def test_SumAndPrfIntensityReducer():
"""Test that the reflection table is reduced on prf and sum intensities"""
reflections = generate_integrated_test_reflections()
reflections = SumAndPrfIntensityReducer.reduce_on_intensities(reflections)
assert list(reflections["intensity.prf.value"]) == [1.0, 2.0, 3.0]
assert list(reflections["intensity.prf.variance"]) == [0.1, 0.2, 0.3]
assert list(reflections["intensity.sum.value"]) == [11.0, 12.0, 13.0]
assert list(reflections["intensity.sum.variance"]) == [1.1, 1.2, 1.3]
reflections["lp"] = flex.double([0.5, 0.5, 1.0])
reflections["partiality"] = flex.double([0.0, 1.0, 1.0])
reflections = SumAndPrfIntensityReducer.apply_scaling_factors(reflections)
assert list(reflections["intensity.prf.value"]) == [1.0, 3.0]
assert list(reflections["intensity.prf.variance"]) == [0.2 * 0.25, 0.3]
assert list(reflections["intensity.sum.value"]) == [6.0, 13.0]
assert list(reflections["intensity.sum.variance"]) == [0.3, 1.3]
# test IsgiI selecting
r = flex.reflection_table()
r["intensity.prf.value"] = flex.double([1.0, 2.0, 3.0])
r["intensity.prf.variance"] = flex.double([1.0, 1.0, 1.0])
r["intensity.sum.value"] = flex.double([1.0, 2.0, 3.0])
r["intensity.sum.variance"] = flex.double([1.0, 1.1, 1.0])
r = SumAndPrfIntensityReducer.filter_on_min_isigi(r, 1.999)
assert list(r["intensity.prf.value"]) == [3.0]
# test bad variance filtering
r = flex.reflection_table()
r["intensity.prf.value"] = flex.double([1.0, 2.0, 3.0])
r["intensity.prf.variance"] = flex.double([0.0, 1.0, 1.0])
r["intensity.sum.value"] = flex.double([1.0, 2.0, 3.0])
r["intensity.sum.variance"] = flex.double([1.0, 0.0, 1.0])
r.set_flags(flex.bool([True, True, True]), r.flags.integrated_prf)
r.set_flags(flex.bool([True, True, True]), r.flags.integrated_sum)
r = SumAndPrfIntensityReducer.filter_bad_variances(r)
assert list(r["intensity.prf.value"]) == [3.0]
# test filtering for export
reflections = generate_integrated_test_reflections()
reflections = SumAndPrfIntensityReducer.filter_for_export(reflections)
assert "intensity.scale.value" not in reflections
assert "intensity.scale.variance" not in reflections
def test_IntensityReducer_instantiations():
"""Test that all classes can be instantiated (have the required implemented
methods) and have an intensities list with at least one str values"""
allowed_intensities = FilterForExportAlgorithm.allowed_intensities
SumIntensityReducer()
assert all(i in allowed_intensities for i in SumIntensityReducer.intensities)
PrfIntensityReducer()
assert all(i in allowed_intensities for i in PrfIntensityReducer.intensities)
SumAndPrfIntensityReducer()
assert all(i in allowed_intensities for i in SumAndPrfIntensityReducer.intensities)
ScaleIntensityReducer()
assert all(i in allowed_intensities for i in ScaleIntensityReducer.intensities)
def setup_dials_models(refls, expts):
### Initial setting up of data
dose = flex.ceil(refls["xyzobs.px.value"].parts()[2])
refls["dose"] = dose.iround()
# want to get scaled data with outliers still present
excluded = refls.get_flags(
refls.flags.excluded_for_scaling) | refls.get_flags(
refls.flags.user_excluded_in_scaling)
good = ~excluded
refls = refls.select(good) # still has outliers
refls = ScaleIntensityReducer.apply_scaling_factors(refls)
scaled_refls = SumAndPrfIntensityReducer.apply_scaling_factors(refls)
sg = expts[0].crystal.get_space_group()
uc = expts[0].crystal.get_unit_cell()
asu_index_s, d_s = map_indices_to_asu(scaled_refls["miller_index"], sg, uc)
anom_index_s, _ = map_indices_to_asu(scaled_refls["miller_index"],
sg,
uc,
anom=True)
### finished initial setting up, now know indices
### Get relevant data and sort by asu index
intensity_s = scaled_refls["intensity.scale.value"]
sigma_s = scaled_refls["intensity.scale.variance"]**0.5
dose_s = scaled_refls["dose"]
integrated_refls = scaled_refls
intensity_u = integrated_refls["intensity.prf.value"]
sigma_u = flex.sqrt(integrated_refls["intensity.prf.variance"])
isel_s = flex.sort_permutation(d_s, reverse=True)
sorted_asu_s = asu_index_s.select(isel_s)
sorted_anom_s = anom_index_s.select(isel_s)
scaled_groups = list(OrderedSet(sorted_asu_s))
outliers = scaled_refls.get_flags(scaled_refls.flags.outlier_in_scaling)
Data = collections.namedtuple(
"Data",
["intensity", "sigma", "dose", "asu_index", "anom_index", "outliers"])
data = Data(
intensity=intensity_s.select(isel_s),
sigma=sigma_s.select(isel_s),
dose=dose_s.select(isel_s),
asu_index=sorted_asu_s,
anom_index=sorted_anom_s,
outliers=outliers.select(isel_s),
)
dials_scaled = DialsScaledModel(data)
unscaled_data = Data(
intensity=intensity_u.select(isel_s),
sigma=sigma_u.select(isel_s),
dose=dose_s.select(isel_s),
asu_index=sorted_asu_s,
anom_index=sorted_anom_s,
outliers=None,
)
dials_unscaled = DialsUnScaledModel(unscaled_data)
return [dials_scaled, dials_unscaled], scaled_groups, uc, sg