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scipPy stats.anglit() | Python

scipy.stats.anglit() is an anglit continuous random variable that is defined with a standard format and some shape parameters to complete its specification.

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 : anglit continuous random variable

Code #1 : Creating anglit continuous random variable




# import scipy
from scipy.stats import anglit
  
numargs = anglit.numargs
[ ] = [0.6, ] * numargs
rv = anglit()
  
print ("RV : \n", rv)


Output :

RV :  
<scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029484AA02E8>

Code #2 : anglit random variates and probability distribution function.




import numpy as np
quantile = np.arange (0.01, 1, 0.1)
   
# Random Variates
R = anglit.rvs(scale = 2,  size = 10)
print ("Random Variates : \n", R)
  
# PDF
R = anglit.pdf(quantile, loc = 0, scale = 1)
print ("\nProbability Distribution : \n", R)


Output :

Random Variates : 
 [-0.73702502 -1.38273136  0.39618481 -0.48434091 -0.85635192 -0.36402882
 -0.21016273  0.53857078  0.96918022 -0.84314795]

Probability Distribution : 
 [0.99980001 0.97589745 0.91308894 0.81387846 0.68222121 0.52336595
 0.34364575 0.15022547 0.         0.        ]
 

Code #3 : Graphical Representation.




import numpy as np
import matplotlib.pyplot as plt
  
distribution = np.linspace(0, np.minimum(rv.dist.b, 5))
print("Distribution : \n", distribution)
  
plot = plt.plot(distribution, rv.pdf(distribution))


Output :

Distribution : 
 [0.         0.01602853 0.03205707 0.0480856  0.06411414 0.08014267
 0.0961712  0.11219974 0.12822827 0.14425681 0.16028534 0.17631387
 0.19234241 0.20837094 0.22439948 0.24042801 0.25645654 0.27248508
 0.28851361 0.30454214 0.32057068 0.33659921 0.35262775 0.36865628
 0.38468481 0.40071335 0.41674188 0.43277042 0.44879895 0.46482748
 0.48085602 0.49688455 0.51291309 0.52894162 0.54497015 0.56099869
 0.57702722 0.59305576 0.60908429 0.62511282 0.64114136 0.65716989
 0.67319843 0.68922696 0.70525549 0.72128403 0.73731256 0.7533411
 0.76936963 0.78539816]

Code #4 : Varying Positional Arguments




import matplotlib.pyplot as plt
import numpy as np
  
x = np.linspace(0, 5, 100)
  
# Varying positional arguments
y1 = anglit.pdf(x, 1, 6)
y2 = anglit.pdf(x, 1, 4)
plt.plot(x, y1, "*", x, y2, "r--")


Output :

Dominic Rubhabha-Wardslaus
Dominic Rubhabha-Wardslaushttp://wardslaus.com
infosec,malicious & dos attacks generator, boot rom exploit philanthropist , wild hacker , game developer,
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