MATLAB(1) User Contributed Perl Documentation MATLAB(1)NAMEPDL::MATLAB - A guide for MATLAB users.
INTRODUCTION
If you are a MATLAB user, this page is for you. It explains the key
differences between MATLAB and PDL to help you get going as quickly as
possible.
This document is not a tutorial. For that, go to PDL::QuickStart. This
document complements the Quick Start guide, as it highlights the key
differences between MATLAB and PDL.
Perl
The key difference between MATLAB and PDL is Perl.
Perl is a general purpose programming language with thousands of
modules freely available on the web. PDL is an extension of Perl. This
gives PDL programs access to more features than most numerical tools
can dream of. At the same time, most syntax differences between MATLAB
and PDL are a result of its Perl foundation.
You do not have to learn much Perl to be effective with PDL. But if you
wish to learn Perl, there is excellent documentation available on-line
(<http://perldoc.perl.org>) or through the command "perldoc perl".
There is also a beginner's portal (<http://perl-begin.org>).
Perl's module repository is called CPAN (<http://www.cpan.org>) and it
has a vast array of modules. Run "perldoc cpan" for more information.
TERMINOLOGY: PIDDLE
MATLAB typically refers to vectors, matrices, and arrays. Perl already
has arrays, and the terms "vector" and "matrix" typically refer to one-
and two-dimensional collections of data. Having no good term to
describe their object, PDL developers coined the term "piddle" to give
a name to their data type.
A piddle consists of a series of numbers organized as an N-dimensional
data set. Piddles provide efficient storage and fast computation of
large N-dimensional matrices. They are highly optimized for numerical
work.
For more information, see "Piddles vs Perl Arrays" later in this
document.
COMMAND WINDOW AND IDE
Unlike MATLAB, PDL does not come with a dedicated IDE. It does however
come with an interactive shell and you can use a Perl IDE to develop
PDL programs.
PDL interactive shell
To start the interactive shell, open a terminal and run "perldl" or
"pdl2". As in MATLAB, the interactive shell is the best way to learn
the language. To exit the shell, type "exit", just like MATLAB.
Writing PDL programs
One popular IDE for Perl is called Padre (<http://padre.perlide.org>).
It is cross platform and easy to use.
Whenever you write a stand-alone PDL program (i.e. outside the "perldl"
or "pdl2" shell) you must start the program with "use PDL;". This
command imports the PDL module into Perl. Here is a sample PDL program:
use PDL; # Import main PDL module.
use PDL::NiceSlice; # Import additional PDL module.
use PDL::AutoLoader; # Import additional PDL module.
$b = pdl [2,3,4]; # Statements end in semicolon.
$A = pdl [ [1,2,3],[4,5,6] ]; # 2-dimensional matrix.
print $A x $b->transpose;
Save this file as "myprogram.pl" and run it with:
perl myprogram.pl
New: Flexible syntax
In current versions of PDL (version 2.4.7 or later) there is a flexible
matrix syntax that can look extremely similar to MATLAB:
1) Use a ';' to delimit rows:
$b = pdl q[ 2,3,4 ];
$A = pdl q[ 1,2,3 ; 4,5,6 ];
2) Use spaces to separate elements:
$b = pdl q[ 2 3 4 ];
$A = pdl q[ 1 2 3 ; 4 5 6 ];
Basically, as long as you put a "q" in front of the opening bracket,
PDL should "do what you mean". So you can write in a syntax that is
more comfortable for you.
MODULES FOR MATLAB USERS
There are two modules that MATLAB users will want to use:
PDL::NiceSlice
Gives PDL a syntax for slices (sub-matrices) that is shorter and
more familiar to MATLAB users.
% MATLAB
b(1:5) --> Selects the first 5 elements from b.
# PDL without NiceSlice
$b->slice("0:4") --> Selects the first 5 elements from $b.
# PDL with NiceSlice
$b(0:4) --> Selects the first 5 elements from $b.
PDL::AutoLoader
Provides a MATLAB-style autoloader for PDL. If an unknown function
"foo()" is called, PDL looks for a file called "foo.pdl". If it
finds one, it reads it.
BASIC FEATURES
This section explains how PDL's syntax differs from MATLAB. Most MATLAB
users will want to start here.
General "gotchas"
Indices
In PDL, indices start at '0' (like C and Java), not 1 (like MATLAB
or FORTRAN). For example, if $b is an array with 5 elements, the
elements would be numbered from 0 to 4.
Displaying an object
MATLAB normally displays object contents automatically. In the PDL
shells you display objects explicitly with the "print" command or
the shortcut "p":
MATLAB:
>> a = 12
a = 12
>> b = 23; % Suppress output.
>>
PDL Shell (perldl or pdl2):
pdl> $a = 12 # No output.
pdl> print $a # Print object.
12
pdl> p $a # "p" is a shorthand for "print" in the shell.
12
pdl>
Creating Piddles
Variables in PDL
Variables always start with the '$' sign.
MATLAB: value = 42
PerlDL: $value = 42
Basic syntax
Use the "pdl" constructor to create a new piddle.
MATLAB: v = [1,2,3,4]
PerlDL: $v = pdl [1,2,3,4]
MATLAB: A = [ 1,2,3 ; 3,4,5 ]
PerlDL: $A = pdl [ [1,2,3] , [3,4,5] ]
Simple matrices
MATLAB PDL
------------
Matrix of ones ones(5) ones 5,5
Matrix of zeros zeros(5) zeros 5,5
Random matrix rand(5) random 5,5
Linear vector 1:5 sequence 5
Notice that in PDL the parenthesis in a function call are often
optional. It is important to keep an eye out for possible
ambiguities. For example:
pdl> p zeros 2, 2 + 2
Should this be interpreted as "zeros(2,2) + 2" or as "zeros 2,
(2+2)"? Both are valid statements:
pdl> p zeros(2,2) + 2
[
[2 2]
[2 2]
]
pdl> p zeros 2, (2+2)
[
[0 0]
[0 0]
[0 0]
[0 0]
]
Rather than trying to memorize Perl's order of precedence, it is
best to use parentheses to make your code unambiguous.
Linearly spaced sequences
MATLAB: >> linspace(2,10,5)
ans = 2 4 6 8 10
PerlDL: pdl> p zeroes(5)->xlinvals(2,10)
[2 4 6 8 10]
Explanation: Start with a 1-dimensional piddle of 5 elements and
give it equally spaced values from 2 to 10.
MATLAB has a single function call for this. On the other hand,
PDL's method is more flexible:
pdl> p zeros(5,5)->xlinvals(2,10)
[
[ 2 4 6 8 10]
[ 2 4 6 8 10]
[ 2 4 6 8 10]
[ 2 4 6 8 10]
[ 2 4 6 8 10]
]
pdl> p zeros(5,5)->ylinvals(2,10)
[
[ 2 2 2 2 2]
[ 4 4 4 4 4]
[ 6 6 6 6 6]
[ 8 8 8 8 8]
[10 10 10 10 10]
]
pdl> p zeros(3,3,3)->zlinvals(2,6)
[
[
[2 2 2]
[2 2 2]
[2 2 2]
]
[
[4 4 4]
[4 4 4]
[4 4 4]
]
[
[6 6 6]
[6 6 6]
[6 6 6]
]
]
Slicing and indices
Extracting a subset from a collection of data is known as slicing.
PDL and MATLAB have a similar syntax for slicing, but there are
two important differences:
1) PDL indices start at 0, as in C and Java. MATLAB starts indices
at 1.
2) In MATLAB you think "rows and columns". In PDL, think "x and
y".
MATLAB PerlDL
------------
>> A pdl> p $A
A = [
1 2 3 [1 2 3]
4 5 6 [4 5 6]
7 8 9 [7 8 9]
]
-------------------------------------------------------
(row = 2, col = 1) (x = 0, y = 1)
>> A(2,1) pdl> p $A(0,1)
ans = [
4 [4]
]
-------------------------------------------------------
(row = 2 to 3, col = 1 to 2) (x = 0 to 1, y = 1 to 2)
>> A(2:3,1:2) pdl> p $A(0:1,1:2)
ans = [
4 5 [4 5]
7 8 [7 8]
]
Warning
When you write a stand-alone PDL program you have to include
the PDL::NiceSlice module. See the previous section "MODULES
FOR MATLAB USERS" for more information.
use PDL; # Import main PDL module.
use PDL::NiceSlice; # Nice syntax for slicing.
use PDL::AutoLoader; # MATLAB-like autoloader.
$A = random 4,4;
print $A(0,1);
Matrix Operations
Matrix multiplication
MATLAB: A * B
PerlDL: $A x $B
Element-wise multiplication
MATLAB: A .* B
PerlDL: $A * $B
Transpose
MATLAB: A'
PerlDL: $A->transpose
Functions that aggregate data
Some functions (like "sum", "max" and "min") aggregate data for an
N-dimensional data set. This is a place where MATLAB and PDL take a
different approach:
In MATLAB, these functions all work along one dimension.
>> A = [ 1,5,4 ; 4,2,1 ]
A = 1 5 4
4 2 1
>> max(A)
ans = 4 5 4
>> max(A')
ans = 5 4
If you want the maximum for the entire data set, you can use
the special A(:) notation which basically turns the entire
data set into a single 1-dimensional vector.
>> max(A(:))
ans = 5
>> A = ones(2,2,2,2)
>> max(A(:))
ans = 1
PDL offers two functions for each feature.
sum vs sumover
avg vs average
max vs maximum
min vs minimum
The long name works over a dimension, while the short name
works over the entire piddle.
pdl> p $A = pdl [ [1,5,4] , [4,2,1] ]
[
[1 5 4]
[4 2 1]
]
pdl> p $A->maximum
[5 4]
pdl> p $A->transpose->maximum
[4 5 4]
pdl> p $A->max
5
pdl> p ones(2,2,2)->max
1
pdl> p ones(2,2,2,2)->max
1
Note Notice that PDL aggregates horizontally while MATLAB aggregates
vertically. In other words:
MATLAB PerlDL
max(A) == $A->transpose->maximum
max(A') == $A->maximum
TIP: In MATLAB you think "rows and columns". In PDL, think "x and
y".
Higher dimensional data sets
A related issue is how MATLAB and PDL understand data sets of higher
dimension. MATLAB was designed for 1D vectors and 2D matrices. Higher
dimensional objects ("N-D arrays") were added on top. In contrast, PDL
was designed for N-dimensional piddles from the start. This leads to a
few surprises in MATLAB that don't occur in PDL:
MATLAB sees a vector as a 2D matrix.
MATLAB PerlDL
------------
>> vector = [1,2,3,4]; pdl> $vector = pdl [1,2,3,4]
>> size(vector) pdl> p $vector->dims
ans = 1 4 4
MATLAB sees "[1,2,3,4]" as a 2D matrix (1x4 matrix). PDL sees it
as a 1D vector: A single dimension of size 4.
But MATLAB ignores the last dimension of a 4x1x1 matrix.
MATLAB PerlDL
------------
>> A = ones(4,1,1); pdl> $A = ones 4,1,1
>> size(A) pdl> p $A->dims
ans = 4 1 4 1 1
And MATLAB treats a 4x1x1 matrix differently from a 1x1x4 matrix.
MATLAB PerlDL
------------
>> A = ones(1,1,4); pdl> $A = ones 1,1,4
>> size(A) pdl> p $A->dims
ans = 1 1 4 1 1 4
MATLAB has no direct syntax for N-D arrays.
pdl> $A = pdl [ [[1,2,3],[4,5,6]], [[2,3,4],[5,6,7]] ]
pdl> p $A->dims
3 2 2
Feature support.
In MATLAB, several features such as sparse matrix support are not
available for N-D arrays. In PDL, just about any feature supported
by 1D and 2D piddles, is equally supported by N-dimensional
piddles. There is usually no distinction.
Loop Structures
Perl has many loop structures, but we will only show the one that is
most familiar to MATLAB users:
MATLAB PerlDL
------------
for i = 1:10 for $i (1..10) {
disp(i) print $i
endfor }
Note Never use for-loops for numerical work. Perl's for-loops are
faster than MATLAB's, but they both pale against a "vectorized"
operation. PDL has many tools that facilitate writing vectorized
programs. These are beyond the scope of this guide. To learn
more, see: PDL::Indexing, PDL::Threading, and PDL::PP.
Likewise, never use 1..10 for numerical work, even outside a for-
loop. 1..10 is a Perl array. Perl arrays are designed for
flexibility, not speed. Use piddles instead. To learn more, see
the next section.
Piddles vs Perl Arrays
It is important to note the difference between a Piddle and a Perl
array. Perl has a general-purpose array object that can hold any type
of element:
@perl_array = 1..10;
@perl_array = ( 12, "Hello" );
@perl_array = ( 1, 2, 3, \@another_perl_array, sequence(5) );
Perl arrays allow you to create powerful data structures (see Data
structures below), but they are not designed for numerical work. For
that, use piddles:
$pdl = pdl [ 1, 2, 3, 4 ];
$pdl = sequence 10_000_000;
$pdl = ones 600, 600;
For example:
$points = pdl 1..10_000_000 # 4.7 seconds
$points = sequence 10_000_000 # milliseconds
TIP: You can use underscores in numbers ("10_000_000" reads better than
10000000).
Conditionals
Perl has many conditionals, but we will only show the one that is most
familiar to MATLAB users:
MATLAB PerlDL
------------
if value > MAX if ($value > $MAX) {
disp("Too large") print "Too large\n";
elseif value < MIN } elsif ($value < $MIN) {
disp("Too small") print "Too small\n";
else } else {
disp("Perfect!") print "Perfect!\n";
end }
Note Here is a "gotcha":
MATLAB: elseif
PerlDL: elsif
If your conditional gives a syntax error, check that you wrote
your "elsif"'s correctly.
TIMTOWDI (There Is More Than One Way To Do It)
One of the most interesting differences between PDL and other tools is
the expressiveness of the Perl language. TIMTOWDI, or "There Is More
Than One Way To Do It", is Perl's motto.
Perl was written by a linguist, and one of its defining properties is
that statements can be formulated in different ways to give the
language a more natural feel. For example, you are unlikely to say to a
friend:
"While I am not finished, I will keep working."
Human language is more flexible than that. Instead, you are more likely
to say:
"I will keep working until I am finished."
Owing to its linguistic roots, Perl is the only programming language
with this sort of flexibility. For example, Perl has traditional while-
loops and if-statements:
while ( ! finished() ) {
keep_working();
}
if ( ! wife_angry() ) {
kiss_wife();
}
But it also offers the alternative until and unless statements:
until ( finished() ) {
keep_working();
}
unless ( wife_angry() ) {
kiss_wife();
}
And Perl allows you to write loops and conditionals in "postfix" form:
keep_working() until finished();
kiss_wife() unless wife_angry();
In this way, Perl often allows you to write more natural, easy to
understand code than is possible in more restrictive programming
languages.
Functions
PDL's syntax for declaring functions differs significantly from
MATLAB's.
MATLAB PerlDL
------------
function retval = foo(x,y) sub foo {
retval = x.**2 + x.*y my ($x, $y) = @_;
endfunction return $x**2 + $x*$y;
}
Don't be intimidated by all the new syntax. Here is a quick run through
a function declaration in PDL:
1) "sub" stands for "subroutine".
2) "my" declares variables to be local to the function.
3) "@_" is a special Perl array that holds all the function parameters.
This might seem like a strange way to do functions, but it allows you
to make functions that take a variable number of parameters. For
example, the following function takes any number of parameters and adds
them together:
sub mysum {
my ($i, $total) = (0, 0);
for $i (@_) {
$total += $i;
}
return $total;
}
4) You can assign values to several variables at once using the syntax:
($a, $b, $c) = (1, 2, 3);
So, in the previous examples:
# This declares two local variables and initializes them to 0.
my ($i, $total) = (0, 0);
# This takes the first two elements of @_ and puts them in $x and $y.
my ($x, $y) = @_;
5) The "return" statement gives the return value of the function, if
any.
ADDITIONAL FEATURES
ASCII File IO
To read data files containing whitespace separated columns of numbers
(as would be read using the MATLAB load command) one uses the PDL rcols
in PDL::IO::Misc. For a general review of the IO functionality
available in PDL, see the documentation for PDL::IO, e.g., "help
PDL::IO" in the pdl2 shell or " pdldoc PDL::IO " from the shell command
line.
Data structures
To create complex data structures, MATLAB uses "cell arrays" and
"structure arrays". Perl's arrays and hashes offer similar
functionality but are more powerful and flexible. This section is only
a quick overview of what Perl has to offer. To learn more about this,
please go to <http://perldoc.perl.org/perldata.html> or run the command
"perldoc perldata".
Arrays
Perl arrays are similar to MATLAB's cell arrays, but more
flexible. For example, in MATLAB, a cell array is still
fundamentally a matrix. It is made of rows, and rows must have the
same length.
MATLAB
------
array = {1, 12, 'hello'; rand(3, 2), ones(3), 'junk'}
=> OK
array = {1, 12, 'hello'; rand(3, 2), ones(3) }
=> ERROR
A Perl array is a general purpose, sequential data structure. It
can contain any data type.
PerlDL
------
@array = ( [1, 12, 'hello'] , [ random(3,2), ones(3,3), 'junk' ] )
=> OK
@array = ( [1, 12, 'hello'] , [ random(3,2), ones(3,3) ] )
=> OK
@array = ( 5 , {'name' => 'Mike'} , [1, 12, 'hello'] )
=> OK
Notice that Perl array's start with the "@" prefix instead of the
"$" used by piddles.
To learn about Perl arrays, please go to
<http://perldoc.perl.org/perldata.html> or run the command
"perldoc perldata".
Hashes
Perl hashes are similar to MATLAB's structure arrays:
MATLAB
------
>> drink = struct('type', 'coke', 'size', 'large', 'myarray', {1,2,3})
>> drink.type = 'sprite'
>> drink.price = 12 % Add new field to structure array.
PerlDL
------
pdl> %drink = ( type => 'coke' , size => 'large', mypiddle => ones(3,3,3) )
pdl> $drink{type} = 'sprite'
pdl> $drink{price} = 12 # Add new field to hash.
Notice that Perl hashes start with the "%" prefix instead of the
"@" for arrays and "$" used by piddles.
To learn about Perl hashes, please go to
<http://perldoc.perl.org/perldata.html> or run the command
"perldoc perldata".
Performance
PDL has powerful performance features, some of which are not normally
available in numerical computation tools. The following pages will
guide you through these features:
PDL::Indexing
Level: Beginner
This beginner tutorial covers the standard "vectorization" feature
that you already know from MATLAB. Use this page to learn how to
avoid for-loops to make your program more efficient.
PDL::Threading
Level: Intermediate
PDL's "vectorization" feature goes beyond what most numerical
software can do. In this tutorial you'll learn how to "thread"
over higher dimensions, allowing you to vectorize your program
further than is possible in MATLAB.
Benchmarks
Level: Intermediate
Perl comes with an easy to use benchmarks module to help you find
how long it takes to execute different parts of your code. It is a
great tool to help you focus your optimization efforts. You can
read about it online (<http://perldoc.perl.org/Benchmark.html>) or
through the command "perldoc Benchmark".
PDL::PP
Level: Advanced
PDL's Pre-Processor is one of PDL's most powerful features. You
write a function definition in special markup and the pre-
processor generates real C code which can be compiled. With PDL:PP
you get the full speed of native C code without having to deal
with the full complexity of the C language.
Plotting
PDL has full-featured plotting abilities. Unlike MATLAB, PDL relies
more on third-party libraries (pgplot and PLplot) for its 2D plotting
features. Its 3D plotting and graphics uses OpenGL for performance and
portability. PDL has three main plotting modules:
PDL::Graphics::PGPLOT
Best for: Plotting 2D functions and data sets.
This is an interface to the venerable PGPLOT library. PGPLOT has
been widely used in the academic and scientific communities for
many years. In part because of its age, PGPLOT has some
limitations compared to newer packages such as PLplot (e.g. no RGB
graphics). But it has many features that still make it popular in
the scientific community.
PDL::Graphics::PLplot
Best for: Plotting 2D functions as well as 2D and 3D data sets.
This is an interface to the PLplot plotting library. PLplot is a
modern, open source library for making scientific plots. It
supports plots of both 2D and 3D data sets. PLplot is best
supported for unix/linux/macosx platforms. It has an active
developers community and support for win32 platforms is improving.
PDL::Graphics::TriD
Best for: Plotting 3D functions.
The native PDL 3D graphics library using OpenGL as a backend for
3D plots and data visualization. With OpenGL, it is easy to
manipulate the resulting 3D objects with the mouse in real time.
Writing GUIs
Through Perl, PDL has access to all the major toolkits for creating a
cross platform graphical user interface. One popular option is wxPerl
(<http://wxperl.sourceforge.net>). These are the Perl bindings for
wxWidgets, a powerful GUI toolkit for writing cross-platform
applications.
wxWidgets is designed to make your application look and feel like a
native application in every platform. For example, the Perl IDE Padre
is written with wxPerl.
Simulink
Simulink is a graphical dynamical system modeler and simulator. It can
be purchased separately as an add-on to MATLAB. PDL and Perl do not
have a direct equivalent to MATLAB's Simulink. If this feature is
important to you, then take a look at Scilab:
<http://www.scilab.org>
Scilab is another numerical analysis software. Like PDL, it is free and
open source. It doesn't have PDL's unique features, but it is very
similar to MATLAB. Scilab comes with Xcos (previously Scicos), a
graphical system modeler and simulator similar to Simulink.
COPYRIGHT
Copyright 2010 Daniel Carrera (dcarrera@gmail.com). You can distribute
and/or modify this document under the same terms as the current Perl
license.
See: http://dev.perl.org/licenses/
Acknowledgements
I'd like to thank David Mertens, Chris Marshall and Sigrid Carrera
for their immense help reviewing earlier drafts of this guide.
Without their hours of work, this document would not be remotely
as useful to MATLAB users as it is today.
perl v5.18.1 2013-05-12 MATLAB(1)
