Wishlist: Multidimensional colormaps #4369
Comments
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All sounds great to me. I see no technical issue, it is just a matter of someone doing this. If we only want to map 2D data -> r 8000 gba than I think a family of normalizers which go from data -> unit-disk (or is unit square more sensible) in the complex plane and then color maps that go from the unit disk -> RGBA would be a good start. If we want to do higher dimensions map to quaternions (or maybe that is getting too fancy?). |
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I guess though we shouldn't limit ourselves to 2d colour spaces. Thinking of colour spaces, have you heard of the L_a_b* colour space? http://en.wikipedia.org/wiki/Lab_color_space as it "aspires to perceptual uniformity" I wonder if this could help influence our choice of colourmaps for our colour revolution... |
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Some other related issues:
import numpy as np
import matplotlib.pyplot as plt
data = np.random.rand(6,5,3)
plt.imshow(data, interpolation='none')
plt.colorbar()
plt.show()If we wanted to plot multi-dimensional colormaps then I guess we need to change the colorbar implementation as well to deal with this... |
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The 'luminescence' in this case is a synonym for monochrome (which coming from a light microscopy background means luminescence on the detector side) so the documentation is fine. |
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See also discussion on Stack Overflow.
I've been doing this "by hand" in Lab space with Actually this is using colorspacious now, and it's cylindrical coordinates, so "Jch space"? Edit: this is now at https://github.com/endolith/complex_colormap |
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+1 wanting this feature. |
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@njsmith, since this involves color spaces and targets neuroscience, perhaps among other applications, do you have some advice? |
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The simplest version of this, via control of alpha, is being revived in #6268. |
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Hey, is anyone working on this? |
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@arshadpatel2001 Have you taken a look at the repository of @endolith posted above, that sounds like a good first step. I don't know if we need to extend the concept of perceptually-uniform to 2d, i.e. if so maybe we take the | grad C | to create a perceptual scalar field? |
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This is essentially being worked on as part of the colormapping work started in #28658 |
xref #3343
TL;DR: It would be nice if it were possible to have color maps for multidimensional data, instead of expecting the color to depend only on a single quantity as is currently the case.
I'm currently working on an application that would like to be able to modulate the color and opacity of an image independently (as in Figure 3.B.b of Allen et al. 2012) so that color indicates a parameter estimate and opacity indicates a statistic assessing the reliability of that estimate:
Currently, this must be accomplished by applying a color map to the parameter estimate, then overwriting the alpha channel with the statistical map, and finally calling
imshowwith the computed RGBA data. (This is what @mwaskom is doing in the referenced issue.) This is a nuisance, and more importantly, it forces all interpolation to be done in RGBA space instead of in data space (xref #5490).Rather than simply making it possible to control alpha independently of RGB, it would make sense to allow color maps that map multidimensional data to RGBA space. Then
imshow's color map for 3-dimensional input can default to id ⊕ 1, and for 4-dimensional input to id, which gets rid of those special cases and makes it possible to have other color maps of those dimensionalities.One could imagine other use cases for this in neuroimaging; for instance, diffusion tensor images are commonly displayed with hue depending on the principal eigenvector of a symmetric tensor field, while luminosity depends on the dispersion of the eigenvalues.
In a completely different direction, one could display a complex-valued function by splitting the complex data into real and imaginary parts, and then applying a two-dimensional color map that sets hue based on the argument and luminosity based on the modulus of the input:
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