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[Wishlist/candidate for gallery?] Diverging colormap taking into account asymmetry of the data about vcenter #12449
Description
I have been using a different approach to achieve what #12419 tries to address (DivergingNorm).
The limitation of using something like DivergingNorm is that it doesn't allow for using color range proportional to the extent of the vmax, vmin w.r.t. vcenter (please see the example below). I have been using the following hackish approach to get over this limitation. This approach also allows for using colorbar extensions with the ability to specify different colors for data > vmax and/or data < vmin [based on my understanding, this won't be possible if DivergingNorm is used].
Would this be a useful addition to the gallery? Does it look like a functionality that I should think of adding to Matplotlib? (Also, please let me know if there is an easier way of achieving the same result).
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import copy
def div_proportional_colormap(cmap='coolwarm', vmin=-1., vmax=1.,
vcenter=0., cmap_name='my_cmap'):
""" Given a diverging colormap, this returns a diverging
colormap with color ranges on either side proportional to
the extent of vmax, vmin w.r.t. vcenter i.e. vmax-vcenter, vcenter-vmin
"""
if isinstance(cmap, str):
cmap = cm.get_cmap(cmap)
max_delta_v= max(vcenter-vmin, vmax-vcenter)
cmap_min = 0.5-0.5*(vcenter-vmin)/max_delta_v
cmap_max = 0.5+0.5*(vmax-vcenter)/max_delta_v
my_colors = cmap(np.linspace(cmap_min, cmap_max, 512))
my_cmap = mcolors.LinearSegmentedColormap.from_list(cmap_name, my_colors)
return my_cmap
x = np.linspace(-2, 7)
y = np.linspace(-1*np.pi, np.pi)
X, Y = np.meshgrid(x, y)
Z = x * np.sin(Y)**2
fig, (ax1, ax2) = plt.subplots(ncols=2)
my_cmap1 = div_proportional_colormap(cmap=plt.cm.coolwarm,
vmin=-2., vmax=6., vcenter=0.,
cmap_name='my_cmap1')
my_cmap2 = copy.copy(my_cmap1)
my_cmap2.set_over('orange')
img1 = ax1.pcolormesh(Z, cmap=my_cmap1, vmin=-2, vmax=6)
cbar1 = fig.colorbar(img1, ax=ax1)
img2 = ax2.pcolormesh(Z, cmap=my_cmap2, vmin=-2, vmax=6)
cbar2 = fig.colorbar(img2, ax=ax2, extend='max')
plt.show()
Activity
jklymak commented on Oct 8, 2018
I’m pretty sure there is an example like this in the new making colormaps tutorial (see the dev docs).
I’m not following the issue with extended data. Does divergingmorm not work w that feature? If true I’m sure that can be fixed
pharshalp commented on Oct 8, 2018
Thanks! I will take a look. By dev docs you mean the ones generated by CircleCI right?
please see the following example which uses the divergingnorm and fails to show the expected results.
jklymak commented on Oct 8, 2018
Ok we can work on over under support for diverging norm.
Dev docs are linked on the front page of the main docs
jklymak commented on Oct 9, 2018
https://matplotlib.org/devdocs/tutorials/colors/colormap-manipulation.html#sphx-glr-tutorials-colors-colormap-manipulation-py
github-actions commented on May 18, 2023
This issue has been marked "inactive" because it has been 365 days since the last comment. If this issue is still present in recent Matplotlib releases, or the feature request is still wanted, please leave a comment and this label will be removed. If there are no updates in another 30 days, this issue will be automatically closed, but you are free to re-open or create a new issue if needed. We value issue reports, and this procedure is meant to help us resurface and prioritize issues that have not been addressed yet, not make them disappear. Thanks for your help!