# Source code for MDAnalysis.coordinates.chain

# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*-
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# MDAnalysis --- https://www.mdanalysis.org
# Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors
# (see the file AUTHORS for the full list of names)
#
# Released under the GNU Public Licence, v2 or any higher version
#
#
# R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler,
# D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein.
# MDAnalysis: A Python package for the rapid analysis of molecular dynamics
# simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th
# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.
# doi: 10.25080/majora-629e541a-00e
#
# N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein.
# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
#

"""\
ChainReader --- :mod:MDAnalysis.coordinates.chain
===================================================

The :class:ChainReader is used by MDAnalysis internally to represent multiple
trajectories as one virtual trajectory. Users typically do not need to use the
:class:ChainReader explicitly and the following documentation is primarily of
interest to developers.

:members:

.. automethod:: _get_local_frame
.. automethod:: _apply
.. automethod:: _get
.. automethod:: _get_same
.. automethod:: _chained_iterator

"""
from __future__ import absolute_import

import warnings

import os.path
import itertools
import bisect
import copy

import numpy as np

from ..lib import util
from ..lib.util import asiterable
from . import base
from . import core

def multi_level_argsort(l):
"""Return indices to sort a multi value tuple. Sorting is done on the first
value of the tuple.

Parameters
----------
l : list

Returns
-------
indices

Example
-------
>>> multi_level_argsort(((0, 2), (4, 9), (0, 4), (7, 9)))
[0, 2, 1, 3]

"""
return [el[0] for el in sorted(enumerate(l), key=lambda x: x[1][0])]

def filter_times(times, dt):
"""Given a list of start and end times this function filters out any duplicate
time steps preferring the last tuple.

Parameters
----------
times : list
sorted list of times
dt : float
timestep between two frames

Returns
-------
list:
indices of times to be used with overlaps removed

Example
-------
>>> filter_times(((0, 3), (0, 3)))
[1, ]
>>> filter_times(((0, 3), (0, 4)))
[1, ]
>>> filter_times(((0, 3), (3, 4)))
[0, 1]
>>> filter_times(((0, 3), (2, 5), (4, 9)))
[1, 2, 3]

"""
# Special cases
if len(times) == 1:
return [0, ]
elif len(times) == 2:
if times[0][0] < times[1][0]:
return [0, 1]
elif np.allclose(times[0][0], times[1][0]):
return [1, ]
else:
return [0, ]
if np.unique(times).size == 2:
return [len(times) - 1, ]

# more then 2 unique time entries

used_idx = [0, ]

for i, (first, middle, last) in enumerate(zip(times[:-2], times[1:-1], times[2:]), start=1):
if np.allclose(first[0], middle[0]):
used_idx[-1] = i
elif not np.allclose(middle[1] - middle[0], dt):
if (middle[0] <= first[1]) and (last[0] <= middle[1]):
used_idx.append(i)
elif (middle[0] <= first[1]):
used_idx.append(i)

# take care of first special case
if times[-2][1] <= times[-1][1]:
used_idx.append(len(times) - 1)

return used_idx

"""
Make a check that  all readers have the same filetype and are  of the
allowed files types. Throws Exception on failure.

Parameters
----------
allowed : list of allowed formats
"""
" when all files are using the same reader. "
"supported for formats: {}".format(allowed))

[docs]class ChainReader(base.ProtoReader): """Reader that concatenates multiple trajectories on the fly. The :class:ChainReader is used by MDAnalysis internally to represent multiple trajectories as one virtual trajectory. Users typically do not need to use the :class:ChainReader explicitly. Chainreader can also handle a continuous trajectory split over several files. To use this pass the continuous == True keyword argument. Setting continuous=True will make the reader choose frames from the set of trajectories in such a way that the trajectory appears to be as continuous in time as possible, i.e. that time is strictly monotonically increasing. This means that there will be no duplicate time frames and no jumps backwards in time. However, there can be gaps in time (e.g., multiple time steps can appear to be missing). Ultimately, it is the user's responsibility to ensure that the input trajectories can be virtually stitched together in a meaningful manner. As an example take the following trajectory that is split into three parts. The column represents the time and the trajectory segments overlap. With the continuous chainreader only the frames marked with a + will be read. :: part01: ++++-- part02: ++++++- part03: ++++++++ .. warning:: The order in which trajectories are given to the chainreader can change what frames are used with the continuous option. The default chainreader will read all frames. The continuous option is currently only supported for XTC and TRR files. Notes ----- The trajectory API attributes exist but most of them only reflect the first trajectory in the list; :attr:ChainReader.n_frames, :attr:ChainReader.n_atoms, and :attr:ChainReader.fixed are properly set, though .. versionchanged:: 0.11.0 Frames now 0-based instead of 1-based .. versionchanged:: 0.13.0 :attr:time now reports the time summed over each trajectory's frames and individual :attr:dt. .. versionchanged:: 0.19.0 added continuous trajectory option .. versionchanged:: 0.19.0 limit output of __repr__ """ format = 'CHAIN' def __init__(self, filenames, skip=1, dt=None, continuous=False, **kwargs): """Set up the chain reader. Parameters ---------- filenames : str or list or sequence file name or list of file names; the reader will open all file names and provide frames in the order of trajectories from the list. Each trajectory must contain the same number of atoms in the same order (i.e. they all must belong to the same topology). The trajectory format is deduced from the extension of each file name. Extension: filenames are either a single file name or list of file names in either plain file names format or (filename, format) tuple combination. This allows explicit setting of the format for each individual trajectory file. skip : int (optional) skip step (also passed on to the individual trajectory readers); must be same for all trajectories dt : float (optional) Passed to individual trajectory readers to enforce a common time difference between frames, in MDAnalysis time units. If not set, each reader's dt will be used (either inferred from the trajectory files, or set to the reader's default) when reporting frame times; note that this might lead an inconsistent time difference between frames. continuous : bool (optional) treat all trajectories as one single long trajectory. Adds several checks; all trajectories have the same dt, they contain at least 2 frames, and they are all of the same file-type. Not implemented for all trajectory formats! This can be used to analyze GROMACS simulations without concatenating them prior to analysis. **kwargs : dict (optional) all other keyword arguments are passed on to each trajectory reader unchanged """ super(ChainReader, self).__init__() filenames = asiterable(filenames) # Override here because single frame readers handle this argument as a # kwarg to a timestep which behaves differently if dt is present or not. if dt is not None: kwargs['dt'] = dt self.readers = [core.reader(filename, **kwargs) for filename in filenames] self.filenames = np.array([fn[0] if isinstance(fn, tuple) else fn for fn in filenames]) # pointer to "active" trajectory index into self.readers self.__active_reader_index = 0 self.skip = skip self.n_atoms = self._get_same('n_atoms') # Translation between virtual frames and frames in individual # trajectories. Assumes that individual trajectories i contain frames # that can be addressed with an index 0 <= f < n_frames[i] # Build a map of frames: ordered list of starting virtual frames; the # index i into this list corresponds to the index into self.readers # # For virtual frame 0 <= k < sum(n_frames) find corresponding # trajectory i and local frame f (i.e. readers[i][f] will correspond to # ChainReader[k]). # build map 'start_frames', which is used by _get_local_frame() n_frames = self._get('n_frames') # [0]: frames are 0-indexed internally # (see Timestep.check_slice_indices()) self._start_frames = np.cumsum([0] + n_frames) self.n_frames = np.sum(n_frames) self.dts = np.array(self._get('dt')) self.total_times = self.dts * n_frames #: source for trajectories frame (fakes trajectory) self.__chained_trajectories_iter = None # calculate new start_frames to have a time continuous trajectory. if continuous: check_allowed_filetypes(self.readers, ['XTC', 'TRR']) if np.any(np.array(n_frames) == 1): raise RuntimeError("ChainReader: Need at least two frames in " "every trajectory with continuous=True") # TODO: allow floating point precision in dt check dt = self._get_same('dt') n_frames = np.asarray(self._get('n_frames')) self.dts = np.ones(self.dts.shape) * dt # the sorting needs to happen on two levels. The first major level # is by start times and the second is by end times. # The second level of sorting is needed for cases like: # [0 1 2 3 4 5 6 7 8 9] [0 1 2 4] # to # [0 1 2 4] [0 1 2 3 4 5 6 7 8 9] # after that sort the chain reader will work times = [] for r in self.readers: r[0] start = r.ts.time r[-1] end = r.ts.time times.append((start, end)) # sort step sort_idx = multi_level_argsort(times) self.readers = [self.readers[i] for i in sort_idx] self.filenames = self.filenames[sort_idx] self.total_times = self.dts * n_frames[sort_idx] # filter step: remove indices if we have complete overlap if len(self.readers) > 1: used_idx = filter_times(np.array(times)[sort_idx], dt) self.readers = [self.readers[i] for i in used_idx] self.filenames = self.filenames[used_idx] self.total_times = self.dts[used_idx] * n_frames[used_idx] # rebuild lookup table sf = [0, ] n_frames = 0 for r1, r2 in zip(self.readers[:-1], self.readers[1:]): r2[0], r1[0] r1_start_time = r1.time start_time = r2.time r1[-1] if r1.time < start_time: warnings.warn("Missing frame in continuous chain", UserWarning) # check for interleaving r1[1] if r1_start_time < start_time < r1.time: raise RuntimeError("ChainReader: Interleaving not supported with continuous=True.") # find end where trajectory was restarted from for ts in r1[::-1]: if ts.time < start_time: break sf.append(sf[-1] + ts.frame + 1) n_frames += ts.frame + 1 n_frames += self.readers[-1].n_frames self._start_frames = sf self.n_frames = n_frames self._sf = sf # make sure that iteration always yields frame 0 # rewind() also sets self.ts self.ts = None self.rewind() @staticmethod def _format_hint(thing): """Can ChainReader read the object *thing* .. versionadded:: 1.0.0 """ return (not isinstance(thing, np.ndarray) and util.iterable(thing) and not util.isstream(thing))
[docs] def _get_local_frame(self, k): """Find trajectory index and trajectory frame for chained frame k. Parameters ---------- k : int Frame k in the chained trajectory can be found in the trajectory at index *i* and frame index *f*. Frames are internally treated as 0-based indices into the trajectory. Returns ------- i : int trajectory f : int frame in trajectory i Raises ------ IndexError for k<0 or i<0. Note ---- Does not check if k is larger than the maximum number of frames in the chained trajectory. """ if k < 0: raise IndexError("Virtual (chained) frames must be >= 0") # trajectory index i i = bisect.bisect_right(self._start_frames, k) - 1 if i < 0: raise IndexError("Cannot find trajectory for virtual frame {0:d}".format(k)) # local frame index f in trajectory i (frame indices are 0-based) f = k - self._start_frames[i] return i, f
# methods that can change with the current reader
[docs] def convert_time_from_native(self, t): return self.active_reader.convert_time_from_native(t)
[docs] def convert_time_to_native(self, t): return self.active_reader.convert_time_to_native(t)
[docs] def convert_pos_from_native(self, x): return self.active_reader.convert_from_native(x)
[docs] def convert_pos_to_native(self, x): return self.active_reader.convert_pos_to_native(x)
def copy(self): new = self.__class__(copy.copy(self.filenames)) # seek the new reader to the same frame we started with new[self.ts.frame] # then copy over the current Timestep in case it has # been modified since initial load new.ts = self.ts.copy() return new # attributes that can change with the current reader @property def filename(self): """Filename of the currently read trajectory""" return self.active_reader.filename # TODO: check that skip_timestep is still supported in all readers # or should this be removed? @property def skip_timestep(self): return self.active_reader.skip_timestep @property def delta(self): return self.active_reader.delta @property def periodic(self): """:attr:periodic attribute of the currently read trajectory""" return self.active_reader.periodic @property def units(self): """:attr:units attribute of the currently read trajectory""" return self.active_reader.units @property def compressed(self): """:attr:compressed attribute of the currently read trajectory""" try: return self.active_reader.compressed except AttributeError: return None @property def frame(self): """Cumulative frame number of the current time step.""" return self.ts.frame @property def time(self): """Cumulative time of the current frame in MDAnalysis time units (typically ps).""" # Before 0.13 we had to distinguish between enforcing a common dt or # summing over each reader's times. # Now each reader is either already instantiated with a common dt, or # left at its default dt. In any case, we sum over individual times. trajindex, subframe = self._get_local_frame(self.frame) return self.total_times[:trajindex].sum() + subframe * self.dts[trajindex]
[docs] def _apply(self, method, **kwargs): """Execute method with kwargs for all readers.""" return [reader.__getattribute__(method)(**kwargs) for reader in self.readers]
[docs] def _get(self, attr): """Get value of attr for all readers.""" return [reader.__getattribute__(attr) for reader in self.readers]
[docs] def _get_same(self, attr): """Verify that attr has the same value for all readers and return value. Parameters ---------- attr : str attribute name Returns ------- value : int or float or str or object common value of the attribute Raises ------ ValueError if not all readers have the same value """ values = np.array(self._get(attr)) value = values[0] if not np.allclose(values, value): bad_traj = np.array(self.filenames)[values != value] raise ValueError("The following trajectories do not have the correct {0} " " ({1}):\n{2}".format(attr, value, bad_traj)) return value
def __activate_reader(self, i): """Make reader i the active reader.""" # private method, not to be used by user to avoid a total mess if not (0 <= i < len(self.readers)): raise IndexError("Reader index must be 0 <= i < {0:d}".format(len(self.readers))) self.__active_reader_index = i @property def active_reader(self): """Reader instance from which frames are currently being read.""" return self.readers[self.__active_reader_index]
[docs] def _read_frame(self, frame): """Position trajectory at frame index frame and return :class:~MDAnalysis.coordinates.base.Timestep. The frame is translated to the corresponding reader and local frame index and the :class:Timestep instance in :attr:ChainReader.ts is updated. Notes ----- frame is 0-based, i.e. the first frame in the trajectory is accessed with frame = 0. See Also -------- :meth:~ChainReader._get_local_frame """ i, f = self._get_local_frame(frame) # seek to (1) reader i and (2) frame f in trajectory i self.__activate_reader(i) self.active_reader[f] # rely on reader to implement __getitem__() # update Timestep self.ts = self.active_reader.ts self.ts.frame = frame # continuous frames, 0-based return self.ts
[docs] def _chained_iterator(self): """Iterator that presents itself as a chained trajectory.""" self._rewind() # must rewind all readers for i in range(self.n_frames): j, f = self._get_local_frame(i) self.__activate_reader(j) self.ts = self.active_reader[f] self.ts.frame = i yield self.ts
def _read_next_timestep(self, ts=None): self.ts = next(self.__chained_trajectories_iter) return self.ts
[docs] def rewind(self): """Set current frame to the beginning.""" self._rewind() self.__chained_trajectories_iter = self._chained_iterator() # set time step for frame 1 self.ts = next(self.__chained_trajectories_iter)
def _rewind(self): """Internal method: Rewind trajectories themselves and trj pointer.""" self._apply('rewind') self.__activate_reader(0)
[docs] def close(self): self._apply('close')
def __iter__(self): """Generator for all frames, starting at frame 1.""" self._rewind() # start from first frame self.__chained_trajectories_iter = self._chained_iterator() for ts in self.__chained_trajectories_iter: yield ts def __repr__(self): if len(self.filenames) > 3: fnames = "{fname} and {nfanmes} more".format( fname=os.path.basename(self.filenames[0]), nfanmes=len(self.filenames) - 1) else: fnames = ", ".join([os.path.basename(fn) for fn in self.filenames]) return ("<{clsname} containing {fname} with {nframes} frames of {natoms} atoms>" "".format( clsname=self.__class__.__name__, fname=fnames, nframes=self.n_frames, natoms=self.n_atoms))
[docs] def add_transformations(self, *transformations): """ Add all transformations to be applied to the trajectory. This function take as list of transformations as an argument. These transformations are functions that will be called by the Reader and given a :class:Timestep object as argument, which will be transformed and returned to the Reader. The transformations can be part of the :mod:~MDAnalysis.transformations module, or created by the user, and are stored as a list transformations. This list can only be modified once, and further calls of this function will raise an exception. .. code-block:: python u = MDAnalysis.Universe(topology, coordinates) workflow = [some_transform, another_transform, this_transform] u.trajectory.add_transformations(*workflow) Parameters ---------- transform_list : list list of all the transformations that will be applied to the coordinates See Also -------- :mod:MDAnalysis.transformations """ #Overrides :meth:~MDAnalysis.coordinates.base.ProtoReader.add_transformations #to avoid unintended behaviour where the coordinates of each frame are transformed #multiple times when iterating over the trajectory. #In this method, the trajectory is modified all at once and once only. super(ChainReader, self).add_transformations(*transformations) for r in self.readers: r.add_transformations(*transformations)
def _apply_transformations(self, ts): """ Applies the transformations to the timestep.""" # Overrides :meth:~MDAnalysis.coordinates.base.ProtoReader.add_transformations # to avoid applying the same transformations multiple times on each frame return ts