Source code for MDAnalysis.transformations.boxdimensions

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Set box dimensions --- :mod:`MDAnalysis.transformations.boxdimensions`

Set dimensions of the simulation box, either to a constant vector across
all timesteps or to a specified vector at each frame.

.. autoclass:: set_dimensions
import numpy as np

from .base import TransformationBase

[docs] class set_dimensions(TransformationBase): """ Set simulation box dimensions. Timestep dimensions are modified in place. Examples -------- e.g. set simulation box dimensions to a vector containing unit cell dimensions [*a*, *b*, *c*, *alpha*, *beta*, *gamma*], lengths *a*, *b*, *c* are in the MDAnalysis length unit (Å), and angles are in degrees. The same dimensions will be used for every frame in the trajectory. .. code-block:: python dim = np.array([2, 2, 2, 90, 90, 90]) transform = mda.transformations.boxdimensions.set_dimensions(dim) u.trajectory.add_transformations(transform) Or e.g. set simulation box dimensions to a vector containing unit cell dimensions [*a*, *b*, *c*, *alpha*, *beta*, *gamma*] at the first frame, and [*2a*, *2b*, *2c*, *alpha*, *beta*, *gamma*] at the second frame. .. code-block:: python dim = np.array([ [2, 2, 2, 90, 90, 90], [4, 4, 4, 90, 90, 90], ]) transform = mda.transformations.boxdimensions.set_dimensions(dim) u.trajectory.add_transformations(transform) Parameters ---------- dimensions: iterable of floats or two-dimensional np.typing.NDArrayLike vector that contains unit cell lengths and angles. Expected shapes are (6, 0) or (1, 6) or (N, 6), where N is the number of frames in the trajectory. If shape is (6, 0) or (1, 6), the same dimensions will be used at every frame in the trajectory. Returns ------- :class:`~MDAnalysis.coordinates.timestep.Timestep` object .. versionchanged:: 2.7.0 Added the option to set varying box dimensions (i.e. an NPT trajectory). """ def __init__(self, dimensions, max_threads=None, parallelizable=True): super().__init__(max_threads=max_threads, parallelizable=parallelizable) self.dimensions = dimensions try: self.dimensions = np.asarray(self.dimensions, np.float32) except ValueError: errmsg = ( f'{self.dimensions} cannot be converted into ' 'np.float32 numpy.ndarray' ) raise ValueError(errmsg) try: self.dimensions = self.dimensions.reshape(-1, 6) except ValueError: errmsg = ( f'{self.dimensions} array does not have valid box ' 'dimension shape.\nSimulation box dimensions are ' 'given by an float array of shape (6, 0), (1, 6), ' 'or (N, 6) where N is the number of frames in the ' 'trajectory and the dimension vector(s) containing ' '3 lengths and 3 angles: ' '[a, b, c, alpha, beta, gamma]' ) raise ValueError(errmsg) def _transform(self, ts): try: ts.dimensions = ( self.dimensions[0] if self.dimensions.shape[0] == 1 else self.dimensions[ts.frame] ) except IndexError as e: raise ValueError( f"Dimensions array has no data for frame {ts.frame}" ) from e return ts