def load(topologies, trajectories, selections, stride, processes):
for top, selection in zip(topologies, selections):
sentinel_trj = md.load(top)
try:
# noop, but causes fast-fail w/bad args.atoms
sentinel_trj.top.select(selection)
except:
raise exception.ImproperlyConfigured(
("The provided selection '{s}' didn't match the topology "
"file, {t}").format(s=selection, t=top))
flat_trjs = []
configs = []
n_inds = None
for topfile, trjset, selection in zip(topologies, trajectories,
selections):
top = md.load(topfile).top
indices = top.select(selection)
if n_inds is not None:
if n_inds != len(indices):
raise exception.ImproperlyConfigured(
("Selection on topology %s selected %s atoms, but "
"other selections selected %s atoms.") %
(topfile, len(indices), n_inds))
n_inds = len(indices)
for trj in trjset:
flat_trjs.append(trj)
configs.append({
'top': top,
'stride': stride,
'atom_indices': indices,
})
logger.info(
"Loading %s trajectories with %s atoms using %s processes "
"(subsampling %s)", len(flat_trjs), len(top.select(selection)),
processes, stride)
assert len(top.select(selection)) > 0, "No atoms selected for clustering"
with timed("Loading took %.1f sec", logger.info):
lengths, xyz = load_as_concatenated(flat_trjs,
args=configs,
processes=processes)
with timed("Turned over array in %.2f min", logging.info):
tmp_xyz = xyz.copy()
del xyz
xyz = tmp_xyz
logger.info("Loaded %s frames.", len(xyz))
return lengths, xyz, top.subset(top.select(selection))
def process_units(timestep=None, infer_timestep=None):
"""Take the timestep parameter and infer_timestep parameters from
the command line arguments and convert it to the string indicating
units (ns) and the factor converting ns to frames.
Parameters
----------
timestep : float
Ratio of ns to frames. This is typically 10 (for 100 ps
timesteps) or 100 (for 10 ps timesteps).
infer_timestep : str, path
Path to a trajectory containing timestep information to infer
the correct timestep from when plotting implied timescales.
"""
if timestep and infer_timestep:
raise exception.ImproperlyConfigured(
'Only one of --timestep and --infer-timestep can be '
'supplied, you supplied both --timestep=%s and '
'--infer-timestep=%s' % (timestep, infer_timestep))
if timestep:
unit_factor = timestep
unit_str = 'ns'
elif infer_timestep:
try:
timestep = md.load(infer_timestep).timestep
except ValueError:
if infer_timestep[-4:] != '.xtc':
raise exception.ImproperlyConfigured(
"Topologyless formats other than XTC are not supported.")
with md.formats.xtc.XTCTrajectoryFile(infer_timestep) as f:
xyz, time, step, box = f.read(n_frames=10)
timesteps = time[1:] - time[0:-1]
assert np.all(timesteps[0] == timesteps)
timestep = timesteps[0]
unit_factor = 1000 / timestep # units are ps
unit_str = 'ns'
else:
unit_factor = 1
unit_str = 'frames'
return unit_factor, unit_str
def calculate_piecewise_helix_vectors(trj,
helix_resnums=None,
helix_start=None,
helix_end=None):
"""Calculates the vectors along specified alpha-helices for each
frame in a trajectory. Vectors are in the direction of the starting
residue to the ending residue.
Parameters
----------
trj : md.Trajectory object
An MDTraj trajectory object containing frames of structures to
compute helix-vectors from.
helix_resnums : array, shape [n_residues, ], optional, default: None
A list of residues that correspond to an alpha-helix. This is
useful if residue numbers within a helix are unordinary. If a
list of residues is not supplied, a start and stop residue can
be specified.
helix_start : int, optional, default: None
The starting residue of the helix.
helix_start : int, optional, default: None
The ending residue of the helix.
Returns
----------
vectors : array, [n_frames, 3]
A list of unit-vectors corresponding to the direction of the
specified alpha-helix for each frame in the trajectory.
center_coords : array, [n_frames, 3]
Each center coordinate of the helix-atoms. Can be used to
reconstruct a line going through the alpha-helix.
"""
if (helix_resnums is None) and ((helix_start is None) or
(helix_end is None)):
raise exception.ImproperlyConfigured(
"Either 'helix_resnums' or 'helix_start' and 'helix_end' "
"are required.")
elif helix_resnums is None:
helix_resnums = np.arange(helix_start, helix_end + 1)
top = trj.topology
backbone_nums = _get_backbone_nums(top, helix_resnums)
backbone_coords = trj.xyz[:, backbone_nums]
vectors = _generate_vectors_from_coords(backbone_coords, n_avg=12)
center_coords = backbone_coords.mean(axis=1)
return vectors, center_coords
def process_command_line(argv):
'''Parse the command line and do a first-pass on processing them into a
format appropriate for the rest of the script.'''
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Cluster a set (or several sets) of trajectories "
"into a single state space based upon RMSD.")
# INPUTS
parser.add_argument(
'--trajectories',
required=True,
nargs="+",
action='append',
help="List of paths to aligned trajectory files to cluster. "
"All file types that MDTraj supports are supported here.")
parser.add_argument(
'--topology',
required=True,
action='append',
dest='topologies',
help="The topology file for the trajectories. This flag must be"
" specified once for each instance of the --trajectories "
"flag. The first --topology flag is taken to be the "
"topology to use for the first instance of the "
"--trajectories flag, and so forth.")
# PARAMETERS
parser.add_argument('--algorithm',
required=True,
choices=["khybrid", "kcenters"],
help="The clustering algorithm to use.")
parser.add_argument(
'--atoms',
action="append",
required=True,
help="The atoms from the trajectories (using MDTraj "
"atom-selection syntax) to cluster based upon. Specify "
"once to apply this selection to every set of "
"trajectories specified by the --trajectories flag, or "
"once for each different topology (i.e. the number of "
"times --trajectories and --topology was specified.)")
parser.add_argument(
'--rmsd-cutoff',
default=None,
type=float,
help="Produce clusters with a maximum distance to cluster "
"center of this value.. Units: nm.")
parser.add_argument('--n-clusters',
default=None,
type=int,
help="Produce at least this number of clusters.")
parser.add_argument(
'--processes',
default=cpu_count(),
type=int,
help="Number processes to use for loading and clustering.")
parser.add_argument(
'--subsample',
default=None,
type=int,
help="Take only every nth frame when loading trajectories. "
"1 implies no subsampling.")
parser.add_argument(
'--no-reassign',
default=False,
action='store_true',
help="Do not do a reassigment step. Ignored if --subsample is "
"not supplied or 1.")
# OUTPUT
parser.add_argument('--distances',
required=True,
action=readable_dir,
help="The location to write the distances file.")
parser.add_argument(
'--centers',
required=True,
action=readable_dir,
help="The location to write the cluster center structures.")
parser.add_argument(
'--assignments',
required=True,
action=readable_dir,
help="The location to write assignments of frames to clusters.")
args = parser.parse_args(argv[1:])
if args.rmsd_cutoff is None and args.n_clusters is None:
raise exception.ImproperlyConfigured(
"At least one of --rmsd-cutoff and --n-clusters is "
"required to cluster.")
if len(args.atoms) == 1:
args.atoms = args.atoms * len(args.trajectories)
elif len(args.atoms) != len(args.trajectories):
raise exception.ImproperlyConfigured(
"Flag --atoms must be provided either once (selection is "
"applied to all trajectories) or the same number of times "
"--trajectories is supplied.")
if len(args.topologies) != len(args.trajectories):
raise exception.ImproperlyConfigured(
"The number of --topology and --trajectory flags must agree.")
if args.algorithm == 'kcenters':
args.Clusterer = KCenters
elif args.algorithm == 'khybrid':
args.Clusterer = KHybrid
if args.subsample is None:
args.subsample = 1
if args.no_reassign and args.subsample == 1:
warnings.warn(
"When subsampling is 1 (or unspecified), --no-reassign has no effect."
)
if args.centers[args.centers.rfind('.'):] == '.h5':
warnings.warn(
"You provided a centers file that looks like it's an h5... "
"centers are saved as pickle. Are you sure this is what you want?")
return args
def process_command_line(argv):
'''Parse the command line and do a first-pass on processing them into a
format appropriate for the rest of the script.'''
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="Compute CARDS matricies for a set of trajectories "
"and save all matrices and dihedral mappings.\n \n"
"Please cite the following papers if you use CARDS with enspara:\n"
"[1] Singh, S. and Bowman, G.R.\n"
" Journal of Chemical Theory and Computation\n"
" 2017 13 (4), 1509-1517\n"
" DOI: 10.1021/acs.jctc.6b01181\n"
"\n"
"[2] Porter,J.R., Zimmerman, M.I., and Bowman G.R.\n"
" bioRxiv 431072; doi: https://doi.org/10.1101/431072\n")
# INPUTS
input_args = parser.add_argument_group("Input Settings")
#input_data_group = parser.add_mutually_exclusive_group(required=True)
input_args.add_argument(
'--trajectories',
required=True,
nargs="+",
action='append',
help="List of paths to aligned trajectory files to cluster. "
"All file types that MDTraj supports are supported here.")
input_args.add_argument('--topology',
required=True,
action='append',
help="The topology file for the trajectories.")
# PARAMETERS
cards_args = parser.add_argument_group("CARDS Settings")
cards_args.add_argument(
'--buffer-size',
default=15,
type=int,
help="Size of buffer zone between rotameric states, in degrees.")
cards_args.add_argument("--processes",
default=max(1,
auto_nprocs() / 4),
type=int,
help="Number of processes to use.")
# OUTPUT
output_args = parser.add_argument_group("Output Settings")
output_args.add_argument(
'--matrices',
required=True,
action=readable_dir,
help="The folder location to write the four CARDS matrices (as pickle)."
)
output_args.add_argument(
'--indices',
required=True,
action=readable_dir,
help="The location to write the dihedral indices file (as CSV).")
args = parser.parse_args(argv[1:])
# CARDS FEATURES
if not (0 < args.buffer_size < 360):
raise exception.ImproperlyConfigured(
"The given buffer size (%s) is not possible." % args.buffer_size)
return args
def process_command_line(argv):
FEATURE_DISTANCES = ['euclidean', 'manhattan']
TRAJECTORY_DISTANCES = ['rmsd']
parser = argparse.ArgumentParser(
prog='cluster',
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Cluster a set (or several sets) of trajectories "
"into a single state space based upon RMSD.")
# INPUTS
input_args = parser.add_argument_group("Input Settings")
input_data_group = parser.add_mutually_exclusive_group(required=True)
input_data_group.add_argument(
"--features",
nargs='+',
help="The h5 file containin observations and features.")
input_data_group.add_argument(
'--trajectories',
nargs="+",
action='append',
help="List of paths to aligned trajectory files to cluster. "
"All file types that MDTraj supports are supported here.")
input_args.add_argument(
'--topology',
action='append',
dest='topologies',
help="The topology file for the trajectories. This flag must be"
" specified once for each instance of the --trajectories "
"flag. The first --topology flag is taken to be the "
"topology to use for the first instance of the "
"--trajectories flag, and so forth.")
# PARAMETERS
cluster_args = parser.add_argument_group("Clustering Settings")
cluster_args.add_argument('--algorithm',
required=True,
choices=["khybrid", "kcenters"],
help="The clustering algorithm to use.")
cluster_args.add_argument(
'--atoms',
action="append",
help="When clustering trajectories, specifies which atoms from the "
"trajectories (using MDTraj atom-selection syntax) to cluster "
"based upon. Specify once to apply this selection to every set "
"of trajectories specified by the --trajectories flag, or "
"once for each different topology (i.e. the number of "
"times --trajectories and --topology was specified.)")
cluster_args.add_argument(
'--cluster-radius',
default=None,
type=float,
help="Produce clusters with a maximum distance to cluster "
"center of this value.")
cluster_args.add_argument('--cluster-number',
default=None,
type=int,
help="Produce at least this number of clusters.")
cluster_args.add_argument(
"--cluster-distance",
default=None,
choices=FEATURE_DISTANCES + TRAJECTORY_DISTANCES,
help="The metric for measuring distances. Some metrics (e.g. rmsd) "
"only apply to trajectories, and others only to features.")
cluster_args.add_argument(
"--cluster-iterations",
default=None,
type=int,
help="The number of refinement iterations to perform. This is only "
"relevant to khybrid clustering.")
cluster_args.add_argument(
'--subsample',
default=1,
type=int,
help="Take only every nth frame when loading trajectories. "
"1 implies no subsampling.")
# OUTPUT
output_args = parser.add_argument_group("Output Settings")
output_args.add_argument(
'--no-reassign',
default=False,
action='store_true',
help="Do not do a reassigment step. Ignored if --subsample is "
"not supplied or 1.")
output_args.add_argument('--distances',
required=True,
action=readable_dir,
help="The location to write the distances file.")
output_args.add_argument(
'--center-features',
required=True,
action=readable_dir,
help="The location to write the cluster center structures.")
output_args.add_argument(
'--assignments',
required=True,
action=readable_dir,
help="The location to write assignments of frames to clusters.")
output_args.add_argument(
"--center-indices",
required=False,
action=readable_dir,
help="Location for cluster center indices output (pickle).")
args = parser.parse_args(argv[1:])
if args.features:
args.features = expand_files([args.features])[0]
if mpi_mode and len(args.features) == 1:
raise exception.ImproperlyConfigured(
'Cannot use ragged array h5 files in MPI mode.')
if args.cluster_distance in FEATURE_DISTANCES:
args.cluster_distance = getattr(libdist, args.cluster_distance)
else:
raise exception.ImproperlyConfigured(
"The given distance (%s) is not compatible with features." %
args.cluster_distance)
if args.subsample != 1 and len(features) == 1:
raise exception.ImproperlyConfigured(
"Subsampling is not supported for h5 inputs.")
# TODO: not necessary if mutually exclusvie above works
if args.trajectories:
raise exception.ImproperlyConfigured(
"--features and --trajectories are mutually exclusive. "
"Either trajectories or features, not both, are clustered.")
if args.topologies:
raise exception.ImproperlyConfigured(
"When --features is specified, --topology is unneccessary.")
if args.atoms:
raise exception.ImproperlyConfigured(
"Option --atoms is only meaningful when clustering "
"trajectories.")
if not args.cluster_distance:
raise exception.ImproperlyConfigured(
"Option --cluster-distance is required when clustering "
"features.")
elif args.trajectories and args.topologies:
args.trajectories = expand_files(args.trajectories)
if not args.cluster_distance or args.cluster_distance == 'rmsd':
args.cluster_distance = md.rmsd
else:
raise exception.ImproperlyConfigured(
"Option --cluster-distance must be rmsd when clustering "
"trajectories.")
if not args.atoms:
raise exception.ImproperlyConfigured(
"Option --atoms is required when clustering trajectories.")
elif len(args.atoms) == 1:
args.atoms = args.atoms * len(args.trajectories)
elif len(args.atoms) != len(args.trajectories):
raise exception.ImproperlyConfigured(
"Flag --atoms must be provided either once (selection is "
"applied to all trajectories) or the same number of times "
"--trajectories is supplied.")
if len(args.topologies) != len(args.trajectories):
raise exception.ImproperlyConfigured(
"The number of --topology and --trajectory flags must agree.")
else:
# CANNOT CLUSTER
raise exception.ImproperlyConfigured(
"Either --features or both of --trajectories and --topologies "
"are required.")
if args.cluster_radius is None and args.cluster_number is None:
raise exception.ImproperlyConfigured(
"At least one of --cluster-radius and --cluster-number is "
"required to cluster.")
if args.algorithm == 'kcenters':
args.Clusterer = KCenters
if args.cluster_iterations is not None:
raise exception.ImproperlyConfigured(
"--cluster-iterations only has an effect when using an "
"interative clustering scheme (e.g. khybrid).")
elif args.algorithm == 'khybrid':
args.Clusterer = KHybrid
if args.no_reassign and args.subsample == 1:
warnings.warn("When subsampling is 1 (or unspecified), "
"--no-reassign has no effect.")
if not args.no_reassign and mpi_mode and args.subsample > 1:
warnings.warn("Reassignment is suppressed in MPI mode.")
args.no_reassign = True
if args.trajectories:
if os.path.splitext(args.center_features)[1] == '.h5':
warnings.warn(
"You provided a centers file that looks like it's an h5... "
"centers are saved as pickle. Are you sure this is what you "
"want?")
else:
if os.path.splitext(args.center_features)[1] != '.npy':
warnings.warn(
"You provided a centers file that looks like it's not "
"an npy, but this is how they are saved. Are you sure "
"this is what you want?")
return args
