scipy.stats.lognorm

scipy.stats.lognorm()

A lognormal 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:

Parameters:

x : array-like

quantiles

q : array-like

lower or upper tail probability

s : 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’)

Methods:

lognorm.rvs(s,loc=0,scale=1,size=1) :

• random variates

lognorm.pdf(x,s,loc=0,scale=1) :

• probability density function

lognorm.cdf(x,s,loc=0,scale=1) :

• cumulative density function

lognorm.sf(x,s,loc=0,scale=1) :

• survival function (1-cdf — sometimes more accurate)

lognorm.ppf(q,s,loc=0,scale=1) :

• percent point function (inverse of cdf — percentiles)

lognorm.isf(q,s,loc=0,scale=1) :

• inverse survival function (inverse of sf)

lognorm.stats(s,loc=0,scale=1,moments=’mv’) :

• mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’)

lognorm.entropy(s,loc=0,scale=1) :

• (differential) entropy of the RV.

lognorm.fit(data,s,loc=0,scale=1) :

• Parameter estimates for lognorm data

Alternatively, the object may be called (as a function) to fix the shape, :

location, and scale parameters returning a “frozen” continuous RV object: :

rv = lognorm(s,loc=0,scale=1) :

• frozen RV object with the same methods but holding the given shape, location, and scale fixed

Examples

>>> import matplotlib.pyplot as plt
>>> numargs = lognorm.numargs
>>> [ s ] = [0.9,]*numargs
>>> rv = lognorm(s)

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 = lognorm.cdf(x,s)
>>> h=plt.semilogy(np.abs(x-lognorm.ppf(prb,c))+1e-20)

Random number generation

>>> R = lognorm.rvs(s,size=100)

Lognormal distribution

lognorm.pdf(x,s) = 1/(s*x*sqrt(2*pi)) * exp(-1/2*(log(x)/s)**2) for x > 0, s > 0.

If log x is normally distributed with mean mu and variance sigma**2, then x is log-normally distributed with shape paramter sigma and scale parameter exp(mu).