def kernapply(x, k, circular=False):
"""Convolve a sequence x with a Kernel k"""
x = flex.double(x).deep_copy()
lenx = len(x)
w = flex.double(lenx, 0.0)
w.set_selected(flex.size_t_range(k.m + 1), k.coef)
sel = lenx -1 - flex.size_t_range(k.m)
w.set_selected(sel, k.coef[1:])
# do convolution in the Fourier domain
fft = fftpack.real_to_complex(lenx)
n = fft.n_real()
m = fft.m_real()
x.extend(flex.double(m-n, 0.))
w.extend(flex.double(m-n, 0.))
conv = fft.forward(x) * fft.forward(w)
# extend result by the reverse conjugate, omitting the DC offset and Nyquist
# frequencies. If fft.n_real() is odd there is no Nyquist term.
end = fft.n_complex() - (fft.n_real() + 1) % 2
conv.extend(flex.conj(conv[1:end]).reversed())
# transform back, take real part and scale
fft = fftpack.complex_to_complex(len(conv))
result = fft.backward(conv).parts()[0] / n
if circular:
return result
else:
return result[(k.m):(lenx-k.m)]
def split_matches_into_blocks(self, nproc=1):
"""Return a list of the matches, split into blocks according to the
gradient_calculation_blocksize parameter and the number of processes (if relevant).
The number of blocks will be set such that the total number of reflections
being processed by concurrent processes does not exceed gradient_calculation_blocksize"""
self.update_matches()
# Need to be able to track the indices of the original matches table for
# scan-varying gradient calculations. A simple and robust (but slightly
# expensive) way to do this is to add an index column to the matches table
self._matches["imatch"] = flex.size_t_range(len(self._matches))
if self._gradient_calculation_blocksize:
nblocks = int(
math.floor(
len(self._matches) * nproc /
self._gradient_calculation_blocksize))
else:
nblocks = nproc
# ensure at least 100 reflections per block
nblocks = min(nblocks, int(len(self._matches) / 100))
nblocks = max(nblocks, 1)
blocksize = int(math.floor(len(self._matches) / nblocks))
blocks = []
for block_num in range(nblocks - 1):
start = block_num * blocksize
end = (block_num + 1) * blocksize
blocks.append(self._matches[start:end])
start = (nblocks - 1) * blocksize
end = len(self._matches)
blocks.append(self._matches[start:end])
return blocks
def predict_for_reflection_table(self, reflections, skip_derivatives=False):
"""perform prediction for all reflections in the supplied table"""
if "entering" not in reflections:
reflections.calculate_entering_flags(self._experiments)
# can only predict for experiments that exist and within the scan range
# any other reflections will be left unchanged
inc = flex.size_t_range(len(reflections))
to_keep = flex.bool(len(inc), False)
for iexp, exp in enumerate(self._experiments):
sel = reflections["id"] == iexp
# keep all reflections if there is no rotation axis
if exp.goniometer is None:
to_keep.set_selected(sel, True)
continue
# trim reflections outside the scan range
phi = reflections["xyzobs.mm.value"].parts()[2]
phi_min, phi_max = exp.scan.get_oscillation_range(deg=False)
passed = (phi >= phi_min) & (phi <= phi_max)
to_keep.set_selected(sel, passed)
# determine indices to include and predict on the subset
inc = inc.select(to_keep)
sub_refl = reflections.select(inc)
preds = self._predict_core(sub_refl, skip_derivatives)
# set updated subset back into place
reflections.set_selected(inc, preds)
return reflections
def __init__(self, constraints, n_full_params):
self._constraints = constraints
# constraints should be a list of EqualShiftConstraint objects
assert len(self._constraints) > 0
self._n_full_params = n_full_params
full_idx = flex.size_t_range(n_full_params)
self._constrained_gps = [c.indices for c in self._constraints]
self._constrained_idx = flex.size_t(
[i for c in self._constrained_gps for i in c])
keep = flex.bool(self._n_full_params, True)
keep.set_selected(self._constrained_idx, False)
self._unconstrained_idx = full_idx.select(keep)
self._n_unconstrained_params = len(self._unconstrained_idx)
def test_MTRIX(self):
'''Test MTRIX record processing'''
# print sys._getframe().f_code.co_name
cau_expected_results = [
[1.0, 1.0, 1.0], [1.0, -1.0, 1.0], [-0.366025, 1.366025, 1.0], [-1.366025, 0.366025, 1.0],
[1.0, 1.5, 1.0], [94.618, -5.253, 91.582],
[94.618, -91.582, -5.253], [51.858229, 79.315053, 91.582], [-42.759771, 84.568053, 91.582],
[94.618, -4.753, 91.582], [62.395, 51.344, 80.786],
[62.395, -80.786, 51.344], [-13.267688, 79.70763, 80.786], [-75.662688, 28.36363, 80.786],
[62.395, 51.844, 80.786], [39.954, 51.526, 72.372],
[39.954, -72.372, 51.526], [-24.645804, 60.364163, 72.372], [-64.599804, 8.838163, 72.372],
[39.954, 52.026, 72.372]]
# use MTRIX data
multimer_data = multimer(
file_name='multimer_test_data.pdb',
reconstruction_type='cau')
cau_multimer_xyz = list(multimer_data.sites_cart())
cau_multimer_xyz.sort()
cau_expected_results.sort()
assert approx_equal(cau_expected_results,cau_multimer_xyz,eps=0.001)
# Test that the number of MTRIX record to process is correct
self.assertEqual(multimer_data.number_of_transforms,4)
# Test getting non-rounded ASU
source_xyz = multimer_data.get_ncs_hierarchy().atoms().extract_xyz()
xyz = apply_transforms(
ncs_coordinates = source_xyz,
ncs_restraints_group_list = multimer_data.get_ncs_restraints_group_list(),
total_asu_length = multimer_data.total_asu_length(),
extended_ncs_selection = flex.size_t_range(source_xyz.size()),
round_coordinates=False)
cau_multimer_xyz = list(xyz)
cau_multimer_xyz.sort()
assert approx_equal(cau_expected_results,cau_multimer_xyz,eps=0.00001)
# Test multimer without rounding
multimer_data = multimer(
file_name='multimer_test_data.pdb',
round_coordinates=False,
reconstruction_type='cau')
cau_multimer_xyz = list(multimer_data.sites_cart())
cau_multimer_xyz.sort()
cau_expected_results.sort()
assert approx_equal(cau_expected_results,cau_multimer_xyz,eps=0.00001)
def rmsd_permutation(O, sites_cart_1, sites_cart_2):
"simple, limited handling of flipped sites"
assert sites_cart_1.size() == len(O.edge_sets)
assert sites_cart_2.size() == len(O.edge_sets)
from scitbx.array_family import flex
result = flex.size_t_range(len(O.edge_sets))
for i, esi in enumerate(O.edge_sets):
if (len(esi) not in [2, 3]): continue
n1 = flex.size_t()
for j in esi:
if (len(O.edge_sets[j]) == 1):
n1.append(j)
if (len(n1) != 2): continue
n1_rev = flex.size_t(reversed(n1))
pair_1 = sites_cart_1.select(n1)
rmsd_1 = pair_1.rms_difference(sites_cart_2.select(n1))
rmsd_2 = pair_1.rms_difference(sites_cart_2.select(n1_rev))
if (rmsd_2 < rmsd_1 * (1 - 1e-6)):
result.set_selected(n1, n1_rev)
return result
def rmsd_permutation(O, sites_cart_1, sites_cart_2):
"simple, limited handling of flipped sites"
assert sites_cart_1.size() == len(O.edge_sets)
assert sites_cart_2.size() == len(O.edge_sets)
from scitbx.array_family import flex
result = flex.size_t_range(len(O.edge_sets))
for i,esi in enumerate(O.edge_sets):
if (len(esi) not in[2, 3]): continue
n1 = flex.size_t()
for j in esi:
if (len(O.edge_sets[j]) == 1):
n1.append(j)
if (len(n1) != 2): continue
n1_rev = flex.size_t(reversed(n1))
pair_1 = sites_cart_1.select(n1)
rmsd_1 = pair_1.rms_difference(sites_cart_2.select(n1))
rmsd_2 = pair_1.rms_difference(sites_cart_2.select(n1_rev))
if (rmsd_2 < rmsd_1*(1-1e-6)):
result.set_selected(n1, n1_rev)
return result
def predict_for_reflection_table(self, reflections):
"""perform prediction for all reflections in the supplied table"""
# set the entering flags if this has not been done
from dials.algorithms.refinement.reflection_manager import calculate_entering_flags
if not reflections.has_key("entering"):
reflections['entering'] = calculate_entering_flags(reflections, self._experiments)
# can only predict for experiments that exist and within the scan range
# any other reflections will be left unchanged
inc = flex.size_t_range(len(reflections))
to_keep = flex.bool(len(inc), False)
for iexp, exp in enumerate(self._experiments):
sel = reflections['id'] == iexp
# keep all reflections if there is no rotation axis
if exp.goniometer is None:
to_keep.set_selected(sel, True)
continue
# trim reflections outside the scan range
phi = reflections['xyzobs.mm.value'].parts()[2]
phi_min, phi_max = exp.scan.get_oscillation_range(deg=False)
passed = (phi >= phi_min) & (phi <= phi_max)
to_keep.set_selected(sel, passed)
# determine indices to include and predict on the subset
inc = inc.select(to_keep)
sub_refl = reflections.select(inc)
preds = self._predict_core(sub_refl)
# set updated subset back into place
reflections.set_selected(inc, preds)
return reflections
def __init__(
self,
hierarchy=None,
# XXX warning, ncs_phil_groups can be changed inside...
ncs_phil_groups=None,
params=None,
log=None,
):
"""
TODO:
1. Transfer get_ncs_info_as_spec() to ncs/ncs.py:ncs
Select method to build ncs_group_object
order of implementation:
1) ncs_phil_groups - user-supplied definitions are filtered
2) hierarchy only - Performing NCS search
Args:
-----
ncs_phil_groups: iotbx.phil.parse(ncs_group_phil_str).extract().ncs_group
chain_max_rmsd (float): limit of rms difference between chains to be considered
as copies
min_percent (float): Threshold for similarity between chains
similarity define as:
(number of matching res) / (number of res in longer chain)
chain_similarity_threshold (float): min similarity between matching chains
residue_match_radius (float): max allow distance difference between pairs of matching
atoms of two residues
"""
self.number_of_ncs_groups = 0 # consider removing/replacing with function
self.ncs_restraints_group_list = class_ncs_restraints_group_list()
# keep hierarchy for writing (To have a source of atoms labels)
self.hierarchy = hierarchy
# residues common to NCS copies. Used for .spec representation
self.common_res_dict = {}
# Collect messages, recommendation and errors
self.messages = '' # Not used outside...
self.old_i_seqs = None
self.original_hierarchy = None
self.truncated_hierarchy = None
self.truncated_h_asc = None
self.chains_info = None
extension = ''
# set search parameters
self.params = params
if self.params is None:
self.params = input.get_default_params().ncs_search
#
if log is None:
self.log = sys.stdout
else:
self.log = log
if hierarchy:
# for a in hierarchy.atoms():
# print "oo", a.i_seq, a.id_str()
# print "====="
hierarchy.atoms().reset_i_seq()
self.original_hierarchy = hierarchy.deep_copy()
self.original_hierarchy.reset_atom_i_seqs()
if self.params.exclude_selection is not None:
# pdb_hierarchy_inp.hierarchy.write_pdb_file("in_ncs_pre_before.pdb")
cache = hierarchy.atom_selection_cache()
sel = cache.selection("not (%s)" %
self.params.exclude_selection)
self.truncated_hierarchy = hierarchy.select(sel)
else:
# this could be to save iseqs but I'm not sure
self.truncated_hierarchy = hierarchy.select(
flex.size_t_range(hierarchy.atoms_size()))
self.old_i_seqs = self.truncated_hierarchy.atoms().extract_i_seq()
# print "self.old_i_seqs", list(self.old_i_seqs)
# self.truncated_hierarchy.atoms().reset_i_seq()
self.truncated_hierarchy.reset_atom_i_seqs()
self.truncated_h_asc = self.truncated_hierarchy.atom_selection_cache(
)
# self.truncated_hierarchy.write_pdb_file("in_ncs_pre_after.pdb")
self.chains_info = ncs_search.get_chains_info(
self.truncated_hierarchy)
if self.truncated_hierarchy.atoms_size() == 0:
return
#
# print "ncs_groups before validation", ncs_phil_groups
validated_ncs_phil_groups = None
validated_ncs_phil_groups = self.validate_ncs_phil_groups(
pdb_h=self.truncated_hierarchy,
ncs_phil_groups=ncs_phil_groups,
asc=self.truncated_h_asc)
if validated_ncs_phil_groups is None:
# print "Last chance, building from hierarchy"
self.build_ncs_obj_from_pdb_asu(pdb_h=self.truncated_hierarchy,
asc=self.truncated_h_asc)
# error handling
if self.ncs_restraints_group_list.get_n_groups() == 0:
print >> self.log, '========== WARNING! ============\n'
print >> self.log, ' No NCS relation were found !!!\n'
print >> self.log, '================================\n'
if self.messages != '':
print >> self.log, self.messages
def tst_functional_gradient_calculator_invalid_arguments():
"""Check errors are raised as expected"""
n_grp = 3
n_dst = 5
n_atm = 10
target_uijs, target_weights, \
base_uijs, base_sels, dataset_hash, \
atomic_base, \
real_group_amps, real_atomic_amps \
= get_optimisation_test_set(n_grp, n_dst, n_atm)
########################################################
# Check expected error messages are raised
########################################################
# Starting values
base_amplitudes_start = flex.double(n_grp * n_dst, 1.0)
wgt_kw_args = dict(
weight_sum_of_amplitudes=0.0,
weight_sum_of_amplitudes_squared=0.0,
weight_sum_of_squared_amplitudes=0.0,
)
# Should not error
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
f, g = f_g_calculator.compute_functional_and_gradients()
# target_uijs
msg = "invalid target_uijs: must be 2-dimensional flex array (currently 3)"
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=flex.sym_mat3_double(flex.grid((n_dst - 1, n_atm, 5)),
(1., 1., 1., 0., 0., 0.)),
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value), '"{}" does not match "{}"'.format(
msg, str(e.value))
# target_weights
msg = "invalid dimension of target_weights (dimension 3): must be same dimension as target_uijs (dimension 2)"
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=flex.double(flex.grid((n_dst, n_atm, 5)), 1.0),
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value)
msg = "incompatible dimension of target_weights (axis 0): must be same size as target_uijs ({} != {})".format(
n_dst, n_dst - 1)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=flex.sym_mat3_double(flex.grid((n_dst - 1, n_atm)),
(1., 1., 1., 0., 0., 0.)),
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value)
msg = "incompatible dimension of target_weights (axis 1): must be same size as target_uijs ({} != {})".format(
n_atm, n_atm + 1)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=flex.sym_mat3_double(flex.grid((n_dst, n_atm + 1)),
(1., 1., 1., 0., 0., 0.)),
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value)
# base components
msg = "invalid input base components. base_amplitudes (length {}), base_uijs (length {}) and base_atom_indices (length {}) must all be the same length"
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=flex.double(n_grp * n_dst - 1, 1.0),
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg.format(n_grp * n_dst - 1, n_grp * n_dst,
n_grp * n_dst) == str(e.value)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs[:-1],
base_atom_indices=base_sels[:-1],
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg.format(n_grp * n_dst, n_grp * n_dst - 1,
n_grp * n_dst - 1) == str(e.value)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs[:-1],
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg.format(n_grp * n_dst, n_grp * n_dst - 1,
n_grp * n_dst) == str(e.value)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels[:-1],
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg.format(n_grp * n_dst, n_grp * n_dst,
n_grp * n_dst - 1) == str(e.value)
msg = "incompatible pair (element 2) in base_uijs/base_atom_indices: pairwise elements must be the same length ({} and {})".format(
n_atm + 1, len(base_uijs[2]))
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs[:2] + [flex.sym_mat3_double(n_atm + 1)] +
base_uijs[3:],
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value)
msg = "incompatible pair (element 2) in base_uijs/base_atom_indices: pairwise elements must be the same length ({} and {})".format(
len(base_sels[2]), n_atm + 1)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels[:2] + [flex.size_t_range(n_atm + 1)] +
base_sels[3:],
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value)
msg = "invalid selection in base_atom_indices ({}): attempting to select atom outside of array (size {})".format(
n_atm, n_atm)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs[:2] + [flex.sym_mat3_double(n_atm + 1)] +
base_uijs[3:],
base_atom_indices=base_sels[:2] + [flex.size_t_range(n_atm + 1)] +
base_sels[3:],
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value)
# dataset_hash
msg = "invalid base_dataset_hash (length {}): must be same length as base_amplitudes, base_uijs & base_atom_indices (length {})".format(
len(dataset_hash) - 1, len(dataset_hash))
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=flex.size_t(list(dataset_hash)[:-1]),
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value), (msg, str(e.value))
msg = "invalid value in base_dataset_hash ({}): attempts to select element outside range of target_uijs (size {})".format(
n_dst - 1, n_dst - 1)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=flex.sym_mat3_double(flex.grid((n_dst - 1, n_atm)),
(1., 1., 1., 0., 0., 0.)),
target_weights=flex.double(flex.grid((n_dst - 1, n_atm)),
1.0), # need to resize this also
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value), (msg, str(e.value))
msg = "Dataset index {} is not present in base_dataset_hash -- this dataset has no base elements associated with it.".format(
n_dst)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=flex.sym_mat3_double(flex.grid((n_dst + 1, n_atm)),
(1., 1., 1., 0., 0., 0.)),
target_weights=flex.double(flex.grid((n_dst + 1, n_atm)),
1.0), # need to resize this also
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
assert msg == str(e.value), (msg, str(e.value))
# atomic uijs
msg = "invalid size of atomic_uijs ({}): must match 2nd dimension of target_uijs ({})".format(
n_atm - 1, n_atm)
with raises(Exception) as e:
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=flex.sym_mat3_double(n_atm - 1),
**wgt_kw_args)
assert msg == str(e.value), (msg, str(e.value))
# atomic mask
f_g_calculator = MultiGroupMultiDatasetUijAmplitudeFunctionalAndGradientCalculator(
target_uijs=target_uijs,
target_weights=target_weights,
base_amplitudes=base_amplitudes_start,
base_uijs=base_uijs,
base_atom_indices=base_sels,
base_dataset_hash=dataset_hash,
atomic_uijs=atomic_base,
**wgt_kw_args)
# should not error
f_g_calculator.set_atomic_optimisation_mask(flex.bool(n_dst, True))
f_g_calculator.set_atomic_optimisation_mask(flex.bool(n_dst, False))
# should error
msg = "Input array (size {}) must be the same length as number of datasets ({})".format(
n_dst - 1, n_dst)
with raises(Exception) as e:
f_g_calculator.set_atomic_optimisation_mask(flex.bool(n_dst - 1, True))
assert msg == str(e.value)
msg = "Input array (size {}) must be the same length as number of datasets ({})".format(
n_dst + 1, n_dst)
with raises(Exception) as e:
f_g_calculator.set_atomic_optimisation_mask(flex.bool(n_dst + 1, True))
assert msg == str(e.value)
# setting amplitudes
# should not error
f_g_calculator.set_current_amplitudes(flex.double(n_grp * n_dst + n_atm))
# should error
msg = "Input array (size {}) must be the same length as current_amplitudes (size {})".format(
n_grp * n_dst + n_atm - 1, n_grp * n_dst + n_atm)
with raises(Exception) as e:
f_g_calculator.set_current_amplitudes(
flex.double(n_grp * n_dst + n_atm - 1))
assert msg == str(e.value)
print('OK')
def make_cif_block(self, experiments, reflections):
"""Write the data to a cif block"""
# Select reflections
selection = reflections.get_flags(reflections.flags.integrated, all=True)
reflections = reflections.select(selection)
# Filter out bad variances and other issues, but don't filter on ice rings
# or alter partialities.
# Assumes you want to apply the lp and dqe corrections to sum and prf
# Do we want to combine partials?
reflections = filter_reflection_table(
reflections,
self.params.intensity,
combine_partials=False,
partiality_threshold=0.0,
d_min=self.params.mtz.d_min,
)
# Get the cif block
cif_block = iotbx.cif.model.block()
# Audit trail
dials_version = dials.util.version.dials_version()
cif_block["_audit.revision_id"] = 1
cif_block["_audit.creation_method"] = dials_version
cif_block["_audit.creation_date"] = datetime.date.today().isoformat()
cif_block["_entry.id"] = "DIALS"
# add software loop
mmcif_software_header = (
"_software.pdbx_ordinal",
"_software.citation_id",
"_software.name", # as defined at [1]
"_software.version",
"_software.type",
"_software.classification",
"_software.description",
)
mmcif_citations_header = (
"_citation.id",
"_citation.journal_abbrev",
"_citation.journal_volume",
"_citation.journal_issue",
"_citation.page_first",
"_citation.page_last",
"_citation.year",
"_citation.title",
)
software_loop = iotbx.cif.model.loop(header=mmcif_software_header)
citations_loop = iotbx.cif.model.loop(header=mmcif_citations_header)
software_loop.add_row(
(
1,
1,
"DIALS",
dials_version,
"package",
"data processing",
"Data processing and integration within the DIALS software package",
)
)
citations_loop.add_row(
(
1,
"Acta Cryst. D",
74,
2,
85,
97,
2018,
"DIALS: implementation and evaluation of a new integration package",
)
)
if "scale" in self.params.intensity:
software_loop.add_row(
(
2,
2,
"DIALS",
dials_version,
"program",
"data scaling",
"Data scaling and merging within the DIALS software package",
)
)
citations_loop.add_row(
(
2,
"Acta Cryst. D",
76,
4,
385,
399,
2020,
"Scaling diffraction data in the DIALS software package: algorithms and new approaches for multi-crystal scaling",
)
)
cif_block.add_loop(software_loop)
cif_block.add_loop(citations_loop)
# Hard coding X-ray
if self.params.mmcif.pdb_version == "v5_next":
cif_block["_pdbx_diffrn_data_section.id"] = "dials"
cif_block["_pdbx_diffrn_data_section.type_scattering"] = "x-ray"
cif_block["_pdbx_diffrn_data_section.type_merged"] = "false"
cif_block["_pdbx_diffrn_data_section.type_scaled"] = str(
"scale" in self.params.intensity
).lower()
# FIXME finish metadata addition - detector and source details needed
# http://mmcif.wwpdb.org/dictionaries/mmcif_pdbx_v50.dic/Categories/index.html
# Add source information;
# _diffrn_source.pdbx_wavelength_list = (list of wavelengths)
# _diffrn_source.source = (general class of source e.g. synchrotron)
# _diffrn_source.type = (specific beamline or instrument e.g DIAMOND BEAMLINE I04)
wls = []
epochs = []
for exp in experiments:
wls.append(round(exp.beam.get_wavelength(), 5))
epochs.append(exp.scan.get_epochs()[0])
unique_wls = set(wls)
cif_block["_exptl_crystal.id"] = 1 # links to crystal_id
cif_block["_diffrn.id"] = 1 # links to diffrn_id
cif_block["_diffrn.crystal_id"] = 1
cif_block["_diffrn_source.diffrn_id"] = 1
cif_block["_diffrn_source.pdbx_wavelength_list"] = ", ".join(
str(w) for w in unique_wls
)
# Add detector information;
# _diffrn_detector.detector = (general class e.g. PIXEL, PLATE etc)
# _diffrn_detector.pdbx_collection_date = (Date of collection yyyy-mm-dd)
# _diffrn_detector.type = (full name of detector e.g. DECTRIS PILATUS3 2M)
# One date is required, so if multiple just use the first date.
min_epoch = min(epochs)
date_str = time.strftime("%Y-%m-%d", time.gmtime(min_epoch))
cif_block["_diffrn_detector.diffrn_id"] = 1
cif_block["_diffrn_detector.pdbx_collection_date"] = date_str
# Write reflection data
# Required columns
header = (
"_pdbx_diffrn_unmerged_refln.reflection_id",
"_pdbx_diffrn_unmerged_refln.scan_id",
"_pdbx_diffrn_unmerged_refln.image_id_begin",
"_pdbx_diffrn_unmerged_refln.image_id_end",
"_pdbx_diffrn_unmerged_refln.index_h",
"_pdbx_diffrn_unmerged_refln.index_k",
"_pdbx_diffrn_unmerged_refln.index_l",
)
extra_items = {
"scales": ("_pdbx_diffrn_unmerged_refln.scale_value", "%5.3f"),
"intensity.scale.value": (
"_pdbx_diffrn_unmerged_refln.intensity_meas",
"%8.3f",
),
"intensity.scale.sigma": (
"_pdbx_diffrn_unmerged_refln.intensity_sigma",
"%8.3f",
),
"intensity.sum.value": (
"_pdbx_diffrn_unmerged_refln.intensity_sum",
"%8.3f",
),
"intensity.sum.sigma": (
"_pdbx_diffrn_unmerged_refln.intensity_sum_sigma",
"%8.3f",
),
"intensity.prf.value": (
"_pdbx_diffrn_unmerged_refln.intensity_prf",
"%8.3f",
),
"intensity.prf.sigma": (
"_pdbx_diffrn_unmerged_refln.intensity_prf_sigma",
"%8.3f",
),
"angle": ("_pdbx_diffrn_unmerged_refln.scan_angle_reflection", "%7.4f"),
"partiality": ("_pdbx_diffrn_unmerged_refln.partiality", "%7.4f"),
}
variables_present = []
if "scale" in self.params.intensity:
reflections["scales"] = 1.0 / reflections["inverse_scale_factor"]
reflections["intensity.scale.sigma"] = flex.sqrt(
reflections["intensity.scale.variance"]
)
variables_present.extend(
["scales", "intensity.scale.value", "intensity.scale.sigma"]
)
if "sum" in self.params.intensity:
reflections["intensity.sum.sigma"] = flex.sqrt(
reflections["intensity.sum.variance"]
)
variables_present.extend(["intensity.sum.value", "intensity.sum.sigma"])
if "profile" in self.params.intensity:
reflections["intensity.prf.sigma"] = flex.sqrt(
reflections["intensity.prf.variance"]
)
variables_present.extend(["intensity.prf.value", "intensity.prf.sigma"])
# Should always exist
reflections["angle"] = reflections["xyzcal.mm"].parts()[2] * RAD2DEG
variables_present.extend(["angle"])
if "partiality" in reflections:
variables_present.extend(["partiality"])
for name in variables_present:
if name in reflections:
header += (extra_items[name][0],)
self._fmt += " " + extra_items[name][1]
if "scale" in self.params.intensity:
# Write dataset_statistics - first make a miller array
crystal_symmetry = cctbxcrystal.symmetry(
space_group=experiments[0].crystal.get_space_group(),
unit_cell=experiments[0].crystal.get_unit_cell(),
)
miller_set = miller.set(
crystal_symmetry=crystal_symmetry,
indices=reflections["miller_index"],
anomalous_flag=False,
)
i_obs = miller.array(miller_set, data=reflections["intensity.scale.value"])
i_obs.set_observation_type_xray_intensity()
i_obs.set_sigmas(reflections["intensity.scale.sigma"])
i_obs.set_info(
miller.array_info(source="DIALS", source_type="reflection_tables")
)
result = dataset_statistics(
i_obs=i_obs,
crystal_symmetry=crystal_symmetry,
use_internal_variance=False,
eliminate_sys_absent=False,
)
merged_block = iotbx.cif.model.block()
merged_block["_reflns.pdbx_ordinal"] = 1
merged_block["_reflns.pdbx_diffrn_id"] = 1
merged_block["_reflns.entry_id"] = "DIALS"
merged_data = result.as_cif_block()
merged_block.update(merged_data)
cif_block.update(merged_block)
# Write the crystal information
# if v5, thats all so return
if self.params.mmcif.pdb_version == "v5":
return cif_block
# continue if v5_next
cif_loop = iotbx.cif.model.loop(
header=(
"_pdbx_diffrn_unmerged_cell.ordinal",
"_pdbx_diffrn_unmerged_cell.crystal_id",
"_pdbx_diffrn_unmerged_cell.wavelength",
"_pdbx_diffrn_unmerged_cell.cell_length_a",
"_pdbx_diffrn_unmerged_cell.cell_length_b",
"_pdbx_diffrn_unmerged_cell.cell_length_c",
"_pdbx_diffrn_unmerged_cell.cell_angle_alpha",
"_pdbx_diffrn_unmerged_cell.cell_angle_beta",
"_pdbx_diffrn_unmerged_cell.cell_angle_gamma",
"_pdbx_diffrn_unmerged_cell.Bravais_lattice",
)
)
crystals = experiments.crystals()
crystal_to_id = {crystal: i + 1 for i, crystal in enumerate(crystals)}
for i, exp in enumerate(experiments):
crystal = exp.crystal
crystal_id = crystal_to_id[crystal]
wavelength = exp.beam.get_wavelength()
a, b, c, alpha, beta, gamma = crystal.get_unit_cell().parameters()
latt_type = str(
bravais_types.bravais_lattice(group=crystal.get_space_group())
)
cif_loop.add_row(
(i + 1, crystal_id, wavelength, a, b, c, alpha, beta, gamma, latt_type)
)
cif_block.add_loop(cif_loop)
# Write the scan information
cif_loop = iotbx.cif.model.loop(
header=(
"_pdbx_diffrn_scan.scan_id",
"_pdbx_diffrn_scan.crystal_id",
"_pdbx_diffrn_scan.image_id_begin",
"_pdbx_diffrn_scan.image_id_end",
"_pdbx_diffrn_scan.scan_angle_begin",
"_pdbx_diffrn_scan.scan_angle_end",
)
)
expid_to_scan_id = {exp.identifier: i + 1 for i, exp in enumerate(experiments)}
for i, exp in enumerate(experiments):
scan = exp.scan
crystal_id = crystal_to_id[exp.crystal]
image_range = scan.get_image_range()
osc_range = scan.get_oscillation_range(deg=True)
cif_loop.add_row(
(
i + 1,
crystal_id,
image_range[0],
image_range[1],
osc_range[0],
osc_range[1],
)
)
cif_block.add_loop(cif_loop)
_, _, _, _, z0, z1 = reflections["bbox"].parts()
h, k, l = [
hkl.iround() for hkl in reflections["miller_index"].as_vec3_double().parts()
]
# make scan id consistent with header as defined above
scan_id = flex.int(reflections.size(), 0)
for id_ in reflections.experiment_identifiers().keys():
expid = reflections.experiment_identifiers()[id_]
sel = reflections["id"] == id_
scan_id.set_selected(sel, expid_to_scan_id[expid])
loop_values = [
flex.size_t_range(1, len(reflections) + 1),
scan_id,
z0,
z1,
h,
k,
l,
] + [reflections[name] for name in variables_present]
cif_loop = iotbx.cif.model.loop(data=dict(zip(header, loop_values)))
cif_block.add_loop(cif_loop)
return cif_block
def __init__(self, x, spans=None, demean=True, detrend=True):
# Ensure x is copied as it will be changed in-place
x = flex.double(x).deep_copy()
n = len(x)
if detrend:
t = flex.size_t_range(n).as_double() + 1 - (n + 1)/2
inv_sumt2 = 1./t.dot(t)
x = x - flex.mean(x) - x.dot(t) * t * inv_sumt2
elif demean:
x -= flex.mean(x)
# determine frequencies
stop = ((n - (n % 2)) // 2) + 1
self.freq = flex.double([i / n for i in range(1, stop)])
fft = fftpack.real_to_complex(n)
n = fft.n_real()
m = fft.m_real()
x.extend(flex.double(m-n, 0.))
xf = fft.forward(x)
# get abs length of complex and normalise by n to get the raw periodogram
spec = flex.norm(xf) / n
if spans is None:
# if not doing smoothing, the spectrum is just the raw periodogram with
# the uninteresting DC offset removed
self.spec = spec[1:]
return
# for smoothing replace the DC offset term and extend the rest of the
# sequence by its reverse conjugate, omitting the Nyquist term if it is
# present
spec[0] = spec[1]
end = fft.n_complex() - (fft.n_real() + 1) % 2
spec.extend(spec[1:end].reversed())
try:
# multiple integer spans
nspans = len(spans)
m = int(spans[0]) // 2
multiple = True
except TypeError:
# single integer span
m = int(spans) // 2
multiple = False
# construct smoothing kernel
k = Kernel('modified.daniell', m)
if multiple:
for i in range(1, nspans):
# construct kernel for convolution
m1 = int(spans[i]) // 2
k1 = Kernel('modified.daniell', m1)
# expand coefficients of k to full kernel and zero pad for smoothing
x1 = flex.double(k1.m, 0.0)
x1.extend(k.coef.reversed())
x1.extend(k.coef[1:])
x1.extend(flex.double(k1.m, 0.0))
# convolve kernels
coef = kernapply(x1, k1, circular=True)
m = len(coef)//2
coef = coef[m:(len(coef))]
k = Kernel(coef=coef)
# apply smoothing kernel
spec = kernapply(spec, k, circular=True)
self.spec = spec[1:fft.n_complex()]
return
def build_i_calc(work_params):
from scitbx.array_family import flex
d_min = work_params.d_min
if (work_params.pdb_file is None):
miller_set = work_params.unit_cell \
.complete_miller_set_with_lattice_symmetry(
d_min=d_min,
anomalous_flag=True).expand_to_p1()
if (work_params.intensity_symmetry is not None):
miller_set = miller_set.customized_copy(
space_group_info=work_params.intensity_symmetry,
anomalous_flag=work_params.anomalous_flag
).unique_under_symmetry()
mt = flex.mersenne_twister(seed=work_params.noise.random_seed)
i_calc_asu = miller_set.array(data=mt.random_double(
size=miller_set.indices().size()))
else:
import iotbx.pdb
pdb_inp = iotbx.pdb.input(file_name=work_params.pdb_file)
xs = pdb_inp.xray_structure_simple().change_basis(
cb_op=work_params.change_of_basis_op_to_niggli_cell)
assert xs.unit_cell().is_similar_to(other=work_params.unit_cell)
_ = work_params.reset_b_factors_value
if (_ is not None):
from cctbx import adptbx
u_iso = adptbx.b_as_u(_)
xs.convert_to_isotropic()
for sc in xs.scatterers():
sc.u_iso = u_iso
miller_set = work_params.unit_cell \
.complete_miller_set_with_lattice_symmetry(
d_min=d_min,
anomalous_flag=work_params.anomalous_flag) \
.expand_to_p1().customized_copy(
space_group_info=xs.space_group_info()) \
.unique_under_symmetry() \
.remove_systematic_absences() \
.map_to_asu()
i_calc_asu = miller_set.structure_factors_from_scatterers(
xray_structure=xs).f_calc().intensities()
if (i_calc_asu.data().size() != 0):
i_calc_max = flex.max(i_calc_asu.data())
if (i_calc_max > 0):
i_calc_asu = i_calc_asu.array(data=i_calc_asu.data() *
(1 / i_calc_max))
i_calc_asu = i_calc_asu.customized_copy(
space_group_info=i_calc_asu.space_group()
.build_derived_reflection_intensity_group(anomalous_flag=True).info()) \
.map_to_asu() \
.complete_array(new_data_value=0)
i_calc_asu = i_calc_asu.sort(by_value="resolution")
assert not i_calc_asu.space_group().is_centric()
assert i_calc_asu.space_group().n_ltr() == 1
assert i_calc_asu.space_group_info().type().is_symmorphic()
if (work_params.force_unit_spot_intensities):
i_calc_asu = i_calc_asu.array(
data=flex.double(i_calc_asu.indices().size(), 1))
i_asu_array = i_calc_asu.customized_copy(
data=flex.size_t_range(i_calc_asu.indices().size()))
if (not i_asu_array.anomalous_flag()):
i_asu_array = i_asu_array.generate_bijvoet_mates()
i_asu_array = i_asu_array.expand_to_p1()
asu_iselection = i_asu_array.data()
i_calc_p1_anom = i_asu_array.customized_copy(
data=i_calc_asu.data().select(asu_iselection))
from libtbx import group_args
return group_args(asu=i_calc_asu,
asu_iselection=asu_iselection,
p1_anom=i_calc_p1_anom)
def write(self, experiments, reflections):
"""
Write the experiments and reflections to file
"""
# if mmcif filename is auto, then choose scaled.cif or integrated.cif
if self.params.mmcif.hklout in (None, Auto, "auto"):
if ("intensity.scale.value"
in reflections) and ("intensity.scale.variance"
in reflections):
filename = "scaled.cif"
logger.info(
"Data appears to be scaled, setting mmcif.hklout = 'scaled.cif'"
)
else:
filename = "integrated.cif"
logger.info(
"Data appears to be unscaled, setting mmcif.hklout = 'integrated.cif'"
)
else:
filename = self.params.mmcif.hklout
# Select reflections
selection = reflections.get_flags(reflections.flags.integrated,
all=True)
reflections = reflections.select(selection)
# Filter out bad variances and other issues, but don't filter on ice rings
# or alter partialities.
# Assumes you want to apply the lp and dqe corrections to sum and prf
# Do we want to combine partials?
reflections = filter_reflection_table(
reflections,
self.params.intensity,
combine_partials=False,
partiality_threshold=0.0,
d_min=self.params.mtz.d_min,
)
# Get the cif block
cif_block = iotbx.cif.model.block()
# Audit trail
dials_version = dials.util.version.dials_version()
cif_block["_audit.creation_method"] = dials_version
cif_block["_audit.creation_date"] = datetime.date.today().isoformat()
cif_block["_computing.data_reduction"] = (
"%s (Winter, G. et al., 2018)" % dials_version)
cif_block[
"_publ.section_references"] = "Winter, G. et al. (2018) Acta Cryst. D74, 85-97."
if "scale" in self.params.intensity:
cif_block[
"_publ.section_references"] += "\nBeilsten-Edmands, J. et al. (2020) Acta Cryst. D76, 385-399."
# Hard coding X-ray
cif_block["_pdbx_diffrn_data_section.id"] = "dials"
cif_block["_pdbx_diffrn_data_section.type_scattering"] = "x-ray"
cif_block["_pdbx_diffrn_data_section.type_merged"] = "false"
cif_block["_pdbx_diffrn_data_section.type_scaled"] = str(
"scale" in self.params.intensity).lower()
# FIXME finish metadata addition - detector and source details needed
# http://mmcif.wwpdb.org/dictionaries/mmcif_pdbx_v50.dic/Categories/index.html
# Add source information;
# _diffrn_source.pdbx_wavelength_list = (list of wavelengths)
# _diffrn_source.source = (general class of source e.g. synchrotron)
# _diffrn_source.type = (specific beamline or instrument e.g DIAMOND BEAMLINE I04)
wls = []
epochs = []
for exp in experiments:
wls.append(round(exp.beam.get_wavelength(), 5))
epochs.append(exp.scan.get_epochs()[0])
unique_wls = set(wls)
cif_block["_diffrn_source.pdbx_wavelength_list"] = ", ".join(
str(w) for w in unique_wls)
# Add detector information;
# _diffrn_detector.detector = (general class e.g. PIXEL, PLATE etc)
# _diffrn_detector.pdbx_collection_date = (Date of collection yyyy-mm-dd)
# _diffrn_detector.type = (full name of detector e.g. DECTRIS PILATUS3 2M)
# One date is required, so if multiple just use the first date.
min_epoch = min(epochs)
date_str = time.strftime("%Y-%m-%d", time.gmtime(min_epoch))
cif_block["_diffrn_detector.pdbx_collection_date"] = date_str
# Write the crystal information
cif_loop = iotbx.cif.model.loop(header=(
"_pdbx_diffrn_unmerged_cell.ordinal",
"_pdbx_diffrn_unmerged_cell.crystal_id",
"_pdbx_diffrn_unmerged_cell.wavelength",
"_pdbx_diffrn_unmerged_cell.cell_length_a",
"_pdbx_diffrn_unmerged_cell.cell_length_b",
"_pdbx_diffrn_unmerged_cell.cell_length_c",
"_pdbx_diffrn_unmerged_cell.cell_angle_alpha",
"_pdbx_diffrn_unmerged_cell.cell_angle_beta",
"_pdbx_diffrn_unmerged_cell.cell_angle_gamma",
"_pdbx_diffrn_unmerged_cell.Bravais_lattice",
))
crystals = experiments.crystals()
crystal_to_id = {crystal: i + 1 for i, crystal in enumerate(crystals)}
for i, exp in enumerate(experiments):
crystal = exp.crystal
crystal_id = crystal_to_id[crystal]
wavelength = exp.beam.get_wavelength()
a, b, c, alpha, beta, gamma = crystal.get_unit_cell().parameters()
latt_type = str(
bravais_types.bravais_lattice(group=crystal.get_space_group()))
cif_loop.add_row((i + 1, crystal_id, wavelength, a, b, c, alpha,
beta, gamma, latt_type))
cif_block.add_loop(cif_loop)
# Write the scan information
cif_loop = iotbx.cif.model.loop(header=(
"_pdbx_diffrn_scan.scan_id",
"_pdbx_diffrn_scan.crystal_id",
"_pdbx_diffrn_scan.image_id_begin",
"_pdbx_diffrn_scan.image_id_end",
"_pdbx_diffrn_scan.scan_angle_begin",
"_pdbx_diffrn_scan.scan_angle_end",
))
for i, exp in enumerate(experiments):
scan = exp.scan
crystal_id = crystal_to_id[exp.crystal]
image_range = scan.get_image_range()
osc_range = scan.get_oscillation_range(deg=True)
cif_loop.add_row((
i + 1,
crystal_id,
image_range[0],
image_range[1],
osc_range[0],
osc_range[1],
))
cif_block.add_loop(cif_loop)
# Make a dict of unit_cell parameters
unit_cell_parameters = {}
if crystal.num_scan_points > 1:
for i in range(crystal.num_scan_points):
a, b, c, alpha, beta, gamma = crystal.get_unit_cell_at_scan_point(
i).parameters()
unit_cell_parameters[i] = (a, b, c, alpha, beta, gamma)
else:
unit_cell_parameters[0] = (a, b, c, alpha, beta, gamma)
# Write reflection data
# Required columns
header = (
"_pdbx_diffrn_unmerged_refln.reflection_id",
"_pdbx_diffrn_unmerged_refln.scan_id",
"_pdbx_diffrn_unmerged_refln.image_id_begin",
"_pdbx_diffrn_unmerged_refln.image_id_end",
"_pdbx_diffrn_unmerged_refln.index_h",
"_pdbx_diffrn_unmerged_refln.index_k",
"_pdbx_diffrn_unmerged_refln.index_l",
)
extra_items = {
"scales": ("_pdbx_diffrn_unmerged_refln.scale_value", "%5.3f"),
"intensity.scale.value": (
"_pdbx_diffrn_unmerged_refln.intensity_meas",
"%8.3f",
),
"intensity.scale.sigma": (
"_pdbx_diffrn_unmerged_refln.intensity_sigma",
"%8.3f",
),
"intensity.sum.value": (
"_pdbx_diffrn_unmerged_refln.intensity_sum",
"%8.3f",
),
"intensity.sum.sigma": (
"_pdbx_diffrn_unmerged_refln.intensity_sum_sigma",
"%8.3f",
),
"intensity.prf.value": (
"_pdbx_diffrn_unmerged_refln.intensity_prf",
"%8.3f",
),
"intensity.prf.sigma": (
"_pdbx_diffrn_unmerged_refln.intensity_prf_sigma",
"%8.3f",
),
"angle":
("_pdbx_diffrn_unmerged_refln.scan_angle_reflection", "%7.4f"),
"partiality": ("_pdbx_diffrn_unmerged_refln.partiality", "%7.4f"),
}
fmt = "%6i %2i %5i %5i %-2i %-2i %-2i"
variables_present = []
if "scale" in self.params.intensity:
reflections["scales"] = 1.0 / reflections["inverse_scale_factor"]
reflections["intensity.scale.sigma"] = flex.sqrt(
reflections["intensity.scale.variance"])
variables_present.extend(
["scales", "intensity.scale.value", "intensity.scale.sigma"])
if "sum" in self.params.intensity:
reflections["intensity.sum.sigma"] = flex.sqrt(
reflections["intensity.sum.variance"])
variables_present.extend(
["intensity.sum.value", "intensity.sum.sigma"])
if "profile" in self.params.intensity:
reflections["intensity.prf.sigma"] = flex.sqrt(
reflections["intensity.prf.variance"])
variables_present.extend(
["intensity.prf.value", "intensity.prf.sigma"])
# Should always exist
reflections["angle"] = reflections["xyzcal.mm"].parts()[2] * RAD2DEG
variables_present.extend(["angle"])
if "partiality" in reflections:
variables_present.extend(["partiality"])
for name in variables_present:
if name in reflections:
header += (extra_items[name][0], )
fmt += " " + extra_items[name][1]
loop_format_strings = {"_pdbx_diffrn_unmerged_refln": fmt}
if "scale" in self.params.intensity:
# Write dataset_statistics - first make a miller array
crystal_symmetry = cctbxcrystal.symmetry(
space_group=experiments[0].crystal.get_space_group(),
unit_cell=experiments[0].crystal.get_unit_cell(),
)
miller_set = miller.set(
crystal_symmetry=crystal_symmetry,
indices=reflections["miller_index"],
anomalous_flag=False,
)
i_obs = miller.array(miller_set,
data=reflections["intensity.scale.value"])
i_obs.set_observation_type_xray_intensity()
i_obs.set_sigmas(reflections["intensity.scale.sigma"])
i_obs.set_info(
miller.array_info(source="DIALS",
source_type="reflection_tables"))
result = dataset_statistics(
i_obs=i_obs,
crystal_symmetry=crystal_symmetry,
use_internal_variance=False,
eliminate_sys_absent=False,
)
cif_block.update(result.as_cif_block())
_, _, _, _, z0, z1 = reflections["bbox"].parts()
h, k, l = [
hkl.iround()
for hkl in reflections["miller_index"].as_vec3_double().parts()
]
loop_values = [
flex.size_t_range(1,
len(reflections) + 1),
reflections["id"] + 1,
z0,
z1,
h,
k,
l,
] + [reflections[name] for name in variables_present]
cif_loop = iotbx.cif.model.loop(data=dict(zip(header, loop_values)))
cif_block.add_loop(cif_loop)
# Add the block
self._cif["dials"] = cif_block
# Print to file
if self.params.mmcif.compress and not filename.endswith(
"." + self.params.mmcif.compress):
filename += "." + self.params.mmcif.compress
if self.params.mmcif.compress == "gz":
open_fn = gzip.open
elif self.params.mmcif.compress == "bz2":
open_fn = bz2.open
elif self.params.mmcif.compress == "xz":
open_fn = lzma.open
else:
open_fn = open
with open_fn(filename, "wt") as fh:
self._cif.show(out=fh, loop_format_strings=loop_format_strings)
# Log
logger.info("Wrote reflections to %s" % filename)
def build_i_calc(work_params):
from scitbx.array_family import flex
d_min = work_params.d_min
if (work_params.pdb_file is None):
miller_set = work_params.unit_cell \
.complete_miller_set_with_lattice_symmetry(
d_min=d_min,
anomalous_flag=True).expand_to_p1()
if (work_params.intensity_symmetry is not None):
miller_set = miller_set.customized_copy(
space_group_info=work_params.intensity_symmetry,
anomalous_flag=work_params.anomalous_flag).unique_under_symmetry()
mt = flex.mersenne_twister(seed=work_params.noise.random_seed)
i_calc_asu = miller_set.array(
data=mt.random_double(size=miller_set.indices().size()))
else:
import iotbx.pdb
pdb_inp = iotbx.pdb.input(file_name=work_params.pdb_file)
xs = pdb_inp.xray_structure_simple().change_basis(
cb_op=work_params.change_of_basis_op_to_niggli_cell)
assert xs.unit_cell().is_similar_to(other=work_params.unit_cell)
_ = work_params.reset_b_factors_value
if (_ is not None):
from cctbx import adptbx
u_iso = adptbx.b_as_u(_)
xs.convert_to_isotropic()
for sc in xs.scatterers():
sc.u_iso = u_iso
miller_set = work_params.unit_cell \
.complete_miller_set_with_lattice_symmetry(
d_min=d_min,
anomalous_flag=work_params.anomalous_flag) \
.expand_to_p1().customized_copy(
space_group_info=xs.space_group_info()) \
.unique_under_symmetry() \
.remove_systematic_absences() \
.map_to_asu()
i_calc_asu = miller_set.structure_factors_from_scatterers(
xray_structure=xs).f_calc().intensities()
if (i_calc_asu.data().size() != 0):
i_calc_max = flex.max(i_calc_asu.data())
if (i_calc_max > 0):
i_calc_asu = i_calc_asu.array(data=i_calc_asu.data() * (1/i_calc_max))
i_calc_asu = i_calc_asu.customized_copy(
space_group_info=i_calc_asu.space_group()
.build_derived_reflection_intensity_group(anomalous_flag=True).info()) \
.map_to_asu() \
.complete_array(new_data_value=0)
i_calc_asu = i_calc_asu.sort(by_value="resolution")
assert not i_calc_asu.space_group().is_centric()
assert i_calc_asu.space_group().n_ltr() == 1
assert i_calc_asu.space_group_info().type().is_symmorphic()
if (work_params.force_unit_spot_intensities):
i_calc_asu = i_calc_asu.array(
data=flex.double(i_calc_asu.indices().size(), 1))
i_asu_array = i_calc_asu.customized_copy(
data=flex.size_t_range(i_calc_asu.indices().size()))
if (not i_asu_array.anomalous_flag()):
i_asu_array = i_asu_array.generate_bijvoet_mates()
i_asu_array = i_asu_array.expand_to_p1()
asu_iselection = i_asu_array.data()
i_calc_p1_anom = i_asu_array.customized_copy(
data=i_calc_asu.data().select(asu_iselection))
from libtbx import group_args
return group_args(
asu=i_calc_asu,
asu_iselection=asu_iselection,
p1_anom=i_calc_p1_anom)
