def __getitem__(self, index):
if self._closed:
raise ValueError('I/O operation on closed file')
if isinstance(index, int):
return self.getFrame(index)
elif isinstance(index, (slice, list, np.ndarray)):
if isinstance(index, slice):
ens = Ensemble('{0:s} ({1[0]:d}:{1[1]:d}:{1[2]:d})'.format(
self._title, index.indices(len(self))))
else:
ens = Ensemble('{0:s} slice'.format(self._title))
ens.setCoords(self.getCoords())
if self._weights is not None:
ens.setWeights(self._weights.copy())
ens.addCoordset(self.getCoordsets(index))
return ens
else:
raise IndexError('invalid index')
def calcOneWayAdaptiveANM(a, b, n_steps, **kwargs):
"""Runs one-way adaptivate ANM. """
n_modes = kwargs.pop('n_modes', 20)
coordsA, coordsB, title, atoms, weights, maskA, maskB, rmsd = checkInput(
a, b, **kwargs)
coordsA = coordsA.copy()
LOGGER.timeit('_prody_calcAdaptiveANM')
n = 0
resetFmin = True
defvecs = []
rmsds = [rmsd]
ensemble = Ensemble(title + '_aANM')
ensemble.setAtoms(atoms)
ensemble.setCoords(coordsB)
ensemble.setWeights(weights)
ensemble.addCoordset(coordsA.copy())
while n < n_steps:
LOGGER.info('\nStarting cycle {0} with initial structure {1}'.format(
n + 1, title))
n_modes = calcStep(coordsA,
coordsB,
n_modes,
ensemble,
defvecs,
rmsds,
mask=maskA,
resetFmin=resetFmin,
**kwargs)
n += 1
resetFmin = False
if n_modes == 0:
LOGGER.report('One-way Adaptive ANM converged in %.2fs.',
'_prody_calcAdaptiveANM')
break
return ensemble
def __getitem__(self, index):
if self._closed:
raise ValueError('I/O operation on closed file')
if isinstance(index, int):
return self.getFrame(index)
elif isinstance(index, (slice, list, ndarray)):
if isinstance(index, slice):
ens = Ensemble('{0} ({1[0]}:{1[1]}:{1[2]})'.format(
self._title, index.indices(len(self))))
else:
ens = Ensemble('{0} slice'.format(self._title))
ens.setCoords(self.getCoords())
if self._weights is not None:
ens.setWeights(self._weights.copy())
ens.addCoordset(self.getCoordsets(index))
ens.setAtoms(self._atoms)
return ens
else:
raise IndexError('invalid index')
def calcBothWaysAdaptiveANM(a, b, n_steps, **kwargs):
"""Runs both-way adaptivate ANM. """
n_modes0 = n_modes = kwargs.pop('n_modes', 20)
coordsA, coordsB, title, atoms, weights, maskA, maskB, rmsd = checkInput(
a, b, **kwargs)
coordsA = coordsA.copy()
coordsB = coordsB.copy()
LOGGER.timeit('_prody_calcAdaptiveANM')
n = 0
resetFmin = True
defvecs = []
rmsds = [rmsd]
ensA = Ensemble('A')
ensA.setCoords(coordsA)
ensA.setWeights(weights)
ensA.addCoordset(coordsA.copy())
ensB = Ensemble('B')
ensB.setCoords(coordsB.copy())
ensB.setWeights(weights)
ensB.addCoordset(coordsB.copy())
while n < n_steps:
LOGGER.info('\nStarting cycle {0} with {1}'.format(
n + 1, getTitle(a, 'structure A')))
n_modes = calcStep(coordsA,
coordsB,
n_modes,
ensA,
defvecs,
rmsds,
mask=maskA,
resetFmin=resetFmin,
**kwargs)
n += 1
resetFmin = False
if n_modes == 0:
break
n = 0
n_modes = n_modes0
resetFmin = True
while n < n_steps:
LOGGER.info('\nStarting cycle {0} with structure {1}'.format(
n + 1, getTitle(b, 'structure B')))
n_modes = calcStep(coordsB,
coordsA,
n_modes,
ensB,
defvecs,
rmsds,
mask=maskB,
resetFmin=resetFmin,
**kwargs)
n += 1
resetFmin = False
if n_modes == 0:
LOGGER.report('Alternating Adaptive ANM converged in %.2fs.',
'_prody_calcAdaptiveANM')
break
ensemble = ensA + ensB[::-1]
ensemble.setTitle(title + '_aANM')
ensemble.setAtoms(atoms)
ensemble.setCoords(ensB.getCoords())
LOGGER.report('Both-way Adaptive ANM converged in %.2fs.',
'_prody_calcAdaptiveANM')
return ensemble