§ È ´i¬ãó2—dZddlmZGd„de¦«ZdS)z°Transformations Base Class --- :mod:`MDAnalysis.transformations.base` ===================================================================== .. autoclass:: TransformationBase é)Úthreadpool_limitscó$—eZdZdZd„Zd„Zd„ZdS)ÚTransformationBasea. Base class for defining on-the-fly transformations The class is designed as a template for creating on-the-fly Transformation classes. This class will 1) set up a context manager framework on limiting the threads per call, which may be the multi-thread OpenBlas backend of NumPy. This backend may kill the performance when subscribing hyperthread or oversubscribing the threads when used together with other parallel engines e.g. Dask. (PR `#2950 `_) Define ``max_threads=1`` when that is the case. 2) set up a boolean attribute `parallelizable` for checking if the transformation can be applied in a **split-apply-combine** parallelism. For example, the :class:`~MDAnalysis.transformations.positionaveraging.PositionAverager` is history-dependent and can not be used in parallel analysis natively. (Issue `#2996 `_) To define a new Transformation, :class:`TransformationBase` has to be subclassed. ``max_threads`` will be set to ``None`` by default, i.e. does not do anything and any settings in the environment such as the environment variable :envvar:`OMP_NUM_THREADS` (see the `OpenMP specification for OMP_NUM_THREADS `_) are used. ``parallelizable`` will be set to ``True`` by default. You may need to double check if it can be used in parallel analysis; if not, override the value to ``False``. Note this attribute is not checked anywhere in MDAnalysis yet. Developers of the parallel analysis have to check it in their own code. .. code-block:: python class NewTransformation(TransformationBase): def __init__(self, ag, parameter, max_threads=1, parallelizable=True): super().__init__(max_threads=max_threads, parallelizable=parallelizable) self.ag = ag self._param = parameter def _transform(self, ts): # REQUIRED ts.positions = some_function(ts, self.ag, self._param) return ts Afterwards the new transformation can be run like this. .. code-block:: python new_transformation = NewTransformation(ag, param) u.trajectory.add_transformations(new_transformation) .. versionadded:: 2.0.0 Add the base class for all transformations to limit threads and check if it can be used in parallel analysis. c ór—| dd¦«|_| dd¦«|_dS)a… Parameters ---------- max_threads: int, optional The maximum thread number can be used. Default is ``None``, which means the default or the external setting. parallelizable: bool, optional A check for if this can be used in split-apply-combine parallel analysis approach. Default is ``True``. Ú max_threadsNÚparallelizableT)Úpoprr)ÚselfÚkwargss úi/srv/www/vhosts/g4struct/public_html/venv/lib/python3.11/site-packages/MDAnalysis/transformations/base.pyÚ__init__zTransformationBase.__init___s7€ð"Ÿ:š: m°TÑ:Ô:ˆÔØ$ŸjšjÐ)9¸4Ñ@Ô@ˆÔÐÐócóˆ—t|j¦«5| |¦«cddd¦«S#1swxYwYdS)zÏThe function that makes transformation can be called as a function The thread limit works as a context manager with given `max_threads` wrapping the real :func:`_transform` function N)rrÚ _transform©r Útss r Ú__call__zTransformationBase.__call__nsˆ€õ ˜tÔ/Ñ 0Ô 0ð 'ð 'Ø—?’? 2Ñ&Ô&ð 'ð 'ð 'ð 'ñ 'ô 'ð 'ð 'ð 'ð 'ð 'ð 'øøøð 'ð 'ð 'ð 'ð 'ð 's •7·;¾;có —td¦«‚)ziTransform the given `Timestep` It deals with the transformation of a single `Timestep`. z!Only implemented in child classes)ÚNotImplementedErrorrs r rzTransformationBase._transformws€õ "Ð"EÑFÔFÐFrN)Ú__name__Ú __module__Ú __qualname__Ú__doc__r rr©rr rr!sT€€€€€ð;ð;ðz Að Að Að'ð'ð'ðGðGðGðGðGrrN)rÚ threadpoolctlrÚobjectrrrr úrskðð.ðð,Ð+Ð+Ð+Ð+Ð+ð[Gð[Gð[Gð[Gð[G˜ñ[Gô[Gð[Gð[Gð[Gr