scipy.stats.powernorm() is a power normal continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution.
Parameters :
q : lower and upper tail probability
x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
size : [tuple of ints, optional] shape or random variates.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).Results : power normal continuous random variable
Code #1 : Creating power normal continuous random variable
# importing library from scipy.stats import powernorm
numargs = powernorm .numargs
a, b = 4.32, 3.18
rv = powernorm (a, b)
print ("RV : \n", rv)
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Output :
RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D836B708
Code #2 : power normal continuous variates and probability distribution
import numpy as np
quantile = np.arange (0.01, 1, 0.1)
# Random Variates R = powernorm.rvs(a, b)
print ("Random Variates : \n", R)
# PDF R = powernorm.pdf(a, b, quantile)
print ("\nProbability Distribution : \n", R)
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Output :
Random Variates : 2.1273001964058893 Probability Distribution : [9.49268896e-16 3.85465279e-15 1.51767596e-14 5.79410238e-14 2.14497868e-13 7.70028464e-13 2.68074679e-12 9.05084823e-12 2.96365464e-11 9.41222378e-11]
Code #3 : Graphical Representation.
import numpy as np
import matplotlib.pyplot as plt
distribution = np.linspace(0, np.minimum(rv.dist.b, 3))
print("Distribution : \n", distribution)
plot = plt.plot(distribution, rv.pdf(distribution))
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Output :
Distribution : [0. 0.04081633 0.08163265 0.12244898 0.16326531 0.20408163 0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959 0.48979592 0.53061224 0.57142857 0.6122449 0.65306122 0.69387755 0.73469388 0.7755102 0.81632653 0.85714286 0.89795918 0.93877551 0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347 1.2244898 1.26530612 1.30612245 1.34693878 1.3877551 1.42857143 1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939 1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735 1.95918367 2. ]
Code #4 : Varying Positional Arguments
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 5, 100)
# Varying positional arguments y1 = powernorm .pdf(x, 1, 3, 5)
y2 = powernorm .pdf(x, 1, 4, 4)
plt.plot(x, y1, "*", x, y2, "r--")
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Output :