scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
Examples
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 = truncnorm.numargs
>>> [ a,b ] = [0.9,]*numargs
>>> rv = truncnorm(a,b)
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
>>> prb = truncnorm.cdf(x,a,b)
>>> h=plt.semilogy(np.abs(x-truncnorm.ppf(prb,a,b))+1e-20)
Random number generation
>>> R = truncnorm.rvs(a,b,size=100)
Truncated Normal distribution.
The standard form of this distribution is a standard normal truncated to the range [a,b] — notice
that a and b are defined over the domain of the standard normal. To convert clip values for a specific
mean and standard deviation use a,b = (myclip_a-my_mean)/my_std, (myclip_b-my_mean)/my_std
596 Chapter 3. Reference