def test_auto_scaling_model():
params = generated_param()
exp = generated_exp(scan=False)
rt = generated_refl()
params.__inject__("model", "auto")
new_exp = create_auto_scaling_model(params, exp, [rt])
assert new_exp[0].scaling_model.id_ == "KB"
params = generated_param(absorption_term=True)
exp = generated_exp(image_range=[1, 5]) # 5 degree wedge
params.__inject__("model", "auto")
new_exp = create_auto_scaling_model(params, exp, [rt])
assert new_exp[0].scaling_model.id_ == "physical"
assert len(new_exp[0].scaling_model.components["scale"].parameters) == 5
assert len(new_exp[0].scaling_model.components["decay"].parameters) == 3
assert "absorption" not in new_exp[0].scaling_model.components
params = generated_param(absorption_term=True)
exp = generated_exp(image_range=[1, 20]) # 20 degree wedge
params.__inject__("model", "auto")
new_exp = create_auto_scaling_model(params, exp, [rt])
assert new_exp[0].scaling_model.id_ == "physical"
assert len(new_exp[0].scaling_model.components["scale"].parameters) == 7
assert len(new_exp[0].scaling_model.components["decay"].parameters) == 6
assert "absorption" not in new_exp[0].scaling_model.components
params = generated_param(absorption_term=True)
exp = generated_exp(image_range=[1, 75]) # 20 degree wedge
params.__inject__("model", "auto")
new_exp = create_auto_scaling_model(params, exp, [rt])
assert new_exp[0].scaling_model.id_ == "physical"
assert len(new_exp[0].scaling_model.components["scale"].parameters) == 12
assert len(new_exp[0].scaling_model.components["decay"].parameters) == 10
assert "absorption" in new_exp[0].scaling_model.components
# Now test overwrite_existing_models option
params.overwrite_existing_models = True
params.model = "KB"
newer_exp = create_scaling_model(params, new_exp, [rt])
assert isinstance(newer_exp[0].scaling_model, KBScalingModel)
def _create_model_and_scaler(self):
"""Create the scaling models and scaler."""
if self.params.model in (None, libtbx.Auto):
self.experiments = create_auto_scaling_model(
self.params, self.experiments, self.reflections
)
else:
self.experiments = create_scaling_model(
self.params, self.experiments, self.reflections
)
logger.info("\nScaling models have been initialised for all experiments.")
logger.info("%s%s%s", "\n", "=" * 80, "\n")
self.experiments = set_image_ranges_in_scaling_models(self.experiments)
self.scaler = create_scaler(self.params, self.experiments, self.reflections)