Using Contrib Color Look-Up Tables

Get Started

Download the sample data: 214 (CAS A)

unix% download_chandra_obsid 214 evt2

Background Information

Many different fields of science rely on different color look-up tables (LUT's): medical imaging, cartography, meteorology, fluid dynamics, as well as astronomy. It is not surprising then that there are many different color maps available. The largest repository seems to be cpt-city which has 100's of different color maps from both the scientific community as well as the realm of artistic/graphic-design. ds9 version 7 now has a direct link to cpt-city and all their color maps are now available in ds9's .sao format.

The color map chosen is up to the user to decide based on what information they are trying to convey. For most scientific purposes, the only common restriction would be that colors are unique: having the color "white" show up at both a low intensity level and a high pixel intensity level in the color map may lead to ambiguity. This is not an absolute rule as one may choose to obscure certain regimes of the data with the same color to draw focus on a different area.

Examples of which color map to use when

If one were to have an image whose pixels represent the incident integrated flux, a colored gradient, one maybe chosen to symbolically match the energy band pass, may be a good choice (Figure 1 and Figure 2). An image showing temperatures is often shown using a rainbow color scheme (Figure 3). And an image showing color, that is the ratio of two images in different energy bands, may best be shown with a color map that smoothly varies between two colors (Figure 4).

Figure 1: Image of Low Energy Events

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Figure 1: Image of Low Energy Events

This smoothed image shows the number of counts at low energies. The color red symbolically represents low energies and the gradation from black through red to white represent intensity.

Figure 2: Image of High Energy Events

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Figure 2: Image of High Energy Events

This smoothed image shows the number of counts at high energies. The color blue symbolically represents high energies and the gradation from black through blue to white represent intensity. Note in particular the bright bulge seen south east of the center (lower right) compared to the low energy image.

Figure 3: Pseudo Temperature Map

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Figure 3: Pseudo Temperature Map

This smoothed image shows the mean energy of the events in each pixel. Red and white pixels represent events with low mean energy (cooler) while green and blue pixels represent regions where events have higher mean energy (hotter) which is concentrated in the same bulge seen in Figure 2.

Figure 4: Hardness Ratio

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Figure 4: Hardness Ratio

The hardness ratio (ratio of high energy to low energy events) is another way to communicate energy dependence in a dataset. As it represents a transition between two states (hard and soft, or high energy and low energy), a color map that varies between two colors is a natural choice. Here again we have chosen a color map with red representing low energies and blue representing high energies.

Contributed Look-Up Tables

These LUT's were extracted from two different software package and the data were reformatted to ds9's .lut format.

• HEAsoft's XImage package, a multi-mission X-ray image display and analysis program
• National Institutes of Health's ImageJ package, a public domain Java image processing program inspired by [National Institutes for Health] Image.

Note: Some of these color maps may be identical or nearly identical to one or more of those already available in ds9, matplotlib, or dmimg2jpg

XImage

Click on colorbars to see example.

ximage_bb blue1 blue2 blue3 blue4 bluebase1 bluebase2 bluebase3 bluebase4 bluebase5 bluebase6 bluebase7 bluebase8 bluebase9 bluyel brown eyellow1 gray1 gray2 green1 green2 green3

green4 green5 green6 green7 green8 green9 invgray invgray1 invspec pink1 pink2 pink3 pink4 pinkbase1 pinkbase2 pinkbase3 pinkbase4 pinkbase5 purby purple1 purple2 purple3

purple4 puryb red1 red2 redbase1 redbase2 redbase3 redbase4 redbase5 redbase6 rgb4 rgb6 spectrum vt1 vt2 vt3 vt4 vt5 vt6 vt7 yellow1 yy

---

ImageJ

Click on colorbars to see example.

000-gray 001-fire 002-spectrum 003-ice 004-phase 005-random 16_colors 16_equal 16_ramps 20_colors 32_colors 5_ramps 6_reserved_colors 6_shades amber auxctq blue_orange blue_orange_icb brain brgbcmyw cells cequal cmy-cyan

cmy-magneta cmy-yellow cmy cold cti_ras edges gem-16 gem-256 gold gyr_centre heart hue hue_ramps_08 hue_ramps_16 icool iman invert_gray isocontour log_down log_up mixed neon-blue neon-green

neon-magenta neon-red pastel rgb-blue rgb-green rgb-red royal sepia siemens smart split_blackblue_redwhite split_blackwhite_ge split_blackwhite_warmmetal split_bluered_warmmetal system_lut thal_16 thal_256 thallium topography unionjack vivid warhol

How to Use Contributed LUT's

All the color look-up tables will be located in $ASCDS_CONTRIB/data. You can access them by name by using the ximage_lut.par and imagej_lut.par parameter files.

unix% plist ximage_lut

Parameters for /soft/ciao-4.4/contrib/param/ximage_lut.par

#
# Color Lookup Tables extracted from NASA XImage package
#
#    http://heasarc.gsfc.nasa.gov/xanadu/ximage/ximage.html
#
# converted to the ".lut" format used by ds9, chips, and dmimg2jpg
#
#
           (bb = ${ASCDS_CONTRIB}/data/bb.lut -> /soft/ciao-4.4/contrib/data/bb.lut) Color Lookup Table
        (blue1 = ${ASCDS_CONTRIB}/data/blue1.lut -> /soft/ciao-4.4/contrib/data/blue1.lut) Color Lookup Table
        (blue2 = ${ASCDS_CONTRIB}/data/blue2.lut -> /soft/ciao-4.4/contrib/data/blue2.lut) Color Lookup Table
        (blue3 = ${ASCDS_CONTRIB}/data/blue3.lut -> /soft/ciao-4.4/contrib/data/blue3.lut) Color Lookup Table
        (blue4 = ${ASCDS_CONTRIB}/data/blue4.lut -> /soft/ciao-4.4/contrib/data/blue4.lut) Color Lookup Table
  ...

unix% plist imagej_lut

Parameters for /export/ciao-4.4/contrib/param/imagej_lut.par

#
#
# Color Lookup Tables extracted from the NIH's ImageJ package
#
#  http://rsbweb.nih.gov/ij/
#  
# converted to the ".lut" format used by ds9, chips, and dmimg2jpg
#
     (000-gray = ${ASCDS_CONTRIB}/data/000-gray.lut -> /export/ciao-4.4/contrib/data/000-gray.lut) Color Lookup Table
     (001-fire = ${ASCDS_CONTRIB}/data/001-fire.lut -> /export/ciao-4.4/contrib/data/001-fire.lut) Color Lookup Table
 (002-spectrum = ${ASCDS_CONTRIB}/data/002-spectrum.lut -> /export/ciao-4.4/contrib/data/002-spectrum.lut) Color Lookup Table
      (003-ice = ${ASCDS_CONTRIB}/data/003-ice.lut -> /export/ciao-4.4/contrib/data/003-ice.lut) Color Lookup Table
   ...

Using contributed LUT's in ds9

As with most things in ds9, there are at least 3 different ways to load a new color map: via the GUI, on the command line, and via XPA. Only the first two are shown here.

On the command line, you use the -cmap load command

unix% ds9 acisf00214N000_evt2.fits -cmap load $ASCDS_CONTRIB/data/heart.lut

Figure 5: ImageJ "heart" LUT in ds9

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Figure 5: ImageJ "heart" LUT in ds9

The "heart" color look-up table from ImageJ being used in ds9. The data have been smoothed with a 3 pixel Gaussian.

Alternatively, in the ds9 GUI, you can load the color map by going to Color -> Colomap Parameters ... In the sub-window, then go to File -> Load Colormap ..., navigate to the CIAO contrib/data directory and select the file name.

When a custom color map is loaded, it will be available under the top level "Color" menu. Prior to version 7, the name of the new color map would simply be appended to the list of those built-in. In version 7, custom color maps have their own sub-menu.

---

Using contributed LUT's in matplotlib

In this example we use the parameter module to retrieve the file name from the imagej_lut.par file. We then parse this file into the red, green, blue, and alpha (transparency) channels used by the ListedColormap class.

% python

import paramio as pio
from pycrates import read_file
import matplotlib.pylab as plt
from matplotlib.colors import ListedColormap

heart_lut = pio.pget("imagej_lut", "heart")
tab = open(heart_lut,"r").readlines()
rgb = [ x.strip().split() for x in tab if not x.startswith("#")]
rgba= [ [float(x[0]),float(x[1]),float(x[2]),1.0] for x in rgb ]
heart_lut_cmap = ListedColormap(rgba)

img = read_file('img_sm_asinh.fits')
imgvals = img.get_image().values
plt.imshow(imgvals, origin='lower', cmap=heart_lut_cmap)
plt.savefig("matplotlib.png")

Figure 6: ImageJ "heart" LUT in matplotlib

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Figure 6: ImageJ "heart" LUT in matplotlib

The "heart" color look-up table from ImageJ being used in matplotlib. The input file was created by applying an asinh transform to a smoothed image from the event file using dmimgcalc.

Note: the data are shown in logical image pixel coordinates.

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Using contributed LUT's in dmimg2jpg

The look-up table can be fed directly to dmimg2jpg. In the follow example we make use of a parameter redirect to locate the file name.

unix% dmimg2jpg infile=img_sm.fits outfile=img_sm.jpg \
  lutfile=")imagej_lut.heart" \
  scalefun=log mode=h clobber=yes

Figure 7: ImageJ "heart" LUT in dmimg2jpg

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Figure 7: ImageJ "heart" LUT in dmimg2jpg

The "heart" color look-up table from ImageJ being used in dmimg2jpg.