def get_filtered_trials(self, include_pdb_hierarchies=False, log=None):
"""
Filter the results based on map statistics and rotamer scoring.
:returns: a list of acceptable trial objects, or if include_pdb_hierarchies
is True, a list of tuples of each trial and the modified pdb
hierarchy.
"""
if (log is None) : log = null_out()
trials = sorted(self.get_trials(), lambda a,b: cmp(b.cc, a.cc))
filtered = []
for k, trial in enumerate(trials):
hierarchy = self.pdb_hierarchy.deep_copy()
if (trial.min_fofc < self.params.min_fofc) or (trial.cc < self.params.cc_min):
print >> log, " discarding trial %d [poor map quality]:" % (k+1)
trial.show_summary(out=log, prefix=" ")
continue
sites = self.sites_start.deep_copy()
sites.set_selected(self.iselection, trial.sites_cart)
hierarchy.atoms().set_xyz(sites)
if (self.params.filter_rotamer_outliers):
n_outliers = alt_confs.score_rotamers(hierarchy=hierarchy,
selection=self.iselection)
if (n_outliers > 0):
print >> log, " discarding trial %d [%d rotamer outlier(s)]:" % \
(k+1, n_outliers)
trial.show_summary(out=log, prefix=" ")
continue
if (include_pdb_hierarchies):
filtered.append((trial, hierarchy))
else :
filtered.append(trial)
return filtered
def get_filtered_trials (self, include_pdb_hierarchies=False, log=None) :
"""
Filter the results based on map statistics and rotamer scoring.
:returns: a list of acceptable trial objects, or if include_pdb_hierarchies
is True, a list of tuples of each trial and the modified pdb
hierarchy.
"""
if (log is None) : log = null_out()
trials = sorted(self.get_trials(), lambda a,b: cmp(b.cc, a.cc))
filtered = []
for k, trial in enumerate(trials) :
hierarchy = self.pdb_hierarchy.deep_copy()
if (trial.min_fofc < self.params.min_fofc) or (trial.cc < self.params.cc_min) :
print >> log, " discarding trial %d [poor map quality]:" % (k+1)
trial.show_summary(out=log, prefix=" ")
continue
sites = self.sites_start.deep_copy()
sites.set_selected(self.iselection, trial.sites_cart)
hierarchy.atoms().set_xyz(sites)
if (self.params.filter_rotamer_outliers) :
n_outliers = alt_confs.score_rotamers(hierarchy=hierarchy,
selection=self.iselection)
if (n_outliers > 0) :
print >> log, " discarding trial %d [%d rotamer outlier(s)]:" % \
(k+1, n_outliers)
trial.show_summary(out=log, prefix=" ")
continue
if (include_pdb_hierarchies) :
filtered.append((trial, hierarchy))
else :
filtered.append(trial)
return filtered
def exercise_utils():
#--- coord_stats_with_flips
pdb_1 = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1639 N PHE A 113 18.514 41.994 54.886 1.00 12.36 N
ATOM 1640 CA PHE A 113 19.737 42.742 54.898 1.00 12.97 C
ATOM 1641 C PHE A 113 19.514 44.206 55.111 1.00 11.88 C
ATOM 1642 O PHE A 113 18.442 44.758 54.823 1.00 13.75 O
ATOM 1643 CB PHE A 113 20.587 42.460 53.684 1.00 20.08 C
ATOM 1644 CG PHE A 113 19.893 42.692 52.399 1.00 17.15 C
ATOM 1645 CD1 PHE A 113 19.182 41.689 51.798 1.00 20.34 C
ATOM 1646 CD2 PHE A 113 20.029 43.895 51.745 1.00 21.22 C
ATOM 1647 CE1 PHE A 113 18.577 41.887 50.601 1.00 23.49 C
ATOM 1648 CE2 PHE A 113 19.411 44.105 50.542 1.00 24.87 C
ATOM 1649 CZ PHE A 113 18.675 43.097 49.976 1.00 21.81 C
ATOM 1650 H PHE A 113 17.875 42.342 54.427 1.00 14.83 H
ATOM 1651 HA PHE A 113 20.250 42.435 55.661 1.00 15.57 H
ATOM 1652 HB2 PHE A 113 21.367 43.036 53.708 1.00 24.10 H
ATOM 1653 HB3 PHE A 113 20.865 41.531 53.707 1.00 24.10 H
ATOM 1654 HD1 PHE A 113 19.099 40.866 52.224 1.00 24.40 H
ATOM 1655 HD2 PHE A 113 20.518 44.581 52.138 1.00 25.47 H
ATOM 1656 HE1 PHE A 113 18.077 41.204 50.215 1.00 28.18 H
ATOM 1657 HE2 PHE A 113 19.488 44.927 50.113 1.00 29.84 H
ATOM 1658 HZ PHE A 113 18.261 43.230 49.154 1.00 26.17 H
""")
pdb_2 = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1639 N PHE A 113 18.514 41.994 54.886 1.00 12.36 N
ATOM 1640 CA PHE A 113 19.737 42.742 54.898 1.00 12.97 C
ATOM 1641 C PHE A 113 19.514 44.206 55.111 1.00 11.88 C
ATOM 1642 O PHE A 113 18.442 44.758 54.823 1.00 13.75 O
ATOM 1643 CB PHE A 113 20.587 42.460 53.684 1.00 20.08 C
ATOM 1644 CG PHE A 113 19.893 42.692 52.399 1.00 17.15 C
ATOM 1645 CD1 PHE A 113 20.174 43.797 51.643 1.00 20.34 C
ATOM 1646 CD2 PHE A 113 18.888 41.845 51.991 1.00 21.22 C
ATOM 1647 CE1 PHE A 113 19.512 44.033 50.483 1.00 23.49 C
ATOM 1648 CE2 PHE A 113 18.224 42.071 50.816 1.00 24.87 C
ATOM 1649 CZ PHE A 113 18.548 43.166 50.057 1.00 21.81 C
ATOM 1650 H PHE A 113 17.875 42.342 54.427 1.00 14.83 H
ATOM 1651 HA PHE A 113 20.250 42.435 55.661 1.00 15.57 H
ATOM 1652 HB2 PHE A 113 21.367 43.036 53.708 1.00 24.10 H
ATOM 1653 HB3 PHE A 113 20.865 41.531 53.707 1.00 24.10 H
ATOM 1654 HD1 PHE A 113 20.840 44.386 51.919 1.00 24.40 H
ATOM 1655 HD2 PHE A 113 18.681 41.096 52.501 1.00 25.47 H
ATOM 1656 HE1 PHE A 113 19.730 44.776 49.967 1.00 28.18 H
ATOM 1657 HE2 PHE A 113 17.560 41.484 50.533 1.00 29.84 H
ATOM 1658 HZ PHE A 113 18.093 43.330 49.263 1.00 26.17 H
""")
hierarchy_1 = pdb_1.hierarchy
hierarchy_2 = pdb_2.hierarchy
pdb_atoms_1 = hierarchy_1.atoms()
sites_1 = pdb_atoms_1.extract_xyz()
sites_2 = hierarchy_2.atoms().extract_xyz()
result = alternate_conformations.coord_stats_with_flips(
sites_1, sites_2, pdb_atoms_1)
assert (approx_equal(result.rmsd, 0.2, eps=0.001))
assert (approx_equal(result.max_dev, 0.452427, eps=0.00001))
#--- get_selection_gap
assert (alternate_conformations.get_selection_gap([1, 2, 3, 4, 5],
[6, 7, 8, 9]) == 0)
assert (alternate_conformations.get_selection_gap([6, 7],
[1, 2, 3, 4, 5]) == 0)
assert (alternate_conformations.get_selection_gap([1, 2, 3, 4, 5],
[7, 8, 9]) == 1)
assert (alternate_conformations.get_selection_gap([1, 2, 3, 4, 5],
[4, 5, 7, 8, 9]) == -2)
assert (alternate_conformations.get_selection_gap([4, 5, 7, 8, 9],
[1, 2, 3, 4]) == -1)
#--- score_rotamers
n_outliers = alternate_conformations.score_rotamers(
hierarchy_2, flex.bool(sites_2.size(), True))
assert (n_outliers == 0)
#--- get_partial_omit_map
xrs = pdb_1.input.xray_structure_simple()
f_calc = abs(xrs.structure_factors(d_min=1.5).f_calc())
f_calc.set_observation_type_xray_amplitude()
flags = f_calc.generate_r_free_flags()
fmodel = mmtbx.utils.fmodel_simple(xray_structures=[xrs],
f_obs=f_calc,
r_free_flags=flags,
scattering_table="n_gaussian",
bulk_solvent_and_scaling=False,
skip_twin_detection=True)
sel = pdb_1.hierarchy.atom_selection_cache().selection("name CZ")
assert (sel.count(True) == 1)
two_fofc_map, fofc_map = alternate_conformations.get_partial_omit_map(
fmodel=fmodel, selection=sel)
site = xrs.sites_frac()[sel.iselection()[0]] # CZ coordinate
assert (fofc_map.tricubic_interpolation(site) > 20)
def exercise_utils () :
#--- coord_stats_with_flips
pdb_1 = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1639 N PHE A 113 18.514 41.994 54.886 1.00 12.36 N
ATOM 1640 CA PHE A 113 19.737 42.742 54.898 1.00 12.97 C
ATOM 1641 C PHE A 113 19.514 44.206 55.111 1.00 11.88 C
ATOM 1642 O PHE A 113 18.442 44.758 54.823 1.00 13.75 O
ATOM 1643 CB PHE A 113 20.587 42.460 53.684 1.00 20.08 C
ATOM 1644 CG PHE A 113 19.893 42.692 52.399 1.00 17.15 C
ATOM 1645 CD1 PHE A 113 19.182 41.689 51.798 1.00 20.34 C
ATOM 1646 CD2 PHE A 113 20.029 43.895 51.745 1.00 21.22 C
ATOM 1647 CE1 PHE A 113 18.577 41.887 50.601 1.00 23.49 C
ATOM 1648 CE2 PHE A 113 19.411 44.105 50.542 1.00 24.87 C
ATOM 1649 CZ PHE A 113 18.675 43.097 49.976 1.00 21.81 C
ATOM 1650 H PHE A 113 17.875 42.342 54.427 1.00 14.83 H
ATOM 1651 HA PHE A 113 20.250 42.435 55.661 1.00 15.57 H
ATOM 1652 HB2 PHE A 113 21.367 43.036 53.708 1.00 24.10 H
ATOM 1653 HB3 PHE A 113 20.865 41.531 53.707 1.00 24.10 H
ATOM 1654 HD1 PHE A 113 19.099 40.866 52.224 1.00 24.40 H
ATOM 1655 HD2 PHE A 113 20.518 44.581 52.138 1.00 25.47 H
ATOM 1656 HE1 PHE A 113 18.077 41.204 50.215 1.00 28.18 H
ATOM 1657 HE2 PHE A 113 19.488 44.927 50.113 1.00 29.84 H
ATOM 1658 HZ PHE A 113 18.261 43.230 49.154 1.00 26.17 H
""")
pdb_2 = iotbx.pdb.hierarchy.input(pdb_string="""\
ATOM 1639 N PHE A 113 18.514 41.994 54.886 1.00 12.36 N
ATOM 1640 CA PHE A 113 19.737 42.742 54.898 1.00 12.97 C
ATOM 1641 C PHE A 113 19.514 44.206 55.111 1.00 11.88 C
ATOM 1642 O PHE A 113 18.442 44.758 54.823 1.00 13.75 O
ATOM 1643 CB PHE A 113 20.587 42.460 53.684 1.00 20.08 C
ATOM 1644 CG PHE A 113 19.893 42.692 52.399 1.00 17.15 C
ATOM 1645 CD1 PHE A 113 20.174 43.797 51.643 1.00 20.34 C
ATOM 1646 CD2 PHE A 113 18.888 41.845 51.991 1.00 21.22 C
ATOM 1647 CE1 PHE A 113 19.512 44.033 50.483 1.00 23.49 C
ATOM 1648 CE2 PHE A 113 18.224 42.071 50.816 1.00 24.87 C
ATOM 1649 CZ PHE A 113 18.548 43.166 50.057 1.00 21.81 C
ATOM 1650 H PHE A 113 17.875 42.342 54.427 1.00 14.83 H
ATOM 1651 HA PHE A 113 20.250 42.435 55.661 1.00 15.57 H
ATOM 1652 HB2 PHE A 113 21.367 43.036 53.708 1.00 24.10 H
ATOM 1653 HB3 PHE A 113 20.865 41.531 53.707 1.00 24.10 H
ATOM 1654 HD1 PHE A 113 20.840 44.386 51.919 1.00 24.40 H
ATOM 1655 HD2 PHE A 113 18.681 41.096 52.501 1.00 25.47 H
ATOM 1656 HE1 PHE A 113 19.730 44.776 49.967 1.00 28.18 H
ATOM 1657 HE2 PHE A 113 17.560 41.484 50.533 1.00 29.84 H
ATOM 1658 HZ PHE A 113 18.093 43.330 49.263 1.00 26.17 H
""")
hierarchy_1 = pdb_1.hierarchy
hierarchy_2 = pdb_2.hierarchy
pdb_atoms_1 = hierarchy_1.atoms()
sites_1 = pdb_atoms_1.extract_xyz()
sites_2 = hierarchy_2.atoms().extract_xyz()
result = alternate_conformations.coord_stats_with_flips(sites_1, sites_2, pdb_atoms_1)
assert (approx_equal(result.rmsd, 0.2, eps=0.001))
assert (approx_equal(result.max_dev, 0.452427, eps=0.00001))
#--- get_selection_gap
assert (alternate_conformations.get_selection_gap([1,2,3,4,5],[6,7,8,9]) == 0)
assert (alternate_conformations.get_selection_gap([6,7], [1,2,3,4,5]) == 0)
assert (alternate_conformations.get_selection_gap([1,2,3,4,5],[7,8,9]) == 1)
assert (alternate_conformations.get_selection_gap([1,2,3,4,5],[4,5,7,8,9]) == -2)
assert (alternate_conformations.get_selection_gap([4,5,7,8,9],[1,2,3,4]) == -1)
#--- score_rotamers
n_outliers = alternate_conformations.score_rotamers(hierarchy_2,
flex.bool(sites_2.size(), True))
assert (n_outliers == 0)
#--- get_partial_omit_map
xrs = pdb_1.input.xray_structure_simple()
f_calc = abs(xrs.structure_factors(d_min=1.5).f_calc())
f_calc.set_observation_type_xray_amplitude()
flags = f_calc.generate_r_free_flags()
fmodel = mmtbx.utils.fmodel_simple(
xray_structures=[xrs],
f_obs=f_calc,
r_free_flags=flags,
scattering_table="n_gaussian",
bulk_solvent_and_scaling=False,
skip_twin_detection=True)
sel = pdb_1.hierarchy.atom_selection_cache().selection("name CZ")
assert (sel.count(True) == 1)
two_fofc_map, fofc_map = alternate_conformations.get_partial_omit_map(
fmodel=fmodel,
selection=sel)
site = xrs.sites_frac()[sel.iselection()[0]] # CZ coordinate
assert (fofc_map.tricubic_interpolation(site) > 20)
def __init__(self,
fmodel,
params,
mp_params,
pdb_hierarchy,
processed_pdb_file,
selection=None,
cif_objects=(),
verbose=True,
debug=False,
out=None):
if (out is None) : out = sys.stdout
adopt_init_args(self, locals())
self.asynchronous_output = False
from mmtbx.rotamer import rotamer_eval
from scitbx.array_family import flex
assert (processed_pdb_file is not None) or (len(pdb_file_names) > 0)
assert (0 < self.params.window_radius <= 4)
self.pdb_hierarchy = pdb_hierarchy
self.processed_pdb_file = processed_pdb_file
self.get_processed_pdb_file(log=out)
self.sites_cart = self.pdb_hierarchy.atoms().extract_xyz().deep_copy()
if (self.selection is None):
self.selection = flex.bool(self.sites_cart.size(), True)
self.min_required_deviation = self.params.min_deviation
if (self.min_required_deviation is Auto):
self.min_required_deviation = fmodel.f_obs().d_min() / 2
self._ensembles = []
self.nproc_1 = self.nproc_2 = 1
two_fofc_map, fofc_map = mmtbx.building.get_difference_maps(fmodel=fmodel)
windows = []
r = rotamer_eval.RotamerEval(data_version="8000")
exclude_resnames = []
if (params.exclude_resnames is not None):
exclude_resnames = [ n.upper() for n in params.exclude_resnames ]
for chain in self.pdb_hierarchy.only_model().chains():
if not chain.is_protein():
continue
residues = chain.residue_groups()
fragments = alt_confs.fragment_single_conformer_chain(residues)
for fragment_residues in fragments :
start = params.window_radius
end = - params.window_radius
for i_res, residue in enumerate(fragment_residues[start:end]):
j_res = i_res + start
atom_groups = residue.atom_groups()
main_conf = atom_groups[0]
if (main_conf.resname.upper() in exclude_resnames):
continue
residue_id = main_conf.id_str()
ag_i_seqs = main_conf.atoms().extract_i_seq()
if (not self.selection.select(ag_i_seqs).all_eq(True)):
continue
if (len(atom_groups) != 1):
if (self.verbose):
print(" residue %s already has multiple conformations"%\
residue_id, file=out)
continue
ag_i_seqs_no_hd = flex.size_t()
for atom in main_conf.atoms():
if (atom.element.strip() not in ["H","D"]):
ag_i_seqs_no_hd.append(atom.i_seq)
# XXX this is probably not optimal; what should I do about the
# adjacent residues? it would be good to check Ramachandran plot too
if (self.params.prefilter.rotameric_only):
n_outliers = alt_confs.score_rotamers(hierarchy=hierarchy,
selection=ag_i_seqs)
if (n_outliers > 0):
if (self.verbose):
print(" residue %s is a rotamer outlier" % residue_id, file=out)
continue
if (self.params.prefilter.use_difference_map):
map_stats = building.local_density_quality(
fofc_map=fofc_map,
two_fofc_map=two_fofc_map,
atom_selection=ag_i_seqs_no_hd,
xray_structure=fmodel.xray_structure,
radius=self.params.prefilter.sampling_radius)
if ((map_stats.number_of_atoms_in_difference_holes() == 0) and
(map_stats.fraction_of_nearby_grid_points_above_cutoff()==0)):
if (self.verbose):
print(" no difference density for %s" % residue_id, file=out)
continue
window_selection = flex.size_t()
offset = - self.params.window_radius
while (offset <= self.params.window_radius):
adjacent_group = fragment_residues[j_res+offset].atom_groups()[0]
window_selection.extend(adjacent_group.atoms().extract_i_seq())
offset += 1
windows.append(residue_window(
residue_id_str=residue_id,
selection=window_selection,
residue_selection=ag_i_seqs_no_hd,
sites_reference=self.sites_cart.select(selection),
window_radius=self.params.window_radius))
if (len(windows) == 0):
raise Sorry("No peptide segments meeting the filtering criteria could "+
"be extracted from the selected atoms.")
else :
print("%d fragments will be refined." % len(windows), file=out)
if (self.mp_params.nproc == 1):
pass
elif (self.mp_params.technology == "multiprocessing"):
if (self.params.n_trials == 1) and (len(self.params.partial_occupancy) == 1):
# only one refinement per window, so parallelize residue iteration
self.nproc_1 = self.mp_params.nproc
else :
# multiple refinements per window, so parallelize at that level
# FIXME actually, this needs to be smarter - if the number of
# available processors is greater than the number of refinements per
# window, it will be more efficient to parallelize the window loop
self.nproc_2 = self.mp_params.nproc
else :
# queuing system, so we can only parallelize residue iteration
self.nproc_1 = self.mp_params.nproc
self.out = null_out()
self.processed_pdb_file = None
print("", file=out)
alt_confs.print_trial_header(out)
ensembles = []
if (self.nproc_1 == 1):
self.asynchronous_output = True
for window in windows :
ens = self.refine_window(window)
ensembles.append(ens)
else :
ensembles = easy_mp.parallel_map(
func=self.refine_window,
iterable=windows,
processes=self.nproc_1,
qsub_command=mp_params.qsub_command,
method=mp_params.technology)
self._ensembles = [ e for e in ensembles if (e is not None) ]
# XXX reassert order
print("", file=out)
if (len(self._ensembles) == 0):
print("WARNING: no ensembles passed filtering step", file=out)
print("", file=out)
self._ensembles.sort(lambda a,b: a.selection[0] < b.selection[0])
self.processed_pdb_file = processed_pdb_file
if (debug):
for k, ens in enumerate(filtered):
pdb_out = ens.dump_pdb_file(
pdb_hierarchy=pdb_hierarchy,
crystal_symmetry=fmodel.f_obs())
print("wrote %s" % pdb_out, file=out)
def __init__ (self,
fmodel,
params,
mp_params,
pdb_hierarchy,
processed_pdb_file,
selection=None,
cif_objects=(),
verbose=True,
debug=False,
out=None) :
if (out is None) : out = sys.stdout
adopt_init_args(self, locals())
self.asynchronous_output = False
from mmtbx.rotamer import rotamer_eval
from scitbx.array_family import flex
assert (processed_pdb_file is not None) or (len(pdb_file_names) > 0)
assert (0 < self.params.window_radius <= 4)
self.pdb_hierarchy = pdb_hierarchy
self.processed_pdb_file = processed_pdb_file
self.get_processed_pdb_file(log=out)
self.sites_cart = self.pdb_hierarchy.atoms().extract_xyz().deep_copy()
if (self.selection is None) :
self.selection = flex.bool(self.sites_cart.size(), True)
self.min_required_deviation = self.params.min_deviation
if (self.min_required_deviation is Auto) :
self.min_required_deviation = fmodel.f_obs().d_min() / 2
self._ensembles = []
self.nproc_1 = self.nproc_2 = 1
two_fofc_map, fofc_map = mmtbx.building.get_difference_maps(fmodel=fmodel)
windows = []
r = rotamer_eval.RotamerEval(data_version="8000")
exclude_resnames = []
if (params.exclude_resnames is not None) :
exclude_resnames = [ n.upper() for n in params.exclude_resnames ]
for chain in self.pdb_hierarchy.only_model().chains() :
main_conf = chain.conformers()[0]
if (not main_conf.is_protein()) :
continue
residues = chain.residue_groups()
fragments = alt_confs.fragment_single_conformer_chain(residues)
for fragment_residues in fragments :
start = params.window_radius
end = - params.window_radius
for i_res, residue in enumerate(fragment_residues[start:end]) :
j_res = i_res + start
atom_groups = residue.atom_groups()
main_conf = atom_groups[0]
if (main_conf.resname.upper() in exclude_resnames) :
continue
residue_id = main_conf.id_str()
ag_i_seqs = main_conf.atoms().extract_i_seq()
if (not self.selection.select(ag_i_seqs).all_eq(True)) :
continue
if (len(atom_groups) != 1) :
if (self.verbose) :
print >> out, " residue %s already has multiple conformations"%\
residue_id
continue
ag_i_seqs_no_hd = flex.size_t()
for atom in main_conf.atoms() :
if (atom.element.strip() not in ["H","D"]) :
ag_i_seqs_no_hd.append(atom.i_seq)
# XXX this is probably not optimal; what should I do about the
# adjacent residues? it would be good to check Ramachandran plot too
if (self.params.prefilter.rotameric_only) :
n_outliers = alt_confs.score_rotamers(hierarchy=hierarchy,
selection=ag_i_seqs)
if (n_outliers > 0) :
if (self.verbose) :
print >> out, " residue %s is a rotamer outlier" % residue_id
continue
if (self.params.prefilter.use_difference_map) :
map_stats = building.local_density_quality(
fofc_map=fofc_map,
two_fofc_map=two_fofc_map,
atom_selection=ag_i_seqs_no_hd,
xray_structure=fmodel.xray_structure,
radius=self.params.prefilter.sampling_radius)
if ((map_stats.number_of_atoms_in_difference_holes() == 0) and
(map_stats.fraction_of_nearby_grid_points_above_cutoff()==0)) :
if (self.verbose) :
print >> out, " no difference density for %s" % residue_id
continue
window_selection = flex.size_t()
offset = - self.params.window_radius
while (offset <= self.params.window_radius) :
adjacent_group = fragment_residues[j_res+offset].atom_groups()[0]
window_selection.extend(adjacent_group.atoms().extract_i_seq())
offset += 1
windows.append(residue_window(
residue_id_str=residue_id,
selection=window_selection,
residue_selection=ag_i_seqs_no_hd,
sites_reference=self.sites_cart.select(selection),
window_radius=self.params.window_radius))
if (len(windows) == 0) :
raise Sorry("No peptide segments meeting the filtering criteria could "+
"be extracted from the selected atoms.")
else :
print >> out, "%d fragments will be refined." % len(windows)
if (self.mp_params.nproc == 1) :
pass
elif (self.mp_params.technology == "multiprocessing") :
if (self.params.n_trials == 1) and (len(self.params.partial_occupancy) == 1) :
# only one refinement per window, so parallelize residue iteration
self.nproc_1 = self.mp_params.nproc
else :
# multiple refinements per window, so parallelize at that level
# FIXME actually, this needs to be smarter - if the number of
# available processors is greater than the number of refinements per
# window, it will be more efficient to parallelize the window loop
self.nproc_2 = self.mp_params.nproc
else :
# queuing system, so we can only parallelize residue iteration
self.nproc_1 = self.mp_params.nproc
self.out = null_out()
self.processed_pdb_file = None
print >> out, ""
alt_confs.print_trial_header(out)
ensembles = []
if (self.nproc_1 == 1) :
self.asynchronous_output = True
for window in windows :
ens = self.refine_window(window)
ensembles.append(ens)
else :
ensembles = easy_mp.parallel_map(
func=self.refine_window,
iterable=windows,
processes=self.nproc_1,
qsub_command=mp_params.qsub_command,
method=mp_params.technology)
self._ensembles = [ e for e in ensembles if (e is not None) ]
# XXX reassert order
print >> out, ""
if (len(self._ensembles) == 0) :
print >> out, "WARNING: no ensembles passed filtering step"
print >> out, ""
self._ensembles.sort(lambda a,b: a.selection[0] < b.selection[0])
self.processed_pdb_file = processed_pdb_file
if (debug) :
for k, ens in enumerate(filtered) :
pdb_out = ens.dump_pdb_file(
pdb_hierarchy=pdb_hierarchy,
crystal_symmetry=fmodel.f_obs())
print >> out, "wrote %s" % pdb_out
