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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
#
# Please cite your use of MDAnalysis in published work:
#
# 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.
.. autoclass:: ChainReader
:members:
.. automethod:: _get_local_frame
.. automethod:: _apply
.. automethod:: _get
.. automethod:: _get_same
.. automethod:: _read_frame
"""
from __future__ import absolute_import
import warnings
import os.path
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
def check_allowed_filetypes(readers, allowed):
"""
Make a check that all readers have the same filetype and are of the
allowed files types. Throws Exception on failure.
Parameters
----------
readers : list of MDA readers
allowed : list of allowed formats
"""
classname = type(readers[0])
only_one_reader = np.all([isinstance(r, classname) for r in readers])
if not only_one_reader:
readernames = [type(r) for r in readers]
raise ValueError("ChainReader: continuous=true only supported"
" when all files are using the same reader. "
"Found: {}".format(readernames))
if readers[0].format not in allowed:
raise NotImplementedError("ChainReader: continuous=True only "
"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
# 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
self.__current_frame = frame
return self.ts
def _read_next_timestep(self, ts=None):
if ts is None:
ts = self.ts
ts = self.__next__()
return ts
[docs] def rewind(self):
"""Set current frame to the beginning."""
self._rewind()
def _rewind(self):
"""Internal method: Rewind trajectories themselves and trj pointer."""
self.__current_frame = -1
self._apply('rewind')
self.__next__()
[docs] def close(self):
self._apply('close')
def __iter__(self):
"""Generator for all frames, starting at frame 0."""
self.__current_frame = -1
# start from first frame
return self
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))
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
def __next__(self):
if self.__current_frame < self.n_frames - 1:
j, f = self._get_local_frame(self.__current_frame + 1)
self.__activate_reader(j)
self.ts = self.active_reader[f]
self.ts.frame = self.__current_frame + 1
self.__current_frame += 1
return self.ts
else:
raise StopIteration()