CODEWITHSUNDEEP
pythonnumpymatplotlib
Cokes
Cokes

Reputation: 4093

Color-coding a histogram

I have a set of N objects with two properties: x and y. I would like to depict the distribution of x with a histogram in MATPLOTLIB using hist(). Easy enough. Now, I would like to color-code EACH bar of the histogram with a color that represents the average value of y in that set with a colormap. Is there an easy way to do this? Here, x and y are both N-d numpy arrays. Thanks!

fig = plt.figure()
n, bins, patches = plt.hist(x, 100, normed=1, histtype='stepfilled')
plt.setp(patches, 'facecolor', 'g', 'alpha', 0.1)
plt.xlabel('x')
plt.ylabel('Normalized frequency')
plt.show()

Upvotes: 1

Views: 3683

Answers (1)

tacaswell
tacaswell

Reputation: 87386

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# set up the bins
Nbins = 10
bins = np.linspace(0, 1, Nbins +1, endpoint=True)
# get some fake data
x = np.random.rand(300)
y = np.arange(300)
# figure out which bin each x goes into
bin_num = np.digitize(x, bins, right=True) - 1
# compute the counts per bin
hist_vals = np.bincount(bin_num)
# set up array for bins
means = np.zeros(Nbins)
# numpy slicing magic to sum the y values by bin
means[bin_num] += y
# take the average
means /= hist_vals

# make the figure/axes objects
fig, ax = plt.subplots(1,1)
# get a color map
my_cmap = cm.get_cmap('jet')
# get normalize function (takes data in range [vmin, vmax] -> [0, 1])
my_norm = Normalize()
# use bar plot 
ax.bar(bins[:-1], hist_vals, color=my_cmap(my_norm(means)), width=np.diff(bins))

# make sure the figure updates
plt.draw()
plt.show()

related: vary the color of each bar in bargraph using particular value

Upvotes: 1

Related Questions