def calculate_gradients(Ih_table):
"""Return a gradient vector on length len(self.apm.x)."""
gsq = flex.pow2(Ih_table.inverse_scale_factors) * Ih_table.weights
sumgsq = gsq * Ih_table.h_index_matrix
prefactor = (
-2.0
* Ih_table.weights
* (
Ih_table.intensities
- (Ih_table.Ih_values * Ih_table.inverse_scale_factors)
)
)
dIh = (
Ih_table.intensities
- (Ih_table.Ih_values * 2.0 * Ih_table.inverse_scale_factors)
) * Ih_table.weights
dIh_by_dpi = calc_dIh_by_dpi(
dIh, sumgsq, Ih_table.h_index_matrix, Ih_table.derivatives.transpose()
)
term_1 = (prefactor * Ih_table.Ih_values) * Ih_table.derivatives
term_2 = (
prefactor * Ih_table.inverse_scale_factors * Ih_table.h_index_matrix
) * dIh_by_dpi
gradient = term_1 + term_2
return gradient
def calculate_gradients(Ih_table):
"""Return a gradient vector on length len(self.apm.x)."""
gsq = np.square(Ih_table.inverse_scale_factors) * Ih_table.weights
sumgsq = flumpy.from_numpy(Ih_table.sum_in_groups(gsq))
prefactor = (-2.0 * Ih_table.weights *
(Ih_table.intensities -
(Ih_table.Ih_values * Ih_table.inverse_scale_factors)))
dIh = flumpy.from_numpy(
(Ih_table.intensities -
(Ih_table.Ih_values * 2.0 * Ih_table.inverse_scale_factors)) *
Ih_table.weights)
dIh_by_dpi = calc_dIh_by_dpi(dIh, sumgsq, Ih_table.h_index_matrix,
Ih_table.derivatives.transpose())
term_1 = (flumpy.from_numpy(prefactor * Ih_table.Ih_values) *
Ih_table.derivatives)
term_2 = (flumpy.from_numpy(
Ih_table.sum_in_groups(prefactor * Ih_table.inverse_scale_factors))
* dIh_by_dpi)
gradient = term_1 + term_2
return gradient