  # scipPy stats.anglit () | python

NumPy | Python Methods and Functions

> 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: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029484AA02E8 & gt; `

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: 