def __init__(self, parameters_dict, configdict, is_scaled=False):
"""Create the phyiscal scaling model components."""
super(PhysicalScalingModel, self).__init__(configdict, is_scaled)
if "scale" in configdict["corrections"]:
scale_setup = parameters_dict["scale"]
self._components["scale"] = SmoothScaleComponent1D(
scale_setup["parameters"], scale_setup["parameter_esds"])
if "decay" in configdict["corrections"]:
decay_setup = parameters_dict["decay"]
self._components["decay"] = SmoothBScaleComponent1D(
decay_setup["parameters"], decay_setup["parameter_esds"])
if "absorption" in configdict["corrections"]:
absorption_setup = parameters_dict["absorption"]
self._components["absorption"] = SHScaleComponent(
absorption_setup["parameters"],
absorption_setup["parameter_esds"])
def test_SmoothScaleFactor1D():
"""Test for the gaussian smoothed 1D scalefactor class."""
SF = SmoothScaleComponent1D(flex.double(5, 1.1))
assert SF.n_params == 5
assert list(SF.parameters) == [1.1, 1.1, 1.1, 1.1, 1.1]
norm_rot = flex.double([0.5, 1.0, 2.5, 0.0])
SF.data = {"x": norm_rot}
SF.update_reflection_data()
assert list(SF.normalised_values[0]) == [0.5, 1.0, 2.5, 0.0]
SF.smoother.set_smoothing(4, 1.0)
assert list(SF.smoother.positions()) == [-0.5, 0.5, 1.5, 2.5, 3.5]
s, d = SF.calculate_scales_and_derivatives()
s2 = SF.calculate_scales()
assert list(s) == list(s2)
assert list(s) == pytest.approx([1.1, 1.1, 1.1, 1.1])
assert d[0, 0] / d[0, 1] == pytest.approx(exp(-1.0) / exp(0.0))
sumexp = exp(-1.0 / 1.0) + exp(-0.0 / 1.0) + exp(
-1.0 / 1.0) # only averages 3 when
# normalised position is exactly on a smoother position.
assert d[0, 1] == pytest.approx((exp(0.0) / sumexp))
T = d.transpose()
assert sum(list(T[:, 0].as_dense_vector())) == 1.0 # should always be 1.0
assert sum(list(T[:, 1].as_dense_vector())) == 1.0
assert sum(list(T[:, 2].as_dense_vector())) == 1.0
assert d[1, 1] == d[1, 2]
assert d[1, 0] == d[1, 3]
s, d = SF.calculate_scales_and_derivatives()
# Test that if one or none in block, then doesn't fail but returns sensible value
SF._normalised_values = [flex.double([0.5])]
SF._n_refl = [1]
assert list(SF.normalised_values[0]) == [0.5]
s, d = SF.calculate_scales_and_derivatives()
s2 = SF.calculate_scales()
assert list(s) == list(s2)
SF._normalised_values = [flex.double()]
SF._n_refl = [0]
assert list(SF.normalised_values[0]) == []
s, d = SF.calculate_scales_and_derivatives()
s2 = SF.calculate_scales()
assert list(s) == list(s2)