scipy stats.arcsine () | python



scipy.stats.arcsine () — it is a continuous random arcsine variable that is defined by 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: arcsine continuous random variable

Code # 1: Generating a continuous random variable arcsine

# scipy import

from scipy.stats import arcsine

 

numargs = arcsine.numargs

[] = [ 0.6 ,] * numargs

rv = arcsine ()

  

print ( " RV: " , rv)

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029484D796D8 & gt; 

Code # 2 : arcsine of random variables and probability distribution function.

quantile = np.arange ( 0.01 , 1 , 0.1 )

 
# Random Variants

R = arcsine.rvs (scale = 2 , size = 10 )

print ( "Random Variates:" , R)

  
# PDF

R = arcsine.pdf (x = quantile, scale = 2 )

print ( "Probability Distribution:" , R)

Exit :

 Random Variates: [1.1735365 8 1.96350916 1.73419819 0.71255312 0.28760466 1.54410451 1.9644408 0.35014597 0.26798525 0.24599504] Probability Distribution: [2.25643896 0.69810843 0.51917523 0.43977033 0.39423905 0.3651505 0.34568283 0.31560195 > 

#libraries

import numpy as np

import matplotlib.pyplot as plt

 

distribution = np.linspace ( 0 , np.minimum (rv.dist.b, 3 ))

print ( "Distribution:" , distribution)

 

plot = plt.plot (distribution, rv .pdf (distribution))

Output:

 Distribution: [0. 0.02040816 0.04081633 0.06122449 0.08163265 0.10204082 0.12244898 0.14285714 0.16326531 0.18367347 0.20408163 0.2244898 0.24489796 0.26530612 0.28571429 0.30612245 0.32653061 0.34693878 0.36734694 0.3877551 0.40816327 0.42857143 0.44897959 0.46938776 0.48979592 0.51020408 0.53061224 0.55102041 0.57142857 0.59183673 0.6122449 0.63265306 0.65306122 0.67346939 0.69387755 0.71428571 0.73469388 0.75510204 0.7755102 0.79591837 0.81632653 0.83673469 0.85714286 0.87755102 0.89795918 0.91836735 0.93877551 0.95918367 0.97959184 1. ] 

Code # 4: change location and scale

from scipy .stats import arcsin e

import matplotlib.pyplot as plt

import numpy as np

a = 2

b = 2

x = np.linspace ( 0 , np.minimum (rv.dist.b, 3 ))

  
# Different location and scale

y1 = arcsine.pdf (x, - 0.1 ,. 8 )

y2 = arcsine.pdf (x, - 3.25 , 3.25 )

plt.plot (x, y1, "*" , x, y2, "r--" )