def test_multiscaler_update_for_minimisation():
"""Test the multiscaler update_for_minimisation method."""
p, e = (generated_param(), generated_exp(2))
p.reflection_selection.method = "use_all"
r1 = generated_refl(id_=0)
r1["intensity.sum.value"] = r1["intensity"]
r1["intensity.sum.variance"] = r1["variance"]
r2 = generated_refl(id_=1)
r2["intensity.sum.value"] = r2["intensity"]
r2["intensity.sum.variance"] = r2["variance"]
p.scaling_options.nproc = 2
p.model = "physical"
exp = create_scaling_model(p, e, [r1, r2])
singlescaler1 = create_scaler(p, [exp[0]], [r1])
singlescaler2 = create_scaler(p, [exp[1]], [r2])
multiscaler = MultiScaler([singlescaler1, singlescaler2])
pmg = ScalingParameterManagerGenerator(
multiscaler.active_scalers,
ScalingTarget,
multiscaler.params.scaling_refinery.refinement_order,
)
multiscaler.single_scalers[0].components["scale"].parameters /= 2.0
multiscaler.single_scalers[1].components["scale"].parameters *= 1.5
apm = pmg.parameter_managers()[0]
multiscaler.update_for_minimisation(apm, 0)
multiscaler.update_for_minimisation(apm, 1)
# bf[0], bf[1] should be list of scales and derivatives
s1, d1 = RefinerCalculator.calculate_scales_and_derivatives(
apm.apm_list[0], 0)
s2, d2 = RefinerCalculator.calculate_scales_and_derivatives(
apm.apm_list[1], 0)
s3, d3 = RefinerCalculator.calculate_scales_and_derivatives(
apm.apm_list[0], 1)
s4, d4 = RefinerCalculator.calculate_scales_and_derivatives(
apm.apm_list[1], 1)
expected_scales_for_block_1 = s1
expected_scales_for_block_1.extend(s2)
expected_scales_for_block_2 = s3
expected_scales_for_block_2.extend(s4)
expected_derivatives_for_block_1 = sparse.matrix(
expected_scales_for_block_1.size(), apm.n_active_params)
expected_derivatives_for_block_2 = sparse.matrix(
expected_scales_for_block_2.size(), apm.n_active_params)
expected_derivatives_for_block_1.assign_block(d1, 0, 0)
expected_derivatives_for_block_1.assign_block(d2, d1.n_rows,
apm.apm_data[1]["start_idx"])
expected_derivatives_for_block_2.assign_block(d3, 0, 0)
expected_derivatives_for_block_2.assign_block(d4, d3.n_rows,
apm.apm_data[1]["start_idx"])
block_list = multiscaler.Ih_table.blocked_data_list
assert block_list[0].inverse_scale_factors == expected_scales_for_block_1
assert block_list[1].inverse_scale_factors == expected_scales_for_block_2
assert block_list[1].derivatives == expected_derivatives_for_block_2
assert block_list[0].derivatives == expected_derivatives_for_block_1
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_RefinerCalculator(small_reflection_table):
"""Test for the RefinerCalculator class. This calculates scale factors and
derivatives for reflections based on the model components."""
# To test the basis function, need a scaling active parameter manager - to set
# this up we need a components dictionary with some reflection data.
# Let's use KB model components for simplicity - and have an extra fake 'abs'
# component.
rt = small_reflection_table
components = {
"scale": SingleScaleFactor(flex.double([1.0])),
"decay": SingleBScaleFactor(flex.double([0.0])),
"abs": SingleScaleFactor(flex.double([1.0])),
} # Create empty components.
components["scale"].data = {"id": rt["id"]}
components["decay"].data = {"d": rt["d"]}
components["abs"].data = {"id": rt["id"]}
for component in components.values():
component.update_reflection_data() # Add some data to components.
apm = scaling_active_parameter_manager(components, ["decay", "scale"])
# First test that scale factors can be successfully updated.
# Manually change the parameters in the apm.
decay = components["decay"] # Define alias
_ = components["scale"] # Define alias
# Note, order of params in apm.x depends on order in scaling model components.
new_B = 1.0
new_S = 2.0
apm.set_param_vals(flex.double([new_S, new_B]))
s, d = RefinerCalculator.calculate_scales_and_derivatives(apm, 0)
slist, dlist = RefinerCalculator._calc_component_scales_derivatives(apm, 0)
# Now test that the inverse scale factor is correctly calculated.
calculated_sfs = s
assert list(calculated_sfs) == pytest.approx(
list(new_S * flex.exp(new_B / (2.0 * flex.pow2(decay.d_values[0])))))
# Now check that the derivative matrix is correctly calculated.
calc_derivs = d
assert calc_derivs[0, 0] == dlist[0][0, 0] * slist[1][0]
assert calc_derivs[1, 0] == dlist[0][1, 0] * slist[1][1]
assert calc_derivs[2, 0] == dlist[0][2, 0] * slist[1][2]
assert calc_derivs[0, 1] == dlist[1][0, 0] * slist[0][0]
assert calc_derivs[1, 1] == dlist[1][1, 0] * slist[0][1]
assert calc_derivs[2, 1] == dlist[1][2, 0] * slist[0][2]
# Repeat the test when there is only one active parameter.
# First reset the parameters
components["decay"].parameters = flex.double([0.0])
components["scale"].parameters = flex.double([1.0])
components["abs"].parameters = flex.double([1.0])
components["decay"].calculate_scales_and_derivatives()
components["scale"].calculate_scales_and_derivatives()
components["abs"].calculate_scales_and_derivatives()
# Now generate a parameter manager for a single component.
apm = scaling_active_parameter_manager(components, ["scale"])
new_S = 2.0
apm.set_param_vals(flex.double(components["scale"].n_params, new_S))
s, d = RefinerCalculator.calculate_scales_and_derivatives(apm, 0)
slist, dlist = RefinerCalculator._calc_component_scales_derivatives(apm, 0)
# Test that the scales and derivatives were correctly calculated
assert list(s) == list([new_S] * slist[0].size())
assert d[0, 0] == dlist[0][0, 0]
assert d[1, 0] == dlist[0][1, 0]
assert d[2, 0] == dlist[0][2, 0]
# Test again for two components.
components["decay"].parameters = flex.double([0.0])
components["scale"].parameters = flex.double([1.0])
components["abs"].parameters = flex.double([1.0])
components["decay"].calculate_scales_and_derivatives()
components["scale"].calculate_scales_and_derivatives()
components["abs"].calculate_scales_and_derivatives()
apm = scaling_active_parameter_manager(components, ["scale", "decay"])
_, __ = RefinerCalculator.calculate_scales_and_derivatives(apm, 0)
# Test for no components
apm = scaling_active_parameter_manager(components, [])
_, d = RefinerCalculator.calculate_scales_and_derivatives(apm, 0)
assert d.n_cols == 0 and d.n_rows == 0
