SciPy

scipy.stats.triang

scipy.stats.triang(*args, **kwds) = <scipy.stats._continuous_distns.triang_gen object>[source]

A triangular continuous random variable.

As an instance of the rv_continuous class, triang 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 triangular distribution can be represented with an up-sloping line from loc to (loc + c*scale) and then downsloping for (loc + c*scale) to (loc + scale).

triang takes c as a shape parameter for \(c\).

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, triang.pdf(x, c, loc, scale) is identically equivalent to triang.pdf(y, c) / scale with y = (x - loc) / scale.

The standard form is in the range [0, 1] with c the mode. The location parameter shifts the start to loc. The scale parameter changes the width from 1 to scale.

Examples

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

Calculate a few first moments:

>>> c = 0.158
>>> mean, var, skew, kurt = triang.stats(c, moments='mvsk')

Display the probability density function (pdf):

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

Check accuracy of cdf and ppf:

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

Generate random numbers:

>>> r = triang.rvs(c, size=1000)

And compare the histogram:

>>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()
../_images/scipy-stats-triang-1.png

Methods

rvs(c, loc=0, scale=1, size=1, random_state=None)

Random variates.

pdf(x, c, loc=0, scale=1)

Probability density function.

logpdf(x, c, loc=0, scale=1)

Log of the probability density function.

cdf(x, c, loc=0, scale=1)

Cumulative distribution function.

logcdf(x, c, loc=0, scale=1)

Log of the cumulative distribution function.

sf(x, c, loc=0, scale=1)

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

logsf(x, c, loc=0, scale=1)

Log of the survival function.

ppf(q, c, loc=0, scale=1)

Percent point function (inverse of cdf — percentiles).

isf(q, c, loc=0, scale=1)

Inverse survival function (inverse of sf).

moment(n, c, loc=0, scale=1)

Non-central moment of order n

stats(c, loc=0, scale=1, moments=’mv’)

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

entropy(c, 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=(c,), 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(c, loc=0, scale=1)

Median of the distribution.

mean(c, loc=0, scale=1)

Mean of the distribution.

var(c, loc=0, scale=1)

Variance of the distribution.

std(c, loc=0, scale=1)

Standard deviation of the distribution.

interval(alpha, c, loc=0, scale=1)

Endpoints of the range that contains alpha percent of the distribution

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