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SCILAB(1)	      User Contributed Perl Documentation	     SCILAB(1)

       PDL::Scilab - A guide for Scilab	users.

       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

       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.

       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
       (<>) or through the command "perldoc perl".
       There is	also a beginner's portal (<>).

       Perl's module repository	is called CPAN (<>) and it
       has a vast array	of modules. Run	"perldoc cpan" for more	information.

       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 "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

       For more	information, see "Piddles vs Perl Arrays" later	in this

       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	(<>).
       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.

	 $b = pdl [2,3,4];		# Statements end in semicolon.
	 $A = pdl [ [1,2,3],[4,5,6] ];	# 2-dimensional	piddle.

	 print $A x $b->transpose;

       Save this file as ""	and run	it with:


   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:

	 $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.

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

	    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
	      $b->slice("0:4")	-->  Selects the first 5 elements from $b.

	      #	PDL with NiceSlice
	      $b(0:4)		-->  Selects the first 5 elements from $b.

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

   General "gotchas"
	    In PDL, indices start at '0' (like C and Java), not	1 (like
	    Scilab).  For example, if $b 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":


	     --> a = 12
	     a =  12.
	     --> b = 23;       // Suppress output.


	     pdl> $a = 12    # No output.
	     pdl> print	$a   # Print object.
	     pdl> p $a	     # "p" is a	shorthand for "print" in the shell.

   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 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 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

	      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]

		 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

		  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.

       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

   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 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 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 piddles,	is
	    equally supported by N-dimensional piddles.	There is usually no

	      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::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

       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

	"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() )	{

	 if ( !	wife_angry() ) {

       But it also offers the alternative until	and unless statements:

	 until ( finished() ) {

	 unless	( wife_angry() ) {

       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

       PDL's syntax for	declaring functions differs significantly from

	 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:

	 ($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

   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 <> or run the
       command "perldoc	perldata".

	    Perl arrays	are similar to Scilab's	lists. They are	both a
	    sequential data structure that can contain any data	type.

	      list( 1, 12, "hello", zeros(3,3) , list( 1, 2) );

	      @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
	    _ or run the command
	    "perldoc perldata".

	    Perl hashes	are similar to Scilab's	structure arrays:

	      --> drink	= struct('type', 'coke', 'size', 'large', 'myarray', ones(3,3,3))
	      --> drink.type = 'sprite'
	      --> drink.price =	12	    // Add new field to	structure array.

	      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
	    _ 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:

	    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.

	    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.

	    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 (<>) or
	    through the	command	"perldoc Benchmark".

	    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.

       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:

	    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.

	    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.

	    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
       (<>). These	are the	Perl bindings for
       wxWidgets, a powerful GUI toolkit for writing cross-platform

       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

       Copyright 2010 Daniel Carrera ( You can distribute
       and/or modify this document under the same terms	as the current Perl


perl v5.32.1			  2018-05-05			     SCILAB(1)


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