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.

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))


Linear normalization, Power law $(\gamma=0.8)$, Power law $(\gamma=0.5)$, Power law $(\gamma=0.3)$


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

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