On-the-fly transformationsΒΆ

An on-the-fly transformation is a function that silently modifies the dynamic data contained in a trajectory Timestep (typically coordinates) as it is loaded into memory. It is called for each current time step to transform data into your desired representation. A transformation function must also return the current Timestep, as transformations are often chained together.

The MDAnalysis.transformations module contains a collection of transformations. For example, fit_rot_trans() can perform a mass-weighted alignment on an AtomGroup to a reference.

In [1]: import MDAnalysis as mda

In [2]: from MDAnalysis.tests.datafiles import TPR, XTC

In [3]: from MDAnalysis import transformations as trans

In [4]: u = mda.Universe(TPR, XTC)

In [5]: protein = u.select_atoms('protein')

In [6]: align_transform = trans.fit_rot_trans(protein, protein, weights=protein.masses)

In [7]: u.trajectory.add_transformations(align_transform)

Other implemented transformations include functions to translate, rotate, fit an AtomGroup to a reference, and wrap or unwrap groups in the unit cell. (Please see the MDAnalysis on-the-fly transformations blog post contains a more complete introduction to these fitting and wrapping functions.)

Although you can only call add_transformations() once, you can pass in multiple transformations in a list, which will be executed in order. For example, the below workflow:

  • makes all molecules whole (unwraps them over periodic boundary conditions)

  • centers the protein in the center of the box

  • wraps water back into the box

# create new Universe for new transformations
In [8]: u = mda.Universe(TPR, XTC)

In [9]: protein = u.select_atoms('protein')

In [10]: water = u.select_atoms('resname SOL')

In [11]: workflow = [trans.unwrap(u.atoms),
   ....:             trans.center_in_box(protein, center='geometry'),
   ....:             trans.wrap(water, compound='residues')]
   ....: 

In [12]: u.trajectory.add_transformations(*workflow)

Please see the full tutorial for more information.

If your transformation does not depend on something within the Universe (e.g. a chosen AtomGroup), you can also create a Universe directly with transformations. The code below translates coordinates 1 angstrom up on the z-axis:

In [13]: u = mda.Universe(TPR, XTC, transformations=[trans.translate([0, 0, 1])])

If you need a different transformation, it is easy to implement your own.

Custom transformationsΒΆ

At its core, a transformation function must only take a Timestep as its input and return the Timestep as the output.

In [14]: import numpy as np

In [15]: def up_by_2(ts):
   ....:     """Translates atoms up by 2 angstrom"""
   ....:     ts.positions += np.array([0.0, 0.0, 0.2])
   ....:     return ts
   ....: 

In [16]: u = mda.Universe(TPR, XTC, transformations=[up_by_2])

If your transformation needs other arguments, you will need to wrap your core transformation with a wrapper function that can accept the other arguments.

In [17]: def up_by_x(x):
   ....:     """Translates atoms up by x angstrom"""
   ....:     def wrapped(ts):
   ....:         """Handles the actual Timestep"""
   ....:         ts.positions += np.array([0.0, 0.0, float(x)])
   ....:         return ts
   ....:     return wrapped
   ....: 

# load Universe with transformations that move it up by 7 angstrom
In [18]: u = mda.Universe(TPR, XTC, transformations=[up_by_x(5), up_by_x(2)])

Alternatively, you can use functools.partial() to substitute the other arguments.

In [19]: import functools

In [20]: def up_by_x(ts, x):
   ....:     ts.positions += np.array([0.0, 0.0, float(x)])
   ....:     return ts
   ....: 

In [21]: up_by_5 = functools.partial(up_by_x, x=5)

In [22]: u = mda.Universe(TPR, XTC, transformations=[up_by_5])

On-the-fly transformation functions can be applied to any property of a Timestep, not just the atom positions. For example, to give each frame of a trajectory a box:

In [23]: def set_box(ts):
   ....:     ts.dimensions = [10, 20, 30, 90, 90, 90]
   ....:     return ts
   ....: 

In [24]: u = mda.Universe(TPR, XTC, transformations=[set_box])