def do_exercise(self, verbose=False):
xs = self.xs
indices = self.miller_indices(xs.space_group_info())
f = structure_factors.f_calc_modulus(xs)
f_sq = structure_factors.f_calc_modulus_squared(xs)
for h in indices:
f.linearise(h)
f_sq.linearise(h)
assert approx_equal_relatively(f.observable ** 2, f_sq.observable, relative_error=1e-10)
grad_f_sq = f_sq.grad_observable
two_f_grad_f = 2 * f.observable * f.grad_observable
flex.compare_derivatives(two_f_grad_f, grad_f_sq, eps=1e-12)
def do_exercise(self, verbose=False):
xs = self.xs
indices = self.miller_indices(xs.space_group_info())
f = structure_factors.f_calc_modulus(xs)
f_sq = structure_factors.f_calc_modulus_squared(xs)
for h in indices:
f.linearise(h)
f_sq.linearise(h)
assert approx_equal_relatively(f.observable**2,
f_sq.observable,
relative_error=1e-10)
grad_f_sq = f_sq.grad_observable
two_f_grad_f = (2 * f.observable * f.grad_observable)
flex.compare_derivatives(two_f_grad_f, grad_f_sq, eps=1e-12)
def compare_analytical_and_finite(f_obs, xray_structure, out):
grads_fin = d_target_d_params_finite(f_obs=f_obs,
xray_structure=xray_structure)
print >> out, "grads_fin:", list(grads_fin)
sf = structure_factors(xray_structure=xray_structure, miller_set=f_obs)
grads_ana = sf.d_target_d_params(f_obs=f_obs, target_type=least_squares)
print >> out, "grads_ana:", list(grads_ana)
flex.compare_derivatives(grads_ana, grads_fin)
curvs_fin = d2_target_d_params_finite(f_obs=f_obs,
xray_structure=xray_structure)
print >> out, "curvs_fin:", list(curvs_fin)
curvs_ana = sf.d2_target_d_params(f_obs=f_obs, target_type=least_squares)
print >> out, "curvs_ana:", list(curvs_ana)
flex.compare_derivatives(curvs_ana.as_1d(), curvs_fin)
print >> out
def compare_analytical_and_finite(f_obs, xray_structure, out):
grads_fin = d_target_d_params_finite(
f_obs=f_obs, xray_structure=xray_structure)
print >> out, "grads_fin:", list(grads_fin)
sf = structure_factors(
xray_structure=xray_structure, miller_set=f_obs)
grads_ana = sf.d_target_d_params(f_obs=f_obs, target_type=least_squares)
print >> out, "grads_ana:", list(grads_ana)
flex.compare_derivatives(grads_ana, grads_fin)
curvs_fin = d2_target_d_params_finite(
f_obs=f_obs, xray_structure=xray_structure)
print >> out, "curvs_fin:", list(curvs_fin)
curvs_ana = sf.d2_target_d_params(f_obs=f_obs, target_type=least_squares)
print >> out, "curvs_ana:", list(curvs_ana)
flex.compare_derivatives(curvs_ana.as_1d(), curvs_fin)
print >> out
def compare_analytical_and_finite(
f_obs,
xray_structure,
gradients_should_be_zero,
eps,
out):
grads_fin = d_target_d_params_finite(
d_order=1, f_obs=f_obs, xray_structure=xray_structure)
print >> out, "grads_fin:", list(grads_fin)
sf = structure_factors(
xray_structure=xray_structure, miller_set=f_obs)
grads_ana = sf.d_target_d_params(f_obs=f_obs, target_type=least_squares)
print >> out, "grads_ana:", list(grads_ana)
if (gradients_should_be_zero):
flex.compare_derivatives(grads_ana, flex.double(grads_ana.size(), 0), eps)
else:
flex.compare_derivatives(grads_ana, grads_fin, eps)
curvs_fin = d_target_d_params_finite(
d_order=2, f_obs=f_obs, xray_structure=xray_structure)
print >> out, "curvs_fin:", list(curvs_fin)
curvs_ana = sf.d2_target_d_params(f_obs=f_obs, target_type=least_squares)
print >> out, "curvs_ana:", list(curvs_ana)
flex.compare_derivatives(curvs_ana.as_1d(), curvs_fin, eps)
assert curvs_ana.matrix_is_symmetric(relative_epsilon=1e-10)
print >> out
#
for i_method,curvs_method in enumerate([
sf.d2_target_d_params_diag,
sf.d2_target_d_params_diag_cpp]):
curvs_diag_ana = curvs_method(f_obs=f_obs, target_type=least_squares)
if (i_method != 0):
flex.compare_derivatives(grads_ana, curvs_diag_ana.grads, eps=1e-12)
curvs_diag_ana = curvs_diag_ana.curvs
assert curvs_diag_ana.size() == curvs_ana.focus()[0]
flex.compare_derivatives(
curvs_ana.matrix_diagonal().as_1d(), curvs_diag_ana, eps=1e-12)
#
if (gradients_should_be_zero):
return flex.max(flex.abs(grads_fin))
