ConvertType will convert any recognized input file type to any specified output type. The executable name is astconvertt with the following general template
$ astconvertt [OPTION...] InputFile [InputFile2] ... [InputFile4]
One line examples:
## Convert an image in FITS to PDF: $ astconvertt image.fits --output=pdf ## Similar to before, but use the Viridis color map: $ astconvertt image.fits --colormap=viridis --output=pdf ## Convert an image in JPEG to FITS (with multiple extensions ## if its color): $ astconvertt image.jpg -oimage.fits ## Use three plain text 2D arrays to create an RGB JPEG output: $ astconvertt f1.txt f2.txt f3.fits -o.jpg ## Use two images and one blank for an RGB EPS output: $ astconvertt M31_r.fits M31_g.fits blank -oeps ## Directly pass input from output of another program through Standard ## input (not a file). $ cat 2darray.txt | astconvertt -oimg.fits
The output’s file format will be interpreted from the value given to the --output option. It can either be given on the command-line or in any of the configuration files (see Configuration files). Note that if the output suffix is not recognized, it will default to plain text format, see Recognized file formats.
At most four input files (one for each color channel for formats that allow it) are allowed in ConvertType. The first input dataset can either be a file or come from Standard input (see Standard input). The order of multiple input files is important. After reading the input file(s) the number of color channels in all the inputs will be used to define which color space to use for the outputs and how each color channel is interpreted.
Some formats can allow more than one color channel (for example in the JPEG format, see Recognized file formats). If there is one input dataset (color channel) the output will be gray-scale, if three input datasets (color channels) are given, they are respectively considered to be the red, green and blue color channels. Finally, if there are four color channels they will be be cyan, magenta, yellow and black (CMYK colors).
The value to --output (or -o) can be either a full file name or just the suffix of the desired output format. In the former case, it will used for the output. In the latter case, the name of the output file will be set based on the automatic output guidelines, see Automatic output. Note that the suffix name can optionally start a . (dot), so for example --output=.jpg and --output=jpg are equivalent. See Recognized file formats.
Besides the common set of options explained in Common options, the options to ConvertType can be classified into input, output and flux related options. The majority of the options are to do with the flux range. 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.
In ConvertType, it is possible to call the HDU option multiple times for the different input FITS or TIFF files in the same order that they are called on the command-line. Note that in the TIFF standard, one ‘directory’ (similar to a FITS HDU) may contain multiple color channels (for example when the image is in RGB).
Except for the fact that multiple calls are possible, this option is identical to the common --hdu in Input/Output options. The number of calls to this option cannot be less than the number of input FITS or TIFF files, but if there are more, the extra HDUs will be ignored, note that they will be read in the order described in Configuration file precedence.
Unlike CFITSIO, libtiff (which is used to read TIFF files) only recognizes numbers (counting from zero, similar to CFITSIO) for ‘directory’ identification. Hence the concept of names is not defined for the directories and the values to this option for TIFF files must be numbers.
The width of the output in centimeters. This is only relevant for those formats that accept such a width (not plain text for example). For most digital purposes, the number of pixels is far more important than the value to this parameter because you can adjust the absolute width (in inches or centimeters) in your document preparation program.
The width of the border to be put around the EPS and PDF outputs in units of PostScript points. There are 72 or 28.35 PostScript points in an inch or centimeter respectively. In other words, there are roughly 3 PostScript points in every millimeter. If you are planning on adding a border, its significance is highly correlated with the value you give to the --widthincm parameter.
Unfortunately in the document structuring convention of the PostScript language, the “bounding box” has to be in units of PostScript points with no fractions allowed.
So the border values only have to be specified in integers.
To have a final border that is thinner than one PostScript point in your document, you can ask for a larger width in ConvertType and then scale down the output EPS or PDF file in your document preparation program.
For example by setting
width in your
includegraphics command in TeX or LaTeX.
Since it is vector graphics, the changes of size have no effect on the quality of your output quality (pixels don’t get different values).
Use Hexadecimal encoding in creating EPS output. By default the ASCII85 encoding is used which provides a much better compression ratio. When converted to PDF (or included in TeX or LaTeX which is finally saved as a PDF file), an efficient binary encoding is used which is far more efficient than both of them. The choice of EPS encoding will thus have no effect on the final PDF.
So if you want to transfer your EPS files (for example if you want to submit your paper to arXiv or journals in PostScript), their storage might become important if you have large images or lots of small ones. By default ASCII85 encoding is used which offers a much better compression ratio (nearly 40 percent) compared to Hexadecimal encoding.
The quality (compression) of the output JPEG file with values from 0 to 100 (inclusive). For other formats the value to this option is ignored. Note that only in gray-scale (when one input color channel is given) will this actually be the exact quality (each pixel will correspond to one input value). If it is in color mode, some degradation will occur. While the JPEG standard does support loss-less graphics, it is not commonly supported.
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 doesn’t 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, Value94 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 don’t 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).
(=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.
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 its given), even if its 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.