def open_structure_factors(session, path, structure_model = None,
over_sampling=2.0, always_raise_errors=True):
if structure_model is None:
raise UserError('Reflection data must be associated with an atomic '
'structure, provided via the structure_model argument.')
from .symmetry import get_map_mgr
mmgr = get_map_mgr(structure_model, create=True)
try:
xmapset = mmgr.add_xmapset_from_file(path, oversampling_rate=over_sampling)
log_str = 'Opened crystallographic dataset from {}\n'.format(path)
if xmapset.experimental_data:
log_str += 'Found experimental reflection data: \n'
log_str += '\n'.join(['\t{}'.format(n) for n in xmapset.experimental_data.keys()])
log_str += '\n'
log_str += 'Rwork: {:.4f}; Rfree: {:.4f}\n'.format(
xmapset.rwork, xmapset.rfree
)
log_str += 'Generated maps: \n{}\n'.format(
'\n'.join(['\t{}'.format(m.name) for m in xmapset]))
log_str += 'Any unwanted maps may be safely closed via the Model panel.'
return [mmgr.crystal_mgr], log_str
except RuntimeError as e:
if always_raise_errors:
raise e
else:
session.logger.warning(str(e))
return None, None
def _fetch(session,
pdb_id,
fetch_source='rcsb',
ignore_cache=False,
structure_factors=False,
over_sampling=2.0,
**kw):
models, status = fetch_func(session,
pdb_id,
ignore_cache=ignore_cache,
**kw)
if structure_factors:
if len(models) != 1:
raise UserError(
'Structure factors can only be used with a single model!')
m = models[0]
sf_file = fetch_structure_factors(session,
pdb_id,
fetch_source=fetch_source,
ignore_cache=ignore_cache,
**kw)
from chimerax.clipper.symmetry import get_map_mgr
mmgr = get_map_mgr(m, create=True)
mmgr.add_xmapset_from_mtz(sf_file, oversampling_rate=over_sampling)
return [mmgr.crystal_mgr], status
return models, status
def _optimize_anisou(self):
import numpy
from numpy import cov
natoms = len(self.model.atoms)
covariances = self._covariances = numpy.empty((natoms, 3, 3))
traj = self._trajectory
for i in range(natoms):
coords = traj[:, i, :]
covariances[i] = numpy.abs(cov(coords, rowvar=False))
#tensors = self._tensors = numpy.empty(covariances.shape)
# for cv, t in zip(covariances, tensors):
# t[:] = numpy.abs(numpy.linalg.inv(cv))
anisou = self._anisou_base = numpy.empty((natoms, 6))
anisou[:, 0] = covariances[:, 0, 0]
anisou[:, 1] = covariances[:, 1, 1]
anisou[:, 2] = covariances[:, 2, 2]
anisou[:, 3] = covariances[:, 0, 1]
anisou[:, 4] = covariances[:, 0, 2]
anisou[:, 5] = covariances[:, 1, 2]
# Convert from B to U
from math import pi
anisou[:] = numpy.sqrt(anisou / (8 * pi**2))
from chimerax.clipper.symmetry import get_map_mgr
map_mgr = get_map_mgr(self.model)
xmapset = self._xmapset = map_mgr.xmapsets[0]
xmapset.triggers.add_handler('maps recalculated', self._map_update_cb)
self._map_update_cb()
def associate_volumes(session, volumes, to_model=None):
if to_model is None:
from chimerax.core.errors import UserError
raise UserError('The toModel argument must be provided!')
from chimerax.clipper.symmetry import get_map_mgr
mgr = get_map_mgr(to_model, create=True)
for v in volumes:
mgr.nxmapset.add_nxmap_handler_from_volume(v)
if mgr.crystal_mgr not in session.models.list():
session.models.add([mgr.crystal_mgr])
def _optimize_u_iso(self):
import numpy
traj = self._trajectory
variance = traj.var(axis=0).sum(axis=1)
from chimerax.clipper.clipper_python import Util
self._u_base = numpy.array([Util.u2b(u)
for u in variance]).astype(numpy.float32)
from chimerax.clipper.symmetry import get_map_mgr
map_mgr = get_map_mgr(self.model)
xmapset = self._xmapset = map_mgr.xmapsets[0]
xmapset.triggers.add_handler('maps recalculated', self._map_update_cb)
self._map_update_cb()