Note

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Exploring normalizations#

Various normalization on a multivariate normal distribution.

importmatplotlib.pyplotaspltimportnumpyasnpfromnumpy.randomimportmultivariate_normalimportmatplotlib.colorsasmcolors# 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)forax,gammainzip(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()
Linear normalization, Power law $(\gamma=0.8)$, Power law $(\gamma=0.5)$, Power law $(\gamma=0.3)$

References

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

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