Loadcv no argument, displays a list of options and asks the user to select by numberīy default, a set of qualitative colors is loaded along with the specified quantitative colormap, comprising eight colorbrewer colors ( ) and 5 intervals of gray from black to white. To improve interaction with loadcsvcolorbar, I recommend adding qualcolors to your IDL startup script. All of the routines should compile on demand and find the files and directories they need automatically. To install, clone the entire repository onto your machine and add it to your IDL !PATH variable. This allows IDL to have access to a decent colorbar and a set of good qualitative colors simultaneously, which is surprisingly difficult in vanilla IDL. These colors are taken from a colorbrewer qualitative scheme ( ), supplemented with some unsaturated grays. By contrast, the new colorbars are perceptually uniform, colorblind friendly, and print correctly in black and white.Īlso included is a series of routines to read in RGB colorbars from CSV files and load them into the current colortable, along with a series of qualitative colors. Aside from the monochromatic and colorbrewer color bars, almost all IDL colorbars suffer from extreme deficiencies in perceptual uniformity and colorblind friendliness. png files located in the IDL_py_png directory, which apply the python-based sequential colormap analysis tools to the IDL builtin maps. These colorbars are better than most of the builtin IDL default colorbars. Included is a new IDL colortable file with the new Matplotlib perceptual colorbars, as designed by Stéfan van der Walt and Nathaniel Smith ( ). These slides are also included as a PDF file in this repository. To learn more about why to use perceptually uniform color maps, as opposed to the more typical rainbow color map ubiquitous in science, look at these slides I made, as well as the embedded references. This repository is designed to get decent colormaps into IDL.
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