Exploring normalizations¶

Various normalization on a multivariate normal distribution.

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
from numpy.random import multivariate_normal

# Fixing random state for reproducibility.
np.random.seed(19680801)

data = np.vstack([
multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
multivariate_normal([30, 20], [[3, 1], [1, 3]], size=1000)
])

gammas = [0.8, 0.5, 0.3]

fig, axs = plt.subplots(nrows=2, ncols=2)

axs[0, 0].set_title('Linear normalization')
axs[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)

for ax, gamma in zip(axs.flat[1:], gammas):
ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma)
ax.hist2d(data[:, 0], data[:, 1], bins=100, norm=mcolors.PowerNorm(gamma))

fig.tight_layout()

plt.show()


References¶

The use of the following functions, methods, classes and modules is shown in this example:

import matplotlib
matplotlib.colors
matplotlib.colors.PowerNorm
matplotlib.axes.Axes.hist2d
matplotlib.pyplot.hist2d


Out:

<function hist2d at 0x7f5f32b7c820>


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