def test_correct(space_group_symbol):
sgi = sgtbx.space_group_info(space_group_symbol)
cs = sgi.any_compatible_crystal_symmetry(volume=1000)
ms = cs.build_miller_set(anomalous_flag=True, d_min=1).expand_to_p1()
# the reciprocal matrix
B = scitbx.matrix.sqr(cs.unit_cell().fractionalization_matrix()).transpose()
crystal = Crystal(B, sgtbx.space_group())
expts = ExperimentList([Experiment(crystal=crystal)])
refl = flex.reflection_table()
refl["miller_index"] = ms.indices()
refl["rlp"] = B.elems * ms.indices().as_vec3_double()
refl["imageset_id"] = flex.int(len(refl))
refl["xyzobs.mm.value"] = flex.vec3_double(len(refl))
non_primitive_basis.correct(expts, refl, assign_indices.AssignIndicesGlobal())
cs_corrected = expts.crystals()[0].get_crystal_symmetry()
assert cs_corrected.change_of_basis_op_to_primitive_setting().is_identity_op()
assert (
cs.change_of_basis_op_to_primitive_setting().apply(ms.indices())
== refl["miller_index"]
)
def run_with_preparsed(experiments, reflections, params):
from dxtbx.model import ExperimentList
from scitbx.math import five_number_summary
print("Found", len(reflections), "reflections", "and", len(experiments),
"experiments")
filtered_reflections = flex.reflection_table()
filtered_experiments = ExperimentList()
skipped_reflections = flex.reflection_table()
skipped_experiments = ExperimentList()
if params.detector is not None:
culled_reflections = flex.reflection_table()
culled_experiments = ExperimentList()
detector = experiments.detectors()[params.detector]
for expt_id, experiment in enumerate(experiments):
refls = reflections.select(reflections['id'] == expt_id)
if experiment.detector is detector:
culled_experiments.append(experiment)
refls['id'] = flex.int(len(refls), len(culled_experiments) - 1)
culled_reflections.extend(refls)
else:
skipped_experiments.append(experiment)
refls['id'] = flex.int(len(refls),
len(skipped_experiments) - 1)
skipped_reflections.extend(refls)
print("RMSD filtering %d experiments using detector %d, out of %d" %
(len(culled_experiments), params.detector, len(experiments)))
reflections = culled_reflections
experiments = culled_experiments
difference_vector_norms = (reflections['xyzcal.mm'] -
reflections['xyzobs.mm.value']).norms()
if params.max_delta is not None:
sel = difference_vector_norms <= params.max_delta
reflections = reflections.select(sel)
difference_vector_norms = difference_vector_norms.select(sel)
data = flex.double()
counts = flex.double()
for i in range(len(experiments)):
dvns = difference_vector_norms.select(reflections['id'] == i)
counts.append(len(dvns))
if len(dvns) == 0:
data.append(0)
continue
rmsd = math.sqrt(flex.sum_sq(dvns) / len(dvns))
data.append(rmsd)
data *= 1000
subset = data.select(counts > 0)
print(len(subset), "experiments with > 0 reflections")
if params.show_plots:
h = flex.histogram(subset, n_slots=40)
fig = plt.figure()
ax = fig.add_subplot('111')
ax.plot(h.slot_centers().as_numpy_array(),
h.slots().as_numpy_array(), '-')
plt.title("Histogram of %d image RMSDs" % len(subset))
fig = plt.figure()
plt.boxplot(subset, vert=False)
plt.title("Boxplot of %d image RMSDs" % len(subset))
plt.show()
outliers = counts == 0
min_x, q1_x, med_x, q3_x, max_x = five_number_summary(subset)
print(
"Five number summary of RMSDs (microns): min %.1f, q1 %.1f, med %.1f, q3 %.1f, max %.1f"
% (min_x, q1_x, med_x, q3_x, max_x))
iqr_x = q3_x - q1_x
cut_x = params.iqr_multiplier * iqr_x
outliers.set_selected(data > q3_x + cut_x, True)
#outliers.set_selected(col < q1_x - cut_x, True) # Don't throw away the images that are outliers in the 'good' direction!
for i in range(len(experiments)):
if outliers[i]:
continue
refls = reflections.select(reflections['id'] == i)
refls['id'] = flex.int(len(refls), len(filtered_experiments))
filtered_reflections.extend(refls)
filtered_experiments.append(experiments[i])
#import IPython;IPython.embed()
zeroes = counts == 0
n_zero = len(counts.select(zeroes))
print(
"Removed %d bad experiments and %d experiments with zero reflections, out of %d (%%%.1f)"
%
(len(experiments) - len(filtered_experiments) - n_zero, n_zero,
len(experiments), 100 *
((len(experiments) - len(filtered_experiments)) / len(experiments))))
if params.detector is not None:
crystals = filtered_experiments.crystals()
for expt_id, experiment in enumerate(skipped_experiments):
if experiment.crystal in crystals:
filtered_experiments.append(experiment)
refls = skipped_reflections.select(
skipped_reflections['id'] == expt_id)
refls['id'] = flex.int(len(refls),
len(filtered_experiments) - 1)
filtered_reflections.extend(refls)
if params.delta_psi_filter is not None:
delta_psi = filtered_reflections['delpsical.rad'] * 180 / math.pi
sel = (delta_psi <= params.delta_psi_filter) & (
delta_psi >= -params.delta_psi_filter)
l = len(filtered_reflections)
filtered_reflections = filtered_reflections.select(sel)
print("Filtering by delta psi, removing %d out of %d reflections" %
(l - len(filtered_reflections), l))
print("Final experiment count", len(filtered_experiments))
return filtered_experiments, filtered_reflections
