This small Fortran colormap fpm library is independent of any graphical toolkit: its main functionnality is converting a real value to RGB values, that you can use with any toolkit offering bitmap drawing. And it offers various methods and options to manage colormaps.

It includes:

• a few basic colormaps: "grey", "inverted_grey", "fire", "rainbow", "inverted_rainbow", "zebra",
• the Dave Green's cubehelix colormap,
• the "magma", "inferno","plasma", "viridis" matplotlib colormaps,
• 62 colormaps of the Scientific colour maps collection v8.0.1 by Fabio Crameri (the discrete palettes were not imported). See Fabio Crameri's poster "Scientific Colour Maps" for more information and my No Bijection! text about the mysteries and wonders of colors.

Assuming your graphical library has a setpixelgb()-like function and z is in the [0, 2] range, you can write something like:

use forcolormap, only: Colormap, colormaps_list, wp
...
type(Colormap) :: cmap
integer  :: red, green, blue
real(wp) :: z, x, y
...
call cmap%set("glasgow", 0.0_wp, 2.0_wp)
...
z = f(x,y)
call cmap%compute_RGB(z, red, green, blue)
call setpixelrgb(x, y, red, green, blue)

Look into the example/demo.f90 file for more usage examples: you can create your own colormap, download a colormap from a text file, etc. The example/demo_reverse.f90 file shows how to use the reverse option to reverse the direction of a colormap.

Note that there is no default colormap as we consider that each user must choose a colormap adapted to the properties of its data. This guideline can help you choosing the right kind of colormap.

You need:

• a modern Fortran compiler, for example GFortran or the Intel ifort/ifx compilers. See the Fortran-lang.org compilers page for other compilers.
• The Fortran Package Manager fpm.
  • For writing PPM files, the library ForImage is used as a fpm dependency.
• Any operating system.

If you have a GitHub account, just clone the repository and launch the demo example:

$ git clone [email protected]:vmagnin/forcolormap.git
$ cd forcolormap
$ fpm run --example demo

The demo is creating PPM files with colormaps and colorbars for all the available colormaps. Other examples are demo_reverse and example1.

To use ForColormap within your own fpm project, add the following lines to your fpm.toml manifest file:

[dependencies]
forcolormap = {git = "https://github.com/vmagnin/forcolormap.git" }

• Create a logo: inspired by Newton/Dark Side of the Moon? Or a rainbow? And using Fortran purple. Or a colored marble?
• Improve the documentation.
  • Include a few images in the README.md file.
  • A table with the characteristics of the colormaps: which are colorblind friendly, perceptually uniform, B&W print safe, etc.
• Colormaps could have an option for logscale.
• A get_colorbar() function could return an array(:,:,1:3) containing the RGB image of the colorbar. The arguments could be the width and height, the direction (horizontal/vertical), etc.
• A save() method could save a colormap as RGB values separated by spaces in a .lut text file.
• Adding functions to create colormaps, for example defined by their two extremes colors: create_linear(color1, color2).

This project is under MIT license.

• For Scientific colour maps, please cite these two items:
  • Crameri, F. (2018a), Scientific colour maps. Zenodo. http:https://doi.org/10.5281/zenodo.1243862
  • Crameri, Fabio, Grace E. Shephard, and Philip J. Heron. “The Misuse of Colour in Science Communication.” Nature Communications 11, no. 1 (October 28, 2020): 5444. https://doi.org/10.1038/s41467-020-19160-7.
• For the matplotlib colormaps, you can cite this webpage https://bids.github.io/colormap/
• For the cubehelix colormap, please cite:
  • Green, D. A. “A Colour Scheme for the Display of Astronomical Intensity Images.” arXiv, August 30, 2011. http:https://arxiv.org/abs/1108.5083.

• Nuñez, Jamie R., Christopher R. Anderton, and Ryan S. Renslow. “Optimizing Colormaps with Consideration for Color Vision Deficiency to Enable Accurate Interpretation of Scientific Data.” Edited by Jesús Malo. PLOS ONE 13, no. 7 (August 1, 2018): e0199239. https://doi.org/10.1371/journal.pone.0199239.
• Rogowitz, Bernice E, and Lloyd A Treinish. “Why Should Engineers and Scientists Be Worried About Color?”
• Thyng, Kristen, Chad Greene, Robert Hetland, Heather Zimmerle, and Steven DiMarco. “True Colors of Oceanography: Guidelines for Effective and Accurate Colormap Selection.” Oceanography 29, no. 3 (September 1, 2016): 9–13. https://doi.org/10.5670/oceanog.2016.66.
• Valeur, Bernard. La couleur dans tous ses éclats. Bibliothèque scientifique. Paris: Belin-"Pour la science", 2011, ISBN 9782701158761.
• Valeur, Bernard. Lumière et luminescence - Ces phénomènes lumineux qui nous entourent. Bibliothèque scientifique. Paris: Belin-"Pour la science", 2005, ISBN 9782701136035.

• https://en.wikipedia.org/wiki/Color_gradient
• https://en.wikipedia.org/wiki/Heat_map
• Ken Hughes, "Default colormaps: Are Parula and Viridis really an improvement over Jet?", posted on October 1, 2019.
• "In Search of a Perfect Colormap", http:https://inversed.ru/Blog_2.htm

• Cubehelix (Dave Green, public domain):
  • https://www.mrao.cam.ac.uk/~dag/CUBEHELIX/
• Scientific colour maps (Fabio Crameri, MIT license):
  • https://www.fabiocrameri.ch/colourmaps/
  • https://s-ink.org/colour-map-guideline
  • https://s-ink.org/scientific-colour-maps
• Matplotlib colormaps (CC0 license / public domain):
  • https://bids.github.io/colormap/
  • Python version: https://github.com/BIDS/colormap/blob/master/colormaps.py
  • Nathaniel Smith and Stéfan van der Walt, A Better Default Colormap for Matplotlib, SciPy 2015: https://www.youtube.com/watch?v=xAoljeRJ3lU
• Colors for data scientists. Generate and refine palettes of optimally distinct colors.