# This module implements trajetories and trajectory generators. # # Written by Konrad Hinsen # """ Trajectory files and their contents """ __docformat__ = 'restructuredtext' from MMTK import Collections, Units, Universe, Utility, \ ParticleProperties, Visualization from Scientific.Geometry import Vector from Scientific import N import copy, os, sys # Report error if the netCDF module is not available. try: from Scientific.IO import NetCDF except ImportError: raise Utility.MMTKError("Trajectories are not available " + "because the netCDF module is missing.") # # Trajectory class # class Trajectory(object): # # Trajectory cache # # This cache is maintained for better efficiency in parallel # processing. The cache contains all trajectories currently open # for reading. When a trajectory object is unpickled, a trajectory # from the cache is reused if possible. This means that """ Trajectory file The data in a trajectory file can be accessed by step or by variable. If t is a Trajectory object, then: * len(t) is the number of steps * t[i] is the data for step i, in the form of a dictionary that maps variable names to data * t[i:j] and t[i:j:n] return a :class:`~MMTK.Trajectory.SubTrajectory` object that refers to a subset of the total number of steps (no data is copied) * t.variable returns the value of the named variable at all time steps. If the variable is a simple scalar, it is read completely and returned as an array. If the variable contains data for each atom, a :class:`~MMTK.Trajectory.TrajectoryVariable` object is returned from which data at specific steps can be obtained by further indexing operations. The routines that generate trajectories decide what variables are used and what they contain. The most frequently used variable is "configuration", which stores the positions of all atoms. Other common variables are "time", "velocities", "temperature", "pressure", and various energy terms whose name end with "_energy". """ def __init__(self, object, filename, mode = 'r', comment = None, double_precision = False, cycle = 0, block_size = 1): """ :param object: the object whose data is stored in the trajectory file. This can be 'None' when opening a file for reading; in that case, a universe object is constructed from the description stored in the trajectory file. This universe object can be accessed via the attribute 'universe' of the trajectory object. :type object: :class:`~MMTK.ChemicalObjects.ChemicalObject` :param filename: the name of the trajectory file :type filename: str :param mode: one of "r" (read-only), "w" (create new file for writing), or "a" (append to existing file or create if the file does not exist) :type mode: str :param comment: optional comment that is stored in the file; allowed only with mode="r" :type comment: str :param double_precision: if True, data in the file is stored using double precision; default is single precision. Note that all I/O via trajectory objects is double precision; conversion from and to single precision file variables is handled automatically. :type double_precision: bool :param cycle: if non-zero, a trajectory is created for a fixed number of steps equal to the value of cycle, and these steps are used cyclically. This is meant for restart trajectories. :type cycle: int :param block_size: an optimization parameter that influences the file structure and the I/O performance for very large files. A block size of 1 is optimal for sequential access to configurations etc., whereas a block size equal to the number of steps is optimal for reading coordinates or scalar variables along the time axis. The default value is 1. Note that older MMTK releases always used a block size of 1 and cannot handle trajectories with different block sizes. :type block_size: int """ filename = os.path.expanduser(filename) self.filename = filename self.mode = mode if object is None and mode == 'r': file = NetCDF.NetCDFFile(filename, 'r') description = file.variables['description'][:].tostring() try: self.block_size = file.dimensions['minor_step_number'] except KeyError: self.block_size = 1 conf = None cell = None if self.block_size == 1: try: conf_var = file.variables['configuration'] conf = conf_var[0, :, :] except KeyError: pass try: cell = file.variables['box_size'][0, :] except KeyError: pass else: try: conf_var = file.variables['configuration'] conf = conf_var[0, :, :, 0] except KeyError: pass try: cell = file.variables['box_size'][0, :, 0] except KeyError: pass file.close() import Skeleton local = {} skeleton = eval(description, vars(Skeleton), local) universe = skeleton.make({}, conf) universe.setCellParameters(cell) object = universe initialize = 1 else: universe = object.universe() if universe is None: raise ValueError("objects not in the same universe") description = None initialize = 0 universe.configuration() if object is universe: index_map = None inverse_map = None else: if mode == 'r': raise ValueError("can't read trajectory for a non-universe") index_map = N.array([a.index for a in object.atomList()]) inverse_map = universe.numberOfPoints()*[None] for i in range(len(index_map)): inverse_map[index_map[i]] = i toplevel = set() for o in Collections.Collection(object): toplevel.add(o.topLevelChemicalObject()) object = Collections.Collection(list(toplevel)) if description is None: description = universe.description(object, inverse_map) import MMTK_trajectory self.trajectory = MMTK_trajectory.Trajectory(universe, description, index_map, filename, mode + 's', double_precision, cycle, block_size) self.universe = universe self.index_map = index_map try: self.block_size = \ self.trajectory.file.dimensions['minor_step_number'] except KeyError: self.block_size = 1 if comment is not None: if mode == 'r': raise IOError('cannot add comment in read-only mode') self.trajectory.file.comment = comment if initialize and conf is not None: self.universe.setFromTrajectory(self) self.particle_trajectory_reader = ParticleTrajectoryReader(self) def __getstate__(self): if self.mode != 'r': raise ValueError("Cannot copy or pickle write-mode trajectories") return self.filename def __setstate__(self, state): self.__init__(None, state) def flush(self): """ Make sure that all data that has been written to the trajectory is also written to the file. """ self.trajectory.flush() def close(self): """ Close the trajectory file. Must be called after writing to ensure that all buffered data is written to the file. No data access is possible after closing a file. """ self.trajectory.close() def __len__(self): return self.trajectory.nsteps def __getitem__(self, item): if not isinstance(item, int): return SubTrajectory(self, N.arange(len(self)))[item] if item < 0: item += len(self) if item >= len(self): raise IndexError data = {} for name, var in self.trajectory.file.variables.items(): if 'step_number' not in var.dimensions: continue if 'atom_number' in var.dimensions: if 'xyz' in var.dimensions: array = ParticleProperties.ParticleVector(self.universe, self.trajectory.readParticleVector(name, item)) else: array = ParticleProperties.ParticleScalar(self.universe, self.trajectory.readParticleScalar(name, item)) else: bs = self.block_size if bs == 1: array = var[item] else: if len(var.shape) == 2: array = var[item/bs, item%bs] else: array = var[item/bs, ..., item%bs] data[name] = 0.+array if data.has_key('configuration'): box = data.get('box_size', None) if box is not None: box = box.astype(N.Float) conf = data['configuration'] data['configuration'] = \ ParticleProperties.Configuration(conf.universe, conf.array, box) return data def __getslice__(self, first, last): return self[(slice(first, last),)] def __getattr__(self, name): try: var = self.trajectory.file.variables[name] except KeyError: raise AttributeError("no variable named " + name) if 'atom_number' in var.dimensions: return TrajectoryVariable(self.universe, self, name) elif 'box_size_length' in var.dimensions: if 'minor_step_number' in var.dimensions: bs = N.transpose(var[:], [0, 2, 1]) bs = N.reshape(bs, (bs.shape[0]*bs.shape[1], bs.shape[2])) return bs[:len(self)] else: return var[:] else: return N.ravel(N.array(var))[:len(self)] def defaultStep(self): try: step = int(self.trajectory.file.last_step[0]) except AttributeError: step = 0 return step def readParticleTrajectory(self, atom, first=0, last=None, skip=1, variable = "configuration"): """ Read trajectory information for a single atom but for multiple time steps. :param atom: the atom whose trajectory is requested :type atom: :class:`~MMTK.ChemicalObjects.Atom` :param first: the number of the first step to be read :type first: int :param last: the number of the first step not to be read. A value of None indicates that the whole trajectory should be read. :type last: int :param skip: the number of steps to skip between two steps read :type skip: int :param variable: the name of the trajectory variable to be read. If the variable is "configuration", the resulting trajectory is made continuous by eliminating all jumps caused by periodic boundary conditions. The pseudo-variable "box_coordinates" can be read to obtain the values of the variable "configuration" scaled to box coordinates. For non-periodic universes there is no difference between box coordinates and real coordinates. :type variable: str :returns: the trajectory for a single atom :rtype: :class:`~MMTK.Trajectory.ParticleTrajectory` """ return ParticleTrajectory(self, atom, first, last, skip, variable) def readRigidBodyTrajectory(self, object, first=0, last=None, skip=1, reference = None): """ Read the positions for an object at multiple time steps and extract the rigid-body motion (center-of-mass position plus orientation as a quaternion) by an optimal-transformation fit. :param object: the object whose rigid-body trajectory is requested :type object: :class:`~MMTK.Collections.GroupOfAtoms` :param first: the number of the first step to be read :type first: int :param last: the number of the first step not to be read. A value of None indicates that the whole trajectory should be read. :type last: int :param skip: the number of steps to skip between two steps read :type skip: int :param reference: the reference configuration for the fit :type reference: :class:`~MMTK.ParticleProperties.Configuration` :returns: the trajectory for a single rigid body :rtype: :class:`~MMTK.Trajectory.RigidBodyTrajectory` """ return RigidBodyTrajectory(self, object, first, last, skip, reference) def variables(self): """ :returns: a list of the names of all variables that are stored in the trajectory :rtype: list of str """ vars = copy.copy(self.trajectory.file.variables.keys()) vars.remove('step') try: vars.remove('description') except ValueError: pass return vars def view(self, first=0, last=None, skip=1, object = None): """ Show an animation of the trajectory using an external visualization program. :param first: the number of the first step in the animation :type first: int :param last: the number of the first step not to include in the animation. A value of None indicates that the whole trajectory should be used. :type last: int :param skip: the number of steps to skip between two steps read :type skip: int :param object: the object to be animated, which must be in the universe stored in the trajectory. None stands for the whole universe. :type object: :class:`~MMTK.Collections.GroupOfAtoms` """ Visualization.viewTrajectory(self, first, last, skip, object) def _boxTransformation(self, pt_in, pt_out, to_box=0): from MMTK_trajectory import boxTransformation try: box_size = self.trajectory.recently_read_box_size except AttributeError: return boxTransformation(self.universe._spec, pt_in, pt_out, box_size, to_box) class SubTrajectory(object): """ Reference to a subset of a trajectory A SubTrajectory object is created by slicing a Trajectory object or another SubTrajectory object. It provides all the operations defined on Trajectory objects. """ def __init__(self, trajectory, indices): self.trajectory = trajectory self.indices = indices self.universe = trajectory.universe def __len__(self): return len(self.indices) def __getitem__(self, item): if isinstance(item, int): return self.trajectory[int(self.indices[item])] else: return SubTrajectory(self.trajectory, self.indices[item]) def __getslice__(self, first, last): return self[(slice(first, last),)] def __getattr__(self, name): return SubVariable(getattr(self.trajectory, name), self.indices) def readParticleTrajectory(self, atom, first=0, last=None, skip=1, variable = "configuration"): if last is None: last = len(self.indices) indices = self.indices[first:last:skip] first = indices[0] last = indices[-1]+1 if len(self.indices) > 1: skip = self.indices[1]-self.indices[0] else: skip = 1 return self.trajectory.readParticleTrajectory(atom, first, last, skip, variable) def readRigidBodyTrajectory(self, object, first=0, last=None, skip=1, reference = None): if last is None: last = len(self.indices) indices = self.indices[first:last:skip] first = indices[0] last = indices[-1]+1 if len(self.indices) > 1: skip = self.indices[1]-self.indices[0] else: skip = 1 return RigidBodyTrajectory(self.trajectory, object, first, last, skip, reference) def variables(self): return self.trajectory.variables() def view(self, first=0, last=None, step=1, subset = None): Visualization.viewTrajectory(self, first, last, step, subset) def close(self): del self.trajectory def _boxTransformation(self, pt_in, pt_out, to_box=0): Trajectory._boxTransformation(self.trajectory, pt_in, pt_out, to_box) # # Trajectory variables # class TrajectoryVariable(object): """ Variable in a trajectory A TrajectoryVariable object is created by extracting a variable from a Trajectory object if that variable contains data for each atom and is thus potentially large. No data is read from the trajectory file when a TrajectoryVariable object is created; the read operation takes place when the TrajectoryVariable is indexed with a specific step number. If t is a TrajectoryVariable object, then: * len(t) is the number of steps * t[i] is the data for step i, in the form of a ParticleScalar, a ParticleVector, or a Configuration object, depending on the variable * t[i:j] and t[i:j:n] return a SubVariable object that refers to a subset of the total number of steps """ def __init__(self, universe, trajectory, name): self.universe = universe self.trajectory = trajectory self.name = name self.var = self.trajectory.trajectory.file.variables[self.name] if self.name == 'configuration': try: self.box_size = \ self.trajectory.trajectory.file.variables['box_size'] except KeyError: self.box_size = None def __len__(self): return len(self.trajectory) def __getitem__(self, item): if not isinstance(item, int): return SubVariable(self, N.arange(len(self)))[item] item = int(item) # gets rid of numpy.intXX objects if item < 0: item = item + len(self.trajectory) if item >= len(self.trajectory): raise IndexError if self.name == 'configuration': if self.box_size is None: box = None elif len(self.box_size.shape) == 3: bs = self.trajectory.block_size box = self.box_size[item/bs, :, item%bs].astype(N.Float) else: box = self.box_size[item].astype(N.Float) array = ParticleProperties.Configuration(self.universe, self.trajectory.trajectory.readParticleVector(self.name, item), box) elif 'xyz' in self.var.dimensions: array = ParticleProperties.ParticleVector(self.universe, self.trajectory.trajectory.readParticleVector(self.name, item)) else: array = ParticleProperties.ParticleScalar(self.universe, self.trajectory.trajectory.readParticleScalar(self.name, item)) return array def __getslice__(self, first, last): return self[(slice(first, last),)] def average(self): sum = self[0] for value in self[1:]: sum = sum + value return sum/len(self) class SubVariable(TrajectoryVariable): """ Reference to a subset of a :class:`~MMTK.Trajectory.TrajectoryVariable` A SubVariable object is created by slicing a TrajectoryVariable object or another SubVariable object. It provides all the operations defined on TrajectoryVariable objects. """ def __init__(self, variable, indices): self.variable = variable self.indices = indices def __len__(self): return len(self.indices) def __getitem__(self, item): if isinstance(item, int): return self.variable[self.indices[item]] else: return SubVariable(self.variable, self.indices[item]) def __getslice__(self, first, last): return self[(slice(first, last),)] # # Trajectory consisting of multiple files # class TrajectorySet(object): """ Trajectory file set A TrajectorySet permits to treat a sequence of trajectory files like a single trajectory for reading data. It behaves exactly like a :class:`~MMTK.Trajectory.Trajectory` object. The trajectory files must all contain data for the same system. The variables stored in the individual files need not be the same, but only variables common to all files can be accessed. Note: depending on how the sequence of trajectories was constructed, the first configuration of each trajectory might be the same as the last one in the preceding trajectory. To avoid counting it twice, specify (filename, 1, None, 1) for all but the first trajectory in the set. """ def __init__(self, object, filenames): """ :param object: the object whose data is stored in the trajectory files. This can be (and usually is) None; in that case, a universe object is constructed from the description stored in the first trajectory file. This universe object can be accessed via the attribute universe of the trajectory set object. :param filenames: a list of trajectory file names or (filename, first_step, last_step, increment) tuples. """ first = filenames[0] if isinstance(first, tuple): first = Trajectory(object, first[0])[first[1]:first[2]:first[3]] else: first = Trajectory(object, first) self.universe = first.universe self.trajectories = [first] self.nsteps = [0, len(first)] self.cell_parameters = [] for file in filenames[1:]: if isinstance(file, tuple): t = Trajectory(self.universe, file[0])[file[1]:file[2]:file[3]] else: t = Trajectory(self.universe, file) self.trajectories.append(t) self.nsteps.append(self.nsteps[-1]+len(t)) try: self.cell_parameters.append(t[0]['box_size']) except KeyError: pass vars = {} for t in self.trajectories: for v in t.variables(): vars[v] = vars.get(v, 0) + 1 self.vars = [] for v, count in vars.items(): if count == len(self.trajectories): self.vars.append(v) def close(self): for t in self.trajectories: t.close() def __len__(self): return self.nsteps[-1] def __getitem__(self, item): if not isinstance(item, int): return SubTrajectory(self, N.arange(len(self)))[item] if item >= len(self): raise IndexError tindex = N.add.reduce(N.greater_equal(item, self.nsteps))-1 return self.trajectories[tindex][item-self.nsteps[tindex]] def __getslice__(self, first, last): return self[(slice(first, last),)] def __getattr__(self, name): if name not in self.vars+['step']: raise AttributeError("no variable named " + name) var = self.trajectories[0].trajectory.file.variables[name] if 'atom_number' in var.dimensions: return TrajectorySetVariable(self.universe, self, name) else: data = [] for t in self.trajectories: var = t.trajectory.file.variables[name] data.append(N.ravel(N.array(var))[:len(t)]) return N.concatenate(data) def readParticleTrajectory(self, atom, first=0, last=None, skip=1, variable = "configuration"): total = None self.steps_read = [] for i in range(len(self.trajectories)): if self.nsteps[i+1] <= first: self.steps_read.append(0) continue if last is not None and self.nsteps[i] >= last: break n = max(0, (self.nsteps[i]-first+skip-1)/skip) start = first+skip*n-self.nsteps[i] n = (self.nsteps[i+1]-first+skip-1)/skip stop = first+skip*n if last is not None: stop = min(stop, last) stop = stop-self.nsteps[i] if start >= 0 and start < self.nsteps[i+1]-self.nsteps[i]: t = self.trajectories[i] pt = t.readParticleTrajectory(atom, start, stop, skip, variable) self.steps_read.append((stop-start)/skip) if total is None: total = pt else: if variable == "configuration" \ and self.cell_parameters[0] is not None: jump = pt.array[0]-total.array[-1] mult = -(jump/self.cell_parameters[i-1]).astype('i') if len(N.nonzero(mult)) > 0: t._boxTransformation(pt.array, pt.array, 1) N.add(pt.array, mult[N.NewAxis, : ], pt.array) t._boxTransformation(pt.array, pt.array, 0) jump = pt.array[0] - total.array[-1] mask = N.less(jump, -0.5*self.cell_parameters[i-1])- \ N.greater(jump, 0.5*self.cell_parameters[i-1]) if len(N.nonzero(mask)) > 0: t._boxTransformation(pt.array, pt.array, 1) N.add(pt.array, mask[N.NewAxis, :], pt.array) t._boxTransformation(pt.array, pt.array, 0) elif variable == "box_coordinates" \ and self.cell_parameters[0] is not None: jump = pt.array[0]-total.array[-1] mult = -jump.astype('i') if len(N.nonzero(mult)) > 0: N.add(pt.array, mult[N.NewAxis, : ], pt.array) jump = pt.array[0] - total.array[-1] mask = N.less(jump, -0.5)- \ N.greater(jump, 0.5) if len(N.nonzero(mask)) > 0: N.add(pt.array, mask[N.NewAxis, :], pt.array) total.array = N.concatenate((total.array, pt.array)) else: self.steps_read.append(0) return total def readRigidBodyTrajectory(self, object, first=0, last=None, skip=1, reference = None): return RigidBodyTrajectory(self, object, first, last, skip, reference) def _boxTransformation(self, pt_in, pt_out, to_box=0): n = 0 for i in range(len(self.steps_read)): t = self.trajectories[i] steps = self.steps_read[i] if steps > 0: t._boxTransformation(pt_in[n:n+steps], pt_out[n:n+steps], to_box) n = n + steps def variables(self): return self.vars def view(self, first=0, last=None, step=1, object = None): Visualization.viewTrajectory(self, first, last, step, object) class TrajectorySetVariable(TrajectoryVariable): """ Variable in a trajectory set A TrajectorySetVariable object is created by extracting a variable from a TrajectorySet object if that variable contains data for each atom and is thus potentially large. It behaves exactly like a TrajectoryVariable object. """ def __init__(self, universe, trajectory_set, name): self.universe = universe self.trajectory_set = trajectory_set self.name = name def __len__(self): return len(self.trajectory_set) def __getitem__(self, item): if not isinstance(item, int): return SubVariable(self, N.arange(len(self)))[item] if item >= len(self.trajectory_set): raise IndexError tindex = N.add.reduce(N.greater_equal(item, self.trajectory_set.nsteps))-1 step = item-self.trajectory_set.nsteps[tindex] t = self.trajectory_set.trajectories[tindex] return getattr(t, self.name)[step] # # Cache for atom trajectories # class ParticleTrajectoryReader(object): def __init__(self, trajectory): self.trajectory = trajectory self.natoms = self.trajectory.universe.numberOfAtoms() self._trajectory = trajectory.trajectory self.cache = {} self.cache_lifetime = 2 def __call__(self, atom, variable, first, last, skip, correct, box): if isinstance(atom, int): index = atom else: index = atom.index if atom.universe() is not self.trajectory.universe: raise ValueError("objects not in the same universe") key = (index, variable, first, last, skip, correct, box) data, count = self.cache.get(key, (None, 0)) if data is not None: self.cache[key] = (data, self.cache_lifetime) return data delete = [] for k, value in self.cache.items(): data, count = value count -= 1 if count == 0: delete.append(k) else: self.cache[k] = (data, count) for k in delete: del self.cache[k] cache_size = min(10, max(1, 100000/max(1, len(self.trajectory)))) natoms = min(cache_size, self.natoms-index) data = self._trajectory.readParticleTrajectories(index, natoms, variable, first, last, skip, correct, box) for i in range(natoms): key = (index+i, variable, first, last, skip, correct, box) self.cache[key] = (data[i], self.cache_lifetime) return data[0] # # Single-atom trajectory # class ParticleTrajectory(object): """ Trajectory data for a single particle A ParticleTrajectory object is created by calling the method :func:`~MMTK.Trajectory.Trajectory.readParticleTrajectory` on a :class:`~MMTK.Trajectory.Trajectory` object. If pt is a ParticleTrajectory object, then * len(pt) is the number of steps stored in it * pt[i] is the value at step i (a vector) """ def __init__(self, trajectory, atom, first=0, last=None, skip=1, variable = "configuration"): if last is None: last = len(trajectory) if variable == "box_coordinates": variable = "configuration" box = 1 else: box = 0 reader = trajectory.particle_trajectory_reader self.array = reader(atom, variable, first, last, skip, variable == "configuration", box) def __len__(self): return self.array.shape[0] def __getitem__(self, index): return Vector(self.array[index]) def translateBy(self, vector): """ Adds a vector to the values at all steps. This does B{not} change the data in the trajectory file. :param vector: the vector to be added :type vector: Scientific.Geometry.Vector """ N.add(self.array, vector.array[N.NewAxis, :], self.array) # # Rigid-body trajectory # class RigidBodyTrajectory(object): """ Rigid-body trajectory data A RigidBodyTrajectory object is created by calling the method :func:`~MMTK.Trajectory.Trajectory.readRigidBodyTrajectory` on a :class:`~MMTK.Trajectory.Trajectory` object. If rbt is a RigidBodyTrajectory object, then * len(rbt) is the number of steps stored in it * rbt[i] is the value at step i (a vector for the center of mass and a quaternion for the orientation) """ def __init__(self, trajectory, object, first=0, last=None, skip=1, reference = None): self.trajectory = trajectory universe = trajectory.universe if last is None: last = len(trajectory) first_conf = trajectory.configuration[first] offset = universe.contiguousObjectOffset([object], first_conf, True) if reference is None: reference = first_conf reference = universe.contiguousObjectConfiguration([object], reference) steps = (last-first+skip-1)/skip mass = object.mass() ref_cms = object.centerOfMass(reference) atoms = object.atomList() possq = N.zeros((steps,), N.Float) cross = N.zeros((steps, 3, 3), N.Float) rcms = N.zeros((steps, 3), N.Float) # cms of the CONTIGUOUS object made of CONTINUOUS atom trajectories for a in atoms: r = trajectory.readParticleTrajectory(a, first, last, skip, "box_coordinates").array w = a._mass/mass N.add(rcms, w*r, rcms) if offset is not None: N.add(rcms, w*offset[a].array, rcms) # relative coords of the CONTIGUOUS reference r_ref = N.zeros((len(atoms), 3), N.Float) for a in range(len(atoms)): r_ref[a] = atoms[a].position(reference).array - ref_cms.array # main loop: storing data needed to fill M matrix for a in range(len(atoms)): r = trajectory.readParticleTrajectory(atoms[a], first, last, skip, "box_coordinates").array r = r - rcms # (a-b)**2 != a**2 - b**2 if offset is not None: N.add(r, offset[atoms[a]].array,r) trajectory._boxTransformation(r, r) w = atoms[a]._mass/mass N.add(possq, w*N.add.reduce(r*r, -1), possq) N.add(possq, w*N.add.reduce(r_ref[a]*r_ref[a],-1), possq) N.add(cross, w*r[:,:,N.NewAxis]*r_ref[N.NewAxis, a,:],cross) self.trajectory._boxTransformation(rcms, rcms) # filling matrix M (formula no 40) k = N.zeros((steps, 4, 4), N.Float) k[:, 0, 0] = -cross[:, 0, 0]-cross[:, 1, 1]-cross[:, 2, 2] k[:, 0, 1] = cross[:, 1, 2]-cross[:, 2, 1] k[:, 0, 2] = cross[:, 2, 0]-cross[:, 0, 2] k[:, 0, 3] = cross[:, 0, 1]-cross[:, 1, 0] k[:, 1, 1] = -cross[:, 0, 0]+cross[:, 1, 1]+cross[:, 2, 2] k[:, 1, 2] = -cross[:, 0, 1]-cross[:, 1, 0] k[:, 1, 3] = -cross[:, 0, 2]-cross[:, 2, 0] k[:, 2, 2] = cross[:, 0, 0]-cross[:, 1, 1]+cross[:, 2, 2] k[:, 2, 3] = -cross[:, 1, 2]-cross[:, 2, 1] k[:, 3, 3] = cross[:, 0, 0]+cross[:, 1, 1]-cross[:, 2, 2] del cross for i in range(1, 4): for j in range(i): k[:, i, j] = k[:, j, i] N.multiply(k, 2., k) for i in range(4): N.add(k[:,i,i], possq, k[:,i,i]) del possq quaternions = N.zeros((steps, 4), N.Float) fit = N.zeros((steps,), N.Float) from Scientific.LA import eigenvectors for i in range(steps): e, v = eigenvectors(k[i]) j = N.argmin(e) if e[j] < 0.: fit[i] = 0. else: fit[i] = N.sqrt(e[j]) if v[j,0] < 0.: quaternions[i] = -v[j] # eliminate jumps else: quaternions[i] = v[j] self.fit = fit self.cms = rcms self.quaternions = quaternions def __len__(self): return self.cms.shape[0] def __getitem__(self, index): from Scientific.Geometry.Quaternion import Quaternion return Vector(self.cms[index]), Quaternion(self.quaternions[index]) # # Type check for trajectory objects # def isTrajectory(object): """ :param object: any Python object :returns: True if object is a trajectory """ import MMTK_trajectory return isinstance(object, (Trajectory, MMTK_trajectory.trajectory_type)) # # Base class for all objects that generate trajectories # class TrajectoryGenerator(object): """ Trajectory generator base class This base class implements the common aspects of everything that generates trajectories: integrators, minimizers, etc. """ def __init__(self, universe, options): self.universe = universe self.options = options def setCallOptions(self, options): self.call_options = options def getActions(self): try: self.actions = self.getOption('actions') except ValueError: self.actions = [] try: if self.getOption('background'): import MMTK_state_accessor self.state_accessor = MMTK_state_accessor.StateAccessor() self.actions.append(self.state_accessor) except ValueError: pass try: steps = self.getOption('steps') except ValueError: steps = None return map(lambda a, t=self, s=steps: a.getSpecificationList(t, s), self.actions) def cleanupActions(self): for a in self.actions: a.cleanup() def getOption(self, option): try: value = self.call_options[option] except KeyError: try: value = self.options[option] except KeyError: try: value = self.default_options[option] except KeyError: raise ValueError('undefined option: ' + option) return value def optionString(self, options): s = '' for o in options: s = s + o + '=' + `self.getOption(o)` + ', ' return s[:-2] def run(self, function, args): if self.getOption('background'): import ThreadManager return ThreadManager.TrajectoryGeneratorThread(self.universe, function, args, self.state_accessor) else: apply(function, args) # # Trajectory action base class # class TrajectoryAction(object): """ Trajectory action base class Subclasses of this base class implement the actions that can be inserted into trajectory generation at regular intervals. """ def __init__(self, first, last, skip): self.first = first self.last = last self.skip = skip spec_type = 'function' def _getSpecificationList(self, trajectory_generator, steps): first = self.first last = self.last if first < 0: first = first + steps if last is None: import MMTK_trajectory last = MMTK_trajectory.maxint elif last < 0: last = last + steps+1 return (self.spec_type, first, last, self.skip) def getSpecificationList(self, trajectory_generator, steps): return self._getSpecificationList(trajectory_generator, steps) \ + (self.Cfunction, self.parameters) def cleanup(self): pass class TrajectoryOutput(TrajectoryAction): """ Trajectory output action A TrajectoryOutput object can be used in the action list of any trajectory-generating operation. It writes any of the available data to a trajectory file. It is possible to use several TrajectoryOutput objects at the same time in order to produce multiple trajectories from a single run. """ def __init__(self, trajectory, data = None, first=0, last=None, skip=1): """ :param trajectory: a trajectory object or a string, which is interpreted as the name of a file that is opened as a trajectory in append mode :param data: a list of data categories. All variables provided by the trajectory generator that fall in any of the listed categories are written to the trajectory file. See the descriptions of the trajectory generators for a list of variables and categories. By default (data = None) the categories "configuration", "energy", "thermodynamic", and "time" are written. :param first: the number of the first step at which the action is run :type first: int :param last: the number of the step at which the action is suspended. A value of None indicates that the action should be applied indefinitely. :type last: int :param skip: the number of steps to skip between two action runs :type skip: int """ TrajectoryAction.__init__(self, first, last, skip) self.destination = trajectory self.categories = data self.must_be_closed = None spec_type = 'trajectory' def getSpecificationList(self, trajectory_generator, steps): if type(self.destination) == type(''): destination = self._setupDestination(self.destination, trajectory_generator.universe) else: destination = self.destination if self.categories is None: categories = self._defaultCategories(trajectory_generator) else: if self.categories == 'all' or self.categories == ['all']: categories = trajectory_generator.available_data else: categories = self.categories for item in categories: if item not in trajectory_generator.available_data: raise ValueError('data item %s is not available' % item) return self._getSpecificationList(trajectory_generator, steps) \ + (destination, categories) def _setupDestination(self, destination, universe): self.must_be_closed = Trajectory(universe, destination, 'a') return self.must_be_closed def cleanup(self): if self.must_be_closed is not None: self.must_be_closed.close() def _defaultCategories(self, trajectory_generator): available = trajectory_generator.available_data return tuple(filter(lambda x, a=available: x in a, self.default_data)) default_data = ['configuration', 'energy', 'thermodynamic', 'time'] class RestartTrajectoryOutput(TrajectoryOutput): """ Restart trajectory output action A RestartTrajectoryOutput object is used in the action list of any trajectory-generating operation. It writes those variables to a trajectory that the trajectory generator declares as necessary for restarting. """ def __init__(self, trajectory, skip=100, length=3): """ :param trajectory: a trajectory object or a string, which is interpreted as the name of a file that is opened as a trajectory in append mode with a cycle length of length and double-precision variables :param skip: the number of steps between two write operations to the restart trajectory :type skip: int :param length: the number of steps stored in the restart trajectory; used only if trajectory is a string """ TrajectoryAction.__init__(self, 0, None, skip) self.destination = trajectory self.categories = None self.length = length def _setupDestination(self, destination, universe): self.must_be_closed = Trajectory(universe, destination, 'a', 'Restart trajectory', 1, self.length) return self.must_be_closed def _defaultCategories(self, trajectory_generator): if trajectory_generator.restart_data is None: raise ValueError("Trajectory generator does not permit restart") return trajectory_generator.restart_data class LogOutput(TrajectoryOutput): """ Protocol file output action A LogOutput object can be used in the action list of any trajectory-generating operation. It writes any of the available data to a text file. """ def __init__(self, file, data = None, first=0, last=None, skip=1): """ :param file: a file object or a string, which is interpreted as the name of a file that is opened in write mode :param data: a list of data categories. All variables provided by the trajectory generator that fall in any of the listed categories are written to the trajectory file. See the descriptions of the trajectory generators for a list of variables and categories. By default (data = None) the categories "configuration", "energy", "thermodynamic", and "time" are written. :param first: the number of the first step at which the action is run :type first: int :param last: the number of the step at which the action is suspended. A value of None indicates that the action should be applied indefinitely. :type last: int :param skip: the number of steps to skip between two action runs :type skip: int """ TrajectoryOutput.__init__(self, file, data, first, last, skip) def _setupDestination(self, destination, universe): self.must_be_closed = open(destination, 'w') return self.must_be_closed spec_type = 'print' default_data = ['energy', 'time'] class StandardLogOutput(LogOutput): """ Standard protocol output action A StandardLogOutput object can be used in the action list of any trajectory-generating operation. It is a specialization of LogOutput to the most common case and writes data in the categories "time" and "energy" to the standard output stream. :param skip: the number of steps to skip between two action runs :type skip: int """ def __init__(self, skip=50): LogOutput.__init__(self, sys.stdout, None, 0, None, skip) # # Snapshot generator # class SnapshotGenerator(TrajectoryGenerator): """ Trajectory generator for single steps A SnapshotGenerator is used for manual assembly of trajectory files. At each call it writes one step to the trajectory, using the current state of the universe (configuration, velocities, etc.) and data provided explicitly with the call. Each call to the SnapshotGenerator object produces one step. All the keyword options can be specified either when creating the generator or when calling it. """ def __init__(self, universe, **options): """ :param universe: the universe on which the generator acts :keyword data: a dictionary that supplies values for variables that are not part of the universe state (e.g. potential energy) :keyword actions: a list of actions to be executed periodically (default is none) """ TrajectoryGenerator.__init__(self, universe, options) self.available_data = [] try: e, g = self.universe.energyAndGradients() except: pass else: self.available_data.append('energy') self.available_data.append('gradients') try: self.universe.configuration() self.available_data.append('configuration') except: pass if self.universe.cellVolume() is not None: self.available_data.append('thermodynamic') if self.universe.velocities() is not None: self.available_data.append('velocities') self.available_data.append('energy') self.available_data.append('thermodynamic') default_options = {'steps': 0, 'actions': []} def __call__(self, **options): self.setCallOptions(options) from MMTK_trajectory import snapshot data = copy.copy(options.get('data', {})) energy_terms = 0 for name in data.keys(): if name == 'time' and 'time' not in self.available_data: self.available_data.append('time') if name[-7:] == '_energy': energy_terms = energy_terms + 1 if 'energy' not in self.available_data: self.available_data.append('energy') if (name == 'temperature' or name == 'pressure') \ and 'thermodynamic' not in self.available_data: self.available_data.append('thermodynamic') if name == 'gradients' and 'gradients' not in self.available_data: self.available_data.append('gradients') actions = self.getActions() for action in actions: categories = action[-1] for c in categories: if c == 'energy' and not data.has_key('kinetic_energy'): v = self.universe.velocities() if v is not None: m = self.universe.masses() e = (v*v*m*0.5).sumOverParticles() data['kinetic_energy'] = e df = self.universe.degreesOfFreedom() data['temperature'] = 2.*e/df/Units.k_B/Units.K if c == 'configuration': if data.has_key('configuration'): data['configuration'] = data['configuration'].array else: data['configuration'] = \ self.universe.configuration().array if c == 'velocities': if data.has_key('velocities'): data['velocities'] = data['velocities'].array else: data['velocities'] = self.universe.velocities().array if c == 'gradients': if data.has_key('gradients'): data['gradients'] = data['gradients'].array p = self.universe.cellParameters() if p is not None: data['box_size'] = p volume = self.universe.cellVolume() if volume is not None: data['volume'] = volume try: m = self.universe.masses() data['masses'] = m.array except: pass snapshot(self.universe, data, actions, energy_terms) # # Trajectory reader (not yet functional...) # if False: class TrajectoryReader(TrajectoryGenerator): def __init__(self, trajectory, options): TrajectoryGenerator.__init__(self, trajectory.universe, options) self.input = trajectory self.available_data = trajectory.variables() default_options = {'trajectory': None, 'log': None, 'options': []} def __call__(self, **options): self.setCallOptions(options) from MMTK_trajectory import readTrajectory readTrajectory(self.universe, self.input.trajectory, [self.getOption('trajectory'), self.getOption('log')] + self.getOption('options')) # # Print information about trajectory file # def trajectoryInfo(filename): """ :param filename: the name of a trajectory file :type filename: str :returns: a string with summarial information about the trajectory """ from Scientific.IO import NetCDF file = NetCDF.NetCDFFile(filename, 'r') nsteps = file.variables['step'].shape[0] if 'minor_step_number' in file.dimensions.keys(): nsteps = nsteps*file.variables['step'].shape[1] s = 'Information about trajectory file ' + filename + ':\n' try: s += file.comment + '\n' except AttributeError: pass s += `file.dimensions['atom_number']` + ' atoms\n' s += `nsteps` + ' steps\n' s += file.history file.close() return s