m where I would like to use A_N looks as follows:
function Gas()
clear all
clc
import Coefficients.* % Does not work
% A simple print
Values.A_N % Does not work
coefficients.A_N % Does not work
Constant.A_N % Does not work
end
Ok so assuming the class coefficients defined as:
classdef coefficients
properties(Constant)
% NIST data
A_N = 28.98641;
end
end
This code must be saved in a file named coefficients.m (The class name and file name have to match to avoid weird effect sometimes).
Then assuming the following Gas.m file:
function Gas()
% Usage with "disp()"
disp('The A_N coefficient taken from NIST data is:')
disp(coefficients.A_N)
% Usage with fprintf
fprintf('\nFrom NIST data, the coefficient A_N = %f\n',coefficients.A_N)
% Usage in calculation (just use it as if it was a variable/constant name
AN2 = coefficients.A_N^2 ;
fprintf('\nA_N coefficient squared = %.2f\n',AN2)
% If you want a shorter notation, you can copy the coefficient value in
% a variable with a shorter name, then use that variable later in code
A_N = coefficients.A_N ;
fprintf('\nA_N coefficient cubed = %.2f\n',A_N^3)
end
Then running this file (calling it from the command line) yields:
>> Gas
The A_N coefficient taken from NIST data is:
28.98641
From NIST data, the coefficient A_N = 28.986410
A_N coefficient squared = 840.21
A_N coefficient cubed = 24354.73
Or if you simply need to access the coefficient in the Matlab console:
>> coefficients.A_N
ans =
28.98641
Now all these examples assume that the class file coefficient.m is visible in the current Matlab scope. For Matlab, it means the file must be in the MATLAB search path (or the current folder works too).
For more info about what is the Matlab search path and how it works you can read:
What Is the MATLAB Search
Path?
Files and Folders that MATLAB
Accesses
In your case, I would make a folder containing all these sorts of classes, then add this folder to the Matlab path, so you never have to worry again about individual script, function or program calling for it.
See this link for tips on using a class definition for this. Highlights of the tips are:
Properties can be accessed directly to get the value
Properties can be added that give the units of the constant (highly advised!)
Comments can be added that act as help text for the constants
The doc command automatically creates a reference page for this classdef
E.g.,
classdef myconstants
properties(Constant)
% g is the gravity of Earth
% (https://en.wikipedia.org/wiki/Gravity_of_Earth)
g = 9.8;
g_units = 'm/s/s';
% c is the speed of light in a vacuum
c = 299792458;
c_units = 'm/s';
end
end
>> help myconstants.g

Arbitrary dimension for CVXGEN program

I would like to solve a QP/LP problem in MatLab using CVXGEN. I have preference for CVXGEN over CVX, since CVXGEN is much faster. In particular, I would like to solve
min f(x) s.t. x in X
where f(x) is in quadratic form and X is compact, convex, and defined by linear functions. The size of the problem varies depending on the run. I would like to automate the procedure as much as possible. To illustrate, an example of a CVXGEN code is:
dimensions
n = 10
end
parameters
Q (n,n) psd # quadratic penalty.
end
variables
x (n)
end
minimize
quad(x, Q)
end​​​​​​​​​​​​​
​This code is inputted at cvxgen.com. On this website, I can then generate the C code which gives me a unique number. I can then compile this into MEX code using the unique number. Last, I can call this MEX code (csolve) from MatLab by running the following code
n=10; % dimension of the problem
params.Q = eye(n,n); % assume that the Hessian is the identity
[vars, status] = csolve(params); % this outputs optimal x* = 0.
This procedure, however, requires for each dimension of the problem n that I want to run, I need to go to cvxgen.com, change n, compile code, then run my MatLab code. Is it possible to let n enter as a parameter? This way, I only need to compile the code once, then in my MatLab code set params.n = n and params.Q = eye(n,n), and then call [vars, status] = csolve(params);.
In short, I do not think it is possible to specify arbitrary dimension on cvxgen.com. I do have one solution: First, in MatLab, allow the user to specify his/her email address and password for cvxgen.com. Next, using system command in MatLab, call a python or javascript program that executes the following steps:
1) Log user into cvxgen.com
2) Goto edit tab on website
3) Copy and paste the cvxgen code
4) Goto generate c tab
5) Click generate code
6) Goto matlab tab
7) Copy unique identifying number for the compiled C code to be downloaded
8) Pass the unique identifying number back to MatLab and compile the Mex files in MatLab by calling the identifying number.
Now the Mex files can be called in MatLab by the following commands:
n=10; % dimension of the problem
params.Q = eye(n,n); % assume that the Hessian is the identity
[vars, status] = csolve(params); % this outputs optimal x* = 0.

MATLAB Cronbach's Alpha if item deleted

I was wondering if there were a way to run a complete Cronbach's Alpha analysis (like that available in 'Reliability Analysis' in SPSS), including an Alpha value if item is deleted.
I've created a Cronbach function from Mathworks File Exchange, giving me:
% Calculate the number of items
k=size(X,2);
% Calculate the variance of the items' sum
VarTotal=nanvar(nansum(X'));
% Calculate the item variance
SumVarX=nansum(nanvar(X));
% Calculate the Cronbach's alpha
a=k/(k-1)*(VarTotal-SumVarX)/VarTotal;
In a 1000x60 matrix, I'd like to know the Alpha when each item across dimension 2 is deleted.
Is there an in-built function for something like this? Is it possible to update this code (or write new code) to that effect?
OK, so it turns out it was simply a case of building the correct for loop.
as(60)=NaN; % preallocate output matrices
varargout(60)=NaN;
for ques = 1:size(twodm,2) % loop across items
cols = 1:size(twodm,2);
cols(ques)=[]; % ques only uses items that aren't `ques`
[as(ques), varargout(ques)] = CronbachAlpha(twodm(:,cols)); % perform the test
end
The function CronbachAlpha was taken from this file, which calculates both standardised and unstandardised Alphas and is better than the one used in the question.

What is your favourite MATLAB/Octave programming trick? [closed]

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I think everyone would agree that the MATLAB language is not pretty, or particularly consistent. But nevermind! We still have to use it to get things done.
What are your favourite tricks for making things easier? Let's have one per answer so people can vote them up if they agree. Also, try to illustrate your answer with an example.
Using the built-in profiler to see where the hot parts of my code are:
profile on
% some lines of code
profile off
profile viewer
or just using the built in tic and toc to get quick timings:
tic;
% some lines of code
toc;
Directly extracting the elements of a matrix that satisfy a particular condition, using logical arrays:
x = rand(1,50) .* 100;
xpart = x( x > 20 & x < 35);
Now xpart contains only those elements of x which lie in the specified range.
Provide quick access to other function documentation by adding a "SEE ALSO" line to the help comments. First, you must include the name of the function in all caps as the first comment line. Do your usual comment header stuff, then put SEE ALSO with a comma separated list of other related functions.
function y = transmog(x)
%TRANSMOG Transmogrifies a matrix X using reverse orthogonal eigenvectors
%
% Usage:
% y = transmog(x)
%
% SEE ALSO
% UNTRANSMOG, TRANSMOG2
When you type "help transmog" at the command line, you will see all the comments in this comment header, with hyperlinks to the comment headers for the other functions listed.
Turn a matrix into a vector using a single colon.
x = rand(4,4);
x(:)
Vectorizing loops. There are lots of ways to do this, and it is entertaining to look for loops in your code and see how they can be vectorized. The performance is astonishingly faster with vector operations!
Anonymous functions, for a few reasons:
to make a quick function for one-off uses, like 3x^2+2x+7. (see listing below) This is useful for functions like quad and fminbnd that take functions as arguments. It's also convenient in scripts (.m files that don't start with a function header) since unlike true functions you can't include subfunctions.
for closures -- although anonymous functions are a little limiting as there doesn't seem to be a way to have assignment within them to mutate state.
.
% quick functions
f = #(x) 3*x.^2 + 2*x + 7;
t = (0:0.001:1);
plot(t,f(t),t,f(2*t),t,f(3*t));
% closures (linfunc below is a function that returns a function,
% and the outer functions arguments are held for the lifetime
% of the returned function.
linfunc = #(m,b) #(x) m*x+b;
C2F = linfunc(9/5, 32);
F2C = linfunc(5/9, -32*5/9);
Matlab's bsxfun, arrayfun, cellfun, and structfun are quite interesting and often save a loop.
M = rand(1000, 1000);
v = rand(1000, 1);
c = bsxfun(#plus, M, v);
This code, for instance, adds column-vector v to each column of matrix M.
Though, in performance critical parts of your application you should benchmark these functions versus the trivial for-loop because often loops are still faster.
LaTeX mode for formulas in graphs: In one of the recent releases (R2006?) you add the additional arguments ,'Interpreter','latex' at the end of a function call and it will use LaTeX rendering. Here's an example:
t=(0:0.001:1);
plot(t,sin(2*pi*[t ; t+0.25]));
xlabel('t');
ylabel('$\hat{y}_k=sin 2\pi (t+{k \over 4})$','Interpreter','latex');
legend({'$\hat{y}_0$','$\hat{y}_1$'},'Interpreter','latex');
Not sure when they added it, but it works with R2006b in the text(), title(), xlabel(), ylabel(), zlabel(), and even legend() functions. Just make sure the syntax you are using is not ambiguous (so with legend() you need to specify the strings as a cell array).
Using xlim and ylim to draw vertical and horizontal lines. Examples:
Draw a horizontal line at y=10:
line(xlim, [10 10])
Draw vertical line at x=5:
line([5 5], ylim)
Here's a quick example:
I find the comma separated list syntax quite useful for building function calls:
% Build a list of args, like so:
args = {'a', 1, 'b', 2};
% Then expand this into arguments:
output = func(args{:})
Here's a bunch of nonobvious functions that are useful from time to time:
mfilename (returns the name of the currently running MATLAB script)
dbstack (gives you access to the names & line numbers of the matlab function stack)
keyboard (stops execution and yields control to the debugging prompt; this is why there's a K in the debug prompt K>>
dbstop error (automatically puts you in debug mode stopped at the line that triggers an error)
I like using function handles for lots of reasons. For one, they are the closest thing I've found in MATLAB to pointers, so you can create reference-like behavior for objects. There are a few neat (and simpler) things you can do with them, too. For example, replacing a switch statement:
switch number,
case 1,
outargs = fcn1(inargs);
case 2,
outargs = fcn2(inargs);
...
end
%
%can be turned into
%
fcnArray = {#fcn1, #fcn2, ...};
outargs = fcnArray{number}(inargs);
I just think little things like that are cool.
Using nargin to set default values for optional arguments and using nargout to set optional output arguments. Quick example
function hLine=myplot(x,y,plotColor,markerType)
% set defaults for optional paramters
if nargin<4, markerType='none'; end
if nargin<3, plotColor='k'; end
hL = plot(x,y,'linetype','-', ...
'color',plotColor, ...
'marker',markerType, ...
'markerFaceColor',plotColor,'markerEdgeColor',plotColor);
% return handle of plot object if required
if nargout>0, hLine = hL; end
Invoking Java code from Matlab
cellfun and arrayfun for automated for loops.
Oh, and reverse an array
v = 1:10;
v_reverse = v(length(v):-1:1);
conditional arguments in the left-hand side of an assignment:
t = (0:0.005:10)';
x = sin(2*pi*t);
x(x>0.5 & t<5) = 0.5;
% This limits all values of x to a maximum of 0.5, where t<5
plot(t,x);
Know your axis properties! There are all sorts of things you can set to tweak the default plotting properties to do what you want:
set(gca,'fontsize',8,'linestyleorder','-','linewidth',0.3,'xtick',1:2:9);
(as an example, sets the fontsize to 8pt, linestyles of all new lines to all be solid and their width 0.3pt, and the xtick points to be [1 3 5 7 9])
Line and figure properties are also useful, but I find myself using axis properties the most.
Be strict with specifying dimensions when using aggregation functions like min, max, mean, diff, sum, any, all,...
For instance the line:
reldiff = diff(a) ./ a(1:end-1)
might work well to compute relative differences of elements in a vector, however in case the vector degenerates to just one element the computation fails:
>> a=rand(1,7);
>> diff(a) ./ a(1:end-1)
ans =
-0.5822 -0.9935 224.2015 0.2708 -0.3328 0.0458
>> a=1;
>> diff(a) ./ a(1:end-1)
??? Error using ==> rdivide
Matrix dimensions must agree.
If you specify the correct dimensions to your functions, this line returns an empty 1-by-0 matrix, which is correct:
>> diff(a, [], 2) ./ a(1, 1:end-1)
ans =
Empty matrix: 1-by-0
>>
The same goes for a min-function which usually computes minimums over columns on a matrix, until the matrix only consists of one row. - Then it will return the minimum over the row unless the dimension parameter states otherwise, and probably break your application.
I can almost guarantee you that consequently setting the dimensions of these aggregation functions will save you quite some debugging work later on.
At least that would have been the case for me. :)
The colon operator for the manipulation of arrays.
#ScottieT812, mentions one: flattening an array, but there's all the other variants of selecting bits of an array:
x=rand(10,10);
flattened=x(:);
Acolumn=x(:,10);
Arow=x(10,:);
y=rand(100);
firstSix=y(1:6);
lastSix=y(end-5:end);
alternate=y(1:2:end);
In order to be able to quickly test a function, I use nargin like so:
function result = multiply(a, b)
if nargin == 0 %no inputs provided, run using defaults for a and b
clc;
disp('RUNNING IN TEST MODE')
a = 1;
b = 2;
end
result = a*b;
Later on, I add a unit test script to test the function for different input conditions.
Using ismember() to merge data organized by text identfiers. Useful when you are analyzing differing periods when entries, in my case company symbols, come and go.
%Merge B into A based on Text identifiers
UniverseA = {'A','B','C','D'};
UniverseB = {'A','C','D'};
DataA = [20 40 60 80];
DataB = [30 50 70];
MergeData = NaN(length(UniverseA),2);
MergeData(:,1) = DataA;
[tf, loc] = ismember(UniverseA, UniverseB);
MergeData(tf,2) = DataB(loc(tf));
MergeData =
20 30
40 NaN
60 50
80 70
Asking 'why' (useful for jarring me out of a Matlab runtime-fail debugging trance at 3am...)
Executing a Simulink model directly from a script (rather than interactively) using the sim command. You can do things like take parameters from a workspace variable, and repeatedly run sim in a loop to simulate something while varying the parameter to see how the behavior changes, and graph the results with whatever graphical commands you like. Much easier than trying to do this interactively, and it gives you much more flexibility than the Simulink "oscilloscope" blocks when visualizing the results. (although you can't use it to see what's going on in realtime while the simulation is running)
A really important thing to know is the DstWorkspace and SrcWorkspace options of the simset command. These control where the "To Workspace" and "From Workspace" blocks get and put their results. Dstworkspace defaults to the current workspace (e.g. if you call sim from inside a function the "To Workspace" blocks will show up as variables accessible from within that same function) but SrcWorkspace defaults to the base workspace and if you want to encapsulate your call to sim you'll want to set SrcWorkspace to current so there is a clean interface to providing/retrieving simulation input parameters and outputs. For example:
function Y=run_my_sim(t,input1,params)
% runs "my_sim.mdl"
% with a From Workspace block referencing I1 as an input signal
% and parameters referenced as fields of the "params" structure
% and output retrieved from a To Workspace block with name O1.
opt = simset('SrcWorkspace','current','DstWorkspace','current');
I1 = struct('time',t,'signals',struct('values',input1,'dimensions',1));
Y = struct;
Y.t = sim('my_sim',t,opt);
Y.output1 = O1.signals.values;
Contour plots with [c,h]=contour and clabel(c,h,'fontsize',fontsize). I usually use the fontsize parameter to reduce the font size so the numbers don't run into each other. This is great for viewing the value of 2-D functions without having to muck around with 3D graphs.
Vectorization:
function iNeedle = findClosest(hay,needle)
%FINDCLOSEST find the indicies of the closest elements in an array.
% Given two vectors [A,B], findClosest will find the indicies of the values
% in vector A closest to the values in vector B.
[hay iOrgHay] = sort(hay(:)'); %#ok must have row vector
% Use histogram to find indices of elements in hay closest to elements in
% needle. The bins are centered on values in hay, with the edges on the
% midpoint between elements.
[iNeedle iNeedle] = histc(needle,[-inf hay+[diff(hay)/2 inf]]); %#ok
% Reversing the sorting.
iNeedle = iOrgHay(iNeedle);
Using persistent (static) variables when running an online algorithm. It may speed up the code in areas like Bayesian machine learning where the model is trained iteratively for the new samples. For example, for computing the independent loglikelihoods, I compute the loglikelihood initially from scratch and update it by summing this previously computed loglikelihood and the additional loglikelihood.
Instead of giving a more specialized machine learning problem, let me give a general online averaging code which I took from here:
function av = runningAverage(x)
% The number of values entered so far - declared persistent.
persistent n;
% The sum of values entered so far - declared persistent.
persistent sumOfX;
if x == 'reset' % Initialise the persistent variables.
n = 0;
sumOfX = 0;
av = 0;
else % A data value has been added.
n = n + 1;
sumOfX = sumOfX + x;
av = sumOfX / n; % Update the running average.
end
Then, the calls will give the following results
runningAverage('reset')
ans = 0
>> runningAverage(5)
ans = 5
>> runningAverage(10)
ans = 7.5000
>> runningAverage(3)
ans = 6
>> runningAverage('reset')
ans = 0
>> runningAverage(8)
ans = 8
I'm surprised that while people mentioned the logical array approach of indexing an array, nobody mentioned the find command.
e.g. if x is an NxMxO array
x(x>20) works by generating an NxMxO logical array and using it to index x (which can be bad if you have large arrays and are looking for a small subset
x(find(x>20)) works by generating list (i.e. 1xwhatever) of indices of x that satisfy x>20, and indexing x by it. "find" should be used more than it is, in my experience.
More what I would call 'tricks'
you can grow/append to arrays and cell arrays if you don't know the size you'll need, by using end + 1 (works with higher dimensions too, so long as the dimensions of the slice match -- so you'll have to initialize x to something other than [] in that case). Not good for numerics but for small dynamic lists of things (or cell arrays), e.g. parsing files.
e.g.
>> x=[1,2,3]
x = 1 2 3
>> x(end+1)=4
x = 1 2 3 4
Another think many people don't know is that for works on any dim 1 array, so to continue the example
>> for n = x;disp(n);end
1
2
3
4
Which means if all you need is the members of x you don't need to index them.
This also works with cell arrays but it's a bit annoying because as it walks them the element is still wrapped in a cell:
>> for el = {1,2,3,4};disp(el);end
[1]
[2]
[3]
[4]
So to get at the elements you have to subscript them
>> for el = {1,2,3,4};disp(el{1});end
1
2
3
4
I can't remember if there is a nicer way around that.
-You can make a Matlab shortcut to an initialization file called startup.m. Here, I define formatting, precision of the output, and plot parameters for my Matlab session (for example, I use a larger plot axis/font size so that .fig's can be seen plainly when I put them in presentations.) See a good blog post from one of the developers about it http://blogs.mathworks.com/loren/2009/03/03/whats-in-your-startupm/ .
-You can load an entire numerical ascii file using the "load" function. This isn't particularly fast, but gets the job done quickly for prototyping (shouldn't that be the Matlab motto?)
-As mentioned, the colon operator and vectorization are lifesavers. Screw loops.
x=repmat([1:10],3,1); % say, x is an example array of data
l=x>=3; % l is a logical vector (1s/0s) to highlight those elements in the array that would meet a certain condition.
N=sum(sum(l));% N is the number of elements that meet that given condition.
cheers -- happy scripting!