=head1 NAME

PDL::Scilab - A guide for Scilab users.

=head1 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.

B<This document is not a tutorial>. For that, go to L<PDL::QuickStart|
PDL::QuickStart>. This document B<complements> the Quick Start guide, as
it highlights the key differences between Scilab and PDL.

=head1 Perl

The key difference between Scilab and PDL is B<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.

B<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 (L<http://perldoc.perl.org>) or through the command C<perldoc perl>.
There is also a beginner's portal (L<http://perl-begin.org>).

Perl's module repository is called CPAN (L<http://www.cpan.org>) and it
has a vast array of modules. Run C<perldoc cpan> for more information.

=head1 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 "I<ndarray>" to give a name to
their data type.

An I<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 "B<ndarrays vs Perl Arrays>" later in this document.

=head1 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.

=head2 PDL interactive shell

To start the interactive shell, open a terminal and run C<perldl> or C<pdl2>.
As in Scilab, the interactive shell is the best way to learn the
language. To exit the shell, type C<exit>, just like Scilab.

=head2 Writing PDL programs

One popular IDE for Perl is called Padre (L<http://padre.perlide.org>).
It is cross platform and easy to use.

Whenever you write a stand-alone PDL program (i.e. outside the
C<perldl> or C<pdl2> shells) you must start the program with C<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 C<myprogram.pl> and run it with:

  perl myprogram.pl

=head2 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 C<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.


=head1 A MODULE FOR SCILAB USERS

Here is a module that Scilab users will want to use:

=over 5

=item L<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.

=back



=head1 BASIC FEATURES

This section explains how PDL's syntax differs from Scilab. Most
Scilab users will want to start here.


=head2 General "gotchas"

=over 5

=item Indices

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

=item Displaying an object

Scilab normally displays object contents automatically. In PDL you display
objects explicitly with the C<print> command or the shortcut C<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

=back



=head2 Creating ndarrays

=over 5

=item Variables in PDL

Variables always start with the '$' sign.

 Scilab:    value  = 42
 PerlDL:    $value = 42

=item Basic syntax

Use the "pdl" constructor to create a new I<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] ]

=item 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 C<zeros(2,2) + 2> or as C<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.

=item 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]

B<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]
   ]
  ]

=item Slicing and indices

Extracting a subset from a collection of data is known as I<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]
                                 ]

=over 5

=item B<Warning>

When you write a stand-alone PDL program you have
to include the L<PDL::NiceSlice> module. See the
previous section "B<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);

=back

=back



=head2 Matrix Operations

=over 10

=item Matrix multiplication

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

=item Element-wise multiplication

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

=item Transpose

 Scilab:    A'
 PerlDL:    $A->transpose

=back


=head2 Functions that aggregate data

Some functions (like C<sum>, C<max> and C<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.


=over 10

=item 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.

=item PDL

PDL offers two functions for each feature.

  sum   vs   sumover
  avg   vs   average
  max   vs   maximum
  min   vs   minimum

The B<long name> works over a dimension, while the B<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

=back


=head2 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:

=over 5

=item 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 C<[1,2,3,4]> as a 2D matrix (1x4 matrix). PDL sees it
as a 1D vector: A single dimension of size 4.

=item 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

=item 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

=item 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

=item 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

=back


=head2 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                 }

=over 5

=item B<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:
L<PDL::Indexing>, L<PDL::Broadcasting>,
and L<PDL::PP>.

Likewise, never use C<1..10> for numerical work, even outside a for-loop.
C<1..10> is a Perl array. Perl arrays are designed for flexibility, not
speed. Use I<ndarrays> instead. To learn more, see the next section.

=back


=head2 ndarrays vs Perl Arrays

It is important to note the difference between an I<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
B<Data structures> below), B<but they are not designed for numerical work>.
For that, use I<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

B<TIP>: You can use underscores in numbers (C<10_000_000> reads better
than C<10000000>).


=head2 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                             }

=over 5

=item B<Note>

Here is a "gotcha":

  Scilab:  elseif
  PerlDL:  elsif

If your conditional gives a syntax error, check that you wrote
your C<elsif>'s correctly.

=back


=head2 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 B<until> and B<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.


=head2 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) "B<sub>" stands for "subroutine".

2) "B<my>" declares variables to be local to the function.

3) "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:

  ($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 "B<return>" statement gives the return value of the function, if any.


=head1 ADDITIONAL FEATURES

=head2 Data structures

To create complex data structures, Scilab uses "I<lists>" and "I<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
L<http://perldoc.perl.org/perldata.html> or run the command C<perldoc perldata>.

=over 5

=item 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.

I<To learn about Perl arrays, please go to L<http://perldoc.perl.org/perldata.html>
or run the command C<perldoc perldata>.>

=item 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.

I<To learn about Perl hashes, please go to L<http://perldoc.perl.org/perldata.html>
or run the command C<perldoc perldata>.>

=back



=head2 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:

=over 5

=item L<PDL::Indexing>

B<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.

=item L<PDL::Broadcasting>

B<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.

=item Benchmarks

B<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 (L<http://perldoc.perl.org/Benchmark.html>) or through the
command C<perldoc Benchmark>.

=item L<PDL::PP>

B<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.

=back



=head2 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:

=over 5

=item L<PDL::Graphics::Simple>

B<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.

=item L<PDL::Graphics::PLplot>

B<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.

=item L<PDL::Graphics::TriD>

B<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.

=back


=head2 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 (L<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 B<Padre> is written with wxPerl.

=head2 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.


=head1 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/

