def test_SingleScaler_update_for_minimisation():
"""Test the update_for_minimisation method of the singlescaler."""
# test_params.scaling_options.nproc = 1
p, e, r = (generated_param(), generated_exp(), generated_refl_2())
exp = create_scaling_model(p, e, r)
p.reflection_selection.method = "use_all"
single_scaler = SingleScaler(p, exp[0], r)
pmg = ScalingParameterManagerGenerator(
single_scaler.active_scalers,
ScalingTarget(),
single_scaler.params.scaling_refinery.refinement_order,
)
single_scaler.components["scale"].parameters /= 2.0
apm = pmg.parameter_managers()[0]
Ih_table = single_scaler.Ih_table.blocked_data_list[0]
Ih_table.calc_Ih()
assert list(Ih_table.inverse_scale_factors) == [1.0, 1.0]
assert list(Ih_table.Ih_values) == [10.0, 1.0]
single_scaler.update_for_minimisation(apm, 0)
# Should set new scale factors, and calculate Ih and weights.
bf = RefinerCalculator.calculate_scales_and_derivatives(apm.apm_list[0], 0)
assert list(Ih_table.inverse_scale_factors) == list(bf[0])
assert list(Ih_table.Ih_values) != [1.0, 10.0]
assert list(Ih_table.Ih_values) == pytest.approx(
list(Ih_table.intensities / bf[0]))
for i in range(Ih_table.derivatives.n_rows):
for j in range(Ih_table.derivatives.n_cols):
assert Ih_table.derivatives[i, j] == pytest.approx(bf[1][i, j])
assert Ih_table.derivatives.non_zeroes == bf[1].non_zeroes
def test_SingleScaler_update_for_minimisation():
"""Test the update_for_minimisation method of the singlescaler."""
# test_params.scaling_options.nproc = 1
p, e, r = (generated_param(), generated_exp(), generated_refl_2())
exp = create_scaling_model(p, e, r)
p.reflection_selection.method = "use_all"
single_scaler = SingleScaler(p, exp[0], r)
apm_fac = create_apm_factory(single_scaler)
single_scaler.components["scale"].parameters /= 2.0
apm = apm_fac.make_next_apm()
Ih_table = single_scaler.Ih_table.blocked_data_list[0]
Ih_table.calc_Ih()
assert list(Ih_table.inverse_scale_factors) == [1.0, 1.0]
assert list(Ih_table.Ih_values) == [10.0, 1.0]
single_scaler.update_for_minimisation(apm, 0)
# Should set new scale factors, and calculate Ih and weights.
bf = basis_function().calculate_scales_and_derivatives(apm.apm_list[0], 0)
assert list(Ih_table.inverse_scale_factors) == list(bf[0])
assert list(Ih_table.Ih_values) != [1.0, 10.0]
assert approx_equal(list(Ih_table.Ih_values),
list(Ih_table.intensities / bf[0]))
for i in range(Ih_table.derivatives.n_rows):
for j in range(Ih_table.derivatives.n_cols):
assert approx_equal(Ih_table.derivatives[i, j], bf[1][i, j])
assert Ih_table.derivatives.non_zeroes == bf[1].non_zeroes
def test_target_gradient_calculation_finite_difference(small_reflection_table,
single_exp,
physical_param):
"""Test the calculated gradients against a finite difference calculation."""
model = PhysicalScalingModel.from_data(physical_param, single_exp,
small_reflection_table)
# need to 'add_data'
model.configure_components(small_reflection_table, single_exp,
physical_param)
model.components["scale"].update_reflection_data()
model.components["decay"].update_reflection_data()
apm = multi_active_parameter_manager(
ScalingTarget(),
[model.components],
[["scale", "decay"]],
scaling_active_parameter_manager,
)
model.components["scale"].inverse_scales = flex.double([2.0, 1.0, 2.0])
model.components["decay"].inverse_scales = flex.double([1.0, 1.0, 0.4])
Ih_table = IhTable([small_reflection_table],
single_exp.crystal.get_space_group())
with patch.object(SingleScaler, "__init__", lambda x, y, z, k: None):
scaler = SingleScaler(None, None, None)
scaler._Ih_table = Ih_table
# Now do finite difference check.
target = ScalingTarget()
scaler.update_for_minimisation(apm, 0)
grad = target.calculate_gradients(scaler.Ih_table.blocked_data_list[0])
res = target.calculate_residuals(scaler.Ih_table.blocked_data_list[0])
assert (res >
1e-8), """residual should not be zero, or the gradient test
below will not really be working!"""
# Now compare to finite difference
f_d_grad = calculate_gradient_fd(target, scaler, apm)
print(list(f_d_grad))
print(list(grad))
assert list(grad) == pytest.approx(list(f_d_grad))
sel = f_d_grad > 1e-8
assert sel, """assert sel has some elements, as finite difference grad should
def test_target_jacobian_calculation_finite_difference(physical_param,
single_exp,
large_reflection_table):
"""Test the calculated jacobian against a finite difference calculation."""
physical_param.physical.decay_correction = False
model = PhysicalScalingModel.from_data(physical_param, single_exp,
large_reflection_table)
# need to 'add_data'
model.configure_components(large_reflection_table, single_exp,
physical_param)
model.components["scale"].update_reflection_data()
apm = multi_active_parameter_manager(
ScalingTarget(),
[model.components],
[["scale"]],
scaling_active_parameter_manager,
)
Ih_table = IhTable([large_reflection_table],
single_exp.crystal.get_space_group())
with patch.object(SingleScaler, "__init__", lambda x, y, z, k: None):
scaler = SingleScaler(None, None, None)
scaler._Ih_table = Ih_table
target = ScalingTarget()
scaler.update_for_minimisation(apm, 0)
fd_jacobian = calculate_jacobian_fd(target, scaler, apm)
r, jacobian, w = target.compute_residuals_and_gradients(
scaler.Ih_table.blocked_data_list[0])
assert r == pytest.approx(
[-50.0 / 3.0, 70.0 / 3.0, -20.0 / 3.0, 12.5, -2.5] +
[-25.0, 0.0, -75.0, 0.0, 200.0])
assert w == pytest.approx(
[0.1, 0.1, 0.1, 0.02, 0.1, 0.02, 0.01, 0.02, 0.01, 0.01])
n_rows = jacobian.n_rows
n_cols = jacobian.n_cols
print(jacobian)
print(fd_jacobian)
for i in range(0, n_rows):
for j in range(0, n_cols):
assert jacobian[i, j] == pytest.approx(fd_jacobian[i, j],
abs=1e-4)