GNU Astronomy Utilities Pixel visualization

The main goal of ConvertType is to visualize pixels to/from print or web friendly formats.

Astronomical data usually have a very large dynamic range (difference between maximum and minimum value) and different subjects might be better demonstrated with a limited flux range.


The color map to visualize a single channel. The first value given to this option is the name of the color map, which is shown below. Some color maps can be configured. In this case, the configuration parameters are optionally given as numbers following the name of the color map for example, see hsv. The table below contains the usable names of the color maps that are currently supported:


Grayscale color map. This color map does not have any parameters. The full dataset range will be scaled to 0 and \(2^8-1=255\) to be stored in the requested format.


Hue, Saturation, Value142 color map. If no values are given after the name (--colormap=hsv), the dataset will be scaled to 0 and 360 for hue covering the full spectrum of colors. However, you can limit the range of hue (to show only a special color range) by explicitly requesting them after the name (for example, --colormap=hsv,20,240).

The mapping of a single-channel dataset to HSV is done through the Hue and Value elements: Lower dataset elements have lower “value” and lower “hue”. This creates darker colors for fainter parts, while also respecting the range of colors.


Viridis is the default colormap of the popular Matplotlib module of Python and available in many other visualization tools like PGFPlots.


The SLS color range, taken from the commonly used SAO DS9. The advantage of this color range is that it starts with black, going into dark blue and finishes with the brighter colors of red and white. So unlike the HSV color range, it includes black and white and brighter colors (like yellow, red) show the larger values.


The inverse of the SLS color map (see above), where the lowest value corresponds to white and the highest value is black. While SLS is good for visualizing on the monitor, SLS-inverse is good for printing.


When there are three input channels and the output is in the FITS format, interpret the three input channels as red, green and blue channels (RGB) and convert them to the hue, saturation, value (HSV) color space.

The currently supported output formats of ConvertType do not have native support for HSV. Therefore this option is only supported when the output is in FITS format and each of the hue, saturation and value arrays can be saved as one FITS extension in the output for further analysis (for example, to select a certain color).

-c STR

(=STR) Change pixel values with the following format "from1:to1, from2:to2,...". This option is very useful in displaying labeled pixels (not actual data images which have noise) like segmentation maps. In labeled images, usually a group of pixels have a fixed integer value. With this option, you can manipulate the labels before the image is displayed to get a better output for print or to emphasize on a particular set of labels and ignore the rest. The labels in the images will be changed in the same order given. By default first the pixel values will be converted then the pixel values will be truncated (see --fluxlow and --fluxhigh).

You can use any number for the values irrespective of your final output, your given values are stored and used in the double precision floating point format. So for example, if your input image has labels from 1 to 20000 and you only want to display those with labels 957 and 11342 then you can run ConvertType with these options:

$ astconvertt --change=957:50000,11342:50001 --fluxlow=5e4 \
   --fluxhigh=1e5 segmentationmap.fits --output=jpg

While the output JPEG format is only 8 bit, this operation is done in an intermediate step which is stored in double precision floating point. The pixel values are converted to 8-bit after all operations on the input fluxes have been complete. By placing the value in double quotes you can use as many spaces as you like for better readability.


Change pixel values (with --change) after truncation of the flux values, by default it is the opposite.


The minimum flux (pixel value) to display in the output image, any pixel value below this value will be set to this value in the output. If the value to this option is the same as --fluxhigh, then no flux truncation will be applied. Note that when multiple channels are given, this value is used for all the color channels.


The maximum flux (pixel value) to display in the output image, see --fluxlow.

-m INT

This is only used for the JPEG and EPS output formats which have an 8-bit space for each channel of each pixel. The maximum value in each pixel can therefore be \(2^8-1=255\). With this option you can change (decrease) the maximum value. By doing so you will decrease the dynamic range. It can be useful if you plan to use those values for other purposes.


Enforce the value of --fluxlow (when it is given), even if it is smaller than the minimum of the dataset and the output is format supporting color. This is particularly useful when you are converting a number of images to a common image format like JPEG or PDF with a single command and want them all to have the same range of colors, independent of the contents of the dataset. Note that if the minimum value is smaller than --fluxlow, then this option is redundant.

By default, when the dataset only has two values, and the output format is PDF or EPS, ConvertType will use the PostScript optimization that allows setting the pixel values per bit, not byte (Recognized file formats). This can greatly help reduce the file size. However, when --fluxlow or --fluxhigh are called, this optimization is disabled: even though there are only two values (is binary), the difference between them does not correspond to the full contrast of black and white.


Similar to --forcemin, but for the maximum.


For 8-bit output types (JPEG, EPS, and PDF for example) the final value that is stored is inverted so white becomes black and vice versa. The reason for this is that astronomical images usually have a very large area of blank sky in them. The result will be that a large are of the image will be black. Note that this behavior is ideal for gray-scale images, if you want a color image, the colors are going to be mixed up.