def create(cls, params, experiment, reflection_table):
"""Return Null Scaler."""
logger.info("Preprocessing target dataset for scaling. \n")
reflection_table = cls.filter_bad_reflections(reflection_table)
variance_mask = reflection_table["variance"] <= 0.0
reflection_table.set_flags(variance_mask,
reflection_table.flags.excluded_for_scaling)
logger.info(
"%s reflections not suitable for scaling\n",
reflection_table.get_flags(
reflection_table.flags.excluded_for_scaling).count(True),
)
cls.ensure_experiment_identifier(experiment, reflection_table)
return NullScaler(params, experiment, reflection_table)
def test_NullScaler():
"""Test for successful creation of NullScaler."""
p, e, r = (generated_param(), generated_exp(), generated_refl())
exp = create_scaling_model(p, e, r)
_ = NullScaler(p, exp[0], r)
