8. Trajectory transformations (“on-the-fly” transformations)
In MDAnalysis, a transformation is a function/function-like class
that modifies the data for the current
Timestep and returns the
Timestep. For instance, coordinate transformations, such as
PBC corrections and molecule fitting are often required for some
analyses and visualization. Transformation functions
transformation_2 in the following
example) can be called by the user for any given
u = MDAnalysis.Universe(topology, trajectory) for ts in u.trajectory: ts = transformation_2(transformation_1(ts))
where they change the coordinates of the timestep
place. There is nothing special about these transformations except
that they have to be written in such a way that they change the
Timestep in place.
As described under Workflows, multiple transformations can be
grouped together and associated with a trajectory so that the
trajectory is transformed on-the-fly, i.e., the data read from the
trajectory file will be changed before it is made available in, say,
MDAnalysis.transformations contains a
collection of transformations (see Transformations in MDAnalysis) that
can be immediately used but one can always write custom
transformations (see Creating transformations).
Instead of manually applying transformations, it is much more convenient to associate a whole workflow of transformations with a trajectory and have the transformations be called automatically.
A workflow is a sequence (tuple or list) of transformation functions that will be applied in this order. For example,
workflow = [transformation_1, transformation_2]
would effectively result in
ts = transformation_2(transformation_1(ts))
for every time step in the trajectory.
One can add a workflow using the
of a trajectory (where the list
workflow is taken from the example
creation using the keyword argument transformations:
u = MDAnalysis.Universe(topology, trajectory, transformations=workflow)
Note that in these two cases, the workflow cannot be changed after having being added.
8.2. Creating transformations
A simple transformation can also be a function that takes a
Timestep as input, modifies it, and
returns it. If it takes no other arguments but a
can be defined as the following example:
def up_by_2(ts): """ Translate all coordinates by 2 angstroms up along the Z dimension. """ ts.positions = ts.positions + np.array([0, 0, 2], dtype=np.float32) return ts
If the transformation requires other arguments besides the
the following two methods can be used to create such transformation:
8.2.1. Creating complex transformation classes
It is implemented by inheriting from
__call__() for the transformation class
and can be applied directly to a
_transform() has to
be defined and include the operations on the
So, a transformation class can be roughly defined as follows:
from MDAnalysis.transformations import TransformationBase class up_by_x_class(TransformationBase): def __init__(self, distance): self.distance = distance def _transform(self, ts): ts.positions = ts.positions + np.array([0, 0, self.distance], dtype=np.float32) return ts
It is the default construction method in
from release 2.0.0 onwards because it can be reliably serialized.
MDAnalysis.transformations.translate for a simple example.
8.2.2. Creating complex transformation closure functions
Transformation can also be a wrapped function takes the
Timestep object as argument.
So in this case, a transformation function (closure) can be roughly defined as follows:
def up_by_x_func(distance): """ Creates a transformation that will translate all coordinates by a given amount along the Z dimension. """ def wrapped(ts): ts.positions = ts.positions + np.array([0, 0, distance], dtype=np.float32) return ts return wrapped
An alternative to using a wrapped function is using partials from
above function can be written as:
import functools def up_by_x(ts, distance): ts.positions = ts.positions + np.array([0, 0, distance], dtype=np.float32) return ts up_by_2 = functools.partial(up_by_x, distance=2)
Although functions (closures) work as transformations, they are not used in
in MDAnalysis from release 2.0.0 onwards because they cannot be reliably
serialized and thus a
Universe with such transformations cannot be
used with common parallelization schemes (e.g., ones based on
For detailed descriptions about how to write a closure-style transformation,
please refer to MDAnalysis 1.x documentation.
8.3. Transformations in MDAnalysis
MDAnalysis.transformations contains transformations that can
be immediately used in your own workflows. In order to use
any of these transformations, the module must first be imported:
A workflow can then be added to a trajectory as described above. Notably,
the parameter max_threads can be defined when creating a transformation
instance to limit the maximum threads.
MDAnalysis.transformations.base.TransformationBase for more details)
Whether a specific transformation can be used along with parallel analysis
can be assessed by checking its
See Currently implemented transformations for more on the existing
8.4. How to transformations
Translating the coordinates of a single frame (although one would normally add the transformation to a workflow, as shown in the subsequent examples):
u = MDAnalysis.Universe(topology, trajectory) new_ts = MDAnalysis.transformations.translate([1,1,1])(u.trajectory.ts)
Create a workflow and add it to the trajectory:
u = MDAnalysis.Universe(topology, trajectory) workflow = [MDAnalysis.transformations.translate([1,1,1]), MDAnalysis.transformations.translate([1,2,3])] u.trajectory.add_transformations(*workflow)
Giving a workflow as a keyword argument when defining the universe:
workflow = [MDAnalysis.transformations.translate([1,1,1]), MDAnalysis.transformations.translate([1,2,3])] u = MDAnalysis.Universe(topology, trajectory, transformations=workflow)
8.5. Building blocks for Transformation Classes
Transformations normally ultilize the power of NumPy to get better performance on array operations. However, when it comes to parallelism, NumPy will sometimes oversubscribe the threads, either by hyper threading (when it uses OpenBlas backend), or by working with other parallel engines (e.g. Dask).
In MDAnalysis, we use threadpoolctl
TransformationBase to control the maximum threads for transformations.
It is also possible to apply a global thread limit by setting the external environmental
OMP_NUM_THREADS=1 MKL_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1
BLIS_NUM_THREADS=1 python script.py. Read more about parallelism and resource management
in scikit-learn documentations.
Users are advised to benchmark code because interaction between different libraries can lead to sub-optimal performance with defaults.
8.6. Currently implemented transformations
- 8.6.1. Trajectory translation —
- 8.6.2. Trajectory rotation —
- 8.6.3. Trajectory Coordinate Averaging —
- 8.6.4. Fitting transformations —
- 8.6.5. Wrap/unwrap transformations —
- 8.6.6. Set box dimensions —