scipy.stats.gilbrat¶
-
scipy.stats.
gilbrat
= <scipy.stats._continuous_distns.gilbrat_gen object>[source]¶ A Gilbrat continuous random variable.
As an instance of the
rv_continuous
class,gilbrat
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
gilbrat
is:\[f(x) = \frac{1}{x \sqrt{2\pi}} \exp(-\frac{1}{2} (\log(x))^2)\]gilbrat
is a special case oflognorm
withs=1
.The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the
loc
andscale
parameters. Specifically,gilbrat.pdf(x, loc, scale)
is identically equivalent togilbrat.pdf(y) / scale
withy = (x - loc) / scale
.Examples
>>> from scipy.stats import gilbrat >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 1)
Calculate a few first moments:
>>> mean, var, skew, kurt = gilbrat.stats(moments='mvsk')
Display the probability density function (
pdf
):>>> x = np.linspace(gilbrat.ppf(0.01), ... gilbrat.ppf(0.99), 100) >>> ax.plot(x, gilbrat.pdf(x), ... 'r-', lw=5, alpha=0.6, label='gilbrat 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 = gilbrat() >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
Check accuracy of
cdf
andppf
:>>> vals = gilbrat.ppf([0.001, 0.5, 0.999]) >>> np.allclose([0.001, 0.5, 0.999], gilbrat.cdf(vals)) True
Generate random numbers:
>>> r = gilbrat.rvs(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(loc=0, scale=1, size=1, random_state=None) Random variates. pdf(x, loc=0, scale=1) Probability density function. logpdf(x, loc=0, scale=1) Log of the probability density function. cdf(x, loc=0, scale=1) Cumulative distribution function. logcdf(x, loc=0, scale=1) Log of the cumulative distribution function. sf(x, loc=0, scale=1) Survival function (also defined as 1 - cdf
, but sf is sometimes more accurate).logsf(x, loc=0, scale=1) Log of the survival function. ppf(q, loc=0, scale=1) Percent point function (inverse of cdf
— percentiles).isf(q, loc=0, scale=1) Inverse survival function (inverse of sf
).moment(n, loc=0, scale=1) Non-central moment of order n stats(loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(loc=0, scale=1) (Differential) entropy of the RV. fit(data, loc=0, scale=1) Parameter estimates for generic data. expect(func, args=(), 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(loc=0, scale=1) Median of the distribution. mean(loc=0, scale=1) Mean of the distribution. var(loc=0, scale=1) Variance of the distribution. std(loc=0, scale=1) Standard deviation of the distribution. interval(alpha, loc=0, scale=1) Endpoints of the range that contains alpha percent of the distribution