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Don't create Legendre polynomial object unless actually needed

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Paul Romano 2019-04-03 08:06:40 -05:00
parent d79524b9ce
commit ae4b0106dc
1 changed files with 18 additions and 17 deletions
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35
openmc/stats/univariate.py
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@ -312,7 +312,7 @@ class Normal(Univariate):
r"""Normally distributed sampling.
The Normal Distribution is characterized by two parameters
:math:`\mu` and :math:`\sigma` and has density function
:math:`\mu` and :math:`\sigma` and has density function
:math:`p(X) dX = 1/(\sqrt{2\pi}\sigma) e^{-(X-\mu)^2/(2\sigma^2)}`
Parameters
@ -325,9 +325,9 @@ class Normal(Univariate):
Attributes
----------
mean_value : float
Mean of the Normal distribution
Mean of the Normal distribution
std_dev : float
Standard deviation of the Normal distribution
Standard deviation of the Normal distribution
"""
def __init__(self, mean_value, std_dev):
@ -380,17 +380,17 @@ class Normal(Univariate):
class Muir(Univariate):
"""Muir energy spectrum.
The Muir energy spectrum is a Gaussian spectrum, but for
The Muir energy spectrum is a Gaussian spectrum, but for
convenience reasons allows the user 3 parameters to define
the distribution, e0 the mean energy of particles, the mass
of reactants m_rat, and the ion temperature kt.
of reactants m_rat, and the ion temperature kt.
Parameters
----------
e0 : float
Mean of the Muir distribution in units of eV
m_rat : float
Ratio of the sum of the masses of the reaction inputs to an
Ratio of the sum of the masses of the reaction inputs to an
AMU
kt : float
Ion temperature for the Muir distribution in units of eV
@ -400,7 +400,7 @@ class Muir(Univariate):
e0 : float
Mean of the Muir distribution in units of eV
m_rat : float
Ratio of the sum of the masses of the reaction inputs to an
Ratio of the sum of the masses of the reaction inputs to an
AMU
kt : float
Ion temperature for the Muir distribution in units of eV
@ -582,26 +582,27 @@ class Legendre(Univariate):
def __init__(self, coefficients):
self.coefficients = coefficients
self._legendre_poly = None
def __call__(self, x):
return self._legendre_polynomial(x)
# Create Legendre polynomial if we haven't yet
if self._legendre_poly is None:
l = np.arange(len(self._coefficients))
coeffs = (2.*l + 1.)/2. * self._coefficients
self._legendre_poly = np.polynomial.Legendre(coeffs)
return self._legendre_poly(x)
def __len__(self):
return len(self._legendre_polynomial.coef)
return len(self._coefficients)
@property
def coefficients(self):
poly = self._legendre_polynomial
l = np.arange(poly.degree() + 1)
return 2./(2.*l + 1.) * poly.coef
return self._coefficients
@coefficients.setter
def coefficients(self, coefficients):
cv.check_type('Legendre expansion coefficients', coefficients,
Iterable, Real)
l = np.arange(len(coefficients))
coeffs = (2.*l + 1.)/2. * np.array(coefficients)
self._legendre_polynomial = np.polynomial.Legendre(coeffs)
self._coefficients = np.asarray(coefficients)
def to_xml_element(self, element_name):
raise NotImplementedError