

PDL::NiceSlice 
Gives PDL a syntax for slices (submatrices) 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 MATLABstyle 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. 
This section explains how PDL’s syntax differs from MATLAB. Most MATLAB users will want to start here.
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> $a = 12 # No output. pdl> print $a # Print object. 12 pdl> p $a # "p" is a shorthand for "print" in the shell. 12 pdl>
Variables in PDL Variables always start with the ’$’ sign.
MATLAB: value = 42 PerlDL: $value = 42Basic 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 5Notice 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 + 2Should 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]<B>ExplanationB>: Start with a 1dimensional 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] ]
<B>WarningB> When you write a standalone PDL program you have to include the PDL::NiceSlice module. See the previous section "<B>MODULES FOR MATLAB USERSB>" for more information.
use PDL; # Import main PDL module. use PDL::NiceSlice; # Nice syntax for slicing. use PDL::AutoLoader; # MATLABlike autoloader. $A = random 4,4; print $A(0,1);
Matrix multiplication MATLAB: A * B PerlDL: $A x $BElementwise multiplication MATLAB: A .* B PerlDL: $A * $BTranspose MATLAB: A PerlDL: $A>transpose
Some functions (like sum, max and min) aggregate data for an Ndimensional 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 4If 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 1dimensional vector.
>> max(A(:)) ans = 5 >> A = ones(2,2,2,2) >> max(A(:)) ans = 1PDL offers two functions for each feature. sum vs sumover avg vs average max vs maximum min vs minimumThe <B>long nameB> works over a dimension, while the <B>short nameB> 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
<B>NoteB> Notice that PDL aggregates horizontally while MATLAB aggregates vertically. In other words:
MATLAB PerlDL max(A) == $A>transpose>maximum max(A) == $A>maximum<B>TIPB>: In MATLAB you think rows and columns. In PDL, think x and y.
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 (ND arrays) were added on top. In contrast, PDL was designed for Ndimensional 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 4MATLAB 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 1And 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 4MATLAB has no direct syntax for ND arrays. pdl> $A = pdl [ [[1,2,3],[4,5,6]], [[2,3,4],[5,6,7]] ] pdl> p $A>dims 3 2 2Feature support. In MATLAB, several features such as sparse matrix support are not available for ND arrays. In PDL, just about any feature supported by 1D and 2D piddles, is equally supported by Ndimensional piddles. There is usually no distinction.
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 }
<B>NoteB> Never use forloops for numerical work. Perl’s forloops 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 forloop. 1..10 is a Perl array. Perl arrays are designed for flexibility, not speed. Use piddles instead. To learn more, see the next section.
It is important to note the difference between a Piddle and a Perl array. Perl has a generalpurpose 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 <B>Data structuresB> below), <B>but they are not designed for numerical workB>. 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 $points = sequence 10_000_000 # milliseconds<B>TIPB>: You can use underscores in numbers (10_000_000 reads better than 10000000).
Perl has many conditionals, but we will only show the one that is most familiar to MATLAB users:
MATLAB PerlDL   if value > MAX if ($value > $MAX) { disp("Too large") print "Too large\n"; elseif value < MIN } elsif ($value < $MIN) { disp("Too small") print "Too small\n"; else } else { disp("Perfect!") print "Perfect!\n"; end }
<B>NoteB> Here is a gotcha:
MATLAB: elseif PerlDL: elsifIf your conditional gives a syntax error, check that you wrote your elsif’s correctly.
One of the most interesting differences between PDL and other tools is the expressiveness of the Perl language. TIMTOWDI, or There Is More Than One Way To Do It, is Perl’s motto.Perl was written by a linguist, and one of its defining properties is that statements can be formulated in different ways to give the language a more natural feel. For example, you are unlikely to say to a friend:
"While I am not finished, I will keep working."Human language is more flexible than that. Instead, you are more likely to say:
"I will keep working until I am finished."Owing to its linguistic roots, Perl is the only programming language with this sort of flexibility. For example, Perl has traditional whileloops and ifstatements:
while ( ! finished() ) { keep_working(); } if ( ! wife_angry() ) { kiss_wife(); }But it also offers the alternative <B>untilB> and <B>unlessB> statements:
until ( finished() ) { keep_working(); } unless ( wife_angry() ) { kiss_wife(); }And Perl allows you to write loops and conditionals in postfix form:
keep_working() until finished(); kiss_wife() unless wife_angry();In this way, Perl often allows you to write more natural, easy to understand code than is possible in more restrictive programming languages.
PDL’s syntax for declaring functions differs significantly from MATLAB’s.
MATLAB PerlDL   function retval = foo(x,y) sub foo { retval = x.**2 + x.*y my ($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) "<B>subB> stands for subroutine".
2) "<B>myB>" declares variables to be local to the function.
3) "<B>B>@_<B>B>" 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 "<B>returnB>" statement gives the return value of the function, if any.
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.
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) } => ERRORA 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] ) => OKNotice 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.
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 <B>LevelB>: Beginner This beginner tutorial covers the standard vectorization feature that you already know from MATLAB. Use this page to learn how to avoid forloops to make your program more efficient.
PDL::Threading <B>LevelB>: 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 <B>LevelB>: 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 <B>LevelB>: Advanced PDL’s PreProcessor is one of PDL’s most powerful features. You write a function definition in special markup and the preprocessor 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.
PDL has fullfeatured plotting abilities. Unlike MATLAB, PDL relies more on thirdparty 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 <B>Best forB>: 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 <B>Best forB>: 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 <B>Best forB>: 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.
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 crossplatform applications.wxWidgets is designed to make your application look and feel like a native application in every platform. For example, the Perl IDE <B>PadreB> is written with wxPerl.
Simulink is a graphical dynamical system modeler and simulator. It can be purchased separately as an addon 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 <B>ScilabB>: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 <B>XcosB> (previously Scicos), a graphical system modeler and simulator similar to Simulink.
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/
<B>AcknowledgementsB> 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.
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