Source code for MDAnalysis.transformations.positionaveraging
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# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
#
"""\
Trajectory Coordinate Averaging --- :mod:`MDAnalysis.transformations.positionaveraging`
=======================================================================================
Averages the coordinates of a given trajectory with the N previous frames.
For frames < N, the average of the frames iterated up to that point will be
returned.
.. autoclass:: PositionAverager
"""
import numpy as np
import warnings
from .base import TransformationBase
[docs]
class PositionAverager(TransformationBase):
"""
Averages the coordinates of a given timestep so that the coordinates
of the AtomGroup correspond to the average positions of the N previous
frames.
For frames < N, the average of the frames iterated up to that point will
be returned.
Example
-------
Average the coordinates of a given AtomGroup over the course of
the previous N frames. For ``N=3``, the output will correspond to the
average of the coordinates over the last 3 frames.
When ``check_reset=True``, the averager will be reset once the iteration is
complete, or if the frames iterated are not sequential.
.. code-block:: python
N=3
transformation = PositionAverager(N, check_reset=True)
u.trajectory.add_transformations(transformation)
for ts in u.trajectory:
print(ts.positions)
In this case, ``ts.positions`` will return the average coordinates of the
last N iterated frames.
When ``check_reset=False``, the average of coordinates from non
sequential timesteps can also be computed. However, the averager must be
manually reset before restarting an iteration. In this case,
``ts.positions`` will return the average coordinates of the last N
iterated frames, despite them not being sequential
(``frames = [0, 7, 1, 6]``).
.. code-block:: python
N=3
transformation = PositionAverager(N, check_reset=False)
u.trajectory.add_transformations(transformation)
frames = [0, 7, 1, 6]
transformation.resetarrays()
for ts in u.trajectory[frames]:
print(ts.positions)
If ``check_reset=True``, the ``PositionAverager`` would have automatically
reset after detecting a non sequential iteration (i.e. when iterating from
frame 7 to frame 1 or when resetting the iterator from frame 6 back to
frame 0).
For frames < N, the average is calculated with the frames iterated up
to that point and thus will not follow the same behaviour as for
frames > N. This can be followed by comparing the number of frames being
used to compute the current averaged frame (``current_avg``) to the one
requested when calling ``PositionAverager`` (``avg_frames``) which in
these examples corresponds to ``N=3``.
.. code-block:: python
N=3
transformation = PositionAverager(N, check_reset=True)
u.trajectory.add_transformations(transformation)
for ts in u.trajectory:
if transformation.current_avg == transformation.avg_frames:
print(ts.positions)
In the case of ``N=3``, as the average is calculated with the frames
iterated up to the current iteration, the first frame returned will
not be averaged. During the first iteration no other frames are stored in
memory thus far and, consequently, ``transformation.current_avg = 1``.
The second frame iterated will return the average of frame 1 and frame 2,
with ``transformation.current_avg = 2``. Only during the third and
following iterations will ``ts.positions`` start returning the average of
the last 3 frames and thus ``transformation.current_avg = 3``
These initial frames are typically not desired during analysis, but one can
easily avoid them, as seen in the previous example with
``if transformation.current_avg == transformation.avg_frames:`` or by
simply removing the first ``avg_frames-1`` frames from the analysis.
Parameters
----------
avg_frames: int
Determines the number of frames to be used for the position averaging.
check_reset: bool, optional
If ``True``, position averaging will be reset and a warning raised
when the trajectory iteration direction changes. If ``False``, position
averaging will not reset, regardless of the iteration.
Returns
-------
MDAnalysis.coordinates.timestep.Timestep
.. versionchanged:: 2.0.0
The transformation was changed to inherit from the base class for
limiting threads and checking if it can be used in parallel analysis.
"""
def __init__(self, avg_frames, check_reset=True,
max_threads=None,
parallelizable=False):
super().__init__(max_threads=max_threads,
parallelizable=parallelizable)
self.avg_frames = avg_frames
self.check_reset = check_reset
self.current_avg = 0
self.resetarrays()
self.current_frame = 0
def resetarrays(self):
self.idx_array = np.empty(self.avg_frames)
self.idx_array[:] = np.nan
def rollidx(self, ts):
self.idx_array = np.roll(self.idx_array, 1)
self.idx_array[0] = ts.frame
def rollposx(self, ts):
try:
self.coord_array.size
except AttributeError:
size = (ts.positions.shape[0], ts.positions.shape[1],
self.avg_frames)
self.coord_array = np.empty(size)
self.coord_array = np.roll(self.coord_array, 1, axis=2)
self.coord_array[..., 0] = ts.positions.copy()
def _transform(self, ts):
# calling the same timestep will not add new data to coord_array
# This can prevent from getting different values when
# call `u.trajectory[i]` multiple times.
if (ts.frame == self.current_frame
and hasattr(self, 'coord_array')
and not np.isnan(self.idx_array).all()):
test = ~np.isnan(self.idx_array)
ts.positions = np.mean(self.coord_array[..., test], axis=2)
return ts
else:
self.current_frame = ts.frame
self.rollidx(ts)
test = ~np.isnan(self.idx_array)
self.current_avg = sum(test)
if self.check_reset:
sign = np.sign(np.diff(self.idx_array[test]))
if not (np.all(sign == 1) or np.all(sign == -1)):
warnings.warn('Cannot average position for non sequential'
'iterations. Averager will be reset.',
Warning)
self.resetarrays()
return self(ts)
self.rollposx(ts)
ts.positions = np.mean(self.coord_array[..., test], axis=2)
return ts