def covariance_matrix(self,
jacobian_transpose=None,
normalised_by_goof=True):
""" The columns of the jacobian_transpose determine which crystallographic
parameters appear in the covariance matrix.
If jacobian_transpose is None, then the covariance matrix returned will
be that for the independent L.S. parameters.
"""
if not self.step_equations().solved:
self.solve()
cov = linalg.inverse_of_u_transpose_u(
self.step_equations().cholesky_factor_packed_u())
cov /= self.sum_w_yo_sq()
if jacobian_transpose is not None:
cov = jacobian_transpose.self_transpose_times_symmetric_times_self(cov)
if normalised_by_goof: cov *= self.restrained_goof()**2
return cov
def covariance_matrix(self,
jacobian_transpose=None,
normalised_by_goof=True):
""" The columns of the jacobian_transpose determine which crystallographic
parameters appear in the covariance matrix.
If jacobian_transpose is None, then the covariance matrix returned will
be that for the independent L.S. parameters.
"""
if not self.step_equations().solved:
self.solve()
cov = linalg.inverse_of_u_transpose_u(
self.step_equations().cholesky_factor_packed_u())
cov /= self.sum_w_yo_sq()
if jacobian_transpose is not None:
cov = jacobian_transpose.self_transpose_times_symmetric_times_self(cov)
if normalised_by_goof: cov *= self.restrained_goof()**2
return cov