# Source code for MDAnalysis.transformations.boxdimensions

    # -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*-
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    # 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.
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    # MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
    # J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
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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

    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