def __init__(self, parameters_dict, configdict, is_scaled=False):
"""Create the array scaling model components."""
super(ArrayScalingModel, self).__init__(configdict, is_scaled)
if not any(i in configdict["corrections"]
for i in ["decay", "absorption"]):
raise ValueError(
"Array model must have at least one of decay or absorption corrections"
)
if "decay" in configdict["corrections"]:
decay_setup = parameters_dict["decay"]
self._components["decay"] = SmoothScaleComponent2D(
decay_setup["parameters"],
shape=(configdict["n_res_param"], configdict["n_time_param"]),
parameter_esds=decay_setup["parameter_esds"],
)
if "absorption" in configdict["corrections"]:
abs_setup = parameters_dict["absorption"]
self._components["absorption"] = SmoothScaleComponent3D(
abs_setup["parameters"],
shape=(
configdict["n_x_param"],
configdict["n_y_param"],
configdict["n_time_param"],
),
parameter_esds=abs_setup["parameter_esds"],
)
if "modulation" in configdict["corrections"]:
mod_setup = parameters_dict["modulation"]
self._components["modulation"] = SmoothScaleComponent2D(
mod_setup["parameters"],
shape=(configdict["n_x_mod_param"],
configdict["n_y_mod_param"]),
parameter_esds=mod_setup["parameter_esds"],
)
def test_SmoothScaleFactor2D():
"""Test the 2D smooth scale factor class."""
with pytest.raises(AssertionError): # Test incorrect shape initialisation
SF = SmoothScaleComponent2D(flex.double(30, 1.1), shape=(5, 5))
SF = SmoothScaleComponent2D(flex.double(30, 1.1), shape=(6, 5))
assert SF.n_x_params == 6
assert SF.n_y_params == 5
assert SF.n_params == 30
assert list(SF.parameters) == [1.1] * 30
norm_rot = flex.double(30, 0.5)
norm_time = flex.double(30, 0.5)
norm_rot[0] = 0.0
norm_time[0] = 0.0
norm_rot[29] = 3.99
norm_time[29] = 2.99
SF.data = {"x": norm_rot, "y": norm_time}
SF.update_reflection_data()
# assert list(SF.normalised_x_values) == list(flex.double(
# [0.5, 0.5, 0.5, 0.0, 0.0, 0.0]))
# assert list(SF.normalised_y_values) == list(flex.double(
# [0.0, 0.0, 0.0, 0.5, 0.5, 0.5]))
SF.smoother.set_smoothing(4, 1.0) # will average 3 in x,y dims.
assert list(SF.smoother.x_positions()) == [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5]
assert list(SF.smoother.y_positions()) == [-0.5, 0.5, 1.5, 2.5, 3.5]
s, d = SF.calculate_scales_and_derivatives()
assert list(s) == pytest.approx([1.1] * 30)
sumexp = exp(0.0) + (4.0 * exp(-1.0 / 1.0)) + (4.0 * exp(-2.0 / 1.0))
assert d[1, 7] == pytest.approx(exp(-0.0) / sumexp)
# Test again with a small number of params to check different behaviour.
SF = SmoothScaleComponent2D(flex.double(6, 1.1), shape=(3, 2))
_ = flex.reflection_table()
norm_rot = flex.double(6, 0.5)
norm_time = flex.double(6, 0.5)
norm_rot[0] = 0.0
norm_time[0] = 0.0
norm_rot[5] = 1.99
norm_time[5] = 0.99
SF.data = {"x": norm_rot, "y": norm_time}
SF.update_reflection_data()
SF.smoother.set_smoothing(4, 1.0) # will average 3,2 in x,y dims.
assert list(SF.smoother.x_positions()) == [0.0, 1.0, 2.0]
assert list(SF.smoother.y_positions()) == [0.0, 1.0]
s, d = SF.calculate_scales_and_derivatives()
s2 = SF.calculate_scales()
assert list(s) == list(s2)
sumexp = (4.0 * exp(-0.5 / 1.0)) + (2.0 * exp(-2.5 / 1.0))
assert d[1, 1] == pytest.approx(exp(-0.5) / sumexp)
# Test that if one or none in block, then doesn't fail but returns sensible value
SF._normalised_x_values = [flex.double([0.5])]
SF._normalised_y_values = [flex.double([0.5])]
SF._n_refl = [1]
assert list(SF.normalised_x_values[0]) == [0.5]
assert list(SF.normalised_y_values[0]) == [0.5]
s, d = SF.calculate_scales_and_derivatives()
s2 = SF.calculate_scales()
assert list(s) == list(s2)
assert list(s) == pytest.approx([1.1])
SF._normalised_x_values = [flex.double()]
SF._normalised_y_values = [flex.double()]
SF._n_refl = [0]
assert list(SF.normalised_x_values[0]) == []
assert list(SF.normalised_y_values[0]) == []
s, d = SF.calculate_scales_and_derivatives()
s2 = SF.calculate_scales()
assert list(s) == list(s2)
assert list(s) == []
assert d == sparse.matrix(0, 0)