def massSqMatrix(self, X):
"""
Calculate the tree-level mass matrix of the scalar field.
This uses :func:`helper_functions.hessianFunction` to calculate the
matrix using finite differences, with differences
given by `self.x_eps`. Note that `self.x_eps` is only used directly
the first time this function is called, so subsequently changing it
will not have an effect.
The resulting matrix will have rank `Ndim`. This function may be useful
for subclasses in finding the boson particle spectrum.
"""
try:
f = self._massSqMatrix
except:
# Create the gradient function
self._massSqMatrix = helper_functions.hessianFunction(
self.V0, self.x_eps, self.Ndim, self.deriv_order)
f = self._massSqMatrix
return f(X)
def d2V(self, X, T):
"""
Calculates the Hessian (second derivative) matrix for the
finite-temperature effective potential.
This uses :func:`helper_functions.hessianFunction` to calculate the
matrix using finite differences, with differences
given by `self.x_eps`. Note that `self.x_eps` is only used directly
the first time this function is called, so subsequently changing it
will not have an effect.
"""
try:
f = self._d2V
except:
# Create the gradient function
self._d2V = helper_functions.hessianFunction(
self.Vtot, self.x_eps, self.Ndim, self.deriv_order)
f = self._d2V
# Need to add extra axes to T since extra axes get added to X in
# the helper function.
T = np.asanyarray(T)[...,np.newaxis]
return f(X,T, False)