scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
Each included continuous distribution is an instance of the class rv_continous:
rv_continuous([momtype, a, b, xa, xb, xtol, ...]) A Generic continuous random variable.
rv_continuous.pdf(x, *args, **kwds) Probability density function at x of the given RV.
rv_continuous.cdf(x, *args, **kwds) Cumulative distribution function at x of the given RV.
rv_continuous.sf(x, *args, **kwds) Survival function (1-cdf) at x of the given RV.
rv_continuous.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV.
rv_continuous.isf(q, *args, **kwds) Inverse survival function at q of the given RV.
rv_continuous.stats(*args, **kwds) Some statistics of the given RV
class rv_continuous(momtype=1, a=None, b=None, xa=-10.0, xb=10.0, xtol=1e-14, badvalue=None,
name=None, longname=None, shapes=None, extradoc=None)
A Generic continuous random variable.
Continuous random variables are defined from a standard form and may require some shape parameters to
complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as
given below:
Examples
Parameters
x : array-like
quantiles
q : array-like
lower or upper tail probability
: array-like
shape parameters
loc : array-like, optional
location parameter (default=0)
scale : array-like, optional
scale parameter (default=1)
size : int or tuple of ints, optional
shape of random variates (default computed from input arguments )
moments : string, optional
composed of letters [’mvsk’] specifying which moments to compute where ‘m’
= mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’)
>>> import matplotlib.pyplot as plt
>>> numargs = generic.numargs
>>> [ ] = [0.9,]*numargs
>>> rv = generic()
Display frozen pdf
>>> x = np.linspace(0,np.minimum(rv.dist.b,3))
>>> h=plt.plot(x,rv.pdf(x))
Check accuracy of cdf and ppf
448 Chapter 3. Reference