def cdf(self, x, *args, **kwds):
"""
Cumulative distribution function at x of the given RV.
Parameters
----------
x : array-like
quantiles
arg1, arg2, arg3,... : array-like
The shape parameter(s) for the distribution (see docstring of the
instance object for more information)
loc : array-like, optional
location parameter (default=0)
scale : array-like, optional
scale parameter (default=1)
Returns
-------
cdf : array-like
Cumulative distribution function evaluated at x
"""
(loc, scale) = map(kwds.get, ['loc', 'scale'])
(args, loc, scale) = self._fix_loc_scale(args, loc, scale)
(x, loc, scale, shape) = map(arr, (x, loc, scale, args[0]))
x = (x - loc) * 1.0 / scale
#
isgamma = (shape > 0) & (scale != 0)
isnorm = (shape == 0) & (scale > 0)
ispe3 = (isnorm | isgamma)
indomain = (x > self.a) & (x < self.b)
toolarge = (x >= self.b)
valid = ispe3 & indomain
output = np.zeros(np.shape(valid), 'd')
np.place(output, (1 - ispe3) * (indomain == indomain), self.badvalue)
np.place(output, toolarge, 1.0)
if any(valid): #call only if at least 1 entry
(x, shape) = argsreduce(valid, *((x,) + (shape,)))
vals = self._cdf(x, shape)
np.place(output, (valid & isgamma),
np.where(scale > 0, vals, 1. - vals))
np.place(output, (valid & isnorm), dist._norm_cdf(x))
if output.ndim == 0:
return output[()]
return output
def _cdf(self, x):
y = (x - self._mu) / self._sigma
return (distributions._norm_cdf(y) +
self._herm_cdf(y) * distributions._norm_pdf(y))