scipy.stats.powerlognorm

scipy.stats.powerlognorm = <scipy.stats._continuous_distns.powerlognorm_gen object at 0x45083fcc>

A power log-normal continuous random variable.

As an instance of the rv_continuous class, powerlognorm object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.

Notes

The probability density function for powerlognorm is:

powerlognorm.pdf(x, c, s) = c / (x*s) * phi(log(x)/s) *
                                        (Phi(-log(x)/s))**(c-1),

where phi is the normal pdf, and Phi is the normal cdf, and x > 0, s, c > 0.

powerlognorm takes c and s as shape parameters.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, powerlognorm.pdf(x, c, s, loc, scale) is identically equivalent to powerlognorm.pdf(y, c, s) / scale with y = (x - loc) / scale.

Examples

>>> from scipy.stats import powerlognorm
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate a few first moments:

>>> c, s = 2.14, 0.446
>>> mean, var, skew, kurt = powerlognorm.stats(c, s, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(powerlognorm.ppf(0.01, c, s),
...                 powerlognorm.ppf(0.99, c, s), 100)
>>> ax.plot(x, powerlognorm.pdf(x, c, s),
...        'r-', lw=5, alpha=0.6, label='powerlognorm pdf')

Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pdf:

>>> rv = powerlognorm(c, s)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = powerlognorm.ppf([0.001, 0.5, 0.999], c, s)
>>> np.allclose([0.001, 0.5, 0.999], powerlognorm.cdf(vals, c, s))
True

Generate random numbers:

>>> r = powerlognorm.rvs(c, s, size=1000)

And compare the histogram:

>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()

(Source code)

Methods

rvs(c, s, loc=0, scale=1, size=1, random_state=None)	Random variates.
pdf(x, c, s, loc=0, scale=1)	Probability density function.
logpdf(x, c, s, loc=0, scale=1)	Log of the probability density function.
cdf(x, c, s, loc=0, scale=1)	Cumulative density function.
logcdf(x, c, s, loc=0, scale=1)	Log of the cumulative density function.
sf(x, c, s, loc=0, scale=1)	Survival function (1 - cdf — sometimes more accurate).
logsf(x, c, s, loc=0, scale=1)	Log of the survival function.
ppf(q, c, s, loc=0, scale=1)	Percent point function (inverse of cdf — percentiles).
isf(q, c, s, loc=0, scale=1)	Inverse survival function (inverse of sf).
moment(n, c, s, loc=0, scale=1)	Non-central moment of order n
stats(c, s, loc=0, scale=1, moments='mv')	Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(c, s, loc=0, scale=1)	(Differential) entropy of the RV.
fit(data, c, s, loc=0, scale=1)	Parameter estimates for generic data.
expect(func, c, s, loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)	Expected value of a function (of one argument) with respect to the distribution.
median(c, s, loc=0, scale=1)	Median of the distribution.
mean(c, s, loc=0, scale=1)	Mean of the distribution.
var(c, s, loc=0, scale=1)	Variance of the distribution.
std(c, s, loc=0, scale=1)	Standard deviation of the distribution.
interval(alpha, c, s, loc=0, scale=1)	Endpoints of the range that contains alpha percent of the distribution