scipy.stats.planck

scipy.stats.planck = <scipy.stats._discrete_distns.planck_gen object>

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

geom

Notes

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_).

Examples

>>> 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)
>>> plt.show()

Check accuracy of cdf and ppf:

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

Generate random numbers:

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

Methods

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(alpha, lambda_, loc=0)	Endpoints of the range that contains fraction alpha [0, 1] of the distribution

scipy.stats.nhypergeom scipy.stats.poisson