def load_data(self):
super(MDL_NODE_SEQUENCE, self).load_data()
filename = None
# Get the name of this animation
self.name = self.data.split()[1][1:-1]
# First, try to find a child node of MDL_NODE_FILE that will tell us the animation file to use
for node in self.nodes:
if type(node) is MDL_NODE_FILE:
# Get the filename
filename = node.filename
break
# If there was no child node of MDL_NODE_FILE, use the sequences name to guess the animation file to use
if filename == None:
filename = self.path + self.name + ".anm"
# Check if this animation file wasn't already loaded
if filename not in self.loaded_animation_files:
print(type(self).__name__ + " Loading file " + filename)
try:
# Create an Animation object and load the ANM file
self.anm = ANM(filename)
except:
print(sys.exc_info()[0])
# Add filename into our loaded animation files
self.loaded_animation_files.append(filename)
def reduceModel(model, atoms, select):
"""Return reduced NMA model. Reduces a :class:`~.NMA` model to a subset of
*atoms* matching *select*. This function behaves differently depending on
the type of the *model* argument. For :class:`.ANM` and :class:`.GNM` or
other :class:`.NMA` models, force constant matrix for system of interest
(specified by the *select*) is derived from the force constant matrix for
the *model* by assuming that for any given displacement of the system of
interest, other atoms move along in such a way as to minimize the potential
energy. This is based on the formulation in [KH00]_. For :class:`.PCA`
models, this function simply takes the sub-covariance matrix for selection.
:arg model: dynamics model
:type model: :class:`.ANM`, :class:`.GNM`, or :class:`.PCA`
:arg atoms: atoms that were used to build the model
:type atoms: :class:`.Atomic`
:arg select: an atom selection or a selection string
:type select: :class:`.Selection`, str
:returns: (:class:`.NMA`, :class:`.Selection`)"""
linalg = importLA()
if not isinstance(model, NMA):
raise TypeError('model must be an NMA instance, not {0:s}'
.format(type(model)))
if not isinstance(atoms, (AtomGroup, AtomSubset, AtomMap)):
raise TypeError('atoms type is not valid')
if len(atoms) <= 1:
raise TypeError('atoms must contain more than 1 atoms')
if isinstance(model, GNM):
matrix = model._kirchhoff
elif isinstance(model, ANM):
matrix = model._hessian
elif isinstance(model, PCA):
matrix = model._cov
else:
raise TypeError('model does not have a valid type derived from NMA')
if matrix is None:
raise ValueError('model matrix (Hessian/Kirchhoff/Covariance) is not '
'built')
if isinstance(select, str):
system = SELECT.getBoolArray(atoms, select)
n_sel = sum(system)
if n_sel == 0:
raise ValueError('select matches 0 atoms')
if len(atoms) == n_sel:
raise ValueError('select matches all atoms')
if isinstance(atoms, AtomGroup):
ag = atoms
which = np.arange(len(atoms))[system]
else:
ag = atoms.getAtomGroup()
which = atoms._getIndices()[system]
sel = Selection(ag, which, select, atoms.getACSIndex())
elif isinstance(select, AtomSubset):
sel = select
if isinstance(atoms, AtomGroup):
if sel.getAtomGroup() != atoms:
raise ValueError('select and atoms do not match')
system = np.zeros(len(atoms), bool)
system[sel._getIndices()] = True
else:
if atoms.getAtomGroup() != sel.getAtomGroup():
raise ValueError('select and atoms do not match')
elif not sel in atoms:
raise ValueError('select is not a subset of atoms')
idxset = set(atoms._getIndices())
system = np.array([idx in idxset for idx in sel._getIndices()])
else:
raise TypeError('select must be a string or a Selection instance')
other = np.invert(system)
if model.is3d():
system = np.tile(system, (3,1)).transpose().flatten()
other = np.tile(other, (3,1)).transpose().flatten()
ss = matrix[system,:][:,system]
if isinstance(model, PCA):
eda = PCA(model.getTitle() + ' reduced')
eda.setCovariance(ss)
return eda, system
so = matrix[system,:][:,other]
os = matrix[other,:][:,system]
oo = matrix[other,:][:,other]
matrix = ss - np.dot(so, np.dot(linalg.inv(oo), os))
if isinstance(model, GNM):
gnm = GNM(model.getTitle() + ' reduced')
gnm.setKirchhoff(matrix)
return gnm, sel
elif isinstance(model, ANM):
anm = ANM(model.getTitle() + ' reduced')
anm.setHessian(matrix)
return anm, sel
elif isinstance(model, PCA):
eda = PCA(model.getTitle() + ' reduced')
eda.setCovariance(matrix)
return eda, sel
def reduceModel(model, atoms, selstr):
"""Return reduced NMA model.
Reduces a :class:`NMA` model to a subset of *atoms* matching a selection
*selstr*. This function behaves differently depending on the type of the
*model* argument. For ANM and GNM or other NMA models, this functions
derives the force constant matrix for system of interest (specified by the
*selstr*) from the force constant matrix for the *model* by assuming that
for any given displacement of the system of interest, the other atoms move
along in such a way as to minimize the potential energy. This is based on
the formulation in in [KH00]_. For PCA models, this function simply takes
the sub-covariance matrix for the selected atoms.
:arg model: dynamics model
:type model: :class:`ANM`, :class:`GNM`, or :class:`PCA`
:arg atoms: atoms that were used to build the model
:arg selstr: a selection string specifying subset of atoms"""
linalg = importLA()
if not isinstance(model, NMA):
raise TypeError("model must be an NMA instance, not {0:s}".format(type(model)))
if not isinstance(atoms, (AtomGroup, AtomSubset, AtomMap)):
raise TypeError("atoms type is not valid")
if len(atoms) <= 1:
raise TypeError("atoms must contain more than 1 atoms")
if isinstance(model, GNM):
matrix = model._kirchhoff
elif isinstance(model, ANM):
matrix = model._hessian
elif isinstance(model, PCA):
matrix = model._cov
else:
raise TypeError("model does not have a valid type derived from NMA")
if matrix is None:
raise ValueError("model matrix (Hessian/Kirchhoff/Covariance) is not " "built")
system = SELECT.getBoolArray(atoms, selstr)
other = np.invert(system)
n_sel = sum(system)
if n_sel == 0:
LOGGER.warning("selection has 0 atoms")
return None
if len(atoms) == n_sel:
LOGGER.warning("selection results in same number of atoms, " "model is not reduced")
return None
if model.is3d():
system = np.tile(system, (3, 1)).transpose().flatten()
other = np.tile(other, (3, 1)).transpose().flatten()
ss = matrix[system, :][:, system]
if isinstance(model, PCA):
eda = PCA(model.getTitle() + " reduced")
eda.setCovariance(ss)
return eda, system
so = matrix[system, :][:, other]
os = matrix[other, :][:, system]
oo = matrix[other, :][:, other]
matrix = ss - np.dot(so, np.dot(linalg.inv(oo), os))
if isinstance(model, GNM):
gnm = GNM(model.getTitle() + " reduced")
gnm.setKirchhoff(matrix)
return gnm, system
elif isinstance(model, ANM):
anm = ANM(model.getTitle() + " reduced")
anm.setHessian(matrix)
return anm, system
elif isinstance(model, PCA):
eda = PCA(model.getTitle() + " reduced")
eda.setCovariance(matrix)
return eda, system