scipPy stats.anglit() | Python Last Updated : 20 Mar, 2019 Comments Improve Suggest changes Like Article Like Report 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 Python3 # 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. Python3 1== 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. Python3 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 Python3 1== 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 : Comment More infoAdvertise with us Next Article scipPy stats.anglit() | Python V vishal3096 Follow Improve Article Tags : Python Python-scipy Python scipy-stats-functions Practice Tags : python Similar Reads scipy stats.erlang() | Python scipy.stats.erlang() : is an Erlang continuous random variable that is defined with a standard format and some shape parameters to complete its specification. it is a special case of the Gamma distribution. Parameters : q : lower and upper tail probability x : quantiles loc : [optional] location par 2 min read scipy stats.gilbrat() | Python scipy.stats.gilbrat() is an Gilbrat 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 2 min read sciPy stats.alpha() | Python scipy.stats.alpha() is an alpha 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 a : shape parameter loc : [optional] location parameter. Default = 0 scale : [opt 1 min read scipy stats.f() | Python scipy.stats.f() is an F 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 a, b : shape parameters x : quantiles loc : [optional] location parameter. Default = 0 scale : [optiona 2 min read scipy stats.chi() | Python scipy.stats.chi() is an chi 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. 2 min read Like