scipy.stats.powerlaw

scipy.stats.powerlaw = <scipy.stats._continuous_distns.powerlaw_gen object>

A power-function continuous random variable.

As an instance of the rv_continuous class, powerlaw 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.

See also

pareto

Notes

The probability density function for powerlaw is:

\[f(x, a) = a x^{a-1}\]

for \(0 \le x \le 1\), \(a > 0\).

powerlaw takes a as a shape parameter for \(a\).

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, powerlaw.pdf(x, a, loc, scale) is identically equivalent to powerlaw.pdf(y, a) / scale with y = (x - loc) / scale. Note that shifting the location of a distribution does not make it a “noncentral” distribution; noncentral generalizations of some distributions are available in separate classes.

For example, the support of powerlaw can be adjusted from the default interval [0, 1] to the interval [c, c+d] by setting loc=c and scale=d. For a power-law distribution with infinite support, see pareto.

powerlaw is a special case of beta with b=1.

Examples

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

Calculate the first four moments:

>>> a = 1.66
>>> mean, var, skew, kurt = powerlaw.stats(a, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(powerlaw.ppf(0.01, a),
...                 powerlaw.ppf(0.99, a), 100)
>>> ax.plot(x, powerlaw.pdf(x, a),
...        'r-', lw=5, alpha=0.6, label='powerlaw 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 = powerlaw(a)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = powerlaw.ppf([0.001, 0.5, 0.999], a)
>>> np.allclose([0.001, 0.5, 0.999], powerlaw.cdf(vals, a))
True

Generate random numbers:

>>> r = powerlaw.rvs(a, size=1000)

And compare the histogram:

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

Methods

rvs(a, loc=0, scale=1, size=1, random_state=None)	Random variates.
pdf(x, a, loc=0, scale=1)	Probability density function.
logpdf(x, a, loc=0, scale=1)	Log of the probability density function.
cdf(x, a, loc=0, scale=1)	Cumulative distribution function.
logcdf(x, a, loc=0, scale=1)	Log of the cumulative distribution function.
sf(x, a, loc=0, scale=1)	Survival function (also defined as 1 - cdf, but sf is sometimes more accurate).
logsf(x, a, loc=0, scale=1)	Log of the survival function.
ppf(q, a, loc=0, scale=1)	Percent point function (inverse of cdf — percentiles).
isf(q, a, loc=0, scale=1)	Inverse survival function (inverse of sf).
moment(order, a, loc=0, scale=1)	Non-central moment of the specified order.
stats(a, loc=0, scale=1, moments=’mv’)	Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(a, loc=0, scale=1)	(Differential) entropy of the RV.
fit(data)	Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments.
expect(func, args=(a,), 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(a, loc=0, scale=1)	Median of the distribution.
mean(a, loc=0, scale=1)	Mean of the distribution.
var(a, loc=0, scale=1)	Variance of the distribution.
std(a, loc=0, scale=1)	Standard deviation of the distribution.
interval(confidence, a, loc=0, scale=1)	Confidence interval with equal areas around the median.