def __init__(self, unmerged_intensities):
self._intensities_original_index = unmerged_intensities
self._observations = {}
from cctbx import miller, sgtbx
sg = self._intensities_original_index.space_group()
sg_type = sg.type()
asu = sgtbx.reciprocal_space_asu(sg_type)
anomalous_flag = self._intensities_original_index.anomalous_flag()
ma = self._intensities_original_index
original_indices = ma.indices()
unique_indices = original_indices.deep_copy()
isym = flex.int(unique_indices.size())
miller.map_to_asu_isym(sg_type, anomalous_flag, unique_indices, isym)
n_plus, n_minus = 0, 0
for iref in range(len(original_indices)):
h_orig = original_indices[iref]
h_uniq = unique_indices[iref]
h_isym = isym[iref]
h_eq = miller.sym_equiv_indices(sg, h_uniq)
if h_eq.is_centric():
flag = observation_group.CENTRIC
else:
asu_which = asu.which(h_uniq)
assert asu_which != 0
if asu_which == 1:
flag = observation_group.PLUS
else:
flag = observation_group.MINUS
h_uniq = tuple(-1*h for h in h_uniq)
group = self._observations.get(h_uniq)
if group is None:
group = observation_group(
h_uniq, is_centric=(flag == observation_group.CENTRIC))
self._observations[h_uniq] = group
if flag == observation_group.MINUS:
n_minus += 1
group.add_iminus(iref)
else:
n_plus += 1
group.add_iplus(iref)
def __init__(self, unmerged_intensities):
self._intensities_original_index = unmerged_intensities
self._observations = {}
from cctbx import miller, sgtbx
sg = self._intensities_original_index.space_group()
sg_type = sg.type()
asu = sgtbx.reciprocal_space_asu(sg_type)
anomalous_flag = self._intensities_original_index.anomalous_flag()
ma = self._intensities_original_index
original_indices = ma.indices()
unique_indices = original_indices.deep_copy()
isym = flex.int(unique_indices.size())
miller.map_to_asu_isym(sg_type, anomalous_flag, unique_indices, isym)
n_plus, n_minus = 0, 0
for iref in range(len(original_indices)):
h_orig = original_indices[iref]
h_uniq = unique_indices[iref]
h_isym = isym[iref]
h_eq = miller.sym_equiv_indices(sg, h_uniq)
if h_eq.is_centric():
flag = observation_group.CENTRIC
else:
asu_which = asu.which(h_uniq)
assert asu_which != 0
if asu_which == 1:
flag = observation_group.PLUS
else:
flag = observation_group.MINUS
h_uniq = tuple(-1*h for h in h_uniq)
group = self._observations.get(h_uniq)
if group is None:
group = observation_group(
h_uniq, is_centric=(flag == observation_group.CENTRIC))
self._observations[h_uniq] = group
if flag == observation_group.MINUS:
n_minus += 1
group.add_iminus(iref)
else:
n_plus += 1
group.add_iplus(iref)
def tst_map_to_asu_isym(anomalous_flag):
from cctbx import sgtbx
from cctbx.miller import map_to_asu_isym
from cctbx.array_family import flex
mi = flex.miller_index()
i = flex.int()
import random
nhkl = 1000
for j in range(nhkl):
hkl = [random.randint(-10, 10) for j in range(3)]
mi.append(hkl)
i.append(0)
spacegroup = sgtbx.space_group_symbols(195).hall()
sg = sgtbx.space_group(spacegroup)
mi_, isym_ = reference_map(sg, mi)
map_to_asu_isym(sg.type(), anomalous_flag, mi, i)
for j in range(nhkl):
assert (i[j] == isym_[j])
def tst_map_to_asu_isym(anomalous_flag):
from cctbx import sgtbx
from cctbx.miller import map_to_asu_isym
from cctbx.array_family import flex
mi = flex.miller_index()
i = flex.int()
import random
nhkl = 1000
for j in range(nhkl):
hkl = [random.randint(-10, 10) for j in range(3)]
mi.append(hkl)
i.append(0)
spacegroup = sgtbx.space_group_symbols(195).hall()
sg = sgtbx.space_group(spacegroup)
mi_, isym_ = reference_map(sg, mi)
map_to_asu_isym(sg.type(), anomalous_flag, mi, i)
for j in range(nhkl):
assert(i[j] == isym_[j])
def writer(
i_obs,
file_object=None,
file_name=None,
batch_numbers=None,
spindle_flags=None,
scale_intensities_for_scalepack_merge=False,
out=sys.stdout):
n_refl = len(i_obs.indices())
#assert (not i_obs.is_unique_set_under_symmetry()) # TT 2014-01-12 not necessary?
assert i_obs.is_xray_intensity_array() and i_obs.sigmas() is not None
# create substitute for batch number and spindle flag - these are impossible
# to determine from the input array
if (type(batch_numbers).__name__ == "array"):
assert batch_numbers.indices().all_eq(i_obs.indices())
batch_numbers = batch_numbers.data()
elif (batch_numbers is None):
batch_numbers = flex.int(n_refl, 0)
if (type(spindle_flags).__name__ == "array"):
assert spindle_flags.indices().all_eq(i_obs.indices())
spindle_flags = spindle_flags.data()
elif (spindle_flags is None):
spindle_flags = flex.int(n_refl, 0)
assert len(batch_numbers) == len(spindle_flags) == n_refl
assert ([file_object, file_name].count(None) == 1)
if (file_object is None):
file_object = open(file_name, "w")
f = file_object
space_group = i_obs.space_group()
# generate array for the centric/I+/I- flag
centric_flags = i_obs.centric_flags().data()
i_obs_asu = i_obs.as_non_anomalous_array().map_to_asu()
i_obs_asu_anom = i_obs.map_to_asu()
friedel_mate_flags = ~(i_obs_asu.indices() == i_obs_asu_anom.indices())
centric_tags = flex.int(n_refl, 1)
centric_tags.set_selected(friedel_mate_flags.iselection(), 2)
centric_tags.set_selected(centric_flags.iselection(), 0)
# generate isym array
uniq_indices = i_obs.indices().deep_copy()
#uniq_indices_anom = i_obs.indices().deep_copy()
isym = flex.int(n_refl, 0)
miller.map_to_asu_isym(space_group.type(), False, uniq_indices, isym)
#miller.map_to_asu_isym(space_group.type(), False, uniq_indices_anom,
# isym)
# write out symmetry operators
n_smx = space_group.n_smx()
f.write("%5d %s\n" % (n_smx, i_obs.space_group_info()))
for smx in space_group.smx():
smx_rot = smx.as_int_array()[0:9]
smx_tra = smx.as_int_array()[9:]
for r in smx_rot :
f.write("%3d" % r)
f.write("\n")
for t in smx_tra :
f.write("%3d" % t)
f.write("\n")
# write out reflections
if scale_intensities_for_scalepack_merge: # 2014-01-07 TT
from iotbx.scalepack.merge import scale_intensities_if_necessary
i_obs=scale_intensities_if_necessary(i_obs,out=out)
from iotbx.scalepack.merge import format_f8_1_or_i8 # Sorry if out of range
for i_refl, (h,k,l) in enumerate(i_obs.indices()):
(h_asu, k_asu, l_asu) = uniq_indices[i_refl]
c_flag = centric_tags[i_refl]
asu_number = abs(isym[i_refl]) + 1 # XXX is this correct?
spindle_flag = spindle_flags[i_refl]
batch = batch_numbers[i_refl]
i_formatted=format_f8_1_or_i8((h,k,l),"intensity",i_obs.data()[i_refl])
s_formatted=format_f8_1_or_i8((h,k,l),"sigma",i_obs.sigmas()[i_refl])
f.write("%4d%4d%4d%4d%4d%4d%6d%2d%2d%3d%s%s\n" %
(h, k, l, h_asu, k_asu, l_asu, batch, c_flag, spindle_flag,
asu_number, i_formatted,s_formatted,))
file_object.close()
def writer (
i_obs,
file_object=None,
file_name=None,
batch_numbers=None,
spindle_flags=None,
scale_intensities_for_scalepack_merge=False,
out=sys.stdout) :
n_refl = len(i_obs.indices())
#assert (not i_obs.is_unique_set_under_symmetry()) # TT 2014-01-12 not necessary?
assert i_obs.is_xray_intensity_array() and i_obs.sigmas() is not None
# create substitute for batch number and spindle flag - these are impossible
# to determine from the input array
if (type(batch_numbers).__name__ == "array") :
assert batch_numbers.indices().all_eq(i_obs.indices())
batch_numbers = batch_numbers.data()
elif (batch_numbers is None) :
batch_numbers = flex.int(n_refl, 0)
if (type(spindle_flags).__name__ == "array") :
assert spindle_flags.indices().all_eq(i_obs.indices())
spindle_flags = spindle_flags.data()
elif (spindle_flags is None) :
spindle_flags = flex.int(n_refl, 0)
assert len(batch_numbers) == len(spindle_flags) == n_refl
assert ([file_object, file_name].count(None) == 1)
if (file_object is None) :
file_object = open(file_name, "w")
f = file_object
space_group = i_obs.space_group()
# generate array for the centric/I+/I- flag
centric_flags = i_obs.centric_flags().data()
i_obs_asu = i_obs.as_non_anomalous_array().map_to_asu()
i_obs_asu_anom = i_obs.map_to_asu()
friedel_mate_flags = ~(i_obs_asu.indices() == i_obs_asu_anom.indices())
centric_tags = flex.int(n_refl, 1)
centric_tags.set_selected(friedel_mate_flags.iselection(), 2)
centric_tags.set_selected(centric_flags.iselection(), 0)
# generate isym array
uniq_indices = i_obs.indices().deep_copy()
#uniq_indices_anom = i_obs.indices().deep_copy()
isym = flex.int(n_refl, 0)
miller.map_to_asu_isym(space_group.type(), False, uniq_indices, isym)
#miller.map_to_asu_isym(space_group.type(), False, uniq_indices_anom,
# isym)
# write out symmetry operators
n_smx = space_group.n_smx()
f.write("%5d %s\n" % (n_smx, i_obs.space_group_info()))
for smx in space_group.smx() :
smx_rot = smx.as_int_array()[0:9]
smx_tra = smx.as_int_array()[9:]
for r in smx_rot :
f.write("%3d" % r)
f.write("\n")
for t in smx_tra :
f.write("%3d" % t)
f.write("\n")
# write out reflections
if scale_intensities_for_scalepack_merge: # 2014-01-07 TT
from iotbx.scalepack.merge import scale_intensities_if_necessary
i_obs=scale_intensities_if_necessary(i_obs,out=out)
from iotbx.scalepack.merge import format_f8_1_or_i8 # Sorry if out of range
for i_refl, (h,k,l) in enumerate(i_obs.indices()) :
(h_asu, k_asu, l_asu) = uniq_indices[i_refl]
c_flag = centric_tags[i_refl]
asu_number = abs(isym[i_refl]) + 1 # XXX is this correct?
spindle_flag = spindle_flags[i_refl]
batch = batch_numbers[i_refl]
i_formatted=format_f8_1_or_i8((h,k,l),"intensity",i_obs.data()[i_refl])
s_formatted=format_f8_1_or_i8((h,k,l),"sigma",i_obs.sigmas()[i_refl])
f.write("%4d%4d%4d%4d%4d%4d%6d%2d%2d%3d%s%s\n" %
(h, k, l, h_asu, k_asu, l_asu, batch, c_flag, spindle_flag,
asu_number, i_formatted,s_formatted,))
file_object.close()
