def run(args):
import iotbx.phil
phil = iotbx.phil.process_command_line(args=args, master_string="""
target_unit_cell = 78,78,37,90,90,90
.type = unit_cell
target_space_group = P43212
.type = space_group
d_min = 2.1
.type = float
plot = False
.type = str
cut_short_at = None
.type = int
""").show()
print
work_params = phil.work.extract()
assert work_params.d_min is not None
print work_params.target_unit_cell
print work_params.target_space_group
from cctbx import miller
miller_set = symmetry(
unit_cell=work_params.target_unit_cell,
space_group_info=work_params.target_space_group
).build_miller_set(
anomalous_flag=True,
d_min=work_params.d_min
)
miller_set.show_summary()
# reality check
#recip_cell_volume = work_params.target_unit_cell.reciprocal().volume()
#recip_sphere_volume = (4/3)*math.pi*math.pow(1./work_params.d_min,3)
#resolution_cells = recip_sphere_volume/recip_cell_volume
#print "Number of asu's in sphere=",resolution_cells/miller_set.size()
results = get_observations(miller_set,phil.remaining_args,work_params)
# Create (and initialise?) arrays for statistics on the set of the
# observed reflections which are present in the reference data set.
completeness = flex.int(miller_set.size())
sum_I = flex.double(miller_set.size())
sum_I_SIGI = flex.double(miller_set.size())
#last = completeness.deep_copy()
for result,filename in results:
result.show_summary()
show_observations(result)
if work_params.plot==True:
#private interface to get the very strong diffraction images
import StringIO
G = StringIO.StringIO()
show_observations(result,out=G)
for line in G.getvalue().split("\n"):
tokens = line.split()
if len(tokens)>6:
try:
if float(tokens[3]) < 2.6 and float(tokens[-1]) > 10:
print "Strong signal",filename,line
except ValueError: pass
print
# Match up the observed intensities against the reference data
# set, i_model, instead of the pre-generated miller set,
# miller_set.
matches = miller.match_indices(
miller_set.indices(),
result.indices())
#for ih,hkl in enumerate(result.indices()):
# print hkl, result.data()[ih]
print
# Update the count for each matched reflection.
completeness += (~matches.single_selection(0)).as_int()
for pair in matches.pairs():
sum_I[pair[0]] += result.data()[pair[1]]
sum_I_SIGI[pair[0]] += (result.data()[pair[1]]/result.sigmas()[pair[1]])
#for ih,hkl in enumerate(miller_set.indices()):
# print "%15s"%str(hkl),"%4d"%last[ih],"%4d"%completeness[ih], sum_I[ih]
#print matches
#help(matches)
#print matches.pair_selection(0)
#for item in matches.pairs(): print item
#print list(miller_set.indices().select(matches.pairs().column(1)))
#print list(~matches.single_selection(0))
#print list(~matches.single_selection(1))
#last = completeness.deep_copy()
#plot_overall_completeness(completeness)
show_overall_observations(miller_set,completeness,sum_I,sum_I_SIGI)
def run(args):
import iotbx.phil
phil = iotbx.phil.process_command_line(
args=args,
master_string="""
target_unit_cell = 78,78,37,90,90,90
.type = unit_cell
target_space_group = P43212
.type = space_group
d_min = 2.1
.type = float
plot = False
.type = str
cut_short_at = None
.type = int
""",
).show()
print
work_params = phil.work.extract()
assert work_params.d_min is not None
print work_params.target_unit_cell
print work_params.target_space_group
from cctbx import miller
miller_set = symmetry(
unit_cell=work_params.target_unit_cell, space_group_info=work_params.target_space_group
).build_miller_set(anomalous_flag=True, d_min=work_params.d_min)
miller_set.show_summary()
# reality check
# recip_cell_volume = work_params.target_unit_cell.reciprocal().volume()
# recip_sphere_volume = (4/3)*math.pi*math.pow(1./work_params.d_min,3)
# resolution_cells = recip_sphere_volume/recip_cell_volume
# print "Number of asu's in sphere=",resolution_cells/miller_set.size()
results = get_observations(miller_set, phil.remaining_args, work_params)
# Create (and initialise?) arrays for statistics on the set of the
# observed reflections which are present in the reference data set.
completeness = flex.int(miller_set.size())
sum_I = flex.double(miller_set.size())
sum_I_SIGI = flex.double(miller_set.size())
# last = completeness.deep_copy()
for result, filename in results:
result.show_summary()
show_observations(result)
if work_params.plot == True:
# private interface to get the very strong diffraction images
import StringIO
G = StringIO.StringIO()
show_observations(result, out=G)
for line in G.getvalue().split("\n"):
tokens = line.split()
if len(tokens) > 6:
try:
if float(tokens[3]) < 2.6 and float(tokens[-1]) > 10:
print "Strong signal", filename, line
except ValueError:
pass
print
# Match up the observed intensities against the reference data
# set, i_model, instead of the pre-generated miller set,
# miller_set.
matches = miller.match_indices(miller_set.indices(), result.indices())
# for ih,hkl in enumerate(result.indices()):
# print hkl, result.data()[ih]
print
# Update the count for each matched reflection.
completeness += (~matches.single_selection(0)).as_int()
for pair in matches.pairs():
sum_I[pair[0]] += result.data()[pair[1]]
sum_I_SIGI[pair[0]] += result.data()[pair[1]] / result.sigmas()[pair[1]]
# for ih,hkl in enumerate(miller_set.indices()):
# print "%15s"%str(hkl),"%4d"%last[ih],"%4d"%completeness[ih], sum_I[ih]
# print matches
# help(matches)
# print matches.pair_selection(0)
# for item in matches.pairs(): print item
# print list(miller_set.indices().select(matches.pairs().column(1)))
# print list(~matches.single_selection(0))
# print list(~matches.single_selection(1))
# last = completeness.deep_copy()
# plot_overall_completeness(completeness)
show_overall_observations(miller_set, completeness, sum_I, sum_I_SIGI)