How to fill histogram with color gradient where a fixed point represents the middle of of colormap - matplotlib

Question

This code
import numpy as np
import matplotlib.pyplot as plt
def randn(n, sigma, mu):
return sigma * np.random.randn(n) + mu
x = randn(1000, 40., -100.)
cm = plt.cm.get_cmap("seismic")
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
_, bins, patches = ax.hist(x,color="r",bins=30)
bin_centers = 0.5*(bins[:-1]+bins[1:])
col = bin_centers - min(bin_centers)
col /= max(col)
for c, p in zip(col, patches):
plt.setp(p, "facecolor", cm(c))
plt.savefig("b.png", dpi=300, bbox_inches="tight")
produces the following histograms
I want to use the diverging colormap seismic and would like to have all bars representing the occurrence of negative numbers to be bluish and all bars representing positive numbers reddish. Around zero the bars should always be white. Therefore the first graph should be mostly reddish and the last one should be mostly bluish. How can I achieve that?

Answer 1

If this is about visual appearance only, you can normalize your colors to the range between the maximum absolute value and its negative counterpart, such that zero is always in the middle (max |bins|).
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = 6.4,4
def randn(n, sigma, mu):
return sigma * np.random.randn(n) + mu
x1 = randn(999, 40., -80)
x2 = randn(750, 40., 80)
x3 = randn(888, 16., -30)
def hist(x, ax=None):
cm = plt.cm.get_cmap("seismic")
ax = ax or plt.gca()
_, bins, patches = ax.hist(x,color="r",bins=30)
bin_centers = 0.5*(bins[:-1]+bins[1:])
maxi = np.abs(bin_centers).max()
norm = plt.Normalize(-maxi,maxi)
for c, p in zip(bin_centers, patches):
plt.setp(p, "facecolor", cm(norm(c)))
fig, axes = plt.subplots(nrows=3, sharex=True)
for x, ax in zip([x1,x2,x3], axes):
hist(x,ax=ax)
plt.show()

Answer 2

I have an alternative answer for a different use case. I wanted to have the different colours from the divergent colormap be dynamically mapped to their respective "width" on either side of the divergence point. Additionally, I wanted to explicitly set the divergence point (in my case, 1).
I achieved this by modifying the answer from #ImportanceofBeingErnest, although in the end I didn't need to do any normalization, I just used two plots on the same figure, and chose the sequential colormaps which, when put end-to-end, re-formed the target divergent colormap.
def hist2(x, vmin, vmax, cmmap_name, ax=None,):
cm = plt.cm.get_cmap(cmmap_name)
ax = ax or plt.gca()
_, bins, patches = ax.hist(x,color="r",bins=50)
bin_centers = 0.5*(bins[:-1]+bins[1:])
norm = plt.Normalize(vmin, vmax)
for c, p in zip(bin_centers, patches):
plt.setp(p, "facecolor", cm(norm(c)))
data = <YOUR DATA>
left_data = [i for i in data if i < <YOUR DIVERGENCE POINT>]
right_data = [i for i in data if i >= <YOUR DIVERGENCE POINT>]
fig, ax = plt.subplots(nrows=1)
hist2(left_data, min(left_data), max(left_data), "YlOrRd_r", ax=ax)
hist2(right_data, min(right_data), max(right_data), "YlGn", ax=ax)
plt.show()
Some of my results: