scipy.stats.planck

scipy.stats.planck = <scipy.stats._discrete_distns.planck_gen object at 0x7f6169c4c290>

A Planck discrete exponential random variable.

Discrete 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

lambda_ : array_like

shape parameters

loc : array_like, optional

location parameter (default=0)

size : int or tuple of ints, optional

shape of random variates (default computed from input arguments )

moments : str, 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 is ‘mv’.

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

location parameters returning a “frozen” discrete RV object:

rv = planck(lambda_, loc=0)

• Frozen RV object with the same methods but holding the given shape and location fixed.

Notes

The probability mass function for planck is:

planck.pmf(k) = (1-exp(-lambda_))*exp(-lambda_*k)

for k*lambda_ >= 0.

planck takes lambda_ as shape parameter.

Examples

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

Calculate a few first 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, 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()

(Source code)

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 variates.
pmf(x, lambda_, loc=0)	Probability mass function.
logpmf(x, lambda_, loc=0)	Log of the probability mass function.
cdf(x, lambda_, loc=0)	Cumulative density function.
logcdf(x, lambda_, loc=0)	Log of the cumulative density function.
sf(x, lambda_, loc=0)	Survival function (1-cdf — sometimes more accurate).
logsf(x, 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, 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 alpha percent of the distribution