def test_quasi_normalisation(simple_reflection_table, test_exp_E2, test_exp_P1):
"""Test the quasi_normalisation function."""
# Test that for small datasets, all Esq values are set to one.
refl = quasi_normalisation(simple_reflection_table, test_exp_E2)
assert list(refl["Esq"]) == [1.0, 1.0, 1.0]
rt = refl_for_norm()
new_rt = quasi_normalisation(rt, test_exp_P1)
for i in range(0, 9):
assert list(new_rt["Esq"][i * 1100 : (i + 1) * 1100]) == pytest.approx(
list(np.linspace(0.9, 1.1, num=1100, endpoint=True))
)
def create(cls, params, experiment, reflection_table, for_multi=False):
"""Perform reflection_table preprocessing and create a SingleScaler."""
cls.ensure_experiment_identifier(params, experiment, reflection_table)
logger.info(
"Preprocessing data for scaling. The id assigned to this \n"
"dataset is %s, and the scaling model type being applied is %s. \n",
list(reflection_table.experiment_identifiers().values())[0],
experiment.scaling_model.id_,
)
reflection_table, reasons = cls.filter_bad_reflections(
reflection_table)
if "inverse_scale_factor" not in reflection_table:
reflection_table["inverse_scale_factor"] = flex.double(
reflection_table.size(), 1.0)
elif (reflection_table["inverse_scale_factor"].count(0.0) ==
reflection_table.size()):
reflection_table["inverse_scale_factor"] = flex.double(
reflection_table.size(), 1.0)
reflection_table = choose_scaling_intensities(
reflection_table, params.reflection_selection.intensity_choice)
excluded_for_scaling = reflection_table.get_flags(
reflection_table.flags.excluded_for_scaling)
user_excluded = reflection_table.get_flags(
reflection_table.flags.user_excluded_in_scaling)
reasons.add_reason("user excluded", user_excluded.count(True))
reasons.add_reason("excluded for scaling",
excluded_for_scaling.count(True))
n_excluded = (excluded_for_scaling | user_excluded).count(True)
if n_excluded == reflection_table.size():
logger.info(
"All reflections were determined to be unsuitable for scaling."
)
logger.info(reasons)
raise BadDatasetForScalingException(
"""Unable to use this dataset for scaling""")
else:
logger.info(
"%s/%s reflections not suitable for scaling\n%s",
n_excluded,
reflection_table.size(),
reasons,
)
if not for_multi:
determine_reflection_selection_parameters(params, [experiment],
[reflection_table])
if params.reflection_selection.method == "intensity_ranges":
reflection_table = quasi_normalisation(reflection_table,
experiment)
if (params.reflection_selection.method
in (None, Auto, "auto", "quasi_random")) or (
experiment.scaling_model.id_ == "physical"
and "absorption" in experiment.scaling_model.components):
if experiment.scan:
# calc theta and phi cryst
reflection_table["phi"] = (
reflection_table["xyzobs.px.value"].parts()[2] *
experiment.scan.get_oscillation()[1])
reflection_table = calc_crystal_frame_vectors(
reflection_table, experiment)
return SingleScaler(params, experiment, reflection_table, for_multi)
def create(cls, params, experiment, reflection_table, for_multi=False):
"""Perform reflection_table preprocessing and create a SingleScaler."""
cls.ensure_experiment_identifier(experiment, reflection_table)
logger.info(
"The scaling model type being applied is %s. \n",
experiment.scaling_model.id_,
)
try:
reflection_table = cls.filter_bad_reflections(
reflection_table,
partiality_cutoff=params.cut_data.partiality_cutoff,
min_isigi=params.cut_data.min_isigi,
intensity_choice=params.reflection_selection.intensity_choice,
)
except ValueError:
raise BadDatasetForScalingException
# combine partial measurements of same reflection, to handle those reflections
# that were split by dials.integrate - changes size of reflection table.
reflection_table = sum_partial_reflections(reflection_table)
if "inverse_scale_factor" not in reflection_table:
reflection_table["inverse_scale_factor"] = flex.double(
reflection_table.size(), 1.0)
elif (reflection_table["inverse_scale_factor"].count(0.0) ==
reflection_table.size()):
reflection_table["inverse_scale_factor"] = flex.double(
reflection_table.size(), 1.0)
reflection_table = choose_initial_scaling_intensities(
reflection_table, params.reflection_selection.intensity_choice)
excluded_for_scaling = reflection_table.get_flags(
reflection_table.flags.excluded_for_scaling)
user_excluded = reflection_table.get_flags(
reflection_table.flags.user_excluded_in_scaling)
reasons = Reasons()
reasons.add_reason("user excluded", user_excluded.count(True))
reasons.add_reason("excluded for scaling",
excluded_for_scaling.count(True))
n_excluded = (excluded_for_scaling | user_excluded).count(True)
if n_excluded == reflection_table.size():
logger.info(
"All reflections were determined to be unsuitable for scaling."
)
logger.info(reasons)
raise BadDatasetForScalingException(
"""Unable to use this dataset for scaling""")
else:
logger.info(
"Excluding %s/%s reflections\n%s",
n_excluded,
reflection_table.size(),
reasons,
)
if params.reflection_selection.method == "intensity_ranges":
reflection_table = quasi_normalisation(reflection_table,
experiment)
if (params.reflection_selection.method
in (None, Auto, "auto", "quasi_random")) or (
experiment.scaling_model.id_ == "physical"
and "absorption" in experiment.scaling_model.components):
if experiment.scan:
reflection_table = calc_crystal_frame_vectors(
reflection_table, experiment)
alignment_axis = (1.0, 0.0, 0.0)
reflection_table["s0c"] = align_axis_along_z(
alignment_axis, reflection_table["s0c"])
reflection_table["s1c"] = align_axis_along_z(
alignment_axis, reflection_table["s1c"])
try:
scaler = SingleScaler(params, experiment, reflection_table,
for_multi)
except BadDatasetForScalingException as e:
raise ValueError(e)
else:
return scaler
