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Converted from Latex to POD by Karl Glazebrook 5/Jan/2012, grep for 'XXX' for conversion comments.

=head1 First Steps with PDL

I<"Maybe there are a few civilizations out there that have decided to stay
home, piddle around and send out some radio waves once in a while.">

I<- Annette Foglino, Space: Is Anyone Out There? Most astronomers say yes, Life, 1 Jul 1989.>

It can be very frustrating to read an introductory book which takes a
long time teaching you the very basics of a topic, in a "Janet and John"
style.  While you wish to learn, you are anxious to see something a bit
more exciting and interesting to see what the language can do.

Fortunately our task in this book on PDL is made very much easier by the
high-level of the language. We can take a tour through PDL, looking at
the advanced features it offers without getting involved in complexity.

The aim of this section is to cover a breadth of PDL features rather
than any in depth, to give the reader a flavour of what he or she can do
using the language and a useful reference for getting started doing real
work. Later sections will focus on looking at the features introduced
here, in more depth.

=head2 Alright, let's I<do> something

We'll assume PDL is correctly installed and set up on your computer
system (see L<http://pdl.perl.org/> for details of obtaining and installing PDL).

For interactive use PDL comes with a program called C<pdl>. This allows
you to type raw PDL (and perl) commands and see the result right away. It
also allows command line recall and editing (via the arrow keys) on most
systems. So we begin by running the C<pdl> program from the system
command line. On a Mac/UNIX/Linux system we would simply type C<pdl>
in a terminal window. On a Windows system we would type C<pdl>
in a command prompt window. If PDL is installed correctly this is
all that is required to bring up C<pdl>.

   myhost% pdl
  perlDL shell v1.354_001
  PDL comes with ABSOLUTELY NO WARRANTY. For details, see the file
  'COPYING' in the PDL distribution. This is free software and you
  are welcome to redistribute it under certain conditions, see
  the same file for details.
  ReadLines, NiceSlice, MultiLines  enabled
  Reading /Users/xxx/.perldlrc...
  Found docs database /usr/lib/perl5/5.12/.../PDL/pdldoc.db
  Type 'help' for online help
  Type 'demo' for online demos
  Loaded PDL v2.4.10 (supports bad values)

We get a whole bunch of informational messages about what it is loading for
startup and the help system. Note; the startup is I<completely> configurable,
an advanced user can completely customize which PDL modules
are loaded. We are left with the 
C<pdl>> prompt at which we can type commands. This kind
of interactive program is called a 'shell'.  There is also C<pdl2>
which is a newer version of the PDL shell with additional features.
It is still under development but completely useable.

Let's create something, and display it:

  pdl> use PDL::Graphics::PGPLOT
  pdl> imag (sin(rvals(200,200)+1))
  Displaying 200 x 200 image from -1 to 0.999999940395355 ... 

The result should look like the image below - a two dimensional C<sin> function.
C<rvals> is a handy PDL function for creating an image whose pixel values are
the radial distance from the central pixel of the image.  With these arguments
it creates a 200 by 200 'radial' image.  (Try 'C<imag(rvals(200,200))>' and you
will see better what we mean!) C<sin()> is the mathematical sine function, this
already exists in perl but in the case of PDL is applied to all 40000 pixels at
once, a topic we will come back to. The C<imag()> function displays the image.
You will see the syntax of perl/PDL is algebraic - by which we mean it is very
similar to C and FORTRAN in how expressions are constructed.  (In fact much
more like C than FORTRAN). It is interesting to reflect on how much C code
would be required to generate the same display, even given the existence of
some convenient graphics library.

=for html <img WIDTH=400 src="firststeps/whirl-sync.png">

Figure of a two dimensional C<sin>  function.

That's all fine. But what if we wanted to achieve the same results in a standalone
perl script? Well it is pretty simple:

  use PDL;
  use PDL::Graphics::PGPLOT;
  imag (sin(rvals(200,200)+1));

That's it. This is a complete perl/PDL program. One could run it by typing 
C<perl filename>. (In fact there are many ways of running it, most systems
allows it to be setup so you can just type I<filename>. See your local
Perl documentation - then the C<perlrun> manual page.)

Two comments:


=item 1.

The statements are all terminated by the 'C<;>' character. Perl is like C
in this regard. When entering code at the C<pdl> command line the final
'C<;>' may be omitted if you wish, note you can also use it to put multiple
statements on one line. In our examples from now on we'll often omit the 
C<pdl> prompt for clarity.

=item 2.

The directive C<use PDL;> tells Perl to load the PDL module, which makes
available all the standard PDL extensions. (Advanced users 
will be interested in knowing
there are other ways of starting PDL which allows one to select which bits
of it you want).


=head2 Whirling through the Whirlpool

Enough about the mechanics of using PDL, let's look at some real data! To work
through these examples exactly you should be able to find them in the directory
C<PDL/Book/> on your system.

We'll be playing with an image of the famous spiral galaxy discovered by
Charles Messier, known to astronomrs as M51 and commonly as the Whirlpool
Galaxy. This is a 'nearby' galaxy, a mere 25 million light years from Earth.
The image file is stored in the 'FITS' format, a common astronomical format,
which is one of the many formats standard PDL can read. (FITS stores more
shades of grey than GIF or JPEG, but PDL can read these formats too).

  pdl> $a = rfits("PDL/Book/m51_raw.fits");
  Reading IMAGE data...
  BITPIX =  -32  size = 262144 pixels 
  Reading  1048576  bytes
  BSCALE =  &&  BZERO = 

This looks pretty simple. As you can probably guess by now C<rfits> is the PDL
function to read a FITS file. This is stored in the perl variable C<$a>.

B<This is an important PDL concept: PDL stores its data arrays in simple perl
variables> (C<$a, $x, $y, $MyData>, etc.).  PDL data arrays are special arrays
which use a more efficient, compact storage than standard perl arrays (C<@a,
@x, ...>) and are much faster to access for numerical computations. To avoid
confusion it is convenient to introduce a special name for them, we call them
I<piddles> (short for 'PDL variables') to distinguish them from ordinary Perl
'arrays', which are in fact really lists.  We'll say more about this later.

Before we start seriously playing around with M51 it is worth noting that we
can also say:

  pdl> $a = rfits "m51_raw.fits";

Note we have now left off the brackets on the C<rfits> function. Perl is rather
simpler than C and allows one to omit the brackets on a function all together.
It assumes all the items in a list are function arguments and can be pretty
convenient. If you are calling more than one function it is however better to
use some brackets so the meaning is clear. For the rules on this 'list
operator' syntax see the Perl syntax documentation.  From now on we'll mostly
use the list operator syntax for conciseness

Let's look at M51:

  pdl> imag $a;

=for html <img WIDTH=600 src="firststeps/whirl-m51.png">

Figure of the raw image C<m51_raw.fits> shown with progressively greater
contrast using the C<imag> command.

A couple of bright spots can be seen, but where is the galaxy? It's the faint
blob in the middle: by default the display range is autoscaled linearly from
the faintest to the brightest pixel, and only the bright star slightly to the
bottom right of the center can be seen without contrast enhancement. We can
easily change that by specifying the black/white data values (Note: C<#> starts
a Perl comment and can be ignored - i.e. no need to type the stuff after it!):

  pdl> imag $a,0,1000; # More contrast
  pdl> imag $a,0,300;  # Even more contrast

You can see that C<imag> takes additional arguments to specify the display
range. In fact C<imag> takes quite a few arguments, many of them optional. By
typing 'C<help imag>' at the C<pdl> prompt we can find out all about the

It is certainly a spiral galaxy with a few foreground stars thrown in for good
measure. But what is that horrible stripey pattern running from bottom right to
top left? That certainly is not part of the galaxy? Well no. What we have here
is the uneven senistivity of the detector used to record the image, a common
artifact in digital imaging. We can correct for this using an image of a
uniformly illuminated screen, what is commonly known as a 'flatfield'.

  pdl> $flat = rfits "m51_flatfield.fits";
  pdl> imag $flat;

This is shown in the next figure. Because the image is of a uniform field,
the actual image reflects the detector sensitivity. To correct our M51
image, we merely have to divide the image by the flatfield:

=for html <img WIDTH=400 src="firststeps/whirl-flat.png">

Figure: The 'flatfield' image showing the detector sensitivity of the raw data.

  pdl> $gal = $a / $flat;
  pdl> imag $gal,0,300;  
  pdl> wfits $gal, 'fixed_gal.fits'; # Save our work as a FITS file

Well that's a lot better.  But think what we have
just done. Both C<$a>  and C<$flat> are I<images>, with 512 pixels by
512 pixels. B<The divide operator 'C</>' has been applied over all
262144 data values in the piddles C<$a>  and C<$flat>.> And it was
pretty fast too - these are what are known as I<vectorised>
operations. In PDL each of these is implemented by heavily optimised
C code, which is what makes PDL very efficient for procession of
large chunks of data. If you did the same operation using normal
perl arrays rather than piddles it would be about ten to twenty times slower
(and use ten times more memory). In fact we can
do whatever arithmetic operations we like on image piddles:

=for html <img WIDTH=400 src="firststeps/whirl-flattened.png">

Figure: The M51 image corrected for the flatfield.

  pdl> $funny = log(($gal/300)**2 - $gal/100  + 4); 
  pdl> imag $funny; # Surprise!

Or on 1-D line piddles. On on 3-D cubic piddles. In fact piddles can support an infinite
number of dimensions (though your computers memory won't). 

B<This the key to PDL: the ability to process large chunks of data at once.>

=head2 Measuring the brightness of M51

How might we extract some useful
scientific information out of this image? A simple
quanitity an astronomer might want to know is how the brightness of the
the 'disk' of the galaxy (the outer region which contains the spiral
arms) compares with the 'bulge' (the compact inner nucleus). Well
let's find out the total sum of all the light in the image:

  pdl> print sum($gal);

C<sum> just sums up all the data values in all the pixels in the 
image - in this case the answer is 17916010. If the image is linear
(which it is) and if it was calibrated (i.e. we knew the relation
between data numbers and brightness units) we could work out the
total brightness. Let's turn it round - we know that M51 has
a luminosity of about 1E36 Watts, so we can work out what
one data value corresponds to in physical units:

  pdl> p 10**36/sum($gal)

This is also about 200 solar luminosities, (Note we have switched to using C<p>
as a shorthand for C<print> - which only works in the C<pdl> and C<pdl2> shells)
which gives 4 billion solar luminosities for the whole galaxy.

OK we do not need PDL for this simple arithmetic, let's get back to
computations that involve the whole image.
How can we get the sum of a piece of an image, e.g. near the centre? 
Well in PDL there is more than one way to do it (Perl aficionados call
this phenomenon TIMTOWTDI). In this case, because we really want
the brightness in a circular aperture, we'll use the C<rvals>

  pdl> $r = rvals $gal;
  pdl> imag $r;

Remember C<rvals>? It replaces all the pixels in an image with its distance
from the centre. We can turn this into a I<mask> with a simple
operation like:

  pdl> $mask = $r<50;
  pdl> imag $mask;

=for html <img WIDTH=400 src="firststeps/whirl-mask.png">

Figure: Using  C<rvals> to generate a mask image to isolate the galaxy bulge 
and disk. Top row: radial gradient image C<$r>, and radial gradient
masked with  less than operator C<$r < 50>. Bottom row: Bulge and disk of
the galaxy.

The Perl I<less than operator> is applied to all pixels in the image.
You can see the result is an image which is 0 on the outskirts and 1 in
the area of the nucleus. We can then simply use the mask image to
isolate in a simple way the bulge and disk components (lower row) and it
is then very easy to find the brightness of both pieces of the M51

 pdl> $bulge = $mask * $gal
 pdl> imag $bulge,0,300
 Displaying 512 x 512 image from 0 to 300 ...
 pdl> print sum $bulge;
 pdl> $disk = $gal * (1-$mask)
 pdl> imag $disk,0,300
 Displaying 512 x 512 image from 0 to 300 ...
 pdl> print sum $disk

You can see that the disk is about 5 times brighter than the bulge in
total, despite its more diffuse appearance. This is typical for
spiral galaxies. We might ask a different question: how does the average
I<surface brightness>, the brightness per unit area on the sky,
compare between bulge and disk? This is again quite straight forward:

  pdl> print sum($bulge)/sum($mask);
  pdl> print sum($disk)/sum(1-$mask);

We work out the area by simply summing up the 0,1 pixels in the mask
image. The answer is the bulge has about 7 times the surface
brightness than the disk - something we might have guessed from
looking at the above figure, which tells astronomers its stellar density is
much higher.

Of course PDL being so powerful, we could have figured this out in one line:

  pdl> print avg($gal->where(rvals($gal)<50)) / avg($gal->where(rvals($gal)>=50))

=head2 Twinkle, twinkle, little star

Let's look at something else, we'll zoom in on a small piece of the image:

  pdl> $section = $gal(337:357,178:198);
  pdl> imag $section; # the bright star

Here we are introducing something new - we can see that PDL supports
I<extensions> to the Perl syntax. We can say C<$var(a:b,c:d...)> to specify
I<multidimensional slices>. In this case we have produced a sub-image ranging
from pixel 337 to 357 along the first dimension, and 178 through 198 along the
second. Remember pdl data dimension indexes start from zero.  We'll talk some
more about I<slicing and dicing> later on. This sub-image happens to contain
a bright star.

At this point you will probably be able to work out for yourself the amount of
light coming from this star, compared to the whole galaxy. (Answer: about 2%)
But let's look at something more involved: the radial profile of the star.
Since stars are a long way away they are almost point sources, but our camera
will blur them out into little disks, and for our analysis we might want an
exact figure for this blurring.

We want to plot all the brightness of all the pixels in this section, against
the distance from the centre. (We've chosen the section to be conveniently
centred on the star, you could think if you want about how you might determine
the centroid automatically using the C<xvals> and C<yvals> functions). Well it
is simple enough to get the distance from the centre:

  pdl> $r = rvals $section;

But to produce a one-dimensional plot of one against the other we need to
reduce the 2D data arrays to one dimension. (i.e our 21 by 21 image section
becomes a 441 element vector). This can be done using the PDL C<clump>
function, which 'clumps' together an arbitrary number of dimensions:

  pdl> $rr  = $r->clump(2); # Clump first two dimensions
  pdl> $sec = $section->clump(2);
  pdl> points $rr, $sec;  # Radial plot

You should see a nice graph with points like those
in the figure below
showing the drop-off
from the bright centre of the star. The blurring is usually measured
by the 'Full Width Half Maximum' (FWHM) - or in plain terms how
fat the profile is across when it drops by half. Looking at Figure 1.6
it looks like this is about 2-3 pixels - pretty compact!

=for html <img WIDTH=400 src="firststeps/whirl-starradial.png">

Figure: Radial light profile of the bright star with fitted curve.

Well we don't just want a guess - let's fit the profile with a function.
These blurring functions are usually represented by the C<Gaussian>
function. PDL comes with a whole variety of general purpose and
special purpose fitting functions which people have written for
their own purposes (and so will you we hope!). Fitting Gaussians
is something that happens rather a lot and there is surpisingly
enough a special function for this very purpose. (One could use
more general fitting packages like C<PDL::Fit::LM> or
C<PDL::Opt::Simplex> but that would require more care).

  pdl> use PDL::Fit::Gaussian;

This loads in the module to do this. PDL, like Perl, is modular. We
don't load all the available modules by default just a convenient
subset. How can we find useful PDL functions and modules? Well
C<help> tells us more about what we already know, to find out
about what we don't know use C<apropos>:

  pdl> apropos gaussian
  PDL::Fit::Gaussian ...
  				Module: routines for fitting gaussians
  PDL::Gaussian   Module: Gaussian distributions.
  fitgauss1d      Fit 1D Gassian to data piddle
  fitgauss1dr     Fit 1D Gassian to radial data piddle
  gefa            Factor a matrix using Gaussian elimination.
  grandom         Constructor which returns piddle of Gaussian random numbers
  ndtri           The value for which the area under the Gaussian probability density function (integrated from minus
  				infinity) is equal to the argument (cf erfi). Works inplace.

This tells us a whole lot about various functions and modules to do with
Gaussians. Note that we can abbreviate C<help> and C<apropos>
with 'C<?>' and 'C<??>' when using the C<pdl> or C<pdl2> shells.

Let's fit a Gaussian:

  pdl> use PDL::Fit::Gaussian;
  pdl> ($peak, $fwhm, $background) = fitgauss1dr($rr, $sec); 
  pdl> p $peak, $fwhm, $background;

C<fitgauss1dr> is a function in the module L<PDL::Fit::Gaussian> which fits
a Gaussian constrained to be radial (i.e. whose peak is at the origin).
You can see that, unlike C and FORTRAN, Perl functions can return
more than one result value. This is pretty convenient. You can see the
FWHM is more like 2.75 pixels. Let's generate a fitted curve with this
functional form.

  pdl> $rrr = sequence(2000)/100;  # Generate radial values 0,0.01,0,02..20
  # Generate gaussian with given FWHM

  pdl> $fit = $peak * exp(-2.772 * ($rrr/$fwhm)**2) + $background;

Note the use of a new function, C<sequence(N)>, which 
generates a new piddle with N values ranging 0..(N-1).
We are simply using this to generate the horizontal axis values
for the plot.  Now let's overlay it on the previous plot.

  pdl> hold; # This command stops new plots starting new pages
  pdl> line $rrr, $fit, {Colour=>2} ; # Line plot

The last C<line> command shows the PDL syntax for optional function
arguments. This is based on the Perl's built in hash syntax. We'll say
more about this later in L<PDL::Book::PGPLOT>.  The result should look a
lot like the figure above.  Not too bad. We could perhaps do a bit
better by exactly centroiding the image but it will do for now.

Let's make a I<simulation> of the 2D stellar image. This is equally

  pdl> $fit2d = $peak * exp(-2.772 * ($r/$fwhm)**2);
  pdl> release; # Back to new page for new plots;
  pdl> imag $fit2d;
  pdl> wfits $fit2d, 'fake_star.fits'; # Save our work

But the figure below is a
boring. So far we have been using simple 2D graphics from the
C<PDL::Graphics::PGPLOT> library. In fact PDL has more
than one graphics library (some see this as a flaw, some
as a feature!). Using the C<PDL::Graphics::TriD> library
which does OpenGL graphics we can look at our simulated
star in 3D (see the right hand panel);

=for html <img WIDTH=600 src="firststeps/whirl-starsim.png">

Figure: Two different views of the 2D simulated Point Spread Function.

   pdl> use PDL::Graphics::TriD; # Load the 3D graphics module
   pdl> imag3d [$fit2d];

If you do this on your computer you should be able to look at the graphic from
different sides by simply dragging in the plot window with the mouse! You can
also zoom in and out with the right mouse button.  Note that C<imag3d> has it's
a rather different syntax for processing it's arguments - for very good reasons
- we'll explore 3D graphics further in L<PDL::Book::TriD>.

Finally here's something interesting. Let's take our fake star and place it
elsewhere on the galaxy image.

  pdl> $newsection = $gal(50:70,70:90);
  pdl> $newsection +=  $fit2d;
  pdl> imag $gal,0,300;

We have a bright new star where none existed before! The C-style C<+=>
increment operator is worth noting - it actually modifies the contents of
C<$newsection> in-place. And because C<$newsection> is a I<slice> of C<$gal>
the change also affects C<$gal>. This is an important property of slices - any
change to the slice affects the I<parent>. This kind of parent/child
relationship is a powerful property of many PDL functions, not just slicing.
What's more in many cases it leads to memory efficiency, when this kind of
linear slice is stored we only store the start/stop/step and not a new copy of
the actual data.

Of course sometimes we DO want a new copy of the actual data, for example if we
plan to do something evil to it. To do this we could use the alternative form:

  pdl> $newsection = $newsection +  $fit2d

Now a new version of C<$newsection>  is created which has nothing to 
do with the original C<$gal>. In fact there is more than one way to do
this as we will see in later chapters.

Just to amuse ourselves, lets write a short script to cover M51 with dozens of fake
stars of random brightnesses:

   use PDL;
   use PDL::Graphics::PGPLOT;
   use PDL::NiceSlice;  # must use in each program file

   srand(42); # Set the random number seed
   $gal  = rfits "fixed_gal.fits";
   $star = rfits "fake_star.fits";
   sub addstar {
      ($x,$y) = @_;
      $xx = $x+20; $yy = $y+20;
      # Note use of slice on the LHS!
      $gal($x:$xx,$y:$yy) += $star * rand(2);

   for (1..100) {
      $x1 = int(rand(470)+10);
      $y1 = int(rand(470)+10);
   imag $gal,0,1000;

This ought to give the casual reader some flavour of the Perl syntax - quite simple
and quite like C except that the entities being manipulated here are entire
arrays of data, not single numbers. The result is shown, for amusement,
in the figure below and takes virtually no time to compute.

=for html <img WIDTH=400 src="firststeps/whirl-fakestars.png">

Figure: M51 covered in fake stars.

=head2 Getting Complex with M51

To conclude this frantic whirl through the possibilities of PDL, let's look at
a moderately complex (sic) example. We'll take M51 and try to enhance it to reveal the
large-scale structure, and then subtract this to reveal small-scale structure.

Just to show off we'll use a method based on the Fourier transform - don't
worry if you don't know much about these, all you need to know is that the
Fourier transform turns the image into an 'inverse' image, with
complex numbers, where each pixel
represents the strength of wavelengths of different scales in the image. 
Let's do it:

  pdl> use PDL::FFT; # Load Fast Fourier Transform package
  pdl> $gal = rfits "fixed_gal.fits";

Now C<$gal> contains real values, to do the Fourier transform it has to
have complex values. We create a variable C<$imag> to hold the imaginary
component and set to zero.(For reasons of efficiency complex numbers
are represented in PDL by seperate real and imaginary arrays - more about this
in Chapter 2.)

  pdl> $imag = $gal * 0;       # Create imaginary component, equal to zero
  pdl> fftnd $gal, $imag;      # Perform Fourier transform

C<fftnd> performs a Fast Fourier Transform, in-place, on arbitrary-dimensioned data (i.e.
it is 'N-dimensional').  You can display C<$gal>  after the FFT but you won't see
much. If at this point we ran C<ifftnd> to invert it we would get the original
C<$gal>  back.

If we want to enhance the large-scale structure we want to make a filter to only
let through low-frequencies:

  pdl> $tmp = rvals($gal)<10;        # Radially-symmetric filter function
  pdl> use PDL::ImageND;             # provides kernctr()
  pdl> $filter = kernctr $tmp, $tmp; # Shift origin to 0,0
  pdl> imag $filter;

=for html <img WIDTH=400 src="firststeps/gal-filter.png">

You can see from the image that C<$filter> is zero everywhere except near the origin
(0,0) (and the 3 reflected corners). As a result it only lets through
low-frequency wavelengths. So we multiply by the filter and  FFT back to
see the result:

  pdl> ($gal2, $imag2) = cmul $gal, $imag, $filter, 0; # Complex multiplication
  pdl> ifftnd $gal2, $imag2;
  pdl> imag $gal2,0,300;

=for html <img WIDTH=400 src="firststeps/whirl-fft.png">

Figure: Fourier filtered smoothed image and contrast enhanced image with 
the smoothed image subtracted.

Well that looks quite a bit different!  Just about all the
high-frequency information has vanished. To see the high-frequency
information we can just subtract our filtered image from the original to
form the right hand image.

  pdl> $orig = rfits "fixed_gal.fits";
  pdl> imag $orig-$gal2,0,100;

=head2 Roundoff

Well that is probably enough abuse of Messier 51. We have demonstrated the ease
of simple and complex data processing with PDL and how PDL fits neatly in to
the Perl syntax as well as extending it.  You have come across basic
arithmetical operations and a scattering of useful functions - and learned how
to find more.  You certainly ought now to have a good feel of what PDL is all
about.  In the next chapter we'll take a more comprehensive look at the basic
parts of PDL that all keen PDL users should know.

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