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"""
========================
Exploring normalizations
========================
Various normalization on a multivariate normal distribution.
"""
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import multivariate_normal
import matplotlib.colors as mcolors
# 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()
# %%
#
# .. admonition:: References
#
# The use of the following functions, methods, classes and modules is shown
# in this example:
#
# - `matplotlib.colors`
# - `matplotlib.colors.PowerNorm`
# - `matplotlib.axes.Axes.hist2d`
# - `matplotlib.pyplot.hist2d`
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