scipPy stats.anglit () | python

> 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: Generating a continuous random variable angled

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

Output:

 RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029484AA02E8" 

Code # 2 : angular random variables and probability distribution function.

import numpy as np quantile = np.arange ( 0.01 , 1 , 0.1 )   # Random Variants R = anglit.rvs (scale = 2 , size = 10 ) print ( "Random Variates:" , R)   # PDF R = anglit.pdf (quantile, loc = 0 , scale = 1 ) print ( "Probability Distribution:" , R) < / p>

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 0.9130.68894 0.813878 3: Graphic representation.  
 
 
 
  
  
  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: Various Positional Arguments  
 
 
 
 
 
  import   numpy as np  
 
  import   matplotlib.pyplot as plt 
 
  
 
  distribution   =   np.linspace (  0  , np.minimum (rv.dist.b,   5  )) 
 
  print   (  "Distribution:"  , distribution ) 
 
  
 
  plot   =   plt.plot (distribution, rv.pdf (distribution)) 
 
           
 
 
 
 
  import   matplotlib.pyplot as plt 
 
  import   numpy as np 
 
  
 
  x   =   np.linspace (  0  ,   5  ,   100  ) 
 
  
  # Various positional arguments 
 
  y1   =   anglit.pdf (x ,   1  ,   6  ) 
 
  y2   =   anglit. pdf (x,   1  ,   4  ) 
 
  plt.plot (x, y1,  " * " , x, y2,  " r-- " ) 
 
  Output: