def test_AnomalousPlotter():
"Make a larger array to allow all plots to be made"
cs = crystal.symmetry(space_group_symbol="P1", unit_cell=(6, 6, 6, 90, 90, 90))
ms = miller.build_set(cs, anomalous_flag=True, d_min=1.0)
indices = ms.indices()
new_indices = flex.miller_index(list(indices) * 10)
new_ms = miller.set(crystal_symmetry=cs, indices=new_indices, anomalous_flag=True)
data = flex.double(float(random.randrange(1, 100)) for _ in range(new_ms.size()))
iobs = miller.array(new_ms, data, sigmas=data)
iobs.change_symmetry(space_group_symbol="P222", merge_non_unique=False)
iobs.set_info(miller.array_info(source="DIALS", source_type="reflection_tables"))
iobs.set_observation_type_xray_intensity()
plotter = AnomalousPlotter(iobs)
d = plotter.make_plots()
expected = ["normal_distribution_plot_highres", "anom_correl_plot"]
keys = d.keys()
for k in expected:
assert k in keys
assert d[k]["data"][0]["x"] # check some data there
plotter = AnomalousPlotter(iobs, strong_cutoff=3.0)
d = plotter.make_plots()
expected = [
"anom_correl_plot",
"anom_scatter_plot_lowres",
"normal_distribution_plot_lowres",
]
keys = list(d.keys())
for k in expected:
assert k in keys
assert d[k]["data"][0]["x"] # check some data there
def make_merging_stats_plots(script):
"""Make merging stats plots for HTML report"""
d = {
"scaling_tables": ([], []),
"resolution_plots": {},
"batch_plots": {},
"misc_plots": {},
"anom_plots": {},
"image_range_tables": [],
}
(
batches,
rvb,
isigivb,
svb,
batch_data,
) = reflection_tables_to_batch_dependent_properties( # pylint: disable=unbalanced-tuple-unpacking
script.reflections,
script.experiments,
script.scaled_miller_array,
)
bm = batch_manager(batches, batch_data)
image_range_tables = make_image_range_table(script.experiments, bm)
if script.merging_statistics_result:
stats = script.merging_statistics_result
anom_stats = script.anom_merging_statistics_result
is_centric = script.scaled_miller_array.space_group().is_centric()
# Now calculate batch data
plotter = ResolutionPlotsAndStats(stats, anom_stats, is_centric)
d["resolution_plots"].update(plotter.make_all_plots())
d["scaling_tables"] = plotter.statistics_tables()
d["batch_plots"].update(scale_rmerge_vs_batch_plot(bm, rvb, svb))
d["batch_plots"].update(i_over_sig_i_vs_batch_plot(bm, isigivb))
plotter = IntensityStatisticsPlots(
script.scaled_miller_array, run_xtriage_analysis=False
)
d["resolution_plots"].update(plotter.generate_resolution_dependent_plots())
if d["resolution_plots"]["cc_one_half"]["data"][2]:
cc_anom = d["resolution_plots"]["cc_one_half"]["data"][2]["y"]
significance = d["resolution_plots"]["cc_one_half"]["data"][3]["y"]
sig = flex.double(cc_anom) > flex.double(significance)
max_anom = 0
for i, v in enumerate(sig):
if v:
max_anom = i
else:
break
d_min = uctbx.d_star_sq_as_d(plotter.binner.limits())[max_anom + 1]
else:
d_min = 0.0
d["misc_plots"].update(plotter.generate_miscellanous_plots())
intensities_anom = script.scaled_miller_array.as_anomalous_array()
intensities_anom = intensities_anom.map_to_asu().customized_copy(
info=script.scaled_miller_array.info()
)
anom_plotter = AnomalousPlotter(intensities_anom, strong_cutoff=d_min)
d["anom_plots"].update(anom_plotter.make_plots())
d["image_range_tables"] = [image_range_tables]
return d
def make_plots(self):
"""Generate tables of overall and resolution-binned merging statistics."""
d = {
"scaling_tables": ([], []),
"resolution_plots": OrderedDict(),
"batch_plots": OrderedDict(),
"misc_plots": OrderedDict(),
"anom_plots": OrderedDict(),
"image_range_tables": [],
}
if "statistics" in self.data:
plotter = ResolutionPlotsAndStats(
self.data["statistics"],
self.data["anomalous_statistics"],
is_centric=self.data["is_centric"],
)
d["resolution_plots"].update(plotter.make_all_plots())
d["scaling_tables"] = plotter.statistics_tables()
d["batch_plots"].update(
scale_rmerge_vs_batch_plot(
self.data["bm"],
self.data["r_merge_vs_batch"],
self.data["scale_vs_batch"],
))
d["batch_plots"].update(
i_over_sig_i_vs_batch_plot(self.data["bm"],
self.data["isigivsbatch"]))
plotter = IntensityStatisticsPlots(
self.data["scaled_miller_array"], run_xtriage_analysis=False)
d["resolution_plots"].update(
plotter.generate_resolution_dependent_plots())
if d["resolution_plots"]["cc_one_half"]["data"][2]:
cc_anom = d["resolution_plots"]["cc_one_half"]["data"][2]["y"]
significance = d["resolution_plots"]["cc_one_half"]["data"][3][
"y"]
sig = flex.double(cc_anom) > flex.double(significance)
max_anom = 0
for i, v in enumerate(sig):
if v:
max_anom = i
else:
break
d_min = uctbx.d_star_sq_as_d(
plotter.binner.limits())[max_anom + 1]
else:
d_min = 0.0
d["misc_plots"].update(plotter.generate_miscellanous_plots())
intensities_anom = self.data[
"scaled_miller_array"].as_anomalous_array()
intensities_anom = intensities_anom.map_to_asu().customized_copy(
info=self.data["scaled_miller_array"].info())
anom_plotter = AnomalousPlotter(intensities_anom,
strong_cutoff=d_min)
d["anom_plots"].update(anom_plotter.make_plots())
d["image_range_tables"] = self.data["image_range_tables"]
return d