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 test_scale_rmerge_vs_batch_plot(batch_manager_fix):
"""Test the scale and rmerge batch plot. Should have the option
to plot without scales (for xia2)."""
bm = batch_manager_fix
rmergevsb = flex.double(range(0, 20))
scalesvsb = flex.double(range(1, 21))
d = scale_rmerge_vs_batch_plot(bm, rmergevsb, scalesvsb)
assert list(d["scale_rmerge_vs_batch"]["data"][0]["x"]) == list(bm.reduced_batches)
assert list(d["scale_rmerge_vs_batch"]["data"][1]["x"]) == list(bm.reduced_batches)
assert list(d["scale_rmerge_vs_batch"]["data"][0]["y"]) == list(scalesvsb)
assert list(d["scale_rmerge_vs_batch"]["data"][1]["y"]) == list(rmergevsb)
d = scale_rmerge_vs_batch_plot(bm, rmergevsb)
assert d["scale_rmerge_vs_batch"]["data"][0] == {}
assert list(d["scale_rmerge_vs_batch"]["data"][1]["x"]) == list(bm.reduced_batches)
assert d["scale_rmerge_vs_batch"]["data"][0] == {}
assert list(d["scale_rmerge_vs_batch"]["data"][1]["y"]) == list(rmergevsb)
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
def batch_dependent_plots(self):
binned_batches, rmerge, isigi, scalesvsbatch = batch_dependent_properties(
self.batches, self.intensities, self.scales)
batches = [{"id": b.id, "range": b.range} for b in self.params.batch]
bm = batch_manager(binned_batches, batches)
d = {}
d.update(i_over_sig_i_vs_batch_plot(bm, isigi))
d.update(scale_rmerge_vs_batch_plot(bm, rmerge, scalesvsbatch))
return d
