"""
========================
Exploring normalizations
========================

Let's explore various normalization on a multivariate normal distribution.

"""

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

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

gammas = [0.8, 0.5, 0.3]

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

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

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

fig.tight_layout()

plt.show() 

　

M:\______\power_norm_demo.py:17: RuntimeWarning: 
    covariance is not symmetric positive-semidefinite.
  multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000) 

E:\______\power_norm_demo.py:28: 
    SyntaxWarning: invalid escape sequence '\g'
  ax.set_title('Power law $(\gamma=%1.1f)$' % gamma)
E:\_____\power_norm_demo.py:17: 
    RuntimeWarning: covariance is not symmetric positive-semidefinite.
  multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000) 

"""
========================
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` 

　

• 2023/11/10 Ver=1.04 Python 3.12.0 (matplotlib 3.8.1)で確認
• 2023/11/10 Ver=1.04 Python 3.11.6 (matplotlib 3.7.1)で確認
• 2023/03/27 Ver=1.03 Python 3.11.2 で確認
• 2020/10/29 Ver=1.01 Python 3.7.8 で確認
• 2018/11/23 Ver=1.01 初版リリース

Top