def exercise_truncate(q_large):
tprs_full = dmtbx.triplet_generator(miller_set=q_large,
discard_weights=True)
tprs = dmtbx.triplet_generator(miller_set=q_large,
amplitudes=q_large.data(),
max_relations_per_reflection=0,
discard_weights=True)
assert tprs.n_relations().all_eq(tprs_full.n_relations())
for n in (1, 10, 100, 1000):
tprs = dmtbx.triplet_generator(miller_set=q_large,
amplitudes=q_large.data(),
max_relations_per_reflection=n,
discard_weights=True)
assert (tprs.n_relations() >= n).all_eq(tprs.n_relations() == n)
n = 3
tprs = dmtbx.triplet_generator(miller_set=q_large,
amplitudes=q_large.data(),
max_relations_per_reflection=n,
discard_weights=True)
n_rel_full = tprs_full.n_relations()
n_rel = tprs.n_relations()
amp = q_large.data()
for ih in range(q_large.indices().size()):
if (n_rel[ih] == n_rel_full[ih]): continue
aa_full = flex.double()
for relation in tprs_full.relations_for(ih):
aa_full.append(amp[relation.ik()] * amp[relation.ihmk()])
aa = flex.double()
for relation in tprs.relations_for(ih):
aa.append(amp[relation.ik()] * amp[relation.ihmk()])
aa_full = aa_full.select(
flex.sort_permutation(data=aa_full, reverse=True))
assert approx_equal(aa_full[:n], aa)
def exercise_truncate(q_large):
tprs_full = dmtbx.triplet_generator(miller_set=q_large, discard_weights=True)
tprs = dmtbx.triplet_generator(
miller_set=q_large, amplitudes=q_large.data(), max_relations_per_reflection=0, discard_weights=True
)
assert tprs.n_relations().all_eq(tprs_full.n_relations())
for n in (1, 10, 100, 1000):
tprs = dmtbx.triplet_generator(
miller_set=q_large, amplitudes=q_large.data(), max_relations_per_reflection=n, discard_weights=True
)
assert (tprs.n_relations() >= n).all_eq(tprs.n_relations() == n)
n = 3
tprs = dmtbx.triplet_generator(
miller_set=q_large, amplitudes=q_large.data(), max_relations_per_reflection=n, discard_weights=True
)
n_rel_full = tprs_full.n_relations()
n_rel = tprs.n_relations()
amp = q_large.data()
for ih in xrange(q_large.indices().size()):
if n_rel[ih] == n_rel_full[ih]:
continue
aa_full = flex.double()
for relation in tprs_full.relations_for(ih):
aa_full.append(amp[relation.ik()] * amp[relation.ihmk()])
aa = flex.double()
for relation in tprs.relations_for(ih):
aa.append(amp[relation.ik()] * amp[relation.ihmk()])
aa_full = aa_full.select(flex.sort_permutation(data=aa_full, reverse=True))
assert approx_equal(aa_full[:n], aa)
def reciprocal_space_squaring(start, selection_fixed, verbose):
tprs = dmtbx.triplet_generator(miller_set=start)
if (0 or verbose):
for ih in range(start.indices()[:1].size()):
for relation in tprs.relations_for(ih):
print(relation.format(start.indices(), ih), end=' ')
if (not relation.is_sigma_2(ih)):
print("not sigma-2", end=' ')
print()
amplitudes = abs(start).data()
if (selection_fixed is not None):
amplitudes.set_selected(~selection_fixed, 0)
input_phases = flex.arg(start.data())
result = tprs.apply_tangent_formula(amplitudes=amplitudes,
phases_rad=input_phases,
selection_fixed=selection_fixed,
use_fixed_only=selection_fixed
is not None)
if (selection_fixed is not None):
assert result.select(selection_fixed).all_eq(
input_phases.select(selection_fixed))
return result
def reciprocal_space_squaring(start, selection_fixed, verbose):
tprs = dmtbx.triplet_generator(miller_set=start)
if 0 or verbose:
for ih in xrange(start.indices()[:1].size()):
for relation in tprs.relations_for(ih):
print relation.format(start.indices(), ih),
if not relation.is_sigma_2(ih):
print "not sigma-2",
print
amplitudes = abs(start).data()
if selection_fixed is not None:
amplitudes.set_selected(~selection_fixed, 0)
input_phases = flex.arg(start.data())
result = tprs.apply_tangent_formula(
amplitudes=amplitudes,
phases_rad=input_phases,
selection_fixed=selection_fixed,
use_fixed_only=selection_fixed is not None,
)
if selection_fixed is not None:
assert result.select(selection_fixed).all_eq(input_phases.select(selection_fixed))
return result
def run():
import sys
reflection_file_name = sys.argv[1]
import iotbx.cif
miller_arrays = iotbx.cif.reader(
file_path=reflection_file_name).as_miller_arrays()
for miller_array in miller_arrays:
s = str(miller_array.info())
if '_meas' in s:
if miller_array.is_xray_intensity_array():
break
elif miller_array.is_xray_amplitude_array():
break
if not ('_meas' in str(miller_array.info())
and (miller_array.is_xray_amplitude_array()
or miller_array.is_xray_intensity_array())):
print "Sorry: CIF does not contain an appropriate miller array"
return
miller_array.show_comprehensive_summary()
print
if (miller_array.is_xray_intensity_array()):
print "Converting intensities to amplitudes."
miller_array = miller_array.as_amplitude_array()
print
miller_array.setup_binner(auto_binning=True)
miller_array.binner().show_summary()
print
all_e_values = miller_array.quasi_normalize_structure_factors().sort(
by_value="data")
large_e_values = all_e_values.select(all_e_values.data() > 1.2)
print "number of large_e_values:", large_e_values.size()
print
from cctbx import dmtbx
triplets = dmtbx.triplet_generator(large_e_values)
from cctbx.array_family import flex
print "triplets per reflection: min,max,mean: %d, %d, %.2f" % (
flex.min(triplets.n_relations()),
flex.max(triplets.n_relations()),
flex.mean(triplets.n_relations().as_double()))
print "total number of triplets:", flex.sum(triplets.n_relations())
print
input_phases = large_e_values \
.random_phases_compatible_with_phase_restrictions()
tangent_formula_phases = input_phases.data()
for i in xrange(10):
tangent_formula_phases = triplets.apply_tangent_formula(
amplitudes=large_e_values.data(),
phases_rad=tangent_formula_phases,
selection_fixed=None,
use_fixed_only=False,
reuse_results=True)
e_map_coeff = large_e_values.phase_transfer(
phase_source=tangent_formula_phases)
from cctbx import maptbx
e_map = e_map_coeff.fft_map(
symmetry_flags=maptbx.use_space_group_symmetry)
e_map.apply_sigma_scaling()
e_map.statistics().show_summary(prefix="e_map ")
print
peak_search = e_map.peak_search(parameters=maptbx.peak_search_parameters(
min_distance_sym_equiv=1.2))
peaks = peak_search.all(max_clusters=10)
print "e_map peak list"
print " fractional coordinates peak height"
for site,height in zip(peaks.sites(), peaks.heights()):
print " (%9.6f, %9.6f, %9.6f)" % site, "%10.3f" % height
print
if (len(sys.argv) > 2):
coordinate_file_name = sys.argv[2]
xray_structure = iotbx.cif.reader(
file_path=coordinate_file_name).build_crystal_structures(
data_block_name="I")
xray_structure.show_summary().show_scatterers()
print
f_calc = abs(miller_array.structure_factors_from_scatterers(
xray_structure=xray_structure,
algorithm="direct").f_calc())
correlation = flex.linear_correlation(f_calc.data(), miller_array.data())
assert correlation.is_well_defined()
print "correlation of f_obs and f_calc: %.4f" % correlation.coefficient()
print
reference_model = xray_structure.as_emma_model()
assert reference_model.unit_cell().is_similar_to(e_map.unit_cell())
assert reference_model.space_group() == e_map.space_group()
from cctbx import euclidean_model_matching as emma
peak_model = emma.model(special_position_settings=reference_model)
for i,site in enumerate(peaks.sites()):
peak_model.add_position(emma.position(label="peak%02d" % i, site=site))
matches = emma.model_matches(
model1=reference_model,
model2=peak_model,
tolerance=1.,
models_are_diffraction_index_equivalent=True)
for match in matches.refined_matches:
match.show()
def run():
import sys
reflection_file_name = sys.argv[1]
import iotbx.cif
miller_arrays = iotbx.cif.reader(
file_path=reflection_file_name).as_miller_arrays()
for miller_array in miller_arrays:
s = str(miller_array.info())
if '_meas' in s:
if miller_array.is_xray_intensity_array():
break
elif miller_array.is_xray_amplitude_array():
break
if not ('_meas' in str(miller_array.info()) and
(miller_array.is_xray_amplitude_array()
or miller_array.is_xray_intensity_array())):
print "Sorry: CIF does not contain an appropriate miller array"
return
miller_array.show_comprehensive_summary()
print
if (miller_array.is_xray_intensity_array()):
print "Converting intensities to amplitudes."
miller_array = miller_array.as_amplitude_array()
print
miller_array.setup_binner(auto_binning=True)
miller_array.binner().show_summary()
print
all_e_values = miller_array.quasi_normalize_structure_factors().sort(
by_value="data")
large_e_values = all_e_values.select(all_e_values.data() > 1.2)
print "number of large_e_values:", large_e_values.size()
print
from cctbx import dmtbx
triplets = dmtbx.triplet_generator(large_e_values)
from cctbx.array_family import flex
print "triplets per reflection: min,max,mean: %d, %d, %.2f" % (
flex.min(triplets.n_relations()), flex.max(triplets.n_relations()),
flex.mean(triplets.n_relations().as_double()))
print "total number of triplets:", flex.sum(triplets.n_relations())
print
input_phases = large_e_values \
.random_phases_compatible_with_phase_restrictions()
tangent_formula_phases = input_phases.data()
for i in xrange(10):
tangent_formula_phases = triplets.apply_tangent_formula(
amplitudes=large_e_values.data(),
phases_rad=tangent_formula_phases,
selection_fixed=None,
use_fixed_only=False,
reuse_results=True)
e_map_coeff = large_e_values.phase_transfer(
phase_source=tangent_formula_phases)
from cctbx import maptbx
e_map = e_map_coeff.fft_map(symmetry_flags=maptbx.use_space_group_symmetry)
e_map.apply_sigma_scaling()
e_map.statistics().show_summary(prefix="e_map ")
print
peak_search = e_map.peak_search(parameters=maptbx.peak_search_parameters(
min_distance_sym_equiv=1.2))
peaks = peak_search.all(max_clusters=10)
print "e_map peak list"
print " fractional coordinates peak height"
for site, height in zip(peaks.sites(), peaks.heights()):
print " (%9.6f, %9.6f, %9.6f)" % site, "%10.3f" % height
print
if (len(sys.argv) > 2):
coordinate_file_name = sys.argv[2]
xray_structure = iotbx.cif.reader(
file_path=coordinate_file_name).build_crystal_structures(
data_block_name="I")
xray_structure.show_summary().show_scatterers()
print
f_calc = abs(
miller_array.structure_factors_from_scatterers(
xray_structure=xray_structure, algorithm="direct").f_calc())
correlation = flex.linear_correlation(f_calc.data(),
miller_array.data())
assert correlation.is_well_defined()
print "correlation of f_obs and f_calc: %.4f" % correlation.coefficient(
)
print
reference_model = xray_structure.as_emma_model()
assert reference_model.unit_cell().is_similar_to(e_map.unit_cell())
assert reference_model.space_group() == e_map.space_group()
from cctbx import euclidean_model_matching as emma
peak_model = emma.model(special_position_settings=reference_model)
for i, site in enumerate(peaks.sites()):
peak_model.add_position(
emma.position(label="peak%02d" % i, site=site))
matches = emma.model_matches(
model1=reference_model,
model2=peak_model,
tolerance=1.,
models_are_diffraction_index_equivalent=True)
for match in matches.refined_matches:
match.show()