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**PROGRAM:**

**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:** **PIDDLE**

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 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 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 "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:

$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.

**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

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

# PDL with NiceSlice

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

**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

$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":

Scilab:

--> a = 12

a = 12.

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

-->

PerlDL:

pdl> $a = 12 # No output.

pdl> print $a # Print object.

12

pdl> p $a # "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 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 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

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 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 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::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 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:

($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 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 for-loops 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 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::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 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**

Copyright 2010 Daniel Carrera ([email protected]). You can distribute and/or modify this

document under the same terms as the current Perl license.

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

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