scipy.stats.halfgennorm () — upper half of a generalized normal continuous random variable. To complete its specific customization, it is defined with a standard format and some shape parameters. The object inherits from it a collection of generic methods and adds specific details to them.

Parameters :

 - & gt;  α:  scale - & gt;  β:  shape - & gt;  μ:  location 

Code # 1: Generating a semi-generalized normal continuous random variable

from scipy.stats import halfgennorm    numargs = halfgennorm.numargs [a] = [ 0.7 ,] * numargs rv = halfgennorm (a)   print ( "RV:" , rv)

Exit:

 RV: scipy.stats._distn_infrastructure.rv_frozen object at 0x0000021FB55D8DD8  
  Code # 2: Semi-Generalized Random Variables and Probability Distribution  
 
 
 
  
 
 
 
 
  import   numpy as np 
 
  quantile   =   np.arange (  0.01  ,   1  ,   0.1  ) 
 
   
  # Random Variants  
 
  R   =   h alfgennorm .rvs (.   2  , scale   =   2  , size   =   10  ) 
 
  print   (  "Random Variates:"  , R) 
 
  
  # PDF  
 
  R   =   halfgennorm .pdf (quantile,.   2  , loc   =   0  , scale   =   1  ) 
 
  print   (  "Probability Distribution:"  , R) 
 
 
  Exit:
 
 Random Variates: [1.41299459e + 03 3.51301175e + 04 1.79981484e + 05 2.90925518e + 02 2.70178121e + 05 1.31706797e + 05 3.25898913e + 01 1.62607410 e + 04 2.02263946e + 04 1.97078668e + 04] Probability Distribution: [0.00559658 0.0043805 0.00400834 0.0037776 0.00360957 0.00347731 0.00336825 0.00327549 0.00319482 0.00312348] 
  Code # 3: Graphic representation.   




  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)) 




  
  Exit :
 Distribution: [0. 0.06122449 0.12244898 0.18367347 0.24489796 0.30612245 0.36734694 0.42857143 0.48979592 0.55102041 0.6122449 0.67346939 0.73469388 0.79591837 0.85714286 0.91836735 0.97959184 1.04081633 1.10204082 1.16326531 1.2244898 1.28571429 1.34693878 1.40816327 1.46938776 1.53061224 1.59183673 1.65306122 1.71428571 1.7755102 1.83673469 1.89795918 1.95918367 2.02040816 2.08163265 2.14285714 2.20408163 2.26530612 2.32653061 2.3877551 2.44897959 2.51020408 2.57142857 2.63265306 2.69387755 2.75510204 2.81632653 2.87755102 2.93877551 3. ] 
  

  Code # 4: Various Positional Arguments  





  import   matplotlib. pyplot as plt 
  import   numpy as np 
  
  x   =   np.linspace (  0  ,   5  ,   100  ) 
  
  # Various positional arguments  

  y1   =   halfgennorm .pdf (x,   1  ,   3  ) 
  y2   =   halfgennorm .pdf (x ,   1  ,   4  ) 
  plt.plot (x, y1,  " * " , x, y2,  " r-- " ) 





  Output: