def _prepare(self):
n_dim = self._n_atoms * 3
self.cov = np.zeros((n_dim, n_dim))
self._ref_atom_positions = self._reference.positions
self._ref_cog = self._reference.center_of_geometry()
self._ref_atom_positions -= self._ref_cog
if self._calc_mean:
interval = int(self.n_frames // 100)
interval = interval if interval > 0 else 1
format = ("Mean Calculation Step"
"%(step)5d/%(numsteps)d [%(percentage)5.1f%%]\r")
mean_pm = ProgressMeter(self.n_frames if self.n_frames else 1,
interval=interval,
verbose=self._verbose,
format=format)
for i, ts in enumerate(
self._u.trajectory[self.start:self.stop:self.step]):
if self.align:
mobile_cog = self._atoms.center_of_geometry()
mobile_atoms, old_rmsd = _fit_to(self._atoms.positions,
self._ref_atom_positions,
self._atoms,
mobile_com=mobile_cog,
ref_com=self._ref_cog)
else:
self.mean += self._atoms.positions.ravel()
mean_pm.echo(i)
self.mean /= self.n_frames
self.mean_atoms = self._atoms
self.mean_atoms.positions = self._atoms.positions
def _prepare(self):
n_dim = self._n_atoms * 3
self.cov = np.zeros((n_dim, n_dim))
self._ref_atom_positions = self._reference.positions
self._ref_cog = self._reference.center_of_geometry()
self._ref_atom_positions -= self._ref_cog
if self._calc_mean:
interval = int(self.n_frames // 100)
interval = interval if interval > 0 else 1
format = ("Mean Calculation Step"
"%(step)5d/%(numsteps)d [%(percentage)5.1f%%]\r")
mean_pm = ProgressMeter(self.n_frames if self.n_frames else 1,
interval=interval, verbose=self._verbose,
format=format)
for i, ts in enumerate(self._u.trajectory[self.start:self.stop:
self.step]):
if self.align:
mobile_cog = self._atoms.center_of_geometry()
mobile_atoms, old_rmsd = _fit_to(self._atoms.positions,
self._ref_atom_positions,
self._atoms,
mobile_com=mobile_cog,
ref_com=self._ref_cog)
else:
self.mean += self._atoms.positions.ravel()
mean_pm.echo(i)
self.mean /= self.n_frames
self.mean_atoms = self._atoms
self.mean_atoms.positions = self._atoms.positions
def _single_frame(self):
if self.align:
mobile_cog = self._atoms.center_of_geometry()
mobile_atoms, old_rmsd = _fit_to(self._atoms.positions,
self._ref_atom_positions,
self._atoms,
mobile_com=mobile_cog,
ref_com=self._ref_cog)
# now all structures are aligned to reference
x = mobile_atoms.positions.ravel()
else:
x = self._atoms.positions.ravel()
x -= self.mean
self.cov += np.dot(x[:, np.newaxis], x[:, np.newaxis].T)
def _single_frame(self):
if self.align:
mobile_cog = self._atoms.center_of_geometry()
mobile_atoms, old_rmsd = _fit_to(self._atoms.positions,
self._ref_atom_positions,
self._atoms,
mobile_com=mobile_cog,
ref_com=self._ref_cog)
# now all structures are aligned to reference
x = mobile_atoms.positions.ravel()
else:
x = self._atoms.positions.ravel()
x -= self.mean
self.cov += np.dot(x[:, np.newaxis], x[:, np.newaxis].T)
def _prepare(self):
# access start index
self._u.trajectory[self.start]
# reference will be start index
self._reference = self._u.select_atoms(self._select)
self._atoms = self._u.select_atoms(self._select)
self._n_atoms = self._atoms.n_atoms
if self._mean is None:
self.mean = np.zeros(self._n_atoms * 3)
self._calc_mean = True
else:
self.mean = self._mean.positions
self._calc_mean = False
if self.n_frames == 1:
raise ValueError('No covariance information can be gathered from a'
'single trajectory frame.\n')
n_dim = self._n_atoms * 3
self.cov = np.zeros((n_dim, n_dim))
self._ref_atom_positions = self._reference.positions
self._ref_cog = self._reference.center_of_geometry()
self._ref_atom_positions -= self._ref_cog
if self._calc_mean:
interval = int(self.n_frames // 100)
interval = interval if interval > 0 else 1
format = ("Mean Calculation Step"
"%(step)5d/%(numsteps)d [%(percentage)5.1f%%]")
mean_pm = ProgressMeter(self.n_frames if self.n_frames else 1,
interval=interval,
verbose=self._verbose,
format=format)
for i, ts in enumerate(
self._u.trajectory[self.start:self.stop:self.step]):
if self.align:
mobile_cog = self._atoms.center_of_geometry()
mobile_atoms, old_rmsd = _fit_to(self._atoms.positions,
self._ref_atom_positions,
self._atoms,
mobile_com=mobile_cog,
ref_com=self._ref_cog)
else:
self.mean += self._atoms.positions.ravel()
mean_pm.echo(i)
self.mean /= self.n_frames
self.mean_atoms = self._atoms
self.mean_atoms.positions = self._atoms.positions
def _prepare(self):
# access start index
self._u.trajectory[self.start]
# reference will be start index
self._reference = self._u.select_atoms(self._select)
self._atoms = self._u.select_atoms(self._select)
self._n_atoms = self._atoms.n_atoms
if self._mean is None:
self.mean = np.zeros(self._n_atoms*3)
self._calc_mean = True
else:
self.mean = self._mean.positions
self._calc_mean = False
if self.n_frames == 1:
raise ValueError('No covariance information can be gathered from a'
'single trajectory frame.\n')
n_dim = self._n_atoms * 3
self.cov = np.zeros((n_dim, n_dim))
self._ref_atom_positions = self._reference.positions
self._ref_cog = self._reference.center_of_geometry()
self._ref_atom_positions -= self._ref_cog
if self._calc_mean:
for ts in ProgressBar(self._u.trajectory[self.start:self.stop:self.step],
verbose=self._verbose, desc="Mean Calculation"):
if self.align:
mobile_cog = self._atoms.center_of_geometry()
mobile_atoms, old_rmsd = _fit_to(self._atoms.positions - mobile_cog,
self._ref_atom_positions,
self._atoms,
mobile_com=mobile_cog,
ref_com=self._ref_cog)
self.mean += self._atoms.positions.ravel()
self.mean /= self.n_frames
self.mean_atoms = self._atoms
self.mean_atoms.positions = self._atoms.positions