def IsotropicFitSegmentOutlierRejection(chain, frag_id1, frag_id2):
segment = chain[frag_id1:frag_id2]
atoms = list(segment.iter_all_atoms())
orig_num_atoms = len(atoms)
rejected = 0
while True:
tls_analyzer = tlsmdmodule.TLSModelAnalyzer()
xlist = atom_selection.chain_to_xmlrpc_list(iter(atoms))
tls_analyzer.set_xmlrpc_chain(xlist)
tlsdict = tls_analyzer.isotropic_fit_segment(frag_id1, frag_id2)
IT, IL, IS, IOrigin = tls_calcs.isotlsdict2tensors(tlsdict)
num_atoms = 0
msd_sum = 0.0
atm_deltab = []
for atm, uiso in TLS.iter_itls_uiso(iter(atoms), IT, IL, IS, IOrigin):
num_atoms += 1
deltab = atm.temp_factor - (Constants.U2B * uiso)
msd_sum += deltab**2
atm_deltab.append((deltab, atm))
sigma = math.sqrt((msd_sum / num_atoms))
sigma2 = 2.0 * sigma
outliers = 0
for deltab, atm in atm_deltab:
if abs(deltab) > sigma2:
atoms.remove(atm)
outliers += 1
rejected += outliers
if outliers == 0 or (num_atoms - outliers) < 10:
console.stdoutln("SEGMENT %s-%s %d->%d" %
(frag_id1, frag_id2, orig_num_atoms,
orig_num_atoms - rejected))
return IT, IL, IS, IOrigin
def fit_tls_parameters(self, chain):
"""Use the non-linear TLS model to calculate tensor values.
"""
tls_group = self.tls_group
## anisotropic model
tlsdict = chain.tls_analyzer.constrained_anisotropic_fit(self.segment_ranges)
T, L, S, origin = tls_calcs.tlsdict2tensors(tlsdict)
tls_group.T = T
tls_group.L = L
tls_group.S = S
tls_group.origin = origin
## isotropic model
itlsdict = chain.tls_analyzer.constrained_isotropic_fit(self.segment_ranges)
IT, IL, IS, IOrigin = tls_calcs.isotlsdict2tensors(itlsdict)
tls_group.itls_T = IT
tls_group.itls_L = IL
tls_group.itls_S = IS
assert numpy.allclose(tls_group.origin, IOrigin)
def fit_tls_parameters(self, chain):
"""Use the non-linear TLS model to calculate tensor values.
"""
tls_group = self.tls_group
## anisotropic model
tlsdict = chain.tls_analyzer.constrained_anisotropic_fit(self.segment_ranges)
T, L, S, origin = tls_calcs.tlsdict2tensors(tlsdict)
tls_group.T = T
tls_group.L = L
tls_group.S = S
tls_group.origin = origin
## isotropic model
itlsdict = chain.tls_analyzer.constrained_isotropic_fit(self.segment_ranges)
IT, IL, IS, IOrigin = tls_calcs.isotlsdict2tensors(itlsdict)
tls_group.itls_T = IT
tls_group.itls_L = IL
tls_group.itls_S = IS
assert numpy.allclose(tls_group.origin, IOrigin)
def IsotropicFitSegmentOutlierRejection(chain, frag_id1, frag_id2):
segment = chain[frag_id1:frag_id2]
atoms = list(segment.iter_all_atoms())
orig_num_atoms = len(atoms)
rejected = 0
while True:
tls_analyzer = tlsmdmodule.TLSModelAnalyzer()
xlist = atom_selection.chain_to_xmlrpc_list(iter(atoms))
tls_analyzer.set_xmlrpc_chain(xlist)
tlsdict = tls_analyzer.isotropic_fit_segment(frag_id1, frag_id2)
IT, IL, IS, IOrigin = tls_calcs.isotlsdict2tensors(tlsdict)
num_atoms = 0
msd_sum = 0.0
atm_deltab = []
for atm, uiso in TLS.iter_itls_uiso(iter(atoms), IT, IL, IS, IOrigin):
num_atoms += 1;
deltab = atm.temp_factor - (Constants.U2B * uiso)
msd_sum += deltab**2
atm_deltab.append((deltab, atm))
sigma = math.sqrt((msd_sum / num_atoms))
sigma2 = 2.0 * sigma
outliers = 0
for deltab, atm in atm_deltab:
if abs(deltab) > sigma2:
atoms.remove(atm)
outliers += 1
rejected += outliers
if outliers == 0 or (num_atoms - outliers) < 10:
console.stdoutln("SEGMENT %s-%s %d->%d" % (
frag_id1, frag_id2, orig_num_atoms, orig_num_atoms - rejected))
return IT, IL, IS, IOrigin
