def to_xyz(self, fn=None):
'''
Method to write the given trajectory to an XYZ file. This method
assumes that the coords attribute has been assigned.
**Optional Arguments**
fn
a string defining the name of the output file
'''
if 'active' in list(self.__dict__.keys()) and not self.active: return
if fn is None:
fn = 'trajectory-%s-%i.xyz' %(self.term.basename.replace('/', '-'),self.term.index)
f = open(fn, 'w')
xyz = XYZWriter(f, [pt[Z].symbol for Z in self.numbers])
for frame, coord in enumerate(self.coords):
xyz.dump('frame %i' %frame, coord)
f.close()
def to_xyz(self, fn=None):
'''
Method to write the given trajectory to an XYZ file. This method
assumes that the coords attribute has been assigned.
**Optional Arguments**
fn
a string defining the name of the output file
'''
if 'active' in list(self.__dict__.keys()) and not self.active: return
if fn is None:
fn = 'trajectory-%s-%i.xyz' %(self.term.basename.replace('/', '-'),self.term.index)
f = open(fn, 'w')
xyz = XYZWriter(f, [pt[Z].symbol for Z in self.numbers])
for frame, coord in enumerate(self.coords):
xyz.dump('frame %i' %frame, coord)
f.close()
def write(self, trajectory, filename):
'''
Method to write the given trajectory to a file
**Arguments**
trajectory
a (F,N,3) numpy array defining the perturbation trajectory.
It contains F frames of (N,3)-dimensional geometry arrays.
filename
a string defining the name of the output file
'''
_f = open(filename, 'w')
xyz = XYZWriter(_f, [pt[Z].symbol for Z in self.system.numbers])
for idx, dx in enumerate(trajectory):
coords = self.system.ref.coords + dx
xyz.dump('frame %i' %idx, coords)
_f.close()
def write_principal_mode(f, f_pca, index, n_frames=100, select=None, mw=True, scaling=1.):
"""
Writes out one xyz file per principal mode given in index
**Arguments:**
f
Path to an h5.File instance containing the original data.
f_pca
Path to an h5.File instance containing the PCA, with reference structure, eigenvalues
and principal modes.
index
An array containing the principal modes which need to be written out.
**Optional arguments:**
n_frames
The number of frames in each xyz file.
select
A list of atom indexes that are considered for the computation
of the spectrum. If not given, all atoms are used.
mw
If mass_weighted is True, the covariance matrix is assumed to be mass-weighted.
scaling
Scaling factor applied to the maximum deviation of the principal mode (i.e. the maximum
principal component for that mode)
"""
# Load in the relevant data
# Atom numbers, masses and initial frame
numbers = f['system/numbers']
masses = f['system/masses']
pos = f['trajectory/pos']
if select is not None:
numbers = numbers[select]
masses = masses[select]
pos = pos[:,select,:]
masses = np.repeat(masses,3)
# Data from the PC analysis
grp = f_pca['pca']
# The selected principal modes
pm = grp['pm'][:,index]
# The corresponding eigenvalues
eigval = grp['eigvals'][index]
# And the principal components
pc = grp['pc'][:,index]
with log.section('PCA'):
for i in range(len(index)):
log('Writing out principal mode %s' %index[i])
if eigval[i] < 0:
Warning('Negative eigenvalue encountered, skipping this entry')
break
# Determine maximum fluctuation (in units of meter*sqrt(kilogram))
max_fluct = np.max(np.abs(pc[:,i]))
# Initialize XYZWriter from molmod package
xw = XYZWriter('pm_%s.xyz' %index[i], [pd[number].symbol for number in numbers])
# Determine index in trajectory closest to rest state, and the corresponding positions
ind_min = np.argmin(np.abs(pc[:,i]))
r_ref = pos[ind_min,:,:]
for j in range(n_frames):
q_var = scaling*pm[:,i]*max_fluct*(2.*j-n_frames)/n_frames
if mw:
q_var /= np.sqrt(masses)
r = r_ref + q_var.reshape(-1,3)
xw.dump('Frame%s' %j, r)
del xw
def write_principal_mode(f,
f_pca,
index,
n_frames=100,
select=None,
mw=True,
scaling=1.):
"""
Writes out one xyz file per principal mode given in index
**Arguments:**
f
Path to an h5.File instance containing the original data.
f_pca
Path to an h5.File instance containing the PCA, with reference structure, eigenvalues
and principal modes.
index
An array containing the principal modes which need to be written out.
**Optional arguments:**
n_frames
The number of frames in each xyz file.
select
A list of atom indexes that are considered for the computation
of the spectrum. If not given, all atoms are used.
mw
If mass_weighted is True, the covariance matrix is assumed to be mass-weighted.
scaling
Scaling factor applied to the maximum deviation of the principal mode (i.e. the maximum
principal component for that mode)
"""
# Load in the relevant data
# Atom numbers, masses and initial frame
numbers = f['system/numbers']
masses = f['system/masses']
pos = f['trajectory/pos']
if select is not None:
numbers = numbers[select]
masses = masses[select]
pos = pos[:, select, :]
masses = np.repeat(masses, 3)
# Data from the PC analysis
grp = f_pca['pca']
# The selected principal modes
pm = grp['pm'][:, index]
# The corresponding eigenvalues
eigval = grp['eigvals'][index]
# And the principal components
pc = grp['pc'][:, index]
with log.section('PCA'):
for i in xrange(len(index)):
log('Writing out principal mode %s' % index[i])
if eigval[i] < 0:
Warning('Negative eigenvalue encountered, skipping this entry')
break
# Determine maximum fluctuation (in units of meter*sqrt(kilogram))
max_fluct = np.max(np.abs(pc[:, i]))
# Initialize XYZWriter from molmod package
xw = XYZWriter('pm_%s.xyz' % index[i],
[pd[number].symbol for number in numbers])
# Determine index in trajectory closest to rest state, and the corresponding positions
ind_min = np.argmin(np.abs(pc[:, i]))
r_ref = pos[ind_min, :, :]
for j in xrange(n_frames):
q_var = scaling * pm[:, i] * max_fluct * (2. * j -
n_frames) / n_frames
if mw:
q_var /= np.sqrt(masses)
r = r_ref + q_var.reshape(-1, 3)
xw.dump('Frame%s' % j, r)
del xw
