def test_plot_array_decay_plot():
"""Test plot generation for array decay correction"""
array_dict = {
"__id__": "array",
"is_scaled": True,
"decay": {
"n_parameters": 20,
"parameters": list(range(1, 21)),
"est_standard_devs": [],
},
"configuration_parameters": {
"corrections": ["decay"],
"time_rot_interval": 10.0,
"n_res_param": 5,
"res_bin_width": 0.1,
"n_time_param": 4,
"resmin": 0.05,
"valid_osc_range": [0, 100],
},
}
array_model = ArrayScalingModel.from_dict(array_dict)
plot = plot_array_decay_plot(array_model)
assert plot["array_decay_plot"]["data"][0]["x"]
assert plot["array_decay_plot"]["data"][0]["y"]
def test_plot_array_absorption_plot():
"""Test plot generation for array absorption_correction"""
array_dict = {
"__id__": "array",
"is_scaled": True,
"absorption": {
"n_parameters": 45,
"parameters": list(range(1, 46)),
"est_standard_devs": [],
},
"configuration_parameters": {
"corrections": ["absorption"],
"time_rot_interval": 10.0,
"n_x_param": 3,
"n_y_param": 3,
"n_time_param": 5,
"xmin": 0,
"x_bin_width": 2,
"ymin": 1,
"y_bin_width": 2,
"valid_osc_range": [0, 100],
},
}
array_model = ArrayScalingModel.from_dict(array_dict)
plot = plot_array_absorption_plot(array_model)
assert plot["array_absorption_plot"]["data"][0]["x"]
assert plot["array_absorption_plot"]["data"][0]["y"]
assert plot["array_absorption_plot"]["data"][0]["z"]
def test_ArrayScalingModel(test_reflections, mock_exp):
"""Test the ArrayScalingModel class."""
configdict = {
"corrections": ["decay", "absorption", "modulation"],
"n_res_param": 2,
"n_time_param": 2,
"resmin": 1.0,
"res_bin_width": 1.0,
"time_norm_fac": 1.0,
"time_rot_interval": 1.0,
"n_x_param": 2,
"n_y_param": 2,
"xmin": 0.0,
"ymin": 0.0,
"x_bin_width": 1.0,
"y_bin_width": 2.0,
"n_x_mod_param": 2,
"n_y_mod_param": 2,
"x_det_bin_width": 2.0,
"y_det_bin_width": 2.0,
}
parameters_dict = {
"decay": {
"parameters": flex.double([1.2, 1.1, 1.0, 0.9]),
"parameter_esds": None,
},
"absorption": {
"parameters": flex.double([0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.1,
0.2]),
"parameter_esds": None,
},
"modulation": {
"parameters": flex.double(4, 0.9),
"parameter_esds": None
},
}
# Test standard factory initialisation
arraymodel = ArrayScalingModel(parameters_dict, configdict)
assert arraymodel.id_ == "array"
assert "decay" in arraymodel.components
assert "absorption" in arraymodel.components
assert "modulation" in arraymodel.components
assert list(
arraymodel.components["decay"].parameters) == [1.2, 1.1, 1.0, 0.9]
assert list(arraymodel.components["absorption"].parameters) == [
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
]
assert list(arraymodel.components["modulation"].parameters) == 4 * [0.9]
# Test configure reflection table
_ = arraymodel.configure_components(test_reflections, mock_exp, [])
# Test from_dict initialisation method for previous model case.
init_dict = arraymodel.to_dict()
new_array_model = ArrayScalingModel.from_dict(init_dict)
assert new_array_model.id_ == "array"
comps = new_array_model.components
assert "modulation" in comps
assert "absorption" in comps
assert "decay" in comps
assert list(comps["decay"].parameters) == [1.2, 1.1, 1.0, 0.9]
assert list(comps["absorption"].parameters) == [
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
]
assert list(comps["modulation"].parameters) == 4 * [0.9]
# Test from_dict initialisation method for another case.
array_dict = {
"__id__": "array",
"is_scaled": True,
"decay": {
"n_parameters": 4,
"parameters": [0.5, 1.0, 0.4, 1.0],
"null_parameter_value": 1.0,
"est_standard_devs": [0.05, 0.1, 0.05, 0.1],
},
"configuration_parameters": {
"corrections": ["decay"],
"n_res_param": 2,
"n_time_param": 2,
"resmin": 1.0,
"res_bin_width": 1.0,
"time_norm_fac": 1.0,
"time_rot_interval": 1.0,
},
}
arraymodel = ArrayScalingModel.from_dict(array_dict)
assert arraymodel.id_ == "array"
comps = arraymodel.components
assert "modulation" not in comps
assert "absorption" not in comps
assert "decay" in comps
assert list(comps["decay"].parameters) == [0.5, 1.0, 0.4, 1.0]
assert list(comps["decay"].parameter_esds) == [0.05, 0.1, 0.05, 0.1]
new_dict = arraymodel.to_dict()
assert new_dict == array_dict
with pytest.raises(RuntimeError):
array_dict["__id__"] = "physical"
arraymodel = ArrayScalingModel.from_dict(array_dict)
assert len(arraymodel.consecutive_refinement_order) == 3
assert "Decay component" in str(arraymodel)
# test limit batch range
configdict = {
"corrections": ["decay", "absorption"],
"n_res_param": 2,
"n_time_param": 3,
"resmin": 1.0,
"res_bin_width": 1.0,
"time_norm_fac": 0.1,
"time_rot_interval": 10.0,
"n_x_param": 2,
"n_y_param": 2,
"xmin": 0.0,
"ymin": 0.0,
"x_bin_width": 1.0,
"y_bin_width": 2.0,
"n_x_mod_param": 2,
"n_y_mod_param": 2,
"x_det_bin_width": 2.0,
"y_det_bin_width": 2.0,
"valid_image_range": (1, 20),
"valid_osc_range": (0, 20),
}
parameters_dict = {
"decay": {
"parameters": flex.double([1.2, 1.1, 1.0, 0.9, 0.8, 0.7]),
"parameter_esds": None,
},
"absorption": {
"parameters":
flex.double(
[0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.3, 0.4, 0.3, 0.4]),
"parameter_esds":
None,
},
}
array = ArrayScalingModel(parameters_dict, configdict)
array.limit_image_range((1, 10))
assert list(array.components["decay"].parameters) == [1.2, 1.1, 1.0, 0.9]
assert list(array.components["absorption"].parameters) == [
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
]
assert array.configdict["n_time_param"] == 2
assert array.configdict["valid_image_range"] == (1, 10)
assert array.configdict["valid_osc_range"] == (0, 10)
def from_dict(d):
"""creates a scaling model from a dict"""
from dials.algorithms.scaling.model.model import ArrayScalingModel
return ArrayScalingModel.from_dict(d)