Default color changes#
As discussed at length elsewhere ,
jet is an
empirically bad colormap and should not be the default colormap.
Due to the position that changing the appearance of the plot breaks
backward compatibility, this change has been put off for far longer
than it should have been. In addition to changing the default color
map we plan to take the chance to change the default color-cycle on
plots and to adopt a different colormap for filled plots (
contourf, etc) and for scatter like plots.
Default heat map colormap#
The choice of a new colormap is fertile ground to bike-shedding ("No, it should be _this_ color") so we have a proposed set criteria (via Nathaniel Smith) to evaluate proposed colormaps.
it should be a sequential colormap, because diverging colormaps are really misleading unless you know where the "center" of the data is, and for a default colormap we generally won't.
it should be perceptually uniform, i.e., human subjective judgments of how far apart nearby colors are should correspond as linearly as possible to the difference between the numerical values they represent, at least locally.
it should have a perceptually uniform luminance ramp, i.e. if you convert to greyscale it should still be uniform. This is useful both in practical terms (greyscale printers are still a thing!) and because luminance is a very strong and natural cue to magnitude.
it should also have some kind of variation in hue, because hue variation is a really helpful additional cue to perception, having two cues is better than one, and there's no reason not to do it.
the hue variation should be chosen to produce reasonable results even for viewers with the more common types of colorblindness. (Which rules out things like red-to-green.)
For bonus points, it would be nice to choose a hue ramp that still works if you throw away the luminance variation, because then we could use the version with varying luminance for 2d plots, and the version with just hue variation for 3d plots. (In 3d plots you really want to reserve the luminance channel for lighting/shading, because your brain is really good at extracting 3d shape from luminance variation. If the 3d surface itself has massively varying luminance then this screws up the ability to see shape.)
Not infringe any existing IP
Default scatter colormap#
For heat-map like applications it can be desirable to cover as much of the luminance scale as possible, however when colormapping markers, having markers too close to white can be a problem. For that reason we propose using a different (but maybe related) colormap to the heat map for marker-based. The design parameters are the same as above, only with a more limited luminance variation.
import numpy as np import matplotlib.pyplot as plt np.random.seed(1234) fig, (ax1, ax2) = plt.subplots(1, 2) N = 50 x = np.random.rand(N) y = np.random.rand(N) colors = np.random.rand(N) area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses ax1.scatter(x, y, s=area, c=colors, alpha=0.5) X,Y = np.meshgrid(np.arange(0, 2*np.pi, .2), np.arange(0, 2*np.pi, .2)) U = np.cos(X) V = np.sin(Y) Q = ax2.quiver(X, Y, U, V, units='width') qd = np.random.rand(np.prod(X.shape)) Q.set_array(qd)
Color cycle / qualitative colormap#
When plotting lines it is frequently desirable to plot multiple lines or artists which need to be distinguishable, but there is no inherent ordering.
import numpy as np import matplotlib.pyplot as plt fig, (ax1, ax2) = plt.subplots(1, 2) x = np.linspace(0, 1, 10) for j in range(10): ax1.plot(x, x * j) th = np.linspace(0, 2*np.pi, 1024) for j in np.linspace(0, np.pi, 10): ax2.plot(th, np.sin(th + j)) ax2.set_xlim(0, 2*np.pi)