def load_frame(self, traj_index, frame_index):
"""Load one or more specified frames.
Example
-------
>>> project = Project.load_from('ProjectInfo.yaml')
>>> foo = project.load_frame(1,10)
>>> bar = Trajectory.read_frame(TrajFilename=project.traj_filename(1),
WhichFrame=10)
>>> np.all(foo['XYZList'] == bar)
True
Parameters
----------
traj_index : int, [int]
Index or indices of the trajectories to pull from
frame_index : int, [int]
Index or indices of the frames to pull from
Returns
-------
traj : msmbuilder.Trajectory
A trajectory object containing the requested frame(s).
"""
if np.isscalar(traj_index) and np.isscalar(frame_index):
xyz = Trajectory.read_frame(TrajFilename=self.traj_filename(traj_index),
WhichFrame=frame_index)
xyzlist = np.array([xyz])
else:
traj_index = np.array(traj_index)
frame_index = np.array(frame_index)
if not (traj_index.ndim == 1 and np.all(traj_index.shape == frame_index.shape)):
raise ValueError('traj_index and frame_index must be 1D and have the same length')
xyzlist = []
for i,j in zip(traj_index, frame_index):
if j >= self.traj_lengths[i]:
raise ValueError('traj %d too short (%d) to contain a frame %d' % (i, self.traj_lengths[i], j))
xyz = Trajectory.read_frame(TrajFilename=self.traj_filename(i),
WhichFrame=j)
xyzlist.append(xyz)
xyzlist = np.array(xyzlist)
conf = self.load_conf()
conf['XYZList'] = xyzlist
return conf
def run(project, assignments, conformations_per_state, states, output_dir):
if states == "all":
states = np.arange(assignments.max() + 1)
inverse_assignments = defaultdict(lambda: [])
for i in xrange(assignments.shape[0]):
for j in xrange(assignments.shape[1]):
inverse_assignments[assignments[i, j]].append((i, j))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
empty_traj = project.empty_traj()
for s in states:
if len(inverse_assignments[s]) == 0:
raise ValueError('No assignments to state! %s' % s)
random.shuffle(inverse_assignments[s])
if len(inverse_assignments[s]) >= conformations_per_state:
confs = inverse_assignments[s][0:conformations_per_state]
else:
confs = inverse_assignments[s]
logger.warning('Not enough assignments in state %s', s)
for i, (traj_ind, frame) in enumerate(confs):
outfile = os.path.join(output_dir, 'State%d-%d.pdb' % (s, i))
if not os.path.exists(outfile):
logger.info('Saving state %d (traj %d, frame %d) as %s', s,
traj_ind, frame, outfile)
traj_filename = project.traj_filename(traj_ind)
xyz = Trajectory.read_frame(traj_filename, frame)
empty_traj['XYZList'] = np.array([xyz])
empty_traj.save_to_pdb(outfile)
else:
logger.warning('Skipping %s. Already exists', outfile)
def run(project, assignments, num_confs_per_state, random_source=None):
"""
Pull random confs from each state in an MSM
Parameters
----------
project : msmbuilder.Project
Used to load up the trajectories, get topology
assignments : np.ndarray, dtype=int
State membership for each frame
num_confs_per_state : int
number of conformations to pull from each state
random_source : numpy.random.RandomState, optional
If supplied, random numbers will be pulled from this random source,
instead of the default, which is np.random. This argument is used
for testing, to ensure that the random number generator always
gives the same stream.
Notes
-----
A new random_source can be initialized by calling numpy.random.RandomState(seed)
with whatever seed you like. See http://stackoverflow.com/questions/5836335/consistenly-create-same-random-numpy-array
for some discussion.
"""
if random_source is None:
random_source = np.random
n_states = max(assignments.flatten()) + 1
logger.info("Pulling %s confs for each of %s confs", num_confs_per_state,
n_states)
inv = MSMLib.invert_assignments(assignments)
xyzlist = []
for s in xrange(n_states):
trj, frame = inv[s]
# trj and frame are a list of indices, such that
# project.load_traj(trj[i])[frame[i]] is a frame assigned to state s
for j in xrange(num_confs_per_state):
r = random_source.randint(len(trj))
xyz = Trajectory.read_frame(project.traj_filename(trj[r]),
frame[r])
xyzlist.append(xyz)
# xyzlist is now a list of (n_atoms, 3) arrays, and we're going
# to stack it along the third dimension
xyzlist = np.dstack(xyzlist)
# load up the conf to get the topology, put then pop in the new coordinates
output = project.load_conf()
output['XYZList'] = xyzlist
return output
def run(project, assignments, num_confs_per_state, random_source=None):
"""
Pull random confs from each state in an MSM
Parameters
----------
project : msmbuilder.Project
Used to load up the trajectories, get topology
assignments : np.ndarray, dtype=int
State membership for each frame
num_confs_per_state : int
number of conformations to pull from each state
random_source : numpy.random.RandomState, optional
If supplied, random numbers will be pulled from this random source,
instead of the default, which is np.random. This argument is used
for testing, to ensure that the random number generator always
gives the same stream.
Notes
-----
A new random_source can be initialized by calling numpy.random.RandomState(seed)
with whatever seed you like. See http://stackoverflow.com/questions/5836335/consistenly-create-same-random-numpy-array
for some discussion.
"""
if random_source is None:
random_source = np.random
n_states = max(assignments.flatten()) + 1
logger.info("Pulling %s confs for each of %s confs", num_confs_per_state, n_states)
inv = MSMLib.invert_assignments(assignments)
xyzlist = []
for s in xrange(n_states):
trj, frame = inv[s]
# trj and frame are a list of indices, such that
# project.load_traj(trj[i])[frame[i]] is a frame assigned to state s
for j in xrange(num_confs_per_state):
r = random_source.randint(len(trj))
xyz = Trajectory.read_frame(project.traj_filename(trj[r]), frame[r])
xyzlist.append(xyz)
# xyzlist is now a list of (n_atoms, 3) arrays, and we're going
# to stack it along the third dimension
xyzlist = np.dstack(xyzlist)
# load up the conf to get the topology, put then pop in the new coordinates
output = project.load_conf()
output['XYZList'] = xyzlist
return output
