scipy.stats.planck = <scipy.stats._discrete_distns.planck_gen object>[source]#

A Planck discrete exponential random variable.

As an instance of the rv_discrete class, planck 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



The probability mass function for planck is:

\[f(k) = (1-\exp(-\lambda)) \exp(-\lambda k)\]

for \(k \ge 0\) and \(\lambda > 0\).

planck takes \(\lambda\) as shape parameter. The Planck distribution can be written as a geometric distribution (geom) with \(p = 1 - \exp(-\lambda)\) shifted by loc = -1.

The probability mass function above is defined in the “standardized” form. To shift distribution use the loc parameter. Specifically, planck.pmf(k, lambda_, loc) is identically equivalent to planck.pmf(k - loc, lambda_).


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

Calculate the first four moments:

>>> lambda_ = 0.51
>>> mean, var, skew, kurt = planck.stats(lambda_, moments='mvsk')

Display the probability mass function (pmf):

>>> x = np.arange(planck.ppf(0.01, lambda_),
...               planck.ppf(0.99, lambda_))
>>> ax.plot(x, planck.pmf(x, lambda_), 'bo', ms=8, label='planck pmf')
>>> ax.vlines(x, 0, planck.pmf(x, lambda_), colors='b', lw=5, alpha=0.5)

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

Freeze the distribution and display the frozen pmf:

>>> rv = planck(lambda_)
>>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1,
...         label='frozen pmf')
>>> ax.legend(loc='best', frameon=False)

Check accuracy of cdf and ppf:

>>> prob = planck.cdf(x, lambda_)
>>> np.allclose(x, planck.ppf(prob, lambda_))

Generate random numbers:

>>> r = planck.rvs(lambda_, size=1000)


rvs(lambda_, loc=0, size=1, random_state=None)

Random variates.

pmf(k, lambda_, loc=0)

Probability mass function.

logpmf(k, lambda_, loc=0)

Log of the probability mass function.

cdf(k, lambda_, loc=0)

Cumulative distribution function.

logcdf(k, lambda_, loc=0)

Log of the cumulative distribution function.

sf(k, lambda_, loc=0)

Survival function (also defined as 1 - cdf, but sf is sometimes more accurate).

logsf(k, lambda_, loc=0)

Log of the survival function.

ppf(q, lambda_, loc=0)

Percent point function (inverse of cdf — percentiles).

isf(q, lambda_, loc=0)

Inverse survival function (inverse of sf).

stats(lambda_, loc=0, moments=’mv’)

Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).

entropy(lambda_, loc=0)

(Differential) entropy of the RV.

expect(func, args=(lambda_,), loc=0, lb=None, ub=None, conditional=False)

Expected value of a function (of one argument) with respect to the distribution.

median(lambda_, loc=0)

Median of the distribution.

mean(lambda_, loc=0)

Mean of the distribution.

var(lambda_, loc=0)

Variance of the distribution.

std(lambda_, loc=0)

Standard deviation of the distribution.

interval(confidence, lambda_, loc=0)

Confidence interval with equal areas around the median.