SciPy

scipy.stats.truncnorm

scipy.stats.truncnorm = <scipy.stats._continuous_distns.truncnorm_gen object at 0x4e17990>[source]

A truncated normal continuous random variable.

Continuous 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

a, b : array_like

shape parameters

loc : array_like, optional

location parameter (default=0)

scale : array_like, optional

scale parameter (default=1)

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=’mv’)

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

location, and scale parameters returning a “frozen” continuous RV object: :

rv = truncnorm(a, b, loc=0, scale=1) :

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

Notes

The standard form of this distribution is a standard normal truncated to the range [a, b] — notice that a and b are defined over the domain of the standard normal. To convert clip values for a specific mean and standard deviation, use:

a, b = (myclip_a - my_mean) / my_std, (myclip_b - my_mean) / my_std

Examples

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

Calculate a few first moments:

>>> a, b = 0.1, 2.0
>>> mean, var, skew, kurt = truncnorm.stats(a, b, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(truncnorm.ppf(0.01, a, b),
...               truncnorm.ppf(0.99, a, b), 100)
>>> ax.plot(x, truncnorm.pdf(x, a, b),
...          'r-', lw=5, alpha=0.6, label='truncnorm pdf')

Alternatively, freeze the distribution and display the frozen pdf:

>>> rv = truncnorm(a, b)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = truncnorm.ppf([0.001, 0.5, 0.999], a, b)
>>> np.allclose([0.001, 0.5, 0.999], truncnorm.cdf(vals, a, b))
True

Generate random numbers:

>>> r = truncnorm.rvs(a, b, size=1000)

And compare the histogram:

>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()

(Source code)

../_images/scipy-stats-truncnorm-1.png

Methods

rvs(a, b, loc=0, scale=1, size=1) Random variates.
pdf(x, a, b, loc=0, scale=1) Probability density function.
logpdf(x, a, b, loc=0, scale=1) Log of the probability density function.
cdf(x, a, b, loc=0, scale=1) Cumulative density function.
logcdf(x, a, b, loc=0, scale=1) Log of the cumulative density function.
sf(x, a, b, loc=0, scale=1) Survival function (1-cdf — sometimes more accurate).
logsf(x, a, b, loc=0, scale=1) Log of the survival function.
ppf(q, a, b, loc=0, scale=1) Percent point function (inverse of cdf — percentiles).
isf(q, a, b, loc=0, scale=1) Inverse survival function (inverse of sf).
moment(n, a, b, loc=0, scale=1) Non-central moment of order n
stats(a, b, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(a, b, loc=0, scale=1) (Differential) entropy of the RV.
fit(data, a, b, loc=0, scale=1) Parameter estimates for generic data.
expect(func, a, b, 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(a, b, loc=0, scale=1) Median of the distribution.
mean(a, b, loc=0, scale=1) Mean of the distribution.
var(a, b, loc=0, scale=1) Variance of the distribution.
std(a, b, loc=0, scale=1) Standard deviation of the distribution.
interval(alpha, a, b, loc=0, scale=1) Endpoints of the range that contains alpha percent of the distribution