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 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: NDARRAY

Scilab 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 "ndarray" to give a name to their data type.

An ndarray consists of a series of numbers organized as an N-dimensional data set. ndarrays provide efficient storage and fast computation of large N-dimensional matrices. They are highly optimized for numerical work.

For more information, see "ndarrays 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 stand-alone 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] ];  # 2-dimensional ndarray.

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 (sub-matrices) 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 shortcut p:

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 ndarrays

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 ndarray.

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 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

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 1-dimensional ndarray 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 stand-alone 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

Element-wise 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 N-dimensional 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 ndarray.

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 N-dimensional ndarrays 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 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 Scilab, several features are not available for N-D arrays. In PDL, just about any feature supported by 1D and 2D ndarrays, is equally supported by N-dimensional ndarrays. 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 for-loops for numerical work. Perl's for-loops 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::Broadcasting, 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 ndarrays instead. To learn more, see the next section.

ndarrays vs Perl Arrays

It is important to note the difference between an ndarray 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 ndarrays:

$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 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 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 ndarrays.

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', myndarray => 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 ndarrays.

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 for-loops to make your program more efficient.

PDL::Broadcasting

Level: Intermediate

PDL's "vectorization" feature goes beyond what most numerical software can do. In this tutorial you'll learn how to "broadcast" 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 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 Scilab, 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::Simple

Best for: Plotting 2D functions and data sets.

Provides a simple library for line graphics and image display, giving a uniform plotting interface to PGPLOT, PLplot, Gnuplot, and Prima.

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.

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

See: http://dev.perl.org/licenses/

To install PDL, copy and paste the appropriate command in to your terminal.

cpanm

cpanm PDL

CPAN shell

perl -MCPAN -e shell
install PDL

For more information on module installation, please visit the detailed CPAN module installation guide.

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