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<title>Color</title>
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vlink="#551a8b">
<h3><img alt="" src="../sun.gif" align="middle" width="100"
height="98"> How it Works</h3>
<blockquote>
<p><b>Table of Contents</b></p>
<a href="#How">How DS9 Renders an Image</a><br>
<a href="#Scales">Scales</a><br>
<a href="#Smoothing">Smoothing</a><br>
<a href="#Contours">Contours</a><br>
<a href="#LargeFiles">Large Files</a><br>
<p><b><a name="How"></a>How DS9 renders an image</b></p>
<p>Here is a short description on how DS9 decides to paint a pixel
a color on the the screen, give an data value... you need a
color scale, a contrast/bias pair for the colorscale, clip
values for the data, a scale distribution, and finally, the
value of the pixel in question.</p>
<blockquote>
<p>Step 1. Select a color scale. A color scale is defined as a
number of colors (RGB triplets). The number of RGB triplets
can vary from just a few to over 200. DS9 contains a number of
predefined color scales (Gray, A, B, I8, ...) or the user may
load his own color scale.</p>
<p>Step 2. Apply a contrast/bias pair. This step takes the
result of step 1 and creates a new array with the
contrast/bias applied. The length of the new array will
between 200 (for pseudocolor) and 4096 (for truecolor).</p>
<p>Step 3. Calculate the data clip values (low/high data
values). The min/max data values may be used or an algorithm
may be used to determine the clip data values.</p>
<p>Step 4. Apply the scale distribution. This involves taking
the result of step 2, and creating yet another array, this
time of size 16384, redistributing the colors, based on the
scale algorithm selected (see <a href="Scales">Scales</a>).</p>
<p>Step 5. Based on your data clip values, and the value of the
pixel you have, index into the result of step 4, and you have
an index into lookup table (for pseudocolor) and an RGB pair
(for truecolor and postscript).</p>
</blockquote>
<p><b><a name="Scales"></a>Scales</b></p>
<p>The <tt>log</tt> function is defined as the following:</p>
<blockquote>
<p><b><img src="img/log.png" alt="log equation" width="78"
height="32"></b></p>
</blockquote>
<p>as <i>x</i> goes from 0 to 1. The user may specify an exponent
<i>a</i> to change the distribution of colors within the
colorbar. The default value of <i>a</i> is 1000. Typically,
optical images respond well at 1000, IR images as low as 100,
and high energy bin tables up to 10000. A value of 10000 closely
matches the <b><tt>log</tt></b> function of SAOImage as defined
as the following:</p>
<blockquote>
<p><b><img src="img/saolog.png" alt="SAOImage log equation"
width="65" height="34"></b></p>
</blockquote>
<p>The <tt>pow</tt> function is defined as the following:</p>
<blockquote>
<p><b><img src="img/pow.png" alt="pow equation" width="51"
height="30"></b></p>
</blockquote>
<p>as <i>x</i> goes from 0 to 1. The user may specify an exponent
<i>a</i> to change the distribution of colors within the
colorbar. The default value of <i>a</i> is 1000.</p>
<p>The <tt>sqrt</tt> scale function is defined as the following:</p>
<blockquote><img src="img/sqrt.png" alt="sqrt equation" width="42"
height="21"><br>
</blockquote>
<p>as <i>x</i> goes from 0 to 1. </p>
<p>The <tt>square</tt> scale function is defined as the
following:</p>
<blockquote><img src="img/square.png" alt="square equation"
width="35" height="21"><br>
</blockquote>
<p>as <i>x</i> goes from 0 to 1.<br>
</p>
<p>The <tt>asinh</tt> scale function is defined as the following:</p>
<blockquote><img alt="asinh" src="img/asinh.png" width="80"
height="29"><br>
</blockquote>
<p>as <i>x</i> goes from 0 to 1. </p>
<p>The <tt>sinh</tt> scale function is defined as the following:</p>
<blockquote><img alt="sinh" src="img/sinh.png" width="69"
height="29"><br>
</blockquote>
<p>as <i>x</i> goes from 0 to 1. </p>
<p>The <tt>histogram equalization</tt> scale function distributes
colors based on the frequency of each data value.</p>
<p><b><a name="Smoothing"></a>Smoothing</b></p>
<p>The user may select one of three types of smoothing kernels.
The parameter, <i>r</i> or <tt>kernel radius</tt>, is defined
as the following:</p>
<blockquote> Boxcar function, where the width = 2<i>r</i>+1<br>
Tophat function, where the radius = <i>r</i> and the diameter
of kernel is 2<i>r</i>+1<br>
Gaussian function, defined as:
<blockquote><img src="img/gauss.png" alt="Gaussian Equation"
width="173" height="38"><br>
</blockquote>
where the mean = 0 and sigma =<i> r</i>/2, and the diameter of
kernel is 2<i>r</i>+1 </blockquote>
<p><b><a name="Contours"></a>Contours</b></p>
<p>The contour algorithm is from an unknown author and originally
came from FV. The difference between the two modes are:<tt><br>
</tt></p>
<blockquote><tt>block</tt> : the image is blocked down before the
contour is generated <br>
<tt>smooth</tt> : the image is smoothed via a Gaussian kernel
before the contour is generated. </blockquote>
<p><tt>block</tt> mode is faster as the smoothing parameter
increases. Inversely, <tt>smooth</tt> mode is much slower as
the smoothing parameter increases.</p>
<p><b><a name="LargeFiles"></a>Large Files</b></p>
There are several factors that determine if DS9 will be able to
load a large file.<br>
<p>32 bit OS vs 64 bit OS : to address very large files, you may
need to use an 64 bit OS with a 64bit version of DS9. 32bit apps
can address up to 4Gb of address space. However, depending on
the OS, this limit may be less. Linux for example, the limit
appears to be ~3GB (the OS and shared libs eat up a lot of
address space). Under 64bit Solaris, 32bit ds9 has a full 4Gb of
space. MacOSX appears to have a limit ~3Gb. Under windows, ~2Gb.</p>
<p>Large File Support: is the ability to sequence thru files
larger than 4Gb. DS9 is compiled with LFS.</p>
<p>File system: the OS file system must be able to support files
larger than 4Gb. Most recent file systems fully support 4GB>.</p>
<p>Memory Management: There are a number of memory management
techniques supported in DS9 that will greatly affect the ability
and speed of loading large files:</p>
<blockquote> <tt>$ ds9 foo.fits # uses mmap</tt><br>
<tt>$ cat foo.fits | ds9 - # allocates memory</tt> <br>
<tt>$ xpaset -p ds9 file foo.fits # uses mmap</tt> <br>
<tt>$ xpaset -p ds9 fits foo.fits # allocates memory</tt><br>
</blockquote>
<p>Memory Map (<tt>mmap</tt>) is very fast, limit is memory
address space (see above). Allocate is very slow, limit is
amount of physical memory + swap partition.</p>
<p>Scanning Data: DS9 needs to determine the min and max data
values to correctly display your image. For large files, this
can take time. You have several options available under the <tt>Scale</tt>
menu:</p>
<blockquote>Scan Data - reads all data<br>
Sample Data - samples every x values (much faster)<br>
Use FITS keyword DATAMIN/MAX or IRAFMIN/MAX - great if in the
header, bad because they are always wrong.<br>
</blockquote>
<p>The best thing to do is to use sample data, and set the data
sample between 10 and 100.</p>
</blockquote>
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