 NAME
 INTRODUCTION
 Perl
 TERMINOLOGY: PIDDLE
 COMMAND WINDOW AND IDE
 A MODULE FOR SCILAB USERS
 BASIC FEATURES
 ADDITIONAL FEATURES
 COPYRIGHT
NAME
PDL::Scilab  A guide for Scilab users.
INTRODUCTION
If you are a Scilab user, this page is for you. It explains the key differences between Scilab 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 Scilab and PDL.
Perl
The key difference between Scilab 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 Scilab 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 online (http://perldoc.perl.org) or through the command perldoc perl
. There is also a beginner's portal (http://perlbegin.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
Scilab typically refers to vectors, matrices, and arrays. Perl already has arrays, and the terms "vector" and "matrix" typically refer to one and twodimensional 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 Ndimensional data set. Piddles provide efficient storage and fast computation of large Ndimensional matrices. They are highly optimized for numerical work.
For more information, see "Piddles vs Perl Arrays" later in this document.
COMMAND WINDOW AND IDE
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 Scilab, the interactive shell is the best way to learn the language. To exit the shell, type exit
, just like Scilab.
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 standalone PDL program (i.e. outside the perldl
or pdl2
shells) 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.
$y = pdl [2,3,4]; # Statements end in semicolon.
$A = pdl [ [1,2,3],[4,5,6] ]; # 2dimensional piddle.
print $A x $y>transpose;
Save this file as myprogram.pl
and run it with:
perl myprogram.pl
New: Flexible syntax
In very recent versions of PDL (version 2.4.7 or later) there is a flexible matrix syntax that can look extremely similar to Scilab:
1) Use a ';' to delimit rows:
$y = pdl q[ 2,3,4 ];
$A = pdl q[ 1,2,3 ; 4,5,6 ];
2) Use spaces to separate elements:
$y = 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.
A MODULE FOR SCILAB USERS
Here is a module that Scilab users will want to use:
 PDL::NiceSlice

Gives PDL a syntax for slices (submatrices) that is shorter and more familiar to Scilab users.
// Scilab b(1:5) > Selects the first 5 elements from b. # PDL without NiceSlice $y>slice("0:4") > Selects the first 5 elements from $y. # PDL with NiceSlice $y(0:4) > Selects the first 5 elements from $y.
BASIC FEATURES
This section explains how PDL's syntax differs from Scilab. Most Scilab users will want to start here.
General "gotchas"
 Indices

In PDL, indices start at '0' (like C and Java), not 1 (like Scilab). For example, if
$y
is an array with 5 elements, the elements would be numbered from 0 to 4.  Displaying an object

Scilab normally displays object contents automatically. In PDL you display objects explicitly with the
print
command or the shortcutp
:Scilab:
> a = 12 a = 12. > b = 23; // Suppress output. >
PerlDL:
pdl> $x = 12 # No output. pdl> print $x # Print object. 12 pdl> p $x # "p" is a shorthand for "print" in the shell. 12
Creating Piddles
 Variables in PDL

Variables always start with the '$' sign.
Scilab: value = 42 PerlDL: $value = 42
 Basic syntax

Use the "pdl" constructor to create a new piddle.
Scilab: v = [1,2,3,4] PerlDL: $v = pdl [1,2,3,4] Scilab: A = [ 1,2,3 ; 3,4,5 ] PerlDL: $A = pdl [ [1,2,3] , [3,4,5] ]
 Simple matrices

Scilab PDL   Matrix of ones ones(5,5) ones 5,5 Matrix of zeros zeros(5,5) zeros 5,5 Random matrix rand(5,5) 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 aszeros 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

Scilab: > 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 1dimensional piddle of 5 elements and give it equally spaced values from 2 to 10.
Scilab 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. The PDL shell and Scilab have a similar syntax for slicing, but there are two important differences:
1) PDL indices start at 0, as in C and Java. Scilab starts indices at 1.
2) In Scilab you think "rows and columns". In PDL, think "x and y".
Scilab 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 standalone PDL program you have to include the PDL::NiceSlice module. See the previous section "MODULES FOR SCILAB USERS" for more information.
use PDL; # Import main PDL module. use PDL::NiceSlice; # Nice syntax for slicing. $A = random 4,4; print $A(0,1);
Matrix Operations
 Matrix multiplication

Scilab: A * B PerlDL: $A x $B
 Elementwise multiplication

Scilab: A .* B PerlDL: $A * $B
 Transpose

Scilab: A' PerlDL: $A>transpose
Functions that aggregate data
Some functions (like sum
, max
and min
) aggregate data for an Ndimensional data set. Scilab and PDL both give you the option to apply these functions to the entire data set or to just one dimension.
 Scilab

In Scilab, these functions work along the entire data set by default, and an optional parameter "r" or "c" makes them act over rows or columns.
> A = [ 1,5,4 ; 4,2,1 ] A = 1. 5. 4. 4. 2. 1. > max(A) ans = 5 > max(A, "r") ans = 4. 5. 4. > max(A, "c") ans = 5. 4.
 PDL

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
Higher dimensional data sets
A related issue is how Scilab and PDL understand data sets of higher dimension. Scilab was designed for 1D vectors and 2D matrices with higher dimensional objects added on top. In contrast, PDL was designed for Ndimensional piddles from the start. This leads to a few surprises in Scilab that don't occur in PDL:
 Scilab sees a vector as a 2D matrix.

Scilab PerlDL   > vector = [1,2,3,4]; pdl> $vector = pdl [1,2,3,4] > size(vector) pdl> p $vector>dims ans = 1 4 4
Scilab 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 Scilab ignores the last dimension of a 4x1x1 matrix.

Scilab 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 Scilab treats a 4x1x1 matrix differently from a 1x1x4 matrix.

Scilab 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
 Scilab 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 2
 Feature support.

In Scilab, several features 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:
Scilab PerlDL   > A = ones(3,3,3); pdl> $A = ones(3,3,3); > A' pdl> transpose $A => ERROR => OK
Loop Structures
Perl has many loop structures, but we will only show the one that is most familiar to Scilab users:
Scilab PerlDL
 
for i = 1:10 for $i (1..10) {
disp(i) print $i
end }
 Note

Never use forloops for numerical work. Perl's forloops are faster than Scilab'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.
Piddles vs Perl Arrays
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 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 Scilab users:
Scilab 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":
Scilab: 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 whileloops and ifstatements:
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 Scilab's.
Scilab 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 $total;
}
4) You can assign values to several variables at once using the syntax:
($x, $y, $z) = (1, 2, 3);
So, in the previous examples:
# This declares two local variables and initializes them to 0.
my ($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
Data structures
To create complex data structures, Scilab uses "lists" and "structs". Perl's arrays and hashes offer similar functionality. 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 Scilab's lists. They are both a sequential data structure that can contain any data type.
Scilab  list( 1, 12, "hello", zeros(3,3) , list( 1, 2) ); PerlDL  @array = ( 1, 12, "hello" , zeros(3,3), [ 1, 2 ] )
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 Scilab's structure arrays:
Scilab  > drink = struct('type', 'coke', 'size', 'large', 'myarray', ones(3,3,3)) > 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 Scilab. Use this page to learn how to avoid forloops 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 Scilab.
 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 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.
Plotting
PDL has fullfeatured plotting abilities. Unlike Scilab, 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

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 crossplatform 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.
Xcos / Scicos
Xcos (formerly Scicos) is a graphical dynamical system modeler and simulator. It is part of the standard Scilab distribution. PDL and Perl do not have a direct equivalent to Scilab's Xcos. If this feature is important to you, you should probably keep a copy of Scilab around for that.
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/