@classmethod
get_inverse_covariances(cls, alphas, transformations): 

Transforms the alphas into proper inverse covariance matrices. Chosen so that in the case that the transformations, Q, diagonalize S we can write QT S Q = A

• :returns: _

@classmethod
get_covariances(cls, alphas, transformations): 

Transforms the alphas into proper inverse covariance matrices. Chosen so that in the case that the transformations, Q, diagonalize S we can write QT S Q = A

• :returns: _

@classmethod
get_momentum_vectors(cls, phases, transformations): 

Transforms the momenta so that they’re aligned along the Gaussian axes

• :returns: _

@classmethod
get_phase_vectors(cls, momenta, transformations): 

Transforms the momenta so that they’re aligned along the Gaussian axes

• :returns: _

@classmethod
get_overlap_gaussians(cls, centers, alphas, transformations, momenta, *, chunk_size=None, rows_cols=None, logger=None, parallelizer=None) -> 'OverlapGaussianData': 

@classmethod
poch(cls, n, m): 

@classmethod
polyint_1D(cls, centers, alphas, n): 

@classmethod
momentum_coeffient(cls, k, n): 

@classmethod
momentum_integral(cls, p, a, k): 

@classmethod
simple_poly_int(cls, n): 

@classmethod
tensor_expansion_integrate(cls, npts, derivs, overlap_data: 'OverlapGaussianData', expansion_type='multicenter', logger=None, reweight=True): 

provides an integral from a polynomial expansion with derivs as an expansion in tensors

• npts: Any

• derivs: Any

• centers: Any

• alphas: Any

• inds: Any

• rot_data: Any

• expansion_type: Any

• logger: Any

• :returns: _

@classmethod
quad_weight_eval(cls, function, d_chunk, w_chunk, ndim, centers, squa): 

@classmethod
quad_nd(cls, centers, alphas, function, flatten=False, degree=3, chunk_size=1000000, normalize=True): 

N-dimensional quadrature

• centers: Any

• alphas: Any

• function: Any

• degree: Any

• :returns: _

@classmethod
rotated_gaussian_quadrature(cls, function, alphas, centers, rotations, inverse, momenta, normalize=True, degree=2): 

@classmethod
quad_integrate(cls, function, overlap_data: 'OverlapGaussianData', degree=2, logger=None): 

Integrate potential over all pairs of Gaussians at once

• degree: Any

• :returns: _

@classmethod
evaluate_overlap(cls, overlap_data: 'OverlapGaussianData', logger=None, return_prefactor=False):